MainoF.RazettiF.Un rinnovato protagonismo per stakeholder e corpi intermedi? Il secondo welfare, tra evoluzioni concettuali e sviluppi empiriciNuove Alleanze Per un Welfare che Cambia. Quarto Rapporto sul Secondo Welfare in ItaliaGiacopelli EditoriTorino, Italy20192347978-88-921-3129-3IstatI Tempi Della Vita Quotidiana: Lavoro, Conciliazione, Parità di Genere e Benessere SoggettivoIstatRome, Italy20191334978-88-458-1971-1OECDThe Pursuit of Gender Equality-An Uphill BattleOECD PublishingParis, France2017214692-64-28131GoriC.MorcianoM.Cash-for-care payments in Europe: Changes in resource allocationSoc. Policy Adm.20195353755010.1111/spol.12498ShutesI.ChiattiC.Migrant labour and the marketisation of care for older people: The employment of migrant care workers by families and service providersJ. Eur. Soc. Policy20122239240510.1177/0958928712449773MartoneV.Modelli di Welfare Emergenti per Una Governance Locale del Lavoro Privato di CuraPPresented at the Espanet ConferenceTorino, Italy18–20 September 2014Available online: https://www.espanet-italia.net/wp-content/uploads/2012/08/images_conferenza2014_sessioni_sessione_18_Martone_Vittorio.pdf(accessed on 7 July 2020)Osservatorio Sulle Prestazioni a Sostegno Della Famiglia DataAvailable online: https%3a%2f%2fwww.inps.it%2fnuovoportaleinps%2fdefault.aspx%3fsPathID%3d%3b0%3b46437%3b%26lastMenu%3d46437%26iMenu%3d12%26p4%3d2(accessed on 8 May 2020)
Flowchart of the rapid review process.
ijerph-17-05511-t001_Table 1
Italian care needs in a comparative perspective: ageing trends, share of population with care responsibility, and share of informal carers across selected European countries.
Country
% of Population Aged 65+
% of Population Aged 18–64 with Care Responsibility for Incapacitated Relatives (2018)
Informal Carers as a % of Total Population (2016)
2018
Difference (2018/2009)
Total
Male
Female
18–35
35–64
65+
Greece
21.8
3.0
10.1
34.0
29.0
39.0
35.0
35.0
34.0
Netherlands
18.9
3.9
10.0
18.0
13.0
23.0
10.0
23.0
17.0
Italy
22.6
2.3
7.7
17.0
16.0
19.0
12.0
20.0
18.0
Spain
19.2
2.6
6.9
16.0
13.0
19.0
14.0
19.0
11.0
Poland
17.1
3.6
6.7
20.0
18.0
21.0
7.0
26.0
20.0
Portugal
21.5
3.5
6.1
13.0
9.0
17.0
8.0
17.0
10.0
Ireland
13.8
2.9
5.7
10.0
9.0
11.0
6.0
13.0
8.0
Malta
18.8
4.6
5.6
26.0
25.0
27.0
19.0
33.0
21.0
EU
19.7
2.4
5.5
17.0
15.0
20.0
13.0
20.0
17.0
France
19.7
3.2
5.3
16.0
13.0
18.0
11.0
18.0
14.0
Belgium
18.7
1.6
4.9
30.0
23.0
36.0
25.0
33.0
30.0
Cyprus
15.9
3.4
4.9
15.0
10.0
20.0
11.0
18.0
13.0
Hungary
18.9
2.5
4.5
18.0
17.0
18.0
8.0
25.0
13.0
Austria
18.7
1.3
4.4
10.0
8.0
12.0
4.0
11.0
15.0
Finland
21.4
4.7
3.7
13.0
10.0
16.0
6.0
16.0
13.0
Sweden
19.8
2.0
3.6
12.0
10.0
15.0
12.0
14.0
10.0
Germany
21.4
1.0
2.9
23.0
20.0
26.0
12.0
28.0
24.0
Romania
18.2
2.1
2.7
9.0
8.0
10.0
5.0
11.0
9.0
Sources: Eurostat, 2019, EQLS 2016.
ijerph-17-05511-t002_Table 2
Experts and stakeholders involved in the study: typologies, number, and abbreviations.
Typologies of Involved Stakeholders
No. of Interviewed Experts
Abbreviations Used in the Text
Universities
3
U1; U2; U3
National research institution and policy maker
1
PM1
Care providers association
1
CP1
Total
5
\\n
ijerph-17-05511-t003_Table 3
Interview framework: research goals, aims and questions.
Research Goals
Aims
Questions
Description of the Occupational Welfare schemes in relation to the Italian Long-Term Care/home care context
To define the main OW schemes applied in Italy in relation to home care
How would you define the OW schemes in Italy?Which OW schemes in Italy are related to LTC provided in the home?How would you define the relationship between OW schemes and LTC provided in based home care?
The impact on various actors’ roles
To understand stakeholder networks and the related roles of eachTo understand the OW schemes impact on home care
What is the role of the different types of stakeholders involved in OW schemes?What is the impact of OW schemes for the main Italian home care stakeholders (informal carers and Migrant care Workers - MCWs)?
Identification of core challenges
To define the challenges of OW schemes applied on home care
What are the main challenges currently facing the OW schemes supporting LTC at home, in your opinion?How can each of these challenges be transformed to allow for the opportunity for innovation?
Formulation of recommendations on innovations and policy
To define what innovative strategies should be used and to define what policymakers can do to support OW schemes supporting home care
What could be some innovative strategies to support and improve OW schemes supporting home care provision in Italy?What main recommendations do you have for policymakers?
OW is Occupational Welfare.
ijerph-17-05511-t004_Table 4
Social protection and LTC expenditure in Italy and in the international context.
Social Protection Expenditure (SPE) 2016
EU
Italy
SPE Total/GDP (%)
28.4
27.1
of which for:
=100.0
=100.0
Health
29.5
23.1
Pensions for retired adults
45.6
57.8
Disability
7.4
5.8
Family support
8.7
6.3
Unemployment support
4.6
5.8
Social exclusion support
4.2
1.0
LTC Expenditure (2017)
OECD *
Italy
Total % of GDP
1.7
0.7
of which for:
=100.0
=100.0
Inpatient long-term care
62.3
51.0
Home-based long-term care
33.2
19.0
Other
4.5
30.1
* Organisation for Economic Co-operation and Development. Source: elaboration by authors based on [28,29].
ijerph-17-05511-t005_Table 5
The Occupational Welfare system in the Italian LTC context: main role played by different stakeholders involved in co-design and implementation.
Stakeholders
Role Played by in OW Path
Regulation
Buyer for Employees
Seller
Provider
Public institutions
X
X
\\n
X
Enterprises
X
X
\\n
\\n
Bilateral bodies
X
X
\\n
\\n
Trade unions
X
X
\\n
\\n
Trade associations
X
X
X
\\n
Providers (profit/NGOs *)
X
X
X
X
* Non-governmental organizations; Source: elaboration by authors based on [31,32,33,34].
ijerph-17-05511-t006_Table 6
Occupational Welfare in Italy: Enterprises and workers involved at the first and second level of contractual agreement.
Contractual Agreements
Enterprises
Workers Beneficiaries
First level (2018)
166,011
2,432,098
Secondary level (2017)
1078
928,260
Total
167,089
3,350,358
Source: [31].
\\n\"\n]"}}},{"rowIdx":436,"cells":{"data":{"kind":"list like","value":["\nInt J Mol SciInt J Mol SciijmsInternational Journal of Molecular Sciences1422-0067MDPI32727099PMC743209310.3390/ijms21155335ijms-21-05335ArticleAn In Vitro Lung System to Assess the Proinflammatory Hazard of Carbon Nanotube Aerosolshttps://orcid.org/0000-0002-6728-4111BarosovaHana12https://orcid.org/0000-0001-9676-7465KarakocakBedia Begum1https://orcid.org/0000-0003-2353-7508SeptiadiDedy1Petri-FinkAlke13StoneVicki4https://orcid.org/0000-0002-7805-9366Rothen-RutishauserBarbara1*BioNanomaterials Group, Adolphe Merkle Institute, University of Fribourg, 1700 Fribourg, Switzerland; hana.barosova@unifr.ch (H.B.); bedia.karakocak@unifr.ch (B.B.K.); dedy.septiadi@unifr.ch (D.S.); alke.fink@unifr.ch (A.P.-F.)Institute of Experimental Medicine of the Czech Academy of Sciences, 142 20 Prague, Czech RepublicDepartment of Chemistry, University of Fribourg, 1700 Fribourg, SwitzerlandInstitute of Biological Chemistry, Biophysics and Bioengineering, Heriot-Watt University, Edinburgh EH14 4AS, UK; v.stone@hw.ac.ukCorrespondence: barbara.rothen@unifr.ch; Tel.: +41-26-300-95022772020820202115533512620202272020© 2020 by the authors.2020Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
In vitro three-dimensional (3D) lung cell models have been thoroughly investigated in recent years and provide a reliable tool to assess the hazard associated with nanomaterials (NMs) released into the air. In this study, a 3D lung co-culture model was optimized to assess the hazard potential of multiwalled carbon nanotubes (MWCNTs), which is known to provoke inflammation and fibrosis, critical adverse outcomes linked to acute and prolonged NM exposure. The lung co-cultures were exposed to MWCNTs at the air-liquid interface (ALI) using the VITROCELL® Cloud system while considering realistic occupational exposure doses. The co-culture model was composed of three human cell lines: alveolar epithelial cells (A549), fibroblasts (MRC-5), and macrophages (differentiated THP-1). The model was exposed to two types of MWCNTs (Mitsui-7 and Nanocyl) at different concentrations (2–10 μg/cm2) to assess the proinflammatory as well as the profibrotic responses after acute (24 h, one exposure) and prolonged (96 h, repeated exposures) exposure cycles. The results showed that acute or prolonged exposure to different concentrations of the tested MWCNTs did not induce cytotoxicity or apparent profibrotic response; however, suggested the onset of proinflammatory response.
Carbon nanotubes (CNTs) exhibit remarkable features, such as thermal stability, electrical conductivity, and outstanding mechanical durability, and therefore are one of the most widely used nanomaterials (NMs) [1], especially in the fields of energy, electronics, and material composites. The promising potential of CNTs in industrial applications increases the demand for CNT production. However, this high demand is expected to lead to increased contamination of the natural eco-system as well as human exposure, which can potentially occur throughout the NMs life cycle, starting from production to their final disposal. More specifically, the biopersistence and high aspect ratio properties of CNTs are a major concern because of the inhalation of NMs in the workplace during production might induce unwanted pulmonary effects [2,3].
CNTs are made of rolled-up graphene sheets—graphene cylinders, typically limited to a few nanometers in diameter, but their length can range from a few micrometers to millimeters [4]. This unique geometry of relatively small diameter and the enormous length, the needle-like shape, have therefore drawn comparisons with asbestos [5]. There are two main classes of CNTs: single-walled carbon nanotubes (SWCNTs), which consist of one graphene cylinder, and multiwalled carbon nanotubes (MWCNTs) comprises two or more of these graphene cylinders [6].
There has been a vast number of investigations on the potential of MWCNTs released into the environment to induce adverse health effects. In the blood of humans exposed to MWCNT in an occupational setting, aberrant changes in mRNA and ncRNA expression profiles have been reported [7]. A growing number of animal studies demonstrate that exposure to MWCNTs potentially triggers airway injury, inflammation, fibrosis, and granuloma [8,9,10,11]. Among different MWCNTs types, Mitsui-7 MWCNTs (from now on referred to as Mitsui-7) proved to induce a progressive fibrotic response in mice [11,12,13]. Although events leading to fibrosis are part of the normal tissue repair process, pulmonary fibrosis usually refers to a pathological condition where the impaired healing process leads to excessive extracellular matrix production. Pulmonary fibrosis develops as a result of the activation of complex intracellular signaling cascades among multiple cell types, including macrophages, epithelial cells, and fibroblasts [14].
Several in vivo studies have been reported investigating the potentially toxic effects of MWCNTs [15,16,17]. But toxicity evaluation using animals raise scientific, ethical, and financial concerns. In vitro human lung cell models are, on the other hand, widely used to investigate the mechanisms of interactions of exposed particles with the cells [18]. Measurement of the corresponding cellular responses come to prominence as a relatively fast alternative for the primary screening of a broad range of NMs [19,20]. Cell culture models can be used to assess several endpoints related to adverse outcomes of exposure to inhaled substances. Monoculture studies are often performed under submerged conditions using human alveolar epithelial cell line A549 to evaluate the epithelial effects [21,22,23,24]. Direct exposures of fibroblasts to CNTs stimulated progressive cell proliferation or collagen production, the known fibrotic markers [25,26]. Submerged exposure of fibroblasts, epithelial cells, and macrophages used in the presented study was previously used as a first screening tool, and it was shown that exposure to CNTs can induce proinflammatory and profibrotic responses [27,28] An increased proinflammatory response upon repeated exposure to CNT aerosols was also observed in a 3D lung co-culture model (in the presence of immune cells) consisting of cell-lines [29] or primary cells including fibroblasts [30].
When assessing the potential effects of CNTs, equivalent human exposure conditions need to be mimicked by considering exposure methods and occupational dosimetry aspects. Most in vivo studies do not consider the real nature of occupational exposures [31], i.e., the prolonged exposure times and overwhelming high doses. The occupational exposures usually occur in a repeated manner over a longer timeframe in manufacturing facilities, and high doses may, therefore, overwhelm the normal defense mechanisms, resulting in significant initial pulmonary inflammation. On the other hand, the majority of the existing in vitro studies focus on acute exposure using relatively high exposure doses but under submerged conditions [32,33,34]. Submerged conditions are physiologically not relevant since lung cells are exposed to air on their apical surface. To investigate the potential adverse effects of the NMs under relevant and realistic conditions, different air-liquid interface (ALI) exposure equipment were developed previously to aerosolize high-aspect-ratio NMs [35] such as CNTs [36,37,38,39], allowing the homogenous spreading of nebulized material onto the cell surface. Furthermore, such equipment can be supplied with a quartz crystal microbalance (QCM), enabling online measurement of the amount of deposited material.
It is crucial to gain insight into the pulmonary hazard of prolonged (subchronic) CNT exposure at repeated doses that can realistically mimic the in vivo conditions. We recently showed that a lower respiratory tract 3D co-culture model with primary human cells, i.e., EpiAlveolar™, could be a promising tool to predict the development of pulmonary fibrosis in response to fibrotic substances such as CNTs [30]. In addition to a proinflammatory and profibrotic response as assessed by cytokine measurements, a disruption in barrier integrity (determined by transepithelial electrical resistance (TEER) measurements) and an increase in alveolar tissue thickening were observed in response to the chemical positive control, transforming growth factor β (TGF-β) [30]. Within the light of this conclusion, the current study was designed to assess the suitability of a 3D lung co-culture model consisting of cell lines (i.e., epithelial cells, macrophages, and fibroblasts) to acute (24 h) and prolonged (96 h) exposures of realistic CNT exposure doses. It is noteworthy that a co-culture model consisting of cell lines can be used for a limited period at ALI due to the issues concerning the overgrowth of the epithelial cells. However, the experiments were designed as a quick screening study to assess the pro-inflammatory and profibrotic cytokine release in response to MWCNT exposure mimicking the occupational exposure conditions.
As a result, this study aimed to expose a 3D lung co-culture model to two different types of MWCNTs, i.e., Mitsui-7 and Nanocyl, as well as silica-based particles as particle controls, i.e., Dörntruper quartz (DQ12) and Min-U-Sil, using a previously developed ALI exposure system at realistic occupational doses while adopting acute (one exposure, sampling after 24 h) and prolonged (five exposures on subsequent days, sampling after 96 h) exposure scenarios. The responsivity of the 3D lung co-culture model as a prescreening tool for possible proinflammatory and profibrotic responses was assessed.
2. Results
The scheme of the experimental setup, as well as the exposure scenario and sample collection, are presented in Figure 1.
2.1. Material Characteristics
To estimate the amount and form of test materials deposited on the cells, transmission electron microscopy (TEM) grids were placed in the VITROCELL® Cloud exposure chamber during the material aerosolization and subsequently analyzed using TEM. The daily short-term exposures of the delivered dose, i.e., each exposure took approximately 10 min; as a result, the 24 h time point corresponds to one exposure, while 96 h corresponds to five exposures. In Figure 2, the TEM images present the amounts comparing daily (24 h) deposition, and deposition after 96 h (i.e., five exposures), and the images confirmed that the VITROCELL® Cloud system is a suitable method for executing repeated dose-dependent deposition studies. The representative TEM images of the deposited samples display the morphological differences among the two MWCNTs samples, i.e., Mitsui-7 and Nanocyl. While Mitsui-7 MWCNTs appear as relatively rare singlets or small bundles of thicker tubes, Nanocyl MWCNTs consist of shorter and thinner tangled tubes. The differences in MWCNTs shapes, i.e., stiff straight tubes vs. tangled tubes has resulted in the presence of a multiple number of walls and thus different consequential diameters. On the other hand, Min-U-Sil quartz particles (for further information, see Table 1) with a tendency to form agglomerates and DQ12 particles presenting a homogenous deposition are also shown in Figure 2. Both Min-U-Sil and DQ12 particles are silica quartz particles, with similar particle sizes (< 5 μm), and similar crystallinity. Min-U-Sil possesses approximately 89% crystallinity, while DQ12 present approximately 73% [40].
The differences in material composition are presented in Table 1. The Min-U-Sil consists of pure silica quartz, whereas DQ12 contains approximately 13% impurities. The information about the tested materials, along with the deposited amounts of each material type measured by a QCM device, corresponding to the daily exposure dose, is provided in Table 1. The weekly deposition dose was calculated as five times the daily dose due to the limitation in the number of exposure chambers available. As a result, the cells were exposed to one type of material, adopting one exposure scenario at a time. After each exposure is complete, the instrument is wiped with pre-wetted tissues with ethanol. Subsequently, another exposure experiment is initiated using a different material for the exposure following a different exposure scenario.
2.2. Cell Line Co-culture Model2.2.1. Cytotoxicity and Cell Morphology
The amount of lactate dehydrogenase (LDH) released into the cell culture medium was assessed as an important cytotoxicity marker (Figure 3A). The data are presented relative to the negative control (bovine serum albumin, BSA-treated samples). No statistically significant (p > 0.05) increase in LDH release was observed for any of the tested materials following the two exposure scenarios. Triton-X (0.2% in phosphate-buffered saline (PBS)) was applied 24 h prior to the sample collection at both time-points as the positive control, which induced a statistically significant (p < 0.05) increase of LDH release.
Cell morphology was inspected by laser scanning microscopy (LSM). The cells were labeled for F-actin and cell nuclei. Additionally, the presence of macrophages on the apical side was confirmed by CD68 staining, and the fibroblasts located at the basal side of the insert were stained for vimentin.
The 3D rendered image of the untreated cells at 24 h time-point, showing the location of macrophages on the apical side of the co-culture model, is presented in Figure 3B. Figure 3C shows cell-cultures fixed at 96 h post-exposure to all materials tested. No difference in cell morphology in Mitsui-7 treated cells was observed compared to samples treated with BSA only (negative control). The cells exposed to DQ12 and Min-U-Sil for 96 h exhibited loss of cellular contact and presented irregular shapes, leading to discontinuity and reduced thickness of the epithelial layer. The cell co-cultures treated with Nanocyl MWCNTs are slightly disorganized with discontinuity in the epithelial layer, and a slight increase in the layer thickness of the fibroblasts. Mitsui-7 MWCNTs exposure proved no effect on the fibroblast cell layer thickness. The XZ-projections show overgrowth of the epithelial layer in all samples, regardless of the sample treatment, which proves in low resemblance of in vivo situation at later (96 h) time-point.
2.2.2. Oxidative Stress Response
The decreased glutathione (GSH) content in the cells was reported as an important marker for oxidative stress. The data is presented as the amount of total GSH (intracellular + extracellular) content relative to the total protein measurements. There was no statistically significant decrease in response to any materials tested or at exposure time-points; however, a tendency in the GSH decrease can be seen for Min-U-Sil and Nanocyl exposure results at both 24 h and 96 h time points (Figure 4A).
2.2.3. Proinflammatory Response
The proinflammatory response was assessed by cytokine release measurements via Enzyme-Linked Immunosorbent Assay (ELISA), specifically for Interleukin-1β (IL-1β) and Interleukin-8 (IL-8) detection, as well as for tumor necrosis factor-α (TNF-α) release (Figure 4B–D). No statistically significant increase was detected for IL-1β and IL-8 release in all tested materials at the end of both exposure periods (Figure 4B,C). On the other hand, the increase in TNF-α release was statistically significant (p < 0.05) for Nanocyl MWCNTs at 24 h post-exposure, while Mitsui-7 MWCNTs induced statistically significant (p < 0.05) release after 96 h post-exposure (Figure 4D). R-848 activates NF-κB, and it was shown that it can trigger IL-1β and TNF-α release in neonates [44]. Therefore, in this study, R-848 was used as a proinflammatory positive control and induced a statistically significant increase after 24 h exposure for IL-1β and TNF-α, and after 96 h for only TNF-α (Figure 4). TNF-α, a potent proinflammatory stimulus [45], was applied as a positive control to induce IL-8 release; a statistically significant (p < 0.05) increase of IL-8 release induced by TNF-α compared to negative control was observed for all exposure time points (24 h, 48 h and 96 h) (Figure 4C).
2.2.4. Profibrotic Response
The profibrotic response was assessed by measuring the cytokine release, specifically TGF-β, platelet-derived growth Factor-AA (PDGF), and Osteopontin (OPN), via ELISA (Figure 5). The underlying drive behind the development of an inflammatory response is to clear the foreign material from the tissue and to eventually initiate the repairing pathway, which is facilitated by growth factors, such as the TGF-β. TGF-β was used as a positive fibrotic control because of its involvement in the development of fibrosis in different organs, disturbances of the homeostatic microenvironment, promotion of cell activation, migration, invasion, and excessive extracellular matrix production [30,46]. No statistically significant increase for any of the cytokine release measurements was observed at any exposure time-points upon exposures to any of the tested materials. The overgrowth of epithelial cells may not allow mediators and cytokines to reach fibroblasts and cell culture medium at the amount, which is relevant for activating fibrotic pathway; for instance, lack of TGF-β does not trigger PDGF and OPN release. R-848 was applied as a positive control to induce an increase in TGF-β release (Figure 5A), and TGF-β was also applied as a positive control to induce the PDGF and OPN release (Figure 5B,C), some trends in the increase of cytokine release can be observed for all positive controls at all investigated time points; however, no statistically significant change was detected.
3. Discussion3.1. Pros and Cons of the Co-culture Model
This study was designed to mimic the subchronic inhalation of different materials. Previous in vivo studies have shown that the investigated MWCNTs can penetrate deep into the alveolar region of lungs and cumulate in interstitium [11,12]. Macrophages play an essential role in the inflammatory response and surface cleaning (by particle uptake [12]) upon particle exposure and subsequently can play a role in the extent of the fibrotic response. As a result, human alveolar epithelial cells, human lung fibroblasts, and human macrophages were used for designing the present co-culture model. This model follows our previous study [27], where all three cell types cultured as monocultures and were exposed to MWCNTs and silica particles, which are also used in this study. The presented co-culture model is cultured at ALI to provide more realistic culturing conditions. It was previously shown that ALI culturing conditions lead to more enhanced protein release compared to the submerged conditions [47]. In this study, cell lines were used for assembling the presented model, as they represent a relatively stable and cost-effective system while providing experimental flexibility compared to the commercially available primary cells. Although primary cells are unique tools for designing long-term experiments with differentiated cells [30,48], they lack the flexibility needed for co-culturing with other cell types, mainly because they usually require a unique media composition.
Furthermore, primary cells are considered a cost-intensive option for researchers, as they either have to be directly isolated from patients while maintaining high purity of the desired cell population or can be obtained as commercially available models. Although an alveolar model consisting of primary cells is commercially available (EpiAlveolar™ [30]), we aimed to present a similar model consisting of cell lines, as a potentially cost-effective tool for enabling rapid pre-screening of particles. This model was designed based on our previous experience with the human alveolar model. We successfully combined human alveolar epithelial cells and human primary immune cells [49] and demonstrated the created model as a powerful tool for screening inflammatory responses upon exposure to various particle types [29,50,51,52]. A summary table (Table 2) is provided to compare the model presented in this study to commercially available 3D lung co-culture models.
3.2. Material Concentrations Used for Cell Exposures
The alveolar mass retention during a full lifetime occupational exposure (45 years) period to CNTs of different sizes was modeled and calculated to be in the range of 12.4 to 46.5 μg/cm2 [53]. In this study, the maximum deposited concentration after five exposures to Nanocyl was 11 μg/cm2 and 20 μg/cm2 for Mitsui-7, respectively, which is in the range of the reported lifetime occupational human exposure to MWCNTs [53]. However, it has to be kept in mind that in this study, the amount of CNTs was deposited within five days as the duration of the experiment is the limiting factor for cell line usage. A549 cells are continuously proliferating cells, and therefore they overgrow into a multilayer after 3 to 4 days in ALI, which negatively affects the physiological relevance of the model. On the other hand, the lowest tested exposure concentration (1 μg/cm2) for Mitsui-7 and Nanocyl corresponds with concentrations used in mice in vivo [13,54].
Moreover, aerosolization of silica quartz particles resulted in an average deposited concentration up to 1 μg/cm2 for DQ12 and 3 μg/cm2 for Min-U-Sil after repeated exposures. The tested exposure concentrations correspond to those used in previous in vitro studies, including human lung models, and induced a pro-inflammatory response [29,48]. Furthermore, these concentrations are comparable to in vivo exposure experiments in rats (3–30 mg/rat, which corresponds to 0.6–6 μg/cm2, deposition calculated based on [55]) [56].
3.3. Oxidative Stress
GSH is known to be essential for several cell processes, mainly interconnected with the maintenance of the thiol-redox status [57]. Therefore, the ratio between the reduced and oxidized forms of GSH is an important indicator of the intracellular redox environment and, at the same time, provides insights regarding cell proliferation, differentiation, and apoptosis [58]. In light of this knowledge, GSH-based oxidative stress measurement assays have been widely implemented [59]. Specifically, GSH measurements in response to exposure to NMs have provided insight regarding the level of the cell’s oxidative stress status [60].
The oxidative stress response upon exposures to silica quartz particles was previously reported in vitro, but not confirmed in vivo [61]. A previous in vitro study showed decreased GSH in fibroblasts 24 h upon exposures to Nanocyl; however, no difference in GSH production was observed 96 h post-exposure compared to the negative control. Min-U-Sil and Mitsui-7 MWCNTs did not induce any significant decrease in GSH either [27]. Similarly, in our study, we did not observe a statistically significant (p > 0.05) decrease in GSH levels for any of the tested materials in both exposure scenarios; however, a tendency in the GSH decrease can be observed for Min-U-Sil and Nanocyl exposure results at both time-points (Figure 4A). This finding is in line with the proteomics investigation of the presented co-culture response upon exposures to Mitsui-7, where no response was observed upon exposure to Mitsui-7 compared to the negative control [47].
3.4. Proinflammatory Response
A proinflammatory cytokine is a type of signaling molecule secreted from certain cell types and promotes inflammation in response to foreign materials, in this case, MWCNTs. Among them, IL-1β, IL-8, and TNF-α are known to play a vital role in mediating the innate immune response and therefore considered biomarkers of nanoimmunotoxicity [62].
Certain particle types have been shown to induce immunotoxicity. Among them, DQ12 is well studied, and have been recruited in in vitro studies as a potent proinflammatory stimulus [29,63]. On the other hand, Min-U-Sil was found to induce increased IL-1β release in macrophage monocultures (differentiated THP-1), but no response in fibroblasts or epithelial cells was reported [27]. In vivo experiments with mice showed a strong inflammatory response upon acute exposure to a high dose of Min-U-Sil [64]. Nanocyl and Mitsui-7 MWCNTs did not induce any proinflammatory response in epithelial cells, fibroblasts, or macrophage monocultures in vitro [27]. Although individual in vitro studies assessing the potential toxicity of MWCNTs of interest did not report proinflammatory responses, it should be noted that these studies were either using monocultures of cells or the exposure conditions were not representative, i.e., submerged conditions as opposed to creating an ALI prior to the exposure.
In this study, we have adapted two exposure scenarios, as explained in the Methods section. The results indicated statistically significant changes in TNF-α secretion levels in response to two different exposure scenarios (acute (24 h) vs. prolonged (96 h)) for both MWCNT types tested. A significant elevation in TNF-α levels for Nanocyl at 24 h post-exposure was detected. In comparison, Mitsui-7 MWCNTs induced a statistically significant (p < 0.05) increase in TNF-α levels 96 h post-exposure (Figure 4D). TNF-α levels specifically play a vital role in proinflammatory response assessment because it is expressed in the early stages of cell inflammation [65]. Therefore, it can be concluded that in a realistic occupational exposure scenario, exposure to MWCNTs does initiate cell inflammation and eventually can result in cell death.
3.5. Profibrotic Response
Exposure to NMs is known to play a role in the development of chronic pulmonary diseases, especially fibrosis [66]. Among all NMs, MWCNTs specifically pose a danger because relatively large quantities are being used in numerous manufacturing practices, and thus will inadvertently lead to human exposure. Therefore, there have been continuous efforts to evaluate the potential link between fibrosis and MWCNTs exposure. It was previously shown that both Mitsui-7 [12,67] and Nanocyl [42] exposures lead to the development of pulmonary fibrosis in rodents. However, it was reported that Nanocyl caused less upregulation of genes involved in fibrosis development compared to long and thick MWCNTs [42]. These results are consistent with an in vitro study investigating epithelial cell (A549), macrophage (differentiated THP-1), and fibroblast (MRC-5) monocultures. Mitsui-7 exposures led to profibrotic response (TGF-β release), and OPN releases in epithelial cells. On the other hand, Nanocyl increased only TGF-β release from fibroblasts [27], which has previously been used in vitro to stimulate the pro-inflammatory response [35,48,63] and in vivo to stimulate the development of pulmonary fibrosis [56,68].
In this study, we did not observe a statistically significant increase in TGF-β, PDGF, or OPN release (Figure 5). Interferon-γ (1 μg/mL) was also used as a positive control to stimulate TGF-β, PDGF, and OPN release (data not shown), but no increase in profibrotic mediators release was detected. In addition to the evaluation of profibrotic mediator releases, we also measured fibrotic markers, the collagen type I and fibronectin release, but did not detect a statistically significant increase in both indicator release levels (data not shown), although this combination of investigated endpoints proved its suitability in predicting profibrotic response in vitro earlier [30]. Furthermore, the use of the SircolTM soluble collagen assay to detect acid and pepsin soluble collagen was considered; however, the presence of serum in the cell culture medium posed interference on the assay results; therefore, the use of this assay was eliminated from the study. It is also noteworthy to state that at the cellular level, proinflammatory mediator detection is considered one of the key events (KE) in the adverse outcome pathway (AOP) for fibrosis [30]. The presented model resembles the alveolar region of the human lungs, and the concept is similar to the EpiAlveolarTM model used in our previous study [30]. Both models use human lung epithelial cells, fibroblasts, and macrophages, while the EpiAlveolarTM model additionally includes endothelial cells. However, the main differences of the two models are the cells used: cell lines vs. primary cells, and the duration of the experiment, i.e., the length of the periods while each cell model remains stable throughout the exposures. For the EpiAlveolarTM model, it was possible to perform exposures over three weeks at ALI, whereas the cell line model only is stable for 3–4 days as the A549 cells start to overgrow at ALI. When comparing the data from both studies, the model with primary cells is more suitable for such an investigation to assess repeated and long-term effects, as it is capable of predicting profibrotic response upon exposure to TGF-β and other NMs. On the other hand, cell line models still offer a cheaper and easy-to-handle option for hazard prescreening of NMs to assess cytotoxicity and inflammatory markers. Finally, the proposed model is not limited to testing potentially hazardous nanomaterials. It can also be implemented to test anti-inflammatory [69,70] and anti-fibrotic [71] nanoformulations in the field of nanomedicine.
4. Materials and Methods4.1. Chemicals and Reagents
All chemicals and reagents used were obtained from Sigma-Aldrich (Buchs, Switzerland) unless stated otherwise.
4.2. Sample Preparation
BSA solution (0.1% in ultrapure H2O) was sterile filtered (0.2 μm pore size; Nalgene, Thermo Scientific, MA, USA). BSA was used as a dispersant for MWCNTs. Mitsui-7 MWCNTs (MWCNTs-7; Mitsui & Co, Tokyo, Japan) and Nanocyl-7000 MWCNTs (referred to as Nanocyl; Nanocyl S.A., Sambreville, Belgium; received from European Commission Joint Research Centre, Ispra, Italy) were dispersed in BSA solution. Briefly, pre-weighted dry MWCNTs powder was heat sterilized at 100 °C overnight, left to cool down, and subsequently 0.1% BSA solution was added to obtain a stock solution with the concentration of 50 μg/mL. This suspension was sonicated for 3 h with continuous shaking to disperse the MWCNTs and subsequently stored at 4 °C until further use.
Two different types of silica quartz particles were used as reference materials. Min-U-Sil is an inert, high purity white crystalline silica recently reported as a potential profibrotic agent [28,72], while Dörntruper quartz (DQ12; composed of 87% crystalline silica and amorphous silica with kaolinite impurities) was reported as a proinflammatory agent in vitro [73], as well as profibrotic agent in vivo [56,74,75]. Both silica materials were dispersed in ultrapure sterile filtered H2O at a concentration of 100 μg/mL.
All stock suspensions were sonicated for 1 h prior to the exposure experiments.
4.3. Material Characterization4.3.1. Electron Microscopy
To investigate the deposition of aerosolized materials, transmission electron microscopy (TEM) 300 mesh carbon-coated copper grids were placed into the exposure chamber prior to the experiment.
TEM grids with deposited material were used without any further treatment. Representative images were captured using a TEM (FEI Tecnai Spirit, Hillsboro, OR, USA) operating at 120 kV and equipped with a Veleta CCD camera (Olympus, Japan).
4.3.2. Endotoxin Content
The endotoxin concentration in the MWCNTs suspensions was measured using the PierceTM LAL Chromogenic Endotoxin Quantitation Kit (ThermoFisher Scientific, Basel, Switzerland), following the manufacturer’s instructions and all suspensions were below 0.5 EU/mL.
4.4. Human Cell Line Co-Culture Model
All cell lines—human fibroblasts MRC-5, human alveolar epithelial cells type II A549 and human monocytes THP-1 were purchased from American Type Culture Collection (ATCC, Manassas, VA, USA) and cultivated according to the ATCC protocol prior assembled into co-culture model.
Human epithelial cells type II (A549 cell line) were cultured in Roswell Park Memorial Institute medium (RPMI 1640) supplemented with 10% Fetal Bovine Serum (FBS), 1% Penicillin/streptomycin and 1% L-Glutamine (all stated chemicals were obtained from Gibco, Gaithersburg, MD, USA). Human monocytes (THP-1 cell line) were cultivated in supplemented RPMI 1640, as mentioned above, with extra 0.05 mM β-mercaptoethanol. To differentiate monocytes into macrophages, prior to co-culture assembling, the THP-1 cells had to be activated by Phorbol 12-Myristate 13-Acetate (PMA; 20 ng/mL in supplemented medium without β-mercaptoethanol) overnight at the density of 4 × 105 cells/mL. Human fibroblasts were cultivated in Minimum Essential Medium (MEM) supplemented with 10% FBS, 1% Penicillin/streptomycin, 1% L-Glutamine, and 1 × Non-Essential Amino Acids (all Gibco, Gaithersburg, MD, USA).
The co-culture model was composed by following the next steps. The Transwell® 12-well inserts (surface area of 0.9 cm2, pores of 0.4 μm diameter, PET membranes; BD Biosciences, Allschwil, Switzerland) were inverted and placed into a sterile petri dish. Then the MRC-5 cells were seeded on the basolateral part of the insert at the density 104 cells/cm2 and incubated for 3 h at 37 °C, 5% CO2 to fully attach to the insert membrane. After the incubation period, inserts were turned back and placed into the 12-well plate containing 1.5 mL supplemented MEM. Subsequently, A549 cells were seeded on the apical side of the insert at the density of 2.8 × 105 cells/cm2 with 1 mL of supplemented RPMI 1640 and incubated for 4 days (d) at 37 °C, 5% CO2. After the 4-d incubation period of A549-MRC-5 co-cultures, differentiated THP-1 in supplemented RPMI 1640 were seeded on the top of A549 cells at the density of 28 × 103 cells/cm2. The plates were then incubated for 2 d at 37 °C, 5% CO2, and subsequently lifted to ALI 24 h prior to the experiment by removing the medium from the apical side and replacing medium from the basolateral side with 0.6 mL of the medium mixture (RPMI:MEM, 1:1).
4.5. The Air-liquid Interface (ALI) Cell Exposure Method
Co-cultures were exposed at the ALI using the VITROCELL® Cloud system (Waldkirch, Germany). Briefly, the exposure system consists of a nebulizer, an exposure chamber as well as a QCM (operated at 5 MHz, detection limit: 0.1 μg/cm2), allowing to measure and record the deposited dose online. For each aerosolization experiment, 200 μL of sample’s stock solution with 2 μL of 0.09% NaCl (NAAPREP® physiological saline, GlaxoSmithKline, Evreux, France) was added to the nebulizer (Aeroneb® Lab, Dangal, Galway, Ireland; mesh size 10 μm for MWCNTs, 4–6 μm for Min-U-SIl and 2.5–6 μm for DQ12). The vibrating perforated membrane at the neck of the nebulizer generates the aerosols and diverts them into the exposure chamber. Inside the chamber, the aerosols in the cloud gently deposit onto the cell surfaces that are maintained at the ALI. The flow rate of nebulizer (the flowrate is preset by the instrument provider, cannot be controlled) was ideal for the aerosols to sufficiently mix within the entire chamber, hence resulting in uniform droplet deposition. VITROCELL® Cloud exposure chamber was wiped with pre-wetted ethanol tissues prior to each exposure experiment.
4.6. Exposure Scenarios
The biological response of the co-culture model was assessed following exposure to all tested materials at the doses presented in Table 1. Two different exposure scenarios were performed, i.e., acute (24 h) exposure and prolonged (96 h) continuous exposure. The cells were exposed apically at the ALI daily and maintained at 37 °C in 5% CO2 throughout each exposure scenario. The cells were exposed to one dose of the material at the acute exposure scenario. In contrast, the cells following the prolonged exposure scenario were exposed to five times the material dose on subsequent days for 96 h in total.
4.7. Biochemical AnalysisCytotoxicity
The LDH release into the supernatant as a result of cell membrane rupture is a well-known indicator of cytotoxicity. The amount of LDH release was evaluated using a commercially available LDH diagnostic kit (Roche Applied Science, Mannheim, Germany), according to the manufacturer’s protocol. Each sample was tested in triplicates; the enzyme activity was measured photometrically. The absorbance was measured at 490 nm with a reference wavelength of 630 nm. LDH values are presented relative to the negative control (BSA-treated cells). Cell cultures exposed apically to 0.2% Triton X-100 for 24 h were used as a positive control.
4.8. Cell Morphology
The cell morphology was evaluated using laser scanning microscopy. At the respective time-points, the cell cultures were fixed for 15 min in 4% paraformaldehyde in PBS at room temperature, subsequently washed 3 × with PBS and stored in PBS at 4 °C until further immunofluorescence staining could occur.
Prior to the immunofluorescence staining, samples were treated with 0.1 M glycine for 15 min and subsequently permeabilized with 0.2% Triton X-100 for another 15 min, both at room temperature. All antibodies were diluted in 0.3% Triton X-100 and 1% BSA in PBS and incubated for 2 h. The cells were first stained with primary antibodies (dilution 1:100) and subsequently with secondary antibodies, 4’,6-diamidin-2-fenylindol (DAPI) and rhodamine-phalloidin (dilution 1:100, Molecular Probes, Eugene, OR, USA). Specifically, anti-CD68 and chicken anti-vimentin were used as the primary antibodies and anti-mouse Alexa 488, anti-chicken Alexa 647 (Abcam, Cambridge, MA, USA), phalloidin-rhodamine and DAPI were used as the fluorophores. Anti-CD68 is used for macrophage staining, anti-vimentin stains intermediate filaments of the fibroblasts, phalloidin-rhodamine stains F-actin cytoskeleton and DAPI stains nucleus. Following antibody incubation, cell culture inserts were embedded in Glycergel (DAKO, Santa Clara, CA, USA). Cell morphology was evaluated by visualization of samples via an inverted laser scanning confocal microscope (LSM) 710 (Axio Observer.Z1, Carl Zeiss, Oberkochen, Germany). Image processing was conducted with IMARIS 3D restoration software (Bitplane AG, Zurich, Switzerland).
4.9. Oxidative Stress
The total amount of reduced intracellular GSH in the cell cultures was quantified using a GSH assay kit (following supplier’s protocol, Cayman Chemical Company, Ann Arbor, MI, USA) as a well-known marker for oxidative stress. Levels of oxidative stress are presented as a ratio of total GSH to the total amount of protein of each sample. Total protein content was measured by the Pierce bicinchoninic acid (BCA) protein assay kit (Pierce Protein Research Products, Thermo Scientific, Rockford, IL, USA) according to the manufacturer guidelines. Tert-Butyl Hydrogen Peroxide (tBHP) at a concentration of 135 mM acted as the positive assay control.
The IL-1β, TNF-α, and IL-8 secretion were assessed using the commercially available DuoSet® ELISA Development diagnostic kit (R&D Systems, Zug, Switzerland), according to the manufacturer’s protocol. Cells treated apically with 1 μg/mL of TNF-α (ImmunoTools, Friesoythe, Germany) served as the positive control for IL-1β (data not shown) and IL-8 assays. R-848 applied apically onto the cell surface at 6 μg/mL was used as a positive control for IL-1β, TNF-α, and IL-8 (data not shown) assays. All positive controls were tested at 24 h, 48 h, and 96 h post-exposure time-points in both co-culture systems.
4.10.2. Profibrotic Response
The TGF-β, PDGF, and osteopontin (OPN) release into the supernatant of exposed cells was quantified using the respective ELISA DuoSet® Development diagnostic kit (R&D Systems, Zug, Switzerland), following the manufacturer’s protocol. Based on the supplier’s protocol, pH activated form of TGF-β was assessed (the activation occurs straight prior to the assay). Cell cultures exposed apically to 1 μg/mL of Interferon-gamma (IFN-γ) were used as a positive control for TGF-β assay (data not shown), while apical cell treatment with 100 ng/mL TGF-β served as the positive control to induce PDGF and OPN release. R-848 applied apically onto the cell surface at 6 μg/mL was used as a positive control for the TGF-β assay. All positive controls were tested at 24 h, 48 h, and 96 h post-exposure time-points in both co-culture systems.
4.11. Statistical Analysis
For each data point, four independent experiments were performed (four biological replicates), one technical replicate per treatment was prepared; each endpoint was measured in triplicates. Statistical analysis was performed using GraphPad Prism 6 (GraphPad Software Inc., La Jolla, CA, USA) software. A parametric one-way analysis of variance (ANOVA) with subsequent Dunnett test was performed. Results were considered significant if p < 0.05.
5. Conclusions
In conclusion, our in vitro co-culture model consisting of three human cell lines relevant to assess inflammation and fibrosis showed a proinflammatory response upon exposure to the positive controls, and a statistically significant TNF-α release was detected after exposure to both MWCNTs tested. On the other hand, no cytotoxicity or profibrotic response was observed in response to exposure to both types of MWCNTs. While the presented model is not suitable to predict the profibrotic response at the 96 h time point, it can be used for both acute (24 h) and prolonged (96 h) proinflammatory response investigations. As a result, our representative 3D lung co-culture model can be used to assess the proinflammatory response to MWCNTs aerosols; if the results induce a positive response, then further investigations involving primary cells allowing a repeated exposure up to several weeks are recommended. Finally, it is important to note that our model is not limited to testing potentially hazardous nanomaterials. A future study is underway to harness this representative lung system to test anti-inflammatory and anti-fibrotic nanoformulations.
Acknowledgments
We kindly thank Miguel Spuch-Calvar, Dimitri Vanhecke, and Barbara Drasler for contributing to the scheme for the exposure scenario (Figure 1). We also thank Martin J.D. Clift, Savvina Chortarea, Monita Sharma, and Amy J. Clippinger for the valuable discussion during the study planning phase.
Supplementary Materials
Supplementary materials can be found at https://www.mdpi.com/1422-0067/21/15/5335/s1. Figure S1. 3D rendered LSM image of the co-culture model at 96 h time-point exposed to BSA. Figure S2. 3D rendered LSM image of the co-culture model at 96 h time-point exposed to Mitsui-7 MWCNTs. Figure S3. 3D rendered LSM image of the co-culture model at 96 h time-point exposed to DQ12. Figure S4. 3D rendered LSM image of the co-culture model at 96 h time-point exposed to Min-U-Sil. Figure S5. 3D rendered LSM image of the co-culture model at 96 h time-point exposed to Nanocyl.
Click here for additional data file.
Author Contributions
Conceptualization, B.R.-R., V.S. and H.B.; methodology, H.B.; software, D.S.; validation, B.H. and B.R.-R.; formal analysis, H.B.; investigation, B.R.-R. and A.P.-F.; resources, B.R.-R. and H.B.; data curation, H.B. and B.B.K.; writing—original draft preparation, H.B.; writing—review and editing, H.B. and B.B.K.; visualization, B.R.-R.; supervision, B.R.-R.; project administration, B.R.-R.; funding acquisition, B.R.-R. All authors have read and agreed to the published version of the manuscript.
Funding
This work was supported by the PETA International Science Consortium Ltd. and the Adolphe Merkle Foundation. In addition, this project has received further funding from the European Union’s Horizon 2020 Research and Innovation Programme, PATROLS—Physiologically Anchored Tools for Realistic Nanomaterial Hazard Assessment, under grant agreement No 760813. B.B.K. sincerely acknowledges the Swiss Government Excellence Postdoctoral Scholarship for Foreign Researchers.
Conflicts of Interest
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
Abbreviations
3D
Three dimensional
ANOVA
Analysis of variance
ATTC
American type culture collection
BCA
Bicinchoninic acid
BSA
Bovine albumin serum
CCD
Charged coupled device
CD
Cluster of differentiation
CNT
Carbon nanotubes
DAPI
4′,6-diamidino-2-phenylindole
ELISA
Enzyme-linked immunosorbent assay
FBS
Fetal bovine serum
GSH
Glutathione
IL
Interleukin
LDH
Lactate dehydrogenase
LSM
Laser scanning microscopy
MEM
Minimum essential medium
MWCNT
Multiwalled carbon nanotubes
NM
Nanomaterial
OPN
Osteopontin
PBS
Phosphate buffered saline
PDGF
Platelet-derived growth factor
PET
Polyethylene terephthalate
PFA
Paraformaldehyde
PMA
Phorbol 12-myristate 13-acetate
QCM
Quartz crystal microbalance
RPMI
Roswell Park Memorial Institute
SEM
Standard error of the mean
tBHP
Tert-butyl hydrogen peroxide
TEER
Transepithelial electric resistance
TEM
Transmission electron microscopy
TGF
Transforming growth factor
TNF
Tumor necrosis factor
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Schematic representation of the experimental setup, exposure scheme, and sample collection.
TEM micrographs represent the amounts of 24 h (1 × exposure) and 96 h (5 × exposures) deposition of Min-U-Sil, DQ12, Nanocyl, and Mitsui-7 particles. The scale bar is 1 μm for Min-U-Sil, DQ12, and Nanocyl samples, and 5 μm for Mitsui-7 CNTs.
Cytotoxicity and cellular morphology of the 3D co-culture model. (A) Cytotoxicity was measured by the LDH assay, data presented in a boxplot with 10–90 percentile. * marks a statistically significant increase compared to the negative control, n = 4. (B) Representative 3D rendered LSM image of the co-culture model at a 24 h time point, presenting the localization of macrophages within the model. Cyan represents cell nuclei, grey shows cytoskeleton, and magenta stains the macrophages (CD68). (C) The LSM images (XY projections) of BSA- and Mitsui-7, DQ12, Min-U-Sil, and Nanocyl-treated samples at 96 h time-point with their corresponding XZ projections showing thickness of the cellular layer, the scale bar is 30 μm. Cyan represents cell nuclei, grey represents cytoskeleton, and red represents vimentin, a type III intermediate filament protein. For 3D rendered LSM images of the co-culture model at 96 h time-point, please refer to the Supplementary Materials, Figures S1–S5. The scale bar is 30 μm.
In response to exposure to the tested materials, the extent of oxidative stress and proinflammatory response is assessed via (A) GSH content; (B) IL-1β; (C) IL-8 and (D) TNF-α release measurements. Data presented in a boxplot with 10–90 percentile. * marks a statistically significant increase compared to the negative control, n = 4.
The profibrotic response measurements of the co-culture model. The responses were investigated via (A) TGF-β release; (B) PDGF release, and (C) OPN release. Data presented in a boxplot with 10–90 percentile.
ijms-21-05335-t001_Table 1
Information about tested materials, daily deposited dose was measured with a quartz crystal microbalance (QCM), the 96 h deposited doses were calculated as five times the 24 h dose.
The comparison of the 3D lung co-culture model presented in this study to commercially available 3D lung co-culture models consisting of primary cells.
Property
The Presented Model in This Study
Commercially Available Models Consisting of Primary Cells [30,48]
Ease of assembling the model
Easy
Sold as ready-to-use
Adjusting/ tuning based on desired investigation endpoints
* Can be included by the end-user, but requires an extensive optimization step as immune cells are cultured in a different and complex cell media as opposed to commercially available models.
\n"],"string":"[\n \"\\nInt J Mol SciInt J Mol SciijmsInternational Journal of Molecular Sciences1422-0067MDPI32727099PMC743209310.3390/ijms21155335ijms-21-05335ArticleAn In Vitro Lung System to Assess the Proinflammatory Hazard of Carbon Nanotube Aerosolshttps://orcid.org/0000-0002-6728-4111BarosovaHana12https://orcid.org/0000-0001-9676-7465KarakocakBedia Begum1https://orcid.org/0000-0003-2353-7508SeptiadiDedy1Petri-FinkAlke13StoneVicki4https://orcid.org/0000-0002-7805-9366Rothen-RutishauserBarbara1*BioNanomaterials Group, Adolphe Merkle Institute, University of Fribourg, 1700 Fribourg, Switzerland; hana.barosova@unifr.ch (H.B.); bedia.karakocak@unifr.ch (B.B.K.); dedy.septiadi@unifr.ch (D.S.); alke.fink@unifr.ch (A.P.-F.)Institute of Experimental Medicine of the Czech Academy of Sciences, 142 20 Prague, Czech RepublicDepartment of Chemistry, University of Fribourg, 1700 Fribourg, SwitzerlandInstitute of Biological Chemistry, Biophysics and Bioengineering, Heriot-Watt University, Edinburgh EH14 4AS, UK; v.stone@hw.ac.ukCorrespondence: barbara.rothen@unifr.ch; Tel.: +41-26-300-95022772020820202115533512620202272020© 2020 by the authors.2020Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
In vitro three-dimensional (3D) lung cell models have been thoroughly investigated in recent years and provide a reliable tool to assess the hazard associated with nanomaterials (NMs) released into the air. In this study, a 3D lung co-culture model was optimized to assess the hazard potential of multiwalled carbon nanotubes (MWCNTs), which is known to provoke inflammation and fibrosis, critical adverse outcomes linked to acute and prolonged NM exposure. The lung co-cultures were exposed to MWCNTs at the air-liquid interface (ALI) using the VITROCELL® Cloud system while considering realistic occupational exposure doses. The co-culture model was composed of three human cell lines: alveolar epithelial cells (A549), fibroblasts (MRC-5), and macrophages (differentiated THP-1). The model was exposed to two types of MWCNTs (Mitsui-7 and Nanocyl) at different concentrations (2–10 μg/cm2) to assess the proinflammatory as well as the profibrotic responses after acute (24 h, one exposure) and prolonged (96 h, repeated exposures) exposure cycles. The results showed that acute or prolonged exposure to different concentrations of the tested MWCNTs did not induce cytotoxicity or apparent profibrotic response; however, suggested the onset of proinflammatory response.
Carbon nanotubes (CNTs) exhibit remarkable features, such as thermal stability, electrical conductivity, and outstanding mechanical durability, and therefore are one of the most widely used nanomaterials (NMs) [1], especially in the fields of energy, electronics, and material composites. The promising potential of CNTs in industrial applications increases the demand for CNT production. However, this high demand is expected to lead to increased contamination of the natural eco-system as well as human exposure, which can potentially occur throughout the NMs life cycle, starting from production to their final disposal. More specifically, the biopersistence and high aspect ratio properties of CNTs are a major concern because of the inhalation of NMs in the workplace during production might induce unwanted pulmonary effects [2,3].
CNTs are made of rolled-up graphene sheets—graphene cylinders, typically limited to a few nanometers in diameter, but their length can range from a few micrometers to millimeters [4]. This unique geometry of relatively small diameter and the enormous length, the needle-like shape, have therefore drawn comparisons with asbestos [5]. There are two main classes of CNTs: single-walled carbon nanotubes (SWCNTs), which consist of one graphene cylinder, and multiwalled carbon nanotubes (MWCNTs) comprises two or more of these graphene cylinders [6].
There has been a vast number of investigations on the potential of MWCNTs released into the environment to induce adverse health effects. In the blood of humans exposed to MWCNT in an occupational setting, aberrant changes in mRNA and ncRNA expression profiles have been reported [7]. A growing number of animal studies demonstrate that exposure to MWCNTs potentially triggers airway injury, inflammation, fibrosis, and granuloma [8,9,10,11]. Among different MWCNTs types, Mitsui-7 MWCNTs (from now on referred to as Mitsui-7) proved to induce a progressive fibrotic response in mice [11,12,13]. Although events leading to fibrosis are part of the normal tissue repair process, pulmonary fibrosis usually refers to a pathological condition where the impaired healing process leads to excessive extracellular matrix production. Pulmonary fibrosis develops as a result of the activation of complex intracellular signaling cascades among multiple cell types, including macrophages, epithelial cells, and fibroblasts [14].
Several in vivo studies have been reported investigating the potentially toxic effects of MWCNTs [15,16,17]. But toxicity evaluation using animals raise scientific, ethical, and financial concerns. In vitro human lung cell models are, on the other hand, widely used to investigate the mechanisms of interactions of exposed particles with the cells [18]. Measurement of the corresponding cellular responses come to prominence as a relatively fast alternative for the primary screening of a broad range of NMs [19,20]. Cell culture models can be used to assess several endpoints related to adverse outcomes of exposure to inhaled substances. Monoculture studies are often performed under submerged conditions using human alveolar epithelial cell line A549 to evaluate the epithelial effects [21,22,23,24]. Direct exposures of fibroblasts to CNTs stimulated progressive cell proliferation or collagen production, the known fibrotic markers [25,26]. Submerged exposure of fibroblasts, epithelial cells, and macrophages used in the presented study was previously used as a first screening tool, and it was shown that exposure to CNTs can induce proinflammatory and profibrotic responses [27,28] An increased proinflammatory response upon repeated exposure to CNT aerosols was also observed in a 3D lung co-culture model (in the presence of immune cells) consisting of cell-lines [29] or primary cells including fibroblasts [30].
When assessing the potential effects of CNTs, equivalent human exposure conditions need to be mimicked by considering exposure methods and occupational dosimetry aspects. Most in vivo studies do not consider the real nature of occupational exposures [31], i.e., the prolonged exposure times and overwhelming high doses. The occupational exposures usually occur in a repeated manner over a longer timeframe in manufacturing facilities, and high doses may, therefore, overwhelm the normal defense mechanisms, resulting in significant initial pulmonary inflammation. On the other hand, the majority of the existing in vitro studies focus on acute exposure using relatively high exposure doses but under submerged conditions [32,33,34]. Submerged conditions are physiologically not relevant since lung cells are exposed to air on their apical surface. To investigate the potential adverse effects of the NMs under relevant and realistic conditions, different air-liquid interface (ALI) exposure equipment were developed previously to aerosolize high-aspect-ratio NMs [35] such as CNTs [36,37,38,39], allowing the homogenous spreading of nebulized material onto the cell surface. Furthermore, such equipment can be supplied with a quartz crystal microbalance (QCM), enabling online measurement of the amount of deposited material.
It is crucial to gain insight into the pulmonary hazard of prolonged (subchronic) CNT exposure at repeated doses that can realistically mimic the in vivo conditions. We recently showed that a lower respiratory tract 3D co-culture model with primary human cells, i.e., EpiAlveolar™, could be a promising tool to predict the development of pulmonary fibrosis in response to fibrotic substances such as CNTs [30]. In addition to a proinflammatory and profibrotic response as assessed by cytokine measurements, a disruption in barrier integrity (determined by transepithelial electrical resistance (TEER) measurements) and an increase in alveolar tissue thickening were observed in response to the chemical positive control, transforming growth factor β (TGF-β) [30]. Within the light of this conclusion, the current study was designed to assess the suitability of a 3D lung co-culture model consisting of cell lines (i.e., epithelial cells, macrophages, and fibroblasts) to acute (24 h) and prolonged (96 h) exposures of realistic CNT exposure doses. It is noteworthy that a co-culture model consisting of cell lines can be used for a limited period at ALI due to the issues concerning the overgrowth of the epithelial cells. However, the experiments were designed as a quick screening study to assess the pro-inflammatory and profibrotic cytokine release in response to MWCNT exposure mimicking the occupational exposure conditions.
As a result, this study aimed to expose a 3D lung co-culture model to two different types of MWCNTs, i.e., Mitsui-7 and Nanocyl, as well as silica-based particles as particle controls, i.e., Dörntruper quartz (DQ12) and Min-U-Sil, using a previously developed ALI exposure system at realistic occupational doses while adopting acute (one exposure, sampling after 24 h) and prolonged (five exposures on subsequent days, sampling after 96 h) exposure scenarios. The responsivity of the 3D lung co-culture model as a prescreening tool for possible proinflammatory and profibrotic responses was assessed.
2. Results
The scheme of the experimental setup, as well as the exposure scenario and sample collection, are presented in Figure 1.
2.1. Material Characteristics
To estimate the amount and form of test materials deposited on the cells, transmission electron microscopy (TEM) grids were placed in the VITROCELL® Cloud exposure chamber during the material aerosolization and subsequently analyzed using TEM. The daily short-term exposures of the delivered dose, i.e., each exposure took approximately 10 min; as a result, the 24 h time point corresponds to one exposure, while 96 h corresponds to five exposures. In Figure 2, the TEM images present the amounts comparing daily (24 h) deposition, and deposition after 96 h (i.e., five exposures), and the images confirmed that the VITROCELL® Cloud system is a suitable method for executing repeated dose-dependent deposition studies. The representative TEM images of the deposited samples display the morphological differences among the two MWCNTs samples, i.e., Mitsui-7 and Nanocyl. While Mitsui-7 MWCNTs appear as relatively rare singlets or small bundles of thicker tubes, Nanocyl MWCNTs consist of shorter and thinner tangled tubes. The differences in MWCNTs shapes, i.e., stiff straight tubes vs. tangled tubes has resulted in the presence of a multiple number of walls and thus different consequential diameters. On the other hand, Min-U-Sil quartz particles (for further information, see Table 1) with a tendency to form agglomerates and DQ12 particles presenting a homogenous deposition are also shown in Figure 2. Both Min-U-Sil and DQ12 particles are silica quartz particles, with similar particle sizes (< 5 μm), and similar crystallinity. Min-U-Sil possesses approximately 89% crystallinity, while DQ12 present approximately 73% [40].
The differences in material composition are presented in Table 1. The Min-U-Sil consists of pure silica quartz, whereas DQ12 contains approximately 13% impurities. The information about the tested materials, along with the deposited amounts of each material type measured by a QCM device, corresponding to the daily exposure dose, is provided in Table 1. The weekly deposition dose was calculated as five times the daily dose due to the limitation in the number of exposure chambers available. As a result, the cells were exposed to one type of material, adopting one exposure scenario at a time. After each exposure is complete, the instrument is wiped with pre-wetted tissues with ethanol. Subsequently, another exposure experiment is initiated using a different material for the exposure following a different exposure scenario.
2.2. Cell Line Co-culture Model2.2.1. Cytotoxicity and Cell Morphology
The amount of lactate dehydrogenase (LDH) released into the cell culture medium was assessed as an important cytotoxicity marker (Figure 3A). The data are presented relative to the negative control (bovine serum albumin, BSA-treated samples). No statistically significant (p > 0.05) increase in LDH release was observed for any of the tested materials following the two exposure scenarios. Triton-X (0.2% in phosphate-buffered saline (PBS)) was applied 24 h prior to the sample collection at both time-points as the positive control, which induced a statistically significant (p < 0.05) increase of LDH release.
Cell morphology was inspected by laser scanning microscopy (LSM). The cells were labeled for F-actin and cell nuclei. Additionally, the presence of macrophages on the apical side was confirmed by CD68 staining, and the fibroblasts located at the basal side of the insert were stained for vimentin.
The 3D rendered image of the untreated cells at 24 h time-point, showing the location of macrophages on the apical side of the co-culture model, is presented in Figure 3B. Figure 3C shows cell-cultures fixed at 96 h post-exposure to all materials tested. No difference in cell morphology in Mitsui-7 treated cells was observed compared to samples treated with BSA only (negative control). The cells exposed to DQ12 and Min-U-Sil for 96 h exhibited loss of cellular contact and presented irregular shapes, leading to discontinuity and reduced thickness of the epithelial layer. The cell co-cultures treated with Nanocyl MWCNTs are slightly disorganized with discontinuity in the epithelial layer, and a slight increase in the layer thickness of the fibroblasts. Mitsui-7 MWCNTs exposure proved no effect on the fibroblast cell layer thickness. The XZ-projections show overgrowth of the epithelial layer in all samples, regardless of the sample treatment, which proves in low resemblance of in vivo situation at later (96 h) time-point.
2.2.2. Oxidative Stress Response
The decreased glutathione (GSH) content in the cells was reported as an important marker for oxidative stress. The data is presented as the amount of total GSH (intracellular + extracellular) content relative to the total protein measurements. There was no statistically significant decrease in response to any materials tested or at exposure time-points; however, a tendency in the GSH decrease can be seen for Min-U-Sil and Nanocyl exposure results at both 24 h and 96 h time points (Figure 4A).
2.2.3. Proinflammatory Response
The proinflammatory response was assessed by cytokine release measurements via Enzyme-Linked Immunosorbent Assay (ELISA), specifically for Interleukin-1β (IL-1β) and Interleukin-8 (IL-8) detection, as well as for tumor necrosis factor-α (TNF-α) release (Figure 4B–D). No statistically significant increase was detected for IL-1β and IL-8 release in all tested materials at the end of both exposure periods (Figure 4B,C). On the other hand, the increase in TNF-α release was statistically significant (p < 0.05) for Nanocyl MWCNTs at 24 h post-exposure, while Mitsui-7 MWCNTs induced statistically significant (p < 0.05) release after 96 h post-exposure (Figure 4D). R-848 activates NF-κB, and it was shown that it can trigger IL-1β and TNF-α release in neonates [44]. Therefore, in this study, R-848 was used as a proinflammatory positive control and induced a statistically significant increase after 24 h exposure for IL-1β and TNF-α, and after 96 h for only TNF-α (Figure 4). TNF-α, a potent proinflammatory stimulus [45], was applied as a positive control to induce IL-8 release; a statistically significant (p < 0.05) increase of IL-8 release induced by TNF-α compared to negative control was observed for all exposure time points (24 h, 48 h and 96 h) (Figure 4C).
2.2.4. Profibrotic Response
The profibrotic response was assessed by measuring the cytokine release, specifically TGF-β, platelet-derived growth Factor-AA (PDGF), and Osteopontin (OPN), via ELISA (Figure 5). The underlying drive behind the development of an inflammatory response is to clear the foreign material from the tissue and to eventually initiate the repairing pathway, which is facilitated by growth factors, such as the TGF-β. TGF-β was used as a positive fibrotic control because of its involvement in the development of fibrosis in different organs, disturbances of the homeostatic microenvironment, promotion of cell activation, migration, invasion, and excessive extracellular matrix production [30,46]. No statistically significant increase for any of the cytokine release measurements was observed at any exposure time-points upon exposures to any of the tested materials. The overgrowth of epithelial cells may not allow mediators and cytokines to reach fibroblasts and cell culture medium at the amount, which is relevant for activating fibrotic pathway; for instance, lack of TGF-β does not trigger PDGF and OPN release. R-848 was applied as a positive control to induce an increase in TGF-β release (Figure 5A), and TGF-β was also applied as a positive control to induce the PDGF and OPN release (Figure 5B,C), some trends in the increase of cytokine release can be observed for all positive controls at all investigated time points; however, no statistically significant change was detected.
3. Discussion3.1. Pros and Cons of the Co-culture Model
This study was designed to mimic the subchronic inhalation of different materials. Previous in vivo studies have shown that the investigated MWCNTs can penetrate deep into the alveolar region of lungs and cumulate in interstitium [11,12]. Macrophages play an essential role in the inflammatory response and surface cleaning (by particle uptake [12]) upon particle exposure and subsequently can play a role in the extent of the fibrotic response. As a result, human alveolar epithelial cells, human lung fibroblasts, and human macrophages were used for designing the present co-culture model. This model follows our previous study [27], where all three cell types cultured as monocultures and were exposed to MWCNTs and silica particles, which are also used in this study. The presented co-culture model is cultured at ALI to provide more realistic culturing conditions. It was previously shown that ALI culturing conditions lead to more enhanced protein release compared to the submerged conditions [47]. In this study, cell lines were used for assembling the presented model, as they represent a relatively stable and cost-effective system while providing experimental flexibility compared to the commercially available primary cells. Although primary cells are unique tools for designing long-term experiments with differentiated cells [30,48], they lack the flexibility needed for co-culturing with other cell types, mainly because they usually require a unique media composition.
Furthermore, primary cells are considered a cost-intensive option for researchers, as they either have to be directly isolated from patients while maintaining high purity of the desired cell population or can be obtained as commercially available models. Although an alveolar model consisting of primary cells is commercially available (EpiAlveolar™ [30]), we aimed to present a similar model consisting of cell lines, as a potentially cost-effective tool for enabling rapid pre-screening of particles. This model was designed based on our previous experience with the human alveolar model. We successfully combined human alveolar epithelial cells and human primary immune cells [49] and demonstrated the created model as a powerful tool for screening inflammatory responses upon exposure to various particle types [29,50,51,52]. A summary table (Table 2) is provided to compare the model presented in this study to commercially available 3D lung co-culture models.
3.2. Material Concentrations Used for Cell Exposures
The alveolar mass retention during a full lifetime occupational exposure (45 years) period to CNTs of different sizes was modeled and calculated to be in the range of 12.4 to 46.5 μg/cm2 [53]. In this study, the maximum deposited concentration after five exposures to Nanocyl was 11 μg/cm2 and 20 μg/cm2 for Mitsui-7, respectively, which is in the range of the reported lifetime occupational human exposure to MWCNTs [53]. However, it has to be kept in mind that in this study, the amount of CNTs was deposited within five days as the duration of the experiment is the limiting factor for cell line usage. A549 cells are continuously proliferating cells, and therefore they overgrow into a multilayer after 3 to 4 days in ALI, which negatively affects the physiological relevance of the model. On the other hand, the lowest tested exposure concentration (1 μg/cm2) for Mitsui-7 and Nanocyl corresponds with concentrations used in mice in vivo [13,54].
Moreover, aerosolization of silica quartz particles resulted in an average deposited concentration up to 1 μg/cm2 for DQ12 and 3 μg/cm2 for Min-U-Sil after repeated exposures. The tested exposure concentrations correspond to those used in previous in vitro studies, including human lung models, and induced a pro-inflammatory response [29,48]. Furthermore, these concentrations are comparable to in vivo exposure experiments in rats (3–30 mg/rat, which corresponds to 0.6–6 μg/cm2, deposition calculated based on [55]) [56].
3.3. Oxidative Stress
GSH is known to be essential for several cell processes, mainly interconnected with the maintenance of the thiol-redox status [57]. Therefore, the ratio between the reduced and oxidized forms of GSH is an important indicator of the intracellular redox environment and, at the same time, provides insights regarding cell proliferation, differentiation, and apoptosis [58]. In light of this knowledge, GSH-based oxidative stress measurement assays have been widely implemented [59]. Specifically, GSH measurements in response to exposure to NMs have provided insight regarding the level of the cell’s oxidative stress status [60].
The oxidative stress response upon exposures to silica quartz particles was previously reported in vitro, but not confirmed in vivo [61]. A previous in vitro study showed decreased GSH in fibroblasts 24 h upon exposures to Nanocyl; however, no difference in GSH production was observed 96 h post-exposure compared to the negative control. Min-U-Sil and Mitsui-7 MWCNTs did not induce any significant decrease in GSH either [27]. Similarly, in our study, we did not observe a statistically significant (p > 0.05) decrease in GSH levels for any of the tested materials in both exposure scenarios; however, a tendency in the GSH decrease can be observed for Min-U-Sil and Nanocyl exposure results at both time-points (Figure 4A). This finding is in line with the proteomics investigation of the presented co-culture response upon exposures to Mitsui-7, where no response was observed upon exposure to Mitsui-7 compared to the negative control [47].
3.4. Proinflammatory Response
A proinflammatory cytokine is a type of signaling molecule secreted from certain cell types and promotes inflammation in response to foreign materials, in this case, MWCNTs. Among them, IL-1β, IL-8, and TNF-α are known to play a vital role in mediating the innate immune response and therefore considered biomarkers of nanoimmunotoxicity [62].
Certain particle types have been shown to induce immunotoxicity. Among them, DQ12 is well studied, and have been recruited in in vitro studies as a potent proinflammatory stimulus [29,63]. On the other hand, Min-U-Sil was found to induce increased IL-1β release in macrophage monocultures (differentiated THP-1), but no response in fibroblasts or epithelial cells was reported [27]. In vivo experiments with mice showed a strong inflammatory response upon acute exposure to a high dose of Min-U-Sil [64]. Nanocyl and Mitsui-7 MWCNTs did not induce any proinflammatory response in epithelial cells, fibroblasts, or macrophage monocultures in vitro [27]. Although individual in vitro studies assessing the potential toxicity of MWCNTs of interest did not report proinflammatory responses, it should be noted that these studies were either using monocultures of cells or the exposure conditions were not representative, i.e., submerged conditions as opposed to creating an ALI prior to the exposure.
In this study, we have adapted two exposure scenarios, as explained in the Methods section. The results indicated statistically significant changes in TNF-α secretion levels in response to two different exposure scenarios (acute (24 h) vs. prolonged (96 h)) for both MWCNT types tested. A significant elevation in TNF-α levels for Nanocyl at 24 h post-exposure was detected. In comparison, Mitsui-7 MWCNTs induced a statistically significant (p < 0.05) increase in TNF-α levels 96 h post-exposure (Figure 4D). TNF-α levels specifically play a vital role in proinflammatory response assessment because it is expressed in the early stages of cell inflammation [65]. Therefore, it can be concluded that in a realistic occupational exposure scenario, exposure to MWCNTs does initiate cell inflammation and eventually can result in cell death.
3.5. Profibrotic Response
Exposure to NMs is known to play a role in the development of chronic pulmonary diseases, especially fibrosis [66]. Among all NMs, MWCNTs specifically pose a danger because relatively large quantities are being used in numerous manufacturing practices, and thus will inadvertently lead to human exposure. Therefore, there have been continuous efforts to evaluate the potential link between fibrosis and MWCNTs exposure. It was previously shown that both Mitsui-7 [12,67] and Nanocyl [42] exposures lead to the development of pulmonary fibrosis in rodents. However, it was reported that Nanocyl caused less upregulation of genes involved in fibrosis development compared to long and thick MWCNTs [42]. These results are consistent with an in vitro study investigating epithelial cell (A549), macrophage (differentiated THP-1), and fibroblast (MRC-5) monocultures. Mitsui-7 exposures led to profibrotic response (TGF-β release), and OPN releases in epithelial cells. On the other hand, Nanocyl increased only TGF-β release from fibroblasts [27], which has previously been used in vitro to stimulate the pro-inflammatory response [35,48,63] and in vivo to stimulate the development of pulmonary fibrosis [56,68].
In this study, we did not observe a statistically significant increase in TGF-β, PDGF, or OPN release (Figure 5). Interferon-γ (1 μg/mL) was also used as a positive control to stimulate TGF-β, PDGF, and OPN release (data not shown), but no increase in profibrotic mediators release was detected. In addition to the evaluation of profibrotic mediator releases, we also measured fibrotic markers, the collagen type I and fibronectin release, but did not detect a statistically significant increase in both indicator release levels (data not shown), although this combination of investigated endpoints proved its suitability in predicting profibrotic response in vitro earlier [30]. Furthermore, the use of the SircolTM soluble collagen assay to detect acid and pepsin soluble collagen was considered; however, the presence of serum in the cell culture medium posed interference on the assay results; therefore, the use of this assay was eliminated from the study. It is also noteworthy to state that at the cellular level, proinflammatory mediator detection is considered one of the key events (KE) in the adverse outcome pathway (AOP) for fibrosis [30]. The presented model resembles the alveolar region of the human lungs, and the concept is similar to the EpiAlveolarTM model used in our previous study [30]. Both models use human lung epithelial cells, fibroblasts, and macrophages, while the EpiAlveolarTM model additionally includes endothelial cells. However, the main differences of the two models are the cells used: cell lines vs. primary cells, and the duration of the experiment, i.e., the length of the periods while each cell model remains stable throughout the exposures. For the EpiAlveolarTM model, it was possible to perform exposures over three weeks at ALI, whereas the cell line model only is stable for 3–4 days as the A549 cells start to overgrow at ALI. When comparing the data from both studies, the model with primary cells is more suitable for such an investigation to assess repeated and long-term effects, as it is capable of predicting profibrotic response upon exposure to TGF-β and other NMs. On the other hand, cell line models still offer a cheaper and easy-to-handle option for hazard prescreening of NMs to assess cytotoxicity and inflammatory markers. Finally, the proposed model is not limited to testing potentially hazardous nanomaterials. It can also be implemented to test anti-inflammatory [69,70] and anti-fibrotic [71] nanoformulations in the field of nanomedicine.
4. Materials and Methods4.1. Chemicals and Reagents
All chemicals and reagents used were obtained from Sigma-Aldrich (Buchs, Switzerland) unless stated otherwise.
4.2. Sample Preparation
BSA solution (0.1% in ultrapure H2O) was sterile filtered (0.2 μm pore size; Nalgene, Thermo Scientific, MA, USA). BSA was used as a dispersant for MWCNTs. Mitsui-7 MWCNTs (MWCNTs-7; Mitsui & Co, Tokyo, Japan) and Nanocyl-7000 MWCNTs (referred to as Nanocyl; Nanocyl S.A., Sambreville, Belgium; received from European Commission Joint Research Centre, Ispra, Italy) were dispersed in BSA solution. Briefly, pre-weighted dry MWCNTs powder was heat sterilized at 100 °C overnight, left to cool down, and subsequently 0.1% BSA solution was added to obtain a stock solution with the concentration of 50 μg/mL. This suspension was sonicated for 3 h with continuous shaking to disperse the MWCNTs and subsequently stored at 4 °C until further use.
Two different types of silica quartz particles were used as reference materials. Min-U-Sil is an inert, high purity white crystalline silica recently reported as a potential profibrotic agent [28,72], while Dörntruper quartz (DQ12; composed of 87% crystalline silica and amorphous silica with kaolinite impurities) was reported as a proinflammatory agent in vitro [73], as well as profibrotic agent in vivo [56,74,75]. Both silica materials were dispersed in ultrapure sterile filtered H2O at a concentration of 100 μg/mL.
All stock suspensions were sonicated for 1 h prior to the exposure experiments.
4.3. Material Characterization4.3.1. Electron Microscopy
To investigate the deposition of aerosolized materials, transmission electron microscopy (TEM) 300 mesh carbon-coated copper grids were placed into the exposure chamber prior to the experiment.
TEM grids with deposited material were used without any further treatment. Representative images were captured using a TEM (FEI Tecnai Spirit, Hillsboro, OR, USA) operating at 120 kV and equipped with a Veleta CCD camera (Olympus, Japan).
4.3.2. Endotoxin Content
The endotoxin concentration in the MWCNTs suspensions was measured using the PierceTM LAL Chromogenic Endotoxin Quantitation Kit (ThermoFisher Scientific, Basel, Switzerland), following the manufacturer’s instructions and all suspensions were below 0.5 EU/mL.
4.4. Human Cell Line Co-Culture Model
All cell lines—human fibroblasts MRC-5, human alveolar epithelial cells type II A549 and human monocytes THP-1 were purchased from American Type Culture Collection (ATCC, Manassas, VA, USA) and cultivated according to the ATCC protocol prior assembled into co-culture model.
Human epithelial cells type II (A549 cell line) were cultured in Roswell Park Memorial Institute medium (RPMI 1640) supplemented with 10% Fetal Bovine Serum (FBS), 1% Penicillin/streptomycin and 1% L-Glutamine (all stated chemicals were obtained from Gibco, Gaithersburg, MD, USA). Human monocytes (THP-1 cell line) were cultivated in supplemented RPMI 1640, as mentioned above, with extra 0.05 mM β-mercaptoethanol. To differentiate monocytes into macrophages, prior to co-culture assembling, the THP-1 cells had to be activated by Phorbol 12-Myristate 13-Acetate (PMA; 20 ng/mL in supplemented medium without β-mercaptoethanol) overnight at the density of 4 × 105 cells/mL. Human fibroblasts were cultivated in Minimum Essential Medium (MEM) supplemented with 10% FBS, 1% Penicillin/streptomycin, 1% L-Glutamine, and 1 × Non-Essential Amino Acids (all Gibco, Gaithersburg, MD, USA).
The co-culture model was composed by following the next steps. The Transwell® 12-well inserts (surface area of 0.9 cm2, pores of 0.4 μm diameter, PET membranes; BD Biosciences, Allschwil, Switzerland) were inverted and placed into a sterile petri dish. Then the MRC-5 cells were seeded on the basolateral part of the insert at the density 104 cells/cm2 and incubated for 3 h at 37 °C, 5% CO2 to fully attach to the insert membrane. After the incubation period, inserts were turned back and placed into the 12-well plate containing 1.5 mL supplemented MEM. Subsequently, A549 cells were seeded on the apical side of the insert at the density of 2.8 × 105 cells/cm2 with 1 mL of supplemented RPMI 1640 and incubated for 4 days (d) at 37 °C, 5% CO2. After the 4-d incubation period of A549-MRC-5 co-cultures, differentiated THP-1 in supplemented RPMI 1640 were seeded on the top of A549 cells at the density of 28 × 103 cells/cm2. The plates were then incubated for 2 d at 37 °C, 5% CO2, and subsequently lifted to ALI 24 h prior to the experiment by removing the medium from the apical side and replacing medium from the basolateral side with 0.6 mL of the medium mixture (RPMI:MEM, 1:1).
4.5. The Air-liquid Interface (ALI) Cell Exposure Method
Co-cultures were exposed at the ALI using the VITROCELL® Cloud system (Waldkirch, Germany). Briefly, the exposure system consists of a nebulizer, an exposure chamber as well as a QCM (operated at 5 MHz, detection limit: 0.1 μg/cm2), allowing to measure and record the deposited dose online. For each aerosolization experiment, 200 μL of sample’s stock solution with 2 μL of 0.09% NaCl (NAAPREP® physiological saline, GlaxoSmithKline, Evreux, France) was added to the nebulizer (Aeroneb® Lab, Dangal, Galway, Ireland; mesh size 10 μm for MWCNTs, 4–6 μm for Min-U-SIl and 2.5–6 μm for DQ12). The vibrating perforated membrane at the neck of the nebulizer generates the aerosols and diverts them into the exposure chamber. Inside the chamber, the aerosols in the cloud gently deposit onto the cell surfaces that are maintained at the ALI. The flow rate of nebulizer (the flowrate is preset by the instrument provider, cannot be controlled) was ideal for the aerosols to sufficiently mix within the entire chamber, hence resulting in uniform droplet deposition. VITROCELL® Cloud exposure chamber was wiped with pre-wetted ethanol tissues prior to each exposure experiment.
4.6. Exposure Scenarios
The biological response of the co-culture model was assessed following exposure to all tested materials at the doses presented in Table 1. Two different exposure scenarios were performed, i.e., acute (24 h) exposure and prolonged (96 h) continuous exposure. The cells were exposed apically at the ALI daily and maintained at 37 °C in 5% CO2 throughout each exposure scenario. The cells were exposed to one dose of the material at the acute exposure scenario. In contrast, the cells following the prolonged exposure scenario were exposed to five times the material dose on subsequent days for 96 h in total.
4.7. Biochemical AnalysisCytotoxicity
The LDH release into the supernatant as a result of cell membrane rupture is a well-known indicator of cytotoxicity. The amount of LDH release was evaluated using a commercially available LDH diagnostic kit (Roche Applied Science, Mannheim, Germany), according to the manufacturer’s protocol. Each sample was tested in triplicates; the enzyme activity was measured photometrically. The absorbance was measured at 490 nm with a reference wavelength of 630 nm. LDH values are presented relative to the negative control (BSA-treated cells). Cell cultures exposed apically to 0.2% Triton X-100 for 24 h were used as a positive control.
4.8. Cell Morphology
The cell morphology was evaluated using laser scanning microscopy. At the respective time-points, the cell cultures were fixed for 15 min in 4% paraformaldehyde in PBS at room temperature, subsequently washed 3 × with PBS and stored in PBS at 4 °C until further immunofluorescence staining could occur.
Prior to the immunofluorescence staining, samples were treated with 0.1 M glycine for 15 min and subsequently permeabilized with 0.2% Triton X-100 for another 15 min, both at room temperature. All antibodies were diluted in 0.3% Triton X-100 and 1% BSA in PBS and incubated for 2 h. The cells were first stained with primary antibodies (dilution 1:100) and subsequently with secondary antibodies, 4’,6-diamidin-2-fenylindol (DAPI) and rhodamine-phalloidin (dilution 1:100, Molecular Probes, Eugene, OR, USA). Specifically, anti-CD68 and chicken anti-vimentin were used as the primary antibodies and anti-mouse Alexa 488, anti-chicken Alexa 647 (Abcam, Cambridge, MA, USA), phalloidin-rhodamine and DAPI were used as the fluorophores. Anti-CD68 is used for macrophage staining, anti-vimentin stains intermediate filaments of the fibroblasts, phalloidin-rhodamine stains F-actin cytoskeleton and DAPI stains nucleus. Following antibody incubation, cell culture inserts were embedded in Glycergel (DAKO, Santa Clara, CA, USA). Cell morphology was evaluated by visualization of samples via an inverted laser scanning confocal microscope (LSM) 710 (Axio Observer.Z1, Carl Zeiss, Oberkochen, Germany). Image processing was conducted with IMARIS 3D restoration software (Bitplane AG, Zurich, Switzerland).
4.9. Oxidative Stress
The total amount of reduced intracellular GSH in the cell cultures was quantified using a GSH assay kit (following supplier’s protocol, Cayman Chemical Company, Ann Arbor, MI, USA) as a well-known marker for oxidative stress. Levels of oxidative stress are presented as a ratio of total GSH to the total amount of protein of each sample. Total protein content was measured by the Pierce bicinchoninic acid (BCA) protein assay kit (Pierce Protein Research Products, Thermo Scientific, Rockford, IL, USA) according to the manufacturer guidelines. Tert-Butyl Hydrogen Peroxide (tBHP) at a concentration of 135 mM acted as the positive assay control.
The IL-1β, TNF-α, and IL-8 secretion were assessed using the commercially available DuoSet® ELISA Development diagnostic kit (R&D Systems, Zug, Switzerland), according to the manufacturer’s protocol. Cells treated apically with 1 μg/mL of TNF-α (ImmunoTools, Friesoythe, Germany) served as the positive control for IL-1β (data not shown) and IL-8 assays. R-848 applied apically onto the cell surface at 6 μg/mL was used as a positive control for IL-1β, TNF-α, and IL-8 (data not shown) assays. All positive controls were tested at 24 h, 48 h, and 96 h post-exposure time-points in both co-culture systems.
4.10.2. Profibrotic Response
The TGF-β, PDGF, and osteopontin (OPN) release into the supernatant of exposed cells was quantified using the respective ELISA DuoSet® Development diagnostic kit (R&D Systems, Zug, Switzerland), following the manufacturer’s protocol. Based on the supplier’s protocol, pH activated form of TGF-β was assessed (the activation occurs straight prior to the assay). Cell cultures exposed apically to 1 μg/mL of Interferon-gamma (IFN-γ) were used as a positive control for TGF-β assay (data not shown), while apical cell treatment with 100 ng/mL TGF-β served as the positive control to induce PDGF and OPN release. R-848 applied apically onto the cell surface at 6 μg/mL was used as a positive control for the TGF-β assay. All positive controls were tested at 24 h, 48 h, and 96 h post-exposure time-points in both co-culture systems.
4.11. Statistical Analysis
For each data point, four independent experiments were performed (four biological replicates), one technical replicate per treatment was prepared; each endpoint was measured in triplicates. Statistical analysis was performed using GraphPad Prism 6 (GraphPad Software Inc., La Jolla, CA, USA) software. A parametric one-way analysis of variance (ANOVA) with subsequent Dunnett test was performed. Results were considered significant if p < 0.05.
5. Conclusions
In conclusion, our in vitro co-culture model consisting of three human cell lines relevant to assess inflammation and fibrosis showed a proinflammatory response upon exposure to the positive controls, and a statistically significant TNF-α release was detected after exposure to both MWCNTs tested. On the other hand, no cytotoxicity or profibrotic response was observed in response to exposure to both types of MWCNTs. While the presented model is not suitable to predict the profibrotic response at the 96 h time point, it can be used for both acute (24 h) and prolonged (96 h) proinflammatory response investigations. As a result, our representative 3D lung co-culture model can be used to assess the proinflammatory response to MWCNTs aerosols; if the results induce a positive response, then further investigations involving primary cells allowing a repeated exposure up to several weeks are recommended. Finally, it is important to note that our model is not limited to testing potentially hazardous nanomaterials. A future study is underway to harness this representative lung system to test anti-inflammatory and anti-fibrotic nanoformulations.
Acknowledgments
We kindly thank Miguel Spuch-Calvar, Dimitri Vanhecke, and Barbara Drasler for contributing to the scheme for the exposure scenario (Figure 1). We also thank Martin J.D. Clift, Savvina Chortarea, Monita Sharma, and Amy J. Clippinger for the valuable discussion during the study planning phase.
Supplementary Materials
Supplementary materials can be found at https://www.mdpi.com/1422-0067/21/15/5335/s1. Figure S1. 3D rendered LSM image of the co-culture model at 96 h time-point exposed to BSA. Figure S2. 3D rendered LSM image of the co-culture model at 96 h time-point exposed to Mitsui-7 MWCNTs. Figure S3. 3D rendered LSM image of the co-culture model at 96 h time-point exposed to DQ12. Figure S4. 3D rendered LSM image of the co-culture model at 96 h time-point exposed to Min-U-Sil. Figure S5. 3D rendered LSM image of the co-culture model at 96 h time-point exposed to Nanocyl.
Click here for additional data file.
Author Contributions
Conceptualization, B.R.-R., V.S. and H.B.; methodology, H.B.; software, D.S.; validation, B.H. and B.R.-R.; formal analysis, H.B.; investigation, B.R.-R. and A.P.-F.; resources, B.R.-R. and H.B.; data curation, H.B. and B.B.K.; writing—original draft preparation, H.B.; writing—review and editing, H.B. and B.B.K.; visualization, B.R.-R.; supervision, B.R.-R.; project administration, B.R.-R.; funding acquisition, B.R.-R. All authors have read and agreed to the published version of the manuscript.
Funding
This work was supported by the PETA International Science Consortium Ltd. and the Adolphe Merkle Foundation. In addition, this project has received further funding from the European Union’s Horizon 2020 Research and Innovation Programme, PATROLS—Physiologically Anchored Tools for Realistic Nanomaterial Hazard Assessment, under grant agreement No 760813. B.B.K. sincerely acknowledges the Swiss Government Excellence Postdoctoral Scholarship for Foreign Researchers.
Conflicts of Interest
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
Abbreviations
3D
Three dimensional
ANOVA
Analysis of variance
ATTC
American type culture collection
BCA
Bicinchoninic acid
BSA
Bovine albumin serum
CCD
Charged coupled device
CD
Cluster of differentiation
CNT
Carbon nanotubes
DAPI
4′,6-diamidino-2-phenylindole
ELISA
Enzyme-linked immunosorbent assay
FBS
Fetal bovine serum
GSH
Glutathione
IL
Interleukin
LDH
Lactate dehydrogenase
LSM
Laser scanning microscopy
MEM
Minimum essential medium
MWCNT
Multiwalled carbon nanotubes
NM
Nanomaterial
OPN
Osteopontin
PBS
Phosphate buffered saline
PDGF
Platelet-derived growth factor
PET
Polyethylene terephthalate
PFA
Paraformaldehyde
PMA
Phorbol 12-myristate 13-acetate
QCM
Quartz crystal microbalance
RPMI
Roswell Park Memorial Institute
SEM
Standard error of the mean
tBHP
Tert-butyl hydrogen peroxide
TEER
Transepithelial electric resistance
TEM
Transmission electron microscopy
TGF
Transforming growth factor
TNF
Tumor necrosis factor
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Schematic representation of the experimental setup, exposure scheme, and sample collection.
TEM micrographs represent the amounts of 24 h (1 × exposure) and 96 h (5 × exposures) deposition of Min-U-Sil, DQ12, Nanocyl, and Mitsui-7 particles. The scale bar is 1 μm for Min-U-Sil, DQ12, and Nanocyl samples, and 5 μm for Mitsui-7 CNTs.
Cytotoxicity and cellular morphology of the 3D co-culture model. (A) Cytotoxicity was measured by the LDH assay, data presented in a boxplot with 10–90 percentile. * marks a statistically significant increase compared to the negative control, n = 4. (B) Representative 3D rendered LSM image of the co-culture model at a 24 h time point, presenting the localization of macrophages within the model. Cyan represents cell nuclei, grey shows cytoskeleton, and magenta stains the macrophages (CD68). (C) The LSM images (XY projections) of BSA- and Mitsui-7, DQ12, Min-U-Sil, and Nanocyl-treated samples at 96 h time-point with their corresponding XZ projections showing thickness of the cellular layer, the scale bar is 30 μm. Cyan represents cell nuclei, grey represents cytoskeleton, and red represents vimentin, a type III intermediate filament protein. For 3D rendered LSM images of the co-culture model at 96 h time-point, please refer to the Supplementary Materials, Figures S1–S5. The scale bar is 30 μm.
In response to exposure to the tested materials, the extent of oxidative stress and proinflammatory response is assessed via (A) GSH content; (B) IL-1β; (C) IL-8 and (D) TNF-α release measurements. Data presented in a boxplot with 10–90 percentile. * marks a statistically significant increase compared to the negative control, n = 4.
The profibrotic response measurements of the co-culture model. The responses were investigated via (A) TGF-β release; (B) PDGF release, and (C) OPN release. Data presented in a boxplot with 10–90 percentile.
ijms-21-05335-t001_Table 1
Information about tested materials, daily deposited dose was measured with a quartz crystal microbalance (QCM), the 96 h deposited doses were calculated as five times the 24 h dose.
The comparison of the 3D lung co-culture model presented in this study to commercially available 3D lung co-culture models consisting of primary cells.
Property
The Presented Model in This Study
Commercially Available Models Consisting of Primary Cells [30,48]
Ease of assembling the model
Easy
Sold as ready-to-use
Adjusting/ tuning based on desired investigation endpoints
* Can be included by the end-user, but requires an extensive optimization step as immune cells are cultured in a different and complex cell media as opposed to commercially available models.
\\n\"\n]"}}},{"rowIdx":437,"cells":{"data":{"kind":"list like","value":["\nInt J Mol SciInt J Mol SciijmsInternational Journal of Molecular Sciences1422-0067MDPI32752130PMC743209410.3390/ijms21155509ijms-21-05509ArticleFlexible NAD+ Binding in Deoxyhypusine Synthase Reflects the Dynamic Hypusine Modification of Translation Factor IF5AChenMeirong12†GaiZuoqi1†‡OkadaChiaki1§YeYuxin1‖https://orcid.org/0000-0003-2854-9225YuJian1https://orcid.org/0000-0003-1687-5904YaoMin1*Faculty of Advanced Life Science, Hokkaido University, Sapporo 060-0810, Japan; chenmr1987@163.com (M.C.); gaizuoqi@126.com (Z.G.); chiaki@hoku-iryo-u.ac.jp (C.O.); yeyx@pkusz.edu.cn (Y.Y.); yu@castor.sci.hokudai.ac.jp (J.Y.)College of Food Science and Technology, Nanjing Agricultural University, Nanjing 210095, ChinaCorrespondence: yao@castor.sci.hokudai.ac.jp; Tel./Fax: +81-11-706-4481
These authors contribute equally to this work.
The present address: School of Life Science and Engineering, Foshan University, Foshan 528231, China.
The present address: School of Pharmaceutical Sciences, Health Sciences University of Hokkaido, Ishikari-Tobetsu 061-0293, Japan.
The present address: School of Chemical Biology and Biotechnology, State Key Laboratory of Chemical Oncogenomics, Peking University Shenzhen Graduate School, Shenzhen 518055, China.
3172020820202115550931520202972020© 2020 by the authors.2020Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
The eukaryotic and archaeal translation factor IF5A requires a post-translational hypusine modification, which is catalyzed by deoxyhypusine synthase (DHS) at a single lysine residue of IF5A with NAD+ and spermidine as cofactors, followed by hydroxylation to form hypusine. While human DHS catalyzed reactions have been well characterized, the mechanism of the hypusination of archaeal IF5A by DHS is not clear. Here we report a DHS structure from Pyrococcus horikoshii OT3 (PhoDHS) at 2.2 Å resolution. The structure reveals two states in a single functional unit (tetramer): two NAD+-bound monomers with the NAD+ and spermidine binding sites observed in multi-conformations (closed and open), and two NAD+-free monomers. The dynamic loop region V288–P299, in the vicinity of the active site, adopts different positions in the closed and open conformations and is disordered when NAD+ is absent. Combined with NAD+ binding analysis, it is clear that PhoDHS can exist in three states: apo, PhoDHS-2 equiv NAD+, and PhoDHS-4 equiv NAD+, which are affected by the NAD+ concentration. Our results demonstrate the dynamic structure of PhoDHS at the NAD+ and spermidine binding site, with conformational changes that may be the response to the local NAD+ concentration, and thus fine-tune the regulation of the translation process via the hypusine modification of IF5A.
Hypusine (N-(4-amino-2-hydroxybutyl) lysine), an unusual amino acid, is formed by the post-translational modification of a lysine residue with the addition of the aminobutyl moiety from the polyamine spermidine. The hypusine modification uniquely occurs in eukaryotic translation factor 5A (eIF5A) precursor proteins [1]. Although eIF5A was originally designed as a translation initial factor, it has been shown that eIF5A is more directly related to elongation rather than the initiation step. The hypusine-modification of eIF5A is involved in the mRNA transportation and translation elongation on the ribosome [2,3]. The binding of eIF5A to translating ribosomes in a hypusine-dependent manner indicates modifications of the specific binding to the translational machinery [4,5], suggesting the important role of eIF5A receiving hypusine modifications in translation processes, which was supported by evidence that the hypusine modification of eIF5A is vital for the growth and survival of eukaryotes, from yeast to mammals [6,7,8]. Previous studies also reported that the hypusine residue is essential for the homodimerization of eIF5A and affects its subcellular location [1,2,9,10].
The hypusine modification of eIF5A is catalyzed by deoxyhypusine synthase (DHS), followed by the hydroxylation of deoxyhypusine by deoxyhypusine hydroxylase (DOHH) [1,11]. DHS catalyzes a NAD+-dependent reaction that synthesizes deoxyhypusine (N-(4-aminobutyl) lysine) by transferring the butylamine moiety of spermidine to a specific lysine residue of eIF5A [12]. Then, deoxyhypusine is hydroxylated by DOHH to form hypusine [13,14,15]. Like eIF5A, DHS is an essential gene, indicating the importance of hypusine modification [16]. Since eIF5A is associated with many diseases, such as diabetes, cancers, malaria, and HIV-1 infections [16], trials aimed at blocking the hypusine modification of eIF5A have been attempted. Specific inhibitors of DHS or DOHH have been reported to affect the arrest of cellular growth, and also alter the proliferation and differentiation of tumor cells [17,18,19]. An extensive biochemical characterization of DHS has been carried out, establishing it as a 40–43 kDa enzyme (the exact size being species-dependent) requiring NAD+ as a cofactor. The mechanism of human deoxyhypusine synthesis by DHS has been proposed as follows [20]: firstly, a hydrogen of spermidine is transferred to NAD+ to generate dehydrospermidine and NADH; the dehydrospermidine then links covalently to a lysine residue (K329 in human DHS) of DHS, forming an enzyme-imine intermediate; finally, DHS catalyzes the formation of deoxyhypusine through the conjugation of the butylamine moiety of dehydrospermidine to the ε-amino group of the lysine residue (K37) of the eIF5A precursor protein.
The reported crystal structures of human DHS show a homotetramer consisting of two tightly associated dimers with four active sites—two near each tight dimer interface [21,22]. Residues from both monomers of a dimer contribute to each of its active sites. In human DHS, four active sites accommodate four molecules of NAD+ (PDB:1DHS) [21,22]. Interestingly, the catalytic sites are blocked by a projecting N-terminal two-turn helix (described as a “ball and chain”) in a cross-monomer manner in the first crystal form of human DHS, obtained upon crystallization at high ionic strengths and a low pH. However, the catalytic site is not obstructed in a structure of human DHS in a ternary complex with NAD+ and GC7 (PDB:1RQD), an analog of spermidine [22]. In this ternary complex structure, GC7 is also located at the interface of the tight dimers and is separated from NAD+ by a loop structure (T307–A319) [22]. The absence of a blockage by the ball and chain structure might suggest that this dynamic part of the enzyme could be involved in the regulation of activity within the functional tetramer. However, this human DHS complex structure without any bound GC7 (PDB:1ROZ), determined upon crystallization at low ionic strengths and pH 8.0 (nearer physiological conditions), lacked the electron density for the N-terminal ball and chain region [22]. In recent research, it has been reported that the N-terminal ball and chain region is important for the assembly of a functional DHS tetramer [23]. This N-terminal region is, therefore, clearly highly dynamic, with the flexibility being for DHS function. Whether it is regulating the active site accessibility in a substrate-dependent manner, binding eIF5A, or conferring sensitivity to cellular conditions, remains unclear. Different from human DHS, the DHS from Trypanosoma brucei exists as a heterotetramer formed by two paralogs, DHSc and DHSp (dimer of heterodimer), with DHSc homologous to human DHS. Each heterodimer contains two NAD+ binding sites, one located in the functional catalytic site while the other is located in a remnant site lacking key catalytic residues [24].
Consistent with archaea sharing a requirement for hypusine for the function of translation factor aIF5A, both the modification [24] and DHS gene homologues [1] have been identified. Furthermore, a study has shown the arrest of archaeal growth in G1 upon treatment with an inhibitor of DHS [25], indicating that DHS is also essential in archaea. However, the mechanism of hypusination in archaea is unclear yet [26]. Homologues of the non-essential second enzyme required for hypusine formation, DOHH, have not been identified. While some archaea may function with a deoxyhypusine modification (rather than hypusine), species are known that form hypusine in the absence of oxygen, which implies a distinct mechanism of hypusine modification in archaea. The structure of an archaeal DHS could provide a better understanding of the process of hypusine modification of aIF5A.
In this study, we determined the crystal structure of DHS from Pyrococcus horikoshii OT3 (PhoDHS), with NAD+-bound, at 2.2 Å resolution. PhoDHS is a tetramer, each monomer sharing a similar fold with its human homologue (PDB ID:1DHS). Interestingly, unlike human and T. brucei DHS, PhoDHS contains only two bound NAD+ molecules and shows a difference in the NAD+-bound and NAD+-free monomeric conformations. In particular, NAD+-bound monomers in the PhoDHS tetramer display different conformations in the region of V288–P299 around the NAD+ binding site. The observations are suggestive of a distinct manner in which NAD+ binds to DHS that could be of relevance to the mechanism and regulation of enzyme activity. Having the states of NAD+-bound and NAD+-free also sheds light on the NAD+-dependent spermidine binding to DHS and raises a possibility that dynamic NAD+ binding in PhoDHS may fine-tune the translation activity of aIF5A via hypusine modification.
2. Results2.1. The Overall Structure of PhoDHS
We determined the crystal structure of PhoDHS at 2.2 Å resolution by using a molecular replacement (Table 1). In an asymmetric unit, the structure of PhsDHS formed a tetramer in accordance with its known biological assembly [21,22], with unbuilt N-terminal 14 and C-terminal 6 of the total 342 residues in each monomer. Additionally, we could not build the parts of two loops in each monomer (E122-D125 and G306–K311 in molA, W290–K311 in molB, E122–V123 and W290–A310 in molC, and W121–K130 and E301–K311 in molD, respectively), because of the lack of electron density. Similar to DHS from other organisms, this tetramer is a dimer of dimers with a point group of D2, generated by two local 2-fold axes that are perpendicular to each other (Figure 1). The monomers of PhoDHS tetramers are more related to each other with a root mean squared deviation (r.m.s.d) between 0.544 Å and 0.606 Å for 276–284 Cα atoms. Based on the buried areas of intermolecular interactions, the tightly bound dimers are defined as molAB and molCD (buried areas > 2700 Å2), while the buried area is less than 1800 Å2 between molA and molD (or between molB and molC). PhoDHS is very similar to human DHS (PDB ID:1DHS), with a r.m.s.d of 1.195 Å for 289 Cα, and is also similar to T. brucei DHS (PDB:6DFT) [24] with a r.m.s.d of 1.39 Å for 275 Cα (Figure 1b). Differences include the N-terminal region (M1–P26) and an insertion (L77–L94) in human DHS. Unlike the inactive human DHS structure, wherein the N-terminal two-turn α helix (A9–L18) blocks the entrance tunnel to the spermidine active site [21,22], the N-terminal region (M1–I14) of PhoDHS is not built due to the poor electron density for this region, suggesting that it is highly flexible and different from human DHS, despite the crystallization conditions being similar to those of inactive human DHS [21]. Given that the active sites are accessible, we assume that the structure of PhoDHS obtained is illustrative of an active form.
Although the overall structures of DHS are similar, there is a significant structural difference in a loop region (V288–P299) that forms part of the active site of PhoDHS. This active site loop is flexible and presents itself in multi-conformations, conf1 and conf2 in molA and molD, respectively, which are likely to represent two snapshots of the active site (Figure S1). In the molB and molC of PhoDHS, the loop region is either disordered, or partially visible. Although only one of two conformations, conformation 1 or conformation 2 (conf1, conf2), was assigned to each molA and molD, a weak density of the alternative conformations could be observed, further suggesting the flexibility of the loop region in PhoDHS. In contrast, this active site loop is stable and present in just one conformation (conf2) in the previously published human and T. brucei DHS structures [24].
Another striking structural difference seen in PhoDHS is at its C-terminus. The C-terminus of PhoDHS (Gly337–Gln342, in all four chains) is invisible in the structure, which could be observed in human and Trypanosoma brucei DHS [24]. A sequence alignment shows that the primary structure of this region is not conserved in PhoDHS, human DHS, and T. brucei DHS (Figure 2). The short helix predicted for PhoDHS is longer in human DHS. The helix is visible in the human DHS structure, though the region retains some intrinsic flexibility, indicating conformational dynamics in the C-terminal region across species. Removal of five residues from the C-terminus in human DHS or 39 residues from the C-terminus of S. cerevisiae DHS impairs activity [26,27]. It is probable that the C-terminus of PhoDHS is also required for full function, and that the intrinsic flexibility of this region, rather than the conserved sequences, is likely important for the activity.
2.2. NAD<sup>+</sup> Binding Manner of PhoDHS
Two bulky electron density blocks shaped as NAD+ were distinguished at the interface between molA and molD of the PhoDHS tetramer after several cycles of refinement and were later assigned as NAD+ (Figure 3a and Figure S2). Those NAD+ molecules were naturally captured during the expression of the enzyme in Escherichia coli, since no NAD+ was added during purification and crystallization processes.
Bound NAD+ is proximal to a loop (V288–P299 (hereafter named “the binding loop”)) that displays very different conformations in each monomer of the PhoDHS tetramer. Notably, in molA and molD, observed to bind NAD+, the binding loop adopts two different conformations (Figure 1a, right): in molA, the binding loop positions away from NAD+ (conf1), while in molD, the binding loop adopts another conformation to access NAD+ (conf2). In contrast, only the second conformation (conf2) of the binding loop was observed in the structures of human and T. brucei DHS (Figure 1b). In the molB and molC of the PhoDHS tetramer, where NAD+ is absent, the binding loop is disordered or only partially visible. We propose that NAD+ binding to PhoDHS induces the stabilization of the flexible loop structure, but that the alternative conformations indicate the action of the loop as a “lid.” In an open conformation (conf1), the active site is exposed to receive NAD+, and the lid is closed over NAD+ in conf2. This controlled switched direction of the binding loop in the presence of NAD+ may be important for the regulation of NAD+ and/or spermidine binding, the “lid” potentially opening and closing in response to the surrounding NAD+ content. In the absence of NAD+, the binding loop is presumably much more flexible in structure and thus invisible in molB and molC. Our PhoDHS structure is the first structure showing distinct differences in the NAD+-bound state of the DHS active site, differences additional to those in the NAD+-free state. This may be an additional pointer to a complex role for conformational dynamics in the function of DHS in the cell, potentially linked to the regulation of the translation process via a hypusine modification of IF5A.
The binding geometry of NAD+ in PhoDHS is similar to that in human DHS [21]. The two NAD+ molecules (NAD1 and NAD2) are embraced individually by molA and molD of PhoDHS in a sandwich binding manner and insert their ends into the pockets of molA, and molD with 2-fold symmetry (Figure 3b). The closest distance between NAD1 and NAD2 is 2.7 Å, where a hydrogen bond can form between the ribose hydroxyl group of each adenosine moiety (Figure 3a). Such interaction between NAD1 and NAD2 stabilizes their binding to DHS, which may explain why, when only two NAD+s are bound, these bind to molAD but not molAB. A cluster of residues from molA and molD compose a binding site for NAD+: NAD1 forms hydrogen bonds with Thr101, Ala102, Gly103, Glu107, Asp214, Ser262, Thr286, Ala287 and Asp320 from molA, and stacks with the imidazole ring of His266 from molD. Interestingly, the main chain groups of Leu294 and Ser295 of the binding loop from molD in conf2 are also involved in a hydrogen bonding network to NAD1. To further characterize the binding mode, we generate the two alternative conformations (conf1, conf2) in both molA and molD by the superimposition of the molA and molD, which shows that the binding loop in conf2 of molA is more tightly associated with the NAD2 (which shares a similarly bound geometry to NAD1) of molD than with NAD1. Except for Ser262, all residues that contact NAD+ are conserved (Figure 2). The crossover of interactions would suggest that molA and molD enhance NAD+ binding to each other, and there is likely to be a synergistic effect between the binding of NAD1 and NAD2, mediated by the flexible loop.
In the previously reported DHS structures [21,22,23], four NAD+ were accommodated individually in four active sites of the tetramer. In our PhoDHS structure, however, the occupation of only two active sites by NAD+ is observed for the first time. To further investigate the NAD+ binding state of the PhoDHS tetramer, we carried out a UV absorption scan using PhoDHS in solution (Figure 3c). There is an obvious difference in the absorption curves of PhoDHS alone and PhoDHS supplemented with NAD+. The match between the absorption scan for PhoDHS + 30 equiv NAD+ (molar ratio of PhoDHS:NAD+ = 1:30) and PhoDHS + two equiv NAD+ (molar ratio 1:2) indicates that PhoDHS can only accommodate two additional NAD+ even if excess NAD+ molecules are available, suggesting that there are two vacant sites in the purified PhoDHS, which become occupied in both conditions. The spectroscopy confirms that there are likely only two out of four binding sites that are occupied by NAD+ in purified PhoDHS in solution, validating the observations from crystallography. The fact that human DHS tetramers bind four NAD+ molecules at high concentrations of NAD+ [21,22] does not exclude the binding of only two NAD+ molecules when the NAD+ concentration is low. Conversely, our spectroscopy data show that PhoDHS can likely bind four NAD+ molecules when sufficient NAD+ is present. The lower binding ratio of NAD+ observed in PhoDHS could infer that DHS may also adopt distinct conformations for NAD+ binding in other species, probably regulated by the local concentration of cellular NAD+, which may offer a control mechanism for IF5A modification and, therefore, activity in cell processes.
2.3. Dynamic Conformation of the Region for Both NAD<sup>+</sup> and Spermidine Binding
As described above, the comparison of PhoDHS monomers reveals the conformational dynamics near the active site. The region V288–K311, located in a valley of the DHS tetramer center, shows either free mobility (and therefore disorder) in the absence of bound NAD+ or two distinct conformations in its presence. In all four monomers, the electron density was lacking for G306–K311, which includes the catalytic Lys307, the imine bond acceptor with spermidine, and thus the aminobutyl donor to aIF5A. In a crystal structure of a human DHS ternary complex, this region is visualized and interacts with NAD+ and a spermidine analog, GC7 [22]. In a recent published paper, it was suggested that no conformational changes occur in the DHS structure upon binding with spermidine [23]. Our identification of the binding loop region V288–P299, acting as a lid over the active site to give open (conf1) and closed (conf2) forms, is indicative of a significant conformational change, at least with respect to NAD binding (Figure 3d). Upon the superposition of the PhoDHS and human DHS (complexed with GC7) structures, we found that GC7 has very close access (about 4.4 Å) to the V288–P299 loop in conf2, but is substantially far away (about 8.2 Å) in conf1 (Figure 3e). Thus, both NAD+ and spermidine can interact with the binding loop in conf2 but not in conf1, suggesting that the flexible binding loop plays a role in accepting substrates, even when NAD+ has bound. We can, therefore, consider three conformational states of this loop in relation to its function (Figure 4): (I) apo form: the disordered structure observed in molB and molC of PhoDHS, prior to NAD+ binding; (II) open form (conf1), the lid is away from the bound NAD+ (observed in molA) in PhoDHS, and is awaiting substrate binding; (III) reaction/closed form (conf2), the lid closes to contact the bound NAD+ (observed in molD) and also spermidine (as indicated in human DHS). Since PhoDHS interacts with NAD+, spermidine, and IF5A via this region, the conformational dynamics are perhaps an unsurprising potential contributor to the achievement of the different conformations for binding different partners. It is also possible, as stated earlier, that the conformational dynamics are a mechanism by which DHS can respond to the concentration of NAD+ and fine-tune the regulation of translation in organisms via the hypusine modification of IF5A.
3. Discussion
Taking the results as a whole, we propose that the binding of NAD+ to PhoDHS is regulated by the NAD+ concentration, which might itself be linked to the requirement for IF5A modification and the subsequent participation in translation. One PhoDHS tetramer binds two NAD+s at a low concentration of NAD+, but may be able to bind two additional NAD+s when supplemented with higher NAD+ concentrations. This new model for NAD+ binding by DHS is shown in Figure 4 with PhoDHS existing in three states: no NAD+, one PhoDHS tetramer binding two NAD+s on one side (molAD or molBC), and one PhoDHS tetramer binding four NAD+s. Our structural data definitively demonstrate the different conformations of loop V288–P299 in each monomer of PhoDHS, with the significant structural stabilization induced by NAD+ binding, which, with the help of a comparison to the human DHS:GC7 structure, is likely stabilized into the final closed conformation (conf2) upon binding spermidine. Indeed, the different conformations of the active site “lid,” or binding loop, observed in our structure are likely to reflect the absence of stabilizing interactions that spermidine binding would provide. Our structure provides a potential explanation for the NAD+-dependent binding of spermidine to DHS [29]. The conformation diversity observed in this region in our structure is compatible with an independence of NAD+ binding to each monomer, suggesting that monomers of DHS may behave equally whether in a tetramer, dimer or heterodimers. Strikingly, our structure presents a simultaneous combination of three different conformations at the spermidine binding site that are reflective of a three-state conformational regulation of DHS by NAD+: the apo form, the open form, and the closed/reaction form. The structure thus highlights the role of conformational dynamics in the regulation of DHS and hypusine modification of aIF5A.
To further address the relationship between the conformational changes of region V288–P299 observed here and the binding of spermidine and IF5A to generate hypusinated IF5A, the complex structure of IF5A-DHS is an important future goal.
4. Materials and Methods4.1. Expression and Purification of PhoDHS
The gene encoding DHS (1–342 aa, 39 kDa) from P. horikoshii OT3 (UniProtKB-O50105) was cloned into a modified pET26b expression vector with a hexahistidine (His6) tag and a TEV cleavage sequence (Glu-Asn-Leu-Tyr-Phe-Gln-Gly) at the N-terminus. For overexpression, the recombinant plasmid containing PhoDHS was transformed into E. coli strain B834 (DE3) pRARE2 by electroporation. The transformants were inoculated into Luria–Bertani (LB) containing 25 μg/mL kanamycin and 34 μg/mL chloramphenicol and cultivated at 30 °C until the optical density at 600 nm (OD600) reached about 0.6. After adding isopropyl β-d-1-thiogalactopyranoside (IPTG) to a final concentration of 1 mM, the cells were grown for an additional 16 h.
The cells were harvested by centrifugation at 4500× g at 25 °C for 20 min, and then washed with cell-wash-buffer (50 mM Tris-HCl, pH 8.0, 50 mM NaCl). The washed cells were disrupted by sonication with cell-lysis buffer (50 mM Tris-HCl, pH 8.5, 500 mM NaCl, 5% glycerol, 0.25 mM DTT, 25 mM imidazole) in the presence of 100 μg/mL deoxyribonuclease I and 50 μg/mL lysozyme, and the cell debris was then removed by centrifugation at 40,000× g for 30 min. The cell lysate was heat-treated at 70 °C for 30 min and centrifuged at 40,000× g for 30 min. The supernatant was filtered through a 0.22 μm filter and then loaded onto a Ni-affinity chromatography column (1 mL Histrap™ HP; GE Healthcare, Chicago, IL, USA) equilibrated with buffer A (50 mM Tris-HCl, pH 8.5, 500 mM NaCl, 5% glycerol, 0.25 mM DTT, 25 mM imidazole). The samples were eluted with a gradient of 25–400 mM imidazole in buffer A. The eluted fractions of the proteins were treated with TEV protease and dialyzed against buffer B (50 mM Tris-HCl, pH 8.5, 25 mM NaCl, 5% glycerol, 0.25 mM DTT, 25 mM imidazole) to remove the cleaved His tag. The dialyzed proteins were purified again using the Ni-affinity column, and the flow-through fraction was collected. The proteins were then purified using a 1 mL RESOURCETM Q column (GE Healthcare) with buffer C (20 mM Tris-HCl, pH 8.5, 25 mM NaCl, 5% glycerol, 0.25 mM DTT). The bound protein was eluted with a linear gradient of NaCl (25–2000 mM). The fractions containing DHS were pooled and then load on to a Superdex 75 16/60 column (GE Healthcare, Chicago, IL, USA) in buffer D (20 mM HEPES, pH 8.0, 200 mM NaCl, 5% glycerol, and 1 mM DTT), and DHS was eluted as a single peak with an estimated molecular weight around 130 kDa, corresponding to the tetramer. Fractions containing purified tetramer were combined and concentrated using VIVAPORE 10/20 (2–20 mL). All steps were monitored by 15% SDS-PAGE.
4.2. Crystallization and Data Collection
Preliminary crystallization trials were performed using a crystallization screening kit from QIAGEN (JCSG I-IV, JCSG + and PEG I&II, Hilden, Germany). A crystallization drop containing 1 μL of protein solution and 1 μL reservoir solution was set up and equilibrated against 100 μL of reservoir solution at 20 °C using the sitting-drop method. Initial crystals were grown in a condition containing 100 mM phosphate–citrate pH 4.2, 40% ethanol, 5% PEG 1000. After the optimization of crystallization conditions with respect to pH and precipitant concentration, well diffracted crystals were obtained in a condition containing 100 mM phosphate–citrate pH 4.8, 32% Ethanol, 5% PEG 1000.
For diffraction data collection, the crystals of PhoDHS were transferred into a cryo-protectant solution containing 15% glycerol in the reservoir solution, then mounted in a loop and flash-cooled in a stream of nitrogen gas at −173 °C. The X-ray diffraction experiment was performed with a wavelength of 1.0 Å at beamline BL41XU in SPring-8 (Hyogo, Japan). The diffraction data were indexed, integrated, scaled, and merged using the HKL2000 package [30]. The crystals belong to space group P212121, with the unit-cell parameters of a = 87.3 Å, b = 89.9 Å, c = 164.8 Å. Using a calculated molecular weight of 38.9 Da, the Matthews coefficient VM value was estimated to be 2.07 Å3Da−1 with 4 PhoDHS molecules in the asymmetric unit, corresponding to a solvent content of 40.56%. A summary of data collection and processing statistics is given in Table 1.
4.3. Structural Determination and Refinement
The structure of PhoDHS was solved by molecular replacement with MOLREP [31], using the protein structure of human DHS (PDB ID:1DHS) as a search model [21], which shows 39.5% amino acid sequence identity with PhoDHS. Initial rigid body refinement was performed with the REFMAC [32] of the CCP4 program suite [33] and the structure was then rebuilt using the program LAFIRE with CNS automatically [34,35]. Structural refinements were further performed using the program phenix_refine of the phenix program suite [36], followed by manual modifications with Coot [37]. After several refinement cycles, the NAD+ molecules were manually built based on 2Fo-Fc and Fo-Fc electron density maps. During the refinement of PhoDHS in complex with NAD+, the NAD+ molecules were rebuilt based on the omit-map several times and the occupancy of NAD+ molecules was adjusted manually following check of Fo-Fc and 2Fo-Fc maps. We also rebuilt the binding loop (V288–P299) several times based on the omit-map and the manually fine-tuned residues occupancy. The Rwork and Rfree factors were monitored during the refinement process, and the latter was calculated from 5% of the reflections. The final structure of PhoDHS was refined to a Rwork/Rfree = 17.52/22.95%, respectively. The summary of refinement statistics is shown in Table 1. The atomic coordinates of PhoDHS have been deposited in the Protein Data Bank under the accession number 7CMC. The buried areas of inter monomer were calculated using PDBePISA [38].
4.4. Analysis of NAD<sup>+</sup> Binding by UV Absorbance Spectroscopy
The purified PhoDHS was mixed with NAD+ (Roche, Nutley, New Jersey, USA) at the molar ratio of 1:30. The complex of PhoDHS and NAD+ was dialyzed in buffer C (20 mM HEPES, pH 8.0, 200 mM NaCl, 5% glycerol, and 1 mM DTT) by using EasySep (TOMY, Tokyo, Japan) to remove free, excess NAD+. The final concentration of the PhoDHS tetramer was about 5.87 μM. The UV absorption spectra of the dialyzed samples were measured by using a DU800 Spectrophotometer (Beckman, Brea, California, USA) at the scanning speed of 1200 nm/min. The dialyzed buffer was taken as the blank. NAD+ at 106 μM in buffer D (20 mM HEPES, pH 8.0, 200 mM NaCl, 5% glycerol, and 1 mM DTT) was used as control. The absorption curve of 2*NAD+ (11.74 μM), a 1:2 molar ratio of PhoDHS:NAD+ was similarly measured using the control curve of NAD+ (106 μM) as the reference.
Acknowledgments
We would like to thank H. Wakabayashi for her work on sample preparation. We thank the beamline staff of Spring-8 for their help with data collection.
Supplementary Materials
Supplementary materials can be found at https://www.mdpi.com/1422-0067/21/15/5509/s1.
Click here for additional data file.
Author Contributions
Conceptualization, M.C., C.O. and M.Y.; Investigation, C.O., Z.G., J.Y.; validation, Z.G., Y.Y. and M.C.; writing—original draft preparation, M.C., Z.G., Y.Y.; writing—review and editing, M.C., M.Y.; supervision, M.Y.; project administration, M.Y.; funding acquisition, M.Y. All authors have read and agreed to the published version of the manuscript.
Funding
This research was funded by Platform Project for Supporting Drug Discovery and Life Science Research (Basis for Supporting Innovative Drug Discovery and Life Science Research (BINDS)) from Japan Agency for Medical Research and Development (AMED) under Grant Number JP18am0101071.
Conflicts of Interest
The authors declare no conflict of interest.
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Crystal structure of deoxyhypusine synthase (DHS). The protein and NAD+ is shown by the ribbon and stick, respectively. (a) Overall structure of Pyrococcus horikoshii OT3 DHS (PhoDHS): (left) A tetramer of PhoDHS in an asymmetric unit with monomeric units colored green (molA), yellow (molB), magenta (molC) and cyan (molD). (right) Superimposition of molD (cyan) onto molA (green). A loop (V288–P299) around the NAD+ binding site is observed in two different conformations (blue and red) in molA (conformation 1, conf1) and molD (conformation 2, conf2), respectively. While the conf1 in molA is away from NAD+, the conf2 in molD moves towards NAD+. (b) Structural superposition of PhoDHS (green) with human (wheat, PDBID:1DHS) and Trypanosoma brucei (light purple, PDBID:6DFT) DHS. The conf1 and conf2 loop in the comparison molecules are labeled (in their respective colors), and the loop in conf1 (red) of molA is added for comparison.
Sequence alignment of PhoDHS with DHS from human and T. brucei. The ESPript 3.0 server was used to output the alignment [28]. The blue box above the sequence indicates that the sequences of at least two out of three organisms share a similarity, while the sequences identical in all the three organisms are marked with red fills. The secondary structure shown by the respective crystal structures for PhoDHS and human DHS are labeled at the top and bottom, respectively. The loop 288–299 with alternative conformations is indicated with grey stars above.
Active site of PhoDHS-bound NAD+. (a) Electron density map for NAD+. NAD+ is superposed with the 2Fo-Fc electron density map contoured at the 1σ level (blue). (b) Binding geometry of NAD+ by PhoDHS. (left) NAD+ is bounded to the interface of the dimer in a sandwich manner. (right) NAD+ extensively interacts with residues from both monomers via a hydrogen bond and electronic interaction. (c) Quantification of NAD+ bound to PhoDHS in solution by UV absorbance spectroscopy. An obvious difference in the absorption curves for PhoDHS alone (no NAD+ added) and supplemented with NAD+ as PhoDHS + 30 equiv NAD+ (molar ratio PhoDHS:NAD of 1:30), shows the binding of NAD+ to vacant sites in purified PhoDHS. The absorption curve of PhoDHS + two equiv NAD+ (molar ratio 1:2) correlates well to the curve PhoDHS + 30 equiv NAD+, indicating that PhoDHS could only accommodate two additional NAD+ even if excessive NAD+ molecules are available. (d) (left) Closed (blue) and open (red) forms of the binding loop V288–P299 with NAD+ bound. (right) Alternative conformations of the loop involved in NAD+ and spermidine binding. (e) Superposition of GC7 onto PhoDHS. GC7 is superposed onto PhoDHS by structural alignment with human DHS in complex with GC7. The loop of two conformations (conf1 and conf2) in molA and molD was generated by the superposition of the two monomers.
A model of the conformational dynamics in DHS, regulated by NAD+. Three binding conformations are shown. On the left, the spermidine binding loop is disordered when there is no NAD+ binding to DHS. In the middle, the loop is partially stabilized and structured in either conf1 or conf2 when NAD+ binds to DHS. The binding of one NAD+ to DHS could promote a second NAD+ to bind to another monomer of the dimer, and binding of both NAD+s would be stabilized via the formation of the hydrogen bond between them. The spermidine binding loop is still disordered in another dimer of the tetramer which remains NAD+-free. On the right, the spermidine binding loop of all four monomers of PhoDHS will be ordered in conf2 in the presence of abundant NAD+, based on the current structure and human DHS structure. Yellow lines: loop in conf1 away from NAD+; blue lines: loop in conf2 towards NAD+; N: NAD+ molecules.
ijms-21-05509-t001_Table 1
Summary of data collection and refinement statistics.
\nData Collection\n
Crystal
Native DHS
Space group
P212121
Beamline
Spring 8 BL41XU
Wavelength (Å)
1.0000
Unit-cell parameter
a = 87.3 Å, b = 89.9 Å, c = 164.8 Å, α = β= γ = 90°
Resolution range (Å)
50–2.20 (2.28–2.20)
Total number of reflections
666,150 (6550)
Completeness (%)
100.0 (100.0)
Redundancy
7.4 (7.4)
I/σ(I)
29.1 (5.2)
R-merge
0.110 (0.479)
\nRefinement Statistics\n
Resolution range (Å)
34.42–2.20
Rwork/Rfree (%)
17.52/22.95
R.m.s.d bond lengths (Å)
0.07
R.m.s.d angles (°)
0.93
No. non-H atoms
\n
Proteins
9817
Water molecules
408
NAD+ molecules
88
Average B value
\n
Protein
35.17
Water molecules
41.81
NAD+ molecules
71.98
Ramachandran Plot (%)
\n
Favored
98.91
Allowed
1.09
Outline
0.00
Values in parentheses are for the highest resolution shell. Rwork = Σhkl ||Fobs|−|Fcalc||/Σhkl |Fobs|, Rfree was calculated for 5% randomly selected test sets that were not used in the refinement.
\n"],"string":"[\n \"\\nInt J Mol SciInt J Mol SciijmsInternational Journal of Molecular Sciences1422-0067MDPI32752130PMC743209410.3390/ijms21155509ijms-21-05509ArticleFlexible NAD+ Binding in Deoxyhypusine Synthase Reflects the Dynamic Hypusine Modification of Translation Factor IF5AChenMeirong12†GaiZuoqi1†‡OkadaChiaki1§YeYuxin1‖https://orcid.org/0000-0003-2854-9225YuJian1https://orcid.org/0000-0003-1687-5904YaoMin1*Faculty of Advanced Life Science, Hokkaido University, Sapporo 060-0810, Japan; chenmr1987@163.com (M.C.); gaizuoqi@126.com (Z.G.); chiaki@hoku-iryo-u.ac.jp (C.O.); yeyx@pkusz.edu.cn (Y.Y.); yu@castor.sci.hokudai.ac.jp (J.Y.)College of Food Science and Technology, Nanjing Agricultural University, Nanjing 210095, ChinaCorrespondence: yao@castor.sci.hokudai.ac.jp; Tel./Fax: +81-11-706-4481
These authors contribute equally to this work.
The present address: School of Life Science and Engineering, Foshan University, Foshan 528231, China.
The present address: School of Pharmaceutical Sciences, Health Sciences University of Hokkaido, Ishikari-Tobetsu 061-0293, Japan.
The present address: School of Chemical Biology and Biotechnology, State Key Laboratory of Chemical Oncogenomics, Peking University Shenzhen Graduate School, Shenzhen 518055, China.
3172020820202115550931520202972020© 2020 by the authors.2020Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
The eukaryotic and archaeal translation factor IF5A requires a post-translational hypusine modification, which is catalyzed by deoxyhypusine synthase (DHS) at a single lysine residue of IF5A with NAD+ and spermidine as cofactors, followed by hydroxylation to form hypusine. While human DHS catalyzed reactions have been well characterized, the mechanism of the hypusination of archaeal IF5A by DHS is not clear. Here we report a DHS structure from Pyrococcus horikoshii OT3 (PhoDHS) at 2.2 Å resolution. The structure reveals two states in a single functional unit (tetramer): two NAD+-bound monomers with the NAD+ and spermidine binding sites observed in multi-conformations (closed and open), and two NAD+-free monomers. The dynamic loop region V288–P299, in the vicinity of the active site, adopts different positions in the closed and open conformations and is disordered when NAD+ is absent. Combined with NAD+ binding analysis, it is clear that PhoDHS can exist in three states: apo, PhoDHS-2 equiv NAD+, and PhoDHS-4 equiv NAD+, which are affected by the NAD+ concentration. Our results demonstrate the dynamic structure of PhoDHS at the NAD+ and spermidine binding site, with conformational changes that may be the response to the local NAD+ concentration, and thus fine-tune the regulation of the translation process via the hypusine modification of IF5A.
Hypusine (N-(4-amino-2-hydroxybutyl) lysine), an unusual amino acid, is formed by the post-translational modification of a lysine residue with the addition of the aminobutyl moiety from the polyamine spermidine. The hypusine modification uniquely occurs in eukaryotic translation factor 5A (eIF5A) precursor proteins [1]. Although eIF5A was originally designed as a translation initial factor, it has been shown that eIF5A is more directly related to elongation rather than the initiation step. The hypusine-modification of eIF5A is involved in the mRNA transportation and translation elongation on the ribosome [2,3]. The binding of eIF5A to translating ribosomes in a hypusine-dependent manner indicates modifications of the specific binding to the translational machinery [4,5], suggesting the important role of eIF5A receiving hypusine modifications in translation processes, which was supported by evidence that the hypusine modification of eIF5A is vital for the growth and survival of eukaryotes, from yeast to mammals [6,7,8]. Previous studies also reported that the hypusine residue is essential for the homodimerization of eIF5A and affects its subcellular location [1,2,9,10].
The hypusine modification of eIF5A is catalyzed by deoxyhypusine synthase (DHS), followed by the hydroxylation of deoxyhypusine by deoxyhypusine hydroxylase (DOHH) [1,11]. DHS catalyzes a NAD+-dependent reaction that synthesizes deoxyhypusine (N-(4-aminobutyl) lysine) by transferring the butylamine moiety of spermidine to a specific lysine residue of eIF5A [12]. Then, deoxyhypusine is hydroxylated by DOHH to form hypusine [13,14,15]. Like eIF5A, DHS is an essential gene, indicating the importance of hypusine modification [16]. Since eIF5A is associated with many diseases, such as diabetes, cancers, malaria, and HIV-1 infections [16], trials aimed at blocking the hypusine modification of eIF5A have been attempted. Specific inhibitors of DHS or DOHH have been reported to affect the arrest of cellular growth, and also alter the proliferation and differentiation of tumor cells [17,18,19]. An extensive biochemical characterization of DHS has been carried out, establishing it as a 40–43 kDa enzyme (the exact size being species-dependent) requiring NAD+ as a cofactor. The mechanism of human deoxyhypusine synthesis by DHS has been proposed as follows [20]: firstly, a hydrogen of spermidine is transferred to NAD+ to generate dehydrospermidine and NADH; the dehydrospermidine then links covalently to a lysine residue (K329 in human DHS) of DHS, forming an enzyme-imine intermediate; finally, DHS catalyzes the formation of deoxyhypusine through the conjugation of the butylamine moiety of dehydrospermidine to the ε-amino group of the lysine residue (K37) of the eIF5A precursor protein.
The reported crystal structures of human DHS show a homotetramer consisting of two tightly associated dimers with four active sites—two near each tight dimer interface [21,22]. Residues from both monomers of a dimer contribute to each of its active sites. In human DHS, four active sites accommodate four molecules of NAD+ (PDB:1DHS) [21,22]. Interestingly, the catalytic sites are blocked by a projecting N-terminal two-turn helix (described as a “ball and chain”) in a cross-monomer manner in the first crystal form of human DHS, obtained upon crystallization at high ionic strengths and a low pH. However, the catalytic site is not obstructed in a structure of human DHS in a ternary complex with NAD+ and GC7 (PDB:1RQD), an analog of spermidine [22]. In this ternary complex structure, GC7 is also located at the interface of the tight dimers and is separated from NAD+ by a loop structure (T307–A319) [22]. The absence of a blockage by the ball and chain structure might suggest that this dynamic part of the enzyme could be involved in the regulation of activity within the functional tetramer. However, this human DHS complex structure without any bound GC7 (PDB:1ROZ), determined upon crystallization at low ionic strengths and pH 8.0 (nearer physiological conditions), lacked the electron density for the N-terminal ball and chain region [22]. In recent research, it has been reported that the N-terminal ball and chain region is important for the assembly of a functional DHS tetramer [23]. This N-terminal region is, therefore, clearly highly dynamic, with the flexibility being for DHS function. Whether it is regulating the active site accessibility in a substrate-dependent manner, binding eIF5A, or conferring sensitivity to cellular conditions, remains unclear. Different from human DHS, the DHS from Trypanosoma brucei exists as a heterotetramer formed by two paralogs, DHSc and DHSp (dimer of heterodimer), with DHSc homologous to human DHS. Each heterodimer contains two NAD+ binding sites, one located in the functional catalytic site while the other is located in a remnant site lacking key catalytic residues [24].
Consistent with archaea sharing a requirement for hypusine for the function of translation factor aIF5A, both the modification [24] and DHS gene homologues [1] have been identified. Furthermore, a study has shown the arrest of archaeal growth in G1 upon treatment with an inhibitor of DHS [25], indicating that DHS is also essential in archaea. However, the mechanism of hypusination in archaea is unclear yet [26]. Homologues of the non-essential second enzyme required for hypusine formation, DOHH, have not been identified. While some archaea may function with a deoxyhypusine modification (rather than hypusine), species are known that form hypusine in the absence of oxygen, which implies a distinct mechanism of hypusine modification in archaea. The structure of an archaeal DHS could provide a better understanding of the process of hypusine modification of aIF5A.
In this study, we determined the crystal structure of DHS from Pyrococcus horikoshii OT3 (PhoDHS), with NAD+-bound, at 2.2 Å resolution. PhoDHS is a tetramer, each monomer sharing a similar fold with its human homologue (PDB ID:1DHS). Interestingly, unlike human and T. brucei DHS, PhoDHS contains only two bound NAD+ molecules and shows a difference in the NAD+-bound and NAD+-free monomeric conformations. In particular, NAD+-bound monomers in the PhoDHS tetramer display different conformations in the region of V288–P299 around the NAD+ binding site. The observations are suggestive of a distinct manner in which NAD+ binds to DHS that could be of relevance to the mechanism and regulation of enzyme activity. Having the states of NAD+-bound and NAD+-free also sheds light on the NAD+-dependent spermidine binding to DHS and raises a possibility that dynamic NAD+ binding in PhoDHS may fine-tune the translation activity of aIF5A via hypusine modification.
2. Results2.1. The Overall Structure of PhoDHS
We determined the crystal structure of PhoDHS at 2.2 Å resolution by using a molecular replacement (Table 1). In an asymmetric unit, the structure of PhsDHS formed a tetramer in accordance with its known biological assembly [21,22], with unbuilt N-terminal 14 and C-terminal 6 of the total 342 residues in each monomer. Additionally, we could not build the parts of two loops in each monomer (E122-D125 and G306–K311 in molA, W290–K311 in molB, E122–V123 and W290–A310 in molC, and W121–K130 and E301–K311 in molD, respectively), because of the lack of electron density. Similar to DHS from other organisms, this tetramer is a dimer of dimers with a point group of D2, generated by two local 2-fold axes that are perpendicular to each other (Figure 1). The monomers of PhoDHS tetramers are more related to each other with a root mean squared deviation (r.m.s.d) between 0.544 Å and 0.606 Å for 276–284 Cα atoms. Based on the buried areas of intermolecular interactions, the tightly bound dimers are defined as molAB and molCD (buried areas > 2700 Å2), while the buried area is less than 1800 Å2 between molA and molD (or between molB and molC). PhoDHS is very similar to human DHS (PDB ID:1DHS), with a r.m.s.d of 1.195 Å for 289 Cα, and is also similar to T. brucei DHS (PDB:6DFT) [24] with a r.m.s.d of 1.39 Å for 275 Cα (Figure 1b). Differences include the N-terminal region (M1–P26) and an insertion (L77–L94) in human DHS. Unlike the inactive human DHS structure, wherein the N-terminal two-turn α helix (A9–L18) blocks the entrance tunnel to the spermidine active site [21,22], the N-terminal region (M1–I14) of PhoDHS is not built due to the poor electron density for this region, suggesting that it is highly flexible and different from human DHS, despite the crystallization conditions being similar to those of inactive human DHS [21]. Given that the active sites are accessible, we assume that the structure of PhoDHS obtained is illustrative of an active form.
Although the overall structures of DHS are similar, there is a significant structural difference in a loop region (V288–P299) that forms part of the active site of PhoDHS. This active site loop is flexible and presents itself in multi-conformations, conf1 and conf2 in molA and molD, respectively, which are likely to represent two snapshots of the active site (Figure S1). In the molB and molC of PhoDHS, the loop region is either disordered, or partially visible. Although only one of two conformations, conformation 1 or conformation 2 (conf1, conf2), was assigned to each molA and molD, a weak density of the alternative conformations could be observed, further suggesting the flexibility of the loop region in PhoDHS. In contrast, this active site loop is stable and present in just one conformation (conf2) in the previously published human and T. brucei DHS structures [24].
Another striking structural difference seen in PhoDHS is at its C-terminus. The C-terminus of PhoDHS (Gly337–Gln342, in all four chains) is invisible in the structure, which could be observed in human and Trypanosoma brucei DHS [24]. A sequence alignment shows that the primary structure of this region is not conserved in PhoDHS, human DHS, and T. brucei DHS (Figure 2). The short helix predicted for PhoDHS is longer in human DHS. The helix is visible in the human DHS structure, though the region retains some intrinsic flexibility, indicating conformational dynamics in the C-terminal region across species. Removal of five residues from the C-terminus in human DHS or 39 residues from the C-terminus of S. cerevisiae DHS impairs activity [26,27]. It is probable that the C-terminus of PhoDHS is also required for full function, and that the intrinsic flexibility of this region, rather than the conserved sequences, is likely important for the activity.
2.2. NAD<sup>+</sup> Binding Manner of PhoDHS
Two bulky electron density blocks shaped as NAD+ were distinguished at the interface between molA and molD of the PhoDHS tetramer after several cycles of refinement and were later assigned as NAD+ (Figure 3a and Figure S2). Those NAD+ molecules were naturally captured during the expression of the enzyme in Escherichia coli, since no NAD+ was added during purification and crystallization processes.
Bound NAD+ is proximal to a loop (V288–P299 (hereafter named “the binding loop”)) that displays very different conformations in each monomer of the PhoDHS tetramer. Notably, in molA and molD, observed to bind NAD+, the binding loop adopts two different conformations (Figure 1a, right): in molA, the binding loop positions away from NAD+ (conf1), while in molD, the binding loop adopts another conformation to access NAD+ (conf2). In contrast, only the second conformation (conf2) of the binding loop was observed in the structures of human and T. brucei DHS (Figure 1b). In the molB and molC of the PhoDHS tetramer, where NAD+ is absent, the binding loop is disordered or only partially visible. We propose that NAD+ binding to PhoDHS induces the stabilization of the flexible loop structure, but that the alternative conformations indicate the action of the loop as a “lid.” In an open conformation (conf1), the active site is exposed to receive NAD+, and the lid is closed over NAD+ in conf2. This controlled switched direction of the binding loop in the presence of NAD+ may be important for the regulation of NAD+ and/or spermidine binding, the “lid” potentially opening and closing in response to the surrounding NAD+ content. In the absence of NAD+, the binding loop is presumably much more flexible in structure and thus invisible in molB and molC. Our PhoDHS structure is the first structure showing distinct differences in the NAD+-bound state of the DHS active site, differences additional to those in the NAD+-free state. This may be an additional pointer to a complex role for conformational dynamics in the function of DHS in the cell, potentially linked to the regulation of the translation process via a hypusine modification of IF5A.
The binding geometry of NAD+ in PhoDHS is similar to that in human DHS [21]. The two NAD+ molecules (NAD1 and NAD2) are embraced individually by molA and molD of PhoDHS in a sandwich binding manner and insert their ends into the pockets of molA, and molD with 2-fold symmetry (Figure 3b). The closest distance between NAD1 and NAD2 is 2.7 Å, where a hydrogen bond can form between the ribose hydroxyl group of each adenosine moiety (Figure 3a). Such interaction between NAD1 and NAD2 stabilizes their binding to DHS, which may explain why, when only two NAD+s are bound, these bind to molAD but not molAB. A cluster of residues from molA and molD compose a binding site for NAD+: NAD1 forms hydrogen bonds with Thr101, Ala102, Gly103, Glu107, Asp214, Ser262, Thr286, Ala287 and Asp320 from molA, and stacks with the imidazole ring of His266 from molD. Interestingly, the main chain groups of Leu294 and Ser295 of the binding loop from molD in conf2 are also involved in a hydrogen bonding network to NAD1. To further characterize the binding mode, we generate the two alternative conformations (conf1, conf2) in both molA and molD by the superimposition of the molA and molD, which shows that the binding loop in conf2 of molA is more tightly associated with the NAD2 (which shares a similarly bound geometry to NAD1) of molD than with NAD1. Except for Ser262, all residues that contact NAD+ are conserved (Figure 2). The crossover of interactions would suggest that molA and molD enhance NAD+ binding to each other, and there is likely to be a synergistic effect between the binding of NAD1 and NAD2, mediated by the flexible loop.
In the previously reported DHS structures [21,22,23], four NAD+ were accommodated individually in four active sites of the tetramer. In our PhoDHS structure, however, the occupation of only two active sites by NAD+ is observed for the first time. To further investigate the NAD+ binding state of the PhoDHS tetramer, we carried out a UV absorption scan using PhoDHS in solution (Figure 3c). There is an obvious difference in the absorption curves of PhoDHS alone and PhoDHS supplemented with NAD+. The match between the absorption scan for PhoDHS + 30 equiv NAD+ (molar ratio of PhoDHS:NAD+ = 1:30) and PhoDHS + two equiv NAD+ (molar ratio 1:2) indicates that PhoDHS can only accommodate two additional NAD+ even if excess NAD+ molecules are available, suggesting that there are two vacant sites in the purified PhoDHS, which become occupied in both conditions. The spectroscopy confirms that there are likely only two out of four binding sites that are occupied by NAD+ in purified PhoDHS in solution, validating the observations from crystallography. The fact that human DHS tetramers bind four NAD+ molecules at high concentrations of NAD+ [21,22] does not exclude the binding of only two NAD+ molecules when the NAD+ concentration is low. Conversely, our spectroscopy data show that PhoDHS can likely bind four NAD+ molecules when sufficient NAD+ is present. The lower binding ratio of NAD+ observed in PhoDHS could infer that DHS may also adopt distinct conformations for NAD+ binding in other species, probably regulated by the local concentration of cellular NAD+, which may offer a control mechanism for IF5A modification and, therefore, activity in cell processes.
2.3. Dynamic Conformation of the Region for Both NAD<sup>+</sup> and Spermidine Binding
As described above, the comparison of PhoDHS monomers reveals the conformational dynamics near the active site. The region V288–K311, located in a valley of the DHS tetramer center, shows either free mobility (and therefore disorder) in the absence of bound NAD+ or two distinct conformations in its presence. In all four monomers, the electron density was lacking for G306–K311, which includes the catalytic Lys307, the imine bond acceptor with spermidine, and thus the aminobutyl donor to aIF5A. In a crystal structure of a human DHS ternary complex, this region is visualized and interacts with NAD+ and a spermidine analog, GC7 [22]. In a recent published paper, it was suggested that no conformational changes occur in the DHS structure upon binding with spermidine [23]. Our identification of the binding loop region V288–P299, acting as a lid over the active site to give open (conf1) and closed (conf2) forms, is indicative of a significant conformational change, at least with respect to NAD binding (Figure 3d). Upon the superposition of the PhoDHS and human DHS (complexed with GC7) structures, we found that GC7 has very close access (about 4.4 Å) to the V288–P299 loop in conf2, but is substantially far away (about 8.2 Å) in conf1 (Figure 3e). Thus, both NAD+ and spermidine can interact with the binding loop in conf2 but not in conf1, suggesting that the flexible binding loop plays a role in accepting substrates, even when NAD+ has bound. We can, therefore, consider three conformational states of this loop in relation to its function (Figure 4): (I) apo form: the disordered structure observed in molB and molC of PhoDHS, prior to NAD+ binding; (II) open form (conf1), the lid is away from the bound NAD+ (observed in molA) in PhoDHS, and is awaiting substrate binding; (III) reaction/closed form (conf2), the lid closes to contact the bound NAD+ (observed in molD) and also spermidine (as indicated in human DHS). Since PhoDHS interacts with NAD+, spermidine, and IF5A via this region, the conformational dynamics are perhaps an unsurprising potential contributor to the achievement of the different conformations for binding different partners. It is also possible, as stated earlier, that the conformational dynamics are a mechanism by which DHS can respond to the concentration of NAD+ and fine-tune the regulation of translation in organisms via the hypusine modification of IF5A.
3. Discussion
Taking the results as a whole, we propose that the binding of NAD+ to PhoDHS is regulated by the NAD+ concentration, which might itself be linked to the requirement for IF5A modification and the subsequent participation in translation. One PhoDHS tetramer binds two NAD+s at a low concentration of NAD+, but may be able to bind two additional NAD+s when supplemented with higher NAD+ concentrations. This new model for NAD+ binding by DHS is shown in Figure 4 with PhoDHS existing in three states: no NAD+, one PhoDHS tetramer binding two NAD+s on one side (molAD or molBC), and one PhoDHS tetramer binding four NAD+s. Our structural data definitively demonstrate the different conformations of loop V288–P299 in each monomer of PhoDHS, with the significant structural stabilization induced by NAD+ binding, which, with the help of a comparison to the human DHS:GC7 structure, is likely stabilized into the final closed conformation (conf2) upon binding spermidine. Indeed, the different conformations of the active site “lid,” or binding loop, observed in our structure are likely to reflect the absence of stabilizing interactions that spermidine binding would provide. Our structure provides a potential explanation for the NAD+-dependent binding of spermidine to DHS [29]. The conformation diversity observed in this region in our structure is compatible with an independence of NAD+ binding to each monomer, suggesting that monomers of DHS may behave equally whether in a tetramer, dimer or heterodimers. Strikingly, our structure presents a simultaneous combination of three different conformations at the spermidine binding site that are reflective of a three-state conformational regulation of DHS by NAD+: the apo form, the open form, and the closed/reaction form. The structure thus highlights the role of conformational dynamics in the regulation of DHS and hypusine modification of aIF5A.
To further address the relationship between the conformational changes of region V288–P299 observed here and the binding of spermidine and IF5A to generate hypusinated IF5A, the complex structure of IF5A-DHS is an important future goal.
4. Materials and Methods4.1. Expression and Purification of PhoDHS
The gene encoding DHS (1–342 aa, 39 kDa) from P. horikoshii OT3 (UniProtKB-O50105) was cloned into a modified pET26b expression vector with a hexahistidine (His6) tag and a TEV cleavage sequence (Glu-Asn-Leu-Tyr-Phe-Gln-Gly) at the N-terminus. For overexpression, the recombinant plasmid containing PhoDHS was transformed into E. coli strain B834 (DE3) pRARE2 by electroporation. The transformants were inoculated into Luria–Bertani (LB) containing 25 μg/mL kanamycin and 34 μg/mL chloramphenicol and cultivated at 30 °C until the optical density at 600 nm (OD600) reached about 0.6. After adding isopropyl β-d-1-thiogalactopyranoside (IPTG) to a final concentration of 1 mM, the cells were grown for an additional 16 h.
The cells were harvested by centrifugation at 4500× g at 25 °C for 20 min, and then washed with cell-wash-buffer (50 mM Tris-HCl, pH 8.0, 50 mM NaCl). The washed cells were disrupted by sonication with cell-lysis buffer (50 mM Tris-HCl, pH 8.5, 500 mM NaCl, 5% glycerol, 0.25 mM DTT, 25 mM imidazole) in the presence of 100 μg/mL deoxyribonuclease I and 50 μg/mL lysozyme, and the cell debris was then removed by centrifugation at 40,000× g for 30 min. The cell lysate was heat-treated at 70 °C for 30 min and centrifuged at 40,000× g for 30 min. The supernatant was filtered through a 0.22 μm filter and then loaded onto a Ni-affinity chromatography column (1 mL Histrap™ HP; GE Healthcare, Chicago, IL, USA) equilibrated with buffer A (50 mM Tris-HCl, pH 8.5, 500 mM NaCl, 5% glycerol, 0.25 mM DTT, 25 mM imidazole). The samples were eluted with a gradient of 25–400 mM imidazole in buffer A. The eluted fractions of the proteins were treated with TEV protease and dialyzed against buffer B (50 mM Tris-HCl, pH 8.5, 25 mM NaCl, 5% glycerol, 0.25 mM DTT, 25 mM imidazole) to remove the cleaved His tag. The dialyzed proteins were purified again using the Ni-affinity column, and the flow-through fraction was collected. The proteins were then purified using a 1 mL RESOURCETM Q column (GE Healthcare) with buffer C (20 mM Tris-HCl, pH 8.5, 25 mM NaCl, 5% glycerol, 0.25 mM DTT). The bound protein was eluted with a linear gradient of NaCl (25–2000 mM). The fractions containing DHS were pooled and then load on to a Superdex 75 16/60 column (GE Healthcare, Chicago, IL, USA) in buffer D (20 mM HEPES, pH 8.0, 200 mM NaCl, 5% glycerol, and 1 mM DTT), and DHS was eluted as a single peak with an estimated molecular weight around 130 kDa, corresponding to the tetramer. Fractions containing purified tetramer were combined and concentrated using VIVAPORE 10/20 (2–20 mL). All steps were monitored by 15% SDS-PAGE.
4.2. Crystallization and Data Collection
Preliminary crystallization trials were performed using a crystallization screening kit from QIAGEN (JCSG I-IV, JCSG + and PEG I&II, Hilden, Germany). A crystallization drop containing 1 μL of protein solution and 1 μL reservoir solution was set up and equilibrated against 100 μL of reservoir solution at 20 °C using the sitting-drop method. Initial crystals were grown in a condition containing 100 mM phosphate–citrate pH 4.2, 40% ethanol, 5% PEG 1000. After the optimization of crystallization conditions with respect to pH and precipitant concentration, well diffracted crystals were obtained in a condition containing 100 mM phosphate–citrate pH 4.8, 32% Ethanol, 5% PEG 1000.
For diffraction data collection, the crystals of PhoDHS were transferred into a cryo-protectant solution containing 15% glycerol in the reservoir solution, then mounted in a loop and flash-cooled in a stream of nitrogen gas at −173 °C. The X-ray diffraction experiment was performed with a wavelength of 1.0 Å at beamline BL41XU in SPring-8 (Hyogo, Japan). The diffraction data were indexed, integrated, scaled, and merged using the HKL2000 package [30]. The crystals belong to space group P212121, with the unit-cell parameters of a = 87.3 Å, b = 89.9 Å, c = 164.8 Å. Using a calculated molecular weight of 38.9 Da, the Matthews coefficient VM value was estimated to be 2.07 Å3Da−1 with 4 PhoDHS molecules in the asymmetric unit, corresponding to a solvent content of 40.56%. A summary of data collection and processing statistics is given in Table 1.
4.3. Structural Determination and Refinement
The structure of PhoDHS was solved by molecular replacement with MOLREP [31], using the protein structure of human DHS (PDB ID:1DHS) as a search model [21], which shows 39.5% amino acid sequence identity with PhoDHS. Initial rigid body refinement was performed with the REFMAC [32] of the CCP4 program suite [33] and the structure was then rebuilt using the program LAFIRE with CNS automatically [34,35]. Structural refinements were further performed using the program phenix_refine of the phenix program suite [36], followed by manual modifications with Coot [37]. After several refinement cycles, the NAD+ molecules were manually built based on 2Fo-Fc and Fo-Fc electron density maps. During the refinement of PhoDHS in complex with NAD+, the NAD+ molecules were rebuilt based on the omit-map several times and the occupancy of NAD+ molecules was adjusted manually following check of Fo-Fc and 2Fo-Fc maps. We also rebuilt the binding loop (V288–P299) several times based on the omit-map and the manually fine-tuned residues occupancy. The Rwork and Rfree factors were monitored during the refinement process, and the latter was calculated from 5% of the reflections. The final structure of PhoDHS was refined to a Rwork/Rfree = 17.52/22.95%, respectively. The summary of refinement statistics is shown in Table 1. The atomic coordinates of PhoDHS have been deposited in the Protein Data Bank under the accession number 7CMC. The buried areas of inter monomer were calculated using PDBePISA [38].
4.4. Analysis of NAD<sup>+</sup> Binding by UV Absorbance Spectroscopy
The purified PhoDHS was mixed with NAD+ (Roche, Nutley, New Jersey, USA) at the molar ratio of 1:30. The complex of PhoDHS and NAD+ was dialyzed in buffer C (20 mM HEPES, pH 8.0, 200 mM NaCl, 5% glycerol, and 1 mM DTT) by using EasySep (TOMY, Tokyo, Japan) to remove free, excess NAD+. The final concentration of the PhoDHS tetramer was about 5.87 μM. The UV absorption spectra of the dialyzed samples were measured by using a DU800 Spectrophotometer (Beckman, Brea, California, USA) at the scanning speed of 1200 nm/min. The dialyzed buffer was taken as the blank. NAD+ at 106 μM in buffer D (20 mM HEPES, pH 8.0, 200 mM NaCl, 5% glycerol, and 1 mM DTT) was used as control. The absorption curve of 2*NAD+ (11.74 μM), a 1:2 molar ratio of PhoDHS:NAD+ was similarly measured using the control curve of NAD+ (106 μM) as the reference.
Acknowledgments
We would like to thank H. Wakabayashi for her work on sample preparation. We thank the beamline staff of Spring-8 for their help with data collection.
Supplementary Materials
Supplementary materials can be found at https://www.mdpi.com/1422-0067/21/15/5509/s1.
Click here for additional data file.
Author Contributions
Conceptualization, M.C., C.O. and M.Y.; Investigation, C.O., Z.G., J.Y.; validation, Z.G., Y.Y. and M.C.; writing—original draft preparation, M.C., Z.G., Y.Y.; writing—review and editing, M.C., M.Y.; supervision, M.Y.; project administration, M.Y.; funding acquisition, M.Y. All authors have read and agreed to the published version of the manuscript.
Funding
This research was funded by Platform Project for Supporting Drug Discovery and Life Science Research (Basis for Supporting Innovative Drug Discovery and Life Science Research (BINDS)) from Japan Agency for Medical Research and Development (AMED) under Grant Number JP18am0101071.
Conflicts of Interest
The authors declare no conflict of interest.
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Crystal structure of deoxyhypusine synthase (DHS). The protein and NAD+ is shown by the ribbon and stick, respectively. (a) Overall structure of Pyrococcus horikoshii OT3 DHS (PhoDHS): (left) A tetramer of PhoDHS in an asymmetric unit with monomeric units colored green (molA), yellow (molB), magenta (molC) and cyan (molD). (right) Superimposition of molD (cyan) onto molA (green). A loop (V288–P299) around the NAD+ binding site is observed in two different conformations (blue and red) in molA (conformation 1, conf1) and molD (conformation 2, conf2), respectively. While the conf1 in molA is away from NAD+, the conf2 in molD moves towards NAD+. (b) Structural superposition of PhoDHS (green) with human (wheat, PDBID:1DHS) and Trypanosoma brucei (light purple, PDBID:6DFT) DHS. The conf1 and conf2 loop in the comparison molecules are labeled (in their respective colors), and the loop in conf1 (red) of molA is added for comparison.
Sequence alignment of PhoDHS with DHS from human and T. brucei. The ESPript 3.0 server was used to output the alignment [28]. The blue box above the sequence indicates that the sequences of at least two out of three organisms share a similarity, while the sequences identical in all the three organisms are marked with red fills. The secondary structure shown by the respective crystal structures for PhoDHS and human DHS are labeled at the top and bottom, respectively. The loop 288–299 with alternative conformations is indicated with grey stars above.
Active site of PhoDHS-bound NAD+. (a) Electron density map for NAD+. NAD+ is superposed with the 2Fo-Fc electron density map contoured at the 1σ level (blue). (b) Binding geometry of NAD+ by PhoDHS. (left) NAD+ is bounded to the interface of the dimer in a sandwich manner. (right) NAD+ extensively interacts with residues from both monomers via a hydrogen bond and electronic interaction. (c) Quantification of NAD+ bound to PhoDHS in solution by UV absorbance spectroscopy. An obvious difference in the absorption curves for PhoDHS alone (no NAD+ added) and supplemented with NAD+ as PhoDHS + 30 equiv NAD+ (molar ratio PhoDHS:NAD of 1:30), shows the binding of NAD+ to vacant sites in purified PhoDHS. The absorption curve of PhoDHS + two equiv NAD+ (molar ratio 1:2) correlates well to the curve PhoDHS + 30 equiv NAD+, indicating that PhoDHS could only accommodate two additional NAD+ even if excessive NAD+ molecules are available. (d) (left) Closed (blue) and open (red) forms of the binding loop V288–P299 with NAD+ bound. (right) Alternative conformations of the loop involved in NAD+ and spermidine binding. (e) Superposition of GC7 onto PhoDHS. GC7 is superposed onto PhoDHS by structural alignment with human DHS in complex with GC7. The loop of two conformations (conf1 and conf2) in molA and molD was generated by the superposition of the two monomers.
A model of the conformational dynamics in DHS, regulated by NAD+. Three binding conformations are shown. On the left, the spermidine binding loop is disordered when there is no NAD+ binding to DHS. In the middle, the loop is partially stabilized and structured in either conf1 or conf2 when NAD+ binds to DHS. The binding of one NAD+ to DHS could promote a second NAD+ to bind to another monomer of the dimer, and binding of both NAD+s would be stabilized via the formation of the hydrogen bond between them. The spermidine binding loop is still disordered in another dimer of the tetramer which remains NAD+-free. On the right, the spermidine binding loop of all four monomers of PhoDHS will be ordered in conf2 in the presence of abundant NAD+, based on the current structure and human DHS structure. Yellow lines: loop in conf1 away from NAD+; blue lines: loop in conf2 towards NAD+; N: NAD+ molecules.
ijms-21-05509-t001_Table 1
Summary of data collection and refinement statistics.
\\nData Collection\\n
Crystal
Native DHS
Space group
P212121
Beamline
Spring 8 BL41XU
Wavelength (Å)
1.0000
Unit-cell parameter
a = 87.3 Å, b = 89.9 Å, c = 164.8 Å, α = β= γ = 90°
Resolution range (Å)
50–2.20 (2.28–2.20)
Total number of reflections
666,150 (6550)
Completeness (%)
100.0 (100.0)
Redundancy
7.4 (7.4)
I/σ(I)
29.1 (5.2)
R-merge
0.110 (0.479)
\\nRefinement Statistics\\n
Resolution range (Å)
34.42–2.20
Rwork/Rfree (%)
17.52/22.95
R.m.s.d bond lengths (Å)
0.07
R.m.s.d angles (°)
0.93
No. non-H atoms
\\n
Proteins
9817
Water molecules
408
NAD+ molecules
88
Average B value
\\n
Protein
35.17
Water molecules
41.81
NAD+ molecules
71.98
Ramachandran Plot (%)
\\n
Favored
98.91
Allowed
1.09
Outline
0.00
Values in parentheses are for the highest resolution shell. Rwork = Σhkl ||Fobs|−|Fcalc||/Σhkl |Fobs|, Rfree was calculated for 5% randomly selected test sets that were not used in the refinement.
\\n\"\n]"}}},{"rowIdx":438,"cells":{"data":{"kind":"list like","value":["\nInt J Environ Res Public HealthInt J Environ Res Public HealthijerphInternational Journal of Environmental Research and Public Health1661-78271660-4601MDPI32707971PMC743209510.3390/ijerph17155279ijerph-17-05279ArticleDecrease in Ambient Fine Particulate Matter during COVID-19 Crisis and Corresponding Health Benefits in Seoul, KoreaHanChangwoo1HongYun-Chul234*Department of Preventive Medicine, Chungnam National University College of Medicine, Daejeon 35015, Korea; cwohan@cnu.ac.krDepartment of Preventive Medicine, Seoul National University College of Medicine, Seoul 03080, KoreaInstitute of Environmental Medicine, Seoul National University Medical Research Center, Seoul 03080, KoreaEnvironmental Health Center, Seoul National University College of Medicine, Seoul 03080, KoreaCorrespondence: ychong1@snu.ac.kr; Tel.: +82-2-740-83942272020820201715527906720202172020© 2020 by the authors.2020Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Both domestic emissions and transported pollutants from neighboring countries affect the ambient fine particulate matter (PM2.5) concentration of Seoul, Korea. Diverse measures to control the coronavirus disease 2019 (COVID-19), such as social distancing and increased telecommuting in Korea and the stringent lockdown measures of China, may reduce domestic emissions and levels of transported pollutants, respectively. In addition, wearing a particulate-filtering respirator may have decreased the absolute PM2.5 exposure level for individuals. Therefore, this study estimated the acute health benefits of PM2.5 reduction and changes in public behavior during the COVID-19 crisis in Seoul, Korea. To calculate the mortality burden attributable to PM2.5, we obtained residents’ registration data, mortality data, and air pollution monitoring data for Seoul from publicly available databases. Relative risks were derived from previous time-series studies. We used the attributable fraction to estimate the number of excessive deaths attributable to acute PM2.5 exposure during January to April, yearly, from 2016 to 2020, and the number of mortalities avoided from PM2.5 reduction and respirator use observed in 2020. The average PM2.5 concentration from January to April in 2020 (25.6 μg/m3) was the lowest in the last 5 years. At least −4.1 μg/m3 (95% CI: −7.2, −0.9) change in ambient PM2.5 in Seoul was observed in 2020 compared to the previous 4 years. Overall, 37.6 (95% CI: 32.6, 42.5) non-accidental; 7.0 (95% CI: 5.7, 8.4) cardiovascular; and 4.7 (95% CI: 3.4, 6.1) respiratory mortalities were avoided due to PM2.5 reduction in 2020. By considering the effects of particulate respirator, decreases of 102.5 (95% CI: 89.0, 115.9) non-accidental; 19.1 (95% CI: 15.6, 22.9) cardiovascular; and 12.9 (95% CI: 9.2, 16.5) respiratory mortalities were estimated. We estimated that 37 lives were saved due to the PM2.5 reduction related to COVID-19 in Seoul, Korea. The health benefit may be greater due to the popular use of particulate-filtering respirators during the COVID-19 crisis. Future studies with daily mortality data are needed to verify our study estimates.
The world is facing one of its gravest challenges due to the coronavirus disease 2019 (COVID-19). After the first unknown pneumonia cases detected in Wuhan, China, in December 2019, scientists identified new species of the zoonotic coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-COV-2), causing COVID-19 [1,2]. COVID-19 may lead to a fatal respiratory disease in the elderly and in persons with preexisting chronic diseases, but the symptoms can be mild in young and healthy individuals [3]. According to the report from the Chinese Center for Disease Control using the 44,672 confirmed COVID-19 cases, the case fatality rate was 2.3%, lower compared to other coronaviruses causing severe acute respiratory syndrome (SARS, 9.6%) and Middle East respiratory syndrome (MERS, 35.5%) [4,5]. However, SARS-COV-2 is highly infectious, with an estimated reproduction number (R0) over 3, causing more mortalities than any other coronaviruses [5,6,7]. Due to the wide geographical spread of the virus and the growing international concern, the World Health Organization (WHO) declared COVID-19 outbreak as a pandemic on 12 April 2020. As of 24 April 2020, COVID-19 has killed more than 190,800 people, with 4.6 million confirmed cases worldwide. Billions of people were instructed or forced to stay at home while countries locked their borders to varying degrees to control COVID-19.
After the first incidence of COVID-19 on 20 January 2020, Korea has controlled the outbreak relatively well compared to other countries. Although there was a surge of COVID-19 in Daegu City, which peaked at over 900 incident cases on 29 February, the daily increase in new cases in Korea had dropped below 10 by the end of April [8]. Without imposing extreme measures to restrict the movement or freedom of the citizens, Korea was able to flatten the curve of the new cases. This was achieved by a massive number of tests conducted for early detection, by applying rigorous epidemiological investigation, and sharing information using the state of the art information and communication technologies [8,9]. These all occurred with the voluntary participation of social distancing and personal hygiene practices (i.e., wearing respirators in public space and washing hands) by the citizens. To address the shortage and ensure the equal distribution of respirators, the Korean government adopted a “5-day rotation system for a respirator” to provide opportunities for people to purchase a respirator at pharmacies on designated days according to their birth years.
Recently, the diverse news media, journal papers, and reports posted on the preprint server medRxic have indicated that the stringent lockdown measures not only helped in controlling the spread of this highly infectious virus, but also helped to decrease the ambient air pollution levels, including particulate matter less than 2.5 μm in diameter (PM2.5) and nitrogen dioxide (NO2) [10,11]. With the lockdown of industrial activities and transportation in China for about 35 days, there was a 30.1 and 9.2 μg/m3 decrease in PM2.5 in Wuhan and Beijing, respectively [12]. One report estimated a 12.9 and 18.9 μg/m3 decrease in NO2 and PM2.5 in China after the massive population quarantine [13]. Satellites of National Aeronautics and Space Administration (NASA) and European Space Agency detected a 10 to 20% decrease in NO2 levels over eastern and central China after the quarantine [14]. The Movement Control Order of the Malaysian government to encourage their citizens to work at home and suspend industrial activities caused a decrease in PM2.5 levels [15]. Lockdown measures such as school closure, work at home, avoiding gatherings, shutting down commerce, and limiting public transportation in the city of Rio de Janeiro and São Paulo State caused a reduction in carbon monoxide and NO2 levels [16,17]. Another report estimated a 9%, 29%, and 11% global reduction in ambient PM2.5, NO2, and ozone levels, respectively, after the COVID-19 crisis [18].
A recent announcement by Korea’s Ministry of Environment revealed a reduction in the average PM2.5 in Korea from December 2019 to April 2020 by 27% (9 μg/m3) compared to that of the previous year [19]. Although the report highlighted the effectiveness of “the seasonal particle pollution measures”, which was newly implemented in December 2019, the marked PM2.5 decrease observed in China as well as around the world suggest that the measures to control COVID-19 may be the largest contributing factor behind the improved air quality. In addition, a recent modeling study showed that around 70% of PM2.5 concentration of Seoul is affected by the emission from China during the days with severe PM2.5 concentration in spring [20].
Therefore, we initiated this study with a reasonable assumption that social distancing and increased telecommuting in Korea, in addition to the stringent lockdown measures and reduced industrial activities in China, may have reduced the PM2.5 levels in Seoul, the capital city of Korea. Furthermore, the respirators supplied and distributed by the Korean government were effective in filtering PM2.5 at least 80% of the 0.6 μm nonoil particulates. Therefore, each person would be exposed to a lower level of PM2.5 compared to the period before the COVID-19 crisis by using the particulate filtering respirator. Based on this assumption, we estimated the acute health benefits of PM2.5 reduction and changes in public behavior (wearing of a respirator) during the COVID-19 crisis using the health impact assessment methodology. The aim of this study was to use currently available data to estimate the acute health benefits of PM2.5 reduction and changes in public behavior, which were changes experienced by Korean citizens in their daily lives during the COVID-19 crisis.
2. Materials and Methods2.1. Study Design
The health impact assessment requires data on exposure, mortality, and the population. Although Korea shares the real-time air pollution monitoring data with the public, the mortality data are not shared simultaneously. Therefore, we estimated the mortality rates over the last 2 years (2019 and 2020) based on the mortality rates of earlier years. With the PM2.5 monitoring data, population data, and estimated mortality rates in Seoul, we estimated the health benefits based on the PM2.5 reduction levels in 2020. By using previous respirator intervention study results [21], we estimated the health benefits of decreased PM2.5 and respirator use in 2020.
To estimate the PM2.5 decrease in year 2020, we first calculated the average PM2.5 concentration during the first 4 months of 2020 and compared it with the concentration in the same months, each year, from 2016 to 2019. By using the attributable fraction (AF) method [22], we estimated the mortality burden attributable to the acute ambient PM2.5 exposure in the first 4 months of each year, and the number of mortalities avoided due to the observed PM2.5 reduction in 2020.
To calculate the mortality burden attributable to PM2.5, we obtained the population, mortality, and air pollution monitoring data of Seoul from publicly available databases. The relative risks (RR) derived from previous time-series studies, which evaluated the association between ambient PM2.5 exposure and cause-specific mortalities were reviewed to retrieve the beta estimates of the concentration–response functions [23,24]. To consider the effects of public respirator use, we referred to previous intervention study, which evaluated the difference between individual PM2.5 exposure level by the particulate respirator use [21].
Several important assumptions were made for our study. Because mortality data were unavailable for the last two years (2019 and 2020), we assumed that mortality rates in Seoul from January to April had not changed in the last 5 years. In Korea, the daily mortality data for a typical year become publicly available at least 1.5 years after the year ends. Therefore, we estimated the mortality rates of Seoul from January to April in 2019 and 2020 by averaging the mortality rates of the same months from 2016 to 2018. Because the mortality rates are closely related to the structure of the population, we also assumed that the composition of the population by age groups had not changed during our study period.
Second, we assumed that the effects of PM2.5 were limited to a single day, ignoring the delayed effect of PM2.5. PM2.5 is known to have lag effects of several days after the exposure [25]. However, because the daily mortality counts were unavailable for the years 2019 and 2020, it was impossible to estimate the daily levels accounting for lag effects of PM2.5. Therefore, we regarded the 4-month period as a whole and calculated the mortality burden for each year using the 4-month averages of PM2.5 concentrations and the estimated mortality rates of Seoul. Assumption focusing on a single day effect of PM2.5 may underestimate the overall mortality burden attributable to PM2.5. However, the avoided number of mortalities due to PM2.5 reduction during the COVID-19 crisis may not be biased because we focused on the changes in PM2.5 levels with the assumption of a linear concentration–response function.
Third, we assumed that the effects of PM2.5 concentration on health outcomes did not change during the study period, despite the changes in personal behaviors (i.e., social distancing and decreased outdoor activities) due to the COVID-19 crisis. We adopted the RRs from the previous time-series studies conducted both in Seoul and that in 652 cities around the globe [23,24]. However, despite assuming that the slope of concentration–response function between PM2.5 and mortality remained unchanged, we assumed that wearing a particulate filtering respirator would decrease the absolute level of PM2.5 exposure of an individual.
This study was exempt from review by the Institutional Review Board of the Seoul National University Hospital, Korea (IRB No.: 2006-122-1133), because data used in our study were de-identified and publicly available.
2.2. Population and Mortality Data
The yearly residents’ registration data for January during the study period (2016 to 2020) and the cause-specific mortality data for January to April (2016 to 2018) were acquired from the publicly available databases, the Korean Statistical Information Service website, and the Korean Statistical Information Service MicroData Integrated Service website [26,27].
We used the International Classification of Disease, 10th revision codes to define the following cause-specific mortalities: non-accidental and specific disease mortality (A00-R00), cardiovascular disease mortality (I00-I99), and respiratory disease mortality (J00-J99). The number of deaths due to these disease categories during the 4 months (January to April) of each year was calculated and divided by the number of registered residents in January of the corresponding year, to calculate the 4-month average in mortality rates of Seoul.
2.3. PM<sub>2.5</sub> and Meteorological Data
Ambient PM2.5 data of Seoul from January 2016 to April 2020 were accessed through the Airkorea website, which provides real-time air pollution monitoring data of Korea [28]. Korea operates 25 air pollution monitoring stations in Seoul, covering 25 basic administration districts. We acquired the hourly PM2.5 monitoring results from each station, and calculated Seoul’s daily and 4-month average of PM2.5 exposure levels for January to April each year from 2016 to 2020. Daily meteorological data such as ambient temperature, relative humidity, and wind speed from January 2016 to April 2020 were accessed through the National Climate Data Center website. The map of Seoul and the locations of the ambient PM2.5 as well as the weather monitoring stations are presented in Figure S1.
2.4. Concentration-Response Functions and Effects of Particulate Matter Filtrating Respirator
The beta estimates of the concentration—response functions were retrieved from the RRs of the previous time-series studies that evaluated the association between ambient PM2.5 exposure and cause-specific mortalities (Supplementary Table S1). We selected two recently published studies—one analyzing the data from cities around the globe, and the other limited to Seoul. In brief, the Multi-City Multi-Country (MCC) collaborative research network gathered the daily mortality rates and PM2.5 data from 499 cities in 16 countries. The researchers found that a 10 μg/m3 increase in a 2-day moving average of ambient PM2.5 was associated with 0.68% (95% confidence interval (CI): 0.59, 0.77); 0.55% (95% CI: 0.45, 0.66); and 0.75% (95% CI: 0.53, 0.95) increase in the daily non-accidental, cardiovascular, and respiratory mortalities, respectively [23].
The study conducted in Seoul used the daily PM2.5 concentrations and mortality data of the city from 2006 to 2012. A 10 μg/m3 increase in ambient PM2.5 was associated with an increase in non-accidental mortality (0.33% (95% CI: 0.01, 0.66)), cardiovascular mortality (0.76% (95% CI: 0.12, 1.41)), and respiratory mortality (1.77% (95% CI: 0.55, 3.01)) on the same day in Seoul [24].
In a previous intervention study with 21 female elderlies in Korea, we evaluated the effects of a particulate-filtering respirator on cardiopulmonary function by using the crossover study design [21]. The subjects were instructed to use (intervention period) or not use the respirator (control period) for six consecutive days and had a medical examination on the last day of each period. By using the disposable particulate respirators (capable of filtering 80% of the 0.6 μm nonoil particulates), we found that the average level of personal exposure to PM2.5 had decreased by 27.4% (9.0 μg/m3 reduction) during the respirator use, and even the outdoor (27.4–28.8 μg/m3) and 24-h personally monitored PM2.5 levels (18.7–20.1 μg/m3) were similar between intervention and control periods. By referring to this value, we estimated that the effects of respirator use during COVID-19 crisis decreased the personal PM2.5 exposure level by 27.4% in addition to the decrease in ambient PM2.5 levels observed during 2020.
2.5. Statistical Analysis
With the estimated number of mortalities and monitored PM2.5 concentrations from January to April each year, we used the AF method to estimate the mortality burden attributable to ambient PM2.5 levels [22].\nExcess deaths by PM2.5=(1−exp−β×ΔC) × Number of deaths
β is the coefficient derived from the RRs in the previous time-series studies and ΔC refers to the changes in the PM2.5 concentrations under different counterfactual scenarios. The number of deaths from January to April for the years 2016 to 2018 was obtained from the mortality database, while those of 2019 and 2020 were calculated by multiplying the number of registered residents with the mortality rates estimated based on the mortality rates of 2016 to 2018.
To calculate the number of excess deaths attributable to the acute PM2.5 exposure from January to April each year from 2016 to 2020, we defined 2.4 μg/m3 as the concentration with the minimum health risk, which is the theoretical minimum risk exposure level, indicating no health benefits for reducing PM2.5 below the level based on prior epidemiological studies [29]. Therefore, ΔC in the equation indicates the difference between average PM2.5 concentrations monitored each year from January to April and 2.4 μg/m3. On the other hand, to calculate the avoided mortality due to PM2.5 reduction in 2020, we defined ΔC as the estimated reduction in PM2.5 from January to April in 2020 compared to the same months in 2016–2019.
To calculate the reduction in PM2.5 in 2020, we used the linear regression models assuming the normal distribution of PM2.5 levels. In model 1, the amount of reduction was estimated based on a simple comparison of the average value for 2020 with the average for the years 2016–2019. In model 2, we adjusted for meteorological variables (daily average temperature, relative humidity, and wind speed), and in model 3, we adjusted for the meteorological variables, years (as a continuous variable), and the months (as a categorical variable). In model 4, we adjusted for the meteorological variables and assumed that the average level of personal exposure to PM2.5 had decreased by 27.4% in 2020, to account for the widespread use of particulate filtrating respirator.
All analyses were conducted with SAS version 9.4 (SAS Institute Inc., Cary, NC, USA), and figures were drawn using the R statistical software (Version 3.6.1; R Foundation for Statistical Computing, Vienna, Austria). The level of statistical significance was set at a p-value of less than 0.05.
3. Results
Table 1 and Figure 1 show the 2016 to 2020 Seoul data for the daily PM2.5 concentrations, ambient temperature, relative humidity, wind speed, and the number of days that the PM2.5 concentration was above the WHO and Korea 24-h average standards. The average ambient temperature from January to April in 2020 was the highest compared to those of the previous 4 years, while the relative humidity and wind speed of 2020 were similar to that of 2016 and 2017.
The average PM2.5 concentration from January to April in 2020 (25.6 μg/m3) was the lowest in the last 5 years. Overall, the PM2.5 concentrations above the WHO (25 μg/m3) and the Korean standards (35 μg/m3) in 2020 were for 55 days (45.5%) and 25 days (20.7%) respectively, which were the least number of days in the past 5 years. Figure 1 shows the dramatic decrease in the daily PM2.5 concentrations as well as the number of days with spiking PM2.5 concentrations in 2020 compared to the previous 4 years.
Table 2 shows the number of registered residents, estimated number of deaths, and mortality rates used in the study. The average number of registered residents in Seoul in January each year was 9,860,115. The average non-accidental, cardiovascular, and respiratory mortalities per 100,000 persons in Seoul were 139.2, 32.0, and 16.1 from January to April in 2016 to 2018, respectively. We used these mortality rates to estimate the number of deaths for 2019 and 2020 assuming that these rates remained unchanged. We estimated that 13,549; 3115; and 1567 persons died in Seoul from January to April in 2020 due to non-accidental, cardiovascular, and respiratory diseases, respectively.
Figure 2, Table S2, and Figure S2 show the number of mortalities attributable to PM2.5 exposure in Seoul from January to April each year from 2016 to 2020. By using the MCC study’s RRs, the daily exposure to PM2.5 in 2020 caused 211.4 (95% CI: 183.7, 239.0); 39.4 (95% CI: 32.3, 47.2); and 26.6 (95% CI: 19.1, 34.0) deaths due to non-accidental, cardiovascular, and respiratory diseases, respectively (Table S2). The mortality attributable to the daily PM2.5 exposure was the lowest in 2020 compared to those of the previous 4 years (Figure 2). The results using RRs from the Seoul study are summarized in Table S2 and Figure S2.
Table 3 shows the estimated PM2.5 reduction levels and the avoided mortality due to the PM2.5 exposure in 2020 compared to those from 2016–2019. By simply comparing the average values, a −5.6 μg/m3 (95% CI: −9.0, −2.3) change in ambient PM2.5 was observed in Seoul from January to April in 2020 compared to the same months in the previous 4 years (model 1). By adjusting for meteorological variables, a −4.1 μg/m3 (95% CI: −7.2, −0.9) change in ambient PM2.5 was estimated (model 2). By further adjusting for years and months, a −15.1 μg/m3 (95% CI: −27.1, −3.2) change in ambient PM2.5 was estimated (model 3). With the conservative estimation of a 4.1 μg/m3 decrease in PM2.5 and RRs from the MCC study, we found that 37.6 (95% CI: 32.6, 42.5) non-accidental; 7.0 (95% CI: 5.7, 8.4) cardiovascular; and 4.7 (95% CI: 3.4, 6.1) respiratory mortalities were avoided because of the reduction in PM2.5 from January to April in 2020 compared to those of the previous 4 years.
By considering the effect of the meteorological variables and the particulate respirator use during 2020 (model 4), we estimated that the personal exposure to ambient PM2.5 decreased by −11.2 μg/m3 (95% CI: −14.3, −8.2) from January to April in 2020 compared to the previous 4 years. The corresponding health benefits were decreases of 102.5 (95% CI: 89.0, 115.9) non-accidental; 19.1 (95% CI: 15.6, 22.9) cardiovascular; and 12.9 (95% CI: 9.2, 16.5) respiratory mortalities.
4. Discussion
We observed at least a 4.1 μg/m3 decrease in ambient PM2.5 concentration in Seoul from January to April in 2020 compared to the same months in 2016–2019. We estimated that 37 persons were saved due to the reduction in PM2.5 during the 4-month period. Because using a particulate-filtrating respirator may decrease the absolute level of PM2.5 exposure for an individual, the health benefit related to air pollution during the COVID-19 crisis may be larger than our current estimation of 37 persons.
There are several possible explanations for the decrease in PM2.5 in Seoul. First, public behavioral changes such as social distancing and reduced outdoor activities to limit COVID-19 transmission may have decreased the air pollution levels. According to the mobility data based on the map navigation application on smartphones, both walking and driving by the public were decreased in Seoul after the COVID-19 crisis (Figure S3). The daily amount of traffics entering the highways and the number of citizens using the Seoul Metropolitan Area subway from January to April in 2020 dropped by 6.1% and 28.4%, respectively, compared to the same months in the period 2016–2019 (Table S3). In addition, industrial activities such as the number of operating factories may have decreased due to the limited consumer demand during COVID-19 crisis, which may have resulted in decreased domestic emissions [30].
Similar improvements in air quality were observed around the world since the COVID-19 crisis, with an estimated reduction of 9%, 29%, and 11% of ambient PM2.5, NO2, and ozone levels, respectively [18]. The reductions in carbon monoxide and NO2 levels were observed after the partial lockdown (school closure, work from home, avoiding gatherings, shutting down commerce, and limiting public transportation) in the city of Rio de Janeiro and São Paulo State [16,17]. During the Movement Control Order (work from home and suspend industrial activities) to isolate the source of COVID-19 in Malaysia, up to a 58.4% decrease in PM2.5 was observed [15]. By analyzing air quality monitoring data of 22 cities in India, 43% and 18% decreases in PM2.5 and NO2 were observed during the COVID-19 crisis [31].
The air pollution levels in Seoul cannot be evaluated without considering the effects of the neighboring countries, China and North Korea. By evaluating the source contribution of PM2.5 on days with severe PM2.5 concentrations (with 24-h average PM2.5 concentration of over 100 μg/m3) in Seoul, China contributed to the PM2.5 concentrations by up to 70% while the domestic contribution was 21% [20]. In addition, around 15% of PM2.5 concentration in Seoul is affected by the emission from North Korea [32]. Among the 1638 mortalities attributable to the acute exposure to high levels of PM2.5 in Korea in 2016, at least 258 and 26 deaths were estimated to have been due to the emissions from China and North Korea, respectively [33].
Because the air quality in China improved dramatically during the COVID-19 quarantine (10 February to 14 April 2020) [12,13,14,34], and considering the fact that China’s contribution to Seoul’s PM2.5 concentration is generally greater in the spring and winter [33], the decrease in PM2.5 observed in Seoul from January to April 2020 may partially be explained by the effects of China’s rigorous quarantine measures and decreased industrial activities during the COVID-19 crisis. If the observed decrease in PM2.5 levels in Seoul in 2020 is indeed due to the changes related to domestic responses as well as China’s response against COVID-19, the estimated mortality benefit from the lowered PM2.5 levels (37 persons) outweighs the number of the direct casualties from COVID-19 in Seoul (2 persons till 30 April 2020). Similar paradoxical phenomena, and a massive decrease in air-pollution-related mortalities and morbidities during the COVID-19 crisis are expected worldwide [14].
Another plausible explanation for the decrease in PM2.5 concentration in Seoul in 2020 is the governmental effort to reduce the domestic sources of PM2.5 [19]. With consultations with relevant ministries, a comprehensive set of measures to control the particulate matter in 2020 to 2024 was finalized on 1 November 2019 [35]. One of the measures is the seasonal management of the domestic sources of PM2.5 from December to April, when PM2.5 levels are usually high [36]. Efforts to reduce the domestic emission of PM2.5 include the shutting down of the coal-fired power plants, voluntary reduction in emissions from business sites, nationwide surveillance of emission sources, designation of low-sailing zones, and transition to low sulfur fuels for ocean vessels. In addition to these comprehensive measures, an increase in rainfall and the number of days with high wind velocity may also help to explain the decrease in PM2.5 observed in Seoul from January to March 2020 [19]. However, the relative humidity and wind speed in 2020 were similar to those of 2016 and 2017; limiting the effects of meteorological factors on PM2.5 reduction.
We may not be able to distinguish the effects of diverse governmental measures from those of domestic and international changes related to COVID-19 on the reduced PM2.5 levels in Seoul. However, it is reasonable to assume that the domestic measures (social distancing and avoidance of outside activities in Korea) and international effects (decreased PM2.5 levels during the quarantine in China) related to COVID-19 may have played a significant role at the same time. We may be able to confirm the effects of COVID-19 by observing the air pollution levels after the COVID-19 measures are lifted and by evaluating whether the effects (decrease in air pollution) cease after the treatment (measures against COVID-19) ends [37].
Although not formally published as a journal article, several reports posted on medRxiv are showing the health benefits of reduced PM2.5 levels after the COVID-19 crisis. One report estimated that the PM2.5 and NO2 levels dropped by 18.9 and 12.9 μg/m3 in China during the COVID-19 quarantine period, which led to a decrease of 3214 PM2.5-related deaths and a decrease of 8911 NO2-related deaths [13]. By analyzing the air pollution data from over 10,000 monitoring stations around the globe, a 9%, 29%, and 11% reduction in ambient PM2.5, NO2, and ozone levels was estimated during February to April 2020, just after the global lockdown in response to COVID-19 [18]. The corresponding health benefits related to the decrease in air pollution levels were 7400 deaths and 6600 pediatric asthma cases.
After the first confirmed case of COVID-19 in Korea on 20 January 2020, the panic buying of particulate filtrating respirator led to instability in supply and demand. To tackle this issue, the Korean government adopted a “5-day rotation system for respirator” to provide equal opportunity for individuals to purchase two particulate-filtering respirators (particulate respirators capable of filtering at least 80% of the 0.6 μm nonoil particulates) per week. As the Korean government instructed its citizens to wear a respirator outside the house to control COVID-19, personal level of exposure to ambient PM2.5 may have decreased from wearing a particulate respirator. With the results of the previous intervention study, we estimated that the effects of respirator use during COVID-19 crisis decreased the personal PM2.5 exposure level by 27.4% in addition to the decrease in ambient PM2.5 levels. We estimated that this decrease led to 102 averted deaths related to PM2.5 during the 4-month period; this is higher compared to the conservative estimation of 37 lives saved with a 4.1 μg/m3 decrease in ambient PM2.5 concentration during the COVID-19 crisis estimated in our study. We believe that using a particulate filtrating respirator not only help to block the transmission of COVID-19, but also helped to limit the adverse health effects of PM2.5.
We have previously estimated that around 12,000 premature deaths were attributable to chronic PM2.5 exposure in Korea in 2015, when the annual average of PM2.5 concentration was 24.4 μg/m3 [38]. We have also estimated that 1763 deaths solely occurred in Seoul. However, our study is different from the previous study in terms of the fact that it addressed the acute effects of PM2.5 exposure by using the RRs from previous time-series studies. With the yearly mortality information and PM2.5 measurement data for the entire year, we may be able to estimate the chronic PM2.5 exposure burden for 2020 and compare the difference with previous years. If reduced PM2.5 levels are maintained throughout 2020, we may be able to see a marked decrease in PM2.5-related burden.
Several limitations should be noted for this study. First, we adopted the RRs from previous time-series studies and ignored the possibility that the association between PM2.5 and health outcomes may have changed during the COVID-19 crisis. The Korean government instructed its citizens to avoid outdoor activities or crowded areas, and to use a respirator outside the house. Due to social distancing and the use of a respirator, the beta coefficient of exposure–response relationship may have changed. If the daily mortality data become available in the future, we may be able to confirm the changes in beta coefficient by comparing the estimates from the time-series analyses before and during the COVID-19 crisis. In addition, although we tried to adjust for the effects of respirator by using the results from previous intervention study, we also assumed that the entire public were using the particulate respirator during the COVID-19 crisis, which is unlikely. Second, we ignored the daily lag effect of PM2.5. Because mortality data are not disclosed simultaneously, we were unable to conduct a day-to-day analysis accounting for the lag effects of PM2.5. With the full mortality data of 2020, a more precise estimation can be conducted by using the daily data rather than using the 4-month (January to April) data as a whole.
Due to insufficient data and assumptions used in our study, our study estimates may be biased. Assumptions regarding mortality rates and concentration-response functions have to be validated with a daily number of mortality data and PM2.5 monitoring data in the future. However, based on the currently available data, our study may offer a glimpse into the acute health benefits of PM2.5 reduction and changes in public behaviors, which are the tangible changes experienced by the citizens in their daily lives during the COVID-19 crisis.
5. Conclusions
We observed at least 4.1 μg/m3 decrease in ambient PM2.5 in Seoul from January to April 2020, and this decrease is believed to be the results of the changes related to COVID-19 crisis. With our conservative estimation, a total of 37 lives were saved due to the PM2.5 reduction in Seoul from January to April 2020 compared to the same period in previous years. However, the health benefits related to the decrease in PM2.5 may be greater because of the popular use of the particulate respirator by the public during the COVID-19 crisis in Korea. We may need to verify our study findings by observing the PM2.5 levels after the COVID-19 crisis and conducting studies with a full set of daily mortality data.
Supplementary Materials
The following are available online at https://www.mdpi.com/1660-4601/17/15/5279/s1, Table S1: Concentration-response functions used in the study; Table S2: Estimated number of mortalities attributable to PM2.5 exposure from January to April in each year from 2016 to 2020; Table S3: The daily average amount of vehicles entering the Seoul Metropolitan Area highways and number of citizens who used subways of Seoul Metropolitan area from January to April each year from 2016 to 2020; Figure S1: Locations of 25 PM2.5 monitoring stations (red circle) and meteorological station (blue circle) covering Seoul; Figure S2: Average PM2.5 concentration of Seoul from January to April and estimated number of mortalities attributable to PM2.5 exposure (RRs from the Seoul study were used for the estimation); Figure S3: Mobility trends of Seoul City (Mobility on 13 January 2020 was regarded as 100%; Data was gathered from the Apple Maps Mobility Trends Reports: https://www.apple.com/covid19/mobility/).
Click here for additional data file.
Author Contributions
Conceptualization, C.H.; Data curation, C.H.; Formal analysis, C.H.; Funding acquisition, C.H.; Investigation, C.H.; Resources, C.H.; Software, C.H.; Supervision, Y.-C.H.; Writing—original draft, C.H.; Writing—review and editing, C.H. and Y.-C.H. All authors have read and agreed to the published version of the manuscript.
Funding
This research was supported by the research fund of Chungnam National University (2020-0892-01).
Conflicts of Interest
The authors declare no conflict of interest.
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PM2.5 concentration in Seoul from year 2016 to 2020 (January to April).
Average PM2.5 concentration of Seoul from January to April of 2016 to 2020 and estimated number of mortalities attributable to PM2.5 exposure (RRs from the MCC study were used for the estimation).
ijerph-17-05279-t001_Table 1
Average daily particulate matter (PM2.5) concentrations, temperature, relative humidity, wind speed, and number of days over the WHO (25 μg/m3) and Republic of Korea (ROK, 35 μg/m3) PM2.5 24-h average standards in Seoul (January to April each year from 2016 to 2020).
Year
Days (N)
PM2.5 (μg/m3) (a)
Temperature (°C) (a)
Relative Humidity (%) (a)
Wind Speed (m/s) (a)
Days over WHO Standard (b)
Days over ROK Standard (b)
2016
121
28.1 (11.1)
4.5 (7.8)
52.7 (14.4)
2.5 (0.7)
64 (52.9)
33 (27.3)
2017
120
31.7 (15.2)
4.6 (7.1)
52.1 (12.7)
2.4 (0.7)
71 (59.2)
40 (33.3)
2018
120
30.6 (18.3)
3.9 (8.4)
51.8 (14.4)
2.0 (0.7)
64 (53.3)
40 (33.3)
2019
120
34.6 (23.6)
4.9 (6.0)
48.8 (14.6)
1.9 (0.6)
63 (52.5)
39 (32.5)
2020
121
25.6 (12.2)
5.8 (5.3)
52.4 (13.5)
2.5 (0.8)
55 (45.5)
25 (20.7)
(a) Mean and standard deviation are presented, (b) Number of days and percentage are presented.
ijerph-17-05279-t002_Table 2
Number of registered population, number of deaths, and mortality rates used in this study.
Year
2016
2017
2018
2019
2020
Registered population at January, Seoul
10,018,537
9,930,478
9,851,767
9,766,288
9,733,509
Non-accidental mortality (A00-R00, January to April)
\n
\n
\n
\n
\n
 Number of deaths
13,776
13,243
14,445
13,595 (a)
13,549 (a)
 Mortality rate (per 100,000)
137.5
133.4
146.6
139.2 (b)
139.2 (b)
Cardiovascular disease mortality (I00-I99, January to April)
\n
\n
\n
\n
\n
 Number of deaths
3080
3129
3316
3125 (a)
3115 (a)
 Mortality rate (per 100,000)
30.7
31.5
33.7
32.0 (b)
32.0 (b)
Respiratory disease mortality (J00-J99, January to April)
\n
\n
\n
\n
\n
 Number of deaths
1533
1462
1789
1572 (a)
1567 (a)
 Mortality rate (per 100,000)
15.3
14.7
18.2
16.1 (b)
16.1 (b)
(a) Calculated based on the estimated mortality rate, (b) Estimated by averaging year 2016–2018 mortality rate.
ijerph-17-05279-t003_Table 3
Estimated PM2.5 reduction levels and avoided mortality due to PM2.5 exposure in January to April of 2020 compared to same month each year from 2016 to 2019.
\n
Model 1 (a)
Model 2 (b)
Model 3 (c)
Model 4 (d)
Reduction of PM2.5 by comparing 2016–2019 and 2020 (μg/m3)
−5.6 (−9.0, −2.3)
−4.1 (−7.2, −0.9)
−15.1 (−27.1, −3.2)
−11.2 (−14.3, −8.2)
Avoided cause-specific deaths
\n
\n
\n
\n
Estimation using RRs from MCC study
\n
\n
\n
\n
 Non-accidental mortality
51.3 (44.6, 58.1)
37.6 (32.6, 42.5)
137.9 (119.8, 156)
102.5 (89.0, 115.9)
 Cardiovascular disease mortality
9.6 (7.8, 11.5)
7 (5.7, 8.4)
25.7 (21.0, 30.8)
19.1 (15.6, 22.9)
 Respiratory disease mortality
6.5 (4.6, 8.3)
4.7 (3.4, 6.1)
17.3 (12.5, 22.2)
12.9 (9.2, 16.5)
Estimation using RRs from Seoul City study
\n
\n
\n
\n
 Non-accidental mortality
25 (0.8, 49.8)
18.3 (0.6, 36.5)
67.2 (2.0, 133.9)
49.9 (1.5, 99.5)
 Cardiovascular disease mortality
13.2 (2.1, 24.3)
9.7 (1.5, 17.8)
35.4 (5.6, 65.2)
26.3 (4.2, 48.5)
 Respiratory disease mortality
15.3 (4.8, 25.8)
11.2 (3.5, 18.9)
41 (12.9, 68.6)
30.5 (9.6, 51.2)
(a) Model 1 estimated by simple comparison of mean value, (b) Model 2 adjusting for daily average temperature, relative humidity, and wind speed, (c) Model 3 adjusting for daily average temperature, relative humidity, wind speed, years (in continuous variable), and months (as categorical variable), (d) Model 4 adjusting for daily average temperature, relative humidity, and wind speed and considered the effect of particulate filtering respirator.
\n"],"string":"[\n \"\\nInt J Environ Res Public HealthInt J Environ Res Public HealthijerphInternational Journal of Environmental Research and Public Health1661-78271660-4601MDPI32707971PMC743209510.3390/ijerph17155279ijerph-17-05279ArticleDecrease in Ambient Fine Particulate Matter during COVID-19 Crisis and Corresponding Health Benefits in Seoul, KoreaHanChangwoo1HongYun-Chul234*Department of Preventive Medicine, Chungnam National University College of Medicine, Daejeon 35015, Korea; cwohan@cnu.ac.krDepartment of Preventive Medicine, Seoul National University College of Medicine, Seoul 03080, KoreaInstitute of Environmental Medicine, Seoul National University Medical Research Center, Seoul 03080, KoreaEnvironmental Health Center, Seoul National University College of Medicine, Seoul 03080, KoreaCorrespondence: ychong1@snu.ac.kr; Tel.: +82-2-740-83942272020820201715527906720202172020© 2020 by the authors.2020Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Both domestic emissions and transported pollutants from neighboring countries affect the ambient fine particulate matter (PM2.5) concentration of Seoul, Korea. Diverse measures to control the coronavirus disease 2019 (COVID-19), such as social distancing and increased telecommuting in Korea and the stringent lockdown measures of China, may reduce domestic emissions and levels of transported pollutants, respectively. In addition, wearing a particulate-filtering respirator may have decreased the absolute PM2.5 exposure level for individuals. Therefore, this study estimated the acute health benefits of PM2.5 reduction and changes in public behavior during the COVID-19 crisis in Seoul, Korea. To calculate the mortality burden attributable to PM2.5, we obtained residents’ registration data, mortality data, and air pollution monitoring data for Seoul from publicly available databases. Relative risks were derived from previous time-series studies. We used the attributable fraction to estimate the number of excessive deaths attributable to acute PM2.5 exposure during January to April, yearly, from 2016 to 2020, and the number of mortalities avoided from PM2.5 reduction and respirator use observed in 2020. The average PM2.5 concentration from January to April in 2020 (25.6 μg/m3) was the lowest in the last 5 years. At least −4.1 μg/m3 (95% CI: −7.2, −0.9) change in ambient PM2.5 in Seoul was observed in 2020 compared to the previous 4 years. Overall, 37.6 (95% CI: 32.6, 42.5) non-accidental; 7.0 (95% CI: 5.7, 8.4) cardiovascular; and 4.7 (95% CI: 3.4, 6.1) respiratory mortalities were avoided due to PM2.5 reduction in 2020. By considering the effects of particulate respirator, decreases of 102.5 (95% CI: 89.0, 115.9) non-accidental; 19.1 (95% CI: 15.6, 22.9) cardiovascular; and 12.9 (95% CI: 9.2, 16.5) respiratory mortalities were estimated. We estimated that 37 lives were saved due to the PM2.5 reduction related to COVID-19 in Seoul, Korea. The health benefit may be greater due to the popular use of particulate-filtering respirators during the COVID-19 crisis. Future studies with daily mortality data are needed to verify our study estimates.
The world is facing one of its gravest challenges due to the coronavirus disease 2019 (COVID-19). After the first unknown pneumonia cases detected in Wuhan, China, in December 2019, scientists identified new species of the zoonotic coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-COV-2), causing COVID-19 [1,2]. COVID-19 may lead to a fatal respiratory disease in the elderly and in persons with preexisting chronic diseases, but the symptoms can be mild in young and healthy individuals [3]. According to the report from the Chinese Center for Disease Control using the 44,672 confirmed COVID-19 cases, the case fatality rate was 2.3%, lower compared to other coronaviruses causing severe acute respiratory syndrome (SARS, 9.6%) and Middle East respiratory syndrome (MERS, 35.5%) [4,5]. However, SARS-COV-2 is highly infectious, with an estimated reproduction number (R0) over 3, causing more mortalities than any other coronaviruses [5,6,7]. Due to the wide geographical spread of the virus and the growing international concern, the World Health Organization (WHO) declared COVID-19 outbreak as a pandemic on 12 April 2020. As of 24 April 2020, COVID-19 has killed more than 190,800 people, with 4.6 million confirmed cases worldwide. Billions of people were instructed or forced to stay at home while countries locked their borders to varying degrees to control COVID-19.
After the first incidence of COVID-19 on 20 January 2020, Korea has controlled the outbreak relatively well compared to other countries. Although there was a surge of COVID-19 in Daegu City, which peaked at over 900 incident cases on 29 February, the daily increase in new cases in Korea had dropped below 10 by the end of April [8]. Without imposing extreme measures to restrict the movement or freedom of the citizens, Korea was able to flatten the curve of the new cases. This was achieved by a massive number of tests conducted for early detection, by applying rigorous epidemiological investigation, and sharing information using the state of the art information and communication technologies [8,9]. These all occurred with the voluntary participation of social distancing and personal hygiene practices (i.e., wearing respirators in public space and washing hands) by the citizens. To address the shortage and ensure the equal distribution of respirators, the Korean government adopted a “5-day rotation system for a respirator” to provide opportunities for people to purchase a respirator at pharmacies on designated days according to their birth years.
Recently, the diverse news media, journal papers, and reports posted on the preprint server medRxic have indicated that the stringent lockdown measures not only helped in controlling the spread of this highly infectious virus, but also helped to decrease the ambient air pollution levels, including particulate matter less than 2.5 μm in diameter (PM2.5) and nitrogen dioxide (NO2) [10,11]. With the lockdown of industrial activities and transportation in China for about 35 days, there was a 30.1 and 9.2 μg/m3 decrease in PM2.5 in Wuhan and Beijing, respectively [12]. One report estimated a 12.9 and 18.9 μg/m3 decrease in NO2 and PM2.5 in China after the massive population quarantine [13]. Satellites of National Aeronautics and Space Administration (NASA) and European Space Agency detected a 10 to 20% decrease in NO2 levels over eastern and central China after the quarantine [14]. The Movement Control Order of the Malaysian government to encourage their citizens to work at home and suspend industrial activities caused a decrease in PM2.5 levels [15]. Lockdown measures such as school closure, work at home, avoiding gatherings, shutting down commerce, and limiting public transportation in the city of Rio de Janeiro and São Paulo State caused a reduction in carbon monoxide and NO2 levels [16,17]. Another report estimated a 9%, 29%, and 11% global reduction in ambient PM2.5, NO2, and ozone levels, respectively, after the COVID-19 crisis [18].
A recent announcement by Korea’s Ministry of Environment revealed a reduction in the average PM2.5 in Korea from December 2019 to April 2020 by 27% (9 μg/m3) compared to that of the previous year [19]. Although the report highlighted the effectiveness of “the seasonal particle pollution measures”, which was newly implemented in December 2019, the marked PM2.5 decrease observed in China as well as around the world suggest that the measures to control COVID-19 may be the largest contributing factor behind the improved air quality. In addition, a recent modeling study showed that around 70% of PM2.5 concentration of Seoul is affected by the emission from China during the days with severe PM2.5 concentration in spring [20].
Therefore, we initiated this study with a reasonable assumption that social distancing and increased telecommuting in Korea, in addition to the stringent lockdown measures and reduced industrial activities in China, may have reduced the PM2.5 levels in Seoul, the capital city of Korea. Furthermore, the respirators supplied and distributed by the Korean government were effective in filtering PM2.5 at least 80% of the 0.6 μm nonoil particulates. Therefore, each person would be exposed to a lower level of PM2.5 compared to the period before the COVID-19 crisis by using the particulate filtering respirator. Based on this assumption, we estimated the acute health benefits of PM2.5 reduction and changes in public behavior (wearing of a respirator) during the COVID-19 crisis using the health impact assessment methodology. The aim of this study was to use currently available data to estimate the acute health benefits of PM2.5 reduction and changes in public behavior, which were changes experienced by Korean citizens in their daily lives during the COVID-19 crisis.
2. Materials and Methods2.1. Study Design
The health impact assessment requires data on exposure, mortality, and the population. Although Korea shares the real-time air pollution monitoring data with the public, the mortality data are not shared simultaneously. Therefore, we estimated the mortality rates over the last 2 years (2019 and 2020) based on the mortality rates of earlier years. With the PM2.5 monitoring data, population data, and estimated mortality rates in Seoul, we estimated the health benefits based on the PM2.5 reduction levels in 2020. By using previous respirator intervention study results [21], we estimated the health benefits of decreased PM2.5 and respirator use in 2020.
To estimate the PM2.5 decrease in year 2020, we first calculated the average PM2.5 concentration during the first 4 months of 2020 and compared it with the concentration in the same months, each year, from 2016 to 2019. By using the attributable fraction (AF) method [22], we estimated the mortality burden attributable to the acute ambient PM2.5 exposure in the first 4 months of each year, and the number of mortalities avoided due to the observed PM2.5 reduction in 2020.
To calculate the mortality burden attributable to PM2.5, we obtained the population, mortality, and air pollution monitoring data of Seoul from publicly available databases. The relative risks (RR) derived from previous time-series studies, which evaluated the association between ambient PM2.5 exposure and cause-specific mortalities were reviewed to retrieve the beta estimates of the concentration–response functions [23,24]. To consider the effects of public respirator use, we referred to previous intervention study, which evaluated the difference between individual PM2.5 exposure level by the particulate respirator use [21].
Several important assumptions were made for our study. Because mortality data were unavailable for the last two years (2019 and 2020), we assumed that mortality rates in Seoul from January to April had not changed in the last 5 years. In Korea, the daily mortality data for a typical year become publicly available at least 1.5 years after the year ends. Therefore, we estimated the mortality rates of Seoul from January to April in 2019 and 2020 by averaging the mortality rates of the same months from 2016 to 2018. Because the mortality rates are closely related to the structure of the population, we also assumed that the composition of the population by age groups had not changed during our study period.
Second, we assumed that the effects of PM2.5 were limited to a single day, ignoring the delayed effect of PM2.5. PM2.5 is known to have lag effects of several days after the exposure [25]. However, because the daily mortality counts were unavailable for the years 2019 and 2020, it was impossible to estimate the daily levels accounting for lag effects of PM2.5. Therefore, we regarded the 4-month period as a whole and calculated the mortality burden for each year using the 4-month averages of PM2.5 concentrations and the estimated mortality rates of Seoul. Assumption focusing on a single day effect of PM2.5 may underestimate the overall mortality burden attributable to PM2.5. However, the avoided number of mortalities due to PM2.5 reduction during the COVID-19 crisis may not be biased because we focused on the changes in PM2.5 levels with the assumption of a linear concentration–response function.
Third, we assumed that the effects of PM2.5 concentration on health outcomes did not change during the study period, despite the changes in personal behaviors (i.e., social distancing and decreased outdoor activities) due to the COVID-19 crisis. We adopted the RRs from the previous time-series studies conducted both in Seoul and that in 652 cities around the globe [23,24]. However, despite assuming that the slope of concentration–response function between PM2.5 and mortality remained unchanged, we assumed that wearing a particulate filtering respirator would decrease the absolute level of PM2.5 exposure of an individual.
This study was exempt from review by the Institutional Review Board of the Seoul National University Hospital, Korea (IRB No.: 2006-122-1133), because data used in our study were de-identified and publicly available.
2.2. Population and Mortality Data
The yearly residents’ registration data for January during the study period (2016 to 2020) and the cause-specific mortality data for January to April (2016 to 2018) were acquired from the publicly available databases, the Korean Statistical Information Service website, and the Korean Statistical Information Service MicroData Integrated Service website [26,27].
We used the International Classification of Disease, 10th revision codes to define the following cause-specific mortalities: non-accidental and specific disease mortality (A00-R00), cardiovascular disease mortality (I00-I99), and respiratory disease mortality (J00-J99). The number of deaths due to these disease categories during the 4 months (January to April) of each year was calculated and divided by the number of registered residents in January of the corresponding year, to calculate the 4-month average in mortality rates of Seoul.
2.3. PM<sub>2.5</sub> and Meteorological Data
Ambient PM2.5 data of Seoul from January 2016 to April 2020 were accessed through the Airkorea website, which provides real-time air pollution monitoring data of Korea [28]. Korea operates 25 air pollution monitoring stations in Seoul, covering 25 basic administration districts. We acquired the hourly PM2.5 monitoring results from each station, and calculated Seoul’s daily and 4-month average of PM2.5 exposure levels for January to April each year from 2016 to 2020. Daily meteorological data such as ambient temperature, relative humidity, and wind speed from January 2016 to April 2020 were accessed through the National Climate Data Center website. The map of Seoul and the locations of the ambient PM2.5 as well as the weather monitoring stations are presented in Figure S1.
2.4. Concentration-Response Functions and Effects of Particulate Matter Filtrating Respirator
The beta estimates of the concentration—response functions were retrieved from the RRs of the previous time-series studies that evaluated the association between ambient PM2.5 exposure and cause-specific mortalities (Supplementary Table S1). We selected two recently published studies—one analyzing the data from cities around the globe, and the other limited to Seoul. In brief, the Multi-City Multi-Country (MCC) collaborative research network gathered the daily mortality rates and PM2.5 data from 499 cities in 16 countries. The researchers found that a 10 μg/m3 increase in a 2-day moving average of ambient PM2.5 was associated with 0.68% (95% confidence interval (CI): 0.59, 0.77); 0.55% (95% CI: 0.45, 0.66); and 0.75% (95% CI: 0.53, 0.95) increase in the daily non-accidental, cardiovascular, and respiratory mortalities, respectively [23].
The study conducted in Seoul used the daily PM2.5 concentrations and mortality data of the city from 2006 to 2012. A 10 μg/m3 increase in ambient PM2.5 was associated with an increase in non-accidental mortality (0.33% (95% CI: 0.01, 0.66)), cardiovascular mortality (0.76% (95% CI: 0.12, 1.41)), and respiratory mortality (1.77% (95% CI: 0.55, 3.01)) on the same day in Seoul [24].
In a previous intervention study with 21 female elderlies in Korea, we evaluated the effects of a particulate-filtering respirator on cardiopulmonary function by using the crossover study design [21]. The subjects were instructed to use (intervention period) or not use the respirator (control period) for six consecutive days and had a medical examination on the last day of each period. By using the disposable particulate respirators (capable of filtering 80% of the 0.6 μm nonoil particulates), we found that the average level of personal exposure to PM2.5 had decreased by 27.4% (9.0 μg/m3 reduction) during the respirator use, and even the outdoor (27.4–28.8 μg/m3) and 24-h personally monitored PM2.5 levels (18.7–20.1 μg/m3) were similar between intervention and control periods. By referring to this value, we estimated that the effects of respirator use during COVID-19 crisis decreased the personal PM2.5 exposure level by 27.4% in addition to the decrease in ambient PM2.5 levels observed during 2020.
2.5. Statistical Analysis
With the estimated number of mortalities and monitored PM2.5 concentrations from January to April each year, we used the AF method to estimate the mortality burden attributable to ambient PM2.5 levels [22].\\nExcess deaths by PM2.5=(1−exp−β×ΔC) × Number of deaths
β is the coefficient derived from the RRs in the previous time-series studies and ΔC refers to the changes in the PM2.5 concentrations under different counterfactual scenarios. The number of deaths from January to April for the years 2016 to 2018 was obtained from the mortality database, while those of 2019 and 2020 were calculated by multiplying the number of registered residents with the mortality rates estimated based on the mortality rates of 2016 to 2018.
To calculate the number of excess deaths attributable to the acute PM2.5 exposure from January to April each year from 2016 to 2020, we defined 2.4 μg/m3 as the concentration with the minimum health risk, which is the theoretical minimum risk exposure level, indicating no health benefits for reducing PM2.5 below the level based on prior epidemiological studies [29]. Therefore, ΔC in the equation indicates the difference between average PM2.5 concentrations monitored each year from January to April and 2.4 μg/m3. On the other hand, to calculate the avoided mortality due to PM2.5 reduction in 2020, we defined ΔC as the estimated reduction in PM2.5 from January to April in 2020 compared to the same months in 2016–2019.
To calculate the reduction in PM2.5 in 2020, we used the linear regression models assuming the normal distribution of PM2.5 levels. In model 1, the amount of reduction was estimated based on a simple comparison of the average value for 2020 with the average for the years 2016–2019. In model 2, we adjusted for meteorological variables (daily average temperature, relative humidity, and wind speed), and in model 3, we adjusted for the meteorological variables, years (as a continuous variable), and the months (as a categorical variable). In model 4, we adjusted for the meteorological variables and assumed that the average level of personal exposure to PM2.5 had decreased by 27.4% in 2020, to account for the widespread use of particulate filtrating respirator.
All analyses were conducted with SAS version 9.4 (SAS Institute Inc., Cary, NC, USA), and figures were drawn using the R statistical software (Version 3.6.1; R Foundation for Statistical Computing, Vienna, Austria). The level of statistical significance was set at a p-value of less than 0.05.
3. Results
Table 1 and Figure 1 show the 2016 to 2020 Seoul data for the daily PM2.5 concentrations, ambient temperature, relative humidity, wind speed, and the number of days that the PM2.5 concentration was above the WHO and Korea 24-h average standards. The average ambient temperature from January to April in 2020 was the highest compared to those of the previous 4 years, while the relative humidity and wind speed of 2020 were similar to that of 2016 and 2017.
The average PM2.5 concentration from January to April in 2020 (25.6 μg/m3) was the lowest in the last 5 years. Overall, the PM2.5 concentrations above the WHO (25 μg/m3) and the Korean standards (35 μg/m3) in 2020 were for 55 days (45.5%) and 25 days (20.7%) respectively, which were the least number of days in the past 5 years. Figure 1 shows the dramatic decrease in the daily PM2.5 concentrations as well as the number of days with spiking PM2.5 concentrations in 2020 compared to the previous 4 years.
Table 2 shows the number of registered residents, estimated number of deaths, and mortality rates used in the study. The average number of registered residents in Seoul in January each year was 9,860,115. The average non-accidental, cardiovascular, and respiratory mortalities per 100,000 persons in Seoul were 139.2, 32.0, and 16.1 from January to April in 2016 to 2018, respectively. We used these mortality rates to estimate the number of deaths for 2019 and 2020 assuming that these rates remained unchanged. We estimated that 13,549; 3115; and 1567 persons died in Seoul from January to April in 2020 due to non-accidental, cardiovascular, and respiratory diseases, respectively.
Figure 2, Table S2, and Figure S2 show the number of mortalities attributable to PM2.5 exposure in Seoul from January to April each year from 2016 to 2020. By using the MCC study’s RRs, the daily exposure to PM2.5 in 2020 caused 211.4 (95% CI: 183.7, 239.0); 39.4 (95% CI: 32.3, 47.2); and 26.6 (95% CI: 19.1, 34.0) deaths due to non-accidental, cardiovascular, and respiratory diseases, respectively (Table S2). The mortality attributable to the daily PM2.5 exposure was the lowest in 2020 compared to those of the previous 4 years (Figure 2). The results using RRs from the Seoul study are summarized in Table S2 and Figure S2.
Table 3 shows the estimated PM2.5 reduction levels and the avoided mortality due to the PM2.5 exposure in 2020 compared to those from 2016–2019. By simply comparing the average values, a −5.6 μg/m3 (95% CI: −9.0, −2.3) change in ambient PM2.5 was observed in Seoul from January to April in 2020 compared to the same months in the previous 4 years (model 1). By adjusting for meteorological variables, a −4.1 μg/m3 (95% CI: −7.2, −0.9) change in ambient PM2.5 was estimated (model 2). By further adjusting for years and months, a −15.1 μg/m3 (95% CI: −27.1, −3.2) change in ambient PM2.5 was estimated (model 3). With the conservative estimation of a 4.1 μg/m3 decrease in PM2.5 and RRs from the MCC study, we found that 37.6 (95% CI: 32.6, 42.5) non-accidental; 7.0 (95% CI: 5.7, 8.4) cardiovascular; and 4.7 (95% CI: 3.4, 6.1) respiratory mortalities were avoided because of the reduction in PM2.5 from January to April in 2020 compared to those of the previous 4 years.
By considering the effect of the meteorological variables and the particulate respirator use during 2020 (model 4), we estimated that the personal exposure to ambient PM2.5 decreased by −11.2 μg/m3 (95% CI: −14.3, −8.2) from January to April in 2020 compared to the previous 4 years. The corresponding health benefits were decreases of 102.5 (95% CI: 89.0, 115.9) non-accidental; 19.1 (95% CI: 15.6, 22.9) cardiovascular; and 12.9 (95% CI: 9.2, 16.5) respiratory mortalities.
4. Discussion
We observed at least a 4.1 μg/m3 decrease in ambient PM2.5 concentration in Seoul from January to April in 2020 compared to the same months in 2016–2019. We estimated that 37 persons were saved due to the reduction in PM2.5 during the 4-month period. Because using a particulate-filtrating respirator may decrease the absolute level of PM2.5 exposure for an individual, the health benefit related to air pollution during the COVID-19 crisis may be larger than our current estimation of 37 persons.
There are several possible explanations for the decrease in PM2.5 in Seoul. First, public behavioral changes such as social distancing and reduced outdoor activities to limit COVID-19 transmission may have decreased the air pollution levels. According to the mobility data based on the map navigation application on smartphones, both walking and driving by the public were decreased in Seoul after the COVID-19 crisis (Figure S3). The daily amount of traffics entering the highways and the number of citizens using the Seoul Metropolitan Area subway from January to April in 2020 dropped by 6.1% and 28.4%, respectively, compared to the same months in the period 2016–2019 (Table S3). In addition, industrial activities such as the number of operating factories may have decreased due to the limited consumer demand during COVID-19 crisis, which may have resulted in decreased domestic emissions [30].
Similar improvements in air quality were observed around the world since the COVID-19 crisis, with an estimated reduction of 9%, 29%, and 11% of ambient PM2.5, NO2, and ozone levels, respectively [18]. The reductions in carbon monoxide and NO2 levels were observed after the partial lockdown (school closure, work from home, avoiding gatherings, shutting down commerce, and limiting public transportation) in the city of Rio de Janeiro and São Paulo State [16,17]. During the Movement Control Order (work from home and suspend industrial activities) to isolate the source of COVID-19 in Malaysia, up to a 58.4% decrease in PM2.5 was observed [15]. By analyzing air quality monitoring data of 22 cities in India, 43% and 18% decreases in PM2.5 and NO2 were observed during the COVID-19 crisis [31].
The air pollution levels in Seoul cannot be evaluated without considering the effects of the neighboring countries, China and North Korea. By evaluating the source contribution of PM2.5 on days with severe PM2.5 concentrations (with 24-h average PM2.5 concentration of over 100 μg/m3) in Seoul, China contributed to the PM2.5 concentrations by up to 70% while the domestic contribution was 21% [20]. In addition, around 15% of PM2.5 concentration in Seoul is affected by the emission from North Korea [32]. Among the 1638 mortalities attributable to the acute exposure to high levels of PM2.5 in Korea in 2016, at least 258 and 26 deaths were estimated to have been due to the emissions from China and North Korea, respectively [33].
Because the air quality in China improved dramatically during the COVID-19 quarantine (10 February to 14 April 2020) [12,13,14,34], and considering the fact that China’s contribution to Seoul’s PM2.5 concentration is generally greater in the spring and winter [33], the decrease in PM2.5 observed in Seoul from January to April 2020 may partially be explained by the effects of China’s rigorous quarantine measures and decreased industrial activities during the COVID-19 crisis. If the observed decrease in PM2.5 levels in Seoul in 2020 is indeed due to the changes related to domestic responses as well as China’s response against COVID-19, the estimated mortality benefit from the lowered PM2.5 levels (37 persons) outweighs the number of the direct casualties from COVID-19 in Seoul (2 persons till 30 April 2020). Similar paradoxical phenomena, and a massive decrease in air-pollution-related mortalities and morbidities during the COVID-19 crisis are expected worldwide [14].
Another plausible explanation for the decrease in PM2.5 concentration in Seoul in 2020 is the governmental effort to reduce the domestic sources of PM2.5 [19]. With consultations with relevant ministries, a comprehensive set of measures to control the particulate matter in 2020 to 2024 was finalized on 1 November 2019 [35]. One of the measures is the seasonal management of the domestic sources of PM2.5 from December to April, when PM2.5 levels are usually high [36]. Efforts to reduce the domestic emission of PM2.5 include the shutting down of the coal-fired power plants, voluntary reduction in emissions from business sites, nationwide surveillance of emission sources, designation of low-sailing zones, and transition to low sulfur fuels for ocean vessels. In addition to these comprehensive measures, an increase in rainfall and the number of days with high wind velocity may also help to explain the decrease in PM2.5 observed in Seoul from January to March 2020 [19]. However, the relative humidity and wind speed in 2020 were similar to those of 2016 and 2017; limiting the effects of meteorological factors on PM2.5 reduction.
We may not be able to distinguish the effects of diverse governmental measures from those of domestic and international changes related to COVID-19 on the reduced PM2.5 levels in Seoul. However, it is reasonable to assume that the domestic measures (social distancing and avoidance of outside activities in Korea) and international effects (decreased PM2.5 levels during the quarantine in China) related to COVID-19 may have played a significant role at the same time. We may be able to confirm the effects of COVID-19 by observing the air pollution levels after the COVID-19 measures are lifted and by evaluating whether the effects (decrease in air pollution) cease after the treatment (measures against COVID-19) ends [37].
Although not formally published as a journal article, several reports posted on medRxiv are showing the health benefits of reduced PM2.5 levels after the COVID-19 crisis. One report estimated that the PM2.5 and NO2 levels dropped by 18.9 and 12.9 μg/m3 in China during the COVID-19 quarantine period, which led to a decrease of 3214 PM2.5-related deaths and a decrease of 8911 NO2-related deaths [13]. By analyzing the air pollution data from over 10,000 monitoring stations around the globe, a 9%, 29%, and 11% reduction in ambient PM2.5, NO2, and ozone levels was estimated during February to April 2020, just after the global lockdown in response to COVID-19 [18]. The corresponding health benefits related to the decrease in air pollution levels were 7400 deaths and 6600 pediatric asthma cases.
After the first confirmed case of COVID-19 in Korea on 20 January 2020, the panic buying of particulate filtrating respirator led to instability in supply and demand. To tackle this issue, the Korean government adopted a “5-day rotation system for respirator” to provide equal opportunity for individuals to purchase two particulate-filtering respirators (particulate respirators capable of filtering at least 80% of the 0.6 μm nonoil particulates) per week. As the Korean government instructed its citizens to wear a respirator outside the house to control COVID-19, personal level of exposure to ambient PM2.5 may have decreased from wearing a particulate respirator. With the results of the previous intervention study, we estimated that the effects of respirator use during COVID-19 crisis decreased the personal PM2.5 exposure level by 27.4% in addition to the decrease in ambient PM2.5 levels. We estimated that this decrease led to 102 averted deaths related to PM2.5 during the 4-month period; this is higher compared to the conservative estimation of 37 lives saved with a 4.1 μg/m3 decrease in ambient PM2.5 concentration during the COVID-19 crisis estimated in our study. We believe that using a particulate filtrating respirator not only help to block the transmission of COVID-19, but also helped to limit the adverse health effects of PM2.5.
We have previously estimated that around 12,000 premature deaths were attributable to chronic PM2.5 exposure in Korea in 2015, when the annual average of PM2.5 concentration was 24.4 μg/m3 [38]. We have also estimated that 1763 deaths solely occurred in Seoul. However, our study is different from the previous study in terms of the fact that it addressed the acute effects of PM2.5 exposure by using the RRs from previous time-series studies. With the yearly mortality information and PM2.5 measurement data for the entire year, we may be able to estimate the chronic PM2.5 exposure burden for 2020 and compare the difference with previous years. If reduced PM2.5 levels are maintained throughout 2020, we may be able to see a marked decrease in PM2.5-related burden.
Several limitations should be noted for this study. First, we adopted the RRs from previous time-series studies and ignored the possibility that the association between PM2.5 and health outcomes may have changed during the COVID-19 crisis. The Korean government instructed its citizens to avoid outdoor activities or crowded areas, and to use a respirator outside the house. Due to social distancing and the use of a respirator, the beta coefficient of exposure–response relationship may have changed. If the daily mortality data become available in the future, we may be able to confirm the changes in beta coefficient by comparing the estimates from the time-series analyses before and during the COVID-19 crisis. In addition, although we tried to adjust for the effects of respirator by using the results from previous intervention study, we also assumed that the entire public were using the particulate respirator during the COVID-19 crisis, which is unlikely. Second, we ignored the daily lag effect of PM2.5. Because mortality data are not disclosed simultaneously, we were unable to conduct a day-to-day analysis accounting for the lag effects of PM2.5. With the full mortality data of 2020, a more precise estimation can be conducted by using the daily data rather than using the 4-month (January to April) data as a whole.
Due to insufficient data and assumptions used in our study, our study estimates may be biased. Assumptions regarding mortality rates and concentration-response functions have to be validated with a daily number of mortality data and PM2.5 monitoring data in the future. However, based on the currently available data, our study may offer a glimpse into the acute health benefits of PM2.5 reduction and changes in public behaviors, which are the tangible changes experienced by the citizens in their daily lives during the COVID-19 crisis.
5. Conclusions
We observed at least 4.1 μg/m3 decrease in ambient PM2.5 in Seoul from January to April 2020, and this decrease is believed to be the results of the changes related to COVID-19 crisis. With our conservative estimation, a total of 37 lives were saved due to the PM2.5 reduction in Seoul from January to April 2020 compared to the same period in previous years. However, the health benefits related to the decrease in PM2.5 may be greater because of the popular use of the particulate respirator by the public during the COVID-19 crisis in Korea. We may need to verify our study findings by observing the PM2.5 levels after the COVID-19 crisis and conducting studies with a full set of daily mortality data.
Supplementary Materials
The following are available online at https://www.mdpi.com/1660-4601/17/15/5279/s1, Table S1: Concentration-response functions used in the study; Table S2: Estimated number of mortalities attributable to PM2.5 exposure from January to April in each year from 2016 to 2020; Table S3: The daily average amount of vehicles entering the Seoul Metropolitan Area highways and number of citizens who used subways of Seoul Metropolitan area from January to April each year from 2016 to 2020; Figure S1: Locations of 25 PM2.5 monitoring stations (red circle) and meteorological station (blue circle) covering Seoul; Figure S2: Average PM2.5 concentration of Seoul from January to April and estimated number of mortalities attributable to PM2.5 exposure (RRs from the Seoul study were used for the estimation); Figure S3: Mobility trends of Seoul City (Mobility on 13 January 2020 was regarded as 100%; Data was gathered from the Apple Maps Mobility Trends Reports: https://www.apple.com/covid19/mobility/).
Click here for additional data file.
Author Contributions
Conceptualization, C.H.; Data curation, C.H.; Formal analysis, C.H.; Funding acquisition, C.H.; Investigation, C.H.; Resources, C.H.; Software, C.H.; Supervision, Y.-C.H.; Writing—original draft, C.H.; Writing—review and editing, C.H. and Y.-C.H. All authors have read and agreed to the published version of the manuscript.
Funding
This research was supported by the research fund of Chungnam National University (2020-0892-01).
Conflicts of Interest
The authors declare no conflict of interest.
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PM2.5 concentration in Seoul from year 2016 to 2020 (January to April).
Average PM2.5 concentration of Seoul from January to April of 2016 to 2020 and estimated number of mortalities attributable to PM2.5 exposure (RRs from the MCC study were used for the estimation).
ijerph-17-05279-t001_Table 1
Average daily particulate matter (PM2.5) concentrations, temperature, relative humidity, wind speed, and number of days over the WHO (25 μg/m3) and Republic of Korea (ROK, 35 μg/m3) PM2.5 24-h average standards in Seoul (January to April each year from 2016 to 2020).
Year
Days (N)
PM2.5 (μg/m3) (a)
Temperature (°C) (a)
Relative Humidity (%) (a)
Wind Speed (m/s) (a)
Days over WHO Standard (b)
Days over ROK Standard (b)
2016
121
28.1 (11.1)
4.5 (7.8)
52.7 (14.4)
2.5 (0.7)
64 (52.9)
33 (27.3)
2017
120
31.7 (15.2)
4.6 (7.1)
52.1 (12.7)
2.4 (0.7)
71 (59.2)
40 (33.3)
2018
120
30.6 (18.3)
3.9 (8.4)
51.8 (14.4)
2.0 (0.7)
64 (53.3)
40 (33.3)
2019
120
34.6 (23.6)
4.9 (6.0)
48.8 (14.6)
1.9 (0.6)
63 (52.5)
39 (32.5)
2020
121
25.6 (12.2)
5.8 (5.3)
52.4 (13.5)
2.5 (0.8)
55 (45.5)
25 (20.7)
(a) Mean and standard deviation are presented, (b) Number of days and percentage are presented.
ijerph-17-05279-t002_Table 2
Number of registered population, number of deaths, and mortality rates used in this study.
Year
2016
2017
2018
2019
2020
Registered population at January, Seoul
10,018,537
9,930,478
9,851,767
9,766,288
9,733,509
Non-accidental mortality (A00-R00, January to April)
\\n
\\n
\\n
\\n
\\n
 Number of deaths
13,776
13,243
14,445
13,595 (a)
13,549 (a)
 Mortality rate (per 100,000)
137.5
133.4
146.6
139.2 (b)
139.2 (b)
Cardiovascular disease mortality (I00-I99, January to April)
\\n
\\n
\\n
\\n
\\n
 Number of deaths
3080
3129
3316
3125 (a)
3115 (a)
 Mortality rate (per 100,000)
30.7
31.5
33.7
32.0 (b)
32.0 (b)
Respiratory disease mortality (J00-J99, January to April)
\\n
\\n
\\n
\\n
\\n
 Number of deaths
1533
1462
1789
1572 (a)
1567 (a)
 Mortality rate (per 100,000)
15.3
14.7
18.2
16.1 (b)
16.1 (b)
(a) Calculated based on the estimated mortality rate, (b) Estimated by averaging year 2016–2018 mortality rate.
ijerph-17-05279-t003_Table 3
Estimated PM2.5 reduction levels and avoided mortality due to PM2.5 exposure in January to April of 2020 compared to same month each year from 2016 to 2019.
\\n
Model 1 (a)
Model 2 (b)
Model 3 (c)
Model 4 (d)
Reduction of PM2.5 by comparing 2016–2019 and 2020 (μg/m3)
−5.6 (−9.0, −2.3)
−4.1 (−7.2, −0.9)
−15.1 (−27.1, −3.2)
−11.2 (−14.3, −8.2)
Avoided cause-specific deaths
\\n
\\n
\\n
\\n
Estimation using RRs from MCC study
\\n
\\n
\\n
\\n
 Non-accidental mortality
51.3 (44.6, 58.1)
37.6 (32.6, 42.5)
137.9 (119.8, 156)
102.5 (89.0, 115.9)
 Cardiovascular disease mortality
9.6 (7.8, 11.5)
7 (5.7, 8.4)
25.7 (21.0, 30.8)
19.1 (15.6, 22.9)
 Respiratory disease mortality
6.5 (4.6, 8.3)
4.7 (3.4, 6.1)
17.3 (12.5, 22.2)
12.9 (9.2, 16.5)
Estimation using RRs from Seoul City study
\\n
\\n
\\n
\\n
 Non-accidental mortality
25 (0.8, 49.8)
18.3 (0.6, 36.5)
67.2 (2.0, 133.9)
49.9 (1.5, 99.5)
 Cardiovascular disease mortality
13.2 (2.1, 24.3)
9.7 (1.5, 17.8)
35.4 (5.6, 65.2)
26.3 (4.2, 48.5)
 Respiratory disease mortality
15.3 (4.8, 25.8)
11.2 (3.5, 18.9)
41 (12.9, 68.6)
30.5 (9.6, 51.2)
(a) Model 1 estimated by simple comparison of mean value, (b) Model 2 adjusting for daily average temperature, relative humidity, and wind speed, (c) Model 3 adjusting for daily average temperature, relative humidity, wind speed, years (in continuous variable), and months (as categorical variable), (d) Model 4 adjusting for daily average temperature, relative humidity, and wind speed and considered the effect of particulate filtering respirator.
\\n\"\n]"}}},{"rowIdx":439,"cells":{"data":{"kind":"list like","value":["\nInt J Environ Res Public HealthInt J Environ Res Public HealthijerphInternational Journal of Environmental Research and Public Health1661-78271660-4601MDPI32751853PMC743209610.3390/ijerph17155541ijerph-17-05541ArticleWellbeing at Work before and during the SARS-COV-2 Pandemic: A Brazilian Nationwide Study among Dietitianshttps://orcid.org/0000-0003-1251-7966MatosRaquel Adjafre da Costa1https://orcid.org/0000-0003-0699-7617AkutsuRita de Cássia Coelho de Almeida1https://orcid.org/0000-0003-0370-3089ZandonadiRenata Puppin1https://orcid.org/0000-0003-3505-4538RochaAda2https://orcid.org/0000-0002-0369-287XBotelhoRaquel Braz Assunção1*Department of Nutrition, Faculty of Health Sciences, University of Brasilia, Brasilia 70910-900, Brazil; raquel.adjafre@gmail.com (R.A.d.C.M.); rita.akutsu@gmail.com (R.d.C.C.d.A.A.); renatapz@yahoo.com.br (R.P.Z.)Faculdade de Ciencias da Nutrição e Alimentação, University of Porto, 4200-464 Porto, Portugal; adarocha@fcna.up.ptCorrespondence: raquelbabotelho@gmail.com3172020820201715554106720202872020© 2020 by the authors.2020Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
This study aimed to evaluate the perceptions of dietitians’ wellbeing at work before and during the SARS-COV-2 pandemic in Brazil. This cross-sectional study was performed using a previously validated instrument to investigate the wellbeing of dietitians at work in Brazil. The questionnaire on the wellbeing of dietitians was composed of 25 items (with a 5-point scale), characteristics, and questions about the SARS-COV-2 period. The application was carried out with GoogleForms® tool from 26 May to 7 June 2020. The weblink to access the research was sent via email, messaging apps, and social networks. Volunteers were recruited nationwide with the help of the Brazilian Dietitians Councils, support groups, as well as media outreach to reach as many dietitians as possible. Volunteers received, along with the research link, the invitation to participate, as well as the consent form. A representative sample of 1359 dietitians from all the Brazilian regions answered the questionnaire—mostly female (92.5%), Catholic (52.9%), from 25 to 39 years old (58.4%), with a partner (63.8%), and with no children (58%). Most of the participants continue working during the pandemic period (83.8%), but they did not have SARS-COV-2 (96%), nor did their family members (80.7%). The wellbeing at work before SARS-COV-2 was 3.88 ± 0.71, statistically different (p < 0.05) from during the pandemic, with the wellbeing of 3.71 ± 0.78. Wellbeing at work was higher before the pandemic for all the analyzed variables. Analyzing variables separately before and during the pandemic, dietitians with partners, children and a Ph.D. presented higher scores for wellbeing at work. Professionals receiving more than five times the minimum wage have higher scores. During the pandemic, better wellbeing was observed for dietitians working remotely.
SARS-COV-2dietitianspandemicwellbeing at work1. Introduction
The world is facing the unexpected SARS-COV-2 pandemic with several consequences for economic, social, mental, environmental, and health aspects. The SARS-COV-2 brought not only the risk of death from the viral infection but also unbearable psychological pressure on people in the world [1]. According to the World of Health Organization (WHO), until June 7, 2020, 6,799,713 cases of SARS-COV-2 were registered and there have been 397,388 deaths in the world [2]. Americas represent almost half of the registered cases (n = 3,234,875 cases; 47.6%) and deaths (n = 179,394 deaths; 45%) in the period. These numbers could be underestimated due to the lack of medical diagnosis at the beginning of the pandemic. Some countries face a few diagnostic tests. In South America, Brazil is the country with the most registered cases of SARS-COV-2 (60.3%, n = 645,771, until 7 June 2020) and deaths (72.7%, n = 35,026 in the same period) [3].
In Brazil, on the frontline of coping with SARS-COV-2, health professionals are acting on different fronts. To date, the future has been uncertain, and healthcare professionals have stepped out of their comfort zones [4]. There are several scenarios faced by health care professionals, especially ones working in clinics or hospitals, with the risk of catching SARS-COV-2, and the ones that are experiencing unemployment and family income reduction without knowing how they will face the economic crisis during and after the pandemic. In this sense, during the pandemic period, they may experience maladaptive psychological consequences of their jobs [5]. Work plays a central role in the individuals’ lives, bringing paradoxical consequences to the social, physical, and psychological integrity of workers [6,7], and low job satisfaction is the leading cause of turnover among health care professionals [8,9]. During the pandemic period, this can get worse.
As well as some other healthcare professionals, during the pandemic, dietitians may be facing some difficulties in their jobs, beyond the traditional ones such as low payment, lack of professional recognition, difficulty in getting their first jobs, and difficulty in geographical mobility [10,11,12,13]. In Brazil, dietitians present a wide range of work options, from directly working in hospitals visiting patients or conducting hospital foodservices, to working in clinics, schools, and commercial restaurants. All of these environments can bring direct contact with possibly infected people, presenting challenges in this new work scenario. The number of patients in the hospitals increased, and the way the food is served needed to change. Restaurants outside the hospitals had to close their doors, leading to unemployment, and facing many risks in reopening when permitted. It is essential to highlight those dietitians, despite being a specific niche of the health system, that are working directly with people infected with Sars-CoV-2. Often, their risks are ignored or underestimated by other health professionals and even by the Federal Government, which can negatively impact wellbeing at work. Recent legislation published on July 8, 2020 (Law No. 14.023) [14] dealing with the protection of professionals exposed to the risks of Sars-Cov-2, did not include dietitians on its lists.
Despite the enormous gathering of scientific data, to date, there is no treatment or vaccine for SARS-COV-2. This scenario of uncertainties about the disease, security itself, and their jobs can affect the perceptions of dietitians about their wellbeing at work. Studies conceive wellbeing at work as a process, defining it as the satisfaction of needs and the fulfillment of individuals’ desires as they fulfill their role in the profession [15,16]. This conception considers the role of work organizations in the health of individuals, and the development of healthy environments enables positive relationships and attitudes. The wellbeing of a professional category can be impacted, among other things, by the general and work values that are expressed in professional practice and social coexistence [16]. Both of them can be facing negative impacts during the SARS-COV-2 pandemic. Wellbeing at work has a significant impact on work performance and quality of life, and it brings paradoxical consequences for the social, physical, and psychological integrity of workers [6,7]. Low wellbeing at work and low job satisfaction are considered the leading causes of turnover among health care professionals [8,9]. Therefore, the knowledge about wellbeing at work is vital to improve the working environment and the quality of the service [8]. Wellbeing at work is related to lower levels of stress, work-related diseases, burnout, depression, and unhealthy personal practices (smoking, drinking, overeating, lack of exercise), and consequently, lower levels of non-communicable chronic diseases (NCD) [17]. There have been studies on the psychological impact of the epidemic on the general public, patients, medical staff, children, and older adults [1,18,19]. However, there is no study on the perception of dietitians’ wellbeing at work during the difficult times of pandemic. The hypothesis is that wellbeing at work will decrease during the pandemic period. In this sense, this study aimed to evaluate dietitians’ perceptions of wellbeing at work before and during the SARS-COV-2 pandemic in Brazil. The professional wellbeing knowledge among dietitians may lead to effective avenues to prevent or manage stress, unhealthy personal practices, and NCD. By evaluating the period before and during the SARS-COV-2 pandemic period, we expect that a clear understanding of the factors that influence dietitians’ wellbeing in these two moments may contribute to helping these professionals to recover after the pandemic period, exploring the areas that were most affected, and working on better professional valorization, improving the public’s trust in dietitians and the dynamics of the interprofessional healthcare team.
2. Materials and Methods2.1. Study Design and Instrument
This exploratory and cross-sectional study was performed using a previously validated instrument [16] to investigate the wellbeing of dietitians before and during the SARS-COV-2 pandemic in Brazil. The questionnaire was composed of 25 items on the wellbeing of dietitians (with a 5-point scale that varies from 1 to 5). It also included characteristics from the original study [16] such as gender, age, marital status, the Brazilian state of current residency, religion, number of individuals living in the house, family income, children, educational level, occupational area as a dietitian, number of workplaces as a dietitian, how long ago graduation ended, and type of university. Researchers included three questions about the SARS-COV-2 period: Do you continue working during the SARS-COV-2 pandemic? Did you test positive for SARS-COV-2? and Did anybody in your family test positive for SARS-COV-2? The complete questionnaire is available in Appendix A.
The instrument application was carried out with GoogleForms® tool (Google LLC, Mountain View, CA, USA) from May 26 to June 7, 2020. The weblink to access the research was sent via email, messaging apps, and social networks. Volunteers were recruited nationwide with the help of the Brazilian Dietitians Councils, support groups, and media outreach to reach as many dietitians as possible. Volunteers received, along with the research link, the invitation to participate, as well as the consent form.
2.2. Participants and Ethics
Dietitians from the entire country were recruited to participate in the study. Researchers wanted to trace the wellbeing at work before and during the SARS-COV-2 of this population group in Brazil. Ethical approval was obtained for this study by the Ethics Committee University of Brasília (protocol No. 54822316.1.00000030). The study was conducted according to the guidelines laid down in the Declaration of Helsinki and followed the Recommendations for the Conduct, Reporting, Editing, and Publication of Scholarly work in Medical Journals.
The sampling size was calculated based on data from the Brazilian Federal Dietitians Council that presents 129,134 registered dietitians [20], considering an error (e) of 3% and a level of significance (α) of 5% [21]. The minimum estimated representative sample size would be of 1059 participants. The inclusion criteria were to be a dietitian and living and working in Brazil.
2.3. Statistical Analysis
Researchers extracted data from the GoogleForms® tool and analyzed using Statistical Package for the Social Sciences—SPSS 24.0 (version 24, SPSS Inc., Chicago, IL, USA). Exploratory and confirmatory analyses were conducted to determine the psychometric quality of the wellbeing at work instrument. We used the Kaiser–Meyer–Olkin (KMO), and Barlett’s sphericity test. For consistency, Cronbach’s alpha was used. Descriptive analyses were used to determine the measures of central tendency and dispersion of the sample. We compared means of the sample through paired t-tests (wellbeing before and during SARS-COV-2) and Analysis of Variance (ANOVA) with Tukey post-hoc.
3. Results
A representative sample of 1359 dietitians from all the 26 Brazilian states and the Federal District and regions answered the questionnaire. Figure 1 shows the distribution of the dietitians and participants by Brazilian regions.
The participants were mostly female (92.5%), Catholic (52.9%), aged from 25 to 39 years old (58.4%), with a partner (63.8%), and with no children (58%) (Table 1). Most of the participants continued working during the pandemic period (83.8%), but had not been diagnosed with SARS-COV-2 (96%) before answering the questionnaire, nor did their family members (80.7%).
Dietitians with less than 1 MW family income are mostly graduates (35.9%) or present specialization/residency (56.4%). However, dietitians with more than 20 MW as a family income have a master’s and Ph.D. (52.2%). Of 144 dietitians with Ph.D., 95.1% work in the teaching area (universities).
Table 2 shows data from the wellbeing at work before and during the pandemic period compared by participants’ characteristics. The instrument presented a KMO of 0.957 and a significant (0.000) Barlett’s sphericity test. For internal consistency, the wellbeing at work instrument presented a Cronbach’s alpha of 0.952. In the communalities, the extraction item was below 0.500 for questions 12 (social relations with my colleagues positively influence my work) and 25 (I consider my workload adequate), and they were not considered for the final score of wellbeing at work. The maintained questions from the instrument were divided into four factors. Factor 1 is related to exterior perception with questions 2, 4, 6, 7, 11 and 15 (Appendix A), factor 2 is concerned about the perception in itself (questions 1, 3, 5, 8, 13, 14, 18 and 21), factor 3 is the task perception (questions 19, 20, 22, 23 and 24), and factor 4 is the perception from the dietitians’ category (questions 9, 10, 16 and 17). Cronbach´s alpha was also calculated for each factor: factor 1, 0.871; factor 2, 0.881; factor 3, 0.881; factor 4, 0.884 (Supplementary File, Table S1).
In general, wellbeing at work before SARS-COV-2 was 3.88 ± 0.71, statistically different (p < 0.05) from during the pandemic, with the wellbeing of 3.71 ± 0.78. A comparison between before the pandemic and during the pandemic showed statistical differences for all variables (worse during the pandemic period) (p < 0.05).
Gender and number of workplaces did not influence wellbeing at work before and during the pandemic period. Individuals with a partner and with children had a better perception of wellbeing at work than the ones with no partner or children. Before and during the pandemic, master’s and Ph.D. individuals presented better wellbeing at work than graduates, and Ph.D. dietitians presented better wellbeing than dietitians with a master’s degree (Table 2).
For both periods, individuals that work in teaching present better wellbeing at work compared to the other areas of dietitians’ practice (clinic, foodservice administration, public health, and others). Before the pandemic period, individuals with family monthly income >5 MW present higher wellbeing at work than the ones up to 5 MW (Table 2). During the pandemic period, the results were a little different, showing differences among individuals up to 3 MW, from 3 to 5 MW and >5 MW, with increasing wellbeing perception with higher family income. Before the pandemic, the time from the undergraduate completion differed from up to 10 years to >15 years. During the pandemic, the higher time from undergraduate completion (>15 years) presented a higher mean of wellbeing at work than the lowest time of completion (≤2 years).
Before the pandemic period, individuals adept in Catholicism and Spiritism had a better perception of wellbeing at work than Protestants. During the pandemic, Catholics did not differ from Protestants, agnostics, or Spiritism followers. However, individuals following the Spiritism religion presented a better perception of wellbeing at work than Protestants (Table 2).
Dietitians that tested positive for SARS-COV-2 (n = 55) were predominantly working in-person (78.2%, n = 43), without or with adaptations (58.2%, n = 32; 20%, n = 11, respectively). Participants who reported not being working during the pandemic period (n = 220) have a job, but they are unable to work in-person or remotely. The wellbeing values were considered for this group of workers because they answered the questions and effectively have a job. There is no difference among perceptions of wellbeing at work between dietitians who had SARS-COV-2 and the ones that did not have, similarly to the results from the ones that had any family member test positive for SARS-COV-2.
When separating the wellbeing at work by factors (Table S1) and comparing by participant´s characteristics, factors 1 and 4 presented the lowest means before and after the pandemic, being lower and statistically different (p < 0.05) during the pandemic. Factor 1 relates to exterior perception and factor 4 to the category perception. Higher scores occurred for the perception of itself and the perception of the task, before and during the pandemic.
4. Discussion
As the world grapples with the impact of the SARS-CoV-2 pandemic, health care workers face extraordinary challenges daily, in different contexts and conditions [22]. The media reports that health care professionals are hugely concerned for the health and wellbeing of their patients, their families, and themselves, facing pandemic issues [22,23]. However, the Brazilian government did not recognize dietitians as part of the health professionals facing the SARS-COV-2 pandemic [14]. These changes in life and work and the lack of recognition and support have a significant impact on their wellbeing, as shown by our results. Among Brazilian dietitians, there was a worse perception of wellbeing at work during the pandemic compared to the period before the pandemic for all variables (p < 0.05) (Table 2). In general, dietitians’ wellbeing at work was positive (above 2.5), which is the midpoint of the scale. The items that obtained the best scores were those that investigate the perception of the importance of the profession for themselves and society. The average scores were above 4.40 before the SARS-COV-2 pandemic and 4.20 during it. The items with the lowest scores and which need to be improved are related to compensation and technological support to perform the tasks assigned to the dietitian. Probably, wage improvement could come through professional qualification, as higher wages were linked to more years of study in our research. Besides, there is a need for an increase in the number of class entities (unions and councils) to fight for better wages of this professional category. In Brazil, even before the SARS-COV-2 pandemic, the country had high unemployment rates [24], a situation worsened by the pandemic, which made it difficult for workers to maintain their family income. Part of the dietitians’ work is in the foodservice area (schools, commercial and institutional restaurants), and most of these are closed or changed the policies of production due to the food safety and workers’ safety conditions.
Wellbeing at work before SARS-COV-2 was 3.88 ± 0.71, statistically different (p < 0.05) from during the pandemic (3.71 ± 0.78). Usually, these conditions would place health care professionals in a situation defined by threat and fear, both of which have been shown to have a detrimental effect on their ability to offer compassionate and person-centered care to their patients [22,25]. In these circumstances, there are high levels of conflict within teams and workplace adversity, leading to a working environment which is perceived as hostile, abusive, and unrewarding [26,27]. As a consequence, workplace adversity can be correlated with a decreased quality of care [28]. In April 2020, the Brazilian Health Ministry requested the registration of all health professionals, including dietitians, to reinforce the fight against SARS-CoV-2, in addition to the usual work of individuals [29]. This reinforcement is to assist managers of the Unified Health System (SUS) in coping with SARS-CoV-2, based on the work capacity of these professionals. It focuses on those who were available to go to the Brazilian states with the greatest need to strengthen health teams [29]. This fact also caused concern in several dietitians who were afraid to work facing SARS-COV-2 patients for various reasons. According to the Brazilian Health Ministry, from the 90,245 Brazilian registered dietitians to reinforce the fight against SARS-CoV-2, 33,624 were willing to work facing SARS-COV-2 [30]. These professionals were registered in the category to receive payment for their work to confront SARS-COV-2. However, the Brazilian Ministry registered other health care professions in a voluntary (unpaid) category of workers, and no dietitian volunteered until 28 April 2020 [30]. It was not stated, but it seems that the dietitians that were willing to work facing SARS-COV-2 were doing it to help the family income, which can potentially worsen their perception of wellbeing at work. Given the unknown and uncontrollable nature of the SARS-COV-2, some health care professionals need to stay away from their home and loved ones, possibly affecting emotional aspects and the relationship with their work [23].
In the pandemic situation, the protection measures for these professionals get worse due to the limitations of social distance. In some cases, this risk is higher, such as in hospitals, emergency services, outpatient clinics, vaccination clinics, screening lines, and other health care settings. In this environment, these professionals are even more exposed when their duty includes providing some assistance to infected people with SARS-COV-2. In foodservices, where these professionals do not work directly with individuals adequately tested for the new coronavirus, they face the same environment with asymptomatic individuals or those in the incubation phase of the disease [31].
Our sample was mostly composed of females (Table 1). The female hegemony among dietitians is common, as shown by other studies in different countries [8,9,13,32,33,34]. Female hegemony in the profession can represent repercussions on career, social prestige, and income [12]. A study showed that dietitians were mostly women and that, although the labor market has grown, the new jobs were mostly (86%) part-time, not only because women need to conciliate career and family care, but also because these positions get lower payment [35]. However, according to our data, gender did not influence wellbeing at work before nor during the pandemic period.
According to a research conducted by the Dietitians Federal Council in 2016/2017 with 1104 dietitians in Brazil, most of them are young, between 25 and 44 years old (81%), a higher percentage than our study (68.3%). Previous studies showed that 73% of the Brazilian dietitians are postgraduates [12,16,36], similar to our data (77.8%, n = 1058). Before and during the pandemic, master’s and Ph.D. individuals presented better wellbeing at work than graduates (Table 2). Most Ph.D. dietitians work in the teaching area, and for both periods, individuals that work in teaching present better wellbeing at work compared to the other areas of dietitians’ practice (clinic, foodservice administration, public health, and others). All schools, including universities, faced changes in how they conducted work. Professors had to search for technological tools and strategies in order to work during the pandemic period, impacting their perception of wellbeing at work, as shown by the lower mean during the pandemic. Undergraduate courses in the health area need practical classes and time inside the hospitals, and it is difficult to return to these activities in-person. The other work areas for dietitians did not present statistically different wellbeing, even for professionals that work in the clinical area and can be inside hospitals.
Before the pandemic period, individuals with family monthly income > 5 MW presented higher wellbeing at work than the ones up to 5 MW (Table 2). During the pandemic period, wellbeing perception increased with higher family income. These data are confirmed by other studies, indicating that lower wages decrease satisfaction at work [13,16,37,38,39]. During the pandemic period, most family members are isolated at home, increasing expenses with bills (water, energy, food, and others). This can influence the difference between the categories of family income compared to the period before the SARS-COV-2. According to the official data [24], unemployment increased in all Brazilian regions with SARS-COV-2, but mainly in the northeast region (from 13.6% in 2019 to 15.6% in 2020), followed by the North (from 10.6% to 11.4%) and the southeast region (from 11.4% to 12.4%), also impacting family income. It is noteworthy that the north and northeast regions were the ones presenting the largest proportional increase in official cases of SARS-COV-2 in Brazil until June 7, 2020 [40].
The Brazilian government published two provisional measures [41,42] during the pandemic changing the work relationship between employers and employees. Employers can reduce employees’ salaries during the SARS-COV-2 crisis, and rules were established for remote work and the suspension of some administrative measures related to safety at work. These legislative measures enable up to a 70% salary reduction and precarious work relationships, reflected by a lower perception of wellbeing at work during the pandemic.
Two characteristics influence wellbeing at work before and during the pandemic: marital status, and children. Dietitians with partners (63.8%) and with children (42%) had higher wellbeing before and during the pandemic. VanderWeele [43] discusses in his article that studies associate marriage with higher life satisfaction and happiness. This association can be related to better mental and physical health and longevity. With time, marriage is associated with a better relationship with others, including work partners, which can influence work wellbeing. VanderWeele [43] also highlights that marriage and family are vital to wellbeing. Despite this study evaluating wellbeing in life, not in the workplace, it could potentially explain higher scores of wellbeing in our study for dietitians with children and partners. Wilcox [44] discusses in his book that marriage is also associated with financial status and education. A meta-analytic study [45] suggested that people who are employed present better life, family, and marital satisfaction. Our study was only conducted with dietitians that have a job, because the instrument is related to wellbeing at work. Therefore, it is not possible to compare this with studies of unemployment. However, they show better wellbeing for people with partners, family, and education, such as our findings for wellbeing at work [46]. Ryff and Heidrich [46] stated that work and education experiences explain differences in the purpose of life. Higher levels of education are associated with happiness and satisfaction and strongly affect income [47]. As already discussed, dietitians with more education and income presented better scores for wellbeing before and during the pandemic.
Regarding religion, worldwide, 15% of people are agnostic [48], higher than in our study (5.2%), but closer to the Brazilian statistics of 8% (IBGE, 2010). Even though research has shown that religion brings higher subjective wellbeing because of social support and meaning in life, wellbeing at work was not lower among agnostic dietitians [49]. Sedikides [50] stated that religion is important for most people´s psychological conditions and subjective wellbeing. There are pieces of evidence that suggest that spirituality is a protective factor for health and psychological problems [51,52]. A study conducted by Ferreira, Pinto e Neto [52] with university students from Portugal, Mozambique, Angola, and Brazil showed that churchgoers present better spiritual wellbeing and better life satisfaction. During the pandemic, churches and temples were closed in many cities and states, and this can explain the lower wellbeing in our study for all the religions during the pandemic.
Dietitians that tested positive for SARS-COV-2 (n = 55) were predominantly working in-person (78.2%, n = 43), without or with adaptations (58.2%, n = 32; 20%, n = 11, respectively). Despite the need for adaptations, people working remotely during the pandemic had better wellbeing at work than the ones that are working in person. Probably, this is due to the possibility of a greater sense of security at home, and being near the family.
The new coronavirus pandemic arrived in Brazil at a time of economic stagnation, problems with the health and social protection systems, difficulties among the food security programs, accelerated increase in poverty, and, especially, extreme poverty, and a significant increase in the homeless population. Since March 2020, Brazil has accumulated a fall in gross domestic product (GDP) [53], and this retreat, partially caused by social isolation, has significantly increased formal and informal unemployment, in addition to precarious labor relations. This new scenario will directly impact dietitian’s work and wellbeing, not only their work conditions, incomes, and uncertainties, but also the feeling of helplessness when facing hunger in the country.
At the same time, the pandemic can bring a search for new strategies for better conditions for health professionals in hospitals and clinics. New routines and behaviors for food production can be developed, not thinking only of food safety inside the production area, but also the attitudes of consumers. Dietitians have the potential to show the importance of their work, to avoid contaminations in foodservices, and bring more discussion about eating habits and immunity.
Dietitians are health professionals at the front line for the population’s nutritional assistance. They work at all levels of complexity in the health care system, and may potentially reduce the risks of disease worsening, and contribute to the recovery of patients affected by Sars-Cov-2. The different spheres (population, governments, and other health professionals) must recognize the relevance of these professionals for public health in the country.
5. Conclusions
These data are essential to evaluating dietitians’ perceptions of wellbeing at work and potentially helping to understand the main challenges supporting them to emerge from the pandemic as a different type of health care practitioner. The instrument presented an excellent KMO and internal consistency as a whole or by its factors. For all the participants’ characteristics, wellbeing decreased during the pandemic, not showing specific influence among the analyzed variables. The hypothesis that the SARS-COV-2 pandemic period influences the wellbeing of Brazilian dietitians was confirmed. However, when evaluating wellbeing separately, before and during the pandemic, dietitians with partners, children, Ph.D., and receiving more than five MW presented higher wellbeing scores at work. During the pandemic period, dietitians working remotely also showed higher wellbeing. Regardless of the period, it is notable that, for dietitians’ wellbeing at work to improve, better compensation for and recognition of the profession is necessary, as well as the conditions for their activities to be carried out. Health policymakers should discuss the role of health professionals as a multidisciplinary team, highlighting the importance of each category for the health system. Much improvement has happened in the health system in Brazil to integrate professionals but, as discussed, dietitians still feel unrecognized for their work. This study can open doors for more research and discussion in the field of wellbeing at work for health professionals, as well as a clear understanding of the factors that influence dietitians’ wellbeing before and during the SARS-COV-2 pandemic, helping these professionals to recover after this period. Therefore, the data could help to explore the areas that most affected (before and during the pandemic period) wellbeing at work, favoring professional valorization. Further studies should be conducted after the pandemic period to evaluate the perceptions of dietitians’ wellbeing at work due to the potential changes in the work environment and conditions in Brazil, and also in other countries, allowing comparisons netweem them.
Acknowledgments
The authors acknowledge the participants, PPGNH/UnB, and CAPES.
Supplementary Materials
The following are available online at https://www.mdpi.com/1660-4601/17/15/5541/s1, Table S1. Wellbeing at work by factors and by socioeconomic and demographic variables of Brazilian dietitians before and during the SARS-COV-2 pandemic period (n = 1359).
Click here for additional data file.
Author Contributions
Conceptualization, R.d.C.C.d.A.A., R.B.A.B., and R.P.Z..; methodology, R.d.C.C.d.A.A., R.B.A.B., A.R., R.A.d.C.M., and R.P.Z.; validation, R.d.C.C.d.A.A.,; formal analysis, R.d.C.C.d.A.A., R.A.d.C.M., R.B.A.B., and R.P.Z..; investigation, R.d.C.C.d.A.A., R.A.d.C.M., R.B.A.B., and R.P.Z.; resources, R.A.d.C.M.; data curation, R.d.C.C.d.A.A., R.A.d.C.M., R.B.A.B., A.R.; writing—original draft preparation, R.d.C.C.d.A.A., R.A.d.C.M., R.B.A.B., and R.P.Z.; writing—review and editing, R.d.C.C.d.A.A., R.A.d.C.M., A.R., R.B.A.B., and R.P.Z.; visualization, A.R.; supervision, R.B.A.B.; project administration, R.d.C.C.d.A.A., R.B.A.B., and R.P.Z.; funding acquisition, R.A.d.C.M. All authors have read and agreed to the published version of the manuscript.
Funding
This research received no external funding.
Conflicts of Interest
The authors declare no conflict of interest.
Appendix A. Brazilian-Portuguese Questionnaire to Evaluate the Dietitians’ Wellbeing at Work before and during the SARS-COV-2 PandemicAppendix A.1. Questionário de Avaliação Do Bem-Estar Do Nutricionista No Trabalho Antes E Durante A Pandemia da Sars-Cov-2/Nutritionist’s Wellbeing Assessment Questionnaire at Work before and during the Sars-Cov-2 Pandemic
A seção seguinte destina-se a recolher dados dos participantes da investigação, com a finalidade de permitir a análise de tendências de respostas em função de características pessoais e do trabalho. Por favor complete o questionário com estes dados, com a garantia de que nenhuma destas informações será utilizada para identificar qualquer participante da investigação./The next section is intended to collect data from the research participants, in order to allow the analysis of trends in responses according to personal and work characteristics. Please complete the questionnaire with this data, ensuring that none of this information will be used to identify any research participants.
Sexo Biológico/Gender
Idade/Age
Estado civil/Marital status
Qual o estado brasileiro de residência atual?/What is your resindency state in Brazil?
Qual a sua religião?/Religion
Quantas pessoas vivem na sua casa incluindo você? Counting with you, how many persons live in the house?
Qual a sua renda familiar?/Family income
Você tem filhos?/Do you have children?
Qual o seu nível educacional?/Educational level
Qual a sua área de atuação? (você pode marcar mais de uma opção)/What is your area of working as a dietitian?
Em quantos locais você exerce atividade como nutricionista?/In how many places do you work as a dietitian?
Há quanto tempo terminou sua graduação em nutrição?/How long ago did you graduate in nutrition?
Em que tipo de instituição cursou sua graduação em nutrição?/In what type of university did you study nutrition?
Você continua trabalhando no período da pandemia da SARS-CoV-2?/Do you comtinue working during SARS-CoV-2 pandemic?
Você testou positivo para a SARS-CoV-2?/Did you test positive for SARS-CoV-2?
Alguma pessoa da sua família testou positivo para a SARS-CoV-2?/Has anyone in your family tested positive for SARS-CoV-2
Appendix A.2. Escala de Bemestar Individual No Trabalho/Wellbeing at Work
Este instrumento pretende avaliar o seu nível de bem-estar no exercício da profissão de nutricionista. Para tanto, você deve avaliar cada uma das afirmativas abaixo, preenchendo os espaços em branco conforme os códigos seguintes:
O trabalho como nutricionista é importante para mim/My work as a dietitian is important for me
Percebo que minha profissão é valorizada onde trabalho/I realize that my profession is valued where I work
Considero que exerço um trabalho importante para a sociedade/I comsider my work important for society
Sou recompensado(a) por minha competência como nutricionista/I am rewarded for my competence as a dietitian
Sou admirado(a) por meus colegas pelo trabalho que faço/I am admired by my colleagues for my work
Tenho liberdade para executar minhas atividades com meu estilo pessoal/I am free to carry out my activities in my personal style
Tenho a infraestrutura material necessária para a execução do meu trabalho/I have the necessary material infrastructure to carry out my work
Tenho a possibilidade de me desenvolver profissionalmente/I have the possibility to improve professionally
Sinto-me seguro(a) com a possibilidade de continuar trabalhando como nutricionista/I feel safe with the possibility of continuing to work as a dietitian
Tenho um bom suporte tecnológico para executar o meu trabalho/I have good technological support to do my job
As relações sociais com meus colegas influenciam positivamente o meu trabalho/Social relations with my colleagues positively influence my work
Sinto-me bem com o relacionamento com meus chefes/I feel good about my relationship with my bosses
Sinto-me bem com o relacionamento com meus subordinados/I feel good about my relationship with my employees
Considero justo o salário que recebo/My salary is fare
Tenho orgulho de pertencer à categoria profissional de nutricionista/I am proud to be part of the dietitans professional category.
Sinto-me bem trabalhando como nutricionista./I feel good to be working as a dietitian
Sou admirado pela sociedade/clientes pelo trabalho que faço/I am admired by society/clients for the work I peform
Considero meu trabalho criativo e estimulante/I consider my work criative and challenging
Quero permanecer sempre trabalhando como nutricionista/I want to continue working as a dietitian
Considero que as tarefas que executo para atender meus clientes são importantes para a qualidade de vida deles/I believe that the tasks I perform to my clients are important to their quality of life
Sinto-me estimulado(a) a estar sempre atualizado(a)/I feel encouraged to always be up to date
Considero que os avanços da ciência da nutrição melhoram o meu desempenho profissional/I believe that advances in nutrition science improve my professional performance
Considero que as novas tecnologias criadas melhoram meu desempenho profissional/I believe that the new technologies improve my professional performance
Considero minha carga horária de trabalho adequada/I consider my workload adequate
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Distribution of dietitians and participants among Brazilian regions.
ijerph-17-05541-t001_Table 1
Characteristics of Brazilian dietitians and SARS-COV-2 questions (n = 1359).
Variable
\nn\n
%
Gender
Female
1258
92.5
Male
102
7.5
Age group
21 to 24 y/o
149
11.0
25 to 29 y/o
275
20.2
30 to 34 y/o
272
20.0
35 to 39 y/o
248
18.2
40 to 44 y/o
134
9.9
45 to 49 y/o
188
6.5
50 to older
194
14.3
Religion
Catholic
720
52.9
Protestant
283
20.8
Spiritism
213
15.7
Agnostic
71
5.2
Others
73
5.4
Level of education (highest degree)
Graduate
302
22.2
Especialization/Residency
677
49.8
Master
237
17.4
PhD
144
10.6
Marital status
Without partner
493
36.3
With partner
867
63.8
Children
Yes
571
42.0
No
789
58.0
Family monthly income
≤1 MW
39
2.9
>1 to 2 MW
111
8.2
>2 to 3 MW
199
14.6
>3 tp 5 MW
297
21.8
>5 to 10 MW
414
30.4
>10 to 20 MW
229
16.8
>20 MW
71
5.2
Number of household members
1
140
10.6
2
366
27.7
3
370
28.1
4
320
24.3
>5
123
9.3
Area of Practice
Clinic
327
24.0
Foodservice administration
173
12.7
Public health
117
8.6
Teaching
99
7.3
Others
72
5.3
More than one area of practice
\n572\n
\n42.1\n
Number of workplaces
1
858
63.1
2
353
26.0
3
85
6.3
>3
64
4.7
Type of institution where you finished your undergraduate degree
Public
656
48.2
Private
704
51.8
Time from the undergraduate completion
≤2 years
288
21.2
>2 to 5 years
232
17.1
>5 to 10 years
255
18.8
>10 to 15 years
253
18.6
>15 years
332
24.4
Do you continue working during SARS-COV-2?
no
220
16.2
yes remotely
486
35.7
yes in person with some adaptations
360
26.5
yes in person
294
21.6
Did you test positive for SARS-COV-2?
No
1304
96.0
Yes
55
4.0
Did any family members test positive for SARS-COV-2?
No
1097
80.7
Yes (does not live with me)
69
5.1
Yes (living with me)
194
14.3
MW—Minimum Wage in Brazil (June 7th 2020)—U$ 213.0.; y/o—years old.
ijerph-17-05541-t002_Table 2
Wellbeing at work by socioeconomic and demographic variables of Brazilian dietitians before and during the pandemic period (n = 1359).
Variable
Before * Pandemic
During * Pandemic
Mean ± SD
Mean ± SD
Gender
Female
3.88 a ± 0.71
3.70 a ± 0.78
Male
3.92 a ± 0.71
3.79 a ± 0.79
Age group
21 to 24 y/o
3.77 abc ± 0.77
3.63 a ± 0.82
25 to 29 y/o
3.79 abc ± 0.76
3.65 a ± 0.80
30 to 34 y/o
3.81 b ± 0.71
3.64 a ± 0.75
35 to 39 y/o
3.94 abc ± 0.65
3.77 ab ± 0.78
40 to 44 y/o
3.90 abc ± 0.70
3.71 ab ± 0.81
45 to 49 y/o
3.98 abc ± 0.63
3.75 ab ± 0.81
50 to older
4.05 ac ± 0.65
3.86 b ± 0.73
Brazilian region
North
3.76 a ± 0.79
3.61 a ± 0.83
Northeast
3.86 ab ± 0.70
3.67 ab ± 0.77
Midwest
3.89 ab ± 0.66
3.69 ab ± 0.76
Southeast
3.95 b ± 0.73
3.81 b ± 0.80
South
3.98 b ± 0.64
3.80 ab ± 0.76
Religion
Catholic
3.91 a ± 0.69
3.73 abc ± 0.77
Protestant
3.72 b ± 0.77
3.59 b ± 0.82
Spiritism
3.99 a ± 0.64
3.83 c ± 0.74
Agnostic
3.89 ab ± 0.70
3.71 abc ± 0.80
Others
3.88 ab ± 0.78
3.68 abc ± 0.88
Level of education (highest degree)
Graduate
3.75 a ± 0.75
3.57 a ± 0.80
Specialization/Residency
3.85 ab ± 0.71
3.68 ab ± 0.79
Master’s
3.97 b ± 0.68
3.78 b ± 0.77
PhD
4.17 c ± 0.55
4.01 c ± 0.65
Marital status
Without partner
3.79 a ± 0.74
3.60 a ± 0.81
With partner
3.93 b ± 0.69
3.77 b ± 0.76
Children
Yes
3.94 a ± 0.68
3.77 a ± 0.75
No
3.84 b ± 0.72
3.66 b ± 0.80
Family monthly income
≤1 MW
3.49 a ± 0.85
3.29 a ± 0.90
>1 to 2 MW
3.53 a ± 0.80
3.33 a ± 0.83
>2 to 3 MW
3.74 a ± 0.79
3.55 a ± 0.84
>3 to 5 MW
3.81 a ± 0.67
3.61 b ± 0.78
>5 to 10 MW
4.01 b ± 0.63
3.86 c ± 0.71
>10 to 20 MW
4.06 b ± 0.61
3.91 c ± 0.70
>20 MW
4.01 b ± 0.69
3.85 c ± 0.78
Area of Practice
Clinic
3.87 a ± 0.73
3.72 ac ± 0.78
Teaching
4.20 b ± 0.55
3.99 b ± 0.69
Foodservice administration
3.73 a ± 0.76
3.58 ac ± 0.83
Public health
3.75 a ± 0.67
3.49 ac ± 0.77
More than one area of practice
3.90 a ± 0.71
3.74 ab ± 0.78
Others
3.90 a ± 0.62
3.69 ac ± 0.75
Number of workplaces
1
3.85 a ± 0.72
3.67 a ± 0.79
2
3.90 a ± 0.68
3.74 a ± 0.75
3
3.99 a ± 0.74
3.82 a ± 0.78
>3
4.02 a ± 0.67
3.82 a ± 0.82
Type of institution where you finished your undergraduate degree
Public
3.92 a ± 0.67
3.74 a ± 0.73
Private
3.84 b ± 0.74
3.68 a ± 0.83
Time from the undergraduate completion
≤2 years
3.76 a ± 0.74
3.59 a ± 0.83
>2 to 5 years
3.80 a ± 0.77
3.66 ab ± 0.79
>5 to 10 years
3.85 a ± 0.72
3.68 ab ± 0.79
>10 to 15 years
3.93 ab ± 0.70
3.76 ab ± 0.80
>15 years
4.03 b ± 0.60
3.83 b ± 0.71
Do you continue working during SARS-COV-2?
No
3.65 *8 ± 0.76
3.33 a ± 0.85
Yes, in person
3.80 *8 ± 0.68
3.65 b ± 0.72
Yes, in person with some adaptations
3.90 *8 ± 0.72
3.74 b ± 0.79
yes remotely
4.02 *8 ± 0.66
3.89 c ± 0.72
Did you test positive for SARS-COV-2?
No
3.89 * ± 0.71
3.71 a ± 0.79
Yes
3.76 * ± 0.71
3.65 a ± 0.70
Did any family members test positive for SARS-COV-2?
No
3.89 * ± 0.70
3.71 a ± 0.78
Yes (does not live with me)
3.88 * ± 0.64
3.74 a ± 0.67
Yes (living with me)
3.84 * ± 0.79
3.68 a ± 0.83
* Comparison between before the pandemic and during the pandemic showed statistical differences for all variables (worse during the pandemic period); Different lowercase letters inside each column and for each variable show statistically different results (p < 0.05); y/o—years old.; MW—Minimum Wage in Brazil (June 7th 2020)—U$ 213.0.
\n"],"string":"[\n \"\\nInt J Environ Res Public HealthInt J Environ Res Public HealthijerphInternational Journal of Environmental Research and Public Health1661-78271660-4601MDPI32751853PMC743209610.3390/ijerph17155541ijerph-17-05541ArticleWellbeing at Work before and during the SARS-COV-2 Pandemic: A Brazilian Nationwide Study among Dietitianshttps://orcid.org/0000-0003-1251-7966MatosRaquel Adjafre da Costa1https://orcid.org/0000-0003-0699-7617AkutsuRita de Cássia Coelho de Almeida1https://orcid.org/0000-0003-0370-3089ZandonadiRenata Puppin1https://orcid.org/0000-0003-3505-4538RochaAda2https://orcid.org/0000-0002-0369-287XBotelhoRaquel Braz Assunção1*Department of Nutrition, Faculty of Health Sciences, University of Brasilia, Brasilia 70910-900, Brazil; raquel.adjafre@gmail.com (R.A.d.C.M.); rita.akutsu@gmail.com (R.d.C.C.d.A.A.); renatapz@yahoo.com.br (R.P.Z.)Faculdade de Ciencias da Nutrição e Alimentação, University of Porto, 4200-464 Porto, Portugal; adarocha@fcna.up.ptCorrespondence: raquelbabotelho@gmail.com3172020820201715554106720202872020© 2020 by the authors.2020Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
This study aimed to evaluate the perceptions of dietitians’ wellbeing at work before and during the SARS-COV-2 pandemic in Brazil. This cross-sectional study was performed using a previously validated instrument to investigate the wellbeing of dietitians at work in Brazil. The questionnaire on the wellbeing of dietitians was composed of 25 items (with a 5-point scale), characteristics, and questions about the SARS-COV-2 period. The application was carried out with GoogleForms® tool from 26 May to 7 June 2020. The weblink to access the research was sent via email, messaging apps, and social networks. Volunteers were recruited nationwide with the help of the Brazilian Dietitians Councils, support groups, as well as media outreach to reach as many dietitians as possible. Volunteers received, along with the research link, the invitation to participate, as well as the consent form. A representative sample of 1359 dietitians from all the Brazilian regions answered the questionnaire—mostly female (92.5%), Catholic (52.9%), from 25 to 39 years old (58.4%), with a partner (63.8%), and with no children (58%). Most of the participants continue working during the pandemic period (83.8%), but they did not have SARS-COV-2 (96%), nor did their family members (80.7%). The wellbeing at work before SARS-COV-2 was 3.88 ± 0.71, statistically different (p < 0.05) from during the pandemic, with the wellbeing of 3.71 ± 0.78. Wellbeing at work was higher before the pandemic for all the analyzed variables. Analyzing variables separately before and during the pandemic, dietitians with partners, children and a Ph.D. presented higher scores for wellbeing at work. Professionals receiving more than five times the minimum wage have higher scores. During the pandemic, better wellbeing was observed for dietitians working remotely.
SARS-COV-2dietitianspandemicwellbeing at work1. Introduction
The world is facing the unexpected SARS-COV-2 pandemic with several consequences for economic, social, mental, environmental, and health aspects. The SARS-COV-2 brought not only the risk of death from the viral infection but also unbearable psychological pressure on people in the world [1]. According to the World of Health Organization (WHO), until June 7, 2020, 6,799,713 cases of SARS-COV-2 were registered and there have been 397,388 deaths in the world [2]. Americas represent almost half of the registered cases (n = 3,234,875 cases; 47.6%) and deaths (n = 179,394 deaths; 45%) in the period. These numbers could be underestimated due to the lack of medical diagnosis at the beginning of the pandemic. Some countries face a few diagnostic tests. In South America, Brazil is the country with the most registered cases of SARS-COV-2 (60.3%, n = 645,771, until 7 June 2020) and deaths (72.7%, n = 35,026 in the same period) [3].
In Brazil, on the frontline of coping with SARS-COV-2, health professionals are acting on different fronts. To date, the future has been uncertain, and healthcare professionals have stepped out of their comfort zones [4]. There are several scenarios faced by health care professionals, especially ones working in clinics or hospitals, with the risk of catching SARS-COV-2, and the ones that are experiencing unemployment and family income reduction without knowing how they will face the economic crisis during and after the pandemic. In this sense, during the pandemic period, they may experience maladaptive psychological consequences of their jobs [5]. Work plays a central role in the individuals’ lives, bringing paradoxical consequences to the social, physical, and psychological integrity of workers [6,7], and low job satisfaction is the leading cause of turnover among health care professionals [8,9]. During the pandemic period, this can get worse.
As well as some other healthcare professionals, during the pandemic, dietitians may be facing some difficulties in their jobs, beyond the traditional ones such as low payment, lack of professional recognition, difficulty in getting their first jobs, and difficulty in geographical mobility [10,11,12,13]. In Brazil, dietitians present a wide range of work options, from directly working in hospitals visiting patients or conducting hospital foodservices, to working in clinics, schools, and commercial restaurants. All of these environments can bring direct contact with possibly infected people, presenting challenges in this new work scenario. The number of patients in the hospitals increased, and the way the food is served needed to change. Restaurants outside the hospitals had to close their doors, leading to unemployment, and facing many risks in reopening when permitted. It is essential to highlight those dietitians, despite being a specific niche of the health system, that are working directly with people infected with Sars-CoV-2. Often, their risks are ignored or underestimated by other health professionals and even by the Federal Government, which can negatively impact wellbeing at work. Recent legislation published on July 8, 2020 (Law No. 14.023) [14] dealing with the protection of professionals exposed to the risks of Sars-Cov-2, did not include dietitians on its lists.
Despite the enormous gathering of scientific data, to date, there is no treatment or vaccine for SARS-COV-2. This scenario of uncertainties about the disease, security itself, and their jobs can affect the perceptions of dietitians about their wellbeing at work. Studies conceive wellbeing at work as a process, defining it as the satisfaction of needs and the fulfillment of individuals’ desires as they fulfill their role in the profession [15,16]. This conception considers the role of work organizations in the health of individuals, and the development of healthy environments enables positive relationships and attitudes. The wellbeing of a professional category can be impacted, among other things, by the general and work values that are expressed in professional practice and social coexistence [16]. Both of them can be facing negative impacts during the SARS-COV-2 pandemic. Wellbeing at work has a significant impact on work performance and quality of life, and it brings paradoxical consequences for the social, physical, and psychological integrity of workers [6,7]. Low wellbeing at work and low job satisfaction are considered the leading causes of turnover among health care professionals [8,9]. Therefore, the knowledge about wellbeing at work is vital to improve the working environment and the quality of the service [8]. Wellbeing at work is related to lower levels of stress, work-related diseases, burnout, depression, and unhealthy personal practices (smoking, drinking, overeating, lack of exercise), and consequently, lower levels of non-communicable chronic diseases (NCD) [17]. There have been studies on the psychological impact of the epidemic on the general public, patients, medical staff, children, and older adults [1,18,19]. However, there is no study on the perception of dietitians’ wellbeing at work during the difficult times of pandemic. The hypothesis is that wellbeing at work will decrease during the pandemic period. In this sense, this study aimed to evaluate dietitians’ perceptions of wellbeing at work before and during the SARS-COV-2 pandemic in Brazil. The professional wellbeing knowledge among dietitians may lead to effective avenues to prevent or manage stress, unhealthy personal practices, and NCD. By evaluating the period before and during the SARS-COV-2 pandemic period, we expect that a clear understanding of the factors that influence dietitians’ wellbeing in these two moments may contribute to helping these professionals to recover after the pandemic period, exploring the areas that were most affected, and working on better professional valorization, improving the public’s trust in dietitians and the dynamics of the interprofessional healthcare team.
2. Materials and Methods2.1. Study Design and Instrument
This exploratory and cross-sectional study was performed using a previously validated instrument [16] to investigate the wellbeing of dietitians before and during the SARS-COV-2 pandemic in Brazil. The questionnaire was composed of 25 items on the wellbeing of dietitians (with a 5-point scale that varies from 1 to 5). It also included characteristics from the original study [16] such as gender, age, marital status, the Brazilian state of current residency, religion, number of individuals living in the house, family income, children, educational level, occupational area as a dietitian, number of workplaces as a dietitian, how long ago graduation ended, and type of university. Researchers included three questions about the SARS-COV-2 period: Do you continue working during the SARS-COV-2 pandemic? Did you test positive for SARS-COV-2? and Did anybody in your family test positive for SARS-COV-2? The complete questionnaire is available in Appendix A.
The instrument application was carried out with GoogleForms® tool (Google LLC, Mountain View, CA, USA) from May 26 to June 7, 2020. The weblink to access the research was sent via email, messaging apps, and social networks. Volunteers were recruited nationwide with the help of the Brazilian Dietitians Councils, support groups, and media outreach to reach as many dietitians as possible. Volunteers received, along with the research link, the invitation to participate, as well as the consent form.
2.2. Participants and Ethics
Dietitians from the entire country were recruited to participate in the study. Researchers wanted to trace the wellbeing at work before and during the SARS-COV-2 of this population group in Brazil. Ethical approval was obtained for this study by the Ethics Committee University of Brasília (protocol No. 54822316.1.00000030). The study was conducted according to the guidelines laid down in the Declaration of Helsinki and followed the Recommendations for the Conduct, Reporting, Editing, and Publication of Scholarly work in Medical Journals.
The sampling size was calculated based on data from the Brazilian Federal Dietitians Council that presents 129,134 registered dietitians [20], considering an error (e) of 3% and a level of significance (α) of 5% [21]. The minimum estimated representative sample size would be of 1059 participants. The inclusion criteria were to be a dietitian and living and working in Brazil.
2.3. Statistical Analysis
Researchers extracted data from the GoogleForms® tool and analyzed using Statistical Package for the Social Sciences—SPSS 24.0 (version 24, SPSS Inc., Chicago, IL, USA). Exploratory and confirmatory analyses were conducted to determine the psychometric quality of the wellbeing at work instrument. We used the Kaiser–Meyer–Olkin (KMO), and Barlett’s sphericity test. For consistency, Cronbach’s alpha was used. Descriptive analyses were used to determine the measures of central tendency and dispersion of the sample. We compared means of the sample through paired t-tests (wellbeing before and during SARS-COV-2) and Analysis of Variance (ANOVA) with Tukey post-hoc.
3. Results
A representative sample of 1359 dietitians from all the 26 Brazilian states and the Federal District and regions answered the questionnaire. Figure 1 shows the distribution of the dietitians and participants by Brazilian regions.
The participants were mostly female (92.5%), Catholic (52.9%), aged from 25 to 39 years old (58.4%), with a partner (63.8%), and with no children (58%) (Table 1). Most of the participants continued working during the pandemic period (83.8%), but had not been diagnosed with SARS-COV-2 (96%) before answering the questionnaire, nor did their family members (80.7%).
Dietitians with less than 1 MW family income are mostly graduates (35.9%) or present specialization/residency (56.4%). However, dietitians with more than 20 MW as a family income have a master’s and Ph.D. (52.2%). Of 144 dietitians with Ph.D., 95.1% work in the teaching area (universities).
Table 2 shows data from the wellbeing at work before and during the pandemic period compared by participants’ characteristics. The instrument presented a KMO of 0.957 and a significant (0.000) Barlett’s sphericity test. For internal consistency, the wellbeing at work instrument presented a Cronbach’s alpha of 0.952. In the communalities, the extraction item was below 0.500 for questions 12 (social relations with my colleagues positively influence my work) and 25 (I consider my workload adequate), and they were not considered for the final score of wellbeing at work. The maintained questions from the instrument were divided into four factors. Factor 1 is related to exterior perception with questions 2, 4, 6, 7, 11 and 15 (Appendix A), factor 2 is concerned about the perception in itself (questions 1, 3, 5, 8, 13, 14, 18 and 21), factor 3 is the task perception (questions 19, 20, 22, 23 and 24), and factor 4 is the perception from the dietitians’ category (questions 9, 10, 16 and 17). Cronbach´s alpha was also calculated for each factor: factor 1, 0.871; factor 2, 0.881; factor 3, 0.881; factor 4, 0.884 (Supplementary File, Table S1).
In general, wellbeing at work before SARS-COV-2 was 3.88 ± 0.71, statistically different (p < 0.05) from during the pandemic, with the wellbeing of 3.71 ± 0.78. A comparison between before the pandemic and during the pandemic showed statistical differences for all variables (worse during the pandemic period) (p < 0.05).
Gender and number of workplaces did not influence wellbeing at work before and during the pandemic period. Individuals with a partner and with children had a better perception of wellbeing at work than the ones with no partner or children. Before and during the pandemic, master’s and Ph.D. individuals presented better wellbeing at work than graduates, and Ph.D. dietitians presented better wellbeing than dietitians with a master’s degree (Table 2).
For both periods, individuals that work in teaching present better wellbeing at work compared to the other areas of dietitians’ practice (clinic, foodservice administration, public health, and others). Before the pandemic period, individuals with family monthly income >5 MW present higher wellbeing at work than the ones up to 5 MW (Table 2). During the pandemic period, the results were a little different, showing differences among individuals up to 3 MW, from 3 to 5 MW and >5 MW, with increasing wellbeing perception with higher family income. Before the pandemic, the time from the undergraduate completion differed from up to 10 years to >15 years. During the pandemic, the higher time from undergraduate completion (>15 years) presented a higher mean of wellbeing at work than the lowest time of completion (≤2 years).
Before the pandemic period, individuals adept in Catholicism and Spiritism had a better perception of wellbeing at work than Protestants. During the pandemic, Catholics did not differ from Protestants, agnostics, or Spiritism followers. However, individuals following the Spiritism religion presented a better perception of wellbeing at work than Protestants (Table 2).
Dietitians that tested positive for SARS-COV-2 (n = 55) were predominantly working in-person (78.2%, n = 43), without or with adaptations (58.2%, n = 32; 20%, n = 11, respectively). Participants who reported not being working during the pandemic period (n = 220) have a job, but they are unable to work in-person or remotely. The wellbeing values were considered for this group of workers because they answered the questions and effectively have a job. There is no difference among perceptions of wellbeing at work between dietitians who had SARS-COV-2 and the ones that did not have, similarly to the results from the ones that had any family member test positive for SARS-COV-2.
When separating the wellbeing at work by factors (Table S1) and comparing by participant´s characteristics, factors 1 and 4 presented the lowest means before and after the pandemic, being lower and statistically different (p < 0.05) during the pandemic. Factor 1 relates to exterior perception and factor 4 to the category perception. Higher scores occurred for the perception of itself and the perception of the task, before and during the pandemic.
4. Discussion
As the world grapples with the impact of the SARS-CoV-2 pandemic, health care workers face extraordinary challenges daily, in different contexts and conditions [22]. The media reports that health care professionals are hugely concerned for the health and wellbeing of their patients, their families, and themselves, facing pandemic issues [22,23]. However, the Brazilian government did not recognize dietitians as part of the health professionals facing the SARS-COV-2 pandemic [14]. These changes in life and work and the lack of recognition and support have a significant impact on their wellbeing, as shown by our results. Among Brazilian dietitians, there was a worse perception of wellbeing at work during the pandemic compared to the period before the pandemic for all variables (p < 0.05) (Table 2). In general, dietitians’ wellbeing at work was positive (above 2.5), which is the midpoint of the scale. The items that obtained the best scores were those that investigate the perception of the importance of the profession for themselves and society. The average scores were above 4.40 before the SARS-COV-2 pandemic and 4.20 during it. The items with the lowest scores and which need to be improved are related to compensation and technological support to perform the tasks assigned to the dietitian. Probably, wage improvement could come through professional qualification, as higher wages were linked to more years of study in our research. Besides, there is a need for an increase in the number of class entities (unions and councils) to fight for better wages of this professional category. In Brazil, even before the SARS-COV-2 pandemic, the country had high unemployment rates [24], a situation worsened by the pandemic, which made it difficult for workers to maintain their family income. Part of the dietitians’ work is in the foodservice area (schools, commercial and institutional restaurants), and most of these are closed or changed the policies of production due to the food safety and workers’ safety conditions.
Wellbeing at work before SARS-COV-2 was 3.88 ± 0.71, statistically different (p < 0.05) from during the pandemic (3.71 ± 0.78). Usually, these conditions would place health care professionals in a situation defined by threat and fear, both of which have been shown to have a detrimental effect on their ability to offer compassionate and person-centered care to their patients [22,25]. In these circumstances, there are high levels of conflict within teams and workplace adversity, leading to a working environment which is perceived as hostile, abusive, and unrewarding [26,27]. As a consequence, workplace adversity can be correlated with a decreased quality of care [28]. In April 2020, the Brazilian Health Ministry requested the registration of all health professionals, including dietitians, to reinforce the fight against SARS-CoV-2, in addition to the usual work of individuals [29]. This reinforcement is to assist managers of the Unified Health System (SUS) in coping with SARS-CoV-2, based on the work capacity of these professionals. It focuses on those who were available to go to the Brazilian states with the greatest need to strengthen health teams [29]. This fact also caused concern in several dietitians who were afraid to work facing SARS-COV-2 patients for various reasons. According to the Brazilian Health Ministry, from the 90,245 Brazilian registered dietitians to reinforce the fight against SARS-CoV-2, 33,624 were willing to work facing SARS-COV-2 [30]. These professionals were registered in the category to receive payment for their work to confront SARS-COV-2. However, the Brazilian Ministry registered other health care professions in a voluntary (unpaid) category of workers, and no dietitian volunteered until 28 April 2020 [30]. It was not stated, but it seems that the dietitians that were willing to work facing SARS-COV-2 were doing it to help the family income, which can potentially worsen their perception of wellbeing at work. Given the unknown and uncontrollable nature of the SARS-COV-2, some health care professionals need to stay away from their home and loved ones, possibly affecting emotional aspects and the relationship with their work [23].
In the pandemic situation, the protection measures for these professionals get worse due to the limitations of social distance. In some cases, this risk is higher, such as in hospitals, emergency services, outpatient clinics, vaccination clinics, screening lines, and other health care settings. In this environment, these professionals are even more exposed when their duty includes providing some assistance to infected people with SARS-COV-2. In foodservices, where these professionals do not work directly with individuals adequately tested for the new coronavirus, they face the same environment with asymptomatic individuals or those in the incubation phase of the disease [31].
Our sample was mostly composed of females (Table 1). The female hegemony among dietitians is common, as shown by other studies in different countries [8,9,13,32,33,34]. Female hegemony in the profession can represent repercussions on career, social prestige, and income [12]. A study showed that dietitians were mostly women and that, although the labor market has grown, the new jobs were mostly (86%) part-time, not only because women need to conciliate career and family care, but also because these positions get lower payment [35]. However, according to our data, gender did not influence wellbeing at work before nor during the pandemic period.
According to a research conducted by the Dietitians Federal Council in 2016/2017 with 1104 dietitians in Brazil, most of them are young, between 25 and 44 years old (81%), a higher percentage than our study (68.3%). Previous studies showed that 73% of the Brazilian dietitians are postgraduates [12,16,36], similar to our data (77.8%, n = 1058). Before and during the pandemic, master’s and Ph.D. individuals presented better wellbeing at work than graduates (Table 2). Most Ph.D. dietitians work in the teaching area, and for both periods, individuals that work in teaching present better wellbeing at work compared to the other areas of dietitians’ practice (clinic, foodservice administration, public health, and others). All schools, including universities, faced changes in how they conducted work. Professors had to search for technological tools and strategies in order to work during the pandemic period, impacting their perception of wellbeing at work, as shown by the lower mean during the pandemic. Undergraduate courses in the health area need practical classes and time inside the hospitals, and it is difficult to return to these activities in-person. The other work areas for dietitians did not present statistically different wellbeing, even for professionals that work in the clinical area and can be inside hospitals.
Before the pandemic period, individuals with family monthly income > 5 MW presented higher wellbeing at work than the ones up to 5 MW (Table 2). During the pandemic period, wellbeing perception increased with higher family income. These data are confirmed by other studies, indicating that lower wages decrease satisfaction at work [13,16,37,38,39]. During the pandemic period, most family members are isolated at home, increasing expenses with bills (water, energy, food, and others). This can influence the difference between the categories of family income compared to the period before the SARS-COV-2. According to the official data [24], unemployment increased in all Brazilian regions with SARS-COV-2, but mainly in the northeast region (from 13.6% in 2019 to 15.6% in 2020), followed by the North (from 10.6% to 11.4%) and the southeast region (from 11.4% to 12.4%), also impacting family income. It is noteworthy that the north and northeast regions were the ones presenting the largest proportional increase in official cases of SARS-COV-2 in Brazil until June 7, 2020 [40].
The Brazilian government published two provisional measures [41,42] during the pandemic changing the work relationship between employers and employees. Employers can reduce employees’ salaries during the SARS-COV-2 crisis, and rules were established for remote work and the suspension of some administrative measures related to safety at work. These legislative measures enable up to a 70% salary reduction and precarious work relationships, reflected by a lower perception of wellbeing at work during the pandemic.
Two characteristics influence wellbeing at work before and during the pandemic: marital status, and children. Dietitians with partners (63.8%) and with children (42%) had higher wellbeing before and during the pandemic. VanderWeele [43] discusses in his article that studies associate marriage with higher life satisfaction and happiness. This association can be related to better mental and physical health and longevity. With time, marriage is associated with a better relationship with others, including work partners, which can influence work wellbeing. VanderWeele [43] also highlights that marriage and family are vital to wellbeing. Despite this study evaluating wellbeing in life, not in the workplace, it could potentially explain higher scores of wellbeing in our study for dietitians with children and partners. Wilcox [44] discusses in his book that marriage is also associated with financial status and education. A meta-analytic study [45] suggested that people who are employed present better life, family, and marital satisfaction. Our study was only conducted with dietitians that have a job, because the instrument is related to wellbeing at work. Therefore, it is not possible to compare this with studies of unemployment. However, they show better wellbeing for people with partners, family, and education, such as our findings for wellbeing at work [46]. Ryff and Heidrich [46] stated that work and education experiences explain differences in the purpose of life. Higher levels of education are associated with happiness and satisfaction and strongly affect income [47]. As already discussed, dietitians with more education and income presented better scores for wellbeing before and during the pandemic.
Regarding religion, worldwide, 15% of people are agnostic [48], higher than in our study (5.2%), but closer to the Brazilian statistics of 8% (IBGE, 2010). Even though research has shown that religion brings higher subjective wellbeing because of social support and meaning in life, wellbeing at work was not lower among agnostic dietitians [49]. Sedikides [50] stated that religion is important for most people´s psychological conditions and subjective wellbeing. There are pieces of evidence that suggest that spirituality is a protective factor for health and psychological problems [51,52]. A study conducted by Ferreira, Pinto e Neto [52] with university students from Portugal, Mozambique, Angola, and Brazil showed that churchgoers present better spiritual wellbeing and better life satisfaction. During the pandemic, churches and temples were closed in many cities and states, and this can explain the lower wellbeing in our study for all the religions during the pandemic.
Dietitians that tested positive for SARS-COV-2 (n = 55) were predominantly working in-person (78.2%, n = 43), without or with adaptations (58.2%, n = 32; 20%, n = 11, respectively). Despite the need for adaptations, people working remotely during the pandemic had better wellbeing at work than the ones that are working in person. Probably, this is due to the possibility of a greater sense of security at home, and being near the family.
The new coronavirus pandemic arrived in Brazil at a time of economic stagnation, problems with the health and social protection systems, difficulties among the food security programs, accelerated increase in poverty, and, especially, extreme poverty, and a significant increase in the homeless population. Since March 2020, Brazil has accumulated a fall in gross domestic product (GDP) [53], and this retreat, partially caused by social isolation, has significantly increased formal and informal unemployment, in addition to precarious labor relations. This new scenario will directly impact dietitian’s work and wellbeing, not only their work conditions, incomes, and uncertainties, but also the feeling of helplessness when facing hunger in the country.
At the same time, the pandemic can bring a search for new strategies for better conditions for health professionals in hospitals and clinics. New routines and behaviors for food production can be developed, not thinking only of food safety inside the production area, but also the attitudes of consumers. Dietitians have the potential to show the importance of their work, to avoid contaminations in foodservices, and bring more discussion about eating habits and immunity.
Dietitians are health professionals at the front line for the population’s nutritional assistance. They work at all levels of complexity in the health care system, and may potentially reduce the risks of disease worsening, and contribute to the recovery of patients affected by Sars-Cov-2. The different spheres (population, governments, and other health professionals) must recognize the relevance of these professionals for public health in the country.
5. Conclusions
These data are essential to evaluating dietitians’ perceptions of wellbeing at work and potentially helping to understand the main challenges supporting them to emerge from the pandemic as a different type of health care practitioner. The instrument presented an excellent KMO and internal consistency as a whole or by its factors. For all the participants’ characteristics, wellbeing decreased during the pandemic, not showing specific influence among the analyzed variables. The hypothesis that the SARS-COV-2 pandemic period influences the wellbeing of Brazilian dietitians was confirmed. However, when evaluating wellbeing separately, before and during the pandemic, dietitians with partners, children, Ph.D., and receiving more than five MW presented higher wellbeing scores at work. During the pandemic period, dietitians working remotely also showed higher wellbeing. Regardless of the period, it is notable that, for dietitians’ wellbeing at work to improve, better compensation for and recognition of the profession is necessary, as well as the conditions for their activities to be carried out. Health policymakers should discuss the role of health professionals as a multidisciplinary team, highlighting the importance of each category for the health system. Much improvement has happened in the health system in Brazil to integrate professionals but, as discussed, dietitians still feel unrecognized for their work. This study can open doors for more research and discussion in the field of wellbeing at work for health professionals, as well as a clear understanding of the factors that influence dietitians’ wellbeing before and during the SARS-COV-2 pandemic, helping these professionals to recover after this period. Therefore, the data could help to explore the areas that most affected (before and during the pandemic period) wellbeing at work, favoring professional valorization. Further studies should be conducted after the pandemic period to evaluate the perceptions of dietitians’ wellbeing at work due to the potential changes in the work environment and conditions in Brazil, and also in other countries, allowing comparisons netweem them.
Acknowledgments
The authors acknowledge the participants, PPGNH/UnB, and CAPES.
Supplementary Materials
The following are available online at https://www.mdpi.com/1660-4601/17/15/5541/s1, Table S1. Wellbeing at work by factors and by socioeconomic and demographic variables of Brazilian dietitians before and during the SARS-COV-2 pandemic period (n = 1359).
Click here for additional data file.
Author Contributions
Conceptualization, R.d.C.C.d.A.A., R.B.A.B., and R.P.Z..; methodology, R.d.C.C.d.A.A., R.B.A.B., A.R., R.A.d.C.M., and R.P.Z.; validation, R.d.C.C.d.A.A.,; formal analysis, R.d.C.C.d.A.A., R.A.d.C.M., R.B.A.B., and R.P.Z..; investigation, R.d.C.C.d.A.A., R.A.d.C.M., R.B.A.B., and R.P.Z.; resources, R.A.d.C.M.; data curation, R.d.C.C.d.A.A., R.A.d.C.M., R.B.A.B., A.R.; writing—original draft preparation, R.d.C.C.d.A.A., R.A.d.C.M., R.B.A.B., and R.P.Z.; writing—review and editing, R.d.C.C.d.A.A., R.A.d.C.M., A.R., R.B.A.B., and R.P.Z.; visualization, A.R.; supervision, R.B.A.B.; project administration, R.d.C.C.d.A.A., R.B.A.B., and R.P.Z.; funding acquisition, R.A.d.C.M. All authors have read and agreed to the published version of the manuscript.
Funding
This research received no external funding.
Conflicts of Interest
The authors declare no conflict of interest.
Appendix A. Brazilian-Portuguese Questionnaire to Evaluate the Dietitians’ Wellbeing at Work before and during the SARS-COV-2 PandemicAppendix A.1. Questionário de Avaliação Do Bem-Estar Do Nutricionista No Trabalho Antes E Durante A Pandemia da Sars-Cov-2/Nutritionist’s Wellbeing Assessment Questionnaire at Work before and during the Sars-Cov-2 Pandemic
A seção seguinte destina-se a recolher dados dos participantes da investigação, com a finalidade de permitir a análise de tendências de respostas em função de características pessoais e do trabalho. Por favor complete o questionário com estes dados, com a garantia de que nenhuma destas informações será utilizada para identificar qualquer participante da investigação./The next section is intended to collect data from the research participants, in order to allow the analysis of trends in responses according to personal and work characteristics. Please complete the questionnaire with this data, ensuring that none of this information will be used to identify any research participants.
Sexo Biológico/Gender
Idade/Age
Estado civil/Marital status
Qual o estado brasileiro de residência atual?/What is your resindency state in Brazil?
Qual a sua religião?/Religion
Quantas pessoas vivem na sua casa incluindo você? Counting with you, how many persons live in the house?
Qual a sua renda familiar?/Family income
Você tem filhos?/Do you have children?
Qual o seu nível educacional?/Educational level
Qual a sua área de atuação? (você pode marcar mais de uma opção)/What is your area of working as a dietitian?
Em quantos locais você exerce atividade como nutricionista?/In how many places do you work as a dietitian?
Há quanto tempo terminou sua graduação em nutrição?/How long ago did you graduate in nutrition?
Em que tipo de instituição cursou sua graduação em nutrição?/In what type of university did you study nutrition?
Você continua trabalhando no período da pandemia da SARS-CoV-2?/Do you comtinue working during SARS-CoV-2 pandemic?
Você testou positivo para a SARS-CoV-2?/Did you test positive for SARS-CoV-2?
Alguma pessoa da sua família testou positivo para a SARS-CoV-2?/Has anyone in your family tested positive for SARS-CoV-2
Appendix A.2. Escala de Bemestar Individual No Trabalho/Wellbeing at Work
Este instrumento pretende avaliar o seu nível de bem-estar no exercício da profissão de nutricionista. Para tanto, você deve avaliar cada uma das afirmativas abaixo, preenchendo os espaços em branco conforme os códigos seguintes:
O trabalho como nutricionista é importante para mim/My work as a dietitian is important for me
Percebo que minha profissão é valorizada onde trabalho/I realize that my profession is valued where I work
Considero que exerço um trabalho importante para a sociedade/I comsider my work important for society
Sou recompensado(a) por minha competência como nutricionista/I am rewarded for my competence as a dietitian
Sou admirado(a) por meus colegas pelo trabalho que faço/I am admired by my colleagues for my work
Tenho liberdade para executar minhas atividades com meu estilo pessoal/I am free to carry out my activities in my personal style
Tenho a infraestrutura material necessária para a execução do meu trabalho/I have the necessary material infrastructure to carry out my work
Tenho a possibilidade de me desenvolver profissionalmente/I have the possibility to improve professionally
Sinto-me seguro(a) com a possibilidade de continuar trabalhando como nutricionista/I feel safe with the possibility of continuing to work as a dietitian
Tenho um bom suporte tecnológico para executar o meu trabalho/I have good technological support to do my job
As relações sociais com meus colegas influenciam positivamente o meu trabalho/Social relations with my colleagues positively influence my work
Sinto-me bem com o relacionamento com meus chefes/I feel good about my relationship with my bosses
Sinto-me bem com o relacionamento com meus subordinados/I feel good about my relationship with my employees
Considero justo o salário que recebo/My salary is fare
Tenho orgulho de pertencer à categoria profissional de nutricionista/I am proud to be part of the dietitans professional category.
Sinto-me bem trabalhando como nutricionista./I feel good to be working as a dietitian
Sou admirado pela sociedade/clientes pelo trabalho que faço/I am admired by society/clients for the work I peform
Considero meu trabalho criativo e estimulante/I consider my work criative and challenging
Quero permanecer sempre trabalhando como nutricionista/I want to continue working as a dietitian
Considero que as tarefas que executo para atender meus clientes são importantes para a qualidade de vida deles/I believe that the tasks I perform to my clients are important to their quality of life
Sinto-me estimulado(a) a estar sempre atualizado(a)/I feel encouraged to always be up to date
Considero que os avanços da ciência da nutrição melhoram o meu desempenho profissional/I believe that advances in nutrition science improve my professional performance
Considero que as novas tecnologias criadas melhoram meu desempenho profissional/I believe that the new technologies improve my professional performance
Considero minha carga horária de trabalho adequada/I consider my workload adequate
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Distribution of dietitians and participants among Brazilian regions.
ijerph-17-05541-t001_Table 1
Characteristics of Brazilian dietitians and SARS-COV-2 questions (n = 1359).
Variable
\\nn\\n
%
Gender
Female
1258
92.5
Male
102
7.5
Age group
21 to 24 y/o
149
11.0
25 to 29 y/o
275
20.2
30 to 34 y/o
272
20.0
35 to 39 y/o
248
18.2
40 to 44 y/o
134
9.9
45 to 49 y/o
188
6.5
50 to older
194
14.3
Religion
Catholic
720
52.9
Protestant
283
20.8
Spiritism
213
15.7
Agnostic
71
5.2
Others
73
5.4
Level of education (highest degree)
Graduate
302
22.2
Especialization/Residency
677
49.8
Master
237
17.4
PhD
144
10.6
Marital status
Without partner
493
36.3
With partner
867
63.8
Children
Yes
571
42.0
No
789
58.0
Family monthly income
≤1 MW
39
2.9
>1 to 2 MW
111
8.2
>2 to 3 MW
199
14.6
>3 tp 5 MW
297
21.8
>5 to 10 MW
414
30.4
>10 to 20 MW
229
16.8
>20 MW
71
5.2
Number of household members
1
140
10.6
2
366
27.7
3
370
28.1
4
320
24.3
>5
123
9.3
Area of Practice
Clinic
327
24.0
Foodservice administration
173
12.7
Public health
117
8.6
Teaching
99
7.3
Others
72
5.3
More than one area of practice
\\n572\\n
\\n42.1\\n
Number of workplaces
1
858
63.1
2
353
26.0
3
85
6.3
>3
64
4.7
Type of institution where you finished your undergraduate degree
Public
656
48.2
Private
704
51.8
Time from the undergraduate completion
≤2 years
288
21.2
>2 to 5 years
232
17.1
>5 to 10 years
255
18.8
>10 to 15 years
253
18.6
>15 years
332
24.4
Do you continue working during SARS-COV-2?
no
220
16.2
yes remotely
486
35.7
yes in person with some adaptations
360
26.5
yes in person
294
21.6
Did you test positive for SARS-COV-2?
No
1304
96.0
Yes
55
4.0
Did any family members test positive for SARS-COV-2?
No
1097
80.7
Yes (does not live with me)
69
5.1
Yes (living with me)
194
14.3
MW—Minimum Wage in Brazil (June 7th 2020)—U$ 213.0.; y/o—years old.
ijerph-17-05541-t002_Table 2
Wellbeing at work by socioeconomic and demographic variables of Brazilian dietitians before and during the pandemic period (n = 1359).
Variable
Before * Pandemic
During * Pandemic
Mean ± SD
Mean ± SD
Gender
Female
3.88 a ± 0.71
3.70 a ± 0.78
Male
3.92 a ± 0.71
3.79 a ± 0.79
Age group
21 to 24 y/o
3.77 abc ± 0.77
3.63 a ± 0.82
25 to 29 y/o
3.79 abc ± 0.76
3.65 a ± 0.80
30 to 34 y/o
3.81 b ± 0.71
3.64 a ± 0.75
35 to 39 y/o
3.94 abc ± 0.65
3.77 ab ± 0.78
40 to 44 y/o
3.90 abc ± 0.70
3.71 ab ± 0.81
45 to 49 y/o
3.98 abc ± 0.63
3.75 ab ± 0.81
50 to older
4.05 ac ± 0.65
3.86 b ± 0.73
Brazilian region
North
3.76 a ± 0.79
3.61 a ± 0.83
Northeast
3.86 ab ± 0.70
3.67 ab ± 0.77
Midwest
3.89 ab ± 0.66
3.69 ab ± 0.76
Southeast
3.95 b ± 0.73
3.81 b ± 0.80
South
3.98 b ± 0.64
3.80 ab ± 0.76
Religion
Catholic
3.91 a ± 0.69
3.73 abc ± 0.77
Protestant
3.72 b ± 0.77
3.59 b ± 0.82
Spiritism
3.99 a ± 0.64
3.83 c ± 0.74
Agnostic
3.89 ab ± 0.70
3.71 abc ± 0.80
Others
3.88 ab ± 0.78
3.68 abc ± 0.88
Level of education (highest degree)
Graduate
3.75 a ± 0.75
3.57 a ± 0.80
Specialization/Residency
3.85 ab ± 0.71
3.68 ab ± 0.79
Master’s
3.97 b ± 0.68
3.78 b ± 0.77
PhD
4.17 c ± 0.55
4.01 c ± 0.65
Marital status
Without partner
3.79 a ± 0.74
3.60 a ± 0.81
With partner
3.93 b ± 0.69
3.77 b ± 0.76
Children
Yes
3.94 a ± 0.68
3.77 a ± 0.75
No
3.84 b ± 0.72
3.66 b ± 0.80
Family monthly income
≤1 MW
3.49 a ± 0.85
3.29 a ± 0.90
>1 to 2 MW
3.53 a ± 0.80
3.33 a ± 0.83
>2 to 3 MW
3.74 a ± 0.79
3.55 a ± 0.84
>3 to 5 MW
3.81 a ± 0.67
3.61 b ± 0.78
>5 to 10 MW
4.01 b ± 0.63
3.86 c ± 0.71
>10 to 20 MW
4.06 b ± 0.61
3.91 c ± 0.70
>20 MW
4.01 b ± 0.69
3.85 c ± 0.78
Area of Practice
Clinic
3.87 a ± 0.73
3.72 ac ± 0.78
Teaching
4.20 b ± 0.55
3.99 b ± 0.69
Foodservice administration
3.73 a ± 0.76
3.58 ac ± 0.83
Public health
3.75 a ± 0.67
3.49 ac ± 0.77
More than one area of practice
3.90 a ± 0.71
3.74 ab ± 0.78
Others
3.90 a ± 0.62
3.69 ac ± 0.75
Number of workplaces
1
3.85 a ± 0.72
3.67 a ± 0.79
2
3.90 a ± 0.68
3.74 a ± 0.75
3
3.99 a ± 0.74
3.82 a ± 0.78
>3
4.02 a ± 0.67
3.82 a ± 0.82
Type of institution where you finished your undergraduate degree
Public
3.92 a ± 0.67
3.74 a ± 0.73
Private
3.84 b ± 0.74
3.68 a ± 0.83
Time from the undergraduate completion
≤2 years
3.76 a ± 0.74
3.59 a ± 0.83
>2 to 5 years
3.80 a ± 0.77
3.66 ab ± 0.79
>5 to 10 years
3.85 a ± 0.72
3.68 ab ± 0.79
>10 to 15 years
3.93 ab ± 0.70
3.76 ab ± 0.80
>15 years
4.03 b ± 0.60
3.83 b ± 0.71
Do you continue working during SARS-COV-2?
No
3.65 *8 ± 0.76
3.33 a ± 0.85
Yes, in person
3.80 *8 ± 0.68
3.65 b ± 0.72
Yes, in person with some adaptations
3.90 *8 ± 0.72
3.74 b ± 0.79
yes remotely
4.02 *8 ± 0.66
3.89 c ± 0.72
Did you test positive for SARS-COV-2?
No
3.89 * ± 0.71
3.71 a ± 0.79
Yes
3.76 * ± 0.71
3.65 a ± 0.70
Did any family members test positive for SARS-COV-2?
No
3.89 * ± 0.70
3.71 a ± 0.78
Yes (does not live with me)
3.88 * ± 0.64
3.74 a ± 0.67
Yes (living with me)
3.84 * ± 0.79
3.68 a ± 0.83
* Comparison between before the pandemic and during the pandemic showed statistical differences for all variables (worse during the pandemic period); Different lowercase letters inside each column and for each variable show statistically different results (p < 0.05); y/o—years old.; MW—Minimum Wage in Brazil (June 7th 2020)—U$ 213.0.
\\n\"\n]"}}},{"rowIdx":440,"cells":{"data":{"kind":"list like","value":["\nInt J Environ Res Public HealthInt J Environ Res Public HealthijerphInternational Journal of Environmental Research and Public Health1661-78271660-4601MDPI32717868PMC743209710.3390/ijerph17155300ijerph-17-05300ArticleAssessment of Attitudes Toward Physical Education by the Implementation of an Extracurricular Program for Obese ChildrenRomero-PérezEna Monserrat1https://orcid.org/0000-0001-6573-6762Núñez EnríquezOscar2*https://orcid.org/0000-0002-8931-1125Gastélum-CuadrasGabriel2Horta-GimMario Alberto1https://orcid.org/0000-0002-7298-9060González-BernalJerónimo J3*https://orcid.org/0000-0002-4389-1777de PazJosé Antonio14Division of Biological Sciences and Health, University of Sonora, 83000 Hermosillo, Sonora, Mexico; ena.romero@unison.mx (E.M.R.-P.); mariohgim@gmail.com (M.A.H.-G.); japazf@unileon.es (J.A.d.P.)Faculty of Physical Culture Sciences, Autonomous University of Chihuahua, 31000 Chihuahua, Chih, Mexico; gastelum@uach.mxDepartment of Health Sciences, University of Burgos, 09001 Burgos, SpainInstitute of Biomedicine, University of León, 24071 León, SpainCorrespondence: onuneze@uach.mx (O.N.E.); jejavier@ubu.es (J.J.G.-B.); Tel.: +34-947-499-108 (J.J.G.-B.)2372020820201715530025620201872020© 2020 by the authors.2020Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
The World Health Organization (WHO) identifies the importance of implementing physical activity programs such as physical education (PE) classes in schools. This study identifies the attitudes of obese children toward PE, before and after participation in a vigorous-intensity physical exercise program without the participation of normal-weight peers using a questionnaire on Attitudes toward Physical Education (CAEF). 98 children between 8–11 years of age were randomized in an Experimental Group (GE) (n = 48) and a Control Group (CG) (n = 47). They were assessed using a questionnaire on Attitudes toward Physical Education (CAEF). All the study participants exhibited a BMI Z-score ≥ 2. Before the intervention, the only difference between boys and girls was “empathy to teacher and physical education subject” (p = 0.001, d de Cohen = 0.72, r = 0.34). The interaction between gender and training was only present in empathy for the teacher, with a medium effect size (η2 = 0.055). The implementation of PE with two hours per week elicits only a few effects over the attitude of obese children, even though with a certain engagement of gender through training in the adjustment of empathy for teachers and the PE class.
childhood obesityattitudesmoderate and vigorous activityphysical education1. Introduction
Obesity has been defined as an increase in body weight resulting from excess fat, which in turn, jeopardizes health significantly [1]. It is a multifactorial disease caused by social, physiological, metabolic, genetic, and psychologic factors; excess food intake and reduced caloric output combine to increase the incidence in children, which rises every day [2]. Among all the health-related and psychological factors affected by obesity, it has been discussed that children and youth present a lack of energy and low self-esteem [3]. The National Health Survey in 2016 confirmed that Mexico has been documenting a growing tendency toward overweight and obesity in school children under the age of 11 years [4].
According to Weigley [5], the Body Mass Index (BMI) is a reasonable estimate of the accumulated fat in the pediatric population. It is, however, difficult to establish cut-off points for diagnosis in children since they undergo a constant height and weight evolution; therefore, for diagnosis, the comparative reference are children of the same age and gender, using percentile tables [6]. This confirms the diagnostic criteria for overweight as any percentile above 85 and obesity as any percentile above 95 [7]. The BMI z-score is also employed in the diagnosis of obesity in children [8] (number of standard deviations above or below the average value of BMI in regards to the group median of the same age and gender). The BMI z-score relates to the percentile, depending on the reference population; the 95 percentile usually corresponds to a BMI z-score of 1.68 [9]. However, the cut-off points used by the WHO for obesity in children are a BMI z-score of 2 [10], which has been used to define obesity in children in the study sample.
Several approaches have been used in Mexico as preventive measures according to the Mexican Official Norm [NOM] [11], which in its suggestions includes the design of low-calorie diet programs along with high-intensity physical activity (PA) as per the needs identified in a community. As such, it appears that the natural scenario, ideal for the application of designed protocols regarding obesity turns out to be the school setting, (by nature the place where a child spends a considerable number of hours a day interacting with a large number of peers) specifically in PE class [12,13,14,15]. This is because there is clear evidence presenting that enforced programs in the schools exert a positive effect on the conduct and healthy lifestyles [16,17,18].
Since its inclusion in the school curriculums, PE has been strongly linked to health [13]. Moreover, this association allowed an understanding of the health benefits and also helped recognize the barriers faced by children and youth engaging in PA settings like physical education. In Mexico, elementary education is provided from first grade (6 years old) to sixth grade (11 years old). Studies demonstrate this stage to be of utmost importance as the acquisition of most healthy habits occurs at this stage [19,20,21]. PE class turns out to be a favorable environment for the enforcement of these habits within the school schedule [22]. It is important to mention that the PE teacher should be one of the main agents of change to facilitate the adoption of healthy habits. The pedagogic approach in these settings must encourage and demonstrate a promising attitude as a way to acquire healthy habits. However, in most cases, a good number of PE classes do not have such emphasis [23].
According to Escarti [24], PE class must be a tool for facilitating instead of suppressing behavior. This is consistent with Kirk’s [25] work, who mentioned that a boring and unexciting environment forms a barrier for children and youth in PE. This means a PE setting should be a space where personal and collective agreements must be made for the achievement of healthy habits within school premises. Thus, having an impact on attitudes that create benefits in the development of school children at a psychological level, in their lifestyles, and their emotional and social aspects is a pre-requisite [17].
In a PE class, obese students may face specific issues that do not represent a definite personality disorder, although, such children more often show a higher rate of psychological barriers in comparison to children with normal weight [26]. Children with obesity tend to show feelings of low self-worth and self-limitations facing social isolation, and stigmatization; sometimes they also develop a feeling that the PE class is a hostile environment where they are victims of bullying and disrespect, thus creating negativity in respect to their self-esteem and body image [3,27,28]. This situation can either be cared for or worsened by the perception of physical education classes these students have, as in case of an inadequate balance between the cooperative learning between teachers and students.
The objectives of the present study include identification of the attitudes of obese boys and girls toward PE class; an analysis of modifications in attitudes of obese boys and girls toward the PE class after participating in a vigorous-intensity physical exercise program without normal-weight peers; and in case of changes, to determine if these changes are the same in boys and girls.
2. Material and Methods
A total of 104 school children were grouped in an experimental and a control group separately, both with children with obesity. The Questionnaire for attitudes toward physical education (CAEF) was used to screen the study participants for a 20-week pre- and post-intervention period [29]. This questionnaire was applied in both groups before and after of intervention.
2.1. Sample
The study participants comprised a total of 104 school children from 3 different elementary schools in the city of Hermosillo, Sonora, and Mexico, aged 8 to 11 years. However, only 95 participants completed the study. The participants had a BMI z-score > 2 and were randomly assigned to either an experimental group (n = 48) and a control group (n = 47) (boys and girls separately), using a computer-generated block randomization sequence (block sizes of 4) (as shown in Table 1). An open invitation was given from the research group to the principals of the schools. As a way to raise awareness on the topic, the open invitation included one presentation per school (three in total) highlighting the benefits of exercise and the risks involved in being overweight and obese during school age. The experimental group was engaged in an exercise program. It was suggested to the control group to engage during the intervention in their regular daily activities; however, the group was offered to enroll in the same exercise program once the 2-week evaluation was complete. The inclusion criteria necessitated the diagnosis of clinical obesity according to the criteria of the WHO at the time of the study, based on a standard deviation of BMI z-score > 2. The exclusion criteria were based on the presence of every single type of disability in the participants that deterred them from performing any physical activities and the presence of chronic conditions like hypothyroidism and uncontrolled juvenile diabetes Type 1. Informed consent was obtained from both the parents and the children. The Bioethics Committee of the University of Sonora supported the study (Reg. DMCS/CBIDMCS/D21).
2.2. Exercise Program
The exercise program was implemented 2 times per week for 20 weeks. A total of 40 sessions for 60 min each were completed and involved varied aerobic and strength activities that worked on conditional and coordination skills, physical displacements with different length and intensity, throws of balls with hands and legs, jumps, and physical activities with the opposition without complete pause between the different activities, at an average intensity during the session with a 79.8% of their maximum heart rate, (estimated by the formula, HRmax = 220-age), which was monitored with a POLAR Model FT7 heart rate monitor (Seppo Säynäjäkangas, Kempele, Filand). These sessions were held in addition to the regular PE classes already established in their school program and only children with obesity participated in the activities of this program.
The exercise program was conducted in the participant’s school. The exercises were taught by the first author and researcher, who is not their regular PE teacher. The program was implemented on separate days such that it did not interfere with their weekly mandatory PE class.
2.3. Instruments
The participants were weighed on a Model 803 digital SECA scale (seca gmbh & co. kg, Hamburg, Germany), with a maximum capacity of 150 kg and a sensitivity of d = 0.1 kg. For measurement of height, a Seca® 213 (seca gmbh & co. kg, Hamburg, Germany) measuring rod with a maximum height of 2 m (6.5 feet) was used. The BMI was calculated using the data thus obtained according to the following formula: Weight/size2 (kg/m2). BMI z-score based on age and gender was calculated using the WHO software version 3.2., 2011 (WHO and UNICEF, Genève, Switzerland) [30].
2.4. Physical Capacity
The physical capacity was assessed by evaluating Curl Up, Shoulder stretch, Push up, Trunk lift, performed using the Fitnessgram methodology [31], along with horizontal jump feet together with a run time of 400 m(s).
2.5. Attitudes toward Physical Education
Questionnaire for attitudes toward physical education [CAEF] [29]: It consists of 56 items wherein the students are enquired about their degree of satisfaction; they can answer on a scale of 1 to 4 where 1 denotes total disagreement and 4 signifies total agreement. This questionnaire has been applied in different studies [32,33,34,35]. Puhl and Heuer [35] mention that children with obesity tend to be bullied in PE class, which in turn affects their future engagement in a healthy lifestyle. Contreras et al. [32] mention similarities stating that peer interaction directly affects their personality development. However, there is no literature about the use of this instrument in a Mexican PE context as yet.
The instrument considers the range of attitudes toward PE in 7 hypothetical factors that are related to the corresponding items for each point.
Assessment of the most essential aspect of the PE, i.e., faculty and subject.
Subject acceptance in terms of its comparison with other subjects.
The validity of the subject and its contents for the integral formation of the student body.
Concerns of PE faculty toward the students.
Performance of PE subject.
Attitude toward PE and sports.
Comparison between PE and sports.
2.6. Statistical Analysis
Statistical analysis was performed with SPSS v.23.0 (SPSS Inc., IBM, Armonk, NY, USA). Data are expressed as mean ± standard deviation. The Kolmogorov–Smirnov test was performed to test the normality assumption. Student’s independent t-tests were used to examine differences between groups and Student’s paired t-tests for pre-post means within each group, and Cohen’s d was used to measure the effect size. The main outcome measure, the modification of attitude of obese boys and girls toward PE classes, was analyzed with the two-way ANOVA (sex and intervention group), interaction analysis partial eta squared (η2) as a measure of effect size. A level of significance was established at p < 0.05.
3. Results
Table 2 depicts the values of participants related to age, weight, size, BMI, BMI z-score, and abdominal perimeter. However, no differences were observed in the baseline values between boys and girls.
Table 3, shows the results of the physical condition tests, before and after the intervention. In EG changes are observed in Curl Up, Shoulder strech and 400 m. In the remaining tests, there were significant changes in both groups.
Figure 1 shows the characteristic elements of the PA program concerning the participating children’s average heart rate throughout the 40 sessions for exercise during 20 weeks.
Table 4 presents the data collected from the different items on the CAEF questionnaire for boys and girls in the baseline; the only difference between them was in the item relating to “the empathy for the teacher and the PE subject”. This factor contains items that describe the children’s ability to establish a good relationship with their PE teacher by identifying a number of his/her traits such as the degree of concern shown toward his/her students, the PE teacher is more “fun” than other teachers, or the student has a better relationship with the PE teacher than with the rest of the teachers.
Table 5 illustrates the values for different items of the questionnaire in all the groups, both before and after the period of intervention. Girls from the GC show a significant difference in the pre and post results on their assessment of the PE subject and teacher, whereas boys from the GC show empathy for the PE teacher and subject.
Table 5 shows the values for the different factors of the questionnaire in all groups, both before and after the intervention period, and the interaction effect of intervention and gender. The organization and concordance results are unchanged and very similar for the control groups; for the experimental groups, both girls and boys showed a decrease in the initial score.
In the difficulty factor, the scores were interpreted in reverse, and it was observed that girls from both the groups (GE and GC) showed higher levels than boys; so for them, the PE class was not difficult; however, after the exercise, the values decreased for girls in the GE, which can be interpreted as an increased perception for the difficulty of the class.
Regarding the empathy factor, the girls in GC did not reflect variations in their scores over time, while the girls in the GE, on the contrary, showed a decrease in results after participating in the PE program, i.e., they reported lowered empathy. Children in both groups acquired quite similar values and modified the teacher’s perception.
In the factor that evaluated physical education as a sport, the participants in the GC showed no difference in these concepts. For girls in the GE, conceptualization about EF class and sport was interchangeable after exercise, whereas boys in this group had similar views but with higher scores, not establishing a clear differentiation between the two activities.
In the preference for PE and Sport in GC vs. GE, participants in GE indicated a decrease in their preference for this subject after the exercise. Boys in the GC had lower values than girls, and the values did not change in post-major. On the other hand, despite having higher values at the beginning of the intervention, participants from the GE showed a tendency of the degree of preference for the PE and sport to worsen after the intervention, significantly in girls.
The usefulness of PE on evaluation reflected the little worth perceived by children from both the groups. However, in children from the GE, a change was reflected after the exercise that was not quite significant, where possible for that group of children; PE is an important subject the usefulness of which would be reflected in the future.
After the intervention period, girls and boys from the GE showed significant changes in their assessment of the PE class. This aspect grouped those test items in which students assigned either more or less importance, variations in degrees of satisfaction with their PE class, whether they consider that the knowledge they are receiving is necessary and important, how fair and unbiased they feel the teacher’s evaluations are, the motivation provided by the teacher, the use of educational materials, and the equal treatment shown by the teacher for boys and girls.
Concerning the subject’s degree of difficulty and the preference for PE and sports, the girls from the GE showed significant differences after the training session. This difficulty was in comparison with other subjects that the children studied every day, and it also involved items related to how easy the children thought it was to pass the course in comparison with other subjects. Their values were interpreted in reverse; therefore, after the moderate to vigorous training, the girls from the GE expressed that the PE class had a higher degree of difficulty after the added training sessions.
Regarding the preference shown by boys and girls toward PE class, significant changes were observed in the girls’ GE after the training since they now preferred to be physically active than to be with friends or watch TV. The remaining factors assessed did not show any differences between the boys and girls as far as gender is concerned.
On analyzing the interaction between gender and training, there was no interaction, except on the empathy for the PE teacher and subject. However, since the effect size (partial eta squared) is considered small when the partial eta squared value is ≤0.02, medium when the value is ≤0.06, and large when the value is ≤0.26, it should be emphasized that even though this interaction is significant, it is also medium and by itself does not explain nine percent of the differences.
Table 5 demonstrates the evaluations of physical tests carried out. Improvements were observed in both the groups in which the lumbar force, the articular amplitude of the shoulder, and the upper and lower extremities were evaluated along with the abdominal force. Although the greatest improvements (delta: post-pre value) were presented in the EG, in the run time of 400 m, the abdominal and lumbar force, as well as a greater decrease in the sum of the cutaneous folds, was noted.
4. Discussion
The improvement in physical fitness was not one of the main objectives of the study, although the PE has been observed to have brought changes in some aspects of physical fitness. Some of these changes could be attributed to the growth and maturation of these children throughout the 20 weeks of the intervention as the GC also showed improvements in the manifestations of upper and lower extremity strength, lumbar strength, shoulder joint width, and decrease in the summation of skin folds. These changes occur widely in the manifestations of physical condition throughout growth [36].
However, the group of children in whom exercise sessions were implemented twice a week showed a higher gain in abdominal strength, 400-m performance, and a greater decrease in subcutaneous fat. Nevertheless, the main results of this research differ from those of Moreno, Rodriguez, and Gutierrez [29] and Cook-Cottone, Case, and Feeley [37] where boys showed a clear preference for attending PE classes in comparison to girls. This is similar to Breslin et al. [38] where they mention that PA is pleasing to both genders; however, vigorous activities requiring a considerable effort are not appealing to girls.
Pre-Post Intragroup Questionnaire
To know the degree to which the differences found between the pre-and-post values of the group that participated in the intervention are attributable to the exercise program, intragroup comparisons were made. On analyzing variations in attitudes toward PE as assessed by the CAEF Questionnaire, it was observed that the consistency with which the teacher organized the class, perception of the teacher’s attire, the time of taking classes, and the fact that the program lacked greater practicality for the group of children show a significant change (p < 0.05), with a significant size of the effect on boys (Cohen’s d-0.72, r-0.52) and girls (Cohen’s d-0.50, r-0.25) essentially, 52% and 25% of pre-post differences in boys and girls, respectively, were explained by exercise intervention.
The perception of the degree of difficulty in the PE class was evaluated on the level of effort that represented to accredit the contents of the subject in comparison to other classes. It was inversely graded and the results showed a significant change (p < 0.05) before and after the intervention in the girls of the GE and the size of the effect (Cohen’s d-0.61, r-0.38). This points out that 38% of these changes could have been developed by the physical exercise program.
The factor related to the degree of preference for PE and sport (relating to the conceptualization that the student has about these two ideas, which sometimes are understood as synonyms) showed how the girls of GE had significant changes (p < 0.05) with an effect size (Cohen’s d = 73, r = 54) that determines how 54% of these changes are attributable to the intervention.
The usefulness of physical education was evaluated through items that questioned the validity of contents during the integral training of the student and were obtained from answers such as “Physical education is boring”, “what I learn in physical education is useless”, etc. It is evaluated in reverse so that high scores on the scale such as those shown by this study, reflect the little usefulness perceived by children of both the groups in this subject. Only the girls in the GC exhibited a significant change (p < 0.05) and showed a size of the effect (Cohen’s d-0.12, r-0.02), and hence, only 2% of these changes should be attributed to the passage of time since they did not participate in the exercise program.
PE elements such as sport and PE utility are factors that showed no significant changes before and after the program, regardless of gender.
The factor empathy reflects the teacher’s ability to engage with the students, considering the teacher “more fun” or the “teacher with whom they relate better than the rest”.
Regarding the attitude before the PE class, only differences in empathy toward the teacher were observed (p = 0.001) between the boys and girls, with a small effect size (Cohen’s d = 0.72, r = 0.34) before the intervention. This means that only 34% of the differences are explained by gender. Regarding the perceived difficulty in PE class, even if not significant, a change in the GE after participating in the PE program was noted; a similar result was obtained with the factor relating to the preference for PE and sports in the trained GE. It is important to mention that difficulty in the perception of the PE class is a factor that children consider to engage and increase their performance [39]. The only factor showing significant changes in the boys and girls from the GE before and after a vigorous exercise program is the one related to empathy for the PE teacher and subject; these results are in accordance with those obtained by Mowatt, DePaw, and Hulac [40].
However, the parameters of evaluation on the part of the students have changed, showing the importance of the teacher’s performance for this purpose in the process of coordinated learning as well as the perception of support in certain psychological needs such as autonomy and social relations and also the role that the teacher plays as a facilitator for these [41].
The recent years have seen a renewed appreciation for the importance of PE class in school settings; the PE teachers have sought specialization and professionalization of their practice, and therefore, the students’ appreciation for the subject holds immense importance. The same was established by O’Brien, Hunter, and Banks [42] clearly showing that the main objective of PE classes is to generate positive attitudes and interest toward it.
The literature now reports strong scientific evidence on the importance of promoting PA programs [17,43,44]. The present research also includes designing programs promoting PE among obese children, which has helped focus on school settings where PE classes are carried out [43]. This suggests, that PE is an ideal place to utilize an appropriate curriculum that is attractive to children, in this case with obesity, promoting engagement in PA for a lifetime [25,44]. It also encourages the analysis of different ways of delivering the planned content, implementing an approach that is not only based on the teacher–student relationship [45,46] but one that promotes a surrounding where all students feel comfortable sharing their abilities and possibilities to create an appropriate pedagogical environment that would help them become active for a lifetime.
Undoubtedly, it would be of great interest to analyze the level of motivation that students possess during the class, in subsequent studies, considering this aspect as a factor that generates a positive attitude and favors participation, especially when it comes to overweight and obese populations.
It is pertinent to highlight the importance of the role played by the PE teacher in promoting positive experiences within the class, which guarantees adherence to the development and maintenance of physical-sports habits [47,48]. This work possesses some limitations, the first one being reduced intervention time, while lack of sensitiveness in the measuring instrument is the second one as insufficient sensitivity of the instrument in detecting change could prove to be a major drawback. Perhaps, adding an interview process would help understand the attitudes toward physical education class in a deeper sense. Besides, the use of heart rate monitoring may not be the best way to quantify the intensity of exercise in children.
5. Conclusions
This paper presents answers to two questions raised by researchers. In regards to whether the training generates change, it can be proved that implementing PE classes through an additional two-hour weekly exercise program of vigorous intensity for obese children only produces little effects on the students’ attitude toward PE. This effect is regardless of gender, even though there is a clear gender interaction in the training, as the only change observed was in the empathy for the teacher and the PE class. However, it is essential to point out that certain limitations such as the location of the school and school-time form barriers for further exploration of different possibilities among obese children in terms of implementation of extra hours every week.
However, this also helped understand that the PE teacher and/or facilitator must use empathy to his/her favor and their good relationship with the students to increase their sensitivity and knowledge. Utilization of an attractive curriculum where the teacher/facilitator designs activities that motivate and encourage students’ participation in intense physical exercises to establish a program with motivating strategies that would promote more participation of obese children can best be implemented by PE classes.
Author Contributions
E.M.R.-P., O.N.E., and J.A.d.P. were responsible for the conception and design of the study. G.G.-C. and M.A.H.-G. were responsible for the literature review. G.G.-C. and M.A.H.-G. designed the exercise program. J.A.d.P. and J.J.G.-B. were responsible for the analysis and interpretation of the data. E.M.R.-P., O.N.E., and G.G.-C. were responsible for drafting the manuscript. E.M.R.-P., O.N.E., and J.J.G.-B. were responsible for critical revision of the article and final approval of the manuscript. All authors have read and agreed to the published version of the manuscript.
Funding
This research received no external funding.
Conflicts of Interest
The authors declare no conflict of interest.
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Mean heart rate per session.
ijerph-17-05300-t001_Table 1
Depiction of the sample according to age and group.
\n
Control Group (CG)
Experimental Group (EG)
Age (year)
8–9
10–11
8–9
10–11
(n)
25
22
25
23
ijerph-17-05300-t002_Table 2
Age and anthropometric characteristics of girls and boys in the sample (Mean and SD).
Variable
Girls
Boys
\np\n
Age (year)
10.3(0.8)
10.4(0.9)
0.684
Weight (kg)
53.4(11.6)
54.0(9.6)
0.284
Height (m)
139.5(9.1)
142.1(7.6)
0.138
BMI (kg/m2)
27.2(3.3)
26.6(3.1)
0.408
Zscore BMI
2.9(0.5)
3.1(0.8)
\n0.031\n
Waist–hip Index
89.4(8.0)
88.8(8.6)
0.712
(Bold and italic = statistically significant).
ijerph-17-05300-t003_Table 3
Evaluation of the physical condition before and after the intervention and the pre-post differences.
Test
CG
\n
\n
\n
EG
\n
\n
\n
\n
Baseline
Post
\np\n
Delta
Baseline
Post
\np\n
Delta
p Delta
Curl Up (n)
8.1(3.9)
8.6(4.2)
0.206
0.5(2.3)
7.0(5.7)
8.5(5.9) *
\n0.034\n
2.0(0.3) #
\n0.000\n
Shoulder strech (cm)
4.9(4.0)
5.6(3.9)
0.282
0.7(1.5)
5.1(7.9)
6.1(6.9) *
\n0.040\n
1.2(2.7)
0.078
Push_up (s)
3.9(2.0)
5.2(1.7) *
\n0.000\n
1.4(1.5)
2.8(2.9)
3.5(2.3) *
\n0.035\n
0.6(2.1)
0.083
400m (s)
127(34.1)
129.8(3.9)
0.255
2.6(17.3)
111.3(26.5)
106.0(26.2) *
\n0.034\n
−5.4(12.8) #
\n0.002\n
Trunk_lift(cm)
32.7(5.2)
34.8(5.0) *
\n0.003\n
2.1(1.9)
32.8(6.3)
34.2(6.2) *
\n0.044\n
1.5(2.5)
0.063
H.jump feet together(m)
0.97(0.16)
0.99(0.17) *
\n0.048\n
0.02 (0.05)
0.97(0.17)
1.00(0.16) *
\n0.047\n
0.03(0.08)
0.137
BMI Zscore
2.9(0.4)
2.7(0.4)
\n0.053\n
−0.2(0.1)
3.1(0.8)
3.0(0.8)
\n0.063\n
−0.1(0.5)
0.074
Pli(mm)
141.2(29.8)
134.9(27.4) *
\n0.033\n
−5.5(12.0)
138.1(27.0)
124.2(30.3) *
\n0.002\n
−14.6(26.3) #
\n0.002\n
p = pre-post intra group; * = p < 0.05; delta = pre-post intra group diference; p delta = inter grup delta; # = p < 0.05 delta from the control group. (Bold and italic = statistically significant).
ijerph-17-05300-t004_Table 4
Data from the questionnaire on attitude toward physical education in girls and boys (Mean and SD)—pre-intervention.
Item
Girls
Boys
\np\n
Subjet organization concordancy
15.2(3.8)
14.5(3.6)
0.373
PE Diifficulty
17.5(3.7)
16.7(3.3)
0.266
Emphaty towards teachers and subject
16.3(3.7)
13.9(2.9)
\n0.001\n
PE as sport
10.5(3.0)
9.9(2.6)
0.284
Preference for PE and Sport
10.5(2.6)
10.9(3.0)
0.499
Utility of PE
22.5(4.7)
22.3(4.5)
0.847
Value subject and PE Teacher
32.2(6.7)
29.9(6.7)
0.091
(Bold and italic = statistically significant).
ijerph-17-05300-t005_Table 5
Pre and post values of the study subjects on each dimension.
Item
\n
Girls
Boys
\n
Control
Experimental
Control
Experimental
\np\n
\nη\n2\n
Subjet organization concordancy
Pretest
14.2(4.1)
16.2(3.4)
12.8(3.3)
16.0(3.2)
0.536
0.04
Postest
14.3(3.9)
14.4(3.8) *
12.9(3.6)
13.5(3.7) *
PE Difficulty
Pretest
17.2(4.4)
17.8(3.0)
16.7(3.5)
16.8(3.2)
0.2
0.056
Postest
17.1(3.9)
15.7(3.8) *
17.0(3.7)
17.5(3.2)
Emphaty towards teachers and subject
Pretest
16.7(3.9)
15.9(3.4)
14.3(2.2)
13.5(3.3)
\n0.021\n
0.055
Postest
16.8(3.5)
14.3(3.9) *
15.1(2.5) *
15.0(342) *
PE as sport
Pretest
11.4(3.1)
9.7(2.7)
9.9(3.3)
10.0(1.8)
0.101
0.028
Postest
11.3(2.7)
8.6(3.3) *
9.7(2.6)
10.8(2.4)
Preference for PE and Sport
Pretest
10.2(2.5)
10.8(2.8)
9.7(2.4)
12.0(3.0)
0.261
0.013
Postest
10.0(2.2)
8.6(3.2) *
9.6(2.4)
10.9(2.7)
Utility of PE
Pretest
24.3(4.9)
20.7(3.)
21.1(4.4)
23.3(4.5)
0.315
0.011
Postest
23.9(5.1)
20.7(4.0)
21.5(4.2)
24.3(5.3)
Value subject and PE Teacher
Pretest
30.5(6.6)
33.9(6.5)
28.6(8.0)
31.0(5.2)
0.077
0.033
Postest
31.3(6.4) *
30.7(8.1)
29.3(3.2)
32.8(7.7)
* p < 0.05 pre-post intra group; p = p value interaction gender × exercise; η2 = effect size of the interaction (Mean and SD). (Bold and italic = statistically significant).
\n"],"string":"[\n \"\\nInt J Environ Res Public HealthInt J Environ Res Public HealthijerphInternational Journal of Environmental Research and Public Health1661-78271660-4601MDPI32717868PMC743209710.3390/ijerph17155300ijerph-17-05300ArticleAssessment of Attitudes Toward Physical Education by the Implementation of an Extracurricular Program for Obese ChildrenRomero-PérezEna Monserrat1https://orcid.org/0000-0001-6573-6762Núñez EnríquezOscar2*https://orcid.org/0000-0002-8931-1125Gastélum-CuadrasGabriel2Horta-GimMario Alberto1https://orcid.org/0000-0002-7298-9060González-BernalJerónimo J3*https://orcid.org/0000-0002-4389-1777de PazJosé Antonio14Division of Biological Sciences and Health, University of Sonora, 83000 Hermosillo, Sonora, Mexico; ena.romero@unison.mx (E.M.R.-P.); mariohgim@gmail.com (M.A.H.-G.); japazf@unileon.es (J.A.d.P.)Faculty of Physical Culture Sciences, Autonomous University of Chihuahua, 31000 Chihuahua, Chih, Mexico; gastelum@uach.mxDepartment of Health Sciences, University of Burgos, 09001 Burgos, SpainInstitute of Biomedicine, University of León, 24071 León, SpainCorrespondence: onuneze@uach.mx (O.N.E.); jejavier@ubu.es (J.J.G.-B.); Tel.: +34-947-499-108 (J.J.G.-B.)2372020820201715530025620201872020© 2020 by the authors.2020Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
The World Health Organization (WHO) identifies the importance of implementing physical activity programs such as physical education (PE) classes in schools. This study identifies the attitudes of obese children toward PE, before and after participation in a vigorous-intensity physical exercise program without the participation of normal-weight peers using a questionnaire on Attitudes toward Physical Education (CAEF). 98 children between 8–11 years of age were randomized in an Experimental Group (GE) (n = 48) and a Control Group (CG) (n = 47). They were assessed using a questionnaire on Attitudes toward Physical Education (CAEF). All the study participants exhibited a BMI Z-score ≥ 2. Before the intervention, the only difference between boys and girls was “empathy to teacher and physical education subject” (p = 0.001, d de Cohen = 0.72, r = 0.34). The interaction between gender and training was only present in empathy for the teacher, with a medium effect size (η2 = 0.055). The implementation of PE with two hours per week elicits only a few effects over the attitude of obese children, even though with a certain engagement of gender through training in the adjustment of empathy for teachers and the PE class.
childhood obesityattitudesmoderate and vigorous activityphysical education1. Introduction
Obesity has been defined as an increase in body weight resulting from excess fat, which in turn, jeopardizes health significantly [1]. It is a multifactorial disease caused by social, physiological, metabolic, genetic, and psychologic factors; excess food intake and reduced caloric output combine to increase the incidence in children, which rises every day [2]. Among all the health-related and psychological factors affected by obesity, it has been discussed that children and youth present a lack of energy and low self-esteem [3]. The National Health Survey in 2016 confirmed that Mexico has been documenting a growing tendency toward overweight and obesity in school children under the age of 11 years [4].
According to Weigley [5], the Body Mass Index (BMI) is a reasonable estimate of the accumulated fat in the pediatric population. It is, however, difficult to establish cut-off points for diagnosis in children since they undergo a constant height and weight evolution; therefore, for diagnosis, the comparative reference are children of the same age and gender, using percentile tables [6]. This confirms the diagnostic criteria for overweight as any percentile above 85 and obesity as any percentile above 95 [7]. The BMI z-score is also employed in the diagnosis of obesity in children [8] (number of standard deviations above or below the average value of BMI in regards to the group median of the same age and gender). The BMI z-score relates to the percentile, depending on the reference population; the 95 percentile usually corresponds to a BMI z-score of 1.68 [9]. However, the cut-off points used by the WHO for obesity in children are a BMI z-score of 2 [10], which has been used to define obesity in children in the study sample.
Several approaches have been used in Mexico as preventive measures according to the Mexican Official Norm [NOM] [11], which in its suggestions includes the design of low-calorie diet programs along with high-intensity physical activity (PA) as per the needs identified in a community. As such, it appears that the natural scenario, ideal for the application of designed protocols regarding obesity turns out to be the school setting, (by nature the place where a child spends a considerable number of hours a day interacting with a large number of peers) specifically in PE class [12,13,14,15]. This is because there is clear evidence presenting that enforced programs in the schools exert a positive effect on the conduct and healthy lifestyles [16,17,18].
Since its inclusion in the school curriculums, PE has been strongly linked to health [13]. Moreover, this association allowed an understanding of the health benefits and also helped recognize the barriers faced by children and youth engaging in PA settings like physical education. In Mexico, elementary education is provided from first grade (6 years old) to sixth grade (11 years old). Studies demonstrate this stage to be of utmost importance as the acquisition of most healthy habits occurs at this stage [19,20,21]. PE class turns out to be a favorable environment for the enforcement of these habits within the school schedule [22]. It is important to mention that the PE teacher should be one of the main agents of change to facilitate the adoption of healthy habits. The pedagogic approach in these settings must encourage and demonstrate a promising attitude as a way to acquire healthy habits. However, in most cases, a good number of PE classes do not have such emphasis [23].
According to Escarti [24], PE class must be a tool for facilitating instead of suppressing behavior. This is consistent with Kirk’s [25] work, who mentioned that a boring and unexciting environment forms a barrier for children and youth in PE. This means a PE setting should be a space where personal and collective agreements must be made for the achievement of healthy habits within school premises. Thus, having an impact on attitudes that create benefits in the development of school children at a psychological level, in their lifestyles, and their emotional and social aspects is a pre-requisite [17].
In a PE class, obese students may face specific issues that do not represent a definite personality disorder, although, such children more often show a higher rate of psychological barriers in comparison to children with normal weight [26]. Children with obesity tend to show feelings of low self-worth and self-limitations facing social isolation, and stigmatization; sometimes they also develop a feeling that the PE class is a hostile environment where they are victims of bullying and disrespect, thus creating negativity in respect to their self-esteem and body image [3,27,28]. This situation can either be cared for or worsened by the perception of physical education classes these students have, as in case of an inadequate balance between the cooperative learning between teachers and students.
The objectives of the present study include identification of the attitudes of obese boys and girls toward PE class; an analysis of modifications in attitudes of obese boys and girls toward the PE class after participating in a vigorous-intensity physical exercise program without normal-weight peers; and in case of changes, to determine if these changes are the same in boys and girls.
2. Material and Methods
A total of 104 school children were grouped in an experimental and a control group separately, both with children with obesity. The Questionnaire for attitudes toward physical education (CAEF) was used to screen the study participants for a 20-week pre- and post-intervention period [29]. This questionnaire was applied in both groups before and after of intervention.
2.1. Sample
The study participants comprised a total of 104 school children from 3 different elementary schools in the city of Hermosillo, Sonora, and Mexico, aged 8 to 11 years. However, only 95 participants completed the study. The participants had a BMI z-score > 2 and were randomly assigned to either an experimental group (n = 48) and a control group (n = 47) (boys and girls separately), using a computer-generated block randomization sequence (block sizes of 4) (as shown in Table 1). An open invitation was given from the research group to the principals of the schools. As a way to raise awareness on the topic, the open invitation included one presentation per school (three in total) highlighting the benefits of exercise and the risks involved in being overweight and obese during school age. The experimental group was engaged in an exercise program. It was suggested to the control group to engage during the intervention in their regular daily activities; however, the group was offered to enroll in the same exercise program once the 2-week evaluation was complete. The inclusion criteria necessitated the diagnosis of clinical obesity according to the criteria of the WHO at the time of the study, based on a standard deviation of BMI z-score > 2. The exclusion criteria were based on the presence of every single type of disability in the participants that deterred them from performing any physical activities and the presence of chronic conditions like hypothyroidism and uncontrolled juvenile diabetes Type 1. Informed consent was obtained from both the parents and the children. The Bioethics Committee of the University of Sonora supported the study (Reg. DMCS/CBIDMCS/D21).
2.2. Exercise Program
The exercise program was implemented 2 times per week for 20 weeks. A total of 40 sessions for 60 min each were completed and involved varied aerobic and strength activities that worked on conditional and coordination skills, physical displacements with different length and intensity, throws of balls with hands and legs, jumps, and physical activities with the opposition without complete pause between the different activities, at an average intensity during the session with a 79.8% of their maximum heart rate, (estimated by the formula, HRmax = 220-age), which was monitored with a POLAR Model FT7 heart rate monitor (Seppo Säynäjäkangas, Kempele, Filand). These sessions were held in addition to the regular PE classes already established in their school program and only children with obesity participated in the activities of this program.
The exercise program was conducted in the participant’s school. The exercises were taught by the first author and researcher, who is not their regular PE teacher. The program was implemented on separate days such that it did not interfere with their weekly mandatory PE class.
2.3. Instruments
The participants were weighed on a Model 803 digital SECA scale (seca gmbh & co. kg, Hamburg, Germany), with a maximum capacity of 150 kg and a sensitivity of d = 0.1 kg. For measurement of height, a Seca® 213 (seca gmbh & co. kg, Hamburg, Germany) measuring rod with a maximum height of 2 m (6.5 feet) was used. The BMI was calculated using the data thus obtained according to the following formula: Weight/size2 (kg/m2). BMI z-score based on age and gender was calculated using the WHO software version 3.2., 2011 (WHO and UNICEF, Genève, Switzerland) [30].
2.4. Physical Capacity
The physical capacity was assessed by evaluating Curl Up, Shoulder stretch, Push up, Trunk lift, performed using the Fitnessgram methodology [31], along with horizontal jump feet together with a run time of 400 m(s).
2.5. Attitudes toward Physical Education
Questionnaire for attitudes toward physical education [CAEF] [29]: It consists of 56 items wherein the students are enquired about their degree of satisfaction; they can answer on a scale of 1 to 4 where 1 denotes total disagreement and 4 signifies total agreement. This questionnaire has been applied in different studies [32,33,34,35]. Puhl and Heuer [35] mention that children with obesity tend to be bullied in PE class, which in turn affects their future engagement in a healthy lifestyle. Contreras et al. [32] mention similarities stating that peer interaction directly affects their personality development. However, there is no literature about the use of this instrument in a Mexican PE context as yet.
The instrument considers the range of attitudes toward PE in 7 hypothetical factors that are related to the corresponding items for each point.
Assessment of the most essential aspect of the PE, i.e., faculty and subject.
Subject acceptance in terms of its comparison with other subjects.
The validity of the subject and its contents for the integral formation of the student body.
Concerns of PE faculty toward the students.
Performance of PE subject.
Attitude toward PE and sports.
Comparison between PE and sports.
2.6. Statistical Analysis
Statistical analysis was performed with SPSS v.23.0 (SPSS Inc., IBM, Armonk, NY, USA). Data are expressed as mean ± standard deviation. The Kolmogorov–Smirnov test was performed to test the normality assumption. Student’s independent t-tests were used to examine differences between groups and Student’s paired t-tests for pre-post means within each group, and Cohen’s d was used to measure the effect size. The main outcome measure, the modification of attitude of obese boys and girls toward PE classes, was analyzed with the two-way ANOVA (sex and intervention group), interaction analysis partial eta squared (η2) as a measure of effect size. A level of significance was established at p < 0.05.
3. Results
Table 2 depicts the values of participants related to age, weight, size, BMI, BMI z-score, and abdominal perimeter. However, no differences were observed in the baseline values between boys and girls.
Table 3, shows the results of the physical condition tests, before and after the intervention. In EG changes are observed in Curl Up, Shoulder strech and 400 m. In the remaining tests, there were significant changes in both groups.
Figure 1 shows the characteristic elements of the PA program concerning the participating children’s average heart rate throughout the 40 sessions for exercise during 20 weeks.
Table 4 presents the data collected from the different items on the CAEF questionnaire for boys and girls in the baseline; the only difference between them was in the item relating to “the empathy for the teacher and the PE subject”. This factor contains items that describe the children’s ability to establish a good relationship with their PE teacher by identifying a number of his/her traits such as the degree of concern shown toward his/her students, the PE teacher is more “fun” than other teachers, or the student has a better relationship with the PE teacher than with the rest of the teachers.
Table 5 illustrates the values for different items of the questionnaire in all the groups, both before and after the period of intervention. Girls from the GC show a significant difference in the pre and post results on their assessment of the PE subject and teacher, whereas boys from the GC show empathy for the PE teacher and subject.
Table 5 shows the values for the different factors of the questionnaire in all groups, both before and after the intervention period, and the interaction effect of intervention and gender. The organization and concordance results are unchanged and very similar for the control groups; for the experimental groups, both girls and boys showed a decrease in the initial score.
In the difficulty factor, the scores were interpreted in reverse, and it was observed that girls from both the groups (GE and GC) showed higher levels than boys; so for them, the PE class was not difficult; however, after the exercise, the values decreased for girls in the GE, which can be interpreted as an increased perception for the difficulty of the class.
Regarding the empathy factor, the girls in GC did not reflect variations in their scores over time, while the girls in the GE, on the contrary, showed a decrease in results after participating in the PE program, i.e., they reported lowered empathy. Children in both groups acquired quite similar values and modified the teacher’s perception.
In the factor that evaluated physical education as a sport, the participants in the GC showed no difference in these concepts. For girls in the GE, conceptualization about EF class and sport was interchangeable after exercise, whereas boys in this group had similar views but with higher scores, not establishing a clear differentiation between the two activities.
In the preference for PE and Sport in GC vs. GE, participants in GE indicated a decrease in their preference for this subject after the exercise. Boys in the GC had lower values than girls, and the values did not change in post-major. On the other hand, despite having higher values at the beginning of the intervention, participants from the GE showed a tendency of the degree of preference for the PE and sport to worsen after the intervention, significantly in girls.
The usefulness of PE on evaluation reflected the little worth perceived by children from both the groups. However, in children from the GE, a change was reflected after the exercise that was not quite significant, where possible for that group of children; PE is an important subject the usefulness of which would be reflected in the future.
After the intervention period, girls and boys from the GE showed significant changes in their assessment of the PE class. This aspect grouped those test items in which students assigned either more or less importance, variations in degrees of satisfaction with their PE class, whether they consider that the knowledge they are receiving is necessary and important, how fair and unbiased they feel the teacher’s evaluations are, the motivation provided by the teacher, the use of educational materials, and the equal treatment shown by the teacher for boys and girls.
Concerning the subject’s degree of difficulty and the preference for PE and sports, the girls from the GE showed significant differences after the training session. This difficulty was in comparison with other subjects that the children studied every day, and it also involved items related to how easy the children thought it was to pass the course in comparison with other subjects. Their values were interpreted in reverse; therefore, after the moderate to vigorous training, the girls from the GE expressed that the PE class had a higher degree of difficulty after the added training sessions.
Regarding the preference shown by boys and girls toward PE class, significant changes were observed in the girls’ GE after the training since they now preferred to be physically active than to be with friends or watch TV. The remaining factors assessed did not show any differences between the boys and girls as far as gender is concerned.
On analyzing the interaction between gender and training, there was no interaction, except on the empathy for the PE teacher and subject. However, since the effect size (partial eta squared) is considered small when the partial eta squared value is ≤0.02, medium when the value is ≤0.06, and large when the value is ≤0.26, it should be emphasized that even though this interaction is significant, it is also medium and by itself does not explain nine percent of the differences.
Table 5 demonstrates the evaluations of physical tests carried out. Improvements were observed in both the groups in which the lumbar force, the articular amplitude of the shoulder, and the upper and lower extremities were evaluated along with the abdominal force. Although the greatest improvements (delta: post-pre value) were presented in the EG, in the run time of 400 m, the abdominal and lumbar force, as well as a greater decrease in the sum of the cutaneous folds, was noted.
4. Discussion
The improvement in physical fitness was not one of the main objectives of the study, although the PE has been observed to have brought changes in some aspects of physical fitness. Some of these changes could be attributed to the growth and maturation of these children throughout the 20 weeks of the intervention as the GC also showed improvements in the manifestations of upper and lower extremity strength, lumbar strength, shoulder joint width, and decrease in the summation of skin folds. These changes occur widely in the manifestations of physical condition throughout growth [36].
However, the group of children in whom exercise sessions were implemented twice a week showed a higher gain in abdominal strength, 400-m performance, and a greater decrease in subcutaneous fat. Nevertheless, the main results of this research differ from those of Moreno, Rodriguez, and Gutierrez [29] and Cook-Cottone, Case, and Feeley [37] where boys showed a clear preference for attending PE classes in comparison to girls. This is similar to Breslin et al. [38] where they mention that PA is pleasing to both genders; however, vigorous activities requiring a considerable effort are not appealing to girls.
Pre-Post Intragroup Questionnaire
To know the degree to which the differences found between the pre-and-post values of the group that participated in the intervention are attributable to the exercise program, intragroup comparisons were made. On analyzing variations in attitudes toward PE as assessed by the CAEF Questionnaire, it was observed that the consistency with which the teacher organized the class, perception of the teacher’s attire, the time of taking classes, and the fact that the program lacked greater practicality for the group of children show a significant change (p < 0.05), with a significant size of the effect on boys (Cohen’s d-0.72, r-0.52) and girls (Cohen’s d-0.50, r-0.25) essentially, 52% and 25% of pre-post differences in boys and girls, respectively, were explained by exercise intervention.
The perception of the degree of difficulty in the PE class was evaluated on the level of effort that represented to accredit the contents of the subject in comparison to other classes. It was inversely graded and the results showed a significant change (p < 0.05) before and after the intervention in the girls of the GE and the size of the effect (Cohen’s d-0.61, r-0.38). This points out that 38% of these changes could have been developed by the physical exercise program.
The factor related to the degree of preference for PE and sport (relating to the conceptualization that the student has about these two ideas, which sometimes are understood as synonyms) showed how the girls of GE had significant changes (p < 0.05) with an effect size (Cohen’s d = 73, r = 54) that determines how 54% of these changes are attributable to the intervention.
The usefulness of physical education was evaluated through items that questioned the validity of contents during the integral training of the student and were obtained from answers such as “Physical education is boring”, “what I learn in physical education is useless”, etc. It is evaluated in reverse so that high scores on the scale such as those shown by this study, reflect the little usefulness perceived by children of both the groups in this subject. Only the girls in the GC exhibited a significant change (p < 0.05) and showed a size of the effect (Cohen’s d-0.12, r-0.02), and hence, only 2% of these changes should be attributed to the passage of time since they did not participate in the exercise program.
PE elements such as sport and PE utility are factors that showed no significant changes before and after the program, regardless of gender.
The factor empathy reflects the teacher’s ability to engage with the students, considering the teacher “more fun” or the “teacher with whom they relate better than the rest”.
Regarding the attitude before the PE class, only differences in empathy toward the teacher were observed (p = 0.001) between the boys and girls, with a small effect size (Cohen’s d = 0.72, r = 0.34) before the intervention. This means that only 34% of the differences are explained by gender. Regarding the perceived difficulty in PE class, even if not significant, a change in the GE after participating in the PE program was noted; a similar result was obtained with the factor relating to the preference for PE and sports in the trained GE. It is important to mention that difficulty in the perception of the PE class is a factor that children consider to engage and increase their performance [39]. The only factor showing significant changes in the boys and girls from the GE before and after a vigorous exercise program is the one related to empathy for the PE teacher and subject; these results are in accordance with those obtained by Mowatt, DePaw, and Hulac [40].
However, the parameters of evaluation on the part of the students have changed, showing the importance of the teacher’s performance for this purpose in the process of coordinated learning as well as the perception of support in certain psychological needs such as autonomy and social relations and also the role that the teacher plays as a facilitator for these [41].
The recent years have seen a renewed appreciation for the importance of PE class in school settings; the PE teachers have sought specialization and professionalization of their practice, and therefore, the students’ appreciation for the subject holds immense importance. The same was established by O’Brien, Hunter, and Banks [42] clearly showing that the main objective of PE classes is to generate positive attitudes and interest toward it.
The literature now reports strong scientific evidence on the importance of promoting PA programs [17,43,44]. The present research also includes designing programs promoting PE among obese children, which has helped focus on school settings where PE classes are carried out [43]. This suggests, that PE is an ideal place to utilize an appropriate curriculum that is attractive to children, in this case with obesity, promoting engagement in PA for a lifetime [25,44]. It also encourages the analysis of different ways of delivering the planned content, implementing an approach that is not only based on the teacher–student relationship [45,46] but one that promotes a surrounding where all students feel comfortable sharing their abilities and possibilities to create an appropriate pedagogical environment that would help them become active for a lifetime.
Undoubtedly, it would be of great interest to analyze the level of motivation that students possess during the class, in subsequent studies, considering this aspect as a factor that generates a positive attitude and favors participation, especially when it comes to overweight and obese populations.
It is pertinent to highlight the importance of the role played by the PE teacher in promoting positive experiences within the class, which guarantees adherence to the development and maintenance of physical-sports habits [47,48]. This work possesses some limitations, the first one being reduced intervention time, while lack of sensitiveness in the measuring instrument is the second one as insufficient sensitivity of the instrument in detecting change could prove to be a major drawback. Perhaps, adding an interview process would help understand the attitudes toward physical education class in a deeper sense. Besides, the use of heart rate monitoring may not be the best way to quantify the intensity of exercise in children.
5. Conclusions
This paper presents answers to two questions raised by researchers. In regards to whether the training generates change, it can be proved that implementing PE classes through an additional two-hour weekly exercise program of vigorous intensity for obese children only produces little effects on the students’ attitude toward PE. This effect is regardless of gender, even though there is a clear gender interaction in the training, as the only change observed was in the empathy for the teacher and the PE class. However, it is essential to point out that certain limitations such as the location of the school and school-time form barriers for further exploration of different possibilities among obese children in terms of implementation of extra hours every week.
However, this also helped understand that the PE teacher and/or facilitator must use empathy to his/her favor and their good relationship with the students to increase their sensitivity and knowledge. Utilization of an attractive curriculum where the teacher/facilitator designs activities that motivate and encourage students’ participation in intense physical exercises to establish a program with motivating strategies that would promote more participation of obese children can best be implemented by PE classes.
Author Contributions
E.M.R.-P., O.N.E., and J.A.d.P. were responsible for the conception and design of the study. G.G.-C. and M.A.H.-G. were responsible for the literature review. G.G.-C. and M.A.H.-G. designed the exercise program. J.A.d.P. and J.J.G.-B. were responsible for the analysis and interpretation of the data. E.M.R.-P., O.N.E., and G.G.-C. were responsible for drafting the manuscript. E.M.R.-P., O.N.E., and J.J.G.-B. were responsible for critical revision of the article and final approval of the manuscript. All authors have read and agreed to the published version of the manuscript.
Funding
This research received no external funding.
Conflicts of Interest
The authors declare no conflict of interest.
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Mean heart rate per session.
ijerph-17-05300-t001_Table 1
Depiction of the sample according to age and group.
\\n
Control Group (CG)
Experimental Group (EG)
Age (year)
8–9
10–11
8–9
10–11
(n)
25
22
25
23
ijerph-17-05300-t002_Table 2
Age and anthropometric characteristics of girls and boys in the sample (Mean and SD).
Variable
Girls
Boys
\\np\\n
Age (year)
10.3(0.8)
10.4(0.9)
0.684
Weight (kg)
53.4(11.6)
54.0(9.6)
0.284
Height (m)
139.5(9.1)
142.1(7.6)
0.138
BMI (kg/m2)
27.2(3.3)
26.6(3.1)
0.408
Zscore BMI
2.9(0.5)
3.1(0.8)
\\n0.031\\n
Waist–hip Index
89.4(8.0)
88.8(8.6)
0.712
(Bold and italic = statistically significant).
ijerph-17-05300-t003_Table 3
Evaluation of the physical condition before and after the intervention and the pre-post differences.
Test
CG
\\n
\\n
\\n
EG
\\n
\\n
\\n
\\n
Baseline
Post
\\np\\n
Delta
Baseline
Post
\\np\\n
Delta
p Delta
Curl Up (n)
8.1(3.9)
8.6(4.2)
0.206
0.5(2.3)
7.0(5.7)
8.5(5.9) *
\\n0.034\\n
2.0(0.3) #
\\n0.000\\n
Shoulder strech (cm)
4.9(4.0)
5.6(3.9)
0.282
0.7(1.5)
5.1(7.9)
6.1(6.9) *
\\n0.040\\n
1.2(2.7)
0.078
Push_up (s)
3.9(2.0)
5.2(1.7) *
\\n0.000\\n
1.4(1.5)
2.8(2.9)
3.5(2.3) *
\\n0.035\\n
0.6(2.1)
0.083
400m (s)
127(34.1)
129.8(3.9)
0.255
2.6(17.3)
111.3(26.5)
106.0(26.2) *
\\n0.034\\n
−5.4(12.8) #
\\n0.002\\n
Trunk_lift(cm)
32.7(5.2)
34.8(5.0) *
\\n0.003\\n
2.1(1.9)
32.8(6.3)
34.2(6.2) *
\\n0.044\\n
1.5(2.5)
0.063
H.jump feet together(m)
0.97(0.16)
0.99(0.17) *
\\n0.048\\n
0.02 (0.05)
0.97(0.17)
1.00(0.16) *
\\n0.047\\n
0.03(0.08)
0.137
BMI Zscore
2.9(0.4)
2.7(0.4)
\\n0.053\\n
−0.2(0.1)
3.1(0.8)
3.0(0.8)
\\n0.063\\n
−0.1(0.5)
0.074
Pli(mm)
141.2(29.8)
134.9(27.4) *
\\n0.033\\n
−5.5(12.0)
138.1(27.0)
124.2(30.3) *
\\n0.002\\n
−14.6(26.3) #
\\n0.002\\n
p = pre-post intra group; * = p < 0.05; delta = pre-post intra group diference; p delta = inter grup delta; # = p < 0.05 delta from the control group. (Bold and italic = statistically significant).
ijerph-17-05300-t004_Table 4
Data from the questionnaire on attitude toward physical education in girls and boys (Mean and SD)—pre-intervention.
Item
Girls
Boys
\\np\\n
Subjet organization concordancy
15.2(3.8)
14.5(3.6)
0.373
PE Diifficulty
17.5(3.7)
16.7(3.3)
0.266
Emphaty towards teachers and subject
16.3(3.7)
13.9(2.9)
\\n0.001\\n
PE as sport
10.5(3.0)
9.9(2.6)
0.284
Preference for PE and Sport
10.5(2.6)
10.9(3.0)
0.499
Utility of PE
22.5(4.7)
22.3(4.5)
0.847
Value subject and PE Teacher
32.2(6.7)
29.9(6.7)
0.091
(Bold and italic = statistically significant).
ijerph-17-05300-t005_Table 5
Pre and post values of the study subjects on each dimension.
Item
\\n
Girls
Boys
\\n
Control
Experimental
Control
Experimental
\\np\\n
\\nη\\n2\\n
Subjet organization concordancy
Pretest
14.2(4.1)
16.2(3.4)
12.8(3.3)
16.0(3.2)
0.536
0.04
Postest
14.3(3.9)
14.4(3.8) *
12.9(3.6)
13.5(3.7) *
PE Difficulty
Pretest
17.2(4.4)
17.8(3.0)
16.7(3.5)
16.8(3.2)
0.2
0.056
Postest
17.1(3.9)
15.7(3.8) *
17.0(3.7)
17.5(3.2)
Emphaty towards teachers and subject
Pretest
16.7(3.9)
15.9(3.4)
14.3(2.2)
13.5(3.3)
\\n0.021\\n
0.055
Postest
16.8(3.5)
14.3(3.9) *
15.1(2.5) *
15.0(342) *
PE as sport
Pretest
11.4(3.1)
9.7(2.7)
9.9(3.3)
10.0(1.8)
0.101
0.028
Postest
11.3(2.7)
8.6(3.3) *
9.7(2.6)
10.8(2.4)
Preference for PE and Sport
Pretest
10.2(2.5)
10.8(2.8)
9.7(2.4)
12.0(3.0)
0.261
0.013
Postest
10.0(2.2)
8.6(3.2) *
9.6(2.4)
10.9(2.7)
Utility of PE
Pretest
24.3(4.9)
20.7(3.)
21.1(4.4)
23.3(4.5)
0.315
0.011
Postest
23.9(5.1)
20.7(4.0)
21.5(4.2)
24.3(5.3)
Value subject and PE Teacher
Pretest
30.5(6.6)
33.9(6.5)
28.6(8.0)
31.0(5.2)
0.077
0.033
Postest
31.3(6.4) *
30.7(8.1)
29.3(3.2)
32.8(7.7)
* p < 0.05 pre-post intra group; p = p value interaction gender × exercise; η2 = effect size of the interaction (Mean and SD). (Bold and italic = statistically significant).
\\n\"\n]"}}},{"rowIdx":441,"cells":{"data":{"kind":"list like","value":["\nInt J Mol SciInt J Mol SciijmsInternational Journal of Molecular Sciences1422-0067MDPI32752303PMC743209810.3390/ijms21155533ijms-21-05533ArticleCytotoxic Effects of Cannabinoids on Human HT-29 Colorectal Adenocarcinoma Cells: Different Mechanisms of THC, CBD, and CB83CerretaniDaniela1CollodelGiulia2BrizziAntonella3FiaschiAnna Ida1MenchiariAndrea4MorettiElena2MoltoniLaura1MicheliLucia1*Department of Medical and Surgical Sciences and Neurosciences, University of Siena, 53100 Siena, Italy; daniela.cerretani@unisi.it (D.C.); annaida.fiaschi@unisi.it (A.I.F.); laura.moltoni@unisi.it (L.M.)Department of Molecular and Developmental Medicine, University of Siena, 53100 Siena, Italy; giulia.collodel@unisi.it (G.C.); elena.moretti@unisi.it (E.M.)Department of Biotechnology, Chemistry and Pharmacy, University of Siena, 53100 Siena, Italy; antonella.brizzi@unisi.itDepartment of Business and Law, University of Siena, 53100 Siena, Italy; andrea.menchiari@unisi.itCorrespondence: lucia.micheli@unisi.it; Tel.: +39-057-723-3285; Fax: +39-057-723-31020182020820202115553315720203072020© 2020 by the authors.2020Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
In this study, we investigated the effects of exposition to IC50 dose for 24 h of a new synthetic cannabinoid (CB83) and of phytocannabinoids Δ9-tetrahydrocannabinol (THC) and cannabidiol (CBD) on HT-29 colorectal carcinoma cells. Cell viability and proliferative activity evaluated using the MTT, lactate dehydrogenase (LDH), and CyQUANT assays showed that cell viability was significantly affected when CB83, THC, and CBD were administered to cells. The results obtained showed that the reduced glutathione/oxidized glutathione ratio was significantly reduced in the cells exposed to CBD and significantly increased in the cells treated with the CB83 when compared to the controls. CBD treatment causes a significant increase in malondialdehyde content. The catalase activity was significantly reduced in HT-29 cells after incubation with CB83, THC, and CBD. The activities of glutathione reductase and glutathione peroxidase were significantly increased in cells exposed to THC and significantly decreased in those treated with CBD. The ascorbic acid content was significantly reduced in cells exposed to CB83, THC, and CBD. The ultrastructural investigation by TEM highlighted a significantly increased percentage of cells apoptotic and necrotic after CB83 exposition. The Annexin V-Propidium Iodide assay showed a significantly increased percentage of cells apoptotic after CB83 exposition and necrotic cells after CBD and THC exposition. Our results proved that only CBD induced oxidative stress in HT-29 colorectal carcinoma cells via CB receptor-independent mechanisms and that CB83 caused a mainly CB2 receptor-mediated antiproliferative effect comparable to 5-Fuorouracil, which is still the mainstay drug in protocols for colorectal cancer.
Cannabinoids obtained from Cannabis sativa and their derivatives produce many biological effects, mainly through interactions with specific receptors such as CB1 and CB2, which have been cloned and characterized [1,2]. Moreover, the orphan G protein coupled receptor 55 (GPR55), the transient receptor potential cation channel subfamily V member 1 (TRPV1), and peroxisome proliferator-activated receptors (PPARs) have been reported as possible receptors for endogenous cannabinoids [3,4]. Given the many effects of cannabinoids and the evidence demonstrated by preclinical studies, it is possible to assume a potential use of these substances in the medical field. To date, cannabinoids have been used in the treatment of nausea and vomiting in cancer patients undergoing chemotherapy, but the use of cannabinoids in oncology is likely to be limited, although there is evidence showing that cannabinoids are able to inhibit cell growth in different cancer cell lines [5] and to exert antitumor effects in experimental animal models [6].
Through cannabinoid receptor and nonreceptor signaling pathways, cannabinoids show specific cytotoxicity against tumor cells while protecting healthy tissue from apoptosis. Bogdanović et al. [7] investigated the proapoptotic and antiproliferative effects of cannabinoids and associated signaling pathways in different cancer cell lines, and it has been demonstrated that natural and synthetic cannabinoids cause a CB1 and/or CB2 receptor-dependent decrease in the proliferation of breast and intestinal cancer cells [5,8]. Cannabinoids impair tumor progressions at various levels. Their main effect is the induction of cancer cell death by apoptosis and the inhibition of cancer cell proliferation. At least one of those actions has been demonstrated in almost all cancer cell types tested [9]. Cannabinoid treatments affect directly the viability of a great variety of cancer cells via the induction of apoptosis or cell cycle arrest [10,11]. The psychotropic cannabinoid, the Δ9-tetrahydrocannabinol (THC, Figure 1), induces apoptosis in a variety of transformed and nontransformed cells, including those of immune origin [10,12]. It was observed that THC treatment induces significant levels of apoptosis in leukemias and lymphocytes in culture, as well as in the murine thymus and spleen [12,13,14], showing that THC may impair T-cell functions through the induction of apoptosis. Moreover, cannabidiol (CBD, Figure 1), a nonpsychotropic cannabinoid, has also been reported to induce apoptosis in several transformed or immortalized cells, including K-ras-thyroid epithelial, C6 glioma, malondhyaldhehyde (MDA)-MB-231 breast carcinoma, HL-60, and Jurkat leukemia cells [5,15,16]. In addition, many evidences suggest that cannabinoids damage tumor angiogenesis and block invasion and metastasis [6,17]. The role of reactive oxygen species (ROS) in regulating apoptosis is supported by many evidences [18], and the production of ROS during apoptosis has been described in various models of apoptotic cell death [19]. Cancer cells seem to possess higher levels of endogenous ROS compared to normal cells, but events that increase ROS levels above a certain threshold seem to be incompatible with the cellular survival. Thus, compounds that increase the ROS level or that impair the cellular antioxidant system will shift the redox balance, inducing cancer cell cytotoxicity [20].
Our previous study in the cannabinoid field led to the development of a new class of synthetic cannabinoid ligands [21,22,23,24,25] chemically characterized by a substituted resorcinol nucleus linked to fatty acid amides. In fact, their structure merges the crucial pharmacophoric requirements for the cannabinoid receptor binding of both THC and anandamide (AEA, Figure 1), the main endogenous cannabinoid, such as a rigid aromatic backbone bearing an alkyl tail and a flexible saturated chain with an amidic head. Among these derivatives, compound CB83 [24], Figure 1, belonging to the 5-(1′,1′-dimethylheptyl)resorcinol class, was selected for its balanced potency (Ki CB1 = 310 nM and Ki CB2 = 30 nM) and selectivity.
The aim of this in vitro study was to investigate the effects of the synthetic cannabinoid CB83 and the traditional phytocannabinoids, THC and CBD, on the viability, proliferation, ultrastructure, and apoptosis in human colorectal carcinoma HT-29 cells. We also evaluated the effect of the treatment with cannabinoids on the HT-29 cellular redox state. For this purpose, we determined the parameters of oxidative stress, such as the ratio of reduced glutathione (GSH) to oxidized glutathione (GSSG); the levels of malondhyaldhehyde (MDA), a marker of lipid peroxidation; the antioxidant ascorbic acid (AA); and the cellular antioxidant enzyme activities, such as catalase (CAT), glutathione peroxidase (GPx), and glutathione reductase (GR). Moreover, we compared the CB effects with those observed with the pyrimidine antagonist 5-Fluorouracil (5FU), which is still the mainstay drug in protocols for colorectal cancer.
2. Results2.1. CB83, THC, CBD, and 5FU Induce Cytotoxicity and Inhibit the Viability of HT-29 Cells
The results from the MTT assay showed that CB83, THC, CBD, and 5FU were cytotoxic and suppressed the viability of HT-29 cells after 24h, with IC50 values of 1.0 ± 0.10 µM, 30.0 ± 1.01 µM, 30.0 ± 3.02 µM, and 34.0 ± 13.89 µM, respectively (Table 1). The HT-29 cells were relatively more sensitive to synthetic cannabinoid CB83, followed by THC, CBD, and 5FU.
In order to verify the cytotoxic activity of CB83, THC, CBD, and 5FU, the viability of HT-29 cells was determined in cellular lysate using the lactate dehydrogenase (LDH) assay. According to the cytotoxic effect found in HT-29 cells, all the substances mentioned above significantly suppressed the viability of the HT-29 cell line after 24 h of treatment, with the maximum effect observed with THC, followed by CB83, 5FU, and CBD, with LDH ratio values of 10.9 ± 0.31, p < 0.001; 5.9 ± 1.01, p < 0.05; 5.9 ± 0.62, p < 0.001; and 5.1 ± 0.72, p < 0.05, respectively vs. the control LDH ratio value (3.0 ± 0.07) (Table 1).
The results of the CyQUANT cell proliferation assay, a highly sensitive, fluorescence-based microplate assay, are shown in Table 1. CB83, THC, CBD, and 5FU cause a significant inhibition of proliferation at 24 h (53.1 ± 5.47, p < 0.001; 70.9 ± 5.59, p < 0.01; 66.8 ± 7.90, p < 0.01; and 57.5 ± 6.15, p < 0.001, respectively) respective to the control (100.0 ± 10.57).
The results achieved using the incubation with CB1 antagonist AM251 and CB2 antagonist AM630 in HT-29 cells exposed to CBD show that the blockade of CB1 and CB2 receptors did not influence the cytotoxic effect of CBD. The treatment of HT-29 cells with THC in the presence of AM251 did not produce significant effects on the cellular viability compared to the control cells, while using AM630 THC showed a significant reduction of cellular viability (p < 0.05 vs. control cells). The treatment of HT-29 cells with CB83 in the presence of CB2 antagonist AM630 did not show significant effects on the cellular viability compared to the control cells, while the cytotoxic effect was maintained in the presence of AM251 (p < 0.05 vs. control cells) (Figure 2).
2.2. Effects of CB83, THC, CBD, and 5FU on the HT-29 Cellular Redox State
In this study we explored the effects of CB83, THC, CBD, and 5FU at the respective IC50 doses on the antioxidant cellular defense system in HT-29 cells. For this purpose, the GSH/GSSG ratio; the AA content; the MDA levels as the index of lipid peroxidation; and the activity of antioxidant enzymes CAT, GR, and GPx were determined in cellular lysates after 24 h of incubation. The GSH/GSSG ratio was essentially unchanged in THC and 5FU-treated HT-29 cells, while in the cells exposed to CBD, the GSH/GSSG ratio was significantly reduced (13.7 ± 0.58; p < 0.05) respective to the control (16.1 ± 1.14). Conversely, the HT-29 cells treated with the CB83 synthetic cannabinoid showed a significant increase in the GSH/GSSG ratio (26.4 ± 0.83; p < 0.001) compared to untreated cells (Figure 3a). The nonenzymatic AA antioxidant content of cells kept for 24 h with CB83, THC, and CBD was significantly reduced (10.5 ± 1.13, 4.8 ± 0.85, and 6.8 ± 0.94 nmol/mL; p < 0.01, p < 0.001, and p < 0.001, respectively) while remaining unchanged in those exposed to 5FU (15.5 ± 1.27 nmol/mL) compared to untreated HT-29 cells (13.7 ± 1.06 nmol/mL) (Figure 3b).
The effects of the treatment of HT-29 with CB83, THC, CBD, and 5FU on MDA levels showed that only the cells exposed to CBD exhibited a significant increase in the MDA content (2.6 ± 0.18 nmol/mL; p < 0.01) respective to the untreated cells (1.6 ± 0.27 nmol/mL) to indicate the presence of oxidative damage (Figure 3c).
The activity of antioxidant enzyme CAT was significantly reduced in HT-29 cells after 24-h incubation with CB83, THC, CBD, and 5FU (121.5 ± 14.03, p < 0.001; 76.5 ± 21.92, p < 0.001; 146.5 ± 17.26, p < 0.05; and 32.9 ± 3.78, p < 0.001 U/mg protein, respectively) respective to the control cells (173.1 ± 3.27 U/mg protein) (Figure 4a).
The GR activity appeared to be unaltered in CB83-treated cells (0.5 ± 0.06 U/mg protein), while it showed a significant increase in HT-29 cells exposed to THC (0.7 ± 0.07 U/mg protein, p < 0.001). Conversely, the cells treated with CBD and 5FU showed a significant decrease in the enzymatic activity (0.1 ± 0.01 U/mg protein, p < 0.01 and 0.2 ± 0.03 U/mg protein, p < 0.01, respectively) compared to the control cells (0.3 ± 0.01 U/mg protein) (Figure 4b).
The GPx activity measured in the cells treated with the CB83 proved to be significantly increased (18.7 ± 1.87 U/mg protein, p < 0.001), as well as in cells exposed to THC (23.5 ± 3.53 U/mg protein, p < 0.001); instead, the HT-29 cells exposed to CBD and 5FU showed a significant decrease in GPx enzymatic activity (9.2 ± 0.79 and 5.2 ± 1.69 U/mg protein, p < 0.05 and p < 0.01, respectively) respective to the control cells (10.8 ± 0.91 U/mg protein) (Figure 4c).
2.3. CB83, THC, CBD, and 5FU Cause Morphological Alterations in HT-29 Cells2.3.1. AnV/PI Assay
The percentage of intact viable cells was significantly decreased in cells treated with 5FU (p < 0.001) and with CB83 (p < 0.01) compared to the control cells, while the viability of cells treated with CBD and with THC did not show significant differences, despite an evident increase with that detected in the controls (Table 2). In particular, a significant increase of AnV-positive cells (p < 0.001) was shown in CB83 cells with respect to the controls, and a significant increase of necrotic cells (PI-positive) was detected in 5FU (p < 0.001), CBD (p < 0.01), and THC (p < 0.05) cells compared to the controls.
2.3.2. Transmission Electron Microscopy (TEM)
TEM analysis highlighted an increased percentage of apoptotic and necrotic cells in all treated samples respective to the controls (Table 3)—in particular, significant values were detected when the cells were treated with 5FU (p < 0.01 and p < 0.001, respectively) and with CB83 (p < 0.001 and p < 0.01, respectively) compared to the control cells.
Normal cells showed normal chromatin textures and cytoplasm containing rough endoplasmic reticulum, Golgi bodies, and mitochondria (Figure 5a). Necrotic cells (Figure 5b) displayed altered chromatin texture and cytoplasm, deeply impaired and devoid of organelles; the plasma membrane was frequently broken. The apoptotic cell characteristics regarded marginated chromatin and swollen mitochondria; moreover, another ultrastructural peculiar alteration was the presence of a very vacuolated cytoplasm (Figure 5c). In all treated samples, an interesting feature was the presence of a percentage (8–12%) of cells showing the cytoplasm rich in enlargements (Figure 5d). This feature was absent in the control cells.
3. Discussion
Cannabinoids act by a modulation of the signaling pathways crucial in the control of cell proliferation and survival, and many in vitro and in vivo experiments have shown that cannabinoids inhibit the proliferation of cancer cells and stimulate autophagy and apoptosis. Here, we studied the effects of cannabinoids THC, CBD, and CB83 on the viability, proliferation, ultrastructure, apoptosis, and cellular redox state of human colorectal carcinoma HT-29 cells.
MTT results, LDH analyses, and the nucleic acid content determination showed that all the cannabinoids tested and 5FU inhibit cellular proliferation. The HT-29 cells were relatively more sensitive to synthetic cannabinoid CB83, followed by THC, CBD, and 5FU. At the same time, only CBD caused oxidative stress in HT-29 cells. Particularly, in CBD-treated HT-29 cells, the decrease in the GSH/GSSG ratio suggests a redox imbalance towards the appearance of oxidative stress, a condition that did not occur in cells treated with THC, CB83, and 5FU. The evaluation of the activity of cellular antioxidant defense enzymes showed that all cannabinoids tested and 5FU reduced the activity of CAT in HT-29 cells, while the activity of GR was significantly increased in cells treated with THC, reduced in HT-29 cells exposed to CBD and 5FU, and unchanged in the CB83 treatment. The GPx activity was significantly increased in HT-29 cells treated with CB83 and THC, whereas it was reduced in cells exposed to CBD and 5FU. Thus, CBD appears to induce oxidative stress in HT-29 cells, possibly through ROS production, which causes GSH consumption and inhibits CAT, GR, and GPx activities. These data were confirmed by the presence, in CBD-treated cells, of significantly higher MDA levels than in control cells, while they remained substantially unchanged in HT-29 cells exposed to THC, CB83, and 5FU.
The decrease of AA, a nonenzymatic antioxidant, in the cells exposed to cannabinoids can be explained by the increased demand for reducing equivalents necessary to maintain GSH levels through the “sparing” effect of AA on GSSG [26]. In HT-29 cells treated with CBD, the request for reducing equivalents was not satisfied, possibly due to the excess of ROS, and the GSH/GSSG ratio remained below the value of the control, contributing to causing oxidative stress. Massi et al. [15] showed that the CBD-dependent production of ROS was accompanied by a reduction in GSH and GSH-related enzymes. The origin of stress induced by CBD came in part from the mitochondria and led to the activation of multiple caspases involved in the intrinsic and extrinsic pathways of apoptosis. The production of ROS is the most supported hypothesis for the CBD-dependent inhibition of cancer cell aggressiveness in experimental models in cultures, [27,28,29] and Singer et al. [30] observed, for the first time in vivo, that the CBD-dependent generation of ROS is, in part, responsible for the antitumor activity of the cannabinoid.
It is reported that CBD has low affinity for cannabinoid receptors and acts independently of them. In fact, CBD seems to interact with other receptors such as TRPV1, GPR55, or PPARs [3,4]. However, other authors suggested that CBD induces apoptosis in cancer cells partially through the direct or indirect activation of CB2 receptors [5,31]. The results obtained using the incubation with CB1 antagonist AM251 and CB2 antagonist AM630 seem to support the hypothesis that CBD-induced cytotoxicity in HT-29 cells occurs through a CB1 and CB2 receptor-independent mechanism and, possibly, via ROS production, which leads to apoptotic cell death [31,32,33]. Regarding the cytotoxic effects induced in HT-29 by THC and CB83, no signs of oxidative stress were evident in the cells treated with these cannabinoids. Furthermore, THC in the presence of AM251 did not produce significant effects on the cellular viability compared to the control cells; while using AM630, THC showed a significant reduction of the cellular viability to demonstrate that it is an agonist of the CB1 receptor, with less efficacy at the CB2 ones [34].
This result is in-line with what was observed by other authors [35], even if it was reported that antitumor activities of THC were associated with both cannabinoid CB1 and CB2 receptors [36,37,38]. Otherwise, the treatment of HT-29 cells with CB83 in the presence of CB2 antagonist AM630 does not show significant effects on the cellular viability compared to the control cells, while the cytotoxic effect is maintained in the presence of AM251, the selective CB1 receptor antagonist. The synthetic cannabinoid CB83 has been shown to have a higher CB2 affinity (Ki = 30 nM), rather than for CB1 receptors (Ki = 310 nM). These data seem to confirm that the cytotoxic effect of CB83 is mainly mediated by CB2 receptors. The inhibition of the cancer cell proliferation and induction of apoptosis in cancer cells by CB1 and CB2 activation is described, and the most upstream key molecule that can initiate death signals appears to be ceramide [39]. The increases of ceramide production via a mechanism that involves tumor necrosis factor-alpha (TNF-α) [34] induces an activation of the endoplasmic reticulum stress-related signaling pathway, which, finally, leads to the activation of the intrinsic apoptosis pathway [31].
The analysis by the Annexin V-Propidium Iodide assay and TEM confirmed that all the tested cannabinoids increased the percentages of the apoptotic and necrotic cells, even if only the synthetic cannabinoid CB83 determined a significant increase of these pathologies. Specifically, this compound resulted as effective as 5FU. These results agree with the data obtained by the MTT test.
Probably through cannabinoid receptors and nonreceptor signaling pathways, cannabinoids showed specific cytotoxicity against tumor cells [7]. By TEM analysis, this cytotoxic effect, particularly evident in samples incubated with CB83, was highlighted by the significant increase of cells with marginated chromatin, swollen mitochondria, and vacuolated cytoplasm or disrupted chromatin and broken plasma membrane, typical features, respectively, of apoptosis and necrosis.
The cannabinoid ligands have been shown to sensitize cancer cells and, synergistically, may interact with members of the TNF receptor, thus suggesting that the combination of cannabinoids with death receptor ligands induces additive or synergistic tumor cell death [40]. Recently, it has been reported that different compounds induced cell death by necroptotic and apoptotic mechanisms in cancer cells, with a concomitant mitochondrial metabolism failure that triggers lower production of ATP and ROS overproduction [41].
4. Materials and Methods4.1. Chemicals
All reagents were purchased from Sigma-Aldrich (St. Louis, MO, USA). All solutions were prepared with deionized water of resistivity no less than 18.2 MΩ·cm–1 (Milli-Q Ultrapure Water System, Merck KGaA, Darmstadt, Germany).
4.2. Cell Cultures
Human colorectal carcinoma HT-29 cells were purchased from ATCC (American Type Culture Collection, Manassas, VA, USA), and they were cultured in Dulbecco’s modified Eagle’s medium (MEM) + GlutaMAX™-1 supplemented with 10% fetal bovine serum (FBS) and 1% penicillin-streptomycin (Gibco/Invitrogen, Carlsbad, CA, USA). The cells were grown in T-75 flasks at 37 °C in a humidified atmosphere with 5% CO2 in the air, and the culture medium was replaced every 2 to 3 days, and passages were performed 1 to 2 days per week.
4.3. Cell Viability Assays4.3.1. Assay for Cytotoxicity (MTT Assay)
To explore the antiproliferative effect on the HT-29 cell line, the cells were exposed to CB83, THC, CBD, and 5FU for 24 h, and the effect on the cell viability was determined using the MTT assay.
The HT-29 cells were preincubated in a 96-well plate at a density of 1.0 × 104 cells/well for 24 h; cells were treated with CB83, THC, CBD, and 5FU at different concentrations (range from 0.1 mM to 0.1 nM) to determine the IC50 values. After incubation for 24 h, the MTT reagent (5 mg/mL) was added to each well, and the plate was incubated for an additional 4 h at 37 °C. At the end of incubation, the media were removed, and the intracellular formazan product was dissolved in 100 μL of isopropyl alcohol. The absorbance of each well was measured at 540 nm using an ELISA reader (iMarkTM; Bio-Rad Laboratories, Inc., Hercules CA, USA), and the MTT reduction rate was calculated by setting each of the control survivals equal to 100%.
In order to investigate the role of the CB receptors, the MTT assay was again performed with specific antagonists of the CB1 and CB2 receptors. The cells were pretreated for 30 min with CB1 and CB2 antagonists AM251 (1 μM) and AM630 (1 μM), respectively, before the addition of CB83, THC, and CBD.
4.3.2. Lactate Dehydrogenase (LDH) Determination
To evaluate the cellular viability in HT-29 cells, the cells were plated at an initial density of 2 × 105 cells/well in 6-well plates, and they were allowed to attach overnight. Afterwards, the medium was removed by aspiration, and the cells were exposed to the IC50 values of CB83, THC, CBD, and 5FU for 24 h. Cells lysate and culture media samples were recovered to the assay LDH activity. LDH catalyzed the conversion of pyruvate to L-lactate, while the reduced nicotinamide adenine dinucleotide (NADH) was oxidized. The rate of oxidation, which is directly proportional to the LDH activity [42], was monitored by measuring the decrease in absorbance at 340 nm using an UV-Vis Spectrophotometer (Lambda 35, Perkin Elmer, Norwalk, CT, USA). Total LDH activity was evaluated by calculation ΔA/min in lysate and media samples. Viability was expressed as the LDH ratio (LDH media/LDH lysate).
4.3.3. Cell Proliferation Assay
The cell viability was determined by using the CyQUANT cell proliferation assay kit, which measures the total nucleic acid content. The cells were plated in 6-well plates at a seeding density of 2 × 105 in 3 mL of culture medium containing 1% FBS. Then, the cells were treated with CB83, THC, CBD, and 5FU for 24 h at the respective IC50 doses. The cells were harvested and were seeded (5000 cells/well) into a 96-well microplate in 200 µL of culture medium containing 1% FBS for 4 h. The medium was aspirated, and the plates frozen at −80 °C until used. The plates were thawed at room temperature and processed according to the manufacturer’s instructions. The analysis was carried out using a VICTOR Multilabel plate reader (excitation 485 nm/emission 520 nm; Perkin Elmer Victor 3V, Waltham, MA, USA).
4.4. Cellular Redox Systems Evaluation
HT-29 cells were exposed to CB83, THC, CBD, and 5FU to evaluate a panel of factors involved in the oxidative stress response.
The cells were plated at an initial density of 2 × 105 cells/well in 6-well plates, allowing them to attach overnight. Afterwards, the medium was removed by aspiration, and the cells were treated to respective doses IC50 of CB83, THC, CBD, and 5FU for 24 h. Then, cells were detached and centrifuged at 1500× g for 2 min. The pellets were resuspended in 1 mL of phosphate-buffered saline (PBS) and exposed to three cycles of freeze-thaw, freezing at −80 °C in the freezer.
The supernatants of cells destroyed by freezing and thawing were aliquoted and processed as described below in the respective methods.
4.4.1. Glutathione Oxidized and Reduced
An aliquot of cell lysates was added to an equal volume of 10% metaphosphoric acid and centrifuged at low speed (2000× g) for 10 min at 0 °C. The supernatant was removed and stored at −80 °C until use. Total GSH and GSSG levels were quantified in the supernatant using a a micro-assay procedure [43] based on an enzymatic method with the reading at 415 nm. Results were expressed in nmol/mg protein.
4.4.2. Proteins Assay
Protein concentrations were determined by the method of Lowry et al. [44], and the calibration curves were prepared with dry bovine serum albumin.
4.4.3. AA Assay
AA levels were measured in the aliquot of cell lysates acidified with metaphosphoric acid using an HPLC method, as described by Ross [45], with minor modifications. The supernatants were filtered (Anotop 0.2 μm, Merck), and 20 μL were injected into a high-performance liquid chromatography (HPLC) column. The AA was quantified by UV reverse-phase HPLC using a Waters 600 E System Controller HPLC (Milford, MA, USA) equipped with a Waters Dual λ 2487 UV detector (Milford, MA, USA) set at 262 nm. A 5-µm ultrasphere ODS column (Beckman, San Ramon, CA, USA) was used with the acetonitrile-water (49/51, v/v) as the mobile phase at the flow rate of 0.8 mL/min. The AA concentrations (nmol/mL) were calculated by peak areas, determined using an Agilent 3395 integrator (Agilent Technologies, Santa Clara, CA, USA).
4.4.4. Malondialdehyde Assessment
The extent of lipid peroxidation in cell lysates was estimated by calculating the MDA levels according the method of Shara et al. [46], with minor modifications. To prevent artifact oxidations of polyunsaturated free fatty acids, immediately after the freezing and thawing, an aliquot of 0.2 mL was added to 0.2 mL of tris-HCl 0.04 M and acetonitrile containing 0.1% butylated hydroxytoluene. After centrifugation at 2000× g for 10 min at 0 °C, the supernatant was frozen at −80 °C until use.
At the time of analysis, the supernatant was derivatized with 2,4-dinitrophenylhydrazine and immediately stirred and extracted with 5 mL of pentane; finally, the samples were dried using nitrogen. A calibration curve with concentrations of MDA in the range from 0.5 nmol/mL to 10 nmol/mL was used.
The MDA hydrazone was quantified by isocratic HPLC using a Waters 600 E System Controller HPLC (Milford, MA, USA) equipped with a Waters Dual λ 2487 UV detector (Milford, MA, USA) set at 307 nm. A 5-µm ultrasphere ODS column C18 (Beckman, San Ramon, CA, USA) was used to separate the hydrazone derivative at the flow rate of 0.8 mL/min with the acetonitrile (45%)-HCl 0.01 N (55%) as the mobile phase. The MDA concentrations (nmol/mL) were calculated by peak areas determined using an Agilent 3395 integrator (Agilent Technologies, Santa Clara, CA, USA).
4.4.5. Catalase Activity
An aliquot of cell lysates was centrifuged at 4000× g for 15 min at 4 °C, and the supernatants were frozen at −80 °C until use. To determine the CAT, a micro-assay procedure was used [47].
This method is based on the reaction of the CAT with methanol in the presence of an optimal concentration of hydrogen peroxide. The formaldehyde production was measured spectrophotometrically at 540 nm with 4-amino-3-hydrazino-5-mercapto-1,2,4-triazole (Purpald) as a chromogen. One unit of CAT activity is defined as the amount of enzyme that will cause the formation of 1 nmol of formaldehyde per minute at 25 °C. The results were expressed as U/mg protein.
4.4.6. Glutathione Reductase Activity
For the evaluation of the GR activity, an aliquot of cell lysates was diluted (1:1) in cold 0.25-M sucrose in 0.1-M phosphate buffer, pH 7.4, and centrifuged at 40,000× g for 20 min at 4 °C. The supernatants were stored at −80 °C until analyzed. The method is based on the increase in absorbance at 415 nm when 5,5′-dithiobis(2-nitrobenzoic acid) is reduced by GSH generated from an excess of GSSG [48].
Samples were prepared in 96-well plates, and absorbance was measured every 30 s for 3 min with a programmable microplate reader. The rate of increase in absorbance was directly proportional to the amount of GR in the sample. The results were expressed as U/mg protein.
4.4.7. Glutathione Peroxidase Assay
The cell lysates were treated using the same procedure as described for GR. The GPx activity is quantitated by measuring the change in absorbance at 340 nm caused by the oxidation of NADPH [49]. One unit of GPx activity is defined as the amount of enzyme that oxidizes 1 µmol of NADPH at 37 °C per minute. Enzyme activity was expressed as U/mg protein.
4.5. Morphological Studies
Apoptosis was initially induced by the incubation of cells 2 × 104 cells/well in complete growing medium for 24 h with the respective IC50 doses of CB83, THC, CBD, and 5FU.
4.5.1. Transmission Electron Microscopy (TEM)
Samples were fixed in cold Karnovsky fixative and maintained at 4 °C for 2 h. Fixed cells were washed in 0.1-M cacodylate buffer (pH 7.2) for 12 h, post-fixed in 1% buffered osmium tetroxide for 1 h at 4 °C, then dehydrated and embedded in Epon Araldite. Ultra-thin sections were cut with a Supernova ultramicrotome (Reickert Jung, Vienna, Austria), mounted on copper grids, stained with uranyl acetate and lead citrate, and then observed and photographed with a Philips CM12 transmission electron microscope (TEM; Philips Scientifics, Eindhoven, The Netherlands and Centro di Microscopie Elettroniche “Laura Bonzi”, ICCOM, Consiglio Nazionale delle Ricerche—CNR, Via Madonna del Piano, 10 Firenze, Italy).
One-hundred ultra-thin cell sections were analyzed from each sample. Major submicroscopic characteristics were recorded by two highly trained examiners who were blind to the experiment applying the same evaluation criteria. Cell conditions (normal, apoptosis, and necrosis) were defined by typical ultrastructural characteristics. Marginated chromatin, cytoplasmatic translucent vacuoles, and swollen and badly assembled mitochondria were the typical ultrastructural markers of apoptosis, whereas cells with a broken plasma membrane and nuclei with disrupted chromatin were affected by necrosis. TEM evaluation was carried out in three different experiments.
4.5.2. Annexin V/Propidium Iodide Assay
The detection of phosphatidylserine (PS) externalization was performed with the Vybrant Apoptosis Assay kit (Invitrogen Ltd., Paisley, UK) made up of Annexin (An) V-fluorescein isothiocyanate (FITC) and propidium iodide (PI), which are able to differentiate viable from apoptotic/necrotic cells. The samples were washed with PBS, centrifuged, and suspended in Annexin-binding buffer (ABB) to obtain a cell density of ~1 × 106. Following the manufacturer’s instructions, 10 µL of conjugated-FITC AnV and 1 µL of PI (100 µg/mL) working solution to each cell suspension were added. Cells were incubated in the dark for 15 min at 37 °C. After a careful wash with ABB, a drop of cell suspension was smeared on each glass slide. Slides were mounted in glycerol containing 5% n-propylgallate. Observations were made with a Leitz Aristoplan (Leica, Wetzlar, Germany) light microscope equipped with a fluorescence apparatus. A total of 300 cells from each sample were counted. By staining cells with FITC-AnV (AnV, green fluorescence) and, simultaneously, with the nonvital dye (PI, red fluorescence), it is possible to recognize intact cells (AnV-negative and PI-negative), early-apoptotic cells with PS externalization (AnV-positive and PI-negative), cells with PS externalization and damaged membranes (AnV-positive and PI-positive), and necrotic cells (AnV-negative and PI-positive). The results were expressed as the percentages of apoptotic cells (green).
4.6. Statistical Analysis
All experiments were independently repeated three times, with multiple replicates within each run. The data represented the mean of three independent experiments in triplicate and were expressed as means ± SD. Statistical analysis was performed using SPSS v.19 Chicago: SPSS Inc. (Chicago, IL, USA).
The IC50 value was defined as the concentration causing proliferation inhibition by 50% compared to the control. The IC50 values are given as mean values ± SD and were calculated according to the method of Litchfield and Wilcoxon [50].
Data was tested for normality using a Kolomogorov-Smirnov Test.
Statistical comparisons were made by one-way ANOVA or Kruskal–Wallis test.
Tukey’s post-hoc test was used under homoscedasticity conditions. Games-Howell post-hoc test was used on violations of the assumption of homoscedasticity. Dunn’s multiple comparisons test for Kruskal–Wallis tests. The values were considered significantly different when p ≤ 0.05.
5. Conclusions
In summary, we found that CB83, a new synthetic cannabinoid characterized by an alkylresorcinol nucleus, has an effect comparable to 5FU, which is still the mainstay drug in protocols for colorectal cancer. CB83 appears to produce its effects mainly through CB2 receptors and is more effective than CBD and THC in the induction of apoptosis and necrosis in HT-29 cells. Furthermore, in this work we observed, like other authors, the cytotoxic effect of CBD on HT-29 cells appears to be independent of the activation of CB1 and CB2 receptors and, instead, to involve the production of ROS and, consequently, oxidative stress, which leads cells to apoptosis and necrosis. Therefore, a combined treatment that exploits the synergistic cytotoxic effects of CB83 and CBD on colon cancer cells could represent an interesting new strategy in colon cancer therapy. Further studies will be needed to evaluate the efficacy and toxicity of CB83 and the CB83 + CBD association in animal models.
Acknowledgments
The authors thank Stefano Menchiari for the excellent technical assistance in making the figures.
Author Contributions
Conceptualization, G.C. and D.C.; methodology, A.I.F. and E.M.; validation, A.I.F.; formal analysis, A.M.; investigation, L.M. (Laura Moltoni); resources, A.B.; data curation, G.C. writing—original draft preparation, D.C.; writing—review and editing, L.M. (Lucia Micheli); supervision, L.M. (Lucia Micheli); project administration, D.C. All authors have read and agreed to the published version of the manuscript.
Funding
This research received no external funding.
Conflicts of Interest
The authors declare no conflict of interest.
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Cytotoxic effects on the cellular viability of HT-29 cells exposed to IC50 of THC, CBD, and CB83 in the presence of AM251 (CB1 antagonist) 1μM and AM630 (CB2 antagonist) 1 μM. The results are expressed as % of control. Data are representative of three independent experiments. Data were statistically evaluated. * p < 0.05 and ** p < 0.01 vs. control.
Nonenzymatic antioxidant contents: (a) glutathione/oxidized glutathione (GSH/GSSG) ratio, (b) ascorbic acid (AA) levels, and (c) malondhyaldhehyde (MDA) levels in HT-29 cells exposed to IC50 of CB83, THC, CBD, and 5-Fluorouracil (5FU). Data are representative of three independent experiments. For each treatment, the experimental measurements (left side with circle marker) and mean values ± S.D. (right side) are reported. Mean value markers represent the significance vs. control (C): triangle for p < 0.05, diamond for p < 0.01, and square for p < 0.001; hyphen if dataset is not significant.
Antioxidant enzymes activity: (a) catalase (CAT), (b) glutathione reductase (GR), and (c) glutathione peroxidase (GPx) in HT-29 cells exposed to IC50 of CB83, THC, CBD, and 5FU. Data are representative of three independent experiments. For each treatment, the experimental measurements (left side with circle marker) and mean values ± S.D. (right side) are reported. Mean value markers represent the significance vs. control (C): triangle for p < 0.05, diamond for p < 0.01, and square for p < 0.001; hyphen if dataset is not significant.
Transmission electron microscopy (TEM) sections of cultured HT-29 cells. (a) Baseline conditions. The cells showed normal nuclei (N), regular chromatin texture, and cytoplasm (Cy) containing the typical organelles’ mitochondria (M). Bar 3.5 μm. (b–d) Incubation with CB83, THC, and CBD. In all samples, a high percentage of cells with necrotic and apoptotic features was described. In (b), a necrotic cell is evidenced in an altered chromatin texture (aN) and a cytoplasm devoid of organelles (aCy) and, in (c), is represented in an apoptotic cell with a cytoplasm rich in vacuoles (V). The cell showed in (d) highlights enlargements in the cytoplasm (arrows). This feature was detected in a percentage of 8–12% in all treated samples. (b,d) Bar 3.5 μm. (c) Bar 3 μm.
ijms-21-05533-t001_Table 1
Results from the MTT, lactate dehydrogenase (LDH), and CyQUANT assays.
Treatment
MTT IC50 (μM)
LDH ratio
CyQUANT %
C
-
3.0 ± 0.07
100.0 ± 10.57
CB83
1.0 ± 0.10
5.9 ± 1.01 *
53.1 ± 5.47 ***
THC
30.0 ±1.01 ##
10.9 ± 0.31 ***
70.9 ± 5.59 **
CBD
30.0 ± 3.02 ##
5.1 ± 0.72 *
66.8 ± 7.90 **
5FU
34.0 ± 13.89 #
5.9 ± 0.62 ***
57.5± 6.15 ***
Data are representative of three independent experiments and are presented as mean ± S.D. Data were statistically evaluated. #\np < 0.05 and ##\np < 0.01 vs. CB83 and * p < 0.05, ** p < 0.01, and *** p < 0.001 vs. control (C). THC: tetrahydrocannabinol, CBD: cannabidiol, and 5FU: 5-Fluorouracil.
ijms-21-05533-t002_Table 2
Results from screening with the Annexin V(AnV)-fluorescein isothiocyanate (FITC) and propidium iodide (PI) assay. Intact cells appeared unstained (AnV- or PI-), apoptotic cells with phosphatidylserine (PS) externalization were green stained with FITC-Annexin (AnV+ PI-), and necrotic cells were red stained (AnV- PI+ or AnV+ PI+) with the broken plasma membrane.
\n
Intact %AnV-PI-
Apoptosis %AnV+PI-
Necrosis %AnV+PI+
C
84.0 ± 1.01
11.0 ± 1.15
3.7 ± 1.15
5FU
38.7 ± 1.53 ***
31.3 ± 1.53
32.3 ± 2.09 ***
CB83
43.3 ± 0.58 **
37.3 ± 1.15 ***
19.3 ± 1.53
CBD
53.0 ± 0.10
20.6 ± 1,16
27.0 ± 1.05 **
THC
56.3 ± 2.52
19.6 ± 2.08
25.0 ± 1.73 *
Data are representative of three independent experiments and are presented as mean ± S.D. Data were statistically evaluated. * p < 0.05, ** p < 0.01, and *** p < 0.001 vs. control (C).
ijms-21-05533-t003_Table 3
Results from screening with a transmission electron microscopy (TEM analysis. One hundred cell sections were analyzed for each sample. A cell was considered apoptotic when marginated chromatin, translucent vacuoles, and swollen and badly assembled mitochondria were detected. A cell was considered necrotic when broken plasma membrane and disrupted chromatin were observed.
\n
Apoptosis %
Necrosis %
C
12.7 ± 2.08
3.3 ± 1.53
5FU
27.3 ± 1.53 **
28.3 ± 1.54 ***
CB83
35.0 ± 2.01 ***
26.0 ± 2.05 **
CBD
22.0 ± 1.10
22.7 ± 2.52
THC
20.0 ± 0.01
20.0 ± 1.73
Data are representative of three independent experiments and are presented as mean ±S.D. Data were statistically evaluated ** p < 0.01, and *** p < 0.001.
\n"],"string":"[\n \"\\nInt J Mol SciInt J Mol SciijmsInternational Journal of Molecular Sciences1422-0067MDPI32752303PMC743209810.3390/ijms21155533ijms-21-05533ArticleCytotoxic Effects of Cannabinoids on Human HT-29 Colorectal Adenocarcinoma Cells: Different Mechanisms of THC, CBD, and CB83CerretaniDaniela1CollodelGiulia2BrizziAntonella3FiaschiAnna Ida1MenchiariAndrea4MorettiElena2MoltoniLaura1MicheliLucia1*Department of Medical and Surgical Sciences and Neurosciences, University of Siena, 53100 Siena, Italy; daniela.cerretani@unisi.it (D.C.); annaida.fiaschi@unisi.it (A.I.F.); laura.moltoni@unisi.it (L.M.)Department of Molecular and Developmental Medicine, University of Siena, 53100 Siena, Italy; giulia.collodel@unisi.it (G.C.); elena.moretti@unisi.it (E.M.)Department of Biotechnology, Chemistry and Pharmacy, University of Siena, 53100 Siena, Italy; antonella.brizzi@unisi.itDepartment of Business and Law, University of Siena, 53100 Siena, Italy; andrea.menchiari@unisi.itCorrespondence: lucia.micheli@unisi.it; Tel.: +39-057-723-3285; Fax: +39-057-723-31020182020820202115553315720203072020© 2020 by the authors.2020Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
In this study, we investigated the effects of exposition to IC50 dose for 24 h of a new synthetic cannabinoid (CB83) and of phytocannabinoids Δ9-tetrahydrocannabinol (THC) and cannabidiol (CBD) on HT-29 colorectal carcinoma cells. Cell viability and proliferative activity evaluated using the MTT, lactate dehydrogenase (LDH), and CyQUANT assays showed that cell viability was significantly affected when CB83, THC, and CBD were administered to cells. The results obtained showed that the reduced glutathione/oxidized glutathione ratio was significantly reduced in the cells exposed to CBD and significantly increased in the cells treated with the CB83 when compared to the controls. CBD treatment causes a significant increase in malondialdehyde content. The catalase activity was significantly reduced in HT-29 cells after incubation with CB83, THC, and CBD. The activities of glutathione reductase and glutathione peroxidase were significantly increased in cells exposed to THC and significantly decreased in those treated with CBD. The ascorbic acid content was significantly reduced in cells exposed to CB83, THC, and CBD. The ultrastructural investigation by TEM highlighted a significantly increased percentage of cells apoptotic and necrotic after CB83 exposition. The Annexin V-Propidium Iodide assay showed a significantly increased percentage of cells apoptotic after CB83 exposition and necrotic cells after CBD and THC exposition. Our results proved that only CBD induced oxidative stress in HT-29 colorectal carcinoma cells via CB receptor-independent mechanisms and that CB83 caused a mainly CB2 receptor-mediated antiproliferative effect comparable to 5-Fuorouracil, which is still the mainstay drug in protocols for colorectal cancer.
Cannabinoids obtained from Cannabis sativa and their derivatives produce many biological effects, mainly through interactions with specific receptors such as CB1 and CB2, which have been cloned and characterized [1,2]. Moreover, the orphan G protein coupled receptor 55 (GPR55), the transient receptor potential cation channel subfamily V member 1 (TRPV1), and peroxisome proliferator-activated receptors (PPARs) have been reported as possible receptors for endogenous cannabinoids [3,4]. Given the many effects of cannabinoids and the evidence demonstrated by preclinical studies, it is possible to assume a potential use of these substances in the medical field. To date, cannabinoids have been used in the treatment of nausea and vomiting in cancer patients undergoing chemotherapy, but the use of cannabinoids in oncology is likely to be limited, although there is evidence showing that cannabinoids are able to inhibit cell growth in different cancer cell lines [5] and to exert antitumor effects in experimental animal models [6].
Through cannabinoid receptor and nonreceptor signaling pathways, cannabinoids show specific cytotoxicity against tumor cells while protecting healthy tissue from apoptosis. Bogdanović et al. [7] investigated the proapoptotic and antiproliferative effects of cannabinoids and associated signaling pathways in different cancer cell lines, and it has been demonstrated that natural and synthetic cannabinoids cause a CB1 and/or CB2 receptor-dependent decrease in the proliferation of breast and intestinal cancer cells [5,8]. Cannabinoids impair tumor progressions at various levels. Their main effect is the induction of cancer cell death by apoptosis and the inhibition of cancer cell proliferation. At least one of those actions has been demonstrated in almost all cancer cell types tested [9]. Cannabinoid treatments affect directly the viability of a great variety of cancer cells via the induction of apoptosis or cell cycle arrest [10,11]. The psychotropic cannabinoid, the Δ9-tetrahydrocannabinol (THC, Figure 1), induces apoptosis in a variety of transformed and nontransformed cells, including those of immune origin [10,12]. It was observed that THC treatment induces significant levels of apoptosis in leukemias and lymphocytes in culture, as well as in the murine thymus and spleen [12,13,14], showing that THC may impair T-cell functions through the induction of apoptosis. Moreover, cannabidiol (CBD, Figure 1), a nonpsychotropic cannabinoid, has also been reported to induce apoptosis in several transformed or immortalized cells, including K-ras-thyroid epithelial, C6 glioma, malondhyaldhehyde (MDA)-MB-231 breast carcinoma, HL-60, and Jurkat leukemia cells [5,15,16]. In addition, many evidences suggest that cannabinoids damage tumor angiogenesis and block invasion and metastasis [6,17]. The role of reactive oxygen species (ROS) in regulating apoptosis is supported by many evidences [18], and the production of ROS during apoptosis has been described in various models of apoptotic cell death [19]. Cancer cells seem to possess higher levels of endogenous ROS compared to normal cells, but events that increase ROS levels above a certain threshold seem to be incompatible with the cellular survival. Thus, compounds that increase the ROS level or that impair the cellular antioxidant system will shift the redox balance, inducing cancer cell cytotoxicity [20].
Our previous study in the cannabinoid field led to the development of a new class of synthetic cannabinoid ligands [21,22,23,24,25] chemically characterized by a substituted resorcinol nucleus linked to fatty acid amides. In fact, their structure merges the crucial pharmacophoric requirements for the cannabinoid receptor binding of both THC and anandamide (AEA, Figure 1), the main endogenous cannabinoid, such as a rigid aromatic backbone bearing an alkyl tail and a flexible saturated chain with an amidic head. Among these derivatives, compound CB83 [24], Figure 1, belonging to the 5-(1′,1′-dimethylheptyl)resorcinol class, was selected for its balanced potency (Ki CB1 = 310 nM and Ki CB2 = 30 nM) and selectivity.
The aim of this in vitro study was to investigate the effects of the synthetic cannabinoid CB83 and the traditional phytocannabinoids, THC and CBD, on the viability, proliferation, ultrastructure, and apoptosis in human colorectal carcinoma HT-29 cells. We also evaluated the effect of the treatment with cannabinoids on the HT-29 cellular redox state. For this purpose, we determined the parameters of oxidative stress, such as the ratio of reduced glutathione (GSH) to oxidized glutathione (GSSG); the levels of malondhyaldhehyde (MDA), a marker of lipid peroxidation; the antioxidant ascorbic acid (AA); and the cellular antioxidant enzyme activities, such as catalase (CAT), glutathione peroxidase (GPx), and glutathione reductase (GR). Moreover, we compared the CB effects with those observed with the pyrimidine antagonist 5-Fluorouracil (5FU), which is still the mainstay drug in protocols for colorectal cancer.
2. Results2.1. CB83, THC, CBD, and 5FU Induce Cytotoxicity and Inhibit the Viability of HT-29 Cells
The results from the MTT assay showed that CB83, THC, CBD, and 5FU were cytotoxic and suppressed the viability of HT-29 cells after 24h, with IC50 values of 1.0 ± 0.10 µM, 30.0 ± 1.01 µM, 30.0 ± 3.02 µM, and 34.0 ± 13.89 µM, respectively (Table 1). The HT-29 cells were relatively more sensitive to synthetic cannabinoid CB83, followed by THC, CBD, and 5FU.
In order to verify the cytotoxic activity of CB83, THC, CBD, and 5FU, the viability of HT-29 cells was determined in cellular lysate using the lactate dehydrogenase (LDH) assay. According to the cytotoxic effect found in HT-29 cells, all the substances mentioned above significantly suppressed the viability of the HT-29 cell line after 24 h of treatment, with the maximum effect observed with THC, followed by CB83, 5FU, and CBD, with LDH ratio values of 10.9 ± 0.31, p < 0.001; 5.9 ± 1.01, p < 0.05; 5.9 ± 0.62, p < 0.001; and 5.1 ± 0.72, p < 0.05, respectively vs. the control LDH ratio value (3.0 ± 0.07) (Table 1).
The results of the CyQUANT cell proliferation assay, a highly sensitive, fluorescence-based microplate assay, are shown in Table 1. CB83, THC, CBD, and 5FU cause a significant inhibition of proliferation at 24 h (53.1 ± 5.47, p < 0.001; 70.9 ± 5.59, p < 0.01; 66.8 ± 7.90, p < 0.01; and 57.5 ± 6.15, p < 0.001, respectively) respective to the control (100.0 ± 10.57).
The results achieved using the incubation with CB1 antagonist AM251 and CB2 antagonist AM630 in HT-29 cells exposed to CBD show that the blockade of CB1 and CB2 receptors did not influence the cytotoxic effect of CBD. The treatment of HT-29 cells with THC in the presence of AM251 did not produce significant effects on the cellular viability compared to the control cells, while using AM630 THC showed a significant reduction of cellular viability (p < 0.05 vs. control cells). The treatment of HT-29 cells with CB83 in the presence of CB2 antagonist AM630 did not show significant effects on the cellular viability compared to the control cells, while the cytotoxic effect was maintained in the presence of AM251 (p < 0.05 vs. control cells) (Figure 2).
2.2. Effects of CB83, THC, CBD, and 5FU on the HT-29 Cellular Redox State
In this study we explored the effects of CB83, THC, CBD, and 5FU at the respective IC50 doses on the antioxidant cellular defense system in HT-29 cells. For this purpose, the GSH/GSSG ratio; the AA content; the MDA levels as the index of lipid peroxidation; and the activity of antioxidant enzymes CAT, GR, and GPx were determined in cellular lysates after 24 h of incubation. The GSH/GSSG ratio was essentially unchanged in THC and 5FU-treated HT-29 cells, while in the cells exposed to CBD, the GSH/GSSG ratio was significantly reduced (13.7 ± 0.58; p < 0.05) respective to the control (16.1 ± 1.14). Conversely, the HT-29 cells treated with the CB83 synthetic cannabinoid showed a significant increase in the GSH/GSSG ratio (26.4 ± 0.83; p < 0.001) compared to untreated cells (Figure 3a). The nonenzymatic AA antioxidant content of cells kept for 24 h with CB83, THC, and CBD was significantly reduced (10.5 ± 1.13, 4.8 ± 0.85, and 6.8 ± 0.94 nmol/mL; p < 0.01, p < 0.001, and p < 0.001, respectively) while remaining unchanged in those exposed to 5FU (15.5 ± 1.27 nmol/mL) compared to untreated HT-29 cells (13.7 ± 1.06 nmol/mL) (Figure 3b).
The effects of the treatment of HT-29 with CB83, THC, CBD, and 5FU on MDA levels showed that only the cells exposed to CBD exhibited a significant increase in the MDA content (2.6 ± 0.18 nmol/mL; p < 0.01) respective to the untreated cells (1.6 ± 0.27 nmol/mL) to indicate the presence of oxidative damage (Figure 3c).
The activity of antioxidant enzyme CAT was significantly reduced in HT-29 cells after 24-h incubation with CB83, THC, CBD, and 5FU (121.5 ± 14.03, p < 0.001; 76.5 ± 21.92, p < 0.001; 146.5 ± 17.26, p < 0.05; and 32.9 ± 3.78, p < 0.001 U/mg protein, respectively) respective to the control cells (173.1 ± 3.27 U/mg protein) (Figure 4a).
The GR activity appeared to be unaltered in CB83-treated cells (0.5 ± 0.06 U/mg protein), while it showed a significant increase in HT-29 cells exposed to THC (0.7 ± 0.07 U/mg protein, p < 0.001). Conversely, the cells treated with CBD and 5FU showed a significant decrease in the enzymatic activity (0.1 ± 0.01 U/mg protein, p < 0.01 and 0.2 ± 0.03 U/mg protein, p < 0.01, respectively) compared to the control cells (0.3 ± 0.01 U/mg protein) (Figure 4b).
The GPx activity measured in the cells treated with the CB83 proved to be significantly increased (18.7 ± 1.87 U/mg protein, p < 0.001), as well as in cells exposed to THC (23.5 ± 3.53 U/mg protein, p < 0.001); instead, the HT-29 cells exposed to CBD and 5FU showed a significant decrease in GPx enzymatic activity (9.2 ± 0.79 and 5.2 ± 1.69 U/mg protein, p < 0.05 and p < 0.01, respectively) respective to the control cells (10.8 ± 0.91 U/mg protein) (Figure 4c).
2.3. CB83, THC, CBD, and 5FU Cause Morphological Alterations in HT-29 Cells2.3.1. AnV/PI Assay
The percentage of intact viable cells was significantly decreased in cells treated with 5FU (p < 0.001) and with CB83 (p < 0.01) compared to the control cells, while the viability of cells treated with CBD and with THC did not show significant differences, despite an evident increase with that detected in the controls (Table 2). In particular, a significant increase of AnV-positive cells (p < 0.001) was shown in CB83 cells with respect to the controls, and a significant increase of necrotic cells (PI-positive) was detected in 5FU (p < 0.001), CBD (p < 0.01), and THC (p < 0.05) cells compared to the controls.
2.3.2. Transmission Electron Microscopy (TEM)
TEM analysis highlighted an increased percentage of apoptotic and necrotic cells in all treated samples respective to the controls (Table 3)—in particular, significant values were detected when the cells were treated with 5FU (p < 0.01 and p < 0.001, respectively) and with CB83 (p < 0.001 and p < 0.01, respectively) compared to the control cells.
Normal cells showed normal chromatin textures and cytoplasm containing rough endoplasmic reticulum, Golgi bodies, and mitochondria (Figure 5a). Necrotic cells (Figure 5b) displayed altered chromatin texture and cytoplasm, deeply impaired and devoid of organelles; the plasma membrane was frequently broken. The apoptotic cell characteristics regarded marginated chromatin and swollen mitochondria; moreover, another ultrastructural peculiar alteration was the presence of a very vacuolated cytoplasm (Figure 5c). In all treated samples, an interesting feature was the presence of a percentage (8–12%) of cells showing the cytoplasm rich in enlargements (Figure 5d). This feature was absent in the control cells.
3. Discussion
Cannabinoids act by a modulation of the signaling pathways crucial in the control of cell proliferation and survival, and many in vitro and in vivo experiments have shown that cannabinoids inhibit the proliferation of cancer cells and stimulate autophagy and apoptosis. Here, we studied the effects of cannabinoids THC, CBD, and CB83 on the viability, proliferation, ultrastructure, apoptosis, and cellular redox state of human colorectal carcinoma HT-29 cells.
MTT results, LDH analyses, and the nucleic acid content determination showed that all the cannabinoids tested and 5FU inhibit cellular proliferation. The HT-29 cells were relatively more sensitive to synthetic cannabinoid CB83, followed by THC, CBD, and 5FU. At the same time, only CBD caused oxidative stress in HT-29 cells. Particularly, in CBD-treated HT-29 cells, the decrease in the GSH/GSSG ratio suggests a redox imbalance towards the appearance of oxidative stress, a condition that did not occur in cells treated with THC, CB83, and 5FU. The evaluation of the activity of cellular antioxidant defense enzymes showed that all cannabinoids tested and 5FU reduced the activity of CAT in HT-29 cells, while the activity of GR was significantly increased in cells treated with THC, reduced in HT-29 cells exposed to CBD and 5FU, and unchanged in the CB83 treatment. The GPx activity was significantly increased in HT-29 cells treated with CB83 and THC, whereas it was reduced in cells exposed to CBD and 5FU. Thus, CBD appears to induce oxidative stress in HT-29 cells, possibly through ROS production, which causes GSH consumption and inhibits CAT, GR, and GPx activities. These data were confirmed by the presence, in CBD-treated cells, of significantly higher MDA levels than in control cells, while they remained substantially unchanged in HT-29 cells exposed to THC, CB83, and 5FU.
The decrease of AA, a nonenzymatic antioxidant, in the cells exposed to cannabinoids can be explained by the increased demand for reducing equivalents necessary to maintain GSH levels through the “sparing” effect of AA on GSSG [26]. In HT-29 cells treated with CBD, the request for reducing equivalents was not satisfied, possibly due to the excess of ROS, and the GSH/GSSG ratio remained below the value of the control, contributing to causing oxidative stress. Massi et al. [15] showed that the CBD-dependent production of ROS was accompanied by a reduction in GSH and GSH-related enzymes. The origin of stress induced by CBD came in part from the mitochondria and led to the activation of multiple caspases involved in the intrinsic and extrinsic pathways of apoptosis. The production of ROS is the most supported hypothesis for the CBD-dependent inhibition of cancer cell aggressiveness in experimental models in cultures, [27,28,29] and Singer et al. [30] observed, for the first time in vivo, that the CBD-dependent generation of ROS is, in part, responsible for the antitumor activity of the cannabinoid.
It is reported that CBD has low affinity for cannabinoid receptors and acts independently of them. In fact, CBD seems to interact with other receptors such as TRPV1, GPR55, or PPARs [3,4]. However, other authors suggested that CBD induces apoptosis in cancer cells partially through the direct or indirect activation of CB2 receptors [5,31]. The results obtained using the incubation with CB1 antagonist AM251 and CB2 antagonist AM630 seem to support the hypothesis that CBD-induced cytotoxicity in HT-29 cells occurs through a CB1 and CB2 receptor-independent mechanism and, possibly, via ROS production, which leads to apoptotic cell death [31,32,33]. Regarding the cytotoxic effects induced in HT-29 by THC and CB83, no signs of oxidative stress were evident in the cells treated with these cannabinoids. Furthermore, THC in the presence of AM251 did not produce significant effects on the cellular viability compared to the control cells; while using AM630, THC showed a significant reduction of the cellular viability to demonstrate that it is an agonist of the CB1 receptor, with less efficacy at the CB2 ones [34].
This result is in-line with what was observed by other authors [35], even if it was reported that antitumor activities of THC were associated with both cannabinoid CB1 and CB2 receptors [36,37,38]. Otherwise, the treatment of HT-29 cells with CB83 in the presence of CB2 antagonist AM630 does not show significant effects on the cellular viability compared to the control cells, while the cytotoxic effect is maintained in the presence of AM251, the selective CB1 receptor antagonist. The synthetic cannabinoid CB83 has been shown to have a higher CB2 affinity (Ki = 30 nM), rather than for CB1 receptors (Ki = 310 nM). These data seem to confirm that the cytotoxic effect of CB83 is mainly mediated by CB2 receptors. The inhibition of the cancer cell proliferation and induction of apoptosis in cancer cells by CB1 and CB2 activation is described, and the most upstream key molecule that can initiate death signals appears to be ceramide [39]. The increases of ceramide production via a mechanism that involves tumor necrosis factor-alpha (TNF-α) [34] induces an activation of the endoplasmic reticulum stress-related signaling pathway, which, finally, leads to the activation of the intrinsic apoptosis pathway [31].
The analysis by the Annexin V-Propidium Iodide assay and TEM confirmed that all the tested cannabinoids increased the percentages of the apoptotic and necrotic cells, even if only the synthetic cannabinoid CB83 determined a significant increase of these pathologies. Specifically, this compound resulted as effective as 5FU. These results agree with the data obtained by the MTT test.
Probably through cannabinoid receptors and nonreceptor signaling pathways, cannabinoids showed specific cytotoxicity against tumor cells [7]. By TEM analysis, this cytotoxic effect, particularly evident in samples incubated with CB83, was highlighted by the significant increase of cells with marginated chromatin, swollen mitochondria, and vacuolated cytoplasm or disrupted chromatin and broken plasma membrane, typical features, respectively, of apoptosis and necrosis.
The cannabinoid ligands have been shown to sensitize cancer cells and, synergistically, may interact with members of the TNF receptor, thus suggesting that the combination of cannabinoids with death receptor ligands induces additive or synergistic tumor cell death [40]. Recently, it has been reported that different compounds induced cell death by necroptotic and apoptotic mechanisms in cancer cells, with a concomitant mitochondrial metabolism failure that triggers lower production of ATP and ROS overproduction [41].
4. Materials and Methods4.1. Chemicals
All reagents were purchased from Sigma-Aldrich (St. Louis, MO, USA). All solutions were prepared with deionized water of resistivity no less than 18.2 MΩ·cm–1 (Milli-Q Ultrapure Water System, Merck KGaA, Darmstadt, Germany).
4.2. Cell Cultures
Human colorectal carcinoma HT-29 cells were purchased from ATCC (American Type Culture Collection, Manassas, VA, USA), and they were cultured in Dulbecco’s modified Eagle’s medium (MEM) + GlutaMAX™-1 supplemented with 10% fetal bovine serum (FBS) and 1% penicillin-streptomycin (Gibco/Invitrogen, Carlsbad, CA, USA). The cells were grown in T-75 flasks at 37 °C in a humidified atmosphere with 5% CO2 in the air, and the culture medium was replaced every 2 to 3 days, and passages were performed 1 to 2 days per week.
4.3. Cell Viability Assays4.3.1. Assay for Cytotoxicity (MTT Assay)
To explore the antiproliferative effect on the HT-29 cell line, the cells were exposed to CB83, THC, CBD, and 5FU for 24 h, and the effect on the cell viability was determined using the MTT assay.
The HT-29 cells were preincubated in a 96-well plate at a density of 1.0 × 104 cells/well for 24 h; cells were treated with CB83, THC, CBD, and 5FU at different concentrations (range from 0.1 mM to 0.1 nM) to determine the IC50 values. After incubation for 24 h, the MTT reagent (5 mg/mL) was added to each well, and the plate was incubated for an additional 4 h at 37 °C. At the end of incubation, the media were removed, and the intracellular formazan product was dissolved in 100 μL of isopropyl alcohol. The absorbance of each well was measured at 540 nm using an ELISA reader (iMarkTM; Bio-Rad Laboratories, Inc., Hercules CA, USA), and the MTT reduction rate was calculated by setting each of the control survivals equal to 100%.
In order to investigate the role of the CB receptors, the MTT assay was again performed with specific antagonists of the CB1 and CB2 receptors. The cells were pretreated for 30 min with CB1 and CB2 antagonists AM251 (1 μM) and AM630 (1 μM), respectively, before the addition of CB83, THC, and CBD.
4.3.2. Lactate Dehydrogenase (LDH) Determination
To evaluate the cellular viability in HT-29 cells, the cells were plated at an initial density of 2 × 105 cells/well in 6-well plates, and they were allowed to attach overnight. Afterwards, the medium was removed by aspiration, and the cells were exposed to the IC50 values of CB83, THC, CBD, and 5FU for 24 h. Cells lysate and culture media samples were recovered to the assay LDH activity. LDH catalyzed the conversion of pyruvate to L-lactate, while the reduced nicotinamide adenine dinucleotide (NADH) was oxidized. The rate of oxidation, which is directly proportional to the LDH activity [42], was monitored by measuring the decrease in absorbance at 340 nm using an UV-Vis Spectrophotometer (Lambda 35, Perkin Elmer, Norwalk, CT, USA). Total LDH activity was evaluated by calculation ΔA/min in lysate and media samples. Viability was expressed as the LDH ratio (LDH media/LDH lysate).
4.3.3. Cell Proliferation Assay
The cell viability was determined by using the CyQUANT cell proliferation assay kit, which measures the total nucleic acid content. The cells were plated in 6-well plates at a seeding density of 2 × 105 in 3 mL of culture medium containing 1% FBS. Then, the cells were treated with CB83, THC, CBD, and 5FU for 24 h at the respective IC50 doses. The cells were harvested and were seeded (5000 cells/well) into a 96-well microplate in 200 µL of culture medium containing 1% FBS for 4 h. The medium was aspirated, and the plates frozen at −80 °C until used. The plates were thawed at room temperature and processed according to the manufacturer’s instructions. The analysis was carried out using a VICTOR Multilabel plate reader (excitation 485 nm/emission 520 nm; Perkin Elmer Victor 3V, Waltham, MA, USA).
4.4. Cellular Redox Systems Evaluation
HT-29 cells were exposed to CB83, THC, CBD, and 5FU to evaluate a panel of factors involved in the oxidative stress response.
The cells were plated at an initial density of 2 × 105 cells/well in 6-well plates, allowing them to attach overnight. Afterwards, the medium was removed by aspiration, and the cells were treated to respective doses IC50 of CB83, THC, CBD, and 5FU for 24 h. Then, cells were detached and centrifuged at 1500× g for 2 min. The pellets were resuspended in 1 mL of phosphate-buffered saline (PBS) and exposed to three cycles of freeze-thaw, freezing at −80 °C in the freezer.
The supernatants of cells destroyed by freezing and thawing were aliquoted and processed as described below in the respective methods.
4.4.1. Glutathione Oxidized and Reduced
An aliquot of cell lysates was added to an equal volume of 10% metaphosphoric acid and centrifuged at low speed (2000× g) for 10 min at 0 °C. The supernatant was removed and stored at −80 °C until use. Total GSH and GSSG levels were quantified in the supernatant using a a micro-assay procedure [43] based on an enzymatic method with the reading at 415 nm. Results were expressed in nmol/mg protein.
4.4.2. Proteins Assay
Protein concentrations were determined by the method of Lowry et al. [44], and the calibration curves were prepared with dry bovine serum albumin.
4.4.3. AA Assay
AA levels were measured in the aliquot of cell lysates acidified with metaphosphoric acid using an HPLC method, as described by Ross [45], with minor modifications. The supernatants were filtered (Anotop 0.2 μm, Merck), and 20 μL were injected into a high-performance liquid chromatography (HPLC) column. The AA was quantified by UV reverse-phase HPLC using a Waters 600 E System Controller HPLC (Milford, MA, USA) equipped with a Waters Dual λ 2487 UV detector (Milford, MA, USA) set at 262 nm. A 5-µm ultrasphere ODS column (Beckman, San Ramon, CA, USA) was used with the acetonitrile-water (49/51, v/v) as the mobile phase at the flow rate of 0.8 mL/min. The AA concentrations (nmol/mL) were calculated by peak areas, determined using an Agilent 3395 integrator (Agilent Technologies, Santa Clara, CA, USA).
4.4.4. Malondialdehyde Assessment
The extent of lipid peroxidation in cell lysates was estimated by calculating the MDA levels according the method of Shara et al. [46], with minor modifications. To prevent artifact oxidations of polyunsaturated free fatty acids, immediately after the freezing and thawing, an aliquot of 0.2 mL was added to 0.2 mL of tris-HCl 0.04 M and acetonitrile containing 0.1% butylated hydroxytoluene. After centrifugation at 2000× g for 10 min at 0 °C, the supernatant was frozen at −80 °C until use.
At the time of analysis, the supernatant was derivatized with 2,4-dinitrophenylhydrazine and immediately stirred and extracted with 5 mL of pentane; finally, the samples were dried using nitrogen. A calibration curve with concentrations of MDA in the range from 0.5 nmol/mL to 10 nmol/mL was used.
The MDA hydrazone was quantified by isocratic HPLC using a Waters 600 E System Controller HPLC (Milford, MA, USA) equipped with a Waters Dual λ 2487 UV detector (Milford, MA, USA) set at 307 nm. A 5-µm ultrasphere ODS column C18 (Beckman, San Ramon, CA, USA) was used to separate the hydrazone derivative at the flow rate of 0.8 mL/min with the acetonitrile (45%)-HCl 0.01 N (55%) as the mobile phase. The MDA concentrations (nmol/mL) were calculated by peak areas determined using an Agilent 3395 integrator (Agilent Technologies, Santa Clara, CA, USA).
4.4.5. Catalase Activity
An aliquot of cell lysates was centrifuged at 4000× g for 15 min at 4 °C, and the supernatants were frozen at −80 °C until use. To determine the CAT, a micro-assay procedure was used [47].
This method is based on the reaction of the CAT with methanol in the presence of an optimal concentration of hydrogen peroxide. The formaldehyde production was measured spectrophotometrically at 540 nm with 4-amino-3-hydrazino-5-mercapto-1,2,4-triazole (Purpald) as a chromogen. One unit of CAT activity is defined as the amount of enzyme that will cause the formation of 1 nmol of formaldehyde per minute at 25 °C. The results were expressed as U/mg protein.
4.4.6. Glutathione Reductase Activity
For the evaluation of the GR activity, an aliquot of cell lysates was diluted (1:1) in cold 0.25-M sucrose in 0.1-M phosphate buffer, pH 7.4, and centrifuged at 40,000× g for 20 min at 4 °C. The supernatants were stored at −80 °C until analyzed. The method is based on the increase in absorbance at 415 nm when 5,5′-dithiobis(2-nitrobenzoic acid) is reduced by GSH generated from an excess of GSSG [48].
Samples were prepared in 96-well plates, and absorbance was measured every 30 s for 3 min with a programmable microplate reader. The rate of increase in absorbance was directly proportional to the amount of GR in the sample. The results were expressed as U/mg protein.
4.4.7. Glutathione Peroxidase Assay
The cell lysates were treated using the same procedure as described for GR. The GPx activity is quantitated by measuring the change in absorbance at 340 nm caused by the oxidation of NADPH [49]. One unit of GPx activity is defined as the amount of enzyme that oxidizes 1 µmol of NADPH at 37 °C per minute. Enzyme activity was expressed as U/mg protein.
4.5. Morphological Studies
Apoptosis was initially induced by the incubation of cells 2 × 104 cells/well in complete growing medium for 24 h with the respective IC50 doses of CB83, THC, CBD, and 5FU.
4.5.1. Transmission Electron Microscopy (TEM)
Samples were fixed in cold Karnovsky fixative and maintained at 4 °C for 2 h. Fixed cells were washed in 0.1-M cacodylate buffer (pH 7.2) for 12 h, post-fixed in 1% buffered osmium tetroxide for 1 h at 4 °C, then dehydrated and embedded in Epon Araldite. Ultra-thin sections were cut with a Supernova ultramicrotome (Reickert Jung, Vienna, Austria), mounted on copper grids, stained with uranyl acetate and lead citrate, and then observed and photographed with a Philips CM12 transmission electron microscope (TEM; Philips Scientifics, Eindhoven, The Netherlands and Centro di Microscopie Elettroniche “Laura Bonzi”, ICCOM, Consiglio Nazionale delle Ricerche—CNR, Via Madonna del Piano, 10 Firenze, Italy).
One-hundred ultra-thin cell sections were analyzed from each sample. Major submicroscopic characteristics were recorded by two highly trained examiners who were blind to the experiment applying the same evaluation criteria. Cell conditions (normal, apoptosis, and necrosis) were defined by typical ultrastructural characteristics. Marginated chromatin, cytoplasmatic translucent vacuoles, and swollen and badly assembled mitochondria were the typical ultrastructural markers of apoptosis, whereas cells with a broken plasma membrane and nuclei with disrupted chromatin were affected by necrosis. TEM evaluation was carried out in three different experiments.
4.5.2. Annexin V/Propidium Iodide Assay
The detection of phosphatidylserine (PS) externalization was performed with the Vybrant Apoptosis Assay kit (Invitrogen Ltd., Paisley, UK) made up of Annexin (An) V-fluorescein isothiocyanate (FITC) and propidium iodide (PI), which are able to differentiate viable from apoptotic/necrotic cells. The samples were washed with PBS, centrifuged, and suspended in Annexin-binding buffer (ABB) to obtain a cell density of ~1 × 106. Following the manufacturer’s instructions, 10 µL of conjugated-FITC AnV and 1 µL of PI (100 µg/mL) working solution to each cell suspension were added. Cells were incubated in the dark for 15 min at 37 °C. After a careful wash with ABB, a drop of cell suspension was smeared on each glass slide. Slides were mounted in glycerol containing 5% n-propylgallate. Observations were made with a Leitz Aristoplan (Leica, Wetzlar, Germany) light microscope equipped with a fluorescence apparatus. A total of 300 cells from each sample were counted. By staining cells with FITC-AnV (AnV, green fluorescence) and, simultaneously, with the nonvital dye (PI, red fluorescence), it is possible to recognize intact cells (AnV-negative and PI-negative), early-apoptotic cells with PS externalization (AnV-positive and PI-negative), cells with PS externalization and damaged membranes (AnV-positive and PI-positive), and necrotic cells (AnV-negative and PI-positive). The results were expressed as the percentages of apoptotic cells (green).
4.6. Statistical Analysis
All experiments were independently repeated three times, with multiple replicates within each run. The data represented the mean of three independent experiments in triplicate and were expressed as means ± SD. Statistical analysis was performed using SPSS v.19 Chicago: SPSS Inc. (Chicago, IL, USA).
The IC50 value was defined as the concentration causing proliferation inhibition by 50% compared to the control. The IC50 values are given as mean values ± SD and were calculated according to the method of Litchfield and Wilcoxon [50].
Data was tested for normality using a Kolomogorov-Smirnov Test.
Statistical comparisons were made by one-way ANOVA or Kruskal–Wallis test.
Tukey’s post-hoc test was used under homoscedasticity conditions. Games-Howell post-hoc test was used on violations of the assumption of homoscedasticity. Dunn’s multiple comparisons test for Kruskal–Wallis tests. The values were considered significantly different when p ≤ 0.05.
5. Conclusions
In summary, we found that CB83, a new synthetic cannabinoid characterized by an alkylresorcinol nucleus, has an effect comparable to 5FU, which is still the mainstay drug in protocols for colorectal cancer. CB83 appears to produce its effects mainly through CB2 receptors and is more effective than CBD and THC in the induction of apoptosis and necrosis in HT-29 cells. Furthermore, in this work we observed, like other authors, the cytotoxic effect of CBD on HT-29 cells appears to be independent of the activation of CB1 and CB2 receptors and, instead, to involve the production of ROS and, consequently, oxidative stress, which leads cells to apoptosis and necrosis. Therefore, a combined treatment that exploits the synergistic cytotoxic effects of CB83 and CBD on colon cancer cells could represent an interesting new strategy in colon cancer therapy. Further studies will be needed to evaluate the efficacy and toxicity of CB83 and the CB83 + CBD association in animal models.
Acknowledgments
The authors thank Stefano Menchiari for the excellent technical assistance in making the figures.
Author Contributions
Conceptualization, G.C. and D.C.; methodology, A.I.F. and E.M.; validation, A.I.F.; formal analysis, A.M.; investigation, L.M. (Laura Moltoni); resources, A.B.; data curation, G.C. writing—original draft preparation, D.C.; writing—review and editing, L.M. (Lucia Micheli); supervision, L.M. (Lucia Micheli); project administration, D.C. All authors have read and agreed to the published version of the manuscript.
Funding
This research received no external funding.
Conflicts of Interest
The authors declare no conflict of interest.
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Cytotoxic effects on the cellular viability of HT-29 cells exposed to IC50 of THC, CBD, and CB83 in the presence of AM251 (CB1 antagonist) 1μM and AM630 (CB2 antagonist) 1 μM. The results are expressed as % of control. Data are representative of three independent experiments. Data were statistically evaluated. * p < 0.05 and ** p < 0.01 vs. control.
Nonenzymatic antioxidant contents: (a) glutathione/oxidized glutathione (GSH/GSSG) ratio, (b) ascorbic acid (AA) levels, and (c) malondhyaldhehyde (MDA) levels in HT-29 cells exposed to IC50 of CB83, THC, CBD, and 5-Fluorouracil (5FU). Data are representative of three independent experiments. For each treatment, the experimental measurements (left side with circle marker) and mean values ± S.D. (right side) are reported. Mean value markers represent the significance vs. control (C): triangle for p < 0.05, diamond for p < 0.01, and square for p < 0.001; hyphen if dataset is not significant.
Antioxidant enzymes activity: (a) catalase (CAT), (b) glutathione reductase (GR), and (c) glutathione peroxidase (GPx) in HT-29 cells exposed to IC50 of CB83, THC, CBD, and 5FU. Data are representative of three independent experiments. For each treatment, the experimental measurements (left side with circle marker) and mean values ± S.D. (right side) are reported. Mean value markers represent the significance vs. control (C): triangle for p < 0.05, diamond for p < 0.01, and square for p < 0.001; hyphen if dataset is not significant.
Transmission electron microscopy (TEM) sections of cultured HT-29 cells. (a) Baseline conditions. The cells showed normal nuclei (N), regular chromatin texture, and cytoplasm (Cy) containing the typical organelles’ mitochondria (M). Bar 3.5 μm. (b–d) Incubation with CB83, THC, and CBD. In all samples, a high percentage of cells with necrotic and apoptotic features was described. In (b), a necrotic cell is evidenced in an altered chromatin texture (aN) and a cytoplasm devoid of organelles (aCy) and, in (c), is represented in an apoptotic cell with a cytoplasm rich in vacuoles (V). The cell showed in (d) highlights enlargements in the cytoplasm (arrows). This feature was detected in a percentage of 8–12% in all treated samples. (b,d) Bar 3.5 μm. (c) Bar 3 μm.
ijms-21-05533-t001_Table 1
Results from the MTT, lactate dehydrogenase (LDH), and CyQUANT assays.
Treatment
MTT IC50 (μM)
LDH ratio
CyQUANT %
C
-
3.0 ± 0.07
100.0 ± 10.57
CB83
1.0 ± 0.10
5.9 ± 1.01 *
53.1 ± 5.47 ***
THC
30.0 ±1.01 ##
10.9 ± 0.31 ***
70.9 ± 5.59 **
CBD
30.0 ± 3.02 ##
5.1 ± 0.72 *
66.8 ± 7.90 **
5FU
34.0 ± 13.89 #
5.9 ± 0.62 ***
57.5± 6.15 ***
Data are representative of three independent experiments and are presented as mean ± S.D. Data were statistically evaluated. #\\np < 0.05 and ##\\np < 0.01 vs. CB83 and * p < 0.05, ** p < 0.01, and *** p < 0.001 vs. control (C). THC: tetrahydrocannabinol, CBD: cannabidiol, and 5FU: 5-Fluorouracil.
ijms-21-05533-t002_Table 2
Results from screening with the Annexin V(AnV)-fluorescein isothiocyanate (FITC) and propidium iodide (PI) assay. Intact cells appeared unstained (AnV- or PI-), apoptotic cells with phosphatidylserine (PS) externalization were green stained with FITC-Annexin (AnV+ PI-), and necrotic cells were red stained (AnV- PI+ or AnV+ PI+) with the broken plasma membrane.
\\n
Intact %AnV-PI-
Apoptosis %AnV+PI-
Necrosis %AnV+PI+
C
84.0 ± 1.01
11.0 ± 1.15
3.7 ± 1.15
5FU
38.7 ± 1.53 ***
31.3 ± 1.53
32.3 ± 2.09 ***
CB83
43.3 ± 0.58 **
37.3 ± 1.15 ***
19.3 ± 1.53
CBD
53.0 ± 0.10
20.6 ± 1,16
27.0 ± 1.05 **
THC
56.3 ± 2.52
19.6 ± 2.08
25.0 ± 1.73 *
Data are representative of three independent experiments and are presented as mean ± S.D. Data were statistically evaluated. * p < 0.05, ** p < 0.01, and *** p < 0.001 vs. control (C).
ijms-21-05533-t003_Table 3
Results from screening with a transmission electron microscopy (TEM analysis. One hundred cell sections were analyzed for each sample. A cell was considered apoptotic when marginated chromatin, translucent vacuoles, and swollen and badly assembled mitochondria were detected. A cell was considered necrotic when broken plasma membrane and disrupted chromatin were observed.
\\n
Apoptosis %
Necrosis %
C
12.7 ± 2.08
3.3 ± 1.53
5FU
27.3 ± 1.53 **
28.3 ± 1.54 ***
CB83
35.0 ± 2.01 ***
26.0 ± 2.05 **
CBD
22.0 ± 1.10
22.7 ± 2.52
THC
20.0 ± 0.01
20.0 ± 1.73
Data are representative of three independent experiments and are presented as mean ±S.D. Data were statistically evaluated ** p < 0.01, and *** p < 0.001.
\\n\"\n]"}}},{"rowIdx":442,"cells":{"data":{"kind":"list like","value":["\nInt J Environ Res Public HealthInt J Environ Res Public HealthijerphInternational Journal of Environmental Research and Public Health1661-78271660-4601MDPI32752122PMC743209910.3390/ijerph17155559ijerph-17-05559ArticleHigher Academic Stress Was Associated with Increased Risk of Overweight and Obesity among College Students in ChinaChenYonghua12†LiuXi1†YanNi1JiaWanru1FanYahui1YanHong1MaLu3*https://orcid.org/0000-0001-7592-9779MaLe1*School of Public Health, Xi’an Jiaotong University Health Science Center, Xi’an 710061, China; chyh_2006@mail.xjtu.edu.cn (Y.C.); lx2142008010@stu.xjtu.edu.cn (X.L.); n19940522@stu.xjtu.edu.cn (N.Y.); jiawanru123@stu.xjtu.edu.cn (W.J.); huikai18@stu.xjtu.edu.cn (Y.F.); yanhonge@mail.xjtu.edu.cn (H.Y.)Research Centre on College Students Ideological Education and Practice, Xi’an Jiaotong University, Xi’an 710061, ChinaGlobal Health Institute, School of Public Health, Xi’an Jiaotong University Health Science Center, Xi’an 710061, ChinaCorrespondence: malu1990@mail.xjtu.edu.cn (L.M.); male@mail.xjtu.edu.cn (L.M.)
The authors contributed equally to this manuscript.
3172020820201715555910520202772020© 2020 by the authors.2020Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
This study examined associations between academic stress and overweight and obesity, and moderation effects of gender, grade, and types of college on such associations. Data on academic stress, negative learning events, weight, and height were self-reported by 27,343 college students in China in 2018. About 23% and 91% of students perceived high academic stress and suffered from at least one negative learning event during the past six months, respectively, especially for females, undergraduates, and students major in humanities and social science subject groups. Perceived academic stress was associated with increased risk of overweight and obesity among all students (OR = 1.05, 95%CI: 1.00–1.10), male (OR = 1.09, 95%CI: 1.03–1.15), undergraduate (OR = 1.06, 95%CI: 1.00–1.11), and students from subordinate universities (OR = 1.13, 95%CI: 1.01–1.26). Negative learning events were associated with increased risk of overweight and obesity among all students (OR = 1.05, 95%CI: 1.01–1.09), undergraduates (OR = 1.05, 95%CI: 1.01–1.09), and students from local universities (OR = 1.07, 95%CI: 1.00–1.14). Interventions are needed to reduce the high academic stress of college students, considering the modifying effects of gender, grade, and college type. Such interventions may further contribute to the prevention of overweight and obesity among college students.
Youth overweight and obesity has become a serious public health concern, and the prevalence has been increasing at an alarming rate especially in developing countries [1]. Among Chinese college students, 22.7% of males and 8.4% of females aged 19–22 years were overweight or obese [2]. Excess weight in college students could increase the risk of developing physical (e.g., type 2 diabetes, hypertension) [3] and psychological health problems (e.g., depression and weight stigma) [4].
High levels of psychological stress have numerous deleterious effects on academic (e.g., impeding learning abilities), physical, and mental health outcomes among university students [5,6]. Psychological stress has been found to be a risk factor of overweight and obesity through multiple interacting biological and behavioral pathways [7]. A meta-analysis of 14 longitudinal studies showed that stresses including general life stress (caregiver stress, major life events) and job strain were positively associated with risk of obesity, albeit with a modest effect size [8]. Furthermore, previous studies suggested that the associations varied by socio-demographic factors. For example, higher perceived life stress was associated with overweight or obesity only among male students [9]. Previous study has indicated that leaving home to attend a post-secondary school, itself, is an important reason for stress, and compared with graduates, the transition mainly affected the freshmen and caused the series of adverse outcomes [10]. Furthermore, for the students in different majors, especially medical, a large number of studies have shown their high level of stress and the effect on both physical health and mental health [11,12].
As one of the most common chronic stressors for college students, prior research has indicated that academic stress may impair self-control and deteriorate health behaviors such as changing the dietary pattern [13], in turn, increasing the risk of overweight and obesity. For example, a laboratory study reported that college students took many more calories, carbohydrates, and sugars when academically stressed [14]. However, no study has focused on the effects of academic stress and negative learning events on overweight and obesity among college students. More and more college students were reported to be plagued with crippling bouts of academic stress, which refers to stress that occurs in the educational field, such as the worrying of the difficult tasks and being assessed by various tests [15], including perceived academic stress and the experience of negative learning events. In China, students’ academic excellence has been a social criterion and even become the only standard for Chinese parents to judge their children [16]. Rapid social changes and the implementation of the one-child policy have contributed to elevate academic and job competition and directly increase college student’s academic pressure [17]. It was indicated that the Chinese college students were confronting the highest level of academic stress compared with their counterparts in Japan and Korea, and tended to report more school-related negative events than those in United States [18,19]. However, no study has investigated the academic stress or negative learning events in Chinese college students until now.
With a large-scale sample of Chinese college students, this study aimed to investigate the associations between academic stress (perceived academic stress and negative learning events) and overweight and obesity. Furthermore, we also examined the potential modifying effects of gender, grade, and college type on such associations. We hypothesized that higher perceived academic stress and negative learning events experiences were associated with increased overweight and obesity among college students in China. Findings would provide insights to design interventions targeting academic stress to fight the obesity epidemic among college students.
2. Materials and Methods2.1. Study Design and Participants
A total of 30,000 students participated in this cross-sectional study in 2018, of which 28,000 were undergraduates and 2000 were graduates. Stratified multilevel cluster random sampling was used to recruit participants at 30 colleges in Shan’xi Province, stratified by college types (subordinates, local, private, and vocational universities). Specifically, 800 undergraduates (200 for each grade from 1 to 4) and 200 graduates were invited in 4 subordinate and 6 local universities, respectively. In the other 6 local colleges and 4 private universities, 1000 undergraduates were recruited, where 300 students were randomly selected from grade 1 and grade 2, while 200 students were selected from grade 3 and grade 4, respectively. The number of the students in the range from grade 1 to 3 who participated in the project were 400, 300, and 300 in vocational universities. All students in the sampled grades were invited to complete the self-administered paper questionnaire in classroom settings in the absence of teachers being supported by a research assistant. It took about 20 min to complete the questionnaire. Trained and experienced research assistants briefed the participants about the study and questions in the questionnaire. Socio-demographic information, perceived academic stress, negative learning events, height, and body weight were used for data analyses. A sample of 27,343 participants (43.5% were boys) with complete key variables data were included in the final data analyses (missing rate = 8.86%).
Consent was sought from the school principals before the survey. It was announced that return of the completed questionnaire implied informed consent by students. No incentive was provided to the participants. The study was approved by the ethical committee of Xi’an Jiao Tong University, bearing registration number 2017-788 on 11 November 2017.
2.2. Variables and Measurements2.2.1. Outcomes
Overweight and obesity: BMI was calculated based on self-reported body weight (kg) divided by self-reported height (m2) squared. Overweight and obesity were defined based on the standard recommended by the Working Group for Obesity in China for Chinese adults (Normal weight: 18.5 kg/m2 < BMI ≤ 23.9 kg/m2, overweight: 24.0 kg/m2 ≤ BMI ≤ 27.9 kg/m2, Obese: BMI ≥ 28.0 kg/m2) [20].
2.2.2. Exposure Variables
The exposure variables were perceived academic stress and negative learning events. Perceived academic stress was measured by one self-reported item “How much academic stress do you feel in the study?” using a 5-point Likert scale where 1 = “No”, 2 = “relatively low”, 3 = “average/general”, 4 = “relatively high” and 5 = “extremely heavy”, with a higher score indicating more perceived academic stress. The response categories were categorized into three categories in the descriptive analyses: low stress including “no = 1” or “relatively light = 2”, medium stress including “average/general = 3”, and high stress including “relatively high = 4” and “extremely heavy = 5”.
Negative learning events were measured by asking the participants whether the four negative learning events happened in the past six months (Response: yes or no). The four negative learning events included: “Failure in the exam or unsatisfactory performance”, “Heavy study load”, “family financial difficulties”, and “Pressure for further study”. Students who answered that these events happened in the past 6 months were asked to rate the degree to which these events affected them using a 5-point Likert scale from 1 = “never” to 5 = “extremely heavy”. The average scores of four events were calculated to indicate the negative impacts of the negative learning events, with higher score indicating more negative impacts.
2.2.3. Covariates
Covariates included gender; major (Science and technology, Medical science, Agronomy, Humanities and social science, Economics and management, and Sports and art); college type (Subordinate universities, Local universities, Private universities, and Vocational universities); grades (undergraduate and graduate); academic attainment (below average, average, and above average); and the characteristics of their parents: paternal and maternal education level (≤junior middle school, senior middle school/vocational schools, and ≥college), and household income (<40,000/year, 50,000–100,000/year, and >100,000/year).
2.3. Statistical Analysis
Descriptive statistics were calculated. A Chi-square test (for categorical variables) and t-tests (for continuous variables) were conducted to test for the differences of perceived academic stress and the impacts of negative learning events across gender, grade, major, and college types.
Linear/logistic mixed-effects models were used to examine the associations of academic stress and negative learning events with overweight and obesity after adjusting for covariates using enter procedure. Effect sizes were presented as a beta coefficient with a standard error or odds ratio and a 95% confidence interval (95% CI). Stratified analyses were conducted by gender, grade, and college types to examine the potential modifying effects. Mixed-effects modeling was used to account for clustering effects. Stata 14 (StataCorp, College Station, TX, USA) was used in data analysis Statistical significance was set at p < 0.05.
3. Results3.1. Characteristics of Participants
The basic characteristics of the 27,343 included college students are shown in Table 1 (n = 11,888 boys (43.4%) and n = 15,295 girls). About 13% of them were overweight or obese. Boys were more likely to be overweight and obese than girls (boys vs. girls: 20.8% vs. 7.3%). Most of them (93.4%) were undergraduate students. Most of the students majored in science and technology, and boys were more likely to study science and technology, while girls were more likely to study humanities and social science. About 90% of the students reported their academic attainment as average or above average. The majority of students’ fathers (83.6%) and mothers (86.5%) only attained junior middle school or lower education, and had a household income <40,000 per year (80.8%).
3.2. Characteristics of Perceived Academic Stress and Negative Learning Events among College Students
The prevalence of perceived high academic stress was 22.9%. Girls (23.3%) or students who study humanities and social science (26.5%) were more likely to report high perceived academic stress compared with boys (22.5%) and other major students (proportions ranged from 20.2% to 23.6%), respectively (Table 2).
Regarding experiencing negative learning events, about 91% of the college students experienced negative learning events in the past 6 months. Females (91.9%), undergraduate students (91.1%), and students studying humanities and social science (92.1%) were more likely to experience negative learning events compared with males (89.4%), graduate students (87.8%), and other major students (proportions ranged from 89.1% to 92.0%), respectively (p < 0.001) (Table 2).
The negative impacts of negative learning events on girls (mean = 8.66), undergraduate students (mean = 8.25), medical science students (mean = 9.22), and vocational university students (mean = 9.11) were significantly (p < 0.001) higher than that of males (mean = 8.27), graduate students (mean = 7.05), other major students (mean score ranged from 8.10 to 8.55), and students of other college types (mean score ranged from 7.93 to 8.48), respectively (Table 2).
3.3. Associations between Perceived Academic Stress and Overweight and Obesity among College Students, Stratified by Gender, Grade and College Types
As shown in Table 3, perceived academic stress was significantly associated with increased risk for overweight or obesity for all students (OR = 1.05, 95%CI: 1.00, 1.39), males (OR = 1.09, 95%CI: 1.03, 1.15), undergraduate students (OR = 1.06, 95%CI: 1.00, 1.11), and students from Subordinate universities (OR = 1.13, 95%CI: 1.01, 1.26) after adjusting for covariates. However, these associations were not significant for females, graduates, and students from other types of college.
3.4. Associations between Negative Learning Events and Overweight and Obesity among College Students, Stratified by Gender, Grade, and College Type
As shown in Table 4, all students who reported a higher negative learning events score were more likely to be overweight or obese (OR = 1.05, 95% CI: 1.01, 1.09). No significant gender difference was found. The undergraduate students but not graduate students who reported higher negative learning events score were more likely to be overweight or obese (OR = 1.05, 95% CI: 1.01, 1.09). Moreover, the association between negative learning events and overweight or obesity was also significant among students in local universities (OR = 1.07, 95% CI: 1.00, 1.14).
4. Discussion
This large-scale study found that both academic stress and negative learning events were positively associated with overweight and obesity among Chinese college students, although the association was modest. Additionally, the study indicated that the association is, to some extent, influenced by some demographic factors: only male students, undergraduates, and students in subordinate universities with higher perceived life stress were more likely to be overweight or obese. The effects of negative learning events on overweight and obesity were only found in undergraduates and students in local universities.
As expected, most of the Chinese college students perceived academic stress, experienced negative learning events, and were impacted by them, which may lead to a series of negative consequences [21]. Consistent with the findings of previous studies [22], this study indicated that female students reported higher levels of academic stress compared to male students. Females may be more sensitive to the stressors; when being in a stressful situation or suffering negative events they may be more easily affected by the problems and more willing to report their emotions [23]. Results of the major differences replicated the previous study which showed that students of medical majors were more academically stressed than students from other majors. Medical students are under much more pressure due to extensive curricula, frequent exams, and strain from working with patients [24]. The study also indicates that undergraduates were more easily likely to encounter negative learning events than graduate students. Graduates may master more professional and time management skills and social support than undergraduates which may help them deal with the negative events and be influenced less.
Psychological stress has been found to be one of the determinants of weight gain through multiple pathways [25]. Biological responses to stress include the impairment of gastrointestinal function that slow gastric emptying and promote energy storage, HPA axis dysregulation, and secretion changes of many biochemical substances that are relevant to weight (e.g., cortisol, leptin, ghrelin and neuropeptide Y) [7]. Moreover, stress can affect behavior such as an increased food assumption and sedentary lifestyle. A study conducted in seven cities of China reported that college students prefer to choose ready-to-eat food and energy-dense foods and increase their food consumption when perceived as having high stress which in turn promotes positive-energy imbalance and increases the risk of obesity [21].
Epidemiological evidence that linked stress as one of the determinants to the development and maintenance of overweight and obesity was accumulating. A cohort study of Hispanic-Latino adults showed that chronic stress is positively related to obesity, with the obese individuals reporting over three chronic stressors [26]. Using data from the Jackson Heart Study, Samson Y et al. showed that African-American adults who perceived higher stress levels were more likely to be obese [27]. The epidemiological data of children and adolescents has also showed an association of higher psychological stress with higher odds of obesity [28]. This study differs in that we focus on the specific academic stressor conducted in Chinese college students, and across these young adults, perceived stress may result in diverse changes in both physiology and behavior compared to children or older adults. In previous studies, multiple stressors often coexist and have greater influence on individuals than one stressor which may explain why the effect of the academic stress on obesity is smaller than prior similar studies [8]. Different measurements of stress and adiposity across the studies may also contribute to the discrepancy. The current study also suggested that a higher incidence and impact of negative learning events may contribute to the likelihood of being overweight and obese. It is possible that people may perceive higher stress after negative events and trigger a stronger stress response which may explain the association [29]. Indeed, negative learning events such as exam failure and financial hardship were often dealt with by avoidance through unhealthy behaviors including overeating, drinking, and smoking more [30]. Although no known researchers have specifically paid attention to negative learning events, the finding concurred with the Dutch study that showed recent stressful life events were positively related to BMI among young adults [31].
Gender-stratified analyses showed that the negative impacts of academic stress on overweight and obesity were more remarkable in male students. The finding is partly consistent with the study in 50 Chinese universities which showed a positive association between perceived life stress and overweight and obesity [9]. Males were found to be more reactive to the stress exposure such as a greater neuroendocrine activation and a high release of cortisol [32]. Moreover, male students are more likely to lose behavior control, eating more and exercising less, while female students seem better able to maintain a healthy lifestyle and a healthy weight when in a stressful situation [33].
Perceived academic stress and negative learning events only affected the risk of obesity for undergraduates, not graduates. The different coping strategies response to stressful events of graduates and undergraduates may explain the difference. It was indicated that undergraduates chose to escape from the plight by internet addiction and overeating, which may lead to overweight or obesity, while graduates master more living skills and prefer to solve the problem in a planned way when being in stressful academic situations [34].
Academic life may account for a significant part of college life for students from subordinate universities and local universities. They may have more excellent academic performance which, in turn, leads to higher expectations, and the failure of exams or worry about further study may have greater influence on them compared to students from other colleges. Hence, the association may be more remarkable than others.
This study has several important strengths. First, the large sample size, about 30,000 college students from different majors, grades, and types of college were recruited in western China which enable us to comprehensively explore the characteristics of academic stress and negative learning events in college students. Another strength was that we analyzed the effects in detail of academic stress and negative learning events on overweight and obesity among college students in different genders, grades, and college types.
The study also has some limitations. First, the cross-sectional design could not address the causal effects of academic stress variables on overweight and obesity among college students. Other factors associated with academic stress (e.g., the intellectual ability and head circumference) [35] and the nutritional status (e.g., dietary intake, physical activity) [36] of college students need to be investigated and considered in future work. It is important to continually monitor changes in academic stress over time alongside changes in nutritional status to observe possible effects of each other in cross-lagged analyses. Second, body weight and height were self-reported, which might have weakened the observed associations. Self-reported height and weight are widely used in population-based studies and are closely correlated to those measured in most cases [37]. This is especially thought to be the case among college students, who pay much attention to their weight status and may measure their weight frequently, and thus may be more likely to report accurately their weight and height. Third, while we observed the association between academic stress and obesity, we did not further explore the mediators of such associations (e.g., food choice, food intake, and physical activities). Future studies may explore the mediators on the associations between academic stress and obesity. Studies that include participants with different educational stages (e.g., primary and secondary school students) are needed to obtain a better understanding of the associations between academic stress and obesity. Longitudinal studies are also warranted to examine the causal relationship of academic stress and negative learning events with overweight and obesity in college students.
5. Conclusions
In conclusion, we found that a majority of college students were exposed to high academic stress and negative learning events. Perceived academic stress and negative learning events could increase the risk of being overweight and obese. Interventions are needed to reduce academic stress among college students and may also reduce the prevalence of overweight and obesity.
Author Contributions
The authors’ responsibilities were as follows: L.M. (Lu Ma) and L.M. (Le Ma) designed the research; Y.C. provided essential materials; X.L., N.Y., W.J., H.Y. performed statistical analyses; Y.F., X.L. drafted the manuscript; X.L. and L.M. (Lu Ma) had primary responsibility for the final content and are the guarantors; all authors critically helped in the interpretation of results, revised the manuscript, and provided relevant intellectual input; and all authors have read and agreed to the published version of the manuscript.
Funding
This research was partly funded by grants from the National Natural Science Foundation of China (NSFC-81473059), the Natural Science Foundation of Shaanxi Province of China (2017JM8041), New-star Plan of Science and Technology of Shaanxi Province (2015LJXX-07), the China Postdoctoral Science Special Foundation (2015T81036), the Fundamental Research Funds for the Central Universities (qngz2016004), and the Nutrition Research Foundation Fund of the Chinese Nutrition Society–DSM Special Research Foundation (CNSDSM 2016–041).
Conflicts of Interest
The authors declare no conflict of interest.
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Behav.20079254855310.1016/j.physbeh.2007.04.03217537466ijerph-17-05559-t001_Table 1
Demographic characteristics of the Chinese college students (Mean (SD)/%) in 2018.
Characteristics
Overall(n = 27,343)
Boys(n = 11,888)
Girls(n = 15,295)
p-Valueacross Gender a
Grade
0.693
 Undergraduate
93.4
93.5
93.4
 Graduate
6.6
6.5
6.6
Major
<0.001
 Science and technology
37.1
57.3
21.8
 Medical science
13.8
8.0
18.1
 Agronomy
3.2
3.7
2.8
 Humanities and social science
19.8
10.7
28.4
 Economics and management
18.4
14.8
22.6
 Sports and art
5.7
5.5
6.3
BMI (kg/m2)
20.9 ± 3.9
21.8 ± 4.3
20.1 ± 3.4
<0.001
Overweight and obesity c
13.2
20.8
7.3
<0.001
Academic attainment
<0.001
 Below average
28.0
3180 (27.0%)
4348 (28.7%)
 Average
58.9
6529 (55.4%)
9318 (61.6%)
 Above average
13.1
2081 (17.7%)
1464 (9.7%)
Paternal education level
<0.001
 ≤Junior middle school
83.6
82.0
84.9
 Senior middle school/vocational schools
14.6
15.9
13.6
 ≥College
1.7
2.0
1.5
Maternal education level
<0.001
 ≤Junior middle school
86.5
85.3
87.5
 Senior middle school/vocational schools
12.1
13.0
11.4
 ≥College
1.4
1.7
1.1
Household income
 <40,000/year
80.8
78.7
82.5
<0.001
 50,000–100,000/year
13.9
14.7
13.3
 >100,000/year
5.3
6.6
4.3
Abbreviation: BMI: body mass index; Undergraduate include undergraduate students in Grade 1, 2, 3, 4, and 5; Graduate include graduate students in Grade 1, 2, and 3. a: p-value was based on Chi-square test for categorical variables and t-tests for continuous variables across genders; c: Overweight and obesity were defined by Working Group for Obesity in China for Chinese adults (overweight: Body mass index: 24.0–27.9 kg/m2; obesity: Body mass index: ≥28 kg/m2).
ijerph-17-05559-t002_Table 2
Characteristics of academic stress and negative learning events among college students in China stratified by gender, grade, major, and college type.
All and Subgroups
Perceived Academic Stress
\np\na\n
Negative Learning Events (Yes/%)
\np\na\n
Impact of Negative Learning Events c
\np\na\n
Low
Medium
High
All
18.9%
58.2%
22.9%
90.7
8.48 ± 4.39
Gender
<0.001
<0.001
 Male
21.5%
56.1%
22.5%
89.4
<0.001
8.27 ± 4.55
 Female
16.9%
59.8%
23.3%
91.9
8.66 ± 4.24
Grade b
0.074
<0.001
<0.001
 Undergraduate
20.3%
55.9%
23.7%
91.1
8.25 ± 4.28
 Graduate
18.5%
56.3%
25.0%
87.8
7.05 ± 4.36
Major
<0.001
<0.001
<0.001
 Science and technology
19.5%
57.5%
23.0%
90.7
8.35 ± 4.40
 Medical science
14.5%
61.9%
23.6%
92.0
9.22 ± 4.27
 Agronomy
21.6%
56.4%
22.0%
89.8
8.10 ± 4.32
 Humanities and social science
16.4%
57.0%
26.5%
92.1
8.51 ± 4.30
 Economics and management
21.2%
58.5%
20.3%
90.1
8.55 ± 4.40
 Sports and art
22.0%
57.9%
20.2%
89.1
7.88 ± 4.37
College type
0.308
0.199
<0.001
 Subordinates universities
22.4%
55.9%
24.7%
91.5
7.93 ± 4.15
 Local universities
19.5%
57.0%
23.4%
91.0
8.36 ± 4.31
 Private universities
18,4%
60.3%
21.3%
90.3
8.48 ± 4.39
 Vocational universities
17.1%
61.1%
21.8%
91.4
9.11 ± 4.39
Variable definition: Score of “impact of negative learning events” was calculated by summing the scores of the four items in the scale, and higher score more negative impact of negative learning events; a: p-value was based on Chi-square test for perceived academic stress and negative learning events across genders, grade, major, and college type. b: The difference of academic stress between graduate and undergraduate was only compared among students from Subordinate and local universities (sample size: 14,494), as only these universities had graduate students. c: The participants were asked to rate the degree to which the four negative learning events affected them using a 5-point Likert scale where “never = 1” to “extremely heavy = 5”. The average scores of four events were calculated to indicate the negative impacts of the negative learning events, with higher score indicating more negative events.
ijerph-17-05559-t003_Table 3
Mixed-effects model for the associations between perceived academic stress and overweight and obesity among college students in China, stratified by gender, grade, and college type (n = 27,343).
All and Subgroups
Overweight and Obesity
OR
95%CI
\np\n
All a
\n1.05\n
\n(1.00, 1.10)\n
\n0.039\n
Gender b
 Male
\n1.09\n
\n(1.03, 1.15)\n
\n0.002\n
 Female
0.949
(0.87, 1.03)
0.235
Grade c
 Undergraduate
\n1.06\n
\n(1.00, 1.11)\n
\n0.032\n
 Graduate
0.98
(0.84, 1.14)
0.753
College type d
 Subordinates universities
\n1.13\n
\n(1.01, 1.26)\n
\n0.038\n
 Local universities
1.02
(0.94, 1.10)
0.659
 Private universities
1.07
(0.95, 1.22)
0.266
 Vocational universities
1.04
(0.96, 1.14)
0.331
Overweight and obesity were defined by Working Group for Obesity in China for Chinese adults (overweight: Body mass index: 24.0–27.9 kg/m2; obesity: Body mass index: ≥28 kg/m2); Non-overweight = 0, overweight or obesity = 1. Linear/logistic mixed-effects model was used to analyze the associations between perceived academic stress and weight status. a: The mixed-effects model adjusted gender, grade, major, college type, father and mother education level, household income, and academic attainment. b: The mixed-effects model adjusted grade, major, college type, father and mother education level, household income, and academic attainment. c: The mixed-effects model adjusted gender, major, college type, father and mother education level, household income, and academic attainment. d: The mixed-effects model adjusted gender, major, grade, father and mother education level, household income, and academic attainment. Numbers in bold indicate statistical significance.
ijerph-17-05559-t004_Table 4
Mixed-effects model for the associations between negative learning events and overweight and obesity among college students in China, stratified by gender, grade, and college type (n = 27,343).
All and Subgroups
Overweight and Obesity
OR
95% CI
\np\n
All a
\n1.05\n
\n(1.01, 1.09)\n
\n0.009\n
Gender b
 Male
1
-
-
 Female
1.02
(0.95, 1.09)
0.608
Grade c
 Undergraduate
\n1.05\n
\n(1.01, 1.09)\n
\n0.011\n
 Graduate
1.05
(0.94, 1.19)
0.352
College type d
 Subordinates universities
1.05
(0.96, 1.16)
0.228
 Local universities
\n1.07\n
\n(1.00, 1.14)\n
\n0.022\n
 Private universities
1.04
(0.95, 1.14)
0.411
 Vocational universities
1.05
(0.98, 1.11)
0.155
Overweight and obesity were defined by Working Group for Obesity in China for Chinese adults (overweight: Body mass index: 24.0–27.9 kg/m2; obesity: Body mass index: 28 kg/m2); Non-overweight = 0, overweight or obesity = 1. Linear/logistic mixed-effects model was used to analyze the associations between negative learning events and weight status. a: The mixed-effects model adjusted gender, grade, major, college type, father and mother education level, household income, and academic attainment. b: The mixed-effects model adjusted grade, major, college type, father and mother education level, household income, and academic attainment. c: The mixed-effects model adjusted gender, major, college type, father and mother education level, household income, and academic attainment. d: The mixed-effects model adjusted gender, major, grade, father and mother education level, household income, and academic attainment. Numbers in bold indicate statistical significance.
\n"],"string":"[\n \"\\nInt J Environ Res Public HealthInt J Environ Res Public HealthijerphInternational Journal of Environmental Research and Public Health1661-78271660-4601MDPI32752122PMC743209910.3390/ijerph17155559ijerph-17-05559ArticleHigher Academic Stress Was Associated with Increased Risk of Overweight and Obesity among College Students in ChinaChenYonghua12†LiuXi1†YanNi1JiaWanru1FanYahui1YanHong1MaLu3*https://orcid.org/0000-0001-7592-9779MaLe1*School of Public Health, Xi’an Jiaotong University Health Science Center, Xi’an 710061, China; chyh_2006@mail.xjtu.edu.cn (Y.C.); lx2142008010@stu.xjtu.edu.cn (X.L.); n19940522@stu.xjtu.edu.cn (N.Y.); jiawanru123@stu.xjtu.edu.cn (W.J.); huikai18@stu.xjtu.edu.cn (Y.F.); yanhonge@mail.xjtu.edu.cn (H.Y.)Research Centre on College Students Ideological Education and Practice, Xi’an Jiaotong University, Xi’an 710061, ChinaGlobal Health Institute, School of Public Health, Xi’an Jiaotong University Health Science Center, Xi’an 710061, ChinaCorrespondence: malu1990@mail.xjtu.edu.cn (L.M.); male@mail.xjtu.edu.cn (L.M.)
The authors contributed equally to this manuscript.
3172020820201715555910520202772020© 2020 by the authors.2020Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
This study examined associations between academic stress and overweight and obesity, and moderation effects of gender, grade, and types of college on such associations. Data on academic stress, negative learning events, weight, and height were self-reported by 27,343 college students in China in 2018. About 23% and 91% of students perceived high academic stress and suffered from at least one negative learning event during the past six months, respectively, especially for females, undergraduates, and students major in humanities and social science subject groups. Perceived academic stress was associated with increased risk of overweight and obesity among all students (OR = 1.05, 95%CI: 1.00–1.10), male (OR = 1.09, 95%CI: 1.03–1.15), undergraduate (OR = 1.06, 95%CI: 1.00–1.11), and students from subordinate universities (OR = 1.13, 95%CI: 1.01–1.26). Negative learning events were associated with increased risk of overweight and obesity among all students (OR = 1.05, 95%CI: 1.01–1.09), undergraduates (OR = 1.05, 95%CI: 1.01–1.09), and students from local universities (OR = 1.07, 95%CI: 1.00–1.14). Interventions are needed to reduce the high academic stress of college students, considering the modifying effects of gender, grade, and college type. Such interventions may further contribute to the prevention of overweight and obesity among college students.
Youth overweight and obesity has become a serious public health concern, and the prevalence has been increasing at an alarming rate especially in developing countries [1]. Among Chinese college students, 22.7% of males and 8.4% of females aged 19–22 years were overweight or obese [2]. Excess weight in college students could increase the risk of developing physical (e.g., type 2 diabetes, hypertension) [3] and psychological health problems (e.g., depression and weight stigma) [4].
High levels of psychological stress have numerous deleterious effects on academic (e.g., impeding learning abilities), physical, and mental health outcomes among university students [5,6]. Psychological stress has been found to be a risk factor of overweight and obesity through multiple interacting biological and behavioral pathways [7]. A meta-analysis of 14 longitudinal studies showed that stresses including general life stress (caregiver stress, major life events) and job strain were positively associated with risk of obesity, albeit with a modest effect size [8]. Furthermore, previous studies suggested that the associations varied by socio-demographic factors. For example, higher perceived life stress was associated with overweight or obesity only among male students [9]. Previous study has indicated that leaving home to attend a post-secondary school, itself, is an important reason for stress, and compared with graduates, the transition mainly affected the freshmen and caused the series of adverse outcomes [10]. Furthermore, for the students in different majors, especially medical, a large number of studies have shown their high level of stress and the effect on both physical health and mental health [11,12].
As one of the most common chronic stressors for college students, prior research has indicated that academic stress may impair self-control and deteriorate health behaviors such as changing the dietary pattern [13], in turn, increasing the risk of overweight and obesity. For example, a laboratory study reported that college students took many more calories, carbohydrates, and sugars when academically stressed [14]. However, no study has focused on the effects of academic stress and negative learning events on overweight and obesity among college students. More and more college students were reported to be plagued with crippling bouts of academic stress, which refers to stress that occurs in the educational field, such as the worrying of the difficult tasks and being assessed by various tests [15], including perceived academic stress and the experience of negative learning events. In China, students’ academic excellence has been a social criterion and even become the only standard for Chinese parents to judge their children [16]. Rapid social changes and the implementation of the one-child policy have contributed to elevate academic and job competition and directly increase college student’s academic pressure [17]. It was indicated that the Chinese college students were confronting the highest level of academic stress compared with their counterparts in Japan and Korea, and tended to report more school-related negative events than those in United States [18,19]. However, no study has investigated the academic stress or negative learning events in Chinese college students until now.
With a large-scale sample of Chinese college students, this study aimed to investigate the associations between academic stress (perceived academic stress and negative learning events) and overweight and obesity. Furthermore, we also examined the potential modifying effects of gender, grade, and college type on such associations. We hypothesized that higher perceived academic stress and negative learning events experiences were associated with increased overweight and obesity among college students in China. Findings would provide insights to design interventions targeting academic stress to fight the obesity epidemic among college students.
2. Materials and Methods2.1. Study Design and Participants
A total of 30,000 students participated in this cross-sectional study in 2018, of which 28,000 were undergraduates and 2000 were graduates. Stratified multilevel cluster random sampling was used to recruit participants at 30 colleges in Shan’xi Province, stratified by college types (subordinates, local, private, and vocational universities). Specifically, 800 undergraduates (200 for each grade from 1 to 4) and 200 graduates were invited in 4 subordinate and 6 local universities, respectively. In the other 6 local colleges and 4 private universities, 1000 undergraduates were recruited, where 300 students were randomly selected from grade 1 and grade 2, while 200 students were selected from grade 3 and grade 4, respectively. The number of the students in the range from grade 1 to 3 who participated in the project were 400, 300, and 300 in vocational universities. All students in the sampled grades were invited to complete the self-administered paper questionnaire in classroom settings in the absence of teachers being supported by a research assistant. It took about 20 min to complete the questionnaire. Trained and experienced research assistants briefed the participants about the study and questions in the questionnaire. Socio-demographic information, perceived academic stress, negative learning events, height, and body weight were used for data analyses. A sample of 27,343 participants (43.5% were boys) with complete key variables data were included in the final data analyses (missing rate = 8.86%).
Consent was sought from the school principals before the survey. It was announced that return of the completed questionnaire implied informed consent by students. No incentive was provided to the participants. The study was approved by the ethical committee of Xi’an Jiao Tong University, bearing registration number 2017-788 on 11 November 2017.
2.2. Variables and Measurements2.2.1. Outcomes
Overweight and obesity: BMI was calculated based on self-reported body weight (kg) divided by self-reported height (m2) squared. Overweight and obesity were defined based on the standard recommended by the Working Group for Obesity in China for Chinese adults (Normal weight: 18.5 kg/m2 < BMI ≤ 23.9 kg/m2, overweight: 24.0 kg/m2 ≤ BMI ≤ 27.9 kg/m2, Obese: BMI ≥ 28.0 kg/m2) [20].
2.2.2. Exposure Variables
The exposure variables were perceived academic stress and negative learning events. Perceived academic stress was measured by one self-reported item “How much academic stress do you feel in the study?” using a 5-point Likert scale where 1 = “No”, 2 = “relatively low”, 3 = “average/general”, 4 = “relatively high” and 5 = “extremely heavy”, with a higher score indicating more perceived academic stress. The response categories were categorized into three categories in the descriptive analyses: low stress including “no = 1” or “relatively light = 2”, medium stress including “average/general = 3”, and high stress including “relatively high = 4” and “extremely heavy = 5”.
Negative learning events were measured by asking the participants whether the four negative learning events happened in the past six months (Response: yes or no). The four negative learning events included: “Failure in the exam or unsatisfactory performance”, “Heavy study load”, “family financial difficulties”, and “Pressure for further study”. Students who answered that these events happened in the past 6 months were asked to rate the degree to which these events affected them using a 5-point Likert scale from 1 = “never” to 5 = “extremely heavy”. The average scores of four events were calculated to indicate the negative impacts of the negative learning events, with higher score indicating more negative impacts.
2.2.3. Covariates
Covariates included gender; major (Science and technology, Medical science, Agronomy, Humanities and social science, Economics and management, and Sports and art); college type (Subordinate universities, Local universities, Private universities, and Vocational universities); grades (undergraduate and graduate); academic attainment (below average, average, and above average); and the characteristics of their parents: paternal and maternal education level (≤junior middle school, senior middle school/vocational schools, and ≥college), and household income (<40,000/year, 50,000–100,000/year, and >100,000/year).
2.3. Statistical Analysis
Descriptive statistics were calculated. A Chi-square test (for categorical variables) and t-tests (for continuous variables) were conducted to test for the differences of perceived academic stress and the impacts of negative learning events across gender, grade, major, and college types.
Linear/logistic mixed-effects models were used to examine the associations of academic stress and negative learning events with overweight and obesity after adjusting for covariates using enter procedure. Effect sizes were presented as a beta coefficient with a standard error or odds ratio and a 95% confidence interval (95% CI). Stratified analyses were conducted by gender, grade, and college types to examine the potential modifying effects. Mixed-effects modeling was used to account for clustering effects. Stata 14 (StataCorp, College Station, TX, USA) was used in data analysis Statistical significance was set at p < 0.05.
3. Results3.1. Characteristics of Participants
The basic characteristics of the 27,343 included college students are shown in Table 1 (n = 11,888 boys (43.4%) and n = 15,295 girls). About 13% of them were overweight or obese. Boys were more likely to be overweight and obese than girls (boys vs. girls: 20.8% vs. 7.3%). Most of them (93.4%) were undergraduate students. Most of the students majored in science and technology, and boys were more likely to study science and technology, while girls were more likely to study humanities and social science. About 90% of the students reported their academic attainment as average or above average. The majority of students’ fathers (83.6%) and mothers (86.5%) only attained junior middle school or lower education, and had a household income <40,000 per year (80.8%).
3.2. Characteristics of Perceived Academic Stress and Negative Learning Events among College Students
The prevalence of perceived high academic stress was 22.9%. Girls (23.3%) or students who study humanities and social science (26.5%) were more likely to report high perceived academic stress compared with boys (22.5%) and other major students (proportions ranged from 20.2% to 23.6%), respectively (Table 2).
Regarding experiencing negative learning events, about 91% of the college students experienced negative learning events in the past 6 months. Females (91.9%), undergraduate students (91.1%), and students studying humanities and social science (92.1%) were more likely to experience negative learning events compared with males (89.4%), graduate students (87.8%), and other major students (proportions ranged from 89.1% to 92.0%), respectively (p < 0.001) (Table 2).
The negative impacts of negative learning events on girls (mean = 8.66), undergraduate students (mean = 8.25), medical science students (mean = 9.22), and vocational university students (mean = 9.11) were significantly (p < 0.001) higher than that of males (mean = 8.27), graduate students (mean = 7.05), other major students (mean score ranged from 8.10 to 8.55), and students of other college types (mean score ranged from 7.93 to 8.48), respectively (Table 2).
3.3. Associations between Perceived Academic Stress and Overweight and Obesity among College Students, Stratified by Gender, Grade and College Types
As shown in Table 3, perceived academic stress was significantly associated with increased risk for overweight or obesity for all students (OR = 1.05, 95%CI: 1.00, 1.39), males (OR = 1.09, 95%CI: 1.03, 1.15), undergraduate students (OR = 1.06, 95%CI: 1.00, 1.11), and students from Subordinate universities (OR = 1.13, 95%CI: 1.01, 1.26) after adjusting for covariates. However, these associations were not significant for females, graduates, and students from other types of college.
3.4. Associations between Negative Learning Events and Overweight and Obesity among College Students, Stratified by Gender, Grade, and College Type
As shown in Table 4, all students who reported a higher negative learning events score were more likely to be overweight or obese (OR = 1.05, 95% CI: 1.01, 1.09). No significant gender difference was found. The undergraduate students but not graduate students who reported higher negative learning events score were more likely to be overweight or obese (OR = 1.05, 95% CI: 1.01, 1.09). Moreover, the association between negative learning events and overweight or obesity was also significant among students in local universities (OR = 1.07, 95% CI: 1.00, 1.14).
4. Discussion
This large-scale study found that both academic stress and negative learning events were positively associated with overweight and obesity among Chinese college students, although the association was modest. Additionally, the study indicated that the association is, to some extent, influenced by some demographic factors: only male students, undergraduates, and students in subordinate universities with higher perceived life stress were more likely to be overweight or obese. The effects of negative learning events on overweight and obesity were only found in undergraduates and students in local universities.
As expected, most of the Chinese college students perceived academic stress, experienced negative learning events, and were impacted by them, which may lead to a series of negative consequences [21]. Consistent with the findings of previous studies [22], this study indicated that female students reported higher levels of academic stress compared to male students. Females may be more sensitive to the stressors; when being in a stressful situation or suffering negative events they may be more easily affected by the problems and more willing to report their emotions [23]. Results of the major differences replicated the previous study which showed that students of medical majors were more academically stressed than students from other majors. Medical students are under much more pressure due to extensive curricula, frequent exams, and strain from working with patients [24]. The study also indicates that undergraduates were more easily likely to encounter negative learning events than graduate students. Graduates may master more professional and time management skills and social support than undergraduates which may help them deal with the negative events and be influenced less.
Psychological stress has been found to be one of the determinants of weight gain through multiple pathways [25]. Biological responses to stress include the impairment of gastrointestinal function that slow gastric emptying and promote energy storage, HPA axis dysregulation, and secretion changes of many biochemical substances that are relevant to weight (e.g., cortisol, leptin, ghrelin and neuropeptide Y) [7]. Moreover, stress can affect behavior such as an increased food assumption and sedentary lifestyle. A study conducted in seven cities of China reported that college students prefer to choose ready-to-eat food and energy-dense foods and increase their food consumption when perceived as having high stress which in turn promotes positive-energy imbalance and increases the risk of obesity [21].
Epidemiological evidence that linked stress as one of the determinants to the development and maintenance of overweight and obesity was accumulating. A cohort study of Hispanic-Latino adults showed that chronic stress is positively related to obesity, with the obese individuals reporting over three chronic stressors [26]. Using data from the Jackson Heart Study, Samson Y et al. showed that African-American adults who perceived higher stress levels were more likely to be obese [27]. The epidemiological data of children and adolescents has also showed an association of higher psychological stress with higher odds of obesity [28]. This study differs in that we focus on the specific academic stressor conducted in Chinese college students, and across these young adults, perceived stress may result in diverse changes in both physiology and behavior compared to children or older adults. In previous studies, multiple stressors often coexist and have greater influence on individuals than one stressor which may explain why the effect of the academic stress on obesity is smaller than prior similar studies [8]. Different measurements of stress and adiposity across the studies may also contribute to the discrepancy. The current study also suggested that a higher incidence and impact of negative learning events may contribute to the likelihood of being overweight and obese. It is possible that people may perceive higher stress after negative events and trigger a stronger stress response which may explain the association [29]. Indeed, negative learning events such as exam failure and financial hardship were often dealt with by avoidance through unhealthy behaviors including overeating, drinking, and smoking more [30]. Although no known researchers have specifically paid attention to negative learning events, the finding concurred with the Dutch study that showed recent stressful life events were positively related to BMI among young adults [31].
Gender-stratified analyses showed that the negative impacts of academic stress on overweight and obesity were more remarkable in male students. The finding is partly consistent with the study in 50 Chinese universities which showed a positive association between perceived life stress and overweight and obesity [9]. Males were found to be more reactive to the stress exposure such as a greater neuroendocrine activation and a high release of cortisol [32]. Moreover, male students are more likely to lose behavior control, eating more and exercising less, while female students seem better able to maintain a healthy lifestyle and a healthy weight when in a stressful situation [33].
Perceived academic stress and negative learning events only affected the risk of obesity for undergraduates, not graduates. The different coping strategies response to stressful events of graduates and undergraduates may explain the difference. It was indicated that undergraduates chose to escape from the plight by internet addiction and overeating, which may lead to overweight or obesity, while graduates master more living skills and prefer to solve the problem in a planned way when being in stressful academic situations [34].
Academic life may account for a significant part of college life for students from subordinate universities and local universities. They may have more excellent academic performance which, in turn, leads to higher expectations, and the failure of exams or worry about further study may have greater influence on them compared to students from other colleges. Hence, the association may be more remarkable than others.
This study has several important strengths. First, the large sample size, about 30,000 college students from different majors, grades, and types of college were recruited in western China which enable us to comprehensively explore the characteristics of academic stress and negative learning events in college students. Another strength was that we analyzed the effects in detail of academic stress and negative learning events on overweight and obesity among college students in different genders, grades, and college types.
The study also has some limitations. First, the cross-sectional design could not address the causal effects of academic stress variables on overweight and obesity among college students. Other factors associated with academic stress (e.g., the intellectual ability and head circumference) [35] and the nutritional status (e.g., dietary intake, physical activity) [36] of college students need to be investigated and considered in future work. It is important to continually monitor changes in academic stress over time alongside changes in nutritional status to observe possible effects of each other in cross-lagged analyses. Second, body weight and height were self-reported, which might have weakened the observed associations. Self-reported height and weight are widely used in population-based studies and are closely correlated to those measured in most cases [37]. This is especially thought to be the case among college students, who pay much attention to their weight status and may measure their weight frequently, and thus may be more likely to report accurately their weight and height. Third, while we observed the association between academic stress and obesity, we did not further explore the mediators of such associations (e.g., food choice, food intake, and physical activities). Future studies may explore the mediators on the associations between academic stress and obesity. Studies that include participants with different educational stages (e.g., primary and secondary school students) are needed to obtain a better understanding of the associations between academic stress and obesity. Longitudinal studies are also warranted to examine the causal relationship of academic stress and negative learning events with overweight and obesity in college students.
5. Conclusions
In conclusion, we found that a majority of college students were exposed to high academic stress and negative learning events. Perceived academic stress and negative learning events could increase the risk of being overweight and obese. Interventions are needed to reduce academic stress among college students and may also reduce the prevalence of overweight and obesity.
Author Contributions
The authors’ responsibilities were as follows: L.M. (Lu Ma) and L.M. (Le Ma) designed the research; Y.C. provided essential materials; X.L., N.Y., W.J., H.Y. performed statistical analyses; Y.F., X.L. drafted the manuscript; X.L. and L.M. (Lu Ma) had primary responsibility for the final content and are the guarantors; all authors critically helped in the interpretation of results, revised the manuscript, and provided relevant intellectual input; and all authors have read and agreed to the published version of the manuscript.
Funding
This research was partly funded by grants from the National Natural Science Foundation of China (NSFC-81473059), the Natural Science Foundation of Shaanxi Province of China (2017JM8041), New-star Plan of Science and Technology of Shaanxi Province (2015LJXX-07), the China Postdoctoral Science Special Foundation (2015T81036), the Fundamental Research Funds for the Central Universities (qngz2016004), and the Nutrition Research Foundation Fund of the Chinese Nutrition Society–DSM Special Research Foundation (CNSDSM 2016–041).
Conflicts of Interest
The authors declare no conflict of interest.
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Demographic characteristics of the Chinese college students (Mean (SD)/%) in 2018.
Characteristics
Overall(n = 27,343)
Boys(n = 11,888)
Girls(n = 15,295)
p-Valueacross Gender a
Grade
0.693
 Undergraduate
93.4
93.5
93.4
 Graduate
6.6
6.5
6.6
Major
<0.001
 Science and technology
37.1
57.3
21.8
 Medical science
13.8
8.0
18.1
 Agronomy
3.2
3.7
2.8
 Humanities and social science
19.8
10.7
28.4
 Economics and management
18.4
14.8
22.6
 Sports and art
5.7
5.5
6.3
BMI (kg/m2)
20.9 ± 3.9
21.8 ± 4.3
20.1 ± 3.4
<0.001
Overweight and obesity c
13.2
20.8
7.3
<0.001
Academic attainment
<0.001
 Below average
28.0
3180 (27.0%)
4348 (28.7%)
 Average
58.9
6529 (55.4%)
9318 (61.6%)
 Above average
13.1
2081 (17.7%)
1464 (9.7%)
Paternal education level
<0.001
 ≤Junior middle school
83.6
82.0
84.9
 Senior middle school/vocational schools
14.6
15.9
13.6
 ≥College
1.7
2.0
1.5
Maternal education level
<0.001
 ≤Junior middle school
86.5
85.3
87.5
 Senior middle school/vocational schools
12.1
13.0
11.4
 ≥College
1.4
1.7
1.1
Household income
 <40,000/year
80.8
78.7
82.5
<0.001
 50,000–100,000/year
13.9
14.7
13.3
 >100,000/year
5.3
6.6
4.3
Abbreviation: BMI: body mass index; Undergraduate include undergraduate students in Grade 1, 2, 3, 4, and 5; Graduate include graduate students in Grade 1, 2, and 3. a: p-value was based on Chi-square test for categorical variables and t-tests for continuous variables across genders; c: Overweight and obesity were defined by Working Group for Obesity in China for Chinese adults (overweight: Body mass index: 24.0–27.9 kg/m2; obesity: Body mass index: ≥28 kg/m2).
ijerph-17-05559-t002_Table 2
Characteristics of academic stress and negative learning events among college students in China stratified by gender, grade, major, and college type.
All and Subgroups
Perceived Academic Stress
\\np\\na\\n
Negative Learning Events (Yes/%)
\\np\\na\\n
Impact of Negative Learning Events c
\\np\\na\\n
Low
Medium
High
All
18.9%
58.2%
22.9%
90.7
8.48 ± 4.39
Gender
<0.001
<0.001
 Male
21.5%
56.1%
22.5%
89.4
<0.001
8.27 ± 4.55
 Female
16.9%
59.8%
23.3%
91.9
8.66 ± 4.24
Grade b
0.074
<0.001
<0.001
 Undergraduate
20.3%
55.9%
23.7%
91.1
8.25 ± 4.28
 Graduate
18.5%
56.3%
25.0%
87.8
7.05 ± 4.36
Major
<0.001
<0.001
<0.001
 Science and technology
19.5%
57.5%
23.0%
90.7
8.35 ± 4.40
 Medical science
14.5%
61.9%
23.6%
92.0
9.22 ± 4.27
 Agronomy
21.6%
56.4%
22.0%
89.8
8.10 ± 4.32
 Humanities and social science
16.4%
57.0%
26.5%
92.1
8.51 ± 4.30
 Economics and management
21.2%
58.5%
20.3%
90.1
8.55 ± 4.40
 Sports and art
22.0%
57.9%
20.2%
89.1
7.88 ± 4.37
College type
0.308
0.199
<0.001
 Subordinates universities
22.4%
55.9%
24.7%
91.5
7.93 ± 4.15
 Local universities
19.5%
57.0%
23.4%
91.0
8.36 ± 4.31
 Private universities
18,4%
60.3%
21.3%
90.3
8.48 ± 4.39
 Vocational universities
17.1%
61.1%
21.8%
91.4
9.11 ± 4.39
Variable definition: Score of “impact of negative learning events” was calculated by summing the scores of the four items in the scale, and higher score more negative impact of negative learning events; a: p-value was based on Chi-square test for perceived academic stress and negative learning events across genders, grade, major, and college type. b: The difference of academic stress between graduate and undergraduate was only compared among students from Subordinate and local universities (sample size: 14,494), as only these universities had graduate students. c: The participants were asked to rate the degree to which the four negative learning events affected them using a 5-point Likert scale where “never = 1” to “extremely heavy = 5”. The average scores of four events were calculated to indicate the negative impacts of the negative learning events, with higher score indicating more negative events.
ijerph-17-05559-t003_Table 3
Mixed-effects model for the associations between perceived academic stress and overweight and obesity among college students in China, stratified by gender, grade, and college type (n = 27,343).
All and Subgroups
Overweight and Obesity
OR
95%CI
\\np\\n
All a
\\n1.05\\n
\\n(1.00, 1.10)\\n
\\n0.039\\n
Gender b
 Male
\\n1.09\\n
\\n(1.03, 1.15)\\n
\\n0.002\\n
 Female
0.949
(0.87, 1.03)
0.235
Grade c
 Undergraduate
\\n1.06\\n
\\n(1.00, 1.11)\\n
\\n0.032\\n
 Graduate
0.98
(0.84, 1.14)
0.753
College type d
 Subordinates universities
\\n1.13\\n
\\n(1.01, 1.26)\\n
\\n0.038\\n
 Local universities
1.02
(0.94, 1.10)
0.659
 Private universities
1.07
(0.95, 1.22)
0.266
 Vocational universities
1.04
(0.96, 1.14)
0.331
Overweight and obesity were defined by Working Group for Obesity in China for Chinese adults (overweight: Body mass index: 24.0–27.9 kg/m2; obesity: Body mass index: ≥28 kg/m2); Non-overweight = 0, overweight or obesity = 1. Linear/logistic mixed-effects model was used to analyze the associations between perceived academic stress and weight status. a: The mixed-effects model adjusted gender, grade, major, college type, father and mother education level, household income, and academic attainment. b: The mixed-effects model adjusted grade, major, college type, father and mother education level, household income, and academic attainment. c: The mixed-effects model adjusted gender, major, college type, father and mother education level, household income, and academic attainment. d: The mixed-effects model adjusted gender, major, grade, father and mother education level, household income, and academic attainment. Numbers in bold indicate statistical significance.
ijerph-17-05559-t004_Table 4
Mixed-effects model for the associations between negative learning events and overweight and obesity among college students in China, stratified by gender, grade, and college type (n = 27,343).
All and Subgroups
Overweight and Obesity
OR
95% CI
\\np\\n
All a
\\n1.05\\n
\\n(1.01, 1.09)\\n
\\n0.009\\n
Gender b
 Male
1
-
-
 Female
1.02
(0.95, 1.09)
0.608
Grade c
 Undergraduate
\\n1.05\\n
\\n(1.01, 1.09)\\n
\\n0.011\\n
 Graduate
1.05
(0.94, 1.19)
0.352
College type d
 Subordinates universities
1.05
(0.96, 1.16)
0.228
 Local universities
\\n1.07\\n
\\n(1.00, 1.14)\\n
\\n0.022\\n
 Private universities
1.04
(0.95, 1.14)
0.411
 Vocational universities
1.05
(0.98, 1.11)
0.155
Overweight and obesity were defined by Working Group for Obesity in China for Chinese adults (overweight: Body mass index: 24.0–27.9 kg/m2; obesity: Body mass index: 28 kg/m2); Non-overweight = 0, overweight or obesity = 1. Linear/logistic mixed-effects model was used to analyze the associations between negative learning events and weight status. a: The mixed-effects model adjusted gender, grade, major, college type, father and mother education level, household income, and academic attainment. b: The mixed-effects model adjusted grade, major, college type, father and mother education level, household income, and academic attainment. c: The mixed-effects model adjusted gender, major, college type, father and mother education level, household income, and academic attainment. d: The mixed-effects model adjusted gender, major, grade, father and mother education level, household income, and academic attainment. Numbers in bold indicate statistical significance.
\\n\"\n]"}}},{"rowIdx":443,"cells":{"data":{"kind":"list like","value":["\nInt J Environ Res Public HealthInt J Environ Res Public HealthijerphInternational Journal of Environmental Research and Public Health1661-78271660-4601MDPI32752119PMC743210010.3390/ijerph17155558ijerph-17-05558ProtocolHealth Promoting School Interventions in Latin America: A Systematic Review Protocol on the Dimensions of the RE-AIM FrameworkBastosPatrícia de Oliveira1CavalcanteAna Suelen Pedroza1PereiraWallingson Michael Gonçalves1de CastroVictor Hugo Santos1https://orcid.org/0000-0002-9483-8060Ferreira JúniorAntonio Rodrigues1https://orcid.org/0000-0003-4239-0716GuerraPaulo Henrique2https://orcid.org/0000-0002-7356-1680da SilvaKelly Samara3da SilvaMaria Rocineide Ferreira1https://orcid.org/0000-0002-4769-4068Barbosa FilhoValter Cordeiro14*Post-graduate Program in Collective Health, Ceara State University, Fortaleza 60714-903, Brazil; paty.bastos@aluno.uece.br (P.d.O.B.); anasuelen15@hotmail.com (A.S.P.C.); wallingsonmichaelgp@hotmail.com (W.M.G.P.); vsantosdecastro@gmail.com (V.H.S.d.C.); arodrigues.junior@uece.br (A.R.F.J.); rocineide.ferreira@uece.br (M.R.F.d.S.)Federal University of Fronteira Sul, Chapeco 89815-899, Brazil; paulo.guerra@uffs.edu.brResearch Center for Physical Activity and Health, Federal University of Santa Catarina, Florianopolis 88040-900, Brazil; kelly.samara@ufsc.brFederal Institute of Education, Science and Technology of Ceara, Aracati 62800-000, BrazilCorrespondence: valtercbf@gmail.com3172020820201715555812620202972020© 2020 by the authors.2020Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Understanding the dimensions of internal and external validities (e.g., using the RE-AIM model: Reach, Effectiveness/Efficacy, Adoption, Implementation, and Maintenance) of school interventions is important to guide research and practice in this context. The aim of this systematic review protocol is to synthesize evidence on the RE-AIM dimensions in interventions based on the Health Promoting School (HPS) approach from the World Health Organization (WHO) in Latin America. Studies of interventions based on HPS-WHO that were carried out in Latin America involving the population of 5 to 18-year-olds will be eligible. Searches in nine electronic databases, a study repository, the gray literature, and the retrieved articles’ reference lists will be performed, without year or publication language limits. Study selection and data extraction will be conducted by independent researchers. Data on intervention implementation will be summarized in categories of HPS-WHO actions: (1) school curriculum, (2) changes in the social and/or physical environment of schools, and (3) actions with families and the community. A previously validated tool will be used to summarize the information on the dimensions of the RE-AIM model. The strengths and limitations of the included studies will be evaluated using the Critical Appraisal Skills Program (CASP) tool, and the confidence level of evidence will be assessed according to the GRADE CERQual tool.
health promotion at schoolschool healthsystematic reviewLatin Americapolicy makingimplementation scienceschool age populationsprogram evaluation1. Introduction
Health promotion in childhood and adolescence has a significant impact on health, society, and the economy [1,2]. Globally, more than 1.1 million deaths due to preventable or treatable causes [3] are estimated among adolescents aged between 10 and 19 years old (more than 3000 per day). The countries of Latin America and the Caribbean are characterized by being the most unequal region in the world in terms of income distribution [4]. This matrix of social inequality results in health inequities, as it directly influences the choice of quality food, housing conditions, risk behaviors and access to health services [5]. Latin American and Caribbean countries stand out on the global scenario with the highest child homicide rates and on worrying estimates of risk factors for the development of chronic non-communicable diseases [2,5,6]. Therefore, confrontation of health harms and health promotion for the young population of this region is imperative in order to decrease these health inequities.
The school has been recognized as an appropriate context for consolidating actions in health promotion and confronting social vulnerabilities suffered in childhood and adolescence [7,8,9]. In 1997, the World Health Organization (WHO) issued the technical report “Promoting Health Through Schools”, which recommends measures and political actions that allow the school to use its full potential to improve the health of children, adolescents, their families, school staff, and the community [10].
Thus, the WHO’s Health Promoting Schools (HPS-WHO) proposal includes a wide school health care model that goes beyond the biomedical perspectives and traditional education [10]. In this perspective, school health intervention strategies should seek planning with a view to education and to health, promoting flexibility in curricula in a participative manner that focuses on meeting students’ well-being and health needs in their totality [7,11,12].
Dimensions related to internal validity (which refers to how true the results of the study are for the studied population) and external validity (aimed at extending the results to other representatives of the population of interest) are important to elucidate the potential impact on public health of populational-scale intervention [13,14]. In this regard, the RE-AIM model (Reach, Effectiveness/Efficacy, Adoption, Implementation, and Maintenance) aims to determine if the dimensions can assist in the evaluation of the potential impact on public health interventions and in the creation and implementation of policies and programs on a large-scale [15,16,17]. One of the main dimensions of the RE-AIM model for stakeholders, police-makers, and practitioners is the implementation [18,19]. Elements such as acceptability, appropriateness, feasibility, fidelity, cost, and penetration are necessary for decision-making on whether or not to carry out new treatments, practices, and health services [20,21]. Knowing these elements can assist administrators in carrying out these actions on a large scale and in analyzing whether these elements have increased the chances of success in the school population’s health [21]. Thus, the RE-AIM model is favorable because it allows a balance between the elements of internal and external validity of interventions at both the organizational and the individual levels [17,22,23], thus its widespread use worldwide [17].
Previous reports have summarized the relevant information about interventions based on HPS-WHO [7,8,11,24,25]. Two studies have synthesized data on the HPS-WHO and showed that interventions promote positive changes in different scholars’ health indicators, such as nutritional status, physical fitness, physical activity, fruit and vegetable intake, tobacco use, and bullying [7,11]. However, only two of the 67 interventions analyzed were carried out in a Latin America context, and these were conducted in Mexico. Another systematic review summarized data of studies focused on the dimensions that may facilitate or hinder the process of implementing interventions based on HPS-WHO worldwide; however, none have been conducted in the region in question [11]. Finally, a systematic review that synthesized studies on HPS-WHO in Latin America from 1996 to 2009 identified only eight studies, but only two of them had experimental designs [9,25].
Considering these gaps, studies in countries and contexts of social, economic, and political vulnerabilities that impact young population’s health need to be encouraged and summarized [1,2,5,6,10,25,26]. In particular, Latin American countries have peculiar political, economic, and social contexts that are different from countries in other regions in which interventions based on HPS-WHO are carried out (mainly North America and Europe), which fact may influence both children and adolescents’ health and how policies and interventions regarding school health promotion are implemented. A synthesis of evidence with methodological rigor and specific data from these countries may guide the different actors involved in their schools (politicians, managers, teachers, family members, and scholars) in the development and implementation of viable strategies for health promotion in Latin American schools.
Therefore, this is a systematic review protocol that will evaluate whether the dimensions of internal and external validities (based on the RE-AIM model) are addressed in interventions based on the HPS-WHO in Latin America. A secondary aim of this protocol is to synthesize information (qualitative and quantitative) on the implementation process of the selected interventions.
2. Materials and Methods2.1. Protocol and Registration
This systematic review was based on the methodological guidelines for evaluation of implementation studies proposed by Cochrane [27,28]. This protocol was registered on the PROSPERO platform (International Prospective Register of Systematic Reviews), under registration number CRD42020168069, with its writing following the guidelines of the Systematic Review and Meta-analysis Protocols (PRISMA-P) (Table S1) [29].
2.2. Elegibility Criteria
Eligibility criteria were organized based on the items suggested by the SPIDER strategy (a version of the PICO strategy adapted for qualitative/mixed-method studies, based on Sample, Phenomenon of Interest, Design, Evaluation, and Research type) [30].
Sample: School-aged children and adolescents (aged 5 to 18 years old or the mean age in this age group) who are students of schools located in Latin American countries. Studies regarding exclusively school staff (for example, teachers) and other members of the school community will not be included.
Phenomenon of Interest: Potential studies must have conducted one or more actions in each of the three dimensions of the HPS-WHO model: (1) school curriculum, (2) changes in the social and/or physical environment of schools, and (3) actions with families and the community [11]. Due to the plurality of possible results, the presence of the term or direct reference to the WHO report will not be required [7]. In addition, strategies should focus on a component of health or well-being (for example, mental health, healthy lifestyle, sexual health, oral health, hygiene, vaccination, substance use, and multi-component interventions) [7].
Evaluation: Studies will be included if they present information on one of the aims of this protocol: (1) internal and external validity (RE-AIM model) and (2) evaluation of the implementation process:
Studies will be considered whether they reported information will include at least one of the five dimensions that represent the RE-AIM model [23,31]. The reach (R) will represent the absolute number, proportion, and representativeness of individuals who are willing to participate in a given intervention or initiative compared to those who have given up or who have not joined. The effectiveness/efficacy (E) will represent the impact or repercussion of an intervention on important outcomes, whether negative, positive, or financial. Adoption (A) will represent the absolute number, proportion, and representativeness of the agents or organizations that are willing to join the program. Implementation (I) will be at the individual level when the agents use the intervention strategies themselves. At the organizational level, it will refer to the loyalty of the action agents to the various stages of an intervention protocol. Finally, maintenance (M) will represent the long-term beneficial effects at the individual level. At the organizational level, it will represent these effects as a program becomes institutionalized over time, thus being part of the routine, practice, or local policy [15,23,31].
The implementation process will consider the taxonomy of Proctor et al. [21]: acceptability, adoption, appropriateness, feasibility, fidelity, implementation cost, penetration, and sustainability. These elements can be measured by evaluating subjects in the intervention plan (children, teachers, school staff, parents, and others), through qualitative, mixed or quantitative methods to measure the results related to the process (if planning or training was carried out, what resources were used, amount, and reach), people/agents (if there was fidelity to the intervention proposal, what adaptations were necessary for each type of context) and products (if objectives were achieved) [20,21].
Research type: Finally, amidst the wide variety of study designs for evaluating the implementation of policies, programs, or practices [19], controlled intervention studies will be included, without necessarily requiring randomization for allocation into groups. They will be included regardless of whether data collection and analysis processes were based on quantitative, qualitative, or mixed methods.
2.3. Information Sources
No limits regarding year, language, or publication status will be applied, provided that they will attend the eligibility criteria.
Searches for potential articles started in May 2020 and will be updated if more than 12 months pass before the publication. The following platforms will be searched:
Electronic databases (n = 9): Medline, PubMed, LILACS, Web of Science, Scopus, PsycINFO, Eric, SciELO, and Cochrane Library;
Gray literature (n = 5): Brazilian Digital Library of Theses and Dissertations (BTDT) (http://bdtd.ibict.br/vufind/), Networked Digital Library of Theses and Dissertations (NDLTD) (http://www.ndltd.org/), International Clinical Trials Registry Platform (ICTRP) (http://apps.who.int/trialsearch/), Brazilian Clinical Trials Registry (REBEC) (http://www.ensaiosclinicos.gov.br/) and Google Scholar (screening of the first 200 results).
Complementary strategies: Search in specialized websites on the theme (www.iuhpe.org; www.ashaweb.org; www.who.int; www.cdc.gov; www.unesco.org; www.freshschools.org; www.hbsc.org; and www.paho.org). Complementary searches will include the screening of the titles in the references of the chosen articles and literature reviews on the theme [7,8,11,22,25,26,32].
2.4. Search Strategy
In order to produce the best results from the search strategy, the terms were tested and defined based on the DeCS (Health Sciences Descriptors) and MeSH (Medical Subject Headings) of the National Library of Medicine, which were aligned with text words from the scientific literature pertinent to the area, as explained in Tables S2 and S3 The search strategy was developed with terms related to the population of interest (children and adolescents from Latin American countries), schools, school health interventions, and experimental studies. Terms were defined in consensus meetings between researchers, including researchers with experience in searching electronic databases, and with the support of reviews on child and adolescent health [1,2], Health Promoting School [10,11,24], and school-based interventions [7]. The Boolean connectors (“OR”, “AND”, and “NOT”) were arranged according to the characteristics and search guidance of each database. Searching in all databases will be performed using terms in English, and databases originated from Brazil (LILACS, SciELO, BTDT, and REBEC) will be searched in Portuguese complementarily. Search strategies were developed acording to the recommendations of the Peer Review of Electronic Search Strategies (PRESS) Statement [33].
2.5. Study Selection
All results will be exported to EndNote Web, and one of the authors (P.B.) will remove any duplicates. Two reviewers (P.B. and A.S) will independently analyze the titles and abstracts of each of the results, which will be selected based on the inclusion criteria. Articles that do not meet these criteria will be excluded. Any discrepancies between the two reviewers regarding the included studies will be discussed; then, consensus will be reached by a third author (V.C.).
After this first search, the full texts will be read independently by the two authors (P.B. and A.S). Disagreements will again be discussed with a third author (V.C.) to reach a consensus. All studies excluded at this stage will be listed and included in the “Table of Excluded Studies”, with statements about the rationale for exclusion. For selected texts that are not available in full, that are unpublished, or that are ongoing studies, the authors will contact the authors to request any necessary information. The PRISMA flow chart will be used to report the study selection process.
2.6. Data Extraction
Data will be extracted by one reviewer and verified by the other (P.B. and A.S.) using a standardized data extraction spreadsheet. Disagreements during this process will be resolved during a consensus meeting involving a third author (V.C.). Authors of unpublished and ongoing studies will be contacted to request any necessary information. Each study included will receive an identifier consisting of the authors’ names and the primary reference year. Publications representing the same study will be combined into a single identifier. For studies with the same authors and the same years, letters in alphabetical order will be added as a third identifier.
Data related to the implementation of HPS-WHO interventions in Latin American schools will be extracted according to the form contained in Table S4. This form was produced according to guidance provided by Moore et al. [20] and Proctor et al. [21] on the evaluation of the implementation of data extraction.
Data regarding intervention studies’ internal and external validity will be extracted. Then, a checklist (Table S5) will be produced according to the RE-AIM model and adapted by Brito et al. [31] for use in systematic reviews, with data about the characterization of the studies added. In total, the checklist will consist of 21 items, which will be related to each segment of the RE-AIM model: Reach (five items), Effectiveness/efficacy (four items), Adoption (six items), Implementation (three items), and Maintenance (three items) [31].
2.7. Assessment of Risk of Bias
The Critical Assessment Skills Program (CASP) quality checklist will be used to assess the risk of bias in the included studies, considering the possible distinctions between the study designs (e.g., randomized controlled trial, cluster randomized controlled trial, and nonrandomized controlled trial) [34]. This tool has been recommended for evidence synthesis that address complex interventions and the evaluation of implementation process [28].
CASP assesses studies’ risk of bias based on 10 questions related to the following domains: objectives, methodology, research design, recruitment strategy, data collection, rigor of data analysis, reflexivity-related issues, ethical issues, discoveries, and contribution to the research. The possible answers to these ten questions are yes, no, unclear, or not applicable.
Two independent reviewers (P.B. and A.S.) will critically assess the risk of bias in studies referred to in the synthesis. A third reviewer (V.C.) will be consulted for resolution of doubts, agreement or consensus. No study will be excluded based on assessment of the risk of bias; on the contrary, the methodological rigor of each study will be considered for the confidence assessments of each finding in the review. Nevertheless, the strengths and methodological limitations of the studies will be discussed among the authors until they reach a consensus. This will be done based on each item and its impact on the main inferences from the studies and the review. In other words, the use of the total scores or the classification of the methodological quality of the included studies will not be applied [28,34].
2.8. Data Synthesis
A descriptive synthesis will summarize information about the methodological and general characteristics of the included studies (for example, year and country of implementation, population group, and intervention strategies).
For the synthesis of evidence on the dimensions of internal and external validity, the scores of the 21-item checklist for each study will be calculated, considering the general and specific score of the domain. The overall quality of the studies will also be assessed and determined based on the reports’ frequency of meeting the 21 items related to the RE-AIM framework. To that end, criteria proposed by Brito et al. [31] will be applied in order to classify the overall score as low (0 to 7 points), moderate (8 to 14 points), or high (15 or higher) quality.
The results of the implementation process will be presented according to the following dimensions: acceptability, adoption, appropriateness, feasibility, fidelity, implementation cost, penetration, and sustainability [20,21]. Quantitative information will be organized in tables and presented in a narrative synthesis (considering a preliminary heterogeneity of data). Qualitative data will be organized using the IRAMUTEQ (R INTERFACE for multidimensional analysis of texts and questionnaires) software in different stages: similarity analysis, word cloud, classic textual statistics, research on group specificities, and descending hierarchical classification. This categorization will be discussed and defined by two researchers (P.B. and A.S.) during the meetings.
Finally, the level of confidence of each finding in the qualitative summaries of the implementation process’s dimensions will be analyzed using GRADE CERQual, © Lewin et al., Oslo, Norway [35]. This tool provides a systematic and transparent framework for assessing the level of confidence in the conclusions of a review based on the following components: (1) methodological limitations, (2) coherence of the review finding, (3) adequacy of data supporting a review finding, and (4) relevance of the included studies for the review question. Four levels are used to describe the overall assessment of confidence: high, moderate, low, and very low, considering the recommendations for this tool. Two researchers will perform this process (P.B. and A.S.) in a consensus meeting to validate the coding.
Potential publication biases will be analyzed narratively. For example, the authors will assess whether information about implementation or scores in the RE-AIM dimensions will be different according to the publication status (journal articles vs. unpublished documents), period, and language of publication. This protocol is exempt from ethical approval since it will be a review of previously published articles. Ethical aspects of the included studies will be reported and discussed based on the CASP domain on ethical issues [34].
3. Final Considerations
The HPS-WHO approach has been recognized as a suitable model in interventions aimed at combining health and education in an integral way in schools [1,7,9,10,25]. Considering the potentiality and particularities of this model in Latin America, we propose this systematic review in order to synthesize the level and quality of evidence on intervention of the HPS model in these countries. By emphasizing the implementation process, we can expose which elements are essential for the assertiveness of future interventions or even political decisions about the student’s health. We expect that the clarity of the evidence on the process of implementing these interventions will provide relevant information for the formulation of interventions and public policies to be widely implemented in schools in Latin America. The main components that favor or hinder its implementation in this context will also be addressed.
Moreover, the evidence-based decision making on health policies and programs in schools in in Latin American countries may consider the results of this protocol because it will summarize dimensions of internal (e.g., whether HPS-WHO intervention may impact students’ health behaviors) and external (e.g., whether HPS-WHO are feasible and suitable for the schools from different areas) validities. Notwithstanding, knowing the quality of the study reports on the elements of internal and external validities in interventions (based on the RE-AIM approach) will indicate which elements need improvement in terms of description and scientific evaluation. This will guide future research and interventions aimed at students’ health in Latin America.
Supplementary Materials
The following are available online at https://www.mdpi.com/1660-4601/17/15/5558/s1. Table S1: Prisma-P Check List. Table S2: Example of search terms, which will be used at Medline/Pubmed. Table S3: Example of search terms, which will be used at Lilacs, Scielo, BTDT and ReBEC. Table S4: Characteristics to be extracted related to implementation. Table S5: Characteristics to be extracted related to RE-AIM.
Click here for additional data file.
Author Contributions
Conceptualization: P.d.O.B., A.S.P.C., W.M.G.P., V.H.S.d.C., and V.C.B.F.; methodology: P.d.O.B., A.S.P.C., W.M.G.P., V.H.S.d.C., and V.C.B.F.; software: P.d.O.B. and A.S.P.C.; validation: P.d.O.B., A.S.P.C., W.M.G.P., V.H.S.d.C., V.C.B.F., A.R.F.J., M.R.F.d.S., P.H.G., and K.S.d.S.; formal analysis: P.d.O.B., A.S.P.C., W.M.G.P., V.H.S.d.C., V.C.B.F., A.R.F.J., M.R.F.d.S., P.H.G., and K.S.d.S.; investigation: P.d.O.B., A.S.P.C., W.M.G.P., V.H.S.d.C., and V.C.B.F.; data curation, P.d.O.B., A.S.P.C., W.M.G.P., V.H.S.d.C., V.C.B.F., A.R.F.J., M.R.F.d.S., P.H.G., and K.S.d.S.; writing—original draft preparation: P.d.O.B., W.M.G.P., and V.C.B.F.; writing—review and editing: P.d.O.B., A.S.P.C., W.M.G.P., V.H.S.d.C., V.C.B.F., A.R.F.J., M.R.F.d.S., P.H.G., and K.S.d.S.; visualization: P.d.O.B., A.S.P.C., W.M.G.P., V.H.S.d.C., V.C.B.F., A.R.F.J., M.R.F.d.S., P.H.G., and K.S.d.S.; supervision: V.C.B.F., A.R.F.J., M.R.F.d.S., P.H.G., and K.S.d.S.; project administration: P.d.O.B., A.S.P.C., V.H.S.d.C., and V.C.B.F.; All authors have read and agreed to the published version of the manuscript.
Funding
This study has no funding for its implementation. Some authors received individual grants from the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior—Brasil Finance Coode 001 (A.S., N. 88882.447752/2019-01; W.M., N. 88882.447540/2019-01; V.H., N. 88887.473351/2020-00). The funding agencies had no participation in the interpretation, analysis, writing, and approval of this manuscript.
Conflicts of Interest
The authors declare no conflicts of interest.
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Sci.2018131310.1186/s13012-017-0689-229384081\n"],"string":"[\n \"\\nInt J Environ Res Public HealthInt J Environ Res Public HealthijerphInternational Journal of Environmental Research and Public Health1661-78271660-4601MDPI32752119PMC743210010.3390/ijerph17155558ijerph-17-05558ProtocolHealth Promoting School Interventions in Latin America: A Systematic Review Protocol on the Dimensions of the RE-AIM FrameworkBastosPatrícia de Oliveira1CavalcanteAna Suelen Pedroza1PereiraWallingson Michael Gonçalves1de CastroVictor Hugo Santos1https://orcid.org/0000-0002-9483-8060Ferreira JúniorAntonio Rodrigues1https://orcid.org/0000-0003-4239-0716GuerraPaulo Henrique2https://orcid.org/0000-0002-7356-1680da SilvaKelly Samara3da SilvaMaria Rocineide Ferreira1https://orcid.org/0000-0002-4769-4068Barbosa FilhoValter Cordeiro14*Post-graduate Program in Collective Health, Ceara State University, Fortaleza 60714-903, Brazil; paty.bastos@aluno.uece.br (P.d.O.B.); anasuelen15@hotmail.com (A.S.P.C.); wallingsonmichaelgp@hotmail.com (W.M.G.P.); vsantosdecastro@gmail.com (V.H.S.d.C.); arodrigues.junior@uece.br (A.R.F.J.); rocineide.ferreira@uece.br (M.R.F.d.S.)Federal University of Fronteira Sul, Chapeco 89815-899, Brazil; paulo.guerra@uffs.edu.brResearch Center for Physical Activity and Health, Federal University of Santa Catarina, Florianopolis 88040-900, Brazil; kelly.samara@ufsc.brFederal Institute of Education, Science and Technology of Ceara, Aracati 62800-000, BrazilCorrespondence: valtercbf@gmail.com3172020820201715555812620202972020© 2020 by the authors.2020Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Understanding the dimensions of internal and external validities (e.g., using the RE-AIM model: Reach, Effectiveness/Efficacy, Adoption, Implementation, and Maintenance) of school interventions is important to guide research and practice in this context. The aim of this systematic review protocol is to synthesize evidence on the RE-AIM dimensions in interventions based on the Health Promoting School (HPS) approach from the World Health Organization (WHO) in Latin America. Studies of interventions based on HPS-WHO that were carried out in Latin America involving the population of 5 to 18-year-olds will be eligible. Searches in nine electronic databases, a study repository, the gray literature, and the retrieved articles’ reference lists will be performed, without year or publication language limits. Study selection and data extraction will be conducted by independent researchers. Data on intervention implementation will be summarized in categories of HPS-WHO actions: (1) school curriculum, (2) changes in the social and/or physical environment of schools, and (3) actions with families and the community. A previously validated tool will be used to summarize the information on the dimensions of the RE-AIM model. The strengths and limitations of the included studies will be evaluated using the Critical Appraisal Skills Program (CASP) tool, and the confidence level of evidence will be assessed according to the GRADE CERQual tool.
health promotion at schoolschool healthsystematic reviewLatin Americapolicy makingimplementation scienceschool age populationsprogram evaluation1. Introduction
Health promotion in childhood and adolescence has a significant impact on health, society, and the economy [1,2]. Globally, more than 1.1 million deaths due to preventable or treatable causes [3] are estimated among adolescents aged between 10 and 19 years old (more than 3000 per day). The countries of Latin America and the Caribbean are characterized by being the most unequal region in the world in terms of income distribution [4]. This matrix of social inequality results in health inequities, as it directly influences the choice of quality food, housing conditions, risk behaviors and access to health services [5]. Latin American and Caribbean countries stand out on the global scenario with the highest child homicide rates and on worrying estimates of risk factors for the development of chronic non-communicable diseases [2,5,6]. Therefore, confrontation of health harms and health promotion for the young population of this region is imperative in order to decrease these health inequities.
The school has been recognized as an appropriate context for consolidating actions in health promotion and confronting social vulnerabilities suffered in childhood and adolescence [7,8,9]. In 1997, the World Health Organization (WHO) issued the technical report “Promoting Health Through Schools”, which recommends measures and political actions that allow the school to use its full potential to improve the health of children, adolescents, their families, school staff, and the community [10].
Thus, the WHO’s Health Promoting Schools (HPS-WHO) proposal includes a wide school health care model that goes beyond the biomedical perspectives and traditional education [10]. In this perspective, school health intervention strategies should seek planning with a view to education and to health, promoting flexibility in curricula in a participative manner that focuses on meeting students’ well-being and health needs in their totality [7,11,12].
Dimensions related to internal validity (which refers to how true the results of the study are for the studied population) and external validity (aimed at extending the results to other representatives of the population of interest) are important to elucidate the potential impact on public health of populational-scale intervention [13,14]. In this regard, the RE-AIM model (Reach, Effectiveness/Efficacy, Adoption, Implementation, and Maintenance) aims to determine if the dimensions can assist in the evaluation of the potential impact on public health interventions and in the creation and implementation of policies and programs on a large-scale [15,16,17]. One of the main dimensions of the RE-AIM model for stakeholders, police-makers, and practitioners is the implementation [18,19]. Elements such as acceptability, appropriateness, feasibility, fidelity, cost, and penetration are necessary for decision-making on whether or not to carry out new treatments, practices, and health services [20,21]. Knowing these elements can assist administrators in carrying out these actions on a large scale and in analyzing whether these elements have increased the chances of success in the school population’s health [21]. Thus, the RE-AIM model is favorable because it allows a balance between the elements of internal and external validity of interventions at both the organizational and the individual levels [17,22,23], thus its widespread use worldwide [17].
Previous reports have summarized the relevant information about interventions based on HPS-WHO [7,8,11,24,25]. Two studies have synthesized data on the HPS-WHO and showed that interventions promote positive changes in different scholars’ health indicators, such as nutritional status, physical fitness, physical activity, fruit and vegetable intake, tobacco use, and bullying [7,11]. However, only two of the 67 interventions analyzed were carried out in a Latin America context, and these were conducted in Mexico. Another systematic review summarized data of studies focused on the dimensions that may facilitate or hinder the process of implementing interventions based on HPS-WHO worldwide; however, none have been conducted in the region in question [11]. Finally, a systematic review that synthesized studies on HPS-WHO in Latin America from 1996 to 2009 identified only eight studies, but only two of them had experimental designs [9,25].
Considering these gaps, studies in countries and contexts of social, economic, and political vulnerabilities that impact young population’s health need to be encouraged and summarized [1,2,5,6,10,25,26]. In particular, Latin American countries have peculiar political, economic, and social contexts that are different from countries in other regions in which interventions based on HPS-WHO are carried out (mainly North America and Europe), which fact may influence both children and adolescents’ health and how policies and interventions regarding school health promotion are implemented. A synthesis of evidence with methodological rigor and specific data from these countries may guide the different actors involved in their schools (politicians, managers, teachers, family members, and scholars) in the development and implementation of viable strategies for health promotion in Latin American schools.
Therefore, this is a systematic review protocol that will evaluate whether the dimensions of internal and external validities (based on the RE-AIM model) are addressed in interventions based on the HPS-WHO in Latin America. A secondary aim of this protocol is to synthesize information (qualitative and quantitative) on the implementation process of the selected interventions.
2. Materials and Methods2.1. Protocol and Registration
This systematic review was based on the methodological guidelines for evaluation of implementation studies proposed by Cochrane [27,28]. This protocol was registered on the PROSPERO platform (International Prospective Register of Systematic Reviews), under registration number CRD42020168069, with its writing following the guidelines of the Systematic Review and Meta-analysis Protocols (PRISMA-P) (Table S1) [29].
2.2. Elegibility Criteria
Eligibility criteria were organized based on the items suggested by the SPIDER strategy (a version of the PICO strategy adapted for qualitative/mixed-method studies, based on Sample, Phenomenon of Interest, Design, Evaluation, and Research type) [30].
Sample: School-aged children and adolescents (aged 5 to 18 years old or the mean age in this age group) who are students of schools located in Latin American countries. Studies regarding exclusively school staff (for example, teachers) and other members of the school community will not be included.
Phenomenon of Interest: Potential studies must have conducted one or more actions in each of the three dimensions of the HPS-WHO model: (1) school curriculum, (2) changes in the social and/or physical environment of schools, and (3) actions with families and the community [11]. Due to the plurality of possible results, the presence of the term or direct reference to the WHO report will not be required [7]. In addition, strategies should focus on a component of health or well-being (for example, mental health, healthy lifestyle, sexual health, oral health, hygiene, vaccination, substance use, and multi-component interventions) [7].
Evaluation: Studies will be included if they present information on one of the aims of this protocol: (1) internal and external validity (RE-AIM model) and (2) evaluation of the implementation process:
Studies will be considered whether they reported information will include at least one of the five dimensions that represent the RE-AIM model [23,31]. The reach (R) will represent the absolute number, proportion, and representativeness of individuals who are willing to participate in a given intervention or initiative compared to those who have given up or who have not joined. The effectiveness/efficacy (E) will represent the impact or repercussion of an intervention on important outcomes, whether negative, positive, or financial. Adoption (A) will represent the absolute number, proportion, and representativeness of the agents or organizations that are willing to join the program. Implementation (I) will be at the individual level when the agents use the intervention strategies themselves. At the organizational level, it will refer to the loyalty of the action agents to the various stages of an intervention protocol. Finally, maintenance (M) will represent the long-term beneficial effects at the individual level. At the organizational level, it will represent these effects as a program becomes institutionalized over time, thus being part of the routine, practice, or local policy [15,23,31].
The implementation process will consider the taxonomy of Proctor et al. [21]: acceptability, adoption, appropriateness, feasibility, fidelity, implementation cost, penetration, and sustainability. These elements can be measured by evaluating subjects in the intervention plan (children, teachers, school staff, parents, and others), through qualitative, mixed or quantitative methods to measure the results related to the process (if planning or training was carried out, what resources were used, amount, and reach), people/agents (if there was fidelity to the intervention proposal, what adaptations were necessary for each type of context) and products (if objectives were achieved) [20,21].
Research type: Finally, amidst the wide variety of study designs for evaluating the implementation of policies, programs, or practices [19], controlled intervention studies will be included, without necessarily requiring randomization for allocation into groups. They will be included regardless of whether data collection and analysis processes were based on quantitative, qualitative, or mixed methods.
2.3. Information Sources
No limits regarding year, language, or publication status will be applied, provided that they will attend the eligibility criteria.
Searches for potential articles started in May 2020 and will be updated if more than 12 months pass before the publication. The following platforms will be searched:
Electronic databases (n = 9): Medline, PubMed, LILACS, Web of Science, Scopus, PsycINFO, Eric, SciELO, and Cochrane Library;
Gray literature (n = 5): Brazilian Digital Library of Theses and Dissertations (BTDT) (http://bdtd.ibict.br/vufind/), Networked Digital Library of Theses and Dissertations (NDLTD) (http://www.ndltd.org/), International Clinical Trials Registry Platform (ICTRP) (http://apps.who.int/trialsearch/), Brazilian Clinical Trials Registry (REBEC) (http://www.ensaiosclinicos.gov.br/) and Google Scholar (screening of the first 200 results).
Complementary strategies: Search in specialized websites on the theme (www.iuhpe.org; www.ashaweb.org; www.who.int; www.cdc.gov; www.unesco.org; www.freshschools.org; www.hbsc.org; and www.paho.org). Complementary searches will include the screening of the titles in the references of the chosen articles and literature reviews on the theme [7,8,11,22,25,26,32].
2.4. Search Strategy
In order to produce the best results from the search strategy, the terms were tested and defined based on the DeCS (Health Sciences Descriptors) and MeSH (Medical Subject Headings) of the National Library of Medicine, which were aligned with text words from the scientific literature pertinent to the area, as explained in Tables S2 and S3 The search strategy was developed with terms related to the population of interest (children and adolescents from Latin American countries), schools, school health interventions, and experimental studies. Terms were defined in consensus meetings between researchers, including researchers with experience in searching electronic databases, and with the support of reviews on child and adolescent health [1,2], Health Promoting School [10,11,24], and school-based interventions [7]. The Boolean connectors (“OR”, “AND”, and “NOT”) were arranged according to the characteristics and search guidance of each database. Searching in all databases will be performed using terms in English, and databases originated from Brazil (LILACS, SciELO, BTDT, and REBEC) will be searched in Portuguese complementarily. Search strategies were developed acording to the recommendations of the Peer Review of Electronic Search Strategies (PRESS) Statement [33].
2.5. Study Selection
All results will be exported to EndNote Web, and one of the authors (P.B.) will remove any duplicates. Two reviewers (P.B. and A.S) will independently analyze the titles and abstracts of each of the results, which will be selected based on the inclusion criteria. Articles that do not meet these criteria will be excluded. Any discrepancies between the two reviewers regarding the included studies will be discussed; then, consensus will be reached by a third author (V.C.).
After this first search, the full texts will be read independently by the two authors (P.B. and A.S). Disagreements will again be discussed with a third author (V.C.) to reach a consensus. All studies excluded at this stage will be listed and included in the “Table of Excluded Studies”, with statements about the rationale for exclusion. For selected texts that are not available in full, that are unpublished, or that are ongoing studies, the authors will contact the authors to request any necessary information. The PRISMA flow chart will be used to report the study selection process.
2.6. Data Extraction
Data will be extracted by one reviewer and verified by the other (P.B. and A.S.) using a standardized data extraction spreadsheet. Disagreements during this process will be resolved during a consensus meeting involving a third author (V.C.). Authors of unpublished and ongoing studies will be contacted to request any necessary information. Each study included will receive an identifier consisting of the authors’ names and the primary reference year. Publications representing the same study will be combined into a single identifier. For studies with the same authors and the same years, letters in alphabetical order will be added as a third identifier.
Data related to the implementation of HPS-WHO interventions in Latin American schools will be extracted according to the form contained in Table S4. This form was produced according to guidance provided by Moore et al. [20] and Proctor et al. [21] on the evaluation of the implementation of data extraction.
Data regarding intervention studies’ internal and external validity will be extracted. Then, a checklist (Table S5) will be produced according to the RE-AIM model and adapted by Brito et al. [31] for use in systematic reviews, with data about the characterization of the studies added. In total, the checklist will consist of 21 items, which will be related to each segment of the RE-AIM model: Reach (five items), Effectiveness/efficacy (four items), Adoption (six items), Implementation (three items), and Maintenance (three items) [31].
2.7. Assessment of Risk of Bias
The Critical Assessment Skills Program (CASP) quality checklist will be used to assess the risk of bias in the included studies, considering the possible distinctions between the study designs (e.g., randomized controlled trial, cluster randomized controlled trial, and nonrandomized controlled trial) [34]. This tool has been recommended for evidence synthesis that address complex interventions and the evaluation of implementation process [28].
CASP assesses studies’ risk of bias based on 10 questions related to the following domains: objectives, methodology, research design, recruitment strategy, data collection, rigor of data analysis, reflexivity-related issues, ethical issues, discoveries, and contribution to the research. The possible answers to these ten questions are yes, no, unclear, or not applicable.
Two independent reviewers (P.B. and A.S.) will critically assess the risk of bias in studies referred to in the synthesis. A third reviewer (V.C.) will be consulted for resolution of doubts, agreement or consensus. No study will be excluded based on assessment of the risk of bias; on the contrary, the methodological rigor of each study will be considered for the confidence assessments of each finding in the review. Nevertheless, the strengths and methodological limitations of the studies will be discussed among the authors until they reach a consensus. This will be done based on each item and its impact on the main inferences from the studies and the review. In other words, the use of the total scores or the classification of the methodological quality of the included studies will not be applied [28,34].
2.8. Data Synthesis
A descriptive synthesis will summarize information about the methodological and general characteristics of the included studies (for example, year and country of implementation, population group, and intervention strategies).
For the synthesis of evidence on the dimensions of internal and external validity, the scores of the 21-item checklist for each study will be calculated, considering the general and specific score of the domain. The overall quality of the studies will also be assessed and determined based on the reports’ frequency of meeting the 21 items related to the RE-AIM framework. To that end, criteria proposed by Brito et al. [31] will be applied in order to classify the overall score as low (0 to 7 points), moderate (8 to 14 points), or high (15 or higher) quality.
The results of the implementation process will be presented according to the following dimensions: acceptability, adoption, appropriateness, feasibility, fidelity, implementation cost, penetration, and sustainability [20,21]. Quantitative information will be organized in tables and presented in a narrative synthesis (considering a preliminary heterogeneity of data). Qualitative data will be organized using the IRAMUTEQ (R INTERFACE for multidimensional analysis of texts and questionnaires) software in different stages: similarity analysis, word cloud, classic textual statistics, research on group specificities, and descending hierarchical classification. This categorization will be discussed and defined by two researchers (P.B. and A.S.) during the meetings.
Finally, the level of confidence of each finding in the qualitative summaries of the implementation process’s dimensions will be analyzed using GRADE CERQual, © Lewin et al., Oslo, Norway [35]. This tool provides a systematic and transparent framework for assessing the level of confidence in the conclusions of a review based on the following components: (1) methodological limitations, (2) coherence of the review finding, (3) adequacy of data supporting a review finding, and (4) relevance of the included studies for the review question. Four levels are used to describe the overall assessment of confidence: high, moderate, low, and very low, considering the recommendations for this tool. Two researchers will perform this process (P.B. and A.S.) in a consensus meeting to validate the coding.
Potential publication biases will be analyzed narratively. For example, the authors will assess whether information about implementation or scores in the RE-AIM dimensions will be different according to the publication status (journal articles vs. unpublished documents), period, and language of publication. This protocol is exempt from ethical approval since it will be a review of previously published articles. Ethical aspects of the included studies will be reported and discussed based on the CASP domain on ethical issues [34].
3. Final Considerations
The HPS-WHO approach has been recognized as a suitable model in interventions aimed at combining health and education in an integral way in schools [1,7,9,10,25]. Considering the potentiality and particularities of this model in Latin America, we propose this systematic review in order to synthesize the level and quality of evidence on intervention of the HPS model in these countries. By emphasizing the implementation process, we can expose which elements are essential for the assertiveness of future interventions or even political decisions about the student’s health. We expect that the clarity of the evidence on the process of implementing these interventions will provide relevant information for the formulation of interventions and public policies to be widely implemented in schools in Latin America. The main components that favor or hinder its implementation in this context will also be addressed.
Moreover, the evidence-based decision making on health policies and programs in schools in in Latin American countries may consider the results of this protocol because it will summarize dimensions of internal (e.g., whether HPS-WHO intervention may impact students’ health behaviors) and external (e.g., whether HPS-WHO are feasible and suitable for the schools from different areas) validities. Notwithstanding, knowing the quality of the study reports on the elements of internal and external validities in interventions (based on the RE-AIM approach) will indicate which elements need improvement in terms of description and scientific evaluation. This will guide future research and interventions aimed at students’ health in Latin America.
Supplementary Materials
The following are available online at https://www.mdpi.com/1660-4601/17/15/5558/s1. Table S1: Prisma-P Check List. Table S2: Example of search terms, which will be used at Medline/Pubmed. Table S3: Example of search terms, which will be used at Lilacs, Scielo, BTDT and ReBEC. Table S4: Characteristics to be extracted related to implementation. Table S5: Characteristics to be extracted related to RE-AIM.
Click here for additional data file.
Author Contributions
Conceptualization: P.d.O.B., A.S.P.C., W.M.G.P., V.H.S.d.C., and V.C.B.F.; methodology: P.d.O.B., A.S.P.C., W.M.G.P., V.H.S.d.C., and V.C.B.F.; software: P.d.O.B. and A.S.P.C.; validation: P.d.O.B., A.S.P.C., W.M.G.P., V.H.S.d.C., V.C.B.F., A.R.F.J., M.R.F.d.S., P.H.G., and K.S.d.S.; formal analysis: P.d.O.B., A.S.P.C., W.M.G.P., V.H.S.d.C., V.C.B.F., A.R.F.J., M.R.F.d.S., P.H.G., and K.S.d.S.; investigation: P.d.O.B., A.S.P.C., W.M.G.P., V.H.S.d.C., and V.C.B.F.; data curation, P.d.O.B., A.S.P.C., W.M.G.P., V.H.S.d.C., V.C.B.F., A.R.F.J., M.R.F.d.S., P.H.G., and K.S.d.S.; writing—original draft preparation: P.d.O.B., W.M.G.P., and V.C.B.F.; writing—review and editing: P.d.O.B., A.S.P.C., W.M.G.P., V.H.S.d.C., V.C.B.F., A.R.F.J., M.R.F.d.S., P.H.G., and K.S.d.S.; visualization: P.d.O.B., A.S.P.C., W.M.G.P., V.H.S.d.C., V.C.B.F., A.R.F.J., M.R.F.d.S., P.H.G., and K.S.d.S.; supervision: V.C.B.F., A.R.F.J., M.R.F.d.S., P.H.G., and K.S.d.S.; project administration: P.d.O.B., A.S.P.C., V.H.S.d.C., and V.C.B.F.; All authors have read and agreed to the published version of the manuscript.
Funding
This study has no funding for its implementation. Some authors received individual grants from the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior—Brasil Finance Coode 001 (A.S., N. 88882.447752/2019-01; W.M., N. 88882.447540/2019-01; V.H., N. 88887.473351/2020-00). The funding agencies had no participation in the interpretation, analysis, writing, and approval of this manuscript.
Conflicts of Interest
The authors declare no conflicts of interest.
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Sci.2018131310.1186/s13012-017-0689-229384081\\n\"\n]"}}},{"rowIdx":444,"cells":{"data":{"kind":"list like","value":["\nInt J Mol SciInt J Mol SciijmsInternational Journal of Molecular Sciences1422-0067MDPI32722576PMC743210110.3390/ijms21155297ijms-21-05297ReviewSubcellular Localization Relevance and Cancer-Associated Mechanisms of Diacylglycerol KinasesFazioAntonietta1†https://orcid.org/0000-0003-0403-9466Owusu ObengEric1†RuscianoIsabella1MarviMaria Vittoria1https://orcid.org/0000-0001-8838-5408ZoliMatteo23MongiorgiSara1https://orcid.org/0000-0001-9684-714XRamazzottiGiulia1FolloMatilde Yung1https://orcid.org/0000-0001-6027-3156McCubreyJames A.4CoccoLucio1ManzoliLucia1*https://orcid.org/0000-0002-6258-5345RattiStefano1Cellular Signalling Laboratory, Department of Biomedical and Neuromotor Sciences (DIBINEM), University of Bologna, Via Irnerio 48, 40126 Bologna, Italy; antonietta.fazio2@unibo.it (A.F.); eric.owusuobeng2@unibo.it (E.O.O.); isabella.rusciano3@unibo.it (I.R.); mariavittoria.marvi2@unibo.it (M.V.M.); s.mongiorgi@unibo.it (S.M.); giulia.ramazzotti@unibo.it (G.R.); matilde.follo@unibo.it (M.Y.F.); lucio.cocco@unibo.it (L.C.); stefano.ratti@unibo.it (S.R.)Center for the Diagnosis and Treatment of Hypothalamic-Pituitary Diseases-Pituitary Unit, IRCCS Istituto delle Scienze Neurologiche di Bologna (Institute of Neurological Sciences of Bologna), 40126 Bologna, Italy; matteo.zoli4@unibo.itDepartment of Biomedical and Neuromotor Sciences (DIBINEM), University of Bologna, 40126 Bologna, ItalyDepartment of Microbiology and Immunology, Brody School of Medicine at East Carolina University, Greenville, NC 27858, USA; MCCUBREYJ@ecu.eduCorrespondence: lucia.manzoli@unibo.it
These authors contributed equally to this work.
2672020820202115529724620202472020© 2020 by the authors.2020Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
An increasing number of reports suggests a significant involvement of the phosphoinositide (PI) cycle in cancer development and progression. Diacylglycerol kinases (DGKs) are very active in the PI cycle. They are a family of ten members that convert diacylglycerol (DAG) into phosphatidic acid (PA), two-second messengers with versatile cellular functions. Notably, some DGK isoforms, such as DGKα, have been reported to possess promising therapeutic potential in cancer therapy. However, further studies are needed in order to better comprehend their involvement in cancer. In this review, we highlight that DGKs are an essential component of the PI cycle that localize within several subcellular compartments, including the nucleus and plasma membrane, together with their PI substrates and that they are involved in mediating major cancer cell mechanisms such as growth and metastasis. DGKs control cancer cell survival, proliferation, and angiogenesis by regulating Akt/mTOR and MAPK/ERK pathways. In addition, some DGKs control cancer cell migration by regulating the activities of the Rho GTPases Rac1 and RhoA.
Phosphoinositides (PIs) represent a tiny component of the total phospholipid content in eukaryotic cell membranes, but they regulate numerous cellular activities such as cell adhesion [1], migration [2], apoptosis [3], vesicular trafficking [4], and post-translational modifications [5]. These processes are consistent with cancer-associated cellular mechanisms. PI metabolism is controlled by several kinases, phosphatases, and phospholipases following their stimulation by different external stimuli. An increasing number of studies report that alterations in the PI cycle, resulting from dysfunctional PI metabolic enzymes, are involved in cancer [6,7,8].
Diacylglycerol kinases (DGKs) are a family of ten PI metabolic kinases (α, β, γ, δ, ε, ζ, η, θ, ι, and κ) that participate in the PI cycle by catalyzing the phosphorylation of diacylglycerol (DAG) to generate phosphatidic acid (PA), hence, regulating these two-second messengers [9]. DAG and PA are involved in the regulation of several critical enzymes, including mammalian target of rapamycin (mTOR) [10], phosphatidylinositol-4-phosphate 5-kinase (PIP5K), the Ras GTPase-activating protein (GAP), rapidly accelerated fibrosarcoma-1 (Raf-1) kinase, protein kinases C (PKCs), mammalian uncoordinated 13 (Unc-13), Chimaerins (which activate Rac GTPase), and rat sarcoma virus guanyl nucleotide-releasing protein (RasGRP), which are involved in cell proliferation and migration [11,12]. Although DGKs can utilize different molecular DAG species independent of PI turnover pathways [13], the conversion of DAG to PA by DGKs represents one of the first steps in PI resynthesis, where phosphatidylinositol 4,5 biphosphate (PtdIns(4,5)P2) levels are replenished [14]. As such, DGK signaling is pivotal in modulating the balance between these two essential bioactive lipids, DAG, and PA.
Accumulating evidence demonstrates that DGKs, as well as phospholipases C (PLCs) and PKCs, are distributed across several subcellular compartments together with their substrates [15,16,17] and, as PLCs and PKCs, they are involved in cell regulation [18,19]. Nuclear localization allows DGKs to participate in a PI cycle which is independent of that of the plasma membrane [20,21]. Therefore, DGK activity may regulate distinct cellular functions and explain the complexities that surround DGK signaling [17]. In fact, DGKs regulate cytokine/growth factor-mediated cell proliferation and migration, cell growth, seizure activity, and insulin receptor-mediated glucose metabolism, suggesting that DGKs may also be involved in several diseases including epilepsy and diabetes [22,23].
Of particular note is the involvement of DGKs in cancer development and progression [24,25,26]. For instance, mutations in the DGKα gene can drive pancreatic cancer [24] as well as promote hepatocellular carcinoma (HCC) progression by activating the mitogen-activated protein kinase (MAPK) pathway [27]. In 3D colon and breast cancer models, DGKα was reported to promote cell survival by regulating Src [28]. For these reasons, there are several reports suggesting that DGKα may be a promising therapeutic target in cancer therapy [7,29]. Meanwhile, in colorectal cancer (CRC), DGKγ plays tumor-suppressive roles, while DGKζ activity promotes tumor progression [26,30,31]. Moreover, DGKη and DGKδ regulate cell growth and proliferation in cervical cancer cell lines [32,33], whereas epigenetic changes in DGKι are reported in glioblastoma and HCC cells [34,35]. Despite the numerous reports of the involvement of DGKs in cancer as well as their clinical potential, the comprehension of the specific cellular functions regulated by DGKs in cancer is not yet complete. This review aims to provide up-to-date knowledge of the regulatory roles played by DGKs in cancer cell survival and metastasis, while also highlighting the downstream regulation of DGKs, the role of their cellular localization and summing up current knowledge on targeting DGKs in cancer therapies.
2. Activation and Regulation of DGK Isozymes
The 10 members of the mammalian DGK family are classified into 5 different subtypes depending on their structural motifs [36]: type I (DGKs α, β, and γ), type II (DGKs δ, η, and κ), type III (DGKε), type IV (DGKs ζ, and ι) and type V (DGKθ). Moreover, some DGK isoforms undergo further alternative splicing which often changes either the distribution or activity of the enzyme [37,38]. Their differences may be attributed to their evolution, in order to regulate specific cellular processes evident in higher vertebrates [39]. DGKs are ubiquitous kinases that are mainly expressed in the brain and hemopoietic tissue [40]. They are prominently distributed across several different regions of the brain, including the cerebellum, hippocampus, and the olfactory bulb, therefore suggesting involvement of DGKs in central nervous system functions [41]. Some DGK isoforms are also expressed in the retina (ε, γ, ι) [41], in striated (δ and ζ) and cardiac muscle (β and ε) [41,42] and in the lungs (α, ε, ζ and η) [43]. So far, it has been shown that different DGK isoforms can be co-expressed in the same tissue and even in the same cell, suggesting that each subtype may carry out tissue or cell-specific functions [41].
Until now, all recognized mammalian DGKs possess two common kinase domains comprising a conserved catalytic domain, which is characterized by a highly conserved ATP binding site and an accessory domain [44]. The possession of additional distinct domains, that seems to have regulatory roles, as shown in DGK family types, contributes to isoform-specific functions and diversity among mammalian DGK isoforms [17]. For instance, type I isoforms (α, β, and γ) participate in calcium (Ca2+) signaling because of their Ca2+ binding motif, while the carboxyl terminus-based sterile alpha motif (SAM) of DGKδ, which is a type II DGK, promotes protein-protein interactions [45]. In addition, DGKδ possesses a PH domain that weakly binds to phosphatidylinositols [46]. The nuclear localization of type IV DGKs (ζ and ι) is enhanced via their nuclear localization sequence (NLS). This domain also serves as substrate for conventional PKCs and is homologous to the phosphorylation domain of the myristoylated alanine-rich kinase substrate (MARCKS) protein. DGKθ, which is the only type V DGK isoform, can be differentiated by its PH domain, three C1 domains, and a Ras-associating domain that mediates Ras signaling [47].
All DGKs possess at least two cysteine-rich regions similar to the DAG-binding C1A and C1B domains of PKC [48]. The C1 regions of DGKs allow membrane binding either through protein interactions, as demonstrated by several DGKs and β-arrestin [49] or through lipid interactions, as shown by DGKα and the lipid product of phosphoinositide 3-kinase (PI3K) [50,51]. C1 domains are recognized phorbol ester or DAG binding regions, but several studies have tried to evaluate the binding potential of the C1 domains of some DGKs to a DAG analog or phorbol ester reported that only the C1A domain of DGK β and γ displays successful binding [52]. Therefore, it is not clear whether the C1 domains of all DGKs can actually bind DAG.
The activation and regulation of DGKs is a very complex process that needs further studies to be fully comprehended [37]. Given the structural and subcellular localization differences, it may be possible that different activation mechanisms exist for each individual DGK isoform. Considering also the ability of DGKs to translocate to different cellular sites, the presence of post-translational modifications and their binding to different cofactors, such as membrane lipids and Ca2+, may produce some degree of diversity in their functions. DAG is accessed by DGKs upon their translocation to DAG-producing cellular membranes, where DGKs are proposed to be activated during agonist or kinase promoted phosphorylation or following their binding to some cofactors or to other proteins [39]. Indeed, some studies reported that the distinct activities observed in the various DGK isoforms may depend on the type of agonist and the cofactors they bind to during their activation [53]. For example, DGKα, which is one of the most studied DGK isoforms, demonstrates this complexity in T lymphocytes. Based on the type of agonist used to activate DGKα in T cells, it translocates to two different membrane compartments: stimulation with interleukin 2 (IL-2) induces the translocation of DGKα from the cytosol to the perinuclear region [54], whereas the translocation from the cytosol to the plasma membrane occurs when DGKα is stimulated by T-cell antigen receptor [55]. Several different cofactors, such as Ca2+, which binds to the EF-hand motif and membrane lipids, including phosphatidylserine (PS), sphingosine, the PI3K lipid products, PtdIns(3,4)P2, and PtdIns(3,4,5)P3 have been reported to modify DGKα activity both in vitro and in vivo [56]. As for other DGK isoforms, activation of DGKδ may be enhanced by the binding of its PH domain to phosphatidylinositols [41], DGKε is inhibited by both PtdIns(4,5)P2 and PS, while DGKζ is activated by both PtdIns(4,5)P2 and PS [57]. Moreover, the protein-protein interaction between DGKθ and RhoA is involved in the regulation of the activity of DGKθ, where its kinase activity is completely reduced by RhoA [17].
Several studies showed that the specificity of DGK activities could also be attributed to its association with or inhibition of DAG-activated proteins, such as the RasGRP proteins [58]. Generally, when DAG is abundant, RasGRPs activate either Rap or Ras, or both, and this mechanism is RasGRP-isoform specific. Consequently, the downstream effects of DGKs diverge because DGK isozymes bind to different isoforms of RasGRP [59]. Hence, the functional specificity of DGKs depends on their interactions. For example, type IV DGKs, ζ, and ι, are all structurally similar, but they induce opposing effects on Ras signaling. DGKζ attenuates Ras signaling both in vitro [58,60] and in vivo [60], whereas DGKι enhances it [59]. These opposing effects were mainly dependent on the ability of DGKs ζ and ι to bind and inhibit specific RasGRP enzymes, respectively RasGRP1 and RasGRP3 [58,59]. Since the activities of DGKs maintain a balance between DAG and PA levels, DGKs can also be associated with proteins whose activities are regulated by PA. In fact, DGKs regulate either directly or indirectly Rac1 [30], mTOR [10], PIP5K type 1α [17], and atypical PKCs [50], all regulated by PA while mediating several essential cellular effects such as cell survival, migration, and vesicle trafficking [11,12].
DGKs were initially described as modulators of the classical and novel PKC family members. However, some DGKs form complexes with certain DAG-sensitive PKC isoforms, thus being regulated by PKC-dependent phosphorylation [36,50]. Indeed, these DGKs and their respective PKC counterparts mutually regulate each other’s enzyme activities, as seen in the case of DGKζ and PKCα. At immune and nervous synapses, the activation of DGKζ and PKCα is mutually regulated by both kinases [50]. At basal conditions, DGKζ phosphorylates DAG and prevents PKCα activation [61] but upon stimulation, there is an overproduction of local DAG levels that is too abundant for DGKζ to phosphorylate, leading to the availability of excess DAG. Consequently, high levels of DAG activate PKCα, which phosphorylates DGKζ, causing their physical dissociation [61] and promoting transient or even prolonged activation of PKCα. In the context of cancer, the DGKζ-PKCα axis could be important in regulating signaling in tumor cells. For instance, DGKζ undergoes a PKCα-dependent phosphorylation to enhance its shuttle from the nucleus to the cytosol [62], a biological process which is implicated in cancer cells responding to stress conditions [63]. Moreover, DGKα is involved in inflammation in tumor cells by positively regulating tumor necrosis factor α (TNFα)-induced nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB) activation via a PKCζ-mediated Ser311 phosphorylation of the NF-κB subunit p65/RelA [64].
Overall, DGKs can be activated by several mechanisms which might be isoform-specific, and they produce different cellular outcomes based on the type of co-factors and proteins they associate with. The concept of DGK-isoform specific functions is supported by mouse knockout studies showing that targeted deletion of specific DGK isoforms leads to a distinct phenotype [59,65,66,67,68].
3. Cellular Localization and Distribution of DGKs
As previously reported, DGKs can translocate to various distinct cellular compartments depending on the type of agonist. This supports the fact that the activities of DGKs may be confined to specific DAG pools produced after receptor activation [17]. This is the case of DGKε, which only phosphorylates DAG species that possess an arachidonoyl group at the sn-2 position [50]. Moreover, recent studies have also shown the presence of an alternative DAG metabolic pathway where individual DGK isoforms phosphorylate different molecular DAG pools or species independently from PI turnover [13]. For instance, DGKδ2 interacts via its SAM domain with the ER enzyme sphingomyelin synthase related protein (SMSr) generating DAG from phosphatidylethanolamine and ceramide [69]. Through a flip-flop mechanism, DAG produced by SMSr in the ER crosses the ER membrane from the lumen region to the cytosolic region to give access to DGKδ2 [69]. A previous work by van der Bend and colleagues showed that receptor activation of DGK in cells induces a physiological DAG generation while treating cells with exogenous PLC induces a global, non-specific DAG production [17,70]. Moreover, receptor activation of DGKs exhibited a significant increase in kinase activity, compared to the kinase activity produced from treating cells with exogenous PLC. Following this observation, the authors suggested that DGKs are activated only in spatially restricted subcellular sites characterized by DAG production because DGKs cannot use DAG pools that are randomly generated in the plasma membrane [17,70].
DGKs usually localize within several cell compartments, with majority localizing at least partially within the plasma membrane. This is either constitutively, as seen in the case of DGKκ [71], or following stimulation with specific agonists, such as DGKδ1, which is translocated to the plasma membrane upon exposure to phorbol esters [72], or DGKα, following engagement of the T cell receptor [55]. Moreover, DGKs θ and ζ are found at the plasma membrane upon activation of some G protein-coupled receptors (Table 1) [73,74]. Several studies showed that the nuclear inositide signaling is involved in regulating essential cellular processes, such as cell cycle and differentiation [75,76,77,78], while it is also implicated in several pathologies, including myelodysplastic syndromes, brain diseases, and cancer [79,80,81,82]. Interestingly, DGKs, which have been discovered in nearly all cell compartments, were also found in the nucleus. Notably, DGKs play a critical role in the PI cycle, and the conversion of DAG to PA by DGKs represents the first step in resynthesizing PIs [14]. In addition, different agonists, such as insulin-like growth factor 1 (IGF-1) or thrombin, can generate DAG in the nucleus but not in the plasma membrane. Moreover, nuclear DAG levels fluctuate independently of extranuclear DAG during cell cycle [20]. Among the nuclear DGKs, DGK α, ζ, and ι translocate in and out of the nucleus [17], while DGKγ is shuttled from the cytoplasm to the nucleus [83] and a significant fraction of DGKθ localize mainly within nuclear speckles [84,85]. DGKθ can also translocate from the cytosol to the plasma membrane upon stimulation by PKCs and activated GPCRs [86]. In addition, nuclear DGKs ζ and ι localize within distinct nuclear compartments [17,84] whereas DGKα localizes mainly at the nuclear periphery [17,71].
DGKs also localize within other organelles and this may be cell type-specific [87,88]. Localization and expression of DGKs in the brain remain elusive. However, DGKε localizes to the subsurface cisterns of cerebellar Purkinje cells and colocalizes with inositol trisphosphate receptor-1 (InsP3R-1) in dendrites and axons of the brain, thus confirming the involvement of DGKε in neuronal and brain functions [89]. Moreover, DGKε can also localize within the endoplasmic reticulum and the plasma membrane [87]. In adrenal cells, a group of PI signaling molecules are expressed significantly in zona glomerulosa cells and medullary chromaffin cells in the adrenal gland. The same study showed that DGKγ localizes to the Golgi complex, DGKε to the plasma membrane, and DGKζ to the nucleus of adrenal cells [88]. Furthermore, DGKs δ and η localize to endosomes [37,45].
Even though the specific functions of DGKs are not clearly known yet, several reports demonstrated the presence of DGK activity in cellular contents containing cytoskeletal components and the possible involvement of DGKs in cytoskeletal remodeling and cellular morphology [90,91]. DGKs can interact with proteins associated with cytoskeletal reorganization, such as Rac and Rho GTPases, PIP5K, Cdc42, and Rho-GDI [17,91,92]. For example, DGKζ binds directly to Rac1 to form a complex with Rho-GDI and PAK1 [93], DGKβ co-localized with actin filaments [94], whereas endogenous DGKζ co-purified with cytoskeletal proteins and localized to the leading edge of C2 myoblasts [42] and glioblastoma cells [58].
4. The Impact of DGKs in the Regulation of Cancer Cell Mechanisms
Despite all the progress made in medicine, cancer remains one of the frequently occurring causes of death in humans. Therefore, it is important to better understand the various mechanisms associated with cancer development and progression, in order to pave the way for new personalized medicine approaches. Interestingly, abnormal levels of the DGK substrate, DAG, are involved in malignant cell transformation, as increased DAG levels induce tumor-promoting effects. Consequently, a decreased expression or activity of DGKs could lead to higher DAG levels that could promote malignant cell transformation (Figure 1) [17,35].
DGKζ activity may explain clearly how DGKs negatively regulate DAG in order to limit the transforming potential of DAG in cancer [37]. Following T cell receptor stimulation, DGKζ negatively regulates RasGRP1, an enzyme involved in cell proliferation. As such, excessive activation of the oncogenic protein Ras is observed in DGKζ-deficient lymphocytes upon T cell receptor stimulation and this correlates with high DAG levels. Even DGKα seems to modulate RasGRP1 and its deletion induces hyperproliferation in T cells [58,65]. Besides, high levels of DGKα expression correlate positively with lung cancer patient survival [95], whereas in HCC, the downregulation of DGKα inhibits cell proliferation and metastasis [27]. Hence, DGKs act as both tumor suppressors and tumor promoters in cancer but this is cancer cell type-dependent.
All these reports demonstrate that DGK signaling may be essential in cancer development and progression. As such, this part of the review highlights the impact of DGKs in the regulation of cell growth, proliferation, and metastasis in cancer.
4.1. Cell Growth and Proliferation in Cancer
Cell growth and proliferation are essential factors in cancer development and progression. These processes have been shown to be regulated by the altered expression and/or activity of cell cycle-related proteins in cancer cells [17,96]. Several reports demonstrated the involvement of the DGK family in cell cycle regulation. For example, DGKs α and ζ play contrasting roles in regulating the cell cycle. DGKζ inhibited the progression of cells from G1 to S phase of the cell cycle [62], while DGKα-induced PA was required for IL-2 mediated progression of cells to the S phase [54]. DGKζ is a negative regulator of cell cycle progression in C2C12 mouse myoblasts: DGKζ overexpression blocked cells at the G1 phase of the cell cycle via its interaction with the Retinoblastoma protein (pRb) which is a tumor suppressor and a cell cycle regulator, and DGKζ downregulation increased the number of cells at both S and G2/M phases of the cell cycle [97]. Interestingly, DGKζ is highly expressed in patient-derived acute myeloid leukemia cells and the knockdown of DGKζ in HL-60 promyelocytic cells induces a cell cycle arrest at the G2M checkpoint, inhibiting cell proliferation while increasing apoptosis (Table 2) [98]. In addition, DGKζ inhibition in U251 and U87 glioblastoma cells caused a marked decrease in cyclin D1 (CCND1) protein expression, which led to an arrest of cells at the G0/G1 phase [99]. Furthermore, major regulators of cancer cell growth and proliferation, such as the phosphorylated forms of Akt and mTOR, were also decreased, resulting in a significant reduction of cell proliferation in DGKζ knockdown cells compared to control cells. The authors also showed in an in vivo model that the tumorigenic capability of glioblastoma cells was reduced when DGKζ expression was decreased. Hence, DGKζ inhibition may confer advantages to glioblastoma patients [99].
In other cancer cells, such as K562 human erythroleukemia cells, DGKα can modulate cell cycle progression by influencing the phosphorylated status of pRb, which subsequently induces cell cycle arrest by impairing the G1/S transition [100]. In HCC cells, DGKα knockdown significantly suppresses cell proliferation, whereas overexpressing wildtype DGKα but not the kinase-dead mutant in the same cells significantly enhances proliferation. Similar results were obtained in HCC xenograft model experiments, where DGKα regulates cell proliferation via activation of the MAPK pathway. Specifically, DGKα downregulation impaired mitogen-activated protein kinase (MEK) and extracellular signal-regulated kinase (ERK) phosphorylation, both of which are crucial in the regulation of cell growth and migration [27]. Moreover, a novel DGKα specific inhibitor CU-3, which was successfully obtained after a high-throughput screening of about 9600 chemical compounds, induced apoptosis in HepG2 HCC cells and HeLa cervical cancer cells, while simultaneously enhancing immune response by promoting IL-2 production [101]. Consistent with these data, it was also reported that silencing or inhibiting DGKα activity with short interfering RNA (siRNA) or small-molecule inhibitor R59022 caused increased death of glioblastoma and melanoma cells by interrupting essential oncogenic pathways [29]. DGKα knockdown decreased both total and phosphorylated forms of mTOR, hypoxia-inducible factor 1-alpha (HIF1α), c-Myc levels, and phosphorylation of Akt in glioblastoma cells. Xenograft experiments also demonstrated that DGKα knockdown and inhibition affects tumor growth, angiogenesis, and survival of mice with intracranial and subcutaneous tumors. Intriguingly, knockdown of DGKα in non-cancerous cells, such as astrocytes and fibroblasts, showed no form of cytotoxicity as revealed in both glioblastoma and melanoma cells. Hence, small-molecule inhibition of DGKα is selectively toxic to human cancer cells but not normal human cells, thus making DGKα inhibition a promising therapeutic target [29].
Several studies demonstrated an active role of DGKα also in Src oncogenic functions [8,28]. Src is a regulator of mitogenic and survival signaling pathways that are downstream of receptor and non-receptor tyrosine kinases, such as the vascular endothelial growth factor receptor (VEGFR), human epidermal growth factor receptor-2 (HER2) and focal adhesion kinase (FAK), which are often aberrantly expressed in colon, breast, and pancreatic cancer [8]. Using 3D colon and breast cancer cell cultures, it was demonstrated that DGKα is essential in cell growth and survival by promoting the stabilization of Src activation. Importantly, DGKα enzymatic activity is necessary for Src activation. Pharmacological or genetic DGKα silencing restricted tumor growth in vivo, thus confirming the function of DGKα in malignant transformation [28].
Furthermore, DGK is involved in the major biological features of the transformed phenotype of Kaposi’s sarcoma (KS) cells, where DGK is essential for cell proliferation and DGK inhibitors could be promising for therapy [104]. Indeed, the DGK pharmacological inhibitor R59949 strongly reduces hepatocyte growth factor (HGF)-induced KS proliferation and anchorage-independent growth without affecting cell survival or the classical Akt and MAPK pathways, which are usually implicated in KS.
On the other hand, further studies showed that CHO-K1 ovary cells expressing the kinase negative mutant of DGKγ exhibited a larger size, slower growth rate, and an extended S phase, suggesting that the increase of cell size was induced by protein synthesis during the extended S phase and that DGKγ regulates cell cycle [61]. Even though the activities of DGKs in cancer seem to support tumor-promoting roles, there is evidence that DGKs can also support tumor suppressor activities [102]. For instance, DGKγ expression is downregulated in HCC tumor tissues and colorectal cancer (CRC) cell lines when compared to non-tumor control tissues, and this correlates with poor clinical outcomes [102]. Interestingly, DGKγ downregulation in HCC is due to epigenetic mutations induced by histone H3 and H4 deacetylation. In addition, an analysis of methylation of the CpG islands of DGK promoter genes in CRC cells revealed that DGKγ is hypermethylated in CRC cells but not in normal colonic tissue, and this corresponds with reduced DGKγ expression in CRC cell lines compared to control cells [26]. However, both constitutively active and kinase-dead DGKγ mutants induced inhibitory effects on CRC cell proliferation [26]. Notably, the ectopic expression of DGKγ in HCC cells decreased cell growth by downregulating glucose transporter 1 (GLUT1) expression and inhibiting cell glycolysis. In fact, GLUT1 expression is high in HCC and promotes tumorigenicity, therefore DGKγ plays tumor suppressor roles in HCC by lowering GLUT1 levels [102].
DGKε activity can also regulate the Ras/RAF/MEK/ERK signaling in cervical cancer cell line models [33]. This pathway plays pivotal roles in the regulation of cell proliferation, survival, and differentiation. The study showed that siRNA downregulation of DGKε impairs the epidermal growth factor (EGF)-activated Ras/RAF/MEK/ERK signaling cascade in HeLa cells. However, the mechanism through which DGKε regulates this pathway is still unknown [33]. Additionally, the potential of DGKη to regulate MAPK signaling, which is a downstream target of epidermal growth factor receptor (EGFR), led a group to study the oncogenic effects of DGKη in lung cancer, which is often characterized by mutations in EGFR and KRAS. The authors reported that silencing DGKη in lung cancer models, characterized by EGFR and KRAS mutations, reduced cancer cell growth while enhancing the cells’ sensitivity to EGFR inhibitor Afatinib [103].
4.2. Cell Migration, Invasiveness, and Metastasis
The motility and invasion of cancer cells from the primary tumor to a distant organ is an essential step in tumor metastasis. This event requires chemotactic migration of cancer cells and crossing of extracellular matrix barriers that surround the tumor [105]. As previously stated, DGKγ plays tumor-suppressive roles in CRC. The ectopic expression of wildtype, as well as kinase active and inactive mutant forms of DGKγ, restricts cell migration and invasion in CRC cells by inhibiting Rac1 activity [26]. Rac1 is a member of the Rho GTPases, which are small GTP-binding proteins that regulate cytoskeletal dynamics and activate essential protein kinases involved in Epithelial to Mesenchymal Transition (EMT) [106]. EMT involves the reprogramming of epithelial cells into mesenchymal cells, leading to morphological changes, specifically more elongated and spindle-like forms with increased migratory and invasive properties [107]. Notably, Rac1 is highly expressed in different stages of colorectal tumors. Its activity in CRC tissues positively correlates with poor prognosis of CRC patients by promoting EMT-mediated invasion of CRC cells via the activation of the signal transducers and activators of transcription 3 (STAT3) pathway [106]. Therefore, it would be interesting to elucidate the mechanisms associated with DGKγ-mediated inhibition of Rac1 activity for potential CRC therapy. DGKγ also plays tumor suppressor roles in HCC cells by reducing cell migration when DGKγ is overexpressed [102]. Conversely, DGKα is highly expressed in HCC and promotes tumorigenicity [27]. In fact, knockdown of DGKα suppresses cell migration by impairing the Ras/RAF/MEK/ERK pathway in HCC cells. The Ras/RAF/MEK/ERK pathway is indeed frequently deregulated in HCC and the activation of this pathway is significantly involved in cancer cell invasion [27]. In fact, ERK signaling is a critical mediator of cell migration, although it is also a classic mediator of cell growth, proliferation, and differentiation. ERK activates several proteins that regulate cell-matrix adhesion, cell protrusion, and retraction, all of which are essential processes recognized during cell motility [108]. Moreover, ERK controls EMT-regulated cell migration through Rac1/Fox01 activation [107]. DGKα activity has also been reported to be a key factor in the migratory and invasive responses induced by several growth factors, including HGF and vascular endothelial growth factor (VEGF) in endothelial, epithelial, and leukemic cells [109,110,111,112]. In line with the potential to regulate migration in endothelial cells by DGKα, a study employing both DGKα specific siRNA and/or DGK pharmacological inhibitor R59949 demonstrated that DGKα activity is a key regulator of migration in Hec-1A endometrial cancer cell line [112]. Inhibition of DGKα indeed reduced cell migration towards estrogen chemoattractant as well as abolished ruffle formation in Hec-1A cells [112]. In addition, DGKα promotes invasive migration in H1299 lung cancer cells and A2780 ovarian carcinoma cells by controlling Rab coupling protein (RCP)-driven integrin trafficking [113]. Furthermore, R59949 significantly reduced HGF-induced motility in KS cell lines with limited effects on cell adhesion and spreading [104], but it did not affect MAPK and Akt signaling pathways.
The downstream product of DGKs, PA has been associated with the receptor tyrosine kinase (RTK) signaling, which is an upstream regulator of the Ras/RAF/MEK/ERK cascade [114]. Another study attributed a potential role of PA in regulating tumor metastasis due to its ability to induce the secretion of Type 1 matrix metalloproteases (MMP1), enzymes able to promote metastasis [115]. However, these studies refer to PA generated by phospholipase D (PLD). Hence, it would be important to understand whether PA generated by DGKs performs the same functions. Interestingly, the application of nanomolar concentrations of PA increased cell migration in invasive MDA-MB-231 human breast cancer cells but had no effect on non-neoplastic control cells. Moreover, applying Clostridium difficile Toxin B to the PA-treated MDA-MB-231 breast cancer cells inhibited Rho activity and was followed by a marked decrease in cell migration [116]. These data strengthen the link between DGK/PA and Rho GTPases in cytoskeletal organization and subsequent cell migration. In addition, PA may be central in the regulation of cell motility by controlling the activity of type I PIP5K isozymes and PtdIns(4,5)P2 [117]. In fact, PA stimulates PIP5K, that participates in actin reorganization by generating PtdIns(4,5)P2, which is a primary regulator of cytoskeletal organization, so that PA signaling is also critical in PtdIns(4,5)P2 resynthesis [117].
A study reported that DGKζ deficiency in fibroblast cells induces a reduction in Rac1 and RhoA activation, as well as a significant reduction in cell migration [118]. Considering this finding, the authors extended their study by elucidating the impact of DGKζ signaling in CRC metastasis [30]. In tumor-derived CRC cell lines, knocking down DGKζ expression produced similar results as those seen in fibroblasts. A significant decrease in Rac1 and RhoA activity in DGKζ knockdown CRC cells was also observed and was followed by a decrease in cell invasion. Concomitantly, DGKζ depletion decreased the invasiveness of prostate cancer and metastatic breast cancer cells [30]. Thus, opposite to the tumor-suppressive roles of DGKγ as described above, DGKζ may also promote tumorigenesis by potentiating cell invasion and migration in several cancer types by regulating Rac1 and RhoA activity. This may be due to the fact that coordinated events between Rac1 and RhoA are necessary for effective migration in cancer metastasis. Indeed, RhoA is involved in the maintenance of actin stress fibers and focal adhesions, while Rac1 regulates the generation of lamellipodia, membrane ruffles formation, and Cdc42 signaling in filopodia production [119].
As for other DGKs, such as DGKδ and DGKι, there are a few reports demonstrating their participation in the development and progression of cancer. Downregulation of DGKδ in cervical and lung adenocarcinoma cell line models induced a downregulation of Akt activity, leading to a decrease in cell migration and proliferation. Moreover, DGKδ can control Akt activity through pleckstrin homology domain leucine-rich repeat protein phosphatase 2 (PHLPP2) [32]. Epigenetic studies have also revealed that DGKι may be methylated in cancer, including glioblastoma and HCC [34,35], while it is still unknown whether DGKι mutations may produce effects directly involved in metastasis of these cancer types.
5. Targeting DGKs in Cancer Therapies
The development of effective therapies to fight cancer continues to be one of the major challenges in modern medicine. Chemotherapy and radiotherapy are somehow successful, but these approaches are non-specific and often lead to short- or long-term adverse effects which usually affect quality of life [7]. Recently, exploring immune-based therapeutic systems, such as blockage of immune checkpoints or adoptive cell transfer (ACT) that stimulate antitumor immunity by targeting and attacking tumor cells, seems promising because of its specificity. The starting point of this immune response is represented by chimeric antigen receptor (CAR)-T cells [120]. Interestingly, incoming reports suggest that targeting DGK activity could be a strong approach to reinforce the anti-tumor functions of T cells [7].
Currently, DGKα holds much promise in cancer therapy [7,29], as its inhibition presents toxicity in various cancer, but not normal human cells [29]. For instance, in T cells, the inhibition of DGKα activity may generate a simultaneous response of reinforcing T cell attack on tumor cells, while directly inhibiting tumor growth [7].
The inhibition of DGKα by R59949 in KS and endometrial cancer cells leads to decreased cell proliferation, growth, and migration [104,112]. On the other hand, using the small molecule inhibitor of DGKs R59022, DGKα was inhibited in glioblastoma, cervical cancer, melanoma, and breast cancer cell lines. In these cells, the percentage of cell death was increased compared to control normal fibroblasts and astrocytes [29]. Indeed, in glioblastoma in vivo tumor models, the same authors showed that DGKα inhibition decreases angiogenesis, tumor growth, and survival of mice with tumors [29]. Targeting DGKs may be important in cancer immunotherapy, as intratumoral injection of DGK knockout T-cells into U87MGvIII glioblastoma tumor models, obtained by CRISPR/Cas9, caused significant suppression of the tumors [25]. More importantly, this result was due to an enhancement in the effector functions of T-cells in the xenograft model. The authors also showed that the CRISPR/Cas9 generated DGK-knockout in CAR-T cells potentiates T-cell functions by increasing cluster of differentiation 3 (CD3) signaling. Consequently, the cells became resistant to immunosuppressive factors such as transforming growth factor-β (TGFβ) and prostaglandin E2, which are known mediators of cancer cell survival [25].
Other studies tested CU-3, a DGK pharmacological inhibitor with a higher specificity for DGKα than R59949 and R59022, mainly due to its specific targeting of the ATP binding site in the catalytic domain of DGKα. This molecule induced apoptosis in several cancer cells, while enhancing immune response [101]. Similarly, compound A, which specifically inhibits type I DGKs and especially DGKα, induced apoptosis and reduced viability of melanoma and several other cancer cell lines [121]. In addition, two novel DGKα inhibitor compounds, namely 11 and 20 (with an IC50 of 1.6 and 1.8 µM respectively, thus representing the most potent DGKα inhibitors until now), decreased cell migration in cancer cells (Figure 2) [122].
On the other hand, Ritanserin, an established serotonin receptor inhibitor, has recently been identified as a DGKα inhibitor. Interestingly, it is more potent than R59022, although these two compounds differ structurally by just a single fluorine [123]. Ritanserin has already been shown to be well-tolerated and safe for human use in clinical trials, potentially paving the way to use it clinically as a DGKα inhibitor [123]. In fact, treatment of several cancer cells with Ritanserin has yielded similar results as other DGK inhibitors [7,124]. For example, the mesenchymal subtypes of lung and pancreatic carcinoma, as well as the mesenchymal subtype of glioblastoma, are sensitive to Ritanserin. Indeed, DGKα inhibition by Ritanserin induced cell death in glioblastoma stem cells and this was partially mediated by apoptosis [124]. Additionally, Ritanserin, as with other small molecule inhibitors of DGKα, also enhanced T cell signaling but failed to promote long-term T-cell activation [125].
6. Conclusions
All the reported studies highlight DGK signaling as a promising target for cancer therapy. However, more studies are needed to fully comprehend DGK specific roles in cancer development and progression. Due to the isoform-specific functions observed in different types of cancer cells and even subcellular sites, it would be crucial to fully understand how the specific DGK isoforms control downstream oncogenic signaling, as these pathways can regulate proliferation, growth, angiogenesis, immunity, and migration. To better understand DGK signaling it would also be essential to explore the potential crosstalk among the various DGK isoforms, the possible redundancy or compensatory functions of other isoforms during the inhibition of one or more DGK isoforms, as well as consider the DGK specific subcellular localization relevance. Moreover, the discovery of more potent DGK-specific isoform inhibitors may be useful to study the isoform-specific functions and develop new cancer targeted therapies. Future studies may also benefit from the combinatorial effect of DGK inhibitors and other standard cancer therapies, such as radiation and chemotherapy. Furthermore, since PA is involved in several cellular processes, the combination of both DGK inhibitors and inhibitors of PA-synthesizing enzymes may prove to be more beneficial in cancer therapy than when used individually. Finally, since recent reports demonstrated that DGKs may have a preference for specific DAG species, which are independent of PI turnover pathways, it would be strategic to further investigate the cellular signaling pathways associated with PA, produced by both PI independent and PI dependent pathways.
Author Contributions
Conceptualization, investigation L.M., L.C. and S.R., formal analysis, A.F., E.O.O., I.R., M.V.M., J.A.M. and M.Z. writing-original draft preparation, A.F. and E.O.O. writing-review and editing, S.M., M.Y.F., G.R., M.Z., J.A.M., S.R., L.C.; and L.M. visualization, I.R. and M.V.M., funding acquisition, L.M., G.R., M.Y.F., S.R. and L.C. All authors have read and agreed to the published version of the manuscript.
Funding
This research was funded by Italian PRIN-MIUR grants (to L.M., G.R., M.Y.F.), Fondazione Cassa di Risparmio di Bologna grant (to S.R.), Fondazione del Monte di Bologna e Ravenna grant and Intesa S. Paolo Foundation grant (to L.C.).
Conflicts of Interest
The authors declare no conflict of interest.
Abbreviations
3D
Three-dimensional
Ca2+
Calcium
ACT
Adoptive cell transfer
AML
Acute myeloid leukemia
CAR
Chimeric antigen receptor
CCND1
Cyclin D1
CD3
Cluster of differentiation 3
CRC
Colorectal cancer
DAG
Diacylglycerol
DGKs
Diacylglycerol kinases
EGF
Epidermal growth factor
EGFR
Epidermal growth factor receptor
EMT
Epithelial to mesenchymal transition
ERK
Extracellular signal-regulated kinase
FAK
Focal adhesion kinase
GAP
GTPase-activating protein
GLUT1
Glucose transporter 1
HCC
Hepatocellular carcinoma
HER2
Human epidermal growth factor receptor-2
HGF
Hepatocyte growth factor
HIF1α
Hypoxia-inducible factor 1-alpha
IL-2
Interleukin 2
InsP3R
Inositol trisphosphate receptor
KS
Kaposi’s sarcoma
MAPK
Mitogen activated protein kinase
MARCKS
Myristoylated alanine rich kinase substrate
MEK
Mitogen-activated protein kinase
MMP1
Type 1 matrix metalloproteases
mTOR
Mammalian target of rapamycin
NLS
Nuclear localization sequence
PA
Phosphatidic acid
PHLPP2
Pleckstrin homology domain leucin-rich repeat protein phosphatase 2
Rat sarcoma virus guanyl nucleotide-releasing protein
RTK
Receptor tyrosine kinase
SAM
Sterile α motif
siRNA
Short interfering RNA
SMSr
Sphingomyelin synthase related protein
STAT3
Signal transducers and activators of transcription 3
TGFβ
Transforming growth factor β
Unc-13
Mammalian uncoordinated 13
VEGF
Vascular endothelial growth factor
VEGFR
Vascular endothelial growth factor receptor
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Cartoon representation of DGK signaling in cancer. Upon their activation, DGKs phosphorylate DAG to form PA, which results in a series of signaling events resulting from the alterations in the signaling of several molecular targets. Several studies reported that DGKs regulate cancer cell survival and proliferation via MAPK and Akt/mTOR pathways. In addition, DGKs regulate migration via the Ras/RAF/MEK/ERK pathway and the Rho GTPase family members. On the other hand, DAG activates several critical proteins including both conventional and novel PKCs, mammalian Unc-13, chimaerins which activate Rac GTPase, and RasGRPs, which are involved in cell proliferation and migration.
Functional effects produced by DGKα inhibition in cancer. The use of DGKα specific or pan inhibitors to inhibit DGKα activity in cancer cells seems to block cancer progression through the inhibition of cancer-promoting mechanisms, such as growth and survival. Intriguingly, inhibition of DGKα activity increases apoptosis in several cancer cell lines and in vivo tumor models, while enhancing T-cell activity or immune response.
ijms-21-05297-t001_Table 1
Distribution of DGKs across distinct subcellular compartments.
Induces cell cycle arrest at G2M, Inhibits cell proliferation and increases apoptosis [98]
\nDGKη\n
Lung cancer
Impairs MAPK signaling [103]
\n"],"string":"[\n \"\\nInt J Mol SciInt J Mol SciijmsInternational Journal of Molecular Sciences1422-0067MDPI32722576PMC743210110.3390/ijms21155297ijms-21-05297ReviewSubcellular Localization Relevance and Cancer-Associated Mechanisms of Diacylglycerol KinasesFazioAntonietta1†https://orcid.org/0000-0003-0403-9466Owusu ObengEric1†RuscianoIsabella1MarviMaria Vittoria1https://orcid.org/0000-0001-8838-5408ZoliMatteo23MongiorgiSara1https://orcid.org/0000-0001-9684-714XRamazzottiGiulia1FolloMatilde Yung1https://orcid.org/0000-0001-6027-3156McCubreyJames A.4CoccoLucio1ManzoliLucia1*https://orcid.org/0000-0002-6258-5345RattiStefano1Cellular Signalling Laboratory, Department of Biomedical and Neuromotor Sciences (DIBINEM), University of Bologna, Via Irnerio 48, 40126 Bologna, Italy; antonietta.fazio2@unibo.it (A.F.); eric.owusuobeng2@unibo.it (E.O.O.); isabella.rusciano3@unibo.it (I.R.); mariavittoria.marvi2@unibo.it (M.V.M.); s.mongiorgi@unibo.it (S.M.); giulia.ramazzotti@unibo.it (G.R.); matilde.follo@unibo.it (M.Y.F.); lucio.cocco@unibo.it (L.C.); stefano.ratti@unibo.it (S.R.)Center for the Diagnosis and Treatment of Hypothalamic-Pituitary Diseases-Pituitary Unit, IRCCS Istituto delle Scienze Neurologiche di Bologna (Institute of Neurological Sciences of Bologna), 40126 Bologna, Italy; matteo.zoli4@unibo.itDepartment of Biomedical and Neuromotor Sciences (DIBINEM), University of Bologna, 40126 Bologna, ItalyDepartment of Microbiology and Immunology, Brody School of Medicine at East Carolina University, Greenville, NC 27858, USA; MCCUBREYJ@ecu.eduCorrespondence: lucia.manzoli@unibo.it
These authors contributed equally to this work.
2672020820202115529724620202472020© 2020 by the authors.2020Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
An increasing number of reports suggests a significant involvement of the phosphoinositide (PI) cycle in cancer development and progression. Diacylglycerol kinases (DGKs) are very active in the PI cycle. They are a family of ten members that convert diacylglycerol (DAG) into phosphatidic acid (PA), two-second messengers with versatile cellular functions. Notably, some DGK isoforms, such as DGKα, have been reported to possess promising therapeutic potential in cancer therapy. However, further studies are needed in order to better comprehend their involvement in cancer. In this review, we highlight that DGKs are an essential component of the PI cycle that localize within several subcellular compartments, including the nucleus and plasma membrane, together with their PI substrates and that they are involved in mediating major cancer cell mechanisms such as growth and metastasis. DGKs control cancer cell survival, proliferation, and angiogenesis by regulating Akt/mTOR and MAPK/ERK pathways. In addition, some DGKs control cancer cell migration by regulating the activities of the Rho GTPases Rac1 and RhoA.
Phosphoinositides (PIs) represent a tiny component of the total phospholipid content in eukaryotic cell membranes, but they regulate numerous cellular activities such as cell adhesion [1], migration [2], apoptosis [3], vesicular trafficking [4], and post-translational modifications [5]. These processes are consistent with cancer-associated cellular mechanisms. PI metabolism is controlled by several kinases, phosphatases, and phospholipases following their stimulation by different external stimuli. An increasing number of studies report that alterations in the PI cycle, resulting from dysfunctional PI metabolic enzymes, are involved in cancer [6,7,8].
Diacylglycerol kinases (DGKs) are a family of ten PI metabolic kinases (α, β, γ, δ, ε, ζ, η, θ, ι, and κ) that participate in the PI cycle by catalyzing the phosphorylation of diacylglycerol (DAG) to generate phosphatidic acid (PA), hence, regulating these two-second messengers [9]. DAG and PA are involved in the regulation of several critical enzymes, including mammalian target of rapamycin (mTOR) [10], phosphatidylinositol-4-phosphate 5-kinase (PIP5K), the Ras GTPase-activating protein (GAP), rapidly accelerated fibrosarcoma-1 (Raf-1) kinase, protein kinases C (PKCs), mammalian uncoordinated 13 (Unc-13), Chimaerins (which activate Rac GTPase), and rat sarcoma virus guanyl nucleotide-releasing protein (RasGRP), which are involved in cell proliferation and migration [11,12]. Although DGKs can utilize different molecular DAG species independent of PI turnover pathways [13], the conversion of DAG to PA by DGKs represents one of the first steps in PI resynthesis, where phosphatidylinositol 4,5 biphosphate (PtdIns(4,5)P2) levels are replenished [14]. As such, DGK signaling is pivotal in modulating the balance between these two essential bioactive lipids, DAG, and PA.
Accumulating evidence demonstrates that DGKs, as well as phospholipases C (PLCs) and PKCs, are distributed across several subcellular compartments together with their substrates [15,16,17] and, as PLCs and PKCs, they are involved in cell regulation [18,19]. Nuclear localization allows DGKs to participate in a PI cycle which is independent of that of the plasma membrane [20,21]. Therefore, DGK activity may regulate distinct cellular functions and explain the complexities that surround DGK signaling [17]. In fact, DGKs regulate cytokine/growth factor-mediated cell proliferation and migration, cell growth, seizure activity, and insulin receptor-mediated glucose metabolism, suggesting that DGKs may also be involved in several diseases including epilepsy and diabetes [22,23].
Of particular note is the involvement of DGKs in cancer development and progression [24,25,26]. For instance, mutations in the DGKα gene can drive pancreatic cancer [24] as well as promote hepatocellular carcinoma (HCC) progression by activating the mitogen-activated protein kinase (MAPK) pathway [27]. In 3D colon and breast cancer models, DGKα was reported to promote cell survival by regulating Src [28]. For these reasons, there are several reports suggesting that DGKα may be a promising therapeutic target in cancer therapy [7,29]. Meanwhile, in colorectal cancer (CRC), DGKγ plays tumor-suppressive roles, while DGKζ activity promotes tumor progression [26,30,31]. Moreover, DGKη and DGKδ regulate cell growth and proliferation in cervical cancer cell lines [32,33], whereas epigenetic changes in DGKι are reported in glioblastoma and HCC cells [34,35]. Despite the numerous reports of the involvement of DGKs in cancer as well as their clinical potential, the comprehension of the specific cellular functions regulated by DGKs in cancer is not yet complete. This review aims to provide up-to-date knowledge of the regulatory roles played by DGKs in cancer cell survival and metastasis, while also highlighting the downstream regulation of DGKs, the role of their cellular localization and summing up current knowledge on targeting DGKs in cancer therapies.
2. Activation and Regulation of DGK Isozymes
The 10 members of the mammalian DGK family are classified into 5 different subtypes depending on their structural motifs [36]: type I (DGKs α, β, and γ), type II (DGKs δ, η, and κ), type III (DGKε), type IV (DGKs ζ, and ι) and type V (DGKθ). Moreover, some DGK isoforms undergo further alternative splicing which often changes either the distribution or activity of the enzyme [37,38]. Their differences may be attributed to their evolution, in order to regulate specific cellular processes evident in higher vertebrates [39]. DGKs are ubiquitous kinases that are mainly expressed in the brain and hemopoietic tissue [40]. They are prominently distributed across several different regions of the brain, including the cerebellum, hippocampus, and the olfactory bulb, therefore suggesting involvement of DGKs in central nervous system functions [41]. Some DGK isoforms are also expressed in the retina (ε, γ, ι) [41], in striated (δ and ζ) and cardiac muscle (β and ε) [41,42] and in the lungs (α, ε, ζ and η) [43]. So far, it has been shown that different DGK isoforms can be co-expressed in the same tissue and even in the same cell, suggesting that each subtype may carry out tissue or cell-specific functions [41].
Until now, all recognized mammalian DGKs possess two common kinase domains comprising a conserved catalytic domain, which is characterized by a highly conserved ATP binding site and an accessory domain [44]. The possession of additional distinct domains, that seems to have regulatory roles, as shown in DGK family types, contributes to isoform-specific functions and diversity among mammalian DGK isoforms [17]. For instance, type I isoforms (α, β, and γ) participate in calcium (Ca2+) signaling because of their Ca2+ binding motif, while the carboxyl terminus-based sterile alpha motif (SAM) of DGKδ, which is a type II DGK, promotes protein-protein interactions [45]. In addition, DGKδ possesses a PH domain that weakly binds to phosphatidylinositols [46]. The nuclear localization of type IV DGKs (ζ and ι) is enhanced via their nuclear localization sequence (NLS). This domain also serves as substrate for conventional PKCs and is homologous to the phosphorylation domain of the myristoylated alanine-rich kinase substrate (MARCKS) protein. DGKθ, which is the only type V DGK isoform, can be differentiated by its PH domain, three C1 domains, and a Ras-associating domain that mediates Ras signaling [47].
All DGKs possess at least two cysteine-rich regions similar to the DAG-binding C1A and C1B domains of PKC [48]. The C1 regions of DGKs allow membrane binding either through protein interactions, as demonstrated by several DGKs and β-arrestin [49] or through lipid interactions, as shown by DGKα and the lipid product of phosphoinositide 3-kinase (PI3K) [50,51]. C1 domains are recognized phorbol ester or DAG binding regions, but several studies have tried to evaluate the binding potential of the C1 domains of some DGKs to a DAG analog or phorbol ester reported that only the C1A domain of DGK β and γ displays successful binding [52]. Therefore, it is not clear whether the C1 domains of all DGKs can actually bind DAG.
The activation and regulation of DGKs is a very complex process that needs further studies to be fully comprehended [37]. Given the structural and subcellular localization differences, it may be possible that different activation mechanisms exist for each individual DGK isoform. Considering also the ability of DGKs to translocate to different cellular sites, the presence of post-translational modifications and their binding to different cofactors, such as membrane lipids and Ca2+, may produce some degree of diversity in their functions. DAG is accessed by DGKs upon their translocation to DAG-producing cellular membranes, where DGKs are proposed to be activated during agonist or kinase promoted phosphorylation or following their binding to some cofactors or to other proteins [39]. Indeed, some studies reported that the distinct activities observed in the various DGK isoforms may depend on the type of agonist and the cofactors they bind to during their activation [53]. For example, DGKα, which is one of the most studied DGK isoforms, demonstrates this complexity in T lymphocytes. Based on the type of agonist used to activate DGKα in T cells, it translocates to two different membrane compartments: stimulation with interleukin 2 (IL-2) induces the translocation of DGKα from the cytosol to the perinuclear region [54], whereas the translocation from the cytosol to the plasma membrane occurs when DGKα is stimulated by T-cell antigen receptor [55]. Several different cofactors, such as Ca2+, which binds to the EF-hand motif and membrane lipids, including phosphatidylserine (PS), sphingosine, the PI3K lipid products, PtdIns(3,4)P2, and PtdIns(3,4,5)P3 have been reported to modify DGKα activity both in vitro and in vivo [56]. As for other DGK isoforms, activation of DGKδ may be enhanced by the binding of its PH domain to phosphatidylinositols [41], DGKε is inhibited by both PtdIns(4,5)P2 and PS, while DGKζ is activated by both PtdIns(4,5)P2 and PS [57]. Moreover, the protein-protein interaction between DGKθ and RhoA is involved in the regulation of the activity of DGKθ, where its kinase activity is completely reduced by RhoA [17].
Several studies showed that the specificity of DGK activities could also be attributed to its association with or inhibition of DAG-activated proteins, such as the RasGRP proteins [58]. Generally, when DAG is abundant, RasGRPs activate either Rap or Ras, or both, and this mechanism is RasGRP-isoform specific. Consequently, the downstream effects of DGKs diverge because DGK isozymes bind to different isoforms of RasGRP [59]. Hence, the functional specificity of DGKs depends on their interactions. For example, type IV DGKs, ζ, and ι, are all structurally similar, but they induce opposing effects on Ras signaling. DGKζ attenuates Ras signaling both in vitro [58,60] and in vivo [60], whereas DGKι enhances it [59]. These opposing effects were mainly dependent on the ability of DGKs ζ and ι to bind and inhibit specific RasGRP enzymes, respectively RasGRP1 and RasGRP3 [58,59]. Since the activities of DGKs maintain a balance between DAG and PA levels, DGKs can also be associated with proteins whose activities are regulated by PA. In fact, DGKs regulate either directly or indirectly Rac1 [30], mTOR [10], PIP5K type 1α [17], and atypical PKCs [50], all regulated by PA while mediating several essential cellular effects such as cell survival, migration, and vesicle trafficking [11,12].
DGKs were initially described as modulators of the classical and novel PKC family members. However, some DGKs form complexes with certain DAG-sensitive PKC isoforms, thus being regulated by PKC-dependent phosphorylation [36,50]. Indeed, these DGKs and their respective PKC counterparts mutually regulate each other’s enzyme activities, as seen in the case of DGKζ and PKCα. At immune and nervous synapses, the activation of DGKζ and PKCα is mutually regulated by both kinases [50]. At basal conditions, DGKζ phosphorylates DAG and prevents PKCα activation [61] but upon stimulation, there is an overproduction of local DAG levels that is too abundant for DGKζ to phosphorylate, leading to the availability of excess DAG. Consequently, high levels of DAG activate PKCα, which phosphorylates DGKζ, causing their physical dissociation [61] and promoting transient or even prolonged activation of PKCα. In the context of cancer, the DGKζ-PKCα axis could be important in regulating signaling in tumor cells. For instance, DGKζ undergoes a PKCα-dependent phosphorylation to enhance its shuttle from the nucleus to the cytosol [62], a biological process which is implicated in cancer cells responding to stress conditions [63]. Moreover, DGKα is involved in inflammation in tumor cells by positively regulating tumor necrosis factor α (TNFα)-induced nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB) activation via a PKCζ-mediated Ser311 phosphorylation of the NF-κB subunit p65/RelA [64].
Overall, DGKs can be activated by several mechanisms which might be isoform-specific, and they produce different cellular outcomes based on the type of co-factors and proteins they associate with. The concept of DGK-isoform specific functions is supported by mouse knockout studies showing that targeted deletion of specific DGK isoforms leads to a distinct phenotype [59,65,66,67,68].
3. Cellular Localization and Distribution of DGKs
As previously reported, DGKs can translocate to various distinct cellular compartments depending on the type of agonist. This supports the fact that the activities of DGKs may be confined to specific DAG pools produced after receptor activation [17]. This is the case of DGKε, which only phosphorylates DAG species that possess an arachidonoyl group at the sn-2 position [50]. Moreover, recent studies have also shown the presence of an alternative DAG metabolic pathway where individual DGK isoforms phosphorylate different molecular DAG pools or species independently from PI turnover [13]. For instance, DGKδ2 interacts via its SAM domain with the ER enzyme sphingomyelin synthase related protein (SMSr) generating DAG from phosphatidylethanolamine and ceramide [69]. Through a flip-flop mechanism, DAG produced by SMSr in the ER crosses the ER membrane from the lumen region to the cytosolic region to give access to DGKδ2 [69]. A previous work by van der Bend and colleagues showed that receptor activation of DGK in cells induces a physiological DAG generation while treating cells with exogenous PLC induces a global, non-specific DAG production [17,70]. Moreover, receptor activation of DGKs exhibited a significant increase in kinase activity, compared to the kinase activity produced from treating cells with exogenous PLC. Following this observation, the authors suggested that DGKs are activated only in spatially restricted subcellular sites characterized by DAG production because DGKs cannot use DAG pools that are randomly generated in the plasma membrane [17,70].
DGKs usually localize within several cell compartments, with majority localizing at least partially within the plasma membrane. This is either constitutively, as seen in the case of DGKκ [71], or following stimulation with specific agonists, such as DGKδ1, which is translocated to the plasma membrane upon exposure to phorbol esters [72], or DGKα, following engagement of the T cell receptor [55]. Moreover, DGKs θ and ζ are found at the plasma membrane upon activation of some G protein-coupled receptors (Table 1) [73,74]. Several studies showed that the nuclear inositide signaling is involved in regulating essential cellular processes, such as cell cycle and differentiation [75,76,77,78], while it is also implicated in several pathologies, including myelodysplastic syndromes, brain diseases, and cancer [79,80,81,82]. Interestingly, DGKs, which have been discovered in nearly all cell compartments, were also found in the nucleus. Notably, DGKs play a critical role in the PI cycle, and the conversion of DAG to PA by DGKs represents the first step in resynthesizing PIs [14]. In addition, different agonists, such as insulin-like growth factor 1 (IGF-1) or thrombin, can generate DAG in the nucleus but not in the plasma membrane. Moreover, nuclear DAG levels fluctuate independently of extranuclear DAG during cell cycle [20]. Among the nuclear DGKs, DGK α, ζ, and ι translocate in and out of the nucleus [17], while DGKγ is shuttled from the cytoplasm to the nucleus [83] and a significant fraction of DGKθ localize mainly within nuclear speckles [84,85]. DGKθ can also translocate from the cytosol to the plasma membrane upon stimulation by PKCs and activated GPCRs [86]. In addition, nuclear DGKs ζ and ι localize within distinct nuclear compartments [17,84] whereas DGKα localizes mainly at the nuclear periphery [17,71].
DGKs also localize within other organelles and this may be cell type-specific [87,88]. Localization and expression of DGKs in the brain remain elusive. However, DGKε localizes to the subsurface cisterns of cerebellar Purkinje cells and colocalizes with inositol trisphosphate receptor-1 (InsP3R-1) in dendrites and axons of the brain, thus confirming the involvement of DGKε in neuronal and brain functions [89]. Moreover, DGKε can also localize within the endoplasmic reticulum and the plasma membrane [87]. In adrenal cells, a group of PI signaling molecules are expressed significantly in zona glomerulosa cells and medullary chromaffin cells in the adrenal gland. The same study showed that DGKγ localizes to the Golgi complex, DGKε to the plasma membrane, and DGKζ to the nucleus of adrenal cells [88]. Furthermore, DGKs δ and η localize to endosomes [37,45].
Even though the specific functions of DGKs are not clearly known yet, several reports demonstrated the presence of DGK activity in cellular contents containing cytoskeletal components and the possible involvement of DGKs in cytoskeletal remodeling and cellular morphology [90,91]. DGKs can interact with proteins associated with cytoskeletal reorganization, such as Rac and Rho GTPases, PIP5K, Cdc42, and Rho-GDI [17,91,92]. For example, DGKζ binds directly to Rac1 to form a complex with Rho-GDI and PAK1 [93], DGKβ co-localized with actin filaments [94], whereas endogenous DGKζ co-purified with cytoskeletal proteins and localized to the leading edge of C2 myoblasts [42] and glioblastoma cells [58].
4. The Impact of DGKs in the Regulation of Cancer Cell Mechanisms
Despite all the progress made in medicine, cancer remains one of the frequently occurring causes of death in humans. Therefore, it is important to better understand the various mechanisms associated with cancer development and progression, in order to pave the way for new personalized medicine approaches. Interestingly, abnormal levels of the DGK substrate, DAG, are involved in malignant cell transformation, as increased DAG levels induce tumor-promoting effects. Consequently, a decreased expression or activity of DGKs could lead to higher DAG levels that could promote malignant cell transformation (Figure 1) [17,35].
DGKζ activity may explain clearly how DGKs negatively regulate DAG in order to limit the transforming potential of DAG in cancer [37]. Following T cell receptor stimulation, DGKζ negatively regulates RasGRP1, an enzyme involved in cell proliferation. As such, excessive activation of the oncogenic protein Ras is observed in DGKζ-deficient lymphocytes upon T cell receptor stimulation and this correlates with high DAG levels. Even DGKα seems to modulate RasGRP1 and its deletion induces hyperproliferation in T cells [58,65]. Besides, high levels of DGKα expression correlate positively with lung cancer patient survival [95], whereas in HCC, the downregulation of DGKα inhibits cell proliferation and metastasis [27]. Hence, DGKs act as both tumor suppressors and tumor promoters in cancer but this is cancer cell type-dependent.
All these reports demonstrate that DGK signaling may be essential in cancer development and progression. As such, this part of the review highlights the impact of DGKs in the regulation of cell growth, proliferation, and metastasis in cancer.
4.1. Cell Growth and Proliferation in Cancer
Cell growth and proliferation are essential factors in cancer development and progression. These processes have been shown to be regulated by the altered expression and/or activity of cell cycle-related proteins in cancer cells [17,96]. Several reports demonstrated the involvement of the DGK family in cell cycle regulation. For example, DGKs α and ζ play contrasting roles in regulating the cell cycle. DGKζ inhibited the progression of cells from G1 to S phase of the cell cycle [62], while DGKα-induced PA was required for IL-2 mediated progression of cells to the S phase [54]. DGKζ is a negative regulator of cell cycle progression in C2C12 mouse myoblasts: DGKζ overexpression blocked cells at the G1 phase of the cell cycle via its interaction with the Retinoblastoma protein (pRb) which is a tumor suppressor and a cell cycle regulator, and DGKζ downregulation increased the number of cells at both S and G2/M phases of the cell cycle [97]. Interestingly, DGKζ is highly expressed in patient-derived acute myeloid leukemia cells and the knockdown of DGKζ in HL-60 promyelocytic cells induces a cell cycle arrest at the G2M checkpoint, inhibiting cell proliferation while increasing apoptosis (Table 2) [98]. In addition, DGKζ inhibition in U251 and U87 glioblastoma cells caused a marked decrease in cyclin D1 (CCND1) protein expression, which led to an arrest of cells at the G0/G1 phase [99]. Furthermore, major regulators of cancer cell growth and proliferation, such as the phosphorylated forms of Akt and mTOR, were also decreased, resulting in a significant reduction of cell proliferation in DGKζ knockdown cells compared to control cells. The authors also showed in an in vivo model that the tumorigenic capability of glioblastoma cells was reduced when DGKζ expression was decreased. Hence, DGKζ inhibition may confer advantages to glioblastoma patients [99].
In other cancer cells, such as K562 human erythroleukemia cells, DGKα can modulate cell cycle progression by influencing the phosphorylated status of pRb, which subsequently induces cell cycle arrest by impairing the G1/S transition [100]. In HCC cells, DGKα knockdown significantly suppresses cell proliferation, whereas overexpressing wildtype DGKα but not the kinase-dead mutant in the same cells significantly enhances proliferation. Similar results were obtained in HCC xenograft model experiments, where DGKα regulates cell proliferation via activation of the MAPK pathway. Specifically, DGKα downregulation impaired mitogen-activated protein kinase (MEK) and extracellular signal-regulated kinase (ERK) phosphorylation, both of which are crucial in the regulation of cell growth and migration [27]. Moreover, a novel DGKα specific inhibitor CU-3, which was successfully obtained after a high-throughput screening of about 9600 chemical compounds, induced apoptosis in HepG2 HCC cells and HeLa cervical cancer cells, while simultaneously enhancing immune response by promoting IL-2 production [101]. Consistent with these data, it was also reported that silencing or inhibiting DGKα activity with short interfering RNA (siRNA) or small-molecule inhibitor R59022 caused increased death of glioblastoma and melanoma cells by interrupting essential oncogenic pathways [29]. DGKα knockdown decreased both total and phosphorylated forms of mTOR, hypoxia-inducible factor 1-alpha (HIF1α), c-Myc levels, and phosphorylation of Akt in glioblastoma cells. Xenograft experiments also demonstrated that DGKα knockdown and inhibition affects tumor growth, angiogenesis, and survival of mice with intracranial and subcutaneous tumors. Intriguingly, knockdown of DGKα in non-cancerous cells, such as astrocytes and fibroblasts, showed no form of cytotoxicity as revealed in both glioblastoma and melanoma cells. Hence, small-molecule inhibition of DGKα is selectively toxic to human cancer cells but not normal human cells, thus making DGKα inhibition a promising therapeutic target [29].
Several studies demonstrated an active role of DGKα also in Src oncogenic functions [8,28]. Src is a regulator of mitogenic and survival signaling pathways that are downstream of receptor and non-receptor tyrosine kinases, such as the vascular endothelial growth factor receptor (VEGFR), human epidermal growth factor receptor-2 (HER2) and focal adhesion kinase (FAK), which are often aberrantly expressed in colon, breast, and pancreatic cancer [8]. Using 3D colon and breast cancer cell cultures, it was demonstrated that DGKα is essential in cell growth and survival by promoting the stabilization of Src activation. Importantly, DGKα enzymatic activity is necessary for Src activation. Pharmacological or genetic DGKα silencing restricted tumor growth in vivo, thus confirming the function of DGKα in malignant transformation [28].
Furthermore, DGK is involved in the major biological features of the transformed phenotype of Kaposi’s sarcoma (KS) cells, where DGK is essential for cell proliferation and DGK inhibitors could be promising for therapy [104]. Indeed, the DGK pharmacological inhibitor R59949 strongly reduces hepatocyte growth factor (HGF)-induced KS proliferation and anchorage-independent growth without affecting cell survival or the classical Akt and MAPK pathways, which are usually implicated in KS.
On the other hand, further studies showed that CHO-K1 ovary cells expressing the kinase negative mutant of DGKγ exhibited a larger size, slower growth rate, and an extended S phase, suggesting that the increase of cell size was induced by protein synthesis during the extended S phase and that DGKγ regulates cell cycle [61]. Even though the activities of DGKs in cancer seem to support tumor-promoting roles, there is evidence that DGKs can also support tumor suppressor activities [102]. For instance, DGKγ expression is downregulated in HCC tumor tissues and colorectal cancer (CRC) cell lines when compared to non-tumor control tissues, and this correlates with poor clinical outcomes [102]. Interestingly, DGKγ downregulation in HCC is due to epigenetic mutations induced by histone H3 and H4 deacetylation. In addition, an analysis of methylation of the CpG islands of DGK promoter genes in CRC cells revealed that DGKγ is hypermethylated in CRC cells but not in normal colonic tissue, and this corresponds with reduced DGKγ expression in CRC cell lines compared to control cells [26]. However, both constitutively active and kinase-dead DGKγ mutants induced inhibitory effects on CRC cell proliferation [26]. Notably, the ectopic expression of DGKγ in HCC cells decreased cell growth by downregulating glucose transporter 1 (GLUT1) expression and inhibiting cell glycolysis. In fact, GLUT1 expression is high in HCC and promotes tumorigenicity, therefore DGKγ plays tumor suppressor roles in HCC by lowering GLUT1 levels [102].
DGKε activity can also regulate the Ras/RAF/MEK/ERK signaling in cervical cancer cell line models [33]. This pathway plays pivotal roles in the regulation of cell proliferation, survival, and differentiation. The study showed that siRNA downregulation of DGKε impairs the epidermal growth factor (EGF)-activated Ras/RAF/MEK/ERK signaling cascade in HeLa cells. However, the mechanism through which DGKε regulates this pathway is still unknown [33]. Additionally, the potential of DGKη to regulate MAPK signaling, which is a downstream target of epidermal growth factor receptor (EGFR), led a group to study the oncogenic effects of DGKη in lung cancer, which is often characterized by mutations in EGFR and KRAS. The authors reported that silencing DGKη in lung cancer models, characterized by EGFR and KRAS mutations, reduced cancer cell growth while enhancing the cells’ sensitivity to EGFR inhibitor Afatinib [103].
4.2. Cell Migration, Invasiveness, and Metastasis
The motility and invasion of cancer cells from the primary tumor to a distant organ is an essential step in tumor metastasis. This event requires chemotactic migration of cancer cells and crossing of extracellular matrix barriers that surround the tumor [105]. As previously stated, DGKγ plays tumor-suppressive roles in CRC. The ectopic expression of wildtype, as well as kinase active and inactive mutant forms of DGKγ, restricts cell migration and invasion in CRC cells by inhibiting Rac1 activity [26]. Rac1 is a member of the Rho GTPases, which are small GTP-binding proteins that regulate cytoskeletal dynamics and activate essential protein kinases involved in Epithelial to Mesenchymal Transition (EMT) [106]. EMT involves the reprogramming of epithelial cells into mesenchymal cells, leading to morphological changes, specifically more elongated and spindle-like forms with increased migratory and invasive properties [107]. Notably, Rac1 is highly expressed in different stages of colorectal tumors. Its activity in CRC tissues positively correlates with poor prognosis of CRC patients by promoting EMT-mediated invasion of CRC cells via the activation of the signal transducers and activators of transcription 3 (STAT3) pathway [106]. Therefore, it would be interesting to elucidate the mechanisms associated with DGKγ-mediated inhibition of Rac1 activity for potential CRC therapy. DGKγ also plays tumor suppressor roles in HCC cells by reducing cell migration when DGKγ is overexpressed [102]. Conversely, DGKα is highly expressed in HCC and promotes tumorigenicity [27]. In fact, knockdown of DGKα suppresses cell migration by impairing the Ras/RAF/MEK/ERK pathway in HCC cells. The Ras/RAF/MEK/ERK pathway is indeed frequently deregulated in HCC and the activation of this pathway is significantly involved in cancer cell invasion [27]. In fact, ERK signaling is a critical mediator of cell migration, although it is also a classic mediator of cell growth, proliferation, and differentiation. ERK activates several proteins that regulate cell-matrix adhesion, cell protrusion, and retraction, all of which are essential processes recognized during cell motility [108]. Moreover, ERK controls EMT-regulated cell migration through Rac1/Fox01 activation [107]. DGKα activity has also been reported to be a key factor in the migratory and invasive responses induced by several growth factors, including HGF and vascular endothelial growth factor (VEGF) in endothelial, epithelial, and leukemic cells [109,110,111,112]. In line with the potential to regulate migration in endothelial cells by DGKα, a study employing both DGKα specific siRNA and/or DGK pharmacological inhibitor R59949 demonstrated that DGKα activity is a key regulator of migration in Hec-1A endometrial cancer cell line [112]. Inhibition of DGKα indeed reduced cell migration towards estrogen chemoattractant as well as abolished ruffle formation in Hec-1A cells [112]. In addition, DGKα promotes invasive migration in H1299 lung cancer cells and A2780 ovarian carcinoma cells by controlling Rab coupling protein (RCP)-driven integrin trafficking [113]. Furthermore, R59949 significantly reduced HGF-induced motility in KS cell lines with limited effects on cell adhesion and spreading [104], but it did not affect MAPK and Akt signaling pathways.
The downstream product of DGKs, PA has been associated with the receptor tyrosine kinase (RTK) signaling, which is an upstream regulator of the Ras/RAF/MEK/ERK cascade [114]. Another study attributed a potential role of PA in regulating tumor metastasis due to its ability to induce the secretion of Type 1 matrix metalloproteases (MMP1), enzymes able to promote metastasis [115]. However, these studies refer to PA generated by phospholipase D (PLD). Hence, it would be important to understand whether PA generated by DGKs performs the same functions. Interestingly, the application of nanomolar concentrations of PA increased cell migration in invasive MDA-MB-231 human breast cancer cells but had no effect on non-neoplastic control cells. Moreover, applying Clostridium difficile Toxin B to the PA-treated MDA-MB-231 breast cancer cells inhibited Rho activity and was followed by a marked decrease in cell migration [116]. These data strengthen the link between DGK/PA and Rho GTPases in cytoskeletal organization and subsequent cell migration. In addition, PA may be central in the regulation of cell motility by controlling the activity of type I PIP5K isozymes and PtdIns(4,5)P2 [117]. In fact, PA stimulates PIP5K, that participates in actin reorganization by generating PtdIns(4,5)P2, which is a primary regulator of cytoskeletal organization, so that PA signaling is also critical in PtdIns(4,5)P2 resynthesis [117].
A study reported that DGKζ deficiency in fibroblast cells induces a reduction in Rac1 and RhoA activation, as well as a significant reduction in cell migration [118]. Considering this finding, the authors extended their study by elucidating the impact of DGKζ signaling in CRC metastasis [30]. In tumor-derived CRC cell lines, knocking down DGKζ expression produced similar results as those seen in fibroblasts. A significant decrease in Rac1 and RhoA activity in DGKζ knockdown CRC cells was also observed and was followed by a decrease in cell invasion. Concomitantly, DGKζ depletion decreased the invasiveness of prostate cancer and metastatic breast cancer cells [30]. Thus, opposite to the tumor-suppressive roles of DGKγ as described above, DGKζ may also promote tumorigenesis by potentiating cell invasion and migration in several cancer types by regulating Rac1 and RhoA activity. This may be due to the fact that coordinated events between Rac1 and RhoA are necessary for effective migration in cancer metastasis. Indeed, RhoA is involved in the maintenance of actin stress fibers and focal adhesions, while Rac1 regulates the generation of lamellipodia, membrane ruffles formation, and Cdc42 signaling in filopodia production [119].
As for other DGKs, such as DGKδ and DGKι, there are a few reports demonstrating their participation in the development and progression of cancer. Downregulation of DGKδ in cervical and lung adenocarcinoma cell line models induced a downregulation of Akt activity, leading to a decrease in cell migration and proliferation. Moreover, DGKδ can control Akt activity through pleckstrin homology domain leucine-rich repeat protein phosphatase 2 (PHLPP2) [32]. Epigenetic studies have also revealed that DGKι may be methylated in cancer, including glioblastoma and HCC [34,35], while it is still unknown whether DGKι mutations may produce effects directly involved in metastasis of these cancer types.
5. Targeting DGKs in Cancer Therapies
The development of effective therapies to fight cancer continues to be one of the major challenges in modern medicine. Chemotherapy and radiotherapy are somehow successful, but these approaches are non-specific and often lead to short- or long-term adverse effects which usually affect quality of life [7]. Recently, exploring immune-based therapeutic systems, such as blockage of immune checkpoints or adoptive cell transfer (ACT) that stimulate antitumor immunity by targeting and attacking tumor cells, seems promising because of its specificity. The starting point of this immune response is represented by chimeric antigen receptor (CAR)-T cells [120]. Interestingly, incoming reports suggest that targeting DGK activity could be a strong approach to reinforce the anti-tumor functions of T cells [7].
Currently, DGKα holds much promise in cancer therapy [7,29], as its inhibition presents toxicity in various cancer, but not normal human cells [29]. For instance, in T cells, the inhibition of DGKα activity may generate a simultaneous response of reinforcing T cell attack on tumor cells, while directly inhibiting tumor growth [7].
The inhibition of DGKα by R59949 in KS and endometrial cancer cells leads to decreased cell proliferation, growth, and migration [104,112]. On the other hand, using the small molecule inhibitor of DGKs R59022, DGKα was inhibited in glioblastoma, cervical cancer, melanoma, and breast cancer cell lines. In these cells, the percentage of cell death was increased compared to control normal fibroblasts and astrocytes [29]. Indeed, in glioblastoma in vivo tumor models, the same authors showed that DGKα inhibition decreases angiogenesis, tumor growth, and survival of mice with tumors [29]. Targeting DGKs may be important in cancer immunotherapy, as intratumoral injection of DGK knockout T-cells into U87MGvIII glioblastoma tumor models, obtained by CRISPR/Cas9, caused significant suppression of the tumors [25]. More importantly, this result was due to an enhancement in the effector functions of T-cells in the xenograft model. The authors also showed that the CRISPR/Cas9 generated DGK-knockout in CAR-T cells potentiates T-cell functions by increasing cluster of differentiation 3 (CD3) signaling. Consequently, the cells became resistant to immunosuppressive factors such as transforming growth factor-β (TGFβ) and prostaglandin E2, which are known mediators of cancer cell survival [25].
Other studies tested CU-3, a DGK pharmacological inhibitor with a higher specificity for DGKα than R59949 and R59022, mainly due to its specific targeting of the ATP binding site in the catalytic domain of DGKα. This molecule induced apoptosis in several cancer cells, while enhancing immune response [101]. Similarly, compound A, which specifically inhibits type I DGKs and especially DGKα, induced apoptosis and reduced viability of melanoma and several other cancer cell lines [121]. In addition, two novel DGKα inhibitor compounds, namely 11 and 20 (with an IC50 of 1.6 and 1.8 µM respectively, thus representing the most potent DGKα inhibitors until now), decreased cell migration in cancer cells (Figure 2) [122].
On the other hand, Ritanserin, an established serotonin receptor inhibitor, has recently been identified as a DGKα inhibitor. Interestingly, it is more potent than R59022, although these two compounds differ structurally by just a single fluorine [123]. Ritanserin has already been shown to be well-tolerated and safe for human use in clinical trials, potentially paving the way to use it clinically as a DGKα inhibitor [123]. In fact, treatment of several cancer cells with Ritanserin has yielded similar results as other DGK inhibitors [7,124]. For example, the mesenchymal subtypes of lung and pancreatic carcinoma, as well as the mesenchymal subtype of glioblastoma, are sensitive to Ritanserin. Indeed, DGKα inhibition by Ritanserin induced cell death in glioblastoma stem cells and this was partially mediated by apoptosis [124]. Additionally, Ritanserin, as with other small molecule inhibitors of DGKα, also enhanced T cell signaling but failed to promote long-term T-cell activation [125].
6. Conclusions
All the reported studies highlight DGK signaling as a promising target for cancer therapy. However, more studies are needed to fully comprehend DGK specific roles in cancer development and progression. Due to the isoform-specific functions observed in different types of cancer cells and even subcellular sites, it would be crucial to fully understand how the specific DGK isoforms control downstream oncogenic signaling, as these pathways can regulate proliferation, growth, angiogenesis, immunity, and migration. To better understand DGK signaling it would also be essential to explore the potential crosstalk among the various DGK isoforms, the possible redundancy or compensatory functions of other isoforms during the inhibition of one or more DGK isoforms, as well as consider the DGK specific subcellular localization relevance. Moreover, the discovery of more potent DGK-specific isoform inhibitors may be useful to study the isoform-specific functions and develop new cancer targeted therapies. Future studies may also benefit from the combinatorial effect of DGK inhibitors and other standard cancer therapies, such as radiation and chemotherapy. Furthermore, since PA is involved in several cellular processes, the combination of both DGK inhibitors and inhibitors of PA-synthesizing enzymes may prove to be more beneficial in cancer therapy than when used individually. Finally, since recent reports demonstrated that DGKs may have a preference for specific DAG species, which are independent of PI turnover pathways, it would be strategic to further investigate the cellular signaling pathways associated with PA, produced by both PI independent and PI dependent pathways.
Author Contributions
Conceptualization, investigation L.M., L.C. and S.R., formal analysis, A.F., E.O.O., I.R., M.V.M., J.A.M. and M.Z. writing-original draft preparation, A.F. and E.O.O. writing-review and editing, S.M., M.Y.F., G.R., M.Z., J.A.M., S.R., L.C.; and L.M. visualization, I.R. and M.V.M., funding acquisition, L.M., G.R., M.Y.F., S.R. and L.C. All authors have read and agreed to the published version of the manuscript.
Funding
This research was funded by Italian PRIN-MIUR grants (to L.M., G.R., M.Y.F.), Fondazione Cassa di Risparmio di Bologna grant (to S.R.), Fondazione del Monte di Bologna e Ravenna grant and Intesa S. Paolo Foundation grant (to L.C.).
Conflicts of Interest
The authors declare no conflict of interest.
Abbreviations
3D
Three-dimensional
Ca2+
Calcium
ACT
Adoptive cell transfer
AML
Acute myeloid leukemia
CAR
Chimeric antigen receptor
CCND1
Cyclin D1
CD3
Cluster of differentiation 3
CRC
Colorectal cancer
DAG
Diacylglycerol
DGKs
Diacylglycerol kinases
EGF
Epidermal growth factor
EGFR
Epidermal growth factor receptor
EMT
Epithelial to mesenchymal transition
ERK
Extracellular signal-regulated kinase
FAK
Focal adhesion kinase
GAP
GTPase-activating protein
GLUT1
Glucose transporter 1
HCC
Hepatocellular carcinoma
HER2
Human epidermal growth factor receptor-2
HGF
Hepatocyte growth factor
HIF1α
Hypoxia-inducible factor 1-alpha
IL-2
Interleukin 2
InsP3R
Inositol trisphosphate receptor
KS
Kaposi’s sarcoma
MAPK
Mitogen activated protein kinase
MARCKS
Myristoylated alanine rich kinase substrate
MEK
Mitogen-activated protein kinase
MMP1
Type 1 matrix metalloproteases
mTOR
Mammalian target of rapamycin
NLS
Nuclear localization sequence
PA
Phosphatidic acid
PHLPP2
Pleckstrin homology domain leucin-rich repeat protein phosphatase 2
Rat sarcoma virus guanyl nucleotide-releasing protein
RTK
Receptor tyrosine kinase
SAM
Sterile α motif
siRNA
Short interfering RNA
SMSr
Sphingomyelin synthase related protein
STAT3
Signal transducers and activators of transcription 3
TGFβ
Transforming growth factor β
Unc-13
Mammalian uncoordinated 13
VEGF
Vascular endothelial growth factor
VEGFR
Vascular endothelial growth factor receptor
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Cartoon representation of DGK signaling in cancer. Upon their activation, DGKs phosphorylate DAG to form PA, which results in a series of signaling events resulting from the alterations in the signaling of several molecular targets. Several studies reported that DGKs regulate cancer cell survival and proliferation via MAPK and Akt/mTOR pathways. In addition, DGKs regulate migration via the Ras/RAF/MEK/ERK pathway and the Rho GTPase family members. On the other hand, DAG activates several critical proteins including both conventional and novel PKCs, mammalian Unc-13, chimaerins which activate Rac GTPase, and RasGRPs, which are involved in cell proliferation and migration.
Functional effects produced by DGKα inhibition in cancer. The use of DGKα specific or pan inhibitors to inhibit DGKα activity in cancer cells seems to block cancer progression through the inhibition of cancer-promoting mechanisms, such as growth and survival. Intriguingly, inhibition of DGKα activity increases apoptosis in several cancer cell lines and in vivo tumor models, while enhancing T-cell activity or immune response.
ijms-21-05297-t001_Table 1
Distribution of DGKs across distinct subcellular compartments.
Induces cell cycle arrest at G2M, Inhibits cell proliferation and increases apoptosis [98]
\\nDGKη\\n
Lung cancer
Impairs MAPK signaling [103]
\\n\"\n]"}}},{"rowIdx":445,"cells":{"data":{"kind":"list like","value":["\nInt J Mol SciInt J Mol SciijmsInternational Journal of Molecular Sciences1422-0067MDPI32731552PMC743210210.3390/ijms21155363ijms-21-05363ArticleChanges in Proteome of Fibroblasts Isolated from Psoriatic Skin LesionsGęgotekAgnieszka1https://orcid.org/0000-0002-8060-7675DominguesPedro2WrońskiAdam3https://orcid.org/0000-0001-5397-7139SkrzydlewskaElżbieta1*Department of Analytical Chemistry, Medical University of Bialystok, Mickiewicza 2D, 15-222 Bialystok, Poland; agnieszka.gegotek@umb.edu.plMass Spectrometry Centre, LAQV REQUIMTE, Department of Chemistry, University of Aveiro, 3810-193 Aveiro, Portugal; p.domingues@ua.ptDermatological Specialized Center “DERMAL” NZOZ in Bialystok, 15-453 Bialystok, Poland; adam.wronski@dermal.plCorrespondence: elzbieta.skrzydlewska@umb.edu.pl; Tel.: +48-85-748-57082872020820202115536306720202772020© 2020 by the authors.2020Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
The dermal fibroblasts are in constant contact with the cells of the immune system and skin epidermis. Therefore, they are essential for the development of lesions in psoriasis. The aim of this study was to assess the changes in the proteomic profile of fibroblasts in the dermis of psoriasis patients, and to discuss the most significant changes and their potential consequences. The proteomic results indicate that fibroblast dysfunction arises from the upregulation of proinflammatory factors and antioxidant proteins, as well as those involved in signal transduction and participating in proteolytic processes. Moreover, downregulated proteins in psoriatic fibroblasts are mainly responsible for the transcription/translation processes, glycolysis/ adenosine triphosphate synthesis and structural molecules. These changes can directly affect intercellular signaling and promote the hyperproliferation of epidermal cells. A better understanding of the metabolic effects of the proteomic changes observed could guide the development of new pharmacotherapies for psoriasis.
psoriasisskin fibroblastsproteomic profileinflammationoxidative conditionsintracellular signal transduction1. Introduction
Psoriasis is a chronic disease that occurs with increasing frequency in developed countries. In European countries and the United States, the prevalence of psoriasis can reach 3% [1,2]. Psoriasis occurs mainly due to a dysfunction of the immune system, and its development might be associated with other diseases, including arthritis, metabolic syndrome, heart disease, polycystic ovarian syndrome, chronic obstructive pulmonary disease and even cancer [3]. However, the main symptom of the disease is excessive skin exfoliation [4]. As a result, psoriasis also affects the psychophysical health of patients by lowering their self-esteem and disrupting their social behavior. Depressive symptoms and sleep disturbances are also common in psoriatic patients [5,6].
The pathogenesis of psoriasis is the result of impaired immunity, and has a genetic component linked to the immune genes and their encoded pathways, as well as to environmental factors such as drugs, smoking, diet, alcohol and mental stress [7]. Regardless of the particular mechanisms involved, psoriasis develops due to the chronic activation of the cells of the peripheral immune system, resulting in the increased proliferation and differentiation of skin cells [8,9]. Significant changes occur in the epidermis, where the accelerated cell cycle of keratinocytes results in intensified keratinization and the formation of cutaneous psoriatic lesions. Epidermal keratinocytes are stimulated to proliferate by signaling molecules, primarily released by lymphocytes. This process has been well examined and described previously [8].
Undoubtedly, the release of signaling molecules that can reach and interact with the epidermis will also have an impact on cells that build the other layers of the skin, including dermal fibroblasts. Under physiological conditions, skin fibroblasts are primarily responsible for the production of collagen and other intercellular matrix substances present in the dermis, which are intimately linked to the condition and function of the skin [10]. However, the metabolic activity of fibroblasts in psoriatic skin has not been extensively studied in recent years, compared to keratinocytes, which have been the subject of extensive research [11,12,13,14,15,16,17,18].
Oxidative stress is a characteristic of the tissue of patients with psoriasis [19]. Currently, it is known that oxidative stress in dermal fibroblasts is higher in scaly skin than in unchanged tissue [20]. It is important to note that the increase in oxidative stress and the decrease in the total antioxidant capacity of dermal fibroblasts are even greater than in the keratinocytes isolated from the same skin biopsy [20]. Under such conditions, the molecules present in intracellular fibroblasts may undergo oxidative modifications, which can trigger an increase in oxidative lipid metabolism [21]. As a result, there is an increase in lipid peroxidation products, including reactive α, β-unsaturated aldehydes and isoprostanes [22]. Additionally, the increase in the enzymatic lipid metabolism of psoriatic fibroblasts promotes the production of bioactive mediators, including eicosanoids, sphingolipids and ceramides. These mediators are involved in skin biology, inflammation and immunity, and even cell apoptosis [23,24].
Increased levels of electrophilic molecules, mainly reactive oxygen species (ROS), as well as reactive aldehydes, especially 4-hydroxynenenal (4-HNE) and malondialdehyde (MDA), can also lead to modifications of proteins in patients with psoriasis. These modifications have been observed in lymphocytes and keratinocytes, and included the formation of protein adducts with lipid peroxidation products [17,25] and a significant increase in protein carbonylation in skin fibroblasts [20]. The presence of these protein modifications in psoriatic fibroblasts also leads to the activation of redox-sensitive signaling pathways, including those that depend on the mitogen-activated protein kinases (mitogen-activated protein kinase (MAPK), p38, extracellular signal-regulated kinase (ERK) and c-Jun N-terminal kinase (JNK)) [21], as well as protein kinase C (PKC) [26]. Consistently, PKC in the cell membranes of psoriatic fibroblasts is significantly activated, which could make these cells very sensitive in response to hormones or growth factors [26]. Moreover, psoriatic fibroblasts, unlike unmodified dermal cells, have been shown to stimulate the proliferation of keratinocytes after receiving activation signals [27]. An example of such action in psoriatic fibroblasts stimulated by inflammatory cytokines is the observation that increased expression of the insulin-like growth factor-I (IGF-I) significantly promotes the proliferation of keratinocytes [28]. Metabolic disturbances in psoriatic fibroblasts also cause increased expression of interleukin 8 (IL-8), resulting in the stimulation of neutrophils, monocytes and T lymphocytes, which migrate into the skin layers [29]. In addition, the changes observed following psoriatic epidermal exfoliation are linked to changes in the metabolism of fibroblasts, not only locally but also in regions distant from the exfoliation site. The expression of factors such as α5 integrin, fibronectin or keratinocyte growth factor (KGF) is high, in particular in non-lesional psoriatic skin fibroblasts [30]. In agreement with this, it is suggested that these factors play a crucial role in the pathogenesis of psoriasis by influencing the inflammation and hyperproliferation of keratinocytes.
The abundance of evidence highlighting the critical role of fibroblasts in the development of psoriasis lesions has led us to investigate in more detail the molecular mechanisms leading to the pathogenesis of the disease. To achieve this, we sought to determine the differences in the proteomic profiles of fibroblasts isolated from the dermis of psoriatic patients, compared to unmodified skin cells.
2. Results
The results presented in this study show that the proteome of fibroblasts isolated from the dermis of psoriatic patients has a different profile than that of control cells. The data obtained from our proteomic analysis allowed us to identify and semi-quantitatively determine the expressions of 718 proteins, 684 of which were found in control fibroblasts, and 690 in cells isolated from the skin of psoriatic patients ( Supplementary Table S1). The distribution of these proteins between the samples is shown on a Venn diagram (Figure 1).
Using principal component analysis (PCA), we found that changes in the proteomic profiles of skin fibroblast cells led to the clustering of the experimental groups (PC1—41.5%, PC2—17.4%). In the case of control fibroblasts, the samples clustered in the left quadrant, while the psoriatic fibroblasts clustered mainly in the lower right quadrant (Figure 2). Statistical analysis indicated that the expressions of 242 of the proteins identified were significantly different between the control fibroblasts and the psoriatic fibroblasts ( Supplementary Table S2). A volcano plot displaying differentially enriched proteins highlighted that the most significantly changed proteins in psoriatic fibroblasts were downregulated; β-catenin (P35222), importin-8 (O15397), protein kinase C (Q05655) and galectin-3 (P17931). The following, on the other hand, were upregulated: keratin (P35527), tubulin (Q9BVA1), 26S proteasome (Q5VWC4), protein transport protein Sec24C (P53992), glutathione S transferase 1 (P08263) and high mobility group protein B2 (P26583) (Figure 3).
The clustering and functions of the 50 most significant proteins were visualized in a two-dimensional hierarchical clustering heat map (Figure 4). The analyzed proteins were divided into two clusters. Cluster 1 contained proteins downregulated in psoriatic fibroblasts compared to controls. These proteins were primarily involved in the transcription/translation processes, protein folding and glycolysis/ATP synthesis, as well as having structural functions. Cluster 2 contained proteins upregulated in psoriasis and had various different biological functions. Some of them were proinflammatory proteins, including NFκB (Q00653), TNFα (P01375) and S100A8/9 (P05109/P06702) (Figure 5A). Another group of proteins upregulated in psoriatic fibroblasts was those with antioxidant properties. These included thioredoxin (Q99757), peroxiredoxin (P32119), glutaredoxin (O76003), Nrf2 (Q16236), glutathione S transferase 1 (P08263) and thioredoxin-dependent peroxide reductase 2 (P30048) (Figure 5B). Finally, psoriatic fibroblasts were characterized by higher expressions of proteins involved in signal transduction (such as 14-3-3 proteins (P31947, P63104), kinases (P55263, P67775, Q5U5J2) and intracellular channel protein 4 (Q6FIC5)) (Figure 6A), intracellular transport (such as the Ran-specific GTPase-activating protein (F6WQW2), GTP-binding nuclear protein Ran (J3KQE5) and transport protein Sec24C (P53992)) (Figure 6B) and the proteins involved in the proteolytic processes (calpain (P07384) and 26S proteasome (Q5VWC4)) (Figure 6C).
3. Discussion
Psoriasis is an immune-mediated disease characterized by increased activation of lymphocytes. Once the lymphocytes have entered the skin, they cause the proliferation, maturation and desquamation of keratinocytes [25]. The resulting skin lesions disrupt the functioning of the epidermis and can lead to a reduction in the quality of life of those affected [5,31]. However, the development of psoriasis is not only linked to changes in the interaction between lymphocytes and keratinocytes; it also involves the activity of other cells of the immune system. For example, the actions of granulocytes [32,33] can significantly alter the profile of the signaling molecules in the plasma of psoriatic patients [34]. Such changes do not exclusively affect keratinocytes, and also have consequences for other skin-building cells, including dermal fibroblasts [24,30,35].
Fibroblasts, as the primary cells of the dermis, are responsible for the synthesis of proteoglycans, glycosaminoglycans and collagen, which are the main components of the extracellular matrix. These cells are essential for maintaining the appropriate thickness of the dermis when the exfoliated epidermis becomes too thin to fulfill a protective function. Therefore, increased collagen biosynthesis in these cells is observed in psoriatic skin [36,37]. Moreover, fibroblasts directly promote the proliferation of keratinocytes by generating and transporting growth factors [27].
These observations highlight the need for further research into how changes in the proteome of these cells could contribute to the development of psoriasis. To date, complex changes in the proteome of psoriatic patients have been identified in keratinocytes and biopsies of the whole skin, as well as in blood cells and plasma [35,38,39,40,41,42,43]. However, in fibroblasts from psoriatic patients, only changes in candidate proteins were analyzed [20,21,26,28,30,44]. Our study investigated changes in the proteome of psoriatic fibroblasts. In this report, we present the most significant modifications and discuss their potential consequences. The changes described include the upregulated proteins that are involved in inflammation, the antioxidant response, signal transduction and proteolytic processes, as well as the downregulated proteins mainly responsible for the transcription/translation processes, glycolysis/ATP synthesis and structural components.
Psoriatic skin exfoliation is intrinsically linked to the response of skin cells to proinflammatory factors generated by immune cells [45]. These agents activate cell membrane receptors leading to stimulation of the intracellular pro-inflammatory pathways. Consequently, the expression of NFκB is increased in dermal fibroblasts, but also in epidermal keratinocytes [46]. The action of NFκB not only leads to the expression of the genes responsible for the inflammatory reaction, but also stimulates other pro-inflammatory factors, including TNFα, which intensify pro-inflammatory signaling within the cell and between adjacent cells. In psoriatic plaques, the skin levels of TNFα are increased, which promotes infiltration by macrophages that also express TNFα [47,48,49]. As a result, keratinocytes are continuously stimulated to proliferate [50]. Until now, there has been no unambiguous data indicating the levels of these factors in psoriatic fibroblasts. However, the presented results allow us to suggest that the increased expression of NFκB and TNFα in fibroblasts can enhance intercellular proinflammatory signaling, thus contributing to the stimulation of keratinocytes proliferation.
Moreover, the increase in the level of pro-inflammatory factors in psoriatic fibroblasts is accompanied by an increased expression of the proteins participating in the proteolytic processes, such as 26S proteasome components and S100A8/A9. The ubiquitin–proteasome pathway activated during the development of psoriasis also plays a central role in the selective degradation of intracellular proteins, including those involved in the control of inflammatory processes [51]. The increase in the level of 26S proteasome subunits observed in this study could contribute to an increased degradation of IκB, which is a cytosolic inhibitor of NFκB [52,53]. As a result, NFκB, by improving the inflammatory response, promotes T cell responses in psoriasis [54]. In vitro and in vivo experiments have shown that 26S proteasome inhibitors inhibit cell proliferation and migration under inflammatory conditions [55], highlighting the potential for the inhibition of the proteasome as a treatment option for inflammatory disorders such as psoriasis [51]. Another protein with proteolytic activity associated with inflammation is a calcium-dependent neutral protease called calpain. The main role of calpain is the regulation of various fundamental cellular functions, such as the cell cycle and apoptosis, but it is also involved in the initiation of inflammation by the degradation of IκB and the activation of NFκB [56]. An increase in the level of calpain in psoriatic patients has already been identified in the skin tissue [57,58]. However, calpain also stimulates the migration of fibroblasts and myoblasts, which is necessary for the treatment of damaged skin [59].
Our data also show an increase in the level of S100A8/A9 proteins in psoriatic fibroblasts. Previous studies have determined that the source of S100A proteins in skin tissue are the activated phagocytes in inflammatory conditions associated with psoriasis lesions [60]. The main role of S100A8/A9 in the psoriatic epidermis is to activate the complement component 3 protein (C3) in keratinocytes. After activation, C3 is translocated to the dermis, where it stimulates immune cells to produce cytokines, interleukins and growth factors, thereby contributing to the development of psoriasis [61,62,63]. It is unknown whether psoriatic fibroblasts can synthesize S100A8/A9 or can only accumulate proteins when produced by other cells. However, our data, which show a significant increase in these proteins in dermal cells, suggest an additional role for fibroblasts in pro-inflammatory signaling, which leads to the hyperproliferation of keratinocytes in psoriasis.
Inflammatory diseases, such as psoriasis, are associated with pro-oxidative conditions, leading to oxidative stress [64,65]. In response, the level and activity of components of the antioxidant system increase in patients with psoriasis [66,67]. Our results confirm that in the fibroblasts of psoriasis patients, one of the main groups of significantly modified proteins is the proteins involved in the antioxidant response. These include the transcription factor Nrf2—a redox-sensitive protein responsible for the expression of cytoprotective proteins. Various investigations into psoriatic keratinocytes have observed changes in Nrf2 levels. One study found that a decrease in the levels of Nrf2 was associated with the development of psoriasis [68], while others observed an increased expression of Nrf2, which led to the elevated expression of keratins and promoted the proliferation of keratinocytes, leading to the pathogenesis of psoriasis [69,70]. The transcriptional activity of Nrf2 leads to the expression of genes coding for antioxidant enzymes, in particular thioredoxin-dependent peroxide reductase and glutathione S transferase 1 [71], the levels of which are increased in psoriatic fibroblasts. A previous study also indicated that the level of these enzymes is increased in fibroblasts under oxidative stress induced by UV, which is probably a defense mechanism against adverse conditions in the cell [72]. Moreover, the increased level of thioredoxin-dependent peroxide reductase is accompanied by a high level of thioredoxin, which is associated with the increased activity of this enzyme. Simultaneously, the levels of peroxiredoxin and glutaredoxin are increased. These proteins can reduce thiol groups in oxidized proteins and also control the peroxide levels induced by cytokines [73]. Previous reports confirm the increase in the mentioned parameters of the antioxidant system in skin biopsies of psoriatic patients [74]. Along with the previously published data, our findings indicate that fibroblasts from psoriasis patients are subject to high levels of oxidative stress, and these cells activate pathways to limit these oxidative conditions.
Signal transduction between cells involved in psoriatic lesion development is one of the fundamental elements to consider in designing effective treatments for psoriasis [75,76,77]. So far, the role of fibroblasts in this intercellular communication has not been described. In this study, we found that fibroblasts in psoriatic skin display the upregulation of 14-3-3 sigma (σ) and zeta/delta (ζ/δ) protein isoforms. Other studies show that 14-3-3 protein levels in psoriatic skin biopsies are changed in various ways, depending on the isoform; 14-3-3τ and σ are upregulated [78,79,80], while 14-3-3β and 14-3-3ζ are downregulated [81]. 14-3-3 is involved in the regulation of transcription and translation through its interaction with DNA/mRNA-binding proteins, such as tristetraprolin (TTP), which induces the destabilization and degradation of cytokine mRNA (including TNFα mRNA). After phosphorylation, TTP can bind to 14-3-3, which inhibits the mRNA-degrading capabilities of TTP. Therefore, in several skin diseases characterized by hyperproliferative keratinocytes, increased levels of 14-3-3 result in the overexpression of cytokines [78]. These changes are accompanied by the upregulation of kinases, as shown in this study and in previous work on a psoriatic skin model [82]. Conversely, in the case of DNA-damage or the overexpression of dysregulated genes, 14–3–3σ is upregulated by a p53-dependent pathway, and partially prevents cells from entering mitosis [83].
In psoriatic skin fibroblasts, the expression of the chloride intracellular channel protein 4 (ClIC4) is increased. The activity of this transmembrane protein is linked to angiogenesis and to the differentiation of keratinocytes [84]. In the case of fibroblasts, the overexpression of ClIC4 leads to the activation of the transforming growth factor-β1 (TGF-β1) and to conversion to myofibroblasts, which is a known feature of diseases characterized by the hyperproliferation of cells, including cancer [44,85]. Consistent with this finding, increased levels of TGF-β1 have been observed in the keratinocytes, plasma and lymphocytes of psoriatic patients [86,87,88]. Therefore, the increased ClIC4 level in fibroblasts from psoriatic patients suggests the role of these cells in enhancing the expression of TGF-β1 in the tissues of people affected by psoriasis.
Psoriatic fibroblasts are also characterized by the increased expression of proteins involved in intracellular transport. Intracellular transport is essential for cells with accelerated proliferation, as well as those that are constantly exposed to the signaling molecules released by cells of the immune system. At the top of the list of the most-changing expressed proteins are the Ran-specific GTPase-activating protein (RANBP1) and the GTP-binding nuclear protein Ran. The actions of these proteins lead to the selective activation of Ran [89], a protein involved in the transport of proteins across the nuclear membrane. Ran carries out nuclear transport by binding to importins or exportins, and thus participates in the activity of regulation of the transcription factor [89]. So far, the increase in the level of Ran and its activators has been identified in activated human lymphocytes T [90]. However, despite the importance of the Ran-related pathway in regulating the cell cycle, it has not been widely analyzed in samples from psoriatic patients. Moreover, the Sec24C protein is also upregulated in psoriatic fibroblasts. The activity of Sec24C facilitates the selection of proteins for transport to the cell nucleus, but is also involved in the transport of proteins to the endoplasmic reticulum [91]. Therefore, Sec24C is responsible for the proper biosynthesis, maturation and secretion of collagen [92], suggesting that its upregulation plays a specific role in the fibroblasts of regularly exfoliated psoriatic skin.
The fibroblasts of psoriatic skin lesions also contain a large group of proteins whose expression is reduced compared to healthy cells. The main proteins are β-catenin, importin-8 and galectin-3. All of these proteins are involved in cell–cell adhesion, which is an essential process in the formation of the skin layers [93,94,95]. Decreased levels of β-catenin have been found in the cytoplasm of keratinocytes in psoriatic skin [93]. β-catenin is responsible for the transmission of the contact inhibition signal, which causes the division of cells to stop. Therefore, its deficiency in psoriasis does not stop cell division. However, in psoriatic cells, β-catenin accumulates in the nucleus, where it can upregulate gene expression and influence cell growth [93]. Conversely, gene expression might also be dysregulated by a decrease in the level of importin-8. This protein is responsible for the transport of mature miRNAs from the cytoplasm to the nucleus, which causes gene silencing [94]. Cells deficient in importin-8 display uncontrolled gene expression [96] which, in the skin, can lead to the hyperproliferation of keratinocytes and the formation of psoriatic lesions.
Another downregulated protein in psoriatic fibroblasts associated with the regulation of gene expression is galectin-3. This protein is strongly expressed in epithelial cells, such as keratinocytes and skin fibroblasts, and is involved in the pathogenesis of inflammatory skin diseases by regulating the functions of immune cells, including lymphocytes [95,97]. Its decreased level in psoriatic keratinocytes has been shown to be a primary mechanism of cellular hyperproliferation, through the activation of the JNK pathway or the accumulation of neutrophils associated with S100A7-9 overexpression [98]. On the other hand, an increase in the levels of galectin-3 in the blood of psoriatic patients leads to inflammation and increased profibrotic activity [99]. However, the exact role of the downregulation of fibroblast galectin-3 in the development of psoriasis remains undefined.
Current evidence indicates that the development of psoriasis is likely associated with changes in the function of the immune system, as well as in skin cells. Psoriatic skin exfoliation is the result of changes in the metabolism of keratinocytes; however, metabolic changes in fibroblasts also contribute significantly to these symptoms.
4. Materials and Methods4.1. Sample Collection and Preparation
Skin biopsy fragments were collected from five untreated patients with a diagnosis of psoriasis vulgaris. The patients were two men and three women; age range 27–48 years, mean 38. They were selected from a cohort of 70 patients because their skin lesions were the most characteristic of typical psoriasis. Biopsies were also taken from five healthy people who had moles, and the adjacent skin removed. These individuals forming a control group were sex-matched to the patient group. They had an age range of 28–50 years, mean 38.
The individuals selected for the patient group had had a diagnosis of plaque psoriasis for at least six months, with at least 10% of the total body surface area affected. The severity of psoriasis was assessed using the PASI score (Psoriasis Area and Severity Index) (median 18; range 12–25). None of the patients or healthy subjects had received topical, injectable or oral medications during the four weeks before the study. Individuals whose history indicated any other disorders were excluded from the study. None of the participants were smokers. All subjects gave their informed consent for inclusion before they participated in the study. The study was conducted in accordance with the Declaration of Helsinki, and the protocol was approved by the Local Bioethics Committee Medical University of Bialystok (Poland), No. R-I-002/502/2015 (17 December 2015). After the biopsy, skin fragments were taken for histopathological examination, and the remaining material was used for molecular analysis.
The samples were washed in PBS with 50 U/mL penicillin and 50 μg/mL streptomycin and incubated overnight at 4 °C in 1 mg/mL dispase to separate the epidermis from the dermis. The obtained dermis was sliced and placed in culture plates in fibroblast culture medium consisting of DMEM (Dulbecco’s Modified Eagle Medium), fetal bovine serum (10%) and penicillin (50 U/mL)/streptomycin (50 μg/mL). Samples were incubated in a humidified atmosphere of 5% CO2 at 37 °C until the fibroblasts emigrating from the slices reached full confluence.
Fibroblasts were collected from the plates by scraping on ice, and were suspending in buffer Tris-HCl (50 mM, pH 7.5, 4 °C) containing 0.1% SDS and protease inhibitor cocktail. All samples were lysed by sonification on ice. The total protein content in the cell lysates was measured using the Bradford assay [100].
4.2. SDS-PAGE and In-Gel Digestion
Each sample (containing 25 μg of protein) was mixed 1:1 with Laemmle buffer, containing 5% 2-mercaptoethanol, and heated at 100 °C for 7 min. After cooling to room temperature, the samples were separated on 12% Tris-Glycine SDS-PAGE gels. Gels were fixed for 1 h in methanol:acetic acid:water (4:1:5) and stained for 4 h with Coomassie Brilliant Blue R-250. All detected bands were cut out of the gel, sliced, and washed out of the dye by acetonitrile (ACN) and 25 mM AMBIC (ammonium bicarbonate). Proteins in each gel fragment were reduced for 1 h with 10 mM DTT, alkylated for 1 h with 50 mM iodoacetamide, and overnight in-gel digested with sequencing grade trypsin (Promega, Madison, WI, USA). The digestion was stopped by the addition of 10% FA (formic acid) and evaporated.
The dried peptides were dissolved in 5% ACN with 0.1% FA immediately before analysis and loaded onto a 150 mm x 75 µm PepMap RSLC capillary analytical C18 column with 2 μm particle size (LC Packings) using Ultimate 3000 HPLC system (Dionex, Idstein, Germany). Peptide separation was done at a flow rate of 0.300 µl/min, and the solvents gradient started at 3 min and was ramped to 60% Buffer B (90% ACN + 0.03% FA) over 60 min. Eluted peptides were analyzed using a Q Exactive HF mass spectrometer with a nanoelectrospray ionization source (ESI) (Thermo Fisher Scientific, Bremen, Germany). The mass spectrometer was externally calibrated and operated in positive and data-dependent modes. Survey MS scans were conducted in the 200–2000 m/z range, with a resolution of 120,000. The top ten most intense ions were fragmented with 30 eV collision energy on an HCD collision cell and analyzed with a resolution of 30,000. A 10 s dynamic exclusion window was applied, and an isolation window of 4 m/z was used to collect suitable tandem mass spectra. The obtained data were acquired with the Xcalibur software version 4.1 (Thermo Fisher Scientific, Bremen, Germany).
4.4. Protein Identification and Label-Free Quantification
For protein identification, Proteome Discoverer 2.0 (Thermo Fisher Scientific, Bremen, Germany) was used with the following search parameters: peptide mass tolerance set to 10 ppm, MS/MS mass tolerance set to 0.02 Da, up to two missed cleavages allowed, cysteine carbamidomethylation/carboxymethylation and methionine oxidation set as a dynamic modification, a minimum peptide length set to 6 amino acids, and the minimum number of identified unique peptides for each protein set to two peptides. Input data were searched against the UniProtKB-SwissProt database (taxonomy: Homo sapiens, release 2019-04). In the case of proteins that were identified in at least 60% of the examined samples from the control or psoriatic group, the missing values were estimated as half of the lowest recorded intensity (half minimum imputation). Other proteins were removed from the analysis as artefacts.
4.5. Statistical Analysis
The analysis of each sample was performed in three independent replicates. Data from individual protein label-free quantifications were log and Z-score transformed. Statistical analysis of data was performed using free available MetaboAnalyst 4.0 software (http://www.metaboanalyst.ca) (Xia Lab, Montreal, Quebec, Canada) [101], RStudio software (R version 3.6.2 (2019-12-12)) [102] and Perseus 1.6.10.43 [103]. Data were analyzed using the standard statistical analysis methods, including univariate analysis (one-way ANOVA), and only proteins with an FDR-corrected significant q-value were taken into account in the discussion. Protein molecular function and protein class were assigned according to the Gene Ontology database in the free available STRING version 11 (https://string-db.org/) (ELIXIR, Hinxton, Cambridgeshire, UK) [104].
5. Conclusions
As this study shows, fibroblast dysfunction results from the upregulation of pro-inflammatory factors and proteins with antioxidant properties, as well as factors involved in signal transduction and participating in proteolytic processes. The changes described may directly affect intercellular signaling and promote the hyperproliferation of epidermal cells. Therefore, a better understanding of their exact molecular mechanisms can contribute to the development of more effective pharmacotherapy.
Acknowledgments
Cooperation between co-authors was financed by the Polish National Agency for Academic Exchange (NAWA) as part of the International Academic Partnerships (PPI/APM/2018/00015/U/001). Thanks are due to the University of Aveiro and FCT/MCT for the financial support to QOPNA ((FCT UID/QUI/00062/2019) and LAQV/REQUIMTE (UIDB/50006/2020), and to RNEM (Rede Nacional de Espectrometria de Massa), Portuguese Mass Spectrometry Network, (LISBOA-01-0145-FEDER-402-022125) through national funds and, where applicable, co-financed by the FEDER (Fundo Europeu de Desenvolvimento Regional), within the PT2020 Partnership Agreement.
Supplementary Materials
The following are available online at https://www.mdpi.com/1422-0067/21/15/5363/s1, Table S1: Names and ID of proteins indicated in fibroblasts isolated from skin of psoriatic patients (n = 5) and healthy people (n = 5). Table S2: The p-values and fold change (FC) for individual statistically significant proteins indicated in fibroblasts isolated from skin of psoriatic patients (n = 5) and healthy people (n = 5).
Click here for additional data file.
Author Contributions
Conceptualization, E.S.; Data curation, A.G. and P.D.; Formal analysis, A.W.; Funding acquisition, E.S.; Investigation, A.G. and A.W.; Methodology, P.D. and A.W.; Project administration, E.S.; Supervision, E.S.; Validation, A.G.; Visualization, A.G.; Writing: original draft, A.G.; Writing: review and editing, P.D. and E.S. All authors have read and agreed to the published version of the manuscript.
Funding
This study was financed by the National Science Centre Poland (NCN) grant no. 2016/23/B/NZ7/02350.
Conflicts of Interest
The authors declare no conflict of interest.
Abbreviations
4-HNE
4-hydroxynenenal
ACN
acetonitrile
AMBIC
ammonium bicarbonate
ANOVA
analysis of variance
C3
component 3
DMEM
Dulbecco’s Modified Eagle Medium
ERK
extracellular signal-regulated kinase
ESI
nanoelectrospray ionization
FA
formic acid
FDR
false discovery rate
HPLC
high performance liquid chromatography
IGF-I
insulin-like growth factor-I
IL-8
interleukin 8
JNK
c-Jun N-terminal kinase
KGF
keratinocyte growth factor
MAPK
mitogen-activated protein kinase
MDA
malondialdehyde
MS
mass spectrometry
NFκB
nuclear factor kappa-light-chain-enhancer of activated B cells
Nrf2
nuclear factor erythroid 2-related factor 2
PASI
Psoriasis Area and Severity Index
PCA
principal component analysis
PKC
protein kinase C
RANBP1
Ran-specific GTPase-activating protein 1
ROS
reactive oxygen species
SDS-PAGE
sodium dodecyl sulfate polyacrylamide gel electrophoresis
TGF-β1
transforming growth factor-β1
TNFα
tumor necrosis factor α.
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Venn diagram showing the number of proteins in fibroblasts isolated from the skin of psoriatic patients (n = 5) and healthy controls (n = 5). The names and ID of all the proteins identified are contained in the Supplementary Table S1.
Principal component analysis (PCA) of the proteins in fibroblasts isolated from skin of psoriatic patients (n = 5) and healthy controls (n = 5).
Volcano plot of fibroblasts proteins isolated from the skin of psoriatic patients (n = 5) and healthy controls (n = 5). Red dots indicate proteins of statistical significance among the groups tested. The p-values and the fold change (FC) for each protein are included in Supplementary Table S2.
Heat map and clustering of the 50 most significantly changing proteins of fibroblasts isolated from the skin of psoriatic patients (n = 5) and healthy controls (n = 5).
The level of significantly changed proteins involved in the antioxidant response (A) and inflammation (B) of fibroblasts isolated from skin of psoriatic patients (n = 5) and healthy controls (n = 5).
The level of significantly changed proteins involved in signal transduction/phosphorylation (A), intracellular transport (B) and proteolysis (C) of fibroblasts isolated from the skin of psoriatic patients (n = 5) and healthy controls (n = 5).
\n"],"string":"[\n \"\\nInt J Mol SciInt J Mol SciijmsInternational Journal of Molecular Sciences1422-0067MDPI32731552PMC743210210.3390/ijms21155363ijms-21-05363ArticleChanges in Proteome of Fibroblasts Isolated from Psoriatic Skin LesionsGęgotekAgnieszka1https://orcid.org/0000-0002-8060-7675DominguesPedro2WrońskiAdam3https://orcid.org/0000-0001-5397-7139SkrzydlewskaElżbieta1*Department of Analytical Chemistry, Medical University of Bialystok, Mickiewicza 2D, 15-222 Bialystok, Poland; agnieszka.gegotek@umb.edu.plMass Spectrometry Centre, LAQV REQUIMTE, Department of Chemistry, University of Aveiro, 3810-193 Aveiro, Portugal; p.domingues@ua.ptDermatological Specialized Center “DERMAL” NZOZ in Bialystok, 15-453 Bialystok, Poland; adam.wronski@dermal.plCorrespondence: elzbieta.skrzydlewska@umb.edu.pl; Tel.: +48-85-748-57082872020820202115536306720202772020© 2020 by the authors.2020Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
The dermal fibroblasts are in constant contact with the cells of the immune system and skin epidermis. Therefore, they are essential for the development of lesions in psoriasis. The aim of this study was to assess the changes in the proteomic profile of fibroblasts in the dermis of psoriasis patients, and to discuss the most significant changes and their potential consequences. The proteomic results indicate that fibroblast dysfunction arises from the upregulation of proinflammatory factors and antioxidant proteins, as well as those involved in signal transduction and participating in proteolytic processes. Moreover, downregulated proteins in psoriatic fibroblasts are mainly responsible for the transcription/translation processes, glycolysis/ adenosine triphosphate synthesis and structural molecules. These changes can directly affect intercellular signaling and promote the hyperproliferation of epidermal cells. A better understanding of the metabolic effects of the proteomic changes observed could guide the development of new pharmacotherapies for psoriasis.
psoriasisskin fibroblastsproteomic profileinflammationoxidative conditionsintracellular signal transduction1. Introduction
Psoriasis is a chronic disease that occurs with increasing frequency in developed countries. In European countries and the United States, the prevalence of psoriasis can reach 3% [1,2]. Psoriasis occurs mainly due to a dysfunction of the immune system, and its development might be associated with other diseases, including arthritis, metabolic syndrome, heart disease, polycystic ovarian syndrome, chronic obstructive pulmonary disease and even cancer [3]. However, the main symptom of the disease is excessive skin exfoliation [4]. As a result, psoriasis also affects the psychophysical health of patients by lowering their self-esteem and disrupting their social behavior. Depressive symptoms and sleep disturbances are also common in psoriatic patients [5,6].
The pathogenesis of psoriasis is the result of impaired immunity, and has a genetic component linked to the immune genes and their encoded pathways, as well as to environmental factors such as drugs, smoking, diet, alcohol and mental stress [7]. Regardless of the particular mechanisms involved, psoriasis develops due to the chronic activation of the cells of the peripheral immune system, resulting in the increased proliferation and differentiation of skin cells [8,9]. Significant changes occur in the epidermis, where the accelerated cell cycle of keratinocytes results in intensified keratinization and the formation of cutaneous psoriatic lesions. Epidermal keratinocytes are stimulated to proliferate by signaling molecules, primarily released by lymphocytes. This process has been well examined and described previously [8].
Undoubtedly, the release of signaling molecules that can reach and interact with the epidermis will also have an impact on cells that build the other layers of the skin, including dermal fibroblasts. Under physiological conditions, skin fibroblasts are primarily responsible for the production of collagen and other intercellular matrix substances present in the dermis, which are intimately linked to the condition and function of the skin [10]. However, the metabolic activity of fibroblasts in psoriatic skin has not been extensively studied in recent years, compared to keratinocytes, which have been the subject of extensive research [11,12,13,14,15,16,17,18].
Oxidative stress is a characteristic of the tissue of patients with psoriasis [19]. Currently, it is known that oxidative stress in dermal fibroblasts is higher in scaly skin than in unchanged tissue [20]. It is important to note that the increase in oxidative stress and the decrease in the total antioxidant capacity of dermal fibroblasts are even greater than in the keratinocytes isolated from the same skin biopsy [20]. Under such conditions, the molecules present in intracellular fibroblasts may undergo oxidative modifications, which can trigger an increase in oxidative lipid metabolism [21]. As a result, there is an increase in lipid peroxidation products, including reactive α, β-unsaturated aldehydes and isoprostanes [22]. Additionally, the increase in the enzymatic lipid metabolism of psoriatic fibroblasts promotes the production of bioactive mediators, including eicosanoids, sphingolipids and ceramides. These mediators are involved in skin biology, inflammation and immunity, and even cell apoptosis [23,24].
Increased levels of electrophilic molecules, mainly reactive oxygen species (ROS), as well as reactive aldehydes, especially 4-hydroxynenenal (4-HNE) and malondialdehyde (MDA), can also lead to modifications of proteins in patients with psoriasis. These modifications have been observed in lymphocytes and keratinocytes, and included the formation of protein adducts with lipid peroxidation products [17,25] and a significant increase in protein carbonylation in skin fibroblasts [20]. The presence of these protein modifications in psoriatic fibroblasts also leads to the activation of redox-sensitive signaling pathways, including those that depend on the mitogen-activated protein kinases (mitogen-activated protein kinase (MAPK), p38, extracellular signal-regulated kinase (ERK) and c-Jun N-terminal kinase (JNK)) [21], as well as protein kinase C (PKC) [26]. Consistently, PKC in the cell membranes of psoriatic fibroblasts is significantly activated, which could make these cells very sensitive in response to hormones or growth factors [26]. Moreover, psoriatic fibroblasts, unlike unmodified dermal cells, have been shown to stimulate the proliferation of keratinocytes after receiving activation signals [27]. An example of such action in psoriatic fibroblasts stimulated by inflammatory cytokines is the observation that increased expression of the insulin-like growth factor-I (IGF-I) significantly promotes the proliferation of keratinocytes [28]. Metabolic disturbances in psoriatic fibroblasts also cause increased expression of interleukin 8 (IL-8), resulting in the stimulation of neutrophils, monocytes and T lymphocytes, which migrate into the skin layers [29]. In addition, the changes observed following psoriatic epidermal exfoliation are linked to changes in the metabolism of fibroblasts, not only locally but also in regions distant from the exfoliation site. The expression of factors such as α5 integrin, fibronectin or keratinocyte growth factor (KGF) is high, in particular in non-lesional psoriatic skin fibroblasts [30]. In agreement with this, it is suggested that these factors play a crucial role in the pathogenesis of psoriasis by influencing the inflammation and hyperproliferation of keratinocytes.
The abundance of evidence highlighting the critical role of fibroblasts in the development of psoriasis lesions has led us to investigate in more detail the molecular mechanisms leading to the pathogenesis of the disease. To achieve this, we sought to determine the differences in the proteomic profiles of fibroblasts isolated from the dermis of psoriatic patients, compared to unmodified skin cells.
2. Results
The results presented in this study show that the proteome of fibroblasts isolated from the dermis of psoriatic patients has a different profile than that of control cells. The data obtained from our proteomic analysis allowed us to identify and semi-quantitatively determine the expressions of 718 proteins, 684 of which were found in control fibroblasts, and 690 in cells isolated from the skin of psoriatic patients ( Supplementary Table S1). The distribution of these proteins between the samples is shown on a Venn diagram (Figure 1).
Using principal component analysis (PCA), we found that changes in the proteomic profiles of skin fibroblast cells led to the clustering of the experimental groups (PC1—41.5%, PC2—17.4%). In the case of control fibroblasts, the samples clustered in the left quadrant, while the psoriatic fibroblasts clustered mainly in the lower right quadrant (Figure 2). Statistical analysis indicated that the expressions of 242 of the proteins identified were significantly different between the control fibroblasts and the psoriatic fibroblasts ( Supplementary Table S2). A volcano plot displaying differentially enriched proteins highlighted that the most significantly changed proteins in psoriatic fibroblasts were downregulated; β-catenin (P35222), importin-8 (O15397), protein kinase C (Q05655) and galectin-3 (P17931). The following, on the other hand, were upregulated: keratin (P35527), tubulin (Q9BVA1), 26S proteasome (Q5VWC4), protein transport protein Sec24C (P53992), glutathione S transferase 1 (P08263) and high mobility group protein B2 (P26583) (Figure 3).
The clustering and functions of the 50 most significant proteins were visualized in a two-dimensional hierarchical clustering heat map (Figure 4). The analyzed proteins were divided into two clusters. Cluster 1 contained proteins downregulated in psoriatic fibroblasts compared to controls. These proteins were primarily involved in the transcription/translation processes, protein folding and glycolysis/ATP synthesis, as well as having structural functions. Cluster 2 contained proteins upregulated in psoriasis and had various different biological functions. Some of them were proinflammatory proteins, including NFκB (Q00653), TNFα (P01375) and S100A8/9 (P05109/P06702) (Figure 5A). Another group of proteins upregulated in psoriatic fibroblasts was those with antioxidant properties. These included thioredoxin (Q99757), peroxiredoxin (P32119), glutaredoxin (O76003), Nrf2 (Q16236), glutathione S transferase 1 (P08263) and thioredoxin-dependent peroxide reductase 2 (P30048) (Figure 5B). Finally, psoriatic fibroblasts were characterized by higher expressions of proteins involved in signal transduction (such as 14-3-3 proteins (P31947, P63104), kinases (P55263, P67775, Q5U5J2) and intracellular channel protein 4 (Q6FIC5)) (Figure 6A), intracellular transport (such as the Ran-specific GTPase-activating protein (F6WQW2), GTP-binding nuclear protein Ran (J3KQE5) and transport protein Sec24C (P53992)) (Figure 6B) and the proteins involved in the proteolytic processes (calpain (P07384) and 26S proteasome (Q5VWC4)) (Figure 6C).
3. Discussion
Psoriasis is an immune-mediated disease characterized by increased activation of lymphocytes. Once the lymphocytes have entered the skin, they cause the proliferation, maturation and desquamation of keratinocytes [25]. The resulting skin lesions disrupt the functioning of the epidermis and can lead to a reduction in the quality of life of those affected [5,31]. However, the development of psoriasis is not only linked to changes in the interaction between lymphocytes and keratinocytes; it also involves the activity of other cells of the immune system. For example, the actions of granulocytes [32,33] can significantly alter the profile of the signaling molecules in the plasma of psoriatic patients [34]. Such changes do not exclusively affect keratinocytes, and also have consequences for other skin-building cells, including dermal fibroblasts [24,30,35].
Fibroblasts, as the primary cells of the dermis, are responsible for the synthesis of proteoglycans, glycosaminoglycans and collagen, which are the main components of the extracellular matrix. These cells are essential for maintaining the appropriate thickness of the dermis when the exfoliated epidermis becomes too thin to fulfill a protective function. Therefore, increased collagen biosynthesis in these cells is observed in psoriatic skin [36,37]. Moreover, fibroblasts directly promote the proliferation of keratinocytes by generating and transporting growth factors [27].
These observations highlight the need for further research into how changes in the proteome of these cells could contribute to the development of psoriasis. To date, complex changes in the proteome of psoriatic patients have been identified in keratinocytes and biopsies of the whole skin, as well as in blood cells and plasma [35,38,39,40,41,42,43]. However, in fibroblasts from psoriatic patients, only changes in candidate proteins were analyzed [20,21,26,28,30,44]. Our study investigated changes in the proteome of psoriatic fibroblasts. In this report, we present the most significant modifications and discuss their potential consequences. The changes described include the upregulated proteins that are involved in inflammation, the antioxidant response, signal transduction and proteolytic processes, as well as the downregulated proteins mainly responsible for the transcription/translation processes, glycolysis/ATP synthesis and structural components.
Psoriatic skin exfoliation is intrinsically linked to the response of skin cells to proinflammatory factors generated by immune cells [45]. These agents activate cell membrane receptors leading to stimulation of the intracellular pro-inflammatory pathways. Consequently, the expression of NFκB is increased in dermal fibroblasts, but also in epidermal keratinocytes [46]. The action of NFκB not only leads to the expression of the genes responsible for the inflammatory reaction, but also stimulates other pro-inflammatory factors, including TNFα, which intensify pro-inflammatory signaling within the cell and between adjacent cells. In psoriatic plaques, the skin levels of TNFα are increased, which promotes infiltration by macrophages that also express TNFα [47,48,49]. As a result, keratinocytes are continuously stimulated to proliferate [50]. Until now, there has been no unambiguous data indicating the levels of these factors in psoriatic fibroblasts. However, the presented results allow us to suggest that the increased expression of NFκB and TNFα in fibroblasts can enhance intercellular proinflammatory signaling, thus contributing to the stimulation of keratinocytes proliferation.
Moreover, the increase in the level of pro-inflammatory factors in psoriatic fibroblasts is accompanied by an increased expression of the proteins participating in the proteolytic processes, such as 26S proteasome components and S100A8/A9. The ubiquitin–proteasome pathway activated during the development of psoriasis also plays a central role in the selective degradation of intracellular proteins, including those involved in the control of inflammatory processes [51]. The increase in the level of 26S proteasome subunits observed in this study could contribute to an increased degradation of IκB, which is a cytosolic inhibitor of NFκB [52,53]. As a result, NFκB, by improving the inflammatory response, promotes T cell responses in psoriasis [54]. In vitro and in vivo experiments have shown that 26S proteasome inhibitors inhibit cell proliferation and migration under inflammatory conditions [55], highlighting the potential for the inhibition of the proteasome as a treatment option for inflammatory disorders such as psoriasis [51]. Another protein with proteolytic activity associated with inflammation is a calcium-dependent neutral protease called calpain. The main role of calpain is the regulation of various fundamental cellular functions, such as the cell cycle and apoptosis, but it is also involved in the initiation of inflammation by the degradation of IκB and the activation of NFκB [56]. An increase in the level of calpain in psoriatic patients has already been identified in the skin tissue [57,58]. However, calpain also stimulates the migration of fibroblasts and myoblasts, which is necessary for the treatment of damaged skin [59].
Our data also show an increase in the level of S100A8/A9 proteins in psoriatic fibroblasts. Previous studies have determined that the source of S100A proteins in skin tissue are the activated phagocytes in inflammatory conditions associated with psoriasis lesions [60]. The main role of S100A8/A9 in the psoriatic epidermis is to activate the complement component 3 protein (C3) in keratinocytes. After activation, C3 is translocated to the dermis, where it stimulates immune cells to produce cytokines, interleukins and growth factors, thereby contributing to the development of psoriasis [61,62,63]. It is unknown whether psoriatic fibroblasts can synthesize S100A8/A9 or can only accumulate proteins when produced by other cells. However, our data, which show a significant increase in these proteins in dermal cells, suggest an additional role for fibroblasts in pro-inflammatory signaling, which leads to the hyperproliferation of keratinocytes in psoriasis.
Inflammatory diseases, such as psoriasis, are associated with pro-oxidative conditions, leading to oxidative stress [64,65]. In response, the level and activity of components of the antioxidant system increase in patients with psoriasis [66,67]. Our results confirm that in the fibroblasts of psoriasis patients, one of the main groups of significantly modified proteins is the proteins involved in the antioxidant response. These include the transcription factor Nrf2—a redox-sensitive protein responsible for the expression of cytoprotective proteins. Various investigations into psoriatic keratinocytes have observed changes in Nrf2 levels. One study found that a decrease in the levels of Nrf2 was associated with the development of psoriasis [68], while others observed an increased expression of Nrf2, which led to the elevated expression of keratins and promoted the proliferation of keratinocytes, leading to the pathogenesis of psoriasis [69,70]. The transcriptional activity of Nrf2 leads to the expression of genes coding for antioxidant enzymes, in particular thioredoxin-dependent peroxide reductase and glutathione S transferase 1 [71], the levels of which are increased in psoriatic fibroblasts. A previous study also indicated that the level of these enzymes is increased in fibroblasts under oxidative stress induced by UV, which is probably a defense mechanism against adverse conditions in the cell [72]. Moreover, the increased level of thioredoxin-dependent peroxide reductase is accompanied by a high level of thioredoxin, which is associated with the increased activity of this enzyme. Simultaneously, the levels of peroxiredoxin and glutaredoxin are increased. These proteins can reduce thiol groups in oxidized proteins and also control the peroxide levels induced by cytokines [73]. Previous reports confirm the increase in the mentioned parameters of the antioxidant system in skin biopsies of psoriatic patients [74]. Along with the previously published data, our findings indicate that fibroblasts from psoriasis patients are subject to high levels of oxidative stress, and these cells activate pathways to limit these oxidative conditions.
Signal transduction between cells involved in psoriatic lesion development is one of the fundamental elements to consider in designing effective treatments for psoriasis [75,76,77]. So far, the role of fibroblasts in this intercellular communication has not been described. In this study, we found that fibroblasts in psoriatic skin display the upregulation of 14-3-3 sigma (σ) and zeta/delta (ζ/δ) protein isoforms. Other studies show that 14-3-3 protein levels in psoriatic skin biopsies are changed in various ways, depending on the isoform; 14-3-3τ and σ are upregulated [78,79,80], while 14-3-3β and 14-3-3ζ are downregulated [81]. 14-3-3 is involved in the regulation of transcription and translation through its interaction with DNA/mRNA-binding proteins, such as tristetraprolin (TTP), which induces the destabilization and degradation of cytokine mRNA (including TNFα mRNA). After phosphorylation, TTP can bind to 14-3-3, which inhibits the mRNA-degrading capabilities of TTP. Therefore, in several skin diseases characterized by hyperproliferative keratinocytes, increased levels of 14-3-3 result in the overexpression of cytokines [78]. These changes are accompanied by the upregulation of kinases, as shown in this study and in previous work on a psoriatic skin model [82]. Conversely, in the case of DNA-damage or the overexpression of dysregulated genes, 14–3–3σ is upregulated by a p53-dependent pathway, and partially prevents cells from entering mitosis [83].
In psoriatic skin fibroblasts, the expression of the chloride intracellular channel protein 4 (ClIC4) is increased. The activity of this transmembrane protein is linked to angiogenesis and to the differentiation of keratinocytes [84]. In the case of fibroblasts, the overexpression of ClIC4 leads to the activation of the transforming growth factor-β1 (TGF-β1) and to conversion to myofibroblasts, which is a known feature of diseases characterized by the hyperproliferation of cells, including cancer [44,85]. Consistent with this finding, increased levels of TGF-β1 have been observed in the keratinocytes, plasma and lymphocytes of psoriatic patients [86,87,88]. Therefore, the increased ClIC4 level in fibroblasts from psoriatic patients suggests the role of these cells in enhancing the expression of TGF-β1 in the tissues of people affected by psoriasis.
Psoriatic fibroblasts are also characterized by the increased expression of proteins involved in intracellular transport. Intracellular transport is essential for cells with accelerated proliferation, as well as those that are constantly exposed to the signaling molecules released by cells of the immune system. At the top of the list of the most-changing expressed proteins are the Ran-specific GTPase-activating protein (RANBP1) and the GTP-binding nuclear protein Ran. The actions of these proteins lead to the selective activation of Ran [89], a protein involved in the transport of proteins across the nuclear membrane. Ran carries out nuclear transport by binding to importins or exportins, and thus participates in the activity of regulation of the transcription factor [89]. So far, the increase in the level of Ran and its activators has been identified in activated human lymphocytes T [90]. However, despite the importance of the Ran-related pathway in regulating the cell cycle, it has not been widely analyzed in samples from psoriatic patients. Moreover, the Sec24C protein is also upregulated in psoriatic fibroblasts. The activity of Sec24C facilitates the selection of proteins for transport to the cell nucleus, but is also involved in the transport of proteins to the endoplasmic reticulum [91]. Therefore, Sec24C is responsible for the proper biosynthesis, maturation and secretion of collagen [92], suggesting that its upregulation plays a specific role in the fibroblasts of regularly exfoliated psoriatic skin.
The fibroblasts of psoriatic skin lesions also contain a large group of proteins whose expression is reduced compared to healthy cells. The main proteins are β-catenin, importin-8 and galectin-3. All of these proteins are involved in cell–cell adhesion, which is an essential process in the formation of the skin layers [93,94,95]. Decreased levels of β-catenin have been found in the cytoplasm of keratinocytes in psoriatic skin [93]. β-catenin is responsible for the transmission of the contact inhibition signal, which causes the division of cells to stop. Therefore, its deficiency in psoriasis does not stop cell division. However, in psoriatic cells, β-catenin accumulates in the nucleus, where it can upregulate gene expression and influence cell growth [93]. Conversely, gene expression might also be dysregulated by a decrease in the level of importin-8. This protein is responsible for the transport of mature miRNAs from the cytoplasm to the nucleus, which causes gene silencing [94]. Cells deficient in importin-8 display uncontrolled gene expression [96] which, in the skin, can lead to the hyperproliferation of keratinocytes and the formation of psoriatic lesions.
Another downregulated protein in psoriatic fibroblasts associated with the regulation of gene expression is galectin-3. This protein is strongly expressed in epithelial cells, such as keratinocytes and skin fibroblasts, and is involved in the pathogenesis of inflammatory skin diseases by regulating the functions of immune cells, including lymphocytes [95,97]. Its decreased level in psoriatic keratinocytes has been shown to be a primary mechanism of cellular hyperproliferation, through the activation of the JNK pathway or the accumulation of neutrophils associated with S100A7-9 overexpression [98]. On the other hand, an increase in the levels of galectin-3 in the blood of psoriatic patients leads to inflammation and increased profibrotic activity [99]. However, the exact role of the downregulation of fibroblast galectin-3 in the development of psoriasis remains undefined.
Current evidence indicates that the development of psoriasis is likely associated with changes in the function of the immune system, as well as in skin cells. Psoriatic skin exfoliation is the result of changes in the metabolism of keratinocytes; however, metabolic changes in fibroblasts also contribute significantly to these symptoms.
4. Materials and Methods4.1. Sample Collection and Preparation
Skin biopsy fragments were collected from five untreated patients with a diagnosis of psoriasis vulgaris. The patients were two men and three women; age range 27–48 years, mean 38. They were selected from a cohort of 70 patients because their skin lesions were the most characteristic of typical psoriasis. Biopsies were also taken from five healthy people who had moles, and the adjacent skin removed. These individuals forming a control group were sex-matched to the patient group. They had an age range of 28–50 years, mean 38.
The individuals selected for the patient group had had a diagnosis of plaque psoriasis for at least six months, with at least 10% of the total body surface area affected. The severity of psoriasis was assessed using the PASI score (Psoriasis Area and Severity Index) (median 18; range 12–25). None of the patients or healthy subjects had received topical, injectable or oral medications during the four weeks before the study. Individuals whose history indicated any other disorders were excluded from the study. None of the participants were smokers. All subjects gave their informed consent for inclusion before they participated in the study. The study was conducted in accordance with the Declaration of Helsinki, and the protocol was approved by the Local Bioethics Committee Medical University of Bialystok (Poland), No. R-I-002/502/2015 (17 December 2015). After the biopsy, skin fragments were taken for histopathological examination, and the remaining material was used for molecular analysis.
The samples were washed in PBS with 50 U/mL penicillin and 50 μg/mL streptomycin and incubated overnight at 4 °C in 1 mg/mL dispase to separate the epidermis from the dermis. The obtained dermis was sliced and placed in culture plates in fibroblast culture medium consisting of DMEM (Dulbecco’s Modified Eagle Medium), fetal bovine serum (10%) and penicillin (50 U/mL)/streptomycin (50 μg/mL). Samples were incubated in a humidified atmosphere of 5% CO2 at 37 °C until the fibroblasts emigrating from the slices reached full confluence.
Fibroblasts were collected from the plates by scraping on ice, and were suspending in buffer Tris-HCl (50 mM, pH 7.5, 4 °C) containing 0.1% SDS and protease inhibitor cocktail. All samples were lysed by sonification on ice. The total protein content in the cell lysates was measured using the Bradford assay [100].
4.2. SDS-PAGE and In-Gel Digestion
Each sample (containing 25 μg of protein) was mixed 1:1 with Laemmle buffer, containing 5% 2-mercaptoethanol, and heated at 100 °C for 7 min. After cooling to room temperature, the samples were separated on 12% Tris-Glycine SDS-PAGE gels. Gels were fixed for 1 h in methanol:acetic acid:water (4:1:5) and stained for 4 h with Coomassie Brilliant Blue R-250. All detected bands were cut out of the gel, sliced, and washed out of the dye by acetonitrile (ACN) and 25 mM AMBIC (ammonium bicarbonate). Proteins in each gel fragment were reduced for 1 h with 10 mM DTT, alkylated for 1 h with 50 mM iodoacetamide, and overnight in-gel digested with sequencing grade trypsin (Promega, Madison, WI, USA). The digestion was stopped by the addition of 10% FA (formic acid) and evaporated.
The dried peptides were dissolved in 5% ACN with 0.1% FA immediately before analysis and loaded onto a 150 mm x 75 µm PepMap RSLC capillary analytical C18 column with 2 μm particle size (LC Packings) using Ultimate 3000 HPLC system (Dionex, Idstein, Germany). Peptide separation was done at a flow rate of 0.300 µl/min, and the solvents gradient started at 3 min and was ramped to 60% Buffer B (90% ACN + 0.03% FA) over 60 min. Eluted peptides were analyzed using a Q Exactive HF mass spectrometer with a nanoelectrospray ionization source (ESI) (Thermo Fisher Scientific, Bremen, Germany). The mass spectrometer was externally calibrated and operated in positive and data-dependent modes. Survey MS scans were conducted in the 200–2000 m/z range, with a resolution of 120,000. The top ten most intense ions were fragmented with 30 eV collision energy on an HCD collision cell and analyzed with a resolution of 30,000. A 10 s dynamic exclusion window was applied, and an isolation window of 4 m/z was used to collect suitable tandem mass spectra. The obtained data were acquired with the Xcalibur software version 4.1 (Thermo Fisher Scientific, Bremen, Germany).
4.4. Protein Identification and Label-Free Quantification
For protein identification, Proteome Discoverer 2.0 (Thermo Fisher Scientific, Bremen, Germany) was used with the following search parameters: peptide mass tolerance set to 10 ppm, MS/MS mass tolerance set to 0.02 Da, up to two missed cleavages allowed, cysteine carbamidomethylation/carboxymethylation and methionine oxidation set as a dynamic modification, a minimum peptide length set to 6 amino acids, and the minimum number of identified unique peptides for each protein set to two peptides. Input data were searched against the UniProtKB-SwissProt database (taxonomy: Homo sapiens, release 2019-04). In the case of proteins that were identified in at least 60% of the examined samples from the control or psoriatic group, the missing values were estimated as half of the lowest recorded intensity (half minimum imputation). Other proteins were removed from the analysis as artefacts.
4.5. Statistical Analysis
The analysis of each sample was performed in three independent replicates. Data from individual protein label-free quantifications were log and Z-score transformed. Statistical analysis of data was performed using free available MetaboAnalyst 4.0 software (http://www.metaboanalyst.ca) (Xia Lab, Montreal, Quebec, Canada) [101], RStudio software (R version 3.6.2 (2019-12-12)) [102] and Perseus 1.6.10.43 [103]. Data were analyzed using the standard statistical analysis methods, including univariate analysis (one-way ANOVA), and only proteins with an FDR-corrected significant q-value were taken into account in the discussion. Protein molecular function and protein class were assigned according to the Gene Ontology database in the free available STRING version 11 (https://string-db.org/) (ELIXIR, Hinxton, Cambridgeshire, UK) [104].
5. Conclusions
As this study shows, fibroblast dysfunction results from the upregulation of pro-inflammatory factors and proteins with antioxidant properties, as well as factors involved in signal transduction and participating in proteolytic processes. The changes described may directly affect intercellular signaling and promote the hyperproliferation of epidermal cells. Therefore, a better understanding of their exact molecular mechanisms can contribute to the development of more effective pharmacotherapy.
Acknowledgments
Cooperation between co-authors was financed by the Polish National Agency for Academic Exchange (NAWA) as part of the International Academic Partnerships (PPI/APM/2018/00015/U/001). Thanks are due to the University of Aveiro and FCT/MCT for the financial support to QOPNA ((FCT UID/QUI/00062/2019) and LAQV/REQUIMTE (UIDB/50006/2020), and to RNEM (Rede Nacional de Espectrometria de Massa), Portuguese Mass Spectrometry Network, (LISBOA-01-0145-FEDER-402-022125) through national funds and, where applicable, co-financed by the FEDER (Fundo Europeu de Desenvolvimento Regional), within the PT2020 Partnership Agreement.
Supplementary Materials
The following are available online at https://www.mdpi.com/1422-0067/21/15/5363/s1, Table S1: Names and ID of proteins indicated in fibroblasts isolated from skin of psoriatic patients (n = 5) and healthy people (n = 5). Table S2: The p-values and fold change (FC) for individual statistically significant proteins indicated in fibroblasts isolated from skin of psoriatic patients (n = 5) and healthy people (n = 5).
Click here for additional data file.
Author Contributions
Conceptualization, E.S.; Data curation, A.G. and P.D.; Formal analysis, A.W.; Funding acquisition, E.S.; Investigation, A.G. and A.W.; Methodology, P.D. and A.W.; Project administration, E.S.; Supervision, E.S.; Validation, A.G.; Visualization, A.G.; Writing: original draft, A.G.; Writing: review and editing, P.D. and E.S. All authors have read and agreed to the published version of the manuscript.
Funding
This study was financed by the National Science Centre Poland (NCN) grant no. 2016/23/B/NZ7/02350.
Conflicts of Interest
The authors declare no conflict of interest.
Abbreviations
4-HNE
4-hydroxynenenal
ACN
acetonitrile
AMBIC
ammonium bicarbonate
ANOVA
analysis of variance
C3
component 3
DMEM
Dulbecco’s Modified Eagle Medium
ERK
extracellular signal-regulated kinase
ESI
nanoelectrospray ionization
FA
formic acid
FDR
false discovery rate
HPLC
high performance liquid chromatography
IGF-I
insulin-like growth factor-I
IL-8
interleukin 8
JNK
c-Jun N-terminal kinase
KGF
keratinocyte growth factor
MAPK
mitogen-activated protein kinase
MDA
malondialdehyde
MS
mass spectrometry
NFκB
nuclear factor kappa-light-chain-enhancer of activated B cells
Nrf2
nuclear factor erythroid 2-related factor 2
PASI
Psoriasis Area and Severity Index
PCA
principal component analysis
PKC
protein kinase C
RANBP1
Ran-specific GTPase-activating protein 1
ROS
reactive oxygen species
SDS-PAGE
sodium dodecyl sulfate polyacrylamide gel electrophoresis
TGF-β1
transforming growth factor-β1
TNFα
tumor necrosis factor α.
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Venn diagram showing the number of proteins in fibroblasts isolated from the skin of psoriatic patients (n = 5) and healthy controls (n = 5). The names and ID of all the proteins identified are contained in the Supplementary Table S1.
Principal component analysis (PCA) of the proteins in fibroblasts isolated from skin of psoriatic patients (n = 5) and healthy controls (n = 5).
Volcano plot of fibroblasts proteins isolated from the skin of psoriatic patients (n = 5) and healthy controls (n = 5). Red dots indicate proteins of statistical significance among the groups tested. The p-values and the fold change (FC) for each protein are included in Supplementary Table S2.
Heat map and clustering of the 50 most significantly changing proteins of fibroblasts isolated from the skin of psoriatic patients (n = 5) and healthy controls (n = 5).
The level of significantly changed proteins involved in the antioxidant response (A) and inflammation (B) of fibroblasts isolated from skin of psoriatic patients (n = 5) and healthy controls (n = 5).
The level of significantly changed proteins involved in signal transduction/phosphorylation (A), intracellular transport (B) and proteolysis (C) of fibroblasts isolated from the skin of psoriatic patients (n = 5) and healthy controls (n = 5).
\\n\"\n]"}}},{"rowIdx":446,"cells":{"data":{"kind":"list like","value":["\nInt J Environ Res Public HealthInt J Environ Res Public HealthijerphInternational Journal of Environmental Research and Public Health1661-78271660-4601MDPI32752247PMC743210310.3390/ijerph17155569ijerph-17-05569ArticleResilient Resources in Youth Athletes and Their Relationship with Anxiety in Different Team Sportshttps://orcid.org/0000-0002-6640-0352González-HernándezJuan1Gomariz-GeaMarcial2https://orcid.org/0000-0002-4317-1665Valero-ValenzuelaAlfonso23https://orcid.org/0000-0002-4595-3994Gómez-LópezManuel23*Department of Personality, Evaluation and Psychological Treatment, University of Granada, 18071 Granada, Spain; jgonzalez@ugr.esDepartment of Physical Activity and Sport, Faculty of Sport Sciences, University of Murcia, Santiago de la Ribera, 30720 Murcia, Spain; marcialrbb@gmail.com (M.G.-G.); avalero@um.es (A.V.-V.)Campus of International Excellence “Mare Nostrum”, University of Murcia, 30720 Murcia, SpainCorrespondence: mgomezlop@um.es; Tel.: +34-868-888-6740182020820201715556929620202972020© 2020 by the authors.2020Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
The objective of this study is to show the links and differences in the expressions of competitive anxiety in the face of the existence of resilient resources in young athletes, according to sporting (years of experience) and personal (gender) characteristics. To meet these aims, the participants answered the Resilience Scale (RS-14) and the Competitive State Anxiety Inventory-2R (CSAI-2R). The sample consisted of 241 adolescent handball and basketball players between 14 and 17 years old. Different analyses were performed, including a differential and multivariate descriptive, a correlation, and a multiple regression. The results showed that anxiety was negatively related to resilience in its acceptance dimension. It was shown that girls showed higher levels of somatic anxiety, while boys showed higher levels of acceptance. Statistically significant differences were found in the resources for acceptance in favor of boys, while there were significantly different indicators in somatic anxiety and self-confidence in favor of girls. The sports experience was positively related to resilience and negatively to anxiety. Although the existence of indicators of cognitive anxiety (e.g., recurrent thoughts or rhyming), coaches and athletes need to understand that they are also indicators of a necessary activation for psychological functioning. Channeling such a process through psychological training of different skills will enhance the capacities for self-confidence.
The ability to obtain an optimal and stable psychological state during competition is of utmost importance to coaches and athletes [1,2,3]. Physical, social, cognitive, and emotional factors factor into such functioning, as these resources restore balance when a young athlete is in the face of adversity [4,5,6]. In this sense, the study of anxiety has been one of the most prolific lines of research, due to the emotional responses it provokes and that can affect athletes’ performance [7].
Anxiety, which is considered a psychological response produced as a consequence of the differences between an athlete’s response capacity and the demands of the environment, accompanied by a high degree of psycho-physiological activation [8], shows functional conditions that facilitate adaptation (e.g., helps to prepare for action) but also other dysfunctions that weaken or hinder behavior (e.g., nervousness, impulsiveness or rumination) [9].
Among the different classifications that exist around this psychological response and the instruments used in research to measure it, we must highlight somatic anxiety (physiological activation that a person perceives when faced with a stressful situation) and cognitive anxiety (negative expectations and concerns about oneself and the current situation and its possible consequences) [10].
In sports situations, somatic anxiety often occurs in competitions perceived as significant where there are uncontrollable elements and under conditions of social evaluation that lead to stress and a hormonal response, such as increased levels of cortisol in saliva, or physiological aspects, such as increased heart rate or muscle tension [11,12]. To understand the relationship between anxiety and sports performance, Martens, Vealey and Burton [13] established the multidimensional theory, in which they proposed that there is a negative relationship between cognitive anxiety and performance on the one hand, and a curvilinear relationship between somatic anxiety and sports performance on the other.
Anxiety is also related to sociodemographic variables such as age and gender in relation with sport contexts, due to it being multifactorial. Different studies carried out in the sports context have shown that the older the athlete, the greater the experience and competitive level in practicing a sport, and the lower their levels of anxiety, because more experienced athletes have greater and better control over their emotions [14,15], greater technical-tactical mastery, and greater experience in stressful and adverse competitive situations that favor self-confidence [16] and cognitive abilities [17]. With regard to gender, after an extensive review of the literature, no studies were found that show statistically significant differences, although it is true that some studies have shown that women have higher levels of anxiety and less self-confidence than men, and that the production of anxiety in men and women can come from different routes. In men it is be produced by the poor management of a situation, and in women by their level of motivation [12,18,19]. Likewise, with regard to the type of sport practiced, the results of the literature review indicate that athletes who practice team sports in which there is physical contact show higher levels of anxiety than athletes who practice individual sports [17,20].
In a confluent direction with anxiety, resilience protects young athletes from stressful situations or threatening scenarios, giving athletes the means to avoid them [21]. Among the many definitions of resilience, Deen, Truner and Wong [22] explained it as a set of cognitive aspects and behavioral responses to acute or chronic adversity. Luthar, Cichetti and Becker [23] defined it as a dynamic process of positive adaptations within a context of adversity. Fletcher and Sarkar [24] argued that resilience is based on overcoming adversity and on positive adaptation. On the other hand, Wu et al. [25] highlighted effective efforts to environmental challenges and ultimate resistance to the unhealthy effects of stress.
Studied in sports contexts, resilience, like anxiety, is a variable that is dependent on different factors, such as emotions, supports, experiences, strategies, motivation, and self-concept [5,26,27], all of which facilitate optimal sports performance. This concept is really important in youth, with important implications in cognitive, physical, and social development that occurs in the adolescence [28], and its influence on growth on the personal level and in sporting abilities [5,29].
As for the relationship between resilience and the sociodemographic variables of age and gender, it has not been possible to establish firm conclusions since resilience has been little studied in the context of sports [30]. However, some studies have shown that athletes are more resilient, which is probably due to their greater experience in the face of adversity both in professionals [15] and youth athletes [28,31,32]. With respect to their relationship with gender, there are contradictions, as there are studies that have not shown significant differences in the levels of resilience between men and women, and others that point to higher levels of resilience in men caused by a better perception of personal competence [33].
With regard to the relationship between resilience and sports variables such as sporting experience or the sports modality practiced, the results of the review carried out on the first of the variables show contradictory results, since there are studies that, although they have not obtained statistically significant outcomes, have found that professional athletes with higher levels of competence are more resilient than amateur athletes [34,35]; on the other hand, there are works that have reflected that athletes with less sporting experience, like youth athletes, had more resilience capacity [32]. As for the sport practiced, different studies have shown that athletes who practice individual sports have higher levels of resilience [28,36].
Self-confidence is understood as conviction (based on what has been learned and lived before) about the ability to perform a certain task successfully. It is evident that young people have less sporting experience and a greater probability of not being confident in their own skills and resources (e.g., several consecutive errors or failures would affect the perception of self-confidence), which would lead to living with greater perceptions of anxiety. However, different aspects could generate their improvement by looking for self-reflection before the tasks [37], reducing the dramatism for the mistake [38] or generating support bonds with colleagues [39] and rational expression of emotions [40].
Moreover, regarding the relationship between anxiety and resilience in sports contexts, it should be noted that based on the literature review, there is little scientific evidence on this issue in adolescent samples and outside the clinical setting. As an example, Martin-Krumm, Sarrazin, Peterson and Famose [41], in a study conducted in France with adolescent children, demonstrated the relationship between resilience and anxiety, since the results reflected that children with a higher level of resilience controlled their anxiety levels better when performing an ability. Likewise, Ortín-Montero, De la Vega and Gonsálvez-Botella [42], in their study about optimism, anxiety, and self-confidence in handball players, showed that the most resilient players faced the competition with greater confidence and with lower levels of anxiety.
Therefore, and on the basis of what has been reviewed so far, the objectives of the study are as follows: (a) to describe the levels of resilience and competitive anxiety, (b) to analyze the differences in terms of gender and years of sports experience, (c) to study the relationship between resiliency resources and competitive anxiety, and (d) to examine the variables that predict both resiliency resources and self-confidence in players.
2. Materials and Methods2.1. Participants
A total of 241 team sports players (handball and basketball) belonging to different clubs in the Murcia region and Valencia communities participated (160 boys, 66.4%; 81 girls, 33.6%), with an average age of 15.36 years (SD = 1.07). According to age, 60.6% (n = 146) of the participants were <15 years, while 39.4% (n = 95) were >15 years old. Most of them stated that they had an average of 1.56 years of experience as a federated player in their sport (SD = 0.50) and they spent 1.93 training sessions per week (SD = 0.36).
2.2. Measurement Instruments
Resilience scale. The Spanish version of the Resilience Scale (RS-14) [43] was used, along with the 14-Item Resilience Scale (ER-14) [44], which is an abbreviated version of the original 25-item version [45]. This scale measures a person’s degree of individual resilience when such is considered a positive personality characteristic, one that allows people to adapt to adverse situations. The scale is composed of 14 items grouped in two dimensions that measure personal competence (11 items: self-confidence, independence, decision, ingenuity and perseverance, etc.; e.g., “I am not afraid to suffer difficulties because I have already experienced them in the past”), and the acceptance of oneself and life (3 items: adaptability, balance, and flexibility and stable life perspective; e.g., “I am a person with adequate self-esteem”). Every item on the scale should correspond with the respondent’s feelings. The answers were on a 7-point Likert-type scale that ranged from strongly disagree (1) to strongly agree (7). The internal consistency for the sample collected was satisfactory (α = 0.80).
Competitive State Anxiety Inventory-2R. The Competitive State Anxiety Inventory-2R (CSAI-2R) [46] comes from the original CSAI-2 version consisting of 27 items [20]. The Spanish version of this second revision, which was validated for athletes, was used in this study [47]. The CSAI-2R is a specific inventory for the sports context, but with a one-dimensional conception of the state of anxiety. The scale is composed of 16 items grouped in three dimensions that measure somatic state anxiety (6 items; e.g., “My heart is racing”), self-confidence (5 items; e.g., “I am confident”), and cognitive state anxiety (5 items; e.g., “I worry that others will be disappointed with my performance”). Each item of the scale had to be answered in terms of how the player felt just before the competition. The answers were collected on a 4-point Likert-type scale ranging from none (1) to very much (4). The internal consistency for the collected sample was satisfactory (α = 0.78).
2.3. Procedure
The study was carried out in different clubs and sport federations of Murcia region and Valencia community (Spain). A letter explaining the objectives of the research and how it was to be carried out, accompanied by two models of informed consent and permission (one for the parents and another for the young athletes), together with a copy of the measures, was sent to the clubs and federations (first for presidents) before the data collection. The questionnaire was administered by the researchers in the sports facilities of the clubs during the training sessions and completed by the participants in 20–30 min. All the participants were informed of the objectives and of their rights as participants in the study, as well as of the voluntary nature and the absolute confidentiality of the answers and handling of the data. It was explained that there were no correct or incorrect answers, so that participants would answer with the most sincerity and honesty. This study was carried out in accordance with the ethical guidelines of the American Psychological Association (APA). The protocol was approved by the Ethics Committee of the Universidad de Murcia (ID: 1494/2017). All subjects gave written informed consent in accordance with the Declaration of Helsinki [48].
2.4. Statistical Analysis
Through the analysis carried out with the statistical software SPSS. 23.0, descriptive measures and homogeneity (X2 Chi-squared) were obtained for the distribution of the observed sample, internal consistency of the instruments (Cronbach’s alpha), differential analysis (t-test, multivariate analysis and effect size with etha-square) to describe any differences in the sample (according to gender and sport experience), correlations, and multiple regressions (5000 bootstrap samples with bias-corrected confidence intervals 95.00) for the determination of the strength of the relationship between the study variables.
3. Results3.1. Descriptive and Differential Analysis
The sample distribution reflected the existence of a statistically significant association between both variables (Table 1) for the achievement of normality, which was assumed to be a link of dependence between the distributions by gender and sport experience (years). Similarly, this circumstance was again obtained when considering the distribution by gender and the hours of weekly training.
As can be seen in Table 2, the sample reflected higher means of self and life acceptance in terms of resilience, while higher means of self-confidence were observed in the perception of anxiety. Participants’ response tendency (asymmetry) tended to be concentrated between 5 and 7 points (Likert response orientation was 1–7) for personal competence as well as for acceptance, with pronounced peaks at 5 and 8 (kurtosis). Likewise, the participants’ response tendency in anxiety perception tended to concentrate (with a Likert response orientation from 1 to 4), between 2 and 3 points for somatic anxiety, and between 2.5 and 3.5 for cognitive anxiety and self-confidence, with pronounced peaks at 2.5 for somatic anxiety and 3 for cognitive anxiety and self-confidence.
Differential analysis (Table 3) showed the existence of statistically significant differences with respect to gender and sports experience. By gender, resources for acceptance (p < 0.02) were shown in favor of girls and self-confidence (p = 0.00) in favor of boys. The effect size of the established comparisons indicated that all the variables are very high, except for competition and cognitive anxiety, aspects that were repeated in the analyses carried out both by gender and by years of sports experience. According to sports experience, the most experienced young athletes showed differences in acceptance (p < 0.03) and self-confidence (p < 0.01).
In order to check whether there were differences in resiliency resources and anxiety responses based on gender and years of sports experience, an analysis of multivariate variance (MANOVA) was carried out with the scores obtained in all the variables under study (Table 4). The results (Pillai’s Trace) indicated statistically significant differences in favor of girls (F(5.232) = 9.08; p < 0.01) with the highest magnitude of the effect (η2 = 731; r = 0.46). Statistically significant differences were shown based on years of sports experience in favor of the younger, most experienced ones, but the interaction with the gender variables was significant (F(5.232) = 7.512; p > 0.03), with a moderate effect size (η2 = 779; r = 0.51). More specifically, analysis of the variance of effects between subjects showed significant differences in competence, acceptance, cognitive anxiety, and self-confidence by gender (in competence and self-confidence in favor of boys, and in cognitive anxiety and acceptance in favor of girls). In the years of sports experience, the differences were in acceptance, cognitive anxiety, and self-confidence in favor of the younger, more experienced ones. No significant differences were shown in the gender*sport experience interaction.
3.2. Correlational Analysis
Statistically significant two-way relationships appeared between the variables under study (Table 5), so that older athletes had higher perceptions of competence as shown in their lower levels of managing resiliently. Acceptance was inversely and significantly related to both cognitive and somatic anxiety. Conversely it was directly related to self-confidence and logically to a high perception of competence. All this led us to think that higher perceptions of resilient acceptance had a direct influence on the athlete’s ability to interpret anxiety. The relationship between the two resilient variables (acceptance and competence) with the development of self-confidence in a young handball player is very significant, in a positive way.
3.3. Multiple Regression Analysis
The results of the regression analysis (Table 6) indicated a model in which as the athletes showed better levels of acceptance, competence, and cognitive anxiety, they also reduced their somatic anxiety indicators, and increased in self-confidence, where F2.239 = 23.59; p < 0.01 explains 33.5% of the total variance.
The results of the regression analysis (Table 7) also indicate a model in which younger athletes had better levels of acceptance and self-confidence and more competence, where F2.239 = 33.96; p < 0.01 explains 42.1% of the total variance. On the other hand, acceptance was achieved through the predictive model established by an increase in competence and self-confidence, and the existence of low levels of cognitive anxiety.
4. Discussion
The objectives of this study were as follows: (a) to describe the levels of resilience and competitive anxiety, (b) to analyze the differences in terms of gender and years of sports experience, (c) to study the relationship between resiliency resources and competitive anxiety, and (d) to examine the variables that predict young players’ resiliency resources and self-confidence.
4.1. Resilience Levels and Competitive Anxiety
The results indicated that both the levels of self-acceptance and life and competence were high. These outcomes imply that athletes best face adversity with high levels of resilience [21]. This finding is also consistent with previous studies, most of which were conducted in team sports such as handball and football with young sport team athletes [28,30,31,32,35]. As for anxiety levels, the results reflected moderate and high levels of self-confidence. It should be noted that moderate levels of anxiety could optimize athletes’ performance [49]. These results are consistent with those found in other studies conducted with samples belonging mostly to individual sports [12,16,18,50] and contradict the high levels of somatic [14,17] and cognitive [19] anxiety reached by others.
4.2. Differences in Levels of Resilience and Competitive Anxiety Depending on Gender and Years of Sports Experience
In relation to resilience, the results showed that acceptance levels were higher in boys, while competence levels were higher in girls. The literature indicates that it is usually boys who show higher levels of resilience due to an increase in physical self-concept and that girls are much more affected by the sports context [51]. In terms of sports experience, it was shown that both acceptance and competence levels were higher in players with greater sports experience. These results are consistent with findings in previous studies in similar samples, most of which were conducted with team sports [31,32].
On the other hand, higher levels of somatic anxiety were found in girls and cognitive anxiety in boys. The literature provides different explanations for why girls tend to have higher levels of anxiety than boys. Abrahamsen et al. [12] attributed the difference to boys’ interpretation of performance; Brunet and Sabiston [18] attributed it to a greater perception of competence and motivation in boys; Peñaloza et al. [50], León-Prados, Fuentes and Calvo [51], and Arbinaga and Caracuel [52] attributed it to girls’ greater concern about the loss of social recognition caused by poor performance. In regard to self-confidence and gender, studies were found that differed from the results reported. Morillo et al. [19] and Peñaloza et al. [50] revealed higher levels of self-confidence in boys. The controversy between the results of the study and those found in the literature may be due to the age difference of the analyzed sample. Finally, in other studies such as the one conducted by Montero, Moreno-Murcia, González, Pulido, and Cervelló [53] there were no statistically significant differences in relation to gender, nor in the levels of anxiety or self-confidence.
As regards the relationship between anxiety levels and sports experience, although no statistically significant results were found, higher levels of somatic anxiety and cognitive anxiety and lower levels of self-confidence were reported in athletes with less sports experience, thus coinciding with findings from previous studies conducted with samples belonging to both team and individual sports. Hanton et al. [14] with adults and Hernández et al. [16] with youths showed that athletes with greater sports experience faced anxiety with higher levels of self-confidence and managed to interpret anxiety better than those with less experience.
4.3. Relationship between Resiliency Resources and Competitive Anxiety
The results of the correlational analysis reflected a negative association between the two dimensions of anxiety (cognitive and somatic anxiety) and of acceptance, showing that competence was negatively related to somatic anxiety and positively to cognitive anxiety. The papers reviewed showed that the two dimensions of resilience were negatively related to those of anxiety [37,38,54,55]. Similar studies were conducted in team sports such as handball, basketball, football, and rugby, and found that youth athletes with higher levels of resilience managed to moderate their anxiety levels significantly, recovering or adapting better to threatening situations [38,41,42,54]. In addition, resilient athletes may be better able to protect themselves in the face of adversity, while those with lower levels may have greater failures, thus causing low self-confidence and higher levels of anxiety [41].
4.4. Resiliency Resources and Self-Confidence Predicting Variables
The regression analysis showed that self-confidence is likely to increase as athletes have better levels of acceptance, competence, and cognitive anxiety and that it is likely to reduce their indicators of somatic anxiety. These results are in line with the findings of Ortin-Montero et al. [42] in their study of handball players, in which the players with higher levels of resilience showed higher levels of self-confidence and coped better with adverse situations.
Furthermore, the regression analysis also indicated that younger athletes have higher levels of competence with higher levels of acceptance and self-confidence. In addition, the acceptance was greater when combined with competence, self-confidence, and less cognitive anxiety. The literature reviewed shows that young athletes tend to be the most resilient [32,55].
The present study, in addition to providing new information on the relationship between resiliency resources and anxiety levels in athletes, has some limitations that should be highlighted. One of them is that the generalization of the results is limited, due to the sample (cadets and juniors), so these results should be considered as preliminary, needing future replication in other categories such as children and other sports, thus covering all ages of adolescence and sports, and both team and individual sports.
In terms of strengths, it is important to highlight the novel information provided in relation to the field of study and the instruments used, as these have been used in a multitude of works and with a high degree of consistency. Likewise, it would be necessary to complement future studies with the analysis of other variables related to anxiety and resilience such as stress and optimism. Finally, another interesting aspect would be to be able to contrast resiliency resources and anxiety levels in collective and individual sports and see the possible differences that are generated.
5. Conclusions
The consequent negative relationship between resilience resources (especially acceptance and competition) and levels of competitive anxiety, is a reflection that to enhance the former makes it possible to regulate up to control the latter. Despite the existence of indicators of cognitive anxiety (e.g., recurrent thoughts or rhyming), coaches and athletes need to understand that they are also indicators of a necessary activation for psychological functioning. Channeling such a process through psychological training of different skills will enhance the capacities for self-confidence. Making young athletes grow up on self-acceptance (e.g., reducing the belief that they cannot lose), for the younger ones, and on the perception of competition (e.g., learning to observe their improvements) already in adolescence, will undoubtedly consolidate key aspects for an effective and adapted psychological response to sports situations.
Highlighting how to work on skills, such as understanding situations of uncertainty, cooperation, or the search for social support at early ages and with strategies that integrate such resources in the dynamics of training and sports growth, will enhance the resources for the much needed coping with stress and anxiety, as well as consolidate beliefs and attitudes that can reduce dysfunctional responses that alter (e.g., blaming others or oneself, creating fears of failure after several mistakes).
Acknowledgments
We are grateful for the support received from the Royal Spanish Handball Federation (RFEBM) which allowed this study to be carried out.
Author Contributions
Conceptualization, J.G.-H. and M.G.-L.; data curation, J.G.-H. and M.G.-G.; formal analysis, J.G.-H.; investigation, M.G.-G. and M.G.-L.; methodology, J.G.-H.; project administration, M.G.-L.; supervision, M.G.-L.; writing—original draft, J.G.-H., M.G.-G., and M.G.-L.; writing—review and editing, J.G.-H., A.V.-V., and M.G.-L. All authors have read and agreed to the published version of the manuscript.
Funding
This research received no external funding.
Conflicts of Interest
The authors declare no conflict of interest.
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Distribution and homogeneity of intervariable variances (gender, sport experience, and weekly training hours).
Chi-squared (X2)2 = 19.26; p = 0.402 *
\nSport Experience\n
\n≤\n5 years (n = 106)\n
\n> 5 years (n = 135)\n
\nMean (SD) age\n
\nn (%)\n
\nMean (SD) age\n
\nn (%)\n
15.67 (0.51)
\n
15.14 (0.49)
\n
Boys = 160 (66.39%)
Boys n (%)
78 (73.58%)
\n
82 (60.74%)
Girls = 81 (33.61%)
Girls n (%)
28 (26.42%)
\n
53 (39.26%)
Chi-squared (X2)2 = 14.39; p = 0.236 *
\nWeekly Training Hours\n
\n≤\n2 h/week (n = 17)\n
\n> 2 h/week (n = 224)\n
\nMean (SD) age\n
\nn (%)\n
\nMean (SD) age\n
\nn (%)\n
15.71 (1.05)
\n
15.33 (1.07)
\n
Boys n (%)
17 (100%)
\n
143 (63.84)
Girls n (%)
0
\n
81 (36.16%)
* p < 0.05.
ijerph-17-05569-t002_Table 2
Means, standard deviations, and measures of dispersion of the variables studied for the sample.
n = 241
Means
Standard Deviation
Asymmetry
Kurtosis
Competence
5.46
0.79
−0.47
0.29
Acceptance
7.37
1.56
−0.20
0.10
Somatic anxiety
2.45
0.77
0.18
−0.85
Self-confidence
3.01
0.59
−0.34
−0.05
Cognitive Anxiety
2.75
0.71
−0.29
−0.37
ijerph-17-05569-t003_Table 3
Differential analysis (t-test), according to the gender and sports experience of the participants.
n = 241; gl2.239
(n = 160)Girls
(n = 81)Boys
\nt(p)\n
\nd\n
(n = 106)≤5 years
(n = 135)>5 years
\nt(p)\n
\nd\n
M (SD)
M (SD)
\n
\n
M (SD)
M (SD)
\n
\n
Competence
5.39 (0.79)
5.58 (0.86)
−3.43
0.40
5.45 (0.81)
5.46 (0.78)
1.03
0.03
Acceptance
7.83 (0.78)
7.05 (0.86)
2.25 (0.02 *)
−0.64
7.33 (0.70)
7.90 (0.64)
−1.62 (0.03 *)
0.07
Somatic anxiety
3.21 (0.73)
3.14 (0.76)
−3.21
0.98
2.48 (0.75)
2.43 (0.78)
1.08
−0.11
Cognitive anxiety
3.04 (0.58)
2.94 (0.62)
−4.13
−0.28
3.03 (0.66)
3.00 (0.54)
1.48
−0.07
Self-confidence
2.61 (0.68)
3.02 (0.68)
−4.32 (0.00 **)
1.01
2.68 (0.73)
3.40 (0.69)
−1.83 (0.01 *)
0.78
* p < 0.05; ** p < 0.01
ijerph-17-05569-t004_Table 4
Analysis of multivariate variance of resiliency resources and anxiety, according to gender and years of federated sport experience.
\n
≤5 Years
>5 Years
F(5.232)Gender
F(5.232)Years
F(5.232)Gender × Years
Boys(n = 78)
Girls(n = 28)
Boys(n = 82)
Girls(n = 53)
M (SD)
M (SD)
M (SD)
M (SD)
Competence
5.85 (0.79)
5.18 (0.83)
5.72 (0.78)
5.33 (0.77)
0.05 *
\n
\n
Acceptance
6.51 (0.59)
7.26 (0.88)
7.32 (0.38)
7.68 (0.52)
0.01 *
0.03 *
\n
Somatic anxiety
2.41 (0.73)
2.64 (0.75)
2.23 (0.73)
2.75 (0.77)
\n
\n
\n
Cognitive anxiety
2.61 (0.73)
2.94 (0.65)
2.93 (0.64)
3.26 (0.70)
0.02 **
0.02 *
\n
Self-confidence
3.10 (0.62)
2.82 (0.72)
3.53 (0.53)
3.21 (0.56)
0.00 **
0.00 **
\n
* p < 0.05; ** p < 0.01
ijerph-17-05569-t005_Table 5
Correlational analysis between resilience dimensions and anxiety responses.
\n
M
1
2
3
4
5
6
1. Somatic anxiety
2.45 (0.77)
1
0.533 **
−0.123
−0.073
−0.193 **
−0.110
2. Cognitive anxiety
3.01 (0.59)
\n
1
0.049
0.013
−0.144 *
−0.125
3. Self-confidence
2.75 (0.71)
\n
\n
1
0.547 **
0.387 **
−0.033
4. Competence
5.46 (0.79)
\n
\n
\n
1
0.494 **
−0.164 *
5. Acceptance
7.37 (1.59)
\n
\n
\n
\n
1
0.051
6. Age
15.36 (1.07)
\n
\n
\n
\n
\n
1
* p < 0.05; ** p < 0.01
ijerph-17-05569-t006_Table 6
Variable predictors of young players’ self-confidence.
Dependent Variable
Step
Independent Variables
Beta
\nt\n
\np\n
Self-confidence
R = 0.579; R2 = 0.335; F2.239(p) = 23.59(0.00)
1
Age
0.023
0.34
0.46
2
Competence
0.354
7.46
0.00 **
3
Acceptance
0.057
2.35
0.01 *
4
Somatic anxiety
−0.100
−2.02
0.04 *
5
Cognitive anxiety
0.112
2.09
0.03 *
* p < 0.05; ** p < 0.01
ijerph-17-05569-t007_Table 7
Predicting variables of resiliency resources in players.
\nDependent Variable\n
\nStep\n
\nIndependent Variables\n
\nBeta\n
\nt\n
\np\n
\nCompetence\n
R = 0.648; R2 = 0.421; F2.239(p) = 33.96 (0.00)
1
Age
−0.123
−3.30
0.63
2
Acceptance
0.177
6.31
0.00 **
3
Somatic anxiety
0.021
0.34
0.73
4
Cognitive anxiety
0.018
0.26
0.79
5
Self-confidence
0.543
7.46
0.00 **
\nDependent Variable\n
\nStep\n
\nIndependent Variables\n
\nBeta\n
\nt\n
\np\n
\nCompetence\n
R = 0.648; R2 = 0.421; F2.239(p) = 33.96 (0.00)
1
Age
−0.150
1.836
0.68
2
Acceptance
0.823
6.308
0.00 **
3
Somatic anxiety
−0.154
−1.159
0.24
4
Cognitive anxiety
−0.217
−1.515
0.02 *
5
Self-confidence
0.407
2.358
0.06 *
* p < 0.05; ** p < 0.01
\n"],"string":"[\n \"\\nInt J Environ Res Public HealthInt J Environ Res Public HealthijerphInternational Journal of Environmental Research and Public Health1661-78271660-4601MDPI32752247PMC743210310.3390/ijerph17155569ijerph-17-05569ArticleResilient Resources in Youth Athletes and Their Relationship with Anxiety in Different Team Sportshttps://orcid.org/0000-0002-6640-0352González-HernándezJuan1Gomariz-GeaMarcial2https://orcid.org/0000-0002-4317-1665Valero-ValenzuelaAlfonso23https://orcid.org/0000-0002-4595-3994Gómez-LópezManuel23*Department of Personality, Evaluation and Psychological Treatment, University of Granada, 18071 Granada, Spain; jgonzalez@ugr.esDepartment of Physical Activity and Sport, Faculty of Sport Sciences, University of Murcia, Santiago de la Ribera, 30720 Murcia, Spain; marcialrbb@gmail.com (M.G.-G.); avalero@um.es (A.V.-V.)Campus of International Excellence “Mare Nostrum”, University of Murcia, 30720 Murcia, SpainCorrespondence: mgomezlop@um.es; Tel.: +34-868-888-6740182020820201715556929620202972020© 2020 by the authors.2020Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
The objective of this study is to show the links and differences in the expressions of competitive anxiety in the face of the existence of resilient resources in young athletes, according to sporting (years of experience) and personal (gender) characteristics. To meet these aims, the participants answered the Resilience Scale (RS-14) and the Competitive State Anxiety Inventory-2R (CSAI-2R). The sample consisted of 241 adolescent handball and basketball players between 14 and 17 years old. Different analyses were performed, including a differential and multivariate descriptive, a correlation, and a multiple regression. The results showed that anxiety was negatively related to resilience in its acceptance dimension. It was shown that girls showed higher levels of somatic anxiety, while boys showed higher levels of acceptance. Statistically significant differences were found in the resources for acceptance in favor of boys, while there were significantly different indicators in somatic anxiety and self-confidence in favor of girls. The sports experience was positively related to resilience and negatively to anxiety. Although the existence of indicators of cognitive anxiety (e.g., recurrent thoughts or rhyming), coaches and athletes need to understand that they are also indicators of a necessary activation for psychological functioning. Channeling such a process through psychological training of different skills will enhance the capacities for self-confidence.
The ability to obtain an optimal and stable psychological state during competition is of utmost importance to coaches and athletes [1,2,3]. Physical, social, cognitive, and emotional factors factor into such functioning, as these resources restore balance when a young athlete is in the face of adversity [4,5,6]. In this sense, the study of anxiety has been one of the most prolific lines of research, due to the emotional responses it provokes and that can affect athletes’ performance [7].
Anxiety, which is considered a psychological response produced as a consequence of the differences between an athlete’s response capacity and the demands of the environment, accompanied by a high degree of psycho-physiological activation [8], shows functional conditions that facilitate adaptation (e.g., helps to prepare for action) but also other dysfunctions that weaken or hinder behavior (e.g., nervousness, impulsiveness or rumination) [9].
Among the different classifications that exist around this psychological response and the instruments used in research to measure it, we must highlight somatic anxiety (physiological activation that a person perceives when faced with a stressful situation) and cognitive anxiety (negative expectations and concerns about oneself and the current situation and its possible consequences) [10].
In sports situations, somatic anxiety often occurs in competitions perceived as significant where there are uncontrollable elements and under conditions of social evaluation that lead to stress and a hormonal response, such as increased levels of cortisol in saliva, or physiological aspects, such as increased heart rate or muscle tension [11,12]. To understand the relationship between anxiety and sports performance, Martens, Vealey and Burton [13] established the multidimensional theory, in which they proposed that there is a negative relationship between cognitive anxiety and performance on the one hand, and a curvilinear relationship between somatic anxiety and sports performance on the other.
Anxiety is also related to sociodemographic variables such as age and gender in relation with sport contexts, due to it being multifactorial. Different studies carried out in the sports context have shown that the older the athlete, the greater the experience and competitive level in practicing a sport, and the lower their levels of anxiety, because more experienced athletes have greater and better control over their emotions [14,15], greater technical-tactical mastery, and greater experience in stressful and adverse competitive situations that favor self-confidence [16] and cognitive abilities [17]. With regard to gender, after an extensive review of the literature, no studies were found that show statistically significant differences, although it is true that some studies have shown that women have higher levels of anxiety and less self-confidence than men, and that the production of anxiety in men and women can come from different routes. In men it is be produced by the poor management of a situation, and in women by their level of motivation [12,18,19]. Likewise, with regard to the type of sport practiced, the results of the literature review indicate that athletes who practice team sports in which there is physical contact show higher levels of anxiety than athletes who practice individual sports [17,20].
In a confluent direction with anxiety, resilience protects young athletes from stressful situations or threatening scenarios, giving athletes the means to avoid them [21]. Among the many definitions of resilience, Deen, Truner and Wong [22] explained it as a set of cognitive aspects and behavioral responses to acute or chronic adversity. Luthar, Cichetti and Becker [23] defined it as a dynamic process of positive adaptations within a context of adversity. Fletcher and Sarkar [24] argued that resilience is based on overcoming adversity and on positive adaptation. On the other hand, Wu et al. [25] highlighted effective efforts to environmental challenges and ultimate resistance to the unhealthy effects of stress.
Studied in sports contexts, resilience, like anxiety, is a variable that is dependent on different factors, such as emotions, supports, experiences, strategies, motivation, and self-concept [5,26,27], all of which facilitate optimal sports performance. This concept is really important in youth, with important implications in cognitive, physical, and social development that occurs in the adolescence [28], and its influence on growth on the personal level and in sporting abilities [5,29].
As for the relationship between resilience and the sociodemographic variables of age and gender, it has not been possible to establish firm conclusions since resilience has been little studied in the context of sports [30]. However, some studies have shown that athletes are more resilient, which is probably due to their greater experience in the face of adversity both in professionals [15] and youth athletes [28,31,32]. With respect to their relationship with gender, there are contradictions, as there are studies that have not shown significant differences in the levels of resilience between men and women, and others that point to higher levels of resilience in men caused by a better perception of personal competence [33].
With regard to the relationship between resilience and sports variables such as sporting experience or the sports modality practiced, the results of the review carried out on the first of the variables show contradictory results, since there are studies that, although they have not obtained statistically significant outcomes, have found that professional athletes with higher levels of competence are more resilient than amateur athletes [34,35]; on the other hand, there are works that have reflected that athletes with less sporting experience, like youth athletes, had more resilience capacity [32]. As for the sport practiced, different studies have shown that athletes who practice individual sports have higher levels of resilience [28,36].
Self-confidence is understood as conviction (based on what has been learned and lived before) about the ability to perform a certain task successfully. It is evident that young people have less sporting experience and a greater probability of not being confident in their own skills and resources (e.g., several consecutive errors or failures would affect the perception of self-confidence), which would lead to living with greater perceptions of anxiety. However, different aspects could generate their improvement by looking for self-reflection before the tasks [37], reducing the dramatism for the mistake [38] or generating support bonds with colleagues [39] and rational expression of emotions [40].
Moreover, regarding the relationship between anxiety and resilience in sports contexts, it should be noted that based on the literature review, there is little scientific evidence on this issue in adolescent samples and outside the clinical setting. As an example, Martin-Krumm, Sarrazin, Peterson and Famose [41], in a study conducted in France with adolescent children, demonstrated the relationship between resilience and anxiety, since the results reflected that children with a higher level of resilience controlled their anxiety levels better when performing an ability. Likewise, Ortín-Montero, De la Vega and Gonsálvez-Botella [42], in their study about optimism, anxiety, and self-confidence in handball players, showed that the most resilient players faced the competition with greater confidence and with lower levels of anxiety.
Therefore, and on the basis of what has been reviewed so far, the objectives of the study are as follows: (a) to describe the levels of resilience and competitive anxiety, (b) to analyze the differences in terms of gender and years of sports experience, (c) to study the relationship between resiliency resources and competitive anxiety, and (d) to examine the variables that predict both resiliency resources and self-confidence in players.
2. Materials and Methods2.1. Participants
A total of 241 team sports players (handball and basketball) belonging to different clubs in the Murcia region and Valencia communities participated (160 boys, 66.4%; 81 girls, 33.6%), with an average age of 15.36 years (SD = 1.07). According to age, 60.6% (n = 146) of the participants were <15 years, while 39.4% (n = 95) were >15 years old. Most of them stated that they had an average of 1.56 years of experience as a federated player in their sport (SD = 0.50) and they spent 1.93 training sessions per week (SD = 0.36).
2.2. Measurement Instruments
Resilience scale. The Spanish version of the Resilience Scale (RS-14) [43] was used, along with the 14-Item Resilience Scale (ER-14) [44], which is an abbreviated version of the original 25-item version [45]. This scale measures a person’s degree of individual resilience when such is considered a positive personality characteristic, one that allows people to adapt to adverse situations. The scale is composed of 14 items grouped in two dimensions that measure personal competence (11 items: self-confidence, independence, decision, ingenuity and perseverance, etc.; e.g., “I am not afraid to suffer difficulties because I have already experienced them in the past”), and the acceptance of oneself and life (3 items: adaptability, balance, and flexibility and stable life perspective; e.g., “I am a person with adequate self-esteem”). Every item on the scale should correspond with the respondent’s feelings. The answers were on a 7-point Likert-type scale that ranged from strongly disagree (1) to strongly agree (7). The internal consistency for the sample collected was satisfactory (α = 0.80).
Competitive State Anxiety Inventory-2R. The Competitive State Anxiety Inventory-2R (CSAI-2R) [46] comes from the original CSAI-2 version consisting of 27 items [20]. The Spanish version of this second revision, which was validated for athletes, was used in this study [47]. The CSAI-2R is a specific inventory for the sports context, but with a one-dimensional conception of the state of anxiety. The scale is composed of 16 items grouped in three dimensions that measure somatic state anxiety (6 items; e.g., “My heart is racing”), self-confidence (5 items; e.g., “I am confident”), and cognitive state anxiety (5 items; e.g., “I worry that others will be disappointed with my performance”). Each item of the scale had to be answered in terms of how the player felt just before the competition. The answers were collected on a 4-point Likert-type scale ranging from none (1) to very much (4). The internal consistency for the collected sample was satisfactory (α = 0.78).
2.3. Procedure
The study was carried out in different clubs and sport federations of Murcia region and Valencia community (Spain). A letter explaining the objectives of the research and how it was to be carried out, accompanied by two models of informed consent and permission (one for the parents and another for the young athletes), together with a copy of the measures, was sent to the clubs and federations (first for presidents) before the data collection. The questionnaire was administered by the researchers in the sports facilities of the clubs during the training sessions and completed by the participants in 20–30 min. All the participants were informed of the objectives and of their rights as participants in the study, as well as of the voluntary nature and the absolute confidentiality of the answers and handling of the data. It was explained that there were no correct or incorrect answers, so that participants would answer with the most sincerity and honesty. This study was carried out in accordance with the ethical guidelines of the American Psychological Association (APA). The protocol was approved by the Ethics Committee of the Universidad de Murcia (ID: 1494/2017). All subjects gave written informed consent in accordance with the Declaration of Helsinki [48].
2.4. Statistical Analysis
Through the analysis carried out with the statistical software SPSS. 23.0, descriptive measures and homogeneity (X2 Chi-squared) were obtained for the distribution of the observed sample, internal consistency of the instruments (Cronbach’s alpha), differential analysis (t-test, multivariate analysis and effect size with etha-square) to describe any differences in the sample (according to gender and sport experience), correlations, and multiple regressions (5000 bootstrap samples with bias-corrected confidence intervals 95.00) for the determination of the strength of the relationship between the study variables.
3. Results3.1. Descriptive and Differential Analysis
The sample distribution reflected the existence of a statistically significant association between both variables (Table 1) for the achievement of normality, which was assumed to be a link of dependence between the distributions by gender and sport experience (years). Similarly, this circumstance was again obtained when considering the distribution by gender and the hours of weekly training.
As can be seen in Table 2, the sample reflected higher means of self and life acceptance in terms of resilience, while higher means of self-confidence were observed in the perception of anxiety. Participants’ response tendency (asymmetry) tended to be concentrated between 5 and 7 points (Likert response orientation was 1–7) for personal competence as well as for acceptance, with pronounced peaks at 5 and 8 (kurtosis). Likewise, the participants’ response tendency in anxiety perception tended to concentrate (with a Likert response orientation from 1 to 4), between 2 and 3 points for somatic anxiety, and between 2.5 and 3.5 for cognitive anxiety and self-confidence, with pronounced peaks at 2.5 for somatic anxiety and 3 for cognitive anxiety and self-confidence.
Differential analysis (Table 3) showed the existence of statistically significant differences with respect to gender and sports experience. By gender, resources for acceptance (p < 0.02) were shown in favor of girls and self-confidence (p = 0.00) in favor of boys. The effect size of the established comparisons indicated that all the variables are very high, except for competition and cognitive anxiety, aspects that were repeated in the analyses carried out both by gender and by years of sports experience. According to sports experience, the most experienced young athletes showed differences in acceptance (p < 0.03) and self-confidence (p < 0.01).
In order to check whether there were differences in resiliency resources and anxiety responses based on gender and years of sports experience, an analysis of multivariate variance (MANOVA) was carried out with the scores obtained in all the variables under study (Table 4). The results (Pillai’s Trace) indicated statistically significant differences in favor of girls (F(5.232) = 9.08; p < 0.01) with the highest magnitude of the effect (η2 = 731; r = 0.46). Statistically significant differences were shown based on years of sports experience in favor of the younger, most experienced ones, but the interaction with the gender variables was significant (F(5.232) = 7.512; p > 0.03), with a moderate effect size (η2 = 779; r = 0.51). More specifically, analysis of the variance of effects between subjects showed significant differences in competence, acceptance, cognitive anxiety, and self-confidence by gender (in competence and self-confidence in favor of boys, and in cognitive anxiety and acceptance in favor of girls). In the years of sports experience, the differences were in acceptance, cognitive anxiety, and self-confidence in favor of the younger, more experienced ones. No significant differences were shown in the gender*sport experience interaction.
3.2. Correlational Analysis
Statistically significant two-way relationships appeared between the variables under study (Table 5), so that older athletes had higher perceptions of competence as shown in their lower levels of managing resiliently. Acceptance was inversely and significantly related to both cognitive and somatic anxiety. Conversely it was directly related to self-confidence and logically to a high perception of competence. All this led us to think that higher perceptions of resilient acceptance had a direct influence on the athlete’s ability to interpret anxiety. The relationship between the two resilient variables (acceptance and competence) with the development of self-confidence in a young handball player is very significant, in a positive way.
3.3. Multiple Regression Analysis
The results of the regression analysis (Table 6) indicated a model in which as the athletes showed better levels of acceptance, competence, and cognitive anxiety, they also reduced their somatic anxiety indicators, and increased in self-confidence, where F2.239 = 23.59; p < 0.01 explains 33.5% of the total variance.
The results of the regression analysis (Table 7) also indicate a model in which younger athletes had better levels of acceptance and self-confidence and more competence, where F2.239 = 33.96; p < 0.01 explains 42.1% of the total variance. On the other hand, acceptance was achieved through the predictive model established by an increase in competence and self-confidence, and the existence of low levels of cognitive anxiety.
4. Discussion
The objectives of this study were as follows: (a) to describe the levels of resilience and competitive anxiety, (b) to analyze the differences in terms of gender and years of sports experience, (c) to study the relationship between resiliency resources and competitive anxiety, and (d) to examine the variables that predict young players’ resiliency resources and self-confidence.
4.1. Resilience Levels and Competitive Anxiety
The results indicated that both the levels of self-acceptance and life and competence were high. These outcomes imply that athletes best face adversity with high levels of resilience [21]. This finding is also consistent with previous studies, most of which were conducted in team sports such as handball and football with young sport team athletes [28,30,31,32,35]. As for anxiety levels, the results reflected moderate and high levels of self-confidence. It should be noted that moderate levels of anxiety could optimize athletes’ performance [49]. These results are consistent with those found in other studies conducted with samples belonging mostly to individual sports [12,16,18,50] and contradict the high levels of somatic [14,17] and cognitive [19] anxiety reached by others.
4.2. Differences in Levels of Resilience and Competitive Anxiety Depending on Gender and Years of Sports Experience
In relation to resilience, the results showed that acceptance levels were higher in boys, while competence levels were higher in girls. The literature indicates that it is usually boys who show higher levels of resilience due to an increase in physical self-concept and that girls are much more affected by the sports context [51]. In terms of sports experience, it was shown that both acceptance and competence levels were higher in players with greater sports experience. These results are consistent with findings in previous studies in similar samples, most of which were conducted with team sports [31,32].
On the other hand, higher levels of somatic anxiety were found in girls and cognitive anxiety in boys. The literature provides different explanations for why girls tend to have higher levels of anxiety than boys. Abrahamsen et al. [12] attributed the difference to boys’ interpretation of performance; Brunet and Sabiston [18] attributed it to a greater perception of competence and motivation in boys; Peñaloza et al. [50], León-Prados, Fuentes and Calvo [51], and Arbinaga and Caracuel [52] attributed it to girls’ greater concern about the loss of social recognition caused by poor performance. In regard to self-confidence and gender, studies were found that differed from the results reported. Morillo et al. [19] and Peñaloza et al. [50] revealed higher levels of self-confidence in boys. The controversy between the results of the study and those found in the literature may be due to the age difference of the analyzed sample. Finally, in other studies such as the one conducted by Montero, Moreno-Murcia, González, Pulido, and Cervelló [53] there were no statistically significant differences in relation to gender, nor in the levels of anxiety or self-confidence.
As regards the relationship between anxiety levels and sports experience, although no statistically significant results were found, higher levels of somatic anxiety and cognitive anxiety and lower levels of self-confidence were reported in athletes with less sports experience, thus coinciding with findings from previous studies conducted with samples belonging to both team and individual sports. Hanton et al. [14] with adults and Hernández et al. [16] with youths showed that athletes with greater sports experience faced anxiety with higher levels of self-confidence and managed to interpret anxiety better than those with less experience.
4.3. Relationship between Resiliency Resources and Competitive Anxiety
The results of the correlational analysis reflected a negative association between the two dimensions of anxiety (cognitive and somatic anxiety) and of acceptance, showing that competence was negatively related to somatic anxiety and positively to cognitive anxiety. The papers reviewed showed that the two dimensions of resilience were negatively related to those of anxiety [37,38,54,55]. Similar studies were conducted in team sports such as handball, basketball, football, and rugby, and found that youth athletes with higher levels of resilience managed to moderate their anxiety levels significantly, recovering or adapting better to threatening situations [38,41,42,54]. In addition, resilient athletes may be better able to protect themselves in the face of adversity, while those with lower levels may have greater failures, thus causing low self-confidence and higher levels of anxiety [41].
4.4. Resiliency Resources and Self-Confidence Predicting Variables
The regression analysis showed that self-confidence is likely to increase as athletes have better levels of acceptance, competence, and cognitive anxiety and that it is likely to reduce their indicators of somatic anxiety. These results are in line with the findings of Ortin-Montero et al. [42] in their study of handball players, in which the players with higher levels of resilience showed higher levels of self-confidence and coped better with adverse situations.
Furthermore, the regression analysis also indicated that younger athletes have higher levels of competence with higher levels of acceptance and self-confidence. In addition, the acceptance was greater when combined with competence, self-confidence, and less cognitive anxiety. The literature reviewed shows that young athletes tend to be the most resilient [32,55].
The present study, in addition to providing new information on the relationship between resiliency resources and anxiety levels in athletes, has some limitations that should be highlighted. One of them is that the generalization of the results is limited, due to the sample (cadets and juniors), so these results should be considered as preliminary, needing future replication in other categories such as children and other sports, thus covering all ages of adolescence and sports, and both team and individual sports.
In terms of strengths, it is important to highlight the novel information provided in relation to the field of study and the instruments used, as these have been used in a multitude of works and with a high degree of consistency. Likewise, it would be necessary to complement future studies with the analysis of other variables related to anxiety and resilience such as stress and optimism. Finally, another interesting aspect would be to be able to contrast resiliency resources and anxiety levels in collective and individual sports and see the possible differences that are generated.
5. Conclusions
The consequent negative relationship between resilience resources (especially acceptance and competition) and levels of competitive anxiety, is a reflection that to enhance the former makes it possible to regulate up to control the latter. Despite the existence of indicators of cognitive anxiety (e.g., recurrent thoughts or rhyming), coaches and athletes need to understand that they are also indicators of a necessary activation for psychological functioning. Channeling such a process through psychological training of different skills will enhance the capacities for self-confidence. Making young athletes grow up on self-acceptance (e.g., reducing the belief that they cannot lose), for the younger ones, and on the perception of competition (e.g., learning to observe their improvements) already in adolescence, will undoubtedly consolidate key aspects for an effective and adapted psychological response to sports situations.
Highlighting how to work on skills, such as understanding situations of uncertainty, cooperation, or the search for social support at early ages and with strategies that integrate such resources in the dynamics of training and sports growth, will enhance the resources for the much needed coping with stress and anxiety, as well as consolidate beliefs and attitudes that can reduce dysfunctional responses that alter (e.g., blaming others or oneself, creating fears of failure after several mistakes).
Acknowledgments
We are grateful for the support received from the Royal Spanish Handball Federation (RFEBM) which allowed this study to be carried out.
Author Contributions
Conceptualization, J.G.-H. and M.G.-L.; data curation, J.G.-H. and M.G.-G.; formal analysis, J.G.-H.; investigation, M.G.-G. and M.G.-L.; methodology, J.G.-H.; project administration, M.G.-L.; supervision, M.G.-L.; writing—original draft, J.G.-H., M.G.-G., and M.G.-L.; writing—review and editing, J.G.-H., A.V.-V., and M.G.-L. All authors have read and agreed to the published version of the manuscript.
Funding
This research received no external funding.
Conflicts of Interest
The authors declare no conflict of interest.
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Distribution and homogeneity of intervariable variances (gender, sport experience, and weekly training hours).
Chi-squared (X2)2 = 19.26; p = 0.402 *
\\nSport Experience\\n
\\n≤\\n5 years (n = 106)\\n
\\n> 5 years (n = 135)\\n
\\nMean (SD) age\\n
\\nn (%)\\n
\\nMean (SD) age\\n
\\nn (%)\\n
15.67 (0.51)
\\n
15.14 (0.49)
\\n
Boys = 160 (66.39%)
Boys n (%)
78 (73.58%)
\\n
82 (60.74%)
Girls = 81 (33.61%)
Girls n (%)
28 (26.42%)
\\n
53 (39.26%)
Chi-squared (X2)2 = 14.39; p = 0.236 *
\\nWeekly Training Hours\\n
\\n≤\\n2 h/week (n = 17)\\n
\\n> 2 h/week (n = 224)\\n
\\nMean (SD) age\\n
\\nn (%)\\n
\\nMean (SD) age\\n
\\nn (%)\\n
15.71 (1.05)
\\n
15.33 (1.07)
\\n
Boys n (%)
17 (100%)
\\n
143 (63.84)
Girls n (%)
0
\\n
81 (36.16%)
* p < 0.05.
ijerph-17-05569-t002_Table 2
Means, standard deviations, and measures of dispersion of the variables studied for the sample.
n = 241
Means
Standard Deviation
Asymmetry
Kurtosis
Competence
5.46
0.79
−0.47
0.29
Acceptance
7.37
1.56
−0.20
0.10
Somatic anxiety
2.45
0.77
0.18
−0.85
Self-confidence
3.01
0.59
−0.34
−0.05
Cognitive Anxiety
2.75
0.71
−0.29
−0.37
ijerph-17-05569-t003_Table 3
Differential analysis (t-test), according to the gender and sports experience of the participants.
n = 241; gl2.239
(n = 160)Girls
(n = 81)Boys
\\nt(p)\\n
\\nd\\n
(n = 106)≤5 years
(n = 135)>5 years
\\nt(p)\\n
\\nd\\n
M (SD)
M (SD)
\\n
\\n
M (SD)
M (SD)
\\n
\\n
Competence
5.39 (0.79)
5.58 (0.86)
−3.43
0.40
5.45 (0.81)
5.46 (0.78)
1.03
0.03
Acceptance
7.83 (0.78)
7.05 (0.86)
2.25 (0.02 *)
−0.64
7.33 (0.70)
7.90 (0.64)
−1.62 (0.03 *)
0.07
Somatic anxiety
3.21 (0.73)
3.14 (0.76)
−3.21
0.98
2.48 (0.75)
2.43 (0.78)
1.08
−0.11
Cognitive anxiety
3.04 (0.58)
2.94 (0.62)
−4.13
−0.28
3.03 (0.66)
3.00 (0.54)
1.48
−0.07
Self-confidence
2.61 (0.68)
3.02 (0.68)
−4.32 (0.00 **)
1.01
2.68 (0.73)
3.40 (0.69)
−1.83 (0.01 *)
0.78
* p < 0.05; ** p < 0.01
ijerph-17-05569-t004_Table 4
Analysis of multivariate variance of resiliency resources and anxiety, according to gender and years of federated sport experience.
\\n
≤5 Years
>5 Years
F(5.232)Gender
F(5.232)Years
F(5.232)Gender × Years
Boys(n = 78)
Girls(n = 28)
Boys(n = 82)
Girls(n = 53)
M (SD)
M (SD)
M (SD)
M (SD)
Competence
5.85 (0.79)
5.18 (0.83)
5.72 (0.78)
5.33 (0.77)
0.05 *
\\n
\\n
Acceptance
6.51 (0.59)
7.26 (0.88)
7.32 (0.38)
7.68 (0.52)
0.01 *
0.03 *
\\n
Somatic anxiety
2.41 (0.73)
2.64 (0.75)
2.23 (0.73)
2.75 (0.77)
\\n
\\n
\\n
Cognitive anxiety
2.61 (0.73)
2.94 (0.65)
2.93 (0.64)
3.26 (0.70)
0.02 **
0.02 *
\\n
Self-confidence
3.10 (0.62)
2.82 (0.72)
3.53 (0.53)
3.21 (0.56)
0.00 **
0.00 **
\\n
* p < 0.05; ** p < 0.01
ijerph-17-05569-t005_Table 5
Correlational analysis between resilience dimensions and anxiety responses.
\\n
M
1
2
3
4
5
6
1. Somatic anxiety
2.45 (0.77)
1
0.533 **
−0.123
−0.073
−0.193 **
−0.110
2. Cognitive anxiety
3.01 (0.59)
\\n
1
0.049
0.013
−0.144 *
−0.125
3. Self-confidence
2.75 (0.71)
\\n
\\n
1
0.547 **
0.387 **
−0.033
4. Competence
5.46 (0.79)
\\n
\\n
\\n
1
0.494 **
−0.164 *
5. Acceptance
7.37 (1.59)
\\n
\\n
\\n
\\n
1
0.051
6. Age
15.36 (1.07)
\\n
\\n
\\n
\\n
\\n
1
* p < 0.05; ** p < 0.01
ijerph-17-05569-t006_Table 6
Variable predictors of young players’ self-confidence.
Dependent Variable
Step
Independent Variables
Beta
\\nt\\n
\\np\\n
Self-confidence
R = 0.579; R2 = 0.335; F2.239(p) = 23.59(0.00)
1
Age
0.023
0.34
0.46
2
Competence
0.354
7.46
0.00 **
3
Acceptance
0.057
2.35
0.01 *
4
Somatic anxiety
−0.100
−2.02
0.04 *
5
Cognitive anxiety
0.112
2.09
0.03 *
* p < 0.05; ** p < 0.01
ijerph-17-05569-t007_Table 7
Predicting variables of resiliency resources in players.
\\nDependent Variable\\n
\\nStep\\n
\\nIndependent Variables\\n
\\nBeta\\n
\\nt\\n
\\np\\n
\\nCompetence\\n
R = 0.648; R2 = 0.421; F2.239(p) = 33.96 (0.00)
1
Age
−0.123
−3.30
0.63
2
Acceptance
0.177
6.31
0.00 **
3
Somatic anxiety
0.021
0.34
0.73
4
Cognitive anxiety
0.018
0.26
0.79
5
Self-confidence
0.543
7.46
0.00 **
\\nDependent Variable\\n
\\nStep\\n
\\nIndependent Variables\\n
\\nBeta\\n
\\nt\\n
\\np\\n
\\nCompetence\\n
R = 0.648; R2 = 0.421; F2.239(p) = 33.96 (0.00)
1
Age
−0.150
1.836
0.68
2
Acceptance
0.823
6.308
0.00 **
3
Somatic anxiety
−0.154
−1.159
0.24
4
Cognitive anxiety
−0.217
−1.515
0.02 *
5
Self-confidence
0.407
2.358
0.06 *
* p < 0.05; ** p < 0.01
\\n\"\n]"}}},{"rowIdx":447,"cells":{"data":{"kind":"list like","value":["\nInt J Environ Res Public HealthInt J Environ Res Public HealthijerphInternational Journal of Environmental Research and Public Health1661-78271660-4601MDPI32751107PMC743210410.3390/ijerph17155454ijerph-17-05454ArticleNon-Cancer Chronic Pain Conditions and Risk for Incident Alzheimer’s Disease and Related Dementias in Community-Dwelling Older Adults: A Population-Based Retrospective Cohort Study of United States Medicare Beneficiaries, 2001–2013KhalidSumaira1*SambamoorthiUsha2InnesKim E.1Department of Epidemiology, West Virginia University School of Public Health, Morgantown, WV 26506, USA; karen.innes@hsc.wvu.eduDepartment of Pharmaceutical Systems and Policy, West Virginia University School of Pharmacy, Morgantown, WV 26506, USA; usambamoorthi@hsc.wvu.eduCorrespondence: sk0055@mix.wvu.edu2972020820201715545424620202372020© 2020 by the authors.2020Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Accumulating evidence suggests that certain chronic pain conditions may increase risk for incident Alzheimer’s disease and related dementias (ADRD). Rigorous longitudinal research remains relatively sparse, and the relation of overall chronic pain condition burden to ADRD risk remains little studied, as has the potential mediating role of sleep and mood disorders. In this retrospective cohort study, we investigated the association of common non-cancer chronic pain conditions (NCPC) at baseline to subsequent risk for incident ADRD, and assessed the potential mediating effects of mood and sleep disorders, using baseline and 2-year follow-up data using 11 pooled cohorts (2001–2013) drawn from the U.S. Medicare Current Beneficiaries Survey (MCBS). The study sample comprised 16,934 community-dwelling adults aged ≥65 and ADRD-free at baseline. NCPC included: headache, osteoarthritis, joint pain, back or neck pain, and neuropathic pain, ascertained using claims data; incident ADRD (N = 1149) was identified using claims and survey data. NCPC at baseline remained associated with incident ADRD after adjustment for sociodemographics, lifestyle characteristics, medical history, medications, and other factors (adjusted odds ratio (AOR) for any vs. no NCPC = 1.21, 95% confidence interval (CI) = 1.04–1.40; p = 0.003); the strength and magnitude of this association rose significantly with increasing number of diagnosed NCPCs (AOR for 4+ vs. 0 conditions = 1.91, CI = 1.31–2.80, p-trend < 0.00001). Inclusion of sleep disorders and/or depression/anxiety modestly reduced these risk estimates. Sensitivity analyses yielded similar findings. NCPC was significantly and positively associated with incident ADRD; this association may be partially mediated by mood and sleep disorders. Additional prospective studies with longer-term follow-up are warranted to confirm and extend our findings.
Alzheimer’s disease and related dementia (ADRD) is a group of serious neurodegenerative disorders characterized by progressive decline in cognitive and psychomotor function [1,2]. Prevalence of ADRD continues to increase worldwide, exacting enormous social, economic and healthcare costs. For example, in the United States, at least 5.8 million adults are living with Alzheimer’s disease (AD), the most common form of dementia, with attributable Medicare costs alone exceeding $146 billion annually; these figures are expected to rise steeply in coming years [3]. ADRD is also a significant contributor to mortality and number of life years lived with disability [3,4]. With no cure for ADRD, and no effective disease-modifying treatments yet available despite decades of clinical research [5], efforts are increasingly shifting to prevention and early intervention, including intensive approaches to identify and target modifiable risk factors for ADRD [6].
To date, several factors have been linked to elevated ADRD risk. These include non-modifiable factors such as advancing age, female sex, rurality, and specific genetic profiles, as well as modifiable risk factors [7]. The latter include: poor education and poverty [7,8]; physical inactivity, midlife obesity, and other lifestyle factors [7,9]; history of head injury or stroke [10,11]; and certain chronic physical health disorders (e.g., diabetes, cardiovascular disease, midlife hypertension, respiratory illness [7,9,12]). Specific mental health conditions (e.g., depression, anxiety) and chronic sleep impairment have also been shown to predict subsequent cognitive decline and conversion to ADRD [13,14,15,16]. In addition, there is growing evidence from observational and experimental research that chronic pain, an increasingly common and highly burdensome condition, may also contribute to elevated risk for neurocognitive impairment and development of ADRD [17,18,19,20,21,22,23].
Although limited at present, there is evidence from longitudinal studies that chronic pain and common non-cancer chronic pain conditions (NCPC) may increase risk for incident cognitive impairment and ADRD [24,25,26,27,28,29,30,31,32,33,34]. For example, recent retrospective cohort investigations of Taiwanese nationals [26,28,29,34] and prospective cohort studies of Japanese and Norwegian elders [25,27,33] have reported significantly increased risk of incident dementia in adults previously diagnosed with fibromyalgia [28], osteoarthritis or knee pain [26,33], and headache [25,27,29,34]. Likewise, findings from a handful of longitudinal studies in US [24,30,32] and British adults [31] suggest that non-specific chronic pain may predict subsequent deterioration in memory [30,32], accelerated cognitive decline [32], new onset cognitive impairment [32], and incident dementia [24,32], although studies varied widely in design, study population, length of follow-up, and measures. However, to our knowledge, no study has investigated the collective and incremental association of common chronic pain conditions to ADRD risk, nor the potential mediating role of depression, anxiety and sleep disorders, conditions strongly and bidirectionally associated with chronic pain and linked to ADRD risk.
In this study, we investigate the association of common NCPC to ADRD risk using multiple retrospective cohorts from a linked database of nationally representative sample of older US Medicare beneficiaries. We hypothesized that the presence of NCPC at baseline will be associated with significantly increased risk of incident ADRD, and that the magnitude of this association will increase with rising number of NCPC at baseline. We further hypothesized that depression, anxiety and/or sleep impairment, will partially mediate the association between NCPC and incident ADRD.
2. Methods2.1. Study Design
Our study used a retrospective cohort design to assess the association of baseline common non-cancer chronic pain conditions (NCPC) to incident ADRD using data from the Medicare Current Beneficiary Survey (MCBS) [35] linked with Medicare fee-for-service claims. MCBS is a nationally representative survey of adults participating in Medicare health insurance program. As detailed below, multiple three-year MCBS cohorts (2001–2013) were pooled to construct the analytic sample for this study.
2.2. Data Source
First initiated in 1991, MCBS has been recruiting survey participants each year using a complex stratified, three-stage probability sampling design described in detail elsewhere [36,37]. MCBS participants undertake structured in-person interviews at baseline and follow-up rounds each year for up to three years. MCBS generates comprehensive cross-sectional and longitudinal data on participants’ health status, health services utilization, prescription medications, and payment sources using a combination of survey and administrative records. Our study used data from MCBS Cost and Use files linked with Medicare fee-for-service claims to ascertain demographics, access to care, lifestyle factors, medical history and medication. Based on the recommendations by MCBS investigators [36,37,38] and sampling strategies documented in prior published studies using MCBS [36,38,39], we combined 11 MCBS cohorts in order to maximize reliability and precision of our study estimates; these included the following cohorts: 2001–2003; 2002–2004; 2003–2005; 2004–2006; 2005–2007; 2006–2008; 2007–2009; 2008–2010; 2009–2011; 2010–2012 and 2011–2013.
2.3. Study Sample
The study sample comprised continuously fee-for-service enrolled community-dwelling Medicare beneficiaries, aged 65 years or over, who had complete information on NCPC status and were still alive at the end of follow-up. Institutionalized participants were excluded, as were participants with diagnosed ADRD at baseline. As depicted in the sample selection flow chart (see supplementary Figure S1), application of all a priori exclusion criteria yielded a final sample size of 16,934 adults (N’s by year = 1874 (2001–2003); 1768 (2002–2004); 1884 (2003–2005); 1766 (2004–2006); 1735 (2005–2007); 1650 (2006–2008); 1495 (2007–2009); 1278 (2008–2010); 1034 (2009–2011); 1159 (2010–2012); and 1291 (2011–2013)).
IRB/ethics approval: The present study was approved as exempt protocol by the WVU Institutional Review Board (IRB), due to the deidentified nature of the data used in the study.
2.4. Dependent Variable: Incident Alzheimer’s Disease and Related Dementia (ADRD) at Follow-Up– Yes/No
The Medicare fee-for-service (FFS) claims for inpatient (IP), skilled nursing facility (SNF), outpatient (OT), home health agency (HHA), and physician office (PO) visits for years 2001–2013 as well as MCBS self-reported Health Status and Functioning files were used to ascertain the presence of ADRD at baseline and follow-up. The presence of ADRD at both baseline (year 1) and follow-up (years 2 and 3) was ascertained using a validated CMS algorithm (Centers for Medicare and Medicaid Services (CMS) Chronic Condition Algorithms) [40] of at least one fee-for-service claim with any of these International Classification of Diseases, ninth Edition, clinical modification (ICD-9-CM) diagnostic codes: 331.0, 331.11, 331.19, 331.2, 331.7, 290.0, 290.10, 290.11, 290.12, 290.13, 290.20, 290.21, 290.3, 290.40, 290.41, 290.42, 290.43, 294.0, 294.10, 294.11, 294.20, 294.21, 294.8, and 797, or an affirmative response to the self-reported Health Status question “Has a doctor ever told you that you had Alzheimer’s?” [41]. Using a combination of claims and survey data to ascertain ADRD has been recommended by MCBS investigators to increase capture of ADRD and has been shown to yield results similar to those of expert in-person-assessment [42].
2.5. Key Independent Variable: Non-Cancer Chronic Pain Condition (NCPC) and Number of NCPCs
Baseline NCPC were identified using Medicare fee-for-service claims. NCPC included five common NCPC (back or neck pain, headache, joint pain, neuropathic pain, and osteoarthritis). The presence of any NCPC was identified using either two outpatient claims (90 days apart) or one inpatient claim using ICD9-CM codes as recommended by the Centers for Medicare and Medicaid Services [40] and consistent with prior studies of NCPC [43,44]. Any NCPC was assessed as a binary variable (yes/no) during baseline. Relative NCPC burden was ascertained with a count variable (0–5 NCPCs).
2.6. Covariates
To account for the influence of potential confounding, specific baseline characteristics known or suspected to be associated with ADRD risk and/or chronic pain based on prior research were selected a priori for inclusion as covariates in our multivariable models. These included age group (65–69, 70–74, 75–79, and ≥80 years), based on prior research indicating the risk in adults doubles approximately every 4 years after age 65 [3,45]. Other biological factors i.e., sex (female/male), and race/ethnicity (Non-Hispanic White, African American, Hispanic, and other); other demographic characteristics, i.e., marital status (married, widowed, divorce/separated, other), educational level (less than high school, high school, some college, college), family income, measured as percentage of federal poverty line (FPL) (poor/low-income (<200%), (middle to high-income (≥200%)); health insurance status (insured by Medicaid (yes/no) or private insurance (yes/no)); rurality (metropolitan areas, yes/no); and lifestyle factors, i.e., smoking status (current smoker, past smoker, never smoked) and body mass index (BMI, kg/m2) (underweight (<18.5), normal (18.5–24.9), overweight (25–29.9), and obese ≥30).
Also included as covariates were other chronic physical health conditions reported at baseline (yes/no). These included hypertension, diabetes, heart disease, respiratory illness, and history of stroke, traumatic brain injury (TBI), and cancer (with the exception of non-melanotic skin cancer), as well as specific auto-immune conditions associated with chronic pain, including rheumatoid arthritis (RA), and systemic lupus erythematosus (SLE). In addition, certain commonly used medications that have been linked to ADRD risk and/or used in chronic pain management were also evaluated as covariates. These included non-steroidal anti-inflammatory drugs (NSAIDS) [46], opioid analgesics [47], benzodiazepines [48] and certain other psychotropic drugs (e.g., selective serotonin reuptake inhibitors (SSRIs), serotonin norepinephrine reuptake inhibitors (SNRIs), monoamino oxidase inhibitors (MAOIs), tricyclic antidepressants (TCAs), and atypical antidepressants) [49]. Benzodiazepine use was evaluated as a separate medication category given that: (1) a significant number of study participants (10%) were prescribed these medications; and (2) prior research has suggested that benzodiazepines not only has multidimensional neurological effects [50,51], but is also associated with elevated risk for ADRD [48].
Mediators: To examine the potential mediating influence of mood and sleep disorders on the relation of NCPC to incident ADRD, we evaluated the effects of diagnosed depression, anxiety and insomnia-related sleep disorders both separately and in combination (see supplementary Table S1 for ICD-9-CM diagnostic codes).
2.7. Statistical Analysis
Potential differences in baseline characteristics by NCPC and ADRD status were determined using Rao-Scott chi-square tests (categorical variables). Logistic regressions were used to assess the unadjusted and independent association of NCPC and NCPC burden to incident ADRD. In these multivariable logistic regressions, we carried out block-wise adjustment for demographic and socioeconomic factors; lifestyle factors and chronic physical health conditions; and analgesics and psychotropic medications to assess the adjusted associations of NCPC to ADRD risk. Similar nested multivariable logistic regression models were used to evaluate potential mediating effects of baseline diagnoses of depression, anxiety, and sleep disorders both individually and in combination. To assess the linear relationship of NCPC to risk of ADRD, regression models with polynomial contrast were used. For our sensitivity analyses, we used multinomial logistic regression to build competing risk of death models with the following four outcomes: ADRD-free and alive at the end of follow-up (N = 15,785) (referent category); ADRD-free and not alive at the end of follow-up (N = 1220); ADRD positive and Alive at the end of follow-up (N = 1149); ADRD positive and not alive at the end of follow-up (N = 259).
All bivariate and multivariable analyses were carried out using MCBS complex survey design sampling weights. MCBS data-cycles are released with cross-sectional as well as longitudinal weights. We used 3-year backward longitudinal weights for analysis of our pooled cohorts to yield up to two years of continuous follow-up. All analyses were performed using SAS survey procedure (SAS version 9.4, SAS Institute, Inc.).
3. Results
Our analytical sample comprised 16,934 eligible participants with a mean age at baseline of 74 ± 0.07 years. The majority of the cohort were female (57%), white (83%), married (56.5%) and reported a family income at or above 200% of the FPL. Study participants were predominantly from metropolitan areas (71%) and most had private insurance (79%).
Overall baseline prevalence of any NCPC in this study was 36% (weighted), with significant differences by cohort (range 31% to 39%, p < 0.0001). Pain burden in this population was high, with 37.5% reporting at least two co-existing NCPCs and 15% indicating three or more NCPCs. A significantly higher percentage of participants with baseline NCPC reported a history of chronic physical health conditions associated with ADRD, including hypertension (82% vs. 68.7%), heart disease (31.5% vs. 19.9%), stroke (14% vs. 10%), diabetes (23.5% vs. 19%) (p’s < 0.0001). Participants with baseline NCPC were also significantly more likely to report mood disorders, including depression (11% vs. 4.5%) and anxiety (7.9% vs. 3.5%), as well as sleep disorders (25.5% vs. 10.2%) when compared to participants without baseline NCPC (Table 1).
Table 2 summarizes the baseline characteristics of the study sample by incident ADRD status. A total of 1149 participants were diagnosed with incident ADRD during the 2-year follow-up period (overall incidence rate = 6.8 per 100 participants). Incident ADRD rates did not differ by study cohorts (range 4.9 to 6.2 per 100, p = 0.9), and were significantly higher in women (6.2% vs. 5.0% in men), and in participants who were black (7.4% vs. 5.5% in Non-Hispanic whites), widowed (8.2% vs. 4.5% in married), and more poorly educated (p’s < 0.0001). The proportion of participants diagnosed with incident ADRD was also significantly higher in those who were <200% of the federal poverty level (7.4% vs. 4.3%), were on Medicaid (9.1% vs. 5.2%) and who lacked private insurance (7.2% vs. 5.3%) (p’s < 0.0001). In addition, the percentage diagnosed with ADRD was significantly greater in those who indicated a history of specific physical health conditions at baseline, including hypertension, heart disease, stroke, and TBI, as well as in those who reported use of opioid analgesics (6.9% vs. 5.4%), benzodiazepines (8.5% vs. 5.4%), or other psychotropics (9.8% vs. 4.8%) (p’s < 0.0001). The proportion of participants with incident ADRD increased significantly with rising number of comorbid physical health conditions (range 4.6% to 10.6%, p < 0.0001). Participants who reported sleep, depression or anxiety disorders at baseline also included a significantly higher proportion of ADRD cases (p’s < 0.0001). Notably, as detailed in Table 2, incident ADRD rates were significantly higher in those who reported any NCPC at baseline (6.6 vs. 4.3 per 100), and rose with increasing number of NCPC conditions, from 6.2 in those with 1 NCPC condition to 17.3 per 100 participants in those with 4 or more NCPC conditions (p’s < 0.0001).
3.1. Association of NCPC to Incident ADRD
In our unadjusted logistic regression model, those with baseline NCPC were 54% more likely to have incident ADRD (odds ratio (OR) = 1.54, 95% confidence interval (CI) = 1.34–1.78, p < 0.0001) (Table 3). Adjustment for demographic and socioeconomic characteristics (sex, race, age, education, poverty, Medicaid insurance, private insurance, and marital status) attenuated but did not eliminate this association (adjusted OR (AOR) = 1.33, 95% confidence interval (CI) =1.14–1.53, p < 0.0001).
The magnitude of this association was modestly reduced after additional adjustment for lifestyle factors and comorbid physical health conditions (AOR = 1.28, CI 1.10–1.48, p = 0.001), and further attenuated by inclusion of medications in the model (AOR adjusted for NSAIDs and opioid analgesics (AOR = 1.25, CI 1.08–1.45, p = 0.003) (Table 3). Further adjustment for benzodiazepines and other psychotropics only slightly reduced the magnitude of this estimate (AOR = 1.21 (1.04–1.40, p = 0.01)). As detailed in Table 3, the association of NCPC to incident ADRD rose significantly in both strength and magnitude with increasing number of pain conditions. Relative to beneficiaries with no NCPC at baseline, those with ≥4 NCPCs were twice as likely to be subsequently diagnosed with ADRD after adjustment for demographics, socioeconomic status, lifestyle factors, and medical conditions (AOR = 1.98, CI = 1.36–2.89, p-trend <0.00001). Further adjustment for analgesic medications only slightly attenuated these associations (AOR for ≥4 vs. no NCPC = 1.91, CI 1.31–2.80, p-trend = 0.0008). When number of NCPCs at baseline were assessed as a continuous variable, risk for incident ADRD increased 12% for each additional NCPC in the fully adjusted model (AOR = 1.12, CI = 1.06–1.20, p = 0.0007).
3.2. Sensitivity Analyses
Analyses using competing risk of death models yielded findings consistent with those from our primary analyses (Table 4). Relative to participants without NCPC at baseline, those with any NCPC and still alive at follow-up were significantly more likely to be diagnosed with incident ADRD after adjustment for demographics, lifestyle characteristics, physical health conditions, analgesics, and other factors (AOR for any NCPC = 1.26, 95% CI = 1.08–1.46, p < 0.003). In contrast, baseline NCPC status was unrelated to death during the follow-up period, either with or without a diagnosis of incident ADRD (AORs, respectively = 0.99 and 0.97, p’s ≥ 0.6). Further adjustment for psychotropics did not appreciably change these estimates. These findings suggest survival bias is unlikely to explain the positive association observed between baseline NCPC and incident ADRD.
3.3. Potential Mediating Effects of Sleep and Mood Disorders
As illustrated in Table 5, inclusion of mood disorders in fully adjusted model modestly attenuated the relation of NCPC to incident ADRD (AORs for any NCPC and 4+ NCPCs vs. no NCPC, respectively = 1.17 (1.01–1.37) and 1.57 (1.06–2.34), p’s ≤ 0.04). Likewise, the addition of sleep disorders to the model attenuated the association of NCPC to ADRD risk (AORs for any NCPC and 4+ NCPCs vs. no NCPC, respectively = 1.20 (1.04–1.39) and 1.66 (1.12–2.45) p’s < 0.02) as did that of both sleep and mood disorders (AORs for any NCPC and 4+ NCPCs, respectively = 1.18 (1.01–1.38) and 1.41 (0.94–2.12)). Adding mood and sleep disorders to models adjusted only for sociodemographics yielded similar risk estimates (AORs for any NCPC = 1.19, CI 1.02–1.47, p < 0.05), again suggesting that mood and sleep disorders may in part mediate the relation of NCPC to incident ADRD. Additional adjustment for benzodiazepines and psychotropics did not appreciably alter these associations (AOR = 1.17, CI = 1.01–1.37, p = 0.04). A number of demographic, lifestyle, and health-related factors remained significantly associated with incident ADRD in our fully adjusted models. These include: black (vs. non-Hispanic white) race (AOR = 1.48, CI 1.09–2.00); age (80+ vs. 65–69, AOR 6.12, CI = 4.7–7.9); poverty (<200% vs. >200%, AOR 1.17, CI = 1.02–1.34); Medicaid insurance (AOR 1.27, 95% CI = 1.01–1.6); BMI (underweight vs. normal, AOR = 1.48, CI = 1.04–2.12); history of stroke (AOR 1.87, CI = 1.6–2.2) or TBI (AOR = 1.52, CI = 1.04–2.23); and psychotropic medications (AOR = 2.04, CI = 1.74–2.39) (p’s < 0.05). Use of neither NSAIDs nor opioids was significantly associated with ADRD risk in the adjusted analyses (AORs respectively = 0.96 (0.80–1.16) and 1.03 (0.87–1.22)), nor was use of benzodiazepines (AOR = 1.17, CI = 0.97–1.40) (p’s > 0.05).
4. Discussion
In this retrospective cohort study of older community-dwelling Medicare fee-for-service enrollees, diagnosed NCPC at baseline remained significantly and positively associated with risk for incident ADRD after adjustment for demographics, socioeconomic factors, medical history, medications, and other factors. The strength and magnitude of this association rose with increasing number of NCPCs, indicating increasing risk for incident ADRD with rising chronic pain condition burden. The relation between NCPC and risk of ADRD appeared to be mediated in part by the presence of sleep and mood disorders. To our knowledge, this investigation is the first large retrospective cohort study to assess the collective and incremental association of NCPC to incident ADRD, and to explore the potential mediating role of mood and sleep disorders in this association.
The significant positive associations NCPC to ADRD risk observed in this study are consistent with those of prior longitudinal studies regarding the relation of specific chronic pain conditions to risk for dementia. Recent large retrospective matched cohort studies in Taiwanese nationals [26,28,29,34,52], prospective cohort investigations in Norwegian adults [25,27] and a small retrospective cohort study of Canadian elders [53] all indicated significantly increased risk for incident all-cause dementia in those diagnosed with headache [25,27,29,34,52,53], OA/knee pain [26,33], and fibromyalgia [28] after adjusting for demographics, comorbid conditions, and other potential confounders. Although the few longitudinal studies investigating the association of non-specific chronic pain to subsequent cognitive deterioration or incident ADRD, all in British [31] and U.S. adults [24,30,32], have varied widely in study population, design, and methodology, results have likewise indicated that severe chronic pain [30,31], persistent pain [32], and/or reported pain interference [24] may predict subsequent worsening in memory [30,31], accelerated cognitive decline [32], and dementia [24,32].
While some longitudinal studies investigating the association between pain and dementia risk have included depression and/or anxiety as covariates in their adjusted models [24,28,29,30,32,34], no studies to our knowledge have explored the potential mediating role of mood disorders or evaluated the influence of sleep impairment on the association of chronic pain to incident ADRD. Sleep and mood disorders are common in older adults, have been strongly and reciprocally associated with chronic pain [54,55], and have been shown to be independent risk factors for ADRD [13,14,15,56,57], suggesting that sleep and mood impairment may mediate the observed associations between pain and ADRD risk. The role of sleep and mood neuromodulators, such as serotonin [58,59], dopamine and histamine [60,61] has been well documented in pain expression, likely contributing to the documented bidirectional relationships of sleep [54] and mood disorders [55,62] to pain conditions. In the current study, inclusion of depression, anxiety, and insomnia-related sleep disorders in the model weakened but did not eliminate the observed associations between NCPC and incident ADRD risk, suggesting a partial mediating role. In agreement with prior research, sleep [15,54] and mood disorders [13,14,57] remained strongly and positively associated with both baseline NCPC and with risk for incident ADRD in this cohort after adjustment for other factors.
NCPC and increased ADRD risk; possible pathways: although the mechanisms underlying the observed association of NCPC to incident ADRD remain speculative, chronic pain may operate via several pathways to increase risk for dementia [21,23,63,64,65]. Adults experiencing chronic pain have demonstrated diminished attention, impaired learning and memory, altered processing speed, reduced psychomotor efficiency, and compromised executive function [17,18,19,22,23], hallmarks of cognitive decline that may ultimately presage the development of cognitive impairment and dementia. Contributing to the documented decline in cognitive function that accompanies chronic pain, NCPC may promote specific adverse neurostructural and neurofunctional changes. For example, experimental and neuroimaging studies have demonstrated neurodegenerative changes in subjects with chronic pain that parallel those observed in ADRD [21,65,66], including reduction in grey matter volume in the amygdala, hippocampus and frontal cortices, the brain regions integrally involved in cognitive and behavioral functioning [66,67]. The increases in both peripheral and systemic inflammation that have been linked to chronic pain [68,69] may contribute to these neurodegenerative changes by contributing to neuroinflammation [63]. For example, chronic pain-induced microglial neuroinflammation has been directly implicated in Alzheimer’s disease pathogenesis via production of amyloid beta plaques and neurofibrillary tangles [21]; persistent inflammation negatively affects neuroplasticity and synaptic performance via reduction in brain-derived neurotrophic factors [66,68,69]. Neuronal receptors in the brain are neither infinite nor mutually exclusive and serve a range of neurologic functions under limited resources. During persistent pain, nerve endings fire rapid pain impulses to the brain for remedial actions, which in turn, exhausts the neuronal resources that are also involved in cognitive functions [69,70,71]. In addition, the presence of chronic pain conditions has been correlated with dysregulation of noradrenergic-modulated endogenous pain autoinhibition [69], which has, in turn, been linked to negative cognitive outcomes, including decline in working and long-term memory [70,71].
In agreement with previously published research [3,7,45], risk for ADRD increased strongly with age in this study, and was elevated among African Americans and in adults who had less education or lower family income, were on Medicaid, or had a history of diabetes, stroke, or TBI. Similarly, the significant positive relationships between baseline sleep and mood disorders and likelihood of incident ADRD observed in this sample of US Medicare beneficiaries are consistent with the findings of numerous prior investigations [14,15]. Both psychotropics and analgesics are frequently employed to manage chronic mood disorders, insomnia, and pain conditions in the older population. NSAIDs have been reported to reduce risk of ADRD in most but not all studies [46], whereas prior research has demonstrated modest or no association of opioid analgesics to ADRD risk [47]. In this study, use of neither NSAIDs nor opioid analgesics was significantly related to risk for ADRD. In agreement with some [49] but not all previous studies [48,72], use of psychotropics, but not benzodiazepines, remained significantly associated with incident ADRD after adjustment for multiple confounders. However, as noted above, adjustment for these medications only slightly attenuated the observed relationships of NCPCs to incident ADRD.
Strengths and Limitations
This study has several strengths, including the population-based design, the use of longitudinal data from multiple cohorts, and the large, nationally representative sample of U.S. community-dwelling elders enrolled in FFS Medicare plans. Comprehensive information was available on a broad range of demographic and lifestyle characteristics, as well as on medical history, medication use, and other factors, allowing us to assess the potential confounding influence of these factors. Furthermore, NCPC’s, medication use, and history of other health conditions were ascertained using claims data and established algorithms. ADRD was identified using a combination of Medicare claims and survey data, likely leading to greater capture of this often under-reported and underdiagnosed outcome [41,73,74]. Notably, previous studies have indicated high specificity (89-95%) and acceptable sensitivity (64-85%) for the ascertainment of ADRD using multiple years of Medicare claims data [41,73,74]. We used a 3-year backward cohort design, with a two-year continuous follow-up and incident ADRD measured at two time points.
Our study also has a number of limitations, including the relatively short follow-up period and lack of information on NCPC duration or on chronic pain symptoms, precluding a more comprehensive assessment of the potential role of chronic pain and chronic pain conditions in the development of ADRD. Given that ADRD is often underdiagnosed and progression is generally slow and insidious, and that the study follow-up period was short, under-ascertainment of ADRD is likely in this study, potentially biasing our risk estimates toward the null. In addition, given that we used a conservative method [75] for ascertaining chronic pain (i.e., ≥1 inpatient visit or two outpatient visits for any chronic pain conditions 90 days apart), and that NCPC is typically under-reported in medical claims data [76,77,78], NCPC may have been under-ascertained in this study, again potentially biasing risk estimates towards the null. Moreover, as sleep and mood disorders often accompany the development of ADRD, these disorders may have reflected prodromal ADRD in some who were diagnosed with incident ADRD. While we were able to adjust for a wide range of potential confounders, including smoking and BMI, we lacked information on certain lifestyle-related and other factors strongly linked to ADRD risk, including alcohol consumption, physical activity, genetic and familial predisposition, and social isolation [7,9]. Due to small cell sizes, we were also unable to adjust for Parkinson’s disease and related movement disorders, conditions which have been linked to chronic pain, as well as to mood and sleep disorders and cognitive impairment [79,80]. However, small cell sizes would also suggest that any residual confounding from these movement disorders is unlikely to explain our findings. Both ADRD and chronic pain have been associated with increased mortality [81,82,83], introducing potential survival bias. However, competing risk of death analyses yielded findings similar to those of our primary analyses, arguing against a substantive effect of survival bias on our study results. Finally, definitive conclusions regarding causality are not possible due to the short follow-up period in this study and the insidious nature of ADRD development and progression. However, while reverse causality cannot be ruled out, a growing literature suggests chronic pain can disrupt neurocognitive function and may increase risk for cognitive decline and incident dementia [18,65,84], whereas evidence for an inverse relationship remains sparse [22].
5. Conclusions
In this large, population-based study in a nationally representative sample of US community-dwelling elders enrolled in FFS Medicare, NCPC at baseline remained significantly and positively associated with risk for incident ADRD after adjustment for demographics, lifestyle factors, medical history, medications, and other factors. This association increased in magnitude with increasing NCPC burden and appeared to be partially mediated by the presence of mood and sleep disorders. Additional large, prospective studies with longer term follow-up are warranted to confirm our findings; to further elucidate the potentially important contribution of chronic pain to accelerated cognitive decline, new onset cognitive impairment and the development of ADRD; to clarify the potential mediating role of sleep and mood disorders; and to explore possible underlying mechanisms.
Acknowledgments
Research reported in this publication was supported in part by the National Institute of General Medical Sciences of the National Institutes of Health (Award Number 2U54GM104942-02), the WVCTSI and the Alzheimer’s Research and Prevention Foundation (ARPF). S.K. received a Fulbright fellowship for doctoral research. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health, the ARPF or the Fulbright fellowship program.
Supplementary Materials
The following are available online at https://www.mdpi.com/1660-4601/17/15/5454/s1, Figure S1: ADRD-NCPC Study Cohort: Medicare Current Beneficiary Survey (MCBS), 2001–2013, Table S1: ICD-9_Clinial Modification (ICD-9-CM) Diagnostic Codes.
Click here for additional data file.
Author Contributions
Conceptualization, design, and methodology, and to the interpretation and presentation of results, S.K., U.S. and K.E.I.; statistical analyses, S.K. and U.S.; manuscript draft, S.K.; subsequent iterations, S.K., U.S. and K.E.I.; critical review of the final draft, U.S. and K.E.I. All authors have read and agreed to the published version of the manuscript.
Funding
This research was funded by the National Institute of General Medical Sciences of the National Institutes of Health (Award Number 2U54GM104942-02), the WVCTSI and the Alzheimer’s Research and Prevention Foundation (ARPF).
Conflicts of Interest
The authors declare no conflict of interest.
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Baseline characteristics by non-cancer chronic pain conditions in older Medicare beneficiaries *, analyzed using linked data from the Medicare Current Beneficiary Survey and Medicare claims, 2001–2013.
Variables
Total
Any NCPC
p-Value §
\n
Yes
No
N ¥
Wt.%
N
Wt.%
N
Wt. %
\nALL¥\n
16,934
100
6369
100.0
10,565
100.0
\n
\nSociodemographics\n
\n
\n
\n
\n
\n
\n
\n
 Sex
\n
\n
\n
\n
\n
\n
<0.0001
 Female
9669
56.9
4215
66.4
5454
51.5
\n
 Male
7265
43.1
2154
33.6
5111
48.5
\n
\nAge in Years\n
\n
\n
\n
\n
\n
\n
<0.0001
 65–69
4188
31.8
1335
26.4
2853
34.9
\n
 70–74
3484
22.5
1230
22.1
2254
22.8
\n
 75–79
3674
21.4
1411
22.8
2263
20.6
\n
 80+
5588
24.3
2393
28.8
3195
21.7
\n
 Race/Ethnicity
\n
\n
\n
\n
\n
\n
0.842
 White
14,085
83.2
5293
83.4
8792
83.0
\n
 Black
1159
6.7
438
6.7
721
6.7
\n
 Hispanic
956
5.6
359
5.7
597
5.6
\n
 Other
709
4.5
268
4.3
441
4.6
\n
 Education
\n
\n
\n
\n
\n
\n
<0.0001
 <High School
4605
25.0
1744
25.4
2861
24.7
\n
 High School
6174
36.4
2349
37.0
3825
36.1
\n
 Some College
2431
15.0
950
15.7
1481
14.6
\n
 College
3675
23.6
1306
21.9
2369
24.6
\n
 Marital Status
\n
\n
\n
\n
\n
\n
<0.0001
 Married
9139
56.5
3208
52.8
5931
58.7
\n
 Widowed
5803
30.6
2491
35.7
3312
27.6
\n
 Divorce/separated
1455
9.5
490
8.3
965
10.2
\n
 Other
532
3.3
179
3.1
353
3.5
\n
 Household Income
\n
\n
\n
\n
\n
\n
<0.0001
 <200% Federal Poverty Level
8260
45.9
3221
48.4
5039
44.6
\n
 ≥200% Federal Poverty Level
8674
54.1
3148
51.6
5526
55.4
\n
\nInsurance\n
\n
\n
\n
\n
\n
\n
\n
 Medicaid
\n
\n
\n
\n
\n
\n
<0.0001
 Yes
2147
11.9
1005
15.4
1142
9.9
\n
 No
14,787
88.1
5364
84.6
9423
90.1
\n
 Private Insurance
\n
\n
\n
\n
\n
\n
0.001
 Yes
13,418
79.4
5147
80.8
8271
78.5
\n
 No
3514
20.6
1221
19.2
2293
21.5
\n
\nResidence\n
\n
\n
\n
\n
\n
\n
\n
 Metropolitan Status
\n
\n
\n
\n
\n
\n
<0.0001
 Metro
11,375
70.6
4415
72.5
6960
69.5
\n
 Rural
5558
29.4
1954
27.5
3604
30.5
\n
\nLifestyle Characteristics\n
\n
\n
\n
\n
\n
\n
\n
 Body Mass Index
\n
\n
\n
\n
\n
\n
<0.0001
 Underweight
312
1.7
100
1.4
212
1.8
\n
 Normal
5867
34.0
1984
30.4
3883
36.0
\n
 Overweight
6709
40.0
2491
39.2
4218
40.5
\n
 Obese
3881
24.3
1734
29.0
2147
21.7
\n
\nMean BMI 27.2 (±0.052)\n
\n
\n
\n
\n
\n
\n
\n
 Smoking Status
\n
\n
\n
\n
\n
\n
<0.0001
 Current
1594
10.2
457
7.8
1137
11.5
\n
 Past
7976
47.5
2965
47.2
5011
47.6
\n
 Never
7326
42.4
2936
45.1
4390
40.8
\n
\nBaseline Health History\n
\n
\n
\n
\n
\n
\n
\n
\nPhysical Health Conditions\n
\n
\n
\n
\n
\n
\n
\n
 Hypertension
\n
\n
\n
\n
\n
\n
<0.0001
 Yes
12,713
73.5
5263
82.0
7450
68.7
\n
 No
4221
26.5
1106
18.0
3115
31.3
\n
 Heart disease
\n
\n
\n
\n
\n
\n
<0.0001
 Yes
4357
24.1
2097
31.5
2260
19.9
\n
 No
12,577
75.9
4272
68.5
8305
80.1
\n
 Stroke
\n
\n
\n
\n
\n
\n
<0.0001
 Yes
2139
11.5
979
14.3
1160
9.9
\n
 No
14,795
88.5
5390
85.7
9405
90.1
\n
 Diabetes
\n
\n
\n
\n
\n
\n
<0.0001
 Yes
3495
20.7
1468
23.5
2027
19.2
\n
 No
13,439
79.3
4901
76.5
8538
80.8
\n
 Respiratory disease
\n
\n
\n
\n
\n
\n
<0.0001
 Yes
2423
14.2
1045
16.7
1378
12.7
\n
 No
14,511
85.8
5324
83.3
9187
87.3
\n
 Cancer
\n
\n
\n
\n
\n
\n
<0.0001
 Yes
7101
40.3
2911
44.4
4190
38.0
\n
 No
9833
59.7
3458
55.6
6375
62.0
\n
 Traumatic Brain Injury
\n
\n
\n
\n
\n
\n
<0.0001
 Yes
221
1.2
146
2.2
75
0.7
\n
 No
16,713
98.8
6223
97.8
10,490
99.3
\n
 Rheumatoid Arthritis
\n
\n
\n
\n
\n
\n
<0.0001
 Yes
508
2.9
349
5.5
159
1.4
\n
 No
16,426
97.1
6020
94.5
10,406
98.6
\n
 Lupus
\n
\n
\n
\n
\n
\n
<0.0001
 Yes
136
0.8
94
1.4
42
0.4
\n
 No
16,798
99.2
6275
98.6
10,523
99.6
\n
\nNumber of NCPCs\n
\n
\n
\n
\n
\n
\n
\n
 0
6385
39.1
-
-
-
-
-
 1
4194
24.4
-
-
-
-
-
 2
3818
22.0
-
-
-
-
-
 3
1908
10.9
-
-
-
-
-
 ≥4
629
3.7
-
-
-
-
-
\nSleep and Mood Disorders\n
\n
 Sleep Disorder
\n
\n
\n
\n
\n
\n
<0.0001
 Yes
2728
15.8
1630
25.5
1098
10.2
\n
 No
14,206
84.2
4739
74.5
9467
89.8
\n
 Depression
\n
\n
\n
\n
\n
\n
<0.0001
 Yes
1158
6.9
686
11.1
472
4.5
\n
 No
15,776
93.1
5683
88.9
10,093
95.5
\n
 Anxiety
\n
\n
\n
\n
\n
\n
<0.0001
 Yes
862
5.1
492
7.9
370
3.5
\n
 No
16,072
94.9
5877
92.1
10,195
96.5
\n
\nMedication Use\n
\n
 NSAIDs
\n
\n
\n
\n
\n
\n
<0.0001
 Yes
3241
18.6
1936
30.2
1305
12.0
\n
 No
13,693
81.4
4433
69.8
9260
88.0
\n
 Opioid Analgesics
\n
\n
\n
\n
\n
\n
<0.0001
 Yes
3353
19.4
2059
32.2
1294
12.0
\n
 No
13,581
80.6
4310
67.8
9271
88.0
\n
 Benzodiazepines
\n
\n
\n
\n
\n
\n
<0.0001
 Yes
1734
9.7
935
14.5
799
7.1
\n
 No
15,200
90.3
5434
85.5
9766
92.9
\n
 Psychotropic medications
\n
\n
\n
\n
\n
\n
<0.0001
 Yes
2871
17.0
1448
23.3
1423
13.5
\n
 No
14,063
83.0
4921
76.7
9142
86.5
\n
* Based on 11 pooled cohorts of 16,934 older community-dwelling adults (age > 65 years), enrolled in fee-for service Medicare, alive at the end of follow-up. ¥ Numbers for some variables may not add up to N = 16,934, due to missing values. § Statistically significant group differences in presence of NCPC were examined with Rao-Scott chi-square test. Abbreviation: NCPC, non-cancer chronic pain conditions; BMI, body mass index; NSAIDs, nonsteroidal anti-inflammatory drugs.
ijerph-17-05454-t002_Table 2
Baseline characteristics by incident Alzheimer’s disease and related dementias (ADRD) in older Medicare beneficiaries *, analyzed using linked data from the Medicare Current Beneficiary Survey and Medicare claims, 2001-2013.
Variables
Incident ADRD
p-Value §
Yes
No
 
N
Wt. %
N
Wt. %
\n
\nALL¥\n
1149
6.8
15,785
93.2
\n
 Any NCPC
\n
\n
\n
\n
<0.0001
 Yes
798
6.6
9677
93.4
\n
 No
351
4.3
6108
95.7
\n
 Number of NCPC’s
\n
\n
\n
\n
<0.0001
 0
351
4.3
6108
95.7
\n
 1
304
6.2
3948
93.8
\n
 2
273
6.0
3605
94.0
\n
 3
155
7.3
1742
92.7
\n
 ≥4
66
17.3
382
82.7
\n
\nSociodemographic\n
\n
\n
\n
\n
\n
 Sex
\n
\n
\n
\n
<0.0001
 Female
725
6.2
8944
93.8
\n
 Male
424
5
6841
95
\n
 Age in Years
\n
\n
\n
\n
<0.0001
 65–69
101
1.9
4087
98.1
\n
 70–74
139
4
3345
96
\n
 75–79
220
5.9
3454
94.1
\n
 80+
689
12
4899
88
\n
 Race/Ethnicity
\n
\n
\n
\n
0.066
 White
922
5.5
13,163
94.5
\n
 Black
106
7.4
1053
92.6
\n
 Hispanic
70
5.9
886
94.1
\n
 Other
51
6.5
658
93.5
\n
 Education
\n
\n
\n
\n
<0.0001
 <High School
417
7.8
4188
92.2
\n
 High School
398
5.5
5776
94.5
\n
 Some College
141
4.8
2290
95.2
\n
 College
186
4.1
3489
95.9
\n
 Marital Status
\n
\n
\n
\n
<0.0001
 Married
494
4.5
8645
95.5
\n
 Widowed
541
8.2
5262
91.8
\n
 Divorce/separated
76
4.4
1379
95.6
\n
 Other
38
6
494
94
\n
 Household Income
\n
\n
\n
\n
<0.0001
 <200% Federal Poverty Level
699
7.4
7561
92.6
\n
 ≥200% Federal Poverty Level
450
4.3
8224
95.7
\n
\nInsurance\n
\n
\n
\n
\n
\n
 Medicaid
\n
\n
\n
\n
<0.0001
 Yes
223
9.1
1924
90.9
\n
 No
926
5.2
13,861
94.8
\n
 Private Insurance
\n
\n
\n
\n
<0.0001
 Yes
847
5.3
12,571
94.7
\n
 No
300
7.2
3214
92.8
\n
\nResidence\n
\n
\n
\n
\n
0.649
 Metropolitan Status
 Metro
787
5.7
10,588
94.3
\n
 Rural
362
5.5
5196
94.5
\n
\nLifestyle Characteristics\n
\n
\n
\n
\n
\n
 Body Mass Index
\n
\n
\n
\n
<0.0001
 Underweight
43
12.2
269
87.8
\n
 Normal
484
6.9
5383
93.1
\n
 Overweight
417
5.2
6292
94.8
\n
 Obese
195
4.3
3686
95.7
\n
 Smoking Status
\n
\n
\n
\n
<0.0001
 Current
96
4.8
1498
95.2
\n
 Past
472
5
7504
95
\n
 Never
580
6.6
6746
93.4
\n
\nBaseline Health History\n
\n
\n
\n
\n
\n
\nPhysical Health Conditions\n
\n
\n
\n
\n
\n
 Hypertension
\n
\n
\n
\n
<0.0001
 Yes
935
6.3
11,778
93.7
\n
 No
214
4.1
4007
95.9
\n
 Heart disease
\n
\n
\n
\n
<0.0001
 Yes
368
7.6
3989
92.4
\n
 No
781
5.1
11,796
94.9
\n
 Stroke
\n
\n
\n
\n
<0.0001
 Yes
281
11.9
1858
88.1
\n
 No
868
4.9
13,927
95.1
\n
 Diabetes
\n
\n
\n
\n
0.032
 Yes
264
6.4
3231
93.6
\n
 No
885
5.5
12,554
94.5
\n
 Respiratory disease
\n
\n
\n
\n
0.576
 Yes
170
5.9
2253
94.1
\n
 No
979
5.6
13,532
94.4
\n
 Cancer
\n
\n
\n
\n
0.043
 Yes
505
6.2
6596
93.8
\n
 No
644
5.3
9189
94.7
\n
 Traumatic Brain Injury
\n
\n
\n
\n
<0.0001
 Yes
30
12.8
191
87.2
\n
 No
1119
5.6
15,594
94.4
\n
 Rheumatoid Arthritis
\n
\n
\n
\n
0.247
 Yes
39
6.8
469
93.2
\n
 No
1110
5.6
15,316
94.4
\n
 Lupus
\n
\n
\n
\n
0.119
 Yes
13
8.9
123
91.1
\n
 No
1136
5.7
15,662
94.3
\n
\nSleep and Mood Disorders\n
\n
\n
\n
\n
\n
 Sleep Disorder
\n
\n
\n
\n
<0.0001
 Yes
267
8.5
2461
91.5
\n
 No
882
5.2
13,324
94.8
\n
 Depression
\n
\n
\n
\n
<0.0001
 Yes
170
12.9
988
87.1
\n
 No
979
5.1
14,797
94.9
\n
 Anxiety
\n
\n
\n
\n
<0.0001
 Yes
109
10.9
753
89.1
\n
 No
1040
5.4
15,032
94.6
\n
\nMedication Use\n
\n
\n
\n
\n
\n
 NSAIDs
\n
\n
\n
\n
0.496
 Yes
226
6
3015
94
\n
 No
923
5.6
12,770
94.4
\n
 Opioid Analgesics
\n
\n
\n
\n
<0.0001
 Yes
266
6.9
3087
93.1
\n
 No
883
5.4
12,698
94.6
\n
 Benzodiazepines
\n
\n
\n
\n
<0.0001
 Yes
167
8.5
1567
91.5
\n
 No
982
5.4
14,218
94.6
\n
* Based on 11 pooled cohorts of 16,934 older community-dwelling adults (age ≥ 65 years), enrolled in fee-for service Medicare, alive at the end of follow-up. ¥ Numbers for some variables may not add up to N = 1149, due to missing values. § Statistically significant group differences in presence of NCPC were examined with Rao-Scott chi-square test. Abbreviations: NCPC, non-cancer chronic pain conditions; ADRD, Alzheimer’s disease and related dementias; NSAIDs, nonsteroidal anti-inflammatory drugs.
ijerph-17-05454-t003_Table 3
Association of baseline non-cancer chronic pain conditions (NCPC) and burden to incident Alzheimer’s disease and related dementias (ADRD) in older Medicare beneficiaries *: analysis using linked Medicare Current Beneficiary Survey and Medicare claims data, 2001–2013 (odds ratios (OR) and adjusted odds ratios (AOR) with 95% confidence intervals (CI) calculated using separate logistic regression models).
NCPC Presence/Number
Unadjusted Model
Model 1 ¥
Model 2 ¥¥
Model 3 ⱡⱡ
OR (95% CI)
\np\n
AOR (95% CI)
\np\n
AOR (95% CI)
\np\n
AOR (95% CI)
\np\n
\nAny NCPC ** vs. None\n
1.54 (1.34,1.78)
<0.0001
1.33 (1.14,1.53)
<0.0001
1.28 (1.10,1.48)
0.001
1.25 (1.08,1.45)
0.003
\nNumber of NCPCs\n
\n
\n
\n
\n
\n
\n
\n
\n
  None (referent)
1.00
\n
1.00
\n
1.00
\n
1.00
\n
  One
1.47 (1.26,1.71)
<0.0001
1.27 (1.08,1.50)
0.0039
1.24 (1.05,1.46)
0.0125
1.23 (1.04,1.45)
0.0179
  Two
1.42 (1.20,1.68)
<0.0001
1.25 (1.05,1.49)
0.0121
1.22 (1.02,1.45)
0.0278
1.20 (1.01,1.43)
0.0435
  Three
1.75 (1.42,2.16)
<0.0001
1.45 (1.17,1.80)
0.0007
1.38 (1.11,1.71)
0.0041
1.34 (1.07,1.68)
0.0122
  Four or more
3.03 (2.14,4.29)
<0.0001
2.32 (1.62,3.31)
<0.0001
1.98 (1.36,2.89)
0.0004
1.91 (1.31,2.80)
0.0008
\n P for linear trend ++\n
\n
<0.0001
\n
<0.0001
\n
<0.0001
\n
<0.0001
\nNumber of NCPCs\n
\n
\n
\n
\n
\n
\n
\n
\n
(per additional NCPC)
1.23 (1.16,1.30)
<0.0001
1.16 (1.09,1.23)
<0.0001
1.13 (1.06,1.20)
0.0002
1.12 (1.05,1.20)
0.0007
* Based on 11 pooled cohorts of 16,934 older community-dwelling adults (age > 65 years), enrolled in fee-for-service Medicare. ** Including back and neck pain, headache and migraine, joint pain, neuropathic pain and osteoarthritis. ¥ Socio-demographics: sex, age, race/ethnicity, education, income, Medicaid insurance, private insurance, marital status, region. ¥¥ Lifestyle and chronic physical health conditions: smoking, body mass index, hypertension, diabetes, heart disease, cancer, respiratory disorder, history of stroke, traumatic brain injury. ⱡⱡ Analgesics: nonsteroidal anti-inflammatory drugs and opioid analgesics. ++ Regression results from polynomial contrast for linear relation indicate a strong linear effect of NCPC on risk of ADRD.
ijerph-17-05454-t004_Table 4
Association of baseline non-cancer chronic pain conditions to incident Alzheimer’s disease and related dementias in older Medicare beneficiaries *: competing risk analysis using linked Medicare Current Beneficiary Survey (MCBS) and Medicare claims data, 2001–2013 (odds ratios (OR) and adjusted odds ratios (AOR) with 95% confidence intervals (CI) calculated from multinomial logistic regression).
Any Non-Cancer Chronic Pain Condition (NCPC) **
Alive at Follow-Up, No ADRD (Referent)
Alive at Follow-Up, Incident ADRD
Died before Follow-Up, Incident ADRD
Died before Follow-Up, No ADRD
OR
\np\n
OR (95% CI)
\np\n
OR (95% CI)
\np\n
OR (95% CI)
\np\n
 Model 1. Unadjusted model
1.00
-
1.55 (1.34,1.78)
<0.0001
1.29 (1.03,1.62)
0.029
1.25 (1.12,1.40)
<0.0001
 Model 2. Adjusted for sociodemographics ¥
1.00
-
1.34 (1.16,1.55)
<0.0001
1.10 (0.87,1.40)
0.42
1.21 (1.07,1.36)
0.0024
 Model 3. Also adjusted for lifestyle and chronic physical health conditions ¥¥
1.00
-
1.28 (1.11,1.49)
0.0009
1.03 (0.79,1.34)
0.8287
1.05 (0.93,1.18)
0.4461
 Model 4. Also adjusted for NSAID and opioid analgesic use
1.00
-
1.26 (1.08,1.46)
0.0026
0.99 (0.75,1.31)
0.96
0.97 (0.85,1.10)
0.60
* Based on 11 pooled cohorts of older community-dwelling adults (age > 65 years), enrolled in fee-for-service Medicare. ** Referent for Any NCPC ‘No NCPC’. ¥ Sex, age, race/ethnicity, education, income, Medicaid insurance, private insurance, marital status, region. ¥¥ Smoking status, body mass index, hypertension, diabetes, heart disease, cancer, respiratory illnesses, history of stroke and traumatic brain injury.
ijerph-17-05454-t005_Table 5
Association of baseline non-cancer chronic pain conditions (NCPC’s) and burden to incident Alzheimer’s disease and related dementias in older Medicare beneficiaries *: analysis using linked Medicare Current Beneficiary Survey and Medicare claims data, 2001–2013: Potential mediating influence of diagnosed mood and sleep disorders (odds ratios (OR) and adjusted odds ratios (AOR) with 95% confidence intervals (CI)).
NCPC Presence/Number
Fully Adjusted Model ¥
Model ¥ + Mood Disorders §
Model ¥ + Sleep Disorders §§
Model ¥ + Mood and Sleep Disorders
OR (95% CI)
\np\n
OR (95% CI)
\np\n
OR (95% CI)
\np\n
OR (95% CI)
\np\n
\nAny NCPC vs. None **\n
1.25 (1.08,1.45)
0.0032
1.17 (1.01,1.37)
0.0388
1.20 (1.04,1.39)
0.0155
1.18 (1.01,1.38)
0.0336
\nNumber of NCPCs\n
 None (referent)
1.00 (referent)
\n
1.00 (referent)
\n
1.00 (referent)
\n
1.00 (referent)
\n
 One
1.23 (1.04,1.45)
0.0179
1.19 (1.01,1.41)
0.0408
1.20 (1.02,1.43)
0.0311
1.17 (0.99,1.39)
0.0594
 Two
1.20 (1.01,1.43)
0.0435
1.13 (0.95,1.36)
0.1722
1.16 (0.97,1.38)
0.1008
1.11 (0.92,1.32)
0.2713
 Three
1.34 (1.07,1.68)
0.0122
1.22 (0.96,1.55)
0.0962
1.25 (0.99,1.58)
0.0589
1.17 (0.92,1.48)
0.2112
 Four or more
1.91 (1.31,2.80)
0.0008
1.57 (1.06,2.34)
0.0255
1.66 (1.12,2.45)
0.0113
1.41 (0.94,2.12)
0.0941
\np for linear trend++\n
\n
<0.0001
\n
<0.0001
\n
<0.0001
\n
<0.0001
\nNumber of NCPCs\n
\n
\n
\n
\n
\n
\n
\n
\n
(per additional NCPC)
1.12 (1.05,1.20)
0.0007
1.08 (1.01,1.16)
0.027
1.09 (1.02,1.16)
0.0101
1.06 (0.99,1.13)
0.1001
* Based on 11 pooled cohorts of older community-dwelling adults (age > 65 years), enrolled in fee-for-service Medicare. ** Including back or neck pain, headache and migraine, joint pain, neuropathic pain, and osteoarthritis. ¥ sex, age, race/ethnicity, education, income, Medicaid insurance, private insurance, marital status, region, smoking status, body mass index, chronic physical health conditions (hypertension, diabetes, heart disease, cancer, respiratory disorder, history of stroke, traumatic brain injury), NSAID and opioid analgesics. § Depression and anxiety; §§ insomnia-related sleep disorders. ++ Regression results from polynomial contrast for linear relation indicate a strong linear effect of NCPC on risk of ADRD.
\n"],"string":"[\n \"\\nInt J Environ Res Public HealthInt J Environ Res Public HealthijerphInternational Journal of Environmental Research and Public Health1661-78271660-4601MDPI32751107PMC743210410.3390/ijerph17155454ijerph-17-05454ArticleNon-Cancer Chronic Pain Conditions and Risk for Incident Alzheimer’s Disease and Related Dementias in Community-Dwelling Older Adults: A Population-Based Retrospective Cohort Study of United States Medicare Beneficiaries, 2001–2013KhalidSumaira1*SambamoorthiUsha2InnesKim E.1Department of Epidemiology, West Virginia University School of Public Health, Morgantown, WV 26506, USA; karen.innes@hsc.wvu.eduDepartment of Pharmaceutical Systems and Policy, West Virginia University School of Pharmacy, Morgantown, WV 26506, USA; usambamoorthi@hsc.wvu.eduCorrespondence: sk0055@mix.wvu.edu2972020820201715545424620202372020© 2020 by the authors.2020Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Accumulating evidence suggests that certain chronic pain conditions may increase risk for incident Alzheimer’s disease and related dementias (ADRD). Rigorous longitudinal research remains relatively sparse, and the relation of overall chronic pain condition burden to ADRD risk remains little studied, as has the potential mediating role of sleep and mood disorders. In this retrospective cohort study, we investigated the association of common non-cancer chronic pain conditions (NCPC) at baseline to subsequent risk for incident ADRD, and assessed the potential mediating effects of mood and sleep disorders, using baseline and 2-year follow-up data using 11 pooled cohorts (2001–2013) drawn from the U.S. Medicare Current Beneficiaries Survey (MCBS). The study sample comprised 16,934 community-dwelling adults aged ≥65 and ADRD-free at baseline. NCPC included: headache, osteoarthritis, joint pain, back or neck pain, and neuropathic pain, ascertained using claims data; incident ADRD (N = 1149) was identified using claims and survey data. NCPC at baseline remained associated with incident ADRD after adjustment for sociodemographics, lifestyle characteristics, medical history, medications, and other factors (adjusted odds ratio (AOR) for any vs. no NCPC = 1.21, 95% confidence interval (CI) = 1.04–1.40; p = 0.003); the strength and magnitude of this association rose significantly with increasing number of diagnosed NCPCs (AOR for 4+ vs. 0 conditions = 1.91, CI = 1.31–2.80, p-trend < 0.00001). Inclusion of sleep disorders and/or depression/anxiety modestly reduced these risk estimates. Sensitivity analyses yielded similar findings. NCPC was significantly and positively associated with incident ADRD; this association may be partially mediated by mood and sleep disorders. Additional prospective studies with longer-term follow-up are warranted to confirm and extend our findings.
Alzheimer’s disease and related dementia (ADRD) is a group of serious neurodegenerative disorders characterized by progressive decline in cognitive and psychomotor function [1,2]. Prevalence of ADRD continues to increase worldwide, exacting enormous social, economic and healthcare costs. For example, in the United States, at least 5.8 million adults are living with Alzheimer’s disease (AD), the most common form of dementia, with attributable Medicare costs alone exceeding $146 billion annually; these figures are expected to rise steeply in coming years [3]. ADRD is also a significant contributor to mortality and number of life years lived with disability [3,4]. With no cure for ADRD, and no effective disease-modifying treatments yet available despite decades of clinical research [5], efforts are increasingly shifting to prevention and early intervention, including intensive approaches to identify and target modifiable risk factors for ADRD [6].
To date, several factors have been linked to elevated ADRD risk. These include non-modifiable factors such as advancing age, female sex, rurality, and specific genetic profiles, as well as modifiable risk factors [7]. The latter include: poor education and poverty [7,8]; physical inactivity, midlife obesity, and other lifestyle factors [7,9]; history of head injury or stroke [10,11]; and certain chronic physical health disorders (e.g., diabetes, cardiovascular disease, midlife hypertension, respiratory illness [7,9,12]). Specific mental health conditions (e.g., depression, anxiety) and chronic sleep impairment have also been shown to predict subsequent cognitive decline and conversion to ADRD [13,14,15,16]. In addition, there is growing evidence from observational and experimental research that chronic pain, an increasingly common and highly burdensome condition, may also contribute to elevated risk for neurocognitive impairment and development of ADRD [17,18,19,20,21,22,23].
Although limited at present, there is evidence from longitudinal studies that chronic pain and common non-cancer chronic pain conditions (NCPC) may increase risk for incident cognitive impairment and ADRD [24,25,26,27,28,29,30,31,32,33,34]. For example, recent retrospective cohort investigations of Taiwanese nationals [26,28,29,34] and prospective cohort studies of Japanese and Norwegian elders [25,27,33] have reported significantly increased risk of incident dementia in adults previously diagnosed with fibromyalgia [28], osteoarthritis or knee pain [26,33], and headache [25,27,29,34]. Likewise, findings from a handful of longitudinal studies in US [24,30,32] and British adults [31] suggest that non-specific chronic pain may predict subsequent deterioration in memory [30,32], accelerated cognitive decline [32], new onset cognitive impairment [32], and incident dementia [24,32], although studies varied widely in design, study population, length of follow-up, and measures. However, to our knowledge, no study has investigated the collective and incremental association of common chronic pain conditions to ADRD risk, nor the potential mediating role of depression, anxiety and sleep disorders, conditions strongly and bidirectionally associated with chronic pain and linked to ADRD risk.
In this study, we investigate the association of common NCPC to ADRD risk using multiple retrospective cohorts from a linked database of nationally representative sample of older US Medicare beneficiaries. We hypothesized that the presence of NCPC at baseline will be associated with significantly increased risk of incident ADRD, and that the magnitude of this association will increase with rising number of NCPC at baseline. We further hypothesized that depression, anxiety and/or sleep impairment, will partially mediate the association between NCPC and incident ADRD.
2. Methods2.1. Study Design
Our study used a retrospective cohort design to assess the association of baseline common non-cancer chronic pain conditions (NCPC) to incident ADRD using data from the Medicare Current Beneficiary Survey (MCBS) [35] linked with Medicare fee-for-service claims. MCBS is a nationally representative survey of adults participating in Medicare health insurance program. As detailed below, multiple three-year MCBS cohorts (2001–2013) were pooled to construct the analytic sample for this study.
2.2. Data Source
First initiated in 1991, MCBS has been recruiting survey participants each year using a complex stratified, three-stage probability sampling design described in detail elsewhere [36,37]. MCBS participants undertake structured in-person interviews at baseline and follow-up rounds each year for up to three years. MCBS generates comprehensive cross-sectional and longitudinal data on participants’ health status, health services utilization, prescription medications, and payment sources using a combination of survey and administrative records. Our study used data from MCBS Cost and Use files linked with Medicare fee-for-service claims to ascertain demographics, access to care, lifestyle factors, medical history and medication. Based on the recommendations by MCBS investigators [36,37,38] and sampling strategies documented in prior published studies using MCBS [36,38,39], we combined 11 MCBS cohorts in order to maximize reliability and precision of our study estimates; these included the following cohorts: 2001–2003; 2002–2004; 2003–2005; 2004–2006; 2005–2007; 2006–2008; 2007–2009; 2008–2010; 2009–2011; 2010–2012 and 2011–2013.
2.3. Study Sample
The study sample comprised continuously fee-for-service enrolled community-dwelling Medicare beneficiaries, aged 65 years or over, who had complete information on NCPC status and were still alive at the end of follow-up. Institutionalized participants were excluded, as were participants with diagnosed ADRD at baseline. As depicted in the sample selection flow chart (see supplementary Figure S1), application of all a priori exclusion criteria yielded a final sample size of 16,934 adults (N’s by year = 1874 (2001–2003); 1768 (2002–2004); 1884 (2003–2005); 1766 (2004–2006); 1735 (2005–2007); 1650 (2006–2008); 1495 (2007–2009); 1278 (2008–2010); 1034 (2009–2011); 1159 (2010–2012); and 1291 (2011–2013)).
IRB/ethics approval: The present study was approved as exempt protocol by the WVU Institutional Review Board (IRB), due to the deidentified nature of the data used in the study.
2.4. Dependent Variable: Incident Alzheimer’s Disease and Related Dementia (ADRD) at Follow-Up– Yes/No
The Medicare fee-for-service (FFS) claims for inpatient (IP), skilled nursing facility (SNF), outpatient (OT), home health agency (HHA), and physician office (PO) visits for years 2001–2013 as well as MCBS self-reported Health Status and Functioning files were used to ascertain the presence of ADRD at baseline and follow-up. The presence of ADRD at both baseline (year 1) and follow-up (years 2 and 3) was ascertained using a validated CMS algorithm (Centers for Medicare and Medicaid Services (CMS) Chronic Condition Algorithms) [40] of at least one fee-for-service claim with any of these International Classification of Diseases, ninth Edition, clinical modification (ICD-9-CM) diagnostic codes: 331.0, 331.11, 331.19, 331.2, 331.7, 290.0, 290.10, 290.11, 290.12, 290.13, 290.20, 290.21, 290.3, 290.40, 290.41, 290.42, 290.43, 294.0, 294.10, 294.11, 294.20, 294.21, 294.8, and 797, or an affirmative response to the self-reported Health Status question “Has a doctor ever told you that you had Alzheimer’s?” [41]. Using a combination of claims and survey data to ascertain ADRD has been recommended by MCBS investigators to increase capture of ADRD and has been shown to yield results similar to those of expert in-person-assessment [42].
2.5. Key Independent Variable: Non-Cancer Chronic Pain Condition (NCPC) and Number of NCPCs
Baseline NCPC were identified using Medicare fee-for-service claims. NCPC included five common NCPC (back or neck pain, headache, joint pain, neuropathic pain, and osteoarthritis). The presence of any NCPC was identified using either two outpatient claims (90 days apart) or one inpatient claim using ICD9-CM codes as recommended by the Centers for Medicare and Medicaid Services [40] and consistent with prior studies of NCPC [43,44]. Any NCPC was assessed as a binary variable (yes/no) during baseline. Relative NCPC burden was ascertained with a count variable (0–5 NCPCs).
2.6. Covariates
To account for the influence of potential confounding, specific baseline characteristics known or suspected to be associated with ADRD risk and/or chronic pain based on prior research were selected a priori for inclusion as covariates in our multivariable models. These included age group (65–69, 70–74, 75–79, and ≥80 years), based on prior research indicating the risk in adults doubles approximately every 4 years after age 65 [3,45]. Other biological factors i.e., sex (female/male), and race/ethnicity (Non-Hispanic White, African American, Hispanic, and other); other demographic characteristics, i.e., marital status (married, widowed, divorce/separated, other), educational level (less than high school, high school, some college, college), family income, measured as percentage of federal poverty line (FPL) (poor/low-income (<200%), (middle to high-income (≥200%)); health insurance status (insured by Medicaid (yes/no) or private insurance (yes/no)); rurality (metropolitan areas, yes/no); and lifestyle factors, i.e., smoking status (current smoker, past smoker, never smoked) and body mass index (BMI, kg/m2) (underweight (<18.5), normal (18.5–24.9), overweight (25–29.9), and obese ≥30).
Also included as covariates were other chronic physical health conditions reported at baseline (yes/no). These included hypertension, diabetes, heart disease, respiratory illness, and history of stroke, traumatic brain injury (TBI), and cancer (with the exception of non-melanotic skin cancer), as well as specific auto-immune conditions associated with chronic pain, including rheumatoid arthritis (RA), and systemic lupus erythematosus (SLE). In addition, certain commonly used medications that have been linked to ADRD risk and/or used in chronic pain management were also evaluated as covariates. These included non-steroidal anti-inflammatory drugs (NSAIDS) [46], opioid analgesics [47], benzodiazepines [48] and certain other psychotropic drugs (e.g., selective serotonin reuptake inhibitors (SSRIs), serotonin norepinephrine reuptake inhibitors (SNRIs), monoamino oxidase inhibitors (MAOIs), tricyclic antidepressants (TCAs), and atypical antidepressants) [49]. Benzodiazepine use was evaluated as a separate medication category given that: (1) a significant number of study participants (10%) were prescribed these medications; and (2) prior research has suggested that benzodiazepines not only has multidimensional neurological effects [50,51], but is also associated with elevated risk for ADRD [48].
Mediators: To examine the potential mediating influence of mood and sleep disorders on the relation of NCPC to incident ADRD, we evaluated the effects of diagnosed depression, anxiety and insomnia-related sleep disorders both separately and in combination (see supplementary Table S1 for ICD-9-CM diagnostic codes).
2.7. Statistical Analysis
Potential differences in baseline characteristics by NCPC and ADRD status were determined using Rao-Scott chi-square tests (categorical variables). Logistic regressions were used to assess the unadjusted and independent association of NCPC and NCPC burden to incident ADRD. In these multivariable logistic regressions, we carried out block-wise adjustment for demographic and socioeconomic factors; lifestyle factors and chronic physical health conditions; and analgesics and psychotropic medications to assess the adjusted associations of NCPC to ADRD risk. Similar nested multivariable logistic regression models were used to evaluate potential mediating effects of baseline diagnoses of depression, anxiety, and sleep disorders both individually and in combination. To assess the linear relationship of NCPC to risk of ADRD, regression models with polynomial contrast were used. For our sensitivity analyses, we used multinomial logistic regression to build competing risk of death models with the following four outcomes: ADRD-free and alive at the end of follow-up (N = 15,785) (referent category); ADRD-free and not alive at the end of follow-up (N = 1220); ADRD positive and Alive at the end of follow-up (N = 1149); ADRD positive and not alive at the end of follow-up (N = 259).
All bivariate and multivariable analyses were carried out using MCBS complex survey design sampling weights. MCBS data-cycles are released with cross-sectional as well as longitudinal weights. We used 3-year backward longitudinal weights for analysis of our pooled cohorts to yield up to two years of continuous follow-up. All analyses were performed using SAS survey procedure (SAS version 9.4, SAS Institute, Inc.).
3. Results
Our analytical sample comprised 16,934 eligible participants with a mean age at baseline of 74 ± 0.07 years. The majority of the cohort were female (57%), white (83%), married (56.5%) and reported a family income at or above 200% of the FPL. Study participants were predominantly from metropolitan areas (71%) and most had private insurance (79%).
Overall baseline prevalence of any NCPC in this study was 36% (weighted), with significant differences by cohort (range 31% to 39%, p < 0.0001). Pain burden in this population was high, with 37.5% reporting at least two co-existing NCPCs and 15% indicating three or more NCPCs. A significantly higher percentage of participants with baseline NCPC reported a history of chronic physical health conditions associated with ADRD, including hypertension (82% vs. 68.7%), heart disease (31.5% vs. 19.9%), stroke (14% vs. 10%), diabetes (23.5% vs. 19%) (p’s < 0.0001). Participants with baseline NCPC were also significantly more likely to report mood disorders, including depression (11% vs. 4.5%) and anxiety (7.9% vs. 3.5%), as well as sleep disorders (25.5% vs. 10.2%) when compared to participants without baseline NCPC (Table 1).
Table 2 summarizes the baseline characteristics of the study sample by incident ADRD status. A total of 1149 participants were diagnosed with incident ADRD during the 2-year follow-up period (overall incidence rate = 6.8 per 100 participants). Incident ADRD rates did not differ by study cohorts (range 4.9 to 6.2 per 100, p = 0.9), and were significantly higher in women (6.2% vs. 5.0% in men), and in participants who were black (7.4% vs. 5.5% in Non-Hispanic whites), widowed (8.2% vs. 4.5% in married), and more poorly educated (p’s < 0.0001). The proportion of participants diagnosed with incident ADRD was also significantly higher in those who were <200% of the federal poverty level (7.4% vs. 4.3%), were on Medicaid (9.1% vs. 5.2%) and who lacked private insurance (7.2% vs. 5.3%) (p’s < 0.0001). In addition, the percentage diagnosed with ADRD was significantly greater in those who indicated a history of specific physical health conditions at baseline, including hypertension, heart disease, stroke, and TBI, as well as in those who reported use of opioid analgesics (6.9% vs. 5.4%), benzodiazepines (8.5% vs. 5.4%), or other psychotropics (9.8% vs. 4.8%) (p’s < 0.0001). The proportion of participants with incident ADRD increased significantly with rising number of comorbid physical health conditions (range 4.6% to 10.6%, p < 0.0001). Participants who reported sleep, depression or anxiety disorders at baseline also included a significantly higher proportion of ADRD cases (p’s < 0.0001). Notably, as detailed in Table 2, incident ADRD rates were significantly higher in those who reported any NCPC at baseline (6.6 vs. 4.3 per 100), and rose with increasing number of NCPC conditions, from 6.2 in those with 1 NCPC condition to 17.3 per 100 participants in those with 4 or more NCPC conditions (p’s < 0.0001).
3.1. Association of NCPC to Incident ADRD
In our unadjusted logistic regression model, those with baseline NCPC were 54% more likely to have incident ADRD (odds ratio (OR) = 1.54, 95% confidence interval (CI) = 1.34–1.78, p < 0.0001) (Table 3). Adjustment for demographic and socioeconomic characteristics (sex, race, age, education, poverty, Medicaid insurance, private insurance, and marital status) attenuated but did not eliminate this association (adjusted OR (AOR) = 1.33, 95% confidence interval (CI) =1.14–1.53, p < 0.0001).
The magnitude of this association was modestly reduced after additional adjustment for lifestyle factors and comorbid physical health conditions (AOR = 1.28, CI 1.10–1.48, p = 0.001), and further attenuated by inclusion of medications in the model (AOR adjusted for NSAIDs and opioid analgesics (AOR = 1.25, CI 1.08–1.45, p = 0.003) (Table 3). Further adjustment for benzodiazepines and other psychotropics only slightly reduced the magnitude of this estimate (AOR = 1.21 (1.04–1.40, p = 0.01)). As detailed in Table 3, the association of NCPC to incident ADRD rose significantly in both strength and magnitude with increasing number of pain conditions. Relative to beneficiaries with no NCPC at baseline, those with ≥4 NCPCs were twice as likely to be subsequently diagnosed with ADRD after adjustment for demographics, socioeconomic status, lifestyle factors, and medical conditions (AOR = 1.98, CI = 1.36–2.89, p-trend <0.00001). Further adjustment for analgesic medications only slightly attenuated these associations (AOR for ≥4 vs. no NCPC = 1.91, CI 1.31–2.80, p-trend = 0.0008). When number of NCPCs at baseline were assessed as a continuous variable, risk for incident ADRD increased 12% for each additional NCPC in the fully adjusted model (AOR = 1.12, CI = 1.06–1.20, p = 0.0007).
3.2. Sensitivity Analyses
Analyses using competing risk of death models yielded findings consistent with those from our primary analyses (Table 4). Relative to participants without NCPC at baseline, those with any NCPC and still alive at follow-up were significantly more likely to be diagnosed with incident ADRD after adjustment for demographics, lifestyle characteristics, physical health conditions, analgesics, and other factors (AOR for any NCPC = 1.26, 95% CI = 1.08–1.46, p < 0.003). In contrast, baseline NCPC status was unrelated to death during the follow-up period, either with or without a diagnosis of incident ADRD (AORs, respectively = 0.99 and 0.97, p’s ≥ 0.6). Further adjustment for psychotropics did not appreciably change these estimates. These findings suggest survival bias is unlikely to explain the positive association observed between baseline NCPC and incident ADRD.
3.3. Potential Mediating Effects of Sleep and Mood Disorders
As illustrated in Table 5, inclusion of mood disorders in fully adjusted model modestly attenuated the relation of NCPC to incident ADRD (AORs for any NCPC and 4+ NCPCs vs. no NCPC, respectively = 1.17 (1.01–1.37) and 1.57 (1.06–2.34), p’s ≤ 0.04). Likewise, the addition of sleep disorders to the model attenuated the association of NCPC to ADRD risk (AORs for any NCPC and 4+ NCPCs vs. no NCPC, respectively = 1.20 (1.04–1.39) and 1.66 (1.12–2.45) p’s < 0.02) as did that of both sleep and mood disorders (AORs for any NCPC and 4+ NCPCs, respectively = 1.18 (1.01–1.38) and 1.41 (0.94–2.12)). Adding mood and sleep disorders to models adjusted only for sociodemographics yielded similar risk estimates (AORs for any NCPC = 1.19, CI 1.02–1.47, p < 0.05), again suggesting that mood and sleep disorders may in part mediate the relation of NCPC to incident ADRD. Additional adjustment for benzodiazepines and psychotropics did not appreciably alter these associations (AOR = 1.17, CI = 1.01–1.37, p = 0.04). A number of demographic, lifestyle, and health-related factors remained significantly associated with incident ADRD in our fully adjusted models. These include: black (vs. non-Hispanic white) race (AOR = 1.48, CI 1.09–2.00); age (80+ vs. 65–69, AOR 6.12, CI = 4.7–7.9); poverty (<200% vs. >200%, AOR 1.17, CI = 1.02–1.34); Medicaid insurance (AOR 1.27, 95% CI = 1.01–1.6); BMI (underweight vs. normal, AOR = 1.48, CI = 1.04–2.12); history of stroke (AOR 1.87, CI = 1.6–2.2) or TBI (AOR = 1.52, CI = 1.04–2.23); and psychotropic medications (AOR = 2.04, CI = 1.74–2.39) (p’s < 0.05). Use of neither NSAIDs nor opioids was significantly associated with ADRD risk in the adjusted analyses (AORs respectively = 0.96 (0.80–1.16) and 1.03 (0.87–1.22)), nor was use of benzodiazepines (AOR = 1.17, CI = 0.97–1.40) (p’s > 0.05).
4. Discussion
In this retrospective cohort study of older community-dwelling Medicare fee-for-service enrollees, diagnosed NCPC at baseline remained significantly and positively associated with risk for incident ADRD after adjustment for demographics, socioeconomic factors, medical history, medications, and other factors. The strength and magnitude of this association rose with increasing number of NCPCs, indicating increasing risk for incident ADRD with rising chronic pain condition burden. The relation between NCPC and risk of ADRD appeared to be mediated in part by the presence of sleep and mood disorders. To our knowledge, this investigation is the first large retrospective cohort study to assess the collective and incremental association of NCPC to incident ADRD, and to explore the potential mediating role of mood and sleep disorders in this association.
The significant positive associations NCPC to ADRD risk observed in this study are consistent with those of prior longitudinal studies regarding the relation of specific chronic pain conditions to risk for dementia. Recent large retrospective matched cohort studies in Taiwanese nationals [26,28,29,34,52], prospective cohort investigations in Norwegian adults [25,27] and a small retrospective cohort study of Canadian elders [53] all indicated significantly increased risk for incident all-cause dementia in those diagnosed with headache [25,27,29,34,52,53], OA/knee pain [26,33], and fibromyalgia [28] after adjusting for demographics, comorbid conditions, and other potential confounders. Although the few longitudinal studies investigating the association of non-specific chronic pain to subsequent cognitive deterioration or incident ADRD, all in British [31] and U.S. adults [24,30,32], have varied widely in study population, design, and methodology, results have likewise indicated that severe chronic pain [30,31], persistent pain [32], and/or reported pain interference [24] may predict subsequent worsening in memory [30,31], accelerated cognitive decline [32], and dementia [24,32].
While some longitudinal studies investigating the association between pain and dementia risk have included depression and/or anxiety as covariates in their adjusted models [24,28,29,30,32,34], no studies to our knowledge have explored the potential mediating role of mood disorders or evaluated the influence of sleep impairment on the association of chronic pain to incident ADRD. Sleep and mood disorders are common in older adults, have been strongly and reciprocally associated with chronic pain [54,55], and have been shown to be independent risk factors for ADRD [13,14,15,56,57], suggesting that sleep and mood impairment may mediate the observed associations between pain and ADRD risk. The role of sleep and mood neuromodulators, such as serotonin [58,59], dopamine and histamine [60,61] has been well documented in pain expression, likely contributing to the documented bidirectional relationships of sleep [54] and mood disorders [55,62] to pain conditions. In the current study, inclusion of depression, anxiety, and insomnia-related sleep disorders in the model weakened but did not eliminate the observed associations between NCPC and incident ADRD risk, suggesting a partial mediating role. In agreement with prior research, sleep [15,54] and mood disorders [13,14,57] remained strongly and positively associated with both baseline NCPC and with risk for incident ADRD in this cohort after adjustment for other factors.
NCPC and increased ADRD risk; possible pathways: although the mechanisms underlying the observed association of NCPC to incident ADRD remain speculative, chronic pain may operate via several pathways to increase risk for dementia [21,23,63,64,65]. Adults experiencing chronic pain have demonstrated diminished attention, impaired learning and memory, altered processing speed, reduced psychomotor efficiency, and compromised executive function [17,18,19,22,23], hallmarks of cognitive decline that may ultimately presage the development of cognitive impairment and dementia. Contributing to the documented decline in cognitive function that accompanies chronic pain, NCPC may promote specific adverse neurostructural and neurofunctional changes. For example, experimental and neuroimaging studies have demonstrated neurodegenerative changes in subjects with chronic pain that parallel those observed in ADRD [21,65,66], including reduction in grey matter volume in the amygdala, hippocampus and frontal cortices, the brain regions integrally involved in cognitive and behavioral functioning [66,67]. The increases in both peripheral and systemic inflammation that have been linked to chronic pain [68,69] may contribute to these neurodegenerative changes by contributing to neuroinflammation [63]. For example, chronic pain-induced microglial neuroinflammation has been directly implicated in Alzheimer’s disease pathogenesis via production of amyloid beta plaques and neurofibrillary tangles [21]; persistent inflammation negatively affects neuroplasticity and synaptic performance via reduction in brain-derived neurotrophic factors [66,68,69]. Neuronal receptors in the brain are neither infinite nor mutually exclusive and serve a range of neurologic functions under limited resources. During persistent pain, nerve endings fire rapid pain impulses to the brain for remedial actions, which in turn, exhausts the neuronal resources that are also involved in cognitive functions [69,70,71]. In addition, the presence of chronic pain conditions has been correlated with dysregulation of noradrenergic-modulated endogenous pain autoinhibition [69], which has, in turn, been linked to negative cognitive outcomes, including decline in working and long-term memory [70,71].
In agreement with previously published research [3,7,45], risk for ADRD increased strongly with age in this study, and was elevated among African Americans and in adults who had less education or lower family income, were on Medicaid, or had a history of diabetes, stroke, or TBI. Similarly, the significant positive relationships between baseline sleep and mood disorders and likelihood of incident ADRD observed in this sample of US Medicare beneficiaries are consistent with the findings of numerous prior investigations [14,15]. Both psychotropics and analgesics are frequently employed to manage chronic mood disorders, insomnia, and pain conditions in the older population. NSAIDs have been reported to reduce risk of ADRD in most but not all studies [46], whereas prior research has demonstrated modest or no association of opioid analgesics to ADRD risk [47]. In this study, use of neither NSAIDs nor opioid analgesics was significantly related to risk for ADRD. In agreement with some [49] but not all previous studies [48,72], use of psychotropics, but not benzodiazepines, remained significantly associated with incident ADRD after adjustment for multiple confounders. However, as noted above, adjustment for these medications only slightly attenuated the observed relationships of NCPCs to incident ADRD.
Strengths and Limitations
This study has several strengths, including the population-based design, the use of longitudinal data from multiple cohorts, and the large, nationally representative sample of U.S. community-dwelling elders enrolled in FFS Medicare plans. Comprehensive information was available on a broad range of demographic and lifestyle characteristics, as well as on medical history, medication use, and other factors, allowing us to assess the potential confounding influence of these factors. Furthermore, NCPC’s, medication use, and history of other health conditions were ascertained using claims data and established algorithms. ADRD was identified using a combination of Medicare claims and survey data, likely leading to greater capture of this often under-reported and underdiagnosed outcome [41,73,74]. Notably, previous studies have indicated high specificity (89-95%) and acceptable sensitivity (64-85%) for the ascertainment of ADRD using multiple years of Medicare claims data [41,73,74]. We used a 3-year backward cohort design, with a two-year continuous follow-up and incident ADRD measured at two time points.
Our study also has a number of limitations, including the relatively short follow-up period and lack of information on NCPC duration or on chronic pain symptoms, precluding a more comprehensive assessment of the potential role of chronic pain and chronic pain conditions in the development of ADRD. Given that ADRD is often underdiagnosed and progression is generally slow and insidious, and that the study follow-up period was short, under-ascertainment of ADRD is likely in this study, potentially biasing our risk estimates toward the null. In addition, given that we used a conservative method [75] for ascertaining chronic pain (i.e., ≥1 inpatient visit or two outpatient visits for any chronic pain conditions 90 days apart), and that NCPC is typically under-reported in medical claims data [76,77,78], NCPC may have been under-ascertained in this study, again potentially biasing risk estimates towards the null. Moreover, as sleep and mood disorders often accompany the development of ADRD, these disorders may have reflected prodromal ADRD in some who were diagnosed with incident ADRD. While we were able to adjust for a wide range of potential confounders, including smoking and BMI, we lacked information on certain lifestyle-related and other factors strongly linked to ADRD risk, including alcohol consumption, physical activity, genetic and familial predisposition, and social isolation [7,9]. Due to small cell sizes, we were also unable to adjust for Parkinson’s disease and related movement disorders, conditions which have been linked to chronic pain, as well as to mood and sleep disorders and cognitive impairment [79,80]. However, small cell sizes would also suggest that any residual confounding from these movement disorders is unlikely to explain our findings. Both ADRD and chronic pain have been associated with increased mortality [81,82,83], introducing potential survival bias. However, competing risk of death analyses yielded findings similar to those of our primary analyses, arguing against a substantive effect of survival bias on our study results. Finally, definitive conclusions regarding causality are not possible due to the short follow-up period in this study and the insidious nature of ADRD development and progression. However, while reverse causality cannot be ruled out, a growing literature suggests chronic pain can disrupt neurocognitive function and may increase risk for cognitive decline and incident dementia [18,65,84], whereas evidence for an inverse relationship remains sparse [22].
5. Conclusions
In this large, population-based study in a nationally representative sample of US community-dwelling elders enrolled in FFS Medicare, NCPC at baseline remained significantly and positively associated with risk for incident ADRD after adjustment for demographics, lifestyle factors, medical history, medications, and other factors. This association increased in magnitude with increasing NCPC burden and appeared to be partially mediated by the presence of mood and sleep disorders. Additional large, prospective studies with longer term follow-up are warranted to confirm our findings; to further elucidate the potentially important contribution of chronic pain to accelerated cognitive decline, new onset cognitive impairment and the development of ADRD; to clarify the potential mediating role of sleep and mood disorders; and to explore possible underlying mechanisms.
Acknowledgments
Research reported in this publication was supported in part by the National Institute of General Medical Sciences of the National Institutes of Health (Award Number 2U54GM104942-02), the WVCTSI and the Alzheimer’s Research and Prevention Foundation (ARPF). S.K. received a Fulbright fellowship for doctoral research. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health, the ARPF or the Fulbright fellowship program.
Supplementary Materials
The following are available online at https://www.mdpi.com/1660-4601/17/15/5454/s1, Figure S1: ADRD-NCPC Study Cohort: Medicare Current Beneficiary Survey (MCBS), 2001–2013, Table S1: ICD-9_Clinial Modification (ICD-9-CM) Diagnostic Codes.
Click here for additional data file.
Author Contributions
Conceptualization, design, and methodology, and to the interpretation and presentation of results, S.K., U.S. and K.E.I.; statistical analyses, S.K. and U.S.; manuscript draft, S.K.; subsequent iterations, S.K., U.S. and K.E.I.; critical review of the final draft, U.S. and K.E.I. All authors have read and agreed to the published version of the manuscript.
Funding
This research was funded by the National Institute of General Medical Sciences of the National Institutes of Health (Award Number 2U54GM104942-02), the WVCTSI and the Alzheimer’s Research and Prevention Foundation (ARPF).
Conflicts of Interest
The authors declare no conflict of interest.
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Baseline characteristics by non-cancer chronic pain conditions in older Medicare beneficiaries *, analyzed using linked data from the Medicare Current Beneficiary Survey and Medicare claims, 2001–2013.
Variables
Total
Any NCPC
p-Value §
\\n
Yes
No
N ¥
Wt.%
N
Wt.%
N
Wt. %
\\nALL¥\\n
16,934
100
6369
100.0
10,565
100.0
\\n
\\nSociodemographics\\n
\\n
\\n
\\n
\\n
\\n
\\n
\\n
 Sex
\\n
\\n
\\n
\\n
\\n
\\n
<0.0001
 Female
9669
56.9
4215
66.4
5454
51.5
\\n
 Male
7265
43.1
2154
33.6
5111
48.5
\\n
\\nAge in Years\\n
\\n
\\n
\\n
\\n
\\n
\\n
<0.0001
 65–69
4188
31.8
1335
26.4
2853
34.9
\\n
 70–74
3484
22.5
1230
22.1
2254
22.8
\\n
 75–79
3674
21.4
1411
22.8
2263
20.6
\\n
 80+
5588
24.3
2393
28.8
3195
21.7
\\n
 Race/Ethnicity
\\n
\\n
\\n
\\n
\\n
\\n
0.842
 White
14,085
83.2
5293
83.4
8792
83.0
\\n
 Black
1159
6.7
438
6.7
721
6.7
\\n
 Hispanic
956
5.6
359
5.7
597
5.6
\\n
 Other
709
4.5
268
4.3
441
4.6
\\n
 Education
\\n
\\n
\\n
\\n
\\n
\\n
<0.0001
 <High School
4605
25.0
1744
25.4
2861
24.7
\\n
 High School
6174
36.4
2349
37.0
3825
36.1
\\n
 Some College
2431
15.0
950
15.7
1481
14.6
\\n
 College
3675
23.6
1306
21.9
2369
24.6
\\n
 Marital Status
\\n
\\n
\\n
\\n
\\n
\\n
<0.0001
 Married
9139
56.5
3208
52.8
5931
58.7
\\n
 Widowed
5803
30.6
2491
35.7
3312
27.6
\\n
 Divorce/separated
1455
9.5
490
8.3
965
10.2
\\n
 Other
532
3.3
179
3.1
353
3.5
\\n
 Household Income
\\n
\\n
\\n
\\n
\\n
\\n
<0.0001
 <200% Federal Poverty Level
8260
45.9
3221
48.4
5039
44.6
\\n
 ≥200% Federal Poverty Level
8674
54.1
3148
51.6
5526
55.4
\\n
\\nInsurance\\n
\\n
\\n
\\n
\\n
\\n
\\n
\\n
 Medicaid
\\n
\\n
\\n
\\n
\\n
\\n
<0.0001
 Yes
2147
11.9
1005
15.4
1142
9.9
\\n
 No
14,787
88.1
5364
84.6
9423
90.1
\\n
 Private Insurance
\\n
\\n
\\n
\\n
\\n
\\n
0.001
 Yes
13,418
79.4
5147
80.8
8271
78.5
\\n
 No
3514
20.6
1221
19.2
2293
21.5
\\n
\\nResidence\\n
\\n
\\n
\\n
\\n
\\n
\\n
\\n
 Metropolitan Status
\\n
\\n
\\n
\\n
\\n
\\n
<0.0001
 Metro
11,375
70.6
4415
72.5
6960
69.5
\\n
 Rural
5558
29.4
1954
27.5
3604
30.5
\\n
\\nLifestyle Characteristics\\n
\\n
\\n
\\n
\\n
\\n
\\n
\\n
 Body Mass Index
\\n
\\n
\\n
\\n
\\n
\\n
<0.0001
 Underweight
312
1.7
100
1.4
212
1.8
\\n
 Normal
5867
34.0
1984
30.4
3883
36.0
\\n
 Overweight
6709
40.0
2491
39.2
4218
40.5
\\n
 Obese
3881
24.3
1734
29.0
2147
21.7
\\n
\\nMean BMI 27.2 (±0.052)\\n
\\n
\\n
\\n
\\n
\\n
\\n
\\n
 Smoking Status
\\n
\\n
\\n
\\n
\\n
\\n
<0.0001
 Current
1594
10.2
457
7.8
1137
11.5
\\n
 Past
7976
47.5
2965
47.2
5011
47.6
\\n
 Never
7326
42.4
2936
45.1
4390
40.8
\\n
\\nBaseline Health History\\n
\\n
\\n
\\n
\\n
\\n
\\n
\\n
\\nPhysical Health Conditions\\n
\\n
\\n
\\n
\\n
\\n
\\n
\\n
 Hypertension
\\n
\\n
\\n
\\n
\\n
\\n
<0.0001
 Yes
12,713
73.5
5263
82.0
7450
68.7
\\n
 No
4221
26.5
1106
18.0
3115
31.3
\\n
 Heart disease
\\n
\\n
\\n
\\n
\\n
\\n
<0.0001
 Yes
4357
24.1
2097
31.5
2260
19.9
\\n
 No
12,577
75.9
4272
68.5
8305
80.1
\\n
 Stroke
\\n
\\n
\\n
\\n
\\n
\\n
<0.0001
 Yes
2139
11.5
979
14.3
1160
9.9
\\n
 No
14,795
88.5
5390
85.7
9405
90.1
\\n
 Diabetes
\\n
\\n
\\n
\\n
\\n
\\n
<0.0001
 Yes
3495
20.7
1468
23.5
2027
19.2
\\n
 No
13,439
79.3
4901
76.5
8538
80.8
\\n
 Respiratory disease
\\n
\\n
\\n
\\n
\\n
\\n
<0.0001
 Yes
2423
14.2
1045
16.7
1378
12.7
\\n
 No
14,511
85.8
5324
83.3
9187
87.3
\\n
 Cancer
\\n
\\n
\\n
\\n
\\n
\\n
<0.0001
 Yes
7101
40.3
2911
44.4
4190
38.0
\\n
 No
9833
59.7
3458
55.6
6375
62.0
\\n
 Traumatic Brain Injury
\\n
\\n
\\n
\\n
\\n
\\n
<0.0001
 Yes
221
1.2
146
2.2
75
0.7
\\n
 No
16,713
98.8
6223
97.8
10,490
99.3
\\n
 Rheumatoid Arthritis
\\n
\\n
\\n
\\n
\\n
\\n
<0.0001
 Yes
508
2.9
349
5.5
159
1.4
\\n
 No
16,426
97.1
6020
94.5
10,406
98.6
\\n
 Lupus
\\n
\\n
\\n
\\n
\\n
\\n
<0.0001
 Yes
136
0.8
94
1.4
42
0.4
\\n
 No
16,798
99.2
6275
98.6
10,523
99.6
\\n
\\nNumber of NCPCs\\n
\\n
\\n
\\n
\\n
\\n
\\n
\\n
 0
6385
39.1
-
-
-
-
-
 1
4194
24.4
-
-
-
-
-
 2
3818
22.0
-
-
-
-
-
 3
1908
10.9
-
-
-
-
-
 ≥4
629
3.7
-
-
-
-
-
\\nSleep and Mood Disorders\\n
\\n
 Sleep Disorder
\\n
\\n
\\n
\\n
\\n
\\n
<0.0001
 Yes
2728
15.8
1630
25.5
1098
10.2
\\n
 No
14,206
84.2
4739
74.5
9467
89.8
\\n
 Depression
\\n
\\n
\\n
\\n
\\n
\\n
<0.0001
 Yes
1158
6.9
686
11.1
472
4.5
\\n
 No
15,776
93.1
5683
88.9
10,093
95.5
\\n
 Anxiety
\\n
\\n
\\n
\\n
\\n
\\n
<0.0001
 Yes
862
5.1
492
7.9
370
3.5
\\n
 No
16,072
94.9
5877
92.1
10,195
96.5
\\n
\\nMedication Use\\n
\\n
 NSAIDs
\\n
\\n
\\n
\\n
\\n
\\n
<0.0001
 Yes
3241
18.6
1936
30.2
1305
12.0
\\n
 No
13,693
81.4
4433
69.8
9260
88.0
\\n
 Opioid Analgesics
\\n
\\n
\\n
\\n
\\n
\\n
<0.0001
 Yes
3353
19.4
2059
32.2
1294
12.0
\\n
 No
13,581
80.6
4310
67.8
9271
88.0
\\n
 Benzodiazepines
\\n
\\n
\\n
\\n
\\n
\\n
<0.0001
 Yes
1734
9.7
935
14.5
799
7.1
\\n
 No
15,200
90.3
5434
85.5
9766
92.9
\\n
 Psychotropic medications
\\n
\\n
\\n
\\n
\\n
\\n
<0.0001
 Yes
2871
17.0
1448
23.3
1423
13.5
\\n
 No
14,063
83.0
4921
76.7
9142
86.5
\\n
* Based on 11 pooled cohorts of 16,934 older community-dwelling adults (age > 65 years), enrolled in fee-for service Medicare, alive at the end of follow-up. ¥ Numbers for some variables may not add up to N = 16,934, due to missing values. § Statistically significant group differences in presence of NCPC were examined with Rao-Scott chi-square test. Abbreviation: NCPC, non-cancer chronic pain conditions; BMI, body mass index; NSAIDs, nonsteroidal anti-inflammatory drugs.
ijerph-17-05454-t002_Table 2
Baseline characteristics by incident Alzheimer’s disease and related dementias (ADRD) in older Medicare beneficiaries *, analyzed using linked data from the Medicare Current Beneficiary Survey and Medicare claims, 2001-2013.
Variables
Incident ADRD
p-Value §
Yes
No
 
N
Wt. %
N
Wt. %
\\n
\\nALL¥\\n
1149
6.8
15,785
93.2
\\n
 Any NCPC
\\n
\\n
\\n
\\n
<0.0001
 Yes
798
6.6
9677
93.4
\\n
 No
351
4.3
6108
95.7
\\n
 Number of NCPC’s
\\n
\\n
\\n
\\n
<0.0001
 0
351
4.3
6108
95.7
\\n
 1
304
6.2
3948
93.8
\\n
 2
273
6.0
3605
94.0
\\n
 3
155
7.3
1742
92.7
\\n
 ≥4
66
17.3
382
82.7
\\n
\\nSociodemographic\\n
\\n
\\n
\\n
\\n
\\n
 Sex
\\n
\\n
\\n
\\n
<0.0001
 Female
725
6.2
8944
93.8
\\n
 Male
424
5
6841
95
\\n
 Age in Years
\\n
\\n
\\n
\\n
<0.0001
 65–69
101
1.9
4087
98.1
\\n
 70–74
139
4
3345
96
\\n
 75–79
220
5.9
3454
94.1
\\n
 80+
689
12
4899
88
\\n
 Race/Ethnicity
\\n
\\n
\\n
\\n
0.066
 White
922
5.5
13,163
94.5
\\n
 Black
106
7.4
1053
92.6
\\n
 Hispanic
70
5.9
886
94.1
\\n
 Other
51
6.5
658
93.5
\\n
 Education
\\n
\\n
\\n
\\n
<0.0001
 <High School
417
7.8
4188
92.2
\\n
 High School
398
5.5
5776
94.5
\\n
 Some College
141
4.8
2290
95.2
\\n
 College
186
4.1
3489
95.9
\\n
 Marital Status
\\n
\\n
\\n
\\n
<0.0001
 Married
494
4.5
8645
95.5
\\n
 Widowed
541
8.2
5262
91.8
\\n
 Divorce/separated
76
4.4
1379
95.6
\\n
 Other
38
6
494
94
\\n
 Household Income
\\n
\\n
\\n
\\n
<0.0001
 <200% Federal Poverty Level
699
7.4
7561
92.6
\\n
 ≥200% Federal Poverty Level
450
4.3
8224
95.7
\\n
\\nInsurance\\n
\\n
\\n
\\n
\\n
\\n
 Medicaid
\\n
\\n
\\n
\\n
<0.0001
 Yes
223
9.1
1924
90.9
\\n
 No
926
5.2
13,861
94.8
\\n
 Private Insurance
\\n
\\n
\\n
\\n
<0.0001
 Yes
847
5.3
12,571
94.7
\\n
 No
300
7.2
3214
92.8
\\n
\\nResidence\\n
\\n
\\n
\\n
\\n
0.649
 Metropolitan Status
 Metro
787
5.7
10,588
94.3
\\n
 Rural
362
5.5
5196
94.5
\\n
\\nLifestyle Characteristics\\n
\\n
\\n
\\n
\\n
\\n
 Body Mass Index
\\n
\\n
\\n
\\n
<0.0001
 Underweight
43
12.2
269
87.8
\\n
 Normal
484
6.9
5383
93.1
\\n
 Overweight
417
5.2
6292
94.8
\\n
 Obese
195
4.3
3686
95.7
\\n
 Smoking Status
\\n
\\n
\\n
\\n
<0.0001
 Current
96
4.8
1498
95.2
\\n
 Past
472
5
7504
95
\\n
 Never
580
6.6
6746
93.4
\\n
\\nBaseline Health History\\n
\\n
\\n
\\n
\\n
\\n
\\nPhysical Health Conditions\\n
\\n
\\n
\\n
\\n
\\n
 Hypertension
\\n
\\n
\\n
\\n
<0.0001
 Yes
935
6.3
11,778
93.7
\\n
 No
214
4.1
4007
95.9
\\n
 Heart disease
\\n
\\n
\\n
\\n
<0.0001
 Yes
368
7.6
3989
92.4
\\n
 No
781
5.1
11,796
94.9
\\n
 Stroke
\\n
\\n
\\n
\\n
<0.0001
 Yes
281
11.9
1858
88.1
\\n
 No
868
4.9
13,927
95.1
\\n
 Diabetes
\\n
\\n
\\n
\\n
0.032
 Yes
264
6.4
3231
93.6
\\n
 No
885
5.5
12,554
94.5
\\n
 Respiratory disease
\\n
\\n
\\n
\\n
0.576
 Yes
170
5.9
2253
94.1
\\n
 No
979
5.6
13,532
94.4
\\n
 Cancer
\\n
\\n
\\n
\\n
0.043
 Yes
505
6.2
6596
93.8
\\n
 No
644
5.3
9189
94.7
\\n
 Traumatic Brain Injury
\\n
\\n
\\n
\\n
<0.0001
 Yes
30
12.8
191
87.2
\\n
 No
1119
5.6
15,594
94.4
\\n
 Rheumatoid Arthritis
\\n
\\n
\\n
\\n
0.247
 Yes
39
6.8
469
93.2
\\n
 No
1110
5.6
15,316
94.4
\\n
 Lupus
\\n
\\n
\\n
\\n
0.119
 Yes
13
8.9
123
91.1
\\n
 No
1136
5.7
15,662
94.3
\\n
\\nSleep and Mood Disorders\\n
\\n
\\n
\\n
\\n
\\n
 Sleep Disorder
\\n
\\n
\\n
\\n
<0.0001
 Yes
267
8.5
2461
91.5
\\n
 No
882
5.2
13,324
94.8
\\n
 Depression
\\n
\\n
\\n
\\n
<0.0001
 Yes
170
12.9
988
87.1
\\n
 No
979
5.1
14,797
94.9
\\n
 Anxiety
\\n
\\n
\\n
\\n
<0.0001
 Yes
109
10.9
753
89.1
\\n
 No
1040
5.4
15,032
94.6
\\n
\\nMedication Use\\n
\\n
\\n
\\n
\\n
\\n
 NSAIDs
\\n
\\n
\\n
\\n
0.496
 Yes
226
6
3015
94
\\n
 No
923
5.6
12,770
94.4
\\n
 Opioid Analgesics
\\n
\\n
\\n
\\n
<0.0001
 Yes
266
6.9
3087
93.1
\\n
 No
883
5.4
12,698
94.6
\\n
 Benzodiazepines
\\n
\\n
\\n
\\n
<0.0001
 Yes
167
8.5
1567
91.5
\\n
 No
982
5.4
14,218
94.6
\\n
* Based on 11 pooled cohorts of 16,934 older community-dwelling adults (age ≥ 65 years), enrolled in fee-for service Medicare, alive at the end of follow-up. ¥ Numbers for some variables may not add up to N = 1149, due to missing values. § Statistically significant group differences in presence of NCPC were examined with Rao-Scott chi-square test. Abbreviations: NCPC, non-cancer chronic pain conditions; ADRD, Alzheimer’s disease and related dementias; NSAIDs, nonsteroidal anti-inflammatory drugs.
ijerph-17-05454-t003_Table 3
Association of baseline non-cancer chronic pain conditions (NCPC) and burden to incident Alzheimer’s disease and related dementias (ADRD) in older Medicare beneficiaries *: analysis using linked Medicare Current Beneficiary Survey and Medicare claims data, 2001–2013 (odds ratios (OR) and adjusted odds ratios (AOR) with 95% confidence intervals (CI) calculated using separate logistic regression models).
NCPC Presence/Number
Unadjusted Model
Model 1 ¥
Model 2 ¥¥
Model 3 ⱡⱡ
OR (95% CI)
\\np\\n
AOR (95% CI)
\\np\\n
AOR (95% CI)
\\np\\n
AOR (95% CI)
\\np\\n
\\nAny NCPC ** vs. None\\n
1.54 (1.34,1.78)
<0.0001
1.33 (1.14,1.53)
<0.0001
1.28 (1.10,1.48)
0.001
1.25 (1.08,1.45)
0.003
\\nNumber of NCPCs\\n
\\n
\\n
\\n
\\n
\\n
\\n
\\n
\\n
  None (referent)
1.00
\\n
1.00
\\n
1.00
\\n
1.00
\\n
  One
1.47 (1.26,1.71)
<0.0001
1.27 (1.08,1.50)
0.0039
1.24 (1.05,1.46)
0.0125
1.23 (1.04,1.45)
0.0179
  Two
1.42 (1.20,1.68)
<0.0001
1.25 (1.05,1.49)
0.0121
1.22 (1.02,1.45)
0.0278
1.20 (1.01,1.43)
0.0435
  Three
1.75 (1.42,2.16)
<0.0001
1.45 (1.17,1.80)
0.0007
1.38 (1.11,1.71)
0.0041
1.34 (1.07,1.68)
0.0122
  Four or more
3.03 (2.14,4.29)
<0.0001
2.32 (1.62,3.31)
<0.0001
1.98 (1.36,2.89)
0.0004
1.91 (1.31,2.80)
0.0008
\\n P for linear trend ++\\n
\\n
<0.0001
\\n
<0.0001
\\n
<0.0001
\\n
<0.0001
\\nNumber of NCPCs\\n
\\n
\\n
\\n
\\n
\\n
\\n
\\n
\\n
(per additional NCPC)
1.23 (1.16,1.30)
<0.0001
1.16 (1.09,1.23)
<0.0001
1.13 (1.06,1.20)
0.0002
1.12 (1.05,1.20)
0.0007
* Based on 11 pooled cohorts of 16,934 older community-dwelling adults (age > 65 years), enrolled in fee-for-service Medicare. ** Including back and neck pain, headache and migraine, joint pain, neuropathic pain and osteoarthritis. ¥ Socio-demographics: sex, age, race/ethnicity, education, income, Medicaid insurance, private insurance, marital status, region. ¥¥ Lifestyle and chronic physical health conditions: smoking, body mass index, hypertension, diabetes, heart disease, cancer, respiratory disorder, history of stroke, traumatic brain injury. ⱡⱡ Analgesics: nonsteroidal anti-inflammatory drugs and opioid analgesics. ++ Regression results from polynomial contrast for linear relation indicate a strong linear effect of NCPC on risk of ADRD.
ijerph-17-05454-t004_Table 4
Association of baseline non-cancer chronic pain conditions to incident Alzheimer’s disease and related dementias in older Medicare beneficiaries *: competing risk analysis using linked Medicare Current Beneficiary Survey (MCBS) and Medicare claims data, 2001–2013 (odds ratios (OR) and adjusted odds ratios (AOR) with 95% confidence intervals (CI) calculated from multinomial logistic regression).
Any Non-Cancer Chronic Pain Condition (NCPC) **
Alive at Follow-Up, No ADRD (Referent)
Alive at Follow-Up, Incident ADRD
Died before Follow-Up, Incident ADRD
Died before Follow-Up, No ADRD
OR
\\np\\n
OR (95% CI)
\\np\\n
OR (95% CI)
\\np\\n
OR (95% CI)
\\np\\n
 Model 1. Unadjusted model
1.00
-
1.55 (1.34,1.78)
<0.0001
1.29 (1.03,1.62)
0.029
1.25 (1.12,1.40)
<0.0001
 Model 2. Adjusted for sociodemographics ¥
1.00
-
1.34 (1.16,1.55)
<0.0001
1.10 (0.87,1.40)
0.42
1.21 (1.07,1.36)
0.0024
 Model 3. Also adjusted for lifestyle and chronic physical health conditions ¥¥
1.00
-
1.28 (1.11,1.49)
0.0009
1.03 (0.79,1.34)
0.8287
1.05 (0.93,1.18)
0.4461
 Model 4. Also adjusted for NSAID and opioid analgesic use
1.00
-
1.26 (1.08,1.46)
0.0026
0.99 (0.75,1.31)
0.96
0.97 (0.85,1.10)
0.60
* Based on 11 pooled cohorts of older community-dwelling adults (age > 65 years), enrolled in fee-for-service Medicare. ** Referent for Any NCPC ‘No NCPC’. ¥ Sex, age, race/ethnicity, education, income, Medicaid insurance, private insurance, marital status, region. ¥¥ Smoking status, body mass index, hypertension, diabetes, heart disease, cancer, respiratory illnesses, history of stroke and traumatic brain injury.
ijerph-17-05454-t005_Table 5
Association of baseline non-cancer chronic pain conditions (NCPC’s) and burden to incident Alzheimer’s disease and related dementias in older Medicare beneficiaries *: analysis using linked Medicare Current Beneficiary Survey and Medicare claims data, 2001–2013: Potential mediating influence of diagnosed mood and sleep disorders (odds ratios (OR) and adjusted odds ratios (AOR) with 95% confidence intervals (CI)).
NCPC Presence/Number
Fully Adjusted Model ¥
Model ¥ + Mood Disorders §
Model ¥ + Sleep Disorders §§
Model ¥ + Mood and Sleep Disorders
OR (95% CI)
\\np\\n
OR (95% CI)
\\np\\n
OR (95% CI)
\\np\\n
OR (95% CI)
\\np\\n
\\nAny NCPC vs. None **\\n
1.25 (1.08,1.45)
0.0032
1.17 (1.01,1.37)
0.0388
1.20 (1.04,1.39)
0.0155
1.18 (1.01,1.38)
0.0336
\\nNumber of NCPCs\\n
 None (referent)
1.00 (referent)
\\n
1.00 (referent)
\\n
1.00 (referent)
\\n
1.00 (referent)
\\n
 One
1.23 (1.04,1.45)
0.0179
1.19 (1.01,1.41)
0.0408
1.20 (1.02,1.43)
0.0311
1.17 (0.99,1.39)
0.0594
 Two
1.20 (1.01,1.43)
0.0435
1.13 (0.95,1.36)
0.1722
1.16 (0.97,1.38)
0.1008
1.11 (0.92,1.32)
0.2713
 Three
1.34 (1.07,1.68)
0.0122
1.22 (0.96,1.55)
0.0962
1.25 (0.99,1.58)
0.0589
1.17 (0.92,1.48)
0.2112
 Four or more
1.91 (1.31,2.80)
0.0008
1.57 (1.06,2.34)
0.0255
1.66 (1.12,2.45)
0.0113
1.41 (0.94,2.12)
0.0941
\\np for linear trend++\\n
\\n
<0.0001
\\n
<0.0001
\\n
<0.0001
\\n
<0.0001
\\nNumber of NCPCs\\n
\\n
\\n
\\n
\\n
\\n
\\n
\\n
\\n
(per additional NCPC)
1.12 (1.05,1.20)
0.0007
1.08 (1.01,1.16)
0.027
1.09 (1.02,1.16)
0.0101
1.06 (0.99,1.13)
0.1001
* Based on 11 pooled cohorts of older community-dwelling adults (age > 65 years), enrolled in fee-for-service Medicare. ** Including back or neck pain, headache and migraine, joint pain, neuropathic pain, and osteoarthritis. ¥ sex, age, race/ethnicity, education, income, Medicaid insurance, private insurance, marital status, region, smoking status, body mass index, chronic physical health conditions (hypertension, diabetes, heart disease, cancer, respiratory disorder, history of stroke, traumatic brain injury), NSAID and opioid analgesics. § Depression and anxiety; §§ insomnia-related sleep disorders. ++ Regression results from polynomial contrast for linear relation indicate a strong linear effect of NCPC on risk of ADRD.
\\n\"\n]"}}},{"rowIdx":448,"cells":{"data":{"kind":"list like","value":["\nInt J Mol SciInt J Mol SciijmsInternational Journal of Molecular Sciences1422-0067MDPI32751945PMC743210510.3390/ijms21155484ijms-21-05484ReviewCurrent Status and Future Perspectives of Checkpoint Inhibitor Immunotherapy for Prostate Cancer: A Comprehensive ReviewKimTae Jin1https://orcid.org/0000-0001-7303-6256KooKyo Chul2*Department of Urology, C.H.A. Bundang Medical Center, University College of Medicine, Seongnam 13496, Korea; tjkim81@cha.ac.krDepartment of Urology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul 06229, KoreaCorrespondence: gckoo@yuhs.ac; Tel.: +82-2-2019-34703172020820202115548403720203072020© 2020 by the authors.2020Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
The clinical spectrum of prostate cancer (PCa) varies from castration-naive to metastatic castration-resistant disease. Despite the administration of androgen synthesis inhibitors and chemotherapy regimens for castration-resistant prostate cancer, the treatment options for this entity are limited. The utilization of the immune system against cancer cells shows potential as a therapeutic modality for various solid tumors and hematologic malignancies. With technological advances over the last decade, immunotherapy has become an integral treatment modality for advanced solid tumors. The feasibility of immunotherapy has shown promise for patients with PCa, and with advances in molecular diagnostic platforms and our understanding of immune mechanisms, immunotherapy is reemerging as a potential treatment modality for PCa. Various combinations of individualized immunotherapy and immune checkpoint blockers with androgen receptor-targeted therapies and conventional cytotoxic agents show promise. This article will review the current status of immunotherapy, including new discoveries and precision approaches to PCa, and discuss future directions in the continuously evolving landscape of immunotherapy.
Prostate cancer (PCa) is the most commonly diagnosed malignancy among men and the second most common cause of cancer-associated death in industrialized nations [1]. The standard primary treatments for localized PCa are radical prostatectomy or radiation therapy (RT) with or without androgen-deprivation therapy (ADT), while the primary treatment modality for PCa patients in the advanced setting is ADT. Despite initial therapeutic responses with ADT, the majority of patients are destined to progress to metastatic castration-resistant PCa (mCRPC) [2]. Therefore, novel therapeutic approaches with durable response rates for advanced disease are warranted.
Current agents approved by the US Food and Drug Administration (FDA) that have shown efficacy in terms of overall survival (OS) in patients with mCRPC include docetaxel, cabazitaxel, radium-223, sipuleucel-T, and androgen receptor axis-targeted agents including abiraterone and enzalutamide [3,4,5,6,7,8]. Since the approval of sipuleucel-T in 2010 [4], there have been significant advancements and innovations in the field of immunotherapy. However, with the exception of sipuleucel-T, no other immunotherapeutic treatment has been approved for the treatment of mCRPC, owing to limited response rates and modest clinical efficacy. Nevertheless, with the introduction of next-generation genomic diagnostic platforms and advancements in our understanding of the molecular pathophysiology, we are now observing a considerable shift in the paradigm of immunotherapy for the treatment of PCa.
This review will highlight the re-emergence of immunotherapeutic approaches in the treatment of PCa, mainly focusing on immune checkpoint inhibitors (ICIs), which have shown the potential to revolutionize the treatment of both localized and advanced PCa.
2. Pathophysiology of Prostate Cancer
The pathogenesis of PCa is a slow, ongoing progression that involves small developing cancers that gradually evolve into different clonal entities with various clinical outcomes [9,10,11]. Previous studies have shown that chronic inflammation is frequent in the prostates of elderly men and that this change is associated with an increased risk of PCa development [12,13]. The underlying mechanisms of chronic prostate inflammation and its clinical relevance to the development of PCa are unclear. Nevertheless, clinical evidence shows that chronic inflammation is a potential risk factor for disease progression and poor clinical outcomes [14,15,16,17].
Cellular pathways that modulate the proliferation, migration, and survival of cancer cells are upregulated through cytokines and signaling molecules, including tumor necrosis factor-alpha (TNF-α), transforming growth factor-beta (TGF-β), C-C motif chemokine ligand 2 (CCL-2), and interleukins (IL-2, IL-6, IL-8, and IL-10) [17]. T cell activity and the associated autoimmune reaction induce epithelial and stromal cell proliferation. Infiltrating B cells are associated with increased antibody production, and recent studies have reported that specific subsets, namely regulatory B cells, may also play a significant role in tumor progression [18,19,20]. Moreover, increased B cell infiltrates have been observed in PCa [21]. Although T and B cells are the immune cell types that are predominantly observed in PCa, various ILs and inflammatory cell cytokines that dwell in the tumor stroma, such as granulocytes, macrophages, natural killer (NK) cells, myeloid-derived suppressor cells (MDSCs), and monocytes, additionally play an essential role in promoting the autocrine or paracrine proliferation of PCa cells [22,23,24,25].
Along with the aforementioned immune cells, androgen receptors (ARs) are a crucial factor in the tumorigenesis of PCa. The AR is a ligand-dependent transcription factor that is densely populated in thymic epithelial cells (TECs) and is essential for the development and function of male accessory sex organs [26,27]. ARs have a crucial role in the proliferation of PCa cells. The downregulation of gene expression required for regular TEC activity results in decreased thymocyte proliferation and an increase in apoptosis, which ultimately leads to thymic involution [27]. AR signaling directly affects the activity of circulating T cells by increasing the transcription of the protein tyrosine phosphatase non-receptor type 1 (PTPN 1). This downregulates the Janus kinase/signal transducers and activators of transcription (JAK/STAT) signaling pathway and subsequently suppress T helper type 1 (Th1) cell differentiation [28]. Moreover, androgen cessation can inversely affect the involution of the thymus by upregulating thymocyte proliferation and differentiation and can subsequently stimulate T cell infiltration [29,30]. Evidence from numerous studies has shown that PCa cells consist of a highly reactive stroma, which is featured by upregulated T cell infiltrates densely populated with regulatory T cells that have immunosuppressive tendencies [31,32].
As integral members of the innate immune system, myeloid cells can be stratified into two individual groups. The first group consists of mononuclear cells such as dendritic cells, macrophages, and monocytes, and the second consists of polymorphonuclear cells, which include basophils, eosinophils, mast cells, and neutrophils. Many studies have shown a tumor-promoting function of terminally differentiated myeloid-derived cells, as they can promote cancer cell proliferation and migration, tumor angiogenesis, and immune suppression [33,34,35,36,37]. In the PCa microenvironment, the cessation of androgen induces CCL2 upregulation and activation of the STAT 3 pathway, which initiates the recruitment of macrophages and induces polarization of M2 macrophages [38,39,40]. Activated M1 macrophages have the ability to promote a Th1 response and can exert an efficient antigen-presenting function with the capability of terminating tumors. However, by initiating the proliferation of epithelial and stromal cells, neovascularization, and suppression of the immune system, the anti-inflammatory characteristics of M2 macrophages stimulate the Th2 response and tissue remodeling, all of which creates favorable changes in the microenvironment for disease progression [41].
3. Immunotherapy Resistance of Prostate Cancer
Various clinical trials of PCa immunotherapy have been targeted at metastatic disease, with specific advances being made for novel therapies for CRPC. Patients with metastatic PCa have a dysfunctional and compromised immune system [42]. The immunogenicity of the cancer cells is the main determining factor in the immunotherapeutic approach to tumor eradication. Cancer cells are integrated into the self-immune system and, therefore, do not express foreign antigens. However, cancer cells exhibit tumor self-antigens, which can be immunogenic. Tumor neoantigens are the result of somatic mutations that are amassed in dividing cancer cells and are clinically correlated with the degree of mutation [43]. The load of tumor mutation has been associated with clinical outcomes of immunotherapy; however, tumor immunogenicity is influenced by numerous components that are regulated by the cancer cells [44,45,46,47,48,49].
Patients with metastatic PCa are known to have disrupted cellular immunity as well as a tumor microenvironment with increased immunosuppressive qualities. The compromised immune system of patients with PCa is characterized by a reduction in NK cell activity and renewal and inhibited expression of CD3 in NK and T cells, which may ultimately result in the reduction of T cell receptors and NK cell-activating receptors [50,51,52]. Moreover, patients with mCRPC also have a reduced number of total T cells [53] and an increased number of myeloid suppressor cells and regulatory T cells in the tumor microenvironment and circulation [54,55,56]. Another potential explanation for the resistance and tolerance to immunotherapy in PCa is the slow disease progression [57,58]. The low mutational tumor burden in patients with PCa may contribute to de novo immunotherapy resistance (Figure 1) [59]. This view, however, is still under debate, as a recent genomic analysis indicated that patients with PCa had a higher tumor mutation burden than patients with renal cell carcinoma [60].
Further research is warranted to elucidate the exact pathological mechanism underlying the resistance of patients with PCa to immunotherapy to pave the way for an effective immunotherapeutic regimen for metastatic PCa.
4. Immune Checkpoint Inhibitors for the Treatment of Prostate Cancer
The development of ICIs has been one of the most dynamic advances during the last decade. To date, sipuleucel-T, a cancer vaccine based on autologous antigen-presenting cell (APC) immunotherapy, is the only immunotherapeutic agent that is FDA-approved for the treatment of PCa [4]. ICIs, which are antibodies inhibiting the immune checkpoint receptors, cytotoxic T lymphocyte-associated protein 4 (CTLA-4), and programmed death 1/programed death ligand 1 (PD-1/PD-L1), have shown potential therapeutic benefits by producing antitumor effects and long-term survival benefits in a broad spectrum of malignancies (Figure 2) [61,62,63]. However, numerous clinical trials in patients with mCRPC utilizing CTLA-4 or PD-1/PD-L1 inhibitors have been unsatisfactory, with limited survival benefit when administered as a monotherapy in unselected patients [64,65,66,67,68]. The sole exception is the FDA-approved usage of pembrolizumab for all malignancies with mismatch repair (MMR) deficiency or a microsatellite instability (MSI)-high status [69,70]. To overcome the therapeutic limitation, combination regimens consisting of ICIs with other modalities, or in a specific subset of patients who would most likely benefit, have resulted in better treatment efficacies in patients with a specific subtype of mCRPC.
4.1. ICI Monotherapy
Ipilimumab, a monoclonal antibody blocking the immune properties of CTLA-4, was the first effective ICI to show potential efficacy for melanoma [71]. The therapeutic success of ipilimumab and its FDA-approval in 2011 triggered an interest in its use in PCa. An open-label phase I/II multicenter clinical trial (NCT00323882) investigated ipilimumab with or without RT in patients with mCRPC [72]. After stratifying the patients into two individual dose escalation arms, the study endpoints included adverse events (AE), prostate-specific antigen (PSA) decline, and tumor response. A select number of patients in both study groups exhibited antitumor activity as measured by PSA response. Eight (16%) patients in the combination arm (10 mg/kg) showed a PSA decline >50%, while one (2.0%) patient showed a complete response (CR) lasting 11 months. Six (12.0%) patients had stable disease which lasted for 2.8 to 6.1 months [72]. Encouraging clinical evidence of antitumor activity in previous studies incited further examination of ipilimumab in several phase III clinical trials. In the CA184-043 phase III trial (NCT00861614), patients with mCRPC who had at least one bone metastasis following taxane-based chemotherapy were allocated to either ipilimumab or placebo following palliative RT every three weeks [67]. Although the study barely missed the primary endpoint in terms of OS between the ipilimumab group and the placebo group (HR 0.85, CI 0.72–1.00; p = 0.053), progression-free survival (PFS) was significantly superior in the ipilimumab group. Moreover, subset analysis revealed that ipilimumab was the most therapeutically efficacious in the subgroup of patients who exhibited favorable prognostic characteristics, including normal serum alkaline phosphatase levels, normal hemoglobin concentration, and no visceral metastases. The OS outcomes in this subgroup of patients were superior with ipilimumab compared to placebo [67]. In a recent randomized, double-blind phase III trial (NCT01057810), ipilimumab monotherapy was compared to a placebo in patients with asymptomatic or minimally symptomatic chemotherapy-naïve mCRPC [65]. The median OS was 28.7 months in the ipilimumab group and 29.7 months in the placebo group (p = 0.367). Median PFS was 5.6 months in the ipilimumab group and 3.8 months in the placebo group (HR = 0.67; 95.87% CI 0.55–0.81). This trial concluded that ipilimumab failed to prolong OS in patients with mCRPC. However, after observing a longer median PFS in the ipilimumab group compared to the placebo group (5.6 months vs. 3.8 months) and a decline in serum PSA values, the investigators concluded that these findings suggest the presence of antitumor activity in a specific subgroup of patients [65]. Indeed, future studies are required to determine how to measure such antitumor activity, preferably with biomarkers, to identify the subgroup of patients who would most benefit from ipilimumab.
Several studies focused on PD-1/PD-L1 inhibition in patients with metastatic PCa. In a phase Ib trial of nivolumab (NCT00730639), 296 patients with non-small cell lung cancer, advanced melanoma, renal cancer carcinoma, or CRPC received nivolumab every two weeks [68]. Favorable objective response rates (ORRs) were shown in patients with melanoma, non-small cell lung cancer, and renal cell carcinoma. However, there were no responses observed in 17 patients with CRPC. PCa samples collected from the patients yielded negative results for PD-L1 expression [68]. Martin et al. focused on the deficiency of PD-L1 expression in PCa and emphasized another challenge in the ongoing battle of PCa treatment [73]. Contrary to the phase I nivolumab trial, the non-randomized phase Ib KEYNOTE-028 (NCT02054806) clinical trial only included patients with PD-L1 expression in ≥1% of tumor or stroma cells [66]. Patients received pembrolizumab 10mg/kg every two weeks for up to two years, with the primary endpoint being ORR. Preliminary results showed an ORR of 17.4%, with 34.8% of patients exhibiting stable disease. The median period of response was 13.5 months [66]. Similar to various solid tumor types, more studies would be needed in order to elucidate the prognostic impact of PD-L1 expression as a biomarker of response to ICIs in patients with PCa.
In general, PCa has a relatively low expression level of PD-L1 [73]. Bishop et al. investigated whether the targets of immunotherapy, namely PD-1, PD-L1/2, and CTLA-4, are overexpressed in patients with mCRPC who were resistant to enzalutamide. The results revealed that tumor resistance to enzalutamide is associated with an upregulation in the expression of PD-L1 [74]. Evidence of antitumor activity was documented in a phase II study in which the anti-PD-1 antibody pembrolizumab was added to patients receiving enzalutamide treatment who had progressive disease [75]. Three (30%) patients experienced a rapid decline in serum PSA level to below 0.2 ng/mL. Two patients with measurable disease at enrolment both showed a partial response. One of the partial responders was revealed to be MSI-positive, which validates the observations of previous reports of MSI as a prognosticator of response to anti-PD-1 antibodies [76]. Although only one patient in the aforementioned trial of pembrolizumab had PCa, this ICI is now approved for the treatment of patients with MSI-positive tumors. Table 1 summarizes clinical trials evaluating the clinical efficacies of ICI monotherapies.
4.2. Combination Immunotherapy Regimens
Several studies have investigated the efficacy of anti-PD-1 inhibitors in combination with various pharmacotherapeutic regimens for the treatment of advanced PCa. The two most promising combination immunotherapeutics for the treatment of mCRPC are the combined usage of two individual ICIs or one ICI with the augmentation of enzalutamide, a novel androgen receptor-axis-targeted agent approved for mCRPC. The phase II CHECKMATE-650 (NCT02985957) clinical trial administered a combination of the PD-1 inhibitor nivolumab with the anti-CTLA4 antibody ipilimumab as a second- or third-line therapy for patients with asymptomatic or minimally symptomatic mCRPC [77]. In this clinical trial, the study population was stratified into two arms based on the previous administration of taxane-based chemotherapy. A total of 78 patients were enrolled, with a minimum observational period of six months. A 10% ORR was observed in the combined ICI arm, which included patients who previously received chemotherapy. However, a superior ORR of 26% was observed in patients who were chemotherapy-naïve [77].
In the setting of PCa in which ICI monotherapy has shown limited success, the combination of nivolumab and ipilimumab showed promising clinical results. Further investigations regarding this combination would likely explore alternative dosages and administration intervals of both ICIs. Ongoing studies are determining whether this combination would be effective for a different subgroup of patients. In 2012, an open-labeled phase I dose-escalation trial (NCT01510288) evaluated ipilimumab in combination with the granulocyte-macrophage colony-stimulating factor-transduced cell-based allogeneic prostate cancer vaccine (GVAX) [78]. Patients with chemotherapy-naïve mCRPC enrolled in the study showed promising antitumor activity, with 25% of the patients exhibiting a PSA decline of greater than 50% and overall good tolerance to the agent [78]. The prospective phase II STARVE-PC trial (NCT02601014) enrolled patients with metastatic PCa with androgen receptor splice variant 7 (AR-V7) positive phenotypes [79]. Patients were administered nivolumab 3 mg/kg with ipilimumab 1 mg/kg every 3 weeks for four doses, and maintenance therapy was performed with nivolumab 3 mg/kg every 2 weeks. For the determination of DNA repair deficiency, targeted next-generation sequencing was utilized. The outcomes were generally more favorable in the patient population with AR-V7-positive PCa with DNA repair deficiency. The currently ongoing single-arm phase II NEPTUNES clinical trial (NCT03061539) is investigating the efficacy of ICI combination therapy in patients with higher tumor mutation burden (TMB) due to a defective DNA damage response (dDDR) or DNA mismatch repair gene mutations (dMMR) and in patients with high tumor-infiltrating lymphocytes [80].
Improvements in our molecular biology knowledge have shown that the AR signaling pathway is a critical element in CRPC progression and is, therefore, a rational target for the treatment of advanced disease and CRPC. Novel combination regimens involving PD-1 inhibitors with enzalutamide have shown promising results in this clinical field [81]. KEYNOTE-365 (NCT02861573) was a phase Ib/II umbrella study that tested various combination therapies in patients with CRPC [82]. The study cohort consisted of 69 patients with mCRPC who have progressed on chemo-naïve abiraterone and were treated with pembrolizumab and enzalutamide. Primary endpoints were safety and PSA response rate. The study results revealed an ORR of 20% and a PSA response rate of 33% [82]. A phase II trial (NCT02312557) enrolled 58 patients with chemotherapy-naïve mCRPC whose disease had progressed during enzalutamide and received four doses of 200mg IV pembrolizumab every three weeks while still on enzalutamide [75]. The primary endpoint was a PSA response greater than 50% (PSA50). Five of 28 (18%) patients achieved PSA50, while three of 12 patients (25%) with measurable disease at baseline had objective responses documented by radiographic imaging [75]. With promising results in the aforementioned clinical trials, KEYNOTE-641 (NCT03834493), a randomized double-blind phase III trial, has started accrual of patients with mCRPC as of July 2019 to evaluate the efficacy and therapeutic safety of the pembrolizumab and enzalutamide combination vs. enzalutamide plus placebo [83]. Table 2 summarizes clinical trials evaluating the clinical efficacies of combination immunotherapies.
4.3. The Utilization of Genomic Selection for Checkpoint Inhibitor Immunotherapy
Another strategy for the use of ICIs in the treatment of PCa focuses on specifically tailored patient selection. This approach of patient selection first started with colon cancer patients, as these patients also exhibit limited treatment response to ICI monotherapy [76]. Investigators noted that patients with dMMR variants of colon cancer have more somatic mutations compared to those with MMR-proficient tumors and higher immune infiltration [76]. A breakthrough study demonstrated the clinical importance of MMR gene mutations and analyzed clinical response to pembrolizumab monotherapy in cancers with and without dMMR variants. Patients with dMMR cancers exhibited an ORR of 40% and a 12-month PFS rate of 78%, while those with MMR-proficient colorectal cancer showed no objective responses and a 12-month PFS rate of 11% [84]. Based on this finding, pembrolizumab was FDA-approved in 2017 for the treatment of unresectable or metastatic solid organ malignancies of any histologic origin exhibiting dMMR or MSI-high expression [85]. This FDA-approval of pembrolizumab was the first cancer treatment to be approved based on a biomarker instead of the primary site of origin [85].
According to a clinical genomic analysis reported by Robinson et al., 3% to 5% of advanced PCa expresses MMR gene mutations, which supports the notion that this disease spectrum could be susceptible to pembrolizumab [11]. To date, retrospective studies have analyzed the therapeutic efficacy of PD-1 inhibitors in patients with mCRPC with dMMR mutations. Antonarakis et al. utilized the Johns Hopkins somatic genomic database for pathogenic loss of function MMR mutations in 13 patients with a deleterious mutation in the MMR gene. Results showed that six (46%) patients had MSH6 mutations, while two of four (50%) patients who had received PD-1 inhibitors showed a clinically meaningful PSA50 [70]. Interestingly, this subset of patients was also observed to be sensitive to standard ADT, exhibiting a ≥90% PSA response rate of 85% and a median PSA PFS of 55 months [70]. In another study, 1551 tumors from 1346 patients with PCa were reviewed. Among 1033 patients who were eligible for the study, 32 (3.1%) patients with mCRPC exhibited MSI-high or dMMR tumors. Among these patients, 11 (34.4%) had been treated with a single-agent anti-PD-1 or PD-L1 treatment. It was observed that six (54.5%) patients achieved PSA50, and four (36.4%) patients showed a radiographic response [69]. Although the MSI-high/dMMR phenotype is uncommon and the subset of this PCa population is small, the biomarker-specific approval of pembrolizumab and the clinical evidence of therapeutic efficacy for this specific subgroup of patients with PCa supports the prospect of tumor sequencing for MMR deficiency in all patients with mCRPC, as recommended in the 2019 version of the National Comprehensive Cancer Network Guidelines [86].
Along with the MSI-high/dMMR genetic expression, clinical trials are ongoing to discover other molecular subtypes that may respond to ICIs. In the open-label phase II KEYNOTE-199 (NCT02787005) trial, the clinical efficacy of pembrolizumab for patients with chemotherapy-resistant mCRPC was assessed in three parallel cohorts [64]. Pembrolizumab monotherapy showed a 12% ORR in patients with somatic ATM or BRCA1/2 mutations, which was higher than the ORR of 4% for patients without the aforementioned mutations [64]. A comparable trend was observed in the CHECKMATE-650 trial, in which a superior response rate was observed in patients with homologous repair-deficient mCRPC [77]. The ImmunoProst (NCT03040791) study is a multicenter, single-arm, open-label phase II trial of nivolumab in patients with mCRPC who have progressed to a previous taxane-based chemotherapy regimen. The study will analyze germline and somatic DNA repair defects in that patient cohort [87]. Another multicenter phase II trial (NCT03248570) is investigating the effects of pembrolizumab in patients with mCRPC stratified according to the presence of defects in DNA damage repair [88].
There is accumulating clinical evidence that a subgroup of patients with CDK12 inactivation may benefit from immunotherapy. Due to numerous focal tandem duplications and increased gene fusion activity, along with an upregulation of neoantigens and T cell infiltration, solid malignancies with CDK12 biallelic mutations have been suggested to harbor a distinctive immune signature [89]. This observation infers the existence of another potential genomic subtype of PCa that could be susceptible to ICIs [90]. Various studies estimate that 4.7% of patients with mCRPC harbor this mutation, making PCa the tumor population with the greatest distribution of biallelic inactivation of CDK12 among all solid tumors [89,91,92]. Wu et al. reported the efficacy of PD-1 inhibitor treatment in four patients with mCRPC harboring CDK12 mutations and showed that two (50%) patients showed antitumor activity in terms of PSA decline [89]. A recently conducted multicenter trial in patients with advanced PCa with loss of function CDK12 mutations evaluated the efficacies of the poly (ADP-ribose) polymerase and PD-1 inhibitors. Three of eight (38%) patients with CDK12-mutated mCRPC had a significant PSA decline, with a median PFS of 6.6 months [93]. The ongoing IMPACT (NCT03570619) trial is investigating whether this subtype of mCRPC is more susceptible to a combination of ICIs consisting of ipilimumab and nivolumab followed by nivolumab monotherapy [94].
With advances in next-generation tumor DNA sequencing, new subsets of patients with PCa are being identified through precision medicine. POLE mutations, or mutations in the exonuclease domain of the proofreading DNA polymerase epsilon enzyme, have been observed in patients with PCa. These mutation phenotypes were observed despite an MSI-stable signature [95]. Although POLE mutations are reported to affect only 0.1% of patients with mCRPC, this gene mutation may have potential therapeutic efficacy with anti-PD1 antibodies due to the vast quantity of predicted mutation-associated neoantigens [96].
Despite recent advances and achievements of checkpoint inhibition and other modalities of immunotherapy, inconsistencies continue to occur between treatment and response across the different tumor spectra. PCa develops complex genotype mutations that significantly affect the outcome of immunotherapy [97]. The classification and identification of patients who will successfully respond to immunotherapy is of paramount importance and is dependent on the future development of biomarkers. It is crucial to establish reliable biomarkers that can aid the initial decision to determine which individual group of patients will be more responsive to a specific mode of immunotherapy. Clinical trials focusing on genomic and DNA transcription data are ongoing to unravel immune signatures to aid the prediction of therapeutic efficacy and response [97,98,99].
4.4. Adoptive Cellular Therapy in the Treatment of Prostate Cancer
Adoptive cellular therapy (ACT) is an emerging treatment strategy in immunotherapy, in which the autologous T cells of the patient are extracted and genetically manipulated prior to reinfusion. Various approaches for ACT, such as engineered T cell receptors, tumor-infiltrating lymphocytes, and chimeric antigen receptors (CAR) T cells, have been developed for the treatment of PCa patients with metastases or in advanced disease settings [100]. Of the aforementioned engineered T cells, CAR-T cells have shown the most therapeutic potential. These cells are genetically constructed to express a specific CAR gene ex vivo to target antigens utilizing an antibody derived single-chain variable fragment (scFv) [101]. After infusion, CAR-T cells activate an inflammatory reaction that results in the cytolysis of the antigen-expressing tumor cell [102].
In patients with PCa, CAR-T cells are used to target prostate-specific membrane antigen (PSMA), which is a type II transmembrane glycoprotein that is specifically upregulated in PCa cells [103]. In a phase I clinical trial conducted by Junghans et al., “designer” CAR-T cells targeting PSMA were continuously infused with a low dose of IL-2 [104]. Three of the five enrolled patients met the 20% engraftment target of CAR-T cells, and there were no anti PSMA toxicities, while two of the three engraftment successful patients exhibited a PSA50 response [104]. In another phase I trial, the safety and dosage of modified autologous PSMA targeted T cells was analyzed. The study concluded that a dosage up of 3 × 107 cells/kg could be safely administered, with persistence of T cell activity lasting for up to 2 weeks [105]. Interestingly, one patient experienced a long-term response over 16 months, which suggests clinical activity [105].
Similar to other cellular-based immunotherapies, CAR-T cell therapy elicits limited therapeutic responses in the treatment of solid tumors, which includes PCa [101]. The major obstacle in CAR-T cell therapy is the tumor microenvironment, which has immunosuppressive properties. To overcome this hindrance, a PSMA directed/TGF-β insensitive CAR-T cell is undergoing a phase I clinical trial (NCT03089203) [106]. The distinctive TGF-β insensitive features of this cell product have the potential to show therapeutic efficacy in the immunosuppressive PCa microenvironment caused by high levels of TGF-β [106]. With the occurrence of neoantigens with resistant clones, further clinical studies and research focusing on next-generation CAR-T cells with multiple scFv targeting multiple tumor-specific antigens are warranted.
5. Conclusions
Considerable progress has been made in the immunotherapeutic approach to the treatment of PCa. However, further research is still warranted to fully understand the immunological mechanisms involved in the PCa microenvironment. For now, the treatment benefits of ICIs show promise and potential. Along with standard monotherapeutic strategies utilizing novel ICIs, the efficacy of the combined usage of two individual ICIs and the administration of one ICI with standard therapies still needs to be elucidated. In the present clinical setting, these regimens will likely be used in some capacity and show therapeutic benefit in a select group of patients with PCa. In the near future, there will be other classes of immunotherapeutic agents, which may become an appropriate treatment for patients with mCRPC. For now, the classification of patients and administration modalities of novel immunotherapeutic agents remain unsolved. Moreover, studies are warranted to investigate how the modification in the sequencing of prior agents could affect the efficacy of the novel agents.
Author Contributions
Conceptualization, T.J.K. and K.C.K.; writing—original draft preparation, T.J.K.; writing—review and editing, K.C.K.; supervision and project administration, K.C.K. All authors have read and agreed to the published version of the manuscript.
Funding
This study was supported by a research grant from the National Research Foundation of Korea (2020R1F1A1073833) and a research grant from Gangnam Severance Hospital Prostate Cancer Center Research Committee (7523110). The authors thank MID (Medical Illustration & Design), a division of the Medical Research Support Services of Yonsei University College of Medicine, for the artistic support related to this work.
Conflicts of Interest
The authors declare no conflict of interest.
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Factors contributing to resistance to immunotherapy. Several potential tumor-related, host-related, and environmental factors may elicit resistance to immunotherapies.
Molecular mechanisms of CTLA4 and PD-1 attenuation of T cell activation. Schematic diagram of the molecular interactions and downstream signaling induced by ligation of CTLA4 and PD-1 by their respective ligands. APC: antigen-presenting cell, CAR: chimeric antigen receptor, CTLA-4: cytotoxic T lymphocyte-associated protein 4, PD-1: programmed death 1, PL-L1: programed death ligand 1, TCR: T cell receptor.
ijms-21-05484-t001_Table 1
Clinical trials evaluating immune checkpoint inhibitor monotherapy for prostate cancer.
Agent
Mechanism
Clinical Phase
Identifier
Indication
Primary Endpoints
Ipilimumab
Immunotherapy + radiotherapy
I/II
NCT00323882 [72]
Metastatic CRPC
AE, PSA response, and tumor response
Ipilimumab
Immunotherapy
III
NCT00861614 (CA184-043) [67]
Metastatic CRPC, post-docetaxel
OS
Ipilimumab
Immunotherapy
III
NCT01057810 [65]
Metastatic CPRC, chemotherapy-naïve
OS
Nivolumab
Immunotherapy
Ib
NCT00730639 (MDX-1106) [68]
CRPC
Safety, antitumor activity, and pharmacokinetic properties
Pembrolizumab
Immunotherapy
Ib
NCT02054806 (KEYNOTE-028) [66]
Advanced prostate cancer with PD-L1 expression ≥ 1% of tumor or stromal cells
\n"],"string":"[\n \"\\nInt J Mol SciInt J Mol SciijmsInternational Journal of Molecular Sciences1422-0067MDPI32751945PMC743210510.3390/ijms21155484ijms-21-05484ReviewCurrent Status and Future Perspectives of Checkpoint Inhibitor Immunotherapy for Prostate Cancer: A Comprehensive ReviewKimTae Jin1https://orcid.org/0000-0001-7303-6256KooKyo Chul2*Department of Urology, C.H.A. Bundang Medical Center, University College of Medicine, Seongnam 13496, Korea; tjkim81@cha.ac.krDepartment of Urology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul 06229, KoreaCorrespondence: gckoo@yuhs.ac; Tel.: +82-2-2019-34703172020820202115548403720203072020© 2020 by the authors.2020Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
The clinical spectrum of prostate cancer (PCa) varies from castration-naive to metastatic castration-resistant disease. Despite the administration of androgen synthesis inhibitors and chemotherapy regimens for castration-resistant prostate cancer, the treatment options for this entity are limited. The utilization of the immune system against cancer cells shows potential as a therapeutic modality for various solid tumors and hematologic malignancies. With technological advances over the last decade, immunotherapy has become an integral treatment modality for advanced solid tumors. The feasibility of immunotherapy has shown promise for patients with PCa, and with advances in molecular diagnostic platforms and our understanding of immune mechanisms, immunotherapy is reemerging as a potential treatment modality for PCa. Various combinations of individualized immunotherapy and immune checkpoint blockers with androgen receptor-targeted therapies and conventional cytotoxic agents show promise. This article will review the current status of immunotherapy, including new discoveries and precision approaches to PCa, and discuss future directions in the continuously evolving landscape of immunotherapy.
Prostate cancer (PCa) is the most commonly diagnosed malignancy among men and the second most common cause of cancer-associated death in industrialized nations [1]. The standard primary treatments for localized PCa are radical prostatectomy or radiation therapy (RT) with or without androgen-deprivation therapy (ADT), while the primary treatment modality for PCa patients in the advanced setting is ADT. Despite initial therapeutic responses with ADT, the majority of patients are destined to progress to metastatic castration-resistant PCa (mCRPC) [2]. Therefore, novel therapeutic approaches with durable response rates for advanced disease are warranted.
Current agents approved by the US Food and Drug Administration (FDA) that have shown efficacy in terms of overall survival (OS) in patients with mCRPC include docetaxel, cabazitaxel, radium-223, sipuleucel-T, and androgen receptor axis-targeted agents including abiraterone and enzalutamide [3,4,5,6,7,8]. Since the approval of sipuleucel-T in 2010 [4], there have been significant advancements and innovations in the field of immunotherapy. However, with the exception of sipuleucel-T, no other immunotherapeutic treatment has been approved for the treatment of mCRPC, owing to limited response rates and modest clinical efficacy. Nevertheless, with the introduction of next-generation genomic diagnostic platforms and advancements in our understanding of the molecular pathophysiology, we are now observing a considerable shift in the paradigm of immunotherapy for the treatment of PCa.
This review will highlight the re-emergence of immunotherapeutic approaches in the treatment of PCa, mainly focusing on immune checkpoint inhibitors (ICIs), which have shown the potential to revolutionize the treatment of both localized and advanced PCa.
2. Pathophysiology of Prostate Cancer
The pathogenesis of PCa is a slow, ongoing progression that involves small developing cancers that gradually evolve into different clonal entities with various clinical outcomes [9,10,11]. Previous studies have shown that chronic inflammation is frequent in the prostates of elderly men and that this change is associated with an increased risk of PCa development [12,13]. The underlying mechanisms of chronic prostate inflammation and its clinical relevance to the development of PCa are unclear. Nevertheless, clinical evidence shows that chronic inflammation is a potential risk factor for disease progression and poor clinical outcomes [14,15,16,17].
Cellular pathways that modulate the proliferation, migration, and survival of cancer cells are upregulated through cytokines and signaling molecules, including tumor necrosis factor-alpha (TNF-α), transforming growth factor-beta (TGF-β), C-C motif chemokine ligand 2 (CCL-2), and interleukins (IL-2, IL-6, IL-8, and IL-10) [17]. T cell activity and the associated autoimmune reaction induce epithelial and stromal cell proliferation. Infiltrating B cells are associated with increased antibody production, and recent studies have reported that specific subsets, namely regulatory B cells, may also play a significant role in tumor progression [18,19,20]. Moreover, increased B cell infiltrates have been observed in PCa [21]. Although T and B cells are the immune cell types that are predominantly observed in PCa, various ILs and inflammatory cell cytokines that dwell in the tumor stroma, such as granulocytes, macrophages, natural killer (NK) cells, myeloid-derived suppressor cells (MDSCs), and monocytes, additionally play an essential role in promoting the autocrine or paracrine proliferation of PCa cells [22,23,24,25].
Along with the aforementioned immune cells, androgen receptors (ARs) are a crucial factor in the tumorigenesis of PCa. The AR is a ligand-dependent transcription factor that is densely populated in thymic epithelial cells (TECs) and is essential for the development and function of male accessory sex organs [26,27]. ARs have a crucial role in the proliferation of PCa cells. The downregulation of gene expression required for regular TEC activity results in decreased thymocyte proliferation and an increase in apoptosis, which ultimately leads to thymic involution [27]. AR signaling directly affects the activity of circulating T cells by increasing the transcription of the protein tyrosine phosphatase non-receptor type 1 (PTPN 1). This downregulates the Janus kinase/signal transducers and activators of transcription (JAK/STAT) signaling pathway and subsequently suppress T helper type 1 (Th1) cell differentiation [28]. Moreover, androgen cessation can inversely affect the involution of the thymus by upregulating thymocyte proliferation and differentiation and can subsequently stimulate T cell infiltration [29,30]. Evidence from numerous studies has shown that PCa cells consist of a highly reactive stroma, which is featured by upregulated T cell infiltrates densely populated with regulatory T cells that have immunosuppressive tendencies [31,32].
As integral members of the innate immune system, myeloid cells can be stratified into two individual groups. The first group consists of mononuclear cells such as dendritic cells, macrophages, and monocytes, and the second consists of polymorphonuclear cells, which include basophils, eosinophils, mast cells, and neutrophils. Many studies have shown a tumor-promoting function of terminally differentiated myeloid-derived cells, as they can promote cancer cell proliferation and migration, tumor angiogenesis, and immune suppression [33,34,35,36,37]. In the PCa microenvironment, the cessation of androgen induces CCL2 upregulation and activation of the STAT 3 pathway, which initiates the recruitment of macrophages and induces polarization of M2 macrophages [38,39,40]. Activated M1 macrophages have the ability to promote a Th1 response and can exert an efficient antigen-presenting function with the capability of terminating tumors. However, by initiating the proliferation of epithelial and stromal cells, neovascularization, and suppression of the immune system, the anti-inflammatory characteristics of M2 macrophages stimulate the Th2 response and tissue remodeling, all of which creates favorable changes in the microenvironment for disease progression [41].
3. Immunotherapy Resistance of Prostate Cancer
Various clinical trials of PCa immunotherapy have been targeted at metastatic disease, with specific advances being made for novel therapies for CRPC. Patients with metastatic PCa have a dysfunctional and compromised immune system [42]. The immunogenicity of the cancer cells is the main determining factor in the immunotherapeutic approach to tumor eradication. Cancer cells are integrated into the self-immune system and, therefore, do not express foreign antigens. However, cancer cells exhibit tumor self-antigens, which can be immunogenic. Tumor neoantigens are the result of somatic mutations that are amassed in dividing cancer cells and are clinically correlated with the degree of mutation [43]. The load of tumor mutation has been associated with clinical outcomes of immunotherapy; however, tumor immunogenicity is influenced by numerous components that are regulated by the cancer cells [44,45,46,47,48,49].
Patients with metastatic PCa are known to have disrupted cellular immunity as well as a tumor microenvironment with increased immunosuppressive qualities. The compromised immune system of patients with PCa is characterized by a reduction in NK cell activity and renewal and inhibited expression of CD3 in NK and T cells, which may ultimately result in the reduction of T cell receptors and NK cell-activating receptors [50,51,52]. Moreover, patients with mCRPC also have a reduced number of total T cells [53] and an increased number of myeloid suppressor cells and regulatory T cells in the tumor microenvironment and circulation [54,55,56]. Another potential explanation for the resistance and tolerance to immunotherapy in PCa is the slow disease progression [57,58]. The low mutational tumor burden in patients with PCa may contribute to de novo immunotherapy resistance (Figure 1) [59]. This view, however, is still under debate, as a recent genomic analysis indicated that patients with PCa had a higher tumor mutation burden than patients with renal cell carcinoma [60].
Further research is warranted to elucidate the exact pathological mechanism underlying the resistance of patients with PCa to immunotherapy to pave the way for an effective immunotherapeutic regimen for metastatic PCa.
4. Immune Checkpoint Inhibitors for the Treatment of Prostate Cancer
The development of ICIs has been one of the most dynamic advances during the last decade. To date, sipuleucel-T, a cancer vaccine based on autologous antigen-presenting cell (APC) immunotherapy, is the only immunotherapeutic agent that is FDA-approved for the treatment of PCa [4]. ICIs, which are antibodies inhibiting the immune checkpoint receptors, cytotoxic T lymphocyte-associated protein 4 (CTLA-4), and programmed death 1/programed death ligand 1 (PD-1/PD-L1), have shown potential therapeutic benefits by producing antitumor effects and long-term survival benefits in a broad spectrum of malignancies (Figure 2) [61,62,63]. However, numerous clinical trials in patients with mCRPC utilizing CTLA-4 or PD-1/PD-L1 inhibitors have been unsatisfactory, with limited survival benefit when administered as a monotherapy in unselected patients [64,65,66,67,68]. The sole exception is the FDA-approved usage of pembrolizumab for all malignancies with mismatch repair (MMR) deficiency or a microsatellite instability (MSI)-high status [69,70]. To overcome the therapeutic limitation, combination regimens consisting of ICIs with other modalities, or in a specific subset of patients who would most likely benefit, have resulted in better treatment efficacies in patients with a specific subtype of mCRPC.
4.1. ICI Monotherapy
Ipilimumab, a monoclonal antibody blocking the immune properties of CTLA-4, was the first effective ICI to show potential efficacy for melanoma [71]. The therapeutic success of ipilimumab and its FDA-approval in 2011 triggered an interest in its use in PCa. An open-label phase I/II multicenter clinical trial (NCT00323882) investigated ipilimumab with or without RT in patients with mCRPC [72]. After stratifying the patients into two individual dose escalation arms, the study endpoints included adverse events (AE), prostate-specific antigen (PSA) decline, and tumor response. A select number of patients in both study groups exhibited antitumor activity as measured by PSA response. Eight (16%) patients in the combination arm (10 mg/kg) showed a PSA decline >50%, while one (2.0%) patient showed a complete response (CR) lasting 11 months. Six (12.0%) patients had stable disease which lasted for 2.8 to 6.1 months [72]. Encouraging clinical evidence of antitumor activity in previous studies incited further examination of ipilimumab in several phase III clinical trials. In the CA184-043 phase III trial (NCT00861614), patients with mCRPC who had at least one bone metastasis following taxane-based chemotherapy were allocated to either ipilimumab or placebo following palliative RT every three weeks [67]. Although the study barely missed the primary endpoint in terms of OS between the ipilimumab group and the placebo group (HR 0.85, CI 0.72–1.00; p = 0.053), progression-free survival (PFS) was significantly superior in the ipilimumab group. Moreover, subset analysis revealed that ipilimumab was the most therapeutically efficacious in the subgroup of patients who exhibited favorable prognostic characteristics, including normal serum alkaline phosphatase levels, normal hemoglobin concentration, and no visceral metastases. The OS outcomes in this subgroup of patients were superior with ipilimumab compared to placebo [67]. In a recent randomized, double-blind phase III trial (NCT01057810), ipilimumab monotherapy was compared to a placebo in patients with asymptomatic or minimally symptomatic chemotherapy-naïve mCRPC [65]. The median OS was 28.7 months in the ipilimumab group and 29.7 months in the placebo group (p = 0.367). Median PFS was 5.6 months in the ipilimumab group and 3.8 months in the placebo group (HR = 0.67; 95.87% CI 0.55–0.81). This trial concluded that ipilimumab failed to prolong OS in patients with mCRPC. However, after observing a longer median PFS in the ipilimumab group compared to the placebo group (5.6 months vs. 3.8 months) and a decline in serum PSA values, the investigators concluded that these findings suggest the presence of antitumor activity in a specific subgroup of patients [65]. Indeed, future studies are required to determine how to measure such antitumor activity, preferably with biomarkers, to identify the subgroup of patients who would most benefit from ipilimumab.
Several studies focused on PD-1/PD-L1 inhibition in patients with metastatic PCa. In a phase Ib trial of nivolumab (NCT00730639), 296 patients with non-small cell lung cancer, advanced melanoma, renal cancer carcinoma, or CRPC received nivolumab every two weeks [68]. Favorable objective response rates (ORRs) were shown in patients with melanoma, non-small cell lung cancer, and renal cell carcinoma. However, there were no responses observed in 17 patients with CRPC. PCa samples collected from the patients yielded negative results for PD-L1 expression [68]. Martin et al. focused on the deficiency of PD-L1 expression in PCa and emphasized another challenge in the ongoing battle of PCa treatment [73]. Contrary to the phase I nivolumab trial, the non-randomized phase Ib KEYNOTE-028 (NCT02054806) clinical trial only included patients with PD-L1 expression in ≥1% of tumor or stroma cells [66]. Patients received pembrolizumab 10mg/kg every two weeks for up to two years, with the primary endpoint being ORR. Preliminary results showed an ORR of 17.4%, with 34.8% of patients exhibiting stable disease. The median period of response was 13.5 months [66]. Similar to various solid tumor types, more studies would be needed in order to elucidate the prognostic impact of PD-L1 expression as a biomarker of response to ICIs in patients with PCa.
In general, PCa has a relatively low expression level of PD-L1 [73]. Bishop et al. investigated whether the targets of immunotherapy, namely PD-1, PD-L1/2, and CTLA-4, are overexpressed in patients with mCRPC who were resistant to enzalutamide. The results revealed that tumor resistance to enzalutamide is associated with an upregulation in the expression of PD-L1 [74]. Evidence of antitumor activity was documented in a phase II study in which the anti-PD-1 antibody pembrolizumab was added to patients receiving enzalutamide treatment who had progressive disease [75]. Three (30%) patients experienced a rapid decline in serum PSA level to below 0.2 ng/mL. Two patients with measurable disease at enrolment both showed a partial response. One of the partial responders was revealed to be MSI-positive, which validates the observations of previous reports of MSI as a prognosticator of response to anti-PD-1 antibodies [76]. Although only one patient in the aforementioned trial of pembrolizumab had PCa, this ICI is now approved for the treatment of patients with MSI-positive tumors. Table 1 summarizes clinical trials evaluating the clinical efficacies of ICI monotherapies.
4.2. Combination Immunotherapy Regimens
Several studies have investigated the efficacy of anti-PD-1 inhibitors in combination with various pharmacotherapeutic regimens for the treatment of advanced PCa. The two most promising combination immunotherapeutics for the treatment of mCRPC are the combined usage of two individual ICIs or one ICI with the augmentation of enzalutamide, a novel androgen receptor-axis-targeted agent approved for mCRPC. The phase II CHECKMATE-650 (NCT02985957) clinical trial administered a combination of the PD-1 inhibitor nivolumab with the anti-CTLA4 antibody ipilimumab as a second- or third-line therapy for patients with asymptomatic or minimally symptomatic mCRPC [77]. In this clinical trial, the study population was stratified into two arms based on the previous administration of taxane-based chemotherapy. A total of 78 patients were enrolled, with a minimum observational period of six months. A 10% ORR was observed in the combined ICI arm, which included patients who previously received chemotherapy. However, a superior ORR of 26% was observed in patients who were chemotherapy-naïve [77].
In the setting of PCa in which ICI monotherapy has shown limited success, the combination of nivolumab and ipilimumab showed promising clinical results. Further investigations regarding this combination would likely explore alternative dosages and administration intervals of both ICIs. Ongoing studies are determining whether this combination would be effective for a different subgroup of patients. In 2012, an open-labeled phase I dose-escalation trial (NCT01510288) evaluated ipilimumab in combination with the granulocyte-macrophage colony-stimulating factor-transduced cell-based allogeneic prostate cancer vaccine (GVAX) [78]. Patients with chemotherapy-naïve mCRPC enrolled in the study showed promising antitumor activity, with 25% of the patients exhibiting a PSA decline of greater than 50% and overall good tolerance to the agent [78]. The prospective phase II STARVE-PC trial (NCT02601014) enrolled patients with metastatic PCa with androgen receptor splice variant 7 (AR-V7) positive phenotypes [79]. Patients were administered nivolumab 3 mg/kg with ipilimumab 1 mg/kg every 3 weeks for four doses, and maintenance therapy was performed with nivolumab 3 mg/kg every 2 weeks. For the determination of DNA repair deficiency, targeted next-generation sequencing was utilized. The outcomes were generally more favorable in the patient population with AR-V7-positive PCa with DNA repair deficiency. The currently ongoing single-arm phase II NEPTUNES clinical trial (NCT03061539) is investigating the efficacy of ICI combination therapy in patients with higher tumor mutation burden (TMB) due to a defective DNA damage response (dDDR) or DNA mismatch repair gene mutations (dMMR) and in patients with high tumor-infiltrating lymphocytes [80].
Improvements in our molecular biology knowledge have shown that the AR signaling pathway is a critical element in CRPC progression and is, therefore, a rational target for the treatment of advanced disease and CRPC. Novel combination regimens involving PD-1 inhibitors with enzalutamide have shown promising results in this clinical field [81]. KEYNOTE-365 (NCT02861573) was a phase Ib/II umbrella study that tested various combination therapies in patients with CRPC [82]. The study cohort consisted of 69 patients with mCRPC who have progressed on chemo-naïve abiraterone and were treated with pembrolizumab and enzalutamide. Primary endpoints were safety and PSA response rate. The study results revealed an ORR of 20% and a PSA response rate of 33% [82]. A phase II trial (NCT02312557) enrolled 58 patients with chemotherapy-naïve mCRPC whose disease had progressed during enzalutamide and received four doses of 200mg IV pembrolizumab every three weeks while still on enzalutamide [75]. The primary endpoint was a PSA response greater than 50% (PSA50). Five of 28 (18%) patients achieved PSA50, while three of 12 patients (25%) with measurable disease at baseline had objective responses documented by radiographic imaging [75]. With promising results in the aforementioned clinical trials, KEYNOTE-641 (NCT03834493), a randomized double-blind phase III trial, has started accrual of patients with mCRPC as of July 2019 to evaluate the efficacy and therapeutic safety of the pembrolizumab and enzalutamide combination vs. enzalutamide plus placebo [83]. Table 2 summarizes clinical trials evaluating the clinical efficacies of combination immunotherapies.
4.3. The Utilization of Genomic Selection for Checkpoint Inhibitor Immunotherapy
Another strategy for the use of ICIs in the treatment of PCa focuses on specifically tailored patient selection. This approach of patient selection first started with colon cancer patients, as these patients also exhibit limited treatment response to ICI monotherapy [76]. Investigators noted that patients with dMMR variants of colon cancer have more somatic mutations compared to those with MMR-proficient tumors and higher immune infiltration [76]. A breakthrough study demonstrated the clinical importance of MMR gene mutations and analyzed clinical response to pembrolizumab monotherapy in cancers with and without dMMR variants. Patients with dMMR cancers exhibited an ORR of 40% and a 12-month PFS rate of 78%, while those with MMR-proficient colorectal cancer showed no objective responses and a 12-month PFS rate of 11% [84]. Based on this finding, pembrolizumab was FDA-approved in 2017 for the treatment of unresectable or metastatic solid organ malignancies of any histologic origin exhibiting dMMR or MSI-high expression [85]. This FDA-approval of pembrolizumab was the first cancer treatment to be approved based on a biomarker instead of the primary site of origin [85].
According to a clinical genomic analysis reported by Robinson et al., 3% to 5% of advanced PCa expresses MMR gene mutations, which supports the notion that this disease spectrum could be susceptible to pembrolizumab [11]. To date, retrospective studies have analyzed the therapeutic efficacy of PD-1 inhibitors in patients with mCRPC with dMMR mutations. Antonarakis et al. utilized the Johns Hopkins somatic genomic database for pathogenic loss of function MMR mutations in 13 patients with a deleterious mutation in the MMR gene. Results showed that six (46%) patients had MSH6 mutations, while two of four (50%) patients who had received PD-1 inhibitors showed a clinically meaningful PSA50 [70]. Interestingly, this subset of patients was also observed to be sensitive to standard ADT, exhibiting a ≥90% PSA response rate of 85% and a median PSA PFS of 55 months [70]. In another study, 1551 tumors from 1346 patients with PCa were reviewed. Among 1033 patients who were eligible for the study, 32 (3.1%) patients with mCRPC exhibited MSI-high or dMMR tumors. Among these patients, 11 (34.4%) had been treated with a single-agent anti-PD-1 or PD-L1 treatment. It was observed that six (54.5%) patients achieved PSA50, and four (36.4%) patients showed a radiographic response [69]. Although the MSI-high/dMMR phenotype is uncommon and the subset of this PCa population is small, the biomarker-specific approval of pembrolizumab and the clinical evidence of therapeutic efficacy for this specific subgroup of patients with PCa supports the prospect of tumor sequencing for MMR deficiency in all patients with mCRPC, as recommended in the 2019 version of the National Comprehensive Cancer Network Guidelines [86].
Along with the MSI-high/dMMR genetic expression, clinical trials are ongoing to discover other molecular subtypes that may respond to ICIs. In the open-label phase II KEYNOTE-199 (NCT02787005) trial, the clinical efficacy of pembrolizumab for patients with chemotherapy-resistant mCRPC was assessed in three parallel cohorts [64]. Pembrolizumab monotherapy showed a 12% ORR in patients with somatic ATM or BRCA1/2 mutations, which was higher than the ORR of 4% for patients without the aforementioned mutations [64]. A comparable trend was observed in the CHECKMATE-650 trial, in which a superior response rate was observed in patients with homologous repair-deficient mCRPC [77]. The ImmunoProst (NCT03040791) study is a multicenter, single-arm, open-label phase II trial of nivolumab in patients with mCRPC who have progressed to a previous taxane-based chemotherapy regimen. The study will analyze germline and somatic DNA repair defects in that patient cohort [87]. Another multicenter phase II trial (NCT03248570) is investigating the effects of pembrolizumab in patients with mCRPC stratified according to the presence of defects in DNA damage repair [88].
There is accumulating clinical evidence that a subgroup of patients with CDK12 inactivation may benefit from immunotherapy. Due to numerous focal tandem duplications and increased gene fusion activity, along with an upregulation of neoantigens and T cell infiltration, solid malignancies with CDK12 biallelic mutations have been suggested to harbor a distinctive immune signature [89]. This observation infers the existence of another potential genomic subtype of PCa that could be susceptible to ICIs [90]. Various studies estimate that 4.7% of patients with mCRPC harbor this mutation, making PCa the tumor population with the greatest distribution of biallelic inactivation of CDK12 among all solid tumors [89,91,92]. Wu et al. reported the efficacy of PD-1 inhibitor treatment in four patients with mCRPC harboring CDK12 mutations and showed that two (50%) patients showed antitumor activity in terms of PSA decline [89]. A recently conducted multicenter trial in patients with advanced PCa with loss of function CDK12 mutations evaluated the efficacies of the poly (ADP-ribose) polymerase and PD-1 inhibitors. Three of eight (38%) patients with CDK12-mutated mCRPC had a significant PSA decline, with a median PFS of 6.6 months [93]. The ongoing IMPACT (NCT03570619) trial is investigating whether this subtype of mCRPC is more susceptible to a combination of ICIs consisting of ipilimumab and nivolumab followed by nivolumab monotherapy [94].
With advances in next-generation tumor DNA sequencing, new subsets of patients with PCa are being identified through precision medicine. POLE mutations, or mutations in the exonuclease domain of the proofreading DNA polymerase epsilon enzyme, have been observed in patients with PCa. These mutation phenotypes were observed despite an MSI-stable signature [95]. Although POLE mutations are reported to affect only 0.1% of patients with mCRPC, this gene mutation may have potential therapeutic efficacy with anti-PD1 antibodies due to the vast quantity of predicted mutation-associated neoantigens [96].
Despite recent advances and achievements of checkpoint inhibition and other modalities of immunotherapy, inconsistencies continue to occur between treatment and response across the different tumor spectra. PCa develops complex genotype mutations that significantly affect the outcome of immunotherapy [97]. The classification and identification of patients who will successfully respond to immunotherapy is of paramount importance and is dependent on the future development of biomarkers. It is crucial to establish reliable biomarkers that can aid the initial decision to determine which individual group of patients will be more responsive to a specific mode of immunotherapy. Clinical trials focusing on genomic and DNA transcription data are ongoing to unravel immune signatures to aid the prediction of therapeutic efficacy and response [97,98,99].
4.4. Adoptive Cellular Therapy in the Treatment of Prostate Cancer
Adoptive cellular therapy (ACT) is an emerging treatment strategy in immunotherapy, in which the autologous T cells of the patient are extracted and genetically manipulated prior to reinfusion. Various approaches for ACT, such as engineered T cell receptors, tumor-infiltrating lymphocytes, and chimeric antigen receptors (CAR) T cells, have been developed for the treatment of PCa patients with metastases or in advanced disease settings [100]. Of the aforementioned engineered T cells, CAR-T cells have shown the most therapeutic potential. These cells are genetically constructed to express a specific CAR gene ex vivo to target antigens utilizing an antibody derived single-chain variable fragment (scFv) [101]. After infusion, CAR-T cells activate an inflammatory reaction that results in the cytolysis of the antigen-expressing tumor cell [102].
In patients with PCa, CAR-T cells are used to target prostate-specific membrane antigen (PSMA), which is a type II transmembrane glycoprotein that is specifically upregulated in PCa cells [103]. In a phase I clinical trial conducted by Junghans et al., “designer” CAR-T cells targeting PSMA were continuously infused with a low dose of IL-2 [104]. Three of the five enrolled patients met the 20% engraftment target of CAR-T cells, and there were no anti PSMA toxicities, while two of the three engraftment successful patients exhibited a PSA50 response [104]. In another phase I trial, the safety and dosage of modified autologous PSMA targeted T cells was analyzed. The study concluded that a dosage up of 3 × 107 cells/kg could be safely administered, with persistence of T cell activity lasting for up to 2 weeks [105]. Interestingly, one patient experienced a long-term response over 16 months, which suggests clinical activity [105].
Similar to other cellular-based immunotherapies, CAR-T cell therapy elicits limited therapeutic responses in the treatment of solid tumors, which includes PCa [101]. The major obstacle in CAR-T cell therapy is the tumor microenvironment, which has immunosuppressive properties. To overcome this hindrance, a PSMA directed/TGF-β insensitive CAR-T cell is undergoing a phase I clinical trial (NCT03089203) [106]. The distinctive TGF-β insensitive features of this cell product have the potential to show therapeutic efficacy in the immunosuppressive PCa microenvironment caused by high levels of TGF-β [106]. With the occurrence of neoantigens with resistant clones, further clinical studies and research focusing on next-generation CAR-T cells with multiple scFv targeting multiple tumor-specific antigens are warranted.
5. Conclusions
Considerable progress has been made in the immunotherapeutic approach to the treatment of PCa. However, further research is still warranted to fully understand the immunological mechanisms involved in the PCa microenvironment. For now, the treatment benefits of ICIs show promise and potential. Along with standard monotherapeutic strategies utilizing novel ICIs, the efficacy of the combined usage of two individual ICIs and the administration of one ICI with standard therapies still needs to be elucidated. In the present clinical setting, these regimens will likely be used in some capacity and show therapeutic benefit in a select group of patients with PCa. In the near future, there will be other classes of immunotherapeutic agents, which may become an appropriate treatment for patients with mCRPC. For now, the classification of patients and administration modalities of novel immunotherapeutic agents remain unsolved. Moreover, studies are warranted to investigate how the modification in the sequencing of prior agents could affect the efficacy of the novel agents.
Author Contributions
Conceptualization, T.J.K. and K.C.K.; writing—original draft preparation, T.J.K.; writing—review and editing, K.C.K.; supervision and project administration, K.C.K. All authors have read and agreed to the published version of the manuscript.
Funding
This study was supported by a research grant from the National Research Foundation of Korea (2020R1F1A1073833) and a research grant from Gangnam Severance Hospital Prostate Cancer Center Research Committee (7523110). The authors thank MID (Medical Illustration & Design), a division of the Medical Research Support Services of Yonsei University College of Medicine, for the artistic support related to this work.
Conflicts of Interest
The authors declare no conflict of interest.
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Urol.20046Suppl. 10S13S1816985927JunghansR.P.MaQ.RathoreR.GomesE.M.BaisA.J.LoA.S.AbediM.DaviesR.A.CabralH.J.Al-HomsiA.S.Phase I Trial of Anti-PSMA Designer CAR-T Cells in Prostate Cancer: Possible Role for Interacting Interleukin 2-T Cell Pharmacodynamics as a Determinant of Clinical ResponseProstate2016761257127010.1002/pros.2321427324746SlovinS.F.WangX.HullingsM.ArauzG.BartidoS.LewisJ.S.SchöderH.ZanzonicoP.ScherH.I.SadelainM.Chimeric antigen receptor (CAR+) modified T cells targeting prostate-specific membrane antigen (PSMA) in patients (pts) with castrate metastatic prostate cancer (CMPC)J. Clin. Oncol.2013317210.1200/jco.2013.31.6_suppl.72NarayanV.GladneyW.PlesaG.VapiwalaN.CarpenterE.MaudeS.L.LalP.LaceyS.F.MelenhorstJ.J.SebroR.A phase I clinical trial of PSMA-directed/TGFβ-insensitive CAR-T cells in metastatic castration-resistant prostate cancerJ. Clin. Oncol.201937TPS34710.1200/JCO.2019.37.7_suppl.TPS347
Factors contributing to resistance to immunotherapy. Several potential tumor-related, host-related, and environmental factors may elicit resistance to immunotherapies.
Molecular mechanisms of CTLA4 and PD-1 attenuation of T cell activation. Schematic diagram of the molecular interactions and downstream signaling induced by ligation of CTLA4 and PD-1 by their respective ligands. APC: antigen-presenting cell, CAR: chimeric antigen receptor, CTLA-4: cytotoxic T lymphocyte-associated protein 4, PD-1: programmed death 1, PL-L1: programed death ligand 1, TCR: T cell receptor.
ijms-21-05484-t001_Table 1
Clinical trials evaluating immune checkpoint inhibitor monotherapy for prostate cancer.
Agent
Mechanism
Clinical Phase
Identifier
Indication
Primary Endpoints
Ipilimumab
Immunotherapy + radiotherapy
I/II
NCT00323882 [72]
Metastatic CRPC
AE, PSA response, and tumor response
Ipilimumab
Immunotherapy
III
NCT00861614 (CA184-043) [67]
Metastatic CRPC, post-docetaxel
OS
Ipilimumab
Immunotherapy
III
NCT01057810 [65]
Metastatic CPRC, chemotherapy-naïve
OS
Nivolumab
Immunotherapy
Ib
NCT00730639 (MDX-1106) [68]
CRPC
Safety, antitumor activity, and pharmacokinetic properties
Pembrolizumab
Immunotherapy
Ib
NCT02054806 (KEYNOTE-028) [66]
Advanced prostate cancer with PD-L1 expression ≥ 1% of tumor or stromal cells
\\n\"\n]"}}},{"rowIdx":449,"cells":{"data":{"kind":"list like","value":["\nInt J Mol SciInt J Mol SciijmsInternational Journal of Molecular Sciences1422-0067MDPI32708014PMC743210610.3390/ijms21155200ijms-21-05200Case ReportDiabetes Mellitus and Hypertension—A Case of Sugar and Salt?SondermannMarcus1https://orcid.org/0000-0002-3289-1050HoleckiMichał2KirschAndrea Marita1BastianManuela3FischerDagmar-Christiane4WillenbergHolger Sven1*Division of Endocrinology and Metabolism, Rostock University Medical Center, 18057 Rostock, Germany; m.sondermann@klinikum-brandenburg.de (M.S.); kirsch_andre@gmx.net (A.M.K.)Department of Internal, Autoimmune and Metabolic Diseases, Medical Faculty in Katowice, Medical University of Silesia, 40-055 Katowice, Poland; holomed@gmail.comInstitute for Clinical Chemistry and Laboratory Medicine, Rostock University Medical Center, 18057 Rostock, Germany; manuela.bastian@med.uni-rostock.deDepartment of Pediatrics, Rostock University Medical Centre, Rostock, 18057 Rostock, Germany; dagmar-christiane.fischer@med.uni-rostock.deCorrespondence: Holger.Willenberg@uni-rostock.de2272020820202115520028520202172020© 2020 by the authors.2020Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
The majority of patients with diabetes mellitus (DM) have hypertension (HTN). A specific mechanism for the development of HTN in DM has not been described. In the Zucker, Endothel, und Salz (sugar, endothelium, and salt) study (ZEuS), indices of glucose metabolism and of volume regulation are recorded. An analysis of these parameters shows that glucose concentrations interfere with plasma osmolality and that changes in glycemic control have a significant impact on fluid status and blood pressure. The results of this study are discussed against the background of the striking similarities between the regulation of sugar and salt blood concentrations, introducing the view that DM is probably a sodium-retention disorder that leads to a state of hypervolemia.
There are multiple conditions and risk factors leading to a chronic increase in blood glucose concentrations. As such, diabetes mellitus (DM) is a heterogeneous group of disorders [1] and in more than 60% of patients with DM, hypertension (HTN) is present as well [2]. DM is more prevalent in the elderly and comes with a typical pattern of clinical and laboratory features at the time of diagnosis [3]. Notably, people above 55 years of age have a 90% chance of developing HTN, while patients with DM experience such a high prevalence of HTN approximately 10 years earlier [4]. A specific mechanism for the development of HTN in DM has not been found and a number of factors have been addressed so far, including overactivity of the sympathetic nervous and the renin–angiotensin–aldosterone systems (RAAS), aberrant renal sodium handling, endothelial dysfunction, blood vessel damage, insulin resistance, chronic inflammation, and activation of adrenal steroid hormone secretion [5,6,7,8].
However, principal considerations on the regulation of glucose and salt plasma concentrations show striking similarities and suggest the view that DM is probably a sodium-retention disorder. As sketched in Figure 1, blood concentrations of glucose and sodium are also increased via hormonal mechanisms involving the adrenal gland and secretion of glucocorticoids and/or mineralocorticoids and bind to their respective receptors. Glucose and sodium bind water, raise the osmotic pressure, and stimulate release of vasopressin, which leads to thirst and reabsorption of water in the kidney. Hypervolemia develops, disguising the total body content of glucose and sodium, and promotes the development of hypertension for which vasopressin concentrations may become inadequately high.
In Figure 1, the thoughts for such an assumption are further developed in additional aspects and substantiated with results collected within the frame of the Zucker, Endothel, und Salz (sugar, endothelium, and salt) study (ZEuS).
2. Results
Within the frame of the ZEuS study, data on plasma osmolality in relation to the results of an oral 75 g glucose challenge (arm A, ZEuS/oral glucose tolerance test (oGTT)) or indices of glucose metabolism and blood pressure (arm B, ZEuS/DM) were obtained (Table 1). All hypertensive patients were on antihypertensive treatment, including beta-blockade (43.3%), anti-angiotensin agents (38.3%), diuretics (30.5%), or calcium channel blockers (20.3%).
Out of 73 individuals undergoing an oral 75 g glucose challenge test, 19 (26%) were diagnosed with impaired glucose tolerance and four (5.5%) with DM. Interestingly, insulin and homeostasis model assessment-insulin resistance (HOMA-IR) values were normally distributed (15.9 ± 11.2 µU/mL and 3.7 ± 2.5, respectively), and correlated well with systolic blood pressure (Person-r = 0.753/p < 0.05 and 0.816/p < 0.01, respectively) and with plasma osmolality (Person-r = 0.719/p < 0.01).
Notably, 20 (27%) had plasma osmolality values higher than 300 mosmol/kg, whereby half of them were in the group of individuals with elevated glucose concentrations after the oral glucose challenge (p < 0.05 in the Chi-square test). There was also a relevant correlation between plasma osmolality and serum copeptin concentrations (Spearman-r = 0.321, p < 0.05) with a steep increase in copeptin concentrations for plasma osmolality values of more than 300 mosmol/kg (Figure 2, Panel a).
Of the 24 patients with DM—all on intensified insulin treatment, including three with an insulin pump—more than 78% had type 2 and 21% had type 1 diabetes, whereby patients with type 1 diabetes were obese and regarded as having double diabetes. As expected, comorbidities were present in a number of patients and distributed as follows: peripheral polyneuropathy in 47.8%, nicotine in 39.1%, chronic kidney injury in 13.0%, glaucoma in 4.3%, and cardiovascular disease in 65%, with parental hypertension in 69.9%. Please see Table 1 for further details.
In these individuals, plasma osmolality showed a significant correlation with systolic (Spearman-r = 0.402, p = 0.05), but not with diastolic blood pressure. Systolic blood pressure did not change significantly during the study (SPB 128.6 ± 11.7 vs. 131.2 ± 19.5 mmHg), and diastolic blood pressure dropped slightly and scarcely failed significance (77.9 ± 8.0 vs. 71.2 ± 7.4 mmHg). In this group of patients with DM, education and changes in insulin treatment led to the expected drop in hemoglobin A1c (HbA1c) (8.40 [7.65–9.05] % vs. 7.25 [6.40–8.05] %; p < 0.001). In 10 of these patients, data on serum N-terminal brain natriuretic peptide (NT-proBNP) concentrations obtained prior and after improved glucose metabolism by the modified insulin therapy revealed a nonsignificant increase of NT-proBNP, in parallel to the intensification of insulin therapy (102 [59.2–260.2] vs. 123 [57.8–278.5] ng/mL). Concentrations of the NT-proBNP, however, were lower the more HbA1c dropped after intervention (Figure 2, Panels b–d). Changes in renin (17.0 [4.2–37.2] vs. 15.8 [8.1–85.4] pg/mL) and aldosterone concentrations (104 [81–231] vs. 149 [75–282] pmol/L) were in favor of relief from volume stress but did not, however, reach statistical significance (the distribution was too wide for the number of patients).
3. Discussion
The majority of patients with DM develop HTN. On one hand, HTN in patients with DM is thought to arise from common risk factors related to the development of DM, e.g., genetic disposition, overactivity of the sympathetic nervous system, chronic inflammation, and excess adrenal steroid hormone secretion, along with RAAS activation as a consequence of surplus adipose tissue and a long list of factors generated by these fat cells [4,5,6,7,8,10]. On the other hand, HTN is also discussed to be already a consequence of diabetes with renal dysfunction, RAAS activation and abnormal renal sodium handling, endothelial dysfunction, blood vessel damage, and chronic inflammation [4,5,6,9]. A certain degree of vagueness may be the reason that until now, DM has not been considered a cause of secondary HTN [11,12].
However, multiple studies have shown that a salt-mediated increase in osmolality triggers the release of arginine-vasopressin [13,14]. Our data on osmolality and copeptin reflect this relationship and the dose-response curve is similar to the one reviewed recently [15]. Interestingly, it is known that acute effects of salt administration on blood pressure are explained by an increase in osmolality [16]. Likewise, vasopressin-deficient Brattleboro rats do not develop hypertension when fed salt and sodium-retaining agents before active vasopressin is administered [10,17,18,19,20]. Similar to the results of our study, the relation of osmolality and blood pressure is also a chronic one. From a physiological and historical point of view, sugar is as much a valuable crystal as is sodium chloride. While salt was once also called “white gold”, 80% of slaves in the Caribbean worked for sugar cane farmers [21]. Both molecules are most likely tasty for evolutionary reasons and extensively used today by the food industry [22]. They bind water, are up-regulated by adrenal gland hormones (sodium by mineralocorticoids, sugar by glucocorticoids), which are synthesized by different enzymes that evolved by gene duplication (aldosterone synthase, CYP11B2, and 11β-hydroxylase, CYP11B1), and they bind to closely related receptors (mineralocorticoid or glucocorticoid receptors, respectively) after inactivation into cortisone or reactivation into cortisol by 11β-hydroxysteroiddehydrogenase type II or type I action, respectively (Figure 1).
However, an important aspect is that sodium and glucose are reclaimed from the kidney filtrate through sodium/glucose cotransporters (SGLT). Thus, as a result of the renal threshold and continuous SGLT action, overfeeding with sugar and hyperglycemia would induce a perpetual recycling process of glucose leading to an increase in total body glucose and to sodium retention. As pointed out in more detail elsewhere, the retention of sodium and glucose results in a state of hypervolemia that develops through induction of thirst and the action of vasopressin, and is reflected by blood pressure, copeptin, and NT-proBNP levels [10,23]. Within this context, it is noteworthy that NT-proBNP concentrations indeed dropped in patients with steep decreases in HbA1c values. Diastolic blood pressure values also declined after the intervention in glucose metabolism, however, they just missed statistical significance. A state of hypervolemia may increase cardiac preload and cause shear stress in vessels, thus promoting chronic heart failure and conditions with inflammation in patients with diabetes or hyperaldosteronism. All in all, this would also explain the excellent cardiovascular outcome of patients in studies with SGLT-2 inhibitors [24] and is not contradicted by the decrease in urinary sodium excretion in patients on long-term treatment with such agents, as the latter resembles a physiological aldosterone escape phenomenon [10].
Problems in volume regulation may also come with the progression of chronic kidney disease (CKD). Thus, our results may change at different stages of disturbed glucose metabolism or in the presence of glucosuria, as well as CKD stages in combination with electrolyte imbalances and sensitivity to aldosterone. Notably, an octet of changes were found to underlie the development of type 2 diabetes mellitus, whereby the increase in glucose reabsorption is one feature only, and therefore, explains the development of hypertension only partially [25,26]. Our findings may also be altered by interference with volume changes in the treatment of CKD or provoked by hyperfiltration in different stages of hypertension. Notably, any therapeutic intervention into the renin-angiotensin-aldosterone system may have an effect that outbalances the salt-retention provoked by reabsorption of glucose and could change our findings. However, because of this, therapeutic interventions to inhibit the activity of the renin-angiotensin-aldosterone system and to lower salt and volume load may be especially effective in the therapy of CKD in individuals with diabetes. Targeting diabetic kidney disease will significantly reduce morbidity, and eventually, mortality. Notably, promising agents have been shown to interfere with inflammatory and hemodynamic pathways [27]. As such, atrasentan and SGLT2 inhibitors decrease albuminuria as well as systolic and diastolic blood pressure indices [27].
However, we noticed a nonsignificant slight elevation of NT-proBNP concentrations with intensified insulin treatment in a portion of our patients. This might explain the hitherto noted weight gain with insulin treatment, and at least in part, the fact that insulin treatment has a U-shaped curve regarding survival [28]. On the basis of these findings and our observations, it might be an option to combine insulin therapy with oral glucose-lowering agents, such as SGLT-2 inhibitors, to improve the outcome.
4. Patients and Methods
In the ZEuS study, the relation of parameters reflecting the glucose metabolism were studied along with indices of salt, water, and volume homeostasis. The study was approved by the institutional review boards and local ethics committees and informed consent was obtained from all individual participants included in the study.
In arm A (ZEuS/oGTT), adult individuals undergoing an oral 75 g glucose load were investigated for anthropometric measures, glucose, insulin, osmolality, and copeptin concentrations at baseline, after 60 as well as 120 min. The main reason was to look for diabetes or other abnormalities in glucose metabolism as well as for the presence of insulin resistance in patients with weight gain or obesity. This program was established simultaneously at the Rostock University Medical Center in Germany and the Medical University of Silesia in Poland. Plasma samples scheduled for determination of copeptin were stored at −80 °C until analysis. The Kryptor system (B.R.A.H.M.S./ThermoFisher Scientific) was used for copeptin measurements and the Osmomat 030 freezing point osmometer (Gonotec, Berlin) for osmolality determinations throughout the study—they were all performed at the Institute for Clinical Chemistry and Laboratory Medicine, Rostock University Medical Center.
In arm B (ZEuS/DM), patients with DM were investigated for anthropometric measures, multiple parameters of glucose metabolism, including HbA1c (photometry), insulin, or C-peptide (both ECLIA Cobas e411, Roche, Mannheim, Germany), and indices of salt-water homeostasis, including renin (Liaison platform, DiaSorin, Saluggia, Italy), aldosterone (Chromogene LC-MS/MS assay), NT-proBNP (Cobas ECLIA), blood pressure, and volume status. In this study arm, patients were included who were in need of specific education and changes in their insulin treatment regiments, e.g., a switch from a conservative to a more intensified form of insulin injection therapy, or from multiple insulin injections to therapy with an insulin pump. Patients with polyuria did not qualify for this study. All in all, 24 adult patients who were capable of full self-management of their diabetes were included and studied before and re-evaluated after 3–6 months of change in therapy. It is important to mention that the medication not changing during the ZEuS/DM study was an exclusion criterion for follow-up in arm B. Furthermore, medically-related forms of diabetes were not included. The aim was to ensure a lifestyle of rather high caloric intake, which was evaluated beforehand.
Methodological limitations are to be seen in the number of patients in the follow-up study, which was caused in part by the exclusion of patients who experienced a change in the blood pressure modifying medication—irrespective of the fact that negative assertions were made on larger case numbers. We also studied indices of glucose and sodium concentrations and not actually total body glucose, total body sodium, or intravascular volume. Apart from these issues, our conclusions therefore maintain a certain degree of interpretation of our results, rather than direct evidence. Furthermore, the osmotic properties of glucose cannot be discerned from the osmotic properties of salt along with the regulatory consequences, when both systems are interrelated as described above. Nevertheless, this close connection should be kept in mind when reading other study results. Since the number of diabetic probands was rather manageable and since we sought to include patients with type 2 DM and/or obesity, we could not differentiate between diabetes types. Therefore, it may well be that our findings cannot be translated to individuals with other forms of DM.
5. Conclusions
With the abovementioned limitations in mind, we conclude that the regulation of glucose concentrations by cortisol has many aspects in common with the regulation of sodium concentrations by aldosterone. Both sugar and salt regulate blood osmolality, which triggers thirst as well as copeptin/vasopressin release and which is related to blood pressure. Through reabsorption of sugar and salt in the kidney, elevations in plasma glucose concentrations promote sodium retention and elevations in NT-proBNP, most likely by fluid load. Thus, DM is probably a sodium retention disorder with all its negative consequences.
Author Contributions
Conceptualization, D.-C.F. and H.S.W.; methodology, M.S., M.H., M.B., D.-C.F., and H.S.W.; software, M.S., A.M.K., and H.S.W.; validation, M.S., A.M.K., M.B., D.-C.F., and H.S.W.; formal analysis, M.S., M.H., A.M.K., M.B., and H.S.W.; investigation, M.S., M.H., and H.S.W.; resources, M.H., D.-C.F. and H.S.W; data curation, M.S., A.M.K., M.B., D.-C.F., and H.S.W.; writing—original draft preparation, H.S.W.; writing—review and editing, all authors; visualization, M.S., A.M.K. and H.S.W.; supervision, M.H., D.-C.F. and H.S.W.; project administration, M.H., D.-C.F. and H.S.W.; funding acquisition, M.H., M.B., D.-C.F. and H.S.W. All authors have read and agreed to the published version of the manuscript.
Funding
This research received no external funding.
Conflicts of Interest
The authors declare no conflict of interest.
Abbreviations
MDPI
Multidisciplinary Digital Publishing Institute
DOAJ
Directory of open access journals
TLA
Three letter acronym
LD
linear dichroism
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Parallelisms in the regulation of glucose and salt blood concentrations. This figure is a further development of the scheme in [9]. Notably, both glucose and sodium are reclaimed in the kidney through the action of sodium-glucose cotransporters.
The ZEuS/oGTT study (arm A) showed that there is a significant correlation between baseline osmolality and copeptin concentrations (a). Patient education and changes in insulin treatment regiments reduced HbA1c values significantly in the ZEuS/DM study (arm B) of 24 patients with diabetes mellitus (b). In 10 of these patients, serum NT-proBNP concentrations were available and shown to rather increase with intensification of insulin therapy but remained lower the more HbA1c values dropped (c), negative figures indicate a smaller difference between pre- and postinterventional values. Plasma osmolality correlated with systolic blood pressure values (d).
ijms-21-05200-t001_Table 1
Baseline characterization of individuals in the two arms of the ZEuS study. While the portion of female patients was higher in arm A intensivation of insulin therapy was more frequently done in male patients (arm B). The patients in arm A were younger and suffered less frequently from hypertension. Their BMI, however, was higher.
Parameter
Age
Gender
BMI
SBP
DBP
Glucose
HbA1c
HTN
(unit)
(years)
(% female)
(kg/m2)
(mmHg)
(mmHg)
(mmol/L)
(%)
(%)
ZEuS/oGTT(arm A, n = 73)
49.1 ± 18.0
71.6
32.2 ± 7.7
131.4 ± 13.6
78.8 ± 10.3
5.2 ± 0.6
5.5 ± 0.4
58.3
ZEuS/DM(arm B, n = 24)
57.8 ± 16.0
20.8
29.8 ± 8.2
128.6 ± 11.7
77.9 ± 8.0
12.1 ± 4.3
9.2 ± 2.7
65.7
Abbreviations: body mass index (BMI), diastolic blood pressure (DBP), diabetes mellitus (DM), hypertension (HTN), oral glucose tolerance test (oGTT), systolic blood pressure (SBP), Zucker, Endothel, und Salz (sugar, endothelium, and salt) study (ZEuS).
\n"],"string":"[\n \"\\nInt J Mol SciInt J Mol SciijmsInternational Journal of Molecular Sciences1422-0067MDPI32708014PMC743210610.3390/ijms21155200ijms-21-05200Case ReportDiabetes Mellitus and Hypertension—A Case of Sugar and Salt?SondermannMarcus1https://orcid.org/0000-0002-3289-1050HoleckiMichał2KirschAndrea Marita1BastianManuela3FischerDagmar-Christiane4WillenbergHolger Sven1*Division of Endocrinology and Metabolism, Rostock University Medical Center, 18057 Rostock, Germany; m.sondermann@klinikum-brandenburg.de (M.S.); kirsch_andre@gmx.net (A.M.K.)Department of Internal, Autoimmune and Metabolic Diseases, Medical Faculty in Katowice, Medical University of Silesia, 40-055 Katowice, Poland; holomed@gmail.comInstitute for Clinical Chemistry and Laboratory Medicine, Rostock University Medical Center, 18057 Rostock, Germany; manuela.bastian@med.uni-rostock.deDepartment of Pediatrics, Rostock University Medical Centre, Rostock, 18057 Rostock, Germany; dagmar-christiane.fischer@med.uni-rostock.deCorrespondence: Holger.Willenberg@uni-rostock.de2272020820202115520028520202172020© 2020 by the authors.2020Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
The majority of patients with diabetes mellitus (DM) have hypertension (HTN). A specific mechanism for the development of HTN in DM has not been described. In the Zucker, Endothel, und Salz (sugar, endothelium, and salt) study (ZEuS), indices of glucose metabolism and of volume regulation are recorded. An analysis of these parameters shows that glucose concentrations interfere with plasma osmolality and that changes in glycemic control have a significant impact on fluid status and blood pressure. The results of this study are discussed against the background of the striking similarities between the regulation of sugar and salt blood concentrations, introducing the view that DM is probably a sodium-retention disorder that leads to a state of hypervolemia.
There are multiple conditions and risk factors leading to a chronic increase in blood glucose concentrations. As such, diabetes mellitus (DM) is a heterogeneous group of disorders [1] and in more than 60% of patients with DM, hypertension (HTN) is present as well [2]. DM is more prevalent in the elderly and comes with a typical pattern of clinical and laboratory features at the time of diagnosis [3]. Notably, people above 55 years of age have a 90% chance of developing HTN, while patients with DM experience such a high prevalence of HTN approximately 10 years earlier [4]. A specific mechanism for the development of HTN in DM has not been found and a number of factors have been addressed so far, including overactivity of the sympathetic nervous and the renin–angiotensin–aldosterone systems (RAAS), aberrant renal sodium handling, endothelial dysfunction, blood vessel damage, insulin resistance, chronic inflammation, and activation of adrenal steroid hormone secretion [5,6,7,8].
However, principal considerations on the regulation of glucose and salt plasma concentrations show striking similarities and suggest the view that DM is probably a sodium-retention disorder. As sketched in Figure 1, blood concentrations of glucose and sodium are also increased via hormonal mechanisms involving the adrenal gland and secretion of glucocorticoids and/or mineralocorticoids and bind to their respective receptors. Glucose and sodium bind water, raise the osmotic pressure, and stimulate release of vasopressin, which leads to thirst and reabsorption of water in the kidney. Hypervolemia develops, disguising the total body content of glucose and sodium, and promotes the development of hypertension for which vasopressin concentrations may become inadequately high.
In Figure 1, the thoughts for such an assumption are further developed in additional aspects and substantiated with results collected within the frame of the Zucker, Endothel, und Salz (sugar, endothelium, and salt) study (ZEuS).
2. Results
Within the frame of the ZEuS study, data on plasma osmolality in relation to the results of an oral 75 g glucose challenge (arm A, ZEuS/oral glucose tolerance test (oGTT)) or indices of glucose metabolism and blood pressure (arm B, ZEuS/DM) were obtained (Table 1). All hypertensive patients were on antihypertensive treatment, including beta-blockade (43.3%), anti-angiotensin agents (38.3%), diuretics (30.5%), or calcium channel blockers (20.3%).
Out of 73 individuals undergoing an oral 75 g glucose challenge test, 19 (26%) were diagnosed with impaired glucose tolerance and four (5.5%) with DM. Interestingly, insulin and homeostasis model assessment-insulin resistance (HOMA-IR) values were normally distributed (15.9 ± 11.2 µU/mL and 3.7 ± 2.5, respectively), and correlated well with systolic blood pressure (Person-r = 0.753/p < 0.05 and 0.816/p < 0.01, respectively) and with plasma osmolality (Person-r = 0.719/p < 0.01).
Notably, 20 (27%) had plasma osmolality values higher than 300 mosmol/kg, whereby half of them were in the group of individuals with elevated glucose concentrations after the oral glucose challenge (p < 0.05 in the Chi-square test). There was also a relevant correlation between plasma osmolality and serum copeptin concentrations (Spearman-r = 0.321, p < 0.05) with a steep increase in copeptin concentrations for plasma osmolality values of more than 300 mosmol/kg (Figure 2, Panel a).
Of the 24 patients with DM—all on intensified insulin treatment, including three with an insulin pump—more than 78% had type 2 and 21% had type 1 diabetes, whereby patients with type 1 diabetes were obese and regarded as having double diabetes. As expected, comorbidities were present in a number of patients and distributed as follows: peripheral polyneuropathy in 47.8%, nicotine in 39.1%, chronic kidney injury in 13.0%, glaucoma in 4.3%, and cardiovascular disease in 65%, with parental hypertension in 69.9%. Please see Table 1 for further details.
In these individuals, plasma osmolality showed a significant correlation with systolic (Spearman-r = 0.402, p = 0.05), but not with diastolic blood pressure. Systolic blood pressure did not change significantly during the study (SPB 128.6 ± 11.7 vs. 131.2 ± 19.5 mmHg), and diastolic blood pressure dropped slightly and scarcely failed significance (77.9 ± 8.0 vs. 71.2 ± 7.4 mmHg). In this group of patients with DM, education and changes in insulin treatment led to the expected drop in hemoglobin A1c (HbA1c) (8.40 [7.65–9.05] % vs. 7.25 [6.40–8.05] %; p < 0.001). In 10 of these patients, data on serum N-terminal brain natriuretic peptide (NT-proBNP) concentrations obtained prior and after improved glucose metabolism by the modified insulin therapy revealed a nonsignificant increase of NT-proBNP, in parallel to the intensification of insulin therapy (102 [59.2–260.2] vs. 123 [57.8–278.5] ng/mL). Concentrations of the NT-proBNP, however, were lower the more HbA1c dropped after intervention (Figure 2, Panels b–d). Changes in renin (17.0 [4.2–37.2] vs. 15.8 [8.1–85.4] pg/mL) and aldosterone concentrations (104 [81–231] vs. 149 [75–282] pmol/L) were in favor of relief from volume stress but did not, however, reach statistical significance (the distribution was too wide for the number of patients).
3. Discussion
The majority of patients with DM develop HTN. On one hand, HTN in patients with DM is thought to arise from common risk factors related to the development of DM, e.g., genetic disposition, overactivity of the sympathetic nervous system, chronic inflammation, and excess adrenal steroid hormone secretion, along with RAAS activation as a consequence of surplus adipose tissue and a long list of factors generated by these fat cells [4,5,6,7,8,10]. On the other hand, HTN is also discussed to be already a consequence of diabetes with renal dysfunction, RAAS activation and abnormal renal sodium handling, endothelial dysfunction, blood vessel damage, and chronic inflammation [4,5,6,9]. A certain degree of vagueness may be the reason that until now, DM has not been considered a cause of secondary HTN [11,12].
However, multiple studies have shown that a salt-mediated increase in osmolality triggers the release of arginine-vasopressin [13,14]. Our data on osmolality and copeptin reflect this relationship and the dose-response curve is similar to the one reviewed recently [15]. Interestingly, it is known that acute effects of salt administration on blood pressure are explained by an increase in osmolality [16]. Likewise, vasopressin-deficient Brattleboro rats do not develop hypertension when fed salt and sodium-retaining agents before active vasopressin is administered [10,17,18,19,20]. Similar to the results of our study, the relation of osmolality and blood pressure is also a chronic one. From a physiological and historical point of view, sugar is as much a valuable crystal as is sodium chloride. While salt was once also called “white gold”, 80% of slaves in the Caribbean worked for sugar cane farmers [21]. Both molecules are most likely tasty for evolutionary reasons and extensively used today by the food industry [22]. They bind water, are up-regulated by adrenal gland hormones (sodium by mineralocorticoids, sugar by glucocorticoids), which are synthesized by different enzymes that evolved by gene duplication (aldosterone synthase, CYP11B2, and 11β-hydroxylase, CYP11B1), and they bind to closely related receptors (mineralocorticoid or glucocorticoid receptors, respectively) after inactivation into cortisone or reactivation into cortisol by 11β-hydroxysteroiddehydrogenase type II or type I action, respectively (Figure 1).
However, an important aspect is that sodium and glucose are reclaimed from the kidney filtrate through sodium/glucose cotransporters (SGLT). Thus, as a result of the renal threshold and continuous SGLT action, overfeeding with sugar and hyperglycemia would induce a perpetual recycling process of glucose leading to an increase in total body glucose and to sodium retention. As pointed out in more detail elsewhere, the retention of sodium and glucose results in a state of hypervolemia that develops through induction of thirst and the action of vasopressin, and is reflected by blood pressure, copeptin, and NT-proBNP levels [10,23]. Within this context, it is noteworthy that NT-proBNP concentrations indeed dropped in patients with steep decreases in HbA1c values. Diastolic blood pressure values also declined after the intervention in glucose metabolism, however, they just missed statistical significance. A state of hypervolemia may increase cardiac preload and cause shear stress in vessels, thus promoting chronic heart failure and conditions with inflammation in patients with diabetes or hyperaldosteronism. All in all, this would also explain the excellent cardiovascular outcome of patients in studies with SGLT-2 inhibitors [24] and is not contradicted by the decrease in urinary sodium excretion in patients on long-term treatment with such agents, as the latter resembles a physiological aldosterone escape phenomenon [10].
Problems in volume regulation may also come with the progression of chronic kidney disease (CKD). Thus, our results may change at different stages of disturbed glucose metabolism or in the presence of glucosuria, as well as CKD stages in combination with electrolyte imbalances and sensitivity to aldosterone. Notably, an octet of changes were found to underlie the development of type 2 diabetes mellitus, whereby the increase in glucose reabsorption is one feature only, and therefore, explains the development of hypertension only partially [25,26]. Our findings may also be altered by interference with volume changes in the treatment of CKD or provoked by hyperfiltration in different stages of hypertension. Notably, any therapeutic intervention into the renin-angiotensin-aldosterone system may have an effect that outbalances the salt-retention provoked by reabsorption of glucose and could change our findings. However, because of this, therapeutic interventions to inhibit the activity of the renin-angiotensin-aldosterone system and to lower salt and volume load may be especially effective in the therapy of CKD in individuals with diabetes. Targeting diabetic kidney disease will significantly reduce morbidity, and eventually, mortality. Notably, promising agents have been shown to interfere with inflammatory and hemodynamic pathways [27]. As such, atrasentan and SGLT2 inhibitors decrease albuminuria as well as systolic and diastolic blood pressure indices [27].
However, we noticed a nonsignificant slight elevation of NT-proBNP concentrations with intensified insulin treatment in a portion of our patients. This might explain the hitherto noted weight gain with insulin treatment, and at least in part, the fact that insulin treatment has a U-shaped curve regarding survival [28]. On the basis of these findings and our observations, it might be an option to combine insulin therapy with oral glucose-lowering agents, such as SGLT-2 inhibitors, to improve the outcome.
4. Patients and Methods
In the ZEuS study, the relation of parameters reflecting the glucose metabolism were studied along with indices of salt, water, and volume homeostasis. The study was approved by the institutional review boards and local ethics committees and informed consent was obtained from all individual participants included in the study.
In arm A (ZEuS/oGTT), adult individuals undergoing an oral 75 g glucose load were investigated for anthropometric measures, glucose, insulin, osmolality, and copeptin concentrations at baseline, after 60 as well as 120 min. The main reason was to look for diabetes or other abnormalities in glucose metabolism as well as for the presence of insulin resistance in patients with weight gain or obesity. This program was established simultaneously at the Rostock University Medical Center in Germany and the Medical University of Silesia in Poland. Plasma samples scheduled for determination of copeptin were stored at −80 °C until analysis. The Kryptor system (B.R.A.H.M.S./ThermoFisher Scientific) was used for copeptin measurements and the Osmomat 030 freezing point osmometer (Gonotec, Berlin) for osmolality determinations throughout the study—they were all performed at the Institute for Clinical Chemistry and Laboratory Medicine, Rostock University Medical Center.
In arm B (ZEuS/DM), patients with DM were investigated for anthropometric measures, multiple parameters of glucose metabolism, including HbA1c (photometry), insulin, or C-peptide (both ECLIA Cobas e411, Roche, Mannheim, Germany), and indices of salt-water homeostasis, including renin (Liaison platform, DiaSorin, Saluggia, Italy), aldosterone (Chromogene LC-MS/MS assay), NT-proBNP (Cobas ECLIA), blood pressure, and volume status. In this study arm, patients were included who were in need of specific education and changes in their insulin treatment regiments, e.g., a switch from a conservative to a more intensified form of insulin injection therapy, or from multiple insulin injections to therapy with an insulin pump. Patients with polyuria did not qualify for this study. All in all, 24 adult patients who were capable of full self-management of their diabetes were included and studied before and re-evaluated after 3–6 months of change in therapy. It is important to mention that the medication not changing during the ZEuS/DM study was an exclusion criterion for follow-up in arm B. Furthermore, medically-related forms of diabetes were not included. The aim was to ensure a lifestyle of rather high caloric intake, which was evaluated beforehand.
Methodological limitations are to be seen in the number of patients in the follow-up study, which was caused in part by the exclusion of patients who experienced a change in the blood pressure modifying medication—irrespective of the fact that negative assertions were made on larger case numbers. We also studied indices of glucose and sodium concentrations and not actually total body glucose, total body sodium, or intravascular volume. Apart from these issues, our conclusions therefore maintain a certain degree of interpretation of our results, rather than direct evidence. Furthermore, the osmotic properties of glucose cannot be discerned from the osmotic properties of salt along with the regulatory consequences, when both systems are interrelated as described above. Nevertheless, this close connection should be kept in mind when reading other study results. Since the number of diabetic probands was rather manageable and since we sought to include patients with type 2 DM and/or obesity, we could not differentiate between diabetes types. Therefore, it may well be that our findings cannot be translated to individuals with other forms of DM.
5. Conclusions
With the abovementioned limitations in mind, we conclude that the regulation of glucose concentrations by cortisol has many aspects in common with the regulation of sodium concentrations by aldosterone. Both sugar and salt regulate blood osmolality, which triggers thirst as well as copeptin/vasopressin release and which is related to blood pressure. Through reabsorption of sugar and salt in the kidney, elevations in plasma glucose concentrations promote sodium retention and elevations in NT-proBNP, most likely by fluid load. Thus, DM is probably a sodium retention disorder with all its negative consequences.
Author Contributions
Conceptualization, D.-C.F. and H.S.W.; methodology, M.S., M.H., M.B., D.-C.F., and H.S.W.; software, M.S., A.M.K., and H.S.W.; validation, M.S., A.M.K., M.B., D.-C.F., and H.S.W.; formal analysis, M.S., M.H., A.M.K., M.B., and H.S.W.; investigation, M.S., M.H., and H.S.W.; resources, M.H., D.-C.F. and H.S.W; data curation, M.S., A.M.K., M.B., D.-C.F., and H.S.W.; writing—original draft preparation, H.S.W.; writing—review and editing, all authors; visualization, M.S., A.M.K. and H.S.W.; supervision, M.H., D.-C.F. and H.S.W.; project administration, M.H., D.-C.F. and H.S.W.; funding acquisition, M.H., M.B., D.-C.F. and H.S.W. All authors have read and agreed to the published version of the manuscript.
Funding
This research received no external funding.
Conflicts of Interest
The authors declare no conflict of interest.
Abbreviations
MDPI
Multidisciplinary Digital Publishing Institute
DOAJ
Directory of open access journals
TLA
Three letter acronym
LD
linear dichroism
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Parallelisms in the regulation of glucose and salt blood concentrations. This figure is a further development of the scheme in [9]. Notably, both glucose and sodium are reclaimed in the kidney through the action of sodium-glucose cotransporters.
The ZEuS/oGTT study (arm A) showed that there is a significant correlation between baseline osmolality and copeptin concentrations (a). Patient education and changes in insulin treatment regiments reduced HbA1c values significantly in the ZEuS/DM study (arm B) of 24 patients with diabetes mellitus (b). In 10 of these patients, serum NT-proBNP concentrations were available and shown to rather increase with intensification of insulin therapy but remained lower the more HbA1c values dropped (c), negative figures indicate a smaller difference between pre- and postinterventional values. Plasma osmolality correlated with systolic blood pressure values (d).
ijms-21-05200-t001_Table 1
Baseline characterization of individuals in the two arms of the ZEuS study. While the portion of female patients was higher in arm A intensivation of insulin therapy was more frequently done in male patients (arm B). The patients in arm A were younger and suffered less frequently from hypertension. Their BMI, however, was higher.
Parameter
Age
Gender
BMI
SBP
DBP
Glucose
HbA1c
HTN
(unit)
(years)
(% female)
(kg/m2)
(mmHg)
(mmHg)
(mmol/L)
(%)
(%)
ZEuS/oGTT(arm A, n = 73)
49.1 ± 18.0
71.6
32.2 ± 7.7
131.4 ± 13.6
78.8 ± 10.3
5.2 ± 0.6
5.5 ± 0.4
58.3
ZEuS/DM(arm B, n = 24)
57.8 ± 16.0
20.8
29.8 ± 8.2
128.6 ± 11.7
77.9 ± 8.0
12.1 ± 4.3
9.2 ± 2.7
65.7
Abbreviations: body mass index (BMI), diastolic blood pressure (DBP), diabetes mellitus (DM), hypertension (HTN), oral glucose tolerance test (oGTT), systolic blood pressure (SBP), Zucker, Endothel, und Salz (sugar, endothelium, and salt) study (ZEuS).
\\n\"\n]"}}},{"rowIdx":450,"cells":{"data":{"kind":"list like","value":["\nInt J Environ Res Public HealthInt J Environ Res Public HealthijerphInternational Journal of Environmental Research and Public Health1661-78271660-4601MDPI32707799PMC743210710.3390/ijerph17155269ijerph-17-05269ArticleMercury Exposure through Fish Consumption in Traditional Communities in the Brazilian Northern AmazonHaconSandra de Souza1*https://orcid.org/0000-0002-8004-9079Oliveira-da-CostaMarcelo2GamaCecile de Souza3FerreiraRenata4BastaPaulo Cesar1SchrammAna1https://orcid.org/0000-0002-1876-782XYokotaDecio4Fundação Oswaldo Cruz, Escola Nacional de Saúde Pública Sérgio Arouca, Rio de Janeiro 21041-210, Brazil; pcbasta@ensp.fiocruz.br (P.C.B.); schrammana@posgrad.ensp.fiocruz.br (A.S.)WWF-Brasil, Brasília 70377-540, Brazil; marcelo@wwf.org.brInstituto de Pesquisas Científicas e Tecnológicas do Amapá, Av. Feliciano Coelho, 1509. Trem, Amapá 68901-025, Brazil; cecile.gama@iepa.ap.gov.brIepé-Instituto de Pesquisa e Formação Indígena, Macapá, Amapá 68908-120, Brazil; renata.ferreira@institutoiepe.org.br (R.F.); decio@institutoiepe.org.br (D.Y.)Correspondence: shacon@ensp.fiocruz.br2272020820201715526929620201672020© 2020 by the authors.2020Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Artisanal small-scale gold mining (ASGM) is the main source of anthropogenic mercury emissions and contamination in Latin America. In the Brazilian northern Amazon, ASGM has contaminated the environment and people over the past century. The main contamination route is through fish consumption, which endangers the food security and livelihoods of traditional communities. Our study aims to assess the potential toxicological health risks caused by the consumption of Hg-contaminated fish across five regions in Amapá State. We sampled 428 fish from 18 sites across inland and coastal aquatic systems. We measured the total mercury content in fish samples, and the results were applied to a mercury exposure risk assessment targeting three distinct groups (adults, women of childbearing age, and children). Mercury contamination was found to exceed the World Health Organization’s safe limit in 28.7% of all fish samples, with higher prevalence in inland zones. Moreover, the local preference for carnivorous fish species presents a serious health risk, particularly for communities near inland rivers in the region. This is the first study to provide clear recommendations for reducing the mercury exposure through fish consumption in Amapá State. It builds scientific evidence that helps decision-makers to implement effective policies for protecting the health of riverine communities.
Artisanal small-scale gold mining (ASGM) activities have significantly expanded in the Amazon over the last two decades. ASGM is a major cause of deforestation and habitat degradation in the Brazilian northern Amazon [1,2,3], particularly on the borders between Guyana, French Guiana, Suriname, and Venezuela, and the Brazilian states of Roraima, Amapá, and Pará (also known as the Guiana Shield Ecoregion) [4]. The ecoregion covers 250 million hectares and contains one of the largest complexes of uninterrupted primary tropical forests on Earth [5]; the region therefore plays a critical role in mitigating global climate change and preserving biodiversity and freshwater ecosystems.
The Brazilian state of Amapá borders French Guiana and Suriname, and almost 72% of its territory is formally protected. It consists of five indigenous territories, four quilombolas territories, and nineteen conservation units (twelve federal, five state, and two municipal), covering a total area of 115.656 km2. These areas are crucial for human livelihoods, especially for communities that rely on their natural resources. However, the protection of these areas has been hampered by weak governance, lack of financial resources for protective management, and different anthropogenic pressures. Among those threats, the widespread use of mercury (Hg) in ASGM has led to the contamination of both the environment and the regional population.
The ASGM sector is the leading source of mercury emissions, contamination, and consumption in Latin America and the Caribbean [6]; thus, the traditional communities that depend on natural resources for their survival are highly vulnerable to ASGM activities. This activity is generally coordinated by illegal networks and conducted by wildcat gold miners, and dealers [7]. ASGM has adversely affected the health of riverside communities—particularly indigenous people [8,9]—that rely on fish as their main protein source [10,11]. Fisheries in the Amazon provide food security and reduce malnutrition rates, as fish are the dominant dietary source of essential amino acids, lipids with fatty acids, minerals, and vitamins [12,13].
In ASGM-dominated regions, human exposure to Hg is mainly through bioaccumulation and biomagnification of methylmercury from fish consumption. In the Brazilian Amazon, indigenous people, riverside communities, fishermen, quilombolas, peasants, and extractivists inhabit the areas near rivers, bays, and streams and are therefore highly exposed to Hg compounds. This reality is aggravated by the social vulnerability of exposed communities, including reduced access to healthcare, formal education, regular income, basic sanitation, and potable water. In addition, these communities suffer from high rates of malnutrition, particularly in children under 5 years of age. They are also vulnerable to a high number of infectious and respiratory diseases (such as malaria, hantavirus pulmonary syndrome, leishmaniasis, yellow fever, etc.) that are exacerbated by both changes in land use and climate [14].
Therefore, mercury contamination in the Amazon is both a national and international concern. In this study, we aimed to develop a better understanding of the risks associated with fish consumption in various regions of the northern Amazon. Although a few studies have shown mercury contamination in fish in the region [5], this is the first to conduct an investigation on human Hg exposure in the study region. We assessed the potential toxicological health risks from mercury exposure through fish consumption for different age groups across five regions in Amapá State.
2. Methods2.1. Study Area
We assessed five regions in the Amapá State territory, including some of the most biodiverse and economically significant river basins in the region (Figure 1). Watersheds in the state play an important social, cultural, and economic role, and fish resources in the region are heavily influenced by the Amazon River and Atlantic Ocean. We chose rivers that bordered protected areas (Table 1) due to the higher occurrence of mineral deposits [15] and higher number of ASGM sites surrounding Amapá’s protected areas [16]. Sampling sites were set up at potential ASGM sites (active and inactive). These sites were identified based on gold mining deforestation maps [3], literature reviews [5,17], the presence of local communities, the influence of coastal tides or inland waters, sampling logistics, the distance to protected areas, and interviews with managers of protected areas and locals with knowledge of the regional gold mining history. Sampling sites 1, 2, and 3 are directly influenced by coastal tides and are therefore considered coastal zones, while sites 4 and 5 are located in inland waters and are therefore referred to as inland zones.
2.2. Fish Sampling and Preparation
Between August 2017 and May 2018, we collected tissue samples from 428 fish from 45 different species at 18 sites across the five regions (Table 1). The fish were caught by local fishermen hired to support the research project. According to the local fishermen, each fish species was captured using specific gear; therefore, gillnets, hooks and hand lines, fishing rods and long lines were used. Each specimen was identified to the species level, and a minimum of 70 g of muscle tissue (free of skin) was extracted from the dorsal region of the fish body. All dissection tools were sterilized before sampling to avoid contamination. Samples were stored in ice boxes and frozen for transportation to the laboratory. The study was conducted in accordance with the Brazilian regulation (IN 03/2014), under the license SISBIO 58296-1.
2.3. Mercury Analysis
Total mercury (THg) was measured by cold vapor atomic fluorescence spectrometry at the Analytical Chemistry Laboratory of the Pontific University of Rio de Janeiro [23]. The analytical quality was determined by control strict blanks with duplicate analysis and compared to the analytical results of certified reference materials (DORM-2, Dogfish Muscle Certified Reference Material for trace elements, National Research Council, Ottawa, ON, Canada). Average recovery values were >96% of the certified value analytical results; and the relative standard deviation (RSD) was <15%. Total mercury analysis from the wet weight was adopted.
2.4. Data Analysis
The data are presented as mean ± standard error and percentiles. We applied the Kruskal–Wallis test to determine the mean differences in THg concentrations between sampling locations and trophic levels.
The risk assessment approach in this study was based on the method used by the US Government Agency for Toxic Substances and Diseases Registry [24]. The method includes four steps: (i) hazard identification, (ii) dose–response assessment, (iii) exposure assessment, and (iv) risk characterization. Most risk assessment studies use secondary data; however, our Hg exposure assessment used information on the most consumed fish species in the region [5] as well as informal interviews with local fishermen and individuals from indigenous and riverine communities.
2.5. Estimated Daily Intake
To assess the health risks of total mercury exposure, we incorporated two scenarios for toxicological risk: (i) the critical scenario and (ii) the current scenario. The critical scenario assumes that some of the local communities—including the indigenous communities—consume larger quantities of fish due to the lack of other protein sources. The current scenario represents the eating habits of most of the communities in the study area. The estimated exposure dose of fish consumers from both coastal and inland zones were divided into three groups: (i) adults of both genders (age: 17 to 75), (ii) women of childbearing age (age: 17 to 49), and (iii) children/juveniles (age: 5 to 16). We estimated their daily intake (EDI) to determine each group’s daily Hg consumption [25]. The average daily fish consumption was based on 16 studies (Table 2) that assessed the dietary patterns and fish consumption of Amazonian communities.
The potential exposure dose (µg/kg/d) was calculated according to Equation (1) [26]:Dpot=C×IR×EF×EDBW×1AT\nwhere Dpot is the potential mercury intake dose (µg/kg/day); C is the total mercury concentration of fish species (µg/g); IR is the fish intake rate (kg/meal); EF is the frequency of exposure (meals /day); ED is the exposure duration (day, week); AT corresponds to the time weighted (day) (time weighted considers the duration of exposure); and BW is the body weight (kg).
The risk ratio was calculated as the ratio between the potential intake dose and the reference dose using the risk quotient equation (Equation (2)):QR = Dpot/RfD\nwhere QR is the risk quotient; Dpot is the potential mercury intake dose (µg/kg/day); and RfD is the reference dose (1.6 μg/kg/week or 0.23 μg/kg/day [25]) of the substance or chemical element (Hg) that can be consumed weekly over a lifetime without appreciable risk to health. A QR of <1 indicates a low likelihood of adverse health effects, while QR > 1 indicates the possibility of adverse non-carcinogenic effects.
2.6. Model Input Variables
The description of the input variables for the mercury exposure model for both the critical and current scenarios are shown in Table 3. We assumed a 50% consumption of fish species in age groups with larger average Hg concentrations (carnivorous and omnivorous) as well as the groups with the lowest Hg concentration (herbivorous and detritivores). The literature review showed a high frequency of fish consumption and a high variability in the quantity of meals, ranging from 1 to 20 meals per week [38,40]. We therefore set an average of 10 meals of fish consumed per week, varying from 160 to 430 g/day, based on the characteristics of each group. We estimated the fish daily intake values based on the literature review (Table 2). The average body weight of the residents in the communities was obtained from research on family budgets that presented the anthropometry and nutritional status of children, adolescents, and adults in the state of Amapá [19].
To quantify exposure, we calculated the mean Hg concentrations of carnivorous and omnivorous fish as well as the 95% confidence interval (CI) of each mean. We also conducted a literature review to determine the frequency of fish meal consumption and estimated potential doses by assigning the 95% upper limit of the mean fish consumption. It was assumed 100% of THg concentration was methylmercury, since methyl Hg constitutes up to 98% of the Hg concentration in fish samples in the Amazon basin. The body weight was the same for both scenarios, and the factor absorption of total Hg as methyl Hg by exposed residents was assumed to be 100% for both scenarios. We used the lognormal function to estimate the exposure dose.
3. Results3.1. Mercury Levels in Fish Species and Trophic Levels
All of the 428 fish sampled in this study had detectable levels of mercury, and 28.7% exceeded the World Health Organization’s (WHO) mercury threshold (0.5 μg/g) for human consumption. The Hg concentration in fish exceeded the safe limit in 77.6% of carnivores, 20% of omnivorous, and 2.4% of herbivores. Carnivorous fish accounted for 76% (n = 325) of the total samples, with average Hg concentrations of 0.4 μg/g (SD = 0.38) and a concentration range of 0.008–2.1 μg/g. Interestingly, the Hg concentration in omnivorous fish averaged 0.60 μg/g (SD = 0.42) and reached a maximum of 1.8 μg/g. This could be explained by changes in fish feeding habits. For instance, fish from the omnivorous species Pimelodus ornatus can change their diet depending on the resources available in the environment. Two herbivorous species showed unexpectedly high Hg concentrations of 1.0 μg/g for Mylesinus paraschombourgkii (flaviano) and 0.85 μg/g for Myloplus ternetzi (pacu). We also observed significant differences in the average Hg concentrations between trophic levels (p < 0.001) (Figure 2).
Four of the seven species with the highest Hg concentrations (Figure 3) are among the most consumed species in the region [5]. The highest level (2.1 μg/g) was detected in Boulengerella cuvieri (pirapucu), followed by Cichla monoculus (tucunaré), and Hoplias aimara (traírão), which are all carnivorous species.
3.2. Mercury Levels at Sampling Sites (Inland and Coastal Zone)
The median Hg contamination of inland fish samples was 0.580 µg/g for carnivores (n = 131), 0.045 µg/g for herbivores (n = 15), and 0.680 µg/g for omnivores (n = 33); samples of detritivorous species were not collected. In coastal zones, the median Hg concentrations were 0.165 µg/g for carnivores (n = 194), 0.019 µg/g for herbivores (n = 5), 0.290 µg/g for omnivores (n = 15), and 0.044 µg/g for detritivores (n = 35). The median Hg levels of carnivorous and omnivorous species showed no statistical differences in both aquatic systems. Hg contamination exceeded the WHO limit in 28.7% of all samples, in 5.2% of coastal samples, and 61.5% of inland samples (p < 0.001) (Figure 4). It is therefore 29 times more likely to observe Hg levels above the safe limit in fish samples from inland areas (95% CI, 15.3–54.6) relative to those in coastal zones. The regional variability of Hg levels in the study region is likely related to the different hydrological, physicochemical, and environmental dynamics between inland and coastal areas.
The highest Hg concentrations were detected in Serrasalmus rhombeus (piranha preta), Pimelodus ornatus (mandi casaca), and Hoplias aimara (traírão) in the inland water zone, and Sciades herzbergii (bagre guribu), Megalops atlanticus (pirapema), and Hoplias malabaricus (traíra) in the coastal zone.
3.3. Health Risk Assessment
Fish from the inland zone had higher levels of Hg compared to fish from the coastal zone (Figure 4b). This is reflected by the potential dose and toxicological QR of total mercury for both exposure scenarios in coastal and inland zones (Table 4). The potential THg dose of the three groups was mostly above the acceptable reference dose of 1.6 μg/kg/week or 0.23 μg/kg/day [25]. In the critical scenario in both zones, the potential THg dose showed high risks of adverse health effects in all groups, regardless of location. The exception was the current scenario for women of childbearing age in the coastal zone (0.08 μg/kg/day). Children/juveniles in the inland zone showed significantly greater risks (p ≤ 0.005) under the critical scenario (55) relative to the current scenario (37.5). Our results highlight the elevated health risks associated with fish consumption in the study area.
4. Discussion
High levels of mercury were detected in different fish species and trophic levels in remote and legally protected areas of the northern Amazon; this suggests a high risk of Hg contamination in local fish consumers. Based on the risk assessment of fish Hg concentrations from 18 sites in Amapá State we recommend a low-risk fish diet for the three different groups. We found that the local preference for carnivorous fish species presents a significant health risk for local communities, particularly in inland water zones. As top predators, carnivorous fish bioaccumulate larger quantities of Hg throughout their lifecycle.
The mercury dynamics in aquatic ecosystems are complex and influenced by many factors, including soil type, river flow, fish trophic level, age of fish, season, pH, the productivity of aquatic ecosystems, characteristics of methylation sites, and others [41,42]. As most of the fish samples (81%) were caught during the dry season (with only one field campaign during the rainy season), our study may be limited by the lack of seasonal data; however, we observed no significant difference in Hg concentration in the fish sampled in the dry and rainy seasons (p ≥ 0.05). Another potential source of bias is the lack of detailed primary data on local eating habits, including dietary patterns and meal composition, particularly with regard to the fish intake dose. To minimize this potential bias, we incorporated a conservative approach by assuming a low daily fish intake in relation to the current exposure scenario and in response to the high frequency and quantity of weekly meals [27,40]; this is due to the high regional heterogeneity in the Amazon region, where fish consumption is higher in some communities due to the lack of alternative protein sources.
Furthermore, erosion can significantly influence THg levels in Amazonian aquatic ecosystems by mobilizing inherited Hg in soils. Consequently, efforts to reduce Hg exposure—particularly in populations downstream of ASGM sites—must also address soil erosion, with a particular focus on vulnerable rivers and streams in riparian forests [43]. In addition, changes in land cover/use leads to frequent forest fires, which releases large quantities of Hg to the atmosphere [44] and aquatic systems [42]. These issues require the political will to implement effective policies to reduce the drivers of deforestation in the Amazon.
Comparing the current and critical scenarios in the coastal zone, we observed no significant difference in the potential doses of children/juveniles based on the acceptable methylmercury dose (1.6 μg/kg/week or 0.23 μg/kg/day) [25]. In contrast, a significant difference was observed in the potential doses of children in the inland zone (p ≤ 0.005). For this group, we assumed children consume the same fish species as adults but in smaller quantities. Furthermore, children are at significant risk of Hg exposure because of their higher Hg doses and lower body weights (approximately half that of adults). The potential dose for the other two groups (adults and pregnant women) in both zones was double the WHO’s daily recommended dose at 0.5 μg/kg/day. The potential dose for women of childbearing age in the coastal zone was within the safe limit at 0.08 μg/kg/day. Nevertheless, under the critical scenario in the coastal zone, the toxicological risks for adults (men and women) was 26 and 20 times higher than the daily reference dose for adults and women of childbearing age, respectively. Our results suggest that both groups in the coastal zone that consume carnivorous fish five times per week on average have significantly higher health risks from THg contamination, even in areas where lower fish Hg levels were observed.
Both scenarios showed a high risk of Hg contamination in the inland zone. The highest risks were observed in children at doses of 37.5 μg/kg/week and 55 μg/kg/week. The consumption of fish with high levels of Hg therefore poses significant health risks for this group. Adults under the current and critical scenarios in both zones had a potential dose of 1.9 μg/kg/day and 9.9 μg/kg/day, respectively, inferring a low risk for the current scenario and very high risk for the critical scenario. For pregnant women, the potential dose was 1.8 μg/kg/day under the current scenario, and the dose for the critical scenario was 4.3 times that of the current scenario. Many studies assessing riverbank populations in the Amazon show that a small proportion of the communities consume carnivorous fish at a frequency of 10 meals a week [15,27,36,39].
These communities have been exposed to Hg contamination for over a century through the ingestion of mercury contaminated fish. This long-term Hg exposure has led to potential adverse health effects, such as fetal impairment in women of childbearing age [45,46,47].
Our findings suggest that the consumption of the carnivorous fish species Cichla monoculus, Hoplias malabaricus, Aspistor quadriscutis, Ageneiosus inermis, Plagioscion auratus, and Sciades herzbergii should not exceed 200 g per week. This is equivalent to a dose of 1.4 μg/kg/week for a 70 kg adult. In contrast, we identified no health risks regarding the consumption of herbivorous (such as Mugil sp. (tainha)) and detritivorous species (such as the Hypostomus sp. (acari/cascudo) and Curimata inornata (branquinha)) in the coastal communities. Moreover, to minimize health risks, Ageneiosus inermis (mandubé), Boulengerella cuvieri (pirapucu), Cichla monoculus (tucunaré), and Hoplias aimara (traírão) should only be consumed once a month. Pregnant women should avoid consuming carnivorous fish and reduce the intake of omnivorous fish during the pregancy.
5. Conclusions
To our knowledge, this is the first study to provide clear recommendations for reducing the Hg exposure through fish consumption in local communities in the northern Amazon. High levels of mercury were detected in different fish species in the study area, suggesting a high risk of Hg contamination in local fish consumers. The highest risk of Hg contamination was observed in children in the inland zone. To minimize health risks, we proposed a maximum weekly fish intake for different trophic levels based on both the toxic characteristics of Hg and human susceptibility. We suggest that the consumption of the carnivorous fish species should not exceed 200 g per week, with special attention to the consumption of mandubé, pirapucu, tucunaré and trairão, that should be consumed once a month.
The study locations were selected in protected and conserved areas, including Brazil’s largest natural reserve (Tumucumaque National Park). These regions provide ecosystem services for traditional communities and maintain their social and cultural values, including the incorporation of fish resources for human livelihoods.
Due to the persistence of Hg in the environment, legal and illegal ASGM activities in Amapá continue to impact the health and livelihoods of traditional communities who are dependent on fish as a primary protein source. This situation has been exacerbated by the global increase in the price of gold, which has driven the expansion of illegal ASGM activities in the Amazon and further threatens the environment and the human rights of local communities. This also inhibits the ability of vulnerable populations to achieve their sustainable development goals.
In this context, it is critical to assess the impacts of Hg contamination on local communities and their environment. There is a strong need to build scientific evidence that helps to develop effective mitigation measures and policies to tackle these issues. It is crucial to assess the impact of mercury contamination on human health at regional scales. Policy makers must also devise and implement effective mitigation measures to guarantee the food security of indigenous communities; this includes the proposition of nutritional alternatives and the moderation of fish consumption without compromising cultural traditions.
Acknowledgments
This research was led by the WWF in close partnership with the Instituto de Pesquisa e Formação Indígena (Iepé), Fundação Oswaldo Cruz, ICMBio, and the Amapá Institute of Scientific and Technological Research (IEPA). We thank the researchers, civil society, fishermen, peasants, quilombolas, indigenous communities, and local and federal institutions who participated in this project. We acknowledge the support from ICMBio in the design and implementation of field work, particularly Iranildo Coutinho, Ricardo Pires, and Christoph Jaster. We thank the Amapá Institute of Scientific and Technological Research (IEPA) and the local inhabitants for their assistance with field work. We also thank Ana Claudia Vasconcellos and André Perisse for participating in the discussions, preparing the database and visiting the indigenous communities and health services in Amapá.
Author Contributions
General coordination: M.O.-d.-C.; conceptualization: M.O.-d.-C., C.d.S.G., R.F. and D.Y.; Field work: C.d.S.G., D.Y. and R.F.; data analysis and method: S.d.S.H., P.C.B. and A.S.; manuscript draft: S.d.S.H., M.O.-d.-C., P.C.B. and A.S.; review: S.d.S.H., M.O.-d.-C., C.d.S.G., R.F., P.C.B., A.S. and D.Y. All authors have read and agreed to the publication of the manuscript.
Funding
This research was funded by the WWF and Iepé with logistical support from Chico Mendes Institute for Biodiversity Conservation (ICMBio).
Conflicts of Interest
There are no conflict of interest to declare.
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Map of the study area indicating the sampling sites, protected areas, and indigenous territories.
Distribution of mercury concentrations in fish (μg/g) by trophic level.
Sankey diagram categorizing the studied fish species and trophic levels by total mercury concentrations (≥0.5 μg/g and ≤0.5 μg/g).
Distribution of mercury concentrations in fish (μg/g) in (a) the sampling sites and (b) the aggregated zones (Kruskal–Wallis: 95% CI, p < 0.001).
ijerph-17-05269-t001_Table 1
Description of the sampling sites and the surrounding regions.
Sites and Main River
Tributaries-Sampling Sites
Coordinates of Sampling Sites
Characteristics of the Region
Correlation to Gold Mining Activity
1-Cassiporé River (CR)
Maruani River
03°32′27.71″ N 51°11′34.23″ W
Coastal zone. Muddy water. High fishing pressure. Influence of tides and pororoca. Protected Areas: Cabo Orange National Park and Amapá State Forest. Communities (pop): Oiapoque, 27.270 a; Calçoene, 11.117 a; Vila Velha, 2.723 b; Cunani (Quilombo) 940 b. Indigenous lands: Juminã, 61 c; Uaçá, 4.462 c; Galibi, 151 d.
Gold mining activity for over 250 years. Influence of Garimpo do Lourenço, located at the headwaters of the Cassiporé River. On-going mining processes in the indigenous land of Uaçá for gold research and exploration.
CR-Bridge
02°57′25.57″ N 51°25′18.10″ W
CR
03°17′16.87″ N 51°10′36.68″ N
CR-Vila Velha
03°08′34.16″ N 51°12′36.16″ W
2-Amapá Grande River (AG)
AG
02°10′16.19″ N 50°47′39.41″ W
Coastal zone. Muddy river bottom. High fishing pressure. Influence of tide and pororoca. Protected Areas: Cariaú River reserve, Maracá-Jipioca Ecological Station. Communities (pop): Calçoene, 11.117 a; Pracuúba, 5.120 a; Amapá, 9.109 a Tartarugalzinho, 17.315 a.
This region is influenced by Garimpo do Tartarugalzinho. At least five other areas in the region constitute environmental liabilities: Bananal, Buracão, Fofoca, Mandiocal and Mineiro.
AG-Confluence
02°07′13.50″ N 50°45′17.52″ W
Flechal
01°53′19.07″ N 50°47′25.27″ W
3-Oiapoque River (OR)
Uaçá River
04°0′11.66″ N 51°28′16.28″ W
Coastal zone. Muddy water. High fishing pressure. Large population in the surroundings. Borders French Guiana. Communities (pop): Oiapoque, 27.270 a Calçoene, 11.117 a. Indigenous lands: Juminã, 121 c; Uaçá: 4 462 c; Galibi: 151 d.
Displacement of illegal ASGM toward French Guiana from 2002. Influence from ASGM sites in the whole basin from both countries.
OR
04°9′49.10″ N 51°37′59.97″ W
OR (Bay)
04°20′9.00″ N 51°34′50.20″ W
4-Araguari River (AR)
Tajauí River
01°23′27.10″ N 52°01′28.02″ W
Inland zone. Sandy River bottom. Illegal fishing.Protected Areas: Tumucumaque National Park, Amapá National Forest. Communities (pop): Pedra Branca do Amapari, 16.502 a; Porto Grande, 21.971 a; Ferreira Gomes, 7.780 a.
Influence of the Amapari, Lourenço, Vila Nova, Capivara, Mururé ASGM sites.
Falsino River
00°58′09.70″ N 51°36′16.90″ W
Mureré River
01°26′16.75″ N 52°03′14.30″ W
Mutum River
01°19′02.84″ N 51°59′23.09″ W
5-Amapari River(AMR)
AMR
01°35′16.86″ N 52°32′38.18″ W
Inland zone. Sandy River bottom. Protected Areas: Tumucumaque Mountains National Park, Amapá National Forest. Communities (pop): Pedra Branca do Amapari, 16.502 a; Serra do Navio, 5.397 a. Indigenous: Wajãpi, 1.200 d.
Four legal active ASGM sites. Illegal gold mining sites within PAs and prospection projects. The Tucano’s Gold Mine (Pedra Branca do Amapari) is the second largest gold mine in Brazil.
Anacuí River
01°36′04.52″ N 52°29′26.99″ W
AMR/Jurupá River
01°11′13.52″ N 52°22′14.66″ W
AMR (Porto Terezinha)
00°52′53.52″ N 52°00′36.84″ W
a [18]. b [19]. c [20]. d [21]. e [22].
ijerph-17-05269-t002_Table 2
Summary of articles that quantified fish consumption in communities in the Brazilian Amazon.
Reference
Reference
Local
Year
Population
\nN\n
Consumption (g/Day; Meals/Week; Daily Eat %)
Average (SD)
Min-Max
[27]
Monte Alegre/PA
1993–1995
Families
35
369
−
[28]
Rio Madeira/RO
1991 and 1993
All ages
607
243 (135)
100–300
[29]
Rio Cuiabá/MT
1995–1996
>12
153
110.4 (152.5)
20–372
[30]
Maroni River, Community Wayana/French Guyana
1997
<1–14Adults (15–45)
109118
195 ± 9372 ± 116
\n
[31]
Rio Negro/AM
1998–1999
Woman (1545–)
31
170.5
23–293
[32]
Alta Floresta/MT
2000–2002
All ages
251
−
3–180
[33]
Indigenous/PA
−
Munduruku
249
30.0 (16.6)
−
Kayabi
47
110.4 (60.6)
−
[34]
Rio Tapajós/PA
2003
Woman > 15
121
124 (65)
−
Men > 15
135
189.7 (105.5)
−
[35]
Lago Puruzinho/PA
2005–2006
All ages
120
406 (204.1)
−
[36]
Madeira River, Curiã Lake, Porto Velho
2009–2011
Grouped Villages
\n
Daily eat (%)5.8%–57.7%
\n
[37]
Madeira River/RO
2015
All
51
34.29
7.1–330
Young
23
21.4
7.1–180
Woman
12
34.3
10–328.6
Adults (1549–)
28
51.1
10–330
[38]
Community of Brasília Legal, Tapajós/PA
2004
Woman
46
4.9 Meals/week
−
Men
22
6 Meals/week
−
[39]
Lower Amazon RiverTrombetas RiverPurus River
2006–2008
Families
85
416.39 (209.12)
−
183
490 (240.69)
−
54
469 (207.72)
−
[40]
Tapajós River–Community of Barreiras/PA
Exposure parameters used for the health risk assessment of residents in areas influenced by coastal tides (sites 1, 2, and 3) and inland zones (sites 4 and 5).
Exposure Parameters by Scenario
Child & Juvenile(5–16)
Women and Men Adults (17–75)
Women ofChildbearing Age (16–49)
Coastal Tidal
Inland
Coastal Tidal
Inland
Coastal Tidal
Inland
Current
[Hg]-omnivorous and carnivorous–Average (SD) µg/g
0.50 (0.37)
0.66 (0.61)
0.50 (0.37)
0.66 (0.61)
0.50 (0.37)
0.66 (0.61)
[Hg]-detritivore and herbivore– Average (SD) µg/g
0.17 (0.18)
0.17 (0.22)
0.17 (0.18)
0.17 (0.22)
0.17 (0.18)
0.17 (0.22)
Average fish consumption (kg/d) adjusted for trophic level
Potential dose (Dpot) and toxicological risk quotient (QR) of total mercury for both exposure scenarios in coastal and inland zones.
Exposure Scenario
Pot. DoseChild & Juvenile(5–16)
Pot. DoseWomen and Men Adults (17–75)
Pot. DoseWoman of Childbearing Age(16–49)
\n
μg/kg/day
μg/kg/week
μg/kg/day
μg/kg/week
μg/kg/day
μg/kg/week
\n
Dpot/d
Dpot/w
Dpot/d
Dpot/w
Dpot/d
Dpot/w
\nCoastal Zone\n
\n
\n
\n
\n
\n
\n
Current scenario
6.8
34
0.5
2.5
0.08
0.4
Critical scenario
6.9
34.5
6.0
30
4.7
23.5
\nInland Zone\n
\n
\n
\n
\n
\n
\n
Current scenario
7.5
37.5
1.9
9.5
1.8
9
Critical scenario
11
55
9.9
49.5
7.7
38.5
\n"],"string":"[\n \"\\nInt J Environ Res Public HealthInt J Environ Res Public HealthijerphInternational Journal of Environmental Research and Public Health1661-78271660-4601MDPI32707799PMC743210710.3390/ijerph17155269ijerph-17-05269ArticleMercury Exposure through Fish Consumption in Traditional Communities in the Brazilian Northern AmazonHaconSandra de Souza1*https://orcid.org/0000-0002-8004-9079Oliveira-da-CostaMarcelo2GamaCecile de Souza3FerreiraRenata4BastaPaulo Cesar1SchrammAna1https://orcid.org/0000-0002-1876-782XYokotaDecio4Fundação Oswaldo Cruz, Escola Nacional de Saúde Pública Sérgio Arouca, Rio de Janeiro 21041-210, Brazil; pcbasta@ensp.fiocruz.br (P.C.B.); schrammana@posgrad.ensp.fiocruz.br (A.S.)WWF-Brasil, Brasília 70377-540, Brazil; marcelo@wwf.org.brInstituto de Pesquisas Científicas e Tecnológicas do Amapá, Av. Feliciano Coelho, 1509. Trem, Amapá 68901-025, Brazil; cecile.gama@iepa.ap.gov.brIepé-Instituto de Pesquisa e Formação Indígena, Macapá, Amapá 68908-120, Brazil; renata.ferreira@institutoiepe.org.br (R.F.); decio@institutoiepe.org.br (D.Y.)Correspondence: shacon@ensp.fiocruz.br2272020820201715526929620201672020© 2020 by the authors.2020Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Artisanal small-scale gold mining (ASGM) is the main source of anthropogenic mercury emissions and contamination in Latin America. In the Brazilian northern Amazon, ASGM has contaminated the environment and people over the past century. The main contamination route is through fish consumption, which endangers the food security and livelihoods of traditional communities. Our study aims to assess the potential toxicological health risks caused by the consumption of Hg-contaminated fish across five regions in Amapá State. We sampled 428 fish from 18 sites across inland and coastal aquatic systems. We measured the total mercury content in fish samples, and the results were applied to a mercury exposure risk assessment targeting three distinct groups (adults, women of childbearing age, and children). Mercury contamination was found to exceed the World Health Organization’s safe limit in 28.7% of all fish samples, with higher prevalence in inland zones. Moreover, the local preference for carnivorous fish species presents a serious health risk, particularly for communities near inland rivers in the region. This is the first study to provide clear recommendations for reducing the mercury exposure through fish consumption in Amapá State. It builds scientific evidence that helps decision-makers to implement effective policies for protecting the health of riverine communities.
Artisanal small-scale gold mining (ASGM) activities have significantly expanded in the Amazon over the last two decades. ASGM is a major cause of deforestation and habitat degradation in the Brazilian northern Amazon [1,2,3], particularly on the borders between Guyana, French Guiana, Suriname, and Venezuela, and the Brazilian states of Roraima, Amapá, and Pará (also known as the Guiana Shield Ecoregion) [4]. The ecoregion covers 250 million hectares and contains one of the largest complexes of uninterrupted primary tropical forests on Earth [5]; the region therefore plays a critical role in mitigating global climate change and preserving biodiversity and freshwater ecosystems.
The Brazilian state of Amapá borders French Guiana and Suriname, and almost 72% of its territory is formally protected. It consists of five indigenous territories, four quilombolas territories, and nineteen conservation units (twelve federal, five state, and two municipal), covering a total area of 115.656 km2. These areas are crucial for human livelihoods, especially for communities that rely on their natural resources. However, the protection of these areas has been hampered by weak governance, lack of financial resources for protective management, and different anthropogenic pressures. Among those threats, the widespread use of mercury (Hg) in ASGM has led to the contamination of both the environment and the regional population.
The ASGM sector is the leading source of mercury emissions, contamination, and consumption in Latin America and the Caribbean [6]; thus, the traditional communities that depend on natural resources for their survival are highly vulnerable to ASGM activities. This activity is generally coordinated by illegal networks and conducted by wildcat gold miners, and dealers [7]. ASGM has adversely affected the health of riverside communities—particularly indigenous people [8,9]—that rely on fish as their main protein source [10,11]. Fisheries in the Amazon provide food security and reduce malnutrition rates, as fish are the dominant dietary source of essential amino acids, lipids with fatty acids, minerals, and vitamins [12,13].
In ASGM-dominated regions, human exposure to Hg is mainly through bioaccumulation and biomagnification of methylmercury from fish consumption. In the Brazilian Amazon, indigenous people, riverside communities, fishermen, quilombolas, peasants, and extractivists inhabit the areas near rivers, bays, and streams and are therefore highly exposed to Hg compounds. This reality is aggravated by the social vulnerability of exposed communities, including reduced access to healthcare, formal education, regular income, basic sanitation, and potable water. In addition, these communities suffer from high rates of malnutrition, particularly in children under 5 years of age. They are also vulnerable to a high number of infectious and respiratory diseases (such as malaria, hantavirus pulmonary syndrome, leishmaniasis, yellow fever, etc.) that are exacerbated by both changes in land use and climate [14].
Therefore, mercury contamination in the Amazon is both a national and international concern. In this study, we aimed to develop a better understanding of the risks associated with fish consumption in various regions of the northern Amazon. Although a few studies have shown mercury contamination in fish in the region [5], this is the first to conduct an investigation on human Hg exposure in the study region. We assessed the potential toxicological health risks from mercury exposure through fish consumption for different age groups across five regions in Amapá State.
2. Methods2.1. Study Area
We assessed five regions in the Amapá State territory, including some of the most biodiverse and economically significant river basins in the region (Figure 1). Watersheds in the state play an important social, cultural, and economic role, and fish resources in the region are heavily influenced by the Amazon River and Atlantic Ocean. We chose rivers that bordered protected areas (Table 1) due to the higher occurrence of mineral deposits [15] and higher number of ASGM sites surrounding Amapá’s protected areas [16]. Sampling sites were set up at potential ASGM sites (active and inactive). These sites were identified based on gold mining deforestation maps [3], literature reviews [5,17], the presence of local communities, the influence of coastal tides or inland waters, sampling logistics, the distance to protected areas, and interviews with managers of protected areas and locals with knowledge of the regional gold mining history. Sampling sites 1, 2, and 3 are directly influenced by coastal tides and are therefore considered coastal zones, while sites 4 and 5 are located in inland waters and are therefore referred to as inland zones.
2.2. Fish Sampling and Preparation
Between August 2017 and May 2018, we collected tissue samples from 428 fish from 45 different species at 18 sites across the five regions (Table 1). The fish were caught by local fishermen hired to support the research project. According to the local fishermen, each fish species was captured using specific gear; therefore, gillnets, hooks and hand lines, fishing rods and long lines were used. Each specimen was identified to the species level, and a minimum of 70 g of muscle tissue (free of skin) was extracted from the dorsal region of the fish body. All dissection tools were sterilized before sampling to avoid contamination. Samples were stored in ice boxes and frozen for transportation to the laboratory. The study was conducted in accordance with the Brazilian regulation (IN 03/2014), under the license SISBIO 58296-1.
2.3. Mercury Analysis
Total mercury (THg) was measured by cold vapor atomic fluorescence spectrometry at the Analytical Chemistry Laboratory of the Pontific University of Rio de Janeiro [23]. The analytical quality was determined by control strict blanks with duplicate analysis and compared to the analytical results of certified reference materials (DORM-2, Dogfish Muscle Certified Reference Material for trace elements, National Research Council, Ottawa, ON, Canada). Average recovery values were >96% of the certified value analytical results; and the relative standard deviation (RSD) was <15%. Total mercury analysis from the wet weight was adopted.
2.4. Data Analysis
The data are presented as mean ± standard error and percentiles. We applied the Kruskal–Wallis test to determine the mean differences in THg concentrations between sampling locations and trophic levels.
The risk assessment approach in this study was based on the method used by the US Government Agency for Toxic Substances and Diseases Registry [24]. The method includes four steps: (i) hazard identification, (ii) dose–response assessment, (iii) exposure assessment, and (iv) risk characterization. Most risk assessment studies use secondary data; however, our Hg exposure assessment used information on the most consumed fish species in the region [5] as well as informal interviews with local fishermen and individuals from indigenous and riverine communities.
2.5. Estimated Daily Intake
To assess the health risks of total mercury exposure, we incorporated two scenarios for toxicological risk: (i) the critical scenario and (ii) the current scenario. The critical scenario assumes that some of the local communities—including the indigenous communities—consume larger quantities of fish due to the lack of other protein sources. The current scenario represents the eating habits of most of the communities in the study area. The estimated exposure dose of fish consumers from both coastal and inland zones were divided into three groups: (i) adults of both genders (age: 17 to 75), (ii) women of childbearing age (age: 17 to 49), and (iii) children/juveniles (age: 5 to 16). We estimated their daily intake (EDI) to determine each group’s daily Hg consumption [25]. The average daily fish consumption was based on 16 studies (Table 2) that assessed the dietary patterns and fish consumption of Amazonian communities.
The potential exposure dose (µg/kg/d) was calculated according to Equation (1) [26]:Dpot=C×IR×EF×EDBW×1AT\\nwhere Dpot is the potential mercury intake dose (µg/kg/day); C is the total mercury concentration of fish species (µg/g); IR is the fish intake rate (kg/meal); EF is the frequency of exposure (meals /day); ED is the exposure duration (day, week); AT corresponds to the time weighted (day) (time weighted considers the duration of exposure); and BW is the body weight (kg).
The risk ratio was calculated as the ratio between the potential intake dose and the reference dose using the risk quotient equation (Equation (2)):QR = Dpot/RfD\\nwhere QR is the risk quotient; Dpot is the potential mercury intake dose (µg/kg/day); and RfD is the reference dose (1.6 μg/kg/week or 0.23 μg/kg/day [25]) of the substance or chemical element (Hg) that can be consumed weekly over a lifetime without appreciable risk to health. A QR of <1 indicates a low likelihood of adverse health effects, while QR > 1 indicates the possibility of adverse non-carcinogenic effects.
2.6. Model Input Variables
The description of the input variables for the mercury exposure model for both the critical and current scenarios are shown in Table 3. We assumed a 50% consumption of fish species in age groups with larger average Hg concentrations (carnivorous and omnivorous) as well as the groups with the lowest Hg concentration (herbivorous and detritivores). The literature review showed a high frequency of fish consumption and a high variability in the quantity of meals, ranging from 1 to 20 meals per week [38,40]. We therefore set an average of 10 meals of fish consumed per week, varying from 160 to 430 g/day, based on the characteristics of each group. We estimated the fish daily intake values based on the literature review (Table 2). The average body weight of the residents in the communities was obtained from research on family budgets that presented the anthropometry and nutritional status of children, adolescents, and adults in the state of Amapá [19].
To quantify exposure, we calculated the mean Hg concentrations of carnivorous and omnivorous fish as well as the 95% confidence interval (CI) of each mean. We also conducted a literature review to determine the frequency of fish meal consumption and estimated potential doses by assigning the 95% upper limit of the mean fish consumption. It was assumed 100% of THg concentration was methylmercury, since methyl Hg constitutes up to 98% of the Hg concentration in fish samples in the Amazon basin. The body weight was the same for both scenarios, and the factor absorption of total Hg as methyl Hg by exposed residents was assumed to be 100% for both scenarios. We used the lognormal function to estimate the exposure dose.
3. Results3.1. Mercury Levels in Fish Species and Trophic Levels
All of the 428 fish sampled in this study had detectable levels of mercury, and 28.7% exceeded the World Health Organization’s (WHO) mercury threshold (0.5 μg/g) for human consumption. The Hg concentration in fish exceeded the safe limit in 77.6% of carnivores, 20% of omnivorous, and 2.4% of herbivores. Carnivorous fish accounted for 76% (n = 325) of the total samples, with average Hg concentrations of 0.4 μg/g (SD = 0.38) and a concentration range of 0.008–2.1 μg/g. Interestingly, the Hg concentration in omnivorous fish averaged 0.60 μg/g (SD = 0.42) and reached a maximum of 1.8 μg/g. This could be explained by changes in fish feeding habits. For instance, fish from the omnivorous species Pimelodus ornatus can change their diet depending on the resources available in the environment. Two herbivorous species showed unexpectedly high Hg concentrations of 1.0 μg/g for Mylesinus paraschombourgkii (flaviano) and 0.85 μg/g for Myloplus ternetzi (pacu). We also observed significant differences in the average Hg concentrations between trophic levels (p < 0.001) (Figure 2).
Four of the seven species with the highest Hg concentrations (Figure 3) are among the most consumed species in the region [5]. The highest level (2.1 μg/g) was detected in Boulengerella cuvieri (pirapucu), followed by Cichla monoculus (tucunaré), and Hoplias aimara (traírão), which are all carnivorous species.
3.2. Mercury Levels at Sampling Sites (Inland and Coastal Zone)
The median Hg contamination of inland fish samples was 0.580 µg/g for carnivores (n = 131), 0.045 µg/g for herbivores (n = 15), and 0.680 µg/g for omnivores (n = 33); samples of detritivorous species were not collected. In coastal zones, the median Hg concentrations were 0.165 µg/g for carnivores (n = 194), 0.019 µg/g for herbivores (n = 5), 0.290 µg/g for omnivores (n = 15), and 0.044 µg/g for detritivores (n = 35). The median Hg levels of carnivorous and omnivorous species showed no statistical differences in both aquatic systems. Hg contamination exceeded the WHO limit in 28.7% of all samples, in 5.2% of coastal samples, and 61.5% of inland samples (p < 0.001) (Figure 4). It is therefore 29 times more likely to observe Hg levels above the safe limit in fish samples from inland areas (95% CI, 15.3–54.6) relative to those in coastal zones. The regional variability of Hg levels in the study region is likely related to the different hydrological, physicochemical, and environmental dynamics between inland and coastal areas.
The highest Hg concentrations were detected in Serrasalmus rhombeus (piranha preta), Pimelodus ornatus (mandi casaca), and Hoplias aimara (traírão) in the inland water zone, and Sciades herzbergii (bagre guribu), Megalops atlanticus (pirapema), and Hoplias malabaricus (traíra) in the coastal zone.
3.3. Health Risk Assessment
Fish from the inland zone had higher levels of Hg compared to fish from the coastal zone (Figure 4b). This is reflected by the potential dose and toxicological QR of total mercury for both exposure scenarios in coastal and inland zones (Table 4). The potential THg dose of the three groups was mostly above the acceptable reference dose of 1.6 μg/kg/week or 0.23 μg/kg/day [25]. In the critical scenario in both zones, the potential THg dose showed high risks of adverse health effects in all groups, regardless of location. The exception was the current scenario for women of childbearing age in the coastal zone (0.08 μg/kg/day). Children/juveniles in the inland zone showed significantly greater risks (p ≤ 0.005) under the critical scenario (55) relative to the current scenario (37.5). Our results highlight the elevated health risks associated with fish consumption in the study area.
4. Discussion
High levels of mercury were detected in different fish species and trophic levels in remote and legally protected areas of the northern Amazon; this suggests a high risk of Hg contamination in local fish consumers. Based on the risk assessment of fish Hg concentrations from 18 sites in Amapá State we recommend a low-risk fish diet for the three different groups. We found that the local preference for carnivorous fish species presents a significant health risk for local communities, particularly in inland water zones. As top predators, carnivorous fish bioaccumulate larger quantities of Hg throughout their lifecycle.
The mercury dynamics in aquatic ecosystems are complex and influenced by many factors, including soil type, river flow, fish trophic level, age of fish, season, pH, the productivity of aquatic ecosystems, characteristics of methylation sites, and others [41,42]. As most of the fish samples (81%) were caught during the dry season (with only one field campaign during the rainy season), our study may be limited by the lack of seasonal data; however, we observed no significant difference in Hg concentration in the fish sampled in the dry and rainy seasons (p ≥ 0.05). Another potential source of bias is the lack of detailed primary data on local eating habits, including dietary patterns and meal composition, particularly with regard to the fish intake dose. To minimize this potential bias, we incorporated a conservative approach by assuming a low daily fish intake in relation to the current exposure scenario and in response to the high frequency and quantity of weekly meals [27,40]; this is due to the high regional heterogeneity in the Amazon region, where fish consumption is higher in some communities due to the lack of alternative protein sources.
Furthermore, erosion can significantly influence THg levels in Amazonian aquatic ecosystems by mobilizing inherited Hg in soils. Consequently, efforts to reduce Hg exposure—particularly in populations downstream of ASGM sites—must also address soil erosion, with a particular focus on vulnerable rivers and streams in riparian forests [43]. In addition, changes in land cover/use leads to frequent forest fires, which releases large quantities of Hg to the atmosphere [44] and aquatic systems [42]. These issues require the political will to implement effective policies to reduce the drivers of deforestation in the Amazon.
Comparing the current and critical scenarios in the coastal zone, we observed no significant difference in the potential doses of children/juveniles based on the acceptable methylmercury dose (1.6 μg/kg/week or 0.23 μg/kg/day) [25]. In contrast, a significant difference was observed in the potential doses of children in the inland zone (p ≤ 0.005). For this group, we assumed children consume the same fish species as adults but in smaller quantities. Furthermore, children are at significant risk of Hg exposure because of their higher Hg doses and lower body weights (approximately half that of adults). The potential dose for the other two groups (adults and pregnant women) in both zones was double the WHO’s daily recommended dose at 0.5 μg/kg/day. The potential dose for women of childbearing age in the coastal zone was within the safe limit at 0.08 μg/kg/day. Nevertheless, under the critical scenario in the coastal zone, the toxicological risks for adults (men and women) was 26 and 20 times higher than the daily reference dose for adults and women of childbearing age, respectively. Our results suggest that both groups in the coastal zone that consume carnivorous fish five times per week on average have significantly higher health risks from THg contamination, even in areas where lower fish Hg levels were observed.
Both scenarios showed a high risk of Hg contamination in the inland zone. The highest risks were observed in children at doses of 37.5 μg/kg/week and 55 μg/kg/week. The consumption of fish with high levels of Hg therefore poses significant health risks for this group. Adults under the current and critical scenarios in both zones had a potential dose of 1.9 μg/kg/day and 9.9 μg/kg/day, respectively, inferring a low risk for the current scenario and very high risk for the critical scenario. For pregnant women, the potential dose was 1.8 μg/kg/day under the current scenario, and the dose for the critical scenario was 4.3 times that of the current scenario. Many studies assessing riverbank populations in the Amazon show that a small proportion of the communities consume carnivorous fish at a frequency of 10 meals a week [15,27,36,39].
These communities have been exposed to Hg contamination for over a century through the ingestion of mercury contaminated fish. This long-term Hg exposure has led to potential adverse health effects, such as fetal impairment in women of childbearing age [45,46,47].
Our findings suggest that the consumption of the carnivorous fish species Cichla monoculus, Hoplias malabaricus, Aspistor quadriscutis, Ageneiosus inermis, Plagioscion auratus, and Sciades herzbergii should not exceed 200 g per week. This is equivalent to a dose of 1.4 μg/kg/week for a 70 kg adult. In contrast, we identified no health risks regarding the consumption of herbivorous (such as Mugil sp. (tainha)) and detritivorous species (such as the Hypostomus sp. (acari/cascudo) and Curimata inornata (branquinha)) in the coastal communities. Moreover, to minimize health risks, Ageneiosus inermis (mandubé), Boulengerella cuvieri (pirapucu), Cichla monoculus (tucunaré), and Hoplias aimara (traírão) should only be consumed once a month. Pregnant women should avoid consuming carnivorous fish and reduce the intake of omnivorous fish during the pregancy.
5. Conclusions
To our knowledge, this is the first study to provide clear recommendations for reducing the Hg exposure through fish consumption in local communities in the northern Amazon. High levels of mercury were detected in different fish species in the study area, suggesting a high risk of Hg contamination in local fish consumers. The highest risk of Hg contamination was observed in children in the inland zone. To minimize health risks, we proposed a maximum weekly fish intake for different trophic levels based on both the toxic characteristics of Hg and human susceptibility. We suggest that the consumption of the carnivorous fish species should not exceed 200 g per week, with special attention to the consumption of mandubé, pirapucu, tucunaré and trairão, that should be consumed once a month.
The study locations were selected in protected and conserved areas, including Brazil’s largest natural reserve (Tumucumaque National Park). These regions provide ecosystem services for traditional communities and maintain their social and cultural values, including the incorporation of fish resources for human livelihoods.
Due to the persistence of Hg in the environment, legal and illegal ASGM activities in Amapá continue to impact the health and livelihoods of traditional communities who are dependent on fish as a primary protein source. This situation has been exacerbated by the global increase in the price of gold, which has driven the expansion of illegal ASGM activities in the Amazon and further threatens the environment and the human rights of local communities. This also inhibits the ability of vulnerable populations to achieve their sustainable development goals.
In this context, it is critical to assess the impacts of Hg contamination on local communities and their environment. There is a strong need to build scientific evidence that helps to develop effective mitigation measures and policies to tackle these issues. It is crucial to assess the impact of mercury contamination on human health at regional scales. Policy makers must also devise and implement effective mitigation measures to guarantee the food security of indigenous communities; this includes the proposition of nutritional alternatives and the moderation of fish consumption without compromising cultural traditions.
Acknowledgments
This research was led by the WWF in close partnership with the Instituto de Pesquisa e Formação Indígena (Iepé), Fundação Oswaldo Cruz, ICMBio, and the Amapá Institute of Scientific and Technological Research (IEPA). We thank the researchers, civil society, fishermen, peasants, quilombolas, indigenous communities, and local and federal institutions who participated in this project. We acknowledge the support from ICMBio in the design and implementation of field work, particularly Iranildo Coutinho, Ricardo Pires, and Christoph Jaster. We thank the Amapá Institute of Scientific and Technological Research (IEPA) and the local inhabitants for their assistance with field work. We also thank Ana Claudia Vasconcellos and André Perisse for participating in the discussions, preparing the database and visiting the indigenous communities and health services in Amapá.
Author Contributions
General coordination: M.O.-d.-C.; conceptualization: M.O.-d.-C., C.d.S.G., R.F. and D.Y.; Field work: C.d.S.G., D.Y. and R.F.; data analysis and method: S.d.S.H., P.C.B. and A.S.; manuscript draft: S.d.S.H., M.O.-d.-C., P.C.B. and A.S.; review: S.d.S.H., M.O.-d.-C., C.d.S.G., R.F., P.C.B., A.S. and D.Y. All authors have read and agreed to the publication of the manuscript.
Funding
This research was funded by the WWF and Iepé with logistical support from Chico Mendes Institute for Biodiversity Conservation (ICMBio).
Conflicts of Interest
There are no conflict of interest to declare.
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Map of the study area indicating the sampling sites, protected areas, and indigenous territories.
Distribution of mercury concentrations in fish (μg/g) by trophic level.
Sankey diagram categorizing the studied fish species and trophic levels by total mercury concentrations (≥0.5 μg/g and ≤0.5 μg/g).
Distribution of mercury concentrations in fish (μg/g) in (a) the sampling sites and (b) the aggregated zones (Kruskal–Wallis: 95% CI, p < 0.001).
ijerph-17-05269-t001_Table 1
Description of the sampling sites and the surrounding regions.
Sites and Main River
Tributaries-Sampling Sites
Coordinates of Sampling Sites
Characteristics of the Region
Correlation to Gold Mining Activity
1-Cassiporé River (CR)
Maruani River
03°32′27.71″ N 51°11′34.23″ W
Coastal zone. Muddy water. High fishing pressure. Influence of tides and pororoca. Protected Areas: Cabo Orange National Park and Amapá State Forest. Communities (pop): Oiapoque, 27.270 a; Calçoene, 11.117 a; Vila Velha, 2.723 b; Cunani (Quilombo) 940 b. Indigenous lands: Juminã, 61 c; Uaçá, 4.462 c; Galibi, 151 d.
Gold mining activity for over 250 years. Influence of Garimpo do Lourenço, located at the headwaters of the Cassiporé River. On-going mining processes in the indigenous land of Uaçá for gold research and exploration.
CR-Bridge
02°57′25.57″ N 51°25′18.10″ W
CR
03°17′16.87″ N 51°10′36.68″ N
CR-Vila Velha
03°08′34.16″ N 51°12′36.16″ W
2-Amapá Grande River (AG)
AG
02°10′16.19″ N 50°47′39.41″ W
Coastal zone. Muddy river bottom. High fishing pressure. Influence of tide and pororoca. Protected Areas: Cariaú River reserve, Maracá-Jipioca Ecological Station. Communities (pop): Calçoene, 11.117 a; Pracuúba, 5.120 a; Amapá, 9.109 a Tartarugalzinho, 17.315 a.
This region is influenced by Garimpo do Tartarugalzinho. At least five other areas in the region constitute environmental liabilities: Bananal, Buracão, Fofoca, Mandiocal and Mineiro.
AG-Confluence
02°07′13.50″ N 50°45′17.52″ W
Flechal
01°53′19.07″ N 50°47′25.27″ W
3-Oiapoque River (OR)
Uaçá River
04°0′11.66″ N 51°28′16.28″ W
Coastal zone. Muddy water. High fishing pressure. Large population in the surroundings. Borders French Guiana. Communities (pop): Oiapoque, 27.270 a Calçoene, 11.117 a. Indigenous lands: Juminã, 121 c; Uaçá: 4 462 c; Galibi: 151 d.
Displacement of illegal ASGM toward French Guiana from 2002. Influence from ASGM sites in the whole basin from both countries.
OR
04°9′49.10″ N 51°37′59.97″ W
OR (Bay)
04°20′9.00″ N 51°34′50.20″ W
4-Araguari River (AR)
Tajauí River
01°23′27.10″ N 52°01′28.02″ W
Inland zone. Sandy River bottom. Illegal fishing.Protected Areas: Tumucumaque National Park, Amapá National Forest. Communities (pop): Pedra Branca do Amapari, 16.502 a; Porto Grande, 21.971 a; Ferreira Gomes, 7.780 a.
Influence of the Amapari, Lourenço, Vila Nova, Capivara, Mururé ASGM sites.
Falsino River
00°58′09.70″ N 51°36′16.90″ W
Mureré River
01°26′16.75″ N 52°03′14.30″ W
Mutum River
01°19′02.84″ N 51°59′23.09″ W
5-Amapari River(AMR)
AMR
01°35′16.86″ N 52°32′38.18″ W
Inland zone. Sandy River bottom. Protected Areas: Tumucumaque Mountains National Park, Amapá National Forest. Communities (pop): Pedra Branca do Amapari, 16.502 a; Serra do Navio, 5.397 a. Indigenous: Wajãpi, 1.200 d.
Four legal active ASGM sites. Illegal gold mining sites within PAs and prospection projects. The Tucano’s Gold Mine (Pedra Branca do Amapari) is the second largest gold mine in Brazil.
Anacuí River
01°36′04.52″ N 52°29′26.99″ W
AMR/Jurupá River
01°11′13.52″ N 52°22′14.66″ W
AMR (Porto Terezinha)
00°52′53.52″ N 52°00′36.84″ W
a [18]. b [19]. c [20]. d [21]. e [22].
ijerph-17-05269-t002_Table 2
Summary of articles that quantified fish consumption in communities in the Brazilian Amazon.
Reference
Reference
Local
Year
Population
\\nN\\n
Consumption (g/Day; Meals/Week; Daily Eat %)
Average (SD)
Min-Max
[27]
Monte Alegre/PA
1993–1995
Families
35
369
−
[28]
Rio Madeira/RO
1991 and 1993
All ages
607
243 (135)
100–300
[29]
Rio Cuiabá/MT
1995–1996
>12
153
110.4 (152.5)
20–372
[30]
Maroni River, Community Wayana/French Guyana
1997
<1–14Adults (15–45)
109118
195 ± 9372 ± 116
\\n
[31]
Rio Negro/AM
1998–1999
Woman (1545–)
31
170.5
23–293
[32]
Alta Floresta/MT
2000–2002
All ages
251
−
3–180
[33]
Indigenous/PA
−
Munduruku
249
30.0 (16.6)
−
Kayabi
47
110.4 (60.6)
−
[34]
Rio Tapajós/PA
2003
Woman > 15
121
124 (65)
−
Men > 15
135
189.7 (105.5)
−
[35]
Lago Puruzinho/PA
2005–2006
All ages
120
406 (204.1)
−
[36]
Madeira River, Curiã Lake, Porto Velho
2009–2011
Grouped Villages
\\n
Daily eat (%)5.8%–57.7%
\\n
[37]
Madeira River/RO
2015
All
51
34.29
7.1–330
Young
23
21.4
7.1–180
Woman
12
34.3
10–328.6
Adults (1549–)
28
51.1
10–330
[38]
Community of Brasília Legal, Tapajós/PA
2004
Woman
46
4.9 Meals/week
−
Men
22
6 Meals/week
−
[39]
Lower Amazon RiverTrombetas RiverPurus River
2006–2008
Families
85
416.39 (209.12)
−
183
490 (240.69)
−
54
469 (207.72)
−
[40]
Tapajós River–Community of Barreiras/PA
Exposure parameters used for the health risk assessment of residents in areas influenced by coastal tides (sites 1, 2, and 3) and inland zones (sites 4 and 5).
Exposure Parameters by Scenario
Child & Juvenile(5–16)
Women and Men Adults (17–75)
Women ofChildbearing Age (16–49)
Coastal Tidal
Inland
Coastal Tidal
Inland
Coastal Tidal
Inland
Current
[Hg]-omnivorous and carnivorous–Average (SD) µg/g
0.50 (0.37)
0.66 (0.61)
0.50 (0.37)
0.66 (0.61)
0.50 (0.37)
0.66 (0.61)
[Hg]-detritivore and herbivore– Average (SD) µg/g
0.17 (0.18)
0.17 (0.22)
0.17 (0.18)
0.17 (0.22)
0.17 (0.18)
0.17 (0.22)
Average fish consumption (kg/d) adjusted for trophic level
Potential dose (Dpot) and toxicological risk quotient (QR) of total mercury for both exposure scenarios in coastal and inland zones.
Exposure Scenario
Pot. DoseChild & Juvenile(5–16)
Pot. DoseWomen and Men Adults (17–75)
Pot. DoseWoman of Childbearing Age(16–49)
\\n
μg/kg/day
μg/kg/week
μg/kg/day
μg/kg/week
μg/kg/day
μg/kg/week
\\n
Dpot/d
Dpot/w
Dpot/d
Dpot/w
Dpot/d
Dpot/w
\\nCoastal Zone\\n
\\n
\\n
\\n
\\n
\\n
\\n
Current scenario
6.8
34
0.5
2.5
0.08
0.4
Critical scenario
6.9
34.5
6.0
30
4.7
23.5
\\nInland Zone\\n
\\n
\\n
\\n
\\n
\\n
\\n
Current scenario
7.5
37.5
1.9
9.5
1.8
9
Critical scenario
11
55
9.9
49.5
7.7
38.5
\\n\"\n]"}}},{"rowIdx":451,"cells":{"data":{"kind":"list like","value":["\nInt J Mol SciInt J Mol SciijmsInternational Journal of Molecular Sciences1422-0067MDPI32751239PMC743210810.3390/ijms21155389ijms-21-05389ReviewGut Microbiota Manipulation as a Tool for Colorectal Cancer Management: Recent Advances in Its Use for Therapeutic PurposesPerilloFederica1†AmorosoChiara1†https://orcid.org/0000-0001-7217-3355StratiFrancesco1*GiuffrèMaria Rita1https://orcid.org/0000-0003-2096-1223Díaz-BasabeAngélica12LattanziGeorgia12https://orcid.org/0000-0002-2541-9428FacciottiFederica1*Department of Experimental Oncology, IEO European Institute of Oncology IRCCS, 20139 Milan, Italy; federica.perillo@ieo.it (F.P.); chiara.amoroso@ieo.it (C.A.); MariaRita.giuffre@ieo.it (M.R.G.); angelicajulieth.diazbasabe@ieo.it (A.D.-B.); georgia.lattanzi@ieo.it (G.L.)Department of Oncology and Hemato-Oncology, Università degli Studi di Milano, 20135 Milan, ItalyCorrespondence: francesco.strati@ieo.it (F.S.); federica.facciotti@ieo.it (F.F.)
These authors equally contributed.
2972020820202115538913720202872020© 2020 by the authors.2020Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Colorectal cancer (CRC) is a multifaceted disease influenced by both environmental and genetic factors. A large body of literature has demonstrated the role of gut microbes in promoting inflammatory responses, creating a suitable microenvironment for the development of skewed interactions between the host and the gut microbiota and cancer initiation. Even if surgery is the primary therapeutic strategy, patients with advanced disease or cancer recurrence after surgery remain difficult to cure. Therefore, the gut microbiota has been proposed as a novel therapeutic target in light of recent promising data in which it seems to modulate the response to cancer immunotherapy. The use of microbe-targeted therapies, including antibiotics, prebiotics, live biotherapeutics, and fecal microbiota transplantation, is therefore considered to support current therapies in CRC management. In this review, we will discuss the importance of host−microbe interactions in CRC and how promoting homeostatic immune responses through microbe-targeted therapies may be useful in preventing/treating CRC development.
Colorectal cancer (CRC) is the third most commonly diagnosed malignancy and the fourth leading cause of cancer death in the world [1]. CRC is a heterogeneous disease with a wide range of long-term outcomes and responses to treatment. Genetic and environmental factors contribute to the etiology and progression of the disease. Several risk factors have been identified, including positive family history, smoking, alcohol intake, lifestyle, and cultural and social practices [2]. Moreover, emerging evidence suggests that diet has an important impact on the risk of CRC development [3]. Over the past three decades, molecular genetic studies have revealed some critical mutations underlying the pathogenesis of the sporadic and inherited forms of CRC [4], including mutational inactivation of the adenomatous polyposis coli (APC) tumor suppressor [5], resulting in overactivation of the Wnt/β-catenin signaling pathway, dysregulated cell proliferation and adenoma development [6], or microsatellite instability, assessed with the detection of mono- and di-nucleotide tracts selected by the National Cancer Institute consensus conference [4,7].
Current treatments for CRC include endoscopic and surgical local excision, downstaging preoperative radiotherapy and systemic therapy, extensive surgery for locoregional and metastatic disease, local ablative therapies for metastases, palliative chemotherapy, targeted therapy, and immunotherapy [8]. Although these treatments have doubled the overall survival of patients up to 3 years for advanced stages of the disease, CRC remains associated with poor prognosis and very low rates of long-term survival [9]. The development and progression of CRC are multi-factorial processes which are associated also with the progressive failure of immunosurveillance, which is the natural and/or therapy-stimulated capacity of the immune system to control cancer progression [10]. However, the role of altered mucosal immune surveillance in the development of CRC has not yet been fully understood [11].
The role of the gut microbiota in cancer biology has been increasingly recognized as an environmental factor favoring CRC development. Indeed, the gut microenvironment harbors a complex microbial ecosystem comprising approximately 3 × 1013 bacteria and other microorganisms such as fungi, phages, archaea, and protists [12], which are confined into the intestinal lumen by the epithelial cell lining, the mucus layers, and the production of antibacterial peptides and bioactive molecules [13]. The mutual interaction between the gut microbiota and the host is further highlighted by its role in inducing immune maturation [14]. For this reason, a CRC-associated microbial dysbiosis can alter the delicate equilibrium between the gut microbiota and the host’s immune system, contributing to cancer initiation and/or progression [15]. The spatial organization of multispecies bacterial communities in higher-order structures, termed biofilms, appears to be indispensable for CRC initiation. According to the adenoma–carcinoma sequence model proposed by Fearon and Vogelstein [16], microbial biofilm may be regarded as an independent “driver” at an early stage of CRC carcinogenesis, before the malignant transformation from adenoma to carcinoma. Here, we will recapitulate how the interconnection between the intestinal microenvironment, CRC onset, and the gut microbiota composition may influence therapies’ outcomes. We will discuss the importance of gut microbiota modulation in CRC patients through dietary interventions as well as microbiome biomodulators, including anti-, pro-, pre-, and post-biotics and fecal microbiota transplantation, and how these therapeutic strategies can help in preventing/treating CRC.
2. Role of Gut Microbiota in CRC2.1. Pro-Tumorigenic Roles of the Gut Microbiota in CRC
The gut microbiota has been linked to carcinogenesis and colorectal tumor progression [15]. Pathobionts (microorganisms commonly living in the gut that become harmful under certain circumstances) colonizing the intestine may produce genotoxins, metabolites that induce DNA damage, which can lead to alterations in the tumor microenvironment (TME), inducing subsequent changes in the abundance of colonic intrinsic pathogenic members (Table 1) [17]. For instance, Escherichia coli is a bacterium commonly found in the human gut. However, some strains are pathogenic and can promote disease via several virulence factors [18]. The E. coli-derived colibactin toxin, encoded by the pathogenicity island pks, is frequently associated with human colorectal carcinogenesis, as demonstrated in human and animal models in which pks+\nE. coli strains induce double-strand DNA breaks, mutations, chromosomal rearrangements, and cell cycle arrest [19]. Accordingly, pks+\nE. coli strains induce single-base substitution/indels mutational patterns, which are predominantly detected in CRC patients [20]. In addition, the enterotoxigenic B. fragilis (ETBF), an anaerobic bacterium found in the human intestinal microbiota, produces an enterotoxin (B. fragilis toxin, BFT), which is highly associated with CRC [21], by activating β-catenin signaling as well as the secretion of interleukin (IL)-8 in colonic epithelial cells, leading to persistent cellular proliferation [22]. The alteration of the gut microbiota composition, known as “dysbiosis”, has been observed in CRC patients [23]. A large body of literature has reported that fecal and intestinal mucosa samples from CRC patients display a lower bacterial diversity compared to healthy individuals [24,25]. In addition, CRC patients show significant alterations in specific bacterial taxa, with a potentially detrimental impact on mucosal immune responses [25]. In particular, the CRC-associated microbiota is characterized by the increased abundance of Enterobacteriaceae, Streptococcus, and typical genera belonging to the oral microbiota such as Fusobacterium, Gemella, Peptostreptococcus, Prevotella, Solobacterium, and Parvimonas [26,27,28]. On the contrary, the Firmicutes phylum (especially the Ruminococcaceae and Lachnospiraceae families) as well as Bifidobacterium, Odoribacter, and Streptococcus are substantially underrepresented in CRC patients [29,30]. In fact, gut microbiota transplant from CRC patients into mice is sufficient to disrupt the intestinal barrier, leading to low-grade inflammation and dysbiosis. Indeed, conventional and germ-free (GF) mice transplanted with stool samples from CRC patients developed high-grade dysplasia and macroscopic polyps concomitantly with a higher proportion of colonic Ki-67 positive proliferating cells as well as increased expression of C-X-C motif chemokine receptor 1, C-X-C motif chemokine receptor 2, IL-17A, IL-22, and IL-23A cytokines. [31]. Similarly, ApcMin/+ mice (carrying a mutation predisposing them to intestinal adenoma and tumor formation) transplanted with feces from CRC patients showed an increase in the number of intestinal tumors, downregulated expression of mucin-2, regenerating islet-derived protein and intestinal secretory immunoglobulin A, upregulation of the NLRP3 inflammasome, and increased production of pro-inflammatory cytokines IL-1β and tumor necrosis factor (TNF)-α. The hyperproliferative and pro-inflammatory phenotype of the CRC associated-mucosa can be attributed to a dysbiotic microbiota, defined as an “imbalanced” gut microbial composition, associated with disease, that promotes the activation of the Wnt signaling pathway [32]. Indeed, alterations of the gut microbiota can also influence tumor development and progression by impairing immunosurveillance [33], leading to features of exhaustion once tumors are established [34], together with a pro-inflammatory phenotype at early stages [23,35,36,37,38]. Fusobacterium nucleatum (Fn) is commonly found in colorectal tissue with high-grade dysplasia as well as in adenomas. It has been shown that Fn modulates several immune responses towards CRC tumor progression, influencing the pre-tumoral environment [39], inducing the production of inflammatory mediators (IL-6, IL-8, IL-1β) [40], transforming growth factor (TGF)-β and TNF-α [41], and suppressing antitumor immunity by the secretion of Fap2, a natural killer inhibitory ligand [42]. Similarly to colibactin and BFT, Fn mediates DNA damage and promotes tumor cell proliferation through the Wnt/β-catenin pathway [43]. Furthermore, bacterial metabolism might affect CRC progression by the production of oxidative stress molecules [30,44,45], promoting chronic inflammation and disrupting intestinal barrier integrity [36,46]. Moreover, choline degradation by gut bacteria contributes to colon cancer via the synthesis of trimethylamine N-oxide, a potential carcinogen [28].
2.2. Anti-Oncogenic Effects of the Gut Microbiota in Colon Cancer
On the other hand, many microbes within the gut microbiota have shown anticancer activities (Table 1). Some lines of evidence have shown that Lactobacillus, Bifidobacterium, and non-enterotoxigenic Bacteroides fragilis (NTBF) can control DNA damage by promoting epithelium renewal through lactic acid production, the modulation of the immune system by reducing the Th17 pool, enhancing major histocompatibility complex-II expression on dendritic cells, and improving natural killer cell and cytotoxic T cell recruitment and cytotoxicity [10,47,48,49,50,51]. Furthermore, the anticancer activities of the gut microbiota are also promoted by short chain fatty acid (SCFA) production. Indeed, one of the most prominent signatures of a healthy microbiota is the presence of SCFA-producing bacteria, such as members of the Lachnospiraceae and Ruminococcaceae families, among others [10,52]. SCFAs, by modulating histone deacetylase inhibitory activity, promote the accumulation and differentiation of Treg cells [53,54,55] controlling tumor progression. Accordingly, two anti-tumorigenic strains of the microbiota recently identified, Faecalibaculum rodentium and its human homologue, Holdemanella biformis, were able to control protein acetylation and tumor cell proliferation through the production of SCFAs, inhibiting the calcineurin and cytoplasmic 3 (NFATc3) activation pathway in mouse and human settings [56].\nijms-21-05389-t001_Table 1
Summary of bacteria known to be involved in colorectal cancer progression and prevention.
Name
(Potential) Role in CRC Oncogenicity
Mechanism of Action
References
Proteobacteria, especially the Enterobacteriaceae family
Pro-oncogenic
Opportunistic pathogens, promotion of inflammation
[22,37]
Escherichia coli
Pro-oncogenic
DNA damage by colibactin, induction of a pro-inflammatory environment
[18,19]
Enterotoxigenic Bacteroides fragilis (ETBF)
Pro-oncogenic
Colon cell hyperproliferation by β-catenin pathway activation and IL-8 production
[20,21,50,57,58,59]
Fusobacterium nucleatum
Pro-oncogenic
Promotion of inflammation, impairment of antitumor immunity, activation of β-catenin pathway, DNA damage
[39,40,41,42]
Ruminococcaceae family
Anti-oncogenic
SCFA production
[9,51]
Lachnospiraceae family
Anti-oncogenic
SCFA production
[9,51]
Bifidobacteria
Anti-oncogenic
SCFA production, reduction of pro-inflammatory cytokines, epithelial cell renewal
[9,45,46,47]
Lactobacilli, including L. casei, L. plantarum, L. rhamnosus, and L. acidophilus
Anti-oncogenic
SCFA production, reduction of pro-inflammatory cytokines, enhancement of antitumor immunity, epithelial cell renewal
[46,47,50]
Non-enterotoxigenic Bacteroides fragilis (NTBF)
Anti-oncogenic
Boost of antitumor immunity, amelioration of inflammation by PSA production
[48,50]
Faecalibaculum rodentium and Holdemanella biformis
Anti-oncogenic
SCFA production
[55]
Akkermansia muciniphila
Anti-oncogenic
SCFA production, regulation of intestinal barrier integrity
[9,60,61]
Enterococcus faecalis
Anti-oncogenic
Improvement of intestinal inflammation
[62]
3. Lifestyle, Diet, and Microbiota in the Early Onset of CRC
Early-onset colorectal cancer (EOCRC) is the second most common cancer and the third leading cause of cancer mortality in people <50 years of age in the USA. The incidence of EOCRC has been on the rise over the past four decades and it is expected to increase by >140% by 2030. At present, different established cancer drivers have been linked to EOCRC including diet, sedentary lifestyle, smoking, and alcohol [63]. The gut microbiota is probably at the intersection of these risk factors and EOCRC. Indeed, the gut microbiome and inflammation are key players and master regulators of CRC onset and progression, as discussed above. The World Cancer Research Foundation (London, UK) and the American Institute for Cancer Research (Washington, DC, USA) consider diet to be one of the most important exogenous factors in CRC etiology [64]. The use of dietary modifications to supplement conventional cancer therapy is, therefore, a practical approach that is receiving growing attention. Dietary composition also dictates nutrient availability in the TME. Manipulation of the metabolic environment of cancer cells markedly changes their metabolic activity, producing shifts in drug sensitivity, proliferation rate, and metabolic requirements. The diet also influences the composition of the gut microbiota and, thus, the effect that gut microbes exert on the above-mentioned mechanisms.
It is now widely recognized that the adoption of a westernized diet rich in red meat and saturated fat and low in fiber exerts a negative effect on intestinal homeostasis [65], also promoting gut dysbiosis and inflammation [50,66,67,68,69,70]. The putative indication of colon cancer risk and a high-fat, low-fiber western diet was evaluated in a two-week diet exchange study among rural Africans, who usually have a low-fat, high-fiber diet, and African Americans, who usually consume the western diet. The diet exchange resulted in remarkable reciprocal changes in microbiota composition, as demonstrated by a shift in African Americans fed with the high fiber diet from Bacteroides and butyrate-producing bacteria (e.g., Roseburia intestinalis and Clostridium symbiosum) towards stronger co-occurrence patterns, including Firmicutes, which are typically associated with complex carbohydrate fermentation. On the other hand, the low-fiber/high-fat intervention was associated with an increase in F. nucleatum. Moreover, the latter were characterized by an increase in proliferative and inflammatory markers such as Ki67 and cluster of differentiation (CD)3+ intraepithelial lymphocytes and CD68+ lamina propria macrophages [71]. It has been postulated that the higher risk of developing CRC through a high-fat diet (HFD) may be due to an increase in the intestinal secretion of primary bile acids, converted into secondary bile acids by the gut microbiota, such as deoxycholic acid and lithocholic acid, which have been associated with a great increase in intestinal tumor formation and inflammatory damage in mice [72].
Changes not only in the diet but also in the use of food additives (used to extend the shelf-life of processed foods) have resulted in a considerable shift in food quality and increased risk of CRC onset. It is well known that nitrite and nitrate consumption, rich in processed meats, can lead to the formation of N-nitroso compounds by gut microbes, some of which are carcinogenic [73]. The health and regulatory issues related to the addition of food ingredients are too vast to cover in this review and have been covered elsewhere [74]; nevertheless, it is worthwhile to consider a few examples of food additives that modulate the gut microbiome and the host inflammatory status, factors associated with CRC development [73]. Monosodium glutamate is an additive used to enhance the flavor of savory foods able to induce obesity and diabetes. Interestingly, monosodium glutamate increases the susceptibility to CRC in models of inflammation-induced colorectal carcinogenesis [75]. Additionally, the food additive titanium dioxide, commonly used as a whitening and brightening agent, promotes colon inflammation and neoplastic lesions in chemically-induced carcinogenesis models [76,77]. Relatively low concentrations of two commonly used emulsifiers, carboxymethylcellulose and polysorbate-80, altered the gut microbiota composition and promoted low-grade intestinal inflammation in animal models [78,79]. These data suggest a role of food additives in the incidence of CRC development in humans. Altogether, these studies support a mechanistic link between the gut microbiota and CRC, since external factors that modulate the gut microbiome include not only stress and dietary factors but also elements previously thought to be disconnected from colon health, such as birth mode, breastfeeding behaviors, and maternal stress and nutrition [80,81,82].
4. Relevance of the Gut Microbiota in the Efficiency of Cancer Therapies
Advancements in CRC pathophysiological understanding have increased the array of treatment options for local and advanced disease, leading to individual therapeutic plans. Surgery is the cornerstone of curative treatment for patients with non-metastasized CRC [83]. In more advanced cases, neoadjuvant treatments, including preoperative chemotherapy, chemoradiotherapy, or radiotherapy, can reduce tumor load and stage and might be necessary to optimize the chances of a successful resection [84]. Current chemotherapies include both single-agent therapy, which is mainly fluoropyrimidine (5-FU)-based, and multiple agent regimens containing oxaliplatin (OX), irinotecan (IRI) and capecitabine (CAP or XELODA or XEL) [85]. Radical surgery and various chemotherapeutic agents can perceptibly create a state of dysbiosis, further exaggerating the influence of deleterious bacteria, reducing efficacy, and exacerbating the toxicity of chemotherapy [86]. In particular, compared with preoperative samples, fecal samples collected postoperatively exhibit a significant decrease in obligate anaerobes, tumor-related bacteria, and butyric acid-producing bacteria. However, a relevant increase in some conditional pathogens, such as Bilophila, Eggerthella, and Anaerostipes, was observed [87]. Moreover, chemotherapy also alters the intestinal microbiota through the so called “rebound effect”, which is characterized by a dramatic increase in pathogens and a shift in lactate-utilizing bacteria from Veillonella to Butyricimonas and Butyricicoccus, as well as a decrease in commensals [87]. Accordingly, stool samples from resected stage III CRC patients characterized by CapeOX chemotherapy-induced diarrhea presented lower bacterial community richness and diversity, with Klebsiella pneumoniae being the most predominant microbial species [88]. Specific members of the gut microbiota have been found to play a vital role in chemoresistance to 5-FU and OX therapy by mediating autophagy [89]. Indeed, the potential relationship between Fn infection and the chemotherapeutic efficacy of 5-FU was investigated both in vitro and in vivo. F. nucleatum load reduced the chemosensitivity of CRC cells to 5-FU by targeting MYD88 innate immune signaling and specific microRNAs responsible for the activation of the autophagy pathway. All these results demonstrate how Fn abundance is well correlated with a lower response in advanced CRC patients to 5-FU-based adjuvant chemotherapy after radical surgery [89,90]. In addition, other gut microbes might aggravate chemotherapy-related adverse reactions via the microbial metabolism of chemotherapy agents [91,92]. For instance, IRI effectiveness is severely limited by gastrointestinal tract toxicity caused by gut bacterial β-glucuronidase enzymes [91,93].
Besides these canonical treatments, other therapies have been explored for CRC management. Targeted therapies include four main groups of drugs: monoclonal antibodies against epidermal growth factor receptor (EGFR) (cetuximab and panitumumab), monoclonal antibodies against vascular endothelial growth factor (VEGF)-A (bevacizumab), fusion proteins that target multiple proangiogenic growth factors (e.g., aflibercept), and small-molecule-based multikinase inhibitors (e.g., regorafenib) [84]. These treatments can work on cancerous cells by directly inhibiting cell proliferation, differentiation, and migration, but they can also alter the TME to slow down tumor growth and promote a stronger immunosurveillance response [85]. Immune escape has been frequently identified in various cancers, including CRC [94]. One major explanation is tumor-related T cell inactivation and exhaustion via activation of co-inhibitory receptors, the so-called immune checkpoint receptors, on the surface of T cells [60], which include programmed cell death protein (PD)-1 and cytotoxic T lymphocyte antigen 4 (CTLA-4). Checkpoint inhibitors are now a standard of care in microsatellite-instable CRC patients [61,95,96]. The gut microbiota is an important player affecting the efficacy of the immune checkpoint blockade [97]. Initial findings by Vetizou et al. showed that the CTLA-4-targeting antibody ipilimumab could treat specific-pathogen-free mice but not GF mice. In addition, antibiotics including ampicillin, colistin, and streptomycin compromise the antitumor effects of this antibody, indicating the key role of the gut microbiota in immunotherapy outcomes [98]. Ipilimumab induces significant changes in the microbiome—in particular, a decrease in the bacterial orders Bacteroidales and Burkholderiales. Similarly, the presence of Bifidobacterium was positively correlated with anti-tumor T-cell responses in melanoma and bladder cancer mouse models treated with an anti-PD-L1 agent [99]. Gut microbes also affect the capacity of cytotoxic T cells to infiltrate the TME. Gut colonization with different Bacteroidales and non-Bacteroidales strains enhanced the efficacy of PD-1 and CTLA-4 monoclonal antibody therapies in GF mice due to the stronger immune-protective infiltration of CD8+ T cells. Unfortunately, the adverse effects of immunotherapy are dominated by autoimmune complications, such as fatal forms of colitis, as seen in metastatic melanoma of Ipilimumab-treated patients, which developed intestinal inflammation within the first 16 weeks of treatment [100].
5. Clinical Applications of the Gut Microbiota Modulation for CRC Prevention and Management
Several approaches, among which dietary interventions, antibiotic treatments, probiotics, prebiotics, and postbiotics, as well as fecal microbiota transplantation (FMT), have been explored to target and modulate gut microbiota composition, including both microbial physiology and/or their metabolites that cause or contribute to CRC directly or indirectly (Figure 1). Various experimental studies have deepened the understanding of the role of gut biomodulators and microbe-based treatments as antineoplastic agents, although a practical clinical application in CRC prevention and management is still largely lacking.
5.1. Dietary Interventions
As mentioned above, diet plays a significant role in shaping the microbiome and, therefore, in the management of CRC. While the impact of a westernized high-fat diet on CRC and on the gut microbiota has been well characterized, the protective effects of grain diets, known to be associated with low CRC risk, remain uncertain at the microbiota level. Yang et al. assessed the capacity of seven different grains to reduce CRC risk in mice fed with an HFD, showing that the consumption of non-glutinous rice, glutinous rice, and sorghum led to the highest reduction in CRC risk. In particular, non-glutinous rice stabilized key altered genera associated with CRC, including Bacteroides, Lactobacillus, Ruminococcus, and Acinetobacter [101]. Moreover, through a prospective cohort study, a diet rich in whole grains and dietary fiber was associated with a lower risk of developing F. nucleatum-positive CRC but not F. nucleatum-negative CRC, supporting a potential role of intestinal microbiota in the development of CRC [102]. Vitamin D supplementation reduces cancer incidence in mouse models of bacteria-driven colitis and CRC [57]. Azoxymethane/dextran sodium sulfate (AOM/DSS)-induced CRC mice fed with high doses of vitamin D showed not only improved body weight gain and less colon shortening but also a lower expression of inflammatory cytokines such as IL-6 and TNF-α. Vitamin D has also a significant regulatory effect on the homeostasis of the microbiota, especially on the regulation of the intestinal barrier integrity mediated by Akkermansia muciniphila, a mucin-utilizing bacterium [58,59].
Since there are no clear guidelines on the type of nutrition that could have a major impact on cancer incidence, various forms of reduced caloric intake, such as fasting, demonstrate a wide range of beneficial effects in preventing malignancies and increasing the efficacy of cancer therapies [103]. One of the main mechanisms through which fasting induces metabolic improvements is certainly mediated by the gut microbiota. For instance, every-other-day fasting led to an alteration of the gut microbiota composition, elevating fermentation products like acetate and lactate. Moreover, this dietary regimen enriched the levels of Firmicutes and also the production of SCFAs, decreasing Bacteroidetes, Actinobacteria, and Tenericutes [104]. In particular, food withdrawal decreased the abundance of potentially pathogenic Proteobacteria while elevating A. muciniphila in mice fed with HFD [105]. Due to various deficiencies in one or both key mitochondrial enzymes, tumors are not able to metabolize ketone bodies as an energetic source. Thus, the administration of a ketogenic diet (KD) may be a reasonable therapeutic strategy to inhibit tumor growth [106]. KDs are low-carbohydrate, high-fat diets, mimicking the metabolic state of fasting by inducing a physiological rise in acetoacetate and beta-hydroxybutyrate [107]. For cancer prevention, a high intake of mono-unsaturated fatty acids and n-3 polyunsaturated fatty acids could be hypothesized to be beneficial for promoting gut health [108], as demonstrated by the delayed tumor growth induced by a KD rich in omega-3 fatty acids in a CRC mouse xenograft model [106]. Lastly, supplementation with α-ketoglutarate, an important intermediary in the nuclear factor kappa light chain enhancer of activated B cells (NF-κB)-mediated inflammatory pathway, offered significant protection against CRC development in mice. Thus, α-ketoglutarate not only exhibited immunomodulatory effects mediated via the downregulation of IL-6, IL-22, TNF-α, and IL-1β cytokines but also minimized the frequency of opportunistic pathogens (Escherichia and Enterococcus), while it increased the populations of Akkermansia, Butyricicoccus, Clostridium, and Ruminococcus, suggesting that dietary α-ketoglutarate intervention may protect against inflammation-related CRC [109].
5.2. Antibiotics
Modulation of the gut microbiota through the use of antibiotics was partially evaluated in CRC, with only few studies present in the literature [110]. Cefoxitin, a semi-synthetic and broad-spectrum cephalosporin, induced a complete and durable clearance of enterotoxigenic B. fragilis colonization in previously ETBF-inoculated mice, with a concomitant decrease in median adenoma formation [111]. Consistent with the pro-tumorigenic Th17 immune response of ETBF [112,113], its eradication was accompanied by an abrupt reduction in colonic IL-17A levels, suggesting that other microbes are implicated in the IL-17 response [111]. Erythromycin has the ability to suppress the transcriptional activity of NF-κB and the activator protein-1 (AP-1), as well as its downstream targets, IL-6 and cyclooxygenase-2 (COX-2), in human CRC cells. Moreover, a reduction in Il-6 and cox-2 mRNA expression was also observed in ApcMin/+ mice, in which the number of intestinal polyps was reduced as well [114]. Berberine (BBR), an isoquinoline molecule with antibacterial activity [115], has been used to treat F. nucleatum colonization in ApcMin/+ mouse models. BBR not only was able to reverse the microbiota imbalance induced by Fn but also blocked the secretion of mucosal immune factors, such as IL-21, IL-22, IL-31, and CD40L. In addition, this compound inhibited the Fn-induced activation of the Janus kinase/signal transducer and activator of transcription (JAK/STAT) and mitogen-activated protein kinase/extracellular signal-regulated kinases (MAPK/ERK) pathway [116]. Moreover, the microbial structure alteration, characterized by the increase in Tenericutes and Verrucomicrobia, was dramatically reversed in Fn-infected mice after BBR intervention, suggesting an antimicrobial intervention as a potential treatment for patients with Fn-associated CRC [117]. Metronidazole has also been explored as an alternative to treat F. nucleatum colonization. This antibiotic reduced the Fn load, cancer cell proliferation, and overall tumor growth in mice bearing colon cancer xenografts [117]. However, antibiotic administration, being the most aggressive means of manipulating gut microbiota composition, has been controversial in its role in cancer management. Although gut microbiome depletion was shown to inhibit cancer progression, accumulating lines of evidence highlight how antibiotics can compromise immunotherapy efficacy or induce disease progression by creating further microbial dysbiosis [118,119].
5.3. Probiotics
For CRC prevention and management, another potential strategy is represented by probiotics. Probiotics are living microorganisms which can confer positive effects on health by impacting on the resident microbiota, intestinal epithelium cells, and, globally, the immune system [120]. Nowadays, several bacterial species are used as probiotics, which are commercially available (Table 2).
Among them, lactic acid bacteria are the most frequently used, not only for their ability to contribute to colonization resistance [121] but also for their immunomodulatory effects [122]. Specific bacterial strains are able to prevent tumor development through the modulation of the immune system in CRC murine models. Oral treatment with Lactobacillus casei BL23, a probiotic strain well known for its anti-inflammatory [123] and anticancer properties [124], significantly protected mice against CRC development. In addition, this probiotic showed not only immunomodulatory effects by downregulating colonic IL-22 but also an antiproliferative effect mediated by the upregulation of caspase (casp)-7, casp-9, and Bik, as well as a decrease in Ki67 expression [125]. Probiotics have also been recently exploited to counteract chemotherapy-dependent dysbiosis, mucositis (inflammatory lesions of the oral and/or gastrointestinal tract caused by high-dose cancer therapies), post-surgical microbiota intestinal alterations, and relapses in CRC patients. Preclinical studies revealed that L. rhamnosus (Lcr35) reduces the severity of diarrhea and intestinal mucositis caused by adjuvant 5-FU-based chemotherapy in mice injected with CT26 colorectal adenocarcinoma cells. Moreover, Lcr35 treatment normalized the increased number of BCL2-associated X protein apoptotic and NF-κB-activated cells as well as the upregulated expression of TNF-α and IL-6 [126]. A recent randomized, double-blind, placebo-controlled trial (NCT03782428) revealed that treatment with six viable microorganisms of Lactobacillus and Bifidobacterium strains significantly reduced the levels of pro-inflammatory cytokines such as TNF-α, IL-6, IL-10, IL-12, IL-17A, IL-17C, and IL-22 and prevented post-surgical complications as well [127]. Probiotics are generally considered safe and well-tolerated for healthy subjects, but in patients with underlying medical conditions, their safety profile is uncertain [62]. Probiotic translocation, which refers to the entry of viable bacteria into extraintestinal sites, leads to the ensuing systemic or localized infections. Indeed, various case reports of probiotic-associated bacteremia, endocarditis, liver abscess, and pneumonia have been published [128]. Nevertheless, another theoretical risk regarding long-term probiotic use is the possible transmission of antibiotic-resistant genes via horizontal gene transfer [62].
5.4. Prebiotics
Prebiotics are non-digestible dietary compounds that stimulate the growth and activity of probiotics, conferring a competitive advantage to commensal bacteria capable of metabolizing these substrates or by increasing the production of beneficial metabolic products, such SCFAs, that result from their fermentation [129,130]. The health benefits of prebiotics goes beyond nutrition and they are gaining popularity among people (Table 3). However, great care must be taken to ensure their therapeutic efficacy, especially regarding intestinal tumors. The chemopreventive effect of galacto-oligosaccharides derived from lactulose revealed a remarkable reduction in colonic tumor numbers in a CRC animal model. Moreover, a significant decrease in pro-inflammatory bacteria was observed, as well as a substantial increase in beneficial populations such as Bifidobacterium [131]. Similarly, ginsenoside-Rb3 and ginsenoside-Rd effectively reduced the size and the number of the polyps and reinstated the dysbiotic gut microbial composition and intestinal microenvironment in ApcMin/+ mice by promoting the growth of SCFA-producing bacteria [132]. Given the encouraging results obtained from studies conducted both in vitro and in vivo, prebiotic administration has also been evaluated in more structured, randomized clinical trials involving CRC patients. A randomized, double-blind, no-treatment parallel control clinical trial involving 140 perioperative CRC patients was performed to investigate the effects of prebiotics containing fructooligosaccharides, xylooligosaccharides, polydextrose, and resistant dextrin on the immune system and the intestinal microbiota. In the preoperative interval, prebiotics upregulated serum levels of IgG, IgM, and transferrin, while in the postoperative period, they enhanced levels of IgG, IgA, CD8+ T cells, and total B lymphocytes. Prebiotic administration increased the abundance of Bifidobacterium and Enterococcus but decreased the abundance of Bacteroides in the preoperative timeframe. On the other hand, in the postoperative period, the abundance of Bacteroides was decreased, while Escherichia-Shigella was increased, suggesting that prebiotic intake is recommended to improve serum immunologic indicators in patients with CRC 7 days before operation, since surgical trauma can alter the gut microbiome [133]. However, recent studies have demonstrated that prebiotic interventions may exert variable effects in different individuals, probably due to differences in the host genetic background, which may plausibly explain the different tumor phenotype, oncogenic pathways, and, subsequently, the response to a specific intervention [134].
5.5. Postbiotics
Postbiotics are chemical compounds of microbial origin including short chain fatty acids, enzymes, peptides, teichoic acids, peptidoglycan-derived muropeptides, endo- and exo-polysaccharides, cell surface proteins, vitamins, plasmalogens, and organic acids [140]. The chemopreventive effects of acetate, butyrate, and propionate mixture were evaluated in AOM/DSS-treated mice, determining a significant reduction in tumor incidence and size. Moreover, SCFAs suppressed pro-inflammatory cytokine expression, including that of IL-6, TNF-α, and IL-17, as well as COX-2 and NF-κB [141]. The prophylactic effects of postbiotics were also shown by the oral intake of mitochonic acid 35, an indole compound, which ameliorated the disease activity index score and survival rate, reducing the macroscopic formation of colonic tumors in murine models of CRC. In addition, it was able to downregulate colonic TNF-α, IL-6, TGF-β1, and fibronectin 1 expression, suggesting its ability to inhibit CRC carcinogenesis [142]. Among the non-viable microbial cells, researchers have exploited the possible therapeutic effects of Enterococcus faecalis. In this regard, it was observed that pre-treatment of THP-1-derived macrophages with heat-killed E. faecalis inhibited NLRP3 inflammasome activation in response to fecal content or commensal microbes, Proteus mirabilis or E. coli, according to the reduction in casp-1 activation and IL-1β maturation. Moreover, experiments in vivo showed that E. faecalis improved the severity of intestinal inflammation, protecting mice from the formation of colon tumors [143]. Interestingly, postbiotics have also been evaluated as adjuvants of anti-cancer therapies. The combined efficacy of L. acidophilus cell lysates with an anti-CTLA-4 monoclonal antibody was tested in vivo. In contrast to anti-CTLA-4 monotherapy, L. acidophilus lysates attenuated body weight loss and the combined administration significantly protected mice against CRC development, suggesting an enhancement of anti-CTLA-4 antitumor activity. Moreover, the synergistic combination led to an increase in CD8+T cells, especially the effector memory T cells, a decrease in Tregs, and it alternatively activated macrophages (M2) in the TME. Additionally, pre-treatment with L. acidophilus lysate in vitro showed an immunomodulatory effect through the inhibition of M2 polarization and of IL-10 production by lipopolysaccharide-activated macrophages. Lastly, the combined administration significantly inhibited the abnormal increase in the relative abundance of Proteobacteria and partly counterbalanced CRC-induced dysbiosis in mice. Thus, CTLA-4 blocking antibodies in combination with the present lysates may be of importance for the development of new therapeutic strategies against CRC to be tested in clinical trials [144]. The postbiotic field is as yet a highly unknown area, considering that the number and diversity of bacterial metabolites are vast. Thus, their safety profile is still under preclinical and clinical evaluation [62].
5.6. FMT
Fecal microbiota transplantation (FMT)—i.e., the transfer of a microbial ecology from a healthy donor into a patient—is currently being explored as a therapeutic strategy to restore normobiosis, the normal state of the human intestinal microbiota, in different pathological contexts [145]. Since CRC is characterized by a status of dysbiosis, FMT is considered as a potential clinical application in patients. To date, FMT has only proved to be highly successful in treating recurrent and antibiotic refractory Clostridiodes difficile (C. difficile) infection, with cure rates of approximately 90% [129]. In a CRC mouse model, FMT was able to normalize the gut microbiota through the reduction of tumor growth. FMT contributed to reducing the levels of inflammation by decreasing IL-1β, IL-6, and TNF-α levels and increasing anti-inflammatory cytokines such as IL-10 and TGF-β through the inhibition of canonical NF-κB activity and cellular proliferation. Moreover, FMT treatment triggered the accumulation of Tregs but not Th1, Th2, and Th17 cells [146]. Additionally, the chemopreventive potential of FMT on FOLFOX-induced mucosal injury was evaluated. Microbiota transplantation reduced the severity of diarrhea and intestinal mucositis, suppressed IL-6 levels, and restored the number of goblet cells, zonula occludens-1, apoptotic, and NF-κB-positive cells, as well as the expression of toll-like receptors and MYD88. All these beneficial effects were accompanied by a restoration of the gut microbiota composition without causing bacteremia [147]. However, it is worth noting that the impact of FMT on the recipient immune system is complicated and unpredictable, and the risk of dissemination of unknown pathogens cannot be prevented. In addition, numerous questions remain to be answered, including the features that a “good donor” should present, the routes of administration, and the long-term effects of this therapy [148].
6. Conclusions and Perspective
The core of CRC carcinogenesis is also defined by gut microbiota metabolic activity and a dysbiotic composition. Hence, a consortium of inflammatory responses, virulence factors, and impaired epithelial signaling create a suitable microenvironment for the development of disrupted and irregular interactions between the host and the gut microbiota [149]. Even if surgery is the primary therapeutic option, patients with advanced disease or cancer recurrence after surgery remain difficult to cure. Since the gut microbiota is gaining more attention, a deeper knowledge of its interaction with the host’s immune system will elucidate the outcomes of cancer therapeutic strategies. Lastly, research is currently assessing the impact of personalized diets and biomodulators to restore a eubiotic condition for the prevention and treatment of CRC [150,151].
Author Contributions
Writing—original draft preparation C.A., F.P.; writing—review and editing F.S., M.R.G., A.D.-B., G.L., F.F.; supervision F.S., F.F.; funding acquisition F.F. All authors have read and agreed to the published version of the manuscript.
Funding
This work was made possible through a grant from AIRC, Associazione Italiana per la Ricerca sul Cancro, to FF (IG-2019 22923).
Conflicts of Interest
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
Abbreviations
Adenomatous polyposis coli
APC
Azoxymethane/dextran sodium sulfate
AOM/ASS
B. fragilis toxin
BFT
Berberine
BBR
Calcineurin and cytoplasmic 3
Nfatc3
Capecitabine
CAP or XELODA or XEL
Capecitabine oxaliplatin
CAPeOX
Caspase
casp
Cluster of differentiation
CD
Colorectal cancer
CRC
Cyclooxygenase-2
COX-2
Cytotoxic T lymphocyte antigen 4
CTLA-4
Early-onset colorectal cancer
EOCRC
Enterotoxigenic B. fragilis
ETBF
Epidermal growth factor receptor
EGFR
Fecal microbiota transplantation
FMT
Fluorouracile
5-FU
Fusobacterium nucleatum
Fn
Germ-free
GF
High fat diet
HFD
Interleukin
IL
Irinotecan
IRI
Janus kinase/signal transducer and activator of transcription
JAK/STAT
Ketogenic diet
KD
Mitogen-activated protein kinase/extracellular signal-regulated kinases
MAPK/ERK
Non-enterotoxigenic Bacteroides fragilis
NTBF
Nuclear factor kappa light chain enhancer of activated B cells
NF-κB
Oxaliplatin
OX
Programmed cell death protein
PD-1
Short chain fatty acids
SCFAs
Transforming growth factor
TGF
Tumor microenvironment
TME
Tumor necrosis factor
TNF
Vascular endothelial growth factor
VEGF
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The gut microbiota can influence colorectal carcinogenesis via a variety of mechanisms, including microbial-derived factors such as metabolites or genotoxins. Skewed host–microbe interactions contribute to the activation of pro-carcinogenic inflammatory pathways that ultimately lead to the progression of CRC. Antibiotics usage is effective in eradicating pathobionts, but its non-selective antimicrobial actions can affect gut homeostasis by also killing health-promoting bacteria and, therefore, reducing its application in CRC management. Prebiotic function fosters probiotic growth. Probiotics act through different anticancerogenic mechanisms: (i) probiotics can inhibit the colonization of pathogenic bacteria, (ii) they can enhance barrier function increasing mucin production and tight junction protein expression, (iii) they promote homeostatic immune responses, contributing to the expansion of anti-inflammatory responses by Treg cells and the modulation of pro-inflammatory cytokine release, (iv) they promote apoptosis on cancer cells. Postbiotics induce selective cytotoxicity against tumor cells as well as the control of tumor cell proliferation by inhibiting NFATc3 activation. Finally, fecal microbiota transplantation (FMT) could be used in CRC management to restore microbiome normobiosis and therefore induce homeostatic immune responses; nevertheless, potential complications associated with FMT include the risk of introducing new pathobionts and the spreading of disease-associated genes.
ijms-21-05389-t002_Table 2
Examples of some commercially available probiotics.
Prebiotic-rich foods and their effects on human health.
Prebiotic
Origin
Clinical Benefit
References
Fructo-Oligosaccharides (FOS)
Vegetables, cereals (onion, garlic, artichokes)
Crohn’s diseaseColitisCRCObesity
[135]
Gluco-Oligosaccharides (GOS)
Legumes (lentils, chickpeas and broad beans)
Crohn’s diseaseColitisObesity
[135,136]
Ginsenoside-Rb3
Panax Ginseng
Myeloid leukemiaCRCHeart failure
[132,137]
Inulin
Asparagus and artichokes
Crohn’s diseaseColitisCRCObesityDiabetes
[138]
Lactulose
Boiled milk
Constipation
[139]
\n"],"string":"[\n \"\\nInt J Mol SciInt J Mol SciijmsInternational Journal of Molecular Sciences1422-0067MDPI32751239PMC743210810.3390/ijms21155389ijms-21-05389ReviewGut Microbiota Manipulation as a Tool for Colorectal Cancer Management: Recent Advances in Its Use for Therapeutic PurposesPerilloFederica1†AmorosoChiara1†https://orcid.org/0000-0001-7217-3355StratiFrancesco1*GiuffrèMaria Rita1https://orcid.org/0000-0003-2096-1223Díaz-BasabeAngélica12LattanziGeorgia12https://orcid.org/0000-0002-2541-9428FacciottiFederica1*Department of Experimental Oncology, IEO European Institute of Oncology IRCCS, 20139 Milan, Italy; federica.perillo@ieo.it (F.P.); chiara.amoroso@ieo.it (C.A.); MariaRita.giuffre@ieo.it (M.R.G.); angelicajulieth.diazbasabe@ieo.it (A.D.-B.); georgia.lattanzi@ieo.it (G.L.)Department of Oncology and Hemato-Oncology, Università degli Studi di Milano, 20135 Milan, ItalyCorrespondence: francesco.strati@ieo.it (F.S.); federica.facciotti@ieo.it (F.F.)
These authors equally contributed.
2972020820202115538913720202872020© 2020 by the authors.2020Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Colorectal cancer (CRC) is a multifaceted disease influenced by both environmental and genetic factors. A large body of literature has demonstrated the role of gut microbes in promoting inflammatory responses, creating a suitable microenvironment for the development of skewed interactions between the host and the gut microbiota and cancer initiation. Even if surgery is the primary therapeutic strategy, patients with advanced disease or cancer recurrence after surgery remain difficult to cure. Therefore, the gut microbiota has been proposed as a novel therapeutic target in light of recent promising data in which it seems to modulate the response to cancer immunotherapy. The use of microbe-targeted therapies, including antibiotics, prebiotics, live biotherapeutics, and fecal microbiota transplantation, is therefore considered to support current therapies in CRC management. In this review, we will discuss the importance of host−microbe interactions in CRC and how promoting homeostatic immune responses through microbe-targeted therapies may be useful in preventing/treating CRC development.
Colorectal cancer (CRC) is the third most commonly diagnosed malignancy and the fourth leading cause of cancer death in the world [1]. CRC is a heterogeneous disease with a wide range of long-term outcomes and responses to treatment. Genetic and environmental factors contribute to the etiology and progression of the disease. Several risk factors have been identified, including positive family history, smoking, alcohol intake, lifestyle, and cultural and social practices [2]. Moreover, emerging evidence suggests that diet has an important impact on the risk of CRC development [3]. Over the past three decades, molecular genetic studies have revealed some critical mutations underlying the pathogenesis of the sporadic and inherited forms of CRC [4], including mutational inactivation of the adenomatous polyposis coli (APC) tumor suppressor [5], resulting in overactivation of the Wnt/β-catenin signaling pathway, dysregulated cell proliferation and adenoma development [6], or microsatellite instability, assessed with the detection of mono- and di-nucleotide tracts selected by the National Cancer Institute consensus conference [4,7].
Current treatments for CRC include endoscopic and surgical local excision, downstaging preoperative radiotherapy and systemic therapy, extensive surgery for locoregional and metastatic disease, local ablative therapies for metastases, palliative chemotherapy, targeted therapy, and immunotherapy [8]. Although these treatments have doubled the overall survival of patients up to 3 years for advanced stages of the disease, CRC remains associated with poor prognosis and very low rates of long-term survival [9]. The development and progression of CRC are multi-factorial processes which are associated also with the progressive failure of immunosurveillance, which is the natural and/or therapy-stimulated capacity of the immune system to control cancer progression [10]. However, the role of altered mucosal immune surveillance in the development of CRC has not yet been fully understood [11].
The role of the gut microbiota in cancer biology has been increasingly recognized as an environmental factor favoring CRC development. Indeed, the gut microenvironment harbors a complex microbial ecosystem comprising approximately 3 × 1013 bacteria and other microorganisms such as fungi, phages, archaea, and protists [12], which are confined into the intestinal lumen by the epithelial cell lining, the mucus layers, and the production of antibacterial peptides and bioactive molecules [13]. The mutual interaction between the gut microbiota and the host is further highlighted by its role in inducing immune maturation [14]. For this reason, a CRC-associated microbial dysbiosis can alter the delicate equilibrium between the gut microbiota and the host’s immune system, contributing to cancer initiation and/or progression [15]. The spatial organization of multispecies bacterial communities in higher-order structures, termed biofilms, appears to be indispensable for CRC initiation. According to the adenoma–carcinoma sequence model proposed by Fearon and Vogelstein [16], microbial biofilm may be regarded as an independent “driver” at an early stage of CRC carcinogenesis, before the malignant transformation from adenoma to carcinoma. Here, we will recapitulate how the interconnection between the intestinal microenvironment, CRC onset, and the gut microbiota composition may influence therapies’ outcomes. We will discuss the importance of gut microbiota modulation in CRC patients through dietary interventions as well as microbiome biomodulators, including anti-, pro-, pre-, and post-biotics and fecal microbiota transplantation, and how these therapeutic strategies can help in preventing/treating CRC.
2. Role of Gut Microbiota in CRC2.1. Pro-Tumorigenic Roles of the Gut Microbiota in CRC
The gut microbiota has been linked to carcinogenesis and colorectal tumor progression [15]. Pathobionts (microorganisms commonly living in the gut that become harmful under certain circumstances) colonizing the intestine may produce genotoxins, metabolites that induce DNA damage, which can lead to alterations in the tumor microenvironment (TME), inducing subsequent changes in the abundance of colonic intrinsic pathogenic members (Table 1) [17]. For instance, Escherichia coli is a bacterium commonly found in the human gut. However, some strains are pathogenic and can promote disease via several virulence factors [18]. The E. coli-derived colibactin toxin, encoded by the pathogenicity island pks, is frequently associated with human colorectal carcinogenesis, as demonstrated in human and animal models in which pks+\\nE. coli strains induce double-strand DNA breaks, mutations, chromosomal rearrangements, and cell cycle arrest [19]. Accordingly, pks+\\nE. coli strains induce single-base substitution/indels mutational patterns, which are predominantly detected in CRC patients [20]. In addition, the enterotoxigenic B. fragilis (ETBF), an anaerobic bacterium found in the human intestinal microbiota, produces an enterotoxin (B. fragilis toxin, BFT), which is highly associated with CRC [21], by activating β-catenin signaling as well as the secretion of interleukin (IL)-8 in colonic epithelial cells, leading to persistent cellular proliferation [22]. The alteration of the gut microbiota composition, known as “dysbiosis”, has been observed in CRC patients [23]. A large body of literature has reported that fecal and intestinal mucosa samples from CRC patients display a lower bacterial diversity compared to healthy individuals [24,25]. In addition, CRC patients show significant alterations in specific bacterial taxa, with a potentially detrimental impact on mucosal immune responses [25]. In particular, the CRC-associated microbiota is characterized by the increased abundance of Enterobacteriaceae, Streptococcus, and typical genera belonging to the oral microbiota such as Fusobacterium, Gemella, Peptostreptococcus, Prevotella, Solobacterium, and Parvimonas [26,27,28]. On the contrary, the Firmicutes phylum (especially the Ruminococcaceae and Lachnospiraceae families) as well as Bifidobacterium, Odoribacter, and Streptococcus are substantially underrepresented in CRC patients [29,30]. In fact, gut microbiota transplant from CRC patients into mice is sufficient to disrupt the intestinal barrier, leading to low-grade inflammation and dysbiosis. Indeed, conventional and germ-free (GF) mice transplanted with stool samples from CRC patients developed high-grade dysplasia and macroscopic polyps concomitantly with a higher proportion of colonic Ki-67 positive proliferating cells as well as increased expression of C-X-C motif chemokine receptor 1, C-X-C motif chemokine receptor 2, IL-17A, IL-22, and IL-23A cytokines. [31]. Similarly, ApcMin/+ mice (carrying a mutation predisposing them to intestinal adenoma and tumor formation) transplanted with feces from CRC patients showed an increase in the number of intestinal tumors, downregulated expression of mucin-2, regenerating islet-derived protein and intestinal secretory immunoglobulin A, upregulation of the NLRP3 inflammasome, and increased production of pro-inflammatory cytokines IL-1β and tumor necrosis factor (TNF)-α. The hyperproliferative and pro-inflammatory phenotype of the CRC associated-mucosa can be attributed to a dysbiotic microbiota, defined as an “imbalanced” gut microbial composition, associated with disease, that promotes the activation of the Wnt signaling pathway [32]. Indeed, alterations of the gut microbiota can also influence tumor development and progression by impairing immunosurveillance [33], leading to features of exhaustion once tumors are established [34], together with a pro-inflammatory phenotype at early stages [23,35,36,37,38]. Fusobacterium nucleatum (Fn) is commonly found in colorectal tissue with high-grade dysplasia as well as in adenomas. It has been shown that Fn modulates several immune responses towards CRC tumor progression, influencing the pre-tumoral environment [39], inducing the production of inflammatory mediators (IL-6, IL-8, IL-1β) [40], transforming growth factor (TGF)-β and TNF-α [41], and suppressing antitumor immunity by the secretion of Fap2, a natural killer inhibitory ligand [42]. Similarly to colibactin and BFT, Fn mediates DNA damage and promotes tumor cell proliferation through the Wnt/β-catenin pathway [43]. Furthermore, bacterial metabolism might affect CRC progression by the production of oxidative stress molecules [30,44,45], promoting chronic inflammation and disrupting intestinal barrier integrity [36,46]. Moreover, choline degradation by gut bacteria contributes to colon cancer via the synthesis of trimethylamine N-oxide, a potential carcinogen [28].
2.2. Anti-Oncogenic Effects of the Gut Microbiota in Colon Cancer
On the other hand, many microbes within the gut microbiota have shown anticancer activities (Table 1). Some lines of evidence have shown that Lactobacillus, Bifidobacterium, and non-enterotoxigenic Bacteroides fragilis (NTBF) can control DNA damage by promoting epithelium renewal through lactic acid production, the modulation of the immune system by reducing the Th17 pool, enhancing major histocompatibility complex-II expression on dendritic cells, and improving natural killer cell and cytotoxic T cell recruitment and cytotoxicity [10,47,48,49,50,51]. Furthermore, the anticancer activities of the gut microbiota are also promoted by short chain fatty acid (SCFA) production. Indeed, one of the most prominent signatures of a healthy microbiota is the presence of SCFA-producing bacteria, such as members of the Lachnospiraceae and Ruminococcaceae families, among others [10,52]. SCFAs, by modulating histone deacetylase inhibitory activity, promote the accumulation and differentiation of Treg cells [53,54,55] controlling tumor progression. Accordingly, two anti-tumorigenic strains of the microbiota recently identified, Faecalibaculum rodentium and its human homologue, Holdemanella biformis, were able to control protein acetylation and tumor cell proliferation through the production of SCFAs, inhibiting the calcineurin and cytoplasmic 3 (NFATc3) activation pathway in mouse and human settings [56].\\nijms-21-05389-t001_Table 1
Summary of bacteria known to be involved in colorectal cancer progression and prevention.
Name
(Potential) Role in CRC Oncogenicity
Mechanism of Action
References
Proteobacteria, especially the Enterobacteriaceae family
Pro-oncogenic
Opportunistic pathogens, promotion of inflammation
[22,37]
Escherichia coli
Pro-oncogenic
DNA damage by colibactin, induction of a pro-inflammatory environment
[18,19]
Enterotoxigenic Bacteroides fragilis (ETBF)
Pro-oncogenic
Colon cell hyperproliferation by β-catenin pathway activation and IL-8 production
[20,21,50,57,58,59]
Fusobacterium nucleatum
Pro-oncogenic
Promotion of inflammation, impairment of antitumor immunity, activation of β-catenin pathway, DNA damage
[39,40,41,42]
Ruminococcaceae family
Anti-oncogenic
SCFA production
[9,51]
Lachnospiraceae family
Anti-oncogenic
SCFA production
[9,51]
Bifidobacteria
Anti-oncogenic
SCFA production, reduction of pro-inflammatory cytokines, epithelial cell renewal
[9,45,46,47]
Lactobacilli, including L. casei, L. plantarum, L. rhamnosus, and L. acidophilus
Anti-oncogenic
SCFA production, reduction of pro-inflammatory cytokines, enhancement of antitumor immunity, epithelial cell renewal
[46,47,50]
Non-enterotoxigenic Bacteroides fragilis (NTBF)
Anti-oncogenic
Boost of antitumor immunity, amelioration of inflammation by PSA production
[48,50]
Faecalibaculum rodentium and Holdemanella biformis
Anti-oncogenic
SCFA production
[55]
Akkermansia muciniphila
Anti-oncogenic
SCFA production, regulation of intestinal barrier integrity
[9,60,61]
Enterococcus faecalis
Anti-oncogenic
Improvement of intestinal inflammation
[62]
3. Lifestyle, Diet, and Microbiota in the Early Onset of CRC
Early-onset colorectal cancer (EOCRC) is the second most common cancer and the third leading cause of cancer mortality in people <50 years of age in the USA. The incidence of EOCRC has been on the rise over the past four decades and it is expected to increase by >140% by 2030. At present, different established cancer drivers have been linked to EOCRC including diet, sedentary lifestyle, smoking, and alcohol [63]. The gut microbiota is probably at the intersection of these risk factors and EOCRC. Indeed, the gut microbiome and inflammation are key players and master regulators of CRC onset and progression, as discussed above. The World Cancer Research Foundation (London, UK) and the American Institute for Cancer Research (Washington, DC, USA) consider diet to be one of the most important exogenous factors in CRC etiology [64]. The use of dietary modifications to supplement conventional cancer therapy is, therefore, a practical approach that is receiving growing attention. Dietary composition also dictates nutrient availability in the TME. Manipulation of the metabolic environment of cancer cells markedly changes their metabolic activity, producing shifts in drug sensitivity, proliferation rate, and metabolic requirements. The diet also influences the composition of the gut microbiota and, thus, the effect that gut microbes exert on the above-mentioned mechanisms.
It is now widely recognized that the adoption of a westernized diet rich in red meat and saturated fat and low in fiber exerts a negative effect on intestinal homeostasis [65], also promoting gut dysbiosis and inflammation [50,66,67,68,69,70]. The putative indication of colon cancer risk and a high-fat, low-fiber western diet was evaluated in a two-week diet exchange study among rural Africans, who usually have a low-fat, high-fiber diet, and African Americans, who usually consume the western diet. The diet exchange resulted in remarkable reciprocal changes in microbiota composition, as demonstrated by a shift in African Americans fed with the high fiber diet from Bacteroides and butyrate-producing bacteria (e.g., Roseburia intestinalis and Clostridium symbiosum) towards stronger co-occurrence patterns, including Firmicutes, which are typically associated with complex carbohydrate fermentation. On the other hand, the low-fiber/high-fat intervention was associated with an increase in F. nucleatum. Moreover, the latter were characterized by an increase in proliferative and inflammatory markers such as Ki67 and cluster of differentiation (CD)3+ intraepithelial lymphocytes and CD68+ lamina propria macrophages [71]. It has been postulated that the higher risk of developing CRC through a high-fat diet (HFD) may be due to an increase in the intestinal secretion of primary bile acids, converted into secondary bile acids by the gut microbiota, such as deoxycholic acid and lithocholic acid, which have been associated with a great increase in intestinal tumor formation and inflammatory damage in mice [72].
Changes not only in the diet but also in the use of food additives (used to extend the shelf-life of processed foods) have resulted in a considerable shift in food quality and increased risk of CRC onset. It is well known that nitrite and nitrate consumption, rich in processed meats, can lead to the formation of N-nitroso compounds by gut microbes, some of which are carcinogenic [73]. The health and regulatory issues related to the addition of food ingredients are too vast to cover in this review and have been covered elsewhere [74]; nevertheless, it is worthwhile to consider a few examples of food additives that modulate the gut microbiome and the host inflammatory status, factors associated with CRC development [73]. Monosodium glutamate is an additive used to enhance the flavor of savory foods able to induce obesity and diabetes. Interestingly, monosodium glutamate increases the susceptibility to CRC in models of inflammation-induced colorectal carcinogenesis [75]. Additionally, the food additive titanium dioxide, commonly used as a whitening and brightening agent, promotes colon inflammation and neoplastic lesions in chemically-induced carcinogenesis models [76,77]. Relatively low concentrations of two commonly used emulsifiers, carboxymethylcellulose and polysorbate-80, altered the gut microbiota composition and promoted low-grade intestinal inflammation in animal models [78,79]. These data suggest a role of food additives in the incidence of CRC development in humans. Altogether, these studies support a mechanistic link between the gut microbiota and CRC, since external factors that modulate the gut microbiome include not only stress and dietary factors but also elements previously thought to be disconnected from colon health, such as birth mode, breastfeeding behaviors, and maternal stress and nutrition [80,81,82].
4. Relevance of the Gut Microbiota in the Efficiency of Cancer Therapies
Advancements in CRC pathophysiological understanding have increased the array of treatment options for local and advanced disease, leading to individual therapeutic plans. Surgery is the cornerstone of curative treatment for patients with non-metastasized CRC [83]. In more advanced cases, neoadjuvant treatments, including preoperative chemotherapy, chemoradiotherapy, or radiotherapy, can reduce tumor load and stage and might be necessary to optimize the chances of a successful resection [84]. Current chemotherapies include both single-agent therapy, which is mainly fluoropyrimidine (5-FU)-based, and multiple agent regimens containing oxaliplatin (OX), irinotecan (IRI) and capecitabine (CAP or XELODA or XEL) [85]. Radical surgery and various chemotherapeutic agents can perceptibly create a state of dysbiosis, further exaggerating the influence of deleterious bacteria, reducing efficacy, and exacerbating the toxicity of chemotherapy [86]. In particular, compared with preoperative samples, fecal samples collected postoperatively exhibit a significant decrease in obligate anaerobes, tumor-related bacteria, and butyric acid-producing bacteria. However, a relevant increase in some conditional pathogens, such as Bilophila, Eggerthella, and Anaerostipes, was observed [87]. Moreover, chemotherapy also alters the intestinal microbiota through the so called “rebound effect”, which is characterized by a dramatic increase in pathogens and a shift in lactate-utilizing bacteria from Veillonella to Butyricimonas and Butyricicoccus, as well as a decrease in commensals [87]. Accordingly, stool samples from resected stage III CRC patients characterized by CapeOX chemotherapy-induced diarrhea presented lower bacterial community richness and diversity, with Klebsiella pneumoniae being the most predominant microbial species [88]. Specific members of the gut microbiota have been found to play a vital role in chemoresistance to 5-FU and OX therapy by mediating autophagy [89]. Indeed, the potential relationship between Fn infection and the chemotherapeutic efficacy of 5-FU was investigated both in vitro and in vivo. F. nucleatum load reduced the chemosensitivity of CRC cells to 5-FU by targeting MYD88 innate immune signaling and specific microRNAs responsible for the activation of the autophagy pathway. All these results demonstrate how Fn abundance is well correlated with a lower response in advanced CRC patients to 5-FU-based adjuvant chemotherapy after radical surgery [89,90]. In addition, other gut microbes might aggravate chemotherapy-related adverse reactions via the microbial metabolism of chemotherapy agents [91,92]. For instance, IRI effectiveness is severely limited by gastrointestinal tract toxicity caused by gut bacterial β-glucuronidase enzymes [91,93].
Besides these canonical treatments, other therapies have been explored for CRC management. Targeted therapies include four main groups of drugs: monoclonal antibodies against epidermal growth factor receptor (EGFR) (cetuximab and panitumumab), monoclonal antibodies against vascular endothelial growth factor (VEGF)-A (bevacizumab), fusion proteins that target multiple proangiogenic growth factors (e.g., aflibercept), and small-molecule-based multikinase inhibitors (e.g., regorafenib) [84]. These treatments can work on cancerous cells by directly inhibiting cell proliferation, differentiation, and migration, but they can also alter the TME to slow down tumor growth and promote a stronger immunosurveillance response [85]. Immune escape has been frequently identified in various cancers, including CRC [94]. One major explanation is tumor-related T cell inactivation and exhaustion via activation of co-inhibitory receptors, the so-called immune checkpoint receptors, on the surface of T cells [60], which include programmed cell death protein (PD)-1 and cytotoxic T lymphocyte antigen 4 (CTLA-4). Checkpoint inhibitors are now a standard of care in microsatellite-instable CRC patients [61,95,96]. The gut microbiota is an important player affecting the efficacy of the immune checkpoint blockade [97]. Initial findings by Vetizou et al. showed that the CTLA-4-targeting antibody ipilimumab could treat specific-pathogen-free mice but not GF mice. In addition, antibiotics including ampicillin, colistin, and streptomycin compromise the antitumor effects of this antibody, indicating the key role of the gut microbiota in immunotherapy outcomes [98]. Ipilimumab induces significant changes in the microbiome—in particular, a decrease in the bacterial orders Bacteroidales and Burkholderiales. Similarly, the presence of Bifidobacterium was positively correlated with anti-tumor T-cell responses in melanoma and bladder cancer mouse models treated with an anti-PD-L1 agent [99]. Gut microbes also affect the capacity of cytotoxic T cells to infiltrate the TME. Gut colonization with different Bacteroidales and non-Bacteroidales strains enhanced the efficacy of PD-1 and CTLA-4 monoclonal antibody therapies in GF mice due to the stronger immune-protective infiltration of CD8+ T cells. Unfortunately, the adverse effects of immunotherapy are dominated by autoimmune complications, such as fatal forms of colitis, as seen in metastatic melanoma of Ipilimumab-treated patients, which developed intestinal inflammation within the first 16 weeks of treatment [100].
5. Clinical Applications of the Gut Microbiota Modulation for CRC Prevention and Management
Several approaches, among which dietary interventions, antibiotic treatments, probiotics, prebiotics, and postbiotics, as well as fecal microbiota transplantation (FMT), have been explored to target and modulate gut microbiota composition, including both microbial physiology and/or their metabolites that cause or contribute to CRC directly or indirectly (Figure 1). Various experimental studies have deepened the understanding of the role of gut biomodulators and microbe-based treatments as antineoplastic agents, although a practical clinical application in CRC prevention and management is still largely lacking.
5.1. Dietary Interventions
As mentioned above, diet plays a significant role in shaping the microbiome and, therefore, in the management of CRC. While the impact of a westernized high-fat diet on CRC and on the gut microbiota has been well characterized, the protective effects of grain diets, known to be associated with low CRC risk, remain uncertain at the microbiota level. Yang et al. assessed the capacity of seven different grains to reduce CRC risk in mice fed with an HFD, showing that the consumption of non-glutinous rice, glutinous rice, and sorghum led to the highest reduction in CRC risk. In particular, non-glutinous rice stabilized key altered genera associated with CRC, including Bacteroides, Lactobacillus, Ruminococcus, and Acinetobacter [101]. Moreover, through a prospective cohort study, a diet rich in whole grains and dietary fiber was associated with a lower risk of developing F. nucleatum-positive CRC but not F. nucleatum-negative CRC, supporting a potential role of intestinal microbiota in the development of CRC [102]. Vitamin D supplementation reduces cancer incidence in mouse models of bacteria-driven colitis and CRC [57]. Azoxymethane/dextran sodium sulfate (AOM/DSS)-induced CRC mice fed with high doses of vitamin D showed not only improved body weight gain and less colon shortening but also a lower expression of inflammatory cytokines such as IL-6 and TNF-α. Vitamin D has also a significant regulatory effect on the homeostasis of the microbiota, especially on the regulation of the intestinal barrier integrity mediated by Akkermansia muciniphila, a mucin-utilizing bacterium [58,59].
Since there are no clear guidelines on the type of nutrition that could have a major impact on cancer incidence, various forms of reduced caloric intake, such as fasting, demonstrate a wide range of beneficial effects in preventing malignancies and increasing the efficacy of cancer therapies [103]. One of the main mechanisms through which fasting induces metabolic improvements is certainly mediated by the gut microbiota. For instance, every-other-day fasting led to an alteration of the gut microbiota composition, elevating fermentation products like acetate and lactate. Moreover, this dietary regimen enriched the levels of Firmicutes and also the production of SCFAs, decreasing Bacteroidetes, Actinobacteria, and Tenericutes [104]. In particular, food withdrawal decreased the abundance of potentially pathogenic Proteobacteria while elevating A. muciniphila in mice fed with HFD [105]. Due to various deficiencies in one or both key mitochondrial enzymes, tumors are not able to metabolize ketone bodies as an energetic source. Thus, the administration of a ketogenic diet (KD) may be a reasonable therapeutic strategy to inhibit tumor growth [106]. KDs are low-carbohydrate, high-fat diets, mimicking the metabolic state of fasting by inducing a physiological rise in acetoacetate and beta-hydroxybutyrate [107]. For cancer prevention, a high intake of mono-unsaturated fatty acids and n-3 polyunsaturated fatty acids could be hypothesized to be beneficial for promoting gut health [108], as demonstrated by the delayed tumor growth induced by a KD rich in omega-3 fatty acids in a CRC mouse xenograft model [106]. Lastly, supplementation with α-ketoglutarate, an important intermediary in the nuclear factor kappa light chain enhancer of activated B cells (NF-κB)-mediated inflammatory pathway, offered significant protection against CRC development in mice. Thus, α-ketoglutarate not only exhibited immunomodulatory effects mediated via the downregulation of IL-6, IL-22, TNF-α, and IL-1β cytokines but also minimized the frequency of opportunistic pathogens (Escherichia and Enterococcus), while it increased the populations of Akkermansia, Butyricicoccus, Clostridium, and Ruminococcus, suggesting that dietary α-ketoglutarate intervention may protect against inflammation-related CRC [109].
5.2. Antibiotics
Modulation of the gut microbiota through the use of antibiotics was partially evaluated in CRC, with only few studies present in the literature [110]. Cefoxitin, a semi-synthetic and broad-spectrum cephalosporin, induced a complete and durable clearance of enterotoxigenic B. fragilis colonization in previously ETBF-inoculated mice, with a concomitant decrease in median adenoma formation [111]. Consistent with the pro-tumorigenic Th17 immune response of ETBF [112,113], its eradication was accompanied by an abrupt reduction in colonic IL-17A levels, suggesting that other microbes are implicated in the IL-17 response [111]. Erythromycin has the ability to suppress the transcriptional activity of NF-κB and the activator protein-1 (AP-1), as well as its downstream targets, IL-6 and cyclooxygenase-2 (COX-2), in human CRC cells. Moreover, a reduction in Il-6 and cox-2 mRNA expression was also observed in ApcMin/+ mice, in which the number of intestinal polyps was reduced as well [114]. Berberine (BBR), an isoquinoline molecule with antibacterial activity [115], has been used to treat F. nucleatum colonization in ApcMin/+ mouse models. BBR not only was able to reverse the microbiota imbalance induced by Fn but also blocked the secretion of mucosal immune factors, such as IL-21, IL-22, IL-31, and CD40L. In addition, this compound inhibited the Fn-induced activation of the Janus kinase/signal transducer and activator of transcription (JAK/STAT) and mitogen-activated protein kinase/extracellular signal-regulated kinases (MAPK/ERK) pathway [116]. Moreover, the microbial structure alteration, characterized by the increase in Tenericutes and Verrucomicrobia, was dramatically reversed in Fn-infected mice after BBR intervention, suggesting an antimicrobial intervention as a potential treatment for patients with Fn-associated CRC [117]. Metronidazole has also been explored as an alternative to treat F. nucleatum colonization. This antibiotic reduced the Fn load, cancer cell proliferation, and overall tumor growth in mice bearing colon cancer xenografts [117]. However, antibiotic administration, being the most aggressive means of manipulating gut microbiota composition, has been controversial in its role in cancer management. Although gut microbiome depletion was shown to inhibit cancer progression, accumulating lines of evidence highlight how antibiotics can compromise immunotherapy efficacy or induce disease progression by creating further microbial dysbiosis [118,119].
5.3. Probiotics
For CRC prevention and management, another potential strategy is represented by probiotics. Probiotics are living microorganisms which can confer positive effects on health by impacting on the resident microbiota, intestinal epithelium cells, and, globally, the immune system [120]. Nowadays, several bacterial species are used as probiotics, which are commercially available (Table 2).
Among them, lactic acid bacteria are the most frequently used, not only for their ability to contribute to colonization resistance [121] but also for their immunomodulatory effects [122]. Specific bacterial strains are able to prevent tumor development through the modulation of the immune system in CRC murine models. Oral treatment with Lactobacillus casei BL23, a probiotic strain well known for its anti-inflammatory [123] and anticancer properties [124], significantly protected mice against CRC development. In addition, this probiotic showed not only immunomodulatory effects by downregulating colonic IL-22 but also an antiproliferative effect mediated by the upregulation of caspase (casp)-7, casp-9, and Bik, as well as a decrease in Ki67 expression [125]. Probiotics have also been recently exploited to counteract chemotherapy-dependent dysbiosis, mucositis (inflammatory lesions of the oral and/or gastrointestinal tract caused by high-dose cancer therapies), post-surgical microbiota intestinal alterations, and relapses in CRC patients. Preclinical studies revealed that L. rhamnosus (Lcr35) reduces the severity of diarrhea and intestinal mucositis caused by adjuvant 5-FU-based chemotherapy in mice injected with CT26 colorectal adenocarcinoma cells. Moreover, Lcr35 treatment normalized the increased number of BCL2-associated X protein apoptotic and NF-κB-activated cells as well as the upregulated expression of TNF-α and IL-6 [126]. A recent randomized, double-blind, placebo-controlled trial (NCT03782428) revealed that treatment with six viable microorganisms of Lactobacillus and Bifidobacterium strains significantly reduced the levels of pro-inflammatory cytokines such as TNF-α, IL-6, IL-10, IL-12, IL-17A, IL-17C, and IL-22 and prevented post-surgical complications as well [127]. Probiotics are generally considered safe and well-tolerated for healthy subjects, but in patients with underlying medical conditions, their safety profile is uncertain [62]. Probiotic translocation, which refers to the entry of viable bacteria into extraintestinal sites, leads to the ensuing systemic or localized infections. Indeed, various case reports of probiotic-associated bacteremia, endocarditis, liver abscess, and pneumonia have been published [128]. Nevertheless, another theoretical risk regarding long-term probiotic use is the possible transmission of antibiotic-resistant genes via horizontal gene transfer [62].
5.4. Prebiotics
Prebiotics are non-digestible dietary compounds that stimulate the growth and activity of probiotics, conferring a competitive advantage to commensal bacteria capable of metabolizing these substrates or by increasing the production of beneficial metabolic products, such SCFAs, that result from their fermentation [129,130]. The health benefits of prebiotics goes beyond nutrition and they are gaining popularity among people (Table 3). However, great care must be taken to ensure their therapeutic efficacy, especially regarding intestinal tumors. The chemopreventive effect of galacto-oligosaccharides derived from lactulose revealed a remarkable reduction in colonic tumor numbers in a CRC animal model. Moreover, a significant decrease in pro-inflammatory bacteria was observed, as well as a substantial increase in beneficial populations such as Bifidobacterium [131]. Similarly, ginsenoside-Rb3 and ginsenoside-Rd effectively reduced the size and the number of the polyps and reinstated the dysbiotic gut microbial composition and intestinal microenvironment in ApcMin/+ mice by promoting the growth of SCFA-producing bacteria [132]. Given the encouraging results obtained from studies conducted both in vitro and in vivo, prebiotic administration has also been evaluated in more structured, randomized clinical trials involving CRC patients. A randomized, double-blind, no-treatment parallel control clinical trial involving 140 perioperative CRC patients was performed to investigate the effects of prebiotics containing fructooligosaccharides, xylooligosaccharides, polydextrose, and resistant dextrin on the immune system and the intestinal microbiota. In the preoperative interval, prebiotics upregulated serum levels of IgG, IgM, and transferrin, while in the postoperative period, they enhanced levels of IgG, IgA, CD8+ T cells, and total B lymphocytes. Prebiotic administration increased the abundance of Bifidobacterium and Enterococcus but decreased the abundance of Bacteroides in the preoperative timeframe. On the other hand, in the postoperative period, the abundance of Bacteroides was decreased, while Escherichia-Shigella was increased, suggesting that prebiotic intake is recommended to improve serum immunologic indicators in patients with CRC 7 days before operation, since surgical trauma can alter the gut microbiome [133]. However, recent studies have demonstrated that prebiotic interventions may exert variable effects in different individuals, probably due to differences in the host genetic background, which may plausibly explain the different tumor phenotype, oncogenic pathways, and, subsequently, the response to a specific intervention [134].
5.5. Postbiotics
Postbiotics are chemical compounds of microbial origin including short chain fatty acids, enzymes, peptides, teichoic acids, peptidoglycan-derived muropeptides, endo- and exo-polysaccharides, cell surface proteins, vitamins, plasmalogens, and organic acids [140]. The chemopreventive effects of acetate, butyrate, and propionate mixture were evaluated in AOM/DSS-treated mice, determining a significant reduction in tumor incidence and size. Moreover, SCFAs suppressed pro-inflammatory cytokine expression, including that of IL-6, TNF-α, and IL-17, as well as COX-2 and NF-κB [141]. The prophylactic effects of postbiotics were also shown by the oral intake of mitochonic acid 35, an indole compound, which ameliorated the disease activity index score and survival rate, reducing the macroscopic formation of colonic tumors in murine models of CRC. In addition, it was able to downregulate colonic TNF-α, IL-6, TGF-β1, and fibronectin 1 expression, suggesting its ability to inhibit CRC carcinogenesis [142]. Among the non-viable microbial cells, researchers have exploited the possible therapeutic effects of Enterococcus faecalis. In this regard, it was observed that pre-treatment of THP-1-derived macrophages with heat-killed E. faecalis inhibited NLRP3 inflammasome activation in response to fecal content or commensal microbes, Proteus mirabilis or E. coli, according to the reduction in casp-1 activation and IL-1β maturation. Moreover, experiments in vivo showed that E. faecalis improved the severity of intestinal inflammation, protecting mice from the formation of colon tumors [143]. Interestingly, postbiotics have also been evaluated as adjuvants of anti-cancer therapies. The combined efficacy of L. acidophilus cell lysates with an anti-CTLA-4 monoclonal antibody was tested in vivo. In contrast to anti-CTLA-4 monotherapy, L. acidophilus lysates attenuated body weight loss and the combined administration significantly protected mice against CRC development, suggesting an enhancement of anti-CTLA-4 antitumor activity. Moreover, the synergistic combination led to an increase in CD8+T cells, especially the effector memory T cells, a decrease in Tregs, and it alternatively activated macrophages (M2) in the TME. Additionally, pre-treatment with L. acidophilus lysate in vitro showed an immunomodulatory effect through the inhibition of M2 polarization and of IL-10 production by lipopolysaccharide-activated macrophages. Lastly, the combined administration significantly inhibited the abnormal increase in the relative abundance of Proteobacteria and partly counterbalanced CRC-induced dysbiosis in mice. Thus, CTLA-4 blocking antibodies in combination with the present lysates may be of importance for the development of new therapeutic strategies against CRC to be tested in clinical trials [144]. The postbiotic field is as yet a highly unknown area, considering that the number and diversity of bacterial metabolites are vast. Thus, their safety profile is still under preclinical and clinical evaluation [62].
5.6. FMT
Fecal microbiota transplantation (FMT)—i.e., the transfer of a microbial ecology from a healthy donor into a patient—is currently being explored as a therapeutic strategy to restore normobiosis, the normal state of the human intestinal microbiota, in different pathological contexts [145]. Since CRC is characterized by a status of dysbiosis, FMT is considered as a potential clinical application in patients. To date, FMT has only proved to be highly successful in treating recurrent and antibiotic refractory Clostridiodes difficile (C. difficile) infection, with cure rates of approximately 90% [129]. In a CRC mouse model, FMT was able to normalize the gut microbiota through the reduction of tumor growth. FMT contributed to reducing the levels of inflammation by decreasing IL-1β, IL-6, and TNF-α levels and increasing anti-inflammatory cytokines such as IL-10 and TGF-β through the inhibition of canonical NF-κB activity and cellular proliferation. Moreover, FMT treatment triggered the accumulation of Tregs but not Th1, Th2, and Th17 cells [146]. Additionally, the chemopreventive potential of FMT on FOLFOX-induced mucosal injury was evaluated. Microbiota transplantation reduced the severity of diarrhea and intestinal mucositis, suppressed IL-6 levels, and restored the number of goblet cells, zonula occludens-1, apoptotic, and NF-κB-positive cells, as well as the expression of toll-like receptors and MYD88. All these beneficial effects were accompanied by a restoration of the gut microbiota composition without causing bacteremia [147]. However, it is worth noting that the impact of FMT on the recipient immune system is complicated and unpredictable, and the risk of dissemination of unknown pathogens cannot be prevented. In addition, numerous questions remain to be answered, including the features that a “good donor” should present, the routes of administration, and the long-term effects of this therapy [148].
6. Conclusions and Perspective
The core of CRC carcinogenesis is also defined by gut microbiota metabolic activity and a dysbiotic composition. Hence, a consortium of inflammatory responses, virulence factors, and impaired epithelial signaling create a suitable microenvironment for the development of disrupted and irregular interactions between the host and the gut microbiota [149]. Even if surgery is the primary therapeutic option, patients with advanced disease or cancer recurrence after surgery remain difficult to cure. Since the gut microbiota is gaining more attention, a deeper knowledge of its interaction with the host’s immune system will elucidate the outcomes of cancer therapeutic strategies. Lastly, research is currently assessing the impact of personalized diets and biomodulators to restore a eubiotic condition for the prevention and treatment of CRC [150,151].
Author Contributions
Writing—original draft preparation C.A., F.P.; writing—review and editing F.S., M.R.G., A.D.-B., G.L., F.F.; supervision F.S., F.F.; funding acquisition F.F. All authors have read and agreed to the published version of the manuscript.
Funding
This work was made possible through a grant from AIRC, Associazione Italiana per la Ricerca sul Cancro, to FF (IG-2019 22923).
Conflicts of Interest
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
Abbreviations
Adenomatous polyposis coli
APC
Azoxymethane/dextran sodium sulfate
AOM/ASS
B. fragilis toxin
BFT
Berberine
BBR
Calcineurin and cytoplasmic 3
Nfatc3
Capecitabine
CAP or XELODA or XEL
Capecitabine oxaliplatin
CAPeOX
Caspase
casp
Cluster of differentiation
CD
Colorectal cancer
CRC
Cyclooxygenase-2
COX-2
Cytotoxic T lymphocyte antigen 4
CTLA-4
Early-onset colorectal cancer
EOCRC
Enterotoxigenic B. fragilis
ETBF
Epidermal growth factor receptor
EGFR
Fecal microbiota transplantation
FMT
Fluorouracile
5-FU
Fusobacterium nucleatum
Fn
Germ-free
GF
High fat diet
HFD
Interleukin
IL
Irinotecan
IRI
Janus kinase/signal transducer and activator of transcription
JAK/STAT
Ketogenic diet
KD
Mitogen-activated protein kinase/extracellular signal-regulated kinases
MAPK/ERK
Non-enterotoxigenic Bacteroides fragilis
NTBF
Nuclear factor kappa light chain enhancer of activated B cells
NF-κB
Oxaliplatin
OX
Programmed cell death protein
PD-1
Short chain fatty acids
SCFAs
Transforming growth factor
TGF
Tumor microenvironment
TME
Tumor necrosis factor
TNF
Vascular endothelial growth factor
VEGF
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The gut microbiota can influence colorectal carcinogenesis via a variety of mechanisms, including microbial-derived factors such as metabolites or genotoxins. Skewed host–microbe interactions contribute to the activation of pro-carcinogenic inflammatory pathways that ultimately lead to the progression of CRC. Antibiotics usage is effective in eradicating pathobionts, but its non-selective antimicrobial actions can affect gut homeostasis by also killing health-promoting bacteria and, therefore, reducing its application in CRC management. Prebiotic function fosters probiotic growth. Probiotics act through different anticancerogenic mechanisms: (i) probiotics can inhibit the colonization of pathogenic bacteria, (ii) they can enhance barrier function increasing mucin production and tight junction protein expression, (iii) they promote homeostatic immune responses, contributing to the expansion of anti-inflammatory responses by Treg cells and the modulation of pro-inflammatory cytokine release, (iv) they promote apoptosis on cancer cells. Postbiotics induce selective cytotoxicity against tumor cells as well as the control of tumor cell proliferation by inhibiting NFATc3 activation. Finally, fecal microbiota transplantation (FMT) could be used in CRC management to restore microbiome normobiosis and therefore induce homeostatic immune responses; nevertheless, potential complications associated with FMT include the risk of introducing new pathobionts and the spreading of disease-associated genes.
ijms-21-05389-t002_Table 2
Examples of some commercially available probiotics.
Prebiotic-rich foods and their effects on human health.
Prebiotic
Origin
Clinical Benefit
References
Fructo-Oligosaccharides (FOS)
Vegetables, cereals (onion, garlic, artichokes)
Crohn’s diseaseColitisCRCObesity
[135]
Gluco-Oligosaccharides (GOS)
Legumes (lentils, chickpeas and broad beans)
Crohn’s diseaseColitisObesity
[135,136]
Ginsenoside-Rb3
Panax Ginseng
Myeloid leukemiaCRCHeart failure
[132,137]
Inulin
Asparagus and artichokes
Crohn’s diseaseColitisCRCObesityDiabetes
[138]
Lactulose
Boiled milk
Constipation
[139]
\\n\"\n]"}}},{"rowIdx":452,"cells":{"data":{"kind":"list like","value":["\nInt J Environ Res Public HealthInt J Environ Res Public HealthijerphInternational Journal of Environmental Research and Public Health1661-78271660-4601MDPI32751437PMC743210910.3390/ijerph17155494ijerph-17-05494ArticleQualitative Approach to Environmental Risk Assessment in Transporthttps://orcid.org/0000-0002-8320-1419DvorakZdenek1*https://orcid.org/0000-0002-4617-0553RehakDavid2DavidAndrej3CekerevacZoran4Faculty of Security Engineering, University of Zilina, 01026 Zilina, SlovakiaFaculty of Safety Engineering, VSB—Technical University of Ostrava, 70030 Ostrava, Czech Republic; david.rehak@vsb.czFaculty of Operation and Economics of Transport and Communications, University of Zilina, 01026 Zilina, Slovakia; andrej.david@fpedas.uniza.skFaculty of Business and Law, Union—Nikola Tesla University, 11000 Belgrade, Serbia; zoran@cekerevac.euCorrespondence: zdenek.dvorak@fbi.uniza.sk; Tel.: +421-415-136-8543072020820201715549429620202772020© 2020 by the authors.2020Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
The purpose of this paper is to present the development of a qualitative approach to environmental risk assessment (QAERA) in transport. The approach is described as a model developed for the future software tool which will be utilizable as a risk decision support system. The basic part is aimed on developing a quantitative environmental risk assessment. Thus, this paper describes a set of 6 pillars of safety and security. Accordingly, the paper contains both chosen safety and security indicators and selected criteria for assessing the risk of launching the environmental change of global model thinking in the transport sector. The environmental risk assessment as a global model of thinking was originally based on historical experience but, nowadays, it is changing. Based on new expert knowledge, more precisely, on input of new global data, paper displays an environmental risk assessment with actual interpretation. The discussion of the paper is oriented to support research results, a new knowledge-oriented approach to global climate changes, using suitable risk assessment methods and technics. The result of the paper is a new approach for the modeling of environmental risk assessment in the transport sector.
risk assessmenttransport sectorenvironmentcommon safety methoddecision support systemexpert evaluation1. Introduction
Transport, as one of the most important sectors of economics, has been developing gradually. Development of mankind is linked to the development of transport systems. The history of transport is mainly associated with the invention of the wheel and the development of inland waterway transport. In addition, maritime transport has played a key role in discovering new continents. With the development of the industrialization of society, railway transport began to develop significantly hand in hand with the discovery of a steam engine. The discovery of an internal combustion and diesel engine led to the development of road transport. The latest results of science and research have been fundamental elements at gradual development of each mode of transport. For many decades, the impact of transport on the environment has not been fundamentally addressed. To be specific, the first research did not take place until the middle of the twentieth century. Globally, the number of vehicles is increasing from year to year, and although the technical parameters of internal combustion engines are improving, global emissions are growing.
Emphasizing the environmental aspect of individual modes of transport should be an important aspect of the transport policy of an advanced society. The European Commission regularly issues recommendations and directives that address some areas of transport development. Individual states should define the priorities of transport development in their transport policy, mainly for ensuring transport services, but also for expected environmental impacts. Essentially, the authors of the article come from central and eastern Europe. In conjunction with this, the examples of the transport policy of the Czech Republic, Slovakia, and Serbia show that these countries have a remarkably similar transport policy, where the aspect of environmental protection plays a less important role.
The traditional scientific research plan has historically been focused on technical and technological processes. The challenge today is security with its multi-level and multi-union complexity. If safety research is understood in the above concept as a multi-level and multi-sectoral problem, then the challenge of addressing the issue of the environmental risk assessment will be met. The methodology will become a key element for setting appropriate indicators that will take into account not only the economic benefits for the company but will also address the societal impact in the social, health, and environmental fields. Drawing on this, simplifying efficiency and risk assessments have now led to absurd shipments of raw materials and semi-finished products in globalized companies, regardless of the full cost.
Current methods of environmental risk assessment are mainly of a quantitative or hybrid nature. In engineering, these methods are most commonly used in industry, where they account for more than 50% of existing risk assessment methods [1]. In the field of transport, these methods are used mainly in the context of the assessment of mobile risk sources [2,3]. However, these methods require a large amount of specific data, which is very time consuming to collect [4]. For this reason, it is convenient to use at first some of the appropriate qualitative methods for the preliminary risk analysis [5,6]. These methods are time-saving but, on the contrary, also quite general.
Referring to the above, it can be stated that even today there is no suitable available method dealing with the qualitative assessment of environmental risks arising from normal traffic. Based on this, the paper aims to present a newly proposed qualitative approach to environmental risk assessment in the transport sector. The essence of this approach is a preliminary environmental risk assessment, which is based on the application of selected safety and security indicators and selected risk assessment criteria in the implementation of environmental change in the global model of thinking in the transport sector.
2. Literature Review
The preparation of a paper focusing on environmental risk assessment in transport requires a broader framework of solutions. Therefore, we primarily examined information sources that play a decisive role in the field of environmental protection. Subsequently, attention was paid to the areas of sustainable development, transport, safety and security.
2.1. Environmental Area
The first paper to be discussed is the paper Biodiversity Loss and its Impact on Humanity, written by a broad team of authors led by Cardinale et al. [7]. The paper has focused on research since 1980, evaluating the last 40 years of life on Earth. Gradually, significant climate changes are taking place, which are accompanied by extreme weather events and often lead to the extinction of some animal and plant species. Entire ecosystems disappear irretrievably. As mentioned in the paper, in 2005, the Millennium Ecosystem Assessment was published for the first time. The condition and trends in the world’s ecosystems, and the services they provide, highlighted two distinct foci of biodiversity and ecosystem functioning (BEF) and biodiversity and ecosystem services (BES) research. Selecting from the published Consensus statements, there is currently clear evidence that biodiversity loss reduces the efficiency of ecological communities that capture biologically important resources, produce biomass, decompose, and recycle biologically important nutrients. Furthermore, there is evidence that greater biodiversity increases the stability of ecosystem functions over time. Moreover, four emerging trends were defined in the research. In our research on environmental risk assessment, we found that diversity effects grow stronger with time, and may increase at larger spatial scales. In BES research—for example, if we want to know how biodiversity affects the ability of ecosystems to remove carbon dioxide from the atmosphere and store carbon in the long term, we need to consider the net impact of biodiversity on photosynthesis (exchange of carbon dioxide for oxygen).
Changes in the environment have taken place continuously in the history of the planet Earth. The research is focused on different parts of the environment. An example to be used in our publication is the article 2500-Year Climate and Environmental Record Inferred from Subfossil Chironomids from Lugu Lake, by the author team led by Chang et al. [8] from 2018. The research focused on multi-proxy indicators for catchment erosion at Lugu Lake puts forward a methodological approach that the issue of environmental change must be considered in a long time series.
Another relevant source is Connor et al. [9] Long-Term Population Dynamics: Theory and Reality in a Peatland Ecosystem, which is focused on the research of environmental change in a selected region. Jimenez and Flores [10] focused on reducing carbon dioxide emissions in large agglomerations, where transport plays a big role. Kondratyev et al. [11] was involved in research of global carbon cycle and climate change. Similarly, Koblen et al. [12] focused on reducing emissions from aviation transport. This research was followed by Koscak et al. [13] which dealt with negative environmental impact of winter airport maintenance.
Within the European Universities Association, there was an established waterborne technology platform, which integrates research activities within maritime transport. The platform has defined seven key trends for maritime transport, which touch on the topic addressed and will be addressed in the paper [14].
2.2. Sustainable Development Area
As a part of global solutions, world conferences on environmental protection were organized in the past. Gradually, this agenda became one of the 17 goals of the transforming world called the 2030 Agenda for Sustainable Development. This agenda was approved by the United Nations and it contains 197 targets in addition to 17 basic goals. Some of them, directly and indirectly, affect the issues of the environment and sustainable transport [15]. Jakobsen [16] subsequently brought a new perspective on ecological economics.
As for these goals and targets, four goals are relevant to our research: Goal 4—Ensure lifelong learning opportunities; Goal 9—Built resilient infrastructure, promote inclusive and sustainable industrialization and foster innovation; Goal 11—Make city and human settlements inclusive safe, resilient, and sustainable; and Goal 13—Take urgent action to combat climate change and its impacts. In the next part, attention will be paid to them [15].
2.3. Transport Area
A lot of research teams around the world are involved in assessing environmental change and its impact on transport and transport infrastructure. Extreme weather events, seismic and geological changes occur in all regions of the world. However, the first limitation of our research is the focus on the central European environment, as examples will be events from the Czech Republic, Slovakia, and Serbia. All three countries have remarkably similar relief. On the whole, there are mountain ranges with transport infrastructure and lowlands with large rivers. The nature of the threats to transport and transport infrastructure is similar. For this reason, the writers focus their research on four basic key components—Modes, Infrastructures, Networks, and Flows (see Figure 1).
As a significant contribution to the field of environmental risk assessment in transport, we present the RAIN project (i.e. Risk Analysis of Infrastructure Networks) solved in the years 2014–2016. The project pointed up the impact of weather extremes on transport and energy. The project resulted in building up a new framework for assessing the risks posed by weather extremes—floods, lightning, landslides, extreme droughts, frosts, and storms [18].
The theoretical framework of risk assessment and information support in railway and road transport was also addressed in the publications Assessment of Critical Infrastructure Elements in Transport [19], Crisis Management Decision Support System in Railway Infrastructure Company [20], and Multi-agent System for Decreasing of Risk in Road Transport [21].
Dzunda et al. [22] dealt with the synthesis of selected aspects of navigation systems to increase air safety. Havel et al. [23] focused their attention on a new route intersection-minimum distance model. Sousek and Dvorak [24] researched the transport of dangerous shipments. Fuchs et al. [25], within the framework of the BIOTRA project and a series of publications, concentrated on the transport of dangerous goods and the assessment of its possible impacts on the environment in the vicinity of roads (line objects). Hoterova and Dvorak [26] discussed comparing CO2 emissions in rail and road transport.
The Water Transport Technology Platform welcomes the Council Conclusions entitled “EU Water Transport Sector—Looking to the future: Towards a carbon-neutral, zero-accident, automated and competitive EU water transport sector”, endorsed by the Council on 5 June 2020. These conclusions are politically very relevant as they clearly demonstrate the views of the Member States on European policy on the future of waterborne transport. The conclusions underline this strategy—the growing importance of the European waterway sector and the need for enhanced research, development, and innovation to achieve the objectives set out in the Council conclusions. Key trends influencing the maritime sector are, e.g., climate change, continuous population growth and urbanization, food and water demand, increasing expectations for health, safety and security and, last but not least, developing countries. On the whole, these constituent factors will increase their share in global economic growth, increase in energy consumption, and fast development of information and communication technologies [27].
2.4. Safety and Security Area
Transport, as a specific area of economic activity, and the provision of transport services for people requires the continuous improvement of all activities and subsystems. During the evaluating of traffic, people focus on attributes such as time, price, quality, safety, and environmental impact. As a part of the evaluation of safety and security aspects, researchers write of the point, line, and area objects. These attributes and aspects were defined within the projects RAIN [18], OKISD (i.e. Critical Infrastructure Protection in Sector Transportation in the Slovak language) [28], and RESILIENCE 2015 [29].
The risk assessment for the environmental area was addressed in the following publications: Dobes et al. [30], Approach of the Czech Republic to the Prevention of Environmentally Oriented Terrorism. Kelemen and Jevcak [31] addressed the specifics of security management in aviation. Kirschenstein et al. [32] discussed research into storms and the resulting threats to air traffic. Polishchuk et al. [33], in their research, dealt with a fuzzy model of risk assessment for environmental for start-up projects in the air transporter sector. Rehak et al. [34] spoke of Safety and Security Issues in Technical Infrastructures.
The decade since 2010 has been aimed at significantly reducing CO2 production by car and aircraft engines. Because of that, many studies were carried out to compare the production of emissions in different modes of transport. The effects of extreme weather events pose a visible response of the Earth to increasing global greenhouse gas emissions. Gradual climate change is mainly reflected in the ever-increasing global temperature, which is leading to the gradual melting of glaciers around the North and South Pole. The significant reduction in the world’s glaciers is also the result of the global deforestation and forest systems. Currently, 40% of the land on Earth is covered by deserts, and their size is growing from year to year. First, according to historical meteorological measurements over the last hundred years, seven of the ten warmest years occurred between 2000 and 2019. Second, according to research by the European Severe Storms Laboratory (next ESSL), extreme weather events increase every year in Europe. Many of these extremes have a devastating impact on transport (relocation of goods and people in vehicles) as well as on transport infrastructure (roads, railways, waterways, and transport facilities). Third, according to the conclusions and recommendations from the RAIN project, it is necessary to take into account more significant extremes of weather in the reconstruction of transport facilities than was the case according to technical standards around 1970 (50 years ago). In our research, the worst-case scenarios for transport infrastructure are landslides, snow avalanches, flooding of transport infrastructure, extremely high and extremely low temperatures, storms, intense storms, and forest fires [35].
3. Materials and Methods
In our research, standard scientific methods were used, from analysis and comparison to synthesis, from top-down solutions to bottom-up solutions. The basis of all methodological procedures is the definition of the object/system where the risks will be assessed. Probabilities and possible implications for the identified typological scenarios are required as input for all scenarios. Thus, risk assessment can rely on the pillars of system security management of the organization (see Figure 2).
A comprehensive assessment of the safety, security, and resilience of transport infrastructure requires new approaches. Our primary objective should be specifically to reduce the volume of transoceanic traffic in the future. All goods and commodities transported over more than 10,000 km should be subject to a special tax. Moreover, the new scientific department of ecological economics recommends re-evaluating long-distance transport. In the current conditions, there are cases where the raw material obtained on one continent is transported to another continent. The product made of the raw material travels thousands of kilometers to another continent, and if not sold there, it is, in turn, transported to another continent. These cases are possible thanks to a non-comprehensive global solution, wherein some countries do not give attention to the environmental impacts of transport [10]. On the contrary, the security solution requires a comprehensive approach, the security pillars shown in the Figure 2 according to the research results must be evaluated using a comprehensive set of measurable indicators.
Figure 2 shows environmental security as one of the pillars of an organization’s security. Environmental risk assessment is part of a general approach to risk assessment. The current basic framework for risk assessment is ISO 31,000 Risk Management—Guidelines [37]. Various techniques and methodologies for risk assessment are used for different areas of society. As part of the methodology aimed at increasing the security of the organization, it is necessary to link these pillars with the PDCA method (i.e. Plan-Do-Check-Act), which sets out to continuously improve business processes.
In addressing environmental risk assessment, it is necessary to build on previously conducted research published in these papers and sources focused on air transport [32,35,38,39,40,41] and other modes of transport [33]. A multi-disciplinary and multi-level approach is the basic method for addressing risks. In the risk assessment, it is important to have relevant statistical files for the last period. Another important part of objective risk assessment is the study of best practices in international research. Some innovative approaches are not based on real research tasks but arise as a real need of society. An example of this is the comparison of CO2 emission from transport published in Norway as a real answer for the discussion of experts. The real measurements that were taken, gave the results shown in Figure 3. The conversion involved the transport of one ton of cargo over one km and, after the calculation, the real emissions in grams were converted. The lowest value was given by a freight train with a diesel drive. In second place, there is a container ship carrying more than two thousand containers. On the opposite side of the line, was the carriage of one ton of cargo by plane.
For the most part, the aim of future research will be the interconnection of individual subsystems, presented by individual pillars of safety/security and their mutual integration. Future security audits must be based on dozens of indicators for each safety/security pillar. The interconnection and thorough evaluation of the measured values will give a clear answer as to which indicators in which pillar appear the weakest and which we must pay primary attention to. The environmental dimension of the solution must include the entire biodiversity of the environment, starting with microorganisms across plants and animals [7].
The growing number of people on the Earth, the occupation of an ever-increasing area of previously untouched nature for agricultural activity, the growth of urban agglomerations, and the depopulation of rural settlements has long been indeterminate. If the principle of sustainable development of society is correct, it cannot be tested in laboratories, but in natural conditions, thus, it is dysfunctional. In relation to this, it is necessary to perceive every extraordinary event, every deviation from normal, as a challenge for the future in this principle. Research teams must continuously prepare scenarios for even the less probable events. Due to this fact, it is necessary to learn from the crown of the crisis and prepare for similar events [15].
The current crisis caused by COVID-19 shows that even the least likely events can cause gigantic material damage, financial and human loss. Based on these facts, it is necessary to look for new approaches to addressing environmental risk assessment. Routine, template approaches obscure the mind and lead us to a dead end.
4. Results
Real research results have been realistically tested by various methods at different levels. One of the possibilities was brainstorming within scientific conferences in combination with a comparison of the results of foreign research projects. The ambition of the authors is to shift knowledge in the field of theory as a theoretical contribution and cooperation with practice as a practical contribution. Another proven method is the preparation of research projects in close cooperation between academia and companies. This trend was applied until 2018. Then, however, there was a turning point, in that currently, projects focused dealing with safety and risks are prepared in cooperation with the state, region, city, municipality with academia, technology companies. Moreover, the fourth group, we invite to cooperate are non-profit stakeholders, associations, and relevant organizations of active people.
This approach brings us significant added value, which lies in a comprehensive understanding of security and risks. All relevant players must be involved in solving these projects. A good example is the current cooperation of regional self-government with the university, companies, state authorities in the region, and interest volunteer associations in the preparation of projects focused on SMART/SAFE city/region.
In this context, the advantage of our faculties is that the authors have been working for a long time on partial methodologies, detailed procedures and solutions that lead to an increase in the level of security. Every year, our security faculties in Zilina and Ostrava discuss dozens of final theses dealing with selected scenarios to prepare the best measures for resolving crisis events.
If we use the division of security areas as presented in Figure 2, one can choose from the reservoirs of partial solutions focused on physical security. These projects have been solved by students in three levels of university studies for more than 10 years. The basic ambition is to prepare security projects designed for all types of facilities, starting with elements of critical infrastructure, across the protection of soft targets to the protection of common facilities according to the requirements of practice.
The second important area where our faculties work closely together and have more than 10 years of results are fire safety projects. These projects are divided into two large groups. Whereas the first one is focused on preventative measures, the second group of projects comprises improving the intervention activities of the fire and rescue corps.
The third area we pay attention to, is the area of occupational safety and health. Primarily, the Faculty of Safety Engineering of the VSB—Technical University of Ostrava has a long tradition of such final theses. This tradition has lasted for more than 20 years. Cooperation in the field of occupational safety and health at the international level also leads to participation in jointly organized conferences and international projects.
The field of environmental safety has markedly a cross-sectional character. Every year, individual final theses are created, the aim of which is to look for methods, processes, and techniques on how to improve environmental protection. The young generation at our faculties is involved in a wide range of youth activities aimed at protecting the environment.
Another important area that has taken on significance within the COVID-19 pandemic is information security. Telework/home office, the use of new communication tools, and the company’s interest in the further development of informatization are the driving force for students to focus their upcoming challenges in the field of cybersecurity in their final theses and projects.
The last presented pillar is technological/technical safety, which is associated with the object/service or real activities. Measures in this area are specified depending on the sector. Within transport, each mode of transport has its own historical development in the field of risk assessment. In the first place, there are space flights, where the required level of safety/acceptable risk has safety indicators set very high. Beside the space flights, attention will also be drawn to the common modes of transport used daily by the population. Next in line is air transport, where previous incidents and air disasters have set safety/security level requirements and safety/security indicators from commonly used modes of transport to the highest level. Safety/risks in air transport are the most important parameter for passengers. As a result of this, in case of any problem before the take-off of the aircraft, each passenger accepts the decision of the captain of the aircraft to postpone the take-off. It is common for passengers who boarded a plane to wait patiently for the captain’s next decision. If they are asked to disembark the aircraft and return to the departure hall, they shall respect such a decision with understanding. In other modes of transport, we will not encounter such an attitude on the part of passengers [12,32,38].
An example of good practice in maritime transport is seaports, which also faces increased control to reduce externalities such as air pollution, noise, and other environmental impacts. Possible sources of emissions in ports include seagoing vessels, port vessels (pilot boats, police boats, pusher tugs), equipment for handling cargo in road transport, semi-trailers, or the trailer sets transporting goods respectively, diesel locomotives, small power plants, industrial, and production centers located in the port.
The emissions from seagoing ships in ports endanger not only the port workers themselves but also the people living near those ports. These emissions cause the population various respiratory diseases such as asthma, lung cancer, and others. For this reason, seaports take various measures to reduce emissions, e.g.,
use of alternative fuels, or fuels that contain a lower percentage of sulfur;
the supply of electricity to vessels at berth in ports (in smaller seaports, where there is not enough electricity capacity, vessels generate electricity themselves by burning fuel oil, or diesel);
improving the exchange of information between vessels and port management centers, which monitor the movement of vessels in ports so that ships can sail at optimum speeds;
only vessels whose engines meet the technical criteria set out in MARPOL Annex VI may sail in the water area of the port;
strengthening the control of vessels by the authorities carrying out checks on their technical condition;
the identification of other areas in the world for the regulation of emissions because of the differences among the countries of the world.
Inland water transport has an exceptionally long history. Risk reduction measures within the technological/technical phase have gradually come with the development of engineering, electronics, energy, and information and communication technologies. At present, however, inland waterway transport lags far behind air transport. In the field of safety, national and international rules and regulations apply. These rules and regulations are accepted, checked, and amended from time to time. Hence, risk assessment in water transport is in its infancy, because some current legal norms and regulations do not accept the current level of knowledge (applies to the countries of central Europe) [43].
Speaking of railway transport, the railway risk assessment was launched within the European Railway Agency by the approval of the Common Safety Methods. A pivotal role of this approach is to find the best practices in other railway companies. The strict separation of the management of railway infrastructure from rail transport has initially led to problems, especially in dealing with the consequences of incidents and railway accidents. The system of renewal of railway infrastructure has for many decades used all the forces and resources of the state railway company for renewal. In addition to this, separating freight and passenger transport from rail infrastructure management has led to complications, prolonging recovery times, and reducing the efficiency of the use of forces and resources. The current situation can be assessed in our countries as follows. Already in the period 1994–1998, the Czech Republic reconstructed a large part of the main transit lines (part of TEN-T) to a speed of 160 km per hour. Currently in the years 2018–2020, it continues in the next phase of reconstruction and modernization of corridor lines. In contrast, Slovakia and Serbia have failed to modernize corridor lines in the last 30 years. The technological progress made by top railway companies in China, Japan, France, and Germany is also an inducement for the countries of central and eastern Europe. The European Railway Agency has defined a role for individual national railway infrastructure administrations and railway undertakings to implement rail risk assessment systems by 2012. Research institutions in the Czech Republic, Slovakia, and Serbia have been intensively involved in this topic. Unfortunately, the transfer of the results of research projects into practice has not taken place yet [19,34,44].
Risk assessment in road transport was addressed in several research projects and the results were published, e.g., in an article by Dvorak et al. [21] and the paper by Fuchs et al. [25]. The development of intelligent transport systems/telematics solutions was started in the Czech Republic, Slovakia, and Serbia around the year 2000. All three countries significantly built the highway network and at the same time implement the results of applied research into the safety of tunnels, which have currently become a good basis for building networks supporting autonomous transport systems [34].
In addition to the area of risk assessment, the writers of the article, along with research teams, attended to the assessment of the vulnerability and resilience of transport and transport infrastructure. OKISD projects [28] were gradually solved, where attention was paid to the foundation of a theoretical framework for the construction of critical infrastructure in transport. The researchers focused mainly on road and rail transport. All workplaces of the Faculty of Security Engineering in Zilina were involved in the solution of the project. Ultimately, the result was more than 100 publications, where the results of the research were presented from different perspectives. A multi-disciplinary and multi-level approach has become the basis of the solution proposed. In the previous phase of the solution, we defined the technical terms that were used in the entire project solution.
The second important contribution was the 7FP RAIN project [18]. Within this project, attention was paid to assessing the risks of extreme weather conditions with priority given to transport infrastructure. As a result of project, vulnerability assessment tools, emphasizing the social vulnerability of society in the event of failures in the energy and transport sectors have been identified.
The most recent project, which brought together the efforts of researchers from Ostrava and Zilina, was the RESILIENCE 2015 project [29]. The project focused on creating a resilience framework for important sectors—energy, transport, and information and communication technologies. Certified methodologies were developed within the project and the results were published in Safety and Security Issues in Technical Infrastructures [34], Dynamic Impact Modeling as a Road Transport Crisis Management Support Tool [45] and in Complex Approach to Assessing Resilience of Critical Infrastructure Elements [46]. The newly developed resilience methodology used a system where resilience consists of adaptability, robustness, and resilience.
Based on the above, it can be stated that the essence of the proposed qualitative approach to environmental risk assessment (QAERA) in transport is the information support of entities in risk management. This approach is determined by 6 pillars of safety and protection of infrastructure elements in the transport sector. More precisely, the environmental risk assessment itself is based on the application of selected safety and security indicators and selected risk assessment criteria in the implementation of environmental change in the global model of thinking in the transport sector. The process of evaluation and use of indicators and criteria is presented in Figure 4.
Step 1: Select the Mode of Transport
The article is devoted to environmental risk assessment in transport and since each mode of transport has certain peculiarities, it is necessary to focus first on one selected mode of transport (air, water, road, or rail).
Step 2: Select the Object Type
After selecting the mode of transport, it is necessary to choose whether the evaluator’s attention will be focused on the transport infrastructure or the transport and transportation processes. When determining reference objects, the division into area (railway stations), line (line sections), or point objects (stop, person, train) is used [46].
The specifics of both types of objects are unambiguous. When assessing threats, the most significant threats to transport infrastructure are, for example, floods and landslides. When assessing threats to transport and transportation processes, the most significant threats include, for example, the intentional act of an employee, the economic crisis or neglecting of work duties.
Step 3: Select Type Object (Criteria)
This step sets out to define the database of type threats (i.e., risk type criteria) of specific reference objects. Events, activation mechanisms, event locations, and causes must be clarified within the database (see Table 1).
After combining the activation mechanism and the source of risk, the following disruptive events arise, for example: malfunction of the security device; immobility of the towing vehicle; illness of staff; panic of the traveling public; panic of railway employees; insolvency of a railway organization; insolvency of debtors of the railway organization; violation of the geometric position of the track; damage to the structure in the body of the track; and fire near the track.
Step 4: Implement Measurable Indicators of Safety/Security Pillars with a Focus on Environmental Risk Assessment
Subsequently, in the fourth step, all safety/security pillars (different viewing angles) must be evaluated. When implementing safety indicators, it is recommended to define 30–50 measurable indicators for each safety/security pillar. As stated above, the paper presents a qualitative approach to environmental risk assessment, which is based on the document of the United Nations [15]:
Goal 4\n
To ensure that all learners acquire the knowledge and skills needed to promote sustainable development.
Goal 9\n
To develop quality, reliable, sustainable, and resilient infrastructure, including regional and transborder infrastructure, to support economic development and human well-being, with a focus on affordable and equitable access for all.
Goal 11\n
To strengthen efforts to protect and safeguard the world’s cultural and natural heritage.
By 2030, to reduce the adverse per capita environmental impact of cities, including by paying special attention to air quality and municipal and other waste management.
By 2030, to provide universal access to safe, inclusive and accessible, green and public spaces, in particular for women and children, older persons, and persons with disabilities.
To support positive economic, social, and environmental links between urban, peri-urban, and rural areas by strengthening national and regional development planning.
By 2020, to substantially increase the number of cities and human settlements adopting and implementing integrated policies and plans towards inclusion, resource efficiency, mitigation and adaptation to climate change, resilience to disasters, and develop and implement, in line with the Sendai Framework for Disaster Risk Reduction 2015–2030, holistic disaster risk management at all levels.
Goal 13\n
To strengthen resilience and adaptive capacity to climate-related hazards and natural disasters in all countries.
To integrate climate change measures into national policies, strategies, and planning.
To improve education, awareness-raising, and human and institutional capacity on climate change mitigation, adaptation, impact reduction, and early warning.
However, the writer’ research was aimed at defining the required number of environmental safety indicators. For this reason, 38 indicators were defined for the real information system. In the following, we introduce proposals concerning some measurable indicators. The evaluation of measurable indicators using the procedure described below will give a clear conclusion about the level of safety of the reference object against a specific source of risk with a proposal for specific measures to reduce it. Examples of some indicators are presented in Table 2, Table 3 and Table 4.
The issue of environmental safety/security indicators is closely related to technical standards, laws, and regulations. Without deep experience in the field of environmental protection, it is not possible to set indicators that will objectively measure environmental security.
Step 5: Evaluation of Measurable Indicators
The same way as the color displays the resulting values in the traffic light (green, orange, and red), it is possible to determine the current level of safety. Briefly, if the values are, for example, on the orange scale, it is appropriate to monitor the indicator. If it is found out that an indicator was included in the green zone during testing, then it is not necessary to conduct safety measures. When entering the orange zone, it is necessary to increase vigilance, limit the speed of traffic, and, if necessary, choose appropriate safety measures to increase safety and security. If the indicator is included in the red zone, it is necessary to stop the transport process and take appropriate safety/security measures immediately.
Step 6: Selection and Implementation of Security Measures
The sixth step is a set of measures based on practical experience. Sets of proposed measures are created for the areas:
technical and technological,
organizational—legal framework,
workforce,
information support.
The technical and technological area is very important, as there is a system of planned inspection and maintenance. Namely, rail transport has a proven system of real technical and technological measures for years to guarantee track safety. Organizational measures are based on four sources. The first source is legal acts—laws, and decrees at the national level, the second is regulations and recommendations at the international level, the third is technical standards, and the fourth is the internal regulations of the infrastructure manager and transport companies. All listed resources are updated at regular intervals. In the area of manpower, there exist measures for the selection, training, and continuous training of employees. That is why railway companies have a long-established training system. For the most part, there are significant changes in the area of information support. Historical security and notification devices have been gradually replaced by high-quality information and communication technology. A current problem in this area is the provision of information security, which is carried out on an ongoing basis in all railway undertakings. Measures in this area focus on people, hardware, software, and data protection (GDPR, sensitive information, and know-how).
Practical application of the decision-making process qualitative approach to environmental risk assessment (QAERA) provides transport. As part of the verification process proposed by QAERA, several scenarios had to be prepared where the whole approach would be tested. The real events from the recent past played decisive role in the selection of scenarios. The first scenario was set by prolonged heavy rainfall which caused large-scale floods. The second scenario for landslides was made up after intense flash floods. The third scenario focused on the intentional act of a railway company employee. Each of these scenarios has a significant environmental impact. Each is relatively likely with a possible high impact on rail transport.
5. Discussion
The qualitative approach to environmental risk assessment in transport is based on long-term research, which relies on the historical basis of our research organizations. The second key part is the company’s interest in environmental safety as one of the priorities [7,8,15,25,30,47]. If we compare different sectors of the national economy, we will find out that the issue of environmental risk assessment is addressed at different levels. Good practice is mostly elaborated in the field of chemical industry and nuclear facilities. In transport, good practice is exemplified in Polishchuk et al. [33] in a fuzzy model of risk assessment for environmental issues. In rail transport, the attention is drawn to the implementation of Common Safety Methods [48], also with reference to examples of good practice.
The current approaches, in which the risk assessment is narrowed down to a defined system, the identification of threats, the analysis of risks, and the design of measures for the selected risk for a specific object/service, are, as mentioned, very narrowed down. The innovativeness of our solution rests on the interconnection of several approaches from vulnerability assessment, alternatively ranging from the assessment of resilience to security indicators as part of the safety/security pillars. The complexity of the solution should take into account all interests—global, national, regional, corporate, and individual. Creating an intelligent/SMART and comprehensive environmental risk assessment solution requires a real scientific basis, long-term experience, and a multidimensional perspective solution [35,49]. Previously implemented projects focused on static evaluation, however, the current trend is focused on the dynamic dimension of the solution [45]. The writers of the article are aware of the need to create a theoretical framework, followed by a methodological procedure and a detailed algorithm of selected scenarios, which will be the basis for the IT solution. In the past, it was not possible to create a comprehensive information system that would provide real-time technology using of the technologies, e.g., the internet of things, big data, and cloud, to gain all the necessary data for decision-making by top managers responsible for environmental risk assessment.
The interconnection of computer science, environmental, and security sciences is a challenging us at present. A multidisciplinary solution, using the knowledge of all relevant disciplines, is a key tool for a comprehensive qualitative approach to environmental risk assessment. Transport, as one of the economic sectors, requires further innovation and research. The real possibility is the continuous improvement of technical and technological processes for individual transport subsystems: transport technology, transport infrastructure, control and information systems in transport, and education and training of transport workers. This original method was intended for the regional and national levels. It does not only address global issues that require a different understanding of current needs and risk management. Moreover, when seeking a global solution, a multi-criteria assessment, which appropriately favors environmental aspects, must be used. Thousands of unused trucks cannot drive on the roads together with the thousands of overloaded trucks. Furthermore, environmentally friendly modes of transport such as water and rail cannot have smaller volumes of transported goods from year to year (situation in central Europe). Furthermore, it is not reasonable to increase the volume of goods transported in maritime transport every year, without full utilization of domestic resources in individual countries.
It is necessary to gradually fulfil the global model of sustainable development [15] step by step so that each activity carried out, reflects the protection of the environment. Promoting globally defined 17 goals and 197 targets are one of the right paths to global environmental protection.
Current quantitative approaches to risk assessment are based on long-term records and available risk data. However, individual countries in this area are creating a database only gradually. Therefore, at this stage of development, the attention of researchers was focused on qualitative evaluation, which also uses other data and thus does not need an extensive database. Thus, the result of this assessment is the identification of risk areas that can be evaluated in the next step using quantitative methods.
6. Conclusions
The paper aimed to present the ideas, reflections, projects, and publications of writers who have long been engaged in research in the fields of transport, safety, and the environment. We are aware of environmental impacts possibly affecting the functioning of transport and the operability of transport infrastructure. With regard to this, they certainly affect people’s health and lives. Every year, millions of people around the world are exposed to new extremes in the weather. Due to them, there is a movement of nations, whose inhabitants are forced to leave their homes and search for new places to live. Globally, the 50 percent limit of urban settlement has been exceeded, thus with more than half of mankind currently living in cities and conurbations with an absolute dependence on the functioning of critical infrastructures.
Research on dynamic resilience has shown us the complexity of current systems, their interdependence and considerable vulnerability. The more complicated and intelligent systems that control the sources of human needs is, the greater the possible impact on people’s lives and the environment.
Safety/security research leads us to ever new challenges how to increase the level of resilience, how to improve the protection of people, companies, institutions, municipalities, cities, regions, and, in general, of the globe. In our research, we have repeatedly encountered the limits of what is feasible, as we solved clearly defined problems and, interestingly, new and new questions have emerged as solutions.
The current limitations of the research play pivotal role, as they need to be based mainly on the multi-specialization of the solution. Creating broad teams of experts in practice and connecting researchers at the international level can bring about a shift in risk assessment in transport. Another limitation is the absence of legal acts that individual countries should issue in order to reduce environmental risks. Last but not least, there is the stereotype that people are used to pollute the environment and do not consider it a major problem. Awareness, education, and training must be coordinated and clear standards for environmental education and training must be defined in the future.
The field of applied research and innovation must be directed to the creation of new approaches, new methodologies, and new techniques in the field of environmental risk in transport. A comprehensive approach and timely identification of sources of risk is key. The writers demonstrated a new approach applicable in environmental risk assessment in transport by proposing safety pillars and a set of indicators.
The issue of environmental risk assessment in the countries of central and eastern Europe is addressed only in research organizations. It cannot be shifted into a real transport policy. It solves other seemingly more important problems—the moral obsolescence of transport infrastructure, non-coordination of individual modes of transport, corrupt behavior, and the absence of SMART solutions/technologies. Research results are rarely used as a basis for government negotiations at national and regional levels. In the area of transport policy, there is a lack of coordination of solutions at national and regional level. To sum up, the proposed approach provides six basic pillars of safety and dozens of indicators that could be part of the transport agenda at local, regional, and national level.
Based on the above, safety/security research requires teamwork, preferably with the representation of experts from different disciplines, because the perception of security is very individual. If we want to bring truly comprehensive solutions, it is necessary to start from all relevant research areas and look for synergistic solutions that can bring a new quality of security to society.
Author Contributions
Conceptualization, Z.D. and D.R.; methodology, Z.D. and D.R.; Validation, Z.D. and D.R.; formal analysis, Z.D., D.R., A.D. and Z.C; resources, Z.D. and D.R.; data curation, Z.D. and D.R.; writing—original draft preparation, Z.D., D.R., A.D. and Z.C.; writing—review and editing, Z.D. and D.R.; visualization, D.R.; supervision, Z.D.; project administration, D.R.; funding acquisition, Z.D. and D.R. All authors have read and agreed to the published version of the manuscript.
Funding
This research was funded by the European Regional Development Fund, grant number 313011T462, and by the Ministry of the Interior of the Czech Republic, grant number VI20192022151.
Conflicts of Interest
The authors declare no conflict of interest.
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Components of transport modes [17].
Security pillars of the organization [36].
Comparison of CO2 emission from the transport of one ton of goods per km [42].
Environmental risk assessment procedure in transport.
ijerph-17-05494-t001_Table 1
Criteria determining the type of risk.
Areas
Criteria
Event naming:
1. Exclusion of traffic—the threat may manifest itself in that it is not possible to operate train transport.2. Change of the schedule—the threat may manifest itself in such a way that it is possible to operate traffic, but to a limited extent.3. Technology change—threats can manifest themselves as an impact on the work system.4. Change in the scope of services—the threat may manifest itself in such a way that the infrastructure is not disrupted, but there has been a change in services.
Activation mechanisms:
1. Mass infection.2. Assault on operating personnel.3. Device malfunction.4. Illness of staff.5. Panic.6. Fire.7. Landslide.8. Flooding of the runway.
Event locations:
1. Train—point object.2. Track section—line object.3. Station—area object.4. Surroundings of the track—area object.
Causes of events:
1. Unintentional errors of transport participants.2. Failure of supplies necessary for operation.3. Problems in state administration.4. Extreme natural phenomena and weather.5. Technical failure.6. Crime.
ijerph-17-05494-t002_Table 2
Indicator—Amount of carbon dioxide (CO2) released into the air.
Value
Verbal Expression
Description
Units/Percentages
1
excellent
Amount of CO2 emissions produced from biomass used as fuel, including CO2 emissions that were not caused by the use of biomass in quantities ranging from 0 to 110 Mg per year.
from 0 to 110 Mg per year
2
good
Amount of CO2 emissions produced from biomass used as fuel, including CO2 emissions that were not caused by the use of biomass in the range of 110 to 162 Mg per year.
from 110 to 162 Mg per year
3
unacceptable
Amount of CO2 emissions produced from biomass used as fuel, including CO2 emissions that were not caused by the use of biomass in quantities exceeding 162 Mg per year.
more than 162 Mg per year
Comment: The indicator presents the measurable production of CO2 emissions from biomass.
ijerph-17-05494-t003_Table 3
Indicator—Amount of investments aimed at environmental protection.
Value
Verbal Expression
Description
Units/Percentages
1
excellent
The railway infrastructure manager spends € 100,000 or more per year on environmental protection.
100,000 euros and more
2
good
The railway infrastructure manager spends between €30,000 and €99,999 per year on environmental protection.
30,000 to 99,999 euros
3
unacceptable
The railway infrastructure manager spends less than €30,000 a year on environmental protection.
less than 30,000 euros
Comment: The indicator presents the amount of investments of the railway infrastructure manager intended for environmental protection.
ijerph-17-05494-t004_Table 4
Indicator—Status of equipment for measuring meteorological data.
Value
Verbal Expression
Description
Units/Percentages
1
excellent
Checks of the functionality of measuring equipment are performed at regular intervals.
90%
2
good
Checks of the functionality of measuring equipment are performed at irregular intervals.
70%
3
unacceptable
The state of functionality of measuring equipment is outdated, the results of measured values are inadequate.
30%
Comment: The indicator presents the status of the meteorological equipment of the infrastructure manager used as a database for telematics solutions in transport.
\n"],"string":"[\n \"\\nInt J Environ Res Public HealthInt J Environ Res Public HealthijerphInternational Journal of Environmental Research and Public Health1661-78271660-4601MDPI32751437PMC743210910.3390/ijerph17155494ijerph-17-05494ArticleQualitative Approach to Environmental Risk Assessment in Transporthttps://orcid.org/0000-0002-8320-1419DvorakZdenek1*https://orcid.org/0000-0002-4617-0553RehakDavid2DavidAndrej3CekerevacZoran4Faculty of Security Engineering, University of Zilina, 01026 Zilina, SlovakiaFaculty of Safety Engineering, VSB—Technical University of Ostrava, 70030 Ostrava, Czech Republic; david.rehak@vsb.czFaculty of Operation and Economics of Transport and Communications, University of Zilina, 01026 Zilina, Slovakia; andrej.david@fpedas.uniza.skFaculty of Business and Law, Union—Nikola Tesla University, 11000 Belgrade, Serbia; zoran@cekerevac.euCorrespondence: zdenek.dvorak@fbi.uniza.sk; Tel.: +421-415-136-8543072020820201715549429620202772020© 2020 by the authors.2020Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
The purpose of this paper is to present the development of a qualitative approach to environmental risk assessment (QAERA) in transport. The approach is described as a model developed for the future software tool which will be utilizable as a risk decision support system. The basic part is aimed on developing a quantitative environmental risk assessment. Thus, this paper describes a set of 6 pillars of safety and security. Accordingly, the paper contains both chosen safety and security indicators and selected criteria for assessing the risk of launching the environmental change of global model thinking in the transport sector. The environmental risk assessment as a global model of thinking was originally based on historical experience but, nowadays, it is changing. Based on new expert knowledge, more precisely, on input of new global data, paper displays an environmental risk assessment with actual interpretation. The discussion of the paper is oriented to support research results, a new knowledge-oriented approach to global climate changes, using suitable risk assessment methods and technics. The result of the paper is a new approach for the modeling of environmental risk assessment in the transport sector.
risk assessmenttransport sectorenvironmentcommon safety methoddecision support systemexpert evaluation1. Introduction
Transport, as one of the most important sectors of economics, has been developing gradually. Development of mankind is linked to the development of transport systems. The history of transport is mainly associated with the invention of the wheel and the development of inland waterway transport. In addition, maritime transport has played a key role in discovering new continents. With the development of the industrialization of society, railway transport began to develop significantly hand in hand with the discovery of a steam engine. The discovery of an internal combustion and diesel engine led to the development of road transport. The latest results of science and research have been fundamental elements at gradual development of each mode of transport. For many decades, the impact of transport on the environment has not been fundamentally addressed. To be specific, the first research did not take place until the middle of the twentieth century. Globally, the number of vehicles is increasing from year to year, and although the technical parameters of internal combustion engines are improving, global emissions are growing.
Emphasizing the environmental aspect of individual modes of transport should be an important aspect of the transport policy of an advanced society. The European Commission regularly issues recommendations and directives that address some areas of transport development. Individual states should define the priorities of transport development in their transport policy, mainly for ensuring transport services, but also for expected environmental impacts. Essentially, the authors of the article come from central and eastern Europe. In conjunction with this, the examples of the transport policy of the Czech Republic, Slovakia, and Serbia show that these countries have a remarkably similar transport policy, where the aspect of environmental protection plays a less important role.
The traditional scientific research plan has historically been focused on technical and technological processes. The challenge today is security with its multi-level and multi-union complexity. If safety research is understood in the above concept as a multi-level and multi-sectoral problem, then the challenge of addressing the issue of the environmental risk assessment will be met. The methodology will become a key element for setting appropriate indicators that will take into account not only the economic benefits for the company but will also address the societal impact in the social, health, and environmental fields. Drawing on this, simplifying efficiency and risk assessments have now led to absurd shipments of raw materials and semi-finished products in globalized companies, regardless of the full cost.
Current methods of environmental risk assessment are mainly of a quantitative or hybrid nature. In engineering, these methods are most commonly used in industry, where they account for more than 50% of existing risk assessment methods [1]. In the field of transport, these methods are used mainly in the context of the assessment of mobile risk sources [2,3]. However, these methods require a large amount of specific data, which is very time consuming to collect [4]. For this reason, it is convenient to use at first some of the appropriate qualitative methods for the preliminary risk analysis [5,6]. These methods are time-saving but, on the contrary, also quite general.
Referring to the above, it can be stated that even today there is no suitable available method dealing with the qualitative assessment of environmental risks arising from normal traffic. Based on this, the paper aims to present a newly proposed qualitative approach to environmental risk assessment in the transport sector. The essence of this approach is a preliminary environmental risk assessment, which is based on the application of selected safety and security indicators and selected risk assessment criteria in the implementation of environmental change in the global model of thinking in the transport sector.
2. Literature Review
The preparation of a paper focusing on environmental risk assessment in transport requires a broader framework of solutions. Therefore, we primarily examined information sources that play a decisive role in the field of environmental protection. Subsequently, attention was paid to the areas of sustainable development, transport, safety and security.
2.1. Environmental Area
The first paper to be discussed is the paper Biodiversity Loss and its Impact on Humanity, written by a broad team of authors led by Cardinale et al. [7]. The paper has focused on research since 1980, evaluating the last 40 years of life on Earth. Gradually, significant climate changes are taking place, which are accompanied by extreme weather events and often lead to the extinction of some animal and plant species. Entire ecosystems disappear irretrievably. As mentioned in the paper, in 2005, the Millennium Ecosystem Assessment was published for the first time. The condition and trends in the world’s ecosystems, and the services they provide, highlighted two distinct foci of biodiversity and ecosystem functioning (BEF) and biodiversity and ecosystem services (BES) research. Selecting from the published Consensus statements, there is currently clear evidence that biodiversity loss reduces the efficiency of ecological communities that capture biologically important resources, produce biomass, decompose, and recycle biologically important nutrients. Furthermore, there is evidence that greater biodiversity increases the stability of ecosystem functions over time. Moreover, four emerging trends were defined in the research. In our research on environmental risk assessment, we found that diversity effects grow stronger with time, and may increase at larger spatial scales. In BES research—for example, if we want to know how biodiversity affects the ability of ecosystems to remove carbon dioxide from the atmosphere and store carbon in the long term, we need to consider the net impact of biodiversity on photosynthesis (exchange of carbon dioxide for oxygen).
Changes in the environment have taken place continuously in the history of the planet Earth. The research is focused on different parts of the environment. An example to be used in our publication is the article 2500-Year Climate and Environmental Record Inferred from Subfossil Chironomids from Lugu Lake, by the author team led by Chang et al. [8] from 2018. The research focused on multi-proxy indicators for catchment erosion at Lugu Lake puts forward a methodological approach that the issue of environmental change must be considered in a long time series.
Another relevant source is Connor et al. [9] Long-Term Population Dynamics: Theory and Reality in a Peatland Ecosystem, which is focused on the research of environmental change in a selected region. Jimenez and Flores [10] focused on reducing carbon dioxide emissions in large agglomerations, where transport plays a big role. Kondratyev et al. [11] was involved in research of global carbon cycle and climate change. Similarly, Koblen et al. [12] focused on reducing emissions from aviation transport. This research was followed by Koscak et al. [13] which dealt with negative environmental impact of winter airport maintenance.
Within the European Universities Association, there was an established waterborne technology platform, which integrates research activities within maritime transport. The platform has defined seven key trends for maritime transport, which touch on the topic addressed and will be addressed in the paper [14].
2.2. Sustainable Development Area
As a part of global solutions, world conferences on environmental protection were organized in the past. Gradually, this agenda became one of the 17 goals of the transforming world called the 2030 Agenda for Sustainable Development. This agenda was approved by the United Nations and it contains 197 targets in addition to 17 basic goals. Some of them, directly and indirectly, affect the issues of the environment and sustainable transport [15]. Jakobsen [16] subsequently brought a new perspective on ecological economics.
As for these goals and targets, four goals are relevant to our research: Goal 4—Ensure lifelong learning opportunities; Goal 9—Built resilient infrastructure, promote inclusive and sustainable industrialization and foster innovation; Goal 11—Make city and human settlements inclusive safe, resilient, and sustainable; and Goal 13—Take urgent action to combat climate change and its impacts. In the next part, attention will be paid to them [15].
2.3. Transport Area
A lot of research teams around the world are involved in assessing environmental change and its impact on transport and transport infrastructure. Extreme weather events, seismic and geological changes occur in all regions of the world. However, the first limitation of our research is the focus on the central European environment, as examples will be events from the Czech Republic, Slovakia, and Serbia. All three countries have remarkably similar relief. On the whole, there are mountain ranges with transport infrastructure and lowlands with large rivers. The nature of the threats to transport and transport infrastructure is similar. For this reason, the writers focus their research on four basic key components—Modes, Infrastructures, Networks, and Flows (see Figure 1).
As a significant contribution to the field of environmental risk assessment in transport, we present the RAIN project (i.e. Risk Analysis of Infrastructure Networks) solved in the years 2014–2016. The project pointed up the impact of weather extremes on transport and energy. The project resulted in building up a new framework for assessing the risks posed by weather extremes—floods, lightning, landslides, extreme droughts, frosts, and storms [18].
The theoretical framework of risk assessment and information support in railway and road transport was also addressed in the publications Assessment of Critical Infrastructure Elements in Transport [19], Crisis Management Decision Support System in Railway Infrastructure Company [20], and Multi-agent System for Decreasing of Risk in Road Transport [21].
Dzunda et al. [22] dealt with the synthesis of selected aspects of navigation systems to increase air safety. Havel et al. [23] focused their attention on a new route intersection-minimum distance model. Sousek and Dvorak [24] researched the transport of dangerous shipments. Fuchs et al. [25], within the framework of the BIOTRA project and a series of publications, concentrated on the transport of dangerous goods and the assessment of its possible impacts on the environment in the vicinity of roads (line objects). Hoterova and Dvorak [26] discussed comparing CO2 emissions in rail and road transport.
The Water Transport Technology Platform welcomes the Council Conclusions entitled “EU Water Transport Sector—Looking to the future: Towards a carbon-neutral, zero-accident, automated and competitive EU water transport sector”, endorsed by the Council on 5 June 2020. These conclusions are politically very relevant as they clearly demonstrate the views of the Member States on European policy on the future of waterborne transport. The conclusions underline this strategy—the growing importance of the European waterway sector and the need for enhanced research, development, and innovation to achieve the objectives set out in the Council conclusions. Key trends influencing the maritime sector are, e.g., climate change, continuous population growth and urbanization, food and water demand, increasing expectations for health, safety and security and, last but not least, developing countries. On the whole, these constituent factors will increase their share in global economic growth, increase in energy consumption, and fast development of information and communication technologies [27].
2.4. Safety and Security Area
Transport, as a specific area of economic activity, and the provision of transport services for people requires the continuous improvement of all activities and subsystems. During the evaluating of traffic, people focus on attributes such as time, price, quality, safety, and environmental impact. As a part of the evaluation of safety and security aspects, researchers write of the point, line, and area objects. These attributes and aspects were defined within the projects RAIN [18], OKISD (i.e. Critical Infrastructure Protection in Sector Transportation in the Slovak language) [28], and RESILIENCE 2015 [29].
The risk assessment for the environmental area was addressed in the following publications: Dobes et al. [30], Approach of the Czech Republic to the Prevention of Environmentally Oriented Terrorism. Kelemen and Jevcak [31] addressed the specifics of security management in aviation. Kirschenstein et al. [32] discussed research into storms and the resulting threats to air traffic. Polishchuk et al. [33], in their research, dealt with a fuzzy model of risk assessment for environmental for start-up projects in the air transporter sector. Rehak et al. [34] spoke of Safety and Security Issues in Technical Infrastructures.
The decade since 2010 has been aimed at significantly reducing CO2 production by car and aircraft engines. Because of that, many studies were carried out to compare the production of emissions in different modes of transport. The effects of extreme weather events pose a visible response of the Earth to increasing global greenhouse gas emissions. Gradual climate change is mainly reflected in the ever-increasing global temperature, which is leading to the gradual melting of glaciers around the North and South Pole. The significant reduction in the world’s glaciers is also the result of the global deforestation and forest systems. Currently, 40% of the land on Earth is covered by deserts, and their size is growing from year to year. First, according to historical meteorological measurements over the last hundred years, seven of the ten warmest years occurred between 2000 and 2019. Second, according to research by the European Severe Storms Laboratory (next ESSL), extreme weather events increase every year in Europe. Many of these extremes have a devastating impact on transport (relocation of goods and people in vehicles) as well as on transport infrastructure (roads, railways, waterways, and transport facilities). Third, according to the conclusions and recommendations from the RAIN project, it is necessary to take into account more significant extremes of weather in the reconstruction of transport facilities than was the case according to technical standards around 1970 (50 years ago). In our research, the worst-case scenarios for transport infrastructure are landslides, snow avalanches, flooding of transport infrastructure, extremely high and extremely low temperatures, storms, intense storms, and forest fires [35].
3. Materials and Methods
In our research, standard scientific methods were used, from analysis and comparison to synthesis, from top-down solutions to bottom-up solutions. The basis of all methodological procedures is the definition of the object/system where the risks will be assessed. Probabilities and possible implications for the identified typological scenarios are required as input for all scenarios. Thus, risk assessment can rely on the pillars of system security management of the organization (see Figure 2).
A comprehensive assessment of the safety, security, and resilience of transport infrastructure requires new approaches. Our primary objective should be specifically to reduce the volume of transoceanic traffic in the future. All goods and commodities transported over more than 10,000 km should be subject to a special tax. Moreover, the new scientific department of ecological economics recommends re-evaluating long-distance transport. In the current conditions, there are cases where the raw material obtained on one continent is transported to another continent. The product made of the raw material travels thousands of kilometers to another continent, and if not sold there, it is, in turn, transported to another continent. These cases are possible thanks to a non-comprehensive global solution, wherein some countries do not give attention to the environmental impacts of transport [10]. On the contrary, the security solution requires a comprehensive approach, the security pillars shown in the Figure 2 according to the research results must be evaluated using a comprehensive set of measurable indicators.
Figure 2 shows environmental security as one of the pillars of an organization’s security. Environmental risk assessment is part of a general approach to risk assessment. The current basic framework for risk assessment is ISO 31,000 Risk Management—Guidelines [37]. Various techniques and methodologies for risk assessment are used for different areas of society. As part of the methodology aimed at increasing the security of the organization, it is necessary to link these pillars with the PDCA method (i.e. Plan-Do-Check-Act), which sets out to continuously improve business processes.
In addressing environmental risk assessment, it is necessary to build on previously conducted research published in these papers and sources focused on air transport [32,35,38,39,40,41] and other modes of transport [33]. A multi-disciplinary and multi-level approach is the basic method for addressing risks. In the risk assessment, it is important to have relevant statistical files for the last period. Another important part of objective risk assessment is the study of best practices in international research. Some innovative approaches are not based on real research tasks but arise as a real need of society. An example of this is the comparison of CO2 emission from transport published in Norway as a real answer for the discussion of experts. The real measurements that were taken, gave the results shown in Figure 3. The conversion involved the transport of one ton of cargo over one km and, after the calculation, the real emissions in grams were converted. The lowest value was given by a freight train with a diesel drive. In second place, there is a container ship carrying more than two thousand containers. On the opposite side of the line, was the carriage of one ton of cargo by plane.
For the most part, the aim of future research will be the interconnection of individual subsystems, presented by individual pillars of safety/security and their mutual integration. Future security audits must be based on dozens of indicators for each safety/security pillar. The interconnection and thorough evaluation of the measured values will give a clear answer as to which indicators in which pillar appear the weakest and which we must pay primary attention to. The environmental dimension of the solution must include the entire biodiversity of the environment, starting with microorganisms across plants and animals [7].
The growing number of people on the Earth, the occupation of an ever-increasing area of previously untouched nature for agricultural activity, the growth of urban agglomerations, and the depopulation of rural settlements has long been indeterminate. If the principle of sustainable development of society is correct, it cannot be tested in laboratories, but in natural conditions, thus, it is dysfunctional. In relation to this, it is necessary to perceive every extraordinary event, every deviation from normal, as a challenge for the future in this principle. Research teams must continuously prepare scenarios for even the less probable events. Due to this fact, it is necessary to learn from the crown of the crisis and prepare for similar events [15].
The current crisis caused by COVID-19 shows that even the least likely events can cause gigantic material damage, financial and human loss. Based on these facts, it is necessary to look for new approaches to addressing environmental risk assessment. Routine, template approaches obscure the mind and lead us to a dead end.
4. Results
Real research results have been realistically tested by various methods at different levels. One of the possibilities was brainstorming within scientific conferences in combination with a comparison of the results of foreign research projects. The ambition of the authors is to shift knowledge in the field of theory as a theoretical contribution and cooperation with practice as a practical contribution. Another proven method is the preparation of research projects in close cooperation between academia and companies. This trend was applied until 2018. Then, however, there was a turning point, in that currently, projects focused dealing with safety and risks are prepared in cooperation with the state, region, city, municipality with academia, technology companies. Moreover, the fourth group, we invite to cooperate are non-profit stakeholders, associations, and relevant organizations of active people.
This approach brings us significant added value, which lies in a comprehensive understanding of security and risks. All relevant players must be involved in solving these projects. A good example is the current cooperation of regional self-government with the university, companies, state authorities in the region, and interest volunteer associations in the preparation of projects focused on SMART/SAFE city/region.
In this context, the advantage of our faculties is that the authors have been working for a long time on partial methodologies, detailed procedures and solutions that lead to an increase in the level of security. Every year, our security faculties in Zilina and Ostrava discuss dozens of final theses dealing with selected scenarios to prepare the best measures for resolving crisis events.
If we use the division of security areas as presented in Figure 2, one can choose from the reservoirs of partial solutions focused on physical security. These projects have been solved by students in three levels of university studies for more than 10 years. The basic ambition is to prepare security projects designed for all types of facilities, starting with elements of critical infrastructure, across the protection of soft targets to the protection of common facilities according to the requirements of practice.
The second important area where our faculties work closely together and have more than 10 years of results are fire safety projects. These projects are divided into two large groups. Whereas the first one is focused on preventative measures, the second group of projects comprises improving the intervention activities of the fire and rescue corps.
The third area we pay attention to, is the area of occupational safety and health. Primarily, the Faculty of Safety Engineering of the VSB—Technical University of Ostrava has a long tradition of such final theses. This tradition has lasted for more than 20 years. Cooperation in the field of occupational safety and health at the international level also leads to participation in jointly organized conferences and international projects.
The field of environmental safety has markedly a cross-sectional character. Every year, individual final theses are created, the aim of which is to look for methods, processes, and techniques on how to improve environmental protection. The young generation at our faculties is involved in a wide range of youth activities aimed at protecting the environment.
Another important area that has taken on significance within the COVID-19 pandemic is information security. Telework/home office, the use of new communication tools, and the company’s interest in the further development of informatization are the driving force for students to focus their upcoming challenges in the field of cybersecurity in their final theses and projects.
The last presented pillar is technological/technical safety, which is associated with the object/service or real activities. Measures in this area are specified depending on the sector. Within transport, each mode of transport has its own historical development in the field of risk assessment. In the first place, there are space flights, where the required level of safety/acceptable risk has safety indicators set very high. Beside the space flights, attention will also be drawn to the common modes of transport used daily by the population. Next in line is air transport, where previous incidents and air disasters have set safety/security level requirements and safety/security indicators from commonly used modes of transport to the highest level. Safety/risks in air transport are the most important parameter for passengers. As a result of this, in case of any problem before the take-off of the aircraft, each passenger accepts the decision of the captain of the aircraft to postpone the take-off. It is common for passengers who boarded a plane to wait patiently for the captain’s next decision. If they are asked to disembark the aircraft and return to the departure hall, they shall respect such a decision with understanding. In other modes of transport, we will not encounter such an attitude on the part of passengers [12,32,38].
An example of good practice in maritime transport is seaports, which also faces increased control to reduce externalities such as air pollution, noise, and other environmental impacts. Possible sources of emissions in ports include seagoing vessels, port vessels (pilot boats, police boats, pusher tugs), equipment for handling cargo in road transport, semi-trailers, or the trailer sets transporting goods respectively, diesel locomotives, small power plants, industrial, and production centers located in the port.
The emissions from seagoing ships in ports endanger not only the port workers themselves but also the people living near those ports. These emissions cause the population various respiratory diseases such as asthma, lung cancer, and others. For this reason, seaports take various measures to reduce emissions, e.g.,
use of alternative fuels, or fuels that contain a lower percentage of sulfur;
the supply of electricity to vessels at berth in ports (in smaller seaports, where there is not enough electricity capacity, vessels generate electricity themselves by burning fuel oil, or diesel);
improving the exchange of information between vessels and port management centers, which monitor the movement of vessels in ports so that ships can sail at optimum speeds;
only vessels whose engines meet the technical criteria set out in MARPOL Annex VI may sail in the water area of the port;
strengthening the control of vessels by the authorities carrying out checks on their technical condition;
the identification of other areas in the world for the regulation of emissions because of the differences among the countries of the world.
Inland water transport has an exceptionally long history. Risk reduction measures within the technological/technical phase have gradually come with the development of engineering, electronics, energy, and information and communication technologies. At present, however, inland waterway transport lags far behind air transport. In the field of safety, national and international rules and regulations apply. These rules and regulations are accepted, checked, and amended from time to time. Hence, risk assessment in water transport is in its infancy, because some current legal norms and regulations do not accept the current level of knowledge (applies to the countries of central Europe) [43].
Speaking of railway transport, the railway risk assessment was launched within the European Railway Agency by the approval of the Common Safety Methods. A pivotal role of this approach is to find the best practices in other railway companies. The strict separation of the management of railway infrastructure from rail transport has initially led to problems, especially in dealing with the consequences of incidents and railway accidents. The system of renewal of railway infrastructure has for many decades used all the forces and resources of the state railway company for renewal. In addition to this, separating freight and passenger transport from rail infrastructure management has led to complications, prolonging recovery times, and reducing the efficiency of the use of forces and resources. The current situation can be assessed in our countries as follows. Already in the period 1994–1998, the Czech Republic reconstructed a large part of the main transit lines (part of TEN-T) to a speed of 160 km per hour. Currently in the years 2018–2020, it continues in the next phase of reconstruction and modernization of corridor lines. In contrast, Slovakia and Serbia have failed to modernize corridor lines in the last 30 years. The technological progress made by top railway companies in China, Japan, France, and Germany is also an inducement for the countries of central and eastern Europe. The European Railway Agency has defined a role for individual national railway infrastructure administrations and railway undertakings to implement rail risk assessment systems by 2012. Research institutions in the Czech Republic, Slovakia, and Serbia have been intensively involved in this topic. Unfortunately, the transfer of the results of research projects into practice has not taken place yet [19,34,44].
Risk assessment in road transport was addressed in several research projects and the results were published, e.g., in an article by Dvorak et al. [21] and the paper by Fuchs et al. [25]. The development of intelligent transport systems/telematics solutions was started in the Czech Republic, Slovakia, and Serbia around the year 2000. All three countries significantly built the highway network and at the same time implement the results of applied research into the safety of tunnels, which have currently become a good basis for building networks supporting autonomous transport systems [34].
In addition to the area of risk assessment, the writers of the article, along with research teams, attended to the assessment of the vulnerability and resilience of transport and transport infrastructure. OKISD projects [28] were gradually solved, where attention was paid to the foundation of a theoretical framework for the construction of critical infrastructure in transport. The researchers focused mainly on road and rail transport. All workplaces of the Faculty of Security Engineering in Zilina were involved in the solution of the project. Ultimately, the result was more than 100 publications, where the results of the research were presented from different perspectives. A multi-disciplinary and multi-level approach has become the basis of the solution proposed. In the previous phase of the solution, we defined the technical terms that were used in the entire project solution.
The second important contribution was the 7FP RAIN project [18]. Within this project, attention was paid to assessing the risks of extreme weather conditions with priority given to transport infrastructure. As a result of project, vulnerability assessment tools, emphasizing the social vulnerability of society in the event of failures in the energy and transport sectors have been identified.
The most recent project, which brought together the efforts of researchers from Ostrava and Zilina, was the RESILIENCE 2015 project [29]. The project focused on creating a resilience framework for important sectors—energy, transport, and information and communication technologies. Certified methodologies were developed within the project and the results were published in Safety and Security Issues in Technical Infrastructures [34], Dynamic Impact Modeling as a Road Transport Crisis Management Support Tool [45] and in Complex Approach to Assessing Resilience of Critical Infrastructure Elements [46]. The newly developed resilience methodology used a system where resilience consists of adaptability, robustness, and resilience.
Based on the above, it can be stated that the essence of the proposed qualitative approach to environmental risk assessment (QAERA) in transport is the information support of entities in risk management. This approach is determined by 6 pillars of safety and protection of infrastructure elements in the transport sector. More precisely, the environmental risk assessment itself is based on the application of selected safety and security indicators and selected risk assessment criteria in the implementation of environmental change in the global model of thinking in the transport sector. The process of evaluation and use of indicators and criteria is presented in Figure 4.
Step 1: Select the Mode of Transport
The article is devoted to environmental risk assessment in transport and since each mode of transport has certain peculiarities, it is necessary to focus first on one selected mode of transport (air, water, road, or rail).
Step 2: Select the Object Type
After selecting the mode of transport, it is necessary to choose whether the evaluator’s attention will be focused on the transport infrastructure or the transport and transportation processes. When determining reference objects, the division into area (railway stations), line (line sections), or point objects (stop, person, train) is used [46].
The specifics of both types of objects are unambiguous. When assessing threats, the most significant threats to transport infrastructure are, for example, floods and landslides. When assessing threats to transport and transportation processes, the most significant threats include, for example, the intentional act of an employee, the economic crisis or neglecting of work duties.
Step 3: Select Type Object (Criteria)
This step sets out to define the database of type threats (i.e., risk type criteria) of specific reference objects. Events, activation mechanisms, event locations, and causes must be clarified within the database (see Table 1).
After combining the activation mechanism and the source of risk, the following disruptive events arise, for example: malfunction of the security device; immobility of the towing vehicle; illness of staff; panic of the traveling public; panic of railway employees; insolvency of a railway organization; insolvency of debtors of the railway organization; violation of the geometric position of the track; damage to the structure in the body of the track; and fire near the track.
Step 4: Implement Measurable Indicators of Safety/Security Pillars with a Focus on Environmental Risk Assessment
Subsequently, in the fourth step, all safety/security pillars (different viewing angles) must be evaluated. When implementing safety indicators, it is recommended to define 30–50 measurable indicators for each safety/security pillar. As stated above, the paper presents a qualitative approach to environmental risk assessment, which is based on the document of the United Nations [15]:
Goal 4\\n
To ensure that all learners acquire the knowledge and skills needed to promote sustainable development.
Goal 9\\n
To develop quality, reliable, sustainable, and resilient infrastructure, including regional and transborder infrastructure, to support economic development and human well-being, with a focus on affordable and equitable access for all.
Goal 11\\n
To strengthen efforts to protect and safeguard the world’s cultural and natural heritage.
By 2030, to reduce the adverse per capita environmental impact of cities, including by paying special attention to air quality and municipal and other waste management.
By 2030, to provide universal access to safe, inclusive and accessible, green and public spaces, in particular for women and children, older persons, and persons with disabilities.
To support positive economic, social, and environmental links between urban, peri-urban, and rural areas by strengthening national and regional development planning.
By 2020, to substantially increase the number of cities and human settlements adopting and implementing integrated policies and plans towards inclusion, resource efficiency, mitigation and adaptation to climate change, resilience to disasters, and develop and implement, in line with the Sendai Framework for Disaster Risk Reduction 2015–2030, holistic disaster risk management at all levels.
Goal 13\\n
To strengthen resilience and adaptive capacity to climate-related hazards and natural disasters in all countries.
To integrate climate change measures into national policies, strategies, and planning.
To improve education, awareness-raising, and human and institutional capacity on climate change mitigation, adaptation, impact reduction, and early warning.
However, the writer’ research was aimed at defining the required number of environmental safety indicators. For this reason, 38 indicators were defined for the real information system. In the following, we introduce proposals concerning some measurable indicators. The evaluation of measurable indicators using the procedure described below will give a clear conclusion about the level of safety of the reference object against a specific source of risk with a proposal for specific measures to reduce it. Examples of some indicators are presented in Table 2, Table 3 and Table 4.
The issue of environmental safety/security indicators is closely related to technical standards, laws, and regulations. Without deep experience in the field of environmental protection, it is not possible to set indicators that will objectively measure environmental security.
Step 5: Evaluation of Measurable Indicators
The same way as the color displays the resulting values in the traffic light (green, orange, and red), it is possible to determine the current level of safety. Briefly, if the values are, for example, on the orange scale, it is appropriate to monitor the indicator. If it is found out that an indicator was included in the green zone during testing, then it is not necessary to conduct safety measures. When entering the orange zone, it is necessary to increase vigilance, limit the speed of traffic, and, if necessary, choose appropriate safety measures to increase safety and security. If the indicator is included in the red zone, it is necessary to stop the transport process and take appropriate safety/security measures immediately.
Step 6: Selection and Implementation of Security Measures
The sixth step is a set of measures based on practical experience. Sets of proposed measures are created for the areas:
technical and technological,
organizational—legal framework,
workforce,
information support.
The technical and technological area is very important, as there is a system of planned inspection and maintenance. Namely, rail transport has a proven system of real technical and technological measures for years to guarantee track safety. Organizational measures are based on four sources. The first source is legal acts—laws, and decrees at the national level, the second is regulations and recommendations at the international level, the third is technical standards, and the fourth is the internal regulations of the infrastructure manager and transport companies. All listed resources are updated at regular intervals. In the area of manpower, there exist measures for the selection, training, and continuous training of employees. That is why railway companies have a long-established training system. For the most part, there are significant changes in the area of information support. Historical security and notification devices have been gradually replaced by high-quality information and communication technology. A current problem in this area is the provision of information security, which is carried out on an ongoing basis in all railway undertakings. Measures in this area focus on people, hardware, software, and data protection (GDPR, sensitive information, and know-how).
Practical application of the decision-making process qualitative approach to environmental risk assessment (QAERA) provides transport. As part of the verification process proposed by QAERA, several scenarios had to be prepared where the whole approach would be tested. The real events from the recent past played decisive role in the selection of scenarios. The first scenario was set by prolonged heavy rainfall which caused large-scale floods. The second scenario for landslides was made up after intense flash floods. The third scenario focused on the intentional act of a railway company employee. Each of these scenarios has a significant environmental impact. Each is relatively likely with a possible high impact on rail transport.
5. Discussion
The qualitative approach to environmental risk assessment in transport is based on long-term research, which relies on the historical basis of our research organizations. The second key part is the company’s interest in environmental safety as one of the priorities [7,8,15,25,30,47]. If we compare different sectors of the national economy, we will find out that the issue of environmental risk assessment is addressed at different levels. Good practice is mostly elaborated in the field of chemical industry and nuclear facilities. In transport, good practice is exemplified in Polishchuk et al. [33] in a fuzzy model of risk assessment for environmental issues. In rail transport, the attention is drawn to the implementation of Common Safety Methods [48], also with reference to examples of good practice.
The current approaches, in which the risk assessment is narrowed down to a defined system, the identification of threats, the analysis of risks, and the design of measures for the selected risk for a specific object/service, are, as mentioned, very narrowed down. The innovativeness of our solution rests on the interconnection of several approaches from vulnerability assessment, alternatively ranging from the assessment of resilience to security indicators as part of the safety/security pillars. The complexity of the solution should take into account all interests—global, national, regional, corporate, and individual. Creating an intelligent/SMART and comprehensive environmental risk assessment solution requires a real scientific basis, long-term experience, and a multidimensional perspective solution [35,49]. Previously implemented projects focused on static evaluation, however, the current trend is focused on the dynamic dimension of the solution [45]. The writers of the article are aware of the need to create a theoretical framework, followed by a methodological procedure and a detailed algorithm of selected scenarios, which will be the basis for the IT solution. In the past, it was not possible to create a comprehensive information system that would provide real-time technology using of the technologies, e.g., the internet of things, big data, and cloud, to gain all the necessary data for decision-making by top managers responsible for environmental risk assessment.
The interconnection of computer science, environmental, and security sciences is a challenging us at present. A multidisciplinary solution, using the knowledge of all relevant disciplines, is a key tool for a comprehensive qualitative approach to environmental risk assessment. Transport, as one of the economic sectors, requires further innovation and research. The real possibility is the continuous improvement of technical and technological processes for individual transport subsystems: transport technology, transport infrastructure, control and information systems in transport, and education and training of transport workers. This original method was intended for the regional and national levels. It does not only address global issues that require a different understanding of current needs and risk management. Moreover, when seeking a global solution, a multi-criteria assessment, which appropriately favors environmental aspects, must be used. Thousands of unused trucks cannot drive on the roads together with the thousands of overloaded trucks. Furthermore, environmentally friendly modes of transport such as water and rail cannot have smaller volumes of transported goods from year to year (situation in central Europe). Furthermore, it is not reasonable to increase the volume of goods transported in maritime transport every year, without full utilization of domestic resources in individual countries.
It is necessary to gradually fulfil the global model of sustainable development [15] step by step so that each activity carried out, reflects the protection of the environment. Promoting globally defined 17 goals and 197 targets are one of the right paths to global environmental protection.
Current quantitative approaches to risk assessment are based on long-term records and available risk data. However, individual countries in this area are creating a database only gradually. Therefore, at this stage of development, the attention of researchers was focused on qualitative evaluation, which also uses other data and thus does not need an extensive database. Thus, the result of this assessment is the identification of risk areas that can be evaluated in the next step using quantitative methods.
6. Conclusions
The paper aimed to present the ideas, reflections, projects, and publications of writers who have long been engaged in research in the fields of transport, safety, and the environment. We are aware of environmental impacts possibly affecting the functioning of transport and the operability of transport infrastructure. With regard to this, they certainly affect people’s health and lives. Every year, millions of people around the world are exposed to new extremes in the weather. Due to them, there is a movement of nations, whose inhabitants are forced to leave their homes and search for new places to live. Globally, the 50 percent limit of urban settlement has been exceeded, thus with more than half of mankind currently living in cities and conurbations with an absolute dependence on the functioning of critical infrastructures.
Research on dynamic resilience has shown us the complexity of current systems, their interdependence and considerable vulnerability. The more complicated and intelligent systems that control the sources of human needs is, the greater the possible impact on people’s lives and the environment.
Safety/security research leads us to ever new challenges how to increase the level of resilience, how to improve the protection of people, companies, institutions, municipalities, cities, regions, and, in general, of the globe. In our research, we have repeatedly encountered the limits of what is feasible, as we solved clearly defined problems and, interestingly, new and new questions have emerged as solutions.
The current limitations of the research play pivotal role, as they need to be based mainly on the multi-specialization of the solution. Creating broad teams of experts in practice and connecting researchers at the international level can bring about a shift in risk assessment in transport. Another limitation is the absence of legal acts that individual countries should issue in order to reduce environmental risks. Last but not least, there is the stereotype that people are used to pollute the environment and do not consider it a major problem. Awareness, education, and training must be coordinated and clear standards for environmental education and training must be defined in the future.
The field of applied research and innovation must be directed to the creation of new approaches, new methodologies, and new techniques in the field of environmental risk in transport. A comprehensive approach and timely identification of sources of risk is key. The writers demonstrated a new approach applicable in environmental risk assessment in transport by proposing safety pillars and a set of indicators.
The issue of environmental risk assessment in the countries of central and eastern Europe is addressed only in research organizations. It cannot be shifted into a real transport policy. It solves other seemingly more important problems—the moral obsolescence of transport infrastructure, non-coordination of individual modes of transport, corrupt behavior, and the absence of SMART solutions/technologies. Research results are rarely used as a basis for government negotiations at national and regional levels. In the area of transport policy, there is a lack of coordination of solutions at national and regional level. To sum up, the proposed approach provides six basic pillars of safety and dozens of indicators that could be part of the transport agenda at local, regional, and national level.
Based on the above, safety/security research requires teamwork, preferably with the representation of experts from different disciplines, because the perception of security is very individual. If we want to bring truly comprehensive solutions, it is necessary to start from all relevant research areas and look for synergistic solutions that can bring a new quality of security to society.
Author Contributions
Conceptualization, Z.D. and D.R.; methodology, Z.D. and D.R.; Validation, Z.D. and D.R.; formal analysis, Z.D., D.R., A.D. and Z.C; resources, Z.D. and D.R.; data curation, Z.D. and D.R.; writing—original draft preparation, Z.D., D.R., A.D. and Z.C.; writing—review and editing, Z.D. and D.R.; visualization, D.R.; supervision, Z.D.; project administration, D.R.; funding acquisition, Z.D. and D.R. All authors have read and agreed to the published version of the manuscript.
Funding
This research was funded by the European Regional Development Fund, grant number 313011T462, and by the Ministry of the Interior of the Czech Republic, grant number VI20192022151.
Conflicts of Interest
The authors declare no conflict of interest.
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Konbin20194931333010.2478/jok-2019-0016ISO 31000Risk Management–GuidelinesInternational Organization for StandardizationGeneva, Switzerland2018KelemenM.SzaboS.VajdovaI.Security Management in the Air Transport: Example of an Interdisciplinary Investigation of Special Security QuestionsProceedings of the International Scientific Conference Challenges to National Defence in Contemporary Geopolitical Situation (CNDCGS 2018)Vilnius, Latvia25–27 April 2018105108NissalkeT.E.The Air Transportation System in the 21st CenturySustainable Built EnvironmentHaghighatF.KimJ.J.United Nations Educational, Scientific and Cultural OrganizationParis, France2009Volume 2365386978-84826-061-0SamehM.M.ScavuzziJ.Environmental Sustainability Measures for AirportsCentre for Research in Air and Space Law/McGill UniversityQuebec, QC, Canada2016VajdovaI.JenčovaE.SzaboS.MelnikovaL.GalandaJ.DobrowolskaM.PlochJ.Environmental Impact of Burning Composite Materials Used in Aircraft Construction on the AirInt. J. Environ. Res. Public Health201916400810.3390/ijerph16204008Faktisk is it True that Emissions from a Container Ship Correspond to 50 Million Cars?Available online: https://www.faktisk.no/artikler/MA/stemmer-det-at-utslippene-fra-et-containerskip-tilsvarer-50-millioner-biler(accessed on 6 June 2020)PavolováH.TobisováA.The Model of Supplier Quality Management in Transport CompanyNase More201360123126LuskovaM.DvorakZ.LeitnerB.Impact of Extreme Weather Events on Land Transport InfrastructureProceedings of the 19th International Scientific Conference Transport Means 2015Kaunas, Lithuania22–23 October 2015306309RehakD.RadimskyM.HromadaM.DvorakZ.Dynamic Impact Modeling as a Road Transport Crisis Management Support ToolAdm. Sci.201992910.3390/admsci9020029RehakD.SenovskyP.HromadaM.LovecekT.Complex Approach to Assessing Resilience of Critical Infrastructure ElementsInt. J. Crit. Infrastruct. Prot.20192512513810.1016/j.ijcip.2019.03.003CrncevicD.Transport Risk AnalysisTransportation Systems and Engineering: Concepts, Methodologies, Tools, and ApplicationsIGI GlobalHershey, PA, USA2015Volume 313110.4018/978-1-4666-8473-7.ch001978-1-4666-8473-7Directive (EU) 2016/798 of the European Parliament and of the Council of 11 May 2016 on Railway SafetyAvailable online: https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX%3A32016L0798(accessed on 27 June 2020)VidrikovaD.BocK.DvorakZ.RehakD.Critical Infrastructure and Integrated ProtectionAssociation of Fire and Safety EngineeringOstrava, Czech Republic2017978-80-7385-190-3
Components of transport modes [17].
Security pillars of the organization [36].
Comparison of CO2 emission from the transport of one ton of goods per km [42].
Environmental risk assessment procedure in transport.
ijerph-17-05494-t001_Table 1
Criteria determining the type of risk.
Areas
Criteria
Event naming:
1. Exclusion of traffic—the threat may manifest itself in that it is not possible to operate train transport.2. Change of the schedule—the threat may manifest itself in such a way that it is possible to operate traffic, but to a limited extent.3. Technology change—threats can manifest themselves as an impact on the work system.4. Change in the scope of services—the threat may manifest itself in such a way that the infrastructure is not disrupted, but there has been a change in services.
Activation mechanisms:
1. Mass infection.2. Assault on operating personnel.3. Device malfunction.4. Illness of staff.5. Panic.6. Fire.7. Landslide.8. Flooding of the runway.
Event locations:
1. Train—point object.2. Track section—line object.3. Station—area object.4. Surroundings of the track—area object.
Causes of events:
1. Unintentional errors of transport participants.2. Failure of supplies necessary for operation.3. Problems in state administration.4. Extreme natural phenomena and weather.5. Technical failure.6. Crime.
ijerph-17-05494-t002_Table 2
Indicator—Amount of carbon dioxide (CO2) released into the air.
Value
Verbal Expression
Description
Units/Percentages
1
excellent
Amount of CO2 emissions produced from biomass used as fuel, including CO2 emissions that were not caused by the use of biomass in quantities ranging from 0 to 110 Mg per year.
from 0 to 110 Mg per year
2
good
Amount of CO2 emissions produced from biomass used as fuel, including CO2 emissions that were not caused by the use of biomass in the range of 110 to 162 Mg per year.
from 110 to 162 Mg per year
3
unacceptable
Amount of CO2 emissions produced from biomass used as fuel, including CO2 emissions that were not caused by the use of biomass in quantities exceeding 162 Mg per year.
more than 162 Mg per year
Comment: The indicator presents the measurable production of CO2 emissions from biomass.
ijerph-17-05494-t003_Table 3
Indicator—Amount of investments aimed at environmental protection.
Value
Verbal Expression
Description
Units/Percentages
1
excellent
The railway infrastructure manager spends € 100,000 or more per year on environmental protection.
100,000 euros and more
2
good
The railway infrastructure manager spends between €30,000 and €99,999 per year on environmental protection.
30,000 to 99,999 euros
3
unacceptable
The railway infrastructure manager spends less than €30,000 a year on environmental protection.
less than 30,000 euros
Comment: The indicator presents the amount of investments of the railway infrastructure manager intended for environmental protection.
ijerph-17-05494-t004_Table 4
Indicator—Status of equipment for measuring meteorological data.
Value
Verbal Expression
Description
Units/Percentages
1
excellent
Checks of the functionality of measuring equipment are performed at regular intervals.
90%
2
good
Checks of the functionality of measuring equipment are performed at irregular intervals.
70%
3
unacceptable
The state of functionality of measuring equipment is outdated, the results of measured values are inadequate.
30%
Comment: The indicator presents the status of the meteorological equipment of the infrastructure manager used as a database for telematics solutions in transport.
\\n\"\n]"}}},{"rowIdx":453,"cells":{"data":{"kind":"list like","value":["\nInt J Environ Res Public HealthInt J Environ Res Public HealthijerphInternational Journal of Environmental Research and Public Health1661-78271660-4601MDPI32722097PMC743211010.3390/ijerph17155325ijerph-17-05325ArticleThe Utility of Virtual Patient Simulations for Clinical Reasoning Educationhttps://orcid.org/0000-0002-9322-8455WatariTakashi1*TokudaYasuharu2OwadaMeiko3OnigataKazumichi1Postgraduate Clinical Training Center, Shimane University Hospital, 89–1, Enya-cho, Izumo shi Shimane 693-8501, Japan; konigata@gmail.comOkinawa Muribushi Project for Teaching Hospitals, Okinawa 901-2132, Japan; yasuharu.tokuda@gmail.comNursing Department, Toho University Hospital Omori Medical Center, Tokyo 143-8541, Japan; meiko.owada@ns.toho-u.ac.jpCorrespondence: wataritari@gmail.com; Tel.: +81-853-20-20052472020820201715532525520202372020© 2020 by the authors.2020Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Virtual Patient Simulations (VPSs) have been cited as a novel learning strategy, but there is little evidence that VPSs yield improvements in clinical reasoning skills and medical knowledge. This study aimed to clarify the effectiveness of VPSs for improving clinical reasoning skills among medical students, and to compare improvements in knowledge or clinical reasoning skills relevant to specific clinical scenarios. We enrolled 210 fourth-year medical students in March 2017 and March 2018 to participate in a real-time pre-post experimental design conducted in a large lecture hall by using a clicker. A VPS program (®Body Interact, Portugal) was implemented for one two-hour class session using the same methodology during both years. A pre–post 20-item multiple-choice questionnaire (10 knowledge and 10 clinical reasoning items) was used to evaluate learning outcomes. A total of 169 students completed the program. Participants showed significant increases in average total post-test scores, both on knowledge items (pre-test: median = 5, mean = 4.78, 95% CI (4.55–5.01); post-test: median = 5, mean = 5.12, 95% CI (4.90–5.43); p-value = 0.003) and clinical reasoning items (pre-test: median = 5, mean = 5.3 95%, CI (4.98–5.58); post-test: median = 8, mean = 7.81, 95% CI (7.57–8.05); p-value < 0.001). Thus, VPS programs could help medical students improve their clinical decision-making skills without lecturer supervision.
Traditional lectures in Japanese medical schools have been primarily conducted as didactic lectures in large classrooms, with little interactivity. According to a previous study, for digital native students, such one-way, passive lectures are not effective and their learning outcomes may be relatively low [1,2,3,4,5]. Further, other reports have indicated that future medical education will make advancements through the implementation of digital tools such as video, audio, and simulators [1,4,6]. In fact, since the 1990s, research has especially focused on the application of virtual simulation technology to medical education [7,8,9,10,11,12]. Currently, virtual simulation including virtual patient is helpful in pre-clinical education, which can now utilize 3D images to teach subjects such as anatomy and pathology [13,14], training for pediatric surgery and laparoscopy [15,16], bioethics [17], and tracheal intubation techniques [18]. The technology has already been applied in the virtual simulation of hearing and vision loss to enhance medical students’ empathy for elderly patients [19]. A systematic review reported that the use of virtual patients can more effectively improve medical students’ skills and achieve at least the same degree of knowledge as traditional methods. The findings suggest that skills can be improved in a targeted way. The improved skills include procedural skills, a mixture of procedural and team skills, and some clinical reasoning skills. Moreover, virtual patient simulations (VPSs) in abnormal clinical scenarios may provide an accelerated breakthrough in improving clinical reasoning education [20]. However, there is inadequate evidence of the usefulness of VPSs for improving clinical reasoning skills for undergraduate medical students without lecturer supervision [21,22]. To the best of our knowledge, few studies have used VPSs to comparatively measure which areas of relevant case knowledge and clinical reasoning skills in symptomatology lead to better outcomes. Thus, this study had two objectives: (1) to clarify the effectiveness of VPSs for developing clinical reasoning skills among medical students without lecturer supervision, and (2) to elucidate whether VPSs improve clinical reasoning skills or knowledge of a particular case. To address these objectives, we used a pre–post experimental design to study a VPS.
2. Materials and Methods2.1. Study Setting and Participants
This pre–post study was conducted at the Shimane University School of Medicine, a national medical school in Japan. It took place during one two-hour introductory class in clinical clerkship education in March 2017 and March 2018. Medical school programs in Japan are six years in length, and the students who participated in the study were at the end of their fourth year of courses, having recently passed the Objective Structured Clinical Examination. About 105 students are enrolled in each year of this medical school, so we arranged to perform the exact intervention in the same setting for a total of 210 people over two years. Before the intervention, we explained to each possible candidate: (1) the research intent, (2) the contents of the class, (3) the expected effects, (4) that data collection would be fully anonymous and collected with the distributed clicker, (5) that participation was free and voluntary, (6) that there was no conflict of interest, and (7) that we would not use these data for student evaluation.
A total of 191 participants attended the classes in this study; 19 of the original candidates did not participate due to absence. Of these, we analyzed a total of 169 participants (88.5%) after excluding 13 participants who did not provide consent and 9 participants who had trouble connecting their clickers and left the class early. The answers were not divided according to sex, as the data were collected anonymously using randomly distributed clickers. However, in 2017 and 2018, the proportion of fourth-year female students was 38% and 36%, respectively.
2.2. Study Design
This was a single center pre–post study of the effects of VPSs on clinical reasoning. A VPS software program (®Body Interact, Coimbra, Portugal) was used as an experimental intervention in both two-hour classes using an experimental design [23]. As the VPS in this study was based on a screen rather than a headset, it can be considered a partial VPS. In this software (Figure 1), the virtual patient shows the dynamic pathophysiological response to user decisions. Participants can act as real clinicians, ordering almost any tests or treatments as needed [23]. We first provided guidance on study participation, confirming that the responses of the participants who consented would be collected using a real-time audience response system (®TurningPoint clicker, Tokyo, Japan), and that the study would exclude the data of the participants who subsequently withdrew consent [24]. An audience response system clicker was randomly distributed to each participant to answer the questions in real time (Figure 2). The data were transmitted and saved to the computer at the front of the room, ensuring anonymity and making it impossible to identify individuals. To evaluate the VPS learning outcomes, we prepared a 20-item multiple-choice question (MCQ) quiz, which included 10 knowledge items and 10 clinical reasoning items for each of two scenarios (Supplementary File S1 List of 20 MCQs). We categorized the questions into knowledge items and clinical reasoning items from the past questions of the Japanese National Medical Practitioners Qualifying Examination in Japan, which were relevant to each scenario. The items were randomly arranged and selected from the Japanese National Medical Practitioners Qualifying Examination. The same MCQ quiz was administered to participants pre-study and post-study to evaluate the VPS outcomes. We explained that the participants could not discuss the answers with others. The difference between the scores indicated what the participants had learned through the intervention.
Two scenarios were used: (1) a 55-year-old male with altered mental status, and (2) a 65-year-old male with acute chest pain. Since the simulation software was partially in English, a faculty member (the same for each class) performed some minimal translations into Japanese as necessary. Another faculty member guided the participants in how to operate the VPS; this faculty member was instructed to intervene as little as possible during the class, especially regarding knowledge and clinical reasoning skills that were directly relevant to the answers for the pre-post tests. For each scenario, participants were allotted 20 min to make a diagnosis and treat the patient. During this period, participants were required to conduct a medical interview, physical examination, interpretation of results, and treatment as if they were practicing physicians. Following each scenario, the participants were automatically presented with a summary of their decision-making. Participants’ pre-post test responses were automatically sent to the computer as a CSV file. To ensure uniform study conditions and reduce confounding factors as much as possible, we used the same large lecture hall and ensured that all participants had the same level of experience in both 2017 and 2018. This was meant to minimize differences in VPS usage time and maintain fairness. The same interventions and methodology were used for all participants.
2.3. Statistical Analyses
We used an interquartile range (IQR) along with the 25th and 75th percentiles to indicate skewed data. The Shapiro–Wilk test was implemented to examine the total score of pre-post test distributions. McNemar’s test was used for paired nominal data pre–post study with Bonferroni correction, while the Wilcoxon signed-rank test was employed for nonparametric repeated measurements with skewed continuous variables. All analyses were performed using Stata statistical software, version 14.0 (Stata 14 Base Reference Manual; Stata Corp., College Station, TX, USA). All tests were two-sided with a p-value < 0.05 (Bonferroni correction in Table 1; p-value < 0.0025), which was considered statistically significant.
2.4. Ethics
The study was conducted in accordance with the Declaration of Helsinki. The Ethics Committee of the Shimane University Hospital did not conduct a review for the following reasons: participants’ personal information was not made available, the research data were automatically converted electronically to nondiscriminating data, participants volunteered and provided informed consent, the safety of VPS research is well-established, VPS research is widely used, and there was no risk of harm to the participants.
3. Results
Compared with pre-test baseline scores, participating students showed significant increase in average total post-test scores (p-value < 0.001). The total pre-test scores showed a median of 10 IQR (8–12), a mean of 10.1, and 95% CI (9.6–10.5); the post-test scores showed a median of 13 IQR (11–15), a mean of 13.0, and 95% CI (12.6–13.4). Figure 3 presents histograms of the pre-test and post-test scores for the 169 participants. The pre-test scores showed a normal distribution, while the post-test scores after the VPS intervention skewed to the right (Shapiro–Wilk test, p-value < 0.022). Furthermore, no significant differences were observed for these fourth-year medical students between the years 2017 (pre-test scores: median = 10 IQR (8–11), mean = 9.7, 95% CI (9.1–10.3); post-test scores: median = 13, IQR (11–15), mean = 12.7, 95% CI (12.1–13.2)) and 2018 (pre-test scores: median = 10 IQR (9–12), mean = 10.4, 95% CI (9.9–10.3); post-test scores: median = 13 IQR (12–16), mean = 13.3, 95% CI (12.7–13.9)).
Table 1 shows that the 10 items related to knowledge (K) and the 10 items related to clinical reasoning (CR), comprising past questions of the Japanese National Examination for Physicians, were randomly arranged. We compared the correct answer rate of the pre-post tests and performed McNemar’s test for each quiz and Bonferroni correction for all 20 items. As a result, 7 of the 10 CR items showed a statistically significant increase in the correct answer rate, whereas only 4 of the 10 K items showed a significant increase. The rate of change between CR items (median = +22.8% IQR 11.8% to 39.1%, mean = 25.3, 95% CI (12.1–38.5)) was significantly higher than that between K items (median = +4.2% IQR −1.8% to 11.8%, mean = 3.84, 95% CI (−5.1 to 12.8)), with p-value < 0.008. Notably, for the CR items, although the pre-test correct answer rate was low, the correct answer rate for some post-test items increased by 50 points or more (i.e., items 1 and 14). The items with a large increase in the correct answer rate included a direct question regarding diagnosis of an altered mental status and management of acute chest pain. On the other hand, some items requiring simple knowledge showed a decrease in the percentage of correct answers.
Figure 4 presents box plots of the scores for the 10 CR items and the 10 K items. CR scores increased significantly (pre-test: median = 5, mean = 5.3 95%, CI (4.98–5.58); post-test: median = 8, mean = 7.81, 95% CI (7.57–8.05); p-value < 0.001). K item scores overall also increased statistically, though the median was unchanged (pre-test: median = 5, mean = 4.78, 95% CI (4.55–5.01); post-test: median = 5, mean = 5.12, 95% CI (4.90–5.43); p-value = 0.003).
4. Discussion
As expected, overall scores were higher after the intervention. This can be explained by the fact that the histogram of overall scores was significantly skewed towards the higher scores. However, as a general rule with certain educational interventions, total scores will always rise immediately afterwards. This study assessed CR outcomes by using a VPS to train participants on diagnosing two clinical scenarios. The results show that VPSs improve CR ability even when used to instruct a large group in a lecture hall. On the other hand, little improvement was observed in K item scores when teacher intervention and instruction were minimized. There are several possible causes for this result. First, even if the participants had already learned the information necessary to answer all K items, these items may be difficult for fourth-year students because they were taken from the Japanese National Medical Practitioners Qualifying Examination, and the students have not yet performed their clinical clerkships. Second, the information is necessary to answer K items encompassed medical knowledge only, and may have been difficult to learn based solely on VPS unless teachers explained the scenario. For example, it is challenging to learn to interpret either electrocardiograms of patients with chest pain or contrast CTs based on anatomical knowledge simply by engaging with a VR program and without reading a textbook or listening to a lecture. Third, improving knowledge item scores in a short period of time may be challenging without essential knowledge of pathology, pharmacology, physiology, and anatomy. Furthermore, the knowledge acquired in this pre-clinical stage may still be perceived as complex when based only on engagement with the VPS. For example, the scores for some of the K items decreased in the opposite direction (items 9 and 18). One possible reason may be that analyzing the chest pain and altered mental status cases through the VPS program caused learner bias due to confusion over each scenario, and the answers were pulled in an unexpected direction. On the other hand, the scores increased for problems related to CR regarding altered mental status and acute chest pain. This is because these scenarios are more closely related to the diagnostic process of listing differential diagnoses for chest pains and determining necessary tests, as well as initially ruling out hypoglycemia for the differential diagnosis of altered mental status. For these reasons, we believe that engaging with a VPS is more useful for learning CR compared to acquiring medical knowledge, and for students to attain clinical experience by repeatedly utilizing the VPS.
For today’s digital native students, we believe it is necessary to implement new learning methods that include video, music, YouTube, and social media, rather than traditional methods [1]. One previous study compared three educational methods for teaching CR: live discussion, watching a video of the discussion, and learning from a textbook [25]. Notably, immediately after the lesson, the students who participated in the live discussion had a statistically significant outcome. However, an evaluation two weeks later showed no significant difference in knowledge retention from watching the discussion video or from participating in the live discussion, and both methods were found to be more effective than textbook learning.
In addition, another randomized controlled trial showed that the authenticity of CR (that is, how similar it is to actual practical experience) in traditional pre-clerkship instruction might not be high, as measured against pre-clerkship and clerkship outcome measures [26].
In this study, since we implemented a nonimmersive simulation without a virtual reality (VR) head set, we cannot consider it a true VR simulation by narrow definition. However, we gathered the participants in a large lecture hall with a shared screen to ensure uniformity of participant experience and to minimize confounding factors as much as possible. A fully immersive VR simulation, using a VR headset, may have a higher educational effect than a large shared screen [27]. It is not easy, however, for all participants to simultaneously use a fully immersive VR simulation, and it currently requires a tremendous amount of money and high-tech equipment [28]. Moreover, another comparative study found no overall significant differences in efficiency of educational outcomes and participant satisfaction between a shared screen and a fully immersive virtual reality simulation [29].
Furthermore, the greatest value of VPS is that the patient is not required to be involved in student training, and thus there is no risk to the patient [2,8,30,31,32,33]. There are also other merits, such as the ability to repeat the learning experience until the educational goal is achieved. Of course, it is not possible to complete all medical instruction through simulation education. However, we believe that if the strengths of traditional educational methods are combined appropriately with the VPS, further educational benefits can be expected.
Limitations
Although we attempted to ensure a uniform study setting, there are some limitations to our study. First, this is not an experiment comparing VPS to other teaching methods. For that reason, we cannot say whether it is better than traditional teaching methods, such as lectures and case discussions. This discussion point is important, and further comparative experiments need to be conducted. Second, the intervention should ideally be reassessed afterwards to ensure that clinical reasoning skills have indeed improved and have been retained. Further, we cannot say whether performing multiple VPS scenarios will actually develop sufficient clinical reasoning skills for a clinician. However, our study could not be followed up because the data were taken anonymously using a clicker. Third, this study was conducted in a Japanese university hospital. Since clinical medical education systems vary from country to country, there is a question of external validity. It is uncertain whether these results are applicable to other countries or institutions. Fourth, variations in the degree of participation may be possible due to the type of VPS used. Further, although we tried to ensure consistency in the study setting, there may have been some unavoidable differences, such as the individual students’ active participation, concentration, and screen visibility due to seating positions. Fifth, about 10% of students were absent since no participation incentives were provided, and this may have created a selection bias. Finally, no similar studies have been conducted, and the validity of the test questions is uncertain, because they were selected from the Japanese National Medical Practitioners Qualifying Examination.
5. Conclusions
Our study suggests that VPS programs are more effective for increasing CR scores than K scores among medical students. VPS software programs could thus help medical students improve their clinical decision-making skills with minimal supervision from lecturers. In summary, the widespread use of VPS software programs in clinical education could help maximize the effectiveness of medical school curriculum.
Acknowledgments
The authors would like to thank Satoru Hattori at Uhida Yoko Co. for providing user information and careful guidance about Body Interact. We also thank all of the medical students who participated in this study.
Supplementary Materials
The following are available online at https://www.mdpi.com/1660-4601/17/15/5325/s1, File S1: List of 20 MCQs.
Click here for additional data file.
Author Contributions
Conceptualization, T.W.; methodology, T.W. and Y.T.; software, T.W. and M.O.; validation, T.W., M.O., and Y.T.; formal analysis, T.W. and Y.T.; investigation, T.W.; resources, T.W.; data curation, T.W. and M.O.; writing—original draft preparation, T.W.; writing—review and editing, T.W. and Y.T.; visualization, T.W.; supervision, Y.T. and K.O.; project administration, T.W. and K.O.; funding acquisition, T.W., and K.O. All authors have read and agreed to the published version of the manuscript.
Funding
This work was funded by JSPS KAKENHI, grant numbers 20H03913.
Conflicts of Interest
The authors declare no conflict of interest.
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The virtual reality simulation software (®Body Interact, Coimbra, Portugal).
Clickers being distributed during the pre-test (March 2018).
Histogram with a bell curve of participant scores (a) on the pre-test and (b) on the post-test.
Total scores on knowledge (a) and clinical reasoning (b) items.
\n"],"string":"[\n \"\\nInt J Environ Res Public HealthInt J Environ Res Public HealthijerphInternational Journal of Environmental Research and Public Health1661-78271660-4601MDPI32722097PMC743211010.3390/ijerph17155325ijerph-17-05325ArticleThe Utility of Virtual Patient Simulations for Clinical Reasoning Educationhttps://orcid.org/0000-0002-9322-8455WatariTakashi1*TokudaYasuharu2OwadaMeiko3OnigataKazumichi1Postgraduate Clinical Training Center, Shimane University Hospital, 89–1, Enya-cho, Izumo shi Shimane 693-8501, Japan; konigata@gmail.comOkinawa Muribushi Project for Teaching Hospitals, Okinawa 901-2132, Japan; yasuharu.tokuda@gmail.comNursing Department, Toho University Hospital Omori Medical Center, Tokyo 143-8541, Japan; meiko.owada@ns.toho-u.ac.jpCorrespondence: wataritari@gmail.com; Tel.: +81-853-20-20052472020820201715532525520202372020© 2020 by the authors.2020Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Virtual Patient Simulations (VPSs) have been cited as a novel learning strategy, but there is little evidence that VPSs yield improvements in clinical reasoning skills and medical knowledge. This study aimed to clarify the effectiveness of VPSs for improving clinical reasoning skills among medical students, and to compare improvements in knowledge or clinical reasoning skills relevant to specific clinical scenarios. We enrolled 210 fourth-year medical students in March 2017 and March 2018 to participate in a real-time pre-post experimental design conducted in a large lecture hall by using a clicker. A VPS program (®Body Interact, Portugal) was implemented for one two-hour class session using the same methodology during both years. A pre–post 20-item multiple-choice questionnaire (10 knowledge and 10 clinical reasoning items) was used to evaluate learning outcomes. A total of 169 students completed the program. Participants showed significant increases in average total post-test scores, both on knowledge items (pre-test: median = 5, mean = 4.78, 95% CI (4.55–5.01); post-test: median = 5, mean = 5.12, 95% CI (4.90–5.43); p-value = 0.003) and clinical reasoning items (pre-test: median = 5, mean = 5.3 95%, CI (4.98–5.58); post-test: median = 8, mean = 7.81, 95% CI (7.57–8.05); p-value < 0.001). Thus, VPS programs could help medical students improve their clinical decision-making skills without lecturer supervision.
Traditional lectures in Japanese medical schools have been primarily conducted as didactic lectures in large classrooms, with little interactivity. According to a previous study, for digital native students, such one-way, passive lectures are not effective and their learning outcomes may be relatively low [1,2,3,4,5]. Further, other reports have indicated that future medical education will make advancements through the implementation of digital tools such as video, audio, and simulators [1,4,6]. In fact, since the 1990s, research has especially focused on the application of virtual simulation technology to medical education [7,8,9,10,11,12]. Currently, virtual simulation including virtual patient is helpful in pre-clinical education, which can now utilize 3D images to teach subjects such as anatomy and pathology [13,14], training for pediatric surgery and laparoscopy [15,16], bioethics [17], and tracheal intubation techniques [18]. The technology has already been applied in the virtual simulation of hearing and vision loss to enhance medical students’ empathy for elderly patients [19]. A systematic review reported that the use of virtual patients can more effectively improve medical students’ skills and achieve at least the same degree of knowledge as traditional methods. The findings suggest that skills can be improved in a targeted way. The improved skills include procedural skills, a mixture of procedural and team skills, and some clinical reasoning skills. Moreover, virtual patient simulations (VPSs) in abnormal clinical scenarios may provide an accelerated breakthrough in improving clinical reasoning education [20]. However, there is inadequate evidence of the usefulness of VPSs for improving clinical reasoning skills for undergraduate medical students without lecturer supervision [21,22]. To the best of our knowledge, few studies have used VPSs to comparatively measure which areas of relevant case knowledge and clinical reasoning skills in symptomatology lead to better outcomes. Thus, this study had two objectives: (1) to clarify the effectiveness of VPSs for developing clinical reasoning skills among medical students without lecturer supervision, and (2) to elucidate whether VPSs improve clinical reasoning skills or knowledge of a particular case. To address these objectives, we used a pre–post experimental design to study a VPS.
2. Materials and Methods2.1. Study Setting and Participants
This pre–post study was conducted at the Shimane University School of Medicine, a national medical school in Japan. It took place during one two-hour introductory class in clinical clerkship education in March 2017 and March 2018. Medical school programs in Japan are six years in length, and the students who participated in the study were at the end of their fourth year of courses, having recently passed the Objective Structured Clinical Examination. About 105 students are enrolled in each year of this medical school, so we arranged to perform the exact intervention in the same setting for a total of 210 people over two years. Before the intervention, we explained to each possible candidate: (1) the research intent, (2) the contents of the class, (3) the expected effects, (4) that data collection would be fully anonymous and collected with the distributed clicker, (5) that participation was free and voluntary, (6) that there was no conflict of interest, and (7) that we would not use these data for student evaluation.
A total of 191 participants attended the classes in this study; 19 of the original candidates did not participate due to absence. Of these, we analyzed a total of 169 participants (88.5%) after excluding 13 participants who did not provide consent and 9 participants who had trouble connecting their clickers and left the class early. The answers were not divided according to sex, as the data were collected anonymously using randomly distributed clickers. However, in 2017 and 2018, the proportion of fourth-year female students was 38% and 36%, respectively.
2.2. Study Design
This was a single center pre–post study of the effects of VPSs on clinical reasoning. A VPS software program (®Body Interact, Coimbra, Portugal) was used as an experimental intervention in both two-hour classes using an experimental design [23]. As the VPS in this study was based on a screen rather than a headset, it can be considered a partial VPS. In this software (Figure 1), the virtual patient shows the dynamic pathophysiological response to user decisions. Participants can act as real clinicians, ordering almost any tests or treatments as needed [23]. We first provided guidance on study participation, confirming that the responses of the participants who consented would be collected using a real-time audience response system (®TurningPoint clicker, Tokyo, Japan), and that the study would exclude the data of the participants who subsequently withdrew consent [24]. An audience response system clicker was randomly distributed to each participant to answer the questions in real time (Figure 2). The data were transmitted and saved to the computer at the front of the room, ensuring anonymity and making it impossible to identify individuals. To evaluate the VPS learning outcomes, we prepared a 20-item multiple-choice question (MCQ) quiz, which included 10 knowledge items and 10 clinical reasoning items for each of two scenarios (Supplementary File S1 List of 20 MCQs). We categorized the questions into knowledge items and clinical reasoning items from the past questions of the Japanese National Medical Practitioners Qualifying Examination in Japan, which were relevant to each scenario. The items were randomly arranged and selected from the Japanese National Medical Practitioners Qualifying Examination. The same MCQ quiz was administered to participants pre-study and post-study to evaluate the VPS outcomes. We explained that the participants could not discuss the answers with others. The difference between the scores indicated what the participants had learned through the intervention.
Two scenarios were used: (1) a 55-year-old male with altered mental status, and (2) a 65-year-old male with acute chest pain. Since the simulation software was partially in English, a faculty member (the same for each class) performed some minimal translations into Japanese as necessary. Another faculty member guided the participants in how to operate the VPS; this faculty member was instructed to intervene as little as possible during the class, especially regarding knowledge and clinical reasoning skills that were directly relevant to the answers for the pre-post tests. For each scenario, participants were allotted 20 min to make a diagnosis and treat the patient. During this period, participants were required to conduct a medical interview, physical examination, interpretation of results, and treatment as if they were practicing physicians. Following each scenario, the participants were automatically presented with a summary of their decision-making. Participants’ pre-post test responses were automatically sent to the computer as a CSV file. To ensure uniform study conditions and reduce confounding factors as much as possible, we used the same large lecture hall and ensured that all participants had the same level of experience in both 2017 and 2018. This was meant to minimize differences in VPS usage time and maintain fairness. The same interventions and methodology were used for all participants.
2.3. Statistical Analyses
We used an interquartile range (IQR) along with the 25th and 75th percentiles to indicate skewed data. The Shapiro–Wilk test was implemented to examine the total score of pre-post test distributions. McNemar’s test was used for paired nominal data pre–post study with Bonferroni correction, while the Wilcoxon signed-rank test was employed for nonparametric repeated measurements with skewed continuous variables. All analyses were performed using Stata statistical software, version 14.0 (Stata 14 Base Reference Manual; Stata Corp., College Station, TX, USA). All tests were two-sided with a p-value < 0.05 (Bonferroni correction in Table 1; p-value < 0.0025), which was considered statistically significant.
2.4. Ethics
The study was conducted in accordance with the Declaration of Helsinki. The Ethics Committee of the Shimane University Hospital did not conduct a review for the following reasons: participants’ personal information was not made available, the research data were automatically converted electronically to nondiscriminating data, participants volunteered and provided informed consent, the safety of VPS research is well-established, VPS research is widely used, and there was no risk of harm to the participants.
3. Results
Compared with pre-test baseline scores, participating students showed significant increase in average total post-test scores (p-value < 0.001). The total pre-test scores showed a median of 10 IQR (8–12), a mean of 10.1, and 95% CI (9.6–10.5); the post-test scores showed a median of 13 IQR (11–15), a mean of 13.0, and 95% CI (12.6–13.4). Figure 3 presents histograms of the pre-test and post-test scores for the 169 participants. The pre-test scores showed a normal distribution, while the post-test scores after the VPS intervention skewed to the right (Shapiro–Wilk test, p-value < 0.022). Furthermore, no significant differences were observed for these fourth-year medical students between the years 2017 (pre-test scores: median = 10 IQR (8–11), mean = 9.7, 95% CI (9.1–10.3); post-test scores: median = 13, IQR (11–15), mean = 12.7, 95% CI (12.1–13.2)) and 2018 (pre-test scores: median = 10 IQR (9–12), mean = 10.4, 95% CI (9.9–10.3); post-test scores: median = 13 IQR (12–16), mean = 13.3, 95% CI (12.7–13.9)).
Table 1 shows that the 10 items related to knowledge (K) and the 10 items related to clinical reasoning (CR), comprising past questions of the Japanese National Examination for Physicians, were randomly arranged. We compared the correct answer rate of the pre-post tests and performed McNemar’s test for each quiz and Bonferroni correction for all 20 items. As a result, 7 of the 10 CR items showed a statistically significant increase in the correct answer rate, whereas only 4 of the 10 K items showed a significant increase. The rate of change between CR items (median = +22.8% IQR 11.8% to 39.1%, mean = 25.3, 95% CI (12.1–38.5)) was significantly higher than that between K items (median = +4.2% IQR −1.8% to 11.8%, mean = 3.84, 95% CI (−5.1 to 12.8)), with p-value < 0.008. Notably, for the CR items, although the pre-test correct answer rate was low, the correct answer rate for some post-test items increased by 50 points or more (i.e., items 1 and 14). The items with a large increase in the correct answer rate included a direct question regarding diagnosis of an altered mental status and management of acute chest pain. On the other hand, some items requiring simple knowledge showed a decrease in the percentage of correct answers.
Figure 4 presents box plots of the scores for the 10 CR items and the 10 K items. CR scores increased significantly (pre-test: median = 5, mean = 5.3 95%, CI (4.98–5.58); post-test: median = 8, mean = 7.81, 95% CI (7.57–8.05); p-value < 0.001). K item scores overall also increased statistically, though the median was unchanged (pre-test: median = 5, mean = 4.78, 95% CI (4.55–5.01); post-test: median = 5, mean = 5.12, 95% CI (4.90–5.43); p-value = 0.003).
4. Discussion
As expected, overall scores were higher after the intervention. This can be explained by the fact that the histogram of overall scores was significantly skewed towards the higher scores. However, as a general rule with certain educational interventions, total scores will always rise immediately afterwards. This study assessed CR outcomes by using a VPS to train participants on diagnosing two clinical scenarios. The results show that VPSs improve CR ability even when used to instruct a large group in a lecture hall. On the other hand, little improvement was observed in K item scores when teacher intervention and instruction were minimized. There are several possible causes for this result. First, even if the participants had already learned the information necessary to answer all K items, these items may be difficult for fourth-year students because they were taken from the Japanese National Medical Practitioners Qualifying Examination, and the students have not yet performed their clinical clerkships. Second, the information is necessary to answer K items encompassed medical knowledge only, and may have been difficult to learn based solely on VPS unless teachers explained the scenario. For example, it is challenging to learn to interpret either electrocardiograms of patients with chest pain or contrast CTs based on anatomical knowledge simply by engaging with a VR program and without reading a textbook or listening to a lecture. Third, improving knowledge item scores in a short period of time may be challenging without essential knowledge of pathology, pharmacology, physiology, and anatomy. Furthermore, the knowledge acquired in this pre-clinical stage may still be perceived as complex when based only on engagement with the VPS. For example, the scores for some of the K items decreased in the opposite direction (items 9 and 18). One possible reason may be that analyzing the chest pain and altered mental status cases through the VPS program caused learner bias due to confusion over each scenario, and the answers were pulled in an unexpected direction. On the other hand, the scores increased for problems related to CR regarding altered mental status and acute chest pain. This is because these scenarios are more closely related to the diagnostic process of listing differential diagnoses for chest pains and determining necessary tests, as well as initially ruling out hypoglycemia for the differential diagnosis of altered mental status. For these reasons, we believe that engaging with a VPS is more useful for learning CR compared to acquiring medical knowledge, and for students to attain clinical experience by repeatedly utilizing the VPS.
For today’s digital native students, we believe it is necessary to implement new learning methods that include video, music, YouTube, and social media, rather than traditional methods [1]. One previous study compared three educational methods for teaching CR: live discussion, watching a video of the discussion, and learning from a textbook [25]. Notably, immediately after the lesson, the students who participated in the live discussion had a statistically significant outcome. However, an evaluation two weeks later showed no significant difference in knowledge retention from watching the discussion video or from participating in the live discussion, and both methods were found to be more effective than textbook learning.
In addition, another randomized controlled trial showed that the authenticity of CR (that is, how similar it is to actual practical experience) in traditional pre-clerkship instruction might not be high, as measured against pre-clerkship and clerkship outcome measures [26].
In this study, since we implemented a nonimmersive simulation without a virtual reality (VR) head set, we cannot consider it a true VR simulation by narrow definition. However, we gathered the participants in a large lecture hall with a shared screen to ensure uniformity of participant experience and to minimize confounding factors as much as possible. A fully immersive VR simulation, using a VR headset, may have a higher educational effect than a large shared screen [27]. It is not easy, however, for all participants to simultaneously use a fully immersive VR simulation, and it currently requires a tremendous amount of money and high-tech equipment [28]. Moreover, another comparative study found no overall significant differences in efficiency of educational outcomes and participant satisfaction between a shared screen and a fully immersive virtual reality simulation [29].
Furthermore, the greatest value of VPS is that the patient is not required to be involved in student training, and thus there is no risk to the patient [2,8,30,31,32,33]. There are also other merits, such as the ability to repeat the learning experience until the educational goal is achieved. Of course, it is not possible to complete all medical instruction through simulation education. However, we believe that if the strengths of traditional educational methods are combined appropriately with the VPS, further educational benefits can be expected.
Limitations
Although we attempted to ensure a uniform study setting, there are some limitations to our study. First, this is not an experiment comparing VPS to other teaching methods. For that reason, we cannot say whether it is better than traditional teaching methods, such as lectures and case discussions. This discussion point is important, and further comparative experiments need to be conducted. Second, the intervention should ideally be reassessed afterwards to ensure that clinical reasoning skills have indeed improved and have been retained. Further, we cannot say whether performing multiple VPS scenarios will actually develop sufficient clinical reasoning skills for a clinician. However, our study could not be followed up because the data were taken anonymously using a clicker. Third, this study was conducted in a Japanese university hospital. Since clinical medical education systems vary from country to country, there is a question of external validity. It is uncertain whether these results are applicable to other countries or institutions. Fourth, variations in the degree of participation may be possible due to the type of VPS used. Further, although we tried to ensure consistency in the study setting, there may have been some unavoidable differences, such as the individual students’ active participation, concentration, and screen visibility due to seating positions. Fifth, about 10% of students were absent since no participation incentives were provided, and this may have created a selection bias. Finally, no similar studies have been conducted, and the validity of the test questions is uncertain, because they were selected from the Japanese National Medical Practitioners Qualifying Examination.
5. Conclusions
Our study suggests that VPS programs are more effective for increasing CR scores than K scores among medical students. VPS software programs could thus help medical students improve their clinical decision-making skills with minimal supervision from lecturers. In summary, the widespread use of VPS software programs in clinical education could help maximize the effectiveness of medical school curriculum.
Acknowledgments
The authors would like to thank Satoru Hattori at Uhida Yoko Co. for providing user information and careful guidance about Body Interact. We also thank all of the medical students who participated in this study.
Supplementary Materials
The following are available online at https://www.mdpi.com/1660-4601/17/15/5325/s1, File S1: List of 20 MCQs.
Click here for additional data file.
Author Contributions
Conceptualization, T.W.; methodology, T.W. and Y.T.; software, T.W. and M.O.; validation, T.W., M.O., and Y.T.; formal analysis, T.W. and Y.T.; investigation, T.W.; resources, T.W.; data curation, T.W. and M.O.; writing—original draft preparation, T.W.; writing—review and editing, T.W. and Y.T.; visualization, T.W.; supervision, Y.T. and K.O.; project administration, T.W. and K.O.; funding acquisition, T.W., and K.O. All authors have read and agreed to the published version of the manuscript.
Funding
This work was funded by JSPS KAKENHI, grant numbers 20H03913.
Conflicts of Interest
The authors declare no conflict of interest.
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A randomised controlled trialBMJ Open20199e02597310.1136/bmjopen-2018-02597331494596DurningS.J.DongT.ArtinoA.R.Jr.LaRochelleJ.PangaroL.N.VleutenC.V.SchuwirthL.Instructional authenticity and clinical reasoning in undergraduate medical education: A 2-year, prospective, randomized trialMil. Med.2012177384310.7205/MILMED-D-12-0023423029859GutiérrezF.PierceJ.VergaraV.M.CoulterR.SalandL.CaudellT.P.GoldsmithT.E.AlversonD.C.The Effect of Degree of Immersion Upon Learning Performance in Virtual Reality Simulations for Medical EducationStud. Health Technol. Inform.200712515516017377256EdvardsenO.SteensrudT.[Virtual Reality in Medical Education]Tidsskr. Nor Laegeforen.19981189029069543805PierceJ.GutiérrezF.VergaraV.M.AlversonD.C.QuallsC.SalandL.GoldsmithT.CaudellT.P.Comparative Usability Studies of Full vs. Partial Immersive Virtual Reality Simulation for Medical Education and TrainingStud. Health Technol. Inform.200813237237718391324EllawayR.Virtual Reality in Medical EducationMed. Teach.20103279179310.3109/0142159X.2010.51322320795817AlmarzooqZ.I.LopesM.KocharA.Virtual Learning During the COVID-19 Pandemic: A Disruptive Technology in Graduate Medical EducationJ. Am. Coll. Cardiol.2020752635263810.1016/j.jacc.2020.04.01532304797CookeS.LemayJ.F.Transforming Medical Assessment: Integrating Uncertainty into the Evaluation of Clinical Reasoning in Medical EducationAcad. Med.20179274675110.1097/ACM.000000000000155928557933KononowiczA.A.WoodhamL.A.EdelbringS.StathakarouN.DaviesD.SaxenaN.CarL.T.Carlstedt-DukeJ.CarJ.ZaryN.Virtual Patient Simulations in Health Professions Education: Systematic Review and Meta-Analysis by the Digital Health Education CollaborationJ. Med. Internet Res.201921e1467610.2196/1467631267981
The virtual reality simulation software (®Body Interact, Coimbra, Portugal).
Clickers being distributed during the pre-test (March 2018).
Histogram with a bell curve of participant scores (a) on the pre-test and (b) on the post-test.
Total scores on knowledge (a) and clinical reasoning (b) items.
\\n\"\n]"}}},{"rowIdx":454,"cells":{"data":{"kind":"list like","value":["\nInt J Environ Res Public HealthInt J Environ Res Public HealthijerphInternational Journal of Environmental Research and Public Health1661-78271660-4601MDPI32731593PMC743211110.3390/ijerph17155439ijerph-17-05439ArticleAssessment of COVID-19 Waste Flows During the Emergency State in Romania and Related Public Health and Environmental ConcernsMihaiFlorin-ConstantinDepartment of Research, Faculty of Geography and Geology, Alexandru Ioan Cuza University, Carol I Blvd, Nr.20 A, RO-700505 Iasi, Romania; mihai.florinconstantin@gmail.com2872020820201715543928620202372020© 2020 by the author.2020Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
This paper provides a rapid assessment method of potentially infectious waste flow related to the coronavirus disease (COVID-19) pandemic in Romania focusing on the emergency state (from 16 March to 14 May 2020) where a national lockdown was in force with restrictive and social distancing measures concerning population mobility and economic activities. Medical and municipal waste management systems are critical services in combating the virus spread in the community. This assessment is useful due to poor available data of medical waste flow in environmental reports and it covers COVID-19 patients, quarantined, and self-isolated persons as the main potential infectious waste sources. The proposed model estimates that COVID-19 related waste flow is 4312 t at the national level from 25 February to 15 June of which 2633 t in the emergency state period. This assessment is correlated with deficiencies of medical and municipal waste management systems in Romania before the COVID-19 pandemic as stress factors of public health and environment. This study points out the main challenges of waste operators and reveals some best practices during this pandemic crisis. Based on the results and discussion section, several recommendations are proposed to COVID-19 waste-related issues and points out the crucial role of the reliable medical and municipal waste database in managing such biologic hazards at national and EU levels. Monitoring of COVID-19 waste flow through such models are important for decision-makers, particularly in low and middle-income countries which are facing waste management deficiencies and gaps in waste statistics, to reduce other contamination risks or related environmental threats.
The geographic expansion of the COVID-19 outbreak around the world forced the World Health Organization (WHO) to declare the pandemic status on 11 March 2020. On 24 March, the United Nations Environment Programme (UNEP) pointed out that waste management is an essential public service in avoiding secondary impacts upon public health and environment and special attention should be paid to handle medical, household and other hazardous items in such a period [1]. Massive amounts of medical waste have already been generated and disposed into the natural environment [2] and waste mismanagement practices can increase the contamination risks in developing countries [3,4] or among urban poor which live in informal settlements [5,6] or among hundreds of millions of people without basic water, sanitation, and waste management facilities in rural areas [7].
Romania declared a state of emergency on 16 March due to the pandemic crisis in which a national lockdown was implemented since the first case of COVID-19 patient was registered on 26 February 2020 [8]. The National Public Health Institute of Romania declared household waste generated in quarantines as infectious waste on 18 March 2020. Therefore, there is a shift from usual municipal waste management practices towards the special collection, transport and disposal requirements of hazardous wastes [9]. On 30 March, the European Center for Disease Prevention and Control (ECDPC) released an infection prevention and control guideline at the household level for suspected or confirmed COVID-19 disease with mild symptoms in self-isolation status [10]. This document provides some recommendations regarding the safe management of household waste. One keynote is to have a double waste bag for used tissues, face masks and other waste, which should be disposed of in the residual bin. The European Commission (EC) released a guideline on 14 April regarding waste management practices in the context of coronavirus crisis [11]. This document requires to maintain quality standards of municipal waste collection services and medical waste treatment and any measures must comply with EU regulations. Zero Waste Europe (ZWE) argues that the COVID-19 crisis should not undermine the EU’s long-term circular economy objectives by returning to business as usual [12]. The transition towards a circular economy should be further supported while 2020 is the first year where the recycling rate of 50% should be achieved by the EU Member States. However, this target is impossible to be achieved in Romania where the recycling rate of municipal waste was around 14% in 2017 [13].
Romania is coping with several mismanagement practices such as poor efficiency of source-separated waste collection schemes in urban areas, the prevalence of landfills as a main waste management option, delays in implementation of regional integrated waste management systems, lack of a reliable waste statistics database, etc. Furthermore, rural Romania is facing illegal waste disposal practices (wild dumps, open burning practices), plastic pollution of freshwater bodies and limited waste collection coverage of the rural population in some counties [14,15]. The circular economy remains underdeveloped despite the promising potential in this area [16], but the new circular economy plan promoted EU could catalyze the effort in the right direction [17]. On the other side, medical waste management issues are less examined in Romania, poor details and waste statistics data provided in environmental reports and several environmental crimes investigated by environmental authorities and revealed to the general public through national and local mass-media with a particular focus to hazardous waste incinerators. Neither the new national waste management plans provide a solid and updated analysis of medical waste management status in Romania [18]. According to WHO, about 85% of medical waste generated by healthcare activities is nonhazardous and the remaining 15% is considered hazardous material that can be infectious, toxic or radioactive [19]. However, COVID-19 related waste flow is considered infectious waste which must be properly treated [20,21], but serious concerns are raised around consumption and management of plastic products associated with personal protective equipment (PPE) [22,23]. A weak infectious waste management system could speed up the growth of COVID-19 in developing countries [24].
On this background, this paper aims: (i) to provide a rapid assessment of potentially infectious waste broken down in three main sources such as COVID-19 patients (hospitalized), people in quarantined sites (abroad working/travel Romanian citizens from countries/regions heavily affected by coronavirus), self-isolated people (persons from abroad or contacts with confirmed COVID-19 cases); (ii) to reveal national and some regional flows of potentially infectious waste generated; (iii) to identify critical issues during the emergency state in Romania related to medical and municipal waste management backgrounds; (iv) to reveal some best practices related to waste management sector; (v) to provide some recommendations related to waste management policies and data support at national and EU levels.
This study intends to provide a rapid assessment method to estimate COVID-19 related waste flow in Central and Eastern European countries where medical and municipal waste management systems must be further improved to comply with EU standards. Furthermore, such analysis is useful in case of low and middle-income countries around the world which face poor waste management infrastructure and gaps in waste statistics data, as an alternative solution to estimate the potential COVID-19 related waste flow necessary for decision-makers to reduce other contamination risk routes and environmental pollution threats.
2. Materials and Methods2.1. COVID-19 Status in Romania
The first case of COVID-19 patient waste detected on 26 February 2020 increasing to 168 cases on 16 March (0 deaths and 9 recovered) when the state of emergency was implemented. The first deaths were registered one week later (9 on 23 March). After the first month of emergency state (15 April) the COVID-19 statistics revealed 7216 (cumulative cases), 419 deaths, 1270 recovered and at the end of the emergency state (and beginning of alert state on 15 May) the situation was 16,247 cumulative cases of which 6020 active cases, 1090 deaths and 9370 recovered. By the end of the first stage of the alert state (15 June) statistics show 22,165 cumulative cases of which 4938 active cases, 1410 deaths and 15,817 recovered.
The peak of active cases was recorded in the second stage of emergency state with over 7000 cases from 23 April to 13 May, dropping during the alert state under 5000 cases from 28 May to 15 June. The number of patients in intensive care units (ICU) increased since 19 March (6 cases) to the peak of 288 cases (22 April), maintain the threshold of plus 200 cases until the end of the emergency state (15 May). However, the cases of intensive care patients decreased in the alert state below 200 cases until 15 June. The number of persons in quarantine has increased since 15 March (2855) to the peak on 9 April (25,586) dropping to 11,631 (28 April) with a slight increase by the end of emergency state 14,441 (15 May) followed by a continuous decreasing trend during the alert state (1331 on 15 June). The number of persons self-isolated increase from 14 March (14,680) to the peak on 29 March (132,641) with a continuous descending trend by the end of emergency state such as 14,789 (15 May). However, there is an ascending trend during the alert state from 19 May (24,415) peaking by the end of month 98,403 (30 May) and maintaining this trend in June surpassing 100,000 persons on 11 June than declining by 15 June (92,734 persons).
These statistics are important in the assessment process of Covid-19 related waste flow in Romania as further described in the following section.
2.2. Estimation of COVID-19 Waste Flow in Healthcare Facilities
High-income countries generate on average up to 0.5 kg of hazardous waste per hospital bed per day while low-income countries generate on average 0.2 kg [19]. On the other side, there are no separate collections (hazardous vs nonhazardous items) in developing countries that increase the ratio of hazardous items through contamination [25]. At the national level, medical waste as follows:Mw = No. of active COVID-19 cases per day × Mwgr (kg∙bed∙day−1)\nwhere Mwgr = medical waste generation rate—1 kg∙bed∙day−1 The number of active cases is available at https://www.graphs.ro/ at the national level. Active cases represent confirmed COVID-19 patients that are treated in hospitals without recovered or dead patients. In general, all COVID-19 cases were treated in hospitals during the emergency state.\nSubnational level (county): Mw = confirmed COVID-19 cases per day × Mwgr (kg∙bed∙day−1).
There is no available information about the active COVID-19 statistics at the county level and some inconsistencies could appear between confirmed cases, deaths, and recovered people at this scale. However, daily data provided by the strategic communication group of the Romanian government are considered for estimation to reduce this aspect. Therefore, COVID-19 medical waste flow is calculated from active cases at national level, whereas subnational is calculated from daily confirmed cases.
The assessment of healthcare waste at subnational levels is more difficult to achieve, the reporting systems are made by regional public health authorities (county departments) to the National Institute of Public Health. However, national authorities stopped reporting data at county levels from 19 March until 2 April. On 20 March, a manifesto initiated by Geo-spatial.org and supported by 17 non-governmental organizations (NGOs) was addressed to national authorities to release such key daily COVID-19 statistics data at county level (including confirmed cases, recovered, deaths, no. of people in quarantine or self-isolated and details about age and gender in each category for data consistency, no. of tests performed each day) for better monitoring of COVID-19 status in Romania and to avoid misinformation and spreading of fake news [26].
The average medical waste generation rate was around 1 kg∙bed∙day−1 in Infectious Diseases Clinical Hospital of Craiova used as the baseline for this paper [27]. This value is consistent with the recommendation of El-Haggar [28] as the average amount of medical waste generated per bed per day.
The highest rate of medical waste 1.814 kg∙bed−1∙day−1 was calculated in the case of the Emergency Clinical Hospital of Craiova [29]. The medical waste generation rates in main hospitals vary between 1–1.8 kg∙bed−1∙day−1 compared to smaller healthcare facilities < 0.5 kg∙bed−1·day−1 [27,29].
In Italy, Vaccari et al. [30] found that the highest medical waste generation in departments of anesthetics (5.96 kg∙bed−1∙day−1) pediatric and intensive care (3.37 kg∙bed−1∙day−1). In Wuhan (China) the amount of medical waste peaked to 240 t per day [24]. Therefore, in the COVID-19 pandemic context, medical waste flow associated with intensive care patients is expected to be higher than the general average of infectious disease and upper limited detected by previous studies in Romania (1.8 kg) is further considered in the analysis.\nMw = No. of COVID-19 patients in ICU. day−1 × 1.8 kg∙bed−1∙day−1
The ICU waste is included in the general COVID-19 medical waste flow as calculated by Equation (1).
2.3. Estimation of Potentially Infectious Waste Generated in Quarantine Places or by Self-Isolated People in Households
Quarantine (14 days) is established for all people who have no symptoms but returning from areas with extended community transmission of new coronavirus (COVID-19 red areas) in special locations (e.g., hotels, touristic pensions) supervised by local authorities and county departments of public health. In the first phase, these locations were served by municipal waste operators, but national authorities considered these wastes as infectious. In this situation, the quarantine places must have contracts with special waste operators licensed to collect and transport such hazardous wastes to waste incinerators.
Self-isolation (14 days) is established for people who do not show symptoms, but: (i) have traveled in the last 14 days to regions/localities in areas affected by COVID-19 other than those with extended community transmission (yellow areas); (ii) direct contact with people with symptoms and who traveled to areas with extended community transmission; (iii) direct contact with people who were confirmed with coronavirus (COVID-19); (iv) family member in above-mentioned cases. These waste generated in such households should be disposed of in residual bins and collected by waste operators and transported to landfills.
The estimation of waste flow data takes into consideration the municipal waste generated in households or quarantine places.
In Romania, municipal waste generation rate is 0.64 kg∙inhab∙day−1 in urban areas and 0.31 kg∙inhab∙day−1 in rural areas according to the new national waste management plan [18].However, regional differences are expected between larger cities (e.g., county capitals) and the other urban areas.
The regional waste management plans of Dolj county points out that Craiova city has a per-capita waste generation rate of 0.7 kg∙inhab∙day−1 compared to other cities 0.55 kg∙inhab∙day−1. [31]. In the latter case, this per-capita generation rate is used as a conservative option to estimate the municipal waste flow in quarantine places and self-isolated cases in households.\nQuarantine/self-isolation waste = no. of people in quarantine/self-isolated (national & subnational levels) × WGR, WGR = waste generation rate (0.55 kg∙inhab∙day−1).
Regional assessments of such wastes depend on available data at county levels regarding the number of persons in quarantine or self-isolated. These data are less available to the public than confirmed cases at the county levels. At the beginning of April, all three main categories (confirmed, quarantine, self-isolated) were available for a short time (see Figure 1). The number of quarantine and isolated are daily provided by the press release notes at the national level (https://stirioficiale.ro/informatii) by the strategic communication group of the Romanian government. Some data are available for certain counties through government representatives at county levels (Prefecture Institution), but the lack of cohesion across all counties reduce the possibility to perform a spatial analysis during all emergency period at subnational levels. In this context, a snapshot of regional disparities is presented for the beginning of April (all three sources of COVID-19 related waste flow) and completed with certain case studies (e.g., Neamt County).
Plastic packaging materials are significant waste streams generated in quarantine places due to the single-use of cups, tableware and dishes according to three daily meals. An increase in plastic and glass was observed in Turin (Italy), but that overall MSW production in March 2020 was 11.5% lower than the one registered in March 2019 [32]. In addition, plastic waste increased in Thailand, but in Indonesia the amounts of waste sent to landfills decreased up to 40% due to the closure of offices, restaurants and industries [24].
2.4. Limitations of the Study
The paper aims to provide a rapid estimation of potentially infectious waste related to coronavirus in Romania at national and subnational levels in the most critical period (emergency state 16 March to 15 May) and the first stage of the alert state (15 May to 15 June). This assessment depends on the reliability of COVID-19 statistics in Romania and the transparency of local and national authorities in this regard as mentioned in Section 2.2 and Section 2.3 (number of confirmed cases, active cases, number of population in quarantine/self-isolate in homes, number of tests, etc.) and on the other hand, the medical waste generation rate (kg∙bed∙day−1) and municipal waste generation rates are taken into consideration and supported by previous studies. Some inconsistences between the reported cases from one county to another could be expected, but daily official statistics are taking into account. In the medium term, experimental studies at various COVID-19 hospitals and quarantined places should be made to adjust waste generation rates and to determine the medical waste composition/municipal waste composition. Another possible objective is to determine the medical waste generation rate broken down per intensive care patients and non-intensive care patients. Such field measurements are necessary for Romania and beyond to update the baseline scenario in both healthcare facilities and households and ultimately to improve the current models. However, special attention should be given in the international comparison of COVID-19 pandemic data due to various reporting systems and the number of tests performed [33].
3. Results and Discussions3.1. COVID-19 Waste Flow at National Level
Table 1 shows that most of the COVID-19 related waste was generated during the emergency state where medical waste (COVID-19 patients) has a ratio of 10.86% and quarantine waste 17.22% of the total waste flow.
These two sources cumulated 739.48 t of waste which should be incinerated according to national authority guidelines adding other 262 t during the alert state. Per the total period 1007.7 t infectious wastes (healthcare facilities + quarantine wastes) should have been collected by special hazardous waste operators and disposed of in the waste incineration plants or using alternative options for medical waste flow as outlined in Section 3.4.
For example, in Jakarta (Indonesia), the amount of medical waste reached 12,740 t 60 days after the virus attacked the region and in Wuhan (China) 5200 t of medical waste was collected from individual containers which served hospitals, isolation areas and shelters [24].
Self-isolation wastes prevailed in all periods as amounts generated, but this waste stream has a lower infectious potential being managed by ordinary municipal waste operators and disposed of in landfills. However, some local authorities treat the self-isolation wastes as quarantine wastes with special transport and disposal treatment.
The ICU patients generated around 6% of total medical waste during the emergency and the alert state states (16 March to 15 June) with no such waste stream before the emergency state. Prior the emergency state, the amounts of potentially infectious waste flow was over 10 times smaller than both emergency and alert states which point out the exponential growth of COVID-19 cases and on the other hand, the large number of people who returned in Romania from countries seriously affected by COVID-19 pandemics such as Italy or Spain.
National Institute of Public Health (NIPH) updated the list of red and yellow areas destinations in which quarantine and self-isolation are compulsory when citizens return in Romania which was regularly updated during the emergency state [34]. The list of extended community transmission comprised on 26 February (first COVID-19 case detected in Romania): Mainland China (Hubei Province), Wenzhou, Hangzhou, Ningbo, Taizhou Cities in Zhejiang Province or one of the 12 localities in Italy (Lombardia and Veneto regions). The list comprises on 15 March 2020 (beginning of emergency state) in (i) red areas (quarantine): Hubei region (China), Italy, Iran, South Korea (Daegu city and Cheongdo County); (ii) yellow areas (self-isolation)—countries with over 500 cases (Austria, Belgium, South Korea, Denmark, Switzerland, France, Germany, Japan, UK, Norway, Netherlands, USA, Spain, Sweden, rest of China. At the end of the emergency state (15 May), the self-isolation is compulsory for all international travels (including family members in the same household). To avoid exposure of family members the traveling person can require to be self-isolated in special quarantine places supervised by authorities.
During the emergency state, the model estimates that the self-isolation waste flow peaked 28 and 29 March over 70 t per day surpassing the threshold of 50 t per day from 25 March to 7 April followed by a descending trend where the daily amounts dropped around 10 t as shown in Figure 1. However, this waste stream regained an ascendant trend during the alert state reaching the threshold of 50 t per day from 26 May to 15 June as shown in Figure 2.
In both periods, the self-isolation waste is the largest COVID-19 related waste flow, but without severe regulations in their collection and treatment practices as the wastes generated by healthcare facilities or quarantine places. Medical waste stream comprised the ITU patients and those with mild symptoms treated in dedicated hospitals. It is estimated that ITU patients generated an average of 0.286 t per day during the emergency state peaking on 22 April (0.548 t) then it dropped to around 0.2–0.3.
The wastes generated by confirmed COVID-19 patients from healthcare facilities are around 4.8 t per day on average during the emergency state peaking on 29 April (7.91 t). However, this peak was not reached in the alert state (15 May to 15 June), but the average was slightly higher around 5.23 t per day. A key aspect is that medical waste flow is larger than quarantine flow from 22 May until the end of the alert state on 15 June. The daily average of quarantine waste is around 3 t per day during the alert state compared to 7.55 t per day during the emergency period. In the latter case, quarantine wastes reached the threshold of 10 t from 6 April to 18 April peaking to 14 t on both 8–9 April. Per total COVID-19 waste flow (medical + quarantine + self-isolation) the daily average is around 44 t per day during the emergency state and around 49 t during the alert state. The total maxim value of COVID-19 related waste flow reached 79.29 t on 29 March (emergency state) and 61.6 t on 12 June (alert state). In the next section, a regional analysis of COVID-19 flow is performed.
3.2. COVID-19 Waste Flow at Subnational Levels
As mentioned to the material and method section, the COVID-19 related data at subnational levels (county data) is difficult to obtain during the emergency state, particularly for quarantine or self-isolation persons. Figure 3 provides a regional snapshot of total potential infectious waste related to COVID-19 in Romania as of 4 April 2020. The map shows regional disparities of such waste flows with a prevalence of Bucharest capital city compared to other counties.
Total potential infectious waste is 73,870.7 kg of which 3386 kg medical waste (COVID-19 patients) and 8566.8 kg quarantine waste. These two waste streams (11,942.8 kg) should be collected by special waste operators and incinerated according to authorities’ recommendations. The rest of the 61,917.9 kg. is self-isolation waste. The medical waste (green section of the pie chart) is the most hazardous flow with larger amounts in Suceava County (967 kg), Bucharest (550 kg), Neamt County (148 kg), Timis County (136 kg), Brasov County (127 kg), Cluj County (121 kg). The lowest values are calculated in the case of Harghita (1 kg), Salaj (5 kg), Valcea (9 kg), etc.
Most quarantine wastes (red section of the pie chart) are generated in Constanta County (539 kg), 400–410 kg (Bucharest, Dolj, Ilfov) which are covered by waste incineration plants. At that time few quarantine wastes (<100 kg) were generated in Hunedoara, Sibiu, Covasna and Harghita counties. Self-isolation wastes are mostly generated in Bucharest city (4354 kg) followed by Arges (3166 kg), Brasov (2607 kg), Mures (2300 kg) which drop below 1000 kg (Dambovita, Valcea, Caras-Severin, Alba, Arad, etc.) lowest values in all three waste categories being in Covasna County.
Three are several counties where medical and quarantine wastes are at the lowest levels compared to self-isolation wastes (e.g., Tulcea, Vaslui, Gorj, etc.) which decrease the contamination risk of spreading COVID-19 disease by infectious waste.
In the case of the Neamt County, the model suggest that medical wastes generated by COVID-19 patients were increasing toward the end of the emergency state as the virus spreading in this area peaking in the last day to 805 kg. On the opposite side, the amounts of self-isolation wastes peaked in the first stage of the emergency state (27–29 March) surpassing 2000 kg of waste per day. The descend trend started by the end of March and the first week of April when the amounts of waste dropped below 500 kg per day until the end of the emergency state (14 May). The high number of self-isolation persons is explained by the large number of people who worked abroad and returned from the yellow areas of Italy or Spain. Fewer persons were quarantined (red zones) which generated fewer wastes (9160 kg) compared to self-isolation persons (37,000 kg). However, quarantine wastes are considered infectious waste which must be transported by special waste operators towards waste incineration plants as a medical waste of COVID-19 patients. In the latter case, it is estimated that the total amount generated during the emergency is almost twice (18,090 kg) than quarantine wastes as cumulative cases are still increasing during the alert state. Infectious wastes (medical + quarantine) reached 27,250.8 kg of total 64,256 kg COVID-19 related waste flow during the emergency state. There is a gap of data between 9 May to 12 May regarding the number of people in quarantine and self-isolation as shown in Figure 4 therefore, the total COVID-19 related flow is difficult to estimate at subnational levels. In the COVID-19 context, waste-related data are scarce and fragmented, and this situation is similar in other countries [24,32]. At the national level, the amounts of quarantine wastes are larger than healthcare wastes during the emergency state, but this situation is changing during the alert state as shown in Table 1.
3.3. Public Health and Environmental Concerns
In the first weeks of COVID-19 cases in Romania, the wastes collected from quarantine places were handled by municipal waste operators and disposed into landfills. Because such wastes were declared by national authorities as infectious wastes (first notification on 18 March 2020) the regional public health departments supervise the waste collection process between these sites and special waste operators licensed to transport hazardous waste to disposal facilities (e.g., hazardous waste incinerators). However, these wastes operators do not have wide geographic coverage and in times of emergency had difficulties to fulfill the requests from hospitals and quarantine sites. As an example, piles of potentially infectious waste bags formed in front of a quarantine site in Galati city because there is only one waste operator authorized to transport such wastes in the South-East Region. Mass media reveals cases where full bins and piles of medical wastes (outdoor) from hospitals remained uncollected in Deva with COVID-19 patients [35].
Such wastes should be temporarily stored in special locations maximum 24 h unless the wastes are stored in a location provided with a cooling system that constantly ensures a temperature of less than 4 °C, a situation in which the storage period can be a maximum of 7 days according to technical norms of Ministry of Health [36].
Ministry of Environment, Water and Forests emitted an order during the emergency state in Romania (2 April 2020) that all public land should be cleared by illegal dumping sites until 30 May 2020. These illegal dumping sites are considered by environmental authorities as an additional factor for the spreading of the coronavirus. Previous controls made by the National Environmental Guard in the first trimester of this year detected several wild dumpsites. Local authorities are responsible to collect and properly dispose of such wastes. Uncontrolled waste disposal practice is a serious environmental issue in Romania where peri-urban and rural regions are the most exposed to such practices [15]. Each spring season, the local authorities must perform sanitation activities around their localities. Seasonal floods feed freshwater pollution of dam lakes or downstream localities. Plastic pollution reached alarming levels in Romania due to waste mismanagement activities [14]. By the end of alert state 15 June 2020, local authorities of Bals city cope with massive plastic pollution and wood wastes carried out by Oltet river from upstream localities due to the summer flash floods.
Sorting stations, where dry recyclables are manually sorted, are an additional source of contamination for workers. Some household items that can be included in the category of infectious waste (napkins, masks, gloves, cutlery, containers, etc.) should not be separately collected, but to be disposed of in the residual bins. Some sorting lines were stopped to avoid the contamination risk of workers due to the presence of such items in recyclables bins. Environmental authorities detected (during March) massive illegal burning practices of hazardous and nonhazardous waste items (e.g., used tires, electric cables) in Ilfov County (Sintesti village of Vidra commune) which polluted the air of Bucharest city. The circular economy is limited in Romania where landfill is still the most prevalent option in case of municipal waste stream despite the most ambitious plan supported by the EU [17].
Medical waste management in Romania is less studied than the municipal waste stream and available data are scarce. However, some studies reveal that improper management of medical wastes can lead to water pollution [37] and other environmental threats [38]. Before the EU accession, the medical waste flow was disposed of in old and improper crematories of hospitals [39]. Despite some early guidelines on healthcare waste management practices [40] the problem of this waste stream is far to be solved across the EU. There is no Europe wide overview of amounts of unused pharmaceuticals and their return rate which poses further risks to the environment [41].
Environmental authorities made field controls on 14 May about the illegal waste disposal of hazardous medical wastes collected from Bucharest hospitals on open dumps in rural areas of Ilfov County as shown in Figure 5. These wastes were collected and transported by a special waste operator under the supervision of environmental authorities.
The problem of pharmaceutical waste-related issues gains attention in recent studies which point out the necessity of cohesion between legislative framework, waste management infrastructure, public policies and awareness campaigns [42,43]. Medical waste disposal is provided also by hazardous waste incinerators. However, hazardous waste incinerators are a controversial topic in the environmental policies of Romania.
In Dolj County, the medical waste incinerator which operates in Sopot commune was fined by the National Environmental Guard (80,000 lei) because of several non-compliances with environmental permits and measures were imposed regarding the proper storage and disposal of waste as well as related to the incinerator furnaces parameters [44].
In Suceava County, the environmental authorities found 1019.8 kg of hazardous medical waste improperly stored in a hall. In 2018, 11 hazardous waste incinerators (which also dispose of the medical waste), 14 treatment facilities (thermal decontamination at low temperatures) and 23 on-site treatment facilities (hospitals) operated in Romania [45]. According to the Ministry of Environment, there are 9 hazardous waste incinerators in Romania which operate in 2020 which can burn 100–500 kg of medical waste per hour and 15 medical waste sterilization facilities which can treat 45 t of medical waste per day in the COVID-19 pandemic context [46].The national waste management plan point out the small number of thermal decontamination treatment facilities at low temperatures of hazardous medical waste (inside the sanitary units or in a centralized system). In Romania, there are 14 counties with no such treatment facilities increasing dependency on hazardous waste incinerators as alternatives for waste disposal options [18]. Environmental NGO’s request national authorities to stop medical and hazardous waste incineration in facilities located near human settlements during the emergency state in Romania because air pollution could increase the COVID-19 cases, particularly in large cities [47]. These organizations draw attention on several environmental concerns such as illegal activities related to recent waste burning practices must be investigated by environmental authorities; to stop the import of any type of waste or second–hand goods; to speed up the acquisition process of monitoring equipment in case of dioxins, furans and PCBs and made that data publicly available.
3.4. Best Practices During the Emergency State Conditions
The Ministry of Health, National Institute of Public Health and the Ministry of Environment, Water and Forests released a notification for the general public regarding the management of municipal waste stream which transposes the UNEP and ECDC guidelines on this matter: (i) municipal waste generated in households with confirmed/suspected COVID-19 cases must be disposed of in the residual bin (without separate collection), the bag must be carefully enclosed without any compression, closed access to animal companions near the waste bags/bins, these items will be collected by municipal waste operators and directed transported to landfills; (ii) municipal waste generated in households without confirmed/suspected cases will be managed as usual with proper separate collection schemes. Clear communication with the general public is an essential step towards sound waste management practices of potential infectious wastes during the COVID-19 pandemic.
The National Institute of Public Health declared household waste generated in quarantine places as infectious wastes (18 March 2020), therefore, a strict waste management procedure must be enforced in this regard. These wastes must be collected by special waste operators and transported at −4 °C to hazardous waste incinerators. Waste operators and/or inter-community development associations that supervise waste management activities at regional level release guidelines with specific requirements on how to handle municipal waste stream in case of confirmed, quarantine or self-isolated citizens [48].
In Bucharest city, local authorities of District 4 and 6 provide special bags for waste collection of quarantine places and from self-isolated persons. District 4 bought two special vehicles (capacity of 840 L each, seven bins of 120 L) dedicated to collect and transport potential infectious wastes at −4 °C to waste incinerators from Dolj and Prahova counties [49]. These vehicles will cover the quarantine site and self-isolated persons with a twice collection frequency per week in District 4 of Bucharest city. The workers have full protection equipment as shown in Figure 6.
Waste workers equipped with PEE equipment are compulsory and they must have access to daily disinfectants. In Neamt County, waste operators changed the waste collection frequency of dry recyclables to once per month in rural areas. In addition, the dry recyclables bags must be enclosed and have indicated by the generator on the date of closing the bag. However, the reduction of waste collection frequency in rural areas can lead to illegal dumping or open burning practices [50].
Optimized routes for medical waste collection at regional levels are necessary to avoid additional risks in the context of COVID-19 pandemic when hospitals generate higher amounts of medical waste and there is increasing pressure on hazardous waste transporters and waste incinerators [51]. During the peak of COVID-19 pandemic, storage and waste disposal facilities are surpassed by the huge amounts of medical wastes in countries like in China and Italy [32,52].
The COVID-19 pandemic reveals that medical waste management treatment facilities must be further developed and to decrease the dependency on hazardous waste incinerators. Civil society and mass-media reveal several serious issues related to public health and environmental pollution of waste incineration plants in Romania in the last years. This waste disposal option is a critical environmental concern around the world [53].
On-site treatment facilities (hospitals) can be cost-efficient compared to the collection, transportation and incineration costs [32,54].
The National Police, National Environmental Guard and Health Inspection performed 283 controls (from 9 June to 1 July) at the sanitary units and at the economic agents that carried out operations in the field of hazardous waste management infested with SARS-CoV-2 virus. This operation led to safely disposing of 210 t hazardous waste, 43 fines (amounting to 454,800 lei) 40 warnings, 6 criminal cases and temporary closure of an incinerator [55].
Alternatives options to waste incineration is mandatory in the medium term in Romania such as [56]: (i) thermal processes at low temperature (105 °C–177 °C) using hot air inactivation equipment, steam equipment, microwave inactivation equipment or inactivation equipment by crushing, steaming, drying process; (ii) chemical processes—medical waste is first crushed and mixed to increase its exposure to the action of chemicals (sodium hypochlorite, peracetic acid or inorganic chemicals) and the disinfectant solution is recycled; (iii) Irradiation-based technologies refer to the exposure of medical waste to the action of electronic particles, cobalt-60 rays or UV rays; (iv) biologic processes use enzymes to break down organic matter. In Romania, the thermal process at low temperatures is the most widespread medical waste treatment as an alternative to waste incineration plants. However, only 6% of healthcare facilities investigated in 2018 (out of 832) treat medical waste in its facilities (thermal process at low temperature) and 15% had contracts with such specialized economic agents [45]. Most healthcare facilities (65%) are reliant on waste operators which transport medical waste flow to hazardous waste incinerators while 14% dispose of their nonhazardous waste through landfilling [45]. Improvements in staff awareness related to proper medical waste management practices and green purchasing suppliers are further steps in reducing pollution threats [57]. Contingency plans to target waste management sector under various scenarios should be continuously developed and adjusted [58].
4. Further Recommendations
Based on medical and municipal waste management issues raised by coronavirus spreading in Romania (Section 3), a set of recommendations is proposed at the EU and Romania levels to better monitor the COVID-19 waste flow, municipal, and medical waste stream in general, as a key support for decision-makers.
4.1. EU Commission
Policy and monitoring issues must be addressed and improved among EU countries under the supervision of the EU Commission to provide reliable data about waste flows during the COVID-19 pandemic in short and medium-term such as:
Special reports about waste management challenges in both medical and municipal waste management sectors associated with COVID-19 pandemic;
Eurostat must include medical waste management statistics as a compulsory special waste stream broken down by waste items (infectious waste included) beside the existing ones (e.g.WEEE, etc.);
Infectious-waste generation rates (kg∙bed∙day−1) and other similar medical waste flows must be determined by experimental studies across EU countries.
In the long term, waste management policies should have the following key objectives:
The European Commission must include the issues of healthcare waste management activities in annual environmental reports and healthcare performance status with separate case reports for each country;
Comprehensive health and waste-related statistics and common policies to cope with future outbreaks;
Circular economy policies must include the medical waste management sector with clear guidelines and best practices;
EU funds to develop and support sound medical waste treatment facilities as an alternative to hazardous waste incineration plants.
4.2. Romania
Poor publicly available data related to medical and municipal waste management flows was the norm before the COVID-19 pandemic in Romania, particularly at the county level, which makes it more difficult to assess the COVID-19 related waste flow in such time of crisis.
This situation was worsening during the emergency state due to the lack of COVID-19 related data at subnational levels as pointed out in Section 2 coupled with waste management deficiencies as highlighted in Section 3. Therefore, in the short and medium-term, national authorities should improve the transparency of key data:
Centralized daily COVID-19 statistics available at county levels including a wider range of indicators (as requested by Geo-spatial.org [26]) with open access data;
Publicly available data about environmental crimes detected by National Environmental Guard regarding waste collection and disposal (incineration/landfilling) of hazardous waste including medical waste flow;
Intensive controls of incinerator’s operations, hazardous waste collection, open burning and illegal dumping activities during COVID-19 pandemic supervised by special environmental commissions with inter-institutional members;
Special reports on such environmental crimes with available data at national and county levels for general public and mass-media;
Regional public health and environmental authorities must cooperate to provide reliable monitoring of hospitals with COVID-19 patients in terms of possible environmental and public health threats associated with medical waste mismanagement practices;
Incinerators and waste operators dealing with hazardous waste (including medical waste fractions) should mandatorily publish on their websites: environmental authorization, annual environmental performance report with detailed statistics about the wastes collected, treated and disposed of. Monthly reports of medical waste collected and treated during the COVID-19 pandemic;
Medical-waste composition data and generation rates supported by experimental studies in each county with special attention to COVID-19 hospitals.
To enhance environmental policy decisions at national and regional levels in a post-COVID-19 pandemic context and to be better prepared for other possible outbreaks, the public access to basic waste related data must be improved:
Regional and local waste management plans must have a special section of medical waste management systems with updated data;
Environmental reports (national and regional levels) must include an updated analysis of medical waste management status in collaboration with the National Public Health Institute;
Special regulations/amendments in reporting waste statistics data and to be available to the general public in electronic format (open data);
Waste statistics should be available on national and subnational levels including NUTS3 regions of Romania;
Waste statistics portal in web GIS format with open data available at the national, county and local administrative levels (LAU-2—cities and communes).
Investments to support alternative and environmentally friendly medical waste treatment facilities to waste incineration plants must be a continual effort in Romania.
5. Conclusions
This paper provides a rapid assessment of potential COVID-19 medical waste flow in Romania during the emergency state (15 March to 14 May) and alert state (15 May to 15 June) at the national level and some regional statistics. This flow is feed by confirmed COVID-19 cases, people in quarantine places, and those suspected and self-isolated in households.
The model estimates a total COVID-19 related flow of 4312 t at the national level from 25 February to 15 June of which 2633 t in the emergency state period.
The self-isolation source dominates in this waste flow for each period analyzed, but this waste stream has the lowest potential infectious risks compared to those generated by healthcare facilities (confirmed COVID-19 patients) and quarantine places. This waste flow is often mixed collected by municipal waste operators and disposed of through landfills. Both healthcare and quarantine wastes are considered infectious wastes by national authorities, therefore, around 1008 t should have been collected by special hazardous waste operators and disposed of in the waste incineration plants or using alternative options for medical waste flow as outlined in Section 3.4. The paper reveals a shift between quarantine and medical wastes in the alert state, where the estimated amounts of waste generated by confirmed COVID-19 patients in healthcare facilities (167.47 t) are larger than those in quarantine places (94.51 t).
There are regional disparities regarding the amounts of infectious waste (healthcare + quarantine sources) compared to self-isolation wastes among Romanian counties as shown by the map (Figure 3), but the capital city of Bucharest is the largest contributor to the overall COVID-19 related waste flow and self-isolation waste on this regional snapshot (4 April). Furthermore, Suceava County is the largest contributor to medical waste and Constanta County for quarantine wastes.
The COVID-19 waste flow cannot be determined for all periods at subnational levels due to the limited data. However, the Neamt County (one of the top five counties affected by COVID-19) is examined as a case study of local COVID-19 related waste flow during the emergency state.
The paper points out several critical issues related to medical and municipal waste management sectors in Romania. Monitoring of COVID-19 waste flow through the proposed model is important for decision-makers, particularly in low and middle-income countries like Romania which are facing waste management deficiencies and gaps in waste statistics, to reduce other contamination risks or related environmental threats. The regional analysis (subnational levels) of COVID-19 related waste flow provided by spatial statistics and thematic cartography is crucial in this regard, but more transparency and public access to such COVID-19 related data are required from national authorities.
The mentioned recommendations would enhance better decisions across EU and Romania in mitigating the waste-related secondary impacts on public health status and environmental factors during such biologic hazards or other possible hazards (natural or anthropogenic) caused by climate change or environmental pollution.
Both medical and municipal waste management services are critical around the world and these systems must cope with serious challenges particularly in low and middle-income countries. Further investigations and research studies are necessary to adjust the COVID-19 waste-related flows in Romania and other countries affected at considerable levels by this pandemic.
Funding
This project is funded by the Ministry of Research and Innovation within Program 1—Development of the national RD system, Subprogram 1.2—Institutional Performance—RDI excellence funding projects, Contract no.34PFE/19.10.2018. (Beneficiary “Alexandru Ioan Cuza” University).
Conflicts of Interest
The author declares no conflicts of interest.
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COVID-19 related waste flow in Romania during the emergency state.
COVID-19 related waste flow in Romania during the alert state.
Regional disparities in COVID19-related waste flows as of 4 April 2020 (emergency state) Counties info: https://en.wikipedia.org/wiki/Counties_of_Romania.
COVID-19 related waste flow in Neamt County during the emergency state.
Illegal dumping of medical waste in Dobroiesti commune, Ilfov County. 14 May 2020 (source: National Environmental Guard).
Collection of COVID-19 infectious waste in District 4 of Bucharest city Source: Bucharest District Hall 4.
ijerph-17-05439-t001_Table 1
COVID-19 waste flow at the national level from 26 February to 15 June 2020.
Period / Tons Generated
ICU Patients RelatedWaste
Medical WasteCOVID-19
QuarantineWaste
Self-Isolation Waste
Total COVID-19 Related Waste
Prior emergency state (26 February–15 March)
0
0.44
5.79
109.72
115.96
Emergency state (16 March–14 May)
17.17
286.04
453.44
1893.56
2633.05
Alert state(15 May–15 June)
10.1
167.47
94.51
1301.81
1563.79
Total period(26 February–15 June)
27.28
453.95
553.75
3305.1
4312.81
\n"],"string":"[\n \"\\nInt J Environ Res Public HealthInt J Environ Res Public HealthijerphInternational Journal of Environmental Research and Public Health1661-78271660-4601MDPI32731593PMC743211110.3390/ijerph17155439ijerph-17-05439ArticleAssessment of COVID-19 Waste Flows During the Emergency State in Romania and Related Public Health and Environmental ConcernsMihaiFlorin-ConstantinDepartment of Research, Faculty of Geography and Geology, Alexandru Ioan Cuza University, Carol I Blvd, Nr.20 A, RO-700505 Iasi, Romania; mihai.florinconstantin@gmail.com2872020820201715543928620202372020© 2020 by the author.2020Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
This paper provides a rapid assessment method of potentially infectious waste flow related to the coronavirus disease (COVID-19) pandemic in Romania focusing on the emergency state (from 16 March to 14 May 2020) where a national lockdown was in force with restrictive and social distancing measures concerning population mobility and economic activities. Medical and municipal waste management systems are critical services in combating the virus spread in the community. This assessment is useful due to poor available data of medical waste flow in environmental reports and it covers COVID-19 patients, quarantined, and self-isolated persons as the main potential infectious waste sources. The proposed model estimates that COVID-19 related waste flow is 4312 t at the national level from 25 February to 15 June of which 2633 t in the emergency state period. This assessment is correlated with deficiencies of medical and municipal waste management systems in Romania before the COVID-19 pandemic as stress factors of public health and environment. This study points out the main challenges of waste operators and reveals some best practices during this pandemic crisis. Based on the results and discussion section, several recommendations are proposed to COVID-19 waste-related issues and points out the crucial role of the reliable medical and municipal waste database in managing such biologic hazards at national and EU levels. Monitoring of COVID-19 waste flow through such models are important for decision-makers, particularly in low and middle-income countries which are facing waste management deficiencies and gaps in waste statistics, to reduce other contamination risks or related environmental threats.
The geographic expansion of the COVID-19 outbreak around the world forced the World Health Organization (WHO) to declare the pandemic status on 11 March 2020. On 24 March, the United Nations Environment Programme (UNEP) pointed out that waste management is an essential public service in avoiding secondary impacts upon public health and environment and special attention should be paid to handle medical, household and other hazardous items in such a period [1]. Massive amounts of medical waste have already been generated and disposed into the natural environment [2] and waste mismanagement practices can increase the contamination risks in developing countries [3,4] or among urban poor which live in informal settlements [5,6] or among hundreds of millions of people without basic water, sanitation, and waste management facilities in rural areas [7].
Romania declared a state of emergency on 16 March due to the pandemic crisis in which a national lockdown was implemented since the first case of COVID-19 patient was registered on 26 February 2020 [8]. The National Public Health Institute of Romania declared household waste generated in quarantines as infectious waste on 18 March 2020. Therefore, there is a shift from usual municipal waste management practices towards the special collection, transport and disposal requirements of hazardous wastes [9]. On 30 March, the European Center for Disease Prevention and Control (ECDPC) released an infection prevention and control guideline at the household level for suspected or confirmed COVID-19 disease with mild symptoms in self-isolation status [10]. This document provides some recommendations regarding the safe management of household waste. One keynote is to have a double waste bag for used tissues, face masks and other waste, which should be disposed of in the residual bin. The European Commission (EC) released a guideline on 14 April regarding waste management practices in the context of coronavirus crisis [11]. This document requires to maintain quality standards of municipal waste collection services and medical waste treatment and any measures must comply with EU regulations. Zero Waste Europe (ZWE) argues that the COVID-19 crisis should not undermine the EU’s long-term circular economy objectives by returning to business as usual [12]. The transition towards a circular economy should be further supported while 2020 is the first year where the recycling rate of 50% should be achieved by the EU Member States. However, this target is impossible to be achieved in Romania where the recycling rate of municipal waste was around 14% in 2017 [13].
Romania is coping with several mismanagement practices such as poor efficiency of source-separated waste collection schemes in urban areas, the prevalence of landfills as a main waste management option, delays in implementation of regional integrated waste management systems, lack of a reliable waste statistics database, etc. Furthermore, rural Romania is facing illegal waste disposal practices (wild dumps, open burning practices), plastic pollution of freshwater bodies and limited waste collection coverage of the rural population in some counties [14,15]. The circular economy remains underdeveloped despite the promising potential in this area [16], but the new circular economy plan promoted EU could catalyze the effort in the right direction [17]. On the other side, medical waste management issues are less examined in Romania, poor details and waste statistics data provided in environmental reports and several environmental crimes investigated by environmental authorities and revealed to the general public through national and local mass-media with a particular focus to hazardous waste incinerators. Neither the new national waste management plans provide a solid and updated analysis of medical waste management status in Romania [18]. According to WHO, about 85% of medical waste generated by healthcare activities is nonhazardous and the remaining 15% is considered hazardous material that can be infectious, toxic or radioactive [19]. However, COVID-19 related waste flow is considered infectious waste which must be properly treated [20,21], but serious concerns are raised around consumption and management of plastic products associated with personal protective equipment (PPE) [22,23]. A weak infectious waste management system could speed up the growth of COVID-19 in developing countries [24].
On this background, this paper aims: (i) to provide a rapid assessment of potentially infectious waste broken down in three main sources such as COVID-19 patients (hospitalized), people in quarantined sites (abroad working/travel Romanian citizens from countries/regions heavily affected by coronavirus), self-isolated people (persons from abroad or contacts with confirmed COVID-19 cases); (ii) to reveal national and some regional flows of potentially infectious waste generated; (iii) to identify critical issues during the emergency state in Romania related to medical and municipal waste management backgrounds; (iv) to reveal some best practices related to waste management sector; (v) to provide some recommendations related to waste management policies and data support at national and EU levels.
This study intends to provide a rapid assessment method to estimate COVID-19 related waste flow in Central and Eastern European countries where medical and municipal waste management systems must be further improved to comply with EU standards. Furthermore, such analysis is useful in case of low and middle-income countries around the world which face poor waste management infrastructure and gaps in waste statistics data, as an alternative solution to estimate the potential COVID-19 related waste flow necessary for decision-makers to reduce other contamination risk routes and environmental pollution threats.
2. Materials and Methods2.1. COVID-19 Status in Romania
The first case of COVID-19 patient waste detected on 26 February 2020 increasing to 168 cases on 16 March (0 deaths and 9 recovered) when the state of emergency was implemented. The first deaths were registered one week later (9 on 23 March). After the first month of emergency state (15 April) the COVID-19 statistics revealed 7216 (cumulative cases), 419 deaths, 1270 recovered and at the end of the emergency state (and beginning of alert state on 15 May) the situation was 16,247 cumulative cases of which 6020 active cases, 1090 deaths and 9370 recovered. By the end of the first stage of the alert state (15 June) statistics show 22,165 cumulative cases of which 4938 active cases, 1410 deaths and 15,817 recovered.
The peak of active cases was recorded in the second stage of emergency state with over 7000 cases from 23 April to 13 May, dropping during the alert state under 5000 cases from 28 May to 15 June. The number of patients in intensive care units (ICU) increased since 19 March (6 cases) to the peak of 288 cases (22 April), maintain the threshold of plus 200 cases until the end of the emergency state (15 May). However, the cases of intensive care patients decreased in the alert state below 200 cases until 15 June. The number of persons in quarantine has increased since 15 March (2855) to the peak on 9 April (25,586) dropping to 11,631 (28 April) with a slight increase by the end of emergency state 14,441 (15 May) followed by a continuous decreasing trend during the alert state (1331 on 15 June). The number of persons self-isolated increase from 14 March (14,680) to the peak on 29 March (132,641) with a continuous descending trend by the end of emergency state such as 14,789 (15 May). However, there is an ascending trend during the alert state from 19 May (24,415) peaking by the end of month 98,403 (30 May) and maintaining this trend in June surpassing 100,000 persons on 11 June than declining by 15 June (92,734 persons).
These statistics are important in the assessment process of Covid-19 related waste flow in Romania as further described in the following section.
2.2. Estimation of COVID-19 Waste Flow in Healthcare Facilities
High-income countries generate on average up to 0.5 kg of hazardous waste per hospital bed per day while low-income countries generate on average 0.2 kg [19]. On the other side, there are no separate collections (hazardous vs nonhazardous items) in developing countries that increase the ratio of hazardous items through contamination [25]. At the national level, medical waste as follows:Mw = No. of active COVID-19 cases per day × Mwgr (kg∙bed∙day−1)\\nwhere Mwgr = medical waste generation rate—1 kg∙bed∙day−1 The number of active cases is available at https://www.graphs.ro/ at the national level. Active cases represent confirmed COVID-19 patients that are treated in hospitals without recovered or dead patients. In general, all COVID-19 cases were treated in hospitals during the emergency state.\\nSubnational level (county): Mw = confirmed COVID-19 cases per day × Mwgr (kg∙bed∙day−1).
There is no available information about the active COVID-19 statistics at the county level and some inconsistencies could appear between confirmed cases, deaths, and recovered people at this scale. However, daily data provided by the strategic communication group of the Romanian government are considered for estimation to reduce this aspect. Therefore, COVID-19 medical waste flow is calculated from active cases at national level, whereas subnational is calculated from daily confirmed cases.
The assessment of healthcare waste at subnational levels is more difficult to achieve, the reporting systems are made by regional public health authorities (county departments) to the National Institute of Public Health. However, national authorities stopped reporting data at county levels from 19 March until 2 April. On 20 March, a manifesto initiated by Geo-spatial.org and supported by 17 non-governmental organizations (NGOs) was addressed to national authorities to release such key daily COVID-19 statistics data at county level (including confirmed cases, recovered, deaths, no. of people in quarantine or self-isolated and details about age and gender in each category for data consistency, no. of tests performed each day) for better monitoring of COVID-19 status in Romania and to avoid misinformation and spreading of fake news [26].
The average medical waste generation rate was around 1 kg∙bed∙day−1 in Infectious Diseases Clinical Hospital of Craiova used as the baseline for this paper [27]. This value is consistent with the recommendation of El-Haggar [28] as the average amount of medical waste generated per bed per day.
The highest rate of medical waste 1.814 kg∙bed−1∙day−1 was calculated in the case of the Emergency Clinical Hospital of Craiova [29]. The medical waste generation rates in main hospitals vary between 1–1.8 kg∙bed−1∙day−1 compared to smaller healthcare facilities < 0.5 kg∙bed−1·day−1 [27,29].
In Italy, Vaccari et al. [30] found that the highest medical waste generation in departments of anesthetics (5.96 kg∙bed−1∙day−1) pediatric and intensive care (3.37 kg∙bed−1∙day−1). In Wuhan (China) the amount of medical waste peaked to 240 t per day [24]. Therefore, in the COVID-19 pandemic context, medical waste flow associated with intensive care patients is expected to be higher than the general average of infectious disease and upper limited detected by previous studies in Romania (1.8 kg) is further considered in the analysis.\\nMw = No. of COVID-19 patients in ICU. day−1 × 1.8 kg∙bed−1∙day−1
The ICU waste is included in the general COVID-19 medical waste flow as calculated by Equation (1).
2.3. Estimation of Potentially Infectious Waste Generated in Quarantine Places or by Self-Isolated People in Households
Quarantine (14 days) is established for all people who have no symptoms but returning from areas with extended community transmission of new coronavirus (COVID-19 red areas) in special locations (e.g., hotels, touristic pensions) supervised by local authorities and county departments of public health. In the first phase, these locations were served by municipal waste operators, but national authorities considered these wastes as infectious. In this situation, the quarantine places must have contracts with special waste operators licensed to collect and transport such hazardous wastes to waste incinerators.
Self-isolation (14 days) is established for people who do not show symptoms, but: (i) have traveled in the last 14 days to regions/localities in areas affected by COVID-19 other than those with extended community transmission (yellow areas); (ii) direct contact with people with symptoms and who traveled to areas with extended community transmission; (iii) direct contact with people who were confirmed with coronavirus (COVID-19); (iv) family member in above-mentioned cases. These waste generated in such households should be disposed of in residual bins and collected by waste operators and transported to landfills.
The estimation of waste flow data takes into consideration the municipal waste generated in households or quarantine places.
In Romania, municipal waste generation rate is 0.64 kg∙inhab∙day−1 in urban areas and 0.31 kg∙inhab∙day−1 in rural areas according to the new national waste management plan [18].However, regional differences are expected between larger cities (e.g., county capitals) and the other urban areas.
The regional waste management plans of Dolj county points out that Craiova city has a per-capita waste generation rate of 0.7 kg∙inhab∙day−1 compared to other cities 0.55 kg∙inhab∙day−1. [31]. In the latter case, this per-capita generation rate is used as a conservative option to estimate the municipal waste flow in quarantine places and self-isolated cases in households.\\nQuarantine/self-isolation waste = no. of people in quarantine/self-isolated (national & subnational levels) × WGR, WGR = waste generation rate (0.55 kg∙inhab∙day−1).
Regional assessments of such wastes depend on available data at county levels regarding the number of persons in quarantine or self-isolated. These data are less available to the public than confirmed cases at the county levels. At the beginning of April, all three main categories (confirmed, quarantine, self-isolated) were available for a short time (see Figure 1). The number of quarantine and isolated are daily provided by the press release notes at the national level (https://stirioficiale.ro/informatii) by the strategic communication group of the Romanian government. Some data are available for certain counties through government representatives at county levels (Prefecture Institution), but the lack of cohesion across all counties reduce the possibility to perform a spatial analysis during all emergency period at subnational levels. In this context, a snapshot of regional disparities is presented for the beginning of April (all three sources of COVID-19 related waste flow) and completed with certain case studies (e.g., Neamt County).
Plastic packaging materials are significant waste streams generated in quarantine places due to the single-use of cups, tableware and dishes according to three daily meals. An increase in plastic and glass was observed in Turin (Italy), but that overall MSW production in March 2020 was 11.5% lower than the one registered in March 2019 [32]. In addition, plastic waste increased in Thailand, but in Indonesia the amounts of waste sent to landfills decreased up to 40% due to the closure of offices, restaurants and industries [24].
2.4. Limitations of the Study
The paper aims to provide a rapid estimation of potentially infectious waste related to coronavirus in Romania at national and subnational levels in the most critical period (emergency state 16 March to 15 May) and the first stage of the alert state (15 May to 15 June). This assessment depends on the reliability of COVID-19 statistics in Romania and the transparency of local and national authorities in this regard as mentioned in Section 2.2 and Section 2.3 (number of confirmed cases, active cases, number of population in quarantine/self-isolate in homes, number of tests, etc.) and on the other hand, the medical waste generation rate (kg∙bed∙day−1) and municipal waste generation rates are taken into consideration and supported by previous studies. Some inconsistences between the reported cases from one county to another could be expected, but daily official statistics are taking into account. In the medium term, experimental studies at various COVID-19 hospitals and quarantined places should be made to adjust waste generation rates and to determine the medical waste composition/municipal waste composition. Another possible objective is to determine the medical waste generation rate broken down per intensive care patients and non-intensive care patients. Such field measurements are necessary for Romania and beyond to update the baseline scenario in both healthcare facilities and households and ultimately to improve the current models. However, special attention should be given in the international comparison of COVID-19 pandemic data due to various reporting systems and the number of tests performed [33].
3. Results and Discussions3.1. COVID-19 Waste Flow at National Level
Table 1 shows that most of the COVID-19 related waste was generated during the emergency state where medical waste (COVID-19 patients) has a ratio of 10.86% and quarantine waste 17.22% of the total waste flow.
These two sources cumulated 739.48 t of waste which should be incinerated according to national authority guidelines adding other 262 t during the alert state. Per the total period 1007.7 t infectious wastes (healthcare facilities + quarantine wastes) should have been collected by special hazardous waste operators and disposed of in the waste incineration plants or using alternative options for medical waste flow as outlined in Section 3.4.
For example, in Jakarta (Indonesia), the amount of medical waste reached 12,740 t 60 days after the virus attacked the region and in Wuhan (China) 5200 t of medical waste was collected from individual containers which served hospitals, isolation areas and shelters [24].
Self-isolation wastes prevailed in all periods as amounts generated, but this waste stream has a lower infectious potential being managed by ordinary municipal waste operators and disposed of in landfills. However, some local authorities treat the self-isolation wastes as quarantine wastes with special transport and disposal treatment.
The ICU patients generated around 6% of total medical waste during the emergency and the alert state states (16 March to 15 June) with no such waste stream before the emergency state. Prior the emergency state, the amounts of potentially infectious waste flow was over 10 times smaller than both emergency and alert states which point out the exponential growth of COVID-19 cases and on the other hand, the large number of people who returned in Romania from countries seriously affected by COVID-19 pandemics such as Italy or Spain.
National Institute of Public Health (NIPH) updated the list of red and yellow areas destinations in which quarantine and self-isolation are compulsory when citizens return in Romania which was regularly updated during the emergency state [34]. The list of extended community transmission comprised on 26 February (first COVID-19 case detected in Romania): Mainland China (Hubei Province), Wenzhou, Hangzhou, Ningbo, Taizhou Cities in Zhejiang Province or one of the 12 localities in Italy (Lombardia and Veneto regions). The list comprises on 15 March 2020 (beginning of emergency state) in (i) red areas (quarantine): Hubei region (China), Italy, Iran, South Korea (Daegu city and Cheongdo County); (ii) yellow areas (self-isolation)—countries with over 500 cases (Austria, Belgium, South Korea, Denmark, Switzerland, France, Germany, Japan, UK, Norway, Netherlands, USA, Spain, Sweden, rest of China. At the end of the emergency state (15 May), the self-isolation is compulsory for all international travels (including family members in the same household). To avoid exposure of family members the traveling person can require to be self-isolated in special quarantine places supervised by authorities.
During the emergency state, the model estimates that the self-isolation waste flow peaked 28 and 29 March over 70 t per day surpassing the threshold of 50 t per day from 25 March to 7 April followed by a descending trend where the daily amounts dropped around 10 t as shown in Figure 1. However, this waste stream regained an ascendant trend during the alert state reaching the threshold of 50 t per day from 26 May to 15 June as shown in Figure 2.
In both periods, the self-isolation waste is the largest COVID-19 related waste flow, but without severe regulations in their collection and treatment practices as the wastes generated by healthcare facilities or quarantine places. Medical waste stream comprised the ITU patients and those with mild symptoms treated in dedicated hospitals. It is estimated that ITU patients generated an average of 0.286 t per day during the emergency state peaking on 22 April (0.548 t) then it dropped to around 0.2–0.3.
The wastes generated by confirmed COVID-19 patients from healthcare facilities are around 4.8 t per day on average during the emergency state peaking on 29 April (7.91 t). However, this peak was not reached in the alert state (15 May to 15 June), but the average was slightly higher around 5.23 t per day. A key aspect is that medical waste flow is larger than quarantine flow from 22 May until the end of the alert state on 15 June. The daily average of quarantine waste is around 3 t per day during the alert state compared to 7.55 t per day during the emergency period. In the latter case, quarantine wastes reached the threshold of 10 t from 6 April to 18 April peaking to 14 t on both 8–9 April. Per total COVID-19 waste flow (medical + quarantine + self-isolation) the daily average is around 44 t per day during the emergency state and around 49 t during the alert state. The total maxim value of COVID-19 related waste flow reached 79.29 t on 29 March (emergency state) and 61.6 t on 12 June (alert state). In the next section, a regional analysis of COVID-19 flow is performed.
3.2. COVID-19 Waste Flow at Subnational Levels
As mentioned to the material and method section, the COVID-19 related data at subnational levels (county data) is difficult to obtain during the emergency state, particularly for quarantine or self-isolation persons. Figure 3 provides a regional snapshot of total potential infectious waste related to COVID-19 in Romania as of 4 April 2020. The map shows regional disparities of such waste flows with a prevalence of Bucharest capital city compared to other counties.
Total potential infectious waste is 73,870.7 kg of which 3386 kg medical waste (COVID-19 patients) and 8566.8 kg quarantine waste. These two waste streams (11,942.8 kg) should be collected by special waste operators and incinerated according to authorities’ recommendations. The rest of the 61,917.9 kg. is self-isolation waste. The medical waste (green section of the pie chart) is the most hazardous flow with larger amounts in Suceava County (967 kg), Bucharest (550 kg), Neamt County (148 kg), Timis County (136 kg), Brasov County (127 kg), Cluj County (121 kg). The lowest values are calculated in the case of Harghita (1 kg), Salaj (5 kg), Valcea (9 kg), etc.
Most quarantine wastes (red section of the pie chart) are generated in Constanta County (539 kg), 400–410 kg (Bucharest, Dolj, Ilfov) which are covered by waste incineration plants. At that time few quarantine wastes (<100 kg) were generated in Hunedoara, Sibiu, Covasna and Harghita counties. Self-isolation wastes are mostly generated in Bucharest city (4354 kg) followed by Arges (3166 kg), Brasov (2607 kg), Mures (2300 kg) which drop below 1000 kg (Dambovita, Valcea, Caras-Severin, Alba, Arad, etc.) lowest values in all three waste categories being in Covasna County.
Three are several counties where medical and quarantine wastes are at the lowest levels compared to self-isolation wastes (e.g., Tulcea, Vaslui, Gorj, etc.) which decrease the contamination risk of spreading COVID-19 disease by infectious waste.
In the case of the Neamt County, the model suggest that medical wastes generated by COVID-19 patients were increasing toward the end of the emergency state as the virus spreading in this area peaking in the last day to 805 kg. On the opposite side, the amounts of self-isolation wastes peaked in the first stage of the emergency state (27–29 March) surpassing 2000 kg of waste per day. The descend trend started by the end of March and the first week of April when the amounts of waste dropped below 500 kg per day until the end of the emergency state (14 May). The high number of self-isolation persons is explained by the large number of people who worked abroad and returned from the yellow areas of Italy or Spain. Fewer persons were quarantined (red zones) which generated fewer wastes (9160 kg) compared to self-isolation persons (37,000 kg). However, quarantine wastes are considered infectious waste which must be transported by special waste operators towards waste incineration plants as a medical waste of COVID-19 patients. In the latter case, it is estimated that the total amount generated during the emergency is almost twice (18,090 kg) than quarantine wastes as cumulative cases are still increasing during the alert state. Infectious wastes (medical + quarantine) reached 27,250.8 kg of total 64,256 kg COVID-19 related waste flow during the emergency state. There is a gap of data between 9 May to 12 May regarding the number of people in quarantine and self-isolation as shown in Figure 4 therefore, the total COVID-19 related flow is difficult to estimate at subnational levels. In the COVID-19 context, waste-related data are scarce and fragmented, and this situation is similar in other countries [24,32]. At the national level, the amounts of quarantine wastes are larger than healthcare wastes during the emergency state, but this situation is changing during the alert state as shown in Table 1.
3.3. Public Health and Environmental Concerns
In the first weeks of COVID-19 cases in Romania, the wastes collected from quarantine places were handled by municipal waste operators and disposed into landfills. Because such wastes were declared by national authorities as infectious wastes (first notification on 18 March 2020) the regional public health departments supervise the waste collection process between these sites and special waste operators licensed to transport hazardous waste to disposal facilities (e.g., hazardous waste incinerators). However, these wastes operators do not have wide geographic coverage and in times of emergency had difficulties to fulfill the requests from hospitals and quarantine sites. As an example, piles of potentially infectious waste bags formed in front of a quarantine site in Galati city because there is only one waste operator authorized to transport such wastes in the South-East Region. Mass media reveals cases where full bins and piles of medical wastes (outdoor) from hospitals remained uncollected in Deva with COVID-19 patients [35].
Such wastes should be temporarily stored in special locations maximum 24 h unless the wastes are stored in a location provided with a cooling system that constantly ensures a temperature of less than 4 °C, a situation in which the storage period can be a maximum of 7 days according to technical norms of Ministry of Health [36].
Ministry of Environment, Water and Forests emitted an order during the emergency state in Romania (2 April 2020) that all public land should be cleared by illegal dumping sites until 30 May 2020. These illegal dumping sites are considered by environmental authorities as an additional factor for the spreading of the coronavirus. Previous controls made by the National Environmental Guard in the first trimester of this year detected several wild dumpsites. Local authorities are responsible to collect and properly dispose of such wastes. Uncontrolled waste disposal practice is a serious environmental issue in Romania where peri-urban and rural regions are the most exposed to such practices [15]. Each spring season, the local authorities must perform sanitation activities around their localities. Seasonal floods feed freshwater pollution of dam lakes or downstream localities. Plastic pollution reached alarming levels in Romania due to waste mismanagement activities [14]. By the end of alert state 15 June 2020, local authorities of Bals city cope with massive plastic pollution and wood wastes carried out by Oltet river from upstream localities due to the summer flash floods.
Sorting stations, where dry recyclables are manually sorted, are an additional source of contamination for workers. Some household items that can be included in the category of infectious waste (napkins, masks, gloves, cutlery, containers, etc.) should not be separately collected, but to be disposed of in the residual bins. Some sorting lines were stopped to avoid the contamination risk of workers due to the presence of such items in recyclables bins. Environmental authorities detected (during March) massive illegal burning practices of hazardous and nonhazardous waste items (e.g., used tires, electric cables) in Ilfov County (Sintesti village of Vidra commune) which polluted the air of Bucharest city. The circular economy is limited in Romania where landfill is still the most prevalent option in case of municipal waste stream despite the most ambitious plan supported by the EU [17].
Medical waste management in Romania is less studied than the municipal waste stream and available data are scarce. However, some studies reveal that improper management of medical wastes can lead to water pollution [37] and other environmental threats [38]. Before the EU accession, the medical waste flow was disposed of in old and improper crematories of hospitals [39]. Despite some early guidelines on healthcare waste management practices [40] the problem of this waste stream is far to be solved across the EU. There is no Europe wide overview of amounts of unused pharmaceuticals and their return rate which poses further risks to the environment [41].
Environmental authorities made field controls on 14 May about the illegal waste disposal of hazardous medical wastes collected from Bucharest hospitals on open dumps in rural areas of Ilfov County as shown in Figure 5. These wastes were collected and transported by a special waste operator under the supervision of environmental authorities.
The problem of pharmaceutical waste-related issues gains attention in recent studies which point out the necessity of cohesion between legislative framework, waste management infrastructure, public policies and awareness campaigns [42,43]. Medical waste disposal is provided also by hazardous waste incinerators. However, hazardous waste incinerators are a controversial topic in the environmental policies of Romania.
In Dolj County, the medical waste incinerator which operates in Sopot commune was fined by the National Environmental Guard (80,000 lei) because of several non-compliances with environmental permits and measures were imposed regarding the proper storage and disposal of waste as well as related to the incinerator furnaces parameters [44].
In Suceava County, the environmental authorities found 1019.8 kg of hazardous medical waste improperly stored in a hall. In 2018, 11 hazardous waste incinerators (which also dispose of the medical waste), 14 treatment facilities (thermal decontamination at low temperatures) and 23 on-site treatment facilities (hospitals) operated in Romania [45]. According to the Ministry of Environment, there are 9 hazardous waste incinerators in Romania which operate in 2020 which can burn 100–500 kg of medical waste per hour and 15 medical waste sterilization facilities which can treat 45 t of medical waste per day in the COVID-19 pandemic context [46].The national waste management plan point out the small number of thermal decontamination treatment facilities at low temperatures of hazardous medical waste (inside the sanitary units or in a centralized system). In Romania, there are 14 counties with no such treatment facilities increasing dependency on hazardous waste incinerators as alternatives for waste disposal options [18]. Environmental NGO’s request national authorities to stop medical and hazardous waste incineration in facilities located near human settlements during the emergency state in Romania because air pollution could increase the COVID-19 cases, particularly in large cities [47]. These organizations draw attention on several environmental concerns such as illegal activities related to recent waste burning practices must be investigated by environmental authorities; to stop the import of any type of waste or second–hand goods; to speed up the acquisition process of monitoring equipment in case of dioxins, furans and PCBs and made that data publicly available.
3.4. Best Practices During the Emergency State Conditions
The Ministry of Health, National Institute of Public Health and the Ministry of Environment, Water and Forests released a notification for the general public regarding the management of municipal waste stream which transposes the UNEP and ECDC guidelines on this matter: (i) municipal waste generated in households with confirmed/suspected COVID-19 cases must be disposed of in the residual bin (without separate collection), the bag must be carefully enclosed without any compression, closed access to animal companions near the waste bags/bins, these items will be collected by municipal waste operators and directed transported to landfills; (ii) municipal waste generated in households without confirmed/suspected cases will be managed as usual with proper separate collection schemes. Clear communication with the general public is an essential step towards sound waste management practices of potential infectious wastes during the COVID-19 pandemic.
The National Institute of Public Health declared household waste generated in quarantine places as infectious wastes (18 March 2020), therefore, a strict waste management procedure must be enforced in this regard. These wastes must be collected by special waste operators and transported at −4 °C to hazardous waste incinerators. Waste operators and/or inter-community development associations that supervise waste management activities at regional level release guidelines with specific requirements on how to handle municipal waste stream in case of confirmed, quarantine or self-isolated citizens [48].
In Bucharest city, local authorities of District 4 and 6 provide special bags for waste collection of quarantine places and from self-isolated persons. District 4 bought two special vehicles (capacity of 840 L each, seven bins of 120 L) dedicated to collect and transport potential infectious wastes at −4 °C to waste incinerators from Dolj and Prahova counties [49]. These vehicles will cover the quarantine site and self-isolated persons with a twice collection frequency per week in District 4 of Bucharest city. The workers have full protection equipment as shown in Figure 6.
Waste workers equipped with PEE equipment are compulsory and they must have access to daily disinfectants. In Neamt County, waste operators changed the waste collection frequency of dry recyclables to once per month in rural areas. In addition, the dry recyclables bags must be enclosed and have indicated by the generator on the date of closing the bag. However, the reduction of waste collection frequency in rural areas can lead to illegal dumping or open burning practices [50].
Optimized routes for medical waste collection at regional levels are necessary to avoid additional risks in the context of COVID-19 pandemic when hospitals generate higher amounts of medical waste and there is increasing pressure on hazardous waste transporters and waste incinerators [51]. During the peak of COVID-19 pandemic, storage and waste disposal facilities are surpassed by the huge amounts of medical wastes in countries like in China and Italy [32,52].
The COVID-19 pandemic reveals that medical waste management treatment facilities must be further developed and to decrease the dependency on hazardous waste incinerators. Civil society and mass-media reveal several serious issues related to public health and environmental pollution of waste incineration plants in Romania in the last years. This waste disposal option is a critical environmental concern around the world [53].
On-site treatment facilities (hospitals) can be cost-efficient compared to the collection, transportation and incineration costs [32,54].
The National Police, National Environmental Guard and Health Inspection performed 283 controls (from 9 June to 1 July) at the sanitary units and at the economic agents that carried out operations in the field of hazardous waste management infested with SARS-CoV-2 virus. This operation led to safely disposing of 210 t hazardous waste, 43 fines (amounting to 454,800 lei) 40 warnings, 6 criminal cases and temporary closure of an incinerator [55].
Alternatives options to waste incineration is mandatory in the medium term in Romania such as [56]: (i) thermal processes at low temperature (105 °C–177 °C) using hot air inactivation equipment, steam equipment, microwave inactivation equipment or inactivation equipment by crushing, steaming, drying process; (ii) chemical processes—medical waste is first crushed and mixed to increase its exposure to the action of chemicals (sodium hypochlorite, peracetic acid or inorganic chemicals) and the disinfectant solution is recycled; (iii) Irradiation-based technologies refer to the exposure of medical waste to the action of electronic particles, cobalt-60 rays or UV rays; (iv) biologic processes use enzymes to break down organic matter. In Romania, the thermal process at low temperatures is the most widespread medical waste treatment as an alternative to waste incineration plants. However, only 6% of healthcare facilities investigated in 2018 (out of 832) treat medical waste in its facilities (thermal process at low temperature) and 15% had contracts with such specialized economic agents [45]. Most healthcare facilities (65%) are reliant on waste operators which transport medical waste flow to hazardous waste incinerators while 14% dispose of their nonhazardous waste through landfilling [45]. Improvements in staff awareness related to proper medical waste management practices and green purchasing suppliers are further steps in reducing pollution threats [57]. Contingency plans to target waste management sector under various scenarios should be continuously developed and adjusted [58].
4. Further Recommendations
Based on medical and municipal waste management issues raised by coronavirus spreading in Romania (Section 3), a set of recommendations is proposed at the EU and Romania levels to better monitor the COVID-19 waste flow, municipal, and medical waste stream in general, as a key support for decision-makers.
4.1. EU Commission
Policy and monitoring issues must be addressed and improved among EU countries under the supervision of the EU Commission to provide reliable data about waste flows during the COVID-19 pandemic in short and medium-term such as:
Special reports about waste management challenges in both medical and municipal waste management sectors associated with COVID-19 pandemic;
Eurostat must include medical waste management statistics as a compulsory special waste stream broken down by waste items (infectious waste included) beside the existing ones (e.g.WEEE, etc.);
Infectious-waste generation rates (kg∙bed∙day−1) and other similar medical waste flows must be determined by experimental studies across EU countries.
In the long term, waste management policies should have the following key objectives:
The European Commission must include the issues of healthcare waste management activities in annual environmental reports and healthcare performance status with separate case reports for each country;
Comprehensive health and waste-related statistics and common policies to cope with future outbreaks;
Circular economy policies must include the medical waste management sector with clear guidelines and best practices;
EU funds to develop and support sound medical waste treatment facilities as an alternative to hazardous waste incineration plants.
4.2. Romania
Poor publicly available data related to medical and municipal waste management flows was the norm before the COVID-19 pandemic in Romania, particularly at the county level, which makes it more difficult to assess the COVID-19 related waste flow in such time of crisis.
This situation was worsening during the emergency state due to the lack of COVID-19 related data at subnational levels as pointed out in Section 2 coupled with waste management deficiencies as highlighted in Section 3. Therefore, in the short and medium-term, national authorities should improve the transparency of key data:
Centralized daily COVID-19 statistics available at county levels including a wider range of indicators (as requested by Geo-spatial.org [26]) with open access data;
Publicly available data about environmental crimes detected by National Environmental Guard regarding waste collection and disposal (incineration/landfilling) of hazardous waste including medical waste flow;
Intensive controls of incinerator’s operations, hazardous waste collection, open burning and illegal dumping activities during COVID-19 pandemic supervised by special environmental commissions with inter-institutional members;
Special reports on such environmental crimes with available data at national and county levels for general public and mass-media;
Regional public health and environmental authorities must cooperate to provide reliable monitoring of hospitals with COVID-19 patients in terms of possible environmental and public health threats associated with medical waste mismanagement practices;
Incinerators and waste operators dealing with hazardous waste (including medical waste fractions) should mandatorily publish on their websites: environmental authorization, annual environmental performance report with detailed statistics about the wastes collected, treated and disposed of. Monthly reports of medical waste collected and treated during the COVID-19 pandemic;
Medical-waste composition data and generation rates supported by experimental studies in each county with special attention to COVID-19 hospitals.
To enhance environmental policy decisions at national and regional levels in a post-COVID-19 pandemic context and to be better prepared for other possible outbreaks, the public access to basic waste related data must be improved:
Regional and local waste management plans must have a special section of medical waste management systems with updated data;
Environmental reports (national and regional levels) must include an updated analysis of medical waste management status in collaboration with the National Public Health Institute;
Special regulations/amendments in reporting waste statistics data and to be available to the general public in electronic format (open data);
Waste statistics should be available on national and subnational levels including NUTS3 regions of Romania;
Waste statistics portal in web GIS format with open data available at the national, county and local administrative levels (LAU-2—cities and communes).
Investments to support alternative and environmentally friendly medical waste treatment facilities to waste incineration plants must be a continual effort in Romania.
5. Conclusions
This paper provides a rapid assessment of potential COVID-19 medical waste flow in Romania during the emergency state (15 March to 14 May) and alert state (15 May to 15 June) at the national level and some regional statistics. This flow is feed by confirmed COVID-19 cases, people in quarantine places, and those suspected and self-isolated in households.
The model estimates a total COVID-19 related flow of 4312 t at the national level from 25 February to 15 June of which 2633 t in the emergency state period.
The self-isolation source dominates in this waste flow for each period analyzed, but this waste stream has the lowest potential infectious risks compared to those generated by healthcare facilities (confirmed COVID-19 patients) and quarantine places. This waste flow is often mixed collected by municipal waste operators and disposed of through landfills. Both healthcare and quarantine wastes are considered infectious wastes by national authorities, therefore, around 1008 t should have been collected by special hazardous waste operators and disposed of in the waste incineration plants or using alternative options for medical waste flow as outlined in Section 3.4. The paper reveals a shift between quarantine and medical wastes in the alert state, where the estimated amounts of waste generated by confirmed COVID-19 patients in healthcare facilities (167.47 t) are larger than those in quarantine places (94.51 t).
There are regional disparities regarding the amounts of infectious waste (healthcare + quarantine sources) compared to self-isolation wastes among Romanian counties as shown by the map (Figure 3), but the capital city of Bucharest is the largest contributor to the overall COVID-19 related waste flow and self-isolation waste on this regional snapshot (4 April). Furthermore, Suceava County is the largest contributor to medical waste and Constanta County for quarantine wastes.
The COVID-19 waste flow cannot be determined for all periods at subnational levels due to the limited data. However, the Neamt County (one of the top five counties affected by COVID-19) is examined as a case study of local COVID-19 related waste flow during the emergency state.
The paper points out several critical issues related to medical and municipal waste management sectors in Romania. Monitoring of COVID-19 waste flow through the proposed model is important for decision-makers, particularly in low and middle-income countries like Romania which are facing waste management deficiencies and gaps in waste statistics, to reduce other contamination risks or related environmental threats. The regional analysis (subnational levels) of COVID-19 related waste flow provided by spatial statistics and thematic cartography is crucial in this regard, but more transparency and public access to such COVID-19 related data are required from national authorities.
The mentioned recommendations would enhance better decisions across EU and Romania in mitigating the waste-related secondary impacts on public health status and environmental factors during such biologic hazards or other possible hazards (natural or anthropogenic) caused by climate change or environmental pollution.
Both medical and municipal waste management services are critical around the world and these systems must cope with serious challenges particularly in low and middle-income countries. Further investigations and research studies are necessary to adjust the COVID-19 waste-related flows in Romania and other countries affected at considerable levels by this pandemic.
Funding
This project is funded by the Ministry of Research and Innovation within Program 1—Development of the national RD system, Subprogram 1.2—Institutional Performance—RDI excellence funding projects, Contract no.34PFE/19.10.2018. (Beneficiary “Alexandru Ioan Cuza” University).
Conflicts of Interest
The author declares no conflicts of interest.
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COVID-19 related waste flow in Romania during the emergency state.
COVID-19 related waste flow in Romania during the alert state.
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ijerph-17-05439-t001_Table 1
COVID-19 waste flow at the national level from 26 February to 15 June 2020.
Period / Tons Generated
ICU Patients RelatedWaste
Medical WasteCOVID-19
QuarantineWaste
Self-Isolation Waste
Total COVID-19 Related Waste
Prior emergency state (26 February–15 March)
0
0.44
5.79
109.72
115.96
Emergency state (16 March–14 May)
17.17
286.04
453.44
1893.56
2633.05
Alert state(15 May–15 June)
10.1
167.47
94.51
1301.81
1563.79
Total period(26 February–15 June)
27.28
453.95
553.75
3305.1
4312.81
\\n\"\n]"}}},{"rowIdx":455,"cells":{"data":{"kind":"list like","value":["\nInt J Environ Res Public HealthInt J Environ Res Public HealthijerphInternational Journal of Environmental Research and Public Health1661-78271660-4601MDPI32751191PMC743211210.3390/ijerph17155463ijerph-17-05463ArticleSynthetic Evaluation of China’s Regional Low-Carbon Economy Challenges by Driver-Pressure-State-Impact-Response ModelPanWenyan1https://orcid.org/0000-0002-5802-0245GulzarMuhammad Awais23*HassanWaseem4School of Safety Science and Emergency Management, Wuhan University of Technology, Wuhan 430070, China; panwenyan@whut.edu.cnUniversity of Waikato Joint Institute, Zhejiang University City College, Hangzhou 310015, ChinaWaikato Management School, University of Waikato, Hamilton 3240, New ZealandNUST Business School, National University of Sciences and Technology, Islamabad 44000, Pakistan; waseem.hassan@nbs.nust.edu.pkCorrespondence: awais.gulzar@waikato.ac.nz2972020820201715546309720202472020© 2020 by the authors.2020Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
The “driver–pressure–state–impact–response” (DPSIR) model has recently become a popular approach to deal with environmental problems. The combination of DPSIR with analytic hierarchy process (AHP) is a useful method to study low-carbon evaluation because the AHP model has a special advantage in multi-indexes evaluation. This paper constructs the low-carbon economy evaluation system and comprehensively evaluates the numerical value of low-carbon economic development of China’s 30 regions from 2000 to 2015 by using the AHP method. It shows that the numerical value of low-carbon economy of China’s 30 regions varies in terms of growth rate. The numerical value of east regional low-carbon economy shows a pattern that is gradually higher than that of the west region. The numerical value of low carbon economic development in the south region is higher than that of the north region by degrees. In addition, based on the model of coordination degree in 2015, the result indicated that the four subsystems have primary coordination in the east area and bare coordination in the central and west areas. It is indicated that the four sub-indexes should be developed at the same pace and promoting the development of a low-carbon economy in the mid-west areas is the key in China. Finally, we proposed that environmental regulations and policies should be formulated to improve coordination in various aspects and various departments. Calculating the degree of low-carbon economic coupling coordination may be helpful for policy makers to formulate effective policies and take actions in the future.
CO2 emissionslow-carbon economyDPSIR modelcoupling coordination model1. Introduction
The 26th meeting of the Conference of the Parties (COP26) of the United Nations Framework Convention on Climate Change (UNFCCC), originally scheduled for November in Glasgow, United Kingdom has been postponed until 2021 [1]. Patricia Espinosa (Executive Secretary of UNFCCC) said COVID-19 is the most urgent threat facing humanity today, but we cannot forget that climate change is the biggest threat over the long term. It clarifies the content of anthropogenic climate change and the political statements of reducing greenhouse gas emissions of each country. An important contribution made by the Paris Agreement (COP21) was the Nationally Determined Contributions (NDCs) [2]. This is the key to achieving long-term goals. The NDCs identified the efforts and commitments of each country to reduce greenhouse gas emissions, improving the impact of climate change. The aim is to work together to respond to the challenge of climate change and the greenhouse effect. The Paris Agreement stipulates that global warming needs to be limited between 1.5 and 2 °C above pre-industrial levels. The European Union (EU) and China as the largest economies and emitters have announced that they will strengthen their collaboration and step-up their efforts to deal with climate change [3]. Greenhouse gas emissions are a very serious environmental problem that the planet is facing today, and it has become a nightmare for countries throughout the world. This issue is mainly attributed to the typical high carbon economy. It is extremely important for the countries around the globe to deal with climate change and achieve a low-carbon economy. In order to explore the corresponding environmental protection measures, the calculation of carbon dioxide emissions and evaluation of low-carbon economic development at the regional level is of great theoretical and practical value [4,5,6,7].
The core objective/essence of low-carbon economy is to reduce CO2 emissions. For the purpose of dealing with the impacts of climatic change on the global economy and human existence, the emission of CO2 in production and consumption must be strictly controlled [8,9,10]. It is indicated that CO2 emissions from burning fossil fuels had increased 49% from the year 1990 to the year 2008 [2]. According to the statistics given by the International Energy Agency [11], China became the world’s largest carbon dioxide emitter and the largest energy consumer [12,13]. All these statistics show that as the global climate problem is becoming more and more serious, the low-carbon economy has become a global hotspot all around the world [1]. In decoupling theory, we know that the amount of materials consumed grows with the economy at the beginning of industrialization. However, this situation will reverse at a certain stage in future, which will realize the growth of economy while decreasing consumption of materials. The meaning on low-carbon economy varies with the places. Different paths and choices should be adopted for CO2 emission reduction to areas that are at different stages of development. For example, the developed countries that have achieved industrialization and high human development must be able to achieve absolute reduction of CO2 emissions. However, in the developing countries that are still in the process of industrialization, due to their booming population and unachieved basic goals of human development, CO2 emissions will continue to grow. Therefore, if they ensure social development along with constant economic growth, the relative reduction of CO2 emissions should be regarded as low-carbon economy. Due to different developmental stages in different regions, the problems they are facing vary greatly. For all these reasons, constructing the low-carbon economy evaluation index system as well as finding specific problems and solutions are of great importance.
The low-carbon economy, which is the outcome of constricting emission requirements and increasing human development at a good level is kind of an economic foundation. The reason we develop a low-carbon economy is to control emissions of CO2, which is a global shared vision [14,15]. Therefore, the low-carbon economy should be at the base of the development of production, environmental protection and resource optimization. A thriving low-carbon economy can be developed only when these factors proceed simultaneously, along with the unification of economy, ecosystem and social benefit. Besides, while we are researching the low-carbon economy, we need to integrate resources science, environmental science and the social sciences effectively. For this purpose, an instructional method is required that can not only specify complex problems but also effectively combine each part. As a result, the DPSIR model serves as a good research tool for the low-carbon economic evaluation.
We aimed to build a comprehensive low-carbon economic system by combining two models, DPSIR and AHP, so as to give a numerical value of the low-carbon economic development of China’s 30 regions from 2000 to 2015. In addition, the spatial distribution was analyzed in the four aspects: “pressure”, “state”, “effect” and “response”. The study largely discussed the applicability of DPSIR model to evaluate China’s regional low-carbon economic challenges; a conceptual model was provided, establishing indicator system–data processing–analysis–making decisions.
2. Theoretical Framework
The DPSIR model is considered as a fundamental and effective tool to thoroughly observe the causation between human activities and the natural environment [16,17]. In the late 1980s, the OECD proposed a Pressure–State–Response (PSR) frame model [18]. On that basis, the United Nations proposed the Driving Force–State–Response frame model. Finally, the European Environment Agency and others combined the benefits of the previous two index systems and proposed a new conceptual frame model: Driving Force-Pressure–State–Impact–Response (DPSIR) [19,20]. It can be seen that the DPSIR model is the expansion and revision of PSR model, which adds the “Driving Force” factor as the cause of the “Pressure” factor and the “Impact” force caused by the environment [21]. The scholars who use the model think that it is people’s activities such as production and consumption that put the natural environment under pressure, consequently leading to change in the natural environment. However, we should respond to the effects that the climate change brings. The DPSIR model is widely used for analyzing problems about resources, environment, economy, and society. For instance, Maxim et al. supplemented the DPSIR model by using the complex system theory, and then, the model was used to analyze the risk of biological diversity in the aspects of policy, society and economy [22]; Ness et al. think the DPSIR model is quite up to the requirements of sustainable Scientific Outlook on Development, and with the help of that, they can research the eutrophication in the Baltic Sea [23]. Atkins et al. create a model which supports marine environment decision by integrating the DPSIR model with features of ecosystem service and social welfare [24]. The DPSIR model was selected due to its simplicity and because it is the most powerful communication tool between environment, economy and society [25,26].
From these aforementioned examples, it is clear that the applicability of DSPIR model is very high and that’s why it is used by many scholars for the analysis of all kinds of resources and in environmental as well as social and economic problems. DPSIR model particularly focuses on economic operations, their effects on the environment and their interrelations. Thus, it is a very systematic, comprehensive, widespread and flexible model.
The “Driving force” indicates the potential cause of the changes in environmental conditions, which mainly refers to the development trend of economic activities, social activities and changes in the industrial structure. The “Pressure” is the influence of human activities on natural environment, energy and resources. The “State” refers to the situation of environment under the above-mentioned pressures and mainly considers regional resource consumption and environment degradation. The “Impact” is the influence of system’s status on environment and social economic structure. Lastly, the “Response” shows the methods and effective policies that people create when facing environmental impact.
Firstly, the analytical framework of a low-carbon economy system is constructed, in which the DPSIR model is used considering human needs, social progress, economic development, energy demand, carbon emissions, resource status, low-carbon consumption and economic development. This progress reflects the features of a low-carbon economy, as a complex system relevant to the mutual effect of human activities and natural resources. Showing the connotation of a scientific outlook on development, the low-carbon economic system should be human-oriented and development-coordinated. The analytical framework of the system based on DPSIR model is shown in Figure 1.
In order to construct a multi-dimensional and scientific evaluation index system that not only can conduct the horizontal comparison in all regions but also the vertical reflection of steady shift, the principles mentioned below must be followed.
For the purpose of ensuring that the evaluation of the system completely and objectively reflects its comprehensiveness and shift result, plenty of work needs to be done. In addition to that, the overlap between indexes must be avoided. Therefore, the index system must be made clear by level diversification and index classification according to system structure.
The evaluation index should also scientifically and moderately reveal the properties and transitional characteristics of a low-carbon economy. Besides, every evaluation with its calculation should be standardized, normalized and clearly defined; even those statistics that cannot be obtained from the existing statistical source can be brought into the index system if they have the ability to reflect the practicality and the embodiment of the low-carbon economy.
As a whole, the system should include various indexes that can affect the level of the low-carbon economy. Although it is impossible to cover all the relevant indexes, the system must be capable of reflecting all aspects that can affect the level of the low-carbon economy in the current social economy. In addition, the selected indexes should reflect the main character and status of the evaluated system from different aspects. In order to make the index system easy to use, while choosing indexes, we must emphasize on their depiction and avoid redundancy.
The worldwide recognized common index must be followed after altering it according to required standards, and concurrently, the internationally accepted names, concepts and calculations must be adopted. In addition to that, whatever the evaluation index, system construct is a global perspective oriented job. Therefore, we must take full account of the dynamic change of the system when designing the index system and embrace innovation.
In this paper, the system is established based on DPSIR model, as shown in Table 1. The “impact” was removed from the DPSIR model because indicators in “impact” overlap with those in “Pressure” and “State”. At the same time, the relevant statistics for economic losses and natural hazard caused by air pollution are hard to get.
The connotation of “Driver” is reflected by setting the “Driver for social development” (D1) and “Driver for economic development” (D2), completely representing the essence of people’s needs and social development of Scientific Outlook on Development. The connotation of “Pressure” is reflected by setting the “resource pressure” (P1) and “environmental pressure” (P2), symbolizing the damages and losses that social progress and human development do to the environment and resources. The connotation of “State” is reflected by setting the “State of low-carbon consumption” (S1) and “State of low-carbon resources” (S2), describing the objective condition of resources and environment in the low-carbon economy system. The connotation of “Response” is reflected by setting the “Scientific Response” (R1), “Human Response” (R2) and “Policy Response” (R3), embodying the positive response of countries towards environmental pollution, resources consumption and ecological destruction. At the same time, the frequently used indexes from the relevant studies about low-carbon economy are selected.
3. Methodology
This paper focuses on the coordination degree among four sub-indexes. Degrees of interaction among the four sub-indexes are defined by their respective characteristics as the degree of coupling coordination. The equation is as follows:T=μd(x)+ϕp(y)+ξs(z)+θp(k)=μ∑i=1mαixi,+ϕ∑i=1nβiyi,+ξ∑i=1jσizi,+θ∑i=1lωiki,\nwhere T represents the development degree of the comprehensive evaluation index, which is the reflection of the development level of low-carbon economy. xi, represents the “Driver” indexes. yi, represents “Pressure” indexes. zi, represents “State” indexes. ki, represents “Response” indexes. The weight values are μ, ϕ, ξ, θ, αi, βi, σi.\nC={d(x)×p(y)×s(z)×p(k)[d(x)+p(y)+s(z)+p(k)4]4}14\nwhere C represents the coupling degree, which is a measure of coordinated development. C ranges from 0 to 1. The greater the coupling degree among the systems the closer the C is to 1. Contrary to it, the smaller the coupling degree among the systems, the closer the C is to 0. Besides, there is no need to develop it while the sub-indexes are irrelevant.\nD=C×T\nD is coupling coordinated degree. T is development degree. It measures the situation as a whole of the development of coordination in the system [27].
The classification criterion of D is as in Table 2.
4. Data and Variables4.1. Measurement of CO<sub>2</sub> Emissions from Energy Consumption
In the current literature and research, the calculation caliber of regional carbon emissions data is difficult to unify. We use the methodology in the IPCC guidelines:C=∑iTi×Fi\nwhere C represents total CO2 emissions from regional energy consumption; i is the type of energy; Ti is the terminal consumption for i; Fi is the coefficient of CO2 emissions for i:Fi=Ai×Bi×(44/12)×103×4186.8×10−9×10−3×Ei\nAi is the coefficient of carbon emissions for i (kgC/GJ); Bi is the oxidation rate in the combustion for i type energy; Ai and Bi are derived from IPCC (2006). In Table 3, Ci is the coefficient of CO2 emissions for i (kgCO2/TJ); Di is the original coefficient for i type of energy (Kg CO2/Kcal); Ei is the calorific value of unit fuel in China.
This paper calculates CO2 emissions in China’s 30 regions (1995–2016). The data for these provinces are derived from China’s Statistical yearbook. The reason we removed the refinery gas and liquefied petroleum gas is because they are difficult to gather, and they are used less. We calculated the total CO2 emissions from 1995 to 2016, which show a tendency to increase year after year (Table 4).
4.2. Other Data and Variables
We evaluate the level of the low-carbon economy development level in China’s 30 regions from 2000 to 2015 as shown in Table 1. The demographic data are obtained from “China Population Statistic Yearbook”, the per capita GDP, the GDP growth and the average income of rural and urban families are obtained from the statistics in “China Statistical Yearbook”. The energy data are obtained through the calculation of statistics in “China Energy Statistical Yearbook” and China’s 30 regions’ statistical yearbooks. The scientific data are attained from “China Statistical Yearbook on Science and Technology”. We can set the 6‰ and 75% as moderate indexes, which is the ideal value of natural population growth rate and urbanization rate.
5. Regional Evaluation of the Low-Carbon Economy5.1. Index Calculation
Urbanization rate (D12). Urbanization rate refers to the percentage that the urban population accounts for the entire population, which witnesses city’s history and development. Since the current statistics do not cover all sub-provincial cities’ statistical data of urbanization rate, this paper adopts the method that “non-agricultural population accounts for total population proportion” to calculate the urbanization rate, which is authorized by police departments. The method can be summarized as “the urbanization rate = non-agricultural population/total population.
Per capita carbon emissions (S11). From 1990s to the year 2000, the global average annual CO2 emission has increased from 22 billion tons to 264 billion tons. Therefore, the primary goal of developing the low-carbon economy is to limit CO2 emissions. Due to unavailability of authorized statistics on carbon emissions, this paper estimates the carbon emissions produced by fossil resources (coal, oil and natural gas) based on other existing statistics. The total carbon emissions = Σ (each resource consumption × carbon emissions per unit of energy consumption), summarized as per capita carbon emissions = total carbon emissions/population.
Carbon emissions of residents’ consumption (S12) and government’s consumption (S13). They refer to the carbon emissions created by government’s and residents’ final consumption on goods and services over a period of time, which can reflect the influence of consumption level and structure on carbon emissions. The government’s consumption on carbon emissions refers to the carbon emissions created by government’s consumption on public services, which can reflect the impact of local government governance level on carbon emissions. We account these two indexes according to the proportion of residents’ consumption in GDP (known as final consumption rate) and carbon intensity per unit of economy. That is, carbon emissions of residents’ consumption = total carbon emissions/residents’ consumption, carbon emissions of government’s consumption = total carbon emissions/government’s consumption.
The efficiency of energy process and conversion (R12). The process of energy conversion is a production link of the energy process system, which is the important index for evaluating the advancement of the energy conversion device, the production technique and the quality of administration. The improvement of efficiency of energy process and conversion means that the low energy input can create high energy output. Its formula is as follows: efficiency of energy process and conversion = energy output/energy input.
Carbon productivity (R13). It is considered as the core index of measuring low carbonization, which is the GDP per unit of carbon emitted. This index links the GDP output with the carbon emissions of energy consumption directly. Therefore, it can visually reflect the carbon use efficiency of the whole social economy and measure the overall level of low-carbon technology. In addition, as a result of its relation with economic structure, the carbon productivity can have the potential of further reduction of carbon emissions intensity of unit consumption when the country’s technology has accumulated to a certain extent. Its formula is carbon productivity = GDP/carbon emissions.
5.2. Dimensionless Evaluation Factors
The collected original data must be dimensionless through range conversion as they cannot be compared directly.
We set xki as the value of the index k that has been normalized in the evaluated area i, and set the vki as the value of the index k in the evaluated area i, the n as the quantity of the evaluated area. Positive data was calculated:xki=vki−min1≤i≤n(vki)max1≤i≤n(vki)−min1≤i≤n(vki)
Negative data was calculated:xki=max1≤i≤n(vki)−vkimax1≤i≤n(vki)−min1≤i≤n(vki)
Regarding, moderate interrelation for those factors, the closer it approached to ideal value, the better. In reference to a large number of previous literature, this paper sets the planning objective of the country’s natural population growth rate in China (V1 = 6‰) as the ideal value of natural population growth rate (D11) and sets the average urbanization rate in developed countries (V2 = 75%) as the ideal value of the urbanization rate (D12). We set xki as the value of the index k that has been normalized in the evaluated area i, set the vki as the value of the index k in the evaluated area i, the vki0 as the ideal value and the n as the quantity of the evaluated area. Moderate data was calculated:xki={1−vki0−vkimax[vki0−min1≤i≤n(vki),max1≤i≤n(vki)−vki0],min1≤i≤n(vki)≤vki≤vkio1−vki0−vkimax[vki0−min1≤i≤n(vki),max1≤i≤n(vki)−vki0],vki0≤vki≤max1≤i≤n(vki)
5.3. Weight of Evaluation Factors
AHP model was developed by Satty [28]; it includes both qualitative and quantitative indicators, and ranks programs based on multiple criteria. It has been widely used in macro (complex and realistic) and human (management subjective) problems [29,30,31]. Nowadays, it is used in many research fields [32,33,34,35].
In this study, the low-carbon economy system includes four levels (Table 1).
If aij is the result of comparing i with j. The matrix is\nA=[a11a12…a1na21a22…a2n…………an1an2…ann]
We employed the Delphi method to analyze the relative importance of D, P, S and R. In this study, we provided a scoring table for 30 experts from the universities, scientific institutions and government decision-making departments. Then we calculated the judgment matrix, obtained the indicator weighting and established pair-wise comparison matrix for A (Table 5).Finally, we found that “State” (S) was crucial to the low-carbon economy level, and it was the base for low-carbon economy, Therefore, weight of S was the highest.
mi is the product of every row: mi=∏i=14aij, (i=1, 2, 3, 4)\nwi¯=mi4; w¯=(w1¯,w2¯,…,wn¯)T\nwi=wi¯∑i=1nwi¯, (i=1, 2, 3, 4); λmax=1n∑i=1n(AW)iwi\nAW=[a11a12…a1na21a22…a2n…………an1an2…ann]×|w1w2•wj|
Finally, we calculated the weight of D, P, S and R were 0.1186, 0.2162, 0.4141 and 0.2511. The eigenvalue λmax = 4.1576.
Then, we tested CI (the consistency index) = 0.0525. CR (the consistency ratio) = 0.059 ≤ 0.10.\nCI=λmax−nn−1CR=CIRI\nRI is used to modify the CI. In this paper, RI = 0.90.
5.4. The synthetic Index and Sub-Index of the Low-Carbon Economy
We calculated the indexes according to the aforesaid principles, then used a composite index that contains rule hierarchy composite index and comprehensive composite index (using the hundred percentage point system). In other words, the method of linear weighted sum is used to calculate index value in all rule hierarchies, and then from its result the low-carbon evaluation index (LCEI) value is obtained.
We use zj to express the numerical value of second grades in the comprehensive system; in order, there are D index, P index, S index and R index. Then we set xk as the dimensionless index after normalization and the wk as the weight of the indexes (Table 1).\nzj=∑xkwk (j=D,P,R,S); LCEI=zD+zP+zS+zR
We present the low-carbon economy level of China’s 30 regions in Table 6.
6. Assessing Coupling Coordination between the Sub-Indexes
As mentioned above, the horizontal comparisons of the coordination degrees are shown in Table 7, the coordination degrees of the 30 regions in China were calculated and analyzed and were based on the model of coordination degree in 2015.
The coupling degree (C) results show a pattern for that central area lower than the west area and the west area lower than the east area. The average degree of the whole country is 0.6723, which is higher than west and central areas but lower than the east area.
The coordination degree (D) results show a pattern for the west area lower than the central area and the central area lower than the east area. The average degree of the whole country is 0.5840, which is higher than the west and central areas but lower than the east area.
Moreover, based on the model of coordination degree in 2015, the coordination degrees of the 30 regions in China were calculated and analyzed.
The results indicated the four sub systems were primary coordination in the east area and bare coordination in the central and west areas in 2015. It indicated that four sub-indexes should be developed at the same pace and be improved based on stable development. The construction of low-carbon economy in mid-west areas is the key in China.
7. Results and Analysis7.1. Results
In Table 8, the numerical value increases gradually; all provinces, municipalities and autonomous regions show varying increasing rates; the level of low-carbon economy varies by province. The index level shows a gradually decreasing trend from southeastern coastal areas to the north on spatial variation.
The Figure 2 reflects the distribution of development capability of regional low-carbon economy in China in 2015. It can be seen from Figure 2 that in the spatial distribution, the numerical value of development capability shows a pattern that indicates the southeast is gradually higher than the north. While the clear winner would be the east, which overall stands on the top. This spatial distributive characteristic is closely related to China’s industrial structure, economic development and resource distribution features. For example, Shanxi and Inner Mongolia are important coal-producing bases where coal mines and resources are so crowded that they have weak low-carbon economy development. However, Guangdong and Fujian relatively have lack of natural resources so that their economy has reached a higher level, and the low-carbon economy develops well. Although Beijing is located in northern China and is very near the coal resource centers geographically, with coal making up such a large proportion in its energy structure, it has a well-developed economy; especially, the proportion of its tertiary industry is over 80%. Its industry is mainly based on modern manufacturing and tech industry; therefore, Beijing is under less pressure on low-carbon economy and It ranks 1st in low-carbon economy. However, industrial manufacturing still accounts for a definite proportion of the industrial structure in Shanghai; as a result, Shanghai is under more pressure on low-carbon economy than Beijing even though its economy is better.
In order to reveal the figures of low-carbon economy in different regions, we used Ward’s method to do cluster analysis on China’s 30 regions based on SPSS statistical software. For instance, the results of 2015 were shown in Table 8.
We found that the 30 regions can be divided into 4 categories. These 4 categories correspond respectively to the 4 statuses: leading status, good status, medium status and poor status. The evaluation results show that the numerical value varies greatly in different regions (Table 9). The leading status is almost all in the east area except Jiangxi, Guangxi and Hunan, which are included in the central area. Shanxi, Inner Mongolia and Ningxia fall into poor status.
Figure 3 and Figure 4 compare the difference of average low-carbon economy level in different regions. They show that the numerical value of east regional low-carbon economy shows a pattern that is gradually higher than the west region. The numerical value of low carbon economic development in the south region is higher than the north region by degrees. Almost all parts of the west are low level. Ningxia is considered to have very low level. We find that the average level has shown a downward trend since 2010. This is due to the decline in economic growth and GDP. Growth rate was 10.3% in 2010 and 7.4% in 2015.
7.2. Analysis of DPSR Sub-Indexes
In view of the driving sub-index, state sub-index and pressure sub-index, there are some differences in all regions, and the leading status is significantly better than the poor status. As seen from the “Response” sub-index, the resource consumption and degree of pollution vary from province to province, as well as pollution abatement and efforts for environmental protection. All these factors reflect that the more efforts the government, system, society and individual make to improve the status of resources, the better the low-carbon economy develops.
The drive sub-index varies greatly from province to province. It shows a pattern with the indication of northeast gradually lower than southeast, which is determined by China’s terrains and regional development policies. From Figure 5, we can see that the regions with high grades of drive sub-index value are all located in coastal areas. This is because these areas have significant regional advantages, which make the input cost of commerce activities and production activities much lower than in other regions. Therefore, their input and output efficiency in economic activities is relatively high. In addition to that, in the earlier stage of reform and opening, China has given special importance to the economic development in coastal areas by providing support in policies. Various regional preferential policies were introduced to form advantages for development; thus, their level of driving force on low-carbon economy is higher than the average level in China.
The situation of the pressure sub-index generally indicates the north being gradually lower than the south, the west gradually lower than the east, and hopping in some areas as shown in (Figure 6), which is determined by various causes of development foundations, regional conditions and development policies in all regions. For quite a long time, the speed of economic development in China’s eastern coastal areas was much faster than in central and western areas. In addition, the coming years offer a critical period for western areas to accelerate industrialization; thus, the low-carbon pressure in east is relatively lower than in the west. We can conclude that the index value in the rule hierarchy of pressure has gradually decreasing effects around the main energy supplement region and the surrounding areas, so Shanxi, Inner Mongolia, Ningxia and Qinghai have the most pressure on low-carbon economy.
The situation of the state sub-index indicates the north being gradually lower than the south; therefore, the ranks of Hainan, Guangxi and Fujian are relatively higher, while Shaannxi, Hunan and Hubei are in the second tier (Figure 7). This is because southeastern coastal areas have good economic development with external supply of energy, oil and natural gas. Hydroelectricity and nuclear electricity occupy a large proportion in the structure of energy consumption, so the low-carbon economy in these regions is in good status. However, although central and southern areas have certain coal resources, the consumption far outstrips the production. As a result, the external supply of energy is badly needed. At the same time, these areas that are rich in forest resources vigorously develop hydroelectricity and pursue rational use of energy, so the numerical value in these regions is better than it is in the north.
The situation of the response sub-index shows a pattern of hopping and overlapping in some areas. Beijing, Shanghai and Guangdong rank the first three in 2015, while in the north, northwest and southeast, the index values are gradually low (Figure 8). This is due to the combined impact of the economic and scientific development level, energy consumption efficiency and industrial structure. According to the statistical yearbook, major consumption of energy in west regions is from coal, and there are more coal resources without desulfurization, which combines with low economic development and inadequate input in environment protection. As a result, the efficiency of energy process and conversion is low, and the productivity of carbon falls behind.
7.3. Analysis of the Degree of Coordination among the Four Sub-Indexes
As mentioned above, the horizontal comparisons of the coordination degrees are shown in Table 8; the coordination degrees of the 30 regions in China were calculated and analyzed and were based on the model of coordination degree in 2015.
The coupling degree (C) results show a pattern for the central area lower than the west area and the west area lower than the east area. The average degree of the whole country is 0.6723, which is higher than the west and central areas but lower than the east area.
The coordination degree (D) results show a pattern for the west area lower than the central area and the central area lower than the east area. The average degree of the whole country is 0.5840, which is higher than the west and central areas but lower than the east area.
Moreover, based on the model of coordination degree in 2015, the coordination degrees of the 30 regions in China were calculated and analyzed.
The results indicated the four sub-systems were primary coordination in the east area and bare coordination in the central and west areas in 2015.In addition, four sub-indexes should be developed at the same pace and improved on the basis of stable development. It shows that promoting the development of a low-carbon economy in mid-west areas is the key in China.
8. Conclusions
Based on the above results, the following conclusions are drawn:
All 30 regions show varying increasing rates. The regional distributions of the sub-indexes are the combined impacts of regional economic development level, resource endowment and policy support. The numerical values in China’s 30 regions vary greatly; as a whole, they indicates the north is gradually lower than the southeastern coastal areas, and the east is higher than the west; the south is higher than the north, and there is overlapping in some areas. Beijing and Shanghai are far ahead while Shanxi, Inner Mongolia and Ningxia are backward.
As compared to the developed countries that have already achieved industrialization, China focuses particularly on achieving sustainable development by combining industrialization with ecological civilization. It is worth noting that China has large regional differences. Compared with the developed regions in the east, there are still a lot of heavy-duty industries in the central and western regions. Industrial structure transformation is more difficult. Therefore, promoting the development of low-carbon economy in mid-west areas is the key in China.
The improvement of people’s living quality and social welfare can best embody the development of low-carbon economy. The result of this study may be helpful for policy makers to formulate effective policies and take actions in the future.
We firmly believe that ecological civilization construction, industrialization and urbanization are not mutually exclusive at all. We must emphasize realizing sustainable development through the low-carbon system while people’s material life has benefited a lot. Therefore, we ought to change the bad social consumption mode, as well as wrong consuming concepts and patterns. Besides, we must make great efforts to turn the low-carbon way of life into a conscious behavior while improving our quality of life and stick to it in concrete activities.
Author Contributions
Conceptualization, W.P, M.A.G and W.H.; methodology, W.P and M.A.G.; formal analysis, W.P.; writing—original draft preparation, W.P.; writing—review and editing, M.A.G and W.H.; supervision, W.P and M.A.G. All authors have read and agreed to the published version of the manuscript.
Funding
This research was funded by the Fundamental Research Funds for the Central University (WUT: 2020VI011) and the National Natural Science Foundation of China (71772143).
Conflicts of Interest
Declare conflicts of interest or state “The authors declare no conflict of interest.”
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The analytical framework of low-carbon economic system based on driver–pressure–state–impact–response (DPSIR) model.
Cluster analysis map of low-carbon economy level of China.
Average low-carbon economy level of China and its three areas.
Comparison of the average low-carbon economy level of the three areas.
Map of drive sub-index in 2000 and 2015.
Map of pressure sub-index in 2000 and 2015.
Map of state sub-index in 2000 and 2015.
Map of response sub-index in 2000 and 2015.
ijerph-17-05463-t001_Table 1
Index system of low-carbon economy.
First Grade
Second Grade
Weight
Third Grade
Weight
Fourth Grade
Weight
Positive or Negative
Comprehensive level of low-carbon economy(A)
Driver (D)
0.1186
Driver for social development (D1)
0.0593
Natural Population Growth (D11, ‰)
0.0272
Moderate
Urbanization Rate (D12, %)
0.0247
Moderate
Engel’s coefficient on urban and rural households (D13, %)
0.0075
Negative
Driver for economic development (D2)
0.0593
per capita GDP (D21, yuan per capita)
0.0183
Positive
GDP growth rate (D22, %)
0.0065
Positive
The average income of rural and urban family (D23, yuan per capita)
0.0345
Positive
Pressure(P)
0.2162
resource pressure (P1)
0.0865
The energy consumption per unit of GDP (P11, tons of coal per 10,000 yuan)
0.0605
Negative
Electricity consumption per capita (P12, kwh per capita)
0.0259
Negative
environmental pressure (P2)
0.1297
The industrial waste-gas discharge per unit of GDP (P21, cubic meter per 10,000 yuan)
0.0741
Negative
SO2 emission per unit of GDP (P22, ton per 10,000 yuan)
0.0371
Negative
Public transportations per 10,000 people (P23)
0.0185
Positive
Status(S)
0.4141
Status of low-carbon consumption (S1)
0.2071
Carbon emissions per capita (S11, ton per capita)
0.1305
Negative
The carbon emissions of residents’ consumption (S12, ton per 10,000 yuan)
0.0541
Negative
The carbon emissions of government consumption (S13, ton per 10,000 yuan)
0.0225
Negative
Status of low-carbon resources (S2)
0.2071
Proportion of consumption of raw coal (S21, %)
0.0941
Negative
Carbon emissions per unit of energy (S22, ton of CO2 per ton of coal)
0.0188
Negative
Forest coverage (S23, %)
0.0941
Positive
Response(R)
0.2511
Scientific Response (R1)
0.1758
Expenditure on science and technology per capita (R11, yuan per capita)
0.0189
Positive
The efficiency of energy process and conversion (R12, %)
0.0181
Positive
Carbon productivity (R13, 10,000 yuan per ton)
0.1387
Positive
Policy Response (R2)
0.0753
The proportion the tertiary industry output value accounts for GDP (R21, %)
0.0442
Positive
The development plan of low-carbon economy (R22)
0.0077
Positive
Policy of carbon tax (R23)
0.0081
Positive
Supervision and statistics system of carbon emissions (R24)
0.0154
Positive
ijerph-17-05463-t002_Table 2
Classification of the coupling coordinated degree.
Coordination Degree
Coordination Level
Coordination Degree
Coordination Level
0.01 < CD2 ≤ 0.10
Extreme disorder ≤
0.51 < CD2 ≤ 0.60
Bare coordination
0.11 <CD2 ≤ 0.20
Serious disorder
0.61 < CD2 ≤ 0.70
Primary coordination
0.21 <CD2 ≤ 0.30
Moderate disorder
0.71 < CD2 ≤0.80
Intermediate coordination
0.31 <CD2 ≤ 0.40
Mild disorder
0.81 < CD2 ≤0.90
Favorable coordination
0.41 <CD2 ≤ 0.50
On the verge of disorder
0.91 < CD2 ≤ 0.10
Quality coordination
ijerph-17-05463-t003_Table 3
Coefficient of CO2 emissions in China.
Energy Type
A
B
C = A × B × (44 / 12) × 1000
D = C × 4186.8 × 10−9 × 10−3
\n"],"string":"[\n \"\\nInt J Environ Res Public HealthInt J Environ Res Public HealthijerphInternational Journal of Environmental Research and Public Health1661-78271660-4601MDPI32751191PMC743211210.3390/ijerph17155463ijerph-17-05463ArticleSynthetic Evaluation of China’s Regional Low-Carbon Economy Challenges by Driver-Pressure-State-Impact-Response ModelPanWenyan1https://orcid.org/0000-0002-5802-0245GulzarMuhammad Awais23*HassanWaseem4School of Safety Science and Emergency Management, Wuhan University of Technology, Wuhan 430070, China; panwenyan@whut.edu.cnUniversity of Waikato Joint Institute, Zhejiang University City College, Hangzhou 310015, ChinaWaikato Management School, University of Waikato, Hamilton 3240, New ZealandNUST Business School, National University of Sciences and Technology, Islamabad 44000, Pakistan; waseem.hassan@nbs.nust.edu.pkCorrespondence: awais.gulzar@waikato.ac.nz2972020820201715546309720202472020© 2020 by the authors.2020Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
The “driver–pressure–state–impact–response” (DPSIR) model has recently become a popular approach to deal with environmental problems. The combination of DPSIR with analytic hierarchy process (AHP) is a useful method to study low-carbon evaluation because the AHP model has a special advantage in multi-indexes evaluation. This paper constructs the low-carbon economy evaluation system and comprehensively evaluates the numerical value of low-carbon economic development of China’s 30 regions from 2000 to 2015 by using the AHP method. It shows that the numerical value of low-carbon economy of China’s 30 regions varies in terms of growth rate. The numerical value of east regional low-carbon economy shows a pattern that is gradually higher than that of the west region. The numerical value of low carbon economic development in the south region is higher than that of the north region by degrees. In addition, based on the model of coordination degree in 2015, the result indicated that the four subsystems have primary coordination in the east area and bare coordination in the central and west areas. It is indicated that the four sub-indexes should be developed at the same pace and promoting the development of a low-carbon economy in the mid-west areas is the key in China. Finally, we proposed that environmental regulations and policies should be formulated to improve coordination in various aspects and various departments. Calculating the degree of low-carbon economic coupling coordination may be helpful for policy makers to formulate effective policies and take actions in the future.
CO2 emissionslow-carbon economyDPSIR modelcoupling coordination model1. Introduction
The 26th meeting of the Conference of the Parties (COP26) of the United Nations Framework Convention on Climate Change (UNFCCC), originally scheduled for November in Glasgow, United Kingdom has been postponed until 2021 [1]. Patricia Espinosa (Executive Secretary of UNFCCC) said COVID-19 is the most urgent threat facing humanity today, but we cannot forget that climate change is the biggest threat over the long term. It clarifies the content of anthropogenic climate change and the political statements of reducing greenhouse gas emissions of each country. An important contribution made by the Paris Agreement (COP21) was the Nationally Determined Contributions (NDCs) [2]. This is the key to achieving long-term goals. The NDCs identified the efforts and commitments of each country to reduce greenhouse gas emissions, improving the impact of climate change. The aim is to work together to respond to the challenge of climate change and the greenhouse effect. The Paris Agreement stipulates that global warming needs to be limited between 1.5 and 2 °C above pre-industrial levels. The European Union (EU) and China as the largest economies and emitters have announced that they will strengthen their collaboration and step-up their efforts to deal with climate change [3]. Greenhouse gas emissions are a very serious environmental problem that the planet is facing today, and it has become a nightmare for countries throughout the world. This issue is mainly attributed to the typical high carbon economy. It is extremely important for the countries around the globe to deal with climate change and achieve a low-carbon economy. In order to explore the corresponding environmental protection measures, the calculation of carbon dioxide emissions and evaluation of low-carbon economic development at the regional level is of great theoretical and practical value [4,5,6,7].
The core objective/essence of low-carbon economy is to reduce CO2 emissions. For the purpose of dealing with the impacts of climatic change on the global economy and human existence, the emission of CO2 in production and consumption must be strictly controlled [8,9,10]. It is indicated that CO2 emissions from burning fossil fuels had increased 49% from the year 1990 to the year 2008 [2]. According to the statistics given by the International Energy Agency [11], China became the world’s largest carbon dioxide emitter and the largest energy consumer [12,13]. All these statistics show that as the global climate problem is becoming more and more serious, the low-carbon economy has become a global hotspot all around the world [1]. In decoupling theory, we know that the amount of materials consumed grows with the economy at the beginning of industrialization. However, this situation will reverse at a certain stage in future, which will realize the growth of economy while decreasing consumption of materials. The meaning on low-carbon economy varies with the places. Different paths and choices should be adopted for CO2 emission reduction to areas that are at different stages of development. For example, the developed countries that have achieved industrialization and high human development must be able to achieve absolute reduction of CO2 emissions. However, in the developing countries that are still in the process of industrialization, due to their booming population and unachieved basic goals of human development, CO2 emissions will continue to grow. Therefore, if they ensure social development along with constant economic growth, the relative reduction of CO2 emissions should be regarded as low-carbon economy. Due to different developmental stages in different regions, the problems they are facing vary greatly. For all these reasons, constructing the low-carbon economy evaluation index system as well as finding specific problems and solutions are of great importance.
The low-carbon economy, which is the outcome of constricting emission requirements and increasing human development at a good level is kind of an economic foundation. The reason we develop a low-carbon economy is to control emissions of CO2, which is a global shared vision [14,15]. Therefore, the low-carbon economy should be at the base of the development of production, environmental protection and resource optimization. A thriving low-carbon economy can be developed only when these factors proceed simultaneously, along with the unification of economy, ecosystem and social benefit. Besides, while we are researching the low-carbon economy, we need to integrate resources science, environmental science and the social sciences effectively. For this purpose, an instructional method is required that can not only specify complex problems but also effectively combine each part. As a result, the DPSIR model serves as a good research tool for the low-carbon economic evaluation.
We aimed to build a comprehensive low-carbon economic system by combining two models, DPSIR and AHP, so as to give a numerical value of the low-carbon economic development of China’s 30 regions from 2000 to 2015. In addition, the spatial distribution was analyzed in the four aspects: “pressure”, “state”, “effect” and “response”. The study largely discussed the applicability of DPSIR model to evaluate China’s regional low-carbon economic challenges; a conceptual model was provided, establishing indicator system–data processing–analysis–making decisions.
2. Theoretical Framework
The DPSIR model is considered as a fundamental and effective tool to thoroughly observe the causation between human activities and the natural environment [16,17]. In the late 1980s, the OECD proposed a Pressure–State–Response (PSR) frame model [18]. On that basis, the United Nations proposed the Driving Force–State–Response frame model. Finally, the European Environment Agency and others combined the benefits of the previous two index systems and proposed a new conceptual frame model: Driving Force-Pressure–State–Impact–Response (DPSIR) [19,20]. It can be seen that the DPSIR model is the expansion and revision of PSR model, which adds the “Driving Force” factor as the cause of the “Pressure” factor and the “Impact” force caused by the environment [21]. The scholars who use the model think that it is people’s activities such as production and consumption that put the natural environment under pressure, consequently leading to change in the natural environment. However, we should respond to the effects that the climate change brings. The DPSIR model is widely used for analyzing problems about resources, environment, economy, and society. For instance, Maxim et al. supplemented the DPSIR model by using the complex system theory, and then, the model was used to analyze the risk of biological diversity in the aspects of policy, society and economy [22]; Ness et al. think the DPSIR model is quite up to the requirements of sustainable Scientific Outlook on Development, and with the help of that, they can research the eutrophication in the Baltic Sea [23]. Atkins et al. create a model which supports marine environment decision by integrating the DPSIR model with features of ecosystem service and social welfare [24]. The DPSIR model was selected due to its simplicity and because it is the most powerful communication tool between environment, economy and society [25,26].
From these aforementioned examples, it is clear that the applicability of DSPIR model is very high and that’s why it is used by many scholars for the analysis of all kinds of resources and in environmental as well as social and economic problems. DPSIR model particularly focuses on economic operations, their effects on the environment and their interrelations. Thus, it is a very systematic, comprehensive, widespread and flexible model.
The “Driving force” indicates the potential cause of the changes in environmental conditions, which mainly refers to the development trend of economic activities, social activities and changes in the industrial structure. The “Pressure” is the influence of human activities on natural environment, energy and resources. The “State” refers to the situation of environment under the above-mentioned pressures and mainly considers regional resource consumption and environment degradation. The “Impact” is the influence of system’s status on environment and social economic structure. Lastly, the “Response” shows the methods and effective policies that people create when facing environmental impact.
Firstly, the analytical framework of a low-carbon economy system is constructed, in which the DPSIR model is used considering human needs, social progress, economic development, energy demand, carbon emissions, resource status, low-carbon consumption and economic development. This progress reflects the features of a low-carbon economy, as a complex system relevant to the mutual effect of human activities and natural resources. Showing the connotation of a scientific outlook on development, the low-carbon economic system should be human-oriented and development-coordinated. The analytical framework of the system based on DPSIR model is shown in Figure 1.
In order to construct a multi-dimensional and scientific evaluation index system that not only can conduct the horizontal comparison in all regions but also the vertical reflection of steady shift, the principles mentioned below must be followed.
For the purpose of ensuring that the evaluation of the system completely and objectively reflects its comprehensiveness and shift result, plenty of work needs to be done. In addition to that, the overlap between indexes must be avoided. Therefore, the index system must be made clear by level diversification and index classification according to system structure.
The evaluation index should also scientifically and moderately reveal the properties and transitional characteristics of a low-carbon economy. Besides, every evaluation with its calculation should be standardized, normalized and clearly defined; even those statistics that cannot be obtained from the existing statistical source can be brought into the index system if they have the ability to reflect the practicality and the embodiment of the low-carbon economy.
As a whole, the system should include various indexes that can affect the level of the low-carbon economy. Although it is impossible to cover all the relevant indexes, the system must be capable of reflecting all aspects that can affect the level of the low-carbon economy in the current social economy. In addition, the selected indexes should reflect the main character and status of the evaluated system from different aspects. In order to make the index system easy to use, while choosing indexes, we must emphasize on their depiction and avoid redundancy.
The worldwide recognized common index must be followed after altering it according to required standards, and concurrently, the internationally accepted names, concepts and calculations must be adopted. In addition to that, whatever the evaluation index, system construct is a global perspective oriented job. Therefore, we must take full account of the dynamic change of the system when designing the index system and embrace innovation.
In this paper, the system is established based on DPSIR model, as shown in Table 1. The “impact” was removed from the DPSIR model because indicators in “impact” overlap with those in “Pressure” and “State”. At the same time, the relevant statistics for economic losses and natural hazard caused by air pollution are hard to get.
The connotation of “Driver” is reflected by setting the “Driver for social development” (D1) and “Driver for economic development” (D2), completely representing the essence of people’s needs and social development of Scientific Outlook on Development. The connotation of “Pressure” is reflected by setting the “resource pressure” (P1) and “environmental pressure” (P2), symbolizing the damages and losses that social progress and human development do to the environment and resources. The connotation of “State” is reflected by setting the “State of low-carbon consumption” (S1) and “State of low-carbon resources” (S2), describing the objective condition of resources and environment in the low-carbon economy system. The connotation of “Response” is reflected by setting the “Scientific Response” (R1), “Human Response” (R2) and “Policy Response” (R3), embodying the positive response of countries towards environmental pollution, resources consumption and ecological destruction. At the same time, the frequently used indexes from the relevant studies about low-carbon economy are selected.
3. Methodology
This paper focuses on the coordination degree among four sub-indexes. Degrees of interaction among the four sub-indexes are defined by their respective characteristics as the degree of coupling coordination. The equation is as follows:T=μd(x)+ϕp(y)+ξs(z)+θp(k)=μ∑i=1mαixi,+ϕ∑i=1nβiyi,+ξ∑i=1jσizi,+θ∑i=1lωiki,\\nwhere T represents the development degree of the comprehensive evaluation index, which is the reflection of the development level of low-carbon economy. xi, represents the “Driver” indexes. yi, represents “Pressure” indexes. zi, represents “State” indexes. ki, represents “Response” indexes. The weight values are μ, ϕ, ξ, θ, αi, βi, σi.\\nC={d(x)×p(y)×s(z)×p(k)[d(x)+p(y)+s(z)+p(k)4]4}14\\nwhere C represents the coupling degree, which is a measure of coordinated development. C ranges from 0 to 1. The greater the coupling degree among the systems the closer the C is to 1. Contrary to it, the smaller the coupling degree among the systems, the closer the C is to 0. Besides, there is no need to develop it while the sub-indexes are irrelevant.\\nD=C×T\\nD is coupling coordinated degree. T is development degree. It measures the situation as a whole of the development of coordination in the system [27].
The classification criterion of D is as in Table 2.
4. Data and Variables4.1. Measurement of CO<sub>2</sub> Emissions from Energy Consumption
In the current literature and research, the calculation caliber of regional carbon emissions data is difficult to unify. We use the methodology in the IPCC guidelines:C=∑iTi×Fi\\nwhere C represents total CO2 emissions from regional energy consumption; i is the type of energy; Ti is the terminal consumption for i; Fi is the coefficient of CO2 emissions for i:Fi=Ai×Bi×(44/12)×103×4186.8×10−9×10−3×Ei\\nAi is the coefficient of carbon emissions for i (kgC/GJ); Bi is the oxidation rate in the combustion for i type energy; Ai and Bi are derived from IPCC (2006). In Table 3, Ci is the coefficient of CO2 emissions for i (kgCO2/TJ); Di is the original coefficient for i type of energy (Kg CO2/Kcal); Ei is the calorific value of unit fuel in China.
This paper calculates CO2 emissions in China’s 30 regions (1995–2016). The data for these provinces are derived from China’s Statistical yearbook. The reason we removed the refinery gas and liquefied petroleum gas is because they are difficult to gather, and they are used less. We calculated the total CO2 emissions from 1995 to 2016, which show a tendency to increase year after year (Table 4).
4.2. Other Data and Variables
We evaluate the level of the low-carbon economy development level in China’s 30 regions from 2000 to 2015 as shown in Table 1. The demographic data are obtained from “China Population Statistic Yearbook”, the per capita GDP, the GDP growth and the average income of rural and urban families are obtained from the statistics in “China Statistical Yearbook”. The energy data are obtained through the calculation of statistics in “China Energy Statistical Yearbook” and China’s 30 regions’ statistical yearbooks. The scientific data are attained from “China Statistical Yearbook on Science and Technology”. We can set the 6‰ and 75% as moderate indexes, which is the ideal value of natural population growth rate and urbanization rate.
5. Regional Evaluation of the Low-Carbon Economy5.1. Index Calculation
Urbanization rate (D12). Urbanization rate refers to the percentage that the urban population accounts for the entire population, which witnesses city’s history and development. Since the current statistics do not cover all sub-provincial cities’ statistical data of urbanization rate, this paper adopts the method that “non-agricultural population accounts for total population proportion” to calculate the urbanization rate, which is authorized by police departments. The method can be summarized as “the urbanization rate = non-agricultural population/total population.
Per capita carbon emissions (S11). From 1990s to the year 2000, the global average annual CO2 emission has increased from 22 billion tons to 264 billion tons. Therefore, the primary goal of developing the low-carbon economy is to limit CO2 emissions. Due to unavailability of authorized statistics on carbon emissions, this paper estimates the carbon emissions produced by fossil resources (coal, oil and natural gas) based on other existing statistics. The total carbon emissions = Σ (each resource consumption × carbon emissions per unit of energy consumption), summarized as per capita carbon emissions = total carbon emissions/population.
Carbon emissions of residents’ consumption (S12) and government’s consumption (S13). They refer to the carbon emissions created by government’s and residents’ final consumption on goods and services over a period of time, which can reflect the influence of consumption level and structure on carbon emissions. The government’s consumption on carbon emissions refers to the carbon emissions created by government’s consumption on public services, which can reflect the impact of local government governance level on carbon emissions. We account these two indexes according to the proportion of residents’ consumption in GDP (known as final consumption rate) and carbon intensity per unit of economy. That is, carbon emissions of residents’ consumption = total carbon emissions/residents’ consumption, carbon emissions of government’s consumption = total carbon emissions/government’s consumption.
The efficiency of energy process and conversion (R12). The process of energy conversion is a production link of the energy process system, which is the important index for evaluating the advancement of the energy conversion device, the production technique and the quality of administration. The improvement of efficiency of energy process and conversion means that the low energy input can create high energy output. Its formula is as follows: efficiency of energy process and conversion = energy output/energy input.
Carbon productivity (R13). It is considered as the core index of measuring low carbonization, which is the GDP per unit of carbon emitted. This index links the GDP output with the carbon emissions of energy consumption directly. Therefore, it can visually reflect the carbon use efficiency of the whole social economy and measure the overall level of low-carbon technology. In addition, as a result of its relation with economic structure, the carbon productivity can have the potential of further reduction of carbon emissions intensity of unit consumption when the country’s technology has accumulated to a certain extent. Its formula is carbon productivity = GDP/carbon emissions.
5.2. Dimensionless Evaluation Factors
The collected original data must be dimensionless through range conversion as they cannot be compared directly.
We set xki as the value of the index k that has been normalized in the evaluated area i, and set the vki as the value of the index k in the evaluated area i, the n as the quantity of the evaluated area. Positive data was calculated:xki=vki−min1≤i≤n(vki)max1≤i≤n(vki)−min1≤i≤n(vki)
Negative data was calculated:xki=max1≤i≤n(vki)−vkimax1≤i≤n(vki)−min1≤i≤n(vki)
Regarding, moderate interrelation for those factors, the closer it approached to ideal value, the better. In reference to a large number of previous literature, this paper sets the planning objective of the country’s natural population growth rate in China (V1 = 6‰) as the ideal value of natural population growth rate (D11) and sets the average urbanization rate in developed countries (V2 = 75%) as the ideal value of the urbanization rate (D12). We set xki as the value of the index k that has been normalized in the evaluated area i, set the vki as the value of the index k in the evaluated area i, the vki0 as the ideal value and the n as the quantity of the evaluated area. Moderate data was calculated:xki={1−vki0−vkimax[vki0−min1≤i≤n(vki),max1≤i≤n(vki)−vki0],min1≤i≤n(vki)≤vki≤vkio1−vki0−vkimax[vki0−min1≤i≤n(vki),max1≤i≤n(vki)−vki0],vki0≤vki≤max1≤i≤n(vki)
5.3. Weight of Evaluation Factors
AHP model was developed by Satty [28]; it includes both qualitative and quantitative indicators, and ranks programs based on multiple criteria. It has been widely used in macro (complex and realistic) and human (management subjective) problems [29,30,31]. Nowadays, it is used in many research fields [32,33,34,35].
In this study, the low-carbon economy system includes four levels (Table 1).
If aij is the result of comparing i with j. The matrix is\\nA=[a11a12…a1na21a22…a2n…………an1an2…ann]
We employed the Delphi method to analyze the relative importance of D, P, S and R. In this study, we provided a scoring table for 30 experts from the universities, scientific institutions and government decision-making departments. Then we calculated the judgment matrix, obtained the indicator weighting and established pair-wise comparison matrix for A (Table 5).Finally, we found that “State” (S) was crucial to the low-carbon economy level, and it was the base for low-carbon economy, Therefore, weight of S was the highest.
mi is the product of every row: mi=∏i=14aij, (i=1, 2, 3, 4)\\nwi¯=mi4; w¯=(w1¯,w2¯,…,wn¯)T\\nwi=wi¯∑i=1nwi¯, (i=1, 2, 3, 4); λmax=1n∑i=1n(AW)iwi\\nAW=[a11a12…a1na21a22…a2n…………an1an2…ann]×|w1w2•wj|
Finally, we calculated the weight of D, P, S and R were 0.1186, 0.2162, 0.4141 and 0.2511. The eigenvalue λmax = 4.1576.
Then, we tested CI (the consistency index) = 0.0525. CR (the consistency ratio) = 0.059 ≤ 0.10.\\nCI=λmax−nn−1CR=CIRI\\nRI is used to modify the CI. In this paper, RI = 0.90.
5.4. The synthetic Index and Sub-Index of the Low-Carbon Economy
We calculated the indexes according to the aforesaid principles, then used a composite index that contains rule hierarchy composite index and comprehensive composite index (using the hundred percentage point system). In other words, the method of linear weighted sum is used to calculate index value in all rule hierarchies, and then from its result the low-carbon evaluation index (LCEI) value is obtained.
We use zj to express the numerical value of second grades in the comprehensive system; in order, there are D index, P index, S index and R index. Then we set xk as the dimensionless index after normalization and the wk as the weight of the indexes (Table 1).\\nzj=∑xkwk (j=D,P,R,S); LCEI=zD+zP+zS+zR
We present the low-carbon economy level of China’s 30 regions in Table 6.
6. Assessing Coupling Coordination between the Sub-Indexes
As mentioned above, the horizontal comparisons of the coordination degrees are shown in Table 7, the coordination degrees of the 30 regions in China were calculated and analyzed and were based on the model of coordination degree in 2015.
The coupling degree (C) results show a pattern for that central area lower than the west area and the west area lower than the east area. The average degree of the whole country is 0.6723, which is higher than west and central areas but lower than the east area.
The coordination degree (D) results show a pattern for the west area lower than the central area and the central area lower than the east area. The average degree of the whole country is 0.5840, which is higher than the west and central areas but lower than the east area.
Moreover, based on the model of coordination degree in 2015, the coordination degrees of the 30 regions in China were calculated and analyzed.
The results indicated the four sub systems were primary coordination in the east area and bare coordination in the central and west areas in 2015. It indicated that four sub-indexes should be developed at the same pace and be improved based on stable development. The construction of low-carbon economy in mid-west areas is the key in China.
7. Results and Analysis7.1. Results
In Table 8, the numerical value increases gradually; all provinces, municipalities and autonomous regions show varying increasing rates; the level of low-carbon economy varies by province. The index level shows a gradually decreasing trend from southeastern coastal areas to the north on spatial variation.
The Figure 2 reflects the distribution of development capability of regional low-carbon economy in China in 2015. It can be seen from Figure 2 that in the spatial distribution, the numerical value of development capability shows a pattern that indicates the southeast is gradually higher than the north. While the clear winner would be the east, which overall stands on the top. This spatial distributive characteristic is closely related to China’s industrial structure, economic development and resource distribution features. For example, Shanxi and Inner Mongolia are important coal-producing bases where coal mines and resources are so crowded that they have weak low-carbon economy development. However, Guangdong and Fujian relatively have lack of natural resources so that their economy has reached a higher level, and the low-carbon economy develops well. Although Beijing is located in northern China and is very near the coal resource centers geographically, with coal making up such a large proportion in its energy structure, it has a well-developed economy; especially, the proportion of its tertiary industry is over 80%. Its industry is mainly based on modern manufacturing and tech industry; therefore, Beijing is under less pressure on low-carbon economy and It ranks 1st in low-carbon economy. However, industrial manufacturing still accounts for a definite proportion of the industrial structure in Shanghai; as a result, Shanghai is under more pressure on low-carbon economy than Beijing even though its economy is better.
In order to reveal the figures of low-carbon economy in different regions, we used Ward’s method to do cluster analysis on China’s 30 regions based on SPSS statistical software. For instance, the results of 2015 were shown in Table 8.
We found that the 30 regions can be divided into 4 categories. These 4 categories correspond respectively to the 4 statuses: leading status, good status, medium status and poor status. The evaluation results show that the numerical value varies greatly in different regions (Table 9). The leading status is almost all in the east area except Jiangxi, Guangxi and Hunan, which are included in the central area. Shanxi, Inner Mongolia and Ningxia fall into poor status.
Figure 3 and Figure 4 compare the difference of average low-carbon economy level in different regions. They show that the numerical value of east regional low-carbon economy shows a pattern that is gradually higher than the west region. The numerical value of low carbon economic development in the south region is higher than the north region by degrees. Almost all parts of the west are low level. Ningxia is considered to have very low level. We find that the average level has shown a downward trend since 2010. This is due to the decline in economic growth and GDP. Growth rate was 10.3% in 2010 and 7.4% in 2015.
7.2. Analysis of DPSR Sub-Indexes
In view of the driving sub-index, state sub-index and pressure sub-index, there are some differences in all regions, and the leading status is significantly better than the poor status. As seen from the “Response” sub-index, the resource consumption and degree of pollution vary from province to province, as well as pollution abatement and efforts for environmental protection. All these factors reflect that the more efforts the government, system, society and individual make to improve the status of resources, the better the low-carbon economy develops.
The drive sub-index varies greatly from province to province. It shows a pattern with the indication of northeast gradually lower than southeast, which is determined by China’s terrains and regional development policies. From Figure 5, we can see that the regions with high grades of drive sub-index value are all located in coastal areas. This is because these areas have significant regional advantages, which make the input cost of commerce activities and production activities much lower than in other regions. Therefore, their input and output efficiency in economic activities is relatively high. In addition to that, in the earlier stage of reform and opening, China has given special importance to the economic development in coastal areas by providing support in policies. Various regional preferential policies were introduced to form advantages for development; thus, their level of driving force on low-carbon economy is higher than the average level in China.
The situation of the pressure sub-index generally indicates the north being gradually lower than the south, the west gradually lower than the east, and hopping in some areas as shown in (Figure 6), which is determined by various causes of development foundations, regional conditions and development policies in all regions. For quite a long time, the speed of economic development in China’s eastern coastal areas was much faster than in central and western areas. In addition, the coming years offer a critical period for western areas to accelerate industrialization; thus, the low-carbon pressure in east is relatively lower than in the west. We can conclude that the index value in the rule hierarchy of pressure has gradually decreasing effects around the main energy supplement region and the surrounding areas, so Shanxi, Inner Mongolia, Ningxia and Qinghai have the most pressure on low-carbon economy.
The situation of the state sub-index indicates the north being gradually lower than the south; therefore, the ranks of Hainan, Guangxi and Fujian are relatively higher, while Shaannxi, Hunan and Hubei are in the second tier (Figure 7). This is because southeastern coastal areas have good economic development with external supply of energy, oil and natural gas. Hydroelectricity and nuclear electricity occupy a large proportion in the structure of energy consumption, so the low-carbon economy in these regions is in good status. However, although central and southern areas have certain coal resources, the consumption far outstrips the production. As a result, the external supply of energy is badly needed. At the same time, these areas that are rich in forest resources vigorously develop hydroelectricity and pursue rational use of energy, so the numerical value in these regions is better than it is in the north.
The situation of the response sub-index shows a pattern of hopping and overlapping in some areas. Beijing, Shanghai and Guangdong rank the first three in 2015, while in the north, northwest and southeast, the index values are gradually low (Figure 8). This is due to the combined impact of the economic and scientific development level, energy consumption efficiency and industrial structure. According to the statistical yearbook, major consumption of energy in west regions is from coal, and there are more coal resources without desulfurization, which combines with low economic development and inadequate input in environment protection. As a result, the efficiency of energy process and conversion is low, and the productivity of carbon falls behind.
7.3. Analysis of the Degree of Coordination among the Four Sub-Indexes
As mentioned above, the horizontal comparisons of the coordination degrees are shown in Table 8; the coordination degrees of the 30 regions in China were calculated and analyzed and were based on the model of coordination degree in 2015.
The coupling degree (C) results show a pattern for the central area lower than the west area and the west area lower than the east area. The average degree of the whole country is 0.6723, which is higher than the west and central areas but lower than the east area.
The coordination degree (D) results show a pattern for the west area lower than the central area and the central area lower than the east area. The average degree of the whole country is 0.5840, which is higher than the west and central areas but lower than the east area.
Moreover, based on the model of coordination degree in 2015, the coordination degrees of the 30 regions in China were calculated and analyzed.
The results indicated the four sub-systems were primary coordination in the east area and bare coordination in the central and west areas in 2015.In addition, four sub-indexes should be developed at the same pace and improved on the basis of stable development. It shows that promoting the development of a low-carbon economy in mid-west areas is the key in China.
8. Conclusions
Based on the above results, the following conclusions are drawn:
All 30 regions show varying increasing rates. The regional distributions of the sub-indexes are the combined impacts of regional economic development level, resource endowment and policy support. The numerical values in China’s 30 regions vary greatly; as a whole, they indicates the north is gradually lower than the southeastern coastal areas, and the east is higher than the west; the south is higher than the north, and there is overlapping in some areas. Beijing and Shanghai are far ahead while Shanxi, Inner Mongolia and Ningxia are backward.
As compared to the developed countries that have already achieved industrialization, China focuses particularly on achieving sustainable development by combining industrialization with ecological civilization. It is worth noting that China has large regional differences. Compared with the developed regions in the east, there are still a lot of heavy-duty industries in the central and western regions. Industrial structure transformation is more difficult. Therefore, promoting the development of low-carbon economy in mid-west areas is the key in China.
The improvement of people’s living quality and social welfare can best embody the development of low-carbon economy. The result of this study may be helpful for policy makers to formulate effective policies and take actions in the future.
We firmly believe that ecological civilization construction, industrialization and urbanization are not mutually exclusive at all. We must emphasize realizing sustainable development through the low-carbon system while people’s material life has benefited a lot. Therefore, we ought to change the bad social consumption mode, as well as wrong consuming concepts and patterns. Besides, we must make great efforts to turn the low-carbon way of life into a conscious behavior while improving our quality of life and stick to it in concrete activities.
Author Contributions
Conceptualization, W.P, M.A.G and W.H.; methodology, W.P and M.A.G.; formal analysis, W.P.; writing—original draft preparation, W.P.; writing—review and editing, M.A.G and W.H.; supervision, W.P and M.A.G. All authors have read and agreed to the published version of the manuscript.
Funding
This research was funded by the Fundamental Research Funds for the Central University (WUT: 2020VI011) and the National Natural Science Foundation of China (71772143).
Conflicts of Interest
Declare conflicts of interest or state “The authors declare no conflict of interest.”
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The analytical framework of low-carbon economic system based on driver–pressure–state–impact–response (DPSIR) model.
Cluster analysis map of low-carbon economy level of China.
Average low-carbon economy level of China and its three areas.
Comparison of the average low-carbon economy level of the three areas.
Map of drive sub-index in 2000 and 2015.
Map of pressure sub-index in 2000 and 2015.
Map of state sub-index in 2000 and 2015.
Map of response sub-index in 2000 and 2015.
ijerph-17-05463-t001_Table 1
Index system of low-carbon economy.
First Grade
Second Grade
Weight
Third Grade
Weight
Fourth Grade
Weight
Positive or Negative
Comprehensive level of low-carbon economy(A)
Driver (D)
0.1186
Driver for social development (D1)
0.0593
Natural Population Growth (D11, ‰)
0.0272
Moderate
Urbanization Rate (D12, %)
0.0247
Moderate
Engel’s coefficient on urban and rural households (D13, %)
0.0075
Negative
Driver for economic development (D2)
0.0593
per capita GDP (D21, yuan per capita)
0.0183
Positive
GDP growth rate (D22, %)
0.0065
Positive
The average income of rural and urban family (D23, yuan per capita)
0.0345
Positive
Pressure(P)
0.2162
resource pressure (P1)
0.0865
The energy consumption per unit of GDP (P11, tons of coal per 10,000 yuan)
0.0605
Negative
Electricity consumption per capita (P12, kwh per capita)
0.0259
Negative
environmental pressure (P2)
0.1297
The industrial waste-gas discharge per unit of GDP (P21, cubic meter per 10,000 yuan)
0.0741
Negative
SO2 emission per unit of GDP (P22, ton per 10,000 yuan)
0.0371
Negative
Public transportations per 10,000 people (P23)
0.0185
Positive
Status(S)
0.4141
Status of low-carbon consumption (S1)
0.2071
Carbon emissions per capita (S11, ton per capita)
0.1305
Negative
The carbon emissions of residents’ consumption (S12, ton per 10,000 yuan)
0.0541
Negative
The carbon emissions of government consumption (S13, ton per 10,000 yuan)
0.0225
Negative
Status of low-carbon resources (S2)
0.2071
Proportion of consumption of raw coal (S21, %)
0.0941
Negative
Carbon emissions per unit of energy (S22, ton of CO2 per ton of coal)
0.0188
Negative
Forest coverage (S23, %)
0.0941
Positive
Response(R)
0.2511
Scientific Response (R1)
0.1758
Expenditure on science and technology per capita (R11, yuan per capita)
0.0189
Positive
The efficiency of energy process and conversion (R12, %)
0.0181
Positive
Carbon productivity (R13, 10,000 yuan per ton)
0.1387
Positive
Policy Response (R2)
0.0753
The proportion the tertiary industry output value accounts for GDP (R21, %)
0.0442
Positive
The development plan of low-carbon economy (R22)
0.0077
Positive
Policy of carbon tax (R23)
0.0081
Positive
Supervision and statistics system of carbon emissions (R24)
0.0154
Positive
ijerph-17-05463-t002_Table 2
Classification of the coupling coordinated degree.
Coordination Degree
Coordination Level
Coordination Degree
Coordination Level
0.01 < CD2 ≤ 0.10
Extreme disorder ≤
0.51 < CD2 ≤ 0.60
Bare coordination
0.11 <CD2 ≤ 0.20
Serious disorder
0.61 < CD2 ≤ 0.70
Primary coordination
0.21 <CD2 ≤ 0.30
Moderate disorder
0.71 < CD2 ≤0.80
Intermediate coordination
0.31 <CD2 ≤ 0.40
Mild disorder
0.81 < CD2 ≤0.90
Favorable coordination
0.41 <CD2 ≤ 0.50
On the verge of disorder
0.91 < CD2 ≤ 0.10
Quality coordination
ijerph-17-05463-t003_Table 3
Coefficient of CO2 emissions in China.
Energy Type
A
B
C = A × B × (44 / 12) × 1000
D = C × 4186.8 × 10−9 × 10−3
\\n\"\n]"}}},{"rowIdx":456,"cells":{"data":{"kind":"list like","value":["\nInt J Mol SciInt J Mol SciijmsInternational Journal of Molecular Sciences1422-0067MDPI32759830PMC743211310.3390/ijms21155571ijms-21-05571ArticleAnti-Angiogenic and Anti-Proliferative Graphene Oxide Nanosheets for Tumor Cell TherapyVerdeValeria1†LongoAnna2†https://orcid.org/0000-0001-6127-5962CucciLorena Maria1https://orcid.org/0000-0002-4269-187XSanfilippoVanessa1https://orcid.org/0000-0003-1556-934XMagrìAntonio3https://orcid.org/0000-0001-5348-5863SatrianoCristina1*AnfusoCarmelina Daniela2LupoGabriella2*https://orcid.org/0000-0002-4193-7612La MendolaDiego4Nano Hybrid BioInterfaces Lab (NHBIL), Department of Chemical Sciences, University of Catania, Viale Andrea Doria 6, 95125 Catania Italy; verdevaleria@virgilio.it (V.V.); lorena.cucci@unict.it (L.M.C.); sanfilippo.vanessa@studium.unict.it (V.S.)Department of Biomedical and Biotechnological Sciences, University of Catania, Via Santa Sofia 97, 95123 Catania, Italy; longo.anna@hotmail.it (A.L.); daniela.anfuso@unict.it (C.D.A.)Institute of Crystallography—National Council of Research, via Paolo Gaifami 18, 95126 Catania, Italy; leotony@unict.itDepartment of Pharmacy, University of Pisa, via Bonanno Pisano 6, 56126 Pisa, Italy; lamendola@farm.unipi.itCorrespondence: csatriano@unict.it (C.S.); gabriella.lupo@unict.it (G.L.); Tel.: +39-095-7385136 (C.S.); +39-095-4781158 (G.L.)
These authors contributed equally to this work.
0482020820202115557121720200182020© 2020 by the authors.2020Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Graphene oxide (GO) is a bidimensional novel material that exhibits high biocompatibility and angiogenic properties, mostly related to the intracellular formation of reactive oxygen species (ROS). In this work, we set up an experimental methodology for the fabrication of GO@peptide hybrids by the immobilization, via irreversible physical adsorption, of the Ac-(GHHPH)4-NH2 peptide sequence, known to mimic the anti-angiogenic domain of the histidine-proline-rich glycoprotein (HPRG). The anti-proliferative capability of the graphene-peptide hybrids were tested in vitro by viability assays on prostate cancer cells (PC-3 line), human neuroblastoma (SH-SY5Y), and human retinal endothelial cells (primary HREC). The anti-angiogenic response of the two cellular models of angiogenesis, namely endothelial and prostate cancer cells, was scrutinized by prostaglandin E2 (PGE2) release and wound scratch assays, to correlate the activation of inflammatory response upon the cell treatments with the GO@peptide nanocomposites to the cell migration processes. Results showed that the GO@peptide nanoassemblies not only effectively induced toxicity in the prostate cancer cells, but also strongly blocked the cell migration and inhibited the prostaglandin-mediated inflammatory process both in PC-3 and in HRECs. Moreover, the cytotoxic mechanism and the internalization efficiency of the theranostic nanoplatforms, investigated by mitochondrial ROS production analyses and confocal microscopy imaging, unraveled a dose-dependent manifold mechanism of action performed by the hybrid nanoassemblies against the PC-3 cells, with the detection of the GO-characteristic cell wrapping and mitochondrial perturbation. The obtained results pointed out to the very promising potential of the synthetized graphene-based hybrids for cancer therapy.
theranosticsnanomaterialsnanomedicineHPRGpeptidescell migrationprostaglandinshuman prostate cancer cellsROSconfocal microscopy1. Introduction
Graphene is a 2-dimensional (2D) carbon nanomaterial consisting of planar sheets of sp2-hybridized carbon atoms arranged in a honeycomb lattice [1] and due to this monoatomic layer structure is the thinnest material produced so far [2]. Over the past 15 years, graphene has attracted the attention of the whole scientific community thanks to its unique mechanical and electrochemical properties, which include high density, chemical inertness, optical transmittance, very high hydrophobicity and molecular barrier abilities [3,4,5]. It is one of the most resistant materials ever tested with tensile strengths greater than 100 GPa and a traction module of 1 TPa [6]. Other remarkable features include the high planar surface (calculated value, 2630 m2/g [7], which allows for its higher drug loading capacity than other nanomaterials [8] and the thermal conductivity (5000 W/mK) [9]. Such properties make graphene particularly promising for applications in several fields, like the aerospace, electronics, energy, mechanical, environmental, and food industries as well as in biomedicine [10]. However, the poor solubility and agglomeration of the nanosheets in solution, caused by van der Waals forces and π–π stacking interactions, greatly limits its uses, makes it difficult to produce and significantly impacts its toxicity [11,12]. The oxygen-functionalized graphene derivative, namely graphene oxide (GO), maintaining a similar hexagonal carbon structure to graphene, overcomes these limits [13]. GO, indeed, shows a high density of functional oxygen groups namely carboxyl (–COOH) and hydroxyl (C–OH) groups, typically located at the edges of the sheets, and carbonyl (C=O) and epoxy groups (C–O–C) on the basal plane of the graphene sheets [14].
The unique chemical and physical properties of GO and its reduced derivative (rGO) have aroused a strong interest especially for biological studies [15], since the presence of defective oxygen-bound sp3 carbon atoms induces a strong hydrophilicity and contributes to the formation of dispersions highly stable colloidal in aqueous solvents, preventing the uncontrolled aggregation of the nanosheets caused by van der Waals and the hydrophobic interactions [16]. Moreover, the presence of hydrophilic functional groups on the GO surface offers high versatility for the derivatization of nanosheets and makes GO a suitable platform for the development of drug delivery systems [17] with potential application in tissue engineering [17], biosensing [18], and bioimaging [19], thus opening new horizons in the field of nanomedicine. In particular, graphene oxide based targeted drug carriers are becoming of great interest in the treatment of cancer diseases [20]. To this respect biological therapies, based on the delivery of biomolecules and gene therapy as well as phototherapies against cancer, including photodynamic and photothermal treatments, which use GO as a carrier, have showed high biocompatibility and good results both in in vitro and in vivo studies [21]. Thus, Liu et al. formulated transferrin modified graphene oxide for glioma-targeted drug delivery [22], Li et al. used functionalized nano-graphene oxide particles for targeted fluorescence imaging and photothermic therapy of glioma U251 cells [23], while Song and colleagues evaluated hyaluronic acid-decorated graphene oxide nanohybrids as carriers for targeted and pH-responsive anticancer therapy [24]. Furthermore, GO shows intrinsic biological properties, including antimicrobial activity [25] and the capability to control the function of immune cells [26] and to modulate angiogenesis. This latter feature provides additional advantages in cancer therapy, since formation of new blood vessels is involved in both tumor growth and development of metastases [21,27]. Hence, the anti-angiogenic action of GO can be very effective to fight cancer. To note, there are plenty of examples in the literature on the development of GO and modified GO platforms for anti-cancer therapy [28,29,30,31].
It has been demonstrated that GO sheets present pro-angiogenic properties at low doses (1–50 ng/mL), due to the controlled production of intracellular reactive oxygen species (ROS) (H2O2 and O2•−) induced by this material, while show anti-angiogenic features at high doses (≥100 ng/mL), attributed to the excessive generation of intracellular ROS [32]. In general, the mechanisms underlying GO toxicity in addition to oxidative stress and excessive ROS production also include DNA damage, apoptosis, autophagy, and immune responses, which widely varied in relation to the physical-chemical properties of GO, such as surface chemistry, layer number, lateral dimension, and purity [33].
The histidine-proline-rich glycoprotein (HPRG) is a single polypeptide chain protein of 70–75 kDa, with a multidomain structure. In humans, the protein is synthesized in the liver and is present in plasma at relatively high concentrations of 100–150 µg/mL (1.5 µM) [34,35]. HPRG ability to simultaneously interact with several ligands suggests that it may act as an adapter molecule which regulates numerous biological processes, including blood coagulation and fibrinolysis, adhesion, and cell migration, as well as anti-/pro-angiogenic activity [36].
Indeed, the HPRG protein promotes angiogenesis by inhibiting the activity of the antiangiogenic thrombospondin-1 (TPS-1) [37], by binding to plasminogen/plasmin onto the surface of endothelial cells as well as by promoting cell migration and invasion [38], which are critical phases of the new blood vessels formation. On the other hand, HPRG has also a demonstrated antiangiogenic activity, mainly localized in its histidine-proline-rich domain (H/P) and occurring by the blocking of the interaction between fibroblast growth factor (FGF-1 and FGF-2) and heparan sulphate in the extracellular matrix (ECM) and the surface of endothelial cells [39].
The Ac–(GHHPH)4–NH2 peptide has been shown to be an active HPRG mimic system, and it has been demonstrated effective as antitumor agent in two syngeneic cancer models, namely Lewis lung cancer (3LL) and melanoma (B16F1) growth in mice [40,41].
Based on these premises, in this work GO was functionalized with a with the Ac–(GHHPH)4GK–NH2 peptide [41] covalently bound to a 5,6-carboxyfluorescein (Fam) moiety, hereinafter named Tetra(HPRG)-Fam. The integration of the therapeutic potential from both GO and the Tetra(HPRG) peptide and the imaging capability through the fluorescence of the dye-labelled peptide makes the hybrid graphene oxide-Tetra(HPRG)Fam system (hereinafter named GO@T) a potential theranostic platform. The physicochemical characterization was carried out by mean of spectroscopic analyses of UV-visible, fluorescence and ATR-FTIR, to scrutinize the hybrid biointerface between the nanosheets and the peptide molecules in terms of electron transfer processes and average peptide molecular structural conformation. In vitro cellular experiments were carried out on human neuroblastoma (SH-SY5Y) and prostate cancer (PC-3) cell lines, as cellular tumor models to test the anti-angiogenic potential of our platforms in anti-cancer therapy. Both cell lines are aggressively-growing tumors, associated also to uncontrolled tumor vascularization [42]. Moreover, PC-3 can form vessel-like structures through a process denoted as vascular mimicry [43]. As endothelial cellular model of angiogenesis, we tested also our nanoformulation on primary human retinal endothelial cells (HREC) [44,45].
Specifically, the nanotoxicity and the pro-or anti-proliferative activity were investigated for GO@T hybrids compared to bare GO sheets and to the fragment peptide. Also, the nanocomposites effect on mitochondrial dysfunctions were scrutinized by measuring the mitochondrial production of O2•− as well as their effects on cellular migration and prostaglandin E2 (PGE2) release. Intracellular imaging using confocal laser scanning microscopy (LSM) was performed to evaluate the mechanism of interaction and internalization of hybrid systems on the studied cancer cell line.
2. Results
UV-visible, circular dichroism (CD), fluorescence and attenuated total reflectance Fourier transform infrared (ATR/FTIR) spectroscopies were used to characterize the hybrid graphene oxide nanosheets functionalized with the Tetra(HPRG)-Fam peptide (samples thereafter named GO@T). Two weight ratios for the peptide/GO mixtures were used for the preparation of the samples, namely, two concentrations of graphene oxide, i.e., 1 mg/mL (GO_A) or 0.5 mg/mL (GO_B) incubated for 2 h under stirring with a fixed concentration of peptide (0.2 mM).
Figure 1 displays the absorption spectra of reference samples of the GO nanosheets and of the Tetra(HPRG)-Fam peptide, both before and after the mixing (Figure 1a) as well as for the corresponding pellets and supernatants recovered after the centrifugation and washing step (Figure 1b), to remove the loosely bound peptide molecules from the GO platforms.
Figure 1a shows, for the Tetra(HPRG)-Fam sample, the characteristic π → π* transition absorption band of the peptide bond in the 180–230 nm range as well as the absorption of the aromatic side-chains of His residues, in the range of 230–300 nm [46]. Moreover, related to the chromophore activity of the Fam moiety [47,48], two weak bands, respectively at 280 nm and in the 350–440 nm region, and a main absorption peak at 497 nm are evident. As to the GO nanosheets, the UV-visible spectra validate the approximation of using the Lambert-Beer law of solutions for these colloidal dispersions. Indeed, both GO_A and GO_B samples exhibit the same spectral features just scaled in a 1 to 0.5 ratio; namely, one absorption peak at 238 nm and a shoulder at around 300 nm, due to the π → π* and n → π* transitions, respectively [49]. The spectra of the hybrid GO@T systems display both hypochromic and bathochromic/hypsochromic shifts compared to the reference GO and Tetra(HPRG)-Fam systems, respectively. Such findings point to the occurrence of electron transfer processes at the interface established between the GO nanosheets and the dye covalently linked to the peptide. To note, the spectra of the mixture systems are mostly given by the sum of the spectra of each component, the GO and the Tetra(HPRG)-Fam, with the GO absorption bands buried by the background signal of the Fam moiety in the UV region. Also, only for GO_B+T the Fam absorption peak being significantly decreased, due to the quenching effect of GO, but with no evidence of shift in the wavelength at the maximum of absorption. These findings point to a lower level of interaction between the GO sheets and the peptide molecules in the mixture than in the hybrid nanocomposite.
After the centrifugation and washing steps (Figure 1b), the spectra of bare GO demonstrate a different relative ratio of sp3 (oxidized defective) to sp2 (graphitic-like) carbon than the pristine GO, as disclosed both by the hypsochromic shift (~6 nm) of the π → π* peak and by the slight hyperchromic shift in the shoulder related to the n → π* transitions, respectively. In particular, the heavier (and larger) GO nanosheets, which accumulate preferentially in the pellet, exhibit a relative ratio of the sp2 to sp3 domains higher than the lighter (and smaller) nanosheets that mostly gathered into the supernatant, and hence have more defective oxygen-containing groups, especially at the edges [50,51]. To note, an additional contribution to these shifts can derive by the decrease of conjugative effect of chromophore aggregation by washing-induced loss of weakly bound oxygen-containing species, which influences the π–π* plasmon peak [52]. Significantly, the pellets of GO_A@T and GO_B@T samples (Figure 1b), which display spectral features of both GO and Tetra(HPRG)-Fam components, do not show significant changes in the peak wavelength position with respect to those of the hybrid systems before the centrifugation and rinsing steps (Figure 1a). These findings point to the successful and irreversible immobilization of the peptide molecules at the GO nanosheets surface.
As to the mixtures, also in this case the spectra evidence an irreversible binding of the Fam-labeled peptide to the GO substrates. For both GO@T hybrids and GO+T mixture samples, the spectra of supernatants collected after first washing step do not display any significant indication of the presence of peptide neither of GO. By using the molar extinction coefficients calculated for Tetra(HPRG)-Fam in the hybrid nanocomposite, a peptide loading of about 95 µM for 1 mg/mL of GO in GO_A@T or GO_A+T and of 88 µM for 1 mg/mL of GO in GO_B@T or GO_B+T could be estimated, respectively.
The analysis of CD spectra for both hybrids and mixture samples, in the comparison with the spectrum of the peptide alone, evidenced: (i) no detectable peptide signals for GO_A@T and GO_A+T (data not shown), likely due to the low relative concentration of the peptide compared to the GO matrix; (ii) a progressive intensity decrease and a red-shift of the minimum band at 202 nm for both GO_B+T (at 206 nm) and GO_B@T (at 207 nm) (Figure 2). The redshift is explained as result of the increase in the polyproline II (PPII) conformers [53]; the intensity change is due to the optical coupling between the peptide chromophore moieties and the GO substrates. Over all, these findings point to a strong interaction between the nanosheets and the Tetra(HPRG)-Fam, which affects at different levels (higher in the hybrids than in the mixtures) the peptide conformational structure and its freedom degrees.
The electron transfer processes at the interface between the GO nanosheets and the Fam-labelled peptide were confirmed by the fluorescence spectra (Figure 3), which display a strong quenching of the Fam peak at 521 nm. In particular, the fluorescein emission decreases of 99.9% in GO_A@T and to 99.1% in GO_B@T samples (see inset Figure 3), respectively.
The characterization by ATR-FTIR analyses (Figure 4) confirmed the effective immobilization of the peptide on the GO nanosheets in the hybrid nanocomposite. As to the uncoated GO, the spectrum displays a broad band in the wavenumber range of 3700–3000 cm−1, assigned to O-H stretching vibrations, as well as two peaks at ~1730 cm−1 and 1620 cm−1, due to the C=O stretching of the carboxylic acid groups and the aromatic C–C stretching, respectively [54]. Moreover, a weak signal at ~1400 cm−1 corresponding to the O–H deformation and an absorption peak at ~1050 cm−1 attributed to the alkoxy C–O stretching vibrations are also visible [55,56]. The spectrum of Tetra(HPRG)-Fam shows characteristic peaks from both the 5,6-carboxyfluorescein unit and the peptide backbone. Specifically, the O–H stretching vibrations in the broad band from 3600 to 3300 cm−1 as well as the signals at 1667 cm−1 and 2852 cm−1, which correspond respectively to the stretching of the C=O of carboxyl groups and the C–H stretching [57] are visible, together with the typical IR bands for amino acids and peptide (the “bio-fingerprint region”) at 1630 cm−1 (amide I) and 1543 cm−1 (amide II) are visible [58]. Moreover, the signals at 1202 cm−1 can be assigned to the C=C stretching and the N–H bending vibrations of the histidine residue, while the weak band in range of 1400 and 1465 cm−1 can be attributed to the C–N stretching of the proline and the sharp peak at 1133 cm−1 corresponds to the C–O stretching vibrations of the peptide [59].
As to the GO@T hybrids, the signals at 1156 and 1080 cm−1, related respectively to the C=C stretching and the N–H bending vibrations of the histidine residue confirm the presence of peptide-related bands in the nanocomposite sample. Such signals are shifted to higher wavenumbers and the peak are broader with respect to those observed for free peptide, thus suggesting the formation of hydrogen binding between the hydrophilic groups of graphene oxide and the side chain imidazole groups of histidine in the peptide [60]. Moreover, the fingerprint signals from the amide I and amide II vibrations are strongly attenuated, likely due to a different conformational state of the peptide immobilized in the adlayer on the GO surface compared to the unbound peptide molecules.
The response of prostate cancer (PC-3), neuroblastoma (SH-SY5Y) and non-tumor endothelial (HREC) cells to the GO@T hybrid nanocomposites was investigated in terms of toxicity through MTT assay. Cell viability assays were performed on cells incubated for 24 h and 48 h with increasing concentrations of GO nanosheets (10, 12, 30 µg/mL), Tetra(HPRG)-Fam (2, 4, 6 µM) and three hybrid (GO@T) and mixture (GO+T) samples, spanning the same concentration range in the GO and the peptide (Figure 5).
In general, the cellular treatments with GO samples resulted not toxic after 24 h of incubation time for all the three cell lines tested. After 48 h of incubation, we detected a statistically significant decrease in cell viability in PC-3 (30 µg/mL GO: 88.5 ± 2.6% of CTRL; ** p < 0.01) and SH-SY5Y (10 µg/mL GO: 78.0 ± 5.1% of CTRL; * p < 0.05; 12 µg/mL GO: 68.1 ± 5.6% of CTRL; ** p < 0.01; 30 µg/mL GO: 50.1 ± 5.9% of CTRL; * p < 0.01;) but not in HRECs. Also, our findings are consistent with many other studies, reporting that GO is non-toxic for multiple cell types [61,62,63].
Interestingly, the treatment with Tetra(HPRG)-Fam alone induced an increased viability both in prostate cancer (Figure 5a, 24 h: ~120% of CTRL: ** p < 0.01; Figure 5d, 48 h: up to ~130% of CTRL: *** p < 0.001) and endothelial cells (Figure 5c, 24 h: ~130% of CTRL: * p < 0.05; Figure 5f, 48 h: ~140% of CTRL: ** p < 0.01) cells, while no significant changes in cell viability were detected in neuroblastoma cells for both 24 h (Figure 5b) and 48 h (Figure 5d) of incubation.
A comparable anti-proliferative effect was induced in PC-3 by the treatment either with the GO@T hybrids or the GO+T mixtures, for both the incubation times of 24 and 48 h. In particular, a statistically significant dose-dependent decrease of viability was detected with respect to both the untreated cells as well as the GO-treated cells, with a maximum of cytotoxicity found for the mixture of 30 µg/mL GO with 6 µM Tetra(HPRG)-Fam (24 h: 54.3 ± 4.8% of CTRL: *** p < 0.001; 48 h: 39.8 ± 2.2% of CTRL: **** p < 0.0001).
As to SH-SY5Y cells, a significant and comparable decrease in cell viability was detected only in the cells incubated for 48 h with the highest concentrations of GO@T hybrid (~80% of CTRL: * p < 0.05) or GO+T mixture (~88–70% of CTRL: * p < 0.05), respectively. To note, the same treatment conditions resulted however proliferative in comparison to the incubations with corresponding positive controls of GO.
The HREC treatments with the highest concentrations of GO@T hybrids resulted anti-proliferative both after 24 h (~70% of CTRL: * p < 0.05) and 48 h (~40% of CTRL: * p < 0.05) of incubation. On the other hand, the endothelial cells incubation with the GO+T mixture at the highest concentrations showed only a slightly proliferative effect at 24 h with respect to the positive control of GO (~120% of CTRL: ##\np < 0.01).
According to the results of anti-proliferative effects induced by GO@T hybrids in PC-3 and HREC but not in SH-SY5Y, the following investigations on the inhibition of cell migration and PGE2 release were carried out only on the prostate cancer and endothelial cells.
The modulation of PC-3 and HREC scratch crossing was monitored for 24 h and 48 h after monolayer wounding (Figure 6).
Representative photographs and the quantitative analysis of wound healing for PC-3 (Figure 6a,c) and HREC (Figure 6b,d) incubated in absence (negative control) or in presence of the positive controls of GO nanosheets (10, 12, 30 µg/mL),Tetra(HPRG)-Fam (2, 4, 6 µM) and the corresponding GO+T mixtures are shown in the comparison with those of cells incubated with the hybrid GO@T samples.
The control untreated PC-3 cells were able to cross the wound at 24 h, until the wound closed after 48 h. Conversely, the migration ability of PC-3 treated with GO was reduced significantly in comparison with control cells. A dose-dependent partial inhibition of cell invasion was observed in presence of Tetra(HPRG)-Fam, especially evident at 24 h of incubation (2 µM: ~70% of CTRL: ** p < 0.01; 4 and 6 µM: ~30% of CTRL: *** p < 0.001) than after 48 h of treatment (2, 4, and 6 µM: ~80% of CTRL: ** p < 0.01). The treatment of PC-3 either with the GO@T hybrids or the GO+T mixtures induced a significant and comparable reduction of the wound closure (at the two highest concentration tested: less than 20% of CTRL: **** p < 0.0001), indicating that the association of the peptide to the GO nanosheets efficiently reduces the PC-3 cells migration, inhibiting their metastatic potential.
HREC migration was modulated differently from cancer cells. Indeed, whereas the cells incubation with GO@T hybrids exhibited a significant inhibitory effect on wound closure for all three concentration tested (less than 20% of CTRL: **** p < 0.0001), and no differences in comparison with the negative control of untreated cells were observed for the cells incubated with the peptide alone, both the GO and the GO+T mixtures showed a similar trend, i.e., a partial inhibition for the lowest concentration (10 µg/mL: ~40% at 24 h and ~60% at 48 h of CTRL: *** p < 0.0001) and a very efficient inhibition of wound closure (less than 20% of CTRL: **** p < 0.0001) at the other two concentrations for both incubation times.
Inflammation is increasingly recognized as a critical mediator of angiogenesis [64]. It has been demonstrated that PGE2 promotes the in vitro tube formation of human microvascular endothelial cells via activation of EP4 receptors and by triggering PKA Cγ signaling pathway, ex vivo vessel outgrowth of aortic rings, and in vivo angiogenesis [65,66,67]. Moreover, during the inflammatory process, immune cells secrete pro-angiogenic factors for the promotion of new vessel formation [64].
The ability of GO@T hybrids to modulate the release of PGE2 in PC-3 and HREC was investigated as marker for the activation of inflammatory processes, which can have a crucial role both in carcinogenesis and cancer progression as well as in the angiogenic switch-on mechanism.
Cell culture supernatants collected after incubation of PC-3 or HREC cells for 48 h in absence (control) or in presence of GO nanosheets, Tetra(HPRG)-Fam, the hybrid GO@T or the mixture GO+T, were assayed for their PGE2 levels (Table 1 and Table 2).
As to prostate cancer cells, upon the incubations with the different samples, the PGE2 production by PC-3 cells decreased in comparison to untreated control cells. Specifically, Table 1 shows a dose-dependent effect (about 35–40%) for the treatments with GO, and a reduction in the PEG2 release by about 26% for Tetra(HPRG)-Fam, and by about 39% for the GO+T mixture, respectively. Finally, the treatments with GO@T hybrids determined a significant decrease by about 60% for all used concentrations.
Analogous investigation on HREC (Table 2) showed that PGE2 levels were significantly reduced after the cellular treatments with GO or GO+T (by 20–29% or 39%, respectively) but not after incubation with the three different concentrations of Tetra(HPRG)-Fam. On the other hand, a significant and large decrease in comparison to untreated cells (by 64%), comparable to that found in the PC-3 case, was detected for HREC cells incubated with GO@T hybrids.
These results confirmed the role played in prostate cancer by PGE2, the most abundant pro-inflammatory mediator, promoting cancer cell invasion by induction of protein and mRNA expression of metalloproteinases, and, in turn, allow for the proliferation and migration of PC-3 cells [68]. Moreover, the nanocomposites of GO and Tetra(HPRG)-Fam peptide, although to different extend, were able to modulate the PGE2 release and thus the new vessel formation as well as the tumor progression. Interestingly, the same trend measured in HREC for both the cell migration and the PEG2 release upon the treatment with the peptide alone, i.e., no significant changes with respect to the negative control of untreated cells, points to a certain role of the switch-on process of angiogenesis in the mechanism involved.
The ability of GO@T to modulate the release of PGE2 in PC3 cell makes them potential anti-inflammatory agents for use in the conditions under which inflammation amplifies the pathological process. In particular, chronic inflammation is a risk factor for the development and progression of prostate cancer [69]. It has been demonstrated in PC3 that PGE2 binds to a prostaglandin transporter which thus determines an increase in their intracellular concentration and, with an intracrine mechanism, they induce the proliferation, migration and invasion of PC-3 tumor cells [70].
To shed light on the molecular mechanisms involved in toxicity induced by the hybrid GO@T platforms, the oxidative stress induced by the production of reactive oxygen species (ROS) was measured through the fluorescent assays of MitoSOX Red, and dichlorohydrofluorescein (DCF), to measure mitochondrial superoxide, and cytosol hydrogen peroxide production, respectively. Hence, PC-3 cells were treated for 24 h with three different concentrations of GO, Tetra(HPRG)-Fam and GO@Tetra(HPRG)-Fam. Figure 7 shows that the PC-3 cells treatment with GO nanosheets increases the ROS in a dose-depended manner, in agreement with the literature on the GO-induced cytotoxicity mechanism by intracellular ROS production [27,49]. On the other hand, no significant ROS production was detected upon the cells treatment by Tetra(HPRG)-Fam as well as by the hybrid GO@T samples in the concentration range tested.
To tackle with the correlation between the pro-tumor effect of PGE2 and the particular ability of GO@T to reduce their synthesis we carried out intracellular imaging LSM to evaluate the cellular uptake of the GO nanosheets decorated by the fluorescent Tetra(HPRG)-Fam peptide as well as to image the perturbation on the mitochondria, as analyzed by the organelle staining by the MitoTracker Deep Red fluorescent probe. Figure 8 shows the representative LSM micrographs of PC-3 cells after 24 h of treatment followed by cellular staining and fixation.
As to the GO-treated cells (Figure 8b), several dark areas in the optical light field micrographs (not observed in the control untreated cells, Figure 8a), show the cellular wrapping by the GO sheets aggregates, which is one of the mechanism invoked for the GO cytoxicity [71]. Interestingly, such GO sheets are detected in close contact to the cell membrane but also intracellularly, as evidenced by the shadowing of the nuclear staining (see arrows in Figure 8a), thus pointing to point to the effective cellular uptake of the GO nanosheets. As to the cells incubated with the free peptide (Figure 8c), a green fluorescence that increases in a dose-dependent manner is clearly visible in the cell cytoplasm and, mostly, accumulating at the cell membrane for the highest peptide concentration tested. Finally, the cells incubated with the three GO@T samples at the different relative concentration of peptide and GO, show a more diffuse green fluorescence inside the cells, as well as peptide-decorated (i.e., green emitting) GO aggregates that wrap/trap the cells in a ‘sheet-form blanket’. To note, the isoelectric pH value of 7.9 calculated for the free peptide at the physiological pH suggests an interaction driven by electrostatic forces with the negatively charged cell membranes. On the other hand, for the GO-immobilized peptide a more effective transport into the cells is offered by the graphene-based platform [20]. These findings validate the self-assembly-based approach used to fabricate the nanocomposite for their use in the theranostics concept.
The quantitative analysis (Figure 8e) confirms the effective internalization of the free peptide in a dose dependent manner, and suggest, by the increase of the detected emission of the MitoTracker Deep Red probe compared to the control untreated cells, a general mitochondrial insult, especially for the bare GO nanosheets and the GO@T hybrid with the lowest concentration of peptide among those tested.
3. Discussion
Small peptide molecules preferentially interact via electrostatic interaction with the edge or basal plane surface of the GO nanosheets. Indeed, the hydrophilic groups of graphene oxide, such as –COOH and –OH, allow for the versatile immobilization of biomolecules via either covalent grafting or non-covalent interactions that include hydrogen bonds, ion–dipole, or van der Waal forces, while the free surface π electrons are capable of forming π–π and CH–π and interactions [49,72]. Noteworthy, aromatic amino acids, such as the histidine included in the aminoacidic sequence of the Tetra(HPRG) peptide, preferably orient themselves in parallel with respect to the basal plane of the graphene sheets, through predominant π–π interactions [73,74].
A strong and irreversible interaction between the physisorbed Tetra(HPRG) peptide and GO was detected by means of UV-visible (Figure 1) and fluorescence (Figure 3) spectroscopies, as demonstrated in terms of electron transfer processes between the nanosheets and the 5,6-carboxyfluorescein fluorophore moiety covalently bound to the Tetra(HPRG) peptide sequence.
In particular, the bathochromic shift (~2 nm) and, most noticeably, the hypochromic shift of the Fam-related main absorption peak at ~500 nm, pointed to the significant decrease, in a GO-dose dependent manner, of the molar extinction coefficient from the value of ε497 = 5.6 × 105 M−1cm−1 of the free peptide to ε499 = 1.3 × 105 M−1cm−1 and ε499 = 1.7 × 105 M−1cm−1 in the GO_A@T and GO_B@T hybrid samples, respectively. Moreover, while the bare GO_A and GO_B nanosheets have similar molar extinction coefficients for the GO characteristic π → π* transition peak, as calculated by using the Lambert-Beer law (ε238 ~3 × 102 mg−1 mL cm−1), the hybrid GO@T samples exhibit both hypsochromic (~16 nm) and hypochromic shifts, the latter effect being depending on the GO concentration, with the values of ε232 = 73 mg−1 mL cm−1 for GO_A@T and ε232 = 93 mg−1 mL cm−1 for GO_B@T estimated from the spectra of the hybrid samples subtracted by that of the free peptide.
GO, which is itself fluorescent, is a quencher for the fluorescence signals of fluorophores, with a quenching efficiency superior to that of conventional organic molecules [75]. Its quenching mechanism is related either to fluorescence resonance energy transfer (FRET) [76] or photo-induced electron transfer (PET) processes [77]. Hence, when the GO nanosheet and the fluorophore molecules are close to each other, the energy or excited electron transfers from the fluorophore (donor) to the GO (acceptor) and the fluorescence signal of the fluorophore is quenched. GO is largely used as energy acceptor for the fabrication of biosensors [78]. Our findings on the strong quenching of the Fam fluorescence (<99% decrease in the emission intensity measured in the absence of GO) in the GO@T hybrids strongly support the successful and irreversible immobilization of the Tetra(HPRG)-Fam molecules on the GO nanosheets (Figure 3).
The CD (Figure 2) and ATR-FTIR (Figure 4) spectra also confirmed the successful immobilization of the peptide molecules at the GO surface, with conformational changes of the peptide molecules due to the strong interaction with the GO nanosheets. In particular, in the infrared spectra, the shifts to higher wavenumbers and the peak broadening of the signals at 1156 and 1080 cm−1, related respectively to the C=C stretching and the N–H bending vibrations of the histidine residue, point out to the formation of hydrogen bonds between the hydrophilic groups of graphene oxide and the side chain imidazole groups of histidine in the peptide [60]. Also, the spectral changes observed in the protein-fingerprint region of amide I and amide II stretching vibrations pointed to significant conformational changes of the peptide molecules in the adlayer at the surface of the GO compared to the unbound form of the free peptide in solution. Specifically, the amide I vibration, absorbing near 1650 cm−1, arises mainly from the C=O stretching with minor contributions from the out-of-phase C–N stretching. The amide II, absorbing near 1550 cm−1, is the combination of N–H and C–N stretching vibration with smaller contributions from the C–O, C–C and N–C stretching vibrations. Both amide I and amide II vibration peaks of proteins may provide valuable structural information and secondary structure prediction [59]. For instance, the amide I peak may encompass different conformational contributions that represent extended strands, β-turns, 3(10)-helix, polyproline I, and polyproline II [79]. The HPRG domain has been reported to have a predominant polyproline II conformation rather than random coil one for the high content of proline residues [80]. We found by CD analyses supporting evidences on the presence of PPII conformers of the free Tetra(HPRG) peptide in equilibrium with the random coil configuration. Indeed, the CD spectrum of the free peptide (Figure 2), shows a broad negative peak at 202 nm, a maximum at 222 nm, and another minimum at 234 nm. This combination has been shown to occur in polypeptides with extended chain polyproline II conformation, whereas the random conformation has the minimum is at 199 nm [81].
To note, the HPRG domain is able to bind different ligands by exploiting the imidazole side chain, partially protonated at physiological pH, and the conformational features of its secondary structure. The HPRG protein has a multidomain structure composed of 2 N-terminal regions (called N1 and N2) homologous to cystatin but lacking the function of inhibitor of cysteine proteinase; a central element, histidine rich region (HRR) flanked by 2 regions rich in proline (PRR1 and PRR2) and a C-terminal domain or (C) [82]. This domain also prevents heparanase-mediated release of angiogenic growth factors from ECM by blocking the heparanase cleavage sites in the EPM heparan sulphate [83] and by binding to tropomyosin on the cell provides antiangiogenic signals to endothelial cells [41]. Interestingly, these processes could be enhanced by high extracellular Zn2+ (generally provided by platelets) and low pH levels, which cause conformational changes in the molecule [34]. Noteworthy, the multidomain structure of HPRG allows for its interaction with a variety of molecules such as heme group, fibrinogen, heparinase, thrombospondin (TSP), divalent metal cations zinc, copper, mercury, cadmium and nickel, but also with associated molecules to cells, including heparan sulphate (HS), tropomyosin ATP synthase, DNA [36]. Specifically, HPRG has been detected on the surface of the cells involved in the immune response such as: Leukocytes, macrophages and monocytes, as well as in the platelet and megakaryocyte granules [35]. Numerous studies have demonstrated the effectiveness of HPRG-derived peptides in inhibiting tumor cell proliferation indirectly, by reducing angiogenesis and starving the tumor. To date, there are no studies conducted on tumor cells after incubation with the Tetra(HPRG) peptide or graphene-based nanocomposites either of the whole protein or of the peptide.
The different viability results (Figure 5) showed by the cells incubated with GO-immobilized peptide, both hybrids GO@T and mixtures GO+T, compared to the free Tetra(HPRG)-Fam peptide could be therefore related to the different binding capabilities of the HPRG backbone in the free and surface-immobilized conformational state.
Specifically, the opposite trends detected on both PC-3 and HREC, i.e., proliferative effect for the peptide alone and anti-proliferative effect for the GO-immobilized peptide, can be related to the equilibrium shift for the latter towards the PPII configuration. To note, according to the confocal imaging analyses (Figure 8), the cells exhibited a dose-dependent cellular uptake of the free peptide with preferential gathering at the cell membrane, while a more diffuse cytoplasm internalization was observed for the GO-immobilized peptides. Accordingly, different signaling pathways resulting in the proliferative or anti-proliferative effects could be activated.
The effects induced by the GO@T hybrids and GO+T mixtures on PC-3 and HREC cell viability confirm that, depending on the incubation time and the dose of treatment, it could be also used as an anti-cancer molecule and not only as a drug-delivery agent [63]. What is more, the combination Tetra(HPRG)-Fam peptide to the GO platform in the GO@T hybrids blocked the cancer cells proliferation to a greater extent than each individual component. In the other hand, in the angiogenic cellular model, the GO@T hybrids inhibit the endothelial cell proliferation while the GO+T mixture do not, thus highlighting a promising potential to block unwanted vessel formation.
The different effects of GO and GO@T hybrids on PC-3, SH-SY5Y and HREC are in agreement with other studies showing that graphene oxide inhibits tumor-sphere formation in different cancer types (breast, ovarian, prostate, lung, and pancreatic cancers, as well as glioblastoma), by inhibiting several signal transduction pathways, but with no toxic for normal fibroblasts [84]. Also, the time- and dose-dependent response on the treatment of SH-SY5Y cells with GO is in agreement with literature data [85] and previous works from this group which demonstrated that the GO toxicity in neuroblastoma cells depends on the functional groups and the oxidation level of GO [27,50]. These findings are in general very promising since they elucidate on the potential of graphene oxide as an effective non-toxic therapeutic strategy for the eradication of cancer cells.
As to the strong inhibitory effect of the migration of PC-3 and HREC cells in the presence of GO or GO@T hybrids (Figure 6), we found a consistent trend with the PEG2 release (see Table 1 and Table 2), with a significant inhibitory effect in both the cell types.
For PC-3, the inhibition in cell migration can be due also to the inhibition of the electron transport chain, as highlighted by the high levels of ROS produced in the cells after incubation with GO (Figure 7). This hypothesis is confirmed by other studies, reporting a blockage of the electrons transfer to the iron-sulfur centers, since GO would subtract electrons, having a higher electron affinity than the iron-sulfur centers [86]. Prolonged damage over time causes inactivation of the free radical scavenger enzymes (superoxide dismutase and glutathione peroxidase) and consequently damage to proteins, DNA and lipids, which greatly thereby influencing the cell metabolism and signaling pathways [84]. Moreover, GO exposure can induce phosphorylation of ERK signaling molecules, which are related to cell cycle regulation, triggering apoptosis [87]. The reduced production of ATP by oxidative phosphorylation would result in an impairment in the F-actin cytoskeleton assembly, which is an ATP-consuming process, thus inhibiting cancer cells migration. Interestingly, the increased ROS levels for cells incubated with the GO@T hybrids is less evident than the treatment with the GO alone. Such a result is most likely due to a protective effect of the peptide which covers the surface of GO sheets thereby reducing the interaction with the oxygen molecules and as consequences the ROS production [88]. The generation of ROS by the GO, indeed, may be mediated by the adsorption of O2 on its surface to form a surface-bound C(O2) intermediate which is capable to oxidize the glutathione enzyme GSH to GSSG, thus restoring the carbon surface to its original state and releasing H2O2 [88].
Since the presence of the peptide in GO@T hybrid led to a reduction in the release of free radicals than the treatment with the GO alone, the inhibition of the PC-3 cells migration in the presence of the hybrid could be due to a mechanism not strictly related to the reduction of the ATP generation. One of the mechanisms proposed in modulating tumor cell migration is the activation of signal pathways, involved in the inflammatory process. It has indeed been demonstrated that PGE2 is the most abundant pro-inflammatory mediator in prostate tissues and its levels are increasing in prostate carcinoma [89]. Moreover, it has been shown that cAMP-PKA/PI3K-Akt signaling pathway, activated in prostate cancer cells by the binding of PGE2 to its EP3 receptor, is involved in PC-3 cells proliferation [68]. The strong inhibition of the PGE2 release by PC-3 (Table 1) and HREC (Table 2) treated with GO@T, may provide novel insight into the therapeutic potential of this hybrid nanocomposite. Indeed, GO triggers profound effects through the metabolic reprogramming of the cell, thereby exerting its anti-inflammatory effects through the downregulation of specific genes, leading to activation of the transcription factor nuclear factor erythroid 2-related factor 2, which inhibited expression of pro-inflammatory cytokines such as IL-1β and IL-6 [90]. A down-regulation by GO, via epigenetic mechanisms, of cyclooxygenase 2 (cox2), releasing PGE2, in human embryonic kidney 293T cells has been demonstrated [91]. The ability of GO to trigger physical interactions between the downstream factors and the cox2 promoter gives GO peculiar characteristics that make it an effective molecule in the treatment of tumor forms. In the same study, aminated GO and poly (acrylic acid)-functionalized GO were demonstrated to reduce the inflammatory response, with a weaker effect on chromatin architecture. Therefore, GO-mediated chromatin interactions may minimize toxicity in practical applications.
The association between Tetra(HPRG) peptide and the ROS-producing GO nanosheets, with the related enhanced cellular uptake and mitochondrial perturbation (Figure 8) are therefore able to strongly inhibit both the tumor cells proliferation and the cell migration process. Mitochondria, in fact, are intracellular organelles involved in energy metabolism, which show a central role in the ATP synthesis. According to the data from mitochondrial organelle staining shown in the Figure 8, the GO@T hybrids at the higher concentrations of peptide and GO interfere less with mitochondrial activity than the peptide and GO alone.
4. Materials and Methods4.1. Chemicals
Graphene oxide water dispersion (0.4 wt% concentration) was purchased from Graphenea Inc., (Gipuzkoa, Spain). TETRA-HPRG (Ac-(GHHPH)4G-NH2 with the addition of 6-Fam (6-carboxyfluorescein) were purchased from Caslo (Kongens Lyngby, Denmark). Phosphate buffered saline purchased by Sigma-Aldrich (St. Louis, MO, USA). Ultrapure Milli-Q water was used (18.2 mΩ∙cm at 25 °C, Millipore, Burlington, MA, USA). RPMI 1640, Dulbecco’s modified eagle medium (DMEM)-F12, fetal bovine serum (FBS), penicillin streptomycin solution and amphotericin solution for cell cultures. 2′,7′-dichlorofluorescein (DCF) were purchased from Sigma-Aldrich (St. Louis, MO, USA). MitoSOX™ red mitochondrial superoxide indicator and 2′-[4-ethoxyphenyl]-5-[4-methyl-1-piperazinyl]-2,5′-bi-1H-benzimidazole trihydrochloride trihydrate (Hoechst33342) were purchased from Thermo Fisher Scientific (Waltham, MA, USA).
4.2. Synthesis of GO@T Hybrid
The aqueous dispersion GO was dried 70 °C, 400 rpm overnight in Termomixer (Thermomixer Comfort model, Eppendorf, Hamburg, Germany) and 3 mg of GO dried were resuspended in Phosphate Buffered Saline (PBS) 10 mM to reach a final concentration of 1 mg/mL. Therefore, to obtain homogeneous dispersion, GO dispersion was sonicated in Labsonic Ultrasound bath at 59 Hz for 120 min maintaining the temperature at 25 °C. The obtained dispersion has a characteristic dark brown coloration. For the synthesis of the GO@T hybrid nanosystem, 20 µL of peptide (10 mM) in Milli-Q ultrapure water was added to 1 mL of the GO dispersion (1 mg/mL or 0.5 mg/mL, for GO_A@T or GO_B@T, respectively). The sample were incubated for 120 min in Thermomixer (Thermomixer Comfort model, Eppendorf) at 37 °C and 400 rpm. To remove the excess of reactants, the synthetized hybrid system was washed with PBS (10 mM) twice by centrifugation (2 min at 2700 RCF). The same washing procedure was also applied to the GO alone.
4.3. UV-Visible and Fluorescence Spectroscopy
UV-visible spectra of the samples in PBS were recorded using spectrometer Lambda S2 (Perkin Elmer, Waltham, MA, USA) and quartz cuvettes with an optical path length of 0.1 cm in the range of 200–700 nm. Fluorescence spectra were recorded on a LS55 (Perkin Elmer, Waltham, MA, USA) fluorimeter using quartz cuvettes with an optical path length of 0.1 cm.
4.4. ATR/FTIR and CD Spectroscopies
The vibrational spectra were recorded using a Perkin Elmer FT-IR spectrophotometer (Spectrum Two, Waltham, MA, USA) in the range of 4000–400 cm−1. To carry out the measures the solid samples of GO and GO@T hybrid dried in Thermomixer (Thermomixer Comfort model, Eppendorf, Hamburg, Germany) at 70 °C and 400 rpm overnight, have been placed on the surface of the crystal and then locked with a “clutch-type” lever before starting the measure. Each spectrum was acquired at a resolution of 2 cm−1 (10 scans).
The CD spectra of samples, both GO@T hybrids and GO+T mixtures, were obtained at 25 °C under a constant flow of nitrogen on a Jasco model 810 spectropolarimeter at a scan rate of 50 nm min−1 and a resolution of 0.1 nm. The path length was 1 cm. The spectra were recorded as average of 10 scans in the 190–260 nm range.
4.5. Cell Cultures and Maintenance
Prostate cancer cells (PC-3) and neuroblastoma (SH-SY5Y) cells were cultured in 25 cm2 tissue-culture treated Corning® flasks (Sigma-Aldrich, St. Louis, MO, USA) in RPMI-1640 and Dulbecco’s modified eagle medium (DMEM)-F12, respectively. The medium was supplemented with 10% v/v fetal bovine serum (FBS), and contained 2 mM L-glutamine, 100 IU/mL penicillin and 0.1 mg/mL streptomycin. HREC cells were cultured in EGM-2 medium supplemented with 5% FBS. Cells were grown in an incubator (Heraeus Hera Cell 150C incubator), under a humidified atmosphere at 37 °C in 5% CO2.
4.6. MTT Assay
For cell viability assays, the 3-[4,5–dimethylthiazol-2-yl]-2,5-diphenyl tetrasodium bromide (MTT) assay was used (Chemicon, Temecula, CA, US). Cell lines were seeded in 96-well plates at the cell density per well of 1.5 × 104 for PC-3 and HREC cells and 2 × 104 for SH-SY5Y cells, respectively. Cells were incubated overnight at 37 °C before experiment. Afterwards, cells were treated for 24 h and 48 h in the absence (negative control) or the presence of GO nanosheets (10, 12, 30 µg/mL), Tetra(HPRG)-Fam peptide (2, 4, 6 µM) and hybrid GO@T samples (10 µg/Ml-2 µM, 12 µg/mL-4 µM, 30 µg/mL-6 µM) and the corresponding GO+T mixtures. After incubation periods, 10 µL of MTT reagent (5 mg/mL) were added to each well and the cells were incubated for 3 h at 37 °C. The formazan crystals were solubilized with 100 µL DMSO and plates were shaken for 10 min. The absorbance was measured at 570 nm with plate reader (Synergy 2-bioTek).
4.7. Cell Migration
PC-3 and HREC migration was measured using a standard scratch assay, performed as previously reported [92]. Cells were incubated for 24 h and 48 h in the absence (negative control) or the presence of GO nanosheets (10, 12, 30 µg/mL), Tetra(HPRG)-Fam peptide (2, 4, 6 µM) and hybrid GO@T samples (10 µg/m-2 µM, 12 µg/mL-4 µM, 30 µg/mL-6 µM) and the corresponding GO+T mixtures. Migration was followed by an inverted Leica DM IRB microscope equipped with CCD camera. Time zero represents the time immediately after the scratch for all conditions.
4.8. PGE<sub>2</sub> Assay
To determine PGE2 release, PC-3 and HREC were incubated for 48 h in the absence (negative control) or the presence of GO nanosheets (10, 12, 30 µg/mL), Tetra(HPRG)-Fam peptide (2, 4, 6 µM) and hybrid GO@T samples (10 µg/mL-2 µM, 12 µg/mL-4 µM, 30 µg/mL-6 µM of GO-peptide concentration). Aliquots of culture medium were analyzed by using ELISA kits (Cayman Chemical, Ann Arbor, MI, USA), according to the manufacturer’s instructions. Three different experiments were analyzed in triplicate.
4.9. Confocal Microscopy Analysis
LSM imaging was performed with an Olympus FV1000 confocal laser scanning microscope (LSM), equipped with diode UV (405 nm, 50 mW), multiline Argon (457 nm, 488 nm, 515 nm, total 30 mW), HeNe(G) (543 nm, 1 mW) and HeNe(R) (633 nm, 1 mW) lasers. An oil immersion objective (60xO PLAPO) and spectral filtering systems were used. The detector gain was fixed at a constant value and images were collected, in sequential mode, randomly all through the area of the well. To perform the experiment PC-3 cells were seeded in glass bottom dishes (WillCo-dish®, Willco Wells, B.V., Amsterdam, The Netherland) with 12 mm of glass diameter at a density of 3 × 104 cells per dish in RPMI-1640 medium supplemented with 1% FBS. Thereafter, cells were treated for 24 h in the absence (negative control) or the presence of GO, Tetra(HPRG)-Fam peptide, the hybrid GO@T and the GO+T mixtures. Before fixing, cells were stained with nuclear dye Hoechst33342 (0.251 µg/mL) and MitoTracker Deep Red (200 nM). Cells were rinsed with fresh PBS and cellular fixation was performed with high purity paraformaldehyde (2% w/v) in PBS (pH 7.3).
4.10. Total and Mitochondrial ROS Production
The oxidative stress induced by the cell treatment with GO, Tetra(HPRG)-Fam and the hybrid GO@T was evaluated through DCF assay and MitoSOX assay on PC-3 cells measuring the level of reactive oxygen species (ROS) produced. To perform the assay, cells were plated into a 96-well plate in complete medium at a density of 10 × 103 cells per well. Cells were treated for 24 h in 3 replicate wells with: GO nanosheets (10, 12, 30 µg/mL), Tetra(HPRG)-Fam peptide (2, 4, 6 µM) and hybrid GO@T samples (10 µg/mL-2 µM, 12 µg/mL-4 µM, 30 µg/mL-6 µM of GO-peptide concentration). The incubation was carried out in complete medium supplemented with 1% FBS. Then, cells were stained with 0.12 µg/mL Hoechst33342 for 20 min, 3 µM 2′,7′-dichlorofluorescein (DCF) for 15 min (total ROS) or 5 µM MitoSOX for 5 min (mitochondrial O2•−) at room temperature and analyzed by measuring the fluorescence emission (λex = 493 nm, λem = 523 nm for the nuclear staining; λex = 493 nm, λem = 523 nm for DCF; λex = 510, λem = 580 for MitoSOX, respectively) using a fluorescence spectrophotometers a LS55 (Perkin Elmer, Waltham, MA, US) and quartz cuvettes with an optical path length of 0.1 cm. Results are normalized to the Hoechst emission and represented as the increase in DCF or MitoSOX signals with respect to the untreated control. Data are presented as the means ± SEM of three replicas.
5. Conclusions
In summary, we assembled here a theranostic platform made of graphene oxide and a tetra repeat sequence of HPRG.
Results of cell viability in human prostate cancer (PC-3), neuroblastoma (SH-SY5Y) and retinal endothelial (HREC) cells incubated with GO-immobilized peptide, both hybrids GO@T and mixtures GO+T, compared to the treatments with the free Tetra(HPRG)-Fam peptide, pointed to different binding capabilities of the HPRG backbone in the free and surface-immobilized conformational state.
In particular, the proliferative effect for the peptide alone and the anti-proliferative effect for the GO-immobilized peptide, detected both in PC-3 and HREC, were related to the predominance of poly-Proline II conformation of the peptide immobilized on the GO nanosheets. Also, results of confocal imaging analyses suggested the activation of different signaling pathways resulting by different peptide internalization and sub-cellular localization. The strong inhibitory effect observed on the migration of PC-3 and HREC cells in the presence of GO or GO@T hybrids was correlated to the PEG2 release.
Such findings may provide novel insights into the therapeutic potential of these nanocomposite. In fact, we demonstrated that a 21-mer peptide analogue mimics the anti-angiogenic activity of the whole HPRG protein and is also able to inhibit tumor growth when associated to the graphene oxide. Such a dual action could represent the basis for obtaining a new, more effective, anticancer therapeutic approach. To this aim, further studies are necessary to deepen the signaling pathway involved in the implementation of the overall function.
Acknowledgments
C.S. and D.L. acknowledge the Consorzio Interuniversitario di Ricerca in Chimica dei Metalli nei Sistemi Biologici (C.I.R.C.M.S.B.), Bari, Italy.
Author Contributions
Conceptualization, C.S., G.L. and D.L.M.; investigation, V.V., A.L., L.M.C., V.S., A.M.; data curation, L.M.C., V.S. and C.S.; writing—original draft preparation, V.V., A.L. and C.D.A.; writing—review and editing, C.S., G.L. and D.L.M. All authors have read and agreed to the published version of the manuscript.
Funding
This research was partially supported by MIUR PRIN grant, 2017WBZFHL), EraNet Cofund-M-ERA.NET 2 (project SmartHyCAR, n. 2471) and University of Catania (Piano della Ricerca di Ateneo, Linea di Intervento 2, 2018–2020).
Conflicts of Interest
The authors declare no conflict of interest.
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(a) UV-visible spectra in phosphate buffer saline solution (PBS, pH = 7.4) of GO_A and GO_B 100× diluted dispersions, both before (dashed lines for GO_A, in black, and GO_B, in grey, respectively) and after 2 h of incubation with the peptide (solid line for GO_A@T, in dark green, and GO_B@T, in light green, respectively). The reference spectra of Tetra(HPRG)-Fam (green, 100× diluted solution of that used for the incubation of GO_A and GO_B samples) and the mixtures immediately the mixing (GO_A+T, in dark green, and GO_B+T, in light green, respectively) are shown for comparison in dot line. (b) UV-visible spectra in PBS of the pellets for hybrid GO_A@T and GO_B@T samples (solid lines), the GO_A+T and GO_B+T mixtures (dot lines) and, for comparison, of bare GO_A and GO_B pellets (dashed lines) after two centrifugation (2700 RCF, 2 min, RT) and washing steps. The spectra of supernatants (dashed dot lines) of GO@T samples collected after the first washing step are included.
CD spectra in phosphate buffer saline solution (PBS, pH = 7.4) of Tetra(HPRG)-Fam (green, solid line, peptide concentration = 1 × 10−5 M) and the pellets (10× dilution) for hybrid GO_B@T (dark green, solid line) and mixture GO_B+T (dark green, dotted line) samples.
Fluorescence spectra (λ excitation = 488 nm) in PBS (pH = 7.4) of free Tetra(HPRG)-Fam peptide (dot green line) and GO@T hybrids (GO_A@T: dark green line; GO_A@T: light green line). In the inset the magnified region for the pellets (solid lines) and supernatants (dashed-dot lines).
Attenuated total reflectance Fourier transform infrared (ATR/FTIR) spectra of graphene oxide (GO) (black curve), Tetra(histidine-proline-rich glycoprotein (HPRG))-Fam (green curve) and GO@T (dark cyan curve) hybrid samples.
Cell viability from MTT assays of prostate cancer (a,d), neuroblastoma (b,e) and endothelial (c,f) cells incubated for 24 h (a–c) or 48 h (d–f) with the hybrid GO@T samples at increasing content of GO nanosheets (10–30 µg/mL concentration range) and Tetra(HPRG)-Fam peptide (3–6 µM concentration range). The negative control of untreated cells (CTRL) and the positive controls of cells treated with the peptide alone or the bare GO or the peptide mixtures are included as reference. Values (means ± SEM) are from three independent experiments. Results are expressed as percentage with respect to CTRL. Student’s t-test was used to compare cell viability measurements in all experimental conditions. (*) p < 0.05, (**) p < 0.01, (***), p < 0.001, (****) p < 0.0001 vs. CTRL, (#) p < 0.05, (##) p < 0.01, (###) p < 0.001, (####) p < 0.0001 vs. the corresponding GO reference sample.
Representative micrographs (a,b) and quantitative analysis of cell migration (c,d) for prostate cancer (a,c) and endothelial (b,d) cells in absence (negative control, CTRL) or in presence of the different treatments at time 0, 24 h and 48 h after scratch: GO nanosheets (10, 12, 30 µg/mL), Tetra(HPRG)-Fam peptide (2, 4, 6 µM), GO@T hybrids (10 µg/mL 2 µM, 12 µg/mL 4 µM, 30 µg/mL 6 µM of GOpeptide concentration) and GO+T mixtures. PC-3 and HREC cells were wounded as described in the Materials and Methods section. The quantitative analysis of migration assay (wound edge advancement in percent vs. time) is expressed as means values (± SEM) from three independent experiments. Statistical analysis was performed by pairwise Student’s T-test results are expressed as percentage of wound closure with respect to time 0. (**) p < 0.01, (***) p < 0.001, (****) p < 0.0001 vs. CTRL, (#) p < 0.05, (##) p < 0.01, (###) p < 0.001 vs. the corresponding GO reference sample.
Ratio of mitochondrial-reactive oxygen species (ROS)/total-ROS levels measured on prostate cancer cells (PC-3) measured by the MitoSOX and dichlorohydrofluorescein (DCF) fluorescence assays. Cells were incubated for 24 h in absence (negative control, CTRL) or in presence of the different treatments: GO nanosheets (10, 12, 30 µg/mL), Tetra(HPRG)-Fam peptide (2, 4, 6 µM) and hybrid GO@T samples (10 µg/mL-2 µM, 12 µg/mL-4 µM, 30 µg/mL-6 µM of GO-peptide concentration). Values (means ± SEM) are from three independent experiments. Statistical analysis was performed by One-way ANOVA test. Symbols indicate the significance versus CTRL: ** p < 0.01. Results are expressed as ratio of MitoSOX with respect to DCF emission intensities.
Laser scanning microscopy (LSM) micrographs of PC-3 cells incubated for 24 h in absence (a: CTRL) or in presence of the different treatments: GO nanosheets (b: 10, 12, 30 µg/mL), Tetra(HPRG)-Fam peptide (c: 2, 4, 6 µM) and hybrid GO@T samples (d: 10 µg/mL-2 µM, 12 µg/mL-4 µM, 30 µg/mL-6 µM of GO-peptide concentration). For each treatment condition, the merged confocal images of nuclei (Hoechst, λex/em = 405/425–450 nm) and mitochondria (MitoTracker Deep Red, λex/em = 633/650–700) (upper panel), and the merged fluorescence image of the dye-labelled peptide (Fam, λex/em = 543/550–600 nm) and the optical bright field micrograph (lower panel) are shown. Scale bar 30 µm. The white arrows guide the eye to the GO sheets. In (e): quantitative analysis of Fam and MitoTracker Deep Red emission intensity. Results are presented as mean +/-SEM from experiments in triplicate and normalized with respect to the control untreated cells. Asterisks represent the correlation significant at the (*) p < 0.05, (**) p < 0.01, (***) p < 0.001 (****) p < 0.0001 vs. CTRL, (One-way ANOVA).
ijms-21-05571-t001_Table 1
Prostaglandin (PGE2) production in PC-3 medium, after different treatments. Values (means ± SEM) are from three independent experiments (n = 3). ANOVA was used to compare PGE2 production in all experimental conditions. * p < 0.01 vs. control cells.
PGE2 production in human retinal endothelial cells (HREC), after different treatments. Values (means ± SEM) are from three independent experiments (n = 3). Student’s t-test used to compare PGE2 production in all experimental conditions. * p < 0.01 vs. control cells.
\n"],"string":"[\n \"\\nInt J Mol SciInt J Mol SciijmsInternational Journal of Molecular Sciences1422-0067MDPI32759830PMC743211310.3390/ijms21155571ijms-21-05571ArticleAnti-Angiogenic and Anti-Proliferative Graphene Oxide Nanosheets for Tumor Cell TherapyVerdeValeria1†LongoAnna2†https://orcid.org/0000-0001-6127-5962CucciLorena Maria1https://orcid.org/0000-0002-4269-187XSanfilippoVanessa1https://orcid.org/0000-0003-1556-934XMagrìAntonio3https://orcid.org/0000-0001-5348-5863SatrianoCristina1*AnfusoCarmelina Daniela2LupoGabriella2*https://orcid.org/0000-0002-4193-7612La MendolaDiego4Nano Hybrid BioInterfaces Lab (NHBIL), Department of Chemical Sciences, University of Catania, Viale Andrea Doria 6, 95125 Catania Italy; verdevaleria@virgilio.it (V.V.); lorena.cucci@unict.it (L.M.C.); sanfilippo.vanessa@studium.unict.it (V.S.)Department of Biomedical and Biotechnological Sciences, University of Catania, Via Santa Sofia 97, 95123 Catania, Italy; longo.anna@hotmail.it (A.L.); daniela.anfuso@unict.it (C.D.A.)Institute of Crystallography—National Council of Research, via Paolo Gaifami 18, 95126 Catania, Italy; leotony@unict.itDepartment of Pharmacy, University of Pisa, via Bonanno Pisano 6, 56126 Pisa, Italy; lamendola@farm.unipi.itCorrespondence: csatriano@unict.it (C.S.); gabriella.lupo@unict.it (G.L.); Tel.: +39-095-7385136 (C.S.); +39-095-4781158 (G.L.)
These authors contributed equally to this work.
0482020820202115557121720200182020© 2020 by the authors.2020Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Graphene oxide (GO) is a bidimensional novel material that exhibits high biocompatibility and angiogenic properties, mostly related to the intracellular formation of reactive oxygen species (ROS). In this work, we set up an experimental methodology for the fabrication of GO@peptide hybrids by the immobilization, via irreversible physical adsorption, of the Ac-(GHHPH)4-NH2 peptide sequence, known to mimic the anti-angiogenic domain of the histidine-proline-rich glycoprotein (HPRG). The anti-proliferative capability of the graphene-peptide hybrids were tested in vitro by viability assays on prostate cancer cells (PC-3 line), human neuroblastoma (SH-SY5Y), and human retinal endothelial cells (primary HREC). The anti-angiogenic response of the two cellular models of angiogenesis, namely endothelial and prostate cancer cells, was scrutinized by prostaglandin E2 (PGE2) release and wound scratch assays, to correlate the activation of inflammatory response upon the cell treatments with the GO@peptide nanocomposites to the cell migration processes. Results showed that the GO@peptide nanoassemblies not only effectively induced toxicity in the prostate cancer cells, but also strongly blocked the cell migration and inhibited the prostaglandin-mediated inflammatory process both in PC-3 and in HRECs. Moreover, the cytotoxic mechanism and the internalization efficiency of the theranostic nanoplatforms, investigated by mitochondrial ROS production analyses and confocal microscopy imaging, unraveled a dose-dependent manifold mechanism of action performed by the hybrid nanoassemblies against the PC-3 cells, with the detection of the GO-characteristic cell wrapping and mitochondrial perturbation. The obtained results pointed out to the very promising potential of the synthetized graphene-based hybrids for cancer therapy.
theranosticsnanomaterialsnanomedicineHPRGpeptidescell migrationprostaglandinshuman prostate cancer cellsROSconfocal microscopy1. Introduction
Graphene is a 2-dimensional (2D) carbon nanomaterial consisting of planar sheets of sp2-hybridized carbon atoms arranged in a honeycomb lattice [1] and due to this monoatomic layer structure is the thinnest material produced so far [2]. Over the past 15 years, graphene has attracted the attention of the whole scientific community thanks to its unique mechanical and electrochemical properties, which include high density, chemical inertness, optical transmittance, very high hydrophobicity and molecular barrier abilities [3,4,5]. It is one of the most resistant materials ever tested with tensile strengths greater than 100 GPa and a traction module of 1 TPa [6]. Other remarkable features include the high planar surface (calculated value, 2630 m2/g [7], which allows for its higher drug loading capacity than other nanomaterials [8] and the thermal conductivity (5000 W/mK) [9]. Such properties make graphene particularly promising for applications in several fields, like the aerospace, electronics, energy, mechanical, environmental, and food industries as well as in biomedicine [10]. However, the poor solubility and agglomeration of the nanosheets in solution, caused by van der Waals forces and π–π stacking interactions, greatly limits its uses, makes it difficult to produce and significantly impacts its toxicity [11,12]. The oxygen-functionalized graphene derivative, namely graphene oxide (GO), maintaining a similar hexagonal carbon structure to graphene, overcomes these limits [13]. GO, indeed, shows a high density of functional oxygen groups namely carboxyl (–COOH) and hydroxyl (C–OH) groups, typically located at the edges of the sheets, and carbonyl (C=O) and epoxy groups (C–O–C) on the basal plane of the graphene sheets [14].
The unique chemical and physical properties of GO and its reduced derivative (rGO) have aroused a strong interest especially for biological studies [15], since the presence of defective oxygen-bound sp3 carbon atoms induces a strong hydrophilicity and contributes to the formation of dispersions highly stable colloidal in aqueous solvents, preventing the uncontrolled aggregation of the nanosheets caused by van der Waals and the hydrophobic interactions [16]. Moreover, the presence of hydrophilic functional groups on the GO surface offers high versatility for the derivatization of nanosheets and makes GO a suitable platform for the development of drug delivery systems [17] with potential application in tissue engineering [17], biosensing [18], and bioimaging [19], thus opening new horizons in the field of nanomedicine. In particular, graphene oxide based targeted drug carriers are becoming of great interest in the treatment of cancer diseases [20]. To this respect biological therapies, based on the delivery of biomolecules and gene therapy as well as phototherapies against cancer, including photodynamic and photothermal treatments, which use GO as a carrier, have showed high biocompatibility and good results both in in vitro and in vivo studies [21]. Thus, Liu et al. formulated transferrin modified graphene oxide for glioma-targeted drug delivery [22], Li et al. used functionalized nano-graphene oxide particles for targeted fluorescence imaging and photothermic therapy of glioma U251 cells [23], while Song and colleagues evaluated hyaluronic acid-decorated graphene oxide nanohybrids as carriers for targeted and pH-responsive anticancer therapy [24]. Furthermore, GO shows intrinsic biological properties, including antimicrobial activity [25] and the capability to control the function of immune cells [26] and to modulate angiogenesis. This latter feature provides additional advantages in cancer therapy, since formation of new blood vessels is involved in both tumor growth and development of metastases [21,27]. Hence, the anti-angiogenic action of GO can be very effective to fight cancer. To note, there are plenty of examples in the literature on the development of GO and modified GO platforms for anti-cancer therapy [28,29,30,31].
It has been demonstrated that GO sheets present pro-angiogenic properties at low doses (1–50 ng/mL), due to the controlled production of intracellular reactive oxygen species (ROS) (H2O2 and O2•−) induced by this material, while show anti-angiogenic features at high doses (≥100 ng/mL), attributed to the excessive generation of intracellular ROS [32]. In general, the mechanisms underlying GO toxicity in addition to oxidative stress and excessive ROS production also include DNA damage, apoptosis, autophagy, and immune responses, which widely varied in relation to the physical-chemical properties of GO, such as surface chemistry, layer number, lateral dimension, and purity [33].
The histidine-proline-rich glycoprotein (HPRG) is a single polypeptide chain protein of 70–75 kDa, with a multidomain structure. In humans, the protein is synthesized in the liver and is present in plasma at relatively high concentrations of 100–150 µg/mL (1.5 µM) [34,35]. HPRG ability to simultaneously interact with several ligands suggests that it may act as an adapter molecule which regulates numerous biological processes, including blood coagulation and fibrinolysis, adhesion, and cell migration, as well as anti-/pro-angiogenic activity [36].
Indeed, the HPRG protein promotes angiogenesis by inhibiting the activity of the antiangiogenic thrombospondin-1 (TPS-1) [37], by binding to plasminogen/plasmin onto the surface of endothelial cells as well as by promoting cell migration and invasion [38], which are critical phases of the new blood vessels formation. On the other hand, HPRG has also a demonstrated antiangiogenic activity, mainly localized in its histidine-proline-rich domain (H/P) and occurring by the blocking of the interaction between fibroblast growth factor (FGF-1 and FGF-2) and heparan sulphate in the extracellular matrix (ECM) and the surface of endothelial cells [39].
The Ac–(GHHPH)4–NH2 peptide has been shown to be an active HPRG mimic system, and it has been demonstrated effective as antitumor agent in two syngeneic cancer models, namely Lewis lung cancer (3LL) and melanoma (B16F1) growth in mice [40,41].
Based on these premises, in this work GO was functionalized with a with the Ac–(GHHPH)4GK–NH2 peptide [41] covalently bound to a 5,6-carboxyfluorescein (Fam) moiety, hereinafter named Tetra(HPRG)-Fam. The integration of the therapeutic potential from both GO and the Tetra(HPRG) peptide and the imaging capability through the fluorescence of the dye-labelled peptide makes the hybrid graphene oxide-Tetra(HPRG)Fam system (hereinafter named GO@T) a potential theranostic platform. The physicochemical characterization was carried out by mean of spectroscopic analyses of UV-visible, fluorescence and ATR-FTIR, to scrutinize the hybrid biointerface between the nanosheets and the peptide molecules in terms of electron transfer processes and average peptide molecular structural conformation. In vitro cellular experiments were carried out on human neuroblastoma (SH-SY5Y) and prostate cancer (PC-3) cell lines, as cellular tumor models to test the anti-angiogenic potential of our platforms in anti-cancer therapy. Both cell lines are aggressively-growing tumors, associated also to uncontrolled tumor vascularization [42]. Moreover, PC-3 can form vessel-like structures through a process denoted as vascular mimicry [43]. As endothelial cellular model of angiogenesis, we tested also our nanoformulation on primary human retinal endothelial cells (HREC) [44,45].
Specifically, the nanotoxicity and the pro-or anti-proliferative activity were investigated for GO@T hybrids compared to bare GO sheets and to the fragment peptide. Also, the nanocomposites effect on mitochondrial dysfunctions were scrutinized by measuring the mitochondrial production of O2•− as well as their effects on cellular migration and prostaglandin E2 (PGE2) release. Intracellular imaging using confocal laser scanning microscopy (LSM) was performed to evaluate the mechanism of interaction and internalization of hybrid systems on the studied cancer cell line.
2. Results
UV-visible, circular dichroism (CD), fluorescence and attenuated total reflectance Fourier transform infrared (ATR/FTIR) spectroscopies were used to characterize the hybrid graphene oxide nanosheets functionalized with the Tetra(HPRG)-Fam peptide (samples thereafter named GO@T). Two weight ratios for the peptide/GO mixtures were used for the preparation of the samples, namely, two concentrations of graphene oxide, i.e., 1 mg/mL (GO_A) or 0.5 mg/mL (GO_B) incubated for 2 h under stirring with a fixed concentration of peptide (0.2 mM).
Figure 1 displays the absorption spectra of reference samples of the GO nanosheets and of the Tetra(HPRG)-Fam peptide, both before and after the mixing (Figure 1a) as well as for the corresponding pellets and supernatants recovered after the centrifugation and washing step (Figure 1b), to remove the loosely bound peptide molecules from the GO platforms.
Figure 1a shows, for the Tetra(HPRG)-Fam sample, the characteristic π → π* transition absorption band of the peptide bond in the 180–230 nm range as well as the absorption of the aromatic side-chains of His residues, in the range of 230–300 nm [46]. Moreover, related to the chromophore activity of the Fam moiety [47,48], two weak bands, respectively at 280 nm and in the 350–440 nm region, and a main absorption peak at 497 nm are evident. As to the GO nanosheets, the UV-visible spectra validate the approximation of using the Lambert-Beer law of solutions for these colloidal dispersions. Indeed, both GO_A and GO_B samples exhibit the same spectral features just scaled in a 1 to 0.5 ratio; namely, one absorption peak at 238 nm and a shoulder at around 300 nm, due to the π → π* and n → π* transitions, respectively [49]. The spectra of the hybrid GO@T systems display both hypochromic and bathochromic/hypsochromic shifts compared to the reference GO and Tetra(HPRG)-Fam systems, respectively. Such findings point to the occurrence of electron transfer processes at the interface established between the GO nanosheets and the dye covalently linked to the peptide. To note, the spectra of the mixture systems are mostly given by the sum of the spectra of each component, the GO and the Tetra(HPRG)-Fam, with the GO absorption bands buried by the background signal of the Fam moiety in the UV region. Also, only for GO_B+T the Fam absorption peak being significantly decreased, due to the quenching effect of GO, but with no evidence of shift in the wavelength at the maximum of absorption. These findings point to a lower level of interaction between the GO sheets and the peptide molecules in the mixture than in the hybrid nanocomposite.
After the centrifugation and washing steps (Figure 1b), the spectra of bare GO demonstrate a different relative ratio of sp3 (oxidized defective) to sp2 (graphitic-like) carbon than the pristine GO, as disclosed both by the hypsochromic shift (~6 nm) of the π → π* peak and by the slight hyperchromic shift in the shoulder related to the n → π* transitions, respectively. In particular, the heavier (and larger) GO nanosheets, which accumulate preferentially in the pellet, exhibit a relative ratio of the sp2 to sp3 domains higher than the lighter (and smaller) nanosheets that mostly gathered into the supernatant, and hence have more defective oxygen-containing groups, especially at the edges [50,51]. To note, an additional contribution to these shifts can derive by the decrease of conjugative effect of chromophore aggregation by washing-induced loss of weakly bound oxygen-containing species, which influences the π–π* plasmon peak [52]. Significantly, the pellets of GO_A@T and GO_B@T samples (Figure 1b), which display spectral features of both GO and Tetra(HPRG)-Fam components, do not show significant changes in the peak wavelength position with respect to those of the hybrid systems before the centrifugation and rinsing steps (Figure 1a). These findings point to the successful and irreversible immobilization of the peptide molecules at the GO nanosheets surface.
As to the mixtures, also in this case the spectra evidence an irreversible binding of the Fam-labeled peptide to the GO substrates. For both GO@T hybrids and GO+T mixture samples, the spectra of supernatants collected after first washing step do not display any significant indication of the presence of peptide neither of GO. By using the molar extinction coefficients calculated for Tetra(HPRG)-Fam in the hybrid nanocomposite, a peptide loading of about 95 µM for 1 mg/mL of GO in GO_A@T or GO_A+T and of 88 µM for 1 mg/mL of GO in GO_B@T or GO_B+T could be estimated, respectively.
The analysis of CD spectra for both hybrids and mixture samples, in the comparison with the spectrum of the peptide alone, evidenced: (i) no detectable peptide signals for GO_A@T and GO_A+T (data not shown), likely due to the low relative concentration of the peptide compared to the GO matrix; (ii) a progressive intensity decrease and a red-shift of the minimum band at 202 nm for both GO_B+T (at 206 nm) and GO_B@T (at 207 nm) (Figure 2). The redshift is explained as result of the increase in the polyproline II (PPII) conformers [53]; the intensity change is due to the optical coupling between the peptide chromophore moieties and the GO substrates. Over all, these findings point to a strong interaction between the nanosheets and the Tetra(HPRG)-Fam, which affects at different levels (higher in the hybrids than in the mixtures) the peptide conformational structure and its freedom degrees.
The electron transfer processes at the interface between the GO nanosheets and the Fam-labelled peptide were confirmed by the fluorescence spectra (Figure 3), which display a strong quenching of the Fam peak at 521 nm. In particular, the fluorescein emission decreases of 99.9% in GO_A@T and to 99.1% in GO_B@T samples (see inset Figure 3), respectively.
The characterization by ATR-FTIR analyses (Figure 4) confirmed the effective immobilization of the peptide on the GO nanosheets in the hybrid nanocomposite. As to the uncoated GO, the spectrum displays a broad band in the wavenumber range of 3700–3000 cm−1, assigned to O-H stretching vibrations, as well as two peaks at ~1730 cm−1 and 1620 cm−1, due to the C=O stretching of the carboxylic acid groups and the aromatic C–C stretching, respectively [54]. Moreover, a weak signal at ~1400 cm−1 corresponding to the O–H deformation and an absorption peak at ~1050 cm−1 attributed to the alkoxy C–O stretching vibrations are also visible [55,56]. The spectrum of Tetra(HPRG)-Fam shows characteristic peaks from both the 5,6-carboxyfluorescein unit and the peptide backbone. Specifically, the O–H stretching vibrations in the broad band from 3600 to 3300 cm−1 as well as the signals at 1667 cm−1 and 2852 cm−1, which correspond respectively to the stretching of the C=O of carboxyl groups and the C–H stretching [57] are visible, together with the typical IR bands for amino acids and peptide (the “bio-fingerprint region”) at 1630 cm−1 (amide I) and 1543 cm−1 (amide II) are visible [58]. Moreover, the signals at 1202 cm−1 can be assigned to the C=C stretching and the N–H bending vibrations of the histidine residue, while the weak band in range of 1400 and 1465 cm−1 can be attributed to the C–N stretching of the proline and the sharp peak at 1133 cm−1 corresponds to the C–O stretching vibrations of the peptide [59].
As to the GO@T hybrids, the signals at 1156 and 1080 cm−1, related respectively to the C=C stretching and the N–H bending vibrations of the histidine residue confirm the presence of peptide-related bands in the nanocomposite sample. Such signals are shifted to higher wavenumbers and the peak are broader with respect to those observed for free peptide, thus suggesting the formation of hydrogen binding between the hydrophilic groups of graphene oxide and the side chain imidazole groups of histidine in the peptide [60]. Moreover, the fingerprint signals from the amide I and amide II vibrations are strongly attenuated, likely due to a different conformational state of the peptide immobilized in the adlayer on the GO surface compared to the unbound peptide molecules.
The response of prostate cancer (PC-3), neuroblastoma (SH-SY5Y) and non-tumor endothelial (HREC) cells to the GO@T hybrid nanocomposites was investigated in terms of toxicity through MTT assay. Cell viability assays were performed on cells incubated for 24 h and 48 h with increasing concentrations of GO nanosheets (10, 12, 30 µg/mL), Tetra(HPRG)-Fam (2, 4, 6 µM) and three hybrid (GO@T) and mixture (GO+T) samples, spanning the same concentration range in the GO and the peptide (Figure 5).
In general, the cellular treatments with GO samples resulted not toxic after 24 h of incubation time for all the three cell lines tested. After 48 h of incubation, we detected a statistically significant decrease in cell viability in PC-3 (30 µg/mL GO: 88.5 ± 2.6% of CTRL; ** p < 0.01) and SH-SY5Y (10 µg/mL GO: 78.0 ± 5.1% of CTRL; * p < 0.05; 12 µg/mL GO: 68.1 ± 5.6% of CTRL; ** p < 0.01; 30 µg/mL GO: 50.1 ± 5.9% of CTRL; * p < 0.01;) but not in HRECs. Also, our findings are consistent with many other studies, reporting that GO is non-toxic for multiple cell types [61,62,63].
Interestingly, the treatment with Tetra(HPRG)-Fam alone induced an increased viability both in prostate cancer (Figure 5a, 24 h: ~120% of CTRL: ** p < 0.01; Figure 5d, 48 h: up to ~130% of CTRL: *** p < 0.001) and endothelial cells (Figure 5c, 24 h: ~130% of CTRL: * p < 0.05; Figure 5f, 48 h: ~140% of CTRL: ** p < 0.01) cells, while no significant changes in cell viability were detected in neuroblastoma cells for both 24 h (Figure 5b) and 48 h (Figure 5d) of incubation.
A comparable anti-proliferative effect was induced in PC-3 by the treatment either with the GO@T hybrids or the GO+T mixtures, for both the incubation times of 24 and 48 h. In particular, a statistically significant dose-dependent decrease of viability was detected with respect to both the untreated cells as well as the GO-treated cells, with a maximum of cytotoxicity found for the mixture of 30 µg/mL GO with 6 µM Tetra(HPRG)-Fam (24 h: 54.3 ± 4.8% of CTRL: *** p < 0.001; 48 h: 39.8 ± 2.2% of CTRL: **** p < 0.0001).
As to SH-SY5Y cells, a significant and comparable decrease in cell viability was detected only in the cells incubated for 48 h with the highest concentrations of GO@T hybrid (~80% of CTRL: * p < 0.05) or GO+T mixture (~88–70% of CTRL: * p < 0.05), respectively. To note, the same treatment conditions resulted however proliferative in comparison to the incubations with corresponding positive controls of GO.
The HREC treatments with the highest concentrations of GO@T hybrids resulted anti-proliferative both after 24 h (~70% of CTRL: * p < 0.05) and 48 h (~40% of CTRL: * p < 0.05) of incubation. On the other hand, the endothelial cells incubation with the GO+T mixture at the highest concentrations showed only a slightly proliferative effect at 24 h with respect to the positive control of GO (~120% of CTRL: ##\\np < 0.01).
According to the results of anti-proliferative effects induced by GO@T hybrids in PC-3 and HREC but not in SH-SY5Y, the following investigations on the inhibition of cell migration and PGE2 release were carried out only on the prostate cancer and endothelial cells.
The modulation of PC-3 and HREC scratch crossing was monitored for 24 h and 48 h after monolayer wounding (Figure 6).
Representative photographs and the quantitative analysis of wound healing for PC-3 (Figure 6a,c) and HREC (Figure 6b,d) incubated in absence (negative control) or in presence of the positive controls of GO nanosheets (10, 12, 30 µg/mL),Tetra(HPRG)-Fam (2, 4, 6 µM) and the corresponding GO+T mixtures are shown in the comparison with those of cells incubated with the hybrid GO@T samples.
The control untreated PC-3 cells were able to cross the wound at 24 h, until the wound closed after 48 h. Conversely, the migration ability of PC-3 treated with GO was reduced significantly in comparison with control cells. A dose-dependent partial inhibition of cell invasion was observed in presence of Tetra(HPRG)-Fam, especially evident at 24 h of incubation (2 µM: ~70% of CTRL: ** p < 0.01; 4 and 6 µM: ~30% of CTRL: *** p < 0.001) than after 48 h of treatment (2, 4, and 6 µM: ~80% of CTRL: ** p < 0.01). The treatment of PC-3 either with the GO@T hybrids or the GO+T mixtures induced a significant and comparable reduction of the wound closure (at the two highest concentration tested: less than 20% of CTRL: **** p < 0.0001), indicating that the association of the peptide to the GO nanosheets efficiently reduces the PC-3 cells migration, inhibiting their metastatic potential.
HREC migration was modulated differently from cancer cells. Indeed, whereas the cells incubation with GO@T hybrids exhibited a significant inhibitory effect on wound closure for all three concentration tested (less than 20% of CTRL: **** p < 0.0001), and no differences in comparison with the negative control of untreated cells were observed for the cells incubated with the peptide alone, both the GO and the GO+T mixtures showed a similar trend, i.e., a partial inhibition for the lowest concentration (10 µg/mL: ~40% at 24 h and ~60% at 48 h of CTRL: *** p < 0.0001) and a very efficient inhibition of wound closure (less than 20% of CTRL: **** p < 0.0001) at the other two concentrations for both incubation times.
Inflammation is increasingly recognized as a critical mediator of angiogenesis [64]. It has been demonstrated that PGE2 promotes the in vitro tube formation of human microvascular endothelial cells via activation of EP4 receptors and by triggering PKA Cγ signaling pathway, ex vivo vessel outgrowth of aortic rings, and in vivo angiogenesis [65,66,67]. Moreover, during the inflammatory process, immune cells secrete pro-angiogenic factors for the promotion of new vessel formation [64].
The ability of GO@T hybrids to modulate the release of PGE2 in PC-3 and HREC was investigated as marker for the activation of inflammatory processes, which can have a crucial role both in carcinogenesis and cancer progression as well as in the angiogenic switch-on mechanism.
Cell culture supernatants collected after incubation of PC-3 or HREC cells for 48 h in absence (control) or in presence of GO nanosheets, Tetra(HPRG)-Fam, the hybrid GO@T or the mixture GO+T, were assayed for their PGE2 levels (Table 1 and Table 2).
As to prostate cancer cells, upon the incubations with the different samples, the PGE2 production by PC-3 cells decreased in comparison to untreated control cells. Specifically, Table 1 shows a dose-dependent effect (about 35–40%) for the treatments with GO, and a reduction in the PEG2 release by about 26% for Tetra(HPRG)-Fam, and by about 39% for the GO+T mixture, respectively. Finally, the treatments with GO@T hybrids determined a significant decrease by about 60% for all used concentrations.
Analogous investigation on HREC (Table 2) showed that PGE2 levels were significantly reduced after the cellular treatments with GO or GO+T (by 20–29% or 39%, respectively) but not after incubation with the three different concentrations of Tetra(HPRG)-Fam. On the other hand, a significant and large decrease in comparison to untreated cells (by 64%), comparable to that found in the PC-3 case, was detected for HREC cells incubated with GO@T hybrids.
These results confirmed the role played in prostate cancer by PGE2, the most abundant pro-inflammatory mediator, promoting cancer cell invasion by induction of protein and mRNA expression of metalloproteinases, and, in turn, allow for the proliferation and migration of PC-3 cells [68]. Moreover, the nanocomposites of GO and Tetra(HPRG)-Fam peptide, although to different extend, were able to modulate the PGE2 release and thus the new vessel formation as well as the tumor progression. Interestingly, the same trend measured in HREC for both the cell migration and the PEG2 release upon the treatment with the peptide alone, i.e., no significant changes with respect to the negative control of untreated cells, points to a certain role of the switch-on process of angiogenesis in the mechanism involved.
The ability of GO@T to modulate the release of PGE2 in PC3 cell makes them potential anti-inflammatory agents for use in the conditions under which inflammation amplifies the pathological process. In particular, chronic inflammation is a risk factor for the development and progression of prostate cancer [69]. It has been demonstrated in PC3 that PGE2 binds to a prostaglandin transporter which thus determines an increase in their intracellular concentration and, with an intracrine mechanism, they induce the proliferation, migration and invasion of PC-3 tumor cells [70].
To shed light on the molecular mechanisms involved in toxicity induced by the hybrid GO@T platforms, the oxidative stress induced by the production of reactive oxygen species (ROS) was measured through the fluorescent assays of MitoSOX Red, and dichlorohydrofluorescein (DCF), to measure mitochondrial superoxide, and cytosol hydrogen peroxide production, respectively. Hence, PC-3 cells were treated for 24 h with three different concentrations of GO, Tetra(HPRG)-Fam and GO@Tetra(HPRG)-Fam. Figure 7 shows that the PC-3 cells treatment with GO nanosheets increases the ROS in a dose-depended manner, in agreement with the literature on the GO-induced cytotoxicity mechanism by intracellular ROS production [27,49]. On the other hand, no significant ROS production was detected upon the cells treatment by Tetra(HPRG)-Fam as well as by the hybrid GO@T samples in the concentration range tested.
To tackle with the correlation between the pro-tumor effect of PGE2 and the particular ability of GO@T to reduce their synthesis we carried out intracellular imaging LSM to evaluate the cellular uptake of the GO nanosheets decorated by the fluorescent Tetra(HPRG)-Fam peptide as well as to image the perturbation on the mitochondria, as analyzed by the organelle staining by the MitoTracker Deep Red fluorescent probe. Figure 8 shows the representative LSM micrographs of PC-3 cells after 24 h of treatment followed by cellular staining and fixation.
As to the GO-treated cells (Figure 8b), several dark areas in the optical light field micrographs (not observed in the control untreated cells, Figure 8a), show the cellular wrapping by the GO sheets aggregates, which is one of the mechanism invoked for the GO cytoxicity [71]. Interestingly, such GO sheets are detected in close contact to the cell membrane but also intracellularly, as evidenced by the shadowing of the nuclear staining (see arrows in Figure 8a), thus pointing to point to the effective cellular uptake of the GO nanosheets. As to the cells incubated with the free peptide (Figure 8c), a green fluorescence that increases in a dose-dependent manner is clearly visible in the cell cytoplasm and, mostly, accumulating at the cell membrane for the highest peptide concentration tested. Finally, the cells incubated with the three GO@T samples at the different relative concentration of peptide and GO, show a more diffuse green fluorescence inside the cells, as well as peptide-decorated (i.e., green emitting) GO aggregates that wrap/trap the cells in a ‘sheet-form blanket’. To note, the isoelectric pH value of 7.9 calculated for the free peptide at the physiological pH suggests an interaction driven by electrostatic forces with the negatively charged cell membranes. On the other hand, for the GO-immobilized peptide a more effective transport into the cells is offered by the graphene-based platform [20]. These findings validate the self-assembly-based approach used to fabricate the nanocomposite for their use in the theranostics concept.
The quantitative analysis (Figure 8e) confirms the effective internalization of the free peptide in a dose dependent manner, and suggest, by the increase of the detected emission of the MitoTracker Deep Red probe compared to the control untreated cells, a general mitochondrial insult, especially for the bare GO nanosheets and the GO@T hybrid with the lowest concentration of peptide among those tested.
3. Discussion
Small peptide molecules preferentially interact via electrostatic interaction with the edge or basal plane surface of the GO nanosheets. Indeed, the hydrophilic groups of graphene oxide, such as –COOH and –OH, allow for the versatile immobilization of biomolecules via either covalent grafting or non-covalent interactions that include hydrogen bonds, ion–dipole, or van der Waal forces, while the free surface π electrons are capable of forming π–π and CH–π and interactions [49,72]. Noteworthy, aromatic amino acids, such as the histidine included in the aminoacidic sequence of the Tetra(HPRG) peptide, preferably orient themselves in parallel with respect to the basal plane of the graphene sheets, through predominant π–π interactions [73,74].
A strong and irreversible interaction between the physisorbed Tetra(HPRG) peptide and GO was detected by means of UV-visible (Figure 1) and fluorescence (Figure 3) spectroscopies, as demonstrated in terms of electron transfer processes between the nanosheets and the 5,6-carboxyfluorescein fluorophore moiety covalently bound to the Tetra(HPRG) peptide sequence.
In particular, the bathochromic shift (~2 nm) and, most noticeably, the hypochromic shift of the Fam-related main absorption peak at ~500 nm, pointed to the significant decrease, in a GO-dose dependent manner, of the molar extinction coefficient from the value of ε497 = 5.6 × 105 M−1cm−1 of the free peptide to ε499 = 1.3 × 105 M−1cm−1 and ε499 = 1.7 × 105 M−1cm−1 in the GO_A@T and GO_B@T hybrid samples, respectively. Moreover, while the bare GO_A and GO_B nanosheets have similar molar extinction coefficients for the GO characteristic π → π* transition peak, as calculated by using the Lambert-Beer law (ε238 ~3 × 102 mg−1 mL cm−1), the hybrid GO@T samples exhibit both hypsochromic (~16 nm) and hypochromic shifts, the latter effect being depending on the GO concentration, with the values of ε232 = 73 mg−1 mL cm−1 for GO_A@T and ε232 = 93 mg−1 mL cm−1 for GO_B@T estimated from the spectra of the hybrid samples subtracted by that of the free peptide.
GO, which is itself fluorescent, is a quencher for the fluorescence signals of fluorophores, with a quenching efficiency superior to that of conventional organic molecules [75]. Its quenching mechanism is related either to fluorescence resonance energy transfer (FRET) [76] or photo-induced electron transfer (PET) processes [77]. Hence, when the GO nanosheet and the fluorophore molecules are close to each other, the energy or excited electron transfers from the fluorophore (donor) to the GO (acceptor) and the fluorescence signal of the fluorophore is quenched. GO is largely used as energy acceptor for the fabrication of biosensors [78]. Our findings on the strong quenching of the Fam fluorescence (<99% decrease in the emission intensity measured in the absence of GO) in the GO@T hybrids strongly support the successful and irreversible immobilization of the Tetra(HPRG)-Fam molecules on the GO nanosheets (Figure 3).
The CD (Figure 2) and ATR-FTIR (Figure 4) spectra also confirmed the successful immobilization of the peptide molecules at the GO surface, with conformational changes of the peptide molecules due to the strong interaction with the GO nanosheets. In particular, in the infrared spectra, the shifts to higher wavenumbers and the peak broadening of the signals at 1156 and 1080 cm−1, related respectively to the C=C stretching and the N–H bending vibrations of the histidine residue, point out to the formation of hydrogen bonds between the hydrophilic groups of graphene oxide and the side chain imidazole groups of histidine in the peptide [60]. Also, the spectral changes observed in the protein-fingerprint region of amide I and amide II stretching vibrations pointed to significant conformational changes of the peptide molecules in the adlayer at the surface of the GO compared to the unbound form of the free peptide in solution. Specifically, the amide I vibration, absorbing near 1650 cm−1, arises mainly from the C=O stretching with minor contributions from the out-of-phase C–N stretching. The amide II, absorbing near 1550 cm−1, is the combination of N–H and C–N stretching vibration with smaller contributions from the C–O, C–C and N–C stretching vibrations. Both amide I and amide II vibration peaks of proteins may provide valuable structural information and secondary structure prediction [59]. For instance, the amide I peak may encompass different conformational contributions that represent extended strands, β-turns, 3(10)-helix, polyproline I, and polyproline II [79]. The HPRG domain has been reported to have a predominant polyproline II conformation rather than random coil one for the high content of proline residues [80]. We found by CD analyses supporting evidences on the presence of PPII conformers of the free Tetra(HPRG) peptide in equilibrium with the random coil configuration. Indeed, the CD spectrum of the free peptide (Figure 2), shows a broad negative peak at 202 nm, a maximum at 222 nm, and another minimum at 234 nm. This combination has been shown to occur in polypeptides with extended chain polyproline II conformation, whereas the random conformation has the minimum is at 199 nm [81].
To note, the HPRG domain is able to bind different ligands by exploiting the imidazole side chain, partially protonated at physiological pH, and the conformational features of its secondary structure. The HPRG protein has a multidomain structure composed of 2 N-terminal regions (called N1 and N2) homologous to cystatin but lacking the function of inhibitor of cysteine proteinase; a central element, histidine rich region (HRR) flanked by 2 regions rich in proline (PRR1 and PRR2) and a C-terminal domain or (C) [82]. This domain also prevents heparanase-mediated release of angiogenic growth factors from ECM by blocking the heparanase cleavage sites in the EPM heparan sulphate [83] and by binding to tropomyosin on the cell provides antiangiogenic signals to endothelial cells [41]. Interestingly, these processes could be enhanced by high extracellular Zn2+ (generally provided by platelets) and low pH levels, which cause conformational changes in the molecule [34]. Noteworthy, the multidomain structure of HPRG allows for its interaction with a variety of molecules such as heme group, fibrinogen, heparinase, thrombospondin (TSP), divalent metal cations zinc, copper, mercury, cadmium and nickel, but also with associated molecules to cells, including heparan sulphate (HS), tropomyosin ATP synthase, DNA [36]. Specifically, HPRG has been detected on the surface of the cells involved in the immune response such as: Leukocytes, macrophages and monocytes, as well as in the platelet and megakaryocyte granules [35]. Numerous studies have demonstrated the effectiveness of HPRG-derived peptides in inhibiting tumor cell proliferation indirectly, by reducing angiogenesis and starving the tumor. To date, there are no studies conducted on tumor cells after incubation with the Tetra(HPRG) peptide or graphene-based nanocomposites either of the whole protein or of the peptide.
The different viability results (Figure 5) showed by the cells incubated with GO-immobilized peptide, both hybrids GO@T and mixtures GO+T, compared to the free Tetra(HPRG)-Fam peptide could be therefore related to the different binding capabilities of the HPRG backbone in the free and surface-immobilized conformational state.
Specifically, the opposite trends detected on both PC-3 and HREC, i.e., proliferative effect for the peptide alone and anti-proliferative effect for the GO-immobilized peptide, can be related to the equilibrium shift for the latter towards the PPII configuration. To note, according to the confocal imaging analyses (Figure 8), the cells exhibited a dose-dependent cellular uptake of the free peptide with preferential gathering at the cell membrane, while a more diffuse cytoplasm internalization was observed for the GO-immobilized peptides. Accordingly, different signaling pathways resulting in the proliferative or anti-proliferative effects could be activated.
The effects induced by the GO@T hybrids and GO+T mixtures on PC-3 and HREC cell viability confirm that, depending on the incubation time and the dose of treatment, it could be also used as an anti-cancer molecule and not only as a drug-delivery agent [63]. What is more, the combination Tetra(HPRG)-Fam peptide to the GO platform in the GO@T hybrids blocked the cancer cells proliferation to a greater extent than each individual component. In the other hand, in the angiogenic cellular model, the GO@T hybrids inhibit the endothelial cell proliferation while the GO+T mixture do not, thus highlighting a promising potential to block unwanted vessel formation.
The different effects of GO and GO@T hybrids on PC-3, SH-SY5Y and HREC are in agreement with other studies showing that graphene oxide inhibits tumor-sphere formation in different cancer types (breast, ovarian, prostate, lung, and pancreatic cancers, as well as glioblastoma), by inhibiting several signal transduction pathways, but with no toxic for normal fibroblasts [84]. Also, the time- and dose-dependent response on the treatment of SH-SY5Y cells with GO is in agreement with literature data [85] and previous works from this group which demonstrated that the GO toxicity in neuroblastoma cells depends on the functional groups and the oxidation level of GO [27,50]. These findings are in general very promising since they elucidate on the potential of graphene oxide as an effective non-toxic therapeutic strategy for the eradication of cancer cells.
As to the strong inhibitory effect of the migration of PC-3 and HREC cells in the presence of GO or GO@T hybrids (Figure 6), we found a consistent trend with the PEG2 release (see Table 1 and Table 2), with a significant inhibitory effect in both the cell types.
For PC-3, the inhibition in cell migration can be due also to the inhibition of the electron transport chain, as highlighted by the high levels of ROS produced in the cells after incubation with GO (Figure 7). This hypothesis is confirmed by other studies, reporting a blockage of the electrons transfer to the iron-sulfur centers, since GO would subtract electrons, having a higher electron affinity than the iron-sulfur centers [86]. Prolonged damage over time causes inactivation of the free radical scavenger enzymes (superoxide dismutase and glutathione peroxidase) and consequently damage to proteins, DNA and lipids, which greatly thereby influencing the cell metabolism and signaling pathways [84]. Moreover, GO exposure can induce phosphorylation of ERK signaling molecules, which are related to cell cycle regulation, triggering apoptosis [87]. The reduced production of ATP by oxidative phosphorylation would result in an impairment in the F-actin cytoskeleton assembly, which is an ATP-consuming process, thus inhibiting cancer cells migration. Interestingly, the increased ROS levels for cells incubated with the GO@T hybrids is less evident than the treatment with the GO alone. Such a result is most likely due to a protective effect of the peptide which covers the surface of GO sheets thereby reducing the interaction with the oxygen molecules and as consequences the ROS production [88]. The generation of ROS by the GO, indeed, may be mediated by the adsorption of O2 on its surface to form a surface-bound C(O2) intermediate which is capable to oxidize the glutathione enzyme GSH to GSSG, thus restoring the carbon surface to its original state and releasing H2O2 [88].
Since the presence of the peptide in GO@T hybrid led to a reduction in the release of free radicals than the treatment with the GO alone, the inhibition of the PC-3 cells migration in the presence of the hybrid could be due to a mechanism not strictly related to the reduction of the ATP generation. One of the mechanisms proposed in modulating tumor cell migration is the activation of signal pathways, involved in the inflammatory process. It has indeed been demonstrated that PGE2 is the most abundant pro-inflammatory mediator in prostate tissues and its levels are increasing in prostate carcinoma [89]. Moreover, it has been shown that cAMP-PKA/PI3K-Akt signaling pathway, activated in prostate cancer cells by the binding of PGE2 to its EP3 receptor, is involved in PC-3 cells proliferation [68]. The strong inhibition of the PGE2 release by PC-3 (Table 1) and HREC (Table 2) treated with GO@T, may provide novel insight into the therapeutic potential of this hybrid nanocomposite. Indeed, GO triggers profound effects through the metabolic reprogramming of the cell, thereby exerting its anti-inflammatory effects through the downregulation of specific genes, leading to activation of the transcription factor nuclear factor erythroid 2-related factor 2, which inhibited expression of pro-inflammatory cytokines such as IL-1β and IL-6 [90]. A down-regulation by GO, via epigenetic mechanisms, of cyclooxygenase 2 (cox2), releasing PGE2, in human embryonic kidney 293T cells has been demonstrated [91]. The ability of GO to trigger physical interactions between the downstream factors and the cox2 promoter gives GO peculiar characteristics that make it an effective molecule in the treatment of tumor forms. In the same study, aminated GO and poly (acrylic acid)-functionalized GO were demonstrated to reduce the inflammatory response, with a weaker effect on chromatin architecture. Therefore, GO-mediated chromatin interactions may minimize toxicity in practical applications.
The association between Tetra(HPRG) peptide and the ROS-producing GO nanosheets, with the related enhanced cellular uptake and mitochondrial perturbation (Figure 8) are therefore able to strongly inhibit both the tumor cells proliferation and the cell migration process. Mitochondria, in fact, are intracellular organelles involved in energy metabolism, which show a central role in the ATP synthesis. According to the data from mitochondrial organelle staining shown in the Figure 8, the GO@T hybrids at the higher concentrations of peptide and GO interfere less with mitochondrial activity than the peptide and GO alone.
4. Materials and Methods4.1. Chemicals
Graphene oxide water dispersion (0.4 wt% concentration) was purchased from Graphenea Inc., (Gipuzkoa, Spain). TETRA-HPRG (Ac-(GHHPH)4G-NH2 with the addition of 6-Fam (6-carboxyfluorescein) were purchased from Caslo (Kongens Lyngby, Denmark). Phosphate buffered saline purchased by Sigma-Aldrich (St. Louis, MO, USA). Ultrapure Milli-Q water was used (18.2 mΩ∙cm at 25 °C, Millipore, Burlington, MA, USA). RPMI 1640, Dulbecco’s modified eagle medium (DMEM)-F12, fetal bovine serum (FBS), penicillin streptomycin solution and amphotericin solution for cell cultures. 2′,7′-dichlorofluorescein (DCF) were purchased from Sigma-Aldrich (St. Louis, MO, USA). MitoSOX™ red mitochondrial superoxide indicator and 2′-[4-ethoxyphenyl]-5-[4-methyl-1-piperazinyl]-2,5′-bi-1H-benzimidazole trihydrochloride trihydrate (Hoechst33342) were purchased from Thermo Fisher Scientific (Waltham, MA, USA).
4.2. Synthesis of GO@T Hybrid
The aqueous dispersion GO was dried 70 °C, 400 rpm overnight in Termomixer (Thermomixer Comfort model, Eppendorf, Hamburg, Germany) and 3 mg of GO dried were resuspended in Phosphate Buffered Saline (PBS) 10 mM to reach a final concentration of 1 mg/mL. Therefore, to obtain homogeneous dispersion, GO dispersion was sonicated in Labsonic Ultrasound bath at 59 Hz for 120 min maintaining the temperature at 25 °C. The obtained dispersion has a characteristic dark brown coloration. For the synthesis of the GO@T hybrid nanosystem, 20 µL of peptide (10 mM) in Milli-Q ultrapure water was added to 1 mL of the GO dispersion (1 mg/mL or 0.5 mg/mL, for GO_A@T or GO_B@T, respectively). The sample were incubated for 120 min in Thermomixer (Thermomixer Comfort model, Eppendorf) at 37 °C and 400 rpm. To remove the excess of reactants, the synthetized hybrid system was washed with PBS (10 mM) twice by centrifugation (2 min at 2700 RCF). The same washing procedure was also applied to the GO alone.
4.3. UV-Visible and Fluorescence Spectroscopy
UV-visible spectra of the samples in PBS were recorded using spectrometer Lambda S2 (Perkin Elmer, Waltham, MA, USA) and quartz cuvettes with an optical path length of 0.1 cm in the range of 200–700 nm. Fluorescence spectra were recorded on a LS55 (Perkin Elmer, Waltham, MA, USA) fluorimeter using quartz cuvettes with an optical path length of 0.1 cm.
4.4. ATR/FTIR and CD Spectroscopies
The vibrational spectra were recorded using a Perkin Elmer FT-IR spectrophotometer (Spectrum Two, Waltham, MA, USA) in the range of 4000–400 cm−1. To carry out the measures the solid samples of GO and GO@T hybrid dried in Thermomixer (Thermomixer Comfort model, Eppendorf, Hamburg, Germany) at 70 °C and 400 rpm overnight, have been placed on the surface of the crystal and then locked with a “clutch-type” lever before starting the measure. Each spectrum was acquired at a resolution of 2 cm−1 (10 scans).
The CD spectra of samples, both GO@T hybrids and GO+T mixtures, were obtained at 25 °C under a constant flow of nitrogen on a Jasco model 810 spectropolarimeter at a scan rate of 50 nm min−1 and a resolution of 0.1 nm. The path length was 1 cm. The spectra were recorded as average of 10 scans in the 190–260 nm range.
4.5. Cell Cultures and Maintenance
Prostate cancer cells (PC-3) and neuroblastoma (SH-SY5Y) cells were cultured in 25 cm2 tissue-culture treated Corning® flasks (Sigma-Aldrich, St. Louis, MO, USA) in RPMI-1640 and Dulbecco’s modified eagle medium (DMEM)-F12, respectively. The medium was supplemented with 10% v/v fetal bovine serum (FBS), and contained 2 mM L-glutamine, 100 IU/mL penicillin and 0.1 mg/mL streptomycin. HREC cells were cultured in EGM-2 medium supplemented with 5% FBS. Cells were grown in an incubator (Heraeus Hera Cell 150C incubator), under a humidified atmosphere at 37 °C in 5% CO2.
4.6. MTT Assay
For cell viability assays, the 3-[4,5–dimethylthiazol-2-yl]-2,5-diphenyl tetrasodium bromide (MTT) assay was used (Chemicon, Temecula, CA, US). Cell lines were seeded in 96-well plates at the cell density per well of 1.5 × 104 for PC-3 and HREC cells and 2 × 104 for SH-SY5Y cells, respectively. Cells were incubated overnight at 37 °C before experiment. Afterwards, cells were treated for 24 h and 48 h in the absence (negative control) or the presence of GO nanosheets (10, 12, 30 µg/mL), Tetra(HPRG)-Fam peptide (2, 4, 6 µM) and hybrid GO@T samples (10 µg/Ml-2 µM, 12 µg/mL-4 µM, 30 µg/mL-6 µM) and the corresponding GO+T mixtures. After incubation periods, 10 µL of MTT reagent (5 mg/mL) were added to each well and the cells were incubated for 3 h at 37 °C. The formazan crystals were solubilized with 100 µL DMSO and plates were shaken for 10 min. The absorbance was measured at 570 nm with plate reader (Synergy 2-bioTek).
4.7. Cell Migration
PC-3 and HREC migration was measured using a standard scratch assay, performed as previously reported [92]. Cells were incubated for 24 h and 48 h in the absence (negative control) or the presence of GO nanosheets (10, 12, 30 µg/mL), Tetra(HPRG)-Fam peptide (2, 4, 6 µM) and hybrid GO@T samples (10 µg/m-2 µM, 12 µg/mL-4 µM, 30 µg/mL-6 µM) and the corresponding GO+T mixtures. Migration was followed by an inverted Leica DM IRB microscope equipped with CCD camera. Time zero represents the time immediately after the scratch for all conditions.
4.8. PGE<sub>2</sub> Assay
To determine PGE2 release, PC-3 and HREC were incubated for 48 h in the absence (negative control) or the presence of GO nanosheets (10, 12, 30 µg/mL), Tetra(HPRG)-Fam peptide (2, 4, 6 µM) and hybrid GO@T samples (10 µg/mL-2 µM, 12 µg/mL-4 µM, 30 µg/mL-6 µM of GO-peptide concentration). Aliquots of culture medium were analyzed by using ELISA kits (Cayman Chemical, Ann Arbor, MI, USA), according to the manufacturer’s instructions. Three different experiments were analyzed in triplicate.
4.9. Confocal Microscopy Analysis
LSM imaging was performed with an Olympus FV1000 confocal laser scanning microscope (LSM), equipped with diode UV (405 nm, 50 mW), multiline Argon (457 nm, 488 nm, 515 nm, total 30 mW), HeNe(G) (543 nm, 1 mW) and HeNe(R) (633 nm, 1 mW) lasers. An oil immersion objective (60xO PLAPO) and spectral filtering systems were used. The detector gain was fixed at a constant value and images were collected, in sequential mode, randomly all through the area of the well. To perform the experiment PC-3 cells were seeded in glass bottom dishes (WillCo-dish®, Willco Wells, B.V., Amsterdam, The Netherland) with 12 mm of glass diameter at a density of 3 × 104 cells per dish in RPMI-1640 medium supplemented with 1% FBS. Thereafter, cells were treated for 24 h in the absence (negative control) or the presence of GO, Tetra(HPRG)-Fam peptide, the hybrid GO@T and the GO+T mixtures. Before fixing, cells were stained with nuclear dye Hoechst33342 (0.251 µg/mL) and MitoTracker Deep Red (200 nM). Cells were rinsed with fresh PBS and cellular fixation was performed with high purity paraformaldehyde (2% w/v) in PBS (pH 7.3).
4.10. Total and Mitochondrial ROS Production
The oxidative stress induced by the cell treatment with GO, Tetra(HPRG)-Fam and the hybrid GO@T was evaluated through DCF assay and MitoSOX assay on PC-3 cells measuring the level of reactive oxygen species (ROS) produced. To perform the assay, cells were plated into a 96-well plate in complete medium at a density of 10 × 103 cells per well. Cells were treated for 24 h in 3 replicate wells with: GO nanosheets (10, 12, 30 µg/mL), Tetra(HPRG)-Fam peptide (2, 4, 6 µM) and hybrid GO@T samples (10 µg/mL-2 µM, 12 µg/mL-4 µM, 30 µg/mL-6 µM of GO-peptide concentration). The incubation was carried out in complete medium supplemented with 1% FBS. Then, cells were stained with 0.12 µg/mL Hoechst33342 for 20 min, 3 µM 2′,7′-dichlorofluorescein (DCF) for 15 min (total ROS) or 5 µM MitoSOX for 5 min (mitochondrial O2•−) at room temperature and analyzed by measuring the fluorescence emission (λex = 493 nm, λem = 523 nm for the nuclear staining; λex = 493 nm, λem = 523 nm for DCF; λex = 510, λem = 580 for MitoSOX, respectively) using a fluorescence spectrophotometers a LS55 (Perkin Elmer, Waltham, MA, US) and quartz cuvettes with an optical path length of 0.1 cm. Results are normalized to the Hoechst emission and represented as the increase in DCF or MitoSOX signals with respect to the untreated control. Data are presented as the means ± SEM of three replicas.
5. Conclusions
In summary, we assembled here a theranostic platform made of graphene oxide and a tetra repeat sequence of HPRG.
Results of cell viability in human prostate cancer (PC-3), neuroblastoma (SH-SY5Y) and retinal endothelial (HREC) cells incubated with GO-immobilized peptide, both hybrids GO@T and mixtures GO+T, compared to the treatments with the free Tetra(HPRG)-Fam peptide, pointed to different binding capabilities of the HPRG backbone in the free and surface-immobilized conformational state.
In particular, the proliferative effect for the peptide alone and the anti-proliferative effect for the GO-immobilized peptide, detected both in PC-3 and HREC, were related to the predominance of poly-Proline II conformation of the peptide immobilized on the GO nanosheets. Also, results of confocal imaging analyses suggested the activation of different signaling pathways resulting by different peptide internalization and sub-cellular localization. The strong inhibitory effect observed on the migration of PC-3 and HREC cells in the presence of GO or GO@T hybrids was correlated to the PEG2 release.
Such findings may provide novel insights into the therapeutic potential of these nanocomposite. In fact, we demonstrated that a 21-mer peptide analogue mimics the anti-angiogenic activity of the whole HPRG protein and is also able to inhibit tumor growth when associated to the graphene oxide. Such a dual action could represent the basis for obtaining a new, more effective, anticancer therapeutic approach. To this aim, further studies are necessary to deepen the signaling pathway involved in the implementation of the overall function.
Acknowledgments
C.S. and D.L. acknowledge the Consorzio Interuniversitario di Ricerca in Chimica dei Metalli nei Sistemi Biologici (C.I.R.C.M.S.B.), Bari, Italy.
Author Contributions
Conceptualization, C.S., G.L. and D.L.M.; investigation, V.V., A.L., L.M.C., V.S., A.M.; data curation, L.M.C., V.S. and C.S.; writing—original draft preparation, V.V., A.L. and C.D.A.; writing—review and editing, C.S., G.L. and D.L.M. All authors have read and agreed to the published version of the manuscript.
Funding
This research was partially supported by MIUR PRIN grant, 2017WBZFHL), EraNet Cofund-M-ERA.NET 2 (project SmartHyCAR, n. 2471) and University of Catania (Piano della Ricerca di Ateneo, Linea di Intervento 2, 2018–2020).
Conflicts of Interest
The authors declare no conflict of interest.
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(a) UV-visible spectra in phosphate buffer saline solution (PBS, pH = 7.4) of GO_A and GO_B 100× diluted dispersions, both before (dashed lines for GO_A, in black, and GO_B, in grey, respectively) and after 2 h of incubation with the peptide (solid line for GO_A@T, in dark green, and GO_B@T, in light green, respectively). The reference spectra of Tetra(HPRG)-Fam (green, 100× diluted solution of that used for the incubation of GO_A and GO_B samples) and the mixtures immediately the mixing (GO_A+T, in dark green, and GO_B+T, in light green, respectively) are shown for comparison in dot line. (b) UV-visible spectra in PBS of the pellets for hybrid GO_A@T and GO_B@T samples (solid lines), the GO_A+T and GO_B+T mixtures (dot lines) and, for comparison, of bare GO_A and GO_B pellets (dashed lines) after two centrifugation (2700 RCF, 2 min, RT) and washing steps. The spectra of supernatants (dashed dot lines) of GO@T samples collected after the first washing step are included.
CD spectra in phosphate buffer saline solution (PBS, pH = 7.4) of Tetra(HPRG)-Fam (green, solid line, peptide concentration = 1 × 10−5 M) and the pellets (10× dilution) for hybrid GO_B@T (dark green, solid line) and mixture GO_B+T (dark green, dotted line) samples.
Fluorescence spectra (λ excitation = 488 nm) in PBS (pH = 7.4) of free Tetra(HPRG)-Fam peptide (dot green line) and GO@T hybrids (GO_A@T: dark green line; GO_A@T: light green line). In the inset the magnified region for the pellets (solid lines) and supernatants (dashed-dot lines).
Attenuated total reflectance Fourier transform infrared (ATR/FTIR) spectra of graphene oxide (GO) (black curve), Tetra(histidine-proline-rich glycoprotein (HPRG))-Fam (green curve) and GO@T (dark cyan curve) hybrid samples.
Cell viability from MTT assays of prostate cancer (a,d), neuroblastoma (b,e) and endothelial (c,f) cells incubated for 24 h (a–c) or 48 h (d–f) with the hybrid GO@T samples at increasing content of GO nanosheets (10–30 µg/mL concentration range) and Tetra(HPRG)-Fam peptide (3–6 µM concentration range). The negative control of untreated cells (CTRL) and the positive controls of cells treated with the peptide alone or the bare GO or the peptide mixtures are included as reference. Values (means ± SEM) are from three independent experiments. Results are expressed as percentage with respect to CTRL. Student’s t-test was used to compare cell viability measurements in all experimental conditions. (*) p < 0.05, (**) p < 0.01, (***), p < 0.001, (****) p < 0.0001 vs. CTRL, (#) p < 0.05, (##) p < 0.01, (###) p < 0.001, (####) p < 0.0001 vs. the corresponding GO reference sample.
Representative micrographs (a,b) and quantitative analysis of cell migration (c,d) for prostate cancer (a,c) and endothelial (b,d) cells in absence (negative control, CTRL) or in presence of the different treatments at time 0, 24 h and 48 h after scratch: GO nanosheets (10, 12, 30 µg/mL), Tetra(HPRG)-Fam peptide (2, 4, 6 µM), GO@T hybrids (10 µg/mL 2 µM, 12 µg/mL 4 µM, 30 µg/mL 6 µM of GOpeptide concentration) and GO+T mixtures. PC-3 and HREC cells were wounded as described in the Materials and Methods section. The quantitative analysis of migration assay (wound edge advancement in percent vs. time) is expressed as means values (± SEM) from three independent experiments. Statistical analysis was performed by pairwise Student’s T-test results are expressed as percentage of wound closure with respect to time 0. (**) p < 0.01, (***) p < 0.001, (****) p < 0.0001 vs. CTRL, (#) p < 0.05, (##) p < 0.01, (###) p < 0.001 vs. the corresponding GO reference sample.
Ratio of mitochondrial-reactive oxygen species (ROS)/total-ROS levels measured on prostate cancer cells (PC-3) measured by the MitoSOX and dichlorohydrofluorescein (DCF) fluorescence assays. Cells were incubated for 24 h in absence (negative control, CTRL) or in presence of the different treatments: GO nanosheets (10, 12, 30 µg/mL), Tetra(HPRG)-Fam peptide (2, 4, 6 µM) and hybrid GO@T samples (10 µg/mL-2 µM, 12 µg/mL-4 µM, 30 µg/mL-6 µM of GO-peptide concentration). Values (means ± SEM) are from three independent experiments. Statistical analysis was performed by One-way ANOVA test. Symbols indicate the significance versus CTRL: ** p < 0.01. Results are expressed as ratio of MitoSOX with respect to DCF emission intensities.
Laser scanning microscopy (LSM) micrographs of PC-3 cells incubated for 24 h in absence (a: CTRL) or in presence of the different treatments: GO nanosheets (b: 10, 12, 30 µg/mL), Tetra(HPRG)-Fam peptide (c: 2, 4, 6 µM) and hybrid GO@T samples (d: 10 µg/mL-2 µM, 12 µg/mL-4 µM, 30 µg/mL-6 µM of GO-peptide concentration). For each treatment condition, the merged confocal images of nuclei (Hoechst, λex/em = 405/425–450 nm) and mitochondria (MitoTracker Deep Red, λex/em = 633/650–700) (upper panel), and the merged fluorescence image of the dye-labelled peptide (Fam, λex/em = 543/550–600 nm) and the optical bright field micrograph (lower panel) are shown. Scale bar 30 µm. The white arrows guide the eye to the GO sheets. In (e): quantitative analysis of Fam and MitoTracker Deep Red emission intensity. Results are presented as mean +/-SEM from experiments in triplicate and normalized with respect to the control untreated cells. Asterisks represent the correlation significant at the (*) p < 0.05, (**) p < 0.01, (***) p < 0.001 (****) p < 0.0001 vs. CTRL, (One-way ANOVA).
ijms-21-05571-t001_Table 1
Prostaglandin (PGE2) production in PC-3 medium, after different treatments. Values (means ± SEM) are from three independent experiments (n = 3). ANOVA was used to compare PGE2 production in all experimental conditions. * p < 0.01 vs. control cells.
PGE2 production in human retinal endothelial cells (HREC), after different treatments. Values (means ± SEM) are from three independent experiments (n = 3). Student’s t-test used to compare PGE2 production in all experimental conditions. * p < 0.01 vs. control cells.
\\n\"\n]"}}},{"rowIdx":457,"cells":{"data":{"kind":"list like","value":["\nFront PsychiatryFront PsychiatryFront. PsychiatryFrontiers in Psychiatry1664-0640Frontiers Media S.A.32848948PMC743211410.3389/fpsyt.2020.00784PsychiatryOriginal ResearchGray Matter Differences Between Premature Pubertal Girls With and Without the Reactivation of the Hypothalamic—Pituitary-Gonadal AxisFuYuchuan\n1\nZhangWenjing\n2\nTaoBo\n2\nYangBeisheng\n2\nYangDi\n3\nXieXiaoling\n1\nLiuPeining\n4\nZhuYaxin\n1\nZhouLu\n1\nChenTao\n1\nLiuXiaozheng\n1\nYanZhihan\n1\n\n*\n\n1\nDepartment of Radiology, Second Affiliated Hospital and Yuying Children’s Hospital of Wenzhou Medical University, Wenzhou, China\n\n2\nDepartment of Radiology, West China Hospital of Sichuan University, Chengdu, China\n\n3\nDepartment of Radiology, Zhejiang Hospital, Hangzhou, China\n\n4\nDepartment of Child Health Care, The Second Affiliated Hospital and Yuying Children’s Hospital of Wenzhou Medical University, Wenzhou, China\n
Edited by: Hesheng Liu, Harvard Medical School, United States
Reviewed by: Ji-Won Chun, Catholic University of Korea, South Korea; Deniz Vatansever, Fudan University, China
This article was submitted to Computational Psychiatry, a section of the journal Frontiers in Psychiatry
118202020201178409120202272020Copyright © 2020 Fu, Zhang, Tao, Yang, Yang, Xie, Liu, Zhu, Zhou, Chen, Liu and Yan2020Fu, Zhang, Tao, Yang, Yang, Xie, Liu, Zhu, Zhou, Chen, Liu and YanThis is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
The onset of puberty and related hormones exerts significant effects on brain morphometric and psychosocial development. The biological mechanisms underlying how the reactivation of the hypothalamic-pituitary-gonadal (HPG) axis and puberty-related hormonal maturation sculpts human brain architecture remain elusive. To address this question, 105 premature pubertal girls (age 8–11 years) without menstruation underwent brain structural scanning on a 3T MR system, and the luteinizing hormone releasing hormone (LHRH) stimulation test was used to identify the reactivation of the HPG axis. Among the 105 girls, 63 were positive for HPG axis reactivation (HPG+), while the others showed negative (HPG-). Cortical thickness was calculated and compared between the two groups after adjusting for age. The brain regions showing inter-group differences were then extracted and correlated with the peak value of serum hormone after the LHRH stimulation test in entire sample. Compared to HPG- girls, HPG+ girls showed reduced cortical thickness mainly in the the right precuneus, right inferior temporal gyrus, and right superior frontal gyrus, while increased cortical thickness primarily in the left superior parietal lobe and right inferior parietal lobe. Linear-regression analysis revealed negative correlations between the cortical thickness of the right inferior parietal lobe with the peak value of FSH and the right precuneus with LH and E. These findings provide evidence to support the notion that the reactivation of HPG axis and changes of hormones during the early phase of hormonal maturation exert influences on the development of gray matter.
cortical thicknessfollicle stimulating hormoneluteinizing hormoneestradiolhypothalamic-pituitary-gonadal axisNatural Science Foundation of Zhejiang Province10.13039/501100004731Introduction
Puberty is a critical period in which dramatic hormone-related changes parallel changes in emotion, behavior, cognition, physical, and brain development (1, 2). Notably, the effect of puberty on psychosocial development in adolescence is believed to vary at different stages, with the early phase of puberty appearing to play a more critical role than those in middle and late adolescence (3). The onset of puberty is often deemed as a trigger of several neuroendocrine-related changes, such as a vast increase in sexual hormone, which occur prior to the physical changes. The released gonadotropin releasing hormone (GnRH) is distributed in the hypothalamus in a pulsatile fashion and initiates a cascade of events that have been recognized as the onset of puberty. Following the secretion of GnRH, the pituitary responds by secreting luteinizing hormone (LH) and follicular stimulating hormone (FSH). These major hormonal changes occur just after the reactivation of the hypothalamic-pituitary-gonadal (HPG) axis, causing gonadal maturation and the production of sex steroids, most notably estrogen (E) and testosterone (TES). The first activation of the HPG axis begins at the prenatal and the early postnatal months. After the first year of birth, the HPG axis lays dormant until the resurrection of the GnRH, which facilitate the onset of puberty (4). The increased secretion of LH from the pituitary gland is the first measurable endocrinological marker of puberty (5). Generally, a serum LH level of ≥5 IU/L using modern ultrasensitive automated chemiluminescence assays after the luteinizing hormone releasing hormone (LHRH) stimulation test from patient samples (either 30 or 60 min) can be recognized as being positive for HPG axis reactivation, otherwise being negative for HPG axis reactivation following previously established clinical criteria (6–9). In the occasional child whose LH is not quite as high, they showed the auxological and clinical signs of puberty, a LHRH stimulation test may be required to confirm the diagnosis. However, sexual maturation in the previous research was defined by using biological features or clinical features [i.e., Tanner stage (10, 11) or the self-report of children (12)]. Therefore, it is necessary to specify the brain regions on which the HPG axis reactivation showed association in early adolescence.
Previous studies have found that brain morphology differs at different stages of puberty. A few studies found that the pubertal stage, as assessed by Tanner staging of sexual maturation, is related to brain morphology. For instance, girls in mid/late puberty had significantly lower right and left cortical gray matter volumes than girls in earlier stages of puberty (13). The gray matter volume of inferior frontal gyrus in adolescents was larger in early pubertal stages compared to that in late puberty (12). The volume in the right hippocampus decreased with increasing Tanner hair stage, while the volume of the right and left amygdala decreased with increasing Tanner breast stage (14). In addition, greater increases in Tanner stage predicted less cortical thickness in the right superior frontal gyrus and right superior temporal gyrus and decreases in the left precuneus surface area in the girls (15). Goddings and her colleagues also demonstrated that the volume of the amygdala and hippocampus increased and the volume of nucleus accumbens, caudate, putamen, and globus pallidus decreased across puberty as measured by Tanner stage (16). While in a large group of 9-year-old twins, including males, the children that showed signs of physical maturation (Tanner stage ≥ 2) was associated with decreased parietal gray matter densities compared to the children without any secondary pubertal signs (Tanner stage ≤ 1) (17). However, how brain development is regulated by pubertal changes, especially at the early stage premenarche, remains poorly understood (14). Previous studies either in animals or humans found that puberty-related hormones were associated with the development of the gray matter in brain. In animal models, puberty-related hormonal changes have been shown to induce changes in brain maturation, including structural reorganization and neuronal circuit pruning, with these effects starting at puberty onset and continuing through adolescence (18–20). Several studies in humans have been conducted to examine the associations between changes in serum hormones and brain structure during adolescence, and the findings were inconsistent. One study examined the influence of sex steroids on structural brain maturation and found that there were no effects of sex steroids on cortical thickness in adolescents aged 8–25 years (12). One study demonstrated that higher TES levels were associated with thinner left calcarine sulcus and right lingual gyrus cortex in girls (21). Another study demonstrated that higher E levels, but not TES, were associated with smaller gray matter density in prefrontal, parietal, and middle temporal areas and greater gray matter density in the middle frontal, inferior temporal, and middle occipital gyri in girls between 10 and 15 years of age (22). Another two studies also showed that changes in the TES and E levels were associated with white matter, gray matter, right amygdala, and bilateral caudate volumes, cortical thickness and surface area (15, 23). Higher FSH levels have also been associated with greater gray matter density in left prefrontal areas, hippocampus, anterior cingulate, precuneus, and right cerebellum among girls aged 9 to 12 years (24). Previous studies did not find an association between LH and gray matter (12, 24, 25); however, a trend for negative association was been suggested between the gray matter density of temporal areas and the LH in the 9-year-old twins (25). Nonetheless, these studies indicated that puberty-related hormones exerted positive or/and negative influences on gray matter structural development and played different roles in brain areas. Due to variable findings in previous studies and the importance of studying gonadotropin concentration at an early stage of hormonal maturation, more research is needed to identify hormone-related influences on brain organization during the initiation of puberty.
To achieve this aim, we enrolled girls aged 8–11 years with thelarche as participants whose bone age and uterine volume were advanced than their chronologic age, seeking to identify the influences of the HPG axis reactivation on brain organization during the initiation of puberty. We conducted high-resolution structural MRI brain scanning in young girls who underwent testing for HPG axis reactivation determined by stimulated LH concentration. We used HPG+ to represent being positive for HPG axis reactivation and HPG- for being negative for HPG axis reactivation. We hypothesized that the girls whose HPG axis reactivation was positive (HPG+) would show cortical thickness differences compared to the girls whose HPG axis reactivation was negative (HPG-), and there were associations between the cortical thickness of several regions showing differences between the two groups and the peak value of puberty-related hormone after the LHRH stimulation test in the entire sample.
MethodsSubjects
This study was approved by the ethics committee of the Second Affiliated Hospital and Yuying Children’s of Wenzhou Medical University. The LHRH stimulation test is an invasive procedure; it needs the intravenous injection of the medication and the collection of the blood sample three times in the test. Therefore, it is difficult to obtain the consent of the guardians to allow healthy girls to take it. We chose to look at these younger girls with thelarche whose bone age and uterine volume were advanced than their chronologic age as study participants. The positive for HPG axis reactivation was diagnosed premature puberty. The girls with premature puberty were recommended to non-clinical intervention such as exercise more and pay attention to their diets and so on and clinical following-up by doctor (6). Their parents also wanted to know their children’s height development thereafter. Because of the HPG-axis reactivation, the process is followed by the activation of the growth axis leading to growth spurts in puberty.
All the subjects provided written informed consent from the guardians before participation. One hundred five right-handed girls aged 8–11 years were recruited via the Child Healthcare Department of the Second Affiliated Hospital and Yuying Children’s of Wenzhou Medical University. The exclusion criteria were as follows: (1) intelligence quotient (IQ) < 70 estimated by the Chinese Wechsler Intelligence Scale for Children (C-WISC) (17), (2) history of premature birth or postterm birth, (3) history of neurological or psychiatric disorders in the participants or their first-degree relatives, (4) history of head trauma, (5) have any systemic physical illness, (6) any medications known to affect brain function or hormone levels, (7) menstruation, and (8) contraindications to MRI scanning. Finally, 105 girls were recruited: 63 were in the HPG+ group, and the others were in the HPG group. Details of the demographics of all subjects are shown in \nTable 1\n.
Demographic and clinical characteristics of girls.
Characteristic
HPG+ (n = 63)
HPG- (n = 42)
\np\n
Mean
SD
Mean
SD
Age (years)
9.13
0.57
8.92
0.36
0.064
BMI (kg/m2)
17.08
2.16
16.21
1.66
0.071
LH(IU/L)
LH max
21.09
15.60
3.36
0.86
<0.000
LH baseline
1.04
1.197
0.117
0.065
<0.000
FSH(IU/L)
FSH max
16.59
6.53
12.94
5.13
<0.001
FSH baseline
4.67
5.06
2.18
0.88
0.0022
E2(pg/ml)
E2max
39.44
15.43
32.45
14.04
<0.010
E2 baseline
37.84
16.17
31.25
14.25
0.043
CBCL
Total score
10.52
10.64
12
10.51
0.156
IQ
91.11
14.50
89.79
12.05
0.12
SD, standard deviation; BMI, body mass index (BMI, calculated as Weight/Height2); HPG+/HPG-, with/without reactivated the HPG-axis; LH, luteinizing hormone; FSH, follicle stimulating hormone; E2, estradiol; TES, testosterone; CBCL, Child Behavior Checklist; IQ, intelligence quotient. p < 0.05 was considered statistically significant in all the tests.
Assessment Scales and Pubertal Assessment
Intelligence tests were performed by means of Wechsler Young Children Scales of Intelligence (C-WISC) in all the subjects to screen for mental retardation before the MRI scanning. Then, assisted by a child care physician, the primary caretaker of each child completed the child behavior checklist (CBCL) (26), which is a self-report questionnaire used to assess the behavioral and emotional problems of the children. The details are shown in \nTable 1\n. Tanner staging was used to examine pubertal status. Mean ages and the standard deviations, the frequencies for Tanner breast stage and hair stage are showed in \nTable 2\n.
Tanner stage.
Tanner stage
Tanner breast (mean age ± SD; frequency)
Tanner hair (mean age ± SD; frequency)
HPG+
HPG-
HPG+
HPG-
1
0
0
9.13 ± 0.5763
8.92 ± 0.3642
2
9.13 ± 0.5763
8.92 ± 0.3642
0
0
Mean age, standard deviations (in years), and frequencies for Tanner breast and hair stages.
Tanner breast 1: preadolescent, 2: appearance of the breast bud. Tanner hair 1: no public hair.
LHRH Stimulation Test
All subjects received the LHRH stimulation test after the imaging data acquisition. Both exams were completed within a week. Following overnight fasting, the participants were told to arrive at the hospital at approximately 8:00 am. Four to five milliliters of blood were collected (0-min sample), and then the LHRH was injected as an intravenous bolus of 2.5 μg/kg (maximum dose < 100 μg) (27) via an indwelling catheter. At 30 and 60 min after injection, 2 ml of blood were drawn again.
The bloods samples from 0 min were sent immediately to the clinical laboratory for analysis, and the 30- and 60-min samples after the LHRH injection were sent immediately to analyze respectively. The samples were centrifuged, separated, and assayed. We assayed LH, FSH, and estradiol (E) using modern ultrasensitive automated chemiluminescence assays, and the lower limit of detection was 0.07 IU/L for LH, 0.3 IU/L for FSH, 10.7 pg/ml for E, and 0.1 ng/ml for TES separately. Intra-assay coefficient of variation (CV) was 3.72 and 3% at 31.98 and 63.02 IU/L for LH, 5.08 and 3.97% at 36.1 and 83.65 IU/L for FSH, 5.55 and 4.61% at 265.22 and 552.67 pg/ml for E, respectively. Inter-assay CV was 5.13 and 4.63% at 30.44 and 59.96 IU/L for LH, 5.58 and 5.07% at 36.68 and 83.91 IU/L for FSH, and 6.08 and 3.81% at 260.37 and 547.73 pg/ml for E, respectively. A serum LH level of ≥5 IU/L after the LHRH stimulation test from patient samples (either 30 or 60 min) is considered evidence of the positive for HPG-axis reactivation otherwise negative for HPG axis reactivation following previously established criteria (6–9). We used HPG+ to represent being positive for HPG axis reactivation and HPG- for being negative for HPG axis reactivation. The peak value of serum puberty-related hormone after the LHRH stimulation test (either 30min or 60min) was used for linear regression analysis. Details of the hormone levels are shown in \nTable 1\n.
Structural Data Acquisition
The participants were scanned on a 3T GE HDxt scanner (General Electric, Milwaukee, Wisconsin) with an eight-channel phase array head coil. High-resolution T1-weighted images were acquired axially for the whole brain with a volumetric three-dimensional fast spoiled gradient recall (FSPGR) sequence. The scan parameters were as follows: TE/TR = 8.88 ms/4.02 ms, flip angle = 15°, FOV = 256 × 256, voxel size = 1 mm × 1 mm × 1 mm, and 160 slices with no gap. T2 FLAIR and T2-weighted images were acquired to screen the brain abnormalities by two experienced neuroradiologists. The quality of all the images was also controlled by them.
Image Processing
The FreeSurfer package (version 5.30, http://surfer.nmr.mgh.harvard.edu/) was used for preprocessing of cortical thickness in high-resolution T1-weighted images. This method has high test-retest reliability (28, 29). The FreeSurfer pipeline performs motion correction on T1-images. The post-processing procedures have been explained in detail in previous studies (30, 31) and contained the following main steps: bias-field correction, Talairach and Tournoux atlas conversion, signal strength standardization, skull-stripped, automatically delineated gray/white matter boundaries, and the pial matter topology correction. Cortical thickness was calculated as the shortest distance between the white matter interface and the pial matter at each vertex across the cortical boundaries (31). These post-processing procedures were performed separately on each cerebral hemisphere.
Statistical Analysis
Differences between HPG (+) and HPG (-) girls on demographic characteristics (e.g., age, BMI, CBCL total score, and IQ) and the hormone concentrations were examined using the independent sample Mann-Whitney U test.
The vertex-wise comparison of the cortical maps was performed on QDEC (Query, Design, Estimate, Contrast) with age as a covariate, and a whole-brain statistical threshold correction was performed using the Monte Carlo simulation method. Statistical significance was set at a cluster-wise–corrected P value < 0.05. A smoothing step using a Gaussian filter with 10-mm full-width at half maximum was initiated to average cortical thickness data across participants in a common spherical coordinate system.
Brain areas with statistical inter-group differences in cortical thickness were selected as brain region of interest (ROI). The average cortical thickness data of the ROI were extracted from the clusters. Correlation analysis between the regional cortical thickness values and stimulated hormone levels was assessed by using a general linear model and age as nuisance variables in the entire sample. Statistical significance was set at alpha < 0.05.
ResultsDemographic Statistics
No statistically significant differences were found in the age (p = 0.064, >0.05), BMI (p = 0.071, >0.05), CBCL total score (p = 0.156, >0.05), IQ (p = 0.12, >0.05), and Tanner breast stage and hair stage between the HPG+ girls and HPG- girls. There were statistically significant differences in the levels of LH (p < 0.0001, <0.05), FSH (p = 0.0030, <0.05), and E (p = 0.0204, <0.05) between the two groups (\nTable 1\n).
Group Differences in Cortical Thickness
Compared to HPG(-) girls, it showed difference in cortical thickness of 10 brain regions in HPG(+) girls, less cortical thickness primarily in the right precuneus (p = 0.002, <0.05), right inferior temporal gyrus (p = 0.006, <0.05), right superior frontal gyrus (p = 0.044, <0.05) and in the left rostral middle frontal gyrus (p = 0.028, <0.05), and higher cortical thickness in the right temporal pole (p = 0.006, <0.05), right inferior parietal lobe (p = 0.003, <0.05), right rostral middle frontal gyrus (p = 0.04, <0.05) and in the left superior parietal lobe (p = 0.001, <0.05), lateral occipital gyrus (p = 0.009, <0.05), and superior temporal gyrus (p = 0.01, <0.05) (\nFigure 1\n and \nTable 3\n)
Differences in cortical thickness between the HPG+ girls and the HPG- girls. Decreased cortical thickness colored by blue and increased cortical thickness colored by red. HPG+ girls show decreased cortical thickness in the left rostral middle frontal gyrus, right precuneus, right inferior temporal gyrus, right superior frontal gyrus and increased cortical thickness in the right temporal pole, right inferior parietal lobe, right rostral middle frontal gyrusin, left superior parietal lobe, left lateral occipital gyrus, left superior temporal gyrus compared to the HPG- girls. Threshold at p < 0.05.
Brain regions with significantly differences in cortical thickness between HPG+ and HPG-.
\nBrain region HPG+ and HPG-\n
\nCluster size (mm2)\n
\nMNI coordinates\n
\nP\n
X
Y
Z
\nLess\n
\nPrecuneus-R\n
116.79
5.8
-65.1
34.3
0.002
\nITG-R\n
70.18
48
-43.9
-16.3
0.006
\nSFG-R\n
23.91
10.2
0.8
51.7
0.044
\nrMFG-L\n
24.30
-28.2
32.0
30.7
0.028
\nHigher\n
\nTemporalpole-R\n
105.91
33.1
14.3
-35.5
0.006
\nIPL-R\n
53.85
47.1
-54.4
44.6
0.003
\nrMFG-R\n
30.43
19.0
55.8
-13.9
0.04
\nSPL-L\n
61.69
-21.4
-51.6
62.1
0.001
\nLOG-L\n
184.07
-35.4
-82.5
-14.9
0.009
\nSTG-L\n
55.43
-57.7
-43.2
15.8
0.01
Less, cortical thickness less in the HPG+ than in the HPG-; higher, cortical thickness higher in the HPG+ than in the HPG-; ITG-R, right inferior temporal gyrus; SFG-R, right superior frontal gyrus; rMFG-L, left rostral middle frontal gyrus; IPL-R, right inferior parietal lobe; rMFG-R, right rostral middle frontal gyrus; SPL-L, left superior parietal lobe; LOG-L, left lateral occipital gyrus; STG-L, left superior temporal gyrus.
Associations Between Hormone Levels and Cortical Thickness in the Entire Sample
We conducted a linear regression analysis to detect hormone-related variation in the cortical thickness of the differential brain region (10 ROIs) in the two groups. The results showed that the peak value of FSH after the LHRH stimulation test was negatively correlated to cortical thickness in the right inferior parietal lobe (r = -0.2630, p = 0.0067, <0.05), the peak value of LH and E were negatively correlation with cortical thickness in the right precuneus (r = -0.2043, p = 0.0365, <0.05; r = -0.2189, p = 0.0249, <0.05) (\nFigure 2\n). After the FDR correction, the results showed that the peak values of FSH after the LHRH stimulation test were negatively correlated to cortical thickness in the right inferior parietal lobe (p = 0.0403, <0.05).
Associations between hormone levels and cortical thickness. (A) Negative correlation between the LH level and the cortical thickness in the right precuneus. (B) Negative correlation between the E levels and the cortical thickness in the right precuneus. (C) Negative correlation between the FSH levels and the cortical thickness in the right inferior parietal lobe.
Discussion
To our knowledge, this study is the first to investigate the impact of HPG axis reactivation defined by LHRH stimulation testing on cortical thickness. Our main findings were (1) HPG+ girls who showed reduced cortical thickness mainly in the right precuneus, right inferior temporal gyrus, right superior frontal gyrus, and the left rostral middle frontal gyrus while increased cortical thickness primarily in the left superior parietal lobe and right inferior parietal lobe, the right temporal pole, right rostral middle frontal gyrus, left lateral occipital gyrus, and left superior temporal gyrus. (2) The correlation analysis revealed negative correlations between the peak value of LH and E after LHRH stimulation test and cortical thickness in the right precuneus, the peak value of FSH, and the right inferior parietal lobe in the two groups. Our results suggest that the reactivation of the HPG axis and related hormonal maturation may be involved in sculpting human brain cortical architecture.
One of the main interesting findings is the bidirectional changes of cortical thickness. In fact, previous studies indicated changes in the gray matter during different stage of puberty. For example, Herting et al. reported greater increases in Tanner stage predicted decreased cortical thickness including the right superior frontal gyrus and right superior temporal gyrus in puberty girls (15). While in a large group of 9-year-old twins, including males, the children that showed any signs of physical maturation (Tanner stage ≥ 2) was associated with decreased parietal gray matter densities compared to the children without any secondary pubertal signs (Tanner stage ≤ 1) (17). No studies have examined the difference in cortical thickness between prepuberty girls and girls of the same age at the onset of puberty. Our findings suggested that the reactivation of the HPG axis played a different role in the cortical thickness. The reactivation of the HPG axis drives dramatic changes in physical appearance, such as facial structure and the appearance of secondary sexual characteristics, and adolescents enter a stage of profound psychological transition in the onset of puberty, which may negatively or positively affect brain maturation. To reach a stable adult role, there are changes in the structure and function of the brain. Furthermore, multiple studies showed the receptors for HPG axis hormones are found in various brain cortical especially for learning and memory (32, 33). Most brain areas in our result were involved in learning and/or memory function. The reactivation of the HPG axis caused a cascade secretion of hormone, which attached to the receptors in the brain cortical. It might be one mechanism that the reactivation of the HPG axis sculpts the brain cortical thickness in our result.
Another important result demonstrates the correlations between the gray matter changes and the level of hormones. Sex steroids are known to affect the brain in two different ways: organizational effects and activational effects. The organizational effects of sex hormones were played by permanently changing the structure of the brain, such as neuronal number, myelination, and dendritic branching (34). Previous animal studies found that E levels could inhibit cortical myelination during puberty (35), and it is also involved in apoptotic processes (36). Another study found that estradiol altered dendritic spine density and dendritic spines in brain regions related to cognition and memory were sensitive to estrogen fluctuations (37). The precuneus was deemed to play a central role in a wide spectrum of highly integrated tasks, including self-centered mental imagery strategies, consciousness, and episodic memory retrieval (38). Yates and Juraska found that puberty ovarian hormone exposure reduced the number of myelinated axons in the splenium of corpus callosum in the rats (18). Koss and the colleagues also found that the number of neurons and glia were increased in the medial prefrontal cortex of female gonadectomy before puberty rats (20). Therefore, we hypothesize that higher estradiol levels might contribute to changes in the cortical thickness of the precuneus by decreasing the cortical number of neurons and/or myelinated axons.
Previous animal and human studies have shown that LH and FSH receptors have been found in various brain areas including the parietal cortex (39, 40). However, how circulating LH and FSH levels exert their effects on cerebral gray matter remains unclear. Bukovsky and colleagues speculated that microglial cells (among cells of the mononuclear phagocyte system) and some nerve cells in the gray matter of the human brain cortex might be influenced by LH. High LH levels might also activate resident macrophages to remove some nerve cells in which the expression of LH receptors was weaker to change gray matter (40). More studies are needed to further investigate the mechanisms. With regard to the relationship between the FSH level and gray matter, previous study showed that the gray matter volume of the right inferior parietal gyrus was decreased in perimenopausal women than premenopausal women (41). As we all know, the serum FSH level in perimenopausal women was obviously increased than in premenopausal. Another study found that the genetic variant for an FSH receptor gene strongly affected the onset of puberty in girls (42).
However, in contrast to our study, previous study of adolescence with an age of 8–25 years reported no effects of sex steroids on cortical thickness (12) and no association between the LH concentrations and gray matter (12, 24, 25). The possible explanations might be that (1) the hormonal level was determined in morning saliva or urine samples in previous studies and (2) the age of the sample. In that study the subjects were between 8 and 25 years. (3) The period the participants enrolled in [before or after hormonal maturation; middle or late puberty (23)] and the handling of confounding factors [stratification by sex or time in menstrual cycle (24)]. However, the onset age of puberty was between 7 and 13 in the girls, and the mean age of menarche was approximately 12 years old (43). To eliminate the effect of the menstrual cycle, the age of the participants was limited to 8–11 years old. All the subjects in our study were girls, none of them had menstruation, and their LH and FSH concentrations were peak-stimulated serum LH and FSH levels after the LHRH stimulation test.
The limitation regarding our study should also be noted. First, the age range of the sample is narrow. The age of the HPG (+) girls was 8 to 11 years, and that of the HPG (-) girls was 8 to 10 years. However, the onset age of puberty was between 7 and 13 in the girls, and the mean age of menarche was approximately 12 years old (43). Hence, to eliminate the effect of the menstrual cycle, the age of the participants was limited to 8 to 11 years old. Second, the serum concentration of LH, FSH, and E were the total hormone levels, encompass both bound and unbound hormone levels. Its precision to analysis the correlation between the puberty-related hormone and the brain regions used the unbound hormone levels. Thirdly, the current study was unable to detect more robust effects of the reactivation of the HPG axis; as we acknowledged, our results didn’t pass the statistical threshold at recommended corrected significance levels, the findings need for replication given the possibility of false-positive results in larger cohorts.
Despite the limitations, it’s important to emphasize the originality of the results by investigate the association of HPG axis reactivation defined by LHRH stimulation testing with cortical thickness in girls at the stage of premenarche. The current results would be useful to study the cellular mechanisms about how the sexual hormone effect the brain gray matter development, especially in the default mode network and executive control network highly associated with psychoneurosis at puberty.
Conclusion
The present study provides evidence that reactivation of the HPG axis is associated with the development of cortical thickness, especially involving the default mode network and executive control network, in puberty along with hormone-related levels. These findings provide evidence for a better understanding of the relevance of the HPG-axis with brain maturation. Future studies should combine behavioral and functional measurements to illuminate the structure–function-hormone relationship.
Data Availability Statement
The datasets generated for this study are available on request to the corresponding author.
Ethics Statement
The studies involving human participants were reviewed and approved by the ethics committee of the Second Affiliated Hospital and Yuying Children’s of Wenzhou Medical University. Written informed consent to participate in this study was provided by the participants’ legal guardian/next of kin.
Author Contributions
ZY conceived the study and designed the protocol. YF, DY, LX, PL, YZ, LZ, and TC did the experiments. WZ, BT, BY, and XL conducted the statistical analyses. YF, WZ, BT, BY, and ZY interpreted study findings and contributed to developing the manuscript. YF wrote the first draft of the manuscript that was revised by all authors.
Funding
This research was supported by Natural Science Foundation of Zhejiang Province (grant number LY19H180003), Zhejiang Medical Health Science and Technology Program (grant numbers 2017KY108 and 2017ZD024), and Wenzhou Municipal Sci-Tech Bureau (grant numbers Y20150291 and Y20190185).
Conflict of Interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
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PsychiatryFrontiers in Psychiatry1664-0640Frontiers Media S.A.32848948PMC743211410.3389/fpsyt.2020.00784PsychiatryOriginal ResearchGray Matter Differences Between Premature Pubertal Girls With and Without the Reactivation of the Hypothalamic—Pituitary-Gonadal AxisFuYuchuan\\n1\\nZhangWenjing\\n2\\nTaoBo\\n2\\nYangBeisheng\\n2\\nYangDi\\n3\\nXieXiaoling\\n1\\nLiuPeining\\n4\\nZhuYaxin\\n1\\nZhouLu\\n1\\nChenTao\\n1\\nLiuXiaozheng\\n1\\nYanZhihan\\n1\\n\\n*\\n\\n1\\nDepartment of Radiology, Second Affiliated Hospital and Yuying Children’s Hospital of Wenzhou Medical University, Wenzhou, China\\n\\n2\\nDepartment of Radiology, West China Hospital of Sichuan University, Chengdu, China\\n\\n3\\nDepartment of Radiology, Zhejiang Hospital, Hangzhou, China\\n\\n4\\nDepartment of Child Health Care, The Second Affiliated Hospital and Yuying Children’s Hospital of Wenzhou Medical University, Wenzhou, China\\n
Edited by: Hesheng Liu, Harvard Medical School, United States
Reviewed by: Ji-Won Chun, Catholic University of Korea, South Korea; Deniz Vatansever, Fudan University, China
This article was submitted to Computational Psychiatry, a section of the journal Frontiers in Psychiatry
118202020201178409120202272020Copyright © 2020 Fu, Zhang, Tao, Yang, Yang, Xie, Liu, Zhu, Zhou, Chen, Liu and Yan2020Fu, Zhang, Tao, Yang, Yang, Xie, Liu, Zhu, Zhou, Chen, Liu and YanThis is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
The onset of puberty and related hormones exerts significant effects on brain morphometric and psychosocial development. The biological mechanisms underlying how the reactivation of the hypothalamic-pituitary-gonadal (HPG) axis and puberty-related hormonal maturation sculpts human brain architecture remain elusive. To address this question, 105 premature pubertal girls (age 8–11 years) without menstruation underwent brain structural scanning on a 3T MR system, and the luteinizing hormone releasing hormone (LHRH) stimulation test was used to identify the reactivation of the HPG axis. Among the 105 girls, 63 were positive for HPG axis reactivation (HPG+), while the others showed negative (HPG-). Cortical thickness was calculated and compared between the two groups after adjusting for age. The brain regions showing inter-group differences were then extracted and correlated with the peak value of serum hormone after the LHRH stimulation test in entire sample. Compared to HPG- girls, HPG+ girls showed reduced cortical thickness mainly in the the right precuneus, right inferior temporal gyrus, and right superior frontal gyrus, while increased cortical thickness primarily in the left superior parietal lobe and right inferior parietal lobe. Linear-regression analysis revealed negative correlations between the cortical thickness of the right inferior parietal lobe with the peak value of FSH and the right precuneus with LH and E. These findings provide evidence to support the notion that the reactivation of HPG axis and changes of hormones during the early phase of hormonal maturation exert influences on the development of gray matter.
cortical thicknessfollicle stimulating hormoneluteinizing hormoneestradiolhypothalamic-pituitary-gonadal axisNatural Science Foundation of Zhejiang Province10.13039/501100004731Introduction
Puberty is a critical period in which dramatic hormone-related changes parallel changes in emotion, behavior, cognition, physical, and brain development (1, 2). Notably, the effect of puberty on psychosocial development in adolescence is believed to vary at different stages, with the early phase of puberty appearing to play a more critical role than those in middle and late adolescence (3). The onset of puberty is often deemed as a trigger of several neuroendocrine-related changes, such as a vast increase in sexual hormone, which occur prior to the physical changes. The released gonadotropin releasing hormone (GnRH) is distributed in the hypothalamus in a pulsatile fashion and initiates a cascade of events that have been recognized as the onset of puberty. Following the secretion of GnRH, the pituitary responds by secreting luteinizing hormone (LH) and follicular stimulating hormone (FSH). These major hormonal changes occur just after the reactivation of the hypothalamic-pituitary-gonadal (HPG) axis, causing gonadal maturation and the production of sex steroids, most notably estrogen (E) and testosterone (TES). The first activation of the HPG axis begins at the prenatal and the early postnatal months. After the first year of birth, the HPG axis lays dormant until the resurrection of the GnRH, which facilitate the onset of puberty (4). The increased secretion of LH from the pituitary gland is the first measurable endocrinological marker of puberty (5). Generally, a serum LH level of ≥5 IU/L using modern ultrasensitive automated chemiluminescence assays after the luteinizing hormone releasing hormone (LHRH) stimulation test from patient samples (either 30 or 60 min) can be recognized as being positive for HPG axis reactivation, otherwise being negative for HPG axis reactivation following previously established clinical criteria (6–9). In the occasional child whose LH is not quite as high, they showed the auxological and clinical signs of puberty, a LHRH stimulation test may be required to confirm the diagnosis. However, sexual maturation in the previous research was defined by using biological features or clinical features [i.e., Tanner stage (10, 11) or the self-report of children (12)]. Therefore, it is necessary to specify the brain regions on which the HPG axis reactivation showed association in early adolescence.
Previous studies have found that brain morphology differs at different stages of puberty. A few studies found that the pubertal stage, as assessed by Tanner staging of sexual maturation, is related to brain morphology. For instance, girls in mid/late puberty had significantly lower right and left cortical gray matter volumes than girls in earlier stages of puberty (13). The gray matter volume of inferior frontal gyrus in adolescents was larger in early pubertal stages compared to that in late puberty (12). The volume in the right hippocampus decreased with increasing Tanner hair stage, while the volume of the right and left amygdala decreased with increasing Tanner breast stage (14). In addition, greater increases in Tanner stage predicted less cortical thickness in the right superior frontal gyrus and right superior temporal gyrus and decreases in the left precuneus surface area in the girls (15). Goddings and her colleagues also demonstrated that the volume of the amygdala and hippocampus increased and the volume of nucleus accumbens, caudate, putamen, and globus pallidus decreased across puberty as measured by Tanner stage (16). While in a large group of 9-year-old twins, including males, the children that showed signs of physical maturation (Tanner stage ≥ 2) was associated with decreased parietal gray matter densities compared to the children without any secondary pubertal signs (Tanner stage ≤ 1) (17). However, how brain development is regulated by pubertal changes, especially at the early stage premenarche, remains poorly understood (14). Previous studies either in animals or humans found that puberty-related hormones were associated with the development of the gray matter in brain. In animal models, puberty-related hormonal changes have been shown to induce changes in brain maturation, including structural reorganization and neuronal circuit pruning, with these effects starting at puberty onset and continuing through adolescence (18–20). Several studies in humans have been conducted to examine the associations between changes in serum hormones and brain structure during adolescence, and the findings were inconsistent. One study examined the influence of sex steroids on structural brain maturation and found that there were no effects of sex steroids on cortical thickness in adolescents aged 8–25 years (12). One study demonstrated that higher TES levels were associated with thinner left calcarine sulcus and right lingual gyrus cortex in girls (21). Another study demonstrated that higher E levels, but not TES, were associated with smaller gray matter density in prefrontal, parietal, and middle temporal areas and greater gray matter density in the middle frontal, inferior temporal, and middle occipital gyri in girls between 10 and 15 years of age (22). Another two studies also showed that changes in the TES and E levels were associated with white matter, gray matter, right amygdala, and bilateral caudate volumes, cortical thickness and surface area (15, 23). Higher FSH levels have also been associated with greater gray matter density in left prefrontal areas, hippocampus, anterior cingulate, precuneus, and right cerebellum among girls aged 9 to 12 years (24). Previous studies did not find an association between LH and gray matter (12, 24, 25); however, a trend for negative association was been suggested between the gray matter density of temporal areas and the LH in the 9-year-old twins (25). Nonetheless, these studies indicated that puberty-related hormones exerted positive or/and negative influences on gray matter structural development and played different roles in brain areas. Due to variable findings in previous studies and the importance of studying gonadotropin concentration at an early stage of hormonal maturation, more research is needed to identify hormone-related influences on brain organization during the initiation of puberty.
To achieve this aim, we enrolled girls aged 8–11 years with thelarche as participants whose bone age and uterine volume were advanced than their chronologic age, seeking to identify the influences of the HPG axis reactivation on brain organization during the initiation of puberty. We conducted high-resolution structural MRI brain scanning in young girls who underwent testing for HPG axis reactivation determined by stimulated LH concentration. We used HPG+ to represent being positive for HPG axis reactivation and HPG- for being negative for HPG axis reactivation. We hypothesized that the girls whose HPG axis reactivation was positive (HPG+) would show cortical thickness differences compared to the girls whose HPG axis reactivation was negative (HPG-), and there were associations between the cortical thickness of several regions showing differences between the two groups and the peak value of puberty-related hormone after the LHRH stimulation test in the entire sample.
MethodsSubjects
This study was approved by the ethics committee of the Second Affiliated Hospital and Yuying Children’s of Wenzhou Medical University. The LHRH stimulation test is an invasive procedure; it needs the intravenous injection of the medication and the collection of the blood sample three times in the test. Therefore, it is difficult to obtain the consent of the guardians to allow healthy girls to take it. We chose to look at these younger girls with thelarche whose bone age and uterine volume were advanced than their chronologic age as study participants. The positive for HPG axis reactivation was diagnosed premature puberty. The girls with premature puberty were recommended to non-clinical intervention such as exercise more and pay attention to their diets and so on and clinical following-up by doctor (6). Their parents also wanted to know their children’s height development thereafter. Because of the HPG-axis reactivation, the process is followed by the activation of the growth axis leading to growth spurts in puberty.
All the subjects provided written informed consent from the guardians before participation. One hundred five right-handed girls aged 8–11 years were recruited via the Child Healthcare Department of the Second Affiliated Hospital and Yuying Children’s of Wenzhou Medical University. The exclusion criteria were as follows: (1) intelligence quotient (IQ) < 70 estimated by the Chinese Wechsler Intelligence Scale for Children (C-WISC) (17), (2) history of premature birth or postterm birth, (3) history of neurological or psychiatric disorders in the participants or their first-degree relatives, (4) history of head trauma, (5) have any systemic physical illness, (6) any medications known to affect brain function or hormone levels, (7) menstruation, and (8) contraindications to MRI scanning. Finally, 105 girls were recruited: 63 were in the HPG+ group, and the others were in the HPG group. Details of the demographics of all subjects are shown in \\nTable 1\\n.
Demographic and clinical characteristics of girls.
Characteristic
HPG+ (n = 63)
HPG- (n = 42)
\\np\\n
Mean
SD
Mean
SD
Age (years)
9.13
0.57
8.92
0.36
0.064
BMI (kg/m2)
17.08
2.16
16.21
1.66
0.071
LH(IU/L)
LH max
21.09
15.60
3.36
0.86
<0.000
LH baseline
1.04
1.197
0.117
0.065
<0.000
FSH(IU/L)
FSH max
16.59
6.53
12.94
5.13
<0.001
FSH baseline
4.67
5.06
2.18
0.88
0.0022
E2(pg/ml)
E2max
39.44
15.43
32.45
14.04
<0.010
E2 baseline
37.84
16.17
31.25
14.25
0.043
CBCL
Total score
10.52
10.64
12
10.51
0.156
IQ
91.11
14.50
89.79
12.05
0.12
SD, standard deviation; BMI, body mass index (BMI, calculated as Weight/Height2); HPG+/HPG-, with/without reactivated the HPG-axis; LH, luteinizing hormone; FSH, follicle stimulating hormone; E2, estradiol; TES, testosterone; CBCL, Child Behavior Checklist; IQ, intelligence quotient. p < 0.05 was considered statistically significant in all the tests.
Assessment Scales and Pubertal Assessment
Intelligence tests were performed by means of Wechsler Young Children Scales of Intelligence (C-WISC) in all the subjects to screen for mental retardation before the MRI scanning. Then, assisted by a child care physician, the primary caretaker of each child completed the child behavior checklist (CBCL) (26), which is a self-report questionnaire used to assess the behavioral and emotional problems of the children. The details are shown in \\nTable 1\\n. Tanner staging was used to examine pubertal status. Mean ages and the standard deviations, the frequencies for Tanner breast stage and hair stage are showed in \\nTable 2\\n.
Tanner stage.
Tanner stage
Tanner breast (mean age ± SD; frequency)
Tanner hair (mean age ± SD; frequency)
HPG+
HPG-
HPG+
HPG-
1
0
0
9.13 ± 0.5763
8.92 ± 0.3642
2
9.13 ± 0.5763
8.92 ± 0.3642
0
0
Mean age, standard deviations (in years), and frequencies for Tanner breast and hair stages.
Tanner breast 1: preadolescent, 2: appearance of the breast bud. Tanner hair 1: no public hair.
LHRH Stimulation Test
All subjects received the LHRH stimulation test after the imaging data acquisition. Both exams were completed within a week. Following overnight fasting, the participants were told to arrive at the hospital at approximately 8:00 am. Four to five milliliters of blood were collected (0-min sample), and then the LHRH was injected as an intravenous bolus of 2.5 μg/kg (maximum dose < 100 μg) (27) via an indwelling catheter. At 30 and 60 min after injection, 2 ml of blood were drawn again.
The bloods samples from 0 min were sent immediately to the clinical laboratory for analysis, and the 30- and 60-min samples after the LHRH injection were sent immediately to analyze respectively. The samples were centrifuged, separated, and assayed. We assayed LH, FSH, and estradiol (E) using modern ultrasensitive automated chemiluminescence assays, and the lower limit of detection was 0.07 IU/L for LH, 0.3 IU/L for FSH, 10.7 pg/ml for E, and 0.1 ng/ml for TES separately. Intra-assay coefficient of variation (CV) was 3.72 and 3% at 31.98 and 63.02 IU/L for LH, 5.08 and 3.97% at 36.1 and 83.65 IU/L for FSH, 5.55 and 4.61% at 265.22 and 552.67 pg/ml for E, respectively. Inter-assay CV was 5.13 and 4.63% at 30.44 and 59.96 IU/L for LH, 5.58 and 5.07% at 36.68 and 83.91 IU/L for FSH, and 6.08 and 3.81% at 260.37 and 547.73 pg/ml for E, respectively. A serum LH level of ≥5 IU/L after the LHRH stimulation test from patient samples (either 30 or 60 min) is considered evidence of the positive for HPG-axis reactivation otherwise negative for HPG axis reactivation following previously established criteria (6–9). We used HPG+ to represent being positive for HPG axis reactivation and HPG- for being negative for HPG axis reactivation. The peak value of serum puberty-related hormone after the LHRH stimulation test (either 30min or 60min) was used for linear regression analysis. Details of the hormone levels are shown in \\nTable 1\\n.
Structural Data Acquisition
The participants were scanned on a 3T GE HDxt scanner (General Electric, Milwaukee, Wisconsin) with an eight-channel phase array head coil. High-resolution T1-weighted images were acquired axially for the whole brain with a volumetric three-dimensional fast spoiled gradient recall (FSPGR) sequence. The scan parameters were as follows: TE/TR = 8.88 ms/4.02 ms, flip angle = 15°, FOV = 256 × 256, voxel size = 1 mm × 1 mm × 1 mm, and 160 slices with no gap. T2 FLAIR and T2-weighted images were acquired to screen the brain abnormalities by two experienced neuroradiologists. The quality of all the images was also controlled by them.
Image Processing
The FreeSurfer package (version 5.30, http://surfer.nmr.mgh.harvard.edu/) was used for preprocessing of cortical thickness in high-resolution T1-weighted images. This method has high test-retest reliability (28, 29). The FreeSurfer pipeline performs motion correction on T1-images. The post-processing procedures have been explained in detail in previous studies (30, 31) and contained the following main steps: bias-field correction, Talairach and Tournoux atlas conversion, signal strength standardization, skull-stripped, automatically delineated gray/white matter boundaries, and the pial matter topology correction. Cortical thickness was calculated as the shortest distance between the white matter interface and the pial matter at each vertex across the cortical boundaries (31). These post-processing procedures were performed separately on each cerebral hemisphere.
Statistical Analysis
Differences between HPG (+) and HPG (-) girls on demographic characteristics (e.g., age, BMI, CBCL total score, and IQ) and the hormone concentrations were examined using the independent sample Mann-Whitney U test.
The vertex-wise comparison of the cortical maps was performed on QDEC (Query, Design, Estimate, Contrast) with age as a covariate, and a whole-brain statistical threshold correction was performed using the Monte Carlo simulation method. Statistical significance was set at a cluster-wise–corrected P value < 0.05. A smoothing step using a Gaussian filter with 10-mm full-width at half maximum was initiated to average cortical thickness data across participants in a common spherical coordinate system.
Brain areas with statistical inter-group differences in cortical thickness were selected as brain region of interest (ROI). The average cortical thickness data of the ROI were extracted from the clusters. Correlation analysis between the regional cortical thickness values and stimulated hormone levels was assessed by using a general linear model and age as nuisance variables in the entire sample. Statistical significance was set at alpha < 0.05.
ResultsDemographic Statistics
No statistically significant differences were found in the age (p = 0.064, >0.05), BMI (p = 0.071, >0.05), CBCL total score (p = 0.156, >0.05), IQ (p = 0.12, >0.05), and Tanner breast stage and hair stage between the HPG+ girls and HPG- girls. There were statistically significant differences in the levels of LH (p < 0.0001, <0.05), FSH (p = 0.0030, <0.05), and E (p = 0.0204, <0.05) between the two groups (\\nTable 1\\n).
Group Differences in Cortical Thickness
Compared to HPG(-) girls, it showed difference in cortical thickness of 10 brain regions in HPG(+) girls, less cortical thickness primarily in the right precuneus (p = 0.002, <0.05), right inferior temporal gyrus (p = 0.006, <0.05), right superior frontal gyrus (p = 0.044, <0.05) and in the left rostral middle frontal gyrus (p = 0.028, <0.05), and higher cortical thickness in the right temporal pole (p = 0.006, <0.05), right inferior parietal lobe (p = 0.003, <0.05), right rostral middle frontal gyrus (p = 0.04, <0.05) and in the left superior parietal lobe (p = 0.001, <0.05), lateral occipital gyrus (p = 0.009, <0.05), and superior temporal gyrus (p = 0.01, <0.05) (\\nFigure 1\\n and \\nTable 3\\n)
Differences in cortical thickness between the HPG+ girls and the HPG- girls. Decreased cortical thickness colored by blue and increased cortical thickness colored by red. HPG+ girls show decreased cortical thickness in the left rostral middle frontal gyrus, right precuneus, right inferior temporal gyrus, right superior frontal gyrus and increased cortical thickness in the right temporal pole, right inferior parietal lobe, right rostral middle frontal gyrusin, left superior parietal lobe, left lateral occipital gyrus, left superior temporal gyrus compared to the HPG- girls. Threshold at p < 0.05.
Brain regions with significantly differences in cortical thickness between HPG+ and HPG-.
\\nBrain region HPG+ and HPG-\\n
\\nCluster size (mm2)\\n
\\nMNI coordinates\\n
\\nP\\n
X
Y
Z
\\nLess\\n
\\nPrecuneus-R\\n
116.79
5.8
-65.1
34.3
0.002
\\nITG-R\\n
70.18
48
-43.9
-16.3
0.006
\\nSFG-R\\n
23.91
10.2
0.8
51.7
0.044
\\nrMFG-L\\n
24.30
-28.2
32.0
30.7
0.028
\\nHigher\\n
\\nTemporalpole-R\\n
105.91
33.1
14.3
-35.5
0.006
\\nIPL-R\\n
53.85
47.1
-54.4
44.6
0.003
\\nrMFG-R\\n
30.43
19.0
55.8
-13.9
0.04
\\nSPL-L\\n
61.69
-21.4
-51.6
62.1
0.001
\\nLOG-L\\n
184.07
-35.4
-82.5
-14.9
0.009
\\nSTG-L\\n
55.43
-57.7
-43.2
15.8
0.01
Less, cortical thickness less in the HPG+ than in the HPG-; higher, cortical thickness higher in the HPG+ than in the HPG-; ITG-R, right inferior temporal gyrus; SFG-R, right superior frontal gyrus; rMFG-L, left rostral middle frontal gyrus; IPL-R, right inferior parietal lobe; rMFG-R, right rostral middle frontal gyrus; SPL-L, left superior parietal lobe; LOG-L, left lateral occipital gyrus; STG-L, left superior temporal gyrus.
Associations Between Hormone Levels and Cortical Thickness in the Entire Sample
We conducted a linear regression analysis to detect hormone-related variation in the cortical thickness of the differential brain region (10 ROIs) in the two groups. The results showed that the peak value of FSH after the LHRH stimulation test was negatively correlated to cortical thickness in the right inferior parietal lobe (r = -0.2630, p = 0.0067, <0.05), the peak value of LH and E were negatively correlation with cortical thickness in the right precuneus (r = -0.2043, p = 0.0365, <0.05; r = -0.2189, p = 0.0249, <0.05) (\\nFigure 2\\n). After the FDR correction, the results showed that the peak values of FSH after the LHRH stimulation test were negatively correlated to cortical thickness in the right inferior parietal lobe (p = 0.0403, <0.05).
Associations between hormone levels and cortical thickness. (A) Negative correlation between the LH level and the cortical thickness in the right precuneus. (B) Negative correlation between the E levels and the cortical thickness in the right precuneus. (C) Negative correlation between the FSH levels and the cortical thickness in the right inferior parietal lobe.
Discussion
To our knowledge, this study is the first to investigate the impact of HPG axis reactivation defined by LHRH stimulation testing on cortical thickness. Our main findings were (1) HPG+ girls who showed reduced cortical thickness mainly in the right precuneus, right inferior temporal gyrus, right superior frontal gyrus, and the left rostral middle frontal gyrus while increased cortical thickness primarily in the left superior parietal lobe and right inferior parietal lobe, the right temporal pole, right rostral middle frontal gyrus, left lateral occipital gyrus, and left superior temporal gyrus. (2) The correlation analysis revealed negative correlations between the peak value of LH and E after LHRH stimulation test and cortical thickness in the right precuneus, the peak value of FSH, and the right inferior parietal lobe in the two groups. Our results suggest that the reactivation of the HPG axis and related hormonal maturation may be involved in sculpting human brain cortical architecture.
One of the main interesting findings is the bidirectional changes of cortical thickness. In fact, previous studies indicated changes in the gray matter during different stage of puberty. For example, Herting et al. reported greater increases in Tanner stage predicted decreased cortical thickness including the right superior frontal gyrus and right superior temporal gyrus in puberty girls (15). While in a large group of 9-year-old twins, including males, the children that showed any signs of physical maturation (Tanner stage ≥ 2) was associated with decreased parietal gray matter densities compared to the children without any secondary pubertal signs (Tanner stage ≤ 1) (17). No studies have examined the difference in cortical thickness between prepuberty girls and girls of the same age at the onset of puberty. Our findings suggested that the reactivation of the HPG axis played a different role in the cortical thickness. The reactivation of the HPG axis drives dramatic changes in physical appearance, such as facial structure and the appearance of secondary sexual characteristics, and adolescents enter a stage of profound psychological transition in the onset of puberty, which may negatively or positively affect brain maturation. To reach a stable adult role, there are changes in the structure and function of the brain. Furthermore, multiple studies showed the receptors for HPG axis hormones are found in various brain cortical especially for learning and memory (32, 33). Most brain areas in our result were involved in learning and/or memory function. The reactivation of the HPG axis caused a cascade secretion of hormone, which attached to the receptors in the brain cortical. It might be one mechanism that the reactivation of the HPG axis sculpts the brain cortical thickness in our result.
Another important result demonstrates the correlations between the gray matter changes and the level of hormones. Sex steroids are known to affect the brain in two different ways: organizational effects and activational effects. The organizational effects of sex hormones were played by permanently changing the structure of the brain, such as neuronal number, myelination, and dendritic branching (34). Previous animal studies found that E levels could inhibit cortical myelination during puberty (35), and it is also involved in apoptotic processes (36). Another study found that estradiol altered dendritic spine density and dendritic spines in brain regions related to cognition and memory were sensitive to estrogen fluctuations (37). The precuneus was deemed to play a central role in a wide spectrum of highly integrated tasks, including self-centered mental imagery strategies, consciousness, and episodic memory retrieval (38). Yates and Juraska found that puberty ovarian hormone exposure reduced the number of myelinated axons in the splenium of corpus callosum in the rats (18). Koss and the colleagues also found that the number of neurons and glia were increased in the medial prefrontal cortex of female gonadectomy before puberty rats (20). Therefore, we hypothesize that higher estradiol levels might contribute to changes in the cortical thickness of the precuneus by decreasing the cortical number of neurons and/or myelinated axons.
Previous animal and human studies have shown that LH and FSH receptors have been found in various brain areas including the parietal cortex (39, 40). However, how circulating LH and FSH levels exert their effects on cerebral gray matter remains unclear. Bukovsky and colleagues speculated that microglial cells (among cells of the mononuclear phagocyte system) and some nerve cells in the gray matter of the human brain cortex might be influenced by LH. High LH levels might also activate resident macrophages to remove some nerve cells in which the expression of LH receptors was weaker to change gray matter (40). More studies are needed to further investigate the mechanisms. With regard to the relationship between the FSH level and gray matter, previous study showed that the gray matter volume of the right inferior parietal gyrus was decreased in perimenopausal women than premenopausal women (41). As we all know, the serum FSH level in perimenopausal women was obviously increased than in premenopausal. Another study found that the genetic variant for an FSH receptor gene strongly affected the onset of puberty in girls (42).
However, in contrast to our study, previous study of adolescence with an age of 8–25 years reported no effects of sex steroids on cortical thickness (12) and no association between the LH concentrations and gray matter (12, 24, 25). The possible explanations might be that (1) the hormonal level was determined in morning saliva or urine samples in previous studies and (2) the age of the sample. In that study the subjects were between 8 and 25 years. (3) The period the participants enrolled in [before or after hormonal maturation; middle or late puberty (23)] and the handling of confounding factors [stratification by sex or time in menstrual cycle (24)]. However, the onset age of puberty was between 7 and 13 in the girls, and the mean age of menarche was approximately 12 years old (43). To eliminate the effect of the menstrual cycle, the age of the participants was limited to 8–11 years old. All the subjects in our study were girls, none of them had menstruation, and their LH and FSH concentrations were peak-stimulated serum LH and FSH levels after the LHRH stimulation test.
The limitation regarding our study should also be noted. First, the age range of the sample is narrow. The age of the HPG (+) girls was 8 to 11 years, and that of the HPG (-) girls was 8 to 10 years. However, the onset age of puberty was between 7 and 13 in the girls, and the mean age of menarche was approximately 12 years old (43). Hence, to eliminate the effect of the menstrual cycle, the age of the participants was limited to 8 to 11 years old. Second, the serum concentration of LH, FSH, and E were the total hormone levels, encompass both bound and unbound hormone levels. Its precision to analysis the correlation between the puberty-related hormone and the brain regions used the unbound hormone levels. Thirdly, the current study was unable to detect more robust effects of the reactivation of the HPG axis; as we acknowledged, our results didn’t pass the statistical threshold at recommended corrected significance levels, the findings need for replication given the possibility of false-positive results in larger cohorts.
Despite the limitations, it’s important to emphasize the originality of the results by investigate the association of HPG axis reactivation defined by LHRH stimulation testing with cortical thickness in girls at the stage of premenarche. The current results would be useful to study the cellular mechanisms about how the sexual hormone effect the brain gray matter development, especially in the default mode network and executive control network highly associated with psychoneurosis at puberty.
Conclusion
The present study provides evidence that reactivation of the HPG axis is associated with the development of cortical thickness, especially involving the default mode network and executive control network, in puberty along with hormone-related levels. These findings provide evidence for a better understanding of the relevance of the HPG-axis with brain maturation. Future studies should combine behavioral and functional measurements to illuminate the structure–function-hormone relationship.
Data Availability Statement
The datasets generated for this study are available on request to the corresponding author.
Ethics Statement
The studies involving human participants were reviewed and approved by the ethics committee of the Second Affiliated Hospital and Yuying Children’s of Wenzhou Medical University. Written informed consent to participate in this study was provided by the participants’ legal guardian/next of kin.
Author Contributions
ZY conceived the study and designed the protocol. YF, DY, LX, PL, YZ, LZ, and TC did the experiments. WZ, BT, BY, and XL conducted the statistical analyses. YF, WZ, BT, BY, and ZY interpreted study findings and contributed to developing the manuscript. YF wrote the first draft of the manuscript that was revised by all authors.
Funding
This research was supported by Natural Science Foundation of Zhejiang Province (grant number LY19H180003), Zhejiang Medical Health Science and Technology Program (grant numbers 2017KY108 and 2017ZD024), and Wenzhou Municipal Sci-Tech Bureau (grant numbers Y20150291 and Y20190185).
Conflict of Interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
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Primary Care (2014) 41(3):465–87.  10.1016/j.pop.2014.05.002\\n\\n\"\n]"}}},{"rowIdx":458,"cells":{"data":{"kind":"list like","value":["\nInt J Mol SciInt J Mol SciijmsInternational Journal of Molecular Sciences1422-0067MDPI32718086PMC743211510.3390/ijms21155238ijms-21-05238ReviewInterleukin-6 in Rheumatoid ArthritisPandolfiFranco1*†https://orcid.org/0000-0001-8638-7565FranzaLaura2†CarusiValentina1AltamuraSimona1AndriolloGloria1NuceraEleonora1Allergy and Clinical Immunology, Fondazione Policlinico Universitario A, Gemelli IRCCS, Catholic University, 00168 Rome, Italy; valentina.carusi@libero.it (V.C.); simona.altamura@guest.policlinicogemelli.it (S.A.); gloria.andriollo@libero.it (G.A.); eleonora.nucera@unicatt.it (E.N.)Emergency Medicine, Fondazione Policlinico Universitario A, Gemelli IRCCS, Università Cattolica del Sacro Cuore, 00168 Rome, Italy; cliodnaghfranza@gmail.comCorrespondence: franco.pandolfi@unicatt.it
These authors contributed equally to this work.
2372020820202115523802720202072020© 2020 by the authors.2020Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
The role of interleukin (IL)-6 in health and disease has been under a lot of scrutiny in recent years, particularly during the recent COVID-19 pandemic. The inflammatory pathways in which IL-6 is involved are also partly responsible of the development and progression of rheumatoid arthritis (RA), opening interesting perspectives in terms of therapy. Anti-IL-6 drugs are being used with variable degrees of success in other diseases and are being tested in RA. Results have been encouraging, particularly when anti-IL-6 has been used with other drugs, such as metothrexate (MTX). In this review we discuss the main immunologic aspects that make anti-IL-6 a good candidate in RA, but despite the main therapeutic options available to target IL-6, no gold standard treatment has been established so far.
IL-6rheumatoid arthritisinflammation1. Interleukin-6 in Health and Disease
Interleukin (IL)-6 is a prototypical cytokine, featuring pleiotropic and redundant functional activity. IL-6 belongs to a family of cytokines, which includes IL-6, IL-11, IL-27, IL-31, oncostatin M (OSM), leukaemia inhibitory factor (LIF), ciliary neurotrophic factor (CNTF), cardiotrophin 1 (CT-1), and cardiotrophin-like cytokine factor 1 (CLCF1); all these cytokines use the common IL-6 signal transducer gp130 [1]. IL-6 production can be induced by both infection and other types of inflammation. Indeed, IL-6 is promptly produced, mainly by macrophages, in response to pathogens or inflammation-related damage-associated molecular patterns [2] and performs a protective function by removing infectious agents and healing damaged tissue via induction of acute phase and immune responses. IL-6 is crucial for both innate and adaptive immunity. Several cell types produce IL-6, including monocytes, T-lymphocytes, fibroblasts, and endothelial cells, and production is strongly enhanced at sites of inflammation [3].
In infections, toll-like receptors (TLRs) directly recognize the bacteria, virus, or fungi and can directly or indirectly, depending on the type of stimuli, induce the production of IL-6 and other inflammatory cytokines, such as IL-1 or tumor necrosis factor α (TNFα), through the nuclear factor-kappa B (NF-kB) signaling pathway. Interestingly, the production of IL-1 and TNFα also stimulate the production of IL-6 [4]. Overall, dysregulated IL-6 production leads to persistent inflammation [5].
IL-6 demonstrates its biological activities only by binding to its specific receptor, IL-6R. Neither IL-6 nor IL-6R have affinity for gp130 (also known as CD130). However, as a complex, IL-6 and IL-6R can bind to and activate the IL-6Rβ-subunit, gp130, leading to its dimerization and intracellular signaling through Janus kinase (JAKs: JAK1, JAK2, and TYK2), in particular, signal transducer and activator of transcription (STAT)1 and 3. STAT1 and 3 are phosphorylated and translocated to the nucleus and induce the transcription of target genes in the nucleus, while SH2 domain-containing protein-tyrosine phosphatase 2 (SHP-2) activates the Ras-MAP kinase pathway [6]. While these molecules activate the transcription process, they also start a negative feedback signaling pathway, which suppresses cytokine signaling 1 (SOCS1) and SOCS3 proteins [4].
IL-6 was thought in the past to target only the liver, since hepatocytes express high IL-6R and gp130, but in recent years it has appeared more and more clear that IL-6 does not work on a single target organ [7]. IL-6 acts as an acute phase protein, enhancing the inflammatory response of the body. Most of its effects are indeed obtained through the liver, where it is processed and starts an inflammatory cascade, inducing C-reactive protein (CRP), serum amyloid A (SAA), fibrinogen, haptoglobin, and α1-antichymotrypsin. IL-6 also triggers a reduction in the levels of albumin, zinc, and iron through various mechanisms [5].
IL-6 also guides cell differentiation in the bone marrow, stimulating the production of neutrophils and megakariocytes, triggering liver production of thrombopoietin, which stimulates the differentiation of megakaryocytes in platelets, with IL-3 [8]. Indeed, like many other inflammatory agents, IL-6 production is also associated to anemia; its effects on hepatocytes include in-vitro production of hepcidin, not yet confirmed in vivo [9].
The effect of IL-6 on the immune system is particularly interesting; its effects are evident both on innate and acquired immunity. In terms of innate immunity, IL-6 is responsible for the maturation of inflammatory infiltrate, promoting neutrophil migration and mononuclear cell infiltration. It also acts as a chemoceptor for monocytes at the site of inflammation [10,11], while in terms of acquired immunity, IL-6 also expresses its action both on T-cells and on B-cells. On the latter, it has a differentiative effect towards active plasma-cells, increasing levels of serum gamma-globulins. Indeed, these two conditions are both present in Castelman’s disease, which is responsive to anti-IL-6 treatment [12]. During the 1980s, IL-6 was indeed known as B cell stimulating factor 2, yet, the role of IL-6 in the production of gamma-globulins, even though relevant, is not essential, as it is for other inflammatory mediators. Gamma-globulin production is indeed a complex process, in which different inflammatory mediators have redundant functions: IL-2 in particular is associated to immunoglobulin production, while IL-4 and TGF-β work as inhibitory molecules [13]. Interestingly, in other contexts, the effect of IL-6 and TGF-β are synergic.
The effects IL-6 has on T-cells are also quite relevant and associated to various pathologies. IL-6 acts mainly on the differentiation of CD4+ T-cells; in particular, it stimulates the Th17 pathway while inhibiting regulatory-T-cells (T-reg) differentiation. Th17 differentiation is important in fighting inflammation and promotes IL-6 production, further enhancing Th17 differentiation. Cytokines produced by Th17 lymphocytes also include TNFα, IL-1β, IL-17, IL-21, and IL-22, which stimulate inflammation and the fibrotic reaction of the tissue [14]. IL-6 on the other hand inhibits the production of T-reg cells through transforming growth factor β (TGFβ). In the right conditions, IL-6 is indeed an autoinflammatory mediator; not only does it reduce the capacity of the body to distinguish self from not self, it also promotes fibrosis and the inflammatory reaction. The importance of its role in many autoimmune diseases has been demonstrated and targeted [15,16].
2. Rheumatoid Arthritis and Immunologic Pathways
Rheumatoid arthritis (RA) is an autoimmune disease characterized by chronic inflammation affecting the joints and cartilage, which can lead to various degrees of osteoarthritis, and it can cause various degrees of disability. Even though the development and progression of RA is still not completely understood, different therapeutic options are available, and they have completely changed the prognosis of the disease [17]. Disease modifying antirheumatic drugs (DMARD), such as methotrexate (MTX), allow patients to keep the disease under control and lead normal lives. Unfortunately, not all patients respond to these drugs and their quality of life is severely impaired [18]. Factors involved in the development of RA can be roughly distinguished into environmental and genetic, with the former influencing the second
Overall, it is widely accepted that RA occurs in genetically predisposed individuals following the interaction with different environmental factors [19,20]. The presence of T-cell receptor (TCR) restriction at the joints has also been described, suggesting T-cell attack of a joint antigen. It may also prove important in terms of predicting poor response to conventional therapy and severity of disease [21,22].
The common amino acid motif (QKRAA) in the HLA-DRB1 region, commonly known as the shared epitope, confers particular susceptibility [23]. Given the importance in determining T-cell repertoire of this region, the central role of T-cells appears obvious.
Joint damage results from the degradation of connective tissue by tissue-destroying matrix metalloproteinases (MMP) and the stimulation of osteoclastogenesis through the receptor activator of the nuclear factor-kB ligand (RANKL).
Among the environmental factors for RA, smoking is an important one, causing alterations in the oral microbiota, which might trigger autoimmune cross-reactions [24]. Something similar also happens with some infections, such as Epstein-Barr and cytomegalovirus infections: These infections over-stimulate the immune system, favoring suppression of T-regs and proliferation of Th17 lymphocytes [25]. In susceptible individuals, the chronic exposure to citrullinated peptides in conditions of chronic inflammation, as in the above-mentioned infections, is a key step in the pathogenesis of RA [26]. The role of the microbiota also needs to be acknowledged: The microbiota is a well-known regulation agent of inflammation and immunity, as it can act as a pro-inflammatory stimulus or reduce systemic inflammation, also modifying the pattern of cytokine production of the organism [27]. In RA, oral microbiota appears to be particularly significant. Indeed, it stimulates both production of aberrant neutrophils, which appear to play, in general, a central role in RA, and also favors cross-reactivity with proteins expressed in the joints [28]. Lactobacillus salivarius is the most represented bacteria in patients suffering from RA, and molecular mimicry of antigens involved in RA has been described. Interestingly, once patients are treated for RA, their oral microbiota changes and becomes similar to that of the general population [29].
From an immunologic point of view, both the adaptive and the innate immune systems are involved. Neutrophils and monocytes are the most important actors as far as innate immunity is concerned in the pathogenesis of RA, as they directly influence the interaction between toll-like-receptors (TLR) and other signaling molecules. In particular, myeloid-related protein (MRP) 8/14 further activates monocytes and creates an inflammatory environment [30]. The role of macrophages has also been acknowledged: They are capable of stimulating TNF and IL-1β production and they activate a wide number of immune effectors. They are also important in terms of clinical presentation, as some of the enzymes they produce are directly involved in cartilage deterioration [31]. As for the adaptive immunity, both B-lymphocytes and T-lymphocytes are involved.
The importance of B-cells in RA has been questioned in the past, but has been confirmed by the efficacy of treating affected patients with rituximab, an anti-CD20 antibody [32]. Plasma cells, which produce antibodies, do not express CD20, proving that the role of B-cells goes beyond autoimmune antibody production. Interestingly, activated CD4+ T cells also stimulate B cells to produce immunoglobulins.
TH17 in RA
The Th1 pathway is traditionally considered the most important in RA, while Th17 CD4 lymphocytes have only recently been acknowledged as important mediators in the pathogenesis of RA [14]. Precursor Th17 lineage cells expressing CD161 or lectin-like transcript 1 (LLT1) accumulate in patients suffering from RA, playing a central role in the pathogenesis [33]. IL-17 production is central in stimulating synovial fibroblasts and it also stimulates osteoclastogenesis in a RANKL-dependent and RANKL-independent fashion: In the first pathway, it acts directly to produce osteolysis. IL-17 also induces osteoblasts to produce RANKL, which act in synergy with TNF-α, resulting in increased osteoclastogenesis. Th-17 also produces IL-21 and 22 and TNF-α, in addition to IL-17, potentially inducing a vicious circle of inflammation [34]. The activation of all these mechanisms seems to be determined at least in part by IL-6, which also determines a reduction in the number of T-regs. Interestingly, IL-6 is further stimulated by IL-17, further maintaining inflammation. The imbalance between the inflammatory pathway and the suppressive one is, indeed, central in the pathogenesis of RA: T-regs in patients with RA appear not only reduced in quantity, but also in quality [35].
In RA, the signaling pathway of the TLRs and the expression of inflammatory cytokines (e.g., IL-1, TNFα, and IL-17) work as IL-6 promoter-activators, as do other factors [36]. Once at the joint, IL-6 has a crucial role in the inflammatory process, in osteoclast-mediated bone resorption and in pannus development; these processes help the development of IgM and IgG rheumatoid factors along with antibodies to citrullinated peptides, which are characteristically increased in RA [37]. At a joint level, IL-6 works on many different paths; its importance has been evidenced in obese patients suffering from osteoarthritis. Obesity is linked to high levels of inflammatory proteins, such as IL-6, in the blood, but also, for instance in the synovial liquid. At this level, inflammatory proteins favor the production of MMPs, particularly, MMP-1, MMP-3, MMP-13, and aggrecanase 1 and 2 (ADAMTS-4, ADAMTS-5) [38]. As previously discussed, MMP activation is also key in the progression of RA.
Other immunologic pathways are also involved in the development of RA and can eventually lead to IL-6 overexpression. For instance, the nuclear factor kappa-light-chain-enhancer of activated B cells (NF-kB) pathway is a key inflammatory mediator in RA, determining an increase in TNFα, which in turn increases IL-6 levels.
IL-6 might also be involved in a more subtle way in the development of RA: It has been observed that IL-6 is involved in cytokine release syndrome complicated by T-cell therapy. Blocking IL-6 in these cases gave good results, proving the central part it plays in inflammatory syndromes [39,40]. IL-6 seems to be responsible for other systemic symptoms associated with RA, in particular in the nervous and cardiovascular systems.
Overall, in patients with RA, elevated serum levels of both IL-6 and IL-6R are found in serum and synovial fluid of affected joints [41].
Other important cytokines that are currently being studied as possible therapeutic targets are IL-17 and granulocyte-macrophage colony-stimulating factor (GM-CSF) [42], further demonstrating the interaction between both innate and adaptive immunity.
Even though RA is usually thought of as a disease affecting mostly the joints, patients who suffer from this disease often face a number of comorbidities and are at a higher risk of developing certain types of disease: Cardiovascular disease is particularly common in this population, for instance, and this seems to be directly affected by IL-6 [43]. A similar situation has been described for psychiatric disorders. Patients suffering from chronic conditions, autoimmune diseases in particular, are known to be at a higher risk of developing a broad range of psychiatric disorders, particularly depression. In this group of patients, those with higher levels of IL-6 and CRP seem to be at a higher risk of developing psychiatric comorbidities [44]. Thus, targeting IL-6 might improve overall health in patients suffering from RA.
Overall, inflammatory cytokines, especially TNF-α, and two interleukins, IL-1β and IL-6, are key in driving inflammation and joint damage. Cytokines such as IL-23, IL-17A, as discussed above, and interferon gamma (IFN-γ) also play crucial roles in the pathogenesis of RA. IL-4 and IL-10, on the other hand, have been suggested to improve arthritis [45]. While there is overall consensus on the inflammatory role of IL-6 in the pathogenesis of RA, there are some studies pointing out that its role is not clear-cut: It has emerged that IL-1 signaling reduces IL-6 signaling in RA, overall worsening patients’ conditions. The possibility of acting directly on IL-1 is thus interesting; at the moment, the most interesting results have been found for patients suffering from other chronic inflammatory diseases, such as type-2 diabetes, in which patients whose IL-1 pathway is blocked may have benefits both in RA and diabetes control [46]. IL-6 acts differently on different cell types, and it can even inhibit fibrosis, but when in a crosstalk with IL-1, its inflammatory characteristics seem to overtake its anti-inflammatory role [47]. Yet, this hypothesis is based primarily on in vitro studies and needs to be confirmed further.
3. Interleukin-6 in Rheumatoid Arthritis and Other Diseases
The IL-6 pathway is involved in different inflammatory diseases and could be a potential target in a vast array of clinical conditions in different branches of medicine [48] spanning RA, cytokine released syndrome in CART cells [49], or COVID-19 infection [50,51].
IL-6 blockade has been successfully used in Castelman’s disease, a lymphoproliferative disorder with a wide spectrum of manifestations, and juvenile idiopathic arthritis (JIA). In some countries it has also been used with various degrees of success in other rheumatologic diseases, such as giant cell arteritis (GCA), Takayasu arteritis (TA), and cytokine releasing syndrome (CRS).
In the past few months, IL-6 has gained visibility because of its effects on COVID-19 infection. COVID-19 is a disease caused by a novel coronavirus and it can cause a vast array of symptoms, ranging from mild, flu-like symptoms to severe respiratory failure [52]. Many different therapeutic approaches have been tried, mostly anti-inflammatory therapies, ranging from steroids to antimalaria drugs; even non-conventional therapies, such as ozone-therapy, have been tested [53]. The most severe symptoms seem to be caused, at least in part, by an autoinflammatory reaction, with characteristics of a cytokine storm [52]: Binding of the novel coronavirus to TLRs causes an increase in the levels of IL-1β, which mediate fibrosis and inflammation of the lung [50]. The role of IL-6 in promoting fibrosis has been studied in other pathologies, particularly in the liver [54]. Blocking IL-6 in this context has proven useful: Patients have been treated with tocilizumab (TCZ), a monoclonal antibody against IL-6R, and results have been encouraging [51]. The role played by IL-6 in COVID-19-related inflammation is confirmed by the fact that patients with higher circulating levels of IL-6 and other inflammatory cytokines, such as persons suffering from Down syndrome, are at a higher risk of developing more severe forms of COVID-19 infection [55].
In the treatment of RA, IL-6 blockade has proven to be very useful for those patients who do not respond to conventional therapy or even less standard ones. For instance, TNF-α had been a promising target in patients with severe RA, but up to two thirds of patients do not have a full response; in this group, targeting IL-6 is not only useful, but even improves the overall response to therapy of these patients [56].
An increase in serum IL-6 and IL-21 levels is associated with markers of B cell activation, and IL-6 is associated with radiographic progression in patients with RA [57]. Based on studies emphasizing the critical role of IL-6 in development and progression of RA, targeting IL-6 has been proposed as a tool to add to the armamentarium to treat RA. Targeting IL-6 can be achieved by either direct targeting of the cytokine or targeting of its receptor. Targeting the receptor (instead of the cytokine itself) may have the advantage of blocking other cytokines of the IL-6 family.
4. Targeting Interleukin-6: Available Options
As discussed above, targeting IL-6 has proven effective in many contexts. Drugs targeting IL-6 can be roughly divided into direct inhibitors and IL-6 receptor inhibitors. The latter offer an advantage, as they have the capacity to also block other interleukins of the same family, such as IL-1, which also play an important role in the pathogenesis of RA [58].
Different types of IL-6 inhibitors will be discussed in the following paragraphs.
TCZ was introduced in 2008 and reduced disease activity in RA with inadequate response to DMARD [59].
In the last few years, several trials in over 23,700 patients have confirmed the efficacy and safety of TCZ (alone or in combination with DMARD) in RA [36], demonstrating the higher efficacy of a combination of DMARDs than MTX on its own.
The randomized OPTION study on 622 patients proved that the use of TCZ reduces signs and symptoms of RA, proving that the inhibition of IL-6 signaling is effective in moderate-to-severe RA [60].
In the TAMARA trial, 286 patients with moderate to severe RA were treated with TCZ and it was concluded that TCZ is effective in improving patients’ condition, the most solid results being seen at about 24 weeks [61].
Different routes of administration of TCZ have also been studied, demonstrating that the sub-cutaneous (SC) route is also safe [59,62,63,64,65,66,67]. Extensive experience has firmly established the short- and long-term efficacy of both intravenous and SC TCZ in adults with moderate-to-severe RA who failed to respond to therapy with synthetic or biologic DMARDs (review in [62]). In one of the first pivotal studies (SAMURAI), it was shown that in 306 patients with active RA, TCZ monotherapy was generally well tolerated and provided radiographic benefit [68]. Another important study, AMBITION, randomized 673 patients, showing that TCZ was better than MTX [69].
The large LITHE study further demonstrated the disease-modifying effects on IL-6R antagonism. The study enrolled 1196 patients, suffering from RA, ranging from moderate to severe forms of the disease. All participants did not show an adequate response to methotrexate (MTX) therapy. During the study, all patients were treated with MTX but they were also randomly divided in a placebo group and a group receiving TCZ (4 or 8 mg/kg, IV) every 4 weeks in combination with MTX. At week 52, patients who were administered TCZ 8 mg/kg showed a mean change in the Genant-modified Sharp, demonstrating a significantly lower radiographic progression of the disease when compared to those on the placebo group (0.29 vs. 1.13; p < 0.0001) [70], further confirming the safety and effectiveness of TCZ. The incidence of adverse effects was 1.2% in patients with no risk factors and 11.2% in patients with three or more risk factors. The most frequent adverse effects were infections, particularly respiratory ones, both bacterial and viral [71]. In this regard, a separate study, which enrolled 63 RA patients, showed that a short course of TCZ increased the risk of hepatitis B reactivation [63].
Another recent paper by Rutherford, 2018 [72] compared the incidence of serious infections in RA patients treated with different DMARDs, even though there was little directly comparative literature of infection risk between TCZ and other drugs. Kerschbaumer et al. studied 19,282 patients with 46,771 global years of follow-up and reported that the incidence of serious infections (SI) was 5.51 cases per 100 patient years for the entire cohort (95% confidence interval (CI) 5.29 to 5.71). Compared with etanercept, TCZ had a higher risk of serious infections (HR 1.22, 95% CI 1.02 to 1.47) especially sepsis and respiratory infections ([73]).
Overall, TCZ has a well-characterized and tolerable safety profile [70].
Another IL-6 R inhibitor, sarilumab (SAR), has also been approved in RA in combination with MTX [74,75,76,77,78]. In the MONARCH study, it was demonstrated that SAR monotherapy was more efficient than adalimumab (an anti-TNFα) monotherapy, improving signs and symptoms and physical functions in patients with RA who could not continue taking MTX [79]. In addition, atlizumab, an IL-6R inhibitor, was proven to be beneficial [80].
As stated above, targeting IL-6 can been achieved by biological reagents, namely monoclonal antibodies directed against IL-6 itself such as siltuximab [81], which is approved for multicentric Castleman’s disease [82] and has shown efficacy in RA [83,84].
Sirukumab is another anti-IL-6 drug, and it has been approved for Castelman’s disease, with positive preliminary data in RA. The SIRROUND-D study showed improvements both in the Clinical Disease Activity Index and clinically. Meaningful improvements in patient-reported outcomes were sustained at week 104 among patients initially randomized to sirukumab [85]. Another randomized study in 38 healthy patients showed similar results, confirming sirukumab’s well-tolerated safety profile [86].
However, the license request for treatment in RA was denied due to excess mortality in the sirukumab arm in comparison to placebo: On 2 Auguest 2017 the Food and Drug Administration (FDA Advisory Committee Meeting denied approval. The majority of the committee (11 versus 2) agreed that it was not clear whether the imbalance in all-cause mortality is actually a side-effect of the drug or rather a consequence of the design of the study. Overall, the committee agreed that more studies should be conducted with sirukumab because, the “safety profile seemed to be consistent with a class effect showing similar adverse effects and laboratory abnormality profiles. However, death rates in sirukumab arms compared with placebo, especially in the controlled period, raised safety concerns, which led to the decision by the FDA to decline the approval of SRK in August 2017. The majority of the committee (11 to 2) did not agree that the safety profile of SRK 50 mg SC every 4 weeks is adequate to support the approval of sirukumab for the treatment of adult patients with moderately to severely active RA who have had an inadequate response or are intolerant to one or more DMARDs”.
Following this decision, the company, albeit convinced that more data may show its efficacy, stopped pursuing this line of research [87].
Clazakizumab is also an anti-IL-6 drug with promising results. In a study with 418 participants, patients with RA received SC clazakizumab once a month at 25, 100, or 200 mg plus MTX, once-monthly only SC clazakizumab at 100 or 200 mg as monotherapy, or MTX plus a placebo (in other words, MTX alone). Patients who received clazakizumab had a significantly greater response at week 12 when compared to patients receiving MTX alone [88].
Olokizumab, also an anti-IL-6 reagent, gave better results than placebo in 119 randomized patients [89].
In addition to the above-mentioned agents, a number of reagents against IL-6R and IL-6 have been developed and used in RA (Table 1).
An IL-6 blockade has proven to be effective not only in classic RA, but also in adult onset Still’s disease. Still’s disease is a rare generalized form of RA, which can present with a vast array of symptoms. Given its rarity, no systemic studies have been conducted on therapeutic strategies, and therapies are based mostly on clinicians’ experience. Targeting IL-1 and TNF-α has proven effective in the context of the articular symptoms of the disease, but, to date, only IL-6 has proven consistently effective in treating systemic symptoms [90].
The safety and efficacy of an IL-6 blockade has also been demonstrated in patients suffering from JIA, a form of RA affecting children and young adults. There are many forms of JIA, some of which closely resemble adult RA, but therapeutic strategies are limited by the young age of patients and differences in the immune systems of children and adults. DMARDs normally used in RA do not always offer satisfactory results. Recently, though, TCZ has been tested in this population and results have been encouraging, demonstrating the central role of IL-6 in autoimmune diseases [91].
Overall, both direct and indirect IL-6 inhibitors have demonstrated acceptable safety profiles, even though some studies have pointed out that mortality rates associated to direct IL-6 inhibition are higher than the ones associated to those of an IL-6 receptor blockade. Studies highlighting this discrepancy, though, compared the mortality rates between those taking an IL-6 inhibitor to those under placebo, so the interpretation of these data is not clear [58].
5. Conclusions
The development of a vast array of reagents against IL-6 and IL-6R is a demonstration per se that the perfect drug to treat RA has not been found yet. Interesting results have indeed been obtained by treating patients with different IL-6 antagonists in combination with different DMARDs: Patients showed improvement in terms of symptoms and quality of life, further proving the central role of IL-6 in the pathogenesis of RA.
Targeting IL-6 or its receptor is a safe and effective treatment, but further studies are required to identify the gold standard of treatment, because direct drug-to-drug comparison trials are still lacking and, even though scarce, there is information suggesting that the safety profile of these drugs may not be fitting for all patients and may even vary from drug to drug. Overall, in those patients who do not adequately respond to classic therapy, anti-IL-6 targeting is an interesting strategy that needs to be evaluated, given its potential capacity to completely modify patients’ prognosis and reduce the burden of disease.
Author Contributions
F.P. has suggested the topic, L.F. has developed the paper, V.C., E.N., S.A. and G.A. have helped review the paper. All authors have read and agree to the published version of the manuscript.
Funding
This research received no external funding
Conflicts of Interest
The authors declare no conflict of interest.
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IL-6 and IL-6R mAbs.
Biological
Other Names
Commercial Name
Producer
Target
Siltuximab
\n
Sylvant **
\n
IL-6
Clazakizumab
LD518 and BMS-945429
\n
Adler Biopharma
IL-6
Olokizumab
\n
\n
UCB
IL-6
Vobarilizumab
\n
\n
\n
Il-6R
Olamkicept
FE-301; FE-999301; TJ-301
\n
Ferring
IL-6R
Sarilumab
\n
Kevzara
Sanofi
IL-6R
Sirukumab **
\n
\n
Janssen
IL-6
Olamkicept
\n
\n
Ferring
\n
Satralizumab
Sapelizumab, SA237
\n
Roche
IL-6R
Tocilizumab
\n
Tocilizumab, Actemra e RoActemra.
Roche
IL-6R
** this drug is approved for multicentric Castleman disease.
\n"],"string":"[\n \"\\nInt J Mol SciInt J Mol SciijmsInternational Journal of Molecular Sciences1422-0067MDPI32718086PMC743211510.3390/ijms21155238ijms-21-05238ReviewInterleukin-6 in Rheumatoid ArthritisPandolfiFranco1*†https://orcid.org/0000-0001-8638-7565FranzaLaura2†CarusiValentina1AltamuraSimona1AndriolloGloria1NuceraEleonora1Allergy and Clinical Immunology, Fondazione Policlinico Universitario A, Gemelli IRCCS, Catholic University, 00168 Rome, Italy; valentina.carusi@libero.it (V.C.); simona.altamura@guest.policlinicogemelli.it (S.A.); gloria.andriollo@libero.it (G.A.); eleonora.nucera@unicatt.it (E.N.)Emergency Medicine, Fondazione Policlinico Universitario A, Gemelli IRCCS, Università Cattolica del Sacro Cuore, 00168 Rome, Italy; cliodnaghfranza@gmail.comCorrespondence: franco.pandolfi@unicatt.it
These authors contributed equally to this work.
2372020820202115523802720202072020© 2020 by the authors.2020Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
The role of interleukin (IL)-6 in health and disease has been under a lot of scrutiny in recent years, particularly during the recent COVID-19 pandemic. The inflammatory pathways in which IL-6 is involved are also partly responsible of the development and progression of rheumatoid arthritis (RA), opening interesting perspectives in terms of therapy. Anti-IL-6 drugs are being used with variable degrees of success in other diseases and are being tested in RA. Results have been encouraging, particularly when anti-IL-6 has been used with other drugs, such as metothrexate (MTX). In this review we discuss the main immunologic aspects that make anti-IL-6 a good candidate in RA, but despite the main therapeutic options available to target IL-6, no gold standard treatment has been established so far.
IL-6rheumatoid arthritisinflammation1. Interleukin-6 in Health and Disease
Interleukin (IL)-6 is a prototypical cytokine, featuring pleiotropic and redundant functional activity. IL-6 belongs to a family of cytokines, which includes IL-6, IL-11, IL-27, IL-31, oncostatin M (OSM), leukaemia inhibitory factor (LIF), ciliary neurotrophic factor (CNTF), cardiotrophin 1 (CT-1), and cardiotrophin-like cytokine factor 1 (CLCF1); all these cytokines use the common IL-6 signal transducer gp130 [1]. IL-6 production can be induced by both infection and other types of inflammation. Indeed, IL-6 is promptly produced, mainly by macrophages, in response to pathogens or inflammation-related damage-associated molecular patterns [2] and performs a protective function by removing infectious agents and healing damaged tissue via induction of acute phase and immune responses. IL-6 is crucial for both innate and adaptive immunity. Several cell types produce IL-6, including monocytes, T-lymphocytes, fibroblasts, and endothelial cells, and production is strongly enhanced at sites of inflammation [3].
In infections, toll-like receptors (TLRs) directly recognize the bacteria, virus, or fungi and can directly or indirectly, depending on the type of stimuli, induce the production of IL-6 and other inflammatory cytokines, such as IL-1 or tumor necrosis factor α (TNFα), through the nuclear factor-kappa B (NF-kB) signaling pathway. Interestingly, the production of IL-1 and TNFα also stimulate the production of IL-6 [4]. Overall, dysregulated IL-6 production leads to persistent inflammation [5].
IL-6 demonstrates its biological activities only by binding to its specific receptor, IL-6R. Neither IL-6 nor IL-6R have affinity for gp130 (also known as CD130). However, as a complex, IL-6 and IL-6R can bind to and activate the IL-6Rβ-subunit, gp130, leading to its dimerization and intracellular signaling through Janus kinase (JAKs: JAK1, JAK2, and TYK2), in particular, signal transducer and activator of transcription (STAT)1 and 3. STAT1 and 3 are phosphorylated and translocated to the nucleus and induce the transcription of target genes in the nucleus, while SH2 domain-containing protein-tyrosine phosphatase 2 (SHP-2) activates the Ras-MAP kinase pathway [6]. While these molecules activate the transcription process, they also start a negative feedback signaling pathway, which suppresses cytokine signaling 1 (SOCS1) and SOCS3 proteins [4].
IL-6 was thought in the past to target only the liver, since hepatocytes express high IL-6R and gp130, but in recent years it has appeared more and more clear that IL-6 does not work on a single target organ [7]. IL-6 acts as an acute phase protein, enhancing the inflammatory response of the body. Most of its effects are indeed obtained through the liver, where it is processed and starts an inflammatory cascade, inducing C-reactive protein (CRP), serum amyloid A (SAA), fibrinogen, haptoglobin, and α1-antichymotrypsin. IL-6 also triggers a reduction in the levels of albumin, zinc, and iron through various mechanisms [5].
IL-6 also guides cell differentiation in the bone marrow, stimulating the production of neutrophils and megakariocytes, triggering liver production of thrombopoietin, which stimulates the differentiation of megakaryocytes in platelets, with IL-3 [8]. Indeed, like many other inflammatory agents, IL-6 production is also associated to anemia; its effects on hepatocytes include in-vitro production of hepcidin, not yet confirmed in vivo [9].
The effect of IL-6 on the immune system is particularly interesting; its effects are evident both on innate and acquired immunity. In terms of innate immunity, IL-6 is responsible for the maturation of inflammatory infiltrate, promoting neutrophil migration and mononuclear cell infiltration. It also acts as a chemoceptor for monocytes at the site of inflammation [10,11], while in terms of acquired immunity, IL-6 also expresses its action both on T-cells and on B-cells. On the latter, it has a differentiative effect towards active plasma-cells, increasing levels of serum gamma-globulins. Indeed, these two conditions are both present in Castelman’s disease, which is responsive to anti-IL-6 treatment [12]. During the 1980s, IL-6 was indeed known as B cell stimulating factor 2, yet, the role of IL-6 in the production of gamma-globulins, even though relevant, is not essential, as it is for other inflammatory mediators. Gamma-globulin production is indeed a complex process, in which different inflammatory mediators have redundant functions: IL-2 in particular is associated to immunoglobulin production, while IL-4 and TGF-β work as inhibitory molecules [13]. Interestingly, in other contexts, the effect of IL-6 and TGF-β are synergic.
The effects IL-6 has on T-cells are also quite relevant and associated to various pathologies. IL-6 acts mainly on the differentiation of CD4+ T-cells; in particular, it stimulates the Th17 pathway while inhibiting regulatory-T-cells (T-reg) differentiation. Th17 differentiation is important in fighting inflammation and promotes IL-6 production, further enhancing Th17 differentiation. Cytokines produced by Th17 lymphocytes also include TNFα, IL-1β, IL-17, IL-21, and IL-22, which stimulate inflammation and the fibrotic reaction of the tissue [14]. IL-6 on the other hand inhibits the production of T-reg cells through transforming growth factor β (TGFβ). In the right conditions, IL-6 is indeed an autoinflammatory mediator; not only does it reduce the capacity of the body to distinguish self from not self, it also promotes fibrosis and the inflammatory reaction. The importance of its role in many autoimmune diseases has been demonstrated and targeted [15,16].
2. Rheumatoid Arthritis and Immunologic Pathways
Rheumatoid arthritis (RA) is an autoimmune disease characterized by chronic inflammation affecting the joints and cartilage, which can lead to various degrees of osteoarthritis, and it can cause various degrees of disability. Even though the development and progression of RA is still not completely understood, different therapeutic options are available, and they have completely changed the prognosis of the disease [17]. Disease modifying antirheumatic drugs (DMARD), such as methotrexate (MTX), allow patients to keep the disease under control and lead normal lives. Unfortunately, not all patients respond to these drugs and their quality of life is severely impaired [18]. Factors involved in the development of RA can be roughly distinguished into environmental and genetic, with the former influencing the second
Overall, it is widely accepted that RA occurs in genetically predisposed individuals following the interaction with different environmental factors [19,20]. The presence of T-cell receptor (TCR) restriction at the joints has also been described, suggesting T-cell attack of a joint antigen. It may also prove important in terms of predicting poor response to conventional therapy and severity of disease [21,22].
The common amino acid motif (QKRAA) in the HLA-DRB1 region, commonly known as the shared epitope, confers particular susceptibility [23]. Given the importance in determining T-cell repertoire of this region, the central role of T-cells appears obvious.
Joint damage results from the degradation of connective tissue by tissue-destroying matrix metalloproteinases (MMP) and the stimulation of osteoclastogenesis through the receptor activator of the nuclear factor-kB ligand (RANKL).
Among the environmental factors for RA, smoking is an important one, causing alterations in the oral microbiota, which might trigger autoimmune cross-reactions [24]. Something similar also happens with some infections, such as Epstein-Barr and cytomegalovirus infections: These infections over-stimulate the immune system, favoring suppression of T-regs and proliferation of Th17 lymphocytes [25]. In susceptible individuals, the chronic exposure to citrullinated peptides in conditions of chronic inflammation, as in the above-mentioned infections, is a key step in the pathogenesis of RA [26]. The role of the microbiota also needs to be acknowledged: The microbiota is a well-known regulation agent of inflammation and immunity, as it can act as a pro-inflammatory stimulus or reduce systemic inflammation, also modifying the pattern of cytokine production of the organism [27]. In RA, oral microbiota appears to be particularly significant. Indeed, it stimulates both production of aberrant neutrophils, which appear to play, in general, a central role in RA, and also favors cross-reactivity with proteins expressed in the joints [28]. Lactobacillus salivarius is the most represented bacteria in patients suffering from RA, and molecular mimicry of antigens involved in RA has been described. Interestingly, once patients are treated for RA, their oral microbiota changes and becomes similar to that of the general population [29].
From an immunologic point of view, both the adaptive and the innate immune systems are involved. Neutrophils and monocytes are the most important actors as far as innate immunity is concerned in the pathogenesis of RA, as they directly influence the interaction between toll-like-receptors (TLR) and other signaling molecules. In particular, myeloid-related protein (MRP) 8/14 further activates monocytes and creates an inflammatory environment [30]. The role of macrophages has also been acknowledged: They are capable of stimulating TNF and IL-1β production and they activate a wide number of immune effectors. They are also important in terms of clinical presentation, as some of the enzymes they produce are directly involved in cartilage deterioration [31]. As for the adaptive immunity, both B-lymphocytes and T-lymphocytes are involved.
The importance of B-cells in RA has been questioned in the past, but has been confirmed by the efficacy of treating affected patients with rituximab, an anti-CD20 antibody [32]. Plasma cells, which produce antibodies, do not express CD20, proving that the role of B-cells goes beyond autoimmune antibody production. Interestingly, activated CD4+ T cells also stimulate B cells to produce immunoglobulins.
TH17 in RA
The Th1 pathway is traditionally considered the most important in RA, while Th17 CD4 lymphocytes have only recently been acknowledged as important mediators in the pathogenesis of RA [14]. Precursor Th17 lineage cells expressing CD161 or lectin-like transcript 1 (LLT1) accumulate in patients suffering from RA, playing a central role in the pathogenesis [33]. IL-17 production is central in stimulating synovial fibroblasts and it also stimulates osteoclastogenesis in a RANKL-dependent and RANKL-independent fashion: In the first pathway, it acts directly to produce osteolysis. IL-17 also induces osteoblasts to produce RANKL, which act in synergy with TNF-α, resulting in increased osteoclastogenesis. Th-17 also produces IL-21 and 22 and TNF-α, in addition to IL-17, potentially inducing a vicious circle of inflammation [34]. The activation of all these mechanisms seems to be determined at least in part by IL-6, which also determines a reduction in the number of T-regs. Interestingly, IL-6 is further stimulated by IL-17, further maintaining inflammation. The imbalance between the inflammatory pathway and the suppressive one is, indeed, central in the pathogenesis of RA: T-regs in patients with RA appear not only reduced in quantity, but also in quality [35].
In RA, the signaling pathway of the TLRs and the expression of inflammatory cytokines (e.g., IL-1, TNFα, and IL-17) work as IL-6 promoter-activators, as do other factors [36]. Once at the joint, IL-6 has a crucial role in the inflammatory process, in osteoclast-mediated bone resorption and in pannus development; these processes help the development of IgM and IgG rheumatoid factors along with antibodies to citrullinated peptides, which are characteristically increased in RA [37]. At a joint level, IL-6 works on many different paths; its importance has been evidenced in obese patients suffering from osteoarthritis. Obesity is linked to high levels of inflammatory proteins, such as IL-6, in the blood, but also, for instance in the synovial liquid. At this level, inflammatory proteins favor the production of MMPs, particularly, MMP-1, MMP-3, MMP-13, and aggrecanase 1 and 2 (ADAMTS-4, ADAMTS-5) [38]. As previously discussed, MMP activation is also key in the progression of RA.
Other immunologic pathways are also involved in the development of RA and can eventually lead to IL-6 overexpression. For instance, the nuclear factor kappa-light-chain-enhancer of activated B cells (NF-kB) pathway is a key inflammatory mediator in RA, determining an increase in TNFα, which in turn increases IL-6 levels.
IL-6 might also be involved in a more subtle way in the development of RA: It has been observed that IL-6 is involved in cytokine release syndrome complicated by T-cell therapy. Blocking IL-6 in these cases gave good results, proving the central part it plays in inflammatory syndromes [39,40]. IL-6 seems to be responsible for other systemic symptoms associated with RA, in particular in the nervous and cardiovascular systems.
Overall, in patients with RA, elevated serum levels of both IL-6 and IL-6R are found in serum and synovial fluid of affected joints [41].
Other important cytokines that are currently being studied as possible therapeutic targets are IL-17 and granulocyte-macrophage colony-stimulating factor (GM-CSF) [42], further demonstrating the interaction between both innate and adaptive immunity.
Even though RA is usually thought of as a disease affecting mostly the joints, patients who suffer from this disease often face a number of comorbidities and are at a higher risk of developing certain types of disease: Cardiovascular disease is particularly common in this population, for instance, and this seems to be directly affected by IL-6 [43]. A similar situation has been described for psychiatric disorders. Patients suffering from chronic conditions, autoimmune diseases in particular, are known to be at a higher risk of developing a broad range of psychiatric disorders, particularly depression. In this group of patients, those with higher levels of IL-6 and CRP seem to be at a higher risk of developing psychiatric comorbidities [44]. Thus, targeting IL-6 might improve overall health in patients suffering from RA.
Overall, inflammatory cytokines, especially TNF-α, and two interleukins, IL-1β and IL-6, are key in driving inflammation and joint damage. Cytokines such as IL-23, IL-17A, as discussed above, and interferon gamma (IFN-γ) also play crucial roles in the pathogenesis of RA. IL-4 and IL-10, on the other hand, have been suggested to improve arthritis [45]. While there is overall consensus on the inflammatory role of IL-6 in the pathogenesis of RA, there are some studies pointing out that its role is not clear-cut: It has emerged that IL-1 signaling reduces IL-6 signaling in RA, overall worsening patients’ conditions. The possibility of acting directly on IL-1 is thus interesting; at the moment, the most interesting results have been found for patients suffering from other chronic inflammatory diseases, such as type-2 diabetes, in which patients whose IL-1 pathway is blocked may have benefits both in RA and diabetes control [46]. IL-6 acts differently on different cell types, and it can even inhibit fibrosis, but when in a crosstalk with IL-1, its inflammatory characteristics seem to overtake its anti-inflammatory role [47]. Yet, this hypothesis is based primarily on in vitro studies and needs to be confirmed further.
3. Interleukin-6 in Rheumatoid Arthritis and Other Diseases
The IL-6 pathway is involved in different inflammatory diseases and could be a potential target in a vast array of clinical conditions in different branches of medicine [48] spanning RA, cytokine released syndrome in CART cells [49], or COVID-19 infection [50,51].
IL-6 blockade has been successfully used in Castelman’s disease, a lymphoproliferative disorder with a wide spectrum of manifestations, and juvenile idiopathic arthritis (JIA). In some countries it has also been used with various degrees of success in other rheumatologic diseases, such as giant cell arteritis (GCA), Takayasu arteritis (TA), and cytokine releasing syndrome (CRS).
In the past few months, IL-6 has gained visibility because of its effects on COVID-19 infection. COVID-19 is a disease caused by a novel coronavirus and it can cause a vast array of symptoms, ranging from mild, flu-like symptoms to severe respiratory failure [52]. Many different therapeutic approaches have been tried, mostly anti-inflammatory therapies, ranging from steroids to antimalaria drugs; even non-conventional therapies, such as ozone-therapy, have been tested [53]. The most severe symptoms seem to be caused, at least in part, by an autoinflammatory reaction, with characteristics of a cytokine storm [52]: Binding of the novel coronavirus to TLRs causes an increase in the levels of IL-1β, which mediate fibrosis and inflammation of the lung [50]. The role of IL-6 in promoting fibrosis has been studied in other pathologies, particularly in the liver [54]. Blocking IL-6 in this context has proven useful: Patients have been treated with tocilizumab (TCZ), a monoclonal antibody against IL-6R, and results have been encouraging [51]. The role played by IL-6 in COVID-19-related inflammation is confirmed by the fact that patients with higher circulating levels of IL-6 and other inflammatory cytokines, such as persons suffering from Down syndrome, are at a higher risk of developing more severe forms of COVID-19 infection [55].
In the treatment of RA, IL-6 blockade has proven to be very useful for those patients who do not respond to conventional therapy or even less standard ones. For instance, TNF-α had been a promising target in patients with severe RA, but up to two thirds of patients do not have a full response; in this group, targeting IL-6 is not only useful, but even improves the overall response to therapy of these patients [56].
An increase in serum IL-6 and IL-21 levels is associated with markers of B cell activation, and IL-6 is associated with radiographic progression in patients with RA [57]. Based on studies emphasizing the critical role of IL-6 in development and progression of RA, targeting IL-6 has been proposed as a tool to add to the armamentarium to treat RA. Targeting IL-6 can be achieved by either direct targeting of the cytokine or targeting of its receptor. Targeting the receptor (instead of the cytokine itself) may have the advantage of blocking other cytokines of the IL-6 family.
4. Targeting Interleukin-6: Available Options
As discussed above, targeting IL-6 has proven effective in many contexts. Drugs targeting IL-6 can be roughly divided into direct inhibitors and IL-6 receptor inhibitors. The latter offer an advantage, as they have the capacity to also block other interleukins of the same family, such as IL-1, which also play an important role in the pathogenesis of RA [58].
Different types of IL-6 inhibitors will be discussed in the following paragraphs.
TCZ was introduced in 2008 and reduced disease activity in RA with inadequate response to DMARD [59].
In the last few years, several trials in over 23,700 patients have confirmed the efficacy and safety of TCZ (alone or in combination with DMARD) in RA [36], demonstrating the higher efficacy of a combination of DMARDs than MTX on its own.
The randomized OPTION study on 622 patients proved that the use of TCZ reduces signs and symptoms of RA, proving that the inhibition of IL-6 signaling is effective in moderate-to-severe RA [60].
In the TAMARA trial, 286 patients with moderate to severe RA were treated with TCZ and it was concluded that TCZ is effective in improving patients’ condition, the most solid results being seen at about 24 weeks [61].
Different routes of administration of TCZ have also been studied, demonstrating that the sub-cutaneous (SC) route is also safe [59,62,63,64,65,66,67]. Extensive experience has firmly established the short- and long-term efficacy of both intravenous and SC TCZ in adults with moderate-to-severe RA who failed to respond to therapy with synthetic or biologic DMARDs (review in [62]). In one of the first pivotal studies (SAMURAI), it was shown that in 306 patients with active RA, TCZ monotherapy was generally well tolerated and provided radiographic benefit [68]. Another important study, AMBITION, randomized 673 patients, showing that TCZ was better than MTX [69].
The large LITHE study further demonstrated the disease-modifying effects on IL-6R antagonism. The study enrolled 1196 patients, suffering from RA, ranging from moderate to severe forms of the disease. All participants did not show an adequate response to methotrexate (MTX) therapy. During the study, all patients were treated with MTX but they were also randomly divided in a placebo group and a group receiving TCZ (4 or 8 mg/kg, IV) every 4 weeks in combination with MTX. At week 52, patients who were administered TCZ 8 mg/kg showed a mean change in the Genant-modified Sharp, demonstrating a significantly lower radiographic progression of the disease when compared to those on the placebo group (0.29 vs. 1.13; p < 0.0001) [70], further confirming the safety and effectiveness of TCZ. The incidence of adverse effects was 1.2% in patients with no risk factors and 11.2% in patients with three or more risk factors. The most frequent adverse effects were infections, particularly respiratory ones, both bacterial and viral [71]. In this regard, a separate study, which enrolled 63 RA patients, showed that a short course of TCZ increased the risk of hepatitis B reactivation [63].
Another recent paper by Rutherford, 2018 [72] compared the incidence of serious infections in RA patients treated with different DMARDs, even though there was little directly comparative literature of infection risk between TCZ and other drugs. Kerschbaumer et al. studied 19,282 patients with 46,771 global years of follow-up and reported that the incidence of serious infections (SI) was 5.51 cases per 100 patient years for the entire cohort (95% confidence interval (CI) 5.29 to 5.71). Compared with etanercept, TCZ had a higher risk of serious infections (HR 1.22, 95% CI 1.02 to 1.47) especially sepsis and respiratory infections ([73]).
Overall, TCZ has a well-characterized and tolerable safety profile [70].
Another IL-6 R inhibitor, sarilumab (SAR), has also been approved in RA in combination with MTX [74,75,76,77,78]. In the MONARCH study, it was demonstrated that SAR monotherapy was more efficient than adalimumab (an anti-TNFα) monotherapy, improving signs and symptoms and physical functions in patients with RA who could not continue taking MTX [79]. In addition, atlizumab, an IL-6R inhibitor, was proven to be beneficial [80].
As stated above, targeting IL-6 can been achieved by biological reagents, namely monoclonal antibodies directed against IL-6 itself such as siltuximab [81], which is approved for multicentric Castleman’s disease [82] and has shown efficacy in RA [83,84].
Sirukumab is another anti-IL-6 drug, and it has been approved for Castelman’s disease, with positive preliminary data in RA. The SIRROUND-D study showed improvements both in the Clinical Disease Activity Index and clinically. Meaningful improvements in patient-reported outcomes were sustained at week 104 among patients initially randomized to sirukumab [85]. Another randomized study in 38 healthy patients showed similar results, confirming sirukumab’s well-tolerated safety profile [86].
However, the license request for treatment in RA was denied due to excess mortality in the sirukumab arm in comparison to placebo: On 2 Auguest 2017 the Food and Drug Administration (FDA Advisory Committee Meeting denied approval. The majority of the committee (11 versus 2) agreed that it was not clear whether the imbalance in all-cause mortality is actually a side-effect of the drug or rather a consequence of the design of the study. Overall, the committee agreed that more studies should be conducted with sirukumab because, the “safety profile seemed to be consistent with a class effect showing similar adverse effects and laboratory abnormality profiles. However, death rates in sirukumab arms compared with placebo, especially in the controlled period, raised safety concerns, which led to the decision by the FDA to decline the approval of SRK in August 2017. The majority of the committee (11 to 2) did not agree that the safety profile of SRK 50 mg SC every 4 weeks is adequate to support the approval of sirukumab for the treatment of adult patients with moderately to severely active RA who have had an inadequate response or are intolerant to one or more DMARDs”.
Following this decision, the company, albeit convinced that more data may show its efficacy, stopped pursuing this line of research [87].
Clazakizumab is also an anti-IL-6 drug with promising results. In a study with 418 participants, patients with RA received SC clazakizumab once a month at 25, 100, or 200 mg plus MTX, once-monthly only SC clazakizumab at 100 or 200 mg as monotherapy, or MTX plus a placebo (in other words, MTX alone). Patients who received clazakizumab had a significantly greater response at week 12 when compared to patients receiving MTX alone [88].
Olokizumab, also an anti-IL-6 reagent, gave better results than placebo in 119 randomized patients [89].
In addition to the above-mentioned agents, a number of reagents against IL-6R and IL-6 have been developed and used in RA (Table 1).
An IL-6 blockade has proven to be effective not only in classic RA, but also in adult onset Still’s disease. Still’s disease is a rare generalized form of RA, which can present with a vast array of symptoms. Given its rarity, no systemic studies have been conducted on therapeutic strategies, and therapies are based mostly on clinicians’ experience. Targeting IL-1 and TNF-α has proven effective in the context of the articular symptoms of the disease, but, to date, only IL-6 has proven consistently effective in treating systemic symptoms [90].
The safety and efficacy of an IL-6 blockade has also been demonstrated in patients suffering from JIA, a form of RA affecting children and young adults. There are many forms of JIA, some of which closely resemble adult RA, but therapeutic strategies are limited by the young age of patients and differences in the immune systems of children and adults. DMARDs normally used in RA do not always offer satisfactory results. Recently, though, TCZ has been tested in this population and results have been encouraging, demonstrating the central role of IL-6 in autoimmune diseases [91].
Overall, both direct and indirect IL-6 inhibitors have demonstrated acceptable safety profiles, even though some studies have pointed out that mortality rates associated to direct IL-6 inhibition are higher than the ones associated to those of an IL-6 receptor blockade. Studies highlighting this discrepancy, though, compared the mortality rates between those taking an IL-6 inhibitor to those under placebo, so the interpretation of these data is not clear [58].
5. Conclusions
The development of a vast array of reagents against IL-6 and IL-6R is a demonstration per se that the perfect drug to treat RA has not been found yet. Interesting results have indeed been obtained by treating patients with different IL-6 antagonists in combination with different DMARDs: Patients showed improvement in terms of symptoms and quality of life, further proving the central role of IL-6 in the pathogenesis of RA.
Targeting IL-6 or its receptor is a safe and effective treatment, but further studies are required to identify the gold standard of treatment, because direct drug-to-drug comparison trials are still lacking and, even though scarce, there is information suggesting that the safety profile of these drugs may not be fitting for all patients and may even vary from drug to drug. Overall, in those patients who do not adequately respond to classic therapy, anti-IL-6 targeting is an interesting strategy that needs to be evaluated, given its potential capacity to completely modify patients’ prognosis and reduce the burden of disease.
Author Contributions
F.P. has suggested the topic, L.F. has developed the paper, V.C., E.N., S.A. and G.A. have helped review the paper. All authors have read and agree to the published version of the manuscript.
Funding
This research received no external funding
Conflicts of Interest
The authors declare no conflict of interest.
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Immunol.20171384986610.1080/1744666X.2017.133299428540751YazılıtaşF.ÖzdelS.Tocilizumab for juvenile idiopathic arthritis: A single-center case seriesSao Paulo Med. J. Rev. Paul. Med.201913751752210.1590/1516-3180.2018.048922071932159638ijms-21-05238-t001_Table 1
IL-6 and IL-6R mAbs.
Biological
Other Names
Commercial Name
Producer
Target
Siltuximab
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Sylvant **
\\n
IL-6
Clazakizumab
LD518 and BMS-945429
\\n
Adler Biopharma
IL-6
Olokizumab
\\n
\\n
UCB
IL-6
Vobarilizumab
\\n
\\n
\\n
Il-6R
Olamkicept
FE-301; FE-999301; TJ-301
\\n
Ferring
IL-6R
Sarilumab
\\n
Kevzara
Sanofi
IL-6R
Sirukumab **
\\n
\\n
Janssen
IL-6
Olamkicept
\\n
\\n
Ferring
\\n
Satralizumab
Sapelizumab, SA237
\\n
Roche
IL-6R
Tocilizumab
\\n
Tocilizumab, Actemra e RoActemra.
Roche
IL-6R
** this drug is approved for multicentric Castleman disease.
\\n\"\n]"}}},{"rowIdx":459,"cells":{"data":{"kind":"list like","value":["\nInt J Mol SciInt J Mol SciijmsInternational Journal of Molecular Sciences1422-0067MDPI32731483PMC743211610.3390/ijms21155355ijms-21-05355ArticleFree Your Mind: Emotional Expressive Flexibility Moderates the Effect of Stress on Post-Traumatic Stress Disorder SymptomsLevy-GigiEinat12*DonnerReut13BonannoGeorge A.4School of Education Bar-Ilan University, Ramat-Gan 52900, Israel; reut.donner@gmail.comGonda Multidisciplinary Brain Research Center, Bar-Ilan University, Ramat-Gan 52900, IsraelPsychology Department, University of Haifa, Haifa 34988, IsraelTeachers College, Columbia University, New York, NY 10027, USA; gab38@columbia.eduCorrespondence: Einat.Levy-Gigi@biu.ac.il; Tel.: +972-3-53179632872020820202115535528620202172020© 2020 by the authors.2020Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Servicemen are exposed to high levels of stress as part of their daily routine, however, studies which tested the relationship between stress and clinical symptoms reached inconsistent results. The present study examines the role of expressive flexibility, which was determined according to the ability to enhance or suppress either negative or positive emotional expression in conflictual situations, as a possible moderator between stress and Post-Traumatic Stress Disorder (PTSD) symptoms. A total of 82 active-duty firefighters (all men, age range = 25–66, M = 33.59, SD = 9.56, range of years in duty service = 2–41, M = 14.37, SD = 11.79), with different duty-related repeated traumatic exposure, participated in the study. We predicted and found that firefighters with low, but not high, expressive flexibility showed a significant positive correlation between duty-related traumatic exposure and PTSD symptomology (t(81) = 3.85, p < 0.001). Hence, the greater the exposure the higher level of symptoms they exhibited. In addition, we found a difference between the moderating roles of suppressing positive and negative emotional expression, as high but not low, ability to suppress the expression of negative emotions (t(81) = 1.76, p > 0.05), as low but not high, ability to suppress the expression of positive emotions (t(81) = 1.6, p > 0.05), served as a protective factor in buffering the deleterious effect of repeated traumatic exposure. The results provide a pivotal support for the growing body of evidence that a flexible emotional profile is an adaptive one, in dealing with negative life events. However, while there is a need to update behavior, the direction of the adaptive update may differ as a function of valance.
The relationship between repeated exposure to trauma and Post-Traumatic Stress Disorder (PTSD) remains unclear, as studies with first responders—who are frequently exposed to traumatic events as part of their occupational routine—have shown inconsistent results. Some studies report significant positive correlation between duty-related traumatic events and PTSD, i.e., the more traumatic events one is exposed to, the higher level of PTSD symptomology, e.g., [1,2]; yet others show no direct effect [3,4,5]. These mixed findings suggest that other variables likely moderate the relationship between trauma exposure and PTSD. Previous studies reveal that emotional and cognitive flexibility may play an important role in this relationship [6,7]. The current study aims to expand these finding by focusing on the moderating role of flexibility in the regulation of emotional expression.
Emotion regulation refers to one’s ability to manage his or her emotional experience, when faced with a given situation [8]. Researchers within the field of emotion regulation differentiate between two main types of regulatory strategies: one, disengagement strategies such as distraction, in which blocking an emotional stimulus from capturing selective attention and thus preventing further cognitive processing; two, engaging strategies such as reappraisal which involves allowing elaborated cognitive processing and providing semantic meaning to the stimulus. Past studies have practiced a categorical approach, which characterized distraction as always maladaptive, and reappraisal as always adaptive, in dealing with all negative situations [9,10,11,12,13]. A more recent approach offers a person–situation interactionist model, which claims there is no “right” or “wrong” regulation strategy; different strategies predict different emotional outcomes, depending on context [8,13]. Specifically, several studies have shown it is better to use reappraisal when faced with low-intensity negative contexts (e.g., when watching a picture of two sad people, use reappraisal by thinking that they support each other and soon would feel better), and distraction when faced with highly aversive situations (use distraction by thinking of yellow triangles when watching a picture of a dead body) [6,8]. Based on this work, investigators have argued that regulatory flexibility, i.e., the ability to choose and apply a proper emotion regulation strategy in a manner that corresponds with contextual demands, plays a crucial role in the successful adaptation to negative events [14].
In the current study, we focused on a unique population of active-duty firefighters. Firefighters are exposed to an annual average of hundreds of duty-related traumatic events, including buildings and factory fires, car accidents and rescuing trapped people (see [6] for estimated number of exposures to different traumatic events in this population). Hence, they represent individuals well with repeated traumatic exposure. A recent study with active-duty firefighters demonstrated that one aspect of regulatory flexibility, related to strategy choice, moderated the relationship between repeated traumatic exposure and PTSD [6]. In this study, participants watched a series of negative computer images, with low-to-high emotional intensity. After watching each image, participants were asked to choose and apply one of two regulation strategies (distraction or reappraisal) in order to cope with the emotions triggered by the image. Participants with high regulatory choice flexibility, who successfully alternated between reappraisal in the low intensity images and distraction in the high intensity images, showed no correlation between the amount of duty-related traumatic events they experienced, and the amount of PTSD symptoms presented. However, participants with low regulatory choice flexibility, who favored only one regulation strategy or alternated between reappraisal and distraction with no regard to the contextual intensity, showed increased levels of PTSD symptomology across exposure.
In addition to the emotional experience, and the ability to regulate it, emotion theorists refer to another element of the “emotion phenomena”: Emotional expression, one’s ability to enhance or suppress an outward emotional reaction, i.e., display more or less emotion than one actually feels, respectively [14]. Previous studies have identified situations in which being highly emotionally expressive was beneficiary—for example, when trying to develop and maintain social interactions [15,16]. Others have found situations in which it is best to be more discrete [17]—for example, during argument mediation (for a full review, see [14]). Expressive flexibility refers to the ability to alternate between enhancement and suppression, in a manner that corresponds with contextual demands. Similar to the person–situation interactionist model discussed earlier, which emphasizes the importance of being flexible and in accord with situational demands when regulating one’s emotional experience, growing evidence suggests the importance of expressive flexibility—as opposed to favoring either the enhancement or the suppression of emotional expression—in successful adjustment to negative events [14,18].
For example, Bonanno et al. [14] tested expressive flexibility among New York students soon after the 9/11 attacks and found it could predict changes in their emotional health and distress levels throughout a two-year time period. In this study, participants were asked to view a set of pleasant and unpleasant emotion-evoking images on a computer monitor. For each image, participants were instructed to either enhance their emotional reactions, suppress their emotion reactions, or simply view the images as they would normally. Facial expressiveness was subsequently coded from videotapes. Expressive enhancement and expressive suppression abilities were then determined for each participant relative to that participant’s own level of expressiveness in the normal view condition. An overall expressive flexibility score was also calculated, representing the ability to both enhance and suppress emotional expression. Both the individual enhancement and suppression ability scores and the overall expressive flexibility score predicted better adjustment, i.e., reduced distress, two years after the 9/11 attacks. A limitation of this study, however, was that it did not examine differences in trauma exposure among participants.
There are also more general limitations of the expressive flexibility paradigm [19]. First, being a laboratory task, it cannot easily be employed for prospective field research. Second, the requirement that participants express or suppress emotion while sitting alone at a computer lacks ecological validity. In order to accommodate these limitations, Burton and Bonanno [19] developed the Flexible Regulation of Emotional Expression (FREE) scale as an easy to use questionnaire measure of expressive flexibility. Crucially, the FREE scale showed good validity, including correlation with expressive flexibility performance in the experimental paradigm.
The current study used the FREE scale to assess expressive flexibility among Israeli firefighters and examine the role of expressive flexibility in the elusive relationship between repeated traumatic exposure and PTSD symptomology. Based on previous findings, our main hypothesis was that expressive flexibility would moderate the relationship between trauma exposure and PTSD. Specifically, we predicted that among individuals with low, but not high, expressive flexibility, repeated traumatic exposure would be related to increased levels of PTSD symptomology. Our second and third hypotheses were that the abilities to flexibly enhance or suppress emotional expression would separately function as moderators, in the same way.
2. Method2.1. Participants
A total of 82 active-duty firefighters (all men, age range = 25–66, M = 33.59, SD = 9.56, 34.1% single; 56.1% married; 9.8% divorced; M years of education = 12.33, SD = 1.01; range years in service 2–41, mean years = 14.37, SD = 11.79), who serve in fire stations in central Israel, volunteered to participate in this cross-sectional study. All the participants hold operational positions and hence they keep a regular fitness routine. Since the vast majority of Israeli firefighters are men, no woman participated in the study. Exclusion criteria for all participants were current or past diagnosis of Axis I and II psychopathology other than PTSD; risk of suicidal/homicidal ideation; any substance dependence or abuse within the past 6 months (excluding nicotine); a history of concussion or other clinically significant head injury including loss of consciousness for over 10 min; or a history of neurological disorders such as epilepsy, multiple sclerosis, stroke, or encephalitis; acute hearing loss or color blindness. The study was conducted in accordance with the Helsinki declaration and was approved by the ethics committee of the Fire and Rescue Service.
2.2. Measures
Duty-related repeated traumatic exposure—repeated traumatic exposure was measured by a self-report exposure questionnaire. In this questionnaire, participants were asked to rate the frequency in which they were personally exposed to 16 different life-threatening, duty-related events during an average year of service (e.g., building fires, car accidents, gas leaks), on a scale of 1 (“was not exposed to at all”) to 6 (“was exposed to on a daily basis”). The number of traumatic duty-related events on an average year (range = 26–66, M = 43.62, SD = 9.57) was then multiplied by each participant’s number of years as an active firefighter (range = 2–41 years, M = 14.37, SD = 11.79), in order to obtain an adequate estimation of cumulative repeated trauma throughout the service (range = 52–2440, M = 671.34, SD = 632.27).
The Flexible Regulation of Emotional Expression (FREE) scale—in the FREE scale [19], participants were instructed to indicate how well they would be able to be more or less expressive, when feeling negative or positive emotions and faced with given, hypothetical social scenarios, on a scale of 1 (“Unable”) to 6 (“Very able”). The scale contains 16 items, which are divided into two subscales: enhancement and suppression. Each subscale is divided into two groups: positive emotional expression, and negative emotional expression. Four items refer to the ability to enhance positive emotional expression (e.g., “You receive a gift from a family member but it’s a shirt you dislike”). Four items refer to the ability to enhance negative emotional expression (e.g., “Your friend is telling you about what a terrible day they had”). Four items refer to the ability to suppress positive emotional expression (e.g., “You are in a training session and you see an accidentally funny typo in the presenter’s slideshow”). Four items refer to the ability to suppress negative emotional expression (e.g., “You have just heard about the death of a close relative right before an important work meeting”). The items of each group were summed into four different scores: positive enhancement, negative enhancement, positive suppression and negative suppression. Both positive and negative enhancement scores were summed into an “enhancement score” (from here on enhancement flexibility); both positive and negative suppression scores were into a “suppression score” (from here on suppression flexibility). Expressive flexibility was calculated by subtracting the absolute value of the average difference between enhancement and suppression scores, from the average sum of the two scores (range = 4.75–10.74, M = 7.77, SD = 1.33). Higher scores indicate high expressive flexibility; lower scores indicate low expressive flexibility.
The PTSD checklist for DSM-5 (PCL-5) questionnaire [20]—this was administered to assess levels of PTSD symptomology (Weathers et al., 2013). In this questionnaire, participants were asked to indicate how much they had been bothered by different problems in the last month, on a scale of 1 (“Not at all”) to 5 (“Extremely”). The PCL-5 questionnaire contains 21 items, which were summed in order to get a total symptom severity score (range = 21–68, M = 29.24, SD = 10.78).
2.3. Procedure
All participants provided a written informed consent at the beginning of the experiment after the nature of the procedure had been fully explained. Firefighters were approached in their fire station, during their shift, and were asked to fill out our three questionnaires in the following order: duty-related traumatic exposure, FREE scale and PCL-5. Firefighter trainees were approached in the National Fire and Rescue Academy in Rishon LeZion during their final week of an advanced training course, and were asked to fill out two questionnaires—FREE scale and PCL-5—in the same order. Each participant performed the experiment individually.
2.4. Data Analysis
Data were analyzed with SPSS (version 25) software. In order to test our hypotheses, regarding the role of expressive flexibility, enhancement flexibility and suppression flexibility in the relationship between repeated traumatic exposure and PTSD symptomology, Hayes’s [21] PROCESS macro for moderator analysis was employed, using 5000 bootstrap resampling for calculation of confidence intervals (Model 1; for the advantages of using this macro see [22]). Duty-related repeated traumatic exposure was treated as the independent variable and PTSD symptomology was treated as the outcome. Expressive flexibility, enhancement flexibility and suppression flexibility were treated, separately, as moderators, in accordance with our three hypotheses.
3. Results
Zero-order correlations between all measures are reported in Table 1. We found a significant positive correlation between duty-related repeated traumatic exposure and PTSD symptomology, a significant negative correlation between enhancement flexibility and PTSD symptomology and a significant negative correlation between expressive flexibility and PTSD symptomology. We found no correlation between enhancement flexibility and suppression flexibility, indicating their independence. Both flexibility variables were positively correlated with expressive flexibility, confirming that higher abilities to enhance or suppress an emotional expression are reflected in a higher expressive flexibility score.
First, we examined our main prediction with expressive flexibility as a possible moderator, in the relationship between repeated traumatic exposure and PTSD symptomology. The estimated coefficients of the main findings and their significance levels are described in Table 2. The general model was significant, R2 = 0.25, F(3,78) = 8.56, p < 0.001. Consistent with our hypothesis, there was a significant interaction between duty-related repeated traumatic exposure and expressive flexibility, which accounted for an additional 3.7% of the variance above. To interpret the interactive effect on PTSD symptomology, we computed bootstrapping confidence intervals (95%) evaluating the magnitude of the relationship between repeated traumatic exposure and PTSD symptoms for individuals with low (−1 SD) and high expressive flexibility (+1 SD). As expected, the results revealed a significant positive relationship between duty-related repeated traumatic exposure and PTSD symptomology for individuals with low expressive flexibility, β = 0.012, 95% CI = 0.006, 0.018, t(81) = 3.85, p < 0.001. However, no such relationship was found among individuals with high expressive flexibility, β = 0.001, 95% CI = −0.01, 0.01, t(81) = 0.12, p > 0.05. These results indicate that among low (but not high) expressive flexibility individuals, an increase in duty-related traumatic exposure is associated with enhanced PTSD symptomology.
The findings regarding the moderating role of enhancement flexibility did not support our second hypothesis. While the general model was significant, R2 = 0.22, F(3,78) = 7.24, p < 0.001, the interaction between duty-related repeated traumatic exposure and enhancement flexibility was not significant, t(81) = −0.56, p > 0.05. Additionally, the results did not support our third hypothesis, regarding the role of suppression flexibility as a possible moderator. While the general model was significant, R2 = 0.19, F(3,78) = 6.22, p < 0.001, the interaction between duty-related repeated traumatic exposure and suppression flexibility was not significant, t(81) = −1, p > 0.05.
To further examine the role of the different components of expressive flexibility in the elusive relationship between duty-related repeated traumatic exposure and PTSD symptomology, we separated between the expression of positive and negative emotions and tested four different variables as possible moderators: negative enhancement, positive enhancement (i.e., the abilities to enhance the expression of positive and negative emotions, respectively), negative suppression and positive suppression (i.e., the ability to suppress the expression of positive and negative emotions, respectively). Both positive enhancement and negative enhancement yielded significant general models, however, the interactions between repeated traumatic exposure and the two variables were not significant.
The findings regarding negative suppression as a possible moderator are reported in Table 3. The general model was significant, R2 = 0.26, F(3,78) = 9, p < 0.001, and there was a significant interaction between repeated exposure and negative suppression which accounted for an additional 4.3% of the variance above. Further examination of this interactive effect revealed a significant positive relationship between repeated duty-related traumatic exposure and PTSD symptomology for individuals with a low ability to suppress the expression of negative emotions, β = 0.012, 95% CI = 0.007, 0.018, t(81) = 4.35, p < 0.001. However, no such relationship was found among individuals with a high ability to suppress the expression of negative emotions, β = 0.004, 95% CI = −0.001, 0.01, t(81) = 1.76, p > 0.05. These results are consistent with our general assumption, as they indicate that among low (but not high) expressive flexibility individuals—i.e., individuals with a low ability to suppress the expression of negative emotions—an increase in duty-related traumatic exposure is associated with enhanced PTSD symptomatology (as described in Figure 1).
The findings regarding positive suppression as a possible moderator are also reported in Table 3. The general model was significant, R2 = 0.23, F(3,78) = 7.79, p < 0.001, and there was a significant interaction between repeated exposure and positive suppression which accounted for an additional 6.4% of the variance above. Further examination of this interactive effect revealed a significant positive relationship between repeated duty-related traumatic exposure and PTSD symptomology for individuals with a high ability to suppress the expression of positive emotions, β = 0.02, 95% CI = 0.01, 0.02, t(81) = 4.3, p < 0.001. However, no such relationship was found among individuals with a low ability to suppress the expression of positive emotions, β = 0.004, 95% CI = −0.001, 0.01, t(81) = 1.6, p > 0.05. These results are inconsistent with our general assumption, as they indicate that among high (but not low) expressive flexibility individuals—i.e., individuals with a high ability to suppress the expression of positive emotions—an increase in duty-related traumatic exposure is associated with enhanced PTSD symptomatology (as described in Figure 2).
This pattern of results indicates that the abilities to suppress the expression of negative and positive emotions have opposing roles in the relationship between repeated traumatic exposure and PTSD symptomology.
4. Discussion
The aim of the current study was to examine the role of expressive flexibility—i.e., the ability to enhance and suppress an outward emotional expression—in the relationship between repeated exposure to traumatic events and PTSD symptomology. In accordance with our prediction, we found that expressive flexibility moderates the relationship between repeated traumatic exposure and PTSD symptoms. Specifically, while individuals with low expressive flexibility showed a positive association between level of repeated exposure and PTSD symptoms, no such relationship was found in individuals with high expressive flexibility. These results are in line with previous studies which showed that high expressive flexibility predicts emotional adaptation following both exposures to general trauma [14,23] or loss of partner [24]. Moreover, it complements and expands previous results on active duty firefighters, which show a similar effect of regulatory choice flexibility [6]. Finally, the results provide additional support for the importance of emotional flexibility in psychological well-being, and—for the first time, to our knowledge—provide specific evidence for the crucial role of expressive flexibility in the successful adaptation to repeated traumatic exposure.
While these results may tell a simple and clear story, focusing on the specific abilities to suppress and enhance emotions, revealed a more complex picture. First, we found that contrary to our prediction, the ability to display more emotion, neither positive nor negative, did not buffer the deleterious effects of repeated exposure to trauma. These results may question the traditional approach, which assumes that expressing emotion is therapeutic and beneficiary, and hence may represent an adaptive way to cope with trauma. Indeed, actively disclosing emotions regarding upsetting life events—whether to a close friend, a therapist or even writing about the experience—is often associated with an immediate emotional relief, and beneficial to mental and physical health [25,26,27,28,29]. Abstaining from emotional expression of past or recent adverse experiences, on the other hand, is commonly linked to poorer health [27,30], increased emotional and physical arousal [30,31] and even to cognitive impairments [32]. However, more recent studies present a different approach. Kennedy-Moore and Watson [26] have pointed out a paradox in expressing negative emotions, suggesting that while disclosure of emotions is an important part of a successful therapeutic process, it may also indicate severe emotional distress, and even serves as a warning sign. Moreover, the relationship between emotional expression and psychological well-being seems more complicated with possible mediators such as different personality traits and coping styles, initial physical state and the level of social support [33,34]. Finally, it was suggested that the personal and social elements of emotional expression play a crucial role in this relationship. Specifically, it was found that sharing emotions is most helpful in reducing distress when it is done in a supportive social environment. However, if those in need or their potential supporters feel uncomfortable communicating emotions, or when the content of the expressed emotion is too intense or overwhelming, the recipients might react in a diminishing or minimizing manner and the person in need would end up feeling dismissed or rejected. Taken together, it is possible that in our study, the intense lifestyle of male, active-duty firefighters, who are repeatedly exposed to overwhelming sights, addresses certain personal coping styles and/or social communication habits in which expressing emotions is not common nor considered as worthwhile. Alternatively, it is possible that while the majority of previous research revealed similar results in firefighters and other servicemen who are repeatedly exposed to trauma [35,36,37,38], future studies may aim to test the moderating role of expressive flexibility in the relationship between trauma exposure and PTSD symptoms in other populations including not only soldiers and police officers, but also men and women civilians who live in conflict zones and experience repeated trauma.
When we tested the general ability to suppress emotional expression, we found no significant effect on the relationship between repeated exposure to trauma and PTSD symptoms. However, further examination revealed an interesting difference between the role of suppressing negative and positive emotions. Specifically, both the ability to suppress negative emotions and the tendency to allow and avoid suppression of positive emotions served as protective factors against the effects of repeated traumatic exposure. These findings may suggest that not only suppressing negative emotions but also allowing positive emotions plays an important role in coping with trauma. Indeed, there is growing evidence for the unique role of positive emotions in stress-related situations. For example, a recent study suggests that it is the lack of positive experiences, and not the excess of negative emotions, that differentiates between individuals with social anxiety disorder and healthy controls [39]. Folkman [40] refers in her review to the important function of positive emotions in restoring physical and psychological coping resources when facing a stressful event. Similarly, the Broaden-and-Build Theory [41,42] emphasizes the crucial role of positive emotions in the down-regulation of sympathetic arousal caused by stress, and the broadening of important coping functions such as attention, creativity and flexible thinking. With that being said, it should be noted that flexibility is the capacity to use the best mechanism, depending on the situation. Thus, enhancement might be best in one situation, or at one point in time in dealing with the situation, while suppression might be best later in the same situation or in another situation. Hence, while some aspects are more related to reduced PTSD symptoms, adaptive behavior is the ability to use enhancement and suppression when they are most appropriate.
The current study has several limitations. First, we did not address the possible relationship between emotional experience and emotional expression. Previous studies which tried to answer this question have yielded inconsistent results. For example, Gross and Levenson [43] showed heightened sympathetic activity among participants who were asked to suppress all outward signs of emotional expression. Interestingly, despite this increase in physiological stress levels, participants did not report an increase in their negative emotional experience. Moreover, in some situations the expressing of negative emotions was linked to a relief from negative emotional experience [33], although this was not the case for all the participants who expressed their aversive feelings. [44,45]. Future studies may aim to test the relationship between expressive flexibility and regulatory flexibility in conditions of repeated traumatic exposure. A positive correlation, for example, would indicate that an improvement in one aspect of emotional flexibility could benefit the other. Both expressive and regulatory flexibility seem to play a crucial role in buffering the effect of traumatic exposure. Hence, a better understanding of the relationship between the two could have important theoretical and therapeutic implications. Another limitation relates to the fact that we tested participants only at one time-point. While such a design serves as a proof of concept and allows to reach important conclusions regarding the associations between the variables, future studies may aim to use a longitudinal approach, in order to base a causal relationship.
The results of the present study may serve to develop interventions which aim to minimize the deleterious effect of repeated trauma both before and during active service. Specifically, it is possible that if first responders will acquire and practice adaptive expressive flexibility skills, it will help them to better cope with stressful situations and to invigorate through positive experiences.
To summarize, the current study reveals that a flexible emotional expressive profile is important when dealing with negative life events. Most importantly, among individuals who are repeatedly exposed to trauma, a resilient expressive profile is not necessarily having high abilities to suppress and enhance positive and negative emotional expressions, but adjusting these abilities in correspondence with the complex context of their lives.
Author Contributions
E.L.-G. and G.A.B. developed the concept of the paper; R.D. administrated the study and analyzed the results under the supervision of E.L.-G.; E.L.-G. and R.D. wrote a first draft of the report and G.A.B. read and commented on it. All authors have read and agreed to the published version of the manuscript.
Funding
The study was funded by the US–Israel Binational Science Foundation (BSF), Grant #2015_143 to E.L.-G. and G.A.B.
Conflicts of Interest
The authors declare no conflict of interest
Abbreviations
PTSD
Post-Traumatic Stress Disorder
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Post-Traumatic Stress Disorder (PTSD) symptomology as a function of duty-related repeated traumatic exposure and negative suppression.
Post-Traumatic Stress Disorder (PTSD) symptomology as a function of duty-related repeated traumatic exposure and positive suppression.
ijms-21-05355-t001_Table 1
Zero-order correlation between enhancement flexibility, suppression flexibility, expressive flexibility, PTSD symptomology and duty-related repeated traumatic exposure.
** p < 0.01. Post-Traumatic Stress Disorder (PTSD).
ijms-21-05355-t002_Table 2
Estimated coefficients, standard errors, and 95% confidence intervals for independent variable and moderator in the model predicting PTSD symptomology.
Variable
\nβ\n
SE
t Value
95% CI
Low
High
Duty-related repeated traumatic exposure
0.006
0.002
3 **
0.002
0.01
Expressive flexibility
−2.26
0.85
−2.65 **
−3.96
−0.56
Interaction
−0.004
0.002
−1.98 *
−0.001
0
β = Unstandardized Estimated Coefficients; SE = Standard Error; CI = Confidence Intervals. * p = 0.05. ** p < 0.01.
ijms-21-05355-t003_Table 3
Estimated coefficients, standard errors, and 95% confidence intervals for independent variable and moderators in the model predicting PTSD symptomology.
\nNegative Suppression as a Moderator\n
\nVariable\n
\nβ\n
\nSE\n
\nt Value\n
\n95% CI\n
\nLow\n
\nHigh\n
Duty-related repeated traumatic exposure
0.001
0.002
4.21 **
0.004
0.012
Negative suppression
−0.55
0.26
−2.14 *
−1.06
−0.04
Interaction
−0.001
0.000
−2.12 *
−0.002
−0.000
\nPositive Suppression as a Moderator\n
\nVariable\n
\nβ\n
\nSE\n
\nt Value\n
\n95% CI\n
\nLow\n
\nHigh\n
Duty-related repeated traumatic exposure
0.01
0.002.
4.77 **
0.006
0.015
Positive suppression
0.23
0.30
0.76
−0.37
0.83
Interaction
0.001
0.00
2.54 *
0.000
0.003
β = Unstandardized Estimated Coefficients; SE = Standard Error; CI = Confidence Intervals. * p < 0.05. ** p < 0.01.
\n"],"string":"[\n \"\\nInt J Mol SciInt J Mol SciijmsInternational Journal of Molecular Sciences1422-0067MDPI32731483PMC743211610.3390/ijms21155355ijms-21-05355ArticleFree Your Mind: Emotional Expressive Flexibility Moderates the Effect of Stress on Post-Traumatic Stress Disorder SymptomsLevy-GigiEinat12*DonnerReut13BonannoGeorge A.4School of Education Bar-Ilan University, Ramat-Gan 52900, Israel; reut.donner@gmail.comGonda Multidisciplinary Brain Research Center, Bar-Ilan University, Ramat-Gan 52900, IsraelPsychology Department, University of Haifa, Haifa 34988, IsraelTeachers College, Columbia University, New York, NY 10027, USA; gab38@columbia.eduCorrespondence: Einat.Levy-Gigi@biu.ac.il; Tel.: +972-3-53179632872020820202115535528620202172020© 2020 by the authors.2020Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Servicemen are exposed to high levels of stress as part of their daily routine, however, studies which tested the relationship between stress and clinical symptoms reached inconsistent results. The present study examines the role of expressive flexibility, which was determined according to the ability to enhance or suppress either negative or positive emotional expression in conflictual situations, as a possible moderator between stress and Post-Traumatic Stress Disorder (PTSD) symptoms. A total of 82 active-duty firefighters (all men, age range = 25–66, M = 33.59, SD = 9.56, range of years in duty service = 2–41, M = 14.37, SD = 11.79), with different duty-related repeated traumatic exposure, participated in the study. We predicted and found that firefighters with low, but not high, expressive flexibility showed a significant positive correlation between duty-related traumatic exposure and PTSD symptomology (t(81) = 3.85, p < 0.001). Hence, the greater the exposure the higher level of symptoms they exhibited. In addition, we found a difference between the moderating roles of suppressing positive and negative emotional expression, as high but not low, ability to suppress the expression of negative emotions (t(81) = 1.76, p > 0.05), as low but not high, ability to suppress the expression of positive emotions (t(81) = 1.6, p > 0.05), served as a protective factor in buffering the deleterious effect of repeated traumatic exposure. The results provide a pivotal support for the growing body of evidence that a flexible emotional profile is an adaptive one, in dealing with negative life events. However, while there is a need to update behavior, the direction of the adaptive update may differ as a function of valance.
The relationship between repeated exposure to trauma and Post-Traumatic Stress Disorder (PTSD) remains unclear, as studies with first responders—who are frequently exposed to traumatic events as part of their occupational routine—have shown inconsistent results. Some studies report significant positive correlation between duty-related traumatic events and PTSD, i.e., the more traumatic events one is exposed to, the higher level of PTSD symptomology, e.g., [1,2]; yet others show no direct effect [3,4,5]. These mixed findings suggest that other variables likely moderate the relationship between trauma exposure and PTSD. Previous studies reveal that emotional and cognitive flexibility may play an important role in this relationship [6,7]. The current study aims to expand these finding by focusing on the moderating role of flexibility in the regulation of emotional expression.
Emotion regulation refers to one’s ability to manage his or her emotional experience, when faced with a given situation [8]. Researchers within the field of emotion regulation differentiate between two main types of regulatory strategies: one, disengagement strategies such as distraction, in which blocking an emotional stimulus from capturing selective attention and thus preventing further cognitive processing; two, engaging strategies such as reappraisal which involves allowing elaborated cognitive processing and providing semantic meaning to the stimulus. Past studies have practiced a categorical approach, which characterized distraction as always maladaptive, and reappraisal as always adaptive, in dealing with all negative situations [9,10,11,12,13]. A more recent approach offers a person–situation interactionist model, which claims there is no “right” or “wrong” regulation strategy; different strategies predict different emotional outcomes, depending on context [8,13]. Specifically, several studies have shown it is better to use reappraisal when faced with low-intensity negative contexts (e.g., when watching a picture of two sad people, use reappraisal by thinking that they support each other and soon would feel better), and distraction when faced with highly aversive situations (use distraction by thinking of yellow triangles when watching a picture of a dead body) [6,8]. Based on this work, investigators have argued that regulatory flexibility, i.e., the ability to choose and apply a proper emotion regulation strategy in a manner that corresponds with contextual demands, plays a crucial role in the successful adaptation to negative events [14].
In the current study, we focused on a unique population of active-duty firefighters. Firefighters are exposed to an annual average of hundreds of duty-related traumatic events, including buildings and factory fires, car accidents and rescuing trapped people (see [6] for estimated number of exposures to different traumatic events in this population). Hence, they represent individuals well with repeated traumatic exposure. A recent study with active-duty firefighters demonstrated that one aspect of regulatory flexibility, related to strategy choice, moderated the relationship between repeated traumatic exposure and PTSD [6]. In this study, participants watched a series of negative computer images, with low-to-high emotional intensity. After watching each image, participants were asked to choose and apply one of two regulation strategies (distraction or reappraisal) in order to cope with the emotions triggered by the image. Participants with high regulatory choice flexibility, who successfully alternated between reappraisal in the low intensity images and distraction in the high intensity images, showed no correlation between the amount of duty-related traumatic events they experienced, and the amount of PTSD symptoms presented. However, participants with low regulatory choice flexibility, who favored only one regulation strategy or alternated between reappraisal and distraction with no regard to the contextual intensity, showed increased levels of PTSD symptomology across exposure.
In addition to the emotional experience, and the ability to regulate it, emotion theorists refer to another element of the “emotion phenomena”: Emotional expression, one’s ability to enhance or suppress an outward emotional reaction, i.e., display more or less emotion than one actually feels, respectively [14]. Previous studies have identified situations in which being highly emotionally expressive was beneficiary—for example, when trying to develop and maintain social interactions [15,16]. Others have found situations in which it is best to be more discrete [17]—for example, during argument mediation (for a full review, see [14]). Expressive flexibility refers to the ability to alternate between enhancement and suppression, in a manner that corresponds with contextual demands. Similar to the person–situation interactionist model discussed earlier, which emphasizes the importance of being flexible and in accord with situational demands when regulating one’s emotional experience, growing evidence suggests the importance of expressive flexibility—as opposed to favoring either the enhancement or the suppression of emotional expression—in successful adjustment to negative events [14,18].
For example, Bonanno et al. [14] tested expressive flexibility among New York students soon after the 9/11 attacks and found it could predict changes in their emotional health and distress levels throughout a two-year time period. In this study, participants were asked to view a set of pleasant and unpleasant emotion-evoking images on a computer monitor. For each image, participants were instructed to either enhance their emotional reactions, suppress their emotion reactions, or simply view the images as they would normally. Facial expressiveness was subsequently coded from videotapes. Expressive enhancement and expressive suppression abilities were then determined for each participant relative to that participant’s own level of expressiveness in the normal view condition. An overall expressive flexibility score was also calculated, representing the ability to both enhance and suppress emotional expression. Both the individual enhancement and suppression ability scores and the overall expressive flexibility score predicted better adjustment, i.e., reduced distress, two years after the 9/11 attacks. A limitation of this study, however, was that it did not examine differences in trauma exposure among participants.
There are also more general limitations of the expressive flexibility paradigm [19]. First, being a laboratory task, it cannot easily be employed for prospective field research. Second, the requirement that participants express or suppress emotion while sitting alone at a computer lacks ecological validity. In order to accommodate these limitations, Burton and Bonanno [19] developed the Flexible Regulation of Emotional Expression (FREE) scale as an easy to use questionnaire measure of expressive flexibility. Crucially, the FREE scale showed good validity, including correlation with expressive flexibility performance in the experimental paradigm.
The current study used the FREE scale to assess expressive flexibility among Israeli firefighters and examine the role of expressive flexibility in the elusive relationship between repeated traumatic exposure and PTSD symptomology. Based on previous findings, our main hypothesis was that expressive flexibility would moderate the relationship between trauma exposure and PTSD. Specifically, we predicted that among individuals with low, but not high, expressive flexibility, repeated traumatic exposure would be related to increased levels of PTSD symptomology. Our second and third hypotheses were that the abilities to flexibly enhance or suppress emotional expression would separately function as moderators, in the same way.
2. Method2.1. Participants
A total of 82 active-duty firefighters (all men, age range = 25–66, M = 33.59, SD = 9.56, 34.1% single; 56.1% married; 9.8% divorced; M years of education = 12.33, SD = 1.01; range years in service 2–41, mean years = 14.37, SD = 11.79), who serve in fire stations in central Israel, volunteered to participate in this cross-sectional study. All the participants hold operational positions and hence they keep a regular fitness routine. Since the vast majority of Israeli firefighters are men, no woman participated in the study. Exclusion criteria for all participants were current or past diagnosis of Axis I and II psychopathology other than PTSD; risk of suicidal/homicidal ideation; any substance dependence or abuse within the past 6 months (excluding nicotine); a history of concussion or other clinically significant head injury including loss of consciousness for over 10 min; or a history of neurological disorders such as epilepsy, multiple sclerosis, stroke, or encephalitis; acute hearing loss or color blindness. The study was conducted in accordance with the Helsinki declaration and was approved by the ethics committee of the Fire and Rescue Service.
2.2. Measures
Duty-related repeated traumatic exposure—repeated traumatic exposure was measured by a self-report exposure questionnaire. In this questionnaire, participants were asked to rate the frequency in which they were personally exposed to 16 different life-threatening, duty-related events during an average year of service (e.g., building fires, car accidents, gas leaks), on a scale of 1 (“was not exposed to at all”) to 6 (“was exposed to on a daily basis”). The number of traumatic duty-related events on an average year (range = 26–66, M = 43.62, SD = 9.57) was then multiplied by each participant’s number of years as an active firefighter (range = 2–41 years, M = 14.37, SD = 11.79), in order to obtain an adequate estimation of cumulative repeated trauma throughout the service (range = 52–2440, M = 671.34, SD = 632.27).
The Flexible Regulation of Emotional Expression (FREE) scale—in the FREE scale [19], participants were instructed to indicate how well they would be able to be more or less expressive, when feeling negative or positive emotions and faced with given, hypothetical social scenarios, on a scale of 1 (“Unable”) to 6 (“Very able”). The scale contains 16 items, which are divided into two subscales: enhancement and suppression. Each subscale is divided into two groups: positive emotional expression, and negative emotional expression. Four items refer to the ability to enhance positive emotional expression (e.g., “You receive a gift from a family member but it’s a shirt you dislike”). Four items refer to the ability to enhance negative emotional expression (e.g., “Your friend is telling you about what a terrible day they had”). Four items refer to the ability to suppress positive emotional expression (e.g., “You are in a training session and you see an accidentally funny typo in the presenter’s slideshow”). Four items refer to the ability to suppress negative emotional expression (e.g., “You have just heard about the death of a close relative right before an important work meeting”). The items of each group were summed into four different scores: positive enhancement, negative enhancement, positive suppression and negative suppression. Both positive and negative enhancement scores were summed into an “enhancement score” (from here on enhancement flexibility); both positive and negative suppression scores were into a “suppression score” (from here on suppression flexibility). Expressive flexibility was calculated by subtracting the absolute value of the average difference between enhancement and suppression scores, from the average sum of the two scores (range = 4.75–10.74, M = 7.77, SD = 1.33). Higher scores indicate high expressive flexibility; lower scores indicate low expressive flexibility.
The PTSD checklist for DSM-5 (PCL-5) questionnaire [20]—this was administered to assess levels of PTSD symptomology (Weathers et al., 2013). In this questionnaire, participants were asked to indicate how much they had been bothered by different problems in the last month, on a scale of 1 (“Not at all”) to 5 (“Extremely”). The PCL-5 questionnaire contains 21 items, which were summed in order to get a total symptom severity score (range = 21–68, M = 29.24, SD = 10.78).
2.3. Procedure
All participants provided a written informed consent at the beginning of the experiment after the nature of the procedure had been fully explained. Firefighters were approached in their fire station, during their shift, and were asked to fill out our three questionnaires in the following order: duty-related traumatic exposure, FREE scale and PCL-5. Firefighter trainees were approached in the National Fire and Rescue Academy in Rishon LeZion during their final week of an advanced training course, and were asked to fill out two questionnaires—FREE scale and PCL-5—in the same order. Each participant performed the experiment individually.
2.4. Data Analysis
Data were analyzed with SPSS (version 25) software. In order to test our hypotheses, regarding the role of expressive flexibility, enhancement flexibility and suppression flexibility in the relationship between repeated traumatic exposure and PTSD symptomology, Hayes’s [21] PROCESS macro for moderator analysis was employed, using 5000 bootstrap resampling for calculation of confidence intervals (Model 1; for the advantages of using this macro see [22]). Duty-related repeated traumatic exposure was treated as the independent variable and PTSD symptomology was treated as the outcome. Expressive flexibility, enhancement flexibility and suppression flexibility were treated, separately, as moderators, in accordance with our three hypotheses.
3. Results
Zero-order correlations between all measures are reported in Table 1. We found a significant positive correlation between duty-related repeated traumatic exposure and PTSD symptomology, a significant negative correlation between enhancement flexibility and PTSD symptomology and a significant negative correlation between expressive flexibility and PTSD symptomology. We found no correlation between enhancement flexibility and suppression flexibility, indicating their independence. Both flexibility variables were positively correlated with expressive flexibility, confirming that higher abilities to enhance or suppress an emotional expression are reflected in a higher expressive flexibility score.
First, we examined our main prediction with expressive flexibility as a possible moderator, in the relationship between repeated traumatic exposure and PTSD symptomology. The estimated coefficients of the main findings and their significance levels are described in Table 2. The general model was significant, R2 = 0.25, F(3,78) = 8.56, p < 0.001. Consistent with our hypothesis, there was a significant interaction between duty-related repeated traumatic exposure and expressive flexibility, which accounted for an additional 3.7% of the variance above. To interpret the interactive effect on PTSD symptomology, we computed bootstrapping confidence intervals (95%) evaluating the magnitude of the relationship between repeated traumatic exposure and PTSD symptoms for individuals with low (−1 SD) and high expressive flexibility (+1 SD). As expected, the results revealed a significant positive relationship between duty-related repeated traumatic exposure and PTSD symptomology for individuals with low expressive flexibility, β = 0.012, 95% CI = 0.006, 0.018, t(81) = 3.85, p < 0.001. However, no such relationship was found among individuals with high expressive flexibility, β = 0.001, 95% CI = −0.01, 0.01, t(81) = 0.12, p > 0.05. These results indicate that among low (but not high) expressive flexibility individuals, an increase in duty-related traumatic exposure is associated with enhanced PTSD symptomology.
The findings regarding the moderating role of enhancement flexibility did not support our second hypothesis. While the general model was significant, R2 = 0.22, F(3,78) = 7.24, p < 0.001, the interaction between duty-related repeated traumatic exposure and enhancement flexibility was not significant, t(81) = −0.56, p > 0.05. Additionally, the results did not support our third hypothesis, regarding the role of suppression flexibility as a possible moderator. While the general model was significant, R2 = 0.19, F(3,78) = 6.22, p < 0.001, the interaction between duty-related repeated traumatic exposure and suppression flexibility was not significant, t(81) = −1, p > 0.05.
To further examine the role of the different components of expressive flexibility in the elusive relationship between duty-related repeated traumatic exposure and PTSD symptomology, we separated between the expression of positive and negative emotions and tested four different variables as possible moderators: negative enhancement, positive enhancement (i.e., the abilities to enhance the expression of positive and negative emotions, respectively), negative suppression and positive suppression (i.e., the ability to suppress the expression of positive and negative emotions, respectively). Both positive enhancement and negative enhancement yielded significant general models, however, the interactions between repeated traumatic exposure and the two variables were not significant.
The findings regarding negative suppression as a possible moderator are reported in Table 3. The general model was significant, R2 = 0.26, F(3,78) = 9, p < 0.001, and there was a significant interaction between repeated exposure and negative suppression which accounted for an additional 4.3% of the variance above. Further examination of this interactive effect revealed a significant positive relationship between repeated duty-related traumatic exposure and PTSD symptomology for individuals with a low ability to suppress the expression of negative emotions, β = 0.012, 95% CI = 0.007, 0.018, t(81) = 4.35, p < 0.001. However, no such relationship was found among individuals with a high ability to suppress the expression of negative emotions, β = 0.004, 95% CI = −0.001, 0.01, t(81) = 1.76, p > 0.05. These results are consistent with our general assumption, as they indicate that among low (but not high) expressive flexibility individuals—i.e., individuals with a low ability to suppress the expression of negative emotions—an increase in duty-related traumatic exposure is associated with enhanced PTSD symptomatology (as described in Figure 1).
The findings regarding positive suppression as a possible moderator are also reported in Table 3. The general model was significant, R2 = 0.23, F(3,78) = 7.79, p < 0.001, and there was a significant interaction between repeated exposure and positive suppression which accounted for an additional 6.4% of the variance above. Further examination of this interactive effect revealed a significant positive relationship between repeated duty-related traumatic exposure and PTSD symptomology for individuals with a high ability to suppress the expression of positive emotions, β = 0.02, 95% CI = 0.01, 0.02, t(81) = 4.3, p < 0.001. However, no such relationship was found among individuals with a low ability to suppress the expression of positive emotions, β = 0.004, 95% CI = −0.001, 0.01, t(81) = 1.6, p > 0.05. These results are inconsistent with our general assumption, as they indicate that among high (but not low) expressive flexibility individuals—i.e., individuals with a high ability to suppress the expression of positive emotions—an increase in duty-related traumatic exposure is associated with enhanced PTSD symptomatology (as described in Figure 2).
This pattern of results indicates that the abilities to suppress the expression of negative and positive emotions have opposing roles in the relationship between repeated traumatic exposure and PTSD symptomology.
4. Discussion
The aim of the current study was to examine the role of expressive flexibility—i.e., the ability to enhance and suppress an outward emotional expression—in the relationship between repeated exposure to traumatic events and PTSD symptomology. In accordance with our prediction, we found that expressive flexibility moderates the relationship between repeated traumatic exposure and PTSD symptoms. Specifically, while individuals with low expressive flexibility showed a positive association between level of repeated exposure and PTSD symptoms, no such relationship was found in individuals with high expressive flexibility. These results are in line with previous studies which showed that high expressive flexibility predicts emotional adaptation following both exposures to general trauma [14,23] or loss of partner [24]. Moreover, it complements and expands previous results on active duty firefighters, which show a similar effect of regulatory choice flexibility [6]. Finally, the results provide additional support for the importance of emotional flexibility in psychological well-being, and—for the first time, to our knowledge—provide specific evidence for the crucial role of expressive flexibility in the successful adaptation to repeated traumatic exposure.
While these results may tell a simple and clear story, focusing on the specific abilities to suppress and enhance emotions, revealed a more complex picture. First, we found that contrary to our prediction, the ability to display more emotion, neither positive nor negative, did not buffer the deleterious effects of repeated exposure to trauma. These results may question the traditional approach, which assumes that expressing emotion is therapeutic and beneficiary, and hence may represent an adaptive way to cope with trauma. Indeed, actively disclosing emotions regarding upsetting life events—whether to a close friend, a therapist or even writing about the experience—is often associated with an immediate emotional relief, and beneficial to mental and physical health [25,26,27,28,29]. Abstaining from emotional expression of past or recent adverse experiences, on the other hand, is commonly linked to poorer health [27,30], increased emotional and physical arousal [30,31] and even to cognitive impairments [32]. However, more recent studies present a different approach. Kennedy-Moore and Watson [26] have pointed out a paradox in expressing negative emotions, suggesting that while disclosure of emotions is an important part of a successful therapeutic process, it may also indicate severe emotional distress, and even serves as a warning sign. Moreover, the relationship between emotional expression and psychological well-being seems more complicated with possible mediators such as different personality traits and coping styles, initial physical state and the level of social support [33,34]. Finally, it was suggested that the personal and social elements of emotional expression play a crucial role in this relationship. Specifically, it was found that sharing emotions is most helpful in reducing distress when it is done in a supportive social environment. However, if those in need or their potential supporters feel uncomfortable communicating emotions, or when the content of the expressed emotion is too intense or overwhelming, the recipients might react in a diminishing or minimizing manner and the person in need would end up feeling dismissed or rejected. Taken together, it is possible that in our study, the intense lifestyle of male, active-duty firefighters, who are repeatedly exposed to overwhelming sights, addresses certain personal coping styles and/or social communication habits in which expressing emotions is not common nor considered as worthwhile. Alternatively, it is possible that while the majority of previous research revealed similar results in firefighters and other servicemen who are repeatedly exposed to trauma [35,36,37,38], future studies may aim to test the moderating role of expressive flexibility in the relationship between trauma exposure and PTSD symptoms in other populations including not only soldiers and police officers, but also men and women civilians who live in conflict zones and experience repeated trauma.
When we tested the general ability to suppress emotional expression, we found no significant effect on the relationship between repeated exposure to trauma and PTSD symptoms. However, further examination revealed an interesting difference between the role of suppressing negative and positive emotions. Specifically, both the ability to suppress negative emotions and the tendency to allow and avoid suppression of positive emotions served as protective factors against the effects of repeated traumatic exposure. These findings may suggest that not only suppressing negative emotions but also allowing positive emotions plays an important role in coping with trauma. Indeed, there is growing evidence for the unique role of positive emotions in stress-related situations. For example, a recent study suggests that it is the lack of positive experiences, and not the excess of negative emotions, that differentiates between individuals with social anxiety disorder and healthy controls [39]. Folkman [40] refers in her review to the important function of positive emotions in restoring physical and psychological coping resources when facing a stressful event. Similarly, the Broaden-and-Build Theory [41,42] emphasizes the crucial role of positive emotions in the down-regulation of sympathetic arousal caused by stress, and the broadening of important coping functions such as attention, creativity and flexible thinking. With that being said, it should be noted that flexibility is the capacity to use the best mechanism, depending on the situation. Thus, enhancement might be best in one situation, or at one point in time in dealing with the situation, while suppression might be best later in the same situation or in another situation. Hence, while some aspects are more related to reduced PTSD symptoms, adaptive behavior is the ability to use enhancement and suppression when they are most appropriate.
The current study has several limitations. First, we did not address the possible relationship between emotional experience and emotional expression. Previous studies which tried to answer this question have yielded inconsistent results. For example, Gross and Levenson [43] showed heightened sympathetic activity among participants who were asked to suppress all outward signs of emotional expression. Interestingly, despite this increase in physiological stress levels, participants did not report an increase in their negative emotional experience. Moreover, in some situations the expressing of negative emotions was linked to a relief from negative emotional experience [33], although this was not the case for all the participants who expressed their aversive feelings. [44,45]. Future studies may aim to test the relationship between expressive flexibility and regulatory flexibility in conditions of repeated traumatic exposure. A positive correlation, for example, would indicate that an improvement in one aspect of emotional flexibility could benefit the other. Both expressive and regulatory flexibility seem to play a crucial role in buffering the effect of traumatic exposure. Hence, a better understanding of the relationship between the two could have important theoretical and therapeutic implications. Another limitation relates to the fact that we tested participants only at one time-point. While such a design serves as a proof of concept and allows to reach important conclusions regarding the associations between the variables, future studies may aim to use a longitudinal approach, in order to base a causal relationship.
The results of the present study may serve to develop interventions which aim to minimize the deleterious effect of repeated trauma both before and during active service. Specifically, it is possible that if first responders will acquire and practice adaptive expressive flexibility skills, it will help them to better cope with stressful situations and to invigorate through positive experiences.
To summarize, the current study reveals that a flexible emotional expressive profile is important when dealing with negative life events. Most importantly, among individuals who are repeatedly exposed to trauma, a resilient expressive profile is not necessarily having high abilities to suppress and enhance positive and negative emotional expressions, but adjusting these abilities in correspondence with the complex context of their lives.
Author Contributions
E.L.-G. and G.A.B. developed the concept of the paper; R.D. administrated the study and analyzed the results under the supervision of E.L.-G.; E.L.-G. and R.D. wrote a first draft of the report and G.A.B. read and commented on it. All authors have read and agreed to the published version of the manuscript.
Funding
The study was funded by the US–Israel Binational Science Foundation (BSF), Grant #2015_143 to E.L.-G. and G.A.B.
Conflicts of Interest
The authors declare no conflict of interest
Abbreviations
PTSD
Post-Traumatic Stress Disorder
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Post-Traumatic Stress Disorder (PTSD) symptomology as a function of duty-related repeated traumatic exposure and negative suppression.
Post-Traumatic Stress Disorder (PTSD) symptomology as a function of duty-related repeated traumatic exposure and positive suppression.
ijms-21-05355-t001_Table 1
Zero-order correlation between enhancement flexibility, suppression flexibility, expressive flexibility, PTSD symptomology and duty-related repeated traumatic exposure.
** p < 0.01. Post-Traumatic Stress Disorder (PTSD).
ijms-21-05355-t002_Table 2
Estimated coefficients, standard errors, and 95% confidence intervals for independent variable and moderator in the model predicting PTSD symptomology.
Variable
\\nβ\\n
SE
t Value
95% CI
Low
High
Duty-related repeated traumatic exposure
0.006
0.002
3 **
0.002
0.01
Expressive flexibility
−2.26
0.85
−2.65 **
−3.96
−0.56
Interaction
−0.004
0.002
−1.98 *
−0.001
0
β = Unstandardized Estimated Coefficients; SE = Standard Error; CI = Confidence Intervals. * p = 0.05. ** p < 0.01.
ijms-21-05355-t003_Table 3
Estimated coefficients, standard errors, and 95% confidence intervals for independent variable and moderators in the model predicting PTSD symptomology.
\\nNegative Suppression as a Moderator\\n
\\nVariable\\n
\\nβ\\n
\\nSE\\n
\\nt Value\\n
\\n95% CI\\n
\\nLow\\n
\\nHigh\\n
Duty-related repeated traumatic exposure
0.001
0.002
4.21 **
0.004
0.012
Negative suppression
−0.55
0.26
−2.14 *
−1.06
−0.04
Interaction
−0.001
0.000
−2.12 *
−0.002
−0.000
\\nPositive Suppression as a Moderator\\n
\\nVariable\\n
\\nβ\\n
\\nSE\\n
\\nt Value\\n
\\n95% CI\\n
\\nLow\\n
\\nHigh\\n
Duty-related repeated traumatic exposure
0.01
0.002.
4.77 **
0.006
0.015
Positive suppression
0.23
0.30
0.76
−0.37
0.83
Interaction
0.001
0.00
2.54 *
0.000
0.003
β = Unstandardized Estimated Coefficients; SE = Standard Error; CI = Confidence Intervals. * p < 0.05. ** p < 0.01.
\\n\"\n]"}}},{"rowIdx":460,"cells":{"data":{"kind":"list like","value":["\nInt J Environ Res Public HealthInt J Environ Res Public HealthijerphInternational Journal of Environmental Research and Public Health1661-78271660-4601MDPI32731381PMC743211710.3390/ijerph17155417ijerph-17-05417ArticleThe Impact of Environmental Social Media Publications on User Satisfaction with and Trust in Tourism BusinessesMartínez-NavalónJuan Gabrielhttps://orcid.org/0000-0001-5951-6392GelashviliVerahttps://orcid.org/0000-0002-9457-7745SauraJosé Ramón*Department of Business Economics, Rey Juan Carlos University, 28033 Madrid, Spain; juangabriel.martinez@urjc.es (J.G.M.-N.); vera.gelashvili@urjc.es (V.G.)Correspondence: joseramon.saura@urjc.es2872020820201715541706720202472020© 2020 by the authors.2020Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
The main aim of the present study was to analyze whether publications related to environmental sustainability in social media directly and positively influence user satisfaction with and trust in tourism businesses. Our second goal was to determine whether the influence of environmental sustainability and satisfaction is moderated by users’ gender. Data collection was performed using a questionnaire. The questionnaire responses were analyzed using the partial least squares-structural equation modeling (PLS-SEM) methodology. The results have shown that there is a positive relationship between environmental sustainability, satisfaction, and trust generated by tourism companies through their publications on social media, and that this relationship is not conditioned by users’ gender. The results of the present study contribute to the literature by bridging the gap in research on tourism enterprises and their strategies regarding social media publications. Our findings also provide important implications related to the content of environmental sustainability strategies and social media communication for tourism companies.
In recent decades, the emergence of social media has provided companies with direct and fast contact with their customers [1]. Nowadays, social media are among the “best possibilities available” means for companies to get in touch with potential customers [2]. By means of social media, companies can announce offers, promote new products and services, get to know about their customers’ preferences, and directly relate their offers [3,4]. Furthermore, several studies have demonstrated that the costs of traditional marketing are considerably higher than social media marketing [5,6,7,8].
Other advantages of using social media for companies include transparency, efficiency, and openness [8,9]. Several studies have highlighted the beneficial role of social media for business, showing that appropriate use of social media can help businesses to capture new customers and turn interested people into future customers [6,10,11,12].
Furthermore, companies also use social media to promote sustainability [8,13]. In recent years, sustainability, a recently emerging concept, has become a popular word among the general public [14,15]. While the meaning of sustainability varies depending on its context of use, such as social, economic, or ecological [16,17], most interpretations of sustainable development converge in that a company’s policies and actions must be respectful of the environment and be socially equitable if they want to contribute to economic growth [18]. That means that sustainable development should be responsible and include conscientious management of the environment to create a positive impact [19]. Accordingly, since sustainability issues are considered to be strategic topics to ensure companies’ success and operability, companies have been trying to strengthen and grow their sustainability [20,21,22]. In the long run, sustainability is a determining factor for firms’ achieving superior competitive advantage and financial performance [23].
Numerous studies have analyzed the sustainability of Spanish companies operating in different sectors [24,25,26]. Many of these studies have highlighted several important benefits that companies can obtain when they apply sustainability in their management model. However, although several studies emphasized the importance of tourism businesses in Spain [27], relevant research on the sustainability of tourism enterprises in Spain remains scarce.
At present, due to the rapid increase in the awareness of the population about sustainability issues, environmental sustainability is a key objective of many tourism companies. Accordingly, in order to follow this trend, along with other modifications [27], companies should modify their sustainability policies.
As demonstrated by Reyes-Menendez et al. [28], most tourism companies in Spain are sustainable, and their stakeholders are aware of it. However, the question is whether investing in environmental sustainability and informing stakeholders about it can increase customer satisfaction with and trust in tourism companies. To date, none of the previous studies has addressed this issue. To bridge this gap in the literature, the present study evaluates the influence of social media publications about sustainability on customer satisfaction with and trust in tourism businesses.
The main aim of the present study is to analyze the impact of social media on promoting environmental sustainability. The two research questions addressed in this study are as follows: (1) Can social media publications by tourism businesses about sustainability influence user satisfaction? (2) If so, can user satisfaction affect user trust in tourism businesses that share publications about sustainability in social media? Accordingly, the present study analyzes whether social media publications related to environmental sustainability directly and positively influence satisfaction and, indirectly and positively, user trust in tourism companies. Specific attention is paid to user gender. The analyses are performed from the user perspective, i.e., using a survey.
The remainder of this paper is structured as follows. Section 2 presents a review of relevant literature on marketing and sustainability in social media. In Section 3, hypotheses are formulated and the sample and methodology are presented. Section 4 reports the results that are discussed further in Section 5. Section 6 concludes.
2. Literature Review2.1. Social Media Marketing
In the 21st century, social media have become a new way of communication that enables people to obtain information and express their beliefs and ideas in a new way [29]. Social platforms, such as Facebook, Twitter, YouTube, Instagram, or LinkedIn, have entered people’s daily lives; today, people increasingly use these media for social interaction and information exchange [30,31]. Nowadays, social media are used in different fields like culture, entertainment, education, politics, economy, or business. In the business area, social media offer business owners’ new ways to communicate, collaborate, create [32], or receive feedback from their users [33,34].
One of the areas where social media are actively employed is marketing [2,3,35,36]. Therefore, since the emergence of social media, companies have changed the way they market their products and services [37]. Marketing through social media has become a tool that helps companies keep in touch with their users, promote the products and services, and transform this relationship into sales in the best case [38].
Many previous studies have demonstrated that using social media marketing can increase companies’ revenue and efficiency, reduce the expenses, help to stay in touch with potential and actual users, increase the impact on promotional activities, and so on [3,4,39]. Another advantage of social media marketing is its higher cost efficiency as compared to traditional marketing [5,7,40]. Owing to the advantages offered by social media marketing, companies are increasingly trying to use this type of marketing to promote their products and services and attract more customers.
However, marketing through social media also has several limitations. For instance, Maecker et al., [11] found that customers of the companies or specific products that use social media marketing have more service requests. Therefore, if enterprises have more service requests, they should invest more money in this area. Furthermore, Todor [40] reported that disadvantages of social media marketing include the lack of user trust due to the large number of frauds related to virtual promotions, cash-on-delivery systems, copyright issues, and so forth. Accordingly, if companies in areas as diverse as tourism, trade, or culture want to enjoy all the advantages offered by the use of marketing through social media, they should carefully consider potential hazards of this channel of marketing.
2.2. Environmental Sustainability in Social Media Marketing of Tourism Industry in Spain
The first official definition of sustainability comes from the United Nations General Assembly Report of the World Commission on Environment and Development. It was initially called “Our common future” and then renamed as the \"Brundtland report\" prepared by Brundtland et al. [41]. The report defines sustainability as “development that meets the needs of the present without compromising the ability of future generations to meet their own needs” (p.39). The report distinguished three different types of sustainability: environmental, economic, and social. According to Basiago [42], economic sustainability is understood as a production system that meets current consumption levels without compromising future needs, while social sustainability consists of a system of social organization that reduces poverty.
Finally, environmental sustainability requires the maintenance and protection of environmental resources for future generations. Therefore, environmental sustainability refers to different production and engineering processes that support and protect the environmental system [43]. Accordingly, environmental sustainability concerns the preservation and protection of natural resources and the development of alternative sources of energy, as well as maintenance of sustainable consumption levels, development of green products, and promotion of biodegradable products.
Since customers are the main marketing variable for the company, and all properly transferred information determines the success or failure of a business, marketing is an important factor related to sustainability [44]. This has given rise to the emergence of the new concept “sustainable marketing”, which is a combination of marketing and sustainability.
Fuller [45] defined sustainable marketing as “the process of planning, implementing, and controlling the development, pricing, promotion, and distribution of products in a manner that satisfies the following three criteria: customer needs are met, organizational goals are attained, and the process is compatible with ecosystem” (p. 4). However, despite the centrality of this concept today, only very few previous studies in the area of marketing have analyzed how enterprises communicate sustainability to the public [46].
Numerous studies demonstrated that social media belong to the most popular marketing tools [2,3,36,39]. Therefore, similarities between sustainable marketing and social media can be detected. For instance, Du et al. [13] pointed out that consumers increasingly seem to prefer eco-friendly offers. Accordingly, companies focus on their new sustainable product performance and pass that information on to their users. Using social media to promote the sustainable marketing of new products and services would be beneficial for companies. Several previous studies have investigated the effect of sustainable marketing through social media and its benefits for the companies [13,47]. However, relevant research on Spain, including its tourism sector that significantly contributes to the country’s economic growth, has been scarce [48].
The tourism industry plays an important role in the Spanish economy, generating a considerable dragging effect on other sectors [49]. According to a report prepared by the business association World Travel and Tourism [50], the tourism sector amounted to 14.6% of Spanish GDP.
In addition, travel and tourism created 1 in 5 new jobs over the last five years. Several authors have studied different aspects of the tourism industry, such as accessible tourism for everyone [51], climate change and its associated risks for the tourism industry [52], the importance of evaluating sustainable tourism and tourist destinations [53], cultural tourism and its evolution in the last years [54], or rural tourism facilities [55]. All these studies have emphasized the importance of the tourism industry for the Spanish economy and society. For instance, Rodríguez-Antón et al. [56] reported that, despite the recent emergence of the phenomenon of the so-called collaborative economy, there has been enormous growth and future potential of the collaborative tourism model in the tourism industry. This implies that the tourism industry will continue to grow positively and contribute to the economic development of the country.
3. Hypotheses Development3.1. Hypotheses
This section presents the hypotheses to be tested in the present study. Numerous previous studies have shown a strong link between marketing and sustainability and have come to the conclusion that environmental sustainability is a crucial factor for companies to create green products, be competitive in the market, increase profits, maintain employee loyalty, or maintain users trust and satisfaction towards them [57,58,59]. For instance, Walsh and Dodds [59] pointed out that the company’s activities have a significant impact on the environment and society in general. Accordingly, customers become increasingly sensible of the environmental, social, and economic implications of their actions towards reasonable consumption and, therefore, put some pressure on companies to implement environmentally friendly policies.
In this connection, a study by Pereira-Moliner et al. [60] showed that environmental management practices positively influence financial performance, market success, and the satisfaction of users and employees. However, few studies directly explored the relationship between environmental sustainability and user satisfaction [31]. Based on the considerations discussed above, the following hypothesis can be formulated:
Social media publications related to environmental sustainability would directly and positively influence user satisfaction.
According to McMurrian and Matulich [61], companies that generate the trust of their users are more likely to expand and improve their income. Numerous previous studies have analyzed user trust in marketing and management [62,63,64]. For instance, a study on travel agencies demonstrated that the more satisfied is a customer in his/her relationship with the travel agency, the more trust s/he attributed to that agency [65]. Likewise, Kassim and Asiah [66] found that customer satisfaction in e-commerce settings has a positive impact on customer trust. Based on the evidence reviewed above, the following hypothesis can be formulated:
Satisfaction of social media users with tourism companies directly and positively influences their trust in those companies.
Furthermore, while previous research on sustainability and satisfaction has demonstrated that more and more consumers support sustainable companies [67,68]. A study by Hou and Elliott [69] showed that compared to men, women are more involved in the search for information, dedicating more time and attention to what they need to know before making their purchase. Similarly, Trivedi and Teichert [70] found gender differences in consumer preferences and purchase intention. Based on this evidence, in the present study, the following hypothesis will be tested:
The influence of environmental sustainability and satisfaction is mediated by user gender.
Based on the hypotheses formulated above, the following model is proposed (see Figure 1).
3.2. Sampling and Methodology
This study aims to investigate whether social media publications about environmental sustainability influence user satisfaction and trust in tourism businesses. To this end, an on-line questionnaire was constructed. Data collection was performed in August 2019. The invitation to participate in the questionnaire was disseminated through e-mail and social media (Twitter, Inc., San Francisco, CA, USA) and Facebook (Facebook, Inc., Menlo Park, CA, USA)). Data collection was supported by active tourism companies and hotels in the province of Albacete (Spain); these companies granted the authors access to their customers through social media. The province of Albacete was chosen because this study is part of a series of investigations that aim to measure different variables in Spanish regions that suffer from the risk of depopulation, and where tourism can be one of the industries that can prevent such depopulation. The advantage of selecting this province is the interest shown by the companies to participate in the research since the results will allow them to improve in the future. However, since Spain is a very touristic country, the results of this study might not be generalizable to less touristic countries.
The questionnaire consisted of two blocks. The first block contained questions about demographic characteristics of the participants; in the second block, the participants were asked to rate a number of items on a 5-point Likert scale (1 = ‘strongly disagree’; 5 = ‘strongly agree’).
For the data analysis and the validation of the hypotheses, the model for the study of structural equations was used, which has its origin in variances (SEM). This model makes it possible to statistically analyze the predicted relationships by predicting the dependent variables, which makes it possible to calculate and quantify the direct and indirect effects of the variables on each other [71,72].
While there are several techniques based on variances, in the present study, the partial least squares (PLS) was used. This technique enables the analysis of composed and factor models, which makes it possible to measure variables and estimate the proposed model [71,73,74].
As argued in many previous studies, the PLS-SEM technique is one of the most complete methods for the analysis of models where relationships between variables are identified and the influence of these among other options is measured [73,75,76,77]. In addition, this technique is used to perform a multi-group analysis [31].
In the present study, a total of 362 questionnaire responses were obtained; however, since some responses were incomplete, only 351 questionnaires were included in the final sample. Therefore, the sample was sufficient for the analysis using the PLS-SEM technique. The use of this technique is also justified in investigating a novel topic on which available literature is scarce and where many different relationships are to be explored. Accordingly, this technique is widely used as an effective tool for exploratory analysis [78]. Using the PLS-SEM technique is also recommended when some of the analyzed variables are composed of dimensions [31], as well as when the proposed model is complex [79]. The PLS-SEM analysis was performed using SmartPLS 3.0 (SmartPLS GmbH, Bönningstedt, Germany), a widely used and reliable software [73,80].
4. Analysis of Results4.1. Descriptive Analysis
As can be seen in Table 1, gender distribution in the sample was balanced (52% women; 48% men). Most participants (52%) were 31–45 years old, followed by those aged 18–30 years old (19.9%) and 46–55 years old (17.6%). Almost two-thirds of the participants (62.4%) were employees, followed by self-employed (16.8%). In terms of time spent using social media, 37% used social media between 30 and 60 min a day, followed by 28.5% who used them for less than 30 min.
In terms of the preferred social platform, the respondents were given the possibility to check more than one option. Most participants (88.3%) used Facebook, followed by Instagram (59.8%) and YouTube (54.4%). Snapchat was regularly used by only 5.1% of the participants.
4.2. Measurement Model Analysis
The analysis using PLS-SEM unfolded in several steps [73]. First, the validation of the measurement scale was carried out. Second, the structural model analysis was performed. Third, the multi-group analysis investigating whether there was a moderation effect in the predicted relations according to user gender was run.
In the present study, the measurement scale was validated twice: first with the items of the multidimensional variable and, second, with the already grouped dimensions. This led to the establishment of the first- and second-order models [81].
In the first step, we validated the measurement scale twice. For the first-order model, all items of the variables were reflective; thus, the criteria to be tested were individual reliability, composite reliability, convergent validity, and discriminant validity.
In the first section, the items favorably outperformed the cutting indices of the first three criteria used, exceeding the value of 0.707 proposed by Carmines and Zeller [82] for individual reliability, Cronbach alpha of 0.70 in the criterion of Nunnally and Bernstein [83] for composite reliability, and 0.5 of the criterion of Fornell and Larcker [84] which sets the minimum level of average variance extracted (AVE) [75]. In the results, the Cronbach alpha values were very close to 0.9, which can incur possible errors [74]. Therefore, the Dijkstra-Henseler indicator (rho_A) was also analyzed, which gives the study results greater robustness, as it is considered the most reliable measure for the analysis of composite reliability [31,85]. The analysis was positive, as all constructs exceeded 0.7. All of the above indicated that all analyzed constructs were reliable and that they accounted for over 50% of the variance of the corresponding items [73]. Said differently, all constructs exceeded the minimum values of composite reliability and convergent validity (see Table 2).
In the last step of the validation of the first-order measurement scale, discriminant validity was analyzed. This was done by means of two analyses. The first analysis was based on Fornell and Larcker’s [84] method that analyzes the amount of variance captured by a variable from its indicators (AVE); this amount must be greater than the variance that this variable shares with other variables in the model [73].
The second analysis, called heterotrait-monotrait (HTMT), allows for a more rigorous analysis of the discriminatory validity criterion [86]. In this last criterion that can be seen in Table 3, not all items exceeded the threshold. Therefore, the items SAT2, HON2, COM1, and COM2 were eliminated (see Table 2).
Once the first-order measurement scale was validated, a grouping of the items of the multidimensional variable was performed, allowing for the second validation of the second-order model. In this model, the dimensions of the multidimensional variable “trust” had a formative character, which is why other analyses were carried out to validate the second-order scale.
First, all criteria previously made for reflective items were studied; however, in the second-order model, all items were kept (Table 4). Subsequently, the analysis was carried out for the formative variable to rule out collinearity problems. This was done by means of the evaluation of the variance inflation factor (VIF; Hair et al. [73,78]). Then, the magnitude of the variable’s weights [87] and significance [81] was evaluated. Table 4 shows the three dimensions of confidence that fully validate the measurement scale.
4.3. Structural Model Analysis
Furthermore, the analysis of the structural model was performed. This analysis aimed to evaluate the predictive capacity of the model and the relations predicted by the hypotheses. Before proceeding to the model analysis, collinearity in the structural model should be ruled out [88]. This is done by means of VIF values of the model which must not exceed 5 [73], followed by the analysis of the algebraic sign, the significance and magnitude of the coefficients: Path coefficients (β), R2 values (variance explained), the size of the effect ƒ2 and the Q2 test (validated cross redundancy) in order to measure the predictive relevance of the model [73,89,90].
With regard to the analysis of the predictive power, i.e., the amount of variance (0.75 relevant, 0.50 moderate, and 0.25 weak; see Hair et al. [78]; Henseler et al. [75]) of a variable explained by another predictive variable (determination coefficient R2), the values obtained for the variables were moderate (60%) for trust and weak (36%) for satisfaction.
Subsequently, the effect size of (f2) was analyzed; this value shows the level to which an exogenous variable contributes to explain an endogenous variable. Table 5 reports the results, and the two studied relationships have a large effect size.
As for the cross-validated redundancy indices (Q2), which help to examine the predictive relevance of the theoretical/structural model [87], these indices were greater than zero. Therefore, the model had a satisfactory predictive relevance.
Therefore, the relationships predicted by Hypotheses 1–2 were significant and had high predictive power and a significant influence effect. Therefore, Hypotheses 1 and 2 were supported by the results. The results of Hypothesis 3 are reported in Section 4.4.
4.4. Multi-Group Moderating Effect
To perform the analysis of the multi-group moderating effect, the measurement invariance test of the compounds must be carried out [31,81]. Such an analysis, called MICOM, is a prerequisite for carrying out a multi-group study [73,91,92]. In the present study, MICOM unfolded in the following three steps. First, we observed that the two subsamples (men and women) were tested for having the same configurational invariance, i.e., on whether they had exactly the same model. Second, compound invariance was searched for. It was stable, as the scores of a compound using the weights of group 1 did not differ from those created using the weights of group 2. Third, 3a and 3b are divided into two parts: 3a analyses equality of variances and 3b analyses equality of measures. Table 6 shows the results of the MICOM analysis.
The results of the MICOM analysis showed complete invariance, suggesting that both subsamples could be grouped together. Once the satisfactory result of the invariance was obtained, the multi-group analysis was performed using two non-parametric techniques, PLS-MGA and PERMUTATIONS, and one parametric technique, the Parametric Test. Table 7 shows that the results obtained using the different analysis techniques of multi-group moderation (Parametric Test, PLS-MGA, and Permutations).
The results of these analyses showed that user gender did not exert a moderating effect on the relationships between satisfaction-trust and environmental sustainability-satisfaction. Therefore, Hypothesis 3 was not supported by the results. Figure 2 shows the model validated for this study.
5. Discussion
This study investigated the relationship between social network publications by tourism companies where these companies promote their sustainable strategies and respect for the environment, on the one hand, and user satisfaction and trust, on the other hand. Supporting H1, our results showed that social media publications related to the environment and sustainability influence user satisfaction. This evidence is consistent with the results reported by Reilly and Hynan [17] and suggests that companies should focus on disseminating and preparing communication plans that support sustainable practices and are respectful towards the environment.
According to Azhar and Fauzan [93], user satisfaction is a key factor that plays a fundamental role in the marketing and development strategies of hotel companies, various tourism businesses, or local businesses that travelers explore while traveling. Similarly, to Saura et al. [34], the results of the present study showed that user satisfaction when browsing through social networks must be positive, as it has a real impact on user engagement with the organization.
Furthermore, supporting H2, our results showed that the satisfaction of social media users with tourism companies directly and positively influences their trust in those companies. This result is consistent with the findings reported by Cori et al. [94], in the political field. Therefore, companies should increase travelers confidence in their sustainable policies, rather than in their messages related to promotions, discounts, or elements that do not add value to the marketing strategy.
In addition, in line with Apperson et al. [95], and Bopp et al. [96], our results showed that, while the strategy of social media publications can improve user trust in tourism businesses, such elements as pollution, society, unsustainable strategies or environmental pollutants in the tourism environment can make tourists feel uncomfortable during their travel.
Based on these findings, it can be concluded that the appropriate content strategy for managers of tourism companies would be to create messages that focus on maintaining values related to the environment [72]. As suggested by Heinonen [97], the messages revealed by companies on social networks can influence user behavior. Another important aspect to consider is how users organize themselves in communities on social networks and interact with the messages that companies launch [98]. These strategies based on social media posts demonstrate user interest in maintaining two-way communication with companies in digital environments [99].
The results of the present study confirm that social media communications should follow a sustainable policy and disseminate relevant environment-related achievements and policies adopted by tourism companies. In this way, users will be able to share the publications with their peers, use those publications for their own purposes, or demonstrate their pride in the fact that they are using the services of an environmentally conscious tourism company to their peers in social network communities.
Finally, our results showed that gender does not moderate user appreciation of social media publications related to sustainability and the environment. Therefore, our findings do not support [100] as the conclusion about gender influence on user behavior in social networks, particularly in the sustainable tourism sector. In contrast, other authors, such as Palos-Sanchez et al. [101], consider the study of gender as an important factor in drawing robust conclusions.
However, considering that our study is the first attempt to investigate the impact of gender on user appreciation of environment- and sustainability-related publications in social media, this topic deserves further in-depth investigation in future research.
As indicated above, social network publications have been studied from different perspectives [102]. However, there is room for improvement to study the feelings of users when their followers support their publications, adding likes, and sharing content on social networks [103].
From this analytical perspective, the study of how users obtain immediate rewards on an emotional level on social media is interesting to set future research objectives in this area. By analyzing these findings, it will be possible to understand how sustainable publications can contribute to creating a positive impact on consumers through social media [104].
In addition, we agree with the results of Tomomi [105], since companies should add environmental supportive content to their communication and marketing plans. A relevant example would be companies’ communicating the content related to supporting sustainable projects and actions, which will both boost their environmental reputation online and make their followers support their content marketing plans by sharing this kind of content.
6. Conclusions
In the present study, the analysis unfolded in the following two blocks. In the first block, the proposed model of how environmental sustainability, directly and indirectly, influences social media users’ satisfaction and trust was analyzed. In a second block, we investigated whether the model can be moderated by user gender. Of note, previous studies that analyzed the relationship between sustainability and social media user satisfaction and trust are scarce, which complicated formulating the hypotheses based on available literature.
The results showed that environmentally sustainable tourism companies generate greater satisfaction among their users. In addition, environmentally sustainable tourism companies also indirectly induced user trust. Therefore, the first two hypotheses tested in this study (H1 and H2) were supported by the results.
However, our prediction that user gender may moderate the aforementioned relationships was not supported by the results. Therefore, H3 had to be rejected.
6.1. Theoretical Implications
As discussed in Section 1, this study aimed to bridge a gap in previous literature by investigating the impact of social media publications by tourism companies on user satisfaction and trust. In doing so, the present study contributes to available knowledge about the role of social media publications in the tourism sector. Furthermore, to the best of our knowledge, the present study is the first to investigate whether user gender plays a role in user evaluation of social media publications related to sustainability and the environment. The results of the present study suggest meaningful directions of further research in this area, including elements such as the organization of users in communities in social networks, the type of interaction according to the message published in the tourism ecosystem, and the concerns and behaviors of users when interacting with this type of content. Our findings also highlight that the content and communication strategies developed by companies in the tourism sector should focus on actions related to environmental sustainability.
6.2. Practical Implications
As for the practical implications for the improvement of the management of tourism companies, it should be pointed out that environmental sustainability is a growing asset within the tourism sector. This implies that an organization committed to environmental sustainability and the one that explicitly reveals this message to its customers will substantially improve user satisfaction and the trust of its stakeholders. The implementation of waste separation policies, the use of products with ecological packaging, reduction of energy costs, and other relevant eco-friendly policies will help the company to pollute less and increase its customers’ purchasing possibilities.
Our findings highlight that the communication strategies of tourism companies should explicitly inform their customers about various sustainability policies implemented. This will improve companies’ public profiles, lead to an increase in the number of customers, and thus expand companies’ influence on society.
Finally, since we did not find any gender differences, managers of tourism companies should take into account that in communicating policies, the strategies should not differentiate between men and women, since both groups have the same perceptions of sustainability and environmental issues. Therefore, campaigns should be carried out in the same way for both genders.
6.3. Limitations and Future Research
The present study has several limitations. First, the size of the sample was rather limited, particularly given that there are possibilities to get larger samples in the tourism industry. Second, due to the paucity of relevant prior research, it was difficult to ground the formulated hypotheses in past work. Thirdly, due to the novelty of the topic, the theoretical framework that the present study is based on is exploratory. In order to expand the present investigation, further research that would replicate the present study design in other sectors or countries, as well as expand the model by adding other marketing variables, such as commitment, loyalty, economic sustainability, or social sustainability.
Author Contributions
Conceptualization, J.G.M.-N., V.G. and J.R.S.; methodology, J.G.M.-N.; software, J.G.M.-N.; validation, V.G. and J.R.S; formal analysis, J.G.M.-N., V.G. and J.R.S.; investigation, J.G.M.-N., V.G. and J.R.S.; data curation, J.G.M.-N.; writing—original draft preparation, J.G.M.-N., V.G. and J.R.S.; writing—review and editing, J.G.M.-N., V.G. and J.R.S; supervision, J.R.S. All authors have read and agreed to the published version of the manuscript.
Funding
This research received no external funding.
Conflicts of Interest
The authors declare no conflict of interest.
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Research model used in the present study.
Proposed research model results.
ijerph-17-05417-t001_Table 1
Characteristics of the study participants (n = 351).
Classification Variable
Variable
Frequency
Percentage
Gender
Female
182
52%
Male
169
48%
Age
18–30
70
19.9%
31–45
184
52.4%
46–55
62
17.6%
56–65
19
5.4%
>65
16
4.6%
Employment
Student
32
9.1%
Housewife/man
14
3.9%
Unemployed
27
7.6%
Employed
219
62.4%
Self-Employed
59
16.8%
Minutes devoted to social media per day
<30
100
28.5%
30–60
130
37%
60–90
40
11.4%
90–120
36
10.3%
>120
45
12.8%
Social media used(multiple responses)
Twitter
126
35.9%
Facebook
310
88.3%
Instagram
210
59.8%
LinkedIn
160
45.6%
YouTube
191
54.4%
Snapchat
18
5.1%
Pinterest
65
18.5%
ijerph-17-05417-t002_Table 2
Measurement items first order.
Constructs
Items
Correlation Loading
CA
CR
rho_A
AVE
Satisfaction
(SAT1) I am satisfied with the knowledge I get from the social media of the tourism companies I follow.
0.885 ***
0.790
0.904
0.820
0.824
(SAT3) The social media of the tourism companies I follow meet my expectations.
0.930 ***
Honesty
(HON1) The tourism companies that I follow on social media keep their promises.
0.933 ***
0.805
0.832
0.832
0.835
(HON3) The social media of the tourism companies I follow are managed in an ethical and transparent way.
0.894 ***
Benevolence
(BEN1) The social media of the tourism companies I follow offer beneficial advice and recommendations.
0.884 ***
0.883
0.928
0.885
0.811
(BEN2) The tourism companies that I follow in social media develop actions taking into account how they will affect their interest groups.
0.871 ***
(BEN3) The tourism companies I follow on social media are concerned about the interests and benefits, both present and future, of their stakeholders.
0.956 ***
Competence
(COM3) The tourism companies that I follow have a knowledge of their users that allows them to adapt to users’ needs.
0.904 ***
1.00
1.00
1.00
1.00
Environmental Sustainability
(SUST1) The tourism companies that I follow on social media have recycling policies.
0.740 ***
0.882
0.914
0.894
0.680
(SOST2) The social media accounts of the tourism companies that I follow promote positive environmental ethics.
0.869 ***
(SUST3) The tourism companies that I follow in social media value and protect the environment.
0.859 ***
(SUST4) The social media of the tourism companies I follow publish pollution awareness messages.
0.834 ***
(SUST5) The social media of the tourism companies that I follow defend the diversity of nature and promote its value and protection.
R2: Trust = 0.607; Satisfaction = 0.360; Adjusted R2: Trust = 0.606; Satisfaction = 0.358; Q2: Trust = 0.460; Satisfaction = 0.275. Students in single queue *** p < 0.001.
ijerph-17-05417-t006_Table 6
Results of invariance measurement testing using permutation.
Constructs
Invariance
Composition Invariance
Partial Invariance
Equal Mean Assessment
Equal Variance Assessment
Full Measurement Invariance Established
C = 1
Confidence Interval
Differences
Confidence Interval
Equal
Differences
Confidence Interval
Equal
TRUST
Yes
0.999
(0.975/1.000)
Yes
−0.043
(−0.219/0.221)
Yes
0.046
(−0.482/0.469)
Yes
Yes
SAT
Yes
1.000
(0.999/1.000)
Yes
−0.144
(−0.225/0.219)
Yes
−0.074
(−0.393/0.390)
Yes
Yes
SUST
Yes
1.000
(0.997/1.000)
Yes
−0.010
(−0.217/0.221)
Yes
−0.197
(−0.403/0.400)
Yes
Yes
Note: SAT = Satisfaction; SUST = Environmental Sustainability.
ijerph-17-05417-t007_Table 7
Result for Hypothesis 3.
\n
Path Coefficient
Confidence Interval(2.5%; 97.5%)
p-Value Difference
\n
Relationship
Men
Women
Difference
\n
PLS-MGA
Permutation Test
Parametric Test
Supported
SAT→TRUST
0.752
0.806
−0.054
(−0.117; 0.118)
0.821
0.360
0.185
NO/NO/NO
SUST→SAT
0.593
0.609
−0.016
(−0.194; 0.192)
0.569
0.872
0.435
NO/NO/NO
Note: SAT = Satisfaction; SUST = Environmental Sustainability.
\n"],"string":"[\n \"\\nInt J Environ Res Public HealthInt J Environ Res Public HealthijerphInternational Journal of Environmental Research and Public Health1661-78271660-4601MDPI32731381PMC743211710.3390/ijerph17155417ijerph-17-05417ArticleThe Impact of Environmental Social Media Publications on User Satisfaction with and Trust in Tourism BusinessesMartínez-NavalónJuan Gabrielhttps://orcid.org/0000-0001-5951-6392GelashviliVerahttps://orcid.org/0000-0002-9457-7745SauraJosé Ramón*Department of Business Economics, Rey Juan Carlos University, 28033 Madrid, Spain; juangabriel.martinez@urjc.es (J.G.M.-N.); vera.gelashvili@urjc.es (V.G.)Correspondence: joseramon.saura@urjc.es2872020820201715541706720202472020© 2020 by the authors.2020Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
The main aim of the present study was to analyze whether publications related to environmental sustainability in social media directly and positively influence user satisfaction with and trust in tourism businesses. Our second goal was to determine whether the influence of environmental sustainability and satisfaction is moderated by users’ gender. Data collection was performed using a questionnaire. The questionnaire responses were analyzed using the partial least squares-structural equation modeling (PLS-SEM) methodology. The results have shown that there is a positive relationship between environmental sustainability, satisfaction, and trust generated by tourism companies through their publications on social media, and that this relationship is not conditioned by users’ gender. The results of the present study contribute to the literature by bridging the gap in research on tourism enterprises and their strategies regarding social media publications. Our findings also provide important implications related to the content of environmental sustainability strategies and social media communication for tourism companies.
In recent decades, the emergence of social media has provided companies with direct and fast contact with their customers [1]. Nowadays, social media are among the “best possibilities available” means for companies to get in touch with potential customers [2]. By means of social media, companies can announce offers, promote new products and services, get to know about their customers’ preferences, and directly relate their offers [3,4]. Furthermore, several studies have demonstrated that the costs of traditional marketing are considerably higher than social media marketing [5,6,7,8].
Other advantages of using social media for companies include transparency, efficiency, and openness [8,9]. Several studies have highlighted the beneficial role of social media for business, showing that appropriate use of social media can help businesses to capture new customers and turn interested people into future customers [6,10,11,12].
Furthermore, companies also use social media to promote sustainability [8,13]. In recent years, sustainability, a recently emerging concept, has become a popular word among the general public [14,15]. While the meaning of sustainability varies depending on its context of use, such as social, economic, or ecological [16,17], most interpretations of sustainable development converge in that a company’s policies and actions must be respectful of the environment and be socially equitable if they want to contribute to economic growth [18]. That means that sustainable development should be responsible and include conscientious management of the environment to create a positive impact [19]. Accordingly, since sustainability issues are considered to be strategic topics to ensure companies’ success and operability, companies have been trying to strengthen and grow their sustainability [20,21,22]. In the long run, sustainability is a determining factor for firms’ achieving superior competitive advantage and financial performance [23].
Numerous studies have analyzed the sustainability of Spanish companies operating in different sectors [24,25,26]. Many of these studies have highlighted several important benefits that companies can obtain when they apply sustainability in their management model. However, although several studies emphasized the importance of tourism businesses in Spain [27], relevant research on the sustainability of tourism enterprises in Spain remains scarce.
At present, due to the rapid increase in the awareness of the population about sustainability issues, environmental sustainability is a key objective of many tourism companies. Accordingly, in order to follow this trend, along with other modifications [27], companies should modify their sustainability policies.
As demonstrated by Reyes-Menendez et al. [28], most tourism companies in Spain are sustainable, and their stakeholders are aware of it. However, the question is whether investing in environmental sustainability and informing stakeholders about it can increase customer satisfaction with and trust in tourism companies. To date, none of the previous studies has addressed this issue. To bridge this gap in the literature, the present study evaluates the influence of social media publications about sustainability on customer satisfaction with and trust in tourism businesses.
The main aim of the present study is to analyze the impact of social media on promoting environmental sustainability. The two research questions addressed in this study are as follows: (1) Can social media publications by tourism businesses about sustainability influence user satisfaction? (2) If so, can user satisfaction affect user trust in tourism businesses that share publications about sustainability in social media? Accordingly, the present study analyzes whether social media publications related to environmental sustainability directly and positively influence satisfaction and, indirectly and positively, user trust in tourism companies. Specific attention is paid to user gender. The analyses are performed from the user perspective, i.e., using a survey.
The remainder of this paper is structured as follows. Section 2 presents a review of relevant literature on marketing and sustainability in social media. In Section 3, hypotheses are formulated and the sample and methodology are presented. Section 4 reports the results that are discussed further in Section 5. Section 6 concludes.
2. Literature Review2.1. Social Media Marketing
In the 21st century, social media have become a new way of communication that enables people to obtain information and express their beliefs and ideas in a new way [29]. Social platforms, such as Facebook, Twitter, YouTube, Instagram, or LinkedIn, have entered people’s daily lives; today, people increasingly use these media for social interaction and information exchange [30,31]. Nowadays, social media are used in different fields like culture, entertainment, education, politics, economy, or business. In the business area, social media offer business owners’ new ways to communicate, collaborate, create [32], or receive feedback from their users [33,34].
One of the areas where social media are actively employed is marketing [2,3,35,36]. Therefore, since the emergence of social media, companies have changed the way they market their products and services [37]. Marketing through social media has become a tool that helps companies keep in touch with their users, promote the products and services, and transform this relationship into sales in the best case [38].
Many previous studies have demonstrated that using social media marketing can increase companies’ revenue and efficiency, reduce the expenses, help to stay in touch with potential and actual users, increase the impact on promotional activities, and so on [3,4,39]. Another advantage of social media marketing is its higher cost efficiency as compared to traditional marketing [5,7,40]. Owing to the advantages offered by social media marketing, companies are increasingly trying to use this type of marketing to promote their products and services and attract more customers.
However, marketing through social media also has several limitations. For instance, Maecker et al., [11] found that customers of the companies or specific products that use social media marketing have more service requests. Therefore, if enterprises have more service requests, they should invest more money in this area. Furthermore, Todor [40] reported that disadvantages of social media marketing include the lack of user trust due to the large number of frauds related to virtual promotions, cash-on-delivery systems, copyright issues, and so forth. Accordingly, if companies in areas as diverse as tourism, trade, or culture want to enjoy all the advantages offered by the use of marketing through social media, they should carefully consider potential hazards of this channel of marketing.
2.2. Environmental Sustainability in Social Media Marketing of Tourism Industry in Spain
The first official definition of sustainability comes from the United Nations General Assembly Report of the World Commission on Environment and Development. It was initially called “Our common future” and then renamed as the \\\"Brundtland report\\\" prepared by Brundtland et al. [41]. The report defines sustainability as “development that meets the needs of the present without compromising the ability of future generations to meet their own needs” (p.39). The report distinguished three different types of sustainability: environmental, economic, and social. According to Basiago [42], economic sustainability is understood as a production system that meets current consumption levels without compromising future needs, while social sustainability consists of a system of social organization that reduces poverty.
Finally, environmental sustainability requires the maintenance and protection of environmental resources for future generations. Therefore, environmental sustainability refers to different production and engineering processes that support and protect the environmental system [43]. Accordingly, environmental sustainability concerns the preservation and protection of natural resources and the development of alternative sources of energy, as well as maintenance of sustainable consumption levels, development of green products, and promotion of biodegradable products.
Since customers are the main marketing variable for the company, and all properly transferred information determines the success or failure of a business, marketing is an important factor related to sustainability [44]. This has given rise to the emergence of the new concept “sustainable marketing”, which is a combination of marketing and sustainability.
Fuller [45] defined sustainable marketing as “the process of planning, implementing, and controlling the development, pricing, promotion, and distribution of products in a manner that satisfies the following three criteria: customer needs are met, organizational goals are attained, and the process is compatible with ecosystem” (p. 4). However, despite the centrality of this concept today, only very few previous studies in the area of marketing have analyzed how enterprises communicate sustainability to the public [46].
Numerous studies demonstrated that social media belong to the most popular marketing tools [2,3,36,39]. Therefore, similarities between sustainable marketing and social media can be detected. For instance, Du et al. [13] pointed out that consumers increasingly seem to prefer eco-friendly offers. Accordingly, companies focus on their new sustainable product performance and pass that information on to their users. Using social media to promote the sustainable marketing of new products and services would be beneficial for companies. Several previous studies have investigated the effect of sustainable marketing through social media and its benefits for the companies [13,47]. However, relevant research on Spain, including its tourism sector that significantly contributes to the country’s economic growth, has been scarce [48].
The tourism industry plays an important role in the Spanish economy, generating a considerable dragging effect on other sectors [49]. According to a report prepared by the business association World Travel and Tourism [50], the tourism sector amounted to 14.6% of Spanish GDP.
In addition, travel and tourism created 1 in 5 new jobs over the last five years. Several authors have studied different aspects of the tourism industry, such as accessible tourism for everyone [51], climate change and its associated risks for the tourism industry [52], the importance of evaluating sustainable tourism and tourist destinations [53], cultural tourism and its evolution in the last years [54], or rural tourism facilities [55]. All these studies have emphasized the importance of the tourism industry for the Spanish economy and society. For instance, Rodríguez-Antón et al. [56] reported that, despite the recent emergence of the phenomenon of the so-called collaborative economy, there has been enormous growth and future potential of the collaborative tourism model in the tourism industry. This implies that the tourism industry will continue to grow positively and contribute to the economic development of the country.
3. Hypotheses Development3.1. Hypotheses
This section presents the hypotheses to be tested in the present study. Numerous previous studies have shown a strong link between marketing and sustainability and have come to the conclusion that environmental sustainability is a crucial factor for companies to create green products, be competitive in the market, increase profits, maintain employee loyalty, or maintain users trust and satisfaction towards them [57,58,59]. For instance, Walsh and Dodds [59] pointed out that the company’s activities have a significant impact on the environment and society in general. Accordingly, customers become increasingly sensible of the environmental, social, and economic implications of their actions towards reasonable consumption and, therefore, put some pressure on companies to implement environmentally friendly policies.
In this connection, a study by Pereira-Moliner et al. [60] showed that environmental management practices positively influence financial performance, market success, and the satisfaction of users and employees. However, few studies directly explored the relationship between environmental sustainability and user satisfaction [31]. Based on the considerations discussed above, the following hypothesis can be formulated:
Social media publications related to environmental sustainability would directly and positively influence user satisfaction.
According to McMurrian and Matulich [61], companies that generate the trust of their users are more likely to expand and improve their income. Numerous previous studies have analyzed user trust in marketing and management [62,63,64]. For instance, a study on travel agencies demonstrated that the more satisfied is a customer in his/her relationship with the travel agency, the more trust s/he attributed to that agency [65]. Likewise, Kassim and Asiah [66] found that customer satisfaction in e-commerce settings has a positive impact on customer trust. Based on the evidence reviewed above, the following hypothesis can be formulated:
Satisfaction of social media users with tourism companies directly and positively influences their trust in those companies.
Furthermore, while previous research on sustainability and satisfaction has demonstrated that more and more consumers support sustainable companies [67,68]. A study by Hou and Elliott [69] showed that compared to men, women are more involved in the search for information, dedicating more time and attention to what they need to know before making their purchase. Similarly, Trivedi and Teichert [70] found gender differences in consumer preferences and purchase intention. Based on this evidence, in the present study, the following hypothesis will be tested:
The influence of environmental sustainability and satisfaction is mediated by user gender.
Based on the hypotheses formulated above, the following model is proposed (see Figure 1).
3.2. Sampling and Methodology
This study aims to investigate whether social media publications about environmental sustainability influence user satisfaction and trust in tourism businesses. To this end, an on-line questionnaire was constructed. Data collection was performed in August 2019. The invitation to participate in the questionnaire was disseminated through e-mail and social media (Twitter, Inc., San Francisco, CA, USA) and Facebook (Facebook, Inc., Menlo Park, CA, USA)). Data collection was supported by active tourism companies and hotels in the province of Albacete (Spain); these companies granted the authors access to their customers through social media. The province of Albacete was chosen because this study is part of a series of investigations that aim to measure different variables in Spanish regions that suffer from the risk of depopulation, and where tourism can be one of the industries that can prevent such depopulation. The advantage of selecting this province is the interest shown by the companies to participate in the research since the results will allow them to improve in the future. However, since Spain is a very touristic country, the results of this study might not be generalizable to less touristic countries.
The questionnaire consisted of two blocks. The first block contained questions about demographic characteristics of the participants; in the second block, the participants were asked to rate a number of items on a 5-point Likert scale (1 = ‘strongly disagree’; 5 = ‘strongly agree’).
For the data analysis and the validation of the hypotheses, the model for the study of structural equations was used, which has its origin in variances (SEM). This model makes it possible to statistically analyze the predicted relationships by predicting the dependent variables, which makes it possible to calculate and quantify the direct and indirect effects of the variables on each other [71,72].
While there are several techniques based on variances, in the present study, the partial least squares (PLS) was used. This technique enables the analysis of composed and factor models, which makes it possible to measure variables and estimate the proposed model [71,73,74].
As argued in many previous studies, the PLS-SEM technique is one of the most complete methods for the analysis of models where relationships between variables are identified and the influence of these among other options is measured [73,75,76,77]. In addition, this technique is used to perform a multi-group analysis [31].
In the present study, a total of 362 questionnaire responses were obtained; however, since some responses were incomplete, only 351 questionnaires were included in the final sample. Therefore, the sample was sufficient for the analysis using the PLS-SEM technique. The use of this technique is also justified in investigating a novel topic on which available literature is scarce and where many different relationships are to be explored. Accordingly, this technique is widely used as an effective tool for exploratory analysis [78]. Using the PLS-SEM technique is also recommended when some of the analyzed variables are composed of dimensions [31], as well as when the proposed model is complex [79]. The PLS-SEM analysis was performed using SmartPLS 3.0 (SmartPLS GmbH, Bönningstedt, Germany), a widely used and reliable software [73,80].
4. Analysis of Results4.1. Descriptive Analysis
As can be seen in Table 1, gender distribution in the sample was balanced (52% women; 48% men). Most participants (52%) were 31–45 years old, followed by those aged 18–30 years old (19.9%) and 46–55 years old (17.6%). Almost two-thirds of the participants (62.4%) were employees, followed by self-employed (16.8%). In terms of time spent using social media, 37% used social media between 30 and 60 min a day, followed by 28.5% who used them for less than 30 min.
In terms of the preferred social platform, the respondents were given the possibility to check more than one option. Most participants (88.3%) used Facebook, followed by Instagram (59.8%) and YouTube (54.4%). Snapchat was regularly used by only 5.1% of the participants.
4.2. Measurement Model Analysis
The analysis using PLS-SEM unfolded in several steps [73]. First, the validation of the measurement scale was carried out. Second, the structural model analysis was performed. Third, the multi-group analysis investigating whether there was a moderation effect in the predicted relations according to user gender was run.
In the present study, the measurement scale was validated twice: first with the items of the multidimensional variable and, second, with the already grouped dimensions. This led to the establishment of the first- and second-order models [81].
In the first step, we validated the measurement scale twice. For the first-order model, all items of the variables were reflective; thus, the criteria to be tested were individual reliability, composite reliability, convergent validity, and discriminant validity.
In the first section, the items favorably outperformed the cutting indices of the first three criteria used, exceeding the value of 0.707 proposed by Carmines and Zeller [82] for individual reliability, Cronbach alpha of 0.70 in the criterion of Nunnally and Bernstein [83] for composite reliability, and 0.5 of the criterion of Fornell and Larcker [84] which sets the minimum level of average variance extracted (AVE) [75]. In the results, the Cronbach alpha values were very close to 0.9, which can incur possible errors [74]. Therefore, the Dijkstra-Henseler indicator (rho_A) was also analyzed, which gives the study results greater robustness, as it is considered the most reliable measure for the analysis of composite reliability [31,85]. The analysis was positive, as all constructs exceeded 0.7. All of the above indicated that all analyzed constructs were reliable and that they accounted for over 50% of the variance of the corresponding items [73]. Said differently, all constructs exceeded the minimum values of composite reliability and convergent validity (see Table 2).
In the last step of the validation of the first-order measurement scale, discriminant validity was analyzed. This was done by means of two analyses. The first analysis was based on Fornell and Larcker’s [84] method that analyzes the amount of variance captured by a variable from its indicators (AVE); this amount must be greater than the variance that this variable shares with other variables in the model [73].
The second analysis, called heterotrait-monotrait (HTMT), allows for a more rigorous analysis of the discriminatory validity criterion [86]. In this last criterion that can be seen in Table 3, not all items exceeded the threshold. Therefore, the items SAT2, HON2, COM1, and COM2 were eliminated (see Table 2).
Once the first-order measurement scale was validated, a grouping of the items of the multidimensional variable was performed, allowing for the second validation of the second-order model. In this model, the dimensions of the multidimensional variable “trust” had a formative character, which is why other analyses were carried out to validate the second-order scale.
First, all criteria previously made for reflective items were studied; however, in the second-order model, all items were kept (Table 4). Subsequently, the analysis was carried out for the formative variable to rule out collinearity problems. This was done by means of the evaluation of the variance inflation factor (VIF; Hair et al. [73,78]). Then, the magnitude of the variable’s weights [87] and significance [81] was evaluated. Table 4 shows the three dimensions of confidence that fully validate the measurement scale.
4.3. Structural Model Analysis
Furthermore, the analysis of the structural model was performed. This analysis aimed to evaluate the predictive capacity of the model and the relations predicted by the hypotheses. Before proceeding to the model analysis, collinearity in the structural model should be ruled out [88]. This is done by means of VIF values of the model which must not exceed 5 [73], followed by the analysis of the algebraic sign, the significance and magnitude of the coefficients: Path coefficients (β), R2 values (variance explained), the size of the effect ƒ2 and the Q2 test (validated cross redundancy) in order to measure the predictive relevance of the model [73,89,90].
With regard to the analysis of the predictive power, i.e., the amount of variance (0.75 relevant, 0.50 moderate, and 0.25 weak; see Hair et al. [78]; Henseler et al. [75]) of a variable explained by another predictive variable (determination coefficient R2), the values obtained for the variables were moderate (60%) for trust and weak (36%) for satisfaction.
Subsequently, the effect size of (f2) was analyzed; this value shows the level to which an exogenous variable contributes to explain an endogenous variable. Table 5 reports the results, and the two studied relationships have a large effect size.
As for the cross-validated redundancy indices (Q2), which help to examine the predictive relevance of the theoretical/structural model [87], these indices were greater than zero. Therefore, the model had a satisfactory predictive relevance.
Therefore, the relationships predicted by Hypotheses 1–2 were significant and had high predictive power and a significant influence effect. Therefore, Hypotheses 1 and 2 were supported by the results. The results of Hypothesis 3 are reported in Section 4.4.
4.4. Multi-Group Moderating Effect
To perform the analysis of the multi-group moderating effect, the measurement invariance test of the compounds must be carried out [31,81]. Such an analysis, called MICOM, is a prerequisite for carrying out a multi-group study [73,91,92]. In the present study, MICOM unfolded in the following three steps. First, we observed that the two subsamples (men and women) were tested for having the same configurational invariance, i.e., on whether they had exactly the same model. Second, compound invariance was searched for. It was stable, as the scores of a compound using the weights of group 1 did not differ from those created using the weights of group 2. Third, 3a and 3b are divided into two parts: 3a analyses equality of variances and 3b analyses equality of measures. Table 6 shows the results of the MICOM analysis.
The results of the MICOM analysis showed complete invariance, suggesting that both subsamples could be grouped together. Once the satisfactory result of the invariance was obtained, the multi-group analysis was performed using two non-parametric techniques, PLS-MGA and PERMUTATIONS, and one parametric technique, the Parametric Test. Table 7 shows that the results obtained using the different analysis techniques of multi-group moderation (Parametric Test, PLS-MGA, and Permutations).
The results of these analyses showed that user gender did not exert a moderating effect on the relationships between satisfaction-trust and environmental sustainability-satisfaction. Therefore, Hypothesis 3 was not supported by the results. Figure 2 shows the model validated for this study.
5. Discussion
This study investigated the relationship between social network publications by tourism companies where these companies promote their sustainable strategies and respect for the environment, on the one hand, and user satisfaction and trust, on the other hand. Supporting H1, our results showed that social media publications related to the environment and sustainability influence user satisfaction. This evidence is consistent with the results reported by Reilly and Hynan [17] and suggests that companies should focus on disseminating and preparing communication plans that support sustainable practices and are respectful towards the environment.
According to Azhar and Fauzan [93], user satisfaction is a key factor that plays a fundamental role in the marketing and development strategies of hotel companies, various tourism businesses, or local businesses that travelers explore while traveling. Similarly, to Saura et al. [34], the results of the present study showed that user satisfaction when browsing through social networks must be positive, as it has a real impact on user engagement with the organization.
Furthermore, supporting H2, our results showed that the satisfaction of social media users with tourism companies directly and positively influences their trust in those companies. This result is consistent with the findings reported by Cori et al. [94], in the political field. Therefore, companies should increase travelers confidence in their sustainable policies, rather than in their messages related to promotions, discounts, or elements that do not add value to the marketing strategy.
In addition, in line with Apperson et al. [95], and Bopp et al. [96], our results showed that, while the strategy of social media publications can improve user trust in tourism businesses, such elements as pollution, society, unsustainable strategies or environmental pollutants in the tourism environment can make tourists feel uncomfortable during their travel.
Based on these findings, it can be concluded that the appropriate content strategy for managers of tourism companies would be to create messages that focus on maintaining values related to the environment [72]. As suggested by Heinonen [97], the messages revealed by companies on social networks can influence user behavior. Another important aspect to consider is how users organize themselves in communities on social networks and interact with the messages that companies launch [98]. These strategies based on social media posts demonstrate user interest in maintaining two-way communication with companies in digital environments [99].
The results of the present study confirm that social media communications should follow a sustainable policy and disseminate relevant environment-related achievements and policies adopted by tourism companies. In this way, users will be able to share the publications with their peers, use those publications for their own purposes, or demonstrate their pride in the fact that they are using the services of an environmentally conscious tourism company to their peers in social network communities.
Finally, our results showed that gender does not moderate user appreciation of social media publications related to sustainability and the environment. Therefore, our findings do not support [100] as the conclusion about gender influence on user behavior in social networks, particularly in the sustainable tourism sector. In contrast, other authors, such as Palos-Sanchez et al. [101], consider the study of gender as an important factor in drawing robust conclusions.
However, considering that our study is the first attempt to investigate the impact of gender on user appreciation of environment- and sustainability-related publications in social media, this topic deserves further in-depth investigation in future research.
As indicated above, social network publications have been studied from different perspectives [102]. However, there is room for improvement to study the feelings of users when their followers support their publications, adding likes, and sharing content on social networks [103].
From this analytical perspective, the study of how users obtain immediate rewards on an emotional level on social media is interesting to set future research objectives in this area. By analyzing these findings, it will be possible to understand how sustainable publications can contribute to creating a positive impact on consumers through social media [104].
In addition, we agree with the results of Tomomi [105], since companies should add environmental supportive content to their communication and marketing plans. A relevant example would be companies’ communicating the content related to supporting sustainable projects and actions, which will both boost their environmental reputation online and make their followers support their content marketing plans by sharing this kind of content.
6. Conclusions
In the present study, the analysis unfolded in the following two blocks. In the first block, the proposed model of how environmental sustainability, directly and indirectly, influences social media users’ satisfaction and trust was analyzed. In a second block, we investigated whether the model can be moderated by user gender. Of note, previous studies that analyzed the relationship between sustainability and social media user satisfaction and trust are scarce, which complicated formulating the hypotheses based on available literature.
The results showed that environmentally sustainable tourism companies generate greater satisfaction among their users. In addition, environmentally sustainable tourism companies also indirectly induced user trust. Therefore, the first two hypotheses tested in this study (H1 and H2) were supported by the results.
However, our prediction that user gender may moderate the aforementioned relationships was not supported by the results. Therefore, H3 had to be rejected.
6.1. Theoretical Implications
As discussed in Section 1, this study aimed to bridge a gap in previous literature by investigating the impact of social media publications by tourism companies on user satisfaction and trust. In doing so, the present study contributes to available knowledge about the role of social media publications in the tourism sector. Furthermore, to the best of our knowledge, the present study is the first to investigate whether user gender plays a role in user evaluation of social media publications related to sustainability and the environment. The results of the present study suggest meaningful directions of further research in this area, including elements such as the organization of users in communities in social networks, the type of interaction according to the message published in the tourism ecosystem, and the concerns and behaviors of users when interacting with this type of content. Our findings also highlight that the content and communication strategies developed by companies in the tourism sector should focus on actions related to environmental sustainability.
6.2. Practical Implications
As for the practical implications for the improvement of the management of tourism companies, it should be pointed out that environmental sustainability is a growing asset within the tourism sector. This implies that an organization committed to environmental sustainability and the one that explicitly reveals this message to its customers will substantially improve user satisfaction and the trust of its stakeholders. The implementation of waste separation policies, the use of products with ecological packaging, reduction of energy costs, and other relevant eco-friendly policies will help the company to pollute less and increase its customers’ purchasing possibilities.
Our findings highlight that the communication strategies of tourism companies should explicitly inform their customers about various sustainability policies implemented. This will improve companies’ public profiles, lead to an increase in the number of customers, and thus expand companies’ influence on society.
Finally, since we did not find any gender differences, managers of tourism companies should take into account that in communicating policies, the strategies should not differentiate between men and women, since both groups have the same perceptions of sustainability and environmental issues. Therefore, campaigns should be carried out in the same way for both genders.
6.3. Limitations and Future Research
The present study has several limitations. First, the size of the sample was rather limited, particularly given that there are possibilities to get larger samples in the tourism industry. Second, due to the paucity of relevant prior research, it was difficult to ground the formulated hypotheses in past work. Thirdly, due to the novelty of the topic, the theoretical framework that the present study is based on is exploratory. In order to expand the present investigation, further research that would replicate the present study design in other sectors or countries, as well as expand the model by adding other marketing variables, such as commitment, loyalty, economic sustainability, or social sustainability.
Author Contributions
Conceptualization, J.G.M.-N., V.G. and J.R.S.; methodology, J.G.M.-N.; software, J.G.M.-N.; validation, V.G. and J.R.S; formal analysis, J.G.M.-N., V.G. and J.R.S.; investigation, J.G.M.-N., V.G. and J.R.S.; data curation, J.G.M.-N.; writing—original draft preparation, J.G.M.-N., V.G. and J.R.S.; writing—review and editing, J.G.M.-N., V.G. and J.R.S; supervision, J.R.S. All authors have read and agreed to the published version of the manuscript.
Funding
This research received no external funding.
Conflicts of Interest
The authors declare no conflict of interest.
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Rev.20163340543110.1108/IMR-09-2014-0304RasoolimaneshS.M.RoldánJ.L.JaafarM.RamayahT.Factors influencing residents’ perceptions toward tourism development: Differences across rural and urban world heritage sitesJ. Travel Res.20165676077510.1177/0047287516662354AzharF.N.FauzanN.The role of Twitter as a social media platforms of central Java government for sustainable tourism developmentSSRN202010.2139/ssrn.3525912CoriL.BianchiF.SprovieriM.CuttittaA.RuggieriS.AlessiA.L.BiondoG.GoriniF.Communication and community involvement to support risk governanceInt. J. Environ. Res. Public Health201916435610.3390/ijerph1622435631717268AppersonA.StellefsonM.PaigeS.ChaneyB.ChaneyJ.D.WangM.Q.MohanA.Facebook groups on chronic obstructive pulmonary disease: Social media content analysisInt. J. Environ. Res. Public Health201916378910.3390/ijerph1620378931600907BoppT.VadeboncoeurJ.StellefsonM.WeinszM.Moving beyond the gym: A content analysis of YouTube as an information resource for physical literacyInt. 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Research model used in the present study.
Proposed research model results.
ijerph-17-05417-t001_Table 1
Characteristics of the study participants (n = 351).
Classification Variable
Variable
Frequency
Percentage
Gender
Female
182
52%
Male
169
48%
Age
18–30
70
19.9%
31–45
184
52.4%
46–55
62
17.6%
56–65
19
5.4%
>65
16
4.6%
Employment
Student
32
9.1%
Housewife/man
14
3.9%
Unemployed
27
7.6%
Employed
219
62.4%
Self-Employed
59
16.8%
Minutes devoted to social media per day
<30
100
28.5%
30–60
130
37%
60–90
40
11.4%
90–120
36
10.3%
>120
45
12.8%
Social media used(multiple responses)
Twitter
126
35.9%
Facebook
310
88.3%
Instagram
210
59.8%
LinkedIn
160
45.6%
YouTube
191
54.4%
Snapchat
18
5.1%
Pinterest
65
18.5%
ijerph-17-05417-t002_Table 2
Measurement items first order.
Constructs
Items
Correlation Loading
CA
CR
rho_A
AVE
Satisfaction
(SAT1) I am satisfied with the knowledge I get from the social media of the tourism companies I follow.
0.885 ***
0.790
0.904
0.820
0.824
(SAT3) The social media of the tourism companies I follow meet my expectations.
0.930 ***
Honesty
(HON1) The tourism companies that I follow on social media keep their promises.
0.933 ***
0.805
0.832
0.832
0.835
(HON3) The social media of the tourism companies I follow are managed in an ethical and transparent way.
0.894 ***
Benevolence
(BEN1) The social media of the tourism companies I follow offer beneficial advice and recommendations.
0.884 ***
0.883
0.928
0.885
0.811
(BEN2) The tourism companies that I follow in social media develop actions taking into account how they will affect their interest groups.
0.871 ***
(BEN3) The tourism companies I follow on social media are concerned about the interests and benefits, both present and future, of their stakeholders.
0.956 ***
Competence
(COM3) The tourism companies that I follow have a knowledge of their users that allows them to adapt to users’ needs.
0.904 ***
1.00
1.00
1.00
1.00
Environmental Sustainability
(SUST1) The tourism companies that I follow on social media have recycling policies.
0.740 ***
0.882
0.914
0.894
0.680
(SOST2) The social media accounts of the tourism companies that I follow promote positive environmental ethics.
0.869 ***
(SUST3) The tourism companies that I follow in social media value and protect the environment.
0.859 ***
(SUST4) The social media of the tourism companies I follow publish pollution awareness messages.
0.834 ***
(SUST5) The social media of the tourism companies that I follow defend the diversity of nature and promote its value and protection.
R2: Trust = 0.607; Satisfaction = 0.360; Adjusted R2: Trust = 0.606; Satisfaction = 0.358; Q2: Trust = 0.460; Satisfaction = 0.275. Students in single queue *** p < 0.001.
ijerph-17-05417-t006_Table 6
Results of invariance measurement testing using permutation.
Constructs
Invariance
Composition Invariance
Partial Invariance
Equal Mean Assessment
Equal Variance Assessment
Full Measurement Invariance Established
C = 1
Confidence Interval
Differences
Confidence Interval
Equal
Differences
Confidence Interval
Equal
TRUST
Yes
0.999
(0.975/1.000)
Yes
−0.043
(−0.219/0.221)
Yes
0.046
(−0.482/0.469)
Yes
Yes
SAT
Yes
1.000
(0.999/1.000)
Yes
−0.144
(−0.225/0.219)
Yes
−0.074
(−0.393/0.390)
Yes
Yes
SUST
Yes
1.000
(0.997/1.000)
Yes
−0.010
(−0.217/0.221)
Yes
−0.197
(−0.403/0.400)
Yes
Yes
Note: SAT = Satisfaction; SUST = Environmental Sustainability.
ijerph-17-05417-t007_Table 7
Result for Hypothesis 3.
\\n
Path Coefficient
Confidence Interval(2.5%; 97.5%)
p-Value Difference
\\n
Relationship
Men
Women
Difference
\\n
PLS-MGA
Permutation Test
Parametric Test
Supported
SAT→TRUST
0.752
0.806
−0.054
(−0.117; 0.118)
0.821
0.360
0.185
NO/NO/NO
SUST→SAT
0.593
0.609
−0.016
(−0.194; 0.192)
0.569
0.872
0.435
NO/NO/NO
Note: SAT = Satisfaction; SUST = Environmental Sustainability.
\\n\"\n]"}}},{"rowIdx":461,"cells":{"data":{"kind":"list like","value":["\nFront PsycholFront PsycholFront. Psychol.Frontiers in Psychology1664-1078Frontiers Media S.A.32849073PMC743211810.3389/fpsyg.2020.01821PsychologyBrief Research ReportSocial Distancing and Stigma: Association Between Compliance With Behavioral Recommendations, Risk Perception, and Stigmatizing Attitudes During the COVID-19 OutbreakTomczykSamuel*RahnMaxiSchmidtSilkeDepartment Health and Prevention, Institute of Psychology, University of Greifswald, Greifswald, Germany
Edited by: Nuno Guerreiro Piçarra, University Institute of Lisbon (ISCTE), Portugal
Reviewed by: Mark Conner, University of Leeds, United Kingdom; Galina Alexandrovna Arina, Lomonosov Moscow State University, Russia; Joseph DeLuca, Icahn School of Medicine at Mount Sinai, United States
*Correspondence: Samuel Tomczyk, samuel.tomczyk@uni-greifswald.de
This article was submitted to Health Psychology, a section of the journal Frontiers in Psychology
11820202020118202011182117420200172020Copyright © 2020 Tomczyk, Rahn and Schmidt.2020Tomczyk, Rahn and SchmidtThis is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Introduction: Following behavioral recommendations is key to successful containment of the COVID-19 pandemic. Therefore, it is important to identify causes and patterns of non-compliance in the population to further optimize risk and health communication.
Methods: A total of 157 participants [80% female; mean age = 27.82 years (SD = 11.01)] were surveyed regarding their intention to comply with behavioral recommendations issued by the German government. Latent class analysis examined patterns of compliance, and subsequent multinomial logistic regression models tested sociodemographic (age, gender, country of origin, level of education, region, and number of persons per household) and psychosocial (knowledge about preventive behaviors, risk perception, stigmatizing attitudes) predictors.
Results: Three latent classes were identified: high compliance (25%) with all recommendations; public compliance (51%), with high compliance regarding public but not personal behaviors; and low compliance (24%) with most recommendations. Compared to high compliance, low compliance was associated with male gender [relative risk ratio (RRR) = 0.08 (0.01; 0.85)], younger age [RRR = 0.72 (0.57; 0.93)], and lower public stigma [RRR = 0.21 (0.05; 0.88)]. Low compliers were also younger than public compliers [RRR = 0.76 (0.59; 0.98)].
Discussion: With 25% of the sample reporting full compliance, and 51% differing in terms of public and personal compliance, these findings challenge the sustainability of strict regulatory measures. Moreover, young males were most likely to express low compliance, stressing the need for selective health promotion efforts. Finally, the positive association between public stigma and compliance points to potential othering effects of stigma during a pandemic, but further longitudinal research is required to examine its impact on health and social processes throughout the pandemic.
COVID-19stigmapublic healthrisk communicationlatent class analysisinfection preventioncross-sectionalIntroduction
The current outbreak of the coronavirus SARS-CoV-2 and the associated disease, COVID-19, is transfixing the world with over 2 million confirmed infections by April 16, 20201. In addition to its physical threat, this outbreak also causes psychological distress, anxiety, and depression (Wang et al., 2020). Moreover, research on the coronavirus-associated SARS pandemic in 2002/2003 points to potentially long-lasting adverse consequences, such as depression, stigmatization, diminished quality of life, and post-traumatic stress (Ko et al., 2006; Lee et al., 2006; Siu, 2008; Gardner and Moallef, 2015).
To contain infectious diseases like COVID-19, experts and government officials alike recommend a series of preventive behaviors, such as hand hygiene, and avoidance behaviors, such as social distancing or (voluntary) quarantine (e.g., Glass et al., 2006; Durham and Casman, 2012; Ding, 2014; Karimi et al., 2015; Weston et al., 2018; Lewnard and Lo, 2020). Previous simulations and current reports affirm that a combination of all strategies has the greatest success rates in containing the disease (Kelso et al., 2009; Kupferschmidt and Cohen, 2020). And yet, successful containment depends on adequate public compliance. While predictors of compliance can be explicated via a behavior theory (e.g., the theory of planned behavior; Ajzen, 1991), and they are well-documented for certain health behaviors (e.g., adherence in chronical illness; Rich et al., 2015), far less is known about compliance in pandemics.
To date, several studies have identified perceived personal risk (i.e., susceptibility, anticipated severity, and anticipatory worry) and knowledge of adaptive behaviors as facilitators of compliance (c. Tang and Wong, 2003, 2005; Cheng and Ng, 2006; Leppin and Aro, 2009; Kwok et al., 2020), although an explicit theoretical framework is often missing (Bish and Michie, 2010). Moreover, barriers to adherence (i.e., non-compliance) have received less attention presumably due to preventive and avoidance behaviors being very easy to carry out.
In a review of 26 studies on preventive behaviors in pandemics (Bish and Michie, 2010), however, compliance rates varied greatly, for example, between 4% for wearing a mask, 41.3% for “one or more specific actions” (Brug et al., 2004), and up to 95% for quarantine (Blendon et al., 2004). Despite the variety of illnesses, time frames, populations, and research methods in these studies, a general implication seems to be that a substantial proportion of the population does not adhere to the recommended behaviors. Composite measures of preventive behaviors revealed even lower compliance: 30.7% of a representative sample in Singapore practiced six or more out of eight (Quah and Hin-Peng, 2004), 48.7% in Hong Kong practiced five or more out of seven (Leung et al., 2003), and 37.8% in England practiced one or more out of three measures (Rubin et al., 2009).
In this respect, a qualitative study on (non)compliance with SARS quarantine identified ethical (e.g., civic duty), legal (e.g., monetary sanctions), and social (e.g., peer pressure) reasons to publicly comply with quarantine, while acceptance of quarantine differed markedly within households and private environments (Cava et al., 2005). Another study also identified practical issues (e.g., disposal of used tissues), selfishness, and responsibility shift (Morrison and Yardley, 2009) as core barriers to compliance. Responsibility shift refers to the belief that infected persons are particularly responsible for (not) spreading the illness, thus protecting others, whereas healthy persons are responsible for protecting themselves from becoming infected, leading to a shift in personal priorities in protective behaviors depending on one’s infection status.
Moreover, sociodemographic variables gender and age (i.e., male, younger age) consistently predicted non-compliance (Leung et al., 2003; Tang and Wong, 2003). This might be connected to a generally lower risk perception, particularly a lower perceived susceptibility, in young males (De Zwart et al., 2009). Regarding educational attainment, higher levels of education have been discussed as barriers to as well as facilitators of behavioral compliance in different populations (Leung et al., 2003; Tang and Wong, 2005; De Zwart et al., 2009; Bish and Michie, 2010).
To capture the existing heterogeneity in (non)compliance, this study utilizes a latent class approach (Collins and Lanza, 2010). Latent classes are often used to analyze behavioral patterns in non-communicable diseases, such as substance use (e.g., Tomczyk et al., 2015, 2016). However, to our knowledge, only one study applied latent class analysis to population behaviors following a novel virus outbreak [i.e., influenza A (H7N9)] in Hong Kong (Liao et al., 2015), despite the method’s statistical advantages in modeling behavioral patterns (e.g., flexibility, integration of measurement error). Liao et al. (2015) identified three latent classes of behavioral compliance, namely, moderate hygiene compliance (moderate personal hygiene, low avoidance behaviors), good hygiene compliance (high personal hygiene, low avoidance), and vigilance (high hygiene and avoidance). Moderate hygiene compliance was the largest class (about 50% of the sample) and was significantly associated with male gender, lower age, poor education, and lower risk perception, thus stressing the need for selective prevention and health promotion.
Finally, the current study also focuses on stigmatizing attitudes in the context of compliance due to the impact of stigma on fear, psychosocial stress, and social rejection during infectious diseases, such as SARS (Sim and Chua, 2004; Lee et al., 2005; Ko et al., 2006; Siu, 2008). Stigmatization can occur at different levels (e.g., individual, social, structural) and is connected to social identity processes (Tajfel and Turner, 1986; Bandura, 1998, 2004; Link and Phelan, 2001), where in-groups (i.e., individuals or groups that a person identifies with) and out-groups (i.e., individuals or groups a person does not identify with) are constructed based on certain characteristics (e.g., profession, illness symptoms). Out-groups are subsequently devaluated, for instance, by being labeled irresponsible or dangerous. This devaluation can further lead to verbal discrimination or interpersonal violence (Parker and Aggleton, 2003; Corrigan et al., 2004). Moreover, public stigma comprises support for a restriction of public opportunities (e.g., vote, utilize health care) for the devaluated out-group, in this instance, symptomatic and/or infected persons. In fact, survivors of the SARS epidemic experienced blame and social rejection (Lee et al., 2005; Mak et al., 2006), while persons of Asian descent reported victimization, regardless of their personal infection status (Zheng et al., 2005). These experiences of being blamed and ostracized oftentimes outlasted the epidemic and were associated with continued psychosocial stress (Brug et al., 2004; Siu, 2008; Jiang et al., 2009). In addition, an increase in influenza infections also corresponded to an increase in stigmatizing attitudes (e.g., a lack of trust, increased hostility) in previous research (Williams and Gonzalez-Medina, 2011).
Furthermore, qualitative studies argue that anticipated stigma might even prohibit personal preventive behaviors during infectious diseases, such as wearing masks, to avoid future stigmatization (Siu, 2008; Jiang et al., 2009); this hypothesis is supported by cross-sectional, quantitative research (Leppin and Aro, 2009). Similarly, perceived differences in responsibility for personal (healthy persons) and public protection (infected persons) during a pandemic (Morrison and Yardley, 2009) might reinforce stigma-associated social identity processes and increase the salience of group differences.
In sum, stigmatization might differentially affect behavioral compliance. On the one hand, it might be beneficial from a prevention perspective by fostering social distancing toward and isolation of infected people, primarily by stigmatizing persons and defining them as a relevant out-group (so-called othering; see Deacon, 2006). On the other hand, it might reduce compliance with official recommendations among stigmatized and/or infected persons due to fear of social isolation, stress, or discrimination (Williams and Gonzalez-Medina, 2011; Smith and Hughes, 2014). Therefore, to investigate compliance and the role of stigmatization during pandemics, this exploratory study aims to:
Examine patterns of intentions to comply with behavioral recommendations to contain the COVID-19 pandemic in the German population via latent class analysis.
Inspect the role of stigma in non-compliance while considering sociodemographic differences, risk perception, and knowledge of adaptive behaviors.
Explore intercultural similarities and differences of compliance by focusing on the German population, whereas previous research mostly focused on Asian populations.
MethodsSample
Via an online survey, a community sample of 157 German adults [80% female; M (SD)age = 27.82 (11.01)] provided information about their knowledge of preventive measures, risk perception, intentions to comply with official behavioral recommendations and guidelines as well as their stigmatizing attitudes toward people suffering from COVID-19. Participants received gift vouchers (€5) as incentives. The survey was conducted via convenience sampling between March 13 and March 27 by placing online advertisements on social media, for instance, on Facebook. During this time, far-reaching social isolation measures were implemented in Germany, for instance, restricting public meetings to two people (except for households) and establishing guidelines for a safety distance of 1.5–2.0 m in public spaces. In addition, behavioral recommendations on personal hygiene and avoidance behaviors were repeatedly and consistently issued by the government. The study procedure included informed consent in alignment with the Declaration of Helsinki and received ethical approval by a local ethics committee (BB 169/18).
Measures
Sociodemographic data comprised age, gender [1 (female), 2 (male)], country of origin [0 (Germany), 1 (other)], level of education [0 (lower secondary education), 1 (higher secondary education, i.e., university entry level), 2 (tertiary education, e.g., bachelor’s degree)], region [0 (rural, i.e., up to 100,000 inhabitants), 1 (urban, i.e., more than 100,000 inhabitants)], and number of persons in one’s household [continuous; recoded as 1 (1), 2 (2), 3 (3 or more)]. For analysis purposes, categorical variables were dummy-coded.
Measures of stigmatizing attitudes were adapted from previous research on mental health stigma, assessing support for discrimination (Schomerus et al., 2007, 2019) with three items (“Persons with COVID-19 should not be allowed to hold public office,” “Persons with COVID-19 should not be allowed to have a driver’s license,” “If persons with COVID-19 do not consent to medical treatment, they should receive compulsory treatment”), and blame (Corrigan et al., 2006; Schomerus et al., 2019) with four items (e.g., “Persons with COVID-19 are to blame for their problems”) rated on a five-point scale each, from 1 (don’t agree at all) to 5 (agree completely). Support for discrimination (Cronbach’s α = 0.71) and blame (α = 0.73) showed satisfactory internal consistency.
Risk perception comprised two items representing cognitive and affective aspects of perceived risk, namely, perceived susceptibility (“How likely will you become infected?”; 0 to 100%) and anticipated fear [“How afraid would you feel if you became infected?”; 1 (not at all) to 5 (very)].
Intentions to comply with official recommendations were assessed by asking participants how likely [1 (not at all) to 5 (very)] they would follow the following nine recommendations: (1) covering mouth and nose with flexed elbow or tissue when coughing or sneezing; (2) avoid handshakes; (3) avoid touching one’s face (i.e., eyes, nose, and mouth) as much as possible; (4) dispose of used tissue immediately and securely; (5) frequent ventilation; (6) increased hand hygiene; (7) stay at home when sick/symptomatic; (8) avoid personal contact to symptomatic persons; (9) avoid mass events. Since strictly following these recommendations is the safest way to contain further spreading of the infection, we recoded items to reflect likelihood of compliance [1 (very high likelihood), 0 (other)]. These nine indicators were then subjected to latent class analysis. In addition, a single item measuring subjective knowledge of adaptive behaviors was rated from 1 (very low) to 5 (very high). All measures are listed in Supplementary Table S1.
Statistical Analysis
Following an inspection of missing data and descriptive data analysis, latent class models were computed to examine patterns of (non)compliance in the population. Subsequent multinomial logistic regression models inspected sociodemographic and psychosocial predictors of compliance patterns. Descriptive data analysis was performed with Stata 15.1 (StataCorp, 2017), and latent class models and multinomial logistic regression models were computed with Mplus 7.4 (Muthén and Muthén, 1998–2015). All analyses were based on α = 0.05.
We estimated latent class models of compliance via robust maximum likelihood estimation with 2,000 sets of random start values. The estimation process started with two latent classes (indicating full compliance and non-compliance), the number of latent classes was subsequently increased up to five, while comparing model fit between models. Model selection considered overall model fit, parameter sparseness, classification quality, and theoretical tenability (Nylund et al., 2007; Tomczyk et al., 2016, 2018). As an overall fit measure, the bootstrapped likelihood ratio test (BLRT) compared the estimated model to a model with one less class: a significant value indicated better fit of the current model. To achieve reliable estimates, we chose 50 random starts with 50 bootstrap draws for each comparison. The Akaike Information Criterion (AIC) and the sample size-adjusted Bayes Information Criterion (BIC) indicated sparseness of the model; a lower value meant a sparser model. Average latent class probabilities (AL) and entropy demonstrated classification quality that is the differentiation between latent classes. Values range between 0 and 1; the closer to 1, the better the fit; an entropy of at least 0.6 pointed to reliable estimates (Asparouhov and Muthén, 2014). Finally, latent classes needed to be interpreted based on the literature and theoretical background. Therefore, the best latent class solution was selected on statistical criteria as well as content validity.
Using the three-step approach (Asparouhov and Muthén, 2014), we calculated multinomial logistic regressions to predict compliance patterns by sociodemographic data and psychological variables (stigmatizing attitudes, risk perception, and subjective knowledge). For each regression model, relative risk ratios (RRRs) including 95% confidence intervals were reported as effect sizes.
ResultsDescriptive Statistics
Missing data were low (37 missing values; 0.01% overall) and equally distributed among variables, suggesting missing at random. Therefore, complete cases were analyzed for descriptive statistics (Schafer, 1999; Dong and Peng, 2013), while full information maximum likelihood was used for latent class estimation. The sample was predominantly female, most persons did not have a migration background, and about a fifth lived in single households. Due to the very high level of education, the variable “education” was dichotomized for further analysis [1 (tertiary), 0 (secondary)]. Intentions to comply were mixed but particularly low for immediate disposal of used tissues, frequent ventilation, and reduced hand-to-face contact (Table 1).
Overview of mean values and relative frequencies of sociodemographic data, risk perception, knowledge, intentions to comply with recommendations, and stigmatizing attitudes in a German community sample (complete cases with listwise deletion; N = 154–157).
Variable
M (SD) or N (%)
Age (range: 18–77)
27.82 (11.01)
Gender
Female
124 (80.0)
Male
31 (20.0)
Level of education
Lower secondary
4 (2.6)
Higher secondary
91 (59.0)
Tertiary
59 (38.3)
Region
Rural
105 (73.2)
Urban
42 (26.8)
Country of origin
Germany
150 (95.5)
Other
7 (4.5)
Persons in one’s household
One
30 (19.5)
Two
63 (38.9)
Three or more
61 (39.6)
Support for discrimination (range: 1–5)
2.50 (0.82)
Blame (range: 1–5)
1.42 (0.54)
Risk perception
Susceptibility (range: 1–100%)
62.17 (20.27)
Fear (range: 1–5)
3.11 (1.05)
Subjective knowledge about adaptive behaviors (range: 1–5)
3.80 (0.76)
Intentions to comply with behavioral recommendations (very high)
(1) Covering mouth and nose when coughing or sneezing
144 (91.7)
(2) Avoid handshakes
121 (77.6)
(3) Avoid touching one’s face as much as possible
28 (17.8)
(4) Dispose of used tissue immediately and securely
81 (52.3)
(5) Frequent ventilation
55 (35.3)
(6) Increased hand hygiene
113 (72.9)
(7) Stay at home when sick
128 (81.5)
(8) Avoid personal contact to symptomatic persons
124 (79.0)
(9) Avoid mass events
128 (81.5)
Latent Class Models
Model fit criteria for latent class models are printed in Table 2. While entropy and information criteria were in favor of a model with four classes, the difference to a three-class model was only marginal (ΔAIC = 0.04; ΔSSABIC = 1.14), and according to the BLRT, the latter was preferable. Moreover, a fourth class would have been very small (n = 6; 4.8%) with similar conditional response probabilities to class 1 of the three-class model. Since it also showed good entropy and latent class separation (ALCP > 0.8) compared to the remaining models, the three-class model was chosen. The following descriptions of latent class counts and proportions are based on most likely latent class membership.
Model fit criteria for latent class models of intentions to comply with behavioral recommendations regarding infection prevention in a German community sample (N = 157).
BLRT, bootstrapped likelihood ratio test; AIC, Akaike Information Criterion; SSABIC, sample size-adjusted Bayes Information Criterion; ALCP, average latent class probabilities. ***p < 0.001; fit criteria indicating the best model are printed in bold.
The first class was labeled “low compliance” (n = 37; 24%), with low to moderate intentions to comply with most recommendations except for covering one’s mouth and nose when sneezing or coughing. The second class was labeled “high compliance” (n = 40; 25%), with high probabilities of following most recommendations and moderate compliance with reducing hand-to-face contact. Finally, the third class, “public compliance” (n = 80; 51%), had high intentions regarding compliance with public and avoidance behaviors (e.g., social distancing) but low intentions regarding personal behaviors (i.e., avoidance of face contact, tissue disposal, frequent ventilation). Conditional response probabilities for each class can be seen in Figure 1.
Conditional response probabilities and latent class proportions of three latent classes of (non)compliance with behavioral recommendations regarding infection prevention in a German community sample (N = 157). The probabilities correspond to the dichotomized likelihood of complying with recommendations [0 (not at all likely to quite likely); 1 (very likely)], thus a higher probability indicates higher compliance.
Multinomial logistic regression compared sociodemographic data, stigmatizing attitudes, knowledge, and risk perception between latent classes (Table 3). To complement multinomial models, detailed descriptive comparisons of latent classes are provided in Supplementary Table S2. Compared to high compliance (class 2), low compliance (class 1) was associated with being male [RRR = 0.08 (0.01; 0.85)], younger [RRR = 0.72 (0.57; 0.93)], and expressing lower support for discrimination [RRR = 0.21 (0.05; 0.88)], whereas public compliance (class 3) and high compliance did not differ on sociodemographic data, stigmatizing attitudes or risk perception, although support for discrimination was considerably lower in public compliers than in high compliers [RRR = 0.27 (0.06; 1.21); p = 0.09]. Furthermore, low compliers were significantly younger [RRR = 0.76 (0.59; 0.98)] than public compliers and, by trend, were less fearful of a possible infection [RRR = 0.46 (0.20; 1.06); p = 0.07].
Multinomial logistic regression of latent classes of intentions to comply with behavioral recommendations regarding infection prevention in a German community sample (N = 157).
Predictor
Public compliance (class 3) vs. high compliance (class 2)
Low compliance (class 1) vs. high compliance (class 2)
Low compliance (class 1) vs. public compliance (class 3)
RRR
95% CI
RRR
95% CI
RRR
95% CI
Age
0.95
0.87
 1.04
0.72*
0.57
 0.93
0.76*
0.59
 0.98
Gender (ref. male)
0.38
0.05
 3.16
0.08*
0.01
 0.85
0.22
0.02
 1.90
Level of education (ref. secondary)
1.20
0.12
11.68
2.82
0.41
19.58
0.44
0.03
 6.60
Region (ref. rural)
3.00
0.36
24.95
3.39
0.37
30.75
0.37
0.02
 5.67
Country of origin (ref. Germany)
0.54
0.08
 3.67
0.25
0.03
 1.76
5.19
0.53
50.83 
Persons per household (ref. One)
Two
3.40
0.68
17.13
0.52
0.07
 4.16
1.00
0.18
 5.49
Three or more
0.15
0.01
 4.12
1.11
0.03
41.84
1.60
0.02
119.22 
Support for discrimination
0.27
0.06
 1.21
0.21*
0.05
 0.88
0.77
0.12
 5.06
Blame
0.94
0.24
 3.67
1.46
0.33
 6.39
1.55
0.28
 8.66
Risk perception
Susceptibility
1.01
0.97
 1.04
1.03
0.99
 1.06
1.02
0.97
 1.06
Fear
1.74
0.61
 4.96
0.80
0.34
 1.89
0.46
0.20
 1.06
Subjective knowledge
0.46
0.13
 1.67
0.25
0.05
 1.26
0.55
0.08
 3.84
RRR, relative risk ratio; 95% CI, 95% confidence interval. Significant coefficients are printed in bold; *p < 0.05.Discussion
As one of the first studies examining patterns of (non)compliance with behavioral recommendations in the general population during the COVID-19 pandemic, this study revealed that only a quarter of the surveyed German population expressed intentions to fully comply with recommendations, while a majority (about 51%) intended to follow some public actions but was less willing to enact personal hygiene behaviors (i.e., swift disposal of tissues, reduction of hand-to-face contact, ventilation). Young males were significantly less likely to comply with recommendations, and aspects of public stigma were also linked to compliance intentions.
In a virus outbreak, such as the COVID-19 pandemic, personal hygiene and social distancing in the general population are paramount to containment of the illness (Wu et al., 2006; Karimi et al., 2015; Weston et al., 2018). And yet, only a minority was ready to comply with the main recommendations, with 25% reaching high compliance in this sample and similar, albeit slightly higher, proportions of 30.7% (Quah and Hin-Peng, 2004), 37.8% (Williams and Gonzalez-Medina, 2011), and 48.7% (Lee et al., 2005) in previous studies. Since Germany was not affected by previous pandemics (e.g., H1N1, SARS) as strongly as Hong Kong, for instance, and measures like wearing face masks are not as common in Europe (e.g., Rubin et al., 2009), we assume the lack of familiarity with such strict preventive measures to be responsible for this lower level of compliance.
Patterns and Predictors of Non-compliance
To further explore cultural differences of compliance during a pandemic and connect our findings to previous research, we compare our findings (Germany) to Liao et al. (2015), who analyzed latent classes of behavior patterns in Hong Kong during a virus outbreak. They also identified three latent classes, with the class moderate hygiene being the largest group, followed by good hygiene and vigilance. Moreover, younger males, persons with lower educational attainment, and lower risk perception were also more likely to belong to the moderate hygiene class (i.e., exhibit low compliance), similar to our findings. This trend of older persons and females reporting higher risk perception and willingness to perform preventive behaviors was consistently found in a variety of health risks (Flynn et al., 1994), among them also pandemics (Bish and Michie, 2010; Kwok et al., 2020), presumably due to a higher perceived susceptibility in these groups. Since older people have a higher risk of manifesting COVID-19 symptoms (Davies et al., 2020), which was promulgated via mass media reports, this might have led to lower susceptibility perceptions among younger people. Across cultures and scenarios, young males tend to report lower risk perception and compliance intentions. By corroborating these associations in the context of COVID-19, our findings stress the need for selective prevention targeting young males to improve their compliance and thereby public health.
Despite these similarities, we observed differing intentions regarding personal hygiene behaviors but overall high intentions to comply with avoidance behaviors, in contrast to Liao et al. (2015). While studies in other Western countries, that is, Canada (Toronto) and the United States (Blendon et al., 2004), also indicated high compliance with quarantine and social distancing strategies, it should be noted that avoidance measures are generally easier to implement than specific preventive behaviors that require personal action (Bish and Michie, 2010). Therefore, it is possible that in this early phase of the COVID-19 outbreak in Germany, personal responsibility was not as salient in the general population. This might be connected to the lack of familiarity with pandemics and appropriate preventive action in the German population. Nevertheless, personal preventive actions may yet increase over time, coinciding with an increase in vigilance, knowledge, and positive attitudes, if supported by concerted action, as suggested by previous SARS outbreak trajectories (Leung et al., 2003, 2005).
To concur, in their analysis of repeated cross-sectional surveys, Liao et al. (2015) observed fairly stable behavioral patterns (i.e., robust latent classes) across time but an increase in public vigilance and perceived threat throughout the epidemic (i.e., an increase in latent class proportions in favor of vigilance). To foster vigilance, the media and governmental institutions are therefore urged to provide clear guidance, openly communicate and justify new measures to increase trust, and strengthen self-efficacy at early stages of a pandemic, as shown in previous health crises (e.g., Seeger, 2006; Bean et al., 2015; Jha et al., 2018).
Non-compliance and Stigmatizing Attitudes
In addition to compliance patterns, this study also examined the impact of stigmatizing attitudes on intentions to comply with behavioral recommendations. While Williams and Gonzalez-Medina (2011) connected an increase in influenza infections to an increase in stigmatizing attitudes, in this study, blame was low (mean = 1.42 on scale of 1–5) and did not predict compliance. Instead, support for discrimination was significantly associated with higher compliance intentions. Drawing on social psychiatric research, this type of discrimination might be described as intentional structural discrimination, where a worldview is actively supported that restricts patients’ rights (by law), for example, regarding their opportunities to vote or to hold public office (Corrigan et al., 2004, 2006; Schomerus et al., 2007). In the context of COVID-19, a support for discrimination implies a desired restriction of access to sociopolitical resources for infected persons.
As a result, while high compliance represents law-abiding and theoretically desirable behavior, its connection to discrimination, particularly in this highly educated sample, is noteworthy. In line with the reasoning behind selfishness and responsibility shift in confronting the SARS pandemic (Morrison and Yardley, 2009), a support for discrimination might indicate a way to maximize differences between relevant in-groups (i.e., responsible, healthy) and out-groups (i.e., irresponsible, reckless) to affirm social identity status (Tajfel and Turner, 1986; Link and Phelan, 2001) and – at least symbolically – reduce the risk of infection. Since blame did not differ between latent classes and was generally low, we assume that in this sample, stigma facilitated othering but not discriminatory action (Deacon, 2006). Although this hypothesis requires further research in larger, longitudinal samples using more elaborate measures of stigmatizing attitudes, it is clearly in line with evidence-based demands of a more nuanced debate of the functional properties of stigmatization and its connection to discrimination in infectious diseases (Deacon, 2006).
Strengths and Limitations
Finally, this study is not without limitations, as the sample is a small convenience sample that is not representative of the German population. In fact, the sample was highly educated, predominantly female, and mostly without migration background. However, we still observed substantial heterogeneity in intentions, despite females and highly educated persons being generally more likely to report high compliance in previous studies. In addition, this study was cross-sectional and exploratory and used short but validated measures of core constructs, hence, effects of risk perception, for example, were not fully explored. Components like anticipatory worry could also affect compliance intentions and should be studied in more detail (Leppin and Aro, 2009). Furthermore, items measuring stigmatizing attitudes were adapted to COVID-19 for this study, therefore, a thorough psychometric validation is necessary. Moreover, we did not assess other important factors that might be connected to (non)compliance, such as ethnicity, interpersonal contact with infected persons, or trust in the government. Finally, we captured behavioral intentions, but we did not assess actual behaviors, as the pandemic had just reached the German population, and official recommendations were first issued at the beginning of data collection. Therefore, future studies should also focus on behavioral performance. When investigating the connection between compliance intentions and behavioral performance, health behaviors models like the theory of planned behavior should be applied to incorporate relevant intermediary variables, such as self-efficacy (Ajzen, 1991; Bish and Michie, 2010). Overall, more comprehensive, longitudinal, and experimental studies are necessary to validate our findings in the context of COVID-19 in diverse populations. Nevertheless, we think this study provides an important look at patterns of compliance at early stages of the COVID-19 outbreak and impactful sociodemographic and attitudinal factors, such as support for discrimination, that underline the need for selective preventive action.
Data Availability Statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation, to any qualified researcher.
Ethics Statement
The studies involving human participants were reviewed and approved by the Ethics Committee of the University Medicine Greifswald, University Medicine Greifswald. The patients/participants provided their written informed consent to participate in this study.
Author Contributions
ST, MR, and SS contributed to the conception and design of the study. ST and MR were responsible for the data collection and statistical analysis. ST wrote the first draft of the manuscript. All authors contributed to the article and approved the submitted version.
Conflict of Interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
We would like to thank Simon Barth, Isabel Buck, and Hanna Groth for their assistance in the collection and preparation of data.
https://coronavirus.jhu.edu/
Supplementary Material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpsyg.2020.01821/full#supplementary-material
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Public Health\n17\n124–129. 10.1177/101053950501700211\n16425657\n"],"string":"[\n \"\\nFront PsycholFront PsycholFront. Psychol.Frontiers in Psychology1664-1078Frontiers Media S.A.32849073PMC743211810.3389/fpsyg.2020.01821PsychologyBrief Research ReportSocial Distancing and Stigma: Association Between Compliance With Behavioral Recommendations, Risk Perception, and Stigmatizing Attitudes During the COVID-19 OutbreakTomczykSamuel*RahnMaxiSchmidtSilkeDepartment Health and Prevention, Institute of Psychology, University of Greifswald, Greifswald, Germany
Edited by: Nuno Guerreiro Piçarra, University Institute of Lisbon (ISCTE), Portugal
Reviewed by: Mark Conner, University of Leeds, United Kingdom; Galina Alexandrovna Arina, Lomonosov Moscow State University, Russia; Joseph DeLuca, Icahn School of Medicine at Mount Sinai, United States
*Correspondence: Samuel Tomczyk, samuel.tomczyk@uni-greifswald.de
This article was submitted to Health Psychology, a section of the journal Frontiers in Psychology
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Introduction: Following behavioral recommendations is key to successful containment of the COVID-19 pandemic. Therefore, it is important to identify causes and patterns of non-compliance in the population to further optimize risk and health communication.
Methods: A total of 157 participants [80% female; mean age = 27.82 years (SD = 11.01)] were surveyed regarding their intention to comply with behavioral recommendations issued by the German government. Latent class analysis examined patterns of compliance, and subsequent multinomial logistic regression models tested sociodemographic (age, gender, country of origin, level of education, region, and number of persons per household) and psychosocial (knowledge about preventive behaviors, risk perception, stigmatizing attitudes) predictors.
Results: Three latent classes were identified: high compliance (25%) with all recommendations; public compliance (51%), with high compliance regarding public but not personal behaviors; and low compliance (24%) with most recommendations. Compared to high compliance, low compliance was associated with male gender [relative risk ratio (RRR) = 0.08 (0.01; 0.85)], younger age [RRR = 0.72 (0.57; 0.93)], and lower public stigma [RRR = 0.21 (0.05; 0.88)]. Low compliers were also younger than public compliers [RRR = 0.76 (0.59; 0.98)].
Discussion: With 25% of the sample reporting full compliance, and 51% differing in terms of public and personal compliance, these findings challenge the sustainability of strict regulatory measures. Moreover, young males were most likely to express low compliance, stressing the need for selective health promotion efforts. Finally, the positive association between public stigma and compliance points to potential othering effects of stigma during a pandemic, but further longitudinal research is required to examine its impact on health and social processes throughout the pandemic.
COVID-19stigmapublic healthrisk communicationlatent class analysisinfection preventioncross-sectionalIntroduction
The current outbreak of the coronavirus SARS-CoV-2 and the associated disease, COVID-19, is transfixing the world with over 2 million confirmed infections by April 16, 20201. In addition to its physical threat, this outbreak also causes psychological distress, anxiety, and depression (Wang et al., 2020). Moreover, research on the coronavirus-associated SARS pandemic in 2002/2003 points to potentially long-lasting adverse consequences, such as depression, stigmatization, diminished quality of life, and post-traumatic stress (Ko et al., 2006; Lee et al., 2006; Siu, 2008; Gardner and Moallef, 2015).
To contain infectious diseases like COVID-19, experts and government officials alike recommend a series of preventive behaviors, such as hand hygiene, and avoidance behaviors, such as social distancing or (voluntary) quarantine (e.g., Glass et al., 2006; Durham and Casman, 2012; Ding, 2014; Karimi et al., 2015; Weston et al., 2018; Lewnard and Lo, 2020). Previous simulations and current reports affirm that a combination of all strategies has the greatest success rates in containing the disease (Kelso et al., 2009; Kupferschmidt and Cohen, 2020). And yet, successful containment depends on adequate public compliance. While predictors of compliance can be explicated via a behavior theory (e.g., the theory of planned behavior; Ajzen, 1991), and they are well-documented for certain health behaviors (e.g., adherence in chronical illness; Rich et al., 2015), far less is known about compliance in pandemics.
To date, several studies have identified perceived personal risk (i.e., susceptibility, anticipated severity, and anticipatory worry) and knowledge of adaptive behaviors as facilitators of compliance (c. Tang and Wong, 2003, 2005; Cheng and Ng, 2006; Leppin and Aro, 2009; Kwok et al., 2020), although an explicit theoretical framework is often missing (Bish and Michie, 2010). Moreover, barriers to adherence (i.e., non-compliance) have received less attention presumably due to preventive and avoidance behaviors being very easy to carry out.
In a review of 26 studies on preventive behaviors in pandemics (Bish and Michie, 2010), however, compliance rates varied greatly, for example, between 4% for wearing a mask, 41.3% for “one or more specific actions” (Brug et al., 2004), and up to 95% for quarantine (Blendon et al., 2004). Despite the variety of illnesses, time frames, populations, and research methods in these studies, a general implication seems to be that a substantial proportion of the population does not adhere to the recommended behaviors. Composite measures of preventive behaviors revealed even lower compliance: 30.7% of a representative sample in Singapore practiced six or more out of eight (Quah and Hin-Peng, 2004), 48.7% in Hong Kong practiced five or more out of seven (Leung et al., 2003), and 37.8% in England practiced one or more out of three measures (Rubin et al., 2009).
In this respect, a qualitative study on (non)compliance with SARS quarantine identified ethical (e.g., civic duty), legal (e.g., monetary sanctions), and social (e.g., peer pressure) reasons to publicly comply with quarantine, while acceptance of quarantine differed markedly within households and private environments (Cava et al., 2005). Another study also identified practical issues (e.g., disposal of used tissues), selfishness, and responsibility shift (Morrison and Yardley, 2009) as core barriers to compliance. Responsibility shift refers to the belief that infected persons are particularly responsible for (not) spreading the illness, thus protecting others, whereas healthy persons are responsible for protecting themselves from becoming infected, leading to a shift in personal priorities in protective behaviors depending on one’s infection status.
Moreover, sociodemographic variables gender and age (i.e., male, younger age) consistently predicted non-compliance (Leung et al., 2003; Tang and Wong, 2003). This might be connected to a generally lower risk perception, particularly a lower perceived susceptibility, in young males (De Zwart et al., 2009). Regarding educational attainment, higher levels of education have been discussed as barriers to as well as facilitators of behavioral compliance in different populations (Leung et al., 2003; Tang and Wong, 2005; De Zwart et al., 2009; Bish and Michie, 2010).
To capture the existing heterogeneity in (non)compliance, this study utilizes a latent class approach (Collins and Lanza, 2010). Latent classes are often used to analyze behavioral patterns in non-communicable diseases, such as substance use (e.g., Tomczyk et al., 2015, 2016). However, to our knowledge, only one study applied latent class analysis to population behaviors following a novel virus outbreak [i.e., influenza A (H7N9)] in Hong Kong (Liao et al., 2015), despite the method’s statistical advantages in modeling behavioral patterns (e.g., flexibility, integration of measurement error). Liao et al. (2015) identified three latent classes of behavioral compliance, namely, moderate hygiene compliance (moderate personal hygiene, low avoidance behaviors), good hygiene compliance (high personal hygiene, low avoidance), and vigilance (high hygiene and avoidance). Moderate hygiene compliance was the largest class (about 50% of the sample) and was significantly associated with male gender, lower age, poor education, and lower risk perception, thus stressing the need for selective prevention and health promotion.
Finally, the current study also focuses on stigmatizing attitudes in the context of compliance due to the impact of stigma on fear, psychosocial stress, and social rejection during infectious diseases, such as SARS (Sim and Chua, 2004; Lee et al., 2005; Ko et al., 2006; Siu, 2008). Stigmatization can occur at different levels (e.g., individual, social, structural) and is connected to social identity processes (Tajfel and Turner, 1986; Bandura, 1998, 2004; Link and Phelan, 2001), where in-groups (i.e., individuals or groups that a person identifies with) and out-groups (i.e., individuals or groups a person does not identify with) are constructed based on certain characteristics (e.g., profession, illness symptoms). Out-groups are subsequently devaluated, for instance, by being labeled irresponsible or dangerous. This devaluation can further lead to verbal discrimination or interpersonal violence (Parker and Aggleton, 2003; Corrigan et al., 2004). Moreover, public stigma comprises support for a restriction of public opportunities (e.g., vote, utilize health care) for the devaluated out-group, in this instance, symptomatic and/or infected persons. In fact, survivors of the SARS epidemic experienced blame and social rejection (Lee et al., 2005; Mak et al., 2006), while persons of Asian descent reported victimization, regardless of their personal infection status (Zheng et al., 2005). These experiences of being blamed and ostracized oftentimes outlasted the epidemic and were associated with continued psychosocial stress (Brug et al., 2004; Siu, 2008; Jiang et al., 2009). In addition, an increase in influenza infections also corresponded to an increase in stigmatizing attitudes (e.g., a lack of trust, increased hostility) in previous research (Williams and Gonzalez-Medina, 2011).
Furthermore, qualitative studies argue that anticipated stigma might even prohibit personal preventive behaviors during infectious diseases, such as wearing masks, to avoid future stigmatization (Siu, 2008; Jiang et al., 2009); this hypothesis is supported by cross-sectional, quantitative research (Leppin and Aro, 2009). Similarly, perceived differences in responsibility for personal (healthy persons) and public protection (infected persons) during a pandemic (Morrison and Yardley, 2009) might reinforce stigma-associated social identity processes and increase the salience of group differences.
In sum, stigmatization might differentially affect behavioral compliance. On the one hand, it might be beneficial from a prevention perspective by fostering social distancing toward and isolation of infected people, primarily by stigmatizing persons and defining them as a relevant out-group (so-called othering; see Deacon, 2006). On the other hand, it might reduce compliance with official recommendations among stigmatized and/or infected persons due to fear of social isolation, stress, or discrimination (Williams and Gonzalez-Medina, 2011; Smith and Hughes, 2014). Therefore, to investigate compliance and the role of stigmatization during pandemics, this exploratory study aims to:
Examine patterns of intentions to comply with behavioral recommendations to contain the COVID-19 pandemic in the German population via latent class analysis.
Inspect the role of stigma in non-compliance while considering sociodemographic differences, risk perception, and knowledge of adaptive behaviors.
Explore intercultural similarities and differences of compliance by focusing on the German population, whereas previous research mostly focused on Asian populations.
MethodsSample
Via an online survey, a community sample of 157 German adults [80% female; M (SD)age = 27.82 (11.01)] provided information about their knowledge of preventive measures, risk perception, intentions to comply with official behavioral recommendations and guidelines as well as their stigmatizing attitudes toward people suffering from COVID-19. Participants received gift vouchers (€5) as incentives. The survey was conducted via convenience sampling between March 13 and March 27 by placing online advertisements on social media, for instance, on Facebook. During this time, far-reaching social isolation measures were implemented in Germany, for instance, restricting public meetings to two people (except for households) and establishing guidelines for a safety distance of 1.5–2.0 m in public spaces. In addition, behavioral recommendations on personal hygiene and avoidance behaviors were repeatedly and consistently issued by the government. The study procedure included informed consent in alignment with the Declaration of Helsinki and received ethical approval by a local ethics committee (BB 169/18).
Measures
Sociodemographic data comprised age, gender [1 (female), 2 (male)], country of origin [0 (Germany), 1 (other)], level of education [0 (lower secondary education), 1 (higher secondary education, i.e., university entry level), 2 (tertiary education, e.g., bachelor’s degree)], region [0 (rural, i.e., up to 100,000 inhabitants), 1 (urban, i.e., more than 100,000 inhabitants)], and number of persons in one’s household [continuous; recoded as 1 (1), 2 (2), 3 (3 or more)]. For analysis purposes, categorical variables were dummy-coded.
Measures of stigmatizing attitudes were adapted from previous research on mental health stigma, assessing support for discrimination (Schomerus et al., 2007, 2019) with three items (“Persons with COVID-19 should not be allowed to hold public office,” “Persons with COVID-19 should not be allowed to have a driver’s license,” “If persons with COVID-19 do not consent to medical treatment, they should receive compulsory treatment”), and blame (Corrigan et al., 2006; Schomerus et al., 2019) with four items (e.g., “Persons with COVID-19 are to blame for their problems”) rated on a five-point scale each, from 1 (don’t agree at all) to 5 (agree completely). Support for discrimination (Cronbach’s α = 0.71) and blame (α = 0.73) showed satisfactory internal consistency.
Risk perception comprised two items representing cognitive and affective aspects of perceived risk, namely, perceived susceptibility (“How likely will you become infected?”; 0 to 100%) and anticipated fear [“How afraid would you feel if you became infected?”; 1 (not at all) to 5 (very)].
Intentions to comply with official recommendations were assessed by asking participants how likely [1 (not at all) to 5 (very)] they would follow the following nine recommendations: (1) covering mouth and nose with flexed elbow or tissue when coughing or sneezing; (2) avoid handshakes; (3) avoid touching one’s face (i.e., eyes, nose, and mouth) as much as possible; (4) dispose of used tissue immediately and securely; (5) frequent ventilation; (6) increased hand hygiene; (7) stay at home when sick/symptomatic; (8) avoid personal contact to symptomatic persons; (9) avoid mass events. Since strictly following these recommendations is the safest way to contain further spreading of the infection, we recoded items to reflect likelihood of compliance [1 (very high likelihood), 0 (other)]. These nine indicators were then subjected to latent class analysis. In addition, a single item measuring subjective knowledge of adaptive behaviors was rated from 1 (very low) to 5 (very high). All measures are listed in Supplementary Table S1.
Statistical Analysis
Following an inspection of missing data and descriptive data analysis, latent class models were computed to examine patterns of (non)compliance in the population. Subsequent multinomial logistic regression models inspected sociodemographic and psychosocial predictors of compliance patterns. Descriptive data analysis was performed with Stata 15.1 (StataCorp, 2017), and latent class models and multinomial logistic regression models were computed with Mplus 7.4 (Muthén and Muthén, 1998–2015). All analyses were based on α = 0.05.
We estimated latent class models of compliance via robust maximum likelihood estimation with 2,000 sets of random start values. The estimation process started with two latent classes (indicating full compliance and non-compliance), the number of latent classes was subsequently increased up to five, while comparing model fit between models. Model selection considered overall model fit, parameter sparseness, classification quality, and theoretical tenability (Nylund et al., 2007; Tomczyk et al., 2016, 2018). As an overall fit measure, the bootstrapped likelihood ratio test (BLRT) compared the estimated model to a model with one less class: a significant value indicated better fit of the current model. To achieve reliable estimates, we chose 50 random starts with 50 bootstrap draws for each comparison. The Akaike Information Criterion (AIC) and the sample size-adjusted Bayes Information Criterion (BIC) indicated sparseness of the model; a lower value meant a sparser model. Average latent class probabilities (AL) and entropy demonstrated classification quality that is the differentiation between latent classes. Values range between 0 and 1; the closer to 1, the better the fit; an entropy of at least 0.6 pointed to reliable estimates (Asparouhov and Muthén, 2014). Finally, latent classes needed to be interpreted based on the literature and theoretical background. Therefore, the best latent class solution was selected on statistical criteria as well as content validity.
Using the three-step approach (Asparouhov and Muthén, 2014), we calculated multinomial logistic regressions to predict compliance patterns by sociodemographic data and psychological variables (stigmatizing attitudes, risk perception, and subjective knowledge). For each regression model, relative risk ratios (RRRs) including 95% confidence intervals were reported as effect sizes.
ResultsDescriptive Statistics
Missing data were low (37 missing values; 0.01% overall) and equally distributed among variables, suggesting missing at random. Therefore, complete cases were analyzed for descriptive statistics (Schafer, 1999; Dong and Peng, 2013), while full information maximum likelihood was used for latent class estimation. The sample was predominantly female, most persons did not have a migration background, and about a fifth lived in single households. Due to the very high level of education, the variable “education” was dichotomized for further analysis [1 (tertiary), 0 (secondary)]. Intentions to comply were mixed but particularly low for immediate disposal of used tissues, frequent ventilation, and reduced hand-to-face contact (Table 1).
Overview of mean values and relative frequencies of sociodemographic data, risk perception, knowledge, intentions to comply with recommendations, and stigmatizing attitudes in a German community sample (complete cases with listwise deletion; N = 154–157).
Variable
M (SD) or N (%)
Age (range: 18–77)
27.82 (11.01)
Gender
Female
124 (80.0)
Male
31 (20.0)
Level of education
Lower secondary
4 (2.6)
Higher secondary
91 (59.0)
Tertiary
59 (38.3)
Region
Rural
105 (73.2)
Urban
42 (26.8)
Country of origin
Germany
150 (95.5)
Other
7 (4.5)
Persons in one’s household
One
30 (19.5)
Two
63 (38.9)
Three or more
61 (39.6)
Support for discrimination (range: 1–5)
2.50 (0.82)
Blame (range: 1–5)
1.42 (0.54)
Risk perception
Susceptibility (range: 1–100%)
62.17 (20.27)
Fear (range: 1–5)
3.11 (1.05)
Subjective knowledge about adaptive behaviors (range: 1–5)
3.80 (0.76)
Intentions to comply with behavioral recommendations (very high)
(1) Covering mouth and nose when coughing or sneezing
144 (91.7)
(2) Avoid handshakes
121 (77.6)
(3) Avoid touching one’s face as much as possible
28 (17.8)
(4) Dispose of used tissue immediately and securely
81 (52.3)
(5) Frequent ventilation
55 (35.3)
(6) Increased hand hygiene
113 (72.9)
(7) Stay at home when sick
128 (81.5)
(8) Avoid personal contact to symptomatic persons
124 (79.0)
(9) Avoid mass events
128 (81.5)
Latent Class Models
Model fit criteria for latent class models are printed in Table 2. While entropy and information criteria were in favor of a model with four classes, the difference to a three-class model was only marginal (ΔAIC = 0.04; ΔSSABIC = 1.14), and according to the BLRT, the latter was preferable. Moreover, a fourth class would have been very small (n = 6; 4.8%) with similar conditional response probabilities to class 1 of the three-class model. Since it also showed good entropy and latent class separation (ALCP > 0.8) compared to the remaining models, the three-class model was chosen. The following descriptions of latent class counts and proportions are based on most likely latent class membership.
Model fit criteria for latent class models of intentions to comply with behavioral recommendations regarding infection prevention in a German community sample (N = 157).
BLRT, bootstrapped likelihood ratio test; AIC, Akaike Information Criterion; SSABIC, sample size-adjusted Bayes Information Criterion; ALCP, average latent class probabilities. ***p < 0.001; fit criteria indicating the best model are printed in bold.
The first class was labeled “low compliance” (n = 37; 24%), with low to moderate intentions to comply with most recommendations except for covering one’s mouth and nose when sneezing or coughing. The second class was labeled “high compliance” (n = 40; 25%), with high probabilities of following most recommendations and moderate compliance with reducing hand-to-face contact. Finally, the third class, “public compliance” (n = 80; 51%), had high intentions regarding compliance with public and avoidance behaviors (e.g., social distancing) but low intentions regarding personal behaviors (i.e., avoidance of face contact, tissue disposal, frequent ventilation). Conditional response probabilities for each class can be seen in Figure 1.
Conditional response probabilities and latent class proportions of three latent classes of (non)compliance with behavioral recommendations regarding infection prevention in a German community sample (N = 157). The probabilities correspond to the dichotomized likelihood of complying with recommendations [0 (not at all likely to quite likely); 1 (very likely)], thus a higher probability indicates higher compliance.
Multinomial logistic regression compared sociodemographic data, stigmatizing attitudes, knowledge, and risk perception between latent classes (Table 3). To complement multinomial models, detailed descriptive comparisons of latent classes are provided in Supplementary Table S2. Compared to high compliance (class 2), low compliance (class 1) was associated with being male [RRR = 0.08 (0.01; 0.85)], younger [RRR = 0.72 (0.57; 0.93)], and expressing lower support for discrimination [RRR = 0.21 (0.05; 0.88)], whereas public compliance (class 3) and high compliance did not differ on sociodemographic data, stigmatizing attitudes or risk perception, although support for discrimination was considerably lower in public compliers than in high compliers [RRR = 0.27 (0.06; 1.21); p = 0.09]. Furthermore, low compliers were significantly younger [RRR = 0.76 (0.59; 0.98)] than public compliers and, by trend, were less fearful of a possible infection [RRR = 0.46 (0.20; 1.06); p = 0.07].
Multinomial logistic regression of latent classes of intentions to comply with behavioral recommendations regarding infection prevention in a German community sample (N = 157).
Predictor
Public compliance (class 3) vs. high compliance (class 2)
Low compliance (class 1) vs. high compliance (class 2)
Low compliance (class 1) vs. public compliance (class 3)
RRR
95% CI
RRR
95% CI
RRR
95% CI
Age
0.95
0.87
 1.04
0.72*
0.57
 0.93
0.76*
0.59
 0.98
Gender (ref. male)
0.38
0.05
 3.16
0.08*
0.01
 0.85
0.22
0.02
 1.90
Level of education (ref. secondary)
1.20
0.12
11.68
2.82
0.41
19.58
0.44
0.03
 6.60
Region (ref. rural)
3.00
0.36
24.95
3.39
0.37
30.75
0.37
0.02
 5.67
Country of origin (ref. Germany)
0.54
0.08
 3.67
0.25
0.03
 1.76
5.19
0.53
50.83 
Persons per household (ref. One)
Two
3.40
0.68
17.13
0.52
0.07
 4.16
1.00
0.18
 5.49
Three or more
0.15
0.01
 4.12
1.11
0.03
41.84
1.60
0.02
119.22 
Support for discrimination
0.27
0.06
 1.21
0.21*
0.05
 0.88
0.77
0.12
 5.06
Blame
0.94
0.24
 3.67
1.46
0.33
 6.39
1.55
0.28
 8.66
Risk perception
Susceptibility
1.01
0.97
 1.04
1.03
0.99
 1.06
1.02
0.97
 1.06
Fear
1.74
0.61
 4.96
0.80
0.34
 1.89
0.46
0.20
 1.06
Subjective knowledge
0.46
0.13
 1.67
0.25
0.05
 1.26
0.55
0.08
 3.84
RRR, relative risk ratio; 95% CI, 95% confidence interval. Significant coefficients are printed in bold; *p < 0.05.Discussion
As one of the first studies examining patterns of (non)compliance with behavioral recommendations in the general population during the COVID-19 pandemic, this study revealed that only a quarter of the surveyed German population expressed intentions to fully comply with recommendations, while a majority (about 51%) intended to follow some public actions but was less willing to enact personal hygiene behaviors (i.e., swift disposal of tissues, reduction of hand-to-face contact, ventilation). Young males were significantly less likely to comply with recommendations, and aspects of public stigma were also linked to compliance intentions.
In a virus outbreak, such as the COVID-19 pandemic, personal hygiene and social distancing in the general population are paramount to containment of the illness (Wu et al., 2006; Karimi et al., 2015; Weston et al., 2018). And yet, only a minority was ready to comply with the main recommendations, with 25% reaching high compliance in this sample and similar, albeit slightly higher, proportions of 30.7% (Quah and Hin-Peng, 2004), 37.8% (Williams and Gonzalez-Medina, 2011), and 48.7% (Lee et al., 2005) in previous studies. Since Germany was not affected by previous pandemics (e.g., H1N1, SARS) as strongly as Hong Kong, for instance, and measures like wearing face masks are not as common in Europe (e.g., Rubin et al., 2009), we assume the lack of familiarity with such strict preventive measures to be responsible for this lower level of compliance.
Patterns and Predictors of Non-compliance
To further explore cultural differences of compliance during a pandemic and connect our findings to previous research, we compare our findings (Germany) to Liao et al. (2015), who analyzed latent classes of behavior patterns in Hong Kong during a virus outbreak. They also identified three latent classes, with the class moderate hygiene being the largest group, followed by good hygiene and vigilance. Moreover, younger males, persons with lower educational attainment, and lower risk perception were also more likely to belong to the moderate hygiene class (i.e., exhibit low compliance), similar to our findings. This trend of older persons and females reporting higher risk perception and willingness to perform preventive behaviors was consistently found in a variety of health risks (Flynn et al., 1994), among them also pandemics (Bish and Michie, 2010; Kwok et al., 2020), presumably due to a higher perceived susceptibility in these groups. Since older people have a higher risk of manifesting COVID-19 symptoms (Davies et al., 2020), which was promulgated via mass media reports, this might have led to lower susceptibility perceptions among younger people. Across cultures and scenarios, young males tend to report lower risk perception and compliance intentions. By corroborating these associations in the context of COVID-19, our findings stress the need for selective prevention targeting young males to improve their compliance and thereby public health.
Despite these similarities, we observed differing intentions regarding personal hygiene behaviors but overall high intentions to comply with avoidance behaviors, in contrast to Liao et al. (2015). While studies in other Western countries, that is, Canada (Toronto) and the United States (Blendon et al., 2004), also indicated high compliance with quarantine and social distancing strategies, it should be noted that avoidance measures are generally easier to implement than specific preventive behaviors that require personal action (Bish and Michie, 2010). Therefore, it is possible that in this early phase of the COVID-19 outbreak in Germany, personal responsibility was not as salient in the general population. This might be connected to the lack of familiarity with pandemics and appropriate preventive action in the German population. Nevertheless, personal preventive actions may yet increase over time, coinciding with an increase in vigilance, knowledge, and positive attitudes, if supported by concerted action, as suggested by previous SARS outbreak trajectories (Leung et al., 2003, 2005).
To concur, in their analysis of repeated cross-sectional surveys, Liao et al. (2015) observed fairly stable behavioral patterns (i.e., robust latent classes) across time but an increase in public vigilance and perceived threat throughout the epidemic (i.e., an increase in latent class proportions in favor of vigilance). To foster vigilance, the media and governmental institutions are therefore urged to provide clear guidance, openly communicate and justify new measures to increase trust, and strengthen self-efficacy at early stages of a pandemic, as shown in previous health crises (e.g., Seeger, 2006; Bean et al., 2015; Jha et al., 2018).
Non-compliance and Stigmatizing Attitudes
In addition to compliance patterns, this study also examined the impact of stigmatizing attitudes on intentions to comply with behavioral recommendations. While Williams and Gonzalez-Medina (2011) connected an increase in influenza infections to an increase in stigmatizing attitudes, in this study, blame was low (mean = 1.42 on scale of 1–5) and did not predict compliance. Instead, support for discrimination was significantly associated with higher compliance intentions. Drawing on social psychiatric research, this type of discrimination might be described as intentional structural discrimination, where a worldview is actively supported that restricts patients’ rights (by law), for example, regarding their opportunities to vote or to hold public office (Corrigan et al., 2004, 2006; Schomerus et al., 2007). In the context of COVID-19, a support for discrimination implies a desired restriction of access to sociopolitical resources for infected persons.
As a result, while high compliance represents law-abiding and theoretically desirable behavior, its connection to discrimination, particularly in this highly educated sample, is noteworthy. In line with the reasoning behind selfishness and responsibility shift in confronting the SARS pandemic (Morrison and Yardley, 2009), a support for discrimination might indicate a way to maximize differences between relevant in-groups (i.e., responsible, healthy) and out-groups (i.e., irresponsible, reckless) to affirm social identity status (Tajfel and Turner, 1986; Link and Phelan, 2001) and – at least symbolically – reduce the risk of infection. Since blame did not differ between latent classes and was generally low, we assume that in this sample, stigma facilitated othering but not discriminatory action (Deacon, 2006). Although this hypothesis requires further research in larger, longitudinal samples using more elaborate measures of stigmatizing attitudes, it is clearly in line with evidence-based demands of a more nuanced debate of the functional properties of stigmatization and its connection to discrimination in infectious diseases (Deacon, 2006).
Strengths and Limitations
Finally, this study is not without limitations, as the sample is a small convenience sample that is not representative of the German population. In fact, the sample was highly educated, predominantly female, and mostly without migration background. However, we still observed substantial heterogeneity in intentions, despite females and highly educated persons being generally more likely to report high compliance in previous studies. In addition, this study was cross-sectional and exploratory and used short but validated measures of core constructs, hence, effects of risk perception, for example, were not fully explored. Components like anticipatory worry could also affect compliance intentions and should be studied in more detail (Leppin and Aro, 2009). Furthermore, items measuring stigmatizing attitudes were adapted to COVID-19 for this study, therefore, a thorough psychometric validation is necessary. Moreover, we did not assess other important factors that might be connected to (non)compliance, such as ethnicity, interpersonal contact with infected persons, or trust in the government. Finally, we captured behavioral intentions, but we did not assess actual behaviors, as the pandemic had just reached the German population, and official recommendations were first issued at the beginning of data collection. Therefore, future studies should also focus on behavioral performance. When investigating the connection between compliance intentions and behavioral performance, health behaviors models like the theory of planned behavior should be applied to incorporate relevant intermediary variables, such as self-efficacy (Ajzen, 1991; Bish and Michie, 2010). Overall, more comprehensive, longitudinal, and experimental studies are necessary to validate our findings in the context of COVID-19 in diverse populations. Nevertheless, we think this study provides an important look at patterns of compliance at early stages of the COVID-19 outbreak and impactful sociodemographic and attitudinal factors, such as support for discrimination, that underline the need for selective preventive action.
Data Availability Statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation, to any qualified researcher.
Ethics Statement
The studies involving human participants were reviewed and approved by the Ethics Committee of the University Medicine Greifswald, University Medicine Greifswald. The patients/participants provided their written informed consent to participate in this study.
Author Contributions
ST, MR, and SS contributed to the conception and design of the study. ST and MR were responsible for the data collection and statistical analysis. ST wrote the first draft of the manuscript. All authors contributed to the article and approved the submitted version.
Conflict of Interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
We would like to thank Simon Barth, Isabel Buck, and Hanna Groth for their assistance in the collection and preparation of data.
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Supplementary Material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpsyg.2020.01821/full#supplementary-material
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Exploratory study on psychosocial impact of the severe acute respiratory syndrome (SARS) outbreak on Chinese students living in Japan.\\nAsia Pac. J. Public Health\\n17\\n124–129. 10.1177/101053950501700211\\n16425657\\n\"\n]"}}},{"rowIdx":462,"cells":{"data":{"kind":"list like","value":["\nFront PsychiatryFront PsychiatryFront. PsychiatryFrontiers in Psychiatry1664-0640Frontiers Media S.A.32848957PMC743211910.3389/fpsyt.2020.00802PsychiatryReviewLocal and Interregional Neurochemical Associations Measured by Magnetic Resonance Spectroscopy for Studying Brain Functions and Psychiatric DisordersShenJun*ShenkarDinaAnLiTomarJyoti SinghMolecular Imaging Branch, National Institute of Mental Health, Bethesda, MD, United States
Edited by: Maria Concepcion Garcia Otaduy, University of São Paulo, Brazil
Reviewed by: Mark Mikkelsen, Johns Hopkins University, United States; Stefania Schiavone, University of Foggia, Italy
*Correspondence: Jun Shen, shenj@intra.nimh.nih.gov
This article was submitted to Molecular Psychiatry, a section of the journal Frontiers in Psychiatry
118202020201180208620202772020Copyright © 2020 Shen, Shenkar, An and Tomar2020Shen, Shenkar, An and TomarThis work is authored by Jun Shen, Dina Shenkar, Li An, and Jyoti Singh Tomar on behalf of the U.S. Government and, as regards Dr. Shen, Dr. Shenkar, Dr. An, Dr. Tomar, and the U.S. Government, is not subject to copyright protection in the United States. Foreign and other copyrights may apply. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Magnetic resonance spectroscopy (MRS) studies have found significant correlations among neurometabolites (e.g., between glutamate and GABA) across individual subjects and altered correlations in neuropsychiatric disorders. In this article, we discuss neurochemical associations among several major neurometabolites which underpin these observations by MRS. We also illustrate the role of spectral editing in eliminating unwanted correlations caused by spectral overlapping. Finally, we describe the prospects of mapping macroscopic neurochemical associations across the brain and characterizing excitation–inhibition balance of neural networks using glutamate- and GABA-editing MRS imaging.
In vivo MRS is the only noninvasive technique that can directly measure brain chemicals in vivo. Using techniques similar to MRI, MRS can measure concentrations of many neurometabolites as well as metabolic fluxes from localized brain regions (1). Over the past decades, MRS studies have found biochemical abnormalities in essentially all neuropsychiatric disorders, providing important insights into our understanding of etiologies and treatments of various brain diseases. These studies have, in most cases, focused on alterations in the concentrations of individual brain neurometabolites by comparing them among different cohorts and/or effects of treatments.
Many significant correlations among neurometabolites and between individual neurometabolites and non-MRS measures of brain function and disorders have been reported more recently. Altered correlations have been found in neuropsychiatric disorders, revealing abnormal neurochemical associations under pathophysiological conditions [e.g., (2–12)]. The strong correlations among N-acetylaspartate (NAA)/choline in precentral gyrus, midcingulate cortex, and thalamus found in healthy subjects were absent in patients with amyotrophic lateral sclerosis (4). Correlation between hippocampal Glx (glutamate + glutamine) and NAA has been demonstrated to be a more sensitive biomarker differentiating between healthy controls and schizophrenia patients than either neurometabolite alone (6, 7). In patients with subclinical hepatic encephalopathy the occipital lobe phosphodiester measured by 31P MRS and Glx levels were found to be negatively correlated (13). Interregional correlations of glutamate and GABA levels have also been reported in many studies [e.g., (8, 14–17)]. These studies have clearly demonstrated that neurochemical associations are abnormally altered in many brain disorders, but the absolute strengths of these correlations measured by different MRS methodologies have been inconsistent or controversial [e.g., (5, 14, 15, 18–23)]. In particular, the effects of spectral overlap on the observed neurometabolite correlations have yet to be illustrated although they can significantly confound the intrinsic neurochemical correlations of interest.
Many studies of neurochemical associations rely on spectral fitting [e.g., (24)] to extract neurometabolite concentrations from overlapping signals. When there is significant spectral overlap between two signals overestimate of one signal is statistically correlated with underestimate of the other signal and vice versa, even when there exists no neurochemical correlation between the two signals. In addition, this type of statistical correlations can propagate due to the intensity constraints imposed by LCModel (24) or overlapping with neurochemically correlated signals. When neurometabolite concentrations are correlated with other measurements (e.g., behavior, resting state fMRI functional connectivity, or gene expression), statistical correlations due to spectral overlap among MRS measurements can also affect correlations between MRS measurements and non-MRS measurements.
In this article we review dominant metabolic pathways connecting major neurometabolites (25–27) which underpin the neurochemical associations detected by MRS correlation studies. Non-MRS neurochemical studies of animal models that found correlated changes in neurometabolite concentrations under various pathophysiological conditions are also discussed. Monte Carlo simulations are performed to demonstrate the existence of statistical correlations that originated from spectral overlap. Finally, we discuss MRS techniques that eliminate spectral overlap and associated statistical correlations. We hope that these discussions will spur interest in developing MRS techniques for mapping neurochemical associations across the brain to facilitate a variety of clinical investigations. In particular, since glutamate and GABA play dominant roles in the excitation–inhibition balance (28, 29) and in many neuropsychiatric disorders (30–33), MRS characterization of glutamate–GABA associations among the nodes of neural networks may provide considerable insight into the interactions between glutamatergic and GABAergic systems and their abnormalities.
Predominant metabolic pathways connecting NAA, glutamate, glutamine, and GABA are reviewed in detail. For clarity, a table summarizing these pathways is provided (Table 1).
Predominant metabolic pathways of NAA, glutamate, glutamine and GABA in brain.
NAA is the most abundant free amino acid derivative in the CNS. NAA is found almost exclusively within the nervous system. In the adult brain, it is mostly confined to neurons. As such, it is of great clinical interest as it has been considered as a neuronal marker for assessment of neuronal viability in a variety of neuropsychiatric disorders using proton MRS. For example, NAA level is markedly reduced within the infarct of stroke patients (34), while a higher NAA level is associated with a better clinical outcome (35). Despite the intense interest in NAA, both its physiological and metabolic roles in normal brain functions as well as in neuropsychiatric disorders remain poorly understood [for a review of the putative role of NAA, see (36)].
Important metabolic associations exist among major neurometabolites observable by MRS through precursor–product relationships and sharing common substrates (26, 37, 38). Glutamate, the most abundant intracellular amino acids in mammals, is a key component of intermediary metabolism and a precursor of numerous cellular components including proteins as well as neurometabolites such as GABA, N-acetylaspartylglutamate (NAAG), and glutathione (26). As glutamate is also the primary excitatory neurotransmitter in the CNS, it is not surprising that the proton MRS has found abnormal glutamate levels in many neuropsychiatric disorders including multiple sclerosis (39), major depression (40), and bipolar disorder (41) where glutamatergic dysfunction is broadly implicated.
Glutamate is primarily synthesized by transamination from α-ketoglutarate catalyzed by aspartate aminotransferase (25):
Glutamate also is produced, to a much lesser extent, from α-ketoglutarate and ammonium via glutamate dehydrogenase, from glutamine via hydrolysis catalyzed by phosphate-activated glutaminase, by other transamination reactions that use α-ketoglutarate as receptor of the amino group, and during protein turnover (42).
The transaminases of importance for maintenance of glutamate homeostasis in the brain are mainly aspartate aminotransferase, branched-chain aminotransferase, and alanine aminotransferase with aspartate aminotransferase dominating overwhelmingly, representing >97% of the glutamate-related aminotransferase activities (26). 13C magnetization transfer MRS experiments have shown that the aspartate aminotransferase reaction is extremely fast in the brain in vivo (43). This rapid transamination by aspartate aminotransferase predominates in the formation of glutamate in the CNS, forming strong metabolic coupling between glutamate and aspartate (25). The tight connection between glutamate and aspartate becomes conspicuous under many pathophysiological conditions where the brain is challenged or perturbed metabolically. For example, during hypoglycemia a decrease in glutamate concentration was accompanied by an increase in aspartate concentration (44–46). Similarly, barbiturate anesthesia and hypothermia were also found to lower the concentration of α-ketoglutarate accompanied by reduced glutamate concentration and increased aspartate concentration (47–49). These correlated changes in glutamate and aspartate were explained by a sizable shift in the aspartate aminotransferase reaction towards aspartate formation at the cost of a reduction in glutamate concentration (25, 26). In contrast, both hypocapnia and hypoxic hypoxia are associated with an increase in glutamate concentration and a reduction in aspartate concentration with the aspartate aminotransferase reaction shifting in the opposite direction (25, 26, 50).
A strong metabolic coupling between glutamate and aspartate mediated by the rapid and ubiquitous aspartate aminotransferase reaction also affects NAA, the dominant signal in proton MRS, as NAA is primarily synthesized from acetyl coenzyme A (CoA) and aspartate by NAA synthase (51):
acetyl-CoA+aspartate→CoA+H\n++NAA
In addition to this indirect connection from glutamate to NAA synthesis via aspartate, both glutamate and NAA are products of NAAG catabolism catalyzed by N-acetylated-α-linked-amino dipeptidase (52–54). The deacetylation of NAA catalyzed by aspartoacylase has also been proposed as a significant metabolic pathway for NAA to act as a reservoir for glutamate in brain (55).
Glutamate–Glutamine Association
In contrast to glutamate, which is predominantly located in glutamatergic neurons, glutamine is primarily an astrocytic chemical. In the MRS literature glutamate + glutamine is often collectively referred to as Glx as at lower magnetic fields it has been difficult to separate the two spectroscopically. Abnormal glutamine concentrations have been found in several brain disorders including cancer, hepatic encephalopathy, and other neuropsychiatric disorders (56, 57).
Although the overall glutamate pool in neural tissues rapidly turns over fueled by primarily glucose under normal physiological conditions, glutamate released from nerve terminals is replenished by astroglial glutamine via the glutamate–glutamine neurotransmitter cycle [Figure 1; (26, 59)]. The negatively charged highly hydrophilic glutamate cannot diffuse across cell membranes. The concentration of glutamate in the extracellular space is extremely low due to its rapid uptake into the astroglia facilitated by high-affinity Na+-dependent transport systems against a large concentration gradient (60–62). Once taken up into the astroglial cells, glutamate is converted into glutamine by glutamine synthetase:
Schematic diagram of the glutamate–glutamine neurotransmitter cycle between neurons and astroglia (58). Glutamate (Glu) is taken up from the synaptic cleft into astroglia. There glutamate is converted to glutamine (Gln) by glutamine synthetase. The inactive glutamine is released by the astroglia, enters the neurons, and then is converted into glutamate by phosphate-activated glutaminase. Glc, glucose; α-KG, α-ketoglutarate; NH3, ammonia.
or oxidized by assimilation into the tricarboxylic acid cycle of astroglial cells (26, 63). Once formed, glutamine readily enters nerve terminals by its own low affinity transport system or by simple diffusion. There the phosphate-activated glutaminase converts it into glutamate (26):
A large number of neurochemical as well as autoradiographic studies have confirmed that glutamate is selectively taken up by astroglial cells and then converted into glutamine, while glutamine preferentially enters the neurons and is converted into glutamate there (64). In vivo\n13C and 15N MRS studies have quantitatively measured the glutamate–glutamine cycling flux in rodent and human brains [e.g., (58, 65–69)]. Results from these studies have demonstrated that the glutamate–glutamine cycle between glutamatergic neurons and astroglia is metabolically significant, providing a major connection between glutamate and glutamine in the brain (70). In addition, over the range of glutamate concentrations found in the nerve terminals, product inhibition appears to be the main mechanism of control of glutaminase activity with glutamate significantly attenuating the activity of glutaminase (71).
The metabolic connection between glutamate and glutamine is manifested in many brain disorders and animal models. For example, elevated ammonia level in the brain is associated with increased glutamine synthesis for ammonia detoxification (67). It has been reported that in hyperammonemia and hepatic encephalopathy the elevation of glutamine level in the brain is accompanied by a reduced glutamate level as glutamate acts as a receptor for the excessive ammonia (72). The changes in glutamate and glutamine levels, however, do not necessarily go in opposite directions. Both glutamate and glutamine are abundant in the brain. Despite efforts to understand the roles of glutamate and glutamine, the reason for maintaining relatively high concentrations of glutamate and glutamine in the brain is still poorly understood. It is possible that a high concentration of glutamate and glutamine facilitates the generally high metabolic activities in the brain because glutamate and glutamine are key components of intermediary metabolism (25). They are also precursors of many other cellular components (26). Their role as “energy reservoirs” is particularly clear when the brain is under metabolic stress. For example, when glucose is scarce, such as in hypoglycemia, both glutamate and glutamine act as energy fuels. As a result, the concentrations of both glutamate and glutamine are reduced in synchrony during hypoglycemia and in many other pathophysiological conditions when normal oxidative metabolism is impaired (45, 73).
Glutamate–GABA Association
While glutamate is the major excitatory neurotransmitter in the mammalian brain, GABA is the major inhibitory neurotransmitter. Since the initial detection of reduced GABA levels in epilepsy patients by MRS (74) and a strong correlation between GABA levels and seizure control in epilepsy patients treated by vigabatrin (75), MRS of GABA has greatly advanced both in terms of MRS methodologies and their clinical applications in studying GABAergic abnormalities in neuropsychiatric disorders.
Like glutamate, GABA metabolism proceeds through important intermediates of the tricarboxylic acid cycle. When GABA was first discovered (76) it was realized that GABA was formed from glutamate. Later studies identified that the principal pathway of GABA goes through α-decarboxylation of glutamate via glutamic acid decarboxylase which converts glutamate directly into GABA (77):
glutamate→GABA + CO2
The source of the GABA precursor is believed to be dominated by neuronal glucose with astroglial glutamine playing a smaller role (78–80). GABA synthesis from putrescine and other polyamines is metabolically insignificant in the brain although polyamines play an important role in the developing brain (81).
A fundamental aspect of glutamate–GABA association is the excitation–inhibition balance in the brain (32, 82) because of their roles as the dominant excitatory and inhibitory neurotransmitters, respectively, in the CNS. Glutamatergic neurons (e.g., cortical pyramidal neurons) receive a significant degree of GABAA-mediated inhibition through interneurons (83). Balanced excitation and inhibition facilitate normal brain functions, and failure to maintain excitation–inhibition balance underlies dysfunction in many brain disorders (32, 33, 84). Many studies have also revealed altered glutamate and GABA. For example, GABA levels were found to decrease, whereas Glx levels increased with increasing visual input in the occipital cortex of healthy subjects (85). Both glutamate and GABA increased following vigorous exercise (86). In autistic patients the frontal lobe [GABA]/[Glu] ratio was found to be significantly lower, suggesting abnormality in the regulation between GABA and glutamate (87). Abnormalities in MRS measures of glutamate and/or GABA in many other neuropsychiatric disorders have also been reported and reviewed [e.g., (40, 88–90)].
Long range excitatory and inhibitory interactions between functionally connected brain regions are well established (91–93). The strong coupling between glutamatergic neurotransmission and total GABA level is supported by a large body of in vitro and in vivo evidence [e.g., (16, 75, 94–100)]. A large number of neuroimaging studies have also shown that total glutamate or Glx concentration is significantly correlated with neural activity or glutamatergic neurotransmission [e.g., (28, 101–103)]. Correlations of total glutamate and total GABA levels locally and among regions functionally connected in a specific neural network have also been reported [e.g., (15–17, 19)].
Statistical Correlations Among MRS Signals Due to Spectral Overlap
Many neurometabolites have similar resonant frequencies, leading to spectral overlap among MRS signals. Effects of spectral overlap on the Cremer–Rao lower bounds of extracted neurometabolites have been analyzed previously (104). Extracting the concentrations of neurometabolites by spectral fitting is essentially mapping the acquired MRS spectrum (spec) into a vector (conc) consisting of concentrations by inverting the matrix equation spec = basis • conc (24). Here, basis is a matrix consisting of basis spectra of the component neurometabolites, which transforms the concentration vector conc into the fitted spectrum that approximates spec in the sense of least squares. Overlapping neurometabolites become statistically correlated through the covariance matrix (COV) of conc (105):
where † denotes Hermitian transposition and σ2 is the noise variance of the measured spectrum spec. The off-diagonal elements (proportional to the square of cross-correlation coefficients) of COV(conc) depend on the frequency separation among the resonances of the neurometabolites in basis. Figure 2 shows Monte Carlo simulations of the correlation between two singlet peaks as a function of their separation in the frequency domain. Figure 2A shows the correlation between conc1 and conc2 across individual fits when spectral overlap between the two signals is minimal. The correlation between conc1 and conc2 when their frequency separation equals their half-height linewidth is plotted in Figure 2B. Finally, the correlation between conc1 and conc2 when their frequency separation equals 0.5* half-height linewidth is plotted in Figure 2C. As shown by Figure 2 statistical correlation between these two neurochemically unrelated signals increases as their spectral overlap increases. Here the large correlation values occur when the two signals happen to exhibit similar chemical shifts, not because one signal influences the other neurochemically. This simple example of two overlapping singlets illustrates a point of caution in the interpretation of neurometabolite correlation results. For multiplets and neurometabolites with multiple resonances, spectral overlap occurs when two resonance lines overlap each other even when the chemical shifts are not very close.
Monte Carlo simulation of statistical correlation between two unrelated Lorentzian singlets with signal-to-noise ratio = 20. The half-height linewidth of the two peaks is 8 Hz. The same spectral fitting process was repeated 1,000 times with the same noise level but different noise realizations. For 40 Hz (A), 8 Hz (B), and 4 Hz (C) frequency separations between the two singlets, the correlation coefficient was found to be −0.04, −0.63, and −0.98, respectively.
The off-diagonal elements of the covariance matrix become highly significant in the presence of severe spectral overlap such as in short echo time MRS spectra. In addition to overlapping neurometabolites, a strong baseline can also cause statistical correlations (106, 107) because the baseline, which arises from macromolecules and/or lipids and residual water, overlaps with essentially all neurometabolite signals. Figure 3 compares spectral fitting of two 3 T short echo time single voxel in vivo spectra by the commercial LCModel software. The spectrum on the left was acquired using short echo time Point RESolved Spectroscopy (PRESS) technique from a cubic voxel in the anterior cingulate cortex of a healthy subject at 3 T (echo time = 35 ms, voxel size = 8 ml). The spectrum on the right was generated by broadening the linewidths of the spectrum on the left by 2.0 Hz and adding random noise to maintain the same signal-to-noise ratio (107). Both spectra in Figure 3 were fitted using LCModel with the same default settings (24). With 2.0 Hz line-broadening the LCModel baseline was conspicuously stronger around the spectral region near 2.35 ppm where glutamate and the aspartyl moiety of NAA resonate. Both NAA and glutamate levels reported by LCModel were lowered by approximately the same amount (~11%) after the line-broadening [n = 10; (107)]. This reduction in the extracted metabolite concentrations was found to be generally more pronounced with greater line-broadening. Although the concentrations of neurometabolites in the two spectra of Figure 3 are identical, the LCModel produced lower neurometabolite levels and a more intense baseline after 2.0 Hz line-broadening. Because of the spectral overlap between baseline and neurometabolites, overestimating (underestimating) the baseline causes underestimating (overestimating) neurometabolites and vice versa. Neurometabolites overlapping with the same broad baseline peak are similarly underestimated (overestimated) due to the broad baseline signals. This in turn, contributes to positive statistical correlations among those neurometabolites regardless of the underlying neurochemical associations.
(A) Single voxel short echo time spectrum acquired from the anterior cingulate cortex of a healthy subject at 3 T. Data was fitted using LCModel. (B) LCModel generated a different baseline from the same data after 2.0 Hz line-broadening and noise injection to maintain the same signal-to-noise ratio. The large change in baseline around 2.35 ppm (marked by an arrow) simultaneously reduced fitted NAA and glutamate concentrations, therefore, causing positive correlation between NAA and glutamate even though the only difference between the two spectra is their linewidth (i.e., no correlations). Reprinted from reference (107) with permission from Elsevier.
Spectral fitting techniques such as the LCModel heavily rely on the linewidth difference between neurometabolites and background signals to separate them. Broad neurometabolite peaks in the presence of a strong baseline, as often seen in clinical short echo time MRS data, can lead to significant quantification errors and unwanted statistical correlations because of the large baseline-metabolite covariances. The results in Figure 3 are also corroborated by an earlier study which quantitatively analyzed the estimation uncertainties caused by the baseline using Cramer–Rao lower bound (CRLB) of the baseline (106), confirming that the estimation uncertainty significantly increases with decreased baseline smoothness and increased spectral linewidths.
Prospects for Mapping Macroscopic Neurochemical Associations By MRS Imaging
Although group comparison of neurometabolite correlations between healthy controls and patients reveals altered neurochemical associations in brain disorders, determining the absolute strength of these correlations is important for interpreting clinical findings (20, 21) and for potentially relaying interregional associations across the brain. As linewidth variations in clinical MRS studies are very common, our analysis of statistical correlations that originated from the spectral overlap in the section Statistical Correlations Among MRS Signals Due to Spectral Overlap has demonstrated that spectral overlap should be eliminated or minimized when the absolute strength of neurometabolite correlations is to be determined. To facilitate measurement of the absolute strength of neurochemical associations, MRS techniques that result in flat baselines and isolated signals of interest would be ideal. Many existing single voxel spectral editing techniques generate flat or weak baselines while eliminating or minimizing overlapping resonances (74, 108–115), which are likely suited for measuring local or intraregional neurochemical associations.
Mapping macroscopic neurochemical associations across the human brain has the exciting potential to broadly impact studies of normal brain functions as well as neuropsychiatric disorders (32, 116, 117). Here we discuss the prospects for measuring interregional excitation–inhibition balance (32, 118–120) by spectroscopic imaging of spectrally resolved glutamate and GABA. Participant motion is a major issue in scanning many patients of neuropsychiatric disorders. Studying these patients using chemical shift imaging is technically challenging because of the relatively long scan time required for phase encoding. As artifacts in chemical shift images caused by motion are hard to detect, they can lead to erroneous diagnosis and data interpretation (121). It is well-known that participant motion inside a magnetic field causes changes in resonant frequencies. Incorporating spectral editing techniques based on highly selective radiofrequency pulses into chemical shift imaging therefore can lead to even larger errors due to the additional effects of carrier frequency mismatch on spectral editing yield.
To minimize error due to unavoidable patient movement during extended scan time necessary for phase encoding, we focus on techniques that can resolve glutamate or GABA in a single shot with relatively weak baselines at 7 T. Spectral isolation of glutamate or GABA accompanied with a weak baseline will minimize the unwanted correlations that originated from spectral overlap (107). The emphasis on minimizing spectral overlap for measuring interregional neurometabolite correlations may seem counterintuitive. However, it is necessary because, for example, overlapping with interregionally correlated signals can relay the correlation to overlapped signals. To spectrally resolve glutamate or GABA over an extended brain region, highly frequency-selective pulses popular for single voxel spectral editing cannot be used because of the unavoidable and significant residual B0 inhomogeneity across a large volume in the brain, especially at high magnetic field strength. Highly frequency-selective pulses are sensitive to patient movement, system instability, and B0 inhomogeneity as they will miss or partially miss the editing target in part of the slice(s) where resonance frequencies are shifted away (122, 123).
Weak or nearly flat baselines are automatically produced at long echo times because of the shorter T2 values of macromolecules (1). Serendipitously, the strongly coupled glutamate H4 (2.35 ppm) forms an intense pseudo singlet at a relatively long echo time (~100 ms) at 7 T (111, 112). The resonances of glutamine H4 at 2.45 ppm and glutathione glutamyl H4 at 2.49 ppm also form pseudo singlets at ~100 ms echo time (see Figure 4). Therefore, glutamate can be spectrally resolved in a single shot without using any spectrally selective pulses at ~100 ms echo time at 7 T (111). At 7 T the multiplet signal of the aspartyl moiety of NAA at 2.49 ppm still overlaps with glutamine H4 and glutathione glutamyl H4 at ~100 ms echo time (111). The overlapping NAA aspartyl moiety signals can be eliminated using a J-suppression pulse acting on the α-H of the aspartyl moiety of NAA at 4.38 ppm (112). The J suppression pulse can be made band-selective with a flat top frequency profile (124) to accommodate variations in B0. Either because no frequency selective pulses are needed (111) or with band-selective J suppression (112), chemical shift imaging of glutamate is feasible at 7 T in the presence of significant patient motion and residual static magnetic field inhomogeneity across the slice(s).
Single voxel 7 T spectrum acquired using a single-shot echo time optimized PRESS sequence (112) without signal averaging. Voxel size = 2 × 2 × 2 cm3. Echo time = 106 ms. Line broadening = 8 Hz. Number of averages = 1. NAA, N-acetylaspartate; Glu, glutamate; Gln, glutamine; GSH, glutathione; tCr, total creatine; tCho, total choline; mI, myo-inositol. Glutamate H4 at 2.35 ppm was spectrally resolved with an approximately flat baseline.
Multiple quantum filtering can be used to edit GABA in a single shot (125–128). It is also possible to generate a flat baseline using multiple quantum filtering (127). Chemical shift imaging of GABA over a large volume in the brain is challenging even at the high magnetic field strength of 7 T as all available GABA editing techniques rely on frequency selective pulses that differentially act on GABA H3 at 1.91 ppm and GABA H4 at 3.02 ppm. At 7 T, the chemical shift dispersion between GABA H3 and GABA H4 is 328 Hz. This relatively large chemical shift difference makes it possible to use band-selective pulses with a flat top (124) to accommodate changes in B0 due to patient movement, system instability, and residual B0 inhomogeneity while still affording a close to uniform editing yield. Figure 5 proposes a band-selective multiple quantum filtering scheme that combines spectral selectivity while allowing signal to shift within the flat passband. This multiple quantum GABA editing scheme is expected to tolerate a large frequency shift with GABA H3 lying within the flat bandwidth and GABA H4 staying outside of the downfield transition band of the band-selective pulse acting on GABA H3.
A proposed scheme for band-selective multiple quantum filtering of GABA. A band-selective refocusing pulse prepares GABA into multiple quantum state while leaving glutathione in single quantum state by avoiding refocusing its cysteinyl α-H at 4.56 ppm. A band-selective 90° pulse converts the double quantum coherence into observable single quantum coherence. The frequency separation between GABA H4 and GABA H3 at 7 T is 164 × 2 = 328 Hz. Asp, aspartate; GSH, glutathione; Cr, creatine. The chemical shifts (in ppm) of the resonance signals are placed below their labels.
Although we have focused on discussing chemical shift imaging of spectrally resolved glutamate and GABA without using highly selective editing pulses for the magnetic field strength of 7 T, similar ideas may also be developed for lower magnetic field strengths such as 3 T. For example, multiecho time averaging at 3 T can isolate glutamate with a nearly flat baseline without using any spectral editing pulses (109). Directly combining this approach with conventional phase-encoding for chemical shift imaging would not be feasible due to the large number [e.g., (32)] of echoes required for each phase encoding step. Instead of using evenly spaced echo times, the averaging effect may be obtained using fewer echo times with numerical optimization. Many fast imaging strategies such as echo-planar readout can also greatly accelerate data acquisition of chemical shift imaging experiments.
The coordinated variations of glutamate and GABA across the brain can be assessed using chemical shift images of spectrally resolved glutamate and GABA. The absolute strengths of interregional correlations of spectrally resolved glutamate and GABA have the exciting potential for characterizing excitatory–inhibitory connections among the nodes of neural networks, therefore providing novel parameters for gauging interregional excitation–inhibition balance, the disruption of which is implicated in many neuropsychiatric disorders [e.g., (33, 103, 117, 129, 130)].
Conclusions
Previous neurochemical studies of animal models have revealed coordinated changes in major neurometabolites under various pathophysiological conditions which were attributed to the well-studied metabolic pathways connecting them. Recent MRS findings of neurometabolite correlations in healthy subjects and altered correlations in patients have further corroborated these associations, and their disruption is a hallmark of many neuropsychiatric disorders. To measure the absolute strength of these correlations, it is necessary to use spectral editing techniques to minimize or eliminate statistical correlations among MRS signals that originated from spectral overlap. Finally, chemical shift imaging of spectrally resolved glutamate and GABA is technically feasible at 7 T. It is hoped that the prospects for eliminating the confounding statistical correlations due to spectral overlap will reduce controversies in the field and generate further interest in characterizing local and interregional neurochemical associations especially glutamate–GABA interactions in the brain for studying neuropsychiatric disorders.
Author Contributions
JS conceived the paper and performed literature search. DS and LA conducted laboratory research. JST performed literature search. JS, LA, and JST wrote the paper. All authors contributed to the article and approved the submitted version.
Funding
This work was supported by the Intramural Research Program of National Institute of Mental Health, NIH.
Conflict of Interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
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PsychiatryFrontiers in Psychiatry1664-0640Frontiers Media S.A.32848957PMC743211910.3389/fpsyt.2020.00802PsychiatryReviewLocal and Interregional Neurochemical Associations Measured by Magnetic Resonance Spectroscopy for Studying Brain Functions and Psychiatric DisordersShenJun*ShenkarDinaAnLiTomarJyoti SinghMolecular Imaging Branch, National Institute of Mental Health, Bethesda, MD, United States
Edited by: Maria Concepcion Garcia Otaduy, University of São Paulo, Brazil
Reviewed by: Mark Mikkelsen, Johns Hopkins University, United States; Stefania Schiavone, University of Foggia, Italy
*Correspondence: Jun Shen, shenj@intra.nimh.nih.gov
This article was submitted to Molecular Psychiatry, a section of the journal Frontiers in Psychiatry
118202020201180208620202772020Copyright © 2020 Shen, Shenkar, An and Tomar2020Shen, Shenkar, An and TomarThis work is authored by Jun Shen, Dina Shenkar, Li An, and Jyoti Singh Tomar on behalf of the U.S. Government and, as regards Dr. Shen, Dr. Shenkar, Dr. An, Dr. Tomar, and the U.S. Government, is not subject to copyright protection in the United States. Foreign and other copyrights may apply. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Magnetic resonance spectroscopy (MRS) studies have found significant correlations among neurometabolites (e.g., between glutamate and GABA) across individual subjects and altered correlations in neuropsychiatric disorders. In this article, we discuss neurochemical associations among several major neurometabolites which underpin these observations by MRS. We also illustrate the role of spectral editing in eliminating unwanted correlations caused by spectral overlapping. Finally, we describe the prospects of mapping macroscopic neurochemical associations across the brain and characterizing excitation–inhibition balance of neural networks using glutamate- and GABA-editing MRS imaging.
In vivo MRS is the only noninvasive technique that can directly measure brain chemicals in vivo. Using techniques similar to MRI, MRS can measure concentrations of many neurometabolites as well as metabolic fluxes from localized brain regions (1). Over the past decades, MRS studies have found biochemical abnormalities in essentially all neuropsychiatric disorders, providing important insights into our understanding of etiologies and treatments of various brain diseases. These studies have, in most cases, focused on alterations in the concentrations of individual brain neurometabolites by comparing them among different cohorts and/or effects of treatments.
Many significant correlations among neurometabolites and between individual neurometabolites and non-MRS measures of brain function and disorders have been reported more recently. Altered correlations have been found in neuropsychiatric disorders, revealing abnormal neurochemical associations under pathophysiological conditions [e.g., (2–12)]. The strong correlations among N-acetylaspartate (NAA)/choline in precentral gyrus, midcingulate cortex, and thalamus found in healthy subjects were absent in patients with amyotrophic lateral sclerosis (4). Correlation between hippocampal Glx (glutamate + glutamine) and NAA has been demonstrated to be a more sensitive biomarker differentiating between healthy controls and schizophrenia patients than either neurometabolite alone (6, 7). In patients with subclinical hepatic encephalopathy the occipital lobe phosphodiester measured by 31P MRS and Glx levels were found to be negatively correlated (13). Interregional correlations of glutamate and GABA levels have also been reported in many studies [e.g., (8, 14–17)]. These studies have clearly demonstrated that neurochemical associations are abnormally altered in many brain disorders, but the absolute strengths of these correlations measured by different MRS methodologies have been inconsistent or controversial [e.g., (5, 14, 15, 18–23)]. In particular, the effects of spectral overlap on the observed neurometabolite correlations have yet to be illustrated although they can significantly confound the intrinsic neurochemical correlations of interest.
Many studies of neurochemical associations rely on spectral fitting [e.g., (24)] to extract neurometabolite concentrations from overlapping signals. When there is significant spectral overlap between two signals overestimate of one signal is statistically correlated with underestimate of the other signal and vice versa, even when there exists no neurochemical correlation between the two signals. In addition, this type of statistical correlations can propagate due to the intensity constraints imposed by LCModel (24) or overlapping with neurochemically correlated signals. When neurometabolite concentrations are correlated with other measurements (e.g., behavior, resting state fMRI functional connectivity, or gene expression), statistical correlations due to spectral overlap among MRS measurements can also affect correlations between MRS measurements and non-MRS measurements.
In this article we review dominant metabolic pathways connecting major neurometabolites (25–27) which underpin the neurochemical associations detected by MRS correlation studies. Non-MRS neurochemical studies of animal models that found correlated changes in neurometabolite concentrations under various pathophysiological conditions are also discussed. Monte Carlo simulations are performed to demonstrate the existence of statistical correlations that originated from spectral overlap. Finally, we discuss MRS techniques that eliminate spectral overlap and associated statistical correlations. We hope that these discussions will spur interest in developing MRS techniques for mapping neurochemical associations across the brain to facilitate a variety of clinical investigations. In particular, since glutamate and GABA play dominant roles in the excitation–inhibition balance (28, 29) and in many neuropsychiatric disorders (30–33), MRS characterization of glutamate–GABA associations among the nodes of neural networks may provide considerable insight into the interactions between glutamatergic and GABAergic systems and their abnormalities.
Predominant metabolic pathways connecting NAA, glutamate, glutamine, and GABA are reviewed in detail. For clarity, a table summarizing these pathways is provided (Table 1).
Predominant metabolic pathways of NAA, glutamate, glutamine and GABA in brain.
NAA is the most abundant free amino acid derivative in the CNS. NAA is found almost exclusively within the nervous system. In the adult brain, it is mostly confined to neurons. As such, it is of great clinical interest as it has been considered as a neuronal marker for assessment of neuronal viability in a variety of neuropsychiatric disorders using proton MRS. For example, NAA level is markedly reduced within the infarct of stroke patients (34), while a higher NAA level is associated with a better clinical outcome (35). Despite the intense interest in NAA, both its physiological and metabolic roles in normal brain functions as well as in neuropsychiatric disorders remain poorly understood [for a review of the putative role of NAA, see (36)].
Important metabolic associations exist among major neurometabolites observable by MRS through precursor–product relationships and sharing common substrates (26, 37, 38). Glutamate, the most abundant intracellular amino acids in mammals, is a key component of intermediary metabolism and a precursor of numerous cellular components including proteins as well as neurometabolites such as GABA, N-acetylaspartylglutamate (NAAG), and glutathione (26). As glutamate is also the primary excitatory neurotransmitter in the CNS, it is not surprising that the proton MRS has found abnormal glutamate levels in many neuropsychiatric disorders including multiple sclerosis (39), major depression (40), and bipolar disorder (41) where glutamatergic dysfunction is broadly implicated.
Glutamate is primarily synthesized by transamination from α-ketoglutarate catalyzed by aspartate aminotransferase (25):
Glutamate also is produced, to a much lesser extent, from α-ketoglutarate and ammonium via glutamate dehydrogenase, from glutamine via hydrolysis catalyzed by phosphate-activated glutaminase, by other transamination reactions that use α-ketoglutarate as receptor of the amino group, and during protein turnover (42).
The transaminases of importance for maintenance of glutamate homeostasis in the brain are mainly aspartate aminotransferase, branched-chain aminotransferase, and alanine aminotransferase with aspartate aminotransferase dominating overwhelmingly, representing >97% of the glutamate-related aminotransferase activities (26). 13C magnetization transfer MRS experiments have shown that the aspartate aminotransferase reaction is extremely fast in the brain in vivo (43). This rapid transamination by aspartate aminotransferase predominates in the formation of glutamate in the CNS, forming strong metabolic coupling between glutamate and aspartate (25). The tight connection between glutamate and aspartate becomes conspicuous under many pathophysiological conditions where the brain is challenged or perturbed metabolically. For example, during hypoglycemia a decrease in glutamate concentration was accompanied by an increase in aspartate concentration (44–46). Similarly, barbiturate anesthesia and hypothermia were also found to lower the concentration of α-ketoglutarate accompanied by reduced glutamate concentration and increased aspartate concentration (47–49). These correlated changes in glutamate and aspartate were explained by a sizable shift in the aspartate aminotransferase reaction towards aspartate formation at the cost of a reduction in glutamate concentration (25, 26). In contrast, both hypocapnia and hypoxic hypoxia are associated with an increase in glutamate concentration and a reduction in aspartate concentration with the aspartate aminotransferase reaction shifting in the opposite direction (25, 26, 50).
A strong metabolic coupling between glutamate and aspartate mediated by the rapid and ubiquitous aspartate aminotransferase reaction also affects NAA, the dominant signal in proton MRS, as NAA is primarily synthesized from acetyl coenzyme A (CoA) and aspartate by NAA synthase (51):
acetyl-CoA+aspartate→CoA+H\\n++NAA
In addition to this indirect connection from glutamate to NAA synthesis via aspartate, both glutamate and NAA are products of NAAG catabolism catalyzed by N-acetylated-α-linked-amino dipeptidase (52–54). The deacetylation of NAA catalyzed by aspartoacylase has also been proposed as a significant metabolic pathway for NAA to act as a reservoir for glutamate in brain (55).
Glutamate–Glutamine Association
In contrast to glutamate, which is predominantly located in glutamatergic neurons, glutamine is primarily an astrocytic chemical. In the MRS literature glutamate + glutamine is often collectively referred to as Glx as at lower magnetic fields it has been difficult to separate the two spectroscopically. Abnormal glutamine concentrations have been found in several brain disorders including cancer, hepatic encephalopathy, and other neuropsychiatric disorders (56, 57).
Although the overall glutamate pool in neural tissues rapidly turns over fueled by primarily glucose under normal physiological conditions, glutamate released from nerve terminals is replenished by astroglial glutamine via the glutamate–glutamine neurotransmitter cycle [Figure 1; (26, 59)]. The negatively charged highly hydrophilic glutamate cannot diffuse across cell membranes. The concentration of glutamate in the extracellular space is extremely low due to its rapid uptake into the astroglia facilitated by high-affinity Na+-dependent transport systems against a large concentration gradient (60–62). Once taken up into the astroglial cells, glutamate is converted into glutamine by glutamine synthetase:
Schematic diagram of the glutamate–glutamine neurotransmitter cycle between neurons and astroglia (58). Glutamate (Glu) is taken up from the synaptic cleft into astroglia. There glutamate is converted to glutamine (Gln) by glutamine synthetase. The inactive glutamine is released by the astroglia, enters the neurons, and then is converted into glutamate by phosphate-activated glutaminase. Glc, glucose; α-KG, α-ketoglutarate; NH3, ammonia.
or oxidized by assimilation into the tricarboxylic acid cycle of astroglial cells (26, 63). Once formed, glutamine readily enters nerve terminals by its own low affinity transport system or by simple diffusion. There the phosphate-activated glutaminase converts it into glutamate (26):
A large number of neurochemical as well as autoradiographic studies have confirmed that glutamate is selectively taken up by astroglial cells and then converted into glutamine, while glutamine preferentially enters the neurons and is converted into glutamate there (64). In vivo\\n13C and 15N MRS studies have quantitatively measured the glutamate–glutamine cycling flux in rodent and human brains [e.g., (58, 65–69)]. Results from these studies have demonstrated that the glutamate–glutamine cycle between glutamatergic neurons and astroglia is metabolically significant, providing a major connection between glutamate and glutamine in the brain (70). In addition, over the range of glutamate concentrations found in the nerve terminals, product inhibition appears to be the main mechanism of control of glutaminase activity with glutamate significantly attenuating the activity of glutaminase (71).
The metabolic connection between glutamate and glutamine is manifested in many brain disorders and animal models. For example, elevated ammonia level in the brain is associated with increased glutamine synthesis for ammonia detoxification (67). It has been reported that in hyperammonemia and hepatic encephalopathy the elevation of glutamine level in the brain is accompanied by a reduced glutamate level as glutamate acts as a receptor for the excessive ammonia (72). The changes in glutamate and glutamine levels, however, do not necessarily go in opposite directions. Both glutamate and glutamine are abundant in the brain. Despite efforts to understand the roles of glutamate and glutamine, the reason for maintaining relatively high concentrations of glutamate and glutamine in the brain is still poorly understood. It is possible that a high concentration of glutamate and glutamine facilitates the generally high metabolic activities in the brain because glutamate and glutamine are key components of intermediary metabolism (25). They are also precursors of many other cellular components (26). Their role as “energy reservoirs” is particularly clear when the brain is under metabolic stress. For example, when glucose is scarce, such as in hypoglycemia, both glutamate and glutamine act as energy fuels. As a result, the concentrations of both glutamate and glutamine are reduced in synchrony during hypoglycemia and in many other pathophysiological conditions when normal oxidative metabolism is impaired (45, 73).
Glutamate–GABA Association
While glutamate is the major excitatory neurotransmitter in the mammalian brain, GABA is the major inhibitory neurotransmitter. Since the initial detection of reduced GABA levels in epilepsy patients by MRS (74) and a strong correlation between GABA levels and seizure control in epilepsy patients treated by vigabatrin (75), MRS of GABA has greatly advanced both in terms of MRS methodologies and their clinical applications in studying GABAergic abnormalities in neuropsychiatric disorders.
Like glutamate, GABA metabolism proceeds through important intermediates of the tricarboxylic acid cycle. When GABA was first discovered (76) it was realized that GABA was formed from glutamate. Later studies identified that the principal pathway of GABA goes through α-decarboxylation of glutamate via glutamic acid decarboxylase which converts glutamate directly into GABA (77):
glutamate→GABA + CO2
The source of the GABA precursor is believed to be dominated by neuronal glucose with astroglial glutamine playing a smaller role (78–80). GABA synthesis from putrescine and other polyamines is metabolically insignificant in the brain although polyamines play an important role in the developing brain (81).
A fundamental aspect of glutamate–GABA association is the excitation–inhibition balance in the brain (32, 82) because of their roles as the dominant excitatory and inhibitory neurotransmitters, respectively, in the CNS. Glutamatergic neurons (e.g., cortical pyramidal neurons) receive a significant degree of GABAA-mediated inhibition through interneurons (83). Balanced excitation and inhibition facilitate normal brain functions, and failure to maintain excitation–inhibition balance underlies dysfunction in many brain disorders (32, 33, 84). Many studies have also revealed altered glutamate and GABA. For example, GABA levels were found to decrease, whereas Glx levels increased with increasing visual input in the occipital cortex of healthy subjects (85). Both glutamate and GABA increased following vigorous exercise (86). In autistic patients the frontal lobe [GABA]/[Glu] ratio was found to be significantly lower, suggesting abnormality in the regulation between GABA and glutamate (87). Abnormalities in MRS measures of glutamate and/or GABA in many other neuropsychiatric disorders have also been reported and reviewed [e.g., (40, 88–90)].
Long range excitatory and inhibitory interactions between functionally connected brain regions are well established (91–93). The strong coupling between glutamatergic neurotransmission and total GABA level is supported by a large body of in vitro and in vivo evidence [e.g., (16, 75, 94–100)]. A large number of neuroimaging studies have also shown that total glutamate or Glx concentration is significantly correlated with neural activity or glutamatergic neurotransmission [e.g., (28, 101–103)]. Correlations of total glutamate and total GABA levels locally and among regions functionally connected in a specific neural network have also been reported [e.g., (15–17, 19)].
Statistical Correlations Among MRS Signals Due to Spectral Overlap
Many neurometabolites have similar resonant frequencies, leading to spectral overlap among MRS signals. Effects of spectral overlap on the Cremer–Rao lower bounds of extracted neurometabolites have been analyzed previously (104). Extracting the concentrations of neurometabolites by spectral fitting is essentially mapping the acquired MRS spectrum (spec) into a vector (conc) consisting of concentrations by inverting the matrix equation spec = basis • conc (24). Here, basis is a matrix consisting of basis spectra of the component neurometabolites, which transforms the concentration vector conc into the fitted spectrum that approximates spec in the sense of least squares. Overlapping neurometabolites become statistically correlated through the covariance matrix (COV) of conc (105):
where † denotes Hermitian transposition and σ2 is the noise variance of the measured spectrum spec. The off-diagonal elements (proportional to the square of cross-correlation coefficients) of COV(conc) depend on the frequency separation among the resonances of the neurometabolites in basis. Figure 2 shows Monte Carlo simulations of the correlation between two singlet peaks as a function of their separation in the frequency domain. Figure 2A shows the correlation between conc1 and conc2 across individual fits when spectral overlap between the two signals is minimal. The correlation between conc1 and conc2 when their frequency separation equals their half-height linewidth is plotted in Figure 2B. Finally, the correlation between conc1 and conc2 when their frequency separation equals 0.5* half-height linewidth is plotted in Figure 2C. As shown by Figure 2 statistical correlation between these two neurochemically unrelated signals increases as their spectral overlap increases. Here the large correlation values occur when the two signals happen to exhibit similar chemical shifts, not because one signal influences the other neurochemically. This simple example of two overlapping singlets illustrates a point of caution in the interpretation of neurometabolite correlation results. For multiplets and neurometabolites with multiple resonances, spectral overlap occurs when two resonance lines overlap each other even when the chemical shifts are not very close.
Monte Carlo simulation of statistical correlation between two unrelated Lorentzian singlets with signal-to-noise ratio = 20. The half-height linewidth of the two peaks is 8 Hz. The same spectral fitting process was repeated 1,000 times with the same noise level but different noise realizations. For 40 Hz (A), 8 Hz (B), and 4 Hz (C) frequency separations between the two singlets, the correlation coefficient was found to be −0.04, −0.63, and −0.98, respectively.
The off-diagonal elements of the covariance matrix become highly significant in the presence of severe spectral overlap such as in short echo time MRS spectra. In addition to overlapping neurometabolites, a strong baseline can also cause statistical correlations (106, 107) because the baseline, which arises from macromolecules and/or lipids and residual water, overlaps with essentially all neurometabolite signals. Figure 3 compares spectral fitting of two 3 T short echo time single voxel in vivo spectra by the commercial LCModel software. The spectrum on the left was acquired using short echo time Point RESolved Spectroscopy (PRESS) technique from a cubic voxel in the anterior cingulate cortex of a healthy subject at 3 T (echo time = 35 ms, voxel size = 8 ml). The spectrum on the right was generated by broadening the linewidths of the spectrum on the left by 2.0 Hz and adding random noise to maintain the same signal-to-noise ratio (107). Both spectra in Figure 3 were fitted using LCModel with the same default settings (24). With 2.0 Hz line-broadening the LCModel baseline was conspicuously stronger around the spectral region near 2.35 ppm where glutamate and the aspartyl moiety of NAA resonate. Both NAA and glutamate levels reported by LCModel were lowered by approximately the same amount (~11%) after the line-broadening [n = 10; (107)]. This reduction in the extracted metabolite concentrations was found to be generally more pronounced with greater line-broadening. Although the concentrations of neurometabolites in the two spectra of Figure 3 are identical, the LCModel produced lower neurometabolite levels and a more intense baseline after 2.0 Hz line-broadening. Because of the spectral overlap between baseline and neurometabolites, overestimating (underestimating) the baseline causes underestimating (overestimating) neurometabolites and vice versa. Neurometabolites overlapping with the same broad baseline peak are similarly underestimated (overestimated) due to the broad baseline signals. This in turn, contributes to positive statistical correlations among those neurometabolites regardless of the underlying neurochemical associations.
(A) Single voxel short echo time spectrum acquired from the anterior cingulate cortex of a healthy subject at 3 T. Data was fitted using LCModel. (B) LCModel generated a different baseline from the same data after 2.0 Hz line-broadening and noise injection to maintain the same signal-to-noise ratio. The large change in baseline around 2.35 ppm (marked by an arrow) simultaneously reduced fitted NAA and glutamate concentrations, therefore, causing positive correlation between NAA and glutamate even though the only difference between the two spectra is their linewidth (i.e., no correlations). Reprinted from reference (107) with permission from Elsevier.
Spectral fitting techniques such as the LCModel heavily rely on the linewidth difference between neurometabolites and background signals to separate them. Broad neurometabolite peaks in the presence of a strong baseline, as often seen in clinical short echo time MRS data, can lead to significant quantification errors and unwanted statistical correlations because of the large baseline-metabolite covariances. The results in Figure 3 are also corroborated by an earlier study which quantitatively analyzed the estimation uncertainties caused by the baseline using Cramer–Rao lower bound (CRLB) of the baseline (106), confirming that the estimation uncertainty significantly increases with decreased baseline smoothness and increased spectral linewidths.
Prospects for Mapping Macroscopic Neurochemical Associations By MRS Imaging
Although group comparison of neurometabolite correlations between healthy controls and patients reveals altered neurochemical associations in brain disorders, determining the absolute strength of these correlations is important for interpreting clinical findings (20, 21) and for potentially relaying interregional associations across the brain. As linewidth variations in clinical MRS studies are very common, our analysis of statistical correlations that originated from the spectral overlap in the section Statistical Correlations Among MRS Signals Due to Spectral Overlap has demonstrated that spectral overlap should be eliminated or minimized when the absolute strength of neurometabolite correlations is to be determined. To facilitate measurement of the absolute strength of neurochemical associations, MRS techniques that result in flat baselines and isolated signals of interest would be ideal. Many existing single voxel spectral editing techniques generate flat or weak baselines while eliminating or minimizing overlapping resonances (74, 108–115), which are likely suited for measuring local or intraregional neurochemical associations.
Mapping macroscopic neurochemical associations across the human brain has the exciting potential to broadly impact studies of normal brain functions as well as neuropsychiatric disorders (32, 116, 117). Here we discuss the prospects for measuring interregional excitation–inhibition balance (32, 118–120) by spectroscopic imaging of spectrally resolved glutamate and GABA. Participant motion is a major issue in scanning many patients of neuropsychiatric disorders. Studying these patients using chemical shift imaging is technically challenging because of the relatively long scan time required for phase encoding. As artifacts in chemical shift images caused by motion are hard to detect, they can lead to erroneous diagnosis and data interpretation (121). It is well-known that participant motion inside a magnetic field causes changes in resonant frequencies. Incorporating spectral editing techniques based on highly selective radiofrequency pulses into chemical shift imaging therefore can lead to even larger errors due to the additional effects of carrier frequency mismatch on spectral editing yield.
To minimize error due to unavoidable patient movement during extended scan time necessary for phase encoding, we focus on techniques that can resolve glutamate or GABA in a single shot with relatively weak baselines at 7 T. Spectral isolation of glutamate or GABA accompanied with a weak baseline will minimize the unwanted correlations that originated from spectral overlap (107). The emphasis on minimizing spectral overlap for measuring interregional neurometabolite correlations may seem counterintuitive. However, it is necessary because, for example, overlapping with interregionally correlated signals can relay the correlation to overlapped signals. To spectrally resolve glutamate or GABA over an extended brain region, highly frequency-selective pulses popular for single voxel spectral editing cannot be used because of the unavoidable and significant residual B0 inhomogeneity across a large volume in the brain, especially at high magnetic field strength. Highly frequency-selective pulses are sensitive to patient movement, system instability, and B0 inhomogeneity as they will miss or partially miss the editing target in part of the slice(s) where resonance frequencies are shifted away (122, 123).
Weak or nearly flat baselines are automatically produced at long echo times because of the shorter T2 values of macromolecules (1). Serendipitously, the strongly coupled glutamate H4 (2.35 ppm) forms an intense pseudo singlet at a relatively long echo time (~100 ms) at 7 T (111, 112). The resonances of glutamine H4 at 2.45 ppm and glutathione glutamyl H4 at 2.49 ppm also form pseudo singlets at ~100 ms echo time (see Figure 4). Therefore, glutamate can be spectrally resolved in a single shot without using any spectrally selective pulses at ~100 ms echo time at 7 T (111). At 7 T the multiplet signal of the aspartyl moiety of NAA at 2.49 ppm still overlaps with glutamine H4 and glutathione glutamyl H4 at ~100 ms echo time (111). The overlapping NAA aspartyl moiety signals can be eliminated using a J-suppression pulse acting on the α-H of the aspartyl moiety of NAA at 4.38 ppm (112). The J suppression pulse can be made band-selective with a flat top frequency profile (124) to accommodate variations in B0. Either because no frequency selective pulses are needed (111) or with band-selective J suppression (112), chemical shift imaging of glutamate is feasible at 7 T in the presence of significant patient motion and residual static magnetic field inhomogeneity across the slice(s).
Single voxel 7 T spectrum acquired using a single-shot echo time optimized PRESS sequence (112) without signal averaging. Voxel size = 2 × 2 × 2 cm3. Echo time = 106 ms. Line broadening = 8 Hz. Number of averages = 1. NAA, N-acetylaspartate; Glu, glutamate; Gln, glutamine; GSH, glutathione; tCr, total creatine; tCho, total choline; mI, myo-inositol. Glutamate H4 at 2.35 ppm was spectrally resolved with an approximately flat baseline.
Multiple quantum filtering can be used to edit GABA in a single shot (125–128). It is also possible to generate a flat baseline using multiple quantum filtering (127). Chemical shift imaging of GABA over a large volume in the brain is challenging even at the high magnetic field strength of 7 T as all available GABA editing techniques rely on frequency selective pulses that differentially act on GABA H3 at 1.91 ppm and GABA H4 at 3.02 ppm. At 7 T, the chemical shift dispersion between GABA H3 and GABA H4 is 328 Hz. This relatively large chemical shift difference makes it possible to use band-selective pulses with a flat top (124) to accommodate changes in B0 due to patient movement, system instability, and residual B0 inhomogeneity while still affording a close to uniform editing yield. Figure 5 proposes a band-selective multiple quantum filtering scheme that combines spectral selectivity while allowing signal to shift within the flat passband. This multiple quantum GABA editing scheme is expected to tolerate a large frequency shift with GABA H3 lying within the flat bandwidth and GABA H4 staying outside of the downfield transition band of the band-selective pulse acting on GABA H3.
A proposed scheme for band-selective multiple quantum filtering of GABA. A band-selective refocusing pulse prepares GABA into multiple quantum state while leaving glutathione in single quantum state by avoiding refocusing its cysteinyl α-H at 4.56 ppm. A band-selective 90° pulse converts the double quantum coherence into observable single quantum coherence. The frequency separation between GABA H4 and GABA H3 at 7 T is 164 × 2 = 328 Hz. Asp, aspartate; GSH, glutathione; Cr, creatine. The chemical shifts (in ppm) of the resonance signals are placed below their labels.
Although we have focused on discussing chemical shift imaging of spectrally resolved glutamate and GABA without using highly selective editing pulses for the magnetic field strength of 7 T, similar ideas may also be developed for lower magnetic field strengths such as 3 T. For example, multiecho time averaging at 3 T can isolate glutamate with a nearly flat baseline without using any spectral editing pulses (109). Directly combining this approach with conventional phase-encoding for chemical shift imaging would not be feasible due to the large number [e.g., (32)] of echoes required for each phase encoding step. Instead of using evenly spaced echo times, the averaging effect may be obtained using fewer echo times with numerical optimization. Many fast imaging strategies such as echo-planar readout can also greatly accelerate data acquisition of chemical shift imaging experiments.
The coordinated variations of glutamate and GABA across the brain can be assessed using chemical shift images of spectrally resolved glutamate and GABA. The absolute strengths of interregional correlations of spectrally resolved glutamate and GABA have the exciting potential for characterizing excitatory–inhibitory connections among the nodes of neural networks, therefore providing novel parameters for gauging interregional excitation–inhibition balance, the disruption of which is implicated in many neuropsychiatric disorders [e.g., (33, 103, 117, 129, 130)].
Conclusions
Previous neurochemical studies of animal models have revealed coordinated changes in major neurometabolites under various pathophysiological conditions which were attributed to the well-studied metabolic pathways connecting them. Recent MRS findings of neurometabolite correlations in healthy subjects and altered correlations in patients have further corroborated these associations, and their disruption is a hallmark of many neuropsychiatric disorders. To measure the absolute strength of these correlations, it is necessary to use spectral editing techniques to minimize or eliminate statistical correlations among MRS signals that originated from spectral overlap. Finally, chemical shift imaging of spectrally resolved glutamate and GABA is technically feasible at 7 T. It is hoped that the prospects for eliminating the confounding statistical correlations due to spectral overlap will reduce controversies in the field and generate further interest in characterizing local and interregional neurochemical associations especially glutamate–GABA interactions in the brain for studying neuropsychiatric disorders.
Author Contributions
JS conceived the paper and performed literature search. DS and LA conducted laboratory research. JST performed literature search. JS, LA, and JST wrote the paper. All authors contributed to the article and approved the submitted version.
Funding
This work was supported by the Intramural Research Program of National Institute of Mental Health, NIH.
Conflict of Interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
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Genet.Frontiers in Genetics1664-8021Frontiers Media S.A.32849776PMC743212010.3389/fgene.2020.00732GeneticsCorrectionCorrigendum: Exact Distribution of Linkage Disequilibrium in the Presence of Mutation, Selection, or Minor Allele Frequency FilteringQuJiayi1KachmanStephen D.2GarrickDorian3FernandoRohan L.4ChengHao1*1Department of Animal Science, University of California, Davis, Davis, CA, United States2Department of Statistics, University of Nebraska Lincoln, Lincoln, NE, United States3School of Agriculture, Massey University, Wellington, New Zealand4Department of Animal Science, Iowa State University, Ames, IA, United States
Edited and reviewed by: Jacob A. Tennessen, Harvard University, United States
*Correspondence: Hao Cheng qtlcheng@ucdavis.edu
This article was submitted to Evolutionary and Population Genetics, a section of the journal Frontiers in Genetics
1182020202011820201173218520201662020Copyright © 2020 Qu, Kachman, Garrick, Fernando and Cheng.2020Qu, Kachman, Garrick, Fernando and ChengThis is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.A Corrigendum on Exact Distribution of Linkage Disequilibrium in the Presence of Mutation, Selection, or Minor Allele Frequency Filtering by Qu, J., Kachman, S. D., Garrick, D., Fernando, R. L., and Cheng, H. (2020). Front. Genet. 11:362. doi: 10.3389/fgene.2020.00362linkage disequilibriumeffective population sizemutation rateselectionminor allele frequency filtering
In the original article, there was a mistake in Figure 1 as published. A wrong graph was used in Figure 1A when c = 0.01. The corrected Figure 1 appears below.
Comparison of Sved's and Hill's approximation to exact distribution of rE2 (scatter points) derived from transition-matrix approach. Mean square errors are shown in the parentheses. “Calibrated” denotes the non-linear regression formula derived from the transition-matrix approach. (A) In the absence of mutation and selection. (B) In the presence of mutation but no selection.
The authors apologize for this error and state that this does not change the scientific conclusions of the article in any way. The original article has been updated.
\n"],"string":"[\n \"\\nFront GenetFront GenetFront. Genet.Frontiers in Genetics1664-8021Frontiers Media S.A.32849776PMC743212010.3389/fgene.2020.00732GeneticsCorrectionCorrigendum: Exact Distribution of Linkage Disequilibrium in the Presence of Mutation, Selection, or Minor Allele Frequency FilteringQuJiayi1KachmanStephen D.2GarrickDorian3FernandoRohan L.4ChengHao1*1Department of Animal Science, University of California, Davis, Davis, CA, United States2Department of Statistics, University of Nebraska Lincoln, Lincoln, NE, United States3School of Agriculture, Massey University, Wellington, New Zealand4Department of Animal Science, Iowa State University, Ames, IA, United States
Edited and reviewed by: Jacob A. Tennessen, Harvard University, United States
*Correspondence: Hao Cheng qtlcheng@ucdavis.edu
This article was submitted to Evolutionary and Population Genetics, a section of the journal Frontiers in Genetics
1182020202011820201173218520201662020Copyright © 2020 Qu, Kachman, Garrick, Fernando and Cheng.2020Qu, Kachman, Garrick, Fernando and ChengThis is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.A Corrigendum on Exact Distribution of Linkage Disequilibrium in the Presence of Mutation, Selection, or Minor Allele Frequency Filtering by Qu, J., Kachman, S. D., Garrick, D., Fernando, R. L., and Cheng, H. (2020). Front. Genet. 11:362. doi: 10.3389/fgene.2020.00362linkage disequilibriumeffective population sizemutation rateselectionminor allele frequency filtering
In the original article, there was a mistake in Figure 1 as published. A wrong graph was used in Figure 1A when c = 0.01. The corrected Figure 1 appears below.
Comparison of Sved's and Hill's approximation to exact distribution of rE2 (scatter points) derived from transition-matrix approach. Mean square errors are shown in the parentheses. “Calibrated” denotes the non-linear regression formula derived from the transition-matrix approach. (A) In the absence of mutation and selection. (B) In the presence of mutation but no selection.
The authors apologize for this error and state that this does not change the scientific conclusions of the article in any way. The original article has been updated.
\\n\"\n]"}}},{"rowIdx":464,"cells":{"data":{"kind":"list like","value":["\nInt J Mol SciInt J Mol SciijmsInternational Journal of Molecular Sciences1422-0067MDPI32751838PMC743212110.3390/ijms21155474ijms-21-05474ArticleMechanosensitivity Is a Characteristic Feature of Cultured Suburothelial Interstitial Cells of the Human Bladderhttps://orcid.org/0000-0003-2484-6994NeuhausJochen1*GonsiorAndreas2ChengSheng1StolzenburgJens-Uwe2BergerFrank Peter3Department of Urology, Research Laboratory, University of Leipzig, Liebigstr. 19, 04103 Leipzig, Germany; chengsheng2005@zju.edu.cnDepartment of Urology, University Hospital Leipzig, Liebigstr. 20, 04103 Leipzig, Germany; andreas.gonsior@medizin.uni-leipzig.de (A.G.); jens-uwe.stolzenburg@uniklinik-leipzig.de (J.-U.S.)Department of Urology, University Hospital Jena, Am Klinikum 1, 07747 Jena, Germany; frank.berger@med.uni-jena.deCorrespondence: jochen.neuhaus@medizin.uni-leipzig.de; Tel.: +49-341-97176883172020820202115547416620202972020© 2020 by the authors.2020Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Bladder dysfunction is characterized by urgency, frequency (pollakisuria, nocturia), and dysuria and may lead to urinary incontinence. Most of these symptoms can be attributed to disturbed bladder sensitivity. There is growing evidence that, besides the urothelium, suburothelial interstitial cells (suICs) are involved in bladder afferent signal processing. The massive expansion of the bladder during the filling phase implicates mechanical stress delivered to the whole bladder wall. Little is known about the reaction of suICs upon mechanical stress. Therefore, we investigated the effects of mechanical stimulation in cultured human suICs. We used fura-2 calcium imaging as a major physiological readout. We found spontaneous intracellular calcium activity in 75 % of the cultured suICs. Defined local pressure application via a glass micropipette led to local increased calcium activity in all stimulated suICs, spreading over the whole cell. A total of 51% of the neighboring cells in a radius of up to 100 µm from the stimulated cell showed an increased activity. Hypotonic ringer and shear stress also induced calcium transients. We found an 18-times increase in syncytial activity compared to unstimulated controls, resulting in an amplification of the primary calcium signal elicited in single cells by 50%. Our results speak in favor of a high sensitivity of suICs for mechanical stress and support the view of a functional syncytium between suICs, which can amplify and distribute local stimuli. Previous studies of connexin expression in the human bladder suggest that this mechanism could also be relevant in normal and pathological function of the bladder in vivo.
The urinary bladder has a dual function, periodical stretching during the filling phase and exhibiting mass contraction for micturition. The realization of this requires a complex coordination with the sphincter systems of the bladder itself, called the vesical or internal sphincter (musculus sphincter vesicae) and the split external or urethral sphincter, comprising an internal smooth muscle part (musculus sphincter urethrae glaber) and an external striated part (musculus sphincter urethrae transversostriatus) [1]. While the neuronal control via spinal cord and higher central nervous system centers are well known [2], the local signal processing in the bladder wall is still enigmatic. Many details of the lower urinary tract function have been learned from diseased states in humans and in animal models, such as bladder outlet obstruction (BOO) and overactive bladder (OAB), and interstitial cystitis/bladder pain processing in the bladder wall is still enigmatic. Many details of the lower urinary tract function have been learned from diseased states in humans and in animal models, such as bladder outlet obstruction (BOO), overactive bladder (OAB), and interstitial cystitis/bladder pain syndrome (IC/BPS) [3,4]. While the urothelium acts as the major sensor of bladder filling, the suburothelial interstitial cells are emerging as important modulators of the afferent signal generation [5]. Twenty-eight transient receptor potential (TRP) channels have been described so far in mammals, of which TRPV1, 2, 4 (vanilloid), TRPM8 (melastatin), and TRPA1 (ankyrin) seem to play an important role in sensory signaling in the bladder [6]. Recently, our own group described TRPA1 expression in interstitial cells (ICs) of the human, guinea pig, and rat, supporting the hypothesis of ICs being mechanosensitive [7]. The role of the ICs in the bladder is still not fully understood and recent studies indicate that there are different subtypes of ICs. The major differences in their distribution define sublayers of the lamina propria, referred to as the upper lamina propria and deeper lamina propria [8]. Recent comparative studies on ICs in human and the major laboratory animals suggest that the understanding of the roles of ICs partly needs revision [7,9,10].
Coupling between ICs, the urothelium, and detrusor smooth muscle cells is another important issue which determines the normal function of the bladder and is significantly altered in disease, as demonstrated in BOO [11,12,13,14,15,16], OAB [17,18], and IC/BPS [19]. Furthermore, cell culture studies showed that cytokines can control connexin (Cx)43 and Cx45 expression in bladder smooth muscle cells and interstitial cells [20,21,22]. Besides the formation of functional syncytia via gap junction coupling, connexins and the closely related family of pannexins can form hemichannels in the plasma membranes, mediating, for example, cellular ATP release [23,24].
In the present study, we used fura-2 calcium imaging as a direct readout for (i) spontaneous Ca2+ activity, (ii) mechanical pressure stimulation, (iii) shear stress, and (iv) hypotonic stretch in cultured human suburothelial interstitial cells of myoid differentiation (suICs). We describe the Ca2+ peak morphology, time resolved intracellular traveling of the Ca2+ wave, and the signal propagation to neighboring cells.
2. Results2.1. Spontaneous Ca<sup>2+</sup> Activity in Cultured Human suICs
After bulk loading of the cells with 2.5 mM of fura-2-acetoxymethylester (fura-2AM), spontaneous Ca2+ activity was recorded for 250 s (n = 11 experiments). In total, 1173 cells were analyzed. At least one transient elevation of the intracellular calcium concentration (calcium transients) was recorded in 879 (75%) of the cells, while 25% of the cells showed no spontaneous calcium activity (Figure 1A). Detailed analysis of the peak characteristics revealed a mean of 0.4 peaks /min (n = 879 cells, SD 0.21, 95% CI (0.42–0.45)). The mean peak amplitude was ΔFI = 200 (n = 1592 peaks, SD 262, 95% CI (188–214)) and the mean peak duration was 38.5 s (n = 1592 peaks, SD 23.8, 95% CI (37–40)). Notably, the area under the curves (AUCs) and the peak amplitudes varied considerably (Figure 1B–E). We found a strong positive correlation between the peak amplitude and the AUC (Spearman’s rho rs = 0.959, p < 0.001) and a significant but very weak correlation between amplitude and peak duration (rs = 0.045, p = 0.045). AUC weakly correlated with the peak duration (rs = 0.267, p = 0.001). While the peaks varied in amplitude and frequency, their morphology was remarkably consistent; a fast rise of [Ca2+]i followed by a slow signal decay (Figure 2) was observed. We observed no plateau phase, but the decay often undulated (Figure 1A, arrows).
To answer the question of whether the amplitude influences the overall shape of the peaks, we investigated the peak morphology by grouping the peaks according to their maximum amplitude into six groups (Table 1) and centering the transients to their maximum. We found that the peak shape was very similar at different amplitudes (Figure 2).
2.2. Single Cell Mechanical Stimulation by Glass Micropipette
We analyzed 14 independent experiments (two cell cultures). Cells were bulk-loaded with fura-2AM, as described in Section 4.4. Ratio images (340 nm/380 nm) were acquired at a rate of 10 frames per second (fps) in seven experiments and at a rate of 4 fps in seven experiments. The fluorescence intensity (FI) was calculated after background correction as FI = F340 nm/F380 nm ×1000. The mean time of observation after initiation of the calcium signal was 128 s (range: 75 s–199 s). The FI was false color-coded (blue = min FI to white = max FI). An example of a typical experiment is depicted in Figure 3. A calcium transient was initiated in each of the 14 experiments. Compared to the spontaneous calcium transients, the transients had higher mean (±SD) amplitudes (2242 ± 967 FI vs. 599 ± 319 FI, p < 0.0001, unpaired t-test), larger AUCs (476737 ± 430178 vs. 41535 ± 37684, p < 0.0001, Mann–Whitney test) and lasted longer (40.99 ± 20.02 vs. 28.29 ± 14.13 s, p = 0.0363, Mann–Whitney test).
The calcium increase spread radially over the whole cell (Figure 4). The peak amplitude showed a linear decay by 50% at a distance of over 80 µm from the site of initiation (Figure 4C). The mean propagation velocity was 32.8 µm/s (n = 32, 95% CI = 12.9–52.6) for region of interest (ROI) distances up to 50 µm (Figure 4D, circles). At higher ROI distances, the velocity was slightly lower (mean = 23.2 µm/s, n = 120, 95% CI = 14.4–32.1); however, the difference was not significant (single-sided Mann–Whitney-Test, p = 0.25).
We evaluated the morphology of the peaks at different distances from the initial calcium signal by averaging peaks measured in seven experiments at 4 fps time resolution. The peak morphology was comparable in respect to signal rise and decay within the 10 s before and after the peak, respectively. However, with increasing distance from the initial peak, the curve flattened out. None of the cells reached the basic pre-stimulation calcium concentration within the observation time of 65 sec (Figure 5).
2.3. Intercellular Propagation of the Ca<sup>2+</sup> Signal
In our experiments, we observed a rise in calcium activity following single cell stimulation in neighboring suICs (Video S1). Therefore, we analyzed this phenomenon in 14 independent experiments (n = 2 cell cultures). The stimulated cell was placed in the center of the field of view after mechanical stimulation of the cell by lowering a glass micropipette onto the cell surface. The micropipette was retracted as soon as a calcium rise was observed in the cell. We recorded at least for 60 s at 4 fps (n = 7) or 10 fps (n = 7). Automatic analysis was done using a self-written Python 3 program [25]. The first ROI (stimulation site) was placed in the stimulated cell and further ROIs were placed in each neighboring cell center close to the nucleus (Figure 6). We calculated the radial distance of the neighboring cells (ROIs) to the stimulation site and grouped the ROIs (group 50: distance ≤ 50 µm etc.).
To reduce background noise, we filtered the peaks for a duration ≥ 10 s and an amplitude ΔFI ≥ 200. All the stimulated cells (14/14) showed a calcium transient. In each case, the first peak occurring in a cell was analyzed. Of the 604 neighboring cells examined, 40% showed a calcium peak within 60 s after stimulation. A representative experiment is depicted in Figure 6C–K.
With growing distance from the stimulation site, we found (i) a reduction in the fraction of active cells, (ii) a smaller peak amplitude, (iii) reduced duration of the calcium transient, (iv) diminished AUC and (v) an increase in the delay of the peak start (Figure 7, Table 2). Within a radius of 100 µm, 51.27% of the neighboring cells showed increased calcium activity, and within 250 µm, 39.24% of the neighboring cells showed increased calcium activity.
For peak analysis, we included only peaks which showed a higher amplitude compared to the spontaneous peaks measured before the stimulation experiment (control in Table 2). The threshold was defined as peak amplitude > 2 * standard deviation of the control. According to this criterion, only approximately 2.5% of the observed peaks should have been spontaneous.
The intercellular propagation velocity of the calcium wave was between 6.5 and 18.5 µm/s in the central 50% of the neighboring cells (median = 9.6, range from 2.7 to 465.9 µm/s). The mean velocity was 25.2 ± 50.4 µm/s, mean ± SD. There was no significant difference between the propagation velocity to close cells within the radius of 50 µm (32.77 ± 55.11 µm/s) or far cells within a distance of > 50 -250 µm (23.23 ± 49.11 µm/s) of the stimulated cell (Figure 7D).
2.4. Shear Stress Evoked Calcium Transients
In addition to the single cell mechanical stimulation, we applied physical deformation of the plasma membrane by changing the flow in the laminar perfusion chamber, generating shear stress. Important for the experimental setup was the habituation of the cells to a constant ringer flow of 1 mL/min for 10 min. In total, we analyzed nine experiments with n = 596 cells (n = 3 cell cultures).
The cells showed a significant increase in their calcium activity in section B, i.e., with increased laminar flow (Figure 8).
Interestingly, the reactions of the cells were not fully synchronized but there was a significant increase in the overall activity (peak frequency, amplitude, AUC and duration; Table 3).
2.5. Hypotonic Stimulation
We used the hypotonic Ringer solution as a different method of mechanical stimulation as it leads to defined reversible swelling of the cells and stress on the cytoskeleton. The experimental setup corresponded to the stimulation by shear stress (see Section 2.4). In brief, the cells were adapted to standard Ringer (309 mOsm/L) at a flow of 1 mL/s for 15 min (Figure 9A). We recorded during the last 3 min of the preincubation. Then, the cells were challenged with either Hypo25 (232 mOsm/L) or Hypo50 (154 mOsm/L) for 3 min Figure 9B) followed by another 3 min recording in 309 mOsm/L Ringer (Figure 9C). We included control experiments with corresponding Ringer changes but using 309 mOsm/L in B and C. We analyzed a total of 111 cells in three independent experiments.
Figure 9 shows a representative experiment (14 cells of each condition included). We found a dramatic concentration dependent increase in the calcium activity elicited by hypotonic Ringer. The number of active cells increased from 5.9% in control Ringer to 100% under hypotonic stimulation; the peaks had a 22- (65-) fold higher amplitude and a 4.4- (5.5-) fold longer duration. This resulted in a 39-fold increase in 232 mOsm/L and a 93-fold increase in 154 mOsm/L in AUC (Table 4).
2.6. Immunocytochemical Characterization of Cultured suICs
We used cell cultures of passage 4-6 to examine the expression of characteristic marker proteins. We performed multiplex immunocytochemical detection of CALR/VIM/aSMA, PDGFRa/aSMA/CALR and PDGFRa/aSMA/Cx43 to define the suIC subtypes. All cells showed VIM-IR, classifying them as mesenchymal ICs (Figure 10B). Most cells were PDGFRa+/aSMA−/CALR+ with medium-sized ovoid nuclei and fusiform cell shapes (Figure 10 and Figure 11, Figure S2). A subpopulation was characterized by small elongated nuclei and at least one long process, which often extended to over 200 µm (Figure 10; Figure 11, arrow-heads). Only a small cell population (about 6%) showed classical myofibroblast differentiation, with strong aSMA staining of actin stress fibers, lacking PDGFRa expression (Figure 10, asterisks). In conclusion, most of the cultured suICs resembled the typical (type 1, PDGFRa+/CALR+/aSMA−) telocyte and a smaller fraction showed the characteristic staining pattern of hybrid (type 2, PDGFRa+/CALR+/aSMA+) telocyte, as defined by Vannucchi and coworkers [26].
In triple immunostaining experiments, we found that Cx43-IR was associated with PDGFRa+/aSMA+ or aSMA− cells. Cx43-IR was significantly lower in myofibroblasts (p = 0.01842, two-sided t-test, data not shown) in typical myofibroblasts showing extensive stress fibers (Figure 11).
3. Discussion
We here demonstrated for the first time that mechanical stimulation of cultured human suburothelial interstitial cells (suICs) evoked intracellular calcium transients. This observation is relevant for the assumed function of the suICs within the afferent signaling pathway in the bladder [27,28,29]. The current view is that the primary sensor of bladder filling—i.e., stretching of the bladder wall—is the urothelium, and it releases adenosine triphosphate (ATP), acetylcholine (ACh), nitric oxide (NO), prostaglandins (PGs), cytokines and many others involved in signal transduction to the ICs and nerve fibers in the lamina propria [30,31].
We observed calcium transients in three different conditions:
(i) Spontaneous calcium transients occurred regularly in the cultured human suICs and were confirmed in previous in vitro and in vivo studies in rats, guinea pigs and humans [29,32,33,34,35].
(ii) Mechanical stimulation of human suICs either by direct indentation of the plasma membrane, swelling of the cells by hypotonic Ringer solution or membrane deformation by sheer stress induced calcium transients. Interestingly, calcium transients were also elicited when releasing the sheer stress by reduction of the flow rate to normal flow. This indicates that the cells are also capable of sensing the changing of the pressure in addition to the force of the pressure.
(iii) Calcium signals propagated not only intracellularly but were transmitted to neighboring cells. This indicates that the suICs formed a functional syncytium, as previously shown [21,22]. Interestingly, we observed two populations of neighboring cells, with approximately 87% of the cells showing a delay of calcium activation after single cell stimulation, resulting in an intercellular signal propagation velocity of about 20 µm/s, while in 20 of 152 cells (13%), the delay was much shorter, with a calculated propagation speed of ≥ 50 µm/s. These cells were similarly found in close proximity to the stimulated cell (<50 µm radius), as well as within a distance of 50 to 250 µm (Figure 7D). We hypothesized that the latter cells were directly connected to the stimulated cell via long thin cell processes, thus resembling telocytes [36], which also have been proposed to be present in the upper lamina propria (ULP) of the human bladder [26,37]. This idea was directly supported by dye coupling experiments, regularly demonstrating cells with long cytoplasmic processes in cultured suICs (Figure 12), and our immunocytochemical experiments indicating the presence of typical type 1 telocytes (PDGFRa+/CALR+/aSMA−) and of PDGFRa+/CALR+/aSMA+ hybrid (type 2) telocytes (Figure 10, Figure 11, Figure S2). We found type 1 telocytes, which were characterized by very small nuclei and small cell bodies showing long thin bipolar cell protrusions spanning up to 200 µm. These cells were observed at a rate of 12.5% in early cell cultures and decreased to around 7% with increasing confluency. Since we used the cell cultures at ≥ 80% confluency (e.g., Figure 4), it is most probable that we did not directly stimulate these cells. However, the small fraction of typical telocytes would well correspond to the observed 13% of cells with short Ca2+ signal delay in up to 250 µm distance (Figure 7D), thus promoting a fast calcium wave distribution over the cellular syncytium. Interestingly, we found no Cx43-IR in typical myofibroblasts accounting for less than 1% of the cells. These very large cells, therefore, would not be considered to contribute to the propagation of the calcium signal.
The intercellular propagation velocities of 32.8 µm/s and data from the literature promote the idea that IP3 rather than Ca2+ proper is responsible for the triggering of calcium transients in neighboring cells. IP3 has an up to 22-times higher diffusion rate and a 10-fold higher lifetime and is thus able to trigger Ca2+ release from neighboring cells in a range of 24 µm from the point of initiation [39,40]. In addition, there is evidence that Ca2+ rapidly closes GJ, possibly via binding to calmodulin (CaM), which in turn closes the GJ channel by binding to Cx43 [41]; review [42].
Interestingly, the propagation velocities of the calcium waves in the cell cultures were well below the velocities reported by Kanai et al. in bladder wholemounts of normal rats (4700 ± 700 µm/s) [29]. However, in contrast to the single cell stimulation in our cell culture experiments, the Ca2+ waves in the bladder wholemounts were initiated by stretching of the whole bladder preparation. As we have demonstrated in our hypotonic experiments, stretch can significantly enhance the spontaneous activity of suIC. Therefore, the cells in the wholemount experiments were pre-stimulated when calcium activity rose at a single site of the active bladder dome, facilitating the rise of [Ca2+]i in neighboring cells and the Ca2+ wave propagation. In addition, synergisms of several signaling mechanisms in vivo—e.g., gap junction coupling, mechanical coupling and fast short-range sensitization of Ca2+ entry channels by the release of soluble activators—could further enhance the Ca2+ wave propagation, as indicated by the higher conduction velocities measured after carbachol stimulation [29].
In a previous study, we found that the same cell cultures used in the present study expressed Cx43 and Cx45, which were differentially regulated by cytokines and growth factors, modulating intercellular coupling [21]. To study the morphology and the Cx43 expression of suICs in situ and in vitro, we used immunohistochemical multiplexing. The interstitial cells directly adjacent to the urothelium presented small cell bodies with two or more processes. Most of them showed a strong expression of Cx43, the molecular correlates of intercellular coupling. The majority of the cells in the ULP stained positive for the cytoskeletal markers vimentin (VIM) and alpha-smooth muscle actin (aSMA), as depicted in the confocal 3D projection (Figure 13, Supplementary Video S2).
Our study provides further evidence for a mechanism of non-neuronal signaling in the human bladder by a network of mesenchymal interstitial cells [5]. The relevance of these cells for the physiologic and pathologic function of the bladder has been implied by evidence at several levels. Their strategical positioning at the border between the urothelial layer and the deep lamina propria is accompanied by characteristic receptor expression [7,9] and by the expression of Cx43, leading to the formation of a functional syncytium in vivo [43].
In the present study, we focused on calcium transients in suICs activated by various mechanical stimuli. Though we did not directly show the involvement of TRPA1 channels in the mechanical activation of calcium transients, it is well documented in the literature that extracellular calcium is essential for eliciting intracellular calcium transients and intracellular calcium oscillations [44,45,46]. Arora and colleagues described how the graded stretch activated slow calcium oscillations in human gingival fibroblasts. These oscillations depended on extracellular [Ca2+]e, actin cytoskeleton and pertussis toxin-sensitive subunits of G-proteins but not on intracellular calcium pools, indicating the involvement of stretch-activated ion channels [46].
Calcium waves arise from massive calcium release from intracellular calcium stores upon activation of inositol triphosphate receptors (IP3Rs) and ryanodine receptors (RyRs) interacting with various members of the TRP family [45,47,48]. In the presence of extracellular Ca2+ and the selective TRPA1 agonists allyl isothiocyanate (AITC) and 15-deoxy-delta-12, 14-prostaglandin J2 (PGJ2) induced intracellular calcium transients in human cardiac fibroblasts, indicating the triggering of intracellular Ca2+ release via extracellular Ca2+ influx [44].
Additional support for the hypothesis of suICs being active elements in afferent signaling processing in the bladder comes from experiments demonstrating increased ATP release from cultured pig bladder ICs by hypotonic stimulation [49]. Our own group has previously shown that human suICs (denoted myofibroblasts in older literature) are highly sensitive to ATP, inducing calcium transients at very low concentrations [33].
In our single cell stimulation experiments, we did not observe any correlation between the direction of the Ringer flow with the spreading of the calcium wave in neighboring cells. Therefore, we conclude that a possible release of ATP from the locally stimulated suIC does not add to the calcium signal measured in our experiments and that the distribution of the signal is truly mediated via the cytoplasmic coupling of the cells.
Cultured suICs express aSMA, allowing cell contraction, especially in the fraction of myofibroblast-like cells forming stress fibers (Figure S1). However, our immunocytochemical experiments indicated that these myofibroblast-like cells are not included in the functional syncytium of the suIC under cell culture conditions. Besides strong aSMA expression, the typical fibroblast-like appearance of this cell—i.e., its large flat cell body with large nuclei and multiple cell processes—has provoked the name “myofibroblast”. Under normal conditions, such cells should not account for the majority of the aSMA+ ICs in the bladder. Therefore, these cells might indeed represent pathological altered suICs and it will be interesting to further investigate their contribution to pathological states of the bladder.
Since most non-myofibroblast suICs (type 2 telocytes) strongly express aSMA in situ and a rise in intracellular calcium can trigger cell contraction, it is conjecturable that local contractions evoked by ATP signaling from the urothelium could also lead to local signaling during the bladder filling phase. Therefore, the role of suICs in afferent signaling could be the enhancement and distribution of local signals over larger areas of the bladder wall. This view is supported by the excellent experiments of Kanai and colleagues in wholemount rat bladders, directly demonstrating the coordinated spreading of local (spontaneous) calcium signals from the dome to the bladder neck. Furthermore, they demonstrated the propagation of the signals from the mucosal initiation site to the serosa of the bladder [43].
If the suICs take part in afferent signal processing in the bladder, one should assume that local stimulation of a single cell, as was conducted in Section 2.2, should influence the calcium activity in the functional syncytium. We therefore compared the activity after stimulation in the neighboring cells with the approximated background activity measured in control experiments. We found that, after mechanical stimulation of a single cell, the syncytial activity (activity in the neighboring cells) increased by 18 times based on the AUCs in the first peaks. The primary calcium signal evoked by a single short-term membrane indentation of a single cell was radially distributed in the cell layer for at least 250 µm, activating an area of approximately 0.2 mm2. The strength of activation decreased with distance from 500% to 200% in a radius of 50 µm and 250 µm, respectively. In total, the primary calcium signal was amplified by 50%. These findings strongly support the notion of an amplifier function of suICs.
The major limitation of this study is the use of a 2D adherent cell culture system. In addition, while we provide some evidence of several subtypes of cells in our cell culture, we did not further characterize these subtypes to allow a more detailed view of the roles of these cells in the bladder. Since the urinary bladder wall is composed of multiple cell types embedded in a complex structured extracellular matrix, the results cannot be transferred directly to the in vivo situation. Physiological stretching of the bladder wall during bladder filling results in multiple cellular interactions, including the release of transmitter substances and mechanical stress. Therefore, the 2D model system can only highlight a small part of the complex sequence of events.
In the future, it will be interesting to investigate the role of suIC functional syncytia in 3D model systems of overactive bladder and especially of interstitial cystitis/bladder pain syndrome (IC/BPS).
4. Materials and Methods4.1. Ethical Statement
This study was approved by the Ethics Committee of the University of Leipzig (ethical approval code: 036-2007, Date of issue: 26.05.2015) and was conducted according to the principles expressed in the World Medical Association Declaration of Helsinki [50]. Written informed consent was obtained from all patients.
4.2. Cell Cultures
Human bladder tissue was collected from patients undergoing cystectomy for bladder carcinoma treatment or due to gynecologic tumors. We were able to culture suICs from five female and three male patients, aged (mean ± SD) 60.2 ± 11.7 yrs and 64.3 ± 6.6 yrs, respectively. Cell cultures of suburothelial interstitial cells (suICs) were established from macroscopic tumor free bladder tissue by sharp microsurgical dissection of the urothelium from the smooth muscle layer of the detrusor, ensuring no contamination with detrusor smooth muscle cells. The tissue was then cut into small fragments of approximately 0.5 mm edge length and cultured in smooth muscle cell growth medium 2 (PromoCell GmbH, Heidelberg, Germany). This medium did not support the growth of urothelial cells and the resulting cell cultures were free of contaminating urothelial cells, as proven by polymerase chain reaction (PCR), Western blot and immunocytochemistry. We described the procedure in detail in a previous study [21]. In brief, the fragments were plated into tissue culture flasks (TPP AG, Trasadingen, Switzerland) and incubated at 37 °C in 5% CO2 humidified atmosphere until reaching 80% confluency. Cell passages 2 to 5 were used in the experiments. For the calcium imaging experiments, the cells were plated onto collagen A (Biochrome AG, Berlin, Germany) coated 13 mm glass cover slips and grown to 80% confluency.
4.3. Chemicals and Solutions
We used a modified Krebs–Ringer solution for the calcium imaging experiment (in mmol/L): CaCl2 1.9; NaCl 120.9; NaHCO3 14.4; KCl 5.9; MgCl2 1.2; NaH2PO4 1.55; Hepes 4.2; Glucose 11.49; pH = 7.2; 309 mOsm/L (all chemicals from Sigma-Aldrich, Steinheim, Germany). All solutions were freshly prepared with cell culture grade distilled water on the day of usage. For hypotonic solutions, we diluted the Krebs–Ringer solution at the ratio of 3:1 and 1:1 to yield 232 mOsm/L and 154 mOsm/L solution, respectively.
4.4. Calcium Imaging
Cells cultured onto glass cover slips were washed 3 times with Krebs–Ringer solution at 37 °C and thereafter incubated for 35 min at 37 °C with 2.5 mMol/L fura-2 AM (fura-2 acetoxymethylester) dissolved in DMSO (dimethylsulfoxid) and 2% pluronic (Invitrogen by Thermo Fisher Scientific, Schwerte, Germany). The cells were washed again in Krebs–Ringer solution and placed into a 37 °C heated lamina flow Diamond Bath Oocyte Recording Chamber (RC-26Z, Warner Instruments, Hamden, CT, USA). To exclude calcium activity caused by the washing and transfer procedure, the cells were adapted to the experimental conditions (37 °C, oxygenation of the Krebs–Ringer solution with 95% O2 and 5% CO2, constant flow of 1 mL/min) for ten minutes before the start of the experiments. We used an IX-71 inverted microscope equipped with a LCPLN20xIR objective (Olympus Microscopy, Hamburg, Germany) and a TILL IMAGO-QE digital camera (TillPhotonics, Gräfelfing, Germany). Experimental control and image acquisition were done using TILLvisION Software V. 4.5 (TillPhotonics). Image series were recorded at 1, 4 or 10 frames per second (fps). Fura-2 fluorescence was excited by 30 ms pulse application of 340nm and 380nm monochromatic light (Polychrome V monochromator, TillPhotonics) and the fluorescence signal was detected at a wavelength of 520 nm. The specific fluorescence intensity (FI) was calculated after subtraction of the background fluorescence: FI = F340 nm/F380 nm × 1000. The FI was color-coded and exported as TIFF video. We used a self-written Python [25] program for analysis of the calcium changes over time (calcium transients). The core of the Python program was the automatic calcium peak detection, which was fitted to cope with the heteromorphic calcium traces. The analysis can be restricted to pre-defined parts of the curves, allowing precise analysis of the peak morphology. For the analysis of intercellular calcium waves, the software allows the definition of a reference (stimulated) cell and calculates the distances of all regions of interest (ROI) and the time course of the calcium signal in relation to this cell. Details are described in the Supplementary Materials.
4.5. Data Analysis and Statistics
We used Microsoft Excel (Microsoft Corporation, Redmond, WA, USA) and Graph Pad Prism 5/8 (GraphPad Software, San Diego, CA, USA) for data processing and statistical analysis (Wilcoxon matched-pairs signed rank test; two-tailed Mann–Whitney test).
4.6. Stimulation Experiments
As a starting point, spontaneous calcium activity was recorded after 10 min of adaptation of the cells before starting the real experiment. We investigated the cells’ reactions to three different mechanical stimuli.
4.6.1. Single Cell Mechanical Stimulation
Single cell mechanical stimulation requires ultraprecise micromanipulators. We used a motorized MP-285 Nano-Micromanipulator (Sutter Instruments, Novato, CA, USA), allowing step sizes down to 40 nm. We mechanically stimulated single cells by defined deflection of the plasma membrane using a glass micropipette with a fine tip, which was closed and rounded to avoid (i) capillary flow-in of ringer and (ii) mechanical injury of the membrane. The membrane deflection was applied by stepwise lowering the micropipette in steps of 40 nm (Figure 14).
4.6.2. Mechanical Stimulation by Shear Stress
Cells were adapted to the experimental conditions by 15 min superfusion with 1 mL/min. We then recorded the calcium activity for 5 min in each section: (A) 1 mL/min, (B) 2 mL/min, (C) 1 mL/min.
4.6.3. Hypotonic Stimulation
After adaptation for 15 min to the experimental conditions (309 mOsm/L, 1 mL/min flow), cells were challenged either with 232 mOsm/L or 154 mOsm/L for 3 min. Recordings of the calcium activity were done for three minutes in each section (A,B,C). For control, the flow was switched to 309 mOsm/L in section B.
4.7. Dye-Coupling Experiments
Single cells were dialyzed for 10 min with 3% lucifer yellow (LY) dissolved in intracellular pipette solution (130 mM/L KCl, 5 mM/L K-pyruvate, 5 mM/L K-oxalacetate, 5 mM/L K-succinate, 10 mM/l HEPES, 0.02 mM EGTA, pH 7.4) [51] by using a 50 MΩ micropipettes for sharp penetration of the cell membrane. LY was injected into the cell by short reproducible pressure pulses by using a PV800 Pneumatic PicoPump (World Precision Instruments, Berlin, Germany), as described previously [52]. LY-fluorescence was visualized using a fluorescein isothiocyanate (FITC) filter set.
4.8. Confocal Imaging
Confocal images of bladder tissue were acquired at a LSM800 (Carl Zeiss Microscopy, Jena, Germany) equipped with a Plan-Apochromat 63x/1.40 Oil DIC M27; in cell cultures, a 40x/0.95 objective was used for acquisition of 5 × 5 large tiles, which were stitched to yield an image of 556 µm × 556 µm. In brief: human bladder tissue was fixed in 4% buffered formalin for 24 h and embedded in paraffin. Ten µm thick paraffin sections were immunofluorescence stained for VIM, aSMA, Cx43 (Table 5). Cell cultures were cultured onto collagen A covered glass coverslips. Cells were fixed for 2 min in –20 °C methanol before processing for primary antibody incubation overnight at 4 °C. Nuclei were stained with DAPI (4′,6-diamidin-2-phenylindol, 1:5000, Carl Roth, Karlsruhe, Germany). Image processing and analysis was done in ImageJ 1.51s [53] for mac and Adobe Photoshop CC V. 20.0.10 (Adobe, San José, CA, USA).
Acknowledgments
The authors want to thank A. Weimann for the excellent technical assistance.
Supplementary Materials
Supplementary materials can be found at https://www.mdpi.com/1422-0067/21/15/5474/s1.
Click here for additional data file.
Author Contributions
Conceptualization, J.N. and F.P.B.; Data curation, A.G. and F.P.B.; Formal analysis, J.N. and F.P.B.; Funding acquisition, J.N.; Investigation, S.C. and F.P.B.; Methodology, J.N., S.C. and F.P.B.; Project administration, J.N.; Resources, J.N., A.G. and J.-U.S.; Software, F.P.B.; Supervision, J.N. and F.P.B.; Validation, J.N., A.G. and F.P.B.; Visualization, J.N. and F.P.B.; Writing—original draft, F.P.B.; Writing—review and editing, J.N., A.G. and J.-U.S. All authors have read and agreed to the published version of the manuscript.
Funding
This research was funded by Deutsche Forschungsgemeinschaft, grant number NE425/4-4. This research received funding from the Leipzig University for Open Access Publishing.
Conflicts of Interest
The authors declare no conflict of interest.
Abbreviations
AUC
area under the curve
aSMA
alpha-smooth muscle actin
BOO
bladder outlet obstruction
CALR
calreticulin
Cx43
connexin 43
DAPI
4′,6-diamidin-2-phenylindol
FITC
fluorescein isothiocyanate
fps
frames per second
fura-2AM
fura-2-acetoxymethylester
IC/BPS
interstitial cystitis/bladder pain syndrome
OAB
overactive bladder
PDGFRa
platelet derived growth factor receptor-alpha
SD
standard deviation
SEM
standard error of the mean
suIC
suburothelial interstitial cells
TRP
transient receptor potential
VIM
vimentin
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Spontaneous calcium activity in cultured suburothelial interstitial cells (suICs). (A) Representative traces of calcium transients measured with the ratiometric fura-2 fluorescent Ca2+ indicator; arrows indicate undulated decay of the amplitued in the red colored trace; (B–E) quantitative analysis of peak frequency (B), peak amplitude (C), peak duration (D) and integrated peak area (area under the curve = AUC (E); inset in (E) explains the peak measures (for detailed information, see Methods section).
Peak morphology of spontaneous Ca2+ transients is homogeneous. Amplitude sorted transients were overlaid at their maximum. Note that the morphology does not alter with the amplitude. Mean (solid lines), standard error of the mean (SEM, dashed lines); only peaks with at least 20 time points were included.
Positioning of the glass micropipette (A, phase contrast); lowering of the micropipette (B, arrow) provoked a local intracellular calcium rise (C) in the foremost quiet cell (arrow); the calcium signal spread across the cell within several seconds (C–F); arrow indicates micropipette tip position; inset in (C) = fluorescence intensity (FI) color-coding.
Intracellular propagation of the calcium signal. (A) Position of the glass micropipette (phase contrast): circles indicate the regions of interest (ROIs) of fluorescence measurements, color corresponds to the color-coding of the time resolved fluorescence intensity traces in (B); scale bar = 200 µm; (B) the fluorescence peak intensity decreased with increasing distance from the site of signal initiation; traces smoothed; (C) peak amplitude (%) versus distance to the site of stimulation (binned data, x-axis = center of bin; bin 5 = > 0 to < 10; bin 20 = ≥ 10 to < 30)—note the linear decay; (D) plotting of the peak delay versus the distance to the site of stimulation showed mainly linear correlation with increasing scattering at higher distances.
Peak morphology of calcium transients depending on the distance to the initial peak. Note that the peaks show similar rising and decay characteristics within 10 s before and after the peak maximum.
Analysis of the intercellular propagation of the calcium signal. (A) Position of the micropipette (stimulation site); ROIs (red circles); white circles indicate sections: 50 µm, 100 µm, 150 µm); nucleus of stimulated cell (white arrow); phase contrast image; scale bar = 50 µm. (B) Typical recording of calcium transients in the stimulated cell (red trace) and the neighboring cells (black traces); recording speed: 10 fps. (C–K) Surface plot of the propagation of the calcium wave over the functional syncytium: (C) pre stimulation; (D) 0.1 s after stimulation; white arrow indicates stimulation site. Note that first calcium peaks in neighboring cells already occurred within 1 s after stimulation (E–G); maximum excitation is seen about 3-5 s after stimulation (I,J); excitation decay is seen after 7 s (K); instant of time since stimulation is indicated bottom right; color-coding ranged from FI = 0–3200 (inset in C).
Analysis of the calcium peaks and transients in neighboring cells compared to the first peaks in the stimulated cells; (A–C) the neighboring cells were grouped into distance groups (upper bin limit indicated); (D) the propagation speed of the calcium signal was compared between near (≤50 µm) and far (>50–250 µm) neighboring cells. Note the split of the y-axis and the bimodal distribution of speeds; controls = cells with spontaneous activity; green dotted line indicates upper 95% CI of the controls.
Effect of shear stress on the calcium activity of cultures suIC. In 5 min recordings, acceleration of the laminar flow from 1ml/s (A) to 2 mL/s (B) led to increased calcium activity. This activity decreased only slowly, as seen in the increased activity recorded 5 min after switching back to 1 mL/min flow (C).
Effect of stimulation with hypotonic Ringer solutions. (A) conditioning of cells by 3 min preincubation in 309 mOsm/L Ringer; (B) 3 min period of challenging the cells with different Ringer solutions; Hypotonic Ringer led to a concentration dependent increase in calcium activity. This increase was almost synchronous and disappeared immediately after switching back to 309 mOsm/L Ringer solution (C). Representative traces of 14 cells per condition are depicted.
Immunocytochemical characterization of the suIC in cell culture. Triple staining of (A) calreticulin (CALR, orange), (B) vimentin (VIM, green) and (C) α-smooth muscle cell actin (aSMA, red); all cells show immunoreactivity (IR) for CALR and VIM, while only very few cells show strong aSMA staining of stress fibers (asterisks); nuclei are stained with 4′,6-diamidin-2-phenylindol (DAPI, blue).
Triple staining of (A) platelet derived growth factor receptor alpha (PDGFRa, orange), (B) aSMA (green) and (C) connexin 43 (Cx43, red). Note the cell membrane associated punctuate or even patchy Cx43-IR present on cells with very small elongated nuclei and small cell bodies (arrows); no Cx43-IR can be attributed to the strongly stained myofibroblast characterized by the presence of stress fibers; nuclei are stained with DAPI (blue); (D) merged image channels.
Lucifer Yellow (LY) injection revealed functional syncytia in cultured suICs. (A) Phase contrast, MP (micro pipette); (B) injected cell filled with LY after 90 s; (C) cell culture with two injected cells (arrows) showing distribution of the LY, defining variably sized functional syncytia; (D) the LY injected cell (arrow) and a direct neighboring cell show bright LY-fluorescence signal; arrow-heads indicate a fine process contacting another cell, spanning a distance of approximately 100 µm; nuclei (DAPI, blue); modified reproduction with permission from Neuhaus et al. 2007 [38].
The 3D projection of a confocal image stack (voxel size (yxz): 0.06 µm × 0.06 µm × 0.39 µm); (A) VIM (green, ex 488 nm); (B) aSMA (red, ex 640 nm); (C) nuclei stained with DAPI (blue, ex 405nm); (D) merged image including Cx43 staining (magenta, excitation at 561 nm). Note that only a few cells are negative for aSMA (arrow); U = urothelium.
Mechanical stimulation using a glass micropipette. The micropipette is placed close to the plasma membrane (A); by stepwise (40 nm steps) lowering of the micropipette, the plasma membrane is dented until a calcium signal is initiated (B,C); red arrows indicate movement of the micropipette (B) and the deflection of the cell membrane (C).
ijms-21-05474-t001_Table 1
Maximum amplitudes of the peak groups.
Group
Mean
SD
\nN\n
1
56.5466
27.46916
1913
2
221.3514
70.75953
582
3
488.9613
72.17626
245
4
743.6639
69.71185
134
5
978.3084
66.73132
70
6
1239.981
66.15836
29
SD = standard deviation; N = number of peaks analyzed.
ijms-21-05474-t002_Table 2
Number of cells observed and number of peaks analyzed.
Cell Group
0
50
100
150
200
250
Control
Distance (µm)
Stimulated
<= 50
>50 ≤ 100
>100 ≤150
>150 ≤ 200
>200≤ 250
NA
Number of cells
14
81
155
213
139
16
619
Active cells (n)
14
44
77
67
41
8
34
Mean (%)
100
54.3
49.7
31.5
29.5
50
5.5
95% CI (%)
\n
43.2–65.4
40.3–50.2
25.2–37.7
21.8–37.2
12.9–51.6
3.7–7.3
Peak ampl. mean
2242
1025
954
887
848
1090
599
95% CI
1683–2800
831–1219
840–1068
766–1008
639–1058
363–1817
489–711
Peak AUC (au)
476737
189641
175652
157005
115,647
97981
41535
95% CI
228,359–725,115
119,516–259,766
139,339–211,966
122,889–191,121
80,430–150,865
9981–185,982
28,387–54,684
Peak duration (s)
40.9
36.8
36.8
33.4
28.1
39.3
28.3
95% CI
29.4–52.6
32.8–40.9
34.1–39.6
30.5–36.3
24.6–31.7
19.4–39.2
23.4–33.2
Peak delay (s)
NA
6.534
11.65
18.34
18.1
15.68
NA
95% CI
4.1–8.9
9.4–13.9
15.4–21.3
12.9–23.3
10.1–21.3
NA = data not available; control = spontaneous peaks before stimulation.
ijms-21-05474-t003_Table 3
Analysis of the shear stress experiments.
Peak
Section A
Section B
Section C
p–Value
Frequency (min−1)
0.62 (522)
0.70 (554)
0.75 (559)
A/B: <0.0001 §
[0.59–0.64]
[0.68–0.73]
[0.72–0.78]
B/C: 0.0039 §
Amplitude (FI)
269.4 (1345)
344.7 (1628)
207.6 (1757)
A/B: <0.0001 $
[245.6–293.19]
[319.4–370.0]
[192.1–223.2]
B/C: <0.0001 $
Duration (s)
34.26 (1345)
39.43 (1628)
35.74 (1757)
A/B: <0.0001 $
[32.99–35.54]
[38.03–40.83]
[34.59–36.90]
B/C: <0.0001 $
AUC
5789 (1345)
8860 (1628)
4602 (1757)
A/B: <0.0001 §
[5149–6430]
[8060–9660]
[4172–5032]
B/C: <0.0001 §
Mean (n), [95% CI]; § Wilcoxon matched pairs signed rank test; $ two-tailed Mann–Whitney test.
ijms-21-05474-t004_Table 4
Analysis of the hypotonic stimulation experiments.
Osmotic Conc.
309
232
154
p–Value
(mOsm/L)
Control
Hypo25
Hypo50
frequency (min–1)
0.02 (44)
0.45 (36)
0.35(58)
Hypo25/Hypo50
[0.0–0.06]
[0.39–0.49]
[0.34–0.36]
<0.0001 $
amplitude (FI)
32.63 (1)
733.2 (47)
2147 (59)
Hypo25/Hypo50
[NA]
[579.8–886.7]
[1891–2403]
<0.0001 $
duration (s)
21 (1)
93 (47)
114.6 (59)
Hypo25/Hypo50
[NA]
[80.19–105.8]
[109.4–119.8]
0.0021 $
AUC
419.6 (1)
37,707 (47)
119,702 (59)
Hypo25/Hypo50
[NA]
[27,896–47,517]
[102,882–136,421]
<0.0001 $
In control, only one cell showed spontaneous activity; mean (n), [95% CI]; $ two-tailed Mann–Whitney test.
ijms-21-05474-t005_Table 5
Primary and secondary antibodies used in confocal imaging experiments.
\n"],"string":"[\n \"\\nInt J Mol SciInt J Mol SciijmsInternational Journal of Molecular Sciences1422-0067MDPI32751838PMC743212110.3390/ijms21155474ijms-21-05474ArticleMechanosensitivity Is a Characteristic Feature of Cultured Suburothelial Interstitial Cells of the Human Bladderhttps://orcid.org/0000-0003-2484-6994NeuhausJochen1*GonsiorAndreas2ChengSheng1StolzenburgJens-Uwe2BergerFrank Peter3Department of Urology, Research Laboratory, University of Leipzig, Liebigstr. 19, 04103 Leipzig, Germany; chengsheng2005@zju.edu.cnDepartment of Urology, University Hospital Leipzig, Liebigstr. 20, 04103 Leipzig, Germany; andreas.gonsior@medizin.uni-leipzig.de (A.G.); jens-uwe.stolzenburg@uniklinik-leipzig.de (J.-U.S.)Department of Urology, University Hospital Jena, Am Klinikum 1, 07747 Jena, Germany; frank.berger@med.uni-jena.deCorrespondence: jochen.neuhaus@medizin.uni-leipzig.de; Tel.: +49-341-97176883172020820202115547416620202972020© 2020 by the authors.2020Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Bladder dysfunction is characterized by urgency, frequency (pollakisuria, nocturia), and dysuria and may lead to urinary incontinence. Most of these symptoms can be attributed to disturbed bladder sensitivity. There is growing evidence that, besides the urothelium, suburothelial interstitial cells (suICs) are involved in bladder afferent signal processing. The massive expansion of the bladder during the filling phase implicates mechanical stress delivered to the whole bladder wall. Little is known about the reaction of suICs upon mechanical stress. Therefore, we investigated the effects of mechanical stimulation in cultured human suICs. We used fura-2 calcium imaging as a major physiological readout. We found spontaneous intracellular calcium activity in 75 % of the cultured suICs. Defined local pressure application via a glass micropipette led to local increased calcium activity in all stimulated suICs, spreading over the whole cell. A total of 51% of the neighboring cells in a radius of up to 100 µm from the stimulated cell showed an increased activity. Hypotonic ringer and shear stress also induced calcium transients. We found an 18-times increase in syncytial activity compared to unstimulated controls, resulting in an amplification of the primary calcium signal elicited in single cells by 50%. Our results speak in favor of a high sensitivity of suICs for mechanical stress and support the view of a functional syncytium between suICs, which can amplify and distribute local stimuli. Previous studies of connexin expression in the human bladder suggest that this mechanism could also be relevant in normal and pathological function of the bladder in vivo.
The urinary bladder has a dual function, periodical stretching during the filling phase and exhibiting mass contraction for micturition. The realization of this requires a complex coordination with the sphincter systems of the bladder itself, called the vesical or internal sphincter (musculus sphincter vesicae) and the split external or urethral sphincter, comprising an internal smooth muscle part (musculus sphincter urethrae glaber) and an external striated part (musculus sphincter urethrae transversostriatus) [1]. While the neuronal control via spinal cord and higher central nervous system centers are well known [2], the local signal processing in the bladder wall is still enigmatic. Many details of the lower urinary tract function have been learned from diseased states in humans and in animal models, such as bladder outlet obstruction (BOO) and overactive bladder (OAB), and interstitial cystitis/bladder pain processing in the bladder wall is still enigmatic. Many details of the lower urinary tract function have been learned from diseased states in humans and in animal models, such as bladder outlet obstruction (BOO), overactive bladder (OAB), and interstitial cystitis/bladder pain syndrome (IC/BPS) [3,4]. While the urothelium acts as the major sensor of bladder filling, the suburothelial interstitial cells are emerging as important modulators of the afferent signal generation [5]. Twenty-eight transient receptor potential (TRP) channels have been described so far in mammals, of which TRPV1, 2, 4 (vanilloid), TRPM8 (melastatin), and TRPA1 (ankyrin) seem to play an important role in sensory signaling in the bladder [6]. Recently, our own group described TRPA1 expression in interstitial cells (ICs) of the human, guinea pig, and rat, supporting the hypothesis of ICs being mechanosensitive [7]. The role of the ICs in the bladder is still not fully understood and recent studies indicate that there are different subtypes of ICs. The major differences in their distribution define sublayers of the lamina propria, referred to as the upper lamina propria and deeper lamina propria [8]. Recent comparative studies on ICs in human and the major laboratory animals suggest that the understanding of the roles of ICs partly needs revision [7,9,10].
Coupling between ICs, the urothelium, and detrusor smooth muscle cells is another important issue which determines the normal function of the bladder and is significantly altered in disease, as demonstrated in BOO [11,12,13,14,15,16], OAB [17,18], and IC/BPS [19]. Furthermore, cell culture studies showed that cytokines can control connexin (Cx)43 and Cx45 expression in bladder smooth muscle cells and interstitial cells [20,21,22]. Besides the formation of functional syncytia via gap junction coupling, connexins and the closely related family of pannexins can form hemichannels in the plasma membranes, mediating, for example, cellular ATP release [23,24].
In the present study, we used fura-2 calcium imaging as a direct readout for (i) spontaneous Ca2+ activity, (ii) mechanical pressure stimulation, (iii) shear stress, and (iv) hypotonic stretch in cultured human suburothelial interstitial cells of myoid differentiation (suICs). We describe the Ca2+ peak morphology, time resolved intracellular traveling of the Ca2+ wave, and the signal propagation to neighboring cells.
2. Results2.1. Spontaneous Ca<sup>2+</sup> Activity in Cultured Human suICs
After bulk loading of the cells with 2.5 mM of fura-2-acetoxymethylester (fura-2AM), spontaneous Ca2+ activity was recorded for 250 s (n = 11 experiments). In total, 1173 cells were analyzed. At least one transient elevation of the intracellular calcium concentration (calcium transients) was recorded in 879 (75%) of the cells, while 25% of the cells showed no spontaneous calcium activity (Figure 1A). Detailed analysis of the peak characteristics revealed a mean of 0.4 peaks /min (n = 879 cells, SD 0.21, 95% CI (0.42–0.45)). The mean peak amplitude was ΔFI = 200 (n = 1592 peaks, SD 262, 95% CI (188–214)) and the mean peak duration was 38.5 s (n = 1592 peaks, SD 23.8, 95% CI (37–40)). Notably, the area under the curves (AUCs) and the peak amplitudes varied considerably (Figure 1B–E). We found a strong positive correlation between the peak amplitude and the AUC (Spearman’s rho rs = 0.959, p < 0.001) and a significant but very weak correlation between amplitude and peak duration (rs = 0.045, p = 0.045). AUC weakly correlated with the peak duration (rs = 0.267, p = 0.001). While the peaks varied in amplitude and frequency, their morphology was remarkably consistent; a fast rise of [Ca2+]i followed by a slow signal decay (Figure 2) was observed. We observed no plateau phase, but the decay often undulated (Figure 1A, arrows).
To answer the question of whether the amplitude influences the overall shape of the peaks, we investigated the peak morphology by grouping the peaks according to their maximum amplitude into six groups (Table 1) and centering the transients to their maximum. We found that the peak shape was very similar at different amplitudes (Figure 2).
2.2. Single Cell Mechanical Stimulation by Glass Micropipette
We analyzed 14 independent experiments (two cell cultures). Cells were bulk-loaded with fura-2AM, as described in Section 4.4. Ratio images (340 nm/380 nm) were acquired at a rate of 10 frames per second (fps) in seven experiments and at a rate of 4 fps in seven experiments. The fluorescence intensity (FI) was calculated after background correction as FI = F340 nm/F380 nm ×1000. The mean time of observation after initiation of the calcium signal was 128 s (range: 75 s–199 s). The FI was false color-coded (blue = min FI to white = max FI). An example of a typical experiment is depicted in Figure 3. A calcium transient was initiated in each of the 14 experiments. Compared to the spontaneous calcium transients, the transients had higher mean (±SD) amplitudes (2242 ± 967 FI vs. 599 ± 319 FI, p < 0.0001, unpaired t-test), larger AUCs (476737 ± 430178 vs. 41535 ± 37684, p < 0.0001, Mann–Whitney test) and lasted longer (40.99 ± 20.02 vs. 28.29 ± 14.13 s, p = 0.0363, Mann–Whitney test).
The calcium increase spread radially over the whole cell (Figure 4). The peak amplitude showed a linear decay by 50% at a distance of over 80 µm from the site of initiation (Figure 4C). The mean propagation velocity was 32.8 µm/s (n = 32, 95% CI = 12.9–52.6) for region of interest (ROI) distances up to 50 µm (Figure 4D, circles). At higher ROI distances, the velocity was slightly lower (mean = 23.2 µm/s, n = 120, 95% CI = 14.4–32.1); however, the difference was not significant (single-sided Mann–Whitney-Test, p = 0.25).
We evaluated the morphology of the peaks at different distances from the initial calcium signal by averaging peaks measured in seven experiments at 4 fps time resolution. The peak morphology was comparable in respect to signal rise and decay within the 10 s before and after the peak, respectively. However, with increasing distance from the initial peak, the curve flattened out. None of the cells reached the basic pre-stimulation calcium concentration within the observation time of 65 sec (Figure 5).
2.3. Intercellular Propagation of the Ca<sup>2+</sup> Signal
In our experiments, we observed a rise in calcium activity following single cell stimulation in neighboring suICs (Video S1). Therefore, we analyzed this phenomenon in 14 independent experiments (n = 2 cell cultures). The stimulated cell was placed in the center of the field of view after mechanical stimulation of the cell by lowering a glass micropipette onto the cell surface. The micropipette was retracted as soon as a calcium rise was observed in the cell. We recorded at least for 60 s at 4 fps (n = 7) or 10 fps (n = 7). Automatic analysis was done using a self-written Python 3 program [25]. The first ROI (stimulation site) was placed in the stimulated cell and further ROIs were placed in each neighboring cell center close to the nucleus (Figure 6). We calculated the radial distance of the neighboring cells (ROIs) to the stimulation site and grouped the ROIs (group 50: distance ≤ 50 µm etc.).
To reduce background noise, we filtered the peaks for a duration ≥ 10 s and an amplitude ΔFI ≥ 200. All the stimulated cells (14/14) showed a calcium transient. In each case, the first peak occurring in a cell was analyzed. Of the 604 neighboring cells examined, 40% showed a calcium peak within 60 s after stimulation. A representative experiment is depicted in Figure 6C–K.
With growing distance from the stimulation site, we found (i) a reduction in the fraction of active cells, (ii) a smaller peak amplitude, (iii) reduced duration of the calcium transient, (iv) diminished AUC and (v) an increase in the delay of the peak start (Figure 7, Table 2). Within a radius of 100 µm, 51.27% of the neighboring cells showed increased calcium activity, and within 250 µm, 39.24% of the neighboring cells showed increased calcium activity.
For peak analysis, we included only peaks which showed a higher amplitude compared to the spontaneous peaks measured before the stimulation experiment (control in Table 2). The threshold was defined as peak amplitude > 2 * standard deviation of the control. According to this criterion, only approximately 2.5% of the observed peaks should have been spontaneous.
The intercellular propagation velocity of the calcium wave was between 6.5 and 18.5 µm/s in the central 50% of the neighboring cells (median = 9.6, range from 2.7 to 465.9 µm/s). The mean velocity was 25.2 ± 50.4 µm/s, mean ± SD. There was no significant difference between the propagation velocity to close cells within the radius of 50 µm (32.77 ± 55.11 µm/s) or far cells within a distance of > 50 -250 µm (23.23 ± 49.11 µm/s) of the stimulated cell (Figure 7D).
2.4. Shear Stress Evoked Calcium Transients
In addition to the single cell mechanical stimulation, we applied physical deformation of the plasma membrane by changing the flow in the laminar perfusion chamber, generating shear stress. Important for the experimental setup was the habituation of the cells to a constant ringer flow of 1 mL/min for 10 min. In total, we analyzed nine experiments with n = 596 cells (n = 3 cell cultures).
The cells showed a significant increase in their calcium activity in section B, i.e., with increased laminar flow (Figure 8).
Interestingly, the reactions of the cells were not fully synchronized but there was a significant increase in the overall activity (peak frequency, amplitude, AUC and duration; Table 3).
2.5. Hypotonic Stimulation
We used the hypotonic Ringer solution as a different method of mechanical stimulation as it leads to defined reversible swelling of the cells and stress on the cytoskeleton. The experimental setup corresponded to the stimulation by shear stress (see Section 2.4). In brief, the cells were adapted to standard Ringer (309 mOsm/L) at a flow of 1 mL/s for 15 min (Figure 9A). We recorded during the last 3 min of the preincubation. Then, the cells were challenged with either Hypo25 (232 mOsm/L) or Hypo50 (154 mOsm/L) for 3 min Figure 9B) followed by another 3 min recording in 309 mOsm/L Ringer (Figure 9C). We included control experiments with corresponding Ringer changes but using 309 mOsm/L in B and C. We analyzed a total of 111 cells in three independent experiments.
Figure 9 shows a representative experiment (14 cells of each condition included). We found a dramatic concentration dependent increase in the calcium activity elicited by hypotonic Ringer. The number of active cells increased from 5.9% in control Ringer to 100% under hypotonic stimulation; the peaks had a 22- (65-) fold higher amplitude and a 4.4- (5.5-) fold longer duration. This resulted in a 39-fold increase in 232 mOsm/L and a 93-fold increase in 154 mOsm/L in AUC (Table 4).
2.6. Immunocytochemical Characterization of Cultured suICs
We used cell cultures of passage 4-6 to examine the expression of characteristic marker proteins. We performed multiplex immunocytochemical detection of CALR/VIM/aSMA, PDGFRa/aSMA/CALR and PDGFRa/aSMA/Cx43 to define the suIC subtypes. All cells showed VIM-IR, classifying them as mesenchymal ICs (Figure 10B). Most cells were PDGFRa+/aSMA−/CALR+ with medium-sized ovoid nuclei and fusiform cell shapes (Figure 10 and Figure 11, Figure S2). A subpopulation was characterized by small elongated nuclei and at least one long process, which often extended to over 200 µm (Figure 10; Figure 11, arrow-heads). Only a small cell population (about 6%) showed classical myofibroblast differentiation, with strong aSMA staining of actin stress fibers, lacking PDGFRa expression (Figure 10, asterisks). In conclusion, most of the cultured suICs resembled the typical (type 1, PDGFRa+/CALR+/aSMA−) telocyte and a smaller fraction showed the characteristic staining pattern of hybrid (type 2, PDGFRa+/CALR+/aSMA+) telocyte, as defined by Vannucchi and coworkers [26].
In triple immunostaining experiments, we found that Cx43-IR was associated with PDGFRa+/aSMA+ or aSMA− cells. Cx43-IR was significantly lower in myofibroblasts (p = 0.01842, two-sided t-test, data not shown) in typical myofibroblasts showing extensive stress fibers (Figure 11).
3. Discussion
We here demonstrated for the first time that mechanical stimulation of cultured human suburothelial interstitial cells (suICs) evoked intracellular calcium transients. This observation is relevant for the assumed function of the suICs within the afferent signaling pathway in the bladder [27,28,29]. The current view is that the primary sensor of bladder filling—i.e., stretching of the bladder wall—is the urothelium, and it releases adenosine triphosphate (ATP), acetylcholine (ACh), nitric oxide (NO), prostaglandins (PGs), cytokines and many others involved in signal transduction to the ICs and nerve fibers in the lamina propria [30,31].
We observed calcium transients in three different conditions:
(i) Spontaneous calcium transients occurred regularly in the cultured human suICs and were confirmed in previous in vitro and in vivo studies in rats, guinea pigs and humans [29,32,33,34,35].
(ii) Mechanical stimulation of human suICs either by direct indentation of the plasma membrane, swelling of the cells by hypotonic Ringer solution or membrane deformation by sheer stress induced calcium transients. Interestingly, calcium transients were also elicited when releasing the sheer stress by reduction of the flow rate to normal flow. This indicates that the cells are also capable of sensing the changing of the pressure in addition to the force of the pressure.
(iii) Calcium signals propagated not only intracellularly but were transmitted to neighboring cells. This indicates that the suICs formed a functional syncytium, as previously shown [21,22]. Interestingly, we observed two populations of neighboring cells, with approximately 87% of the cells showing a delay of calcium activation after single cell stimulation, resulting in an intercellular signal propagation velocity of about 20 µm/s, while in 20 of 152 cells (13%), the delay was much shorter, with a calculated propagation speed of ≥ 50 µm/s. These cells were similarly found in close proximity to the stimulated cell (<50 µm radius), as well as within a distance of 50 to 250 µm (Figure 7D). We hypothesized that the latter cells were directly connected to the stimulated cell via long thin cell processes, thus resembling telocytes [36], which also have been proposed to be present in the upper lamina propria (ULP) of the human bladder [26,37]. This idea was directly supported by dye coupling experiments, regularly demonstrating cells with long cytoplasmic processes in cultured suICs (Figure 12), and our immunocytochemical experiments indicating the presence of typical type 1 telocytes (PDGFRa+/CALR+/aSMA−) and of PDGFRa+/CALR+/aSMA+ hybrid (type 2) telocytes (Figure 10, Figure 11, Figure S2). We found type 1 telocytes, which were characterized by very small nuclei and small cell bodies showing long thin bipolar cell protrusions spanning up to 200 µm. These cells were observed at a rate of 12.5% in early cell cultures and decreased to around 7% with increasing confluency. Since we used the cell cultures at ≥ 80% confluency (e.g., Figure 4), it is most probable that we did not directly stimulate these cells. However, the small fraction of typical telocytes would well correspond to the observed 13% of cells with short Ca2+ signal delay in up to 250 µm distance (Figure 7D), thus promoting a fast calcium wave distribution over the cellular syncytium. Interestingly, we found no Cx43-IR in typical myofibroblasts accounting for less than 1% of the cells. These very large cells, therefore, would not be considered to contribute to the propagation of the calcium signal.
The intercellular propagation velocities of 32.8 µm/s and data from the literature promote the idea that IP3 rather than Ca2+ proper is responsible for the triggering of calcium transients in neighboring cells. IP3 has an up to 22-times higher diffusion rate and a 10-fold higher lifetime and is thus able to trigger Ca2+ release from neighboring cells in a range of 24 µm from the point of initiation [39,40]. In addition, there is evidence that Ca2+ rapidly closes GJ, possibly via binding to calmodulin (CaM), which in turn closes the GJ channel by binding to Cx43 [41]; review [42].
Interestingly, the propagation velocities of the calcium waves in the cell cultures were well below the velocities reported by Kanai et al. in bladder wholemounts of normal rats (4700 ± 700 µm/s) [29]. However, in contrast to the single cell stimulation in our cell culture experiments, the Ca2+ waves in the bladder wholemounts were initiated by stretching of the whole bladder preparation. As we have demonstrated in our hypotonic experiments, stretch can significantly enhance the spontaneous activity of suIC. Therefore, the cells in the wholemount experiments were pre-stimulated when calcium activity rose at a single site of the active bladder dome, facilitating the rise of [Ca2+]i in neighboring cells and the Ca2+ wave propagation. In addition, synergisms of several signaling mechanisms in vivo—e.g., gap junction coupling, mechanical coupling and fast short-range sensitization of Ca2+ entry channels by the release of soluble activators—could further enhance the Ca2+ wave propagation, as indicated by the higher conduction velocities measured after carbachol stimulation [29].
In a previous study, we found that the same cell cultures used in the present study expressed Cx43 and Cx45, which were differentially regulated by cytokines and growth factors, modulating intercellular coupling [21]. To study the morphology and the Cx43 expression of suICs in situ and in vitro, we used immunohistochemical multiplexing. The interstitial cells directly adjacent to the urothelium presented small cell bodies with two or more processes. Most of them showed a strong expression of Cx43, the molecular correlates of intercellular coupling. The majority of the cells in the ULP stained positive for the cytoskeletal markers vimentin (VIM) and alpha-smooth muscle actin (aSMA), as depicted in the confocal 3D projection (Figure 13, Supplementary Video S2).
Our study provides further evidence for a mechanism of non-neuronal signaling in the human bladder by a network of mesenchymal interstitial cells [5]. The relevance of these cells for the physiologic and pathologic function of the bladder has been implied by evidence at several levels. Their strategical positioning at the border between the urothelial layer and the deep lamina propria is accompanied by characteristic receptor expression [7,9] and by the expression of Cx43, leading to the formation of a functional syncytium in vivo [43].
In the present study, we focused on calcium transients in suICs activated by various mechanical stimuli. Though we did not directly show the involvement of TRPA1 channels in the mechanical activation of calcium transients, it is well documented in the literature that extracellular calcium is essential for eliciting intracellular calcium transients and intracellular calcium oscillations [44,45,46]. Arora and colleagues described how the graded stretch activated slow calcium oscillations in human gingival fibroblasts. These oscillations depended on extracellular [Ca2+]e, actin cytoskeleton and pertussis toxin-sensitive subunits of G-proteins but not on intracellular calcium pools, indicating the involvement of stretch-activated ion channels [46].
Calcium waves arise from massive calcium release from intracellular calcium stores upon activation of inositol triphosphate receptors (IP3Rs) and ryanodine receptors (RyRs) interacting with various members of the TRP family [45,47,48]. In the presence of extracellular Ca2+ and the selective TRPA1 agonists allyl isothiocyanate (AITC) and 15-deoxy-delta-12, 14-prostaglandin J2 (PGJ2) induced intracellular calcium transients in human cardiac fibroblasts, indicating the triggering of intracellular Ca2+ release via extracellular Ca2+ influx [44].
Additional support for the hypothesis of suICs being active elements in afferent signaling processing in the bladder comes from experiments demonstrating increased ATP release from cultured pig bladder ICs by hypotonic stimulation [49]. Our own group has previously shown that human suICs (denoted myofibroblasts in older literature) are highly sensitive to ATP, inducing calcium transients at very low concentrations [33].
In our single cell stimulation experiments, we did not observe any correlation between the direction of the Ringer flow with the spreading of the calcium wave in neighboring cells. Therefore, we conclude that a possible release of ATP from the locally stimulated suIC does not add to the calcium signal measured in our experiments and that the distribution of the signal is truly mediated via the cytoplasmic coupling of the cells.
Cultured suICs express aSMA, allowing cell contraction, especially in the fraction of myofibroblast-like cells forming stress fibers (Figure S1). However, our immunocytochemical experiments indicated that these myofibroblast-like cells are not included in the functional syncytium of the suIC under cell culture conditions. Besides strong aSMA expression, the typical fibroblast-like appearance of this cell—i.e., its large flat cell body with large nuclei and multiple cell processes—has provoked the name “myofibroblast”. Under normal conditions, such cells should not account for the majority of the aSMA+ ICs in the bladder. Therefore, these cells might indeed represent pathological altered suICs and it will be interesting to further investigate their contribution to pathological states of the bladder.
Since most non-myofibroblast suICs (type 2 telocytes) strongly express aSMA in situ and a rise in intracellular calcium can trigger cell contraction, it is conjecturable that local contractions evoked by ATP signaling from the urothelium could also lead to local signaling during the bladder filling phase. Therefore, the role of suICs in afferent signaling could be the enhancement and distribution of local signals over larger areas of the bladder wall. This view is supported by the excellent experiments of Kanai and colleagues in wholemount rat bladders, directly demonstrating the coordinated spreading of local (spontaneous) calcium signals from the dome to the bladder neck. Furthermore, they demonstrated the propagation of the signals from the mucosal initiation site to the serosa of the bladder [43].
If the suICs take part in afferent signal processing in the bladder, one should assume that local stimulation of a single cell, as was conducted in Section 2.2, should influence the calcium activity in the functional syncytium. We therefore compared the activity after stimulation in the neighboring cells with the approximated background activity measured in control experiments. We found that, after mechanical stimulation of a single cell, the syncytial activity (activity in the neighboring cells) increased by 18 times based on the AUCs in the first peaks. The primary calcium signal evoked by a single short-term membrane indentation of a single cell was radially distributed in the cell layer for at least 250 µm, activating an area of approximately 0.2 mm2. The strength of activation decreased with distance from 500% to 200% in a radius of 50 µm and 250 µm, respectively. In total, the primary calcium signal was amplified by 50%. These findings strongly support the notion of an amplifier function of suICs.
The major limitation of this study is the use of a 2D adherent cell culture system. In addition, while we provide some evidence of several subtypes of cells in our cell culture, we did not further characterize these subtypes to allow a more detailed view of the roles of these cells in the bladder. Since the urinary bladder wall is composed of multiple cell types embedded in a complex structured extracellular matrix, the results cannot be transferred directly to the in vivo situation. Physiological stretching of the bladder wall during bladder filling results in multiple cellular interactions, including the release of transmitter substances and mechanical stress. Therefore, the 2D model system can only highlight a small part of the complex sequence of events.
In the future, it will be interesting to investigate the role of suIC functional syncytia in 3D model systems of overactive bladder and especially of interstitial cystitis/bladder pain syndrome (IC/BPS).
4. Materials and Methods4.1. Ethical Statement
This study was approved by the Ethics Committee of the University of Leipzig (ethical approval code: 036-2007, Date of issue: 26.05.2015) and was conducted according to the principles expressed in the World Medical Association Declaration of Helsinki [50]. Written informed consent was obtained from all patients.
4.2. Cell Cultures
Human bladder tissue was collected from patients undergoing cystectomy for bladder carcinoma treatment or due to gynecologic tumors. We were able to culture suICs from five female and three male patients, aged (mean ± SD) 60.2 ± 11.7 yrs and 64.3 ± 6.6 yrs, respectively. Cell cultures of suburothelial interstitial cells (suICs) were established from macroscopic tumor free bladder tissue by sharp microsurgical dissection of the urothelium from the smooth muscle layer of the detrusor, ensuring no contamination with detrusor smooth muscle cells. The tissue was then cut into small fragments of approximately 0.5 mm edge length and cultured in smooth muscle cell growth medium 2 (PromoCell GmbH, Heidelberg, Germany). This medium did not support the growth of urothelial cells and the resulting cell cultures were free of contaminating urothelial cells, as proven by polymerase chain reaction (PCR), Western blot and immunocytochemistry. We described the procedure in detail in a previous study [21]. In brief, the fragments were plated into tissue culture flasks (TPP AG, Trasadingen, Switzerland) and incubated at 37 °C in 5% CO2 humidified atmosphere until reaching 80% confluency. Cell passages 2 to 5 were used in the experiments. For the calcium imaging experiments, the cells were plated onto collagen A (Biochrome AG, Berlin, Germany) coated 13 mm glass cover slips and grown to 80% confluency.
4.3. Chemicals and Solutions
We used a modified Krebs–Ringer solution for the calcium imaging experiment (in mmol/L): CaCl2 1.9; NaCl 120.9; NaHCO3 14.4; KCl 5.9; MgCl2 1.2; NaH2PO4 1.55; Hepes 4.2; Glucose 11.49; pH = 7.2; 309 mOsm/L (all chemicals from Sigma-Aldrich, Steinheim, Germany). All solutions were freshly prepared with cell culture grade distilled water on the day of usage. For hypotonic solutions, we diluted the Krebs–Ringer solution at the ratio of 3:1 and 1:1 to yield 232 mOsm/L and 154 mOsm/L solution, respectively.
4.4. Calcium Imaging
Cells cultured onto glass cover slips were washed 3 times with Krebs–Ringer solution at 37 °C and thereafter incubated for 35 min at 37 °C with 2.5 mMol/L fura-2 AM (fura-2 acetoxymethylester) dissolved in DMSO (dimethylsulfoxid) and 2% pluronic (Invitrogen by Thermo Fisher Scientific, Schwerte, Germany). The cells were washed again in Krebs–Ringer solution and placed into a 37 °C heated lamina flow Diamond Bath Oocyte Recording Chamber (RC-26Z, Warner Instruments, Hamden, CT, USA). To exclude calcium activity caused by the washing and transfer procedure, the cells were adapted to the experimental conditions (37 °C, oxygenation of the Krebs–Ringer solution with 95% O2 and 5% CO2, constant flow of 1 mL/min) for ten minutes before the start of the experiments. We used an IX-71 inverted microscope equipped with a LCPLN20xIR objective (Olympus Microscopy, Hamburg, Germany) and a TILL IMAGO-QE digital camera (TillPhotonics, Gräfelfing, Germany). Experimental control and image acquisition were done using TILLvisION Software V. 4.5 (TillPhotonics). Image series were recorded at 1, 4 or 10 frames per second (fps). Fura-2 fluorescence was excited by 30 ms pulse application of 340nm and 380nm monochromatic light (Polychrome V monochromator, TillPhotonics) and the fluorescence signal was detected at a wavelength of 520 nm. The specific fluorescence intensity (FI) was calculated after subtraction of the background fluorescence: FI = F340 nm/F380 nm × 1000. The FI was color-coded and exported as TIFF video. We used a self-written Python [25] program for analysis of the calcium changes over time (calcium transients). The core of the Python program was the automatic calcium peak detection, which was fitted to cope with the heteromorphic calcium traces. The analysis can be restricted to pre-defined parts of the curves, allowing precise analysis of the peak morphology. For the analysis of intercellular calcium waves, the software allows the definition of a reference (stimulated) cell and calculates the distances of all regions of interest (ROI) and the time course of the calcium signal in relation to this cell. Details are described in the Supplementary Materials.
4.5. Data Analysis and Statistics
We used Microsoft Excel (Microsoft Corporation, Redmond, WA, USA) and Graph Pad Prism 5/8 (GraphPad Software, San Diego, CA, USA) for data processing and statistical analysis (Wilcoxon matched-pairs signed rank test; two-tailed Mann–Whitney test).
4.6. Stimulation Experiments
As a starting point, spontaneous calcium activity was recorded after 10 min of adaptation of the cells before starting the real experiment. We investigated the cells’ reactions to three different mechanical stimuli.
4.6.1. Single Cell Mechanical Stimulation
Single cell mechanical stimulation requires ultraprecise micromanipulators. We used a motorized MP-285 Nano-Micromanipulator (Sutter Instruments, Novato, CA, USA), allowing step sizes down to 40 nm. We mechanically stimulated single cells by defined deflection of the plasma membrane using a glass micropipette with a fine tip, which was closed and rounded to avoid (i) capillary flow-in of ringer and (ii) mechanical injury of the membrane. The membrane deflection was applied by stepwise lowering the micropipette in steps of 40 nm (Figure 14).
4.6.2. Mechanical Stimulation by Shear Stress
Cells were adapted to the experimental conditions by 15 min superfusion with 1 mL/min. We then recorded the calcium activity for 5 min in each section: (A) 1 mL/min, (B) 2 mL/min, (C) 1 mL/min.
4.6.3. Hypotonic Stimulation
After adaptation for 15 min to the experimental conditions (309 mOsm/L, 1 mL/min flow), cells were challenged either with 232 mOsm/L or 154 mOsm/L for 3 min. Recordings of the calcium activity were done for three minutes in each section (A,B,C). For control, the flow was switched to 309 mOsm/L in section B.
4.7. Dye-Coupling Experiments
Single cells were dialyzed for 10 min with 3% lucifer yellow (LY) dissolved in intracellular pipette solution (130 mM/L KCl, 5 mM/L K-pyruvate, 5 mM/L K-oxalacetate, 5 mM/L K-succinate, 10 mM/l HEPES, 0.02 mM EGTA, pH 7.4) [51] by using a 50 MΩ micropipettes for sharp penetration of the cell membrane. LY was injected into the cell by short reproducible pressure pulses by using a PV800 Pneumatic PicoPump (World Precision Instruments, Berlin, Germany), as described previously [52]. LY-fluorescence was visualized using a fluorescein isothiocyanate (FITC) filter set.
4.8. Confocal Imaging
Confocal images of bladder tissue were acquired at a LSM800 (Carl Zeiss Microscopy, Jena, Germany) equipped with a Plan-Apochromat 63x/1.40 Oil DIC M27; in cell cultures, a 40x/0.95 objective was used for acquisition of 5 × 5 large tiles, which were stitched to yield an image of 556 µm × 556 µm. In brief: human bladder tissue was fixed in 4% buffered formalin for 24 h and embedded in paraffin. Ten µm thick paraffin sections were immunofluorescence stained for VIM, aSMA, Cx43 (Table 5). Cell cultures were cultured onto collagen A covered glass coverslips. Cells were fixed for 2 min in –20 °C methanol before processing for primary antibody incubation overnight at 4 °C. Nuclei were stained with DAPI (4′,6-diamidin-2-phenylindol, 1:5000, Carl Roth, Karlsruhe, Germany). Image processing and analysis was done in ImageJ 1.51s [53] for mac and Adobe Photoshop CC V. 20.0.10 (Adobe, San José, CA, USA).
Acknowledgments
The authors want to thank A. Weimann for the excellent technical assistance.
Supplementary Materials
Supplementary materials can be found at https://www.mdpi.com/1422-0067/21/15/5474/s1.
Click here for additional data file.
Author Contributions
Conceptualization, J.N. and F.P.B.; Data curation, A.G. and F.P.B.; Formal analysis, J.N. and F.P.B.; Funding acquisition, J.N.; Investigation, S.C. and F.P.B.; Methodology, J.N., S.C. and F.P.B.; Project administration, J.N.; Resources, J.N., A.G. and J.-U.S.; Software, F.P.B.; Supervision, J.N. and F.P.B.; Validation, J.N., A.G. and F.P.B.; Visualization, J.N. and F.P.B.; Writing—original draft, F.P.B.; Writing—review and editing, J.N., A.G. and J.-U.S. All authors have read and agreed to the published version of the manuscript.
Funding
This research was funded by Deutsche Forschungsgemeinschaft, grant number NE425/4-4. This research received funding from the Leipzig University for Open Access Publishing.
Conflicts of Interest
The authors declare no conflict of interest.
Abbreviations
AUC
area under the curve
aSMA
alpha-smooth muscle actin
BOO
bladder outlet obstruction
CALR
calreticulin
Cx43
connexin 43
DAPI
4′,6-diamidin-2-phenylindol
FITC
fluorescein isothiocyanate
fps
frames per second
fura-2AM
fura-2-acetoxymethylester
IC/BPS
interstitial cystitis/bladder pain syndrome
OAB
overactive bladder
PDGFRa
platelet derived growth factor receptor-alpha
SD
standard deviation
SEM
standard error of the mean
suIC
suburothelial interstitial cells
TRP
transient receptor potential
VIM
vimentin
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Spontaneous calcium activity in cultured suburothelial interstitial cells (suICs). (A) Representative traces of calcium transients measured with the ratiometric fura-2 fluorescent Ca2+ indicator; arrows indicate undulated decay of the amplitued in the red colored trace; (B–E) quantitative analysis of peak frequency (B), peak amplitude (C), peak duration (D) and integrated peak area (area under the curve = AUC (E); inset in (E) explains the peak measures (for detailed information, see Methods section).
Peak morphology of spontaneous Ca2+ transients is homogeneous. Amplitude sorted transients were overlaid at their maximum. Note that the morphology does not alter with the amplitude. Mean (solid lines), standard error of the mean (SEM, dashed lines); only peaks with at least 20 time points were included.
Positioning of the glass micropipette (A, phase contrast); lowering of the micropipette (B, arrow) provoked a local intracellular calcium rise (C) in the foremost quiet cell (arrow); the calcium signal spread across the cell within several seconds (C–F); arrow indicates micropipette tip position; inset in (C) = fluorescence intensity (FI) color-coding.
Intracellular propagation of the calcium signal. (A) Position of the glass micropipette (phase contrast): circles indicate the regions of interest (ROIs) of fluorescence measurements, color corresponds to the color-coding of the time resolved fluorescence intensity traces in (B); scale bar = 200 µm; (B) the fluorescence peak intensity decreased with increasing distance from the site of signal initiation; traces smoothed; (C) peak amplitude (%) versus distance to the site of stimulation (binned data, x-axis = center of bin; bin 5 = > 0 to < 10; bin 20 = ≥ 10 to < 30)—note the linear decay; (D) plotting of the peak delay versus the distance to the site of stimulation showed mainly linear correlation with increasing scattering at higher distances.
Peak morphology of calcium transients depending on the distance to the initial peak. Note that the peaks show similar rising and decay characteristics within 10 s before and after the peak maximum.
Analysis of the intercellular propagation of the calcium signal. (A) Position of the micropipette (stimulation site); ROIs (red circles); white circles indicate sections: 50 µm, 100 µm, 150 µm); nucleus of stimulated cell (white arrow); phase contrast image; scale bar = 50 µm. (B) Typical recording of calcium transients in the stimulated cell (red trace) and the neighboring cells (black traces); recording speed: 10 fps. (C–K) Surface plot of the propagation of the calcium wave over the functional syncytium: (C) pre stimulation; (D) 0.1 s after stimulation; white arrow indicates stimulation site. Note that first calcium peaks in neighboring cells already occurred within 1 s after stimulation (E–G); maximum excitation is seen about 3-5 s after stimulation (I,J); excitation decay is seen after 7 s (K); instant of time since stimulation is indicated bottom right; color-coding ranged from FI = 0–3200 (inset in C).
Analysis of the calcium peaks and transients in neighboring cells compared to the first peaks in the stimulated cells; (A–C) the neighboring cells were grouped into distance groups (upper bin limit indicated); (D) the propagation speed of the calcium signal was compared between near (≤50 µm) and far (>50–250 µm) neighboring cells. Note the split of the y-axis and the bimodal distribution of speeds; controls = cells with spontaneous activity; green dotted line indicates upper 95% CI of the controls.
Effect of shear stress on the calcium activity of cultures suIC. In 5 min recordings, acceleration of the laminar flow from 1ml/s (A) to 2 mL/s (B) led to increased calcium activity. This activity decreased only slowly, as seen in the increased activity recorded 5 min after switching back to 1 mL/min flow (C).
Effect of stimulation with hypotonic Ringer solutions. (A) conditioning of cells by 3 min preincubation in 309 mOsm/L Ringer; (B) 3 min period of challenging the cells with different Ringer solutions; Hypotonic Ringer led to a concentration dependent increase in calcium activity. This increase was almost synchronous and disappeared immediately after switching back to 309 mOsm/L Ringer solution (C). Representative traces of 14 cells per condition are depicted.
Immunocytochemical characterization of the suIC in cell culture. Triple staining of (A) calreticulin (CALR, orange), (B) vimentin (VIM, green) and (C) α-smooth muscle cell actin (aSMA, red); all cells show immunoreactivity (IR) for CALR and VIM, while only very few cells show strong aSMA staining of stress fibers (asterisks); nuclei are stained with 4′,6-diamidin-2-phenylindol (DAPI, blue).
Triple staining of (A) platelet derived growth factor receptor alpha (PDGFRa, orange), (B) aSMA (green) and (C) connexin 43 (Cx43, red). Note the cell membrane associated punctuate or even patchy Cx43-IR present on cells with very small elongated nuclei and small cell bodies (arrows); no Cx43-IR can be attributed to the strongly stained myofibroblast characterized by the presence of stress fibers; nuclei are stained with DAPI (blue); (D) merged image channels.
Lucifer Yellow (LY) injection revealed functional syncytia in cultured suICs. (A) Phase contrast, MP (micro pipette); (B) injected cell filled with LY after 90 s; (C) cell culture with two injected cells (arrows) showing distribution of the LY, defining variably sized functional syncytia; (D) the LY injected cell (arrow) and a direct neighboring cell show bright LY-fluorescence signal; arrow-heads indicate a fine process contacting another cell, spanning a distance of approximately 100 µm; nuclei (DAPI, blue); modified reproduction with permission from Neuhaus et al. 2007 [38].
The 3D projection of a confocal image stack (voxel size (yxz): 0.06 µm × 0.06 µm × 0.39 µm); (A) VIM (green, ex 488 nm); (B) aSMA (red, ex 640 nm); (C) nuclei stained with DAPI (blue, ex 405nm); (D) merged image including Cx43 staining (magenta, excitation at 561 nm). Note that only a few cells are negative for aSMA (arrow); U = urothelium.
Mechanical stimulation using a glass micropipette. The micropipette is placed close to the plasma membrane (A); by stepwise (40 nm steps) lowering of the micropipette, the plasma membrane is dented until a calcium signal is initiated (B,C); red arrows indicate movement of the micropipette (B) and the deflection of the cell membrane (C).
ijms-21-05474-t001_Table 1
Maximum amplitudes of the peak groups.
Group
Mean
SD
\\nN\\n
1
56.5466
27.46916
1913
2
221.3514
70.75953
582
3
488.9613
72.17626
245
4
743.6639
69.71185
134
5
978.3084
66.73132
70
6
1239.981
66.15836
29
SD = standard deviation; N = number of peaks analyzed.
ijms-21-05474-t002_Table 2
Number of cells observed and number of peaks analyzed.
Cell Group
0
50
100
150
200
250
Control
Distance (µm)
Stimulated
<= 50
>50 ≤ 100
>100 ≤150
>150 ≤ 200
>200≤ 250
NA
Number of cells
14
81
155
213
139
16
619
Active cells (n)
14
44
77
67
41
8
34
Mean (%)
100
54.3
49.7
31.5
29.5
50
5.5
95% CI (%)
\\n
43.2–65.4
40.3–50.2
25.2–37.7
21.8–37.2
12.9–51.6
3.7–7.3
Peak ampl. mean
2242
1025
954
887
848
1090
599
95% CI
1683–2800
831–1219
840–1068
766–1008
639–1058
363–1817
489–711
Peak AUC (au)
476737
189641
175652
157005
115,647
97981
41535
95% CI
228,359–725,115
119,516–259,766
139,339–211,966
122,889–191,121
80,430–150,865
9981–185,982
28,387–54,684
Peak duration (s)
40.9
36.8
36.8
33.4
28.1
39.3
28.3
95% CI
29.4–52.6
32.8–40.9
34.1–39.6
30.5–36.3
24.6–31.7
19.4–39.2
23.4–33.2
Peak delay (s)
NA
6.534
11.65
18.34
18.1
15.68
NA
95% CI
4.1–8.9
9.4–13.9
15.4–21.3
12.9–23.3
10.1–21.3
NA = data not available; control = spontaneous peaks before stimulation.
ijms-21-05474-t003_Table 3
Analysis of the shear stress experiments.
Peak
Section A
Section B
Section C
p–Value
Frequency (min−1)
0.62 (522)
0.70 (554)
0.75 (559)
A/B: <0.0001 §
[0.59–0.64]
[0.68–0.73]
[0.72–0.78]
B/C: 0.0039 §
Amplitude (FI)
269.4 (1345)
344.7 (1628)
207.6 (1757)
A/B: <0.0001 $
[245.6–293.19]
[319.4–370.0]
[192.1–223.2]
B/C: <0.0001 $
Duration (s)
34.26 (1345)
39.43 (1628)
35.74 (1757)
A/B: <0.0001 $
[32.99–35.54]
[38.03–40.83]
[34.59–36.90]
B/C: <0.0001 $
AUC
5789 (1345)
8860 (1628)
4602 (1757)
A/B: <0.0001 §
[5149–6430]
[8060–9660]
[4172–5032]
B/C: <0.0001 §
Mean (n), [95% CI]; § Wilcoxon matched pairs signed rank test; $ two-tailed Mann–Whitney test.
ijms-21-05474-t004_Table 4
Analysis of the hypotonic stimulation experiments.
Osmotic Conc.
309
232
154
p–Value
(mOsm/L)
Control
Hypo25
Hypo50
frequency (min–1)
0.02 (44)
0.45 (36)
0.35(58)
Hypo25/Hypo50
[0.0–0.06]
[0.39–0.49]
[0.34–0.36]
<0.0001 $
amplitude (FI)
32.63 (1)
733.2 (47)
2147 (59)
Hypo25/Hypo50
[NA]
[579.8–886.7]
[1891–2403]
<0.0001 $
duration (s)
21 (1)
93 (47)
114.6 (59)
Hypo25/Hypo50
[NA]
[80.19–105.8]
[109.4–119.8]
0.0021 $
AUC
419.6 (1)
37,707 (47)
119,702 (59)
Hypo25/Hypo50
[NA]
[27,896–47,517]
[102,882–136,421]
<0.0001 $
In control, only one cell showed spontaneous activity; mean (n), [95% CI]; $ two-tailed Mann–Whitney test.
ijms-21-05474-t005_Table 5
Primary and secondary antibodies used in confocal imaging experiments.
\\n\"\n]"}}},{"rowIdx":465,"cells":{"data":{"kind":"list like","value":["\nInt J Environ Res Public HealthInt J Environ Res Public HealthijerphInternational Journal of Environmental Research and Public Health1661-78271660-4601MDPI32751311PMC743212210.3390/ijerph17155477ijerph-17-05477ArticleCorrelation between COVID-19 Morbidity and Mortality Rates in Japan and Local Population Density, Temperature, and Absolute Humidityhttps://orcid.org/0000-0001-6595-0742KoderaSachiko1https://orcid.org/0000-0001-6571-9807RashedEssam A.12https://orcid.org/0000-0001-8336-1140HirataAkimasa13*Department of Electrical and Mechanical Engineering, Nagoya Institute of Technology, Nagoya 466-8555, Japan; kodera@nitech.ac.jp (S.K.); essam.rashed@nitech.ac.jp (E.A.R.)Department of Mathematics, Faculty of Science, Suez Canal University, Ismailia 41522, EgyptCenter of Biomedical Physics and Information Technology, Nagoya Institute of Technology, Nagoya 466-8555, JapanCorrespondence: ahirata@nitech.ac.jp; Tel.: +81-52-735-79162972020820201715547722620202772020© 2020 by the authors.2020Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
This study analyzed the morbidity and mortality rates of the coronavirus disease (COVID-19) pandemic in different prefectures of Japan. Under the constraint that daily maximum confirmed deaths and daily maximum cases should exceed 4 and 10, respectively, 14 prefectures were included, and cofactors affecting the morbidity and mortality rates were evaluated. In particular, the number of confirmed deaths was assessed, excluding cases of nosocomial infections and nursing home patients. The correlations between the morbidity and mortality rates and population density were statistically significant (p-value < 0.05). In addition, the percentage of elderly population was also found to be non-negligible. Among weather parameters, the maximum temperature and absolute humidity averaged over the duration were found to be in modest correlation with the morbidity and mortality rates. Lower morbidity and mortality rates were observed for higher temperature and absolute humidity. Multivariate linear regression considering these factors showed that the adjusted determination coefficient for the confirmed cases was 0.693 in terms of population density, elderly percentage, and maximum absolute humidity (p-value < 0.01). These findings could be useful for intervention planning during future pandemics, including a potential second COVID-19 outbreak.
The COVID-19 outbreak was first reported in China in 2019 [1,2] and spread worldwide in early 2020. Japan declared a state of emergency in seven (of 47) prefectures on 7 April 2020 and extended it to all prefectures on 13 April 2020. The state of emergency was withdrawn on 25 May 2020. During this state of emergency, unlike many other countries where city lockdowns were enforced, in Japan, citizens self-isolated. The mortality rate (per population) in Japan is relatively low compared to the global rate; the total number of confirmed deaths in Japan is 846 (25 May 2020), corresponding to 6.72 per million people [3]. Although a straightforward comparison is infeasible, this number is smaller than that of many other countries with the same order of magnitude of population: 541, 504, 435, and 295 in Italy, the United Kingdom, France, and the United States, respectively, but larger than 5.18 and 4.0 in Indonesia and Australia, respectively (25 July 2020).
An additional difficulty in understanding the morbidity rate is the unreliability of the diagnosis of COVID-19. The number of polymerase chain reaction (PCR) tests, a simple and cost-effective method, is limited in Japan, partly because of its reliability. Therefore, chest CT is used for a fast-track, highly accurate diagnosis [4]. Some patients do not exhibit any common pandemic symptoms [5], thereby complicating morbidity rate assessment in different areas (e.g., population composition).
Morbidity and mortality statistics have been updated every day in each prefecture in Japan, which provides a good opportunity for local studies. A notable feature of Japan is that no medical collapse has been reported. In addition, due to the health insurance system, free medical care for COVID-19 has been offered. Thus, the reliability of the mortality rate is more accurate than that of the morbidity rate, because patients with weak or mild symptoms may not be tested. However, to avoid nosocomial infections and medical resource shortages, it was suggested that people with specific symptoms (e.g., fever with temperature >37.5 °C for no more than four consecutive days) stay home and avoid seeking medical attention, unless they had been in close contact with an infected person(s) or had recently visited foreign countries. Such a policy may result in longer latency in the reported cases.
In general, coronaviruses are considered to spread mainly by respiratory droplets and contact via droplets [6]. Droplets tend to fall to the ground close to the infected host. Droplet transmission is typically limited to short distances, generally less than 2 m. There exist some hypotheses about transmission due to airborne transmission that remain in flight for one hour or longer [7]. For both mechanisms, the ambient condition potentially influences the duration of droplet and airborne spread. Several co-factors potentially influence COVID-19 morbidity/mortality rates [8,9,10,11,12,13,14]); among them, ambient conditions have been considered here.
The effect of ambient temperature on the mortality was discussed in Wuhan [8]. Positive and negative associations were found between daily COVID-19 death counts and daily temperature difference and absolute humidity, respectively. The effect of high temperature and humidity on the transmission of COVID-19 was discussed using relative humidity as a measure [9]. Their finding suggested that high temperature and humidity may suppress COVID transmission. Furthermore, the effect of weather on COVID-19 cases employing a case in Jakarta was presented in [10]. They reported that only average temperature is correlated with the pandemic spread. The effect of ambient temperature on the confirmed cases was discussed in more than 100 Chinese cities, and it was concluded that there is no evidence supporting that COVID-19 case counts would decline when the weather becomes warmer [12]. The effect of ambient temperature and absolute humidity on the confirmed cases was investigated in cities in China, and the researchers commented that the epidemic might gradually ease partially due to rising temperatures [13]. Instead, no correlation with UV and temperature on the transmission of COVID-19 was reported in [15].
Following Chinese studies, case studies in different countries have been reported. Briz-Redón and Serrano-Aroca [16] evaluated the spatiotemporal analysis of temperature in the cases of early COVID-19 evolution in Spain. Pirouz et al. [17] discussed the correlation between daily confirmed cases and temperature, humidity, and velocity with multivariate analysis in Italy. Application of neural networks for its estimation is also discussed in [18]. A similar attempt has been made in Oslo, Norway [19]. Recent studies have confirmed the effect of temperature and relative humidity on morbidity rates in Brazil [20,21]. From these studies, it is difficult to derive a consistent conclusion on the effect of the weather on the spread of COVID-19. Studies of influenza suggested the importance of ambient conditions for its spread: lower spread for higher humidity (e.g., [22,23]).
Studies with wider scopes included global data analysis, discussing how temperature and humidity are correlated with the infection and fatality rates of the COVID-19 pandemic [24,25]. The region of interest is wide (country level) in these studies, and thus it is not directly applicable to the ruling or regulation. In addition, some modeling studies have been proposed. However, parameter setting is not easy for this type of novel virus spread [14,26]; in most modeling studies, the parameters relating to the weather or population cannot be given explicitly. Instead, the effect of population density on the spreading effect of the epidemic has been discussed under some assumptions [27].
Nevertheless, none of the aforementioned research and modeling studies simultaneously considered the impact of population density and ambient conditions. A question that arises here is To what extent do ambient conditions and population density influence morbidity and mortality rates in different cities? Unlike the aforementioned studies, a major feature of Japan is the relative homogeneity of the health insurance and care system without medical collapse during this pandemic. In addition, the difference in household wealth is relatively small in Japan [28]. The average annual salary per population is USD 34,400 to 39,900 (USD 1 = JPY 107). The standard deviation of household consumption in each prefecture is 10% or less [28]. With all these demographic factors, the data sample discussed here provides a convenient case study with less bias. In a recent study [29], we examined the time course of the morbidity rate of different prefectures in Japan and found that the durations of the spread and decay stages can be characterized by population density, temperature, and absolute humidity. An additional factor would be the ratio of the elderly to the entire population; in Japan, this ratio reached 28.4% [30], which is ranked the highest globally.
This novel study aimed to evaluate the effect of ambient temperature and humidity on mortality and morbidity rates in different prefectures in Japan. Additionally, it considered the influence of population density and composition. To the best of the authors’ knowledge, this is the first study to highlight the environmental factors’ effect during COVID-19 in Japan. The model of Japan provides an interesting case study for different factors, as the medical service and social reaction is almost uniform nationwide and high-quality data were recorded properly. If the correlation of the pandemic with population density and ambient conditions is significant, the findings will be useful to set the level and duration for a strict lockdown period for each city considering the environmental factors and in planning future pandemic measures.
The organization of this study is as follows. In Section 2, the data sources of COVID-19 in Japan and weather data are mentioned. Then, the statistical method for data processing is explained briefly. In Section 3, effect of population density, elderly population, and ambient conditions on the morbidity/mortality rates are evaluated statistically. Based on the evaluation, multivariate linear regression has been conducted to estimate the morbidity/mortality rate from these parameters. In Section 4, provides discussion of the results including the limitation. The conclusion is given in Section 5.
2. Material and Methods2.1. Data Source
In this study, we used three datasets. The first involved the confirmed daily positive cases and deaths in each prefecture [31]. This dataset is based on the report by the Ministry of Health Labor and Welfare [32]. We used time-integrated data until 25 May 2020—when the emergency state was terminated. According to the dataset, 16 prefectures had confirmed total deaths and daily positive counts higher than 4 and 10, respectively. These prefectures were defined as infected. The remaining prefectures were excluded due to a lower number of infected cases, which is simply because of the self-isolation, including the discouragement from moving to other prefectures after the declaration. In Japan, to avoid nosocomial infections and medical resource shortages, it was suggested that people with symptoms (e.g., fever >37.5 °C for no more than four consecutive days) stay home and not seek immediate medical attention unless they had been in close contact with infected people or had recently visited a foreign country. Some patients have been reported to be asymptomatic [5], making the statistical study of COVID-19 more complex. Then, the positive rate of the test varied from 2.2% to 34.8% for different prefectures. Unlike other diseases, the number of confirmed cases/deaths are counted even when patients are found to be infected after their death. For this data collection, we use the mortality rate in this study as a metric rather than the case fatality rate.
For comparison, two sets of data were prepared: (1) the number of confirmed deaths excluding and including nosocomial/nursing facility infection, and (2) the total confirmed positive cases. This is to avoid the data of cluster infection for high-risk groups, resulting in a higher possibility of death.
Note that among some of the 16 selected prefectures, as shown in Figure 1, the number of victims due to nosocomial/nursing facility infection was not always reported. Thus, such prefectures were excluded from the comparisons. Among others, the area of Hokkaido is one to two magnitudes larger than the other prefectures. Thus, several peaks in the number of cases are observed in different cities with larger distances between them than those in other adjacent prefectures. In the Gunma prefecture, three confirmed deaths occurred, except for in Isezaki City, where substantial nosocomial infections were reported (15 victims). Thus, we used data from the Gunma prefecture, excluding Isezaki City, for accurate comparison of mortality rates. In addition, Saitama was also excluded since it did not report humidity data (see below for the third dataset). Two prefectures with unclear nosocomial/nursing facility infections were also excluded.
The second dataset comprises the population and the area of the prefectures. Based on the evidence that more than 90% of the victims are older than 60 years and because the retirement age in Japan is 65, which may potentially influence morbidity rates, we set the threshold as 65. For a total of 14 prefectures and one city, the first and second datasets are listed in Table 1. Note that the rationale for choosing 25 May as a reference date is the end of state of emergency, and then, the daily confirmed death over Japan was 20 (128 million population); the daily confirmed death was smaller than 100 for one month after that.
The third dataset comprises weather data for each prefecture. They are extracted from the weather reports generated by the Japan Meteorological Agency [33]. In our previous study, we had studied the correlation between environmental conditions and the duration of the pandemic from its spread to decay periods [29]. We extended this investigation to the prefectures defined in Table 1. We then estimated the start and end dates of the spread and decay stages, as defined in Table 2. To validate the effect of ambient factors in different phases of the pandemic, we computed ambient features for three time frames: during the spreading stage DS (from TSS to TSE), during the decaying stage DD (from TDS to TDE), and during both stages (from TSS to TDE).
To consider the mortality rate, which is affected by many factors, it should be noted that the metrics are averaged over the duration of the spread stage, decay stage, and the entire period. The duration-averaged values of temperature, absolute humidity, wind velocity, and daylight hours were calculated from the data available from the internet site mentioned above, as listed in Table 3. The latitude of Japan considered here is N 33°36′ (Fukuoka) to N 36°35′ (Ishikawa), except for Okinawa of N 26°12′), and thus, total solar radiation may be marginally influenced with this measure.
2.2. Statistical Analysis
A statistical study was conducted to analyze the correlation of different factors on both mortality and morbidity rates. The software JMP (SAS Institute, Cary, NC, USA) was used in this study. In order to specify dominant factors influencing the rates, p-value was used. We determined the pairwise correlations by calculating the Spearman’s rank correlation between the number of confirmed positive cases, confirmed death cases, and different environmental and demographic parameters. Correlation matrix with partial correlation probability and CI of correlation were calculated. After that, with the same software, multivariate analysis [34] was conducted in terms of the factors. We considered linear regression for data least-squares fitting after considering multicollinearity. Statistical significance was accepted at p < 0.05.
3. Results3.1. Effect of Population Density and Elderly Population
Figure 2 shows the relationship between confirmed positive cases and confirmed deaths, including and excluding nosocomial infections and nursing home patients. A modest correlation was observed between positive cases per million and population density (R2 = 0.394), whereas a slight and mild correlation was observed for confirmed deaths (R2 = 0.097) and excluding nosocomial infection (R2 = 0.259). This result suggests that population density should be considered as a factor that implicitly represents social distancing, as is similar to our previous study that discussed the pandemic’s duration [29].
When the cases and deaths for the elderly population were considered, the same tendency was observed; R2 = 0.363 for cases, and R2 = 0.078 and R2 = 0.210 for deaths with and without nosocomial infections (not shown to avoid repetition), respectively. Instead, as shown in Figure 3, the morbidity and mortality rates normalized by population density are modestly correlated with the percentage of the elderly, especially for confirmed deaths excluding nosocomial infections (R2 = 0.482). This factor is thus considered in the multivariate analysis study presented later.
3.2. Effect of Ambient Conditions
Several ambient factors potentially influence morbidity and mortality rates. Our study considered temperature and absolute humidity. Most previous studies reported the maximum, average, or difference (diurnal variation range) of ambient temperature (e.g., see [8] and [35]). Our study also considered the minimum temperature. Recent reports on influenza suggest the importance of absolute humidity rather than its relative value [22,23]; however, we considered the maximum, average, minimum, and difference values of absolute humidity as parameters. The daily average wind velocity and daylight hours were also considered. Regression analysis was conducted for all metrics averaged over the duration of the spread and decay stages and the total duration.
Table 4 lists the coefficients of determination for different metrics. For most parameters, the averaged values over the total stage provided the highest correlation rather than those over the other two durations. As an example, Figure 4 shows the correlation between the number of confirmed positive cases and fatality normalized by the population density and the daily maximum temperature and diurnal absolute humidity. A moderate correlation was observed among the daily maximum temperature, diurnal absolute humidity, and cases per population density. Table 5 lists the Spearman’s rank correlation for different parameters. The ambient factor was normalized by population density as aforementioned. A moderate correlation was also observed with the daily maximum temperature, daily maximum, and diurnal absolute humidity and percentage of elderly population. Correlation was weak with wind velocity and daylight hours.
3.3. Multivariate Linear Regression
In this subsection, the morbidity/mortality rates are estimated in terms of different factors. In Section 3.1, population density and percentage of the elderly were found to be modest, at least non-negligible factors for multivariate analysis [34]. In Section 3.2, maximum temperature and absolute humidity difference were found to be relatively important. No consistency was observed between mortality and morbidity rates. The data in Ishikawa and Toyama prefectures were considered as outliers from hierarchical clustering (see also [29]).
The difference in absolute humidity is derived from the maximum and minimum absolute humidity; at least two parameters are needed. In addition, the maximum temperature is also related to the maximum absolute humidity. In terms of variance inflation factors (VIFs), the multicollinearity was evaluated. The threshold value to differentiate small from large is generally taken as 10 [36]. From this analysis, a set of population density, elderly percentage, and absolute humidity provided estimation without multicollinearity: VIF < 3.78 for spread duration, VIF < 3.23 for decay duration, and VIF < 3.68 for total duration. Note that the maximum ambient temperature was excluded due to strong correlation with absolute humidity.
Figure 5 shows the multivariate linear regression of cases and deaths per million. Table 6 shows the determination coefficients for the three durations. As shown in Figure 5, the predicted and actual data are of good correlation with the averaged value over three stages. The highest contribution rates were the population density in the multivariate analysis (74.4%, 80.0%, and 84.5% in the cases per million, deaths per million including, and excluding nosocomial infection, respectively).
4. Discussion
In this study, we analyzed the morbidity and mortality rates in different prefectures in Japan, where the number of confirmed deaths and daily confirmed positive counts were higher than 4 and 10, respectively. A major feature of Japan was the relative homogeneity of the health insurance and care system without medical collapse during this pandemic, in addition to household wealth. The Japanese strategy included identifying infection clusters at an early stage, to the best possible extent. However, the criteria for conducting tests (diagnosis) on potential patients may not be uniform in different prefectures; some patients may exhibit weak symptoms. Thus, after retracting the state of emergency on 25 May 2020, we processed the data for morbidity and mortality rates in 14 prefectures.
The morbidity/mortality rates were then shown to be proportional to the population density. In previous studies, this factor was not considered [12] nor was correlation between different cities considered [17]. After excluding the number of confirmed deaths in cluster infections related to hospital and care services, we observed modest correlation among different cities in terms of population density. It is worth noting that no strict closure was applied in Japan. Next, we found a good correlation between population density and the spread of COVID-19. This finding implicitly represents social distancing. In Tokyo and Osaka, which are considered among cities with the highest population densities worldwide, infection is potentially more likely to occur compared to other less dense regions. However, this may not be the case reported in other countries where strict lockdown was implemented. In Wuhan (China), the duration of the decaying stage was only 10 days, with almost no contact during the period. However, such strict lockdown may not be allowed in most countries to avoid severe social and economic damage. Therefore, this study demonstrates that population density should be considered for avoiding potential spread in future pandemics. Moreover, this finding may be useful to improve the simulation model of epidemic transmission [37,38]. The maximum temperature and absolute humidity differences were the dominant ambient factors characterizing morbidity and mortality rates. As shown in Figure 4, cases and deaths in Ishikawa and Toyama prefectures have a different tendency than that in other prefectures—as COVID-19 occurred in a very limited area in these prefectures. In general, for higher temperature and absolute humidity, the morbidity and mortality rates were decreased. For example, the population density of Hyogo (650.4 capita/km2) is nearly equal to that of Okinawa (637.5 capita/km2). However, the total cases in Hyogo were 8.6 times that of Okinawa. The daily maximum temperature in Hyogo was 7 °C lower than in Okinawa. This relationship can be observed in other prefectures but not all due to mild correlation with weather condition. The reason for higher correlation with absolute humidity difference is unclear. However, one potential reason would be the relatively small variation in a limited period (from mid-March to mid-May). Further study of key factors would be needed. The ambient conditions in Okinawa prefecture differ the most from those of other prefectures in Japan. If the data of Okinawa are excluded, the correlation of confirmed cases and deaths improved. In particular, the total cases and deaths normalized by population have a mild correlation with the maximum temperature and absolute humidity averaged over spread duration (from 0.13 < R2 < 0.18 to 0.37 < R2 < 0.55).
The effect of ambient conditions on the morbidity and mortality rates was shown to be modest over multiple prefecture studies. As mentioned in the introduction, this was a controversial COVID-19 issue. Our study hypothesized that this may be caused by population density, which was not considered in previous studies, as well as the uniformity of the policy, health insurance system, household wealth, etc.
The morbidity and mortality rates were roughly derived via multivariate analysis. Note that the ambient parameters are cross-correlated with each other, and thus further research and investigation are needed. Their adjusted-R2 was almost the same; 0.69 (p < 0.01) for positive cases, and those for confirmed deaths including and excluding nosocomial infection were 0.53 (p < 0.05) and 0.15 (p = 0.25), respectively. This statistical finding may be improved for modeling studies. The correlation with the mortality rate excluding nosocomial infection was relatively low, suggesting that nosocomial infection would be a part of COVID-19 transmission at least in Japan.
Unlike previous studies that discussed the correlation with ambient condition in each city (e.g., [17]), our study explores common factors over 14 prefectures, resulting in lower p-value as compared to such studies. In such cases, the uncertainty of measured ambient condition would also be another factor to influence the correlation. For example, no correlation with ambient condition was observed in the analysis of 122 cities in China [12].
Note that according to the record of the Ministry of Health in Japan, no pandemic has been reported in the last 50 years [39]. Thus, a comparison with other epidemics is infeasible. Common influenza has been recorded, but only at fixed points (hospitals), making proper comparison difficult [39]. However, the finding of this study that presents the effect of population density and ambient conditions may be useful when considering measures for potential future pandemics.
5. Conclusions
A mild correlation was found of mortality and morbidity rates with the population density and the percentage of the elderly population, in addition to maximum absolute humidity averaged over the spread stage under Japanese policy. The multivariate linear regression provided adjusted coefficients of determination, which were 0.69 and 0.53 (p < 0.05) for positive cases and confirmed deaths, respectively. Our results suggested that with population and weather data, we can estimate the number of cases and deaths, at least in Japanese cities. Although the date and duration of the pandemic were different even in Japan, our estimation presented mild correlation, providing useful information for the planning of policy and medical resources. With our findings, more customized guidelines can be developed, specific to where and when different measures can be applied to restrict the adverse effects caused by a potential pandemic in the future, including a second wave of COVID-19. The limitation of this study is that the weather data in different prefectures are similar to each other due to the limited period (March to May 2020), and thus further data are needed for a general conclusion. The controversy in previous studies may be potentially caused by the population density and elderly population percentage, as those were not considered in most studies. Thus, these factors should be included for proper comparisons with the tendencies of international cities.
Author Contributions
Conceptualization, A.H.; data curation, S.K. and E.A.R.; formal analysis, S.K., E.A.R. and A.H.; investigation, S.K. and E.A.R.; methodology, A.H.; project administration, A.H.; resources, E.A.R.; software, S.K. and E.A.R.; validation, E.A.R.; visualization, S.K.; writing—original draft, A.H.; writing—review and editing, S.K., E.A.R. and A.H. All authors have read and agreed to the published version of the manuscript.
Funding
This research received no external funding.
Conflicts of Interest
The authors declare no conflict of interest.
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Public Health202017535410.3390/ijerph17155354Statistics Bureau of JapanPopulation EstimatesAvailable online: https://www.stat.go.jp/index.html(accessed on 2 June 2020)Toyo Keizai OnlineCoronavirus Disease (COVID-19) Situation Report in JapanAvailable online: https://toyokeizai.net/sp/visual/tko/covid19/en.html(accessed on 31 May 2020)Ministry of Health Labour and WelfareAbout Coronavirus Disease 2019 (COVID-19)Available online: https://www.mhlw.go.jp/stf/seisakunitsuite/bunya/newpage_00032.html(accessed on 26 May 2020)Japan Meteorological Agency Weather/EarthquakeAvailable online: https://www.jma.go.jp/jma/indexe.html(accessed on 29 May 2020)RencherA.C.Methods of Multivariate AnalysisJohn Wiley & SonsHoboken, NJ, USA2003Volume 492MetzJ.A.FinnA.Influenza and humidity–Why a bit more damp may be good for you!J. Infect.201571S54S5810.1016/j.jinf.2015.04.01325917802AlinA.MulticollinearityWiley Interdiscip. Rev. Comput. Stat.2010237037410.1002/wics.84DaleyD.J.GaniJ.Epidemic Modelling: An IntroductionCambridge University PressCambridge, UK2001Volume 15SinghR.AdhikariR.Age-structured impact of social distancing on the COVID-19 epidemic in IndiaAvailable online: https://arxiv.org/abs/2003.12055(accessed on 29 July 2020)Ministry of Health Labour and WelfarePandemic Influenza and Avian InfluenzaAvailable online: https://www.mhlw.go.jp/english/topics/influenza/(accessed on 4 July 2020)
Map of Japan with prefectures included (excluded) in this study highlighted with red (blue) color.
Correlation between population density and number of confirmed positive cases and fatality. The number of (a) positive cases, confirmed deaths (b) including and (c) excluding those caused by nosocomial infection.
Correlation between the number of confirmed positive cases and fatality normalized by the population density and the percentage of the elderly population. The number of (a) positive cases, confirmed deaths (b) including and (c) excluding those caused by nosocomial infection.
Correlation between the number of confirmed positive cases and fatality normalized by the population density and (a–c) the daily maximum temperature and (d–f) diurnal absolute humidity averaged over total duration. The number of (a,d) positive cases, confirmed deaths (b,e) including and (c,f) excluding those caused by nosocomial infection.
Multivariate linear regression with population density, elderly percentage, daily maximum absolute humidity averaged over spread duration. The number of (a) positive cases, and confirmed deaths (b) including and (c) excluding those caused by nosocomial infection.
ijerph-17-05477-t001_Table 1
Population and population density of 14 prefectures, in addition to the percentage of the elderly population, where confirmed deaths and daily confirmed positives are larger than 4 and 10, respectively. The data of confirmed cases and deaths were counted until 25 May 2020.
Prefectures
Population (×1000)
Density (capita/km2)
Total Cases
Confirmed Deaths
Confirmed Deaths (Ex.) †
Cases/1M
Elderly (>65 years) (%)
Aichi
7552
1460.0
507
34
16
67.1
25.1
Chiba
6259
1217.4
904
44
27
144.4
27.8
Fukuoka
5104
1024.8
672
25
20
131.7
27.9
Gifu
1987
187.3
150
7
7
75.5
30.1
Gunma
1942
304.6
149
19
19
76.7
29.9
Hyogo
5466
650.4
699
40
33
127.9
29.1
Ibaraki
2860
470.4
168
10
10
58.7
29.5
Ishikawa
1138
271.7
296
24
6
260.1
29.6
Kanagawa
9198
3807.5
1336
76
59
145.2
25.3
Kyoto
2583
560.1
358
15
15
138.6
29.2
Okinawa
1453
637.5
81
6
6
55.7
22.2
Osaka
8809
4631.0
1781
80
45
202.2
27.6
Tokyo
13,921
6354.8
5170
292
210
371.4
23.1
Toyama
1044
245.6
227
21
10
217.4
32.3
† Excluding nosocomial infection in confirmed deaths.
ijerph-17-05477-t002_Table 2
Starting and terminating dates of the spread and decay stages of COVID-19 in different prefectures in Japan. TSS (TDS) and TSE (TDE) denote the start and end dates for the spread (decay) stages of the pandemic.
Prefectures
Spread Stage
Decay Stage
\nTSS\n
\nTSE\n
\nTDS\n
\nTDE\n
Aichi
22-February
30-March
1-April
27-April
Chiba
19-March
2-April
13-April
5-May
Fukuoka
22-March
1-April
9-April
27-April
Gifu
25-March
4-April
6-April
17-April
Gunma
25-March
5-April
9-April
22-April
Hyogo
19-March
4-April
7-April
4-May
Ibaraki
16-March
28-March
8-April
23-April
Ishikawa
24-March
3-April
8-April
8-May
Kanagawa
19-March
3-April
11-April
19-May
Kyoto
16-March
2-April
5-April
9-May
Okinawa
28-March
3-April
10-April
25-April
Osaka
18-March
6-April
13-April
6-May
Tokyo
17-March
3-April
10-April
7-May
Toyama
1-April
13-April
18-April
30-April
ijerph-17-05477-t003_Table 3
Duration-averaged temperature (T), absolute humidity (H), wind velocity (Vair), and daylight hours (DL) in each prefecture. DS and DD represent time frames during the spread and decay stages of the pandemic, respectively, as listed in Table 2. Tave, Tmax, and Tmin represent the daily average, maximum, and minimum temperatures, respectively. Have, Hmax, and Hmin represent the daily average, maximum, and minimum absolute humidity values, respectively. Vair represents the daily averaged wind velocity.
Spread Duration (DS)
\nPrefectures\n
\nTave\n
\nTmax\n
\nTmin\n
\nTdiff\n
\nHave\n
\nHmax\n
\nHmin\n
\nHdiff\n
\nVair\n
\nDL\n
Aichi
10.1
14.8
6.0
8.8
5.9
7.9
4.4
3.5
3.3
5.3
Chiba
12.4
16.1
8.1
8.1
6.6
9.5
4.6
4.9
4.5
4.5
Fukuoka
14.2
17.5
11.3
6.2
8.8
11.0
6.9
4.1
3.1
3.5
Gifu
12.0
16.4
7.7
8.7
6.7
8.4
4.9
3.5
2.7
4.2
Gunma
10.6
15.3
5.4
9.9
5.7
7.5
4.6
3.0
2.6
4.4
Hyogo
12.7
16.4
9.1
7.3
7.2
9.6
5.3
4.2
3.7
5.1
Ibaraki
10.3
17.1
3.4
13.7
5.7
8.5
3.7
4.8
2.7
7.0
Ishikawa
9.9
14.7
5.7
9.0
5.9
7.2
4.1
3.1
4.6
4.4
Kanagawa
12.4
16.7
8.0
8.7
6.8
9.7
4.7
5.0
4.4
4.6
Kyoto
11.5
16.6
6.8
9.8
6.4
8.6
4.7
3.9
2.4
4.6
Okinawa
21.3
24.0
18.8
5.1
14.7
17.4
12.4
5.0
4.3
1.9
Osaka
12.7
17.0
8.9
8.1
6.7
8.9
5.1
3.9
2.6
5.1
Tokyo
11.7
16.7
6.7
10.0
6.4
9.3
4.5
4.8
3.3
5.4
Toyama
9.7
14.6
5.2
9.4
6.3
7.7
4.7
3.0
3.2
4.4
\nDecay Duration (DD)\n
\n
\nTave\n
\nTmax\n
\nTmin\n
\nTdiff\n
\nHave\n
\nHmax\n
\nHmin\n
\nHdiff\n
\nVair\n
\nDL\n
Aichi
13.0
18.3
8.6
9.7
6.5
8.4
4.9
3.5
3.9
6.1
Chiba
15.1
19.1
11.2
7.8
8.4
10.3
6.4
3.9
4.4
4.6
Fukuoka
14.0
17.5
10.9
6.6
7.3
9.4
5.7
3.7
3.6
4.7
Gifu
12.6
18.2
7.7
10.6
5.1
6.6
3.6
3.0
3.5
6.5
Gunma
11.5
16.3
7.2
9.1
6.3
8.4
4.9
3.4
3.2
5.2
Hyogo
15.5
19.0
12.4
6.6
8.1
9.5
6.0
3.5
4.0
5.3
Ibaraki
10.8
15.6
6.4
9.1
6.5
8.2
4.9
3.3
3.5
4.7
Ishikawa
13.1
17.3
9.2
8.1
7.0
8.7
5.4
3.3
4.4
4.5
Kanagawa
16.6
20.7
13.0
7.7
9.8
11.7
7.7
4.0
3.9
4.9
Kyoto
14.7
20.1
10.0
10.1
7.1
9.0
5.3
3.7
2.5
5.0
Okinawa
19.8
22.1
17.6
4.5
11.8
14.1
10.0
4.0
5.0
3.2
Osaka
16.2
20.6
12.3
8.3
8.1
10.2
6.3
3.9
2.7
5.3
Tokyo
14.4
19.2
9.9
9.3
8.6
10.7
6.7
3.9
3.2
5.0
Toyama
12.1
17.6
7.7
9.9
7.5
9.1
5.8
3.3
3.7
3.9
\nAll Duration, from TSS to TDE\n
\n
\nTave\n
\nTmax\n
\nTmin\n
\nTdiff\n
\nHave\n
\nHmax\n
\nHmin\n
\nHdiff\n
\nVair\n
\nDL\n
Aichi
11.3
16.2
7.1
6.2
8.2
4.6
3.5
11.3
3.5
5.5
Chiba
13.8
17.8
9.6
7.3
9.7
5.4
4.3
13.8
4.3
4.9
Fukuoka
14.9
19.0
11.4
8.6
10.8
6.8
3.9
14.9
3.3
5.5
Gifu
12.2
17.2
7.7
5.8
7.4
4.2
3.3
12.2
3.2
5.5
Gunma
11.1
16.0
6.2
5.9
7.7
4.6
3.1
11.1
2.9
5.2
Hyogo
14.2
17.8
10.9
7.5
9.3
5.5
3.8
14.2
3.8
5.6
Ibaraki
10.4
15.8
4.8
6.2
8.3
4.4
3.9
10.4
2.9
5.5
Ishikawa
12.1
16.3
8.0
6.6
8.1
4.9
3.2
12.1
4.2
4.7
Kanagawa
15.1
19.4
11.2
8.6
10.8
6.6
4.3
15.1
4.0
5.0
Kyoto
13.6
18.9
8.8
6.8
8.8
5.0
3.8
13.6
2.4
4.9
Okinawa
20.1
22.4
17.9
12.5
14.8
10.6
4.1
20.1
4.6
2.5
Osaka
14.3
18.6
10.4
7.2
9.3
5.6
3.7
14.3
2.6
5.4
Tokyo
13.3
18.3
8.5
7.6
9.9
5.7
4.2
13.3
3.2
5.3
Toyama
10.9
16.1
6.2
6.8
8.4
5.2
3.2
10.9
3.4
4.3
ijerph-17-05477-t004_Table 4
Coefficient of determination for different metrics: (i) cases, (ii) death, (iii) death excluding nosocomial infection, (iv) cases normalized by density, (v) death normalized density, and (vi) death excluding nosocomial infection normalized by population density.
\n
(i)
(ii)
(iii)
(iv)
(v)
(vi)
Population density
0.393
0.097
0.259
—
—
—
Elderly density
0.363
0.078
0.210
0.225
0.185
0.295
Elderly percentage
0.009
0.014
0.007
0.405
0.360
0.482
\nTave\n
\nDS\n
0.073
0.143
0.041
0.151
0.157
0.122
\n
\nDD\n
0.000
0.035
0.011
0.164
0.173
0.274
\n
Total
0.009
0.075
0.020
0.158
0.173
0.216
\nTmax\n
\nDS\n
0.089
0.161
0.035
0.175
0.181
0.130
\n
\nDD\n
0.008
0.019
0.001
0.143
0.166
0.229
\n
Total
0.003
0.081
0.006
0.202
0.229
0.242
\nTmin\n
\nDS\n
0.053
0.114
0.054
0.105
0.116
0.112
\n
\nDD\n
0.001
0.041
0.019
0.147
0.147
0.246
\n
Total
0.013
0.069
0.034
0.122
0.134
0.192
\nTdiff\n
\nDS\n
0.007
0.027
0.047
0.015
0.021
0.043
\n
\nDD\n
0.026
0.042
0.048
0.071
0.055
0.128
\n
Total
0.026
0.042
0.078
0.036
0.036
0.101
\nHave\n
\nDS\n
0.076
0.091
0.043
0.055
0.055
0.048
\n
\nDD\n
0.017
0.002
0.019
0.099
0.061
0.142
\n
Total
0.006
0.026
0.004
0.095
0.080
0.127
\nHmax\n
\nDS\n
0.069
0.123
0.032
0.152
0.149
0.131
\n
\nDD\n
0.016
0.001
0.019
0.127
0.081
0.160
\n
Total
0.005
0.038
0.003
0.160
0.138
0.191
\nHmin\n
\nDS\n
0.086
0.084
0.036
0.044
0.039
0.025
\n
\nDD\n
0.011
0.002
0.016
0.089
0.051
0.117
\n
Total
0.011
0.024
0.004
0.079
0.060
0.089
\nHdiff\n
\nDS\n
0.001
0.107
0.002
0.463
0.488
0.546
\n
\nDD\n
0.052
0.001
0.031
0.347
0.277
0.384
\n
Total
0.006
0.074
0.000
0.485
0.509
0.635
\nVair\n
\nDS\n
0.034
0.058
0.007
0.020
0.022
0.044
\n
\nDD\n
0.035
0.000
0.091
0.023
0.027
0.003
\n
Total
0.001
0.008
0.032
0.015
0.017
0.015
\nDL\n
\nDS\n
0.023
0.007
0.012
0.012
0.010
0.014
\n
\nDD\n
0.021
0.077
0.025
0.045
0.086
0.018
\n
Total
0.008
0.007
0.000
0.035
0.053
0.029
ijerph-17-05477-t005_Table 5
Spearman’s rank correlation for cases normalized by density, death normalized density, and death, excluding nosocomial infection normalized by population density.
Parameters
\n
Cases/Density
Deaths/Density
Deaths/Density (Ex.)
\n
\n
\np\n
p-value
\np\n
p-value
\np\n
p-value
Elderly percentage
0.864
<0.0001
0.824
<0.001
0.842
<0.001
\nTave\n
−0.456
0.101
−0.489
0.076
−0.456
0.101
0.101
\n
−0.565
<0.05
−0.539
<0.05
−0.543
<0.05
<0.005
\n
−0.503
0.067
−0.543
<0.05
−0.508
0.064
0.064
\nTmax\n
−0.526
0.050
−0.551
<0.05
−0.471
0.089
0.089
\n
−0.631
<0.05
−0.574
<0.05
−0.560
<0.05
<0.005
\n
Total
−0.475
0.086
−0.535
<0.05
−0.473
0.088
\nTmin\n
\nDS\n
−0.385
0.175
−0.446
0.110
−0.442
0.114
\n
\nDD\n
−0.524
0.055
−0.506
0.065
−0.511
0.062
\n
Total
−0.429
0.126
−0.477
0.084
−0.453
0.104
\nTdiff\n
\nDS\n
0.234
0.422
0.280
0.333
0.311
0.280
\n
\nDD\n
0.317
0.269
0.273
0.345
0.289
0.317
\n
Total
0.315
0.273
0.326
0.255
0.375
0.187
\nHave\n
\nDS\n
−0.314
0.275
−0.353
0.215
−0.331
0.248
\n
\nDD\n
−0.560
<0.05
−0.465
0.094
−0.469
0.091
\n
Total
−0.496
0.071
−0.476
0.085
−0.450
0.107
\nHmax\n
\nDS\n
−0.578
<0.05
−0.569
<0.05
−0.534
<0.05
\n
\nDD\n
−0.570
<0.05
−0.497
0.070
−0.488
0.076
\n
Total
−0.601
<0.05
−0.579
<0.05
−0.542
<0.05
\nHmin\n
\nDS\n
−0.080
0.787
−0.113
0.701
−0.060
0.839
\n
\nDD\n
−0.532
0.050
−0.439
0.116
−0.444
0.112
\n
Total
−0.495
0.072
−0.493
0.073
−0.453
0.104
\nHdiff\n
\nDS\n
−0.665
<0.01
−0.583
<0.05
−0.579
<0.05
\n
\nDD\n
−0.777
<0.005
−0.736
<0.005
−0.699
<0.01
\n
Total
−0.669
<0.01
−0.636
<0.05
−0.623
<0.05
\nVair\n
\nDS\n
−0.160
0.584
−0.081
0.782
−0.187
0.523
\n
\nDD\n
−0.024
0.935
0.077
0.794
−0.029
0.923
\n
Total
−0.108
0.714
−0.007
0.982
−0.103
0.725
\nDL\n
\nDS\n
−0.464
0.095
−0.411
0.144
−0.446
0.110
\n
\nDD\n
−0.169
0.563
−0.222
0.446
−0.231
0.427
\n
Total
−0.191
0.513
−0.301
0.296
−0.319
0.267
ijerph-17-05477-t006_Table 6
Coefficients of determination and adjusted R2 values for multivariate linear regression.
\n
Cases
Deaths
Deaths (Ex.) †
R2
adj. R2
p-Value
R2
adj. R2
p-Value
R2
adj. R2
p-Value
\nDS\n
0.777
0.693
<0.01
0.659
0.532
<0.05
0.384
0.153
0.251
\nDD\n
0.773
0.688
<0.01
0.653
0.523
<0.05
0.383
0.151
0.253
Total
0.776
0.692
<0.01
0.662
0.536
<0.05
0.386
0.155
0.249
† Excluding nosocomial infection in confirmed deaths.
\n"],"string":"[\n \"\\nInt J Environ Res Public HealthInt J Environ Res Public HealthijerphInternational Journal of Environmental Research and Public Health1661-78271660-4601MDPI32751311PMC743212210.3390/ijerph17155477ijerph-17-05477ArticleCorrelation between COVID-19 Morbidity and Mortality Rates in Japan and Local Population Density, Temperature, and Absolute Humidityhttps://orcid.org/0000-0001-6595-0742KoderaSachiko1https://orcid.org/0000-0001-6571-9807RashedEssam A.12https://orcid.org/0000-0001-8336-1140HirataAkimasa13*Department of Electrical and Mechanical Engineering, Nagoya Institute of Technology, Nagoya 466-8555, Japan; kodera@nitech.ac.jp (S.K.); essam.rashed@nitech.ac.jp (E.A.R.)Department of Mathematics, Faculty of Science, Suez Canal University, Ismailia 41522, EgyptCenter of Biomedical Physics and Information Technology, Nagoya Institute of Technology, Nagoya 466-8555, JapanCorrespondence: ahirata@nitech.ac.jp; Tel.: +81-52-735-79162972020820201715547722620202772020© 2020 by the authors.2020Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
This study analyzed the morbidity and mortality rates of the coronavirus disease (COVID-19) pandemic in different prefectures of Japan. Under the constraint that daily maximum confirmed deaths and daily maximum cases should exceed 4 and 10, respectively, 14 prefectures were included, and cofactors affecting the morbidity and mortality rates were evaluated. In particular, the number of confirmed deaths was assessed, excluding cases of nosocomial infections and nursing home patients. The correlations between the morbidity and mortality rates and population density were statistically significant (p-value < 0.05). In addition, the percentage of elderly population was also found to be non-negligible. Among weather parameters, the maximum temperature and absolute humidity averaged over the duration were found to be in modest correlation with the morbidity and mortality rates. Lower morbidity and mortality rates were observed for higher temperature and absolute humidity. Multivariate linear regression considering these factors showed that the adjusted determination coefficient for the confirmed cases was 0.693 in terms of population density, elderly percentage, and maximum absolute humidity (p-value < 0.01). These findings could be useful for intervention planning during future pandemics, including a potential second COVID-19 outbreak.
The COVID-19 outbreak was first reported in China in 2019 [1,2] and spread worldwide in early 2020. Japan declared a state of emergency in seven (of 47) prefectures on 7 April 2020 and extended it to all prefectures on 13 April 2020. The state of emergency was withdrawn on 25 May 2020. During this state of emergency, unlike many other countries where city lockdowns were enforced, in Japan, citizens self-isolated. The mortality rate (per population) in Japan is relatively low compared to the global rate; the total number of confirmed deaths in Japan is 846 (25 May 2020), corresponding to 6.72 per million people [3]. Although a straightforward comparison is infeasible, this number is smaller than that of many other countries with the same order of magnitude of population: 541, 504, 435, and 295 in Italy, the United Kingdom, France, and the United States, respectively, but larger than 5.18 and 4.0 in Indonesia and Australia, respectively (25 July 2020).
An additional difficulty in understanding the morbidity rate is the unreliability of the diagnosis of COVID-19. The number of polymerase chain reaction (PCR) tests, a simple and cost-effective method, is limited in Japan, partly because of its reliability. Therefore, chest CT is used for a fast-track, highly accurate diagnosis [4]. Some patients do not exhibit any common pandemic symptoms [5], thereby complicating morbidity rate assessment in different areas (e.g., population composition).
Morbidity and mortality statistics have been updated every day in each prefecture in Japan, which provides a good opportunity for local studies. A notable feature of Japan is that no medical collapse has been reported. In addition, due to the health insurance system, free medical care for COVID-19 has been offered. Thus, the reliability of the mortality rate is more accurate than that of the morbidity rate, because patients with weak or mild symptoms may not be tested. However, to avoid nosocomial infections and medical resource shortages, it was suggested that people with specific symptoms (e.g., fever with temperature >37.5 °C for no more than four consecutive days) stay home and avoid seeking medical attention, unless they had been in close contact with an infected person(s) or had recently visited foreign countries. Such a policy may result in longer latency in the reported cases.
In general, coronaviruses are considered to spread mainly by respiratory droplets and contact via droplets [6]. Droplets tend to fall to the ground close to the infected host. Droplet transmission is typically limited to short distances, generally less than 2 m. There exist some hypotheses about transmission due to airborne transmission that remain in flight for one hour or longer [7]. For both mechanisms, the ambient condition potentially influences the duration of droplet and airborne spread. Several co-factors potentially influence COVID-19 morbidity/mortality rates [8,9,10,11,12,13,14]); among them, ambient conditions have been considered here.
The effect of ambient temperature on the mortality was discussed in Wuhan [8]. Positive and negative associations were found between daily COVID-19 death counts and daily temperature difference and absolute humidity, respectively. The effect of high temperature and humidity on the transmission of COVID-19 was discussed using relative humidity as a measure [9]. Their finding suggested that high temperature and humidity may suppress COVID transmission. Furthermore, the effect of weather on COVID-19 cases employing a case in Jakarta was presented in [10]. They reported that only average temperature is correlated with the pandemic spread. The effect of ambient temperature on the confirmed cases was discussed in more than 100 Chinese cities, and it was concluded that there is no evidence supporting that COVID-19 case counts would decline when the weather becomes warmer [12]. The effect of ambient temperature and absolute humidity on the confirmed cases was investigated in cities in China, and the researchers commented that the epidemic might gradually ease partially due to rising temperatures [13]. Instead, no correlation with UV and temperature on the transmission of COVID-19 was reported in [15].
Following Chinese studies, case studies in different countries have been reported. Briz-Redón and Serrano-Aroca [16] evaluated the spatiotemporal analysis of temperature in the cases of early COVID-19 evolution in Spain. Pirouz et al. [17] discussed the correlation between daily confirmed cases and temperature, humidity, and velocity with multivariate analysis in Italy. Application of neural networks for its estimation is also discussed in [18]. A similar attempt has been made in Oslo, Norway [19]. Recent studies have confirmed the effect of temperature and relative humidity on morbidity rates in Brazil [20,21]. From these studies, it is difficult to derive a consistent conclusion on the effect of the weather on the spread of COVID-19. Studies of influenza suggested the importance of ambient conditions for its spread: lower spread for higher humidity (e.g., [22,23]).
Studies with wider scopes included global data analysis, discussing how temperature and humidity are correlated with the infection and fatality rates of the COVID-19 pandemic [24,25]. The region of interest is wide (country level) in these studies, and thus it is not directly applicable to the ruling or regulation. In addition, some modeling studies have been proposed. However, parameter setting is not easy for this type of novel virus spread [14,26]; in most modeling studies, the parameters relating to the weather or population cannot be given explicitly. Instead, the effect of population density on the spreading effect of the epidemic has been discussed under some assumptions [27].
Nevertheless, none of the aforementioned research and modeling studies simultaneously considered the impact of population density and ambient conditions. A question that arises here is To what extent do ambient conditions and population density influence morbidity and mortality rates in different cities? Unlike the aforementioned studies, a major feature of Japan is the relative homogeneity of the health insurance and care system without medical collapse during this pandemic. In addition, the difference in household wealth is relatively small in Japan [28]. The average annual salary per population is USD 34,400 to 39,900 (USD 1 = JPY 107). The standard deviation of household consumption in each prefecture is 10% or less [28]. With all these demographic factors, the data sample discussed here provides a convenient case study with less bias. In a recent study [29], we examined the time course of the morbidity rate of different prefectures in Japan and found that the durations of the spread and decay stages can be characterized by population density, temperature, and absolute humidity. An additional factor would be the ratio of the elderly to the entire population; in Japan, this ratio reached 28.4% [30], which is ranked the highest globally.
This novel study aimed to evaluate the effect of ambient temperature and humidity on mortality and morbidity rates in different prefectures in Japan. Additionally, it considered the influence of population density and composition. To the best of the authors’ knowledge, this is the first study to highlight the environmental factors’ effect during COVID-19 in Japan. The model of Japan provides an interesting case study for different factors, as the medical service and social reaction is almost uniform nationwide and high-quality data were recorded properly. If the correlation of the pandemic with population density and ambient conditions is significant, the findings will be useful to set the level and duration for a strict lockdown period for each city considering the environmental factors and in planning future pandemic measures.
The organization of this study is as follows. In Section 2, the data sources of COVID-19 in Japan and weather data are mentioned. Then, the statistical method for data processing is explained briefly. In Section 3, effect of population density, elderly population, and ambient conditions on the morbidity/mortality rates are evaluated statistically. Based on the evaluation, multivariate linear regression has been conducted to estimate the morbidity/mortality rate from these parameters. In Section 4, provides discussion of the results including the limitation. The conclusion is given in Section 5.
2. Material and Methods2.1. Data Source
In this study, we used three datasets. The first involved the confirmed daily positive cases and deaths in each prefecture [31]. This dataset is based on the report by the Ministry of Health Labor and Welfare [32]. We used time-integrated data until 25 May 2020—when the emergency state was terminated. According to the dataset, 16 prefectures had confirmed total deaths and daily positive counts higher than 4 and 10, respectively. These prefectures were defined as infected. The remaining prefectures were excluded due to a lower number of infected cases, which is simply because of the self-isolation, including the discouragement from moving to other prefectures after the declaration. In Japan, to avoid nosocomial infections and medical resource shortages, it was suggested that people with symptoms (e.g., fever >37.5 °C for no more than four consecutive days) stay home and not seek immediate medical attention unless they had been in close contact with infected people or had recently visited a foreign country. Some patients have been reported to be asymptomatic [5], making the statistical study of COVID-19 more complex. Then, the positive rate of the test varied from 2.2% to 34.8% for different prefectures. Unlike other diseases, the number of confirmed cases/deaths are counted even when patients are found to be infected after their death. For this data collection, we use the mortality rate in this study as a metric rather than the case fatality rate.
For comparison, two sets of data were prepared: (1) the number of confirmed deaths excluding and including nosocomial/nursing facility infection, and (2) the total confirmed positive cases. This is to avoid the data of cluster infection for high-risk groups, resulting in a higher possibility of death.
Note that among some of the 16 selected prefectures, as shown in Figure 1, the number of victims due to nosocomial/nursing facility infection was not always reported. Thus, such prefectures were excluded from the comparisons. Among others, the area of Hokkaido is one to two magnitudes larger than the other prefectures. Thus, several peaks in the number of cases are observed in different cities with larger distances between them than those in other adjacent prefectures. In the Gunma prefecture, three confirmed deaths occurred, except for in Isezaki City, where substantial nosocomial infections were reported (15 victims). Thus, we used data from the Gunma prefecture, excluding Isezaki City, for accurate comparison of mortality rates. In addition, Saitama was also excluded since it did not report humidity data (see below for the third dataset). Two prefectures with unclear nosocomial/nursing facility infections were also excluded.
The second dataset comprises the population and the area of the prefectures. Based on the evidence that more than 90% of the victims are older than 60 years and because the retirement age in Japan is 65, which may potentially influence morbidity rates, we set the threshold as 65. For a total of 14 prefectures and one city, the first and second datasets are listed in Table 1. Note that the rationale for choosing 25 May as a reference date is the end of state of emergency, and then, the daily confirmed death over Japan was 20 (128 million population); the daily confirmed death was smaller than 100 for one month after that.
The third dataset comprises weather data for each prefecture. They are extracted from the weather reports generated by the Japan Meteorological Agency [33]. In our previous study, we had studied the correlation between environmental conditions and the duration of the pandemic from its spread to decay periods [29]. We extended this investigation to the prefectures defined in Table 1. We then estimated the start and end dates of the spread and decay stages, as defined in Table 2. To validate the effect of ambient factors in different phases of the pandemic, we computed ambient features for three time frames: during the spreading stage DS (from TSS to TSE), during the decaying stage DD (from TDS to TDE), and during both stages (from TSS to TDE).
To consider the mortality rate, which is affected by many factors, it should be noted that the metrics are averaged over the duration of the spread stage, decay stage, and the entire period. The duration-averaged values of temperature, absolute humidity, wind velocity, and daylight hours were calculated from the data available from the internet site mentioned above, as listed in Table 3. The latitude of Japan considered here is N 33°36′ (Fukuoka) to N 36°35′ (Ishikawa), except for Okinawa of N 26°12′), and thus, total solar radiation may be marginally influenced with this measure.
2.2. Statistical Analysis
A statistical study was conducted to analyze the correlation of different factors on both mortality and morbidity rates. The software JMP (SAS Institute, Cary, NC, USA) was used in this study. In order to specify dominant factors influencing the rates, p-value was used. We determined the pairwise correlations by calculating the Spearman’s rank correlation between the number of confirmed positive cases, confirmed death cases, and different environmental and demographic parameters. Correlation matrix with partial correlation probability and CI of correlation were calculated. After that, with the same software, multivariate analysis [34] was conducted in terms of the factors. We considered linear regression for data least-squares fitting after considering multicollinearity. Statistical significance was accepted at p < 0.05.
3. Results3.1. Effect of Population Density and Elderly Population
Figure 2 shows the relationship between confirmed positive cases and confirmed deaths, including and excluding nosocomial infections and nursing home patients. A modest correlation was observed between positive cases per million and population density (R2 = 0.394), whereas a slight and mild correlation was observed for confirmed deaths (R2 = 0.097) and excluding nosocomial infection (R2 = 0.259). This result suggests that population density should be considered as a factor that implicitly represents social distancing, as is similar to our previous study that discussed the pandemic’s duration [29].
When the cases and deaths for the elderly population were considered, the same tendency was observed; R2 = 0.363 for cases, and R2 = 0.078 and R2 = 0.210 for deaths with and without nosocomial infections (not shown to avoid repetition), respectively. Instead, as shown in Figure 3, the morbidity and mortality rates normalized by population density are modestly correlated with the percentage of the elderly, especially for confirmed deaths excluding nosocomial infections (R2 = 0.482). This factor is thus considered in the multivariate analysis study presented later.
3.2. Effect of Ambient Conditions
Several ambient factors potentially influence morbidity and mortality rates. Our study considered temperature and absolute humidity. Most previous studies reported the maximum, average, or difference (diurnal variation range) of ambient temperature (e.g., see [8] and [35]). Our study also considered the minimum temperature. Recent reports on influenza suggest the importance of absolute humidity rather than its relative value [22,23]; however, we considered the maximum, average, minimum, and difference values of absolute humidity as parameters. The daily average wind velocity and daylight hours were also considered. Regression analysis was conducted for all metrics averaged over the duration of the spread and decay stages and the total duration.
Table 4 lists the coefficients of determination for different metrics. For most parameters, the averaged values over the total stage provided the highest correlation rather than those over the other two durations. As an example, Figure 4 shows the correlation between the number of confirmed positive cases and fatality normalized by the population density and the daily maximum temperature and diurnal absolute humidity. A moderate correlation was observed among the daily maximum temperature, diurnal absolute humidity, and cases per population density. Table 5 lists the Spearman’s rank correlation for different parameters. The ambient factor was normalized by population density as aforementioned. A moderate correlation was also observed with the daily maximum temperature, daily maximum, and diurnal absolute humidity and percentage of elderly population. Correlation was weak with wind velocity and daylight hours.
3.3. Multivariate Linear Regression
In this subsection, the morbidity/mortality rates are estimated in terms of different factors. In Section 3.1, population density and percentage of the elderly were found to be modest, at least non-negligible factors for multivariate analysis [34]. In Section 3.2, maximum temperature and absolute humidity difference were found to be relatively important. No consistency was observed between mortality and morbidity rates. The data in Ishikawa and Toyama prefectures were considered as outliers from hierarchical clustering (see also [29]).
The difference in absolute humidity is derived from the maximum and minimum absolute humidity; at least two parameters are needed. In addition, the maximum temperature is also related to the maximum absolute humidity. In terms of variance inflation factors (VIFs), the multicollinearity was evaluated. The threshold value to differentiate small from large is generally taken as 10 [36]. From this analysis, a set of population density, elderly percentage, and absolute humidity provided estimation without multicollinearity: VIF < 3.78 for spread duration, VIF < 3.23 for decay duration, and VIF < 3.68 for total duration. Note that the maximum ambient temperature was excluded due to strong correlation with absolute humidity.
Figure 5 shows the multivariate linear regression of cases and deaths per million. Table 6 shows the determination coefficients for the three durations. As shown in Figure 5, the predicted and actual data are of good correlation with the averaged value over three stages. The highest contribution rates were the population density in the multivariate analysis (74.4%, 80.0%, and 84.5% in the cases per million, deaths per million including, and excluding nosocomial infection, respectively).
4. Discussion
In this study, we analyzed the morbidity and mortality rates in different prefectures in Japan, where the number of confirmed deaths and daily confirmed positive counts were higher than 4 and 10, respectively. A major feature of Japan was the relative homogeneity of the health insurance and care system without medical collapse during this pandemic, in addition to household wealth. The Japanese strategy included identifying infection clusters at an early stage, to the best possible extent. However, the criteria for conducting tests (diagnosis) on potential patients may not be uniform in different prefectures; some patients may exhibit weak symptoms. Thus, after retracting the state of emergency on 25 May 2020, we processed the data for morbidity and mortality rates in 14 prefectures.
The morbidity/mortality rates were then shown to be proportional to the population density. In previous studies, this factor was not considered [12] nor was correlation between different cities considered [17]. After excluding the number of confirmed deaths in cluster infections related to hospital and care services, we observed modest correlation among different cities in terms of population density. It is worth noting that no strict closure was applied in Japan. Next, we found a good correlation between population density and the spread of COVID-19. This finding implicitly represents social distancing. In Tokyo and Osaka, which are considered among cities with the highest population densities worldwide, infection is potentially more likely to occur compared to other less dense regions. However, this may not be the case reported in other countries where strict lockdown was implemented. In Wuhan (China), the duration of the decaying stage was only 10 days, with almost no contact during the period. However, such strict lockdown may not be allowed in most countries to avoid severe social and economic damage. Therefore, this study demonstrates that population density should be considered for avoiding potential spread in future pandemics. Moreover, this finding may be useful to improve the simulation model of epidemic transmission [37,38]. The maximum temperature and absolute humidity differences were the dominant ambient factors characterizing morbidity and mortality rates. As shown in Figure 4, cases and deaths in Ishikawa and Toyama prefectures have a different tendency than that in other prefectures—as COVID-19 occurred in a very limited area in these prefectures. In general, for higher temperature and absolute humidity, the morbidity and mortality rates were decreased. For example, the population density of Hyogo (650.4 capita/km2) is nearly equal to that of Okinawa (637.5 capita/km2). However, the total cases in Hyogo were 8.6 times that of Okinawa. The daily maximum temperature in Hyogo was 7 °C lower than in Okinawa. This relationship can be observed in other prefectures but not all due to mild correlation with weather condition. The reason for higher correlation with absolute humidity difference is unclear. However, one potential reason would be the relatively small variation in a limited period (from mid-March to mid-May). Further study of key factors would be needed. The ambient conditions in Okinawa prefecture differ the most from those of other prefectures in Japan. If the data of Okinawa are excluded, the correlation of confirmed cases and deaths improved. In particular, the total cases and deaths normalized by population have a mild correlation with the maximum temperature and absolute humidity averaged over spread duration (from 0.13 < R2 < 0.18 to 0.37 < R2 < 0.55).
The effect of ambient conditions on the morbidity and mortality rates was shown to be modest over multiple prefecture studies. As mentioned in the introduction, this was a controversial COVID-19 issue. Our study hypothesized that this may be caused by population density, which was not considered in previous studies, as well as the uniformity of the policy, health insurance system, household wealth, etc.
The morbidity and mortality rates were roughly derived via multivariate analysis. Note that the ambient parameters are cross-correlated with each other, and thus further research and investigation are needed. Their adjusted-R2 was almost the same; 0.69 (p < 0.01) for positive cases, and those for confirmed deaths including and excluding nosocomial infection were 0.53 (p < 0.05) and 0.15 (p = 0.25), respectively. This statistical finding may be improved for modeling studies. The correlation with the mortality rate excluding nosocomial infection was relatively low, suggesting that nosocomial infection would be a part of COVID-19 transmission at least in Japan.
Unlike previous studies that discussed the correlation with ambient condition in each city (e.g., [17]), our study explores common factors over 14 prefectures, resulting in lower p-value as compared to such studies. In such cases, the uncertainty of measured ambient condition would also be another factor to influence the correlation. For example, no correlation with ambient condition was observed in the analysis of 122 cities in China [12].
Note that according to the record of the Ministry of Health in Japan, no pandemic has been reported in the last 50 years [39]. Thus, a comparison with other epidemics is infeasible. Common influenza has been recorded, but only at fixed points (hospitals), making proper comparison difficult [39]. However, the finding of this study that presents the effect of population density and ambient conditions may be useful when considering measures for potential future pandemics.
5. Conclusions
A mild correlation was found of mortality and morbidity rates with the population density and the percentage of the elderly population, in addition to maximum absolute humidity averaged over the spread stage under Japanese policy. The multivariate linear regression provided adjusted coefficients of determination, which were 0.69 and 0.53 (p < 0.05) for positive cases and confirmed deaths, respectively. Our results suggested that with population and weather data, we can estimate the number of cases and deaths, at least in Japanese cities. Although the date and duration of the pandemic were different even in Japan, our estimation presented mild correlation, providing useful information for the planning of policy and medical resources. With our findings, more customized guidelines can be developed, specific to where and when different measures can be applied to restrict the adverse effects caused by a potential pandemic in the future, including a second wave of COVID-19. The limitation of this study is that the weather data in different prefectures are similar to each other due to the limited period (March to May 2020), and thus further data are needed for a general conclusion. The controversy in previous studies may be potentially caused by the population density and elderly population percentage, as those were not considered in most studies. Thus, these factors should be included for proper comparisons with the tendencies of international cities.
Author Contributions
Conceptualization, A.H.; data curation, S.K. and E.A.R.; formal analysis, S.K., E.A.R. and A.H.; investigation, S.K. and E.A.R.; methodology, A.H.; project administration, A.H.; resources, E.A.R.; software, S.K. and E.A.R.; validation, E.A.R.; visualization, S.K.; writing—original draft, A.H.; writing—review and editing, S.K., E.A.R. and A.H. All authors have read and agreed to the published version of the manuscript.
Funding
This research received no external funding.
Conflicts of Interest
The authors declare no conflict of interest.
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Public Health202017535410.3390/ijerph17155354Statistics Bureau of JapanPopulation EstimatesAvailable online: https://www.stat.go.jp/index.html(accessed on 2 June 2020)Toyo Keizai OnlineCoronavirus Disease (COVID-19) Situation Report in JapanAvailable online: https://toyokeizai.net/sp/visual/tko/covid19/en.html(accessed on 31 May 2020)Ministry of Health Labour and WelfareAbout Coronavirus Disease 2019 (COVID-19)Available online: https://www.mhlw.go.jp/stf/seisakunitsuite/bunya/newpage_00032.html(accessed on 26 May 2020)Japan Meteorological Agency Weather/EarthquakeAvailable online: https://www.jma.go.jp/jma/indexe.html(accessed on 29 May 2020)RencherA.C.Methods of Multivariate AnalysisJohn Wiley & SonsHoboken, NJ, USA2003Volume 492MetzJ.A.FinnA.Influenza and humidity–Why a bit more damp may be good for you!J. Infect.201571S54S5810.1016/j.jinf.2015.04.01325917802AlinA.MulticollinearityWiley Interdiscip. Rev. Comput. Stat.2010237037410.1002/wics.84DaleyD.J.GaniJ.Epidemic Modelling: An IntroductionCambridge University PressCambridge, UK2001Volume 15SinghR.AdhikariR.Age-structured impact of social distancing on the COVID-19 epidemic in IndiaAvailable online: https://arxiv.org/abs/2003.12055(accessed on 29 July 2020)Ministry of Health Labour and WelfarePandemic Influenza and Avian InfluenzaAvailable online: https://www.mhlw.go.jp/english/topics/influenza/(accessed on 4 July 2020)
Map of Japan with prefectures included (excluded) in this study highlighted with red (blue) color.
Correlation between population density and number of confirmed positive cases and fatality. The number of (a) positive cases, confirmed deaths (b) including and (c) excluding those caused by nosocomial infection.
Correlation between the number of confirmed positive cases and fatality normalized by the population density and the percentage of the elderly population. The number of (a) positive cases, confirmed deaths (b) including and (c) excluding those caused by nosocomial infection.
Correlation between the number of confirmed positive cases and fatality normalized by the population density and (a–c) the daily maximum temperature and (d–f) diurnal absolute humidity averaged over total duration. The number of (a,d) positive cases, confirmed deaths (b,e) including and (c,f) excluding those caused by nosocomial infection.
Multivariate linear regression with population density, elderly percentage, daily maximum absolute humidity averaged over spread duration. The number of (a) positive cases, and confirmed deaths (b) including and (c) excluding those caused by nosocomial infection.
ijerph-17-05477-t001_Table 1
Population and population density of 14 prefectures, in addition to the percentage of the elderly population, where confirmed deaths and daily confirmed positives are larger than 4 and 10, respectively. The data of confirmed cases and deaths were counted until 25 May 2020.
Prefectures
Population (×1000)
Density (capita/km2)
Total Cases
Confirmed Deaths
Confirmed Deaths (Ex.) †
Cases/1M
Elderly (>65 years) (%)
Aichi
7552
1460.0
507
34
16
67.1
25.1
Chiba
6259
1217.4
904
44
27
144.4
27.8
Fukuoka
5104
1024.8
672
25
20
131.7
27.9
Gifu
1987
187.3
150
7
7
75.5
30.1
Gunma
1942
304.6
149
19
19
76.7
29.9
Hyogo
5466
650.4
699
40
33
127.9
29.1
Ibaraki
2860
470.4
168
10
10
58.7
29.5
Ishikawa
1138
271.7
296
24
6
260.1
29.6
Kanagawa
9198
3807.5
1336
76
59
145.2
25.3
Kyoto
2583
560.1
358
15
15
138.6
29.2
Okinawa
1453
637.5
81
6
6
55.7
22.2
Osaka
8809
4631.0
1781
80
45
202.2
27.6
Tokyo
13,921
6354.8
5170
292
210
371.4
23.1
Toyama
1044
245.6
227
21
10
217.4
32.3
† Excluding nosocomial infection in confirmed deaths.
ijerph-17-05477-t002_Table 2
Starting and terminating dates of the spread and decay stages of COVID-19 in different prefectures in Japan. TSS (TDS) and TSE (TDE) denote the start and end dates for the spread (decay) stages of the pandemic.
Prefectures
Spread Stage
Decay Stage
\\nTSS\\n
\\nTSE\\n
\\nTDS\\n
\\nTDE\\n
Aichi
22-February
30-March
1-April
27-April
Chiba
19-March
2-April
13-April
5-May
Fukuoka
22-March
1-April
9-April
27-April
Gifu
25-March
4-April
6-April
17-April
Gunma
25-March
5-April
9-April
22-April
Hyogo
19-March
4-April
7-April
4-May
Ibaraki
16-March
28-March
8-April
23-April
Ishikawa
24-March
3-April
8-April
8-May
Kanagawa
19-March
3-April
11-April
19-May
Kyoto
16-March
2-April
5-April
9-May
Okinawa
28-March
3-April
10-April
25-April
Osaka
18-March
6-April
13-April
6-May
Tokyo
17-March
3-April
10-April
7-May
Toyama
1-April
13-April
18-April
30-April
ijerph-17-05477-t003_Table 3
Duration-averaged temperature (T), absolute humidity (H), wind velocity (Vair), and daylight hours (DL) in each prefecture. DS and DD represent time frames during the spread and decay stages of the pandemic, respectively, as listed in Table 2. Tave, Tmax, and Tmin represent the daily average, maximum, and minimum temperatures, respectively. Have, Hmax, and Hmin represent the daily average, maximum, and minimum absolute humidity values, respectively. Vair represents the daily averaged wind velocity.
Spread Duration (DS)
\\nPrefectures\\n
\\nTave\\n
\\nTmax\\n
\\nTmin\\n
\\nTdiff\\n
\\nHave\\n
\\nHmax\\n
\\nHmin\\n
\\nHdiff\\n
\\nVair\\n
\\nDL\\n
Aichi
10.1
14.8
6.0
8.8
5.9
7.9
4.4
3.5
3.3
5.3
Chiba
12.4
16.1
8.1
8.1
6.6
9.5
4.6
4.9
4.5
4.5
Fukuoka
14.2
17.5
11.3
6.2
8.8
11.0
6.9
4.1
3.1
3.5
Gifu
12.0
16.4
7.7
8.7
6.7
8.4
4.9
3.5
2.7
4.2
Gunma
10.6
15.3
5.4
9.9
5.7
7.5
4.6
3.0
2.6
4.4
Hyogo
12.7
16.4
9.1
7.3
7.2
9.6
5.3
4.2
3.7
5.1
Ibaraki
10.3
17.1
3.4
13.7
5.7
8.5
3.7
4.8
2.7
7.0
Ishikawa
9.9
14.7
5.7
9.0
5.9
7.2
4.1
3.1
4.6
4.4
Kanagawa
12.4
16.7
8.0
8.7
6.8
9.7
4.7
5.0
4.4
4.6
Kyoto
11.5
16.6
6.8
9.8
6.4
8.6
4.7
3.9
2.4
4.6
Okinawa
21.3
24.0
18.8
5.1
14.7
17.4
12.4
5.0
4.3
1.9
Osaka
12.7
17.0
8.9
8.1
6.7
8.9
5.1
3.9
2.6
5.1
Tokyo
11.7
16.7
6.7
10.0
6.4
9.3
4.5
4.8
3.3
5.4
Toyama
9.7
14.6
5.2
9.4
6.3
7.7
4.7
3.0
3.2
4.4
\\nDecay Duration (DD)\\n
\\n
\\nTave\\n
\\nTmax\\n
\\nTmin\\n
\\nTdiff\\n
\\nHave\\n
\\nHmax\\n
\\nHmin\\n
\\nHdiff\\n
\\nVair\\n
\\nDL\\n
Aichi
13.0
18.3
8.6
9.7
6.5
8.4
4.9
3.5
3.9
6.1
Chiba
15.1
19.1
11.2
7.8
8.4
10.3
6.4
3.9
4.4
4.6
Fukuoka
14.0
17.5
10.9
6.6
7.3
9.4
5.7
3.7
3.6
4.7
Gifu
12.6
18.2
7.7
10.6
5.1
6.6
3.6
3.0
3.5
6.5
Gunma
11.5
16.3
7.2
9.1
6.3
8.4
4.9
3.4
3.2
5.2
Hyogo
15.5
19.0
12.4
6.6
8.1
9.5
6.0
3.5
4.0
5.3
Ibaraki
10.8
15.6
6.4
9.1
6.5
8.2
4.9
3.3
3.5
4.7
Ishikawa
13.1
17.3
9.2
8.1
7.0
8.7
5.4
3.3
4.4
4.5
Kanagawa
16.6
20.7
13.0
7.7
9.8
11.7
7.7
4.0
3.9
4.9
Kyoto
14.7
20.1
10.0
10.1
7.1
9.0
5.3
3.7
2.5
5.0
Okinawa
19.8
22.1
17.6
4.5
11.8
14.1
10.0
4.0
5.0
3.2
Osaka
16.2
20.6
12.3
8.3
8.1
10.2
6.3
3.9
2.7
5.3
Tokyo
14.4
19.2
9.9
9.3
8.6
10.7
6.7
3.9
3.2
5.0
Toyama
12.1
17.6
7.7
9.9
7.5
9.1
5.8
3.3
3.7
3.9
\\nAll Duration, from TSS to TDE\\n
\\n
\\nTave\\n
\\nTmax\\n
\\nTmin\\n
\\nTdiff\\n
\\nHave\\n
\\nHmax\\n
\\nHmin\\n
\\nHdiff\\n
\\nVair\\n
\\nDL\\n
Aichi
11.3
16.2
7.1
6.2
8.2
4.6
3.5
11.3
3.5
5.5
Chiba
13.8
17.8
9.6
7.3
9.7
5.4
4.3
13.8
4.3
4.9
Fukuoka
14.9
19.0
11.4
8.6
10.8
6.8
3.9
14.9
3.3
5.5
Gifu
12.2
17.2
7.7
5.8
7.4
4.2
3.3
12.2
3.2
5.5
Gunma
11.1
16.0
6.2
5.9
7.7
4.6
3.1
11.1
2.9
5.2
Hyogo
14.2
17.8
10.9
7.5
9.3
5.5
3.8
14.2
3.8
5.6
Ibaraki
10.4
15.8
4.8
6.2
8.3
4.4
3.9
10.4
2.9
5.5
Ishikawa
12.1
16.3
8.0
6.6
8.1
4.9
3.2
12.1
4.2
4.7
Kanagawa
15.1
19.4
11.2
8.6
10.8
6.6
4.3
15.1
4.0
5.0
Kyoto
13.6
18.9
8.8
6.8
8.8
5.0
3.8
13.6
2.4
4.9
Okinawa
20.1
22.4
17.9
12.5
14.8
10.6
4.1
20.1
4.6
2.5
Osaka
14.3
18.6
10.4
7.2
9.3
5.6
3.7
14.3
2.6
5.4
Tokyo
13.3
18.3
8.5
7.6
9.9
5.7
4.2
13.3
3.2
5.3
Toyama
10.9
16.1
6.2
6.8
8.4
5.2
3.2
10.9
3.4
4.3
ijerph-17-05477-t004_Table 4
Coefficient of determination for different metrics: (i) cases, (ii) death, (iii) death excluding nosocomial infection, (iv) cases normalized by density, (v) death normalized density, and (vi) death excluding nosocomial infection normalized by population density.
\\n
(i)
(ii)
(iii)
(iv)
(v)
(vi)
Population density
0.393
0.097
0.259
—
—
—
Elderly density
0.363
0.078
0.210
0.225
0.185
0.295
Elderly percentage
0.009
0.014
0.007
0.405
0.360
0.482
\\nTave\\n
\\nDS\\n
0.073
0.143
0.041
0.151
0.157
0.122
\\n
\\nDD\\n
0.000
0.035
0.011
0.164
0.173
0.274
\\n
Total
0.009
0.075
0.020
0.158
0.173
0.216
\\nTmax\\n
\\nDS\\n
0.089
0.161
0.035
0.175
0.181
0.130
\\n
\\nDD\\n
0.008
0.019
0.001
0.143
0.166
0.229
\\n
Total
0.003
0.081
0.006
0.202
0.229
0.242
\\nTmin\\n
\\nDS\\n
0.053
0.114
0.054
0.105
0.116
0.112
\\n
\\nDD\\n
0.001
0.041
0.019
0.147
0.147
0.246
\\n
Total
0.013
0.069
0.034
0.122
0.134
0.192
\\nTdiff\\n
\\nDS\\n
0.007
0.027
0.047
0.015
0.021
0.043
\\n
\\nDD\\n
0.026
0.042
0.048
0.071
0.055
0.128
\\n
Total
0.026
0.042
0.078
0.036
0.036
0.101
\\nHave\\n
\\nDS\\n
0.076
0.091
0.043
0.055
0.055
0.048
\\n
\\nDD\\n
0.017
0.002
0.019
0.099
0.061
0.142
\\n
Total
0.006
0.026
0.004
0.095
0.080
0.127
\\nHmax\\n
\\nDS\\n
0.069
0.123
0.032
0.152
0.149
0.131
\\n
\\nDD\\n
0.016
0.001
0.019
0.127
0.081
0.160
\\n
Total
0.005
0.038
0.003
0.160
0.138
0.191
\\nHmin\\n
\\nDS\\n
0.086
0.084
0.036
0.044
0.039
0.025
\\n
\\nDD\\n
0.011
0.002
0.016
0.089
0.051
0.117
\\n
Total
0.011
0.024
0.004
0.079
0.060
0.089
\\nHdiff\\n
\\nDS\\n
0.001
0.107
0.002
0.463
0.488
0.546
\\n
\\nDD\\n
0.052
0.001
0.031
0.347
0.277
0.384
\\n
Total
0.006
0.074
0.000
0.485
0.509
0.635
\\nVair\\n
\\nDS\\n
0.034
0.058
0.007
0.020
0.022
0.044
\\n
\\nDD\\n
0.035
0.000
0.091
0.023
0.027
0.003
\\n
Total
0.001
0.008
0.032
0.015
0.017
0.015
\\nDL\\n
\\nDS\\n
0.023
0.007
0.012
0.012
0.010
0.014
\\n
\\nDD\\n
0.021
0.077
0.025
0.045
0.086
0.018
\\n
Total
0.008
0.007
0.000
0.035
0.053
0.029
ijerph-17-05477-t005_Table 5
Spearman’s rank correlation for cases normalized by density, death normalized density, and death, excluding nosocomial infection normalized by population density.
Parameters
\\n
Cases/Density
Deaths/Density
Deaths/Density (Ex.)
\\n
\\n
\\np\\n
p-value
\\np\\n
p-value
\\np\\n
p-value
Elderly percentage
0.864
<0.0001
0.824
<0.001
0.842
<0.001
\\nTave\\n
−0.456
0.101
−0.489
0.076
−0.456
0.101
0.101
\\n
−0.565
<0.05
−0.539
<0.05
−0.543
<0.05
<0.005
\\n
−0.503
0.067
−0.543
<0.05
−0.508
0.064
0.064
\\nTmax\\n
−0.526
0.050
−0.551
<0.05
−0.471
0.089
0.089
\\n
−0.631
<0.05
−0.574
<0.05
−0.560
<0.05
<0.005
\\n
Total
−0.475
0.086
−0.535
<0.05
−0.473
0.088
\\nTmin\\n
\\nDS\\n
−0.385
0.175
−0.446
0.110
−0.442
0.114
\\n
\\nDD\\n
−0.524
0.055
−0.506
0.065
−0.511
0.062
\\n
Total
−0.429
0.126
−0.477
0.084
−0.453
0.104
\\nTdiff\\n
\\nDS\\n
0.234
0.422
0.280
0.333
0.311
0.280
\\n
\\nDD\\n
0.317
0.269
0.273
0.345
0.289
0.317
\\n
Total
0.315
0.273
0.326
0.255
0.375
0.187
\\nHave\\n
\\nDS\\n
−0.314
0.275
−0.353
0.215
−0.331
0.248
\\n
\\nDD\\n
−0.560
<0.05
−0.465
0.094
−0.469
0.091
\\n
Total
−0.496
0.071
−0.476
0.085
−0.450
0.107
\\nHmax\\n
\\nDS\\n
−0.578
<0.05
−0.569
<0.05
−0.534
<0.05
\\n
\\nDD\\n
−0.570
<0.05
−0.497
0.070
−0.488
0.076
\\n
Total
−0.601
<0.05
−0.579
<0.05
−0.542
<0.05
\\nHmin\\n
\\nDS\\n
−0.080
0.787
−0.113
0.701
−0.060
0.839
\\n
\\nDD\\n
−0.532
0.050
−0.439
0.116
−0.444
0.112
\\n
Total
−0.495
0.072
−0.493
0.073
−0.453
0.104
\\nHdiff\\n
\\nDS\\n
−0.665
<0.01
−0.583
<0.05
−0.579
<0.05
\\n
\\nDD\\n
−0.777
<0.005
−0.736
<0.005
−0.699
<0.01
\\n
Total
−0.669
<0.01
−0.636
<0.05
−0.623
<0.05
\\nVair\\n
\\nDS\\n
−0.160
0.584
−0.081
0.782
−0.187
0.523
\\n
\\nDD\\n
−0.024
0.935
0.077
0.794
−0.029
0.923
\\n
Total
−0.108
0.714
−0.007
0.982
−0.103
0.725
\\nDL\\n
\\nDS\\n
−0.464
0.095
−0.411
0.144
−0.446
0.110
\\n
\\nDD\\n
−0.169
0.563
−0.222
0.446
−0.231
0.427
\\n
Total
−0.191
0.513
−0.301
0.296
−0.319
0.267
ijerph-17-05477-t006_Table 6
Coefficients of determination and adjusted R2 values for multivariate linear regression.
\\n
Cases
Deaths
Deaths (Ex.) †
R2
adj. R2
p-Value
R2
adj. R2
p-Value
R2
adj. R2
p-Value
\\nDS\\n
0.777
0.693
<0.01
0.659
0.532
<0.05
0.384
0.153
0.251
\\nDD\\n
0.773
0.688
<0.01
0.653
0.523
<0.05
0.383
0.151
0.253
Total
0.776
0.692
<0.01
0.662
0.536
<0.05
0.386
0.155
0.249
† Excluding nosocomial infection in confirmed deaths.
\\n\"\n]"}}},{"rowIdx":466,"cells":{"data":{"kind":"list like","value":["\nInt J Environ Res Public HealthInt J Environ Res Public HealthijerphInternational Journal of Environmental Research and Public Health1661-78271660-4601MDPI32751203PMC743212310.3390/ijerph17155464ijerph-17-05464ArticlePrevalence of Misophonia and Correlates of Its Symptoms among Inpatients with Depressionhttps://orcid.org/0000-0001-8161-3367SiepsiakMarta1*SobczakAnna Maria2https://orcid.org/0000-0003-2216-5888BohaterewiczBartosz2CichockiŁukasz3https://orcid.org/0000-0002-3349-6562DraganWojciech Łukasz1Faculty of Psychology, University of Warsaw, 00-183 Warsaw, Poland; wdragan@psych.uw.edu.plDepartment of Cognitive Neuroscience and Neuroergonomics, Institute of Applied Psychology, Jagiellonian University, 30-348 Cracow, Poland; ansonsobczak@gmail.com (A.M.S.); bohaterewicz@gmail.com (B.B.)Department of Psychiatry, Andrzej Frycz Modrzewski Cracow Academy, 30-705 Cracow, Poland; lwcichocki@gmail.comCorrespondence: marta.siepsiak@psych.uw.edu.pl2972020820201715546416620201672020© 2020 by the authors.2020Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Misophonia is an underexplored condition that significantly decreases the quality of life of those who suffer from it. It has neurological and physiological correlates and is associated with a variety of psychiatric symptoms; however, a growing body of data suggests that it is a discrete disorder. While comorbid diagnoses among people with misophonia have been a matter of research interest for many years there is no data on the frequency of misophonia among people with psychiatric disorders. This could be the next step to reveal additional mechanisms underlying misophonia. Until recently, the use of a variety of non-validated questionnaires and the dominance of internet-based studies have been also a major obstacles to a proper definition of misophonia. A total of 94 inpatients diagnosed with depression were assessed for misophonia with face-to-face interviews as well as with MisoQuest—a validated misophonia questionnaire. The prevalence of misophonia among these patients and the congruence of MisoQuest with face-to-face interviews were evaluated. Additionally, the patients filled in a series of questionnaires that measured a variety of psychiatric symptoms and psychological traits. Anxiety, depression, impulsivity, somatic pain, vegetative symptoms, post-traumatic stress disorder (PTSD) symptoms, gender, and age were analyzed in relation to the severity of symptoms of misophonia. Between 8.5 to 12.76% of inpatients with depression were diagnosed with misophonia (depending on measurement and inclusion criteria). MisoQuest accuracy was equal to 92.55%, sensitivity-66.67% and specificity-96.34%. Severity of misophonia symptoms was positively correlated to the greatest extent with anxiety. Moderate positive correlation was also found between severity of misophonia symptoms and depressive symptoms, intrusions, and somatic pain; a weak positive correlation was found between severity of misophonia and non-planning impulsivity, motor impulsivity, avoidance, and vegetative symptoms. There was no relationship between the severity of misophonia symptoms and attentional impulsivity or the age of participants.
misophoniadepressionanxietyimpulsivityPTSDMisoQuestquestionnaire for assessing misophoniamisophonia prevalence1. Introduction
Misophonia is a kind of decreased sound tolerance [1,2] which significantly influences the quality of life of those who suffer from it. It is still underexplored and does not have either an International Classification of Diseases (ICD-11) or Diagnostic and Statistical Manual of Mental Disorders (DSM-5) classification. When people affected by misophonia are exposed to certain sound stimuli particular to each individual (e.g., sniffing, breathing, or tapping), they experience unwanted emotions (such as anger, disgust, irritation, or anxiety) which they usually consider excessive [3,4,5,6]. Moreover, they report physical sensations such as pressure in the chest, shoulders, head, or whole body as well as an increase in body temperature, pain, or breathing difficulties [3,7]. Although recent studies indicate that misophonia is a rather unique disorder, it is strongly associated with a variety of psychiatric symptoms [8,9,10]. Some results have indicated that anxiety symptoms act as a mediator of anger outbursts among individuals with misophonia [11]. These results have been replicated on a group of Chinese students [12]. Furthermore, anxiety symptoms turned out to be strongly related to the severity of misophonia in a study conducted on a population of Singaporean patients with various psychiatric diagnoses [13]. Moreover, patients with misophonia had significantly more symptoms of anxiety than the control group in the study of Schröder et al. [14]. We hypothesized that in our sample of patients with a diagnosis of depression, anxiety symptoms would be positively associated with severity of misophonia symptoms.
Rouw and Erfanian [5] found that post-traumatic stress disorder (PTSD) was one of the most common diagnoses in people with misophonia (occurring in 12% of cases). Remarkably, they noted that PTSD was the only comorbid disorder related to the severity of misophonia symptoms. Other studies have also found PTSD to be one of the most common comorbid disorders, being present in from 15.38% [9] to 30% [15] of cases. Its presence was associated with the severity of misophonia symptoms. Therefore, we assumed in this study that there would be a positive correlation between the severity of misophonia symptoms and PTSD symptoms, such as hyperarousal, avoidance, and intrusions.
Other research and observations on misophonia have led researchers to conclude that misophonia is strongly associated with obsessive-compulsive personality disorder (OCPD) and obsessive-compulsive disorder (OCD) [6,11,16]. The available data gives frequencies of the occurrence of OCD in people with misophonia at 2.4% [6], 11.53% [9], and 28% [15] while as many as 52.4% of people characterized with misophonia meet DSM diagnostic criteria for OCPD [6]. However, the occurrence of OCD characteristics such as obsession, compulsivity, and impulsivity [6] in misophonia is yet to be explored, as the data remain inconsistent. In a study by Cusack et al. [8], obsessive symptoms were more related to misophonia than compulsive symptoms. In contrast, the results of McKay et al. 2018 showed that, while some symptoms of OCD, e.g., ordering and harm avoidance, were higher among people with misophonia, other symptoms such as obsessions, neutralizing, and washing were lower [10]. Likewise, Schröderet al. 2013 state that impulsivity is an actual link between obsessive compulsive spectrum disorder and misophonia [6]. Taking into consideration the aforementioned reports, this study focused on impulsivity as a trait.
There is a large body of data indicating that people with OCD have increased impulsivity [17,18]; however, this relation appears to be quite complex [19]. While some aspects of impulsivity are positively correlated with OCD, others are associated negatively or not associated at all [20,21,22]. Moreover, self-report studies on people with OCD can be biased; therefore, they may not exactly reflect neurocognitive impulsivity [23]. Due to these disputes and discrepancies in the data on the role of impulsivity in OCD and because our goal was to explore the psychological construct of impulsivity rather than its comorbidity with other disorders, we excluded people with a diagnosis of OCD from this study. We suspected that, in spite of the exclusion of people with OCD, there would be a positive correlation between impulsivity and severity of misophonia symptoms.
Another disorder frequently reported among people with misophonia is depression, which was diagnosed in 22% of a sample of 50 people suffering from misophonia [15] and in 9.61% of a sample of 52 [9]. Both studies revealed a positive correlation between depression and the severity of misophonia symptoms. An internet-based study conducted on students of psychology also confirmed relationship between misophonia and symptoms of depression [11,12]. Another self-report study found that depression was reported by 13% of participants with misophonia [5]. In this study, we wanted to investigate whether in a clinical group of people with diagnoses of depression there is a positive correlation between severity of depressive symptoms and misophonia symptoms.
A growing body of data shows that being annoyed by noise is related to symptoms of anxiety and depression [24,25], while symptoms of depression has been found in a third of patients with tinnitus or hyperacusis [26]. Additionally, sound sensitivity has been shown to be associated with multiple vegetative symptoms of depression and pain [27,28,29]. In this study, we wanted to explore whether vegetative symptoms and pain are also associated with severity of misophonia symptoms.
In 2018, Queck et al. investigated factors related to the severity of misophonia symptoms in psychiatric patients, half of whom were diagnosed with depression [13]. Likewise, we decided to narrow down the research group to inpatients diagnosed with depression in order to avoid potential confounding effects due to other disorders. While there is no evidence that misophonia coexists with any particular disorder, at least 50% of people with misophonia suffer from some other psychiatric condition. Finding a trait or set of characteristics related to the severity of misophonia could shed light on its underlying mechanisms. Therefore, we explored how misophonia symptoms are related to psychological constructs rather than nosological entities in a homogenous group of psychiatric patients. We focused on attentional, motor, and non-planning impulsivity, symptoms of anxiety, alongside intrusions, hyperarousal and avoidance as components of PTSD. We also investigated the intensity of depressive symptoms, as the research group consisted of people who had been diagnosed with depression. Additionally, we controlled for somatic pain and vegetative symptoms that could impact auditory sensitivity.
Apart from the above, the main goal of this study was to assess the prevalence of misophonia among the patients with the diagnosis of depression. While there is already some data on the percentage of people with misophonia who suffer from depression, there is a gap in knowledge on how many people with depression suffer from misophonia. Revealing this relationship could be an additional cue on possible mechanisms underlying misophonia. Additionally, we aimed to assess the external validity of MisoQuest [30], a new questionnaire for assessing misophonia.
2. Materials and Methods
This study was approved by The Ethics Committee at the Faculty of Psychology at the University of Warsaw (Psychologiczne korelaty mizofonii u osób z depresją, 21/05/2019).
2.1. Participants
A total of 100 patients with a current diagnosis of depression were enrolled in the study. Data was missing for six participants, so 94 participants were analyzed. The psychiatric diagnoses were made by a team of hospital psychiatrists based on ICD-10 classification, prior to and independent of the study. Exclusion criteria were as follows: (a) cognitive impairment, (b) psychosis, (c) obsessive-compulsive disorder, (d) drug and/or alcohol addiction, and (e) experiencing a bipolar depressive episode. The reason the participants were in a patient unit was depression, although we excluded from the study participants with psychotic depression, depression in a progress of bipolar disorder, and when the state of depression was mainly a consequence of an alcoholic addiction. Patients were mainly treated for depression, but some of them had also diagnoses of personality or anxiety disorders. The time of their hospitalization was between two weeks and two months, and we recruited them with the help of their psychiatric doctors, who recommended patients who fulfilled the criteria of participation for this study.
Both male and female participants were recruited from psychiatric care wards in Kraków (Dr Jozef Babinski Clinical Hospital) and Warsaw (Institute of Psychiatry and Neurology). Participants’ ages ranged from 18 to 79 years old (M = 39.95, SD = 14.9). There was no difference in the average age (t(55) = 0.08; p = 0.935) of the men (M = 39.58, SD = 13.38) and women (M = 39.90, SD = 16.17). There were slightly more women than men in this study (45.7%; missing data for 19.1% of participants).
2.2. Assessments
The participants were individually evaluated by researchers trained in the diagnosis of misophonia using the measures described below.
Misophonia was identified using both the MisoQuest questionnaire [30] and a semi-structured interview based on Schröder et al.’s diagnostic criteria for misophonia [6]. During the interview, the diagnostician asked whether the patients agree or not with statements (see Supplementary File S1) derived directly from the diagnostic criteria. After each statement, according to the situation, the researcher asked additional questions in order to eliminate possible misunderstandings and to verify whether the given symptoms have clinical meaning. MisoQuest, a 14-item self-report questionnaire for assessing misophonia, was developed and validated on a Polish population. It has excellent psychometric properties and, therefore, was suitable for the purposes of this study. Previously [30], the cutoff for clinically significant misophonia symptoms was not specified. In this study, we proposed a cutoff of 61 out of 70 points. This derives from the difference between the mean score (M = 65.72) and the standard deviation (SD = 4.3) for people classified as having misophonia in the first MisoQuest validation study conducted in 2018. The same cutoff has been chosen to identify people with misophonia in an ongoing study further assessing the external validity of MisoQuest as well as in all other studies using MisoQuest associated with the “Psychological and psychophysiological correlates of misophonia” project being conducted at the Faculty of Psychology at the University of Warsaw.
Impulsivity was evaluated with the Barratt Impulsivity Scale (BIS-11)—a 30-item self-report questionnaire which assesses three aspects of impulsivity: attentional, motor, and non-planning. The Polish adaptation was used in this study [31].
Severity of depressive and anxiety symptoms was assessed with The Hospital Anxiety Depression Scale (HADS) [32,33] and The Symptom Checklist-27-plus (SCL-27-plus). The HADS consists of 14 items, 7 of which relate to anxiety and 7 of which relate to depression. The SCL-27-plus [34,35] measures the intensity of depressive symptoms, both those currently experienced and those which have occurred throughout one’s life as well as vegetative symptoms, agoraphobia, social phobia, and somatic pain.
PTSD components were identified using the Impact Event Scale-Revised (IES-R)—a 22-item self-reported questionnaire proposed by Weiss & Marmar [36] and adapted to Polish by Juczyński and Ogińska-Bulik [37]. The IES-R is used as a screening tool for an initial diagnosis of PTSD as it contains three subscales that include the symptoms of PTSD: intrusion, hyperarousal, and avoidance. Intrusion refers to recurring images, dreams, perceptual thoughts, or impressions connected with trauma. Hyperarousal is characterized by heightened alertness, anxiety, impatience, and difficulties concentrating. Avoidance manifests in attempts to eschew thoughts, emotions, or conversations connected to the trauma.
2.3. Data Analysis
Patients with misophonia were identified based on MisoQuest results and face-to-face interviews in order to evaluate the prevalence of misophonia as well as the external validity of MisoQuest. Next, the sensitivity, specificity, and accuracy of MisoQuest were calculated with exact Clopper–Pearson confidence intervals.
In order to check the difference in severity of misophonia symptoms between men and women, a t-test was conducted on the entire sample (94 participants). Next, Pearson correlations between the severity of misophonia symptoms (measured by MisoQuest) and all the factors described above, as well as the age of the participants, were calculated on the entire sample.
Row data is provided in Supplementary File S2.
3. Results3.1. Prevalence of Misophonia among Inpatients with Depression
Eight out of 94 subjects were diagnosed with misophonia (8.5%), meeting criteria for misophonia in both face-to-face interview and MisoQuest.
According to the assumed cutoff in MisoQuest (61 out of 70 points), 11 patients should be classified as having misophonia (11.7%). However, two of them (61 and 62 points) did not meet the criteria in the face-to-face interview, and in one case, the symptoms were explained by PTSD (62 points). There were also 4 patients (52, 59, 59, and 60 points) who met criteria for mild misophonia [6] in face-to-face interviews but did not report a significant severity of misophonia symptoms on MisoQuest.
3.2. External Validity of MisoQuest
The data from the questionnaire—MisoQuest were compared with face-to-face misophonia diagnosis. For sensitivity, specificity and accuracy of MisoQuest, see Table 1.
3.3. Gender Differences in Misophonia Symptoms Severity and Correlates of Severity of Misophonia Symptoms
Women (M = 44.72; SD = 14.66) manifested a significantly higher severity of misophonia symptoms (t(74) = 2.5; p = 0.015; Cohen’s d = 0.57) than men (M = 35.52; SD = 17.48).
There was no correlation between age and severity of symptoms of misophonia. The severity of misophonia symptoms was moderately correlated with symptoms of anxiety and severity of depression. A moderate correlation was also found with intrusions and arousal, while avoidance correlated weakly. There was a weak correlation with non-planning impulsivity and motor impulsivity and no correlation with attentional impulsivity. Moderate and weak correlation with somatic pain and vegetative symptoms was found (Table 2).
4. Discussion
This study showed that the prevalence of people with misophonia among patients with depression ranges from 8.5 to 12.76%. Additionally, comparing MisoQuest to face-to-face interviews allowed us to verify its external validity in a population of patients, proving its high accuracy. Severity of misophonia symptoms was positively correlated to the greatest extent with anxiety. Moreover, severity of misophonia symptoms was weakly to moderately associated with a variety of symptoms of pathologies and traits (impulsivity).
A total of 8.5% of patients met the criteria for misophonia. If we included the 4 cases with mild misophonia, with very limited impact on one’s life, this would be 12.76%. This is the first study investigating the prevalence of misophonia among inpatients with psychiatric diagnoses and also the first study, on any population, to evaluate the prevalence of misophonia using face-to-face interviews. These percentages of individuals found to have misophonia are half those found in other studies [11,12]. However, it is not possible to draw a conclusion from this comparison. The difference in the percentage of people with misophonia is probably due to differences in the measurement tools used. We believe that previously used misophonia questionnaires might have also captured more general sound sensitivities and considered lower severities of symptoms to constitute misophonia.
Analysis of Pearson’s correlation revealed a moderate association between depressive symptoms and severity of misophonia. The relation between misophonia and depression and its symptoms is already supported by data from several studies [5,11,15]. Chronic stress might link these phenomena, as it is strongly associated with misophonia and, at the same time, is one of the leading causes of depression [38]. Reactions characteristic of misophonia—tension related to trigger sounds and, consequently, avoidance of many social situations—can lead to overactivity of the hypothalamic–pituitary–adrenal tract [39,40]. Although it is still hypothetical that depression and depressive symptoms can be caused by misophonia, this notion could be supported by comparing our results with those of other studies: depression appears to be more prevalent among people with misophonia than misophonia is among people with depression. Nonetheless, it is highly possible that this difference is due to the use of different diagnostic criteria (MisoQuest vs. the Misophonia Questionnaire) as well as the way the data was gathered (internet-based study vs. face-to-face interviews). Therefore, the exploration of the incidence of misophonia, measured with comparable criteria and questionnaires, among homogenous groups of patients as well as in the general population should be done in the future. This could help to reveal the direction of the relation between misophonia and depression as well as other disorders.
This is yet another study indicating the meaningful role of anxiety in misophonia. Anxiety was correlated to a greater degree with misophonia severity than were any of the other measured variables. These results are similar to those of Queck et al. [13], who found that anxiety was the only significant factor in multivariate regression, predicting misophonia severity in a group of psychiatric patients. While the relation between misophonia and symptoms of anxiety as a pathology is already well documented, anxiety disorders (such as generalized anxiety disorder, panic disorder, or social phobia) are not the disorders most frequently found alongside misophonia and are not reported as those most significantly related with increased misophonia symptoms. The exceptions to this are OCD, in which the role in misophonia is still disputed, and PTSD, which is an event-related disorder, in contrast to other anxiety disorders where the more diffuse nature of the anxiety facilitates interaction with misophonia. At the same time, there is also significant data on the role of increased neuroticism in misophonia [14,41]. As the role of anxiety in predicting the severity of misophonia symptoms seems to be already well described, a deeper investigation into anxiety as a trait and personality characteristics such as neuroticism versus anxiety disorders could be the next step in unveiling the mechanisms of misophonia related to anxiety.
Excluding OCD patients allowed us to assess self-described impulsivity without the risk of overlap. As a result, unlike attentional impulsivity, non-planning impulsivity and motor impulsivity were weakly but significantly correlated with misophonia symptoms. Notably, attentional impulsivity was one of only two variables (the other being age) in this study that were not correlated with the severity of misophonia; the other aspects of impulsivity were much less associated with severity of misophonia than were all the other variables. Hence, the role of impulsivity was undermined in this study, but nonetheless, strong conclusions about impulsivity should not be drawn—this relationship needs further exploration.
Regarding symptoms of PTSD, intrusions and arousal were moderately associated with misophonia symptoms while avoidance was only weakly correlated. There were 6 participants (7.4%) who reported that they had been diagnosed by their psychiatrist with PTSD; their average score on MisoQuest was 10 points higher (M = 48.83, SD = 12.37) than patients without such a diagnosis (M = 38.52, SD = 16.85). The increased severity of misophonia symptoms in people with PTSD has already been reported by Rouw and Erfanian [5]. Unfortunately, our study did not include enough participants diagnosed with PTSD to conduct a statistical analysis and to draw reliable conclusions. Nonetheless, no patients with PTSD were diagnosed with misophonia in the face-to-face interviews. Therefore, our results undermine the potential relationship between misophonia and PTSD. These results might rather indicate relations between more general sound sensitivity and psychological distress and hyperarousal [42,43].
Vegetative symptoms were found to be weakly yet significantly correlated with the severity of misophonia symptoms, while somatic pain was found to be moderately significantly correlated. Although many studies discuss the vegetative symptoms experienced by people with misophonia in the presence of their misophonic triggers, to the best of our knowledge, this is the first study to measure the association between the above symptoms and misophonia severity in general. We are not surprised with these results. We expected that vegetative symptoms and somatic pain would be related to misophonia symptom severity to a certain but limited extent based on the associations between sound sensitivity and somatic disturbances and pain discussed in the Introduction section. Future research should check whether these variables are more related to general sound sensitivity than to misophonia. It is possible that, because misophonia is a more specific condition than general sound sensitivity/hyperacusis, the presence of pain and vegetative symptoms could be lower in misophonia.
Women manifested a significantly higher severity of misophonia symptoms than did men. Our results are in line with recent research conducted by Queck et al. [13] or Erfanian et al. [9]. However, we draw different conclusions to previous studies [11,12] which found no gender differences in severity of misophonia. Gender differences in accepted standards for certain scales that are used in clinical practice are usually not taken into account in research studies. As with other constructs, the standards for genders might differ also for misophonia.
The severity of misophonia symptoms was not associated with the age of participants. However, much age data was missing, which could have affected the results. In a study by Queck et al. [13] on patients with psychiatric diagnoses, a negative correlation between the severity of misophonia symptoms and age was observed, but only before adjusting for confounding factors. Similarly, there was no relation with age in the study by Wu et al. [11]. Nonetheless, the data are inconclusive: in the study conducted by Rouw & Erfanian [5], while severity of misophonia symptoms decreased with age and there was negative correlation with a small effect between age and severity of misophonia, they noted that it increased in the two oldest age categories (over 65). Thus, the association between age and severity of misophonia needs further exploration.
Assessment of MisoQuest’s additional external validity showed that, with its high accuracy, it is a good tool for assessing misophonia. It has lower sensitivity than specificity, which means that there is a risk of false negative cases. In fact, 4 patients diagnosed in face-to-face interviews with misophonia with limited impact on one’s life did not reach the cutoff for misophonia in MisoQuest. Results slightly below or above the cutoff for misophonia should be evaluated carefully, especially in populations of patients with psychiatric diagnoses. MisoQuest was created with an aim to identify people whose quality of life is significantly lower because of misophonia [30], and apparently, it captures more severe cases. Further assessment of validity of MisoQuest is needed on the general population.
Limitations
This study has two meaningful weaknesses. The major drawback of the study is the significant amount of missing data (age and gender) that was a result of mistakes during the data collection. Another limitation is that caution must be used when comparing these data with those of other studies (especially studies in which only questionnaires were used, with no face-to-face interviews). This is because MisoQuest is based on a slightly different approach to misophonia than the Misophonia Questionnaire (MQ), which has, to date, been used in the majority of studies. At the time of conducting this study, there was no Polish version of the MQ available. In the future, it would be worthwhile to compare characteristics of people diagnosed with misophonia by those two different questionnaires.
5. Conclusions
The prevalence of misophonia among patients with depression was evaluated in this study and stands at 8.5–12.76%. Additionally, the results show that MisoQuest can be helpful for identifying misophonia among patients with psychiatric diagnoses. The severity of misophonia symptoms was associated weakly to moderately with various symptoms and psychological characteristics and was the most associated with anxiety. Anxiety, however, is a more general psychological construct that not only is related to multiple psychiatric conditions but also for which intensity differs based on the temperamental characteristics of individuals. The aforementioned results are in line with other studies, suggesting that misophonia might be an independent disorder.
Supplementary Materials
The following are available online at https://www.mdpi.com/1660-4601/17/15/5464/s1, File S1: Semi-structured interview; File S2: Row data.
Click here for additional data file.
Author Contributions
Conceptualization, M.S., A.M.S., and B.B.; methodology, M.S., A.M.S., B.B., and W.Ł.D.; formal analysis, M.S.; investigation, M.S., A.M.S., B.B., and Ł.C.; writing—original draft preparation, M.S. and A.M.S.; writing—review and editing, B.B., Ł.C., and W.Ł.D.; supervision, W.Ł.D.; project administration, M.S., A.M.S., B.B., and Ł.C.; funding acquisition, M.S. All authors have read and agreed to the published version of the manuscript.
Funding
This work was supported by the Faculty of Psychology, University of Warsaw, from the funds awarded by the Ministry of Science and Higher Education in the form of a subsidy for the maintenance and development of research potential in 2020 (501-D125-01-1250000-5011000251), the Polish National Science Center grant number 2018/29/N/HS6/01108 and the Foundation for Polish Science (FNP) project (Bio-inspired Artificial Neural Networks ID: POIR.04.04.00-00-14DE/18-00.
Conflicts of Interest
The authors declare no conflict of interest.
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Neurosci.201026058360010.1007/s00406-010-0108-z20174927DanielsE.C.RodriguezA.ZabelinaD.L.Severity of misophonia symptoms is associated with worse cognitive control when exposed to misophonia trigger soundsPLoS ONE202015e022711810.1371/journal.pone.022711831945068CallahanM.L.BinderL.M.O’NeilM.E.ZaccariB.RoostM.S.GolshanS.HuckansM.FannJ.R.StorzbachD.Sensory sensitivity in operation enduring freedom/operation Iraqi freedom veterans with and without blast exposure and mild traumatic brain injuryAppl. Neuropsychol.20182512613610.1080/23279095.2016.1261867CallahanM.L.StorzbachD.Sensory sensitivity and posttraumatic stress disorder in blast exposed veterans with mild traumatic brain injuryAppl. Neuropsychol.20192636537310.1080/23279095.2018.143317929465307ijerph-17-05464-t001_Table 1
Sensitivity, specificity, and accuracy of MisoQuest.
MisoQuest
Value %
(95% CI)
Sensitivity
66.67
(34.89–90.08)
Specificity
96.34
(89.68–99.24)
Accuracy
92.55
(85.26–96.95)
ijerph-17-05464-t002_Table 2
Correlations with severity of misophonia symptoms assessed with MisoQuest.
\n
Age
HADS Anxiety
BIS Attentional
BIS Motor
BIS Non-Planning
SCL Vegetative
SCL Pain
HADS Depression
IES Intrusions
IES Arousal
IES Avoidance
MisoQuest Pearson’s correlation p-value
0.024
0.444
−0.178
0.205
0.204
0.276
0.307
0.325
0.324
0.325
0.263
0.856
0.000
0.086
0.047
0.049
0.007
0.003
0.001
0.001
0.001
0.010
HADS—Hospital Anxiety and Depression scale; BIS—Barrat Impulsivity Scale; SCL—Symptom Checklist Scale; IES—Impact of Event Scale.
\n"],"string":"[\n \"\\nInt J Environ Res Public HealthInt J Environ Res Public HealthijerphInternational Journal of Environmental Research and Public Health1661-78271660-4601MDPI32751203PMC743212310.3390/ijerph17155464ijerph-17-05464ArticlePrevalence of Misophonia and Correlates of Its Symptoms among Inpatients with Depressionhttps://orcid.org/0000-0001-8161-3367SiepsiakMarta1*SobczakAnna Maria2https://orcid.org/0000-0003-2216-5888BohaterewiczBartosz2CichockiŁukasz3https://orcid.org/0000-0002-3349-6562DraganWojciech Łukasz1Faculty of Psychology, University of Warsaw, 00-183 Warsaw, Poland; wdragan@psych.uw.edu.plDepartment of Cognitive Neuroscience and Neuroergonomics, Institute of Applied Psychology, Jagiellonian University, 30-348 Cracow, Poland; ansonsobczak@gmail.com (A.M.S.); bohaterewicz@gmail.com (B.B.)Department of Psychiatry, Andrzej Frycz Modrzewski Cracow Academy, 30-705 Cracow, Poland; lwcichocki@gmail.comCorrespondence: marta.siepsiak@psych.uw.edu.pl2972020820201715546416620201672020© 2020 by the authors.2020Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Misophonia is an underexplored condition that significantly decreases the quality of life of those who suffer from it. It has neurological and physiological correlates and is associated with a variety of psychiatric symptoms; however, a growing body of data suggests that it is a discrete disorder. While comorbid diagnoses among people with misophonia have been a matter of research interest for many years there is no data on the frequency of misophonia among people with psychiatric disorders. This could be the next step to reveal additional mechanisms underlying misophonia. Until recently, the use of a variety of non-validated questionnaires and the dominance of internet-based studies have been also a major obstacles to a proper definition of misophonia. A total of 94 inpatients diagnosed with depression were assessed for misophonia with face-to-face interviews as well as with MisoQuest—a validated misophonia questionnaire. The prevalence of misophonia among these patients and the congruence of MisoQuest with face-to-face interviews were evaluated. Additionally, the patients filled in a series of questionnaires that measured a variety of psychiatric symptoms and psychological traits. Anxiety, depression, impulsivity, somatic pain, vegetative symptoms, post-traumatic stress disorder (PTSD) symptoms, gender, and age were analyzed in relation to the severity of symptoms of misophonia. Between 8.5 to 12.76% of inpatients with depression were diagnosed with misophonia (depending on measurement and inclusion criteria). MisoQuest accuracy was equal to 92.55%, sensitivity-66.67% and specificity-96.34%. Severity of misophonia symptoms was positively correlated to the greatest extent with anxiety. Moderate positive correlation was also found between severity of misophonia symptoms and depressive symptoms, intrusions, and somatic pain; a weak positive correlation was found between severity of misophonia and non-planning impulsivity, motor impulsivity, avoidance, and vegetative symptoms. There was no relationship between the severity of misophonia symptoms and attentional impulsivity or the age of participants.
misophoniadepressionanxietyimpulsivityPTSDMisoQuestquestionnaire for assessing misophoniamisophonia prevalence1. Introduction
Misophonia is a kind of decreased sound tolerance [1,2] which significantly influences the quality of life of those who suffer from it. It is still underexplored and does not have either an International Classification of Diseases (ICD-11) or Diagnostic and Statistical Manual of Mental Disorders (DSM-5) classification. When people affected by misophonia are exposed to certain sound stimuli particular to each individual (e.g., sniffing, breathing, or tapping), they experience unwanted emotions (such as anger, disgust, irritation, or anxiety) which they usually consider excessive [3,4,5,6]. Moreover, they report physical sensations such as pressure in the chest, shoulders, head, or whole body as well as an increase in body temperature, pain, or breathing difficulties [3,7]. Although recent studies indicate that misophonia is a rather unique disorder, it is strongly associated with a variety of psychiatric symptoms [8,9,10]. Some results have indicated that anxiety symptoms act as a mediator of anger outbursts among individuals with misophonia [11]. These results have been replicated on a group of Chinese students [12]. Furthermore, anxiety symptoms turned out to be strongly related to the severity of misophonia in a study conducted on a population of Singaporean patients with various psychiatric diagnoses [13]. Moreover, patients with misophonia had significantly more symptoms of anxiety than the control group in the study of Schröder et al. [14]. We hypothesized that in our sample of patients with a diagnosis of depression, anxiety symptoms would be positively associated with severity of misophonia symptoms.
Rouw and Erfanian [5] found that post-traumatic stress disorder (PTSD) was one of the most common diagnoses in people with misophonia (occurring in 12% of cases). Remarkably, they noted that PTSD was the only comorbid disorder related to the severity of misophonia symptoms. Other studies have also found PTSD to be one of the most common comorbid disorders, being present in from 15.38% [9] to 30% [15] of cases. Its presence was associated with the severity of misophonia symptoms. Therefore, we assumed in this study that there would be a positive correlation between the severity of misophonia symptoms and PTSD symptoms, such as hyperarousal, avoidance, and intrusions.
Other research and observations on misophonia have led researchers to conclude that misophonia is strongly associated with obsessive-compulsive personality disorder (OCPD) and obsessive-compulsive disorder (OCD) [6,11,16]. The available data gives frequencies of the occurrence of OCD in people with misophonia at 2.4% [6], 11.53% [9], and 28% [15] while as many as 52.4% of people characterized with misophonia meet DSM diagnostic criteria for OCPD [6]. However, the occurrence of OCD characteristics such as obsession, compulsivity, and impulsivity [6] in misophonia is yet to be explored, as the data remain inconsistent. In a study by Cusack et al. [8], obsessive symptoms were more related to misophonia than compulsive symptoms. In contrast, the results of McKay et al. 2018 showed that, while some symptoms of OCD, e.g., ordering and harm avoidance, were higher among people with misophonia, other symptoms such as obsessions, neutralizing, and washing were lower [10]. Likewise, Schröderet al. 2013 state that impulsivity is an actual link between obsessive compulsive spectrum disorder and misophonia [6]. Taking into consideration the aforementioned reports, this study focused on impulsivity as a trait.
There is a large body of data indicating that people with OCD have increased impulsivity [17,18]; however, this relation appears to be quite complex [19]. While some aspects of impulsivity are positively correlated with OCD, others are associated negatively or not associated at all [20,21,22]. Moreover, self-report studies on people with OCD can be biased; therefore, they may not exactly reflect neurocognitive impulsivity [23]. Due to these disputes and discrepancies in the data on the role of impulsivity in OCD and because our goal was to explore the psychological construct of impulsivity rather than its comorbidity with other disorders, we excluded people with a diagnosis of OCD from this study. We suspected that, in spite of the exclusion of people with OCD, there would be a positive correlation between impulsivity and severity of misophonia symptoms.
Another disorder frequently reported among people with misophonia is depression, which was diagnosed in 22% of a sample of 50 people suffering from misophonia [15] and in 9.61% of a sample of 52 [9]. Both studies revealed a positive correlation between depression and the severity of misophonia symptoms. An internet-based study conducted on students of psychology also confirmed relationship between misophonia and symptoms of depression [11,12]. Another self-report study found that depression was reported by 13% of participants with misophonia [5]. In this study, we wanted to investigate whether in a clinical group of people with diagnoses of depression there is a positive correlation between severity of depressive symptoms and misophonia symptoms.
A growing body of data shows that being annoyed by noise is related to symptoms of anxiety and depression [24,25], while symptoms of depression has been found in a third of patients with tinnitus or hyperacusis [26]. Additionally, sound sensitivity has been shown to be associated with multiple vegetative symptoms of depression and pain [27,28,29]. In this study, we wanted to explore whether vegetative symptoms and pain are also associated with severity of misophonia symptoms.
In 2018, Queck et al. investigated factors related to the severity of misophonia symptoms in psychiatric patients, half of whom were diagnosed with depression [13]. Likewise, we decided to narrow down the research group to inpatients diagnosed with depression in order to avoid potential confounding effects due to other disorders. While there is no evidence that misophonia coexists with any particular disorder, at least 50% of people with misophonia suffer from some other psychiatric condition. Finding a trait or set of characteristics related to the severity of misophonia could shed light on its underlying mechanisms. Therefore, we explored how misophonia symptoms are related to psychological constructs rather than nosological entities in a homogenous group of psychiatric patients. We focused on attentional, motor, and non-planning impulsivity, symptoms of anxiety, alongside intrusions, hyperarousal and avoidance as components of PTSD. We also investigated the intensity of depressive symptoms, as the research group consisted of people who had been diagnosed with depression. Additionally, we controlled for somatic pain and vegetative symptoms that could impact auditory sensitivity.
Apart from the above, the main goal of this study was to assess the prevalence of misophonia among the patients with the diagnosis of depression. While there is already some data on the percentage of people with misophonia who suffer from depression, there is a gap in knowledge on how many people with depression suffer from misophonia. Revealing this relationship could be an additional cue on possible mechanisms underlying misophonia. Additionally, we aimed to assess the external validity of MisoQuest [30], a new questionnaire for assessing misophonia.
2. Materials and Methods
This study was approved by The Ethics Committee at the Faculty of Psychology at the University of Warsaw (Psychologiczne korelaty mizofonii u osób z depresją, 21/05/2019).
2.1. Participants
A total of 100 patients with a current diagnosis of depression were enrolled in the study. Data was missing for six participants, so 94 participants were analyzed. The psychiatric diagnoses were made by a team of hospital psychiatrists based on ICD-10 classification, prior to and independent of the study. Exclusion criteria were as follows: (a) cognitive impairment, (b) psychosis, (c) obsessive-compulsive disorder, (d) drug and/or alcohol addiction, and (e) experiencing a bipolar depressive episode. The reason the participants were in a patient unit was depression, although we excluded from the study participants with psychotic depression, depression in a progress of bipolar disorder, and when the state of depression was mainly a consequence of an alcoholic addiction. Patients were mainly treated for depression, but some of them had also diagnoses of personality or anxiety disorders. The time of their hospitalization was between two weeks and two months, and we recruited them with the help of their psychiatric doctors, who recommended patients who fulfilled the criteria of participation for this study.
Both male and female participants were recruited from psychiatric care wards in Kraków (Dr Jozef Babinski Clinical Hospital) and Warsaw (Institute of Psychiatry and Neurology). Participants’ ages ranged from 18 to 79 years old (M = 39.95, SD = 14.9). There was no difference in the average age (t(55) = 0.08; p = 0.935) of the men (M = 39.58, SD = 13.38) and women (M = 39.90, SD = 16.17). There were slightly more women than men in this study (45.7%; missing data for 19.1% of participants).
2.2. Assessments
The participants were individually evaluated by researchers trained in the diagnosis of misophonia using the measures described below.
Misophonia was identified using both the MisoQuest questionnaire [30] and a semi-structured interview based on Schröder et al.’s diagnostic criteria for misophonia [6]. During the interview, the diagnostician asked whether the patients agree or not with statements (see Supplementary File S1) derived directly from the diagnostic criteria. After each statement, according to the situation, the researcher asked additional questions in order to eliminate possible misunderstandings and to verify whether the given symptoms have clinical meaning. MisoQuest, a 14-item self-report questionnaire for assessing misophonia, was developed and validated on a Polish population. It has excellent psychometric properties and, therefore, was suitable for the purposes of this study. Previously [30], the cutoff for clinically significant misophonia symptoms was not specified. In this study, we proposed a cutoff of 61 out of 70 points. This derives from the difference between the mean score (M = 65.72) and the standard deviation (SD = 4.3) for people classified as having misophonia in the first MisoQuest validation study conducted in 2018. The same cutoff has been chosen to identify people with misophonia in an ongoing study further assessing the external validity of MisoQuest as well as in all other studies using MisoQuest associated with the “Psychological and psychophysiological correlates of misophonia” project being conducted at the Faculty of Psychology at the University of Warsaw.
Impulsivity was evaluated with the Barratt Impulsivity Scale (BIS-11)—a 30-item self-report questionnaire which assesses three aspects of impulsivity: attentional, motor, and non-planning. The Polish adaptation was used in this study [31].
Severity of depressive and anxiety symptoms was assessed with The Hospital Anxiety Depression Scale (HADS) [32,33] and The Symptom Checklist-27-plus (SCL-27-plus). The HADS consists of 14 items, 7 of which relate to anxiety and 7 of which relate to depression. The SCL-27-plus [34,35] measures the intensity of depressive symptoms, both those currently experienced and those which have occurred throughout one’s life as well as vegetative symptoms, agoraphobia, social phobia, and somatic pain.
PTSD components were identified using the Impact Event Scale-Revised (IES-R)—a 22-item self-reported questionnaire proposed by Weiss & Marmar [36] and adapted to Polish by Juczyński and Ogińska-Bulik [37]. The IES-R is used as a screening tool for an initial diagnosis of PTSD as it contains three subscales that include the symptoms of PTSD: intrusion, hyperarousal, and avoidance. Intrusion refers to recurring images, dreams, perceptual thoughts, or impressions connected with trauma. Hyperarousal is characterized by heightened alertness, anxiety, impatience, and difficulties concentrating. Avoidance manifests in attempts to eschew thoughts, emotions, or conversations connected to the trauma.
2.3. Data Analysis
Patients with misophonia were identified based on MisoQuest results and face-to-face interviews in order to evaluate the prevalence of misophonia as well as the external validity of MisoQuest. Next, the sensitivity, specificity, and accuracy of MisoQuest were calculated with exact Clopper–Pearson confidence intervals.
In order to check the difference in severity of misophonia symptoms between men and women, a t-test was conducted on the entire sample (94 participants). Next, Pearson correlations between the severity of misophonia symptoms (measured by MisoQuest) and all the factors described above, as well as the age of the participants, were calculated on the entire sample.
Row data is provided in Supplementary File S2.
3. Results3.1. Prevalence of Misophonia among Inpatients with Depression
Eight out of 94 subjects were diagnosed with misophonia (8.5%), meeting criteria for misophonia in both face-to-face interview and MisoQuest.
According to the assumed cutoff in MisoQuest (61 out of 70 points), 11 patients should be classified as having misophonia (11.7%). However, two of them (61 and 62 points) did not meet the criteria in the face-to-face interview, and in one case, the symptoms were explained by PTSD (62 points). There were also 4 patients (52, 59, 59, and 60 points) who met criteria for mild misophonia [6] in face-to-face interviews but did not report a significant severity of misophonia symptoms on MisoQuest.
3.2. External Validity of MisoQuest
The data from the questionnaire—MisoQuest were compared with face-to-face misophonia diagnosis. For sensitivity, specificity and accuracy of MisoQuest, see Table 1.
3.3. Gender Differences in Misophonia Symptoms Severity and Correlates of Severity of Misophonia Symptoms
Women (M = 44.72; SD = 14.66) manifested a significantly higher severity of misophonia symptoms (t(74) = 2.5; p = 0.015; Cohen’s d = 0.57) than men (M = 35.52; SD = 17.48).
There was no correlation between age and severity of symptoms of misophonia. The severity of misophonia symptoms was moderately correlated with symptoms of anxiety and severity of depression. A moderate correlation was also found with intrusions and arousal, while avoidance correlated weakly. There was a weak correlation with non-planning impulsivity and motor impulsivity and no correlation with attentional impulsivity. Moderate and weak correlation with somatic pain and vegetative symptoms was found (Table 2).
4. Discussion
This study showed that the prevalence of people with misophonia among patients with depression ranges from 8.5 to 12.76%. Additionally, comparing MisoQuest to face-to-face interviews allowed us to verify its external validity in a population of patients, proving its high accuracy. Severity of misophonia symptoms was positively correlated to the greatest extent with anxiety. Moreover, severity of misophonia symptoms was weakly to moderately associated with a variety of symptoms of pathologies and traits (impulsivity).
A total of 8.5% of patients met the criteria for misophonia. If we included the 4 cases with mild misophonia, with very limited impact on one’s life, this would be 12.76%. This is the first study investigating the prevalence of misophonia among inpatients with psychiatric diagnoses and also the first study, on any population, to evaluate the prevalence of misophonia using face-to-face interviews. These percentages of individuals found to have misophonia are half those found in other studies [11,12]. However, it is not possible to draw a conclusion from this comparison. The difference in the percentage of people with misophonia is probably due to differences in the measurement tools used. We believe that previously used misophonia questionnaires might have also captured more general sound sensitivities and considered lower severities of symptoms to constitute misophonia.
Analysis of Pearson’s correlation revealed a moderate association between depressive symptoms and severity of misophonia. The relation between misophonia and depression and its symptoms is already supported by data from several studies [5,11,15]. Chronic stress might link these phenomena, as it is strongly associated with misophonia and, at the same time, is one of the leading causes of depression [38]. Reactions characteristic of misophonia—tension related to trigger sounds and, consequently, avoidance of many social situations—can lead to overactivity of the hypothalamic–pituitary–adrenal tract [39,40]. Although it is still hypothetical that depression and depressive symptoms can be caused by misophonia, this notion could be supported by comparing our results with those of other studies: depression appears to be more prevalent among people with misophonia than misophonia is among people with depression. Nonetheless, it is highly possible that this difference is due to the use of different diagnostic criteria (MisoQuest vs. the Misophonia Questionnaire) as well as the way the data was gathered (internet-based study vs. face-to-face interviews). Therefore, the exploration of the incidence of misophonia, measured with comparable criteria and questionnaires, among homogenous groups of patients as well as in the general population should be done in the future. This could help to reveal the direction of the relation between misophonia and depression as well as other disorders.
This is yet another study indicating the meaningful role of anxiety in misophonia. Anxiety was correlated to a greater degree with misophonia severity than were any of the other measured variables. These results are similar to those of Queck et al. [13], who found that anxiety was the only significant factor in multivariate regression, predicting misophonia severity in a group of psychiatric patients. While the relation between misophonia and symptoms of anxiety as a pathology is already well documented, anxiety disorders (such as generalized anxiety disorder, panic disorder, or social phobia) are not the disorders most frequently found alongside misophonia and are not reported as those most significantly related with increased misophonia symptoms. The exceptions to this are OCD, in which the role in misophonia is still disputed, and PTSD, which is an event-related disorder, in contrast to other anxiety disorders where the more diffuse nature of the anxiety facilitates interaction with misophonia. At the same time, there is also significant data on the role of increased neuroticism in misophonia [14,41]. As the role of anxiety in predicting the severity of misophonia symptoms seems to be already well described, a deeper investigation into anxiety as a trait and personality characteristics such as neuroticism versus anxiety disorders could be the next step in unveiling the mechanisms of misophonia related to anxiety.
Excluding OCD patients allowed us to assess self-described impulsivity without the risk of overlap. As a result, unlike attentional impulsivity, non-planning impulsivity and motor impulsivity were weakly but significantly correlated with misophonia symptoms. Notably, attentional impulsivity was one of only two variables (the other being age) in this study that were not correlated with the severity of misophonia; the other aspects of impulsivity were much less associated with severity of misophonia than were all the other variables. Hence, the role of impulsivity was undermined in this study, but nonetheless, strong conclusions about impulsivity should not be drawn—this relationship needs further exploration.
Regarding symptoms of PTSD, intrusions and arousal were moderately associated with misophonia symptoms while avoidance was only weakly correlated. There were 6 participants (7.4%) who reported that they had been diagnosed by their psychiatrist with PTSD; their average score on MisoQuest was 10 points higher (M = 48.83, SD = 12.37) than patients without such a diagnosis (M = 38.52, SD = 16.85). The increased severity of misophonia symptoms in people with PTSD has already been reported by Rouw and Erfanian [5]. Unfortunately, our study did not include enough participants diagnosed with PTSD to conduct a statistical analysis and to draw reliable conclusions. Nonetheless, no patients with PTSD were diagnosed with misophonia in the face-to-face interviews. Therefore, our results undermine the potential relationship between misophonia and PTSD. These results might rather indicate relations between more general sound sensitivity and psychological distress and hyperarousal [42,43].
Vegetative symptoms were found to be weakly yet significantly correlated with the severity of misophonia symptoms, while somatic pain was found to be moderately significantly correlated. Although many studies discuss the vegetative symptoms experienced by people with misophonia in the presence of their misophonic triggers, to the best of our knowledge, this is the first study to measure the association between the above symptoms and misophonia severity in general. We are not surprised with these results. We expected that vegetative symptoms and somatic pain would be related to misophonia symptom severity to a certain but limited extent based on the associations between sound sensitivity and somatic disturbances and pain discussed in the Introduction section. Future research should check whether these variables are more related to general sound sensitivity than to misophonia. It is possible that, because misophonia is a more specific condition than general sound sensitivity/hyperacusis, the presence of pain and vegetative symptoms could be lower in misophonia.
Women manifested a significantly higher severity of misophonia symptoms than did men. Our results are in line with recent research conducted by Queck et al. [13] or Erfanian et al. [9]. However, we draw different conclusions to previous studies [11,12] which found no gender differences in severity of misophonia. Gender differences in accepted standards for certain scales that are used in clinical practice are usually not taken into account in research studies. As with other constructs, the standards for genders might differ also for misophonia.
The severity of misophonia symptoms was not associated with the age of participants. However, much age data was missing, which could have affected the results. In a study by Queck et al. [13] on patients with psychiatric diagnoses, a negative correlation between the severity of misophonia symptoms and age was observed, but only before adjusting for confounding factors. Similarly, there was no relation with age in the study by Wu et al. [11]. Nonetheless, the data are inconclusive: in the study conducted by Rouw & Erfanian [5], while severity of misophonia symptoms decreased with age and there was negative correlation with a small effect between age and severity of misophonia, they noted that it increased in the two oldest age categories (over 65). Thus, the association between age and severity of misophonia needs further exploration.
Assessment of MisoQuest’s additional external validity showed that, with its high accuracy, it is a good tool for assessing misophonia. It has lower sensitivity than specificity, which means that there is a risk of false negative cases. In fact, 4 patients diagnosed in face-to-face interviews with misophonia with limited impact on one’s life did not reach the cutoff for misophonia in MisoQuest. Results slightly below or above the cutoff for misophonia should be evaluated carefully, especially in populations of patients with psychiatric diagnoses. MisoQuest was created with an aim to identify people whose quality of life is significantly lower because of misophonia [30], and apparently, it captures more severe cases. Further assessment of validity of MisoQuest is needed on the general population.
Limitations
This study has two meaningful weaknesses. The major drawback of the study is the significant amount of missing data (age and gender) that was a result of mistakes during the data collection. Another limitation is that caution must be used when comparing these data with those of other studies (especially studies in which only questionnaires were used, with no face-to-face interviews). This is because MisoQuest is based on a slightly different approach to misophonia than the Misophonia Questionnaire (MQ), which has, to date, been used in the majority of studies. At the time of conducting this study, there was no Polish version of the MQ available. In the future, it would be worthwhile to compare characteristics of people diagnosed with misophonia by those two different questionnaires.
5. Conclusions
The prevalence of misophonia among patients with depression was evaluated in this study and stands at 8.5–12.76%. Additionally, the results show that MisoQuest can be helpful for identifying misophonia among patients with psychiatric diagnoses. The severity of misophonia symptoms was associated weakly to moderately with various symptoms and psychological characteristics and was the most associated with anxiety. Anxiety, however, is a more general psychological construct that not only is related to multiple psychiatric conditions but also for which intensity differs based on the temperamental characteristics of individuals. The aforementioned results are in line with other studies, suggesting that misophonia might be an independent disorder.
Supplementary Materials
The following are available online at https://www.mdpi.com/1660-4601/17/15/5464/s1, File S1: Semi-structured interview; File S2: Row data.
Click here for additional data file.
Author Contributions
Conceptualization, M.S., A.M.S., and B.B.; methodology, M.S., A.M.S., B.B., and W.Ł.D.; formal analysis, M.S.; investigation, M.S., A.M.S., B.B., and Ł.C.; writing—original draft preparation, M.S. and A.M.S.; writing—review and editing, B.B., Ł.C., and W.Ł.D.; supervision, W.Ł.D.; project administration, M.S., A.M.S., B.B., and Ł.C.; funding acquisition, M.S. All authors have read and agreed to the published version of the manuscript.
Funding
This work was supported by the Faculty of Psychology, University of Warsaw, from the funds awarded by the Ministry of Science and Higher Education in the form of a subsidy for the maintenance and development of research potential in 2020 (501-D125-01-1250000-5011000251), the Polish National Science Center grant number 2018/29/N/HS6/01108 and the Foundation for Polish Science (FNP) project (Bio-inspired Artificial Neural Networks ID: POIR.04.04.00-00-14DE/18-00.
Conflicts of Interest
The authors declare no conflict of interest.
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Sensitivity, specificity, and accuracy of MisoQuest.
MisoQuest
Value %
(95% CI)
Sensitivity
66.67
(34.89–90.08)
Specificity
96.34
(89.68–99.24)
Accuracy
92.55
(85.26–96.95)
ijerph-17-05464-t002_Table 2
Correlations with severity of misophonia symptoms assessed with MisoQuest.
\\n
Age
HADS Anxiety
BIS Attentional
BIS Motor
BIS Non-Planning
SCL Vegetative
SCL Pain
HADS Depression
IES Intrusions
IES Arousal
IES Avoidance
MisoQuest Pearson’s correlation p-value
0.024
0.444
−0.178
0.205
0.204
0.276
0.307
0.325
0.324
0.325
0.263
0.856
0.000
0.086
0.047
0.049
0.007
0.003
0.001
0.001
0.001
0.010
HADS—Hospital Anxiety and Depression scale; BIS—Barrat Impulsivity Scale; SCL—Symptom Checklist Scale; IES—Impact of Event Scale.
\\n\"\n]"}}},{"rowIdx":467,"cells":{"data":{"kind":"list like","value":["\nInt J Mol SciInt J Mol SciijmsInternational Journal of Molecular Sciences1422-0067MDPI32751332PMC743212410.3390/ijms21155395ijms-21-05395ArticlePolymorphisms in the Angiogenesis-Related Genes EFNB2, MMP2 and JAG1 Are Associated with Survival of Colorectal Cancer Patientshttps://orcid.org/0000-0003-4430-296XSchererDominique12DeutelmoserHeike12BalavarcaYesilda1https://orcid.org/0000-0002-6096-1052TothReka13HabermannNina14BuckKatharina1KapElisabeth Johanna5BotmaAkke15SeiboldPetra5JansenLina6Lorenzo BermejoJusto2WeiglKorbinian6https://orcid.org/0000-0002-7238-6956BennerAxel7HoffmeisterMichael6UlrichAlexis89https://orcid.org/0000-0002-6129-1572BrennerHermann1610BurwinkelBarbara1112https://orcid.org/0000-0001-8919-1971Chang-ClaudeJenny513https://orcid.org/0000-0001-7641-059XUlrichCornelia M.11415*Division of Preventive Oncology, National Center for Tumor Diseases (NCT) and German Cancer Research Center (DKFZ), 69117 Heidelberg, Germany; scherer@imbi.uni-heidelberg.de (D.S.); heike.deutelmoser@nct-heidelberg.de (H.D.); ybalavarca@gmail.com (Y.B.); r.toth@dkfz-heidelberg.de (R.T.); nina.habermann@embl.de (N.H.); katharina.buck@gmx.de (K.B.); akkebotma@hotmail.com (A.B.); h.brenner@dkfz-heidelberg.de (H.B.)Institute of Medical Biometry and Informatics, University of Heidelberg, 69117 Heidelberg, Germany; lorenzo@imbi.uni-heidelberg.deDivision of Cancer Epigenomics and Cancer Risk Factors, German Cancer Research Center (DKFZ), 69117 Heidelberg, GermanyEuropean Molecular Biology Laboratory (EMBL), Genome Biology, 69117 Heidelberg, GermanyDivision of Cancer Epidemiology, German Cancer Research Center (DKFZ), 69117 Heidelberg, Germany; lisannekap@gmail.com (E.J.K.); p.seibold@Dkfz-Heidelberg.de (P.S.); j.chang-claude@dkfz-heidelberg.de (J.C.-C.)Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), 69117 Heidelberg, Germany; l.jansen@Dkfz-Heidelberg.de (L.J.); k.weigl@dkfz-heidelberg.de (K.W.); m.hoffmeister@dkfz-heidelberg.de (M.H.)Division of Biostatistics, German Cancer Research Center (DKFZ), 69117 Heidelberg, Germany; benner@dkfz-heidelberg.deDepartment of General, Visceral and Transplantation Surgery, University Hospital Heidelberg, 69117 Heidelberg, Germany; aulrich@lukasneuss.deChirurgische Klinik I, Lukaskrankenhaus Neuss, 41464 Neuss, GermanyGerman Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), 69117 Heidelberg, GermanyDivision of Molecular Epidemiology, German Cancer Research Center (DKFZ), 69117 Heidelberg, Germany; B.Burwinkel@dkfz-heidelberg.deDivision Molecular Biology of Breast Cancer, Department of Gynecology and Obstetrics, University of Heidelberg, 69117 Heidelberg, GermanyCancer Epidemiology Group, University Cancer Center Hamburg, University Medical Center Hamburg-Eppendorf, 20246 Hamburg, GermanyHuntsman Cancer Institute, Salt Lake City, UT 84112, USADepartment of Population Health Sciences, University of Utah, Salt Lake City, UT 84112, USACorrespondence: Neli.ulrich@hci.utah.edu; Tel.: +1-801-213-57162972020820202115539503720202272020© 2020 by the authors.2020Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
An individual’s inherited genetic variation may contribute to the ‘angiogenic switch’, which is essential for blood supply and tumor growth of microscopic and macroscopic tumors. Polymorphisms in angiogenesis-related genes potentially predispose to colorectal cancer (CRC) or affect the survival of CRC patients. We investigated the association of 392 single nucleotide polymorphisms (SNPs) in 33 angiogenesis-related genes with CRC risk and survival of CRC patients in 1754 CRC cases and 1781 healthy controls within DACHS (Darmkrebs: Chancen der Verhütung durch Screening), a German population-based case-control study. Odds ratios and 95% confidence intervals (CI) were estimated from unconditional logistic regression to test for genetic associations with CRC risk. The Cox proportional hazard model was used to estimate hazard ratios (HR) and 95% CIs for survival. Multiple testing was adjusted for by a false discovery rate. No variant was associated with CRC risk. Variants in EFNB2, MMP2 and JAG1 were significantly associated with overall survival. The association of the EFNB2 tagging SNP rs9520090 (p < 0.0001) was confirmed in two validation datasets (p-values: 0.01 and 0.05). The associations of the tagging SNPs rs6040062 in JAG1 (p-value 0.0003) and rs2241145 in MMP2 (p-value 0.0005) showed the same direction of association with overall survival in the first and second validation sets, respectively, although they did not reach significance (p-values: 0.09 and 0.25, respectively). EFNB2, MMP2 and JAG1 are known for their functional role in angiogenesis and the present study points to novel evidence for the impact of angiogenesis-related genetic variants on the CRC outcome.
Colorectal cancer (CRC) is the second most common cancer and the second leading cause of cancer death among men and women throughout the world asserting major public health problems [1]. Disease risk and prognosis are to a large proportion modifiable with obesity, red meat consumption and sedentary lifestyle increasing the risk and physical activity, NSAID (non-steroidal anti-inflammatory drug) use and fiber intake being protective [2,3,4]. However, there is also a genetic component to the disease, which is estimated to explain up to 35% of the heritability in colorectal cancer risk [5,6]. Some studies point to genetic associations with the CRC outcome [7,8,9]. However, evidence is still limited.
Angiogenesis, the generation of new blood vessels from pre-existing vessels, is crucial for tumor growth, progression and metastasis as it provides nutrients and oxygen to the growing tumor [10]. While in normal tissue, angiogenesis is tightly controlled and only transiently turned on, in carcinogenic progression an ‘angiogenic switch’ perpetuates the formation of new blood vessels [11].
VEGF (vascular endothelial growth factor) as one of the most potent endothelial cell mitogens is a major driver of angiogenesis. It can act in an autocrine or paracrine manner through interaction with its receptors (VEGFR1-3, also named FLT1 (fms related tyrosine kinase 1), KDR (kinase insert domain receptor) and FLT4 (fms related tyrosine kinase 4), respectively) and co-receptors (neuropilins, NRPs) [12]. Such interactions may further be regulated through ephrinB2 (EFNB2) and finally stimulate proliferation, migration of endothelial cells, cell degradation, remodeling of the extracellular matrix (ECM) and angiogenesis [13]. Ephrins and their receptors (Eph receptors) are crucial in numerous biological processes [14]. Their bidirectional signaling triggers cascades in both, the receptor- and the ligand-bearing cells and deregulated Eph/ephrin activation as well as altered expression of the receptors or their ligands have been frequently reported in a variety of tumors including colorectal neoplasms [14,15]. Based on these observations, targeted interference with Eph/ephrin signaling has been proposed for cancer therapy [16].
Matrix metalloproteinases (MMPs) are critical in ECM remodeling [13,17]. In addition to their capability to promote bioavailability of VEGF, the proteolytic activities of MMPs facilitate protein degradation and support vessel formation [18]. VEGF signaling also regulates DLL4 expression, which is one of two key ligands of NOTCH4 (notch receptor 4), the second ligand being JAG1 (jagged canonical Notch ligand 1) [19]. Concurrently, NOTCH4 promotes VEGFR expression [20].
An individual’s inherited genetic background may modify angiogenesis-related signaling cascades that affect blood supply of premalignant lesions and/or macroscopic tumors [21]. We hypothesize that this may result in altered cancer risk or disease outcome after colorectal cancer diagnosis.
In the present study, we investigated the association between angiogenesis-related genetic variants and risk of CRC, overall survival (OS) and recurrence-free survival (RFS) of colorectal cancer patients in 1754 CRC cases and 1781 healthy controls using a comprehensive targeted genotyping approach and validated some of the associations.
2. Results
In this study 392 SNPs in 33 angiogenesis-related genes (Table S1) were investigated for their association with risk of colorectal cancer and survival of colorectal cancer patients in a population of 1754 colorectal cancer cases and 1781 healthy individuals (Tables S2, S3 and S4, respectively). Characteristics of the study population are presented in Table 1.
2.1. Risk
Higher education and the use of NSAIDs were associated with reduced risk of colorectal cancer. In addition, cases were more likely to smoke regularly, consume higher amounts of red meat and alcohol and were more often diabetic and obese (Table 1a).
No variant was associated with the risk of colorectal cancer after adjustment for multiple testing (Table S2). Furthermore, we did not observe statistically significant interactions of any of the investigated polymorphisms with NSAID use or smoking in relation to the risk of colorectal cancer.
2.2. Overall and Recurrence-Free Survival
During a median follow up of five years, 538 patients died and 468 had a recurrent event. Deceased patients were older, diagnosed with higher disease stages and tumor grade. They were less likely to consume alcohol and to be overweight or obese (Table 1b).
Eleven tagging SNPs in four genes (EFNB2, MMP2, JAG1 and KDR) were significantly associated with overall survival (Table 2, Figure S1 and Table S3) of colorectal cancer patients. Five variants in EFNB2, none of which were in high LD (r2 < 0.8), were associated with overall survival. The strongest associations with overall survival were observed for the protective variant rs2391333 (HRC > T: 0.77, 95% CI: 0.68–0.87, p-value: <0.0001) and for the risk variant rs9520090 (HRG > C: 1.29, 95% CI: 1.15–1.46, p-value: <0.0001). Furthermore, rs9520087 (HRG > A: 1.24, 95% CI: 1.10–1.40, p-value: 0.0006), rs9520088 (HRTT vs. TC/CC: 1.37, 95% CI: 1.15–1.62, p-value: 0.0003) and rs2057408 (HRAA vs. AG/GG: 1.35, 95% CI: 1.14–1.62, p-value: 0.0008) were significantly associated with worse overall survival (Table 2a). All associations with overall survival remained statistically significant after correction for multiple testing (q < 0.05).
Four tagging SNPs (none in high LD with r2 < 0.8) in the region of MMP2 were significantly associated with overall survival of colorectal cancer patients (Table 2a). Rs17301608 was significantly associated with poorer OS (HRA > G: 1.25, 95% CI: 1.11–1.41, p-value: 0.0003, Table 2a). The variants rs2241145 (HRG > C: 1.23, 95% CI: 1.09–1.38, p-value: 0.0005) and rs1561219 (HRC > A: 1.32, 95% CI: 1.11–1.56, p-value: 0.0009) were also associated with decreased overall survival. In contrast, the variant rs243847 significantly improved overall survival (HRT > C: 0.81, 95% CI: 0.71–0.92, p-value: 0.0010). All associations remained statistically significant after multiple testing correction (q < 0.05).
One intronic variant in JAG1 (rs6040062) was significantly associated with shorter overall survival (HRG > C: 1.36, 95% CI: 1.15–1.61, p-value: 0.0003, Table 2a) and recurrence-free survival (HRG > C: 1.31, 95% CI: 1.09–1.57, p-value: 0.004, Table S4) of colorectal cancer patients. Only the association with overall survival remained significant after multiple testing correction (q < 0.05).
Finally, the V297I variant (rs2305948) in KDR was associated with decreased overall survival (HRCC vs. CT/TT: 1.28, 95% CI: 1.04–1.57, p-value: 0.02, Table 2b). For its deleterious and probably damaging SIFT and PolyPhen scores, respectively, this polymorphism was a selected candidate.
SNPs in MMP2, EFNB2 and JAG1, which were significantly associated with patients’ survival, were subsequently analyzed in a first validation set of 2165 (for MMP2 and JAG1), and 1638 (for EFNB2) DACHS colorectal cancer patients and a second validations set of 372 colorectal cancer patients from TCGA where genome-wide data was available.
In the first validation set of imputed genotypes from the DACHS study with a median follow up of five years, the association with rs9520090 in EFNB2 was validated in a dominant model (HRGG vs. GC/CC: 1.22, 95% CI: 1.00–1.47, p-value: 0.05). The variant rs6040062 in JAG1 showed a similar direction (HR: 1.10) in the validation set, however the association was not significant (p-value: 0.25). No other association was validated in this dataset (Table S5).
For the second validation set, we retrieved data of 372 colorectal cancer patients with a median follow up of 0.6 years from The Cancer Genome Atlas (TCGA) Colon Adenocarcinoma (COAD) and Rectal Adenocarcinoma (READ) to validate significant associations in an external dataset. The variant rs7983579, which is in LD with rs9520090 (r2 = 0.8) in EFNB2, was significantly associated with the overall survival of colorectal cancer patients (HRT > G: 1.76, 95% CI: 1.14–2.73, p-value: 0.01). The variant rs9520090 itself showed the same direction of association with overall survival, although it did not reach significance (HRG > C: 1.28, 95% CI: 0.84–1.94, p-value: 0.25). Furthermore, the variants rs9923304 (HRT > C: 1.49, 95% CI: 0.94–2.38, p-value: 0.09) and rs1477017 (HRA > G: 1.42, 95% CI: 0.91–2.22, p-value: 0.12) in MMP2 linked to rs2241145 (r2 = 0.83) and rs17301608 (r2 = 0.93), respectively, were marginally associated with overall survival. No TCGA data were available for variants in JAG1.
The gene-environment interaction between overall survival and smoking, NSAID use, adjuvant 5-FU-based chemotherapy and microsatellite status was investigated for all variants (Table S7). Some indication for interaction was observed for two tagging SNPs in ETS1 (rs11221322 and rs7121854: pinteraction: 0.0004 and 0.001, respectively) and the candidate missense variant in TEK V486I (pinteraction: 0.003) with MSI (microsatellite instability), for the candidate missense variant KDR Q472H (rs1870377; pinteraction: 0.03) with smoking, for the candidate missense variant TEK V486I (rs1334811, pinteraction: 0.03) with NSAID use and for the IL10 candidate rs1800890 (pinteraction: 0.01) with adjuvant 5-FU-based chemotherapy for overall survival of colorectal cancer patients. These associations did not remain significant after adjustment for multiple testing.
3. Discussion
Genetic variation may contribute to an ‘angiogenic switch’, which is essential for blood supply and tumor growth in microscopic and macroscopic tumors. In the present study, we investigated the association of polymorphisms in angiogenesis-related genes with the risk of colorectal cancer as well as with overall survival and recurrence-free survival of colorectal cancer patients in the DACHS population-based case-control study using 1754 CRC cases and 1781 controls. We chose a targeted candidate gene approach to obtain a more complete coverage of the hypothesized genes and avoided potentially high numbers of false-negative findings, which are often the result of large-scale genotyping studies. We observed significant associations between variants in EFNB2, MMP2 and JAG1 and overall survival. Some of the associations could be validated.
Ephrins are membrane-associated ligands that function through interaction with their receptors, erythropoietin-producing hepatocellular (Eph) receptor tyrosine kinase. The unique ligand-receptor interaction, which requires cell-cell contact, can trigger bidirectional signaling, with effects in both the ligand- and the receptor-bearing cell [22]. Downstream or upstream signaling cascades regulate cell proliferation, migration, adhesion, differentiation, survival and angiogenesis and are crucial for homeostasis in epithelial tissues. In this study, we observed significant associations between five variants in ephrin B2 (EFNB2) and OS of colorectal cancer patients. Rs9520087 is located in the 3′UTR of EFNB2, which may suggest regulatory effects on gene expression of EFNB2, however, the in silico functional follow up did not reveal any associations with gene expression. It is not linked to any known variant and it is covered only by a small number of genome-wide genotyping arrays. Imputation of the variant in a validation set of 2165 CRC cases resulted in 10% of inaccurately imputed alleles, which may be the reason why this variant has not been identified earlier [23]. Similarly, other EFNB2 variants are only present on a limited number of genome-wide genotyping arrays. Rs3742159, linked to rs9520088 (r2 = 1) displayed a RegulomeDB rank of 3a. It is located within a region, which likely affects binding of transcription factors, one of which was identified to be NFKB [24]. None of the other linked SNPs seems to be functionally important. The SNPs rs2391333 and rs9520090 modify the methylation status of the site chr13:107163855 (methylation probe cg05493945) within EFNB2. Whether this results in changed gene expression of EFNB2 needs to be further investigated. A number of studies have shown that EFNB2 and its receptor EPHB4 are transcriptionally upregulated in colon cancer carcinoma cell lines and in colon cancer tissue as compared to adjacent mucosa, supporting a role of EFNB2 in carcinogenesis potentially through modulation of angiogenic signals, even though the exact mechanisms remain to be fully elucidated [25]. No other gene, which may be relevant for cancer progression is located in the vicinity of this region, thus it seems plausible, that the identified associations with survival of colorectal cancer patients are linked to EFNB2.
We observed a statistically significant association with OS of colorectal cancer patients for four MMP2 tagging SNPs (rs17301608, rs2241145, rs1561217 and rs243847), all located in introns or in non-coding exon regions, while a previous association of the MMP2 rs243865 (−1306 C > T-in our study the linked SNP rs243866 was genotyped, r2: 0.99) in smaller studies of OS or CRC risk was not confirmed [26,27]. The major task of MMPs is the degradation of proteins, which is an essential step in many hallmarks of cancer in regulating cell growth, differentiation, apoptosis, migration, invasion, immune surveillance and cancer angiogenesis [18]. Consequently, MMPs play a crucial role in malignant transformation and cancer progression. To our knowledge, neither of the polymorphisms or any of the linked variants has previously been reported for their association with OS of colorectal cancer patients. In silico functional follow up of rs17301608 revealed that three of the linked variants (rs1477017, rs11646643 and rs9302671; r2 > 0.8) ranked low (1f) at RegulomeDB, supporting a regulatory role. They likely affect the binding of transcription factors (TF), potentially of NR2F2 (nuclear receptor subfamily 2 group F member 2), which was previously shown to regulate cell growth, invasiveness, metastasis and angiogenesis [28,29]. Furthermore, all three variants linked to rs17301608 are reported expression quantitative trait loci (eQTLs) for LPCAT2 (lysophosphatidylcholine acyltransferase 2), which was previously associated with the progression of breast cancer, ovarian cancer and CRC in human tissue and in cell lines [30]. Finally, data from TCGA supports the function of rs2241145 and rs17301608 as eQTLs on LPCAT2 expression in prostate adenocarcinoma [31].
Aberrant NOTCH signaling promotes the development and progression of colorectal cancer and is partly driven by the overexpression of JAG1, a ligand of NOTCH4 [32]. We identified one tagging SNP (rs6040062) in JAG1, which was associated with shorter OS of colorectal cancer patients. The variant and its linked SNPs rs3748480 (r2: 1.0) and rs112946915 (r2: 0.97) are intronic. The lowest functional RegulomeDB rank was reached by rs112946915 (3a), which modifies binding motifs for transcription factors STAT1 and IRF1 among others. JAG1 has been considered as an attractive target for colorectal cancer therapy, as it seems to be a more specific target than other members of the NOTCH pathway [33].
Our results are based on more than 1700 CRC cases and 1700 healthy controls, with comprehensive clinical, epidemiological and demographic data, which is a major strength of the study. The detailed information on lifestyle factors as well as the availability of clinical patients’ characteristics enabled us to adequately account for relevant environmental factors and test for effect modification. Although our sample size was fairly large, some of the interaction analyses were based on small strata. Unfortunately, the MSI status was missing for 22% of the population and 9% of investigated tumors were MSI-high. Thus, associations should be considered with caution. Furthermore, we had access to two validation sets and validated the association of the EFNB2 tagging SNP rs9520090 (or its linked variant rs7983579) with the overall survival of colorectal cancer. In addition, tagging SNPs in JAG1 and MMP2 showed the same direction of association with overall survival in the first and second validation set, respectively.
The signals of the first validation set were weaker than we expected considering that the discovery and first validation sets were derived from the same study population. We thus compared both sample sets with respect to genetic, epidemiological and clinical data. For up to 67 individuals, imputed genotypes from genome-wide data and at the same time directly genotyped genotypes were available. We observed a discrepancy of 5%–25% between the directly genotyped and imputed genotypes, suggesting an insufficient imputation accuracy for these regions and supporting the choice of a targeted candidate gene approach. The accuracy of imputed genotypes strongly relies on linkage disequilibrium (LD), consequently they depend on the genotypes measured within the same LD-block. EFNB2 and JAG are located in regions of rather low LD, which reduces the probability to correctly impute missing genotypes [34]. We further observed differences between the two study sample sets with respect to overall survival, alcohol consumption and tumor grade of patients. We adjusted for these variables, however, other confounders not captured within this study may explain the discrepant association strengths between the two study samples. While 98% of participants reported to be of German nationality, the ethnic background of study participants was not assessed as part of this study. We cannot rule out an effect of ethnicity on the identified associations, even though this is likely to be very small, because they are expected to be of Caucasian origin.
In conclusion, we identified novel genetic associations with the overall survival of colorectal cancer patients. To our knowledge, no previous study has reported associations between SNPs in EFNB2 or JAG1 with the CRC outcome, both genes known for their functional role in angiogenesis and their impact on disease progression. The present study cannot point to the causal variant or elucidate the functional role of the identified polymorphisms and additional investigations in other populations are required to further validate these associations and to unravel the causal variants and their functionalities.
4. Materials and Methods 4.1. Study Population
The study population includes patients with colorectal cancer who participated in a long-term follow-up study of patients of the German population-based case-control study DACHS (“Darmkrebs: Chancen der Verhuetung durch Screening”; described in detail elsewhere) [35,36,37,38]. In brief, colorectal cancer patients with a primary, confirmed diagnosis of colorectal cancer were recruited from hospitals of the Rhein-Neckar-Odenwald region 2003–2007 if they were aged 30 years or older, resident in the study region and able to complete an in-person interview. Baseline interviews were performed using standardized questionnaires to collect information on demographic and established or suggested colorectal cancer risk factors as well as possible prognostic factors. Follow-up information on vital status was collected at three, five and ten years after diagnosis. Causes of death were verified by death certificates and coded based on ICD−10 classifications. Information on recurrences and secondary tumors were collected from general practitioners and specialists as applicable. In addition, clinical and histological data were extracted from patient and pathological records. Patients without baseline blood samples (post-diagnostic) or without mouthwash (<1%) were excluded from the study.
Population controls were randomly selected from lists of residents of the population registries of the cities and counties. Controls were matched to cases by sex, county of residence and 5-year age group. Controls were eligible if they were aged 30 years or older with no history of colorectal cancer.
The study was approved by the ethics committee (approved on 6 December 2001, project identification code 310/2001) of the University of Heidelberg and State Medical Boards of Baden-Wuerttemberg and Rhineland-Palatinate and was conducted in agreement with the Helsinki Declaration. Written informed consent was provided by all participants at the baseline and during follow-up.
4.2. Single Nucleotide Polymorphism (SNP) Selection and Genotyping
We selected 33 genes related to angiogenesis based on a comprehensive literature search, gene ontology data bases and external expertise. The genes comprised: AKT1, ANGPT1, ANGPT2, DKK4, DLL1, DLL4, EFNB2, EPHB4, ETS1, FGF2, FLT1, FLT4, HIF1A, ID1, JAG1, KDR, MAPK1, MMP2, MMP9, NOTCH4, NRP1, NRP2, PDGFA, PDGFRB, PGF, PIK3CA, PIK3CG, TEK, TIE1, VEGFA, VEGFB, VEGFC and VHL (Table S1).
We used Haploview 4.2 (Broad Institute, Cambridge, MA, USA) to identify tagging SNPs located in the assigned genes as well as in the 5′ and 3′ flanking regions (up to 10 kb) for coverage of the genetic variation across the gene. The tagging SNP selection was based on the HapMap project (CEU [Utah Residents (CEPH) with Northern and Western European Ancestry] population, Phase II/Release 24), using a pairwise tagging approach applying r2 ≥ 0.8 as the cutoff.
Genomic DNA was extracted from EDTA (Ethylenediaminetetraacetic acid) anticoagulated blood or mouthwash samples using the FlexiGene DNA kit (Qiagen GmbH, Hilden, Germany) and quantified using Quant-iT PicoGreen dsDNA reagent and kit (Invitrogen/Life Technologies, Darmstadt, Germany). Genotyping was performed using the Illumina GoldenGate assay or iPLEX assay (Sequenom, Hamburg, Germany) for the MassArray system. For quality control, all assays were validated using the 72 CEPH controls, distributed randomly among the study samples. Each assay batch contained negative and positive controls and 5% of the total number of samples were re-genotyped to confirm reproducibility. Call rates for all genotypes exceeded 96%. The concordance for blinded duplicates was >99.7%. Deviation from the Hardy-Weinberg equilibrium was evaluated using a χ2 statistic in all control subjects. A total of 392 single nucleotide polymorphisms in 33 genes were investigated in this study, which passed quality control, were not in linkage disequilibrium (LD) and had a cell count of at least 10.
4.3. Imputation
Imputation of environmental factors was performed using multiple imputation as most variables, except for tumor grade (11%), had only few missing values (<1%). For grading, we compared no imputation and multiple imputation of grading with best-case (all missing values set to grade 1) and worst-case (all missing values set to grade 4) resulting in similar effect estimates (<10% difference). Genotype imputation in the DACHS study was performed by using IMPUTE2 and the 1000 Genomes reference panel [39,40].
4.4. Statistical Analysis
The associations between polymorphisms and the risk of colorectal cancer were assessed using unconditional logistic regression models estimating odds ratios (OR) and 95% confidence intervals (CI). Associations were also adjusted by relevant environmental factors, which were obtained using the backward elimination procedure. These factors were: age (<60, 60–69, 70–79 and 80+ years), sex, alcohol consumption (no alcohol consumption, 0.1–5.7, 5.7–13.3, 13.3–28.6 and ≥28.6 g alcohol/day during lifetime), BMI category 5–14 years ago (≤18.5 kg/m2, 18.5 to < 25kg/m2 (reference category), 25 to <30 kg/m2 and ≥30 kg/m2), first degree family history of CRC (yes, no/unknown), education (low, medium and high), prior colonoscopy (ever colorectal endoscopy after consideration of endoscopy report, yes, no/unknown), diabetes, regular smoking (never, yes), NSAID use (never, ever used NSAIDs more than once/month ≥1 year) and red meat consumption (low, medium and high).
For each polymorphism, the risk of colorectal cancer was estimated using log-additive, and dominant models. Differences in baseline characteristics between deceased and non-deceased patients were tested using the χ2 statistic and t-tests for categorical and continuous variables, respectively. As an outcome, time to survival and recurrence-free survival times were calculated as the time from the diagnosis to the event of interest (death and recurrence or death, respectively) or censoring. Median follow-up time was calculated using the reverse Kaplan-Meier estimator and the cumulative survival curves for overall survival were generated using the Kaplan-Meier curves [41].
Cox proportional hazard models were used to estimate hazard ratios (HR) for overall survival and recurrence-free survival, and their 95% CIs associated with genetic variants. Dominant and log-additive modes of inheritance were assumed for the genetic variants. The models also included adjustment for relevant environmental and clinical factors obtained from a backward elimination procedure. The analyses were adjusted for age (<60, 60–70, 70–80 and 80+ years), sex, stage (I-IV), tumor grade (1 and 2 versus 3 and 4), current BMI (<18.5 kg/m2, 18.5 to <25 kg/m2 (reference category), 25 to <30 kg/m2 and ≥30 kg/m2), and alcohol intake (no alcohol intake and quartiles: 0.1–6.1, 6.1–15.6, 15.6–32.6 and ≥32.6 g alcohol/day in the last year).
The effect modification was tested by using multiplicative interaction terms and stratified analyses for adjuvant 5-FU-based chemotherapy (yes/no) and microsatellite status (high/low; only for overall survival), NSAID use (never, ever used NSAIDs more than once/month ≥1 year), and smoking (never/yes).
All statistical analyses were two-sided with a significance level of 0.05 and were performed using SAS (version 9.3, SAS Institute, Cary, NC, USA) and R (version 3.0.2, R Foundation for Statistical Computing, Vienna, Austria).
The false discovery rate (FDR, indicated as the q-value) correction method was used to adjust for multiple testing of non-candidate polymorphisms [42].
In silico functional follow up of the SNPs (Table S6) that were statistically significantly associated with risk of CRC or overall survival of CRC patients was performed using Haploreg, LDlink, RegulomeDB, PancanQTL, Pancan-meQTL, GTExPortal and (variant effect predictor) VEP [24,31,43,44,45,46,47].
4.5. Validation Sets
Validation of significant associations was performed using two datasets. The first set included DNA samples from an independent sample of 2165 DACHS colorectal cancer patients recruited 2007–2010, which were genotyped using Illumina’s Global Screening Array, OmniExpress BeadChip and OncoArray as described previously [48]. Genotypes were imputed separately for each gene (±1.5 MB) and for each array using the Michigan Imputation Server [49]. The Haplotype Reference Consortium (HRC) reference panel (HRC r1.1 2016) has been used for imputation and Eagle for phasing. Subsequently, the imputed genotypes of each array were combined. The second set included genotype data from The Cancer Genome Atlas (TCGA) Colon Adenocarcinoma (COAD) cohort and Rectal Adenocarcinoma (TCGA-READ) cohort, which were downloaded from the NCI Genomic Data Commons.
A statistical analysis from our respective findings was performed for both validation datasets as described above. TCGA analyses were adjusted for the available variables age, sex and stage.
The data that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
We thank all participants of the DACHS study, the interviewers, as well as the physicians and staff of the following hospitals and cooperating institutions: Chirurgische Universitätsklinik Heidelberg, Klinik am Gesundbrunnen Heilbronn, St. Vincentiuskrankenhaus Speyer, St. Josefskrankenhaus Heidelberg, Chirurgische Universitätsklinik Mannheim, Diakonissenkrankenhaus Speyer, Krankenhaus Salem Heidelberg, Kreiskrankenhaus Schwetzingen, St. Marienund St. Annastiftkrankenhaus Ludwigshafen, Klinikum Ludwigshafen, Stadtklinik Frankenthal, Diakoniekrankenhaus Mannheim, Kreiskrankenhaus Sinsheim, Klinikum am Plattenwald Bad Friedrichshall, Kreiskrankenhaus Weinheim, Kreiskrankenhaus Eberbach, Kreiskrankenhaus Buchen, Kreiskrankenhaus Mosbach, Enddarmzentrum Mannheim, Kreiskrankenhaus Brackenheim, and the Cancer Registry of Rhineland-Palatinate. We thank the microarray unit of the Genomics and Proteomics Core Facility of the German Cancer Research Center for genotyping, particularly Matthias Schick, and also thank Muhabbet Celik, Ursula Eilber, Sabine Behrens and Ute Handte-Daub for excellent technical assistance. The results shown here are in part based upon data generated by the TCGA Research Network: https://www.cancer.gov/tcga.
Supplementary Materials
Supplementary Materials can be found at https://www.mdpi.com/1422-0067/21/15/5395/s1.
Click here for additional data file.
Author Contributions
D.S. prepared the statistical analysis plan, interpreted the data and drafted the manuscript. Y.B., H.D. and K.B. analyzed the data, P.S., B.B., R.T., N.H., A.B. (Akke Botma) and E.J.K. contributed to SNP selection, A.B. ( Axel Benner) and J.L.B. provided statistical support. A.U., K.W., L.J., J.C.-C., M.H., H.B., designed and implemented the different studies included in this analysis. C.M.U. conceived the study, provided input to interpreting the data and writing the manuscript. All authors discussed the results, provided critical feedback and approved the final manuscript. All authors have read and agreed to the published version of the manuscript.
Funding
D. Scherer was supported by an Olympia Morata fellowship of the Medical Faculty, University of Heidelberg and the German Federal Ministry of Education and Research (01KT1510). The DACHS study was supported by grants from the German Research Council (Deutsche Forschungsgemeinschaft, grant numbers BR 1704/6–1, BR 1704/6–3, BR 1704/6–4, BR 1704/6–6, CH 117/1–1, HO 5117/2–1, HE 5998/2–1, KL 2354/3–1, RO 2270/8–1, and BR 1704/17–1), and the German Federal Ministry of Education and Research (grant numbers 01KH0404, 01ER0814, 01ER0815, 01ER1505A and 01ER1505B). CM Ulrich was supported by R01 CA189184, U01 CA206110 and R01 CA 207371.
Conflicts of Interest
The authors declare no conflict of interest.
Abbreviations
CRC
Colorectal cancer
OS
Overall survival
RFS
Recurrence-free survival
SNP
Single nucleotide polymorphism
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Cancer200611963063510.1002/ijc.2187516496385HowieB.N.DonnellyP.MarchiniJ.A flexible and accurate genotype imputation method for the next generation of genome-wide association studiesPLoS Genet.20095100052910.1371/journal.pgen.1000529HowieB.MarchiniJ.StephensM.Genotype imputation with thousands of genomesG3 Genes Genomes Genet.2011145747010.1534/g3.111.001198SchemperM.SmithT.L.A note on quantifying follow-up in studies of failure timeControl Clin. Trials199617434334610.1016/0197-2456(96)00075-X8889347BenjaminiY.HochbergY.Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple TestingJ. R. Stat. Soc.19955728930010.1111/j.2517-6161.1995.tb02031.xVariant Effect PredictorAvailable online: http://www.ensembl.org/Homo_sapiens/Tools/VEP?db=core(accessed on 23 April 2019)WardL.D.KellisM.HaploReg: A resource for exploring chromatin states, conservation, and regulatory motif alterations within sets of genetically linked variantsNucleic Acids Res.20124093093410.1093/nar/gkr91722064851KeenJ.C.MooreH.M.The Genotype-Tissue Expression (GTEx) Project: Linking Clinical Data with Molecular Analysis to Advance Personalized MedicineJ. Pers. Med.20155222910.3390/jpm501002225809799GongJ.WanH.MeiS.RuanH.ZhangZ.LiuC.GuoA.Y.DiaoL.MiaoX.HanL.Pancan-meQTL: A database to systematically evaluate the effects of genetic variants on methylation in human cancerNucleic Acids Res.2019471066107210.1093/nar/gky814MachielaM.J.ChanockS.J.LDlink: A web-based application for exploring population-specific haplotype structure and linking correlated alleles of possible functional variantsBioinformatics2015313555355710.1093/bioinformatics/btv40226139635PetersU.JiaoS.SchumacherF.R.HutterC.M.AragakiA.K.BaronJ.A.BerndtS.I.BezieauS.BrennerH.ButterbachK.Identification of Genetic Susceptibility Loci for Colorectal Tumors in a Genome-Wide Meta-analysisGastroenterology201314479980710.1053/j.gastro.2012.12.02023266556DasS.ForerL.SchonherrS.SidoreC.LockeA.E.KwongA.VriezeS.I.ChewE.Y.LevyS.McGueM.Next-generation genotype imputation service and methodsNat. Genet.2016481284128710.1038/ng.365627571263
Characteristics of the study population. (a) Total population; (b) Case population.
ijms-21-05395-t001a_Table 1
(a)
\n
\n
Controls
Cases
p-Value
\nGender (Male/Female)\n
\n
1066/715
1027/727
0.43
\nAge (Mean ± SD)\n
\n
68.72 ± 10.18
68.25 ± 10.42
0.32
\nFirst Degree Relatives With CRC\n
n (available)
1781
1754
0.009
Yes (%)
11.3
14.3
No (%)
88.7
85.8
\nEducation\n
n (available)
1781
1754
<0.0001
Low (%)
53.3
62.9
Medium (%)
25.3
20.5
High (%)
21.4
16.6
\nRed meat Consumption\n
n (available)
1781
1754
<0.0001
Low (%)
10.7
8.2
Medium (%)
85.1
84.7
High (%)
4.2
7.1
\nAlcohol Consumption (g/day)\n
n (available)
1781
1754
0.012
no intake
14.7
16.3
0.1–5.7
23.4
21.0
5.7–13.3
21.1
20.8
13.3–28.6
23.6
21.1
≥28.6
17.2
20.9
\nRegular Smoking\n
n (available)
1781
1754
0.011
Never (%)
52.2
47.9
Current (%)
47.8
52.1
\nDiabetes\n
n (available)
1781
1754
0.0003
Yes (%)
13.7
18.2
No (%)
86.4
81.8
\nBMI (kg/m2)\n
n (available)
1781
1754
<0.0001
<18.5 (%)
0.7
0.97
18.5–25 (%)
37.1
31.07
25–30 (%)
47.0
47.32
>30 (%)
15.2
20.64
\nNSAID Use\n
n (available)
1781
1754
<0.0001
No (%)
63.1
74.0
Yes (%)
37.0
26.0
\nEver Colonoscopy\n
n (available)
1781
1754
<0.0001
No (%)
46.9
79.2
Yes (%)
53.1
20.8
ijms-21-05395-t001b_Table 1
(b)
\n
\n
Overall Survival (Discovery)
Overall Survival (Validation)
\nOverall n/Events\n
\n
1675/538
2165/689
\n
\n
HR (95% CI)
\np\n
HR (95% CI)
\np\n
\nGender\n
Male
[1]
\n
[1]
\n
Female
0.89 (0.75–1.05)
0.16
1.27 (1.09–1.49)
0.003
\nAge Category\n
<60
[1]
\n
[1]
\n
60–69
1.24 (0.94–1.62)
0.13
1.31 (1.00–1.72)
0.047
70–79
1.45 (1.10–1.90)
0.007
1.84 (1.43–2.36)
<0.0001
>80
2.84 (2.14–3.77)
<0.0001
3.40 (2.63–4.39)
<0.0001
\nStage at Diagnosis\n
I
[1]
\n
[1]
\n
II
1.53 (1.10–2.12)
0.01
1.81 (1.38–2.37)
<0.0001
III
2.73 (2.01–3.69)
<0.0001
2.70 (2.08–3.52)
<0.0001
IV
13.28 (9.81–17.98)
<0.0001
13.33 (10.26–17.31)
<0.0001
\nGrade\n
G1-G2
[1]
\n
[1]
\n
G3-G4
1.27 (1.06–1.52)
0.01
1.85 (1.57–2.18)
<0.0001
\nAlcohol Consumption\n\n(g/day)\n
no intake
[1]
\n
[1]
\n
0.1–6.1
0.69 (0.53–0.88)
0.003
0.79 (0.64–0.98)
0.03
6.1–15.6
0.70 (0.54–0.91)
0.007
0.76 (0.61–0.95)
0.02
15.6–32.6
0.67 (0.52–0.86)
0.002
0.88 (0.70–1.09)
0.24
≥32.6
0.74 (0.58–0.96)
0.02
0.93 (0.73–1.18)
0.54
\nBMI (kg/m2)\n
18.5–25>
[1]
\n
[1]
\n
<18.5
1.72 (1.075–2.75)
0.02
1.18 (0.71–1.95)
0.53
25–30>
0.77 (0.64–0.93)
0.007
0.76 (0.65–0.90)
0.002
≥30
0.71 (0.56–0.91)
0.007
0.78 (0.63–0.96)
0.02
Abbreviations: HR: hazard ratio; CI: confidence interval; BMI: body mass index.
Significant associations of genetic variants in MMP2, EFNB2 and JAG1 with overall and recurrence-free survival of colorectal cancer patients in the discovery cohort. (a) Associations of tagging SNPs; (b) Association of candidate SNP.
ijms-21-05395-t002a_Table 2
(a)
\n
\n
\n
\n
\n
\n
Overall Survival
\n
\n
Recurrence-Free Survival
Gene
SNP
Location
Alleles
Alive
Deceased
HR 1 (95%-CI)
\np\n
\nq\n
Alive
Deceased
HR 1 (95%-CI)
\np\n
\nq\n
\nEFNB2\n
rs2391333
intronic
CC
455
223
[1]
\n
\n
474
204
[1]
\n
\n
CT
526
259
0.77 (0.68–0.87)
<0.0001
0.008
538
247
0.85 (0.74–0.97)
0.02
0.73
TT
174
64
168
70
CT/TT
700
323
0.76 (0.64–0.90)
0.002
0.060
706
317
0.83 (0.69–0.99)
0.05
0.91
rs9520090
intronic
GG
338
134
[1]
\n
\n
339
133
[1]
\n
\n
GC
580
270
1.29 (1.15–1.46)
<0.0001
0.008
586
264
1.20 (1.06–1.37)
0.005
0.68
CC
237
142
255
124
GC/GC
817
412
1.42 (1.16–1.73)
0.0006
0.04
841
388
1.2 (1.03–1.56)
0.03
0.68
rs9520087
3′UTR
GG
369
148
[1]
\n
\n
371
146
[1]
\n
\n
GA
562
297
1.24 (1.10–1.40)
0.0006
0.04
575
284
1.15 (1.00–1.31)
0.05
0.86
AA
224
101
234
91
GA/AA
786
398
1.42 (1.17–1.71)
0.0003
0.03
809
375
1.28 (1.05–1.57)
0.14
0.68
rs2057408
downstream
AA
509
204
[1]
\n
\n
513
200
[1]
\n
\n
AG
488
271
1.19 (1.05–1.34)
0.006
0.13
507
252
1.13 (0.99–1.29)
0.07
0.87
GG
158
71
160
69
AG/GG
646
342
1.35 (1.14–1.62)
0.0008
0.04
667
321
1.26 (1.04–1.52)
0.02
0.68
rs9520088
intronic
TT
638
269
[1]
\n
\n
641
266
[1]
\n
\n
TC
429
239
1.24 (1.08–1.41)
0.002
0.06
446
222
1.15 (0.99–1.33)
0.07
0.87
CC
88
38
93
33
TC/CC
517
277
1.37 (1.15–1.62)
0.0003
0.03
539
255
1.24 (1.03–1.49)
0.02
0.68
\nMMP2\n
rs17301608
intronic
AA
432
180
[1]
\n
\n
431
181
[1]
\n
\n
AG
540
267
1.25 (1.11–1.41)
0.0003
0.03
551
256
1.11 (0.98–1.27)
0.11
0.87
GG
183
99
198
84
AG/GG
723
366
1.42 (1.18–1.71)
0.0002
0.03
749
340
1.23 (1.01–1.49)
0.04
0.91
rs1561219
intronic
CC
877
378
[1]
\n
\n
877
378
[1]
\n
\n
CA
255
160
1.32 (1.11–1.56)
0.0009
0.04
277
138
1.11 (0.92–1.34)
0.26
0.89
AA
23
8
26
5
CA/AA
278
168
1.37 (1.14–1.65)
0.0009
0.04
303
143
1.17 (0.95–1.40)
0.14
0.91
rs2241145
intronic
GG
341
133
[1]
\n
\n
334
140
[1]
\n
\n
GC
547
281
1.23 (1.09–1.38)
0.0005
0.04
566
262
1.11 (0.98–1.26)
0.11
0.87
CC
267
132
280
119
\n
\n
\n
GC/CC
814
413
1.50 (1.23–1.83)
<0.0001
0.03
846
381
1.24 (1.01–1.52)
0.05
0.91
rs243847
intronic
TT
432
213
[1]
\n
\n
448
197
[1]
\n
\n
TC
542
267
0.81 (0.71–0.92)
0.001
0.04
555
254
0.88 (0.76–1.00)
0.05
0.87
\n
CC
181
66
177
70
\n
TC/CC
723
333
0.82 (0.68–0.97)
0.024
0.32
732
324
0.86 (0.71–1.04)
0.11
0.91
\nJAG1\n
rs6040062
intronic
TT
876
391
[1]
\n
\n
886
381
[1]
\n
\n
TG
259
142
1.36 (1.15–1.61)
0.0003
0.02
274
127
1.31 (1.09–1.57)
0.004
0.68
GG
20
13
20
13
TG/GG
279
155
1.38 (1.14–1.67)
0.0007
0.04
294
140
1.29 (1.05–1.59)
0.01
0.68
ijms-21-05395-t002b_Table 2
(b)
\n
\n
\n
\n
\n
\n
Overall Survival
\n
\n
Recurrence-Free Survival
Gene
SNP
Location
Alleles
Alive
Deceased
HR 1 (95%-CI)
\np\n
\nq\n
Alive
Deceased
HR 1 (95%-CI)
\np\n
\nq\n
\nKDR\n
rs2305948
V297I
CC
934
424
[1]
\n
\n
944
414
[1]
\n
\n
CT
209
113
1.27 (1.06–1.53)
0.01
NA
225
97
1.14 (0.92–1.41)
0.23
NA
TT
12
9
11
10
CT/TT
221
122
1.28 (1.04–1.57)
0.02
NA
236
107
1.09 (0.86–1.37)
0.47
NA
1 Hazard ratios for log-additive and dominant models, adjusted for age, sex, stage, grade, BMI, alcohol consumption. Abbreviations: SNP: single nucleotide polymorphism; HR: hazard ration; CI: confidence interval; NA: not applicable.
\n"],"string":"[\n \"\\nInt J Mol SciInt J Mol SciijmsInternational Journal of Molecular Sciences1422-0067MDPI32751332PMC743212410.3390/ijms21155395ijms-21-05395ArticlePolymorphisms in the Angiogenesis-Related Genes EFNB2, MMP2 and JAG1 Are Associated with Survival of Colorectal Cancer Patientshttps://orcid.org/0000-0003-4430-296XSchererDominique12DeutelmoserHeike12BalavarcaYesilda1https://orcid.org/0000-0002-6096-1052TothReka13HabermannNina14BuckKatharina1KapElisabeth Johanna5BotmaAkke15SeiboldPetra5JansenLina6Lorenzo BermejoJusto2WeiglKorbinian6https://orcid.org/0000-0002-7238-6956BennerAxel7HoffmeisterMichael6UlrichAlexis89https://orcid.org/0000-0002-6129-1572BrennerHermann1610BurwinkelBarbara1112https://orcid.org/0000-0001-8919-1971Chang-ClaudeJenny513https://orcid.org/0000-0001-7641-059XUlrichCornelia M.11415*Division of Preventive Oncology, National Center for Tumor Diseases (NCT) and German Cancer Research Center (DKFZ), 69117 Heidelberg, Germany; scherer@imbi.uni-heidelberg.de (D.S.); heike.deutelmoser@nct-heidelberg.de (H.D.); ybalavarca@gmail.com (Y.B.); r.toth@dkfz-heidelberg.de (R.T.); nina.habermann@embl.de (N.H.); katharina.buck@gmx.de (K.B.); akkebotma@hotmail.com (A.B.); h.brenner@dkfz-heidelberg.de (H.B.)Institute of Medical Biometry and Informatics, University of Heidelberg, 69117 Heidelberg, Germany; lorenzo@imbi.uni-heidelberg.deDivision of Cancer Epigenomics and Cancer Risk Factors, German Cancer Research Center (DKFZ), 69117 Heidelberg, GermanyEuropean Molecular Biology Laboratory (EMBL), Genome Biology, 69117 Heidelberg, GermanyDivision of Cancer Epidemiology, German Cancer Research Center (DKFZ), 69117 Heidelberg, Germany; lisannekap@gmail.com (E.J.K.); p.seibold@Dkfz-Heidelberg.de (P.S.); j.chang-claude@dkfz-heidelberg.de (J.C.-C.)Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), 69117 Heidelberg, Germany; l.jansen@Dkfz-Heidelberg.de (L.J.); k.weigl@dkfz-heidelberg.de (K.W.); m.hoffmeister@dkfz-heidelberg.de (M.H.)Division of Biostatistics, German Cancer Research Center (DKFZ), 69117 Heidelberg, Germany; benner@dkfz-heidelberg.deDepartment of General, Visceral and Transplantation Surgery, University Hospital Heidelberg, 69117 Heidelberg, Germany; aulrich@lukasneuss.deChirurgische Klinik I, Lukaskrankenhaus Neuss, 41464 Neuss, GermanyGerman Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), 69117 Heidelberg, GermanyDivision of Molecular Epidemiology, German Cancer Research Center (DKFZ), 69117 Heidelberg, Germany; B.Burwinkel@dkfz-heidelberg.deDivision Molecular Biology of Breast Cancer, Department of Gynecology and Obstetrics, University of Heidelberg, 69117 Heidelberg, GermanyCancer Epidemiology Group, University Cancer Center Hamburg, University Medical Center Hamburg-Eppendorf, 20246 Hamburg, GermanyHuntsman Cancer Institute, Salt Lake City, UT 84112, USADepartment of Population Health Sciences, University of Utah, Salt Lake City, UT 84112, USACorrespondence: Neli.ulrich@hci.utah.edu; Tel.: +1-801-213-57162972020820202115539503720202272020© 2020 by the authors.2020Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
An individual’s inherited genetic variation may contribute to the ‘angiogenic switch’, which is essential for blood supply and tumor growth of microscopic and macroscopic tumors. Polymorphisms in angiogenesis-related genes potentially predispose to colorectal cancer (CRC) or affect the survival of CRC patients. We investigated the association of 392 single nucleotide polymorphisms (SNPs) in 33 angiogenesis-related genes with CRC risk and survival of CRC patients in 1754 CRC cases and 1781 healthy controls within DACHS (Darmkrebs: Chancen der Verhütung durch Screening), a German population-based case-control study. Odds ratios and 95% confidence intervals (CI) were estimated from unconditional logistic regression to test for genetic associations with CRC risk. The Cox proportional hazard model was used to estimate hazard ratios (HR) and 95% CIs for survival. Multiple testing was adjusted for by a false discovery rate. No variant was associated with CRC risk. Variants in EFNB2, MMP2 and JAG1 were significantly associated with overall survival. The association of the EFNB2 tagging SNP rs9520090 (p < 0.0001) was confirmed in two validation datasets (p-values: 0.01 and 0.05). The associations of the tagging SNPs rs6040062 in JAG1 (p-value 0.0003) and rs2241145 in MMP2 (p-value 0.0005) showed the same direction of association with overall survival in the first and second validation sets, respectively, although they did not reach significance (p-values: 0.09 and 0.25, respectively). EFNB2, MMP2 and JAG1 are known for their functional role in angiogenesis and the present study points to novel evidence for the impact of angiogenesis-related genetic variants on the CRC outcome.
Colorectal cancer (CRC) is the second most common cancer and the second leading cause of cancer death among men and women throughout the world asserting major public health problems [1]. Disease risk and prognosis are to a large proportion modifiable with obesity, red meat consumption and sedentary lifestyle increasing the risk and physical activity, NSAID (non-steroidal anti-inflammatory drug) use and fiber intake being protective [2,3,4]. However, there is also a genetic component to the disease, which is estimated to explain up to 35% of the heritability in colorectal cancer risk [5,6]. Some studies point to genetic associations with the CRC outcome [7,8,9]. However, evidence is still limited.
Angiogenesis, the generation of new blood vessels from pre-existing vessels, is crucial for tumor growth, progression and metastasis as it provides nutrients and oxygen to the growing tumor [10]. While in normal tissue, angiogenesis is tightly controlled and only transiently turned on, in carcinogenic progression an ‘angiogenic switch’ perpetuates the formation of new blood vessels [11].
VEGF (vascular endothelial growth factor) as one of the most potent endothelial cell mitogens is a major driver of angiogenesis. It can act in an autocrine or paracrine manner through interaction with its receptors (VEGFR1-3, also named FLT1 (fms related tyrosine kinase 1), KDR (kinase insert domain receptor) and FLT4 (fms related tyrosine kinase 4), respectively) and co-receptors (neuropilins, NRPs) [12]. Such interactions may further be regulated through ephrinB2 (EFNB2) and finally stimulate proliferation, migration of endothelial cells, cell degradation, remodeling of the extracellular matrix (ECM) and angiogenesis [13]. Ephrins and their receptors (Eph receptors) are crucial in numerous biological processes [14]. Their bidirectional signaling triggers cascades in both, the receptor- and the ligand-bearing cells and deregulated Eph/ephrin activation as well as altered expression of the receptors or their ligands have been frequently reported in a variety of tumors including colorectal neoplasms [14,15]. Based on these observations, targeted interference with Eph/ephrin signaling has been proposed for cancer therapy [16].
Matrix metalloproteinases (MMPs) are critical in ECM remodeling [13,17]. In addition to their capability to promote bioavailability of VEGF, the proteolytic activities of MMPs facilitate protein degradation and support vessel formation [18]. VEGF signaling also regulates DLL4 expression, which is one of two key ligands of NOTCH4 (notch receptor 4), the second ligand being JAG1 (jagged canonical Notch ligand 1) [19]. Concurrently, NOTCH4 promotes VEGFR expression [20].
An individual’s inherited genetic background may modify angiogenesis-related signaling cascades that affect blood supply of premalignant lesions and/or macroscopic tumors [21]. We hypothesize that this may result in altered cancer risk or disease outcome after colorectal cancer diagnosis.
In the present study, we investigated the association between angiogenesis-related genetic variants and risk of CRC, overall survival (OS) and recurrence-free survival (RFS) of colorectal cancer patients in 1754 CRC cases and 1781 healthy controls using a comprehensive targeted genotyping approach and validated some of the associations.
2. Results
In this study 392 SNPs in 33 angiogenesis-related genes (Table S1) were investigated for their association with risk of colorectal cancer and survival of colorectal cancer patients in a population of 1754 colorectal cancer cases and 1781 healthy individuals (Tables S2, S3 and S4, respectively). Characteristics of the study population are presented in Table 1.
2.1. Risk
Higher education and the use of NSAIDs were associated with reduced risk of colorectal cancer. In addition, cases were more likely to smoke regularly, consume higher amounts of red meat and alcohol and were more often diabetic and obese (Table 1a).
No variant was associated with the risk of colorectal cancer after adjustment for multiple testing (Table S2). Furthermore, we did not observe statistically significant interactions of any of the investigated polymorphisms with NSAID use or smoking in relation to the risk of colorectal cancer.
2.2. Overall and Recurrence-Free Survival
During a median follow up of five years, 538 patients died and 468 had a recurrent event. Deceased patients were older, diagnosed with higher disease stages and tumor grade. They were less likely to consume alcohol and to be overweight or obese (Table 1b).
Eleven tagging SNPs in four genes (EFNB2, MMP2, JAG1 and KDR) were significantly associated with overall survival (Table 2, Figure S1 and Table S3) of colorectal cancer patients. Five variants in EFNB2, none of which were in high LD (r2 < 0.8), were associated with overall survival. The strongest associations with overall survival were observed for the protective variant rs2391333 (HRC > T: 0.77, 95% CI: 0.68–0.87, p-value: <0.0001) and for the risk variant rs9520090 (HRG > C: 1.29, 95% CI: 1.15–1.46, p-value: <0.0001). Furthermore, rs9520087 (HRG > A: 1.24, 95% CI: 1.10–1.40, p-value: 0.0006), rs9520088 (HRTT vs. TC/CC: 1.37, 95% CI: 1.15–1.62, p-value: 0.0003) and rs2057408 (HRAA vs. AG/GG: 1.35, 95% CI: 1.14–1.62, p-value: 0.0008) were significantly associated with worse overall survival (Table 2a). All associations with overall survival remained statistically significant after correction for multiple testing (q < 0.05).
Four tagging SNPs (none in high LD with r2 < 0.8) in the region of MMP2 were significantly associated with overall survival of colorectal cancer patients (Table 2a). Rs17301608 was significantly associated with poorer OS (HRA > G: 1.25, 95% CI: 1.11–1.41, p-value: 0.0003, Table 2a). The variants rs2241145 (HRG > C: 1.23, 95% CI: 1.09–1.38, p-value: 0.0005) and rs1561219 (HRC > A: 1.32, 95% CI: 1.11–1.56, p-value: 0.0009) were also associated with decreased overall survival. In contrast, the variant rs243847 significantly improved overall survival (HRT > C: 0.81, 95% CI: 0.71–0.92, p-value: 0.0010). All associations remained statistically significant after multiple testing correction (q < 0.05).
One intronic variant in JAG1 (rs6040062) was significantly associated with shorter overall survival (HRG > C: 1.36, 95% CI: 1.15–1.61, p-value: 0.0003, Table 2a) and recurrence-free survival (HRG > C: 1.31, 95% CI: 1.09–1.57, p-value: 0.004, Table S4) of colorectal cancer patients. Only the association with overall survival remained significant after multiple testing correction (q < 0.05).
Finally, the V297I variant (rs2305948) in KDR was associated with decreased overall survival (HRCC vs. CT/TT: 1.28, 95% CI: 1.04–1.57, p-value: 0.02, Table 2b). For its deleterious and probably damaging SIFT and PolyPhen scores, respectively, this polymorphism was a selected candidate.
SNPs in MMP2, EFNB2 and JAG1, which were significantly associated with patients’ survival, were subsequently analyzed in a first validation set of 2165 (for MMP2 and JAG1), and 1638 (for EFNB2) DACHS colorectal cancer patients and a second validations set of 372 colorectal cancer patients from TCGA where genome-wide data was available.
In the first validation set of imputed genotypes from the DACHS study with a median follow up of five years, the association with rs9520090 in EFNB2 was validated in a dominant model (HRGG vs. GC/CC: 1.22, 95% CI: 1.00–1.47, p-value: 0.05). The variant rs6040062 in JAG1 showed a similar direction (HR: 1.10) in the validation set, however the association was not significant (p-value: 0.25). No other association was validated in this dataset (Table S5).
For the second validation set, we retrieved data of 372 colorectal cancer patients with a median follow up of 0.6 years from The Cancer Genome Atlas (TCGA) Colon Adenocarcinoma (COAD) and Rectal Adenocarcinoma (READ) to validate significant associations in an external dataset. The variant rs7983579, which is in LD with rs9520090 (r2 = 0.8) in EFNB2, was significantly associated with the overall survival of colorectal cancer patients (HRT > G: 1.76, 95% CI: 1.14–2.73, p-value: 0.01). The variant rs9520090 itself showed the same direction of association with overall survival, although it did not reach significance (HRG > C: 1.28, 95% CI: 0.84–1.94, p-value: 0.25). Furthermore, the variants rs9923304 (HRT > C: 1.49, 95% CI: 0.94–2.38, p-value: 0.09) and rs1477017 (HRA > G: 1.42, 95% CI: 0.91–2.22, p-value: 0.12) in MMP2 linked to rs2241145 (r2 = 0.83) and rs17301608 (r2 = 0.93), respectively, were marginally associated with overall survival. No TCGA data were available for variants in JAG1.
The gene-environment interaction between overall survival and smoking, NSAID use, adjuvant 5-FU-based chemotherapy and microsatellite status was investigated for all variants (Table S7). Some indication for interaction was observed for two tagging SNPs in ETS1 (rs11221322 and rs7121854: pinteraction: 0.0004 and 0.001, respectively) and the candidate missense variant in TEK V486I (pinteraction: 0.003) with MSI (microsatellite instability), for the candidate missense variant KDR Q472H (rs1870377; pinteraction: 0.03) with smoking, for the candidate missense variant TEK V486I (rs1334811, pinteraction: 0.03) with NSAID use and for the IL10 candidate rs1800890 (pinteraction: 0.01) with adjuvant 5-FU-based chemotherapy for overall survival of colorectal cancer patients. These associations did not remain significant after adjustment for multiple testing.
3. Discussion
Genetic variation may contribute to an ‘angiogenic switch’, which is essential for blood supply and tumor growth in microscopic and macroscopic tumors. In the present study, we investigated the association of polymorphisms in angiogenesis-related genes with the risk of colorectal cancer as well as with overall survival and recurrence-free survival of colorectal cancer patients in the DACHS population-based case-control study using 1754 CRC cases and 1781 controls. We chose a targeted candidate gene approach to obtain a more complete coverage of the hypothesized genes and avoided potentially high numbers of false-negative findings, which are often the result of large-scale genotyping studies. We observed significant associations between variants in EFNB2, MMP2 and JAG1 and overall survival. Some of the associations could be validated.
Ephrins are membrane-associated ligands that function through interaction with their receptors, erythropoietin-producing hepatocellular (Eph) receptor tyrosine kinase. The unique ligand-receptor interaction, which requires cell-cell contact, can trigger bidirectional signaling, with effects in both the ligand- and the receptor-bearing cell [22]. Downstream or upstream signaling cascades regulate cell proliferation, migration, adhesion, differentiation, survival and angiogenesis and are crucial for homeostasis in epithelial tissues. In this study, we observed significant associations between five variants in ephrin B2 (EFNB2) and OS of colorectal cancer patients. Rs9520087 is located in the 3′UTR of EFNB2, which may suggest regulatory effects on gene expression of EFNB2, however, the in silico functional follow up did not reveal any associations with gene expression. It is not linked to any known variant and it is covered only by a small number of genome-wide genotyping arrays. Imputation of the variant in a validation set of 2165 CRC cases resulted in 10% of inaccurately imputed alleles, which may be the reason why this variant has not been identified earlier [23]. Similarly, other EFNB2 variants are only present on a limited number of genome-wide genotyping arrays. Rs3742159, linked to rs9520088 (r2 = 1) displayed a RegulomeDB rank of 3a. It is located within a region, which likely affects binding of transcription factors, one of which was identified to be NFKB [24]. None of the other linked SNPs seems to be functionally important. The SNPs rs2391333 and rs9520090 modify the methylation status of the site chr13:107163855 (methylation probe cg05493945) within EFNB2. Whether this results in changed gene expression of EFNB2 needs to be further investigated. A number of studies have shown that EFNB2 and its receptor EPHB4 are transcriptionally upregulated in colon cancer carcinoma cell lines and in colon cancer tissue as compared to adjacent mucosa, supporting a role of EFNB2 in carcinogenesis potentially through modulation of angiogenic signals, even though the exact mechanisms remain to be fully elucidated [25]. No other gene, which may be relevant for cancer progression is located in the vicinity of this region, thus it seems plausible, that the identified associations with survival of colorectal cancer patients are linked to EFNB2.
We observed a statistically significant association with OS of colorectal cancer patients for four MMP2 tagging SNPs (rs17301608, rs2241145, rs1561217 and rs243847), all located in introns or in non-coding exon regions, while a previous association of the MMP2 rs243865 (−1306 C > T-in our study the linked SNP rs243866 was genotyped, r2: 0.99) in smaller studies of OS or CRC risk was not confirmed [26,27]. The major task of MMPs is the degradation of proteins, which is an essential step in many hallmarks of cancer in regulating cell growth, differentiation, apoptosis, migration, invasion, immune surveillance and cancer angiogenesis [18]. Consequently, MMPs play a crucial role in malignant transformation and cancer progression. To our knowledge, neither of the polymorphisms or any of the linked variants has previously been reported for their association with OS of colorectal cancer patients. In silico functional follow up of rs17301608 revealed that three of the linked variants (rs1477017, rs11646643 and rs9302671; r2 > 0.8) ranked low (1f) at RegulomeDB, supporting a regulatory role. They likely affect the binding of transcription factors (TF), potentially of NR2F2 (nuclear receptor subfamily 2 group F member 2), which was previously shown to regulate cell growth, invasiveness, metastasis and angiogenesis [28,29]. Furthermore, all three variants linked to rs17301608 are reported expression quantitative trait loci (eQTLs) for LPCAT2 (lysophosphatidylcholine acyltransferase 2), which was previously associated with the progression of breast cancer, ovarian cancer and CRC in human tissue and in cell lines [30]. Finally, data from TCGA supports the function of rs2241145 and rs17301608 as eQTLs on LPCAT2 expression in prostate adenocarcinoma [31].
Aberrant NOTCH signaling promotes the development and progression of colorectal cancer and is partly driven by the overexpression of JAG1, a ligand of NOTCH4 [32]. We identified one tagging SNP (rs6040062) in JAG1, which was associated with shorter OS of colorectal cancer patients. The variant and its linked SNPs rs3748480 (r2: 1.0) and rs112946915 (r2: 0.97) are intronic. The lowest functional RegulomeDB rank was reached by rs112946915 (3a), which modifies binding motifs for transcription factors STAT1 and IRF1 among others. JAG1 has been considered as an attractive target for colorectal cancer therapy, as it seems to be a more specific target than other members of the NOTCH pathway [33].
Our results are based on more than 1700 CRC cases and 1700 healthy controls, with comprehensive clinical, epidemiological and demographic data, which is a major strength of the study. The detailed information on lifestyle factors as well as the availability of clinical patients’ characteristics enabled us to adequately account for relevant environmental factors and test for effect modification. Although our sample size was fairly large, some of the interaction analyses were based on small strata. Unfortunately, the MSI status was missing for 22% of the population and 9% of investigated tumors were MSI-high. Thus, associations should be considered with caution. Furthermore, we had access to two validation sets and validated the association of the EFNB2 tagging SNP rs9520090 (or its linked variant rs7983579) with the overall survival of colorectal cancer. In addition, tagging SNPs in JAG1 and MMP2 showed the same direction of association with overall survival in the first and second validation set, respectively.
The signals of the first validation set were weaker than we expected considering that the discovery and first validation sets were derived from the same study population. We thus compared both sample sets with respect to genetic, epidemiological and clinical data. For up to 67 individuals, imputed genotypes from genome-wide data and at the same time directly genotyped genotypes were available. We observed a discrepancy of 5%–25% between the directly genotyped and imputed genotypes, suggesting an insufficient imputation accuracy for these regions and supporting the choice of a targeted candidate gene approach. The accuracy of imputed genotypes strongly relies on linkage disequilibrium (LD), consequently they depend on the genotypes measured within the same LD-block. EFNB2 and JAG are located in regions of rather low LD, which reduces the probability to correctly impute missing genotypes [34]. We further observed differences between the two study sample sets with respect to overall survival, alcohol consumption and tumor grade of patients. We adjusted for these variables, however, other confounders not captured within this study may explain the discrepant association strengths between the two study samples. While 98% of participants reported to be of German nationality, the ethnic background of study participants was not assessed as part of this study. We cannot rule out an effect of ethnicity on the identified associations, even though this is likely to be very small, because they are expected to be of Caucasian origin.
In conclusion, we identified novel genetic associations with the overall survival of colorectal cancer patients. To our knowledge, no previous study has reported associations between SNPs in EFNB2 or JAG1 with the CRC outcome, both genes known for their functional role in angiogenesis and their impact on disease progression. The present study cannot point to the causal variant or elucidate the functional role of the identified polymorphisms and additional investigations in other populations are required to further validate these associations and to unravel the causal variants and their functionalities.
4. Materials and Methods 4.1. Study Population
The study population includes patients with colorectal cancer who participated in a long-term follow-up study of patients of the German population-based case-control study DACHS (“Darmkrebs: Chancen der Verhuetung durch Screening”; described in detail elsewhere) [35,36,37,38]. In brief, colorectal cancer patients with a primary, confirmed diagnosis of colorectal cancer were recruited from hospitals of the Rhein-Neckar-Odenwald region 2003–2007 if they were aged 30 years or older, resident in the study region and able to complete an in-person interview. Baseline interviews were performed using standardized questionnaires to collect information on demographic and established or suggested colorectal cancer risk factors as well as possible prognostic factors. Follow-up information on vital status was collected at three, five and ten years after diagnosis. Causes of death were verified by death certificates and coded based on ICD−10 classifications. Information on recurrences and secondary tumors were collected from general practitioners and specialists as applicable. In addition, clinical and histological data were extracted from patient and pathological records. Patients without baseline blood samples (post-diagnostic) or without mouthwash (<1%) were excluded from the study.
Population controls were randomly selected from lists of residents of the population registries of the cities and counties. Controls were matched to cases by sex, county of residence and 5-year age group. Controls were eligible if they were aged 30 years or older with no history of colorectal cancer.
The study was approved by the ethics committee (approved on 6 December 2001, project identification code 310/2001) of the University of Heidelberg and State Medical Boards of Baden-Wuerttemberg and Rhineland-Palatinate and was conducted in agreement with the Helsinki Declaration. Written informed consent was provided by all participants at the baseline and during follow-up.
4.2. Single Nucleotide Polymorphism (SNP) Selection and Genotyping
We selected 33 genes related to angiogenesis based on a comprehensive literature search, gene ontology data bases and external expertise. The genes comprised: AKT1, ANGPT1, ANGPT2, DKK4, DLL1, DLL4, EFNB2, EPHB4, ETS1, FGF2, FLT1, FLT4, HIF1A, ID1, JAG1, KDR, MAPK1, MMP2, MMP9, NOTCH4, NRP1, NRP2, PDGFA, PDGFRB, PGF, PIK3CA, PIK3CG, TEK, TIE1, VEGFA, VEGFB, VEGFC and VHL (Table S1).
We used Haploview 4.2 (Broad Institute, Cambridge, MA, USA) to identify tagging SNPs located in the assigned genes as well as in the 5′ and 3′ flanking regions (up to 10 kb) for coverage of the genetic variation across the gene. The tagging SNP selection was based on the HapMap project (CEU [Utah Residents (CEPH) with Northern and Western European Ancestry] population, Phase II/Release 24), using a pairwise tagging approach applying r2 ≥ 0.8 as the cutoff.
Genomic DNA was extracted from EDTA (Ethylenediaminetetraacetic acid) anticoagulated blood or mouthwash samples using the FlexiGene DNA kit (Qiagen GmbH, Hilden, Germany) and quantified using Quant-iT PicoGreen dsDNA reagent and kit (Invitrogen/Life Technologies, Darmstadt, Germany). Genotyping was performed using the Illumina GoldenGate assay or iPLEX assay (Sequenom, Hamburg, Germany) for the MassArray system. For quality control, all assays were validated using the 72 CEPH controls, distributed randomly among the study samples. Each assay batch contained negative and positive controls and 5% of the total number of samples were re-genotyped to confirm reproducibility. Call rates for all genotypes exceeded 96%. The concordance for blinded duplicates was >99.7%. Deviation from the Hardy-Weinberg equilibrium was evaluated using a χ2 statistic in all control subjects. A total of 392 single nucleotide polymorphisms in 33 genes were investigated in this study, which passed quality control, were not in linkage disequilibrium (LD) and had a cell count of at least 10.
4.3. Imputation
Imputation of environmental factors was performed using multiple imputation as most variables, except for tumor grade (11%), had only few missing values (<1%). For grading, we compared no imputation and multiple imputation of grading with best-case (all missing values set to grade 1) and worst-case (all missing values set to grade 4) resulting in similar effect estimates (<10% difference). Genotype imputation in the DACHS study was performed by using IMPUTE2 and the 1000 Genomes reference panel [39,40].
4.4. Statistical Analysis
The associations between polymorphisms and the risk of colorectal cancer were assessed using unconditional logistic regression models estimating odds ratios (OR) and 95% confidence intervals (CI). Associations were also adjusted by relevant environmental factors, which were obtained using the backward elimination procedure. These factors were: age (<60, 60–69, 70–79 and 80+ years), sex, alcohol consumption (no alcohol consumption, 0.1–5.7, 5.7–13.3, 13.3–28.6 and ≥28.6 g alcohol/day during lifetime), BMI category 5–14 years ago (≤18.5 kg/m2, 18.5 to < 25kg/m2 (reference category), 25 to <30 kg/m2 and ≥30 kg/m2), first degree family history of CRC (yes, no/unknown), education (low, medium and high), prior colonoscopy (ever colorectal endoscopy after consideration of endoscopy report, yes, no/unknown), diabetes, regular smoking (never, yes), NSAID use (never, ever used NSAIDs more than once/month ≥1 year) and red meat consumption (low, medium and high).
For each polymorphism, the risk of colorectal cancer was estimated using log-additive, and dominant models. Differences in baseline characteristics between deceased and non-deceased patients were tested using the χ2 statistic and t-tests for categorical and continuous variables, respectively. As an outcome, time to survival and recurrence-free survival times were calculated as the time from the diagnosis to the event of interest (death and recurrence or death, respectively) or censoring. Median follow-up time was calculated using the reverse Kaplan-Meier estimator and the cumulative survival curves for overall survival were generated using the Kaplan-Meier curves [41].
Cox proportional hazard models were used to estimate hazard ratios (HR) for overall survival and recurrence-free survival, and their 95% CIs associated with genetic variants. Dominant and log-additive modes of inheritance were assumed for the genetic variants. The models also included adjustment for relevant environmental and clinical factors obtained from a backward elimination procedure. The analyses were adjusted for age (<60, 60–70, 70–80 and 80+ years), sex, stage (I-IV), tumor grade (1 and 2 versus 3 and 4), current BMI (<18.5 kg/m2, 18.5 to <25 kg/m2 (reference category), 25 to <30 kg/m2 and ≥30 kg/m2), and alcohol intake (no alcohol intake and quartiles: 0.1–6.1, 6.1–15.6, 15.6–32.6 and ≥32.6 g alcohol/day in the last year).
The effect modification was tested by using multiplicative interaction terms and stratified analyses for adjuvant 5-FU-based chemotherapy (yes/no) and microsatellite status (high/low; only for overall survival), NSAID use (never, ever used NSAIDs more than once/month ≥1 year), and smoking (never/yes).
All statistical analyses were two-sided with a significance level of 0.05 and were performed using SAS (version 9.3, SAS Institute, Cary, NC, USA) and R (version 3.0.2, R Foundation for Statistical Computing, Vienna, Austria).
The false discovery rate (FDR, indicated as the q-value) correction method was used to adjust for multiple testing of non-candidate polymorphisms [42].
In silico functional follow up of the SNPs (Table S6) that were statistically significantly associated with risk of CRC or overall survival of CRC patients was performed using Haploreg, LDlink, RegulomeDB, PancanQTL, Pancan-meQTL, GTExPortal and (variant effect predictor) VEP [24,31,43,44,45,46,47].
4.5. Validation Sets
Validation of significant associations was performed using two datasets. The first set included DNA samples from an independent sample of 2165 DACHS colorectal cancer patients recruited 2007–2010, which were genotyped using Illumina’s Global Screening Array, OmniExpress BeadChip and OncoArray as described previously [48]. Genotypes were imputed separately for each gene (±1.5 MB) and for each array using the Michigan Imputation Server [49]. The Haplotype Reference Consortium (HRC) reference panel (HRC r1.1 2016) has been used for imputation and Eagle for phasing. Subsequently, the imputed genotypes of each array were combined. The second set included genotype data from The Cancer Genome Atlas (TCGA) Colon Adenocarcinoma (COAD) cohort and Rectal Adenocarcinoma (TCGA-READ) cohort, which were downloaded from the NCI Genomic Data Commons.
A statistical analysis from our respective findings was performed for both validation datasets as described above. TCGA analyses were adjusted for the available variables age, sex and stage.
The data that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
We thank all participants of the DACHS study, the interviewers, as well as the physicians and staff of the following hospitals and cooperating institutions: Chirurgische Universitätsklinik Heidelberg, Klinik am Gesundbrunnen Heilbronn, St. Vincentiuskrankenhaus Speyer, St. Josefskrankenhaus Heidelberg, Chirurgische Universitätsklinik Mannheim, Diakonissenkrankenhaus Speyer, Krankenhaus Salem Heidelberg, Kreiskrankenhaus Schwetzingen, St. Marienund St. Annastiftkrankenhaus Ludwigshafen, Klinikum Ludwigshafen, Stadtklinik Frankenthal, Diakoniekrankenhaus Mannheim, Kreiskrankenhaus Sinsheim, Klinikum am Plattenwald Bad Friedrichshall, Kreiskrankenhaus Weinheim, Kreiskrankenhaus Eberbach, Kreiskrankenhaus Buchen, Kreiskrankenhaus Mosbach, Enddarmzentrum Mannheim, Kreiskrankenhaus Brackenheim, and the Cancer Registry of Rhineland-Palatinate. We thank the microarray unit of the Genomics and Proteomics Core Facility of the German Cancer Research Center for genotyping, particularly Matthias Schick, and also thank Muhabbet Celik, Ursula Eilber, Sabine Behrens and Ute Handte-Daub for excellent technical assistance. The results shown here are in part based upon data generated by the TCGA Research Network: https://www.cancer.gov/tcga.
Supplementary Materials
Supplementary Materials can be found at https://www.mdpi.com/1422-0067/21/15/5395/s1.
Click here for additional data file.
Author Contributions
D.S. prepared the statistical analysis plan, interpreted the data and drafted the manuscript. Y.B., H.D. and K.B. analyzed the data, P.S., B.B., R.T., N.H., A.B. (Akke Botma) and E.J.K. contributed to SNP selection, A.B. ( Axel Benner) and J.L.B. provided statistical support. A.U., K.W., L.J., J.C.-C., M.H., H.B., designed and implemented the different studies included in this analysis. C.M.U. conceived the study, provided input to interpreting the data and writing the manuscript. All authors discussed the results, provided critical feedback and approved the final manuscript. All authors have read and agreed to the published version of the manuscript.
Funding
D. Scherer was supported by an Olympia Morata fellowship of the Medical Faculty, University of Heidelberg and the German Federal Ministry of Education and Research (01KT1510). The DACHS study was supported by grants from the German Research Council (Deutsche Forschungsgemeinschaft, grant numbers BR 1704/6–1, BR 1704/6–3, BR 1704/6–4, BR 1704/6–6, CH 117/1–1, HO 5117/2–1, HE 5998/2–1, KL 2354/3–1, RO 2270/8–1, and BR 1704/17–1), and the German Federal Ministry of Education and Research (grant numbers 01KH0404, 01ER0814, 01ER0815, 01ER1505A and 01ER1505B). CM Ulrich was supported by R01 CA189184, U01 CA206110 and R01 CA 207371.
Conflicts of Interest
The authors declare no conflict of interest.
Abbreviations
CRC
Colorectal cancer
OS
Overall survival
RFS
Recurrence-free survival
SNP
Single nucleotide polymorphism
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Characteristics of the study population. (a) Total population; (b) Case population.
ijms-21-05395-t001a_Table 1
(a)
\\n
\\n
Controls
Cases
p-Value
\\nGender (Male/Female)\\n
\\n
1066/715
1027/727
0.43
\\nAge (Mean ± SD)\\n
\\n
68.72 ± 10.18
68.25 ± 10.42
0.32
\\nFirst Degree Relatives With CRC\\n
n (available)
1781
1754
0.009
Yes (%)
11.3
14.3
No (%)
88.7
85.8
\\nEducation\\n
n (available)
1781
1754
<0.0001
Low (%)
53.3
62.9
Medium (%)
25.3
20.5
High (%)
21.4
16.6
\\nRed meat Consumption\\n
n (available)
1781
1754
<0.0001
Low (%)
10.7
8.2
Medium (%)
85.1
84.7
High (%)
4.2
7.1
\\nAlcohol Consumption (g/day)\\n
n (available)
1781
1754
0.012
no intake
14.7
16.3
0.1–5.7
23.4
21.0
5.7–13.3
21.1
20.8
13.3–28.6
23.6
21.1
≥28.6
17.2
20.9
\\nRegular Smoking\\n
n (available)
1781
1754
0.011
Never (%)
52.2
47.9
Current (%)
47.8
52.1
\\nDiabetes\\n
n (available)
1781
1754
0.0003
Yes (%)
13.7
18.2
No (%)
86.4
81.8
\\nBMI (kg/m2)\\n
n (available)
1781
1754
<0.0001
<18.5 (%)
0.7
0.97
18.5–25 (%)
37.1
31.07
25–30 (%)
47.0
47.32
>30 (%)
15.2
20.64
\\nNSAID Use\\n
n (available)
1781
1754
<0.0001
No (%)
63.1
74.0
Yes (%)
37.0
26.0
\\nEver Colonoscopy\\n
n (available)
1781
1754
<0.0001
No (%)
46.9
79.2
Yes (%)
53.1
20.8
ijms-21-05395-t001b_Table 1
(b)
\\n
\\n
Overall Survival (Discovery)
Overall Survival (Validation)
\\nOverall n/Events\\n
\\n
1675/538
2165/689
\\n
\\n
HR (95% CI)
\\np\\n
HR (95% CI)
\\np\\n
\\nGender\\n
Male
[1]
\\n
[1]
\\n
Female
0.89 (0.75–1.05)
0.16
1.27 (1.09–1.49)
0.003
\\nAge Category\\n
<60
[1]
\\n
[1]
\\n
60–69
1.24 (0.94–1.62)
0.13
1.31 (1.00–1.72)
0.047
70–79
1.45 (1.10–1.90)
0.007
1.84 (1.43–2.36)
<0.0001
>80
2.84 (2.14–3.77)
<0.0001
3.40 (2.63–4.39)
<0.0001
\\nStage at Diagnosis\\n
I
[1]
\\n
[1]
\\n
II
1.53 (1.10–2.12)
0.01
1.81 (1.38–2.37)
<0.0001
III
2.73 (2.01–3.69)
<0.0001
2.70 (2.08–3.52)
<0.0001
IV
13.28 (9.81–17.98)
<0.0001
13.33 (10.26–17.31)
<0.0001
\\nGrade\\n
G1-G2
[1]
\\n
[1]
\\n
G3-G4
1.27 (1.06–1.52)
0.01
1.85 (1.57–2.18)
<0.0001
\\nAlcohol Consumption\\n\\n(g/day)\\n
no intake
[1]
\\n
[1]
\\n
0.1–6.1
0.69 (0.53–0.88)
0.003
0.79 (0.64–0.98)
0.03
6.1–15.6
0.70 (0.54–0.91)
0.007
0.76 (0.61–0.95)
0.02
15.6–32.6
0.67 (0.52–0.86)
0.002
0.88 (0.70–1.09)
0.24
≥32.6
0.74 (0.58–0.96)
0.02
0.93 (0.73–1.18)
0.54
\\nBMI (kg/m2)\\n
18.5–25>
[1]
\\n
[1]
\\n
<18.5
1.72 (1.075–2.75)
0.02
1.18 (0.71–1.95)
0.53
25–30>
0.77 (0.64–0.93)
0.007
0.76 (0.65–0.90)
0.002
≥30
0.71 (0.56–0.91)
0.007
0.78 (0.63–0.96)
0.02
Abbreviations: HR: hazard ratio; CI: confidence interval; BMI: body mass index.
Significant associations of genetic variants in MMP2, EFNB2 and JAG1 with overall and recurrence-free survival of colorectal cancer patients in the discovery cohort. (a) Associations of tagging SNPs; (b) Association of candidate SNP.
ijms-21-05395-t002a_Table 2
(a)
\\n
\\n
\\n
\\n
\\n
\\n
Overall Survival
\\n
\\n
Recurrence-Free Survival
Gene
SNP
Location
Alleles
Alive
Deceased
HR 1 (95%-CI)
\\np\\n
\\nq\\n
Alive
Deceased
HR 1 (95%-CI)
\\np\\n
\\nq\\n
\\nEFNB2\\n
rs2391333
intronic
CC
455
223
[1]
\\n
\\n
474
204
[1]
\\n
\\n
CT
526
259
0.77 (0.68–0.87)
<0.0001
0.008
538
247
0.85 (0.74–0.97)
0.02
0.73
TT
174
64
168
70
CT/TT
700
323
0.76 (0.64–0.90)
0.002
0.060
706
317
0.83 (0.69–0.99)
0.05
0.91
rs9520090
intronic
GG
338
134
[1]
\\n
\\n
339
133
[1]
\\n
\\n
GC
580
270
1.29 (1.15–1.46)
<0.0001
0.008
586
264
1.20 (1.06–1.37)
0.005
0.68
CC
237
142
255
124
GC/GC
817
412
1.42 (1.16–1.73)
0.0006
0.04
841
388
1.2 (1.03–1.56)
0.03
0.68
rs9520087
3′UTR
GG
369
148
[1]
\\n
\\n
371
146
[1]
\\n
\\n
GA
562
297
1.24 (1.10–1.40)
0.0006
0.04
575
284
1.15 (1.00–1.31)
0.05
0.86
AA
224
101
234
91
GA/AA
786
398
1.42 (1.17–1.71)
0.0003
0.03
809
375
1.28 (1.05–1.57)
0.14
0.68
rs2057408
downstream
AA
509
204
[1]
\\n
\\n
513
200
[1]
\\n
\\n
AG
488
271
1.19 (1.05–1.34)
0.006
0.13
507
252
1.13 (0.99–1.29)
0.07
0.87
GG
158
71
160
69
AG/GG
646
342
1.35 (1.14–1.62)
0.0008
0.04
667
321
1.26 (1.04–1.52)
0.02
0.68
rs9520088
intronic
TT
638
269
[1]
\\n
\\n
641
266
[1]
\\n
\\n
TC
429
239
1.24 (1.08–1.41)
0.002
0.06
446
222
1.15 (0.99–1.33)
0.07
0.87
CC
88
38
93
33
TC/CC
517
277
1.37 (1.15–1.62)
0.0003
0.03
539
255
1.24 (1.03–1.49)
0.02
0.68
\\nMMP2\\n
rs17301608
intronic
AA
432
180
[1]
\\n
\\n
431
181
[1]
\\n
\\n
AG
540
267
1.25 (1.11–1.41)
0.0003
0.03
551
256
1.11 (0.98–1.27)
0.11
0.87
GG
183
99
198
84
AG/GG
723
366
1.42 (1.18–1.71)
0.0002
0.03
749
340
1.23 (1.01–1.49)
0.04
0.91
rs1561219
intronic
CC
877
378
[1]
\\n
\\n
877
378
[1]
\\n
\\n
CA
255
160
1.32 (1.11–1.56)
0.0009
0.04
277
138
1.11 (0.92–1.34)
0.26
0.89
AA
23
8
26
5
CA/AA
278
168
1.37 (1.14–1.65)
0.0009
0.04
303
143
1.17 (0.95–1.40)
0.14
0.91
rs2241145
intronic
GG
341
133
[1]
\\n
\\n
334
140
[1]
\\n
\\n
GC
547
281
1.23 (1.09–1.38)
0.0005
0.04
566
262
1.11 (0.98–1.26)
0.11
0.87
CC
267
132
280
119
\\n
\\n
\\n
GC/CC
814
413
1.50 (1.23–1.83)
<0.0001
0.03
846
381
1.24 (1.01–1.52)
0.05
0.91
rs243847
intronic
TT
432
213
[1]
\\n
\\n
448
197
[1]
\\n
\\n
TC
542
267
0.81 (0.71–0.92)
0.001
0.04
555
254
0.88 (0.76–1.00)
0.05
0.87
\\n
CC
181
66
177
70
\\n
TC/CC
723
333
0.82 (0.68–0.97)
0.024
0.32
732
324
0.86 (0.71–1.04)
0.11
0.91
\\nJAG1\\n
rs6040062
intronic
TT
876
391
[1]
\\n
\\n
886
381
[1]
\\n
\\n
TG
259
142
1.36 (1.15–1.61)
0.0003
0.02
274
127
1.31 (1.09–1.57)
0.004
0.68
GG
20
13
20
13
TG/GG
279
155
1.38 (1.14–1.67)
0.0007
0.04
294
140
1.29 (1.05–1.59)
0.01
0.68
ijms-21-05395-t002b_Table 2
(b)
\\n
\\n
\\n
\\n
\\n
\\n
Overall Survival
\\n
\\n
Recurrence-Free Survival
Gene
SNP
Location
Alleles
Alive
Deceased
HR 1 (95%-CI)
\\np\\n
\\nq\\n
Alive
Deceased
HR 1 (95%-CI)
\\np\\n
\\nq\\n
\\nKDR\\n
rs2305948
V297I
CC
934
424
[1]
\\n
\\n
944
414
[1]
\\n
\\n
CT
209
113
1.27 (1.06–1.53)
0.01
NA
225
97
1.14 (0.92–1.41)
0.23
NA
TT
12
9
11
10
CT/TT
221
122
1.28 (1.04–1.57)
0.02
NA
236
107
1.09 (0.86–1.37)
0.47
NA
1 Hazard ratios for log-additive and dominant models, adjusted for age, sex, stage, grade, BMI, alcohol consumption. Abbreviations: SNP: single nucleotide polymorphism; HR: hazard ration; CI: confidence interval; NA: not applicable.
\\n\"\n]"}}},{"rowIdx":468,"cells":{"data":{"kind":"list like","value":["\nInt J Mol SciInt J Mol SciijmsInternational Journal of Molecular Sciences1422-0067MDPI32752176PMC743212510.3390/ijms21155514ijms-21-05514ReviewUpdate on Cuticular Wax Biosynthesis and Its Roles in Plant Disease ResistanceWangXiaoyu†KongLingyao†ZhiPengfeihttps://orcid.org/0000-0001-6672-4704ChangCheng*College of Life Sciences, Qingdao University, Qingdao 266071, Shandong, China; 2017020884@qdu.edu.cn (X.W.); konglingyao@126.com (L.K.); 2017020872@qdu.edu.cn (P.Z.)Correspondence: cc@qdu.edu.cn; Tel.: +86-532-85953227
These authors contributed equally to this work.
0182020820202115551429620203072020© 2020 by the authors.2020Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
The aerial surface of higher plants is covered by a hydrophobic layer of cuticular waxes to protect plant tissues against enormous environmental challenges including the infection of various pathogens. As the first contact site between plants and pathogens, the layer of cuticular waxes could function as a plant physical barrier that limits the entry of pathogens, acts as a reservoir of signals to trigger plant defense responses, and even gives cues exploited by pathogens to initiate their infection processes. Past decades have seen unprecedented proceedings in understanding the molecular mechanisms underlying the biosynthesis of plant cuticular waxes and their functions regulating plant–pathogen interactions. In this review, we summarized the recent progress in the molecular biology of cuticular wax biosynthesis and highlighted its multiple roles in plant disease resistance against bacterial, fungal, and insect pathogens.
In the natural environment, plants encounter various pathogens such as viruses, bacteria, fungi, and even herbivorous insects, which seriously threaten plant growth and crop production. It was estimated that these pathogens have contributed to at least 20% of yield loss in important crops including wheat, rice, maize, potatoes, and soybeans [1,2]. Therefore, diseases caused by pathogenic microorganisms and herbivores are major factors affecting agriculture [3,4,5,6]. Unlike animals that could escape from predators and pathogens, plants must withstand pathogen attacks at the sites of growth [1,2]. Increasing evidence from studies in the model plant Arabidopsis thaliana revealed that plants have acquired a battery of sophisticated defense mechanisms to defend themselves against pathogen attacks during their co-evolution with various pathogens [7].
As the preformed defense, physical barriers such as spines, hairs, trichomes, thorns, and cuticles cover the aerial parts of plants [8,9]. In contrast, plant innate immunity acts as an example of induced defense. For instance, pattern-triggered immunity (PTI) was induced by the recognition of chemical molecules in the pathogen, microbial, and/or damage-associated molecular patterns (PAMPs, MAMPs, and/or DAMPs, respectively), and these chemical molecules are released during pathogen infection and recognized by pattern recognition receptors (PRRs) [10,11]. Generally, PTI is involved in a wide range of defenses, including the production of reactive oxygen species (ROS) and the cascade of mitogen-activated protein kinases (MAPKs) [12,13,14,15]. To interfere with pattern-triggered immunity, pathogens usually secrete effectors, which could be recognized by specific resistance (R) proteins, inducing effector-triggered immunity (ETI) [16,17,18,19,20]. Compared with PTI, ETI usually triggers the hypersensitive response (HR) and systemic acquired resistance (SAR) in host plants [21,22,23,24,25]. Traditionally, breeding for disease resistance in crops such as wheat, rice, potato, barley, and even soybean mainly relies on host resistance regulated by Resistance (R) genes [26]. However, in many cases, due to the variation of new pathogen strains, the resistance mediated by the R gene is less effective in the field [26]. These new pathogen strains can escape the recognition of the R gene and the R gene-mediated downstream defense [26]. Increasing evidence from studies in model plants and crops such as Arabidopsis, tomato (Solanum lycopersicum), and rice have revealed that phytohormones and their signaling pathways usually function at the downstream of pattern-triggered immunity and effector-triggered immunity to regulate plant defense, which were also considered to be effective plant defense mechanisms [27,28]. For instance, phytohormones salicylic acid (SA), jasmonic acid (JA), and ethylene (ET) are well known as the main regulators in plant defense responses [29]. In addition, phytohormones abscisic acid (ABA), auxin, gibberellin (GA), brassinosteroid (BR), and cytokinin (CK) also act as indirect factors involved in plant-pathogen interactions [27,28].
As the outmost layer exposed to the environment, cuticle covers plant aerial organs and protects plant tissues against enormous environmental challenges such as dehydration, excessive UV radiation, mechanical damage, and even pathogen infections, which have been summarized in prior reviews [30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45]. In addition to its protective roles, cuticle also gets involved in regulating plant development [33]. Although the composition of the cuticle varies among plant species, tissues, developmental stages, and even environmental conditions, plant cuticle is mainly composed of a cutin scaffold impregnated by and covered with cuticular waxes [46,47]. As a mixture of very-long-chain (VLC, >C20) fatty acids and their derivatives, cuticular waxes play multiple roles, from acting as a plant physical barrier, limiting the entry of pathogens, to functioning as a cue exploited by pathogens to initiate their prepenetration and infection processes in regulating the plant-pathogen interactions. Therefore, cuticular waxes have gained increasing attention in the study of plant disease resistance [9,48]. In this review, we summarized the recent advances in the molecular biology of cuticular wax biosynthesis and discussed their multiple roles in plant disease resistance against bacterial, fungal, and insect pathogens.
2. Molecular Mechanism of Cuticular Wax Biosynthesis
Plant cuticular waxes are organic solvent-extractable complex mixtures comprising very-long-chain fatty acids and their derivatives, such as aldehydes, alkanes, primary and secondary alcohols, esters, and ketones [49,50,51]. In some plant species, secondary metabolites such as flavonoids, pentacyclic triterpenoids, and tocopherols have also been identified as wax components [49,50,51]. In the model plant Arabidopsis, cuticular wax biosynthetic mechanisms have been characterized with the contribution of wax biosynthetic mutants and transcriptomic/proteomic analysis [49,50,51].
As summarized in Figure 1, the biosynthesis of cuticular waxes in Arabidopsis can be divided into three steps: (1) the de novo synthesis of C16 or C18 fatty acids; (2) the extension of C16 and C18 fatty acids to form very-long-chain fatty acids (VLCFAs), which are used as direct precursors for wax synthesis in the endoplasmic reticulum (ER); and (3) the synthesis of derivatives of VLCFAs, such as aldehydes, alcohols, alkanes, ketones, and esters [49,51]. In the plastids of epidermal cells, acetyl coenzyme A (CoA) was converted into CoA-C2 by the catalysis of fatty acid synthetase complex (FAS), and after many reaction cycles, it can generate C16 or C18 acyl-acyl carrier protein (ACP), which was hydrolyzed by a fatty acyl-ACP thioesterase (FAT) to produce C16 or C18 fatty acids (Figure 1). These C16 or C18 fatty acids were activated to acyl-CoAs by the long-chain acyl-coenzyme A synthases (LACSs) and then transported to the endoplasmic reticulum (ER) [51,52]. C16 and C18 acyl-CoAs served as precursors for the formation of very-long-chain acyl-CoAs (up to C34), which was catalyzed by the enzymes of the fatty acid elongase (FAE) complex and the ECERIFERUM2 (CER2) proteins (Figure 1) [53,54]. In the FAE complex, β-ketoacyl-CoA synthase (KCS), β-ketoacyl-CoA reductase (KCR), 3-hydroxyacyl-CoA dehydratase (HCD), and enoyl-CoA reductase (ECR) catalyzed the sequential condensation/reduction/dehydration/reduction reactions in the formation of very-long-chain acyl-CoAs (Figure 1) [55,56,57,58]. These elongated very-long-chain acyl-CoAs were then modified into alkanes by the ECERIFERUM1 (CER1)/ECERIFERUM3 (CER3)/CYTOCHROME B5 (CYTB5) complex in an alkane-forming pathway, and these very-long-chain alkanes could be further oxidized to secondary alcohols and ketones by the CYP95A family cytochrome P450 enzymes MIDCHAIN ALKANE HYDROXYLASE1 (MAH1) (Figure 1) [59,60,61]. In the alcohol-forming pathway, very-long-chain acyl-CoAs were converted into the n-6 monounsaturated fatty acids by the acyl desaturase ECERIFERUM17 (CER17), which was followed by the formation of primary alcohols catalyzed by the fatty acyl-CoA reductase ECERIFERUM4 (CER4) (Figure 1) [62,63]. In addition, the Arabidopsis WAX SYNTHASE/ACYL-COA: DIACYLGLYCEROL ACYLTRANSFERASE 1 (WSD1) catalyzed the formation of wax esters through using acyl-CoAs and primary alcohols as precursors in the alcohol-forming pathway (Figure 1) [64]. These generated waxes components were transported from the ER to the plasma membrane (PM)via the Golgi and trans-Golgi network (TGN)-trafficking pathways, and finally exported out of the plant cell to the cuticle via the PM-localized ATP binding cassette G (ABCG) subfamily half transporters and the lipid transfer proteins (LTPs) (Figure 1) [49,50,51].
Increasing evidence from studies in Arabidopsis revealed that the biosynthesis of cuticular waxes is regulated by multiple transcriptional regulators [49,50,51]. As the first reported transcriptional regulator, the Arabidopsis APETALA2-Ethylene responsive factor (AP2-EREBP)-type transcription factor WAX INDUCER1/SHINE1 (WIN1/SHN1) and its close homologs SHN2 and SHN3 activated cuticular wax biosynthesis by upregulation of biosynthesis genes β-Ketoacyl-CoA synthase 1 (KCS1), CER1, and CER2 [65,66]. Similarly, Myeloblastosis (MYB) family transcription factors MYB94 and MYB96 potentiated the cuticular wax biosynthesis under drought by directly activating expression of KCS2, ECR, CER2, and WSD1 genes in Arabidopsis [67,68]. In contrast, the Arabidopsis AP2/ERF-type transcription factor DECREASE WAX BIOSYNTHESIS (DEWAX) was reported to negatively regulate cuticular wax biosynthesis in the light/dark cycle by directly suppressing long chain acyl-CoA synthase 2 (LACS2), CER1, and ECR genes [69,70]. Moreover, the biosynthesis of cuticular wax in Arabidopsis is regulated at the post-transcriptional and post-translational levels. For instance, ECERIFERUM7 (CER7), a core subunit of the exosome, regulated the accumulation of trans-acting small interfering RNA class of small RNAs involved in direct silencing of CER3 in Arabidopsis [71]. Another recent study in Arabidopsis revealed that CER16, a protein with no known domains or motifs, also inhibited post-transcriptional gene silencing of CER3 to regulate alkane biosynthesis [72]. In addition, the Kelch repeat F-box protein SMALL AND GLOSSY LEAVES1 (SAGL1) mediated proteasome-dependent degradation of CER3, thereby negatively regulating cuticular wax biosynthesis in Arabidopsis [73].
3. Regulation of Plant–Bacterial Pathogen Interaction by Cuticular Waxes
On the plant cuticle, bacterial pathogens usually produce extracellular polymeric substances and form large aggregates to help them to withstand the harsh surrounding conditions. It is well known that the bacterial pathogen Pseudomonas syringae can produce syringafactin, a compound with surfactant properties, to facilitate its motility and increase the permeability of Arabidopsis cuticle [74]. Increasing evidence has revealed that the survival and infection of bacterial pathogens on plant surfaces are affected by the integrity and permeability of the plant cuticular wax layer. A more permeable plant cuticular wax layer could lead to either enhanced resistance or susceptibility to pathogen infections [48]. For instance, Arabidopsis sma4 (symptoms to multiple-regulated avr4) is a loss-of-function mutant of the SMA4 gene, which encodes the cuticular wax biosynthetic component LACS2 [75]. Tang et al. found that the sma4 mutant exhibited enhanced susceptibility to the hemibiotrophic bacterial pathogen Pseudomonas syringae pv tomato strain DC3000 (Pst DC3000) but displayed enhanced resistance against the necrotrophic fungal pathogen Botrytis cinerea (B. cinerea) [76].
MYB family transcription factors have been reported to be involved in plant cuticular wax biosynthesis and disease resistance against bacterial pathogens [48,51]. For instance, Arabidopsis transcription factor MYB30 functions as a positive regulator of a cell death pathway, conditioning the hypersensitive response [77]. Raffaele et al. demonstrated that the exacerbated hypersensitive response phenotype of MYB30-overexpressing Arabidopsis lines was altered by the loss of function of the acyl-ACP thioesterase gene acyl-ACP thioesterase B (FATB), which causes severe defects in the supply of fatty acids for the biosynthesis of very-long-chain fatty acids, suggesting that MYB30 modulates hypersensitive response via controlling the biosynthesis of very-long-chain fatty acids in Arabidopsis [77]. Similarly, Zhang et al. reported that ectopic expression of apple (Medicago truncatula) MdMYB30, which encodes an R2R3 MYB transcription factor, in Arabidopsis increased the transcription levels of wax biosynthesis-related genes, including wax synthesis regulatory gene 1 (AtWRI1), AtWIN1, AtKCS1, acyl-CoA binding protein 1 (AtACBP1), AtLACS2, AtSHINE2, and AtSHINE3 [78]. The accumulation of wax compositions, such as C29 alkanes, C31 alcohols, C29 aldehydes, C16 fatty acids, C29 ketones, and C29 and C30 esters were significantly enhanced in MdMYB30-ectopic-expression Arabidopsis lines [78]. Interestingly, MdMYB30 also contributed to the increased resistance against Pst DC3000 in Arabidopsis, suggesting that the changed epicuticular wax content and composition might cause disease resistance against the bacterial pathogen Pst DC3000 [78]. In addition, MYB96, another MYB transcription factor (TF) in Arabidopsis, directly binds to the promoters of wax biosynthetic genes including KCS1, KCS2/DAISY, KCS6, KCR1, and CER3, and actives the expression of these genes under drought- and ABA-inducible conditions [79]. Notably, MYB96 activation tagging Arabidopsis lines showed increased wax accumulation and enhanced resistance to Pst DC3000 by potentiating the SA biosynthesis [80]. However, the accumulation of cuticular wax components does not necessarily contribute to the plant disease resistance against bacterial pathogens. For instance, VLC alkanes were accumulated but the susceptibility to Pst DC3000 was enhanced in the Arabidopsis lines over-expressing CER1 [59]. Interestingly, increasing evidence from studies in fungal pathogens Blumeria and Colletotrichum revealed that certain wax components such as very-long-chain aldehyde and terpenoids could be exploited by certain fungal pathogens to trigger their infection processes, but other components, including free fatty acid RR (resistance-related) metabolites, contribute to plant resistance against fungal pathogens. Therefore, characterizing the function of specific wax components in regulating bacterial growth and infections, as well as plant defense responses, might contribute to understanding the roles of cuticular waxes in the regulation of plant–bacterial pathogen interactions in the future research.
4. Regulation of Plant–Fungal Pathogen Interaction by Cuticular Waxes
During the infection process, fungal pathogens could synthesize and secrete hydrolytic enzymes such as cutinases and lipases to degrade the cuticular wax layer [81]. For instance, cutinase2 (CUT2) gene in rice blast (Magnaporthe grisea) is activated during appressorial development and fungal penetration [82]. During these processes, fungal pathogens would be recognized by the plant immune systems and trigger the immune responses. Several Arabidopsis plants over-accumulating fungal cutinase exhibited increased cuticle permeability and enhanced resistance to fungal pathogens. For example, the Arabidopsis AP2/ERF-type transcription factor DECREASE WAX BIOSYNTHESIS (DEWAX) negatively regulates cuticular wax biosynthesis by suppressing cuticular wax biosynthesis genes (CER1, LACS2, ATP-citrate lyase A2 and ECR) [83]. Interestingly, over-expression DEWAX in Arabidopsis led to enhanced disease resistance against grey mildew (Botrytis cinerea) [69]. Further analyses revealed that DEWAX acts as a transcriptional activator, binding to the promoters of defense-related genes including plant defensin 1.2a (PDF1.2a), indole glucosinolate O-methyltransferase 1 (IGMT1), and peroxidase 37 (PRX37), and upregulating the expression of these genes in Arabidopsis [69]. These results suggest that cuticle wax biosynthesis genes could regulate plant disease resistance through direct targeting defense-related genes. Indeed, the Arabidopsis sma4 mutant plants display increased resistance to grey mildew (B. cinerea), and these processes were independent of jasmonic acid (JA)- and ethylene (ET)-signaling pathways [76].
Interestingly, Arabidopsis mutants such as long-chain acyl-CoA synthetase-2 (lacs-2) and -3, and myeloblast transcription factor 96 (myb96) with increased cuticle permeability exhibited enhanced disease resistance against grey mildew (B. cinerea) [76,80]. L’Haridon et al. reported that a permeable cuticle in Arabidopsis is associated with the release of reactive oxygen species (ROS) and induction of innate immunity, demonstrating the importance of fungal suppression by reactive oxygen species formation [84]. In contrast, Cui et al. demonstrated that Botrytis immunity conferred by cuticle permeability can be genetically uncoupled from phosphatase2c-regulated abscisic acid (ABA) sensitivity but requires negative regulation of a parallel ABA-dependent cell death pathway [85]. In addition, several studies revealed that cuticular wax accumulation also contributes to disease resistance against fungal pathogens. For instance, Zhang et al. showed that the apple (M. truncatula) transcription factor MdMYB30 could bind to the promoter region of MdKCS1 to activate its expression and induce wax biosynthesis [78]. Notably, the infection of apple canker pathogen Botryosphaeria dothidea could induce the accumulation of wax crystals and transcription of pathogenesis-related genes, such as MdNPR1, MdPR1, MdPR5, MdEDS1, and MdPAL at B. dothidea injection sites in MdMYB30-overexpression apple lines [78]. Consistently, MdMYB30-overexpression transgenic apple calli exhibited strengthened resistance against apple canker (B. dothidea). These results indicated that MdMYB30 positively modulates waxes biosynthesis of apple fruit and enhances apple resistance to certain fungal pathogens [78].
Powdery mildew caused by the fungal pathogen Blumeria graminis is a devastating disease in barley and wheat. Increasing evidence has revealed that B. graminis could utilize plant cuticular wax components to initiate their prepenetration processes such as conidial germination and appressorial development [86,87,88,89,90,91,92]. Hansjakob et al. reported that very-long-chain aldehydes could stimulate the in vitro conidial germination of B. graminis in a dose-dependent manner [86,87]. Recently, Wang et al. and Kong et al. reported that the silencing of the wheat 3-ketoacyl-CoA synthase (TaKCS6) and enoyl-CoA reductase (TaECR) led to the reduction of cuticular wax load and attenuated conidial germination of Blumeria graminis f.sp. tritici (Bgt). Interestingly, the Bgt germination penalty on the TaKCS6- or TaECR-silenced wheat plants could be fully restored by the application of wild-type cuticular waxes or very-long-chain aldehydes, suggesting that the very-long-chain aldehydes were the wax signals provided by TaKCS6 and TaECR for stimulating Bgt conidia germination in bread wheat [91,92]. In Arabidopsis, Inada and Savory reported that the powdery mildew pathogen Golovinomyces orontii (G. orontii) could infect the mature rosette leaves of Arabidopsis, but its prepenetration processes such as conidial germination and appressorial formation were strongly inhibited on stems, fruits, and roots of Arabidopsis [93]. In addition, they found that inhibition of prepenetration processes of powdery mildew pathogen G. orontii on Arabidopsis stems was more severe in the mutant cer3 but not in cer1, which is consistent with the fact that CER1 gets involved in the biosynthesis of very-long-chain alkanes, but CER3 mediates the formation of very-long-chain aldehydes [93]. Therefore, stimulating germination of powdery mildew conidia by very-long-chain aldehydes might be conserved during the interactions of powdery mildew pathogens with monocots and dicots.
Fusarium head blight (FHB) caused by the fungal pathogen Fusarium graminearum is another devastating disease in wheat and barley. Silencing of barley WAX INDUCER1 (HvWIN1), a gene essential for the regulation of cuticular wax biosynthesis, resulted in enhanced susceptibility to FHB [94]. Further study showed the contents of free fatty acid RR (resistance-related) metabolites such as linoleic and palmitic acids, and the transcript abundance of genes involved in cuticular wax biosynthesis, including CYP86A2, CYP89A2, and LACS2, were significantly reduced in the HvWIN1-silenced barley leaves upon pathogen inoculation, suggesting that HvWIN1 regulates the expression of free fatty acid biosynthesis genes to reinforce cuticle to resist head blight in barley [94].
As a typical fungal pathogen, rust has evolved special mechanisms for invading plants. Upon rust infection, fungal urediniospores need to attach to the surface of leaves and subsequently form germ tubes. In general, rust pathogen requires specific plant surface topography and chemical signals to trigger the formation of prepenetration structures [95,96]. Barrel medic (Medicago truncatula) gene IRG1, encoding a Cys(2) His(2) zinc finger transcription factor, contributes to the plant nonhost resistance to fungal pathogens. Phakopsora pachyrhizi and Puccinia emaculata are the main pathogens causing soybean (Glycine max) and switchgrass (Panicum virgatum) rust, respectively [97,98]. The barrel medic inhibitor of rust germ tube differentation1 (irg1) mutant showed retarded prepenetration structures of two rust pathogens, P. pachyrhizi and P. emaculata, and one anthracnose pathogen, Colletotrichum trifoli [99]. Further analyses revealed that abaxial epicuticular wax crystals were completely lost and surface hydrophobicity was reduced in barrel medic (M. truncatula) irg1 mutant [99]. Meanwhile, the compositions of epicuticular waxes were changed in irg1 mutant, with fewer C30 primary alcohols as well as more C29 and C30 alkanes [99]. Transcriptome analysis found that ECERIFERUM 4, an enzyme involved in primary alcohol biosynthesis, and MYB96, a major transcription factor regulating wax biosynthesis, were downregulated in irg1 mutant, suggesting that IRG1 executes a regulating role in the biosynthesis of epidermal wax, which might affect the germination and appressorial formation of nonhost fungal pathogens in barrel medic (M. truncatula) [99]. Similarly, a barley gene, CYP96B22, encoding a putative cytochrome P450 monooxygenase, was also known to be involved in cuticular wax biosynthesis [100]. The expression of CYP96B22 was induced by the inoculation of rice blast Magnaporthe orzae at the nonhost barley leaves [100]. Meanwhile, the silencing of CYP96B22 using barley stripe mosaic virus-mediated gene silencing (BSMV-VIGS) led to a decrease in penetration resistance of barley plants to host and nonhost isolates of blast Magnaporthe, suggesting that CYP96B22 plays a role in the disease resistance against rice blast M. orzae infection [100]. Further studying the roles of IRG1 and CYP96B22 might help us to improve understanding of the significance of cuticular wax deposition on plant disease resistance against fungal infection in the future.
5. Regulation of Plant–Insect Interaction by Cuticular Waxes
Growing in their natural environment, plants are usually attacked by a variety of herbivorous insects. It was estimated that insect infestation leads to yield losses of more than 20% in wheat, soybean, and cotton [101]. As summarized in a prior review, plant–insect interactions are regulated by plant cuticular waxes at multiple levels [102]. First, cuticular waxes contribute to the slippery nature of plant surfaces, thus affecting plant-insect interactions [96]. For instance, cuticular waxes coating stems of many Macaranga ant plants (Euphorbiaceae) contain a large amount of triterpenoids, rendering the surface very slippery for most insects and allowing its symbiotic ants to survive in a competitor-free environment [103,104]. Second, certain cuticular wax components such as long-chain alkanes could be exploited by insects for host selection [105,106,107,108,109]. Spencer J.L. reported that the addition of long-chain alkanes in sinigrin and cabbage homogenates could stimulate oviposition by the diamondback moth Plutella xylostella [110]. Third, egg deposition of insects could significantly affect the composition of plant cuticular waxes, which could be sensed by egg parasitoids [111]. Blenn et al. reported that oviposition by the large cabbage white butterfly Pieris brassicae led to changes in the amounts of the wax composition such as the fatty acids tetracosanoic acid and tetratriacontanoic acid in Arabidopsis, and that the tetracosanoic acid could attract the egg parasitoid Trichogramma brassicae [111].
In addition to studies on the correlation of insect behaviors and plant wax compositions, the plant transcriptomic analysis also contributes to our understanding of the regulation of plant-insect interaction by cuticular waxes. For instance, tea green leafhopper, Empoasca (Matsumurasca) onukii Matsuda, is one of the most harmful pests to tea plants (Camellia sinensis), seriously threatening tea yield and quality [112]. A recent transcriptomic analysis revealed that genes involved in cuticle wax biosynthesis were significantly upregulated by tea green leafhopper infestation in tea plants [112]. In particular, the transcription level of a CER1 homolog involved in the formation of cuticular wax alkane was most significantly elevated, and C29 alkanes in tea leaf waxes were increased [112]. These results suggested that the CER1 homolog plays a pivotal role in tea wax alkane formation and is probably involved in responding to tea green leafhopper and other environmental stresses. Therefore, it is intriguing to characterize the function of other cuticular wax biosynthesis genes in regulating plant-insect interaction in future research.
6. Conclusions and Perspectives
In this review, we discussed the recent progress in the understanding of plant cuticular wax biosynthesis and its important roles in regulating plant-pathogen interactions (as summarized in Figure 1). Although past decades have seen a great advance in understanding the function of cuticular wax biosynthesis genes in model plants, we still have a long way to go towards fully understanding the biosynthesis of plant cuticular wax. For instance, exact enzymes mediating biosynthesis of certain cuticular wax components such as very-long-chain aldehydes need to be identified. Furthermore, biosynthesis of cuticular wax shares many precursors and energies with metabolisms of other substances such as saccharides, lipids, and even amino acids, but how plants orchestrate these biosynthetic processes is still unknown. In addition, increasing evidence has revealed that the biosynthesis of cuticular wax is regulated by developmental signals and environmental conditions, and their underlying mechanisms also remain to be disclosed.
As summarized in Table 1, many cuticular wax biosynthesis genes get involved in plant–pathogen interaction. As we know, the mixture of cuticular waxes is mainly composed of very-long-chain fatty acids and their derivatives, such as aldehydes, alkanes, primary and secondary alcohols, esters, and ketones, but the exact cuticular wax components responsible for limiting pathogen infection or inducing plant defense responses remains to be identified. Although it was demonstrated that very-long-chain aldehydes function as wax signals to trigger the conidial germination and appresorial development of B. graminis in barley and wheat, wherein the chemical regulation seems to be very specific between plant-pathogen interactions. Therefore, identifying these cuticular wax components that induce plant defense or pathogen infection and characterizing their underlying mechanisms would certainly improve our understanding of the function of cuticular wax biosynthesis in plant disease resistance. In addition, most of our knowledge about plant cuticular wax biosynthesis and their roles in plant disease resistance come from the study of the model plant-pathogen interaction, such as Arabidopsis and the fungal pathogen Botrytis cinerea, or bacterial pathogen Pst DC3000. However, the roles and mechanisms of cuticular waxes in regulating crop-pathogen interactions remain to be explored in future research.
Our knowledge of cuticular wax biosynthesis and their roles in plant–pathogen interactions could bring us valuable information to develop new strategies for crop protection. For instance, based on an understanding about the cuticular wax biosynthetic pathway and the function of exact cuticular wax components in plant-pathogen interaction, we could employ genome-editing systems such as the clustered regularly interspaced short palindromic repeats and CRISPR associated protein 9 (CRISPR-Cas9) system to create genome-edited crops producing the “ideal” layer of cuticular waxes without wax cues exploited by pathogens [113]. In addition, the knowledge of cuticular wax biosynthesis and their functions in plant-pathogen interaction would help us to synthesize the “elicitor” cuticular wax components to prime plant defense responses and limit pathogen infection.
Author Contributions
C.C., L.K., X.W., and P.Z. wrote this manuscript. All authors have read and agreed to the published version of the manuscript.
Funding
This research was funded by the National Natural Science Foundation of China (31701412, 31701986), the Natural Science Foundation of Shandong Province (ZR2017BC109), and the Qingdao Science and Technology Bureau Fund (17-1-1-50-jch, 18-2-2-51-jch).
Conflicts of Interest
The authors declare no conflict of interest.
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A simplified model for the cuticular wax biosynthesis and its roles in regulating plant–pathogen interactions. The biosynthesis of the cuticular wax mixture starts from the elongation of C16 or C18 fatty acid-coenzyme A (CoA) by fatty acid elongase (FAE) complex and ECERIFERUM2 (CER2) proteins. The elongated very-long-chain (VLC) acyl-CoAs are then modified into aldehydes, alkanes, secondary alcohols, and ketones by an alkane-forming pathway (shown in blue) or into primary alcohols and wax esters by an alcohol-forming pathway (shown in pink). Names shown in red denote proteins involved in the regulation of plant-pathogen interactions. For steps involving multiple paralogs, only the gene subfamily name is given in black. Circle, square, and triangle denote plant-bacterial pathogen interaction, plant-fungal pathogen interaction, and plant–insect interaction, respectively. Positive and negative regulations of plant–pathogen interaction are individually shown in green and red colors, respectively. The model for the cuticular wax biosynthesis was built on Yeast and Rose. 2013, and Lewandowska et al., 2020 [8,49].
ijms-21-05514-t001_Table 1
Cuticular wax biosynthesis genes involved in plant-pathogen interactions.
Gene Name
Gene Product
Gene Product Family
Plant Species
Function of Gene Product
Involvement of Gene Product in Plant-Pathogen Interaction and Evidence
Reference
\nDEWAX\n
DEWAX
AP2/ERF-type transcription factor
\nArabidopsis thaliana\n
Transcriptional suppression of cuticular waxes biosynthesis genes
DEWAX acts as transcriptional activator of defense-related genes and positively regulates disease resistance against Botrytis cinerea.
[69]
\nSMA4\n
LACS2
Long chain acyl-CoA synthetase
\nArabidopsis thaliana\n
Biosynthesis of C16 or C18 acyl-CoAs
sma4 mutant exhibited enhanced susceptibility to bacterial pathogen Pst DC3000 but enhanced resistance against fungal pathogen B. cinerea.
[76]
\nMYB30\n
MYB30
R2R3-type MYB family transcription factor
\nArabidopsis thaliana\n
Transcriptional activation of cuticular waxes biosynthesis genes
Hypersensitive response was exacerbated in MYB30-overexpressing lines.
[77]
\nMdMYB30\n
MdMYB30
R2R3-type MYB family transcription factor
\nMalus domestica\n
Transcriptional activation of cuticular waxes biosynthesis genes
Ectopic expression of MdMYB30 in Arabidopsis increases resistance to Pst DC3000.
[78]
\nMYB96\n
MYB96
R2R3-type MYB family transcription factor
\nArabidopsis thaliana\n
Transcriptional activation of cuticular waxes biosynthesis genes
MYB96 activation-tagging Arabidopsis lines exhibited enhanced resistance to Pst DC3000 by potentiating SA biosynthesis.
[80]
\nCER1\n
CER1
VLC-aldehyde decarbonylase putative
\nArabidopsis thaliana\n
Formation of VLC alkanes
The susceptibility to Pst DC3000 were enhanced in the Arabidopsis plants over-expressing CER1.
[59]
\nTaKCS6\n
TaKCS6
3-Ketoacyl-CoA synthase
\nTriticum aestivum\n
Biosynthesis of VLC acyl-CoAs
Silencing of TaKCS6 attenuated Bgt conidia germination in bread wheat.
[91]
\nTaECR\n
TaECR
Enoyl-CoA reductase
\nTriticum aestivum\n
Biosynthesis of VLC acyl-CoAs
Silencing of TaECR attenuated Bgt conidia germination in bread wheat.
[92]
\nCER3\n
CER3
VLC-acyl-CoA reductase putative
\nArabidopsis thaliana\n
Formation of VLC alkanes
The inhibition of prepenetration processes of Golovinomyces orontii on Arabidopsis stems is more severe in the mutant cer3.
[93]
\nHvWIN1\n
HvWIN1
AP2-EREBP-type transcription factor
\nHordeum vulgare\n
Transcriptional activation of cuticular waxes biosynthesis genes
Silencing of HvWIN1 resulted in enhanced susceptibility to FHB.
[94]
\nIRG1\n
IRG1
Cys2His2 zinc finger transcription factor
\nMedicago truncatula\n
Formation of epicuticular wax crystals
irg1 mutant showed retarded prepenetration of two rust pathogens and one anthracnose pathogen.
[99]
\nCYP96B2\n
CYP96B2
Cytochrome P450 monooxygenase putative
\nHordeum vulgare\n
Cuticular waxes biosynthesis
Silencing of CYP96B22 led to a decrease in penetration resistance of barley plants to blast pathogen Magnaporthe.
[100]
\n"],"string":"[\n \"\\nInt J Mol SciInt J Mol SciijmsInternational Journal of Molecular Sciences1422-0067MDPI32752176PMC743212510.3390/ijms21155514ijms-21-05514ReviewUpdate on Cuticular Wax Biosynthesis and Its Roles in Plant Disease ResistanceWangXiaoyu†KongLingyao†ZhiPengfeihttps://orcid.org/0000-0001-6672-4704ChangCheng*College of Life Sciences, Qingdao University, Qingdao 266071, Shandong, China; 2017020884@qdu.edu.cn (X.W.); konglingyao@126.com (L.K.); 2017020872@qdu.edu.cn (P.Z.)Correspondence: cc@qdu.edu.cn; Tel.: +86-532-85953227
These authors contributed equally to this work.
0182020820202115551429620203072020© 2020 by the authors.2020Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
The aerial surface of higher plants is covered by a hydrophobic layer of cuticular waxes to protect plant tissues against enormous environmental challenges including the infection of various pathogens. As the first contact site between plants and pathogens, the layer of cuticular waxes could function as a plant physical barrier that limits the entry of pathogens, acts as a reservoir of signals to trigger plant defense responses, and even gives cues exploited by pathogens to initiate their infection processes. Past decades have seen unprecedented proceedings in understanding the molecular mechanisms underlying the biosynthesis of plant cuticular waxes and their functions regulating plant–pathogen interactions. In this review, we summarized the recent progress in the molecular biology of cuticular wax biosynthesis and highlighted its multiple roles in plant disease resistance against bacterial, fungal, and insect pathogens.
In the natural environment, plants encounter various pathogens such as viruses, bacteria, fungi, and even herbivorous insects, which seriously threaten plant growth and crop production. It was estimated that these pathogens have contributed to at least 20% of yield loss in important crops including wheat, rice, maize, potatoes, and soybeans [1,2]. Therefore, diseases caused by pathogenic microorganisms and herbivores are major factors affecting agriculture [3,4,5,6]. Unlike animals that could escape from predators and pathogens, plants must withstand pathogen attacks at the sites of growth [1,2]. Increasing evidence from studies in the model plant Arabidopsis thaliana revealed that plants have acquired a battery of sophisticated defense mechanisms to defend themselves against pathogen attacks during their co-evolution with various pathogens [7].
As the preformed defense, physical barriers such as spines, hairs, trichomes, thorns, and cuticles cover the aerial parts of plants [8,9]. In contrast, plant innate immunity acts as an example of induced defense. For instance, pattern-triggered immunity (PTI) was induced by the recognition of chemical molecules in the pathogen, microbial, and/or damage-associated molecular patterns (PAMPs, MAMPs, and/or DAMPs, respectively), and these chemical molecules are released during pathogen infection and recognized by pattern recognition receptors (PRRs) [10,11]. Generally, PTI is involved in a wide range of defenses, including the production of reactive oxygen species (ROS) and the cascade of mitogen-activated protein kinases (MAPKs) [12,13,14,15]. To interfere with pattern-triggered immunity, pathogens usually secrete effectors, which could be recognized by specific resistance (R) proteins, inducing effector-triggered immunity (ETI) [16,17,18,19,20]. Compared with PTI, ETI usually triggers the hypersensitive response (HR) and systemic acquired resistance (SAR) in host plants [21,22,23,24,25]. Traditionally, breeding for disease resistance in crops such as wheat, rice, potato, barley, and even soybean mainly relies on host resistance regulated by Resistance (R) genes [26]. However, in many cases, due to the variation of new pathogen strains, the resistance mediated by the R gene is less effective in the field [26]. These new pathogen strains can escape the recognition of the R gene and the R gene-mediated downstream defense [26]. Increasing evidence from studies in model plants and crops such as Arabidopsis, tomato (Solanum lycopersicum), and rice have revealed that phytohormones and their signaling pathways usually function at the downstream of pattern-triggered immunity and effector-triggered immunity to regulate plant defense, which were also considered to be effective plant defense mechanisms [27,28]. For instance, phytohormones salicylic acid (SA), jasmonic acid (JA), and ethylene (ET) are well known as the main regulators in plant defense responses [29]. In addition, phytohormones abscisic acid (ABA), auxin, gibberellin (GA), brassinosteroid (BR), and cytokinin (CK) also act as indirect factors involved in plant-pathogen interactions [27,28].
As the outmost layer exposed to the environment, cuticle covers plant aerial organs and protects plant tissues against enormous environmental challenges such as dehydration, excessive UV radiation, mechanical damage, and even pathogen infections, which have been summarized in prior reviews [30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45]. In addition to its protective roles, cuticle also gets involved in regulating plant development [33]. Although the composition of the cuticle varies among plant species, tissues, developmental stages, and even environmental conditions, plant cuticle is mainly composed of a cutin scaffold impregnated by and covered with cuticular waxes [46,47]. As a mixture of very-long-chain (VLC, >C20) fatty acids and their derivatives, cuticular waxes play multiple roles, from acting as a plant physical barrier, limiting the entry of pathogens, to functioning as a cue exploited by pathogens to initiate their prepenetration and infection processes in regulating the plant-pathogen interactions. Therefore, cuticular waxes have gained increasing attention in the study of plant disease resistance [9,48]. In this review, we summarized the recent advances in the molecular biology of cuticular wax biosynthesis and discussed their multiple roles in plant disease resistance against bacterial, fungal, and insect pathogens.
2. Molecular Mechanism of Cuticular Wax Biosynthesis
Plant cuticular waxes are organic solvent-extractable complex mixtures comprising very-long-chain fatty acids and their derivatives, such as aldehydes, alkanes, primary and secondary alcohols, esters, and ketones [49,50,51]. In some plant species, secondary metabolites such as flavonoids, pentacyclic triterpenoids, and tocopherols have also been identified as wax components [49,50,51]. In the model plant Arabidopsis, cuticular wax biosynthetic mechanisms have been characterized with the contribution of wax biosynthetic mutants and transcriptomic/proteomic analysis [49,50,51].
As summarized in Figure 1, the biosynthesis of cuticular waxes in Arabidopsis can be divided into three steps: (1) the de novo synthesis of C16 or C18 fatty acids; (2) the extension of C16 and C18 fatty acids to form very-long-chain fatty acids (VLCFAs), which are used as direct precursors for wax synthesis in the endoplasmic reticulum (ER); and (3) the synthesis of derivatives of VLCFAs, such as aldehydes, alcohols, alkanes, ketones, and esters [49,51]. In the plastids of epidermal cells, acetyl coenzyme A (CoA) was converted into CoA-C2 by the catalysis of fatty acid synthetase complex (FAS), and after many reaction cycles, it can generate C16 or C18 acyl-acyl carrier protein (ACP), which was hydrolyzed by a fatty acyl-ACP thioesterase (FAT) to produce C16 or C18 fatty acids (Figure 1). These C16 or C18 fatty acids were activated to acyl-CoAs by the long-chain acyl-coenzyme A synthases (LACSs) and then transported to the endoplasmic reticulum (ER) [51,52]. C16 and C18 acyl-CoAs served as precursors for the formation of very-long-chain acyl-CoAs (up to C34), which was catalyzed by the enzymes of the fatty acid elongase (FAE) complex and the ECERIFERUM2 (CER2) proteins (Figure 1) [53,54]. In the FAE complex, β-ketoacyl-CoA synthase (KCS), β-ketoacyl-CoA reductase (KCR), 3-hydroxyacyl-CoA dehydratase (HCD), and enoyl-CoA reductase (ECR) catalyzed the sequential condensation/reduction/dehydration/reduction reactions in the formation of very-long-chain acyl-CoAs (Figure 1) [55,56,57,58]. These elongated very-long-chain acyl-CoAs were then modified into alkanes by the ECERIFERUM1 (CER1)/ECERIFERUM3 (CER3)/CYTOCHROME B5 (CYTB5) complex in an alkane-forming pathway, and these very-long-chain alkanes could be further oxidized to secondary alcohols and ketones by the CYP95A family cytochrome P450 enzymes MIDCHAIN ALKANE HYDROXYLASE1 (MAH1) (Figure 1) [59,60,61]. In the alcohol-forming pathway, very-long-chain acyl-CoAs were converted into the n-6 monounsaturated fatty acids by the acyl desaturase ECERIFERUM17 (CER17), which was followed by the formation of primary alcohols catalyzed by the fatty acyl-CoA reductase ECERIFERUM4 (CER4) (Figure 1) [62,63]. In addition, the Arabidopsis WAX SYNTHASE/ACYL-COA: DIACYLGLYCEROL ACYLTRANSFERASE 1 (WSD1) catalyzed the formation of wax esters through using acyl-CoAs and primary alcohols as precursors in the alcohol-forming pathway (Figure 1) [64]. These generated waxes components were transported from the ER to the plasma membrane (PM)via the Golgi and trans-Golgi network (TGN)-trafficking pathways, and finally exported out of the plant cell to the cuticle via the PM-localized ATP binding cassette G (ABCG) subfamily half transporters and the lipid transfer proteins (LTPs) (Figure 1) [49,50,51].
Increasing evidence from studies in Arabidopsis revealed that the biosynthesis of cuticular waxes is regulated by multiple transcriptional regulators [49,50,51]. As the first reported transcriptional regulator, the Arabidopsis APETALA2-Ethylene responsive factor (AP2-EREBP)-type transcription factor WAX INDUCER1/SHINE1 (WIN1/SHN1) and its close homologs SHN2 and SHN3 activated cuticular wax biosynthesis by upregulation of biosynthesis genes β-Ketoacyl-CoA synthase 1 (KCS1), CER1, and CER2 [65,66]. Similarly, Myeloblastosis (MYB) family transcription factors MYB94 and MYB96 potentiated the cuticular wax biosynthesis under drought by directly activating expression of KCS2, ECR, CER2, and WSD1 genes in Arabidopsis [67,68]. In contrast, the Arabidopsis AP2/ERF-type transcription factor DECREASE WAX BIOSYNTHESIS (DEWAX) was reported to negatively regulate cuticular wax biosynthesis in the light/dark cycle by directly suppressing long chain acyl-CoA synthase 2 (LACS2), CER1, and ECR genes [69,70]. Moreover, the biosynthesis of cuticular wax in Arabidopsis is regulated at the post-transcriptional and post-translational levels. For instance, ECERIFERUM7 (CER7), a core subunit of the exosome, regulated the accumulation of trans-acting small interfering RNA class of small RNAs involved in direct silencing of CER3 in Arabidopsis [71]. Another recent study in Arabidopsis revealed that CER16, a protein with no known domains or motifs, also inhibited post-transcriptional gene silencing of CER3 to regulate alkane biosynthesis [72]. In addition, the Kelch repeat F-box protein SMALL AND GLOSSY LEAVES1 (SAGL1) mediated proteasome-dependent degradation of CER3, thereby negatively regulating cuticular wax biosynthesis in Arabidopsis [73].
3. Regulation of Plant–Bacterial Pathogen Interaction by Cuticular Waxes
On the plant cuticle, bacterial pathogens usually produce extracellular polymeric substances and form large aggregates to help them to withstand the harsh surrounding conditions. It is well known that the bacterial pathogen Pseudomonas syringae can produce syringafactin, a compound with surfactant properties, to facilitate its motility and increase the permeability of Arabidopsis cuticle [74]. Increasing evidence has revealed that the survival and infection of bacterial pathogens on plant surfaces are affected by the integrity and permeability of the plant cuticular wax layer. A more permeable plant cuticular wax layer could lead to either enhanced resistance or susceptibility to pathogen infections [48]. For instance, Arabidopsis sma4 (symptoms to multiple-regulated avr4) is a loss-of-function mutant of the SMA4 gene, which encodes the cuticular wax biosynthetic component LACS2 [75]. Tang et al. found that the sma4 mutant exhibited enhanced susceptibility to the hemibiotrophic bacterial pathogen Pseudomonas syringae pv tomato strain DC3000 (Pst DC3000) but displayed enhanced resistance against the necrotrophic fungal pathogen Botrytis cinerea (B. cinerea) [76].
MYB family transcription factors have been reported to be involved in plant cuticular wax biosynthesis and disease resistance against bacterial pathogens [48,51]. For instance, Arabidopsis transcription factor MYB30 functions as a positive regulator of a cell death pathway, conditioning the hypersensitive response [77]. Raffaele et al. demonstrated that the exacerbated hypersensitive response phenotype of MYB30-overexpressing Arabidopsis lines was altered by the loss of function of the acyl-ACP thioesterase gene acyl-ACP thioesterase B (FATB), which causes severe defects in the supply of fatty acids for the biosynthesis of very-long-chain fatty acids, suggesting that MYB30 modulates hypersensitive response via controlling the biosynthesis of very-long-chain fatty acids in Arabidopsis [77]. Similarly, Zhang et al. reported that ectopic expression of apple (Medicago truncatula) MdMYB30, which encodes an R2R3 MYB transcription factor, in Arabidopsis increased the transcription levels of wax biosynthesis-related genes, including wax synthesis regulatory gene 1 (AtWRI1), AtWIN1, AtKCS1, acyl-CoA binding protein 1 (AtACBP1), AtLACS2, AtSHINE2, and AtSHINE3 [78]. The accumulation of wax compositions, such as C29 alkanes, C31 alcohols, C29 aldehydes, C16 fatty acids, C29 ketones, and C29 and C30 esters were significantly enhanced in MdMYB30-ectopic-expression Arabidopsis lines [78]. Interestingly, MdMYB30 also contributed to the increased resistance against Pst DC3000 in Arabidopsis, suggesting that the changed epicuticular wax content and composition might cause disease resistance against the bacterial pathogen Pst DC3000 [78]. In addition, MYB96, another MYB transcription factor (TF) in Arabidopsis, directly binds to the promoters of wax biosynthetic genes including KCS1, KCS2/DAISY, KCS6, KCR1, and CER3, and actives the expression of these genes under drought- and ABA-inducible conditions [79]. Notably, MYB96 activation tagging Arabidopsis lines showed increased wax accumulation and enhanced resistance to Pst DC3000 by potentiating the SA biosynthesis [80]. However, the accumulation of cuticular wax components does not necessarily contribute to the plant disease resistance against bacterial pathogens. For instance, VLC alkanes were accumulated but the susceptibility to Pst DC3000 was enhanced in the Arabidopsis lines over-expressing CER1 [59]. Interestingly, increasing evidence from studies in fungal pathogens Blumeria and Colletotrichum revealed that certain wax components such as very-long-chain aldehyde and terpenoids could be exploited by certain fungal pathogens to trigger their infection processes, but other components, including free fatty acid RR (resistance-related) metabolites, contribute to plant resistance against fungal pathogens. Therefore, characterizing the function of specific wax components in regulating bacterial growth and infections, as well as plant defense responses, might contribute to understanding the roles of cuticular waxes in the regulation of plant–bacterial pathogen interactions in the future research.
4. Regulation of Plant–Fungal Pathogen Interaction by Cuticular Waxes
During the infection process, fungal pathogens could synthesize and secrete hydrolytic enzymes such as cutinases and lipases to degrade the cuticular wax layer [81]. For instance, cutinase2 (CUT2) gene in rice blast (Magnaporthe grisea) is activated during appressorial development and fungal penetration [82]. During these processes, fungal pathogens would be recognized by the plant immune systems and trigger the immune responses. Several Arabidopsis plants over-accumulating fungal cutinase exhibited increased cuticle permeability and enhanced resistance to fungal pathogens. For example, the Arabidopsis AP2/ERF-type transcription factor DECREASE WAX BIOSYNTHESIS (DEWAX) negatively regulates cuticular wax biosynthesis by suppressing cuticular wax biosynthesis genes (CER1, LACS2, ATP-citrate lyase A2 and ECR) [83]. Interestingly, over-expression DEWAX in Arabidopsis led to enhanced disease resistance against grey mildew (Botrytis cinerea) [69]. Further analyses revealed that DEWAX acts as a transcriptional activator, binding to the promoters of defense-related genes including plant defensin 1.2a (PDF1.2a), indole glucosinolate O-methyltransferase 1 (IGMT1), and peroxidase 37 (PRX37), and upregulating the expression of these genes in Arabidopsis [69]. These results suggest that cuticle wax biosynthesis genes could regulate plant disease resistance through direct targeting defense-related genes. Indeed, the Arabidopsis sma4 mutant plants display increased resistance to grey mildew (B. cinerea), and these processes were independent of jasmonic acid (JA)- and ethylene (ET)-signaling pathways [76].
Interestingly, Arabidopsis mutants such as long-chain acyl-CoA synthetase-2 (lacs-2) and -3, and myeloblast transcription factor 96 (myb96) with increased cuticle permeability exhibited enhanced disease resistance against grey mildew (B. cinerea) [76,80]. L’Haridon et al. reported that a permeable cuticle in Arabidopsis is associated with the release of reactive oxygen species (ROS) and induction of innate immunity, demonstrating the importance of fungal suppression by reactive oxygen species formation [84]. In contrast, Cui et al. demonstrated that Botrytis immunity conferred by cuticle permeability can be genetically uncoupled from phosphatase2c-regulated abscisic acid (ABA) sensitivity but requires negative regulation of a parallel ABA-dependent cell death pathway [85]. In addition, several studies revealed that cuticular wax accumulation also contributes to disease resistance against fungal pathogens. For instance, Zhang et al. showed that the apple (M. truncatula) transcription factor MdMYB30 could bind to the promoter region of MdKCS1 to activate its expression and induce wax biosynthesis [78]. Notably, the infection of apple canker pathogen Botryosphaeria dothidea could induce the accumulation of wax crystals and transcription of pathogenesis-related genes, such as MdNPR1, MdPR1, MdPR5, MdEDS1, and MdPAL at B. dothidea injection sites in MdMYB30-overexpression apple lines [78]. Consistently, MdMYB30-overexpression transgenic apple calli exhibited strengthened resistance against apple canker (B. dothidea). These results indicated that MdMYB30 positively modulates waxes biosynthesis of apple fruit and enhances apple resistance to certain fungal pathogens [78].
Powdery mildew caused by the fungal pathogen Blumeria graminis is a devastating disease in barley and wheat. Increasing evidence has revealed that B. graminis could utilize plant cuticular wax components to initiate their prepenetration processes such as conidial germination and appressorial development [86,87,88,89,90,91,92]. Hansjakob et al. reported that very-long-chain aldehydes could stimulate the in vitro conidial germination of B. graminis in a dose-dependent manner [86,87]. Recently, Wang et al. and Kong et al. reported that the silencing of the wheat 3-ketoacyl-CoA synthase (TaKCS6) and enoyl-CoA reductase (TaECR) led to the reduction of cuticular wax load and attenuated conidial germination of Blumeria graminis f.sp. tritici (Bgt). Interestingly, the Bgt germination penalty on the TaKCS6- or TaECR-silenced wheat plants could be fully restored by the application of wild-type cuticular waxes or very-long-chain aldehydes, suggesting that the very-long-chain aldehydes were the wax signals provided by TaKCS6 and TaECR for stimulating Bgt conidia germination in bread wheat [91,92]. In Arabidopsis, Inada and Savory reported that the powdery mildew pathogen Golovinomyces orontii (G. orontii) could infect the mature rosette leaves of Arabidopsis, but its prepenetration processes such as conidial germination and appressorial formation were strongly inhibited on stems, fruits, and roots of Arabidopsis [93]. In addition, they found that inhibition of prepenetration processes of powdery mildew pathogen G. orontii on Arabidopsis stems was more severe in the mutant cer3 but not in cer1, which is consistent with the fact that CER1 gets involved in the biosynthesis of very-long-chain alkanes, but CER3 mediates the formation of very-long-chain aldehydes [93]. Therefore, stimulating germination of powdery mildew conidia by very-long-chain aldehydes might be conserved during the interactions of powdery mildew pathogens with monocots and dicots.
Fusarium head blight (FHB) caused by the fungal pathogen Fusarium graminearum is another devastating disease in wheat and barley. Silencing of barley WAX INDUCER1 (HvWIN1), a gene essential for the regulation of cuticular wax biosynthesis, resulted in enhanced susceptibility to FHB [94]. Further study showed the contents of free fatty acid RR (resistance-related) metabolites such as linoleic and palmitic acids, and the transcript abundance of genes involved in cuticular wax biosynthesis, including CYP86A2, CYP89A2, and LACS2, were significantly reduced in the HvWIN1-silenced barley leaves upon pathogen inoculation, suggesting that HvWIN1 regulates the expression of free fatty acid biosynthesis genes to reinforce cuticle to resist head blight in barley [94].
As a typical fungal pathogen, rust has evolved special mechanisms for invading plants. Upon rust infection, fungal urediniospores need to attach to the surface of leaves and subsequently form germ tubes. In general, rust pathogen requires specific plant surface topography and chemical signals to trigger the formation of prepenetration structures [95,96]. Barrel medic (Medicago truncatula) gene IRG1, encoding a Cys(2) His(2) zinc finger transcription factor, contributes to the plant nonhost resistance to fungal pathogens. Phakopsora pachyrhizi and Puccinia emaculata are the main pathogens causing soybean (Glycine max) and switchgrass (Panicum virgatum) rust, respectively [97,98]. The barrel medic inhibitor of rust germ tube differentation1 (irg1) mutant showed retarded prepenetration structures of two rust pathogens, P. pachyrhizi and P. emaculata, and one anthracnose pathogen, Colletotrichum trifoli [99]. Further analyses revealed that abaxial epicuticular wax crystals were completely lost and surface hydrophobicity was reduced in barrel medic (M. truncatula) irg1 mutant [99]. Meanwhile, the compositions of epicuticular waxes were changed in irg1 mutant, with fewer C30 primary alcohols as well as more C29 and C30 alkanes [99]. Transcriptome analysis found that ECERIFERUM 4, an enzyme involved in primary alcohol biosynthesis, and MYB96, a major transcription factor regulating wax biosynthesis, were downregulated in irg1 mutant, suggesting that IRG1 executes a regulating role in the biosynthesis of epidermal wax, which might affect the germination and appressorial formation of nonhost fungal pathogens in barrel medic (M. truncatula) [99]. Similarly, a barley gene, CYP96B22, encoding a putative cytochrome P450 monooxygenase, was also known to be involved in cuticular wax biosynthesis [100]. The expression of CYP96B22 was induced by the inoculation of rice blast Magnaporthe orzae at the nonhost barley leaves [100]. Meanwhile, the silencing of CYP96B22 using barley stripe mosaic virus-mediated gene silencing (BSMV-VIGS) led to a decrease in penetration resistance of barley plants to host and nonhost isolates of blast Magnaporthe, suggesting that CYP96B22 plays a role in the disease resistance against rice blast M. orzae infection [100]. Further studying the roles of IRG1 and CYP96B22 might help us to improve understanding of the significance of cuticular wax deposition on plant disease resistance against fungal infection in the future.
5. Regulation of Plant–Insect Interaction by Cuticular Waxes
Growing in their natural environment, plants are usually attacked by a variety of herbivorous insects. It was estimated that insect infestation leads to yield losses of more than 20% in wheat, soybean, and cotton [101]. As summarized in a prior review, plant–insect interactions are regulated by plant cuticular waxes at multiple levels [102]. First, cuticular waxes contribute to the slippery nature of plant surfaces, thus affecting plant-insect interactions [96]. For instance, cuticular waxes coating stems of many Macaranga ant plants (Euphorbiaceae) contain a large amount of triterpenoids, rendering the surface very slippery for most insects and allowing its symbiotic ants to survive in a competitor-free environment [103,104]. Second, certain cuticular wax components such as long-chain alkanes could be exploited by insects for host selection [105,106,107,108,109]. Spencer J.L. reported that the addition of long-chain alkanes in sinigrin and cabbage homogenates could stimulate oviposition by the diamondback moth Plutella xylostella [110]. Third, egg deposition of insects could significantly affect the composition of plant cuticular waxes, which could be sensed by egg parasitoids [111]. Blenn et al. reported that oviposition by the large cabbage white butterfly Pieris brassicae led to changes in the amounts of the wax composition such as the fatty acids tetracosanoic acid and tetratriacontanoic acid in Arabidopsis, and that the tetracosanoic acid could attract the egg parasitoid Trichogramma brassicae [111].
In addition to studies on the correlation of insect behaviors and plant wax compositions, the plant transcriptomic analysis also contributes to our understanding of the regulation of plant-insect interaction by cuticular waxes. For instance, tea green leafhopper, Empoasca (Matsumurasca) onukii Matsuda, is one of the most harmful pests to tea plants (Camellia sinensis), seriously threatening tea yield and quality [112]. A recent transcriptomic analysis revealed that genes involved in cuticle wax biosynthesis were significantly upregulated by tea green leafhopper infestation in tea plants [112]. In particular, the transcription level of a CER1 homolog involved in the formation of cuticular wax alkane was most significantly elevated, and C29 alkanes in tea leaf waxes were increased [112]. These results suggested that the CER1 homolog plays a pivotal role in tea wax alkane formation and is probably involved in responding to tea green leafhopper and other environmental stresses. Therefore, it is intriguing to characterize the function of other cuticular wax biosynthesis genes in regulating plant-insect interaction in future research.
6. Conclusions and Perspectives
In this review, we discussed the recent progress in the understanding of plant cuticular wax biosynthesis and its important roles in regulating plant-pathogen interactions (as summarized in Figure 1). Although past decades have seen a great advance in understanding the function of cuticular wax biosynthesis genes in model plants, we still have a long way to go towards fully understanding the biosynthesis of plant cuticular wax. For instance, exact enzymes mediating biosynthesis of certain cuticular wax components such as very-long-chain aldehydes need to be identified. Furthermore, biosynthesis of cuticular wax shares many precursors and energies with metabolisms of other substances such as saccharides, lipids, and even amino acids, but how plants orchestrate these biosynthetic processes is still unknown. In addition, increasing evidence has revealed that the biosynthesis of cuticular wax is regulated by developmental signals and environmental conditions, and their underlying mechanisms also remain to be disclosed.
As summarized in Table 1, many cuticular wax biosynthesis genes get involved in plant–pathogen interaction. As we know, the mixture of cuticular waxes is mainly composed of very-long-chain fatty acids and their derivatives, such as aldehydes, alkanes, primary and secondary alcohols, esters, and ketones, but the exact cuticular wax components responsible for limiting pathogen infection or inducing plant defense responses remains to be identified. Although it was demonstrated that very-long-chain aldehydes function as wax signals to trigger the conidial germination and appresorial development of B. graminis in barley and wheat, wherein the chemical regulation seems to be very specific between plant-pathogen interactions. Therefore, identifying these cuticular wax components that induce plant defense or pathogen infection and characterizing their underlying mechanisms would certainly improve our understanding of the function of cuticular wax biosynthesis in plant disease resistance. In addition, most of our knowledge about plant cuticular wax biosynthesis and their roles in plant disease resistance come from the study of the model plant-pathogen interaction, such as Arabidopsis and the fungal pathogen Botrytis cinerea, or bacterial pathogen Pst DC3000. However, the roles and mechanisms of cuticular waxes in regulating crop-pathogen interactions remain to be explored in future research.
Our knowledge of cuticular wax biosynthesis and their roles in plant–pathogen interactions could bring us valuable information to develop new strategies for crop protection. For instance, based on an understanding about the cuticular wax biosynthetic pathway and the function of exact cuticular wax components in plant-pathogen interaction, we could employ genome-editing systems such as the clustered regularly interspaced short palindromic repeats and CRISPR associated protein 9 (CRISPR-Cas9) system to create genome-edited crops producing the “ideal” layer of cuticular waxes without wax cues exploited by pathogens [113]. In addition, the knowledge of cuticular wax biosynthesis and their functions in plant-pathogen interaction would help us to synthesize the “elicitor” cuticular wax components to prime plant defense responses and limit pathogen infection.
Author Contributions
C.C., L.K., X.W., and P.Z. wrote this manuscript. All authors have read and agreed to the published version of the manuscript.
Funding
This research was funded by the National Natural Science Foundation of China (31701412, 31701986), the Natural Science Foundation of Shandong Province (ZR2017BC109), and the Qingdao Science and Technology Bureau Fund (17-1-1-50-jch, 18-2-2-51-jch).
Conflicts of Interest
The authors declare no conflict of interest.
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A simplified model for the cuticular wax biosynthesis and its roles in regulating plant–pathogen interactions. The biosynthesis of the cuticular wax mixture starts from the elongation of C16 or C18 fatty acid-coenzyme A (CoA) by fatty acid elongase (FAE) complex and ECERIFERUM2 (CER2) proteins. The elongated very-long-chain (VLC) acyl-CoAs are then modified into aldehydes, alkanes, secondary alcohols, and ketones by an alkane-forming pathway (shown in blue) or into primary alcohols and wax esters by an alcohol-forming pathway (shown in pink). Names shown in red denote proteins involved in the regulation of plant-pathogen interactions. For steps involving multiple paralogs, only the gene subfamily name is given in black. Circle, square, and triangle denote plant-bacterial pathogen interaction, plant-fungal pathogen interaction, and plant–insect interaction, respectively. Positive and negative regulations of plant–pathogen interaction are individually shown in green and red colors, respectively. The model for the cuticular wax biosynthesis was built on Yeast and Rose. 2013, and Lewandowska et al., 2020 [8,49].
ijms-21-05514-t001_Table 1
Cuticular wax biosynthesis genes involved in plant-pathogen interactions.
Gene Name
Gene Product
Gene Product Family
Plant Species
Function of Gene Product
Involvement of Gene Product in Plant-Pathogen Interaction and Evidence
Reference
\\nDEWAX\\n
DEWAX
AP2/ERF-type transcription factor
\\nArabidopsis thaliana\\n
Transcriptional suppression of cuticular waxes biosynthesis genes
DEWAX acts as transcriptional activator of defense-related genes and positively regulates disease resistance against Botrytis cinerea.
[69]
\\nSMA4\\n
LACS2
Long chain acyl-CoA synthetase
\\nArabidopsis thaliana\\n
Biosynthesis of C16 or C18 acyl-CoAs
sma4 mutant exhibited enhanced susceptibility to bacterial pathogen Pst DC3000 but enhanced resistance against fungal pathogen B. cinerea.
[76]
\\nMYB30\\n
MYB30
R2R3-type MYB family transcription factor
\\nArabidopsis thaliana\\n
Transcriptional activation of cuticular waxes biosynthesis genes
Hypersensitive response was exacerbated in MYB30-overexpressing lines.
[77]
\\nMdMYB30\\n
MdMYB30
R2R3-type MYB family transcription factor
\\nMalus domestica\\n
Transcriptional activation of cuticular waxes biosynthesis genes
Ectopic expression of MdMYB30 in Arabidopsis increases resistance to Pst DC3000.
[78]
\\nMYB96\\n
MYB96
R2R3-type MYB family transcription factor
\\nArabidopsis thaliana\\n
Transcriptional activation of cuticular waxes biosynthesis genes
MYB96 activation-tagging Arabidopsis lines exhibited enhanced resistance to Pst DC3000 by potentiating SA biosynthesis.
[80]
\\nCER1\\n
CER1
VLC-aldehyde decarbonylase putative
\\nArabidopsis thaliana\\n
Formation of VLC alkanes
The susceptibility to Pst DC3000 were enhanced in the Arabidopsis plants over-expressing CER1.
[59]
\\nTaKCS6\\n
TaKCS6
3-Ketoacyl-CoA synthase
\\nTriticum aestivum\\n
Biosynthesis of VLC acyl-CoAs
Silencing of TaKCS6 attenuated Bgt conidia germination in bread wheat.
[91]
\\nTaECR\\n
TaECR
Enoyl-CoA reductase
\\nTriticum aestivum\\n
Biosynthesis of VLC acyl-CoAs
Silencing of TaECR attenuated Bgt conidia germination in bread wheat.
[92]
\\nCER3\\n
CER3
VLC-acyl-CoA reductase putative
\\nArabidopsis thaliana\\n
Formation of VLC alkanes
The inhibition of prepenetration processes of Golovinomyces orontii on Arabidopsis stems is more severe in the mutant cer3.
[93]
\\nHvWIN1\\n
HvWIN1
AP2-EREBP-type transcription factor
\\nHordeum vulgare\\n
Transcriptional activation of cuticular waxes biosynthesis genes
Silencing of HvWIN1 resulted in enhanced susceptibility to FHB.
[94]
\\nIRG1\\n
IRG1
Cys2His2 zinc finger transcription factor
\\nMedicago truncatula\\n
Formation of epicuticular wax crystals
irg1 mutant showed retarded prepenetration of two rust pathogens and one anthracnose pathogen.
[99]
\\nCYP96B2\\n
CYP96B2
Cytochrome P450 monooxygenase putative
\\nHordeum vulgare\\n
Cuticular waxes biosynthesis
Silencing of CYP96B22 led to a decrease in penetration resistance of barley plants to blast pathogen Magnaporthe.
[100]
\\n\"\n]"}}},{"rowIdx":469,"cells":{"data":{"kind":"list like","value":["\nInt J Environ Res Public HealthInt J Environ Res Public HealthijerphInternational Journal of Environmental Research and Public Health1661-78271660-4601MDPI32717867PMC743212610.3390/ijerph17155299ijerph-17-05299ArticleEnhanced Biodegradation of Phthalic Acid Esters’ Derivatives by Plasticizer-Degrading Bacteria (Burkholderia cepacia, Archaeoglobus fulgidus, Pseudomonas aeruginosa) Using a Correction 3D-QSAR Modelhttps://orcid.org/0000-0002-1075-8603ZhangHaigang*ZhaoChengjiNaHuiAlan G. MacDiarmid Institute, College of Chemistry, Jilin University, Changchun 130012, China; zhaochengji@jlu.edu.cn (C.Z.); huina@jlu.edu.cn (H.N.)Correspondence: zhanghg18@mails.jlu.edu.cn; Tel.: +86-0431-85168870; Fax: +86-0431-851688702372020820201715529927620202172020© 2020 by the authors.2020Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
A phthalic acid ester’s (PAEs) comprehensive biodegradability three-dimensional structure-activity relationship (3D-QSAR) model was established, to design environmentally friendly PAE derivatives, which could be simultaneously degraded by plasticizer-degrading bacteria, such as Burkholderia cepacia, Archaeoglobus fulgidus, and Pseudomonas aeruginosa. Only three derivatives of diethyl phthalate (DEP (DEP-27, DEP-28 and DEP-29)) were suited for their functionality and environmental friendliness, which had an improved stability in the environment and improved the characteristics (bio-toxicity, bioaccumulation, persistence, and long-range migration) of the persistent organic pollutants (POPs). The simulation inference of the microbial degradation path before and after DEP modification and the calculation of the reaction energy barrier exhibited the energy barrier for degradation being reduced after DEP modification and was consistent with the increased ratio of comprehensive biodegradability. This confirmed the effectiveness of the comparative molecular similarity index analysis (CoMSIA) model of the PAE’s comprehensive biodegradability. In addition, a molecular dynamics simulation revealed that the binding of the DEP-29 derivative with the three plasticizer-degradation enzymes increased significantly. DEP-29 could be used as a methyl phthalate derivative that synergistically degrades with microplastics, providing directional selection and theoretical designing for plasticizer replacement.
In industrial production, a plasticizer is an indispensable part of microplastics and increases the flexibility and durability of plastic products. The phthalic acid esters (PAEs) are the most widely used plasticizers [1]. About 8.4 million tons of PAE plasticizers are used annually, accounting for 70% of its total use worldwide. Among these, diethyl phthalate (DEP) accounts for a high proportion [2]. The extensive use of phthalate plasticizers creates great commercial value, though it also poses environmental health risks that we cannot ignore. PAE plasticizers are not directly connected to plastic polymers, and they easily release into the environment during use [3]. Agricultural plastic film is one of the primary sources of microplastics in the soil. The PAE content in a vegetable greenhouse base could reach up to 9.68 mg/kg [4]. The PAEs in microplastics belong to refractory organic compounds, which are detected in soil [5], rivers [6], drinking water [7], food [8], household garbage [9], sewage sludge [10], industrial wastewater [11], marine sediment [12] and landfill leach [13]. Toxicological studies proved that PAE plasticizers remaining in the environment could enter human and animal bodies through inhalation, diet and skin contact, causing great harm to human health and environmental safety [14]. Although PAEs are not acutely toxic to organisms, exposure to large doses could lead to teratogenic, carcinogenic and mutagenic effects in animals [15]. In addition, animal experiments confirmed that some phthalates cause liver and kidney damage in animals [16]. The US Environmental Protection Agency, the European Union, and the China National Environmental Monitoring Center have listed PAEs as the priority pollutants [17]. The degradation and transformation of phthalate plasticizers in environmental residual has become a topic of interest in recent years.
Microbial degradation is a key process that mineralizes the organic pollutants in the environment. Hoellein et al. [18] studied the biological effects of microplastics in the water surrounding sewage treatment plants. They observed that only specific microorganisms could affect migration behavior and the biodegradability of plasticizer. During microbial degradation, the PAEs could simultaneously be degraded by a variety of plasticizer-degrading bacteria. A study reported that 50% of the PAEs degraded after an inoculation of aerobic microorganisms in sewage sludge for 28 days [19]. Sugatt et al. [20] studied the biodegradability of 14 PAE plasticizers commercially used in microplastics using a shake flask test. They detected the PAE plasticizers were sensitive to the mixed microbial community in the natural environment, and they gradually degraded and mineralized. To improve the microbiological degradation efficiency of plasticizers, several scientists attempted to isolate a single species of bacteria with a high-efficiency of degradation from nature. Wu et al. [21] screened and isolated Ochrobactrum lupini and Agrobacterium tumefaciens that used dibutyl phthalate (DBP) as their only carbon source and energy source from polluted river sludge and completely mineralized it. The degrading enzymes (esterase, carboxylesterase and hydratase) play a crucial role in the synergistic degradation of PAE plasticizers in microplastics [22]. Under different environmental conditions, microorganisms degrade the PAE plasticizers, but the degradation cycle is relatively long [23]. Therefore, it is of immense significance to improve the biodegradability of the PAE plasticizers to inhibit their adverse effects on the environment and human health.
In this paper, we employed the range normalization method combined with the entropy weight method to characterize the comprehensive biodegradability of PAEs by enzymes from three plasticizer-degrading bacteria [24]. We constructed a three-dimensional structure-activity relationship (3D-QSAR) model of the comprehensive biodegradability of the PAEs. DEP was taken as the target molecule for molecular modification, and we screened for DEP derivatives suitable for degradation by plasticizer-degrading bacteria. Further, we evaluated the DEP derivatives for their functionality and environmental friendliness, providing directional selection for plasticizer replacement.
2. Materials and Methods2.1. Data Source
The Burkholderia, Archaeoglobus [22] and Pseudomonas [25] species are typical plasticizer-degrading bacteria that effectively degrade PAE plasticizers. We obtained the structures of phthalate dioxygenase reductase from Burkholderia cepacia (PDB ID: 2PIA), esterase from Archaeoglobus fulgidus (PDB ID: 2ZYI) and carboxylesterase from Pseudomonas aeruginosa (PDB ID: 3CN7) from the Protein Data Bank (PDB (http://www.rcsb.org/pdb)) [26]. Using the molecular docking module in Sybyl-x2.0 software, we docked 17 different PAEs with the three degrading enzymes to obtain the corresponding scoring function value [27], which formed the source of the degradability data of the PAEs.
2.2. Construction of the Comparative Molecular Similarity Index Analysis (CoMSIA) Model of the Comprehensive Biodegradability of PAEs
We employed the range normalization method to standardize the degradation data of the three plasticizer-degrading bacteria and the entropy weight method to objectively weigh the standardized data [28]. Finally, we obtained the value of the comprehensive biodegradability of the PAE molecules, degraded by the three bacteria. The following are the range normalization formulas of the scoring function values of 17 PAE molecules after docking with the three plasticizer-degrading enzymes:\nYij=1−a+aXij−XminjXmaxj−Xminj\nYij=1−a+aXmaxj−XijXmaxj−Xminj
Equation (1) is the processing formula for the positive index, where a higher index is desirable. Equation (2) is the processing formula for the negative index, where a lower index is desirable. Among them, i represents the different PAEs (i = 1, 2, 3, …, 17); j represents the degradability index of the different degradative enzymes (j = 1, 2, 3; corresponding to the enzymes from B. cepacia, P. aeruginosa and A. fulgidus, respectively); Yij is the standardized value of each scoring function; Xij is the scoring function value of the j-th biodegradability index of the i-th PAE; Xmaxj and Xminj are the maximum and minimum values of the corresponding indexes, a ∈ (0,1), which are generally 0.9. The scoring function value characterizes the index of microbial degradability with a higher score being desirable. Therefore, we used the processing formula for the positive index to standardize the microbial degradability data.
The entropy weight method is an objective method for weight processing [29], which uses information entropy for processing the weight. The calculation steps are as follows:
Step 1: calculate the information entropy (Ej) of the j-th biodegradability index of the i-th PAE using the following formula:Ej=−k⋅∑i=1mYij⋅lnYij\nwhere k = 1/lnm, m is the total number of PAE molecules (i.e., 17), and Ej is the information entropy of the j-th biodegradability index.
Step 2: calculate the difference coefficient (Hj) of the j-th biodegradability index, reflecting the difference in the degree of each index using the following formula:Hj=1−Ej\nwhere Hj is the difference coefficient of the j-th biodegradability index.
Step 3: normalize the index difference coefficient (Hj) to estimate the weight (Wj) of each biodegradability index using the following formula:Wj=Hj∑j=1nHj\nwhere Wj is the weight of the j-th biodegradability index, and n is the total number of biodegradability indicators (i.e., 3).
Step 4: calculate the comprehensive value of the biodegradability of the three plasticizer-degrading bacteria for each PAE using the following formula:Zin=∑j=1nWj⋅Yij\nwhere Zin is the comprehensive value of the biodegradability of the three plasticizer-degrading bacteria of the i-th PAE, and n is the total number of biodegradability indicators (i.e., 3).
The molecular structure of the PAEs and the comprehensive biodegradability CoMSIA model of the three plasticizer-degrading bacteria were mainly constructed by using the 3D-QSAR module in Sybyl-x 2.0 software (Tripos, Princeton, NJ, USA). The minimize module in the software optimized the molecular structure of the PAEs [30].To obtain a stable conformation of the lowest energy state of the molecule, the Powell’s conjugate gradient method was employed and combined with the Tripos molecular force field. The latter sets the charge of the molecule to the Gasterger–Hückle charge [31]. The energy convergence standard was adjusted to 0.005 kcal mol−1, and the number of iterations was set to 10,000 times [32]. In this paper, the diundecyl phthalate (DUP) with the highest comprehensive value of the biodegradability index of the three plasticizer-degrading bacteria was used as the template molecule (Figure 1). The Align Database module of the software was selected to select the common part of the optimized PAE molecular structure as the common skeleton for superposition [33].
Sixteen molecules were randomly selected from the comprehensive biodegradation values of 17 PAEs molecules and three plasticizer degrading bacteria. According to the ratio of 4:1, 13 PAE molecules were used as the training set, and the remaining three PAE molecules were used as the test set. The template molecule DUP was used in both the test set and the training set. The least squares method (PLS) was used to analyze the training set compounds. The training set was cross-validated under the extraction method module to obtain the cross-validation coefficient (q2) and the optimal number of principal components (n). The training set compounds were analyzed using the no validation regression to obtain the cross-validation coefficient (R2), standard deviation (SEE), test value (F) and the contribution rate of the force field (stereo field, electrostatic field). The scrambling stability test assessed the robustness of the model by evaluating the scrambling stability test parameters (Q2), cross-validated standard error of prediction (cSDEP), and dQ2/dr2yy. The cross-validation method assessed the external prediction ability of the built model to the test set compounds with the external verification parameter (r2pred). Based on the information prompted by the contour maps of the constructed CoMSIA model, the substitution sites and substitution groups that had a greater impact on the comprehensive score value were screened, forming the theoretical basis for the subsequent molecular modification. In this paper, DEP was used as the target molecule for modification, which is the most widely used and frequently detected in the environment. Figure 2 displays the molecular structure of the target molecule DEP.
2.3. Evaluation of Functionality and Environmental Friendliness of DEP Derivatives Based on Density Functional Theory (DFT)
Stability was used as an evaluation index for the molecular functionality of the DEP derivatives [34] with the frequency, total energy and energy gap as the parameters. The above parameters were calculated by Gaussian 2009 software (Gaussian Inc., Wallingford, CT, USA) based on density functional theory (DFT) at the unit level of b3pw91/6-31G* [35]; environmental friendliness was evaluated by the four persistent organic pollutants’ (POPs) characteristics of toxicity (LC50), persistence (logt1/2), bioconcentration (logBCF) and migration (−logPL) [36].
3. Results and Discussion3.1. Construction and Evaluation of the CoMSIA Model for the Comprehensive Biodegradability of the PAEs by Plasticizer-Degrading Bacteria3.1.1. Calculation of the Comprehensive Biodegradation Values of the PAEs by Three Plasticizer-Degrading Bacteria of PAE Molecules
The docking score values, range normalized conversion values, weight and comprehensive biodegradation values of the combination of PAE molecules with phthalate dioxygenase reductase from B. cepacia (PDB ID: 2PIA), A. fulgidus (PDB ID: 2ZYI) and P. aeruginosa (PDB ID: 3CN7) are listed in Table 1.
3.1.2. Construction of the CoMSIA Model for the Comprehensive Biodegradability of the PAE Molecules by Three Plasticizer-Degrading Bacteria
DUP exhibited the highest comprehensive biodegradability among all the PAEs. It was the template molecule for the common skeleton superpositioning of the training set compounds to construct a CoMSIA model of the comprehensive biodegradability of the PAEs by the three plasticizer-degrading bacteria. In Table 1, a and b represent the training set and the test set of the model, respectively. Table 2 lists the evaluation parameters of the CoMSIA model. The cross-validation coefficient (q2), calculated by the cross-validation analysis, was 0.731 (>0.5), and the principal component number (n) of the model was 8. Both of these indicate that the CoMSIA model had good internal prediction capabilities [37]. The standard deviation (SEE) calculated by the non-cross-validation analysis was 0.013 (<0.95), the test value (F) was 578.738, and the non-cross-validation coefficient (R2) was 0.999 (>0.9). These indicate that the model had a high fitting ability [38]. The perturbation stability test parameter (Q2) was 0.85, cSDEP was 0.251, and dq2/dr2yy was 1.488, indicating that the model had a high stability [39]. The external validation coefficient (r2pred), estimated by the external validation analysis of the test set compound, was 0.761 (>0.6), indicating that the model exhibited a good external prediction ability [40]. In addition, the contribution rate of each force field was 34.7% in the steric field (S), 11.1% in the electrostatic field (E), 46.8% in the hydrophobic field (H), 7.4% in the hydrogen bond acceptor field (D) and 0.0% in the hydrogen bond donor field (A).
3.1.3. Contour Map Analysis of the CoMSIA Model
The contribution rates of the steric, electrostatic field and hydrophobic field were the highest. Therefore, the force field information of the electrostatic and hydrophobic fields in the contour maps of the comprehensive biodegradability of the DEP molecule was analyzed (Figure 3). The steric field information map (Figure 3a) displays that introducing small-volume groups into the yellow region could improve the comprehensive biodegradation value of the DEP molecules by the three plasticizer-degrading bacteria. In other words, the groups with volumes less than that of -CH2CH3 were introduced at the C1 position. In the electrostatic field (Figure 3b), introducing positively charged groups into the blue region or negatively charged groups into the red region could effectively improve the comprehensive biodegradability of the DEP molecules [41]. However, the red region was located in the common skeleton and was difficult to replace and modify. Therefore, a group with a positive charge greater than -H introduced into the C1 position could achieve the purpose of increasing the comprehensive biodegradability. In the hydrophobic field (Figure 3c), introducing strong hydrophilic substituents in the white area at the C2 position was conducive to increase the comprehensive biodegradability of the DEP molecules. In summary, to improve the comprehensive biodegradability of DEP, it was necessary to modify its structure by introducing single and double substitutions: (i) at the C1 position, a group with a volume smaller than -CH2CH3 and a group with an electropositivity greater than -H, and (ii) at the C2 position, a hydrophilic group.
3.2. Molecular Modification of DEP for Enhanced Biodegradability Based on the CoMSIA Model3.2.1. Molecular Modification and Prediction of Comprehensive Biodegradability of DEP
According to the force field information from the contour maps of the CoMSIA model, the groups was substituted at the C1 and C2 positions to conduct single and double substitutions on the DEP molecule. The groups with volumes smaller than -CH2CH3 (-CH3, -OH, -H, -CN, -NH2, -CHO) and groups with an electropositivity greater than -H (-CH3, -CH2CH3, -CH(CH3)2, -C(CH3)3) were introduced at the C1 position. At the C2 position, the groups with higher hydrophilicities (-OH, -CHO, -COOH, -NH2, -COCH3, -CONH2) were introduced. These substitutions would improve the comprehensive biodegradability of derivative molecules. Thus, a total of 30 kinds of DEP derivatives were designed accordingly. Using the constructed CoMSIA model to predict the comprehensive biological properties of the modified derivatives, it was found that the comprehensive biodegradability of most of the DEP derivatives enhanced significantly (Table 3). Among them, the comprehensive biodegradability of 12 DEP derivatives increased by more than 15%, and the comprehensive biodegradation value of DEP-26 molecules had the highest growth rate, reaching 50.37%. DEP-23 molecules had the same substitution groups at site C2 as in the DEP-26 molecules. However, the H atom at site C1 (H1 atom) in DEP-26 resulted in a higher comprehensive biodegradation value than the corresponding substituent in DEP-23. DEP-21 was introduced to more hydrophilic groups, increasing its comprehensive biodegradability value to more than that of DEP-15. In summary, the prediction of the comprehensive biodegradability of the DEP derivatives by the three plasticizer-degrading bacteria was consistent with the information presented in the contour maps of the CoMSIA model.
3.2.2. Verification of the CoMSIA Model for the Comprehensive Biodegradability of PAEs Molecules
In this paper, Sybyl-x2.0 software was also used to construct the 3D-QSAR model of the single biodegradability of the PAEs by the three plasticizer-degrading bacteria (Table 4). The DEP derivative molecules were screened through the biodegradable 3D-QSAR model of the PAEs’ molecular plasticizer-degrading bacteria, whose degradation efficiency ratio was close to the weight ratios of the entropy weight method in the comprehensive biodegradability model (Table 3). The docking score of the DEP derivative with phthalate dioxygenase reductase was 4.939–6.725, with a growth rate of up to 20.74%. Compared with the growth rate before modification, the docking score values of the DEP derivative binding with esterase ranged from −2.46% to 27.71%. The scoring functions for carboxylesterase and DEP derivatives exhibited a significant increase, with the growth rate ranging from 15.86% to 24.96%. The ratio of the increase in the biodegradation effect value of DEP-23–DEP-30 by three plasticizer-degrading bacteria was the closest to that calculated and weighted by the entropy weight method. These results verified that the CoMSIA model effectively provides the biodegradability information of DEP derivatives when degraded by three plasticizer-degrading bacteria. Additionally, this result demonstrated that the CoMSIA model modified by the entropy weight method is reliable with a good predictive ability, and can be applied to the design modifications in PAE molecules.
3.2.3. Evaluation of the Functionality and Environmental Friendliness of DEP Derivatives
Molecular stability was the primary index for evaluating the functionality of DEP derivatives, characterized by the energy value, energy gap value, and positive frequency value. The persistent organic pollutant (POP) characteristics determined the environmental friendliness of the DEP derivatives, before and after modification, by evaluating their bio-toxicity (logLC50), bioaccumulation (logBCF), persistence (logt1/2) and long-range migration (logKOA) [36]. The energy value was inversely proportional to the stability of the molecules in the environment [42]. A higher positive frequency value indicated that the DEP derivative molecules could exist in the environment [43]. The bio-toxicity and long-range migration of the DEP derivatives were inversely proportional to the predicted logLC50 and logKOA values, respectively. On the contrary, the bioaccumulation and persistence of the DEP derivatives were directly proportional to the predicted logBCF and logt1/2 values, respectively. Table 5 summarizes the predicted values of the parameters evaluating the functionality and environmental friendliness of the DEP derivatives, before and after modification.
Table 5 displays that the energy values of the DEP derivatives were significantly lower than that of the DEP molecule, with a reduction of 19.91–37.39%. The energy gap values did not change significantly before and after modification, indicating that the modified DEP derivatives were highly stable. In addition, the positive frequency values of the DEP derivatives were greater than zero, indicating that the modified molecules could exist in the environment. Among the eight DEP derivatives, only DEP-27, DEP-28, and DEP-29 exhibited no significant changes in their bio-toxicity compared with the unmodified molecule. The bio-toxicity of the remaining five derivatives significantly increased, with the highest increase-rate going up to 38.91%. An organic compound with a logBCF value less than 100 does not easily accumulate in organisms and has a small impact on the environment [44]. Except for the DEP-25 and DEP-26 molecules, the logBCF values of the DEP derivatives were much lower than 100. Among them, the logBCF values of DEP-27, DEP-28, and DEP-29 after modification did not change significantly. The predicted half-lives of the DEP derivatives were slightly higher than that of DEP, indicating that the degradability of these derivatives improved marginally. The predicted logKOA values of the eight DEP derivatives were significantly higher than that of DEP, suggesting that their mobilities were decreasing. In conclusion, among the eight DEP derivatives, only DEP-27, DEP-28 and DEP-29 exhibited significantly higher biodegradation values, and their functionalities and environmental friendliness were also better than the unmodified DEP molecule.
3.3. Analysis of the Microbial Degradation Mechanism of DEP and Its Derivatives Based on a Microbial Degradation Path Simulation3.3.1. Simulation of Microbial Degradation of DEP and Its Derivative Molecules
Plasticizer residue poses a serious threat to the environment. Among the possible ways of degradation, microbial degradation is the primary elimination process of PAE plasticizers. It achieves the purpose of recycling elements and balancing the ecosystem [45]. Gram-negative and Gram-positive bacteria degrade PAEs via different pathways, but both eventually form protocatechate [46]. Protocatechate can convert into pyruvate, succinate, and oxaloacetate, entering the tricarboxylic acid cycle and finally mineralizing into CO2 and H2O [47]. Ren et al. [48] found that esterase plays a crucial role in the microbial degradation of PAEs while studying their microbial degradation mechanism. Ester bond hydrolysis forms the key initial step in the microbial degradation of PAEs [49]. Based on the microbial degradation path of PAEs, the screened DEP derivative molecules (DEP-27, DEP-28 and DEP-29) were taken as examples to simulate and derive the microbial degradation path before and after DEP molecular modification (Figure 4). A Gaussian calculation was carried out for the energy barrier of the reaction to compare the difficulty of the biodegradation process before and after the DEP molecular modification (Table 6).
As shown in Figure 4, the microbial degradation transformation paths of DEP and its derivatives are divided into the transformation paths of Gram-negative bacteria and Gram-positive bacteria. Under the action of the plasticizer-degrading enzymes, DEP first hydrolyzed to phthalate monoesters (M0-1) and then hydrolyzed to form phthalate (M0-2). Phthalate 4,5-dioxygenase in Gram-negative bacteria oxidized the hydroxyl phthalate to produce 4,5-dihydroxyphthalic acid (M0-3), which decarboxylated to protocatechate (M0-5) [50]. Gram-positive bacteria hydrolyzed phthalic acid at C3 and C4 to produce 3, 4-dihydroxyphthalic acid (M0-4), which decarboxylated to protocatechate (M0-5) [51]. Finally, protocatechate mineralized into CO2 and H2O through the tricarboxylic acid cycle, culminating in the complete degradation of PAEs [47]. When the C2 site of DEP was replaced with -CONH2, the resulting derivative was difficult to hydrolyze into phthalic acid [52]. So the degradation process was slightly different from that of DEP.
3.3.2. Calculation of the Reaction Energy Barrier for Microbial Degradation Transformation Paths of DEP and Its Derivatives
The reaction energy barrier required for the microbial degradation of the three DEP derivatives was lower than that required for the degradation of DEP (Table 6). A smaller reaction barrier is desirable as it improves the likelihood of the reaction path to occur. This indicates that the microorganisms could easily degrade the three DEP derivatives compared with DEP. The change rates of the reaction energy barrier for DEP-27, DEP-28, and DEP-29 were −20.15%, −23.42%, and −30.26%, respectively. These were consistent with the enhanced comprehensive biodegradability predicted by the comprehensive biodegradability model (DEP-27: 23.33%, DEP-28: 27.04% and DEP-29: 31.85%). This validates the reliability of the comprehensive biodegradation model, and indicates that the biodegradability of DEP can improve by molecular modification.
3.3.3. Simulation and Verification of the Molecular Dynamics of the Microbial Degradation of DEP and Its Derivatives
In this paper, GROMACS 4.6.5 software was used to simulate the molecular dynamics (MD) of the ligand complex structures of DEP and its derivatives in Sybyl-x2.0, docked with phthalate dioxygenase reductase, esterase and acid esterase. The Poisson–Boltzmann surface area (MM-PBSA) method was used to calculate the binding free energy in the molecular dynamics [53]. Compared with the scoring function of molecular docking, the degree of binding energy characterized the biodegradability of DEP molecules before and after modification (Table 7).
Higher docking scores, indicate a stronger binding of the molecules to the degradative enzyme [54]. Additionally, the binding energy is directly proportional to the affinity between the molecule and the degrading enzyme [55], indicating a higher biodegradability of the molecule. Compared with the DEP molecules, DEP-27 and DEP-28 had lower scoring functions and higher free energy of binding to the degrading enzymes (Table 7). The scoring functions of only DEP-29 binding to the three degrading enzymes increased significantly, and its binding free energy also decreased significantly. The results demonstrated that DEP-29 was the only derivative that could easily bind to the three degrading enzymes at the same time and that the degree of binding was significant. This not only proves the rationality of the comprehensive biodegradation model of PAEs to modify the derivatives, but also forms the means to screen the PAE derivatives that synergistically degrade with microplastics.
4. Conclusions
In this paper, a 3D-QSAR model of the comprehensive biodegradability of the phthalic acid esters (PAEs) was constructed by combining the range normalization and entropy weight methods. In combination with molecular modifications, this model was successfully applied to design environmentally friendly PAEs that can co-degrade with microplastics. These PAE derivatives significantly improve the biodegradability of PAE plasticizers in microplastics, and can reduce the residual PAEs in the natural environment, relieve the adverse effects of plasticizer residues in the human body and environment, and provide the directional selection and theoretical support for the replacement of plasticizers. Further research work will be focused on whether the designed PAE derivatives could be degraded synergistically with the plasticizer-degrading bacteria or not, and the synthesis and biodegradation of these derivatives would provide an effective verification for the molecular modification method available in this study.
Author Contributions
Conceptualization, H.Z. and C.Z.; methodology, H.Z.; software, H.Z.; validation, C.Z. and H.N.; formal analysis, H.Z.; investigation, H.Z.; resources, H.Z.; data curation, H.Z.; writing—original draft preparation, H.Z.; writing—review and editing, H.Z., C.Z.; visualization, H.Z.; supervision, C.Z.; project administration, C.Z. All authors have read and agreed to the published version of the manuscript.
Funding
This research received no external funding.
Conflicts of Interest
The authors declare no conflict of interest.
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Molecular structure of the template molecule diundecyl phthalate (DUP).
Molecular structure of the target molecule diethyl phthalate (DEP).
The contour maps from the CoMSIA model of DEP, (a) steric field, (b) electrostatic field and (c) hydrophobic field.
Schematic diagram of transformation paths of microbial degradation of DEP and its derivative molecules. A: The transformation path of gram-negative bacteria; B: the transformation path of Gram-positive bacteria; M0: the microbial degradation products of DEP; Mr: the microbial degradation products of DEP derivative molecules; R: -CH3, -CH2CH3, -CH (CH3)2; r = 1, 2, 3.
ijerph-17-05299-t001_Table 1
Biodegradability score values and comprehensive biodegradability values of the phthalic acid ester (PAE) molecules by three plasticizer-degrading bacteria.
Compounds
Docking Score Value of 2PIA
Converted Values of 2PIA
Docking Score Value of 2ZYI
Converted Values of 2ZYI
Docking Score Value of 3CN7
Converted Values of 3CN7
Comprehensive Biodegradation Values
BBP a
7.070
0.509
1.908
0.100
5.116
0.380
0.323
DAP a
5.918
0.385
5.461
0.357
5.275
0.409
0.380
DBP b
5.754
0.367
5.551
0.364
6.047
0.547
0.408
DEP a
5.574
0.347
4.711
0.303
3.548
0.100
0.272
DHP a
7.455
0.551
7.221
0.485
6.964
0.711
0.564
DIBP a
4.824
0.266
5.081
0.330
5.006
0.361
0.313
DIHP a
8.634
0.679
10.570
0.727
7.870
0.872
0.743
DIHXP
7.462
0.552
10.548
0.726
6.660
0.656
0.643
DIPP a
6.313
0.427
5.916
0.390
6.061
0.549
0.442
DIPRP a
5.339
0.322
4.609
0.296
5.885
0.518
0.358
DMEP b
7.085
0.511
4.088
0.258
6.876
0.695
0.459
DMP a
3.293
0.100
4.624
0.297
3.940
0.170
0.191
DNOP a
10.420
0.873
4.797
0.309
8.333
0.955
0.679
DPP a
5.484
0.338
4.970
0.322
6.313
0.594
0.393
DPRP b
6.273
0.423
6.681
0.446
4.746
0.314
0.406
DTDP a
9.112
0.731
13.722
0.956
8.584
1.000
0.880
DUP *
11.595
1.000
14.334
1.000
8.561
0.996
0.999
\nEj\n
1.799
1.792
1.497
−
\nHj\n
0.799
0.792
0.497
−
\nWj\n
38.28%
37.93%
23.79%
−
a: Training set; b: test set; *: template molecule.
ijerph-17-05299-t002_Table 2
Evaluation parameters of the comparative molecular similarity index analysis (CoMSIA) model for the biodegradability of the PAEs by three plasticizer-degrading bacteria.
Model
q2
\nn\n
SEE
R2
F
r2pred
Q2
cSDEP
dq2/dr2yy
S
E
H
D
A
CoMSIA
0.731
8
0.013
0.999
578.738
0.761
0.385
0.251
1.488
34.7%
11.1%
46.8%
7.4%
0.0%
ijerph-17-05299-t003_Table 3
Prediction of the CoMSIA model of comprehensive and the single biodegradability of DEP derivatives and their change ratios.
No.
Substituent Group
Comprehensive Biodegradation Values
Change Rate(%)
Docking Score Value of 2PIA
Change Rate(%)
Docking Score Value of 2ZYI
Change Rate(%)
Docking Score Value of 3CN7
Change Rate(%)
Ratio
DEP
\n
0.27
\n
5.57
\n
4.71
\n
3.55
\n
38.28:37.93:23.79
DEP-1
H1-CH3
0.293
8.52%
5.637
1.20%
5.232
11.08%
4.319
21.66%
−
DEP-2
H1-CH2CH3
0.311
15.19%
5.762
3.45%
5.353
13.65%
4.329
21.94%
−
DEP-3
H1-CH(CH3)2
0.328
21.48%
5.876
5.49%
5.397
14.59%
4.399
23.92%
−
DEP-4
H1-C(CH3)3
0.349
29.26%
5.976
7.29%
5.673
20.45%
4.36
22.82%
−
DEP-5
C1-CH3
0.269
−0.37%
5.500
−1.26%
4.827
2.48%
4.262
20.06%
−
DEP-6
C1-OH
0.228
−15.56%
4.828
−13.32%
4.838
2.72%
4.224
18.99%
−
DEP-7
C1-H
0.272
0.74%
5.059
−9.17%
4.784
1.57%
4.372
23.15%
−
DEP-8
C1-CN
0.255
−5.56%
5.266
−5.46%
4.874
3.48%
4.172
17.52%
−
DEP-9
C1-NH2
0.225
−16.67%
4.967
−10.83%
4.677
−0.70%
4.327
21.89%
−
DEP-10
C1-CHO
0.255
−5.56%
4.938
−11.35%
5.077
7.79%
4.113
15.86%
−
DEP-11
C2-OH
0.249
−7.78%
5.658
1.58%
4.594
−2.46%
4.29
20.85%
−
DEP-12
C2-CHO
0.262
−2.96%
5.737
3.00%
4.871
3.42%
4.282
20.62%
−
DEP-13
C2-COOH
0.269
−0.37%
5.892
5.78%
4.921
4.48%
4.312
21.46%
−
DEP-14
C2-NH2
0.256
−5.19%
5.504
−1.18%
4.911
4.27%
4.199
18.28%
−
DEP-15
C2-COCH3
0.289
7.04%
6.109
9.68%
4.969
5.50%
4.378
23.32%
−
DEP-16
C2-CONH2
0.235
−12.96%
5.755
3.32%
4.776
1.40%
4.436
24.96%
−
DEP-17
C2-(OH)2
0.267
−1.11%
5.484
−1.54%
5.495
16.67%
4.170
17.46%
−
DEP-18
C2-(CHO)2
0.267
−1.11%
5.409
−2.89%
5.73
21.66%
4.131
16.37%
−
DEP-19
C2-(COOH)2
0.284
5.19%
5.621
0.92%
5.826
23.69%
4.119
16.03%
−
DEP-20
C2-(NH2)2
0.259
−4.07%
5.444
−2.26%
5.434
15.37%
4.295
20.99%
−
DEP-21
C2-(COCH3)2
0.310
14.81%
5.799
4.11%
6.015
27.71%
4.188
17.97%
−
DEP-22
C2-(CONH2)2
0.261
−3.33%
5.362
−3.73%
5.946
26.24%
4.175
17.61%
−
DEP-23
H1-CH3-C2-COCH3
0.357
32.22%
6.317
13.41%
5.547
17.77%
4.198
18.25%
27.13:35.95:36.92
DEP-24
H1-CH2CH3-C2-COCH3
0.377
39.63%
6.453
15.85%
5.725
21.55%
4.169
17.44%
28.91:39.30:31.80
DEP-25
H1-CH(CH3)2-C2-COCH3
0.389
44.07%
6.565
17.86%
5.708
21.19%
4.200
18.31%
31.14:36.94:31.92
DEP-26
H1-C(CH3)3-C2-COCH3
0.406
50.37%
6.725
20.74%
5.787
22.87%
4.282
20.62%
32.29:35.60:32.11
DEP-27
H1-CH3-C2-CONH2
0.333
23.33%
6.077
9.10%
5.778
22.68%
4.129
16.31%
18.93:47.15:33.92
DEP-28
H1-CH2CH3-C2-CONH2
0.343
27.04%
6.151
10.43%
5.898
25.22%
4.130
16.34%
20.06:48.51:31.42
DEP-29
H1-CH(CH3)2-C2-CONH2
0.356
31.85%
6.263
12.44%
5.879
24.82%
4.144
16.73%
23.04:45.97:30.99
DEP-30
H1-C(CH3)3-C2-CONH2
0.370
37.04%
6.382
14.58%
5.953
26.39%
4.230
19.15%
24.25:43.89:31.86
Note: H1 stands for the H atom on the C1 site.
ijerph-17-05299-t004_Table 4
Evaluation parameters of B. cepacia (a), A. fulgidus (b), P. aeruginosa (c). Biodegradation 3D-QSAR model of PAEs.
Model
3D-QSAR
q2
n
SEE
R2
F
r2pred
Q2
cSDEP
dq2/dr2yy
a
CoMSIA
0.627
8
0.172
0.998
284.548
0.657
0.559
2.78
0.958
b
CoMFA
0.697
3
0.518
0.986
207.862
0.918
0.422
3.306
1.372
c
CoMFA
0.68
10
0.001
1
465107.312
0.618
0.491
3.021
0.690
ijerph-17-05299-t005_Table 5
Prediction parameters for DEP’s molecular function and environmental friendliness before and after modification.
No.
Total Energy (a.u.)
Change Rate(%)
Energy Gap (eV)
Frequency (cm−1)
Bio-Toxicity(logLC50)
Change Rate(%)
Bioaccumulation (logBCF)
BCF
Persistence(logt1/2)
Change Rate(%)
Long-Range Migration (logKOA)
Change Rate(%)
DEP
−766.62
\n
5.32
24.02
1.100
\n
1.264
18.37
3.156
\n
7.505
\n
DEP-23
−919.26
−19.91%
5.15
15.09
0.781
29.00%
1.879
75.68
3.271
−3.64%
8.540
13.79%
DEP-24
−958.57
−25.04%
5.16
18.09
0.744
32.36%
1.992
98.17
3.250
−2.98%
8.546
13.87%
DEP-25
−997.89
−30.17%
5.14
17.65
0.737
33.00%
2.041
109.90
3.230
−2.34%
8.563
14.10%
DEP-26
−1037.2
−35.30%
5.06
15.38
0.672
38.91%
2.116
130.62
3.215
−1.87%
8.766
16.80%
DEP-27
−935.32
−22.01%
5.00
19.32
1.070
2.73%
1.569
37.07
3.386
−7.29%
8.329
10.98%
DEP-28
−974.64
−27.13%
4.97
16.33
1.047
4.82%
1.600
39.81
3.378
−7.03%
8.352
11.29%
DEP-29
−1013.95
−32.26%
4.96
16.24
1.038
5.64%
1.653
44.98
3.357
−6.37%
8.374
11.58%
DEP-30
−1053.26
−37.39%
4.94
15.99
0.923
16.09%
1.716
52.00
3.349
−6.12%
8.489
13.11%
ijerph-17-05299-t006_Table 6
Calculation of the reaction energy barrier of microbial degradation transformation paths of DEP and its derivatives.
DEP
Change Rate(%)
DEP-27
Change Rate(%)
Path
Reactants
ReactionProducts
Energy Barrier (kJ/mol)
Total Energy Barrier (kJ/mol)
Reactants
ReactionProducts
Energy Barrier (kJ/mol)
Total Energy Barrier (kJ/mol)
Path1
DEP
M0-1
27.57
127.33
−
DEP-27
M1-1
28.88
113.16
−11.13
M0-1
M0-2
15.75
M1-1
M1-2
13.92
M0-2
M0-3
53.82
M1-2
M1-3
56.97
M0-3
M0-5
30.19
M1-3
M1-5
13.39
Path2
DEP
M0-1
27.57
139.68
−
DEP-27
M1-1
28.88
127.07
−9.02
M0-1
M0-2
15.75
M1-1
M1-2
13.92
M0-2
M0-4
68.79
M1-2
M1-4
72.99
M0-4
M0-5
27.57
M1-4
M1-5
11.29
\n
\n
Total Change rate (%)
−20.15
\nDEP-28\n
\nChange Rate\n\n(%)\n
\nDEP-29\n
\nChange Rate\n\n(%)\n
\nPath\n
\nReactants\n
\nReaction\n\nProducts\n
\nEnergy Barrier (kJ/mol)\n
\nTotal Energy Barrier (kJ/mol)\n
\nPath\n
\nReactants\n
\nReaction\n\nProducts\n
\nEnergy Barrier (kJ/mol)\n
Path1
DEP-28
M2-1
24.94
110.98
−12.84
DEP-29
M3-1
17.07
99.77
−21.65
M2-1
M2-2
15.67
M3-1
M3-2
12.34
M2-2
M2-3
56.97
M3-2
M3-3
56.97
M2-3
M2-5
13.39
M3-3
M3-5
13.39
Path2
DEP-28
M2-1
24.94
124.90
−10.58
DEP-29
M3-1
17.07
113.68
−18.61
M2-1
M2-2
15.67
M3-1
M3-2
12.34
M2-2
M2-4
72.99
M3-2
M3-4
72.99
M2-4
M2-5
11.29
M3-4
M3-5
11.29
Total Change rate (%)
−23.42
Total Change rate (%)
−30.26
ijerph-17-05299-t007_Table 7
Molecular docking scores and molecular dynamics simulation of the binding energy calculation for DEP and its derivative molecules.
No.
2PIA
2ZYI
3CN7
Docking Score Value
△Gbind (kJ/mol)
Docking Score Value
△Gbind (kJ/mol)
Docking Score Value
△Gbind (kJ/mol)
DEP
5.574
−62.400
4.711
−138.694
3.548
−108.742
DEP-27
5.323↓
−126.613↓
3.491↓
−158.330↓
5.661↑
−102.247↑
DEP-28
5.793↑
−74.505↓
3.993↓
−138.588↑
6.139↑
−136.861↓
DEP-29
5.717↑
−108.149↓
7.535↑
−177.961↓
7.042↑
−160.312↓
\n"],"string":"[\n \"\\nInt J Environ Res Public HealthInt J Environ Res Public HealthijerphInternational Journal of Environmental Research and Public Health1661-78271660-4601MDPI32717867PMC743212610.3390/ijerph17155299ijerph-17-05299ArticleEnhanced Biodegradation of Phthalic Acid Esters’ Derivatives by Plasticizer-Degrading Bacteria (Burkholderia cepacia, Archaeoglobus fulgidus, Pseudomonas aeruginosa) Using a Correction 3D-QSAR Modelhttps://orcid.org/0000-0002-1075-8603ZhangHaigang*ZhaoChengjiNaHuiAlan G. MacDiarmid Institute, College of Chemistry, Jilin University, Changchun 130012, China; zhaochengji@jlu.edu.cn (C.Z.); huina@jlu.edu.cn (H.N.)Correspondence: zhanghg18@mails.jlu.edu.cn; Tel.: +86-0431-85168870; Fax: +86-0431-851688702372020820201715529927620202172020© 2020 by the authors.2020Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
A phthalic acid ester’s (PAEs) comprehensive biodegradability three-dimensional structure-activity relationship (3D-QSAR) model was established, to design environmentally friendly PAE derivatives, which could be simultaneously degraded by plasticizer-degrading bacteria, such as Burkholderia cepacia, Archaeoglobus fulgidus, and Pseudomonas aeruginosa. Only three derivatives of diethyl phthalate (DEP (DEP-27, DEP-28 and DEP-29)) were suited for their functionality and environmental friendliness, which had an improved stability in the environment and improved the characteristics (bio-toxicity, bioaccumulation, persistence, and long-range migration) of the persistent organic pollutants (POPs). The simulation inference of the microbial degradation path before and after DEP modification and the calculation of the reaction energy barrier exhibited the energy barrier for degradation being reduced after DEP modification and was consistent with the increased ratio of comprehensive biodegradability. This confirmed the effectiveness of the comparative molecular similarity index analysis (CoMSIA) model of the PAE’s comprehensive biodegradability. In addition, a molecular dynamics simulation revealed that the binding of the DEP-29 derivative with the three plasticizer-degradation enzymes increased significantly. DEP-29 could be used as a methyl phthalate derivative that synergistically degrades with microplastics, providing directional selection and theoretical designing for plasticizer replacement.
In industrial production, a plasticizer is an indispensable part of microplastics and increases the flexibility and durability of plastic products. The phthalic acid esters (PAEs) are the most widely used plasticizers [1]. About 8.4 million tons of PAE plasticizers are used annually, accounting for 70% of its total use worldwide. Among these, diethyl phthalate (DEP) accounts for a high proportion [2]. The extensive use of phthalate plasticizers creates great commercial value, though it also poses environmental health risks that we cannot ignore. PAE plasticizers are not directly connected to plastic polymers, and they easily release into the environment during use [3]. Agricultural plastic film is one of the primary sources of microplastics in the soil. The PAE content in a vegetable greenhouse base could reach up to 9.68 mg/kg [4]. The PAEs in microplastics belong to refractory organic compounds, which are detected in soil [5], rivers [6], drinking water [7], food [8], household garbage [9], sewage sludge [10], industrial wastewater [11], marine sediment [12] and landfill leach [13]. Toxicological studies proved that PAE plasticizers remaining in the environment could enter human and animal bodies through inhalation, diet and skin contact, causing great harm to human health and environmental safety [14]. Although PAEs are not acutely toxic to organisms, exposure to large doses could lead to teratogenic, carcinogenic and mutagenic effects in animals [15]. In addition, animal experiments confirmed that some phthalates cause liver and kidney damage in animals [16]. The US Environmental Protection Agency, the European Union, and the China National Environmental Monitoring Center have listed PAEs as the priority pollutants [17]. The degradation and transformation of phthalate plasticizers in environmental residual has become a topic of interest in recent years.
Microbial degradation is a key process that mineralizes the organic pollutants in the environment. Hoellein et al. [18] studied the biological effects of microplastics in the water surrounding sewage treatment plants. They observed that only specific microorganisms could affect migration behavior and the biodegradability of plasticizer. During microbial degradation, the PAEs could simultaneously be degraded by a variety of plasticizer-degrading bacteria. A study reported that 50% of the PAEs degraded after an inoculation of aerobic microorganisms in sewage sludge for 28 days [19]. Sugatt et al. [20] studied the biodegradability of 14 PAE plasticizers commercially used in microplastics using a shake flask test. They detected the PAE plasticizers were sensitive to the mixed microbial community in the natural environment, and they gradually degraded and mineralized. To improve the microbiological degradation efficiency of plasticizers, several scientists attempted to isolate a single species of bacteria with a high-efficiency of degradation from nature. Wu et al. [21] screened and isolated Ochrobactrum lupini and Agrobacterium tumefaciens that used dibutyl phthalate (DBP) as their only carbon source and energy source from polluted river sludge and completely mineralized it. The degrading enzymes (esterase, carboxylesterase and hydratase) play a crucial role in the synergistic degradation of PAE plasticizers in microplastics [22]. Under different environmental conditions, microorganisms degrade the PAE plasticizers, but the degradation cycle is relatively long [23]. Therefore, it is of immense significance to improve the biodegradability of the PAE plasticizers to inhibit their adverse effects on the environment and human health.
In this paper, we employed the range normalization method combined with the entropy weight method to characterize the comprehensive biodegradability of PAEs by enzymes from three plasticizer-degrading bacteria [24]. We constructed a three-dimensional structure-activity relationship (3D-QSAR) model of the comprehensive biodegradability of the PAEs. DEP was taken as the target molecule for molecular modification, and we screened for DEP derivatives suitable for degradation by plasticizer-degrading bacteria. Further, we evaluated the DEP derivatives for their functionality and environmental friendliness, providing directional selection for plasticizer replacement.
2. Materials and Methods2.1. Data Source
The Burkholderia, Archaeoglobus [22] and Pseudomonas [25] species are typical plasticizer-degrading bacteria that effectively degrade PAE plasticizers. We obtained the structures of phthalate dioxygenase reductase from Burkholderia cepacia (PDB ID: 2PIA), esterase from Archaeoglobus fulgidus (PDB ID: 2ZYI) and carboxylesterase from Pseudomonas aeruginosa (PDB ID: 3CN7) from the Protein Data Bank (PDB (http://www.rcsb.org/pdb)) [26]. Using the molecular docking module in Sybyl-x2.0 software, we docked 17 different PAEs with the three degrading enzymes to obtain the corresponding scoring function value [27], which formed the source of the degradability data of the PAEs.
2.2. Construction of the Comparative Molecular Similarity Index Analysis (CoMSIA) Model of the Comprehensive Biodegradability of PAEs
We employed the range normalization method to standardize the degradation data of the three plasticizer-degrading bacteria and the entropy weight method to objectively weigh the standardized data [28]. Finally, we obtained the value of the comprehensive biodegradability of the PAE molecules, degraded by the three bacteria. The following are the range normalization formulas of the scoring function values of 17 PAE molecules after docking with the three plasticizer-degrading enzymes:\\nYij=1−a+aXij−XminjXmaxj−Xminj\\nYij=1−a+aXmaxj−XijXmaxj−Xminj
Equation (1) is the processing formula for the positive index, where a higher index is desirable. Equation (2) is the processing formula for the negative index, where a lower index is desirable. Among them, i represents the different PAEs (i = 1, 2, 3, …, 17); j represents the degradability index of the different degradative enzymes (j = 1, 2, 3; corresponding to the enzymes from B. cepacia, P. aeruginosa and A. fulgidus, respectively); Yij is the standardized value of each scoring function; Xij is the scoring function value of the j-th biodegradability index of the i-th PAE; Xmaxj and Xminj are the maximum and minimum values of the corresponding indexes, a ∈ (0,1), which are generally 0.9. The scoring function value characterizes the index of microbial degradability with a higher score being desirable. Therefore, we used the processing formula for the positive index to standardize the microbial degradability data.
The entropy weight method is an objective method for weight processing [29], which uses information entropy for processing the weight. The calculation steps are as follows:
Step 1: calculate the information entropy (Ej) of the j-th biodegradability index of the i-th PAE using the following formula:Ej=−k⋅∑i=1mYij⋅lnYij\\nwhere k = 1/lnm, m is the total number of PAE molecules (i.e., 17), and Ej is the information entropy of the j-th biodegradability index.
Step 2: calculate the difference coefficient (Hj) of the j-th biodegradability index, reflecting the difference in the degree of each index using the following formula:Hj=1−Ej\\nwhere Hj is the difference coefficient of the j-th biodegradability index.
Step 3: normalize the index difference coefficient (Hj) to estimate the weight (Wj) of each biodegradability index using the following formula:Wj=Hj∑j=1nHj\\nwhere Wj is the weight of the j-th biodegradability index, and n is the total number of biodegradability indicators (i.e., 3).
Step 4: calculate the comprehensive value of the biodegradability of the three plasticizer-degrading bacteria for each PAE using the following formula:Zin=∑j=1nWj⋅Yij\\nwhere Zin is the comprehensive value of the biodegradability of the three plasticizer-degrading bacteria of the i-th PAE, and n is the total number of biodegradability indicators (i.e., 3).
The molecular structure of the PAEs and the comprehensive biodegradability CoMSIA model of the three plasticizer-degrading bacteria were mainly constructed by using the 3D-QSAR module in Sybyl-x 2.0 software (Tripos, Princeton, NJ, USA). The minimize module in the software optimized the molecular structure of the PAEs [30].To obtain a stable conformation of the lowest energy state of the molecule, the Powell’s conjugate gradient method was employed and combined with the Tripos molecular force field. The latter sets the charge of the molecule to the Gasterger–Hückle charge [31]. The energy convergence standard was adjusted to 0.005 kcal mol−1, and the number of iterations was set to 10,000 times [32]. In this paper, the diundecyl phthalate (DUP) with the highest comprehensive value of the biodegradability index of the three plasticizer-degrading bacteria was used as the template molecule (Figure 1). The Align Database module of the software was selected to select the common part of the optimized PAE molecular structure as the common skeleton for superposition [33].
Sixteen molecules were randomly selected from the comprehensive biodegradation values of 17 PAEs molecules and three plasticizer degrading bacteria. According to the ratio of 4:1, 13 PAE molecules were used as the training set, and the remaining three PAE molecules were used as the test set. The template molecule DUP was used in both the test set and the training set. The least squares method (PLS) was used to analyze the training set compounds. The training set was cross-validated under the extraction method module to obtain the cross-validation coefficient (q2) and the optimal number of principal components (n). The training set compounds were analyzed using the no validation regression to obtain the cross-validation coefficient (R2), standard deviation (SEE), test value (F) and the contribution rate of the force field (stereo field, electrostatic field). The scrambling stability test assessed the robustness of the model by evaluating the scrambling stability test parameters (Q2), cross-validated standard error of prediction (cSDEP), and dQ2/dr2yy. The cross-validation method assessed the external prediction ability of the built model to the test set compounds with the external verification parameter (r2pred). Based on the information prompted by the contour maps of the constructed CoMSIA model, the substitution sites and substitution groups that had a greater impact on the comprehensive score value were screened, forming the theoretical basis for the subsequent molecular modification. In this paper, DEP was used as the target molecule for modification, which is the most widely used and frequently detected in the environment. Figure 2 displays the molecular structure of the target molecule DEP.
2.3. Evaluation of Functionality and Environmental Friendliness of DEP Derivatives Based on Density Functional Theory (DFT)
Stability was used as an evaluation index for the molecular functionality of the DEP derivatives [34] with the frequency, total energy and energy gap as the parameters. The above parameters were calculated by Gaussian 2009 software (Gaussian Inc., Wallingford, CT, USA) based on density functional theory (DFT) at the unit level of b3pw91/6-31G* [35]; environmental friendliness was evaluated by the four persistent organic pollutants’ (POPs) characteristics of toxicity (LC50), persistence (logt1/2), bioconcentration (logBCF) and migration (−logPL) [36].
3. Results and Discussion3.1. Construction and Evaluation of the CoMSIA Model for the Comprehensive Biodegradability of the PAEs by Plasticizer-Degrading Bacteria3.1.1. Calculation of the Comprehensive Biodegradation Values of the PAEs by Three Plasticizer-Degrading Bacteria of PAE Molecules
The docking score values, range normalized conversion values, weight and comprehensive biodegradation values of the combination of PAE molecules with phthalate dioxygenase reductase from B. cepacia (PDB ID: 2PIA), A. fulgidus (PDB ID: 2ZYI) and P. aeruginosa (PDB ID: 3CN7) are listed in Table 1.
3.1.2. Construction of the CoMSIA Model for the Comprehensive Biodegradability of the PAE Molecules by Three Plasticizer-Degrading Bacteria
DUP exhibited the highest comprehensive biodegradability among all the PAEs. It was the template molecule for the common skeleton superpositioning of the training set compounds to construct a CoMSIA model of the comprehensive biodegradability of the PAEs by the three plasticizer-degrading bacteria. In Table 1, a and b represent the training set and the test set of the model, respectively. Table 2 lists the evaluation parameters of the CoMSIA model. The cross-validation coefficient (q2), calculated by the cross-validation analysis, was 0.731 (>0.5), and the principal component number (n) of the model was 8. Both of these indicate that the CoMSIA model had good internal prediction capabilities [37]. The standard deviation (SEE) calculated by the non-cross-validation analysis was 0.013 (<0.95), the test value (F) was 578.738, and the non-cross-validation coefficient (R2) was 0.999 (>0.9). These indicate that the model had a high fitting ability [38]. The perturbation stability test parameter (Q2) was 0.85, cSDEP was 0.251, and dq2/dr2yy was 1.488, indicating that the model had a high stability [39]. The external validation coefficient (r2pred), estimated by the external validation analysis of the test set compound, was 0.761 (>0.6), indicating that the model exhibited a good external prediction ability [40]. In addition, the contribution rate of each force field was 34.7% in the steric field (S), 11.1% in the electrostatic field (E), 46.8% in the hydrophobic field (H), 7.4% in the hydrogen bond acceptor field (D) and 0.0% in the hydrogen bond donor field (A).
3.1.3. Contour Map Analysis of the CoMSIA Model
The contribution rates of the steric, electrostatic field and hydrophobic field were the highest. Therefore, the force field information of the electrostatic and hydrophobic fields in the contour maps of the comprehensive biodegradability of the DEP molecule was analyzed (Figure 3). The steric field information map (Figure 3a) displays that introducing small-volume groups into the yellow region could improve the comprehensive biodegradation value of the DEP molecules by the three plasticizer-degrading bacteria. In other words, the groups with volumes less than that of -CH2CH3 were introduced at the C1 position. In the electrostatic field (Figure 3b), introducing positively charged groups into the blue region or negatively charged groups into the red region could effectively improve the comprehensive biodegradability of the DEP molecules [41]. However, the red region was located in the common skeleton and was difficult to replace and modify. Therefore, a group with a positive charge greater than -H introduced into the C1 position could achieve the purpose of increasing the comprehensive biodegradability. In the hydrophobic field (Figure 3c), introducing strong hydrophilic substituents in the white area at the C2 position was conducive to increase the comprehensive biodegradability of the DEP molecules. In summary, to improve the comprehensive biodegradability of DEP, it was necessary to modify its structure by introducing single and double substitutions: (i) at the C1 position, a group with a volume smaller than -CH2CH3 and a group with an electropositivity greater than -H, and (ii) at the C2 position, a hydrophilic group.
3.2. Molecular Modification of DEP for Enhanced Biodegradability Based on the CoMSIA Model3.2.1. Molecular Modification and Prediction of Comprehensive Biodegradability of DEP
According to the force field information from the contour maps of the CoMSIA model, the groups was substituted at the C1 and C2 positions to conduct single and double substitutions on the DEP molecule. The groups with volumes smaller than -CH2CH3 (-CH3, -OH, -H, -CN, -NH2, -CHO) and groups with an electropositivity greater than -H (-CH3, -CH2CH3, -CH(CH3)2, -C(CH3)3) were introduced at the C1 position. At the C2 position, the groups with higher hydrophilicities (-OH, -CHO, -COOH, -NH2, -COCH3, -CONH2) were introduced. These substitutions would improve the comprehensive biodegradability of derivative molecules. Thus, a total of 30 kinds of DEP derivatives were designed accordingly. Using the constructed CoMSIA model to predict the comprehensive biological properties of the modified derivatives, it was found that the comprehensive biodegradability of most of the DEP derivatives enhanced significantly (Table 3). Among them, the comprehensive biodegradability of 12 DEP derivatives increased by more than 15%, and the comprehensive biodegradation value of DEP-26 molecules had the highest growth rate, reaching 50.37%. DEP-23 molecules had the same substitution groups at site C2 as in the DEP-26 molecules. However, the H atom at site C1 (H1 atom) in DEP-26 resulted in a higher comprehensive biodegradation value than the corresponding substituent in DEP-23. DEP-21 was introduced to more hydrophilic groups, increasing its comprehensive biodegradability value to more than that of DEP-15. In summary, the prediction of the comprehensive biodegradability of the DEP derivatives by the three plasticizer-degrading bacteria was consistent with the information presented in the contour maps of the CoMSIA model.
3.2.2. Verification of the CoMSIA Model for the Comprehensive Biodegradability of PAEs Molecules
In this paper, Sybyl-x2.0 software was also used to construct the 3D-QSAR model of the single biodegradability of the PAEs by the three plasticizer-degrading bacteria (Table 4). The DEP derivative molecules were screened through the biodegradable 3D-QSAR model of the PAEs’ molecular plasticizer-degrading bacteria, whose degradation efficiency ratio was close to the weight ratios of the entropy weight method in the comprehensive biodegradability model (Table 3). The docking score of the DEP derivative with phthalate dioxygenase reductase was 4.939–6.725, with a growth rate of up to 20.74%. Compared with the growth rate before modification, the docking score values of the DEP derivative binding with esterase ranged from −2.46% to 27.71%. The scoring functions for carboxylesterase and DEP derivatives exhibited a significant increase, with the growth rate ranging from 15.86% to 24.96%. The ratio of the increase in the biodegradation effect value of DEP-23–DEP-30 by three plasticizer-degrading bacteria was the closest to that calculated and weighted by the entropy weight method. These results verified that the CoMSIA model effectively provides the biodegradability information of DEP derivatives when degraded by three plasticizer-degrading bacteria. Additionally, this result demonstrated that the CoMSIA model modified by the entropy weight method is reliable with a good predictive ability, and can be applied to the design modifications in PAE molecules.
3.2.3. Evaluation of the Functionality and Environmental Friendliness of DEP Derivatives
Molecular stability was the primary index for evaluating the functionality of DEP derivatives, characterized by the energy value, energy gap value, and positive frequency value. The persistent organic pollutant (POP) characteristics determined the environmental friendliness of the DEP derivatives, before and after modification, by evaluating their bio-toxicity (logLC50), bioaccumulation (logBCF), persistence (logt1/2) and long-range migration (logKOA) [36]. The energy value was inversely proportional to the stability of the molecules in the environment [42]. A higher positive frequency value indicated that the DEP derivative molecules could exist in the environment [43]. The bio-toxicity and long-range migration of the DEP derivatives were inversely proportional to the predicted logLC50 and logKOA values, respectively. On the contrary, the bioaccumulation and persistence of the DEP derivatives were directly proportional to the predicted logBCF and logt1/2 values, respectively. Table 5 summarizes the predicted values of the parameters evaluating the functionality and environmental friendliness of the DEP derivatives, before and after modification.
Table 5 displays that the energy values of the DEP derivatives were significantly lower than that of the DEP molecule, with a reduction of 19.91–37.39%. The energy gap values did not change significantly before and after modification, indicating that the modified DEP derivatives were highly stable. In addition, the positive frequency values of the DEP derivatives were greater than zero, indicating that the modified molecules could exist in the environment. Among the eight DEP derivatives, only DEP-27, DEP-28, and DEP-29 exhibited no significant changes in their bio-toxicity compared with the unmodified molecule. The bio-toxicity of the remaining five derivatives significantly increased, with the highest increase-rate going up to 38.91%. An organic compound with a logBCF value less than 100 does not easily accumulate in organisms and has a small impact on the environment [44]. Except for the DEP-25 and DEP-26 molecules, the logBCF values of the DEP derivatives were much lower than 100. Among them, the logBCF values of DEP-27, DEP-28, and DEP-29 after modification did not change significantly. The predicted half-lives of the DEP derivatives were slightly higher than that of DEP, indicating that the degradability of these derivatives improved marginally. The predicted logKOA values of the eight DEP derivatives were significantly higher than that of DEP, suggesting that their mobilities were decreasing. In conclusion, among the eight DEP derivatives, only DEP-27, DEP-28 and DEP-29 exhibited significantly higher biodegradation values, and their functionalities and environmental friendliness were also better than the unmodified DEP molecule.
3.3. Analysis of the Microbial Degradation Mechanism of DEP and Its Derivatives Based on a Microbial Degradation Path Simulation3.3.1. Simulation of Microbial Degradation of DEP and Its Derivative Molecules
Plasticizer residue poses a serious threat to the environment. Among the possible ways of degradation, microbial degradation is the primary elimination process of PAE plasticizers. It achieves the purpose of recycling elements and balancing the ecosystem [45]. Gram-negative and Gram-positive bacteria degrade PAEs via different pathways, but both eventually form protocatechate [46]. Protocatechate can convert into pyruvate, succinate, and oxaloacetate, entering the tricarboxylic acid cycle and finally mineralizing into CO2 and H2O [47]. Ren et al. [48] found that esterase plays a crucial role in the microbial degradation of PAEs while studying their microbial degradation mechanism. Ester bond hydrolysis forms the key initial step in the microbial degradation of PAEs [49]. Based on the microbial degradation path of PAEs, the screened DEP derivative molecules (DEP-27, DEP-28 and DEP-29) were taken as examples to simulate and derive the microbial degradation path before and after DEP molecular modification (Figure 4). A Gaussian calculation was carried out for the energy barrier of the reaction to compare the difficulty of the biodegradation process before and after the DEP molecular modification (Table 6).
As shown in Figure 4, the microbial degradation transformation paths of DEP and its derivatives are divided into the transformation paths of Gram-negative bacteria and Gram-positive bacteria. Under the action of the plasticizer-degrading enzymes, DEP first hydrolyzed to phthalate monoesters (M0-1) and then hydrolyzed to form phthalate (M0-2). Phthalate 4,5-dioxygenase in Gram-negative bacteria oxidized the hydroxyl phthalate to produce 4,5-dihydroxyphthalic acid (M0-3), which decarboxylated to protocatechate (M0-5) [50]. Gram-positive bacteria hydrolyzed phthalic acid at C3 and C4 to produce 3, 4-dihydroxyphthalic acid (M0-4), which decarboxylated to protocatechate (M0-5) [51]. Finally, protocatechate mineralized into CO2 and H2O through the tricarboxylic acid cycle, culminating in the complete degradation of PAEs [47]. When the C2 site of DEP was replaced with -CONH2, the resulting derivative was difficult to hydrolyze into phthalic acid [52]. So the degradation process was slightly different from that of DEP.
3.3.2. Calculation of the Reaction Energy Barrier for Microbial Degradation Transformation Paths of DEP and Its Derivatives
The reaction energy barrier required for the microbial degradation of the three DEP derivatives was lower than that required for the degradation of DEP (Table 6). A smaller reaction barrier is desirable as it improves the likelihood of the reaction path to occur. This indicates that the microorganisms could easily degrade the three DEP derivatives compared with DEP. The change rates of the reaction energy barrier for DEP-27, DEP-28, and DEP-29 were −20.15%, −23.42%, and −30.26%, respectively. These were consistent with the enhanced comprehensive biodegradability predicted by the comprehensive biodegradability model (DEP-27: 23.33%, DEP-28: 27.04% and DEP-29: 31.85%). This validates the reliability of the comprehensive biodegradation model, and indicates that the biodegradability of DEP can improve by molecular modification.
3.3.3. Simulation and Verification of the Molecular Dynamics of the Microbial Degradation of DEP and Its Derivatives
In this paper, GROMACS 4.6.5 software was used to simulate the molecular dynamics (MD) of the ligand complex structures of DEP and its derivatives in Sybyl-x2.0, docked with phthalate dioxygenase reductase, esterase and acid esterase. The Poisson–Boltzmann surface area (MM-PBSA) method was used to calculate the binding free energy in the molecular dynamics [53]. Compared with the scoring function of molecular docking, the degree of binding energy characterized the biodegradability of DEP molecules before and after modification (Table 7).
Higher docking scores, indicate a stronger binding of the molecules to the degradative enzyme [54]. Additionally, the binding energy is directly proportional to the affinity between the molecule and the degrading enzyme [55], indicating a higher biodegradability of the molecule. Compared with the DEP molecules, DEP-27 and DEP-28 had lower scoring functions and higher free energy of binding to the degrading enzymes (Table 7). The scoring functions of only DEP-29 binding to the three degrading enzymes increased significantly, and its binding free energy also decreased significantly. The results demonstrated that DEP-29 was the only derivative that could easily bind to the three degrading enzymes at the same time and that the degree of binding was significant. This not only proves the rationality of the comprehensive biodegradation model of PAEs to modify the derivatives, but also forms the means to screen the PAE derivatives that synergistically degrade with microplastics.
4. Conclusions
In this paper, a 3D-QSAR model of the comprehensive biodegradability of the phthalic acid esters (PAEs) was constructed by combining the range normalization and entropy weight methods. In combination with molecular modifications, this model was successfully applied to design environmentally friendly PAEs that can co-degrade with microplastics. These PAE derivatives significantly improve the biodegradability of PAE plasticizers in microplastics, and can reduce the residual PAEs in the natural environment, relieve the adverse effects of plasticizer residues in the human body and environment, and provide the directional selection and theoretical support for the replacement of plasticizers. Further research work will be focused on whether the designed PAE derivatives could be degraded synergistically with the plasticizer-degrading bacteria or not, and the synthesis and biodegradation of these derivatives would provide an effective verification for the molecular modification method available in this study.
Author Contributions
Conceptualization, H.Z. and C.Z.; methodology, H.Z.; software, H.Z.; validation, C.Z. and H.N.; formal analysis, H.Z.; investigation, H.Z.; resources, H.Z.; data curation, H.Z.; writing—original draft preparation, H.Z.; writing—review and editing, H.Z., C.Z.; visualization, H.Z.; supervision, C.Z.; project administration, C.Z. All authors have read and agreed to the published version of the manuscript.
Funding
This research received no external funding.
Conflicts of Interest
The authors declare no conflict of interest.
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Molecular structure of the template molecule diundecyl phthalate (DUP).
Molecular structure of the target molecule diethyl phthalate (DEP).
The contour maps from the CoMSIA model of DEP, (a) steric field, (b) electrostatic field and (c) hydrophobic field.
Schematic diagram of transformation paths of microbial degradation of DEP and its derivative molecules. A: The transformation path of gram-negative bacteria; B: the transformation path of Gram-positive bacteria; M0: the microbial degradation products of DEP; Mr: the microbial degradation products of DEP derivative molecules; R: -CH3, -CH2CH3, -CH (CH3)2; r = 1, 2, 3.
ijerph-17-05299-t001_Table 1
Biodegradability score values and comprehensive biodegradability values of the phthalic acid ester (PAE) molecules by three plasticizer-degrading bacteria.
Compounds
Docking Score Value of 2PIA
Converted Values of 2PIA
Docking Score Value of 2ZYI
Converted Values of 2ZYI
Docking Score Value of 3CN7
Converted Values of 3CN7
Comprehensive Biodegradation Values
BBP a
7.070
0.509
1.908
0.100
5.116
0.380
0.323
DAP a
5.918
0.385
5.461
0.357
5.275
0.409
0.380
DBP b
5.754
0.367
5.551
0.364
6.047
0.547
0.408
DEP a
5.574
0.347
4.711
0.303
3.548
0.100
0.272
DHP a
7.455
0.551
7.221
0.485
6.964
0.711
0.564
DIBP a
4.824
0.266
5.081
0.330
5.006
0.361
0.313
DIHP a
8.634
0.679
10.570
0.727
7.870
0.872
0.743
DIHXP
7.462
0.552
10.548
0.726
6.660
0.656
0.643
DIPP a
6.313
0.427
5.916
0.390
6.061
0.549
0.442
DIPRP a
5.339
0.322
4.609
0.296
5.885
0.518
0.358
DMEP b
7.085
0.511
4.088
0.258
6.876
0.695
0.459
DMP a
3.293
0.100
4.624
0.297
3.940
0.170
0.191
DNOP a
10.420
0.873
4.797
0.309
8.333
0.955
0.679
DPP a
5.484
0.338
4.970
0.322
6.313
0.594
0.393
DPRP b
6.273
0.423
6.681
0.446
4.746
0.314
0.406
DTDP a
9.112
0.731
13.722
0.956
8.584
1.000
0.880
DUP *
11.595
1.000
14.334
1.000
8.561
0.996
0.999
\\nEj\\n
1.799
1.792
1.497
−
\\nHj\\n
0.799
0.792
0.497
−
\\nWj\\n
38.28%
37.93%
23.79%
−
a: Training set; b: test set; *: template molecule.
ijerph-17-05299-t002_Table 2
Evaluation parameters of the comparative molecular similarity index analysis (CoMSIA) model for the biodegradability of the PAEs by three plasticizer-degrading bacteria.
Model
q2
\\nn\\n
SEE
R2
F
r2pred
Q2
cSDEP
dq2/dr2yy
S
E
H
D
A
CoMSIA
0.731
8
0.013
0.999
578.738
0.761
0.385
0.251
1.488
34.7%
11.1%
46.8%
7.4%
0.0%
ijerph-17-05299-t003_Table 3
Prediction of the CoMSIA model of comprehensive and the single biodegradability of DEP derivatives and their change ratios.
No.
Substituent Group
Comprehensive Biodegradation Values
Change Rate(%)
Docking Score Value of 2PIA
Change Rate(%)
Docking Score Value of 2ZYI
Change Rate(%)
Docking Score Value of 3CN7
Change Rate(%)
Ratio
DEP
\\n
0.27
\\n
5.57
\\n
4.71
\\n
3.55
\\n
38.28:37.93:23.79
DEP-1
H1-CH3
0.293
8.52%
5.637
1.20%
5.232
11.08%
4.319
21.66%
−
DEP-2
H1-CH2CH3
0.311
15.19%
5.762
3.45%
5.353
13.65%
4.329
21.94%
−
DEP-3
H1-CH(CH3)2
0.328
21.48%
5.876
5.49%
5.397
14.59%
4.399
23.92%
−
DEP-4
H1-C(CH3)3
0.349
29.26%
5.976
7.29%
5.673
20.45%
4.36
22.82%
−
DEP-5
C1-CH3
0.269
−0.37%
5.500
−1.26%
4.827
2.48%
4.262
20.06%
−
DEP-6
C1-OH
0.228
−15.56%
4.828
−13.32%
4.838
2.72%
4.224
18.99%
−
DEP-7
C1-H
0.272
0.74%
5.059
−9.17%
4.784
1.57%
4.372
23.15%
−
DEP-8
C1-CN
0.255
−5.56%
5.266
−5.46%
4.874
3.48%
4.172
17.52%
−
DEP-9
C1-NH2
0.225
−16.67%
4.967
−10.83%
4.677
−0.70%
4.327
21.89%
−
DEP-10
C1-CHO
0.255
−5.56%
4.938
−11.35%
5.077
7.79%
4.113
15.86%
−
DEP-11
C2-OH
0.249
−7.78%
5.658
1.58%
4.594
−2.46%
4.29
20.85%
−
DEP-12
C2-CHO
0.262
−2.96%
5.737
3.00%
4.871
3.42%
4.282
20.62%
−
DEP-13
C2-COOH
0.269
−0.37%
5.892
5.78%
4.921
4.48%
4.312
21.46%
−
DEP-14
C2-NH2
0.256
−5.19%
5.504
−1.18%
4.911
4.27%
4.199
18.28%
−
DEP-15
C2-COCH3
0.289
7.04%
6.109
9.68%
4.969
5.50%
4.378
23.32%
−
DEP-16
C2-CONH2
0.235
−12.96%
5.755
3.32%
4.776
1.40%
4.436
24.96%
−
DEP-17
C2-(OH)2
0.267
−1.11%
5.484
−1.54%
5.495
16.67%
4.170
17.46%
−
DEP-18
C2-(CHO)2
0.267
−1.11%
5.409
−2.89%
5.73
21.66%
4.131
16.37%
−
DEP-19
C2-(COOH)2
0.284
5.19%
5.621
0.92%
5.826
23.69%
4.119
16.03%
−
DEP-20
C2-(NH2)2
0.259
−4.07%
5.444
−2.26%
5.434
15.37%
4.295
20.99%
−
DEP-21
C2-(COCH3)2
0.310
14.81%
5.799
4.11%
6.015
27.71%
4.188
17.97%
−
DEP-22
C2-(CONH2)2
0.261
−3.33%
5.362
−3.73%
5.946
26.24%
4.175
17.61%
−
DEP-23
H1-CH3-C2-COCH3
0.357
32.22%
6.317
13.41%
5.547
17.77%
4.198
18.25%
27.13:35.95:36.92
DEP-24
H1-CH2CH3-C2-COCH3
0.377
39.63%
6.453
15.85%
5.725
21.55%
4.169
17.44%
28.91:39.30:31.80
DEP-25
H1-CH(CH3)2-C2-COCH3
0.389
44.07%
6.565
17.86%
5.708
21.19%
4.200
18.31%
31.14:36.94:31.92
DEP-26
H1-C(CH3)3-C2-COCH3
0.406
50.37%
6.725
20.74%
5.787
22.87%
4.282
20.62%
32.29:35.60:32.11
DEP-27
H1-CH3-C2-CONH2
0.333
23.33%
6.077
9.10%
5.778
22.68%
4.129
16.31%
18.93:47.15:33.92
DEP-28
H1-CH2CH3-C2-CONH2
0.343
27.04%
6.151
10.43%
5.898
25.22%
4.130
16.34%
20.06:48.51:31.42
DEP-29
H1-CH(CH3)2-C2-CONH2
0.356
31.85%
6.263
12.44%
5.879
24.82%
4.144
16.73%
23.04:45.97:30.99
DEP-30
H1-C(CH3)3-C2-CONH2
0.370
37.04%
6.382
14.58%
5.953
26.39%
4.230
19.15%
24.25:43.89:31.86
Note: H1 stands for the H atom on the C1 site.
ijerph-17-05299-t004_Table 4
Evaluation parameters of B. cepacia (a), A. fulgidus (b), P. aeruginosa (c). Biodegradation 3D-QSAR model of PAEs.
Model
3D-QSAR
q2
n
SEE
R2
F
r2pred
Q2
cSDEP
dq2/dr2yy
a
CoMSIA
0.627
8
0.172
0.998
284.548
0.657
0.559
2.78
0.958
b
CoMFA
0.697
3
0.518
0.986
207.862
0.918
0.422
3.306
1.372
c
CoMFA
0.68
10
0.001
1
465107.312
0.618
0.491
3.021
0.690
ijerph-17-05299-t005_Table 5
Prediction parameters for DEP’s molecular function and environmental friendliness before and after modification.
No.
Total Energy (a.u.)
Change Rate(%)
Energy Gap (eV)
Frequency (cm−1)
Bio-Toxicity(logLC50)
Change Rate(%)
Bioaccumulation (logBCF)
BCF
Persistence(logt1/2)
Change Rate(%)
Long-Range Migration (logKOA)
Change Rate(%)
DEP
−766.62
\\n
5.32
24.02
1.100
\\n
1.264
18.37
3.156
\\n
7.505
\\n
DEP-23
−919.26
−19.91%
5.15
15.09
0.781
29.00%
1.879
75.68
3.271
−3.64%
8.540
13.79%
DEP-24
−958.57
−25.04%
5.16
18.09
0.744
32.36%
1.992
98.17
3.250
−2.98%
8.546
13.87%
DEP-25
−997.89
−30.17%
5.14
17.65
0.737
33.00%
2.041
109.90
3.230
−2.34%
8.563
14.10%
DEP-26
−1037.2
−35.30%
5.06
15.38
0.672
38.91%
2.116
130.62
3.215
−1.87%
8.766
16.80%
DEP-27
−935.32
−22.01%
5.00
19.32
1.070
2.73%
1.569
37.07
3.386
−7.29%
8.329
10.98%
DEP-28
−974.64
−27.13%
4.97
16.33
1.047
4.82%
1.600
39.81
3.378
−7.03%
8.352
11.29%
DEP-29
−1013.95
−32.26%
4.96
16.24
1.038
5.64%
1.653
44.98
3.357
−6.37%
8.374
11.58%
DEP-30
−1053.26
−37.39%
4.94
15.99
0.923
16.09%
1.716
52.00
3.349
−6.12%
8.489
13.11%
ijerph-17-05299-t006_Table 6
Calculation of the reaction energy barrier of microbial degradation transformation paths of DEP and its derivatives.
DEP
Change Rate(%)
DEP-27
Change Rate(%)
Path
Reactants
ReactionProducts
Energy Barrier (kJ/mol)
Total Energy Barrier (kJ/mol)
Reactants
ReactionProducts
Energy Barrier (kJ/mol)
Total Energy Barrier (kJ/mol)
Path1
DEP
M0-1
27.57
127.33
−
DEP-27
M1-1
28.88
113.16
−11.13
M0-1
M0-2
15.75
M1-1
M1-2
13.92
M0-2
M0-3
53.82
M1-2
M1-3
56.97
M0-3
M0-5
30.19
M1-3
M1-5
13.39
Path2
DEP
M0-1
27.57
139.68
−
DEP-27
M1-1
28.88
127.07
−9.02
M0-1
M0-2
15.75
M1-1
M1-2
13.92
M0-2
M0-4
68.79
M1-2
M1-4
72.99
M0-4
M0-5
27.57
M1-4
M1-5
11.29
\\n
\\n
Total Change rate (%)
−20.15
\\nDEP-28\\n
\\nChange Rate\\n\\n(%)\\n
\\nDEP-29\\n
\\nChange Rate\\n\\n(%)\\n
\\nPath\\n
\\nReactants\\n
\\nReaction\\n\\nProducts\\n
\\nEnergy Barrier (kJ/mol)\\n
\\nTotal Energy Barrier (kJ/mol)\\n
\\nPath\\n
\\nReactants\\n
\\nReaction\\n\\nProducts\\n
\\nEnergy Barrier (kJ/mol)\\n
Path1
DEP-28
M2-1
24.94
110.98
−12.84
DEP-29
M3-1
17.07
99.77
−21.65
M2-1
M2-2
15.67
M3-1
M3-2
12.34
M2-2
M2-3
56.97
M3-2
M3-3
56.97
M2-3
M2-5
13.39
M3-3
M3-5
13.39
Path2
DEP-28
M2-1
24.94
124.90
−10.58
DEP-29
M3-1
17.07
113.68
−18.61
M2-1
M2-2
15.67
M3-1
M3-2
12.34
M2-2
M2-4
72.99
M3-2
M3-4
72.99
M2-4
M2-5
11.29
M3-4
M3-5
11.29
Total Change rate (%)
−23.42
Total Change rate (%)
−30.26
ijerph-17-05299-t007_Table 7
Molecular docking scores and molecular dynamics simulation of the binding energy calculation for DEP and its derivative molecules.
No.
2PIA
2ZYI
3CN7
Docking Score Value
△Gbind (kJ/mol)
Docking Score Value
△Gbind (kJ/mol)
Docking Score Value
△Gbind (kJ/mol)
DEP
5.574
−62.400
4.711
−138.694
3.548
−108.742
DEP-27
5.323↓
−126.613↓
3.491↓
−158.330↓
5.661↑
−102.247↑
DEP-28
5.793↑
−74.505↓
3.993↓
−138.588↑
6.139↑
−136.861↓
DEP-29
5.717↑
−108.149↓
7.535↑
−177.961↓
7.042↑
−160.312↓
\\n\"\n]"}}},{"rowIdx":470,"cells":{"data":{"kind":"list like","value":["\nFront Plant SciFront Plant SciFront. Plant Sci.Frontiers in Plant Science1664-462XFrontiers Media S.A.32849733PMC743212710.3389/fpls.2020.01184Plant ScienceOriginal ResearchGenome-Wide Association Mapping Identifies Novel Loci for Quantitative Resistance to Blackleg Disease in CanolaRamanHarsh\n1\n\n*\nMcVittieBrett\n1\nPirathibanRamethaa\n2\nRamanRosy\n1\nZhangYuanyuan\n3\nBarbulescuDenise M.\n4\nQiuYu\n1\nLiuShengyi\n3\nCullisBrian\n2\n\n1\nNSW Department of Primary Industries, Wagga Wagga Agricultural Institute, Wagga Wagga, NSW, Australia\n\n2\nCentre for Bioinformatics and Biometrics, National Institute for Applied Statistics Research Australia, University of Wollongong, Wollongong, NSW, Australia\n\n3\nOil Crops Research Institute, Chinese Academy of Agricultural Sciences, Wuhan, China\n\n4\nDepartment of Jobs, Precincts and Regions, Agriculture Victoria, Horsham, VIC, Australia\n
Edited by: Ryo Fujimoto, Kobe University, Japan
Reviewed by: Habibur Rahman, University of Alberta, Canada; Takahiro Kawanabe, Tokai University, Japan
This article was submitted to Plant Breeding, a section of the journal Frontiers in Plant Science
1182020202011118407520202172020Copyright © 2020 Raman, McVittie, Pirathiban, Raman, Zhang, Barbulescu, Qiu, Liu and Cullis2020Raman, McVittie, Pirathiban, Raman, Zhang, Barbulescu, Qiu, Liu and CullisThis is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Blackleg disease, caused by the fungal pathogen Leptosphaeria maculans, continues to be a major concern for sustainable production of canola (Brassica napus L.) in many parts of the world. The deployment of effective quantitative resistance (QR) is recognized as a durable strategy in providing natural defense to pathogens. Herein, we uncover loci for resistance to blackleg in a genetically diverse panel of canola accessions by exploiting historic recombination events which occurred during domestication and selective breeding by genome-wide association analysis (GWAS). We found extensive variation in resistance to blackleg at the adult plant stage, including for upper canopy infection. Using the linkage disequilibrium and genetic relationship estimates from 12,414 high quality SNPs, GWAS identified 59 statistically significant and “suggestive” SNPs on 17 chromosomes of B. napus genome that underlie variation in resistance to blackleg, evaluated under field and shade-house conditions. Each of the SNP association accounted for up to 25.1% of additive genetic variance in resistance among diverse panel of accessions. To understand the homology of QR genomic regions with Arabidopsis thaliana genome, we searched the synteny between QR regions with 22 ancestral blocks of Brassicaceae. Comparative analyses revealed that 25 SNP associations for QR were localized in nine ancestral blocks, as a result of genomic rearrangements. We further showed that phenological traits such as flowering time, plant height, and maturity confound the genetic variation in resistance. Altogether, these findings provided new insights on the complex genetic control of the blackleg resistance and further expanded our understanding of its genetic architecture.
natural variationresistance to blacklegLeptosphaeria maculanscanolagenome-wide association analysislinkage disequilibriumGrains Research and Development Corporation10.13039/501100000980DAN00117, DAN00208, DAN1701Introduction
Canola, a polyploid crop (Brassica napus L, 2n = 4× = 36, genome AnAnCnCn) that was only domesticated about 500 years ago (Gómez-Campo and Prakash, 1999), has now taken central stage for meeting global demands of healthy vegetable oil for human consumption. Currently, it is grown on over 37 M ha worldwide with production of 75 m tonnes (http://www.fao.org/faostat/). Blackleg, caused by the fungal pathogen Leptosphaeria maculans, is one of the most serious diseases of canola in many parts of the world and accounts for significant yield losses (Sosnowski et al., 2004; Fitt et al., 2006). The infection occurs at various stages of plant development and causes lesions on underground and the above ground parts; cotyledon, leaf, stem, branches, flowers, and siliques (Sprague et al., 2018). Current disease management strategies include deployment of host resistance, mediated by qualitative (race-specific R genes) and uncharacterized quantitative resistance (race nonspecific QTL, QR) genes, crop rotations, and application of fungicides.
Several sources of qualitative and quantitative resistance to blackleg has been identified in Brassica species (Mithen et al., 1987; Chevre et al., 1996; Chèvre et al., 1997; Christianson et al., 2006; Rimmer, 2006; Leflon et al., 2007; Raman et al., 2013; Fredua-Agyeman et al., 2014; Gaebelein et al., 2019); and several of them were deployed by canola breeders to develop resistant cultivars. Genetics underlying both types of resistance has been investigated in different structured mapping populations and unstructured diverse panels of canola (Rimmer, 2006; Delourme et al., 2011; Raman et al., 2013). However, loci associated with R gene mediated resistance are understood in greater detail compared to QR (Larkan et al., 2013; Larkan et al., 2014; Larkan et al., 2019). Though, incorporation of the R genes has been effective in mitigating risks for yield loss due to blackleg; many of them have become ineffective over-time, when deployed singly or in “stack” (Rouxel et al., 2003; Li et al., 2006; Sprague et al., 2006; Van De Wouw et al., 2014). In contrast, QR is recognized for its durability in providing natural defense to pathogens, including L. maculans, as it conveys incomplete resistance, probably due to decreased selective pressure to overcome host resistance (Johnson, 1984; Boyd, 2006; Fukuoka et al., 2009; Delourme et al., 2014).
Several hundred quantitative trait loci (QTL) for QR were identified in mapping populations following classical QTL (Delourme et al., 2006; Kaur et al., 2009; Jestin et al., 2011; Raman et al., 2012a; Raman et al., 2012b; Fopa Fomeju et al., 2014; Jestin et al., 2015; Larkan et al., 2016a; Raman et al., 2016; Kumar et al., 2018; Raman et al., 2018; Raman et al., 2020) and genome-wide association (GWAS) approaches in canola (Jestin et al., 2011; Fopa Fomeju et al., 2014; Fopa Fomeju et al., 2015; Rahman et al., 2016; Raman et al., 2016; Kumar et al., 2018). However, only a limited number of QTL provided stable QR to blackleg across environments (Pilet et al., 2001; Huang et al., 2016; Larkan et al., 2016a; Raman et al., 2018). Combining QR loci with complementary mode of action and combining QR with R genes could provide effective and durable resistance (Brun et al., 2010; Michelmore et al., 2017; Pilet-Nayel et al., 2017; Rimbaud et al., 2018). The latter approach has not been extremely successful in some parts of Australia, particularly when pathogen pressure is too high under field conditions (Raman et al., 2012b; Raman et al., 2020). Sources of effective QR resistance and underlying mode of action for QR genes is largely unknown in canola. Despite of immense interest in exploiting QR in canola breeding programs, comprehensive GWAS studies using larger sets of diverse panel and markers, and field-based assessments of resistance to blackleg across multiple locations and environments were lacking. In addition, upper canopy infection (UCI); characterized by lesions on siliques, pedicel, branches, and stem, at terminal stages of plant development, has become another concern for Australian canola industry in recent years (Sprague et al., 2018). Currently, there is a very limited knowledge on the extent of genetic variation for resistance to UCI in canola germplasm as well as underlying loci involved in resistance. Delineation of loci involved in multifaceted components of resistance and their uniqueness/redundancy in canola germplasm would provide potential genetic solution to reduce yield losses, caused by blackleg disease.
Herein, we assess a diverse panel of canola accessions across multiple environments and reveal significant marker-trait associations for resistance, evaluated as plant survival/mortality, internal infection (stem/crown canker) and severity of infection in upper canopy. To understand whether duplicated homoeologous regions are involved in QR, we searched the synteny between QR regions with 22 ancestral blocks (AK) of Brassicaceae (Schranz et al., 2006; Delourme et al., 2013; Fopa Fomeju et al., 2014). This analysis revealed that majority of QR regions have originated as a result of homoeologous and paralogous exchanges. We further showed that phenological traits such as flowering time, plant height and maturity confound the genetic variation for resistance. Our results provide new insights into the genetic and developmental basis for variation in QR among diverse panel of accessions. Identification of genetic variation for resistance to stem canker and UCI, in combination with molecular tools would enable the incorporation of QR into the elite germplasm, to develop new cultivars with overall reduced susceptibility to blackleg infection in canola.
Material and MethodsDiversity Panel
A diverse panel of 421 accessions, comprising 395 of Brassica napus, 21 of B. napus/Brassica juncea derivatives, one of B. juncea and four of Brassica carinata was used in this study (\nSupplementary Table 1\n). The B. napus panel includes 368 homozygous doubled haploid (DH) diverse accessions, representing different geographical locations, that were utilized earlier for mapping loci associated with photoperiodic response and flowering time (Raman et al., 2019). The DH accessions were generated via the microspore culture technique at the Haplotech Inc. (Manitoba, Canada).
Phenotypic Evaluation of Diverse DH Accessions for Resistance
The diverse panel of accessions was evaluated for resistance to L. maculans in five experiments. Two experiments were conducted under semi-controlled shade-house conditions (SH16, SH17) across two years (2016 and 2017) at Grains Innovation Park, Horsham, Victoria, Australia (36°43’14.6”S 142°10’24.5”E) and three experiments (FT17, FT18 and FT19) were conducted under field conditions across three years (2017, 2018 and 2019) at Wagga Wagga Agricultural Institute (WWAI), Wagga Wagga, NSW, Australia (35°02′27.0″S 147°19′12.6″E).
Phenotyping for Resistance With Ascospore Shower Test (Shade-House Experiments)Experimental Design
The shade-house experiments included a total of 327 DH accessions in two runs: in SH16, 244 accessions together with 10 current commercial cultivars (as “check”) were evaluated whereas in SH17, the remaining 77 accessions along with the same 10 “check” cultivars were evaluated (\nSupplementary Table 1\n). Connectivity between both experiments with 10 check cultivars and four DH accessions (46C04, ATR-Beacon, ATR-Signal and ATR-Hyden) that were used in SH16 experiment ensured that the assessment of disease expression is consistent across both experiments (\nSupplementary Table 2\n). In the SH16 experiment, the accessions were arranged in a Completely Randomized Design with three pot replicates for each accession (\nSupplementary Figure 1A\n). A total of 762 pots were located in three adjacent bays. Each bay consisted of a rectangular array of pots; each bay had either four or two columns (herein referred to as ranges) with a varying number of rows (80, 79, 32, and 31, respectively). Individual pots were the experimental units where, each pot contained 4 plants of the same accession. In the SH17 experiment, accessions were allocated to pots within three (complete) blocks. These blocks were located within one rectangular array of pots (Bay) with four ranges and 66 rows. The whole experiment had a total of 264 pots (\nSupplementary Figure 1B\n).
Ascospores, released from a mixed stubble were used as inoculum for the “ascospore shower” test (Huang et al., 2006b). Stubble was collected from 2015 commercial crops of Australian canola cultivars: ATR-Gem (group A: Rlm1, Cudal, NSW), CB-Telfer (group B: Rlm4, Arthurton, South Australia), ATR-Stingray (group C: Rlm3, Minyip, Victoria), Hyola450TT (group ABD: Rlm1, Rlm4, LepR3, Streatham, Victoria), Hyola650TT (group ABD, Grenfell, NSW), T28156 (group F: Rlm6, unreleased breeding line, Parkes, NSW) and ATR-Marlin (group S: Rlm1, LepR3, Parkes, NSW). Details of the “ascospore shower” test are given in Marcroft et al. (2012). Essentially, seedlings of test accessions were grown in punnets for approximately 10 to 15 days and then four seedlings/accession were subjected to the “ascospore shower.” The seedlings were placed onto trays and were then transferred to two growth (humidity) chambers, where the relative humidity was set at 100% at 20°C, for 48 h to encourage disease progression. After 21 days of inoculation, seedlings were transplanted into plastic pots (20 cm diameter) in a shade-house, each pot containing 4 plants per accession and subsequently raised according to the described experimental designs until the physiological maturity stage (GS 83-85). Plants were cut at the crowns and their cross-sections were assessed visually for disease severity as described previously (Marcroft et al., 2012).
Phenotypic Measurements of Diversity Panel Under Natural Field Conditions
A subset of the diverse panel, comprising 300 homozygous accessions (\nSupplementary Table 1\n) was evaluated for resistance in blackleg nursery containing mixed stubble of triazine tolerant (TT) cultivars (Raman et al., 2018) and Westar stubble at the experimental farm of WWAI (35°02’27.0”S 147°19’12.6”E).
Experimental Design
In each of the three field experiments, accessions were allocated to plots within two (complete) blocks. Plots consisted of a single row of at most 100 plants. These plants were grown in canola stubble of mixed TT cultivars and Westar as described in Raman et al. (2018). Each complete block comprised a rectangular array of plots with 30 rows by 10 columns separated by a buffer row of SturtTT (see \nSupplementary Figure 1C\n). The experiments were carried-out in canola growing seasons (April-November). The blackleg nursery was irrigated frequently (5-6 times, approximately 150 mm water applied) using lateral move, in addition to natural rainfall. The weather data and conditions for both experiments is presented in \nSupplementary Figure 2\n. Plant emergence was recorded by counting number of plants present in each plot after 45 days of sowing. Number of surviving plants were recorded thrice at growth stage (GS) 15-16 (after 5 weeks of emergence), GS 60-65 (at flowering stage) and at the GS 83-85 (physiological maturity). Plant survival was calculated as the proportion between number of plants at emergence and at maturity. At the physiological maturity, ten plants per accession were cut at the crown and visually assessed for internal stem infection on a 0 to 100 percentage scale based on area showing necrosis (Raman et al., 2012b).
Resistance for UCI severity was assessed in the two experiments conducted in 2018 and 2019. Number of plants infected and the extent of UCI on ten randomly selected plants from each plot were scored. An ordinal disease severity rating scale was used based on visual assessment; 0: immune reaction (no visible symptoms of UCI); (1) disease present on up to 25% of plant tissue (small lesions, resistant to UCI); (2) disease present on 26%–50% of plant tissue; (3) disease present on 51%–75% of plant tissue, (4) disease present on >75% but less than 90% of plant tissue and (5) 100% susceptibility, affecting all tissues, characterized with extensive discoloration, snapping-off vegetative (main and secondary branches) and reproductive structures (siliques).
Measurement of Phenological Traits
Each field experiment was also assessed for three key developmental traits: flowering time, plant height and maturity. Flowering time (days to first flower, GS 65) was recorded, as the difference between the date when 50% of plants in a plot had the first flower opened and the day of sowing. The date of first flower was recorded for each plot three times in a week. Plant height (cm) was measured from top to base with a scale at the silique-filling stage (GS 80) from five plants selected randomly in the middle of each plot while, plant maturity (0–9 scale) was recorded, just 1–2 days before the assessment of blackleg severity.
SNP Genotyping and Selection of Marker Panel
DNA was genotyped with Illumina Infinium SNP markers (Clarke et al., 2016), as detailed in our earlier study (Raman et al., 2019). Across all accessions and SNPs, there was a total of 2.7% missing values. Missing values were imputed using the k nearest neighbor method (Troyanskaya et al., 2001) using the Pedicure package (Butler, 2019) in R statistical computing environment (R Core Team, 2019). In this method, a missing value for a marker is replaced by the mean of its k nearest neighbors with nonmissing data for that individual. SNPs that had <80% call rate, and <2% MAF (minor allele frequency) were discarded prior to GWAS analysis. We examined redundant SNPs based on the Hamming distance between marker pairs. Sets of markers, whose Hamming distance was less than a threshold of 0.001 were considered as identical and were removed prior to analysis. We also removed SNPs which could not be anchored to the 19 linkage groups of An, and Cn subgenomes (plus Ann_random and Cnn_random linkage groups) of reference sequenced genome of B. napus cv. “Darmor-bzh” version 4.1. A final set of 12,414 high quality SNPs for 344 accessions were selected for GWAS analysis.
Statistical Analysis
The approach used herein for the determination of genomic regions that influence the expression of the traits associated with blackleg resistance is an extension of the whole genome, single step QTL analysis developed by Verbyla et al. (2007). Our approach uses an alternative working model which assumes the variance of each set of markers on different linkage groups (LGs) have different variances. That is if α is the r-vector of marker effects presented in map order, then our working model assumes that α is Gaussian, mean zero, variance
where c is the number of LGs, ri is the number of markers in the ith LG, Iri is the identity matrix of order ri, ⊕ is the direct sum operator and r= ∑i=1cri. The working model of Verbyla et al. (2007) assumes that σαi2= σα2, for i = 1,…, c. Hence their model is equivalent to the standard GBLUP model used in models for genomic selection (Tolhurst et al., 2019).
Construction of the appropriate genetic and nongenetic working model requires the inclusion of terms which represent the plot structure of the experiment(s), as well as accounting for other significant sources of nongenetic variation which occur in either shade-house studies or field trials (Gilmour et al., 1997; Smith et al., 2006). The working model (referred to as model M1) must also include terms which partition residual variance for those traits where the experimental unit is not the observational unit (Bailey, 2008). These traits include internal infection, for FT17, FT18, and FT19; internal infection and mortality for SH16, and SH17; and UCI and plant height for FT18 and FT19. All models also include a term representing polygenic effects, and the integrity of the experimental designs are preserved by including an additional term as a fixed effect, representing the effects of those accessions which were not genotyped but had phenotypic data (Tolhurst et al., 2019). All trials and traits were analyzed individually except the two runs of the shade-house experiments SH16 and SH17 which were analyzed jointly considering years as “environments”.
The models were fitted either as a linear mixed model (LMM) using residual maximum likelihood (REML) (Patterson and Thompson, 1971) for the traits internal infection, UCI, days to flower, plant height and maturity which were considered to be approximately Gaussian on a suitable transformed scale, or as a generalized linear mixed model (GLMM) using penalized quasi likelihood (Breslow and Clayton, 1993) for the traits: plant survival and mortality. All analyses were conducted using ASReml-R (Butler et al., 2018), which provides REML estimates of variance parameters, empirical best linear unbiased predictions (EBLUPs) of random effects and empirical best linear unbiased estimates (EBLUEs) of fixed effects.
Heritability (reliability) for each experiment and trait was computed using a generalized definition for heritability developed using the methods of Cullis et al. (2006):
h2=1− PEV/(2θgt2)
where PEV is the average pair-wise prediction error variance of genotype effects and θgt2 is the genetic variance of trial t. Pair-wise marker additive genetic correlations (ra) between traits were computed using the EBLUPs of the marker additive genetic effects of those accessions evaluated for each trait.
GWAS Approach
Using the working model, M1, genome scans for each linkage group with a positive REML estimate of the marker variance was performed. During this scan, each marker was fitted as a fixed effect, the other markers on the same linkage group were dropped from the model, the marker variances for the other linkage groups were held fixed at the values from the base working model while all other variance parameters were re-estimated. Potential QTLs were chosen from this scan, along with their positions and LOD scores, i.e., –log\n10(P – value). The potential set of markers was then thinned using a modification of the approach of Benjamini and Hochberg (1995). Given the strong dependence of the tests within a linkage group, the squared LD (r2) was computed for each pair of markers and any markers with an estimated LD of greater than 0.7 and within 100 kb, were deemed to be within the same linkage block. Markers which were in the same linkage block and also chosen as potential QTLs were thinned again, to leave only one potential QTL within any given linkage block. The linkage block marker was chosen as the marker with the lowest P-value and the least number of missing values, for that linkage block. The remaining set of markers obtained from this were then included as the baseline multi-QTL model which included all of the same terms as the working baseline model as well as the set of QTLs fitted as fixed effects. Markers were dropped from this baseline multi-QTL model using a Bonferroni based backward elimination approach until all remaining markers were significant at a significance level of 0.05. All remaining markers in the fixed component of the final multi-QTL model are reported here as putative QTLs. Of which the markers with P-value ≤ 0.001, i.e., LOD score ≥ 3 are considered as “significant,” and the markers with 0.001 < P value < 0.05 are considered as “suggestive” (Rexroad et al., 2012).
Putative Candidate Genes for Resistance
Marker sequences that revealed significant associations with quantitative resistance to blackleg in this GWAS study and previous studies (Jestin et al., 2011; Fopa Fomeju et al., 2014; Fopa Fomeju et al., 2015; Larkan et al., 2016a; Raman et al., 2016; Kumar et al., 2018) were aligned by searching their physical positions with the reference Darmor-bzh gene assembly version 4.1 (Chalhoub et al., 2014). Biological functions of those genes in relation to R and QR loci were described.
Comparative Mapping of SNP Associations in Relation to AK of Brassicaceae and Disease Resistance <italic>R</italic> Genes
Marker sequences of significant SNP associations for resistance were aligned with the sequence of A. thaliana to identify the location of QR loci and its relationship with AK of Brassicaceae, as described previously (Zou et al., 2016). Brassica napus\nR genes (Chalhoub et al., 2014; Alamery et al., 2018) that map in the vicinity of genomic regions associated with resistance to blackleg were also searched for their synteny with AK blocks.
ResultsGenetic Variation for Resistance to Blackleg
Despite of the extreme high day-temperatures and drought conditions across field environments (\nSupplementary Figure 2\n), we were able to raise blackleg nurseries, suitable for evaluation of germplasm for resistance. Frequent overhead watering, with the lateral move irrigator ensured that condition for blackleg infection were congenial throughout the growing season. Across environments, blackleg infection occurred, right from the cotyledon stage (after 4–6 weeks of seedling emergence) to silique maturation (\nSupplementary Figure 3A\n). The maximum disease expression for UCI was observed at the silique maturation stage, when the disease severity for UCI was assessed. Frequency distributions exhibited that severity of blackleg scores, measured as internal infection and plant mortality, were variable across environments; blackleg severity was higher under shade-house (ascospore shower test) compared to disease nurseries under field conditions (\nFigures 1A, B\n).
Variation for resistance to blackleg disease, caused by the fungus, Leptosphaeria maculans in canola. Box plots showing variation for resistance to blackleg disease, measured as per cent internal infection (A) and as plant survival/mortality (B) assessed across five environments. In total, disease severity scores from 421 diverse accessions are evaluated across five environments (for details of accessions evaluated, see \nSupplementary Table 1\n). (C) The frequency distribution of upper canopy infection scores among diverse accessions. (D) Accessions showing extremes in resistance and susceptibility to upper canopy blackleg infection, evaluated across two field environments (2018, 2019) at Wagga Wagga, NSW. Variation for blackleg severity was assessed under field conditions (in blackleg nurseries) and under shade-house conditions (with ascospore shower method). (E) Scatter plots showing relationship between plant mortality and internal infection among diverse accessions, assessed for resistance using ascospore shower test. Stubble collected from commercial crops of Australian canola cultivars: ATR-Gem (group A; Cudal, NSW), CB-Telfer (group B, Arthurton, South Australia), ATR-Stingray (group C, Minyip, Victoria), Hyola450TT (group ABD, Streatham, Victoria), Hyola650TT (group ABD, Grenfell, NSW), T28156 (group F, unreleased breeding line, Parkes, NSW) and ATR-Marlin (group S, Parkes, NSW) grown in 2015, was used for “ascospore shower.” Empirical best linear unbiased predictions (EBLUPs) of genetic effects are plotted.
Joint analysis of the two shade-house experiments, where accessions were evaluated with the “ascospore shower” method, revealed that the majority of them were susceptible to pathotypes, present on the infested stubble derived from the Australian canola cultivars: ATR-Gem, CB-Telfer, ATR-Stingray, Hyola450TT, Hyola650TT, T28156 and ATR-Marlin (\nSupplementary Table 3\n). We identified 21 accessions of canola, and one accession of Ethiopian mustard (B. carinata, 2n =4x =34, subgenome BcBcCcCc, ATC93184) that had ≤10% internal infection in the shade-house experiments (\nSupplementary Table 3\n). Resistant B. napus accessions were of Australian (43C80CL, 46C04, AGA99, ATR-Beacon, ATR-Mako, ATR-Signal, AV-Ruby, BononzaTT, HurricaneTT, ScaddanTT, Surpass501TT, ThunderTT, WarriorCL, RQ01-02), European (Global, Columbus, Beluga, Lindore) and Asian (Gan-You and Tosharsu) origins. The level of resistance in 16 accessions was similar to contemporary and modern commercial cultivars: GT42 (Rlm1,4,6), Hyola575 (LepR1, Rlm1) and Hyola970 (Rlm7) (\nSupplementary Table 3\n). Several cultivars, carrying the race-specific resistance genes, such as Stingray (Rlm3,9), Ripper (Rlm2,4,9) and CB-Telfer (Rlm4) were susceptible under shade-house and field conditions, suggesting that major genes were ineffective in conferring resistance, while some of the resistant accessions such as Scaddan (Rlm1,4), Bonanza (Rlm2,9) and ThunderTT (Rlm4,9) had a very low scores for internal infection when challenged with ascospores released from cultivars carrying Rlm1, Rlm2, Rlm4 and Rlm9 genes (either single or in stack), hinting that those accessions may have loci for quantitative disease resistance or a combination of both novel R and QR loci, this requires further verification. Diverse panel of canola accession showed a continuous distribution of disease severity scores for UCI, suggesting that multiple genes control resistance to UCI (\nFigure 1C\n). Among different accessions, Beluga, Gundula, Jet Neuf - a donor source of quantitative resistance in Darmor-bzh and other winter cultivars (Pilet et al., 2001), Lindore, Rafal, and two accessions of B. carinata (ATC94047, ATC94458) were highly resistant to UCI, while P3083, Ding474 and Wesbell were rated as highly susceptible (\nFigure 1D\n).
Our analysis showed that the significant source of genetic variation in blackleg resistance was from the additive component, i.e., genetic markers; VAFa, which ranged from 33.8% for plant survival (FT17) to 100% for internal infection (SH17, FT17) and mortality (SH17) (\nTable 1\n). The values for the reliabilities (broad sense genomic heritability) for each trait measured were generally high (\nTable 2\n) and found to be trait and environment dependent. For example, plant survival had low reliability (42%) in 2018, but higher values were obtained in 2017 (87%) and in 2019 (99%) environments. Flowering time had higher values for reliability across all environments (97% to 98%) compared to other traits. High reliability values suggest that the variation in different traits measured is heritable and therefore is suitable for genetic analysis as well as for exploiting in the canola breeding programs for enhancing genetic gains.
Summary of the residual maximum likelihood (REML) estimates for additive and nonadditive (residual) genetic variance from the models before (Base marker model: M1) and after identifying putative quantitative trait loci (QTL) (Final multi QTL model: M2) for each of the trait and trial.
Trait
Experiment
Additive (M1)
Additive (M2)
Nonadditive (M1)
Nonadditive (M2)
VAFa (%)
VAFm (%)
Internal infection
SH16
1.46
0.91
0.27
0.30
84.4
28.3
Internal infection
SH17
5.21
2.81
0.00
0.00
100.0
43.6
Internal infection
SH16/17
2.68
1.66
0.17
0.10
94.0
37.0
Internal infection
FT17
0.86
0.64
0.00
0.00
100.0
22.6
Internal infection
FT18
1.35
0.67
0.04
0.00
97.4
51.8
Internal infection
FT19
3.58
2.48
0.750
0.75
82.7
28.5
UCI
FT18
0.27
0.06
0.17
0.19
61.2
47.7
UCI
FT19
0.24
0.06
0.05
0.07
81.5
59.0
Height
FT18
129.75
46.42
51.53
55.69
71.6
43.2
Height
FT19
117.81
63.68
56.17
53.41
67.7
31.2
DTF
FT17
36.50
20.90
5.09
5.12
87.8
34.4
DTF
FT18
158.48
68.18
13.41
13.96
91.1
48.9
DTF
FT19
48.56
23.67
7.12
7.06
87.5
39.8
Plant survival
FT17
0.31
0.27
0.61
0.58
33.8
7.8
Plant survival
FT18
0.67
0.42
0.23
0.12
74.1
38.7
Plant survival
FT19
0.57
0.42
0.57
0.44
49.7
27.2
Mortality
SH16
0.68
0.32
0.21
0.16
76.0
46.9
Mortality
SH17
2.79
1.92
0.00
0.00
100.0
27.8
Mortality
SH16/17
1.04
0.55
0.19
0.21
84.6
35.6
VAFa is the percentage of total genetic variance accounted by the markers. VAFm shows the percentage of genetic variance accounted by the identified putative QTLs. Resistance to blackleg was assessed as internal infection (stem canker, %) and plant survival/mortality (0–1) across five environments in 2016, 2017, 2018, and 2019. SH, shade-house; FT, Field; DTF, Days to flower; UCI, Upper canopy infection (1-5 scale); SH16/17, joint analysis of SH16 and SH17.
Summary of heritability, mean, minimum and maximum values of the predicted means for each trait and trial in the diverse panel of canola accessions.
Phenotypic Environment
Year
Trait
Trial
Heritability (%)
Mean
Minimum
Maximum
Shade-house
2016
Internal infection
SH16
80
83.79
11.67
98.88
2017
Internal infection
SH17
84
74.26
3.81
98.84
2016
Mortality
SH16
72
0.46
0.13
0.85
2017
Mortality
SH17
77
0.34
0.06
0.90
2016/2017
Internal infection
SH16/17
81
51.89
0.82
86.35
2016/2017
Mortality
SH16/17
75
0.50
0.10
0.92
Field
2017
Internal infection
FT17
66
4.75
0.55
24.47
2018
Internal infection
FT18
67
22.45
0.60
91.84
2019
Internal infection
FT19
81
22.45
0.60
91.84
2018
UCI
FT18
74
1.99
0.18
4.41
2019
UCI
FT19
76
1.76
0.34
4.45
2018
Height
FT18
89
128.98
91.86
160.44
2019
Height
FT19
90
90.35
28.46
123.36
2017
DTF
FT17
98
120.39
109.20
139.57
2018
DTF
FT18
98
122.17
102.95
155.60
2019
DTF
FT19
97
115.42
100.81
139.35
2017
Plant survival
FT17
87
0.81
0.23
0.98
2018
Plant survival
FT18
42
0.98
0.86
0.99
2019
Plant survival
FT19
99
0.66
0.08
0.98
Resistance to blackleg was assessed as internal infection (%) and plant survival/mortality (proportion) across five environments in 2016, 2017, 2018, and 2019. SH, shade-house; FT, Field; DTF, Days to flower; UCI, Upper canopy infection (1–5 scale). Data on SH16/17 is based on the joint analysis of SH16 and SH17 trials.
Genetic Correlation Between Blackleg Traits
We investigated the pair-wise additive genetic correlation between different measures of blackleg scores among accessions. There was a high positive correlation (ra = 0.91) between internal infection and mortality in ascospore shower test (\nFigure 1E\n). Likewise, internal infection scores showed negative correlation with plant survival in 2017 (ra = −0.56), 2018 (ra = −0.54) and in 2019 field environments (ra = −0.66) (\nFigures 2A–C\n). Resistance to UCI, evaluated across two years also showed a negative correlation with plant survival across both environments (2018; ra = −0.50, 2019; ra = −0.42). To determine the effectiveness of quantitative resistance across environments (field and shade-house), we further investigated the relationship between internal infection (stem canker) scores among accessions. We observed a high additive genetic correlation (ra = 0.60 to 0.77), suggesting that diverse accessions have effective resistance to blackleg (\nFigure 2D\n).
Genetic (additive) correlations for resistance to blackleg evaluated in disease nurseries across three environments in 2017 (A), 2018 (B), and 2019 (C) at Wagga Wagga, NSW, Australia. Durability of resistance (assessed as internal infection) across three field experiments and shade-house (joint analysis of 2016 and 2017 experiments) also shown (D) Blackleg severity was evaluated on the basis of plant survival (proportion), internal infection (crown canker, %) and upper canopy infection (UCI, 1–5 scale) among diverse accessions (for details, see \nSupplementary Table 1\n). Plant development traits: plant height (cm) was measured for five plants per plot; maturity was scored for each plot; flowering time (days to first flower, GS 65) was determined, when 50% of plants have first open flower from the day of sowing as days to flower. Pair-wise correlations (ra) between traits were computed using the EBLUPs of the marker additive genetic effects.
Genetic Control of Blackleg Resistance
To gain an understanding of genetic loci controlling variation in QR against L.\nmaculans, we performed GWAS analysis. This was accomplished by using 12,414 genome-wide distributed SNPs across all 19 linkage groups (and Ann_random and Cnn_random linkage groups) of B. napus (\nFigure 3A\n). Marker density on the An subgenome was higher (n = 6,480) compared to the Cn subgenome (n = 5,939) and covered 0.77 Gb of the reference Darmor-bzh genome (\nSupplementary Table 4\n). The LD was estimated at the whole genome level, using the full set of SNP markers, for each chromosome and both An and Cn subgenomes (\nFigures 3B, C\n, \nSupplementary Figure 4\n). The LD decay (r\n2 = 0.2) of the An and Cn subgenomes varied from 0.6 Mb to 2 Mb, respectively. After thinning markers controlling for false discovery rate, and accounting LD among diverse accessions, GWAS revealed 26 highly significant (LOD score of ≥3, P ≤ 0.001) and 33 “suggestive” (LOD score of <3, 0.001 < P < 0.05) associations for resistance to L. maculans, scored as per cent internal canker infection, plant survival and UCI on 17 chromosomes of canola, with the exception of C07 and C08 (\nSupplementary Table 5\n). SNP markers explained 7.8 to 59.0% of genetic variance for resistance to blackleg (\nTable 1\n).
Distribution of SNP markers (A) and LD (r\n2) among diverse accessions of canola across 19 linkage groups of B. napus (A01-C09) and unanchored An_random (Ann) and Cn_random (Cnn) linkage groups. LD decay across the An subgenome (B) and Cn subgenome (C). LD decay occurs (r\n2 = 0.2) approximately 0.61 Mb and 1 Mb in An and Cn subgenomes, respectively. The r\n2 is estimated for each pair of SNPs used for GWAS.
SNP Associations Detected for Resistance Using “Ascospore Shower” Screens
Joint analysis of the two shade-house experiments identified 18 (four significant and 14 suggestive) SNP associations for resistance against pathotypes present on stubble of groups A, B, C, D, E, F, and S commercial canola cultivars (\nTable 3\n; \nSupplementary Table 5\n). For plant mortality, eight SNP associations, comprising two significant and six “suggestive,” were identified on chromosomes A01, C04, and C06 (\nFigure 4A\n, \nSupplementary Table 5\n). The maximum genetic variance (9.1%) was explained by each of the two “suggestive” SNPs, Bn-A01-p6747706 (on A01) and Bn-scaff_16576_1-p256367 on chromosome C04. For internal infection, 10 SNP associations; two significant and eight “suggestive,” were identified on chromosomes A01, A03, A04, C01, C05, and C06 (\nFigure 4B\n). The maximum genetic variance (8.3%) was explained by the Bn-scaff_15838_1-p1403775 SNP on C01, followed by Bn-scaff_17088_3-p242403 on C06 (6.4%). As both mortality/plant survival and internal infection (stem canker) phenotypes were highly correlated (ra = 0.91, \nFigure 1E\n), we identified two genomic regions that were collocated on chromosome A01 (\nSupplementary Table 5\n). To determine the effect of population size on marker-trait association, we undertook GWAS analysis for each environment independently of ascospore shower screens, conducted across the two experiments (SH16 and SH17). In the SH16 experiment, four significant SNP associations were identified: two were identified for resistance to internal infection on chromosome A01 within genomic interval of 6.17 Mb to 10.87 Mb, while, four SNP associations were identified for plant survival on A01, A03, and C06 (\nTable 3\n, \nSupplementary Table 5\n). In SH17 experiment, two significant SNP associations were identified: the first for internal infection on A03 (0.94 Mb), and second for plant survival on A06 (1.58 Mb) and none of them were detected in SH16 (\nSupplementary Table 5\n). This discrepancy was likely due to the effective population size affecting both population structure as well as local LD among accessions. Overlaying results from single experiments (SH16 and SH17) with from joint analysis (SH16/SH17), we found that two regions for resistance on A01 (5.3 Mb; 6.17 Mb); one region on A03 (14.8 Mb); and one region on C06 (33.2 to 33.5 Mb, within LD) were detected across individual and joint analyses (\nSupplementary Table 5\n).
Genome-wide association analysis showing significant statistical association between Illumina SNP markers and resistance to L. maculans.
Trait
SNP marker
Experiment
Linkage group
Position (bp)
\nP-value
LOD
\nR\n2 (%)
\nMortality\n
\nBn-A01-p6747706\n
\nSH16\n
\nA01\n
\n6173561\n
\n0.001\n
\n3.11\n
\n9.6\n
\nInternal infection\n
\nBn-A01-p6747706\n
\nSH16\n
\nA01\n
\n6173561\n
\n0\n
\n3.33\n
\n6.5\n
Internal infection
Bn-A01-p12622123
SH16
A01
10872512
0.001
3.27
5.1
Plant survival
Bn-A02-p27743910
FT18
A02
24724645
0.001
3.02
9.7
Internal infection
Bn-A03-p1308820
SH17
A03
945040
0.001
3.01
16.5
Internal infection
Bn-A03-p4352837
FT19
A03
3885180
0
3.59
6.9
Upper canopy infection
Bn-A03-p8475614
FT19
A03
7780069
0
5.05
19.2
Mortality
Bn-A03-p15815965
SH16
A03
14892225
0
4.26
10.1
\nUpper canopy infection\n
\nBn-A04-p2448580\n
\nFT19\n
\nA04\n
\n2133626\n
\n0\n
\n5.97\n
\n23.4\n
\nUpper canopy infection\n
\nBn-A04-p2713564\n
\nFT18\n
\nA04\n
\n2419904\n
\n0\n
\n3.51\n
\n10.2\n
Upper canopy infection
Bn-A04-p4099958
FT19
A04
4169810
0
4
11.7
Upper canopy infection
Bn-A05-p22141046
FT19
A05
20252157
0
6.73
25.1
Mortality
Bn-A06-p18000461
SH17
A06
1585510
0
3.8
8
Plant survival
Bn-scaff_15705_1-p84236
FT19
A06
19564715
0
4.53
2.9
Internal infection
Bn-A07-p11185122
FT18
A07
12477068
0
3.46
13.5
Upper canopy infection
Bn-A02-p771951
FT18
A07
13400889
0.001
3.01
7
Internal infection
Bn-A07-p16036626
FT19
A07
17951152
0
4.57
8
Upper canopy infection
Bn-A07-p18226326
FT18
A07
20118101
0.001
2.96
11.6
Internal infection
Bn-A09-p10092753
FT17
A09
9086363
0
4.17
13.9
Internal infection
Bn-A09-p20157367
FT18
A09
17156001
0
3.73
10.8
Plant survival
Bn-A10-p2048923
FT19
A10
1671039
0
3.4
6.3
Internal infection
Bn-A10-p15021776
FT18
A10
14967128
0
4.7
13.3
Plant survival
Bn-scaff_15838_1-p790905
FT18
C01
1224850
0.001
3
11.9
Internal infection
Bn-scaff_22790_1-p1264986
SH16/17
C01
16610817
0
4.99
3.1
Plant survival
Bn-scaff_15712_6-p292471
FT17
C02
37086215
0
5.01
7.7
Internal infection
Bn-scaff_18917_1-p533499
FT17
C03
29919378
0
3.51
10.9
Internal infection
Bn-A05-p17894045
FT18
C05
33067591
0
4.11
13.5
Internal infection
Bn-scaff_20294_1-p250512
SH16/17
C06
3244625
0
3.39
3.4
Mortality
Bn-scaff_15746_1-p145442
SH16/17
C06
21016294
0
3.53
7.5
Mortality
Bn-scaff_23957_1-p151052
SH16
C06
30618173
0.001
3.26
3.5
\nMortality\n
\nBn-scaff_25466_1-p33838\n
\nSH16/17\n
\nC06\n
\n33209209\n
\n0\n
\n3.51\n
\n6.3\n
\nMortality\n
\nBn-scaff_20294_1-p395143\n
\nSH16\n
\nC06\n
\n33566786\n
\n0\n
\n3.86\n
\n22\n
Internal infection
Bn-scaff_17554_1-p19888
FT19
C09
39720977
0
4.3
8.1
Upper canopy infection
Bn-scaff_15576_1-p115266
FT19
C09
41180043
0.001
3.27
14
Upper canopy infection
Bn-scaff_16361_1-p1072715
FT18
Cnn_random
62936531
0
4.33
12.8
Resistance was assessed as plant mortality (proportion) and internal infection (%) using “ascospore shower” test under shade-house conditions across 2016 (SH16) and 2017 (SH17) whereas it was assessed as plant survival (proportion), internal stem infection (%) and upper canopy infection (1–5 scale) across three field environments (2017: FT17, 2018: FT18, and 2019: FT19). Only markers that revealed the LOD score ≥3 are shown herein. Marker associations with LOD scores of <3 are given in a \nSupplementary Table 5\n. The physical positions of SNPs are based on the map position on the Darmor-bzh gene assembly (Clarke et al., 2016). Associations in bold represent to those which appeared across experiments and in LD. The LD was estimated in the diverse panel of canola accessions. R2 and SH16/17 refer to the percentage of genetic variance explained by the SNP marker and joint analysis of SH16 and SH17 experiments, respectively.
Genetic loci associated with resistance to blackleg, assessed using “ascospore shower test” in diverse accessions of canola. Manhattan plots showing LOD scores (-log10\nP) for association between Illumina SNP and disease severity: plant mortality (A) and internal infection (B) in 285 (genotyped) of 327 canola accessions (for details, see \nSupplementary Table 1\n). LOD scores presented in the Manhattan plots are from Base marker model M1. The red dash line indicates the threshold value for highly significant SNPs at LOD ≥ 3. Positions (kb) of the highly significant (LOD ≥ 3, P ≤ 0.001) and “suggestive” (LOD < 3, 0.001 < P < 0.05) markers from the final multi-QTL model (M2) are labeled. The physical positions of SNPs are based on the map position on the Darmor-bzh gene assembly (Clarke et al., 2016, \nSupplementary Table 5\n). (C) Box plot showing the distribution of the EBLUPs for internal infection partitioned into allele combinations for the SNP markers that are significantly associated with resistance (LOD ≥ 3) assessed under shade-house conditions with “ascospore shower” test in a diverse panel of GWAS accessions. Major (present in high proportion) and minor (present in low proportion) allele for each marker are shown in different colors. Number of alleles (n) for each significant SNPs associated with resistance are also shown. Distributions of SNP marker Bn-A03-p13088220 allele represent to SH17 experiment, where 68 canola accessions were evaluated for resistance to blackleg using the “ascospore shower test.”.
To identify SNP markers suitable for tracking QR genes in B. napus germplasm, we compared relationship between marker alleles that showed significant associations to L. maculans with “ascospore shower” and variation in resistance to blackleg, assessed by the severity of internal infection. Our analysis showed that single SNP association could not sufficiently account for resistance. However, using multiple alleles for different QTL regions, resistance could be tracked in a diverse panel of canola accessions (\nFigure 4C\n). For example, SNP alleles present in a greater proportion (TT, Bn-scaff-20294-1-p250512) and in smaller proportion on chromosomes A01 (GG, Bn-A01-p6747706), A03 (GG, Bn-A03-p1308820), and C01 (GG, Bn-scaff-22790-1-p1264986), contributed in increasing resistance to internal infection under shade-house conditions.
SNP Associations for Plant Survival in Field
GWAS identified 13 SNPs (five significant and eight “suggestive”) that were associated with resistance to L. maculans, based on the plant survival scores under field conditions across three environments (\nFigure 5A\n, \nSupplementary Table 5\n). Of the five significant associations (LOD ≥3) identified on A02, A06, A10, C01, and C02 chromosomes, the SNP Bn-scaff_15838_1-p790905 on C01 explained the maximum genetic variance (11.9%) for plant survival, followed by Bn-A02-p27743910 (9.7%) on A02 (\nTable 3\n). In total, GWAS associations explained 7.8% (2017) to 38.7% (2018) of the genetic variance (\nTable 1\n). Two genomic regions, the first delimited with two SNPs, Bn-A06-p17332795 and Bn-A06-p17042021, and the second region by the Bn-scaff_15705_1-p84236 and Bn-A06-p18432812 markers on A06, were detected within 0.26 Mb (in LD) across 2018 and 2019 environments (\nSupplementary Table 5\n).
Manhattan plots showing genomic regions associated with resistance to blackleg in diverse accessions. Resistance was assessed by plant survival (A), internal infection (B) in blackleg nursery across three field environments in three consecutive years (2017 to 2019) at Wagga Wagga, while upper canopy infection (C) was assessed across two years (2018 and 2019). LOD scores (-log10\nP) for association between Illumina SNP and disease severity in diverse canola accessions are shown on y-axis. LOD scores presented in the Manhattan plots are from Base marker model M1. The red dash line indicates the threshold value for significant SNPs at LOD ≥ 3. Positions (kb) of the highly significant (LOD ≥ 3, P ≤ 0.001) and “suggestive” (LOD < 3, 0.001 < P < 0.05) markers from the final multi-QTL model (M2) are labeled. The physical positions of SNPs (x-axis) are based on the map position on the Darmor-bzh genome assembly (Clarke et al., 2016, for detail, see \nSupplementary Table 5\n). (D) Circos plot with 19 chromosomes showing localization of quantitative trait loci (QTL) associated with resistance to blackleg and phenological components in a diverse panel of canola accessions on the 19 chromosomes of canola. Physical positions of SNP markers were inferred on the positions of the Darmor-bzh reference genome assembly. Scatter plots and lines represent LOD scores and R2 values of SNP associations for different traits evaluated in a GWAS panel. Lines represent to R2 values (inner most shell). Outer most shell represents to different traits (I: internal infection; S: plant survival, M: plant mortality, Mat: plant maturity; H: plant height; U: upper canopy infection; F: flowering time), evaluated in a GWAS panel. The second outer most shell illustrates the ancestral blocks. Localization of SNP markers associated with resistance to blackleg is only shown on the ancestral blocks of Brassicaceae, as defined by Schranz et al., 2006 by aligning A. thaliana genes to the Darmor reference genome.
SNP Associations for Resistance to Internal Infection in Field
Seventeen SNP associations (nine significant and eight “suggestive”) were identified for resistance to internal infection across three field environments (2017-2019) on chromosomes A03, A07, A08, A09, A10, C03, C05, and C09 (\nFigure 5B\n, \nSupplementary Table 5\n). SNP marker, Bn-A09-p10092753 on chromosome A09 accounted for 13.9% of genetic variation for internal infection (\nTable 3\n). Of the SNP associations, 82% of them (14/17) were mapped on the An subgenome, suggesting that the A genome is the richer reservoir of blackleg resistance genes compared to the Cn subgenome. This is consistent with earlier findings that majority of R genes (Rlm1, Rlm2, Rlm3, Rlm4, Rlm7, Rlm9, Rlm12, LepR1-4) identified so far, are mapped on the An subgenome, while only a few, Rlm13 is mapped on the Cn subgenome (Raman et al, unpublished). Among SNP associations, only one genomic region, delimited with Bn-A07-p15812852 to Bn-A07-p16036626 (17.7 to 17.9 Mb sequence of Darmor-bzh) on chromosome A07 was detected repeatedly in LD across two field environments (FT18 and FT19; \nSupplementary Table 5\n).
SNP Associations for Resistance to UCI
Herein, we identified 14 associations (eight significant and six “suggestive”) for resistance against UCI on A03, A04, A05, A07, and C09 and Cnn_random contig of Darmor-bzh assembly (\nTable 3\n, \nFigure 5C\n). The number of SNP associations varied from 6 to 8, depending upon the phenotypic environment. Of the SNP associations, Bn-A05-p22141046 on A05 accounted for the maximum genetic variance (R\n2 = 25.1%), followed by Bn-A04-p2448580 (R\n2 = 23.4%) on A04. More than 42% of the genetic associations (6/14) were detected on A04, suggesting that multiple loci for resistance are localized on this chromosome. Across environments, one genomic region of the Darmor-bzh sequence (2.13 to 2.42 Mb); demarked with markers: Bn-A04-p2448580/Bn-A04-p2713564 was repeatedly detected in LD on A04 and accounted for up to 23.4% of the genetic variance (\nTable 3\n, \nSupplementary Table 5\n). Identification of multiple genomic regions associated with resistance, having small additive allelic effects, suggests that UCI is a quantitative trait and is controlled by multiple loci in canola.
Hot-Spot Genetic Regions for Resistance to Blackleg
To identify hot-spot genomic regions for resistance to blackleg, we compared the physical positions of sequences of SNPs that reveal significant associations for plant survival, stem canker and UCI in diverse panel of canola accessions. The An subgenome had 39 SNP associations, while 20 associations were detected on the Cn subgenome (\nFigure 5D\n). Among the 19 linkage groups, A04, and A07 had 2.3 to 2.7 times more associations (QTL) for resistance to blackleg, compared to average genome-wide SNP (2.95/linkage group) associations (\nSupplementary Figure 5\n).
Significantly Associated SNPs Overlap With Previously Identified QR and Known <italic>R</italic> Genes
To determine the uniqueness/redundancy of loci involved in QR resistance, we compared the physical positions of SNP associations identified in this study with the previous studies (Jestin et al., 2011; Fopa Fomeju et al., 2014; Larkan et al., 2016a; Rahman et al., 2016; Raman et al., 2016; Kumar et al., 2018; Raman et al., 2018; Raman et al., 2020). We identified several marker loci for QR to blackleg on chromosomes A01, A02, A03, A04, A05, A06, A07, A08, A09, A10, C01, C02, C05, and C06 that were mapped near the SNP associations identified in the present study (\nFigure 6\n). This suggests that QR loci identified here are relevant to international germplasm, comprising spring, semispring/winter and winter types. We also identified new QR loci on chromosomes A01 (10.8 Mb), A04 (1.1 Mb to 2.41 Mb), A06 (5.88 Mb), A08 (1.30 Mb, 16.94 Mb), A10 (1.67 Mb), C01 (1.80 Mb, 161.61 Mb), C03 (29.91 Mb), and C05 (7.03 Mb). These loci provide new targets for genetic improvement in canola breeding programs.
Comparison between genomic regions identified in a diverse panel of canola (this study) and previous studies. Marker loci highlighted in different colors represent to different studies. For clarity, we showed only the highly significant SNP associations for resistance (\nTables 3 \nand \n4\n) here. Candidate genes associated with R genes which map within 0.25 Mb are shown here. For details, see \nSupplementary Table 6\n.
To identify plausible candidate genes in the QR genomic regions, we compared the physical locations of annotated R genes in the Darmor-bzh assembly (Chalhoub et al., 2014; Alamery et al., 2018). Forty-two R genes were located within 1 Mb of QR loci (\nSupplementary Table 6\n) of which 12 disease resistance R genes were located within 200 kb from the SNP associations (QTLs) on A01, A06, A07, A10, C01, C05; C06, and C09 (\nTable 4\n). These annotated R genes encode disease resistance proteins with the TIR-NSB-LRR and non-TIT-NBS-LRR (CNL), RLP and NB-ARC-LRR motifs, which are implicated in ETI (Jones and Dangl, 2006).
Annotated disease resistance genes located in the vicinity of quantitative resistance to blackleg disease in canola grown across different environments.
Trait
SNP marker
Linkage group
Position
\nP-value
LOD
\nR\n2 (%)
Candidate R gene
Distance from candidate gene
\nR gene motif
(bp)
ID
Start
End
(bp)
Mortality (SH16/17)
Bn-A01-p23905191
A01
20068468
0.007
2.19
4.40
BnaA01g28560D
19873137
19874480
195331
CC – NBS
Plant survival (FT18)
Bn-A02-p27743910
A02
24724645
0.001
3.02
9.66
BnaA02g36900D (A2_random)
24581776
24580933
142869
TIR-NBS-LRR
Plant survival (FT19)
Bn-A06-p18000403
A06
1585452
0.000
4.26
1.71
BnaA06g39650D (A6_ran) BnaA06g02940D
16553271789462
16586001792348
69817204,010
CC-NBS-LRR
UCIS (FT18)
Bn-A02-p771951
A07
13400889
0.000
3.54
7.02
BnaA07g15400D
13339537
13343565
57,324
TIR-NBS-LRR
Internal infection (FT18)
Bn-A07-p16036626
A07
17951152
0.001
3.10
0.56
BnaA07g24250DBnaA07g24260D
1813890918149353
1813748618145885
193,122187757
NBS - LRRTIR-NBS-LRR
Internal infection (FT18)
Bn-A10-p9802574
A10
11216126
0.001
3.22
4.78
BnaA10g13740D
11035464
11038549
177,577
NB-ARC domain-containing protein
Plant survival (FT18)
Bn-scaff_15838_1-p790905
C01
1224850
0.001
3.21
12.84
chrC01_random-snap.407 BnaC01g02460D
12462301302496
12440361303729
2138077,646
TIR-NBS-LRR
Internal infection (SH16/17)
Bn-scaff_18181_1-p622258
C05
7039786
0.001
2.97
4.08
BnaC05g12130D
7046069
7053047
6,283
CC-NBS-LRR
Internal infection (SH16/17)
Bn-scaff_20294_1-p250512
C06
3244625
0.000
3.39
3.39
BnaC06g43800D (c06_random)
3240778
3,241,715
3847
TIR-NBS-LRR
Mortality (SH16)
Bn-scaff_20294_1-p395143
C06
33566786
0.002
2.69
16.03
BnaC06g34000D
33631584
33634387
67,601
TIR-NBS-LRR
Internal infection (FT19)
Bn-scaff_17554_1-p19888
C09
39720977
0.000
4.44
8.03
BnaC09g36260D
39561973
39561973
159004
NB-ARC-LRR, RLK
UCI (FT19)
Bn-scaff_15576_1-p115266
C09
41180043
0.001
3.28
14.03
BnaC09g38280D
41200106
41205204
20,063
Zinc finger
Only SNP associations that were detected within 200 kb are shown herein. Detailed information is presented in a \nSupplementary Table 5\n. Data on SH16 and SH17 experiments relates to ascospore shower test, while FT18, FT18, and FT19 experiments relate to field screening of GWAS panel. Data on SH16/17 is based on the joint analysis of SH16 and SH17 trials. R2 refers to the percentage of genetic variance explained by the SNP marker.
Several QR Loci of Canola May Represent to Ancestral Genes of Brassicaceae
To determine and verify whether QR genomic regions are located in the homoeologous regions of B. napus genome, we searched the synteny between genomic regions that showed significant associations with QR, using SNP markers as proxies, and 22 AK blocks of Brassicaceae (Schranz et al., 2006). We delimited 37 SNP associations (identified on chromosomes A01, A02, A03, A04, A05, A06, A07, A009, A10, C001, C02, C3, C04, C05, C06, and C09) in the 17 AK blocks: A, B, C, D, E, F, H, I, J, K, N, O, Q, R, S, T, U, and X (\nFigure 5D\n). Six SNP associations, which were mapped on chromosomes A03, A04, A09, A10, C04 and C06 could not be located to the AK blocks (\nSupplementary Table 7\n). Of the 44 significant SNP associations for resistance, 25 (56.7%) were localized in the duplicated regions of the 9 AK blocks; the maximum SNP associations (n = 6) were located in each of the blocks: E, F, and U, while the minimum associations (n = 1) were identified in each of the B, D, H, I, K, and T blocks (\nSupplementary Table 7\n). In addition to the E, J, R, and U blocks identified for resistance to blackleg in European canola (Fopa Fomeju et al., 2015), we also mapped SNP association in the duplicated regions on A, B, C, D, F, H, I, K, N, Q, S, T and X AK (\nSupplementary Table 7\n). In this study, no SNP association for resistance could be assigned to the W block (Fopa Fomeju et al., 2014). In addition, one SNP association for resistance, assessed based on plant mortality on chromosome C04 could not be mapped onto the same ancestral karyotype J block, identified in a previous study (Fopa Fomeju et al., 2015).
We further investigated the locations of genomic regions for QR with subgenomic structure of AK blocks, determined on the level of fractionation (Parkin, 2011; Parkin et al., 2014). The maximum SNP associations were present in the least fractionated subgenome (LF = 18), followed by seven associations in the moderately fractionated subgenome (MF1) whereas, the minimum SNP associations (6) were in the most fractionated subgenome (MF2). Consistent with a previous study (Fopa Fomeju et al., 2015), one to six homoeologous regions for QR were identified in the LF, MF1, and MF2 subgenomes of duplicated regions of E, J, R, U, and W (\nSupplementary Table 7\n). For example, we identified SNP association for UCI to 39.93 Mb region on chromosome C09 (R block, LF subgenome). The same regions for QR was also identified at 39.93 Mb region on chromosome C09 in a B. napus GWAS panel of European origin (Fopa Fomeju et al., 2015). At least 18 disease resistance genes of A. thaliana (localized in AK blocks C, D, E, F, H, I, J, N, Q, R, S, U, and X) were located within 100 kb regions, suggesting that these R genes were retained in B. napus genome, despite of long term genome fractionation, evolution and domestication (Chalhoub et al., 2014; Parkin et al., 2014). This study and the previously reported ones revealed that majority of QR regions have originated as a result of homoeologous and paralogous exchanges occurred between A. thaliana and B. napus genomes.
Relationship Between Resistance and Phenological Traits
Since resistance to blackleg seems to be conditioned with other plant developmental traits such as plant height and maturity (Pilet et al., 2001; Delourme et al., 2004; Huang et al., 2016; Raman et al., 2018), we scored these traits in the replicated trials conducted under field conditions (2017–2019). Pair-wise correlations of the additive genetic effects between different traits: blackleg resistance, flowering time, plant height and maturity are presented in \nFigure 2\n. Utilizing the data on the genetic variation in the developmental traits, we further (i) located loci associated with flowering time, plant height and maturity in the diversity panel and (ii) investigated their physical localization in relation to significant loci associated with resistance to blackleg. GWAS analysis identified 30 SNP associations for flowering time on chromosomes A01, A02, A03, A10, C03, and C07 (\nSupplementary Table 5\n, \nSupplementary Figure 6\n); at least one of them is in the vicinity of associations for QR to blackleg on A02 (24.6 to 24.7 Mb). This genomic region maps within 27.5 kb from the AGAMOUS-LIKE 69/MADS AFFECTING FLOWERING 4; MAF4, AT5G65070, 24.6 Mb on the Darmor assembly). Likewise, GWAS identified 13 SNP associations for plant height on chromosomes A02, A08, A10, C03, C06, and C07 (\nFigure 5D\n, \nSupplementary Figure 6\n). Of which, one marker association on A02 that was identified with flowering time (24.6 to 24.7 Mb), was also located in the same genomic region for plant survival in 2018 field environment (\nSupplementary Table 5\n). No QTL for plant height was detected on chromosome A06, where QR locus for blackleg was identified earlier in the Darmor-bzh/Yudal population (Huang et al., 2016; Kumar et al., 2018; Raman et al., 2018). Of the 14 SNP associations identified on A05, A08, A10, Ann_random, C02, C05 and C09 for plant maturity, possibly four genomic regions on chromosomes A08 (1.03 Mb to 1.30 Mb), A10 (1.61 to 1.67 Mb, and 14.6 to 14.9 Mb), and C09 (39.7 Mb) were detected near the SNP maker loci for resistance to blackleg (\nSupplementary Table 5\n). Moderate correlation between phenotypes and co-location of QTLs for resistance to blackleg and phenological traits (\nFigures 2A–C\n and \nFigure 5D\n) suggest that genes, underlying QTLs are either linked or may have pleiotropic allelic effects. It was difficult to measure causal relationship between plant development traits and resistance, as blackleg disease may have impacted their expression.
We looked if variation in flowering modulate blackleg infection. To understand this, we compared genetic variation in flowering time revealed in FT17 and FT18 experiments (this study) and previous experiments conducted at WWAI in the same environments (2017, 2018) on the same set of 300 diverse accessions (Raman et al., 2019). We observed a high correlation across environments (r = 0.98). The possibility of involvement of flowering time loci was also excluded by comparing the physical locations of SNP associations for flowering time, which were identified in the same set of GWAS panel evaluated under the similar field conditions, in the absence of blackleg disease; similar SNP-trait associations were detected, as found in experiments conducted in disease nurseries. This observation suggests variation in flowering time did not significantly affect blackleg disease in experimental conditions used in this study.
DiscussionAscospore Shower and Field Screens Identify Additional Sources for Quantitative Resistance
In this study, we included a larger set of germplasm as compared to previous studies (Delourme et al., 2006; Kaur et al., 2009; Light et al., 2011; Raman et al., 2012b; Larkan et al., 2016a; Raman et al., 2016; Raman et al., 2018) and identified additional sources for resistance (\nFigure 1C\n, \nSupplementary Table 3\n). Optimal conditions for blackleg development are required for assessing variation in resistance. For example, under high disease pressure, it may be difficult to reveal variation in resistance to blackleg, as L. maculans is a hemibiotroph and can colonize on resistant accessions of canola and related species. We observed moderately high values for reliability, which are comparable to previous studies (Pilet et al., 1998; Raman et al., 2012b; Jestin et al., 2015; Larkan et al., 2016a; Raman et al., 2016; Raman et al., 2018; Raman et al., 2019b) (\nTable 2\n). Low reliability values (12%) observed in some environments could be due to empirical visual assessment of resistance and genotype x environment interactions, as observed in canola DH populations from Skipton/Ag-Spectrum and RP04/Ag-Outback and Darmor-bzh/Yudal (Raman et al., 2012b; Huang et al., 2016; Raman et al., 2020). Under moderate disease pressure, we could assess genetic variation in resistance to UCI to blackleg consistently across environments (reliability scores: 74 to 76%, \nTable 2\n). Hereby, we emphasize the genetic variation in resistance to UCI among the accessions of the diverse panel and report several sources of resistance and susceptibility (\nSupplementary Table 3\n). This information can be useful for (i) understanding the genetic basis of resistance to UCI in mapping populations as well as (ii) developing protocols for high-throughput phenotyping for comprehensive assessment of UCI. The resistant accessions can further be exploited in breeding programs for developing improved cultivars with overall resistance to blackleg in canola and its related species.
Genome-Wide Distributed Loci Are Associated With Quantitative Resistance
We have delineated numerous loci for resistance to blackleg in a diverse panel of canola using a GWAS approach, although causative SNPs are not identified in this study. Majority of the Australian canola germplasm has either qualitative and/or quantitative “background” resistance. In fact, it is not common to find Australian cultivars that do not have either qualitative and/or quantitative resistance, as it has been the “backbone” trait of canola industry. Therefore, it is difficult to access Australian germplasm (with effective population size for GWAS analysis) that does not have R genes and utilize for the identification of QR genes comprehensively.
Most of the significant SNP associations identified in our screens do not correspond to the “known” R genes reported on chromosomes A01, A02, A06, A07, and A10 (Raman et al., 2013; Raman et al., 2016) and were not consistently detected across environments (\nTable 3\n). The results indicate that environment played a significant role in quantitative resistance expression. Similar observations were made in previous QTL and GWAS studies (Pilet et al., 2001; Jestin et al., 2011; Jestin et al., 2015; Huang et al., 2016; Kumar et al., 2018; Raman et al., 2018). Presence of SNP associations accounting low genetic variance (despite of moderate to high reliability, \nTable 3\n) on multiple genomic regions also indicate that QR is governed by quantitative genes (polygenic) rather than monogenic R genes, as the latter provide the complete resistance and account for larger proportion of genetic variance (Raman et al., 2012a; Raman et al., 2012b; Larkan et al., 2016b; Raman et al., 2018; Raman et al., 2020). We also observed that some SNPs accounted for variable LOD scores and genotypic variance (\nSupplementary Table 5\n). For example, Bn-A07-p16036626 on A07 (17.9 Mb) had “suggestive” LOD score of 1.82 in 2018, while the same SNP association had significant LOD score of 4.52 in 2019, suggesting that significance of trait-marker association relies on environment (phenotypic conditions including pathogen population) and “suggestive” associations should not be discounted in genetic improvement programs. More than 50% (32/59) of the SNP associations, detected on A01, A02, A03, A04, A05, A06, A07, A09, C02, C05, C06, and C09 chromosomes, were identified within 100 kb distance from the previously identified QTLs (\nSupplementary Table 6\n, \nFigure 6\n), at least in one of the mapping populations (Larkan et al., 2016a; Raman et al., 2016; Kumar et al., 2018; Raman et al., 2018; Raman et al., 2020). Other SNPs associations on A04, A10 and C01 were mapped 0.5 to 1 Mb from QTL identified in Aviso/Bristol, Canberra/Bristol, Darmor/Bristol, and Grizzly/Bristol populations (\nSupplementary Table 6\n). These results suggest the common QR loci for resistance to blackleg in the winter (Jestin et al., 2011) and diverse panel of B. napus germplasm utilized in this study. Unlike previous studies (Jestin et al., 2011; Larkan et al., 2016a; Kumar et al., 2018), significant QTL (LOD ≥3) on chromosome A08 could not be detected in our GWAS panel. However, there were “suggestive” associations (with LOD <3) on A08. This could be attributed to L. maculans population structure, environmental conditions and/or the origin of germplasm. Previous studies (Jestin et al., 2011; Fopa Fomeju et al., 2015; Kumar et al., 2018) utilized a smaller set of accessions of winter rapeseed for GWAS which were evaluated under European phenotypic environments, whereas herein we used a larger diverse set of germplasm comprising spring, semispring/winter and winter types under Australian growing environments.
Some of the SNP associations for QR to blackleg were present in the vicinity of known R genes (Rlm3, Rlm4, Rlm7, and Rlm9) on chromosomes A07 (17.7 to 17.9 Mb), LepR3/Rlm2 on A10 (14.4 Mb), LepR1 on A02, and LepR4 on chromosome A06 (Yu et al., 2008; Yu et al., 2013; Larkan et al., 2016b; Raman et al., 2016; Raman et al., 2018). The 17.7 to 17.9 Mb genomic region of A07 represents to a cluster of R genes and maps approximately 2Mb apart the Rlm9, encoding Wall-Associated Kinase like protein (BnaA07g20220D, 15.91 Mb) that was recently cloned in canola. (Larkan et al., 2019). Previously, Rlm9 was mapped to a 15.95 Mb region in a DH population from Darmor-bzh/Yudal population (Raman et al., 2018). The genetic region, delineated by the Bn-A07-p18226326 marker at the 20.1 Mb on the Darmor assembly for UCI could represent the Rlm1 gene in the AK block, E (subgenome LF). It was interesting that the SNP associations on A07 (Bn-A07-p15812852/Bn-A07-p16036626); 17.7 to 17.9 Mb for resistance to internal infection (\nSupplementary Table 5\n) were not detected with ascospore shower test. This was due to the utilization of stubbles derived from cultivars having Rlm1/Rlm3/Rlm4/Rlm7/Rlm9 and unknown QR genes, which likely have rendered Rlm genes on A07 ineffective, leading to no variation for genetic analyses to detect. Effectiveness of qualitative R genes under field conditions identified hereby is in contrast to earlier studies, where the effectiveness of Rlm3, Rlm4, and Rlm9 was questioned under the Australian field conditions (Light et al., 2011; Raman et al., 2012a; Larkan et al., 2016a; Raman et al., 2016; Raman et al., 2018; Raman et al., 2020). This may have occurred due to fungal population structure and environmental conditions, which may have impacted the frequency and distribution of Avirulence genes in the L. maculans population in the disease nurseries. It is reiterated that field environments across 2017, 2018 and 2019 were less conducive for the development of high disease pressure (\nSupplementary Figures 2A, B\n), due to record excessive drought conditions and high temperatures (plus 40°C during day time).The strong effect of weather conditions including the temperature on the severity of blackleg (stem canker) is reported previously (Evans et al., 2008). Further work is required to established whether there is any interaction between QR and R genes (described above) and or QR identified under field conditions is due to the genetic effect of R genes (e.g. Rlm12 and QTL region on A01; 5.35Mb to 6.33Mb on A02, Rlm2/LepR3 and QTL region 14.6 Mb to 15.3Mb on A10; \nSupplementary Table 6\n).
Our findings suggest that the “ascospore shower” test using ascospores released from mixed stubble sources (from cultivars carrying R genes such as Rlm1, Rlm4, Rlm6, LepR1, and LepR3, used in this study) was found to be suitable for the identification of QR genes. In the present study, we identified several genomic regions for QR which were detected variably across environments. For example, we identified genomic regions for resistance on chromosome A01, including Rlm12 under shade-house conditions, which were not detected under field conditions. These results suggest that complementary approaches should be explored for genetic analysis of QR to blackleg. Our findings and previous studies revealed that variation in quantitative resistance is due to quantitative genes, distributed genome-wide with smaller allelic effects, suggesting that variation in resistance has evolved in several independent genes in canola germplasm.
GWAS Analysis Reveals Genomic Regions for Resistance to UCI
GWAS analysis has been extensively used to detect marker associations with trait of interest, especially which are in LD with QTL. Relying on this approach with genetic relationship estimates, we detected 16 associations between SNPs and UCI. Majority (56%) of the SNP associations were detected on chromosome A04. Detection of multiple SNPs in small genomic interval on A04 suggest that this region harbors gene(s) for resistance to UCI and is one of the candidates for fine mapping. In addition, SNP marker Bn-A03-p8475614, accounting for 19.2% of genetic variance was mapped in the proximity of QTL involved in resistance to blackleg (~180 kb) on chromosome A03 (7.59 Mb, Raman et al, 2016). In earlier studies, significant marker associations for resistance to both single spore isolates of L. maculans and stem canker were also detected on the homoeologous group 3 (A03/C03) chromosome (Jestin et al., 2015; Raman et al., 2016). Two genomic regions (13.4 Mb and 20.1 Mb) for resistance to UCI were identified on chromosome A07. The 13.4 Mb locus was mapped ~129.7 kb apart from the GWAS association for resistance at the cotyledon stage (Raman et al., 2016). One of the disease resistance genes, BnaA07g15400D (GSBRNA2T00098616001) was also located 57.3 kb proximal to this QR locus (\nSupplementary Table 5\n), implying that this genomic region is likely to be involved in resistance to blackleg. The 20.1 Mb genomic region for UCI may represent to the Rlm1 gene. The physical position of Rlm1 in the GWAS panel is consistent with our results on the mapping of Rlm1 in a high-resolution mapping population of canola (Raman et al, unpublished). This current study also hints that the R gene mediated resistances to internal infection (mapped to the 17.7 Mb to 17.9 Mb of the Darmor-bzh sequence, Rlm3,4/7/9) and to UCI (mapped to the 20.11 Mb, Rlm1), are stable under high temperatures in field and is not prone to high temperatures. These results are in contrast with a previous study which reported that Rlm6 gene is not stable under high temperatures (Huang et al., 2006a).
Consistent with our earlier and other GWAS studies (Rahman et al., 2016; Raman et al., 2016), we identified several significant associations for field resistance (disease nurseries in field and ascospore shower in shade-house) near genomic regions identified with single spore isolates. In addition, we detected genomic regions for QR near R genes, suggesting that mechanisms underlying host Pathogen-Associated Molecular Pattern triggered Immunity (PTI) and Effector-Triggered Immunity (ETI) are shared for qualitative and quantitative resistance (Thomma et al., 2011). Pathogens like L. maculans must overcome PTI to colonize on host and then to interact with ETI for defense response. It is not established here which R/QR loci are responsible for PTI or weaker ETI (Doehlemann and Hemetsberger, 2013). It is also possible that some of the genomic regions identified here could be associated with qualitative resistance due to (i) the genetic effects of R genes, (ii) ubiquitous presence of races of L. maculans in a canola crop and (iii) Avr genes that are in low frequency and not subjected to mutations, leading to virulence toward R genes.
Possible Role of Plant Developmental Traits on Blackleg Resistance
There was a positive relationship between UCI, flowering time, maturity, and plant height in 2018, however no such consistent relationship was found in 2019. This could have been attributed to the water stress and high temperatures in 2019 (\nSupplementary Figure 2\n). Moderate temperatures and high humidity are known to favor blackleg disease (Ghanbarnia et al., 2009). One genomic region for plant survival on A02, demarked with markers: Bn-A02-p27327804 (24.6 Mb), Bn-A02-p27336483 (24.6 Mb) and Bn-A02-p27743910 (24.7 Mb) was mapped within 100 kb from the SNP associations for flowering time and plant height (\nSupplementary Table 6\n). These results suggest that phenology genes may have pleiotropic effects on the level of blackleg expression.
Conclusions
In summary, we have delineated natural variation for resistance to blackleg in a diverse panel of canola. The specific accessions identified herein provide a useful resource for improving resistance to blackleg, and increasing genetic diversity in resistance genes, which can be exploited in the breeding programs. GWAS analysis identified 59 significant and “suggestive” loci for resistance to blackleg under shade-house and field conditions. These loci can only explain a small proportion of the genetic variance in resistance to blackleg; the sizable proportion of variance is accounted by G× E interaction, indicating that additional loci remain to be identified. Further research is required to assess the stability of QTL associations across canola growing regions in Australia and elsewhere. Given the adaptive nature of L. maculans, it is also important to demonstrate the uniqueness of genes conferring effective quantitative resistance to blackleg in Brassica species to expand the genetic base of canola - one of the most recent domesticated oil seed crops in recent history. As this and previous studies showed that QR is controlled by several genes with smaller allelic effects, introgression of a large number of loci is difficult via traditional marker-assisted selection (Fikere et al., 2018). However, favorable alleles for stable QR including for plant height and flowering time, which don’t confound the expression of QR can be enriched in the elite breeding lines using genomic selection approaches. Altogether with the results of previous studies, our results suggest that the gene network involved in QR is rather complex and evolved in a multifaceted manner to unleash genomic variation that contributed to natural variation in resistance to blackleg disease in canola.
Data Availability Statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
Author Contributions
HR conceived the research, planned experiments and wrote the paper. HR, BM, and RR conducted research and contributed to phenotyping. BC developed the statistical methods for GWAS analysis. RP performed statistical analysis and GWAS analysis in consultation with HR. SM and DB evaluated accessions for resistance using ascospore shower, in consultation with HR. YZ, SL, and HR performed comparative mapping of significantly associated SNP markers. All authors contributed to the article and approved the submitted version.
Funding
This study was supported by the NSW Department of Primary Industries, the Grains Research and Development Corporation, and Agriculture Victoria Research (AVR under the investments in National Brassica Germplasm Improvement Program project to HR (DAN00117, DAN00208, and DAN1707).
Conflict of Interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Acknowledgments
We thank Dean MacCullum, Hannah Roe, Shane Hildebrand, and Bashar Thejer for technical assistance in the field. Authors are thankful to Dr Steve Marcroft, Marcroft Grains Pathology, Horsham for evaluating canola accessions using ascospore shower test.
Supplementary Material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpls.2020.01184/full#supplementary-material\n
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Plant Sci.Frontiers in Plant Science1664-462XFrontiers Media S.A.32849733PMC743212710.3389/fpls.2020.01184Plant ScienceOriginal ResearchGenome-Wide Association Mapping Identifies Novel Loci for Quantitative Resistance to Blackleg Disease in CanolaRamanHarsh\\n1\\n\\n*\\nMcVittieBrett\\n1\\nPirathibanRamethaa\\n2\\nRamanRosy\\n1\\nZhangYuanyuan\\n3\\nBarbulescuDenise M.\\n4\\nQiuYu\\n1\\nLiuShengyi\\n3\\nCullisBrian\\n2\\n\\n1\\nNSW Department of Primary Industries, Wagga Wagga Agricultural Institute, Wagga Wagga, NSW, Australia\\n\\n2\\nCentre for Bioinformatics and Biometrics, National Institute for Applied Statistics Research Australia, University of Wollongong, Wollongong, NSW, Australia\\n\\n3\\nOil Crops Research Institute, Chinese Academy of Agricultural Sciences, Wuhan, China\\n\\n4\\nDepartment of Jobs, Precincts and Regions, Agriculture Victoria, Horsham, VIC, Australia\\n
Edited by: Ryo Fujimoto, Kobe University, Japan
Reviewed by: Habibur Rahman, University of Alberta, Canada; Takahiro Kawanabe, Tokai University, Japan
This article was submitted to Plant Breeding, a section of the journal Frontiers in Plant Science
1182020202011118407520202172020Copyright © 2020 Raman, McVittie, Pirathiban, Raman, Zhang, Barbulescu, Qiu, Liu and Cullis2020Raman, McVittie, Pirathiban, Raman, Zhang, Barbulescu, Qiu, Liu and CullisThis is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Blackleg disease, caused by the fungal pathogen Leptosphaeria maculans, continues to be a major concern for sustainable production of canola (Brassica napus L.) in many parts of the world. The deployment of effective quantitative resistance (QR) is recognized as a durable strategy in providing natural defense to pathogens. Herein, we uncover loci for resistance to blackleg in a genetically diverse panel of canola accessions by exploiting historic recombination events which occurred during domestication and selective breeding by genome-wide association analysis (GWAS). We found extensive variation in resistance to blackleg at the adult plant stage, including for upper canopy infection. Using the linkage disequilibrium and genetic relationship estimates from 12,414 high quality SNPs, GWAS identified 59 statistically significant and “suggestive” SNPs on 17 chromosomes of B. napus genome that underlie variation in resistance to blackleg, evaluated under field and shade-house conditions. Each of the SNP association accounted for up to 25.1% of additive genetic variance in resistance among diverse panel of accessions. To understand the homology of QR genomic regions with Arabidopsis thaliana genome, we searched the synteny between QR regions with 22 ancestral blocks of Brassicaceae. Comparative analyses revealed that 25 SNP associations for QR were localized in nine ancestral blocks, as a result of genomic rearrangements. We further showed that phenological traits such as flowering time, plant height, and maturity confound the genetic variation in resistance. Altogether, these findings provided new insights on the complex genetic control of the blackleg resistance and further expanded our understanding of its genetic architecture.
natural variationresistance to blacklegLeptosphaeria maculanscanolagenome-wide association analysislinkage disequilibriumGrains Research and Development Corporation10.13039/501100000980DAN00117, DAN00208, DAN1701Introduction
Canola, a polyploid crop (Brassica napus L, 2n = 4× = 36, genome AnAnCnCn) that was only domesticated about 500 years ago (Gómez-Campo and Prakash, 1999), has now taken central stage for meeting global demands of healthy vegetable oil for human consumption. Currently, it is grown on over 37 M ha worldwide with production of 75 m tonnes (http://www.fao.org/faostat/). Blackleg, caused by the fungal pathogen Leptosphaeria maculans, is one of the most serious diseases of canola in many parts of the world and accounts for significant yield losses (Sosnowski et al., 2004; Fitt et al., 2006). The infection occurs at various stages of plant development and causes lesions on underground and the above ground parts; cotyledon, leaf, stem, branches, flowers, and siliques (Sprague et al., 2018). Current disease management strategies include deployment of host resistance, mediated by qualitative (race-specific R genes) and uncharacterized quantitative resistance (race nonspecific QTL, QR) genes, crop rotations, and application of fungicides.
Several sources of qualitative and quantitative resistance to blackleg has been identified in Brassica species (Mithen et al., 1987; Chevre et al., 1996; Chèvre et al., 1997; Christianson et al., 2006; Rimmer, 2006; Leflon et al., 2007; Raman et al., 2013; Fredua-Agyeman et al., 2014; Gaebelein et al., 2019); and several of them were deployed by canola breeders to develop resistant cultivars. Genetics underlying both types of resistance has been investigated in different structured mapping populations and unstructured diverse panels of canola (Rimmer, 2006; Delourme et al., 2011; Raman et al., 2013). However, loci associated with R gene mediated resistance are understood in greater detail compared to QR (Larkan et al., 2013; Larkan et al., 2014; Larkan et al., 2019). Though, incorporation of the R genes has been effective in mitigating risks for yield loss due to blackleg; many of them have become ineffective over-time, when deployed singly or in “stack” (Rouxel et al., 2003; Li et al., 2006; Sprague et al., 2006; Van De Wouw et al., 2014). In contrast, QR is recognized for its durability in providing natural defense to pathogens, including L. maculans, as it conveys incomplete resistance, probably due to decreased selective pressure to overcome host resistance (Johnson, 1984; Boyd, 2006; Fukuoka et al., 2009; Delourme et al., 2014).
Several hundred quantitative trait loci (QTL) for QR were identified in mapping populations following classical QTL (Delourme et al., 2006; Kaur et al., 2009; Jestin et al., 2011; Raman et al., 2012a; Raman et al., 2012b; Fopa Fomeju et al., 2014; Jestin et al., 2015; Larkan et al., 2016a; Raman et al., 2016; Kumar et al., 2018; Raman et al., 2018; Raman et al., 2020) and genome-wide association (GWAS) approaches in canola (Jestin et al., 2011; Fopa Fomeju et al., 2014; Fopa Fomeju et al., 2015; Rahman et al., 2016; Raman et al., 2016; Kumar et al., 2018). However, only a limited number of QTL provided stable QR to blackleg across environments (Pilet et al., 2001; Huang et al., 2016; Larkan et al., 2016a; Raman et al., 2018). Combining QR loci with complementary mode of action and combining QR with R genes could provide effective and durable resistance (Brun et al., 2010; Michelmore et al., 2017; Pilet-Nayel et al., 2017; Rimbaud et al., 2018). The latter approach has not been extremely successful in some parts of Australia, particularly when pathogen pressure is too high under field conditions (Raman et al., 2012b; Raman et al., 2020). Sources of effective QR resistance and underlying mode of action for QR genes is largely unknown in canola. Despite of immense interest in exploiting QR in canola breeding programs, comprehensive GWAS studies using larger sets of diverse panel and markers, and field-based assessments of resistance to blackleg across multiple locations and environments were lacking. In addition, upper canopy infection (UCI); characterized by lesions on siliques, pedicel, branches, and stem, at terminal stages of plant development, has become another concern for Australian canola industry in recent years (Sprague et al., 2018). Currently, there is a very limited knowledge on the extent of genetic variation for resistance to UCI in canola germplasm as well as underlying loci involved in resistance. Delineation of loci involved in multifaceted components of resistance and their uniqueness/redundancy in canola germplasm would provide potential genetic solution to reduce yield losses, caused by blackleg disease.
Herein, we assess a diverse panel of canola accessions across multiple environments and reveal significant marker-trait associations for resistance, evaluated as plant survival/mortality, internal infection (stem/crown canker) and severity of infection in upper canopy. To understand whether duplicated homoeologous regions are involved in QR, we searched the synteny between QR regions with 22 ancestral blocks (AK) of Brassicaceae (Schranz et al., 2006; Delourme et al., 2013; Fopa Fomeju et al., 2014). This analysis revealed that majority of QR regions have originated as a result of homoeologous and paralogous exchanges. We further showed that phenological traits such as flowering time, plant height and maturity confound the genetic variation for resistance. Our results provide new insights into the genetic and developmental basis for variation in QR among diverse panel of accessions. Identification of genetic variation for resistance to stem canker and UCI, in combination with molecular tools would enable the incorporation of QR into the elite germplasm, to develop new cultivars with overall reduced susceptibility to blackleg infection in canola.
Material and MethodsDiversity Panel
A diverse panel of 421 accessions, comprising 395 of Brassica napus, 21 of B. napus/Brassica juncea derivatives, one of B. juncea and four of Brassica carinata was used in this study (\\nSupplementary Table 1\\n). The B. napus panel includes 368 homozygous doubled haploid (DH) diverse accessions, representing different geographical locations, that were utilized earlier for mapping loci associated with photoperiodic response and flowering time (Raman et al., 2019). The DH accessions were generated via the microspore culture technique at the Haplotech Inc. (Manitoba, Canada).
Phenotypic Evaluation of Diverse DH Accessions for Resistance
The diverse panel of accessions was evaluated for resistance to L. maculans in five experiments. Two experiments were conducted under semi-controlled shade-house conditions (SH16, SH17) across two years (2016 and 2017) at Grains Innovation Park, Horsham, Victoria, Australia (36°43’14.6”S 142°10’24.5”E) and three experiments (FT17, FT18 and FT19) were conducted under field conditions across three years (2017, 2018 and 2019) at Wagga Wagga Agricultural Institute (WWAI), Wagga Wagga, NSW, Australia (35°02′27.0″S 147°19′12.6″E).
Phenotyping for Resistance With Ascospore Shower Test (Shade-House Experiments)Experimental Design
The shade-house experiments included a total of 327 DH accessions in two runs: in SH16, 244 accessions together with 10 current commercial cultivars (as “check”) were evaluated whereas in SH17, the remaining 77 accessions along with the same 10 “check” cultivars were evaluated (\\nSupplementary Table 1\\n). Connectivity between both experiments with 10 check cultivars and four DH accessions (46C04, ATR-Beacon, ATR-Signal and ATR-Hyden) that were used in SH16 experiment ensured that the assessment of disease expression is consistent across both experiments (\\nSupplementary Table 2\\n). In the SH16 experiment, the accessions were arranged in a Completely Randomized Design with three pot replicates for each accession (\\nSupplementary Figure 1A\\n). A total of 762 pots were located in three adjacent bays. Each bay consisted of a rectangular array of pots; each bay had either four or two columns (herein referred to as ranges) with a varying number of rows (80, 79, 32, and 31, respectively). Individual pots were the experimental units where, each pot contained 4 plants of the same accession. In the SH17 experiment, accessions were allocated to pots within three (complete) blocks. These blocks were located within one rectangular array of pots (Bay) with four ranges and 66 rows. The whole experiment had a total of 264 pots (\\nSupplementary Figure 1B\\n).
Ascospores, released from a mixed stubble were used as inoculum for the “ascospore shower” test (Huang et al., 2006b). Stubble was collected from 2015 commercial crops of Australian canola cultivars: ATR-Gem (group A: Rlm1, Cudal, NSW), CB-Telfer (group B: Rlm4, Arthurton, South Australia), ATR-Stingray (group C: Rlm3, Minyip, Victoria), Hyola450TT (group ABD: Rlm1, Rlm4, LepR3, Streatham, Victoria), Hyola650TT (group ABD, Grenfell, NSW), T28156 (group F: Rlm6, unreleased breeding line, Parkes, NSW) and ATR-Marlin (group S: Rlm1, LepR3, Parkes, NSW). Details of the “ascospore shower” test are given in Marcroft et al. (2012). Essentially, seedlings of test accessions were grown in punnets for approximately 10 to 15 days and then four seedlings/accession were subjected to the “ascospore shower.” The seedlings were placed onto trays and were then transferred to two growth (humidity) chambers, where the relative humidity was set at 100% at 20°C, for 48 h to encourage disease progression. After 21 days of inoculation, seedlings were transplanted into plastic pots (20 cm diameter) in a shade-house, each pot containing 4 plants per accession and subsequently raised according to the described experimental designs until the physiological maturity stage (GS 83-85). Plants were cut at the crowns and their cross-sections were assessed visually for disease severity as described previously (Marcroft et al., 2012).
Phenotypic Measurements of Diversity Panel Under Natural Field Conditions
A subset of the diverse panel, comprising 300 homozygous accessions (\\nSupplementary Table 1\\n) was evaluated for resistance in blackleg nursery containing mixed stubble of triazine tolerant (TT) cultivars (Raman et al., 2018) and Westar stubble at the experimental farm of WWAI (35°02’27.0”S 147°19’12.6”E).
Experimental Design
In each of the three field experiments, accessions were allocated to plots within two (complete) blocks. Plots consisted of a single row of at most 100 plants. These plants were grown in canola stubble of mixed TT cultivars and Westar as described in Raman et al. (2018). Each complete block comprised a rectangular array of plots with 30 rows by 10 columns separated by a buffer row of SturtTT (see \\nSupplementary Figure 1C\\n). The experiments were carried-out in canola growing seasons (April-November). The blackleg nursery was irrigated frequently (5-6 times, approximately 150 mm water applied) using lateral move, in addition to natural rainfall. The weather data and conditions for both experiments is presented in \\nSupplementary Figure 2\\n. Plant emergence was recorded by counting number of plants present in each plot after 45 days of sowing. Number of surviving plants were recorded thrice at growth stage (GS) 15-16 (after 5 weeks of emergence), GS 60-65 (at flowering stage) and at the GS 83-85 (physiological maturity). Plant survival was calculated as the proportion between number of plants at emergence and at maturity. At the physiological maturity, ten plants per accession were cut at the crown and visually assessed for internal stem infection on a 0 to 100 percentage scale based on area showing necrosis (Raman et al., 2012b).
Resistance for UCI severity was assessed in the two experiments conducted in 2018 and 2019. Number of plants infected and the extent of UCI on ten randomly selected plants from each plot were scored. An ordinal disease severity rating scale was used based on visual assessment; 0: immune reaction (no visible symptoms of UCI); (1) disease present on up to 25% of plant tissue (small lesions, resistant to UCI); (2) disease present on 26%–50% of plant tissue; (3) disease present on 51%–75% of plant tissue, (4) disease present on >75% but less than 90% of plant tissue and (5) 100% susceptibility, affecting all tissues, characterized with extensive discoloration, snapping-off vegetative (main and secondary branches) and reproductive structures (siliques).
Measurement of Phenological Traits
Each field experiment was also assessed for three key developmental traits: flowering time, plant height and maturity. Flowering time (days to first flower, GS 65) was recorded, as the difference between the date when 50% of plants in a plot had the first flower opened and the day of sowing. The date of first flower was recorded for each plot three times in a week. Plant height (cm) was measured from top to base with a scale at the silique-filling stage (GS 80) from five plants selected randomly in the middle of each plot while, plant maturity (0–9 scale) was recorded, just 1–2 days before the assessment of blackleg severity.
SNP Genotyping and Selection of Marker Panel
DNA was genotyped with Illumina Infinium SNP markers (Clarke et al., 2016), as detailed in our earlier study (Raman et al., 2019). Across all accessions and SNPs, there was a total of 2.7% missing values. Missing values were imputed using the k nearest neighbor method (Troyanskaya et al., 2001) using the Pedicure package (Butler, 2019) in R statistical computing environment (R Core Team, 2019). In this method, a missing value for a marker is replaced by the mean of its k nearest neighbors with nonmissing data for that individual. SNPs that had <80% call rate, and <2% MAF (minor allele frequency) were discarded prior to GWAS analysis. We examined redundant SNPs based on the Hamming distance between marker pairs. Sets of markers, whose Hamming distance was less than a threshold of 0.001 were considered as identical and were removed prior to analysis. We also removed SNPs which could not be anchored to the 19 linkage groups of An, and Cn subgenomes (plus Ann_random and Cnn_random linkage groups) of reference sequenced genome of B. napus cv. “Darmor-bzh” version 4.1. A final set of 12,414 high quality SNPs for 344 accessions were selected for GWAS analysis.
Statistical Analysis
The approach used herein for the determination of genomic regions that influence the expression of the traits associated with blackleg resistance is an extension of the whole genome, single step QTL analysis developed by Verbyla et al. (2007). Our approach uses an alternative working model which assumes the variance of each set of markers on different linkage groups (LGs) have different variances. That is if α is the r-vector of marker effects presented in map order, then our working model assumes that α is Gaussian, mean zero, variance
where c is the number of LGs, ri is the number of markers in the ith LG, Iri is the identity matrix of order ri, ⊕ is the direct sum operator and r= ∑i=1cri. The working model of Verbyla et al. (2007) assumes that σαi2= σα2, for i = 1,…, c. Hence their model is equivalent to the standard GBLUP model used in models for genomic selection (Tolhurst et al., 2019).
Construction of the appropriate genetic and nongenetic working model requires the inclusion of terms which represent the plot structure of the experiment(s), as well as accounting for other significant sources of nongenetic variation which occur in either shade-house studies or field trials (Gilmour et al., 1997; Smith et al., 2006). The working model (referred to as model M1) must also include terms which partition residual variance for those traits where the experimental unit is not the observational unit (Bailey, 2008). These traits include internal infection, for FT17, FT18, and FT19; internal infection and mortality for SH16, and SH17; and UCI and plant height for FT18 and FT19. All models also include a term representing polygenic effects, and the integrity of the experimental designs are preserved by including an additional term as a fixed effect, representing the effects of those accessions which were not genotyped but had phenotypic data (Tolhurst et al., 2019). All trials and traits were analyzed individually except the two runs of the shade-house experiments SH16 and SH17 which were analyzed jointly considering years as “environments”.
The models were fitted either as a linear mixed model (LMM) using residual maximum likelihood (REML) (Patterson and Thompson, 1971) for the traits internal infection, UCI, days to flower, plant height and maturity which were considered to be approximately Gaussian on a suitable transformed scale, or as a generalized linear mixed model (GLMM) using penalized quasi likelihood (Breslow and Clayton, 1993) for the traits: plant survival and mortality. All analyses were conducted using ASReml-R (Butler et al., 2018), which provides REML estimates of variance parameters, empirical best linear unbiased predictions (EBLUPs) of random effects and empirical best linear unbiased estimates (EBLUEs) of fixed effects.
Heritability (reliability) for each experiment and trait was computed using a generalized definition for heritability developed using the methods of Cullis et al. (2006):
h2=1− PEV/(2θgt2)
where PEV is the average pair-wise prediction error variance of genotype effects and θgt2 is the genetic variance of trial t. Pair-wise marker additive genetic correlations (ra) between traits were computed using the EBLUPs of the marker additive genetic effects of those accessions evaluated for each trait.
GWAS Approach
Using the working model, M1, genome scans for each linkage group with a positive REML estimate of the marker variance was performed. During this scan, each marker was fitted as a fixed effect, the other markers on the same linkage group were dropped from the model, the marker variances for the other linkage groups were held fixed at the values from the base working model while all other variance parameters were re-estimated. Potential QTLs were chosen from this scan, along with their positions and LOD scores, i.e., –log\\n10(P – value). The potential set of markers was then thinned using a modification of the approach of Benjamini and Hochberg (1995). Given the strong dependence of the tests within a linkage group, the squared LD (r2) was computed for each pair of markers and any markers with an estimated LD of greater than 0.7 and within 100 kb, were deemed to be within the same linkage block. Markers which were in the same linkage block and also chosen as potential QTLs were thinned again, to leave only one potential QTL within any given linkage block. The linkage block marker was chosen as the marker with the lowest P-value and the least number of missing values, for that linkage block. The remaining set of markers obtained from this were then included as the baseline multi-QTL model which included all of the same terms as the working baseline model as well as the set of QTLs fitted as fixed effects. Markers were dropped from this baseline multi-QTL model using a Bonferroni based backward elimination approach until all remaining markers were significant at a significance level of 0.05. All remaining markers in the fixed component of the final multi-QTL model are reported here as putative QTLs. Of which the markers with P-value ≤ 0.001, i.e., LOD score ≥ 3 are considered as “significant,” and the markers with 0.001 < P value < 0.05 are considered as “suggestive” (Rexroad et al., 2012).
Putative Candidate Genes for Resistance
Marker sequences that revealed significant associations with quantitative resistance to blackleg in this GWAS study and previous studies (Jestin et al., 2011; Fopa Fomeju et al., 2014; Fopa Fomeju et al., 2015; Larkan et al., 2016a; Raman et al., 2016; Kumar et al., 2018) were aligned by searching their physical positions with the reference Darmor-bzh gene assembly version 4.1 (Chalhoub et al., 2014). Biological functions of those genes in relation to R and QR loci were described.
Comparative Mapping of SNP Associations in Relation to AK of Brassicaceae and Disease Resistance <italic>R</italic> Genes
Marker sequences of significant SNP associations for resistance were aligned with the sequence of A. thaliana to identify the location of QR loci and its relationship with AK of Brassicaceae, as described previously (Zou et al., 2016). Brassica napus\\nR genes (Chalhoub et al., 2014; Alamery et al., 2018) that map in the vicinity of genomic regions associated with resistance to blackleg were also searched for their synteny with AK blocks.
ResultsGenetic Variation for Resistance to Blackleg
Despite of the extreme high day-temperatures and drought conditions across field environments (\\nSupplementary Figure 2\\n), we were able to raise blackleg nurseries, suitable for evaluation of germplasm for resistance. Frequent overhead watering, with the lateral move irrigator ensured that condition for blackleg infection were congenial throughout the growing season. Across environments, blackleg infection occurred, right from the cotyledon stage (after 4–6 weeks of seedling emergence) to silique maturation (\\nSupplementary Figure 3A\\n). The maximum disease expression for UCI was observed at the silique maturation stage, when the disease severity for UCI was assessed. Frequency distributions exhibited that severity of blackleg scores, measured as internal infection and plant mortality, were variable across environments; blackleg severity was higher under shade-house (ascospore shower test) compared to disease nurseries under field conditions (\\nFigures 1A, B\\n).
Variation for resistance to blackleg disease, caused by the fungus, Leptosphaeria maculans in canola. Box plots showing variation for resistance to blackleg disease, measured as per cent internal infection (A) and as plant survival/mortality (B) assessed across five environments. In total, disease severity scores from 421 diverse accessions are evaluated across five environments (for details of accessions evaluated, see \\nSupplementary Table 1\\n). (C) The frequency distribution of upper canopy infection scores among diverse accessions. (D) Accessions showing extremes in resistance and susceptibility to upper canopy blackleg infection, evaluated across two field environments (2018, 2019) at Wagga Wagga, NSW. Variation for blackleg severity was assessed under field conditions (in blackleg nurseries) and under shade-house conditions (with ascospore shower method). (E) Scatter plots showing relationship between plant mortality and internal infection among diverse accessions, assessed for resistance using ascospore shower test. Stubble collected from commercial crops of Australian canola cultivars: ATR-Gem (group A; Cudal, NSW), CB-Telfer (group B, Arthurton, South Australia), ATR-Stingray (group C, Minyip, Victoria), Hyola450TT (group ABD, Streatham, Victoria), Hyola650TT (group ABD, Grenfell, NSW), T28156 (group F, unreleased breeding line, Parkes, NSW) and ATR-Marlin (group S, Parkes, NSW) grown in 2015, was used for “ascospore shower.” Empirical best linear unbiased predictions (EBLUPs) of genetic effects are plotted.
Joint analysis of the two shade-house experiments, where accessions were evaluated with the “ascospore shower” method, revealed that the majority of them were susceptible to pathotypes, present on the infested stubble derived from the Australian canola cultivars: ATR-Gem, CB-Telfer, ATR-Stingray, Hyola450TT, Hyola650TT, T28156 and ATR-Marlin (\\nSupplementary Table 3\\n). We identified 21 accessions of canola, and one accession of Ethiopian mustard (B. carinata, 2n =4x =34, subgenome BcBcCcCc, ATC93184) that had ≤10% internal infection in the shade-house experiments (\\nSupplementary Table 3\\n). Resistant B. napus accessions were of Australian (43C80CL, 46C04, AGA99, ATR-Beacon, ATR-Mako, ATR-Signal, AV-Ruby, BononzaTT, HurricaneTT, ScaddanTT, Surpass501TT, ThunderTT, WarriorCL, RQ01-02), European (Global, Columbus, Beluga, Lindore) and Asian (Gan-You and Tosharsu) origins. The level of resistance in 16 accessions was similar to contemporary and modern commercial cultivars: GT42 (Rlm1,4,6), Hyola575 (LepR1, Rlm1) and Hyola970 (Rlm7) (\\nSupplementary Table 3\\n). Several cultivars, carrying the race-specific resistance genes, such as Stingray (Rlm3,9), Ripper (Rlm2,4,9) and CB-Telfer (Rlm4) were susceptible under shade-house and field conditions, suggesting that major genes were ineffective in conferring resistance, while some of the resistant accessions such as Scaddan (Rlm1,4), Bonanza (Rlm2,9) and ThunderTT (Rlm4,9) had a very low scores for internal infection when challenged with ascospores released from cultivars carrying Rlm1, Rlm2, Rlm4 and Rlm9 genes (either single or in stack), hinting that those accessions may have loci for quantitative disease resistance or a combination of both novel R and QR loci, this requires further verification. Diverse panel of canola accession showed a continuous distribution of disease severity scores for UCI, suggesting that multiple genes control resistance to UCI (\\nFigure 1C\\n). Among different accessions, Beluga, Gundula, Jet Neuf - a donor source of quantitative resistance in Darmor-bzh and other winter cultivars (Pilet et al., 2001), Lindore, Rafal, and two accessions of B. carinata (ATC94047, ATC94458) were highly resistant to UCI, while P3083, Ding474 and Wesbell were rated as highly susceptible (\\nFigure 1D\\n).
Our analysis showed that the significant source of genetic variation in blackleg resistance was from the additive component, i.e., genetic markers; VAFa, which ranged from 33.8% for plant survival (FT17) to 100% for internal infection (SH17, FT17) and mortality (SH17) (\\nTable 1\\n). The values for the reliabilities (broad sense genomic heritability) for each trait measured were generally high (\\nTable 2\\n) and found to be trait and environment dependent. For example, plant survival had low reliability (42%) in 2018, but higher values were obtained in 2017 (87%) and in 2019 (99%) environments. Flowering time had higher values for reliability across all environments (97% to 98%) compared to other traits. High reliability values suggest that the variation in different traits measured is heritable and therefore is suitable for genetic analysis as well as for exploiting in the canola breeding programs for enhancing genetic gains.
Summary of the residual maximum likelihood (REML) estimates for additive and nonadditive (residual) genetic variance from the models before (Base marker model: M1) and after identifying putative quantitative trait loci (QTL) (Final multi QTL model: M2) for each of the trait and trial.
Trait
Experiment
Additive (M1)
Additive (M2)
Nonadditive (M1)
Nonadditive (M2)
VAFa (%)
VAFm (%)
Internal infection
SH16
1.46
0.91
0.27
0.30
84.4
28.3
Internal infection
SH17
5.21
2.81
0.00
0.00
100.0
43.6
Internal infection
SH16/17
2.68
1.66
0.17
0.10
94.0
37.0
Internal infection
FT17
0.86
0.64
0.00
0.00
100.0
22.6
Internal infection
FT18
1.35
0.67
0.04
0.00
97.4
51.8
Internal infection
FT19
3.58
2.48
0.750
0.75
82.7
28.5
UCI
FT18
0.27
0.06
0.17
0.19
61.2
47.7
UCI
FT19
0.24
0.06
0.05
0.07
81.5
59.0
Height
FT18
129.75
46.42
51.53
55.69
71.6
43.2
Height
FT19
117.81
63.68
56.17
53.41
67.7
31.2
DTF
FT17
36.50
20.90
5.09
5.12
87.8
34.4
DTF
FT18
158.48
68.18
13.41
13.96
91.1
48.9
DTF
FT19
48.56
23.67
7.12
7.06
87.5
39.8
Plant survival
FT17
0.31
0.27
0.61
0.58
33.8
7.8
Plant survival
FT18
0.67
0.42
0.23
0.12
74.1
38.7
Plant survival
FT19
0.57
0.42
0.57
0.44
49.7
27.2
Mortality
SH16
0.68
0.32
0.21
0.16
76.0
46.9
Mortality
SH17
2.79
1.92
0.00
0.00
100.0
27.8
Mortality
SH16/17
1.04
0.55
0.19
0.21
84.6
35.6
VAFa is the percentage of total genetic variance accounted by the markers. VAFm shows the percentage of genetic variance accounted by the identified putative QTLs. Resistance to blackleg was assessed as internal infection (stem canker, %) and plant survival/mortality (0–1) across five environments in 2016, 2017, 2018, and 2019. SH, shade-house; FT, Field; DTF, Days to flower; UCI, Upper canopy infection (1-5 scale); SH16/17, joint analysis of SH16 and SH17.
Summary of heritability, mean, minimum and maximum values of the predicted means for each trait and trial in the diverse panel of canola accessions.
Phenotypic Environment
Year
Trait
Trial
Heritability (%)
Mean
Minimum
Maximum
Shade-house
2016
Internal infection
SH16
80
83.79
11.67
98.88
2017
Internal infection
SH17
84
74.26
3.81
98.84
2016
Mortality
SH16
72
0.46
0.13
0.85
2017
Mortality
SH17
77
0.34
0.06
0.90
2016/2017
Internal infection
SH16/17
81
51.89
0.82
86.35
2016/2017
Mortality
SH16/17
75
0.50
0.10
0.92
Field
2017
Internal infection
FT17
66
4.75
0.55
24.47
2018
Internal infection
FT18
67
22.45
0.60
91.84
2019
Internal infection
FT19
81
22.45
0.60
91.84
2018
UCI
FT18
74
1.99
0.18
4.41
2019
UCI
FT19
76
1.76
0.34
4.45
2018
Height
FT18
89
128.98
91.86
160.44
2019
Height
FT19
90
90.35
28.46
123.36
2017
DTF
FT17
98
120.39
109.20
139.57
2018
DTF
FT18
98
122.17
102.95
155.60
2019
DTF
FT19
97
115.42
100.81
139.35
2017
Plant survival
FT17
87
0.81
0.23
0.98
2018
Plant survival
FT18
42
0.98
0.86
0.99
2019
Plant survival
FT19
99
0.66
0.08
0.98
Resistance to blackleg was assessed as internal infection (%) and plant survival/mortality (proportion) across five environments in 2016, 2017, 2018, and 2019. SH, shade-house; FT, Field; DTF, Days to flower; UCI, Upper canopy infection (1–5 scale). Data on SH16/17 is based on the joint analysis of SH16 and SH17 trials.
Genetic Correlation Between Blackleg Traits
We investigated the pair-wise additive genetic correlation between different measures of blackleg scores among accessions. There was a high positive correlation (ra = 0.91) between internal infection and mortality in ascospore shower test (\\nFigure 1E\\n). Likewise, internal infection scores showed negative correlation with plant survival in 2017 (ra = −0.56), 2018 (ra = −0.54) and in 2019 field environments (ra = −0.66) (\\nFigures 2A–C\\n). Resistance to UCI, evaluated across two years also showed a negative correlation with plant survival across both environments (2018; ra = −0.50, 2019; ra = −0.42). To determine the effectiveness of quantitative resistance across environments (field and shade-house), we further investigated the relationship between internal infection (stem canker) scores among accessions. We observed a high additive genetic correlation (ra = 0.60 to 0.77), suggesting that diverse accessions have effective resistance to blackleg (\\nFigure 2D\\n).
Genetic (additive) correlations for resistance to blackleg evaluated in disease nurseries across three environments in 2017 (A), 2018 (B), and 2019 (C) at Wagga Wagga, NSW, Australia. Durability of resistance (assessed as internal infection) across three field experiments and shade-house (joint analysis of 2016 and 2017 experiments) also shown (D) Blackleg severity was evaluated on the basis of plant survival (proportion), internal infection (crown canker, %) and upper canopy infection (UCI, 1–5 scale) among diverse accessions (for details, see \\nSupplementary Table 1\\n). Plant development traits: plant height (cm) was measured for five plants per plot; maturity was scored for each plot; flowering time (days to first flower, GS 65) was determined, when 50% of plants have first open flower from the day of sowing as days to flower. Pair-wise correlations (ra) between traits were computed using the EBLUPs of the marker additive genetic effects.
Genetic Control of Blackleg Resistance
To gain an understanding of genetic loci controlling variation in QR against L.\\nmaculans, we performed GWAS analysis. This was accomplished by using 12,414 genome-wide distributed SNPs across all 19 linkage groups (and Ann_random and Cnn_random linkage groups) of B. napus (\\nFigure 3A\\n). Marker density on the An subgenome was higher (n = 6,480) compared to the Cn subgenome (n = 5,939) and covered 0.77 Gb of the reference Darmor-bzh genome (\\nSupplementary Table 4\\n). The LD was estimated at the whole genome level, using the full set of SNP markers, for each chromosome and both An and Cn subgenomes (\\nFigures 3B, C\\n, \\nSupplementary Figure 4\\n). The LD decay (r\\n2 = 0.2) of the An and Cn subgenomes varied from 0.6 Mb to 2 Mb, respectively. After thinning markers controlling for false discovery rate, and accounting LD among diverse accessions, GWAS revealed 26 highly significant (LOD score of ≥3, P ≤ 0.001) and 33 “suggestive” (LOD score of <3, 0.001 < P < 0.05) associations for resistance to L. maculans, scored as per cent internal canker infection, plant survival and UCI on 17 chromosomes of canola, with the exception of C07 and C08 (\\nSupplementary Table 5\\n). SNP markers explained 7.8 to 59.0% of genetic variance for resistance to blackleg (\\nTable 1\\n).
Distribution of SNP markers (A) and LD (r\\n2) among diverse accessions of canola across 19 linkage groups of B. napus (A01-C09) and unanchored An_random (Ann) and Cn_random (Cnn) linkage groups. LD decay across the An subgenome (B) and Cn subgenome (C). LD decay occurs (r\\n2 = 0.2) approximately 0.61 Mb and 1 Mb in An and Cn subgenomes, respectively. The r\\n2 is estimated for each pair of SNPs used for GWAS.
SNP Associations Detected for Resistance Using “Ascospore Shower” Screens
Joint analysis of the two shade-house experiments identified 18 (four significant and 14 suggestive) SNP associations for resistance against pathotypes present on stubble of groups A, B, C, D, E, F, and S commercial canola cultivars (\\nTable 3\\n; \\nSupplementary Table 5\\n). For plant mortality, eight SNP associations, comprising two significant and six “suggestive,” were identified on chromosomes A01, C04, and C06 (\\nFigure 4A\\n, \\nSupplementary Table 5\\n). The maximum genetic variance (9.1%) was explained by each of the two “suggestive” SNPs, Bn-A01-p6747706 (on A01) and Bn-scaff_16576_1-p256367 on chromosome C04. For internal infection, 10 SNP associations; two significant and eight “suggestive,” were identified on chromosomes A01, A03, A04, C01, C05, and C06 (\\nFigure 4B\\n). The maximum genetic variance (8.3%) was explained by the Bn-scaff_15838_1-p1403775 SNP on C01, followed by Bn-scaff_17088_3-p242403 on C06 (6.4%). As both mortality/plant survival and internal infection (stem canker) phenotypes were highly correlated (ra = 0.91, \\nFigure 1E\\n), we identified two genomic regions that were collocated on chromosome A01 (\\nSupplementary Table 5\\n). To determine the effect of population size on marker-trait association, we undertook GWAS analysis for each environment independently of ascospore shower screens, conducted across the two experiments (SH16 and SH17). In the SH16 experiment, four significant SNP associations were identified: two were identified for resistance to internal infection on chromosome A01 within genomic interval of 6.17 Mb to 10.87 Mb, while, four SNP associations were identified for plant survival on A01, A03, and C06 (\\nTable 3\\n, \\nSupplementary Table 5\\n). In SH17 experiment, two significant SNP associations were identified: the first for internal infection on A03 (0.94 Mb), and second for plant survival on A06 (1.58 Mb) and none of them were detected in SH16 (\\nSupplementary Table 5\\n). This discrepancy was likely due to the effective population size affecting both population structure as well as local LD among accessions. Overlaying results from single experiments (SH16 and SH17) with from joint analysis (SH16/SH17), we found that two regions for resistance on A01 (5.3 Mb; 6.17 Mb); one region on A03 (14.8 Mb); and one region on C06 (33.2 to 33.5 Mb, within LD) were detected across individual and joint analyses (\\nSupplementary Table 5\\n).
Genome-wide association analysis showing significant statistical association between Illumina SNP markers and resistance to L. maculans.
Trait
SNP marker
Experiment
Linkage group
Position (bp)
\\nP-value
LOD
\\nR\\n2 (%)
\\nMortality\\n
\\nBn-A01-p6747706\\n
\\nSH16\\n
\\nA01\\n
\\n6173561\\n
\\n0.001\\n
\\n3.11\\n
\\n9.6\\n
\\nInternal infection\\n
\\nBn-A01-p6747706\\n
\\nSH16\\n
\\nA01\\n
\\n6173561\\n
\\n0\\n
\\n3.33\\n
\\n6.5\\n
Internal infection
Bn-A01-p12622123
SH16
A01
10872512
0.001
3.27
5.1
Plant survival
Bn-A02-p27743910
FT18
A02
24724645
0.001
3.02
9.7
Internal infection
Bn-A03-p1308820
SH17
A03
945040
0.001
3.01
16.5
Internal infection
Bn-A03-p4352837
FT19
A03
3885180
0
3.59
6.9
Upper canopy infection
Bn-A03-p8475614
FT19
A03
7780069
0
5.05
19.2
Mortality
Bn-A03-p15815965
SH16
A03
14892225
0
4.26
10.1
\\nUpper canopy infection\\n
\\nBn-A04-p2448580\\n
\\nFT19\\n
\\nA04\\n
\\n2133626\\n
\\n0\\n
\\n5.97\\n
\\n23.4\\n
\\nUpper canopy infection\\n
\\nBn-A04-p2713564\\n
\\nFT18\\n
\\nA04\\n
\\n2419904\\n
\\n0\\n
\\n3.51\\n
\\n10.2\\n
Upper canopy infection
Bn-A04-p4099958
FT19
A04
4169810
0
4
11.7
Upper canopy infection
Bn-A05-p22141046
FT19
A05
20252157
0
6.73
25.1
Mortality
Bn-A06-p18000461
SH17
A06
1585510
0
3.8
8
Plant survival
Bn-scaff_15705_1-p84236
FT19
A06
19564715
0
4.53
2.9
Internal infection
Bn-A07-p11185122
FT18
A07
12477068
0
3.46
13.5
Upper canopy infection
Bn-A02-p771951
FT18
A07
13400889
0.001
3.01
7
Internal infection
Bn-A07-p16036626
FT19
A07
17951152
0
4.57
8
Upper canopy infection
Bn-A07-p18226326
FT18
A07
20118101
0.001
2.96
11.6
Internal infection
Bn-A09-p10092753
FT17
A09
9086363
0
4.17
13.9
Internal infection
Bn-A09-p20157367
FT18
A09
17156001
0
3.73
10.8
Plant survival
Bn-A10-p2048923
FT19
A10
1671039
0
3.4
6.3
Internal infection
Bn-A10-p15021776
FT18
A10
14967128
0
4.7
13.3
Plant survival
Bn-scaff_15838_1-p790905
FT18
C01
1224850
0.001
3
11.9
Internal infection
Bn-scaff_22790_1-p1264986
SH16/17
C01
16610817
0
4.99
3.1
Plant survival
Bn-scaff_15712_6-p292471
FT17
C02
37086215
0
5.01
7.7
Internal infection
Bn-scaff_18917_1-p533499
FT17
C03
29919378
0
3.51
10.9
Internal infection
Bn-A05-p17894045
FT18
C05
33067591
0
4.11
13.5
Internal infection
Bn-scaff_20294_1-p250512
SH16/17
C06
3244625
0
3.39
3.4
Mortality
Bn-scaff_15746_1-p145442
SH16/17
C06
21016294
0
3.53
7.5
Mortality
Bn-scaff_23957_1-p151052
SH16
C06
30618173
0.001
3.26
3.5
\\nMortality\\n
\\nBn-scaff_25466_1-p33838\\n
\\nSH16/17\\n
\\nC06\\n
\\n33209209\\n
\\n0\\n
\\n3.51\\n
\\n6.3\\n
\\nMortality\\n
\\nBn-scaff_20294_1-p395143\\n
\\nSH16\\n
\\nC06\\n
\\n33566786\\n
\\n0\\n
\\n3.86\\n
\\n22\\n
Internal infection
Bn-scaff_17554_1-p19888
FT19
C09
39720977
0
4.3
8.1
Upper canopy infection
Bn-scaff_15576_1-p115266
FT19
C09
41180043
0.001
3.27
14
Upper canopy infection
Bn-scaff_16361_1-p1072715
FT18
Cnn_random
62936531
0
4.33
12.8
Resistance was assessed as plant mortality (proportion) and internal infection (%) using “ascospore shower” test under shade-house conditions across 2016 (SH16) and 2017 (SH17) whereas it was assessed as plant survival (proportion), internal stem infection (%) and upper canopy infection (1–5 scale) across three field environments (2017: FT17, 2018: FT18, and 2019: FT19). Only markers that revealed the LOD score ≥3 are shown herein. Marker associations with LOD scores of <3 are given in a \\nSupplementary Table 5\\n. The physical positions of SNPs are based on the map position on the Darmor-bzh gene assembly (Clarke et al., 2016). Associations in bold represent to those which appeared across experiments and in LD. The LD was estimated in the diverse panel of canola accessions. R2 and SH16/17 refer to the percentage of genetic variance explained by the SNP marker and joint analysis of SH16 and SH17 experiments, respectively.
Genetic loci associated with resistance to blackleg, assessed using “ascospore shower test” in diverse accessions of canola. Manhattan plots showing LOD scores (-log10\\nP) for association between Illumina SNP and disease severity: plant mortality (A) and internal infection (B) in 285 (genotyped) of 327 canola accessions (for details, see \\nSupplementary Table 1\\n). LOD scores presented in the Manhattan plots are from Base marker model M1. The red dash line indicates the threshold value for highly significant SNPs at LOD ≥ 3. Positions (kb) of the highly significant (LOD ≥ 3, P ≤ 0.001) and “suggestive” (LOD < 3, 0.001 < P < 0.05) markers from the final multi-QTL model (M2) are labeled. The physical positions of SNPs are based on the map position on the Darmor-bzh gene assembly (Clarke et al., 2016, \\nSupplementary Table 5\\n). (C) Box plot showing the distribution of the EBLUPs for internal infection partitioned into allele combinations for the SNP markers that are significantly associated with resistance (LOD ≥ 3) assessed under shade-house conditions with “ascospore shower” test in a diverse panel of GWAS accessions. Major (present in high proportion) and minor (present in low proportion) allele for each marker are shown in different colors. Number of alleles (n) for each significant SNPs associated with resistance are also shown. Distributions of SNP marker Bn-A03-p13088220 allele represent to SH17 experiment, where 68 canola accessions were evaluated for resistance to blackleg using the “ascospore shower test.”.
To identify SNP markers suitable for tracking QR genes in B. napus germplasm, we compared relationship between marker alleles that showed significant associations to L. maculans with “ascospore shower” and variation in resistance to blackleg, assessed by the severity of internal infection. Our analysis showed that single SNP association could not sufficiently account for resistance. However, using multiple alleles for different QTL regions, resistance could be tracked in a diverse panel of canola accessions (\\nFigure 4C\\n). For example, SNP alleles present in a greater proportion (TT, Bn-scaff-20294-1-p250512) and in smaller proportion on chromosomes A01 (GG, Bn-A01-p6747706), A03 (GG, Bn-A03-p1308820), and C01 (GG, Bn-scaff-22790-1-p1264986), contributed in increasing resistance to internal infection under shade-house conditions.
SNP Associations for Plant Survival in Field
GWAS identified 13 SNPs (five significant and eight “suggestive”) that were associated with resistance to L. maculans, based on the plant survival scores under field conditions across three environments (\\nFigure 5A\\n, \\nSupplementary Table 5\\n). Of the five significant associations (LOD ≥3) identified on A02, A06, A10, C01, and C02 chromosomes, the SNP Bn-scaff_15838_1-p790905 on C01 explained the maximum genetic variance (11.9%) for plant survival, followed by Bn-A02-p27743910 (9.7%) on A02 (\\nTable 3\\n). In total, GWAS associations explained 7.8% (2017) to 38.7% (2018) of the genetic variance (\\nTable 1\\n). Two genomic regions, the first delimited with two SNPs, Bn-A06-p17332795 and Bn-A06-p17042021, and the second region by the Bn-scaff_15705_1-p84236 and Bn-A06-p18432812 markers on A06, were detected within 0.26 Mb (in LD) across 2018 and 2019 environments (\\nSupplementary Table 5\\n).
Manhattan plots showing genomic regions associated with resistance to blackleg in diverse accessions. Resistance was assessed by plant survival (A), internal infection (B) in blackleg nursery across three field environments in three consecutive years (2017 to 2019) at Wagga Wagga, while upper canopy infection (C) was assessed across two years (2018 and 2019). LOD scores (-log10\\nP) for association between Illumina SNP and disease severity in diverse canola accessions are shown on y-axis. LOD scores presented in the Manhattan plots are from Base marker model M1. The red dash line indicates the threshold value for significant SNPs at LOD ≥ 3. Positions (kb) of the highly significant (LOD ≥ 3, P ≤ 0.001) and “suggestive” (LOD < 3, 0.001 < P < 0.05) markers from the final multi-QTL model (M2) are labeled. The physical positions of SNPs (x-axis) are based on the map position on the Darmor-bzh genome assembly (Clarke et al., 2016, for detail, see \\nSupplementary Table 5\\n). (D) Circos plot with 19 chromosomes showing localization of quantitative trait loci (QTL) associated with resistance to blackleg and phenological components in a diverse panel of canola accessions on the 19 chromosomes of canola. Physical positions of SNP markers were inferred on the positions of the Darmor-bzh reference genome assembly. Scatter plots and lines represent LOD scores and R2 values of SNP associations for different traits evaluated in a GWAS panel. Lines represent to R2 values (inner most shell). Outer most shell represents to different traits (I: internal infection; S: plant survival, M: plant mortality, Mat: plant maturity; H: plant height; U: upper canopy infection; F: flowering time), evaluated in a GWAS panel. The second outer most shell illustrates the ancestral blocks. Localization of SNP markers associated with resistance to blackleg is only shown on the ancestral blocks of Brassicaceae, as defined by Schranz et al., 2006 by aligning A. thaliana genes to the Darmor reference genome.
SNP Associations for Resistance to Internal Infection in Field
Seventeen SNP associations (nine significant and eight “suggestive”) were identified for resistance to internal infection across three field environments (2017-2019) on chromosomes A03, A07, A08, A09, A10, C03, C05, and C09 (\\nFigure 5B\\n, \\nSupplementary Table 5\\n). SNP marker, Bn-A09-p10092753 on chromosome A09 accounted for 13.9% of genetic variation for internal infection (\\nTable 3\\n). Of the SNP associations, 82% of them (14/17) were mapped on the An subgenome, suggesting that the A genome is the richer reservoir of blackleg resistance genes compared to the Cn subgenome. This is consistent with earlier findings that majority of R genes (Rlm1, Rlm2, Rlm3, Rlm4, Rlm7, Rlm9, Rlm12, LepR1-4) identified so far, are mapped on the An subgenome, while only a few, Rlm13 is mapped on the Cn subgenome (Raman et al, unpublished). Among SNP associations, only one genomic region, delimited with Bn-A07-p15812852 to Bn-A07-p16036626 (17.7 to 17.9 Mb sequence of Darmor-bzh) on chromosome A07 was detected repeatedly in LD across two field environments (FT18 and FT19; \\nSupplementary Table 5\\n).
SNP Associations for Resistance to UCI
Herein, we identified 14 associations (eight significant and six “suggestive”) for resistance against UCI on A03, A04, A05, A07, and C09 and Cnn_random contig of Darmor-bzh assembly (\\nTable 3\\n, \\nFigure 5C\\n). The number of SNP associations varied from 6 to 8, depending upon the phenotypic environment. Of the SNP associations, Bn-A05-p22141046 on A05 accounted for the maximum genetic variance (R\\n2 = 25.1%), followed by Bn-A04-p2448580 (R\\n2 = 23.4%) on A04. More than 42% of the genetic associations (6/14) were detected on A04, suggesting that multiple loci for resistance are localized on this chromosome. Across environments, one genomic region of the Darmor-bzh sequence (2.13 to 2.42 Mb); demarked with markers: Bn-A04-p2448580/Bn-A04-p2713564 was repeatedly detected in LD on A04 and accounted for up to 23.4% of the genetic variance (\\nTable 3\\n, \\nSupplementary Table 5\\n). Identification of multiple genomic regions associated with resistance, having small additive allelic effects, suggests that UCI is a quantitative trait and is controlled by multiple loci in canola.
Hot-Spot Genetic Regions for Resistance to Blackleg
To identify hot-spot genomic regions for resistance to blackleg, we compared the physical positions of sequences of SNPs that reveal significant associations for plant survival, stem canker and UCI in diverse panel of canola accessions. The An subgenome had 39 SNP associations, while 20 associations were detected on the Cn subgenome (\\nFigure 5D\\n). Among the 19 linkage groups, A04, and A07 had 2.3 to 2.7 times more associations (QTL) for resistance to blackleg, compared to average genome-wide SNP (2.95/linkage group) associations (\\nSupplementary Figure 5\\n).
Significantly Associated SNPs Overlap With Previously Identified QR and Known <italic>R</italic> Genes
To determine the uniqueness/redundancy of loci involved in QR resistance, we compared the physical positions of SNP associations identified in this study with the previous studies (Jestin et al., 2011; Fopa Fomeju et al., 2014; Larkan et al., 2016a; Rahman et al., 2016; Raman et al., 2016; Kumar et al., 2018; Raman et al., 2018; Raman et al., 2020). We identified several marker loci for QR to blackleg on chromosomes A01, A02, A03, A04, A05, A06, A07, A08, A09, A10, C01, C02, C05, and C06 that were mapped near the SNP associations identified in the present study (\\nFigure 6\\n). This suggests that QR loci identified here are relevant to international germplasm, comprising spring, semispring/winter and winter types. We also identified new QR loci on chromosomes A01 (10.8 Mb), A04 (1.1 Mb to 2.41 Mb), A06 (5.88 Mb), A08 (1.30 Mb, 16.94 Mb), A10 (1.67 Mb), C01 (1.80 Mb, 161.61 Mb), C03 (29.91 Mb), and C05 (7.03 Mb). These loci provide new targets for genetic improvement in canola breeding programs.
Comparison between genomic regions identified in a diverse panel of canola (this study) and previous studies. Marker loci highlighted in different colors represent to different studies. For clarity, we showed only the highly significant SNP associations for resistance (\\nTables 3 \\nand \\n4\\n) here. Candidate genes associated with R genes which map within 0.25 Mb are shown here. For details, see \\nSupplementary Table 6\\n.
To identify plausible candidate genes in the QR genomic regions, we compared the physical locations of annotated R genes in the Darmor-bzh assembly (Chalhoub et al., 2014; Alamery et al., 2018). Forty-two R genes were located within 1 Mb of QR loci (\\nSupplementary Table 6\\n) of which 12 disease resistance R genes were located within 200 kb from the SNP associations (QTLs) on A01, A06, A07, A10, C01, C05; C06, and C09 (\\nTable 4\\n). These annotated R genes encode disease resistance proteins with the TIR-NSB-LRR and non-TIT-NBS-LRR (CNL), RLP and NB-ARC-LRR motifs, which are implicated in ETI (Jones and Dangl, 2006).
Annotated disease resistance genes located in the vicinity of quantitative resistance to blackleg disease in canola grown across different environments.
Trait
SNP marker
Linkage group
Position
\\nP-value
LOD
\\nR\\n2 (%)
Candidate R gene
Distance from candidate gene
\\nR gene motif
(bp)
ID
Start
End
(bp)
Mortality (SH16/17)
Bn-A01-p23905191
A01
20068468
0.007
2.19
4.40
BnaA01g28560D
19873137
19874480
195331
CC – NBS
Plant survival (FT18)
Bn-A02-p27743910
A02
24724645
0.001
3.02
9.66
BnaA02g36900D (A2_random)
24581776
24580933
142869
TIR-NBS-LRR
Plant survival (FT19)
Bn-A06-p18000403
A06
1585452
0.000
4.26
1.71
BnaA06g39650D (A6_ran) BnaA06g02940D
16553271789462
16586001792348
69817204,010
CC-NBS-LRR
UCIS (FT18)
Bn-A02-p771951
A07
13400889
0.000
3.54
7.02
BnaA07g15400D
13339537
13343565
57,324
TIR-NBS-LRR
Internal infection (FT18)
Bn-A07-p16036626
A07
17951152
0.001
3.10
0.56
BnaA07g24250DBnaA07g24260D
1813890918149353
1813748618145885
193,122187757
NBS - LRRTIR-NBS-LRR
Internal infection (FT18)
Bn-A10-p9802574
A10
11216126
0.001
3.22
4.78
BnaA10g13740D
11035464
11038549
177,577
NB-ARC domain-containing protein
Plant survival (FT18)
Bn-scaff_15838_1-p790905
C01
1224850
0.001
3.21
12.84
chrC01_random-snap.407 BnaC01g02460D
12462301302496
12440361303729
2138077,646
TIR-NBS-LRR
Internal infection (SH16/17)
Bn-scaff_18181_1-p622258
C05
7039786
0.001
2.97
4.08
BnaC05g12130D
7046069
7053047
6,283
CC-NBS-LRR
Internal infection (SH16/17)
Bn-scaff_20294_1-p250512
C06
3244625
0.000
3.39
3.39
BnaC06g43800D (c06_random)
3240778
3,241,715
3847
TIR-NBS-LRR
Mortality (SH16)
Bn-scaff_20294_1-p395143
C06
33566786
0.002
2.69
16.03
BnaC06g34000D
33631584
33634387
67,601
TIR-NBS-LRR
Internal infection (FT19)
Bn-scaff_17554_1-p19888
C09
39720977
0.000
4.44
8.03
BnaC09g36260D
39561973
39561973
159004
NB-ARC-LRR, RLK
UCI (FT19)
Bn-scaff_15576_1-p115266
C09
41180043
0.001
3.28
14.03
BnaC09g38280D
41200106
41205204
20,063
Zinc finger
Only SNP associations that were detected within 200 kb are shown herein. Detailed information is presented in a \\nSupplementary Table 5\\n. Data on SH16 and SH17 experiments relates to ascospore shower test, while FT18, FT18, and FT19 experiments relate to field screening of GWAS panel. Data on SH16/17 is based on the joint analysis of SH16 and SH17 trials. R2 refers to the percentage of genetic variance explained by the SNP marker.
Several QR Loci of Canola May Represent to Ancestral Genes of Brassicaceae
To determine and verify whether QR genomic regions are located in the homoeologous regions of B. napus genome, we searched the synteny between genomic regions that showed significant associations with QR, using SNP markers as proxies, and 22 AK blocks of Brassicaceae (Schranz et al., 2006). We delimited 37 SNP associations (identified on chromosomes A01, A02, A03, A04, A05, A06, A07, A009, A10, C001, C02, C3, C04, C05, C06, and C09) in the 17 AK blocks: A, B, C, D, E, F, H, I, J, K, N, O, Q, R, S, T, U, and X (\\nFigure 5D\\n). Six SNP associations, which were mapped on chromosomes A03, A04, A09, A10, C04 and C06 could not be located to the AK blocks (\\nSupplementary Table 7\\n). Of the 44 significant SNP associations for resistance, 25 (56.7%) were localized in the duplicated regions of the 9 AK blocks; the maximum SNP associations (n = 6) were located in each of the blocks: E, F, and U, while the minimum associations (n = 1) were identified in each of the B, D, H, I, K, and T blocks (\\nSupplementary Table 7\\n). In addition to the E, J, R, and U blocks identified for resistance to blackleg in European canola (Fopa Fomeju et al., 2015), we also mapped SNP association in the duplicated regions on A, B, C, D, F, H, I, K, N, Q, S, T and X AK (\\nSupplementary Table 7\\n). In this study, no SNP association for resistance could be assigned to the W block (Fopa Fomeju et al., 2014). In addition, one SNP association for resistance, assessed based on plant mortality on chromosome C04 could not be mapped onto the same ancestral karyotype J block, identified in a previous study (Fopa Fomeju et al., 2015).
We further investigated the locations of genomic regions for QR with subgenomic structure of AK blocks, determined on the level of fractionation (Parkin, 2011; Parkin et al., 2014). The maximum SNP associations were present in the least fractionated subgenome (LF = 18), followed by seven associations in the moderately fractionated subgenome (MF1) whereas, the minimum SNP associations (6) were in the most fractionated subgenome (MF2). Consistent with a previous study (Fopa Fomeju et al., 2015), one to six homoeologous regions for QR were identified in the LF, MF1, and MF2 subgenomes of duplicated regions of E, J, R, U, and W (\\nSupplementary Table 7\\n). For example, we identified SNP association for UCI to 39.93 Mb region on chromosome C09 (R block, LF subgenome). The same regions for QR was also identified at 39.93 Mb region on chromosome C09 in a B. napus GWAS panel of European origin (Fopa Fomeju et al., 2015). At least 18 disease resistance genes of A. thaliana (localized in AK blocks C, D, E, F, H, I, J, N, Q, R, S, U, and X) were located within 100 kb regions, suggesting that these R genes were retained in B. napus genome, despite of long term genome fractionation, evolution and domestication (Chalhoub et al., 2014; Parkin et al., 2014). This study and the previously reported ones revealed that majority of QR regions have originated as a result of homoeologous and paralogous exchanges occurred between A. thaliana and B. napus genomes.
Relationship Between Resistance and Phenological Traits
Since resistance to blackleg seems to be conditioned with other plant developmental traits such as plant height and maturity (Pilet et al., 2001; Delourme et al., 2004; Huang et al., 2016; Raman et al., 2018), we scored these traits in the replicated trials conducted under field conditions (2017–2019). Pair-wise correlations of the additive genetic effects between different traits: blackleg resistance, flowering time, plant height and maturity are presented in \\nFigure 2\\n. Utilizing the data on the genetic variation in the developmental traits, we further (i) located loci associated with flowering time, plant height and maturity in the diversity panel and (ii) investigated their physical localization in relation to significant loci associated with resistance to blackleg. GWAS analysis identified 30 SNP associations for flowering time on chromosomes A01, A02, A03, A10, C03, and C07 (\\nSupplementary Table 5\\n, \\nSupplementary Figure 6\\n); at least one of them is in the vicinity of associations for QR to blackleg on A02 (24.6 to 24.7 Mb). This genomic region maps within 27.5 kb from the AGAMOUS-LIKE 69/MADS AFFECTING FLOWERING 4; MAF4, AT5G65070, 24.6 Mb on the Darmor assembly). Likewise, GWAS identified 13 SNP associations for plant height on chromosomes A02, A08, A10, C03, C06, and C07 (\\nFigure 5D\\n, \\nSupplementary Figure 6\\n). Of which, one marker association on A02 that was identified with flowering time (24.6 to 24.7 Mb), was also located in the same genomic region for plant survival in 2018 field environment (\\nSupplementary Table 5\\n). No QTL for plant height was detected on chromosome A06, where QR locus for blackleg was identified earlier in the Darmor-bzh/Yudal population (Huang et al., 2016; Kumar et al., 2018; Raman et al., 2018). Of the 14 SNP associations identified on A05, A08, A10, Ann_random, C02, C05 and C09 for plant maturity, possibly four genomic regions on chromosomes A08 (1.03 Mb to 1.30 Mb), A10 (1.61 to 1.67 Mb, and 14.6 to 14.9 Mb), and C09 (39.7 Mb) were detected near the SNP maker loci for resistance to blackleg (\\nSupplementary Table 5\\n). Moderate correlation between phenotypes and co-location of QTLs for resistance to blackleg and phenological traits (\\nFigures 2A–C\\n and \\nFigure 5D\\n) suggest that genes, underlying QTLs are either linked or may have pleiotropic allelic effects. It was difficult to measure causal relationship between plant development traits and resistance, as blackleg disease may have impacted their expression.
We looked if variation in flowering modulate blackleg infection. To understand this, we compared genetic variation in flowering time revealed in FT17 and FT18 experiments (this study) and previous experiments conducted at WWAI in the same environments (2017, 2018) on the same set of 300 diverse accessions (Raman et al., 2019). We observed a high correlation across environments (r = 0.98). The possibility of involvement of flowering time loci was also excluded by comparing the physical locations of SNP associations for flowering time, which were identified in the same set of GWAS panel evaluated under the similar field conditions, in the absence of blackleg disease; similar SNP-trait associations were detected, as found in experiments conducted in disease nurseries. This observation suggests variation in flowering time did not significantly affect blackleg disease in experimental conditions used in this study.
DiscussionAscospore Shower and Field Screens Identify Additional Sources for Quantitative Resistance
In this study, we included a larger set of germplasm as compared to previous studies (Delourme et al., 2006; Kaur et al., 2009; Light et al., 2011; Raman et al., 2012b; Larkan et al., 2016a; Raman et al., 2016; Raman et al., 2018) and identified additional sources for resistance (\\nFigure 1C\\n, \\nSupplementary Table 3\\n). Optimal conditions for blackleg development are required for assessing variation in resistance. For example, under high disease pressure, it may be difficult to reveal variation in resistance to blackleg, as L. maculans is a hemibiotroph and can colonize on resistant accessions of canola and related species. We observed moderately high values for reliability, which are comparable to previous studies (Pilet et al., 1998; Raman et al., 2012b; Jestin et al., 2015; Larkan et al., 2016a; Raman et al., 2016; Raman et al., 2018; Raman et al., 2019b) (\\nTable 2\\n). Low reliability values (12%) observed in some environments could be due to empirical visual assessment of resistance and genotype x environment interactions, as observed in canola DH populations from Skipton/Ag-Spectrum and RP04/Ag-Outback and Darmor-bzh/Yudal (Raman et al., 2012b; Huang et al., 2016; Raman et al., 2020). Under moderate disease pressure, we could assess genetic variation in resistance to UCI to blackleg consistently across environments (reliability scores: 74 to 76%, \\nTable 2\\n). Hereby, we emphasize the genetic variation in resistance to UCI among the accessions of the diverse panel and report several sources of resistance and susceptibility (\\nSupplementary Table 3\\n). This information can be useful for (i) understanding the genetic basis of resistance to UCI in mapping populations as well as (ii) developing protocols for high-throughput phenotyping for comprehensive assessment of UCI. The resistant accessions can further be exploited in breeding programs for developing improved cultivars with overall resistance to blackleg in canola and its related species.
Genome-Wide Distributed Loci Are Associated With Quantitative Resistance
We have delineated numerous loci for resistance to blackleg in a diverse panel of canola using a GWAS approach, although causative SNPs are not identified in this study. Majority of the Australian canola germplasm has either qualitative and/or quantitative “background” resistance. In fact, it is not common to find Australian cultivars that do not have either qualitative and/or quantitative resistance, as it has been the “backbone” trait of canola industry. Therefore, it is difficult to access Australian germplasm (with effective population size for GWAS analysis) that does not have R genes and utilize for the identification of QR genes comprehensively.
Most of the significant SNP associations identified in our screens do not correspond to the “known” R genes reported on chromosomes A01, A02, A06, A07, and A10 (Raman et al., 2013; Raman et al., 2016) and were not consistently detected across environments (\\nTable 3\\n). The results indicate that environment played a significant role in quantitative resistance expression. Similar observations were made in previous QTL and GWAS studies (Pilet et al., 2001; Jestin et al., 2011; Jestin et al., 2015; Huang et al., 2016; Kumar et al., 2018; Raman et al., 2018). Presence of SNP associations accounting low genetic variance (despite of moderate to high reliability, \\nTable 3\\n) on multiple genomic regions also indicate that QR is governed by quantitative genes (polygenic) rather than monogenic R genes, as the latter provide the complete resistance and account for larger proportion of genetic variance (Raman et al., 2012a; Raman et al., 2012b; Larkan et al., 2016b; Raman et al., 2018; Raman et al., 2020). We also observed that some SNPs accounted for variable LOD scores and genotypic variance (\\nSupplementary Table 5\\n). For example, Bn-A07-p16036626 on A07 (17.9 Mb) had “suggestive” LOD score of 1.82 in 2018, while the same SNP association had significant LOD score of 4.52 in 2019, suggesting that significance of trait-marker association relies on environment (phenotypic conditions including pathogen population) and “suggestive” associations should not be discounted in genetic improvement programs. More than 50% (32/59) of the SNP associations, detected on A01, A02, A03, A04, A05, A06, A07, A09, C02, C05, C06, and C09 chromosomes, were identified within 100 kb distance from the previously identified QTLs (\\nSupplementary Table 6\\n, \\nFigure 6\\n), at least in one of the mapping populations (Larkan et al., 2016a; Raman et al., 2016; Kumar et al., 2018; Raman et al., 2018; Raman et al., 2020). Other SNPs associations on A04, A10 and C01 were mapped 0.5 to 1 Mb from QTL identified in Aviso/Bristol, Canberra/Bristol, Darmor/Bristol, and Grizzly/Bristol populations (\\nSupplementary Table 6\\n). These results suggest the common QR loci for resistance to blackleg in the winter (Jestin et al., 2011) and diverse panel of B. napus germplasm utilized in this study. Unlike previous studies (Jestin et al., 2011; Larkan et al., 2016a; Kumar et al., 2018), significant QTL (LOD ≥3) on chromosome A08 could not be detected in our GWAS panel. However, there were “suggestive” associations (with LOD <3) on A08. This could be attributed to L. maculans population structure, environmental conditions and/or the origin of germplasm. Previous studies (Jestin et al., 2011; Fopa Fomeju et al., 2015; Kumar et al., 2018) utilized a smaller set of accessions of winter rapeseed for GWAS which were evaluated under European phenotypic environments, whereas herein we used a larger diverse set of germplasm comprising spring, semispring/winter and winter types under Australian growing environments.
Some of the SNP associations for QR to blackleg were present in the vicinity of known R genes (Rlm3, Rlm4, Rlm7, and Rlm9) on chromosomes A07 (17.7 to 17.9 Mb), LepR3/Rlm2 on A10 (14.4 Mb), LepR1 on A02, and LepR4 on chromosome A06 (Yu et al., 2008; Yu et al., 2013; Larkan et al., 2016b; Raman et al., 2016; Raman et al., 2018). The 17.7 to 17.9 Mb genomic region of A07 represents to a cluster of R genes and maps approximately 2Mb apart the Rlm9, encoding Wall-Associated Kinase like protein (BnaA07g20220D, 15.91 Mb) that was recently cloned in canola. (Larkan et al., 2019). Previously, Rlm9 was mapped to a 15.95 Mb region in a DH population from Darmor-bzh/Yudal population (Raman et al., 2018). The genetic region, delineated by the Bn-A07-p18226326 marker at the 20.1 Mb on the Darmor assembly for UCI could represent the Rlm1 gene in the AK block, E (subgenome LF). It was interesting that the SNP associations on A07 (Bn-A07-p15812852/Bn-A07-p16036626); 17.7 to 17.9 Mb for resistance to internal infection (\\nSupplementary Table 5\\n) were not detected with ascospore shower test. This was due to the utilization of stubbles derived from cultivars having Rlm1/Rlm3/Rlm4/Rlm7/Rlm9 and unknown QR genes, which likely have rendered Rlm genes on A07 ineffective, leading to no variation for genetic analyses to detect. Effectiveness of qualitative R genes under field conditions identified hereby is in contrast to earlier studies, where the effectiveness of Rlm3, Rlm4, and Rlm9 was questioned under the Australian field conditions (Light et al., 2011; Raman et al., 2012a; Larkan et al., 2016a; Raman et al., 2016; Raman et al., 2018; Raman et al., 2020). This may have occurred due to fungal population structure and environmental conditions, which may have impacted the frequency and distribution of Avirulence genes in the L. maculans population in the disease nurseries. It is reiterated that field environments across 2017, 2018 and 2019 were less conducive for the development of high disease pressure (\\nSupplementary Figures 2A, B\\n), due to record excessive drought conditions and high temperatures (plus 40°C during day time).The strong effect of weather conditions including the temperature on the severity of blackleg (stem canker) is reported previously (Evans et al., 2008). Further work is required to established whether there is any interaction between QR and R genes (described above) and or QR identified under field conditions is due to the genetic effect of R genes (e.g. Rlm12 and QTL region on A01; 5.35Mb to 6.33Mb on A02, Rlm2/LepR3 and QTL region 14.6 Mb to 15.3Mb on A10; \\nSupplementary Table 6\\n).
Our findings suggest that the “ascospore shower” test using ascospores released from mixed stubble sources (from cultivars carrying R genes such as Rlm1, Rlm4, Rlm6, LepR1, and LepR3, used in this study) was found to be suitable for the identification of QR genes. In the present study, we identified several genomic regions for QR which were detected variably across environments. For example, we identified genomic regions for resistance on chromosome A01, including Rlm12 under shade-house conditions, which were not detected under field conditions. These results suggest that complementary approaches should be explored for genetic analysis of QR to blackleg. Our findings and previous studies revealed that variation in quantitative resistance is due to quantitative genes, distributed genome-wide with smaller allelic effects, suggesting that variation in resistance has evolved in several independent genes in canola germplasm.
GWAS Analysis Reveals Genomic Regions for Resistance to UCI
GWAS analysis has been extensively used to detect marker associations with trait of interest, especially which are in LD with QTL. Relying on this approach with genetic relationship estimates, we detected 16 associations between SNPs and UCI. Majority (56%) of the SNP associations were detected on chromosome A04. Detection of multiple SNPs in small genomic interval on A04 suggest that this region harbors gene(s) for resistance to UCI and is one of the candidates for fine mapping. In addition, SNP marker Bn-A03-p8475614, accounting for 19.2% of genetic variance was mapped in the proximity of QTL involved in resistance to blackleg (~180 kb) on chromosome A03 (7.59 Mb, Raman et al, 2016). In earlier studies, significant marker associations for resistance to both single spore isolates of L. maculans and stem canker were also detected on the homoeologous group 3 (A03/C03) chromosome (Jestin et al., 2015; Raman et al., 2016). Two genomic regions (13.4 Mb and 20.1 Mb) for resistance to UCI were identified on chromosome A07. The 13.4 Mb locus was mapped ~129.7 kb apart from the GWAS association for resistance at the cotyledon stage (Raman et al., 2016). One of the disease resistance genes, BnaA07g15400D (GSBRNA2T00098616001) was also located 57.3 kb proximal to this QR locus (\\nSupplementary Table 5\\n), implying that this genomic region is likely to be involved in resistance to blackleg. The 20.1 Mb genomic region for UCI may represent to the Rlm1 gene. The physical position of Rlm1 in the GWAS panel is consistent with our results on the mapping of Rlm1 in a high-resolution mapping population of canola (Raman et al, unpublished). This current study also hints that the R gene mediated resistances to internal infection (mapped to the 17.7 Mb to 17.9 Mb of the Darmor-bzh sequence, Rlm3,4/7/9) and to UCI (mapped to the 20.11 Mb, Rlm1), are stable under high temperatures in field and is not prone to high temperatures. These results are in contrast with a previous study which reported that Rlm6 gene is not stable under high temperatures (Huang et al., 2006a).
Consistent with our earlier and other GWAS studies (Rahman et al., 2016; Raman et al., 2016), we identified several significant associations for field resistance (disease nurseries in field and ascospore shower in shade-house) near genomic regions identified with single spore isolates. In addition, we detected genomic regions for QR near R genes, suggesting that mechanisms underlying host Pathogen-Associated Molecular Pattern triggered Immunity (PTI) and Effector-Triggered Immunity (ETI) are shared for qualitative and quantitative resistance (Thomma et al., 2011). Pathogens like L. maculans must overcome PTI to colonize on host and then to interact with ETI for defense response. It is not established here which R/QR loci are responsible for PTI or weaker ETI (Doehlemann and Hemetsberger, 2013). It is also possible that some of the genomic regions identified here could be associated with qualitative resistance due to (i) the genetic effects of R genes, (ii) ubiquitous presence of races of L. maculans in a canola crop and (iii) Avr genes that are in low frequency and not subjected to mutations, leading to virulence toward R genes.
Possible Role of Plant Developmental Traits on Blackleg Resistance
There was a positive relationship between UCI, flowering time, maturity, and plant height in 2018, however no such consistent relationship was found in 2019. This could have been attributed to the water stress and high temperatures in 2019 (\\nSupplementary Figure 2\\n). Moderate temperatures and high humidity are known to favor blackleg disease (Ghanbarnia et al., 2009). One genomic region for plant survival on A02, demarked with markers: Bn-A02-p27327804 (24.6 Mb), Bn-A02-p27336483 (24.6 Mb) and Bn-A02-p27743910 (24.7 Mb) was mapped within 100 kb from the SNP associations for flowering time and plant height (\\nSupplementary Table 6\\n). These results suggest that phenology genes may have pleiotropic effects on the level of blackleg expression.
Conclusions
In summary, we have delineated natural variation for resistance to blackleg in a diverse panel of canola. The specific accessions identified herein provide a useful resource for improving resistance to blackleg, and increasing genetic diversity in resistance genes, which can be exploited in the breeding programs. GWAS analysis identified 59 significant and “suggestive” loci for resistance to blackleg under shade-house and field conditions. These loci can only explain a small proportion of the genetic variance in resistance to blackleg; the sizable proportion of variance is accounted by G× E interaction, indicating that additional loci remain to be identified. Further research is required to assess the stability of QTL associations across canola growing regions in Australia and elsewhere. Given the adaptive nature of L. maculans, it is also important to demonstrate the uniqueness of genes conferring effective quantitative resistance to blackleg in Brassica species to expand the genetic base of canola - one of the most recent domesticated oil seed crops in recent history. As this and previous studies showed that QR is controlled by several genes with smaller allelic effects, introgression of a large number of loci is difficult via traditional marker-assisted selection (Fikere et al., 2018). However, favorable alleles for stable QR including for plant height and flowering time, which don’t confound the expression of QR can be enriched in the elite breeding lines using genomic selection approaches. Altogether with the results of previous studies, our results suggest that the gene network involved in QR is rather complex and evolved in a multifaceted manner to unleash genomic variation that contributed to natural variation in resistance to blackleg disease in canola.
Data Availability Statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
Author Contributions
HR conceived the research, planned experiments and wrote the paper. HR, BM, and RR conducted research and contributed to phenotyping. BC developed the statistical methods for GWAS analysis. RP performed statistical analysis and GWAS analysis in consultation with HR. SM and DB evaluated accessions for resistance using ascospore shower, in consultation with HR. YZ, SL, and HR performed comparative mapping of significantly associated SNP markers. All authors contributed to the article and approved the submitted version.
Funding
This study was supported by the NSW Department of Primary Industries, the Grains Research and Development Corporation, and Agriculture Victoria Research (AVR under the investments in National Brassica Germplasm Improvement Program project to HR (DAN00117, DAN00208, and DAN1707).
Conflict of Interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Acknowledgments
We thank Dean MacCullum, Hannah Roe, Shane Hildebrand, and Bashar Thejer for technical assistance in the field. Authors are thankful to Dr Steve Marcroft, Marcroft Grains Pathology, Horsham for evaluating canola accessions using ascospore shower test.
Supplementary Material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpls.2020.01184/full#supplementary-material\\n
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BMC Genomics\\n17, 1–14. 10.1186/s12864-015-2343-1\\n26818753\\n\"\n]"}}},{"rowIdx":471,"cells":{"data":{"kind":"list like","value":["\nInt J Mol SciInt J Mol SciijmsInternational Journal of Molecular Sciences1422-0067MDPI32722327PMC743212810.3390/ijms21155270ijms-21-05270ReviewGingival Crevicular Fluid Peptidome Profiling in Healthy and in Periodontal DiseasesPreianòMariaimmacolata1†SavinoRocco2†VillellaChiara1https://orcid.org/0000-0002-4236-7367PelaiaCorrado1TerraccianoRosa3*Department of Health Sciences, University “Magna Græcia”, 88100 Catanzaro, Italy; preiano@unicz.it (M.P.); villella@unicz.it (C.V.); pelaia.corrado@gmail.com (C.P.)Department of Medical and Surgical Sciences, University “Magna Græcia”, 88100 Catanzaro, Italy; savino@unicz.itDepartment of Experimental and Clinical Medicine, University “Magna Græcia”, 88100 Catanzaro, ItalyCorrespondence: terracciano@unicz.it; Tel.: +39-0961-3694085
Co-first author, these authors contributed equally to this work.
2472020820202115527016620202272020© 2020 by the authors.2020Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Given its intrinsic nature, gingival crevicular fluid (GCF) is an attractive source for the discovery of novel biomarkers of periodontal diseases. GCF contains antimicrobial peptides and small proteins which could play a role in specific immune-inflammatory responses to guarantee healthy gingival status and to prevent periodontal diseases. Presently, several proteomics studies have been performed leading to increased coverage of the GCF proteome, however fewer efforts have been done to explore its natural peptides. To fill such gap, this review provides an overview of the mass spectrometric platforms and experimental designs aimed at GCF peptidome profiling, including our own data and experiences gathered from over several years of matrix-assisted laser desorption ionization/time of flight mass spectrometry (MALDI-TOF MS) based approach in this field. These tools might be useful for capturing snapshots containing diagnostic clinical information on an individual and population scale, which may be used as a specific code not only for the diagnosis of the nature or the stage of the inflammatory process in periodontal disease, but more importantly, for its prognosis, which is still an unmet medical need. As a matter of fact, current peptidomics investigations suffer from a lack of standardized procedures, posing a serious problem for data interpretation. Descriptions of the efforts to address such concerns will be highlighted.
gingival crevicular fluidMALDI-TOFpeptidesantimicrobial peptidespeptidomicsperiodontal diseasesmass spectrometry1. Introduction1.1. Gingivitis and Periodontitis
Periodontal diseases are complex, multifactorial, highly prevalent chronic inflammatory disorders affecting the tooth supporting tissues. Among them, periodontitis is a complex immune–inflammatory disease representing a significant health issue due to its high prevalence worldwide with approximately 10–15% of individuals being affected by severe forms [1]. It is characterized by microbially-associated, host-mediated inflammation that, in the absence of advanced treatment, could lead to a loss of periodontal attachment and also to alveolar bone resorption, resulting in deep periodontal lesions that may cause significant tooth loss in the advanced stage of pathology [2]. The accumulation of activated inflammatory cells like polynuclear leukocytes, macrophages, and lymphocytes in response to dental plaque amplifies the inflammatory process by the release of pro-inflammatory mediators [3]. Instead, gingivitis is recognized as a reversible condition, associated with microbial plaque accumulation and gingival inflammation that often resolves after restoration of oral hygiene procedures but, if untreated, may ultimately progress to periodontitis, thus representing the transitional phase between health and periodontal disease [1]. Therefore, a crucial interest has been growing in delineating the mechanism underlying this inflammatory stage, before it evolves to periodontitis. Periodontitis diagnosis is based on clinical examination, medical and dental history, tooth mobility, and radiographic assessment. Clinical evaluation is based on the assessment parameters, such oral hygiene, gingival status, probing depth, bleeding on probing, clinical attachment loss, alveolar bone status, but also on procedures such as periodontal microbiology testing, systemic health profiling by blood analysis, and finally, histological studies [4]. The main limit of most of these clinical methods is a certain degree of subjectivity, since they are examiner dependent. In addition, the biological events which trigger the progression from health to disease conditions are still unclear, therefore there is an urgent need to discover and validate reliable biomarkers able to diagnose early stage of pathology, possibly before clinical manifestations of periodontitis, in order to allow a more effective prompt therapeutic intervention.
Diagnostic tests are generally not necessary to discriminate between an inflamed site and a healthy site because clinical indices of inflammation, determined as described above, correspond well enough with periodontium histological inflammation. However, from a diagnostic point of view, the ability to discriminate between an inflamed-progressive (active) site and a less critical inflamed but stable site is of paramount importance [5,6]. Now, it is well accepted that both the outcomes of the immune/inflammatory response present in saliva/gingival crevicular fluid (GCF) and the impact of behavioral and environmental factors deeply affect the clinical picture of periodontopathy [7]. Following the “omics” burst, many researchers have tried to unravel molecular markers of periodontitis, identifying several genes, transcripts, proteins, and metabolites related to the pathology. Therefore, in the attempt to screen molecular profiles of periodontal disease, GCF has gained much attention, as will be discussed in the following sections.
1.2. Nature and Function of GCF
GCF is released in the dental crevice or gingival sulcus, defined as the space between the tooth and the overlying unattached gingiva. It mirrors serum composition because it not only derives from the gingival plexus of blood capillaries in the gingival corium, but also contains molecules derived from both host tissues and subgingival plaque, as well as a number of different cells, including epithelial cells, leukocytes and bacteria from the adjacent plaque mass [8]. In contrast, specialized glands, in particular the major salivary glands (submandibular, sublingual and parotid glands), and also roughly 600 minor salivary glands that are dispersed throughout the oral mucosa, have evolved to secrete saliva in the mouth. Obviously, GCF flows from the dental crevice into the buccal cavity, mixing with saliva and becoming part of it. Therefore, saliva is an aqueous solution (water content of 99%) containing electrolytes, small organic molecules, and nucleic acids, but most of all, proteins. In fact, over 3000 proteins have been found in saliva, the most represented being mucins, amylases, proline-rich proteins, cystatins, statherin, and histatins [4].
GCF exists as a serum transudate, changing into an inflammatory exudate as the inflammatory events progress [4]. In fact, this fluid is described as a mixture of molecules derived from the blood, host tissues, and subgingival plaque, and it contains electrolytes, small organic molecules, and various kind of proteins, among which antibodies, cytokines, bacterial antigens, and finally, enzymes of both host and bacterial origin [9]. Cytological examination of the GCF also demonstrates that it contains a variety of different cell types, including bacteria from the neighboring plaque mass, desquamated epithelial cells (which are passively washed out of the dental crevice into the oral cavity by the outward GCF flow), and transmigrating leukocytes (among which, lymphocytes monocytes/macrophages, and polymorphonuclear cells). Erythrocyte detection in the GCF should be considered an auxiliary finding, indicating injury to the microvessels that is typical of progressive inflammatory process [10]. With the onset of inflammation, the GCF flow rate increases and its composition begins to look like that of an inflammatory exudate. The augmented GCF flow contributes to host immune defense by flushing both bacteria and their metabolites away from the dental crevice. The main route for the diffusion of GCF is through the basement membrane, and subsequently, through the junctional epithelium into the dental crevice [11]. A particularly attractive feature of GCF is that this fluid is “periodontal specific” as it stems from a local site deeply involved in the manifestation of periodontal disease. For this reason it is considered to represent a faithful mirror of the periodontal health state of an individual [4]. Due to its composition, GCF might be a source of new biomarkers of periodontitis/gingivitis. The oral cavity is indeed a reserve of the microbiome and, when adverse changes occur within the oral cavity, it results in pathological changes, such as gingivitis, periodontitis, and dental caries [8]. As a biomonitoring fluid, GCF may play a key role both in the diagnosis and in the prognosis of oral diseases, in particular for periodontal diseases. It can be included among the most non-traumatic proximal fluid to obtain information about periodontal tissue status, including the conditions of the connective tissue and the level of hard destruction [12]. A non-invasive GCF collection (paper strip, paper points) technique helps obtaining samples of all age groups of human subjects, allowing also multiple sites sampling within the oral cavity [13]. So far, diverse inflammatory mediators have been isolated from GCF, including cytokines, phosphatase, proteinase and local tissue degradation products [14]. Several proteomics studies in the last decade shed light on GCF as potential source of biomarkers of both gingivitis and periodontitis and provided evidence for its diagnostic usefulness. The differential expression level of several proteins, such as defensins, annexins, plastin-2, lipocalin-2, and S100-P, was reported in the GCF between health and disease [15].
1.3. Low Molecular Weight Profiling of GCF
Despite the fact that naturally occurring peptides present in bodily fluids are known to play both local and/or systemic functions, only a few proteomics/peptidomics investigations targeted the GCF peptidome [6,16,17,18,19,20,21,22,23,24,25,26,27,28,29]. Even with such limited studies, the knowledge of the biological mechanisms that can be gained from peptidomics applied both to the identification of markers of disease and to the development of therapies which has encouraged the use of in the context of progress made in this field [6,16,17,18,19,20,21,22,23,24,25,26,27,28,29]. In the last decade, the impact of proteomics to the understanding of human GCF composition has progressively increased, underlining the potential diagnostic role of this fluid as invaluable source of periodontal disease-specific markers [6,14,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47].
With the progress of bottom-up MS-based approaches, enormous amounts of data have been generated, especially concerning the protein content of GCF, and extraordinary attention has been paid to comparative analysis in order to identify differentially expressed proteins between healthy and periodontal disease [48,49]. While from one point of view, it appears remarkable to categorize these studies as based on discovery or on quantitative proteomics approaches, as recently outlined by both Bostanci and colleagues [15,48] and Tsuchida and collaborators [49], it could also be of interest to appraise the efforts done by the proteomics community to assess the invaluable role played by naturally occurring peptides in GCF. Therefore, in the present review we highlight peptidomics studies which offer significant opportunities to detect and to identify peptides and small proteins in GCF of interest for periodontal diseases (Scheme 1, Table 1 and Table 2). A special focus will be given to the study design of several investigations, which are described from the collection of the samples to their processing and to the MS platform and strategy adopted, all with the aim to better address current limitations which hurdle GCF peptidomics research. Concerning the methodological strategy, all the studies reviewed here were carried out with MS profiling approaches in which protein content of GCF did not undergo protease digestion before MS analysis. The absence of a digestion step preceding the MS analysis overcome the intrinsic limitation of not discriminating between the naturally occurring peptides found in GCF and those artificially generated by proteolysis in the preceding step required for a bottom up protocol. The relevance of the top down profiling approaches reviewed here resides therefore in the fact that such investigations might reveal in a more direct and clear manner specific proteins/peptides functions and mechanisms of actions, as top down approaches preserve valuable information about all of proteoforms including proteolytic cleavage from larger proteins by the action of proteases of human or bacterial origin.
Generally, the peptide profiling investigations that we summarize in this review make use of surface enhanced laser desorption ionization (SELDI) [17,18] and matrix-assisted laser desorption ionization (MALDI)-MS [6,19,20,21,22,23,24,25,26] approaches which result more suitable and easily applicable for the analysis of natural peptides of GCF (Table 1). However, few investigations based on electrospray ionization (ESI)-MS have also been carried out to explore peptidic components and small proteins found in GCF in a top-down fashion [27,28,29] (Table 1).
.
2. An Overview of MS Profiling Tools and Investigations to Detect GCF Peptides
Some, but not many investigations based on MS have been performed to explore the GCF peptidome both under physiological [20,22,23,27,28] and pathological conditions [6,16,17,18,19,21,24,25,26,29]. Targeted identifications of specific peptidic component in GCF have been reported by Lundy et al. [16]. In this study, Calgranulin A (S100-A8) was purified by high-performance liquid chromatography (HPLC) and then identified using N-terminal amino-acid sequencing. S100-A8 is expressed in cells of myeloid origin and secreted in the case of cell damage or activation of phagocytes and monocytes. It exerts many roles in periodontal inflammation including chemotaxis, phagocytosis, or inhibition of microbial growth [16]. The amount of Calgranulin A was determined in GCF from healthy, gingivitis and periodontitis sites both in periodontitis patients (n = 15) and also from controls (n = 5) with healthy gingival status. The results showed significant differences in the amounts of Calgranulin A in relation to disease status, with higher expression levels in periodontitis and gingivitis sites than in the healthy sites in both periodontitis subjects and controls (Table 2) [16].
2.1. SELDI MS GCF Peptidome Profiling
In SELDI MS, samples to be analyzed are applied directly to a biochip coated with specific chemical “bait” matrices, which may be comprised of supports routinely used in chromatographic techniques (anionic, cationic, hydrophobic surfaces, etc.) or biochemical bait molecules, for instance purified ligands, proteins (like antibodies, receptors) or also DNA oligonucleotides [50]. The protein population bound to the biochip can then be washed with different kinds of washing buffers, in such a way that only the proteins sharing common chemical features remain on the surface and, subsequently, the mass map of all proteins retained is obtained at the same time. This analysis gives rise to a protein “fingerprint” formed by the exact molecular weight of each and every single protein first bound and then ionized off the specific bait surface used. SELDI-time of flight (TOF) MS, is one of the best analytical techniques for profiling both peptides and also low MW proteins (<20 kDa). Indeed, it has been extensively used in proteomics studies and, due to its high throughput, it became a promising tool for the identification of diagnostic patterns of disease [51,52,53]. However, this technology suffers a number of drawbacks preventing its widespread application as a routine platform in clinical diagnostics [54]. One of the main disadvantages of SELDI-TOF MS in peptidomics analysis is that its resolution is generally not very high (i.e., several different peptides can be present within each single ion peak, thus preventing direct assignment of the ion peaks of interest) [55]. Another point of weakness of the platform is the poor reproducibility, both within and also between laboratories. In this respect, a number of efforts have already been made to set up standardized protocols among the various research groups [56]. Another limitation of SELDI-TOF instruments is that they are not able to directly identify putative biomarkers as they lack MS/MS capabilities [57]. Finally, concerns can also be risen related to both the sensitivity of the technique and also the specificity of the biomarkers discovered by SELDI analysis [54].
The first peptidomics study on GCF was pioneered by Diamond and colleagues [18]. With the aim to understand the physiologic role of beta-defensins in the oral environment, Diamond and colleagues examined the media of cultured human oral keratinocytes and GCF by ProteinChip Array SELDI-TOF MS technology. The authors found that both the 47-amino acid form of human β-Defensin 1 (hBD-1, m/z = 5069) and the 41-amino acid form of human β-Defensin 2 (hBD-2, m/z = 4330) were best detected in GCF by SELDI-TOF measurements when a chip (among the others) coated with a hydrophilic phase was used. Peptides with the predicted masses of the alpha-defensins HNP-1 (m/z = 3442), HNP-2 (m/z = 3371) and HNP-3 (m/z = 3486) were also detected. However, only the identity of hBD-1 and hBD-2 was indirectly confirmed by immunoaffinity capture with anti-hBD-1 and anti-hBD-2 polyclonal antibodies on the ProteinChip surface, although hBD-1, as the authors hypothesized, was found in several smaller forms suggesting extracellular proteolysis. Immunoaffinity capture experiments unmasked inter-individual variation between the two subjects (n = 2) in the relative amount of hBD-2 and the hBD-1 peptides. Specifically, more hBD-2 and alpha-defensins, with little hBD-1, was seen in the case of the subject with greater inflammation. β-Defensins are secreted by gingival epithelial cells and exert combined antimicrobial, chemotactic, and anti-inflammatory properties [18]
With the same technology, some years later, Dommisch and colleagues optimized the experimental conditions to obtain the simultaneous detection of human neutrophil antimicrobial peptides (AMPs) in GCF as well as human cathelicidin LL-37 in SELDI-TOF profiles [17]. LL-37 is secreted by neutrophils and plays a key role in innate immune responses [17]. In this study, they compared GCF specimen from healthy periodontal sites to those from sites with early gingival inflammation. As each single donor also provided GCF samples from healthy periodontal sites as an internal control, GCF samples from sites diagnosed as gingivitis were analyzed individually in a donor-specific manner, thus allowing relative quantification. The authors observed that donors (four out of eight donors) that showed significantly higher intensity peaks corresponding to the mass of LL-37 (p = 0.01) also displayed greater (although not statistically significant) intensity peaks corresponding to the molecular masses of HNP-1 and HNP-2 in samples obtained from inflamed as compared to healthy periodontal sites (see Table 2). However, also in this SELDI study, the main limitations are due to the loss of unambiguous sequence-based identifications of the putative m/z peaks detected in the mass spectra and to the inherent semi quantitative assay.
2.2. MALDI MS GCF Peptidome Profiling
In the MALDI-TOF MS, a large excess of matrix (which is a small organic aromatic UV absorbing molecule) is used in comparison to the amount of the analytes (in this case peptides that need to be analyzed). Peptides are co-crystallized with the matrix into a solid-phase onto a stainless-steel target plate. When a laser beam hits the surface of the solid mixture, the matrix absorbs the laser energy and transfers it to the protonated peptides. Simultaneously, the rapid heating determines desorption of both the matrix and the newly formed [M + H]+ protonated peptides directly into the gas phase. MALDI ion source is commonly coupled to TOF mass analyzer [58]. In the recent mass analyzers, the ions generated in the source are then accelerated to a fixed amount of kinetic energy and travel along a flight tube. Having all the same kinetic energy, the small ions have a higher velocity and are therefore recorded by a detector before the larger ones. As a consequence, each m/z value displayed in a TOF spectrum is proportional to the time required to reach the detector for each given analyte.
Specifically, MALDI-TOF MS is a very versatile and fast analytical platform for analyzing both small and large polypeptides contained in various kind of biological samples. Some intrinsic properties of this analytical technique include low sample consumption, simple sample preparation, remarkable tolerance toward salts and buffers, high degree of automation and high throughput analysis and, finally, high sensitivity and mass accuracy. All these features make MALDI-TOF MS highly suitable for diagnostic purposes [59,60,61,62,63]. The versatility of the MALDI-TOF MS platform was exploited in different approaches to analyze GCF peptidome [6,19,20,21,22,23,24,25,26].
Lundy and colleagues used MALDI-TOF MS to qualitatively analyze neutrophil alpha-defensins in unfractionated GCF samples [19]. Starting from the rationale that the triad of alpha-defensins peptides (HNP-1, HNP-2, and HNP-3), due to their similar primary sequence, might show an equal ionization efficiency during the MALDI-TOF MS measurements, these investigators assumed that the ion intensities of the peaks representing the alpha-defensins paralleled the relative amount of these peptides. Therefore, they assessed the relative intensity of the peaks corresponding to HNP-1, HNP-2 and HNP-3 detected in the MALDI-TOF mass spectra of the unfractionated GCF collected from 12 periodontally healthy and 11 periodontally diseased subjects. From a comparison of the relative abundance of defensins, they found that the peak corresponding to the mass of HNP-1 was always the highest of the triplet while the peak matching with HNP-3 was always the lowest. These findings are consistent with the speculation that HNP-2 could be produced selectively by proteolysis of HNP-3, but not of HNP-1 [64]. Additionally, on the basis of ion intensity scores, the authors observed that the defensins were more abundant in a higher proportion of the healthy sites in comparison to the diseased (although the differences were not statistically significant). Therefore, they hypothesized that high expression of HNPs in GCF of periodontally healthy gingivae serves as barrier against bacterial colonization. These findings on defensins expression based on MALDI-MS data from Lundy [19] were however in contrast with those observed later by Dommisch [17] in the SELDI-based investigations (Table 2).
Wen et al. performed a profiling proteomic approach using GCF from 20 pubertal and 20 post-pubertal subjects in order to identify novel candidate biomarkers in GCF useful for the diagnosis of pubertal growth peak [20]. The GCF samples were collected by paper points and stored at −80 °C until MALDI-TOF analysis. Sample processing was performed using TFA, without the use of a protease inhibitor cocktail (PIC) (see Table 1). The unfractionated samples were analyzed directly by MALDI-TOF MS. The statistical analysis was performed after normalization for peak intensity and the results showed that six peptides were significantly different between the two groups. In particular, the peaks at m/z 1660.2, 1783.0 and 6108.9 were upregulated in the pubertal group compared to the post-pubertal group, while the peaks at m/z 5064.9, 2912.5 and 4178.6 were downregulated in the pubertal group compared to the post-pubertal group. Although primary sequence of these potential candidate biomarkers has not been identified, still the study provided a statistical model based on MALDI-TOF MS data able to detect the pubertal growth peak, demonstrating the potential use of this approach as a periodontal diagnostic tool. It should be taken into account that normalization for protein concentration was not performed, nor was the time of storage at −80 °C indicated.
Ngo and colleagues were the first to apply the sequencing capability of MALDI-TOF/TOF MS to identify the endogenous peptides of GCF collected from inflamed sites [6]. By means of this technology, 33 endogenous peptides were identified with a molecular weight ranging from 1067 Da to 3700 Da. In particular, of these 33 peptides, 10 peptides were identified in unfractionated GCF from single sites from direct TOF/TOF sequencing experiments while the other 23 peptides were identified in GCF samples pooled from multiple inflamed sites and fractionated by nano-HPLC. Several peptides resulted to be fragments derived from salivary precursor proteins, possibly reflecting the protease activity in the periodontal pocket (see Table 1). In this study, however, the identification of the three putative alpha-defensins peaks (HNP1–3) (m/z = 3370.9, m/z = 3442.2, and m/z = 3486.5), detected in the MALDI-TOF spectra and also revealed by previous investigations [17,19] was complicated because precursors ions did not fragment by collision-induced dissociation in MS/MS mode, due to the presence of the three characteristic internal disulfide bonds inside defensins. Therefore, the authors validated a putative assignment by using a reducing agent and showing that the resulting MALDI-TOF mass spectra displayed a +6 Da shift only in the mass of three peaks, finding consistent with the reduction of three internal disulfide bonds. Other peptides and small proteins were also identified alternatively by SDS-PAGE followed by in-gel digestion and MS analyses using both nano liquid chromatography (LC)-ESI-MS/MS and MALDI-TOF/TOF MS. All the peptides and small proteins of this study are summarized in Table 1, while medium-size and larger proteins, also identified by the authors, are not listed in Table 1 because they’re outside of the scope of the present review.
Later on, the same group demonstrated that the combined use of MALDI-TOF MS with bioinformatics may allow the site-specific prediction of periodontal disease progression in a cohort of forty-one periodontal maintenance subjects [21]. Each individual and specific site were followed over 12 months, with clinical measurements taken both at baseline and then every three months, collecting GCF to be analyzed at each visit [21]. The authors compared the MALDI-TOF MS spectra of GCF of both healthy and diseased sites from patients with moderate to severe chronic periodontitis. The purpose of the study was to use these spectra to create models for disease diagnosis. In particular, to pursue this, they used a genetic algorithm to create a pattern analysis-based model to predict specifically sites which were undergoing attachment loss. Interestingly, the statistical algorithm showed that there was more similarity in the GCF profile between patients, than between sites with different severity of inflammation. However, even with these differences, MALDI profiles may be taken into account for the prediction of periodontal attachment loss at a site level with a very significant positive predictive value and, most importantly, before it can be measured by clinical examination. This valuable result can be achieved with one single test on each individual GCF sample. Remarkably, this was the first study which explored and preliminarily demonstrated the potential use of MALDI-TOF MS technology in the field of periodontal disease diagnostics.
Our group set up a standardized protocol for both GCF collection from healthy gingival tissue belonging to subjects diagnosed healthy by clinical examination and further GCF processing [22]. Pre-analytical and analytical variables expected to influence the GCF peptidome profiling were assessed by MALDI-TOF MS analysis. More specifically, in a comparative study, we explored how the robustness and reproducibility of MALDI-TOF MS profiles are influenced by the use of paper points vs. paper strips, by the conditions of centrifugal elution and by the utilization of PIC (Table 1). Moreover, the effects of both sinapinic acid composition and sample-to-matrix ratio in MALDI-TOF MS analysis were systematically estimated, with the aim of optimizing both the quality and robustness of signals in GCF MALDI-TOF spectra. Definitively, a simple and high throughput procedure was optimized to rapidly obtain reproducible peptidome profiles of GCF from healthy sites of clinically healthy subjects. Sample composition and dilution severely affected the peptidome profiles obtained in MALDI-TOF analysis, therefore in this study, the optimal elution volume was evaluated in the assessment of a standardized procedure. Among other findings, elution by TFA generated richer gingival fingerprints compared to those obtained by the use of acetic acid. PIC supplementation and centrifugation speed did not change GCF profiles. Matrix composition and matrix/sample volume ratio were also assessed to obtain robustness of the MS analysis ensuring CV less than 10% for peak area and signal-to-noise ratio. These results are of interest for standardizing both sample collection and handling methods for GCF peptidomic-based biomarker identification, although we are aware that the procedure we set up may have to be re-visited for clinical conditions differing from a healthy status (for instance, gingival inflammation or periodontal disease), in which it is known that the nature of the GCF changes dramatically.
As diagnostic peptide fingerprints may vary as a function of GCF collection, handling, and storage, our research group analyzed if storage conditions have an influence on the quality and the reproducibility of MALDI-TOF profiles for this biological fluid [23]. Indeed, our data strongly suggest that sample storage conditions indeed affect the GCF peptide pattern over time in a quite significant manner. Specifically, a procedure of immediate extraction of GCF from paper strips, followed by storage of the extracted material, generates a lower amount of variation in molecular profiles as compared to the extraction performed after the storage on paper strips. Indeed, significant spectral changes were observed for GCF samples stored at −20 °C directly on the paper strips for three months and then extracted, in comparison to samples extracted immediately and then stored for three months in the same conditions. Moreover, a significant decrease in the peak area of HNP-3, of S100-A8 and of both full-length S100-A9 and its truncated form was detected after 3 months even at −80 °C when storage was on paper strips. The artifacts found in the “paper-stored GCF” profile may not only influence the pattern-based biomarker discovery, but also make its use not suitable for in vitro diagnostic test targeting. So S100-A8 and S100-A9 are proposed as periodontal disease potential diagnostic biomarkers [49]. This study definitively shows that the signatures closest to those obtained with immediate elution and analysis were attained for the GCF samples eluted in TFA and then immediately stored at temperatures not higher than −80 °C for at most one month. The information gained from our findings on protein/patterns stability after storage may be useful in defining standardized protocols enabling optimal preservation of GCF samples.
Very recently, our group proposed a fast and easy to perform method for processing GCF before MS analysis, thus minimizing the risk of false positive detection due to degradation of particularly sensitive molecular species within GCF samples [24]. In this investigation, twenty subjects (ten healthy and ten suffering from gingivitis) were recruited in order to perform comparative proteomics profiling studies between healthy and gingivitis subjects (Table 1 and Table 2). So, we proposed a standardized methodology for GCF analysis by MALDI-TOF/TOF-MS in order to identify a characteristic peptide signature of gingivitis. Storage times were established of one and three months concerning GCF specimen to analyze, in order to better evaluate any degradation and changes in GCF peptidome patterns. Comparative analysis of normalized peak area from peptidome profiles showed a pattern of five m/z peaks (3371, 4136, 4964, 13,153, and 13,458) which discriminate the two groups in a statistically significant manner. Specifically, the normalized peak area for m/z 3371, 4136, and 13,458 was significantly higher in GCF from gingivitis subjects compared to GCF from the healthy group, while an inverse trend was observed for m/z = 4964 and for m/z = 13,153 (see Table 2). The peak with m/z = 3371 was identified as the HNP-2 belonging to the triplet of alpha-defensins. Curiously, no significant change was observed for HNP-1 and HNP-3 between healthy and gingivitis groups. The other four peptides were identified as the C-terminal fragment of alpha-1-antitrypsin (AAT), namely C-36 peptide (m/z = 4136), as the β-thymosin (m/z = 4964) and, finally, two different post-translational modifications (PTMs) of the full-length S100-A9 protein were found (m/z = 13153 and m/z = 13458) (see Table 1). The study also demonstrated the emerging role of MALDI-TOF MS in the high-throughput characterization of naturally occurring peptides from complex bodily fluid and its potential to directly identify constituents of the GCF peptidome and their proteoforms.
In a very recent study, Tang et al. analyzed GCF, saliva, and serum samples from 50 gender- and age-matched subjects suffering from chronic periodontitis (n = 17) and gingivitis (n = 17), and from healthy subjects (n = 16), in order to identify potential biomarkers of periodontal diseases by using MALDI-TOF MS [25]. GCF samples were collected by paper strips, immediately deposited into a tube containing PBS; supernatants were recovered after centrifugation and stored at −80 °C (Table 1). GCF, saliva and serum samples were fractionated using a weak cation exchange magnetic beads and analyzed immediately on a MALDI-TOF mass spectrometer or stored at −20 °C and analyzed within 24 h. In the comparison between chronic periodontitis and healthy groups, four GCF peptides were significantly higher in chronic periodontitis group compared with healthy (3434.4 Da, 4126.6 Da, 5407.7 Da, and 5416.0 Da) (see Table 2). Among these, only the peak at m/z 3434.4 was successfully identified to be derived from haptoglobin by nano-LC/ESI-MS/MS analysis and its upregulation in chronic periodontitis patients was confirmed also for saliva samples. The authors identify other two peptides derived from haptoglobin both in serum and saliva (3874.9 Da and 1147.1 Da respectively) with an increased expression level in chronic periodontitis and gingivitis patients, thus confirming the results obtained with GCF. One strength of this study relies on the possibility to obtain specific molecular signatures for a given bodily fluid and then to compare the potential biomarkers identified across different bodily fluids. However, an important limitation, as pointed out by the authors themselves, is the absence of any kind of normalization for protein concentration. Furthermore, samples stored for the same amount of time need to be compared in order to ensure the robustness of the results; indeed, as discussed above, the protein profile of a sample analyzed immediately by MS may not be comparable to that of the same sample stored at −20 °C and analyzed within 24 h [23,24].
On the basis of blind MALDI-TOF analysis presently used in clinical microbiology for conventional bacterial strain identification, Antezack and colleagues adapted this analytical approach to MALDI peptide profiles acquired from GCF, saliva and dental plaque with the aim to develop a rapid diagnostic test for periodontitis [26]. They compared the MALDI spectra to assess if a classification may be performed among individuals according to their periodontal status. In particular, the authors used the binary discriminant analysis method [65] to evaluate differences between the groups of periodontitis (n = 67) and healthy periodontal (n = 74) subjects on the basis of a discriminant peak list (Table 1). Based on the differentially expressed peaks from MALDI peptide fingerprints, a diagnostic decision tree for periodontitis was built for each type of sample. In the case of GCF, a pattern of nine peaks was chosen to set up a decision tree with a specificity of 98% and a sensitivity of 96%. In a blind experiment GCF data demonstrated the ability to segregate periodontitis patients from control individuals showing a specificity of 75.7% (±0.195) and a sensitivity of 79.6% (±0.188). Although the authors claim their study as the first “to demonstrate that MALDI-TOF MS differentiates periodontitis from healthy periodontium by blind identification of specific patterns in mass signals from protein profiles in saliva, GCF and dental plaque” [26], the investigation suffers several limitations, in particular the absence of preliminary experiments designed to assess the impact of short-term storage at 4 °C of the analyzed samples and the total absence of quantification of total protein concentration in the same samples. In fact, concerning the first limitation (impact of short-term storage at 4 °C) although data in the literature show the small molecules like uric acid or cortisol are stable when saliva was stored for at 4 °C for four weeks [66,67] this may absolutely not hold true for protein or peptide components in the same fluid [23,24]. Further studies are absolutely necessary in order to increase the robustness of the diagnostic approach.
2.3. ESI MS GCF Peptidome Profiling
Unlike the MALDI source which ionizes and sublimates the samples out of a crystalline, dry matrix by laser, the ESI source ionizes the analytes out of a solution and is therefore usually coupled to LC instruments [68]. In the ESI source, the solution containing peptides analytes is passed through a needle to which a high voltage (~2–5 kV) is applied [69]. This leads to the generation of a spray of small charged droplets. As the solvent evaporates, desolvation of peptide/protein-solvent droplets resulted in a multicharged ions formations. The ions are then accelerated into the mass analyzer for subsequent measurement of their mass-to-charge ratios and of ion intensity. A mathematical deconvolution performed by the software on the ESI spectrum allows the mass of the analyte to be determined with an accuracy of roughly 1/10,000 amu. The combined information of both the time of elution (obtained, when possible, by using protein standards) and precise mass value of the protein quite often allows non ambiguous identifications to be obtained. This platform may be used successfully for the analysis of small complex samples because it allows high sensitivity detection of different analytes at the same time.
The mass analyzers routinely used with ESI are quadrupole, linear ion trap, and Orbitrap, however we concisely describe only the last two analyzers, those used in the peptidomic investigations here reviewed. Briefly, in a linear ion trap, ions are confined in radial direction by a two-dimensional radio frequency field, and in axial direction by stopping potentials applied to end electrodes [70]. The trapped ions are manipulated employing direct current and radio frequency electric fields in a series of carefully timed events. They can work as stand-alone mass spectrometers or into hybrid configurations combined with high-resolution mass analyzers. A correlation between the concentration of a given peptide and the ionic current, which is measured by the ion trap mass spectrometer, is however possible only under very reproducible conditions. Indeed, as the ionic current is directly proportional to the protonation level of the peptide, it is necessary to standardize the treatment of the samples. The linear ion trap mass spectrometer has become popular because of its robustness, fast scan speed, sensitivity, user friendliness and relatively low cost. However, resolution, mass accuracy and dynamic range are still not comparable to other mass spectrometers such as Orbitrap instruments [71]. Orbitrap is a benchtop instrument that is cost effective, accessible, and applicable in hybrid architectures. The Orbitrap MS is composed of a barrel-like outer electrode and a spindle-like central electrode [72]. In this electric field, ions rotate around the central electrode while oscillating down the length of the electrode; the frequency of these harmonic ion oscillations, undergone by the orbitally trapped ions, is used for m/z measurement [72]. The main advantage of the Orbitrap analyzers are enhanced resolution (the highest available for a benchtop mass spectrometer mostly in hybrid architectures), high mass accuracy, high sensitivity, wide dynamic range, fast acquisition rates and multi-stage MS(n) capabilities for tandem MS experiments [71,73]
Pisano and colleagues were the first group to analyze GCF by reverse-phase (RP)-HPLC coupled to ESI MS [27]. The authors analyzed the acidic-soluble protein content of GCF collected under physiological conditions. After the chromatographic separation, the eluate was directly introduced into an ion-trap mass spectrometer through ESI. Concerning peptides and small proteins, the HNPs 1–3 (alpha-defensins 1, 2, and 3) were the principal components detected, while only minor amounts of HNP-4, statherin, PB peptide, cystatin A were observed. Additionally, other unidentified components were detected with low mass values of 1067.4, 1109.4, and 1151.4 amu (probably corresponding to the same molecule showing a different degree of acetylation) and other peaks with mass values of 4135.0, 4936.4, 4964.0 and 4980.0 amu. This was also the first study to analytically demonstrate that GCF represents a physiological entity distinct from saliva. In fact, the authors did not detect the presence in chromatogram of the major proteins/peptides characteristic of human saliva such as acidic and basic proline-rich proteins. Although this approach does not allow for the detection of proteins that are not soluble in acidic solution, it contributed to the initial effort to discover the still under-represented GCF peptidome by an innovative MS-top down approach.
The same group (Inzitari et al.) demonstrated later, not only by HPLC-ESI-MS but also by immunohistochemical analysis, that gingival sulcus is the main source of Tβ4 in the oral cavity and that GFC contains high levels of Tβ4 and also of a second member of the same peptide family, namely Tβ10 [28]. Of note, these peptides play a key role in diverse cellular functions such as tissue development, migration, angiogenesis, and wound healing [28].
Very recently, Dassatti and colleagues also performed a pilot study analyzing the GCF proteomic profile by RP-HPLC coupled to ESI MS. GCF samples were collected during the first 15 post-partum days from 10 female patients with a clinical scenario of gingivitis and periodontitis (assessed by clinical periodontal parameters), and from the same patients after three months in order to evaluate the effects of a professional oral hygiene session and to analyze the associated variations of proteomic profile of GCF. A control group was created in order to compare the results with GCF samples from 10 not pregnant fertile women [29]. The analysis of clinical periodontal parameters at three months post-partum revealed the therapeutic support of the professional oral hygiene session performed during the first visit. Proteomic profiling of GCF, performed by RP-HPLC-ESI coupled to a hybrid ion trap-Orbitrap mass spectrometer, highlighted the presence of peptides playing a key role during the inflammatory process and exerting defense mechanisms. These peptides, were then identified by MS/MS experiments as α-defensins, thymosin-β4 with one of its fragments (21–44), thymosin-β10, fibrinopeptide A and its fragments (fragments 21–35 and 22–35) and fibrinopeptide B (Table 1). The levels of all the defensins (α-defensins 1–4) and of all the fibrinopeptides, were always higher during post-partum gingivitis and periodontitis, compared to the three months recall group and the control group (Table 2). The value of thymosin-β4 decreased in the group of patients with periodontitis, while it was significantly increased in the three months recall group. A similar trend was also observed for thymosin-β10 (Table 2). Otherwise, the expression level of thymosin β4 fragment (21–44) was higher during post-partum periodontitis compared to the three months recall group and the control group (Table 2). These findings demonstrate that pregnant women may be at an increased risk of pathological dental conditions and underscore the importance of a good oral hygiene procedures in helping to prevent problems with dental health. It is also interesting to associate the study of the expression level of peptides involved in the inflammatory process and in defense mechanisms with the variation of the clinical parameters normally used for the evaluation of the gingival health status. However, the paper lacks some important information about sample collection and processing.
3. The GCF Antimicrobial Peptidome
This section will focus in particular on the main findings concerning naturally occurring peptides, identified in GCF by profiling strategies performed without a previous proteolysis step propaedeutic to mass analysis. Considerations about their putative role and how their analysis might lead to the identification of novel biomarkers of periodontal health or disease will be discussed. As emerged from the literature reviewed in the previous sections, the top-down analysis of GCF peptides and their proteoforms (as they occurred in their intact form) poses several challenges for MS-based proteomics platforms. One of the main limitation arises in such cases from the short sequences of detected peptides, therefore the probability to find in their sequence basic aminoacidic residues is very low and as consequence those peptides may not be favorably charged for MS detection, as they are often singly charged and more difficult to efficiently fragment. Additionally, unlike bottom up approaches, the absence of Lys and Arg as non-tryptic peptides, hinders their detection. Furthermore, the conventional database search approach, which is normal routine in proteomics, is not suitable in this case. However these issues are extensively addressed also elsewhere [74,75]. Although definitive identification of naturally occurring peptides is particularly challenging, some MS instruments for example MALDI-TOF/TOF and ESI-ion trap or ESI-ion trap/Orbitrap provide not only a rapid screening of proteoforms [18,22,23,27], but also their sequencing and identifications [6,24,25,28,29]. In the light of the above considerations, it is worth highlighting important results from several groups; for example Ngo and colleagues detected and identified for the first time histone protein or peptide in GCF by MALDI-TOF/TOF (Table 1) [6]. Interestingly, histone fragments, have been shown to have antibacterial activity [76]. Other examples are the discovery of haptoglobin fragment in GCF by Tang et al. [25] (Table 1) as well as fibrinopeptide fragments by Dassatti et al. [29] (Table 1). It would possible to speculate that these fragments might play a more specific role in GCF compared to those played by their precursor protein. It is well known that haptoglobin is an acute-phase protein, which promotes anti-inflammation activities and could also act indirectly as an antioxidant and bacteriostatic agent [77]. Its expression increases in response to injury or infection as it contributes to the recovery of homeostasis after systemic or local infection [77]. Therefore, the authors hypothesized that the up-regulation of haptoglobin derived fragment levels in chronic periodontitis patients compared to healthy subjects (Table 2) may reflect the anti-inflammatory and reestablishment process of the periodontium. Fibrinogen and fibrinopeptides are involved in hemostasis, wound healing, inflammation, angiogenesis, vascularization after injury, and other biological functions [78]. So, the authors speculated that the increased levels in periodontal disease conditions of fibrinopeptide A and its fragments along with fibrinopeptide B (see Table 2) may be due to their involvement in the healing process and revascularization of the area affected by periodontal disease.
Another point worthy of note was the detection of Thymosin β4, together with its sulfoxide form, and of Thymosin β10 by Inzitari and colleagues [28] which demonstrated that gingival sulcus is the main source of Tβ4 in the oral cavity and that GFC is the main source of Tβ4 and Tβ10 [28]. Although several bottom-up approaches documented the presence of Thymosin β4, they failed to identify Thymosin β10 or the sulfoxide form of Thymosin β4 [14,32,39,42,45]. These peptides have multiple diverse cellular functions, including tissue development, migration, angiogenesis, and wound healing [79]. In particular, multifunctional roles are attributed to Tβ4 in protecting cells against damage by acting as antimicrobial, anti-inflammatory, and anti-apoptotic agent [80].
Our group detected two proteoforms of S100-A9 with m/z = 13,153 and 13,458 and the oxidized forms of S100-A8 [23,24]. S100-A9 together with S100 A8 belong to the damage-associated molecular pattern family members of danger signals that initiate and amplify local inflammation and innate immune responses [81]. S100-A9 is frequently associated with S100-A8 to form the S100-A8/S100-A9 heterodimer, known as calprotectin, with anti-microbial, chemotactic, pro-apoptotic and anti-proliferative activities [81]. Several studies shed light on the key role of calprotectin and its subunits in the pathobiology of a number of inflammatory conditions including periodontal diseases [49,81,82]. In addition the GCF calprotectin levels were positively correlated with clinical parameters of periodontitis [81]. Specifically, in our comparative study, we detected the acetylated form (m/z = 13153) and the acetylated and glutathionylated form of the S100-A9 (m/z = 13458) [24]. The first was found decreased in gingivitis patients compared to the healthy controls, while the latter was found increased [24]. It is common knowledge that GCF contains significant levels of reduced glutathione which is responsible for the local antioxidant capacity, typical of periodontally healthy subjects [83] and that glutathionylation of S100-A9 alters its ability to form complexes with S100-A8, to bind endothelial cells, and limits neutrophil migration in inflammatory lesions [84]. Based on these results, our group speculated that the glutathionylation in S100-A9 may result in a protective effect against oxidative process at the site of inflammation thus providing a coherent explanation to the observed reversed ratio between the m/z = 13,458 (both glutathionylated and acetylated) and the m/z = 13,153 (acetylated only) forms of S100-A9 peptide in gingivitis patients compared to healthy subjects [24].
Lundy and colleagues, in line with other transcriptomics and proteomics studies [4], found increased expression levels of S100-A8 in periodontitis and gingivitis sites compared to the healthy ones (Table 2) [16]. However, it is worth noting that, in some comparative proteomics studies, S100-A8 and S100-A9 are not differentially expressed, even if detected [32,33,34,41], suggesting various hypotheses as already reported elsewhere [23] and discussed in the next sections.
Among several AMPs detected by the MS profiling platforms, here described and summarized in Table 1 and Table 2, important families such as human alpha-defensins (HNPs 1–4) and human beta-defensins (hBD-1 and hBD-2) and also the cathelicidin antimicrobial LL-37 peptide were studied.
Concerning the alpha defensins (HNPs 1–4) the results of comparative studies between healthy and gingivitis groups or healthy and periodontitis groups (Table 2) showed that defensins, with the exclusion of one study [19], are generally increased in periodontal conditions (gingivitis and periodontitis, Table 2). Similarly to other AMPs, defensins exert their protective role against microbes entering the oral cavity resulting in effective control of infections. However, even with a broad bactericidal effect, HNPs have been demonstrated to be rather ineffective in vitro against various periodontal pathogens, at least at concentrations in which they are found in healthy gingival crevice [85]. Increased expression of α-defensins may induce tissue damage both directly, by their observed cytotoxic effects on cells [86], and indirectly, by competing with neutrophil elastase for the binding of α-1-antitrypsin (AAT), an inhibitor of neutrophil elastase [87]. Consequently, defensins may influence the balance between proteases and their inhibitors and increased defensins levels observed both in peptidomics studies (Table 2) [17,18,24,29] and also in proteomics investigations [34,38,40] may cause augmented proteolytic activity, thus contributing to the inflammation and the tissue damage observed in gingivitis. As shown in Table 2, in our studies we detected significantly increased levels of only HNP-2 in the gingivitis group while no significant change was observed for HNP-1 and HNP-3 between healthy and gingivitis groups [24]. Our results were partially in agreement with the findings of Dommisch et al. who observed increased levels of HNP-2 and, to a lesser extent, of HNP-1 (although both not statistically significant) in gingivitis sites in comparison to the healthy sites while no variation of HNP-3 was reported [17]. However, the general observed trend of increase of defensins in periodontal diseases was inverted in the study of Lundy and colleagues as they found that the defensins expressions were more abundant in a higher proportion of the healthy sites in comparison to the diseased (although the differences were not statistically significant) [19].
Interestingly, Diamond and colleagues detected in GCF not only the alfa defensins, but also the β -defensins hBD-1 and hBD-2, although hBD-1 was also found “in several smaller forms” suggesting extracellular proteolysis [18]. It is interesting to note that a higher amount of both hBD-2 and α-defensins, with little hBD-1, was observed in the subject with higher levels of inflammation (individual 1, Table 2). This observation is in line with increased levels of both hBD-2 and neutrophils in inflammation. β-defensins are small cationic peptides produced by gingival epithelium that are involved in the innate host defense against the bacterial challenge, which is continuously present in the oral cavity [88]. β-defensins play a key role in the awakening of the innate immune response to increased bacterial exposure in the gingival epithelium and exert combined antimicrobial, chemotactic and anti-inflammatory properties. These peptides contribute to the healing process of gingival wounds, regenerating the damaged epithelium by promoting the attachment and proliferation of fibroblasts on the diseased root surfaces [88].
Another relevant antimicrobial peptide detected by Dommisch and colleagues is the human cathelicidin LL-37, a peptide of 37 amino acid starting with two leucine residues which has a broad spectrum of antimicrobial activity [89]. It is stored as biologically inactive precursor in the secondary granules of neutrophils and it acquires its antimicrobial potency after proteolysis exerted by proteinase 3. LL-37 regulates inflammatory and immune responses, promotes angiogenesis and wound healing and, finally, also neutralizes lipopolysaccharides. The increased expression of this peptide in samples obtained from sites characterized by gingivitis compared to healthy periodontal sites (Table 2), together with the increased expression in the same diseased sites of HNP-1 and HNP-2, induced Dommisch and colleagues to speculate that LL-37 might be a good indicator of the antimicrobial capabilities of the gingival apparatus provided by neutrophils and that there might be an association between a parallel secretion of these AMPs due to the increased numbers of neutrophils in inflammation.
Our group identified for the first time the sequence of the peptide m/z = 4136 already detected in GCF in previous top down proteomics studies, but not structurally elucidated [27]. Despite technical issues, due not only to the complexity of GCF mixture but also to the high molecular weight of the precursor ion (m/z = 4136), a direct high-energy CID fragmentation of the selected peak allowed us to identify it as C-36 peptide corresponding to the residue fragment 383–418 of AAT protein [24] (Table 1). This peptide showed a statistically significant increase in gingivitis group compared to healthy group as previously described in the Section 2.2 [24] (Table 2). Interestingly, C-36 peptide deriving from AAT, exerts significant pro-inflammatory activity [90]. In fact, it was found expressed in human lung tissue as pro-inflammatory activator of human monocytes [91]. Moreover, the proteolytic cleavage of AAT at sites of inflammation may reverse the anti-inflammatory effect of AAT thus contributing to neutrophil recruitment and activation [92]. Considering that inflamed sites are populated by several bacterial species secreting proteolytic enzymes able to generate protein-breakdown products [93], it is tempting to speculate that the C-36 peptide, which was found increased in gingivitis patients as compared to healthy subjects [24], might be derived by AAT bacterial proteolytic breakdown. It should be stressed that a bottom-up LC MS/MS approach would have failed to address the levels of AAT endogenous fragment, due to the requirement for the proteomic mixture to be digested before it can be mass analyzed, whereas our top down MALDI-TOF platform allowed for the detection of increased levels of C-36 peptide in the GCF of gingivitis patients [24].
All these findings further demonstrate the utility of peptidomics profiling platforms for screening and detecting proteoforms, including products of specific proteolytic cleavages, which can have different effects and important biological roles on periodontal diseases processes. The ability to characterize these species as they occur in GCF is essential for understanding the mechanism of the pathobiological response to oral diseases.
4. Relevance and Challenge of Pre-Analytical and Analytical Variables
To date, the effects of different sampling protocols and the influence of both pre-analytical and analytical conditions on GCF proteomic profiling have not been extensively addressed in many of the proteomics and peptidomics investigations which analyzed this specific biological fluid. The inherent limitations of GCF, due to its tiny amount in healthy gums as well as its heterogeneity in periodontal diseases, pose many concerns especially in comparative studies, which require rigorous protocols for protein recovery and normalization of MS data. Other concerns arise from artefacts due to storage conditions. Furthermore, in many cases, investigations which are biased by inter-individual variability of GCF samples, were performed on too small size groups to guarantee sufficiently robust statistical analysis. Last but not least, the comparison of the results obtained among several research groups is not so straightforward and the strategy adopted (bottom-up or top-down) should also be taken into account.
4.1. Quantitation Issues
More than five decades of scientific literature have definitively demonstrated that different sampling methodologies, different sampling times and different processing protocols significantly affected both the quality and the quantity of GCF samples [94]. Up to date, only a few quantitative proteomics investigations have been performed with the aim to differentiate GCF protein profiles in periodontal health and disease [14,30,33,38,39,40,44]. In MALDI-TOF MS, but also in other MS platforms, the ionization is influenced not only by the analyte concentration, but also by its primary structure (in fact, the presence of one or more basic residues in a peptide/protein promotes the ionization). Therefore, in the MS investigations which analyze peptides without an hydrolysis preceding step, MS spectra and MS tandem spectra could result poor and in such cases the spectra interpretation may result challenging in comparison to those obtained by bottom-up procedures in which the presence of tryptic peptides facilitates the ionization and the fragmentation step in MS/MS analysis. The lack in some peptides of a positive charge on the N-terminal residue due to PTMs (acetylation/pyroglutamylation) and the simultaneous absence of basic residues such as Lys, Arg, or His hinder the detection of these peptides [74,75].
Concerning peptidomics studies, the quest for quantitative approach is stringently desirable in order to make published data comparable between each other. The lack of data uniformity is due to several factors, including the difficulty in accurately measuring the small volumes of GCF obtained (especially for healthy sites) and the differences both in collection methodology and processing protocols, as pointed out in Table 1. An additional problem, which complicates the analysis and standardization issues in GCF analysis, is the amount of volume used to elute the sample collected on paper strips or on paper points. In particular, this is an important issue in the case of SELDI and MALDI based investigations (Table 1). In fact, it is well accepted that MALDI-TOF MS spectra of complex bodily fluids are heavily influenced by the extent of sample dilution [63,95,96]. To address this issue in our investigations, experiments at different elution volumes have been performed and both the number of peaks and total peak area in MALDI-TOF MS spectra have been assessed [22]. Indeed, the observation that more concentrated solutions showed both a lower number of peaks and a lower total peak area in comparison to more diluted samples was the most evident and expected behavior; not surprisingly, this was consistent with the MALDI ionization process. Furthermore, since GCF is a complex biological sample for the presence of endogenous peptides and proteins of different structures and different concentrations, the occurrence of ion suppression effects may also take place [97,98].
The effect of heterogeneity of both sample/matrix and ion signals determines poor MALDI data reproducibility, making this kind of analysis unsuitable for quantitative purposes [62]. In order to make MS profiling analysis more robust the use of internal standard and/or a better assessment of peak area normalization are required. Several studies have shown that with the appropriate internal standard, linear calibration curves can be generated providing a correct quantification especially for small molecules and peptides, even in complex biological matrices [62,99,100,101]. The use of internal standard can be avoided when a linear correlation between analyte concentration and ion counts can be demonstrated [102]. A possible alternative to the utilization of an internal standard is the use of Ionic-Liquid Matrices [103].
4.2. PIC
The presence in the GCF of active proteases, of both human and bacterial origin might constitute serious drawbacks making GCF peptidomics analysis very challenging. As in saliva and in other bodily fluids, the degradation and/or protease activity in GCF should be taken in great account. In light of possible protease activity, in such peptidomics studies after sample collection the use of protease inhibitors or of other precautions to prevent protease activity might be highly recommended. Concerning the solutions or buffer used to elute GCF collected on paper strips, in our experience, the use of acidic aqueous solutions is preferable to neutral or basic aqueous/organic ones, because acidic elution quenches protease activity, preserving proteins from potential protease degradations [27,104]. It is interesting to underline that the use of acidic buffer in peptidomics investigations might turn useful also for other reasons. In fact, high molecular weight proteins such as mucins, lactoferrin, α-amylases, and myeloperoxidases, found to be present in GCF [14,30,31,33,34,36,37,38,40,41,42,43,44,45,46,47] and which could hinder the detection of peptidic components [105], are insoluble in acidic conditions [27]. Studies on peptidome stability of GCF samples with PIC supplementation at neutral or basic pH are not yet reported in literature. Many research groups make use of acidic buffer to elute or dilute GCF immediately after the collection [6,17,18,20,21,22,23,24,27,28] and, as indicated in Table 1, for all of the investigations reviewed, the use [22,23,24] or not [6,16,17,18,19,20,21,25,26,27,28,29] of PIC is shown.
The levels of the peptides present in bodily fluids might reflect the activity of protease(s) generating them, which could in turn be influenced by various biological events. Thus, the amount of certain peptides can be used as indirect sensors of the biological state of an individual and, in the end, could provide invaluable information for clinical diagnoses [57]. Among the peptidomics studies reviewed here, many investigations have shown in GCF the presence of peptide fragments derived by proteolytic cleavage from larger proteins such as Fibrinopeptide A fragments (21–35) and (22–35) and Fibrinopeptide B [29], haptoglobin derived peptide [25], Thymosin β-4 fragment (21–44) [29], hBD-1 derived peptides [18] and peptides fragments derived from salivary and serum precursor proteins [6], C36 peptide AAT [24]. Many of these peptides, arising from precursor proteins after exquisitely specific proteolytic cleavages, could exert significant biological activity against various kinds of microorganisms as already outlined in previous section. However, these findings currently remain only mere hypotheses and targeted experiments are required in order to support these possible speculations.
Careful attention should be used in the interpretation of the results of all the studies in which neither protease inhibitors nor other precautions to prevent protease activity were used during sample collection, as the presence of fragments identified as putative biomarkers of pathology may simply be due to the action of uninhibited proteases.
Ultimately, the presence of glutathione in stored GCF determines glutathionylation patterns of such protein target [23] lowering the concentration of the protein as demonstrated in our previous work [23]. The above considerations, together with the degradation patterns observed during storage in various conditions [23,24], underline the requirement to better asses the concentration and storage issues (see below) not only in top down but also in bottom up approaches in order to make the results comparable among each other for a consistency in data interpretation.
4.3. Storage
Among the various pre-analytical issues that deeply affect the peptide profiles of GCF, the storage conditions may strongly compromise the correct interpretation of data analysis if not properly assessed. To address this issue, our group studied the stability of GCF MALDI-TOF profiles after sample collection under different storage conditions [23,24]. Specifically, the storage of GCF immediately extracted from paper strips was found to generate less variations in molecular profiles compared to those obtained when the extraction is performed after the storage. In fact, significant spectral changes were detected for those samples stored at −20 °C directly on the paper strips and extracted after three months, in comparison to the freshly extracted control [23]. Still, even in the case of GCF samples immediately extracted from paper strips, a significant decrease in the peak area of HNP-3, S100-A8, full-length S100-A9, and its truncated form were detected after three months of storage, even at a temperature as low as −80 °C [23].
Based on the above findings, we conclude that the storage conditions may strongly compromise the correct data interpretation, since alterations of “stored GCF” profile may influence the pattern-based biomarker discovery. As a consequence, the samples to be compared (healthy versus periodontal diseases) should be stored for the same amount of time in order to minimize the chance that the results of the study may be biased by potential artifacts.
4.4. The Inter-Individual Variability
Considering the intrinsic inter-individual variability of GCF samples, large cohorts are necessary in order to compare the proteomes in the presence or absence of periodontitis. As reported in Table 2, only few case-control reports describing differences between healthy and gingivitis [24] or healthy and periodontitis subjects [19,25] or sites [16,17,29] were reported; moreover only one experimental model of feasibility blinded test [26] emerged (Table 2).
With the exception of the studies by Tang and colleagues (n = 50) [25] and by Antezack et al. (n = 141) [26], a limitation of the reports summarized in Table 2 is the small sample size.
It is quite conceivable that the subset of potential biomarkers identified in a small sample size might not be confirmed on a larger cohort of patients. Conversely, the use of larger sample sizes should provide increased statistical power allowing the identification of additional biomarkers. Investigations on larger cohorts could provide a more accurate qualitative interpretation of peptide signatures, for well-defined clinical applications.
5. Bioinformatics Approaches
It should be considered that the majority of data in spectral libraries are built from in-silico enzymatic digestion of proteins, limiting the analysis only to those peptides derived from expected enzyme cleavage sites and limiting the number of PTMs considered; for this reason the datasets are difficult to leverage for peptidomics [74]. Moreover, the limited length of the aminoacidic sequences makes peptidomics spectral data analysis and interpretation more challenging in comparison to conventional bottom-up proteomics. As already underlined in Section 3, the studies here reported relate to ‘non-tryptic’ peptides which generate MS/MS patterns less informative in comparison to the ‘tryptic digested peptides’. In fact, due to their intrinsic nature, the absence of basic Lysine or Arginine at the C-terminal sequence might hurdle the fragmentation process and, as a consequence, generation of poor MS/MS spectra could be observed. In line with these observations, the same bioinformatics strategies used for bottom-up approaches do not perform efficiently when less predictive and informative MS/MS patterns are obtained from endogenous peptides [75]. It is important to highlight that, unlike bottom-up proteomics, software solutions for peptidome characterization are not fully developed and data analysis often requires laborious manual interpretation and rigorous validation especially when the identification is based on a single peptide [74,75]. De novo sequencing algorithms assisted by classical database search programs for sensitive and accurate peptide identification are also currently available [106,107]. De novo sequence assignments (when manually performed) often require skilled and experienced personnel and, besides fragmentation patterns, further acquired confirmations for identification can possibly be the assessment of ion intensities and in the case of LC-ESI experiments, evaluation of accurate retention times [27]. The use of software for predicted fragmentations is one of most cost-effective way to validate the identification [108]. For example, concerning both MALDI-TOF/TOF and ESI experiments, when the automated software identify with low confidence endogenous peptide and their fragments, in order to overcame these difficulties and to reach high confidence identification/validation, the experimental mass value of the peptide derived from unspecific cleavage of precursor protein/endogenous peptide can be compared with average theoretical mass values using the FindPept or PeptideMass software (available at http://www.expasy.org/tools) and the experimental MS/MS spectrum can be compared to the MS/MS spectrum generated from the Protein Prospector (http://prospector.ucsf.edu/). Furthermore, several tools have been developed for peptide mapping such as iPiG, EnzymePredictor, PatternLab, Peptigram, UStags, PepNovo, and MS-Tag [74].
6. Conclusions
Peptidomics research represents one of the most interesting and challenging area of proteomics. GCF peptidome could be a goldmine for the discovery of novel biomarkers of periodontal diseases. While many proteomics-based bottom-up approaches have been pursued, resulting in a burgeoning of proteins occurring in GCF, still these approaches are not adequate enough to completely disclose all the proteoforms present in the sample. On the other hand, peptidomics investigations, mainly based on profiling (top-down) strategies, which are well suited for proteoforms detection, are very few and therefore, up to date, the picture of naturally occurring peptides and their role in GCF is still incomplete. In this scenario, the main limitations are due to the lack of standardization of pre-analytical and analytical variables affecting the different processing steps from the sample collection, to the potential artefacts coming from storage of GCF and from the protein concentration normalization and, finally, to the robustness of MS analysis. So, differences derived from heterogeneity in collection, handling, and storage make the results difficult to compare and to establish their correct interpretation due to their inconsistency. Controversies arising from defensins and calgranulins are clear examples of these data misinterpretations.
With the appropriate standardization, MS profiling strategies will allow to harvest intact peptide signatures from GCF which could help to delineate the subtle boundaries existing among the different stages of gingival inflammation. Obviously, each single biomarker forming the signature can be validated by more traditional biochemical approaches like, for instance, Western blot analysis or ELISA assays, which both present an advantage in terms of exquisite specificity of purposely developed monoclonal antibodies raised against the peptide identified by MS. This will open the possibility to recognize a specific phenotypic pattern for the early diagnosis and the progression to periodontal diseases. Moreover, an increased coverage of both GCF peptidome and proteome might assist the comprehension of molecular mechanisms underpinning the pathogenesis of periodontitis. Although the focus of this review is on periodontal diseases, in recent years it was proposed that GCF and saliva should be kept in consideration also in “the wider contexts of oral and systemic health” [109]. Indeed, it has been reported that saliva has shown potential for the development of diagnostic assays for systemic pathologies such as acquired immunodeficiencies, diabetes mellitus, Sjögren syndrome, cerebrovascular/cardiovascular diseases, systemic sclerosis, and even for cancers like breast cancer and, not surprisingly, more localized tumors such as tumors of oral cavity, salivary gland tumor larynx carcinoma, and head and neck carcinoma ([4] and references therein). Considering that GCF has a different origin compared to saliva (it is not secreted from the salivary glands) it is not surprising that these two biofluids do not have a perfect overlap as diagnostic predictors in systemic health, however it has been reported that periodontal diseases (focus of this review) and the consequent periodontal inflammation can have an effect on the progression of important systemic disorders, such as cardiovascular and cerebrovascular diseases [94]. In light of these considerations, MS profiling strategies will allow to harvest intact peptide signatures from GCF which can be used for diagnostics and prognostics assays for important systemic disorders, such as cardiovascular and cerebrovascular diseases.
Acknowledgments
In this section you can acknowledge any support given which is not covered by the author contribution or funding sections. This may include administrative and technical support, or donations in kind (e.g., materials used for experiments).
Author Contributions
Conceptualization, R.T.; writing-original draft preparation, M.P., R.S., C.V., and R.T.; writing-reviewing and editing, M.P., R.S., C.V., C.P., and R.T.; table preparation, M.P. and R.S.; supervision, R.S. and R.T. All authors have read and agreed to the published version of the manuscript.
Funding
This work was supported in part by funding from the Department of Health Sciences of the University of Catanzaro. MP has been supported by funding from research grant PON-MIUR 03PE000_78—Nutramed. CV has been supported by a fellowship of the Ph.D. Program in Life Sciences.
Conflicts of Interest
The authors declare no conflict of interest.
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Proteom.201211M111–M01058710.1074/mcp.M111.01058722186715MaB.ZhangK.HendrieC.LiangC.LiM.Doherty-KirbyA.LajoieG.PEAKS: Powerful software for peptide de novo sequencing by tandem mass spectrometryRapid Commun. Mass Spectrom.2003172337234210.1002/rcm.119614558135BudamguntaH.OlexioukV.LuytenW.SchildermansK.MaesE.BoonenK.MenschaertG.BaggermanG.Comprehensive Peptide Analysis of Mouse Brain Striatum Identifies Novel sORF-Encoded PolypeptidesProteomics201818e170021810.1002/pmic.20170021829710410TaylorJ.J.PreshawP.M.Gingival crevicular fluid and salivaPeriodontology 200020167071010.1111/prd.1211826662478Scheme and Tablesijms-21-05270-sch001_Scheme 1
Overview of the main informational data extrapolated by peptidomics experiments on gingival crevicular fluid (GCF). Peptidomics workflow, without a digestion step, allows to preserve the endogenous information of peptides from GCF, including post-translational modifications and proteolytic products.
Paper strips/2.5%TFA; PIC and NO PIC; Unfractionated.
−20 °C/1 or 3 months, −80 °C/1 or 3 months and immediately analyzed
Yes
Yes
MALDI-TOF/TOF
α-defensins 1–4, hBD-2, Thymosin β4, S100-A8 and its oxidized forms, S100-A9 and its isoforms, Lysozyme C
Preianò et al. [24]
Paper strips/2.5%TFA; PIC and NO PIC, Unfractionated.
−20 °C/1 month, −80 °C/1 month and immediately analyzed.
Yes
Yes
MALDI-TOF/TOF
α-defensins 1–3, hBD-2, C-36 peptide of AAT, Thymosin β4, Thymosin β10, S100-A8 and its oxidized forms, S100-A9 and its isoforms, Lysozyme C
Tang et al. [25]
Paper strips/phosphate-buffered saline; NO PIC; Weak cation exchange magnetic beads.
−80 °C, time not specified (c).
No
Yes
MALDI-TOF
Haptoglobin derived peptide and other unidentified peptides: m/z = 4126.6, 5407.7, 5416.0
Antezack et al. [26]
Paper points/HPLC-grade water; NO PIC; Unfractionated.
Paper cones/Buffer not specified; Use of PIC not specified; RP-HPLC.
−80 °C, time not specified.
No
Yes
ESI-Ion Trap/Orbitrap
α-defensins 1–4, Thymosin β4, Thymosin β4 fragment (21–44), Thymosin β10, Fibrinopeptide A, Fibrinopeptide A fragments (21–35) and (22–35), Fibrinopeptide B
(a) Peak normalization is not required as it is a targeted study. (b) Protein quantitation: samples from healthy periodontal sites served as internal control (baseline) for quantitative analysis. (c) After storage at −80 °C, GCF samples were fractioned using a weak cation exchange magnetic bead kit and then immediately analyzed on MALDI-TOF or stored at −20 °C and analyzed within 24 h.
ijms-21-05270-t002_Table 2
Naturally occurring peptides found in gingival crevicular fluid (GCF) from different study groups. Expression levels are shown as increased (↑) or decreased (↓), as determined by the referred investigation in relation to the appropriate control group. H = Healthy, G = Gingivitis, P = periodontitis, CP = Chronic periodontitis.
Naturally Occurring Peptides
Methods
Study Groupsand Number of Subjects (n)
Study Groups and Protein Expression Level
Ref.
C-36 AAT
MALDI-TOF
H (10) vs. G (10)
↓H/↑G *
Preianò et al. [24]
Cathelicidin antimicrobial peptide LL-37
SELDI-TOF
H sites (n = 8) and G sites (n = 8) in subjects with good general health (4).
↓H sites/↑G sites in H *
Dommisch et al. [17]
Fibrinopeptide A
ESI-Ion Trap/Orbitrap
\nG and P sites in women after pregnancy (10) and not pregnant women as H controls (10).\n
H, G and P sites in patients with P (15) and H sites in H subjects (5).
↓H/↑G sites in P *↓H/↑P sites in P *↓H sites in P/↑G sites in P *↓H sites in P/↑P sites in P *
Lundy et al. [16]
S100-A9
MALDI-TOF
H (10) vs. G (10)
↑H/↓G *
Preianò et al. [24]
S100-A9 Glutathionylated
MALDI-TOF
H (10) vs. G (10)
↓H/↑G *
Preianò et al. [24]
Thymosin β-4
MALDI-TOF
H (10) vs. G (10)
↑H/↓G *
Preianò et al. [24]
ESI-Ion Trap/Orbitrap
G and P sites in women after pregnancy (10) and not pregnant women as H controls (10).
\n"],"string":"[\n \"\\nInt J Mol SciInt J Mol SciijmsInternational Journal of Molecular Sciences1422-0067MDPI32722327PMC743212810.3390/ijms21155270ijms-21-05270ReviewGingival Crevicular Fluid Peptidome Profiling in Healthy and in Periodontal DiseasesPreianòMariaimmacolata1†SavinoRocco2†VillellaChiara1https://orcid.org/0000-0002-4236-7367PelaiaCorrado1TerraccianoRosa3*Department of Health Sciences, University “Magna Græcia”, 88100 Catanzaro, Italy; preiano@unicz.it (M.P.); villella@unicz.it (C.V.); pelaia.corrado@gmail.com (C.P.)Department of Medical and Surgical Sciences, University “Magna Græcia”, 88100 Catanzaro, Italy; savino@unicz.itDepartment of Experimental and Clinical Medicine, University “Magna Græcia”, 88100 Catanzaro, ItalyCorrespondence: terracciano@unicz.it; Tel.: +39-0961-3694085
Co-first author, these authors contributed equally to this work.
2472020820202115527016620202272020© 2020 by the authors.2020Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Given its intrinsic nature, gingival crevicular fluid (GCF) is an attractive source for the discovery of novel biomarkers of periodontal diseases. GCF contains antimicrobial peptides and small proteins which could play a role in specific immune-inflammatory responses to guarantee healthy gingival status and to prevent periodontal diseases. Presently, several proteomics studies have been performed leading to increased coverage of the GCF proteome, however fewer efforts have been done to explore its natural peptides. To fill such gap, this review provides an overview of the mass spectrometric platforms and experimental designs aimed at GCF peptidome profiling, including our own data and experiences gathered from over several years of matrix-assisted laser desorption ionization/time of flight mass spectrometry (MALDI-TOF MS) based approach in this field. These tools might be useful for capturing snapshots containing diagnostic clinical information on an individual and population scale, which may be used as a specific code not only for the diagnosis of the nature or the stage of the inflammatory process in periodontal disease, but more importantly, for its prognosis, which is still an unmet medical need. As a matter of fact, current peptidomics investigations suffer from a lack of standardized procedures, posing a serious problem for data interpretation. Descriptions of the efforts to address such concerns will be highlighted.
gingival crevicular fluidMALDI-TOFpeptidesantimicrobial peptidespeptidomicsperiodontal diseasesmass spectrometry1. Introduction1.1. Gingivitis and Periodontitis
Periodontal diseases are complex, multifactorial, highly prevalent chronic inflammatory disorders affecting the tooth supporting tissues. Among them, periodontitis is a complex immune–inflammatory disease representing a significant health issue due to its high prevalence worldwide with approximately 10–15% of individuals being affected by severe forms [1]. It is characterized by microbially-associated, host-mediated inflammation that, in the absence of advanced treatment, could lead to a loss of periodontal attachment and also to alveolar bone resorption, resulting in deep periodontal lesions that may cause significant tooth loss in the advanced stage of pathology [2]. The accumulation of activated inflammatory cells like polynuclear leukocytes, macrophages, and lymphocytes in response to dental plaque amplifies the inflammatory process by the release of pro-inflammatory mediators [3]. Instead, gingivitis is recognized as a reversible condition, associated with microbial plaque accumulation and gingival inflammation that often resolves after restoration of oral hygiene procedures but, if untreated, may ultimately progress to periodontitis, thus representing the transitional phase between health and periodontal disease [1]. Therefore, a crucial interest has been growing in delineating the mechanism underlying this inflammatory stage, before it evolves to periodontitis. Periodontitis diagnosis is based on clinical examination, medical and dental history, tooth mobility, and radiographic assessment. Clinical evaluation is based on the assessment parameters, such oral hygiene, gingival status, probing depth, bleeding on probing, clinical attachment loss, alveolar bone status, but also on procedures such as periodontal microbiology testing, systemic health profiling by blood analysis, and finally, histological studies [4]. The main limit of most of these clinical methods is a certain degree of subjectivity, since they are examiner dependent. In addition, the biological events which trigger the progression from health to disease conditions are still unclear, therefore there is an urgent need to discover and validate reliable biomarkers able to diagnose early stage of pathology, possibly before clinical manifestations of periodontitis, in order to allow a more effective prompt therapeutic intervention.
Diagnostic tests are generally not necessary to discriminate between an inflamed site and a healthy site because clinical indices of inflammation, determined as described above, correspond well enough with periodontium histological inflammation. However, from a diagnostic point of view, the ability to discriminate between an inflamed-progressive (active) site and a less critical inflamed but stable site is of paramount importance [5,6]. Now, it is well accepted that both the outcomes of the immune/inflammatory response present in saliva/gingival crevicular fluid (GCF) and the impact of behavioral and environmental factors deeply affect the clinical picture of periodontopathy [7]. Following the “omics” burst, many researchers have tried to unravel molecular markers of periodontitis, identifying several genes, transcripts, proteins, and metabolites related to the pathology. Therefore, in the attempt to screen molecular profiles of periodontal disease, GCF has gained much attention, as will be discussed in the following sections.
1.2. Nature and Function of GCF
GCF is released in the dental crevice or gingival sulcus, defined as the space between the tooth and the overlying unattached gingiva. It mirrors serum composition because it not only derives from the gingival plexus of blood capillaries in the gingival corium, but also contains molecules derived from both host tissues and subgingival plaque, as well as a number of different cells, including epithelial cells, leukocytes and bacteria from the adjacent plaque mass [8]. In contrast, specialized glands, in particular the major salivary glands (submandibular, sublingual and parotid glands), and also roughly 600 minor salivary glands that are dispersed throughout the oral mucosa, have evolved to secrete saliva in the mouth. Obviously, GCF flows from the dental crevice into the buccal cavity, mixing with saliva and becoming part of it. Therefore, saliva is an aqueous solution (water content of 99%) containing electrolytes, small organic molecules, and nucleic acids, but most of all, proteins. In fact, over 3000 proteins have been found in saliva, the most represented being mucins, amylases, proline-rich proteins, cystatins, statherin, and histatins [4].
GCF exists as a serum transudate, changing into an inflammatory exudate as the inflammatory events progress [4]. In fact, this fluid is described as a mixture of molecules derived from the blood, host tissues, and subgingival plaque, and it contains electrolytes, small organic molecules, and various kind of proteins, among which antibodies, cytokines, bacterial antigens, and finally, enzymes of both host and bacterial origin [9]. Cytological examination of the GCF also demonstrates that it contains a variety of different cell types, including bacteria from the neighboring plaque mass, desquamated epithelial cells (which are passively washed out of the dental crevice into the oral cavity by the outward GCF flow), and transmigrating leukocytes (among which, lymphocytes monocytes/macrophages, and polymorphonuclear cells). Erythrocyte detection in the GCF should be considered an auxiliary finding, indicating injury to the microvessels that is typical of progressive inflammatory process [10]. With the onset of inflammation, the GCF flow rate increases and its composition begins to look like that of an inflammatory exudate. The augmented GCF flow contributes to host immune defense by flushing both bacteria and their metabolites away from the dental crevice. The main route for the diffusion of GCF is through the basement membrane, and subsequently, through the junctional epithelium into the dental crevice [11]. A particularly attractive feature of GCF is that this fluid is “periodontal specific” as it stems from a local site deeply involved in the manifestation of periodontal disease. For this reason it is considered to represent a faithful mirror of the periodontal health state of an individual [4]. Due to its composition, GCF might be a source of new biomarkers of periodontitis/gingivitis. The oral cavity is indeed a reserve of the microbiome and, when adverse changes occur within the oral cavity, it results in pathological changes, such as gingivitis, periodontitis, and dental caries [8]. As a biomonitoring fluid, GCF may play a key role both in the diagnosis and in the prognosis of oral diseases, in particular for periodontal diseases. It can be included among the most non-traumatic proximal fluid to obtain information about periodontal tissue status, including the conditions of the connective tissue and the level of hard destruction [12]. A non-invasive GCF collection (paper strip, paper points) technique helps obtaining samples of all age groups of human subjects, allowing also multiple sites sampling within the oral cavity [13]. So far, diverse inflammatory mediators have been isolated from GCF, including cytokines, phosphatase, proteinase and local tissue degradation products [14]. Several proteomics studies in the last decade shed light on GCF as potential source of biomarkers of both gingivitis and periodontitis and provided evidence for its diagnostic usefulness. The differential expression level of several proteins, such as defensins, annexins, plastin-2, lipocalin-2, and S100-P, was reported in the GCF between health and disease [15].
1.3. Low Molecular Weight Profiling of GCF
Despite the fact that naturally occurring peptides present in bodily fluids are known to play both local and/or systemic functions, only a few proteomics/peptidomics investigations targeted the GCF peptidome [6,16,17,18,19,20,21,22,23,24,25,26,27,28,29]. Even with such limited studies, the knowledge of the biological mechanisms that can be gained from peptidomics applied both to the identification of markers of disease and to the development of therapies which has encouraged the use of in the context of progress made in this field [6,16,17,18,19,20,21,22,23,24,25,26,27,28,29]. In the last decade, the impact of proteomics to the understanding of human GCF composition has progressively increased, underlining the potential diagnostic role of this fluid as invaluable source of periodontal disease-specific markers [6,14,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47].
With the progress of bottom-up MS-based approaches, enormous amounts of data have been generated, especially concerning the protein content of GCF, and extraordinary attention has been paid to comparative analysis in order to identify differentially expressed proteins between healthy and periodontal disease [48,49]. While from one point of view, it appears remarkable to categorize these studies as based on discovery or on quantitative proteomics approaches, as recently outlined by both Bostanci and colleagues [15,48] and Tsuchida and collaborators [49], it could also be of interest to appraise the efforts done by the proteomics community to assess the invaluable role played by naturally occurring peptides in GCF. Therefore, in the present review we highlight peptidomics studies which offer significant opportunities to detect and to identify peptides and small proteins in GCF of interest for periodontal diseases (Scheme 1, Table 1 and Table 2). A special focus will be given to the study design of several investigations, which are described from the collection of the samples to their processing and to the MS platform and strategy adopted, all with the aim to better address current limitations which hurdle GCF peptidomics research. Concerning the methodological strategy, all the studies reviewed here were carried out with MS profiling approaches in which protein content of GCF did not undergo protease digestion before MS analysis. The absence of a digestion step preceding the MS analysis overcome the intrinsic limitation of not discriminating between the naturally occurring peptides found in GCF and those artificially generated by proteolysis in the preceding step required for a bottom up protocol. The relevance of the top down profiling approaches reviewed here resides therefore in the fact that such investigations might reveal in a more direct and clear manner specific proteins/peptides functions and mechanisms of actions, as top down approaches preserve valuable information about all of proteoforms including proteolytic cleavage from larger proteins by the action of proteases of human or bacterial origin.
Generally, the peptide profiling investigations that we summarize in this review make use of surface enhanced laser desorption ionization (SELDI) [17,18] and matrix-assisted laser desorption ionization (MALDI)-MS [6,19,20,21,22,23,24,25,26] approaches which result more suitable and easily applicable for the analysis of natural peptides of GCF (Table 1). However, few investigations based on electrospray ionization (ESI)-MS have also been carried out to explore peptidic components and small proteins found in GCF in a top-down fashion [27,28,29] (Table 1).
.
2. An Overview of MS Profiling Tools and Investigations to Detect GCF Peptides
Some, but not many investigations based on MS have been performed to explore the GCF peptidome both under physiological [20,22,23,27,28] and pathological conditions [6,16,17,18,19,21,24,25,26,29]. Targeted identifications of specific peptidic component in GCF have been reported by Lundy et al. [16]. In this study, Calgranulin A (S100-A8) was purified by high-performance liquid chromatography (HPLC) and then identified using N-terminal amino-acid sequencing. S100-A8 is expressed in cells of myeloid origin and secreted in the case of cell damage or activation of phagocytes and monocytes. It exerts many roles in periodontal inflammation including chemotaxis, phagocytosis, or inhibition of microbial growth [16]. The amount of Calgranulin A was determined in GCF from healthy, gingivitis and periodontitis sites both in periodontitis patients (n = 15) and also from controls (n = 5) with healthy gingival status. The results showed significant differences in the amounts of Calgranulin A in relation to disease status, with higher expression levels in periodontitis and gingivitis sites than in the healthy sites in both periodontitis subjects and controls (Table 2) [16].
2.1. SELDI MS GCF Peptidome Profiling
In SELDI MS, samples to be analyzed are applied directly to a biochip coated with specific chemical “bait” matrices, which may be comprised of supports routinely used in chromatographic techniques (anionic, cationic, hydrophobic surfaces, etc.) or biochemical bait molecules, for instance purified ligands, proteins (like antibodies, receptors) or also DNA oligonucleotides [50]. The protein population bound to the biochip can then be washed with different kinds of washing buffers, in such a way that only the proteins sharing common chemical features remain on the surface and, subsequently, the mass map of all proteins retained is obtained at the same time. This analysis gives rise to a protein “fingerprint” formed by the exact molecular weight of each and every single protein first bound and then ionized off the specific bait surface used. SELDI-time of flight (TOF) MS, is one of the best analytical techniques for profiling both peptides and also low MW proteins (<20 kDa). Indeed, it has been extensively used in proteomics studies and, due to its high throughput, it became a promising tool for the identification of diagnostic patterns of disease [51,52,53]. However, this technology suffers a number of drawbacks preventing its widespread application as a routine platform in clinical diagnostics [54]. One of the main disadvantages of SELDI-TOF MS in peptidomics analysis is that its resolution is generally not very high (i.e., several different peptides can be present within each single ion peak, thus preventing direct assignment of the ion peaks of interest) [55]. Another point of weakness of the platform is the poor reproducibility, both within and also between laboratories. In this respect, a number of efforts have already been made to set up standardized protocols among the various research groups [56]. Another limitation of SELDI-TOF instruments is that they are not able to directly identify putative biomarkers as they lack MS/MS capabilities [57]. Finally, concerns can also be risen related to both the sensitivity of the technique and also the specificity of the biomarkers discovered by SELDI analysis [54].
The first peptidomics study on GCF was pioneered by Diamond and colleagues [18]. With the aim to understand the physiologic role of beta-defensins in the oral environment, Diamond and colleagues examined the media of cultured human oral keratinocytes and GCF by ProteinChip Array SELDI-TOF MS technology. The authors found that both the 47-amino acid form of human β-Defensin 1 (hBD-1, m/z = 5069) and the 41-amino acid form of human β-Defensin 2 (hBD-2, m/z = 4330) were best detected in GCF by SELDI-TOF measurements when a chip (among the others) coated with a hydrophilic phase was used. Peptides with the predicted masses of the alpha-defensins HNP-1 (m/z = 3442), HNP-2 (m/z = 3371) and HNP-3 (m/z = 3486) were also detected. However, only the identity of hBD-1 and hBD-2 was indirectly confirmed by immunoaffinity capture with anti-hBD-1 and anti-hBD-2 polyclonal antibodies on the ProteinChip surface, although hBD-1, as the authors hypothesized, was found in several smaller forms suggesting extracellular proteolysis. Immunoaffinity capture experiments unmasked inter-individual variation between the two subjects (n = 2) in the relative amount of hBD-2 and the hBD-1 peptides. Specifically, more hBD-2 and alpha-defensins, with little hBD-1, was seen in the case of the subject with greater inflammation. β-Defensins are secreted by gingival epithelial cells and exert combined antimicrobial, chemotactic, and anti-inflammatory properties [18]
With the same technology, some years later, Dommisch and colleagues optimized the experimental conditions to obtain the simultaneous detection of human neutrophil antimicrobial peptides (AMPs) in GCF as well as human cathelicidin LL-37 in SELDI-TOF profiles [17]. LL-37 is secreted by neutrophils and plays a key role in innate immune responses [17]. In this study, they compared GCF specimen from healthy periodontal sites to those from sites with early gingival inflammation. As each single donor also provided GCF samples from healthy periodontal sites as an internal control, GCF samples from sites diagnosed as gingivitis were analyzed individually in a donor-specific manner, thus allowing relative quantification. The authors observed that donors (four out of eight donors) that showed significantly higher intensity peaks corresponding to the mass of LL-37 (p = 0.01) also displayed greater (although not statistically significant) intensity peaks corresponding to the molecular masses of HNP-1 and HNP-2 in samples obtained from inflamed as compared to healthy periodontal sites (see Table 2). However, also in this SELDI study, the main limitations are due to the loss of unambiguous sequence-based identifications of the putative m/z peaks detected in the mass spectra and to the inherent semi quantitative assay.
2.2. MALDI MS GCF Peptidome Profiling
In the MALDI-TOF MS, a large excess of matrix (which is a small organic aromatic UV absorbing molecule) is used in comparison to the amount of the analytes (in this case peptides that need to be analyzed). Peptides are co-crystallized with the matrix into a solid-phase onto a stainless-steel target plate. When a laser beam hits the surface of the solid mixture, the matrix absorbs the laser energy and transfers it to the protonated peptides. Simultaneously, the rapid heating determines desorption of both the matrix and the newly formed [M + H]+ protonated peptides directly into the gas phase. MALDI ion source is commonly coupled to TOF mass analyzer [58]. In the recent mass analyzers, the ions generated in the source are then accelerated to a fixed amount of kinetic energy and travel along a flight tube. Having all the same kinetic energy, the small ions have a higher velocity and are therefore recorded by a detector before the larger ones. As a consequence, each m/z value displayed in a TOF spectrum is proportional to the time required to reach the detector for each given analyte.
Specifically, MALDI-TOF MS is a very versatile and fast analytical platform for analyzing both small and large polypeptides contained in various kind of biological samples. Some intrinsic properties of this analytical technique include low sample consumption, simple sample preparation, remarkable tolerance toward salts and buffers, high degree of automation and high throughput analysis and, finally, high sensitivity and mass accuracy. All these features make MALDI-TOF MS highly suitable for diagnostic purposes [59,60,61,62,63]. The versatility of the MALDI-TOF MS platform was exploited in different approaches to analyze GCF peptidome [6,19,20,21,22,23,24,25,26].
Lundy and colleagues used MALDI-TOF MS to qualitatively analyze neutrophil alpha-defensins in unfractionated GCF samples [19]. Starting from the rationale that the triad of alpha-defensins peptides (HNP-1, HNP-2, and HNP-3), due to their similar primary sequence, might show an equal ionization efficiency during the MALDI-TOF MS measurements, these investigators assumed that the ion intensities of the peaks representing the alpha-defensins paralleled the relative amount of these peptides. Therefore, they assessed the relative intensity of the peaks corresponding to HNP-1, HNP-2 and HNP-3 detected in the MALDI-TOF mass spectra of the unfractionated GCF collected from 12 periodontally healthy and 11 periodontally diseased subjects. From a comparison of the relative abundance of defensins, they found that the peak corresponding to the mass of HNP-1 was always the highest of the triplet while the peak matching with HNP-3 was always the lowest. These findings are consistent with the speculation that HNP-2 could be produced selectively by proteolysis of HNP-3, but not of HNP-1 [64]. Additionally, on the basis of ion intensity scores, the authors observed that the defensins were more abundant in a higher proportion of the healthy sites in comparison to the diseased (although the differences were not statistically significant). Therefore, they hypothesized that high expression of HNPs in GCF of periodontally healthy gingivae serves as barrier against bacterial colonization. These findings on defensins expression based on MALDI-MS data from Lundy [19] were however in contrast with those observed later by Dommisch [17] in the SELDI-based investigations (Table 2).
Wen et al. performed a profiling proteomic approach using GCF from 20 pubertal and 20 post-pubertal subjects in order to identify novel candidate biomarkers in GCF useful for the diagnosis of pubertal growth peak [20]. The GCF samples were collected by paper points and stored at −80 °C until MALDI-TOF analysis. Sample processing was performed using TFA, without the use of a protease inhibitor cocktail (PIC) (see Table 1). The unfractionated samples were analyzed directly by MALDI-TOF MS. The statistical analysis was performed after normalization for peak intensity and the results showed that six peptides were significantly different between the two groups. In particular, the peaks at m/z 1660.2, 1783.0 and 6108.9 were upregulated in the pubertal group compared to the post-pubertal group, while the peaks at m/z 5064.9, 2912.5 and 4178.6 were downregulated in the pubertal group compared to the post-pubertal group. Although primary sequence of these potential candidate biomarkers has not been identified, still the study provided a statistical model based on MALDI-TOF MS data able to detect the pubertal growth peak, demonstrating the potential use of this approach as a periodontal diagnostic tool. It should be taken into account that normalization for protein concentration was not performed, nor was the time of storage at −80 °C indicated.
Ngo and colleagues were the first to apply the sequencing capability of MALDI-TOF/TOF MS to identify the endogenous peptides of GCF collected from inflamed sites [6]. By means of this technology, 33 endogenous peptides were identified with a molecular weight ranging from 1067 Da to 3700 Da. In particular, of these 33 peptides, 10 peptides were identified in unfractionated GCF from single sites from direct TOF/TOF sequencing experiments while the other 23 peptides were identified in GCF samples pooled from multiple inflamed sites and fractionated by nano-HPLC. Several peptides resulted to be fragments derived from salivary precursor proteins, possibly reflecting the protease activity in the periodontal pocket (see Table 1). In this study, however, the identification of the three putative alpha-defensins peaks (HNP1–3) (m/z = 3370.9, m/z = 3442.2, and m/z = 3486.5), detected in the MALDI-TOF spectra and also revealed by previous investigations [17,19] was complicated because precursors ions did not fragment by collision-induced dissociation in MS/MS mode, due to the presence of the three characteristic internal disulfide bonds inside defensins. Therefore, the authors validated a putative assignment by using a reducing agent and showing that the resulting MALDI-TOF mass spectra displayed a +6 Da shift only in the mass of three peaks, finding consistent with the reduction of three internal disulfide bonds. Other peptides and small proteins were also identified alternatively by SDS-PAGE followed by in-gel digestion and MS analyses using both nano liquid chromatography (LC)-ESI-MS/MS and MALDI-TOF/TOF MS. All the peptides and small proteins of this study are summarized in Table 1, while medium-size and larger proteins, also identified by the authors, are not listed in Table 1 because they’re outside of the scope of the present review.
Later on, the same group demonstrated that the combined use of MALDI-TOF MS with bioinformatics may allow the site-specific prediction of periodontal disease progression in a cohort of forty-one periodontal maintenance subjects [21]. Each individual and specific site were followed over 12 months, with clinical measurements taken both at baseline and then every three months, collecting GCF to be analyzed at each visit [21]. The authors compared the MALDI-TOF MS spectra of GCF of both healthy and diseased sites from patients with moderate to severe chronic periodontitis. The purpose of the study was to use these spectra to create models for disease diagnosis. In particular, to pursue this, they used a genetic algorithm to create a pattern analysis-based model to predict specifically sites which were undergoing attachment loss. Interestingly, the statistical algorithm showed that there was more similarity in the GCF profile between patients, than between sites with different severity of inflammation. However, even with these differences, MALDI profiles may be taken into account for the prediction of periodontal attachment loss at a site level with a very significant positive predictive value and, most importantly, before it can be measured by clinical examination. This valuable result can be achieved with one single test on each individual GCF sample. Remarkably, this was the first study which explored and preliminarily demonstrated the potential use of MALDI-TOF MS technology in the field of periodontal disease diagnostics.
Our group set up a standardized protocol for both GCF collection from healthy gingival tissue belonging to subjects diagnosed healthy by clinical examination and further GCF processing [22]. Pre-analytical and analytical variables expected to influence the GCF peptidome profiling were assessed by MALDI-TOF MS analysis. More specifically, in a comparative study, we explored how the robustness and reproducibility of MALDI-TOF MS profiles are influenced by the use of paper points vs. paper strips, by the conditions of centrifugal elution and by the utilization of PIC (Table 1). Moreover, the effects of both sinapinic acid composition and sample-to-matrix ratio in MALDI-TOF MS analysis were systematically estimated, with the aim of optimizing both the quality and robustness of signals in GCF MALDI-TOF spectra. Definitively, a simple and high throughput procedure was optimized to rapidly obtain reproducible peptidome profiles of GCF from healthy sites of clinically healthy subjects. Sample composition and dilution severely affected the peptidome profiles obtained in MALDI-TOF analysis, therefore in this study, the optimal elution volume was evaluated in the assessment of a standardized procedure. Among other findings, elution by TFA generated richer gingival fingerprints compared to those obtained by the use of acetic acid. PIC supplementation and centrifugation speed did not change GCF profiles. Matrix composition and matrix/sample volume ratio were also assessed to obtain robustness of the MS analysis ensuring CV less than 10% for peak area and signal-to-noise ratio. These results are of interest for standardizing both sample collection and handling methods for GCF peptidomic-based biomarker identification, although we are aware that the procedure we set up may have to be re-visited for clinical conditions differing from a healthy status (for instance, gingival inflammation or periodontal disease), in which it is known that the nature of the GCF changes dramatically.
As diagnostic peptide fingerprints may vary as a function of GCF collection, handling, and storage, our research group analyzed if storage conditions have an influence on the quality and the reproducibility of MALDI-TOF profiles for this biological fluid [23]. Indeed, our data strongly suggest that sample storage conditions indeed affect the GCF peptide pattern over time in a quite significant manner. Specifically, a procedure of immediate extraction of GCF from paper strips, followed by storage of the extracted material, generates a lower amount of variation in molecular profiles as compared to the extraction performed after the storage on paper strips. Indeed, significant spectral changes were observed for GCF samples stored at −20 °C directly on the paper strips for three months and then extracted, in comparison to samples extracted immediately and then stored for three months in the same conditions. Moreover, a significant decrease in the peak area of HNP-3, of S100-A8 and of both full-length S100-A9 and its truncated form was detected after 3 months even at −80 °C when storage was on paper strips. The artifacts found in the “paper-stored GCF” profile may not only influence the pattern-based biomarker discovery, but also make its use not suitable for in vitro diagnostic test targeting. So S100-A8 and S100-A9 are proposed as periodontal disease potential diagnostic biomarkers [49]. This study definitively shows that the signatures closest to those obtained with immediate elution and analysis were attained for the GCF samples eluted in TFA and then immediately stored at temperatures not higher than −80 °C for at most one month. The information gained from our findings on protein/patterns stability after storage may be useful in defining standardized protocols enabling optimal preservation of GCF samples.
Very recently, our group proposed a fast and easy to perform method for processing GCF before MS analysis, thus minimizing the risk of false positive detection due to degradation of particularly sensitive molecular species within GCF samples [24]. In this investigation, twenty subjects (ten healthy and ten suffering from gingivitis) were recruited in order to perform comparative proteomics profiling studies between healthy and gingivitis subjects (Table 1 and Table 2). So, we proposed a standardized methodology for GCF analysis by MALDI-TOF/TOF-MS in order to identify a characteristic peptide signature of gingivitis. Storage times were established of one and three months concerning GCF specimen to analyze, in order to better evaluate any degradation and changes in GCF peptidome patterns. Comparative analysis of normalized peak area from peptidome profiles showed a pattern of five m/z peaks (3371, 4136, 4964, 13,153, and 13,458) which discriminate the two groups in a statistically significant manner. Specifically, the normalized peak area for m/z 3371, 4136, and 13,458 was significantly higher in GCF from gingivitis subjects compared to GCF from the healthy group, while an inverse trend was observed for m/z = 4964 and for m/z = 13,153 (see Table 2). The peak with m/z = 3371 was identified as the HNP-2 belonging to the triplet of alpha-defensins. Curiously, no significant change was observed for HNP-1 and HNP-3 between healthy and gingivitis groups. The other four peptides were identified as the C-terminal fragment of alpha-1-antitrypsin (AAT), namely C-36 peptide (m/z = 4136), as the β-thymosin (m/z = 4964) and, finally, two different post-translational modifications (PTMs) of the full-length S100-A9 protein were found (m/z = 13153 and m/z = 13458) (see Table 1). The study also demonstrated the emerging role of MALDI-TOF MS in the high-throughput characterization of naturally occurring peptides from complex bodily fluid and its potential to directly identify constituents of the GCF peptidome and their proteoforms.
In a very recent study, Tang et al. analyzed GCF, saliva, and serum samples from 50 gender- and age-matched subjects suffering from chronic periodontitis (n = 17) and gingivitis (n = 17), and from healthy subjects (n = 16), in order to identify potential biomarkers of periodontal diseases by using MALDI-TOF MS [25]. GCF samples were collected by paper strips, immediately deposited into a tube containing PBS; supernatants were recovered after centrifugation and stored at −80 °C (Table 1). GCF, saliva and serum samples were fractionated using a weak cation exchange magnetic beads and analyzed immediately on a MALDI-TOF mass spectrometer or stored at −20 °C and analyzed within 24 h. In the comparison between chronic periodontitis and healthy groups, four GCF peptides were significantly higher in chronic periodontitis group compared with healthy (3434.4 Da, 4126.6 Da, 5407.7 Da, and 5416.0 Da) (see Table 2). Among these, only the peak at m/z 3434.4 was successfully identified to be derived from haptoglobin by nano-LC/ESI-MS/MS analysis and its upregulation in chronic periodontitis patients was confirmed also for saliva samples. The authors identify other two peptides derived from haptoglobin both in serum and saliva (3874.9 Da and 1147.1 Da respectively) with an increased expression level in chronic periodontitis and gingivitis patients, thus confirming the results obtained with GCF. One strength of this study relies on the possibility to obtain specific molecular signatures for a given bodily fluid and then to compare the potential biomarkers identified across different bodily fluids. However, an important limitation, as pointed out by the authors themselves, is the absence of any kind of normalization for protein concentration. Furthermore, samples stored for the same amount of time need to be compared in order to ensure the robustness of the results; indeed, as discussed above, the protein profile of a sample analyzed immediately by MS may not be comparable to that of the same sample stored at −20 °C and analyzed within 24 h [23,24].
On the basis of blind MALDI-TOF analysis presently used in clinical microbiology for conventional bacterial strain identification, Antezack and colleagues adapted this analytical approach to MALDI peptide profiles acquired from GCF, saliva and dental plaque with the aim to develop a rapid diagnostic test for periodontitis [26]. They compared the MALDI spectra to assess if a classification may be performed among individuals according to their periodontal status. In particular, the authors used the binary discriminant analysis method [65] to evaluate differences between the groups of periodontitis (n = 67) and healthy periodontal (n = 74) subjects on the basis of a discriminant peak list (Table 1). Based on the differentially expressed peaks from MALDI peptide fingerprints, a diagnostic decision tree for periodontitis was built for each type of sample. In the case of GCF, a pattern of nine peaks was chosen to set up a decision tree with a specificity of 98% and a sensitivity of 96%. In a blind experiment GCF data demonstrated the ability to segregate periodontitis patients from control individuals showing a specificity of 75.7% (±0.195) and a sensitivity of 79.6% (±0.188). Although the authors claim their study as the first “to demonstrate that MALDI-TOF MS differentiates periodontitis from healthy periodontium by blind identification of specific patterns in mass signals from protein profiles in saliva, GCF and dental plaque” [26], the investigation suffers several limitations, in particular the absence of preliminary experiments designed to assess the impact of short-term storage at 4 °C of the analyzed samples and the total absence of quantification of total protein concentration in the same samples. In fact, concerning the first limitation (impact of short-term storage at 4 °C) although data in the literature show the small molecules like uric acid or cortisol are stable when saliva was stored for at 4 °C for four weeks [66,67] this may absolutely not hold true for protein or peptide components in the same fluid [23,24]. Further studies are absolutely necessary in order to increase the robustness of the diagnostic approach.
2.3. ESI MS GCF Peptidome Profiling
Unlike the MALDI source which ionizes and sublimates the samples out of a crystalline, dry matrix by laser, the ESI source ionizes the analytes out of a solution and is therefore usually coupled to LC instruments [68]. In the ESI source, the solution containing peptides analytes is passed through a needle to which a high voltage (~2–5 kV) is applied [69]. This leads to the generation of a spray of small charged droplets. As the solvent evaporates, desolvation of peptide/protein-solvent droplets resulted in a multicharged ions formations. The ions are then accelerated into the mass analyzer for subsequent measurement of their mass-to-charge ratios and of ion intensity. A mathematical deconvolution performed by the software on the ESI spectrum allows the mass of the analyte to be determined with an accuracy of roughly 1/10,000 amu. The combined information of both the time of elution (obtained, when possible, by using protein standards) and precise mass value of the protein quite often allows non ambiguous identifications to be obtained. This platform may be used successfully for the analysis of small complex samples because it allows high sensitivity detection of different analytes at the same time.
The mass analyzers routinely used with ESI are quadrupole, linear ion trap, and Orbitrap, however we concisely describe only the last two analyzers, those used in the peptidomic investigations here reviewed. Briefly, in a linear ion trap, ions are confined in radial direction by a two-dimensional radio frequency field, and in axial direction by stopping potentials applied to end electrodes [70]. The trapped ions are manipulated employing direct current and radio frequency electric fields in a series of carefully timed events. They can work as stand-alone mass spectrometers or into hybrid configurations combined with high-resolution mass analyzers. A correlation between the concentration of a given peptide and the ionic current, which is measured by the ion trap mass spectrometer, is however possible only under very reproducible conditions. Indeed, as the ionic current is directly proportional to the protonation level of the peptide, it is necessary to standardize the treatment of the samples. The linear ion trap mass spectrometer has become popular because of its robustness, fast scan speed, sensitivity, user friendliness and relatively low cost. However, resolution, mass accuracy and dynamic range are still not comparable to other mass spectrometers such as Orbitrap instruments [71]. Orbitrap is a benchtop instrument that is cost effective, accessible, and applicable in hybrid architectures. The Orbitrap MS is composed of a barrel-like outer electrode and a spindle-like central electrode [72]. In this electric field, ions rotate around the central electrode while oscillating down the length of the electrode; the frequency of these harmonic ion oscillations, undergone by the orbitally trapped ions, is used for m/z measurement [72]. The main advantage of the Orbitrap analyzers are enhanced resolution (the highest available for a benchtop mass spectrometer mostly in hybrid architectures), high mass accuracy, high sensitivity, wide dynamic range, fast acquisition rates and multi-stage MS(n) capabilities for tandem MS experiments [71,73]
Pisano and colleagues were the first group to analyze GCF by reverse-phase (RP)-HPLC coupled to ESI MS [27]. The authors analyzed the acidic-soluble protein content of GCF collected under physiological conditions. After the chromatographic separation, the eluate was directly introduced into an ion-trap mass spectrometer through ESI. Concerning peptides and small proteins, the HNPs 1–3 (alpha-defensins 1, 2, and 3) were the principal components detected, while only minor amounts of HNP-4, statherin, PB peptide, cystatin A were observed. Additionally, other unidentified components were detected with low mass values of 1067.4, 1109.4, and 1151.4 amu (probably corresponding to the same molecule showing a different degree of acetylation) and other peaks with mass values of 4135.0, 4936.4, 4964.0 and 4980.0 amu. This was also the first study to analytically demonstrate that GCF represents a physiological entity distinct from saliva. In fact, the authors did not detect the presence in chromatogram of the major proteins/peptides characteristic of human saliva such as acidic and basic proline-rich proteins. Although this approach does not allow for the detection of proteins that are not soluble in acidic solution, it contributed to the initial effort to discover the still under-represented GCF peptidome by an innovative MS-top down approach.
The same group (Inzitari et al.) demonstrated later, not only by HPLC-ESI-MS but also by immunohistochemical analysis, that gingival sulcus is the main source of Tβ4 in the oral cavity and that GFC contains high levels of Tβ4 and also of a second member of the same peptide family, namely Tβ10 [28]. Of note, these peptides play a key role in diverse cellular functions such as tissue development, migration, angiogenesis, and wound healing [28].
Very recently, Dassatti and colleagues also performed a pilot study analyzing the GCF proteomic profile by RP-HPLC coupled to ESI MS. GCF samples were collected during the first 15 post-partum days from 10 female patients with a clinical scenario of gingivitis and periodontitis (assessed by clinical periodontal parameters), and from the same patients after three months in order to evaluate the effects of a professional oral hygiene session and to analyze the associated variations of proteomic profile of GCF. A control group was created in order to compare the results with GCF samples from 10 not pregnant fertile women [29]. The analysis of clinical periodontal parameters at three months post-partum revealed the therapeutic support of the professional oral hygiene session performed during the first visit. Proteomic profiling of GCF, performed by RP-HPLC-ESI coupled to a hybrid ion trap-Orbitrap mass spectrometer, highlighted the presence of peptides playing a key role during the inflammatory process and exerting defense mechanisms. These peptides, were then identified by MS/MS experiments as α-defensins, thymosin-β4 with one of its fragments (21–44), thymosin-β10, fibrinopeptide A and its fragments (fragments 21–35 and 22–35) and fibrinopeptide B (Table 1). The levels of all the defensins (α-defensins 1–4) and of all the fibrinopeptides, were always higher during post-partum gingivitis and periodontitis, compared to the three months recall group and the control group (Table 2). The value of thymosin-β4 decreased in the group of patients with periodontitis, while it was significantly increased in the three months recall group. A similar trend was also observed for thymosin-β10 (Table 2). Otherwise, the expression level of thymosin β4 fragment (21–44) was higher during post-partum periodontitis compared to the three months recall group and the control group (Table 2). These findings demonstrate that pregnant women may be at an increased risk of pathological dental conditions and underscore the importance of a good oral hygiene procedures in helping to prevent problems with dental health. It is also interesting to associate the study of the expression level of peptides involved in the inflammatory process and in defense mechanisms with the variation of the clinical parameters normally used for the evaluation of the gingival health status. However, the paper lacks some important information about sample collection and processing.
3. The GCF Antimicrobial Peptidome
This section will focus in particular on the main findings concerning naturally occurring peptides, identified in GCF by profiling strategies performed without a previous proteolysis step propaedeutic to mass analysis. Considerations about their putative role and how their analysis might lead to the identification of novel biomarkers of periodontal health or disease will be discussed. As emerged from the literature reviewed in the previous sections, the top-down analysis of GCF peptides and their proteoforms (as they occurred in their intact form) poses several challenges for MS-based proteomics platforms. One of the main limitation arises in such cases from the short sequences of detected peptides, therefore the probability to find in their sequence basic aminoacidic residues is very low and as consequence those peptides may not be favorably charged for MS detection, as they are often singly charged and more difficult to efficiently fragment. Additionally, unlike bottom up approaches, the absence of Lys and Arg as non-tryptic peptides, hinders their detection. Furthermore, the conventional database search approach, which is normal routine in proteomics, is not suitable in this case. However these issues are extensively addressed also elsewhere [74,75]. Although definitive identification of naturally occurring peptides is particularly challenging, some MS instruments for example MALDI-TOF/TOF and ESI-ion trap or ESI-ion trap/Orbitrap provide not only a rapid screening of proteoforms [18,22,23,27], but also their sequencing and identifications [6,24,25,28,29]. In the light of the above considerations, it is worth highlighting important results from several groups; for example Ngo and colleagues detected and identified for the first time histone protein or peptide in GCF by MALDI-TOF/TOF (Table 1) [6]. Interestingly, histone fragments, have been shown to have antibacterial activity [76]. Other examples are the discovery of haptoglobin fragment in GCF by Tang et al. [25] (Table 1) as well as fibrinopeptide fragments by Dassatti et al. [29] (Table 1). It would possible to speculate that these fragments might play a more specific role in GCF compared to those played by their precursor protein. It is well known that haptoglobin is an acute-phase protein, which promotes anti-inflammation activities and could also act indirectly as an antioxidant and bacteriostatic agent [77]. Its expression increases in response to injury or infection as it contributes to the recovery of homeostasis after systemic or local infection [77]. Therefore, the authors hypothesized that the up-regulation of haptoglobin derived fragment levels in chronic periodontitis patients compared to healthy subjects (Table 2) may reflect the anti-inflammatory and reestablishment process of the periodontium. Fibrinogen and fibrinopeptides are involved in hemostasis, wound healing, inflammation, angiogenesis, vascularization after injury, and other biological functions [78]. So, the authors speculated that the increased levels in periodontal disease conditions of fibrinopeptide A and its fragments along with fibrinopeptide B (see Table 2) may be due to their involvement in the healing process and revascularization of the area affected by periodontal disease.
Another point worthy of note was the detection of Thymosin β4, together with its sulfoxide form, and of Thymosin β10 by Inzitari and colleagues [28] which demonstrated that gingival sulcus is the main source of Tβ4 in the oral cavity and that GFC is the main source of Tβ4 and Tβ10 [28]. Although several bottom-up approaches documented the presence of Thymosin β4, they failed to identify Thymosin β10 or the sulfoxide form of Thymosin β4 [14,32,39,42,45]. These peptides have multiple diverse cellular functions, including tissue development, migration, angiogenesis, and wound healing [79]. In particular, multifunctional roles are attributed to Tβ4 in protecting cells against damage by acting as antimicrobial, anti-inflammatory, and anti-apoptotic agent [80].
Our group detected two proteoforms of S100-A9 with m/z = 13,153 and 13,458 and the oxidized forms of S100-A8 [23,24]. S100-A9 together with S100 A8 belong to the damage-associated molecular pattern family members of danger signals that initiate and amplify local inflammation and innate immune responses [81]. S100-A9 is frequently associated with S100-A8 to form the S100-A8/S100-A9 heterodimer, known as calprotectin, with anti-microbial, chemotactic, pro-apoptotic and anti-proliferative activities [81]. Several studies shed light on the key role of calprotectin and its subunits in the pathobiology of a number of inflammatory conditions including periodontal diseases [49,81,82]. In addition the GCF calprotectin levels were positively correlated with clinical parameters of periodontitis [81]. Specifically, in our comparative study, we detected the acetylated form (m/z = 13153) and the acetylated and glutathionylated form of the S100-A9 (m/z = 13458) [24]. The first was found decreased in gingivitis patients compared to the healthy controls, while the latter was found increased [24]. It is common knowledge that GCF contains significant levels of reduced glutathione which is responsible for the local antioxidant capacity, typical of periodontally healthy subjects [83] and that glutathionylation of S100-A9 alters its ability to form complexes with S100-A8, to bind endothelial cells, and limits neutrophil migration in inflammatory lesions [84]. Based on these results, our group speculated that the glutathionylation in S100-A9 may result in a protective effect against oxidative process at the site of inflammation thus providing a coherent explanation to the observed reversed ratio between the m/z = 13,458 (both glutathionylated and acetylated) and the m/z = 13,153 (acetylated only) forms of S100-A9 peptide in gingivitis patients compared to healthy subjects [24].
Lundy and colleagues, in line with other transcriptomics and proteomics studies [4], found increased expression levels of S100-A8 in periodontitis and gingivitis sites compared to the healthy ones (Table 2) [16]. However, it is worth noting that, in some comparative proteomics studies, S100-A8 and S100-A9 are not differentially expressed, even if detected [32,33,34,41], suggesting various hypotheses as already reported elsewhere [23] and discussed in the next sections.
Among several AMPs detected by the MS profiling platforms, here described and summarized in Table 1 and Table 2, important families such as human alpha-defensins (HNPs 1–4) and human beta-defensins (hBD-1 and hBD-2) and also the cathelicidin antimicrobial LL-37 peptide were studied.
Concerning the alpha defensins (HNPs 1–4) the results of comparative studies between healthy and gingivitis groups or healthy and periodontitis groups (Table 2) showed that defensins, with the exclusion of one study [19], are generally increased in periodontal conditions (gingivitis and periodontitis, Table 2). Similarly to other AMPs, defensins exert their protective role against microbes entering the oral cavity resulting in effective control of infections. However, even with a broad bactericidal effect, HNPs have been demonstrated to be rather ineffective in vitro against various periodontal pathogens, at least at concentrations in which they are found in healthy gingival crevice [85]. Increased expression of α-defensins may induce tissue damage both directly, by their observed cytotoxic effects on cells [86], and indirectly, by competing with neutrophil elastase for the binding of α-1-antitrypsin (AAT), an inhibitor of neutrophil elastase [87]. Consequently, defensins may influence the balance between proteases and their inhibitors and increased defensins levels observed both in peptidomics studies (Table 2) [17,18,24,29] and also in proteomics investigations [34,38,40] may cause augmented proteolytic activity, thus contributing to the inflammation and the tissue damage observed in gingivitis. As shown in Table 2, in our studies we detected significantly increased levels of only HNP-2 in the gingivitis group while no significant change was observed for HNP-1 and HNP-3 between healthy and gingivitis groups [24]. Our results were partially in agreement with the findings of Dommisch et al. who observed increased levels of HNP-2 and, to a lesser extent, of HNP-1 (although both not statistically significant) in gingivitis sites in comparison to the healthy sites while no variation of HNP-3 was reported [17]. However, the general observed trend of increase of defensins in periodontal diseases was inverted in the study of Lundy and colleagues as they found that the defensins expressions were more abundant in a higher proportion of the healthy sites in comparison to the diseased (although the differences were not statistically significant) [19].
Interestingly, Diamond and colleagues detected in GCF not only the alfa defensins, but also the β -defensins hBD-1 and hBD-2, although hBD-1 was also found “in several smaller forms” suggesting extracellular proteolysis [18]. It is interesting to note that a higher amount of both hBD-2 and α-defensins, with little hBD-1, was observed in the subject with higher levels of inflammation (individual 1, Table 2). This observation is in line with increased levels of both hBD-2 and neutrophils in inflammation. β-defensins are small cationic peptides produced by gingival epithelium that are involved in the innate host defense against the bacterial challenge, which is continuously present in the oral cavity [88]. β-defensins play a key role in the awakening of the innate immune response to increased bacterial exposure in the gingival epithelium and exert combined antimicrobial, chemotactic and anti-inflammatory properties. These peptides contribute to the healing process of gingival wounds, regenerating the damaged epithelium by promoting the attachment and proliferation of fibroblasts on the diseased root surfaces [88].
Another relevant antimicrobial peptide detected by Dommisch and colleagues is the human cathelicidin LL-37, a peptide of 37 amino acid starting with two leucine residues which has a broad spectrum of antimicrobial activity [89]. It is stored as biologically inactive precursor in the secondary granules of neutrophils and it acquires its antimicrobial potency after proteolysis exerted by proteinase 3. LL-37 regulates inflammatory and immune responses, promotes angiogenesis and wound healing and, finally, also neutralizes lipopolysaccharides. The increased expression of this peptide in samples obtained from sites characterized by gingivitis compared to healthy periodontal sites (Table 2), together with the increased expression in the same diseased sites of HNP-1 and HNP-2, induced Dommisch and colleagues to speculate that LL-37 might be a good indicator of the antimicrobial capabilities of the gingival apparatus provided by neutrophils and that there might be an association between a parallel secretion of these AMPs due to the increased numbers of neutrophils in inflammation.
Our group identified for the first time the sequence of the peptide m/z = 4136 already detected in GCF in previous top down proteomics studies, but not structurally elucidated [27]. Despite technical issues, due not only to the complexity of GCF mixture but also to the high molecular weight of the precursor ion (m/z = 4136), a direct high-energy CID fragmentation of the selected peak allowed us to identify it as C-36 peptide corresponding to the residue fragment 383–418 of AAT protein [24] (Table 1). This peptide showed a statistically significant increase in gingivitis group compared to healthy group as previously described in the Section 2.2 [24] (Table 2). Interestingly, C-36 peptide deriving from AAT, exerts significant pro-inflammatory activity [90]. In fact, it was found expressed in human lung tissue as pro-inflammatory activator of human monocytes [91]. Moreover, the proteolytic cleavage of AAT at sites of inflammation may reverse the anti-inflammatory effect of AAT thus contributing to neutrophil recruitment and activation [92]. Considering that inflamed sites are populated by several bacterial species secreting proteolytic enzymes able to generate protein-breakdown products [93], it is tempting to speculate that the C-36 peptide, which was found increased in gingivitis patients as compared to healthy subjects [24], might be derived by AAT bacterial proteolytic breakdown. It should be stressed that a bottom-up LC MS/MS approach would have failed to address the levels of AAT endogenous fragment, due to the requirement for the proteomic mixture to be digested before it can be mass analyzed, whereas our top down MALDI-TOF platform allowed for the detection of increased levels of C-36 peptide in the GCF of gingivitis patients [24].
All these findings further demonstrate the utility of peptidomics profiling platforms for screening and detecting proteoforms, including products of specific proteolytic cleavages, which can have different effects and important biological roles on periodontal diseases processes. The ability to characterize these species as they occur in GCF is essential for understanding the mechanism of the pathobiological response to oral diseases.
4. Relevance and Challenge of Pre-Analytical and Analytical Variables
To date, the effects of different sampling protocols and the influence of both pre-analytical and analytical conditions on GCF proteomic profiling have not been extensively addressed in many of the proteomics and peptidomics investigations which analyzed this specific biological fluid. The inherent limitations of GCF, due to its tiny amount in healthy gums as well as its heterogeneity in periodontal diseases, pose many concerns especially in comparative studies, which require rigorous protocols for protein recovery and normalization of MS data. Other concerns arise from artefacts due to storage conditions. Furthermore, in many cases, investigations which are biased by inter-individual variability of GCF samples, were performed on too small size groups to guarantee sufficiently robust statistical analysis. Last but not least, the comparison of the results obtained among several research groups is not so straightforward and the strategy adopted (bottom-up or top-down) should also be taken into account.
4.1. Quantitation Issues
More than five decades of scientific literature have definitively demonstrated that different sampling methodologies, different sampling times and different processing protocols significantly affected both the quality and the quantity of GCF samples [94]. Up to date, only a few quantitative proteomics investigations have been performed with the aim to differentiate GCF protein profiles in periodontal health and disease [14,30,33,38,39,40,44]. In MALDI-TOF MS, but also in other MS platforms, the ionization is influenced not only by the analyte concentration, but also by its primary structure (in fact, the presence of one or more basic residues in a peptide/protein promotes the ionization). Therefore, in the MS investigations which analyze peptides without an hydrolysis preceding step, MS spectra and MS tandem spectra could result poor and in such cases the spectra interpretation may result challenging in comparison to those obtained by bottom-up procedures in which the presence of tryptic peptides facilitates the ionization and the fragmentation step in MS/MS analysis. The lack in some peptides of a positive charge on the N-terminal residue due to PTMs (acetylation/pyroglutamylation) and the simultaneous absence of basic residues such as Lys, Arg, or His hinder the detection of these peptides [74,75].
Concerning peptidomics studies, the quest for quantitative approach is stringently desirable in order to make published data comparable between each other. The lack of data uniformity is due to several factors, including the difficulty in accurately measuring the small volumes of GCF obtained (especially for healthy sites) and the differences both in collection methodology and processing protocols, as pointed out in Table 1. An additional problem, which complicates the analysis and standardization issues in GCF analysis, is the amount of volume used to elute the sample collected on paper strips or on paper points. In particular, this is an important issue in the case of SELDI and MALDI based investigations (Table 1). In fact, it is well accepted that MALDI-TOF MS spectra of complex bodily fluids are heavily influenced by the extent of sample dilution [63,95,96]. To address this issue in our investigations, experiments at different elution volumes have been performed and both the number of peaks and total peak area in MALDI-TOF MS spectra have been assessed [22]. Indeed, the observation that more concentrated solutions showed both a lower number of peaks and a lower total peak area in comparison to more diluted samples was the most evident and expected behavior; not surprisingly, this was consistent with the MALDI ionization process. Furthermore, since GCF is a complex biological sample for the presence of endogenous peptides and proteins of different structures and different concentrations, the occurrence of ion suppression effects may also take place [97,98].
The effect of heterogeneity of both sample/matrix and ion signals determines poor MALDI data reproducibility, making this kind of analysis unsuitable for quantitative purposes [62]. In order to make MS profiling analysis more robust the use of internal standard and/or a better assessment of peak area normalization are required. Several studies have shown that with the appropriate internal standard, linear calibration curves can be generated providing a correct quantification especially for small molecules and peptides, even in complex biological matrices [62,99,100,101]. The use of internal standard can be avoided when a linear correlation between analyte concentration and ion counts can be demonstrated [102]. A possible alternative to the utilization of an internal standard is the use of Ionic-Liquid Matrices [103].
4.2. PIC
The presence in the GCF of active proteases, of both human and bacterial origin might constitute serious drawbacks making GCF peptidomics analysis very challenging. As in saliva and in other bodily fluids, the degradation and/or protease activity in GCF should be taken in great account. In light of possible protease activity, in such peptidomics studies after sample collection the use of protease inhibitors or of other precautions to prevent protease activity might be highly recommended. Concerning the solutions or buffer used to elute GCF collected on paper strips, in our experience, the use of acidic aqueous solutions is preferable to neutral or basic aqueous/organic ones, because acidic elution quenches protease activity, preserving proteins from potential protease degradations [27,104]. It is interesting to underline that the use of acidic buffer in peptidomics investigations might turn useful also for other reasons. In fact, high molecular weight proteins such as mucins, lactoferrin, α-amylases, and myeloperoxidases, found to be present in GCF [14,30,31,33,34,36,37,38,40,41,42,43,44,45,46,47] and which could hinder the detection of peptidic components [105], are insoluble in acidic conditions [27]. Studies on peptidome stability of GCF samples with PIC supplementation at neutral or basic pH are not yet reported in literature. Many research groups make use of acidic buffer to elute or dilute GCF immediately after the collection [6,17,18,20,21,22,23,24,27,28] and, as indicated in Table 1, for all of the investigations reviewed, the use [22,23,24] or not [6,16,17,18,19,20,21,25,26,27,28,29] of PIC is shown.
The levels of the peptides present in bodily fluids might reflect the activity of protease(s) generating them, which could in turn be influenced by various biological events. Thus, the amount of certain peptides can be used as indirect sensors of the biological state of an individual and, in the end, could provide invaluable information for clinical diagnoses [57]. Among the peptidomics studies reviewed here, many investigations have shown in GCF the presence of peptide fragments derived by proteolytic cleavage from larger proteins such as Fibrinopeptide A fragments (21–35) and (22–35) and Fibrinopeptide B [29], haptoglobin derived peptide [25], Thymosin β-4 fragment (21–44) [29], hBD-1 derived peptides [18] and peptides fragments derived from salivary and serum precursor proteins [6], C36 peptide AAT [24]. Many of these peptides, arising from precursor proteins after exquisitely specific proteolytic cleavages, could exert significant biological activity against various kinds of microorganisms as already outlined in previous section. However, these findings currently remain only mere hypotheses and targeted experiments are required in order to support these possible speculations.
Careful attention should be used in the interpretation of the results of all the studies in which neither protease inhibitors nor other precautions to prevent protease activity were used during sample collection, as the presence of fragments identified as putative biomarkers of pathology may simply be due to the action of uninhibited proteases.
Ultimately, the presence of glutathione in stored GCF determines glutathionylation patterns of such protein target [23] lowering the concentration of the protein as demonstrated in our previous work [23]. The above considerations, together with the degradation patterns observed during storage in various conditions [23,24], underline the requirement to better asses the concentration and storage issues (see below) not only in top down but also in bottom up approaches in order to make the results comparable among each other for a consistency in data interpretation.
4.3. Storage
Among the various pre-analytical issues that deeply affect the peptide profiles of GCF, the storage conditions may strongly compromise the correct interpretation of data analysis if not properly assessed. To address this issue, our group studied the stability of GCF MALDI-TOF profiles after sample collection under different storage conditions [23,24]. Specifically, the storage of GCF immediately extracted from paper strips was found to generate less variations in molecular profiles compared to those obtained when the extraction is performed after the storage. In fact, significant spectral changes were detected for those samples stored at −20 °C directly on the paper strips and extracted after three months, in comparison to the freshly extracted control [23]. Still, even in the case of GCF samples immediately extracted from paper strips, a significant decrease in the peak area of HNP-3, S100-A8, full-length S100-A9, and its truncated form were detected after three months of storage, even at a temperature as low as −80 °C [23].
Based on the above findings, we conclude that the storage conditions may strongly compromise the correct data interpretation, since alterations of “stored GCF” profile may influence the pattern-based biomarker discovery. As a consequence, the samples to be compared (healthy versus periodontal diseases) should be stored for the same amount of time in order to minimize the chance that the results of the study may be biased by potential artifacts.
4.4. The Inter-Individual Variability
Considering the intrinsic inter-individual variability of GCF samples, large cohorts are necessary in order to compare the proteomes in the presence or absence of periodontitis. As reported in Table 2, only few case-control reports describing differences between healthy and gingivitis [24] or healthy and periodontitis subjects [19,25] or sites [16,17,29] were reported; moreover only one experimental model of feasibility blinded test [26] emerged (Table 2).
With the exception of the studies by Tang and colleagues (n = 50) [25] and by Antezack et al. (n = 141) [26], a limitation of the reports summarized in Table 2 is the small sample size.
It is quite conceivable that the subset of potential biomarkers identified in a small sample size might not be confirmed on a larger cohort of patients. Conversely, the use of larger sample sizes should provide increased statistical power allowing the identification of additional biomarkers. Investigations on larger cohorts could provide a more accurate qualitative interpretation of peptide signatures, for well-defined clinical applications.
5. Bioinformatics Approaches
It should be considered that the majority of data in spectral libraries are built from in-silico enzymatic digestion of proteins, limiting the analysis only to those peptides derived from expected enzyme cleavage sites and limiting the number of PTMs considered; for this reason the datasets are difficult to leverage for peptidomics [74]. Moreover, the limited length of the aminoacidic sequences makes peptidomics spectral data analysis and interpretation more challenging in comparison to conventional bottom-up proteomics. As already underlined in Section 3, the studies here reported relate to ‘non-tryptic’ peptides which generate MS/MS patterns less informative in comparison to the ‘tryptic digested peptides’. In fact, due to their intrinsic nature, the absence of basic Lysine or Arginine at the C-terminal sequence might hurdle the fragmentation process and, as a consequence, generation of poor MS/MS spectra could be observed. In line with these observations, the same bioinformatics strategies used for bottom-up approaches do not perform efficiently when less predictive and informative MS/MS patterns are obtained from endogenous peptides [75]. It is important to highlight that, unlike bottom-up proteomics, software solutions for peptidome characterization are not fully developed and data analysis often requires laborious manual interpretation and rigorous validation especially when the identification is based on a single peptide [74,75]. De novo sequencing algorithms assisted by classical database search programs for sensitive and accurate peptide identification are also currently available [106,107]. De novo sequence assignments (when manually performed) often require skilled and experienced personnel and, besides fragmentation patterns, further acquired confirmations for identification can possibly be the assessment of ion intensities and in the case of LC-ESI experiments, evaluation of accurate retention times [27]. The use of software for predicted fragmentations is one of most cost-effective way to validate the identification [108]. For example, concerning both MALDI-TOF/TOF and ESI experiments, when the automated software identify with low confidence endogenous peptide and their fragments, in order to overcame these difficulties and to reach high confidence identification/validation, the experimental mass value of the peptide derived from unspecific cleavage of precursor protein/endogenous peptide can be compared with average theoretical mass values using the FindPept or PeptideMass software (available at http://www.expasy.org/tools) and the experimental MS/MS spectrum can be compared to the MS/MS spectrum generated from the Protein Prospector (http://prospector.ucsf.edu/). Furthermore, several tools have been developed for peptide mapping such as iPiG, EnzymePredictor, PatternLab, Peptigram, UStags, PepNovo, and MS-Tag [74].
6. Conclusions
Peptidomics research represents one of the most interesting and challenging area of proteomics. GCF peptidome could be a goldmine for the discovery of novel biomarkers of periodontal diseases. While many proteomics-based bottom-up approaches have been pursued, resulting in a burgeoning of proteins occurring in GCF, still these approaches are not adequate enough to completely disclose all the proteoforms present in the sample. On the other hand, peptidomics investigations, mainly based on profiling (top-down) strategies, which are well suited for proteoforms detection, are very few and therefore, up to date, the picture of naturally occurring peptides and their role in GCF is still incomplete. In this scenario, the main limitations are due to the lack of standardization of pre-analytical and analytical variables affecting the different processing steps from the sample collection, to the potential artefacts coming from storage of GCF and from the protein concentration normalization and, finally, to the robustness of MS analysis. So, differences derived from heterogeneity in collection, handling, and storage make the results difficult to compare and to establish their correct interpretation due to their inconsistency. Controversies arising from defensins and calgranulins are clear examples of these data misinterpretations.
With the appropriate standardization, MS profiling strategies will allow to harvest intact peptide signatures from GCF which could help to delineate the subtle boundaries existing among the different stages of gingival inflammation. Obviously, each single biomarker forming the signature can be validated by more traditional biochemical approaches like, for instance, Western blot analysis or ELISA assays, which both present an advantage in terms of exquisite specificity of purposely developed monoclonal antibodies raised against the peptide identified by MS. This will open the possibility to recognize a specific phenotypic pattern for the early diagnosis and the progression to periodontal diseases. Moreover, an increased coverage of both GCF peptidome and proteome might assist the comprehension of molecular mechanisms underpinning the pathogenesis of periodontitis. Although the focus of this review is on periodontal diseases, in recent years it was proposed that GCF and saliva should be kept in consideration also in “the wider contexts of oral and systemic health” [109]. Indeed, it has been reported that saliva has shown potential for the development of diagnostic assays for systemic pathologies such as acquired immunodeficiencies, diabetes mellitus, Sjögren syndrome, cerebrovascular/cardiovascular diseases, systemic sclerosis, and even for cancers like breast cancer and, not surprisingly, more localized tumors such as tumors of oral cavity, salivary gland tumor larynx carcinoma, and head and neck carcinoma ([4] and references therein). Considering that GCF has a different origin compared to saliva (it is not secreted from the salivary glands) it is not surprising that these two biofluids do not have a perfect overlap as diagnostic predictors in systemic health, however it has been reported that periodontal diseases (focus of this review) and the consequent periodontal inflammation can have an effect on the progression of important systemic disorders, such as cardiovascular and cerebrovascular diseases [94]. In light of these considerations, MS profiling strategies will allow to harvest intact peptide signatures from GCF which can be used for diagnostics and prognostics assays for important systemic disorders, such as cardiovascular and cerebrovascular diseases.
Acknowledgments
In this section you can acknowledge any support given which is not covered by the author contribution or funding sections. This may include administrative and technical support, or donations in kind (e.g., materials used for experiments).
Author Contributions
Conceptualization, R.T.; writing-original draft preparation, M.P., R.S., C.V., and R.T.; writing-reviewing and editing, M.P., R.S., C.V., C.P., and R.T.; table preparation, M.P. and R.S.; supervision, R.S. and R.T. All authors have read and agreed to the published version of the manuscript.
Funding
This work was supported in part by funding from the Department of Health Sciences of the University of Catanzaro. MP has been supported by funding from research grant PON-MIUR 03PE000_78—Nutramed. CV has been supported by a fellowship of the Ph.D. Program in Life Sciences.
Conflicts of Interest
The authors declare no conflict of interest.
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Overview of the main informational data extrapolated by peptidomics experiments on gingival crevicular fluid (GCF). Peptidomics workflow, without a digestion step, allows to preserve the endogenous information of peptides from GCF, including post-translational modifications and proteolytic products.
Paper strips/2.5%TFA; PIC and NO PIC; Unfractionated.
−20 °C/1 or 3 months, −80 °C/1 or 3 months and immediately analyzed
Yes
Yes
MALDI-TOF/TOF
α-defensins 1–4, hBD-2, Thymosin β4, S100-A8 and its oxidized forms, S100-A9 and its isoforms, Lysozyme C
Preianò et al. [24]
Paper strips/2.5%TFA; PIC and NO PIC, Unfractionated.
−20 °C/1 month, −80 °C/1 month and immediately analyzed.
Yes
Yes
MALDI-TOF/TOF
α-defensins 1–3, hBD-2, C-36 peptide of AAT, Thymosin β4, Thymosin β10, S100-A8 and its oxidized forms, S100-A9 and its isoforms, Lysozyme C
Tang et al. [25]
Paper strips/phosphate-buffered saline; NO PIC; Weak cation exchange magnetic beads.
−80 °C, time not specified (c).
No
Yes
MALDI-TOF
Haptoglobin derived peptide and other unidentified peptides: m/z = 4126.6, 5407.7, 5416.0
Antezack et al. [26]
Paper points/HPLC-grade water; NO PIC; Unfractionated.
Paper cones/Buffer not specified; Use of PIC not specified; RP-HPLC.
−80 °C, time not specified.
No
Yes
ESI-Ion Trap/Orbitrap
α-defensins 1–4, Thymosin β4, Thymosin β4 fragment (21–44), Thymosin β10, Fibrinopeptide A, Fibrinopeptide A fragments (21–35) and (22–35), Fibrinopeptide B
(a) Peak normalization is not required as it is a targeted study. (b) Protein quantitation: samples from healthy periodontal sites served as internal control (baseline) for quantitative analysis. (c) After storage at −80 °C, GCF samples were fractioned using a weak cation exchange magnetic bead kit and then immediately analyzed on MALDI-TOF or stored at −20 °C and analyzed within 24 h.
ijms-21-05270-t002_Table 2
Naturally occurring peptides found in gingival crevicular fluid (GCF) from different study groups. Expression levels are shown as increased (↑) or decreased (↓), as determined by the referred investigation in relation to the appropriate control group. H = Healthy, G = Gingivitis, P = periodontitis, CP = Chronic periodontitis.
Naturally Occurring Peptides
Methods
Study Groupsand Number of Subjects (n)
Study Groups and Protein Expression Level
Ref.
C-36 AAT
MALDI-TOF
H (10) vs. G (10)
↓H/↑G *
Preianò et al. [24]
Cathelicidin antimicrobial peptide LL-37
SELDI-TOF
H sites (n = 8) and G sites (n = 8) in subjects with good general health (4).
↓H sites/↑G sites in H *
Dommisch et al. [17]
Fibrinopeptide A
ESI-Ion Trap/Orbitrap
\\nG and P sites in women after pregnancy (10) and not pregnant women as H controls (10).\\n
H, G and P sites in patients with P (15) and H sites in H subjects (5).
↓H/↑G sites in P *↓H/↑P sites in P *↓H sites in P/↑G sites in P *↓H sites in P/↑P sites in P *
Lundy et al. [16]
S100-A9
MALDI-TOF
H (10) vs. G (10)
↑H/↓G *
Preianò et al. [24]
S100-A9 Glutathionylated
MALDI-TOF
H (10) vs. G (10)
↓H/↑G *
Preianò et al. [24]
Thymosin β-4
MALDI-TOF
H (10) vs. G (10)
↑H/↓G *
Preianò et al. [24]
ESI-Ion Trap/Orbitrap
G and P sites in women after pregnancy (10) and not pregnant women as H controls (10).
\\n\"\n]"}}},{"rowIdx":472,"cells":{"data":{"kind":"list like","value":["\nFront PsycholFront PsycholFront. Psychol.Frontiers in Psychology1664-1078Frontiers Media S.A.32849080PMC743212910.3389/fpsyg.2020.01838PsychologyOriginal ResearchPredicting Late Adolescent Anxiety From Early Adolescent Environmental Stress Exposure: Cognitive Control as MediatorTsaiNancy1*JaeggiSusanne M.2EcclesJacquelynne S.2AthertonOlivia E.3RobinsRichard W.31McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, United States2School of Education, University of California, Irvine, Irvine, CA, United States3Department of Psychology, University of California, Davis, Davis, CA, United States
Edited by: Mathias Weymar, University of Potsdam, Germany
Reviewed by: Andrea T. Shafer, National Institute on Aging, National Institutes of Health (NIH), United States; Swann Pichon, Université de Genève, Switzerland
*Correspondence: Nancy Tsai, ntsai@mit.edu
This article was submitted to Emotion Science, a section of the journal Frontiers in Psychology
1182020202011183803320200372020Copyright © 2020 Tsai, Jaeggi, Eccles, Atherton and Robins.2020Tsai, Jaeggi, Eccles, Atherton and RobinsThis is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Early exposure to stressful life events is associated with greater risk of chronic diseases and mental health problems, including anxiety. However, there is significant variation in how individuals respond to environmental adversity, perhaps due to individual differences in processing and regulating emotional information. Differences in cognitive control – processes necessary for implementing goal directed behavior – have been linked to both stress exposure and anxiety, but the directionality of these links is unclear. The present study investigated the longitudinal pathway of environmental stress exposure during early adolescence on later adolescent anxiety, and the possible mediating mechanism of cognitive control. Participants were 674 Mexican-origin adolescents (meanage = 10.8 years, 50% male) enrolled in the California Families Project, an ongoing longitudinal study of Mexican-origin families. In the current analysis, we examined self-reports of environmental stressors at age 14 (Time 1), cognitive control at age 16 (Time 2), and anxiety at age 18 (Time 3). Structural equation modeling revealed that environmental stressors (Time 1) had both direct and indirect effects on later anxiety (Time 3) through their effects on cognitive control (Time 2), even when accounting for prior levels of anxiety (Time 2). Cognitive control accounted for 18% of the association between environmental stressors and adolescent anxiety: an increase in stressors decreased cognitive control (β = −0.20, p < 0.001), however, cognitive control buffers against anxiety (β = −0.10, p = 0.004). These findings deepen our understanding of the mechanisms underlying the development of anxiety and highlight the importance of cognitive control as a potential protective factor.
Exposure to stressful life events is associated with greater risk of developing chronic diseases and mental health disorders, including anxiety – the most prevalent psychiatric disorder experienced by youth (Pérez-Edgar and Fox, 2005; Pine, 2007; Rapee et al., 2009). However, there is significant variation in how individuals respond to environmental adversity, and those individual differences might be related to the processing and regulation of information. These cognitive control processes necessary for implementing goal-directed behavior – an umbrella term for a collection of related yet distinct processes that are also labeled effortful control, executive function, and self-regulation, depending on the field of study (Zhou et al., 2012) – have been significantly associated with anxiety, as well as a range of other psychiatric disorders such as depression and substance abuse (e.g., Baler and Volkow, 2006; Hirsch and Mathews, 2012; Zainal and Newman, 2018). Cognitive models of generalized anxiety disorder have converged on cognitive control as a potential mechanism of psychopathology (Joormann, 2006; Joorman et al., 2009; Hirsch and Mathews, 2012). For example, the Attentional Control Theory (Eysenck and Derakshan, 2011) and the Processing Efficiency Theory (Eysenck and Calvo, 1992) were put forth to explain the reallocation of cognitive resources when processes such as inhibition, stress, and negative thoughts co-occur. These theories postulate that compromised cognitive control is linked to excessive and uncontrollable worry, a core symptom of anxiety. Indeed, cross-sectional studies have found an association between a range of cognitive control functions and anxiety disorders (e.g., Toren et al., 2000; Muris and Ollendick, 2005; Fujii et al., 2013), as well as the degree of cognitive control impairment being commensurate with the severity of anxiety among patients with generalized anxiety disorder (Hallion et al., 2017). The few studies examining longitudinal associations have found that executive function is related to anxiety problems in an adolescent population two years later (Han et al., 2016) and in adults nine years later (Zainal and Newman, 2018). The scarcity of studies examining longitudinal associations between of cognitive control and anxiety begs the question of directionality and whether cognitive control is an underlying mechanism that might mediate the effect of stress exposure on the development of anxiety.
Stress and cognitive control are processed by closely related neural systems (e.g., Herman et al., 2005), leading some researchers to speculate that stress exposure during childhood and adolescence, sensitive periods of neurocognitive development, can compromise the development of the neural regions that underlie the development of cognitive control (Shonkoff and Phillips, 2000; Lupien et al., 2009). For example, a longitudinal study of infants raised in a predominantly low-income environment found that the chronic physical and psychosocial stress exposure associated with poverty predicted later executive function in pre-kindergarten (Berry et al., 2012). In their longitudinal study examining childhood poverty (age 9) and later adult emotion regulation (age 24), Kim et al. (2013) found that cumulative chronic stress mediated the relationship, mimicking findings highlighting the mediating role of stress between childhood poverty and later cognitive control (e.g., Evans and Schamberg, 2009; Blair et al., 2011; Evans and Fuller-Rowell, 2013; Kim et al., 2018). These findings underscore the link between early stress exposure to later diminished executive function abilities (Gunnar et al., 2009; Blair, 2010; Ursache et al., 2013), but whether these relationships together explain anxiety outcomes remains unclear.
Although the aforementioned links all strongly suggest a mechanism by which early experiences of stress contribute to anxiety outcomes, few published studies to date have explicitly examined the relationship between stress exposure, cognitive control, and anxiety together. In a recent study, Huh et al. (2017) found mediating effects of emotion regulation (i.e., cognitive control in emotionally salient contexts) on the relationship between acute childhood stressors and adult anxiety symptoms in a clinical population using a cross-sectional design. Similarly, Affrunti and Woodruff-Borden (2015) found that executive functions mediated the relationship between fear perception and anxiety in 7- to 10-year-old children. With a short-term longitudinal design, Gulley et al. (2016) examined the interaction of effortful control and stressors on the development of anxiety over a 3-month period finding that, at low levels of stress, high level of effortful control protected against the development of anxious symptoms. With little to almost no prospective studies to draw from, some have speculated that adolescents with more effective cognitive control abilities are better able to process negative emotional information, which in turn lowers their risk for psychopathology (Martel et al., 2007; Micco et al., 2009). Similarly, Nigg (2006) theorizes that anxiety arises from experiences of both negative affect and impaired cognitive control. That is, greater exposure to stressors paired with lower levels of cognitive control may contribute to increased anxiety and depression (Anthony et al., 2002; Eisenberg et al., 2005). However, no studies to date have tested these theories by examining the longitudinal relations between early environmental stress exposure, cognitive control, and later anxiety, and thus, the directionality of these relationships remains unclear and beckons the need for further research.
In the present study, we conducted a prospective mediation analysis to evaluate the effect of environmental stress exposure during early adolescence on late adolescent anxiety and examine the possible mediating mechanism of cognitive control. Given previous findings, we hypothesize that:
Increased stress exposure is associated with higher levels of anxiety.
This relation between stress exposure and anxiety is partially mediated by cognitive control, with increased stress exposure leading to impaired cognitive control, whereas cognitive control in turn buffers against the development of anxiety.
Previous research examining the association between environmental stress exposure and anxiety has often operationalized environmental stress as poverty, leaving a vast range of other possible environmental stressors overlooked and/or under examined. Thus, the present study uses data from a sample of Mexican-origin youth in the United States who face unique challenges and may experience greater exposure to adversity ranging from fewer material and emotional resources to increased exposure to discrimination and neighborhood violence, and more chaotic and less stable home environments (Evans and Kantrowitz, 2002; Evans and Kim, 2010), experiences that can cause chronic stress beyond those of financial origins. Moreover, data from this sample of youth provide an opportunity to examine changes in cognition and anxiety in the context of adolescence – a unique developmental time period marked by notable neurocognitive changes and heightened prevalence of stress-related psychological disorders (Merikangas et al., 2010; Romeo, 2017). Thus, it is critical for research to elucidate the potential risk factors that lead to the development of these disorders during this period of enhanced vulnerability.
Materials and MethodsParticipants
Participants were 674 Mexican-origin adolescents (meanage = 10.8 years, 50% male) enrolled in the California Families Project, an ongoing 12-year longitudinal study of Mexican-origin families (for additional details about the study, see Martin et al., 2019; Atherton et al., 2020). Of the 674 participants, 551 participants had longitudinal data for all our variables of interest at ages 14 (Time 1), 16 years (Time 2), and 18 years (Time 3) and were included in the analysis. Potential participants were drawn at random from rosters of students from the Sacramento and Woodland, CA, school districts. To be eligible for participation in this study, the focal child had to be in the fifth grade, of Mexican origin, and living with his/her biological mother; 72.6% of the eligible families consented to participate in the study, which was granted approval by the Institutional Review Board of University of California, Davis.
MeasuresEnvironmental Stressors
We measured environmental stress exposure using a composite of three separate scales that were all administered at age 14: neighborhood criminal events, neighborhood quality dissatisfaction, and adolescent reports of discrimination experiences. Similar to other composite measures such as socioeconomic status, which is often defined as a composite of occupation, education, and income, our measure of environmental stress exposure is a formative construct: the events are largely independent of each other but collectively contribute to the construct (see Edwards and Bagozzi, 2000). Therefore, environmental stress exposure was calculated by summing the average scores of all three risk factors. Additive indices of cumulative stress exposure are robust and consistently predict mental health outcomes better than indices of singular stress exposure or alternative multiple stress exposure metrics (Evans et al., 2013; Kim et al., 2013; Evans and Cassells, 2014) and have been established as a reasonable method in capturing the confluence of physical and psychosocial challenges associated with adolescent adversity (Evans and English, 2002).
Neighborhood Criminal Events Scale
The adolescent reported on neighborhood-level violence using the Neighborhood Criminal Events Scale, which consists of 10 items. These items assess the extent to which there is violence and disorder in the neighborhood (Aneshensel and Sucoff, 1996; Bowen and Chapman, 1996; Sampson et al., 1997; Cutrona et al., 2000; Ross and Jang, 2000). The scale includes items, such as “How often did [violent crimes including stabbings, shootings, and violent assaults] happen in your neighborhood in the past year?” and “How often did [kids sell illegal drugs] in your neighborhood in the past year?” Ratings were made on a four-point scale ranging from 1 (almost never or never) to 4 (almost always to always). Higher scores indicated greater exposure to crime. The scale demonstrated good internal reliability (α = 0.88).
Neighborhood Quality Dissatisfaction
The adolescent reported on his/her personal evaluation of attractiveness of the neighborhood using an abbreviated version of Neighborhood Quality Evaluation Scale (Roosa et al., 2005), which consists of six items. A typical item is “Your neighborhood is clean and attractive” and “Overall, you are satisfied with your neighborhood.” Ratings were made on a four-point scale ranging from 1 (not at all true) to 4 (very true). Higher scores indicated higher perceptions of neighborhood quality. The average score was then reversed to reflect negative neighborhood quality, with higher scores indicating poorer perceptions of neighborhood quality, which was then used as part of the cumulative stressor score. This scale demonstrated excellent internal reliability (α = 0.93).
Perceived Ethnic Discrimination
The adolescent reported his/her perceived personal experiences with ethnic discrimination using four items, which were adapted for use in the La Familia Project (Johnston and Delgado, 2004) from questions on the Racism in the Workplace Scale (Hughes and Dodge, 1997) and Schedule of Sexist Events (Klonoff and Landrine, 1995). Sample items include “You have heard your teachers at school making jokes or saying bad things about [Mexicans/Mexican–Americans]” and “Teachers think kids who speak Spanish don’t do as well at school.” Ratings were made on a four-point Likert scale, ranging from 1 (almost never or never) to 4 (almost always or always). Higher scores indicated greater experiences of discrimination. The scale demonstrated adequate internal reliability (α = 0.68).
Cognitive Control
Adolescents completed the effortful control scale (16 items) from the short form of the Early Adolescent Temperament Questionnaire – Revised when the adolescent was 16 years old (EATQ-R; Ellis and Rothbart, 2001). The 16-item EATQ-R scale assesses various aspects of cognitive control including the capacity to perform an action when there is a strong tendency to avoid it, the capacity to focus and shift attention when desired, and the capacity to suppress and regulate dominant impulses. This scale includes items such as “When someone tells you to stop doing something, it is easy for you to stop.” and “You pay close attention when someone tells you how to do something.” Ratings were made on a four-point scale ranging from 1 (not at all true of you) to 4 (very true of you). Higher scores indicated greater cognitive control. The full scale demonstrated adequate reliability (α = 0.65). Cognitive control assessed at age 16 (Time 2) was included as our mediator of interest.
Anxiety
Anxiety was assessed using the Mini-Mood and Anxiety Symptom Questionnaire (Casillas and Clark, 2000). For our measure of anxiety, we composited the anxiety (three items; “How much have you felt keyed up or on edge”) and anxious arousal (10 items; “Have you had trouble swallowing”) items into an overall anxiety scale. Participants rated how much they “felt or experienced” each symptom “during the past week” using a four-point scale ranging from 1 (not at all) to 4 (very much). Higher scores indicated more anxiety. The scale demonstrated good reliability (α = 0.87). Anxiety assessed at age 16 (Time 2) was included as a covariate given that our outcome of interest was anxiety at age 18 (Time 3).
Analytical Approach
Our prospective mediation analysis was framed around three time points (Time 1, 2, 3) in order to capture a full prospective mediation model. Several considerations informed the development of our analytical model: (1) the temporal sequence of variables required in a mediation model; (2) the need to account for the stability of anxiety over time, to ensure that the effects of environmental stress and cognitive control are, in fact, prospectively predicting anxiety (and not just due to the fact that anxiety symptoms are stable across adolescence); and (3) the equivalent distance of time between measurements. Our final model was determined by these constraints and captures the development of these constructs during the peak of adolescence. Thus, we examined self-reports of environmental stressors at age 14 (Time 1), cognitive control at age 16 (Time 2), and anxiety at age 18 (Time 3), while including anxiety at age 16 as a covariate1.
To address our research questions, we conducted a prospective mediation analysis using SEM in Stata Version 13 (StataCorp, 2013). Bootstrapping procedures in SEM were used to test the significance of the mediation effects of cognitive control. In this study, 200 bootstrapping samples were generated from the original data set by random sampling to determine indirect effects of mediating variables and analyze the corresponding confidence intervals. This statistical approach is considered to be a robust method of analyzing indirect effects (Hayes, 2009).
Results
Descriptive statistics, correlations, and α reliability estimates for all study variables were calculated prior to addressing our research questions and are displayed in Table 1. The hypothesized structural model comprised three observed variables: environmental stressors at age 14 (Time 1), cognitive control at age 16 (Time 2), and anxiety at age 18 (Time 3). In addition, we included anxiety at age 16 (Time 2) as a predictor of anxiety at age 18 (Time 3) in order to account for the fact that anxiety symptoms are likely stable over time and allow us to draw stronger inferences about prospective effects.
Mean, Standard Deviations (SD), and pairwise correlations among study measures.
Measure
Time at measurement
Age atmeasurement
Mean
SD
1
2
3
1. Environmental stressors
T1
14
1.67
0.44
2. Cognitive control
T2
16
2.93
0.37
−0.19**
3. Anxiety at Age 16
T2
16
1.3
0.3
0.18**
−0.33**
4. Anxiety at age 18
T3
18
1.22
0.26
0.17**
−0.21**
0.35**
The presentation of the pairwise correlations focus on the measures at timepoints relevant to the analysis. Correlation is significant at **p < 0.001.
We hypothesized that individuals with higher levels of environmental stress exposure would later report higher levels of anxiety, as compared with peers with lower levels of environmental stress exposure (Hypothesis 1). Furthermore, we predicted that this effect would be mediated by cognitive control (Hypothesis 2). Indeed, structural equation modeling revealed that cumulative environmental stressors at age 14 had both direct (path c′, Figure 1B) and indirect (paths a and b, Figure 1B) effects on later anxiety at age 18 through their effects on cognitive control at age 16 even when previously reported anxiety at age 16 was included as a covariate. Figure 1 shows the results of a test of the full model, including the total (Figure 1A) and indirect effects (Figure 1B) among cumulative environmental stressors, cognitive control, and anxiety.
Path model showing the effect of cumulative environmental stressors on anxiety mediated by cognitive control. Total effects model (A) and indirect effects model (B) demonstrate the cognitive control at age 16 partially mediated the relation between environmental stressors at age 14 and anxiety at age 18. Anxiety at age 16 is included as a covariale. Residual variances of cognitive control and anxiety at age 16 are correlated to account for association due to unmeasured common causes (r = −0.03). Both standardized and unstandardized coefficients are shown. p < 0.05, ∗∗p < 0.01, ∗∗∗p < 0.001.
Consistent with Hypothesis 1, our results show that adolescents who report higher levels of cumulative environmental stressors at age 14 later reported greater anxiety at age 18 (c; β = 0.11, B = 0.06, z = 2.68, p = 0.02). Consistent with Hypothesis 2, which predicts that cognitive control would mediate the relation between cumulative environmental stressors and early adulthood anxiety, our results demonstrate a significant effect of environmental stressors on cognitive control (a; β = −0.20, B = −0.17, z = −4.52, p < 0.001), of cognitive control on anxiety (b; β = −0.10, B = −0.07, z = −2.91, p = 0.004), and of environmental stressors on anxiety (c′; β = 0.09, B = 0.05, z = 2.00, p = 0.05). These effects remained statistically significant even while controlling for anxiety at age 16, which suggests that cognitive control as a mediator is prospectively predicting anxiety at age 18 over and above prior levels of anxiety. That is, those reporting higher levels of environmental stressors tended to have lower cognitive control. Higher cognitive control, in turn, was associated with lower levels of late adolescent anxiety. The bootstrapped unstandardized indirect effect was B = 0.012, confidence interval [0.002, 0.02], and thus, the indirect effect was statistically significant. As a partial mediator, cognitive control accounted for 18% of the association between environmental cumulative stressors and adolescent anxiety (indirect effect/total effect): individuals reporting higher levels of cumulative environmental stress exposure tended to have decreased cognitive control (β = −0.20, B = −0.17, p < 0.001), cognitive control in turn was associated with decreased anxiety (β = −0.10, B = −0.07, p = 0.004). Taken together, these findings are consistent with the hypothesis that cumulative environmental stress exposure is associated with later anxiety at least in part because stress exposure impairs cognitive control, a critical factor in buffering against the development of anxiety.
Discussion
The purpose of the current study was to investigate the potential meditational role of cognitive control in the longitudinal relation between cumulative environmental stress exposure and the development of late adolescent anxiety. Given previous research, we tested the hypotheses that (1) increased stress exposure would be associated with higher levels of anxiety, and (2) this association would be partially mediated by cognitive control, with increased stress exposure being associated with impaired cognitive control, which in turn is linked to increased anxiety.
In line with our first hypothesis, our findings revealed a statistically significant positive association between early adolescent cumulative environmental stress exposure and later adolescent anxiety, albeit with β = 0.11, the effect is considered small (small = 2%, medium = 15%, and large = 25%; Lachowicz et al., 2018). This is consistent with a large body of research demonstrating a link between early exposure to adverse experiences and a range of later physical and mental health outcomes (see Nusslock and Miller, 2016, for review). However, our findings point to the importance of examining stress exposure from different sources. Previous studies have examined child maltreatment, poverty, family instability, socioeconomic status, and trauma to operationalize stress and adversity. In our unique sample, we touched upon a small fraction of the breadth of stressors one may be exposed to during development. We included reports of discrimination, exposure to criminal activity, and neighborhood quality in our measure of cumulative environmental stress. Sources of stress are wide ranging – from health inequalities to experiences of racism and discrimination – therefore, we urge these diverse experiences of stress to be reflected in future research and to be considered for their potential cumulative effects.
In line with our second hypothesis, we found the aforementioned relationship was partially explained by adolescent cognitive control. Specifically, our findings demonstrated that cognitive control mediated the relation between cumulative environmental stress exposure at age 14 and anxiety at age 18: those with greater exposure to environmental stressors tended to then have lower cognitive control (medium effect; β = −0.20), but higher cognitive control, in turn, was associated with lower levels of late adolescent anxiety (small effect; β = −0.10). In fact, even after controlling for anxiety at age 16, our tested model demonstrates that cognitive control accounts for 18% of the total effect between environmental stress exposure at age 14 and anxiety at age 18, which indicates a medium proportion of explained variance (Lachowicz et al., 2018). As the first study to examine the three constructs in a prospective, longitudinal manner, our results converge with evidence from developmental psychology, public health, and neuroscience to chronicle the role of social systems in shaping the development of our mental and emotional health. More importantly, our findings uniquely identify cognitive control as an underlying mechanism, a protective factor that is both vulnerable to the influences of environmental stress yet potentially buffers against these deleterious effects on anxiety outcomes. Thus, efforts to mitigate mental health outcomes for youth ought to consider the role and malleability of cognitive control. Although results from cognitive control interventions are mixed (see Au et al., 2015 for meta-analysis), a growing number of interventions studies have shown some promise in improving mental health outcomes (Owens et al., 2013; Koster et al., 2017; Jopling et al., 2020). The prospect of optimizing this function is critical in promoting resilience, particularly during adolescence, a unique period of neurocognitive development and enhanced vulnerability.
It is important to note that despite the fact that the above relations were statistically significant, their β values ranged from small to medium. Specifically, the relation between cumulative environmental stress at age 14 and later anxiety at age 18 may be meaningful but smaller than expected given the findings from previous literature. It is important to note, however, that previous findings were either cross-sectional in nature and/or examined only two constructs, which may magnify the strength of the relationships. A longitudinal study examining poverty, chronic stress, and later cognitive control – as indexed by neural activity – reported similar β values for poverty and cognitive control ranging between 0.03 and 0.05, and for chronic stress and cognitive control ranging between −0.13 and −0.14 (Kim et al., 2013), which closely mirrors our findings for the direct effects of path a and c′ (Figure 1). As such, modest values may reflect the challenges in isolating causal mechanisms that are inherent to longitudinal work, where the dynamic relationship of variables gets diluted over time as other factors come into play. For example, our findings demonstrate that concurrent measurement of cognitive control and anxiety at age 16 leads to a stronger relationship (r = −0.33, p < 0.001) than cognitive control at age 16 and anxiety at age 18 (r = −0.21, p < 0.001). Note, however, that the indirect effect of cognitive control accounted for 18% of the total relationship, despite the weakened association over the 2 years and controlling for anxiety at age 16.
The present results should be considered in light of a few limitations. First, our measure of environmental stressors aimed to encapsulate the cumulative effects of stress through measures of environmental adversity. The range of measures included – from perceived discrimination to neighborhood quality – resulted in modest correlation between measures. However, this modest correlation may reflect the methodological issues of assessing environmental stress. Measurement has taken on a variety of forms in attempt to capture the broad range of physical to psychosocial sources (see Evans and English, 2002 for review). One promising approach indexes environmental stress exposure as a cumulative construct in attempt to capture the confluence of multiple external demands that may lead to suboptimal outcomes for youth. Literature on chronic stress shows that the quantity of risk factors encountered, as captured by a cumulative index, and not the particular type that seems to better predict outcomes (Kraemer et al., 2005; Sameroff, 2006; Evans and Cassells, 2014). With our cumulative score from three questionnaires, the self-reported levels of chronic stress were low in our sample (mean = 1.67, range = 0–4), which could reflect measurement issues and/or the possibility that our sample was not exposed to high levels of environmental stress. Future work would benefit from including a greater breadth of measures for a more robust index.
Second, our measure of cognitive control relied on self-report. In a meta-analysis of 282 studies of self-control, correlations within and across types of self-control measures were weak (Duckworth and Kern, 2011). Future work including some combination of behavioral, observational, and self-report may improve measurement validity. Lastly, the direction of the relationship between cognitive control and anxiety is arguable. That is, there is literature indicating impaired cognitive control causes anxiety (Abravanel and Sinha, 2015), anxiety causes impaired cognitive control (Edwards et al., 2016), or that cognitive control moderates the relationship between stress and adversity on poor mental health outcomes (Extremera and Rey, 2015). Although correlational in nature, our novel findings hint at the first causal effect – that is, greater cognitive control is associated with later decreased anxiety – but all three effects have not been adequately examined together (as competing or complementary processes) in a longitudinal context.
The current study set out to synthesize findings from stress, cognition, and mental health literature and test previously untested theories on the directionality of these relationships during adolescence. As the first prospective longitudinal study in this area, our results deepen our understanding of the mechanism underlying early stress exposure and the development of anxiety during a developmentally sensitive period. More importantly, our findings underscore the importance of preserving cognitive control as a means of combating mental health disorders and as a possible protective factor in promoting resilience.
Data Availability Statement
The datasets generated for this study will not be made publicly available due to confidentiality reasons. Data is available upon application through administrators of the California Families Project.
Ethics Statement
The studies involving human participants were reviewed and approved by the Institutional Review Board of University of California, Davis. Written informed consent to participate in this study was provided by the participants’ legal guardian/next of kin.
Author Contributions
NT, SJ, JE, and RR contributed to the theoretical development of the study. OA supplied resources needed for study analysis. NT performed the data analysis and interpretation under the supervision of SJ, JE, and RR. NT drafted the manuscript. SJ, JE, RR, and OA provided revisions. All authors approved the final version of the manuscript for submission.
Conflict of Interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Funding. This study was supported by award no. MRP-17-454825 from the University of California Consortium Adolescence Seed Grant. The California Families Project was supported by a grant from the National Institute on Drug Abuse (DA017902) to RR.
Sex was also examined as a second covariate but did not change any of the findings (i.e., all significant effects remained significant), and thus was omitted from the final model presented here.
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Psychol.Frontiers in Psychology1664-1078Frontiers Media S.A.32849080PMC743212910.3389/fpsyg.2020.01838PsychologyOriginal ResearchPredicting Late Adolescent Anxiety From Early Adolescent Environmental Stress Exposure: Cognitive Control as MediatorTsaiNancy1*JaeggiSusanne M.2EcclesJacquelynne S.2AthertonOlivia E.3RobinsRichard W.31McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, United States2School of Education, University of California, Irvine, Irvine, CA, United States3Department of Psychology, University of California, Davis, Davis, CA, United States
Edited by: Mathias Weymar, University of Potsdam, Germany
Reviewed by: Andrea T. Shafer, National Institute on Aging, National Institutes of Health (NIH), United States; Swann Pichon, Université de Genève, Switzerland
*Correspondence: Nancy Tsai, ntsai@mit.edu
This article was submitted to Emotion Science, a section of the journal Frontiers in Psychology
1182020202011183803320200372020Copyright © 2020 Tsai, Jaeggi, Eccles, Atherton and Robins.2020Tsai, Jaeggi, Eccles, Atherton and RobinsThis is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Early exposure to stressful life events is associated with greater risk of chronic diseases and mental health problems, including anxiety. However, there is significant variation in how individuals respond to environmental adversity, perhaps due to individual differences in processing and regulating emotional information. Differences in cognitive control – processes necessary for implementing goal directed behavior – have been linked to both stress exposure and anxiety, but the directionality of these links is unclear. The present study investigated the longitudinal pathway of environmental stress exposure during early adolescence on later adolescent anxiety, and the possible mediating mechanism of cognitive control. Participants were 674 Mexican-origin adolescents (meanage = 10.8 years, 50% male) enrolled in the California Families Project, an ongoing longitudinal study of Mexican-origin families. In the current analysis, we examined self-reports of environmental stressors at age 14 (Time 1), cognitive control at age 16 (Time 2), and anxiety at age 18 (Time 3). Structural equation modeling revealed that environmental stressors (Time 1) had both direct and indirect effects on later anxiety (Time 3) through their effects on cognitive control (Time 2), even when accounting for prior levels of anxiety (Time 2). Cognitive control accounted for 18% of the association between environmental stressors and adolescent anxiety: an increase in stressors decreased cognitive control (β = −0.20, p < 0.001), however, cognitive control buffers against anxiety (β = −0.10, p = 0.004). These findings deepen our understanding of the mechanisms underlying the development of anxiety and highlight the importance of cognitive control as a potential protective factor.
Exposure to stressful life events is associated with greater risk of developing chronic diseases and mental health disorders, including anxiety – the most prevalent psychiatric disorder experienced by youth (Pérez-Edgar and Fox, 2005; Pine, 2007; Rapee et al., 2009). However, there is significant variation in how individuals respond to environmental adversity, and those individual differences might be related to the processing and regulation of information. These cognitive control processes necessary for implementing goal-directed behavior – an umbrella term for a collection of related yet distinct processes that are also labeled effortful control, executive function, and self-regulation, depending on the field of study (Zhou et al., 2012) – have been significantly associated with anxiety, as well as a range of other psychiatric disorders such as depression and substance abuse (e.g., Baler and Volkow, 2006; Hirsch and Mathews, 2012; Zainal and Newman, 2018). Cognitive models of generalized anxiety disorder have converged on cognitive control as a potential mechanism of psychopathology (Joormann, 2006; Joorman et al., 2009; Hirsch and Mathews, 2012). For example, the Attentional Control Theory (Eysenck and Derakshan, 2011) and the Processing Efficiency Theory (Eysenck and Calvo, 1992) were put forth to explain the reallocation of cognitive resources when processes such as inhibition, stress, and negative thoughts co-occur. These theories postulate that compromised cognitive control is linked to excessive and uncontrollable worry, a core symptom of anxiety. Indeed, cross-sectional studies have found an association between a range of cognitive control functions and anxiety disorders (e.g., Toren et al., 2000; Muris and Ollendick, 2005; Fujii et al., 2013), as well as the degree of cognitive control impairment being commensurate with the severity of anxiety among patients with generalized anxiety disorder (Hallion et al., 2017). The few studies examining longitudinal associations have found that executive function is related to anxiety problems in an adolescent population two years later (Han et al., 2016) and in adults nine years later (Zainal and Newman, 2018). The scarcity of studies examining longitudinal associations between of cognitive control and anxiety begs the question of directionality and whether cognitive control is an underlying mechanism that might mediate the effect of stress exposure on the development of anxiety.
Stress and cognitive control are processed by closely related neural systems (e.g., Herman et al., 2005), leading some researchers to speculate that stress exposure during childhood and adolescence, sensitive periods of neurocognitive development, can compromise the development of the neural regions that underlie the development of cognitive control (Shonkoff and Phillips, 2000; Lupien et al., 2009). For example, a longitudinal study of infants raised in a predominantly low-income environment found that the chronic physical and psychosocial stress exposure associated with poverty predicted later executive function in pre-kindergarten (Berry et al., 2012). In their longitudinal study examining childhood poverty (age 9) and later adult emotion regulation (age 24), Kim et al. (2013) found that cumulative chronic stress mediated the relationship, mimicking findings highlighting the mediating role of stress between childhood poverty and later cognitive control (e.g., Evans and Schamberg, 2009; Blair et al., 2011; Evans and Fuller-Rowell, 2013; Kim et al., 2018). These findings underscore the link between early stress exposure to later diminished executive function abilities (Gunnar et al., 2009; Blair, 2010; Ursache et al., 2013), but whether these relationships together explain anxiety outcomes remains unclear.
Although the aforementioned links all strongly suggest a mechanism by which early experiences of stress contribute to anxiety outcomes, few published studies to date have explicitly examined the relationship between stress exposure, cognitive control, and anxiety together. In a recent study, Huh et al. (2017) found mediating effects of emotion regulation (i.e., cognitive control in emotionally salient contexts) on the relationship between acute childhood stressors and adult anxiety symptoms in a clinical population using a cross-sectional design. Similarly, Affrunti and Woodruff-Borden (2015) found that executive functions mediated the relationship between fear perception and anxiety in 7- to 10-year-old children. With a short-term longitudinal design, Gulley et al. (2016) examined the interaction of effortful control and stressors on the development of anxiety over a 3-month period finding that, at low levels of stress, high level of effortful control protected against the development of anxious symptoms. With little to almost no prospective studies to draw from, some have speculated that adolescents with more effective cognitive control abilities are better able to process negative emotional information, which in turn lowers their risk for psychopathology (Martel et al., 2007; Micco et al., 2009). Similarly, Nigg (2006) theorizes that anxiety arises from experiences of both negative affect and impaired cognitive control. That is, greater exposure to stressors paired with lower levels of cognitive control may contribute to increased anxiety and depression (Anthony et al., 2002; Eisenberg et al., 2005). However, no studies to date have tested these theories by examining the longitudinal relations between early environmental stress exposure, cognitive control, and later anxiety, and thus, the directionality of these relationships remains unclear and beckons the need for further research.
In the present study, we conducted a prospective mediation analysis to evaluate the effect of environmental stress exposure during early adolescence on late adolescent anxiety and examine the possible mediating mechanism of cognitive control. Given previous findings, we hypothesize that:
Increased stress exposure is associated with higher levels of anxiety.
This relation between stress exposure and anxiety is partially mediated by cognitive control, with increased stress exposure leading to impaired cognitive control, whereas cognitive control in turn buffers against the development of anxiety.
Previous research examining the association between environmental stress exposure and anxiety has often operationalized environmental stress as poverty, leaving a vast range of other possible environmental stressors overlooked and/or under examined. Thus, the present study uses data from a sample of Mexican-origin youth in the United States who face unique challenges and may experience greater exposure to adversity ranging from fewer material and emotional resources to increased exposure to discrimination and neighborhood violence, and more chaotic and less stable home environments (Evans and Kantrowitz, 2002; Evans and Kim, 2010), experiences that can cause chronic stress beyond those of financial origins. Moreover, data from this sample of youth provide an opportunity to examine changes in cognition and anxiety in the context of adolescence – a unique developmental time period marked by notable neurocognitive changes and heightened prevalence of stress-related psychological disorders (Merikangas et al., 2010; Romeo, 2017). Thus, it is critical for research to elucidate the potential risk factors that lead to the development of these disorders during this period of enhanced vulnerability.
Materials and MethodsParticipants
Participants were 674 Mexican-origin adolescents (meanage = 10.8 years, 50% male) enrolled in the California Families Project, an ongoing 12-year longitudinal study of Mexican-origin families (for additional details about the study, see Martin et al., 2019; Atherton et al., 2020). Of the 674 participants, 551 participants had longitudinal data for all our variables of interest at ages 14 (Time 1), 16 years (Time 2), and 18 years (Time 3) and were included in the analysis. Potential participants were drawn at random from rosters of students from the Sacramento and Woodland, CA, school districts. To be eligible for participation in this study, the focal child had to be in the fifth grade, of Mexican origin, and living with his/her biological mother; 72.6% of the eligible families consented to participate in the study, which was granted approval by the Institutional Review Board of University of California, Davis.
MeasuresEnvironmental Stressors
We measured environmental stress exposure using a composite of three separate scales that were all administered at age 14: neighborhood criminal events, neighborhood quality dissatisfaction, and adolescent reports of discrimination experiences. Similar to other composite measures such as socioeconomic status, which is often defined as a composite of occupation, education, and income, our measure of environmental stress exposure is a formative construct: the events are largely independent of each other but collectively contribute to the construct (see Edwards and Bagozzi, 2000). Therefore, environmental stress exposure was calculated by summing the average scores of all three risk factors. Additive indices of cumulative stress exposure are robust and consistently predict mental health outcomes better than indices of singular stress exposure or alternative multiple stress exposure metrics (Evans et al., 2013; Kim et al., 2013; Evans and Cassells, 2014) and have been established as a reasonable method in capturing the confluence of physical and psychosocial challenges associated with adolescent adversity (Evans and English, 2002).
Neighborhood Criminal Events Scale
The adolescent reported on neighborhood-level violence using the Neighborhood Criminal Events Scale, which consists of 10 items. These items assess the extent to which there is violence and disorder in the neighborhood (Aneshensel and Sucoff, 1996; Bowen and Chapman, 1996; Sampson et al., 1997; Cutrona et al., 2000; Ross and Jang, 2000). The scale includes items, such as “How often did [violent crimes including stabbings, shootings, and violent assaults] happen in your neighborhood in the past year?” and “How often did [kids sell illegal drugs] in your neighborhood in the past year?” Ratings were made on a four-point scale ranging from 1 (almost never or never) to 4 (almost always to always). Higher scores indicated greater exposure to crime. The scale demonstrated good internal reliability (α = 0.88).
Neighborhood Quality Dissatisfaction
The adolescent reported on his/her personal evaluation of attractiveness of the neighborhood using an abbreviated version of Neighborhood Quality Evaluation Scale (Roosa et al., 2005), which consists of six items. A typical item is “Your neighborhood is clean and attractive” and “Overall, you are satisfied with your neighborhood.” Ratings were made on a four-point scale ranging from 1 (not at all true) to 4 (very true). Higher scores indicated higher perceptions of neighborhood quality. The average score was then reversed to reflect negative neighborhood quality, with higher scores indicating poorer perceptions of neighborhood quality, which was then used as part of the cumulative stressor score. This scale demonstrated excellent internal reliability (α = 0.93).
Perceived Ethnic Discrimination
The adolescent reported his/her perceived personal experiences with ethnic discrimination using four items, which were adapted for use in the La Familia Project (Johnston and Delgado, 2004) from questions on the Racism in the Workplace Scale (Hughes and Dodge, 1997) and Schedule of Sexist Events (Klonoff and Landrine, 1995). Sample items include “You have heard your teachers at school making jokes or saying bad things about [Mexicans/Mexican–Americans]” and “Teachers think kids who speak Spanish don’t do as well at school.” Ratings were made on a four-point Likert scale, ranging from 1 (almost never or never) to 4 (almost always or always). Higher scores indicated greater experiences of discrimination. The scale demonstrated adequate internal reliability (α = 0.68).
Cognitive Control
Adolescents completed the effortful control scale (16 items) from the short form of the Early Adolescent Temperament Questionnaire – Revised when the adolescent was 16 years old (EATQ-R; Ellis and Rothbart, 2001). The 16-item EATQ-R scale assesses various aspects of cognitive control including the capacity to perform an action when there is a strong tendency to avoid it, the capacity to focus and shift attention when desired, and the capacity to suppress and regulate dominant impulses. This scale includes items such as “When someone tells you to stop doing something, it is easy for you to stop.” and “You pay close attention when someone tells you how to do something.” Ratings were made on a four-point scale ranging from 1 (not at all true of you) to 4 (very true of you). Higher scores indicated greater cognitive control. The full scale demonstrated adequate reliability (α = 0.65). Cognitive control assessed at age 16 (Time 2) was included as our mediator of interest.
Anxiety
Anxiety was assessed using the Mini-Mood and Anxiety Symptom Questionnaire (Casillas and Clark, 2000). For our measure of anxiety, we composited the anxiety (three items; “How much have you felt keyed up or on edge”) and anxious arousal (10 items; “Have you had trouble swallowing”) items into an overall anxiety scale. Participants rated how much they “felt or experienced” each symptom “during the past week” using a four-point scale ranging from 1 (not at all) to 4 (very much). Higher scores indicated more anxiety. The scale demonstrated good reliability (α = 0.87). Anxiety assessed at age 16 (Time 2) was included as a covariate given that our outcome of interest was anxiety at age 18 (Time 3).
Analytical Approach
Our prospective mediation analysis was framed around three time points (Time 1, 2, 3) in order to capture a full prospective mediation model. Several considerations informed the development of our analytical model: (1) the temporal sequence of variables required in a mediation model; (2) the need to account for the stability of anxiety over time, to ensure that the effects of environmental stress and cognitive control are, in fact, prospectively predicting anxiety (and not just due to the fact that anxiety symptoms are stable across adolescence); and (3) the equivalent distance of time between measurements. Our final model was determined by these constraints and captures the development of these constructs during the peak of adolescence. Thus, we examined self-reports of environmental stressors at age 14 (Time 1), cognitive control at age 16 (Time 2), and anxiety at age 18 (Time 3), while including anxiety at age 16 as a covariate1.
To address our research questions, we conducted a prospective mediation analysis using SEM in Stata Version 13 (StataCorp, 2013). Bootstrapping procedures in SEM were used to test the significance of the mediation effects of cognitive control. In this study, 200 bootstrapping samples were generated from the original data set by random sampling to determine indirect effects of mediating variables and analyze the corresponding confidence intervals. This statistical approach is considered to be a robust method of analyzing indirect effects (Hayes, 2009).
Results
Descriptive statistics, correlations, and α reliability estimates for all study variables were calculated prior to addressing our research questions and are displayed in Table 1. The hypothesized structural model comprised three observed variables: environmental stressors at age 14 (Time 1), cognitive control at age 16 (Time 2), and anxiety at age 18 (Time 3). In addition, we included anxiety at age 16 (Time 2) as a predictor of anxiety at age 18 (Time 3) in order to account for the fact that anxiety symptoms are likely stable over time and allow us to draw stronger inferences about prospective effects.
Mean, Standard Deviations (SD), and pairwise correlations among study measures.
Measure
Time at measurement
Age atmeasurement
Mean
SD
1
2
3
1. Environmental stressors
T1
14
1.67
0.44
2. Cognitive control
T2
16
2.93
0.37
−0.19**
3. Anxiety at Age 16
T2
16
1.3
0.3
0.18**
−0.33**
4. Anxiety at age 18
T3
18
1.22
0.26
0.17**
−0.21**
0.35**
The presentation of the pairwise correlations focus on the measures at timepoints relevant to the analysis. Correlation is significant at **p < 0.001.
We hypothesized that individuals with higher levels of environmental stress exposure would later report higher levels of anxiety, as compared with peers with lower levels of environmental stress exposure (Hypothesis 1). Furthermore, we predicted that this effect would be mediated by cognitive control (Hypothesis 2). Indeed, structural equation modeling revealed that cumulative environmental stressors at age 14 had both direct (path c′, Figure 1B) and indirect (paths a and b, Figure 1B) effects on later anxiety at age 18 through their effects on cognitive control at age 16 even when previously reported anxiety at age 16 was included as a covariate. Figure 1 shows the results of a test of the full model, including the total (Figure 1A) and indirect effects (Figure 1B) among cumulative environmental stressors, cognitive control, and anxiety.
Path model showing the effect of cumulative environmental stressors on anxiety mediated by cognitive control. Total effects model (A) and indirect effects model (B) demonstrate the cognitive control at age 16 partially mediated the relation between environmental stressors at age 14 and anxiety at age 18. Anxiety at age 16 is included as a covariale. Residual variances of cognitive control and anxiety at age 16 are correlated to account for association due to unmeasured common causes (r = −0.03). Both standardized and unstandardized coefficients are shown. p < 0.05, ∗∗p < 0.01, ∗∗∗p < 0.001.
Consistent with Hypothesis 1, our results show that adolescents who report higher levels of cumulative environmental stressors at age 14 later reported greater anxiety at age 18 (c; β = 0.11, B = 0.06, z = 2.68, p = 0.02). Consistent with Hypothesis 2, which predicts that cognitive control would mediate the relation between cumulative environmental stressors and early adulthood anxiety, our results demonstrate a significant effect of environmental stressors on cognitive control (a; β = −0.20, B = −0.17, z = −4.52, p < 0.001), of cognitive control on anxiety (b; β = −0.10, B = −0.07, z = −2.91, p = 0.004), and of environmental stressors on anxiety (c′; β = 0.09, B = 0.05, z = 2.00, p = 0.05). These effects remained statistically significant even while controlling for anxiety at age 16, which suggests that cognitive control as a mediator is prospectively predicting anxiety at age 18 over and above prior levels of anxiety. That is, those reporting higher levels of environmental stressors tended to have lower cognitive control. Higher cognitive control, in turn, was associated with lower levels of late adolescent anxiety. The bootstrapped unstandardized indirect effect was B = 0.012, confidence interval [0.002, 0.02], and thus, the indirect effect was statistically significant. As a partial mediator, cognitive control accounted for 18% of the association between environmental cumulative stressors and adolescent anxiety (indirect effect/total effect): individuals reporting higher levels of cumulative environmental stress exposure tended to have decreased cognitive control (β = −0.20, B = −0.17, p < 0.001), cognitive control in turn was associated with decreased anxiety (β = −0.10, B = −0.07, p = 0.004). Taken together, these findings are consistent with the hypothesis that cumulative environmental stress exposure is associated with later anxiety at least in part because stress exposure impairs cognitive control, a critical factor in buffering against the development of anxiety.
Discussion
The purpose of the current study was to investigate the potential meditational role of cognitive control in the longitudinal relation between cumulative environmental stress exposure and the development of late adolescent anxiety. Given previous research, we tested the hypotheses that (1) increased stress exposure would be associated with higher levels of anxiety, and (2) this association would be partially mediated by cognitive control, with increased stress exposure being associated with impaired cognitive control, which in turn is linked to increased anxiety.
In line with our first hypothesis, our findings revealed a statistically significant positive association between early adolescent cumulative environmental stress exposure and later adolescent anxiety, albeit with β = 0.11, the effect is considered small (small = 2%, medium = 15%, and large = 25%; Lachowicz et al., 2018). This is consistent with a large body of research demonstrating a link between early exposure to adverse experiences and a range of later physical and mental health outcomes (see Nusslock and Miller, 2016, for review). However, our findings point to the importance of examining stress exposure from different sources. Previous studies have examined child maltreatment, poverty, family instability, socioeconomic status, and trauma to operationalize stress and adversity. In our unique sample, we touched upon a small fraction of the breadth of stressors one may be exposed to during development. We included reports of discrimination, exposure to criminal activity, and neighborhood quality in our measure of cumulative environmental stress. Sources of stress are wide ranging – from health inequalities to experiences of racism and discrimination – therefore, we urge these diverse experiences of stress to be reflected in future research and to be considered for their potential cumulative effects.
In line with our second hypothesis, we found the aforementioned relationship was partially explained by adolescent cognitive control. Specifically, our findings demonstrated that cognitive control mediated the relation between cumulative environmental stress exposure at age 14 and anxiety at age 18: those with greater exposure to environmental stressors tended to then have lower cognitive control (medium effect; β = −0.20), but higher cognitive control, in turn, was associated with lower levels of late adolescent anxiety (small effect; β = −0.10). In fact, even after controlling for anxiety at age 16, our tested model demonstrates that cognitive control accounts for 18% of the total effect between environmental stress exposure at age 14 and anxiety at age 18, which indicates a medium proportion of explained variance (Lachowicz et al., 2018). As the first study to examine the three constructs in a prospective, longitudinal manner, our results converge with evidence from developmental psychology, public health, and neuroscience to chronicle the role of social systems in shaping the development of our mental and emotional health. More importantly, our findings uniquely identify cognitive control as an underlying mechanism, a protective factor that is both vulnerable to the influences of environmental stress yet potentially buffers against these deleterious effects on anxiety outcomes. Thus, efforts to mitigate mental health outcomes for youth ought to consider the role and malleability of cognitive control. Although results from cognitive control interventions are mixed (see Au et al., 2015 for meta-analysis), a growing number of interventions studies have shown some promise in improving mental health outcomes (Owens et al., 2013; Koster et al., 2017; Jopling et al., 2020). The prospect of optimizing this function is critical in promoting resilience, particularly during adolescence, a unique period of neurocognitive development and enhanced vulnerability.
It is important to note that despite the fact that the above relations were statistically significant, their β values ranged from small to medium. Specifically, the relation between cumulative environmental stress at age 14 and later anxiety at age 18 may be meaningful but smaller than expected given the findings from previous literature. It is important to note, however, that previous findings were either cross-sectional in nature and/or examined only two constructs, which may magnify the strength of the relationships. A longitudinal study examining poverty, chronic stress, and later cognitive control – as indexed by neural activity – reported similar β values for poverty and cognitive control ranging between 0.03 and 0.05, and for chronic stress and cognitive control ranging between −0.13 and −0.14 (Kim et al., 2013), which closely mirrors our findings for the direct effects of path a and c′ (Figure 1). As such, modest values may reflect the challenges in isolating causal mechanisms that are inherent to longitudinal work, where the dynamic relationship of variables gets diluted over time as other factors come into play. For example, our findings demonstrate that concurrent measurement of cognitive control and anxiety at age 16 leads to a stronger relationship (r = −0.33, p < 0.001) than cognitive control at age 16 and anxiety at age 18 (r = −0.21, p < 0.001). Note, however, that the indirect effect of cognitive control accounted for 18% of the total relationship, despite the weakened association over the 2 years and controlling for anxiety at age 16.
The present results should be considered in light of a few limitations. First, our measure of environmental stressors aimed to encapsulate the cumulative effects of stress through measures of environmental adversity. The range of measures included – from perceived discrimination to neighborhood quality – resulted in modest correlation between measures. However, this modest correlation may reflect the methodological issues of assessing environmental stress. Measurement has taken on a variety of forms in attempt to capture the broad range of physical to psychosocial sources (see Evans and English, 2002 for review). One promising approach indexes environmental stress exposure as a cumulative construct in attempt to capture the confluence of multiple external demands that may lead to suboptimal outcomes for youth. Literature on chronic stress shows that the quantity of risk factors encountered, as captured by a cumulative index, and not the particular type that seems to better predict outcomes (Kraemer et al., 2005; Sameroff, 2006; Evans and Cassells, 2014). With our cumulative score from three questionnaires, the self-reported levels of chronic stress were low in our sample (mean = 1.67, range = 0–4), which could reflect measurement issues and/or the possibility that our sample was not exposed to high levels of environmental stress. Future work would benefit from including a greater breadth of measures for a more robust index.
Second, our measure of cognitive control relied on self-report. In a meta-analysis of 282 studies of self-control, correlations within and across types of self-control measures were weak (Duckworth and Kern, 2011). Future work including some combination of behavioral, observational, and self-report may improve measurement validity. Lastly, the direction of the relationship between cognitive control and anxiety is arguable. That is, there is literature indicating impaired cognitive control causes anxiety (Abravanel and Sinha, 2015), anxiety causes impaired cognitive control (Edwards et al., 2016), or that cognitive control moderates the relationship between stress and adversity on poor mental health outcomes (Extremera and Rey, 2015). Although correlational in nature, our novel findings hint at the first causal effect – that is, greater cognitive control is associated with later decreased anxiety – but all three effects have not been adequately examined together (as competing or complementary processes) in a longitudinal context.
The current study set out to synthesize findings from stress, cognition, and mental health literature and test previously untested theories on the directionality of these relationships during adolescence. As the first prospective longitudinal study in this area, our results deepen our understanding of the mechanism underlying early stress exposure and the development of anxiety during a developmentally sensitive period. More importantly, our findings underscore the importance of preserving cognitive control as a means of combating mental health disorders and as a possible protective factor in promoting resilience.
Data Availability Statement
The datasets generated for this study will not be made publicly available due to confidentiality reasons. Data is available upon application through administrators of the California Families Project.
Ethics Statement
The studies involving human participants were reviewed and approved by the Institutional Review Board of University of California, Davis. Written informed consent to participate in this study was provided by the participants’ legal guardian/next of kin.
Author Contributions
NT, SJ, JE, and RR contributed to the theoretical development of the study. OA supplied resources needed for study analysis. NT performed the data analysis and interpretation under the supervision of SJ, JE, and RR. NT drafted the manuscript. SJ, JE, RR, and OA provided revisions. All authors approved the final version of the manuscript for submission.
Conflict of Interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Funding. This study was supported by award no. MRP-17-454825 from the University of California Consortium Adolescence Seed Grant. The California Families Project was supported by a grant from the National Institute on Drug Abuse (DA017902) to RR.
Sex was also examined as a second covariate but did not change any of the findings (i.e., all significant effects remained significant), and thus was omitted from the final model presented here.
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These authors contributed equally to this work.
0382020820202115555222720200182020© 2020 by the authors.2020Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Late embryogenesis abundant (LEA) group 1 (LEA_1) proteins are intrinsically disordered proteins (IDPs) that play important roles in protecting plants from abiotic stress. Their protective function, at a molecular level, has not yet been fully elucidated, but several studies suggest their involvement in membrane stabilization under stress conditions. In this paper, the soybean LEA_1 protein PM1 and its truncated forms (PM1-N: N-terminal half; PM1-C: C-terminal half) were tested for the ability to protect liposomes against damage induced by freeze-thaw stress. Turbidity measurement and light microscopy showed that full-length PM1 and PM1-N, but not PM1-C, can prevent freeze-thaw-induced aggregation of POPC (1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine) liposomes and native thylakoid membranes, isolated from spinach leaves (Spinacia oleracea). Particle size distribution analysis by dynamic light scattering (DLS) further confirmed that PM1 and PM1-N can prevent liposome aggregation during freeze-thaw. Furthermore, PM1 or PM1-N could significantly inhibit membrane fusion of liposomes, but not reduce the leakage of their contents following freezing stress. The results of proteolytic digestion and circular dichroism experiments suggest that PM1 and PM1-N proteins bind mainly on the surface of the POPC liposome. We propose that, through its N-terminal region, PM1 functions as a membrane-stabilizing protein during abiotic stress, and might inhibit membrane fusion and aggregation of vesicles or other endomembrane structures within the plant cell.
Plants have developed various adaptations to maintain normal physiological processes under adverse abiotic stress, such as soil salinity, drought and extreme temperatures. For instance, it has been demonstrated experimentally that late embryogenesis abundant (LEA) proteins are involved in plant tolerance to abiotic stress [1,2,3]. Based on Pfam domains within their sequences, LEA proteins can be classified into eight subgroups [3]. Most research has focused on the protective functions of the LEA_4, LEA_5 and dehydrin subgroups, generating data on expression profile, cellular localization, and the role of the respective genes and proteins. There is relatively little research on the protective roles of other groups, including the LEA_1 proteins, which are highly expressed in mature seeds and also accumulate in vegetative tissues under stress conditions [4,5,6]. Some genetic studies have been performed showing that the over-expression of LEA_1 genes, such as LEA4-1 from Brassica napus, BhLEA1 and BhLEA2 from Boea hygrometrica, AtLEA4-5 from Arabidopsis thaliana, and XsLEA1-8 from Xerophyta schlechteri, confer tolerance to salt, drought and osmotic stress in transgenic plants [5,6,7]. The over-expression of recombinant Gastrodia elata GeLEA1-1 or Xerophyta schlechteri XsLEA1-8 enhances Escherichia coli viability under low-temperature or heat stress [7,8]. In addition, in vitro, the recombinant LEA_1 proteins AtLEA4-2 and AtLEA4-5 (both A. thaliana) and XsLEA1-8 can preserve lactate dehydrogenase activity and prevent this enzyme aggregating during freeze–thaw, heat, desiccation and oxidative stress [7,9,10]. Liu et al. suggested that BhLEA1 and BhLEA2 play a general protective role in the plant cell during dehydration stress and increase membrane and protein stability, as indicated by the relatively low electrolyte leakage and higher SOD and POD activity in transgenic plant leaves [5]. In contrast, A. thaliana basic protein LEA18 (LEA_1) specifically aggregates and destabilizes negatively charged liposomes, which suggests that LEA18 does not function as a membrane-stabilizing protein, but could modulate membrane stability depending on membrane composition [11].
The LEA proteins are a broad family of proteins, many of which are thought to be intrinsically disordered proteins (IDPs) with “moonlighting” activity [3,10]. However, to our knowledge, relatively few individual LEA proteins have been shown to have multifunctional protective activities. One example of a multifunctional protein, PM1, a LEA_1 protein from soybean, can interact with a range of other molecules, such as non-reducing sugars, poly-l-lysine and phospholipids, during dehydration. PM1 is likely to be an important component of the cellular, organic glass that may stabilize desiccation-sensitive proteins and membranes in stressed plants [12]. PM1 contains a high proportion of basic amino acids, including 13 lysine, 5 arginine and 10 histidine residues, but these are distributed unequally along the length of the protein. There are 12 lysine residues and 3 arginine residues located in the N-terminal half (residues 1–84), while 9 histidine residues and 2 arginine residues are located in the C-terminal region (residues 85–173). In previous papers, we have demonstrated that PM1 protein can bind some metal ions (Fe3+, Ni2+, Cu2+ and Zn2+) and so play an important protective role in reducing oxidative damage and ion toxicity in plants exposed to abiotic stress [13]. In addition, PM1 or its truncated version, PM1-C (C-terminal half only), but not PM1-N, can form oligomers and high molecular weight (HMW) complexes via its C-terminal histidine residues both in vitro and in planta. Crucially, binding of Cu2+ at high concentrations, which takes place through the same histidine residues, seems to promote oligomerization and the formation of HMW complexes by PM1 [14].
In the present paper, we investigate whether soybean LEA_1 protein PM1 and its two truncated forms, PM1-N and PM1-C, possess the ability to suppress freeze-thaw-induced damage of liposomes. We show that only PM1 and PM1-N can effectively prevent aggregation of POPC liposomes caused by freeze-thaw treatment. Furthermore, we show that, although PM1 and PM1-N inhibit membrane fusion events, they do not reduce leakage of POPC liposomes. On the basis of these observations, we propose that the N-terminal region of PM1 protein has a membrane stabilizing function during abiotic stress.
2. Results2.1. PM1 and PM1-N Proteins Inhibit the Freeze-Thaw-Induced Increase in Turbidity of a Liposome Suspension
The cell membrane is composed of lipids (phospholipids and cholesterol), proteins and carbohydrate groups. Among the phospholipids, phosphatidylcholine (PC) represents a major component of biological membranes and the PC, POPC (1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine), can be used to produce liposomes that model cell membranes. Light scattering due to aggregation of POPC liposomes can be measured by apparent absorbance at 400 nm [15]. The OD400 of a freshly prepared POPC liposome suspension was ~0.3 and the addition of one of the proteins thaumatin, PM1, PM1-N or PM1-C to POPC liposomes did not obviously affect their turbidity before exposure to stress (Figure 1).
The turbidity, as shown by OD400, of the liposome suspension increased markedly to 0.8 after three freeze-thaw cycles, indicating that the freeze-thaw treatment causes liposome aggregation. When 0.8 mg/mL thaumatin (molar ratio of protein:lipid~1:3300) or 0.8 mg/mL PM1-C protein (molar ratio of protein:lipid~1:1650) was added to the POPC liposome suspension, its OD400 increased to 0.6 after the freeze-thaw treatment, which is significantly different (p < 0.01) to that of liposomes only. Thaumatin was chosen as a negative control in the following study because of having no known function in membrane protection [11] and its similar molecular size to PM1 protein. There was no statistical difference in turbidity between liposome/PM1-C and liposome/thaumatin. However, when PM1 or PM1-N protein (0.04–0.4 mg/mL) was included in the liposome suspension, the turbidity increase after freeze-thaw was appreciably suppressed and the extent of this suppression was dependent on the concentration of the added protein, being significantly (p < 0.05) or extremely significantly different (p < 0.01) from that of thaumatin. When the concentration of PM1 (molar ratio of protein:lipid ~1:3300) or PM1-N protein (molar ratio of protein:lipid ~1:1650) reached 0.8 mg/mL, the post-treatment turbidity was 0.4 at OD400, i.e., similar to that before freeze-thaw.
2.2. PM1 and PM1-N Proteins Can Inhibit or Prevent Freeze-Thaw-Induced Aggregation of Liposomes and Isolated Thylakoid membranes
The inhibitory effect of PM1, PM1-N or PM1-C protein on liposome aggregation can be followed by light microscopy. Before freeze-thaw treatment, POPC liposomes were small in size and evenly distributed in the suspension (Figure 2A). The addition of one of thaumatin, PM1, PM1-N or PM1-C proteins to POPC liposomes did not cause aggregation before freeze-thaw treatment, but in the absence of added proteins, liposome aggregation could be seen to occur after freeze-thaw treatment. With either 0.8 mg/mL thaumatin (negative control) or 0.8 mg/mL PM1-C protein in the suspension, the liposomes still aggregated after freeze-thaw. In contrast, when 0.8 mg/mL PM1 or 0.8 mg/mL PM1-N protein was added to liposomes, the suspension remained homogeneous even after freeze-thaw.
Next, the anti-aggregation effect of the PM1, PM1-N or PM1-C proteins on natural biological material was tested using thylakoid membranes isolated from spinach leaves (Spinacia oleracea). Under the light microscope, the thylakoid membranes appeared as evenly distributed, small particles in suspension (Figure 2B). The addition of thaumatin, PM1, PM1-N or PM1-C proteins individually to thylakoid membranes did not cause aggregation. However, after freeze-thaw treatment, the thylakoid membranes aggregated markedly, as was apparent by light microscopy, and even being directly visible to the naked eye. The addition of 0.8 mg/mL thaumatin (negative control) or 0.8 mg/mL PM1-C protein did not prevent the formation of thylakoid aggregates when the suspension was subjected to freeze-thaw. In contrast, when 0.8 mg/mL PM1 or 0.8 mg/mL PM1-N protein was added to the thylakoid suspension, there was almost no aggregation after freeze-thaw.
2.3. The Addition of Either PM1 or PM1-N Protein Can Stabilize the Liposome Particle Size
The particle size distribution of POPC liposomes was measured and a single peak was obtained by dynamic light scattering (DLS) centering around 130 nm before freeze-thaw treatment (Figure 3). The addition of thaumatin, PM1, PM1-N or PM1-C individually to liposomes did not affect the particle size distribution (Figure 3A). After freeze-thaw treatment, another peak of 800 nm was observed besides the main peak of 130 nm, indicating that freeze-thaw can cause liposome adhesion, involving liposome fusion and/or aggregation. When either 0.8 mg/mL thaumatin or 0.8 mg/mL PM1-C protein was added to the liposome suspension, a peak at 250–270 nm was present besides the main peak of 130 nm. In contrast, when either 0.8 mg/mL PM1 or 0.8 mg/mL PM1-N protein was added to the liposome suspension, a small peak of particle size ~50 nm emerged besides the main peak of 140 nm (Figure 3B).
Together, the above results suggest that PM1 and PM1-N, but not PM1-C, can inhibit aggregation of liposomes and thylakoids caused by freeze-thaw treatment; the effect was concentration-dependent within a certain range and also specific. Furthermore, PM1 and PM1-N protein can prevent membrane fusion in POPC liposomes due to freeze-thaw.
2.4. PM1 and PM1-N Proteins Cannot Prevent Freeze-Thaw-Induced Leakage of Liposomes
Freeze-thaw treatment can cause the collapse of liposome membranes, causing leakage of liposome contents. To obtain a deeper insight into the protective effects of PM1 protein and its N-terminal region, a leakage experiment, using the fluorescent probe, carboxyfluorescein (CF), trapped within liposomes, was carried out according to [16]. As shown in Figure 4, the leakage rate of pure POPC liposomes after freeze-thaw treatment was 91.9%, suggesting that freeze-thaw almost entirely destroys their integrity. In the presence of the proteins (all at 0.8 mg/mL) used in this study, the post-treatment leakage rates of POPC liposomes were as follows: thaumatin, 86.8%; PM1, 80.1%; PM1-N, 80.5%; PM1-C, 79.6%. There was no statistical difference in CF leakage rates between any of the liposome/protein combinations and the liposome-only samples, suggesting that PM1 and its truncated versions prevent leakage no better than thaumatin or, indeed, no protein (Figure 4).
2.5. PM1 and PM1-N Proteins Can Effectively Inhibit or Prevent Membrane Fusion
The leakage of soluble contents from liposomes is often accompanied by membrane fusion [17]. The degree of fusion of the membrane can be assessed quantitatively by fluorescence resonance energy transfer (FRET) [18]. In the present paper, POPC liposomes whose membranes contained a pair of fluorescently labeled phospholipids (Rh-PE and NBD-PE) that can undergo FRET were mixed with unlabeled liposomes. When membrane fusion between labeled and unlabeled liposomes occurs, the degree of FRET between Rh-PE and NBD-PE will be reduced, due to dilution of the fluorescent probes. As shown as Figure 5, about 41.6% of liposomes underwent membrane fusion after freeze-thaw. When 0.8 mg/mL thaumatin was added to the liposome suspension, the degree of membrane fusion was 37.1% after freeze-thaw is not significantly different from that of liposomes only. When 0.8 mg/mL PM1-C protein was added to the liposome suspension, the degree of membrane fusion was 31.7% after freeze-thaw, showing a significant difference (p < 0.05) with liposomes only or thaumatin as negative control. However, in the presence of 0.8 mg/mL PM1 or 0.8 mg/mL PM1-N, the degree of membrane fusion reduced markedly to 10.6% or 8.6%, respectively, showing significant differences (p < 0.01) to liposomes only or thaumatin as negative control. Thus, the ability of PM1 to reduce membrane fusion in POPC liposomes caused by freeze-thaw largely resides in its N-terminal sequence.
The above results show that PM1 or PM1-N protein do not reduce the leakage of CF from POPC liposomes subjected to freeze-thaw, although they can significantly inhibit membrane fusion. Compared to thaumatin protein as negative control, on the other hand, PM1-C protein did not reduce leakage, and only reduced membrane fusion of POPC liposomes to some extent.
2.6. The Presence of POPC Liposomes Does Not Affect Digestion of PM1 by Trypsin
PM1 is an IDP, a group of proteins that are on the whole degraded ~100 times faster than folded proteins [19]. Liposomes can increase the folding of IDPs, and this can be measured by a limited proteolysis test [20,21]. Trypsin is a Lys-specific protease that can recognize the Lys residues located exclusively within the N-terminal half of the soybean PM1 sequence. Besides the main band at ~20 kDa, the purified PM1 protein contains several smaller bands, which also can be detected by PM1 specific antibody [14], probably indicating a small amount of degradation in protein preparation. Figure 6A shows that PM1 was readily and gradually degraded by trypsin over 30 min, as indicated by the main band at ~20 kDa becoming weaker and by the appearance of smaller bands below 17 kDa. This pattern of degradation did not change in the presence of POPC liposomes, suggesting that the structure of PM1 protein remains disordered state and is not influenced by the presence of liposomes.
2.7. The Presence of POPC Liposomes Does Not Change the Secondary Structure of PM1 Protein
It has been reported that Zea mays dehydrin DHN1 and A. thaliana dehydrin Lti30 bind the negatively charged head groups of phospholipids and that this is accompanied by an increase in α-helicity of both LEA proteins [15,22,23,24]. We tested the effect of POPC liposomes on PM1 secondary structure using circular dichroism (CD) (Figure 6B). We observed that PM1 protein remained unstructured both before, and after, freeze-thaw in the absence or presence of liposomes, as indicated by an ellipticity minimum at 198 nm.
Together, the results of trypsin digestion and CD analysis suggest that PM1 interacts mainly with the surface of the uncharged POPC liposomes.
3. Discussion
LEA proteins, a large and highly diverse protein family, accumulate during seed desiccation in the later stages of embryogenesis. Given the diversity and compartmentalization of LEA proteins in plant tissues, a detailed understanding of their function involves characterization of each protein individually. The over-expression of some LEA protein genes in transgenic plants confers tolerance to low-temperature stress. For example, over-expression of A. thaliana COR15A and COR15B (LEA_4) in Arabidopsis or of Citrus unshiu CuCOR19 (dehydrin) in tobacco (Nicotiana tabacum) increases the freezing tolerance of the resulting transgenic plants, as indicated by reduced electrolyte leakage compared to controls [25,26,27]. In vitro, COR15A and COR15B stabilize liposomes, either in terms of freeze-induced solute leakage or membrane fusion [26]. Similarly, Pisum sativum LEAM (LEA_4) or the Artemia franciscana AfrLEA2 and AfrLEA3m (LEA_4) increase liposome stability after freeze-thawing, as illustrated by a reduced leakage of entrapped CF molecules [28,29]. In contrast, three A. thaliana LEA proteins, LEA1, LEA26 and LEA27 (LEA_2), increase CF leakage from liposomes after freeze-thaw treatment [16]. Arabidopsis Lti30 (dehydrin) assembles lipid vesicles and native thylakoid membranes into aggregates by a possible role in membrane cross-linking [15], while Arabidopsis LEA18 protein (LEA_1) increases CF leakage, liposome membrane fusion and liposome aggregation under non-freezing conditions [11].
Shih et al. reported that soybean PM1 protein can interact with POPC lipids to maintain the liquid-crystal phase over a wide temperature range in the dry state [12]. In the present paper, we found that at high concentrations PM1/PM1-N protein specifically binds to or interacts with the surface of liposome membranes in solution and in the frozen state. For this reason, we speculate that, in a solution of PM1 or PM1-N, the liposomes are kept separate from each other by the protein molecules. During freeze-thaw, liposomes break down or fragment such that their contents (CF in our experiments) leak out [26]. In the absence of PM1 and PM1-N proteins, the resulting membrane fragments can fuse and POPC liposomes aggregate to such an extent that they become visible under the light microscope and even to the naked eye. In the presence of PM1 and PM1-N, the adherence of liposome membranes was reduced and membrane fusion and liposome aggregation was reduced, even though leakage of CF contents was not prevented. Therefore, our observations support a protection mechanism for PM1 on cells and cell membranes that is very similar to the “molecular shield” model. This model was proposed by Wise and Tunnacliffe to explain the protective function of LEA proteins towards other proteins under water stress conditions [30].
Some dehydrins, such as Zea mays DHN1, Thellungiella salsuginea TsDHN1 and TsDHN2, and A. thaliana ERD14 and Lti30, bind phospholipids in vitro [15,19,22,23,31]. These interactions between dehydrins and phospholipid molecules are driven by electrostatics and depend on the positively charged residues in the characteristic lysine-rich K-segment of the dehydrins, which pair with the negatively charged head groups of the lipids [24]. Ananlysis of the amino acid composition of soybean basic PM1 protein shows that the N-terminal is also rich in lysine residues and has +6 positively charged amino acids in solution at pH 7.4. It is reasonable to speculate that the N-terminal region of PM1 might interact with the surface of liposomes, especially with the negatively charged head groups of phospholipid molecules.
On the other hand, the C-terminal region of PM1 contains 9 histidine residues and has only +1 positively charged amino acids at pH 7.4. In contrast to the PM1 and PM1-N proteins, PM1-C cannot effectively prevent liposome aggregation and membrane fusion during freeze-thaw. This could be because PM1-C, with many fewer charged residues, can only attach to the liposome surface with low affinity. Therefore, it is not as effective at keeping liposomes separate and consequently preventing them from aggregating. Therefore, the anti-aggregation function of PM1 protein on cell membranes is likely mainly conferred by its N-terminal region.
A. thaliana basic LEA18 protein and soybean PM1 protein both belong to the LEA_1 subgroup of LEA proteins. However, there is only 30% similarity between the two protein sequences. Intriguingly, LEA18 does not function as a membrane-stabilizing protein, in contrast to PM1. Instead, the LEA18 protein causes aggregation and destabilization of negatively charged liposomes [11].
We have carried out a series of studies on the protective functions of soybean PM1 protein. The amino acid sequence shows that its histidine residues are mainly located in the C-terminal half, while its lysine residues are mostly in the N-terminal region. The C-terminal region has the potential to bind metal ions, scavenge hydroxyl radicals, and form oligomers and HMW complexes. These functions are directly related to the number of histidine residues in the C-terminal region [13,14]. In contrast to the disordered nature of the C-terminus, the highly conserved N-terminal half of PM1 can be induced to form significant levels of α-helical structure by SDS or trifluoroethanol [14]. At the same time, this N-terminal region of PM1 has a protective effect on LDH activity (Figure A1) and can also effectively prevent liposome aggregation. Both these functions may relate to the number of lysine residues in the N-terminal region. It has been speculated that the two halves of PM1 could perform different functions by binding different targets. The conformational flexibility and structural plasticity of PM1, as an IDP, may be the molecular basis for its multiple protective functions.
4. Materials and Methods 4.1. Protein Over-Expression and Purification
Immature soybean (G. max L. Merr. cv Bainong 6#) seeds were collected from pods 35–45 d after flowering and then total RNA was extracted from plant material with Trizol reagent (TAKARA, Otsu, Japan). The full-length ORF of the PM1 gene was amplified by RT-PCR using the PrimeScriptTM one-step RT-PCR kit (TAKARA, Otsu, Japan). Constructs containing the full-length soybean PM1, PM1-N and PM1-C sequences were described previously [13,14]. Recombinant PM1, PM1-N and PM1-C proteins were purified using affinity chromatography, and His-tags were removed by incubation with thrombin as previously described [14]. The proteins were lyophilized and then stored at −80°C for later use. The proteins were resuspended in a suitable buffer before use.
4.2. Preparation of Liposomes
POPC (1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine) was obtained from Avanti Polar Lipids (Alabaster, AL, USA). Large unilaminar vesicles (LUVs, 100 nm) of POPC were prepared by the extrusion method described previously [15]. Briefly, the lipids were dissolved in chloroform, and lipid mixtures were dried under a gentle liquid nitrogen flow and subsequently rehydrated in 10 mM Na2HPO4-KH2PO4 buffer, pH7.4. The lipid solution was extruded in an Avanti Mini Extruder (100 nm polycarbonate filter), repeated 15 times and then liposomes were collected.
For the leakage experiment, liposomes were made according to [16]. Briefly, an appropriate amount of POPC lipid was hydrated in buffer (250 μL 100 mM CF (carboxyfluorescein) in 10 mM Na2HPO4-KH2PO4 buffer, pH 7.4). The lipid solution was extruded as above and liposomes were collected. To remove external CF, the lipid samples were passed through a Sephadex G-25 column (NAP-5, GE Healthcare, Milwaukee, WI, USA) in buffer (10 mM Na2HPO4-KH2PO4, 0.1 mM EDTA and 50 mM NaCl).
To measure membrane fusion, two liposome samples were prepared: One containing 1 mol % of both NBD-PE and Rh-PE, while the other contained only unlabeled lipids. After extrusion, the two samples were mixed at a 1:9 (labeled:unlabeled) ratio. All liposomes were diluted to 20 mg/mL before use.
4.3. Thylakoid Membrane Preparation
Thylakoids were isolated from spinach (Spinacia oleracea) as previously described [32]. Briefly, 40 g spinach leaves and 100 mL cold A buffer (0.3 M Suc, 50 mM Na-phosphate, pH 7.4, and 5 mM MgCl2) was mixed for 5 × 10 s with a coldmixer. The solution was filtered with 50 μM nylon mesh and centrifuged at 3000 rpm for 3 min. The pellet was suspended in 30 mL A buffer and centrifuged at 4500 rpm for 5 min. The solution was homogenized in 30 mL B buffer (10 mM phosphate, pH 7.4, 5 mM MgCl2, and 5 mM NaCl) and centrifuged at 4500 rpm for 5 min. The pellet (about 200 mg) was homogenized in 10 mL buffer (0.1 M Suc, 10 mM phosphate, pH 7.4, 5 mM MgCl2 and 5 mM NaCl).
4.4. Freeze-Thaw Treatment of Liposomes
Either liposomes (20 mg/mL) or thylakoid membranes (about 20 mg/mL) were mixed with the same volume of the PM1, PM1-N and PM1-C proteins at concentrations of 0.08, 0.2, 0.8 or 1.6 mg/mL or the negative control protein thaumatin (from Thaumatococcus daniellii; Sigma, St. Louis, MO, USA) at a concentration of 1.6 mg/mL in 10 mM Na2HPO4-KH2PO4 buffer, pH 7.4, or with buffer alone. The freeze-thaw treatment was performed as previously described [26]; samples were then frozen in an ethylene glycol bath at −20 °C for 2 h and allowed to thaw for 30 min at 28°C. This freeze-thaw cycle was repeated up to three times.
4.5. Turbidity Measurement and Dynamic Light Scattering (DLS)
A simple aggregation assay was performed by measuring apparent absorbance [33], namely turbidity, due to light scattering at 400 nm (denoted as OD400), with an Ultro Spec 2000 (GE Healthcare, Milwaukee, WI, USA). In addition, particle size distribution in suspension was measured using a DLS analyzer (Zetaplus Zeta Potential Analyzer, Brookhaven Instruments, Holtsville, NY, USA) [11]. For both of these measurements, the samples were diluted to avoid saturation.
4.6. Light Microscopy
For light microscopy, 6 μL samples were placed on a glass slide, and images were examined using the optical microscope Olympus BX51 (Olympus Corporation, Tokyo, Japan).
4.7. CF Leakage Experiments
The leakage experiments were performed as previously reported [16]. Same of 12 μL were diluted with 300 μL Na2HPO4-KH2PO4 buffer in 96-well plates. CF fluorescence was measured with a Varioskan Flash multimode reader (Thermo Scientific, Waltham, MA, USA) with the following settings: Excitation 444 nm; emission 555 nm. While fluorescence is strongly quenched at the high concentration inside intact liposomes, fluorescence will increase when CF is released into the surrounding buffer. The 100% fluorescence level for leakage (i.e., complete release of entrapped CF) was obtained by detergent lysis of the liposomes with 5 μL 0.1% Triton X-100 solution.
4.8. Membrane Fusion Measurements
Membrane fusion was measured as a reduction of fluorescence resonance energy transfer (FRET) between Rh-PE and NBD-PE by measuring the increase in NBD fluorescence, due to the dilution of the probes after fusion of labeled, and unlabeled, liposome samples [18]. This is using a Hitachi F-4500 fluorescence instrument (Hitachi, Tokyo, Japan) at an excitation wavelength of 450 nm and an emission wavelength of 530 nm. The 100% fusion level (i.e., maximal NBD fluorescence in each sample) was determined after lysis of the liposomes with Triton X-100 solution.
4.9. Limited Proteolysis
Protease sensitivity was tested with trypsin [15]. Typically, PM1 (1 mg/mL) was mixed with the same volume of liposomes (1 mg/mL). To start the digestion, trypsin was added at a mass ratio of 1:200, trypsin to PM1. After the samples were incubated at room temperature for 0–30 min, the reactions were terminated by adding 1 mM phenylmethanesulfonyl fluorid, a trypsin inhibitor. All samples were mixed with 5×loading buffer, heated at 95 °C for 5 min and then run on a 15% ready-made SDS-PAGE gel.
4.10. Far UV-Circular Dichroism Spectroscopy
PM1 protein (20 μM) was mixed with the same volume of liposomes (2 mg/mL) or with pure buffer with or without freeze-thaw treatment. Far UV-circular dichroism (CD) spectra were recorded using a Jasco J-815 CD spectropolarimeter (JASCO Analytical Instruments, Tokyo, Japan). The acquisition parameters were 0.5 nm resolution, 1.0 nm bandwidth, 0.5 s response and 250–190 nm wavelength range. Three spectra were averaged and smoothed to reduce noise.
4.11. In Vitro Lactate Dehydrogenase Assays
Lactate dehydrogenase (LDH) assays were adapted from [34]. In short, LDH from rabbit muscle (Roche, Mannheim, Germany) was diluted in 100 mM sodium phosphate buffer (pH 7.0) to a final concentration of 0.357 μM. PM1, PM1-N and PM1-C proteins or the negative control protein lysozyme were added to equal volumes of LDH at molar ratios of 1:1 (test protein: LDH). The enzyme with and without added proteins was frozen in an ethylene glycol bath at −20 °C for 2 h and allowed to thaw for 30 min at 28 °C. This freeze-thaw cycle was repeated up to five times. The activity of LDH was assayed according to [34].
4.12. Statistics
p-Value was determined using SPSS 9 software (SPSS, Inc., Chicago, IL, USA). One-way ANOVA and Tukey post hoc test using InStat3 (GraphPad Software, San Diego, CA, USA) were performed for all the experiments. All experiments were performed at least in triplicate.
Acknowledgments
We thank the Instrumental Analysis Center of Shenzhen University (Lihu Campus) for providing research instruments.
Author Contributions
G.L. and Y.Z. (Yizhi Zheng) designed the project. L.C. performed most of the experiments, and Y.S. participated in the experiments. Y.Z. (Yongdong Zou) and J.H. analyzed the leakage experiments and the FRET data. L.C. and Y.S. wrote the draft of the manuscript, J.H., Y.Z. (Yizhi Zheng) and G.L. revised the draft. Funding acquisition and project administration were done by Y.L., Y.Z. (Yizhi Zheng) and G.L. All authors have read and approved the submitted vision of the manuscript.
Funding
This research was funded by National Nature Science Foundation of China (Grant No. 31370289 and 31600203) and Shenzhen Fundamental Research fund (Grant No.JCYJ20170818142241972).
Conflicts of Interest
The authors declare no conflict of interest.
Abbreviations
LEA
late embryogenesis abundant
IDP
intrinsically disordered protein
DLS
dynamic light scattering
PC
phosphatidylcholine
POPC
1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine
CF
carboxyfluorescein
HMW
high molecular weight
LUV
large unilaminar vesicle
FRET
fluorescence resonance energy transfer
CD
circular dichroism
LDH
lactate dehydrogenase
Appendix A
To validate whether PM1 could protect cellular proteins under stress conditions, we analyzed the protective effects of PM1, PM1-N or PM1-C protein on LDH during freeze−thaw cycles. Figure A1. shows that, PM1 and PM1-N, but not PM1-C gave a significantly better protection of LDH against freeze−thaw-induced inactivation (p < 0.01).
Protective effect of PM1, PM1-N and PM1-C on LDH. LDH was subjected to freeze-thaw in the absence or presence of PM1, PM1-N or PM1-C, or lysozyme as a negative control protein. The samples were frozen in an ethylene glycol bath at −20 °Cfor 2 h and allowed to thaw for 30 min at 28 °C. This freeze-thaw cycle wasrepeated up to five times. **denotes significance at p < 0.01 and “ns” denotes not significant using one-way ANOVA, plus a Tukey post-hoc test.
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PM1 and its N-terminal region prevent freeze-thaw-induced liposome aggregation. Turbidity of POPC liposomes before and after freeze-thaw treatment in the presence and absence of PM1, PM1-N or PM1-C. For the freeze-thaw test, the samples were frozen in an ethylene glycol bath at −20 °C for 2 h and allowed to thaw for 30 min at 28 °C. This freeze-thaw cycle was repeated up to three times. ** denotes significance at p < 0.01, * denotes p < 0.05 and “ns” denotes not significant using one-way ANOVA, plus a Tukey post-hoc test.
PM1 and its N-terminal region prevent POPC liposome or thylakoid aggregation resulting from freeze-thaw treatment as assessed by light microscopy. (A) POPC liposomes; (B) spinach leaf thylakoids. Bars in (A) and (B) represent 100 μm. FT: freeze-thaw treatment.
Size distribution of POPC liposomes before and after freeze-thaw treatment in the presence and absence of PM1, PM1-N or PM1-C. (A) Size distribution of POPC liposomes before treatment; (B) size distribution of POPC liposomes after freeze-thaw treatment.
Influence of PM1, PM1-N and PM1-C on CF leakage from liposomes after freeze-thaw treatment. POPC liposomes were subjected to freeze-thaw in the absence or presence of PM1, PM1-N or PM1-C, or thaumatin as a negative control protein. “ns” denotes not significant using one-way ANOVA, plus a Tukey post-hoc test.
Effect of PM1, PM1-N and PM1-C on membrane fusion caused by freeze-thaw treatment. POPC liposomes were subjected to freeze-thaw in the absence or presence of PM1, PM1-N or PM1-C, or thaumatin as a negative control protein. Membrane fusion was determined by a FRET assay. ** denotes significance at p < 0.01and * denotes p < 0.05 using one-way ANOVA, plus a Tukey post-hoc test.
PM1 does not gain structure in the presence of liposomes. (A) Digestion of PM1 by trypsin in the presence or absence of POPC liposomes. (B) CD spectra of PM1 alone and PM1 in the presence of liposomes before and after freeze-thaw treatment.
\n"],"string":"[\n \"\\nInt J Mol SciInt J Mol SciijmsInternational Journal of Molecular Sciences1422-0067MDPI32756462PMC743213010.3390/ijms21155552ijms-21-05552ArticleThe N-Terminal Region of Soybean PM1 Protein Protects Liposomes during Freeze-ThawChenLiyi1†SunYajun1†LiuYun1ZouYongdong2HuangJianzi1ZhengYizhi1https://orcid.org/0000-0003-1335-4775LiuGuobao1*Guangdong Provincial Key Laboratory for Plant Epigenetics, College of Life Sciences and Oceanography, Shenzhen University, Shenzhen 518060, China; chenly@tsinghua-sz.org (L.C.); sunyajun43@163.com (Y.S.); sunshine@szu.edu.cn (Y.L.); biohjz@szu.edu.cn (J.H.); yzzheng@szu.edu.cn (Y.Z.)The Instrumental Analysis Center of Shenzhen University (Lihu Campus), Shenzhen University, Shenzhen 518060, China; zouyd@szu.edu.cnCorrespondence: liugb@szu.edu.cn; Tel.: +86-755-26558081
These authors contributed equally to this work.
0382020820202115555222720200182020© 2020 by the authors.2020Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Late embryogenesis abundant (LEA) group 1 (LEA_1) proteins are intrinsically disordered proteins (IDPs) that play important roles in protecting plants from abiotic stress. Their protective function, at a molecular level, has not yet been fully elucidated, but several studies suggest their involvement in membrane stabilization under stress conditions. In this paper, the soybean LEA_1 protein PM1 and its truncated forms (PM1-N: N-terminal half; PM1-C: C-terminal half) were tested for the ability to protect liposomes against damage induced by freeze-thaw stress. Turbidity measurement and light microscopy showed that full-length PM1 and PM1-N, but not PM1-C, can prevent freeze-thaw-induced aggregation of POPC (1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine) liposomes and native thylakoid membranes, isolated from spinach leaves (Spinacia oleracea). Particle size distribution analysis by dynamic light scattering (DLS) further confirmed that PM1 and PM1-N can prevent liposome aggregation during freeze-thaw. Furthermore, PM1 or PM1-N could significantly inhibit membrane fusion of liposomes, but not reduce the leakage of their contents following freezing stress. The results of proteolytic digestion and circular dichroism experiments suggest that PM1 and PM1-N proteins bind mainly on the surface of the POPC liposome. We propose that, through its N-terminal region, PM1 functions as a membrane-stabilizing protein during abiotic stress, and might inhibit membrane fusion and aggregation of vesicles or other endomembrane structures within the plant cell.
Plants have developed various adaptations to maintain normal physiological processes under adverse abiotic stress, such as soil salinity, drought and extreme temperatures. For instance, it has been demonstrated experimentally that late embryogenesis abundant (LEA) proteins are involved in plant tolerance to abiotic stress [1,2,3]. Based on Pfam domains within their sequences, LEA proteins can be classified into eight subgroups [3]. Most research has focused on the protective functions of the LEA_4, LEA_5 and dehydrin subgroups, generating data on expression profile, cellular localization, and the role of the respective genes and proteins. There is relatively little research on the protective roles of other groups, including the LEA_1 proteins, which are highly expressed in mature seeds and also accumulate in vegetative tissues under stress conditions [4,5,6]. Some genetic studies have been performed showing that the over-expression of LEA_1 genes, such as LEA4-1 from Brassica napus, BhLEA1 and BhLEA2 from Boea hygrometrica, AtLEA4-5 from Arabidopsis thaliana, and XsLEA1-8 from Xerophyta schlechteri, confer tolerance to salt, drought and osmotic stress in transgenic plants [5,6,7]. The over-expression of recombinant Gastrodia elata GeLEA1-1 or Xerophyta schlechteri XsLEA1-8 enhances Escherichia coli viability under low-temperature or heat stress [7,8]. In addition, in vitro, the recombinant LEA_1 proteins AtLEA4-2 and AtLEA4-5 (both A. thaliana) and XsLEA1-8 can preserve lactate dehydrogenase activity and prevent this enzyme aggregating during freeze–thaw, heat, desiccation and oxidative stress [7,9,10]. Liu et al. suggested that BhLEA1 and BhLEA2 play a general protective role in the plant cell during dehydration stress and increase membrane and protein stability, as indicated by the relatively low electrolyte leakage and higher SOD and POD activity in transgenic plant leaves [5]. In contrast, A. thaliana basic protein LEA18 (LEA_1) specifically aggregates and destabilizes negatively charged liposomes, which suggests that LEA18 does not function as a membrane-stabilizing protein, but could modulate membrane stability depending on membrane composition [11].
The LEA proteins are a broad family of proteins, many of which are thought to be intrinsically disordered proteins (IDPs) with “moonlighting” activity [3,10]. However, to our knowledge, relatively few individual LEA proteins have been shown to have multifunctional protective activities. One example of a multifunctional protein, PM1, a LEA_1 protein from soybean, can interact with a range of other molecules, such as non-reducing sugars, poly-l-lysine and phospholipids, during dehydration. PM1 is likely to be an important component of the cellular, organic glass that may stabilize desiccation-sensitive proteins and membranes in stressed plants [12]. PM1 contains a high proportion of basic amino acids, including 13 lysine, 5 arginine and 10 histidine residues, but these are distributed unequally along the length of the protein. There are 12 lysine residues and 3 arginine residues located in the N-terminal half (residues 1–84), while 9 histidine residues and 2 arginine residues are located in the C-terminal region (residues 85–173). In previous papers, we have demonstrated that PM1 protein can bind some metal ions (Fe3+, Ni2+, Cu2+ and Zn2+) and so play an important protective role in reducing oxidative damage and ion toxicity in plants exposed to abiotic stress [13]. In addition, PM1 or its truncated version, PM1-C (C-terminal half only), but not PM1-N, can form oligomers and high molecular weight (HMW) complexes via its C-terminal histidine residues both in vitro and in planta. Crucially, binding of Cu2+ at high concentrations, which takes place through the same histidine residues, seems to promote oligomerization and the formation of HMW complexes by PM1 [14].
In the present paper, we investigate whether soybean LEA_1 protein PM1 and its two truncated forms, PM1-N and PM1-C, possess the ability to suppress freeze-thaw-induced damage of liposomes. We show that only PM1 and PM1-N can effectively prevent aggregation of POPC liposomes caused by freeze-thaw treatment. Furthermore, we show that, although PM1 and PM1-N inhibit membrane fusion events, they do not reduce leakage of POPC liposomes. On the basis of these observations, we propose that the N-terminal region of PM1 protein has a membrane stabilizing function during abiotic stress.
2. Results2.1. PM1 and PM1-N Proteins Inhibit the Freeze-Thaw-Induced Increase in Turbidity of a Liposome Suspension
The cell membrane is composed of lipids (phospholipids and cholesterol), proteins and carbohydrate groups. Among the phospholipids, phosphatidylcholine (PC) represents a major component of biological membranes and the PC, POPC (1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine), can be used to produce liposomes that model cell membranes. Light scattering due to aggregation of POPC liposomes can be measured by apparent absorbance at 400 nm [15]. The OD400 of a freshly prepared POPC liposome suspension was ~0.3 and the addition of one of the proteins thaumatin, PM1, PM1-N or PM1-C to POPC liposomes did not obviously affect their turbidity before exposure to stress (Figure 1).
The turbidity, as shown by OD400, of the liposome suspension increased markedly to 0.8 after three freeze-thaw cycles, indicating that the freeze-thaw treatment causes liposome aggregation. When 0.8 mg/mL thaumatin (molar ratio of protein:lipid~1:3300) or 0.8 mg/mL PM1-C protein (molar ratio of protein:lipid~1:1650) was added to the POPC liposome suspension, its OD400 increased to 0.6 after the freeze-thaw treatment, which is significantly different (p < 0.01) to that of liposomes only. Thaumatin was chosen as a negative control in the following study because of having no known function in membrane protection [11] and its similar molecular size to PM1 protein. There was no statistical difference in turbidity between liposome/PM1-C and liposome/thaumatin. However, when PM1 or PM1-N protein (0.04–0.4 mg/mL) was included in the liposome suspension, the turbidity increase after freeze-thaw was appreciably suppressed and the extent of this suppression was dependent on the concentration of the added protein, being significantly (p < 0.05) or extremely significantly different (p < 0.01) from that of thaumatin. When the concentration of PM1 (molar ratio of protein:lipid ~1:3300) or PM1-N protein (molar ratio of protein:lipid ~1:1650) reached 0.8 mg/mL, the post-treatment turbidity was 0.4 at OD400, i.e., similar to that before freeze-thaw.
2.2. PM1 and PM1-N Proteins Can Inhibit or Prevent Freeze-Thaw-Induced Aggregation of Liposomes and Isolated Thylakoid membranes
The inhibitory effect of PM1, PM1-N or PM1-C protein on liposome aggregation can be followed by light microscopy. Before freeze-thaw treatment, POPC liposomes were small in size and evenly distributed in the suspension (Figure 2A). The addition of one of thaumatin, PM1, PM1-N or PM1-C proteins to POPC liposomes did not cause aggregation before freeze-thaw treatment, but in the absence of added proteins, liposome aggregation could be seen to occur after freeze-thaw treatment. With either 0.8 mg/mL thaumatin (negative control) or 0.8 mg/mL PM1-C protein in the suspension, the liposomes still aggregated after freeze-thaw. In contrast, when 0.8 mg/mL PM1 or 0.8 mg/mL PM1-N protein was added to liposomes, the suspension remained homogeneous even after freeze-thaw.
Next, the anti-aggregation effect of the PM1, PM1-N or PM1-C proteins on natural biological material was tested using thylakoid membranes isolated from spinach leaves (Spinacia oleracea). Under the light microscope, the thylakoid membranes appeared as evenly distributed, small particles in suspension (Figure 2B). The addition of thaumatin, PM1, PM1-N or PM1-C proteins individually to thylakoid membranes did not cause aggregation. However, after freeze-thaw treatment, the thylakoid membranes aggregated markedly, as was apparent by light microscopy, and even being directly visible to the naked eye. The addition of 0.8 mg/mL thaumatin (negative control) or 0.8 mg/mL PM1-C protein did not prevent the formation of thylakoid aggregates when the suspension was subjected to freeze-thaw. In contrast, when 0.8 mg/mL PM1 or 0.8 mg/mL PM1-N protein was added to the thylakoid suspension, there was almost no aggregation after freeze-thaw.
2.3. The Addition of Either PM1 or PM1-N Protein Can Stabilize the Liposome Particle Size
The particle size distribution of POPC liposomes was measured and a single peak was obtained by dynamic light scattering (DLS) centering around 130 nm before freeze-thaw treatment (Figure 3). The addition of thaumatin, PM1, PM1-N or PM1-C individually to liposomes did not affect the particle size distribution (Figure 3A). After freeze-thaw treatment, another peak of 800 nm was observed besides the main peak of 130 nm, indicating that freeze-thaw can cause liposome adhesion, involving liposome fusion and/or aggregation. When either 0.8 mg/mL thaumatin or 0.8 mg/mL PM1-C protein was added to the liposome suspension, a peak at 250–270 nm was present besides the main peak of 130 nm. In contrast, when either 0.8 mg/mL PM1 or 0.8 mg/mL PM1-N protein was added to the liposome suspension, a small peak of particle size ~50 nm emerged besides the main peak of 140 nm (Figure 3B).
Together, the above results suggest that PM1 and PM1-N, but not PM1-C, can inhibit aggregation of liposomes and thylakoids caused by freeze-thaw treatment; the effect was concentration-dependent within a certain range and also specific. Furthermore, PM1 and PM1-N protein can prevent membrane fusion in POPC liposomes due to freeze-thaw.
2.4. PM1 and PM1-N Proteins Cannot Prevent Freeze-Thaw-Induced Leakage of Liposomes
Freeze-thaw treatment can cause the collapse of liposome membranes, causing leakage of liposome contents. To obtain a deeper insight into the protective effects of PM1 protein and its N-terminal region, a leakage experiment, using the fluorescent probe, carboxyfluorescein (CF), trapped within liposomes, was carried out according to [16]. As shown in Figure 4, the leakage rate of pure POPC liposomes after freeze-thaw treatment was 91.9%, suggesting that freeze-thaw almost entirely destroys their integrity. In the presence of the proteins (all at 0.8 mg/mL) used in this study, the post-treatment leakage rates of POPC liposomes were as follows: thaumatin, 86.8%; PM1, 80.1%; PM1-N, 80.5%; PM1-C, 79.6%. There was no statistical difference in CF leakage rates between any of the liposome/protein combinations and the liposome-only samples, suggesting that PM1 and its truncated versions prevent leakage no better than thaumatin or, indeed, no protein (Figure 4).
2.5. PM1 and PM1-N Proteins Can Effectively Inhibit or Prevent Membrane Fusion
The leakage of soluble contents from liposomes is often accompanied by membrane fusion [17]. The degree of fusion of the membrane can be assessed quantitatively by fluorescence resonance energy transfer (FRET) [18]. In the present paper, POPC liposomes whose membranes contained a pair of fluorescently labeled phospholipids (Rh-PE and NBD-PE) that can undergo FRET were mixed with unlabeled liposomes. When membrane fusion between labeled and unlabeled liposomes occurs, the degree of FRET between Rh-PE and NBD-PE will be reduced, due to dilution of the fluorescent probes. As shown as Figure 5, about 41.6% of liposomes underwent membrane fusion after freeze-thaw. When 0.8 mg/mL thaumatin was added to the liposome suspension, the degree of membrane fusion was 37.1% after freeze-thaw is not significantly different from that of liposomes only. When 0.8 mg/mL PM1-C protein was added to the liposome suspension, the degree of membrane fusion was 31.7% after freeze-thaw, showing a significant difference (p < 0.05) with liposomes only or thaumatin as negative control. However, in the presence of 0.8 mg/mL PM1 or 0.8 mg/mL PM1-N, the degree of membrane fusion reduced markedly to 10.6% or 8.6%, respectively, showing significant differences (p < 0.01) to liposomes only or thaumatin as negative control. Thus, the ability of PM1 to reduce membrane fusion in POPC liposomes caused by freeze-thaw largely resides in its N-terminal sequence.
The above results show that PM1 or PM1-N protein do not reduce the leakage of CF from POPC liposomes subjected to freeze-thaw, although they can significantly inhibit membrane fusion. Compared to thaumatin protein as negative control, on the other hand, PM1-C protein did not reduce leakage, and only reduced membrane fusion of POPC liposomes to some extent.
2.6. The Presence of POPC Liposomes Does Not Affect Digestion of PM1 by Trypsin
PM1 is an IDP, a group of proteins that are on the whole degraded ~100 times faster than folded proteins [19]. Liposomes can increase the folding of IDPs, and this can be measured by a limited proteolysis test [20,21]. Trypsin is a Lys-specific protease that can recognize the Lys residues located exclusively within the N-terminal half of the soybean PM1 sequence. Besides the main band at ~20 kDa, the purified PM1 protein contains several smaller bands, which also can be detected by PM1 specific antibody [14], probably indicating a small amount of degradation in protein preparation. Figure 6A shows that PM1 was readily and gradually degraded by trypsin over 30 min, as indicated by the main band at ~20 kDa becoming weaker and by the appearance of smaller bands below 17 kDa. This pattern of degradation did not change in the presence of POPC liposomes, suggesting that the structure of PM1 protein remains disordered state and is not influenced by the presence of liposomes.
2.7. The Presence of POPC Liposomes Does Not Change the Secondary Structure of PM1 Protein
It has been reported that Zea mays dehydrin DHN1 and A. thaliana dehydrin Lti30 bind the negatively charged head groups of phospholipids and that this is accompanied by an increase in α-helicity of both LEA proteins [15,22,23,24]. We tested the effect of POPC liposomes on PM1 secondary structure using circular dichroism (CD) (Figure 6B). We observed that PM1 protein remained unstructured both before, and after, freeze-thaw in the absence or presence of liposomes, as indicated by an ellipticity minimum at 198 nm.
Together, the results of trypsin digestion and CD analysis suggest that PM1 interacts mainly with the surface of the uncharged POPC liposomes.
3. Discussion
LEA proteins, a large and highly diverse protein family, accumulate during seed desiccation in the later stages of embryogenesis. Given the diversity and compartmentalization of LEA proteins in plant tissues, a detailed understanding of their function involves characterization of each protein individually. The over-expression of some LEA protein genes in transgenic plants confers tolerance to low-temperature stress. For example, over-expression of A. thaliana COR15A and COR15B (LEA_4) in Arabidopsis or of Citrus unshiu CuCOR19 (dehydrin) in tobacco (Nicotiana tabacum) increases the freezing tolerance of the resulting transgenic plants, as indicated by reduced electrolyte leakage compared to controls [25,26,27]. In vitro, COR15A and COR15B stabilize liposomes, either in terms of freeze-induced solute leakage or membrane fusion [26]. Similarly, Pisum sativum LEAM (LEA_4) or the Artemia franciscana AfrLEA2 and AfrLEA3m (LEA_4) increase liposome stability after freeze-thawing, as illustrated by a reduced leakage of entrapped CF molecules [28,29]. In contrast, three A. thaliana LEA proteins, LEA1, LEA26 and LEA27 (LEA_2), increase CF leakage from liposomes after freeze-thaw treatment [16]. Arabidopsis Lti30 (dehydrin) assembles lipid vesicles and native thylakoid membranes into aggregates by a possible role in membrane cross-linking [15], while Arabidopsis LEA18 protein (LEA_1) increases CF leakage, liposome membrane fusion and liposome aggregation under non-freezing conditions [11].
Shih et al. reported that soybean PM1 protein can interact with POPC lipids to maintain the liquid-crystal phase over a wide temperature range in the dry state [12]. In the present paper, we found that at high concentrations PM1/PM1-N protein specifically binds to or interacts with the surface of liposome membranes in solution and in the frozen state. For this reason, we speculate that, in a solution of PM1 or PM1-N, the liposomes are kept separate from each other by the protein molecules. During freeze-thaw, liposomes break down or fragment such that their contents (CF in our experiments) leak out [26]. In the absence of PM1 and PM1-N proteins, the resulting membrane fragments can fuse and POPC liposomes aggregate to such an extent that they become visible under the light microscope and even to the naked eye. In the presence of PM1 and PM1-N, the adherence of liposome membranes was reduced and membrane fusion and liposome aggregation was reduced, even though leakage of CF contents was not prevented. Therefore, our observations support a protection mechanism for PM1 on cells and cell membranes that is very similar to the “molecular shield” model. This model was proposed by Wise and Tunnacliffe to explain the protective function of LEA proteins towards other proteins under water stress conditions [30].
Some dehydrins, such as Zea mays DHN1, Thellungiella salsuginea TsDHN1 and TsDHN2, and A. thaliana ERD14 and Lti30, bind phospholipids in vitro [15,19,22,23,31]. These interactions between dehydrins and phospholipid molecules are driven by electrostatics and depend on the positively charged residues in the characteristic lysine-rich K-segment of the dehydrins, which pair with the negatively charged head groups of the lipids [24]. Ananlysis of the amino acid composition of soybean basic PM1 protein shows that the N-terminal is also rich in lysine residues and has +6 positively charged amino acids in solution at pH 7.4. It is reasonable to speculate that the N-terminal region of PM1 might interact with the surface of liposomes, especially with the negatively charged head groups of phospholipid molecules.
On the other hand, the C-terminal region of PM1 contains 9 histidine residues and has only +1 positively charged amino acids at pH 7.4. In contrast to the PM1 and PM1-N proteins, PM1-C cannot effectively prevent liposome aggregation and membrane fusion during freeze-thaw. This could be because PM1-C, with many fewer charged residues, can only attach to the liposome surface with low affinity. Therefore, it is not as effective at keeping liposomes separate and consequently preventing them from aggregating. Therefore, the anti-aggregation function of PM1 protein on cell membranes is likely mainly conferred by its N-terminal region.
A. thaliana basic LEA18 protein and soybean PM1 protein both belong to the LEA_1 subgroup of LEA proteins. However, there is only 30% similarity between the two protein sequences. Intriguingly, LEA18 does not function as a membrane-stabilizing protein, in contrast to PM1. Instead, the LEA18 protein causes aggregation and destabilization of negatively charged liposomes [11].
We have carried out a series of studies on the protective functions of soybean PM1 protein. The amino acid sequence shows that its histidine residues are mainly located in the C-terminal half, while its lysine residues are mostly in the N-terminal region. The C-terminal region has the potential to bind metal ions, scavenge hydroxyl radicals, and form oligomers and HMW complexes. These functions are directly related to the number of histidine residues in the C-terminal region [13,14]. In contrast to the disordered nature of the C-terminus, the highly conserved N-terminal half of PM1 can be induced to form significant levels of α-helical structure by SDS or trifluoroethanol [14]. At the same time, this N-terminal region of PM1 has a protective effect on LDH activity (Figure A1) and can also effectively prevent liposome aggregation. Both these functions may relate to the number of lysine residues in the N-terminal region. It has been speculated that the two halves of PM1 could perform different functions by binding different targets. The conformational flexibility and structural plasticity of PM1, as an IDP, may be the molecular basis for its multiple protective functions.
4. Materials and Methods 4.1. Protein Over-Expression and Purification
Immature soybean (G. max L. Merr. cv Bainong 6#) seeds were collected from pods 35–45 d after flowering and then total RNA was extracted from plant material with Trizol reagent (TAKARA, Otsu, Japan). The full-length ORF of the PM1 gene was amplified by RT-PCR using the PrimeScriptTM one-step RT-PCR kit (TAKARA, Otsu, Japan). Constructs containing the full-length soybean PM1, PM1-N and PM1-C sequences were described previously [13,14]. Recombinant PM1, PM1-N and PM1-C proteins were purified using affinity chromatography, and His-tags were removed by incubation with thrombin as previously described [14]. The proteins were lyophilized and then stored at −80°C for later use. The proteins were resuspended in a suitable buffer before use.
4.2. Preparation of Liposomes
POPC (1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine) was obtained from Avanti Polar Lipids (Alabaster, AL, USA). Large unilaminar vesicles (LUVs, 100 nm) of POPC were prepared by the extrusion method described previously [15]. Briefly, the lipids were dissolved in chloroform, and lipid mixtures were dried under a gentle liquid nitrogen flow and subsequently rehydrated in 10 mM Na2HPO4-KH2PO4 buffer, pH7.4. The lipid solution was extruded in an Avanti Mini Extruder (100 nm polycarbonate filter), repeated 15 times and then liposomes were collected.
For the leakage experiment, liposomes were made according to [16]. Briefly, an appropriate amount of POPC lipid was hydrated in buffer (250 μL 100 mM CF (carboxyfluorescein) in 10 mM Na2HPO4-KH2PO4 buffer, pH 7.4). The lipid solution was extruded as above and liposomes were collected. To remove external CF, the lipid samples were passed through a Sephadex G-25 column (NAP-5, GE Healthcare, Milwaukee, WI, USA) in buffer (10 mM Na2HPO4-KH2PO4, 0.1 mM EDTA and 50 mM NaCl).
To measure membrane fusion, two liposome samples were prepared: One containing 1 mol % of both NBD-PE and Rh-PE, while the other contained only unlabeled lipids. After extrusion, the two samples were mixed at a 1:9 (labeled:unlabeled) ratio. All liposomes were diluted to 20 mg/mL before use.
4.3. Thylakoid Membrane Preparation
Thylakoids were isolated from spinach (Spinacia oleracea) as previously described [32]. Briefly, 40 g spinach leaves and 100 mL cold A buffer (0.3 M Suc, 50 mM Na-phosphate, pH 7.4, and 5 mM MgCl2) was mixed for 5 × 10 s with a coldmixer. The solution was filtered with 50 μM nylon mesh and centrifuged at 3000 rpm for 3 min. The pellet was suspended in 30 mL A buffer and centrifuged at 4500 rpm for 5 min. The solution was homogenized in 30 mL B buffer (10 mM phosphate, pH 7.4, 5 mM MgCl2, and 5 mM NaCl) and centrifuged at 4500 rpm for 5 min. The pellet (about 200 mg) was homogenized in 10 mL buffer (0.1 M Suc, 10 mM phosphate, pH 7.4, 5 mM MgCl2 and 5 mM NaCl).
4.4. Freeze-Thaw Treatment of Liposomes
Either liposomes (20 mg/mL) or thylakoid membranes (about 20 mg/mL) were mixed with the same volume of the PM1, PM1-N and PM1-C proteins at concentrations of 0.08, 0.2, 0.8 or 1.6 mg/mL or the negative control protein thaumatin (from Thaumatococcus daniellii; Sigma, St. Louis, MO, USA) at a concentration of 1.6 mg/mL in 10 mM Na2HPO4-KH2PO4 buffer, pH 7.4, or with buffer alone. The freeze-thaw treatment was performed as previously described [26]; samples were then frozen in an ethylene glycol bath at −20 °C for 2 h and allowed to thaw for 30 min at 28°C. This freeze-thaw cycle was repeated up to three times.
4.5. Turbidity Measurement and Dynamic Light Scattering (DLS)
A simple aggregation assay was performed by measuring apparent absorbance [33], namely turbidity, due to light scattering at 400 nm (denoted as OD400), with an Ultro Spec 2000 (GE Healthcare, Milwaukee, WI, USA). In addition, particle size distribution in suspension was measured using a DLS analyzer (Zetaplus Zeta Potential Analyzer, Brookhaven Instruments, Holtsville, NY, USA) [11]. For both of these measurements, the samples were diluted to avoid saturation.
4.6. Light Microscopy
For light microscopy, 6 μL samples were placed on a glass slide, and images were examined using the optical microscope Olympus BX51 (Olympus Corporation, Tokyo, Japan).
4.7. CF Leakage Experiments
The leakage experiments were performed as previously reported [16]. Same of 12 μL were diluted with 300 μL Na2HPO4-KH2PO4 buffer in 96-well plates. CF fluorescence was measured with a Varioskan Flash multimode reader (Thermo Scientific, Waltham, MA, USA) with the following settings: Excitation 444 nm; emission 555 nm. While fluorescence is strongly quenched at the high concentration inside intact liposomes, fluorescence will increase when CF is released into the surrounding buffer. The 100% fluorescence level for leakage (i.e., complete release of entrapped CF) was obtained by detergent lysis of the liposomes with 5 μL 0.1% Triton X-100 solution.
4.8. Membrane Fusion Measurements
Membrane fusion was measured as a reduction of fluorescence resonance energy transfer (FRET) between Rh-PE and NBD-PE by measuring the increase in NBD fluorescence, due to the dilution of the probes after fusion of labeled, and unlabeled, liposome samples [18]. This is using a Hitachi F-4500 fluorescence instrument (Hitachi, Tokyo, Japan) at an excitation wavelength of 450 nm and an emission wavelength of 530 nm. The 100% fusion level (i.e., maximal NBD fluorescence in each sample) was determined after lysis of the liposomes with Triton X-100 solution.
4.9. Limited Proteolysis
Protease sensitivity was tested with trypsin [15]. Typically, PM1 (1 mg/mL) was mixed with the same volume of liposomes (1 mg/mL). To start the digestion, trypsin was added at a mass ratio of 1:200, trypsin to PM1. After the samples were incubated at room temperature for 0–30 min, the reactions were terminated by adding 1 mM phenylmethanesulfonyl fluorid, a trypsin inhibitor. All samples were mixed with 5×loading buffer, heated at 95 °C for 5 min and then run on a 15% ready-made SDS-PAGE gel.
4.10. Far UV-Circular Dichroism Spectroscopy
PM1 protein (20 μM) was mixed with the same volume of liposomes (2 mg/mL) or with pure buffer with or without freeze-thaw treatment. Far UV-circular dichroism (CD) spectra were recorded using a Jasco J-815 CD spectropolarimeter (JASCO Analytical Instruments, Tokyo, Japan). The acquisition parameters were 0.5 nm resolution, 1.0 nm bandwidth, 0.5 s response and 250–190 nm wavelength range. Three spectra were averaged and smoothed to reduce noise.
4.11. In Vitro Lactate Dehydrogenase Assays
Lactate dehydrogenase (LDH) assays were adapted from [34]. In short, LDH from rabbit muscle (Roche, Mannheim, Germany) was diluted in 100 mM sodium phosphate buffer (pH 7.0) to a final concentration of 0.357 μM. PM1, PM1-N and PM1-C proteins or the negative control protein lysozyme were added to equal volumes of LDH at molar ratios of 1:1 (test protein: LDH). The enzyme with and without added proteins was frozen in an ethylene glycol bath at −20 °C for 2 h and allowed to thaw for 30 min at 28 °C. This freeze-thaw cycle was repeated up to five times. The activity of LDH was assayed according to [34].
4.12. Statistics
p-Value was determined using SPSS 9 software (SPSS, Inc., Chicago, IL, USA). One-way ANOVA and Tukey post hoc test using InStat3 (GraphPad Software, San Diego, CA, USA) were performed for all the experiments. All experiments were performed at least in triplicate.
Acknowledgments
We thank the Instrumental Analysis Center of Shenzhen University (Lihu Campus) for providing research instruments.
Author Contributions
G.L. and Y.Z. (Yizhi Zheng) designed the project. L.C. performed most of the experiments, and Y.S. participated in the experiments. Y.Z. (Yongdong Zou) and J.H. analyzed the leakage experiments and the FRET data. L.C. and Y.S. wrote the draft of the manuscript, J.H., Y.Z. (Yizhi Zheng) and G.L. revised the draft. Funding acquisition and project administration were done by Y.L., Y.Z. (Yizhi Zheng) and G.L. All authors have read and approved the submitted vision of the manuscript.
Funding
This research was funded by National Nature Science Foundation of China (Grant No. 31370289 and 31600203) and Shenzhen Fundamental Research fund (Grant No.JCYJ20170818142241972).
Conflicts of Interest
The authors declare no conflict of interest.
Abbreviations
LEA
late embryogenesis abundant
IDP
intrinsically disordered protein
DLS
dynamic light scattering
PC
phosphatidylcholine
POPC
1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine
CF
carboxyfluorescein
HMW
high molecular weight
LUV
large unilaminar vesicle
FRET
fluorescence resonance energy transfer
CD
circular dichroism
LDH
lactate dehydrogenase
Appendix A
To validate whether PM1 could protect cellular proteins under stress conditions, we analyzed the protective effects of PM1, PM1-N or PM1-C protein on LDH during freeze−thaw cycles. Figure A1. shows that, PM1 and PM1-N, but not PM1-C gave a significantly better protection of LDH against freeze−thaw-induced inactivation (p < 0.01).
Protective effect of PM1, PM1-N and PM1-C on LDH. LDH was subjected to freeze-thaw in the absence or presence of PM1, PM1-N or PM1-C, or lysozyme as a negative control protein. The samples were frozen in an ethylene glycol bath at −20 °Cfor 2 h and allowed to thaw for 30 min at 28 °C. This freeze-thaw cycle wasrepeated up to five times. **denotes significance at p < 0.01 and “ns” denotes not significant using one-way ANOVA, plus a Tukey post-hoc test.
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PM1 and its N-terminal region prevent freeze-thaw-induced liposome aggregation. Turbidity of POPC liposomes before and after freeze-thaw treatment in the presence and absence of PM1, PM1-N or PM1-C. For the freeze-thaw test, the samples were frozen in an ethylene glycol bath at −20 °C for 2 h and allowed to thaw for 30 min at 28 °C. This freeze-thaw cycle was repeated up to three times. ** denotes significance at p < 0.01, * denotes p < 0.05 and “ns” denotes not significant using one-way ANOVA, plus a Tukey post-hoc test.
PM1 and its N-terminal region prevent POPC liposome or thylakoid aggregation resulting from freeze-thaw treatment as assessed by light microscopy. (A) POPC liposomes; (B) spinach leaf thylakoids. Bars in (A) and (B) represent 100 μm. FT: freeze-thaw treatment.
Size distribution of POPC liposomes before and after freeze-thaw treatment in the presence and absence of PM1, PM1-N or PM1-C. (A) Size distribution of POPC liposomes before treatment; (B) size distribution of POPC liposomes after freeze-thaw treatment.
Influence of PM1, PM1-N and PM1-C on CF leakage from liposomes after freeze-thaw treatment. POPC liposomes were subjected to freeze-thaw in the absence or presence of PM1, PM1-N or PM1-C, or thaumatin as a negative control protein. “ns” denotes not significant using one-way ANOVA, plus a Tukey post-hoc test.
Effect of PM1, PM1-N and PM1-C on membrane fusion caused by freeze-thaw treatment. POPC liposomes were subjected to freeze-thaw in the absence or presence of PM1, PM1-N or PM1-C, or thaumatin as a negative control protein. Membrane fusion was determined by a FRET assay. ** denotes significance at p < 0.01and * denotes p < 0.05 using one-way ANOVA, plus a Tukey post-hoc test.
PM1 does not gain structure in the presence of liposomes. (A) Digestion of PM1 by trypsin in the presence or absence of POPC liposomes. (B) CD spectra of PM1 alone and PM1 in the presence of liposomes before and after freeze-thaw treatment.
\\n\"\n]"}}},{"rowIdx":474,"cells":{"data":{"kind":"list like","value":["\nInt J Mol SciInt J Mol SciijmsInternational Journal of Molecular Sciences1422-0067MDPI32717877PMC743213110.3390/ijms21155213ijms-21-05213ArticleThe In Vitro Effect of Prostaglandin E2 and F2α on the Chemerin System in the Porcine Endometrium during GestationDobrzynKamil*†https://orcid.org/0000-0002-8677-5282KiezunMarta†ZaobidnaEwaKisielewskaKatarzynaRytelewskaEdytaGudelskaMarlenaKopijGrzegorzBorsKingaSzymanskaKarolinaKaminskaBarbarahttps://orcid.org/0000-0003-1643-026XKaminskiTadeuszhttps://orcid.org/0000-0001-5364-1387SmolinskaNina*Department of Animal Anatomy and Physiology, Faculty of Biology and Biotechnology, University of Warmia and Mazury in Olsztyn, Oczapowskiego 1A, 10-719 Olsztyn-Kortowo, Poland; marta.kiezun@uwm.edu.pl (M.K.); ewa.zaobidna@uwm.edu.pl (E.Z.); katarzyna.kisielewska@uwm.edu.pl (K.K.); edyta.rytelewska@uwm.edu.pl (E.R.); marlena.gudelska@uwm.edu.pl (M.G.); grzegorz.kopij@uwm.edu.pl (G.K.); kinga.bors@uwm.edu.pl (K.B.); karolina.szymanska@uwm.edu.pl (K.S.); barbara.kaminska@uwm.edu.pl (B.K.); tkam@uwm.edu.pl (T.K.)Correspondence: kamil.dobrzyn@uwm.edu.pl (K.D.); nina.smolinska@uwm.edu.pl (N.S.)
These authors contributed equally to this work.
2372020820202115521321520202172020© 2020 by the authors.2020Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Chemerin belongs to the group of adipocyte-derived hormones known as adipokines, which are responsible mainly for the control of energy homeostasis. Adipokine exerts its influence through three receptors: Chemokine-like receptor 1 (CMKLR1), G protein-coupled receptor 1 (GPR1), and C-C motif chemokine receptor-like 2 (CCRL2). A growing body of evidence indicates that chemerin participates in the regulation of the female reproductive system. According to the literature, the expression of chemerin and its receptors in reproductive structures depends on the local hormonal milieu. The aim of this study was to investigate the in vitro effect of prostaglandins E2 (PGE2) and F2α (PGF2α) on chemerin and chemerin receptor (chemerin system) mRNAs (qPCR) and proteins (ELISA, Western blotting) in endometrial tissue explants collected from early-pregnant gilts. Both PGE2 and PGF2α significantly influenced the expression of the chemerin gene, hormone secretion, and the expression of chemerin receptor genes and proteins. The influence of both prostaglandins on the expression of the chemerin system varied between different stages of gestation. This is the first study to describe the modulatory effect of PGE2 and PGF2α on the expression of the chemerin system in the porcine uterus during early gestation.
early pregnancychemerinchemerin receptorsporcine uterusPGE2PGF2α1. Introduction
The relationship between nutritional status and reproductive success in animals has been studied for many years. Adipokines are a group of adipocyte-derived agents that are responsible mainly for the regulation of energy metabolism, and they are perceived as the key link between metabolic status and reproductive functions. The adipokine family includes chemerin, the product of the retinoic acid receptor responder 2 gene (RARRES2), also known as tazarotene-induced gene 2 (TIG2) [1]. Chemerin circulates in the blood as prochemerin, which consists of 143 amino acids and originates from a 163 amino acid precursor known as pre-prochemerin [2]. Interestingly, chemerin has been found to exert pleiotropic effects, including modulation of insulin sensitivity that affects food intake, energy homeostasis, and adipose tissue function [3,4]. Chemerin has been found to have a chemotactic properties for immune cells, through promoting the immune cells migration to the inflammation sites. It has also been reported that chemerin may inhibit pro-inflammatory cytokines actions, such as interleukin-6 (IL-6) and tumor necrosis factor-α (TNF-α) [3,5]. In the body, chemerin binds to three G-protein coupled receptors: Chemokine-like receptor 1 (CMKLR1), G protein-coupled receptor 1 (GPR1), and C-C motif chemokine receptor-like 2 (CCRL2). Chemokine-like receptor 1, also known as ChemR23 in humans, has been most extensively studied. G protein-coupled receptor 1 is structurally similar to CMKLR1, but its role has not been elucidated to date. It is believed that GPR1 has different functions than those of CMKLR1 because both receptors are expressed in different tissues. Chemokine-like receptor 1 has been detected mainly in macrophages, natural killer cells, plasmacytoid dendritic cells, and myeloid dendritic cells, whereas GPR1 is expressed in cells related to the central nervous system [6,7,8]. CMKLR1 has been found to use MAPK/ERK1/2, AMPK, and PI3K/Akt kinases to transduce signals, whereas GPR1 acts mainly through the AMPK signaling pathway [9]. The third receptor, CCRL2, differs from CMKLR1 and GPR1. C-C motif chemokine receptor-like 2 is unable to transduce the signal, but it binds the N-terminal region of chemerin and exposes the C-terminal region of the hormone molecule to CMKLR1 that is expressed on the membranes of the neighboring cells [10]. The expression of the CCLR2 gene and protein was confirmed in neutrophils, T cells, and macrophages [11].
A growing body of evidence suggests that chemerin may be responsible for the control of reproductive functions. It has been indicated that the inhibition of the chemerin signaling via the CMKLR1 receptor in the mice decidua results in embryo abortion [12]. Furthermore, it has been observed that in women who had early spontaneous abortions, the concentrations of chemerin in the blood plasma were decreased, whereas the expression of CMKLR1 in the decidua was found to be elevated [12]. The expression of the chemerin system (a collective term for chemerin and its receptors) was confirmed in brain structures responsible for reproductive functions, as well as in the lower branches of the hypothalamic-pituitary-ovarian axis and the female reproductive tract. Chemerin expression was confirmed in the hypothalami of mice and pigs [13,14]. Chemerin receptor expression was reported in porcine and bovine pituitaries and hypothalami [14,15,16]. It has been suggested that chemerin plays an important role in the structural remodeling of the hypothalamus, and may be responsible for the regulation of the secretion of hypothalamic hormones connected with feeding behavior [13,17]. The expression of chemerin and its receptors in the porcine hypothalamic structures responsible for gonadoliberin (GnRH) production, as well as in the pituitary gonadotrophs suggests the potential involvement of the adipokine in the regulation of the reproductive functions [14,15]. Chemerin system expression was observed in the human uterus [18,19], in human and rat placenta [1,20], in the ovaries of women and rodents [1,21,22], and in porcine ovaries, uteri, trophoblasts, and conceptuses [23,24]. The expression of the chemerin system components in the endometrium, trophoblasts, placentas, and conceptuses suggests that the adipokine may play an important role in the foeto–maternal crosstalk during early pregnancy, as well as, through the placenta, across the whole gestation period. Chemerin decreases the in vitro steroidogenesis of granulosa cells, blocks oocyte meiotic progression in cattle, and suppresses FSH-induced progesterone (P4) and estradiol (E2) secretion in rat preantral follicles and granulosa cells [21,22,25]. Chemerin also reduced the hCG-induced production of P4 in cultured follicles and corpora lutea of mice [26]. Our previous research revealed changes in the expression of the chemerin system in porcine uterine and ovarian tissues and demonstrated changes in serum chemerin concentrations during the estrous cycle and early gestation. These findings suggest that chemerin system expression may be dependent on the hormonal milieu [14,23,24]. Similar observations were made by Yang et al., [26] who reported that chemerin and GPR1 expression in the ovaries of mice varied during the estrous cycle. Treatment involving PGF2α decreased GPR1 receptor expression at both gene and protein levels. The above findings supported the formulation of the research hypothesis postulating that prostaglandins, the key factors that regulate uterine functions in early pregnancy, affect the endometrial expression of the chemerin system. Therefore, the aim of the present study was to investigate the influence of prostaglandin E2 (PGE2) and F2α (PGF2α) on the endometrial expression of chemerin, CMKLR1, GPR1, and CCRL2 genes, and on the concentrations of chemerin receptor proteins and chemerin secretion by endometrial tissue explants during early gestation.
2. Results2.1. The Effect of PGE<sub>2</sub> on Chemerin Gene Expression and Protein Secretion by the Endometrial Tissue Explants
In the endometrium, on days 10 to 11 of gestation, PGE2 at the dose of 250 ng/mL enhanced, but at the dose of 100 ng/mL decreased chemerin gene expression (Figure 1A). On days 12 to 13 of gestation, PGE2 at all the tested doses caused an increase in chemerin mRNA content (Figure 1C). On days 15 to 16 of pregnancy, PGE2 enhanced chemerin gene expression (100 ng/mL), but decreased the hormone secretion (100 and 500 ng/mL; Figure 1E,F). Prostaglandin E2 suppressed the endometrial chemerin gene expression on days 27 to 28 of pregnancy (100, 250, 500 ng/mL), as well as on days 10 to 11 of the estrous cycle (250 ng/mL; Figure 1G,I; p < 0.05).
In general, PGE2 treatment resulted in an increase of chemerin mRNA expression on days 12 to 13 and 15 to 16 of pregnancy and a decrease on days 27 to 28 of gestation and days 10 to 11 of the cycle. PGE2 decreased chemerin protein secretion on days 15 to 16 of gestation.
2.2. The Effect of PGE<sub>2</sub> on CMKLR1 Gene and Protein Expression in the Endometrial Tissue Explants
In the endometrium, on days 10 to 11 of gestation, PGE2 (100, 250, 500 ng/mL) caused a decrease in CMKLR1 gene expression (Figure 2A). During the same days, PGE2 at the dose of 250 ng/mL increased, whereas at doses of 100 and 500 ng/mL decreased the receptor protein concentration (Figure 2B). On days 12 to 13 of pregnancy, PGE2 increased (250, 500 ng/mL) CMKLR1 mRNA content and decreased (100, 250, 500 ng/mL) the receptor protein concentration (Figure 2C,D). On days 15 to 16 of gestation, PGE2 at the dose of 250 ng/mL enhanced, but at the dose of 100 ng/mL suppressed CMKLR1 mRNA content (Figure 2E). During the same days, PGE2 caused a decrease in the receptor protein concentration (100, 250, 500 ng/mL; Figure 2F). On days 27 to 28 of pregnancy, PGE2 inhibited CMKLR1 gene expression (250 ng/mL) and enhanced the receptor protein concentration (100, 250, 500 ng/mL; Figure 2G,H). On days 10 to 11 of the estrous cycle, PGE2 at all tested doses caused an increase in CMKLR1 gene expression but decreased the receptor protein concentration (Figure 2J; p < 0.05).
In general, PGE2 decreased CMKLR1 gene expression on days 10 to 11 and 27 to 28 of gestation and protein expression on days 12 to 13 and 15 to 16 of gestation, and 10 to 11 of the cycle. PGE2 enhanced the receptor gene expression on days 12 to 13 of gestation and 10 to 11 of the cycle, and increased CMKLR1 protein content on days 27 to 28 of pregnancy.
2.3. The Effect of PGE<sub>2</sub> on GPR1 Gene and Protein Expression in the Endometrial Tissue Explants
On days 10 to 11 of gestation, PGE2 at all tested doses caused a decrease in GPR1 expression at both the gene and protein levels (Figure 3A,B). On days 12 to 13 of gestation, PGE2 at the dose of 500 ng/mL increased, but at the doses of 100 and 250 ng/mL inhibited GPR1 gene expression. During the same days, PGE2 (250 ng/mL) caused an increase in the endometrial GPR1 protein concentration (Figure 3C,D). On days 15 to 16 of pregnancy, PGE2 caused a decrease in GPR1 mRNA content (100, 250, 500 ng/mL) and increased the receptor protein concentration (100, 500 ng/mL; Figure 3E,F). On days 27 to 28 of pregnancy, PGE2 at all the tested doses caused a decrease in the receptor gene expression and provoked an increase in GPR1 protein concentration (Figure 3G,H). On days 10 to 11 of the estrous cycle, PGE2 at the doses of 250 and 500 ng/mL enhanced, but at the dose of 100 ng/mL inhibited the endometrial GPR1 expression. During the same period, PGE2 at the dose of 100 ng/mL was found to increase the receptor protein concentration (Figure 3I,J; p < 0.05).
Generally, PGE2 decreased the receptor mRNA content on days 10 to 11, 15 to 16, and 27 to 28 of pregnancy and GPR1 protein concentration on days 10 to 11 of pregnancy. The prostaglandin increased GPR1 protein concentration on days 12 to 13, 15 to 16, and 27 to 28 of pregnancy, and 10 to 11 of the cycle.
2.4. The Effect of PGE<sub>2</sub> on CCRL2 Gene and Protein Expression in the Endometrial Tissue Explants
On days 10 to 11 of gestation, PGE2 was found to exert an inhibitory effect on the endometrial expression of CCRL2 at both the gene (100, 250, 500 ng/mL) and protein (100, 250 ng/mL) levels (Figure 4A,B). On days 12 to 13 of gestation, PGE2 caused a decrease (100, 250, 500 ng/mL) in CCRL2 expression, but increased (100, 250 ng/mL) the receptor protein concentration (Figure 4C,D). On days 15 to 16 of pregnancy, PGE2 at the dose of 500 ng/mL caused an increase in CCRL2 protein concentration (Figure 4F). On days 27 to 28 of pregnancy, PGE2 at the dose of 100 ng/mL increased, whereas at the dose of 500 ng/mL decreased CCRL2 mRNA content (Figure 4G). On these days, PGE2 at all the tested doses caused an increase in the endometrial CCRL2 protein concentration (Figure 4H). On days 10 to 11 of the estrous cycle, PGE2 at the dose of 250 ng/mL enhanced, but at the dose of 100 ng/mL inhibited CCRL2 expression (Figure 4I). On these days, PGE2 was observed to decrease (100, 250, 500 ng/mL) the endometrial CCRL2 protein concentration (Figure 4J; p < 0.05).
In summary, PGE2 inhibited CCRL2 gene expression on days 10 to 11 and 12 to 13 of gestation, and the receptor protein content on days 10 to 11 of pregnancy and 10 to 11 of the cycle. On days 12 to 13, 15 to 16, and 27 to 28, PGE2 stimulated CCRL2 protein expression.
2.5. The Effect of PGF<sub>2α</sub> on Chemerin Gene Expression and Protein Secretion by the Endometrial Tissue Explants
In the endometrium, on days 10 to 11 of pregnancy, PGF2α caused an increase (250, 500 ng/mL) in chemerin gene expression (Figure 5A). On days 12 to 13 of pregnancy, PGF2α caused an increase in chemerin mRNA content (100, 250, 500 ng/mL), but decreased (500 ng/mL) its protein secretion (Figure 5C,D). On days 15 to 16 of gestation, PGF2α caused a decrease in chemerin gene expression (100, 500 ng/mL), as well as its protein secretion (100, 250, 500 ng/mL; Figure 5E,F). On days 27 to 28 of pregnancy, PGF2α also decreased both chemerin gene expression (100, 250, 500 ng/mL) and protein secretion (250 ng/mL) (Figure 5G,H; p < 0.05).
Briefly, PGF2α decreased chemerin mRNA content on days 15 to 16 and 27 to 28 of pregnancy, and protein secretion on days 12 to 13, 15 to 16, and 27 to 28 of pregnancy. On days 10 to 11 and 12 to 13 of gestation, the prostaglandin enhanced chemerin gene expression.
2.6. The Effect of PGF<sub>2α</sub> on CMKLR1 Gene and Protein Expression in the Endometrial Tissue Explants
On days 10 to 11 of gestation, PGF2α caused a decrease (100, 250, 500 ng/mL) in CMKLR1 expression and increased (250, 500 ng/mL) the receptor protein concentration (Figure 6A,B). On days 12 to 13 of pregnancy, PGF2α at the dose of 250 ng/mL enhanced CMKLR1 expression (Figure 6C). On those days, PGF2α at the dose of 500 ng/mL increased, but at the doses of 100 and 250 ng/mL, decreased the receptor protein concentration (Figure 6D). On days 15 to 16 of pregnancy, PGF2α caused a decrease in CMKLR1 expression at both the gene (250, 500 ng/mL) and protein (100, 250 ng/mL) levels (Figure 6E,F). On days 27 to 28 of pregnancy, PGF2α at the dose of 100 ng/mL enhanced, but at the dose of 500 ng/mL inhibited CMKLR1 expression (Figure 6G). On those days, PGF2α at the doses of 250 and 500 ng/mL was found to enhance the receptor protein expression (Figure 6H). On days 10 to 11 of the estrous cycle, PGF2α provoked an increase in both CMKLR1 gene (100, 250, 500 ng/mL) and protein (500 ng/mL) expression (Figure 6I,J; p < 0.05).
Summarizing, PGF2α suppressed CMKLR1 gene expression on days 10 to 11 and 15 to 16 of pregnancy, and the receptor protein expression on days 15 to 16 of pregnancy. On days 12 to 13 of gestation and 10 to 11 of the cycle, PGF2α enhanced the receptor gene expression, whereas on days 10 to 11, 27 to 28 of pregnancy and 10 to 11 of the cycle, it increased CMKlR1 protein expression.
2.7. The Effect of PGF<sub>2α</sub> on GPR1 Gene and Protein Expression in the Endometrial Tissue Explants
In the endometrium, on days 10 to 11 of gestation, PGF2α at all the tested doses caused a decrease in GPR1 expression at both gene and protein levels (Figure 7A,B). On days 12 to 13 of gestation, PGF2α at the dose of 250 ng/mL increased, whereas at the doses of 100 and 500 ng/mL decreased GPR1 expression (Figure 7C). On those days, PGF2α at the dose of 500 ng/mL enhanced the receptor protein expression (Figure 7D). On days 15 to 16 of pregnancy, PGF2α caused a decrease (100, 250, 500 ng/mL) in GPR1 expression but increased (250 ng/mL) the endometrial receptor protein concentration (Figure 7E,F). On days 27 to 28 of pregnancy, PGF2α at the dose of 250 ng/mL increased, whereas at the doses of 100 and 500 ng/mL decreased the GPR1 mRNA content (Figure 7G). On those days, PGF2α at all the tested doses decreased the receptor protein concentration (Figure 7H). On days 10 to 11 of the estrous cycle, PGF2α enhanced (250, 500 ng/mL) GPR1 expression and decreased (500 ng/mL) the receptor protein concentration (Figure 7I,J; p < 0.05).
Generally, PGF2α decreased GPR1 gene expression on days 10 to 11 and 15 to 16 of pregnancy and the receptor protein content on days 10 to 11 and 27 to 28 of gestation and 10 to 11 of the cycle. On days 10 to 11 of the cycle PGF2α increased GPR1 gene expression, and on days 12 to 13 and 15 to 16 of pregnancy, it increased the receptor protein content.
2.8. The Effect of PGF<sub>2α</sub> on CCRL2 Gene and Protein Expression in the Endometrial Tissue Explants
In the endometrium, on days 10 to 11 of gestation, PGF2α enhanced (100 ng/mL) CCRL2 expression and decreased (100, 250, 500 ng/mL) the receptor protein concentration (Figure 8A,B). On days 12 to 13 of pregnancy, PGF2α decreased (100, 500 ng/mL) CCRL2 expression and increased (100, 250, 500 ng/mL) the receptor protein concentration (Figure 8C,D). On days 15 to 16 of pregnancy, PGF2α caused a decrease in CCRL2 expression at both the gene (100 ng/mL) and protein (100, 250, 500 ng/mL) levels (Figure 8E,F). On days 27 to 28 of gestation, PGF2α provoked an increase in CCRL2 expression (100, 500 ng/mL), as well as in the receptor protein concentration (100, 250 ng/mL; Figure 8G,H). On days 10 to 11 of the estrous cycle, PGF2α at all tested doses stimulated CCRL2 expression, but decreased the receptor protein concentration (Figure 8I,J; p < 0.05).
In general, PGF2α caused a decrease in CCRL2 gene expression on days 12 to 13 and 15 to 16 of gestation, and in the receptor protein content on days 10 to 11 and 15 to 16 of gestation and 10 to 11 of the cycle. On days 10 to 11 and 27 to 28 of pregnancy and 10 to 11 of the cycle, PGF2α enhanced CCRL2 gene expression, whereas on days 12 to 13 and 27 to 28 of pregnancy, it increased the receptor protein content.
3. Discussion
The expression of the chemerin system in the porcine reproductive tract during the estrous cycle and early gestation has been reported previously [23]. Chemerin, CMKLR1, GPR1, and CCRL2 have been detected in both the endometrium and the myometrium. However, the relationship between the chemerin system and uterine-derived factors responsible for the regulation of reproductive functions, such as steroid hormones and prostaglandins, has not been previously investigated. This is the first study to demonstrate the modulatory influence of PGE2 and PGF2α on chemerin system expression in the endometrium of early-pregnant gilts. The obtained results indicate the dose-dependent influence of prostaglandins (PGs) on the endometrial expression of chemerin system at both the gene and protein levels. Our unpublished results (Smolinska et al., unpublished) indicate that chemerin may also enhance the expression of the key enzymes responsible for the synthesis of prostaglandins such as microsomal prostaglandin E synthase-1 (mPGES-1), cyclooxygenase-2 (COX-2), prostaglandin F synthase (PGFS), and prostaglandin E 9-keto-reductase (CBR1), as well as modulate the secretion of PGE2 and PGF2α, depending on the adipokine dose and the pregnancy period. The above points to the presence of a complex regulatory loop between chemerin and prostaglandins in the porcine endometrium. This study revealed differences in the expression patterns of chemerin system genes and the corresponding proteins. The observed differences in the expression patterns of mRNA and the corresponding proteins are not surprising. Schwanhäusser et al. [27] reported that the differences in mRNA expression in mammals explained approximately 40% of the variation in protein levels, and the coefficient of determination (R2) between the content of mRNA and protein was only 0.41. The R2 value increased to 0.95 when translation rate constants were taken into account. These suggest that the protein abundance may be regulated mainly by the translation efficiency. The presented mechanism may be beneficial for the metabolism of cell because of the savings in the substrates for protein synthesis and the minimization in the energy expenditure associated with the translation process. The discrepancies between gene and protein expression may also arise from the differences in the stability of proteins and gene transcripts, transcriptional and post-transcriptional regulation, inhibition of post-transcriptional processes through high mRNA content, or attenuation of gene expression due to high protein concentrations, as well as due to miRNA-induced mRNA degradation [28,29,30]. The observed changes in the expression of the endometrial chemerin system under the influence of prostaglandins could be explained by variations in the expression of prostaglandin receptors during the analyzed pregnancy periods. The concentrations of PGE2 and PGF2α receptors fluctuate during pregnancy in mice and during the estrous cycle and early gestation in pigs [31,32,33]. In pigs, differences were observed in the endometrial content of PGF2α receptor (PTGFR) protein during the estrous cycle and gestation [32]. Estradiol and PGE2, the main factors responsible for the maternal recognition of pregnancy, stimulate the endometrial expression of PGE2 receptors PTGER2 and PTGER4 [33]. Varied responses to prostaglandins may also be explained by differences in the activity of prostaglandin 9-ketoreductase (CBR1) in explants collected during different gestation periods. The prostaglandin 9-ketoreductase enzyme converts PGE2 into PGF2α. The expression of the CBR1 gene and protein was confirmed in the porcine uterus during early pregnancy and the oestrous cycle [34], which could point to the conversion of prostaglandins in tissue explants during the studied periods. Therefore, the differences in the concentration of chemerin system elements may be explained by the characteristic response for each of the studied dose of PGs. Beside the concentration and ratio of both prostaglandins, their receptors expression patterns, which are strongly connected with the individual periods of early gestation, should also be considered as an important factor that may affect chemerin and its receptors’ mRNA and protein content. The differences observed in the present study could also be attributed to changes in the hormonal microenvironment induced by different concentration patterns of steroid hormones, endogenous prostaglandins, as well as the occurrence of accompanying processes characteristic for the studied periods.
Early pregnancy in pigs is one of the most dynamic and critical periods during gestation. Around day 11 of porcine gestation, migrating conceptuses begin to secrete E2, which initiates a number of processes related to the maternal recognition of pregnancy [35]. During this time, the direction of PGF2α secretion is altered from endocrine to exocrine, and PGE2 production is stimulated, which promotes the secretory functions of the corpus luteum and ensures the proper course of gestation [36,37]. During our previous study we indicated the changes of chemerin system protein expression across early gestation which may be caused by a number of factors present in the uterine microenvironment [23]. The analysis of the results presented herein indicates many similarities between the basal and PG-stimulated chemerin system expression patterns on the corresponding days of gestation. The comparison of the response patterns indicates that changes in the basal chemerin system expression during early pregnancy seem to be connected with the local presence of PGE2 and PGF2α. Furthermore, due to a fact that PGs are secreted in a pulsatile manner, and their secretion patterns during early pregnancy are similar, the PGE2:PGF2α ratio is another agent, which should be taken into consideration [38]. Beside its luteoprotective effects, the redirection of prostaglandin secretion also promotes endometrial reconstruction and modulation of the angiogenesis process during the peri-implantation period [32,39,40]. The chemerin system seems to be involved in the stimulation of angiogenesis. Chemerin has been found to promote angiogenesis in vivo in mice and in vitro in the human umbilical vein endothelial cell line. Similar effects were observed in the human microvascular endothelial cell line [41,42]. Furthermore, it has also been found that chemerin, besides its influence on the vascularization process, may also, mainly via CMKLR1 receptor, influence the vascular tone and blood pressure [43]. Our unpublished data also indicate that chemerin enhances the endometrial secretion of several angiogenic factors, including vascular endothelial growth factors (VEGFA and VEGFB), placental growth factor, and basic fibroblast growth factor, as well as modulates the expression of their receptor proteins, depending on chemerin dose and the stage of gestation. In the present study, PGE2 decreased CMKLR1 protein concentrations on days 12 to 13 and 15 to 16 of gestation, whereas increased CCRL2 protein expression. PGF2α generally exerts a similar effect on the CMKLR1 receptor on days 12 to 16 of gestation and enhances CCRL2 protein content on days 12 to 13. Both prostaglandins exerted the opposite effects on GPR1 protein expression on days 12 to 16 of gestation. Chemerin promotes angiogenesis mainly via the CMKLR1 receptor [41], whereas the GPR1 receptor is responsible mainly for glucose homeostasis [44], which could suggest that by suppressing CMKLR1 and promoting GPR1 expression, prostaglandins could regulate tissue remodeling, prevent excessive vascular development, and ensure the availability of nutrients for implanting embryos. These findings could be confirmed by the observation that the lack of GPR1 expression in pregnant mice resulted in glucose intolerance, disrupted lipid metabolism, and decreased insulin levels [45]. We propose that the regulation of these processes may take place not only through the inhibition of CMKLR1 receptor expression, but also via the stimulation of CCRL2 protein expression. An increase of CCRL2 protein content may result in an increased chemerin uptake from the circulating blood and, in consequence, enhanced GPR1 receptor expression in the tissue. Interestingly, both prostaglandins stimulated the expression of CMKLR1 and CCRL2 proteins on days 27 to 28 of gestation, which implies that the chemerin system could be involved in intensive angiogenesis processes during this period. The present results suggest that the chemerin system and prostaglandins could be a part of a mechanism that regulates endometrial tissue vascularization and, consequently, nutrient availability.
In mammals, gestation is a unique period during which the maternal organism accepts conceptuses carrying different genetic material. Developing conceptuses harbor only one half of genetic material which is identical to the maternal DNA, and they are perceived as natural semi-allografts. The presence of foreign genetic material in the uterine environment raises the risk of immune system mobilization and, consequently, embryo rejection. The suppression of immune activity in the pregnant uterus is a natural mechanism that prevents embryo rejection. Implanting conceptuses secrete a number of factors that modulate the maternal immune system, including interleukin 1β (IL1β), interferons δ and γ, interleukin 6 (IL6), epidermal growth factor, transforming growth factor β, and leukemia inhibitory factor (LIF), which are crucial for maternal–fetal crosstalk [46,47,48,49,50]. However, these processes must be strictly controlled to prevent conceptus rejection [51,52]. Progesterone and PGE2 are the key factors that suppress the immune system during gestation. The presence of PGE2 during the first trimester of human gestation restrains the activation of maternal leukocytes in the decidua, which inhibits their anti-trophoblast killer function [53]. In contrast, PGF2α is an important inflammatory mediator in the reproductive system [54]. Chemerin is also a potential modulator of the immune response. Chemerin acts through CMKLR1 to promote chemotaxis of immature dendritic cells (DCs) and macrophages [6]. Chemerin levels were elevated in tissues and fluids during inflammation, and CMKLR1-expressing immune cells were involved in several chronic inflammatory diseases [5]. However, chemerin can also exert anti-inflammatory effects. Cash et al. (2008) reported that chemerin significantly downgraded neutrophil and monocyte recruitment and decreased proinflammatory cytokine expression in mice [55]. Our unpublished data indicate that chemerin may modulate the endometrial secretion of various cytokines, including interleukins 1β, IL6 and 8, tumor necrosis factor α (TNFα), transforming growth factor α (TGFα), and LIF. We revealed that chemerin may inhibit and/or enhance the secretion of IL8, IL1β, IL6, TNFα, TGFα, and LIF in a dose- and time-dependent manner. Chemerin not only influences cytokine secretion, but also controls cytokine activity by regulating the expression of cytokine receptors in endometrial tissue. We found that the effect of chemerin on the cytokine receptor expression in the endometrium varied depending on the pregnancy period and the dose of the adipokine (Smolinska et al., unpublished). Due to the fact that chemerin acts as the chemoattractant for the immune cells, one of the most important sources of cytokines, further studies concerning the role of this adipokine in the endometrial immune cells recruitment are necessary to receive the full picture of its effect on the uterine immunological milieu during the early gestation period. The current study revealed that PGE2 inhibits chemerin secretion on days 15 to 16 of pregnancy and stimulates CCRL2 protein expression, whereas PGF2α exerts the same effect on days 12 to 28 of gestation in the case of chemerin and promotes the expression of CCRL2 on days 12 to 13 and 27 to 28. During implantation, on days 15 to 16 of pregnancy, both prostaglandins could inhibit chemerin production to prevent the recruitment of immune cells and pregnancy failure. Furthermore, the enhanced expression of CCRL2 protein in the tissue may result in further chemerin level reduction. In pigs, the conceptuses initiate an acute-phase inflammatory response during implantation and placental attachment; therefore, PGF2α could inhibit chemerin secretion during the maternal recognition of pregnancy and implantation to counteract its anti-inflammatory effects and establish a supportive environment for gestation [56].
4. Materials and Methods4.1. Animals and Tissue Collection
Twenty-five mature crossbred gilts (Large White × Polish Landrace, 130–140 kg, of weight and age of 7–8 months) were randomly assigned to one of the following experimental groups (n = 5 per group): Gilts on days 10 to 11 (transuterine migration of embryos), 12 to 13 (maternal recognition of pregnancy), 15 to 16 (beginning of implantation), and 27 to 28 (end of implantation) of pregnancy, and days 10 to 11 of the estrous cycle (mid-luteal phase; the activity of corpora lutea (CL) is comparable to the CL activity during pregnancy). The quantity of CLs and embryos are specified in the Supplementary Table S1. Animals were maintained and fed in accordance with current Polish standards. The daily dose of compound feed was approximately 2.7 kg/gilt at 32.4 MJ of metabolizable energy intake (12 MJ per kg of feed). The feed contained 135 g/kg of total digestible protein, 5.4 g/kg methionine and cystine, 7.3 g/kg lysine, 2.1 g/kg tryptophan, 4.8 g/kg threonine, 5.7 g/kg total phosphorus, 8.9 g/kg calcium, 1.7 g/kg sodium, 10% fiber, and the addition of other macro- and microelements. All individuals were given access to fresh water and forage ad libitum. Cyclic gilts were monitored daily for estrus behavior in the presence of boar. The day of the onset of the second estrus was recognized as day 0 of the cycle. The phase of the estrus cycle was confirmed on the basis of the ovary morphology [57]. The inseminations were conducted by natural mating with the use of the same, crossbreed boar. The insemination was carried out on the first or second day of the estrous cycle, and the first day after coitus was counted as a first day of gestation. The phase of gestation was further confirmed based on the conceptuses morphology [58]. Additionally, the phase of the estrous cycle and pregnancy was confirmed by determining the level P4, as described in Nitkiewicz et al. [59] and Dobrzyn et al. [60]. The concentrations of P4 in the porcine plasma and uterine luminal flushing are detailed in the Supplementary Table S1. The post-mortem-obtained uteri were transported immediately in ice-cold PBS supplemented with antibiotic-antimycotic solution (Sigma-Aldrich, Saint Louis, MO, USA) for an in vitro tissue culture procedure. Figure 9 presents the experimental design scheme.
4.2. Endometrial Explant Cultures
The in vitro cultures of endometrial explants were conducted as described by Smolinska et al. [61]. In order to investigate the impact of PGE2 and PGF2α on the chemerin system expression, after preincubation (2 h) in phenol-red free medium M199 (Sigma-Aldrich, USA), explants were incubated in the presence of PGE2 (100, 250, 500 ng/mL; Sigma-Aldrich, USA), PGF2α (100, 250, 500 ng/mL; Sigma-Aldrich, USA), or without any treatment (control group) for 24 h (37 °C, 95% O2, 5% CO2). The doses of PGE2 and PGF2α were chosen based on the work of Morgan et al. [62] and Gregoraszczuk and Michas [63]. Morgan et al. measured PGF2α levels in porcine uterine flushing on day 11 of gestation under exposure to estradiol valerate. The concentration of PGF2α in the control group was 198 ng/mL on the above-mentioned day. Since both PGs are secreted in a pulsatile manner and their concentrations are not constant, their physiological levels could be determined in the range of 100–500 ng/mL. The cultures were run in five separate experiments per group (n = 5, one gilt per each experiment) and each treatment combination was prepared in duplicates. The lactate dehydrogenase (LDH) activity measurement after preincubation and incubation periods was used to define the viability of tissue explants. The analysis was performed using Liquick Cor-LDH kit (Cormay, Poland) in accordance with the manufacturer’s instructions. The obtained LDH activity in the culture media was compared to the enzyme activity in the fully disintegrated tissue (maximal LDH release, positive control). The mean activity of LDH in the culture media after 24 h of incubation was 184 ± 14 U/L (0.93% of maximal LDH release).
4.3. Total RNA Isolation and Reverse Transcription
Total RNA from endometrial tissue explants was isolated using TRI Reagent® RNA Isolation Reagent (Sigma-Aldrich) following the producer’s instructions. The quantity and purity of the obtained RNA samples were inspected spectrophotometrically using Infinite M200 Pro (Tecan, Mannedorf, Switzerland). One microgram of each RNA sample was reverse transcribed (RT) with the use of Omniscript RT Kit (Qiagen, Germantown, MD, USA) in the presence of 0.5 μg oligo(dT)15 (Roche, Basel, Switzerland) in a final volume of 20 μL. The RT reaction was conducted at 37 °C for 1 h and was terminated by the incubation at 93 °C for 5 min.
4.4. Quantitative Real-Time PCR Analysis
Quantitative real-time PCR (qPCR) analysis was carried out using Power SYBR Green Master Mix (Applied Biosystems, Carlsbad, CA, USA) as described by Smolinska et al. [14]. Specific primer pairs used to amplify parts of chemerin, CMKLR1, GPR1, CCRL2, cyclophilin (PPIA), and β-actin (ACTB) genes are included in Table 1. The constitutively expressed genes PPIA and ACTB were used as the internal controls to verify the method. Our preliminary studies revealed that the endometrial PPIA and ACTB expression was stable during the estrous cycle and pregnancy, as well as with and without treatments. Quantitative real-time PCR reaction mixtures contained: cDNA, primers, Power SYBR Green PCR Master Mix (12.5 μL; Applied Biosystems), and RNase-free water (to the final volume of 25 μL). In negative controls, the cDNA was substituted by water, or RT was not performed before qPCR. All reactions were run in duplicates. The specificity of the reaction was confirmed at the end of the run by the analysis of the melting-curve. The purity of the amplification product was confirmed by agarose gel electrophoresis. The calculation of chemerin and chemerin receptors genes’ relative expression was conducted with the use of the comparative cycle threshold method (ΔΔCT) and normalized using the geometrical means of the reference genes Ct values.
4.5. Enzyme-Linked Immunosorbent Assay (ELISA) of Chemerin
Chemerin concentrations in the culture media were determined using a commercial ELISA kit (FineTest, Wuhan, China) following the manufacturer’s protocol. The range of standard curve was 0.156–10 ng/mL. The sensitivity of the assay was approximately 0.1 ng/mL. The sensitivity of the assays was defined as the lowest protein concentration that could be differentiated from zero samples. Absorbance was measured at 450 nm with the use of Infinite M200 PRO reader with Tecan i-control software (Tecan, Switzerland). The data were linearized by plotting the log of chemerin concentration versus the log of the optical density, and the best fit line was determined by regression analysis. Intra- and inter-assay coefficients of variation of the assay were 3.22% ± 0.39 and 8.1%, respectively.
The basal concentrations of chemerin in the uterine luminal flushing were determined by [23] and are detailed in the Supplementary Table S1.
4.6. Protein Isolation and Western Blotting
The endometrial tissue explants were homogenized in T-PER Tissue Protein Extraction Reagent (Thermo Fischer Scientific, Waltham, MA, USA) in the presence of peptidase and phosphatase inhibitors (Sigma-Aldrich). The lysates were cleared by double centrifugation at 10,000× g for 5 min. The protein concentrations were measured with the use of Bradford dye-binding procedure with the dilutions of bovine serum albumin (BSA) as standards.
Western blotting analysis was performed as described by Smolinska et al. [14]. Endometrial tissue lysates (40 μg) from control, PGE2-, and PGF2α-treated samples were resolved by SDS-PAGE electrophoresis in the 12.5% polyacrylamide gels and transferred onto PVDF membrane (Whatman, USA). Subsequently, membranes were blocked for 1 h in Tris-buffered saline Tween-20 containing 5% skimmed milk powder. After blocking, membranes were incubated for 12 h at 4 °C with rabbit polyclonal antibodies to CMKLR1 (1:1000; ab230442; Abcam, UK), mouse polyclonal antibodies to GPR1 (1:500; ab169331; Abcam, UK), rabbit polyclonal antibodies to CCRL2 (1:600; ab85224; Abcam, UK), and rabbit polyclonal antibodies to actin (1:200; A2066; Sigma-Aldrich, USA). Actin was used as a control to normalize the results of chemerin receptors protein concentration. Subsequently, to identify immunoreactive products, membranes were incubated for 1.5 h at RT with goat anti-rabbit IgG for CMKLR1, CCRL2, and actin (1:5000; sc-2054; Santa Cruz, USA), and goat anti-mouse IgG for GPR1 (1:2500; 115-035-003; Jackson ImmunoResearch Laboratories, Baltimore Pike, PA, USA) conjugated with horseradish peroxidase (HRP). For negative control blots, primary antibodies were substituted by nonspecific fetal calf serum (MP Biomedicals, Santa Ana, CA, USA). Immunocomplexes were visualized with Immobilon Western Chemiluminescent HRP Substrate (Merck Millipore, Kenilworth, NJ, USA) on the G: Box EF Gel Documentation System (Syngene, Cambridge, UK). The same protocol was performed in relation to the adipose tissue used as the positive controls. The results were quantified by densitometric analysis of immunoblots with the use of Image Studio Lite version 5.2 software (LI-COR, Lincoln, NE, USA). Data were expressed as the ratio of chemerin receptors proteins relative to actin protein in arbitrary optical density units.
4.7. Statistical Analysis
Statistica software (StatSoft Inc., Tulsa, OK, USA) was used to perform the statistical analysis of results. All variables were analyzed using descriptive statistics (mean, standard deviation, sample minimum, and sample maximum). To determine the differences in genes expression and proteins concentration between control groups and PGE2 or PGF2α treated groups, a one-way ANOVA followed by Duncan’s post-hoc test were used. Results were presented as the means ± S.E.M. from five independent observations. Values for p < 0.05 were considered statistically significant.
5. Conclusions
This is the first study to demonstrate that prostaglandins affect the expression of the chemerin system in the porcine uterus during early gestation. The presented results indicate that the analyzed prostaglandins exert different effects on the endometrial chemerin system expression, which depend on the dose of the used PGs. Furthermore, we revealed that the response pattern for PGs is specific for each of the studied pregnancy periods. The above findings confirm our hypothesis assuming the regulatory influence of PGs on chemerin system expression in the porcine early-pregnant uterus. In the light of the existing knowledge, the presented findings also suggest that the chemerin system could be an important element of the regulatory mechanism responsible for the proper course of gestation, and that its expression depends on the local hormonal microenvironment.
Supplementary Materials
Supplementary materials can be found at https://www.mdpi.com/1422-0067/21/15/5213/s1. Supplementary Table S1. Progesterone and chemerin concentrations, as well as and number of corpora lutea (CL) and embryos in the research groups. Supplementary Figure S1. The standard curve of Chemerin ELISA kit used in the study.
Click here for additional data file.
Author Contributions
Conceptualization, N.S., T.K., K.D., M.K.; methodology, N.S., M.K., and K.D.; validation, M.K., K.D., E.R.; formal analysis, N.S., M.G., K.K., E.Z.; investigation, M.K., K.D., N.S., E.R., K.K., E.Z.; resources, K.B., G.K., K.S.; data curation, M.K., and B.K.; writing—original draft preparation, M.K. and K.D.; writing—review and editing, M.K., N.S., T.K., B.K., K.D.; supervision, N.S. and T.K.; project administration, N.S.; funding acquisition, N.S. All authors have read and agreed to the published version of the manuscript.
Funding
This research was supported by the National Science Centre (projects no: 2017/25/B/NZ9/00040).
Conflicts of Interest
The authors declare no competing financial interests.
Animal Welfare Statement
The authors confirm that the ethical policies of the journal, as noted on the journal’s author guidelines page, have been adhered to. All studies were carried out in accordance with the Polish Act on the protection of animals used for scientific or educational purposes of the 15th of January 2015 (Polish Journal of Law of 2015 item 266), as well as directive 2010/63/EU of the European Parliament of the 22nd of September 2010 on the protection of animals used for scientific purposes.
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The influence of prostaglandin E2 (PGE2; 100, 250, 500 ng/mL) on chemerin mRNA expression (A,C,E,G,I) and chemerin protein secretion (B,D,F,H,J) in the porcine endometrial tissue explants on days 10 to 11, 12 to 13, 15 to 16 and 27 to 28 of the pregnancy, and on days 10 to 11 of the estrous cycle. The gene expression was determined by quantitative real-time PCR. The protein secretion was determined by an ELISA test. Results are reported as the means ± S.E.M. (n = 5). Bars with different superscripts differ (p < 0.05).
The influence of prostaglandin E2 (PGE2; 100, 250, 500 ng/mL) on chemokine-like receptor 1 (CMKLR1) mRNA (A,C,E,G,I) and protein (B,D,F,H,J) expression in the porcine endometrium on days 10 to 11, 12 to 13, 15 to 16, and 27 to 28 of the pregnancy, and on days 10 to 11 of the estrous cycle. The gene expression was determined by quantitative real-time PCR. The protein concentration was determined by the western blotting analysis; upper panels: Representative immunoblots; lower panels: Densitometric analysis of CMKLR1 protein relative to actin protein. Results are reported as the means ± S.E.M. (n = 5). Bars with different superscripts differ (p < 0.05).
The influence of prostaglandin E2 (PGE2; 100, 250, 500 ng/mL) on G protein-coupled receptor 1 (GPR1) mRNA (A,C,E,G,I) and protein (B,D,F,H,J) expression in the porcine endometrium on days 10 to 11, 12 to 13, 15 to 16, and 27 to 28 of the pregnancy, and on days 10 to 11 of the estrous cycle. The gene expression was determined by quantitative real-time PCR. The protein concentration was determined by the western blotting analysis; upper panels: Representative immunoblots; lower panels: Densitometric analysis of GPR1 protein relative to actin protein. Results are reported as the means ± S.E.M. (n = 5). Bars with different superscripts differ (p < 0.05).
The influence of prostaglandin E2 (PGE2; 100, 250, 500 ng/mL) on C-C motif chemokine receptor like 2 (CCRL2) mRNA (A,C,E,G,I) and protein (B,D,F,H,J) expression in the porcine endometrium on days 10 to 11, 12 to 13, 15 to 16, and 27 to 28 of the pregnancy, and on days 10 to 11 of the estrous cycle. The gene expression was determined by quantitative real-time PCR. The protein concentration was determined by the western blotting analysis; upper panels: Representative immunoblots; lower panels: Densitometric analysis of CCRL2 protein relative to actin protein. Results are reported as the means ± S.E.M. (n = 5). Bars with different superscripts differ (p < 0.05).
The influence of prostaglandin F2α (PGF2α; 100, 250, 500 ng/mL) on chemerin mRNA expression (A,C,E,G,I) and chemerin protein secretion (B,D,F,H,J) in the porcine endometrial tissue explants on days 10 to 11, 12 to 13, 15 to 16, and 27 to 28 of the pregnancy, and on days 10 to 11 of the estrous cycle. The gene expression was determined by quantitative real-time PCR. The proteins secretion was determined by an ELISA test. Results are reported as the means ± S.E.M. (n = 5). Bars with different superscripts differ (p < 0.05).
The influence of prostaglandin F2α (PGF2α; 100, 250, 500 ng/mL) on chemokine-like receptor 1 (CMKLR1) mRNA (A,C,E,G,I) and protein (B,D,F,H,J) expression in the porcine endometrium on days 10 to 11, 12 to 13, 15 to 16, and 27 to 28 of the pregnancy, and on days 10 to 11 of the estrous cycle. The gene expression was determined by quantitative real-time PCR. The protein concentration was determined by the western blotting analysis; upper panels: Representative immunoblots; lower panels: Densitometric analysis of CMKLR1 protein relative to actin protein. Results are reported as the means ± S.E.M. (n = 5). Bars with different superscripts differ (p < 0.05).
The influence of prostaglandin F2α (PGF2α; 100, 250, 500 ng/mL) on G protein-coupled receptor 1 (GPR1) mRNA (A,C,E,G,I) and protein (B,D,F,H,J) expression in the porcine endometrium on days 10 to 11, 12 to 13, 15 to 16, and 27 to 28 of the pregnancy, and on days 10 to 11 of the estrous cycle. The gene expression was determined by quantitative real-time PCR. The protein concentration was determined by the western blotting analysis; upper panels: Representative immunoblots; lower panels: Densitometric analysis of GPR1 protein relative to actin protein. Results are reported as the means ± S.E.M. (n = 5). Bars with different superscripts differ (p < 0.05).
The influence of prostaglandin F2α (PGF2α; 100, 250, 500 ng/mL) on C-C motif chemokine receptor like 2 (CCRL2) mRNA (A,C,E,G,I) and protein (B,D,F,H,J) expression in the porcine endometrium on days 10 to 11, 12 to 13, 15 to 16, and 27 to 28 of the pregnancy, and on days 10 to 11 of the estrous cycle. The gene expression was determined by quantitative real-time PCR. The protein concentration was determined by the western blotting analysis; upper panels: Representative immunoblots; lower panels: Densitometric analysis of CCRL2 protein relative to actin protein. Results are reported as the means ± S.E.M. (n = 5). Bars with different superscripts differ (p < 0.05).
Experimental design graph.
ijms-21-05213-t001_Table 1
Characteristics of the primers used in the study of gene expression in porcine endometrial explants. RARRES2: Chemerin; CCRL2: C-C motif chemokine receptor like 2; CMLKR1: chemokine-like receptor 1; GPR1: G protein-coupled receptor 1; PPIA: Cyclophilin; ACTB: β-actin; F: Forward; R: Reverse.
\n"],"string":"[\n \"\\nInt J Mol SciInt J Mol SciijmsInternational Journal of Molecular Sciences1422-0067MDPI32717877PMC743213110.3390/ijms21155213ijms-21-05213ArticleThe In Vitro Effect of Prostaglandin E2 and F2α on the Chemerin System in the Porcine Endometrium during GestationDobrzynKamil*†https://orcid.org/0000-0002-8677-5282KiezunMarta†ZaobidnaEwaKisielewskaKatarzynaRytelewskaEdytaGudelskaMarlenaKopijGrzegorzBorsKingaSzymanskaKarolinaKaminskaBarbarahttps://orcid.org/0000-0003-1643-026XKaminskiTadeuszhttps://orcid.org/0000-0001-5364-1387SmolinskaNina*Department of Animal Anatomy and Physiology, Faculty of Biology and Biotechnology, University of Warmia and Mazury in Olsztyn, Oczapowskiego 1A, 10-719 Olsztyn-Kortowo, Poland; marta.kiezun@uwm.edu.pl (M.K.); ewa.zaobidna@uwm.edu.pl (E.Z.); katarzyna.kisielewska@uwm.edu.pl (K.K.); edyta.rytelewska@uwm.edu.pl (E.R.); marlena.gudelska@uwm.edu.pl (M.G.); grzegorz.kopij@uwm.edu.pl (G.K.); kinga.bors@uwm.edu.pl (K.B.); karolina.szymanska@uwm.edu.pl (K.S.); barbara.kaminska@uwm.edu.pl (B.K.); tkam@uwm.edu.pl (T.K.)Correspondence: kamil.dobrzyn@uwm.edu.pl (K.D.); nina.smolinska@uwm.edu.pl (N.S.)
These authors contributed equally to this work.
2372020820202115521321520202172020© 2020 by the authors.2020Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Chemerin belongs to the group of adipocyte-derived hormones known as adipokines, which are responsible mainly for the control of energy homeostasis. Adipokine exerts its influence through three receptors: Chemokine-like receptor 1 (CMKLR1), G protein-coupled receptor 1 (GPR1), and C-C motif chemokine receptor-like 2 (CCRL2). A growing body of evidence indicates that chemerin participates in the regulation of the female reproductive system. According to the literature, the expression of chemerin and its receptors in reproductive structures depends on the local hormonal milieu. The aim of this study was to investigate the in vitro effect of prostaglandins E2 (PGE2) and F2α (PGF2α) on chemerin and chemerin receptor (chemerin system) mRNAs (qPCR) and proteins (ELISA, Western blotting) in endometrial tissue explants collected from early-pregnant gilts. Both PGE2 and PGF2α significantly influenced the expression of the chemerin gene, hormone secretion, and the expression of chemerin receptor genes and proteins. The influence of both prostaglandins on the expression of the chemerin system varied between different stages of gestation. This is the first study to describe the modulatory effect of PGE2 and PGF2α on the expression of the chemerin system in the porcine uterus during early gestation.
early pregnancychemerinchemerin receptorsporcine uterusPGE2PGF2α1. Introduction
The relationship between nutritional status and reproductive success in animals has been studied for many years. Adipokines are a group of adipocyte-derived agents that are responsible mainly for the regulation of energy metabolism, and they are perceived as the key link between metabolic status and reproductive functions. The adipokine family includes chemerin, the product of the retinoic acid receptor responder 2 gene (RARRES2), also known as tazarotene-induced gene 2 (TIG2) [1]. Chemerin circulates in the blood as prochemerin, which consists of 143 amino acids and originates from a 163 amino acid precursor known as pre-prochemerin [2]. Interestingly, chemerin has been found to exert pleiotropic effects, including modulation of insulin sensitivity that affects food intake, energy homeostasis, and adipose tissue function [3,4]. Chemerin has been found to have a chemotactic properties for immune cells, through promoting the immune cells migration to the inflammation sites. It has also been reported that chemerin may inhibit pro-inflammatory cytokines actions, such as interleukin-6 (IL-6) and tumor necrosis factor-α (TNF-α) [3,5]. In the body, chemerin binds to three G-protein coupled receptors: Chemokine-like receptor 1 (CMKLR1), G protein-coupled receptor 1 (GPR1), and C-C motif chemokine receptor-like 2 (CCRL2). Chemokine-like receptor 1, also known as ChemR23 in humans, has been most extensively studied. G protein-coupled receptor 1 is structurally similar to CMKLR1, but its role has not been elucidated to date. It is believed that GPR1 has different functions than those of CMKLR1 because both receptors are expressed in different tissues. Chemokine-like receptor 1 has been detected mainly in macrophages, natural killer cells, plasmacytoid dendritic cells, and myeloid dendritic cells, whereas GPR1 is expressed in cells related to the central nervous system [6,7,8]. CMKLR1 has been found to use MAPK/ERK1/2, AMPK, and PI3K/Akt kinases to transduce signals, whereas GPR1 acts mainly through the AMPK signaling pathway [9]. The third receptor, CCRL2, differs from CMKLR1 and GPR1. C-C motif chemokine receptor-like 2 is unable to transduce the signal, but it binds the N-terminal region of chemerin and exposes the C-terminal region of the hormone molecule to CMKLR1 that is expressed on the membranes of the neighboring cells [10]. The expression of the CCLR2 gene and protein was confirmed in neutrophils, T cells, and macrophages [11].
A growing body of evidence suggests that chemerin may be responsible for the control of reproductive functions. It has been indicated that the inhibition of the chemerin signaling via the CMKLR1 receptor in the mice decidua results in embryo abortion [12]. Furthermore, it has been observed that in women who had early spontaneous abortions, the concentrations of chemerin in the blood plasma were decreased, whereas the expression of CMKLR1 in the decidua was found to be elevated [12]. The expression of the chemerin system (a collective term for chemerin and its receptors) was confirmed in brain structures responsible for reproductive functions, as well as in the lower branches of the hypothalamic-pituitary-ovarian axis and the female reproductive tract. Chemerin expression was confirmed in the hypothalami of mice and pigs [13,14]. Chemerin receptor expression was reported in porcine and bovine pituitaries and hypothalami [14,15,16]. It has been suggested that chemerin plays an important role in the structural remodeling of the hypothalamus, and may be responsible for the regulation of the secretion of hypothalamic hormones connected with feeding behavior [13,17]. The expression of chemerin and its receptors in the porcine hypothalamic structures responsible for gonadoliberin (GnRH) production, as well as in the pituitary gonadotrophs suggests the potential involvement of the adipokine in the regulation of the reproductive functions [14,15]. Chemerin system expression was observed in the human uterus [18,19], in human and rat placenta [1,20], in the ovaries of women and rodents [1,21,22], and in porcine ovaries, uteri, trophoblasts, and conceptuses [23,24]. The expression of the chemerin system components in the endometrium, trophoblasts, placentas, and conceptuses suggests that the adipokine may play an important role in the foeto–maternal crosstalk during early pregnancy, as well as, through the placenta, across the whole gestation period. Chemerin decreases the in vitro steroidogenesis of granulosa cells, blocks oocyte meiotic progression in cattle, and suppresses FSH-induced progesterone (P4) and estradiol (E2) secretion in rat preantral follicles and granulosa cells [21,22,25]. Chemerin also reduced the hCG-induced production of P4 in cultured follicles and corpora lutea of mice [26]. Our previous research revealed changes in the expression of the chemerin system in porcine uterine and ovarian tissues and demonstrated changes in serum chemerin concentrations during the estrous cycle and early gestation. These findings suggest that chemerin system expression may be dependent on the hormonal milieu [14,23,24]. Similar observations were made by Yang et al., [26] who reported that chemerin and GPR1 expression in the ovaries of mice varied during the estrous cycle. Treatment involving PGF2α decreased GPR1 receptor expression at both gene and protein levels. The above findings supported the formulation of the research hypothesis postulating that prostaglandins, the key factors that regulate uterine functions in early pregnancy, affect the endometrial expression of the chemerin system. Therefore, the aim of the present study was to investigate the influence of prostaglandin E2 (PGE2) and F2α (PGF2α) on the endometrial expression of chemerin, CMKLR1, GPR1, and CCRL2 genes, and on the concentrations of chemerin receptor proteins and chemerin secretion by endometrial tissue explants during early gestation.
2. Results2.1. The Effect of PGE<sub>2</sub> on Chemerin Gene Expression and Protein Secretion by the Endometrial Tissue Explants
In the endometrium, on days 10 to 11 of gestation, PGE2 at the dose of 250 ng/mL enhanced, but at the dose of 100 ng/mL decreased chemerin gene expression (Figure 1A). On days 12 to 13 of gestation, PGE2 at all the tested doses caused an increase in chemerin mRNA content (Figure 1C). On days 15 to 16 of pregnancy, PGE2 enhanced chemerin gene expression (100 ng/mL), but decreased the hormone secretion (100 and 500 ng/mL; Figure 1E,F). Prostaglandin E2 suppressed the endometrial chemerin gene expression on days 27 to 28 of pregnancy (100, 250, 500 ng/mL), as well as on days 10 to 11 of the estrous cycle (250 ng/mL; Figure 1G,I; p < 0.05).
In general, PGE2 treatment resulted in an increase of chemerin mRNA expression on days 12 to 13 and 15 to 16 of pregnancy and a decrease on days 27 to 28 of gestation and days 10 to 11 of the cycle. PGE2 decreased chemerin protein secretion on days 15 to 16 of gestation.
2.2. The Effect of PGE<sub>2</sub> on CMKLR1 Gene and Protein Expression in the Endometrial Tissue Explants
In the endometrium, on days 10 to 11 of gestation, PGE2 (100, 250, 500 ng/mL) caused a decrease in CMKLR1 gene expression (Figure 2A). During the same days, PGE2 at the dose of 250 ng/mL increased, whereas at doses of 100 and 500 ng/mL decreased the receptor protein concentration (Figure 2B). On days 12 to 13 of pregnancy, PGE2 increased (250, 500 ng/mL) CMKLR1 mRNA content and decreased (100, 250, 500 ng/mL) the receptor protein concentration (Figure 2C,D). On days 15 to 16 of gestation, PGE2 at the dose of 250 ng/mL enhanced, but at the dose of 100 ng/mL suppressed CMKLR1 mRNA content (Figure 2E). During the same days, PGE2 caused a decrease in the receptor protein concentration (100, 250, 500 ng/mL; Figure 2F). On days 27 to 28 of pregnancy, PGE2 inhibited CMKLR1 gene expression (250 ng/mL) and enhanced the receptor protein concentration (100, 250, 500 ng/mL; Figure 2G,H). On days 10 to 11 of the estrous cycle, PGE2 at all tested doses caused an increase in CMKLR1 gene expression but decreased the receptor protein concentration (Figure 2J; p < 0.05).
In general, PGE2 decreased CMKLR1 gene expression on days 10 to 11 and 27 to 28 of gestation and protein expression on days 12 to 13 and 15 to 16 of gestation, and 10 to 11 of the cycle. PGE2 enhanced the receptor gene expression on days 12 to 13 of gestation and 10 to 11 of the cycle, and increased CMKLR1 protein content on days 27 to 28 of pregnancy.
2.3. The Effect of PGE<sub>2</sub> on GPR1 Gene and Protein Expression in the Endometrial Tissue Explants
On days 10 to 11 of gestation, PGE2 at all tested doses caused a decrease in GPR1 expression at both the gene and protein levels (Figure 3A,B). On days 12 to 13 of gestation, PGE2 at the dose of 500 ng/mL increased, but at the doses of 100 and 250 ng/mL inhibited GPR1 gene expression. During the same days, PGE2 (250 ng/mL) caused an increase in the endometrial GPR1 protein concentration (Figure 3C,D). On days 15 to 16 of pregnancy, PGE2 caused a decrease in GPR1 mRNA content (100, 250, 500 ng/mL) and increased the receptor protein concentration (100, 500 ng/mL; Figure 3E,F). On days 27 to 28 of pregnancy, PGE2 at all the tested doses caused a decrease in the receptor gene expression and provoked an increase in GPR1 protein concentration (Figure 3G,H). On days 10 to 11 of the estrous cycle, PGE2 at the doses of 250 and 500 ng/mL enhanced, but at the dose of 100 ng/mL inhibited the endometrial GPR1 expression. During the same period, PGE2 at the dose of 100 ng/mL was found to increase the receptor protein concentration (Figure 3I,J; p < 0.05).
Generally, PGE2 decreased the receptor mRNA content on days 10 to 11, 15 to 16, and 27 to 28 of pregnancy and GPR1 protein concentration on days 10 to 11 of pregnancy. The prostaglandin increased GPR1 protein concentration on days 12 to 13, 15 to 16, and 27 to 28 of pregnancy, and 10 to 11 of the cycle.
2.4. The Effect of PGE<sub>2</sub> on CCRL2 Gene and Protein Expression in the Endometrial Tissue Explants
On days 10 to 11 of gestation, PGE2 was found to exert an inhibitory effect on the endometrial expression of CCRL2 at both the gene (100, 250, 500 ng/mL) and protein (100, 250 ng/mL) levels (Figure 4A,B). On days 12 to 13 of gestation, PGE2 caused a decrease (100, 250, 500 ng/mL) in CCRL2 expression, but increased (100, 250 ng/mL) the receptor protein concentration (Figure 4C,D). On days 15 to 16 of pregnancy, PGE2 at the dose of 500 ng/mL caused an increase in CCRL2 protein concentration (Figure 4F). On days 27 to 28 of pregnancy, PGE2 at the dose of 100 ng/mL increased, whereas at the dose of 500 ng/mL decreased CCRL2 mRNA content (Figure 4G). On these days, PGE2 at all the tested doses caused an increase in the endometrial CCRL2 protein concentration (Figure 4H). On days 10 to 11 of the estrous cycle, PGE2 at the dose of 250 ng/mL enhanced, but at the dose of 100 ng/mL inhibited CCRL2 expression (Figure 4I). On these days, PGE2 was observed to decrease (100, 250, 500 ng/mL) the endometrial CCRL2 protein concentration (Figure 4J; p < 0.05).
In summary, PGE2 inhibited CCRL2 gene expression on days 10 to 11 and 12 to 13 of gestation, and the receptor protein content on days 10 to 11 of pregnancy and 10 to 11 of the cycle. On days 12 to 13, 15 to 16, and 27 to 28, PGE2 stimulated CCRL2 protein expression.
2.5. The Effect of PGF<sub>2α</sub> on Chemerin Gene Expression and Protein Secretion by the Endometrial Tissue Explants
In the endometrium, on days 10 to 11 of pregnancy, PGF2α caused an increase (250, 500 ng/mL) in chemerin gene expression (Figure 5A). On days 12 to 13 of pregnancy, PGF2α caused an increase in chemerin mRNA content (100, 250, 500 ng/mL), but decreased (500 ng/mL) its protein secretion (Figure 5C,D). On days 15 to 16 of gestation, PGF2α caused a decrease in chemerin gene expression (100, 500 ng/mL), as well as its protein secretion (100, 250, 500 ng/mL; Figure 5E,F). On days 27 to 28 of pregnancy, PGF2α also decreased both chemerin gene expression (100, 250, 500 ng/mL) and protein secretion (250 ng/mL) (Figure 5G,H; p < 0.05).
Briefly, PGF2α decreased chemerin mRNA content on days 15 to 16 and 27 to 28 of pregnancy, and protein secretion on days 12 to 13, 15 to 16, and 27 to 28 of pregnancy. On days 10 to 11 and 12 to 13 of gestation, the prostaglandin enhanced chemerin gene expression.
2.6. The Effect of PGF<sub>2α</sub> on CMKLR1 Gene and Protein Expression in the Endometrial Tissue Explants
On days 10 to 11 of gestation, PGF2α caused a decrease (100, 250, 500 ng/mL) in CMKLR1 expression and increased (250, 500 ng/mL) the receptor protein concentration (Figure 6A,B). On days 12 to 13 of pregnancy, PGF2α at the dose of 250 ng/mL enhanced CMKLR1 expression (Figure 6C). On those days, PGF2α at the dose of 500 ng/mL increased, but at the doses of 100 and 250 ng/mL, decreased the receptor protein concentration (Figure 6D). On days 15 to 16 of pregnancy, PGF2α caused a decrease in CMKLR1 expression at both the gene (250, 500 ng/mL) and protein (100, 250 ng/mL) levels (Figure 6E,F). On days 27 to 28 of pregnancy, PGF2α at the dose of 100 ng/mL enhanced, but at the dose of 500 ng/mL inhibited CMKLR1 expression (Figure 6G). On those days, PGF2α at the doses of 250 and 500 ng/mL was found to enhance the receptor protein expression (Figure 6H). On days 10 to 11 of the estrous cycle, PGF2α provoked an increase in both CMKLR1 gene (100, 250, 500 ng/mL) and protein (500 ng/mL) expression (Figure 6I,J; p < 0.05).
Summarizing, PGF2α suppressed CMKLR1 gene expression on days 10 to 11 and 15 to 16 of pregnancy, and the receptor protein expression on days 15 to 16 of pregnancy. On days 12 to 13 of gestation and 10 to 11 of the cycle, PGF2α enhanced the receptor gene expression, whereas on days 10 to 11, 27 to 28 of pregnancy and 10 to 11 of the cycle, it increased CMKlR1 protein expression.
2.7. The Effect of PGF<sub>2α</sub> on GPR1 Gene and Protein Expression in the Endometrial Tissue Explants
In the endometrium, on days 10 to 11 of gestation, PGF2α at all the tested doses caused a decrease in GPR1 expression at both gene and protein levels (Figure 7A,B). On days 12 to 13 of gestation, PGF2α at the dose of 250 ng/mL increased, whereas at the doses of 100 and 500 ng/mL decreased GPR1 expression (Figure 7C). On those days, PGF2α at the dose of 500 ng/mL enhanced the receptor protein expression (Figure 7D). On days 15 to 16 of pregnancy, PGF2α caused a decrease (100, 250, 500 ng/mL) in GPR1 expression but increased (250 ng/mL) the endometrial receptor protein concentration (Figure 7E,F). On days 27 to 28 of pregnancy, PGF2α at the dose of 250 ng/mL increased, whereas at the doses of 100 and 500 ng/mL decreased the GPR1 mRNA content (Figure 7G). On those days, PGF2α at all the tested doses decreased the receptor protein concentration (Figure 7H). On days 10 to 11 of the estrous cycle, PGF2α enhanced (250, 500 ng/mL) GPR1 expression and decreased (500 ng/mL) the receptor protein concentration (Figure 7I,J; p < 0.05).
Generally, PGF2α decreased GPR1 gene expression on days 10 to 11 and 15 to 16 of pregnancy and the receptor protein content on days 10 to 11 and 27 to 28 of gestation and 10 to 11 of the cycle. On days 10 to 11 of the cycle PGF2α increased GPR1 gene expression, and on days 12 to 13 and 15 to 16 of pregnancy, it increased the receptor protein content.
2.8. The Effect of PGF<sub>2α</sub> on CCRL2 Gene and Protein Expression in the Endometrial Tissue Explants
In the endometrium, on days 10 to 11 of gestation, PGF2α enhanced (100 ng/mL) CCRL2 expression and decreased (100, 250, 500 ng/mL) the receptor protein concentration (Figure 8A,B). On days 12 to 13 of pregnancy, PGF2α decreased (100, 500 ng/mL) CCRL2 expression and increased (100, 250, 500 ng/mL) the receptor protein concentration (Figure 8C,D). On days 15 to 16 of pregnancy, PGF2α caused a decrease in CCRL2 expression at both the gene (100 ng/mL) and protein (100, 250, 500 ng/mL) levels (Figure 8E,F). On days 27 to 28 of gestation, PGF2α provoked an increase in CCRL2 expression (100, 500 ng/mL), as well as in the receptor protein concentration (100, 250 ng/mL; Figure 8G,H). On days 10 to 11 of the estrous cycle, PGF2α at all tested doses stimulated CCRL2 expression, but decreased the receptor protein concentration (Figure 8I,J; p < 0.05).
In general, PGF2α caused a decrease in CCRL2 gene expression on days 12 to 13 and 15 to 16 of gestation, and in the receptor protein content on days 10 to 11 and 15 to 16 of gestation and 10 to 11 of the cycle. On days 10 to 11 and 27 to 28 of pregnancy and 10 to 11 of the cycle, PGF2α enhanced CCRL2 gene expression, whereas on days 12 to 13 and 27 to 28 of pregnancy, it increased the receptor protein content.
3. Discussion
The expression of the chemerin system in the porcine reproductive tract during the estrous cycle and early gestation has been reported previously [23]. Chemerin, CMKLR1, GPR1, and CCRL2 have been detected in both the endometrium and the myometrium. However, the relationship between the chemerin system and uterine-derived factors responsible for the regulation of reproductive functions, such as steroid hormones and prostaglandins, has not been previously investigated. This is the first study to demonstrate the modulatory influence of PGE2 and PGF2α on chemerin system expression in the endometrium of early-pregnant gilts. The obtained results indicate the dose-dependent influence of prostaglandins (PGs) on the endometrial expression of chemerin system at both the gene and protein levels. Our unpublished results (Smolinska et al., unpublished) indicate that chemerin may also enhance the expression of the key enzymes responsible for the synthesis of prostaglandins such as microsomal prostaglandin E synthase-1 (mPGES-1), cyclooxygenase-2 (COX-2), prostaglandin F synthase (PGFS), and prostaglandin E 9-keto-reductase (CBR1), as well as modulate the secretion of PGE2 and PGF2α, depending on the adipokine dose and the pregnancy period. The above points to the presence of a complex regulatory loop between chemerin and prostaglandins in the porcine endometrium. This study revealed differences in the expression patterns of chemerin system genes and the corresponding proteins. The observed differences in the expression patterns of mRNA and the corresponding proteins are not surprising. Schwanhäusser et al. [27] reported that the differences in mRNA expression in mammals explained approximately 40% of the variation in protein levels, and the coefficient of determination (R2) between the content of mRNA and protein was only 0.41. The R2 value increased to 0.95 when translation rate constants were taken into account. These suggest that the protein abundance may be regulated mainly by the translation efficiency. The presented mechanism may be beneficial for the metabolism of cell because of the savings in the substrates for protein synthesis and the minimization in the energy expenditure associated with the translation process. The discrepancies between gene and protein expression may also arise from the differences in the stability of proteins and gene transcripts, transcriptional and post-transcriptional regulation, inhibition of post-transcriptional processes through high mRNA content, or attenuation of gene expression due to high protein concentrations, as well as due to miRNA-induced mRNA degradation [28,29,30]. The observed changes in the expression of the endometrial chemerin system under the influence of prostaglandins could be explained by variations in the expression of prostaglandin receptors during the analyzed pregnancy periods. The concentrations of PGE2 and PGF2α receptors fluctuate during pregnancy in mice and during the estrous cycle and early gestation in pigs [31,32,33]. In pigs, differences were observed in the endometrial content of PGF2α receptor (PTGFR) protein during the estrous cycle and gestation [32]. Estradiol and PGE2, the main factors responsible for the maternal recognition of pregnancy, stimulate the endometrial expression of PGE2 receptors PTGER2 and PTGER4 [33]. Varied responses to prostaglandins may also be explained by differences in the activity of prostaglandin 9-ketoreductase (CBR1) in explants collected during different gestation periods. The prostaglandin 9-ketoreductase enzyme converts PGE2 into PGF2α. The expression of the CBR1 gene and protein was confirmed in the porcine uterus during early pregnancy and the oestrous cycle [34], which could point to the conversion of prostaglandins in tissue explants during the studied periods. Therefore, the differences in the concentration of chemerin system elements may be explained by the characteristic response for each of the studied dose of PGs. Beside the concentration and ratio of both prostaglandins, their receptors expression patterns, which are strongly connected with the individual periods of early gestation, should also be considered as an important factor that may affect chemerin and its receptors’ mRNA and protein content. The differences observed in the present study could also be attributed to changes in the hormonal microenvironment induced by different concentration patterns of steroid hormones, endogenous prostaglandins, as well as the occurrence of accompanying processes characteristic for the studied periods.
Early pregnancy in pigs is one of the most dynamic and critical periods during gestation. Around day 11 of porcine gestation, migrating conceptuses begin to secrete E2, which initiates a number of processes related to the maternal recognition of pregnancy [35]. During this time, the direction of PGF2α secretion is altered from endocrine to exocrine, and PGE2 production is stimulated, which promotes the secretory functions of the corpus luteum and ensures the proper course of gestation [36,37]. During our previous study we indicated the changes of chemerin system protein expression across early gestation which may be caused by a number of factors present in the uterine microenvironment [23]. The analysis of the results presented herein indicates many similarities between the basal and PG-stimulated chemerin system expression patterns on the corresponding days of gestation. The comparison of the response patterns indicates that changes in the basal chemerin system expression during early pregnancy seem to be connected with the local presence of PGE2 and PGF2α. Furthermore, due to a fact that PGs are secreted in a pulsatile manner, and their secretion patterns during early pregnancy are similar, the PGE2:PGF2α ratio is another agent, which should be taken into consideration [38]. Beside its luteoprotective effects, the redirection of prostaglandin secretion also promotes endometrial reconstruction and modulation of the angiogenesis process during the peri-implantation period [32,39,40]. The chemerin system seems to be involved in the stimulation of angiogenesis. Chemerin has been found to promote angiogenesis in vivo in mice and in vitro in the human umbilical vein endothelial cell line. Similar effects were observed in the human microvascular endothelial cell line [41,42]. Furthermore, it has also been found that chemerin, besides its influence on the vascularization process, may also, mainly via CMKLR1 receptor, influence the vascular tone and blood pressure [43]. Our unpublished data also indicate that chemerin enhances the endometrial secretion of several angiogenic factors, including vascular endothelial growth factors (VEGFA and VEGFB), placental growth factor, and basic fibroblast growth factor, as well as modulates the expression of their receptor proteins, depending on chemerin dose and the stage of gestation. In the present study, PGE2 decreased CMKLR1 protein concentrations on days 12 to 13 and 15 to 16 of gestation, whereas increased CCRL2 protein expression. PGF2α generally exerts a similar effect on the CMKLR1 receptor on days 12 to 16 of gestation and enhances CCRL2 protein content on days 12 to 13. Both prostaglandins exerted the opposite effects on GPR1 protein expression on days 12 to 16 of gestation. Chemerin promotes angiogenesis mainly via the CMKLR1 receptor [41], whereas the GPR1 receptor is responsible mainly for glucose homeostasis [44], which could suggest that by suppressing CMKLR1 and promoting GPR1 expression, prostaglandins could regulate tissue remodeling, prevent excessive vascular development, and ensure the availability of nutrients for implanting embryos. These findings could be confirmed by the observation that the lack of GPR1 expression in pregnant mice resulted in glucose intolerance, disrupted lipid metabolism, and decreased insulin levels [45]. We propose that the regulation of these processes may take place not only through the inhibition of CMKLR1 receptor expression, but also via the stimulation of CCRL2 protein expression. An increase of CCRL2 protein content may result in an increased chemerin uptake from the circulating blood and, in consequence, enhanced GPR1 receptor expression in the tissue. Interestingly, both prostaglandins stimulated the expression of CMKLR1 and CCRL2 proteins on days 27 to 28 of gestation, which implies that the chemerin system could be involved in intensive angiogenesis processes during this period. The present results suggest that the chemerin system and prostaglandins could be a part of a mechanism that regulates endometrial tissue vascularization and, consequently, nutrient availability.
In mammals, gestation is a unique period during which the maternal organism accepts conceptuses carrying different genetic material. Developing conceptuses harbor only one half of genetic material which is identical to the maternal DNA, and they are perceived as natural semi-allografts. The presence of foreign genetic material in the uterine environment raises the risk of immune system mobilization and, consequently, embryo rejection. The suppression of immune activity in the pregnant uterus is a natural mechanism that prevents embryo rejection. Implanting conceptuses secrete a number of factors that modulate the maternal immune system, including interleukin 1β (IL1β), interferons δ and γ, interleukin 6 (IL6), epidermal growth factor, transforming growth factor β, and leukemia inhibitory factor (LIF), which are crucial for maternal–fetal crosstalk [46,47,48,49,50]. However, these processes must be strictly controlled to prevent conceptus rejection [51,52]. Progesterone and PGE2 are the key factors that suppress the immune system during gestation. The presence of PGE2 during the first trimester of human gestation restrains the activation of maternal leukocytes in the decidua, which inhibits their anti-trophoblast killer function [53]. In contrast, PGF2α is an important inflammatory mediator in the reproductive system [54]. Chemerin is also a potential modulator of the immune response. Chemerin acts through CMKLR1 to promote chemotaxis of immature dendritic cells (DCs) and macrophages [6]. Chemerin levels were elevated in tissues and fluids during inflammation, and CMKLR1-expressing immune cells were involved in several chronic inflammatory diseases [5]. However, chemerin can also exert anti-inflammatory effects. Cash et al. (2008) reported that chemerin significantly downgraded neutrophil and monocyte recruitment and decreased proinflammatory cytokine expression in mice [55]. Our unpublished data indicate that chemerin may modulate the endometrial secretion of various cytokines, including interleukins 1β, IL6 and 8, tumor necrosis factor α (TNFα), transforming growth factor α (TGFα), and LIF. We revealed that chemerin may inhibit and/or enhance the secretion of IL8, IL1β, IL6, TNFα, TGFα, and LIF in a dose- and time-dependent manner. Chemerin not only influences cytokine secretion, but also controls cytokine activity by regulating the expression of cytokine receptors in endometrial tissue. We found that the effect of chemerin on the cytokine receptor expression in the endometrium varied depending on the pregnancy period and the dose of the adipokine (Smolinska et al., unpublished). Due to the fact that chemerin acts as the chemoattractant for the immune cells, one of the most important sources of cytokines, further studies concerning the role of this adipokine in the endometrial immune cells recruitment are necessary to receive the full picture of its effect on the uterine immunological milieu during the early gestation period. The current study revealed that PGE2 inhibits chemerin secretion on days 15 to 16 of pregnancy and stimulates CCRL2 protein expression, whereas PGF2α exerts the same effect on days 12 to 28 of gestation in the case of chemerin and promotes the expression of CCRL2 on days 12 to 13 and 27 to 28. During implantation, on days 15 to 16 of pregnancy, both prostaglandins could inhibit chemerin production to prevent the recruitment of immune cells and pregnancy failure. Furthermore, the enhanced expression of CCRL2 protein in the tissue may result in further chemerin level reduction. In pigs, the conceptuses initiate an acute-phase inflammatory response during implantation and placental attachment; therefore, PGF2α could inhibit chemerin secretion during the maternal recognition of pregnancy and implantation to counteract its anti-inflammatory effects and establish a supportive environment for gestation [56].
4. Materials and Methods4.1. Animals and Tissue Collection
Twenty-five mature crossbred gilts (Large White × Polish Landrace, 130–140 kg, of weight and age of 7–8 months) were randomly assigned to one of the following experimental groups (n = 5 per group): Gilts on days 10 to 11 (transuterine migration of embryos), 12 to 13 (maternal recognition of pregnancy), 15 to 16 (beginning of implantation), and 27 to 28 (end of implantation) of pregnancy, and days 10 to 11 of the estrous cycle (mid-luteal phase; the activity of corpora lutea (CL) is comparable to the CL activity during pregnancy). The quantity of CLs and embryos are specified in the Supplementary Table S1. Animals were maintained and fed in accordance with current Polish standards. The daily dose of compound feed was approximately 2.7 kg/gilt at 32.4 MJ of metabolizable energy intake (12 MJ per kg of feed). The feed contained 135 g/kg of total digestible protein, 5.4 g/kg methionine and cystine, 7.3 g/kg lysine, 2.1 g/kg tryptophan, 4.8 g/kg threonine, 5.7 g/kg total phosphorus, 8.9 g/kg calcium, 1.7 g/kg sodium, 10% fiber, and the addition of other macro- and microelements. All individuals were given access to fresh water and forage ad libitum. Cyclic gilts were monitored daily for estrus behavior in the presence of boar. The day of the onset of the second estrus was recognized as day 0 of the cycle. The phase of the estrus cycle was confirmed on the basis of the ovary morphology [57]. The inseminations were conducted by natural mating with the use of the same, crossbreed boar. The insemination was carried out on the first or second day of the estrous cycle, and the first day after coitus was counted as a first day of gestation. The phase of gestation was further confirmed based on the conceptuses morphology [58]. Additionally, the phase of the estrous cycle and pregnancy was confirmed by determining the level P4, as described in Nitkiewicz et al. [59] and Dobrzyn et al. [60]. The concentrations of P4 in the porcine plasma and uterine luminal flushing are detailed in the Supplementary Table S1. The post-mortem-obtained uteri were transported immediately in ice-cold PBS supplemented with antibiotic-antimycotic solution (Sigma-Aldrich, Saint Louis, MO, USA) for an in vitro tissue culture procedure. Figure 9 presents the experimental design scheme.
4.2. Endometrial Explant Cultures
The in vitro cultures of endometrial explants were conducted as described by Smolinska et al. [61]. In order to investigate the impact of PGE2 and PGF2α on the chemerin system expression, after preincubation (2 h) in phenol-red free medium M199 (Sigma-Aldrich, USA), explants were incubated in the presence of PGE2 (100, 250, 500 ng/mL; Sigma-Aldrich, USA), PGF2α (100, 250, 500 ng/mL; Sigma-Aldrich, USA), or without any treatment (control group) for 24 h (37 °C, 95% O2, 5% CO2). The doses of PGE2 and PGF2α were chosen based on the work of Morgan et al. [62] and Gregoraszczuk and Michas [63]. Morgan et al. measured PGF2α levels in porcine uterine flushing on day 11 of gestation under exposure to estradiol valerate. The concentration of PGF2α in the control group was 198 ng/mL on the above-mentioned day. Since both PGs are secreted in a pulsatile manner and their concentrations are not constant, their physiological levels could be determined in the range of 100–500 ng/mL. The cultures were run in five separate experiments per group (n = 5, one gilt per each experiment) and each treatment combination was prepared in duplicates. The lactate dehydrogenase (LDH) activity measurement after preincubation and incubation periods was used to define the viability of tissue explants. The analysis was performed using Liquick Cor-LDH kit (Cormay, Poland) in accordance with the manufacturer’s instructions. The obtained LDH activity in the culture media was compared to the enzyme activity in the fully disintegrated tissue (maximal LDH release, positive control). The mean activity of LDH in the culture media after 24 h of incubation was 184 ± 14 U/L (0.93% of maximal LDH release).
4.3. Total RNA Isolation and Reverse Transcription
Total RNA from endometrial tissue explants was isolated using TRI Reagent® RNA Isolation Reagent (Sigma-Aldrich) following the producer’s instructions. The quantity and purity of the obtained RNA samples were inspected spectrophotometrically using Infinite M200 Pro (Tecan, Mannedorf, Switzerland). One microgram of each RNA sample was reverse transcribed (RT) with the use of Omniscript RT Kit (Qiagen, Germantown, MD, USA) in the presence of 0.5 μg oligo(dT)15 (Roche, Basel, Switzerland) in a final volume of 20 μL. The RT reaction was conducted at 37 °C for 1 h and was terminated by the incubation at 93 °C for 5 min.
4.4. Quantitative Real-Time PCR Analysis
Quantitative real-time PCR (qPCR) analysis was carried out using Power SYBR Green Master Mix (Applied Biosystems, Carlsbad, CA, USA) as described by Smolinska et al. [14]. Specific primer pairs used to amplify parts of chemerin, CMKLR1, GPR1, CCRL2, cyclophilin (PPIA), and β-actin (ACTB) genes are included in Table 1. The constitutively expressed genes PPIA and ACTB were used as the internal controls to verify the method. Our preliminary studies revealed that the endometrial PPIA and ACTB expression was stable during the estrous cycle and pregnancy, as well as with and without treatments. Quantitative real-time PCR reaction mixtures contained: cDNA, primers, Power SYBR Green PCR Master Mix (12.5 μL; Applied Biosystems), and RNase-free water (to the final volume of 25 μL). In negative controls, the cDNA was substituted by water, or RT was not performed before qPCR. All reactions were run in duplicates. The specificity of the reaction was confirmed at the end of the run by the analysis of the melting-curve. The purity of the amplification product was confirmed by agarose gel electrophoresis. The calculation of chemerin and chemerin receptors genes’ relative expression was conducted with the use of the comparative cycle threshold method (ΔΔCT) and normalized using the geometrical means of the reference genes Ct values.
4.5. Enzyme-Linked Immunosorbent Assay (ELISA) of Chemerin
Chemerin concentrations in the culture media were determined using a commercial ELISA kit (FineTest, Wuhan, China) following the manufacturer’s protocol. The range of standard curve was 0.156–10 ng/mL. The sensitivity of the assay was approximately 0.1 ng/mL. The sensitivity of the assays was defined as the lowest protein concentration that could be differentiated from zero samples. Absorbance was measured at 450 nm with the use of Infinite M200 PRO reader with Tecan i-control software (Tecan, Switzerland). The data were linearized by plotting the log of chemerin concentration versus the log of the optical density, and the best fit line was determined by regression analysis. Intra- and inter-assay coefficients of variation of the assay were 3.22% ± 0.39 and 8.1%, respectively.
The basal concentrations of chemerin in the uterine luminal flushing were determined by [23] and are detailed in the Supplementary Table S1.
4.6. Protein Isolation and Western Blotting
The endometrial tissue explants were homogenized in T-PER Tissue Protein Extraction Reagent (Thermo Fischer Scientific, Waltham, MA, USA) in the presence of peptidase and phosphatase inhibitors (Sigma-Aldrich). The lysates were cleared by double centrifugation at 10,000× g for 5 min. The protein concentrations were measured with the use of Bradford dye-binding procedure with the dilutions of bovine serum albumin (BSA) as standards.
Western blotting analysis was performed as described by Smolinska et al. [14]. Endometrial tissue lysates (40 μg) from control, PGE2-, and PGF2α-treated samples were resolved by SDS-PAGE electrophoresis in the 12.5% polyacrylamide gels and transferred onto PVDF membrane (Whatman, USA). Subsequently, membranes were blocked for 1 h in Tris-buffered saline Tween-20 containing 5% skimmed milk powder. After blocking, membranes were incubated for 12 h at 4 °C with rabbit polyclonal antibodies to CMKLR1 (1:1000; ab230442; Abcam, UK), mouse polyclonal antibodies to GPR1 (1:500; ab169331; Abcam, UK), rabbit polyclonal antibodies to CCRL2 (1:600; ab85224; Abcam, UK), and rabbit polyclonal antibodies to actin (1:200; A2066; Sigma-Aldrich, USA). Actin was used as a control to normalize the results of chemerin receptors protein concentration. Subsequently, to identify immunoreactive products, membranes were incubated for 1.5 h at RT with goat anti-rabbit IgG for CMKLR1, CCRL2, and actin (1:5000; sc-2054; Santa Cruz, USA), and goat anti-mouse IgG for GPR1 (1:2500; 115-035-003; Jackson ImmunoResearch Laboratories, Baltimore Pike, PA, USA) conjugated with horseradish peroxidase (HRP). For negative control blots, primary antibodies were substituted by nonspecific fetal calf serum (MP Biomedicals, Santa Ana, CA, USA). Immunocomplexes were visualized with Immobilon Western Chemiluminescent HRP Substrate (Merck Millipore, Kenilworth, NJ, USA) on the G: Box EF Gel Documentation System (Syngene, Cambridge, UK). The same protocol was performed in relation to the adipose tissue used as the positive controls. The results were quantified by densitometric analysis of immunoblots with the use of Image Studio Lite version 5.2 software (LI-COR, Lincoln, NE, USA). Data were expressed as the ratio of chemerin receptors proteins relative to actin protein in arbitrary optical density units.
4.7. Statistical Analysis
Statistica software (StatSoft Inc., Tulsa, OK, USA) was used to perform the statistical analysis of results. All variables were analyzed using descriptive statistics (mean, standard deviation, sample minimum, and sample maximum). To determine the differences in genes expression and proteins concentration between control groups and PGE2 or PGF2α treated groups, a one-way ANOVA followed by Duncan’s post-hoc test were used. Results were presented as the means ± S.E.M. from five independent observations. Values for p < 0.05 were considered statistically significant.
5. Conclusions
This is the first study to demonstrate that prostaglandins affect the expression of the chemerin system in the porcine uterus during early gestation. The presented results indicate that the analyzed prostaglandins exert different effects on the endometrial chemerin system expression, which depend on the dose of the used PGs. Furthermore, we revealed that the response pattern for PGs is specific for each of the studied pregnancy periods. The above findings confirm our hypothesis assuming the regulatory influence of PGs on chemerin system expression in the porcine early-pregnant uterus. In the light of the existing knowledge, the presented findings also suggest that the chemerin system could be an important element of the regulatory mechanism responsible for the proper course of gestation, and that its expression depends on the local hormonal microenvironment.
Supplementary Materials
Supplementary materials can be found at https://www.mdpi.com/1422-0067/21/15/5213/s1. Supplementary Table S1. Progesterone and chemerin concentrations, as well as and number of corpora lutea (CL) and embryos in the research groups. Supplementary Figure S1. The standard curve of Chemerin ELISA kit used in the study.
Click here for additional data file.
Author Contributions
Conceptualization, N.S., T.K., K.D., M.K.; methodology, N.S., M.K., and K.D.; validation, M.K., K.D., E.R.; formal analysis, N.S., M.G., K.K., E.Z.; investigation, M.K., K.D., N.S., E.R., K.K., E.Z.; resources, K.B., G.K., K.S.; data curation, M.K., and B.K.; writing—original draft preparation, M.K. and K.D.; writing—review and editing, M.K., N.S., T.K., B.K., K.D.; supervision, N.S. and T.K.; project administration, N.S.; funding acquisition, N.S. All authors have read and agreed to the published version of the manuscript.
Funding
This research was supported by the National Science Centre (projects no: 2017/25/B/NZ9/00040).
Conflicts of Interest
The authors declare no competing financial interests.
Animal Welfare Statement
The authors confirm that the ethical policies of the journal, as noted on the journal’s author guidelines page, have been adhered to. All studies were carried out in accordance with the Polish Act on the protection of animals used for scientific or educational purposes of the 15th of January 2015 (Polish Journal of Law of 2015 item 266), as well as directive 2010/63/EU of the European Parliament of the 22nd of September 2010 on the protection of animals used for scientific purposes.
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The influence of prostaglandin E2 (PGE2; 100, 250, 500 ng/mL) on chemerin mRNA expression (A,C,E,G,I) and chemerin protein secretion (B,D,F,H,J) in the porcine endometrial tissue explants on days 10 to 11, 12 to 13, 15 to 16 and 27 to 28 of the pregnancy, and on days 10 to 11 of the estrous cycle. The gene expression was determined by quantitative real-time PCR. The protein secretion was determined by an ELISA test. Results are reported as the means ± S.E.M. (n = 5). Bars with different superscripts differ (p < 0.05).
The influence of prostaglandin E2 (PGE2; 100, 250, 500 ng/mL) on chemokine-like receptor 1 (CMKLR1) mRNA (A,C,E,G,I) and protein (B,D,F,H,J) expression in the porcine endometrium on days 10 to 11, 12 to 13, 15 to 16, and 27 to 28 of the pregnancy, and on days 10 to 11 of the estrous cycle. The gene expression was determined by quantitative real-time PCR. The protein concentration was determined by the western blotting analysis; upper panels: Representative immunoblots; lower panels: Densitometric analysis of CMKLR1 protein relative to actin protein. Results are reported as the means ± S.E.M. (n = 5). Bars with different superscripts differ (p < 0.05).
The influence of prostaglandin E2 (PGE2; 100, 250, 500 ng/mL) on G protein-coupled receptor 1 (GPR1) mRNA (A,C,E,G,I) and protein (B,D,F,H,J) expression in the porcine endometrium on days 10 to 11, 12 to 13, 15 to 16, and 27 to 28 of the pregnancy, and on days 10 to 11 of the estrous cycle. The gene expression was determined by quantitative real-time PCR. The protein concentration was determined by the western blotting analysis; upper panels: Representative immunoblots; lower panels: Densitometric analysis of GPR1 protein relative to actin protein. Results are reported as the means ± S.E.M. (n = 5). Bars with different superscripts differ (p < 0.05).
The influence of prostaglandin E2 (PGE2; 100, 250, 500 ng/mL) on C-C motif chemokine receptor like 2 (CCRL2) mRNA (A,C,E,G,I) and protein (B,D,F,H,J) expression in the porcine endometrium on days 10 to 11, 12 to 13, 15 to 16, and 27 to 28 of the pregnancy, and on days 10 to 11 of the estrous cycle. The gene expression was determined by quantitative real-time PCR. The protein concentration was determined by the western blotting analysis; upper panels: Representative immunoblots; lower panels: Densitometric analysis of CCRL2 protein relative to actin protein. Results are reported as the means ± S.E.M. (n = 5). Bars with different superscripts differ (p < 0.05).
The influence of prostaglandin F2α (PGF2α; 100, 250, 500 ng/mL) on chemerin mRNA expression (A,C,E,G,I) and chemerin protein secretion (B,D,F,H,J) in the porcine endometrial tissue explants on days 10 to 11, 12 to 13, 15 to 16, and 27 to 28 of the pregnancy, and on days 10 to 11 of the estrous cycle. The gene expression was determined by quantitative real-time PCR. The proteins secretion was determined by an ELISA test. Results are reported as the means ± S.E.M. (n = 5). Bars with different superscripts differ (p < 0.05).
The influence of prostaglandin F2α (PGF2α; 100, 250, 500 ng/mL) on chemokine-like receptor 1 (CMKLR1) mRNA (A,C,E,G,I) and protein (B,D,F,H,J) expression in the porcine endometrium on days 10 to 11, 12 to 13, 15 to 16, and 27 to 28 of the pregnancy, and on days 10 to 11 of the estrous cycle. The gene expression was determined by quantitative real-time PCR. The protein concentration was determined by the western blotting analysis; upper panels: Representative immunoblots; lower panels: Densitometric analysis of CMKLR1 protein relative to actin protein. Results are reported as the means ± S.E.M. (n = 5). Bars with different superscripts differ (p < 0.05).
The influence of prostaglandin F2α (PGF2α; 100, 250, 500 ng/mL) on G protein-coupled receptor 1 (GPR1) mRNA (A,C,E,G,I) and protein (B,D,F,H,J) expression in the porcine endometrium on days 10 to 11, 12 to 13, 15 to 16, and 27 to 28 of the pregnancy, and on days 10 to 11 of the estrous cycle. The gene expression was determined by quantitative real-time PCR. The protein concentration was determined by the western blotting analysis; upper panels: Representative immunoblots; lower panels: Densitometric analysis of GPR1 protein relative to actin protein. Results are reported as the means ± S.E.M. (n = 5). Bars with different superscripts differ (p < 0.05).
The influence of prostaglandin F2α (PGF2α; 100, 250, 500 ng/mL) on C-C motif chemokine receptor like 2 (CCRL2) mRNA (A,C,E,G,I) and protein (B,D,F,H,J) expression in the porcine endometrium on days 10 to 11, 12 to 13, 15 to 16, and 27 to 28 of the pregnancy, and on days 10 to 11 of the estrous cycle. The gene expression was determined by quantitative real-time PCR. The protein concentration was determined by the western blotting analysis; upper panels: Representative immunoblots; lower panels: Densitometric analysis of CCRL2 protein relative to actin protein. Results are reported as the means ± S.E.M. (n = 5). Bars with different superscripts differ (p < 0.05).
Experimental design graph.
ijms-21-05213-t001_Table 1
Characteristics of the primers used in the study of gene expression in porcine endometrial explants. RARRES2: Chemerin; CCRL2: C-C motif chemokine receptor like 2; CMLKR1: chemokine-like receptor 1; GPR1: G protein-coupled receptor 1; PPIA: Cyclophilin; ACTB: β-actin; F: Forward; R: Reverse.
\\n\"\n]"}}},{"rowIdx":475,"cells":{"data":{"kind":"list like","value":["\nInt J Environ Res Public HealthInt J Environ Res Public HealthijerphInternational Journal of Environmental Research and Public Health1661-78271660-4601MDPI32722623PMC743213210.3390/ijerph17155374ijerph-17-05374ArticleReduced Gray Matter Volume and Risk of Falls in Parkinson’s Disease with Dementia Patients: A Voxel-Based Morphometry Studyhttps://orcid.org/0000-0003-3883-2104ChengKai-Lun123†LinLi-Han4†https://orcid.org/0000-0002-6386-2850ChenPo-Cheng5https://orcid.org/0000-0002-3875-6677ChiangPi-Ling4ChenYueh-Sheng4ChenHsiu-Ling4ChenMeng-Hsiang4ChouKun-Hsien67LiShau-Hsuan8LuCheng-Hsien9LinWei-Che4*Department of Medical Imaging, Chung Shan Medical University Hospital, Taichung 402, Taiwan; chengkailun108@gmail.comSchool of Medical Imaging and Radiological Sciences, Chung Shan Medical University, Taichung 402, TaiwanDepartment of Veterinary Medicine, National Chung Hsing University, Taichung 402, TaiwanDepartment of Diagnostic Radiology, Kaohsiung Chang Gung Memorial Hospital, and Chang Gung University College of Medicine, Kaohsiung 833, Taiwan; cimetidine.tw@yahoo.com.tw (L.-H.L.); lovage@cgmh.org.tw (P.-L.C.); yssamchen@gmail.com (Y.-S.C.); suring.tw@gmail.com (H.-L.C.); sperfect1101@gmail.com (M.-H.C.)Department of Physical Medicine and Rehabilitation, Kaohsiung Chang Gung Memorial Hospital, and Chang Gung University College of Medicine, Kaohsiung 833, Taiwan; ben0922852179@gmail.comBrain Research Center, National Yang-Ming University, Taipei 112, Taiwan; dargonchow@gmail.comInstitute of Neuroscience, National Yang-Ming University, Taipei 112, TaiwanDepartment of Oncology and Hematology, Kaohsiung Chang Gung Memorial Hospital, and Chang Gung University College of Medicine, Kaohsiung 833, Taiwan; lee0624@adm.cgmh.org.twDepartment of Neurology, Kaohsiung Chang Gung Memorial Hospital, and Chang Gung University College of Medicine, Kaohsiung 833, Taiwan; chlu99@cgmh.org.twCorrespondence: u64lin@yahoo.com.tw; Tel.: +886-7-731-7123
These authors contributed equally to this work.
2672020820201715537415620202372020© 2020 by the authors.2020Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Purpose: Risk of falls is a common sequela affecting patients with Parkinson’s disease (PD). Although motor impairment and dementia are correlated with falls, associations of brain structure and cognition deficits with falls remain unclear. Material and Methods: Thirty-five PD patients with dementia (PDD), and 37 age- and sex-matched healthy subjects were recruited for this study. All participants received structural magnetic resonance imaging (MRI) scans, and disease severity and cognitive evaluations. Additionally, patient fall history was recorded. Regional structural differences between PDD with and without fall groups were performed using voxel-based morphometry processing. Stepwise logistic regression analysis was used to predict the fall risk in PDD patients. Results: The results revealed that 48% of PDD patients experienced falls. Significantly lower gray matter volume (GMV) in the left calcarine and right inferior frontal gyrus in PDD patients with fall compared to PDD patients without fall were noted. The PDD patients with fall exhibited worse UPDRS-II scores compared to PDD patients without fall and were negatively correlated with lower GMV in the left calcarine (p/r = 0.004/−0.492). Furthermore, lower GMV in the left calcarine and right inferior frontal gyrus correlated with poor attention and executive functional test scores. Multiple logistic regression analysis showed that the left calcarine was the only variable (p = 0.004, 95% CI = 0.00–0.00) negatively associated with the fall event. Conclusions: PDD patients exhibiting impaired motor function, lower GMV in the left calcarine and right inferior frontal gyrus, and notable cognitive deficits may have increased risk of falls.
Common symptoms of Parkinson’s disease with dementia (PDD), including tremors, bradykinesia, rigidity, postural instability, memory impairment, and visual hallucinations can result in an increased risk of falls [1]. Postural instability, especially during gait initiation, is considered one of the primary factors leading to falls in patients with PDD. The instability worsens with visual impairment, as visual input contributes significantly to the maintenance of upright posture when walking. Other motor network disruptions, and cognitive decline may also affect patient balance, further increasing the risk of falls. Meanwhile, repeated falls can result in fractures requiring hospitalization, and in more serious cases, may be fatal, particularly in the elderly and female [2]. Furthermore, long-term complications include atrophy caused by disuse, lifestyle disruptions caused by fear of falls, or possible need for institutionalization [3]. To date, however, the relationship between disease severity or cognitive function and risks of falls in PDD patients has not yet to be further investigated.
In recent years, the application of neuroimaging in exploring the etiology of neurodegenerative diseases has gained in popularity, with Parkinson’s disease (PD) being one of the most commonly studied disorders. These imaging methods have contributed significantly to a broadening of our understanding of the disease, beyond simply impaired dopaminergic transmissions [4]. In fact, some neuroimaging studies have reported alterations in networks related to motor and cognitive symptoms [5,6,7,8,9,10]. Radiotracer imaging, such as positron emission tomography (PET) or single-photon emission computed tomography (SPECT), can be used to study the dopaminergic and other neurochemical systems [11]; while widely available magnetic resonance imaging (MRI) has demonstrated effectiveness at measuring anatomical changes of the brain, with several MRI studies reporting cortical atrophy in Parkinson’s patients with and without dementia [12,13,14,15], decreasing gray matter volume (GMV) of left calcarine in patients with impaired cognition in Huntington’s disease [16], or that of right inferior frontal gyrus in unaffected siblings of schizophrenia patients [17]. However, clarification of the pathophysiology within the brain and the association with risks of fall in PDD patients require further investigation.
In this study, we aim to relate the clinical disease severity and cognitive function of patients with PDD to the MRI findings. We hypothesized that PDD patients with histories of falls demonstrated relatively diminished physical and cognitive functions, which would be associated with distinct brain regions, different from those regions affected in PDD patients without histories of falls.
2. Materials and Methods2.1. Subjects
Thirty-five patients with PDD (8 men and 27 women; mean age, 64.00 ± 5.85 years) and 37 age- and sex-matched healthy subjects (10 men and 27 women; mean age, 63.14 ± 5.34 years) were recruited for the study. Patients received MRI scans and neuropsychological tests (NPT) at the time of Parkinson disease diagnosis, while the control group received scans and tests at the initial enrollment in the study. Exclusion criteria for this study included a history of neurologic or psychiatric illness, the presence of developmental disorders, use of medication for unrelated conditions, and head injuries. The 35 PDD patients were further divided into two subgroups, based on the presence or absence of a history of falls (18 in the non-fall group, 3 men and 15 women; mean age, 62.78 ± 5.61 years; and 17 in the fall group, 5 men and 12 women; mean age, 65.29 ± 5.99 years). The Chang Gung Memorial Hospital Institutional Review Committees approved the study (IRB is 103-6906A3), and written informed consents were obtained from all subjects.
2.2. Assessment of Clinical Disease Severity
The clinical features recorded were age at enrollment (or age at the time of the first reported symptom attributable to the disease), education level, and history of falls. Data on falls were prospectively collected from either the clinical records starting at enrollment or information provided by patients during the study period. The Morse fall scale assessed the risk of falling for hospital in-patients or those in long-term care and included considerations such as presence/absence of intravenous therapy [18]. Since all patients were enrolled in the study from the Neurology Out-patient Clinic, the scale was slightly modified by deletion of the item, “intravenous therapy or not”. The severity of PDD was graded according to the scores of the Unified Parkinson’s Disease Rating Scale (UPDRS), the Hoehn and Yahr staging, and activity of daily living assessment [19,20]. An experienced neurology nursing specialist who was blinded to the patients’ clinical and biochemical data was trained to measure these functional scores at the time of enrollment.
2.3. Neuropsychological Tests (NPT)
The NPT battery focusing on attention, executive function, speech and language, memory, and visuospatial functions were performed by a clinical psychologist blinded to each patient’s status. Attention was evaluated by letter number sequencing and digit span score from the Wechsler Adult Intelligence Scale-III (WAIS-III) [21], and by orientation score and attention score from the Cognitive Ability Screening Instrument (CASI) [22]. Executive functions were assessed using arithmetic, picture arrangement, digit symbol coding, and matrix reasoning scores from the WAIS-III, as well as abstract thinking scores from the CASI. Memory functions were assessed using short- and long-term memory scores from the CASI, and information scores from the WAIS-III. Speech and language ability were assessed using vocabulary, comprehension, and similarity scores from the WAIS-III, and language and semantic fluency scores from the CASI. Visuospatial functions were assessed using picture completion and block design scores from the WAIS-III and drawing score from the CASI. Raw scores for each NPT were transformed to z scores based on normative data. Cognitive domain scores were calculated by averaging z scores for NPT within specific domains, thereby accounting for any unequal distribution of tests per domain [23,24]. Impairment was defined as a z score ≤ 2 standard deviation (SD) compared to normal subjects for a given domain. PDD was defined as: (1) a diagnosis of idiopathic PD; (2) PD developed prior to the onset of dementia; (3) Mini-Mental State Examination (MMSE) < 26; (4) impairment in at least two of the neuro-psychological battery domains [25].
2.4. MRI Image Acquisition
All MRI scans were performed on the same 3-Tesla GE Signa whole-body MRI system (General Electric Healthcare, Milwaukee, WI, USA), equipped with an eight-channel head coil. A T1-weighted three-dimensional fluid-attenuated inversion-recovery fast spoiled gradient-recalled echo pulse sequence was used with the following imaging parameters for each participant and time-point: TR/TE/TI = 9.5/3.9/450 ms; flip angle, 15; NEX = 1; matrix size = 512 × 512; voxel size = 0.47 × 0.47 × 1.3 mm3; and 110 axial slices. An experienced neuroradiologist, blinded to the participants’ status, visually examined all the MRI scans to verify they were free from gross anatomical abnormalities. Subsequently, none of the participants in the study were excluded.
2.5. Voxel-Based Morphometry Processing
All structural MRI were post-processed by the same neuroradiologist, unaware of patients’ information. Voxel-based morphometry (VBM) was performed using Statistical Parametric Mapping software (SPM12) [1] running on Matlab 2015b (Matworks, Natick, MA, USA). The details of image processing and analysis of regional GMV differences were described in the Supplementary Materials (Method: Voxel-based morphometry processing and analysis).
2.6. Statistical Analysis2.6.1. Analysis of Demographic Data between Groups
Statistical analysis was performed using the statistics computer software SPSS 18 (SPSS Inc., Chicago, IL, USA). Descriptive statistics were expressed as mean ± SD for continuous variables, and as numbers for categorical variables. The clinical data of PDD patients and normal subjects were analyzed by chi-square test or independent t test where appropriate. One-way analysis of covariance (ANCOVA) was used to compare NPT with age, sex, and education as covariates. The threshold for statistical significance was p < 0.05.
2.6.2. Relationships among Disease Severity, Cognition Function, and Gray Matter Volume
For further relationship analysis, the regional GMV was extracted from the clusters with significant statistical difference from the PDD group comparisons.
Partial correlation analysis, adjusted for age and sex, was performed to correlate GMVs showing significant differences in the PDD with fall and without fall groups with disease severity and NPT. Significance was set at a Bonferroni corrected p < 0.05, accounting for multiple region of interest (ROI) comparisons [26].
Stepwise logistic regression analysis with forward method was used to evaluate the relationships among disease severity, NPT, and areas with significant GMV differences in the PDD with fall and without fall groups, with adjustments made for other potential confounding factors.
3. Results3.1. Clinical Characteristics, NPT, and Disease Severity among Groups
The demographic characteristics and NPT of PDD patients and normal controls are shown in Table 1. Except for age and sex, there were significant differences in education duration, MMSE, and NPT between the PDD patients and normal controls. All patients, regardless of fall status, performed significantly worse than those in the control group in all NPT (p < 0.05) (Table 1). There were no significant differences in MMSE and NPT between the PDD with fall group and the without fall group.
The disease severity of PDD patients with and without fall groups are shown in Table 2. Except for UPDRS-II (F = 2.540; p = 0.028), there were no significant differences in UPDRS, UPDRS I, UPDRS III, Hoehn and Yahr staging, activity of daily living assessment, and Morse fall scale between the PDD with fall and without fall groups.
3.2. Regional Gray Matter Volume (GMV) Aberrations among Groups
The regions with significant GMV differences are presented in Table 3.
3.2.1. Comparison between all PDD Patients and Normal Controls
All PDD patients exhibited lower NPT results, and significantly lower GMV in the right superior temporal gyrus, left putamen, bilateral cerebellum, bilateral middle frontal gyri, right fusiform, bilateral precentral gyri, left superior frontal gyrus, left middle occipital gyrus, and left superior medial frontal gyrus (uncorrected p < 0.001) compared to the control group.
3.2.2. Comparison between PDD without Fall and Control Groups, and between PDD with Fall and Control Groups
By using a p-value of 0.001 (uncorrected), the PDD with fall group displayed lower GMV in the left middle temporal gyrus, left cuneus, left medial frontal gyrus, left middle occipital gyrus, bilateral inferior parietal gyri, right middle occipital gyrus, left middle temporal gyrus, right cingulate gyrus, and right anterior cingulate. Meanwhile, the PDD without fall group had lower GMV in the right cerebellum, and right superior temporal gyrus compared to the control group (uncorrected p < 0.001).
3.2.3. Comparison between the PDD with Fall and without Fall Groups
Based on a nonstationary cluster extent threshold of p < 0.05 corrected for multiple comparisons with FWE, there was a significantly lower GMV in the left calcarine and right inferior frontal gyrus in PDD patient with fall group compared to PDD patients without fall group.
By using an uncorrected p-value of 0.001, PDD with fall group showed significantly lower GMV in the left calcarine, right inferior frontal gyrus, left inferior temporal gyrus, right precentral gyrus, left inferior frontal gyrus, right middle temporal gyrus, right inferior frontal gyrus, left Rolandic operculum, left caudate head, and right middle occipital gyrus compared to PDD without fall group (Figure 1).
3.3. Relationship among Disease Severity Profiles, NPT, and Extracted Regional GMV from PDD Patients
UPDRS-II was the only disease severity score showing significant difference between the PDD with fall and without fall groups; moreover, it exhibited a significantly negative correlation with a low GMV in the left calcarine (p/r = 0.004/−0.492) (Figure 2).
There are positive correlations between NPT (attention and executive function) and the significantly lower GMV in PDD with fall group compared to PDD without fall group. The poor attention domain correlated with low GMV in the left calcarine (p/r = 0.002/0.520) and right inferior frontal gyrus (p/r = 0.003/0.503); while the low executive function correlated with low GMV in the left calcarine (p/r = 0.007/0.460) (Figure 2).
Multiple logistic regression analysis with forward method showed that the left calcarine was the only variable (p = 0.004, B: −37.6, 95% CI = 0.00−0.00) negatively associated with the fall event, meaning that lower GMV of the left calcarine could predict the occurrence of a fall event.
4. Discussion4.1. Brief Summary and Potential Implications
There were significant differences in education level and the NPT between the PDD patients and controls, but no significant differences in the NPT between the PDD patients with and without fall groups. In the assessment of disease severity, the PDD with fall group only had significantly higher scores in UPDRS-II compared to the PDD without fall group. In the imaging study, all PDD patients showed significantly lower GMV in the right superior temporal gyrus, left putamen, bilateral cerebellum, bilateral middle frontal gyri, right fusiform, bilateral precentral gyri, left superior frontal gyrus, left middle occipital gyrus, and left superior medial frontal gyrus compared to the control group. There was a significantly lower GMV in left calcarine, and right inferior frontal gyrus in the PDD with fall group compared to the PDD without fall group. In terms of relationships, UPDRS-II showed a negative correlation with GMV in the left calcarine. Attention function was positively correlated with the GMV in the left calcarine, and right inferior frontal gyrus, while executive function was positively correlated with the GMV only in the left calcarine. Of note, the regression analysis showed that the GMV of the left calcarine was negatively associated with fall events.
Studies have reported the use of NPT to assess clinical characteristics of PDD patients [27,28,29]. Although differences existed in the evaluation tools, declining functions in many domains of NPT were noted in PDD patients, similar to the results reported herein. However, the specific domain accounting for falls has yet to be clarified. Although Papapetropoulos et al. [30] reported that PDD was one of the independent risk factors attributing to falls, no NPT were performed in the study. Another study found that mild cognitive impairment was related to increasing risk of fall, but the cognitive functions of the participants were rated by Clinical Dementia Rating Scale, which focused on the ability for self-care [31]. Meanwhile, Allcock et al. [32] reported on a relationship between impaired attention and fall frequency due to increasing difficulty in performing concurrent tasks and compensatory movements to prevent falls in PD patients. One systematic review also supported the concept that cognitive impairment, especially in the domains of attention and executive function, can disturb multitasking and thereby result in falls [33]. In this study, complete NP tests were assessed for all participants, and the PDD patients had significantly lower z scores compared to the controls. Although the z scores of most domains of the NPT in the PDD with fall group appeared to be lower than that of the PDD without fall group, no statistical difference was noted, possibly attributable to the small sample size.
In this study, multiple disease severity scales, including UPDRS, UPDRS I, II, III, Hoehn and Yahr stage, activity of daily living, and Morse fall scale, were assessed for all PDD participants. Although the disease severity in the PDD with fall group appeared to be severer than that of the PDD without fall group, no statistical difference was noted except for UPDRS-II, possibly attributable to the small sample size or similarity of disease severity of PDD participants. One systematic review reported that disease severity as measured by Hoehn and Yahr stage or by the UPDRS was found to be significantly associated with recurrent falls in PD [34]. Matinolli et al. [35] reported that UPDRS-II was a significant risk factor for predicting fall in PD, similar to the results reported herein. The UPDRS-II, specific for motor aspects of experiences of daily living, has 13 self-assessed items, including freezing when walking, a phenomenon closely related to fall. These results demonstrate UPDRS-II might be a tool to assess the fall risk in PD.
Many structural MRI studies have reported on the existence of cortical atrophy in PD patients, particularly atrophy of the hippocampus and the amygdala in patients with PD with or without dementia [12,13,14,15]. Recently, VBM brain MRI has been used in the study of patients with PD. These studies have demonstrated that patients with PDD present with limbic and widespread neocortical gray matter loss, while Parkinson’s patients without dementia mainly present with atrophy in the frontal and temporal regions [36,37,38,39]. Cristina et al. had found that PDD with visual hallucination presented gray matter volumetric reductions in the left orbitofrontal lobe when compared to normal subjects [2]. In the present study, the GMV of most neocortex regions of the PDD patients were less than those of the control group. The PDD with fall group mainly showed significantly decreased GMV in the left frontal, temporal, and occipital lobes, compared to the control group, similar to the results of other studies. Prior to this study, no imaging study had focused specifically on regions of the brain associated with fall risk. However, this study finds that the PDD with fall group had significantly less GMV in the left calcarine and right inferior frontal gyrus than that of the PDD without fall group. This finding is similar to a study comparing the brain structures of PD patients with and without rapid eye movement sleep behavior disorder [40]. Furthermore, the association between sleep disorders and falls in PD patients has been reported in a separate case-control study [41]. It is believed that sleep disorders can increase the risk of falls via several mechanisms, such as poor attention, slower reaction time, and impaired balance.
The calcarine region, part of the occipital lobe, is where the primary visual cortex is concentrated. The relationship between visuospatial ability and motor function has been demonstrated in previous studies [42,43]. Consistent with a previous study [44], this study demonstrates a negative correlation in PD patients between the GMV of the left calcarine and the UPDRS-II, which tests and rates the motor aspects associated with daily activities. Moreover, we identify associations between the GMV of the left calcarine and attention, implying a relationship between visual function and attention in PDD patients. Stuart et al. [45] conducted a cohort study and built a structural equation model showing associations between attention, visual function, saccade frequency, and gait impairment in PD patients. The abovementioned factors may result in poor executive function. Of note, the association between the GMV of the right inferior frontal gyrus and executive function is exhibited in this study, consistent with a previous imaging study which reported on the important role of the right inferior frontal gyrus in executive control [46]. Taken together, the results of the present study and separate studies suggest that visual function and specific domains of cognitive impairment play critical roles in fall events.
4.2. Limitations
Although this study demonstrates that specific domains of cognitive impairment and brain regions are reported to be related to falls in PDD patients, some limitations need to be addressed. First, this study applies a cross-sectional design, and further causal relationships should be investigated by future cohort study. Second, the small sample size limits the statistical power of demonstrating differences among groups. Third, the male to female odds ratio of developing dementia in PD patients between 65 and 69 years old has been reported to be 2:1 by a large cross-sectional study [47]; however, in this study, the number of female participants is higher than that of the male participants. This kind of selection bias may deviate our results from the real-world evidence, and future investigators should consider this when designing any future clinical trial.
5. Conclusions
Those PDD patients with falls may suffer from higher degrees of structural and functional degeneration of the brain, especially in the calcarine and inferior frontal regions. These brain regions play important roles in attention and visual function, and aberrations in these regions may be used to predict future risk of falls in PDD patients. The findings of this study suggest that closer attention to these comorbidities be paid in clinical practice for early intervention and prevention of fall events.
Supplementary Materials
The following are available online at https://www.mdpi.com/1660-4601/17/15/5374/s1, Method: Voxel-based morphometry processing and analysis.
Click here for additional data file.
Author Contributions
Conceptualization, K.-L.C., L.-H.L. and W.-C.L.; Data curation, K.-L.C. and C.-H.L.; Formal analysis, K.-L.C. and P.-L.C.; Funding acquisition, C.-H.L. and W.-C.L.; Methodology, P.-L.C. and K.-H.C.; Resources, Y.-S.C., M.-H.C., C.-H.L. and W.-C.L.; Software, H.-L.C. and S.-H.L.; Supervision, L.-H.L., P.-C.C., Y.-S.C., H.-L.C., M.-H.C., S.-H.L. and W.-C.L.; Writing—original draft, K.-L.C.; Writing—review & editing, P.-L.C. and W.-C.L. All authors have read and agreed to the published version of the manuscript.
Funding
The authors declare no competing financial interests. This study received funding from the National Science Council of Taiwan (MOST 106-2314-B-182A-031-MY2, to W.-C.L.) and from Chang Gang Memorial Hospital (CMRPG8H0951 and CMRPG8I0371 to W.-C.L.)
Conflicts of Interest
The authors declare no conflict of interest.
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Lower gray matter volumes (GMVs) in PDD patients with fall vs without fall, with highlighted significant areas. Compared to PDD patients without fall, PDD patients with fall showed significantly lower GMV in the left inferior temporal gyrus, right inferior frontal gyrus, left caudate head, right middle occipital gyrus, left inferior frontal gyrus, left Rolandic operculum, right inferior frontal gyrus, right middle temporal gyrus, left calcarine, and right precentral gyrus. Among these areas, the clusters of left calcarine and R inferior frontal gyrus survived from statistical threshold of FWE-corrected p < 0.05.
Correlations between NP tests (attention and executive) and UPDRS-II and regional GMV. (a) Left calcarine and attention. (b) Left calcarine and executive. (c) left calcarine and UPDRS-II. (d) Right inferior frontal gyrus and attention.
ijerph-17-05374-t001_Table 1
Demographic characteristics of PDD patients and normal controls.
Demographics
Normal Group
PDD
F +
p-Value +
With Fall
Without Fall
All Patients
Number of cases
37
17
18
35
\n
\n
Sex (n = men/women)
10/27
5 / 12
3 / 15
8/27
\n
0.630
Ages (years)
63.1 ± 5.34
65.3 ± 5.99
62.8 ± 5.61
64.0 ± 5.85
1.112
0.335
Education
9.95 ± 4.73 #
5.53 ± 4.14 #
6.72 ± 4.78
6.14 ± 4.45
6.416
0.003
MMSE
26.7 ± 2.22§#
19.2 ± 4.10 #
21.1 ± 4.30 §
20.1 ± 4.25
36.481
<0.001
\nNeuropsychological domains\n
Attention
0.46 ± 0.48 § #
−0.70 ±-0.66 #
−0.30 ± 0.72 §
−0.49 ± 0.71
5.871
<0.001
Executive function
0.43 ± 0.76 § #
−0.54 ± 0.62 #
−0.41 ± 0.65 §
−0.48 ± 0.63
1.461
<0.001
Memory
0.41 ± 0.53 § #
−0.54 ± 0.85 #
−0.34 ± 0.65 §
−0.43 ± 0.75
2.140
0.001
Speech and language
0.46 ± 0.71 § #
−0.52 ± 0.58 #
−0.50 ± 0.69 §
−0.51 ± 0.63
0.317
<0.001
Visuospatial function
0.46 ± 0.62 § #
−0.45 ± 0.77 #
−0.53 ± 0.74 §
−0.49 ± 0.74
1.054
<0.001
F+ and p\n+ represent the comparison between all PDD patients vs. the control group. Sex was compared by chi-square test. Age, education, and MMSE data were compared by independent t test. NP data were compared by ANCOVA after controlling for age, sex, and education. Data are presented as mean ± SD. # Significant differences between the control group and the PDD with fall group. § Significant differences between the control group and the PDD without fall group.
ijerph-17-05374-t002_Table 2
Disease severity of PDD patients.
Disease Severity
PD with Dementia
F +
p-Value +
With Fall
Without Fall
All Patients
\nNumber of Cases\n
17
18
35
\n
\n
Unified Parkinson’s Disease Rating Scale (UPDRS) a
57.0 ± 28.3
42.6 ± 22.9
49.6 ± 26.3
0.559
0.106
UPDRS I b
4.88 ± 2.91
3.72 ± 2.95
4.28 ± 2.95
0.040
0.250
UPDRS-II c
16.7 ± 9.98
10.5 ± 5.54
13.5 ± 8.49
2.540
0.028 *
UPDRS III d
35.4 ± 16.8
28.3 ± 16.1
31.8 ± 16.6
0.010
0.213
Hoehn and Yahr staging
2.53 ± 0.89
2.14 ± 0.98
2.33 ± 0.95
0.294
0.228
Activity of daily living
76.5 ± 15.0
80.6 ± 16.3
78.6 ± 15.6
0.022
0.446
Morse fall scale
62.9 ± 27.7
49.7 ± 26.8
56.1 ± 27.7
0.102
0.161
F + and p\n+ represent the comparison between PDD with fall group vs. PDD without fall group. a Total UPDRS score is the combined sum of parts I, II, and III. Theoretical minimum and maximum values are 0 and 176, respectively (176 represents the worst disability and 0 no disability).b I. Mentation, behavior, and mood. Theoretical minimum and maximum values are 0 and 16, respectively. (16 represents the worst disability and 0 no disability). c II. Activities of daily living (ADL). Theoretical minimum and maximum values are 0 and 52, respectively. (52 represents the worst disability and 0 no disability). d III. Motor examination. Theoretical minimum and maximum values are 0 and 108, respectively. (108 represents the worst disability and 0 no disability). UPDRS, UPDRS I/II/III, Hoehn and Yahr staging, Activity of daily living, and Morse fall scale were compared by independent t test. Data are presented as mean ± SD.* Significant differences between the PDD with fall group and PDD without fall group.
ijerph-17-05374-t003_Table 3
Regions of statistically significant lower GMV in PD with dementia patients compared to those of control.
Gray Matter Volume
Anatomical Regions
x
y
z
Cluster Size
T-Value
\nNormal > all PDD\n
\n
R superior temporal gyrus *
59
−11
0
14770
5.90
\n
L putamen *
−27
15
6
10899
5.68
\n
R cerebellum *
20
−74
−47
14753
5.54
\n
R middle frontal gyrus *
29
50
8
591
5.40
\n
L middle frontal gyrus *
−29
42
30
304
4.94
\n
R fusiform
41
−57
−18
289
4.55
\n
R precentral gyrus
53
-8
47
711
4.51
\n
L precentral gyrus
−44
6
47
646
4.42
\n
L superior frontal gyrus
−18
60
8
445
4.26
\n
L middle occipital gyrus
−33
−81
14
195
4.20
\n
L superior medial frontal gyrus
−11
4
−20
238
4.11
\n
L cerebellum
−30
−68
-36
648
3.52
\nNormal > PDD with fall\n
\n
L middle temporal gyrus *
−47
2
−35
53327
7.82
\n
L cuneus *
−5
−86
3
28263
7.14
\n
L medial frontal gyrus *
−12
39
18
2027
5.34
\n
L middle occipital gyrus
−33
−81
14
641
6.10
\n
R inferior parietal gyrus
57
−56
44
270
5.23
\n
L inferior parietal gyrus
−59
−47
45
281
4.79
\n
R middle occipital gyrus
42
−80
8
532
4.67
\n
L middle temporal gyrus
−41
−65
12
441
4.40
\n
R cingulate gyrus
14
11
44
165
3.92
\n
R anterior cingulate
14
44
3
234
3.87
\nNormal > PDD without fall\n
\n
R cerebellum
17
−74
−45
1860
4.00
\n
R superior temporal gyrus
62
2
−3
236
3.63
\nPDD with fall < PDD without fall\n
\n
L calcarine *
−9
−68
20
3735
5.97
\n
R inferior frontal gyrus *(pars triangularis)
48
20
17
333
6.01
\n
L inferior temporal gyrus
−47
3
−35
717
4.97
\n
R precentral gyrus
41
−15
38
363
4.77
\n
L inferior frontal gyrus (pars opercularis)
−47
15
15
444
4.73
\n
R middle temporal gyrus
50
-53
20
390
4.52
\n
R inferior frontal gyrus (pars orbitalis)
30
35
−14
385
4.32
\n
L Rolandic operculum
−42
2
15
312
4.06
\n
L caudate head
−14
15
3
248
3.82
\n
R middle occipital gyrus
41
−78
9
205
3.72
Voxel-based morphometry of PDD patients compared to those of controls at an uncorrected p-Value (<0.001). * Indicated for statistical threshold: uncorrected p < 0.001 with a cluster extent correction family-wise error (FWE)-corrected p-Value < 0.05. (x, y, and z) refer to the Montreal Neurological Institute coordinates. The T-value is determined by dividing the estimated regression coefficient by its standard error. Abbreviations: R Right; L Left.
\n"],"string":"[\n \"\\nInt J Environ Res Public HealthInt J Environ Res Public HealthijerphInternational Journal of Environmental Research and Public Health1661-78271660-4601MDPI32722623PMC743213210.3390/ijerph17155374ijerph-17-05374ArticleReduced Gray Matter Volume and Risk of Falls in Parkinson’s Disease with Dementia Patients: A Voxel-Based Morphometry Studyhttps://orcid.org/0000-0003-3883-2104ChengKai-Lun123†LinLi-Han4†https://orcid.org/0000-0002-6386-2850ChenPo-Cheng5https://orcid.org/0000-0002-3875-6677ChiangPi-Ling4ChenYueh-Sheng4ChenHsiu-Ling4ChenMeng-Hsiang4ChouKun-Hsien67LiShau-Hsuan8LuCheng-Hsien9LinWei-Che4*Department of Medical Imaging, Chung Shan Medical University Hospital, Taichung 402, Taiwan; chengkailun108@gmail.comSchool of Medical Imaging and Radiological Sciences, Chung Shan Medical University, Taichung 402, TaiwanDepartment of Veterinary Medicine, National Chung Hsing University, Taichung 402, TaiwanDepartment of Diagnostic Radiology, Kaohsiung Chang Gung Memorial Hospital, and Chang Gung University College of Medicine, Kaohsiung 833, Taiwan; cimetidine.tw@yahoo.com.tw (L.-H.L.); lovage@cgmh.org.tw (P.-L.C.); yssamchen@gmail.com (Y.-S.C.); suring.tw@gmail.com (H.-L.C.); sperfect1101@gmail.com (M.-H.C.)Department of Physical Medicine and Rehabilitation, Kaohsiung Chang Gung Memorial Hospital, and Chang Gung University College of Medicine, Kaohsiung 833, Taiwan; ben0922852179@gmail.comBrain Research Center, National Yang-Ming University, Taipei 112, Taiwan; dargonchow@gmail.comInstitute of Neuroscience, National Yang-Ming University, Taipei 112, TaiwanDepartment of Oncology and Hematology, Kaohsiung Chang Gung Memorial Hospital, and Chang Gung University College of Medicine, Kaohsiung 833, Taiwan; lee0624@adm.cgmh.org.twDepartment of Neurology, Kaohsiung Chang Gung Memorial Hospital, and Chang Gung University College of Medicine, Kaohsiung 833, Taiwan; chlu99@cgmh.org.twCorrespondence: u64lin@yahoo.com.tw; Tel.: +886-7-731-7123
These authors contributed equally to this work.
2672020820201715537415620202372020© 2020 by the authors.2020Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Purpose: Risk of falls is a common sequela affecting patients with Parkinson’s disease (PD). Although motor impairment and dementia are correlated with falls, associations of brain structure and cognition deficits with falls remain unclear. Material and Methods: Thirty-five PD patients with dementia (PDD), and 37 age- and sex-matched healthy subjects were recruited for this study. All participants received structural magnetic resonance imaging (MRI) scans, and disease severity and cognitive evaluations. Additionally, patient fall history was recorded. Regional structural differences between PDD with and without fall groups were performed using voxel-based morphometry processing. Stepwise logistic regression analysis was used to predict the fall risk in PDD patients. Results: The results revealed that 48% of PDD patients experienced falls. Significantly lower gray matter volume (GMV) in the left calcarine and right inferior frontal gyrus in PDD patients with fall compared to PDD patients without fall were noted. The PDD patients with fall exhibited worse UPDRS-II scores compared to PDD patients without fall and were negatively correlated with lower GMV in the left calcarine (p/r = 0.004/−0.492). Furthermore, lower GMV in the left calcarine and right inferior frontal gyrus correlated with poor attention and executive functional test scores. Multiple logistic regression analysis showed that the left calcarine was the only variable (p = 0.004, 95% CI = 0.00–0.00) negatively associated with the fall event. Conclusions: PDD patients exhibiting impaired motor function, lower GMV in the left calcarine and right inferior frontal gyrus, and notable cognitive deficits may have increased risk of falls.
Common symptoms of Parkinson’s disease with dementia (PDD), including tremors, bradykinesia, rigidity, postural instability, memory impairment, and visual hallucinations can result in an increased risk of falls [1]. Postural instability, especially during gait initiation, is considered one of the primary factors leading to falls in patients with PDD. The instability worsens with visual impairment, as visual input contributes significantly to the maintenance of upright posture when walking. Other motor network disruptions, and cognitive decline may also affect patient balance, further increasing the risk of falls. Meanwhile, repeated falls can result in fractures requiring hospitalization, and in more serious cases, may be fatal, particularly in the elderly and female [2]. Furthermore, long-term complications include atrophy caused by disuse, lifestyle disruptions caused by fear of falls, or possible need for institutionalization [3]. To date, however, the relationship between disease severity or cognitive function and risks of falls in PDD patients has not yet to be further investigated.
In recent years, the application of neuroimaging in exploring the etiology of neurodegenerative diseases has gained in popularity, with Parkinson’s disease (PD) being one of the most commonly studied disorders. These imaging methods have contributed significantly to a broadening of our understanding of the disease, beyond simply impaired dopaminergic transmissions [4]. In fact, some neuroimaging studies have reported alterations in networks related to motor and cognitive symptoms [5,6,7,8,9,10]. Radiotracer imaging, such as positron emission tomography (PET) or single-photon emission computed tomography (SPECT), can be used to study the dopaminergic and other neurochemical systems [11]; while widely available magnetic resonance imaging (MRI) has demonstrated effectiveness at measuring anatomical changes of the brain, with several MRI studies reporting cortical atrophy in Parkinson’s patients with and without dementia [12,13,14,15], decreasing gray matter volume (GMV) of left calcarine in patients with impaired cognition in Huntington’s disease [16], or that of right inferior frontal gyrus in unaffected siblings of schizophrenia patients [17]. However, clarification of the pathophysiology within the brain and the association with risks of fall in PDD patients require further investigation.
In this study, we aim to relate the clinical disease severity and cognitive function of patients with PDD to the MRI findings. We hypothesized that PDD patients with histories of falls demonstrated relatively diminished physical and cognitive functions, which would be associated with distinct brain regions, different from those regions affected in PDD patients without histories of falls.
2. Materials and Methods2.1. Subjects
Thirty-five patients with PDD (8 men and 27 women; mean age, 64.00 ± 5.85 years) and 37 age- and sex-matched healthy subjects (10 men and 27 women; mean age, 63.14 ± 5.34 years) were recruited for the study. Patients received MRI scans and neuropsychological tests (NPT) at the time of Parkinson disease diagnosis, while the control group received scans and tests at the initial enrollment in the study. Exclusion criteria for this study included a history of neurologic or psychiatric illness, the presence of developmental disorders, use of medication for unrelated conditions, and head injuries. The 35 PDD patients were further divided into two subgroups, based on the presence or absence of a history of falls (18 in the non-fall group, 3 men and 15 women; mean age, 62.78 ± 5.61 years; and 17 in the fall group, 5 men and 12 women; mean age, 65.29 ± 5.99 years). The Chang Gung Memorial Hospital Institutional Review Committees approved the study (IRB is 103-6906A3), and written informed consents were obtained from all subjects.
2.2. Assessment of Clinical Disease Severity
The clinical features recorded were age at enrollment (or age at the time of the first reported symptom attributable to the disease), education level, and history of falls. Data on falls were prospectively collected from either the clinical records starting at enrollment or information provided by patients during the study period. The Morse fall scale assessed the risk of falling for hospital in-patients or those in long-term care and included considerations such as presence/absence of intravenous therapy [18]. Since all patients were enrolled in the study from the Neurology Out-patient Clinic, the scale was slightly modified by deletion of the item, “intravenous therapy or not”. The severity of PDD was graded according to the scores of the Unified Parkinson’s Disease Rating Scale (UPDRS), the Hoehn and Yahr staging, and activity of daily living assessment [19,20]. An experienced neurology nursing specialist who was blinded to the patients’ clinical and biochemical data was trained to measure these functional scores at the time of enrollment.
2.3. Neuropsychological Tests (NPT)
The NPT battery focusing on attention, executive function, speech and language, memory, and visuospatial functions were performed by a clinical psychologist blinded to each patient’s status. Attention was evaluated by letter number sequencing and digit span score from the Wechsler Adult Intelligence Scale-III (WAIS-III) [21], and by orientation score and attention score from the Cognitive Ability Screening Instrument (CASI) [22]. Executive functions were assessed using arithmetic, picture arrangement, digit symbol coding, and matrix reasoning scores from the WAIS-III, as well as abstract thinking scores from the CASI. Memory functions were assessed using short- and long-term memory scores from the CASI, and information scores from the WAIS-III. Speech and language ability were assessed using vocabulary, comprehension, and similarity scores from the WAIS-III, and language and semantic fluency scores from the CASI. Visuospatial functions were assessed using picture completion and block design scores from the WAIS-III and drawing score from the CASI. Raw scores for each NPT were transformed to z scores based on normative data. Cognitive domain scores were calculated by averaging z scores for NPT within specific domains, thereby accounting for any unequal distribution of tests per domain [23,24]. Impairment was defined as a z score ≤ 2 standard deviation (SD) compared to normal subjects for a given domain. PDD was defined as: (1) a diagnosis of idiopathic PD; (2) PD developed prior to the onset of dementia; (3) Mini-Mental State Examination (MMSE) < 26; (4) impairment in at least two of the neuro-psychological battery domains [25].
2.4. MRI Image Acquisition
All MRI scans were performed on the same 3-Tesla GE Signa whole-body MRI system (General Electric Healthcare, Milwaukee, WI, USA), equipped with an eight-channel head coil. A T1-weighted three-dimensional fluid-attenuated inversion-recovery fast spoiled gradient-recalled echo pulse sequence was used with the following imaging parameters for each participant and time-point: TR/TE/TI = 9.5/3.9/450 ms; flip angle, 15; NEX = 1; matrix size = 512 × 512; voxel size = 0.47 × 0.47 × 1.3 mm3; and 110 axial slices. An experienced neuroradiologist, blinded to the participants’ status, visually examined all the MRI scans to verify they were free from gross anatomical abnormalities. Subsequently, none of the participants in the study were excluded.
2.5. Voxel-Based Morphometry Processing
All structural MRI were post-processed by the same neuroradiologist, unaware of patients’ information. Voxel-based morphometry (VBM) was performed using Statistical Parametric Mapping software (SPM12) [1] running on Matlab 2015b (Matworks, Natick, MA, USA). The details of image processing and analysis of regional GMV differences were described in the Supplementary Materials (Method: Voxel-based morphometry processing and analysis).
2.6. Statistical Analysis2.6.1. Analysis of Demographic Data between Groups
Statistical analysis was performed using the statistics computer software SPSS 18 (SPSS Inc., Chicago, IL, USA). Descriptive statistics were expressed as mean ± SD for continuous variables, and as numbers for categorical variables. The clinical data of PDD patients and normal subjects were analyzed by chi-square test or independent t test where appropriate. One-way analysis of covariance (ANCOVA) was used to compare NPT with age, sex, and education as covariates. The threshold for statistical significance was p < 0.05.
2.6.2. Relationships among Disease Severity, Cognition Function, and Gray Matter Volume
For further relationship analysis, the regional GMV was extracted from the clusters with significant statistical difference from the PDD group comparisons.
Partial correlation analysis, adjusted for age and sex, was performed to correlate GMVs showing significant differences in the PDD with fall and without fall groups with disease severity and NPT. Significance was set at a Bonferroni corrected p < 0.05, accounting for multiple region of interest (ROI) comparisons [26].
Stepwise logistic regression analysis with forward method was used to evaluate the relationships among disease severity, NPT, and areas with significant GMV differences in the PDD with fall and without fall groups, with adjustments made for other potential confounding factors.
3. Results3.1. Clinical Characteristics, NPT, and Disease Severity among Groups
The demographic characteristics and NPT of PDD patients and normal controls are shown in Table 1. Except for age and sex, there were significant differences in education duration, MMSE, and NPT between the PDD patients and normal controls. All patients, regardless of fall status, performed significantly worse than those in the control group in all NPT (p < 0.05) (Table 1). There were no significant differences in MMSE and NPT between the PDD with fall group and the without fall group.
The disease severity of PDD patients with and without fall groups are shown in Table 2. Except for UPDRS-II (F = 2.540; p = 0.028), there were no significant differences in UPDRS, UPDRS I, UPDRS III, Hoehn and Yahr staging, activity of daily living assessment, and Morse fall scale between the PDD with fall and without fall groups.
3.2. Regional Gray Matter Volume (GMV) Aberrations among Groups
The regions with significant GMV differences are presented in Table 3.
3.2.1. Comparison between all PDD Patients and Normal Controls
All PDD patients exhibited lower NPT results, and significantly lower GMV in the right superior temporal gyrus, left putamen, bilateral cerebellum, bilateral middle frontal gyri, right fusiform, bilateral precentral gyri, left superior frontal gyrus, left middle occipital gyrus, and left superior medial frontal gyrus (uncorrected p < 0.001) compared to the control group.
3.2.2. Comparison between PDD without Fall and Control Groups, and between PDD with Fall and Control Groups
By using a p-value of 0.001 (uncorrected), the PDD with fall group displayed lower GMV in the left middle temporal gyrus, left cuneus, left medial frontal gyrus, left middle occipital gyrus, bilateral inferior parietal gyri, right middle occipital gyrus, left middle temporal gyrus, right cingulate gyrus, and right anterior cingulate. Meanwhile, the PDD without fall group had lower GMV in the right cerebellum, and right superior temporal gyrus compared to the control group (uncorrected p < 0.001).
3.2.3. Comparison between the PDD with Fall and without Fall Groups
Based on a nonstationary cluster extent threshold of p < 0.05 corrected for multiple comparisons with FWE, there was a significantly lower GMV in the left calcarine and right inferior frontal gyrus in PDD patient with fall group compared to PDD patients without fall group.
By using an uncorrected p-value of 0.001, PDD with fall group showed significantly lower GMV in the left calcarine, right inferior frontal gyrus, left inferior temporal gyrus, right precentral gyrus, left inferior frontal gyrus, right middle temporal gyrus, right inferior frontal gyrus, left Rolandic operculum, left caudate head, and right middle occipital gyrus compared to PDD without fall group (Figure 1).
3.3. Relationship among Disease Severity Profiles, NPT, and Extracted Regional GMV from PDD Patients
UPDRS-II was the only disease severity score showing significant difference between the PDD with fall and without fall groups; moreover, it exhibited a significantly negative correlation with a low GMV in the left calcarine (p/r = 0.004/−0.492) (Figure 2).
There are positive correlations between NPT (attention and executive function) and the significantly lower GMV in PDD with fall group compared to PDD without fall group. The poor attention domain correlated with low GMV in the left calcarine (p/r = 0.002/0.520) and right inferior frontal gyrus (p/r = 0.003/0.503); while the low executive function correlated with low GMV in the left calcarine (p/r = 0.007/0.460) (Figure 2).
Multiple logistic regression analysis with forward method showed that the left calcarine was the only variable (p = 0.004, B: −37.6, 95% CI = 0.00−0.00) negatively associated with the fall event, meaning that lower GMV of the left calcarine could predict the occurrence of a fall event.
4. Discussion4.1. Brief Summary and Potential Implications
There were significant differences in education level and the NPT between the PDD patients and controls, but no significant differences in the NPT between the PDD patients with and without fall groups. In the assessment of disease severity, the PDD with fall group only had significantly higher scores in UPDRS-II compared to the PDD without fall group. In the imaging study, all PDD patients showed significantly lower GMV in the right superior temporal gyrus, left putamen, bilateral cerebellum, bilateral middle frontal gyri, right fusiform, bilateral precentral gyri, left superior frontal gyrus, left middle occipital gyrus, and left superior medial frontal gyrus compared to the control group. There was a significantly lower GMV in left calcarine, and right inferior frontal gyrus in the PDD with fall group compared to the PDD without fall group. In terms of relationships, UPDRS-II showed a negative correlation with GMV in the left calcarine. Attention function was positively correlated with the GMV in the left calcarine, and right inferior frontal gyrus, while executive function was positively correlated with the GMV only in the left calcarine. Of note, the regression analysis showed that the GMV of the left calcarine was negatively associated with fall events.
Studies have reported the use of NPT to assess clinical characteristics of PDD patients [27,28,29]. Although differences existed in the evaluation tools, declining functions in many domains of NPT were noted in PDD patients, similar to the results reported herein. However, the specific domain accounting for falls has yet to be clarified. Although Papapetropoulos et al. [30] reported that PDD was one of the independent risk factors attributing to falls, no NPT were performed in the study. Another study found that mild cognitive impairment was related to increasing risk of fall, but the cognitive functions of the participants were rated by Clinical Dementia Rating Scale, which focused on the ability for self-care [31]. Meanwhile, Allcock et al. [32] reported on a relationship between impaired attention and fall frequency due to increasing difficulty in performing concurrent tasks and compensatory movements to prevent falls in PD patients. One systematic review also supported the concept that cognitive impairment, especially in the domains of attention and executive function, can disturb multitasking and thereby result in falls [33]. In this study, complete NP tests were assessed for all participants, and the PDD patients had significantly lower z scores compared to the controls. Although the z scores of most domains of the NPT in the PDD with fall group appeared to be lower than that of the PDD without fall group, no statistical difference was noted, possibly attributable to the small sample size.
In this study, multiple disease severity scales, including UPDRS, UPDRS I, II, III, Hoehn and Yahr stage, activity of daily living, and Morse fall scale, were assessed for all PDD participants. Although the disease severity in the PDD with fall group appeared to be severer than that of the PDD without fall group, no statistical difference was noted except for UPDRS-II, possibly attributable to the small sample size or similarity of disease severity of PDD participants. One systematic review reported that disease severity as measured by Hoehn and Yahr stage or by the UPDRS was found to be significantly associated with recurrent falls in PD [34]. Matinolli et al. [35] reported that UPDRS-II was a significant risk factor for predicting fall in PD, similar to the results reported herein. The UPDRS-II, specific for motor aspects of experiences of daily living, has 13 self-assessed items, including freezing when walking, a phenomenon closely related to fall. These results demonstrate UPDRS-II might be a tool to assess the fall risk in PD.
Many structural MRI studies have reported on the existence of cortical atrophy in PD patients, particularly atrophy of the hippocampus and the amygdala in patients with PD with or without dementia [12,13,14,15]. Recently, VBM brain MRI has been used in the study of patients with PD. These studies have demonstrated that patients with PDD present with limbic and widespread neocortical gray matter loss, while Parkinson’s patients without dementia mainly present with atrophy in the frontal and temporal regions [36,37,38,39]. Cristina et al. had found that PDD with visual hallucination presented gray matter volumetric reductions in the left orbitofrontal lobe when compared to normal subjects [2]. In the present study, the GMV of most neocortex regions of the PDD patients were less than those of the control group. The PDD with fall group mainly showed significantly decreased GMV in the left frontal, temporal, and occipital lobes, compared to the control group, similar to the results of other studies. Prior to this study, no imaging study had focused specifically on regions of the brain associated with fall risk. However, this study finds that the PDD with fall group had significantly less GMV in the left calcarine and right inferior frontal gyrus than that of the PDD without fall group. This finding is similar to a study comparing the brain structures of PD patients with and without rapid eye movement sleep behavior disorder [40]. Furthermore, the association between sleep disorders and falls in PD patients has been reported in a separate case-control study [41]. It is believed that sleep disorders can increase the risk of falls via several mechanisms, such as poor attention, slower reaction time, and impaired balance.
The calcarine region, part of the occipital lobe, is where the primary visual cortex is concentrated. The relationship between visuospatial ability and motor function has been demonstrated in previous studies [42,43]. Consistent with a previous study [44], this study demonstrates a negative correlation in PD patients between the GMV of the left calcarine and the UPDRS-II, which tests and rates the motor aspects associated with daily activities. Moreover, we identify associations between the GMV of the left calcarine and attention, implying a relationship between visual function and attention in PDD patients. Stuart et al. [45] conducted a cohort study and built a structural equation model showing associations between attention, visual function, saccade frequency, and gait impairment in PD patients. The abovementioned factors may result in poor executive function. Of note, the association between the GMV of the right inferior frontal gyrus and executive function is exhibited in this study, consistent with a previous imaging study which reported on the important role of the right inferior frontal gyrus in executive control [46]. Taken together, the results of the present study and separate studies suggest that visual function and specific domains of cognitive impairment play critical roles in fall events.
4.2. Limitations
Although this study demonstrates that specific domains of cognitive impairment and brain regions are reported to be related to falls in PDD patients, some limitations need to be addressed. First, this study applies a cross-sectional design, and further causal relationships should be investigated by future cohort study. Second, the small sample size limits the statistical power of demonstrating differences among groups. Third, the male to female odds ratio of developing dementia in PD patients between 65 and 69 years old has been reported to be 2:1 by a large cross-sectional study [47]; however, in this study, the number of female participants is higher than that of the male participants. This kind of selection bias may deviate our results from the real-world evidence, and future investigators should consider this when designing any future clinical trial.
5. Conclusions
Those PDD patients with falls may suffer from higher degrees of structural and functional degeneration of the brain, especially in the calcarine and inferior frontal regions. These brain regions play important roles in attention and visual function, and aberrations in these regions may be used to predict future risk of falls in PDD patients. The findings of this study suggest that closer attention to these comorbidities be paid in clinical practice for early intervention and prevention of fall events.
Supplementary Materials
The following are available online at https://www.mdpi.com/1660-4601/17/15/5374/s1, Method: Voxel-based morphometry processing and analysis.
Click here for additional data file.
Author Contributions
Conceptualization, K.-L.C., L.-H.L. and W.-C.L.; Data curation, K.-L.C. and C.-H.L.; Formal analysis, K.-L.C. and P.-L.C.; Funding acquisition, C.-H.L. and W.-C.L.; Methodology, P.-L.C. and K.-H.C.; Resources, Y.-S.C., M.-H.C., C.-H.L. and W.-C.L.; Software, H.-L.C. and S.-H.L.; Supervision, L.-H.L., P.-C.C., Y.-S.C., H.-L.C., M.-H.C., S.-H.L. and W.-C.L.; Writing—original draft, K.-L.C.; Writing—review & editing, P.-L.C. and W.-C.L. All authors have read and agreed to the published version of the manuscript.
Funding
The authors declare no competing financial interests. This study received funding from the National Science Council of Taiwan (MOST 106-2314-B-182A-031-MY2, to W.-C.L.) and from Chang Gang Memorial Hospital (CMRPG8H0951 and CMRPG8I0371 to W.-C.L.)
Conflicts of Interest
The authors declare no conflict of interest.
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Lower gray matter volumes (GMVs) in PDD patients with fall vs without fall, with highlighted significant areas. Compared to PDD patients without fall, PDD patients with fall showed significantly lower GMV in the left inferior temporal gyrus, right inferior frontal gyrus, left caudate head, right middle occipital gyrus, left inferior frontal gyrus, left Rolandic operculum, right inferior frontal gyrus, right middle temporal gyrus, left calcarine, and right precentral gyrus. Among these areas, the clusters of left calcarine and R inferior frontal gyrus survived from statistical threshold of FWE-corrected p < 0.05.
Correlations between NP tests (attention and executive) and UPDRS-II and regional GMV. (a) Left calcarine and attention. (b) Left calcarine and executive. (c) left calcarine and UPDRS-II. (d) Right inferior frontal gyrus and attention.
ijerph-17-05374-t001_Table 1
Demographic characteristics of PDD patients and normal controls.
Demographics
Normal Group
PDD
F +
p-Value +
With Fall
Without Fall
All Patients
Number of cases
37
17
18
35
\\n
\\n
Sex (n = men/women)
10/27
5 / 12
3 / 15
8/27
\\n
0.630
Ages (years)
63.1 ± 5.34
65.3 ± 5.99
62.8 ± 5.61
64.0 ± 5.85
1.112
0.335
Education
9.95 ± 4.73 #
5.53 ± 4.14 #
6.72 ± 4.78
6.14 ± 4.45
6.416
0.003
MMSE
26.7 ± 2.22§#
19.2 ± 4.10 #
21.1 ± 4.30 §
20.1 ± 4.25
36.481
<0.001
\\nNeuropsychological domains\\n
Attention
0.46 ± 0.48 § #
−0.70 ±-0.66 #
−0.30 ± 0.72 §
−0.49 ± 0.71
5.871
<0.001
Executive function
0.43 ± 0.76 § #
−0.54 ± 0.62 #
−0.41 ± 0.65 §
−0.48 ± 0.63
1.461
<0.001
Memory
0.41 ± 0.53 § #
−0.54 ± 0.85 #
−0.34 ± 0.65 §
−0.43 ± 0.75
2.140
0.001
Speech and language
0.46 ± 0.71 § #
−0.52 ± 0.58 #
−0.50 ± 0.69 §
−0.51 ± 0.63
0.317
<0.001
Visuospatial function
0.46 ± 0.62 § #
−0.45 ± 0.77 #
−0.53 ± 0.74 §
−0.49 ± 0.74
1.054
<0.001
F+ and p\\n+ represent the comparison between all PDD patients vs. the control group. Sex was compared by chi-square test. Age, education, and MMSE data were compared by independent t test. NP data were compared by ANCOVA after controlling for age, sex, and education. Data are presented as mean ± SD. # Significant differences between the control group and the PDD with fall group. § Significant differences between the control group and the PDD without fall group.
ijerph-17-05374-t002_Table 2
Disease severity of PDD patients.
Disease Severity
PD with Dementia
F +
p-Value +
With Fall
Without Fall
All Patients
\\nNumber of Cases\\n
17
18
35
\\n
\\n
Unified Parkinson’s Disease Rating Scale (UPDRS) a
57.0 ± 28.3
42.6 ± 22.9
49.6 ± 26.3
0.559
0.106
UPDRS I b
4.88 ± 2.91
3.72 ± 2.95
4.28 ± 2.95
0.040
0.250
UPDRS-II c
16.7 ± 9.98
10.5 ± 5.54
13.5 ± 8.49
2.540
0.028 *
UPDRS III d
35.4 ± 16.8
28.3 ± 16.1
31.8 ± 16.6
0.010
0.213
Hoehn and Yahr staging
2.53 ± 0.89
2.14 ± 0.98
2.33 ± 0.95
0.294
0.228
Activity of daily living
76.5 ± 15.0
80.6 ± 16.3
78.6 ± 15.6
0.022
0.446
Morse fall scale
62.9 ± 27.7
49.7 ± 26.8
56.1 ± 27.7
0.102
0.161
F + and p\\n+ represent the comparison between PDD with fall group vs. PDD without fall group. a Total UPDRS score is the combined sum of parts I, II, and III. Theoretical minimum and maximum values are 0 and 176, respectively (176 represents the worst disability and 0 no disability).b I. Mentation, behavior, and mood. Theoretical minimum and maximum values are 0 and 16, respectively. (16 represents the worst disability and 0 no disability). c II. Activities of daily living (ADL). Theoretical minimum and maximum values are 0 and 52, respectively. (52 represents the worst disability and 0 no disability). d III. Motor examination. Theoretical minimum and maximum values are 0 and 108, respectively. (108 represents the worst disability and 0 no disability). UPDRS, UPDRS I/II/III, Hoehn and Yahr staging, Activity of daily living, and Morse fall scale were compared by independent t test. Data are presented as mean ± SD.* Significant differences between the PDD with fall group and PDD without fall group.
ijerph-17-05374-t003_Table 3
Regions of statistically significant lower GMV in PD with dementia patients compared to those of control.
Gray Matter Volume
Anatomical Regions
x
y
z
Cluster Size
T-Value
\\nNormal > all PDD\\n
\\n
R superior temporal gyrus *
59
−11
0
14770
5.90
\\n
L putamen *
−27
15
6
10899
5.68
\\n
R cerebellum *
20
−74
−47
14753
5.54
\\n
R middle frontal gyrus *
29
50
8
591
5.40
\\n
L middle frontal gyrus *
−29
42
30
304
4.94
\\n
R fusiform
41
−57
−18
289
4.55
\\n
R precentral gyrus
53
-8
47
711
4.51
\\n
L precentral gyrus
−44
6
47
646
4.42
\\n
L superior frontal gyrus
−18
60
8
445
4.26
\\n
L middle occipital gyrus
−33
−81
14
195
4.20
\\n
L superior medial frontal gyrus
−11
4
−20
238
4.11
\\n
L cerebellum
−30
−68
-36
648
3.52
\\nNormal > PDD with fall\\n
\\n
L middle temporal gyrus *
−47
2
−35
53327
7.82
\\n
L cuneus *
−5
−86
3
28263
7.14
\\n
L medial frontal gyrus *
−12
39
18
2027
5.34
\\n
L middle occipital gyrus
−33
−81
14
641
6.10
\\n
R inferior parietal gyrus
57
−56
44
270
5.23
\\n
L inferior parietal gyrus
−59
−47
45
281
4.79
\\n
R middle occipital gyrus
42
−80
8
532
4.67
\\n
L middle temporal gyrus
−41
−65
12
441
4.40
\\n
R cingulate gyrus
14
11
44
165
3.92
\\n
R anterior cingulate
14
44
3
234
3.87
\\nNormal > PDD without fall\\n
\\n
R cerebellum
17
−74
−45
1860
4.00
\\n
R superior temporal gyrus
62
2
−3
236
3.63
\\nPDD with fall < PDD without fall\\n
\\n
L calcarine *
−9
−68
20
3735
5.97
\\n
R inferior frontal gyrus *(pars triangularis)
48
20
17
333
6.01
\\n
L inferior temporal gyrus
−47
3
−35
717
4.97
\\n
R precentral gyrus
41
−15
38
363
4.77
\\n
L inferior frontal gyrus (pars opercularis)
−47
15
15
444
4.73
\\n
R middle temporal gyrus
50
-53
20
390
4.52
\\n
R inferior frontal gyrus (pars orbitalis)
30
35
−14
385
4.32
\\n
L Rolandic operculum
−42
2
15
312
4.06
\\n
L caudate head
−14
15
3
248
3.82
\\n
R middle occipital gyrus
41
−78
9
205
3.72
Voxel-based morphometry of PDD patients compared to those of controls at an uncorrected p-Value (<0.001). * Indicated for statistical threshold: uncorrected p < 0.001 with a cluster extent correction family-wise error (FWE)-corrected p-Value < 0.05. (x, y, and z) refer to the Montreal Neurological Institute coordinates. The T-value is determined by dividing the estimated regression coefficient by its standard error. Abbreviations: R Right; L Left.
\\n\"\n]"}}},{"rowIdx":476,"cells":{"data":{"kind":"list like","value":["\nFront OncolFront OncolFront. Oncol.Frontiers in Oncology2234-943XFrontiers Media S.A.32850301PMC743213310.3389/fonc.2020.00872OncologyOriginal ResearchRadiomic-Based Quantitative CT Analysis of Pure Ground-Glass Nodules to Predict the Invasiveness of Lung AdenocarcinomaXuFangyi1ZhuWenchao1ShenYao12WangJian3XuRui45OuteshChooah1SongLijiang6GanYi7PuCailing1HuHongjie1*1Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China2Department of Radiology, Yinzhou Hospital Affiliated With the School of Medicine of Ningbo University, Ningbo, China3Department of Radiology, Tongde Hospital of Zhejiang Province, Hangzhou, China4DUT-RU International School of Information Science & Engineering, Dalian University of Technology, Dalian, China5DUT-RU Co-Research Center of Advanced ICT for Active Life, Dalian, China6Department of Cardiothoracic Surgery, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China7Department of Pathology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
Edited by: Marco Lucchi, University of Pisa, Italy
Reviewed by: Ahmad Chaddad, Guilin University of Electronic Technology, China; Niha Beig, Case Western Reserve University, United States; Shuling Chen, First Affiliated Hospital of Sun Yat-sen University, China
*Correspondence: Hongjie Hu hongjiehu@zju.edu.cn
This article was submitted to Thoracic Oncology, a section of the journal Frontiers in Oncology
1182020202010872191220190452020Copyright © 2020 Xu, Zhu, Shen, Wang, Xu, Outesh, Song, Gan, Pu and Hu.2020Xu, Zhu, Shen, Wang, Xu, Outesh, Song, Gan, Pu and HuThis is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Objectives: To investigate the performance of radiomic-based quantitative analysis on CT images in predicting invasiveness of lung adenocarcinoma manifesting as pure ground-glass nodules (pGGNs).
Methods: A total of 275 lung adenocarcinoma cases, with 322 pGGNs resected surgically and confirmed pathologically, from January 2015 to October 2017 were enrolled in this retrospective study. All nodules were split into training and test cohorts randomly with a ratio of 4:1 to establish models to predict between pGGN-like adenocarcinoma in situ (AIS)/minimally invasive adenocarcinoma (MIA) and invasive adenocarcinoma (IVA). Radiomic feature extraction was performed using Pyradiomics with semi-automatically segmented tumor regions on CT scans that were contoured with an in-house plugin for 3D-Slicer. Random forest (RF) and support vector machine (SVM) were used for feature selection and predictive model building in the training cohort. Three different predictive models containing conventional, radiomic, and combined models were built on the basis of the selected clinical, radiological, and radiomic features. The predictive performance of each model was evaluated through the receiver operating characteristic curve (ROC) and the area under the curve (AUC). The predictive performance of two radiologists (A and B) and our radiomic predictive model were further investigated in the test cohort to see if radiomic predictive model could improve radiologists' performance in prediction between pGGN-like AIS/MIA and IVA.
Results: Among 322 nodules, 48 (14.9%) were AIS and 102 (31.7%) were MIA with 172 (53.4%) for IVA. Age, diameter, density, and nine meaningful radiomic features were selected for model building in the training cohort. Three predictive models showed good performance in prediction between pGGN-like AIS/MIA and IVA (AUC > 0.8, P < 0.05) in both training and test cohorts. The AUC values in the test cohort were 0.824 (95% CI, 0.723–0.924), 0.833 (95% CI, 0.733–0.934), and 0.848 (95% CI, 0.750–0.946) for conventional, radiomic, and combined models, respectively. The predictive accuracy was 73.44 and 59.38% for radiologist A and radiologist B in the test cohort and was improved dramatically to 79.69 and 75.00% with the aid of our radiomic predictive model.
Conclusion: The predictive models built in our study showed good predictive power with good accuracy and sensitivity, which provided a non-invasive, convenient, economic, and repeatable way for the prediction between IVA and AIS/MIA representing as pGGNs. The radiomic predictive model outperformed two radiologists in predicting pGGN-like AIS/MIA and IVA, and could significantly improve the predictive performance of the two radiologists, especially radiologist B with less experience in medical imaging diagnosis. The selected radiomic features in our research did not provide more useful information to improve the combined predictive model's performance.
A new classification for lung adenocarcinoma was proposed in 2011 by the International Association for the Study of Lung Cancer/American Thoracic Society/European Respiratory Society (IASLC/ATS/ERS) (1), which was also issued as the 4th edition WHO lung cancer classification in 2015 (2). According to the new classification, lung adenocarcinoma can be divided into preinvasive lesion, minimally invasive adenocarcinoma (MIA), and invasive adenocarcinoma (IVA), and preinvasive lesion includes atypical adenomatous hyperplasia (AAH) and adenocarcinoma in situ (AIS) (1). The improvement of medical technology and the generalization of lung cancer screening project have led more attention to pure ground-glass nodules (pGGNs) detected on computed tomography (CT) images (3, 4).
Approximately 20% of lung adenocarcinoma including AIS, MIA, and even some early-stage IVA could present as pGGNs on CT images (4), which makes it quite difficult for radiologists and clinicians to make a precise diagnosis with conventional radiological parameters like size, density, etc. Kakinuma et al. reported that growth was observed in approximately 10% of pGGNs ≤5 mm, of which 1% would develop into IVA or MIA in their study (5). In another study, 57.8% of pGGNs showed growth during follow-up and 26.3% of them were adenocarcinoma (6). Eguchi et al. examined 124 cases with pGGNs, and 64 pGGNs (51.6%) showed growth during their 2-year follow-up (7). Several previous research revealed that nearly 50% of pGGNs were invasive lesions (3, 8–10). In clinical practice, pGGNs are usually prescribed to be followed up but data above demonstrated that more detailed diagnosis and more individualized management should be made for pGGNs.
Compared with IVA, AIS, and MIA are considered as indolent lung adenocarcinoma because of the excellent prognosis (11, 12). AIS/MIA could be followed up or treated with sublobar resection while more aggressive surgical interventions should be taken for IVA (13, 14). Several previous studies revealed that the 5-year survival rates of AIS and MIA could be 100% and near 100% with a complete resection while that of IVA in stage Ia is no more than 75% (11, 13, 14). Thus, it might provide some guidance for clinical therapeutic decision-making if pGGN-like IVA could be figured out on preoperative CT images.
There were many studies investigating the difference in radiological features among lung adenocarcinoma subtypes. Wang et al. found that the mean CT attenuation and lesion size differed significantly between MIA and non-invasive lesions and internal air bronchograms were more often seen in adenocarcinoma (15). Several investigations reported that the notched signs, spiculations, bubbly lucencies, and rapid volume expansion were more common in IVA (15–17). However, those radiological features could be subtly different because of the small size of pGGN-like adenocarcinoma. Furthermore, the assessment of those parameters tends to be subjective, which could be influenced by radiologists' experience and diagnostic ability. Percutaneous biopsy is one method used in clinical practice to determine the nature of pulmonary nodules, which could provide relatively accurate pathological information. However, percutaneous biopsy is an invasive operation, and patients may have some operation-related complications (18, 19). Considering the heterogeneity in adenocarcinoma, small pieces of tissue obtained by biopsy cannot represent the characteristics of the whole lesion (20). What is more, in some cases, it is difficult to complete biopsy due to patient's physical condition and bad cooperation as well as the location and size of nodules (20, 21). Thus, the accurate diagnosis of pGGNs remains a key point and a challenge in the field of medical imaging diagnosis.
Radiomics is an emerging subject that could extract a large amount of invisible features from medical images for clinical decision-making (20, 22). Radiomics has had remarkable progress in central nervous system malignancies, thoracic imaging diagnosis, discrimination of hepatic mass, and some other diseases (23–26). Chaddad et al. performed retrospective analysis involving 315 patients diagnosed as non-small cell lung cancer (NSCLC) and significant correlation was observed between radiomic features and survival (27). Also, radiomics' promising performance in the distinction of benign and malignant pulmonary nodules and the discrimination of adenocarcinoma subtypes had been validated in several researches (11, 28, 29). However, in most previous radiomic studies, all types of pulmonary nodules including solid and subsolid nodules were recruited as the study population. Few studies focused on the use of machine learning in early-stage lung adenocarcinoma representing as pGGNs, which are usually very difficult to manage. Since the diagnosis of solid components in pulmonary nodules on CT images is relatively uncomplicated while pGGNs remain a big challenge for medical imaging diagnosis, we aimed to explore the potential value of radiomic-based quantitative analysis to predict the invasiveness of pGGN-like adenocarcinoma to establish a comprehensive predictive model for clinical decision-making.
Materials and Methods
This retrospective study was approved by the institutional review committee of the Sir Run Run Shaw Hospital (No. 20190520-162) with an abstention of informed consents from all the patients involved according to the guidelines of the Council for International Organizations of Medical Science (CIOMS).
Patients
We reviewed all the materials of 1,610 patients undergoing surgical resection for primary lung adenocarcinoma with complete clinical data and preoperative CT images from January 2015 to October 2017 in Sir Run Run Shaw Hospital, and reinterpreted the preoperative CT images from the Picture Archiving and Communication Systems (PACS) one by one. Clinical data like age, gender, and smoking status of all cases were collected from digital records. Patients who met any one of the following criteria were excluded: (1) nodules with solid components (n = 776), (2) nodule diameter >3 cm (n = 292), (3) patients with a history of other malignant diseases (n = 87), (4) CT images with bad quality (n = 70), and (5) patients who accepted thoracic surgical intervention, radiation, or any chemotherapeutics (n = 110).
Finally, 275 patients (72 men and 203 women, age range, 25~78 years) with 322 pGGNs (82 men and 240 women, age range, 25~78 years) were enrolled into this retrospective study (detailed in Figure 1 and Table 1). The median time from the last preoperative CT scan to surgery was 6 (0–92) days.
The flowchart of patient selection.
Analysis of clinical and radiological features of pGGNs in the training and test cohort.
Training cohort
Test cohort
Total
AIS/MIA
IVA
P
Total
AIS/MIA
IVA
P
258
120 (46.5%)
138 (53.5%)
64
30 (46.9%)
34 (53.1%)
Gender
    Male
70 (27.1%)
31 (25.8%)
39 (28.3%)
0.662
12 (18.8%)
3 (10.0%)
9 (26.5%)
0.092
    Female
188 (72.9%)
89 (74.2%)
99 (71.7%)
52 (81.3%)
27 (90.0%)
25 (73.5%)
Age (years)
53 (25, 78)
51 (25, 69)
55 (26, 78)
0.001
54 (30, 72)
51 (31, 72)
55 (30, 72)
0.106#
Smoking history
    Never
230 (89.1%)
109 (90.8%)
121 (87.7%)
0.417
59 (92.2%)
29 (96.7%)
30 (88.2%)
0.360*
    Current or ever
28 (10.9%)
11 (9.2%)
17 (12.3%)
5 (7.8%)
1 (3.3%)
4 (11.8%)
    Diameter (cm)
0.94 (0.24, 2.94)
0.76 (0.24, 1.92)
1.13 (0.51, 2.94)
<0.001
0.93 (0.34, 2.04)
0.70 (0.34, 1.41)
1.09 (0.61, 2.04)
<0.001#
Density (HU)
−583.0 (−829.2, −122.5)
−615.0 (−801.7, −122.5)
−529.2 (−829.2, −176.3)
<0.001
−525.8 (−763.2, −179.8)
−544.7 (−763.2, −298.9)
−509.0 (−739.1, −179.8)
0.088#
Two-sample t-test.
Fisher exact probability test.
Continuous variables were represented in median (minimum, maximum) without any special statement.
pGGNs, pure ground-glass nodules; AIS, adenocarcinoma in situ; MIA, minimally invasive adenocarcinoma; IVA, invasive adenocarcinoma.
Histological Evaluation
All surgical specimens were fixed with formalin and stained with hematoxylin–eosin (HE). Two pathologists evaluated all slides using a multi-headed microscope and discussed about the diagnosis until a consensus was reached. According to the classification of lung adenocarcinoma issued in the 4th edition WHO lung cancer classification in 2015, each nodule was classified as AIS, MIA, and IVA (26, 30). Each histological pattern presented in targeted lesion including lepidic, acinar, solid, papillary, and micropapillary patterns was recorded in 5% increments (30, 31).
Image Acquisition
All the plain CT images were obtained using multidetector computed tomography scanners (Siemens SOMATOM Definition Flash, Siemens FORCE CT, Siemens Sensation 16, Siemens Definition AS 40, and GE LightSpeed VCT). The protocol parameters for each scanning were detailed in Supplemental Table 1. Plain spiral acquisitions were obtained from thoracic inlet to lung bases on patients accepting breath-hold training. Images were reconstructed using a standard reconstruction kernels in lung window settings (mean, −500 HU; width, 1,500 HU). All images underwent multi-planar reconstruction (MPR) including coronal and sagittal reconstruction utilizing the post-processing station.
Nodule Analysis and Segmentation
All transverse CT images were interpreted jointly by two radiologists who were both blind to the clinical and pathological information of all cases (radiologist A, with 10 years' experience in thoracic imaging diagnosis; radiologist B, with 2 years' experience in medical imaging diagnosis) on our professional reading screen. Conventional quantitative radiological features that were widely used in clinical diagnosis involving diameter (cm) and density (Hounsfield Unit, HU) were determined for each nodule. Nodule diameter was measured on the average of long- and short-axis diameters, both of which should be obtained on the same transverse revealing the greatest dimensions. The nodule density was measured at three different parts of each nodule avoiding vessels and bronchus and the mean value of the three results was calculated. The mean value of diameter and density measured by the two radiologists was calculated for our study.
Segmentation data consisted of all the 322 pGGNs. Plain CT images were loaded into 3D-slicer (http://www.slicer.org) (32), an open source image processing software, implemented with in-house algorithm for automatic nodule detection and segmentation. Radiologist B would verify the regions of interest (ROIs) of automatic segmentation and made some modifications when the ROIs were not satisfactory. Radiologist A would have a second review for the results of radiologist B's semi-automatic segmentation. A consensus would be achieved via negotiation between two radiologists for each case when meeting a collision on reviewing. While modifying ROIs, two radiologists would delineate manually around the nodule boundary on each section avoiding the bronchus and vessels as much as they could.
Radiomic Feature Extraction and Predictive Models Building
Segmentation data were analyzed with Pyradiomics to extract radiomic features describing tumor phenotypes (33). All the segmentation data had a voxel resampling of 0.7 × 0.7 × 0.7 mm3 for standardization to reduce the impact from the heterogeneity of image acquisition. In the end, we obtained nine types totaling 960 radiomic features for each nodule, which have been listed in Supplemental Table 2. Features are commonly grouped as follows: (1) first-order statistical features: these describe the voxel intensity distribution in the delineated ROI. They are usually calculated on the basis of the intensity histogram, including energy, entropy, standard deviation, skewness, kurtosis, uniformity, mean, minimum, and maximum intensity values and so on (20, 26). (2) Shape-based features: descriptors of the two- and three-dimensional size and shape of the ROI. (3) Textural features: these contain gray level co-occurrence matrix (GLCM), gray level run length matrix (GLRLM), gray level size zone matrix (GLSZM), neighboring gray size zone matrix (NGZDM), and gray-level dependence matrix (GLDM). They are computed on the analysis of the three-dimensional directions within the tumor and the consideration of the spatial location of each voxel in the ROI (26). (4) Transformed features: features in the first and third groups extracted from images applied with a series of wavelet or Laplacian-of-Gaussian filtration.
Random forest (RF) and support vector machine (SVM) with cross-validation (CV) was used for radiomic feature selection and predictive model building to distinct pGGN-like IVA from indolent adenocarcinoma (AIS/MIA). Multivariate models were made in training cohort and were tested in a separate test cohort. All pGGNs were split into training and test cohorts randomly by a ratio of 4:1. Three predictive models were created in our research: (1) Conventional (selected clinical and radiological quantitative features), (2) Radiomic (selected radiomic features), and (3) Combined (selected conventional and radiomic features) predictive models. Subsequently, a binary analysis in which pGGN-like IVA was set as positive while AIS/MIA was thought as negative was applied to compare the predictive performance between radiomic predictive model and two radiologists (A and B) in the test cohort. Two weeks later, two radiologists, knowing the performance of our radiomic predictive model and its diagnosis for each pGGN in the test cohort, reevaluated all pGGNs in the test cohort.
Statistical Analysis
All the statistical analysis was applied using SPSS 25.0 (IBM, Armonk, NY, USA) and MedCalc 15.8 (MedCalc Software, Acacialaan 22, Ostend, Belgium). Tables and figures in our study were made with GraphPad Prism 5 (GraphPad Software Inc., San Diego, CA, USA) and Microsoft Office 2019 (Microsoft, Redmond, WDC, USA).
Thirty pGGNs were selected randomly to test the repeatability of nodule diameter and density measurement. Radiologist A and B did the measurement work of those 30 pGGNs, respectively. Two weeks later, radiologist B measured the diameter and density of these 30 pGGNs, again according to the same measurement criteria. Inter-/intra-observer correlation coefficient (ICC) was calculated for repeatability assessment.
For the assessment of clinical, quantitative radiological, and selected radiomic features, chi-square test or Fisher exact probability test was utilized for categorical variables. Two-sample t-test was adopted, if the continuous variables met the normal test and variance homogeneity test; otherwise, Wilcoxon signed-rank test was used. Predictive power of each predictive model was evaluated using receiver operating characteristic (ROC) curve and area under the curve (AUC). Models with an AUC > 0.50 and a P < 0.05 were thought to be predictive. McNemar's test and Kappa analysis were used to compare the binary diagnosis of two radiologists and radiomic predictive model.
Results
The measurement of nodule diameter and density between senior radiologist A and junior radiologist B was highly consistent (ICC > 0.9, P < 0.05). Two weeks later, radiologist B did the measurement for the 30 selected pGGNs, and the ICC values were up to 0.955 (P < 0.05) and 0.984 (P < 0.05) for diameter and density measurement.
Clinical Data and Conventional Image Features
A total of 322 pGGNs were recruited into this study with 80% in the training cohort and 20% in the test cohort. The analysis of clinical and quantitative radiological features in the training and test cohort were listed in Table 1. In the training cohort, the median age was 53 years (age range, 25–78 years) and the majority of cases were female (72.9%) with 28 (10.9%) having a smoking history. In the test cohort, 52 (81.3%) were female with a median age of 54 years (age range, 30–72 years) and 59 (92.2%) never smoke. Diameter showed statistical discrepancy between AIS/MIA and IVA in both training and test cohort (P < 0.001) while nothing significantly different existed in gender and smoking status between the two groups. Age and density exhibited evident difference in training cohort between AIS/MIA and IVA (P = 0.01 and P < 0.001), but no significant difference (P > 0.05) appeared between the two groups in test cohort.
Radiomic Feature Selection and Predictive Model Building
A RF algorithm with 4-fold cross-validation was taken to calculate the contribution value of each radiomic feature in the training cohort for the prediction of pGGN-like IVA from AIS/MIA. Predictive model building was performed using SVM also combined with 4-fold cross-validation. All the extracted radiomic features were listed in descending order by the contribution value for the classifier and were added one by one as the input for the SVM model training in each iteration of cross-validation calculation (detailed in Figure 2). In the process of gradual accumulation of model training, the overall accuracy of the training cohort was recorded. When sequencing extracted radiomic features by their contribution value for the classifier in each iteration, we found that the first 20 radiomic features contributed much more than other features whose contribution values were <0.01 and even close to 0. Meanwhile, the first 20 radiomic features could increase the classifying performance dramatically while inclusion of extra features did not make a big difference to the performance of the SVM classifier in each iteration. To improve the generalization of our predictive model, only radiomic features appearing more than three times in the first 20 of contribution value rank in four iterations of the 4-fold cross validation were selected for final model building. Those radiomic features that were used for final predictive model building incorporated four from GLCM, four from GLSZM, and one from GLRLM. All the nine features are detailed in Table 2.
The flowchart of the whole study.
Nine selected radiomic features.
Selected radiomic feature
Radiomic group
Filter associated
R1
Maximum probability
GLCM
wavelet-LLL
R2
Joint entropy
GLCM
None
R3
Sum entropy
GLCM
wavelet-LLL
R4
Joint energy
GLCM
None
R5
Gray level Non-uniformity
GLSZM
log-sigma-1-0-mm-3D
R6
Gray level Non-uniformity
GLSZM
wavelet-LLH
R7
Gray level Non-uniformity
GLSZM
log-sigma-3-0-mm-3D
R8
Size zone Non-uniformity
GLSZM
None
R9
Low gray level run emphasis
GLRLM
log-sigma-3-0-mm-3D
GLCM, gray level co-occurrence matrix; GLSZM, gray level size zone matrix; GLRLM, gray level run length matrix.
We first investigated whether the nine advanced radiomic features could discriminate IVA from indolent adenocarcinoma (AIS/MIA) representing as pGGNs. Figure 3 shows the comparison of those nine radiomic features for the distinction of pGGN-like AIS/MIA and IVA in training and test cohorts. All the selected radiomic features revealed significant difference between AIS/MIA and IVA in both training and test cohorts (P < 0.001). Among the nine radiomic features, three (Maximum Probability, Joint Energy, and Low Gray Level Run Emphasis) had larger median values for AIS/MIA while the median values of six other features (Joint Entropy, Sum Entropy, Gray Level Non-Uniformity, and Three Different Filtered Size Zone Non-Uniformity) were higher for IVA than that for AIS/MIA in both training and test cohorts (detailed in Supplemental Table 3).
(A–I) Selected radiomic features in training and test cohort. Nine selected radiomic features showed significant difference between AIS/MIA and IVA in both training and test cohorts. Maximum Probability, Joint Energy, and Low Gray Level Run Emphasis had larger median values for AIS/MIA, while the median values of Joint Entropy, Sum Entropy, Gray Level Non-Uniformity, and Size Zone Non-uniformity were higher for IVA in both training and test cohorts.
Multivariate predictive models were created for each set of features, involving conventional (age, diameter, and density), radiomic (nine predictive features), and the combined (conventional and selected radiomic features) sets. The three different models presented good predictive power (AUC > 0.8, P < 0.05) in both training and test cohorts as shown in Figure 4, Supplemental Table 4. Then, DeLong's test (34, 35) was applied to complete the pairwise predictive performance comparison among the three models, in which no significant difference was observed (P > 0.05). Figure 5 showed comprehensive parameters including accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), misdiagnosis rate (MR), and missed diagnosis rate (MDR) of the three predictive models' and two radiologists' binary diagnosis in the test cohort (detailed in Supplemental Table 5). The accuracy was 76.56, 71.88, and 78.13% for radiomic, conventional, and combined predictive models but no big difference was noted in comprehensive assessment among the three models (Figure 5), which was consistent with the results of DeLong's test above. In a word, no matter what features were used for model training (conventional or radiomic features), predictive models built with machine learning algorithm could predict pGGN-like IVA from AIS/MIA well. The combination of conventional and radiomic features could further improve the diagnosis accuracy of predictive model, but the improvement was not statistically significant in our study.
The ROC analysis of the three different predictive model. Three predictive models presented good performance in discrimination between pGGN-like IVA and AIS/MIA. (A), the predictive performance of three models in the 693 training cohort; (B), the predictive performance of three models in the testing cohort.
The binary diagnosis of predictive models and two radiologists. (A), the first binary diagnosis of the senior radiologist A with 10-year experience in thoracic imaging diagnosis in test cohort; (B), the first binary diagnosis of the junior radiologist B with 2-year experience in medical imaging diagnosis in test cohort; R, the binary diagnosis of radiomic predictive model in test cohort; A+R, the second binary diagnosis of radiologist A in test cohort with the aid of radiomic predictive model; B+R, the second binary diagnosis of radiologist B in test cohort with the aid of radiomic predictive model. PPV, positive predictive value; NPV, negative predictive value; MR, misdiagnosis rate; MDR, missed diagnosis rate.
To investigate whether the radiomic predictive model could help radiologists improve their predictive performance, we then compared the diagnosis of radiomic predictive model and two radiologists (Figure 5, Table 3). Radiologist A performed better than radiologist B with higher diagnostic accuracy, sensitivity, specificity, PPV, and NPV. Either accuracy or sensitivity, radiomic predictive model outperformed radiologist A with the cost of decreased specificity. Significant difference was observed between the binary diagnosis of radiomic predictive model and that of two radiologists (A vs. R, χ2 = 7.563, P = 0.004, B vs. R, χ2 = 4, P = 0.043). Generally speaking, Radiomic predictive model showed better performance than two radiologists in the prediction between pGGN-like IVA and AIS/MIA. Two radiologists dramatically improved their diagnostic accuracy to 79.69 and 75.00% with the aid of radiomic predictive model (A vs. A+R, χ2 = 4.9, P = 0.021, B vs. B+R, χ2 = 5.042, P = 0.023). The comparison of the second diagnosis of two radiologists revealed that when having the guidance from radiomic predictive model, no statistical difference existed between two radiologists in prediction of pGGN-like IVA and AIS/MIA (A+R vs. B+R, χ2 = 1.455, P = 0.227).
Performance comparison between radiomic predictive model and two radiologists in test cohort.
McNemar's test
Kappa analysis
χ2
P
κ
P
A vs. B
0
1.000
0.316
0.018
A vs. R
7.563
0.004
0.517
<0.001
B vs. R
4
0.043
0.241
0.070
A vs. A+R
4.9
0.021
0.690
<0.001
B vs. B+R
5.042
0.023
0.275
0.022
A+R vs. B+R
1.455
0.227
0.654
<0.001
A, the first binary diagnosis of the senior radiologist A with 10-year experience in thoracic imaging diagnosis in test cohort; B, the first binary diagnosis of the junior radiologist B with 2-year experience in medical imaging diagnosis in test cohort; R, the binary diagnosis of radiomic predictive model in test cohort; A+R, the second binary diagnosis of radiologist A in the test cohort with the aid of radiomic predictive model; B+R, the second binary diagnosis of radiologist B in the test cohort with the aid of radiomic predictive model.
Discussion
When it comes to pure ground-glass pulmonary nodules, clinicians tend to choose follow-up as their first choice for management. However, according to previous studies (4, 5, 31), a certain proportion of IVA that needs surgical treatment could be pGGNs on CT scans. If a pGGN-like IVA was misdiagnosed as a benign lesion or indolent adenocarcinoma and was given a prescription of follow-up, it might progress during the interval or even metastasize and miss the optimal time for surgical intervention. Conventional radiological features like lobulated signs, spiculations, bubble lucencies, and pleura traction have been demonstrated to be helpful to differentiate the malignancy of pulmonary nodules and the invasiveness of lung adenocarcinoma (36, 37). However, pGGN-like lung adenocarcinoma tends to be in small volume with a large similarity in morphological characteristics and the assessment of the conventional radiological features is easy to be affected by the subjectivity of doctors, it remains a challenge to make a precise judgement for pGGNs without surgical intervention. Percutaneous biopsies may be helpful in determining the nature of pulmonary nodules. Nevertheless, it is an invasive tissue extraction method with the possibility of postoperative complications, and due to the tumor heterogeneity, it is not persuasive to represent the characteristics of the entire lesion with only a tiny tissue (20, 38). In some cases, because of nodules' location and size, percutaneous biopsies may not be a good choice for diagnosis. Thus, the diagnosis of pGGNs remains a thorny point for clinical research.
Heidinger et al. reported that two-dimensional diameter could provide enough information for pulmonary nodule risk classification in their quantitative analysis based on CT images (4, 39). In our study, the diameter of pGGNs showed a significantly different distribution between IVA and indolent adenocarcinoma (AIS/MIA), which was consistent with previous reports. Kitami et al. acclaimed that almost all the pGGNs with a diameter <10 mm and a density of no more than −600 HU were demonstrated to be preinvasive lesions in their study (40). The medians of density in our training and test cohort were −583.0 and −525.8 HU, both slightly higher than −600 HU, which might have something to do with the difference in lung adenocarcinoma classification. We classified MIA into indolent adenocarcinoma, which might lead to the increase in density medians.
Radiomics is a new quantitative image analysis approach that allows thorough exploration in medical images and has attracted more and more attention in the field of medicine in recent years. In this study, we obtained plenty of quantitative radiomic features (960 for each nodule) from routine thoracic CT images using machine learning techniques and completed the quantitative analysis of pGGN-like adenocarcinoma classification on the basis of conventional clinical, quantitative radiological, and selected radiomic features. Nine selected radiomic features demonstrated good performance in distinction of the IVA and indolent adenocarcinoma representing pGGNs on unenhanced CT images. Nine selected radiomic features in our study consisted of four GLCM-based features, four GLSZM-based features, and one GLRLM-based feature, which were analyzed as follows:
(1) Four radiomic features from GLCM: Maximum Probability, Joint Entropy, Sum Entropy, and Joint Energy. GLCM was used to compare the gray level correlation between two points in a certain distance in a spatial position, which reflects the comprehensive information about pixel distribution including direction, distance, gray value, and the pattern of gray level arrangement (41, 42). Maximum Probability refers to a pair of pixels with the highest frequency in a GLCM (43, 44). Entropy is a parameter describing the complexity of an image, which means the larger the entropy value of an image is, the more complex the image is (43). Energy is related to the uniformity of gray level distribution and the roughness of image texture with a larger energy indicating a more regular and more stable texture (42, 43). In our study, Maximum Probability and Joint Energy got higher median values for AIS/MIA in the training as well as test cohort while Joint Entropy and Sum Entropy in IVA were higher than that in AIS/MIA in both training and test cohort. This might have something to do with that AIS/MIA tend to be homogeneous, which results in a higher probability of finding pixels with same distribution pattern in AIS/MIA and the different entropy and energy values between AIS/MIA and IVA.
(2) Four radiomic features from GLSZM and one from GLRLM: Gray Level Non-Uniformity filtered with LOG or wavelet algorithm, Size Zone Non-Uniformity, and Low Gray Level Run Emphasis. GLSZM refers to the number of pixels that share the same gray level intensity and the same arrangement pattern in an image while GLRLM calculates the number of pixels with the same gray level value and distribution pattern in a certain direction (45–47). Gray Level Non-Uniformity and Size Zone Non-Uniformity indicate the variability of gray level and size zone volumes in an image, with a higher value referring to more heterogeneity in ROIs (46, 47). In our study, the median values of Gray Level Non-Uniformity and Size Zone Non-Uniformity were higher for IVA, which might be related to the fact that IVA tends to be more heterogeneous. Low Gray Level Run Emphasis analyzes the distribution of low gray level values in an image (47). The homogeneity and relatively lower average density of AIS/MIA might lead to the higher median value of Low Gray Level Run Emphasis for AIS/MIA in our study.
Chen et al. picked 76 features meaningful for the distinction of malignancy of pulmonary nodules from 750 extracted radiomic features and built a predictive model whose accuracy was up to 84% using four selected advanced features (20). Yagi et al. carried out the texture analysis of high-resolution computed tomography (HRCT) and found that 90th percentile and entropy performed well in discrimination between AIS/MIA and IVA with an AUC value of 0.90 (95% CI: 0.84–0.95) (13). Three different predictive models set with clinical, radiological, and nine selected radiomic features from 960 features extracted from unenhanced CT images in our study all presented good predictive power in the discrimination between AIS/MIA (indolent adenocarcinoma) and IVA (AUC > 0.8, P < 0.05). She et al. extracted radiomic features from radiological data of 402 cases (207 for training and 195 for test) diagnosed with lung adenocarcinoma and selected five meaningful radiomic features to build a predictive model that outperformed significantly the predictive model only built with conventional radiological features including nodule diameter for the discrimination between IVA and AIS/MIA (11). However, no apparent difference existed between conventional and radiomic predictive models in our study (P > 0.05). Combining conventional and radiomic features could improve the AUC value of combined predictive model, but it was not statistically significant. This might be caused by the difference of our study population. All types of pulmonary nodules including solid and subsolid nodules, which have heterogeneous internal density in lesions, were used for She's study. Therefore, compared with conventional radiological features such as diameter and density, radiomic features could more thoroughly analyze the variability and distribution of gray level intensity in ROI, which would provide more valuable information for improvement in diagnostic performance of predictive models. However, the relatively obscure variability of gray level intensity in pGGNs might result in the limited reference value for radiomic features in prediction between pGGN-like IVA and AIS/MIA, which potentially led to the similar diagnostic performance among our three predictive models. Nevertheless, predictive models built using either radiomic or conventional radiological features presented good performance in distinction between pGGN-like IVA and AIS/MIA, which further confirmed the possibility of machine learning methods for the differentiation of the invasion of pGGN-like lung adenocarcinoma. In conclusion, the predictive models established in our study could still provide certain guidance for clinicians to make accurate diagnosis.
To assess whether the radiomic predictive model could improve radiologists' performance in diagnosis of pGGN-like lung adenocarcinoma, we further compared the dichotomous diagnosis of the radiomic predictive model and two radiologists. There was a dramatic difference between radiologist A and radiologist B in the values of diagnostic accuracy, sensitivity, and specificity. The Kappa analysis revealed bad consistency between the results of the two radiologists (κ = 0.316, P = 0.018) while McNemar's test between that of two radiologists showed no significant difference (P < 0.05). This was related to the mechanisms of two statistical methods. McNemar's test only compares results with collision in two diagnostic methods instead of using comprehensive data acquired in a study while Kappa analysis calculates the consistency in all data (48–50). Figure 6 shows the mechanism of McNemar's test and the formula for calculating the value of χ2. A small (b – c) leads to a small χ2-value, which results in a P-value of more than 0.05 no matter whether the data have actual clinical significance or not. In our test cohort, 32.8% (21/64) of the pGGNs had diverse diagnoses from two radiologists, while the value of (b – c) is only 1, which resulted in a value of 0 for χ2. In this special situation, McNemar's test and Kappa analysis should be combined with actual data distribution to complete the comparison of two radiologists' diagnosis. The comprehensive analysis showed that the diagnostic ability of senior radiologist A was higher than that of junior radiologist B and the radiomic predictive model outperformed two radiologists. When having the diagnosis of the radiomic predictive model for reference, two radiologists could significantly improve their performance in prediction pGGN-like IVA from AIS/MIA. What is more, no significant difference existed between the second diagnosis from two radiologists with the aid of the radiomic predictive model. The predictive model built using selected radiomic features in our research could obviously improve the ability of radiologists in prediction between pGGN-like IVA and AIS/MIA, especially for the junior radiologist; it could help radiologist B reach the level of the senior radiologist A's diagnostic ability, which had certain potential clinical meaning.
(A) The mechanism of McNemar's test. (B) The calculation formula for χ2-value. Formula (1) would be used if (b + c) ≥40; otherwise, formula (2) should be chosen.
There were also some limitations in our study. First, this was a retrospective study in which all the cases were sorted according to rigorous exclusion criteria. There was certain inherent selection bias in it. Meanwhile, 322 pGGNs were not large enough for this quantitative study, compared with 960 features for each nodule. Further data collection including data from other clinic centers would be done to evaluate these models' predictive reproducibility. Second, the feature selection driven by restrictive algorithm (<1% features remaining after) might lead to a certain loss of potential predictive features for distinction. Despite the significant feature reduction, we were still able to find predictive features with high robustness. Third, limitations of this trial included the lack of standardization in image acquisition completed on various CT scanners. A voxel-resampling was applied to reduce the influence from the variability in image acquisition protocols in our study, but the above problem may still have a certain impact on the feature selection and model building. Thus, the standardization of image acquisition and establishment of database with high quality are urgently required for radiomic research. Finally, we chose a semi-automatic method to complete the pGGN segmentation. Though consistent segmentation for each pGGN had been reached through negotiation by two radiologists, there was still some interobserver difference existing in this procedure. A reliable automatic segmentation algorithm that can be applied in clinical practice is still to be developed.
Conclusion
Nine selected radiomic features in our research showed different distribution between IVA and AIS/MIA, which could provide some guidance for clinical practice. The predictive models established using conventional clinical, radiological, or radiomic features could help to distinguish the invasiveness of pGGN-like lung adenocarcinoma, but radiomic features could not offer more meaningful information to improve the performance of the combined model created in our study. The diagnostic performance of the radiomic predictive model established in our study was better than that of the two radiologists, and the predictive model could provide auxiliary information for radiologists (especially for junior radiologists) to improve their diagnostic ability in discrimination between pGGN-like IVA and AIS/MIA.
Data Availability Statement
The datasets generated for this study are available on request to the corresponding author.
Ethics Statement
This study was approved by the Institutional Review Committee of the Sir Run Run Shaw Hospital.
Author Contributions
FX and WZ conceived and designed this study. FX, YS, and CP applied the data collection for this study. FX and YS did the data reproof work during the manscript review and now they are collecting new data for further exploration. FX and JW did the segmentation work for each pGGN recruited for this study. WZ and RX contributed to the algorithm design for this study. YG completed the pathological interpretation of all pGGNs in this study. FX drafted the manuscript. LS and CQ and HH contributed equally to the manuscript review.
Conflict of Interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Funding. This research was supported by Zhejiang Provincial Natural Science Foundation of China under Grant No. LQ20F030018, Key Research and Development Program of Zhejiang Province under Grant No. 2019C03064, Program Co-sponsored by Province and Ministry under Grant No. WKJ-ZJ-1926 and National Natural Science Foundation of China (NSFC) under Grant No. 61772106.
Supplementary Material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fonc.2020.00872/full#supplementary-material
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Oncol.Frontiers in Oncology2234-943XFrontiers Media S.A.32850301PMC743213310.3389/fonc.2020.00872OncologyOriginal ResearchRadiomic-Based Quantitative CT Analysis of Pure Ground-Glass Nodules to Predict the Invasiveness of Lung AdenocarcinomaXuFangyi1ZhuWenchao1ShenYao12WangJian3XuRui45OuteshChooah1SongLijiang6GanYi7PuCailing1HuHongjie1*1Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China2Department of Radiology, Yinzhou Hospital Affiliated With the School of Medicine of Ningbo University, Ningbo, China3Department of Radiology, Tongde Hospital of Zhejiang Province, Hangzhou, China4DUT-RU International School of Information Science & Engineering, Dalian University of Technology, Dalian, China5DUT-RU Co-Research Center of Advanced ICT for Active Life, Dalian, China6Department of Cardiothoracic Surgery, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China7Department of Pathology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
Edited by: Marco Lucchi, University of Pisa, Italy
Reviewed by: Ahmad Chaddad, Guilin University of Electronic Technology, China; Niha Beig, Case Western Reserve University, United States; Shuling Chen, First Affiliated Hospital of Sun Yat-sen University, China
*Correspondence: Hongjie Hu hongjiehu@zju.edu.cn
This article was submitted to Thoracic Oncology, a section of the journal Frontiers in Oncology
1182020202010872191220190452020Copyright © 2020 Xu, Zhu, Shen, Wang, Xu, Outesh, Song, Gan, Pu and Hu.2020Xu, Zhu, Shen, Wang, Xu, Outesh, Song, Gan, Pu and HuThis is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Objectives: To investigate the performance of radiomic-based quantitative analysis on CT images in predicting invasiveness of lung adenocarcinoma manifesting as pure ground-glass nodules (pGGNs).
Methods: A total of 275 lung adenocarcinoma cases, with 322 pGGNs resected surgically and confirmed pathologically, from January 2015 to October 2017 were enrolled in this retrospective study. All nodules were split into training and test cohorts randomly with a ratio of 4:1 to establish models to predict between pGGN-like adenocarcinoma in situ (AIS)/minimally invasive adenocarcinoma (MIA) and invasive adenocarcinoma (IVA). Radiomic feature extraction was performed using Pyradiomics with semi-automatically segmented tumor regions on CT scans that were contoured with an in-house plugin for 3D-Slicer. Random forest (RF) and support vector machine (SVM) were used for feature selection and predictive model building in the training cohort. Three different predictive models containing conventional, radiomic, and combined models were built on the basis of the selected clinical, radiological, and radiomic features. The predictive performance of each model was evaluated through the receiver operating characteristic curve (ROC) and the area under the curve (AUC). The predictive performance of two radiologists (A and B) and our radiomic predictive model were further investigated in the test cohort to see if radiomic predictive model could improve radiologists' performance in prediction between pGGN-like AIS/MIA and IVA.
Results: Among 322 nodules, 48 (14.9%) were AIS and 102 (31.7%) were MIA with 172 (53.4%) for IVA. Age, diameter, density, and nine meaningful radiomic features were selected for model building in the training cohort. Three predictive models showed good performance in prediction between pGGN-like AIS/MIA and IVA (AUC > 0.8, P < 0.05) in both training and test cohorts. The AUC values in the test cohort were 0.824 (95% CI, 0.723–0.924), 0.833 (95% CI, 0.733–0.934), and 0.848 (95% CI, 0.750–0.946) for conventional, radiomic, and combined models, respectively. The predictive accuracy was 73.44 and 59.38% for radiologist A and radiologist B in the test cohort and was improved dramatically to 79.69 and 75.00% with the aid of our radiomic predictive model.
Conclusion: The predictive models built in our study showed good predictive power with good accuracy and sensitivity, which provided a non-invasive, convenient, economic, and repeatable way for the prediction between IVA and AIS/MIA representing as pGGNs. The radiomic predictive model outperformed two radiologists in predicting pGGN-like AIS/MIA and IVA, and could significantly improve the predictive performance of the two radiologists, especially radiologist B with less experience in medical imaging diagnosis. The selected radiomic features in our research did not provide more useful information to improve the combined predictive model's performance.
A new classification for lung adenocarcinoma was proposed in 2011 by the International Association for the Study of Lung Cancer/American Thoracic Society/European Respiratory Society (IASLC/ATS/ERS) (1), which was also issued as the 4th edition WHO lung cancer classification in 2015 (2). According to the new classification, lung adenocarcinoma can be divided into preinvasive lesion, minimally invasive adenocarcinoma (MIA), and invasive adenocarcinoma (IVA), and preinvasive lesion includes atypical adenomatous hyperplasia (AAH) and adenocarcinoma in situ (AIS) (1). The improvement of medical technology and the generalization of lung cancer screening project have led more attention to pure ground-glass nodules (pGGNs) detected on computed tomography (CT) images (3, 4).
Approximately 20% of lung adenocarcinoma including AIS, MIA, and even some early-stage IVA could present as pGGNs on CT images (4), which makes it quite difficult for radiologists and clinicians to make a precise diagnosis with conventional radiological parameters like size, density, etc. Kakinuma et al. reported that growth was observed in approximately 10% of pGGNs ≤5 mm, of which 1% would develop into IVA or MIA in their study (5). In another study, 57.8% of pGGNs showed growth during follow-up and 26.3% of them were adenocarcinoma (6). Eguchi et al. examined 124 cases with pGGNs, and 64 pGGNs (51.6%) showed growth during their 2-year follow-up (7). Several previous research revealed that nearly 50% of pGGNs were invasive lesions (3, 8–10). In clinical practice, pGGNs are usually prescribed to be followed up but data above demonstrated that more detailed diagnosis and more individualized management should be made for pGGNs.
Compared with IVA, AIS, and MIA are considered as indolent lung adenocarcinoma because of the excellent prognosis (11, 12). AIS/MIA could be followed up or treated with sublobar resection while more aggressive surgical interventions should be taken for IVA (13, 14). Several previous studies revealed that the 5-year survival rates of AIS and MIA could be 100% and near 100% with a complete resection while that of IVA in stage Ia is no more than 75% (11, 13, 14). Thus, it might provide some guidance for clinical therapeutic decision-making if pGGN-like IVA could be figured out on preoperative CT images.
There were many studies investigating the difference in radiological features among lung adenocarcinoma subtypes. Wang et al. found that the mean CT attenuation and lesion size differed significantly between MIA and non-invasive lesions and internal air bronchograms were more often seen in adenocarcinoma (15). Several investigations reported that the notched signs, spiculations, bubbly lucencies, and rapid volume expansion were more common in IVA (15–17). However, those radiological features could be subtly different because of the small size of pGGN-like adenocarcinoma. Furthermore, the assessment of those parameters tends to be subjective, which could be influenced by radiologists' experience and diagnostic ability. Percutaneous biopsy is one method used in clinical practice to determine the nature of pulmonary nodules, which could provide relatively accurate pathological information. However, percutaneous biopsy is an invasive operation, and patients may have some operation-related complications (18, 19). Considering the heterogeneity in adenocarcinoma, small pieces of tissue obtained by biopsy cannot represent the characteristics of the whole lesion (20). What is more, in some cases, it is difficult to complete biopsy due to patient's physical condition and bad cooperation as well as the location and size of nodules (20, 21). Thus, the accurate diagnosis of pGGNs remains a key point and a challenge in the field of medical imaging diagnosis.
Radiomics is an emerging subject that could extract a large amount of invisible features from medical images for clinical decision-making (20, 22). Radiomics has had remarkable progress in central nervous system malignancies, thoracic imaging diagnosis, discrimination of hepatic mass, and some other diseases (23–26). Chaddad et al. performed retrospective analysis involving 315 patients diagnosed as non-small cell lung cancer (NSCLC) and significant correlation was observed between radiomic features and survival (27). Also, radiomics' promising performance in the distinction of benign and malignant pulmonary nodules and the discrimination of adenocarcinoma subtypes had been validated in several researches (11, 28, 29). However, in most previous radiomic studies, all types of pulmonary nodules including solid and subsolid nodules were recruited as the study population. Few studies focused on the use of machine learning in early-stage lung adenocarcinoma representing as pGGNs, which are usually very difficult to manage. Since the diagnosis of solid components in pulmonary nodules on CT images is relatively uncomplicated while pGGNs remain a big challenge for medical imaging diagnosis, we aimed to explore the potential value of radiomic-based quantitative analysis to predict the invasiveness of pGGN-like adenocarcinoma to establish a comprehensive predictive model for clinical decision-making.
Materials and Methods
This retrospective study was approved by the institutional review committee of the Sir Run Run Shaw Hospital (No. 20190520-162) with an abstention of informed consents from all the patients involved according to the guidelines of the Council for International Organizations of Medical Science (CIOMS).
Patients
We reviewed all the materials of 1,610 patients undergoing surgical resection for primary lung adenocarcinoma with complete clinical data and preoperative CT images from January 2015 to October 2017 in Sir Run Run Shaw Hospital, and reinterpreted the preoperative CT images from the Picture Archiving and Communication Systems (PACS) one by one. Clinical data like age, gender, and smoking status of all cases were collected from digital records. Patients who met any one of the following criteria were excluded: (1) nodules with solid components (n = 776), (2) nodule diameter >3 cm (n = 292), (3) patients with a history of other malignant diseases (n = 87), (4) CT images with bad quality (n = 70), and (5) patients who accepted thoracic surgical intervention, radiation, or any chemotherapeutics (n = 110).
Finally, 275 patients (72 men and 203 women, age range, 25~78 years) with 322 pGGNs (82 men and 240 women, age range, 25~78 years) were enrolled into this retrospective study (detailed in Figure 1 and Table 1). The median time from the last preoperative CT scan to surgery was 6 (0–92) days.
The flowchart of patient selection.
Analysis of clinical and radiological features of pGGNs in the training and test cohort.
Training cohort
Test cohort
Total
AIS/MIA
IVA
P
Total
AIS/MIA
IVA
P
258
120 (46.5%)
138 (53.5%)
64
30 (46.9%)
34 (53.1%)
Gender
    Male
70 (27.1%)
31 (25.8%)
39 (28.3%)
0.662
12 (18.8%)
3 (10.0%)
9 (26.5%)
0.092
    Female
188 (72.9%)
89 (74.2%)
99 (71.7%)
52 (81.3%)
27 (90.0%)
25 (73.5%)
Age (years)
53 (25, 78)
51 (25, 69)
55 (26, 78)
0.001
54 (30, 72)
51 (31, 72)
55 (30, 72)
0.106#
Smoking history
    Never
230 (89.1%)
109 (90.8%)
121 (87.7%)
0.417
59 (92.2%)
29 (96.7%)
30 (88.2%)
0.360*
    Current or ever
28 (10.9%)
11 (9.2%)
17 (12.3%)
5 (7.8%)
1 (3.3%)
4 (11.8%)
    Diameter (cm)
0.94 (0.24, 2.94)
0.76 (0.24, 1.92)
1.13 (0.51, 2.94)
<0.001
0.93 (0.34, 2.04)
0.70 (0.34, 1.41)
1.09 (0.61, 2.04)
<0.001#
Density (HU)
−583.0 (−829.2, −122.5)
−615.0 (−801.7, −122.5)
−529.2 (−829.2, −176.3)
<0.001
−525.8 (−763.2, −179.8)
−544.7 (−763.2, −298.9)
−509.0 (−739.1, −179.8)
0.088#
Two-sample t-test.
Fisher exact probability test.
Continuous variables were represented in median (minimum, maximum) without any special statement.
pGGNs, pure ground-glass nodules; AIS, adenocarcinoma in situ; MIA, minimally invasive adenocarcinoma; IVA, invasive adenocarcinoma.
Histological Evaluation
All surgical specimens were fixed with formalin and stained with hematoxylin–eosin (HE). Two pathologists evaluated all slides using a multi-headed microscope and discussed about the diagnosis until a consensus was reached. According to the classification of lung adenocarcinoma issued in the 4th edition WHO lung cancer classification in 2015, each nodule was classified as AIS, MIA, and IVA (26, 30). Each histological pattern presented in targeted lesion including lepidic, acinar, solid, papillary, and micropapillary patterns was recorded in 5% increments (30, 31).
Image Acquisition
All the plain CT images were obtained using multidetector computed tomography scanners (Siemens SOMATOM Definition Flash, Siemens FORCE CT, Siemens Sensation 16, Siemens Definition AS 40, and GE LightSpeed VCT). The protocol parameters for each scanning were detailed in Supplemental Table 1. Plain spiral acquisitions were obtained from thoracic inlet to lung bases on patients accepting breath-hold training. Images were reconstructed using a standard reconstruction kernels in lung window settings (mean, −500 HU; width, 1,500 HU). All images underwent multi-planar reconstruction (MPR) including coronal and sagittal reconstruction utilizing the post-processing station.
Nodule Analysis and Segmentation
All transverse CT images were interpreted jointly by two radiologists who were both blind to the clinical and pathological information of all cases (radiologist A, with 10 years' experience in thoracic imaging diagnosis; radiologist B, with 2 years' experience in medical imaging diagnosis) on our professional reading screen. Conventional quantitative radiological features that were widely used in clinical diagnosis involving diameter (cm) and density (Hounsfield Unit, HU) were determined for each nodule. Nodule diameter was measured on the average of long- and short-axis diameters, both of which should be obtained on the same transverse revealing the greatest dimensions. The nodule density was measured at three different parts of each nodule avoiding vessels and bronchus and the mean value of the three results was calculated. The mean value of diameter and density measured by the two radiologists was calculated for our study.
Segmentation data consisted of all the 322 pGGNs. Plain CT images were loaded into 3D-slicer (http://www.slicer.org) (32), an open source image processing software, implemented with in-house algorithm for automatic nodule detection and segmentation. Radiologist B would verify the regions of interest (ROIs) of automatic segmentation and made some modifications when the ROIs were not satisfactory. Radiologist A would have a second review for the results of radiologist B's semi-automatic segmentation. A consensus would be achieved via negotiation between two radiologists for each case when meeting a collision on reviewing. While modifying ROIs, two radiologists would delineate manually around the nodule boundary on each section avoiding the bronchus and vessels as much as they could.
Radiomic Feature Extraction and Predictive Models Building
Segmentation data were analyzed with Pyradiomics to extract radiomic features describing tumor phenotypes (33). All the segmentation data had a voxel resampling of 0.7 × 0.7 × 0.7 mm3 for standardization to reduce the impact from the heterogeneity of image acquisition. In the end, we obtained nine types totaling 960 radiomic features for each nodule, which have been listed in Supplemental Table 2. Features are commonly grouped as follows: (1) first-order statistical features: these describe the voxel intensity distribution in the delineated ROI. They are usually calculated on the basis of the intensity histogram, including energy, entropy, standard deviation, skewness, kurtosis, uniformity, mean, minimum, and maximum intensity values and so on (20, 26). (2) Shape-based features: descriptors of the two- and three-dimensional size and shape of the ROI. (3) Textural features: these contain gray level co-occurrence matrix (GLCM), gray level run length matrix (GLRLM), gray level size zone matrix (GLSZM), neighboring gray size zone matrix (NGZDM), and gray-level dependence matrix (GLDM). They are computed on the analysis of the three-dimensional directions within the tumor and the consideration of the spatial location of each voxel in the ROI (26). (4) Transformed features: features in the first and third groups extracted from images applied with a series of wavelet or Laplacian-of-Gaussian filtration.
Random forest (RF) and support vector machine (SVM) with cross-validation (CV) was used for radiomic feature selection and predictive model building to distinct pGGN-like IVA from indolent adenocarcinoma (AIS/MIA). Multivariate models were made in training cohort and were tested in a separate test cohort. All pGGNs were split into training and test cohorts randomly by a ratio of 4:1. Three predictive models were created in our research: (1) Conventional (selected clinical and radiological quantitative features), (2) Radiomic (selected radiomic features), and (3) Combined (selected conventional and radiomic features) predictive models. Subsequently, a binary analysis in which pGGN-like IVA was set as positive while AIS/MIA was thought as negative was applied to compare the predictive performance between radiomic predictive model and two radiologists (A and B) in the test cohort. Two weeks later, two radiologists, knowing the performance of our radiomic predictive model and its diagnosis for each pGGN in the test cohort, reevaluated all pGGNs in the test cohort.
Statistical Analysis
All the statistical analysis was applied using SPSS 25.0 (IBM, Armonk, NY, USA) and MedCalc 15.8 (MedCalc Software, Acacialaan 22, Ostend, Belgium). Tables and figures in our study were made with GraphPad Prism 5 (GraphPad Software Inc., San Diego, CA, USA) and Microsoft Office 2019 (Microsoft, Redmond, WDC, USA).
Thirty pGGNs were selected randomly to test the repeatability of nodule diameter and density measurement. Radiologist A and B did the measurement work of those 30 pGGNs, respectively. Two weeks later, radiologist B measured the diameter and density of these 30 pGGNs, again according to the same measurement criteria. Inter-/intra-observer correlation coefficient (ICC) was calculated for repeatability assessment.
For the assessment of clinical, quantitative radiological, and selected radiomic features, chi-square test or Fisher exact probability test was utilized for categorical variables. Two-sample t-test was adopted, if the continuous variables met the normal test and variance homogeneity test; otherwise, Wilcoxon signed-rank test was used. Predictive power of each predictive model was evaluated using receiver operating characteristic (ROC) curve and area under the curve (AUC). Models with an AUC > 0.50 and a P < 0.05 were thought to be predictive. McNemar's test and Kappa analysis were used to compare the binary diagnosis of two radiologists and radiomic predictive model.
Results
The measurement of nodule diameter and density between senior radiologist A and junior radiologist B was highly consistent (ICC > 0.9, P < 0.05). Two weeks later, radiologist B did the measurement for the 30 selected pGGNs, and the ICC values were up to 0.955 (P < 0.05) and 0.984 (P < 0.05) for diameter and density measurement.
Clinical Data and Conventional Image Features
A total of 322 pGGNs were recruited into this study with 80% in the training cohort and 20% in the test cohort. The analysis of clinical and quantitative radiological features in the training and test cohort were listed in Table 1. In the training cohort, the median age was 53 years (age range, 25–78 years) and the majority of cases were female (72.9%) with 28 (10.9%) having a smoking history. In the test cohort, 52 (81.3%) were female with a median age of 54 years (age range, 30–72 years) and 59 (92.2%) never smoke. Diameter showed statistical discrepancy between AIS/MIA and IVA in both training and test cohort (P < 0.001) while nothing significantly different existed in gender and smoking status between the two groups. Age and density exhibited evident difference in training cohort between AIS/MIA and IVA (P = 0.01 and P < 0.001), but no significant difference (P > 0.05) appeared between the two groups in test cohort.
Radiomic Feature Selection and Predictive Model Building
A RF algorithm with 4-fold cross-validation was taken to calculate the contribution value of each radiomic feature in the training cohort for the prediction of pGGN-like IVA from AIS/MIA. Predictive model building was performed using SVM also combined with 4-fold cross-validation. All the extracted radiomic features were listed in descending order by the contribution value for the classifier and were added one by one as the input for the SVM model training in each iteration of cross-validation calculation (detailed in Figure 2). In the process of gradual accumulation of model training, the overall accuracy of the training cohort was recorded. When sequencing extracted radiomic features by their contribution value for the classifier in each iteration, we found that the first 20 radiomic features contributed much more than other features whose contribution values were <0.01 and even close to 0. Meanwhile, the first 20 radiomic features could increase the classifying performance dramatically while inclusion of extra features did not make a big difference to the performance of the SVM classifier in each iteration. To improve the generalization of our predictive model, only radiomic features appearing more than three times in the first 20 of contribution value rank in four iterations of the 4-fold cross validation were selected for final model building. Those radiomic features that were used for final predictive model building incorporated four from GLCM, four from GLSZM, and one from GLRLM. All the nine features are detailed in Table 2.
The flowchart of the whole study.
Nine selected radiomic features.
Selected radiomic feature
Radiomic group
Filter associated
R1
Maximum probability
GLCM
wavelet-LLL
R2
Joint entropy
GLCM
None
R3
Sum entropy
GLCM
wavelet-LLL
R4
Joint energy
GLCM
None
R5
Gray level Non-uniformity
GLSZM
log-sigma-1-0-mm-3D
R6
Gray level Non-uniformity
GLSZM
wavelet-LLH
R7
Gray level Non-uniformity
GLSZM
log-sigma-3-0-mm-3D
R8
Size zone Non-uniformity
GLSZM
None
R9
Low gray level run emphasis
GLRLM
log-sigma-3-0-mm-3D
GLCM, gray level co-occurrence matrix; GLSZM, gray level size zone matrix; GLRLM, gray level run length matrix.
We first investigated whether the nine advanced radiomic features could discriminate IVA from indolent adenocarcinoma (AIS/MIA) representing as pGGNs. Figure 3 shows the comparison of those nine radiomic features for the distinction of pGGN-like AIS/MIA and IVA in training and test cohorts. All the selected radiomic features revealed significant difference between AIS/MIA and IVA in both training and test cohorts (P < 0.001). Among the nine radiomic features, three (Maximum Probability, Joint Energy, and Low Gray Level Run Emphasis) had larger median values for AIS/MIA while the median values of six other features (Joint Entropy, Sum Entropy, Gray Level Non-Uniformity, and Three Different Filtered Size Zone Non-Uniformity) were higher for IVA than that for AIS/MIA in both training and test cohorts (detailed in Supplemental Table 3).
(A–I) Selected radiomic features in training and test cohort. Nine selected radiomic features showed significant difference between AIS/MIA and IVA in both training and test cohorts. Maximum Probability, Joint Energy, and Low Gray Level Run Emphasis had larger median values for AIS/MIA, while the median values of Joint Entropy, Sum Entropy, Gray Level Non-Uniformity, and Size Zone Non-uniformity were higher for IVA in both training and test cohorts.
Multivariate predictive models were created for each set of features, involving conventional (age, diameter, and density), radiomic (nine predictive features), and the combined (conventional and selected radiomic features) sets. The three different models presented good predictive power (AUC > 0.8, P < 0.05) in both training and test cohorts as shown in Figure 4, Supplemental Table 4. Then, DeLong's test (34, 35) was applied to complete the pairwise predictive performance comparison among the three models, in which no significant difference was observed (P > 0.05). Figure 5 showed comprehensive parameters including accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), misdiagnosis rate (MR), and missed diagnosis rate (MDR) of the three predictive models' and two radiologists' binary diagnosis in the test cohort (detailed in Supplemental Table 5). The accuracy was 76.56, 71.88, and 78.13% for radiomic, conventional, and combined predictive models but no big difference was noted in comprehensive assessment among the three models (Figure 5), which was consistent with the results of DeLong's test above. In a word, no matter what features were used for model training (conventional or radiomic features), predictive models built with machine learning algorithm could predict pGGN-like IVA from AIS/MIA well. The combination of conventional and radiomic features could further improve the diagnosis accuracy of predictive model, but the improvement was not statistically significant in our study.
The ROC analysis of the three different predictive model. Three predictive models presented good performance in discrimination between pGGN-like IVA and AIS/MIA. (A), the predictive performance of three models in the 693 training cohort; (B), the predictive performance of three models in the testing cohort.
The binary diagnosis of predictive models and two radiologists. (A), the first binary diagnosis of the senior radiologist A with 10-year experience in thoracic imaging diagnosis in test cohort; (B), the first binary diagnosis of the junior radiologist B with 2-year experience in medical imaging diagnosis in test cohort; R, the binary diagnosis of radiomic predictive model in test cohort; A+R, the second binary diagnosis of radiologist A in test cohort with the aid of radiomic predictive model; B+R, the second binary diagnosis of radiologist B in test cohort with the aid of radiomic predictive model. PPV, positive predictive value; NPV, negative predictive value; MR, misdiagnosis rate; MDR, missed diagnosis rate.
To investigate whether the radiomic predictive model could help radiologists improve their predictive performance, we then compared the diagnosis of radiomic predictive model and two radiologists (Figure 5, Table 3). Radiologist A performed better than radiologist B with higher diagnostic accuracy, sensitivity, specificity, PPV, and NPV. Either accuracy or sensitivity, radiomic predictive model outperformed radiologist A with the cost of decreased specificity. Significant difference was observed between the binary diagnosis of radiomic predictive model and that of two radiologists (A vs. R, χ2 = 7.563, P = 0.004, B vs. R, χ2 = 4, P = 0.043). Generally speaking, Radiomic predictive model showed better performance than two radiologists in the prediction between pGGN-like IVA and AIS/MIA. Two radiologists dramatically improved their diagnostic accuracy to 79.69 and 75.00% with the aid of radiomic predictive model (A vs. A+R, χ2 = 4.9, P = 0.021, B vs. B+R, χ2 = 5.042, P = 0.023). The comparison of the second diagnosis of two radiologists revealed that when having the guidance from radiomic predictive model, no statistical difference existed between two radiologists in prediction of pGGN-like IVA and AIS/MIA (A+R vs. B+R, χ2 = 1.455, P = 0.227).
Performance comparison between radiomic predictive model and two radiologists in test cohort.
McNemar's test
Kappa analysis
χ2
P
κ
P
A vs. B
0
1.000
0.316
0.018
A vs. R
7.563
0.004
0.517
<0.001
B vs. R
4
0.043
0.241
0.070
A vs. A+R
4.9
0.021
0.690
<0.001
B vs. B+R
5.042
0.023
0.275
0.022
A+R vs. B+R
1.455
0.227
0.654
<0.001
A, the first binary diagnosis of the senior radiologist A with 10-year experience in thoracic imaging diagnosis in test cohort; B, the first binary diagnosis of the junior radiologist B with 2-year experience in medical imaging diagnosis in test cohort; R, the binary diagnosis of radiomic predictive model in test cohort; A+R, the second binary diagnosis of radiologist A in the test cohort with the aid of radiomic predictive model; B+R, the second binary diagnosis of radiologist B in the test cohort with the aid of radiomic predictive model.
Discussion
When it comes to pure ground-glass pulmonary nodules, clinicians tend to choose follow-up as their first choice for management. However, according to previous studies (4, 5, 31), a certain proportion of IVA that needs surgical treatment could be pGGNs on CT scans. If a pGGN-like IVA was misdiagnosed as a benign lesion or indolent adenocarcinoma and was given a prescription of follow-up, it might progress during the interval or even metastasize and miss the optimal time for surgical intervention. Conventional radiological features like lobulated signs, spiculations, bubble lucencies, and pleura traction have been demonstrated to be helpful to differentiate the malignancy of pulmonary nodules and the invasiveness of lung adenocarcinoma (36, 37). However, pGGN-like lung adenocarcinoma tends to be in small volume with a large similarity in morphological characteristics and the assessment of the conventional radiological features is easy to be affected by the subjectivity of doctors, it remains a challenge to make a precise judgement for pGGNs without surgical intervention. Percutaneous biopsies may be helpful in determining the nature of pulmonary nodules. Nevertheless, it is an invasive tissue extraction method with the possibility of postoperative complications, and due to the tumor heterogeneity, it is not persuasive to represent the characteristics of the entire lesion with only a tiny tissue (20, 38). In some cases, because of nodules' location and size, percutaneous biopsies may not be a good choice for diagnosis. Thus, the diagnosis of pGGNs remains a thorny point for clinical research.
Heidinger et al. reported that two-dimensional diameter could provide enough information for pulmonary nodule risk classification in their quantitative analysis based on CT images (4, 39). In our study, the diameter of pGGNs showed a significantly different distribution between IVA and indolent adenocarcinoma (AIS/MIA), which was consistent with previous reports. Kitami et al. acclaimed that almost all the pGGNs with a diameter <10 mm and a density of no more than −600 HU were demonstrated to be preinvasive lesions in their study (40). The medians of density in our training and test cohort were −583.0 and −525.8 HU, both slightly higher than −600 HU, which might have something to do with the difference in lung adenocarcinoma classification. We classified MIA into indolent adenocarcinoma, which might lead to the increase in density medians.
Radiomics is a new quantitative image analysis approach that allows thorough exploration in medical images and has attracted more and more attention in the field of medicine in recent years. In this study, we obtained plenty of quantitative radiomic features (960 for each nodule) from routine thoracic CT images using machine learning techniques and completed the quantitative analysis of pGGN-like adenocarcinoma classification on the basis of conventional clinical, quantitative radiological, and selected radiomic features. Nine selected radiomic features demonstrated good performance in distinction of the IVA and indolent adenocarcinoma representing pGGNs on unenhanced CT images. Nine selected radiomic features in our study consisted of four GLCM-based features, four GLSZM-based features, and one GLRLM-based feature, which were analyzed as follows:
(1) Four radiomic features from GLCM: Maximum Probability, Joint Entropy, Sum Entropy, and Joint Energy. GLCM was used to compare the gray level correlation between two points in a certain distance in a spatial position, which reflects the comprehensive information about pixel distribution including direction, distance, gray value, and the pattern of gray level arrangement (41, 42). Maximum Probability refers to a pair of pixels with the highest frequency in a GLCM (43, 44). Entropy is a parameter describing the complexity of an image, which means the larger the entropy value of an image is, the more complex the image is (43). Energy is related to the uniformity of gray level distribution and the roughness of image texture with a larger energy indicating a more regular and more stable texture (42, 43). In our study, Maximum Probability and Joint Energy got higher median values for AIS/MIA in the training as well as test cohort while Joint Entropy and Sum Entropy in IVA were higher than that in AIS/MIA in both training and test cohort. This might have something to do with that AIS/MIA tend to be homogeneous, which results in a higher probability of finding pixels with same distribution pattern in AIS/MIA and the different entropy and energy values between AIS/MIA and IVA.
(2) Four radiomic features from GLSZM and one from GLRLM: Gray Level Non-Uniformity filtered with LOG or wavelet algorithm, Size Zone Non-Uniformity, and Low Gray Level Run Emphasis. GLSZM refers to the number of pixels that share the same gray level intensity and the same arrangement pattern in an image while GLRLM calculates the number of pixels with the same gray level value and distribution pattern in a certain direction (45–47). Gray Level Non-Uniformity and Size Zone Non-Uniformity indicate the variability of gray level and size zone volumes in an image, with a higher value referring to more heterogeneity in ROIs (46, 47). In our study, the median values of Gray Level Non-Uniformity and Size Zone Non-Uniformity were higher for IVA, which might be related to the fact that IVA tends to be more heterogeneous. Low Gray Level Run Emphasis analyzes the distribution of low gray level values in an image (47). The homogeneity and relatively lower average density of AIS/MIA might lead to the higher median value of Low Gray Level Run Emphasis for AIS/MIA in our study.
Chen et al. picked 76 features meaningful for the distinction of malignancy of pulmonary nodules from 750 extracted radiomic features and built a predictive model whose accuracy was up to 84% using four selected advanced features (20). Yagi et al. carried out the texture analysis of high-resolution computed tomography (HRCT) and found that 90th percentile and entropy performed well in discrimination between AIS/MIA and IVA with an AUC value of 0.90 (95% CI: 0.84–0.95) (13). Three different predictive models set with clinical, radiological, and nine selected radiomic features from 960 features extracted from unenhanced CT images in our study all presented good predictive power in the discrimination between AIS/MIA (indolent adenocarcinoma) and IVA (AUC > 0.8, P < 0.05). She et al. extracted radiomic features from radiological data of 402 cases (207 for training and 195 for test) diagnosed with lung adenocarcinoma and selected five meaningful radiomic features to build a predictive model that outperformed significantly the predictive model only built with conventional radiological features including nodule diameter for the discrimination between IVA and AIS/MIA (11). However, no apparent difference existed between conventional and radiomic predictive models in our study (P > 0.05). Combining conventional and radiomic features could improve the AUC value of combined predictive model, but it was not statistically significant. This might be caused by the difference of our study population. All types of pulmonary nodules including solid and subsolid nodules, which have heterogeneous internal density in lesions, were used for She's study. Therefore, compared with conventional radiological features such as diameter and density, radiomic features could more thoroughly analyze the variability and distribution of gray level intensity in ROI, which would provide more valuable information for improvement in diagnostic performance of predictive models. However, the relatively obscure variability of gray level intensity in pGGNs might result in the limited reference value for radiomic features in prediction between pGGN-like IVA and AIS/MIA, which potentially led to the similar diagnostic performance among our three predictive models. Nevertheless, predictive models built using either radiomic or conventional radiological features presented good performance in distinction between pGGN-like IVA and AIS/MIA, which further confirmed the possibility of machine learning methods for the differentiation of the invasion of pGGN-like lung adenocarcinoma. In conclusion, the predictive models established in our study could still provide certain guidance for clinicians to make accurate diagnosis.
To assess whether the radiomic predictive model could improve radiologists' performance in diagnosis of pGGN-like lung adenocarcinoma, we further compared the dichotomous diagnosis of the radiomic predictive model and two radiologists. There was a dramatic difference between radiologist A and radiologist B in the values of diagnostic accuracy, sensitivity, and specificity. The Kappa analysis revealed bad consistency between the results of the two radiologists (κ = 0.316, P = 0.018) while McNemar's test between that of two radiologists showed no significant difference (P < 0.05). This was related to the mechanisms of two statistical methods. McNemar's test only compares results with collision in two diagnostic methods instead of using comprehensive data acquired in a study while Kappa analysis calculates the consistency in all data (48–50). Figure 6 shows the mechanism of McNemar's test and the formula for calculating the value of χ2. A small (b – c) leads to a small χ2-value, which results in a P-value of more than 0.05 no matter whether the data have actual clinical significance or not. In our test cohort, 32.8% (21/64) of the pGGNs had diverse diagnoses from two radiologists, while the value of (b – c) is only 1, which resulted in a value of 0 for χ2. In this special situation, McNemar's test and Kappa analysis should be combined with actual data distribution to complete the comparison of two radiologists' diagnosis. The comprehensive analysis showed that the diagnostic ability of senior radiologist A was higher than that of junior radiologist B and the radiomic predictive model outperformed two radiologists. When having the diagnosis of the radiomic predictive model for reference, two radiologists could significantly improve their performance in prediction pGGN-like IVA from AIS/MIA. What is more, no significant difference existed between the second diagnosis from two radiologists with the aid of the radiomic predictive model. The predictive model built using selected radiomic features in our research could obviously improve the ability of radiologists in prediction between pGGN-like IVA and AIS/MIA, especially for the junior radiologist; it could help radiologist B reach the level of the senior radiologist A's diagnostic ability, which had certain potential clinical meaning.
(A) The mechanism of McNemar's test. (B) The calculation formula for χ2-value. Formula (1) would be used if (b + c) ≥40; otherwise, formula (2) should be chosen.
There were also some limitations in our study. First, this was a retrospective study in which all the cases were sorted according to rigorous exclusion criteria. There was certain inherent selection bias in it. Meanwhile, 322 pGGNs were not large enough for this quantitative study, compared with 960 features for each nodule. Further data collection including data from other clinic centers would be done to evaluate these models' predictive reproducibility. Second, the feature selection driven by restrictive algorithm (<1% features remaining after) might lead to a certain loss of potential predictive features for distinction. Despite the significant feature reduction, we were still able to find predictive features with high robustness. Third, limitations of this trial included the lack of standardization in image acquisition completed on various CT scanners. A voxel-resampling was applied to reduce the influence from the variability in image acquisition protocols in our study, but the above problem may still have a certain impact on the feature selection and model building. Thus, the standardization of image acquisition and establishment of database with high quality are urgently required for radiomic research. Finally, we chose a semi-automatic method to complete the pGGN segmentation. Though consistent segmentation for each pGGN had been reached through negotiation by two radiologists, there was still some interobserver difference existing in this procedure. A reliable automatic segmentation algorithm that can be applied in clinical practice is still to be developed.
Conclusion
Nine selected radiomic features in our research showed different distribution between IVA and AIS/MIA, which could provide some guidance for clinical practice. The predictive models established using conventional clinical, radiological, or radiomic features could help to distinguish the invasiveness of pGGN-like lung adenocarcinoma, but radiomic features could not offer more meaningful information to improve the performance of the combined model created in our study. The diagnostic performance of the radiomic predictive model established in our study was better than that of the two radiologists, and the predictive model could provide auxiliary information for radiologists (especially for junior radiologists) to improve their diagnostic ability in discrimination between pGGN-like IVA and AIS/MIA.
Data Availability Statement
The datasets generated for this study are available on request to the corresponding author.
Ethics Statement
This study was approved by the Institutional Review Committee of the Sir Run Run Shaw Hospital.
Author Contributions
FX and WZ conceived and designed this study. FX, YS, and CP applied the data collection for this study. FX and YS did the data reproof work during the manscript review and now they are collecting new data for further exploration. FX and JW did the segmentation work for each pGGN recruited for this study. WZ and RX contributed to the algorithm design for this study. YG completed the pathological interpretation of all pGGNs in this study. FX drafted the manuscript. LS and CQ and HH contributed equally to the manuscript review.
Conflict of Interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Funding. This research was supported by Zhejiang Provincial Natural Science Foundation of China under Grant No. LQ20F030018, Key Research and Development Program of Zhejiang Province under Grant No. 2019C03064, Program Co-sponsored by Province and Ministry under Grant No. WKJ-ZJ-1926 and National Natural Science Foundation of China (NSFC) under Grant No. 61772106.
Supplementary Material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fonc.2020.00872/full#supplementary-material
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Plant Sci.Frontiers in Plant Science1664-462XFrontiers Media S.A.32849756PMC743213410.3389/fpls.2020.01222Plant ScienceOriginal ResearchSpatial Patterns and Drivers of Angiosperm Sexual Systems in China Differ Between Woody and Herbaceous SpeciesWangYunyun12LyuTong23LuoAo2LiYaoqi2LiuYunpeng2FreckletonRobert P.4LiuShuguang1WangZhiheng2*1National Engineering Laboratory for Applied Technology of Forestry & Ecology in Southern China, and College of Life Science and Technology, Central South University of Forest and Technology, Changsha, China2Institute of Ecology and Key Laboratory for Earth Surface Processes of the Ministry of Education, College of Urban and Environmental Sciences, Peking University, Beijing, China3School of Urban Planning and Design, Shenzhen Graduate School, Peking University, Shenzhen, China4Department of Animal and Plant Sciences, University of Sheffield, Sheffield, United Kingdom
Edited by: Hang Sun, Chinese Academy of Sciences, China
Reviewed by: Hong Qian, Illinois State Museum, United States; Liangsheng Zhang, Zhejiang University, China
This article was submitted to Functional Plant Ecology, a section of the journal Frontiers in Plant Science
1182020202011122210320202772020Copyright © 2020 Wang, Lyu, Luo, Li, Liu, Freckleton, Liu and Wang2020Wang, Lyu, Luo, Li, Liu, Freckleton, Liu and WangThis is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Plant sexual systems play an important role in the evolution of angiosperm diversity. However, large-scale patterns in the frequencies of sexual systems (i.e. dioecy, monoecy, and hermaphroditism) and their drivers for species with different growth forms remain poorly known. Here, using a newly compiled database on the sexual systems and distributions of 19780 angiosperm species in China, we map the large-scale geographical patterns in frequencies of the sexual systems of woody and herbaceous species separately. We use these data to test the following two hypotheses: (1) the prevalence of sexual systems differs between woody and herbaceous assemblies because woody plants have taller canopies and are found in warm and humid climates; (2) the relative contributions of different drivers (specifically climate, evolutionary age, and mature plant height) to these patterns differ between woody and herbaceous species. We show that geographical patterns in proportions of different sexual systems (especially dioecy) differ between woody and herbaceous species. Geographical variations in sexual systems of woody species were influenced by climate, evolutionary age and plant height. In contrast, these have only weakly significant effects on the patterns of sexual systems of herbaceous species. We suggest that differences between species with woody and herbaceous growth forms in terms of biogeographic patterns of sexual systems, and their drivers, may reflect their differences in physiological and ecological adaptions, as well as the coevolution of sexual system with vegetative traits in response to environmental changes.
angiospermssexual systemsgrowth formclimate changegeographical patternmacro evo-devoplant heightChinaNational Natural Science Foundation of China10.13039/50110000180931901216, 31988102, 31911530102Introduction
The sexual systems of plant species play a significant role in the evolution of angiosperm diversity (Charlesworth and Wright, 2001; Nazareno et al., 2013; Käfer et al., 2014; Sabath et al., 2016; Rosche et al., 2018), variations in reproductive strategies (Bawa, 1980), and population and community dynamics in response to climate changes (Queenborough et al., 2009; Wang et al., 2018). Understanding the spatial distribution of plant sexual systems at large scales is vital for understanding the functional biogeography of reproductive traits. Previous studies have shown that the geographical distributions of angiosperm sexual systems may ultimately reflect coevolution with vegetative traits that are tied closely to sexual systems (i.e. growth form, Vamosi et al., 2003; Moeller et al., 2017; Wang et al., 2020). Nonetheless, the determinants of geographical patterns of sexual systems remain controversial.
The evolution of plant sexual systems has been widely hypothesized to be closely linked to the evolution of plant growth forms (e.g. woody vs. herbaceous growth forms, Bawa et al., 1985; Renner and Ricklefs, 1995; Vamosi et al., 2003; Vamosi and Vamosi, 2004; Mitchell and Diggle, 2005; Soza et al., 2012). Generally, dioecy is significantly more frequent in woody flowering plant species with long lifespan than in other growth forms (Renner and Ricklefs, 1995; Sakai et al., 1995; Vamosi et al., 2003; Ward et al., 2005). In contrast, hermaphroditism is more common in herbaceous species with short lifespan (Senarath, 2008; Moeller et al., 2017). This is likely because obligately outcrossing dioecious species need a long lifespan to find mates, reproduce and complete their life cycle (Morgan et al., 1997; Aarssen, 2000). In contrast, hermaphrodites, being able to self-pollinate, are likely to accumulate more genetic load and suffer from an increased turnover rate (Klekowski and Godfrey, 1989; Chen and Li, 2008). Moreover, growth form may affect relationships between traits and the environment (Fitzjohn et al., 2014). For example, based on more than 88,400 species and six plant traits, Šímová et al. (2018) have found significant differences in trait-climate correlations between woody and herbaceous species. However, the biogeographical distributions of sexual systems of different growth forms across large-scale environmental gradients remain poorly understood.
Plant sexual systems and sexual reproduction are sensitive to climate variations (Hedhly et al., 2009; Eckert et al., 2010). Recent studies indicate that reductions in precipitation can change the allocation of resources to male and female functions within individual plants (Hultine et al., 2016), which further influences the expression and composition of plant sexual system in local floras. For example, dioecy is more common in humid areas, especially in rainforest trees (Lloyd, 1980; Sakai et al., 1995; Friedman and Barrett, 2009; Vary et al., 2011), although aridity has been shown to have contributed to the evolutionary transition from hermaphroditism to dioecy (Ashman, 2006). Climate warming can also affect the composition of sexual systems in local floras by causing a mismatch between the timing of flowering and pollinator abundance/presence, which may lead to declines of plants populations (Etterson and Mazer, 2016). Moreover, different growth forms have contrasting climate preferences. Warm and humid climates are more suitable for woody plants (Wang et al., 2011), while herbaceous plants often have broader climate adaptations and hence have widespread distributions (Curtis and Bradley, 2016). Therefore, the influences of climate on sexual system compositions may differ between woody and herbaceous species. However, the role of climate in driving spatial variations in sexual system compositions of woody and herbaceous species remains poorly understood (Gamble et al., 2018).
Evolutionary history might be expected to influence the distribution of sexual systems (Obbard et al., 2006; Barrett, 2013; Käfer et al., 2014). Self-pollinated populations likely accumulate harmful mutants, which may increase their extinction rate (Etterson and Mazer, 2016). Therefore, self-pollinated species may be more common in temperate environments with more short-lived species and younger flora than in tropical environments. In contrast, dioecy (obligate outcrossing) can reduce the expression of recessive deleterious mutations, which tends to reduce their extinction rate (Obbard et al., 2006). Therefore, dioecy may be more frequent in regions with older floras. These findings suggest that the composition of sexual systems within communities is likely associated with the evolutionary ages of species in a region. Additionally, woody and herbaceous species differ significantly in their evolutionary rates (Laroche et al., 1997; Kay et al., 2006). Woody species have longer reproductive cycles and lifespans, and tend to accumulate genetic changes more slowly than herbaceous species. Differences in evolutionary rates may lead to differences in the speed of climate-niche evolution between these two growth forms. For example, using more than 5,000 plant species, Smith and Beaulieu (2009) found that woody plants adapted to new climates at a rate two to ten times slower than herbs over the course of their evolution. Therefore, exploring the differences between the effects of evolutionary time on frequencies of sexual systems of woody and herbaceous species would improve our understanding of the evolutionary mechanisms underlying large-scale patterns in sexual system composition.
Here, using data on sexual systems and spatial distributions of 19,780 angiosperm species across China, we compare the geographical patterns in plant sexual system compositions between woody and herbaceous species, and explore the ecological and evolutionary determinants of these patterns. Specifically, we test the following hypotheses: (1) the geographical patterns in sexual system composition differ between woody and herbaceous species; (2) the relative contributions of different drivers (specifically climate, evolutionary age, and mature plant height) to these patterns differ between woody and herbaceous species. Dioecious species are more common in humid areas and in floras with older and more woody species, whereas hermaphroditic species are more common in temperate arid areas of northwestern China and in floras with younger and more herbaceous species.
Materials and MethodsSexual System
We compiled a database on the sexual systems of angiosperms in China using published sources: Flora of China (Wu et al., 1994-2013), Flora Republicae Popularis Sinicae (126 issues of 80 volumes), Seeds of Woody Plants in China (Chen and Huang, 2001), and efloras (http://efloras.org/). The Tree of Sex (Ashman et al., 2014), TYR Plant Trait Database (www.try-db.org; Kattge et al., 2011), Botanical Information and Ecology Network (BIEN, Enquist et al., 2016, http://bien.nceas.ucsb.edu/bien/biendata/bien-3/) and journal publications (Sabath et al., 2016; Goldberg et al., 2017) were used to check and supplement the sexual system information in the database. For species with information of sexual systems from multiple sources, conflicting records were checked and removed. In total, we compiled data of sexual systems for 19,780 species from 2,506 genera and 262 families in China (Table 1A).
The correlations between the geographical patterns in proportions of sexual systems among different growth forms.
Sexual System
All-Wood
All-Herb
Wood-Herb
Hermaphroditism
0.535***
0.702**
0.304*
Dioecy
0.172ns
0.636*
−0.068ns
Monoecy
0.593*
0.888***
0.382**
All, all species; Wood, woody species; Herb, herbaceous species. The significance was estimated using the Dutilleul’s modified t test, which accounts for the inflation of significance due to spatial autocorrelation (Package “SpatialPack”; Dutilleul et al., 1993).
Significance codes: ***P < 0.001, **P < 0.01, *P < 0.05, ‘.’ P < 0.1. The text is bolded when the correlation is significant.
We classified all species into three categories according to their sexual systems following Cardoso et al. (2018): dioecy (separate male and female individuals), monoecy (having pistils and stamens on separate flowers of the same plant), and hermaphrodites (having both male and female sex organs on the same flower). The category of dioecy includes the androdioecious, gynodioecious, and polygamodioecious species, while monoecy includes the monoecious, andromonoecious, and gynomonoecious species. Considering that a few species vary intraspecifically in their sexual systems (e.g., Dorken et al., 2017) in response to local abiotic or biotic conditions (e.g., climate variables or pollinator densities; Barrett and Harder, 2017), we excluded such species from the final dataset used in this study.
Species Distribution Data
Data on the distributions of plant species were compiled from all national, provincial and local floras, including Flora of China (both Chinese and English versions), Higher Plants of China, The Atlas of Woody Plants in China (Fang et al., 2011; see Wang et al., 2009 for more information), etc. From these resources, we compiled only species distribution records at county-level or finer scales. Species distributions were further supplemented with data from National Specimen Information Infrastructure (NSII, http://www.nsii.org.cn/), and screened. NSII included over fifteen million specimen records that are published recently. In total, our dataset consists the distributions of 19,780 Chinese plant species with data for sexual systems.
Species distributions were rasterized to a grid with a spatial resolution of 100 × 100 km to eliminate the potential bias of area on subsequent analyses. We removed the grid cells with less than 50% of their area along the country borders and coastal areas. In all, 949 grid cells were included in the analyses reported below.
Growth Form
Data on the growth forms of all angiosperm species in China were compiled from the Flora of China (Wu et al., 1994-2013) and Flora Reipublicae Popularis Bulgaricae (Kuzmanov and Kožuharov, 1968). All species were categorized into two growth forms: “woody” and “herbaceous”. Woody species included trees, shrubs and woody lianas, whereas “herbaceous” species included those recorded as herbs, herbaceous lianas, and subshrubs. Species with information on both sexual systems and geographical distributions included 10,200 woody species, 9,479 herbaceous species, and 101 undefined.
Climate Data
Climate can influence flower morphology through its influences on reproductive performance (e.g., pollen production) during the flowering period (Zhang, 2006), and hence could influence sexual systems of plants. Moreover, the reproductive (gametophytic) phase in flowering plants is often highly sensitive to temperature or water stresses. Thus we selected four variables to evaluate the influences of climate, namely, mean temperature of coldest quarter (MTCQ), mean precipitation of warmest quarter (MPWQ), actual evapotranspiration (AET), and aridity index (AI). AET reflects the influences of both water availability and environmental energy, and is significantly associated with ecosystem primary productivity. AET was calculated using the Thornthwaite equation (Thornthwaite and Hare, 1955) from the average monthly temperature and precipitation that were obtained from the WorldClim database (Hijmans et al., 2005). AI reflects the level of aridity and was calculated as the ratio of mean annual precipitation to annual potential evapotranspiration. Higher AI values represent lower aridity. The average and the full range of values for each climatic variable within each grid cell were estimated with the zonal statistics tool in ArcGIS 10.0.
Genus Age
To evaluate the effect of evolutionary time (i.e., genus age) on biogeographical patterns in sexual system compositions, we used the average genus age derived from three phylogenies including Zanne et al. (2014), Lu et al. (2018), and Smith and Brown (2018). This is because (1) different dating methods may lead to different estimations of genus ages; and (2) incomplete phylogenies tend to overestimate the ages of some genera when their close relatives are missed from the phylogenies, and none of these phylogenies is complete at the genus level. For comparison, we also repeated all analyses using the genus ages of each phylogeny separately. It is noteworthy that the geographical patterns in mean genus age per grid cell estimated using the genus ages derived from each phylogeny were consistent with each other (Figure 1A). Moreover, all major findings based on these four age estimations were also highly consistent. Therefore, we included the results based on the average genus ages across the three phylogenies in the main text, and those based on the genus ages derived from each phylogeny in the supplementary materials for comparison.
Spatial patterns in proportions of angiosperm species with different sexual systems at 100 × 100 km scale. From top down, the three rows represent the proportions of species with dioecy, monoecy, and hermaphroditism respectively. From left to right, the three columns represent all species, woody species and herbaceous species respectively. Grid cells without data are shown in white. The patterns in the proportions of sexual systems for woody species were updated from Wang et al., 2020.
Mature Plant Height
Plant mature height and longevity of plant species are expected to be positively associated (Marbà et al., 2007; Moles and Leishman, 2008; Moles et al., 2009). Here we used mature height as a proxy for longevity to test the association between sexual system and longevity. Mature height of plant species was obtained from Flora of China (http://frps.eflora.cn/, accessed in November 2013; http://www.efloras.org/flora_page.aspx?flora_id=2, accessed in February 2014). As lower and upper limits of mature height are normally reported for most species in floras, we used the average of lower and upper limits to represent the mature height of species. Species without erect stems (e.g., woody lianas, scandent shrubs, climbers or epiphytes) were excluded from our analyses following Moles et al. (2009). For each growth-form, we averaged the mature height across species within each grid cell to evaluate the effect of plant height on the biogeographical patterns of sexual system compositions (proportions of the three sexual systems per grid cell).
Statistical Analyses
First, based on the data of sexual systems, growth forms, and distributions of all species, we calculated the proportions of species with different sexual systems within each grid cell for all species together and for each growth form separately. We used Pearson correlation coefficients to evaluate the similarity between the geographical patterns in the proportions of different sexual systems of woody and herbaceous species (e.g. woody dioecy vs. herbaceous dioecy, woody monoecy vs. herbaceous monoecy, woody hermaphroditism vs. herbaceous hermaphroditism) and used Dutilleul’s t test to evaluate the significance of these correlations due the influences of spatial autocorrelation on significance tests (using R package of “SpatialPack”; Dutilleul et al., 1993).
We used generalized linear models (GLMs) with quasi-Poisson residuals to evaluate the explanatory power of each predictor on the proportions of sexual systems per grid cell and this was conducted separately for each of the three species groups (i.e. overall, woody, and herbaceous). The climate variable (i.e., MTCQ, MPWQ, AET, and AI), genus age, and plant height were used as predictors and the proportions of each sexual systems per grid cell were used as response variables. The explanatory power of each variable was estimated as the adjusted R2adj (%) of the GLM model. Modified t tests were used to correct for the effect of spatial autocorrelation on p values (Clifford et al., 1989).
To evaluate the influences of growth forms on the relationships between the proportion of sexual systems and different predictors, we first built spatial linear models (SLM) for the combined dataset of sexual system for both growth forms together using spatial simultaneous autoregressive error (SAR) models. SLMs could account for the effects of residual spatial autocorrelation on the significance tests of regression slopes (Kissling and Carl, 2008). The climate variables (i.e., AET, AI, MTCQ, and MPWQ), genus age, plant height, growth form (i.e., woody vs. herbaceous), and interaction terms between growth form and other predictors were included as the explanatory variables. All the predictors were standardized before fitting the models. Similarly, we also conducted SLMs for each growth form separately. Moran’s I was estimated for the residuals of SLMs, and indicated no spatial autocorrelations.
All analyses were performed in R 3.4.3 (Core Team R, 2017).
ResultsPatterns in the Proportions of Sexual Systems for Different Growth Forms
The spatial patterns in the proportions of hermaphroditic and monoecious species per grid cell were consistent between woody and herbaceous growth forms (r = 0.304 and p < 0.05 for proportion of hermaphroditic species; 0.382 and p < 0.05 for proportion of monoecious species; Table 1). For all species and both growth forms, the proportion of hermaphroditic species per grid cell was higher in northwest China than in other regions, while the proportion of monoecious species was higher in eastern China (Figure 1). In contrast, spatial patterns in the proportion of dioecious species were not significantly correlated between woody and herbaceous growth forms (P > 0.05; Table 1). The woody dioecious species were the most frequent in northeast China and medium in southern China, while herbaceous dioecious species were the most frequent in southwest China (Figure 1). Growth form was the strongest predictor for the proportions of all three sexual systems, and the effects of growth form on the proportions of sexual systems were higher than the other predictors by one order of magnitude (Tables 2, 3, and A2; Figure 2).
The slopes of growth forms (woody vs. herbaceous), climate, genus age, plant height, and their interaction on proportions of sexual systems (i.e. dioecy, monoecy, and hermaphroditism) evaluated using spatial linear models (SLMs) with simultaneous autoregressive errors (SAR).
Variables
Dioecy
Monoecy
Hermaphroditism
Growth Form
0.169
−0.180
−0.0192
Height
−0.0403
0.112
−0.0473
AET
0.0101
0.00622
−0.00657
AI
0.00673
0.00100
−0.00174
MTCQ
0.00937
−0.00933
−0.000256
MPWQ
−0.0135
−0.0000633
0.000311
Genus Age
0.00334
−0.00214
0.000283
Growth Form: AET
−0.0000394
−0.00167
0.000299
Growth Form: AI
−0.0142
−0.0103
0.0244
Growth Form: MTCQ
−0.0333
0.0145
0.0205
Growth Form: MPWQ
0.0282
0.0195
−0.0475
Growth Form: age
0.0585
0.0266
−0.0831
Growth Form: height
0.0507
−0.113
0.0423
R2
0.922
0.723
0.829
All continuous predictors were standardized prior analysis. The regression coefficients (slopes) with p < 0.05 is shown in bold. AET, actual evapotranspiration; AI, aridity index; MPWQ, mean precipitation of warmest quarter; MTCQ, mean temperature of coldest quarter; Age, evolutionary age. The genus age was calculated using the average genus age extracted from all three phylogenies including Zanne et al. (2014), Lu et al. (2018), Smith and Brown (2018).
The slopes and significance of different predictors on proportions of sexual systems evaluated using spatial linear models (SLMs) with simultaneous autoregressive errors (SAR).
Variable
All
Herb
Wood
Herma.
Dioecy
Monoecy
Herma.
Dioecy
Monoecy
Herma.
Dioecy
Monoecy
AET
−0.0173
0.0120
0.00866
−0.00707
0.00258
0.00446
−0.0183
0.0137
0.00442
AI
−0.00219
0.00740
−0.00410
−0.00250
0.00206
0.000435
0.0126
−0.00434
−0.00840
MTCQ
0.00738
0.00455
−0.0112
−0.000611
0.00260
−0.00193
0.0187
−0.0249
0.00619
MPWQ
0.00130
−0.0154
0.00807
0.00138
−0.000430
−0.000962
−0.0279
0.00875
0.0195
Age
−0.00518
0.00994
−0.00585
0.000157
0.00428
−0.00444
−0.0378
0.0270
0.0108
Height
−0.0228
0.0221
0.00500
−0.00429
−0.00174
0.00598
−−0.00588
0.00885
-0.00301
R2
0.831
0.906
0.206
0.320
0.474
0.217
0.770
0.582
0.499
The regression coefficients (slopes) with p < 0.05 is shown in bold. Here genus age was average genus of the three phylogenies including Zanne et al. (2014), Lu et al. (2018), and Smith and Brown (2018).
Proportions of species with different sexual systems among different growth forms in China. D, Dioecy; M, monoecy; H, hermaphroditism.
Effects of Climate on Distributions of Proportions of Sexual Systems
The proportions of all three sexual systems were relatively weakly related to climate for herbaceous species than for both all species and woody species (Table 3). The growth form–climate interaction terms were significant in almost all cases (Table 2). This indicates that the relationship between proportions of sexual systems and particular climate variables differed significantly between woody and herbaceous assemblages.
The effects of climate on proportions of sexual systems were significantly greater for woody species than for herbaceous species. For woody species, the proportion of hermaphroditism decreased with AET and MPWQ but increased with AI and MTCQ, while the proportion of dioecious showed almost opposite trends (Table 3). For herbaceous species, all three sexual systems were weakly correlated with climate (R2 ranged from 0.217 to 0.474 in Table 3).
Effects of Genus Age on Geographical Distributions of Sexual Systems
Genus age was consistently a significant predictor of sexual system frequencies for woody species, but had only weak effect on herbaceous species. For woody species, the proportion of hermaphroditic was negatively correlated with genus age, while the proportion of dioecious species and monoecious species were positively correlated with genus age (all P < 0.05, Table 3). The explanatory power of genus age was much stronger on the proportions of dioecious and hermaphroditic species than on monoecious species (Table 3).
Effects of Mature Plant Height on the Geographical Frequency of Sexual Systems
Mature plant height per grid cell was a significant predictor for geographical patterns in the proportions of almost all sexual systems examined here (Tables 2 and 3), but its effect was much stronger for woody species than for herbaceous species (Tables 3 and A2). For both growth forms, the proportion of hermaphroditic species decreased. For woody species, the proportion of dioecious species increased but the monoecious decreased with plant height per grid cell, whereas herbaceous species showed opposite trend (Table 3).
Discussion
Because different sexual systems are associated with variation of plant growth form, there are large-differences in the distributions of plant sexual systems between contrasting woody and herbaceous species. Compared with Wang et al. (2020), we advanced our understanding of the spatial distribution of plant sexual systems and the determinants underlying these patterns, especially between growth forms. Growth form significantly differed the relationships between geographic patterns in the proportions of all three sexual systems with different biotic and abiotic factors. Climate, evolutionary age, and mature plant height were all significantly correlates of the distributions of sexual systems of woody species than herbaceous ones. These results are partially consistent with the existing evidence that species diversity of woody plants is more closely related with family age and climate than that of herbaceous plants (Oberle et al., 2009; Hawkins et al., 2011).
Effect of Plant Height on Proportions of Sexual Systems
Plant height was a significant predictor of the proportions of the three sexual systems in almost all cases, which partly confirmed previous hypotheses about the coevolution between sexual systems and growth forms (Renner and Ricklefs, 1995; Vamosi et al., 2003). Both plant height and sexual systems could reflect the adaptation and evolutionary trend of plant life-history strategies to a certain extent, and they also tend to coevolve (Renner, 2014; Wang et al., 2020). Owing to limited mating opportunities, selection has tended to favor dioecy in long-lived woody species which are tall (Renner and Ricklefs, 1995; Obbard et al., 2006). Taller species with height dimorphism and greater dispersal investment may also benefit dioecious species (e.g. Salicaceae) by improving the efficiency in pollen and seed dispersal, which further promote the maintenance and continuation of species populations (Pickup and Barrett, 2012; Thomson et al., 2018). In contrast, hermaphroditism tends to be associated with short-lifespan species with low height because hermaphroditic species can self-pollinate and thus tend to accumulate much more deleterious mutations (Etterson and Mazer, 2016), and which may have higher mortality and extinction risks (Obbard et al., 2006; Cheptou, 2019).
The explanatory power of plant height on the frequency of sexual systems was stronger for woody than for herbaceous species. This may be likely because woody and herbaceous plant species have different adaptive strategies in resource allocation between reproductive and vegetative tissues (Abrahamson and Gadgil, 1973; Bonser and Aarssen, 2003). Woody species may have more resources for the investment to sexual systems and reproduction (Porth and El-Kassaby, 2014), while herbaceous species (e.g. the annual Mercurialis annua) tend to exhibit short life-cycles, fast rates of population but limited resource accumulation (Lanfear et al., 2013).
The Effects of Climate on the Proportions of Sexual Systems
The proportion of hermaphroditic species for all species combined was high in areas with high AI or MTCQ, consistent with Wang et al. (2020). The proportion of dioecious species among all species combined (e.g. Anaphalis) was higher in southern China than in other regions. This finding supports the hypothesis that dioecy is more common in tropical and subtropical floras where climate is warm and humid (Freeman et al., 1976; Bawa, 1980; Bawa et al., 1985; Sakai et al., 1995; Vary et al., 2011). In contrast, the proportion of woody dioecious species (e.g. Rhamnus and Acer) is higher in Northeast China with relatively higher AET and MPWQ compared to other regions in China. These results reveal that the differences in requirements for hydrothermal conditions between woody and herbaceous growth forms may influence their geographical patterns in proportions of sexual systems, consistent with our hypothesis. Generally, woody species often have deep roots, and herbaceous species have relatively shallow roots. Therefore, herbaceous species have lower efficiencies in soil water usage and are more sensitive to reduced water supply compared with woody species (Franco, 2002). Correspondingly, the interaction terms between climate and growth form in the spatial linear models explaining the frequencies of different sexual systems were significant in most cases. The differences in the effects of climate on proportions of sexual systems between woody and herbaceous growth forms may be partly due to the coevolution of sexual systems with growth forms (Renner and Ricklefs, 1995; Vamosi et al., 2003; Case and Barrett, 2004). Moreover, our results further corroborate previous findings that climate may have influenced the spatial pattern of sexual systems via its effect on the composition of plant growth forms (Wang et al., 2020).
We also found that the sexual system composition of woody species was more closely associated with contemporary climate than that of herbaceous species, which is consistent with previous studies at broad scales (Oberle et al., 2009; Wang et al., 2009). Compared with herbaceous species that can be protected from frost by being annual/biennial or by the production of underground buds, woody plant species typically with longer life cycles and large sizes expose their stems and buds (with reproductive organs) in climate. Thus climate has been found to have significant influences on the spatial pattern of woody species (Currie and Paquin, 1987) compared with herbaceous species. These results suggest that the differences in the effects of climate on geographical patterns in proportions of sexual systems for woody and herbaceous species may also be associated with the differences in ecophysiological strategies related to resource acquisition and utilization between growth forms.
Effect of Evolutionary Time on Proportions of Sexual Systems
For both woody and all species, the proportion of dioecious species was positively correlated with mean genus age per grid cell, which support our hypothesis that dioecy tends to be maintained in regions with older floras. Similarly, the proportion of woody monoecious species was also positively correlated with mean genus age. These results partly support and expand the hypothesis that dioecy and monoecy are often found to be significantly associated with each other across phylogenies of woody angiosperms (Renner and Ricklefs, 1995). In contrast, the proportions of sexual systems for herbaceous species were weakly correlated with mean genus age (e.g. Apiaceae, Asteraceae, Cyperaceae), which might be due to the higher population turnover rate and faster adaptation to climate of herbaceous species than those of woody species (e.g. Acer and Salicaceae, Obbard et al., 2006; Smith and Beaulieu, 2009). Moreover, the effect of genus age on the proportions of woody sexual systems was much stronger than on those of herbaceous species. The differences in the effect of evolutionary age on proportions of sexual systems between woody and herbaceous growth forms might reflect their differences in evolutionary rates (i.e. speciation and extinction rates). Molecular studies have revealed that compared with herbaceous species, woody species such as trees tend to have longer generation times and lower evolutionary rate (Smith and Beaulieu, 2009), and thus may have lower chances to transit between sexual systems in response to environmental changes.
Conclusions
Here we mapped the geographical distributions of the frequencies of sexual systems and compared the effects of abiotic and biotic factors on sexual system frequencies for both woody and herbaceous flowering plant species in China. The results suggest that woody and herbaceous species differed significantly in their patterns in the frequencies of sexual systems, and also in the determinants underlying these patterns. The proportions of sexual systems of woody species were more strongly influenced by climate, evolutionary age, and mature height, whereas those of herbaceous species were less influenced by these factors. These findings shed light on previous hypotheses about the association between sexual systems and growth forms, and further demonstrate the necessity to differentiate woody and herbaceous growth forms when investigating the evolution and ecology of sexual systems. It is noteworthy that sexual systems may also vary intraspecifically across populations of a single species (e.g. Mercurialis annua, Dorken et al., 2017). Exploring the intraspecific sexual system variations for species with different growth forms along environmental gradients would further improve our understanding on the drivers of sexual system variations. Our study also suggests that compared with both dioecious and hermaphroditic species, more attention should be paid to monoecious species from both evolutionary and ecological perspectives. This study improves our understanding of the mechanisms underlying geographical patterns of reproductive trait diversity across different growth forms and their responses to climate.
Data Availability Statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation, to any qualified researcher.
Author Contributions
ZW and YW conceived the idea and design of the study. YW, TL, AL, YLi, and YLiu organized the database. YW and TL performed the statistical analysis. YW wrote the first draft of the manuscript. ZW has comprehensively revised the article. All authors contributed to the article and approved the submitted version.
Funding
This work was supported by the National Key Research Development Program of China (#2017YFA0605101; #2018YFA0606104), the Strategic Priority Research Program of Chinese Academy of Sciences (#XDB31000000), and National Natural Science Foundation of China (#31901216, #31988102, #31911530102).
Conflict of Interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Supplementary Material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpls.2020.01222/full#supplementary-material
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Plant Sci.Frontiers in Plant Science1664-462XFrontiers Media S.A.32849756PMC743213410.3389/fpls.2020.01222Plant ScienceOriginal ResearchSpatial Patterns and Drivers of Angiosperm Sexual Systems in China Differ Between Woody and Herbaceous SpeciesWangYunyun12LyuTong23LuoAo2LiYaoqi2LiuYunpeng2FreckletonRobert P.4LiuShuguang1WangZhiheng2*1National Engineering Laboratory for Applied Technology of Forestry & Ecology in Southern China, and College of Life Science and Technology, Central South University of Forest and Technology, Changsha, China2Institute of Ecology and Key Laboratory for Earth Surface Processes of the Ministry of Education, College of Urban and Environmental Sciences, Peking University, Beijing, China3School of Urban Planning and Design, Shenzhen Graduate School, Peking University, Shenzhen, China4Department of Animal and Plant Sciences, University of Sheffield, Sheffield, United Kingdom
Edited by: Hang Sun, Chinese Academy of Sciences, China
Reviewed by: Hong Qian, Illinois State Museum, United States; Liangsheng Zhang, Zhejiang University, China
This article was submitted to Functional Plant Ecology, a section of the journal Frontiers in Plant Science
1182020202011122210320202772020Copyright © 2020 Wang, Lyu, Luo, Li, Liu, Freckleton, Liu and Wang2020Wang, Lyu, Luo, Li, Liu, Freckleton, Liu and WangThis is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Plant sexual systems play an important role in the evolution of angiosperm diversity. However, large-scale patterns in the frequencies of sexual systems (i.e. dioecy, monoecy, and hermaphroditism) and their drivers for species with different growth forms remain poorly known. Here, using a newly compiled database on the sexual systems and distributions of 19780 angiosperm species in China, we map the large-scale geographical patterns in frequencies of the sexual systems of woody and herbaceous species separately. We use these data to test the following two hypotheses: (1) the prevalence of sexual systems differs between woody and herbaceous assemblies because woody plants have taller canopies and are found in warm and humid climates; (2) the relative contributions of different drivers (specifically climate, evolutionary age, and mature plant height) to these patterns differ between woody and herbaceous species. We show that geographical patterns in proportions of different sexual systems (especially dioecy) differ between woody and herbaceous species. Geographical variations in sexual systems of woody species were influenced by climate, evolutionary age and plant height. In contrast, these have only weakly significant effects on the patterns of sexual systems of herbaceous species. We suggest that differences between species with woody and herbaceous growth forms in terms of biogeographic patterns of sexual systems, and their drivers, may reflect their differences in physiological and ecological adaptions, as well as the coevolution of sexual system with vegetative traits in response to environmental changes.
angiospermssexual systemsgrowth formclimate changegeographical patternmacro evo-devoplant heightChinaNational Natural Science Foundation of China10.13039/50110000180931901216, 31988102, 31911530102Introduction
The sexual systems of plant species play a significant role in the evolution of angiosperm diversity (Charlesworth and Wright, 2001; Nazareno et al., 2013; Käfer et al., 2014; Sabath et al., 2016; Rosche et al., 2018), variations in reproductive strategies (Bawa, 1980), and population and community dynamics in response to climate changes (Queenborough et al., 2009; Wang et al., 2018). Understanding the spatial distribution of plant sexual systems at large scales is vital for understanding the functional biogeography of reproductive traits. Previous studies have shown that the geographical distributions of angiosperm sexual systems may ultimately reflect coevolution with vegetative traits that are tied closely to sexual systems (i.e. growth form, Vamosi et al., 2003; Moeller et al., 2017; Wang et al., 2020). Nonetheless, the determinants of geographical patterns of sexual systems remain controversial.
The evolution of plant sexual systems has been widely hypothesized to be closely linked to the evolution of plant growth forms (e.g. woody vs. herbaceous growth forms, Bawa et al., 1985; Renner and Ricklefs, 1995; Vamosi et al., 2003; Vamosi and Vamosi, 2004; Mitchell and Diggle, 2005; Soza et al., 2012). Generally, dioecy is significantly more frequent in woody flowering plant species with long lifespan than in other growth forms (Renner and Ricklefs, 1995; Sakai et al., 1995; Vamosi et al., 2003; Ward et al., 2005). In contrast, hermaphroditism is more common in herbaceous species with short lifespan (Senarath, 2008; Moeller et al., 2017). This is likely because obligately outcrossing dioecious species need a long lifespan to find mates, reproduce and complete their life cycle (Morgan et al., 1997; Aarssen, 2000). In contrast, hermaphrodites, being able to self-pollinate, are likely to accumulate more genetic load and suffer from an increased turnover rate (Klekowski and Godfrey, 1989; Chen and Li, 2008). Moreover, growth form may affect relationships between traits and the environment (Fitzjohn et al., 2014). For example, based on more than 88,400 species and six plant traits, Šímová et al. (2018) have found significant differences in trait-climate correlations between woody and herbaceous species. However, the biogeographical distributions of sexual systems of different growth forms across large-scale environmental gradients remain poorly understood.
Plant sexual systems and sexual reproduction are sensitive to climate variations (Hedhly et al., 2009; Eckert et al., 2010). Recent studies indicate that reductions in precipitation can change the allocation of resources to male and female functions within individual plants (Hultine et al., 2016), which further influences the expression and composition of plant sexual system in local floras. For example, dioecy is more common in humid areas, especially in rainforest trees (Lloyd, 1980; Sakai et al., 1995; Friedman and Barrett, 2009; Vary et al., 2011), although aridity has been shown to have contributed to the evolutionary transition from hermaphroditism to dioecy (Ashman, 2006). Climate warming can also affect the composition of sexual systems in local floras by causing a mismatch between the timing of flowering and pollinator abundance/presence, which may lead to declines of plants populations (Etterson and Mazer, 2016). Moreover, different growth forms have contrasting climate preferences. Warm and humid climates are more suitable for woody plants (Wang et al., 2011), while herbaceous plants often have broader climate adaptations and hence have widespread distributions (Curtis and Bradley, 2016). Therefore, the influences of climate on sexual system compositions may differ between woody and herbaceous species. However, the role of climate in driving spatial variations in sexual system compositions of woody and herbaceous species remains poorly understood (Gamble et al., 2018).
Evolutionary history might be expected to influence the distribution of sexual systems (Obbard et al., 2006; Barrett, 2013; Käfer et al., 2014). Self-pollinated populations likely accumulate harmful mutants, which may increase their extinction rate (Etterson and Mazer, 2016). Therefore, self-pollinated species may be more common in temperate environments with more short-lived species and younger flora than in tropical environments. In contrast, dioecy (obligate outcrossing) can reduce the expression of recessive deleterious mutations, which tends to reduce their extinction rate (Obbard et al., 2006). Therefore, dioecy may be more frequent in regions with older floras. These findings suggest that the composition of sexual systems within communities is likely associated with the evolutionary ages of species in a region. Additionally, woody and herbaceous species differ significantly in their evolutionary rates (Laroche et al., 1997; Kay et al., 2006). Woody species have longer reproductive cycles and lifespans, and tend to accumulate genetic changes more slowly than herbaceous species. Differences in evolutionary rates may lead to differences in the speed of climate-niche evolution between these two growth forms. For example, using more than 5,000 plant species, Smith and Beaulieu (2009) found that woody plants adapted to new climates at a rate two to ten times slower than herbs over the course of their evolution. Therefore, exploring the differences between the effects of evolutionary time on frequencies of sexual systems of woody and herbaceous species would improve our understanding of the evolutionary mechanisms underlying large-scale patterns in sexual system composition.
Here, using data on sexual systems and spatial distributions of 19,780 angiosperm species across China, we compare the geographical patterns in plant sexual system compositions between woody and herbaceous species, and explore the ecological and evolutionary determinants of these patterns. Specifically, we test the following hypotheses: (1) the geographical patterns in sexual system composition differ between woody and herbaceous species; (2) the relative contributions of different drivers (specifically climate, evolutionary age, and mature plant height) to these patterns differ between woody and herbaceous species. Dioecious species are more common in humid areas and in floras with older and more woody species, whereas hermaphroditic species are more common in temperate arid areas of northwestern China and in floras with younger and more herbaceous species.
Materials and MethodsSexual System
We compiled a database on the sexual systems of angiosperms in China using published sources: Flora of China (Wu et al., 1994-2013), Flora Republicae Popularis Sinicae (126 issues of 80 volumes), Seeds of Woody Plants in China (Chen and Huang, 2001), and efloras (http://efloras.org/). The Tree of Sex (Ashman et al., 2014), TYR Plant Trait Database (www.try-db.org; Kattge et al., 2011), Botanical Information and Ecology Network (BIEN, Enquist et al., 2016, http://bien.nceas.ucsb.edu/bien/biendata/bien-3/) and journal publications (Sabath et al., 2016; Goldberg et al., 2017) were used to check and supplement the sexual system information in the database. For species with information of sexual systems from multiple sources, conflicting records were checked and removed. In total, we compiled data of sexual systems for 19,780 species from 2,506 genera and 262 families in China (Table 1A).
The correlations between the geographical patterns in proportions of sexual systems among different growth forms.
Sexual System
All-Wood
All-Herb
Wood-Herb
Hermaphroditism
0.535***
0.702**
0.304*
Dioecy
0.172ns
0.636*
−0.068ns
Monoecy
0.593*
0.888***
0.382**
All, all species; Wood, woody species; Herb, herbaceous species. The significance was estimated using the Dutilleul’s modified t test, which accounts for the inflation of significance due to spatial autocorrelation (Package “SpatialPack”; Dutilleul et al., 1993).
Significance codes: ***P < 0.001, **P < 0.01, *P < 0.05, ‘.’ P < 0.1. The text is bolded when the correlation is significant.
We classified all species into three categories according to their sexual systems following Cardoso et al. (2018): dioecy (separate male and female individuals), monoecy (having pistils and stamens on separate flowers of the same plant), and hermaphrodites (having both male and female sex organs on the same flower). The category of dioecy includes the androdioecious, gynodioecious, and polygamodioecious species, while monoecy includes the monoecious, andromonoecious, and gynomonoecious species. Considering that a few species vary intraspecifically in their sexual systems (e.g., Dorken et al., 2017) in response to local abiotic or biotic conditions (e.g., climate variables or pollinator densities; Barrett and Harder, 2017), we excluded such species from the final dataset used in this study.
Species Distribution Data
Data on the distributions of plant species were compiled from all national, provincial and local floras, including Flora of China (both Chinese and English versions), Higher Plants of China, The Atlas of Woody Plants in China (Fang et al., 2011; see Wang et al., 2009 for more information), etc. From these resources, we compiled only species distribution records at county-level or finer scales. Species distributions were further supplemented with data from National Specimen Information Infrastructure (NSII, http://www.nsii.org.cn/), and screened. NSII included over fifteen million specimen records that are published recently. In total, our dataset consists the distributions of 19,780 Chinese plant species with data for sexual systems.
Species distributions were rasterized to a grid with a spatial resolution of 100 × 100 km to eliminate the potential bias of area on subsequent analyses. We removed the grid cells with less than 50% of their area along the country borders and coastal areas. In all, 949 grid cells were included in the analyses reported below.
Growth Form
Data on the growth forms of all angiosperm species in China were compiled from the Flora of China (Wu et al., 1994-2013) and Flora Reipublicae Popularis Bulgaricae (Kuzmanov and Kožuharov, 1968). All species were categorized into two growth forms: “woody” and “herbaceous”. Woody species included trees, shrubs and woody lianas, whereas “herbaceous” species included those recorded as herbs, herbaceous lianas, and subshrubs. Species with information on both sexual systems and geographical distributions included 10,200 woody species, 9,479 herbaceous species, and 101 undefined.
Climate Data
Climate can influence flower morphology through its influences on reproductive performance (e.g., pollen production) during the flowering period (Zhang, 2006), and hence could influence sexual systems of plants. Moreover, the reproductive (gametophytic) phase in flowering plants is often highly sensitive to temperature or water stresses. Thus we selected four variables to evaluate the influences of climate, namely, mean temperature of coldest quarter (MTCQ), mean precipitation of warmest quarter (MPWQ), actual evapotranspiration (AET), and aridity index (AI). AET reflects the influences of both water availability and environmental energy, and is significantly associated with ecosystem primary productivity. AET was calculated using the Thornthwaite equation (Thornthwaite and Hare, 1955) from the average monthly temperature and precipitation that were obtained from the WorldClim database (Hijmans et al., 2005). AI reflects the level of aridity and was calculated as the ratio of mean annual precipitation to annual potential evapotranspiration. Higher AI values represent lower aridity. The average and the full range of values for each climatic variable within each grid cell were estimated with the zonal statistics tool in ArcGIS 10.0.
Genus Age
To evaluate the effect of evolutionary time (i.e., genus age) on biogeographical patterns in sexual system compositions, we used the average genus age derived from three phylogenies including Zanne et al. (2014), Lu et al. (2018), and Smith and Brown (2018). This is because (1) different dating methods may lead to different estimations of genus ages; and (2) incomplete phylogenies tend to overestimate the ages of some genera when their close relatives are missed from the phylogenies, and none of these phylogenies is complete at the genus level. For comparison, we also repeated all analyses using the genus ages of each phylogeny separately. It is noteworthy that the geographical patterns in mean genus age per grid cell estimated using the genus ages derived from each phylogeny were consistent with each other (Figure 1A). Moreover, all major findings based on these four age estimations were also highly consistent. Therefore, we included the results based on the average genus ages across the three phylogenies in the main text, and those based on the genus ages derived from each phylogeny in the supplementary materials for comparison.
Spatial patterns in proportions of angiosperm species with different sexual systems at 100 × 100 km scale. From top down, the three rows represent the proportions of species with dioecy, monoecy, and hermaphroditism respectively. From left to right, the three columns represent all species, woody species and herbaceous species respectively. Grid cells without data are shown in white. The patterns in the proportions of sexual systems for woody species were updated from Wang et al., 2020.
Mature Plant Height
Plant mature height and longevity of plant species are expected to be positively associated (Marbà et al., 2007; Moles and Leishman, 2008; Moles et al., 2009). Here we used mature height as a proxy for longevity to test the association between sexual system and longevity. Mature height of plant species was obtained from Flora of China (http://frps.eflora.cn/, accessed in November 2013; http://www.efloras.org/flora_page.aspx?flora_id=2, accessed in February 2014). As lower and upper limits of mature height are normally reported for most species in floras, we used the average of lower and upper limits to represent the mature height of species. Species without erect stems (e.g., woody lianas, scandent shrubs, climbers or epiphytes) were excluded from our analyses following Moles et al. (2009). For each growth-form, we averaged the mature height across species within each grid cell to evaluate the effect of plant height on the biogeographical patterns of sexual system compositions (proportions of the three sexual systems per grid cell).
Statistical Analyses
First, based on the data of sexual systems, growth forms, and distributions of all species, we calculated the proportions of species with different sexual systems within each grid cell for all species together and for each growth form separately. We used Pearson correlation coefficients to evaluate the similarity between the geographical patterns in the proportions of different sexual systems of woody and herbaceous species (e.g. woody dioecy vs. herbaceous dioecy, woody monoecy vs. herbaceous monoecy, woody hermaphroditism vs. herbaceous hermaphroditism) and used Dutilleul’s t test to evaluate the significance of these correlations due the influences of spatial autocorrelation on significance tests (using R package of “SpatialPack”; Dutilleul et al., 1993).
We used generalized linear models (GLMs) with quasi-Poisson residuals to evaluate the explanatory power of each predictor on the proportions of sexual systems per grid cell and this was conducted separately for each of the three species groups (i.e. overall, woody, and herbaceous). The climate variable (i.e., MTCQ, MPWQ, AET, and AI), genus age, and plant height were used as predictors and the proportions of each sexual systems per grid cell were used as response variables. The explanatory power of each variable was estimated as the adjusted R2adj (%) of the GLM model. Modified t tests were used to correct for the effect of spatial autocorrelation on p values (Clifford et al., 1989).
To evaluate the influences of growth forms on the relationships between the proportion of sexual systems and different predictors, we first built spatial linear models (SLM) for the combined dataset of sexual system for both growth forms together using spatial simultaneous autoregressive error (SAR) models. SLMs could account for the effects of residual spatial autocorrelation on the significance tests of regression slopes (Kissling and Carl, 2008). The climate variables (i.e., AET, AI, MTCQ, and MPWQ), genus age, plant height, growth form (i.e., woody vs. herbaceous), and interaction terms between growth form and other predictors were included as the explanatory variables. All the predictors were standardized before fitting the models. Similarly, we also conducted SLMs for each growth form separately. Moran’s I was estimated for the residuals of SLMs, and indicated no spatial autocorrelations.
All analyses were performed in R 3.4.3 (Core Team R, 2017).
ResultsPatterns in the Proportions of Sexual Systems for Different Growth Forms
The spatial patterns in the proportions of hermaphroditic and monoecious species per grid cell were consistent between woody and herbaceous growth forms (r = 0.304 and p < 0.05 for proportion of hermaphroditic species; 0.382 and p < 0.05 for proportion of monoecious species; Table 1). For all species and both growth forms, the proportion of hermaphroditic species per grid cell was higher in northwest China than in other regions, while the proportion of monoecious species was higher in eastern China (Figure 1). In contrast, spatial patterns in the proportion of dioecious species were not significantly correlated between woody and herbaceous growth forms (P > 0.05; Table 1). The woody dioecious species were the most frequent in northeast China and medium in southern China, while herbaceous dioecious species were the most frequent in southwest China (Figure 1). Growth form was the strongest predictor for the proportions of all three sexual systems, and the effects of growth form on the proportions of sexual systems were higher than the other predictors by one order of magnitude (Tables 2, 3, and A2; Figure 2).
The slopes of growth forms (woody vs. herbaceous), climate, genus age, plant height, and their interaction on proportions of sexual systems (i.e. dioecy, monoecy, and hermaphroditism) evaluated using spatial linear models (SLMs) with simultaneous autoregressive errors (SAR).
Variables
Dioecy
Monoecy
Hermaphroditism
Growth Form
0.169
−0.180
−0.0192
Height
−0.0403
0.112
−0.0473
AET
0.0101
0.00622
−0.00657
AI
0.00673
0.00100
−0.00174
MTCQ
0.00937
−0.00933
−0.000256
MPWQ
−0.0135
−0.0000633
0.000311
Genus Age
0.00334
−0.00214
0.000283
Growth Form: AET
−0.0000394
−0.00167
0.000299
Growth Form: AI
−0.0142
−0.0103
0.0244
Growth Form: MTCQ
−0.0333
0.0145
0.0205
Growth Form: MPWQ
0.0282
0.0195
−0.0475
Growth Form: age
0.0585
0.0266
−0.0831
Growth Form: height
0.0507
−0.113
0.0423
R2
0.922
0.723
0.829
All continuous predictors were standardized prior analysis. The regression coefficients (slopes) with p < 0.05 is shown in bold. AET, actual evapotranspiration; AI, aridity index; MPWQ, mean precipitation of warmest quarter; MTCQ, mean temperature of coldest quarter; Age, evolutionary age. The genus age was calculated using the average genus age extracted from all three phylogenies including Zanne et al. (2014), Lu et al. (2018), Smith and Brown (2018).
The slopes and significance of different predictors on proportions of sexual systems evaluated using spatial linear models (SLMs) with simultaneous autoregressive errors (SAR).
Variable
All
Herb
Wood
Herma.
Dioecy
Monoecy
Herma.
Dioecy
Monoecy
Herma.
Dioecy
Monoecy
AET
−0.0173
0.0120
0.00866
−0.00707
0.00258
0.00446
−0.0183
0.0137
0.00442
AI
−0.00219
0.00740
−0.00410
−0.00250
0.00206
0.000435
0.0126
−0.00434
−0.00840
MTCQ
0.00738
0.00455
−0.0112
−0.000611
0.00260
−0.00193
0.0187
−0.0249
0.00619
MPWQ
0.00130
−0.0154
0.00807
0.00138
−0.000430
−0.000962
−0.0279
0.00875
0.0195
Age
−0.00518
0.00994
−0.00585
0.000157
0.00428
−0.00444
−0.0378
0.0270
0.0108
Height
−0.0228
0.0221
0.00500
−0.00429
−0.00174
0.00598
−−0.00588
0.00885
-0.00301
R2
0.831
0.906
0.206
0.320
0.474
0.217
0.770
0.582
0.499
The regression coefficients (slopes) with p < 0.05 is shown in bold. Here genus age was average genus of the three phylogenies including Zanne et al. (2014), Lu et al. (2018), and Smith and Brown (2018).
Proportions of species with different sexual systems among different growth forms in China. D, Dioecy; M, monoecy; H, hermaphroditism.
Effects of Climate on Distributions of Proportions of Sexual Systems
The proportions of all three sexual systems were relatively weakly related to climate for herbaceous species than for both all species and woody species (Table 3). The growth form–climate interaction terms were significant in almost all cases (Table 2). This indicates that the relationship between proportions of sexual systems and particular climate variables differed significantly between woody and herbaceous assemblages.
The effects of climate on proportions of sexual systems were significantly greater for woody species than for herbaceous species. For woody species, the proportion of hermaphroditism decreased with AET and MPWQ but increased with AI and MTCQ, while the proportion of dioecious showed almost opposite trends (Table 3). For herbaceous species, all three sexual systems were weakly correlated with climate (R2 ranged from 0.217 to 0.474 in Table 3).
Effects of Genus Age on Geographical Distributions of Sexual Systems
Genus age was consistently a significant predictor of sexual system frequencies for woody species, but had only weak effect on herbaceous species. For woody species, the proportion of hermaphroditic was negatively correlated with genus age, while the proportion of dioecious species and monoecious species were positively correlated with genus age (all P < 0.05, Table 3). The explanatory power of genus age was much stronger on the proportions of dioecious and hermaphroditic species than on monoecious species (Table 3).
Effects of Mature Plant Height on the Geographical Frequency of Sexual Systems
Mature plant height per grid cell was a significant predictor for geographical patterns in the proportions of almost all sexual systems examined here (Tables 2 and 3), but its effect was much stronger for woody species than for herbaceous species (Tables 3 and A2). For both growth forms, the proportion of hermaphroditic species decreased. For woody species, the proportion of dioecious species increased but the monoecious decreased with plant height per grid cell, whereas herbaceous species showed opposite trend (Table 3).
Discussion
Because different sexual systems are associated with variation of plant growth form, there are large-differences in the distributions of plant sexual systems between contrasting woody and herbaceous species. Compared with Wang et al. (2020), we advanced our understanding of the spatial distribution of plant sexual systems and the determinants underlying these patterns, especially between growth forms. Growth form significantly differed the relationships between geographic patterns in the proportions of all three sexual systems with different biotic and abiotic factors. Climate, evolutionary age, and mature plant height were all significantly correlates of the distributions of sexual systems of woody species than herbaceous ones. These results are partially consistent with the existing evidence that species diversity of woody plants is more closely related with family age and climate than that of herbaceous plants (Oberle et al., 2009; Hawkins et al., 2011).
Effect of Plant Height on Proportions of Sexual Systems
Plant height was a significant predictor of the proportions of the three sexual systems in almost all cases, which partly confirmed previous hypotheses about the coevolution between sexual systems and growth forms (Renner and Ricklefs, 1995; Vamosi et al., 2003). Both plant height and sexual systems could reflect the adaptation and evolutionary trend of plant life-history strategies to a certain extent, and they also tend to coevolve (Renner, 2014; Wang et al., 2020). Owing to limited mating opportunities, selection has tended to favor dioecy in long-lived woody species which are tall (Renner and Ricklefs, 1995; Obbard et al., 2006). Taller species with height dimorphism and greater dispersal investment may also benefit dioecious species (e.g. Salicaceae) by improving the efficiency in pollen and seed dispersal, which further promote the maintenance and continuation of species populations (Pickup and Barrett, 2012; Thomson et al., 2018). In contrast, hermaphroditism tends to be associated with short-lifespan species with low height because hermaphroditic species can self-pollinate and thus tend to accumulate much more deleterious mutations (Etterson and Mazer, 2016), and which may have higher mortality and extinction risks (Obbard et al., 2006; Cheptou, 2019).
The explanatory power of plant height on the frequency of sexual systems was stronger for woody than for herbaceous species. This may be likely because woody and herbaceous plant species have different adaptive strategies in resource allocation between reproductive and vegetative tissues (Abrahamson and Gadgil, 1973; Bonser and Aarssen, 2003). Woody species may have more resources for the investment to sexual systems and reproduction (Porth and El-Kassaby, 2014), while herbaceous species (e.g. the annual Mercurialis annua) tend to exhibit short life-cycles, fast rates of population but limited resource accumulation (Lanfear et al., 2013).
The Effects of Climate on the Proportions of Sexual Systems
The proportion of hermaphroditic species for all species combined was high in areas with high AI or MTCQ, consistent with Wang et al. (2020). The proportion of dioecious species among all species combined (e.g. Anaphalis) was higher in southern China than in other regions. This finding supports the hypothesis that dioecy is more common in tropical and subtropical floras where climate is warm and humid (Freeman et al., 1976; Bawa, 1980; Bawa et al., 1985; Sakai et al., 1995; Vary et al., 2011). In contrast, the proportion of woody dioecious species (e.g. Rhamnus and Acer) is higher in Northeast China with relatively higher AET and MPWQ compared to other regions in China. These results reveal that the differences in requirements for hydrothermal conditions between woody and herbaceous growth forms may influence their geographical patterns in proportions of sexual systems, consistent with our hypothesis. Generally, woody species often have deep roots, and herbaceous species have relatively shallow roots. Therefore, herbaceous species have lower efficiencies in soil water usage and are more sensitive to reduced water supply compared with woody species (Franco, 2002). Correspondingly, the interaction terms between climate and growth form in the spatial linear models explaining the frequencies of different sexual systems were significant in most cases. The differences in the effects of climate on proportions of sexual systems between woody and herbaceous growth forms may be partly due to the coevolution of sexual systems with growth forms (Renner and Ricklefs, 1995; Vamosi et al., 2003; Case and Barrett, 2004). Moreover, our results further corroborate previous findings that climate may have influenced the spatial pattern of sexual systems via its effect on the composition of plant growth forms (Wang et al., 2020).
We also found that the sexual system composition of woody species was more closely associated with contemporary climate than that of herbaceous species, which is consistent with previous studies at broad scales (Oberle et al., 2009; Wang et al., 2009). Compared with herbaceous species that can be protected from frost by being annual/biennial or by the production of underground buds, woody plant species typically with longer life cycles and large sizes expose their stems and buds (with reproductive organs) in climate. Thus climate has been found to have significant influences on the spatial pattern of woody species (Currie and Paquin, 1987) compared with herbaceous species. These results suggest that the differences in the effects of climate on geographical patterns in proportions of sexual systems for woody and herbaceous species may also be associated with the differences in ecophysiological strategies related to resource acquisition and utilization between growth forms.
Effect of Evolutionary Time on Proportions of Sexual Systems
For both woody and all species, the proportion of dioecious species was positively correlated with mean genus age per grid cell, which support our hypothesis that dioecy tends to be maintained in regions with older floras. Similarly, the proportion of woody monoecious species was also positively correlated with mean genus age. These results partly support and expand the hypothesis that dioecy and monoecy are often found to be significantly associated with each other across phylogenies of woody angiosperms (Renner and Ricklefs, 1995). In contrast, the proportions of sexual systems for herbaceous species were weakly correlated with mean genus age (e.g. Apiaceae, Asteraceae, Cyperaceae), which might be due to the higher population turnover rate and faster adaptation to climate of herbaceous species than those of woody species (e.g. Acer and Salicaceae, Obbard et al., 2006; Smith and Beaulieu, 2009). Moreover, the effect of genus age on the proportions of woody sexual systems was much stronger than on those of herbaceous species. The differences in the effect of evolutionary age on proportions of sexual systems between woody and herbaceous growth forms might reflect their differences in evolutionary rates (i.e. speciation and extinction rates). Molecular studies have revealed that compared with herbaceous species, woody species such as trees tend to have longer generation times and lower evolutionary rate (Smith and Beaulieu, 2009), and thus may have lower chances to transit between sexual systems in response to environmental changes.
Conclusions
Here we mapped the geographical distributions of the frequencies of sexual systems and compared the effects of abiotic and biotic factors on sexual system frequencies for both woody and herbaceous flowering plant species in China. The results suggest that woody and herbaceous species differed significantly in their patterns in the frequencies of sexual systems, and also in the determinants underlying these patterns. The proportions of sexual systems of woody species were more strongly influenced by climate, evolutionary age, and mature height, whereas those of herbaceous species were less influenced by these factors. These findings shed light on previous hypotheses about the association between sexual systems and growth forms, and further demonstrate the necessity to differentiate woody and herbaceous growth forms when investigating the evolution and ecology of sexual systems. It is noteworthy that sexual systems may also vary intraspecifically across populations of a single species (e.g. Mercurialis annua, Dorken et al., 2017). Exploring the intraspecific sexual system variations for species with different growth forms along environmental gradients would further improve our understanding on the drivers of sexual system variations. Our study also suggests that compared with both dioecious and hermaphroditic species, more attention should be paid to monoecious species from both evolutionary and ecological perspectives. This study improves our understanding of the mechanisms underlying geographical patterns of reproductive trait diversity across different growth forms and their responses to climate.
Data Availability Statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation, to any qualified researcher.
Author Contributions
ZW and YW conceived the idea and design of the study. YW, TL, AL, YLi, and YLiu organized the database. YW and TL performed the statistical analysis. YW wrote the first draft of the manuscript. ZW has comprehensively revised the article. All authors contributed to the article and approved the submitted version.
Funding
This work was supported by the National Key Research Development Program of China (#2017YFA0605101; #2018YFA0606104), the Strategic Priority Research Program of Chinese Academy of Sciences (#XDB31000000), and National Natural Science Foundation of China (#31901216, #31988102, #31911530102).
Conflict of Interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Supplementary Material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpls.2020.01222/full#supplementary-material
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(2007). “Flora of China,” in Clusiaceae through Araliaceae, vol. 13 (Beijing: Science Press, and St. Louis: Missouri Botanical Garden Press).\\n\\n\"\n]"}}},{"rowIdx":478,"cells":{"data":{"kind":"list like","value":["\nFront PsycholFront PsycholFront. Psychol.Frontiers in Psychology1664-1078Frontiers Media S.A.32849113PMC743213510.3389/fpsyg.2020.01903PsychologyOriginal ResearchThe Impact of Organizational Psychology Factors for the Cross-Border Legal Service EntrepreneursXuChengjin1*ZhangZhe21School of Law, Shandong Normal University, Jinan, China2School of Economics and Management, Beijing Jiaotong University, Beijing, China
Edited by: Chih-Hung Yuan, University of Electronic Science and Technology of China, China
Reviewed by: Apple Liu, Sultan Idris Education University, Malaysia; Qian Song, Pukyong National University, South Korea
This article was submitted to Organizational Psychology, a section of the journal Frontiers in Psychology
1182020202011190323420201072020Copyright © 2020 Xu and Zhang.2020Xu and ZhangThis is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Trade friction has always been a prominent feature in the current economic development of the world. Its impacts on multinational enterprises are self-evident, but its psychological effects on the multinational entrepreneurs are still unclear. In order to understand the impacts of trade friction on psychological effects of multinational legal service entrepreneurs, 305 multinational entrepreneurs were selected in this study for questionnaire survey, and Spearman’s correlation and regression models were used to analyze the correlation among economic pressure, the thought of recession, self-efficacy, and social support. The structural equation model was used to analyze the influence path of economic pressure and social support on the thought of entrepreneurial recession, as well as the influence path of multinational entrepreneurship orientation and value-chain potential on the international performance. The results show that economic pressure is significantly and positively correlated with the thought of recession and self-efficacy extremely and significantly and negatively correlated with objective support and support utilization extremely; social support will reverse the thought of entrepreneurial recession caused by the economic pressure; the indirect impact path coefficient of social support utilization in economic pressure and entrepreneurial recession is – 0.281; the indirect impact path coefficient of value-chain potential in multinational entrepreneurial motivation and international performance is – 0.424. It shows that trade friction will indirectly trigger the thought of entrepreneurial recession of entrepreneurs by reducing their economic incomes. Besides, the social support utilization can significantly regulate the relationship between the economic pressure and the thought of entrepreneurial recession. Therefore, the value-chain potential plays an intermediary role in multinational entrepreneurial motivation and international performance.
trade frictioneconomic pressurestructural equation modelsocial supportthe thought of entrepreneurial recession of entrepreneursvalue-chain potentialIntroduction
In recent years, there is an upsurge of entrepreneurship in China. Nevertheless, there is a serious problem of oversupply in all fields; especially in the traditional fields, due to the increasing competitiveness in domestic market of China, it is difficult for more and more entrepreneurs to get success in the domestic market (Verbeke and Yuan, 2013). There are often many business opportunities in overseas markets, especially in developing countries. Therefore, Chinese entrepreneurs have shifted their entrepreneurial perspective to overseas markets in order to achieve success through overseas entrepreneurship. Multinational enterprise becomes one of the important forms of economic globalization, so it has been widely concerned by experts and scholars (Dimitratos et al., 2014; Da Silva Lopes et al., 2019). In order to enter the overseas market, multinational enterprises have made a lot of foreign investment, which can not only promote local economic development and employment but also benefit from technology spillovers (Tippmann et al., 2018). However, the work performance of multinational enterprises will also be affected by factors such as competition effects and government subsidy policies (Emelifeonwu and Valk, 2019). In addition, the entrepreneurship of multinational enterprises has to consider the profit transfer, fair trading, and other related points between the two countries. When there is a problem, there will be a “trade friction” (Jiang et al., 2019). China has now become the third largest trading country in the world, causing a shock on the original interest pattern, so the trade friction is inevitable. However, it is still unknown whether the trade frictions will affect the psychological effects of multinational entrepreneurs and in turn affect the work performance of these entrepreneurs.
With the deepening of economic globalization, the trend of entrepreneurial strategy and international diversification strategy are considered as key factors for multinational enterprises to succeed in the global market, and related studies have shown that the trend entrepreneurial strategy has a directly positive correlation with the entrepreneurial performance of multinational enterprises (McGee and Peterson, 2019; Palmer et al., 2019). The entrepreneur is the core of entrepreneurship, so the physical and psychological factors of entrepreneurs are critical, and the individual characteristics of entrepreneurs are the direct constraints of entrepreneurial activities in a specific entrepreneurial environment (Yueh et al., 2020). Studies have shown that the individual characteristics of entrepreneurs can also affect the entrepreneurial performance. Therefore, it is of great significance to explore the impacts of the trend of entrepreneurial strategy and the individual characteristics of entrepreneur on the work performance of multinational enterprises for enterprises to continue the multinational entrepreneurial activities and promote the development of enterprises (Dijkhuizen et al., 2018; Howell, 2018). Most of the entrepreneurial researches in the past had been dedicated to the outputs of small businesses and startups, instead of focusing on international acquisition scenarios.
In order to explore the impacts of the trend of internationalization on the psychological effects and work performance of multinational entrepreneurs, the legal service entrepreneurs of knowledge-based service companies were thus selected as the research objects. The impacts of trade friction on the psychological effects of multinational entrepreneurs and the impact mechanism of multinational entrepreneurial orientation on the international performance were explored in this study firstly. Finally, the validity and authenticity of hypotheses proposed in this study were verified through actual investigations. The results of this study aimed to lay a foundation for understanding the psychological effects of multinational entrepreneurs and promoting the international work performance of multinational enterprises.
Literature OverviewPsychological Effect of Entrepreneurs
Entrepreneurship can help entrepreneurs achieve their personal ideals and promote economic development and technological innovation. Researches in entrepreneurship-related fields had been conducted from the perspectives of management, psychology, and sociology. Lee et al. (2019) explored the differences between family and non-family senior management teams in organizational and psychological ownership of work, and the results showed that there was no significant difference between them and no significant impact on the entrepreneurship of the enterprise. Hooshangi and Loewenstein (2018) found that reduction in the investment opportunities could threaten the entrepreneur’s willingness to invest, and reduced investment costs would inhibit the investment decisions of pioneer entrepreneurs, while loose proprietary systems could have a positive impact on psychology of entrepreneurship. Zou et al. (2016) explored the different interests and goals of the inherent conflict between entrepreneurs and venture capitalists based on the psychological capital and then put forward a series of theoretical propositions about solving venture capital and strategic countermeasures. These studies revealed that the individual characteristics of entrepreneurs had a significant impact on entrepreneurial decision-making and entrepreneurship. Kautto (2019) found that the psychosocial impact of exogenous policy intervention could promote the transfer of entrepreneurship. Muhammad et al. (2020) indicated that external pressure had a positive and direct impact on behaviors of entrepreneurs and that the sustainable orientation of strategy can provide differentiation for enterprisers. In summary, psychological differences of individuals and external pressures affect the behaviors of entrepreneurs, yet the impacts of changes in the external environment and policies on the psychological effects of multinational entrepreneurs still need to be further explored.
Impacts of International Performance of Multinational Entrepreneurs
Entrepreneurship is a significant form to achieve the social and economic development. In exploring the international entrepreneurship, it is very important to improve the performance of multinational enterprise. In order to explore the impact of strategic responsibilities of the multinational enterprises on the performance of their subsidiaries, Sarabi et al. (2020) found through building a model that the entrepreneurial leadership of the chief executive officer (CEO) of a subsidiary can improve the enterprise’s performance. Boone et al. (2019) revealed that the entrepreneurial spirit and the innovation enthusiasm of the top management team could affect the performance of the enterprise. In order to explore the impact of external crises on the performance of subsidiaries of the multinational enterprises, Martins et al. (2019) found that the sense of crisis affected the business performance of multinational enterprises through verification of the least-square structural equation model. Collings et al. (2019) revealed that the multinational strategy determined the goals of the global talent management system and also affected the performances of enterprises. From the perspective of individual human resources, it indicated that the personal performance could be improved by enlarging the human resources. Fernandes et al. (2018) constructed the framework of the multinational entrepreneur to affect the working performance, and the results showed that the entrepreneurial spirit can promote the working performance through management. Based on the above researches, it can be found that individual characteristics and strategic decisions of entrepreneurs can affect the work performance of multinational enterprises. However, the existing research lacks a definition of entrepreneurship-oriented dimension and its impact on the international performance of multinational companies.
To sum up, the multinational legal service entrepreneurs were taken as the research objects in this paper. In the form of questionnaire, the impact path of the economic pressure and social support of the trade friction on the entrepreneurial recession of entrepreneurs and the impact path of multinational entrepreneurial orientation and value-chain potential on the international performance of enterprises were analyzed. In this way, this study will lay the theoretical foundation for finding out the way to relieve from the psychological pressure of multinational entrepreneur and improving their working performance.
Materials and MethodsResearch Objects
A total of 310 Chinese entrepreneurs of legal services who started businesses in the United States, Canada, Indonesia, Brazil, and the United Kingdom were selected as the research objects, the questionnaires were distributed online from June 2019 to December 2019, and 305 questionnaires were returned, so the response rate was 98.39%. The 305 subjects were 21–38 years old, with an average age of 26.88 ± 5.52. Studies show that the risk aversion of female entrepreneurs is higher than that of male and that self-efficiency has a strong predictive effect on the entrepreneurial behavior. Therefore, the gender, age, and self-efficiency were taken as the control variables for subsequent analysis.
Model Building of Effect on Entrepreneur’s Recession From Economic Pressure and Social Support From the Trade Friction
It is believed in this study that the economic pressure brought about by the trade friction could cause the entrepreneur to experience some negative emotions (anxiety, despair, depression, etc.). Based on the theory of learned helplessness, the theoretical model was built for effect of the economic pressure and social support caused by the trade friction on the entrepreneur’s thought of recession. The theoretical model is shown in Figure 1.
Model of effect of the entrepreneur’s thought of recession.
According to Figure 1, the economic pressure is an independent variable, entrepreneurial recession psychology is the dependent variable, and the sub-dimension under the social support is the intermediary variable. Therefore, the following hypotheses were proposed in this study:
The economic pressure could be promoted significantly by the trade friction;
The thought of entrepreneurial recession could be promoted significantly by the economic pressure;
The promotion of economic pressure on the thought of entrepreneurial recession could be regulated reversely by the social support;
The promotion of economic pressure on the thought of entrepreneurial recession could be regulated reversely by the subjective support;
The promotion of economic pressure on the thought of entrepreneurial recession could be regulated reversely by the objective support;
The promotion of economic pressure on the thought of entrepreneurial recession could be regulated reversely by the support utilization.
Model Building of Correlation on the Multinational Entrepreneurial Orientation and the International Performance
In order to explore the correlation on the multinational entrepreneurial orientation and international performance, the theoretical model shown in Figure 2 below was proposed in this study by taking the global value chain as the intermediary variable.
Model of effect of the entrepreneurship international performance.
Based on the above model, the below hypotheses are proposed in this study:
The international performance could be promoted by the multinational entrepreneurial orientation;
The international performance could be promoted by innovation in the multinational entrepreneurial orientation;
The international performance could be promoted by antecedence in the multinational entrepreneurial orientation;
The international performance could be promoted by adventure in the multinational entrepreneurial orientation;
The international performance could be performed by the value-chain potential;
The international performance could be performed by complexity of the trade;
The international performance could be performed by codability of the product;
The international performance could be performed by the supply capacity;
The value-chain potential is the intermediary variable for multinational entrepreneurial orientation to affect the international performance.
Design of Evaluation Tools
The evaluation scales used in this study were all widely used, reliable, and valid evaluation tools in line with the actual conditions.
Evaluation Tools of the Social Support
The Social Support Rating Scale (SSRS) was used by Basinska and Rozkwitalska (2020). The scale included 10 items and 3 dimensions, which were the degree of subjective support, degree of objective support, and degree of support utilization. In addition, the Likert-4 rating method (1–4 scores indicate from “no” to “yes”) was used for evaluation in this scale. The subjective support included the support at the emotional level or resources from the relatives, friends, social networks, etc. The objective support refers to the actual support resources and also includes the material support provided by individuals, organizations, or the society. The support utilization refers to the utilization degree of the actual or perceived resource by individuals.
Evaluation Tools of the Economic Pressure
The Economic Pressure Rating Scale (EPRS) (Pollack et al., 2012) was compiled to evaluate the economic pressure of entrepreneur under the trade friction. The scale was supplemented by that “the recent economic environment has negatively affected my company,” “the business has been in trouble in the past year,” and “I have to bear a lot of pressures and responsibilities, and play a lot of roles.” The Likert-7 rating method (1–7 points indicate from “strongly disagree” to “strongly agree”) was used for evaluation in this scale.
Evaluation Tools of Entrepreneurial Recession
The exiting entrepreneurship assessment scale was used for recompilation and was applied to evaluate the entrepreneur’s thought of recession (Schuster et al., 2013). Three items were used for the evaluation, namely, “in the next 1 year, I will try my best to quit my current entrepreneurial activities,” “if I continue my business, I will be very worried,” and “continuing to start a business is not as passionate as when I started my business.” The Likert-7 rating method (1–7 points indicate from “strongly disagree” to “strongly agree”) was used for evaluation in this scale.
Evaluation Tools of Self-Efficiency
The entrepreneurship self-efficacy assessment scale (Chen et al., 2019) was used for recompilation and was applied to evaluate the entrepreneur’s self-efficacy. In this study, 17 items were used for the evaluation. The Likert-5 rating method (1–5 points indicate from “strongly disagree” to “strongly agree”) was used for evaluation in this scale.
Evaluation Tools of Multinational Entrepreneurial Orientation
Based on the three dimensions of innovativeness, proactiveness, and risk taking and the “9-item semantic difference scale” (Williams and Lee, 2009), the scale was improved by combining with the internationalization perspective of the multinational enterprises. The improved scale could be seen as below Table 1. When this scale was used for evaluation, it was indicated by “yes” for 1 score or “no” for 0 score of the evaluation.
Evaluation scale of the multinational entrepreneurial orientation.
Dimensionality
No.
Item
Innovation
1
Focusing on marketing, products, or services? Or focusing on investment and leadership and innovation of the R & D and technology
2
Having new products/services?
3
Having great changes or the product/service path?
Proactiveness
4
Response to the competitor, or initiated by itself?
5
The first to introduce the new product/service and the technology on management and operation?
6
Avoiding competitive conflicts in the form of “peaceful coexistence” as often?
Risk-taking
7
Being inclined to the international project with low risk?
8
Achieving the goals step by step in the international environment?
9
Reducing the error rate of decision with an attitude of await-and-see?
Evaluation Tools of Value-Chain Potential
The value-chain potential was measured based on the trade complexity, product codability, and supply capacity (Rashid et al., 2019). The finally designed scale is shown in Table 2. When this scale was used for evaluation, it was indicated by “yes” for 1 score or “no” for 0 score of the evaluation.
Evaluation scale of value-chain potential.
Dimensionality
No.
Item
Trade complexity
1
Having the need for information exchange by exchange, communication, and share
2
High complexity for having the need for information exchange by exchange, communication, and share
Product codability
3
Quantity of information on related design or quality besides the formal documents
4
High complexity for information on related design or quality besides the formal documents
Supply capacity
5
Strong acceptance to demand changes of the market compared with other competitors
6
Strong acceptance to higher requirements from the service-provided side compared with other competitors
7
High assets proprietary of the company
8
High ability to bargain in the product trade
Evaluation Tools of International Performance
Based on the research results of other scholars (Abosede et al., 2018; Karami and Tang, 2019), the international performance was evaluated based on the four dimensions of foreign sales growth, changes in international market share, international investment return rate, and foreign operation satisfaction. The finally designed evaluation scale is shown in Table 3 below. When this scale was used for evaluation, it was indicated by “yes” for 1 score or “no” for 0 score of the evaluation.
Evaluation scale of the international performance.
No.
Item
1
Rapid increase of the company’s overseas sales revenue in recent years
2
Rapider increase of the company’s sales revenue in the oversea markets than other major competitors
3
Rapid increase of overseas sales profits in recent years
4
Rapider increase of the company’s sales profits in the oversea markets than other major competitors
5
Rapid increase of the company’s overseas market shares in recent years
6
Rapider increase of the company’s overseas market shares than other major competitors
7
Higher return rate of the company’s oversea investment in recent years than other major competitors
8
Significant increase of the company’s sales rate in oversea market
9
High operational satisfaction of the company in oversea market in recent years
10
High satisfaction feedback of the company from the clients
Statistical Analysis
The statistical processing was performed by SPSS 19.0. The validity tests of the evaluation tools were performed by using the Bartlett’s spherical test and KMO value (it was deemed as appropriation when KMO value was higher than 0.7, and it was deemed as appropriation when Bartlett’s spherical test p < 0.05). The reliability tests of the evaluation tools were performed by using the Cronbach’s α (it was deemed as high reliability when Cronbach’s α was higher than 0.7). The principal component analysis (PCA) was used to extract the factors, whose eigenvalue was higher than 1, in the variable tables. The descriptive statistics was used for statistical analysis of the sample distribution of each variable. The correlation of each variable was analyzed with the Pearson correlation. Hypotheses proposed in this study were verified by the process of hierarchical regression. When P < 0.05, it is considered that it has a statistical significance.
ResultsAnalysis on the Current Status of the Global and China-Related Trade Friction
Comparing the changes of the total number of the global and China-related trade frictions from 2009 to 2019, Figure 3 indicates that in the past 10 years, the trade frictions in the world have been more than 150 cases and those in China have been more than 70 cases. In 2019, the China-related trade frictions accounted for 30.46% of the cases in the whole world. Overall, the number of China-related trade friction showed a high trend all the time. The annual growth rate of the China-related trade friction showed the same trend as the annual growth rate of trade friction all over the world.
Change trends of total number of the global and China-related trade frictions from 2009 to 2019.
Comparing the proportions of the number of the global and China-related trade frictions from 2009 to 2019, Figure 4 shows that among all the global and China-related trade frictions, the anti-dumping cases accounted for the largest proportion, which was 80% (2363 cases) and 67% (715 cases), respectively, while the proportions of anti-subsidy were 12% (344 cases) and 14% (145 cases), respectively, and the proportions of safeguard precaution (8% (222 cases) and 9% (201 cases) had no great difference. In addition, there were also some trade frictions related to the special safeguards, accounting for 0% (10 cases) and 1% (10 cases), respectively. It shows that most countries in the world took the boycott measures such as increased taxation currently when dumping products from outside the country on the market. At the same time, the importing country offset the subsidies for imported products by imposing countervailing measures, price commitments, or import restrictions in order to protect the domestic industries and restore fair competition.
Proportions of the number of the global and China-related trade frictions from 2009 to 2019 [Figure (A) is the proportion of the global trade frictions; Figure (B) is the proportion of China-related trade frictions].
The top 10 complaining countries and the top 10 complained industries in the China-related trade frictions in 2009–2019 were compared. It could be seen from Figure 5A that, among the top 10 complaining countries, the cases both in the United States and in India were more than 150, and the number of complaints in the past 10 years was much higher than that in the European Union (EU) and other countries or organizations. It could be seen from Figure 5B that, among the top 10 complained industries, the numbers of metal product industry (197 cases), steel industry (196 cases), and chemical raw materials and product industries (164 cases) are much higher than those in other industries, and the most complained industries belong to the manufacturing industry.
The top 10 complaining countries and the complained industries in the China-related trade frictions in 2009–2019. Figure (A) is the top 10 complaining countries; Figure (B) is the top 10 complained countries; A is for the fabricated metal industry; B is for the steel industry; C is for the chemical raw material and product industry; D is for the non-metallic product industry; E is for the textile industry; F is for the electrical industry; G is for the paper manufacturing industry; H is for the non-ferrous metal industry; I is for the food industry; and J is for the rubber product industry.
Descriptive Statistical Analysis on Basic Personal Information of the Entrepreneur
Then, a descriptive statistical analysis on the basic personal information of the entrepreneur was performed based on 305 entrepreneurs of legal services surveyed in this study. The results are shown in Figure 6 below. It could be seen that, among all the 305 entrepreneurs of legal services, the proportion in male is higher than that in female, which is 61% (187 cases) and 39% (118 cases), respectively, mainly aging range of 26–30 years with 126 cases (41%), and most are masters and above (212 cases, 70%).
Statistical results of basic data of the subjects [Figure (A) is the sex ratio; Figure (B) is the age range; and Figure (C) is the academic qualification].
Results on Reliability and Validity Test of the Evaluation Tools
First, the reliability and validity test of the scales used in this study was performed. It could be seen from Table 4 that the Cronbach’s α is higher than 0.7, KMO is higher than 0.7, and p < 0.05 in the Bartlett spherical test. It shows that the evaluation tools used in this study have high reliability and validity, which lays the reliability of the subsequent evaluation results.
Results on reliability and validity test of the evaluation tools.
Scale
Cronbach’sα
KMO
Bartlett’s spherical test
χ 2
df
p
Social support
0.815
0.713
720.955
44
0.000
Economic pressure
0.809
0.713
241.955
3
0.000
Entrepreneurial recession
0.821
0.711
225.782
3
0.000
Self-efficacy
0.952
0.712
267.893
3
0.000
Multinational entrepreneurial orientation
0.833
0.713
268.392
4
0.000
Value-chain potential
0.862
0.712
305.171
5
0.000
International performance
0.934
0.713
556.272
3
0.000
Results of Correlation Analysis of Various Variables
Then, the correlation between the thought of entrepreneurial recession, the impact of entrepreneurial work performance, and other variables in this study were compared. The results are shown in Tables 5, 6 below. It could be seen from Table 4 that the economic pressure has an extremely significant positive correlation with the thought of recession and self-efficacy (p < 0.01) and has an extremely significant negative correlation with the objective support and support utilization (p < 0.01). The thought of recession has an extremely significant positive correlation with the self-efficacy (p < 0.01) and has a significant negative correlation with the objective support and support utilization (p < 0.05). The self-efficacy has a significant negative correlation with the subjective support and objective support (p < 0.05) and has an extremely significant negative correlation with the support utilization (p < 0.01). The subjective support has an extremely significant negative correlation with the objective support and support utilization (p < 0.01). There is an extremely significant positive correlation between the objective support and support utilization (p < 0.01).
Correlation analysis on related variables of the thought of entrepreneurial recession.
Variable
Economic pressure
Thought of recession
Self-efficacy
Subjective support
Objective support
Support utilization
Economic pressure
1
Thought of recession
0.432**
1
Self-efficacy
0.321**
0.452**
1
Subjective support
0.032
–0.101
−0.173*
1
Objective support
−0.544**
−0.193*
−0.207*
−0.282**
1
Support utilization
−0.501**
−0.704**
−0.686**
−0.457**
0.458**
1
*Correlation was significant at the 0.05 level (2-tailed); **Correlation was significant at the 0.01 level (2-tailed).
Correlation analysis of related variables of the entrepreneurial working performance.
Variable
Multinational entrepreneurial orientation
Innovation
Proactiveness
Risk-bearing
Value-chain potential
Transaction complexity
Product codability
Supply capacity
International performance
Multinational entrepreneurial orientation
1
Innovation
0.820**
1
Proactiveness
0.810**
0.545**
1
Risk-bearing
0.808**
0.467**
0.455**
1
Value-chain potential
0.520**
0.460**
0.502**
0.348**
1
Transaction complexity
0.398**
0.387**
0.362**
0.298**
0.832**
1
Product codability
0.326**
0.178*
0.332**
0.274**
0.577**
0.202**
1
Supply capacity
0.487**
0.434**
0.454**
0.303**
0.862**
0.622**
0.274**
1
International performance
0.566**
0.551**
0.508**
0.347**
0.678**
0.567**
0.263**
0.656**
1
*Correlation was significant at the 0.05 level (2-tailed); **Correlation was significant at the 0.01 level (2-tailed).
It could be seen from Table 6 that the multinational entrepreneurial guide and its sub-dimensions (innovation, proactiveness, and risk-taking), value-chain potential, and its sub-dimensions (transaction complexity, product codability, and supply capacity) have a significant correlation with the international performance (p < 0.05).
Verification Results of the Proposed Hypotheses
The hypotheses proposed earlier in this study were analyzed by the regression analysis. It could be seen from Table 7 that: a. The trade friction has an extremely significant positive impact on the economic pressure (β = 0.206; p < 0.01). b. The economic pressure has an extremely significant positive effect on the thought of entrepreneurial recession (β = 0.244; p < 0.01). c. The social support × economic pressure can negatively affect the thought of entrepreneurial recession, that is, the social support can regulate the thought of entrepreneurial recession caused by the economic pressure reversely (β = −0.192; p < 0.01). c1. and c2. The subjective support and objective support cannot regulate the thought of entrepreneurial recession caused by the economic pressure significantly (β = −0.078, −0.032; p > 0.05). c3. The support utilization can regulate the thought of entrepreneurial recession caused by the economic pressure (β = −0.281; p < 0.01). d. The multinational entrepreneurial orientation has a significant positive impact on the international performance (β = 0.532; p < 0.01). d1. The innovation dimension in multinational entrepreneurship has an extremely significant positive impact on the international performance (β = 0.288; p < 0.01). d2. The proactiveness dimension in multinational entrepreneurship has a significant positive impact on the international performance (β = 0.227; p < 0.01). d3. The risk-taking in the multinational entrepreneurship cannot affect the international performance (β = 0.036; p > 0.05). e. The value-chain potential has an extremely significant positive impact on the international performance (β = 0.798; p < 0.01). e1. The transaction complexity has an extremely significant positive impact on the international performance (β = 0.182; p < 0.01). e2. The product codability cannot affect the international performance (β = −0.092; p > 0.05). e3. The supply capacity has an extremely significant positive effect on the international performance (β = 0.424; p < 0.01). In addition, it is also found in this study that the multinational entrepreneurial orientation can affect the value-chain potential significantly (β = 0.415; p < 0.01).
Analysis results of regression analysis models.
Model
Independent variable
Dependent variable
β
△R2
p
1
Trade friction
Economic pressure
0.206
0.344
0.000
2
Economic pressure
Thought of entrepreneurial recession
0.244
0.022
0.000
3
Economic pressure × social support
Thought of entrepreneurial recession
–0.192
0.021
0.000
4
Economic pressure × subjective support
Thought of entrepreneurial recession
–0.078
0.003
0.173
5
Economic pressure × objective support
Thought of entrepreneurial recession
–0.032
0.003
0.532
6
Economic pressure × support utilization
Thought of entrepreneurial recession
–0.281
0.032
0.000
7
Multinational entrepreneurial orientation
International performance
0.523
0.307
0.000
8
Innovation
International performance
0.288
0.322
0.000
9
proactiveness
International performance
0.227
0.314
0.000
10
Risk-bearing
International performance
0.036
0.004
0.102
11
Value-chain potential
International performance
0.798
0.443
0.000
12
Trade complexity
International performance
0.182
0.318
0.000
13
Product codability
International performance
–0.092
0.003
0.098
14
Supply capacity
International performance
0.424
0.261
0.000
15
Multinational entrepreneurial orientation
Value-chain potential
0.415
0.257
0.000
× indicates that the social support and its sub-dimension exerts the intermediary role for impact of economic pressure on the thought of entrepreneurial recession.
It could be seen from Table 8 that all the hypotheses are true except c1., C2., d3., and e2. Therefore, the result graph of the impact path proposed in this study has to be redrawn based on the verification results of the hypotheses.
Verification results of the hypotheses.
No.
Hypotheses
Verification result
a.
The economic pressure could be promoted significantly by the trade friction.
Support
b.
The thought of entrepreneurial recession could be promoted significantly by the economic pressure.
Support
c.
The promotion of economic pressure on the thought of entrepreneurial recession could be regulated reversely by the social support.
Support
c1.
The promotion of economic pressure on the thought of entrepreneurial recession could be regulated reversely by the subjective support.
Not support
c2.
The promotion of economic pressure on the thought of entrepreneurial recession could be regulated reversely by the objective support.
Note support
c3.
The promotion of economic pressure on the thought entrepreneurial recession could be regulated reversely by the support utilization.
Support
d.
The international performance could be promoted by the multinational entrepreneurial orientation.
Support
d1.
The international performance could be promoted by innovation in the multinational entrepreneurial orientation.
Support
d2.
The international performance could be promoted by antecedence in the multinational entrepreneurial orientation.
Support
d3.
The international performance could be promoted by adventure in the multinational entrepreneurial orientation.
Not support
e.
The international performance could be performed by the value-chain potential.
Support
e1.
The international performance could be performed by complexity of the trade.
Support
e2.
The international performance could be performed by codability of the product.
Not support
e3.
The international performance could be performed by the supply capacity.
Support
f
The value-chain potential is the intermediary variable for multinational entrepreneurial orientation to affect the international performance.
Support
The impact path of the regulated economic pressure and social support based on the trade friction on the thought of entrepreneurial recession is shown in Figure 7 below. It can be seen that the economic pressure could be impacted positively by the trade friction, while the occurrence of the thought of entrepreneurial recession could be impacted positively by the economic pressure. However, the thought of entrepreneurial recession caused by the economic pressure could be regulated by the social support and its support utilization.
Impact paths of the economic pressure and social support based on the trade friction on the thought of entrepreneurial recession. ** represents P < 0.01.
The impact paths of the regulated multinational entrepreneurial motivation, value-chain potential, and international performance are shown in Figure 8 below. It can be seen that the international performance could be promoted by the multinational entrepreneurial motivation and its innovation and proactiveness, and the international performance could be promoted by the transaction complexity and supply capacity of the value-chain potential. The value-chain potential can play a role of intermediation in the relationship between the multinational entrepreneurial motivation and international performance.
Impact path of the multinational entrepreneurial orientation, value-chain potential, and international performance. ** represents P < 0.01.
Discussion
In this study, it is found that the United States, European Union, Association of Southeast Asian Nations, and Canada are main exporters of China, so the current export markets of China will continue to be the “main battlefield” of the trade friction in the future (Lawrence, 2018). On the other hand, exports from China to the emerging markets such as Russia, India, and Mexico also grow at 25% year by year, so the trade relations with the emerging markets will also be in the form of “no major problems and constant minor issues.” It is found that greater economic pressure to persons of multinational legal service can be promoted by the trade friction. Some early statistics in WTO show that 1 of 7 trade frictions is China-related (Holladay et al., 2017). In addition, after the trade frictions were analyzed in the whole world in the past 10 years, it is found that most trade frictions were China-related, and the China-related trade friction in 2019 only was more than 30%. With gradual development of the foreign trade, the trade friction has extended from a single product to the entire industry. Therefore, in the future, it should not only consider quelling trade disputes but also consider policy formulation and policy level of the trade friction (Ji et al., 2020).
The protection policies for the knowledge-based enterprises in various countries are not less than those for the manufacturing industry, and the incidents caused by trade friction for the knowledge-service enterprises entering their own countries are possible. Therefore, the legal service entrepreneurs have to bear greater economic pressure, so the trade friction may cause certain economic pressure on multinational entrepreneurs (Cui et al., 2019; Liu et al., 2019). When there are events such as trade friction that are beyond the scope of the entrepreneur’s own tolerance/control, they will have a helpless feeling. This negative emotion will greatly affect the entrepreneur’s pursuit of a sense of accomplishment, so excessive economic pressure will result in the psychological effect of entrepreneurial recession (Ryff, 2019). In the process of multinational entrepreneurship, even if there are a lot of subjective or objective social supports in the society, the entrepreneur fails to apply these supports in practice, then it will have no great significance for the economic pressure and psychological pressure of the entrepreneur. Thus, the support utilization in social support can regulate the thought of entrepreneurial recession caused by the economic pressure (Klyver et al., 2017; Monica et al., 2018). It indicated that entrepreneurs can improve the economic pressure caused by trade frictions in enterprises through reasonable utilization of the social support during the multinational entrepreneurship and then adjust their psychological effects. In the global market, the innovation of products, services, and management is the source of enterprise value. The advanced behavior of enterprise will bring first-mover advantages, which is conducive to seize the market share and thus obtain higher profits. Therefore, the innovation and advancement in the multinational entrepreneurial orientation can promote the international performance of enterprises (Álvarez Torres et al., 2019; Guzmán et al., 2019). In the process of product transactions, the multinational companies are committed to the development and improvement of services and other technologies, increase the complexity of market information in the transaction process, and improve the knowledge and other aspects. In this way, it can increase the initiative of the enterprise and satisfy the needs of customers to a greater extent. Therefore, the complexity of product transactions and supply capacity of product in the value-chain potential of entrepreneurship of the multinational enterprises can promote the international performance of enterprises (Bos et al., 2017; Ferreira et al., 2018; Biberhofer et al., 2019).
It is found that the entrepreneur’s thought of entrepreneurial recession can be affected positively by the size of economic pressure, which is similar to the results of Morris et al. (2020). Then, the regulation role of the social support and its sub-dimensions of subjective support, objective support, and support utilization to the thought of recession caused by the economic pressure was explored. The results of this study show that the stronger the social support, the weaker the relationship between thought of entrepreneurial recession caused by the economic pressure. The reasons for the above results may be that the research objects of this study are the multinational entrepreneurs. No matter how much subjective or objective support it has in the society, such support has to be applied in practice to improve the economic pressure of the enterprise and then reduce the thought of entrepreneurial recession (Alby et al., 2019), which is consistent with the results in this study that the subjective support and objective support cannot regulate the impact of economic pressure on the thought of entrepreneurial recession reversely. It is found in this study that the international performance could be promoted significantly by the multinational entrepreneurial orientation, and the effects of innovation and proactiveness in the multinational entrepreneurial orientation are significant. It is consistent with the results of Augustie and Saad (2019). The international performance cannot be impacted significantly by the risk-taking in the multinational entrepreneurial orientation. This may be because this experience in overseas operations of entrepreneurs is relatively weak in the international market, and the company’s management level is relatively insufficient, so that many problems behind the value-chain potential cannot be treated and solved skillfully and quickly. The value-chain potential is a key factor to drive the international performance (Vonsée et al., 2019). It is found in this study that the international performance can be promoted by the value-chain potential and its transaction complexity and supply capacity. This is because, in the transaction process, the enterprise is committed to the development and improvement of the services and other technologies, enhancement on complexity of the market information, and improvement of the knowledge. In this way, the initiative of the enterprise can be increased, and thus the clients can be satisfied to a larger extend. Entrepreneurship is a very complex and dynamic activity, and the difficulties experienced by multinational entrepreneurs are more than those experienced by domestic entrepreneurs (Boone et al., 2019). Regardless of any entrepreneurial behavior or activity, it is germinated in a specific entrepreneurial environment and developed step by step. Therefore, the entrepreneurial activities are inevitably affected by the entrepreneurial environment (Biryukov et al., 2020). In addition, it is found based on the codability of the products in this study that the codability of products can suppress the falseness of international performance. It is because codability of the products can facilitate the product or service management provided by the company in multinational enterprises, so that it is convenient to estimate and choose. Therefore, this variable has no inhibitory effect on the international performance of multinational enterprises.
Conclusion
With assistance of statistical methods combined with questionnaire surveys, regression model, and structural equation models, this study analyzed the impact mechanism of trade friction on the mentality of multinational entrepreneurs. It was found that trade friction can trigger the thought of entrepreneurial recession of entrepreneurs indirectly by reducing their income. The social support utilization can significantly regulate the relationship between economic pressure and the thought of entrepreneurial recession. The multinational entrepreneurship motivation can promote the international performance of multinational enterprises through the value-chain potential. It fails to consider other factors of the external environment (such as policies, etc.) and the interactions from the personal internal factors to the thought of entrepreneurial recession and the entrepreneurial working performance. Accordingly, the quantity of samples has to be increased in the future and to take specific research in a certain area to explore the effects on external environment and the internal factors of individuals on the thought of entrepreneurial recession and entrepreneurial working performance. In conclusion, the thought of entrepreneurial recession of the entrepreneur can be reduced and the subjective initiative of the entrepreneur can be played by using the social support to the maximal extent. International performance of the enterprise can be improved by complying with the international market and improving the service ability. Results in this study can provide a basis for understanding the impact mechanism of the thought of entrepreneurial recession of entrepreneur of multinational legal service and its work performance.
Data Availability Statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation, to any qualified researcher.
Ethics Statement
The studies involving human participants were reviewed and approved by the Shandong Normal University Ethics Committee. The patients/participants provided their written informed consent to participate in this study. Written informed consent was obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article.
Author Contributions
CX wrote the manuscript. ZZ reviewed the manuscript. Both authors contributed to the article and approved the submitted version.
Conflict of Interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Funding. This research was funded by the Shandong Normal University Young Teachers Research Project (Humanities and Social Sciences).
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Psychol.Frontiers in Psychology1664-1078Frontiers Media S.A.32849113PMC743213510.3389/fpsyg.2020.01903PsychologyOriginal ResearchThe Impact of Organizational Psychology Factors for the Cross-Border Legal Service EntrepreneursXuChengjin1*ZhangZhe21School of Law, Shandong Normal University, Jinan, China2School of Economics and Management, Beijing Jiaotong University, Beijing, China
Edited by: Chih-Hung Yuan, University of Electronic Science and Technology of China, China
Reviewed by: Apple Liu, Sultan Idris Education University, Malaysia; Qian Song, Pukyong National University, South Korea
This article was submitted to Organizational Psychology, a section of the journal Frontiers in Psychology
1182020202011190323420201072020Copyright © 2020 Xu and Zhang.2020Xu and ZhangThis is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Trade friction has always been a prominent feature in the current economic development of the world. Its impacts on multinational enterprises are self-evident, but its psychological effects on the multinational entrepreneurs are still unclear. In order to understand the impacts of trade friction on psychological effects of multinational legal service entrepreneurs, 305 multinational entrepreneurs were selected in this study for questionnaire survey, and Spearman’s correlation and regression models were used to analyze the correlation among economic pressure, the thought of recession, self-efficacy, and social support. The structural equation model was used to analyze the influence path of economic pressure and social support on the thought of entrepreneurial recession, as well as the influence path of multinational entrepreneurship orientation and value-chain potential on the international performance. The results show that economic pressure is significantly and positively correlated with the thought of recession and self-efficacy extremely and significantly and negatively correlated with objective support and support utilization extremely; social support will reverse the thought of entrepreneurial recession caused by the economic pressure; the indirect impact path coefficient of social support utilization in economic pressure and entrepreneurial recession is – 0.281; the indirect impact path coefficient of value-chain potential in multinational entrepreneurial motivation and international performance is – 0.424. It shows that trade friction will indirectly trigger the thought of entrepreneurial recession of entrepreneurs by reducing their economic incomes. Besides, the social support utilization can significantly regulate the relationship between the economic pressure and the thought of entrepreneurial recession. Therefore, the value-chain potential plays an intermediary role in multinational entrepreneurial motivation and international performance.
trade frictioneconomic pressurestructural equation modelsocial supportthe thought of entrepreneurial recession of entrepreneursvalue-chain potentialIntroduction
In recent years, there is an upsurge of entrepreneurship in China. Nevertheless, there is a serious problem of oversupply in all fields; especially in the traditional fields, due to the increasing competitiveness in domestic market of China, it is difficult for more and more entrepreneurs to get success in the domestic market (Verbeke and Yuan, 2013). There are often many business opportunities in overseas markets, especially in developing countries. Therefore, Chinese entrepreneurs have shifted their entrepreneurial perspective to overseas markets in order to achieve success through overseas entrepreneurship. Multinational enterprise becomes one of the important forms of economic globalization, so it has been widely concerned by experts and scholars (Dimitratos et al., 2014; Da Silva Lopes et al., 2019). In order to enter the overseas market, multinational enterprises have made a lot of foreign investment, which can not only promote local economic development and employment but also benefit from technology spillovers (Tippmann et al., 2018). However, the work performance of multinational enterprises will also be affected by factors such as competition effects and government subsidy policies (Emelifeonwu and Valk, 2019). In addition, the entrepreneurship of multinational enterprises has to consider the profit transfer, fair trading, and other related points between the two countries. When there is a problem, there will be a “trade friction” (Jiang et al., 2019). China has now become the third largest trading country in the world, causing a shock on the original interest pattern, so the trade friction is inevitable. However, it is still unknown whether the trade frictions will affect the psychological effects of multinational entrepreneurs and in turn affect the work performance of these entrepreneurs.
With the deepening of economic globalization, the trend of entrepreneurial strategy and international diversification strategy are considered as key factors for multinational enterprises to succeed in the global market, and related studies have shown that the trend entrepreneurial strategy has a directly positive correlation with the entrepreneurial performance of multinational enterprises (McGee and Peterson, 2019; Palmer et al., 2019). The entrepreneur is the core of entrepreneurship, so the physical and psychological factors of entrepreneurs are critical, and the individual characteristics of entrepreneurs are the direct constraints of entrepreneurial activities in a specific entrepreneurial environment (Yueh et al., 2020). Studies have shown that the individual characteristics of entrepreneurs can also affect the entrepreneurial performance. Therefore, it is of great significance to explore the impacts of the trend of entrepreneurial strategy and the individual characteristics of entrepreneur on the work performance of multinational enterprises for enterprises to continue the multinational entrepreneurial activities and promote the development of enterprises (Dijkhuizen et al., 2018; Howell, 2018). Most of the entrepreneurial researches in the past had been dedicated to the outputs of small businesses and startups, instead of focusing on international acquisition scenarios.
In order to explore the impacts of the trend of internationalization on the psychological effects and work performance of multinational entrepreneurs, the legal service entrepreneurs of knowledge-based service companies were thus selected as the research objects. The impacts of trade friction on the psychological effects of multinational entrepreneurs and the impact mechanism of multinational entrepreneurial orientation on the international performance were explored in this study firstly. Finally, the validity and authenticity of hypotheses proposed in this study were verified through actual investigations. The results of this study aimed to lay a foundation for understanding the psychological effects of multinational entrepreneurs and promoting the international work performance of multinational enterprises.
Literature OverviewPsychological Effect of Entrepreneurs
Entrepreneurship can help entrepreneurs achieve their personal ideals and promote economic development and technological innovation. Researches in entrepreneurship-related fields had been conducted from the perspectives of management, psychology, and sociology. Lee et al. (2019) explored the differences between family and non-family senior management teams in organizational and psychological ownership of work, and the results showed that there was no significant difference between them and no significant impact on the entrepreneurship of the enterprise. Hooshangi and Loewenstein (2018) found that reduction in the investment opportunities could threaten the entrepreneur’s willingness to invest, and reduced investment costs would inhibit the investment decisions of pioneer entrepreneurs, while loose proprietary systems could have a positive impact on psychology of entrepreneurship. Zou et al. (2016) explored the different interests and goals of the inherent conflict between entrepreneurs and venture capitalists based on the psychological capital and then put forward a series of theoretical propositions about solving venture capital and strategic countermeasures. These studies revealed that the individual characteristics of entrepreneurs had a significant impact on entrepreneurial decision-making and entrepreneurship. Kautto (2019) found that the psychosocial impact of exogenous policy intervention could promote the transfer of entrepreneurship. Muhammad et al. (2020) indicated that external pressure had a positive and direct impact on behaviors of entrepreneurs and that the sustainable orientation of strategy can provide differentiation for enterprisers. In summary, psychological differences of individuals and external pressures affect the behaviors of entrepreneurs, yet the impacts of changes in the external environment and policies on the psychological effects of multinational entrepreneurs still need to be further explored.
Impacts of International Performance of Multinational Entrepreneurs
Entrepreneurship is a significant form to achieve the social and economic development. In exploring the international entrepreneurship, it is very important to improve the performance of multinational enterprise. In order to explore the impact of strategic responsibilities of the multinational enterprises on the performance of their subsidiaries, Sarabi et al. (2020) found through building a model that the entrepreneurial leadership of the chief executive officer (CEO) of a subsidiary can improve the enterprise’s performance. Boone et al. (2019) revealed that the entrepreneurial spirit and the innovation enthusiasm of the top management team could affect the performance of the enterprise. In order to explore the impact of external crises on the performance of subsidiaries of the multinational enterprises, Martins et al. (2019) found that the sense of crisis affected the business performance of multinational enterprises through verification of the least-square structural equation model. Collings et al. (2019) revealed that the multinational strategy determined the goals of the global talent management system and also affected the performances of enterprises. From the perspective of individual human resources, it indicated that the personal performance could be improved by enlarging the human resources. Fernandes et al. (2018) constructed the framework of the multinational entrepreneur to affect the working performance, and the results showed that the entrepreneurial spirit can promote the working performance through management. Based on the above researches, it can be found that individual characteristics and strategic decisions of entrepreneurs can affect the work performance of multinational enterprises. However, the existing research lacks a definition of entrepreneurship-oriented dimension and its impact on the international performance of multinational companies.
To sum up, the multinational legal service entrepreneurs were taken as the research objects in this paper. In the form of questionnaire, the impact path of the economic pressure and social support of the trade friction on the entrepreneurial recession of entrepreneurs and the impact path of multinational entrepreneurial orientation and value-chain potential on the international performance of enterprises were analyzed. In this way, this study will lay the theoretical foundation for finding out the way to relieve from the psychological pressure of multinational entrepreneur and improving their working performance.
Materials and MethodsResearch Objects
A total of 310 Chinese entrepreneurs of legal services who started businesses in the United States, Canada, Indonesia, Brazil, and the United Kingdom were selected as the research objects, the questionnaires were distributed online from June 2019 to December 2019, and 305 questionnaires were returned, so the response rate was 98.39%. The 305 subjects were 21–38 years old, with an average age of 26.88 ± 5.52. Studies show that the risk aversion of female entrepreneurs is higher than that of male and that self-efficiency has a strong predictive effect on the entrepreneurial behavior. Therefore, the gender, age, and self-efficiency were taken as the control variables for subsequent analysis.
Model Building of Effect on Entrepreneur’s Recession From Economic Pressure and Social Support From the Trade Friction
It is believed in this study that the economic pressure brought about by the trade friction could cause the entrepreneur to experience some negative emotions (anxiety, despair, depression, etc.). Based on the theory of learned helplessness, the theoretical model was built for effect of the economic pressure and social support caused by the trade friction on the entrepreneur’s thought of recession. The theoretical model is shown in Figure 1.
Model of effect of the entrepreneur’s thought of recession.
According to Figure 1, the economic pressure is an independent variable, entrepreneurial recession psychology is the dependent variable, and the sub-dimension under the social support is the intermediary variable. Therefore, the following hypotheses were proposed in this study:
The economic pressure could be promoted significantly by the trade friction;
The thought of entrepreneurial recession could be promoted significantly by the economic pressure;
The promotion of economic pressure on the thought of entrepreneurial recession could be regulated reversely by the social support;
The promotion of economic pressure on the thought of entrepreneurial recession could be regulated reversely by the subjective support;
The promotion of economic pressure on the thought of entrepreneurial recession could be regulated reversely by the objective support;
The promotion of economic pressure on the thought of entrepreneurial recession could be regulated reversely by the support utilization.
Model Building of Correlation on the Multinational Entrepreneurial Orientation and the International Performance
In order to explore the correlation on the multinational entrepreneurial orientation and international performance, the theoretical model shown in Figure 2 below was proposed in this study by taking the global value chain as the intermediary variable.
Model of effect of the entrepreneurship international performance.
Based on the above model, the below hypotheses are proposed in this study:
The international performance could be promoted by the multinational entrepreneurial orientation;
The international performance could be promoted by innovation in the multinational entrepreneurial orientation;
The international performance could be promoted by antecedence in the multinational entrepreneurial orientation;
The international performance could be promoted by adventure in the multinational entrepreneurial orientation;
The international performance could be performed by the value-chain potential;
The international performance could be performed by complexity of the trade;
The international performance could be performed by codability of the product;
The international performance could be performed by the supply capacity;
The value-chain potential is the intermediary variable for multinational entrepreneurial orientation to affect the international performance.
Design of Evaluation Tools
The evaluation scales used in this study were all widely used, reliable, and valid evaluation tools in line with the actual conditions.
Evaluation Tools of the Social Support
The Social Support Rating Scale (SSRS) was used by Basinska and Rozkwitalska (2020). The scale included 10 items and 3 dimensions, which were the degree of subjective support, degree of objective support, and degree of support utilization. In addition, the Likert-4 rating method (1–4 scores indicate from “no” to “yes”) was used for evaluation in this scale. The subjective support included the support at the emotional level or resources from the relatives, friends, social networks, etc. The objective support refers to the actual support resources and also includes the material support provided by individuals, organizations, or the society. The support utilization refers to the utilization degree of the actual or perceived resource by individuals.
Evaluation Tools of the Economic Pressure
The Economic Pressure Rating Scale (EPRS) (Pollack et al., 2012) was compiled to evaluate the economic pressure of entrepreneur under the trade friction. The scale was supplemented by that “the recent economic environment has negatively affected my company,” “the business has been in trouble in the past year,” and “I have to bear a lot of pressures and responsibilities, and play a lot of roles.” The Likert-7 rating method (1–7 points indicate from “strongly disagree” to “strongly agree”) was used for evaluation in this scale.
Evaluation Tools of Entrepreneurial Recession
The exiting entrepreneurship assessment scale was used for recompilation and was applied to evaluate the entrepreneur’s thought of recession (Schuster et al., 2013). Three items were used for the evaluation, namely, “in the next 1 year, I will try my best to quit my current entrepreneurial activities,” “if I continue my business, I will be very worried,” and “continuing to start a business is not as passionate as when I started my business.” The Likert-7 rating method (1–7 points indicate from “strongly disagree” to “strongly agree”) was used for evaluation in this scale.
Evaluation Tools of Self-Efficiency
The entrepreneurship self-efficacy assessment scale (Chen et al., 2019) was used for recompilation and was applied to evaluate the entrepreneur’s self-efficacy. In this study, 17 items were used for the evaluation. The Likert-5 rating method (1–5 points indicate from “strongly disagree” to “strongly agree”) was used for evaluation in this scale.
Evaluation Tools of Multinational Entrepreneurial Orientation
Based on the three dimensions of innovativeness, proactiveness, and risk taking and the “9-item semantic difference scale” (Williams and Lee, 2009), the scale was improved by combining with the internationalization perspective of the multinational enterprises. The improved scale could be seen as below Table 1. When this scale was used for evaluation, it was indicated by “yes” for 1 score or “no” for 0 score of the evaluation.
Evaluation scale of the multinational entrepreneurial orientation.
Dimensionality
No.
Item
Innovation
1
Focusing on marketing, products, or services? Or focusing on investment and leadership and innovation of the R & D and technology
2
Having new products/services?
3
Having great changes or the product/service path?
Proactiveness
4
Response to the competitor, or initiated by itself?
5
The first to introduce the new product/service and the technology on management and operation?
6
Avoiding competitive conflicts in the form of “peaceful coexistence” as often?
Risk-taking
7
Being inclined to the international project with low risk?
8
Achieving the goals step by step in the international environment?
9
Reducing the error rate of decision with an attitude of await-and-see?
Evaluation Tools of Value-Chain Potential
The value-chain potential was measured based on the trade complexity, product codability, and supply capacity (Rashid et al., 2019). The finally designed scale is shown in Table 2. When this scale was used for evaluation, it was indicated by “yes” for 1 score or “no” for 0 score of the evaluation.
Evaluation scale of value-chain potential.
Dimensionality
No.
Item
Trade complexity
1
Having the need for information exchange by exchange, communication, and share
2
High complexity for having the need for information exchange by exchange, communication, and share
Product codability
3
Quantity of information on related design or quality besides the formal documents
4
High complexity for information on related design or quality besides the formal documents
Supply capacity
5
Strong acceptance to demand changes of the market compared with other competitors
6
Strong acceptance to higher requirements from the service-provided side compared with other competitors
7
High assets proprietary of the company
8
High ability to bargain in the product trade
Evaluation Tools of International Performance
Based on the research results of other scholars (Abosede et al., 2018; Karami and Tang, 2019), the international performance was evaluated based on the four dimensions of foreign sales growth, changes in international market share, international investment return rate, and foreign operation satisfaction. The finally designed evaluation scale is shown in Table 3 below. When this scale was used for evaluation, it was indicated by “yes” for 1 score or “no” for 0 score of the evaluation.
Evaluation scale of the international performance.
No.
Item
1
Rapid increase of the company’s overseas sales revenue in recent years
2
Rapider increase of the company’s sales revenue in the oversea markets than other major competitors
3
Rapid increase of overseas sales profits in recent years
4
Rapider increase of the company’s sales profits in the oversea markets than other major competitors
5
Rapid increase of the company’s overseas market shares in recent years
6
Rapider increase of the company’s overseas market shares than other major competitors
7
Higher return rate of the company’s oversea investment in recent years than other major competitors
8
Significant increase of the company’s sales rate in oversea market
9
High operational satisfaction of the company in oversea market in recent years
10
High satisfaction feedback of the company from the clients
Statistical Analysis
The statistical processing was performed by SPSS 19.0. The validity tests of the evaluation tools were performed by using the Bartlett’s spherical test and KMO value (it was deemed as appropriation when KMO value was higher than 0.7, and it was deemed as appropriation when Bartlett’s spherical test p < 0.05). The reliability tests of the evaluation tools were performed by using the Cronbach’s α (it was deemed as high reliability when Cronbach’s α was higher than 0.7). The principal component analysis (PCA) was used to extract the factors, whose eigenvalue was higher than 1, in the variable tables. The descriptive statistics was used for statistical analysis of the sample distribution of each variable. The correlation of each variable was analyzed with the Pearson correlation. Hypotheses proposed in this study were verified by the process of hierarchical regression. When P < 0.05, it is considered that it has a statistical significance.
ResultsAnalysis on the Current Status of the Global and China-Related Trade Friction
Comparing the changes of the total number of the global and China-related trade frictions from 2009 to 2019, Figure 3 indicates that in the past 10 years, the trade frictions in the world have been more than 150 cases and those in China have been more than 70 cases. In 2019, the China-related trade frictions accounted for 30.46% of the cases in the whole world. Overall, the number of China-related trade friction showed a high trend all the time. The annual growth rate of the China-related trade friction showed the same trend as the annual growth rate of trade friction all over the world.
Change trends of total number of the global and China-related trade frictions from 2009 to 2019.
Comparing the proportions of the number of the global and China-related trade frictions from 2009 to 2019, Figure 4 shows that among all the global and China-related trade frictions, the anti-dumping cases accounted for the largest proportion, which was 80% (2363 cases) and 67% (715 cases), respectively, while the proportions of anti-subsidy were 12% (344 cases) and 14% (145 cases), respectively, and the proportions of safeguard precaution (8% (222 cases) and 9% (201 cases) had no great difference. In addition, there were also some trade frictions related to the special safeguards, accounting for 0% (10 cases) and 1% (10 cases), respectively. It shows that most countries in the world took the boycott measures such as increased taxation currently when dumping products from outside the country on the market. At the same time, the importing country offset the subsidies for imported products by imposing countervailing measures, price commitments, or import restrictions in order to protect the domestic industries and restore fair competition.
Proportions of the number of the global and China-related trade frictions from 2009 to 2019 [Figure (A) is the proportion of the global trade frictions; Figure (B) is the proportion of China-related trade frictions].
The top 10 complaining countries and the top 10 complained industries in the China-related trade frictions in 2009–2019 were compared. It could be seen from Figure 5A that, among the top 10 complaining countries, the cases both in the United States and in India were more than 150, and the number of complaints in the past 10 years was much higher than that in the European Union (EU) and other countries or organizations. It could be seen from Figure 5B that, among the top 10 complained industries, the numbers of metal product industry (197 cases), steel industry (196 cases), and chemical raw materials and product industries (164 cases) are much higher than those in other industries, and the most complained industries belong to the manufacturing industry.
The top 10 complaining countries and the complained industries in the China-related trade frictions in 2009–2019. Figure (A) is the top 10 complaining countries; Figure (B) is the top 10 complained countries; A is for the fabricated metal industry; B is for the steel industry; C is for the chemical raw material and product industry; D is for the non-metallic product industry; E is for the textile industry; F is for the electrical industry; G is for the paper manufacturing industry; H is for the non-ferrous metal industry; I is for the food industry; and J is for the rubber product industry.
Descriptive Statistical Analysis on Basic Personal Information of the Entrepreneur
Then, a descriptive statistical analysis on the basic personal information of the entrepreneur was performed based on 305 entrepreneurs of legal services surveyed in this study. The results are shown in Figure 6 below. It could be seen that, among all the 305 entrepreneurs of legal services, the proportion in male is higher than that in female, which is 61% (187 cases) and 39% (118 cases), respectively, mainly aging range of 26–30 years with 126 cases (41%), and most are masters and above (212 cases, 70%).
Statistical results of basic data of the subjects [Figure (A) is the sex ratio; Figure (B) is the age range; and Figure (C) is the academic qualification].
Results on Reliability and Validity Test of the Evaluation Tools
First, the reliability and validity test of the scales used in this study was performed. It could be seen from Table 4 that the Cronbach’s α is higher than 0.7, KMO is higher than 0.7, and p < 0.05 in the Bartlett spherical test. It shows that the evaluation tools used in this study have high reliability and validity, which lays the reliability of the subsequent evaluation results.
Results on reliability and validity test of the evaluation tools.
Scale
Cronbach’sα
KMO
Bartlett’s spherical test
χ 2
df
p
Social support
0.815
0.713
720.955
44
0.000
Economic pressure
0.809
0.713
241.955
3
0.000
Entrepreneurial recession
0.821
0.711
225.782
3
0.000
Self-efficacy
0.952
0.712
267.893
3
0.000
Multinational entrepreneurial orientation
0.833
0.713
268.392
4
0.000
Value-chain potential
0.862
0.712
305.171
5
0.000
International performance
0.934
0.713
556.272
3
0.000
Results of Correlation Analysis of Various Variables
Then, the correlation between the thought of entrepreneurial recession, the impact of entrepreneurial work performance, and other variables in this study were compared. The results are shown in Tables 5, 6 below. It could be seen from Table 4 that the economic pressure has an extremely significant positive correlation with the thought of recession and self-efficacy (p < 0.01) and has an extremely significant negative correlation with the objective support and support utilization (p < 0.01). The thought of recession has an extremely significant positive correlation with the self-efficacy (p < 0.01) and has a significant negative correlation with the objective support and support utilization (p < 0.05). The self-efficacy has a significant negative correlation with the subjective support and objective support (p < 0.05) and has an extremely significant negative correlation with the support utilization (p < 0.01). The subjective support has an extremely significant negative correlation with the objective support and support utilization (p < 0.01). There is an extremely significant positive correlation between the objective support and support utilization (p < 0.01).
Correlation analysis on related variables of the thought of entrepreneurial recession.
Variable
Economic pressure
Thought of recession
Self-efficacy
Subjective support
Objective support
Support utilization
Economic pressure
1
Thought of recession
0.432**
1
Self-efficacy
0.321**
0.452**
1
Subjective support
0.032
–0.101
−0.173*
1
Objective support
−0.544**
−0.193*
−0.207*
−0.282**
1
Support utilization
−0.501**
−0.704**
−0.686**
−0.457**
0.458**
1
*Correlation was significant at the 0.05 level (2-tailed); **Correlation was significant at the 0.01 level (2-tailed).
Correlation analysis of related variables of the entrepreneurial working performance.
Variable
Multinational entrepreneurial orientation
Innovation
Proactiveness
Risk-bearing
Value-chain potential
Transaction complexity
Product codability
Supply capacity
International performance
Multinational entrepreneurial orientation
1
Innovation
0.820**
1
Proactiveness
0.810**
0.545**
1
Risk-bearing
0.808**
0.467**
0.455**
1
Value-chain potential
0.520**
0.460**
0.502**
0.348**
1
Transaction complexity
0.398**
0.387**
0.362**
0.298**
0.832**
1
Product codability
0.326**
0.178*
0.332**
0.274**
0.577**
0.202**
1
Supply capacity
0.487**
0.434**
0.454**
0.303**
0.862**
0.622**
0.274**
1
International performance
0.566**
0.551**
0.508**
0.347**
0.678**
0.567**
0.263**
0.656**
1
*Correlation was significant at the 0.05 level (2-tailed); **Correlation was significant at the 0.01 level (2-tailed).
It could be seen from Table 6 that the multinational entrepreneurial guide and its sub-dimensions (innovation, proactiveness, and risk-taking), value-chain potential, and its sub-dimensions (transaction complexity, product codability, and supply capacity) have a significant correlation with the international performance (p < 0.05).
Verification Results of the Proposed Hypotheses
The hypotheses proposed earlier in this study were analyzed by the regression analysis. It could be seen from Table 7 that: a. The trade friction has an extremely significant positive impact on the economic pressure (β = 0.206; p < 0.01). b. The economic pressure has an extremely significant positive effect on the thought of entrepreneurial recession (β = 0.244; p < 0.01). c. The social support × economic pressure can negatively affect the thought of entrepreneurial recession, that is, the social support can regulate the thought of entrepreneurial recession caused by the economic pressure reversely (β = −0.192; p < 0.01). c1. and c2. The subjective support and objective support cannot regulate the thought of entrepreneurial recession caused by the economic pressure significantly (β = −0.078, −0.032; p > 0.05). c3. The support utilization can regulate the thought of entrepreneurial recession caused by the economic pressure (β = −0.281; p < 0.01). d. The multinational entrepreneurial orientation has a significant positive impact on the international performance (β = 0.532; p < 0.01). d1. The innovation dimension in multinational entrepreneurship has an extremely significant positive impact on the international performance (β = 0.288; p < 0.01). d2. The proactiveness dimension in multinational entrepreneurship has a significant positive impact on the international performance (β = 0.227; p < 0.01). d3. The risk-taking in the multinational entrepreneurship cannot affect the international performance (β = 0.036; p > 0.05). e. The value-chain potential has an extremely significant positive impact on the international performance (β = 0.798; p < 0.01). e1. The transaction complexity has an extremely significant positive impact on the international performance (β = 0.182; p < 0.01). e2. The product codability cannot affect the international performance (β = −0.092; p > 0.05). e3. The supply capacity has an extremely significant positive effect on the international performance (β = 0.424; p < 0.01). In addition, it is also found in this study that the multinational entrepreneurial orientation can affect the value-chain potential significantly (β = 0.415; p < 0.01).
Analysis results of regression analysis models.
Model
Independent variable
Dependent variable
β
△R2
p
1
Trade friction
Economic pressure
0.206
0.344
0.000
2
Economic pressure
Thought of entrepreneurial recession
0.244
0.022
0.000
3
Economic pressure × social support
Thought of entrepreneurial recession
–0.192
0.021
0.000
4
Economic pressure × subjective support
Thought of entrepreneurial recession
–0.078
0.003
0.173
5
Economic pressure × objective support
Thought of entrepreneurial recession
–0.032
0.003
0.532
6
Economic pressure × support utilization
Thought of entrepreneurial recession
–0.281
0.032
0.000
7
Multinational entrepreneurial orientation
International performance
0.523
0.307
0.000
8
Innovation
International performance
0.288
0.322
0.000
9
proactiveness
International performance
0.227
0.314
0.000
10
Risk-bearing
International performance
0.036
0.004
0.102
11
Value-chain potential
International performance
0.798
0.443
0.000
12
Trade complexity
International performance
0.182
0.318
0.000
13
Product codability
International performance
–0.092
0.003
0.098
14
Supply capacity
International performance
0.424
0.261
0.000
15
Multinational entrepreneurial orientation
Value-chain potential
0.415
0.257
0.000
× indicates that the social support and its sub-dimension exerts the intermediary role for impact of economic pressure on the thought of entrepreneurial recession.
It could be seen from Table 8 that all the hypotheses are true except c1., C2., d3., and e2. Therefore, the result graph of the impact path proposed in this study has to be redrawn based on the verification results of the hypotheses.
Verification results of the hypotheses.
No.
Hypotheses
Verification result
a.
The economic pressure could be promoted significantly by the trade friction.
Support
b.
The thought of entrepreneurial recession could be promoted significantly by the economic pressure.
Support
c.
The promotion of economic pressure on the thought of entrepreneurial recession could be regulated reversely by the social support.
Support
c1.
The promotion of economic pressure on the thought of entrepreneurial recession could be regulated reversely by the subjective support.
Not support
c2.
The promotion of economic pressure on the thought of entrepreneurial recession could be regulated reversely by the objective support.
Note support
c3.
The promotion of economic pressure on the thought entrepreneurial recession could be regulated reversely by the support utilization.
Support
d.
The international performance could be promoted by the multinational entrepreneurial orientation.
Support
d1.
The international performance could be promoted by innovation in the multinational entrepreneurial orientation.
Support
d2.
The international performance could be promoted by antecedence in the multinational entrepreneurial orientation.
Support
d3.
The international performance could be promoted by adventure in the multinational entrepreneurial orientation.
Not support
e.
The international performance could be performed by the value-chain potential.
Support
e1.
The international performance could be performed by complexity of the trade.
Support
e2.
The international performance could be performed by codability of the product.
Not support
e3.
The international performance could be performed by the supply capacity.
Support
f
The value-chain potential is the intermediary variable for multinational entrepreneurial orientation to affect the international performance.
Support
The impact path of the regulated economic pressure and social support based on the trade friction on the thought of entrepreneurial recession is shown in Figure 7 below. It can be seen that the economic pressure could be impacted positively by the trade friction, while the occurrence of the thought of entrepreneurial recession could be impacted positively by the economic pressure. However, the thought of entrepreneurial recession caused by the economic pressure could be regulated by the social support and its support utilization.
Impact paths of the economic pressure and social support based on the trade friction on the thought of entrepreneurial recession. ** represents P < 0.01.
The impact paths of the regulated multinational entrepreneurial motivation, value-chain potential, and international performance are shown in Figure 8 below. It can be seen that the international performance could be promoted by the multinational entrepreneurial motivation and its innovation and proactiveness, and the international performance could be promoted by the transaction complexity and supply capacity of the value-chain potential. The value-chain potential can play a role of intermediation in the relationship between the multinational entrepreneurial motivation and international performance.
Impact path of the multinational entrepreneurial orientation, value-chain potential, and international performance. ** represents P < 0.01.
Discussion
In this study, it is found that the United States, European Union, Association of Southeast Asian Nations, and Canada are main exporters of China, so the current export markets of China will continue to be the “main battlefield” of the trade friction in the future (Lawrence, 2018). On the other hand, exports from China to the emerging markets such as Russia, India, and Mexico also grow at 25% year by year, so the trade relations with the emerging markets will also be in the form of “no major problems and constant minor issues.” It is found that greater economic pressure to persons of multinational legal service can be promoted by the trade friction. Some early statistics in WTO show that 1 of 7 trade frictions is China-related (Holladay et al., 2017). In addition, after the trade frictions were analyzed in the whole world in the past 10 years, it is found that most trade frictions were China-related, and the China-related trade friction in 2019 only was more than 30%. With gradual development of the foreign trade, the trade friction has extended from a single product to the entire industry. Therefore, in the future, it should not only consider quelling trade disputes but also consider policy formulation and policy level of the trade friction (Ji et al., 2020).
The protection policies for the knowledge-based enterprises in various countries are not less than those for the manufacturing industry, and the incidents caused by trade friction for the knowledge-service enterprises entering their own countries are possible. Therefore, the legal service entrepreneurs have to bear greater economic pressure, so the trade friction may cause certain economic pressure on multinational entrepreneurs (Cui et al., 2019; Liu et al., 2019). When there are events such as trade friction that are beyond the scope of the entrepreneur’s own tolerance/control, they will have a helpless feeling. This negative emotion will greatly affect the entrepreneur’s pursuit of a sense of accomplishment, so excessive economic pressure will result in the psychological effect of entrepreneurial recession (Ryff, 2019). In the process of multinational entrepreneurship, even if there are a lot of subjective or objective social supports in the society, the entrepreneur fails to apply these supports in practice, then it will have no great significance for the economic pressure and psychological pressure of the entrepreneur. Thus, the support utilization in social support can regulate the thought of entrepreneurial recession caused by the economic pressure (Klyver et al., 2017; Monica et al., 2018). It indicated that entrepreneurs can improve the economic pressure caused by trade frictions in enterprises through reasonable utilization of the social support during the multinational entrepreneurship and then adjust their psychological effects. In the global market, the innovation of products, services, and management is the source of enterprise value. The advanced behavior of enterprise will bring first-mover advantages, which is conducive to seize the market share and thus obtain higher profits. Therefore, the innovation and advancement in the multinational entrepreneurial orientation can promote the international performance of enterprises (Álvarez Torres et al., 2019; Guzmán et al., 2019). In the process of product transactions, the multinational companies are committed to the development and improvement of services and other technologies, increase the complexity of market information in the transaction process, and improve the knowledge and other aspects. In this way, it can increase the initiative of the enterprise and satisfy the needs of customers to a greater extent. Therefore, the complexity of product transactions and supply capacity of product in the value-chain potential of entrepreneurship of the multinational enterprises can promote the international performance of enterprises (Bos et al., 2017; Ferreira et al., 2018; Biberhofer et al., 2019).
It is found that the entrepreneur’s thought of entrepreneurial recession can be affected positively by the size of economic pressure, which is similar to the results of Morris et al. (2020). Then, the regulation role of the social support and its sub-dimensions of subjective support, objective support, and support utilization to the thought of recession caused by the economic pressure was explored. The results of this study show that the stronger the social support, the weaker the relationship between thought of entrepreneurial recession caused by the economic pressure. The reasons for the above results may be that the research objects of this study are the multinational entrepreneurs. No matter how much subjective or objective support it has in the society, such support has to be applied in practice to improve the economic pressure of the enterprise and then reduce the thought of entrepreneurial recession (Alby et al., 2019), which is consistent with the results in this study that the subjective support and objective support cannot regulate the impact of economic pressure on the thought of entrepreneurial recession reversely. It is found in this study that the international performance could be promoted significantly by the multinational entrepreneurial orientation, and the effects of innovation and proactiveness in the multinational entrepreneurial orientation are significant. It is consistent with the results of Augustie and Saad (2019). The international performance cannot be impacted significantly by the risk-taking in the multinational entrepreneurial orientation. This may be because this experience in overseas operations of entrepreneurs is relatively weak in the international market, and the company’s management level is relatively insufficient, so that many problems behind the value-chain potential cannot be treated and solved skillfully and quickly. The value-chain potential is a key factor to drive the international performance (Vonsée et al., 2019). It is found in this study that the international performance can be promoted by the value-chain potential and its transaction complexity and supply capacity. This is because, in the transaction process, the enterprise is committed to the development and improvement of the services and other technologies, enhancement on complexity of the market information, and improvement of the knowledge. In this way, the initiative of the enterprise can be increased, and thus the clients can be satisfied to a larger extend. Entrepreneurship is a very complex and dynamic activity, and the difficulties experienced by multinational entrepreneurs are more than those experienced by domestic entrepreneurs (Boone et al., 2019). Regardless of any entrepreneurial behavior or activity, it is germinated in a specific entrepreneurial environment and developed step by step. Therefore, the entrepreneurial activities are inevitably affected by the entrepreneurial environment (Biryukov et al., 2020). In addition, it is found based on the codability of the products in this study that the codability of products can suppress the falseness of international performance. It is because codability of the products can facilitate the product or service management provided by the company in multinational enterprises, so that it is convenient to estimate and choose. Therefore, this variable has no inhibitory effect on the international performance of multinational enterprises.
Conclusion
With assistance of statistical methods combined with questionnaire surveys, regression model, and structural equation models, this study analyzed the impact mechanism of trade friction on the mentality of multinational entrepreneurs. It was found that trade friction can trigger the thought of entrepreneurial recession of entrepreneurs indirectly by reducing their income. The social support utilization can significantly regulate the relationship between economic pressure and the thought of entrepreneurial recession. The multinational entrepreneurship motivation can promote the international performance of multinational enterprises through the value-chain potential. It fails to consider other factors of the external environment (such as policies, etc.) and the interactions from the personal internal factors to the thought of entrepreneurial recession and the entrepreneurial working performance. Accordingly, the quantity of samples has to be increased in the future and to take specific research in a certain area to explore the effects on external environment and the internal factors of individuals on the thought of entrepreneurial recession and entrepreneurial working performance. In conclusion, the thought of entrepreneurial recession of the entrepreneur can be reduced and the subjective initiative of the entrepreneur can be played by using the social support to the maximal extent. International performance of the enterprise can be improved by complying with the international market and improving the service ability. Results in this study can provide a basis for understanding the impact mechanism of the thought of entrepreneurial recession of entrepreneur of multinational legal service and its work performance.
Data Availability Statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation, to any qualified researcher.
Ethics Statement
The studies involving human participants were reviewed and approved by the Shandong Normal University Ethics Committee. The patients/participants provided their written informed consent to participate in this study. Written informed consent was obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article.
Author Contributions
CX wrote the manuscript. ZZ reviewed the manuscript. Both authors contributed to the article and approved the submitted version.
Conflict of Interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Funding. This research was funded by the Shandong Normal University Young Teachers Research Project (Humanities and Social Sciences).
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Edited by: Ryo Fujimoto, Kobe University, Japan
Reviewed by: Kaoru Tonosaki, Yokohama City University, Japan; Muhammad Qasim Shahid, South China Agricultural University, China
This article was submitted to Plant Breeding, a section of the journal Frontiers in Plant Science
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Ploidy manipulation is an efficient technique for the development of novel phenotypes in plant breeding. However, in rice (Oryza sativa L.), severe seed sterility has been considered a barrier preventing cultivation of autotetraploids since the 1930s. Recently, a series of studies identified two fertile autotetraploids, identified herein as the PMeS (Polyploid Meiosis Stability) and Neo-Tetraploid lines. Here, we summarize their characteristics, focusing on the recovery of seed fertility, and discuss potential future directions of study in this area, providing a comprehensive understanding of current progress in the study of fertile tetraploid rice, a classical, but promising, concept for rice breeding.
ricetetraploidfertilitypollenseedJapan Society for the Promotion of Science10.13039/50110000169118K05565Introduction
The phenomenon of polyploidy, which refers to the multiplication of chromosome sets within cells, often doubling a normal (diploid) set into a quadruple (tetraploid) set, is a widespread and distinctive feature of the higher plants (Stebbins, 1950). Polyploids are traditionally classified into autopolyploids, which originate from a single parent species (xx to xxxx), and allopolyploids, which originate from two hybridizing species (xx + yy to xxyy) (Clark and Donghe, 2018). In plants, polyploidization occurs frequently, and 25%–30% of flowering plants are suggested to be current polyploids, which have not yet diploidized (Van de Peer et al., 2017). The adaptive potential of polyploidy has long been discussed. Van de Peer et al. (2017) summarized the adaptive potential of polyploids with respect to the effects of interaction between species (i.e., changes in modes of reproduction and changes in interactions with pollinators and herbivores), environmental robustness (i.e., increasing tolerance to abiotic stresses), and species diversification. These changes in adaptive potential are reported to be caused by changes in the genomes/transcriptomes of the polyploid, including structural changes in genomes/genes, and transcriptional and functional alterations in duplicated genes (for a detailed review of these changes, see Renny-Byfield and Wendel (2014)]. Because the function of a duplicated gene should be redundant at the time of polyploidization, it can accumulate mutations, which may affect its function without reducing the fitness of the individual (Blanc and Wolfe, 2004). It is therefore widely accepted that polyploids have greater mutational robustness than diploids (Van de Peer et al., 2009). Fawcett et al. (2009) showed that the majority of the most recent duplication events in flowering plants are clustered in time and seem to coincide with the period of the most recent mass extinction. This suggests that polyploid plants coped better than diploid plants with the markedly changed environments of that period (Van de Peer et al., 2009).
In crop species, it has been frequently observed that polyploidization causes plants to grow larger, more quickly, and with higher yields, compared to their diploid relatives (Renny-Bayfield and Wendel, 2014), although some studies suggest that polyploidization did not have a selective advantage over diploid genomes during the domestication process (Hilu, 1993). In addition to improved crop yield, recent studies have shown that polyploidy generates novel or diversified characteristics such as changes to grain threshing properties (Zhang et al., 2011) and grain hardness (Chantret et al., 2004) in wheat, flowering time in sunflower (Blackman et al., 2010), growing condition in coffee (Combes et al., 2012), and quality of fiber in cotton (Jiang et al., 1998). These studies prompted us to survey the potential capacity for polyploidization in rice, a staple food for four billion people globally (CGIAR, http://ricecrp.org/), as a means of providing new genetic materials for future crop improvement. In this review, we focus on autotetraploid rice, especially its fertility, because low seed fertility has been regarded as a barrier obstructing its use. In the course of summarizing two pioneering studies on autotetraploid rice with high seed fertility, we will discuss potential future directions for ploidy manipulation in rice, a classical but promising concept.
History of the Study of Tetraploid Rice
Asian cultivated rice (Oryza sativa L.) is a diploid species with two sets of 12 chromosomes (2n = 24). [Although Asian cultivated rice may have experienced whole genome duplication more than 96 million years ago (Guo et al., 2019), this species is regarded as diploid, as it did not experience a “recent” polyploidization. In this review, we therefore regard Asian cultivated rice as a diploid species and tetraploid rice as containing two sets of 24 chromosomes]. As far as we know, no autotetraploid or allotetraploid rice varieties are used in agriculture. The first report of spontaneously autotetraploid rice was by Nakamori (1933), 2 decades after the determination of its chromosome number (Kuwada, 1910). Soon after the 1933 report, Ichijima (1934) reported artificially induced tetraploids. Some subsequent studies concerning the artificial induction of tetraploidy in rice have been reported (Cua, 1950; Oka, 1953). According to He et al. (2010), research on polyploid rice breeding was initiated in 1953 in China.
Low Seed Fertility: A Barrier Preventing Tetraploid Rice Use
In general, polyploidization confers greater stress tolerance by fostering slower development, delayed reproduction, longer life span, improved defense against pathogens and herbivores, larger seeds, and lower reproductive effort with greater emphasis on vegetative reproduction [Hilu, 1993, see also review by Levin (1983)]. In rice, large grain and leaf size are often observed in autotetraploid plants. In addition, Tu et al. (2014) reported higher salt tolerance in autotetraploid rice than in diploid rice. These studies suggest the potential usefulness of autotetraploid rice for breeding. However, low numbers of spikelets per panicle and low fertility have frequently observed in tetraploid rice throughout its known existence. Oka (1954) surveyed the phenotypes of a diverse set of autotetraploid rice varieties. Although greater vegetative than reproductive growth was not specifically indicated (Table 1), he showed that seed fertility in tetraploid varieties ranged from 0% to 55% (Oka, 1954; Oka, 1955). This low seed set rate has been the main barrier preventing use of autotetraploid for rice breeding (Cai et al., 2007).
General characteristics of tetraploid rice lines (summary of Oka, 1954).
Traits
Characteristics in tetraploids
Panicle length
Increased (0.95 to 1.30)
Grain length
Increased (1.15 to 1.30)
Awn
Developed
Plant height
Reduced (0.65 to 1.00)
Spikelet number per panicle
Reduced (0.50 to 0.90)
Panicle number per plant
Reduced (0.40 to 0.70)
Seed fertility
0% to 55%
Pollen fertility
55% to 95%
Numbers in parenthesis indicate ratio of phenotype value of tetraploids compared to diploids.
Lower polyploid fertilities have been considered to be due to abnormal behavior of the chromosomes during meiosis (Bomblies et al., 2016). In the first division of meiosis, a pair of homologous chromosomes forms a bivalent, which later segregates. However, in polyploids, especially autopolyploids, the proper formation of bivalents is often inhibited, because homology among three or more chromosomes means that they fail to provide the special intrinsic cues necessary for normal, diploid-like segregation (Bomblies et al., 2016). In autotetraploid rice, chromosomal behavior at meiosis has been surveyed by Cai et al. (2007) and Wu et al. (2014). Cai et al. (2007) found that a tetraploid line, Dure-4X, displays abnormal meiotic behaviors including a higher rate of multivalents, univalents, and trivalents during prophase, lagging chromosomes during metaphase, and micronuclei during anaphase and telophase. In addition to such abnormalities, asynchrony of chromosomes and abnormal cell shape are also observed in meiosis in pollen mother cells in tetraploids with low fertility (Wu et al., 2014). These studies suggested that abnormal chromosomal behavior is one of the reasons for low seed fertility in autotetraploid rice. Recent studies have revealed the differences of epigenetic state and transcriptomes between the diploid and tetraploid rice (Xu et al., 2014; Zhang et al., 2015; Li et al., 2018). Zhang et al. (2015) suggested that the increased methylation level of transposable elements (TE) alters the expression of genes near by the TE in autotetraploid rice. Transcriptomic differences, including the expression of small RNAs, long non-coding RNAs, meiosis-related genes, and carbohydrate metabolism-related genes have been suggested to be related to meiotic abnormalities and the subsequent reduction of pollen fertility in autotetraploid rice (Li et al., 2016; Li et al., 2017; Chen et al., 2018; Li et al., 2020).
PMeS (Polyploid Meiosis Stability) and Neo-Tetraploidy: Emerging Fertile Autotetraploid Rice
Although its low seed fertility has long been a barrier preventing the use of autotetraploid rice in breeding, two new autotetraploid rice series with high seed and pollen fertilities, identified herein as the PMeS and Neo-Tetraploid lines, were developed recently (Table 2). The PMeS lines have been developed and analyzed by Cai et al. (2007) and subsequent studies (He et al., 2010; He Y. C. et al., 2011; Tu et al., 2014). They derive from the progenies of crosses between indica and japonica rice subspecies. Unlike other autotetraploid rice lines, PMeS lines do not show abnormal chromosomal behavior at meiosis. In addition, their pollen development pattern appears normal at all stages, lacking the many abnormalities seen in other autotetraploid rice lines (He et al., 2010). These results suggest that stable meiosis, timely tapetum degradation, and normal mitochondrial development were the critical factors ensuring the high pollen fertility of PMeS lines (He et al., 2010). Recently, Xiong et al. (2019) revealed that the rice homolog of the gene MND1, which plays a role in meiosis in yeast and Arabidopsis, also plays a crucial role in improving the seed set rate in PMeS lines. They showed that the OsMND1 expression level in panicles of a PMeS line was higher than in the diploid lines or in the other autotetraploid line with low seed set rate and OsMND1 overexpression improved pollen fertility and seed set. The meiotic behavioral observations suggested that the effects of OsMND1 might be maintaining the balance of synapsis and recombination (Xiong et al., 2019). However, it is still unknown why the expression level of OsMND1 was lower in autotetraploid line with low seed set rate than the PMeS line. More research is necessary for a full understanding of the molecular mechanism of normal meiosis in PMeS lines (Xiong et al., 2019).
Comparison of two fertile tetraploid rice series.
 
Neo-Tetraploid lines
PMeS lines
Line name
Huaduo1, 2, 3, 4, 5, 8a,b,c,d
Sg99012, HN-2026-4Xe,f
Cross combination
Inter-subspecific (japonica and indica)
Inter-subspecific (japonica and indica)f
Seed set
more than 68%a,b
Over 80%e,f
Pollen fertility
more than 92%a
84.15% (stained), 78.52% (germinated)g
Meiotic behavior in male gametes
Few abnormality were detecteda
Normale
Seed set of F1
83.07% (when crossed with japonica varieties)a
67.18% (when crossed with Ballila-4X)e
 
76.45% (when crossed with indica varieties)a
 
aBei et al., 2019, bGuo et al., 2017, cChen et al., 2019, dGhaleb et al., 2020, eHe Y. C. et al., 2011, fCai et al., 2007, gHe et al., 2010.
The other autotetraploid rice varieties with high seed and pollen fertilities were named the Neo-Tetraploid lines by Guo et al. (2017). These lines derived from the progenies of crosses between T44 (96025) and T45 (Jackson-4X) and showed a seed set rate of more than 80%, while their parents (T44 and T45) showed less than 32% (Guo et al., 2017; Bei et al., 2019). Cytological observation of the meiotic stages in Neo-Tetraploid rice lines showed that fewer abnormalities in these lines than their autotetraploid parents, which displayed different chromosomal configurations at diakinesis (Bei et al., 2019). Surprisingly, a total of 324 genes in the Neo-Tetraploid rice genome showed new mutations, which do not exist in its parents’ genomes (Bei et al., 2019). By means of a transcriptome analysis, Bei et al. (2019) suggested that genomic structural reprogramming, DNA variations, and differential expression of some important meiosis- and epigenetics-related genes might be associated with the high fertility of Neo-Tetraploid lines.
Autotetraploid Rice and Hybrid Sterility
Autotetraploid rice varieties with high seed fertilities are also considered to be important resources for hybrid rice breeding (Guo et al., 2017). Interestingly, both PMeS and Neo-Tetraploid lines produce F1 progenies with high seed setting rates when crossed with other autotetraploid rice lines (Table 2, He Y. C. et al., 2011; Guo et al., 2017). In PMeS lines, He Y. C. et al. (2011) showed that the seed set rate of an F1 hybrid of Balilla-4X × HN2026-4X (a PMeS line) was 67.18%, while it was 37.26% in the F1 of Balilla-4X × NJ11-4X (a non-PMeS line). They suggested that the normal meiotic behavior in lines with a PMeS background led to the high frequency of successful fertilization, normal embryo development, and high seed set rate observed in its F1 progeny.
In Neo-Tetraploid lines, the agronomic traits of various autotetraploid hybrids were examined by Guo et al. (2017). They found that the seed setting rate in the hybrids of Neo-Tetraploid rice was significantly higher than in the hybrids generated from other autotetraploid lines. Their transcriptome analysis revealed that genes expressed differently by the hybrid and its parents included meiosis stage-specific- and meiosis-related genes, such as RAD51 and SMC2. Because RAD51 and SMC2 are involved in recombination between homologous chromosome and control of chromosomal structure, respectively, during meiosis (Aya et al., 2011), their transcriptional changes should affect to the fertility observed in autotetraploid hybrids. However, the understanding of the cause of transcriptional changes in hybrids still remains a challenging.
The seed fertility of autotetraploid hybrids is also controlled by several hybrid sterility loci. At such loci in rice, several alleles exist; some of them interact in the heterozygous state to cause abortion of gametes (see the review of Koide et al., 2008b). At this locus, the allele that does not cause abortion of gametes is called the neutral allele (Ikehashi and Araki, 1986; Koide et al., 2008a). He et al. (2011b) and Wu et al. (2015) showed that interaction between three hybrid sterility loci (Sa, Sb, and Sc) has significant effects on the pollen fertility of autotetraploid hybrids, and that pollen fertility further decreased with increasing allelic interaction. They also found abnormal chromosomal behavior in the hybrids with low pollen fertility and suggested that the gene interactions of hybrid sterility loci tend to increase the chromosomal abnormalities that cause the partial abortion of male gametes, leading to the decline in seed set of the autotetraploid rice hybrids (He et al., 2011b). Such abnormalities in chromosomal behavior were reduced in hybrids with neutral alleles (San and Sbn) at loci Sa and Sb, respectively (Wu et al., 2017). Chen et al. (2019) developed hybrids between the Neo-Tetraploid line and autotetraploid varieties with neutral alleles. They found an improvement of seed set rate and suggested that meiosis-related and meiosis-specific genes, and those related to saccharide metabolism and starch synthase, were involved in these heterozygote-specific phenotypes (Chen et al., 2019).
Discussions and Conclusions
These two series of pioneer studies on the PMeS and Neo-Tetraploid lines have opened the door to the use of autotetraploid varieties in rice breeding and cultivation. To facilitate the development of fertile autotetraploid lines, further research aimed at understanding the origin of the genes/quantitative trait loci (QTLs) responsible for high fertility in these tetraploid rice varieties will be necessary. Both the PMeS and Neo-Tetraploid rice lines have been developed from the progenies of crosses between the indica and japonica rice subspecies. Since the parental autotetraploid varieties show seed sterility, one possibility for the origin of high-fertility genes/QTLs in the new varieties’ progenies is the nature of interactions between genes (or genomic regions) derived from different autotetraploid parents. If so, a QTL analysis of high seed fertility could be performed by developing recombinant inbred lines derived from crosses between autotetraploids. The Neo-Tetraploid lines show different expression of some important meiosis stage specific- and meiosis-related genes (Guo et al., 2017; Bei et al., 2019). Genes/QTLs responsible for high seed fertility might control the expression of these. Interestingly, the PMeS and Neo-Tetraploid lines show high seed fertility not only when selfing, but also in hybrids with other autotetraploid rice strains (He Y. C. et al., 2011; Guo et al., 2017). These results suggest that the genes/QTLs responsible for high seed fertility in PMeS and Neo-Tetraploid lines act dominantly, suppressing unstable chromosomal behavior during meiosis in hybrids. If this is so, the question arises as to why the initial hybrids of indica and japonica used for developing the PMeS and Neo-Tetraploid lines did not show high seed fertility. Another possibility for the origin of genes/QTLs responsible for high seed fertility is the new appearance of these genes/QTLs during the line selection process. The finding of new mutations, absent from the parental genome, in a Neo-Tetraploid line (Bei et al., 2019) may support this scenario. The activation of TEs followed by alteration of the epigenetic state (Zhang et al., 2015; Guo et al., 2017; Bei et al., 2019) may cause new appearance of genes/QTLs in tetraploid rice. Further study is necessary to unclear why many such mutations would occur in the autotetraploid lines.
Although their origin is unclear, identifying the genes responsible for high seed fertility in the PMeS and Neo-Tetraploid lines will enable the transfer of this technique to other autotetraploid rice varieties through marker-assisted selection or genome editing. A diverse set of fertile autotetraploid rice varieties will greatly increase their potential usefulness in future agriculture. Ploidy manipulation, a classical concept, will revive in the field of rice breeding in the near future.
Author Contributions
YKo, DK, and YKi contributed to conceptualization of this review. YKo wrote original draft. DK and YKi reviewed and edited the original manuscript.
Funding
YKo and YKi are funded by JSPS KAKENHI Grant Numbers 18K05565 and 19H00937, respectively.
Conflict of Interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Acknowledgments
We thank to I. Takamure for his support for the project. No conflict of interest declared.
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Edited by: Ryo Fujimoto, Kobe University, Japan
Reviewed by: Kaoru Tonosaki, Yokohama City University, Japan; Muhammad Qasim Shahid, South China Agricultural University, China
This article was submitted to Plant Breeding, a section of the journal Frontiers in Plant Science
1182020202011123122420202772020Copyright © 2020 Koide, Kuniyoshi and Kishima2020Koide, Kuniyoshi and KishimaThis is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Ploidy manipulation is an efficient technique for the development of novel phenotypes in plant breeding. However, in rice (Oryza sativa L.), severe seed sterility has been considered a barrier preventing cultivation of autotetraploids since the 1930s. Recently, a series of studies identified two fertile autotetraploids, identified herein as the PMeS (Polyploid Meiosis Stability) and Neo-Tetraploid lines. Here, we summarize their characteristics, focusing on the recovery of seed fertility, and discuss potential future directions of study in this area, providing a comprehensive understanding of current progress in the study of fertile tetraploid rice, a classical, but promising, concept for rice breeding.
ricetetraploidfertilitypollenseedJapan Society for the Promotion of Science10.13039/50110000169118K05565Introduction
The phenomenon of polyploidy, which refers to the multiplication of chromosome sets within cells, often doubling a normal (diploid) set into a quadruple (tetraploid) set, is a widespread and distinctive feature of the higher plants (Stebbins, 1950). Polyploids are traditionally classified into autopolyploids, which originate from a single parent species (xx to xxxx), and allopolyploids, which originate from two hybridizing species (xx + yy to xxyy) (Clark and Donghe, 2018). In plants, polyploidization occurs frequently, and 25%–30% of flowering plants are suggested to be current polyploids, which have not yet diploidized (Van de Peer et al., 2017). The adaptive potential of polyploidy has long been discussed. Van de Peer et al. (2017) summarized the adaptive potential of polyploids with respect to the effects of interaction between species (i.e., changes in modes of reproduction and changes in interactions with pollinators and herbivores), environmental robustness (i.e., increasing tolerance to abiotic stresses), and species diversification. These changes in adaptive potential are reported to be caused by changes in the genomes/transcriptomes of the polyploid, including structural changes in genomes/genes, and transcriptional and functional alterations in duplicated genes (for a detailed review of these changes, see Renny-Byfield and Wendel (2014)]. Because the function of a duplicated gene should be redundant at the time of polyploidization, it can accumulate mutations, which may affect its function without reducing the fitness of the individual (Blanc and Wolfe, 2004). It is therefore widely accepted that polyploids have greater mutational robustness than diploids (Van de Peer et al., 2009). Fawcett et al. (2009) showed that the majority of the most recent duplication events in flowering plants are clustered in time and seem to coincide with the period of the most recent mass extinction. This suggests that polyploid plants coped better than diploid plants with the markedly changed environments of that period (Van de Peer et al., 2009).
In crop species, it has been frequently observed that polyploidization causes plants to grow larger, more quickly, and with higher yields, compared to their diploid relatives (Renny-Bayfield and Wendel, 2014), although some studies suggest that polyploidization did not have a selective advantage over diploid genomes during the domestication process (Hilu, 1993). In addition to improved crop yield, recent studies have shown that polyploidy generates novel or diversified characteristics such as changes to grain threshing properties (Zhang et al., 2011) and grain hardness (Chantret et al., 2004) in wheat, flowering time in sunflower (Blackman et al., 2010), growing condition in coffee (Combes et al., 2012), and quality of fiber in cotton (Jiang et al., 1998). These studies prompted us to survey the potential capacity for polyploidization in rice, a staple food for four billion people globally (CGIAR, http://ricecrp.org/), as a means of providing new genetic materials for future crop improvement. In this review, we focus on autotetraploid rice, especially its fertility, because low seed fertility has been regarded as a barrier obstructing its use. In the course of summarizing two pioneering studies on autotetraploid rice with high seed fertility, we will discuss potential future directions for ploidy manipulation in rice, a classical but promising concept.
History of the Study of Tetraploid Rice
Asian cultivated rice (Oryza sativa L.) is a diploid species with two sets of 12 chromosomes (2n = 24). [Although Asian cultivated rice may have experienced whole genome duplication more than 96 million years ago (Guo et al., 2019), this species is regarded as diploid, as it did not experience a “recent” polyploidization. In this review, we therefore regard Asian cultivated rice as a diploid species and tetraploid rice as containing two sets of 24 chromosomes]. As far as we know, no autotetraploid or allotetraploid rice varieties are used in agriculture. The first report of spontaneously autotetraploid rice was by Nakamori (1933), 2 decades after the determination of its chromosome number (Kuwada, 1910). Soon after the 1933 report, Ichijima (1934) reported artificially induced tetraploids. Some subsequent studies concerning the artificial induction of tetraploidy in rice have been reported (Cua, 1950; Oka, 1953). According to He et al. (2010), research on polyploid rice breeding was initiated in 1953 in China.
Low Seed Fertility: A Barrier Preventing Tetraploid Rice Use
In general, polyploidization confers greater stress tolerance by fostering slower development, delayed reproduction, longer life span, improved defense against pathogens and herbivores, larger seeds, and lower reproductive effort with greater emphasis on vegetative reproduction [Hilu, 1993, see also review by Levin (1983)]. In rice, large grain and leaf size are often observed in autotetraploid plants. In addition, Tu et al. (2014) reported higher salt tolerance in autotetraploid rice than in diploid rice. These studies suggest the potential usefulness of autotetraploid rice for breeding. However, low numbers of spikelets per panicle and low fertility have frequently observed in tetraploid rice throughout its known existence. Oka (1954) surveyed the phenotypes of a diverse set of autotetraploid rice varieties. Although greater vegetative than reproductive growth was not specifically indicated (Table 1), he showed that seed fertility in tetraploid varieties ranged from 0% to 55% (Oka, 1954; Oka, 1955). This low seed set rate has been the main barrier preventing use of autotetraploid for rice breeding (Cai et al., 2007).
General characteristics of tetraploid rice lines (summary of Oka, 1954).
Traits
Characteristics in tetraploids
Panicle length
Increased (0.95 to 1.30)
Grain length
Increased (1.15 to 1.30)
Awn
Developed
Plant height
Reduced (0.65 to 1.00)
Spikelet number per panicle
Reduced (0.50 to 0.90)
Panicle number per plant
Reduced (0.40 to 0.70)
Seed fertility
0% to 55%
Pollen fertility
55% to 95%
Numbers in parenthesis indicate ratio of phenotype value of tetraploids compared to diploids.
Lower polyploid fertilities have been considered to be due to abnormal behavior of the chromosomes during meiosis (Bomblies et al., 2016). In the first division of meiosis, a pair of homologous chromosomes forms a bivalent, which later segregates. However, in polyploids, especially autopolyploids, the proper formation of bivalents is often inhibited, because homology among three or more chromosomes means that they fail to provide the special intrinsic cues necessary for normal, diploid-like segregation (Bomblies et al., 2016). In autotetraploid rice, chromosomal behavior at meiosis has been surveyed by Cai et al. (2007) and Wu et al. (2014). Cai et al. (2007) found that a tetraploid line, Dure-4X, displays abnormal meiotic behaviors including a higher rate of multivalents, univalents, and trivalents during prophase, lagging chromosomes during metaphase, and micronuclei during anaphase and telophase. In addition to such abnormalities, asynchrony of chromosomes and abnormal cell shape are also observed in meiosis in pollen mother cells in tetraploids with low fertility (Wu et al., 2014). These studies suggested that abnormal chromosomal behavior is one of the reasons for low seed fertility in autotetraploid rice. Recent studies have revealed the differences of epigenetic state and transcriptomes between the diploid and tetraploid rice (Xu et al., 2014; Zhang et al., 2015; Li et al., 2018). Zhang et al. (2015) suggested that the increased methylation level of transposable elements (TE) alters the expression of genes near by the TE in autotetraploid rice. Transcriptomic differences, including the expression of small RNAs, long non-coding RNAs, meiosis-related genes, and carbohydrate metabolism-related genes have been suggested to be related to meiotic abnormalities and the subsequent reduction of pollen fertility in autotetraploid rice (Li et al., 2016; Li et al., 2017; Chen et al., 2018; Li et al., 2020).
PMeS (Polyploid Meiosis Stability) and Neo-Tetraploidy: Emerging Fertile Autotetraploid Rice
Although its low seed fertility has long been a barrier preventing the use of autotetraploid rice in breeding, two new autotetraploid rice series with high seed and pollen fertilities, identified herein as the PMeS and Neo-Tetraploid lines, were developed recently (Table 2). The PMeS lines have been developed and analyzed by Cai et al. (2007) and subsequent studies (He et al., 2010; He Y. C. et al., 2011; Tu et al., 2014). They derive from the progenies of crosses between indica and japonica rice subspecies. Unlike other autotetraploid rice lines, PMeS lines do not show abnormal chromosomal behavior at meiosis. In addition, their pollen development pattern appears normal at all stages, lacking the many abnormalities seen in other autotetraploid rice lines (He et al., 2010). These results suggest that stable meiosis, timely tapetum degradation, and normal mitochondrial development were the critical factors ensuring the high pollen fertility of PMeS lines (He et al., 2010). Recently, Xiong et al. (2019) revealed that the rice homolog of the gene MND1, which plays a role in meiosis in yeast and Arabidopsis, also plays a crucial role in improving the seed set rate in PMeS lines. They showed that the OsMND1 expression level in panicles of a PMeS line was higher than in the diploid lines or in the other autotetraploid line with low seed set rate and OsMND1 overexpression improved pollen fertility and seed set. The meiotic behavioral observations suggested that the effects of OsMND1 might be maintaining the balance of synapsis and recombination (Xiong et al., 2019). However, it is still unknown why the expression level of OsMND1 was lower in autotetraploid line with low seed set rate than the PMeS line. More research is necessary for a full understanding of the molecular mechanism of normal meiosis in PMeS lines (Xiong et al., 2019).
Comparison of two fertile tetraploid rice series.
 
Neo-Tetraploid lines
PMeS lines
Line name
Huaduo1, 2, 3, 4, 5, 8a,b,c,d
Sg99012, HN-2026-4Xe,f
Cross combination
Inter-subspecific (japonica and indica)
Inter-subspecific (japonica and indica)f
Seed set
more than 68%a,b
Over 80%e,f
Pollen fertility
more than 92%a
84.15% (stained), 78.52% (germinated)g
Meiotic behavior in male gametes
Few abnormality were detecteda
Normale
Seed set of F1
83.07% (when crossed with japonica varieties)a
67.18% (when crossed with Ballila-4X)e
 
76.45% (when crossed with indica varieties)a
 
aBei et al., 2019, bGuo et al., 2017, cChen et al., 2019, dGhaleb et al., 2020, eHe Y. C. et al., 2011, fCai et al., 2007, gHe et al., 2010.
The other autotetraploid rice varieties with high seed and pollen fertilities were named the Neo-Tetraploid lines by Guo et al. (2017). These lines derived from the progenies of crosses between T44 (96025) and T45 (Jackson-4X) and showed a seed set rate of more than 80%, while their parents (T44 and T45) showed less than 32% (Guo et al., 2017; Bei et al., 2019). Cytological observation of the meiotic stages in Neo-Tetraploid rice lines showed that fewer abnormalities in these lines than their autotetraploid parents, which displayed different chromosomal configurations at diakinesis (Bei et al., 2019). Surprisingly, a total of 324 genes in the Neo-Tetraploid rice genome showed new mutations, which do not exist in its parents’ genomes (Bei et al., 2019). By means of a transcriptome analysis, Bei et al. (2019) suggested that genomic structural reprogramming, DNA variations, and differential expression of some important meiosis- and epigenetics-related genes might be associated with the high fertility of Neo-Tetraploid lines.
Autotetraploid Rice and Hybrid Sterility
Autotetraploid rice varieties with high seed fertilities are also considered to be important resources for hybrid rice breeding (Guo et al., 2017). Interestingly, both PMeS and Neo-Tetraploid lines produce F1 progenies with high seed setting rates when crossed with other autotetraploid rice lines (Table 2, He Y. C. et al., 2011; Guo et al., 2017). In PMeS lines, He Y. C. et al. (2011) showed that the seed set rate of an F1 hybrid of Balilla-4X × HN2026-4X (a PMeS line) was 67.18%, while it was 37.26% in the F1 of Balilla-4X × NJ11-4X (a non-PMeS line). They suggested that the normal meiotic behavior in lines with a PMeS background led to the high frequency of successful fertilization, normal embryo development, and high seed set rate observed in its F1 progeny.
In Neo-Tetraploid lines, the agronomic traits of various autotetraploid hybrids were examined by Guo et al. (2017). They found that the seed setting rate in the hybrids of Neo-Tetraploid rice was significantly higher than in the hybrids generated from other autotetraploid lines. Their transcriptome analysis revealed that genes expressed differently by the hybrid and its parents included meiosis stage-specific- and meiosis-related genes, such as RAD51 and SMC2. Because RAD51 and SMC2 are involved in recombination between homologous chromosome and control of chromosomal structure, respectively, during meiosis (Aya et al., 2011), their transcriptional changes should affect to the fertility observed in autotetraploid hybrids. However, the understanding of the cause of transcriptional changes in hybrids still remains a challenging.
The seed fertility of autotetraploid hybrids is also controlled by several hybrid sterility loci. At such loci in rice, several alleles exist; some of them interact in the heterozygous state to cause abortion of gametes (see the review of Koide et al., 2008b). At this locus, the allele that does not cause abortion of gametes is called the neutral allele (Ikehashi and Araki, 1986; Koide et al., 2008a). He et al. (2011b) and Wu et al. (2015) showed that interaction between three hybrid sterility loci (Sa, Sb, and Sc) has significant effects on the pollen fertility of autotetraploid hybrids, and that pollen fertility further decreased with increasing allelic interaction. They also found abnormal chromosomal behavior in the hybrids with low pollen fertility and suggested that the gene interactions of hybrid sterility loci tend to increase the chromosomal abnormalities that cause the partial abortion of male gametes, leading to the decline in seed set of the autotetraploid rice hybrids (He et al., 2011b). Such abnormalities in chromosomal behavior were reduced in hybrids with neutral alleles (San and Sbn) at loci Sa and Sb, respectively (Wu et al., 2017). Chen et al. (2019) developed hybrids between the Neo-Tetraploid line and autotetraploid varieties with neutral alleles. They found an improvement of seed set rate and suggested that meiosis-related and meiosis-specific genes, and those related to saccharide metabolism and starch synthase, were involved in these heterozygote-specific phenotypes (Chen et al., 2019).
Discussions and Conclusions
These two series of pioneer studies on the PMeS and Neo-Tetraploid lines have opened the door to the use of autotetraploid varieties in rice breeding and cultivation. To facilitate the development of fertile autotetraploid lines, further research aimed at understanding the origin of the genes/quantitative trait loci (QTLs) responsible for high fertility in these tetraploid rice varieties will be necessary. Both the PMeS and Neo-Tetraploid rice lines have been developed from the progenies of crosses between the indica and japonica rice subspecies. Since the parental autotetraploid varieties show seed sterility, one possibility for the origin of high-fertility genes/QTLs in the new varieties’ progenies is the nature of interactions between genes (or genomic regions) derived from different autotetraploid parents. If so, a QTL analysis of high seed fertility could be performed by developing recombinant inbred lines derived from crosses between autotetraploids. The Neo-Tetraploid lines show different expression of some important meiosis stage specific- and meiosis-related genes (Guo et al., 2017; Bei et al., 2019). Genes/QTLs responsible for high seed fertility might control the expression of these. Interestingly, the PMeS and Neo-Tetraploid lines show high seed fertility not only when selfing, but also in hybrids with other autotetraploid rice strains (He Y. C. et al., 2011; Guo et al., 2017). These results suggest that the genes/QTLs responsible for high seed fertility in PMeS and Neo-Tetraploid lines act dominantly, suppressing unstable chromosomal behavior during meiosis in hybrids. If this is so, the question arises as to why the initial hybrids of indica and japonica used for developing the PMeS and Neo-Tetraploid lines did not show high seed fertility. Another possibility for the origin of genes/QTLs responsible for high seed fertility is the new appearance of these genes/QTLs during the line selection process. The finding of new mutations, absent from the parental genome, in a Neo-Tetraploid line (Bei et al., 2019) may support this scenario. The activation of TEs followed by alteration of the epigenetic state (Zhang et al., 2015; Guo et al., 2017; Bei et al., 2019) may cause new appearance of genes/QTLs in tetraploid rice. Further study is necessary to unclear why many such mutations would occur in the autotetraploid lines.
Although their origin is unclear, identifying the genes responsible for high seed fertility in the PMeS and Neo-Tetraploid lines will enable the transfer of this technique to other autotetraploid rice varieties through marker-assisted selection or genome editing. A diverse set of fertile autotetraploid rice varieties will greatly increase their potential usefulness in future agriculture. Ploidy manipulation, a classical concept, will revive in the field of rice breeding in the near future.
Author Contributions
YKo, DK, and YKi contributed to conceptualization of this review. YKo wrote original draft. DK and YKi reviewed and edited the original manuscript.
Funding
YKo and YKi are funded by JSPS KAKENHI Grant Numbers 18K05565 and 19H00937, respectively.
Conflict of Interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Acknowledgments
We thank to I. Takamure for his support for the project. No conflict of interest declared.
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Neurol.Frontiers in Neurology1664-2295Frontiers Media S.A.32849224PMC743213710.3389/fneur.2020.00770NeurologyBrief Research ReportEmpiric Recurrence Risk Estimates for Chronic Tic Disorders: Implications for Genetic CounselingHeimanGary A.1*RispoliJessica1SeymourChristine1LeckmanJames F.23KingRobert A.2FernandezThomas V.231Department of Genetics and the Human Genetics Institute of New Jersey, Rutgers, The State University of New Jersey, Piscataway, NJ, United States2Child Study Center, Yale University School of Medicine, New Haven, CT, United States3Department of Psychiatry, Yale University School of Medicine, New Haven, CT, United States
Edited by: Giuseppe De Michele, University of Naples Federico II, Italy
Reviewed by: Oksana Suchowersky, University of Alberta, Canada; Jong-Min Kim, Seoul National University Bundang Hospital, South Korea
*Correspondence: Gary A. Heiman heiman@dls.rutgers.edu
This article was submitted to Movement Disorders, a section of the journal Frontiers in Neurology
118202020201177011220202262020Copyright © 2020 Heiman, Rispoli, Seymour, Leckman, King and Fernandez.2020Heiman, Rispoli, Seymour, Leckman, King and FernandezThis is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Background: Tourette disorder (TD) and other chronic tic disorders are neurodevelopmental/neuropsychiatric disorders characterized by motor and/or vocal tics. Family studies indicate that TD strongly aggregates within families and that other chronic tic disorders are biologically related such that studies typically combine them into any chronic tic disorder (CTD). Because of stigma, bullying, and comorbidity with other neuropsychiatric disorders, CTDs can severely impact the quality of life of individuals with these disorders.
Objectives: The genetic architecture of CTDs is complex and heterogeneous, involving a myriad of genetic variants. Thus, providing familial recurrence risks is based on empirical recurrence risk estimates rather than genetic testing. Because empiric recurrence risks for CTDs have not been published, the purpose of this study is to calculate and report these recurrence risks estimates.
Methods: Based on population prevalence and increased risk to different relatives from a large population-based family study, we calculated the empiric recurrent risk estimate for each relative type (full sibling, parents, offspring, all first-degree, and all second-degree).
Results: The recurrence risk estimate for CTDs in first-degree relatives is 29.9% [95% confidence interval (CI) = 23.2–38.5%]. The risk is higher in males, 33.7% (95% CI = 26.2–43.3%), than females, 24.3% (95% CI = 18.9–31.3%).
Conclusions: Given the complex, heterogeneous genetic architecture of CTDs, individuals concerned about recurrence risk should be referred to genetic counseling. Such counseling should include discussion of the derivation and limitations of these empiric recurrence risk estimates, including the upper and lower limits of the range of risk.
Tourette and other chronic tic disorders are neurodevelopmental/neuropsychiatric disorders characterized by motor and/or vocal tics that make their appearance before the age of 18 years. While the diagnostic criteria for Tourette disorder (TD) require the presence of multiple motor and at least one vocal tic over at least 1 year, the criteria for chronic motor tic (CMTD) or chronic vocal tic (CVTD) disorders require at least 1 year of having either motor or vocal tics, respectively, but not both, during the individual's lifetime (1, 2). Tic severity symptoms characteristically wax and wane throughout the day, weeks, months, and years (3). The 95% lifetime prevalence confidence interval (CI) of TD ranges from 0.32 to 0.85% (4), and combined, the prevalence CI of all chronic tic disorders (CTDs) ranges from 0.92 to 2.83% (5). Chronic tic disorders are found across most ethnic groups worldwide and are more prevalent in males than females (3–4:1) (6, 7). While follow-up studies suggest a third of individuals report tic resolution into adulthood (8), objective videotape evaluations show that nonetheless some of these individuals still manifest tics (9). Over and above their psychosocial sequelae such as stigma or bullying, CTDs can severely impact quality of life because of a high comorbidity rate with other neuropsychiatric disorders including obsessive–compulsive disorder (OCD, 50.0%) and attention-deficit/hyperactivity disorder (ADHD, 54.3%) (6, 10, 11).
Family studies consistently show that TD strongly aggregates within families and that TD, CMTD, and CVTD are biologically sufficiently related that genetic studies typically combine them into a category of any CTD rather than analyzing them separately (6, 7, 11–16). Heritability studies clearly indicate a genetic contribution to the etiology of CTDs, with estimates ranging from 25 to 50% in twin studies (13, 15, 17) to 77% in a population-based familial-clustering study (18). Thus, CTDs are among the most heritable neuropsychiatric conditions (18). The relationship between tic severity and genetic predisposition in these studies is mixed. While proband severity is not associated with the number of affected relatives (19), proband severity is associated with having at least one affected relative (i.e., positive family history) (20). However, tic severity has low heritability (21) and varies considerably within families (22, 23). There is little evidence of assortative mating among individuals with tic disorders (24). However, when present, bilineal transmission has been associated with a higher level of tic severity in the offspring (24). In addition, multiple studies also indicate that OCD is genetically related to CTDs (11, 13, 25, 26) with both shared and distinct genetic risk factors (27).
Because family and twin studies show that CTDs are familial and have, at least in part, a genetic causation, molecular genetic studies have been conducted using different study designs (28). Initially, when it was thought that highly penetrant mutations were necessary and sufficient to cause CTD, linkage studies were conducted using large multiplex families. While these studies often had intriguing initial findings, they could not be replicated or localized to a distinct genetic locus (7, 12). Later, the “common variant–common disease” hypothesis and the “rare variant–common disease” hypothesis paradigms led, respectively, to genome-wide association studies (GWASs) (16, 29) and whole-exome sequencing (WES) studies (14, 30). In the GWASs of CTDs, only one single-nucleotide polymorphism surpassed the genome-wide significance threshold, but this finding was not replicated in an independent cohort (16). However, polygenic risk scores, based on GWAS results, are able to predict tic disorders in independent samples (16, 31). Two recent WES studies, searching for de novo damaging variants (variants predicted to disrupt protein function or probably-damaging missense variants), have yielded important insights into the genetic architecture of CTDs (14, 30). First, they estimate 400–500 genes that increase risk for CTDs when mutated in the form of a damaging high-penetrance variant. Second, 10–12% of CTDs are due to de novo damaging variants, including de novo copy number variants (CNVs). While de novo in one individual, these individuals can subsequently pass the variants on to their offspring. Finally, some of the risk genes found so far in WES studies are involved in cell polarity, suggesting that CTDs may be caused by a disruption in neurons arriving at the correct location and making correct connections during neurodevelopment. Taken together, these molecular genetic studies suggest that CTDs are caused by variants in many different genes, some with rare damaging high-penetrance variants, including CNVs, which may interact with a background genetic risk from common low-penetrance variants. Additionally, CTDs may be caused by an additive effect of common low-penetrance variants in many genes (i.e., polygenic), which may or may not overlap with the same genes that harbor rare high-penetrance variants. Future studies may also document the “co-action” of specific gene variants (32). Specific gene variants may also interact with environmental factors such as prenatal maternal smoking (33).
Given the complex and heterogeneous genetic architecture of CTDs, providing familial recurrence risks is challenging. Currently, there are no genetic tests available to determine individualized familial recurrence risk to relatives. Instead, available recurrence risks are empirical estimations based on results from population prevalence and family studies rather than a single risk based on Mendelian inheritance. Because these studies provide risk ranges (i.e., CIs), this makes providing empiric recurrence risk to at-risk individuals even more challenging (34, 35). The purpose of this article is to provide the empirical recurrence risk estimates for CTDs for different familial relationships, as these recurrence risks have not been previously reported. The process of how to optimally present these empiric risk estimates to individuals seeking genetic counseling is beyond the scope of this article and can be found elsewhere (35). However, in the discussion, we present the implications for genetic counseling for CTDs, based on current knowledge of genetic architecture and from empiric recurrence estimates.
Materials and MethodsGeneral Population Prevalence of CTDs
There are two published meta-analyses of the general population lifetime prevalence of TD (4, 5). One of these (5) provides estimates for TD and all CTDs combined. We used the CTDs general population prevalence data from this study (5) to calculate the empiric recurrence risk estimates. This meta-analysis found an overall general population prevalence for CTDs of 1.6% (95% CI = 0.9–2.8) with a higher prevalence in males (1.8%; 95% CI = 0.7–4.4%) than females (1.3%; 95% CI = 0.8–2.0%) (5).
Family Study Data: Increased Risk of CTDs in Relatives
Despite many family studies [for a review, see (12)], there is no published meta-analysis of increased risk of CTDs in relatives. We based our calculation of empiric recurrence risk on a recent family study because it is population-based, it has the largest sample size (n = 4,826), and it includes all CTDs rather than only TD (18).
Calculation of Empiric Recurrence Risk Estimates for CTDs
For each relative type (full sibling, parents, offspring, all first-degree, and all second-degree), we multiplied the CTD general population prevalence estimate (5) by the reported increased CTD risk to each relative type (18). We also provided the range of risk by multiplying the population prevalence by the 95% CI for CTD increased risk to each relative type. This was done overall as well as separately for each sex.
Results
Overall, based on general population prevalence and increased risk to relatives, the recurrence risk estimate for CTDs in any first-degree relative is 29.9% (95% CI = 23.2–38.5%) (Table 1). The risk is higher in males, 33.7% (95% CI = 26.2–43.3%), than females, 24.3% (95% CI = 18.9–31.3%). For second-degree relatives, the recurrence risk estimates are considerably lower (7.4%; 95% CI = 5.1–10.4%), also higher in males (8.3%; 95% CI = 5.8–11.7%) than in females (6.0%; 95% CI = 4.2–8.5%).
Empiric recurrence risk estimate for chronic tic disorders based on general population prevalence and increased risk to relatives.
Relative type
General population prevalence (5)
Increased risk to relative OR (95% CI) (18)
Recurrence risk % (95% CI)
A. OVERALL
All 1st degree
1.6%
18.7 (14.5–24.1)
29.9 (23.2–38.5)
  Full sibling
1.6%
17.7 (12.9–24.2)
28.3 (20.6–38.7)
  Parents
1.6%
21.1 (11.2–39.7)
33.8 (17.9–63.5)
  Offspring
1.6%
24.7 (12.4–49.3)
39.5 (19.9–78.9)
All 2nd degree
1.6%
4.6 (3.2–6.5)
7.4 (5.1–10.4)
B. MALES ONLY
All 1st degree
1.8%
18.7 (14.5–24.1)
33.7 (26.2–43.3)
  Full sibling
1.8%
17.7 (12.9–24.2)
31.9 (23.2–43.6)
  Parents
1.8%
21.1 (11.2–39.7)
38.0 (20.1–71.4)
  Offspring
1.8%
24.7 (12.4–49.3)
44.5 (22.4–88.7)
All 2nd degree
1.8%
4.6 (3.2–6.5)
8.3 (5.8–11.7)
C. FEMALES ONLY
All 1st degree
1.3%
18.7 (14.5–24.1)
24.3 (18.9–31.3)
  Full sibling
1.3%
17.7 (12.9–24.2)
23.0 (16.8–31.5)
  Parents
1.3%
21.1 (11.2–39.7)
27.4 (14.5–51.6)
  Offspring
1.3%
24.7 (12.4–49.3)
32.1 (16.1–64.1)
All 2nd degree
1.3%
4.6 (3.2–6.5)
6.0 (4.2–8.5)
Discussion
For CTDs, the empiric 95% CI recurrence risk estimates for different first-degree relatives (sibling, parents, and offspring) range from 28.3 (sibling) to 39.5% (offspring). The higher population prevalence in males than in females (3–4:1) led to a higher recurrence risk for any first-degree relative in males (33.7% in males vs. 24.3% in females). These recurrence risks estimates are in line with the recurrence risk to offspring of parents with other severe psychiatric diagnoses (schizophrenia, bipolar disorder, major depressive disorder), based on a meta-analysis of high-risk family studies (36). In that study, children of a parent with one of these diagnoses had a 32% chance of having a psychotic or major mood disorder by early adulthood. On the other hand, our CTD empiric recurrence risks are higher than those for schizophrenia and bipolar disorder historically quoted for genetic counselors (37). However, the meta-analysis was published more recently than the genetic counseling quoted risks, and this could explain the difference. In support of the higher risks from the meta-analysis, a recent systematic review of multiple familial high-risk longitudinal studies found that adult offspring of one or more parents with schizophrenia had 15–40% risk of developing a psychotic disorder (38). Thus, the recurrence risks for CTDs are in line with the risks from these other neuropsychiatric disorders.
Our findings should be considered in the context of certain limitations. The prevalence estimate used in this study was from the 2012 meta-analysis by Knight et al. (5). A subsequent meta-analysis, published in 2014, by Scharf et al. (4), included additional studies and had a slightly lower prevalence of TD (Scharf and colleagues' TD prevalence = 0.5%; 95% CI = 0.3–0.9%, compared with Knight and colleagues' TD prevalence 0.8%; 95% CI = 0.4–1.5%). While Scharf and colleagues' article did not report other CTDs, the lower TD prevalence may indicate that the true CTD prevalence and thus the empiric recurrence estimates reported here are slightly lower. On the other hand, a recent meta-analysis of prevalence studies conducted in China, which was not included in either of the prior meta-analyses, found a prevalence of TD plus other CTDs that was similar to the prevalence in the study of Knight et al. (1.5% combined for Chinese study vs. 1.6% in Knight and colleagues' study). Thus, far, there has not been a meta-analysis of family studies. While we used the results from the largest general population family study to date, this is only an estimate of the true underlying increased risk to relatives. Combining multiple family studies would reduce the size of the 95% CI (e.g., offspring 95% CI = 19.9–78.9%). In addition, our reliance on the data from the Swedish national registries to estimate empiric recurrence risk may also be somewhat problematic as the registries overrepresent the more severe or complex cases (18) but may miss less severe cases (that never seek clinical attention) and hence may not generalize to milder forms of the disorder.
In summary, CTDs have a complex heterogeneous etiology that involves hundreds of genes, some with rare high-penetrance mutations and others with common low-penetrance variants that act in an additive fashion. In this article, the empiric recurrence risk estimates range between 30 and 40% for specific first-degree relatives (or all first-degree relatives combined), but the CIs are wide. The recurrence risk estimates for second-degree relatives are considerably lower (7.4%; 95% CI = 5.1–10.4%). The recurrence risks are higher in males than females. These recurrence risks are in line with published risks for other neuropsychiatric disorders, including schizophrenia (36, 38).
Genetic Counseling Implications
Genetic counseling is a process that helps individuals understand the medical implications, including recurrence risk, for a variety of inherited disorders. The goal of this article was to provide the empiric recurrence risk estimates for CTDs, not the process of how to provide these risk estimates during a genetic counseling session. For an excellent article discussing the process of psychiatric genetic counseling, see Inglis et al. (35).
Previous work has suggested that patients with a family history of psychiatric disorders and other multifactorial disorders are interested in, and benefit from, genetic counseling, despite the challenges and limitations of precise risk assessment (35, 39). It is yet to be explored which families with CTDs might be interested in genetic counseling. However, recent insights into the genetic architecture of CTDs have implications for recurrence risk estimation for families seeking information and thus make genetic counseling for CTDs possible. Presently, genetic counseling for CTDs using the empiric recurrence risks found in this article would follow a comparable model, outlined by Inglis et al. (35), which details an approach for providing psychiatric genetic counseling. Similar to genetic counseling for CTDs, psychiatric genetic counseling often involves providing empiric recurrence risk estimates. That article (35) provides detailed descriptions of (a) exploring personal and family histories, (b) establishing a shared understanding and expectations for counseling, (c) discussing the known and unknown etiology of psychiatric illness, (d) discussing protective factors, (e) communicating risk to patients, and (f) deriving estimates of probabilities for children to develop the illness. Additionally, this article also provides text for clinicians to use for different issues that might arise in a genetic counseling setting [Tables 1–3 Inglis et al. (35)].
Table 2 outlines the key points for couples or patients seeking genetic counseling for CTDs. The key points of a consultation with a genetic counselor include receiving education regarding the hereditary nature of CTDs, understanding its complex and heterogeneous genetic architecture, and comprehending the lack of available genetic testing. Although empiric recurrence risk estimates are available, there is currently no genetic testing that would provide individualized recurrence risk estimates for CTDs or that would rule out any predisposing factor. Genetic counselors are trained to provide a balanced summary of the current understanding of the underlying genetic etiology while highlighting the limitations of such information. Physicians and other medical professionals are encouraged to refer interested parents or patients to genetic counseling. Information surrounding the recurrence risk of CTDs is expected to evolve over time, and genetic counselors are able to provide the most up-to-date information to families at risk.
Key counseling points for a chronic tic disorder (CTD) genetic counseling appointment.
Prevalence is higher in males than females (3–4:1).
CTDs are highly heritable.
Complex and heterogeneous etiology with hundreds of genes involved, both rare high-penetrance and common low-penetrance variants, and these may interact with environmental factors in utero.
Ten percent to 12% of CTDs are due to de novo mutations, and these de novo mutations in one generation can be passed on to children in the next generation.
Lifetime worst-ever tic severity may be higher when there is at least one other family member is affected, but the number of relatives with tics does not predict severity. Worst-ever tic severity varies widely within family members (i.e., severity does not breed true). Also, there is limited evidence that if both parents are affected, their children will have more severe tics.
At present, genetic testing for Tourette disorder and other CTDs has limited utility.
The empiric recurrence risk estimates for CTD found in this study are an average risk across all family types, including families in which only one individual has a CTD and families in which multiple individuals have had CTD. The 95% confidence intervals show the range of uncertainty around an average value and not meant to imply a range that is clinically applicable to a certain family type. That is, we currently do not have a system to categorize families with higher or lower risk within these intervals.
One aspect that we did not address is the risk to relatives for the associated disorders that are known to be often clinically comorbid with CTDs, such as ADHD or OCD, but that may be at increased risk even in offspring without tics (10, 25). Furthermore, because genetic counseling is often sought in the context of couples contemplating conceiving in the context of a family history of CTD, preconceived notions about the quality of life with CTD may affect such decisions and be heavily colored by the specific experiences of affected family members. The quality of life over the life span of individuals with CTDs varies greatly with their clinical specifics, especially the presence or absence of comorbidities such as ADHD or OCD, age, family support, and treatment (40, 41). Although these are not the purview of the genetic counselor per se, it is important that individuals and families with CTD be connected with clinicians with expertise in these disorders who can help to mitigate their potentially deleterious impact.
Data Availability Statement
All datasets generated for this study are included in the article.
Ethics Statement
Ethical approval and written informed consent were not required as per local legislation and national guidelines.
Author Contributions
GH, JR, CS, JL, RK, and TF contributed to the design, organization, and conception of the work. GH and TF contributed to the statistical analysis. GH, JR, and TF contributed to the interpretation of the data. GH, JR, and RK contributed to drafting the manuscript. All authors reviewed, critiqued, and gave final approval for the version to be published and agreed to be accountable for all aspects of the work.
Conflict of Interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Funding. This study was supported by grants from the National Institute of Mental Health (R01MH115958 to GH) and the New Jersey Center for Tourette Syndrome and Associated Disorders (NJCTS; to GH).
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(2016) 50:82–93. 10.1177/000486741559442926169656\n"],"string":"[\n \"\\nFront NeurolFront NeurolFront. Neurol.Frontiers in Neurology1664-2295Frontiers Media S.A.32849224PMC743213710.3389/fneur.2020.00770NeurologyBrief Research ReportEmpiric Recurrence Risk Estimates for Chronic Tic Disorders: Implications for Genetic CounselingHeimanGary A.1*RispoliJessica1SeymourChristine1LeckmanJames F.23KingRobert A.2FernandezThomas V.231Department of Genetics and the Human Genetics Institute of New Jersey, Rutgers, The State University of New Jersey, Piscataway, NJ, United States2Child Study Center, Yale University School of Medicine, New Haven, CT, United States3Department of Psychiatry, Yale University School of Medicine, New Haven, CT, United States
Edited by: Giuseppe De Michele, University of Naples Federico II, Italy
Reviewed by: Oksana Suchowersky, University of Alberta, Canada; Jong-Min Kim, Seoul National University Bundang Hospital, South Korea
*Correspondence: Gary A. Heiman heiman@dls.rutgers.edu
This article was submitted to Movement Disorders, a section of the journal Frontiers in Neurology
118202020201177011220202262020Copyright © 2020 Heiman, Rispoli, Seymour, Leckman, King and Fernandez.2020Heiman, Rispoli, Seymour, Leckman, King and FernandezThis is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Background: Tourette disorder (TD) and other chronic tic disorders are neurodevelopmental/neuropsychiatric disorders characterized by motor and/or vocal tics. Family studies indicate that TD strongly aggregates within families and that other chronic tic disorders are biologically related such that studies typically combine them into any chronic tic disorder (CTD). Because of stigma, bullying, and comorbidity with other neuropsychiatric disorders, CTDs can severely impact the quality of life of individuals with these disorders.
Objectives: The genetic architecture of CTDs is complex and heterogeneous, involving a myriad of genetic variants. Thus, providing familial recurrence risks is based on empirical recurrence risk estimates rather than genetic testing. Because empiric recurrence risks for CTDs have not been published, the purpose of this study is to calculate and report these recurrence risks estimates.
Methods: Based on population prevalence and increased risk to different relatives from a large population-based family study, we calculated the empiric recurrent risk estimate for each relative type (full sibling, parents, offspring, all first-degree, and all second-degree).
Results: The recurrence risk estimate for CTDs in first-degree relatives is 29.9% [95% confidence interval (CI) = 23.2–38.5%]. The risk is higher in males, 33.7% (95% CI = 26.2–43.3%), than females, 24.3% (95% CI = 18.9–31.3%).
Conclusions: Given the complex, heterogeneous genetic architecture of CTDs, individuals concerned about recurrence risk should be referred to genetic counseling. Such counseling should include discussion of the derivation and limitations of these empiric recurrence risk estimates, including the upper and lower limits of the range of risk.
Tourette and other chronic tic disorders are neurodevelopmental/neuropsychiatric disorders characterized by motor and/or vocal tics that make their appearance before the age of 18 years. While the diagnostic criteria for Tourette disorder (TD) require the presence of multiple motor and at least one vocal tic over at least 1 year, the criteria for chronic motor tic (CMTD) or chronic vocal tic (CVTD) disorders require at least 1 year of having either motor or vocal tics, respectively, but not both, during the individual's lifetime (1, 2). Tic severity symptoms characteristically wax and wane throughout the day, weeks, months, and years (3). The 95% lifetime prevalence confidence interval (CI) of TD ranges from 0.32 to 0.85% (4), and combined, the prevalence CI of all chronic tic disorders (CTDs) ranges from 0.92 to 2.83% (5). Chronic tic disorders are found across most ethnic groups worldwide and are more prevalent in males than females (3–4:1) (6, 7). While follow-up studies suggest a third of individuals report tic resolution into adulthood (8), objective videotape evaluations show that nonetheless some of these individuals still manifest tics (9). Over and above their psychosocial sequelae such as stigma or bullying, CTDs can severely impact quality of life because of a high comorbidity rate with other neuropsychiatric disorders including obsessive–compulsive disorder (OCD, 50.0%) and attention-deficit/hyperactivity disorder (ADHD, 54.3%) (6, 10, 11).
Family studies consistently show that TD strongly aggregates within families and that TD, CMTD, and CVTD are biologically sufficiently related that genetic studies typically combine them into a category of any CTD rather than analyzing them separately (6, 7, 11–16). Heritability studies clearly indicate a genetic contribution to the etiology of CTDs, with estimates ranging from 25 to 50% in twin studies (13, 15, 17) to 77% in a population-based familial-clustering study (18). Thus, CTDs are among the most heritable neuropsychiatric conditions (18). The relationship between tic severity and genetic predisposition in these studies is mixed. While proband severity is not associated with the number of affected relatives (19), proband severity is associated with having at least one affected relative (i.e., positive family history) (20). However, tic severity has low heritability (21) and varies considerably within families (22, 23). There is little evidence of assortative mating among individuals with tic disorders (24). However, when present, bilineal transmission has been associated with a higher level of tic severity in the offspring (24). In addition, multiple studies also indicate that OCD is genetically related to CTDs (11, 13, 25, 26) with both shared and distinct genetic risk factors (27).
Because family and twin studies show that CTDs are familial and have, at least in part, a genetic causation, molecular genetic studies have been conducted using different study designs (28). Initially, when it was thought that highly penetrant mutations were necessary and sufficient to cause CTD, linkage studies were conducted using large multiplex families. While these studies often had intriguing initial findings, they could not be replicated or localized to a distinct genetic locus (7, 12). Later, the “common variant–common disease” hypothesis and the “rare variant–common disease” hypothesis paradigms led, respectively, to genome-wide association studies (GWASs) (16, 29) and whole-exome sequencing (WES) studies (14, 30). In the GWASs of CTDs, only one single-nucleotide polymorphism surpassed the genome-wide significance threshold, but this finding was not replicated in an independent cohort (16). However, polygenic risk scores, based on GWAS results, are able to predict tic disorders in independent samples (16, 31). Two recent WES studies, searching for de novo damaging variants (variants predicted to disrupt protein function or probably-damaging missense variants), have yielded important insights into the genetic architecture of CTDs (14, 30). First, they estimate 400–500 genes that increase risk for CTDs when mutated in the form of a damaging high-penetrance variant. Second, 10–12% of CTDs are due to de novo damaging variants, including de novo copy number variants (CNVs). While de novo in one individual, these individuals can subsequently pass the variants on to their offspring. Finally, some of the risk genes found so far in WES studies are involved in cell polarity, suggesting that CTDs may be caused by a disruption in neurons arriving at the correct location and making correct connections during neurodevelopment. Taken together, these molecular genetic studies suggest that CTDs are caused by variants in many different genes, some with rare damaging high-penetrance variants, including CNVs, which may interact with a background genetic risk from common low-penetrance variants. Additionally, CTDs may be caused by an additive effect of common low-penetrance variants in many genes (i.e., polygenic), which may or may not overlap with the same genes that harbor rare high-penetrance variants. Future studies may also document the “co-action” of specific gene variants (32). Specific gene variants may also interact with environmental factors such as prenatal maternal smoking (33).
Given the complex and heterogeneous genetic architecture of CTDs, providing familial recurrence risks is challenging. Currently, there are no genetic tests available to determine individualized familial recurrence risk to relatives. Instead, available recurrence risks are empirical estimations based on results from population prevalence and family studies rather than a single risk based on Mendelian inheritance. Because these studies provide risk ranges (i.e., CIs), this makes providing empiric recurrence risk to at-risk individuals even more challenging (34, 35). The purpose of this article is to provide the empirical recurrence risk estimates for CTDs for different familial relationships, as these recurrence risks have not been previously reported. The process of how to optimally present these empiric risk estimates to individuals seeking genetic counseling is beyond the scope of this article and can be found elsewhere (35). However, in the discussion, we present the implications for genetic counseling for CTDs, based on current knowledge of genetic architecture and from empiric recurrence estimates.
Materials and MethodsGeneral Population Prevalence of CTDs
There are two published meta-analyses of the general population lifetime prevalence of TD (4, 5). One of these (5) provides estimates for TD and all CTDs combined. We used the CTDs general population prevalence data from this study (5) to calculate the empiric recurrence risk estimates. This meta-analysis found an overall general population prevalence for CTDs of 1.6% (95% CI = 0.9–2.8) with a higher prevalence in males (1.8%; 95% CI = 0.7–4.4%) than females (1.3%; 95% CI = 0.8–2.0%) (5).
Family Study Data: Increased Risk of CTDs in Relatives
Despite many family studies [for a review, see (12)], there is no published meta-analysis of increased risk of CTDs in relatives. We based our calculation of empiric recurrence risk on a recent family study because it is population-based, it has the largest sample size (n = 4,826), and it includes all CTDs rather than only TD (18).
Calculation of Empiric Recurrence Risk Estimates for CTDs
For each relative type (full sibling, parents, offspring, all first-degree, and all second-degree), we multiplied the CTD general population prevalence estimate (5) by the reported increased CTD risk to each relative type (18). We also provided the range of risk by multiplying the population prevalence by the 95% CI for CTD increased risk to each relative type. This was done overall as well as separately for each sex.
Results
Overall, based on general population prevalence and increased risk to relatives, the recurrence risk estimate for CTDs in any first-degree relative is 29.9% (95% CI = 23.2–38.5%) (Table 1). The risk is higher in males, 33.7% (95% CI = 26.2–43.3%), than females, 24.3% (95% CI = 18.9–31.3%). For second-degree relatives, the recurrence risk estimates are considerably lower (7.4%; 95% CI = 5.1–10.4%), also higher in males (8.3%; 95% CI = 5.8–11.7%) than in females (6.0%; 95% CI = 4.2–8.5%).
Empiric recurrence risk estimate for chronic tic disorders based on general population prevalence and increased risk to relatives.
Relative type
General population prevalence (5)
Increased risk to relative OR (95% CI) (18)
Recurrence risk % (95% CI)
A. OVERALL
All 1st degree
1.6%
18.7 (14.5–24.1)
29.9 (23.2–38.5)
  Full sibling
1.6%
17.7 (12.9–24.2)
28.3 (20.6–38.7)
  Parents
1.6%
21.1 (11.2–39.7)
33.8 (17.9–63.5)
  Offspring
1.6%
24.7 (12.4–49.3)
39.5 (19.9–78.9)
All 2nd degree
1.6%
4.6 (3.2–6.5)
7.4 (5.1–10.4)
B. MALES ONLY
All 1st degree
1.8%
18.7 (14.5–24.1)
33.7 (26.2–43.3)
  Full sibling
1.8%
17.7 (12.9–24.2)
31.9 (23.2–43.6)
  Parents
1.8%
21.1 (11.2–39.7)
38.0 (20.1–71.4)
  Offspring
1.8%
24.7 (12.4–49.3)
44.5 (22.4–88.7)
All 2nd degree
1.8%
4.6 (3.2–6.5)
8.3 (5.8–11.7)
C. FEMALES ONLY
All 1st degree
1.3%
18.7 (14.5–24.1)
24.3 (18.9–31.3)
  Full sibling
1.3%
17.7 (12.9–24.2)
23.0 (16.8–31.5)
  Parents
1.3%
21.1 (11.2–39.7)
27.4 (14.5–51.6)
  Offspring
1.3%
24.7 (12.4–49.3)
32.1 (16.1–64.1)
All 2nd degree
1.3%
4.6 (3.2–6.5)
6.0 (4.2–8.5)
Discussion
For CTDs, the empiric 95% CI recurrence risk estimates for different first-degree relatives (sibling, parents, and offspring) range from 28.3 (sibling) to 39.5% (offspring). The higher population prevalence in males than in females (3–4:1) led to a higher recurrence risk for any first-degree relative in males (33.7% in males vs. 24.3% in females). These recurrence risks estimates are in line with the recurrence risk to offspring of parents with other severe psychiatric diagnoses (schizophrenia, bipolar disorder, major depressive disorder), based on a meta-analysis of high-risk family studies (36). In that study, children of a parent with one of these diagnoses had a 32% chance of having a psychotic or major mood disorder by early adulthood. On the other hand, our CTD empiric recurrence risks are higher than those for schizophrenia and bipolar disorder historically quoted for genetic counselors (37). However, the meta-analysis was published more recently than the genetic counseling quoted risks, and this could explain the difference. In support of the higher risks from the meta-analysis, a recent systematic review of multiple familial high-risk longitudinal studies found that adult offspring of one or more parents with schizophrenia had 15–40% risk of developing a psychotic disorder (38). Thus, the recurrence risks for CTDs are in line with the risks from these other neuropsychiatric disorders.
Our findings should be considered in the context of certain limitations. The prevalence estimate used in this study was from the 2012 meta-analysis by Knight et al. (5). A subsequent meta-analysis, published in 2014, by Scharf et al. (4), included additional studies and had a slightly lower prevalence of TD (Scharf and colleagues' TD prevalence = 0.5%; 95% CI = 0.3–0.9%, compared with Knight and colleagues' TD prevalence 0.8%; 95% CI = 0.4–1.5%). While Scharf and colleagues' article did not report other CTDs, the lower TD prevalence may indicate that the true CTD prevalence and thus the empiric recurrence estimates reported here are slightly lower. On the other hand, a recent meta-analysis of prevalence studies conducted in China, which was not included in either of the prior meta-analyses, found a prevalence of TD plus other CTDs that was similar to the prevalence in the study of Knight et al. (1.5% combined for Chinese study vs. 1.6% in Knight and colleagues' study). Thus, far, there has not been a meta-analysis of family studies. While we used the results from the largest general population family study to date, this is only an estimate of the true underlying increased risk to relatives. Combining multiple family studies would reduce the size of the 95% CI (e.g., offspring 95% CI = 19.9–78.9%). In addition, our reliance on the data from the Swedish national registries to estimate empiric recurrence risk may also be somewhat problematic as the registries overrepresent the more severe or complex cases (18) but may miss less severe cases (that never seek clinical attention) and hence may not generalize to milder forms of the disorder.
In summary, CTDs have a complex heterogeneous etiology that involves hundreds of genes, some with rare high-penetrance mutations and others with common low-penetrance variants that act in an additive fashion. In this article, the empiric recurrence risk estimates range between 30 and 40% for specific first-degree relatives (or all first-degree relatives combined), but the CIs are wide. The recurrence risk estimates for second-degree relatives are considerably lower (7.4%; 95% CI = 5.1–10.4%). The recurrence risks are higher in males than females. These recurrence risks are in line with published risks for other neuropsychiatric disorders, including schizophrenia (36, 38).
Genetic Counseling Implications
Genetic counseling is a process that helps individuals understand the medical implications, including recurrence risk, for a variety of inherited disorders. The goal of this article was to provide the empiric recurrence risk estimates for CTDs, not the process of how to provide these risk estimates during a genetic counseling session. For an excellent article discussing the process of psychiatric genetic counseling, see Inglis et al. (35).
Previous work has suggested that patients with a family history of psychiatric disorders and other multifactorial disorders are interested in, and benefit from, genetic counseling, despite the challenges and limitations of precise risk assessment (35, 39). It is yet to be explored which families with CTDs might be interested in genetic counseling. However, recent insights into the genetic architecture of CTDs have implications for recurrence risk estimation for families seeking information and thus make genetic counseling for CTDs possible. Presently, genetic counseling for CTDs using the empiric recurrence risks found in this article would follow a comparable model, outlined by Inglis et al. (35), which details an approach for providing psychiatric genetic counseling. Similar to genetic counseling for CTDs, psychiatric genetic counseling often involves providing empiric recurrence risk estimates. That article (35) provides detailed descriptions of (a) exploring personal and family histories, (b) establishing a shared understanding and expectations for counseling, (c) discussing the known and unknown etiology of psychiatric illness, (d) discussing protective factors, (e) communicating risk to patients, and (f) deriving estimates of probabilities for children to develop the illness. Additionally, this article also provides text for clinicians to use for different issues that might arise in a genetic counseling setting [Tables 1–3 Inglis et al. (35)].
Table 2 outlines the key points for couples or patients seeking genetic counseling for CTDs. The key points of a consultation with a genetic counselor include receiving education regarding the hereditary nature of CTDs, understanding its complex and heterogeneous genetic architecture, and comprehending the lack of available genetic testing. Although empiric recurrence risk estimates are available, there is currently no genetic testing that would provide individualized recurrence risk estimates for CTDs or that would rule out any predisposing factor. Genetic counselors are trained to provide a balanced summary of the current understanding of the underlying genetic etiology while highlighting the limitations of such information. Physicians and other medical professionals are encouraged to refer interested parents or patients to genetic counseling. Information surrounding the recurrence risk of CTDs is expected to evolve over time, and genetic counselors are able to provide the most up-to-date information to families at risk.
Key counseling points for a chronic tic disorder (CTD) genetic counseling appointment.
Prevalence is higher in males than females (3–4:1).
CTDs are highly heritable.
Complex and heterogeneous etiology with hundreds of genes involved, both rare high-penetrance and common low-penetrance variants, and these may interact with environmental factors in utero.
Ten percent to 12% of CTDs are due to de novo mutations, and these de novo mutations in one generation can be passed on to children in the next generation.
Lifetime worst-ever tic severity may be higher when there is at least one other family member is affected, but the number of relatives with tics does not predict severity. Worst-ever tic severity varies widely within family members (i.e., severity does not breed true). Also, there is limited evidence that if both parents are affected, their children will have more severe tics.
At present, genetic testing for Tourette disorder and other CTDs has limited utility.
The empiric recurrence risk estimates for CTD found in this study are an average risk across all family types, including families in which only one individual has a CTD and families in which multiple individuals have had CTD. The 95% confidence intervals show the range of uncertainty around an average value and not meant to imply a range that is clinically applicable to a certain family type. That is, we currently do not have a system to categorize families with higher or lower risk within these intervals.
One aspect that we did not address is the risk to relatives for the associated disorders that are known to be often clinically comorbid with CTDs, such as ADHD or OCD, but that may be at increased risk even in offspring without tics (10, 25). Furthermore, because genetic counseling is often sought in the context of couples contemplating conceiving in the context of a family history of CTD, preconceived notions about the quality of life with CTD may affect such decisions and be heavily colored by the specific experiences of affected family members. The quality of life over the life span of individuals with CTDs varies greatly with their clinical specifics, especially the presence or absence of comorbidities such as ADHD or OCD, age, family support, and treatment (40, 41). Although these are not the purview of the genetic counselor per se, it is important that individuals and families with CTD be connected with clinicians with expertise in these disorders who can help to mitigate their potentially deleterious impact.
Data Availability Statement
All datasets generated for this study are included in the article.
Ethics Statement
Ethical approval and written informed consent were not required as per local legislation and national guidelines.
Author Contributions
GH, JR, CS, JL, RK, and TF contributed to the design, organization, and conception of the work. GH and TF contributed to the statistical analysis. GH, JR, and TF contributed to the interpretation of the data. GH, JR, and RK contributed to drafting the manuscript. All authors reviewed, critiqued, and gave final approval for the version to be published and agreed to be accountable for all aspects of the work.
Conflict of Interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Funding. This study was supported by grants from the National Institute of Mental Health (R01MH115958 to GH) and the New Jersey Center for Tourette Syndrome and Associated Disorders (NJCTS; to GH).
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(2016) 50:82–93. 10.1177/000486741559442926169656\\n\"\n]"}}},{"rowIdx":481,"cells":{"data":{"kind":"list like","value":["\nFront ImmunolFront ImmunolFront. Immunol.Frontiers in Immunology1664-3224Frontiers Media S.A.32849666PMC743213810.3389/fimmu.2020.02014ImmunologyPerspectiveUnderstanding the Pathophysiology of COVID-19: Could the Contact System Be the Key?MeiniSimone1*ZanichelliAndrea2SbrojavaccaRodolfo3IuriFederico4RobertsAnna Teresa5SuffrittiChiara2TasciniCarlo31Internal Medicine Unit, Azienda USL Toscana Centro, Santa Maria Annunziata Hospital, Florence, Italy2General Medicine Unit, ASST Fatebenefratelli Sacco, Ospedale Luigi Sacco-Università degli Studi di Milano, Milan, Italy3Infectious Diseases Clinic, Santa Maria Misericordia Hospital, Università degli Studi di Udine, Udine, Italy4Department of Emergency, Santa Maria Misericordia Hospital, Università degli Studi di Udine, Udine, Italy5Medical Department, Azienda USL Toscana Nord-Ovest, Pisa, Italy
Edited by: Gennady Bocharov, Institute of Numerical Mathematics (RAS), Russia
Reviewed by: Klaus T. Preissner, University of Giessen, Germany; Zhenlong Liu, McGill University, Canada
This article was submitted to Viral Immunology, a section of the journal Frontiers in Immunology
11820202020118202011201416420202472020Copyright © 2020 Meini, Zanichelli, Sbrojavacca, Iuri, Roberts, Suffritti and Tascini.2020Meini, Zanichelli, Sbrojavacca, Iuri, Roberts, Suffritti and TasciniThis is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
To date the pathophysiology of COVID-19 remains unclear: this represents a factor determining the current lack of effective treatments. In this paper, we hypothesized a complex host response to SARS-CoV-2, with the Contact System (CS) playing a pivotal role in innate immune response. CS is linked with different proteolytic defense systems operating in human vasculature: the Kallikrein–Kinin (KKS), the Coagulation/Fibrinolysis and the Renin–Angiotensin (RAS) Systems. We investigated the role of the mediators involved. CS consists of Factor XII (FXII) and plasma prekallikrein (complexed to high-molecular-weight kininogen-HK). Autoactivation of FXII by contact with SARS-CoV-2 could lead to activation of intrinsic coagulation, with fibrin formation (microthrombosis), and fibrinolysis, resulting in increased D-dimer levels. Activation of kallikrein by activated FXII leads to production of bradykinin (BK) from HK. BK binds to B2-receptors, mediating vascular permeability, vasodilation and edema. B1-receptors, binding the metabolite [des-Arg9]-BK (DABK), are up-regulated during infections and mediate lung inflammatory responses. BK could play a relevant role in COVID-19 as already described for other viral models. Angiotensin-Converting-Enzyme (ACE) 2 displays lung protective effects: it inactivates DABK and converts Angiotensin II (Ang II) into Angiotensin-(1-7) and Angiotensin I into Angiotensin-(1-9). SARS-CoV-2 binds to ACE2 for cell entry, downregulating it: an impaired DABK inactivation could lead to an enhanced activity of B1-receptors, and the accumulation of Ang II, through a negative feedback loop, may result in decreased ACE activity, with consequent increase of BK. Therapies targeting the CS, the KKS and action of BK could be effective for the treatment of COVID-19.
Starting in December 2019 in Wuhan (Hubei Province, China), a novel coronavirus, designated SARS-CoV-2, has caused an international outbreak of a respiratory illness (COVID-19), rapidly evolving into a pandemic. The clinical spectrum of SARS-CoV-2 infection varies from asymptomatic or self-limiting mild forms, occurring in most cases, to severe progressive pneumonia with acute respiratory distress syndrome (ARDS), and death. In a yet to be defined percentage of cases, after about one week, there is a sudden and unpredictable worsening of clinical conditions (1).
At present, there is no vaccine or pharmacological treatments of proven efficacy for COVID-19 (2) and further investigation on effective drugs is required to face the current pandemic. One factor determining the lack of effective treatments is that the pathophysiology of COVID-19 remains largely unclear.
In this review, we try to address the complex link between the pathophysiology of COVID-19 and the different proteolytic defense systems operating in human vasculature, investigating the role of the mediators involved and speculating on the possibility of pharmacological modulation.
Clinical and Laboratory Findings in Patients With COVID-19
COVID-19 is mainly a respiratory illness, but a wide variety of clinical manifestations have been described, including the central nervous (3) and the digestive (4) systems. Most symptomatic COVID-19 patients display manifestations such as fever (98.6%), dry cough (59.4%), dyspnea (31.2%), myalgias (34.8%), sore throat (17.4%), diarrhea (10.1%), and other (5). Olfactory and gustatory dysfunctions are common symptoms, occurring in about 50% of patients and often presenting early in the clinical course (6, 7). Low blood pressure values are frequently observed in hospitalized patients: Wang et al. (5) in their cohort reported a median of mean arterial pressure values of 90 mmHg despite 31% of patients having a history of hypertension.
The predominant findings of lung Computed Tomography are images of bilateral, peripheral and basal ground-glass opacities, crazy-paving pattern, consolidations, often in association (8), and ultrasonography precociously demonstrates a lung interstitial syndrome (9). These findings are consistent with a lung injury characterized by increased permeability, leaky blood vessels and edema, and have been confirmed by the histopathological data obtained from the lungs of patients who died from COVID-19, showing diffuse alveolar damage with necrosis of alveolar lining cells, pneumocyte type 2 hyperplasia, linear intra-alveolar fibrin deposition and increased lung weight due to edema; in addition, thrombi in pulmonary arteries with a diameter of 1–2 mm, without complete luminal obstruction, and massive alveolar capillary microthrombi were observed (10).
Concerning laboratory findings, an increase of lactate dehydrogenase levels and lymphocytopenia are common. Elevated levels of serum ferritin, as commonly found in viral infections, are detected in most patients (11). Levels of interleukin-6 (IL-6) are typically in the upper limit of the reference range and appear to correlate with disease severity (12, 13). IL-6-induced high levels of C-reactive protein (CRP) are typically more related to bacterial rather than to viral infections (11): in COVID-19 patients CRP values are very variable. Even in non-critical patients, high D-dimer levels are found in most patients. Prothrombin time is often slightly increased.
Levels of inflammatory and coagulation biomarkers vary considerably among patients with COVID-19, suggesting the existence of different biochemical/clinical phenotypes, in which the predominant systems involved and the inflammatory and coagulopathy response patterns differ.
From a pathogenetic point of view, it is clear that a link (to date not yet fully clarified) exists between the clinical manifestations and alterations of the inflammatory and coagulation systems, and that these different systems are only apparently unrelated.
The Role of the Contact System in the Pathophysiology of COVID-19
The Contact System (CS) is part of the innate immune system and of inflammatory response mechanism against artificial material, misfolded and foreign proteins and microorganisms (including viruses), found in the intravascular compartment. It remains to be clarified whether contact factors bind and activate directly on the viral surface or on infected cells (14).
The main proteins of the CS are the Factor XII (FXII), the prekallikrein (PK) and the high-molecular-weight kininogen (HK). These proteins are produced by the liver and circulate as zymogens into the bloodstream. Virtually all plasma PK circulates in complex with HK.
Auto-activation of FXII to FXIIa by contact with a variety of artificial and biological negatively charged surfaces, including microorganisms, gives rise to CS cascade. Biological substances with the potential to support its activation include: DNA, RNA, polyphosphates retained on activated platelet surface, aggregated proteins, neutrophil extracellular traps (NETs) and ferritin (15–19). Kannemeier et al. (20) presented evidence that different forms of eukaryotic and prokaryotic RNA serve as promoters of blood coagulation, enhancing auto-activation of proteases of the CS, such as FXII and FXI. As the extracellular RNA derived from damaged or necrotic cells represented a “foreign surface” able to activate the CS, it could be speculated that the same process may be initiated by viral RNA. In addition, at times of cellular stress (i.e., hypoxia, hyperthermia, oxygen radical production) such as that observed during COVID-19, endogenous “alarmins” named “Danger-Associated Molecular Patterns” (DAMPs) are released from necrotic cells. These molecules are able to initiate appropriate defense reactions associated with “sterile” inflammation and tissue repair, engaging the “Pattern-Recognition Receptors” (PRRs), such as the cell membrane and endosomal Toll-like receptors (TLRs) (21). Moreover, during viral infections, TLRs represent a host primary line of defense for pathogen sensing, due to their properties to bind diverse exogenous ligands (the “Pathogen-Associated Molecular Patterns,” PAMPs), including viral RNA (21); DAMPs and PAMPs are able to activate the FXII and the CS.
HK, complexed with PK, binds to these “surfaces”: the domain 5 is the artificial surface-binding region of HK, while the domain 6 binds PK and FXI in order to initiate the intrinsic coagulation (16). After HK binds to a surface, PK is exposed to conversion to plasma kallikrein (KAL) by FXIIa: the binding induces a conformational change in PK so that it acquires enzymatic activity and can stoichiometrically cleave HK (22). In turn, KAL cleaves and activates more FXII, in a powerful positive feedback loop (14).
In addition, a vessel wall-associated serine protease, prolyl-carboxypeptidase (PRCP), is able to activate PK to KAL independent of FXIIa (16).
The CS is involved in inflammation and in coagulation: when sufficient amounts of FXII are activated, FXIIa also activates FXI (to FXIa), and the intrinsic (or contact) coagulation pathway can start, leading to subsequent thrombin activation and fibrin formation. KAL can influence the fibrinolytic pathway by activating plasminogen into plasmin, thus leading also to fibrin degradation (23). D-dimer is a soluble fibrin degradation product deriving from the plasmin-mediated degradation of cross-linked fibrin: it can therefore be considered a biomarker of concomitant activation of both coagulation and fibrinolysis (24).
It should be remembered that plasmin can also activate FXII (25), and that FXIIa can act as a plasminogen activator too (26): it has been speculated that in the very early stages of in vivo contact activation, when PK has yet to become activated, plasmin could have an initiating role (26), however it should be noted that plasmin is hardly present in plasma as an active protease due to the very effective action of its specific inhibitor, the α2-anti-plasmin: plasmin is protected from inactivation by this inhibitor only when bound to fibrin.
The coagulation cascade can be modernly considered as a component and one of the intravascular effectors of innate immunity (immunothrombosis) (27). It is debatable whether the main physiological function of FXII is the activation of the intrinsic coagulation pathway, or if to be a component of the CS should be considered its main physiological function. For physiological hemostasis to occur, FXII auto-activation is dispensable (21). The FXII-induced intrinsic coagulation pathway is involved in pathological thrombus formation but is not associated with abnormal hemostasis: FXII-deficient subjects present in fact a normal hemostatic capacity (28, 29). Challenging the concept of the coagulation balance, targeting FXII or its activator polyphosphate can provide protection from thromboembolic diseases (and modulate immunothrombosis) without interfering with hemostasis and increasing the risk of bleeding (14, 16, 18, 30, 31).
COVID-19 is a condition clearly characterized by coagulopathy, as testified by the extensive microthrombosis reported in lung autopsies (10), and the high levels of D-dimer displayed by most patients indirectly testify the hyperactivation of both coagulation and fibrinolysis, and overwhelming immunothrombosis. It should be remembered that low-molecular weight heparins (LMWHs) have been extensively used in hospitalized COVID-19 patients for preventing venous thromboembolism and thrombotic complications, and are currently investigated in randomized controlled trials (i.e., ClinicalTrials.gov Identifier: NCT04401293).
It is interesting to note that in the physiological state FXII acts as a growth factor promoting angiogenesis and wound repair (32), but pathologically it can promote lung fibroblast proliferation leading to pulmonary fibrosis (33): COVID-19 may also evolve into pulmonary fibrosis.
The archetypal contact activation disease state is sepsis from any etiology. There is no specific data on the model of SARS-CoV-2, but data may be gathered from other viral models. It is known that herpes simplex virus type-1 (HSV1) can trigger and amplify coagulation through the contact phase and intrinsic coagulation pathway: both an inhibitor of FXIIa (corn trypsin inhibitor), and anti-FXII, anti-KAL and anti-FXI antibodies were able to inhibit HSV1-initiated clotting (34). Moreover, PK and FXII levels are significantly lower in patients with dengue hemorrhagic fever (DHF), probably due to activation and consumption (35).
It has been mentioned that CS is part of the innate immune system: it is known that non-structural protein 3 (nsp3) of coronaviruses results able to block the host innate immune response (36), and other nsp play a role in evading host recognition (37).
The Kallikrein–Kinin System
The Kallikrein–Kinin System (KKS) is mainly a host inflammatory response mechanism, and although KKS and CS overlap and interact in the intravascular compartment (plasma KAL is part of both systems), the use of the two terms has different implications. Activation of KKS finally leads to the liberation of bradykinin (BK), and plays an essential role in inflammation, but not in blood coagulation (16).
Upon activation by FXIIa, KAL cleaves HK, releasing from its domain 4 the nonapeptide bradykinin (BK-1-9 or BK) (38); BK is converted by a carboxypeptidase to [des-Arg9]-BK (BK-1-8 or DABK), an active metabolite (39). During inflammation, plasmin potentiates the cleavage of HK by KAL, thus enhancing BK production (40).
BK and DABK bind to two pharmacologically distinct G protein-coupled receptors: the bradykinin B2 receptor (B2R), whose ligand is BK, and the B1 receptor (B1R), whose main agonist is DABK (39). The B2R is widely and constitutively expressed in mammalian cells (e.g., endothelial and smooth muscle cells), whereas the B1R is mostly inducible under the effect of cytokines during infections and immunopathology (41).
After binding through its B2R, BK activates signaling pathways resulting in increased vascular permeability, vasodilation, edema formation, hypotension, pain, fever (14): all typical clinical features of COVID-19. BK is one of the most potent vasodilatory substances in humans: it is known that the BK-mediated angioedema is responsible for a very high percentage of serious morbidity and mortality (42). BK is also one of the most potent inflammatory mediators, able to stimulate the production of superoxide radicals and nitric oxide and to modulate the mobilization and release of histamine, arachidonic acid, prostaglandin E2, prostacyclin, pro-inflammatory interleukin-1, and tumor necrosis factor (TNF)-alpha (41). Thereafter, BK has shown to increase IL-6 production via B2R in colorectal cancer cell (43), and the B2R-antagonist icatibant was able to inhibit the BK-induced IL-6 release (44). This effect is interesting: also chloroquine, that has been extensively used and investigated for COVID-19 treatment, was able to reduce IL-6 production by monocytes/macrophages (45). BK also stimulates tissue plasminogen activator (t-PA) release from human endothelium through a B2R-dependent mechanism: this effect was significantly reduced in smokers (46). A strong link between KKS and the renin–angiotensin system (RAS) is testified by the fact that B2R forms homo- and heterodimers with several receptors of the RAS, that are important for some physiologic functions, including thrombosis risk regulation. The B2R also complexes with endothelial cell nitric oxide synthase, while the B1R couples with inducible nitric oxide synthase (16).
B1R mediates several responses including vasodilation, hypotension, and increased vascular permeability (41): all typical features of COVID-19.
Human kallikreins have been detected in many tissues (47), including the epithelia of the upper and lower respiratory tract: there are in fact two classical pathways for the generation of kinins, the plasma and the tissue KKS. As the substrate of plasma KAL is HK (leading to BK), the substrate of tissue kallikreins is the low-molecular-weight kininogen, leading to formation of the decapeptide Lys-bradykinin or kallidin (KD). A carboxypeptidase leads to the formation of the active metabolite [des-Arg10]-KD (DAKD) from KD. KD mainly binds to B2R, while B1R has a high affinity for DAKD (48).
It is not known if SARS-CoV-2 infection is specifically associated with kinins dysregulation, but this happens in several viral models. Low levels of HK have been observed in DHF patients, perhaps due to proteolysis and generation of BK (49). Taylor et al. (50) previously described a novel mechanism of hantavirus-induced vascular leakage involving activation of the KKS, showing that incubation of FXII, PK, and HK with hantavirus-infected endothelial cells results in increased cleavage of HK, higher enzymatic activities of FXII/KAL and increased liberation of BK, that dramatically increased cell permeability. Furthermore, the alterations in permeability could be prevented using inhibitors directly blocking BK binding, the activity of FXII, or the activity of KAL (50). Infection of guinea pigs by nasal instillation of parainfluenza-3 virus induced airway hyperreactivity and influx of inflammatory cells into lung tissues, and these responses were attenuated by B2R-antagonists (51). Tissue kallikrein 1 was shown to intervene early during influenza infection, enhancing the antiviral defense, and the decreased expression observed in patients with chronic obstructive pulmonary disease could contribute to the less favorable evolution of influenza in this group (52).
Therefore, the KKS appears to be involved in vascular leakage and inflammatory response observed during different viral infections (14). We can speculate that modulation of the CS and the KKS may limit the evolution towards a frankly dysregulated host response also in SARS-CoV-2 infection.
Moreover, a role of BK in COVID-19 pathogenesis is suggested by several clinical features and symptoms observed in patients: given the close interconnection with the RAS, these aspects will be further discussed in the next chapter.
The Renin–Angiotensin System (RAS) and the Interplay With KKS
The renin–angiotensin system (RAS) is classically known for its effects on the cardiovascular system and fluid homeostasis, but it has become clear that the RAS is present in many tissues, where evidently has a role to play (53).
Starting from angiotensinogen, whose primary source is the liver, the RAS leads to the production of the multi-functional peptide hormone Angiotensin II (Ang II). Renin first catalyzes the cleavage of the peptide Angiotensin I (Ang I) from the N-terminus of the angiotensinogen molecule, then, sequentially, the dicarboxyl-peptidase angiotensin converting enzyme (ACE) removes two amino-acids from the C-terminus of Ang I to form Ang II (54). Ang II exerts its main functions binding to two specific G-protein coupled receptors: the ATII type 1 receptor (AT1R) and ATII type 2 receptor (AT2R) (54).
ACE is present in many tissues and is particularly abundant on the endothelium of the lungs: it is mainly anchored to the plasma membrane through a single trans-membrane domain, but a soluble form has also been described (53).
Apart from its well-known role as a peptidyl-dipeptidase forming Ang II, ACE is also described as a kininase II, able to inactivate BK, as well as KD (53). The affinity of ACE appears to be higher for BK than for Ang I, suggesting that ACE-inhibition may really involve the BK degradation more than the Ang II production (55). BK-evoked sensitization of airway sensory nerves is believed to be the main mechanism for ACE-inhibitor-induced dry cough (56): considering that dry cough is very frequently observed in COVID-19 patients, this pathway could in part explain the pathogenesis of this symptom. Additionally, a role of the BK has been hypothesized also for gustatory and olfactory dysfunctions (7); again, ACE-inhibitors can cause olfactory dysfunction (57).
In addition, over the last 20 years, knowledge of the biology and physiology of another enzyme besides ACE, the angiotensin converting enzyme 2 (ACE2), has accumulated (58): ACE2 is widely expressed, including type 2 alveolar epithelial cells, endothelial cells and enterocytes (10, 58).
Both ACE and ACE2 act as zinc metallopeptidases (ACE2 only acts as a carboxypeptidase), but differ for substrate specificities, displaying counterbalancing roles in the RAS.
ACE2 converts Ang I into Angiotensin (1-9), and Ang II into Angiotensin (1-7); unlike ACE, ACE2 does not cleave BK, and is insensitive to conventional ACE-inhibitors (58). Ang II can be converted to angiotensin (1-7) also by PRCP in the low-pH areas of the kidney (59).
Angiotensin 1-7, acting on Mas receptor, exerts vasodilatory effects, thus diminishing and opposing the vasoconstrictor effect, mainly AT1R-mediated, of Ang II; moreover, it displays anti-fibrotic, anti-oxidant and anti-hypertrophic protective properties (58).
Therefore, ACE2 expression seems to protect from lung injury. Sodhi et al. (39) observed that a reduction in pulmonary ACE2 activity contributes to the pathogenesis of lung inflammation, resulting in prompt onset of neutrophil infiltration and more severe inflammation. Imai et al. (60) showed that the loss of ACE2 expression in acute lung injury leads to leaky pulmonary blood vessels through AT1R stimulation, while the AT2R protects against lung injury during sepsis. Angiotensin 1-9 has shown beneficial biological effects via the AT2R, resulting in protective effects on cardiac and vascular remodeling (58) and against pulmonary arterial hypertension, inflammation and fibrosis (61).
SARS-CoV-2 binds ACE2 for host cell entry, through the binding of its major spike glycoprotein (S1) to the N-terminal region of the receptor (62); chloroquine seems to interfere with ACE2 glycosylation, thus possibly preventing SARS-CoV-2 binding to target cells (63). Following binding with SARS-CoV-2, a loss of ACE2 function occurs, driven by endocytosis and activation of proteolytic cleavage and processing (58, 62). It can be assumed that this downregulation may be involved in the pathophysiology of COVID-19 and its manifestations. DABK is a substrate of ACE2, and the attenuation of ACE2 activity leads to impaired DABK inactivation and thus to enhanced B1R signaling.
In a mouse model, the lack of ACE2 function with consequent accumulation of Ang II, through a negative feedback loop, resulted in a secondary reduction of ACE activity (at the molecular level, Ang II downregulates renal ACE gene and enzymatic activity levels, as well as renin gene expression): these crosstalk effects between ACE2 and ACE appeared to be sex-dependent and more evident in males (64). It is known that COVID-19 affects male patients in a larger percentage (65) and with worse outcomes (66). The reduced activity of ACE is also expected to result in further BK accumulation. Moreover, it has been recognized in vitro that Ang II, through the stimulation of AT2R, is associated with increased expression of PRCP, leading to a KAL-mediated increased formation of BK (67).
Since SARS-CoV-2 binds to ACE2 receptors to enter host cells, and intravenous infusion of ACE-inhibitors and angiotensin receptor blockers (ARBs) in experimental animal models increased the amount of ACE2 receptors in the cardiopulmonary circulation, it has been speculated that patients chronically taking these drugs may be at increased risk of worse outcomes from COVID-19 (68). However, to date, there are no conclusive data demonstrating beneficial or adverse outcomes with background use of ACE-inhibitors, ARBs or other RAS antagonists among COVID-19 patients with a history of cardiovascular disease treated with these drugs (69–71). For the pathophysiological considerations previously made, however, in our opinion, it remains debatable if ACE-inhibitors, for their action on BK, should be temporarily suspended during the acute phase of illness, especially in the case of low blood pressure values.
Finally, it is interesting to observe that in an experimental malaria model (Plasmodium parasites during blood stages release kinins), exposure to captopril (an ACE-inhibitor that leads to the reduction of BK degradation) resulted in death in mice, while the concomitant administration of chloroquine protected them. B1R-knockout mice presented a significant reduction of survival when compared with wild-type mice, unlike the B2R-knockout ones (72). In this inflammation/infection model, chloroquine-induced upregulation of B1R expression proved protective: the full meaning of this result is unclear but might indicate that the selective inhibition of B2R could represent a rational modulation of dysregulated BK pathway during infection. Could the same considerations apply to COVID-19?
C1-Inhibitor and Its Potential Role in Viral Infections
Hereditary angioedema (HAE) represents the archetypal KKS disorder and can be due to a deficiency of C1-INH (Type 1), an abnormal C1-INH molecule (Type 2), or a gain-in-function of FXII with consequent plasma C1-INH consumption (Type 3) (73). Thrombin formation is not considered a feature of this disorder: even if patients with acute attacks have elevated D-dimer levels, they do not display an increased thrombotic risk (74). Clinical pictures of activation of CS and KKS without (such as HAE) and with thrombin formation (such as sepsis) can be in fact distinguished (16): COVID-19 evidently falls into the latter group.
C1-INH is a protein able to inhibit multiple serine proteases involved in the CS, KKS, Complement, Fibrinolysis, and Coagulation Systems: through the inhibition of C1r and C1s subcomponents of C1 complex, FXIIa, and KAL, C1-INH prevents the activation of CS and KKS. The N-terminal end (non-serpin domain) confers to C1-INH the capacity to bind lipopolysaccharides and E-selectin: owing to this moiety, C1-INH can also intervene in the regulation of inflammatory reactions (75). Moreover, C1-INH inhibits selectin-mediated leukocyte adhesion, regardless of its protease inhibitory activity (76).
Wygrecka et al. (77) showed that C1-INH is able to inhibit the cytotoxic activity of extracellular histones (that play a determining role in pulmonary injury leading to ARDS) and the release of several cytokines, such as TNF-alpha, IL-1b, and IL-6. It is interesting to note that accumulation of extracellular histones has been detected during infection due to influenza virus, and anti-histone antibodies have led to a marked decrease in the lung damage consisting of widespread pulmonary microvascular thrombosis, endothelial necrosis, hemorrhagic effusions and edema (78). These histopathological findings are observed also in COVID-19, although there are several differences compared to the influenza model (10) whose discussion goes beyond the scope of this review. Although there is actually no specific evidence regarding SARS-CoV-2 infection, it can be assumed that C1-INH might have beneficial effects also in this case, both through the inhibition of the CS and KKS, especially regarding the BK-induced vascular leakage and edema formation, and its anti-inflammatory activity mediated by inhibition of complement activation and histone toxicity.
Figure 1 shows the interconnection between the different human proteolytic systems operating in the vasculature, proposing a picture of an integrated host response to SARS-CoV-2 infection.
The Contact System (CS) as a plausible link between Coagulation/Fibrinolysis, Kallikrein–Kinin and Renin–Angiotensin Systems in the pathobiology of SARS-CoV-2 infection (based on the hypothesis of the activation of FXII and HK by SARS-CoV-2, directly or following cell invasion and/or damage). Black arrows indicate enzymatic activation, while red lines represent inhibition or degradation/downregulation. The sequences of black arrows imply the involvement of other molecules not shown to activate the molecule indicated by the final arrow. Dashed red lines imply the involvement of other molecules not shown to inhibit the molecule indicated at the end of the line. Gray arrows indicate the transformation of one molecule into another. ACE, angiotensin converting enzyme; ACE2, angiotensin converting enzyme 2; Ang I, Angiotensin I; Ang II, Angiotensin II; Ang (1-7), Angiotensin 1-7; Ang (1-9), Angiotensin 1-9; AT1R, ATII type 1 receptor; AT2R, ATII type 2 receptor; B1R, B1 receptor; B2R, B2 receptor; BK, bradykinin; C1-INH, C1-inhibitor; DABK, [des-Arg9]-BK; FXI, coagulation factor XI; FXII, coagulation factor XII; HK, high-molecular-weight kininogen; IL-6, interleukin-6; KAL, plasma kallikrein; MasR, Mas receptor; PK, plasma prekallikrein; t-PA, tissue plasminogen activator.
Table 1 lists some available drugs potentially representing effective therapeutic approaches in COVID-19, by modulation of the pathways and systems whose involvement has been hypothesized in its pathogenesis.
Potential therapeutic approaches able to modulate the systems involved in the pathogenesis of COVID-19.
Drug
Mechanism of action
Labeled indication
Potential role in COVID-19
Contact system
C1-inhibitor
Inhibition of CS, Coagulation/Fibrinolytic systems, complement and KKS
Treatment and prevention of angioedema attacks in hereditary angioedema
Inhibition of all systems involved
Anti-factor XII (FXII) antibody
Monoclonal antibody inhibiting FXII
Phase II study ongoing; phase III study planned. Studied indication: prevention of angioedema attacks
Inhibition of FXII and consequently of CS and KKS
Prevention and treatment of thrombosis, without increasing bleeding risk (Action on Coagulation system)
Kallikrein–kinin system
Icatibant
Bradykinin type 2 receptor (B2R)-antagonist
Treatment of acute attacks in hereditary angioedema
Inhibition of pro-inflammatory and vasoactive actions of BK
Lanadelumab
Monoclonal antibody inhibiting plasma KAL
Prevention of attacks of hereditary angioedema
Inhibition of KKS and BK generation
Ecallantide
Inhibition of plasma KAL
Treatment of acute attacks in hereditary angioedema
Inhibition of KKS and BK generation
Coagulation system
Low-Molecular-Weight Heparin/Fondaparinux
Catalyzed inhibition of activated coagulation factor X by antithrombin
Prevention and treatment of venous thromboembolism
Prevention and treatment of thrombosis and pulmonary embolism (consider bleeding risk)
Anti-factor XII (FXII) antibody
See above
See above
See above
Discussion and Conclusion
The hypothesis of the involvement of different human proteolytic defense systems operating in the vasculature in the pathogenesis of COVID-19 has recently been proposed also by other authors. van de Veerdonk et al. (79) hypothesized that a kinin-dependent local lung angioedema via B1R and eventually B2R is an important feature of COVID-19 and proposed that blocking the B2R and inhibiting plasma KAL activity might be beneficial in early disease, preventing ARDS. Roche and Roche (80) emphasized the pivotal role of BK and DABK, suggesting that the B2R-antagonist icatibant might be able to interrupt the dysregulated pathway, thereby improving clinical outcomes. Colarusso et al. (23) proposed instead to block pharmacologically the KKS upstream of the BK, by means of lanadelumab. Regarding B1R-antagonists, several companies have in past developed orally available molecules, and some of these entered phase II clinical trials, but none have been developed further; possible reasons for this failure may be inefficacy in humans due to species differences, or human specific adverse effects (48).
In our opinion, the rational for modulating these pathways is strong but to date few data for COVID-19 are available. However, the exceptional nature of this pandemic and the lack of effective interventions of proven efficacy makes it necessary to explore further therapeutic possibilities.
Understanding the pathogenetic mechanisms underlying COVID-19 is crucial for the development of new effective therapeutic approaches modulating the CS, the KKS, the RAS and the Coagulation/Fibrinolysis System. The KKS inhibitors lanadelumab and ecallantide, licensed for the treatment of HAE, and several oral KKS inhibitors in clinical development, should be assessed for their efficacy in the treatment of patients with COVID-19. The same holds for icatibant, a selective B2R antagonist used for on demand treatment in HAE. Other promising CS-linked targets or mediators that should be explored in COVID-19 include anti-FXIIa antibodies and C1-INH. This pathophysiological therapeutic approach could be of great value also for other viral infections.
Data Availability Statement
Publicly available datasets were analyzed in this study. This data can be found at the appropriate doi link of every cited article.
Author Contributions
SM, AZ, RS, FI, and CT: conceptualization. SM, AZ, RS, FI, CS, AR, and CT: formal analysis. AZ: funding acquisition. SM, AZ, and CT: investigation and project administration. SM, AZ, RS, FI, AR, CS, and CT: methodology and resources. SM, AZ, AR, and CT: writing – original draft preparation and writing – review and editing. All authors contributed to the article and approved the submitted version.
Conflict of Interest
AZ received speaker/consultancy fees and/or was a member of medical/advisory boards for CSL Behring, Shire/Takeda, and SOBI. All the authors declare that they have no financial competing interests about the topic of this article, except for the publishing support and journal styling services, that were provided by SEEd Medical Publishers and funded by CSL Behring, Italy.
Funding. The CSL Behring funded the publishing support and journal styling services, but had no role in the conduct of the research, preparation of the article, in study design, in the collection, analysis and interpretation of data, in the writing of the report, and in the decision to submit the article for publication.
We acknowledge SEEd Medical Publishers, that provided publishing support and journal styling services.
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(2018) 188:135–48. 10.1016/j.ajpath.2017.09.014\n29107075van de VeerdonkFLNeteaMGvan DeurenMvan der MeerJWde MastOBrüggemannRJ\nKallikrein-kinin blockade in patients With COVID-19 to prevent acute respiratory distress syndrome.\nElife. (2020) 9:e57555. 10.7554/eLife.57555\n32338605RocheJARocheR.\nA hypothesized role for dysregulated bradykinin signaling in COVID-19 respiratory complications.\nFASEB J. (2020) 00:1–5. 10.1096/fj.202000967\n32359101AbbreviationsACE
Angiotensin-Converting Enzyme
ACE2
Angiotensin-Converting Enzyme 2
Ang I
Angiotensin I
Ang II
Angiotensin II
Ang (1-7)
Angiotensin 1-7
Ang (1-9)
Angiotensin 1-9
ARB
angiotensin receptor blocker
ARDS
Acute respiratory distress syndrome
AT1R
Angiotensin II type 1 receptor
AT2R
Angiotensin II type 2 receptor
BK
Bradykinin
B1R
Bradykinin B1 receptor
B2R
Bradykinin B2 receptor
COVID-19
COronaVIrus Disease 2019
CS
Contact System
C1-INH
C1-inhibitor
DABK
[des-Arg9]-Bradykinin
DAKD
[des-Arg10]-Kallidin
DAMPs
Danger-Associated Molecular Patterns
DHF
dengue hemorrhagic fever
FXI
Factor XI
FXII
Factor XII
HK
High-molecular-weight Kininogen
HSV1
Herpes simplex virus type-1
IL-6
Interleukin-6
KAL
Kallikrein
KD
Kallidin
KKS
Kallikrein–Kinin System
LMWH
Low-Molecular Weight Heparin
PAMPs
Pathogen-Associated Molecular Patterns
PK
Prekallikrein
PRCP
Prolyl-carboxypeptidase
PRR
Pattern-Recognition Receptor
RAS
Renin–Angiotensin System
SARS-CoV-2
Severe Acute Respiratory Syndrome – Coronavirus – 2
TLR
Toll-like receptor
TNF-alpha
tumor necrosis factor-alpha
t-PA
tissue-Plasminogen Activator.
\n"],"string":"[\n \"\\nFront ImmunolFront ImmunolFront. Immunol.Frontiers in Immunology1664-3224Frontiers Media S.A.32849666PMC743213810.3389/fimmu.2020.02014ImmunologyPerspectiveUnderstanding the Pathophysiology of COVID-19: Could the Contact System Be the Key?MeiniSimone1*ZanichelliAndrea2SbrojavaccaRodolfo3IuriFederico4RobertsAnna Teresa5SuffrittiChiara2TasciniCarlo31Internal Medicine Unit, Azienda USL Toscana Centro, Santa Maria Annunziata Hospital, Florence, Italy2General Medicine Unit, ASST Fatebenefratelli Sacco, Ospedale Luigi Sacco-Università degli Studi di Milano, Milan, Italy3Infectious Diseases Clinic, Santa Maria Misericordia Hospital, Università degli Studi di Udine, Udine, Italy4Department of Emergency, Santa Maria Misericordia Hospital, Università degli Studi di Udine, Udine, Italy5Medical Department, Azienda USL Toscana Nord-Ovest, Pisa, Italy
Edited by: Gennady Bocharov, Institute of Numerical Mathematics (RAS), Russia
Reviewed by: Klaus T. Preissner, University of Giessen, Germany; Zhenlong Liu, McGill University, Canada
This article was submitted to Viral Immunology, a section of the journal Frontiers in Immunology
11820202020118202011201416420202472020Copyright © 2020 Meini, Zanichelli, Sbrojavacca, Iuri, Roberts, Suffritti and Tascini.2020Meini, Zanichelli, Sbrojavacca, Iuri, Roberts, Suffritti and TasciniThis is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
To date the pathophysiology of COVID-19 remains unclear: this represents a factor determining the current lack of effective treatments. In this paper, we hypothesized a complex host response to SARS-CoV-2, with the Contact System (CS) playing a pivotal role in innate immune response. CS is linked with different proteolytic defense systems operating in human vasculature: the Kallikrein–Kinin (KKS), the Coagulation/Fibrinolysis and the Renin–Angiotensin (RAS) Systems. We investigated the role of the mediators involved. CS consists of Factor XII (FXII) and plasma prekallikrein (complexed to high-molecular-weight kininogen-HK). Autoactivation of FXII by contact with SARS-CoV-2 could lead to activation of intrinsic coagulation, with fibrin formation (microthrombosis), and fibrinolysis, resulting in increased D-dimer levels. Activation of kallikrein by activated FXII leads to production of bradykinin (BK) from HK. BK binds to B2-receptors, mediating vascular permeability, vasodilation and edema. B1-receptors, binding the metabolite [des-Arg9]-BK (DABK), are up-regulated during infections and mediate lung inflammatory responses. BK could play a relevant role in COVID-19 as already described for other viral models. Angiotensin-Converting-Enzyme (ACE) 2 displays lung protective effects: it inactivates DABK and converts Angiotensin II (Ang II) into Angiotensin-(1-7) and Angiotensin I into Angiotensin-(1-9). SARS-CoV-2 binds to ACE2 for cell entry, downregulating it: an impaired DABK inactivation could lead to an enhanced activity of B1-receptors, and the accumulation of Ang II, through a negative feedback loop, may result in decreased ACE activity, with consequent increase of BK. Therapies targeting the CS, the KKS and action of BK could be effective for the treatment of COVID-19.
Starting in December 2019 in Wuhan (Hubei Province, China), a novel coronavirus, designated SARS-CoV-2, has caused an international outbreak of a respiratory illness (COVID-19), rapidly evolving into a pandemic. The clinical spectrum of SARS-CoV-2 infection varies from asymptomatic or self-limiting mild forms, occurring in most cases, to severe progressive pneumonia with acute respiratory distress syndrome (ARDS), and death. In a yet to be defined percentage of cases, after about one week, there is a sudden and unpredictable worsening of clinical conditions (1).
At present, there is no vaccine or pharmacological treatments of proven efficacy for COVID-19 (2) and further investigation on effective drugs is required to face the current pandemic. One factor determining the lack of effective treatments is that the pathophysiology of COVID-19 remains largely unclear.
In this review, we try to address the complex link between the pathophysiology of COVID-19 and the different proteolytic defense systems operating in human vasculature, investigating the role of the mediators involved and speculating on the possibility of pharmacological modulation.
Clinical and Laboratory Findings in Patients With COVID-19
COVID-19 is mainly a respiratory illness, but a wide variety of clinical manifestations have been described, including the central nervous (3) and the digestive (4) systems. Most symptomatic COVID-19 patients display manifestations such as fever (98.6%), dry cough (59.4%), dyspnea (31.2%), myalgias (34.8%), sore throat (17.4%), diarrhea (10.1%), and other (5). Olfactory and gustatory dysfunctions are common symptoms, occurring in about 50% of patients and often presenting early in the clinical course (6, 7). Low blood pressure values are frequently observed in hospitalized patients: Wang et al. (5) in their cohort reported a median of mean arterial pressure values of 90 mmHg despite 31% of patients having a history of hypertension.
The predominant findings of lung Computed Tomography are images of bilateral, peripheral and basal ground-glass opacities, crazy-paving pattern, consolidations, often in association (8), and ultrasonography precociously demonstrates a lung interstitial syndrome (9). These findings are consistent with a lung injury characterized by increased permeability, leaky blood vessels and edema, and have been confirmed by the histopathological data obtained from the lungs of patients who died from COVID-19, showing diffuse alveolar damage with necrosis of alveolar lining cells, pneumocyte type 2 hyperplasia, linear intra-alveolar fibrin deposition and increased lung weight due to edema; in addition, thrombi in pulmonary arteries with a diameter of 1–2 mm, without complete luminal obstruction, and massive alveolar capillary microthrombi were observed (10).
Concerning laboratory findings, an increase of lactate dehydrogenase levels and lymphocytopenia are common. Elevated levels of serum ferritin, as commonly found in viral infections, are detected in most patients (11). Levels of interleukin-6 (IL-6) are typically in the upper limit of the reference range and appear to correlate with disease severity (12, 13). IL-6-induced high levels of C-reactive protein (CRP) are typically more related to bacterial rather than to viral infections (11): in COVID-19 patients CRP values are very variable. Even in non-critical patients, high D-dimer levels are found in most patients. Prothrombin time is often slightly increased.
Levels of inflammatory and coagulation biomarkers vary considerably among patients with COVID-19, suggesting the existence of different biochemical/clinical phenotypes, in which the predominant systems involved and the inflammatory and coagulopathy response patterns differ.
From a pathogenetic point of view, it is clear that a link (to date not yet fully clarified) exists between the clinical manifestations and alterations of the inflammatory and coagulation systems, and that these different systems are only apparently unrelated.
The Role of the Contact System in the Pathophysiology of COVID-19
The Contact System (CS) is part of the innate immune system and of inflammatory response mechanism against artificial material, misfolded and foreign proteins and microorganisms (including viruses), found in the intravascular compartment. It remains to be clarified whether contact factors bind and activate directly on the viral surface or on infected cells (14).
The main proteins of the CS are the Factor XII (FXII), the prekallikrein (PK) and the high-molecular-weight kininogen (HK). These proteins are produced by the liver and circulate as zymogens into the bloodstream. Virtually all plasma PK circulates in complex with HK.
Auto-activation of FXII to FXIIa by contact with a variety of artificial and biological negatively charged surfaces, including microorganisms, gives rise to CS cascade. Biological substances with the potential to support its activation include: DNA, RNA, polyphosphates retained on activated platelet surface, aggregated proteins, neutrophil extracellular traps (NETs) and ferritin (15–19). Kannemeier et al. (20) presented evidence that different forms of eukaryotic and prokaryotic RNA serve as promoters of blood coagulation, enhancing auto-activation of proteases of the CS, such as FXII and FXI. As the extracellular RNA derived from damaged or necrotic cells represented a “foreign surface” able to activate the CS, it could be speculated that the same process may be initiated by viral RNA. In addition, at times of cellular stress (i.e., hypoxia, hyperthermia, oxygen radical production) such as that observed during COVID-19, endogenous “alarmins” named “Danger-Associated Molecular Patterns” (DAMPs) are released from necrotic cells. These molecules are able to initiate appropriate defense reactions associated with “sterile” inflammation and tissue repair, engaging the “Pattern-Recognition Receptors” (PRRs), such as the cell membrane and endosomal Toll-like receptors (TLRs) (21). Moreover, during viral infections, TLRs represent a host primary line of defense for pathogen sensing, due to their properties to bind diverse exogenous ligands (the “Pathogen-Associated Molecular Patterns,” PAMPs), including viral RNA (21); DAMPs and PAMPs are able to activate the FXII and the CS.
HK, complexed with PK, binds to these “surfaces”: the domain 5 is the artificial surface-binding region of HK, while the domain 6 binds PK and FXI in order to initiate the intrinsic coagulation (16). After HK binds to a surface, PK is exposed to conversion to plasma kallikrein (KAL) by FXIIa: the binding induces a conformational change in PK so that it acquires enzymatic activity and can stoichiometrically cleave HK (22). In turn, KAL cleaves and activates more FXII, in a powerful positive feedback loop (14).
In addition, a vessel wall-associated serine protease, prolyl-carboxypeptidase (PRCP), is able to activate PK to KAL independent of FXIIa (16).
The CS is involved in inflammation and in coagulation: when sufficient amounts of FXII are activated, FXIIa also activates FXI (to FXIa), and the intrinsic (or contact) coagulation pathway can start, leading to subsequent thrombin activation and fibrin formation. KAL can influence the fibrinolytic pathway by activating plasminogen into plasmin, thus leading also to fibrin degradation (23). D-dimer is a soluble fibrin degradation product deriving from the plasmin-mediated degradation of cross-linked fibrin: it can therefore be considered a biomarker of concomitant activation of both coagulation and fibrinolysis (24).
It should be remembered that plasmin can also activate FXII (25), and that FXIIa can act as a plasminogen activator too (26): it has been speculated that in the very early stages of in vivo contact activation, when PK has yet to become activated, plasmin could have an initiating role (26), however it should be noted that plasmin is hardly present in plasma as an active protease due to the very effective action of its specific inhibitor, the α2-anti-plasmin: plasmin is protected from inactivation by this inhibitor only when bound to fibrin.
The coagulation cascade can be modernly considered as a component and one of the intravascular effectors of innate immunity (immunothrombosis) (27). It is debatable whether the main physiological function of FXII is the activation of the intrinsic coagulation pathway, or if to be a component of the CS should be considered its main physiological function. For physiological hemostasis to occur, FXII auto-activation is dispensable (21). The FXII-induced intrinsic coagulation pathway is involved in pathological thrombus formation but is not associated with abnormal hemostasis: FXII-deficient subjects present in fact a normal hemostatic capacity (28, 29). Challenging the concept of the coagulation balance, targeting FXII or its activator polyphosphate can provide protection from thromboembolic diseases (and modulate immunothrombosis) without interfering with hemostasis and increasing the risk of bleeding (14, 16, 18, 30, 31).
COVID-19 is a condition clearly characterized by coagulopathy, as testified by the extensive microthrombosis reported in lung autopsies (10), and the high levels of D-dimer displayed by most patients indirectly testify the hyperactivation of both coagulation and fibrinolysis, and overwhelming immunothrombosis. It should be remembered that low-molecular weight heparins (LMWHs) have been extensively used in hospitalized COVID-19 patients for preventing venous thromboembolism and thrombotic complications, and are currently investigated in randomized controlled trials (i.e., ClinicalTrials.gov Identifier: NCT04401293).
It is interesting to note that in the physiological state FXII acts as a growth factor promoting angiogenesis and wound repair (32), but pathologically it can promote lung fibroblast proliferation leading to pulmonary fibrosis (33): COVID-19 may also evolve into pulmonary fibrosis.
The archetypal contact activation disease state is sepsis from any etiology. There is no specific data on the model of SARS-CoV-2, but data may be gathered from other viral models. It is known that herpes simplex virus type-1 (HSV1) can trigger and amplify coagulation through the contact phase and intrinsic coagulation pathway: both an inhibitor of FXIIa (corn trypsin inhibitor), and anti-FXII, anti-KAL and anti-FXI antibodies were able to inhibit HSV1-initiated clotting (34). Moreover, PK and FXII levels are significantly lower in patients with dengue hemorrhagic fever (DHF), probably due to activation and consumption (35).
It has been mentioned that CS is part of the innate immune system: it is known that non-structural protein 3 (nsp3) of coronaviruses results able to block the host innate immune response (36), and other nsp play a role in evading host recognition (37).
The Kallikrein–Kinin System
The Kallikrein–Kinin System (KKS) is mainly a host inflammatory response mechanism, and although KKS and CS overlap and interact in the intravascular compartment (plasma KAL is part of both systems), the use of the two terms has different implications. Activation of KKS finally leads to the liberation of bradykinin (BK), and plays an essential role in inflammation, but not in blood coagulation (16).
Upon activation by FXIIa, KAL cleaves HK, releasing from its domain 4 the nonapeptide bradykinin (BK-1-9 or BK) (38); BK is converted by a carboxypeptidase to [des-Arg9]-BK (BK-1-8 or DABK), an active metabolite (39). During inflammation, plasmin potentiates the cleavage of HK by KAL, thus enhancing BK production (40).
BK and DABK bind to two pharmacologically distinct G protein-coupled receptors: the bradykinin B2 receptor (B2R), whose ligand is BK, and the B1 receptor (B1R), whose main agonist is DABK (39). The B2R is widely and constitutively expressed in mammalian cells (e.g., endothelial and smooth muscle cells), whereas the B1R is mostly inducible under the effect of cytokines during infections and immunopathology (41).
After binding through its B2R, BK activates signaling pathways resulting in increased vascular permeability, vasodilation, edema formation, hypotension, pain, fever (14): all typical clinical features of COVID-19. BK is one of the most potent vasodilatory substances in humans: it is known that the BK-mediated angioedema is responsible for a very high percentage of serious morbidity and mortality (42). BK is also one of the most potent inflammatory mediators, able to stimulate the production of superoxide radicals and nitric oxide and to modulate the mobilization and release of histamine, arachidonic acid, prostaglandin E2, prostacyclin, pro-inflammatory interleukin-1, and tumor necrosis factor (TNF)-alpha (41). Thereafter, BK has shown to increase IL-6 production via B2R in colorectal cancer cell (43), and the B2R-antagonist icatibant was able to inhibit the BK-induced IL-6 release (44). This effect is interesting: also chloroquine, that has been extensively used and investigated for COVID-19 treatment, was able to reduce IL-6 production by monocytes/macrophages (45). BK also stimulates tissue plasminogen activator (t-PA) release from human endothelium through a B2R-dependent mechanism: this effect was significantly reduced in smokers (46). A strong link between KKS and the renin–angiotensin system (RAS) is testified by the fact that B2R forms homo- and heterodimers with several receptors of the RAS, that are important for some physiologic functions, including thrombosis risk regulation. The B2R also complexes with endothelial cell nitric oxide synthase, while the B1R couples with inducible nitric oxide synthase (16).
B1R mediates several responses including vasodilation, hypotension, and increased vascular permeability (41): all typical features of COVID-19.
Human kallikreins have been detected in many tissues (47), including the epithelia of the upper and lower respiratory tract: there are in fact two classical pathways for the generation of kinins, the plasma and the tissue KKS. As the substrate of plasma KAL is HK (leading to BK), the substrate of tissue kallikreins is the low-molecular-weight kininogen, leading to formation of the decapeptide Lys-bradykinin or kallidin (KD). A carboxypeptidase leads to the formation of the active metabolite [des-Arg10]-KD (DAKD) from KD. KD mainly binds to B2R, while B1R has a high affinity for DAKD (48).
It is not known if SARS-CoV-2 infection is specifically associated with kinins dysregulation, but this happens in several viral models. Low levels of HK have been observed in DHF patients, perhaps due to proteolysis and generation of BK (49). Taylor et al. (50) previously described a novel mechanism of hantavirus-induced vascular leakage involving activation of the KKS, showing that incubation of FXII, PK, and HK with hantavirus-infected endothelial cells results in increased cleavage of HK, higher enzymatic activities of FXII/KAL and increased liberation of BK, that dramatically increased cell permeability. Furthermore, the alterations in permeability could be prevented using inhibitors directly blocking BK binding, the activity of FXII, or the activity of KAL (50). Infection of guinea pigs by nasal instillation of parainfluenza-3 virus induced airway hyperreactivity and influx of inflammatory cells into lung tissues, and these responses were attenuated by B2R-antagonists (51). Tissue kallikrein 1 was shown to intervene early during influenza infection, enhancing the antiviral defense, and the decreased expression observed in patients with chronic obstructive pulmonary disease could contribute to the less favorable evolution of influenza in this group (52).
Therefore, the KKS appears to be involved in vascular leakage and inflammatory response observed during different viral infections (14). We can speculate that modulation of the CS and the KKS may limit the evolution towards a frankly dysregulated host response also in SARS-CoV-2 infection.
Moreover, a role of BK in COVID-19 pathogenesis is suggested by several clinical features and symptoms observed in patients: given the close interconnection with the RAS, these aspects will be further discussed in the next chapter.
The Renin–Angiotensin System (RAS) and the Interplay With KKS
The renin–angiotensin system (RAS) is classically known for its effects on the cardiovascular system and fluid homeostasis, but it has become clear that the RAS is present in many tissues, where evidently has a role to play (53).
Starting from angiotensinogen, whose primary source is the liver, the RAS leads to the production of the multi-functional peptide hormone Angiotensin II (Ang II). Renin first catalyzes the cleavage of the peptide Angiotensin I (Ang I) from the N-terminus of the angiotensinogen molecule, then, sequentially, the dicarboxyl-peptidase angiotensin converting enzyme (ACE) removes two amino-acids from the C-terminus of Ang I to form Ang II (54). Ang II exerts its main functions binding to two specific G-protein coupled receptors: the ATII type 1 receptor (AT1R) and ATII type 2 receptor (AT2R) (54).
ACE is present in many tissues and is particularly abundant on the endothelium of the lungs: it is mainly anchored to the plasma membrane through a single trans-membrane domain, but a soluble form has also been described (53).
Apart from its well-known role as a peptidyl-dipeptidase forming Ang II, ACE is also described as a kininase II, able to inactivate BK, as well as KD (53). The affinity of ACE appears to be higher for BK than for Ang I, suggesting that ACE-inhibition may really involve the BK degradation more than the Ang II production (55). BK-evoked sensitization of airway sensory nerves is believed to be the main mechanism for ACE-inhibitor-induced dry cough (56): considering that dry cough is very frequently observed in COVID-19 patients, this pathway could in part explain the pathogenesis of this symptom. Additionally, a role of the BK has been hypothesized also for gustatory and olfactory dysfunctions (7); again, ACE-inhibitors can cause olfactory dysfunction (57).
In addition, over the last 20 years, knowledge of the biology and physiology of another enzyme besides ACE, the angiotensin converting enzyme 2 (ACE2), has accumulated (58): ACE2 is widely expressed, including type 2 alveolar epithelial cells, endothelial cells and enterocytes (10, 58).
Both ACE and ACE2 act as zinc metallopeptidases (ACE2 only acts as a carboxypeptidase), but differ for substrate specificities, displaying counterbalancing roles in the RAS.
ACE2 converts Ang I into Angiotensin (1-9), and Ang II into Angiotensin (1-7); unlike ACE, ACE2 does not cleave BK, and is insensitive to conventional ACE-inhibitors (58). Ang II can be converted to angiotensin (1-7) also by PRCP in the low-pH areas of the kidney (59).
Angiotensin 1-7, acting on Mas receptor, exerts vasodilatory effects, thus diminishing and opposing the vasoconstrictor effect, mainly AT1R-mediated, of Ang II; moreover, it displays anti-fibrotic, anti-oxidant and anti-hypertrophic protective properties (58).
Therefore, ACE2 expression seems to protect from lung injury. Sodhi et al. (39) observed that a reduction in pulmonary ACE2 activity contributes to the pathogenesis of lung inflammation, resulting in prompt onset of neutrophil infiltration and more severe inflammation. Imai et al. (60) showed that the loss of ACE2 expression in acute lung injury leads to leaky pulmonary blood vessels through AT1R stimulation, while the AT2R protects against lung injury during sepsis. Angiotensin 1-9 has shown beneficial biological effects via the AT2R, resulting in protective effects on cardiac and vascular remodeling (58) and against pulmonary arterial hypertension, inflammation and fibrosis (61).
SARS-CoV-2 binds ACE2 for host cell entry, through the binding of its major spike glycoprotein (S1) to the N-terminal region of the receptor (62); chloroquine seems to interfere with ACE2 glycosylation, thus possibly preventing SARS-CoV-2 binding to target cells (63). Following binding with SARS-CoV-2, a loss of ACE2 function occurs, driven by endocytosis and activation of proteolytic cleavage and processing (58, 62). It can be assumed that this downregulation may be involved in the pathophysiology of COVID-19 and its manifestations. DABK is a substrate of ACE2, and the attenuation of ACE2 activity leads to impaired DABK inactivation and thus to enhanced B1R signaling.
In a mouse model, the lack of ACE2 function with consequent accumulation of Ang II, through a negative feedback loop, resulted in a secondary reduction of ACE activity (at the molecular level, Ang II downregulates renal ACE gene and enzymatic activity levels, as well as renin gene expression): these crosstalk effects between ACE2 and ACE appeared to be sex-dependent and more evident in males (64). It is known that COVID-19 affects male patients in a larger percentage (65) and with worse outcomes (66). The reduced activity of ACE is also expected to result in further BK accumulation. Moreover, it has been recognized in vitro that Ang II, through the stimulation of AT2R, is associated with increased expression of PRCP, leading to a KAL-mediated increased formation of BK (67).
Since SARS-CoV-2 binds to ACE2 receptors to enter host cells, and intravenous infusion of ACE-inhibitors and angiotensin receptor blockers (ARBs) in experimental animal models increased the amount of ACE2 receptors in the cardiopulmonary circulation, it has been speculated that patients chronically taking these drugs may be at increased risk of worse outcomes from COVID-19 (68). However, to date, there are no conclusive data demonstrating beneficial or adverse outcomes with background use of ACE-inhibitors, ARBs or other RAS antagonists among COVID-19 patients with a history of cardiovascular disease treated with these drugs (69–71). For the pathophysiological considerations previously made, however, in our opinion, it remains debatable if ACE-inhibitors, for their action on BK, should be temporarily suspended during the acute phase of illness, especially in the case of low blood pressure values.
Finally, it is interesting to observe that in an experimental malaria model (Plasmodium parasites during blood stages release kinins), exposure to captopril (an ACE-inhibitor that leads to the reduction of BK degradation) resulted in death in mice, while the concomitant administration of chloroquine protected them. B1R-knockout mice presented a significant reduction of survival when compared with wild-type mice, unlike the B2R-knockout ones (72). In this inflammation/infection model, chloroquine-induced upregulation of B1R expression proved protective: the full meaning of this result is unclear but might indicate that the selective inhibition of B2R could represent a rational modulation of dysregulated BK pathway during infection. Could the same considerations apply to COVID-19?
C1-Inhibitor and Its Potential Role in Viral Infections
Hereditary angioedema (HAE) represents the archetypal KKS disorder and can be due to a deficiency of C1-INH (Type 1), an abnormal C1-INH molecule (Type 2), or a gain-in-function of FXII with consequent plasma C1-INH consumption (Type 3) (73). Thrombin formation is not considered a feature of this disorder: even if patients with acute attacks have elevated D-dimer levels, they do not display an increased thrombotic risk (74). Clinical pictures of activation of CS and KKS without (such as HAE) and with thrombin formation (such as sepsis) can be in fact distinguished (16): COVID-19 evidently falls into the latter group.
C1-INH is a protein able to inhibit multiple serine proteases involved in the CS, KKS, Complement, Fibrinolysis, and Coagulation Systems: through the inhibition of C1r and C1s subcomponents of C1 complex, FXIIa, and KAL, C1-INH prevents the activation of CS and KKS. The N-terminal end (non-serpin domain) confers to C1-INH the capacity to bind lipopolysaccharides and E-selectin: owing to this moiety, C1-INH can also intervene in the regulation of inflammatory reactions (75). Moreover, C1-INH inhibits selectin-mediated leukocyte adhesion, regardless of its protease inhibitory activity (76).
Wygrecka et al. (77) showed that C1-INH is able to inhibit the cytotoxic activity of extracellular histones (that play a determining role in pulmonary injury leading to ARDS) and the release of several cytokines, such as TNF-alpha, IL-1b, and IL-6. It is interesting to note that accumulation of extracellular histones has been detected during infection due to influenza virus, and anti-histone antibodies have led to a marked decrease in the lung damage consisting of widespread pulmonary microvascular thrombosis, endothelial necrosis, hemorrhagic effusions and edema (78). These histopathological findings are observed also in COVID-19, although there are several differences compared to the influenza model (10) whose discussion goes beyond the scope of this review. Although there is actually no specific evidence regarding SARS-CoV-2 infection, it can be assumed that C1-INH might have beneficial effects also in this case, both through the inhibition of the CS and KKS, especially regarding the BK-induced vascular leakage and edema formation, and its anti-inflammatory activity mediated by inhibition of complement activation and histone toxicity.
Figure 1 shows the interconnection between the different human proteolytic systems operating in the vasculature, proposing a picture of an integrated host response to SARS-CoV-2 infection.
The Contact System (CS) as a plausible link between Coagulation/Fibrinolysis, Kallikrein–Kinin and Renin–Angiotensin Systems in the pathobiology of SARS-CoV-2 infection (based on the hypothesis of the activation of FXII and HK by SARS-CoV-2, directly or following cell invasion and/or damage). Black arrows indicate enzymatic activation, while red lines represent inhibition or degradation/downregulation. The sequences of black arrows imply the involvement of other molecules not shown to activate the molecule indicated by the final arrow. Dashed red lines imply the involvement of other molecules not shown to inhibit the molecule indicated at the end of the line. Gray arrows indicate the transformation of one molecule into another. ACE, angiotensin converting enzyme; ACE2, angiotensin converting enzyme 2; Ang I, Angiotensin I; Ang II, Angiotensin II; Ang (1-7), Angiotensin 1-7; Ang (1-9), Angiotensin 1-9; AT1R, ATII type 1 receptor; AT2R, ATII type 2 receptor; B1R, B1 receptor; B2R, B2 receptor; BK, bradykinin; C1-INH, C1-inhibitor; DABK, [des-Arg9]-BK; FXI, coagulation factor XI; FXII, coagulation factor XII; HK, high-molecular-weight kininogen; IL-6, interleukin-6; KAL, plasma kallikrein; MasR, Mas receptor; PK, plasma prekallikrein; t-PA, tissue plasminogen activator.
Table 1 lists some available drugs potentially representing effective therapeutic approaches in COVID-19, by modulation of the pathways and systems whose involvement has been hypothesized in its pathogenesis.
Potential therapeutic approaches able to modulate the systems involved in the pathogenesis of COVID-19.
Drug
Mechanism of action
Labeled indication
Potential role in COVID-19
Contact system
C1-inhibitor
Inhibition of CS, Coagulation/Fibrinolytic systems, complement and KKS
Treatment and prevention of angioedema attacks in hereditary angioedema
Inhibition of all systems involved
Anti-factor XII (FXII) antibody
Monoclonal antibody inhibiting FXII
Phase II study ongoing; phase III study planned. Studied indication: prevention of angioedema attacks
Inhibition of FXII and consequently of CS and KKS
Prevention and treatment of thrombosis, without increasing bleeding risk (Action on Coagulation system)
Kallikrein–kinin system
Icatibant
Bradykinin type 2 receptor (B2R)-antagonist
Treatment of acute attacks in hereditary angioedema
Inhibition of pro-inflammatory and vasoactive actions of BK
Lanadelumab
Monoclonal antibody inhibiting plasma KAL
Prevention of attacks of hereditary angioedema
Inhibition of KKS and BK generation
Ecallantide
Inhibition of plasma KAL
Treatment of acute attacks in hereditary angioedema
Inhibition of KKS and BK generation
Coagulation system
Low-Molecular-Weight Heparin/Fondaparinux
Catalyzed inhibition of activated coagulation factor X by antithrombin
Prevention and treatment of venous thromboembolism
Prevention and treatment of thrombosis and pulmonary embolism (consider bleeding risk)
Anti-factor XII (FXII) antibody
See above
See above
See above
Discussion and Conclusion
The hypothesis of the involvement of different human proteolytic defense systems operating in the vasculature in the pathogenesis of COVID-19 has recently been proposed also by other authors. van de Veerdonk et al. (79) hypothesized that a kinin-dependent local lung angioedema via B1R and eventually B2R is an important feature of COVID-19 and proposed that blocking the B2R and inhibiting plasma KAL activity might be beneficial in early disease, preventing ARDS. Roche and Roche (80) emphasized the pivotal role of BK and DABK, suggesting that the B2R-antagonist icatibant might be able to interrupt the dysregulated pathway, thereby improving clinical outcomes. Colarusso et al. (23) proposed instead to block pharmacologically the KKS upstream of the BK, by means of lanadelumab. Regarding B1R-antagonists, several companies have in past developed orally available molecules, and some of these entered phase II clinical trials, but none have been developed further; possible reasons for this failure may be inefficacy in humans due to species differences, or human specific adverse effects (48).
In our opinion, the rational for modulating these pathways is strong but to date few data for COVID-19 are available. However, the exceptional nature of this pandemic and the lack of effective interventions of proven efficacy makes it necessary to explore further therapeutic possibilities.
Understanding the pathogenetic mechanisms underlying COVID-19 is crucial for the development of new effective therapeutic approaches modulating the CS, the KKS, the RAS and the Coagulation/Fibrinolysis System. The KKS inhibitors lanadelumab and ecallantide, licensed for the treatment of HAE, and several oral KKS inhibitors in clinical development, should be assessed for their efficacy in the treatment of patients with COVID-19. The same holds for icatibant, a selective B2R antagonist used for on demand treatment in HAE. Other promising CS-linked targets or mediators that should be explored in COVID-19 include anti-FXIIa antibodies and C1-INH. This pathophysiological therapeutic approach could be of great value also for other viral infections.
Data Availability Statement
Publicly available datasets were analyzed in this study. This data can be found at the appropriate doi link of every cited article.
Author Contributions
SM, AZ, RS, FI, and CT: conceptualization. SM, AZ, RS, FI, CS, AR, and CT: formal analysis. AZ: funding acquisition. SM, AZ, and CT: investigation and project administration. SM, AZ, RS, FI, AR, CS, and CT: methodology and resources. SM, AZ, AR, and CT: writing – original draft preparation and writing – review and editing. All authors contributed to the article and approved the submitted version.
Conflict of Interest
AZ received speaker/consultancy fees and/or was a member of medical/advisory boards for CSL Behring, Shire/Takeda, and SOBI. All the authors declare that they have no financial competing interests about the topic of this article, except for the publishing support and journal styling services, that were provided by SEEd Medical Publishers and funded by CSL Behring, Italy.
Funding. The CSL Behring funded the publishing support and journal styling services, but had no role in the conduct of the research, preparation of the article, in study design, in the collection, analysis and interpretation of data, in the writing of the report, and in the decision to submit the article for publication.
We acknowledge SEEd Medical Publishers, that provided publishing support and journal styling services.
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(2020) 00:1–5. 10.1096/fj.202000967\\n32359101AbbreviationsACE
Angiotensin-Converting Enzyme
ACE2
Angiotensin-Converting Enzyme 2
Ang I
Angiotensin I
Ang II
Angiotensin II
Ang (1-7)
Angiotensin 1-7
Ang (1-9)
Angiotensin 1-9
ARB
angiotensin receptor blocker
ARDS
Acute respiratory distress syndrome
AT1R
Angiotensin II type 1 receptor
AT2R
Angiotensin II type 2 receptor
BK
Bradykinin
B1R
Bradykinin B1 receptor
B2R
Bradykinin B2 receptor
COVID-19
COronaVIrus Disease 2019
CS
Contact System
C1-INH
C1-inhibitor
DABK
[des-Arg9]-Bradykinin
DAKD
[des-Arg10]-Kallidin
DAMPs
Danger-Associated Molecular Patterns
DHF
dengue hemorrhagic fever
FXI
Factor XI
FXII
Factor XII
HK
High-molecular-weight Kininogen
HSV1
Herpes simplex virus type-1
IL-6
Interleukin-6
KAL
Kallikrein
KD
Kallidin
KKS
Kallikrein–Kinin System
LMWH
Low-Molecular Weight Heparin
PAMPs
Pathogen-Associated Molecular Patterns
PK
Prekallikrein
PRCP
Prolyl-carboxypeptidase
PRR
Pattern-Recognition Receptor
RAS
Renin–Angiotensin System
SARS-CoV-2
Severe Acute Respiratory Syndrome – Coronavirus – 2
TLR
Toll-like receptor
TNF-alpha
tumor necrosis factor-alpha
t-PA
tissue-Plasminogen Activator.
\\n\"\n]"}}},{"rowIdx":482,"cells":{"data":{"kind":"list like","value":["\nJ Med Internet ResJ. Med. Internet ResJMIRJournal of Medical Internet Research1439-44561438-8871JMIR PublicationsToronto, Canada32687479PMC7432139v22i8e1837410.2196/18374Original PaperOriginal PaperRejected Online Feedback From a Swiss Physician Rating Website Between 2008 and 2017: Analysis of 2352 RatingsEysenbachGuntherBrunsonEmilySchulzPeterKaliyadanFerozeMcLennanStuartMBHL, PhDhttps://orcid.org/0000-0002-2019-625312Institute of History and Ethics in MedicineTechnical University of MunichIsmaninger Straße 22Munich, 81675Germany49 89 4140 4041stuart.mclennan@tum.de\n\nInstitute of History and Ethics in Medicine\nTechnical University of Munich\nMunich\nGermany\n\n\nInstitute for Biomedical Ethics\nUniversity of Basel\nBasel\nSwitzerland\nCorresponding Author: Stuart McLennan stuart.mclennan@tum.de82020382020228e183742222020154202022520201162020©Stuart McLennan. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 03.08.2020.2020This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on http://www.jmir.org/, as well as this copyright and license information must be included.Background
Previous research internationally has only analyzed publicly available feedback on physician rating websites (PRWs). However, it appears that many PRWs are not publishing all the feedback they receive. Analysis of this rejected feedback could provide a better understanding of the types of feedback that are currently not published and whether this is appropriate.
Objective
The aim of this study was to examine (1) the number of patient feedback rejected from the Swiss PRW Medicosearch, (2) the evaluation tendencies of the rejected patient feedback, and (3) the types of issues raised in the rejected narrative comments.
Methods
The Swiss PRW Medicosearch provided all the feedback that had been rejected between September 16, 2008, and September 22, 2017. The feedback were analyzed and classified according to a theoretical categorization framework of physician-, staff-, and practice-related issues.
Results
Between September 16, 2008, and September 22, 2017, Medicosearch rejected a total of 2352 patient feedback. The majority of feedback rejected (1754/2352, 74.6%) had narrative comments in the German language. However, 11.9% (279/2352) of the rejected feedback only provided a quantitative rating with no narrative comment. Overall, 25% (588/2352) of the rejected feedback were positive, 18.7% (440/2352) were neutral, and 56% (1316/2352) were negative. The average rating of the rejected feedback was 2.8 (SD 1.4). In total, 44 subcategories addressing the physician (n=20), staff (n=9), and practice (n=15) were identified. In total, 3804 distinct issues were identified within the 44 subcategories of the categorization framework; 75% (2854/3804) of the issues were related to the physician, 6.4% (242/3804) were related to the staff, and 18.6% (708/3804) were related to the practice. Frequently mentioned issues identified from the rejected feedback included (1) satisfaction with treatment (533/1903, 28%); (2) the overall assessment of the physician (392/1903, 20.6%); (3) recommending the physician (345/1903, 18.1%); (4) the physician’s communication (261/1903, 13.7%); (5) the physician’s caring attitude (220/1903, 11.6%); and (6) the physician’s friendliness (203/1903, 10.6%).
Conclusions
It is unclear why the majority of the feedback were rejected. This is problematic and raises concerns that online patient feedback are being inappropriately manipulated. If online patient feedback is going to be collected, there needs to be clear policies and practices about how this is handled. It cannot be left to the whims of PRWs, who may have financial incentives to suppress negative feedback, to decide which feedback is or is not published online. Further research is needed to examine how many PRWs are using criteria for determining which feedback is published or not, what those criteria are, and what measures PRWs are using to address the manipulation of online patient feedback.
There remains relevant unwarranted variation in health care systems and deficiencies regarding all key aspects of health care [1]. Members of the public, however, have traditionally had few ways of knowing who the “good” health care organizations and professionals are [2]. As part of a wider move toward transparency, public reporting activities have been developed in a number of countries with the aim of providing information about health care organizations or professionals to the public to correct this asymmetry of information in order to inform patient decision-making and drive quality improvement [3-6].
One type of public reporting activity that has been developed in recent decades is physician rating websites (PRWs) [7-10]. PRWs represent a “bottom-up” approach to public reporting, allowing users to post ratings and comments regarding their physician as a source of information for others [11-14]. Although patients have always been able to share their opinions about their physicians with others, the ability to share these opinions via the internet and social media now means that these opinions have the potential to reach a far wider audience. With a growing number of patients utilizing the internet in relation to health care [15], it is expected that PRWs will play an increasingly important role.
A recent systematic search of PRWs internationally analyzed 143 different websites from 12 countries [16] and found that the majority (76.9%) of websites provided options to give feedback both on a predefined quantitative rating scale and as narrative comments. Previous research internationally has often focused on analyzing the ratings and comments publicly available on PRWs. This research has reported that many PRWs have incomplete lists of physicians, a low number of physicians rated, and a low number of ratings per physician that are overwhelmingly positive, which has raised concerns about the representativeness, validity, and usefulness of information on PRWs [14,17]. Furthermore, the medical profession has often expressed concerns that feedback on PRWs will be manipulated for “doctor bashing” or defamation [10].
The first PRWs in Switzerland were established in 2008, at the same time as many international PRWs. However, in comparison with other countries that have established PRWs, there has been limited research conducted on PRWs in Switzerland. This author recently conducted a study involving a random stratified sample of 966 physicians generated from the regions of Zürich and Geneva [18,19]. Selected physicians were searched on a total of four websites (OkDoc, Medicosearch, DocApp, and Google) between November 2017 and July 2018, and it was recorded whether the physician could be found. Moreover, the physician’s rating, the number of ratings and narrative comments, and the text of narrative comments were recorded. As far as the author is aware, this was the first inclusion of Google in a study examining physician ratings internationally.
With regard to the frequency of quantitative ratings and narrative comments on Swiss PRWs, similar issues as those identified in the international literature were found. Many of the selected physicians could not be identified (the proportion of physicians who could be identified ranged from 42.4% on OkDoc to 87.3% on DocApp), few of the identifiable physicians had been rated quantitatively (4.5% on DocApp to 49.8% on Google) or received a narrative comment (4.5% on DocApp to 31.2% on Google) at least once, rated physicians had, on average, a low number of quantitative ratings (1.47 ratings on OkDoc to 3.74 rating on Google) and narrative comments (1.23 comments on OkDoc to 3.03 comments on Google), and all three websites that allowed quantitative ratings had very positive average ratings on a 5-star rating scale (DocApp, 4.71; Medicosearch, 4.69; and Google, 4.41) [18].
With regard to the contents of narrative comments, it was found that the selected physicians had a total of 849 comments [19]. Narrative comments were analyzed and classified according to a theoretical categorization framework previously developed by Emmert et al [10]. In total, 43 subcategories addressing the physician, staff, and practice were identified. None of the PRWs’ comments covered all 43 subcategories of the categorization framework; comments on Google covered 86% of the subcategories, those on Medicosearch covered 72%, those on DocApp covered 60%, and those on OkDoc covered 56%. In total, 2441 distinct issues were identified within the 43 subcategories of the categorization framework; 83.65% of the issues were related to the physician, 6.63% were related to the staff, and 9.70% were related to the practice. Overall, 95% of the subcategories of the categorization framework and 81.60% of the distinct issues identified were concerning aspects of performance (interpersonal skills of the physician and staff, infrastructure, and organization and management of the practice) considered assessable by patients [19]. Furthermore, this research raised concerns that user feedback is being suppressed by Swiss PRWs [18,19], which risks undermining the overall aim of PRWs of providing a reliable source of unbiased information regarding patients’ experiences and satisfaction with physicians.
As far as this author is aware, previous research internationally has only analyzed publicly available feedback on PRWs. However, as it appears that many PRWs are not publishing all the feedback they receive. Analysis of this rejected feedback could provide a better understanding of the types of feedback that are currently not published and whether this is appropriate. This study therefore aimed to examine (1) the number of patient feedback rejected from the Swiss PRW Medicosearch, (2) the evaluation tendencies of the rejected patient feedback, and (3) the types of issues raised in the rejected narrative comments. Gaining a better understanding of feedback rejected by PRWs may help to identify issues in the way PRWs are currently determining which feedback are and are not to be publicly published.
MethodsSample
Switzerland is a Central European country with a population of about 8.4 million people and 4 official languages (German, French, Italian, and Romansh). The Swiss health care system is highly complex and decentralized, and all Swiss residents are required to purchase basic mandatory health insurance that is offered by competing nonprofit insurers. Mandatory health insurance covers most general practitioner and specialist services, and people not enrolled in managed care plans generally have free choice of professionals [18]. The first PRWs in Switzerland, OkDoc and Medicosearch, were established in 2008. A systematic web-based search conducted in June 2016 identified that the websites DocApp and Google also allow users to view quantitative ratings and/or narrative comments about Swiss physicians in a structured manner without having to open an account or log onto the website [18,19]. It appears that other websites have also subsequently started to allow users to view quantitative ratings and/or narrative comments about Swiss physicians (eg, DeinDoktor and Doctena) [19]. Nevertheless, out of the dedicated Swiss PRWs, Medicosearch appears to be one of the best established and used [18,19]. Medicosearch allows users to search for physicians by location and specialty. Physician profiles provide general information about the physician (specialties, languages spoken, and contact details). In recent years, Medicosearch has shifted its business strategy toward online appointments, where physicians pay a fee and their booking systems are integrated with Medicosearch, allowing patients to book an appointment with a physician directly on Medicosearch. Users can also leave reviews of physicians, but Medicosearch requires both a quantitative rating (5-star rating scale) and a narrative comment in every patient feedback. Although Medicosearch allows negative comments, it informs the concerned physician before publishing it on the website, so that the physician can decide whether to activate the negative feedback function. If the physician refuses, the feedback function is deactivated, removing positive comments as well [19].
As part of a larger project examining Swiss PRWs, the author approached the CEO of Medicosearch, Beat Burger. Discussions confirmed that Medicosearch does not publish all the feedback they receive. The author enquired about the possibility of receiving these rejected feedback for analysis. Medicosearch agreed to provide the rejected feedback to the author in an anonymous form. On October 24, 2017, Medicosearch sent the author an excel file that included all the feedback that Medicosearch had rejected between September 16, 2008, and September 22, 2017. The details of the rated physicians were not included. Medicosearch did not provide any reasons for why the feedback were rejected. The following data were imported into a Statistical Package for the Social Sciences (SPSS version 26 for Windows, IBM Corporation) file: the date the feedback was created, the quantitative rating out of 5, and the narrative comments provided under “title” and “description.” In early 2019, the author inquired with Medicosearch whether it would be possible to receive the rejected feedback from September 23, 2017, to December 31, 2018. Medicosearch told the author on April 11, 2019, that owing to new data-protection rules, Medicosearch had deleted all rejected feedback, and therefore, it was not possible to provide an updated file.
Data Analysis
Medicosearch uses a 5-star rating scale; a rating of 4 to 5 stars was considered a positive rating, 3 stars was considered a neutral rating, and 1 to 2 stars was considered a negative rating. The content of each narrative comment was analyzed and classified by the author according to a theoretical categorization framework of physician-, staff-, and practice-related issues. The categorization framework from Emmert et al was initially used [10], with modifications where necessary. This included removing categories that were not identified in the comments, adding categories that were identified but were not adequately covered by the previous framework, and separating categories (eg, friendliness and caring attitude) that were discussed in comments as distinct issues. Narrative comments were analyzed in their original language. Descriptive statistics included means and standard deviations for continuous variables and percentages for categorical variables. To analyze whether differences existed between German and French comments, chi-squared tests were used. All analyses were performed with the significance level α set to .05 and two-tailed tests, using SPSS version 26.
ResultsCharacteristics of Ratings
Between September 16, 2008, and September 22, 2017, Medicosearch rejected a total of 2352 patient feedback (Table 1).
The majority of feedback rejected (1754/2352, 74.6%) had narrative comments in German (Table 2). Other rejected feedback had narrative comments in French (275/2352, 11.7%), Italian (31/2352, 1.3%), English (12/2352, 0.5%), and Spanish (1/2352, 0.04%). However, 11.9% (279/2352) of the rejected feedback only provided a quantitative rating with no narrative comment.
Overall, 25% (588/2352) of the quantitative ratings were positive, 18.7% (440/2352) were neutral, and 56% (1316/2352) were negative (Table 3). Additionally, the average rating of the rejected feedback was 2.8 (SD 1.4).
Distribution of rejected feedback according to year.
Distribution of comments (year)a (N=2352), n (%)
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
26(1.1)
259(11.0)
232(9.9)
344(14.6)
392(16.7)
321(13.6)
268(11.4)
142(6.0)
236(10.0)
132(5.6)
aFrom September 16, 2008, to September 22, 2017.
Distribution of rejected feedback according to language.
Language (N=2352), n (%)
German
French
Italian
English
Spanish
Missing
1754 (74.6)
275 (11.7)
31 (1.3)
12 (0.5)
1 (0.04)
279 (11.9)
Quantitative rating evaluation results.
Measure
Languagea
Total (N=2352), n (%)
German (N=1754), n (%)
French (N=275), n (%)
Italian (N=31), n (%)
English (N=12), n (%)
Spanish (N=1), n (%)
Missing (N=279), n (%)
\n\n
\nEvaluation\n
\n\n
\n\n
\n\n
\n\n
\n\n
\n\n
\n\n
\n\n
Positive
399 (22.7)
81 (29.5)
4 (12.9)
4 (33.3)
1/1 (100)
99 (35.5)
588 (25.0)
\n\n
Neutral
296 (16.9)
59 (21.5)
6 (19.4)
1 (8.3)
0 (0)
77 (26.3)
440 (18.7%)
\n\n
Negative
1065 (60.4)
135 (49.1)
21 (67.7)
7 (58.3)
0 (0)
88 (30)
1316 (56%)
\n\n
Missing
0 (0)
0 (0)
0 (0)
0 (0)
0 (0)
8 (2.9)
8 (0.3)
Average rating (SD)
2.7 (1.3)
3.0 (1.4)
2.4 (1.3)
2.9 (1.5)
5.0
3.2 (1.4)
2.8 (1.4)
Of the 2073 ratings that provided narrative comments, analysis found that a total of 170 comments were not feedback from a patient concerning the physician, staff, or practice (92 comments were not comprehensible, 29 comments were explicitly labelled as test ratings, 15 comments were about the PRW, 10 comments reported that the person had not visited the physician yet, 10 comments simply reported that the physician’s details were not up to date, eight comments were abusive, four comments were second-hand reports, two comments were asking for advice regarding their or their family member’s condition, and two comments were not concerning a visit to a physician). Consequently, this feedback was excluded from the categorization framework.
The 1903 included narrative comments had a mean length of 158 characters (SD 214), ranging from 1 to 2788 characters. There was a significant difference in the mean character length between positive comments (mean 88, SD 130) and negative comments (mean 193, SD 241) (t1314=−11, P<.001). There was no significant difference in the mean character length between German comments (mean 158, SD 205) and French comments (mean 154, SD 206) (t1862=0.2, P=.82).
Categorization of Issues
Content analysis of the included 1903 narrative comments identified 44 subcategories addressing the physician (n=20), staff (n=9), and practice (n=15) (Textbox 1).
Categorization framework.
\nPhysician (n=20)\n
Satisfaction with treatment; overall assessment; recommendation; communication; caring attitude; friendliness; competence; treatment cost/billing; being taken seriously; time spent with patient; trust; professionalism; cooperation with medical specialists; alternative medicine; telephone availability; privacy; health insurance differentiation; patient involvement; individualized service; child friendliness
\nStaff (n=9)\n
Friendliness; overall assessment; service/assistance; communication; professionalism; availability by telephone; time spent with patient; health insurance differentiation; trust
\nPractice (n=15)\n
Overall assessment; waiting time within the practice; atmosphere; organization; ability to get appointment; equipment; recommendation; consultation hours; location; waiting room entertainment; parking space; availability by telephone; privacy; barrier-free access; online appointment
In total, 3804 distinct issues were identified within the 44 subcategories of the categorization framework; 75% (2854/3804) of the issues were related to the physician, 6.4% (242/3804) were related to the staff, and 18.6% (708/3804) were related to the practice (Table 4). The most frequent issue mentioned in the rejected comments was satisfaction with treatment (533/1903, 28%); 73.2% (390/533) of these ratings were negative. Other frequently mentioned issues regarding the physician were as follows: 20.6% (392/1903) of comments provided an overall assessment of the physician (53.8% negative); 18.1% (345/1903) provided a recommendation regarding the physician (76.2% negative); 13.7% (261/1903) referred to the physician’s communication (77.8% negative); 11.6% (220/1903) referred to the physician’s caring attitude (75.9% negative); and 10.6% (203/1903) referred to the physician’s friendliness (73.9% negative). In relation to staff issues, the most frequently mentioned issue was regarding the staffs’ friendliness (109/1903; 5.7%); 43.1% of these ratings were negative. Concerning practice issues, the frequently mentioned issues were as follows: 15.5% (295/1903) of the comments provided an overall assessment (38.6% positive), 8.1% (155/1903) referred to the waiting time within the practice (58.7% negative), and 5% (96/1903) referred to the atmosphere of the practice (40.6% positive).
However, there were some significant differences between German and French comments in a number of subcategories (Multimedia Appendix 1). For instance, German comments referred significantly more often to the physician’s friendliness (χ21=5.9, P=.01), being taken seriously by the physician (χ21=8.5, P=.002), staff friendliness (χ21=7.0, P=.005), waiting time in the practice (χ21=6.3, P=.01), and practice atmosphere (χ21=6.6, P=.007).
Categorization of issues.
Issue
Total (N=1903), n (%)
Quantitative rating evaluation
Positive, n (%)
Neutral, n (%)
Negative, n (%)
\nPhysician\n
\n\n
\n\n
\n\n
\n\n
\n\n
Satisfaction with treatment
533 (28.0)
82 (15.4)
61 (11.4)
390 (73.2)
\n\n
Overall assessment
392 (20.6)
122 (31.0)
59 (15.1)
211 (53.8)
\n\n
Recommendation
345 (18.1)
35 (10.1)
47 (13.6)
263 (76.2)
\n\n
Communication
261 (13.7)
39 (14.9)
19 (7.3)
203 (77.8)
\n\n
Caring attitude
220 (11.6)
40 (18.2)
13 (5.9)
167 (75.9)
\n\n
Friendliness
203 (10.6)
35 (17.2)
18 (8.9)
150 (73.9)
\n\n
Treatment cost/billing
173 (9.1)
8 (4.6)
30 (17.3)
135 (78.0)
\n\n
Competence
170 (8.9)
43 (25.3)
13 (7.6)
114 (67.1)
\n\n
Being taken seriously
141 (7.4)
10 (7.1)
10 (7.1)
121 (85.8)
\n\n
Time spent with patient
136 (7.1)
17 (12.5)
18 (13.2)
101 (74.3)
\n\n
Trust
133 (6.9)
44 (33.1)
11 (8.3)
78 (58.6)
\n\n
Professionalism
97 (5.1)
11 (11.3)
6 (6.2)
80 (82.5)
\n\n
Cooperation with medical specialists
13 (0.7)
4 (30.8)
0
9 (69.2)
\n\n
Alternative medicine
8 (0.4)
6 (75.0)
0
2 (25.0)
\n\n
Telephone availability
8 (0.4)
1 (12.5)
3 (37.5)
4 (50.0)
\n\n
Privacy
7 (0.4)
0
2 (28.6)
5 (71.4)
\n\n
Health insurance differentiation
6 (0.3)
0
0
6 (100)
\n\n
Patient involvement
5 (0.3)
0
1 (20.0)
4 (80.0)
\n\n
Individualized service
2 (0.1)
0
0
2 (100)
\n\n
Child friendliness
1 (0.04)
0
0
1 (100)
\nStaff\n
\n\n
\n\n
\n\n
\n\n
\n\n
Friendliness
109 (5.7)
39 (35.8)
23 (21.1)
47 (43.1)
\n\n
Overall assessment
60 (3.2)
19 (31.7)
18 (30.0)
23 (38.3)
\n\n
Service/assistance
31 (1.6)
11 (35.5)
3 (9.7)
17 (54.8)
\n\n
Communication
22 (1.2)
4 (18.2)
7 (31.8)
11 (50.0)
\n\n
Professionalism
10 (0.5)
2 (20.0)
2 (20.0)
6 (60.0)
\n\n
Availability by telephone
6 (0.3)
1 (16.7)
4 (66.7)
1 (16.7)
\n\n
Time spent with patient
2 (0.1)
1 (50)
0
1 (50.0)
\n\n
Health insurance differentiation
1 (0.04)
0
1 (100)
0
\n\n
Trust
1 (0.04)
0
0
1 (100)
\nPractice\n
\n\n
\n\n
\n\n
\n\n
\n\n
Overall assessment
295 (15.5)
114 (38.6)
90 (30.5)
91 (30.8)
\n\n
Waiting time within practice
155 (8.1)
22 (14.2)
42 (27.1)
91 (58.7)
\n\n
Atmosphere
96 (5.0)
39 (40.6)
29 (30.2)
28 (29.2)
\n\n
Organization
37 (1.9)
7 (18.9)
11 (29.7)
19 (51.4)
\n\n
Ability to get appointment
36 (1.9)
4 (11.1)
11 (30.6)
21 (58.3)
\n\n
Equipment
31 (1.6)
8 (25.8)
10 (32.3)
13 (41.9)
\n\n
Recommendation
25 (1.3)
4 (16.0)
5 (20.0)
16 (64.0)
\n\n
Consultation hours
8 (0.4)
3 (37.5)
3 (37.5)
2 (25.0)
\n\n
Location
7 (0.4)
2 (28.6)
2 (28.6)
3 (42.9)
\n\n
Waiting room entertainment
5 (0.3)
4 (80.0)
1 (20.0)
0
\n\n
Parking space
4 (0.2)
4 (100)
0
0
\n\n
Availability by telephone
3 (0.2)
1 (33.3)
1 (33.3)
1 (33.3)
\n\n
Privacy
3 (0.2)
0
3 (100)
0
\n\n
Barrier-free access
2 (0.1)
0
1 (50.0)
1 (50.0)
\n\n
Online appointment
1 (0.04)
0
1 (100)
0
DiscussionPrincipal Findings
As far as this author is aware, this is the first study internationally to examine feedback that has been rejected from a PRW. The key findings of this study are as follows: (1) the Swiss PRW Medicosearch rejected a total of 2352 patient feedback between September 16, 2008, and September 22, 2017, (2) just over half of all the rejected feedback were negative, and (3) the most frequently mentioned issue in the rejected feedback was satisfaction with treatment. Medicosearch has shown a lot of transparency in providing this rejected feedback for analysis. It is, however, unclear why the majority of the feedback were rejected. This is problematic and raises concerns that online patient feedback are being inappropriately manipulated.
Medicosearch did not provide the reasons why it rejected the feedback, and as far as this author is aware, Medicosearch does not use formal criteria for determining which feedback should be published or rejected. Of the 2352 ratings rejected by Medicosearch, 170 comments were excluded from the categorization framework for various reasons. For example, the feedback were not comprehensible, were explicitly labelled as test ratings, were about the PRW, etc. These would also appear to be legitimate reasons for Medicosearch to reject the feedback. However, the appropriateness of rejecting the remaining 92.7% of feedback is less clear, particularly as they appear to be qualitatively the same as the published feedback for a sample of Swiss physicians recently analyzed [19].
Twelve percent of the rejected feedback only provided a quantitative rating with no narrative comment. Medicosearch requires that both a quantitative rating and a narrative comment be provided in every patient feedback, and this is likely the reason for rejecting this feedback. Narrative comments often provide a richer source of information than quantitative ratings [10]; however, making narrative comments mandatory seems inappropriate. Some patients may not be willing or able to describe what happened in a narrative comment, but may still want to share their satisfaction with their physician with others. It is unclear why these ratings should simply be excluded because the patient did not want to also write a narrative comment.
In terms of the evaluation tendencies of online patient feedback, previous Swiss and international research has found that the published online patient feedback on PRWs is overwhelmingly positive [8,10,14,17-32]. However, recent research also raised concerns that negative feedback is being suppressed by Swiss PRWs [19]. For instance, the PRW OkDoc explicitly states on its website that any negative comments will be deleted, and while Medicosearch allows negative comments, it informs the concerned physician before publishing it online, so the physician can decide whether to activate the negative feedback function [19]. There was therefore an expectation that the majority of the rejected feedback would be negative. However, this analysis of 2352 rejected feedback from Medicosearch found that just over half of all rejected feedback were negative, and the average rejected rating was 2.8 out of 5.
The proportion of rejected negative feedback, however, is substantially higher than the proportion of negative feedback that has been published in the international literature [8,10,14,17-32]. Analysis of the published feedback for a sample of Swiss physicians also reported that only 4.3% (10/234) of the feedback published on Medicosearch was negative and that the average rating was 4.68 out of 5. It is unclear why there is such a large discrepancy between published and rejected negative feedback. It has previously been suggested that Switzerland’s restrictive legal framework regarding data protection may be having a big impact on the types of online patient feedback that are published [18]. However, it may also be that PRWs like Medicosearch are also deciding themselves not to publish a lot of the negative feedback they receive owing to conflicts of interest. Medicosearch has shifted its business strategy toward online appointments, where physicians pay a fee and their booking systems are integrated with Medicosearch, which allows patients to book an appointment with a physician directly on Medicosearch. Consequently, Medicosearch will likely be reluctant to upset paying physicians by publishing too much negative feedback, as their business is now reliant on physicians using their online appointment system.
Users of PRWs can also manipulate online patient feedback, and there is some indication that physicians or practice staff sometimes pose as patients on PRWs to post either positive comments about themselves or negative comments about competitors [33]. Indeed, 25% (588/2352) of the rejected feedback were positive, and it is possible some of these were rejected because Medicosearch suspected that these were fake reviews. However, without a clear and consistent way to determine which feedback is rejected, there is a danger that feedback will be inappropriately rejected.
With regard to the contents of narrative comments, the most frequently mentioned issues identified from the rejected feedback included (1) satisfaction with treatment; (2) the overall assessment of the physician; (3) recommending the physician; (4) the physician’s communication; (5) the physician’s caring attitude; and (6) the physician’s friendliness. In comparison, the top five mentioned issues identified in the analysis of the published feedback for a sample of Swiss physicians were (1) the overall assessment of the physician and the physician’s competence; (2) the physician’s communication; (3) recommending the physician; (4) the physician’s friendliness; and (5) the physician’s caring attitude [19]. This suggests that online patient feedback is raising similar issues, regardless of whether it is published or rejected. Indeed, just like in the analysis of the published feedback for a sample of Swiss physicians [19], it is important to recognize that 95% (42/44) of the subcategories of the categorization framework and 81.5% (3101/3804) of the distinct issues identified were concerning aspects of performance (interpersonal skills of the physician and staff, infrastructure, and organization and management of the practice) that are considered to be assessable by patients.
If online patient feedback is going to be collected, there needs to be clear policies and practices about how this is handled. It cannot be left to the whims of PRWs, who may have financial incentives to suppress negative feedback, to decide which feedback is or is not published online. It has previously been recommended that “there is a need for consensus-based criteria that applies to all Swiss PRWs for determining which comments are and are not to be publicly published and which are clearly publicized so users of PRWs are aware of it” [19]. This analysis of 2352 rejected feedback from Medicosearch further highlights the need for such a consensus-based criteria. To support this, further research is needed to examine how many Swiss PRWs are using criteria for determining which feedback is published or not, what those criteria are, and what measures PRWs are using to address the manipulation of online patient feedback. It appears that research examining these issues would be helpful in most countries that have PRWs.
Limitations
This study has some limitations that should be taken into account when interpreting the results. First, it is unknown how many patient feedback Medicosearch received in total during the time period covered. The author contacted Medicosearch asking for this information but never received a response, and the information is not freely available on the website. It would be helpful to know the proportion of patient feedback that is being rejected. Second, the sample of rejected feedback was only taken from one Swiss PRW. Although Medicosearch is one of the oldest and most used Swiss PRWs, it is unclear how generalizable the results are to other PRWs and other countries. Future research examining whether PRWs are using criteria for determining which feedback is published or not should include all Swiss PRWs. Third, the specialty and sociodemographic information of the rated physicians are unknown, and there may be important differences between the different specialties and physicians. Finally, the sociodemographic information of the rating patients is unknown and may not be representative of Swiss patients in general.
This work was funded by the Swiss Academy of Medical Sciences’ Käthe-Zingg-Schwichtenberg-Fonds, which had no role in the project design; in the collection, analysis, or interpretation of data; in the writing of the article; or in the decision to submit the paper for publication.
Conflicts of Interest: None declared.
Appendix
Categorization of issues by language.
AbbreviationsPRW
physician rating website
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Med. Internet ResJMIRJournal of Medical Internet Research1439-44561438-8871JMIR PublicationsToronto, Canada32687479PMC7432139v22i8e1837410.2196/18374Original PaperOriginal PaperRejected Online Feedback From a Swiss Physician Rating Website Between 2008 and 2017: Analysis of 2352 RatingsEysenbachGuntherBrunsonEmilySchulzPeterKaliyadanFerozeMcLennanStuartMBHL, PhDhttps://orcid.org/0000-0002-2019-625312Institute of History and Ethics in MedicineTechnical University of MunichIsmaninger Straße 22Munich, 81675Germany49 89 4140 4041stuart.mclennan@tum.de\\n\\nInstitute of History and Ethics in Medicine\\nTechnical University of Munich\\nMunich\\nGermany\\n\\n\\nInstitute for Biomedical Ethics\\nUniversity of Basel\\nBasel\\nSwitzerland\\nCorresponding Author: Stuart McLennan stuart.mclennan@tum.de82020382020228e183742222020154202022520201162020©Stuart McLennan. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 03.08.2020.2020This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on http://www.jmir.org/, as well as this copyright and license information must be included.Background
Previous research internationally has only analyzed publicly available feedback on physician rating websites (PRWs). However, it appears that many PRWs are not publishing all the feedback they receive. Analysis of this rejected feedback could provide a better understanding of the types of feedback that are currently not published and whether this is appropriate.
Objective
The aim of this study was to examine (1) the number of patient feedback rejected from the Swiss PRW Medicosearch, (2) the evaluation tendencies of the rejected patient feedback, and (3) the types of issues raised in the rejected narrative comments.
Methods
The Swiss PRW Medicosearch provided all the feedback that had been rejected between September 16, 2008, and September 22, 2017. The feedback were analyzed and classified according to a theoretical categorization framework of physician-, staff-, and practice-related issues.
Results
Between September 16, 2008, and September 22, 2017, Medicosearch rejected a total of 2352 patient feedback. The majority of feedback rejected (1754/2352, 74.6%) had narrative comments in the German language. However, 11.9% (279/2352) of the rejected feedback only provided a quantitative rating with no narrative comment. Overall, 25% (588/2352) of the rejected feedback were positive, 18.7% (440/2352) were neutral, and 56% (1316/2352) were negative. The average rating of the rejected feedback was 2.8 (SD 1.4). In total, 44 subcategories addressing the physician (n=20), staff (n=9), and practice (n=15) were identified. In total, 3804 distinct issues were identified within the 44 subcategories of the categorization framework; 75% (2854/3804) of the issues were related to the physician, 6.4% (242/3804) were related to the staff, and 18.6% (708/3804) were related to the practice. Frequently mentioned issues identified from the rejected feedback included (1) satisfaction with treatment (533/1903, 28%); (2) the overall assessment of the physician (392/1903, 20.6%); (3) recommending the physician (345/1903, 18.1%); (4) the physician’s communication (261/1903, 13.7%); (5) the physician’s caring attitude (220/1903, 11.6%); and (6) the physician’s friendliness (203/1903, 10.6%).
Conclusions
It is unclear why the majority of the feedback were rejected. This is problematic and raises concerns that online patient feedback are being inappropriately manipulated. If online patient feedback is going to be collected, there needs to be clear policies and practices about how this is handled. It cannot be left to the whims of PRWs, who may have financial incentives to suppress negative feedback, to decide which feedback is or is not published online. Further research is needed to examine how many PRWs are using criteria for determining which feedback is published or not, what those criteria are, and what measures PRWs are using to address the manipulation of online patient feedback.
There remains relevant unwarranted variation in health care systems and deficiencies regarding all key aspects of health care [1]. Members of the public, however, have traditionally had few ways of knowing who the “good” health care organizations and professionals are [2]. As part of a wider move toward transparency, public reporting activities have been developed in a number of countries with the aim of providing information about health care organizations or professionals to the public to correct this asymmetry of information in order to inform patient decision-making and drive quality improvement [3-6].
One type of public reporting activity that has been developed in recent decades is physician rating websites (PRWs) [7-10]. PRWs represent a “bottom-up” approach to public reporting, allowing users to post ratings and comments regarding their physician as a source of information for others [11-14]. Although patients have always been able to share their opinions about their physicians with others, the ability to share these opinions via the internet and social media now means that these opinions have the potential to reach a far wider audience. With a growing number of patients utilizing the internet in relation to health care [15], it is expected that PRWs will play an increasingly important role.
A recent systematic search of PRWs internationally analyzed 143 different websites from 12 countries [16] and found that the majority (76.9%) of websites provided options to give feedback both on a predefined quantitative rating scale and as narrative comments. Previous research internationally has often focused on analyzing the ratings and comments publicly available on PRWs. This research has reported that many PRWs have incomplete lists of physicians, a low number of physicians rated, and a low number of ratings per physician that are overwhelmingly positive, which has raised concerns about the representativeness, validity, and usefulness of information on PRWs [14,17]. Furthermore, the medical profession has often expressed concerns that feedback on PRWs will be manipulated for “doctor bashing” or defamation [10].
The first PRWs in Switzerland were established in 2008, at the same time as many international PRWs. However, in comparison with other countries that have established PRWs, there has been limited research conducted on PRWs in Switzerland. This author recently conducted a study involving a random stratified sample of 966 physicians generated from the regions of Zürich and Geneva [18,19]. Selected physicians were searched on a total of four websites (OkDoc, Medicosearch, DocApp, and Google) between November 2017 and July 2018, and it was recorded whether the physician could be found. Moreover, the physician’s rating, the number of ratings and narrative comments, and the text of narrative comments were recorded. As far as the author is aware, this was the first inclusion of Google in a study examining physician ratings internationally.
With regard to the frequency of quantitative ratings and narrative comments on Swiss PRWs, similar issues as those identified in the international literature were found. Many of the selected physicians could not be identified (the proportion of physicians who could be identified ranged from 42.4% on OkDoc to 87.3% on DocApp), few of the identifiable physicians had been rated quantitatively (4.5% on DocApp to 49.8% on Google) or received a narrative comment (4.5% on DocApp to 31.2% on Google) at least once, rated physicians had, on average, a low number of quantitative ratings (1.47 ratings on OkDoc to 3.74 rating on Google) and narrative comments (1.23 comments on OkDoc to 3.03 comments on Google), and all three websites that allowed quantitative ratings had very positive average ratings on a 5-star rating scale (DocApp, 4.71; Medicosearch, 4.69; and Google, 4.41) [18].
With regard to the contents of narrative comments, it was found that the selected physicians had a total of 849 comments [19]. Narrative comments were analyzed and classified according to a theoretical categorization framework previously developed by Emmert et al [10]. In total, 43 subcategories addressing the physician, staff, and practice were identified. None of the PRWs’ comments covered all 43 subcategories of the categorization framework; comments on Google covered 86% of the subcategories, those on Medicosearch covered 72%, those on DocApp covered 60%, and those on OkDoc covered 56%. In total, 2441 distinct issues were identified within the 43 subcategories of the categorization framework; 83.65% of the issues were related to the physician, 6.63% were related to the staff, and 9.70% were related to the practice. Overall, 95% of the subcategories of the categorization framework and 81.60% of the distinct issues identified were concerning aspects of performance (interpersonal skills of the physician and staff, infrastructure, and organization and management of the practice) considered assessable by patients [19]. Furthermore, this research raised concerns that user feedback is being suppressed by Swiss PRWs [18,19], which risks undermining the overall aim of PRWs of providing a reliable source of unbiased information regarding patients’ experiences and satisfaction with physicians.
As far as this author is aware, previous research internationally has only analyzed publicly available feedback on PRWs. However, as it appears that many PRWs are not publishing all the feedback they receive. Analysis of this rejected feedback could provide a better understanding of the types of feedback that are currently not published and whether this is appropriate. This study therefore aimed to examine (1) the number of patient feedback rejected from the Swiss PRW Medicosearch, (2) the evaluation tendencies of the rejected patient feedback, and (3) the types of issues raised in the rejected narrative comments. Gaining a better understanding of feedback rejected by PRWs may help to identify issues in the way PRWs are currently determining which feedback are and are not to be publicly published.
MethodsSample
Switzerland is a Central European country with a population of about 8.4 million people and 4 official languages (German, French, Italian, and Romansh). The Swiss health care system is highly complex and decentralized, and all Swiss residents are required to purchase basic mandatory health insurance that is offered by competing nonprofit insurers. Mandatory health insurance covers most general practitioner and specialist services, and people not enrolled in managed care plans generally have free choice of professionals [18]. The first PRWs in Switzerland, OkDoc and Medicosearch, were established in 2008. A systematic web-based search conducted in June 2016 identified that the websites DocApp and Google also allow users to view quantitative ratings and/or narrative comments about Swiss physicians in a structured manner without having to open an account or log onto the website [18,19]. It appears that other websites have also subsequently started to allow users to view quantitative ratings and/or narrative comments about Swiss physicians (eg, DeinDoktor and Doctena) [19]. Nevertheless, out of the dedicated Swiss PRWs, Medicosearch appears to be one of the best established and used [18,19]. Medicosearch allows users to search for physicians by location and specialty. Physician profiles provide general information about the physician (specialties, languages spoken, and contact details). In recent years, Medicosearch has shifted its business strategy toward online appointments, where physicians pay a fee and their booking systems are integrated with Medicosearch, allowing patients to book an appointment with a physician directly on Medicosearch. Users can also leave reviews of physicians, but Medicosearch requires both a quantitative rating (5-star rating scale) and a narrative comment in every patient feedback. Although Medicosearch allows negative comments, it informs the concerned physician before publishing it on the website, so that the physician can decide whether to activate the negative feedback function. If the physician refuses, the feedback function is deactivated, removing positive comments as well [19].
As part of a larger project examining Swiss PRWs, the author approached the CEO of Medicosearch, Beat Burger. Discussions confirmed that Medicosearch does not publish all the feedback they receive. The author enquired about the possibility of receiving these rejected feedback for analysis. Medicosearch agreed to provide the rejected feedback to the author in an anonymous form. On October 24, 2017, Medicosearch sent the author an excel file that included all the feedback that Medicosearch had rejected between September 16, 2008, and September 22, 2017. The details of the rated physicians were not included. Medicosearch did not provide any reasons for why the feedback were rejected. The following data were imported into a Statistical Package for the Social Sciences (SPSS version 26 for Windows, IBM Corporation) file: the date the feedback was created, the quantitative rating out of 5, and the narrative comments provided under “title” and “description.” In early 2019, the author inquired with Medicosearch whether it would be possible to receive the rejected feedback from September 23, 2017, to December 31, 2018. Medicosearch told the author on April 11, 2019, that owing to new data-protection rules, Medicosearch had deleted all rejected feedback, and therefore, it was not possible to provide an updated file.
Data Analysis
Medicosearch uses a 5-star rating scale; a rating of 4 to 5 stars was considered a positive rating, 3 stars was considered a neutral rating, and 1 to 2 stars was considered a negative rating. The content of each narrative comment was analyzed and classified by the author according to a theoretical categorization framework of physician-, staff-, and practice-related issues. The categorization framework from Emmert et al was initially used [10], with modifications where necessary. This included removing categories that were not identified in the comments, adding categories that were identified but were not adequately covered by the previous framework, and separating categories (eg, friendliness and caring attitude) that were discussed in comments as distinct issues. Narrative comments were analyzed in their original language. Descriptive statistics included means and standard deviations for continuous variables and percentages for categorical variables. To analyze whether differences existed between German and French comments, chi-squared tests were used. All analyses were performed with the significance level α set to .05 and two-tailed tests, using SPSS version 26.
ResultsCharacteristics of Ratings
Between September 16, 2008, and September 22, 2017, Medicosearch rejected a total of 2352 patient feedback (Table 1).
The majority of feedback rejected (1754/2352, 74.6%) had narrative comments in German (Table 2). Other rejected feedback had narrative comments in French (275/2352, 11.7%), Italian (31/2352, 1.3%), English (12/2352, 0.5%), and Spanish (1/2352, 0.04%). However, 11.9% (279/2352) of the rejected feedback only provided a quantitative rating with no narrative comment.
Overall, 25% (588/2352) of the quantitative ratings were positive, 18.7% (440/2352) were neutral, and 56% (1316/2352) were negative (Table 3). Additionally, the average rating of the rejected feedback was 2.8 (SD 1.4).
Distribution of rejected feedback according to year.
Distribution of comments (year)a (N=2352), n (%)
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
26(1.1)
259(11.0)
232(9.9)
344(14.6)
392(16.7)
321(13.6)
268(11.4)
142(6.0)
236(10.0)
132(5.6)
aFrom September 16, 2008, to September 22, 2017.
Distribution of rejected feedback according to language.
Language (N=2352), n (%)
German
French
Italian
English
Spanish
Missing
1754 (74.6)
275 (11.7)
31 (1.3)
12 (0.5)
1 (0.04)
279 (11.9)
Quantitative rating evaluation results.
Measure
Languagea
Total (N=2352), n (%)
German (N=1754), n (%)
French (N=275), n (%)
Italian (N=31), n (%)
English (N=12), n (%)
Spanish (N=1), n (%)
Missing (N=279), n (%)
\\n\\n
\\nEvaluation\\n
\\n\\n
\\n\\n
\\n\\n
\\n\\n
\\n\\n
\\n\\n
\\n\\n
\\n\\n
Positive
399 (22.7)
81 (29.5)
4 (12.9)
4 (33.3)
1/1 (100)
99 (35.5)
588 (25.0)
\\n\\n
Neutral
296 (16.9)
59 (21.5)
6 (19.4)
1 (8.3)
0 (0)
77 (26.3)
440 (18.7%)
\\n\\n
Negative
1065 (60.4)
135 (49.1)
21 (67.7)
7 (58.3)
0 (0)
88 (30)
1316 (56%)
\\n\\n
Missing
0 (0)
0 (0)
0 (0)
0 (0)
0 (0)
8 (2.9)
8 (0.3)
Average rating (SD)
2.7 (1.3)
3.0 (1.4)
2.4 (1.3)
2.9 (1.5)
5.0
3.2 (1.4)
2.8 (1.4)
Of the 2073 ratings that provided narrative comments, analysis found that a total of 170 comments were not feedback from a patient concerning the physician, staff, or practice (92 comments were not comprehensible, 29 comments were explicitly labelled as test ratings, 15 comments were about the PRW, 10 comments reported that the person had not visited the physician yet, 10 comments simply reported that the physician’s details were not up to date, eight comments were abusive, four comments were second-hand reports, two comments were asking for advice regarding their or their family member’s condition, and two comments were not concerning a visit to a physician). Consequently, this feedback was excluded from the categorization framework.
The 1903 included narrative comments had a mean length of 158 characters (SD 214), ranging from 1 to 2788 characters. There was a significant difference in the mean character length between positive comments (mean 88, SD 130) and negative comments (mean 193, SD 241) (t1314=−11, P<.001). There was no significant difference in the mean character length between German comments (mean 158, SD 205) and French comments (mean 154, SD 206) (t1862=0.2, P=.82).
Categorization of Issues
Content analysis of the included 1903 narrative comments identified 44 subcategories addressing the physician (n=20), staff (n=9), and practice (n=15) (Textbox 1).
Categorization framework.
\\nPhysician (n=20)\\n
Satisfaction with treatment; overall assessment; recommendation; communication; caring attitude; friendliness; competence; treatment cost/billing; being taken seriously; time spent with patient; trust; professionalism; cooperation with medical specialists; alternative medicine; telephone availability; privacy; health insurance differentiation; patient involvement; individualized service; child friendliness
\\nStaff (n=9)\\n
Friendliness; overall assessment; service/assistance; communication; professionalism; availability by telephone; time spent with patient; health insurance differentiation; trust
\\nPractice (n=15)\\n
Overall assessment; waiting time within the practice; atmosphere; organization; ability to get appointment; equipment; recommendation; consultation hours; location; waiting room entertainment; parking space; availability by telephone; privacy; barrier-free access; online appointment
In total, 3804 distinct issues were identified within the 44 subcategories of the categorization framework; 75% (2854/3804) of the issues were related to the physician, 6.4% (242/3804) were related to the staff, and 18.6% (708/3804) were related to the practice (Table 4). The most frequent issue mentioned in the rejected comments was satisfaction with treatment (533/1903, 28%); 73.2% (390/533) of these ratings were negative. Other frequently mentioned issues regarding the physician were as follows: 20.6% (392/1903) of comments provided an overall assessment of the physician (53.8% negative); 18.1% (345/1903) provided a recommendation regarding the physician (76.2% negative); 13.7% (261/1903) referred to the physician’s communication (77.8% negative); 11.6% (220/1903) referred to the physician’s caring attitude (75.9% negative); and 10.6% (203/1903) referred to the physician’s friendliness (73.9% negative). In relation to staff issues, the most frequently mentioned issue was regarding the staffs’ friendliness (109/1903; 5.7%); 43.1% of these ratings were negative. Concerning practice issues, the frequently mentioned issues were as follows: 15.5% (295/1903) of the comments provided an overall assessment (38.6% positive), 8.1% (155/1903) referred to the waiting time within the practice (58.7% negative), and 5% (96/1903) referred to the atmosphere of the practice (40.6% positive).
However, there were some significant differences between German and French comments in a number of subcategories (Multimedia Appendix 1). For instance, German comments referred significantly more often to the physician’s friendliness (χ21=5.9, P=.01), being taken seriously by the physician (χ21=8.5, P=.002), staff friendliness (χ21=7.0, P=.005), waiting time in the practice (χ21=6.3, P=.01), and practice atmosphere (χ21=6.6, P=.007).
Categorization of issues.
Issue
Total (N=1903), n (%)
Quantitative rating evaluation
Positive, n (%)
Neutral, n (%)
Negative, n (%)
\\nPhysician\\n
\\n\\n
\\n\\n
\\n\\n
\\n\\n
\\n\\n
Satisfaction with treatment
533 (28.0)
82 (15.4)
61 (11.4)
390 (73.2)
\\n\\n
Overall assessment
392 (20.6)
122 (31.0)
59 (15.1)
211 (53.8)
\\n\\n
Recommendation
345 (18.1)
35 (10.1)
47 (13.6)
263 (76.2)
\\n\\n
Communication
261 (13.7)
39 (14.9)
19 (7.3)
203 (77.8)
\\n\\n
Caring attitude
220 (11.6)
40 (18.2)
13 (5.9)
167 (75.9)
\\n\\n
Friendliness
203 (10.6)
35 (17.2)
18 (8.9)
150 (73.9)
\\n\\n
Treatment cost/billing
173 (9.1)
8 (4.6)
30 (17.3)
135 (78.0)
\\n\\n
Competence
170 (8.9)
43 (25.3)
13 (7.6)
114 (67.1)
\\n\\n
Being taken seriously
141 (7.4)
10 (7.1)
10 (7.1)
121 (85.8)
\\n\\n
Time spent with patient
136 (7.1)
17 (12.5)
18 (13.2)
101 (74.3)
\\n\\n
Trust
133 (6.9)
44 (33.1)
11 (8.3)
78 (58.6)
\\n\\n
Professionalism
97 (5.1)
11 (11.3)
6 (6.2)
80 (82.5)
\\n\\n
Cooperation with medical specialists
13 (0.7)
4 (30.8)
0
9 (69.2)
\\n\\n
Alternative medicine
8 (0.4)
6 (75.0)
0
2 (25.0)
\\n\\n
Telephone availability
8 (0.4)
1 (12.5)
3 (37.5)
4 (50.0)
\\n\\n
Privacy
7 (0.4)
0
2 (28.6)
5 (71.4)
\\n\\n
Health insurance differentiation
6 (0.3)
0
0
6 (100)
\\n\\n
Patient involvement
5 (0.3)
0
1 (20.0)
4 (80.0)
\\n\\n
Individualized service
2 (0.1)
0
0
2 (100)
\\n\\n
Child friendliness
1 (0.04)
0
0
1 (100)
\\nStaff\\n
\\n\\n
\\n\\n
\\n\\n
\\n\\n
\\n\\n
Friendliness
109 (5.7)
39 (35.8)
23 (21.1)
47 (43.1)
\\n\\n
Overall assessment
60 (3.2)
19 (31.7)
18 (30.0)
23 (38.3)
\\n\\n
Service/assistance
31 (1.6)
11 (35.5)
3 (9.7)
17 (54.8)
\\n\\n
Communication
22 (1.2)
4 (18.2)
7 (31.8)
11 (50.0)
\\n\\n
Professionalism
10 (0.5)
2 (20.0)
2 (20.0)
6 (60.0)
\\n\\n
Availability by telephone
6 (0.3)
1 (16.7)
4 (66.7)
1 (16.7)
\\n\\n
Time spent with patient
2 (0.1)
1 (50)
0
1 (50.0)
\\n\\n
Health insurance differentiation
1 (0.04)
0
1 (100)
0
\\n\\n
Trust
1 (0.04)
0
0
1 (100)
\\nPractice\\n
\\n\\n
\\n\\n
\\n\\n
\\n\\n
\\n\\n
Overall assessment
295 (15.5)
114 (38.6)
90 (30.5)
91 (30.8)
\\n\\n
Waiting time within practice
155 (8.1)
22 (14.2)
42 (27.1)
91 (58.7)
\\n\\n
Atmosphere
96 (5.0)
39 (40.6)
29 (30.2)
28 (29.2)
\\n\\n
Organization
37 (1.9)
7 (18.9)
11 (29.7)
19 (51.4)
\\n\\n
Ability to get appointment
36 (1.9)
4 (11.1)
11 (30.6)
21 (58.3)
\\n\\n
Equipment
31 (1.6)
8 (25.8)
10 (32.3)
13 (41.9)
\\n\\n
Recommendation
25 (1.3)
4 (16.0)
5 (20.0)
16 (64.0)
\\n\\n
Consultation hours
8 (0.4)
3 (37.5)
3 (37.5)
2 (25.0)
\\n\\n
Location
7 (0.4)
2 (28.6)
2 (28.6)
3 (42.9)
\\n\\n
Waiting room entertainment
5 (0.3)
4 (80.0)
1 (20.0)
0
\\n\\n
Parking space
4 (0.2)
4 (100)
0
0
\\n\\n
Availability by telephone
3 (0.2)
1 (33.3)
1 (33.3)
1 (33.3)
\\n\\n
Privacy
3 (0.2)
0
3 (100)
0
\\n\\n
Barrier-free access
2 (0.1)
0
1 (50.0)
1 (50.0)
\\n\\n
Online appointment
1 (0.04)
0
1 (100)
0
DiscussionPrincipal Findings
As far as this author is aware, this is the first study internationally to examine feedback that has been rejected from a PRW. The key findings of this study are as follows: (1) the Swiss PRW Medicosearch rejected a total of 2352 patient feedback between September 16, 2008, and September 22, 2017, (2) just over half of all the rejected feedback were negative, and (3) the most frequently mentioned issue in the rejected feedback was satisfaction with treatment. Medicosearch has shown a lot of transparency in providing this rejected feedback for analysis. It is, however, unclear why the majority of the feedback were rejected. This is problematic and raises concerns that online patient feedback are being inappropriately manipulated.
Medicosearch did not provide the reasons why it rejected the feedback, and as far as this author is aware, Medicosearch does not use formal criteria for determining which feedback should be published or rejected. Of the 2352 ratings rejected by Medicosearch, 170 comments were excluded from the categorization framework for various reasons. For example, the feedback were not comprehensible, were explicitly labelled as test ratings, were about the PRW, etc. These would also appear to be legitimate reasons for Medicosearch to reject the feedback. However, the appropriateness of rejecting the remaining 92.7% of feedback is less clear, particularly as they appear to be qualitatively the same as the published feedback for a sample of Swiss physicians recently analyzed [19].
Twelve percent of the rejected feedback only provided a quantitative rating with no narrative comment. Medicosearch requires that both a quantitative rating and a narrative comment be provided in every patient feedback, and this is likely the reason for rejecting this feedback. Narrative comments often provide a richer source of information than quantitative ratings [10]; however, making narrative comments mandatory seems inappropriate. Some patients may not be willing or able to describe what happened in a narrative comment, but may still want to share their satisfaction with their physician with others. It is unclear why these ratings should simply be excluded because the patient did not want to also write a narrative comment.
In terms of the evaluation tendencies of online patient feedback, previous Swiss and international research has found that the published online patient feedback on PRWs is overwhelmingly positive [8,10,14,17-32]. However, recent research also raised concerns that negative feedback is being suppressed by Swiss PRWs [19]. For instance, the PRW OkDoc explicitly states on its website that any negative comments will be deleted, and while Medicosearch allows negative comments, it informs the concerned physician before publishing it online, so the physician can decide whether to activate the negative feedback function [19]. There was therefore an expectation that the majority of the rejected feedback would be negative. However, this analysis of 2352 rejected feedback from Medicosearch found that just over half of all rejected feedback were negative, and the average rejected rating was 2.8 out of 5.
The proportion of rejected negative feedback, however, is substantially higher than the proportion of negative feedback that has been published in the international literature [8,10,14,17-32]. Analysis of the published feedback for a sample of Swiss physicians also reported that only 4.3% (10/234) of the feedback published on Medicosearch was negative and that the average rating was 4.68 out of 5. It is unclear why there is such a large discrepancy between published and rejected negative feedback. It has previously been suggested that Switzerland’s restrictive legal framework regarding data protection may be having a big impact on the types of online patient feedback that are published [18]. However, it may also be that PRWs like Medicosearch are also deciding themselves not to publish a lot of the negative feedback they receive owing to conflicts of interest. Medicosearch has shifted its business strategy toward online appointments, where physicians pay a fee and their booking systems are integrated with Medicosearch, which allows patients to book an appointment with a physician directly on Medicosearch. Consequently, Medicosearch will likely be reluctant to upset paying physicians by publishing too much negative feedback, as their business is now reliant on physicians using their online appointment system.
Users of PRWs can also manipulate online patient feedback, and there is some indication that physicians or practice staff sometimes pose as patients on PRWs to post either positive comments about themselves or negative comments about competitors [33]. Indeed, 25% (588/2352) of the rejected feedback were positive, and it is possible some of these were rejected because Medicosearch suspected that these were fake reviews. However, without a clear and consistent way to determine which feedback is rejected, there is a danger that feedback will be inappropriately rejected.
With regard to the contents of narrative comments, the most frequently mentioned issues identified from the rejected feedback included (1) satisfaction with treatment; (2) the overall assessment of the physician; (3) recommending the physician; (4) the physician’s communication; (5) the physician’s caring attitude; and (6) the physician’s friendliness. In comparison, the top five mentioned issues identified in the analysis of the published feedback for a sample of Swiss physicians were (1) the overall assessment of the physician and the physician’s competence; (2) the physician’s communication; (3) recommending the physician; (4) the physician’s friendliness; and (5) the physician’s caring attitude [19]. This suggests that online patient feedback is raising similar issues, regardless of whether it is published or rejected. Indeed, just like in the analysis of the published feedback for a sample of Swiss physicians [19], it is important to recognize that 95% (42/44) of the subcategories of the categorization framework and 81.5% (3101/3804) of the distinct issues identified were concerning aspects of performance (interpersonal skills of the physician and staff, infrastructure, and organization and management of the practice) that are considered to be assessable by patients.
If online patient feedback is going to be collected, there needs to be clear policies and practices about how this is handled. It cannot be left to the whims of PRWs, who may have financial incentives to suppress negative feedback, to decide which feedback is or is not published online. It has previously been recommended that “there is a need for consensus-based criteria that applies to all Swiss PRWs for determining which comments are and are not to be publicly published and which are clearly publicized so users of PRWs are aware of it” [19]. This analysis of 2352 rejected feedback from Medicosearch further highlights the need for such a consensus-based criteria. To support this, further research is needed to examine how many Swiss PRWs are using criteria for determining which feedback is published or not, what those criteria are, and what measures PRWs are using to address the manipulation of online patient feedback. It appears that research examining these issues would be helpful in most countries that have PRWs.
Limitations
This study has some limitations that should be taken into account when interpreting the results. First, it is unknown how many patient feedback Medicosearch received in total during the time period covered. The author contacted Medicosearch asking for this information but never received a response, and the information is not freely available on the website. It would be helpful to know the proportion of patient feedback that is being rejected. Second, the sample of rejected feedback was only taken from one Swiss PRW. Although Medicosearch is one of the oldest and most used Swiss PRWs, it is unclear how generalizable the results are to other PRWs and other countries. Future research examining whether PRWs are using criteria for determining which feedback is published or not should include all Swiss PRWs. Third, the specialty and sociodemographic information of the rated physicians are unknown, and there may be important differences between the different specialties and physicians. Finally, the sociodemographic information of the rating patients is unknown and may not be representative of Swiss patients in general.
This work was funded by the Swiss Academy of Medical Sciences’ Käthe-Zingg-Schwichtenberg-Fonds, which had no role in the project design; in the collection, analysis, or interpretation of data; in the writing of the article; or in the decision to submit the paper for publication.
Conflicts of Interest: None declared.
Appendix
Categorization of issues by language.
AbbreviationsPRW
physician rating website
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Immunol.Frontiers in Immunology1664-3224Frontiers Media S.A.32849667PMC743214010.3389/fimmu.2020.02020ImmunologyOriginal ResearchThe Safety and Efficacy of Live Viral Vaccines in Patients With Cartilage-Hair HypoplasiaVakkilainenSvetlana123*KleinoIivari4HonkanenJarno3SaloHarri5KainulainenLeena6GräsbeckMichaela7KekäläinenEliisa48MäkitieOuti123910KlemettiPaula11Children’s Hospital, Pediatric Research Center, University of Helsinki and Helsinki University Hospital, Helsinki, Finland2Folkhälsan Research Center, Institute of Genetics, Helsinki, Finland3Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland4Translational Immunology Research Program, University of Helsinki, Helsinki, Finland5Children’s Hospital, Clinicum, University of Helsinki, Helsinki, Finland6Department of Pediatrics and Adolescents, Turku University Hospital, University of Turku, Turku, Finland7Department of Pediatrics, Kymenlaakso Central Hospital, Kotka, Finland8Helsinki University Hospital, HUSLAB, Division of Clinical Microbiology, Helsinki, Finland9Department of Molecular Medicine and Surgery and Center for Molecular Medicine, Karolinska Institutet, Stockholm, Sweden10Department of Clinical Genetics, Karolinska University Hospital, Stockholm, Sweden
Edited by: Yu Lung Lau, The University of Hong Kong, Hong Kong
Reviewed by: Taizo Wada, Kanazawa University, Japan; Surjit Singh, Post Graduate Institute of Medical Education and Research (PGIMER), India
This article was submitted to Primary Immunodeficiencies, a section of the journal Frontiers in Immunology
1182020202011202010620202772020Copyright © 2020 Vakkilainen, Kleino, Honkanen, Salo, Kainulainen, Gräsbeck, Kekäläinen, Mäkitie and Klemetti.2020Vakkilainen, Kleino, Honkanen, Salo, Kainulainen, Gräsbeck, Kekäläinen, Mäkitie and KlemettiThis is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.Background
Live viral vaccines are generally contraindicated in patients with combined immunodeficiency including cartilage-hair hypoplasia (CHH); however, they may be tolerated in milder syndromes. We evaluated the safety and efficacy of live viral vaccines in patients with CHH.
Methods
We analyzed hospital and immunization records of 104 patients with CHH and measured serum antibodies to measles, mumps, rubella, and varicella zoster virus (VZV) in all patients who agreed to blood sampling (n = 50). We conducted a clinical trial (ClinicalTrials.gov identifier: NCT02383797) of live VZV vaccine on five subjects with CHH who lacked varicella history, had no clinical symptoms of immunodeficiency, and were seronegative for VZV; humoral and cellular immunologic responses were assessed post-immunization.
Results
A large proportion of patients have been immunized with live viral vaccines, including measles-mumps-rubella (MMR) (n = 40, 38%) and VZV (n = 10, 10%) vaccines, with no serious adverse events. Of the 50 patients tested for antibodies, previous immunization has been documented with MMR (n = 22), rubella (n = 2) and measles (n = 1) vaccines. Patients with CHH demonstrated seropositivity rates of 96%/75%/91% to measles, mumps and rubella, respectively, measured at a medium of 24 years post-immunization. Clinical trial participants developed humoral and cellular responses to VZV vaccine. One trial participant developed post-immunization rash and knee swelling, both resolved without treatment.
Conclusion
No serious adverse events have been recorded after immunization with live viral vaccines in Finnish patients with CHH. Patients generate humoral and cellular immune response to live viral vaccines. Immunization with live vaccines may be considered in selected CHH patients with no or clinically mild immunodeficiency.
Cartilage-hair hypoplasia (CHH, MIM #250250) is a rare skeletal dysplasia with combined immunodeficiency. CHH is caused by mutations in the RMRP gene, encoding the RNA component of the mitochondrial RNA-processing endoribonuclease RNase MPR (1). Impaired RMRP functioning results in cell cycle disturbances, altered telomere biology and changes in gene regulation (2–4). Patients with CHH demonstrate highly variable degree of immune defect, ranging from asymptomatic lymphopenia to severe combined immunodeficiency necessitating hematopoietic stem cell transplantation (5, 6).
The first report on CHH among the Amish described fatal outcomes after varicella zoster virus (VZV) primary infection (7). Since then, several CHH patients with severe varicella, but not fatalities, have been reported (8–12). In the large Finnish case series published in 1990s, only two out of 56 patients with a history of varicella had required hospitalization (13). In a more recent Amish series of 25 patients, neither antiviral medications nor hospitalizations were needed for varicella (14).
Patients with CHH can develop fatal complications after administration of live viral vaccines against smallpox or polio (15, 16). The development of humoral or cellular response after live vaccine administration has not been previously assessed in CHH. CD4+ cells producing interferon gamma (IFN-γ) contribute significantly to the immunity from VZV, and measurement of IFN-γ response represents a simple method for evaluation of VZV-specific cellular immunity (17, 18).
Live vaccines are generally contraindicated in patients with combined immunodeficiency, although they can be tolerated in milder syndromes (19). Measles-mumps-rubella (MMR) and VZV vaccines are safe in children with partial DiGeorge syndrome who have CD4 + T cell counts of ≥500 cells/mm3 (19). In children infected with human immunodeficiency virus, live vaccines are considered safe if CD4 + T cell count is >200 cells/mm3 or >15% (19).
In order to enable evidence-based decisions on the immunization of patients with CHH, we collected clinical data on the course of live-vaccine preventable diseases and analyzed vaccination and serologic data for the administered live viral vaccines in a large cohort of Finnish CHH patients, and then conducted a clinical trial with live VZV vaccine in this patient population.
Materials and Methods
The study protocol and the clinical trial (ClinicalTrials.gov identifier: NCT02383797, registered on March 9, 2015) were approved by the Institutional Ethics Committee at Helsinki University Hospital and University of Helsinki, Finland. Additional approval for the clinical trial was obtained from the Finnish Medicines Agency (FIMEA). The study was conducted in accordance with the Declaration of Helsinki and national and institutional standards. All participants and/or caregivers signed informed consents.
Clinical and Laboratory Data
Clinical data, including data on live-vaccine preventable diseases, were obtained from the patients directly, as well as retrospectively from health records, as part of our previous studies exploring characteristics and natural course of CHH in the Finnish cohort (5, 13, 20, 21). Only provider-recorded data in the patients’ immunization certificates were considered reliable and were included in the analysis of immunizations. The researchers had access to data on all hospitalizations of the study patients, beginning from 1969, via the Health Registers described in detail previously (21).
Serum samples were obtained from patients during clinical visits as part of our previous study (5). All patients who agreed to blood sampling were included, except those with ongoing immunoglobulin replacement therapy. The patients were not selected on the basis of the history of live-vaccine preventable diseases or immunization with live vaccines. Samples were analyzed by enzyme immunoassays for the presence of antibodies to measles, mumps, rubella, and VZVs. Reference values of the local laboratory (HUSLAB) were applied, and the titers were considered protective if over 15 EIU for rubella and VZVs, while for antibodies against measles and mumps viruses, qualitative assays were used.
Clinical Trial of Live Varicella Vaccine
We conducted an open-label clinical trial on live VZV vaccine (Figure 1). Of the 120 patients with CHH in the Finnish Skeletal Dysplasia Register, we recruited five patients. Inclusion criteria were: (1) genetically confirmed CHH, (2) age >12 months, and (3) no history of varicella. Exclusion criteria were (1) history of immunization against VZV, (2) positive serum antibodies for VZV, (3) low CD4 + T cell counts (<15% or <200 cells/mm3), (4) clinical signs of severe immunodeficiency, or (5) ongoing immunoglobulin replacement therapy.
Recruitment to the clinical trial of safety and efficacy of live varicella zoster virus vaccine in patients with cartilage-hair hypoplasia.
In 2015–2017, all recruited patients were given one or two doses of Varilrix®, a live-attenuated VZV Oka strain vaccine, subcutaneously, by the nurses in the health care facilities. One dose contained at least 103.3 plaque forming units of VZV. A second dose was administered if the serologic response after primary immunization was negative.
Blood samples were drawn pre-immunization and 4-6 weeks post-immunization for evaluation of humoral and cellular responses. Humoral responses were assessed by measurement of serum VZV-specific antibodies by enzyme immunoassay in HUSLAB. Cellular responses were assessed by elispot using cryopreserved peripheral blood mononuclear cells (PBMC). Control PBMC samples were obtained from (1) healthy adult volunteers with a history of varicella disease, (2) CHH families, healthy siblings who had been immunized against varicella, and 3) one patient with CHH and a history of varicella.
Peripheral blood mononuclear cells were isolated from heparinized blood samples by Ficoll centrifugation. The isolated cells were counted and frozen in RPMI1640 cell culture medium containing 10% heat-inactivated human male serum (Innovative Research, Peary Court Novi, MI, United States) and 10% Dimethyl sulfoxide. Freezing of the isolated PBMCs to −80°C was done gradually in a temperature-controlled way in 1.8 ml cryotubes using isopropanol-filled cryofreezing containers. The next day the frozen cells were transferred to automated liquid nitrogen freezer (AGA, Finland) for long term storage at −180°C until use.
Peripheral blood mononuclear cells from patients with CHH and controls were subjected to IFN-γ elispot antigen response analysis (3420-4HST-2, MabTech, Nacka Strand, Sweden). The elispot was performed according to manufacturer’s protocol with 24 h stimulation with VZV antigen preparation (PR-BA104, Jena Bioscience, Löbstedter, Germany). Shortly, thawed PBMC were cultured on IFN-γ elispot strips in CTL-test medium (CTLT-010, Cellular Technology Limited, OH, United States) in 5% CO2 humidified incubator at 37°C. For antigen response, cells in triplicate wells were stimulated with 2 ug of VZV antigen and compared to CTL-test only negative controls. MabTech anti-CD3 (1:1000) was used as positive control to TCR-stimulant for T cells. The IFN-γ spots were made visible with streptavidin-HRP enzymatic assay (3420-4HST-2, MabTech) and the number of IFN-γ spots were counted with elispot reader system (Elispot Reader System ELRIFL04, AID, Strassberg, Germany) with cutoff settings for a size and staining intensity of the positive spots deriving from the corresponding positive and negative control spots. The identity of patient and control samples was blinded during the elispot analysis. The statistical analysis of the elispot data (Paired-Wilcoxon) was done in RStudio (1.25) running R (3.6.2) with packages (rstatix, ggpubr, and tidyverse) available in r-project (cran.r-project.org). If patient had received two doses of vaccine, the data from the sample collected after the second dose was used in the analysis. One sample (vaccinated healthy control) was dropped from the data due to very low level of reactive T cells in anti-CD3 positive control.
All patients/caregivers were instructed to report any adverse reactions immediately to the researchers and were additionally contacted by phone 4–6 weeks post-immunization to record potential adverse events. In addition, patient records of all clinical trial participants were assessed at the time of writing (3–5 years post-immunization) to check for possible long-term adverse events or breakthrough VZV infection.
ResultsCohort Description
Of the identified 120 patients with CHH in Finland, informed consent for the use of clinical and laboratory data was obtained from 104 patients during our previous studies (Figure 1). All patients were either homozygous or compound heterozygous for the g.71A > G RMRP mutation.
The clinical characteristics of this cohort have been reported in our recent publication (20), According to our clinical immunodeficiency categorization (21) and based on data on childhood symptoms available in 103/104 patients, 62 patients were classified as clinically asymptomatic, another 28 individuals demonstrated symptoms of humoral immunodeficiency with recurrent respiratory tract infections and/or sepsis, and the remaining 13 patients were regarded as having combined immunodeficiency in childhood, based on the additional features of autoimmunity or opportunistic infections. During lifetime, twelve patients had received immunoglobulin replacement therapy and one patient had undergone a hematopoietic stem cell transplant. Retrospective laboratory data demonstrated lymphopenia in 36/83 (43%) patients in childhood and in 37/74 (50%) patients in adulthood.
Live-Vaccine Preventable Diseases in Patients With CHH
A significant number of patients reported history of measles (n = 32), mumps (n = 24) and/or rubella (n = 18) (Table 1), that, in the majority of patients, was also confirmed from the hospital records. All cases occurred in unvaccinated patients, prior to the 1980s, and had been diagnosed clinically. No hospitalizations were linked to measles, mumps, or rubella. Also, no long-term complications have been documented, including no cases of measles-associated subacute sclerosing panencephalitis or rubella-associated granulomatous skin lesions.
Live vaccine preventable disease history, live vaccine immunization history and serological testing in 104 patients with CHH.
Disease
Vaccine
Clinical history of disease (n)
Written evidence of immunization (n)
Complications post-immunization (n)
Positive serologya [n/tested (%)]
Measles
Measles
32
12
0
44/50 (88)
Mumps
Mumps
24
NA
NA
33/48 (69)
Rubella
Rubella
18
8
0
40/48 (83)
MMR
NA
40
0
NA
Polio
Sabin
0
37
0
n/a
Rota virus
Rota virus
n/a
2
0
n/a
Smallpox
Smallpox
0
22
0
n/a
Tuberculosis
BCG
1
78
0
n/a
Varicella
VZVb
73
10
1c
49/50 (98)d
Yellow fever
Yellow fever
0
1
0
n/a
BCG bacillus Calmette–Guérin vaccine, MMR measles, mumps and rubella combined vaccine, n number of patients, n/a not accessed, NA not applicable, VZV varicella zoster virus. aPost-vaccination serology results only were reported in those immunized against varicella. bPatients enrolled in the clinical trial were also included. cComplications are described in detail under the section Clinical trial of live varicella zoster virus vaccine in patients with CHH. dSeronegative patient had no history of varicella or VZV vaccine administration, but deceased before enrollment into VZV vaccine clinical trial.
A history of varicella was reported in 73 patients with CHH. In addition, five more patients were seropositive for VZV despite negative clinical history. Of these 78 patients, seven were hospitalized for varicella due to severe disease, at least two of whom had concomitant pneumonia, all seven recovered. In addition, nine more patients were treated with acyclovir immediately after the onset of rash, which successfully prevented the severe course of varicella, and eight more individuals were administered VZV-specific immunoglobulin after a contact with varicella and did not develop disease.
Immunization With Live Viral Vaccines in Patients With CHH
Historically, measles vaccine was used in Finland in 1975–1982, rubella vaccine in 1975–1988, and MMR vaccine has been used since 1982. Written evidence of immunization was available in 90/104 patients, and the majority of them (96%, 86/90) have been vaccinated with one or more live vaccines (Table 1). No hospitalizations or serious complications were reported after any live vaccine administration, not even after immunization against smallpox, polio and yellow fever.
For measles-containing vaccines, one patient received the first dose in adulthood, otherwise primary immunization was performed in childhood, at median age of 1.7 years (range 1.1–12.6 years).
Prior to the initiation of our clinical trial, we were aware of five patients with CHH successfully immunized with live VZV vaccine during the second year of life, with no adverse events.
Serology for Measles, Mumps, Rubella, and Varicella in Patients With CHH
Of the 50 patients tested for antibodies against measles (n = 50), mumps (n = 48), rubella (n = 48), and varicella (n = 50) viruses, previous immunization has been documented with MMR (n = 22), VZV (n = 9), rubella (n = 2) and measles (n = 1) vaccines.
For varicella, all patients with a history of disease or immunization showed durable humoral response with 100% (44/44) of seropositivity when tested (Table 2). For measles, 100% (15/15) of patients with a history of disease had measurable antibodies, as had 96% (22/23) immunized patients (Table 2). For mumps, seropositivity was observed in 86% (12/14) of patients with a history of disease and in 75% (15/20) of immunized patients (Table 2). For rubella, these proportions were 100% (13/13) and 91% (20/22), respectively (Table 2). In terms of the clinical staging of immunodeficiency, all seronegative patients were asymptomatic, except for two patients with combined immunodeficiency, of whom one did not develop antibodies against measles and the other against mumps after MMR vaccination.
Results of antibody measurements for measles, mumps, rubella (MMR) and varicella zoster viruses from serum samples of patients with cartilage-hair hypoplasia.
Disease
Clinical history
Immunized
Serology positive [n/tested (%)]
Measles
Yes: 32
Yes: 4
3/3 (100)
No/no data: 28
12/12 (100)
No/no data: 72
Yes: 39
19/20 (95)
No/no data: 34
10/15 (67)
Mumps
Yes: 24
Yes: 2
1/1 (100)
No/no data: 22
11/13 (85)
No/no data: 80
Yes: 37
15/20 (75)
No/no data: 43
6/14 (43)
Rubella
Yes: 18
Yes: 4
2/2 (100)
No/no data: 14
11/11 (100)
No/no data: 86
Yes: 43
18/20 (90)
No/no data: 43
9/15 (60)
Varicella
Yes: 73
Yes: 0
–
No/no data 73
35/35 (100)
No/no data: 31
Yes: 10
9/9 (100)
No/no data: 21
5/6 (83)
Median time between the last dose of measle-containing vaccines and serum sampling for serology was 24.6 years (range 0.3–36.9 years). This median time interval was similar for MMR and for rubella-containing vaccines (24.7 and 24.9 years, respectively). All five patients immunized with live VZV vaccine, had detectable antibodies against VZV in serum 5–19 years (median 17 years) post-immunization.
Clinical Trial of Live Varicella Zoster Virus Vaccine in Patients With CHH
Table 3 summarizes the results of the clinical trial on live VZV vaccine in five patients with CHH. We immunized four children and one adult, all with clinically silent immunodeficiency. Three patients were lymphopenic, with various abnormalities in lymphocyte subpopulations. Lymphocyte responses to stimulation with mitogens were normal in all patients. All five patients had normal levels of immunoglobulin G.
Summary of the clinical trial of live varicella zoster virus vaccine in patients with cartilage-hair hypoplasia.
Patient
1
2
3
4
5
Age at primary immunization, years
1⋅5
2⋅7
6
16
45
Symptoms of combined immunodeficiency prior to immunizationa
None
None
None
None
None
Laboratory parameters prior to immunization
Total lymphocyte count, cells/mm3
2112
3020
960
1210
1590
CD3 + T cell count, cells/mm3
1150
2240
580
810
1200
CD4 + T cell count, cells/mm3
827
1482
294
543
625
CD8 + T cell count, cells/mm3
140
530
110
140
590
CD19 + B cell count, cells/mm3
290
580
120
130
160
CD16/56 + NK cell count, cells/mm3
310
110
170
90
160
Lymphocyte response to stimulation with PHA
Normal
Normal
Normal
Normal
Normal
Number of vaccine doses
2
2
1
2
1
Time interval between vaccine doses, months
6
6
n/a
4.5
n/a
VZV-specific immunoglobulin G after the first dose
neg
neg
pos
neg
pos
VZV-specific immunoglobulin G after the second dose
pos
pos
NA
pos
NA
Cellular response by elispot after the first dose
neg
pos
pos
pos
pos
Cellular response by elispot after the second dose
pos
n/a
NA
pos
NA
Vaccine adverse events
None
None
None
None
Yesb
Numbers in bold represent cell counts below the normal range for age. n/a not available, NA – not applicable, neg – negative, PHA – phytohemagglutinin, pos – positive, VZV – varicella zoster virus. aIncluding severe or opportunistic infections, autoimmunity, or malignancy. bPost-immunization adverse events (rash and knee swelling) are described in detail in text.
None of the four immunized children developed adverse events. All five patients are well 2.8–4.5 years post-immunization, and no breakthrough varicella has been reported. Patient 5 reported a self-limited upper respiratory tract infection two weeks post-immunization. He then developed a rash during the third week post-immunization when few painless yellow-reddish round papules and ulcers (but not vesicles) 0.5 cm in size appeared on his abdomen, neck, and head within several days. The rash persisted unchanged for a week and then gradually disappeared. In addition, the patient developed swelling and morning stiffness in his right knee three weeks post-immunization, which did not disturb his walking and cycling. The patient declined antiviral and anti-inflammatory therapy and reported no complaints on the follow-up seven weeks post-immunization. There was no viremia at the time of rash, confirmed by negative blood VZV nucleic acid testing.
Three patients who did not develop measurable humoral response after the first vaccine dose demonstrated seroconversion after the second immunization. All patients also demonstrated the development of cellular response post-immunization when measured by elispot, 4/5 after the first vaccine dose (Figure 2).
Varicella zoster virus antigen response in CHH patient samples. Peripheral blood mononuclear cells of naïve and post-vaccine paired patient samples showed statistically significant induction of cellular immunity in interferon-γ elispot antigen assay. The level of induction (mean, black lines) was similar to a single healthy control.
Discussion
This is the first study describing the administration of live viral vaccines in a large cohort of patients with CHH. We demonstrate the safety and efficacy of MMR and VZV vaccines in this patient population, based on the retrospective data on provider-recorded immunizations, absence of breakthrough infections or hospitalizations linked to immunizations, as well as the durable serologic response. We also present the results of a clinical trial of live VZV vaccine in patients with CHH, which showed the generation of both humoral and cellular immune responses. Among the participants of our clinical trial, the incidence of varicella-like post-vaccination rash was 20% (1/5), compared with the reported rates of 0–7% in healthy individuals (22).
Prior to the 1980s, when MMR immunization was introduced in Finland, many patients with CHH had contracted MMR-diseases with uneventful course. This is in line with the generally milder immunodeficiency phenotype in Finnish individuals with CHH who rarely develop severe opportunistic infections (21). However, varicella clearly is an exception, as many patients with CHH suffer from prolonged rash and fever and some develop pneumonia and require hospitalization (13). Many patients with CHH are pre-emptively treated with acyclovir and given VZV immunoglobulin for the post-exposure prophylaxis. Immunization against VZV may obviate the need for prophylaxis, antiviral treatment and hospitalizations. The same considerations can be applied to the MMR vaccine.
Patients with CHH mount robust humoral immune response to measles and varicella (both natural infection and immunization), which remains measurable for decades. Antibodies to mumps either do not develop or wane over time in a significant proportion (14–25%) of patients with CHH, raising the concern about insufficient immunity against mumps. Also, rarely, patients with CHH may not develop seropositivity for rubella after MMR administration. Therefore, antibody measurement should be routinely used to assess vaccination responses in patients with CHH and booster doses should be given in case of insufficient immunity.
The pattern of seropositivity to MMR and VZV in patients with CHH are similar to those in healthy individuals, with good responses to measles, varicella and rubella, but lower rates of seropositivity to mumps (23, 24). However, in our clinical trial of VZV vaccine, 60% (3/5) of participants failed to develop antibodies against VZV after the single vaccine dose, while in healthy children this proportion is 24% (25).
This is the first study describing the production of IFN-γ in stimulated PBMC from patients with CHH. Our assessment of cellular response to VZV vaccine demonstrate that patients with CHH develop cellular immunity probably comparable to healthy immunized controls. The comparison was problematic due to the small number of healthy immunized controls. Antibodies are essential to neutralize pathogens in extracellular space. However, cellular immunity is critical for controlling and containing intracellular pathogens, especially viruses with the ability to form latency, such as VZV. Thus, the protection against VZV is provided by multitude of simultaneously acting immunological mechanisms (26). Here, we observed clear cellular responses in two CHH patients after the first vaccine dose despite no development of antibodies to VZV. This may be explained by delayed antibody production consistent with combined immunodeficiency in CHH. The activation of cellular response against VZV in CHH patients suggests that these individuals may have sufficient immune protection against VZV despite the decreased humoral response. Our data support the recommendation of two doses of VZV vaccine, not single dose, to ensure optimal immune response of both, humoral and cellular, arms of the immune system.
Although there were no reported long-term consequences of live vaccines in our cohort, clinicians and patients should be aware of the risk of delayed development of vaccine-strain rubella virus-associated skin granulomas in patients with CHH (27). In addition, there were no reactivation of vaccine-strain VZV in our patients, possibly reflecting the effective cellular immunity against VZV. However, such a reactivation, occurring even years after primary immunization, and accompanied by central nervous system infection, has been described, both in immunocompetent and immunocompromised adolescents (28).
Laboratory parameters do not correlate with clinical severity or prognosis in CHH (21), and our study reports successful immunization of lymphopenic patients, therefore mild lymphopenia itself should not be an absolute contraindication for live vaccine administration in CHH. Clinicians can utilize laboratory criteria used in the management of patients with human immunodeficiency virus infection or patients with partial DiGeorge syndrome to make decisions on immunization in patients with CHH. Importantly, all vaccinated patients in our clinical trial demonstrated normal lymphocyte proliferation responses to mitogens prior to immunization. Immunization may be considered in clinically asymptomatic patients with CHH or those with mild recurrent respiratory tract infections and no other clinical features of immunodeficiency. However, individuals considered for the immunization should be selected cautiously and the qualitative assessment of lymphocyte subsets and function should be performed prior to immunization. Even though several patients with clinical features of combined immunodeficiency have been immunized with MMR successfully, we advise against live vaccine administration to these patients.
The strengths of our study are (1) the large number of patients for whom clinical and laboratory data were available, (2) written evidence of immunization in the majority of the study participants, (3) long-term follow-up data and assessment of serologic response to immunization in the long-term, as well as (4) assessment of both, humoral and cellular immune responses in a clinical trial. The drawbacks of our clinical trial include small sample size and relatively short follow-up time, calling for further studies with longer follow-up to evaluate the efficacy and long-term safety of VZV vaccine.
In conclusion, we provide retrospective and prospective data on the safety and efficacy of live viral vaccines in Finnish patients with CHH. Immunization with live vaccines should be considered in selected CHH patients with clinically mild immunodeficiency.
Data Availability Statement
All datasets presented in this study are included in the article/supplementary material.
Ethics Statement
The studies involving human participants were reviewed and approved by Institutional Ethics Committee at the Helsinki University Hospital and University of Helsinki, Finland. Written informed consent to participate in this study was provided by the participants’ legal guardian/next of kin.
Author Contributions
SV, PK, and OM planned the study. SV, PK, LK, and MG carried out a clinical trial. JH and HS prepared PBMC for further analysis. EK and IK planned and performed the elispot analysis. SV drafted the manuscript. All authors contributed to the writing and critical review of the manuscript and approved the final version.
Conflict of Interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Funding. This work was supported by the Sigrid Jusélius Foundation to OM, the Academy of Finland to OM, the Folkhälsan Research Foundation to OM, the Novo Nordisk Foundation to OM, the Helsinki University Hospital Research Funds to OM, the Swedish Childhood Cancer Foundation to OM, the Foundation for Pediatric Research to OM, and the Doctoral School in Health Sciences at the University of Helsinki to SV.
We thank laboratory assistants Maria Kiikeri and Sinikka Tsupari for their help with PBMC separation. We are thankful to all the patients who participated in this study.
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(2019) 10:956. 10.3389/fimmu.2019.00956\n31118935BuchbinderDHauckFAlbertMHRackABakhtiarSShcherbinaA\nRubella virus-associated cutaneous granulomatous disease: a unique complication in immune-deficient patients, not limited to DNA repair disorders.\nJ Clin Immunol. (2019) 39:81–9. 10.1007/s10875-018-0581-0\n30607663HarringtonWEMatóSBurroughsLCarpenterPAGershonASchmidDS\nVaccine oka varicella meningitis in two adolescents.\nPediatrics. (2019) 144:e20191522. 10.1542/peds.2019-1522\n31776194AbbreviationsCHH
cartilage-hair hypoplasia
IFN-γ
interferon gamma
MMR
measles, mumps, rubella
PBMC
peripheral blood mononuclear cells
VZV
varicella oster virus.
\n"],"string":"[\n \"\\nFront ImmunolFront ImmunolFront. Immunol.Frontiers in Immunology1664-3224Frontiers Media S.A.32849667PMC743214010.3389/fimmu.2020.02020ImmunologyOriginal ResearchThe Safety and Efficacy of Live Viral Vaccines in Patients With Cartilage-Hair HypoplasiaVakkilainenSvetlana123*KleinoIivari4HonkanenJarno3SaloHarri5KainulainenLeena6GräsbeckMichaela7KekäläinenEliisa48MäkitieOuti123910KlemettiPaula11Children’s Hospital, Pediatric Research Center, University of Helsinki and Helsinki University Hospital, Helsinki, Finland2Folkhälsan Research Center, Institute of Genetics, Helsinki, Finland3Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland4Translational Immunology Research Program, University of Helsinki, Helsinki, Finland5Children’s Hospital, Clinicum, University of Helsinki, Helsinki, Finland6Department of Pediatrics and Adolescents, Turku University Hospital, University of Turku, Turku, Finland7Department of Pediatrics, Kymenlaakso Central Hospital, Kotka, Finland8Helsinki University Hospital, HUSLAB, Division of Clinical Microbiology, Helsinki, Finland9Department of Molecular Medicine and Surgery and Center for Molecular Medicine, Karolinska Institutet, Stockholm, Sweden10Department of Clinical Genetics, Karolinska University Hospital, Stockholm, Sweden
Edited by: Yu Lung Lau, The University of Hong Kong, Hong Kong
Reviewed by: Taizo Wada, Kanazawa University, Japan; Surjit Singh, Post Graduate Institute of Medical Education and Research (PGIMER), India
This article was submitted to Primary Immunodeficiencies, a section of the journal Frontiers in Immunology
1182020202011202010620202772020Copyright © 2020 Vakkilainen, Kleino, Honkanen, Salo, Kainulainen, Gräsbeck, Kekäläinen, Mäkitie and Klemetti.2020Vakkilainen, Kleino, Honkanen, Salo, Kainulainen, Gräsbeck, Kekäläinen, Mäkitie and KlemettiThis is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.Background
Live viral vaccines are generally contraindicated in patients with combined immunodeficiency including cartilage-hair hypoplasia (CHH); however, they may be tolerated in milder syndromes. We evaluated the safety and efficacy of live viral vaccines in patients with CHH.
Methods
We analyzed hospital and immunization records of 104 patients with CHH and measured serum antibodies to measles, mumps, rubella, and varicella zoster virus (VZV) in all patients who agreed to blood sampling (n = 50). We conducted a clinical trial (ClinicalTrials.gov identifier: NCT02383797) of live VZV vaccine on five subjects with CHH who lacked varicella history, had no clinical symptoms of immunodeficiency, and were seronegative for VZV; humoral and cellular immunologic responses were assessed post-immunization.
Results
A large proportion of patients have been immunized with live viral vaccines, including measles-mumps-rubella (MMR) (n = 40, 38%) and VZV (n = 10, 10%) vaccines, with no serious adverse events. Of the 50 patients tested for antibodies, previous immunization has been documented with MMR (n = 22), rubella (n = 2) and measles (n = 1) vaccines. Patients with CHH demonstrated seropositivity rates of 96%/75%/91% to measles, mumps and rubella, respectively, measured at a medium of 24 years post-immunization. Clinical trial participants developed humoral and cellular responses to VZV vaccine. One trial participant developed post-immunization rash and knee swelling, both resolved without treatment.
Conclusion
No serious adverse events have been recorded after immunization with live viral vaccines in Finnish patients with CHH. Patients generate humoral and cellular immune response to live viral vaccines. Immunization with live vaccines may be considered in selected CHH patients with no or clinically mild immunodeficiency.
Cartilage-hair hypoplasia (CHH, MIM #250250) is a rare skeletal dysplasia with combined immunodeficiency. CHH is caused by mutations in the RMRP gene, encoding the RNA component of the mitochondrial RNA-processing endoribonuclease RNase MPR (1). Impaired RMRP functioning results in cell cycle disturbances, altered telomere biology and changes in gene regulation (2–4). Patients with CHH demonstrate highly variable degree of immune defect, ranging from asymptomatic lymphopenia to severe combined immunodeficiency necessitating hematopoietic stem cell transplantation (5, 6).
The first report on CHH among the Amish described fatal outcomes after varicella zoster virus (VZV) primary infection (7). Since then, several CHH patients with severe varicella, but not fatalities, have been reported (8–12). In the large Finnish case series published in 1990s, only two out of 56 patients with a history of varicella had required hospitalization (13). In a more recent Amish series of 25 patients, neither antiviral medications nor hospitalizations were needed for varicella (14).
Patients with CHH can develop fatal complications after administration of live viral vaccines against smallpox or polio (15, 16). The development of humoral or cellular response after live vaccine administration has not been previously assessed in CHH. CD4+ cells producing interferon gamma (IFN-γ) contribute significantly to the immunity from VZV, and measurement of IFN-γ response represents a simple method for evaluation of VZV-specific cellular immunity (17, 18).
Live vaccines are generally contraindicated in patients with combined immunodeficiency, although they can be tolerated in milder syndromes (19). Measles-mumps-rubella (MMR) and VZV vaccines are safe in children with partial DiGeorge syndrome who have CD4 + T cell counts of ≥500 cells/mm3 (19). In children infected with human immunodeficiency virus, live vaccines are considered safe if CD4 + T cell count is >200 cells/mm3 or >15% (19).
In order to enable evidence-based decisions on the immunization of patients with CHH, we collected clinical data on the course of live-vaccine preventable diseases and analyzed vaccination and serologic data for the administered live viral vaccines in a large cohort of Finnish CHH patients, and then conducted a clinical trial with live VZV vaccine in this patient population.
Materials and Methods
The study protocol and the clinical trial (ClinicalTrials.gov identifier: NCT02383797, registered on March 9, 2015) were approved by the Institutional Ethics Committee at Helsinki University Hospital and University of Helsinki, Finland. Additional approval for the clinical trial was obtained from the Finnish Medicines Agency (FIMEA). The study was conducted in accordance with the Declaration of Helsinki and national and institutional standards. All participants and/or caregivers signed informed consents.
Clinical and Laboratory Data
Clinical data, including data on live-vaccine preventable diseases, were obtained from the patients directly, as well as retrospectively from health records, as part of our previous studies exploring characteristics and natural course of CHH in the Finnish cohort (5, 13, 20, 21). Only provider-recorded data in the patients’ immunization certificates were considered reliable and were included in the analysis of immunizations. The researchers had access to data on all hospitalizations of the study patients, beginning from 1969, via the Health Registers described in detail previously (21).
Serum samples were obtained from patients during clinical visits as part of our previous study (5). All patients who agreed to blood sampling were included, except those with ongoing immunoglobulin replacement therapy. The patients were not selected on the basis of the history of live-vaccine preventable diseases or immunization with live vaccines. Samples were analyzed by enzyme immunoassays for the presence of antibodies to measles, mumps, rubella, and VZVs. Reference values of the local laboratory (HUSLAB) were applied, and the titers were considered protective if over 15 EIU for rubella and VZVs, while for antibodies against measles and mumps viruses, qualitative assays were used.
Clinical Trial of Live Varicella Vaccine
We conducted an open-label clinical trial on live VZV vaccine (Figure 1). Of the 120 patients with CHH in the Finnish Skeletal Dysplasia Register, we recruited five patients. Inclusion criteria were: (1) genetically confirmed CHH, (2) age >12 months, and (3) no history of varicella. Exclusion criteria were (1) history of immunization against VZV, (2) positive serum antibodies for VZV, (3) low CD4 + T cell counts (<15% or <200 cells/mm3), (4) clinical signs of severe immunodeficiency, or (5) ongoing immunoglobulin replacement therapy.
Recruitment to the clinical trial of safety and efficacy of live varicella zoster virus vaccine in patients with cartilage-hair hypoplasia.
In 2015–2017, all recruited patients were given one or two doses of Varilrix®, a live-attenuated VZV Oka strain vaccine, subcutaneously, by the nurses in the health care facilities. One dose contained at least 103.3 plaque forming units of VZV. A second dose was administered if the serologic response after primary immunization was negative.
Blood samples were drawn pre-immunization and 4-6 weeks post-immunization for evaluation of humoral and cellular responses. Humoral responses were assessed by measurement of serum VZV-specific antibodies by enzyme immunoassay in HUSLAB. Cellular responses were assessed by elispot using cryopreserved peripheral blood mononuclear cells (PBMC). Control PBMC samples were obtained from (1) healthy adult volunteers with a history of varicella disease, (2) CHH families, healthy siblings who had been immunized against varicella, and 3) one patient with CHH and a history of varicella.
Peripheral blood mononuclear cells were isolated from heparinized blood samples by Ficoll centrifugation. The isolated cells were counted and frozen in RPMI1640 cell culture medium containing 10% heat-inactivated human male serum (Innovative Research, Peary Court Novi, MI, United States) and 10% Dimethyl sulfoxide. Freezing of the isolated PBMCs to −80°C was done gradually in a temperature-controlled way in 1.8 ml cryotubes using isopropanol-filled cryofreezing containers. The next day the frozen cells were transferred to automated liquid nitrogen freezer (AGA, Finland) for long term storage at −180°C until use.
Peripheral blood mononuclear cells from patients with CHH and controls were subjected to IFN-γ elispot antigen response analysis (3420-4HST-2, MabTech, Nacka Strand, Sweden). The elispot was performed according to manufacturer’s protocol with 24 h stimulation with VZV antigen preparation (PR-BA104, Jena Bioscience, Löbstedter, Germany). Shortly, thawed PBMC were cultured on IFN-γ elispot strips in CTL-test medium (CTLT-010, Cellular Technology Limited, OH, United States) in 5% CO2 humidified incubator at 37°C. For antigen response, cells in triplicate wells were stimulated with 2 ug of VZV antigen and compared to CTL-test only negative controls. MabTech anti-CD3 (1:1000) was used as positive control to TCR-stimulant for T cells. The IFN-γ spots were made visible with streptavidin-HRP enzymatic assay (3420-4HST-2, MabTech) and the number of IFN-γ spots were counted with elispot reader system (Elispot Reader System ELRIFL04, AID, Strassberg, Germany) with cutoff settings for a size and staining intensity of the positive spots deriving from the corresponding positive and negative control spots. The identity of patient and control samples was blinded during the elispot analysis. The statistical analysis of the elispot data (Paired-Wilcoxon) was done in RStudio (1.25) running R (3.6.2) with packages (rstatix, ggpubr, and tidyverse) available in r-project (cran.r-project.org). If patient had received two doses of vaccine, the data from the sample collected after the second dose was used in the analysis. One sample (vaccinated healthy control) was dropped from the data due to very low level of reactive T cells in anti-CD3 positive control.
All patients/caregivers were instructed to report any adverse reactions immediately to the researchers and were additionally contacted by phone 4–6 weeks post-immunization to record potential adverse events. In addition, patient records of all clinical trial participants were assessed at the time of writing (3–5 years post-immunization) to check for possible long-term adverse events or breakthrough VZV infection.
ResultsCohort Description
Of the identified 120 patients with CHH in Finland, informed consent for the use of clinical and laboratory data was obtained from 104 patients during our previous studies (Figure 1). All patients were either homozygous or compound heterozygous for the g.71A > G RMRP mutation.
The clinical characteristics of this cohort have been reported in our recent publication (20), According to our clinical immunodeficiency categorization (21) and based on data on childhood symptoms available in 103/104 patients, 62 patients were classified as clinically asymptomatic, another 28 individuals demonstrated symptoms of humoral immunodeficiency with recurrent respiratory tract infections and/or sepsis, and the remaining 13 patients were regarded as having combined immunodeficiency in childhood, based on the additional features of autoimmunity or opportunistic infections. During lifetime, twelve patients had received immunoglobulin replacement therapy and one patient had undergone a hematopoietic stem cell transplant. Retrospective laboratory data demonstrated lymphopenia in 36/83 (43%) patients in childhood and in 37/74 (50%) patients in adulthood.
Live-Vaccine Preventable Diseases in Patients With CHH
A significant number of patients reported history of measles (n = 32), mumps (n = 24) and/or rubella (n = 18) (Table 1), that, in the majority of patients, was also confirmed from the hospital records. All cases occurred in unvaccinated patients, prior to the 1980s, and had been diagnosed clinically. No hospitalizations were linked to measles, mumps, or rubella. Also, no long-term complications have been documented, including no cases of measles-associated subacute sclerosing panencephalitis or rubella-associated granulomatous skin lesions.
Live vaccine preventable disease history, live vaccine immunization history and serological testing in 104 patients with CHH.
Disease
Vaccine
Clinical history of disease (n)
Written evidence of immunization (n)
Complications post-immunization (n)
Positive serologya [n/tested (%)]
Measles
Measles
32
12
0
44/50 (88)
Mumps
Mumps
24
NA
NA
33/48 (69)
Rubella
Rubella
18
8
0
40/48 (83)
MMR
NA
40
0
NA
Polio
Sabin
0
37
0
n/a
Rota virus
Rota virus
n/a
2
0
n/a
Smallpox
Smallpox
0
22
0
n/a
Tuberculosis
BCG
1
78
0
n/a
Varicella
VZVb
73
10
1c
49/50 (98)d
Yellow fever
Yellow fever
0
1
0
n/a
BCG bacillus Calmette–Guérin vaccine, MMR measles, mumps and rubella combined vaccine, n number of patients, n/a not accessed, NA not applicable, VZV varicella zoster virus. aPost-vaccination serology results only were reported in those immunized against varicella. bPatients enrolled in the clinical trial were also included. cComplications are described in detail under the section Clinical trial of live varicella zoster virus vaccine in patients with CHH. dSeronegative patient had no history of varicella or VZV vaccine administration, but deceased before enrollment into VZV vaccine clinical trial.
A history of varicella was reported in 73 patients with CHH. In addition, five more patients were seropositive for VZV despite negative clinical history. Of these 78 patients, seven were hospitalized for varicella due to severe disease, at least two of whom had concomitant pneumonia, all seven recovered. In addition, nine more patients were treated with acyclovir immediately after the onset of rash, which successfully prevented the severe course of varicella, and eight more individuals were administered VZV-specific immunoglobulin after a contact with varicella and did not develop disease.
Immunization With Live Viral Vaccines in Patients With CHH
Historically, measles vaccine was used in Finland in 1975–1982, rubella vaccine in 1975–1988, and MMR vaccine has been used since 1982. Written evidence of immunization was available in 90/104 patients, and the majority of them (96%, 86/90) have been vaccinated with one or more live vaccines (Table 1). No hospitalizations or serious complications were reported after any live vaccine administration, not even after immunization against smallpox, polio and yellow fever.
For measles-containing vaccines, one patient received the first dose in adulthood, otherwise primary immunization was performed in childhood, at median age of 1.7 years (range 1.1–12.6 years).
Prior to the initiation of our clinical trial, we were aware of five patients with CHH successfully immunized with live VZV vaccine during the second year of life, with no adverse events.
Serology for Measles, Mumps, Rubella, and Varicella in Patients With CHH
Of the 50 patients tested for antibodies against measles (n = 50), mumps (n = 48), rubella (n = 48), and varicella (n = 50) viruses, previous immunization has been documented with MMR (n = 22), VZV (n = 9), rubella (n = 2) and measles (n = 1) vaccines.
For varicella, all patients with a history of disease or immunization showed durable humoral response with 100% (44/44) of seropositivity when tested (Table 2). For measles, 100% (15/15) of patients with a history of disease had measurable antibodies, as had 96% (22/23) immunized patients (Table 2). For mumps, seropositivity was observed in 86% (12/14) of patients with a history of disease and in 75% (15/20) of immunized patients (Table 2). For rubella, these proportions were 100% (13/13) and 91% (20/22), respectively (Table 2). In terms of the clinical staging of immunodeficiency, all seronegative patients were asymptomatic, except for two patients with combined immunodeficiency, of whom one did not develop antibodies against measles and the other against mumps after MMR vaccination.
Results of antibody measurements for measles, mumps, rubella (MMR) and varicella zoster viruses from serum samples of patients with cartilage-hair hypoplasia.
Disease
Clinical history
Immunized
Serology positive [n/tested (%)]
Measles
Yes: 32
Yes: 4
3/3 (100)
No/no data: 28
12/12 (100)
No/no data: 72
Yes: 39
19/20 (95)
No/no data: 34
10/15 (67)
Mumps
Yes: 24
Yes: 2
1/1 (100)
No/no data: 22
11/13 (85)
No/no data: 80
Yes: 37
15/20 (75)
No/no data: 43
6/14 (43)
Rubella
Yes: 18
Yes: 4
2/2 (100)
No/no data: 14
11/11 (100)
No/no data: 86
Yes: 43
18/20 (90)
No/no data: 43
9/15 (60)
Varicella
Yes: 73
Yes: 0
–
No/no data 73
35/35 (100)
No/no data: 31
Yes: 10
9/9 (100)
No/no data: 21
5/6 (83)
Median time between the last dose of measle-containing vaccines and serum sampling for serology was 24.6 years (range 0.3–36.9 years). This median time interval was similar for MMR and for rubella-containing vaccines (24.7 and 24.9 years, respectively). All five patients immunized with live VZV vaccine, had detectable antibodies against VZV in serum 5–19 years (median 17 years) post-immunization.
Clinical Trial of Live Varicella Zoster Virus Vaccine in Patients With CHH
Table 3 summarizes the results of the clinical trial on live VZV vaccine in five patients with CHH. We immunized four children and one adult, all with clinically silent immunodeficiency. Three patients were lymphopenic, with various abnormalities in lymphocyte subpopulations. Lymphocyte responses to stimulation with mitogens were normal in all patients. All five patients had normal levels of immunoglobulin G.
Summary of the clinical trial of live varicella zoster virus vaccine in patients with cartilage-hair hypoplasia.
Patient
1
2
3
4
5
Age at primary immunization, years
1⋅5
2⋅7
6
16
45
Symptoms of combined immunodeficiency prior to immunizationa
None
None
None
None
None
Laboratory parameters prior to immunization
Total lymphocyte count, cells/mm3
2112
3020
960
1210
1590
CD3 + T cell count, cells/mm3
1150
2240
580
810
1200
CD4 + T cell count, cells/mm3
827
1482
294
543
625
CD8 + T cell count, cells/mm3
140
530
110
140
590
CD19 + B cell count, cells/mm3
290
580
120
130
160
CD16/56 + NK cell count, cells/mm3
310
110
170
90
160
Lymphocyte response to stimulation with PHA
Normal
Normal
Normal
Normal
Normal
Number of vaccine doses
2
2
1
2
1
Time interval between vaccine doses, months
6
6
n/a
4.5
n/a
VZV-specific immunoglobulin G after the first dose
neg
neg
pos
neg
pos
VZV-specific immunoglobulin G after the second dose
pos
pos
NA
pos
NA
Cellular response by elispot after the first dose
neg
pos
pos
pos
pos
Cellular response by elispot after the second dose
pos
n/a
NA
pos
NA
Vaccine adverse events
None
None
None
None
Yesb
Numbers in bold represent cell counts below the normal range for age. n/a not available, NA – not applicable, neg – negative, PHA – phytohemagglutinin, pos – positive, VZV – varicella zoster virus. aIncluding severe or opportunistic infections, autoimmunity, or malignancy. bPost-immunization adverse events (rash and knee swelling) are described in detail in text.
None of the four immunized children developed adverse events. All five patients are well 2.8–4.5 years post-immunization, and no breakthrough varicella has been reported. Patient 5 reported a self-limited upper respiratory tract infection two weeks post-immunization. He then developed a rash during the third week post-immunization when few painless yellow-reddish round papules and ulcers (but not vesicles) 0.5 cm in size appeared on his abdomen, neck, and head within several days. The rash persisted unchanged for a week and then gradually disappeared. In addition, the patient developed swelling and morning stiffness in his right knee three weeks post-immunization, which did not disturb his walking and cycling. The patient declined antiviral and anti-inflammatory therapy and reported no complaints on the follow-up seven weeks post-immunization. There was no viremia at the time of rash, confirmed by negative blood VZV nucleic acid testing.
Three patients who did not develop measurable humoral response after the first vaccine dose demonstrated seroconversion after the second immunization. All patients also demonstrated the development of cellular response post-immunization when measured by elispot, 4/5 after the first vaccine dose (Figure 2).
Varicella zoster virus antigen response in CHH patient samples. Peripheral blood mononuclear cells of naïve and post-vaccine paired patient samples showed statistically significant induction of cellular immunity in interferon-γ elispot antigen assay. The level of induction (mean, black lines) was similar to a single healthy control.
Discussion
This is the first study describing the administration of live viral vaccines in a large cohort of patients with CHH. We demonstrate the safety and efficacy of MMR and VZV vaccines in this patient population, based on the retrospective data on provider-recorded immunizations, absence of breakthrough infections or hospitalizations linked to immunizations, as well as the durable serologic response. We also present the results of a clinical trial of live VZV vaccine in patients with CHH, which showed the generation of both humoral and cellular immune responses. Among the participants of our clinical trial, the incidence of varicella-like post-vaccination rash was 20% (1/5), compared with the reported rates of 0–7% in healthy individuals (22).
Prior to the 1980s, when MMR immunization was introduced in Finland, many patients with CHH had contracted MMR-diseases with uneventful course. This is in line with the generally milder immunodeficiency phenotype in Finnish individuals with CHH who rarely develop severe opportunistic infections (21). However, varicella clearly is an exception, as many patients with CHH suffer from prolonged rash and fever and some develop pneumonia and require hospitalization (13). Many patients with CHH are pre-emptively treated with acyclovir and given VZV immunoglobulin for the post-exposure prophylaxis. Immunization against VZV may obviate the need for prophylaxis, antiviral treatment and hospitalizations. The same considerations can be applied to the MMR vaccine.
Patients with CHH mount robust humoral immune response to measles and varicella (both natural infection and immunization), which remains measurable for decades. Antibodies to mumps either do not develop or wane over time in a significant proportion (14–25%) of patients with CHH, raising the concern about insufficient immunity against mumps. Also, rarely, patients with CHH may not develop seropositivity for rubella after MMR administration. Therefore, antibody measurement should be routinely used to assess vaccination responses in patients with CHH and booster doses should be given in case of insufficient immunity.
The pattern of seropositivity to MMR and VZV in patients with CHH are similar to those in healthy individuals, with good responses to measles, varicella and rubella, but lower rates of seropositivity to mumps (23, 24). However, in our clinical trial of VZV vaccine, 60% (3/5) of participants failed to develop antibodies against VZV after the single vaccine dose, while in healthy children this proportion is 24% (25).
This is the first study describing the production of IFN-γ in stimulated PBMC from patients with CHH. Our assessment of cellular response to VZV vaccine demonstrate that patients with CHH develop cellular immunity probably comparable to healthy immunized controls. The comparison was problematic due to the small number of healthy immunized controls. Antibodies are essential to neutralize pathogens in extracellular space. However, cellular immunity is critical for controlling and containing intracellular pathogens, especially viruses with the ability to form latency, such as VZV. Thus, the protection against VZV is provided by multitude of simultaneously acting immunological mechanisms (26). Here, we observed clear cellular responses in two CHH patients after the first vaccine dose despite no development of antibodies to VZV. This may be explained by delayed antibody production consistent with combined immunodeficiency in CHH. The activation of cellular response against VZV in CHH patients suggests that these individuals may have sufficient immune protection against VZV despite the decreased humoral response. Our data support the recommendation of two doses of VZV vaccine, not single dose, to ensure optimal immune response of both, humoral and cellular, arms of the immune system.
Although there were no reported long-term consequences of live vaccines in our cohort, clinicians and patients should be aware of the risk of delayed development of vaccine-strain rubella virus-associated skin granulomas in patients with CHH (27). In addition, there were no reactivation of vaccine-strain VZV in our patients, possibly reflecting the effective cellular immunity against VZV. However, such a reactivation, occurring even years after primary immunization, and accompanied by central nervous system infection, has been described, both in immunocompetent and immunocompromised adolescents (28).
Laboratory parameters do not correlate with clinical severity or prognosis in CHH (21), and our study reports successful immunization of lymphopenic patients, therefore mild lymphopenia itself should not be an absolute contraindication for live vaccine administration in CHH. Clinicians can utilize laboratory criteria used in the management of patients with human immunodeficiency virus infection or patients with partial DiGeorge syndrome to make decisions on immunization in patients with CHH. Importantly, all vaccinated patients in our clinical trial demonstrated normal lymphocyte proliferation responses to mitogens prior to immunization. Immunization may be considered in clinically asymptomatic patients with CHH or those with mild recurrent respiratory tract infections and no other clinical features of immunodeficiency. However, individuals considered for the immunization should be selected cautiously and the qualitative assessment of lymphocyte subsets and function should be performed prior to immunization. Even though several patients with clinical features of combined immunodeficiency have been immunized with MMR successfully, we advise against live vaccine administration to these patients.
The strengths of our study are (1) the large number of patients for whom clinical and laboratory data were available, (2) written evidence of immunization in the majority of the study participants, (3) long-term follow-up data and assessment of serologic response to immunization in the long-term, as well as (4) assessment of both, humoral and cellular immune responses in a clinical trial. The drawbacks of our clinical trial include small sample size and relatively short follow-up time, calling for further studies with longer follow-up to evaluate the efficacy and long-term safety of VZV vaccine.
In conclusion, we provide retrospective and prospective data on the safety and efficacy of live viral vaccines in Finnish patients with CHH. Immunization with live vaccines should be considered in selected CHH patients with clinically mild immunodeficiency.
Data Availability Statement
All datasets presented in this study are included in the article/supplementary material.
Ethics Statement
The studies involving human participants were reviewed and approved by Institutional Ethics Committee at the Helsinki University Hospital and University of Helsinki, Finland. Written informed consent to participate in this study was provided by the participants’ legal guardian/next of kin.
Author Contributions
SV, PK, and OM planned the study. SV, PK, LK, and MG carried out a clinical trial. JH and HS prepared PBMC for further analysis. EK and IK planned and performed the elispot analysis. SV drafted the manuscript. All authors contributed to the writing and critical review of the manuscript and approved the final version.
Conflict of Interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Funding. This work was supported by the Sigrid Jusélius Foundation to OM, the Academy of Finland to OM, the Folkhälsan Research Foundation to OM, the Novo Nordisk Foundation to OM, the Helsinki University Hospital Research Funds to OM, the Swedish Childhood Cancer Foundation to OM, the Foundation for Pediatric Research to OM, and the Doctoral School in Health Sciences at the University of Helsinki to SV.
We thank laboratory assistants Maria Kiikeri and Sinikka Tsupari for their help with PBMC separation. We are thankful to all the patients who participated in this study.
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(2009) 351:71–4. 10.1016/j.jim.2009.09.010\\n19818791SmithJGLiuXKaufholdRMClairJCaulfieldMJ.\\nDevelopment and validation of a gamma interferon ELISPOT assay for quantitation of cellular immune responses to varicella-zoster virus.\\nClin Diagn Labor Immunol. (2001) 8:871–9. 10.1128/CDLI.8.5.871-879.2001\\n11527795SobhABonillaFA.\\nVaccination in primary immunodeficiency disorders.\\nJ Allergy Clin Immunol Pract. (2016) 4:1066–75. 10.1016/j.jaip.2016.09.012\\n27836056VakkilainenSMakitieRKlemettiPValtaHTaskinenMHusebyeES\\nA wide spectrum of autoimmune manifestations and other symptoms suggesting immune dysregulation in patients with cartilage-hair hypoplasia.\\nFront Immunol. (2018) 9:2468. 10.3389/fimmu.2018.02468\\n30410491VakkilainenSTaskinenMKlemettiPPukkalaEMäkitieO.\\nA 30-year prospective follow-up study reveals risk factors for early death in cartilage-hair hypoplasia.\\nFront Immunol. (2019) 10:1581. 10.3389/fimmu.2019.01581\\n31379817KrethHWLeeBWKosuwonPSalazarJGloriani-BarzagaNBockHL\\nSixteen years of global experience with the first refrigerator-stable varicella vaccine (Varilrix).\\nBioDrugs. (2008) 22:387–402. 10.2165/0063030-200822060-00005\\n18998756FedeliUZanettiCSaiaB.\\nSusceptibility of healthcare workers to measles, mumps rubella and varicella.\\nJ Hosp Infect. (2002) 51:133–5. 10.1053/jhin.2002.1222\\n12090801KennedyRBOvsyannikovaIGThomasALarrabeeBRRubinSPolandGA.\\nDifferential durability of immune responses to measles and mumps following MMR vaccination.\\nVaccine. (2019) 37:1775–84. 10.1016/j.vaccine.2019.02.030\\n30797639MichalikDESteinbergSPLaRussaPSEdwardsKMWrightPFArvinAM\\nPrimary vaccine failure after 1 Dose of varicella vaccine in healthy children.\\nJ Infect Dis. (2008) 197:944–9. 10.1086/529043\\n18419532SlifkaMKAmannaIJ.\\nRole of multivalency and antigenic threshold in generating protective antibody responses.\\nFront Immunol. (2019) 10:956. 10.3389/fimmu.2019.00956\\n31118935BuchbinderDHauckFAlbertMHRackABakhtiarSShcherbinaA\\nRubella virus-associated cutaneous granulomatous disease: a unique complication in immune-deficient patients, not limited to DNA repair disorders.\\nJ Clin Immunol. (2019) 39:81–9. 10.1007/s10875-018-0581-0\\n30607663HarringtonWEMatóSBurroughsLCarpenterPAGershonASchmidDS\\nVaccine oka varicella meningitis in two adolescents.\\nPediatrics. (2019) 144:e20191522. 10.1542/peds.2019-1522\\n31776194AbbreviationsCHH
cartilage-hair hypoplasia
IFN-γ
interferon gamma
MMR
measles, mumps, rubella
PBMC
peripheral blood mononuclear cells
VZV
varicella oster virus.
\\n\"\n]"}}},{"rowIdx":484,"cells":{"data":{"kind":"list like","value":["\nJMIR Ment HealthJMHJMIR Mental Health2368-7959JMIR PublicationsToronto, Canada32559173PMC7432141v7i8e1977810.2196/19778Personal PerspectivePersonal PerspectivePatient Innovation in Investigating the Effects of Environmental Pollution in Schizophrenia: Case Report of Digital Phenotyping Beyond AppsFulfordDanielCampelloneTimMoteJasmineVaidyamAdityaMS1https://orcid.org/0000-0002-2900-4561RouxSpencerMS1https://orcid.org/0000-0001-5374-6859TorousJohnMDhttps://orcid.org/0000-0002-5362-79371Division of Digital PsychiatryBeth Isreal Deaconess Medical CenterHarvard Medical School330 Brookline AvenueBoston, MA, 02215United States1 7143359858jtorous@bidmc.harvard.edu\n\nDivision of Digital Psychiatry\nBeth Isreal Deaconess Medical Center\nHarvard Medical School\nBoston, MA\nUnited States\nCorresponding Author: John Torous jtorous@bidmc.harvard.edu8202038202078e197783042020215202017620201862020©Aditya Vaidyam, Spencer Roux, John Torous. Originally published in JMIR Mental Health (http://mental.jmir.org), 03.08.2020.2020This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Mental Health, is properly cited. The complete bibliographic information, a link to the original publication on http://mental.jmir.org/, as well as this copyright and license information must be included.
This patient perspective highlights the role of patients in the innovation and codesign of digital mental health technology. Though digital mental health apps have evolved and become highly functional, many still act as data collection silos without adequate support for patients to understand and investigate potentially meaningful inferences in their own data. Few digital health platforms respect the patient’s agency and curiosity, allowing the individual to wear the hat of researcher and data scientist and share their experiences and insight with their clinicians. This case is cowritten with an individual with lived experiences of schizophrenia who has decided to openly share their name and experiences to share with others the methods and results of their curiosity and encourage and inspire others to follow their curiosity as well.
digital mental healthmHealthappsserious mental illnessschizophreniapsychiatrydigital phenotypingIntroduction
Digital health software, including apps, are designed to serve people and help improve clinical outcomes. Although apps have become more functional and able to capture both surveys and sensor information (ie, digital phenotyping [1]), technical limitations in smartphone software and hardware limit potential data capture. Highlighting the potential of health software beyond smartphones (apps), this case report demonstrates the potential for such software to transition between different devices and hardware toward better serving people. Building off a prior case report where an individual started with an app to track symptoms but found a tally counter more useful [2], here we discuss a case where an app was used at first, followed by a transition to a novel device that proved to be a more comprehensive solution.
Brief Case
Spencer Roux (henceforth referred to as SR) is a male in his thirties who was diagnosed with schizophrenia in 2013. His symptoms are currently well managed with exercise, lifestyle interventions, and medications. Seeking to constantly improve his condition and curious about the impact of various environmental aspects on his mental health, SR sought to quantify such environmental aspects. SR also considered tracking noise level, temperature, and ambient lighting, the latter serving as a proxy for sunlight.
Mental health concerns related to environmental variations, including global warming, climate change, and pollution are rising while pollution itself is a known trigger for brain inflammation [3-5], which may be related to the onset and exacerbation of numerous illnesses. The strongest available evidence is for the relationship between pollution and depression [6] but emerging evidence indicates that ambient pollution could be associated with hospitalization in illnesses such as schizophrenia [7,8]. However, tracking environmental conditions such as pollution is not possible today through the use of common smartphone apps, as no major smartphone devices support indoor precision air quality sensors. Although it is possible to purchase such sensors individually, there is no off-the-shelf indoor precision air quality sensor that also tracks mental health available on the market today.
Though he had no prior knowledge of this literature, SR wanted to assess the relationship between air quality and the number of auditory hallucinations he experienced day-to-day. As his symptoms are well controlled, he believed this was a practical time to learn more about environmental factors that may be related to his symptoms. Previously, SR used a tally counter device to identify a relationship between his auditory hallucination count and the number of times he responded to his hallucinations [2], and here sought to capture new data. As with the tally counter, he wanted to track his symptoms in a manner that was inconspicuous and easy to engage with.
The Nordic Thingy prototyping platform (Figure 1; Nordic Semiconductor) was temporarily provided to SR at no cost as it would be suitable for his needs and is compatible with the LAMP platform, which enables visualization and statistical inference. The LAMP platform has been used in prior clinical cases for similar purposes [9], using only sensors available on commercial smartphone devices. The Nordic Thingy prototyping platform has numerous sensors not available in a smartphone that are able to achieve a high fidelity of measurement recording. The device was preloaded with a long-term evolution (LTE) data plan. The sensors include the following: LTE, Bluetooth, Wi-Fi, near-field communication (NFC), button, temperature, humidity, air quality, air pressure, ambient color, ambient light, high-g accelerometer, low-power accelerometer, global position system (GPS), digital microphone, radio frequency (RF) antenna, barometer, altitude, 9-axis motion tracker. It also has a multicolor light emitting diode (LED) and digital buzzer (a low-quality speaker) for responding to the user if necessary.
The size of a Nordic Thingy shown in comparison to a smartphone device. The primary input method is the surface containing the Nordic Semiconductors logo, which acts as a pressure-insensitive button pad.
Due to its literal “black box” nature, it was not distracting to use, and was convenient and easy to manage. Noting its primary purpose as a prototyping system, it was purchased from a Nordic Semiconductors distributor for US $120. As the Nordic Thingy can capture data but does not offer data management, integration, or visualization, the LAMP platform was then installed to enable digital mental health–related functionality [10,11]. The Nordic Thingy was charged roughly alongside SR's smartphone and captured or measured the environment of SR's home throughout the day, for most of which SR was present. The button press was used to record auditory hallucinations, similar to the tally counter SR had used previously.
SR independently collected over 200,000 data points via the Nordic Thingy in the span of seven days, producing about 65 MB of uncompressed data. The humidity, temperature, air pressure, and air quality data appeared as sensor data in the LAMP platform, and the button presses appeared as active data in the form of responses to a survey with a single yes or no question. With the assistance of the LAMP programming interfaces, the data were plotted and are shown in Figure 2.
Though his findings showed no significant relationship between indoor air quality and auditory hallucinations (McNemar chi-squared test with continuity correction = 3179.4, df=1, P<.001), SR was able to devise a hypothesis, perform an experiment, analyze the data, and produce a tangible outcome within the span of 1 week. The installation of the LAMP platform on a non–smartphone device proved useful in yielding high-quality data suitable for actionable digital phenotyping as well as data visualization.
SR's experimental data. The red lines indicate a button press, the blue line indicates indoor air quality, and the purple line indicates the threshold for indoor air quality >100; higher values indicate poorer air quality.
Brief Discussion
As a single case report, SR's example highlights the potential for innovation in and codesign of digital mental health technology by those with lived experiences of serious mental illness. As technology and sensors evolve, it is possible to collect novel data streams like pollution on a highly personalized scale. Understanding the role of digital mental health technology beyond just apps and ensuring it is flexible enough to move between devices and contexts ensures that both patients and clinicians can take full advantage of these advances in technology. The design and evolution of digital mental health tools and platforms will likely rapidly evolve toward devices similar to the one used in this case report, and must involve individuals such as SR, whose lived experiences offer insight into both the problems and solutions encountered when building and using such systems.
As novel sensors like those used in this case report continue to add new accessible data, it is also critical for the field to understand their clinical uses, personal meaning, and therapeutic targets. Although it is easy to use such data in machine learning algorithms or artificial intelligence systems, starting with patient-centric clinical use cases can minimize bias, increase utility, and accelerate clinical translation. From an experimental methods standpoint, it is also important to consider privacy, safety, and ethics when developing individual study protocols, ensuring that all parties involved obtain recorded consent and respect the wishes of other parties. Furthermore, as seen in this case example, there was actually no need for advanced analytical methods beyond simple visualization and a statistical test, highlighting how the right data used in the right clinical case can itself be definitive.
As SR's case yet again shows, a smartphone app may not always be the solution. Although smartphones and wearables are considered highly accessible and usable devices that are practically always within reach, the potential of other digital hardware should not be ignored. The software can be the same, as demonstrated by the use of the LAMP platform on a non–smartphone device. Focusing less on the device and more on how the software serves unique clinical purposes offers a useful reminder toward focusing on fluid and interoperable technology that is not tied to any one platform or device. SR's case shows the merits of using non–smartphone devices for their specialized sensors and unobtrusive behavior.
Through a unique experimental journey, SR's case presents a clear message to other curious and innovative individuals with lived experiences, as well as creators and implementors of digital mental health technologies. Clinicians may also choose to actively take part or passively encourage innovation, lending clinical context and insight in either case. Through creativity, innovation, and in small part the accessibility and actionability of data, individuals given the right tools can understand and impact their own mental health.
Conflicts of Interest: JT reports unrelated research support for Otuska. The remaining authors declare no conflicts of interest.
AbbreviationsGPS
global positioning system
LED
light emitting diode
LTE
long-term evolution
NFC
near-field communication
RF
radio frequency
InselTRDigital phenotyping: a global tool for psychiatryWorld Psychiatry2018100717327627710.1002/wps.2055010.1002/wps.205503019210330192103TorousJRouxSPatient-Driven Innovation for Mobile Mental Health Technology: Case Report of Symptom Tracking in SchizophreniaJMIR Ment Health201770643e2710.2196/mental.79112868438628684386JayarajRLRodriguezEAWangYBlockMLOutdoor Ambient Air Pollution and Neurodegenerative Diseases: the Neuroinflammation HypothesisCurr Environ Health Rep20176254216617910.1007/s40572-017-0142-32844464528444645BlockMLCalderón-GarcidueñasLAir pollution: mechanisms of neuroinflammation and CNS diseaseTrends Neurosci200993295061610.1016/j.tins.2009.05.0091971618719716187BabadjouniRMHodisDMRadwanskiRDurazoRPatelALiuQMackWJClinical effects of air pollution on the central nervous system; a reviewJ Clin Neurosci2017943162410.1016/j.jocn.2017.04.0282852889628528896BuoliMGrassiSCaldiroliACarnevaliGSMucciFIodiceSCantoneLPergoliLBollatiVIs there a link between air pollution and mental disorders?Environ Int2018911815416810.1016/j.envint.2018.05.0442988376229883762DuanJChengQLuoXBaiLZhangHWangSXuZGaoJZhangYSuHIs the serious ambient air pollution associated with increased admissions for schizophrenia?Sci Total Environ20181210644141910.1016/j.scitotenv.2018.06.2182998008029980080BaiLZhangYChengQSuHTraffic-related air pollution and hospital admissions for schizophrenia: a time-series study2018 Conference on Environment and Health-Precision Environmental Health: A Collection of Papers on the Challenges of Interdisciplinary Cooperation2018WisniewskiHHensonPTorousJUsing a Smartphone App to Identify Clinically Relevant Behavior Trends Symptom Report, Cognition Scores, and Exercise Levels: A Case SeriesFront Psychiatry20199231065210.3389/fpsyt.2019.0065210.3389/fpsyt.2019.006523160796031607960TorousJWisniewskiHBirdBCarpenterEDavidGElejaldeEFulfordDGuimondSHaysRHensonPHoffmanLLimCMenonMNoelVPearsonJPetersonRSusheelaATroyHVaidyamAWeizenbaumENaslundJaKeshavanMCreating a Digital Health Smartphone App and Digital Phenotyping Platform for Mental Health and Diverse Healthcare Needs: an Interdisciplinary and Collaborative ApproachJ Technol Behav Sci201942742738510.1007/s41347-019-00095-w10.1007/s41347-019-00095-wVaidyamAHalamkaJTorousJActionable digital phenotyping: a framework for the delivery of just-in-time and longitudinal interventions in clinical healthcaremHealth201985252510.21037/mhealth.2019.07.0410.21037/mhealth.2019.07.043155927031559270\n"],"string":"[\n \"\\nJMIR Ment HealthJMHJMIR Mental Health2368-7959JMIR PublicationsToronto, Canada32559173PMC7432141v7i8e1977810.2196/19778Personal PerspectivePersonal PerspectivePatient Innovation in Investigating the Effects of Environmental Pollution in Schizophrenia: Case Report of Digital Phenotyping Beyond AppsFulfordDanielCampelloneTimMoteJasmineVaidyamAdityaMS1https://orcid.org/0000-0002-2900-4561RouxSpencerMS1https://orcid.org/0000-0001-5374-6859TorousJohnMDhttps://orcid.org/0000-0002-5362-79371Division of Digital PsychiatryBeth Isreal Deaconess Medical CenterHarvard Medical School330 Brookline AvenueBoston, MA, 02215United States1 7143359858jtorous@bidmc.harvard.edu\\n\\nDivision of Digital Psychiatry\\nBeth Isreal Deaconess Medical Center\\nHarvard Medical School\\nBoston, MA\\nUnited States\\nCorresponding Author: John Torous jtorous@bidmc.harvard.edu8202038202078e197783042020215202017620201862020©Aditya Vaidyam, Spencer Roux, John Torous. Originally published in JMIR Mental Health (http://mental.jmir.org), 03.08.2020.2020This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Mental Health, is properly cited. The complete bibliographic information, a link to the original publication on http://mental.jmir.org/, as well as this copyright and license information must be included.
This patient perspective highlights the role of patients in the innovation and codesign of digital mental health technology. Though digital mental health apps have evolved and become highly functional, many still act as data collection silos without adequate support for patients to understand and investigate potentially meaningful inferences in their own data. Few digital health platforms respect the patient’s agency and curiosity, allowing the individual to wear the hat of researcher and data scientist and share their experiences and insight with their clinicians. This case is cowritten with an individual with lived experiences of schizophrenia who has decided to openly share their name and experiences to share with others the methods and results of their curiosity and encourage and inspire others to follow their curiosity as well.
digital mental healthmHealthappsserious mental illnessschizophreniapsychiatrydigital phenotypingIntroduction
Digital health software, including apps, are designed to serve people and help improve clinical outcomes. Although apps have become more functional and able to capture both surveys and sensor information (ie, digital phenotyping [1]), technical limitations in smartphone software and hardware limit potential data capture. Highlighting the potential of health software beyond smartphones (apps), this case report demonstrates the potential for such software to transition between different devices and hardware toward better serving people. Building off a prior case report where an individual started with an app to track symptoms but found a tally counter more useful [2], here we discuss a case where an app was used at first, followed by a transition to a novel device that proved to be a more comprehensive solution.
Brief Case
Spencer Roux (henceforth referred to as SR) is a male in his thirties who was diagnosed with schizophrenia in 2013. His symptoms are currently well managed with exercise, lifestyle interventions, and medications. Seeking to constantly improve his condition and curious about the impact of various environmental aspects on his mental health, SR sought to quantify such environmental aspects. SR also considered tracking noise level, temperature, and ambient lighting, the latter serving as a proxy for sunlight.
Mental health concerns related to environmental variations, including global warming, climate change, and pollution are rising while pollution itself is a known trigger for brain inflammation [3-5], which may be related to the onset and exacerbation of numerous illnesses. The strongest available evidence is for the relationship between pollution and depression [6] but emerging evidence indicates that ambient pollution could be associated with hospitalization in illnesses such as schizophrenia [7,8]. However, tracking environmental conditions such as pollution is not possible today through the use of common smartphone apps, as no major smartphone devices support indoor precision air quality sensors. Although it is possible to purchase such sensors individually, there is no off-the-shelf indoor precision air quality sensor that also tracks mental health available on the market today.
Though he had no prior knowledge of this literature, SR wanted to assess the relationship between air quality and the number of auditory hallucinations he experienced day-to-day. As his symptoms are well controlled, he believed this was a practical time to learn more about environmental factors that may be related to his symptoms. Previously, SR used a tally counter device to identify a relationship between his auditory hallucination count and the number of times he responded to his hallucinations [2], and here sought to capture new data. As with the tally counter, he wanted to track his symptoms in a manner that was inconspicuous and easy to engage with.
The Nordic Thingy prototyping platform (Figure 1; Nordic Semiconductor) was temporarily provided to SR at no cost as it would be suitable for his needs and is compatible with the LAMP platform, which enables visualization and statistical inference. The LAMP platform has been used in prior clinical cases for similar purposes [9], using only sensors available on commercial smartphone devices. The Nordic Thingy prototyping platform has numerous sensors not available in a smartphone that are able to achieve a high fidelity of measurement recording. The device was preloaded with a long-term evolution (LTE) data plan. The sensors include the following: LTE, Bluetooth, Wi-Fi, near-field communication (NFC), button, temperature, humidity, air quality, air pressure, ambient color, ambient light, high-g accelerometer, low-power accelerometer, global position system (GPS), digital microphone, radio frequency (RF) antenna, barometer, altitude, 9-axis motion tracker. It also has a multicolor light emitting diode (LED) and digital buzzer (a low-quality speaker) for responding to the user if necessary.
The size of a Nordic Thingy shown in comparison to a smartphone device. The primary input method is the surface containing the Nordic Semiconductors logo, which acts as a pressure-insensitive button pad.
Due to its literal “black box” nature, it was not distracting to use, and was convenient and easy to manage. Noting its primary purpose as a prototyping system, it was purchased from a Nordic Semiconductors distributor for US $120. As the Nordic Thingy can capture data but does not offer data management, integration, or visualization, the LAMP platform was then installed to enable digital mental health–related functionality [10,11]. The Nordic Thingy was charged roughly alongside SR's smartphone and captured or measured the environment of SR's home throughout the day, for most of which SR was present. The button press was used to record auditory hallucinations, similar to the tally counter SR had used previously.
SR independently collected over 200,000 data points via the Nordic Thingy in the span of seven days, producing about 65 MB of uncompressed data. The humidity, temperature, air pressure, and air quality data appeared as sensor data in the LAMP platform, and the button presses appeared as active data in the form of responses to a survey with a single yes or no question. With the assistance of the LAMP programming interfaces, the data were plotted and are shown in Figure 2.
Though his findings showed no significant relationship between indoor air quality and auditory hallucinations (McNemar chi-squared test with continuity correction = 3179.4, df=1, P<.001), SR was able to devise a hypothesis, perform an experiment, analyze the data, and produce a tangible outcome within the span of 1 week. The installation of the LAMP platform on a non–smartphone device proved useful in yielding high-quality data suitable for actionable digital phenotyping as well as data visualization.
SR's experimental data. The red lines indicate a button press, the blue line indicates indoor air quality, and the purple line indicates the threshold for indoor air quality >100; higher values indicate poorer air quality.
Brief Discussion
As a single case report, SR's example highlights the potential for innovation in and codesign of digital mental health technology by those with lived experiences of serious mental illness. As technology and sensors evolve, it is possible to collect novel data streams like pollution on a highly personalized scale. Understanding the role of digital mental health technology beyond just apps and ensuring it is flexible enough to move between devices and contexts ensures that both patients and clinicians can take full advantage of these advances in technology. The design and evolution of digital mental health tools and platforms will likely rapidly evolve toward devices similar to the one used in this case report, and must involve individuals such as SR, whose lived experiences offer insight into both the problems and solutions encountered when building and using such systems.
As novel sensors like those used in this case report continue to add new accessible data, it is also critical for the field to understand their clinical uses, personal meaning, and therapeutic targets. Although it is easy to use such data in machine learning algorithms or artificial intelligence systems, starting with patient-centric clinical use cases can minimize bias, increase utility, and accelerate clinical translation. From an experimental methods standpoint, it is also important to consider privacy, safety, and ethics when developing individual study protocols, ensuring that all parties involved obtain recorded consent and respect the wishes of other parties. Furthermore, as seen in this case example, there was actually no need for advanced analytical methods beyond simple visualization and a statistical test, highlighting how the right data used in the right clinical case can itself be definitive.
As SR's case yet again shows, a smartphone app may not always be the solution. Although smartphones and wearables are considered highly accessible and usable devices that are practically always within reach, the potential of other digital hardware should not be ignored. The software can be the same, as demonstrated by the use of the LAMP platform on a non–smartphone device. Focusing less on the device and more on how the software serves unique clinical purposes offers a useful reminder toward focusing on fluid and interoperable technology that is not tied to any one platform or device. SR's case shows the merits of using non–smartphone devices for their specialized sensors and unobtrusive behavior.
Through a unique experimental journey, SR's case presents a clear message to other curious and innovative individuals with lived experiences, as well as creators and implementors of digital mental health technologies. Clinicians may also choose to actively take part or passively encourage innovation, lending clinical context and insight in either case. Through creativity, innovation, and in small part the accessibility and actionability of data, individuals given the right tools can understand and impact their own mental health.
Conflicts of Interest: JT reports unrelated research support for Otuska. The remaining authors declare no conflicts of interest.
AbbreviationsGPS
global positioning system
LED
light emitting diode
LTE
long-term evolution
NFC
near-field communication
RF
radio frequency
InselTRDigital phenotyping: a global tool for psychiatryWorld Psychiatry2018100717327627710.1002/wps.2055010.1002/wps.205503019210330192103TorousJRouxSPatient-Driven Innovation for Mobile Mental Health Technology: Case Report of Symptom Tracking in SchizophreniaJMIR Ment Health201770643e2710.2196/mental.79112868438628684386JayarajRLRodriguezEAWangYBlockMLOutdoor Ambient Air Pollution and Neurodegenerative Diseases: the Neuroinflammation HypothesisCurr Environ Health Rep20176254216617910.1007/s40572-017-0142-32844464528444645BlockMLCalderón-GarcidueñasLAir pollution: mechanisms of neuroinflammation and CNS diseaseTrends Neurosci200993295061610.1016/j.tins.2009.05.0091971618719716187BabadjouniRMHodisDMRadwanskiRDurazoRPatelALiuQMackWJClinical effects of air pollution on the central nervous system; a reviewJ Clin Neurosci2017943162410.1016/j.jocn.2017.04.0282852889628528896BuoliMGrassiSCaldiroliACarnevaliGSMucciFIodiceSCantoneLPergoliLBollatiVIs there a link between air pollution and mental disorders?Environ Int2018911815416810.1016/j.envint.2018.05.0442988376229883762DuanJChengQLuoXBaiLZhangHWangSXuZGaoJZhangYSuHIs the serious ambient air pollution associated with increased admissions for schizophrenia?Sci Total Environ20181210644141910.1016/j.scitotenv.2018.06.2182998008029980080BaiLZhangYChengQSuHTraffic-related air pollution and hospital admissions for schizophrenia: a time-series study2018 Conference on Environment and Health-Precision Environmental Health: A Collection of Papers on the Challenges of Interdisciplinary Cooperation2018WisniewskiHHensonPTorousJUsing a Smartphone App to Identify Clinically Relevant Behavior Trends Symptom Report, Cognition Scores, and Exercise Levels: A Case SeriesFront Psychiatry20199231065210.3389/fpsyt.2019.0065210.3389/fpsyt.2019.006523160796031607960TorousJWisniewskiHBirdBCarpenterEDavidGElejaldeEFulfordDGuimondSHaysRHensonPHoffmanLLimCMenonMNoelVPearsonJPetersonRSusheelaATroyHVaidyamAWeizenbaumENaslundJaKeshavanMCreating a Digital Health Smartphone App and Digital Phenotyping Platform for Mental Health and Diverse Healthcare Needs: an Interdisciplinary and Collaborative ApproachJ Technol Behav Sci201942742738510.1007/s41347-019-00095-w10.1007/s41347-019-00095-wVaidyamAHalamkaJTorousJActionable digital phenotyping: a framework for the delivery of just-in-time and longitudinal interventions in clinical healthcaremHealth201985252510.21037/mhealth.2019.07.0410.21037/mhealth.2019.07.043155927031559270\\n\"\n]"}}},{"rowIdx":485,"cells":{"data":{"kind":"list like","value":["\nJMIR Med InformJMIJMIR Medical Informatics2291-9694JMIR PublicationsToronto, Canada32744510PMC7432142v8i8e2007110.2196/20071Original PaperOriginal PaperImproving Diagnostic Classification of Stillbirths and Neonatal Deaths Using ICD-PM (International Classification of Diseases for Perinatal Mortality) Codes: Validation StudyEysenbachGuntherDella MeaVincenzoLukHiu MeiMD, MMed1https://orcid.org/0000-0001-8912-5552AllansonEmmaPhD, FRANZCOG2https://orcid.org/0000-0002-4786-4354MingWai-KitMPH, MD, PhD3https://orcid.org/0000-0002-8846-7515LeungWing CheongMD, FRCOGhttps://orcid.org/0000-0003-1851-21381Department of Obstetrics and GynaecologyKwong Wah HospitalHong Kong SARChina (Hong Kong)852 35175091leungwc@ha.org.hk\n\nDepartment of Obstetrics and Gynaecology\nKwong Wah Hospital\nHong Kong SAR\nChina (Hong Kong)\n\n\nInstitute of Health Research\nUniversity of Notre Dame, Fremantle\nWestern Australia\nAustralia\n\n\nDepartment of Public Health and Preventive Medicine\nSchool of Medicine\nJinan University\nGuangzhou\nChina\nCorresponding Author: Wing Cheong Leung leungwc@ha.org.hk8202038202088e2007111520204620208620202562020©Hiu Mei Luk, Emma Allanson, Wai-Kit Ming, Wing Cheong Leung. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 03.08.2020.2020This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Medical Informatics, is properly cited. The complete bibliographic information, a link to the original publication on http://medinform.jmir.org/, as well as this copyright and license information must be included.Background
Stillbirths and neonatal deaths have long been imperfectly classified and recorded worldwide. In Hong Kong, the current code system is deficient (>90% cases with unknown causes) in providing the diagnoses of perinatal mortality cases.
Objective
The objective of this study was to apply the International Classification of Diseases for Perinatal Mortality (ICD-PM) system to existing perinatal death data. Further, the aim was to assess whether there was any change in the classifications of perinatal deaths compared with the existing classification system and identify any areas in which future interventions can be made.
Methods
We applied the ICD-PM (with International Statistical Classification of Diseases and Related Health Problems, 10th Revision) code system to existing perinatal death data in Kwong Wah Hospital, Hong Kong, to improve diagnostic classification. The study included stillbirths (after 24 weeks gestation) and neonatal deaths (from birth to 28 days). The retrospective data (5 years) from May 1, 2012, to April 30, 2017, were recoded by the principal investigator (HML) applying the ICD-PM, then validated by an overseas expert (EA) after she reviewed the detailed case summaries. The prospective application of ICD-PM from May 1, 2017, to April 30, 2019, was performed during the monthly multidisciplinary perinatal meetings and then also validated by EA for agreement.
Results
We analyzed the data of 34,920 deliveries, and 119 cases were included for analysis (92 stillbirths and 27 neonatal deaths). The overall agreement with EA of our codes using the ICD-PM was 93.2% (111/119); 92% (78/85) for the 5 years of retrospective codes and 97% (33/34) for the 2 years of prospective codes (P=.44). After the application of the ICD-PM, the overall proportion of unknown causes of perinatal mortality dropped from 34.5% (41/119) to 10.1% (12/119) of cases (P<.001).
Conclusions
Using the ICD-PM would lead to a better classification of perinatal deaths, reduce the proportion of unknown diagnoses, and clearly link the maternal conditions with these perinatal deaths.
More than 5 million perinatal deaths occur globally each year, and this largely silent epidemic significantly impacts and burdens families [1,2]. Despite this, perinatal deaths have long been frequently invisible and poorly recorded worldwide.
A large proportion of stillbirth and neonatal death cases take place in less developed countries [3]. Classification methods used in these countries can be obscure. The numbers of skilled medical personnel are often inadequate, which contributes to less than comprehensive recording of clinical data at the time of stillbirth and neonatal deaths [4,5]. In contrast, stillbirth affects proportionally fewer births in high-income countries, and data related to these deaths tend to be comprehensively recorded [6]. Many high-income countries have developed their own classification systems for perinatal deaths; for example, the United Kingdom uses the Codac system [7], Sweden has the Stockholm system [8], Australia and New Zealand use the Perinatal Society of Australia and New Zealand perinatal death classification system [9], and the Netherlands uses the Tulip classification system [10]. In the United States, the Stillbirth Collaborative Research Network developed the initial causes of fetal death system [11]. While we see an annual reduction in the rate in stillbirth globally [12], this trend differs widely among high- and low- income countries. An internationally recognized classification system of perinatal mortality would be invaluable so that the data could easily be compared between different countries to facilitate classification and research in driving programs to reduce the overall perinatal mortality.
Based on the International Statistical Classification of Diseases and Related Health Problems, 10th Revision (ICD-10), the International Classification of Diseases for Perinatal Mortality (ICD-PM) was released by the World Health Organization in August 2016 [13]. It is the first perinatal death classification system developed to be used worldwide. This system links the cause of perinatal death, using ICD-10 codes [14], separated by timing of death, with the maternal conditions that contribute to perinatal death.
Currently, in Hong Kong under the Hospital Authority, the coding system (for stillbirths only) used [15] is a direct one (ie, if the cause of the stillbirth could be identified, it would be stated directly such as congenital abnormalities, pregnancy-induced hypertension, cord accident, antepartum hemorrhage, maternal disease). The remaining cases would be placed in the categories unclassified, unexplained, and miscellaneous/uninvestigated, which essentially refer to unknown causes. The problem is that up to 92.8% (800/862) of stillbirths from 2012 to 2018 were classified under these 3 categories [15]. This phenomenon has significantly impaired the potential of using this perinatal database to further study and design programs to reduce perinatal mortality.
We hypothesized that by using a globally applicable classification system such as ICD-PM that recognizes stillbirths and neonatal deaths together with the contributing maternal conditions, coding of stillbirth and neonatal death could be significantly improved. Therefore, a validation study was performed to apply the ICD-PM coding system to our stillbirths and neonatal death cases.
Outcome
The primary outcome was the reduction in unknown causes for stillbirth and neonatal death after applying the ICD-PM coding system. The secondary outcome was the percentage agreement with expert (EA) in applying ICD-PM codes.
MethodsRecruitment
The ICD-PM system, the World Health Organization application of the ICD-10 to deaths during the perinatal period, was applied to existing perinatal death data from May 1, 2012, to April 30, 2019, in Kwong Wah Hospital, a regional public hospital with 34,920 deliveries during the 7-year study period.
Selection Criteria
Inclusion criteria:
Stillbirth cases diagnosed after 24 completed weeks gestation
Neonatal death cases within 28 days of birth
Exclusion criteria:
Miscarriage at less than 24 completed weeks of gestation
Termination of pregnancy due to fetal anomalies or maternal abnormal medical conditions
Ethics
Ethics approval for the study was granted by the Kowloon Central/Kowloon East Research Ethics Committee (KC/KE-19-0193/ER-1). As this clinical study did not involve active patient participation, no patient consent was required.
Diagnostic Classification
In our department, every case of stillbirth and neonatal death is discussed in our monthly perinatal meetings attended by consultants, specialists, trainees, and senior labor ward midwives and senior nurses from the departments of obstetrics and gynecology and pediatrics. A detailed case summary is presented by the resident trainee directly involved in the clinical management of that particular case. Diagnosis of the cause of stillbirth and neonatal death is based on the clinical findings and investigation results.
We performed routine investigations for stillbirths, including:
Maternal: complete blood counts; liver and renal function tests; urate; clotting profile; thyroid function test; lupus anticoagulant; anticardiolipin antibodies; antinuclear antibodies +/– anti-ds DNA; rheumatoid factor; Kleihauer test; toxoplasmosis, rubella, cytomegalovirus, and herpes simplex virus tests; oral glucose tolerance test; hemoglobin A1c; high vaginal swab; midstream urine for culture; and others.
Placenta: swabs for culture, histopathology, karyotyping (if the stillbirth showed dysmorphic features)
Stillbirth: body surface swabs for culture, autopsy (with consent from parents)
Data Validation
Our validation study started in 2017 and was divided into two parts.
Retrospective Validation Study (Five Years)
The recoding of the retrospective stillbirth and neonatal death data from May 1, 2012, to April 30, 2017, was performed by the principal investigator (HML) using the ICD-10 [14] to get a specific ICD-10 code that was then converted to the ICD-PM categories [13].
Our ICD-10 and ICD-PM codes together with the detailed case summaries for each stillbirth and neonatal death case were then forwarded to an overseas expert (EA; via emails without patient identity) for validation of our codes based on the detailed case summaries, further discussion, and verification. EA has extensive experience using the ICD-PM [1-4]. The proportion of ICD-PM codes EA disagreed with was noted.
Prospective Validation Study (Two Years)
The prospective application of ICD-10 and then ICD-PM was performed from May 1, 2017, to April 30, 2019. The coding for each stillbirth and neonatal death case was validated during our monthly perinatal meetings.
Our ICD-PM codes together with the detailed case summaries in this prospective case series were also forwarded to the overseas expert (EA) to be checked for agreement.
Statistical Analysis
All frequency data were analyzed by summary statistics. SPSS Statistics version 25.0 (IBM Corporation) was used for the analysis. The Pearson chi-square test was used where appropriate, such as to determine whether there was a significant difference between the frequency of unknown causes of death classified in our original Hospital Authority coding system and the newly applied ICD-PM coding system. P<.05 was considered statistically significant.
Results
We analyzed data for 34,920 deliveries, and 119 cases were included for analysis (92 stillbirths and 27 neonatal deaths):
Retrospective ICD-PM codes (5 years; May 1, 2012, to April 30, 2017)—total 85 cases
Prospective ICD-PM codes (2 years; May 1, 2017, to April 30, 2019)—total 34 cases
All stillbirth cases had the full set of routine investigations described under Methods. However, only 25.2% (30/119) of cases had a postmortem examination.
EA verified every single one of the 119 stillbirth and neonatal death cases during the study period. The overall agreement rate of our codes using ICD-10 and then ICD-PM was 93.2% (111/119 cases) with EA: 92% (78/85 cases) for the 5 years of retrospective cases and 97% (33/34 cases) for the 2 years of prospective cases (P=.44). It was interesting and educational to look at how EA disagreed with our codes (Table 1).
Table 2 illustrates the application of the ICD-PM for perinatal death and maternal condition in stillbirth and neonatal death cases, respectively. In the ICD-PM, there are 6 groups of antepartum causes for stillbirth (A1 to A6), 7 groups of intrapartum causes for stillbirth (I1 to I7), 11 groups of causes for neonatal death (N1 to N11), and 5 groups of maternal conditions (M1 to M5) to be associated with stillbirth or neonatal death.
The most common causes for antepartum stillbirths were A3 (antepartum hypoxia, 24/91, 26%), followed by A5 (disorders related to fetal growth, 17/91, 19%), and A1 (congenital malformations, deformations, and chromosomal abnormalities, 10/91, 11%). In this case series, there was only one intrapartum stillbirth (I1). The most common corresponding maternal conditions were M1 (complications of placenta, cord, and membranes, 39/91, 43%), followed by M4 (maternal medical and surgical conditions, 21/91, 23%), and M2 (maternal complications of pregnancy, 12/91, 13%). The most common associations were A3;M1 (20/91, 22%), A6;M5 (12/91, 13%), and A6;M4 (9/91, 10%).
On the other hand, the most common causes for neonatal deaths were N9 (low birth weight and prematurity, 9/27, 33%), followed by N8 (neonatal conditions, 5/27, 19%), N6 (infection, 4/27, 15%), and N1 (congenital malformations, deformations and chromosomal abnormalities, also 4/27, 15%). The most common corresponding maternal conditions were M1 (complications of placenta, cord, and membranes, 10/27, 37%), followed by M2 (maternal complications of pregnancy, 6/27, 22%) and M4 (maternal medical and surgical conditions, 5/27, 19%). The most common associations were N9;M1 (3/27, 11%), N9;M4 (3/27, 11%), and N6; M1 (3/27, 11%).
The 8 cases in which the overseas expert (EA) disagreed with our codes.
Case number
Our original codes
Our ICD-PMa codes
EA’s codes
Comment
1
Unknown
A6; M1
A6; M5
EA: wonder if the placental pathology showing chorioamnionitis (M1) is related to the stillbirth in the absence of other evidence
9
Preeclampsia with placenta abruptio
A3; M2
A3; M4
EA: would classify this as M4 (PETb) as the fetus died before the mother, likely as a result of the PET, rather than the maternal death (M2) being the cause of the fetal death
EA: Do you think the hypoplastic adrenals (A1) was the cause of death? Or hypoxia as a result of the TTTS (M1) with the hypoplastic adrenals being a secondary issue?
19
Unknown
A6; M1
A6; M5
EA: As long as you are certain of the chorioamnionitis (M1) again, or is this a postmortem change (M5) between fetal death and delivery?
23
Unknown
A5; M4
A6; M4
EA: The birthweight is surely to be expected with the delay between fetal death and delivery, and there is no evidence of IUGRd (A5); keep M4 (GDMe).
31
Unknown
A6; M1
A6; M5
EA: Are you confident of the chorioamnionitis (M1) as the cause of death?
71
Fetal syndromal abnormality
A1; M3
A1; M5
EA: The cesarean delivery was done after the fetal death. Cesarean delivery as a cause is more when there are complications (M3) from the cesarean delivery that lead to the fetal death
89
Cord accident, drug addict
A3; M4
A3; M1
EA: Cord accident (A3; M1) as the cause of fetal death rather than mother is drug addict (M4)
aICD-PM: International Classification of Disease for Perinatal Mortality.
bPET: pre-eclampsia.
cTTTS: twin-twin transfusion syndrome.
dIUGR: intrauterine growth restriction.
eGDM: gestational diabetes mellitus.
ICD-PM codes for stillbirths (antepartum [A] and intrapartum [I]) and neonatal [N] deaths with maternal conditions (n=119).
Perinatal cause of death
Maternal condition
\n\n
M1: complications of placenta, cord, and membrane
M2: maternal complications of pregnancy
M3: other complications of labor and delivery
M4: maternal medical and surgical conditions
M5: no maternal conditions
Total (%)
Antenatal death (A)
\n\n
\n\n
\n\n
\n\n
\n\n
\n\n
\n\n
A1: congenital malformations, deformations, and chromosomal abnormalities
2
2
0
2
4
10 (11.0)
\n\n
A2: infection
6
0
0
0
1
7 (7.7)
\n\n
A3: antepartum hypoxia
20
1
0
3
0
24 (26.4)
\n\n
A4: other specified antepartum disorder
1
0
0
1
0
2 (2.2)
\n\n
A5: disorders related to fetal growth
6
5
0
6
0
17 (18.7)
\n\n
A6: fetal death of unspecified cause
4
4
2
9
12
31 (34.0)
\n\n
Total (%)
39 (42.9)
12 (13.2)
2 (2.2)
21 (23.0)
17 (18.7)
91 (100.0)
Intrapartum death (I)
\n\n
\n\n
\n\n
\n\n
\n\n
\n\n
\n\n
I1: congenital malformations, deformations, and chromosomal abnormalities
0
0
1
0
0
1 (100.0)
\n\n
I2: birth trauma
0
0
0
0
0
0 (0.0)
\n\n
I3: acute intrapartum event
0
0
0
0
0
0 (0.0)
\n\n
I4: infection
0
0
0
0
0
0 (0.0)
\n\n
I5: other specified intrapartum disorder
0
0
0
0
0
0 (0.0)
\n\n
I6: disorders related to fetal growth
0
0
0
0
0
0 (0.0)
\n\n
I7: intrapartum death of unspecified cause
0
0
0
0
0
0 (0.0)
\n\n
Total (%)
0
0
1 (100.0)
0
0
1 (100.0)
Neonatal death (N)
\n\n
\n\n
\n\n
\n\n
\n\n
\n\n
\n\n
N1: congenital malformations, deformations, and chromosomal abnormalities
1
1
2
0
0
4 (14.8)
\n\n
N2: disorders related to fetal growth
0
0
0
1
0
1 (3.7)
\n\n
N3: birth trauma
0
0
0
0
0
0 (0.0)
\n\n
N4: complications of intrapartum events
1
0
0
0
0
1 (3.7)
\n\n
N5: convulsions and disorders of cerebral status
0
0
0
0
0
0 (0.0)
\n\n
N6: infection
3
1
0
0
0
4 (14.8)
\n\n
N7: respiratory and cardiovascular disorders
0
2
1
0
0
3 (11.1)
\n\n
N8: neonatal conditions
2
1
0
1
1
5 (18.5)
\n\n
N9: low birth weight and prematurity
3
1
1
3
1
9 (33.4)
\n\n
N10: miscellaneous
0
0
0
0
0
0 (0.0)
\n\n
N11: neonatal death of unspecified cause
0
0
0
0
0
0 (0.0)
\n\n
Total (%)
10 (37.1)
6 (22.2)
4 (14.8)
5 (18.5)
2 (7.4)
27 (100.0)
Before the application of the ICD-PM coding system, 34.5% (41/119) of stillbirths and neonatal deaths were classified as having unknown causes. After the application of the ICD-PM system, the cases with unknown causes of perinatal death dropped to 10.1% (12/119) cases (P<.001). In our study, all neonatal deaths had specific causes identified.
After retrospectively applying ICD-10 and ICD-PM codes in the cases with unknown causes (30/85, 35%) of stillbirth from our original classification on the 85 cases from 2012 to 2017 (retrospective validation study), we can further reduce the unknown causes (ie, A6;M5) to 8% (7/85) of cases (P<.001). Table 3 showed how we could change the 23 unknown (30 minus 7) to known causes using ICD-PM.
In the retrospective 5-year validation study (May 1, 2012, to April 30, 2017), 23/30 unknown causes of stillbirth can be assigned a diagnosis category after applying ICD-PM code.
Case number
Original codes
Remarks
ICD-PMa codes
3
Unknown
Funisitis, GDMb
A2; M1
8
Unknown
Maternal hypothyroidism
A6; M4
14
Unknown
History of deep vein thrombosis, placental histopathology: fetal thrombotic vasculopathy and placenta infarction
A3; M1
23
Unknown
480 g at 28+ weeks, GDM
A6; M4
28
Unknown
375 g at 24+ weeks, placenta: thrombotic vasculopathy
A5; M1
32
Unknown
Placenta: focal fetal thrombotic vasculopathy
A6; M1
39
Unknown
GDM
A6; M4
48
Unknown
GDM
A6; M4
50
Unknown
2520 g at 39 weeks; placenta: thrombotic vasculopathy
Maternal infection with fever; placenta: focal intervillous thrombus
A2; M1
85
Unknown
Placenta: minor infarcted villi
A6; M1
aICD-PM: International Classification of Diseases for Perinatal Mortality.
bGDM: gestational diabetes mellitus.
cDM: diabetes mellitus.
DiscussionPrincipal Findings
Perinatal deaths remain problematic worldwide. Before the advent of the ICD-PM, there were several independent systems in high-income countries and few systems in low- and middle- income countries, causing significant disparity in perinatal death data recorded among countries [16]. The classification system used in Hong Kong Hospital Authority obstetrics units is no better, with 92.8% of cases having unknown causes [15]. It seems that we were somehow reluctant to give a specific diagnosis under the original coding system unless the cause was crystal clear. Hence detailed and accurate information that can be retrieved from our original classification of perinatal mortality cases was scarce. The ICD-PM is the first system that addresses the issue internationally. ICD-10 and ICD-PM are user-friendly as shown by the high level of agreement (93.2% overall) between our codes with that by the international expert (EA). The agreement with EA of our codes using ICD-PM was even higher, reaching 97.1% for the 2 years of prospective cases compared with 91.8% for the 5 years of retrospective cases, although not statistically significant (P=.44). This further supports our observation that the final consensus code made during perinatal meetings with multidisciplinary discussion on the perinatal death cases gives the most accurate answer, although our principal investigator (HML) had already done a good job with the 5-year retrospective codes.
The ICD-PM is designed to be used for classifying stillbirths and neonatal deaths at three levels. First, it identifies the time of death as antepartum (before the onset of labor), intrapartum (during labor but before delivery), or neonatal (up to day 7 of postnatal life, can be extended to late neonatal deaths, so as within 28 days of life, like in our classification). Second, it is multilayered such that the depth of classification can reflect the locally available intensity of investigation. In our Hospital Authority obstetrics units, the investigations for stillbirths and neonatal deaths are quite in-depth, as described under Methods, but this was not be reflected by the original classification system. Third, the ICD-PM links the contributing maternal condition, if any, with perinatal death. This is very important because most of these contributing maternal conditions would not have shown up in our original coding system. Ultimately, the ICD-PM classification at all three levels allows easy identification of where a program intervention should be targeted in order to improve the perinatal outcomes.
After the application of the ICD-PM system, the ratio of unknown causes of perinatal mortality dropped from 34.5% to 10.1%, which was statistically significant (P<.001). A total of 77% of stillbirth cases initially classified as unknown were now assigned a diagnostic category after applying the ICD-PM (Table 3). In our study, all neonatal deaths had specific causes identified. In other words, the ICD-PM is more useful for coding stillbirths than neonatal deaths in our locality.
The main benefit of using ICD-10 and ICD-PM codes was to have a better understanding of the perinatal deaths in terms of the timing of death, the depth of investigations, and any contributing maternal conditions. We also consider the ICD-PM code to be user-friendly for changing an existing local perinatal death classification to one that is global and can be compared with international data. Using the ICD-PM code can lead to better classification of perinatal deaths, reduce the proportion of unknown diagnoses, and clearly link the maternal conditions with these perinatal deaths. There are some countries such as the United Kingdom [3], South Africa [3,16,17], India [18], and Colombia [19] using the ICD-PM to interpret the perinatal mortality data which showed that the ICD-PM classification was feasible and enabling the characterization of perinatal mortality. This is the first validation study demonstrating the application of the ICD-PM coding system in Hong Kong.
Limitations
However, there are limitations to our study. The study was focused on one hospital only, so the sample size was small. The improvement in coding might be related to the enthusiastic principal investigator (HML) as the coder in the retrospective validation study; the code in the prospective validation study was done during our monthly perinatal meetings with multidisciplinary input from various stakeholders, which is our usual practice before and after the study. Whether there is an overall Hawthorne effect (improving performance of coding during the study period) could be seen by continuous monitoring of the diagnostic classification of stillbirths and neonatal deaths after the ICD-PM is formally used for coding from 2020 onward.
Further study can be performed in all Hong Kong Hospital Authority obstetrics units using the ICD-10 and ICD-PM so as to draw an overall picture of perinatal mortality across the territory.
Conclusions
The ICD-PM is a user-friendly system that can enhance the understanding of data [5]. Using the ICD-PM coding system could lead to a more comprehensive classification of perinatal deaths, reduce the proportion of unknown causes as well as providing better linkage to maternal conditions in these perinatal deaths. The ICD-PM classifications are more extensive in covering diagnostic categories, with more specific details. Implementing this new coding system in Hong Kong Hospital Authority obstetrics units will be of great help in improving clinical practice and reducing perinatal mortality in the long run.
Authors' Contributions: All authors had full access to the data, contributed to the study, approved the final version for publication, and take responsibility for its accuracy and integrity. HML and WCL were responsible for concept or design of the study. HML acquired the data and drafted the article. All authors contributed to the analysis or interpretation of data. WCL, EA, and WKM performed critical revision for important intellectual content.
Conflicts of Interest: None declared.
AbbreviationsICD-10
International Statistical Classification of Diseases and Related Health Problems, 10th Revision
ICD-PM
International Classification of Diseases for Perinatal Mortality
AllansonERTunçalpOGardosiJPattinsonRCFrancisAVogelJPErwichJFlenadyVJFrøenJFNeilsonJQuachAChouDMathaiMSayLGülmezogluAMOptimising the International Classification of Diseases to identify the maternal condition in the case of perinatal deathBJOG201611123122037204610.1111/1471-0528.1424610.1111/1471-0528.142462752755027527550AllansonERTunçalpOGardosiJPattinsonRCVogelJPErwichJFlenadyVJFrøenJFNeilsonJQuachAFrancisAChouDMathaiMSayLGülmezogluAMGiving a voice to millions: developing the WHO application of ICD-10 to deaths during the perinatal period: ICD-PMBJOG201611123121896189910.1111/1471-0528.1424310.1111/1471-0528.142432752695727526957AllansonERTunçalpOGardosiJPattinsonRCFrancisAVogelJPErwichJFlenadyVJFrøenJFNeilsonJQuachAChouDMathaiMSayLGülmezogluAMThe WHO application of ICD-10 to deaths during the perinatal period (ICD-PM): results from pilot database testing in South Africa and United KingdomBJOG201611123122019202810.1111/1471-0528.1424410.1111/1471-0528.142442752712227527122AllansonERVogelJPTunçalpOGardosiJPattinsonRCFrancisAErwichJFlenadyVJFrøenJFNeilsonJQuachAChouDMathaiMSayLGülmezogluAMApplication of ICD-PM to preterm-related neonatal deaths in South Africa and United KingdomBJOG201611123122029203610.1111/1471-0528.1424510.1111/1471-0528.142452752739027527390AminuMvan den BroekNStillbirth in low- and middle-income countries: addressing the 'silent epidemic'Int Health201970111423723910.1093/inthealth/ihz0153108189331081893FlenadyVWojcieszekAMMiddletonPEllwoodDErwichJJCooryMKhongTYSilverRMSmithGCSBoyleFMLawnJEBlencoweHLeisherSHGrossMMHoreyDFarralesLBloomfieldFMcCowanLBrownSJJosephKSZeitlinJReinebrantHERavaldiCVannacciACassidyJCassidyPFarquharCWallaceESiassakosDHeazellAEPStoreyCSadlerLPetersenSFrøenJFGoldenbergRLLancet Ending Preventable Stillbirths study groupLancet Stillbirths In High-Income Countries Investigator GroupStillbirths: recall to action in high-income countriesLancet20162133871001969170210.1016/S0140-6736(15)01020-X2679407026794070FrøenJFPinarHFlenadyVBahrinSCharlesAChaukeLDayKDukeCWFacchinettiFFrettsRCGardenerGGilshenanKGordijnSJGordonAGuyonGHarrisonCKoshyRPattinsonRCPeterssonKRussellLSaastadESmithGCSTorabiRCauses of death and associated conditions (Codac): a utilitarian approach to the classification of perinatal deathsBMC Pregnancy Childbirth200961092210.1186/1471-2393-9-221951522819515228VarliIHPeterssonKBottingaRBremmeKHofsjöAHolmMHolsteCKublickasMNormanMPiloCRoosNSundbergAWolffKPapadogiannakisNThe Stockholm classification of stillbirthActa Obstet Gynecol Scand200887111202121210.1080/000163408024602711895120718951207LuJRMcCowanLA comparison of the Perinatal Society of Australia and New Zealand-Perinatal Death Classification system and relevant condition at death stillbirth classification systemsAust N Z J Obstet Gynaecol20091049546747110.1111/j.1479-828X.2009.01066.x1978072719780727KortewegFJGordijnSJTimmerAErwichJJHMBergmanKABoumanKRaviseJMHeringaMPHolmJPThe Tulip classification of perinatal mortality: introduction and multidisciplinary inter-rater agreementBJOG20064113439340110.1111/j.1471-0528.2006.00881.x10.1111/j.1471-0528.2006.00881.x1655365116553651BoydTKWrightCAOdendaalHJElliottAJSensMAFolkerthRDRobertsDJKinneyHCPASS NetworkThe stillbirth classification system for the safe passage studyPediatr Dev Pathol201720212013210.1177/10935266166862512832696328326963International Stillbirth Alliance Collaborative for Improving Classification of Perinatal DeathsFlenadyVWojcieszekAMEllwoodDLeisherSHErwichJJHMDraperESMcClureEMReinebrantHEOatsJMcCowanLKentALGardenerGGordonATudehopeDSiassakosDStoreyCZuccolloJDahlstromJEGoldKJGordijnSPetterssonKMassonVPattinsonRGardosiJKhongTYFrøenJFSilverRMClassification of causes and associated conditions for stillbirths and neonatal deathsSemin Fetal Neonatal 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One2019145e021586410.1371/journal.pone.02158643107111131071111SharmaBSiwatchSKakkarNSuriVRainaAAggarwalNClassifying stillbirths in a tertiary care hospital of India: International Classification of Disease-Perinatal Mortality (ICD-PM) versus cause of death-associated condition (CODAC) systemJ Obstet Gynaecol20204291510.1080/01443615.2020.173601632347769Salazar-BarrientosMZuleta-TobónJJ[Application of the International Classification of Diseases for Perinatal Mortality (ICD-PM) to vital statistics records for the purpose of classifying perinatal deaths in Antioquia, Colombia]Rev Colomb Obstet Ginecol20191270422824210.18597/rcog.34063214223832142238\n"],"string":"[\n \"\\nJMIR Med InformJMIJMIR Medical Informatics2291-9694JMIR PublicationsToronto, Canada32744510PMC7432142v8i8e2007110.2196/20071Original PaperOriginal PaperImproving Diagnostic Classification of Stillbirths and Neonatal Deaths Using ICD-PM (International Classification of Diseases for Perinatal Mortality) Codes: Validation StudyEysenbachGuntherDella MeaVincenzoLukHiu MeiMD, MMed1https://orcid.org/0000-0001-8912-5552AllansonEmmaPhD, FRANZCOG2https://orcid.org/0000-0002-4786-4354MingWai-KitMPH, MD, PhD3https://orcid.org/0000-0002-8846-7515LeungWing CheongMD, FRCOGhttps://orcid.org/0000-0003-1851-21381Department of Obstetrics and GynaecologyKwong Wah HospitalHong Kong SARChina (Hong Kong)852 35175091leungwc@ha.org.hk\\n\\nDepartment of Obstetrics and Gynaecology\\nKwong Wah Hospital\\nHong Kong SAR\\nChina (Hong Kong)\\n\\n\\nInstitute of Health Research\\nUniversity of Notre Dame, Fremantle\\nWestern Australia\\nAustralia\\n\\n\\nDepartment of Public Health and Preventive Medicine\\nSchool of Medicine\\nJinan University\\nGuangzhou\\nChina\\nCorresponding Author: Wing Cheong Leung leungwc@ha.org.hk8202038202088e2007111520204620208620202562020©Hiu Mei Luk, Emma Allanson, Wai-Kit Ming, Wing Cheong Leung. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 03.08.2020.2020This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Medical Informatics, is properly cited. The complete bibliographic information, a link to the original publication on http://medinform.jmir.org/, as well as this copyright and license information must be included.Background
Stillbirths and neonatal deaths have long been imperfectly classified and recorded worldwide. In Hong Kong, the current code system is deficient (>90% cases with unknown causes) in providing the diagnoses of perinatal mortality cases.
Objective
The objective of this study was to apply the International Classification of Diseases for Perinatal Mortality (ICD-PM) system to existing perinatal death data. Further, the aim was to assess whether there was any change in the classifications of perinatal deaths compared with the existing classification system and identify any areas in which future interventions can be made.
Methods
We applied the ICD-PM (with International Statistical Classification of Diseases and Related Health Problems, 10th Revision) code system to existing perinatal death data in Kwong Wah Hospital, Hong Kong, to improve diagnostic classification. The study included stillbirths (after 24 weeks gestation) and neonatal deaths (from birth to 28 days). The retrospective data (5 years) from May 1, 2012, to April 30, 2017, were recoded by the principal investigator (HML) applying the ICD-PM, then validated by an overseas expert (EA) after she reviewed the detailed case summaries. The prospective application of ICD-PM from May 1, 2017, to April 30, 2019, was performed during the monthly multidisciplinary perinatal meetings and then also validated by EA for agreement.
Results
We analyzed the data of 34,920 deliveries, and 119 cases were included for analysis (92 stillbirths and 27 neonatal deaths). The overall agreement with EA of our codes using the ICD-PM was 93.2% (111/119); 92% (78/85) for the 5 years of retrospective codes and 97% (33/34) for the 2 years of prospective codes (P=.44). After the application of the ICD-PM, the overall proportion of unknown causes of perinatal mortality dropped from 34.5% (41/119) to 10.1% (12/119) of cases (P<.001).
Conclusions
Using the ICD-PM would lead to a better classification of perinatal deaths, reduce the proportion of unknown diagnoses, and clearly link the maternal conditions with these perinatal deaths.
More than 5 million perinatal deaths occur globally each year, and this largely silent epidemic significantly impacts and burdens families [1,2]. Despite this, perinatal deaths have long been frequently invisible and poorly recorded worldwide.
A large proportion of stillbirth and neonatal death cases take place in less developed countries [3]. Classification methods used in these countries can be obscure. The numbers of skilled medical personnel are often inadequate, which contributes to less than comprehensive recording of clinical data at the time of stillbirth and neonatal deaths [4,5]. In contrast, stillbirth affects proportionally fewer births in high-income countries, and data related to these deaths tend to be comprehensively recorded [6]. Many high-income countries have developed their own classification systems for perinatal deaths; for example, the United Kingdom uses the Codac system [7], Sweden has the Stockholm system [8], Australia and New Zealand use the Perinatal Society of Australia and New Zealand perinatal death classification system [9], and the Netherlands uses the Tulip classification system [10]. In the United States, the Stillbirth Collaborative Research Network developed the initial causes of fetal death system [11]. While we see an annual reduction in the rate in stillbirth globally [12], this trend differs widely among high- and low- income countries. An internationally recognized classification system of perinatal mortality would be invaluable so that the data could easily be compared between different countries to facilitate classification and research in driving programs to reduce the overall perinatal mortality.
Based on the International Statistical Classification of Diseases and Related Health Problems, 10th Revision (ICD-10), the International Classification of Diseases for Perinatal Mortality (ICD-PM) was released by the World Health Organization in August 2016 [13]. It is the first perinatal death classification system developed to be used worldwide. This system links the cause of perinatal death, using ICD-10 codes [14], separated by timing of death, with the maternal conditions that contribute to perinatal death.
Currently, in Hong Kong under the Hospital Authority, the coding system (for stillbirths only) used [15] is a direct one (ie, if the cause of the stillbirth could be identified, it would be stated directly such as congenital abnormalities, pregnancy-induced hypertension, cord accident, antepartum hemorrhage, maternal disease). The remaining cases would be placed in the categories unclassified, unexplained, and miscellaneous/uninvestigated, which essentially refer to unknown causes. The problem is that up to 92.8% (800/862) of stillbirths from 2012 to 2018 were classified under these 3 categories [15]. This phenomenon has significantly impaired the potential of using this perinatal database to further study and design programs to reduce perinatal mortality.
We hypothesized that by using a globally applicable classification system such as ICD-PM that recognizes stillbirths and neonatal deaths together with the contributing maternal conditions, coding of stillbirth and neonatal death could be significantly improved. Therefore, a validation study was performed to apply the ICD-PM coding system to our stillbirths and neonatal death cases.
Outcome
The primary outcome was the reduction in unknown causes for stillbirth and neonatal death after applying the ICD-PM coding system. The secondary outcome was the percentage agreement with expert (EA) in applying ICD-PM codes.
MethodsRecruitment
The ICD-PM system, the World Health Organization application of the ICD-10 to deaths during the perinatal period, was applied to existing perinatal death data from May 1, 2012, to April 30, 2019, in Kwong Wah Hospital, a regional public hospital with 34,920 deliveries during the 7-year study period.
Selection Criteria
Inclusion criteria:
Stillbirth cases diagnosed after 24 completed weeks gestation
Neonatal death cases within 28 days of birth
Exclusion criteria:
Miscarriage at less than 24 completed weeks of gestation
Termination of pregnancy due to fetal anomalies or maternal abnormal medical conditions
Ethics
Ethics approval for the study was granted by the Kowloon Central/Kowloon East Research Ethics Committee (KC/KE-19-0193/ER-1). As this clinical study did not involve active patient participation, no patient consent was required.
Diagnostic Classification
In our department, every case of stillbirth and neonatal death is discussed in our monthly perinatal meetings attended by consultants, specialists, trainees, and senior labor ward midwives and senior nurses from the departments of obstetrics and gynecology and pediatrics. A detailed case summary is presented by the resident trainee directly involved in the clinical management of that particular case. Diagnosis of the cause of stillbirth and neonatal death is based on the clinical findings and investigation results.
We performed routine investigations for stillbirths, including:
Maternal: complete blood counts; liver and renal function tests; urate; clotting profile; thyroid function test; lupus anticoagulant; anticardiolipin antibodies; antinuclear antibodies +/– anti-ds DNA; rheumatoid factor; Kleihauer test; toxoplasmosis, rubella, cytomegalovirus, and herpes simplex virus tests; oral glucose tolerance test; hemoglobin A1c; high vaginal swab; midstream urine for culture; and others.
Placenta: swabs for culture, histopathology, karyotyping (if the stillbirth showed dysmorphic features)
Stillbirth: body surface swabs for culture, autopsy (with consent from parents)
Data Validation
Our validation study started in 2017 and was divided into two parts.
Retrospective Validation Study (Five Years)
The recoding of the retrospective stillbirth and neonatal death data from May 1, 2012, to April 30, 2017, was performed by the principal investigator (HML) using the ICD-10 [14] to get a specific ICD-10 code that was then converted to the ICD-PM categories [13].
Our ICD-10 and ICD-PM codes together with the detailed case summaries for each stillbirth and neonatal death case were then forwarded to an overseas expert (EA; via emails without patient identity) for validation of our codes based on the detailed case summaries, further discussion, and verification. EA has extensive experience using the ICD-PM [1-4]. The proportion of ICD-PM codes EA disagreed with was noted.
Prospective Validation Study (Two Years)
The prospective application of ICD-10 and then ICD-PM was performed from May 1, 2017, to April 30, 2019. The coding for each stillbirth and neonatal death case was validated during our monthly perinatal meetings.
Our ICD-PM codes together with the detailed case summaries in this prospective case series were also forwarded to the overseas expert (EA) to be checked for agreement.
Statistical Analysis
All frequency data were analyzed by summary statistics. SPSS Statistics version 25.0 (IBM Corporation) was used for the analysis. The Pearson chi-square test was used where appropriate, such as to determine whether there was a significant difference between the frequency of unknown causes of death classified in our original Hospital Authority coding system and the newly applied ICD-PM coding system. P<.05 was considered statistically significant.
Results
We analyzed data for 34,920 deliveries, and 119 cases were included for analysis (92 stillbirths and 27 neonatal deaths):
Retrospective ICD-PM codes (5 years; May 1, 2012, to April 30, 2017)—total 85 cases
Prospective ICD-PM codes (2 years; May 1, 2017, to April 30, 2019)—total 34 cases
All stillbirth cases had the full set of routine investigations described under Methods. However, only 25.2% (30/119) of cases had a postmortem examination.
EA verified every single one of the 119 stillbirth and neonatal death cases during the study period. The overall agreement rate of our codes using ICD-10 and then ICD-PM was 93.2% (111/119 cases) with EA: 92% (78/85 cases) for the 5 years of retrospective cases and 97% (33/34 cases) for the 2 years of prospective cases (P=.44). It was interesting and educational to look at how EA disagreed with our codes (Table 1).
Table 2 illustrates the application of the ICD-PM for perinatal death and maternal condition in stillbirth and neonatal death cases, respectively. In the ICD-PM, there are 6 groups of antepartum causes for stillbirth (A1 to A6), 7 groups of intrapartum causes for stillbirth (I1 to I7), 11 groups of causes for neonatal death (N1 to N11), and 5 groups of maternal conditions (M1 to M5) to be associated with stillbirth or neonatal death.
The most common causes for antepartum stillbirths were A3 (antepartum hypoxia, 24/91, 26%), followed by A5 (disorders related to fetal growth, 17/91, 19%), and A1 (congenital malformations, deformations, and chromosomal abnormalities, 10/91, 11%). In this case series, there was only one intrapartum stillbirth (I1). The most common corresponding maternal conditions were M1 (complications of placenta, cord, and membranes, 39/91, 43%), followed by M4 (maternal medical and surgical conditions, 21/91, 23%), and M2 (maternal complications of pregnancy, 12/91, 13%). The most common associations were A3;M1 (20/91, 22%), A6;M5 (12/91, 13%), and A6;M4 (9/91, 10%).
On the other hand, the most common causes for neonatal deaths were N9 (low birth weight and prematurity, 9/27, 33%), followed by N8 (neonatal conditions, 5/27, 19%), N6 (infection, 4/27, 15%), and N1 (congenital malformations, deformations and chromosomal abnormalities, also 4/27, 15%). The most common corresponding maternal conditions were M1 (complications of placenta, cord, and membranes, 10/27, 37%), followed by M2 (maternal complications of pregnancy, 6/27, 22%) and M4 (maternal medical and surgical conditions, 5/27, 19%). The most common associations were N9;M1 (3/27, 11%), N9;M4 (3/27, 11%), and N6; M1 (3/27, 11%).
The 8 cases in which the overseas expert (EA) disagreed with our codes.
Case number
Our original codes
Our ICD-PMa codes
EA’s codes
Comment
1
Unknown
A6; M1
A6; M5
EA: wonder if the placental pathology showing chorioamnionitis (M1) is related to the stillbirth in the absence of other evidence
9
Preeclampsia with placenta abruptio
A3; M2
A3; M4
EA: would classify this as M4 (PETb) as the fetus died before the mother, likely as a result of the PET, rather than the maternal death (M2) being the cause of the fetal death
EA: Do you think the hypoplastic adrenals (A1) was the cause of death? Or hypoxia as a result of the TTTS (M1) with the hypoplastic adrenals being a secondary issue?
19
Unknown
A6; M1
A6; M5
EA: As long as you are certain of the chorioamnionitis (M1) again, or is this a postmortem change (M5) between fetal death and delivery?
23
Unknown
A5; M4
A6; M4
EA: The birthweight is surely to be expected with the delay between fetal death and delivery, and there is no evidence of IUGRd (A5); keep M4 (GDMe).
31
Unknown
A6; M1
A6; M5
EA: Are you confident of the chorioamnionitis (M1) as the cause of death?
71
Fetal syndromal abnormality
A1; M3
A1; M5
EA: The cesarean delivery was done after the fetal death. Cesarean delivery as a cause is more when there are complications (M3) from the cesarean delivery that lead to the fetal death
89
Cord accident, drug addict
A3; M4
A3; M1
EA: Cord accident (A3; M1) as the cause of fetal death rather than mother is drug addict (M4)
aICD-PM: International Classification of Disease for Perinatal Mortality.
bPET: pre-eclampsia.
cTTTS: twin-twin transfusion syndrome.
dIUGR: intrauterine growth restriction.
eGDM: gestational diabetes mellitus.
ICD-PM codes for stillbirths (antepartum [A] and intrapartum [I]) and neonatal [N] deaths with maternal conditions (n=119).
Perinatal cause of death
Maternal condition
\\n\\n
M1: complications of placenta, cord, and membrane
M2: maternal complications of pregnancy
M3: other complications of labor and delivery
M4: maternal medical and surgical conditions
M5: no maternal conditions
Total (%)
Antenatal death (A)
\\n\\n
\\n\\n
\\n\\n
\\n\\n
\\n\\n
\\n\\n
\\n\\n
A1: congenital malformations, deformations, and chromosomal abnormalities
2
2
0
2
4
10 (11.0)
\\n\\n
A2: infection
6
0
0
0
1
7 (7.7)
\\n\\n
A3: antepartum hypoxia
20
1
0
3
0
24 (26.4)
\\n\\n
A4: other specified antepartum disorder
1
0
0
1
0
2 (2.2)
\\n\\n
A5: disorders related to fetal growth
6
5
0
6
0
17 (18.7)
\\n\\n
A6: fetal death of unspecified cause
4
4
2
9
12
31 (34.0)
\\n\\n
Total (%)
39 (42.9)
12 (13.2)
2 (2.2)
21 (23.0)
17 (18.7)
91 (100.0)
Intrapartum death (I)
\\n\\n
\\n\\n
\\n\\n
\\n\\n
\\n\\n
\\n\\n
\\n\\n
I1: congenital malformations, deformations, and chromosomal abnormalities
0
0
1
0
0
1 (100.0)
\\n\\n
I2: birth trauma
0
0
0
0
0
0 (0.0)
\\n\\n
I3: acute intrapartum event
0
0
0
0
0
0 (0.0)
\\n\\n
I4: infection
0
0
0
0
0
0 (0.0)
\\n\\n
I5: other specified intrapartum disorder
0
0
0
0
0
0 (0.0)
\\n\\n
I6: disorders related to fetal growth
0
0
0
0
0
0 (0.0)
\\n\\n
I7: intrapartum death of unspecified cause
0
0
0
0
0
0 (0.0)
\\n\\n
Total (%)
0
0
1 (100.0)
0
0
1 (100.0)
Neonatal death (N)
\\n\\n
\\n\\n
\\n\\n
\\n\\n
\\n\\n
\\n\\n
\\n\\n
N1: congenital malformations, deformations, and chromosomal abnormalities
1
1
2
0
0
4 (14.8)
\\n\\n
N2: disorders related to fetal growth
0
0
0
1
0
1 (3.7)
\\n\\n
N3: birth trauma
0
0
0
0
0
0 (0.0)
\\n\\n
N4: complications of intrapartum events
1
0
0
0
0
1 (3.7)
\\n\\n
N5: convulsions and disorders of cerebral status
0
0
0
0
0
0 (0.0)
\\n\\n
N6: infection
3
1
0
0
0
4 (14.8)
\\n\\n
N7: respiratory and cardiovascular disorders
0
2
1
0
0
3 (11.1)
\\n\\n
N8: neonatal conditions
2
1
0
1
1
5 (18.5)
\\n\\n
N9: low birth weight and prematurity
3
1
1
3
1
9 (33.4)
\\n\\n
N10: miscellaneous
0
0
0
0
0
0 (0.0)
\\n\\n
N11: neonatal death of unspecified cause
0
0
0
0
0
0 (0.0)
\\n\\n
Total (%)
10 (37.1)
6 (22.2)
4 (14.8)
5 (18.5)
2 (7.4)
27 (100.0)
Before the application of the ICD-PM coding system, 34.5% (41/119) of stillbirths and neonatal deaths were classified as having unknown causes. After the application of the ICD-PM system, the cases with unknown causes of perinatal death dropped to 10.1% (12/119) cases (P<.001). In our study, all neonatal deaths had specific causes identified.
After retrospectively applying ICD-10 and ICD-PM codes in the cases with unknown causes (30/85, 35%) of stillbirth from our original classification on the 85 cases from 2012 to 2017 (retrospective validation study), we can further reduce the unknown causes (ie, A6;M5) to 8% (7/85) of cases (P<.001). Table 3 showed how we could change the 23 unknown (30 minus 7) to known causes using ICD-PM.
In the retrospective 5-year validation study (May 1, 2012, to April 30, 2017), 23/30 unknown causes of stillbirth can be assigned a diagnosis category after applying ICD-PM code.
Case number
Original codes
Remarks
ICD-PMa codes
3
Unknown
Funisitis, GDMb
A2; M1
8
Unknown
Maternal hypothyroidism
A6; M4
14
Unknown
History of deep vein thrombosis, placental histopathology: fetal thrombotic vasculopathy and placenta infarction
A3; M1
23
Unknown
480 g at 28+ weeks, GDM
A6; M4
28
Unknown
375 g at 24+ weeks, placenta: thrombotic vasculopathy
A5; M1
32
Unknown
Placenta: focal fetal thrombotic vasculopathy
A6; M1
39
Unknown
GDM
A6; M4
48
Unknown
GDM
A6; M4
50
Unknown
2520 g at 39 weeks; placenta: thrombotic vasculopathy
Maternal infection with fever; placenta: focal intervillous thrombus
A2; M1
85
Unknown
Placenta: minor infarcted villi
A6; M1
aICD-PM: International Classification of Diseases for Perinatal Mortality.
bGDM: gestational diabetes mellitus.
cDM: diabetes mellitus.
DiscussionPrincipal Findings
Perinatal deaths remain problematic worldwide. Before the advent of the ICD-PM, there were several independent systems in high-income countries and few systems in low- and middle- income countries, causing significant disparity in perinatal death data recorded among countries [16]. The classification system used in Hong Kong Hospital Authority obstetrics units is no better, with 92.8% of cases having unknown causes [15]. It seems that we were somehow reluctant to give a specific diagnosis under the original coding system unless the cause was crystal clear. Hence detailed and accurate information that can be retrieved from our original classification of perinatal mortality cases was scarce. The ICD-PM is the first system that addresses the issue internationally. ICD-10 and ICD-PM are user-friendly as shown by the high level of agreement (93.2% overall) between our codes with that by the international expert (EA). The agreement with EA of our codes using ICD-PM was even higher, reaching 97.1% for the 2 years of prospective cases compared with 91.8% for the 5 years of retrospective cases, although not statistically significant (P=.44). This further supports our observation that the final consensus code made during perinatal meetings with multidisciplinary discussion on the perinatal death cases gives the most accurate answer, although our principal investigator (HML) had already done a good job with the 5-year retrospective codes.
The ICD-PM is designed to be used for classifying stillbirths and neonatal deaths at three levels. First, it identifies the time of death as antepartum (before the onset of labor), intrapartum (during labor but before delivery), or neonatal (up to day 7 of postnatal life, can be extended to late neonatal deaths, so as within 28 days of life, like in our classification). Second, it is multilayered such that the depth of classification can reflect the locally available intensity of investigation. In our Hospital Authority obstetrics units, the investigations for stillbirths and neonatal deaths are quite in-depth, as described under Methods, but this was not be reflected by the original classification system. Third, the ICD-PM links the contributing maternal condition, if any, with perinatal death. This is very important because most of these contributing maternal conditions would not have shown up in our original coding system. Ultimately, the ICD-PM classification at all three levels allows easy identification of where a program intervention should be targeted in order to improve the perinatal outcomes.
After the application of the ICD-PM system, the ratio of unknown causes of perinatal mortality dropped from 34.5% to 10.1%, which was statistically significant (P<.001). A total of 77% of stillbirth cases initially classified as unknown were now assigned a diagnostic category after applying the ICD-PM (Table 3). In our study, all neonatal deaths had specific causes identified. In other words, the ICD-PM is more useful for coding stillbirths than neonatal deaths in our locality.
The main benefit of using ICD-10 and ICD-PM codes was to have a better understanding of the perinatal deaths in terms of the timing of death, the depth of investigations, and any contributing maternal conditions. We also consider the ICD-PM code to be user-friendly for changing an existing local perinatal death classification to one that is global and can be compared with international data. Using the ICD-PM code can lead to better classification of perinatal deaths, reduce the proportion of unknown diagnoses, and clearly link the maternal conditions with these perinatal deaths. There are some countries such as the United Kingdom [3], South Africa [3,16,17], India [18], and Colombia [19] using the ICD-PM to interpret the perinatal mortality data which showed that the ICD-PM classification was feasible and enabling the characterization of perinatal mortality. This is the first validation study demonstrating the application of the ICD-PM coding system in Hong Kong.
Limitations
However, there are limitations to our study. The study was focused on one hospital only, so the sample size was small. The improvement in coding might be related to the enthusiastic principal investigator (HML) as the coder in the retrospective validation study; the code in the prospective validation study was done during our monthly perinatal meetings with multidisciplinary input from various stakeholders, which is our usual practice before and after the study. Whether there is an overall Hawthorne effect (improving performance of coding during the study period) could be seen by continuous monitoring of the diagnostic classification of stillbirths and neonatal deaths after the ICD-PM is formally used for coding from 2020 onward.
Further study can be performed in all Hong Kong Hospital Authority obstetrics units using the ICD-10 and ICD-PM so as to draw an overall picture of perinatal mortality across the territory.
Conclusions
The ICD-PM is a user-friendly system that can enhance the understanding of data [5]. Using the ICD-PM coding system could lead to a more comprehensive classification of perinatal deaths, reduce the proportion of unknown causes as well as providing better linkage to maternal conditions in these perinatal deaths. The ICD-PM classifications are more extensive in covering diagnostic categories, with more specific details. Implementing this new coding system in Hong Kong Hospital Authority obstetrics units will be of great help in improving clinical practice and reducing perinatal mortality in the long run.
Authors' Contributions: All authors had full access to the data, contributed to the study, approved the final version for publication, and take responsibility for its accuracy and integrity. HML and WCL were responsible for concept or design of the study. HML acquired the data and drafted the article. All authors contributed to the analysis or interpretation of data. WCL, EA, and WKM performed critical revision for important intellectual content.
Conflicts of Interest: None declared.
AbbreviationsICD-10
International Statistical Classification of Diseases and Related Health Problems, 10th Revision
ICD-PM
International Classification of Diseases for Perinatal Mortality
AllansonERTunçalpOGardosiJPattinsonRCFrancisAVogelJPErwichJFlenadyVJFrøenJFNeilsonJQuachAChouDMathaiMSayLGülmezogluAMOptimising the International Classification of Diseases to identify the maternal condition in the case of perinatal deathBJOG201611123122037204610.1111/1471-0528.1424610.1111/1471-0528.142462752755027527550AllansonERTunçalpOGardosiJPattinsonRCVogelJPErwichJFlenadyVJFrøenJFNeilsonJQuachAFrancisAChouDMathaiMSayLGülmezogluAMGiving a voice to millions: developing the WHO application of ICD-10 to deaths during the perinatal period: ICD-PMBJOG201611123121896189910.1111/1471-0528.1424310.1111/1471-0528.142432752695727526957AllansonERTunçalpOGardosiJPattinsonRCFrancisAVogelJPErwichJFlenadyVJFrøenJFNeilsonJQuachAChouDMathaiMSayLGülmezogluAMThe WHO application of ICD-10 to deaths during the perinatal period (ICD-PM): results from pilot database testing in South Africa and United KingdomBJOG201611123122019202810.1111/1471-0528.1424410.1111/1471-0528.142442752712227527122AllansonERVogelJPTunçalpOGardosiJPattinsonRCFrancisAErwichJFlenadyVJFrøenJFNeilsonJQuachAChouDMathaiMSayLGülmezogluAMApplication of ICD-PM to preterm-related neonatal deaths in South Africa and United KingdomBJOG201611123122029203610.1111/1471-0528.1424510.1111/1471-0528.142452752739027527390AminuMvan den BroekNStillbirth in low- and middle-income countries: addressing the 'silent epidemic'Int Health201970111423723910.1093/inthealth/ihz0153108189331081893FlenadyVWojcieszekAMMiddletonPEllwoodDErwichJJCooryMKhongTYSilverRMSmithGCSBoyleFMLawnJEBlencoweHLeisherSHGrossMMHoreyDFarralesLBloomfieldFMcCowanLBrownSJJosephKSZeitlinJReinebrantHERavaldiCVannacciACassidyJCassidyPFarquharCWallaceESiassakosDHeazellAEPStoreyCSadlerLPetersenSFrøenJFGoldenbergRLLancet Ending Preventable Stillbirths study groupLancet Stillbirths In High-Income Countries Investigator GroupStillbirths: recall to action in high-income countriesLancet20162133871001969170210.1016/S0140-6736(15)01020-X2679407026794070FrøenJFPinarHFlenadyVBahrinSCharlesAChaukeLDayKDukeCWFacchinettiFFrettsRCGardenerGGilshenanKGordijnSJGordonAGuyonGHarrisonCKoshyRPattinsonRCPeterssonKRussellLSaastadESmithGCSTorabiRCauses of death and associated conditions (Codac): a utilitarian approach to the classification of perinatal deathsBMC Pregnancy Childbirth200961092210.1186/1471-2393-9-221951522819515228VarliIHPeterssonKBottingaRBremmeKHofsjöAHolmMHolsteCKublickasMNormanMPiloCRoosNSundbergAWolffKPapadogiannakisNThe Stockholm classification of stillbirthActa Obstet Gynecol Scand200887111202121210.1080/000163408024602711895120718951207LuJRMcCowanLA comparison of the Perinatal Society of Australia and New Zealand-Perinatal Death Classification system and relevant condition at death stillbirth classification systemsAust N Z J Obstet Gynaecol20091049546747110.1111/j.1479-828X.2009.01066.x1978072719780727KortewegFJGordijnSJTimmerAErwichJJHMBergmanKABoumanKRaviseJMHeringaMPHolmJPThe Tulip classification of perinatal mortality: introduction and multidisciplinary inter-rater agreementBJOG20064113439340110.1111/j.1471-0528.2006.00881.x10.1111/j.1471-0528.2006.00881.x1655365116553651BoydTKWrightCAOdendaalHJElliottAJSensMAFolkerthRDRobertsDJKinneyHCPASS NetworkThe stillbirth classification system for the safe passage studyPediatr Dev Pathol201720212013210.1177/10935266166862512832696328326963International Stillbirth Alliance Collaborative for Improving Classification of Perinatal DeathsFlenadyVWojcieszekAMEllwoodDLeisherSHErwichJJHMDraperESMcClureEMReinebrantHEOatsJMcCowanLKentALGardenerGGordonATudehopeDSiassakosDStoreyCZuccolloJDahlstromJEGoldKJGordijnSPetterssonKMassonVPattinsonRGardosiJKhongTYFrøenJFSilverRMClassification of causes and associated conditions for stillbirths and neonatal deathsSemin Fetal Neonatal Med2017622317618510.1016/j.siny.2017.02.0092828599028285990The WHO application of ICD-10 to deaths during the perinatal period: ICD-PM20162020-07-02GenevaWorld Health Organizationhttps://apps.who.int/iris/bitstream/handle/10665/249515/9789241549752-eng.pdf;jsessionid=A18FDCCE417B827672FB54E387A8CC63?sequence=1International Statistical Classification of Diseases and Related Health Problems, 10th Revision ICD-10 Version: 20192020-07-02https://icd.who.int/browse10/2019/enThe Hong Kong Hospital Authority Annual Obstetric Reports 2012-20182020-07-02https://www.ekg.org.hk/html/gateway/obs-gyn/aor2018.pdfLavinTAllansonERNedkoffLPreenDBPattinsonRCApplying the international classification of diseases to perinatal mortality data, South AfricaBull World Health Organ20181201961280681610.2471/BLT.17.2066313050502830505028AminuMMathaiMvan den BroekNApplication of the ICD-PM classification system to stillbirth in four sub-Saharan African countriesPLoS One2019145e021586410.1371/journal.pone.02158643107111131071111SharmaBSiwatchSKakkarNSuriVRainaAAggarwalNClassifying stillbirths in a tertiary care hospital of India: International Classification of Disease-Perinatal Mortality (ICD-PM) versus cause of death-associated condition (CODAC) systemJ Obstet Gynaecol20204291510.1080/01443615.2020.173601632347769Salazar-BarrientosMZuleta-TobónJJ[Application of the International Classification of Diseases for Perinatal Mortality (ICD-PM) to vital statistics records for the purpose of classifying perinatal deaths in Antioquia, Colombia]Rev Colomb Obstet Ginecol20191270422824210.18597/rcog.34063214223832142238\\n\"\n]"}}},{"rowIdx":486,"cells":{"data":{"kind":"list like","value":["\nFront PsychiatryFront PsychiatryFront. PsychiatryFrontiers in Psychiatry1664-0640Frontiers Media S.A.33192625PMC743214310.3389/fpsyt.2020.00757PsychiatryOriginal ResearchPlasma microRNA Array Analysis Identifies Overexpressed miR-19b-3p as a Biomarker of Bipolar Depression Distinguishing From Unipolar DepressionChenYu\n1\nShiJiabo\n1\nLiuHaiyan\n1\nWangQiang\n2\nChenXiangxiang\n1\nTangHao\n1\nYanRui\n1\nYaoZhijian\n1\n\n3\n\n4\n\n*\nLuQing\n4\n\n5\n\n*\n\n1\nDepartment of Psychiatry, The Affifiliated Brain Hospital of Nanjing Medical University, Nanjing Medical University, Nanjing, China\n\n2\nDepartment of Medical Psychology; Nanjing Drum Tower Hospital, Medical School of Nanjing University, Nanjing, China\n\n3\nDepartment of Psychiatry, Nanjing Brain Hospital, Medical School of Nanjing University, Nanjing, China\n\n4\nSchool of Biological Sciences & Medical Engineering, Southeast University, Nanjing, China\n\n5\nChild Development and Learning Science, Key Laboratory of Ministry of Education, Nanjing, China\n
This article was submitted to Mood and Anxiety Disorders, a section of the journal Frontiers in Psychiatry
118202020201175727920191672020Copyright © 2020 Chen, Shi, Liu, Wang, Chen, Tang, Yan, Yao and Lu2020Chen, Shi, Liu, Wang, Chen, Tang, Yan, Yao and LuThis is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.Objectives
The clinical characteristics of bipolar disorder (current major depressive episode) (BD) overlap with unipolar depressive disorder (UD), which makes it difficult to perform an accurate diagnosis. We identified plasma microRNAs (miRNAs) that distinguished BD from UD and explored the relationship between miRNA expression levels and clinical characteristics.
Methods
Total miRNAs from blood plasma from seven UD patients, seven BD patients, and six controls were analyzed. The identified miRNAs were validated in a separate population group. Depression severity and early life adversities were assessed. Bioinformatic analysis was conducted to investigate the target genes that were identified and the pathways associated with the altered miRNAs.
Results
Compared to controls, 42 miRNAs were differentially expressed in patients. miR-19b-3p, miR-3921, and miR-1180-3p were selected to validate the microarray results. Only miR-19b-3p was validated as down-regulated in patients. The primary predicted genes associated with miR-19b-3p were MAPK1, PTEN, and PRKAA1. The most relevant KEGG pathways included mTOR, FoxO, and the PI3-K/Akt signaling pathway. BD patients were more likely to have higher expression levels of miR-19b-3p and more severe childhood trauma experience compared to UD patients.
Conclusions
Plasma miR-19b-3p is a potential non-invasive biomarker that might be useful in distinguishing UD from BD. miR-19b3p was predicted to be involved in the pathway of inflammatory dysregulation associated with experiencing early childhood trauma.
The clinical manifestations of bipolar disorder (current depressive episode) (BD) and unipolar depressive disorder (UD) have a number of similar characteristics. Both disorders present a substantial public health burden due to their high prevalence, recurrence, and degree of disability. Although the theoretical basis of clinical psychiatry has been developed for more than half a century from Kraepelin’s conceptual foundations of mental illness to descriptive symptomatological conception, it still is not easy to clearly distinguish BD from UD. An episode of major depression may occur in the early stage of BD such that the patient is misdiagnosed with UD due to the lack of any history of hypomanic or manic episodes. It is possible for clinicians to change a diagnosis over time when evidence of hypomanic or manic episodes appears. The time-delay gap between the onset and the accurate diagnosis of BD, on average, is approximately five to ten years (1). Delayed diagnosis of BD may lead to a deleterious outcome through the use of antidepressant monotherapy, which would increase the risk of ‘switching’ into hypomanic or manic episodes (2, 3). Approximately 40% to 70% of BD patients have to confront the problem of receiving an appropriate diagnosis as early as possible (4, 5). It is imperative for clinicians to make a precise diagnosis to discern BD from UD. A series of clinical assessments have been administered, including sub-threshold hypomania, atypical depressive symptoms, and other clinical characteristics (6–8). Nonetheless, it is not enough to effectively distinguish the disorders using only these clinical symptoms.
Given the significant heritability of UD and BD, candidate genes for both diseases have been identified (9–11). One genome-wide association study (GWAS) that focused on major depression and included over 135,000 individuals identified 44 loci with genome-wide significance (12). Another GWAS that included over 20,000 BD patients found 30 significant genomic loci (13). However, these GWAS presented poor replication and failed to find shared or distinct genetic markers of affective disorder. One reason for this failure is that psychiatric diseases result from environmental causes as well as predisposing genetic factors. Epigenetic mechanisms indicate the interaction of genetic and environmental factors (G*E interaction). Given the role of non-coding RNAs as post-transcriptional regulators of gene expression, microRNAs are considered to be one of the G*E pathological features of affective disorders. Thus, the use of microRNA expression is of considerable interest to detect and distinguish UD from BD (14–17).
MicroRNAs (miRNAs) are small, endogenously-expressed, non-coding RNAs (~22 nucleotides) that can repress translation to inhibit protein synthesis or promote degradation of target mRNAs with complementary sequences (18). One miRNA can target hundreds of different mRNAs, and multiple miRNAs may regulate a single mRNA. Nearly 70% of mammalian miRNAs are expressed in the brain that generally negatively regulate target gene expression. Emerging studies have demonstrated that miRNAs, which are regarded as “neural communication sculptors,” play an essential role in the proliferation, differentiation, and migration of neurons and participate in the regulation of neuropsychiatric disorders (19, 20). Zhao et al. reported that sevoflurane-induced upregulation of miR-19-3p in neonatal rats post-transcriptionally inhibited protein translation of CCNA2, which contributed to the impairment of learning and memory (21). Clinical studies have reported altered miRNA expression in different brain regions of patients with schizophrenia, bipolar disorders, and major depressive disorder (22, 23).
MiRNAs are also released and circulating in the serum, plasma, and other body fluids. The signatures of circulating miRNAs may be potentially useful non-invasive biomarkers for disease, such as psychiatric disorders. For example, circulating miRNAs were shown to be changed by electroconvulsive shock therapy in psychotic depression (24). Walker et al. identified differential miRNAs (miR-15b, miR-132, and miR-652) in whole-blood samples of bipolar disorder comparing to healthy controls (25). Another study found a set of circulating miRNAs (let-7a-5p, let-7d-5p, let-7f-5p, miR-24-3p, and miR-425-3p) that were specifically altered in major depressive patients, five miRNA transcripts (miR-140-3p, miR-30d-5p, miR-330-5p, miR-378-5p, and miR-21-3p) were specifically altered in bipolar disorder patients, and two miRNAs (miR-330-3p and miR-345-5p) were altered in both diseases (26). Though the potential use of circulating miRNAs for psychiatric disorders screening has emerged, whether circulating miRNAs can be used as biomarkers for distinguishing bipolar depression from unipolar depression is still unclear.
Among the numerous adverse environmental conditions that exist, early childhood trauma, such as emotional abuse or neglect, physical abuse or neglect, and sexual abuse, is the greatest risk factor for the onset and development of depression (27). Patients diagnosed with UD or BD experienced more childhood trauma than healthy subjects (28, 29). Neuroimaging studies have shown that early life adversities were associated with structural and functional abnormalities in specific brain regions that are involved in cognitive processing and emotional regulation (30). Growing evidence supports a direct association between exposure to childhood trauma and elevated levels of inflammatory biomarkers, such as C-reactive protein, interleukin-6, and white blood cell counts (31). The impact of early life adversity and a dysregulated inflammatory profile would persist into adulthood, leading to greater vulnerability to depression (32, 33). It is hypothesized that the activation of immunological processes might be the mediator between childhood trauma and psychopathological outcomes (34).
During the process of adaptation to environmental perturbations by the individual, gene expression is modulated dynamically to optimize responses to stress. Emerging evidence indicates that miRNAs are ideally positioned to coordinate the genomic response to stress (35). Stress-induced alterations in miRNA expression affect multiple biological processes, including neurotransmission, cytokine production, and inflammation. Understanding the role of miRNAs in regulating stress-induced gene expression could have diagnostic benefits in mood disorders. To date, no studies have been conducted that compare the interactions between alterations in miRNA expression and early life adversities in UD and BD patients. The present study used an array to assess genome-wide plasma miRNA expression in UD and BD patients in combination with the assessment of the environmental risk factor of childhood trauma. The results of this study present a novel and comprehensive molecular signature that can contribute to the differential pathogenesis of UD and BD.
Materials and MethodsParticipants
Patients who were experiencing a major depressive episode were recruited from the Department of Psychiatry of the Affiliated Nanjing Brain Hospital of Nanjing Medical University from 2015 to 2016. The inclusion criteria for patients were as follows. (i) The patients received a diagnosis from a senior psychiatrist of major depressive disorder (unipolar depression, UD) or bipolar disorder (type I or type II) (BD) according to the Structured Clinical Interview for DSM-IV (SCID-IV). (ii) The patients received a 24-item Hamilton Depression Rating Scale (HAMD-24) score equal to or greater than 20. (iii) The age of the patients was between 18 and 55 years. (iv) The patients did not receive any psychotropic medications (including antipsychotics, antidepressants, mood stabilizers, and benzodiazepines) for at least four weeks. The exclusion criteria included the following. (i) The patients were diagnosed with other DSM-IV psychiatric disorders. (ii) The patients had a history of severe head injury. (iii) The patients were diagnosed with any neurological diseases or severe physical diseases, as evaluated by laboratory tests or personal history. (iv) The patients had a history of alcohol or substance dependence or abuse. (v) The patients were pregnant or lactating. All patients who were included in the study were followed up every six months until December 2018. The different types of early life stress were assessed among all patients when they entered the study using the Childhood Trauma Questionnaire (CTQ) (36). At every follow-up time point, the UD patients were administered the 32-item hypomania checklist (HCL-32) to screen for hypomania symptoms (37). If the HCL-32 score was greater than 14 points, the patient’s diagnosis was switched to BD, and the patient was excluded from the study.
The healthy control group included individuals who were matched to the patients with respect to age, gender, and education. The control subjects were recruited from communities in Nanjing from 2015 to 2016 and were screened using the Mini International Neuropsychiatric Interview (MINI) (38). Healthy controls were excluded if they had any history of psychiatric disorders or had any family history of mental disorders in their first-degree relatives.
All subjects were genetically unrelated, ethnic Han Chinese, with at least six years of education. Each subject donated 5 ml of venous blood at the time of their recruitment. A two-phase study was designed. First, in the screening phase, we performed peripheral miRNA profiling using Affymetrix chips for UD patients, BD patients, and healthy controls who were randomly selected from the sample set. In the second, independent validation phase, we examined the expression levels of identified miRNAs in all participants and analyzed the results of the miRNA arrays using the Gene Ontology and KEGG Pathway assays. Finally, we assessed the shared and distinct correlations between the expression of selected miRNAs and childhood traumatic experiences in UD and BD patients.
This study was approved by the institutional review board of the Affiliated Nanjing Brain Hospital of Nanjing Medical University, and written informed consent was obtained from each participant.
Plasma Preparation and RNA Isolation
The plasma was separated from venous blood within 24h after collection by centrifugation at 12,000 r.p.m. for 15 min. The supernatant from the plasma samples was stored in 300 µl aliquots at −80°C in RNase-free microtubes until it was used for miRNA extraction. A modified method was utilized to isolate total RNA. Briefly, Trizol reagent (Invitrogen, Carlsbad, CA, USA) was used to break down cells and cellular components in the plasma, and total RNA was extracted and purified using a miRNeasy Serum/Plasma Kit (Qiagen, Valencia, CA, USA) according to the manufacturer’s instructions. RNA quality and quantity were evaluated using a NanoDrop ND-1000 spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA).
miRNA Array
During the screening phase, a volume corresponding to 500 ng of total RNA from each blood sample was processed using a FlashTag Biotin HSR RNA Labeling kit (Affymetrix, Santa Clara, CA, USA), following the manufacturer’s protocol. The RNA was subsequently hybridized onto Affymetrix GeneChip miRNA 4.0 Arrays that each contained 2,578 human miRNA sequences (Affymetrix, Santa Clara, CA, USA). The GeneChip miRNA 4.0 Arrays were washed and stained using aFluidics station 450 and a GeneChip Scanner 3000 7G (Affymetrix, Santa Clara, CA, USA), respectively.
qRT-PCR Validation
At the validation phase, quantitative real-time PCR (qRT-PCR) was performed using Taqman microRNA probes (Applied Biosystems Inc, CA, USA) to confirm the candidate miRNAs identified on the microarrays. Total RNA was reverse transcribed to complementary DNA using a miRNA 1st Strand cDNA Synthesis Kit, stem-loop RT primers (Vazyme, Nanjing, China), and the GeneAmp 9700 PCR System (Thermo Scientific, MA, USA). The reactions began in a 384-well optical plate at 95°C for 5 minutes, followed by 40 cycles of 95°C for 10 seconds and 60°C for 30 seconds. The quantitative detection of the miRNAs was performed using the miRNA Universal SYBR qPCR Master Mix (Vazyme, Nanjing, China) and implemented on the Agilent AriaMx platform (Agilent Technologies, Palo Alto, CA, USA). All reactions, including no-template controls, were performed in triplicate. To calculate the relative expression levels of the target miRNAs, U6 was used as the control miRNA for plasma samples (the sequences of the primers listed in \nSupplemental data 1\n).
Data Analysis
All statistical analyses were performed using R (version 3.5.0) and SPSS (version 24.0). Demographic and clinical characteristics among the three groups were compared using χ2 tests, the Student’s t-test, or one-way ANOVA.
CEL-files of raw data were produced using Affymetrix GeneChip Command Console Software, Version 4.0 (Affymetrix, Santa Clara, CA, USA). Arrays were normalized using quantile normalization, and the probe values were log2 transformed. All data have been submitted to the GEO repository (code number: GSE152267).
We utilized one-way ANOVA to detect differently expressed miRNAs among all three groups, and miRNAs were chosen as candidates for further confirmation by individual qRT-PCR according to the following three criteria: (i) p-value <0.05 for the ANOVA; (ii) assessed using Fisher’s Least Significant Difference test as the Post Hoc test for multiple comparisons; (iii) fold changes (FCs) ≥3/2 or ≤2/3 between every two groups (BD vs. controls, UD vs. controls, and BD vs. UD).
Concerning qRT-PCR validation, the Ct values were normalized according to the delta Ct (ΔCt) method based on the internal reference, U6. The relative expression levels of target miRNAs were calculated using 2-ΔΔCT, which were analyzed using the Student’s t-test for independent samples (39). Binary logistic regression was applied to determine correlations between miRNA expression levels and childhood traumatic variables. A p-value<0.05 was statistically significant for all analyses.
Target Gene Prediction and Pathway Analysis
Bioinformatic analysis (Genminix Informatics Ltd., Shanghai, China) was performed for the miRNAs expressed in significant amounts. TargetScan (http://www.targetscan.org/) and miRanda (http://www.microrna.org/microrna/hom.do) were used to predict target genes for the validated candidate miRNAs, and only genes predicted by both databases were retained. To identify the potential biological mechanism of differentially expressed miRNAs among the BD, UD, and healthy controls, we performed gene ontology (GO, http://www.geneontology.org/) and Kyoto Encyclopedia of Genes and Genomes (KEGG, http://www.genome.jp/kegg/) pathway enrichment analysis with the David web application (https://david.ncifcrf.gov). Fisher’s exact test was employed to determine whether a gene set was enriched for a specific gene using GO terms or KEGG pathways compared to the background information. The p-value was corrected for false discovery rates (FDR). GO terms with a p-value<0.01 and a KEGG pathway with a p-value<0.05 were considered significant. The miRNA-mRNA-gene network of miR-19b-3p was obtained by using Cytoscape (Version 2.8.2).
ResultsDemographic and Clinical Characteristics of the Subjects
All subjects were followed up every six months to confirm whether they experienced any hypomania or mania episodes until December 2018. During three years of follow up, six UD patients (two males and four females, with a mean age of 26.2 ± 10.0 years) experienced a manic episode that lasted more than seven days. These six patients were excluded from the study since their diagnosis was switched from UD to BD. Thus, 32 UD patients and 27 BD patients were included in the final analysis (see \nTable 1\n). There was no statistical difference between these two groups regarding age, gender, education, or family history (p>0.05). The UD group recruited more first-episode patients compared to the BD group, while patients in the BD group had a longer duration of illness than the UD group. Clinical assessments showed that there were no significant differences in the total scores for HAMD between the two diagnostic groups. At the same time, the UD patients had higher anxiety or somatization scores (p<0.05) and sleep disturbance scores (p<0.01) compared to BD patients.
Demographic and clinical characteristics of unipolar depression, bipolar depression, and healthy controls.
Seven UD patients, seven BD patients, and six healthy controls were randomly selected from the total sample to undergo microarray chip inspection. There were no differences in the subjects’ demographic characteristics, the overall severity of depression, or childhood traumatic experiences (see \nSupplementary data 2\n). Total RNA extracted from peripheral venous blood was analyzed using Affymetrix miRNA 4.0 Arrays. The altered miRNA expression in UD and BD patients compared to healthy controls are listed in \nSupplementary data 3\n. Seventeen miRNAs were down-regulated, and 25 miRNAs were up-regulated (p<0.05).
Hierarchical clustering was carried out for all covered human mature miRNAs and pre-miRNAs, and significant differences in expression values were observed between the patients diagnosed with UD and BD and healthy controls. (\nFigure 1\n). Following hierarchical clustering, the array results were narrowed down to dysregulated miRNAs with a p-value < 0.05 from one-way ANOVA analysis of the three groups and a fold change ≥3/2 or ≤2/3 for all the two-group comparisons. Based on these criteria, three representative miRNAs (miR-19b-3p, miR-3921, and miR-1180-3p) were selected for PCR to validate the microarray results.
\n(A) Volcano map of differentially expressed miRNAs. The horizontal axis is the logarithm of the fold change value based on 2, and the vertical axis is the negative logarithm of the p value based on 10. Blue dots represent down-regulated miRNAs with statistically significant differences in patients comparing healthy subjects. Red dots represent miRNAs that are up-regulated in patients, and grey dots represent miRNAs that have no significant differences in expression values between the patients and healthy controls. (B) Heat map of differentially expressed miRNAs. The abscissa represents the subject names between groups, and the ordinate represents the differentially expressed miRNAs. Differentiated miRNAs are expressed in red with high expression values and those with low expression values are in green.
Verification Using Quantitative Real-Time PCR
Quantitative real-time PCR (qRT-PCR) was performed to verify the differentially expressed miRNAs in an expanded sample comprised of 32 UD patients and 27 BD patients. Only miRNAs with a fold change ≥3/2 or ≤2/3 and a p-value < 0.05 from Student’s t-tests for each two-group comparison were validated. Of these, miR-19b-3p was verified to be down-regulated (see \nFigure 2\n).
Scatter plot figures show qRT-PCR validation of the differential expression of three miRNAs among three groups (unipolar patients (UD), bipolar patients (BD), and healthy controls (HC) (A) miR-19b-3p, (B) miR-3921, (C) miR-1180-3p). The values of 2-ΔΔCT represent the expressed level of filtered miRNAs. A p-value of < 0.05 means statistically significant.
Microarray-Based Gene Ontology and Signal Pathway Analysis
Based on the GO and KEGG signal pathway analyses, we first predicted mRNAs that could be regulated by miR-19b-3p. Then we performed an enrichment analysis to infer the putative biological pathways that might be involved in the miRNA regulation. We detected 288 mRNAs regulated by miR-19b-3p (see \nSupplementary Data 4\n). Significant gene functions and pathways putatively altered in UD and BD patients were selected based on the standards of p<0.01 (GO) and p<0.05 (KEGG). GO results revealed that most functions associated with miR-19b-3p regulation were related to signal transduction, neuron growth, cell differentiation, and apoptosis (\nFigure 3A\n). The most relevant KEGG signal pathways were involved in enrichment of biological processes, which included mTOR, autophagy, FoxO, prolactin, p53, and the PI3-K/Akt signaling pathway (\nFigure 3B\n). A more intuitive miRNA-mRNA-gene network diagram was produced to investigate the potential role of miR-19b-3p in the pathogenesis of depression (\nFigure 3C\n). The most relevant target genes were MAPK1, PTEN, TGFBR2, PRKAA1, PIK3R3, and RAF1, which were involved in multiple pathways.
\n(A) GO analysis of target genes predicted by miR-19b-3p. The ordinate is the name of the target gene function, and the abscissa is the negative logarithm of p value (-Lgp). The larger the -Lgp value, the smaller the p value and the higher the significance level of the target gene function. (B) KEGG signal pathway of target genes predicted by miR-19b-3p. The ordinate is the name of the target gene signal pathway, and the abscissa is the negative logarithm of p value (-Lgp). The larger the -Lgp value, the smaller the p value and the higher the significance level of the target gene signal pathway. (C) MicroRNA-Gene network diagram of miR-19b-3p. The blue square in the figure refers to miR-19b-3p, the red circle refers to target genes, and the green arrow refers to the signal pathways involved in the target gene. Lines represent the regulatory relationship between miR-19b-3p and target genes.
Correlation Between miRNA Expression and Stress-Related Psychological Variables
A binary logistic regression model was produced to examine the correlation between miR-19b-3p expression and exposure to childhood trauma in patients with UD and BD, with adjustments for demographic variables. The results showed that the expression level of miR-19b-3p (OR=5.717, 95%CI:1.497–21.835, p=0.011) and the overall severity of childhood trauma (OR=1.099, 95%CI:1.012-1.192, p=0.025) were significantly associated with greater risk of BD. Among the sub-types of childhood trauma (emotional abuse, physical abuse, sexual abuse, emotional neglect, and physical neglect), there was a weak association between physical neglect and UD (OR=0.773, 95%CI:0.598-0.999, p=0.049) (\nTable 2\n). The other specific types of childhood trauma were not significantly correlated with any morbid status.
The logistic regression of miR-19b-3p and childhood trauma characteristics between the patients with UD and BD.
B
\np value
OR
95%CI
miR-19b-3p
1.743
0.011*
5.717
1.497
21.835
Total score of CTQ
0.094
0.025*
1.099
1.012
1.192
Physical neglect
−0.258
0.049*
0.773
0.598
0.999
UD, unipolar depression; BD, bipolar depression; CTQ, childhood trauma questionnaire. Physical neglect: a subtype of childhood trauma. *p < 0.05
Discussion
In the present study, we systematically investigated the genome-wide miRNA expression profile in plasma from patients with UD and BD as compared to healthy subjects. Among 42 differentially expressed miRNAs, three miRNAs (miR-19b-3p, miR-3921, and miR-1180-3p) were selected to validate the microarray chip results. The novel miR-3921 was significantly up-regulated in the UD group but was not verified. miR-1180-3p was up-regulated in UD and BD patients but also was not verified. To date, no published reports have associated these two miRNAs with psychiatric disorders. However, miR-19b-3p was significantly down-regulated in the patient group, which indicates that peripheral miRNAs are possible non-invasive biomarkers with the necessary diagnostic accuracy. Furthermore, a specific miRNA related to psychological factors might help differentiate BD from UD. The BD patients were more likely to exhibit the combination of over-expressed miR-19b-3p and more severe childhood trauma when UD and BD patients were compared.
miR-19b-3p and Its Pathology in Psychiatric Diseases
MiR-19b-3p belongs to the miR-17/92 cluster, which exerts powerful effects on lymphocyte development, proliferation, activation, differentiation, and cytokine production (40, 41). Gantier et al. first reported that miR-19b regulated the activity of nuclear factor-κB (NF-κB) signaling in inflammation (42). Over-expression of miR-19b-3p inhibited the production of IL-6 and IL-8, and the interaction of miR-19b-3p with its direct target gene, G protein-coupled receptor kinase 6 (GRK6), was discovered to affect inflammation (43).
TNFAIP3, which is negatively regulated by miR-19b-3p, is widely recognized as an important regulator of inflammation (44). An intriguing role for miR-19b-3p is to regulate the neuroinflammatory response induced by the Japanese encephalitis virus (JEV) via enhancement of NF-κB signaling (45). Dwivedi et al. reported that miR-19b was over-expressed in the prefrontal cortex of rats with stress-induced depression with chronic administration of exogenous corticosterone (46). Although the epigenetic mechanisms of miR-19b-3p in psychiatric disorders remain unclear, various studies point to the effects of this miRNA in the communication between the immune system and the brain, which is known as a new area in psychiatry, specifically, immunopsychiatry.
The Target Genes of miR-19b-3p in Unipolar Depression and Bipolar Disorder
Our in silico results predicted a series of target genes and biological pathways for miR-19b-3p. Since patients with major depression who experienced childhood trauma were susceptible to immune dysregulation, we first check the biological processes associated with miR-19b-3p in immunomodulation between UD patients and BD patients. GO analysis predicted that the target gene function of miR-19b-3p, was enriched in the Wnt signaling pathway. It is consistent with a previous study that dysregulated expression of Wnt-related genes was shown in BD (47). Numerous studies have shown that suppression of Wnt signaling could induce both manic and depressive behaviors and exacerbate a proinflammatory state leading to increased neuronal apoptosis (48). In our study, the Wnt pathway was the most significantly enriched biological function, indicating its potential role in identifying UD and BD patients.
KEGG enrichment analyses predicted that the most significant target gene pathway was the mammalian rapamycin (mTOR) signaling pathway. Accumulating evidences suggested that mTOR signaling was dysregulated in depression (49). Activated mTOR signaling might be related to antidepressant-like effects in the hippocampus by modulating inflammatory cytokines such as interleukin-1β (IL-1β), interleukin-6 (IL-6), and tumor necrosis factor-α (TNF-α) (50). Autophagy signaling was the second, riched pathway regulated by miR-19b-3p. Dysregulation of autophagy leads to various disease manifestations, such as inflammation, metabolic alterations, and neurodegeneration (51). Some antidepressants induce inflammatory suppression through decreased serum levels of IL-1β and IL-18 and decreased NLRP3 protein expression via the autophagy pathway (52). Recent findings indicated that the forkhead box O (FoxO) signaling pathway is involved in the development of major depression and constitutes a potential therapeutic target in the treatment of depression (53). Although there is no evidence for FoxO involvement in BD, our results suggested that this pathway played an important role via miR-19b-3p in both UD and BD.
The prolactin pathway was identified in the differentiation between UD and BD. Hyperprolactinemia is reported commonly in subjects with a psychotic disorder which could due to stress, while information regarding mood disorder patients was particularly lacking (54). The p53 signaling, identified differently between UD and BD patients in our study, has not been reported to be associated with psychiatric disorders previously. Although the phosphatidylinositol 3-kinase (PI3K)-Akt signaling pathway was less strongly associated with miR-19b-3p expression, it contains the largest number of target genes. PI3K-Akt signaling is involved in the inhibition of the inflammatory response of lipoteichoic acid-stimulated macrophages (55). Reports indicate that some antidepressants used in clinical practice exert therapeutic efficacy via the promotion of the hippocampal PI3k-Akt-mTOR signaling pathway (56).
Mitogen-activated protein kinase 1 (MAPK1) had the highest predictive value among all the target genes of miR-19b-3p. Emerging evidence has revealed that alteration of MAPK1 is associated with psychiatric disorders, such as major depressive disorders, bipolar disorder and schizophrenia (57, 58). MAPK1 is highly expressed in the prefrontal cortex and hippocampus, and can modulate neuronal growth and differentiation, synaptic plasticity, and inflammatory processes via mTOR, FoxO, and other signaling pathways (59, 60).
Phosphatase and tensin homolog (PTEN) is a significant target of miR-19b-3p, and it directly regulates the PI3-K-Akt-mTOR signaling pathway, which is regarded as the most critical pathway for many neurobiological functions in the brain. PTEN regulates neuron cell size and affects dendritic growth, and it also acts as a significant tumor suppressor gene through the modulation of the inflammatory process (61, 62). Altered expression of PTEN in the blood is considered a biomarker for suicidal tendencies (63), as well as in the prefrontal cortex and hippocampus of suicide victims (64). The possibility of using miR-19b-3p and its target genes, such as PTEN to identify stress-related neuropathology in mood disorders, is encouraging. An upstream molecule in the mTOR pathway (PRKAA1) was down-regulated by miR-181a and facilitated hippocampal fear memory consolidation (65). Fear inhibition is related to trauma events (66). Therefore, PRKAA1 is one of significant target genes of miR-19b-3p that could be associated with stress-induced bipolar patients via activation of the mTOR pathway.
Childhood Traumatic Exposure and miRNA Alterations in Depressive Patients
Early life adversities, such as emotional, physical, and sexual abuse or neglect in childhood, are associated with poor psychological health outcomes in adults. Childhood traumatic experiences play an important role in developing BD, induction of more severe clinical symptoms, impairing emotion regulation and cognitive function, and lead to a much higher risk for suicide (28). Recent studies have explored the epigenetic mechanisms between childhood trauma and major depression, as well as schizophrenia (14). Our findings provide a significant association between childhood traumatic experiences and altered expression of miR-19b-3p in BD. Based on our bioanalysis results, plasma miR-19b-3p might be a biomarker associated with the impact of childhood trauma on UD and BD through the involvement of inflammatory processes.
Increased inflammation has been described in healthy individuals exposed to childhood trauma, suggesting a potentially causal role in the future onset of depression (67, 68). The Dwivedi group focused on epigenetic mechanisms of depression and suicide and recently reported that proinflammatory cytokines (e.g., TNF-α) and miR-19a-3b were up-regulated in the dorsolateral prefrontal cortex (DLPFC) in suicide victims, and both molecules were increased in the peripheral blood mononuclear cells of depressed patients with severe suicidal ideation (69).
Another important finding showed that unipolar patients with the lowest expression of peripheral miR-19p-3b among the three groups experienced more physical neglect, which has not been reported previously. The effects of neglect can be as traumatic as or even more traumatic than the effects of abuse, and the traumatic effects persist into adulthood (70). Neglect has several forms. Physical neglect could prompt the individual to develop psychological unavailability, which impacts the development of depressive symptoms. Further research is needed on possible epigenetic mechanisms of neglect associated with miRNAs.
Strengths and Limitations
This study has some strengths. First, we stringently recruited subjects in the screening phase. There were nearly identical clinical manifestations of the unipolar and bipolar patients, which could exclude possible confounding caused by distinct profiling between the two groups. Second, the differentiated miRNAs were systematically screened through the whole miRNome profiling on unipolar and bipolar depressive patients and healthy controls and was validated in a larger independent sample to increase the reliability of the results. Third, we provided an in-depth discussion of the possible miRNA targets and the integration of potential molecular mechanisms with environmental factors. We explored the possible use of peripheral miRNAs as non-invasive diagnostic biomarkers of mood disorders from an immune-psychiatry perspective and prompted further research on the role of miR-19p-3b in depression.
Nevertheless, the relationship of miRNA expression in plasma and the brain is not clear, since we only observed alterations in peripheral miRNAs. It is a concern that there were two selected miRNAs eliminated in the validation phase, which might be due to the limited sample size. A miRNA panel would be a more robust method of diagnosis of UD or BD. We did not examine any circulating inflammatory factors that were potentially related to the disorders. Therefore, the association between miRNA expression and immunological regulation remains unclear. A well-designed study would need to be conducted to develop a psychoneuroimmunology network of childhood trauma, immunological indices, altered miRNAs, and the disorder.
Conclusions
This study showed that the expression profiling of plasma miRNAs was altered both in UD and BD. miR-19b-3p was down-regulated in patients with depression and was validated to be over-expressed in BD but not UD. The target genes for miR-19b-3p were primarily enriched in the Wnt signaling and mTOR pathways. These miRNA gene targets elucidate the pathways and mechanisms involved in neuroimmunology pathways and depression pathogenesis.
Data Availability Statement
All data have been submitted to the GEO repository (code number: GSE152267).
Ethics Statement
The studies involving human participants were reviewed and approved by Affiliated Brain Hospital of Nanjing Medical University ethics committee. The patients/participants provided their written informed consent to participate in this study.
Author Contributions
YC: collected data, conducted the statistical analysis, drafted the manuscript, edited, and submitted the manuscript. JS, HL, QW, XC, HT, RY: collected data, reviewed, and revised the manuscript. QL: statistical analysis, critically reviewed, edited, and revised the manuscript. ZY: conceptualized and designed the study, critically reviewed, and revised the manuscript.
Funding
This work was supported by National Key R&D Program of China under grant number 2018YFC1314600; The National Natural Science Foundation of China under grant number 81871066,81571639; Jiangsu Provincial Medical Innovation Team of the Project of Invigorating Health Care through Science, Technology and Education under grant number CXTDC2016004; Jiangsu Provincial key research and development program under grant number BE2018609; Jiangsu Provincial Medical Youth Talent-The Project of Invigorating Health Care through Science, Technology and Education, QNRC2016049; The Science and Technology Program of Nanjing, 201803030.
Conflict of Interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Acknowledgments
We thank all subjects for participating in this study.
Supplementary Material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpsyt.2020.00757/full#supplementary-material\n
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Dev Psychopathol (1997) 9(4):855–79. 10.1017/S0954579497001478\n\n"],"string":"[\n \"\\nFront PsychiatryFront PsychiatryFront. PsychiatryFrontiers in Psychiatry1664-0640Frontiers Media S.A.33192625PMC743214310.3389/fpsyt.2020.00757PsychiatryOriginal ResearchPlasma microRNA Array Analysis Identifies Overexpressed miR-19b-3p as a Biomarker of Bipolar Depression Distinguishing From Unipolar DepressionChenYu\\n1\\nShiJiabo\\n1\\nLiuHaiyan\\n1\\nWangQiang\\n2\\nChenXiangxiang\\n1\\nTangHao\\n1\\nYanRui\\n1\\nYaoZhijian\\n1\\n\\n3\\n\\n4\\n\\n*\\nLuQing\\n4\\n\\n5\\n\\n*\\n\\n1\\nDepartment of Psychiatry, The Affifiliated Brain Hospital of Nanjing Medical University, Nanjing Medical University, Nanjing, China\\n\\n2\\nDepartment of Medical Psychology; Nanjing Drum Tower Hospital, Medical School of Nanjing University, Nanjing, China\\n\\n3\\nDepartment of Psychiatry, Nanjing Brain Hospital, Medical School of Nanjing University, Nanjing, China\\n\\n4\\nSchool of Biological Sciences & Medical Engineering, Southeast University, Nanjing, China\\n\\n5\\nChild Development and Learning Science, Key Laboratory of Ministry of Education, Nanjing, China\\n
This article was submitted to Mood and Anxiety Disorders, a section of the journal Frontiers in Psychiatry
118202020201175727920191672020Copyright © 2020 Chen, Shi, Liu, Wang, Chen, Tang, Yan, Yao and Lu2020Chen, Shi, Liu, Wang, Chen, Tang, Yan, Yao and LuThis is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.Objectives
The clinical characteristics of bipolar disorder (current major depressive episode) (BD) overlap with unipolar depressive disorder (UD), which makes it difficult to perform an accurate diagnosis. We identified plasma microRNAs (miRNAs) that distinguished BD from UD and explored the relationship between miRNA expression levels and clinical characteristics.
Methods
Total miRNAs from blood plasma from seven UD patients, seven BD patients, and six controls were analyzed. The identified miRNAs were validated in a separate population group. Depression severity and early life adversities were assessed. Bioinformatic analysis was conducted to investigate the target genes that were identified and the pathways associated with the altered miRNAs.
Results
Compared to controls, 42 miRNAs were differentially expressed in patients. miR-19b-3p, miR-3921, and miR-1180-3p were selected to validate the microarray results. Only miR-19b-3p was validated as down-regulated in patients. The primary predicted genes associated with miR-19b-3p were MAPK1, PTEN, and PRKAA1. The most relevant KEGG pathways included mTOR, FoxO, and the PI3-K/Akt signaling pathway. BD patients were more likely to have higher expression levels of miR-19b-3p and more severe childhood trauma experience compared to UD patients.
Conclusions
Plasma miR-19b-3p is a potential non-invasive biomarker that might be useful in distinguishing UD from BD. miR-19b3p was predicted to be involved in the pathway of inflammatory dysregulation associated with experiencing early childhood trauma.
The clinical manifestations of bipolar disorder (current depressive episode) (BD) and unipolar depressive disorder (UD) have a number of similar characteristics. Both disorders present a substantial public health burden due to their high prevalence, recurrence, and degree of disability. Although the theoretical basis of clinical psychiatry has been developed for more than half a century from Kraepelin’s conceptual foundations of mental illness to descriptive symptomatological conception, it still is not easy to clearly distinguish BD from UD. An episode of major depression may occur in the early stage of BD such that the patient is misdiagnosed with UD due to the lack of any history of hypomanic or manic episodes. It is possible for clinicians to change a diagnosis over time when evidence of hypomanic or manic episodes appears. The time-delay gap between the onset and the accurate diagnosis of BD, on average, is approximately five to ten years (1). Delayed diagnosis of BD may lead to a deleterious outcome through the use of antidepressant monotherapy, which would increase the risk of ‘switching’ into hypomanic or manic episodes (2, 3). Approximately 40% to 70% of BD patients have to confront the problem of receiving an appropriate diagnosis as early as possible (4, 5). It is imperative for clinicians to make a precise diagnosis to discern BD from UD. A series of clinical assessments have been administered, including sub-threshold hypomania, atypical depressive symptoms, and other clinical characteristics (6–8). Nonetheless, it is not enough to effectively distinguish the disorders using only these clinical symptoms.
Given the significant heritability of UD and BD, candidate genes for both diseases have been identified (9–11). One genome-wide association study (GWAS) that focused on major depression and included over 135,000 individuals identified 44 loci with genome-wide significance (12). Another GWAS that included over 20,000 BD patients found 30 significant genomic loci (13). However, these GWAS presented poor replication and failed to find shared or distinct genetic markers of affective disorder. One reason for this failure is that psychiatric diseases result from environmental causes as well as predisposing genetic factors. Epigenetic mechanisms indicate the interaction of genetic and environmental factors (G*E interaction). Given the role of non-coding RNAs as post-transcriptional regulators of gene expression, microRNAs are considered to be one of the G*E pathological features of affective disorders. Thus, the use of microRNA expression is of considerable interest to detect and distinguish UD from BD (14–17).
MicroRNAs (miRNAs) are small, endogenously-expressed, non-coding RNAs (~22 nucleotides) that can repress translation to inhibit protein synthesis or promote degradation of target mRNAs with complementary sequences (18). One miRNA can target hundreds of different mRNAs, and multiple miRNAs may regulate a single mRNA. Nearly 70% of mammalian miRNAs are expressed in the brain that generally negatively regulate target gene expression. Emerging studies have demonstrated that miRNAs, which are regarded as “neural communication sculptors,” play an essential role in the proliferation, differentiation, and migration of neurons and participate in the regulation of neuropsychiatric disorders (19, 20). Zhao et al. reported that sevoflurane-induced upregulation of miR-19-3p in neonatal rats post-transcriptionally inhibited protein translation of CCNA2, which contributed to the impairment of learning and memory (21). Clinical studies have reported altered miRNA expression in different brain regions of patients with schizophrenia, bipolar disorders, and major depressive disorder (22, 23).
MiRNAs are also released and circulating in the serum, plasma, and other body fluids. The signatures of circulating miRNAs may be potentially useful non-invasive biomarkers for disease, such as psychiatric disorders. For example, circulating miRNAs were shown to be changed by electroconvulsive shock therapy in psychotic depression (24). Walker et al. identified differential miRNAs (miR-15b, miR-132, and miR-652) in whole-blood samples of bipolar disorder comparing to healthy controls (25). Another study found a set of circulating miRNAs (let-7a-5p, let-7d-5p, let-7f-5p, miR-24-3p, and miR-425-3p) that were specifically altered in major depressive patients, five miRNA transcripts (miR-140-3p, miR-30d-5p, miR-330-5p, miR-378-5p, and miR-21-3p) were specifically altered in bipolar disorder patients, and two miRNAs (miR-330-3p and miR-345-5p) were altered in both diseases (26). Though the potential use of circulating miRNAs for psychiatric disorders screening has emerged, whether circulating miRNAs can be used as biomarkers for distinguishing bipolar depression from unipolar depression is still unclear.
Among the numerous adverse environmental conditions that exist, early childhood trauma, such as emotional abuse or neglect, physical abuse or neglect, and sexual abuse, is the greatest risk factor for the onset and development of depression (27). Patients diagnosed with UD or BD experienced more childhood trauma than healthy subjects (28, 29). Neuroimaging studies have shown that early life adversities were associated with structural and functional abnormalities in specific brain regions that are involved in cognitive processing and emotional regulation (30). Growing evidence supports a direct association between exposure to childhood trauma and elevated levels of inflammatory biomarkers, such as C-reactive protein, interleukin-6, and white blood cell counts (31). The impact of early life adversity and a dysregulated inflammatory profile would persist into adulthood, leading to greater vulnerability to depression (32, 33). It is hypothesized that the activation of immunological processes might be the mediator between childhood trauma and psychopathological outcomes (34).
During the process of adaptation to environmental perturbations by the individual, gene expression is modulated dynamically to optimize responses to stress. Emerging evidence indicates that miRNAs are ideally positioned to coordinate the genomic response to stress (35). Stress-induced alterations in miRNA expression affect multiple biological processes, including neurotransmission, cytokine production, and inflammation. Understanding the role of miRNAs in regulating stress-induced gene expression could have diagnostic benefits in mood disorders. To date, no studies have been conducted that compare the interactions between alterations in miRNA expression and early life adversities in UD and BD patients. The present study used an array to assess genome-wide plasma miRNA expression in UD and BD patients in combination with the assessment of the environmental risk factor of childhood trauma. The results of this study present a novel and comprehensive molecular signature that can contribute to the differential pathogenesis of UD and BD.
Materials and MethodsParticipants
Patients who were experiencing a major depressive episode were recruited from the Department of Psychiatry of the Affiliated Nanjing Brain Hospital of Nanjing Medical University from 2015 to 2016. The inclusion criteria for patients were as follows. (i) The patients received a diagnosis from a senior psychiatrist of major depressive disorder (unipolar depression, UD) or bipolar disorder (type I or type II) (BD) according to the Structured Clinical Interview for DSM-IV (SCID-IV). (ii) The patients received a 24-item Hamilton Depression Rating Scale (HAMD-24) score equal to or greater than 20. (iii) The age of the patients was between 18 and 55 years. (iv) The patients did not receive any psychotropic medications (including antipsychotics, antidepressants, mood stabilizers, and benzodiazepines) for at least four weeks. The exclusion criteria included the following. (i) The patients were diagnosed with other DSM-IV psychiatric disorders. (ii) The patients had a history of severe head injury. (iii) The patients were diagnosed with any neurological diseases or severe physical diseases, as evaluated by laboratory tests or personal history. (iv) The patients had a history of alcohol or substance dependence or abuse. (v) The patients were pregnant or lactating. All patients who were included in the study were followed up every six months until December 2018. The different types of early life stress were assessed among all patients when they entered the study using the Childhood Trauma Questionnaire (CTQ) (36). At every follow-up time point, the UD patients were administered the 32-item hypomania checklist (HCL-32) to screen for hypomania symptoms (37). If the HCL-32 score was greater than 14 points, the patient’s diagnosis was switched to BD, and the patient was excluded from the study.
The healthy control group included individuals who were matched to the patients with respect to age, gender, and education. The control subjects were recruited from communities in Nanjing from 2015 to 2016 and were screened using the Mini International Neuropsychiatric Interview (MINI) (38). Healthy controls were excluded if they had any history of psychiatric disorders or had any family history of mental disorders in their first-degree relatives.
All subjects were genetically unrelated, ethnic Han Chinese, with at least six years of education. Each subject donated 5 ml of venous blood at the time of their recruitment. A two-phase study was designed. First, in the screening phase, we performed peripheral miRNA profiling using Affymetrix chips for UD patients, BD patients, and healthy controls who were randomly selected from the sample set. In the second, independent validation phase, we examined the expression levels of identified miRNAs in all participants and analyzed the results of the miRNA arrays using the Gene Ontology and KEGG Pathway assays. Finally, we assessed the shared and distinct correlations between the expression of selected miRNAs and childhood traumatic experiences in UD and BD patients.
This study was approved by the institutional review board of the Affiliated Nanjing Brain Hospital of Nanjing Medical University, and written informed consent was obtained from each participant.
Plasma Preparation and RNA Isolation
The plasma was separated from venous blood within 24h after collection by centrifugation at 12,000 r.p.m. for 15 min. The supernatant from the plasma samples was stored in 300 µl aliquots at −80°C in RNase-free microtubes until it was used for miRNA extraction. A modified method was utilized to isolate total RNA. Briefly, Trizol reagent (Invitrogen, Carlsbad, CA, USA) was used to break down cells and cellular components in the plasma, and total RNA was extracted and purified using a miRNeasy Serum/Plasma Kit (Qiagen, Valencia, CA, USA) according to the manufacturer’s instructions. RNA quality and quantity were evaluated using a NanoDrop ND-1000 spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA).
miRNA Array
During the screening phase, a volume corresponding to 500 ng of total RNA from each blood sample was processed using a FlashTag Biotin HSR RNA Labeling kit (Affymetrix, Santa Clara, CA, USA), following the manufacturer’s protocol. The RNA was subsequently hybridized onto Affymetrix GeneChip miRNA 4.0 Arrays that each contained 2,578 human miRNA sequences (Affymetrix, Santa Clara, CA, USA). The GeneChip miRNA 4.0 Arrays were washed and stained using aFluidics station 450 and a GeneChip Scanner 3000 7G (Affymetrix, Santa Clara, CA, USA), respectively.
qRT-PCR Validation
At the validation phase, quantitative real-time PCR (qRT-PCR) was performed using Taqman microRNA probes (Applied Biosystems Inc, CA, USA) to confirm the candidate miRNAs identified on the microarrays. Total RNA was reverse transcribed to complementary DNA using a miRNA 1st Strand cDNA Synthesis Kit, stem-loop RT primers (Vazyme, Nanjing, China), and the GeneAmp 9700 PCR System (Thermo Scientific, MA, USA). The reactions began in a 384-well optical plate at 95°C for 5 minutes, followed by 40 cycles of 95°C for 10 seconds and 60°C for 30 seconds. The quantitative detection of the miRNAs was performed using the miRNA Universal SYBR qPCR Master Mix (Vazyme, Nanjing, China) and implemented on the Agilent AriaMx platform (Agilent Technologies, Palo Alto, CA, USA). All reactions, including no-template controls, were performed in triplicate. To calculate the relative expression levels of the target miRNAs, U6 was used as the control miRNA for plasma samples (the sequences of the primers listed in \\nSupplemental data 1\\n).
Data Analysis
All statistical analyses were performed using R (version 3.5.0) and SPSS (version 24.0). Demographic and clinical characteristics among the three groups were compared using χ2 tests, the Student’s t-test, or one-way ANOVA.
CEL-files of raw data were produced using Affymetrix GeneChip Command Console Software, Version 4.0 (Affymetrix, Santa Clara, CA, USA). Arrays were normalized using quantile normalization, and the probe values were log2 transformed. All data have been submitted to the GEO repository (code number: GSE152267).
We utilized one-way ANOVA to detect differently expressed miRNAs among all three groups, and miRNAs were chosen as candidates for further confirmation by individual qRT-PCR according to the following three criteria: (i) p-value <0.05 for the ANOVA; (ii) assessed using Fisher’s Least Significant Difference test as the Post Hoc test for multiple comparisons; (iii) fold changes (FCs) ≥3/2 or ≤2/3 between every two groups (BD vs. controls, UD vs. controls, and BD vs. UD).
Concerning qRT-PCR validation, the Ct values were normalized according to the delta Ct (ΔCt) method based on the internal reference, U6. The relative expression levels of target miRNAs were calculated using 2-ΔΔCT, which were analyzed using the Student’s t-test for independent samples (39). Binary logistic regression was applied to determine correlations between miRNA expression levels and childhood traumatic variables. A p-value<0.05 was statistically significant for all analyses.
Target Gene Prediction and Pathway Analysis
Bioinformatic analysis (Genminix Informatics Ltd., Shanghai, China) was performed for the miRNAs expressed in significant amounts. TargetScan (http://www.targetscan.org/) and miRanda (http://www.microrna.org/microrna/hom.do) were used to predict target genes for the validated candidate miRNAs, and only genes predicted by both databases were retained. To identify the potential biological mechanism of differentially expressed miRNAs among the BD, UD, and healthy controls, we performed gene ontology (GO, http://www.geneontology.org/) and Kyoto Encyclopedia of Genes and Genomes (KEGG, http://www.genome.jp/kegg/) pathway enrichment analysis with the David web application (https://david.ncifcrf.gov). Fisher’s exact test was employed to determine whether a gene set was enriched for a specific gene using GO terms or KEGG pathways compared to the background information. The p-value was corrected for false discovery rates (FDR). GO terms with a p-value<0.01 and a KEGG pathway with a p-value<0.05 were considered significant. The miRNA-mRNA-gene network of miR-19b-3p was obtained by using Cytoscape (Version 2.8.2).
ResultsDemographic and Clinical Characteristics of the Subjects
All subjects were followed up every six months to confirm whether they experienced any hypomania or mania episodes until December 2018. During three years of follow up, six UD patients (two males and four females, with a mean age of 26.2 ± 10.0 years) experienced a manic episode that lasted more than seven days. These six patients were excluded from the study since their diagnosis was switched from UD to BD. Thus, 32 UD patients and 27 BD patients were included in the final analysis (see \\nTable 1\\n). There was no statistical difference between these two groups regarding age, gender, education, or family history (p>0.05). The UD group recruited more first-episode patients compared to the BD group, while patients in the BD group had a longer duration of illness than the UD group. Clinical assessments showed that there were no significant differences in the total scores for HAMD between the two diagnostic groups. At the same time, the UD patients had higher anxiety or somatization scores (p<0.05) and sleep disturbance scores (p<0.01) compared to BD patients.
Demographic and clinical characteristics of unipolar depression, bipolar depression, and healthy controls.
Seven UD patients, seven BD patients, and six healthy controls were randomly selected from the total sample to undergo microarray chip inspection. There were no differences in the subjects’ demographic characteristics, the overall severity of depression, or childhood traumatic experiences (see \\nSupplementary data 2\\n). Total RNA extracted from peripheral venous blood was analyzed using Affymetrix miRNA 4.0 Arrays. The altered miRNA expression in UD and BD patients compared to healthy controls are listed in \\nSupplementary data 3\\n. Seventeen miRNAs were down-regulated, and 25 miRNAs were up-regulated (p<0.05).
Hierarchical clustering was carried out for all covered human mature miRNAs and pre-miRNAs, and significant differences in expression values were observed between the patients diagnosed with UD and BD and healthy controls. (\\nFigure 1\\n). Following hierarchical clustering, the array results were narrowed down to dysregulated miRNAs with a p-value < 0.05 from one-way ANOVA analysis of the three groups and a fold change ≥3/2 or ≤2/3 for all the two-group comparisons. Based on these criteria, three representative miRNAs (miR-19b-3p, miR-3921, and miR-1180-3p) were selected for PCR to validate the microarray results.
\\n(A) Volcano map of differentially expressed miRNAs. The horizontal axis is the logarithm of the fold change value based on 2, and the vertical axis is the negative logarithm of the p value based on 10. Blue dots represent down-regulated miRNAs with statistically significant differences in patients comparing healthy subjects. Red dots represent miRNAs that are up-regulated in patients, and grey dots represent miRNAs that have no significant differences in expression values between the patients and healthy controls. (B) Heat map of differentially expressed miRNAs. The abscissa represents the subject names between groups, and the ordinate represents the differentially expressed miRNAs. Differentiated miRNAs are expressed in red with high expression values and those with low expression values are in green.
Verification Using Quantitative Real-Time PCR
Quantitative real-time PCR (qRT-PCR) was performed to verify the differentially expressed miRNAs in an expanded sample comprised of 32 UD patients and 27 BD patients. Only miRNAs with a fold change ≥3/2 or ≤2/3 and a p-value < 0.05 from Student’s t-tests for each two-group comparison were validated. Of these, miR-19b-3p was verified to be down-regulated (see \\nFigure 2\\n).
Scatter plot figures show qRT-PCR validation of the differential expression of three miRNAs among three groups (unipolar patients (UD), bipolar patients (BD), and healthy controls (HC) (A) miR-19b-3p, (B) miR-3921, (C) miR-1180-3p). The values of 2-ΔΔCT represent the expressed level of filtered miRNAs. A p-value of < 0.05 means statistically significant.
Microarray-Based Gene Ontology and Signal Pathway Analysis
Based on the GO and KEGG signal pathway analyses, we first predicted mRNAs that could be regulated by miR-19b-3p. Then we performed an enrichment analysis to infer the putative biological pathways that might be involved in the miRNA regulation. We detected 288 mRNAs regulated by miR-19b-3p (see \\nSupplementary Data 4\\n). Significant gene functions and pathways putatively altered in UD and BD patients were selected based on the standards of p<0.01 (GO) and p<0.05 (KEGG). GO results revealed that most functions associated with miR-19b-3p regulation were related to signal transduction, neuron growth, cell differentiation, and apoptosis (\\nFigure 3A\\n). The most relevant KEGG signal pathways were involved in enrichment of biological processes, which included mTOR, autophagy, FoxO, prolactin, p53, and the PI3-K/Akt signaling pathway (\\nFigure 3B\\n). A more intuitive miRNA-mRNA-gene network diagram was produced to investigate the potential role of miR-19b-3p in the pathogenesis of depression (\\nFigure 3C\\n). The most relevant target genes were MAPK1, PTEN, TGFBR2, PRKAA1, PIK3R3, and RAF1, which were involved in multiple pathways.
\\n(A) GO analysis of target genes predicted by miR-19b-3p. The ordinate is the name of the target gene function, and the abscissa is the negative logarithm of p value (-Lgp). The larger the -Lgp value, the smaller the p value and the higher the significance level of the target gene function. (B) KEGG signal pathway of target genes predicted by miR-19b-3p. The ordinate is the name of the target gene signal pathway, and the abscissa is the negative logarithm of p value (-Lgp). The larger the -Lgp value, the smaller the p value and the higher the significance level of the target gene signal pathway. (C) MicroRNA-Gene network diagram of miR-19b-3p. The blue square in the figure refers to miR-19b-3p, the red circle refers to target genes, and the green arrow refers to the signal pathways involved in the target gene. Lines represent the regulatory relationship between miR-19b-3p and target genes.
Correlation Between miRNA Expression and Stress-Related Psychological Variables
A binary logistic regression model was produced to examine the correlation between miR-19b-3p expression and exposure to childhood trauma in patients with UD and BD, with adjustments for demographic variables. The results showed that the expression level of miR-19b-3p (OR=5.717, 95%CI:1.497–21.835, p=0.011) and the overall severity of childhood trauma (OR=1.099, 95%CI:1.012-1.192, p=0.025) were significantly associated with greater risk of BD. Among the sub-types of childhood trauma (emotional abuse, physical abuse, sexual abuse, emotional neglect, and physical neglect), there was a weak association between physical neglect and UD (OR=0.773, 95%CI:0.598-0.999, p=0.049) (\\nTable 2\\n). The other specific types of childhood trauma were not significantly correlated with any morbid status.
The logistic regression of miR-19b-3p and childhood trauma characteristics between the patients with UD and BD.
B
\\np value
OR
95%CI
miR-19b-3p
1.743
0.011*
5.717
1.497
21.835
Total score of CTQ
0.094
0.025*
1.099
1.012
1.192
Physical neglect
−0.258
0.049*
0.773
0.598
0.999
UD, unipolar depression; BD, bipolar depression; CTQ, childhood trauma questionnaire. Physical neglect: a subtype of childhood trauma. *p < 0.05
Discussion
In the present study, we systematically investigated the genome-wide miRNA expression profile in plasma from patients with UD and BD as compared to healthy subjects. Among 42 differentially expressed miRNAs, three miRNAs (miR-19b-3p, miR-3921, and miR-1180-3p) were selected to validate the microarray chip results. The novel miR-3921 was significantly up-regulated in the UD group but was not verified. miR-1180-3p was up-regulated in UD and BD patients but also was not verified. To date, no published reports have associated these two miRNAs with psychiatric disorders. However, miR-19b-3p was significantly down-regulated in the patient group, which indicates that peripheral miRNAs are possible non-invasive biomarkers with the necessary diagnostic accuracy. Furthermore, a specific miRNA related to psychological factors might help differentiate BD from UD. The BD patients were more likely to exhibit the combination of over-expressed miR-19b-3p and more severe childhood trauma when UD and BD patients were compared.
miR-19b-3p and Its Pathology in Psychiatric Diseases
MiR-19b-3p belongs to the miR-17/92 cluster, which exerts powerful effects on lymphocyte development, proliferation, activation, differentiation, and cytokine production (40, 41). Gantier et al. first reported that miR-19b regulated the activity of nuclear factor-κB (NF-κB) signaling in inflammation (42). Over-expression of miR-19b-3p inhibited the production of IL-6 and IL-8, and the interaction of miR-19b-3p with its direct target gene, G protein-coupled receptor kinase 6 (GRK6), was discovered to affect inflammation (43).
TNFAIP3, which is negatively regulated by miR-19b-3p, is widely recognized as an important regulator of inflammation (44). An intriguing role for miR-19b-3p is to regulate the neuroinflammatory response induced by the Japanese encephalitis virus (JEV) via enhancement of NF-κB signaling (45). Dwivedi et al. reported that miR-19b was over-expressed in the prefrontal cortex of rats with stress-induced depression with chronic administration of exogenous corticosterone (46). Although the epigenetic mechanisms of miR-19b-3p in psychiatric disorders remain unclear, various studies point to the effects of this miRNA in the communication between the immune system and the brain, which is known as a new area in psychiatry, specifically, immunopsychiatry.
The Target Genes of miR-19b-3p in Unipolar Depression and Bipolar Disorder
Our in silico results predicted a series of target genes and biological pathways for miR-19b-3p. Since patients with major depression who experienced childhood trauma were susceptible to immune dysregulation, we first check the biological processes associated with miR-19b-3p in immunomodulation between UD patients and BD patients. GO analysis predicted that the target gene function of miR-19b-3p, was enriched in the Wnt signaling pathway. It is consistent with a previous study that dysregulated expression of Wnt-related genes was shown in BD (47). Numerous studies have shown that suppression of Wnt signaling could induce both manic and depressive behaviors and exacerbate a proinflammatory state leading to increased neuronal apoptosis (48). In our study, the Wnt pathway was the most significantly enriched biological function, indicating its potential role in identifying UD and BD patients.
KEGG enrichment analyses predicted that the most significant target gene pathway was the mammalian rapamycin (mTOR) signaling pathway. Accumulating evidences suggested that mTOR signaling was dysregulated in depression (49). Activated mTOR signaling might be related to antidepressant-like effects in the hippocampus by modulating inflammatory cytokines such as interleukin-1β (IL-1β), interleukin-6 (IL-6), and tumor necrosis factor-α (TNF-α) (50). Autophagy signaling was the second, riched pathway regulated by miR-19b-3p. Dysregulation of autophagy leads to various disease manifestations, such as inflammation, metabolic alterations, and neurodegeneration (51). Some antidepressants induce inflammatory suppression through decreased serum levels of IL-1β and IL-18 and decreased NLRP3 protein expression via the autophagy pathway (52). Recent findings indicated that the forkhead box O (FoxO) signaling pathway is involved in the development of major depression and constitutes a potential therapeutic target in the treatment of depression (53). Although there is no evidence for FoxO involvement in BD, our results suggested that this pathway played an important role via miR-19b-3p in both UD and BD.
The prolactin pathway was identified in the differentiation between UD and BD. Hyperprolactinemia is reported commonly in subjects with a psychotic disorder which could due to stress, while information regarding mood disorder patients was particularly lacking (54). The p53 signaling, identified differently between UD and BD patients in our study, has not been reported to be associated with psychiatric disorders previously. Although the phosphatidylinositol 3-kinase (PI3K)-Akt signaling pathway was less strongly associated with miR-19b-3p expression, it contains the largest number of target genes. PI3K-Akt signaling is involved in the inhibition of the inflammatory response of lipoteichoic acid-stimulated macrophages (55). Reports indicate that some antidepressants used in clinical practice exert therapeutic efficacy via the promotion of the hippocampal PI3k-Akt-mTOR signaling pathway (56).
Mitogen-activated protein kinase 1 (MAPK1) had the highest predictive value among all the target genes of miR-19b-3p. Emerging evidence has revealed that alteration of MAPK1 is associated with psychiatric disorders, such as major depressive disorders, bipolar disorder and schizophrenia (57, 58). MAPK1 is highly expressed in the prefrontal cortex and hippocampus, and can modulate neuronal growth and differentiation, synaptic plasticity, and inflammatory processes via mTOR, FoxO, and other signaling pathways (59, 60).
Phosphatase and tensin homolog (PTEN) is a significant target of miR-19b-3p, and it directly regulates the PI3-K-Akt-mTOR signaling pathway, which is regarded as the most critical pathway for many neurobiological functions in the brain. PTEN regulates neuron cell size and affects dendritic growth, and it also acts as a significant tumor suppressor gene through the modulation of the inflammatory process (61, 62). Altered expression of PTEN in the blood is considered a biomarker for suicidal tendencies (63), as well as in the prefrontal cortex and hippocampus of suicide victims (64). The possibility of using miR-19b-3p and its target genes, such as PTEN to identify stress-related neuropathology in mood disorders, is encouraging. An upstream molecule in the mTOR pathway (PRKAA1) was down-regulated by miR-181a and facilitated hippocampal fear memory consolidation (65). Fear inhibition is related to trauma events (66). Therefore, PRKAA1 is one of significant target genes of miR-19b-3p that could be associated with stress-induced bipolar patients via activation of the mTOR pathway.
Childhood Traumatic Exposure and miRNA Alterations in Depressive Patients
Early life adversities, such as emotional, physical, and sexual abuse or neglect in childhood, are associated with poor psychological health outcomes in adults. Childhood traumatic experiences play an important role in developing BD, induction of more severe clinical symptoms, impairing emotion regulation and cognitive function, and lead to a much higher risk for suicide (28). Recent studies have explored the epigenetic mechanisms between childhood trauma and major depression, as well as schizophrenia (14). Our findings provide a significant association between childhood traumatic experiences and altered expression of miR-19b-3p in BD. Based on our bioanalysis results, plasma miR-19b-3p might be a biomarker associated with the impact of childhood trauma on UD and BD through the involvement of inflammatory processes.
Increased inflammation has been described in healthy individuals exposed to childhood trauma, suggesting a potentially causal role in the future onset of depression (67, 68). The Dwivedi group focused on epigenetic mechanisms of depression and suicide and recently reported that proinflammatory cytokines (e.g., TNF-α) and miR-19a-3b were up-regulated in the dorsolateral prefrontal cortex (DLPFC) in suicide victims, and both molecules were increased in the peripheral blood mononuclear cells of depressed patients with severe suicidal ideation (69).
Another important finding showed that unipolar patients with the lowest expression of peripheral miR-19p-3b among the three groups experienced more physical neglect, which has not been reported previously. The effects of neglect can be as traumatic as or even more traumatic than the effects of abuse, and the traumatic effects persist into adulthood (70). Neglect has several forms. Physical neglect could prompt the individual to develop psychological unavailability, which impacts the development of depressive symptoms. Further research is needed on possible epigenetic mechanisms of neglect associated with miRNAs.
Strengths and Limitations
This study has some strengths. First, we stringently recruited subjects in the screening phase. There were nearly identical clinical manifestations of the unipolar and bipolar patients, which could exclude possible confounding caused by distinct profiling between the two groups. Second, the differentiated miRNAs were systematically screened through the whole miRNome profiling on unipolar and bipolar depressive patients and healthy controls and was validated in a larger independent sample to increase the reliability of the results. Third, we provided an in-depth discussion of the possible miRNA targets and the integration of potential molecular mechanisms with environmental factors. We explored the possible use of peripheral miRNAs as non-invasive diagnostic biomarkers of mood disorders from an immune-psychiatry perspective and prompted further research on the role of miR-19p-3b in depression.
Nevertheless, the relationship of miRNA expression in plasma and the brain is not clear, since we only observed alterations in peripheral miRNAs. It is a concern that there were two selected miRNAs eliminated in the validation phase, which might be due to the limited sample size. A miRNA panel would be a more robust method of diagnosis of UD or BD. We did not examine any circulating inflammatory factors that were potentially related to the disorders. Therefore, the association between miRNA expression and immunological regulation remains unclear. A well-designed study would need to be conducted to develop a psychoneuroimmunology network of childhood trauma, immunological indices, altered miRNAs, and the disorder.
Conclusions
This study showed that the expression profiling of plasma miRNAs was altered both in UD and BD. miR-19b-3p was down-regulated in patients with depression and was validated to be over-expressed in BD but not UD. The target genes for miR-19b-3p were primarily enriched in the Wnt signaling and mTOR pathways. These miRNA gene targets elucidate the pathways and mechanisms involved in neuroimmunology pathways and depression pathogenesis.
Data Availability Statement
All data have been submitted to the GEO repository (code number: GSE152267).
Ethics Statement
The studies involving human participants were reviewed and approved by Affiliated Brain Hospital of Nanjing Medical University ethics committee. The patients/participants provided their written informed consent to participate in this study.
Author Contributions
YC: collected data, conducted the statistical analysis, drafted the manuscript, edited, and submitted the manuscript. JS, HL, QW, XC, HT, RY: collected data, reviewed, and revised the manuscript. QL: statistical analysis, critically reviewed, edited, and revised the manuscript. ZY: conceptualized and designed the study, critically reviewed, and revised the manuscript.
Funding
This work was supported by National Key R&D Program of China under grant number 2018YFC1314600; The National Natural Science Foundation of China under grant number 81871066,81571639; Jiangsu Provincial Medical Innovation Team of the Project of Invigorating Health Care through Science, Technology and Education under grant number CXTDC2016004; Jiangsu Provincial key research and development program under grant number BE2018609; Jiangsu Provincial Medical Youth Talent-The Project of Invigorating Health Care through Science, Technology and Education, QNRC2016049; The Science and Technology Program of Nanjing, 201803030.
Conflict of Interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Acknowledgments
We thank all subjects for participating in this study.
Supplementary Material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpsyt.2020.00757/full#supplementary-material\\n
Click here for additional data file.
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Plant Sci.Frontiers in Plant Science1664-462XFrontiers Media S.A.32849706PMC743214410.3389/fpls.2020.01148Plant ScienceMethodsMathematical Modeling of Growth and Paclitaxel Biosynthesis in Corylus avellana Cell Culture Responding to Fungal Elicitors Using Multilayer Perceptron-Genetic AlgorithmSalehiMina1*FarhadiSiamak1MoieniAhmad1*SafaieNaser2*AhmadiHamed31Department of Plant Genetics and Breeding, Faculty of Agriculture, Tarbiat Modares University, Tehran, Iran2Department of Plant Pathology, Faculty of Agriculture, Tarbiat Modares University, Tehran, Iran3Bioscience and Agriculture Modeling Research Unit, Department of Poultry Science, Tarbiat Modares University, Tehran, Iran
Edited by: Xianwen Ren, Peking University, China
Reviewed by: Bingqiang Liu, Shandong University, China; Junjie Yue, Institute of Biotechnology (CAAS), China
*Correspondence: Mina Salehi, mina.salehi@modares.ac.ir; salehi.minasm@gmail.com; Ahmad Moieni, moieni_a@modares.ac.ir; Naser Safaie, nsafaie@modares.ac.ir
This article was submitted to Computational Genomics, a section of the journal Frontiers in Plant Science
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Paclitaxel is the top-selling anticancer medicine in the world. In vitro culture of Corylus avellana has been made known as a promising and inexpensive strategy for producing paclitaxel. Fungal elicitors have been named as the most efficient strategy for enhancing the biosynthesis of secondary metabolites in plant cell culture. In this study, endophytic fungal strain HEF17 was isolated from C. avellana and identified as Camarosporomyces flavigenus. C. avellana cell suspension culture (CSC) elicited with cell extract (CE) and culture filtrate (CF) derived from strain HEF17, either individually or combined treatment, in mid and late log phase was processed for modeling and optimizing growth and paclitaxel biosynthesis regarding CE and CF concentration levels, elicitor adding day, and CSC harvesting time using multilayer perceptron-genetic algorithm (MLP-GA). The results displayed higher accuracy of MLP-GA models (0.89–0.95) than regression models (0.56–0.85). The great accordance between the predicted and observed values of output variables (dry weight, intracellular, extracellular and total yield of paclitaxel, and also extracellular paclitaxel portion) for both training and testing subsets supported the excellent performance of developed MLP-GA models. MLP-GA method presented a promising tool for selecting the optimal conditions for maximum paclitaxel biosynthesis. An Excel® estimator, HCC-paclitaxel, was designed based on MLP-GA model as an easy-to-use tool for predicting paclitaxel biosynthesis in C. avellana CSC responding to fungal elicitors.
Paclitaxel is a potent mitotic inhibitor that is utilized for treating breast, lung and ovarian cancers, and Kaposi’s sarcoma (Weaver, 2014), so that it has been entitled the top-selling anticancer medicine in the world (Goodman and Walsh, 2001). Also, this impactful chemotherapeutic agent is used for off-label treatment of endometrial, gastroesophageal, prostate, cervical, and head and neck cancers (Weaver, 2014). Invaluable secondary metabolite “paclitaxel” was initially extracted from Taxus bark (Wheeler et al., 1992). But harvesting the bark of these valuable species in the natural areas speedily exceeded levels deemed as a sustainable one, and critical over-harvesting has caused Taxus wild populations to be on the brink of extinction worldwide (Shinwari and Qaiser, 2011). Plant cell factories are a promising environmentally sustainable alternative to paclitaxel mass production (Salehi et al., 2017; Espinosa-Leal et al., 2018; Salehi et al., 2019b; Salehi et al., 2019c). The rising demand for paclitaxel and Taxus recalcitrant behavior under in vitro conditions have caused extensive effort toward finding alternatives for producing this invaluable secondary metabolite.
In vitro culture of hazel (Corylus avellana, European filbert) has been made known as a promising and inexpensive strategy for producing paclitaxel (Gallego et al., 2017; Salehi et al., 2017; Salehi et al., 2018c; Salehi et al., 2019b; Salehi et al., 2019c; Farhadi et al., 2020; Salehi et al., 2020). Biosynthesizing bioactive compounds in plants is influenced by various factors (Torkamani et al., 2014; Salehi et al., 2017; Salehi et al., 2018a; Salehi et al., 2018b; Salehi et al., 2018c; Salehi et al., 2019a; Salehi et al., 2019b; Salehi et al., 2019c; Salehi et al., 2019d). Previous studies (Salehi et al., 2019b; Salehi et al., 2019c; Farhadi et al., 2020; Salehi et al., 2020) demonstrated the positive influences of cell extract (CE) and culture filtrate (CF) of endophytic fungi on paclitaxel biosynthesis in cell suspension culture (CSC) of C. avellana. Fungal elicitor type, concentration and adding time, and also exposure time of cell culture (CSC harvesting time) should be optimized to achieve the maximum biosynthesis of paclitaxel in C. avellana CSC (Salehi et al., 2019b; Salehi et al., 2019c; Farhadi et al., 2020; Salehi et al., 2020). Precise analysis of the effects of these factors and their optimal selection would pave the way for the commercialization of bioprocessing C. avellana cells toward paclitaxel mass production. Paclitaxel biosynthesis and its elicitation are complex biological processes since they are affected by several factors and their nonlinear interactions. Optimizing these mentioned factors by experimenting is laborious, costly, and time-consuming. The mathematical models can effectively predict the optimized conditions for a multifactorial process (Struik et al., 2005; Gallego et al., 2011) such as paclitaxel biosynthesis.
Artificial intelligence (AI) technology is the algorithm capable of complex and intelligent computing similar to the routine performance of the human brain (Agatonovic-Kustrin and Beresford, 2000). Artificial neural network (ANN) is an AI method discovering complex nonlinear relationships among input (factors) and output (parameters) data (Patnaik, 1999; Plumb et al., 2005). Indeed, ANN is a brain-inspired method that imitates the way that the human brain works (Agatonovic-Kustrin and Beresford, 2000). It processes information and makes decision in systems involving vagueness and uncertainty (Patnaik, 1999; Gago et al., 2010). This technology has been widely used as a predictive instrument in a broad range of fields including ecology, food science, agriculture, environmental sciences, plant biology, pharmaceutical research, and biotechnology (Hilbert and Ostendorf, 2001; Daniel et al., 2008; Huang, 2009; Arab et al., 2018; Hesami et al., 2019a; Hesami et al., 2019b; Hesami et al., 2019c; Sheikhi et al., 2020). Multilayer perceptron (MLP), one of the most popular types of ANN, exhibits superior predictive ability as compared to traditional statistical methods to approximate the mathematical functions for analyzing and interpreting different unforeseeable data sets (Ahmadi and Golian, 2011; Jamshidi et al., 2016). However, training and designing of ANN face several problems. One of the biggest problems is assigning the weights in ANN structure which displays the direct influence on model performance. Basically, the network architecture and learning algorithm parameters control the weights. Also, other network parameters including the number of memory taps, the number of hidden layers and nodes and learning rates could influence ANN performance (Tahmasebi and Hezarkhani, 2009). To overcome these mentioned problems, ANN is hybridized with other optimization methods including genetic algorithm (GA) (Plumb et al., 2005; Shao et al., 2007; Ahmadi and Golian, 2011; Eftekhari et al., 2018; Sheikhi et al., 2020).
GA is the evolutionary algorithm making superb solutions to problems and has been applied for bioprocess optimization in plant biology (Osama et al., 2015; Jamshidi et al., 2016; Arab et al., 2018). Indeed, GA is a search algorithm inspired by natural selection and genetics concepts (Holland, 1992). The fundamental principles of GA are the creation of an initial population of search solutions (chromosomes), and then elite search solutions were selected for crossover using a roulette wheel selection method, which will ultimately be the best solution (fittest chromosome) (optimal value) among them (Figure 1).
Steps of operation of multilayer perceptron-genetics algorithm (MLP-GA) intelligence.
Multilayer perceptron-genetic algorithm (MLP-GA), integrating MLP with GA (Figure 1), causes achieving an accurate model for prediction and optimization of biological process (Jamshidi et al., 2016; Arab et al., 2018; Eftekhari et al., 2018).
The objectives of this research were (a) to isolate endophytic fungi from C. avellana grown in Iran, (b) to develop regression and MLP-GA models to predict output variables “dry weight (DW), intracellular paclitaxel, extracellular paclitaxel, total yield of paclitaxel and extracellular paclitaxel portion” based on input variables “CE and CF concentration levels, elicitor adding day, and CSC harvesting time”, (c) to compare regression and MLP-GA performance in term of prediction accuracy of output variables, (d) to optimize the mentioned factors for maximum biosynthesis of paclitaxel, (e) to detect the most important factors for maximum biosynthesis of paclitaxel, and (f) to design an Excel® estimator which can easily be applied to predict the total yield of paclitaxel in C. avellana CSC based on input variables.
Materials and MethodsIsolation of Endophytic Fungi
Healthy samples of the bud, stem, bark, and leaves were obtained from C. avellana grown in Iran during June to September 2018. The surfaces of the samples were sterilized as described by Salehi et al. (2018c; 2019b). The surface-sterilized plant samples were cut and transferred on PDAC [potato dextrose agar (PDA); supplemented with 250 mg l−1 Chloramphenicol] in unique Petri dishes (100 × 15 mm), incubated at 25 °C. After the growth of endophytic fungi, the pure cultures of the isolates were established by hyphal tip culture (Strobel et al., 1996). All fungal endophytes were numbered as HEF# series and stored at 4 °C.
Molecular Identification of Endophytic Fungus
Fungal endophyte was cultured in potato dextrose broth (PDB) and incubated in a shaker incubator at 25 °C and 110 rpm for 10 days. The extraction of fungal genomic DNA was done as described by Salehi et al. (2018c; 2019b). The partial sequences of internal transcribed spacer (ITS) fragments (ITS1-5.8S-ITS2) and actin gene (ACT) were used to obtain DNA sequence information. ITS fragments were amplified using universal primers ITS1 and ITS4 (White et al., 1990) and ACT using primer pair ACT-512F and ACT-783R (Carbone and Kohn, 1999). PCR reaction mixtures (25 µl) consisted of 1 µl genomic DNA (~100 ng), 1 µl forward and reverse primers (10 pM), and 12.5 µl Premix Taq (TaKaRa Biotechnology Ltd., Japan), and 10.5 µl PCR ultrapure water. PCR reaction programs were an initial denaturation at 94 °C for 3 min, followed by 30 cycles of denaturation (94 °C for 30 s), annealing [56 °C (ITS) and 59 °C (ACT) for 30 s], extension (72 °C for 1 min), and a final extension at 72 °C for 5 min. PCR product analysis and purification, sequencing and the phylogenetic analysis were made as described previously (Salehi et al., 2018c; Salehi et al., 2019b).
Elicitation of <italic>C. avellana</italic> Cell Culture
C. avellana CSC was established as described by Salehi et al. (2017; 2018c; 2019b; 2019c). The elicitors (CE and CF) were prepared as described previously (Salehi et al., 2019b). For elicitation, 1.5 ± 0.1 g of C. avellana cells (fresh mass) was cultured in 100 ml flasks containing 30 ml MS medium supplemented with 2 mg l−1 2,4-D and 0.2 mg l−1 BAP.
Based on our previous studies (Salehi et al., 2019b; Salehi et al., 2019c; Farhadi et al., 2020), three concentrations [2.5, 5, and 10% (v/v)] of fungal elicitors “CE:CF (100:0, 75:25, 50:50, 25:75, 0:100 v/v)” and also mid (day 13) and late (day 17) log phase of C. avellana cell cultures were selected for adding fungal elicitors. Control received an equal volume of water (for CE)/PDB (for CF).
Cell Growth Measurement
Cell growth was determined by the measurement of cell dry weight (DW). Cell biomass was separated from the culture medium by filtration (Whatman No. 1) and washed with distilled water to remove the residual medium, afterward freeze-dried to a constant weight by a vacuum-freeze drier.
Quantification of Paclitaxel
C. avellana cells were separated from the culture medium by a filter paper (Whatman No. 1). Intracellular and extracellular paclitaxel were extracted from the cells and culture broth using a procedure described by Salehi et al. (2017; 2018c; 2019b). Filtering all samples was performed by 0.22 µm cellulose acetate syringe filters before HPLC analysis. Paclitaxel in the samples was analyzed by HPLC (Waters, USA) with a C18 analysis column (Machereye-Nagel EC 250/4.6 Nucleodur). Each sample (20 µl) was injected and detected at 230 nm using a UV detector. The mobile phase was methanol:water (80:20 v/v) at a flow rate of 1.0 ml min−1. The quantification of paclitaxel was based on an external standard of genuine paclitaxel (Sigma).
Experimental Design
The experiment was conducted based on randomized complete block design (RCBD) with factorial arrangement, three factors containing elicitor type with 10 levels [CE:CF (100:0, 75:25, 50:50, 25:75, 0:100 v/v) and water:PDB (100:0, 75:25, 50:50, 25:75, 0:100 v/v)], concentration with three levels [2.5, 5, and 10% (v/v)], elicitor adding day with two levels (days 13 and 17), and three replicates. The cultures were harvested at 2-day intervals after elicitation until the 23rd day.
Model Development
The data were randomly divided into a training subset (70%) and a testing subset (30%). The training subset was applied to develop multiple linear regression (MLR) and backward regression and also MLP-GA models, and testing subset was applied to test the predictability of developed models (Shao et al., 2006).
Regression Analysis
Regression analysis is one of well-known predictive modeling methods. The popularity of these models may be assigned to model parameter interpretability and its ease of use. Here, MLR and backward regression models were used to predict DW, intracellular, extracellular and total yield of paclitaxel, and also extracellular paclitaxel portion. Significance level for the independent variables to include in the model was set at 0.05.
To determine which model component is more important during the modeling process, sensitivity analysis was performed on developed regression models using analysis of variance (ANOVA) and absolute t value (|t value|) corresponding to model coefficients. It is noteworthy that a more important model component displays a higher |t value| (Ahmadi and Golian, 2011; Ahmadi and Rodehutscord, 2017).
Multilayer Perceptron (MLP) Model
Three-layered feed forward back-propagation neural network was used to define the influences of CE and CF concentration levels, elicitor adding day, and CSC harvesting time on DW, paclitaxel biosynthesis (intracellular, extracellular and total), and extracellular paclitaxel portion. Transfer functions for hidden and output layers were hyperbolic tangent sigmoid (tansig) and linear (purelin), respectively.
ANN capability to process the information is determined by its architecture. Evolutionary algorithms are used for searching the optimal architecture design (Yao, 1999).
Genetic Algorithm (GA)
The high number of hidden neurons leads to prolong the training time and also overfits the data. Too few hidden neurons lead to a low accuracy rate (Matignon, 2005). GA was used (i) to determine optimal MLP architecture design including the optimal numbers of neurons, and (ii) to optimize the values of input variables (CE and CF concentration, elicitor adding day, and CSC harvesting time) in developed MLP-GA models for maximum paclitaxel biosynthesis and its secretion. An initial population of 50, crossover rate of 0.85, generation number of 500 and mutation rate of 0.01 (Haupt and Haupt, 2004; Abramson, 2007) were set to establish fittest MLP structure and optimize input variables for maximum output variables.
The performance of MLP-GA models is determined by root mean square error (RMSE) and coefficient of determination (R2) as reported by Ahmadi (2017), as well as mean absolute percentage error (MAPE) [Eq. (1)].
Where “yact” are the actual values, “yest” are the predicted values, and “n” is the number of data.
Sensitivity Analysis of the Models
Sensitivity analysis was done on MLP-GA models to determine the importance degree of the factors (CE and CF concentration levels, elicitor adding day, and CSC harvesting time) on the model parameters (DW, paclitaxel biosynthesis, and its secretion). The sensitivity of DW, paclitaxel biosynthesis (intracellular, extracellular, and total yield), and extracellular paclitaxel portion was determined by the criteria including variable sensitivity error (VSE) value displaying the performance (RMSE) of MLP-GA model when that particular input variable is unavailable in the model. Variable sensitivity ratio (VSR) value was calculated as the ratio of VSE and MLP-GA model error (RMSE value) when all input variables are available. Finally, calculated VSR values were rescaled within the range [0, 1]. The input variable with higher VSR was considered as the higher important variable in the model (Ahmadi and Golian, 2011).
The mathematical codes for the development and evaluation of MLR, backward regression, and MLP-GA models were written using MATLAB (Matlab, 2010) software, and the graphs were made by GraphPad Prism 5 (GraphPad Prism 5, 2005) software. “ANNGA_opt” program coded by MATLAB can be downloaded from https://github.com/hahmadima/ANNGA_opt.
ResultsIdentification of Endophytic Fungus
Strain HEF17 was isolated from the leaf of C. avellana and identified as Camarosporomyces flavigenus by analysis of the sequences of actin gene (Figure 2). Accession numbers used for phylogenetic study were reported by De Gruyter et al. (2013). This is the first report of this endophytic fungus on C. avellana tree (matrix nova). The partial sequences of ITS rDNA and ACT obtained from C. flavigenus strain HEF17 were deposited in GenBank (NCBI) under accession numbers MT176168 and MT224136, respectively.
Molecular identification of strain HEF17 based on the analysis of the sequences of actin gene. The tree was rooted to Plenodomus lingam (CBS 147.24).
Regression Analysis
Goodness of fit displayed no difference regarding the accuracy of MLR and backward regression for all output variables, 0.66, 0.56, 0.61, 0.58, and 0.85 for DW, intracellular paclitaxel, extracellular paclitaxel, total yield of paclitaxel, and extracellular paclitaxel portion, respectively, for the training subset (Table 1). Accordingly, the results of backward regression showed that elicitor adding day and CSC harvesting time are only parameters among the four above-mentioned input variables which influenced DW (Table 1). All input variables including CE and CF concentration levels, elicitor adding day and CSC harvesting time are important factors influencing intracellular, extracellular, and total yield of paclitaxel, and also paclitaxel secretion from cells to the culture medium (Table 1). R2 values for DW, intracellular paclitaxel, extracellular paclitaxel, total yield of paclitaxel, and extracellular paclitaxel portion were estimated 0.64, 0.58, 0.61, 0.61, and 0.85, respectively, for the testing subset (Figure 3).
Backward regression models for estimating growth, paclitaxel biosynthesis, and secretion in Corylus avellana cell suspension culture (CSC) treated with fungal elicitors using cell extract (CE) and culture filtrate (CF) concentration levels [% (v/v)], elicitor adding day, and CSC harvesting time (day).
Measured factors
Variablea\n
Coeffcient
Standard error
t value
Dry weight (g l-1)
Intercept
-0.8950
0.5350
-1.67
Elicitor adding day
0.0995
0.0325
3.06
CSC harvesting time
0.4862
0.0239
20.34
R2
0.6635
RMSE
0.9642
MAPE
4.6650
Intracellular paclitaxel (µg g-1 DW)
Intercept
-4.7600
1.3200
-3.60
CE concentration level
0.4393
0.0531
8.28
CF concentration level
0.7936
0.0528
15.03
Elicitor adding day
0.8277
0.0787
10.52
CSC harvesting time
-0.2170
0.0578
-3.75
R2
0.5560
RMSE
2.3318
MAPE
59.1400
Extracellular paclitaxel (µg l-1)
Intercept
-123.5000
8.9600
-13.78
CE concentration level
2.6740
0.3600
7.44
CF concentration level
4.7370
0.3580
13.24
Elicitor adding day
5.8400
0.5330
10.96
CSC harvesting time
2.5470
0.3920
6.50
R2
0.6060
RMSE
15.7977
MAPE
65.60000
Total yield of paclitaxel (µg l-1)
Intercept
-242.0000
23.0
-10.51
CE concentration level
7.1130
0.924
7.70
CF concentration level
12.7280
0.920
13.84
Elicitor adding day
15.5000
1.37
11.31
CSC harvesting time
3.0100
1.01
2.99
R2
0.5803
RMSE
40.5987
MAPE
67.7200
Extracellularpaclitaxel portion(%)
Intercept
-18.6000
1.4200
-13.07
CE concentration level
0.1976
0.0571
3.46
CF concentration level
0.3167
0.0569
5.57
Elicitor adding day
0.3119
0.0847
3.68
CSC harvesting time
2.2211
0.0623
35.68
R2
0.8549
RMSE
2.51070
MAPE
21.18
Significant (p ≤ 0.05) variables included in the model. R2, coefficient of determination; RMSE, root mean square error; MAPE, mean absolute percentage error.
Scatter plot of actual data against predicted values of dry weight, intracellular, extracellular and total yield of paclitaxel, and extracellular paclitaxel portion in Corylus avellana cell cultures using backward regression models in the testing subset. The solid line shows fitted simple regression line on scatter points.
Goodness of fit of DW model and absolute t values (Table 1) showed that out of the investigated input variables, CSC harvesting time (|t value| = 20.3) was the most important parameter affecting DW, followed by elicitor adding day (|t value| = 3.1). Accordingly, CF concentration level (|t value| = 15.0) displayed the highest effect on intracellular paclitaxel, followed by elicitor adding day (|t value| = 10.5), CE concentration level (|t value| = 8.3) and CSC harvesting time (|t value| = 3.8). Also, CF concentration level (|t value| = 13.2) was the most effective component on extracellular paclitaxel, followed by elicitor adding day (|t value| = 11.0), CE concentration level (|t value| = 7.4) and CSC harvesting time (|t value| = 6.5). Furthermore, CF concentration level (|t value| = 13.8) was the most important factor influencing total yield of paclitaxel, followed by elicitor adding day (|t value| = 11.3), CE concentration level (|t value| = 7.7) and CSC harvesting time (|t value| = 3.0). Additionally, CSC harvesting time exhibited the highest effect on extracellular paclitaxel portion (|t value| = 35.7), followed by CF concentration level (|t value| = 5.6), elicitor adding day (|t value| = 3.7) and CE concentration level (|t value| = 3.5) (Table 1).
Multilayer Perceptron-Genetics Algorithm Analysis
Initially, CE and CF concentration levels, elicitor adding day and CSC harvesting time were used as input variables and DW, intracellular, extracellular and total yield of paclitaxel, and also extracellular paclitaxel portion as output variables. Then, output variables were predicted according to developed MLP-GA models. To evaluate the performance of developed MLP-GA models, the predicted values were plotted against the observed values of training (Figure 4A) and testing (Figure 4B) subsets. The great accordance between the predicted and observed values of DW, intracellular, extracellular and total yield of paclitaxel, and also extracellular paclitaxel portion was observed for both training and testing subsets (Figure 4). Goodness of fit of developed MLP-GA models showed that the developed models could accurately (R2 = 0.90, 0.89, 0.92, 0.95, and 0.91) (Table 2) predict DW, intracellular, extracellular and total yield of paclitaxel, and also extracellular paclitaxel portion of the testing subset, not used during the training processes (Figure 4). Also, developed MLP-GA models displayed the balanced statistical values for both training and testing subsets (Table 2).
Scatter plot of actual data against predicted values of dry weight, intracellular, extracellular and total yield of paclitaxel and extracellular paclitaxel portion in Corylus avellana cell cultures using multilayer perceptron-genetics algorithm (MLP-GA) models in training (A) and testing (B) subsets. The solid line shows fitted simple regression line on scatter points.
Statistics and information on multilayer perceptron-genetics algorithm (MLP-GA) models for growth, paclitaxel biosynthesis and secretion in Corylus avellana cell culture.
Measured factors
Neuron number
Training subsets
Testing subsets
R2
RMSE
MAPE
R2
RMSE
MAPE
Dry weight
5
0.90
0.53
0.45028
0.90
0.54
0.48036
Intracellular paclitaxel
6
0.90
1.14
0.86184
0.89
0.98
0.78311
Extracellular paclitaxel
7
0.95
6.78
4.45092
0.92
5.77
4.12229
Total yield of paclitaxel
7
0.95
14.27
10.9185
0.95
12.87
9.85688
Extracellular paclitaxel portion
5
0.92
1.87
1.54511
0.91
1.89
1.59910
R2, coefficient of determination; RMSE, root mean square error; MAPE, mean absolute percentage error.
Sensitivity Analysis of the Models
To rank the input variables based on their relative importance in the model, VSRs were estimated using all data lines (training and testing subsets). VSRs were obtained for each of output variables (DW, intracellular, extracellular and total yield of paclitaxel, and also extracellular paclitaxel portion) regarding CE and CF concentration levels, elicitor adding day and CSC harvesting time (Table 3). Analysis of DW model indicated that DW of C. avellana cells was more sensitive to CSC harvesting time (VSR = 0.990), followed by elicitor adding day (VSR = 0.010), CE and CF concentration levels (VSR = 0.004). Intracellular paclitaxel displayed more sensitivity to CE concentration level (VSR = 0.530), followed by CF concentration level (VSR = 0.460), elicitor adding day (VSR = 0.180), and CSC harvesting time (VSR = 0.100). Extracellular paclitaxel showed more sensitivity to CSC harvesting time (VSR = 0.660), followed by CF concentration level (VSR = 0.250), CE concentration level (VSR = 0.110), and elicitor adding day (VSR = 0.100). Accordingly, total yield of paclitaxel exhibited more sensitivity to CE concentration level (VSR = 0.720), followed by CF concentration level (VSR = 0.500), CSC harvesting time (VSR = 0.190), and elicitor adding day (VSR = 0.070). Also, extracellular paclitaxel portion displayed more sensitivity to CSC harvesting time (VSR = 0.810), followed by elicitor adding day (VSR = 0.120), CE concentration level (VSR = 0.080), and CF concentration level (VSR = 0.050) (Table 3).
Importance (according to sensitivity analysis) and optimal levels of the different input variables including cell extract (CE), culture filtrate (CF) concentration levels (% (v/v)), elicitor adding day and cell suspension culture (CSC) harvesting time (day) for achieving maximum growth, paclitaxel biosynthesis and secretion in Corylus avellana CSC using multilayer perceptron-genetics algorithm (MLP-GA) models.
Criteria
Variable
Importance value(according to VSRa)
Optimal level
Output Optimal
Dry weight (g l-1)
CE concentration level
0.004
5.67
12.04
CF concentration level
0.004
0.60
Elicitor adding day
0.010
15.17
CSC harvesting time
0.990
20.78
Intracellular paclitaxel (µg g-1 DW)
CE concentration level
0.530
3.37
17.74
CF concentration level
0.460
5.33
Elicitor adding day
0.180
17.00
CSC harvesting time
0.100
20.27
Extracellular paclitaxel (µg l-1)
CE concentration level
0.110
5.29
124.52
CF concentration level
0.250
5.75
Elicitor adding day
0.100
17.00
CSC harvesting time
0.660
20.90
Total yield of paclitaxel (µg l-1)
CE concentration level
0.720
3.33
369.67
CF concentration level
0.500
5.25
Elicitor adding day
0.070
17.00
CSC harvesting time
0.190
20.95
Extracellular paclitaxel portion (%)
CE concentration level
0.080
4.51
48.07
CF concentration level
0.050
5.10
Elicitor adding day
0.120
17.00
CSC harvesting time
0.810
23.00
Relative indication of the ratio between the variable sensitivity error and the error of the model when all variables are available. Calculated VSR values were rescaled within range [0, 1].
Model Optimization
The optimization analysis on developed MLP-GA models was performed using GA to determine the optimal levels of input variables for achieving maximum growth, paclitaxel biosynthesis, and its secretion in C. avellana CSC (Table 3). The optimization results showed that adding 6.27% (v/v) of 90CE:10CF containing 5.67% (v/v) CE and 0.6% (v/v) CF on 15th day and harvesting CSC 134 h and 38 min after elicitation could result in maximum DW (12.04 g l−1) (Table 3). The highest content of intracellular paclitaxel (17.74 µg g−1 DW) may be produced by adding 8.70% (v/v) of 39CE:61CF containing 3.37% (v/v) CE and 5.33% (v/v) CF on 17th day and harvesting CSC 78 h and 29 min after elicitation (Table 3). Also, the results showed that highest extracellular paclitaxel (124.52 µg l−1) can be produced by adding 11.13% (v/v) of 48CE:52CF containing 5.29% (v/v) CE and 5.75% (v/v) CF on 17th day and harvesting CSC 93 h and 36 min after elicitation (Table 3). Additionally, CSC exposed with 8.58% (v/v) of 39CE:61CF containing 3.33% (v/v) CE and 5.25% (v/v) CF on 17th day and harvesting it 94 h and 48 min after elicitation may obtain the highest total yield of paclitaxel (369.67 µg l−1) (Table 3). The results of MLP-GA model optimization displayed that adding 9.61% (v/v) of 47CE:53CF containing 4.51% (v/v) CE and 5.10% (v/v) CF on 17th day and harvesting CSC 144 h after elicitation may lead to highest extracellular paclitaxel portion (48.07) (Table 3).
Comparison of MLP-GA and Backward Regression Models
The statistical values for MLP-GA models displayed higher prediction accuracy as compared to regression models as estimated R2 for MLP-GA vs. regression models were: DW = 0.90 vs. 0.66, intracellular paclitaxel = 0.90 vs. 0.56, extracellular paclitaxel = 0.93 vs. 0.61, total yield of paclitaxel = 0.95 vs. 0.58, and extracellular paclitaxel portion = 0.92 vs. 0.85 (Tables 1 and 2). In the end, an Excel® total paclitaxel estimator, namely, HCC-paclitaxel, was created using developed MLP-GA model (Figure 5). The mentioned estimator was presented as supplementary material.
HCC-paclitaxel: an Excel® estimator for predicting total paclitaxel value in Corylus avellana cell culture responding fungal elicitors using multilayer perceptron-genetic algorithm (MLP-GA) model. CE, cell extract; CF, culture filtrate. This estimator was presented as Supplementary Material.
Discussion
Predicting the optimal amount of the effective factors on paclitaxel biosynthesis is highly promising and essential for its production increment and cost decrement. This is the first study on predicting the optimal conditions for maximum paclitaxel biosynthesis in C. avellana CSC exposed to fungal elicitors using the mathematical model. To accurately predict the optimal amounts of effective factors (CE and CF concentration levels, elicitor adding day, and CSC harvesting time) on paclitaxel biosynthesis in C. avellana CSC, using a trustworthy modeling system is essential.
In this study, regression and MLP-GA modeling were applied to evaluate the relationships among four studied factors “CE and CF concentration levels, elicitor adding day, and CSC harvesting time” and the parameters “DW, intracellular, extracellular, and total yield of paclitaxel and extracellular paclitaxel portion”, and also the possibility of predicting the growth and paclitaxel biosynthesis by the determined factors. Such mathematical predictions have not been described in this area. Higher accuracy of MLP-GA models as compared to regression models (Tables 1 and 2) was also reported in previous studies (Jamshidi et al., 2016; Eftekhari et al., 2018).
The fit of regression models was presented by R2 (Figure 3) for testing subset, suggesting these models can explain 64, 58, 61, 61 and 85% of the variability in DW, intracellular paclitaxel, extracellular paclitaxel, total yield of paclitaxel and paclitaxel extracellular portion, respectively, when they face unseen data.
Our results suggested that MLP-GA models could accurately predict DW, intracellular paclitaxel, extracellular paclitaxel, total yield of paclitaxel and extracellular paclitaxel portion (R2 = 0.90, 0.89, 0.92, 0.94, and 0.91, respectively) in the testing subset (Figure 4), not used in the training process. Also, the small number of hidden neuron and also closing the errors of training and testing subsets to each other (Table 2) suggested that overlearning had not arisen in the training process, and developed MLP-GA models displayed good generalizability when they faced unseen data (Lou and Nakai, 2001; Ahmadi and Golian, 2011). Based on RMSE, R2 and MAPE of the training and testing subsets (Table 2), it can be concluded that tansig activation function effectively worked for modeling over all experiments. Small RMSE and MAPE (Table 2) showed the high potential of MLP-GA models in predicting output variables.
Regardless of previous studies on the effects of CE and CF concentration levels, elicitor adding day and CSC harvesting time on paclitaxel biosynthesis and secretion, there remains the question to be answered: which input variables are the most important in paclitaxel biosynthesis? As previously mentioned, sensitivity analysis displayed that CE and CF concentration levels are the most important variables affecting total yield of paclitaxel (Table 3). Endophytic fungi synthesize microbe-associated molecular patterns (MAMPs). The receptors localized on plant cell surface recognize MAMPs and thus induce plant defense system (Ausubel, 2005). Some of these MAMPs are found only in CE, a number of these exist only in CF, and others are found in both CE and CF with different concentrations (Figure 6). Therefore, paclitaxel biosynthesis elicitation potential of these fungal elicitors (CE and CF) is different. Extracellular paclitaxel content is important for paclitaxel production in a continuous system. Sensitivity analysis displayed that CSC harvesting time is the most important factor affecting extracellular paclitaxel (Table 3). Paclitaxel biosynthesis is the complex biological process that requires the accurate techniques for modeling and optimization. MLP-GA has been efficiently used to solve problems with extremely difficult and unknown solution in various fields (Jamshidi et al., 2016; Arab et al., 2018; Eftekhari et al., 2018; Sheikhi et al., 2020). A growing interest in ANN has mostly been because of its power in solving the problems in a broad range of fields, their ability for modeling nonlinear and complex relationships, prediction ability of the unseen relationships on the unseen data, and having no need of a specification of data statistical distribution (Mahanta, 2017).
Schematic design of microbe-associated molecular patterns (MAMPs) found in cell extract and culture filtrate, and total yield of paclitaxel in Corylus avellana cell cultures exposed with different concentrations of these fungal elicitors derived from Camarosporomyces flavigenus.
According to the high prediction accuracy of the training and testing subsets, it can be concluded that developed MLP-GA could accurately predict DW, paclitaxel biosynthesis, and secretion in C. avellana CSC.
Publishing developed MLP-GA models needs to share the connection weight matrices, which running ANN models requires the especial software. Therefore, we share developed MLP-GA model predicting total paclitaxel with the readers as HCC-paclitaxel Excel® estimator (Figure 5).
Conclusion
This research applied mathematical approaches for modeling and optimizing paclitaxel biosynthesis in C. avellana cell culture treated with fungal elicitors for the first time. The great accordance between the predicted and observed values of the output variables (DW, intracellular, extracellular and total yield of paclitaxel, and also extracellular paclitaxel portion) supported the excellent performance of developed MLP-GA models. HCC-paclitaxel Excel® estimator presents an easy-to-use tool to predict total yield of paclitaxel in C. avellana cell culture treated with fungal elicitors using MLP-GA model.
Author Contributions
MS directed the research, carried out all experiments and analyses. AM directed in vitro cell culture and elicitation experiment. NS directed the sections related to fungal elicitation. HA performed data modeling. MS and SF interpreted the results and wrote the manuscript. All authors read and approved the final manuscript.
Funding
The authors acknowledge Iran National Science Foundation (INSF, No. 97010721), and Research Deputy of Tarbiat Modares University, Tehran for financial support of this research project.
Conflict of Interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
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Plant Sci.Frontiers in Plant Science1664-462XFrontiers Media S.A.32849706PMC743214410.3389/fpls.2020.01148Plant ScienceMethodsMathematical Modeling of Growth and Paclitaxel Biosynthesis in Corylus avellana Cell Culture Responding to Fungal Elicitors Using Multilayer Perceptron-Genetic AlgorithmSalehiMina1*FarhadiSiamak1MoieniAhmad1*SafaieNaser2*AhmadiHamed31Department of Plant Genetics and Breeding, Faculty of Agriculture, Tarbiat Modares University, Tehran, Iran2Department of Plant Pathology, Faculty of Agriculture, Tarbiat Modares University, Tehran, Iran3Bioscience and Agriculture Modeling Research Unit, Department of Poultry Science, Tarbiat Modares University, Tehran, Iran
Edited by: Xianwen Ren, Peking University, China
Reviewed by: Bingqiang Liu, Shandong University, China; Junjie Yue, Institute of Biotechnology (CAAS), China
*Correspondence: Mina Salehi, mina.salehi@modares.ac.ir; salehi.minasm@gmail.com; Ahmad Moieni, moieni_a@modares.ac.ir; Naser Safaie, nsafaie@modares.ac.ir
This article was submitted to Computational Genomics, a section of the journal Frontiers in Plant Science
1182020202011114822420201472020Copyright © 2020 Salehi, Farhadi, Moieni, Safaie and Ahmadi2020Salehi, Farhadi, Moieni, Safaie and AhmadiThis is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Paclitaxel is the top-selling anticancer medicine in the world. In vitro culture of Corylus avellana has been made known as a promising and inexpensive strategy for producing paclitaxel. Fungal elicitors have been named as the most efficient strategy for enhancing the biosynthesis of secondary metabolites in plant cell culture. In this study, endophytic fungal strain HEF17 was isolated from C. avellana and identified as Camarosporomyces flavigenus. C. avellana cell suspension culture (CSC) elicited with cell extract (CE) and culture filtrate (CF) derived from strain HEF17, either individually or combined treatment, in mid and late log phase was processed for modeling and optimizing growth and paclitaxel biosynthesis regarding CE and CF concentration levels, elicitor adding day, and CSC harvesting time using multilayer perceptron-genetic algorithm (MLP-GA). The results displayed higher accuracy of MLP-GA models (0.89–0.95) than regression models (0.56–0.85). The great accordance between the predicted and observed values of output variables (dry weight, intracellular, extracellular and total yield of paclitaxel, and also extracellular paclitaxel portion) for both training and testing subsets supported the excellent performance of developed MLP-GA models. MLP-GA method presented a promising tool for selecting the optimal conditions for maximum paclitaxel biosynthesis. An Excel® estimator, HCC-paclitaxel, was designed based on MLP-GA model as an easy-to-use tool for predicting paclitaxel biosynthesis in C. avellana CSC responding to fungal elicitors.
Paclitaxel is a potent mitotic inhibitor that is utilized for treating breast, lung and ovarian cancers, and Kaposi’s sarcoma (Weaver, 2014), so that it has been entitled the top-selling anticancer medicine in the world (Goodman and Walsh, 2001). Also, this impactful chemotherapeutic agent is used for off-label treatment of endometrial, gastroesophageal, prostate, cervical, and head and neck cancers (Weaver, 2014). Invaluable secondary metabolite “paclitaxel” was initially extracted from Taxus bark (Wheeler et al., 1992). But harvesting the bark of these valuable species in the natural areas speedily exceeded levels deemed as a sustainable one, and critical over-harvesting has caused Taxus wild populations to be on the brink of extinction worldwide (Shinwari and Qaiser, 2011). Plant cell factories are a promising environmentally sustainable alternative to paclitaxel mass production (Salehi et al., 2017; Espinosa-Leal et al., 2018; Salehi et al., 2019b; Salehi et al., 2019c). The rising demand for paclitaxel and Taxus recalcitrant behavior under in vitro conditions have caused extensive effort toward finding alternatives for producing this invaluable secondary metabolite.
In vitro culture of hazel (Corylus avellana, European filbert) has been made known as a promising and inexpensive strategy for producing paclitaxel (Gallego et al., 2017; Salehi et al., 2017; Salehi et al., 2018c; Salehi et al., 2019b; Salehi et al., 2019c; Farhadi et al., 2020; Salehi et al., 2020). Biosynthesizing bioactive compounds in plants is influenced by various factors (Torkamani et al., 2014; Salehi et al., 2017; Salehi et al., 2018a; Salehi et al., 2018b; Salehi et al., 2018c; Salehi et al., 2019a; Salehi et al., 2019b; Salehi et al., 2019c; Salehi et al., 2019d). Previous studies (Salehi et al., 2019b; Salehi et al., 2019c; Farhadi et al., 2020; Salehi et al., 2020) demonstrated the positive influences of cell extract (CE) and culture filtrate (CF) of endophytic fungi on paclitaxel biosynthesis in cell suspension culture (CSC) of C. avellana. Fungal elicitor type, concentration and adding time, and also exposure time of cell culture (CSC harvesting time) should be optimized to achieve the maximum biosynthesis of paclitaxel in C. avellana CSC (Salehi et al., 2019b; Salehi et al., 2019c; Farhadi et al., 2020; Salehi et al., 2020). Precise analysis of the effects of these factors and their optimal selection would pave the way for the commercialization of bioprocessing C. avellana cells toward paclitaxel mass production. Paclitaxel biosynthesis and its elicitation are complex biological processes since they are affected by several factors and their nonlinear interactions. Optimizing these mentioned factors by experimenting is laborious, costly, and time-consuming. The mathematical models can effectively predict the optimized conditions for a multifactorial process (Struik et al., 2005; Gallego et al., 2011) such as paclitaxel biosynthesis.
Artificial intelligence (AI) technology is the algorithm capable of complex and intelligent computing similar to the routine performance of the human brain (Agatonovic-Kustrin and Beresford, 2000). Artificial neural network (ANN) is an AI method discovering complex nonlinear relationships among input (factors) and output (parameters) data (Patnaik, 1999; Plumb et al., 2005). Indeed, ANN is a brain-inspired method that imitates the way that the human brain works (Agatonovic-Kustrin and Beresford, 2000). It processes information and makes decision in systems involving vagueness and uncertainty (Patnaik, 1999; Gago et al., 2010). This technology has been widely used as a predictive instrument in a broad range of fields including ecology, food science, agriculture, environmental sciences, plant biology, pharmaceutical research, and biotechnology (Hilbert and Ostendorf, 2001; Daniel et al., 2008; Huang, 2009; Arab et al., 2018; Hesami et al., 2019a; Hesami et al., 2019b; Hesami et al., 2019c; Sheikhi et al., 2020). Multilayer perceptron (MLP), one of the most popular types of ANN, exhibits superior predictive ability as compared to traditional statistical methods to approximate the mathematical functions for analyzing and interpreting different unforeseeable data sets (Ahmadi and Golian, 2011; Jamshidi et al., 2016). However, training and designing of ANN face several problems. One of the biggest problems is assigning the weights in ANN structure which displays the direct influence on model performance. Basically, the network architecture and learning algorithm parameters control the weights. Also, other network parameters including the number of memory taps, the number of hidden layers and nodes and learning rates could influence ANN performance (Tahmasebi and Hezarkhani, 2009). To overcome these mentioned problems, ANN is hybridized with other optimization methods including genetic algorithm (GA) (Plumb et al., 2005; Shao et al., 2007; Ahmadi and Golian, 2011; Eftekhari et al., 2018; Sheikhi et al., 2020).
GA is the evolutionary algorithm making superb solutions to problems and has been applied for bioprocess optimization in plant biology (Osama et al., 2015; Jamshidi et al., 2016; Arab et al., 2018). Indeed, GA is a search algorithm inspired by natural selection and genetics concepts (Holland, 1992). The fundamental principles of GA are the creation of an initial population of search solutions (chromosomes), and then elite search solutions were selected for crossover using a roulette wheel selection method, which will ultimately be the best solution (fittest chromosome) (optimal value) among them (Figure 1).
Steps of operation of multilayer perceptron-genetics algorithm (MLP-GA) intelligence.
Multilayer perceptron-genetic algorithm (MLP-GA), integrating MLP with GA (Figure 1), causes achieving an accurate model for prediction and optimization of biological process (Jamshidi et al., 2016; Arab et al., 2018; Eftekhari et al., 2018).
The objectives of this research were (a) to isolate endophytic fungi from C. avellana grown in Iran, (b) to develop regression and MLP-GA models to predict output variables “dry weight (DW), intracellular paclitaxel, extracellular paclitaxel, total yield of paclitaxel and extracellular paclitaxel portion” based on input variables “CE and CF concentration levels, elicitor adding day, and CSC harvesting time”, (c) to compare regression and MLP-GA performance in term of prediction accuracy of output variables, (d) to optimize the mentioned factors for maximum biosynthesis of paclitaxel, (e) to detect the most important factors for maximum biosynthesis of paclitaxel, and (f) to design an Excel® estimator which can easily be applied to predict the total yield of paclitaxel in C. avellana CSC based on input variables.
Materials and MethodsIsolation of Endophytic Fungi
Healthy samples of the bud, stem, bark, and leaves were obtained from C. avellana grown in Iran during June to September 2018. The surfaces of the samples were sterilized as described by Salehi et al. (2018c; 2019b). The surface-sterilized plant samples were cut and transferred on PDAC [potato dextrose agar (PDA); supplemented with 250 mg l−1 Chloramphenicol] in unique Petri dishes (100 × 15 mm), incubated at 25 °C. After the growth of endophytic fungi, the pure cultures of the isolates were established by hyphal tip culture (Strobel et al., 1996). All fungal endophytes were numbered as HEF# series and stored at 4 °C.
Molecular Identification of Endophytic Fungus
Fungal endophyte was cultured in potato dextrose broth (PDB) and incubated in a shaker incubator at 25 °C and 110 rpm for 10 days. The extraction of fungal genomic DNA was done as described by Salehi et al. (2018c; 2019b). The partial sequences of internal transcribed spacer (ITS) fragments (ITS1-5.8S-ITS2) and actin gene (ACT) were used to obtain DNA sequence information. ITS fragments were amplified using universal primers ITS1 and ITS4 (White et al., 1990) and ACT using primer pair ACT-512F and ACT-783R (Carbone and Kohn, 1999). PCR reaction mixtures (25 µl) consisted of 1 µl genomic DNA (~100 ng), 1 µl forward and reverse primers (10 pM), and 12.5 µl Premix Taq (TaKaRa Biotechnology Ltd., Japan), and 10.5 µl PCR ultrapure water. PCR reaction programs were an initial denaturation at 94 °C for 3 min, followed by 30 cycles of denaturation (94 °C for 30 s), annealing [56 °C (ITS) and 59 °C (ACT) for 30 s], extension (72 °C for 1 min), and a final extension at 72 °C for 5 min. PCR product analysis and purification, sequencing and the phylogenetic analysis were made as described previously (Salehi et al., 2018c; Salehi et al., 2019b).
Elicitation of <italic>C. avellana</italic> Cell Culture
C. avellana CSC was established as described by Salehi et al. (2017; 2018c; 2019b; 2019c). The elicitors (CE and CF) were prepared as described previously (Salehi et al., 2019b). For elicitation, 1.5 ± 0.1 g of C. avellana cells (fresh mass) was cultured in 100 ml flasks containing 30 ml MS medium supplemented with 2 mg l−1 2,4-D and 0.2 mg l−1 BAP.
Based on our previous studies (Salehi et al., 2019b; Salehi et al., 2019c; Farhadi et al., 2020), three concentrations [2.5, 5, and 10% (v/v)] of fungal elicitors “CE:CF (100:0, 75:25, 50:50, 25:75, 0:100 v/v)” and also mid (day 13) and late (day 17) log phase of C. avellana cell cultures were selected for adding fungal elicitors. Control received an equal volume of water (for CE)/PDB (for CF).
Cell Growth Measurement
Cell growth was determined by the measurement of cell dry weight (DW). Cell biomass was separated from the culture medium by filtration (Whatman No. 1) and washed with distilled water to remove the residual medium, afterward freeze-dried to a constant weight by a vacuum-freeze drier.
Quantification of Paclitaxel
C. avellana cells were separated from the culture medium by a filter paper (Whatman No. 1). Intracellular and extracellular paclitaxel were extracted from the cells and culture broth using a procedure described by Salehi et al. (2017; 2018c; 2019b). Filtering all samples was performed by 0.22 µm cellulose acetate syringe filters before HPLC analysis. Paclitaxel in the samples was analyzed by HPLC (Waters, USA) with a C18 analysis column (Machereye-Nagel EC 250/4.6 Nucleodur). Each sample (20 µl) was injected and detected at 230 nm using a UV detector. The mobile phase was methanol:water (80:20 v/v) at a flow rate of 1.0 ml min−1. The quantification of paclitaxel was based on an external standard of genuine paclitaxel (Sigma).
Experimental Design
The experiment was conducted based on randomized complete block design (RCBD) with factorial arrangement, three factors containing elicitor type with 10 levels [CE:CF (100:0, 75:25, 50:50, 25:75, 0:100 v/v) and water:PDB (100:0, 75:25, 50:50, 25:75, 0:100 v/v)], concentration with three levels [2.5, 5, and 10% (v/v)], elicitor adding day with two levels (days 13 and 17), and three replicates. The cultures were harvested at 2-day intervals after elicitation until the 23rd day.
Model Development
The data were randomly divided into a training subset (70%) and a testing subset (30%). The training subset was applied to develop multiple linear regression (MLR) and backward regression and also MLP-GA models, and testing subset was applied to test the predictability of developed models (Shao et al., 2006).
Regression Analysis
Regression analysis is one of well-known predictive modeling methods. The popularity of these models may be assigned to model parameter interpretability and its ease of use. Here, MLR and backward regression models were used to predict DW, intracellular, extracellular and total yield of paclitaxel, and also extracellular paclitaxel portion. Significance level for the independent variables to include in the model was set at 0.05.
To determine which model component is more important during the modeling process, sensitivity analysis was performed on developed regression models using analysis of variance (ANOVA) and absolute t value (|t value|) corresponding to model coefficients. It is noteworthy that a more important model component displays a higher |t value| (Ahmadi and Golian, 2011; Ahmadi and Rodehutscord, 2017).
Multilayer Perceptron (MLP) Model
Three-layered feed forward back-propagation neural network was used to define the influences of CE and CF concentration levels, elicitor adding day, and CSC harvesting time on DW, paclitaxel biosynthesis (intracellular, extracellular and total), and extracellular paclitaxel portion. Transfer functions for hidden and output layers were hyperbolic tangent sigmoid (tansig) and linear (purelin), respectively.
ANN capability to process the information is determined by its architecture. Evolutionary algorithms are used for searching the optimal architecture design (Yao, 1999).
Genetic Algorithm (GA)
The high number of hidden neurons leads to prolong the training time and also overfits the data. Too few hidden neurons lead to a low accuracy rate (Matignon, 2005). GA was used (i) to determine optimal MLP architecture design including the optimal numbers of neurons, and (ii) to optimize the values of input variables (CE and CF concentration, elicitor adding day, and CSC harvesting time) in developed MLP-GA models for maximum paclitaxel biosynthesis and its secretion. An initial population of 50, crossover rate of 0.85, generation number of 500 and mutation rate of 0.01 (Haupt and Haupt, 2004; Abramson, 2007) were set to establish fittest MLP structure and optimize input variables for maximum output variables.
The performance of MLP-GA models is determined by root mean square error (RMSE) and coefficient of determination (R2) as reported by Ahmadi (2017), as well as mean absolute percentage error (MAPE) [Eq. (1)].
Where “yact” are the actual values, “yest” are the predicted values, and “n” is the number of data.
Sensitivity Analysis of the Models
Sensitivity analysis was done on MLP-GA models to determine the importance degree of the factors (CE and CF concentration levels, elicitor adding day, and CSC harvesting time) on the model parameters (DW, paclitaxel biosynthesis, and its secretion). The sensitivity of DW, paclitaxel biosynthesis (intracellular, extracellular, and total yield), and extracellular paclitaxel portion was determined by the criteria including variable sensitivity error (VSE) value displaying the performance (RMSE) of MLP-GA model when that particular input variable is unavailable in the model. Variable sensitivity ratio (VSR) value was calculated as the ratio of VSE and MLP-GA model error (RMSE value) when all input variables are available. Finally, calculated VSR values were rescaled within the range [0, 1]. The input variable with higher VSR was considered as the higher important variable in the model (Ahmadi and Golian, 2011).
The mathematical codes for the development and evaluation of MLR, backward regression, and MLP-GA models were written using MATLAB (Matlab, 2010) software, and the graphs were made by GraphPad Prism 5 (GraphPad Prism 5, 2005) software. “ANNGA_opt” program coded by MATLAB can be downloaded from https://github.com/hahmadima/ANNGA_opt.
ResultsIdentification of Endophytic Fungus
Strain HEF17 was isolated from the leaf of C. avellana and identified as Camarosporomyces flavigenus by analysis of the sequences of actin gene (Figure 2). Accession numbers used for phylogenetic study were reported by De Gruyter et al. (2013). This is the first report of this endophytic fungus on C. avellana tree (matrix nova). The partial sequences of ITS rDNA and ACT obtained from C. flavigenus strain HEF17 were deposited in GenBank (NCBI) under accession numbers MT176168 and MT224136, respectively.
Molecular identification of strain HEF17 based on the analysis of the sequences of actin gene. The tree was rooted to Plenodomus lingam (CBS 147.24).
Regression Analysis
Goodness of fit displayed no difference regarding the accuracy of MLR and backward regression for all output variables, 0.66, 0.56, 0.61, 0.58, and 0.85 for DW, intracellular paclitaxel, extracellular paclitaxel, total yield of paclitaxel, and extracellular paclitaxel portion, respectively, for the training subset (Table 1). Accordingly, the results of backward regression showed that elicitor adding day and CSC harvesting time are only parameters among the four above-mentioned input variables which influenced DW (Table 1). All input variables including CE and CF concentration levels, elicitor adding day and CSC harvesting time are important factors influencing intracellular, extracellular, and total yield of paclitaxel, and also paclitaxel secretion from cells to the culture medium (Table 1). R2 values for DW, intracellular paclitaxel, extracellular paclitaxel, total yield of paclitaxel, and extracellular paclitaxel portion were estimated 0.64, 0.58, 0.61, 0.61, and 0.85, respectively, for the testing subset (Figure 3).
Backward regression models for estimating growth, paclitaxel biosynthesis, and secretion in Corylus avellana cell suspension culture (CSC) treated with fungal elicitors using cell extract (CE) and culture filtrate (CF) concentration levels [% (v/v)], elicitor adding day, and CSC harvesting time (day).
Measured factors
Variablea\\n
Coeffcient
Standard error
t value
Dry weight (g l-1)
Intercept
-0.8950
0.5350
-1.67
Elicitor adding day
0.0995
0.0325
3.06
CSC harvesting time
0.4862
0.0239
20.34
R2
0.6635
RMSE
0.9642
MAPE
4.6650
Intracellular paclitaxel (µg g-1 DW)
Intercept
-4.7600
1.3200
-3.60
CE concentration level
0.4393
0.0531
8.28
CF concentration level
0.7936
0.0528
15.03
Elicitor adding day
0.8277
0.0787
10.52
CSC harvesting time
-0.2170
0.0578
-3.75
R2
0.5560
RMSE
2.3318
MAPE
59.1400
Extracellular paclitaxel (µg l-1)
Intercept
-123.5000
8.9600
-13.78
CE concentration level
2.6740
0.3600
7.44
CF concentration level
4.7370
0.3580
13.24
Elicitor adding day
5.8400
0.5330
10.96
CSC harvesting time
2.5470
0.3920
6.50
R2
0.6060
RMSE
15.7977
MAPE
65.60000
Total yield of paclitaxel (µg l-1)
Intercept
-242.0000
23.0
-10.51
CE concentration level
7.1130
0.924
7.70
CF concentration level
12.7280
0.920
13.84
Elicitor adding day
15.5000
1.37
11.31
CSC harvesting time
3.0100
1.01
2.99
R2
0.5803
RMSE
40.5987
MAPE
67.7200
Extracellularpaclitaxel portion(%)
Intercept
-18.6000
1.4200
-13.07
CE concentration level
0.1976
0.0571
3.46
CF concentration level
0.3167
0.0569
5.57
Elicitor adding day
0.3119
0.0847
3.68
CSC harvesting time
2.2211
0.0623
35.68
R2
0.8549
RMSE
2.51070
MAPE
21.18
Significant (p ≤ 0.05) variables included in the model. R2, coefficient of determination; RMSE, root mean square error; MAPE, mean absolute percentage error.
Scatter plot of actual data against predicted values of dry weight, intracellular, extracellular and total yield of paclitaxel, and extracellular paclitaxel portion in Corylus avellana cell cultures using backward regression models in the testing subset. The solid line shows fitted simple regression line on scatter points.
Goodness of fit of DW model and absolute t values (Table 1) showed that out of the investigated input variables, CSC harvesting time (|t value| = 20.3) was the most important parameter affecting DW, followed by elicitor adding day (|t value| = 3.1). Accordingly, CF concentration level (|t value| = 15.0) displayed the highest effect on intracellular paclitaxel, followed by elicitor adding day (|t value| = 10.5), CE concentration level (|t value| = 8.3) and CSC harvesting time (|t value| = 3.8). Also, CF concentration level (|t value| = 13.2) was the most effective component on extracellular paclitaxel, followed by elicitor adding day (|t value| = 11.0), CE concentration level (|t value| = 7.4) and CSC harvesting time (|t value| = 6.5). Furthermore, CF concentration level (|t value| = 13.8) was the most important factor influencing total yield of paclitaxel, followed by elicitor adding day (|t value| = 11.3), CE concentration level (|t value| = 7.7) and CSC harvesting time (|t value| = 3.0). Additionally, CSC harvesting time exhibited the highest effect on extracellular paclitaxel portion (|t value| = 35.7), followed by CF concentration level (|t value| = 5.6), elicitor adding day (|t value| = 3.7) and CE concentration level (|t value| = 3.5) (Table 1).
Multilayer Perceptron-Genetics Algorithm Analysis
Initially, CE and CF concentration levels, elicitor adding day and CSC harvesting time were used as input variables and DW, intracellular, extracellular and total yield of paclitaxel, and also extracellular paclitaxel portion as output variables. Then, output variables were predicted according to developed MLP-GA models. To evaluate the performance of developed MLP-GA models, the predicted values were plotted against the observed values of training (Figure 4A) and testing (Figure 4B) subsets. The great accordance between the predicted and observed values of DW, intracellular, extracellular and total yield of paclitaxel, and also extracellular paclitaxel portion was observed for both training and testing subsets (Figure 4). Goodness of fit of developed MLP-GA models showed that the developed models could accurately (R2 = 0.90, 0.89, 0.92, 0.95, and 0.91) (Table 2) predict DW, intracellular, extracellular and total yield of paclitaxel, and also extracellular paclitaxel portion of the testing subset, not used during the training processes (Figure 4). Also, developed MLP-GA models displayed the balanced statistical values for both training and testing subsets (Table 2).
Scatter plot of actual data against predicted values of dry weight, intracellular, extracellular and total yield of paclitaxel and extracellular paclitaxel portion in Corylus avellana cell cultures using multilayer perceptron-genetics algorithm (MLP-GA) models in training (A) and testing (B) subsets. The solid line shows fitted simple regression line on scatter points.
Statistics and information on multilayer perceptron-genetics algorithm (MLP-GA) models for growth, paclitaxel biosynthesis and secretion in Corylus avellana cell culture.
Measured factors
Neuron number
Training subsets
Testing subsets
R2
RMSE
MAPE
R2
RMSE
MAPE
Dry weight
5
0.90
0.53
0.45028
0.90
0.54
0.48036
Intracellular paclitaxel
6
0.90
1.14
0.86184
0.89
0.98
0.78311
Extracellular paclitaxel
7
0.95
6.78
4.45092
0.92
5.77
4.12229
Total yield of paclitaxel
7
0.95
14.27
10.9185
0.95
12.87
9.85688
Extracellular paclitaxel portion
5
0.92
1.87
1.54511
0.91
1.89
1.59910
R2, coefficient of determination; RMSE, root mean square error; MAPE, mean absolute percentage error.
Sensitivity Analysis of the Models
To rank the input variables based on their relative importance in the model, VSRs were estimated using all data lines (training and testing subsets). VSRs were obtained for each of output variables (DW, intracellular, extracellular and total yield of paclitaxel, and also extracellular paclitaxel portion) regarding CE and CF concentration levels, elicitor adding day and CSC harvesting time (Table 3). Analysis of DW model indicated that DW of C. avellana cells was more sensitive to CSC harvesting time (VSR = 0.990), followed by elicitor adding day (VSR = 0.010), CE and CF concentration levels (VSR = 0.004). Intracellular paclitaxel displayed more sensitivity to CE concentration level (VSR = 0.530), followed by CF concentration level (VSR = 0.460), elicitor adding day (VSR = 0.180), and CSC harvesting time (VSR = 0.100). Extracellular paclitaxel showed more sensitivity to CSC harvesting time (VSR = 0.660), followed by CF concentration level (VSR = 0.250), CE concentration level (VSR = 0.110), and elicitor adding day (VSR = 0.100). Accordingly, total yield of paclitaxel exhibited more sensitivity to CE concentration level (VSR = 0.720), followed by CF concentration level (VSR = 0.500), CSC harvesting time (VSR = 0.190), and elicitor adding day (VSR = 0.070). Also, extracellular paclitaxel portion displayed more sensitivity to CSC harvesting time (VSR = 0.810), followed by elicitor adding day (VSR = 0.120), CE concentration level (VSR = 0.080), and CF concentration level (VSR = 0.050) (Table 3).
Importance (according to sensitivity analysis) and optimal levels of the different input variables including cell extract (CE), culture filtrate (CF) concentration levels (% (v/v)), elicitor adding day and cell suspension culture (CSC) harvesting time (day) for achieving maximum growth, paclitaxel biosynthesis and secretion in Corylus avellana CSC using multilayer perceptron-genetics algorithm (MLP-GA) models.
Criteria
Variable
Importance value(according to VSRa)
Optimal level
Output Optimal
Dry weight (g l-1)
CE concentration level
0.004
5.67
12.04
CF concentration level
0.004
0.60
Elicitor adding day
0.010
15.17
CSC harvesting time
0.990
20.78
Intracellular paclitaxel (µg g-1 DW)
CE concentration level
0.530
3.37
17.74
CF concentration level
0.460
5.33
Elicitor adding day
0.180
17.00
CSC harvesting time
0.100
20.27
Extracellular paclitaxel (µg l-1)
CE concentration level
0.110
5.29
124.52
CF concentration level
0.250
5.75
Elicitor adding day
0.100
17.00
CSC harvesting time
0.660
20.90
Total yield of paclitaxel (µg l-1)
CE concentration level
0.720
3.33
369.67
CF concentration level
0.500
5.25
Elicitor adding day
0.070
17.00
CSC harvesting time
0.190
20.95
Extracellular paclitaxel portion (%)
CE concentration level
0.080
4.51
48.07
CF concentration level
0.050
5.10
Elicitor adding day
0.120
17.00
CSC harvesting time
0.810
23.00
Relative indication of the ratio between the variable sensitivity error and the error of the model when all variables are available. Calculated VSR values were rescaled within range [0, 1].
Model Optimization
The optimization analysis on developed MLP-GA models was performed using GA to determine the optimal levels of input variables for achieving maximum growth, paclitaxel biosynthesis, and its secretion in C. avellana CSC (Table 3). The optimization results showed that adding 6.27% (v/v) of 90CE:10CF containing 5.67% (v/v) CE and 0.6% (v/v) CF on 15th day and harvesting CSC 134 h and 38 min after elicitation could result in maximum DW (12.04 g l−1) (Table 3). The highest content of intracellular paclitaxel (17.74 µg g−1 DW) may be produced by adding 8.70% (v/v) of 39CE:61CF containing 3.37% (v/v) CE and 5.33% (v/v) CF on 17th day and harvesting CSC 78 h and 29 min after elicitation (Table 3). Also, the results showed that highest extracellular paclitaxel (124.52 µg l−1) can be produced by adding 11.13% (v/v) of 48CE:52CF containing 5.29% (v/v) CE and 5.75% (v/v) CF on 17th day and harvesting CSC 93 h and 36 min after elicitation (Table 3). Additionally, CSC exposed with 8.58% (v/v) of 39CE:61CF containing 3.33% (v/v) CE and 5.25% (v/v) CF on 17th day and harvesting it 94 h and 48 min after elicitation may obtain the highest total yield of paclitaxel (369.67 µg l−1) (Table 3). The results of MLP-GA model optimization displayed that adding 9.61% (v/v) of 47CE:53CF containing 4.51% (v/v) CE and 5.10% (v/v) CF on 17th day and harvesting CSC 144 h after elicitation may lead to highest extracellular paclitaxel portion (48.07) (Table 3).
Comparison of MLP-GA and Backward Regression Models
The statistical values for MLP-GA models displayed higher prediction accuracy as compared to regression models as estimated R2 for MLP-GA vs. regression models were: DW = 0.90 vs. 0.66, intracellular paclitaxel = 0.90 vs. 0.56, extracellular paclitaxel = 0.93 vs. 0.61, total yield of paclitaxel = 0.95 vs. 0.58, and extracellular paclitaxel portion = 0.92 vs. 0.85 (Tables 1 and 2). In the end, an Excel® total paclitaxel estimator, namely, HCC-paclitaxel, was created using developed MLP-GA model (Figure 5). The mentioned estimator was presented as supplementary material.
HCC-paclitaxel: an Excel® estimator for predicting total paclitaxel value in Corylus avellana cell culture responding fungal elicitors using multilayer perceptron-genetic algorithm (MLP-GA) model. CE, cell extract; CF, culture filtrate. This estimator was presented as Supplementary Material.
Discussion
Predicting the optimal amount of the effective factors on paclitaxel biosynthesis is highly promising and essential for its production increment and cost decrement. This is the first study on predicting the optimal conditions for maximum paclitaxel biosynthesis in C. avellana CSC exposed to fungal elicitors using the mathematical model. To accurately predict the optimal amounts of effective factors (CE and CF concentration levels, elicitor adding day, and CSC harvesting time) on paclitaxel biosynthesis in C. avellana CSC, using a trustworthy modeling system is essential.
In this study, regression and MLP-GA modeling were applied to evaluate the relationships among four studied factors “CE and CF concentration levels, elicitor adding day, and CSC harvesting time” and the parameters “DW, intracellular, extracellular, and total yield of paclitaxel and extracellular paclitaxel portion”, and also the possibility of predicting the growth and paclitaxel biosynthesis by the determined factors. Such mathematical predictions have not been described in this area. Higher accuracy of MLP-GA models as compared to regression models (Tables 1 and 2) was also reported in previous studies (Jamshidi et al., 2016; Eftekhari et al., 2018).
The fit of regression models was presented by R2 (Figure 3) for testing subset, suggesting these models can explain 64, 58, 61, 61 and 85% of the variability in DW, intracellular paclitaxel, extracellular paclitaxel, total yield of paclitaxel and paclitaxel extracellular portion, respectively, when they face unseen data.
Our results suggested that MLP-GA models could accurately predict DW, intracellular paclitaxel, extracellular paclitaxel, total yield of paclitaxel and extracellular paclitaxel portion (R2 = 0.90, 0.89, 0.92, 0.94, and 0.91, respectively) in the testing subset (Figure 4), not used in the training process. Also, the small number of hidden neuron and also closing the errors of training and testing subsets to each other (Table 2) suggested that overlearning had not arisen in the training process, and developed MLP-GA models displayed good generalizability when they faced unseen data (Lou and Nakai, 2001; Ahmadi and Golian, 2011). Based on RMSE, R2 and MAPE of the training and testing subsets (Table 2), it can be concluded that tansig activation function effectively worked for modeling over all experiments. Small RMSE and MAPE (Table 2) showed the high potential of MLP-GA models in predicting output variables.
Regardless of previous studies on the effects of CE and CF concentration levels, elicitor adding day and CSC harvesting time on paclitaxel biosynthesis and secretion, there remains the question to be answered: which input variables are the most important in paclitaxel biosynthesis? As previously mentioned, sensitivity analysis displayed that CE and CF concentration levels are the most important variables affecting total yield of paclitaxel (Table 3). Endophytic fungi synthesize microbe-associated molecular patterns (MAMPs). The receptors localized on plant cell surface recognize MAMPs and thus induce plant defense system (Ausubel, 2005). Some of these MAMPs are found only in CE, a number of these exist only in CF, and others are found in both CE and CF with different concentrations (Figure 6). Therefore, paclitaxel biosynthesis elicitation potential of these fungal elicitors (CE and CF) is different. Extracellular paclitaxel content is important for paclitaxel production in a continuous system. Sensitivity analysis displayed that CSC harvesting time is the most important factor affecting extracellular paclitaxel (Table 3). Paclitaxel biosynthesis is the complex biological process that requires the accurate techniques for modeling and optimization. MLP-GA has been efficiently used to solve problems with extremely difficult and unknown solution in various fields (Jamshidi et al., 2016; Arab et al., 2018; Eftekhari et al., 2018; Sheikhi et al., 2020). A growing interest in ANN has mostly been because of its power in solving the problems in a broad range of fields, their ability for modeling nonlinear and complex relationships, prediction ability of the unseen relationships on the unseen data, and having no need of a specification of data statistical distribution (Mahanta, 2017).
Schematic design of microbe-associated molecular patterns (MAMPs) found in cell extract and culture filtrate, and total yield of paclitaxel in Corylus avellana cell cultures exposed with different concentrations of these fungal elicitors derived from Camarosporomyces flavigenus.
According to the high prediction accuracy of the training and testing subsets, it can be concluded that developed MLP-GA could accurately predict DW, paclitaxel biosynthesis, and secretion in C. avellana CSC.
Publishing developed MLP-GA models needs to share the connection weight matrices, which running ANN models requires the especial software. Therefore, we share developed MLP-GA model predicting total paclitaxel with the readers as HCC-paclitaxel Excel® estimator (Figure 5).
Conclusion
This research applied mathematical approaches for modeling and optimizing paclitaxel biosynthesis in C. avellana cell culture treated with fungal elicitors for the first time. The great accordance between the predicted and observed values of the output variables (DW, intracellular, extracellular and total yield of paclitaxel, and also extracellular paclitaxel portion) supported the excellent performance of developed MLP-GA models. HCC-paclitaxel Excel® estimator presents an easy-to-use tool to predict total yield of paclitaxel in C. avellana cell culture treated with fungal elicitors using MLP-GA model.
Author Contributions
MS directed the research, carried out all experiments and analyses. AM directed in vitro cell culture and elicitation experiment. NS directed the sections related to fungal elicitation. HA performed data modeling. MS and SF interpreted the results and wrote the manuscript. All authors read and approved the final manuscript.
Funding
The authors acknowledge Iran National Science Foundation (INSF, No. 97010721), and Research Deputy of Tarbiat Modares University, Tehran for financial support of this research project.
Conflict of Interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
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Public HealthFrontiers in Public Health2296-2565Frontiers Media S.A.32850612PMC743214510.3389/fpubh.2020.00461Public HealthOriginal ResearchA Predicting Nomogram for Mortality in Patients With COVID-19PanDeng1ChengDandan2CaoYiwei1HuChuan3ZouFenglin4YuWencheng1*XuTao1*1Department of Pulmonary and Critical Care Medicine, Affiliated Hospital of Qingdao University, Qingdao, China2Department of Hematology, Tongji Medical College, Tongji Hospital, Huazhong University of Science and Technology, Wuhan, China3Department of Joint Surgery, Affiliated Hospital of Qingdao University, Qingdao, China4Department of Biliary-Pancreatic Surgery, Tongji Medical College, Tongji Hospital, Huazhong University of Science and Technology, Wuhan, China
This article was submitted to Life-Course Epidemiology and Social Inequalities, a section of the journal Frontiers in Public Health
118202020201182020846112520202272020Copyright © 2020 Pan, Cheng, Cao, Hu, Zou, Yu and Xu.2020Pan, Cheng, Cao, Hu, Zou, Yu and XuThis is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Background: The global COVID-19 epidemic remains severe, with the cumulative global death toll reaching more than 207,170 as of May 2, 2020 (1).
Purpose: Our research objective is to establish a reliable nomogram to predict mortality in COVID-19 patients. The nomogram can help us distinguish between patients who are at high risk of death and need close attention.
Patients and Methods: For the single-center retrospective study, we collected 21 cases of patients who died in the critical illness area of the Optical Valley Branch of Tongji Hospital, Huazhong University of Science and Technology, from February 9 to March 10. Additionally, we selected 99 patients discharged during this period for analysis. The nomogram was constructed to predict the mortality for COVID-19 patients using the primary group of 120 patients and was validated using an independent cohort of 84 patients. We used multivariable logistic regression analysis to construct the prediction model. The nomogram was evaluated for calibration, differentiation, and clinical usefulness.
Results: The predictors included in the nomogram were c-reactive protein, PaO2/FiO2, and cTnI. The areas under the curves of the nomogram were 0.988 (95% CI: 0.972–1.000) and 0.956 (95% CI, 0.874–1.000) in the primary and validation groups, respectively. Decision curve analysis suggests that the nomogram may have clinical usefulness.
Conclusion: This study provides a nomogram containing c-reactive protein, PaO2/FiO2, and cTnI that can be conveniently used to predict individual mortality in COVID-19 patients. Next, we will collect as many cases as possible from multiple centers to build a more reliable nomogram to predict mortality for COVID-19 patients.
nomogrampredictmortalityCOVID-19patientsQingdao City Science and Technology Special20-4-1-5-nshIntroduction
Since the outbreak of COVID-19 in Wuhan, Hubei province, in December 2019, it has had a major impact on people's lives, health, and property safety. It has rapidly expanded to 34 provincial divisions of China (2–4). As of April 28, 2020, the total number of confirmed cases in Wuhan has reached 68,128, accounting for 80.75% of the total number of confirmed cases in China, and 4,512 people have died in Wuhan, accounting for 97.18% of the total number of deaths in China (5).
SARS-CoV-2 is a binuclear virus that has a wide clinical spectrum of infection (6–8). In the past 2 months, only a few studies have analyzed prognostic factors for death, with repeated emphasis on factors such as age, lymphocytosis, leukocytosis, and elevated ALT (7). Thus, some of these potential prognostic factors need further validation, and a scoring system that accurately predicts the possible mortality risk is needed. Nomograms are used for multiple indicators to diagnose or predict disease onset or progression, making prognostic model results easier to read. Therefore, nomograms have been gradually applied in medical research and clinical practice (9, 10).
Here, we randomly selected some patients with definite clinical outcome (death or discharge) who had been admitted to the critical care unit of the Optical Valley Branch of Tongji Hospital, Huazhong University of Science and Technology, as of March 10, 2020 (11). The aim of our study was to establish a nomogram that incorporated demographics, clinical characteristics, and laboratory results to predict individual mortality in COVID-19 patients.
Materials and MethodsPatient Selection
We selected COVID-19 patients admitted to the critical illness area of the Optical Valley Branch of Tongji Hospital, Huazhong University of Science and Technology, from February 9, 2020, to March 10, 2020.
Patients were enrolled in this cohort if they: (1) were more than 18 years old; (2) were diagnosed with COVID-19 by multiplex real-time RT-PCR; (3) had complete clinical records of medical history and laboratory results. Patients were excluded if they: (1) were suspected with COVID-19; (2) had incomplete clinical records of medical history and laboratory results.
Study Variables
We collected demographics, comorbidities, routine laboratory tests, immunological indicators, radiological images, and inpatient treatments (12). For each patient, the baseline characters were screened: gender, age, leukocytes, platelet–lymphocyte ratio (PLR), neutrophil–lymphocyte ratio (NLR), Charlson Comorbidity Index (CCI), CURB-65 score, PaO2/FiO2, interleukin-1β (IL-1β), interleukin-2R (IL-2R), interleukin-6 (IL-6), interleukin-8 (IL-8), interleukin-10 (IL-10), tumor necrosis factor-α (TNF-α), cardiac troponin I (cTnI), brain natriuretic peptide (BNP), calcitonin, c-reactive protein (CRP), lactate dehydrogenase (LDH), d-dimer, fibrinogen, history of hypertension, heart failure, coronary heart disease (CHD), diabetes, cough, expectoration, diarrhea, shortness of breath, body temperature above 38°C, and X-ray or CT findings.
Construction of the Nomogram
We used the chi-square test for univariate analysis. Multivariable regression models were developed by incorporating meaningful factors in univariate analyses. Univariate and multivariate regression analysis was used to analyze the risk factors in the primary cohort (13). Then we construct a nomogram on the basis of the multivariate logistic regression model.
Statistical Analysis
Patients' demographic and clinical characteristics were analyzed using descriptive methods, with standard summary statistics including the median, interquartile range (IQR), and proportions. Categorical variables were processed by Chi-square or Fisher's exact tests, as appropriate. We tested the accuracy of the nomograms by discrimination and calibration both in the primary and the validation cohort. We used the receiver operating characteristic curve (ROC) to assess the discriminative ability of the nomogram and then assessed the area under the curve (AUC) (14, 15). Calibration curves were used to compare the association between actual outcomes and predicted probabilities (16). Decision curve analysis (DCA) was performed by calculating the net benefits for a range of threshold probabilities to evaluate the clinical utility of the nomogram (17, 18). Statistical analysis was performed using SPSS 23 (IBM, Chicago, IL, USA) and R version 3.4.4. All statistical tests were two-tailed, and a P < 0.05 was considered to be statistically significant.
ResultsPatient Characteristics
Between February 9, 2020, and March 10, 2020, from more than 600 patients in the critical illness area of the Optical Valley Branch of Tongji Hospital, 120 cases were selected for inclusion in the study by means of a random number table. The average age of the 120 patients was 62.16 years old, and there were 50 females and 70 males (19). The majority of the patients were over 60 years old (69.20%). Among the 120 cases, 90 (75.0%) developed cough symptoms with or without expectoration, 62 (51.7%) had high fever (≥38°C), 68 (56.7%) had mild shortness of breath, and 15 (12.5%) developed diarrhea. The vast majority of patients exhibited bilateral invasion in chest X-ray and CT, mostly with a patchy and ground-glass appearance (Table 1).
Results of the univariate association analyses.
Characteristics
Death set
Survival set
Z-value
P-value
Age, year
70 (66, 78.5)
64 (51, 70)
−3.273
0.001
Sex (male)
17
53
5.358
0.021
Sex (female)
4
46
WBC
9.01 (5.42, 14.62)
5.47 (4.49, 6.81)
−3.398
0.001
NLR
10.44 (5.70, 25.10)
2.50 (1.80, 3.84)
−5.688
0.000
PLR
305.88 (143.23, 352.80)
204.40 (149.43, 312.73)
−1.433
0.152
PCT
0.29 (0.15, 0.57)
0.06 (0.05, 0.07)
−6.560
0.000
CRP
113.3 (63.55, 145.4)
8.9 (2.4, 32.4)
−5.888
0.000
LDH
566 (327.5, 667.5)
245 (190, 285)
−5.046
0.000
Fibrinogen
4.9 (3.22, 6.19)
5.18 (4.1, 5.85)
−0.597
0.550
PaO2/FiO2
105.43 (76.05, 161.98)
331.76 (233.89, 390.54)
−6.351
0.000
Serumcalcium(L)
19
77
1.043
0.307
TNI(H)
11
5
29.615
0.000
BNP(H)
18
23
30.070
0.000
CURB-65(0–1)
10
93
26.879
0.000
CURB-65(2–5)
11
6
CCI index(0–2)
18
96
2.555
0.110
CCI index(3-)
3
3
IL-1β(H)
4
16
0.000
1.000
IL-2R(H)
17
34
15.554
0.000
IL-6(H)
19
36
20.434
0.000
IL-8(H)
4
4
4.091
0.043
IL-10(H)
8
8
11.034
0.001
TNF-α(H)
16
45
6.549
0.010
D-dimer(H)
21
68
8.866
0.003
Hypertension
13
31
6.982
0.008
Heart failure
1
0
0.175
CHD
4
5
3.083
0.079
Diabetes
4
14
0.055
0.814
Cough
14
76
0.943
0.332
Expectoration
7
44
0.875
0.350
Diarrhea
2
13
0.008
0.928
≥38°C
11
51
0.005
0.943
GGO
8
30
0.486
0.486
Consolidation
0
13
1.883
0.170
Pathy
15
77
0.116
0.733
Fibrosis
0
3
1.000
Independent Prognostic Factors
We used univariate and multivariate analyses to identify the prognostic factors (20, 21). The univariate analysis revealed that age (p = 0.001), Male/Female (p = 0.021), WBC (p = 0.001), NLR (p = 0.000), PCT (p = 0.000), CRP (p = 0.000), LDH (p = 0.000), PaO2/FiO2 (p = 0.000), Serum calcium (p = 0.307), cTnI (p = 0.000), BNP (p = 0.000), CURB-65 (p = 0.000), IL-2R (p = 0.000), IL-6 (p = 0.000), IL-8 (p = 0.043), IL-10 (p = 0.001), TNF-α (p = 0.01), D-dimer (p = 0.003), and Hypertension (p = 0.008) were significant prognostic factors. The multivariate analysis revealed that CRP (p = 0.004), PaO2/FiO2 (p = 0.002), and cTnI (p = 0.016) were independent prognostic factors for predicting the mortality of COVID-19 patients (22).
Development and Validation of a Nomogram
Multivariate logistic regression analysis determined that CRP, PaO2/FiO2, and cTnI were independent predictors (Table 2). The model with three independent predictors is established and represented by a nomogram (Figure 1).
Multivariate logistic regression of death in COVID-19 patients.
B
SE
Wald
OR(95%CI)
P-value
CRP
0.037
0.013
8.271
1.037 (1.012, 1.064)
0.004
PaO2/FiO2
−0.045
0.015
9.144
0.956 (0.929, 0.984)
0.002
TNI
5.417
2.244
5.830
225.296 (2.773, 18303.1)
0.016
Nomogram predicting mortality for COVID-19 patients with three available factors: CRP, PaO2/FiO2, and cTnI.
We predicted the probability of death in patients with COVID-19 by calculating the sum of points in the nomogram corresponding to patient characteristics. Regarding the clinical application of this nomogram, we assumed that, if a patient's CRP was 20 mg/L, the PaO2/FiO2 was 350, and the cTnI was increased, then the corresponding score of this patient was 3+33+26, a total of 62 points, so the corresponding risk of death of this patient was between 0.001 and 0.01, and this patient was relatively safe.
The AUC for the prediction nomogram was 0.988, 95% CI, 0.972–1.000 (23). The AUC for CRP was 0.910. The AUC for PaO2/FiO2 was 0.942. The AUC for cTnI was 0.737. The nomogram decision curve analysis is shown in Figure 2. The calibration curve of the nomogram for the probability of mortality demonstrated good agreement between prediction and observation in the primary cohort (Figure 2) (24). The decision curve showed that if the threshold probability of a patient or doctor is >0%, using the nomogram to predict the mortality for COVID-19 patients adds more benefit. Within this range, net benefit was comparable, with several overlaps, on the basis of the nomogram (25).
From left to right: ROC curves of the nomogram and the three factors; calibration curve of the nomogram in the cohort; decision curve analysis for the nomogram. The y-axis measures the net benefit. The red line represents the nomogram. The gray line represents the assumption that all patients have died. The thin black link represents the assumption that no patients have died. The net benefit was calculated by subtracting the proportion of all patients who are false positive from the proportion who are true positive, weighting by the relative harm of forgoing treatment compared with the negative consequences of unnecessary treatment.
Validation was performed by using another 84 COVID-19 patients. In the validation cohort, the independent risk factors included in the nomogram were examined. The AUC of the nomogram was 0.956 (95% CI: 0.874–1.000). Figure 3 shows that the calibration plot for the probability of mortality demonstrated good agreement between the prediction by nomogram and actual observation.
From left to right: ROC curves of the Nomogram and the three factors; calibration curve of the nomogram in the cohort; decision curve analysis for the nomogram.
Discussion
Since 2003, coronavirus has caused a number of major public health events (26, 27). Generally, it was not until the outbreak of severe acute respiratory syndrome (SARS) in Guangdong, China, in 2002 and 2003 that the coronavirus was found to be highly pathogenic to humans. Another highly pathogenic coronavirus is the Middle East respiratory syndrome (MERS) coronavirus, which emerged in 2012 in Middle Eastern countries (28–31). COVID-19 is another highly pathogenic coronavirus that has been added to human history. Although COVID-19 has a mortality rate of <3% compared to SARS (9.6%) and MERS (34%), its effects remain extremely sudden and devastating (32, 33).
Previous studies have shown that coronaviruses can cause respiratory and intestinal infections (34). According to recent reports, the most common symptoms of COVID-19 patients are fever, cough, myalgia, and fatigue, while the less common are expectoration, hemoptysis, and diarrhea (35–39). Most patients have mild symptoms and good prognosis. Mild patients may have only a low fever and mild weakness, with no obvious signs of pneumonia (40). In the late stage of infection, a variety of complications occur in critically ill patients, including ARDS, septic shock, refractory metabolic acidosis, acute myocardial injury, disseminated intravascular coagulation, and even death (5, 6). Therefore, early diagnosis and early assessment of the patient's prognosis are crucial for controlling the outbreak.
Corticosteroids were used extensively during the outbreaks of severe SARS and MERS and are being applied in COVID-19 patients. Corticosteroids suppress inflammation in the lungs but also inhibit immune responses and pathogen clearance at the same time (41). The clinical benefit of corticosteroids in COVID-19 needs further clinical trials to confirm (42, 43).
In previous studies, several prognostic models have been developed. For example, the NLR was the most effective prognostic factor affecting prognosis for severe COVID-19 patients (44).
In our study, this nomogram based on three variables, CRP, PaO2/FiO2, and cTnI, can provide a more accurate assessment and prediction of mortality at admission for COVID-19 patients. If patients with high mortality rates are properly and promptly identified, they should be more likely to benefit from nutritional support in nursing care and close attention in clinical care, which will ultimately have a positive impact on their recovery (45, 46).
The variables in the nomogram we constructed were easily accessible from routine clinical work. As a result, clinicians can use this simple, intuitive predictive tool to draw a few lines in a few seconds to make a quick assessment of a patient's prognosis with no computational difficulty. Additionally, our model can help clinical workers rationally allocate medical resources to reduce the fatality rate of COVID-19 when medical resources are scarce.
There are still some limitations to our study. First, the study was at a single center with a small sample, and the next step is to include as many cases as possible from multiple centers to build a more reliable predicting nomogram for mortality in COVID-19 patients. Second, we excluded patients whose case data were incomplete, which could have resulted in selection bias.
Conclusions
To sum up, to better identify the prognosis of COVID-19 patients, identify severe patients as early as possible, improve the cure rate, and reduce the fatality rate, we constructed a nomogram based on 120 patients to predict mortality. The proposed nomogram considered three independent risk factors, namely, CRP, PaO2/FiO2, and cTnI. We have confirmed that this nomogram has excellent discrimination and clinical availability.
Data Availability Statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
Ethics Statement
The studies involving human participants were reviewed and approved by Medical ethics committee of affiliated hospital of Qingdao university. The ethics committee waived the requirement of written informed consent for participation. Written informed consent was obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article.
Author Contributions
DP: data analysis and visualization. DP, DC, YC, and CH: writing original draft. FZ, TX, and WY: review and editing. All authors contributed to the article and approved the submitted version.
Conflict of Interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Thanks to the medical workers who have contributed to the epidemic and those in all industries involved in the prevention and control of the epidemic. Finally, we pay tribute to our patients.
Funding. This work was supported by Qingdao City Science and Technology Special Fund No.20-4-1-5-nsh.
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Public HealthFrontiers in Public Health2296-2565Frontiers Media S.A.32850612PMC743214510.3389/fpubh.2020.00461Public HealthOriginal ResearchA Predicting Nomogram for Mortality in Patients With COVID-19PanDeng1ChengDandan2CaoYiwei1HuChuan3ZouFenglin4YuWencheng1*XuTao1*1Department of Pulmonary and Critical Care Medicine, Affiliated Hospital of Qingdao University, Qingdao, China2Department of Hematology, Tongji Medical College, Tongji Hospital, Huazhong University of Science and Technology, Wuhan, China3Department of Joint Surgery, Affiliated Hospital of Qingdao University, Qingdao, China4Department of Biliary-Pancreatic Surgery, Tongji Medical College, Tongji Hospital, Huazhong University of Science and Technology, Wuhan, China
This article was submitted to Life-Course Epidemiology and Social Inequalities, a section of the journal Frontiers in Public Health
118202020201182020846112520202272020Copyright © 2020 Pan, Cheng, Cao, Hu, Zou, Yu and Xu.2020Pan, Cheng, Cao, Hu, Zou, Yu and XuThis is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Background: The global COVID-19 epidemic remains severe, with the cumulative global death toll reaching more than 207,170 as of May 2, 2020 (1).
Purpose: Our research objective is to establish a reliable nomogram to predict mortality in COVID-19 patients. The nomogram can help us distinguish between patients who are at high risk of death and need close attention.
Patients and Methods: For the single-center retrospective study, we collected 21 cases of patients who died in the critical illness area of the Optical Valley Branch of Tongji Hospital, Huazhong University of Science and Technology, from February 9 to March 10. Additionally, we selected 99 patients discharged during this period for analysis. The nomogram was constructed to predict the mortality for COVID-19 patients using the primary group of 120 patients and was validated using an independent cohort of 84 patients. We used multivariable logistic regression analysis to construct the prediction model. The nomogram was evaluated for calibration, differentiation, and clinical usefulness.
Results: The predictors included in the nomogram were c-reactive protein, PaO2/FiO2, and cTnI. The areas under the curves of the nomogram were 0.988 (95% CI: 0.972–1.000) and 0.956 (95% CI, 0.874–1.000) in the primary and validation groups, respectively. Decision curve analysis suggests that the nomogram may have clinical usefulness.
Conclusion: This study provides a nomogram containing c-reactive protein, PaO2/FiO2, and cTnI that can be conveniently used to predict individual mortality in COVID-19 patients. Next, we will collect as many cases as possible from multiple centers to build a more reliable nomogram to predict mortality for COVID-19 patients.
nomogrampredictmortalityCOVID-19patientsQingdao City Science and Technology Special20-4-1-5-nshIntroduction
Since the outbreak of COVID-19 in Wuhan, Hubei province, in December 2019, it has had a major impact on people's lives, health, and property safety. It has rapidly expanded to 34 provincial divisions of China (2–4). As of April 28, 2020, the total number of confirmed cases in Wuhan has reached 68,128, accounting for 80.75% of the total number of confirmed cases in China, and 4,512 people have died in Wuhan, accounting for 97.18% of the total number of deaths in China (5).
SARS-CoV-2 is a binuclear virus that has a wide clinical spectrum of infection (6–8). In the past 2 months, only a few studies have analyzed prognostic factors for death, with repeated emphasis on factors such as age, lymphocytosis, leukocytosis, and elevated ALT (7). Thus, some of these potential prognostic factors need further validation, and a scoring system that accurately predicts the possible mortality risk is needed. Nomograms are used for multiple indicators to diagnose or predict disease onset or progression, making prognostic model results easier to read. Therefore, nomograms have been gradually applied in medical research and clinical practice (9, 10).
Here, we randomly selected some patients with definite clinical outcome (death or discharge) who had been admitted to the critical care unit of the Optical Valley Branch of Tongji Hospital, Huazhong University of Science and Technology, as of March 10, 2020 (11). The aim of our study was to establish a nomogram that incorporated demographics, clinical characteristics, and laboratory results to predict individual mortality in COVID-19 patients.
Materials and MethodsPatient Selection
We selected COVID-19 patients admitted to the critical illness area of the Optical Valley Branch of Tongji Hospital, Huazhong University of Science and Technology, from February 9, 2020, to March 10, 2020.
Patients were enrolled in this cohort if they: (1) were more than 18 years old; (2) were diagnosed with COVID-19 by multiplex real-time RT-PCR; (3) had complete clinical records of medical history and laboratory results. Patients were excluded if they: (1) were suspected with COVID-19; (2) had incomplete clinical records of medical history and laboratory results.
Study Variables
We collected demographics, comorbidities, routine laboratory tests, immunological indicators, radiological images, and inpatient treatments (12). For each patient, the baseline characters were screened: gender, age, leukocytes, platelet–lymphocyte ratio (PLR), neutrophil–lymphocyte ratio (NLR), Charlson Comorbidity Index (CCI), CURB-65 score, PaO2/FiO2, interleukin-1β (IL-1β), interleukin-2R (IL-2R), interleukin-6 (IL-6), interleukin-8 (IL-8), interleukin-10 (IL-10), tumor necrosis factor-α (TNF-α), cardiac troponin I (cTnI), brain natriuretic peptide (BNP), calcitonin, c-reactive protein (CRP), lactate dehydrogenase (LDH), d-dimer, fibrinogen, history of hypertension, heart failure, coronary heart disease (CHD), diabetes, cough, expectoration, diarrhea, shortness of breath, body temperature above 38°C, and X-ray or CT findings.
Construction of the Nomogram
We used the chi-square test for univariate analysis. Multivariable regression models were developed by incorporating meaningful factors in univariate analyses. Univariate and multivariate regression analysis was used to analyze the risk factors in the primary cohort (13). Then we construct a nomogram on the basis of the multivariate logistic regression model.
Statistical Analysis
Patients' demographic and clinical characteristics were analyzed using descriptive methods, with standard summary statistics including the median, interquartile range (IQR), and proportions. Categorical variables were processed by Chi-square or Fisher's exact tests, as appropriate. We tested the accuracy of the nomograms by discrimination and calibration both in the primary and the validation cohort. We used the receiver operating characteristic curve (ROC) to assess the discriminative ability of the nomogram and then assessed the area under the curve (AUC) (14, 15). Calibration curves were used to compare the association between actual outcomes and predicted probabilities (16). Decision curve analysis (DCA) was performed by calculating the net benefits for a range of threshold probabilities to evaluate the clinical utility of the nomogram (17, 18). Statistical analysis was performed using SPSS 23 (IBM, Chicago, IL, USA) and R version 3.4.4. All statistical tests were two-tailed, and a P < 0.05 was considered to be statistically significant.
ResultsPatient Characteristics
Between February 9, 2020, and March 10, 2020, from more than 600 patients in the critical illness area of the Optical Valley Branch of Tongji Hospital, 120 cases were selected for inclusion in the study by means of a random number table. The average age of the 120 patients was 62.16 years old, and there were 50 females and 70 males (19). The majority of the patients were over 60 years old (69.20%). Among the 120 cases, 90 (75.0%) developed cough symptoms with or without expectoration, 62 (51.7%) had high fever (≥38°C), 68 (56.7%) had mild shortness of breath, and 15 (12.5%) developed diarrhea. The vast majority of patients exhibited bilateral invasion in chest X-ray and CT, mostly with a patchy and ground-glass appearance (Table 1).
Results of the univariate association analyses.
Characteristics
Death set
Survival set
Z-value
P-value
Age, year
70 (66, 78.5)
64 (51, 70)
−3.273
0.001
Sex (male)
17
53
5.358
0.021
Sex (female)
4
46
WBC
9.01 (5.42, 14.62)
5.47 (4.49, 6.81)
−3.398
0.001
NLR
10.44 (5.70, 25.10)
2.50 (1.80, 3.84)
−5.688
0.000
PLR
305.88 (143.23, 352.80)
204.40 (149.43, 312.73)
−1.433
0.152
PCT
0.29 (0.15, 0.57)
0.06 (0.05, 0.07)
−6.560
0.000
CRP
113.3 (63.55, 145.4)
8.9 (2.4, 32.4)
−5.888
0.000
LDH
566 (327.5, 667.5)
245 (190, 285)
−5.046
0.000
Fibrinogen
4.9 (3.22, 6.19)
5.18 (4.1, 5.85)
−0.597
0.550
PaO2/FiO2
105.43 (76.05, 161.98)
331.76 (233.89, 390.54)
−6.351
0.000
Serumcalcium(L)
19
77
1.043
0.307
TNI(H)
11
5
29.615
0.000
BNP(H)
18
23
30.070
0.000
CURB-65(0–1)
10
93
26.879
0.000
CURB-65(2–5)
11
6
CCI index(0–2)
18
96
2.555
0.110
CCI index(3-)
3
3
IL-1β(H)
4
16
0.000
1.000
IL-2R(H)
17
34
15.554
0.000
IL-6(H)
19
36
20.434
0.000
IL-8(H)
4
4
4.091
0.043
IL-10(H)
8
8
11.034
0.001
TNF-α(H)
16
45
6.549
0.010
D-dimer(H)
21
68
8.866
0.003
Hypertension
13
31
6.982
0.008
Heart failure
1
0
0.175
CHD
4
5
3.083
0.079
Diabetes
4
14
0.055
0.814
Cough
14
76
0.943
0.332
Expectoration
7
44
0.875
0.350
Diarrhea
2
13
0.008
0.928
≥38°C
11
51
0.005
0.943
GGO
8
30
0.486
0.486
Consolidation
0
13
1.883
0.170
Pathy
15
77
0.116
0.733
Fibrosis
0
3
1.000
Independent Prognostic Factors
We used univariate and multivariate analyses to identify the prognostic factors (20, 21). The univariate analysis revealed that age (p = 0.001), Male/Female (p = 0.021), WBC (p = 0.001), NLR (p = 0.000), PCT (p = 0.000), CRP (p = 0.000), LDH (p = 0.000), PaO2/FiO2 (p = 0.000), Serum calcium (p = 0.307), cTnI (p = 0.000), BNP (p = 0.000), CURB-65 (p = 0.000), IL-2R (p = 0.000), IL-6 (p = 0.000), IL-8 (p = 0.043), IL-10 (p = 0.001), TNF-α (p = 0.01), D-dimer (p = 0.003), and Hypertension (p = 0.008) were significant prognostic factors. The multivariate analysis revealed that CRP (p = 0.004), PaO2/FiO2 (p = 0.002), and cTnI (p = 0.016) were independent prognostic factors for predicting the mortality of COVID-19 patients (22).
Development and Validation of a Nomogram
Multivariate logistic regression analysis determined that CRP, PaO2/FiO2, and cTnI were independent predictors (Table 2). The model with three independent predictors is established and represented by a nomogram (Figure 1).
Multivariate logistic regression of death in COVID-19 patients.
B
SE
Wald
OR(95%CI)
P-value
CRP
0.037
0.013
8.271
1.037 (1.012, 1.064)
0.004
PaO2/FiO2
−0.045
0.015
9.144
0.956 (0.929, 0.984)
0.002
TNI
5.417
2.244
5.830
225.296 (2.773, 18303.1)
0.016
Nomogram predicting mortality for COVID-19 patients with three available factors: CRP, PaO2/FiO2, and cTnI.
We predicted the probability of death in patients with COVID-19 by calculating the sum of points in the nomogram corresponding to patient characteristics. Regarding the clinical application of this nomogram, we assumed that, if a patient's CRP was 20 mg/L, the PaO2/FiO2 was 350, and the cTnI was increased, then the corresponding score of this patient was 3+33+26, a total of 62 points, so the corresponding risk of death of this patient was between 0.001 and 0.01, and this patient was relatively safe.
The AUC for the prediction nomogram was 0.988, 95% CI, 0.972–1.000 (23). The AUC for CRP was 0.910. The AUC for PaO2/FiO2 was 0.942. The AUC for cTnI was 0.737. The nomogram decision curve analysis is shown in Figure 2. The calibration curve of the nomogram for the probability of mortality demonstrated good agreement between prediction and observation in the primary cohort (Figure 2) (24). The decision curve showed that if the threshold probability of a patient or doctor is >0%, using the nomogram to predict the mortality for COVID-19 patients adds more benefit. Within this range, net benefit was comparable, with several overlaps, on the basis of the nomogram (25).
From left to right: ROC curves of the nomogram and the three factors; calibration curve of the nomogram in the cohort; decision curve analysis for the nomogram. The y-axis measures the net benefit. The red line represents the nomogram. The gray line represents the assumption that all patients have died. The thin black link represents the assumption that no patients have died. The net benefit was calculated by subtracting the proportion of all patients who are false positive from the proportion who are true positive, weighting by the relative harm of forgoing treatment compared with the negative consequences of unnecessary treatment.
Validation was performed by using another 84 COVID-19 patients. In the validation cohort, the independent risk factors included in the nomogram were examined. The AUC of the nomogram was 0.956 (95% CI: 0.874–1.000). Figure 3 shows that the calibration plot for the probability of mortality demonstrated good agreement between the prediction by nomogram and actual observation.
From left to right: ROC curves of the Nomogram and the three factors; calibration curve of the nomogram in the cohort; decision curve analysis for the nomogram.
Discussion
Since 2003, coronavirus has caused a number of major public health events (26, 27). Generally, it was not until the outbreak of severe acute respiratory syndrome (SARS) in Guangdong, China, in 2002 and 2003 that the coronavirus was found to be highly pathogenic to humans. Another highly pathogenic coronavirus is the Middle East respiratory syndrome (MERS) coronavirus, which emerged in 2012 in Middle Eastern countries (28–31). COVID-19 is another highly pathogenic coronavirus that has been added to human history. Although COVID-19 has a mortality rate of <3% compared to SARS (9.6%) and MERS (34%), its effects remain extremely sudden and devastating (32, 33).
Previous studies have shown that coronaviruses can cause respiratory and intestinal infections (34). According to recent reports, the most common symptoms of COVID-19 patients are fever, cough, myalgia, and fatigue, while the less common are expectoration, hemoptysis, and diarrhea (35–39). Most patients have mild symptoms and good prognosis. Mild patients may have only a low fever and mild weakness, with no obvious signs of pneumonia (40). In the late stage of infection, a variety of complications occur in critically ill patients, including ARDS, septic shock, refractory metabolic acidosis, acute myocardial injury, disseminated intravascular coagulation, and even death (5, 6). Therefore, early diagnosis and early assessment of the patient's prognosis are crucial for controlling the outbreak.
Corticosteroids were used extensively during the outbreaks of severe SARS and MERS and are being applied in COVID-19 patients. Corticosteroids suppress inflammation in the lungs but also inhibit immune responses and pathogen clearance at the same time (41). The clinical benefit of corticosteroids in COVID-19 needs further clinical trials to confirm (42, 43).
In previous studies, several prognostic models have been developed. For example, the NLR was the most effective prognostic factor affecting prognosis for severe COVID-19 patients (44).
In our study, this nomogram based on three variables, CRP, PaO2/FiO2, and cTnI, can provide a more accurate assessment and prediction of mortality at admission for COVID-19 patients. If patients with high mortality rates are properly and promptly identified, they should be more likely to benefit from nutritional support in nursing care and close attention in clinical care, which will ultimately have a positive impact on their recovery (45, 46).
The variables in the nomogram we constructed were easily accessible from routine clinical work. As a result, clinicians can use this simple, intuitive predictive tool to draw a few lines in a few seconds to make a quick assessment of a patient's prognosis with no computational difficulty. Additionally, our model can help clinical workers rationally allocate medical resources to reduce the fatality rate of COVID-19 when medical resources are scarce.
There are still some limitations to our study. First, the study was at a single center with a small sample, and the next step is to include as many cases as possible from multiple centers to build a more reliable predicting nomogram for mortality in COVID-19 patients. Second, we excluded patients whose case data were incomplete, which could have resulted in selection bias.
Conclusions
To sum up, to better identify the prognosis of COVID-19 patients, identify severe patients as early as possible, improve the cure rate, and reduce the fatality rate, we constructed a nomogram based on 120 patients to predict mortality. The proposed nomogram considered three independent risk factors, namely, CRP, PaO2/FiO2, and cTnI. We have confirmed that this nomogram has excellent discrimination and clinical availability.
Data Availability Statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
Ethics Statement
The studies involving human participants were reviewed and approved by Medical ethics committee of affiliated hospital of Qingdao university. The ethics committee waived the requirement of written informed consent for participation. Written informed consent was obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article.
Author Contributions
DP: data analysis and visualization. DP, DC, YC, and CH: writing original draft. FZ, TX, and WY: review and editing. All authors contributed to the article and approved the submitted version.
Conflict of Interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Thanks to the medical workers who have contributed to the epidemic and those in all industries involved in the prevention and control of the epidemic. Finally, we pay tribute to our patients.
Funding. This work was supported by Qingdao City Science and Technology Special Fund No.20-4-1-5-nsh.
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Psychol.Frontiers in Psychology1664-1078Frontiers Media S.A.32849153PMC743214610.3389/fpsyg.2020.01969PsychologyBrief Research ReportBeyond the Lab: Empirically Supported Treatments in the Real WorldSchneiderRenee A.1*GrassoJoseph R.1ChenShih Yin1ChenConnie12ReillyErin D.3KocherBob141Lyra Health, Burlingame, CA, United States2Department of General Internal Medicine, University of California, San Francisco, San Francisco, CA, United States3Department of Psychiatry, University of Massachusetts System, Boston, MA, United States4School of Medicine, Stanford University, Stanford, CA, United States
Edited by: Emily K. Sandoz, University of Louisiana at Lafayette, United States
Reviewed by: John Young, University of Mississippi, United States; Brandon Gaudiano, Brown University, United States
*Correspondence: Renee A. Schneider, renee@lyraclinical.com
This article was submitted to Psychology for Clinical Settings, a section of the journal Frontiers in Psychology
1182020202011196917420201672020Copyright © 2020 Schneider, Grasso, Chen, Chen, Reilly and Kocher.2020Schneider, Grasso, Chen, Chen, Reilly and KocherThis is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Laboratory studies of empirically supported treatments (ESTs) for mental health problems achieve much higher rates of clinical improvement than has been observed following treatment in the community. This discrepancy is likely to due to limited reliance on ESTs by therapists outside of academia. Concerns about the generalizability of ESTs to patients in the community, who may have comorbid problems, likely limit rates of adoption. The present study examined the impact of ESTs delivered in the real-world for 1,256 adults who received services through an employee assistance program specializing in the delivery of ESTs. Rates of anxiety and depression decreased significantly, following treatment with an EST, and 898 (71.5%) patients demonstrated reliable improvement. Even among patients comorbid for depression and anxiety at baseline, over half reported reliable improvement in both disorders. Findings suggest ESTs can be effectively delivered outside of academic RCTs. However, additional research is needed to understand and overcome barriers to disseminating ESTs to the broader community.
empirically supported treatmentevidence-based treatmentdepressionanxietymental health problems
Empirically supported therapies (ESTs) are behavioral health interventions that have been rigorously tested in randomized controlled trials (RCTs) or a series of well-designed single-subject experiments and have demonstrated efficacy when compared to a control or active treatment condition (Chambless and Hollon, 1998; Tolin et al., 2015). ESTs have been developed for a range of behavioral health problems, including the most common disorders, depression and anxiety. In particular, cognitive behavioral therapy (CBT), cognitive therapy (CT), and interpersonal psychotherapy have all demonstrated efficacy in the treatment of moderate or severe depression, with evidence suggesting that CBT and CT may be as efficacious as antidepressant medication (Shapiro et al., 1994; Gloaguen et al., 1998; DeRubeis et al., 2005). Multiple meta-analyses also support the efficacy of CBT or behavioral therapies for the treatment of anxiety disorders, including panic, obsessive-compulsive disorder, social anxiety, post-traumatic stress disorder, and generalized anxiety disorder (Deacon and Abramowitz, 2004).
Reliable improvement, defined as symptom improvement not accounted for by measurement error alone (Jacobson and Truax, 1991), is the standard by which ESTs are often measured. Laboratory studies of ESTs, in which efficacy is tested in an RCT, demonstrate rates of reliable improvement at or greater than 50% (Hofmann et al., 2012; Loerinc et al., 2015). However, findings indicate that among adults treated with psychotherapy under naturalistic conditions in the community, fewer than 30% achieve reliable improvement (Hansen et al., 2002).
One explanation for the discrepancy in observed outcomes may be due to limited adoption of ESTs by providers in the community. Despite efforts to disseminate ESTs more broadly, they are underutilized outside of academia (Stewart and Chambless, 2007). Concerns about the external validity of RCTs are frequently cited by clinicians in the community (Nelson and Steele, 2007; Kazdin, 2008), who worry that ESTs are infeasible to implement in real-world settings (Addis et al., 1999) and that subjects in RCTs differ in important ways from patients in community settings (Westen and Morrison, 2001). In particular, it has been hypothesized that including patients with psychological comorbidities, which tends to be the norm in real-world clinical settings, could reduce the impact of ESTs (Shadish et al., 2000).
Unfortunately, treatment studies often focus only on a single diagnosis, with comorbid psychiatric diagnoses considered exclusion criteria, thus diminishing their application to real-world settings (Goldstein-Piekarski et al., 2016). There are only a small number of RCTs examining EST effectiveness with more real-world–like samples. For example, Craigie and Nathan (2009) found that both individual and group CBT could be effectively used to treat depression in a sample of adult community outpatients with high rates of comorbidity. Barlow et al. (2017) similarly demonstrated that ESTs could be effective in the treatment of adult anxiety disorders when applied to patients with multiple psychiatric diagnoses.
However, other studies on the generalizability of ESTs have produced mixed results, with some research suggesting that ESTs are less potent when delivered outside of more controlled settings (e.g., Weisz et al., 2006; Jonsson et al., 2015). Consistent with these findings, it has been suggested that as study conditions more closely resemble the real world, the efficacy of ESTs may diminish (Westen and Morrison, 2001; Stewart and Chambless, 2009). For example, when providers are not required to use a treatment manual or when treatment fidelity is not measured, CBT may be less potent, as therapists may “drift” from standardized practices (Waller, 2009). Further, therapists in well-controlled RCTs often receive more intensive training in ESTs and clinical consultation than therapists practicing in the community (Weisz et al., 2006; Becker and Stirman, 2011), which may affect treatment fidelity and outcomes.
The EST literature has been criticized for focusing on efficacy in tightly controlled clinical trials at the expense of real-world generalizability (Tolin et al., 2015), and there are few studies that examine the efficacy of ESTs under more naturalistic conditions. In particular, only a handful of studies have looked at how adult patients with psychological comorbidities fare when treated with an EST. In addition, most studies of ESTs rely on a randomized controlled design, which may limit the generalizability of findings as not all patients are willing to be randomized to conditions (Tolin et al., 2015). Studies of the portability of ESTs outside of academia are often conducted in the context of extensive clinical training and oversight, something that is generally lacking in the community. In revising the criteria for ESTs, Tolin et al. (2015) encouraged research that (1) did not involve randomizing subjects to conditions, (2) was conducted by clinicians outside of academia, and (3) involved patients with behavioral health comorbidities.
This present study applies a retrospective design to build on the criteria outlined by Tolin et al. (2015) to better understand whether ESTs for behavioral health problems are effective under real-world conditions. We examined rates of reliable improvement among patients with depression or anxiety who received an EST from a community therapist. Because research on the efficacy of ESTs for patients with psychological comorbidities is limited, we also report separately rates of reliable improvement among patients who started treatment with both depression and anxiety.
MethodsParticipants
Adult patients, 18 years or older, who started individual therapy between July 1, 2018 and May 31, 2019, were included in the present study. All patients in the study were referred to a community therapist by an employee assistance program (EAP) that partners with therapists who utilize ESTs. Patients were employees or dependents of customers who had purchased the EAP. Patients were included in the study if they scored in the clinical range on a measure of depression or anxiety and completed a baseline assessment within 2 weeks of their first appointment (Figure 1). Patients were sent electronically secure assessment questionnaires every 4 weeks following their first appointment. Baseline assessments were compared to the most recent assessment to estimate the impact of treatment. This study was deemed exempt from human patients review by the Western Institutional Review Board.
Participant flow through the study.
Therapists
All therapists in the present study were in community private or group practices and had agreed to join the network of an EAP that specializes in referring patients to providers who practice ESTs. Prior to joining the EAP network, a vetting team reviewed each provider’s public presence (e.g., website) and application, if available, to determine whether they likely utilized ESTs. Those providers who passed this initial step were invited to participate in a clinical vetting interview designed to test their knowledge of and ability to apply ESTs. Sample components of the clinical interview include asking prospective therapists about their theoretical orientation, the therapies and interventions they use, how they measure treatment progress, the average length of treatment, and how they adapt treatment plans based on a patient’s response to treatment. In particular, we were looking for providers who used ESTs as defined by Chambless and Hollon (1998) and Tolin et al. (2015), used validated measures to assess treatment progress and outcomes, and practiced short-term therapy in contrast to treatments of indeterminate length. Only those therapists who passed the rigorous clinical vetting interview were invited to join the network. Historically, approximately 5% of providers who applied to join the network have passed the review and vetting interview. Providers included in the study were compensated monetarily, as per standard community practice.
Measures
Assessments consisted of the Patient Health Questionnaire 9 (PHQ-9) and Generalized Anxiety Disorder scale (GAD-7), well-validated measures of depression and anxiety (Kroenke et al., 2001; Spitzer et al., 2006; Plummer et al., 2016). The PHQ-9 includes nine items rated on a 4-point scale, with scores ranging from 0 to a maximum of 27. The GAD-7 includes seven items rated on a 4-point scale, with scores ranging from 0 to 21. Both measures have been shown to be sensitive to treatment changes over time in psychiatric populations (Beard and Björgvinsson, 2014). For inclusion in the study, a clinical cutoff of 10 was used for the PHQ-9, as research suggests that patients who score at or greater than 10 are very likely to meet the criteria for major depression (Kroenke et al., 2001). A clinical cutoff of 8 was used for the GAD-7, as research suggests that scores at or greater than 8 are highly likely to correspond to an anxiety disorder diagnosis (Kroenke et al., 2007; Plummer et al., 2016).
Analyses
Multiple regression analyses were conducted to examine a possible association between number of sessions and reduction in PHQ-9 and GAD-7 scores, respectively, after controlling for baseline scores. Paired t-tests were used to test differences in PHQ-9 and GAD-7 scores between baseline and follow-up. For each measure, we compared the baseline assessment score to the last available assessment and calculated Cohen’s drm, a conservative measure of effect size for within-subjects designs that controls for correlation between measurements (Lakens, 2013). We calculated the number of patients who demonstrated reliable improvement on either measure, using the RC index proposed by Jacobson and Truax (1991). The RC index allowed us to determine whether a patient made substantial improvement in symptomatology, beyond what could be attributed to measurement error. Consistent with previous research (Gyani et al., 2013), we used an RC index of 6 for the PHQ-9 and 4 for the GAD-7. We also calculated the percentage of patients who recovered, meaning they moved from the clinical range to below the clinical cutoff on either measure. In addition, we calculated the number of patients who demonstrated reliable recovery (Gyani et al., 2013) in that they both demonstrated reliable improvement and recovered on either the PHQ-9 or GAD-7. Finally, to better assess the impact of treatment on patients with psychological comorbidity, rates of reliable improvement and recovery are reported separately for patients who started in the clinical range on both the PHQ-9 and GAD-7.
Results
Of the 1,256 patients included in the analyses, 54.3% (n = 682) identified as female, 33.3% (n = 418) identified as male, 12.3% (n = 155) did not specify gender, and gender was missing for 1 patient. Sixty-eight percent of patients were between the ages of 18 and 34 years. The mean age of patients was 32.8 (SD = 8.7) years (Table 1).
Characteristics of subjects and therapists.
Subjects n (%)
Gender
Female
682 (54.3)
Male
418 (33.3)
Unspecified
155 (12.3)
Missing
1 (0.1)
Age group, years
18–24
153 (12.2)
24–34
699 (55.7)
35–44
275 (21.9)
45–54
86 (6.8)
55–64
41 (3.3)
=65
2 (0.2)
Clinical presentation
PHQ-9 =10
845 (67.3)
GAD-7 = 8
1,097(87.3)
PHQ-9 and GAD-7 in clinical range
686 (54.6)
Therapists n (%)
Therapist credential
Doctoral level
242 (43.3)
Master’s level
317 (56.7)
Therapist years of experience
<5
137 (24.5)
5–10
195 (34.9)
10–15
108 (19.3)
15–20
64 (11.4)
>20
52 (9.3)
Missing
3 (0.5)
The 1,256 patients saw 559 separate therapists (Table 1). On average, each therapist saw 2.1 (SD = 1.6) patients. Approximately 43.3% of therapists had a doctoral degree; 56.7% of therapists had a master’s degree (e.g., LMFT, LCSW, LPCC). Therapists delivered ESTs, as per their normal practice, with the exception that patients could receive up to a pre-specified number of session visits per calendar year, with the maximum number of sessions being 25. The average number of sessions delivered across the course of treatment was 9.4 (SD = 7.13). The average number of weeks patients spent in treatment was 13.1 (SD = 10.4). The number of sessions was inversely correlated with depression and anxiety at follow-up, in that patients with more sessions showed greater improvement on the PHQ-9 [β = −0.05, t(1253) = 2.60, p = 0.01] and the GAD-7 [β = −0.05, t(1094) = 2.65, p < 0.01], after controlling for baseline scores.
Independent-samples t-tests were conducted to compare baseline severities on the GAD-7 and PHQ-9 for patients who did and did not complete a second outcome assessment. In terms of baseline PHQ-9 scores, there was no significant difference in severity for patients who completed a second outcome (mean = 14.30, SD = 3.79) and patients who did not complete a second outcome (mean = 14.65, SD = 3.83); t(1418) = −1.69, p = 0.09. Similarly, there was not a significant difference in the severity of baseline GAD-7 scores for patients who completed a second outcome (mean = 12.69, SD = 3.61) and patients who did not complete a second outcome (mean = 12.70, SD = 3.62); t(1860) = −0.05, p = 0.96.
Depression Symptoms
Of the 1,256 patients, 845 (67.3%) scored in the clinical range on the PHQ-9 at baseline. The baseline average score on the PHQ-9 was 14.30 (SD = 3.79), corresponding to the moderate range of depression severity. Results of paired t-tests revealed that among patients who started in the clinical range, depression scores decreased significantly between baseline and follow-up (mean = 7.58, SD = 4.45), with patients improving an average of 6.7 points on the PHQ-9 [95% confidence interval (CI), 6.4–7.1], t(844) = 39.00, p < 0.001. Cohen’s d suggested a large treatment effect size on depression (drm = 1.62). Reliable improvement in depression scores was observed in 509 patients (60.2%), and 602 patients (71.2%) recovered on the PHQ-9 (Table 2). A total of 448 patients (53.0%) demonstrated reliable recovery on the PHQ-9, defined as meeting criteria for both reliable improvement and recovery.
Clinical change and recovery.
n (%)
Baseline positive screening for depression (n = 845)
Baseline positive screening for anxiety (n = 1,097)
Baseline positive screening for depression or anxiety (n = 1,256)
Baseline positive screening for depression and anxiety (n = 686)
Reliable improvement
509 (60.2)
750 (68.4)
898 (71.5)
361 (52.6)
Recovery
602 (71.2)
682 (62.2)
934 (74.4)
350 (51.0)
Reliable recovery*
448 (53.0)
595 (54.2)
774 (61.6)
269 (39.2)
Reliable improvement or recovery
663 (78.5)
837 (76.3)
1,039(82.7)
442 (64.4)
* Reliable recovery is defined as reliable improvement and recovery.Anxiety Symptoms
At baseline, 1,097 patients (87.3%) scored in the clinical range on the GAD-7. The average baseline score on the GAD-7 was 12.69 (SD = 3.61), corresponding to the moderate range of anxiety severity. Anxiety scores decreased an average of 5.6 points (95% CI, 5.3–5.9) between baseline and follow-up (mean = 7.07, SD = 4.35), and results of paired t-tests revealed that this difference was statistically significant, t(1096) = 38.66, p < 0.001. Cohen’s d suggested a large treatment effect size on anxiety (drm = 1.40). Of the 1,097 patients with clinical scores on the GAD-7 at baseline, 750 (68.4%) met the criteria for reliable improvement, and 682 patients (62.2%) recovered (Table 2). Reliable recovery from anxiety was observed in 595 patients (54.2%).
Depression and Anxiety Symptoms
A total of 686 patients (54.6%) scored in the clinical range on both measures at baseline, suggesting they were comorbid for depression and anxiety. Of these, 361 patients (52.6%) showed reliable improvement on both measures, and 350 (51.0%) recovered on both measures (Table 2). Approximately 269 (39.2%) of the 686 patients demonstrated reliable recovery from both depression and anxiety.
Discussion
Findings presented here demonstrate that ESTs can be efficacious under real-world conditions and deliver results that are comparable to those observed in RCTs (e.g., Hofmann et al., 2012). Among patients receiving an EST from a community provider, levels of depression and anxiety significantly decreased over the course of treatment. Of note, more than half of patients who were comorbid for depression and anxiety at baseline made meaningful improvement in both areas. In utilizing the criteria outlined by Tolin et al. (2015), this study further supports the efficacy of ESTs and extends their usefulness to settings outside of academia.
It is widely known that a gap exists between academia and real-world clinical practice, with the majority of providers in the community relying on prior experience and professional preferences, rather than research data, to inform their clinical decisions (Stewart and Chambless, 2007; Lilienfeld et al., 2013). One reason that community providers cite for rejecting ESTs is a concern that subjects in RCTs are not representative of patients in the real world who may have higher rates of comorbidity (Shafran et al., 2009). Contrary to this hypothesis, findings presented here are consistent with previous research (Craigie and Nathan, 2009; Barlow et al., 2017) and suggest that, even for patients with psychiatric comorbidities, ESTs can produce significant symptom reduction. Further, treatment effectiveness was comparable to efficacy rates seen in RCTs (e.g., Hofmann et al., 2012) despite the lack of clinical oversight and standardized training in ESTs that are characteristic of most studies examining EST effectiveness in the community.
Some limitations to the present study should be considered. In particular, there was no measure of treatment fidelity, so we cannot be certain whether ESTs were delivered with strict adherence to treatment manuals. However, this limitation allowed us to examine how ESTs perform when delivered under more naturalistic conditions by therapists with heterogeneous training in ESTs and use of varied ESTs. It is also possible that therapists combined elements of different ESTs, and in fact, the research suggests that this approach may be associated with better outcomes for patients with psychological comorbidities (Chorpita et al., 2013).
It would have been helpful to capture what other comorbid behavioral health conditions patients may have been experiencing and more specific details on the types of anxiety or depressive disorders patients had. In addition, we did not account for any other treatments patients may have been receiving that could also have produced a change in depression or anxiety. Further, because this was a naturalistic study, the timing of when baseline and follow-up measures were completed varied across patients. It is possible that the last outcome measure we have for some patients was collected midtreatment, and we would have seen even higher rates of reliable improvement and recovery if all patients completed the final outcome survey immediately after the end of treatment. It should also be noted that this was not an intent-to-treat analysis, and there may have been important differences between those patients who completed outcomes assessments and those who did not or those who dropped out of treatment prematurely, possibly skewing findings in a more positive direction.
Finally, because of a possible therapist selection bias, it is unknown whether these results are generalizable to ESTs delivered by community providers who have not undergone extensive vetting. In the majority of studies testing the generalizability of ESTs beyond academia or with patients who have psychological comorbidities, providers receive considerable clinical training, and treatment fidelity is measured in an ongoing way (e.g., Weisz et al., 2006; Barlow et al., 2017). Additional research is therefore needed to determine whether most providers can deliver ESTs in the community without substantial clinical vetting or oversight.
Further research is also needed to understand which ESTs are most easily transported to community settings and what modifications, if any, therapists in the community must make to improve patient acceptability. In traditional research studies, there may be a self-selection bias favoring patients who are interested in more structured or novel interventions and who are better-educated around what ESTs typically entail. Patients in the community may be less familiar with therapy, in general, and likely have very different expectations for therapy relative to those participating in a research study at an academic center. Therapists in the community may be adapting ESTs to make them more appealing to patients, and an understanding of these adaptations could improve efforts to disseminate ESTs more broadly.
Despite the limitations, this study addresses an important gap in the empirical research on the external validity of ESTs. Given that research demonstrates that ESTs are effective outside of academia (e.g., Weisz et al., 2006; Craigie and Nathan, 2009), translational studies aimed at understanding and overcoming barriers to the adoption of ESTs in the real world are an important next step. Understanding what changes therapists in the community may make to ESTs to improve acceptability and how those changes affect treatment efficacy may enhance dissemination of ESTs outside of academic settings and increase the sustainability of EST implementation in clinical practice.
Data Availability Statement
All datasets generated for this study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.
Ethics Statement
The study was granted exempt status by the Western IRB. Written informed consent from the participants was not required to participate in this study in accordance with the national legislation and the institutional requirements.
Author Contributions
RS wrote the initial draft of the manuscript. JG, CC, ER, and BK contributed to the development and editing of the manuscript. SC did the statistical analyses. All authors contributed to the article and approved the submitted version.
Conflict of Interest
RS, JG, SC, BK, and CC were employed by or have equity in Lyra Health, Inc. The authors declare that this study received funding from Lyra Health, Inc. The funder had the following involvement in the study: funding of data collection process; employment of co-authors who designed the study, conducted analyses, and prepared the manuscript.
Funding. This research was funded by the Lyra Health.
We would like to express their appreciation for the therapists in the Lyra network who work every day to support patients in leading more fulfilling, productive lives.
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Psychol.Frontiers in Psychology1664-1078Frontiers Media S.A.32849153PMC743214610.3389/fpsyg.2020.01969PsychologyBrief Research ReportBeyond the Lab: Empirically Supported Treatments in the Real WorldSchneiderRenee A.1*GrassoJoseph R.1ChenShih Yin1ChenConnie12ReillyErin D.3KocherBob141Lyra Health, Burlingame, CA, United States2Department of General Internal Medicine, University of California, San Francisco, San Francisco, CA, United States3Department of Psychiatry, University of Massachusetts System, Boston, MA, United States4School of Medicine, Stanford University, Stanford, CA, United States
Edited by: Emily K. Sandoz, University of Louisiana at Lafayette, United States
Reviewed by: John Young, University of Mississippi, United States; Brandon Gaudiano, Brown University, United States
*Correspondence: Renee A. Schneider, renee@lyraclinical.com
This article was submitted to Psychology for Clinical Settings, a section of the journal Frontiers in Psychology
1182020202011196917420201672020Copyright © 2020 Schneider, Grasso, Chen, Chen, Reilly and Kocher.2020Schneider, Grasso, Chen, Chen, Reilly and KocherThis is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Laboratory studies of empirically supported treatments (ESTs) for mental health problems achieve much higher rates of clinical improvement than has been observed following treatment in the community. This discrepancy is likely to due to limited reliance on ESTs by therapists outside of academia. Concerns about the generalizability of ESTs to patients in the community, who may have comorbid problems, likely limit rates of adoption. The present study examined the impact of ESTs delivered in the real-world for 1,256 adults who received services through an employee assistance program specializing in the delivery of ESTs. Rates of anxiety and depression decreased significantly, following treatment with an EST, and 898 (71.5%) patients demonstrated reliable improvement. Even among patients comorbid for depression and anxiety at baseline, over half reported reliable improvement in both disorders. Findings suggest ESTs can be effectively delivered outside of academic RCTs. However, additional research is needed to understand and overcome barriers to disseminating ESTs to the broader community.
empirically supported treatmentevidence-based treatmentdepressionanxietymental health problems
Empirically supported therapies (ESTs) are behavioral health interventions that have been rigorously tested in randomized controlled trials (RCTs) or a series of well-designed single-subject experiments and have demonstrated efficacy when compared to a control or active treatment condition (Chambless and Hollon, 1998; Tolin et al., 2015). ESTs have been developed for a range of behavioral health problems, including the most common disorders, depression and anxiety. In particular, cognitive behavioral therapy (CBT), cognitive therapy (CT), and interpersonal psychotherapy have all demonstrated efficacy in the treatment of moderate or severe depression, with evidence suggesting that CBT and CT may be as efficacious as antidepressant medication (Shapiro et al., 1994; Gloaguen et al., 1998; DeRubeis et al., 2005). Multiple meta-analyses also support the efficacy of CBT or behavioral therapies for the treatment of anxiety disorders, including panic, obsessive-compulsive disorder, social anxiety, post-traumatic stress disorder, and generalized anxiety disorder (Deacon and Abramowitz, 2004).
Reliable improvement, defined as symptom improvement not accounted for by measurement error alone (Jacobson and Truax, 1991), is the standard by which ESTs are often measured. Laboratory studies of ESTs, in which efficacy is tested in an RCT, demonstrate rates of reliable improvement at or greater than 50% (Hofmann et al., 2012; Loerinc et al., 2015). However, findings indicate that among adults treated with psychotherapy under naturalistic conditions in the community, fewer than 30% achieve reliable improvement (Hansen et al., 2002).
One explanation for the discrepancy in observed outcomes may be due to limited adoption of ESTs by providers in the community. Despite efforts to disseminate ESTs more broadly, they are underutilized outside of academia (Stewart and Chambless, 2007). Concerns about the external validity of RCTs are frequently cited by clinicians in the community (Nelson and Steele, 2007; Kazdin, 2008), who worry that ESTs are infeasible to implement in real-world settings (Addis et al., 1999) and that subjects in RCTs differ in important ways from patients in community settings (Westen and Morrison, 2001). In particular, it has been hypothesized that including patients with psychological comorbidities, which tends to be the norm in real-world clinical settings, could reduce the impact of ESTs (Shadish et al., 2000).
Unfortunately, treatment studies often focus only on a single diagnosis, with comorbid psychiatric diagnoses considered exclusion criteria, thus diminishing their application to real-world settings (Goldstein-Piekarski et al., 2016). There are only a small number of RCTs examining EST effectiveness with more real-world–like samples. For example, Craigie and Nathan (2009) found that both individual and group CBT could be effectively used to treat depression in a sample of adult community outpatients with high rates of comorbidity. Barlow et al. (2017) similarly demonstrated that ESTs could be effective in the treatment of adult anxiety disorders when applied to patients with multiple psychiatric diagnoses.
However, other studies on the generalizability of ESTs have produced mixed results, with some research suggesting that ESTs are less potent when delivered outside of more controlled settings (e.g., Weisz et al., 2006; Jonsson et al., 2015). Consistent with these findings, it has been suggested that as study conditions more closely resemble the real world, the efficacy of ESTs may diminish (Westen and Morrison, 2001; Stewart and Chambless, 2009). For example, when providers are not required to use a treatment manual or when treatment fidelity is not measured, CBT may be less potent, as therapists may “drift” from standardized practices (Waller, 2009). Further, therapists in well-controlled RCTs often receive more intensive training in ESTs and clinical consultation than therapists practicing in the community (Weisz et al., 2006; Becker and Stirman, 2011), which may affect treatment fidelity and outcomes.
The EST literature has been criticized for focusing on efficacy in tightly controlled clinical trials at the expense of real-world generalizability (Tolin et al., 2015), and there are few studies that examine the efficacy of ESTs under more naturalistic conditions. In particular, only a handful of studies have looked at how adult patients with psychological comorbidities fare when treated with an EST. In addition, most studies of ESTs rely on a randomized controlled design, which may limit the generalizability of findings as not all patients are willing to be randomized to conditions (Tolin et al., 2015). Studies of the portability of ESTs outside of academia are often conducted in the context of extensive clinical training and oversight, something that is generally lacking in the community. In revising the criteria for ESTs, Tolin et al. (2015) encouraged research that (1) did not involve randomizing subjects to conditions, (2) was conducted by clinicians outside of academia, and (3) involved patients with behavioral health comorbidities.
This present study applies a retrospective design to build on the criteria outlined by Tolin et al. (2015) to better understand whether ESTs for behavioral health problems are effective under real-world conditions. We examined rates of reliable improvement among patients with depression or anxiety who received an EST from a community therapist. Because research on the efficacy of ESTs for patients with psychological comorbidities is limited, we also report separately rates of reliable improvement among patients who started treatment with both depression and anxiety.
MethodsParticipants
Adult patients, 18 years or older, who started individual therapy between July 1, 2018 and May 31, 2019, were included in the present study. All patients in the study were referred to a community therapist by an employee assistance program (EAP) that partners with therapists who utilize ESTs. Patients were employees or dependents of customers who had purchased the EAP. Patients were included in the study if they scored in the clinical range on a measure of depression or anxiety and completed a baseline assessment within 2 weeks of their first appointment (Figure 1). Patients were sent electronically secure assessment questionnaires every 4 weeks following their first appointment. Baseline assessments were compared to the most recent assessment to estimate the impact of treatment. This study was deemed exempt from human patients review by the Western Institutional Review Board.
Participant flow through the study.
Therapists
All therapists in the present study were in community private or group practices and had agreed to join the network of an EAP that specializes in referring patients to providers who practice ESTs. Prior to joining the EAP network, a vetting team reviewed each provider’s public presence (e.g., website) and application, if available, to determine whether they likely utilized ESTs. Those providers who passed this initial step were invited to participate in a clinical vetting interview designed to test their knowledge of and ability to apply ESTs. Sample components of the clinical interview include asking prospective therapists about their theoretical orientation, the therapies and interventions they use, how they measure treatment progress, the average length of treatment, and how they adapt treatment plans based on a patient’s response to treatment. In particular, we were looking for providers who used ESTs as defined by Chambless and Hollon (1998) and Tolin et al. (2015), used validated measures to assess treatment progress and outcomes, and practiced short-term therapy in contrast to treatments of indeterminate length. Only those therapists who passed the rigorous clinical vetting interview were invited to join the network. Historically, approximately 5% of providers who applied to join the network have passed the review and vetting interview. Providers included in the study were compensated monetarily, as per standard community practice.
Measures
Assessments consisted of the Patient Health Questionnaire 9 (PHQ-9) and Generalized Anxiety Disorder scale (GAD-7), well-validated measures of depression and anxiety (Kroenke et al., 2001; Spitzer et al., 2006; Plummer et al., 2016). The PHQ-9 includes nine items rated on a 4-point scale, with scores ranging from 0 to a maximum of 27. The GAD-7 includes seven items rated on a 4-point scale, with scores ranging from 0 to 21. Both measures have been shown to be sensitive to treatment changes over time in psychiatric populations (Beard and Björgvinsson, 2014). For inclusion in the study, a clinical cutoff of 10 was used for the PHQ-9, as research suggests that patients who score at or greater than 10 are very likely to meet the criteria for major depression (Kroenke et al., 2001). A clinical cutoff of 8 was used for the GAD-7, as research suggests that scores at or greater than 8 are highly likely to correspond to an anxiety disorder diagnosis (Kroenke et al., 2007; Plummer et al., 2016).
Analyses
Multiple regression analyses were conducted to examine a possible association between number of sessions and reduction in PHQ-9 and GAD-7 scores, respectively, after controlling for baseline scores. Paired t-tests were used to test differences in PHQ-9 and GAD-7 scores between baseline and follow-up. For each measure, we compared the baseline assessment score to the last available assessment and calculated Cohen’s drm, a conservative measure of effect size for within-subjects designs that controls for correlation between measurements (Lakens, 2013). We calculated the number of patients who demonstrated reliable improvement on either measure, using the RC index proposed by Jacobson and Truax (1991). The RC index allowed us to determine whether a patient made substantial improvement in symptomatology, beyond what could be attributed to measurement error. Consistent with previous research (Gyani et al., 2013), we used an RC index of 6 for the PHQ-9 and 4 for the GAD-7. We also calculated the percentage of patients who recovered, meaning they moved from the clinical range to below the clinical cutoff on either measure. In addition, we calculated the number of patients who demonstrated reliable recovery (Gyani et al., 2013) in that they both demonstrated reliable improvement and recovered on either the PHQ-9 or GAD-7. Finally, to better assess the impact of treatment on patients with psychological comorbidity, rates of reliable improvement and recovery are reported separately for patients who started in the clinical range on both the PHQ-9 and GAD-7.
Results
Of the 1,256 patients included in the analyses, 54.3% (n = 682) identified as female, 33.3% (n = 418) identified as male, 12.3% (n = 155) did not specify gender, and gender was missing for 1 patient. Sixty-eight percent of patients were between the ages of 18 and 34 years. The mean age of patients was 32.8 (SD = 8.7) years (Table 1).
Characteristics of subjects and therapists.
Subjects n (%)
Gender
Female
682 (54.3)
Male
418 (33.3)
Unspecified
155 (12.3)
Missing
1 (0.1)
Age group, years
18–24
153 (12.2)
24–34
699 (55.7)
35–44
275 (21.9)
45–54
86 (6.8)
55–64
41 (3.3)
=65
2 (0.2)
Clinical presentation
PHQ-9 =10
845 (67.3)
GAD-7 = 8
1,097(87.3)
PHQ-9 and GAD-7 in clinical range
686 (54.6)
Therapists n (%)
Therapist credential
Doctoral level
242 (43.3)
Master’s level
317 (56.7)
Therapist years of experience
<5
137 (24.5)
5–10
195 (34.9)
10–15
108 (19.3)
15–20
64 (11.4)
>20
52 (9.3)
Missing
3 (0.5)
The 1,256 patients saw 559 separate therapists (Table 1). On average, each therapist saw 2.1 (SD = 1.6) patients. Approximately 43.3% of therapists had a doctoral degree; 56.7% of therapists had a master’s degree (e.g., LMFT, LCSW, LPCC). Therapists delivered ESTs, as per their normal practice, with the exception that patients could receive up to a pre-specified number of session visits per calendar year, with the maximum number of sessions being 25. The average number of sessions delivered across the course of treatment was 9.4 (SD = 7.13). The average number of weeks patients spent in treatment was 13.1 (SD = 10.4). The number of sessions was inversely correlated with depression and anxiety at follow-up, in that patients with more sessions showed greater improvement on the PHQ-9 [β = −0.05, t(1253) = 2.60, p = 0.01] and the GAD-7 [β = −0.05, t(1094) = 2.65, p < 0.01], after controlling for baseline scores.
Independent-samples t-tests were conducted to compare baseline severities on the GAD-7 and PHQ-9 for patients who did and did not complete a second outcome assessment. In terms of baseline PHQ-9 scores, there was no significant difference in severity for patients who completed a second outcome (mean = 14.30, SD = 3.79) and patients who did not complete a second outcome (mean = 14.65, SD = 3.83); t(1418) = −1.69, p = 0.09. Similarly, there was not a significant difference in the severity of baseline GAD-7 scores for patients who completed a second outcome (mean = 12.69, SD = 3.61) and patients who did not complete a second outcome (mean = 12.70, SD = 3.62); t(1860) = −0.05, p = 0.96.
Depression Symptoms
Of the 1,256 patients, 845 (67.3%) scored in the clinical range on the PHQ-9 at baseline. The baseline average score on the PHQ-9 was 14.30 (SD = 3.79), corresponding to the moderate range of depression severity. Results of paired t-tests revealed that among patients who started in the clinical range, depression scores decreased significantly between baseline and follow-up (mean = 7.58, SD = 4.45), with patients improving an average of 6.7 points on the PHQ-9 [95% confidence interval (CI), 6.4–7.1], t(844) = 39.00, p < 0.001. Cohen’s d suggested a large treatment effect size on depression (drm = 1.62). Reliable improvement in depression scores was observed in 509 patients (60.2%), and 602 patients (71.2%) recovered on the PHQ-9 (Table 2). A total of 448 patients (53.0%) demonstrated reliable recovery on the PHQ-9, defined as meeting criteria for both reliable improvement and recovery.
Clinical change and recovery.
n (%)
Baseline positive screening for depression (n = 845)
Baseline positive screening for anxiety (n = 1,097)
Baseline positive screening for depression or anxiety (n = 1,256)
Baseline positive screening for depression and anxiety (n = 686)
Reliable improvement
509 (60.2)
750 (68.4)
898 (71.5)
361 (52.6)
Recovery
602 (71.2)
682 (62.2)
934 (74.4)
350 (51.0)
Reliable recovery*
448 (53.0)
595 (54.2)
774 (61.6)
269 (39.2)
Reliable improvement or recovery
663 (78.5)
837 (76.3)
1,039(82.7)
442 (64.4)
* Reliable recovery is defined as reliable improvement and recovery.Anxiety Symptoms
At baseline, 1,097 patients (87.3%) scored in the clinical range on the GAD-7. The average baseline score on the GAD-7 was 12.69 (SD = 3.61), corresponding to the moderate range of anxiety severity. Anxiety scores decreased an average of 5.6 points (95% CI, 5.3–5.9) between baseline and follow-up (mean = 7.07, SD = 4.35), and results of paired t-tests revealed that this difference was statistically significant, t(1096) = 38.66, p < 0.001. Cohen’s d suggested a large treatment effect size on anxiety (drm = 1.40). Of the 1,097 patients with clinical scores on the GAD-7 at baseline, 750 (68.4%) met the criteria for reliable improvement, and 682 patients (62.2%) recovered (Table 2). Reliable recovery from anxiety was observed in 595 patients (54.2%).
Depression and Anxiety Symptoms
A total of 686 patients (54.6%) scored in the clinical range on both measures at baseline, suggesting they were comorbid for depression and anxiety. Of these, 361 patients (52.6%) showed reliable improvement on both measures, and 350 (51.0%) recovered on both measures (Table 2). Approximately 269 (39.2%) of the 686 patients demonstrated reliable recovery from both depression and anxiety.
Discussion
Findings presented here demonstrate that ESTs can be efficacious under real-world conditions and deliver results that are comparable to those observed in RCTs (e.g., Hofmann et al., 2012). Among patients receiving an EST from a community provider, levels of depression and anxiety significantly decreased over the course of treatment. Of note, more than half of patients who were comorbid for depression and anxiety at baseline made meaningful improvement in both areas. In utilizing the criteria outlined by Tolin et al. (2015), this study further supports the efficacy of ESTs and extends their usefulness to settings outside of academia.
It is widely known that a gap exists between academia and real-world clinical practice, with the majority of providers in the community relying on prior experience and professional preferences, rather than research data, to inform their clinical decisions (Stewart and Chambless, 2007; Lilienfeld et al., 2013). One reason that community providers cite for rejecting ESTs is a concern that subjects in RCTs are not representative of patients in the real world who may have higher rates of comorbidity (Shafran et al., 2009). Contrary to this hypothesis, findings presented here are consistent with previous research (Craigie and Nathan, 2009; Barlow et al., 2017) and suggest that, even for patients with psychiatric comorbidities, ESTs can produce significant symptom reduction. Further, treatment effectiveness was comparable to efficacy rates seen in RCTs (e.g., Hofmann et al., 2012) despite the lack of clinical oversight and standardized training in ESTs that are characteristic of most studies examining EST effectiveness in the community.
Some limitations to the present study should be considered. In particular, there was no measure of treatment fidelity, so we cannot be certain whether ESTs were delivered with strict adherence to treatment manuals. However, this limitation allowed us to examine how ESTs perform when delivered under more naturalistic conditions by therapists with heterogeneous training in ESTs and use of varied ESTs. It is also possible that therapists combined elements of different ESTs, and in fact, the research suggests that this approach may be associated with better outcomes for patients with psychological comorbidities (Chorpita et al., 2013).
It would have been helpful to capture what other comorbid behavioral health conditions patients may have been experiencing and more specific details on the types of anxiety or depressive disorders patients had. In addition, we did not account for any other treatments patients may have been receiving that could also have produced a change in depression or anxiety. Further, because this was a naturalistic study, the timing of when baseline and follow-up measures were completed varied across patients. It is possible that the last outcome measure we have for some patients was collected midtreatment, and we would have seen even higher rates of reliable improvement and recovery if all patients completed the final outcome survey immediately after the end of treatment. It should also be noted that this was not an intent-to-treat analysis, and there may have been important differences between those patients who completed outcomes assessments and those who did not or those who dropped out of treatment prematurely, possibly skewing findings in a more positive direction.
Finally, because of a possible therapist selection bias, it is unknown whether these results are generalizable to ESTs delivered by community providers who have not undergone extensive vetting. In the majority of studies testing the generalizability of ESTs beyond academia or with patients who have psychological comorbidities, providers receive considerable clinical training, and treatment fidelity is measured in an ongoing way (e.g., Weisz et al., 2006; Barlow et al., 2017). Additional research is therefore needed to determine whether most providers can deliver ESTs in the community without substantial clinical vetting or oversight.
Further research is also needed to understand which ESTs are most easily transported to community settings and what modifications, if any, therapists in the community must make to improve patient acceptability. In traditional research studies, there may be a self-selection bias favoring patients who are interested in more structured or novel interventions and who are better-educated around what ESTs typically entail. Patients in the community may be less familiar with therapy, in general, and likely have very different expectations for therapy relative to those participating in a research study at an academic center. Therapists in the community may be adapting ESTs to make them more appealing to patients, and an understanding of these adaptations could improve efforts to disseminate ESTs more broadly.
Despite the limitations, this study addresses an important gap in the empirical research on the external validity of ESTs. Given that research demonstrates that ESTs are effective outside of academia (e.g., Weisz et al., 2006; Craigie and Nathan, 2009), translational studies aimed at understanding and overcoming barriers to the adoption of ESTs in the real world are an important next step. Understanding what changes therapists in the community may make to ESTs to improve acceptability and how those changes affect treatment efficacy may enhance dissemination of ESTs outside of academic settings and increase the sustainability of EST implementation in clinical practice.
Data Availability Statement
All datasets generated for this study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.
Ethics Statement
The study was granted exempt status by the Western IRB. Written informed consent from the participants was not required to participate in this study in accordance with the national legislation and the institutional requirements.
Author Contributions
RS wrote the initial draft of the manuscript. JG, CC, ER, and BK contributed to the development and editing of the manuscript. SC did the statistical analyses. All authors contributed to the article and approved the submitted version.
Conflict of Interest
RS, JG, SC, BK, and CC were employed by or have equity in Lyra Health, Inc. The authors declare that this study received funding from Lyra Health, Inc. The funder had the following involvement in the study: funding of data collection process; employment of co-authors who designed the study, conducted analyses, and prepared the manuscript.
Funding. This research was funded by the Lyra Health.
We would like to express their appreciation for the therapists in the Lyra network who work every day to support patients in leading more fulfilling, productive lives.
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Biol.Frontiers in Cell and Developmental Biology2296-634XFrontiers Media S.A.32850817PMC743214710.3389/fcell.2020.00696Cell and Developmental BiologyReviewRelevance Function of Linc-ROR in the Pathogenesis of CancerChenWenjian1†YangJunfa23†FangHui4†LiLei5SunJun1*1Anhui Provincial Children’s Hospital, Affiliated to Anhui Medical University, Hefei, China2Key Laboratory of Anti-inflammatory and Immune Medicine, Ministry of Education, Institute of Clinical Pharmacology, Anhui Medical University, Hefei, China3School of Pharmacy, Anhui Medical University, Hefei, China4Department of Pharmacology, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, China5The Affiliated Hospital of Anhui Medical University, Hefei, China
Edited by: Sridhar Muthusami, Karpagam Academy of Higher Education, India
Reviewed by: Anca Maria Cimpean, Victor Babes University of Medicine and Pharmacy, Romania; Omar Torres-Quesada, University of Innsbruck, Austria
*Correspondence: Jun Sun, sunjun500@aliyun.com; sunjun14190@163.com
†These authors have contributed equally to this work
This article was submitted to Molecular and Cellular Oncology, a section of the journal Frontiers in Cell and Developmental Biology
11820202020869604420200972020Copyright © 2020 Chen, Yang, Fang, Li and Sun.2020Chen, Yang, Fang, Li and SunThis is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Long non-coding RNAs (lncRNAs) are the key components of non-coding RNAs (ncRNAs) with a length of 200 nucleotides. They are transcribed from the so-called “dark matter” of the genome. Increasing evidence have shown that lncRNAs play an important role in the pathophysiology of human diseases, particularly in the development and progression of tumors. Linc-ROR, as a new intergenic non-protein coding RNA, has been considered to be a pivotal regulatory factor that affects the occurrence and development of human tumors, including breast cancer (BC), colorectal cancer (CRC), pancreatic cancer (PC), hepatocellular carcinoma (HCC), and so on. Dysregulation of Linc-ROR has been closely related to advanced clinicopathological factors predicting a poor prognosis. Because linc-ROR can regulate cell proliferation, apoptosis, migration, and invasion, it can thus be used as a potential biomarker for patients with tumors and has potential clinical significance as a therapeutic target. This article reviewed the role of linc-ROR in the development of tumors, its related molecular mechanisms, and clinical values.
lncRNAsncRNAslinc-RORcancersbiomarkerNatural Science Foundation of Anhui Province10.13039/501100003995Introduction
Cancer is a serious disease that affects human health, being one of the main causes of death all over the worldwide. According to research in 2018, 59% of new tumor cases and 70% of the cancer-associated deaths occurred in low-income and developing countries (Kumar and Sharawat, 2018; Noorolyai et al., 2019). Owing to a shortage in effective screening methods and lack of identification of early symptoms, most patients were already in advanced stages when they were diagnosed with cancer (Bray et al., 2018; Koo et al., 2018). Additionally, some clinical studies have shown that polarity and adhesion of cancer cells was decreased, leading to heir increased mobility and invasion, which is a key step in the development of cancer (Yan et al., 2018). Therefore, studies have shown that the high mobility of cancer cells is the main factor leading to high mortality rates in patients with cancer. Currently, there are many ways employed in the treatment of cancer, including surgery, radiotherapy, chemotherapy, biotherapy and targeted therapy (Nie et al., 2016). However, in the past 5 years, the survival rate of patients with cancers remains dismal (Nakashima, 2018). Therefore, in the process of developing human antitumor strategies, it is particularly important to find new early biomarkers and thus identify potential regulatory mechanisms to improve the survival rate of patients with cancers.
Over the past 2 decades, ncRNAs constitute more than 90% of the RNAs made from the human genome, but most of the > 50,000 known non-coding RNAs (ncRNAs) have been discovered and remain largely unstudied (Bhan et al., 2017; Slack and Chinnaiyan, 2019). Transfer RNA (tRNA) (89%) and ribosomal RNA (rRNA) (8.9%) constitute the majority of ncRNAs, followed in abundance by messenger RNAs (mRNAs) (0.9%). Thus, the remaining ncRNAs, including circular RNA (circRNA), small nuclear RNA (snRNA), small nucleolar RNA (snoRNA), microRNA (miRNA), and long non-coding RNA (lncRNA) together account for ∼1% of total ncRNA. Despite their low abundance, these ncRNAs have been reported to play critical roles in transcription, post-transcriptional processing, and translation such as epigenetics, post-transcriptional regulation, chromatin modification, and regulation of the cell cycle (Huarte, 2015; Kondo et al., 2017; Peng et al., 2017b). Additionally, because ncRNAs can be packaged into extracellular vesicles (EV), including exosomes (Meldolesi, 2018), they have been shown to provide a mechanism for intercellular communication through the transfer of miRNA and lncRNA to recipient cells both locally and systemically (Sun et al., 2018). It is important to note that the expression of ncRNAs, their post-transcriptional modification (particularly lncRNAs), and their subcellular distribution have been shown to be important to when assigning their potential function (Palazzo and Lee, 2015). Recently, next-generation sequencing and bioinformatics technology have revealed that circRNAs play crucial role in diagnosis and prognosis of various diseases (Pamudurti et al., 2017). Briefly, circRNAs are single-stranded transcripts generated by back-splicing (Jeck and Sharpless, 2014), with covalently linked head-to-tail closed loop structures with neither 5′–3′ polarity nor a polyadenylated tail (Memczak et al., 2013), that range in length from a few hundred to thousands of nucleotides, and are widely expressed in mammals, thereby showing higher stability compared to that in linear RNAs (Chen J. et al., 2017), and exhibiting a cell-type- or developmental-stage-specific expression pattern (Barrett and Salzman, 2016; Wang J. J. et al., 2019). Many functions of circRNAs have also been identified, including their role as miRNA sponges, binding to RNA-binding proteins and protein decoys, and functioning as regulators of transcription (Hansen et al., 2013; Du et al., 2016; Yang Y. et al., 2018). Interestingly, many circRNAs have been shown to be dysregulated in pathophysiological processes, and circRNAs are known to regulate the expression of gene by acting as miRNA sponges, in a mechanism that is termed as competitive endogenous RNA (ceRNA) mechanism (Zheng et al., 2016; Wang J. J. et al., 2019). For example, circMTO1 have been demonstrated to harbor conventional miRNA binding sites and has been identified as an inhibitor of miRNA-9 in hepatocellular carcinoma (HCC) (Han et al., 2017). Additionally, our previous study has demonstrated that miRNA plays a role in limiting the development of liver fibrosis by markedly blocking the activation and proliferation of hepatic stellate cells (HSCs), suggesting that miRNAs might be involved in the development and progression of several forms of cancers (Yang J. et al., 2017; Yang et al., 2019). Of note, lncRNAs, which are mainly transcribed by RNA polymerase II, are a new kind of ncRNA that are longer than 200 nucleotides (Ma et al., 2016). Owing to the lack of open reading frames, lncRNAs have extremely limited or no protein coding capacity (Ruan et al., 2016; Li J. et al., 2017). These new regulators were initially regarded as transcriptional noise with no specific biological functions (Kim and Sung, 2012). Recently, our laboratory found that epigenetic silencing of lncRNA ANRIL promoted the progression of liver fibrosis, thereby indicating that lncRNAs were associated with the progression of cancers (Yang et al., 2020). Interestingly, increasing evidence have shown that cellular events, including differentiation, proliferation, invasion, apoptosis, and migration, have all been associated with lncRNAs (Guttman et al., 2011). Additionally, there has been new evidence suggesting that lncRNAs may regulate a variety of biological and disease processes, from gene transcription and translation to post-translational modifications (Davalos and Esteller, 2019; Pang et al., 2019). More importantly, lncRNAs have been reported to be used as tumor suppressor genes or oncogenes, thus affecting the proliferation and metastasis of various types of tumors during tumorigenesis (Chen Q. N. et al., 2017; Lu et al., 2017). Subsequent studies have demonstrated that lncRNAs may serve as ceRNAs for miRNAs and in chromatin remodeling during the development of cancers (Huang et al., 2019; Wang C. J. et al., 2019). Figure 1 illustrates the functions of lncRNAs at the molecular level. Regarding certain cancer-associated human lncRNAs, it was demonstrated that linc-ROR was demonstrated to be predominantly upregulated in tumors (Peng et al., 2017a). The abnormal expression of linc-ROR in tumors has been suggested to be one of the main leading factors driving the development. The main ways to revert this effect would be to affect cell growth, migration, and invasion, thus leading to the inhibition of epithelial-mesenchymal transition (EMT), enhancement of the sensitivity to chemotherapy, etc. (Chen et al., 2016a; Zhao et al., 2017). For example, the expression level of linc-ROR in HCC tissues was inhibited compared with the adjacent tissues. At the same time, the downregulation of linc-ROR was linked to the aggressive process of the disease in patients with HCC. Furthermore, the ability of migration and invasion of HCC cells may be delayed by the low expression level of linc-ROR. In this review, we attempted to introduce the latest research on the biological effects, potential clinical applications, and molecular mechanisms of linc-ROR in human tumors and discuss its prognostic and therapeutic values.
Paradigms for the function of long ncRNAs. Recent studies have identified a variety of regulatory paradigms for the mechanism by which long ncRNAs function, many of which are highlighted here. Transcription from an upstream non-coding promoter (pink) can negatively (1) or positively (2) affect the expression of the downstream gene (purple) by inhibiting the recruitment of RNA polymerase II or inducing chromatin remodeling, respectively. (3) An antisense transcript (orange) is able to hybridize to the overlapping sense transcript (purple) and block the recognition of the splice sites by the spliceosome, thus resulting in an alternatively spliced transcript. (4) Alternatively, hybridization of the sense and antisense transcripts can allow Dicer to generate endogenous siRNAs. By binding to specific protein partners, a non-coding transcript (blue) can modulate the activity of the protein (5), serve as a structural component that allows the formation of a larger RNA-protein complex (6), or alter where the protein localizes in the cell (7). (8) Long ncRNAs (green) can be processed to yield small RNAs, such as miRNAs, piRNAs, and other less well-characterized classes of small transcripts.
Overview of Linc-Ror
Among lncRNAs, linc-ROR is a novel and important carcinogenic 2.6 kb lncRNA located in chromosome 18, which was initially identified as a highly expressed transcript of pluripotent and embryonic stem cells (Chen et al., 2016b). Studies found that the octamer-binding transcription factor 4 (OCT4), SRY-box transcription factor 2 (SOX2), and Nanog homeobox (Nanog) key pluripotency factors could regulate linc-ROR (Wang et al., 2013). However, linc-ROR was also found to be expressed in several organs including lung, liver, breast, and colon. Since its discovery, research in this field has been extensively expanded during the past 6 years, revealing the important role of linc-ROR in tumorigenesis. Additionally, upregulation of linc-ROR has been suggested to mediate the re-expression of fetal and cardiomyocyte hypertrophy-related genes (Wang et al., 2013; Li et al., 2016). Many reports regarding linc-ROR and tumorigenesis in recent years, have revealed that the upregulation of linc-ROR is positively correlated with the clinicopathological characteristics and poor prognosis of tumors, including the stages of advanced tumor node metastasis (TNM), positive lymph node metastasis (LNM), and lower survival rate but higher recurrence rate.
Current evidence have strongly indicated that linc-ROR may exert an impact on a variety of cancers (Pan et al., 2016). Furthermore, both tumorigenesis and metastasis have been shown to be induced by linc-ROR via activation of the EMT in various cancers (Hou et al., 2014; Huang et al., 2016; Zhan et al., 2016). For example, linc-ROR was demonstrated to be upregulated, thereby promoting EMT in HCC (Li J. et al., 2017). Besides, it was also reported that self-renewal and differentiation of glioma stem cells was significantly affected by linc-ROR (Zhang et al., 2012; Feng et al., 2015). More importantly, the chemoresistance of pancreatic cancer (PC) and breast cancer (BC) (Li et al., 2016) as well as radio-resistance of colorectal cancer (CRC) cells were observed to be elevated by linc-ROR (Yang P. et al., 2017). Moreover, linc-ROR has also been shown to exert a significantly effect on the stem cell-like characteristics and tumorigenic potential of PC. Recently, it was also reported that linc-ROR could be used as a biomarker in the field of diagnosis and prognosis of BC and oral cancer (Arunkumar et al., 2017; Zhao et al., 2017). Notably, increasing studies showed that linc-ROR could be used as a ceRNA, thus exerting its impact in the post-transcriptional network of tumor pathogenesis. For example, in triple-negative BC, linc-ROR has been shown to serve as a ceRNA, therefore promoting the migration and invasion of BC cells (Signal et al., 2016). Overall, linc-ROR is a typical lncRNA that plays important regulatory roles in interaction with miRNAs and maintenance of stem cell pluripotency, triggering the EMT as well. Furthermore, linc-ROR has also been involved in various key roles under hypoxia and in the promotion of tumorigenesis (Figure 2). Therefore, linc-ROR may be considered as an oncogene affecting the progression of tumor and a promising predictor for the poor prognosis in patients with cancer. The transition of linc-ROR from basic research to clinical application requires further investigations as early as possible.
Linc-ROR is a typical lncRNA that plays important regulatory roles in interacting with miRNAs and maintaining stem cell pluripotency, as well as triggering the EMT as well. Linc-ROR is also involved in various key roles under various stresses and in epigenetic regulation.
Regulatory Role of Linc-Ror in Various Types of Cancer
Increasing evidence has shown that the linc-ROR was abnormal expression in many cancers, and its dysregulation was associated with cellular functions (Fu et al., 2017; Spinelli et al., 2018). Additionally, studies found that the expression level of linc-ROR was substantially upregulated in samples of papillary thyroid carcinomas (PTCs) and PTCs cell lines as well as in metastatic PTCs samples and PTCs cell lines (Zhang et al., 2018). Simultaneously, cell migration and invasion could be regulated by linc-ROR via affecting EMT (Pastushenko and Blanpain, 2019). More importantly, studies demonstrated that linc-ROR was abnormally expressed in several cancers and led to elevated the invasion and metastasis of cancer cells to promoting the progression of tumors (Hashemian et al., 2019; Li et al., 2020b, c). This review summarizes the status of linc-ROR research in various human cancers and discusses its mechanism and clinical significance in the development and progression of tumor. The expression pattern, functional role, and regulatory mechanism of linc-ROR are summarized in Table 1 and depicted in Figure 3.
Underlying molecular mechanisms of linc-ROR in multiple cancers. (A) Linc-ROR binds to miR-205 to upregulate ZEB2, while it also regulated the expression of EMT markers. (B) Linc-ROR could interact with miR-145 to inhibit FSCN1 and upregulated EMT-associated proteins while it decreases G0/G1 arrest and facilitated drug resistance. (C) Linc-ROR facilitated EMT through upregulate EZHZ and regulated ZEB2 by competitively binding to miR-145, and ZEB2 overexpression leads to increased EMT. In addition, linc-ROR binds to miR-876-5p to upregulate FOXM1. (D) Linc-ROR downregulated through EMT production and repress the expression of miR-145. (E) Linc-ROR binds to miR-6833-3p, while also regulating the process of EMT. (F) Linc-ROR upregulated ZEB1 to attenuate the expression of p53, while also decreasing the expression of miR-145 to increase the level of Nanog and reduce that of miR-124 to suppress PKM2. Detailed mechanisms of linc-ROR in other cancers are provided in the review.
Breast Cancer
BC, which accounts for a quarter of all female cancer cases, is the most commonly diagnosed cancer and the leading cause of cancer-associated deaths among women worldwide (Li Z. et al., 2019). In 2018, it was estimated that there would be 2.1 million or so newly diagnosed cases of female BC (Bray et al., 2018; Hannafon et al., 2019). The main risk factors for BC, which is the difficult to change due to prolonged exposure to endogenous hormones, is difficult to control (Rudel et al., 2014; Bray et al., 2018). However, comprehensive treatment approaches have resulted in relatively good clinical outcomes for some patients with BC (Rudel et al., 2014; Goel et al., 2016; Kumler et al., 2016). Nevertheless, it has been reported that about one-third of the patients with BC have the potential for cell metastasis, chemotherapy resistance, and even recurrence (Goel et al., 2016; Kumler et al., 2016). Hence, there is an urgent need to develop new therapies targeting various molecular mechanisms of tumorigenesis for the treatment of BC.
Hou et al. investigated the expression level of linc-ROR in 94 patients (Hou et al., 2018). Their results revealed that the level of linc-ROR was increased in BC tissues. Moreover, the level of the expression of linc-ROR in the peripheral blood of the patients with BC was shown to be closely related to TNM phase and LNM. In addition, the wound-healing assay showed that overexpression of linc-ROR increased BC cells (MCF10A) mobility. Transwell assay revealed that linc-ROR overexpression remarkably increased the migration ability (Hou et al., 2018). More importantly, they found that ectopic expression of linc-ROR induced an EMT program in MCF10A cells. Fluorescence activated cell sorter analysis demonstrated that the subpopulation of the stem cell phenotype CD44high/CD24low was elevated in MCF10A cells transfected with linc-ROR plasmid. Mechanistically, the results of bioinformatic analysis and RNA immunoprecipitation analysis from Hou et al. demonstrated that linc-ROR functions as a ceRNA to regulate miR-205 activity toward prevention of the degradation of transcripts of miR-205 target genes, such as ZEB1 and ZEB2, from degradation. Additionally, it was shown that the expression levels of miR-205 members were decreased upon linc-ROR overexpression in MCF10A cells (Hou et al., 2018). More importantly, Zhao et al. recently demonstrated that CRISPR/Cas9-generated BRCA1-knockdown adipose-derived stem cells stimulated a more aggressive behavior in BC cells than wild-type adipose-derived stem cells (Zhao et al., 2019). Therefore, we believe that CRISPR/Cas9 may be used to in the treatment of BC by inhibiting the expression of linc-ROR during the progression of BC. Conclusively, the linc-ROR/miRNAs axis has been reported to closely affect the occurrence and development of BC.
Pancreatic Cancer
PC is the fourth most common cause of cancer-related mortality worldwide, leading to approximately 227,000 deaths annually (Siegel et al., 2016; Sabater et al., 2018). The 5−year relative survival of patients with PC remained at approximately 8% for 2005–2011 (Siegel et al., 2016). Hence, PC has been proposed to be one of the top two cancers in terms of fatalities in the next decade (Rahib et al., 2014). Surgical resection remains the exclusive potential curative treatment (Xiong et al., 2017). However, approximately half of the patients present with metastasis at the time of diagnosis, missing the opportunity for an effective treatment (Vincent et al., 2011; Xiong et al., 2017). A growing body of literature has demonstrated that both metastasis and limited effective biomarker for the diagnosis and treatment are the main obstacles for the efficient medical therapy of PC (Vincent et al., 2011; Boj et al., 2015; Basuroy et al., 2018). Thus, it is an absolute necessity to identify potential biomarkers and therapeutic targets in PC.
Zhan et al. (2016) have highlighted the oncogenic effects of linc-ROR in the initiation and progression of PC. Their study demonstrated that the level of linc-ROR was significantly elevated in PC tissues (Zhan et al., 2016). Moreover, the wound-healing assay and Boyden’s chamber assay results showed that linc-ROR silencing reduced the migratory capability and metastasis of PC cells (Zhan et al., 2016). Another study by Chen et al. showed that the proliferation rates of sh-ROR-cells in which the level of linc-ROR was suppressed were evidently lower than those of sh-NC-cells. This result was confirmed by colony formation assay, suggesting that linc-ROR accelerated the growth of PC cells (Chen et al., 2016a). Interestingly, silencing of linc-ROR was shown to result in increased levels of the epithelial markers E-cadherin and α-catenin, and decreased levels of mesenchymal markers N-cadherin and vimentin, indicating that linc-ROR plays an important role in the regulation of EMT in PC cells (Zhan et al., 2016; Chen et al., 2020). More importantly, microarray analysis identified ZEB1 as potential target gene of linc-ROR. Further, the expression of linc-ROR and ZEB1 were observed to be negatively correlated with that of p53, suggesting that linc-ROR might mediate migration, and metastasis in PC cells may partly via activation of ZEB1 through the inhibition of the expression of p53 (Zhan et al., 2016). Interestingly, the fluorescence in situ hybridization and luciferase reporter assay results showed that the expression of linc-ROR was demonstrated to be negatively correlated to that of miR-145. MiR-145 can induce posttranscriptional silencing of its targeted genes by binding to the mRNA 3’-UTR or linc-ROR specific sites, indicating that linc-ROR can act as a ceRNA to decrease miR-145 in PC cells, thereby activating expression of Nanog, thus leading to the proliferation of pancreatic cancer stem cells (PCSCs) (Gao et al., 2016). Additionally, Li et al. (2016) further proved that the impact of linc-ROR can be partly reversed by overexpression of miR-124. Consistently, linc-ROR was observed to exhibited a negative correlation with the expression of miR-124 (Li et al., 2016). Hence, a linc-ROR/miR-124/PTBP1/PKM2 axis was identified in PC, shedding new light on the lncRNA-based diagnosis and therapeutic approaches in PC (Li et al., 2016, 2020b). Notably, recent studies showed that PC cell-derived EVs could be used as effective carriers of paclitaxel to their parental cells, thereby bringing the drug into cells through an endocytic pathway and increasing its cytotoxicity (Saari et al., 2015). Additionally, it was demonstrated that vesicle-containing ncRNAs could serve as EV-associated PC detection markers (Worst et al., 2019). Thus, the presence of linc-RORs in EVs from patients with PC could serve as a potential diagnostic biomarker and a novel target for the therapy of patients with PC; this is worthy of further and wider research attention.
Hepatocellular Carcinoma
As the sixth most international commonly occurring cancer in 2018, HCC has become the fourth cause of cancer-associated deaths worldwide. It has been estimated that 841,000 new cases and 782,000 deaths will occur each year (Bray et al., 2018). Briefly, HCC has been reported to account for 75–80% of all the liver cancer cases, half of which have been detected in China (Omata et al., 2017; Bray et al., 2018). As such, HCC poses a huge threat to the worldwide health, especially that of the Chinese people (Omata et al., 2017). About 70% of the patients is expected to recrudescent within 5 years after hepatectomy, and 30% of the patients will die from this tumor (Vigano et al., 2015). Therefore, on the basis of studying the pathogenesis of HCC, it is apparent to look for more effective molecular markers and therapeutic targets for the management of HCC.
Li C. et al. (2017) and Chen et al. (2018) reported that the expression level of linc-ROR was obviously elevated in 20 HCC tissues and four cell lines compared to the corresponding non-tumor tissues and normal liver cell lines, respectively, suggesting that linc-ROR might be critical regulator in the progression of HCC. Furthermore, biological function assay demonstrated that linc-ROR could play promoting role in regulating migration and invasion of HCC (Chen et al., 2018). Moreover, downregulation of linc-ROR could result in a significant increase in G1/G0 phase and an obvious decrease in S phase (Li C. et al., 2017). More importantly, silencing of linc-ROR could lead to the increased expression of E−cadherin and decreased expression level of N−cadherin in HCC cell lines. Li C. et al. (2017) further confirmed that linc-ROR could bind to the zeste homolog 2 (EZH2), thereby affecting the expression of E-cadherin, further indicating that linc-ROR could regulate the progression of EMT. Moreover, linc-ROR was further determined to be associated with DNA repair. Currently, mounting studies have identified reliable indicators of DNA damage, such as phosphorylated histone H2AX (γ-H2AX). Chen et al. (2018) uncover that overexpression of linc-ROR could obviously decrease the expression level of γ-H2AX, illuminating the suppressive effects of the overexpression of linc-ROR on DNA repair in HCC.
Further research demonstrated that linc-ROR could interact with miR−145 and dramatically downregulate the expression of miR−145 in HCC cells (Li C. et al., 2017). The above-mentioned results revealed that linc-ROR might play a promoting role in the proliferation, migration, invasion, and EMT of the HCC cell, which was contrary to the influence of miR−145 enrichment (Li C. et al., 2017). It suggested that overexpressed miR−145 could effectively reverse the promotion of HCC tumorigenesis induced by the overexpression of linc-ROR (Li C. et al., 2017). Li and his colleagues proposed a mechanistic model that linc-ROR promotes HCC tumorigenesis and autophagy partly through negatively regulating the expression of miR−145. Aside from that, it has been reported that miR-145 represses EMT, tumor migration, and invasion by directly targeting the 3’-UTRs of ZEB2 in the tumor. The decrease in miR−145 and increase in ZEB2 can obviously reversed the inhibition of cell migration and invasion mediated by the linc-ROR knockdown. Therefore, it was suggested that targeting the linc-ROR/miR−145/ZEB2 axis might represent a novel therapeutic application in HCC (Li C. et al., 2017). Similarly, Zhi et al. (2019) similarly showed that the migration and invasion of cells was reduced by the knockdown of linc-ROR. Moreover, they further confirmed that FOXM1-mediated activation of linc-ROR contributes to the poor sensitivity of HCC cells to sorafenib via partially regulating the miR-876-5p/FOXM1 axis, which forms a positive-feedback loop. Further evaluation of the regulatory mechanism involving this axis may provide new insights for exploring a potential therapeutic strategy for the management of HCC (Zhi et al., 2019). Consequently, these studies may offer new insights regarding the pathology of HCC and provide potential strategies for lncRNA-directed treatment. However, both the in vivo influence and other underlying mechanisms of linc-ROR still remain to be determined and clarified in the future research.
Colorectal Cancer
There are approximately 1.3 million new CRC cases and 690,000 CRC-related deaths worldwide each year, thus making CRC the third most common cancer in the world (Torre et al., 2015). Although the treatment of CRC has significantly improved in recent decades, the prognosis remains unsatisfactory, especially in case of advanced tumors with distant metastases (Bogousslavsky, 1984; Torre et al., 2015). Current studies results showed that approximately 25% of cases with CRC have synchronous liver metastases during the time of diagnosis (Kawaguchi et al., 2019). These patients have inherently low survival rates of less than 10% within 5 years, with an even worse prognosis (Hu et al., 2019; Kawaguchi et al., 2019). Thus, there is an urgent need to better understand the progression of CRC and to identify novel and sensitive biomarkers for the diagnosis and treatment of patients with CRC.
Yang et al. detected the expression of linc-ROR in 30 CRC tissues compared to normal tissues by using qRT-PCR. They found that the expression of linc-ROR was remarkably increased in CRC tissues compared with normal tissues. Similarly, linc-ROR was shown to be overexpressed in five CRC cell lines (Yan and Sun, 2018; Li et al., 2020a). Then, they also performed a series of functional assays to clarify the biological effects of the aberrant expression of linc-ROR on proliferation, viability, apoptosis, migration, and invasion of CRC cells. Knockdown of linc-ROR was shown to effectively inhibit the proliferation of CRC cells, whereas its overexpression obviously increased the proliferative capacity of CRC cells. Accordingly, silencing of linc-ROR strongly inhibited the migratory and invasive abilities of CRC cells, compared with that in the control cells (Li et al., 2020a). In contrast, the migratory and invasive ability of cells was activated following the overexpression of linc-ROR. The MTS (3-(4,5-dimethylthiazol-2-yl)-5-(3-carboxymethoxyphenyl)-2-(4- sulfophenyl)-2H-tetrazolium) assay results showed that the overexpression of linc-ROR could enhance the viability of CRC cells. Furthermore, the flow cytometric analysis results revealed that the percentage of apoptotic cells in linc-ROR overexpression group was reduced by 9.74% ± 2.13%, indicating that the overexpression of linc-ROR could inhibit apoptosis in the CRC cell lines (Li et al., 2020a). More importantly, a recent study revealed the role of linc-ROR in the EMT. It was revealed that the upregulation of linc-ROR could increase the expression of N-cadherin and Vimentin as well as decrease the level of E-cadherin, leading to the promotion of the progression of EMT (Zhou et al., 2016; Yan and Sun, 2018). Meanwhile, the high expression of linc-ROR in CRC was also confirmed by Hu et al. (2019). Mechanistically, Li et al. (2020a) proved that that linc-ROR could bind to miR-6833-3p, which was determined to be significantly downregulated in CRC tissues. Additionally, a negative correlation was exhibited between the expression of linc-ROR and miR-6833-3p in BC tissues. Anti-AGO2 RNA immunoprecipitation assay further confirmed these results (Li et al., 2020a). Besides, rescue assays demonstrated that downregulation of miR-6833-3p could partly reversed the inhibition of tumorigenesis induced by linc-ROR knockdown in BC cells. Li and his colleagues uncovered that linc-ROR exerted its oncogenic role through negatively regulating the expression of miR-6833-3p during the progression of CRC, which might give new insights into molecular diagnosis and treatment (Li et al., 2020a). In addition, Li et al. (2020a) further uncovered that linc-ROR could mediate the expression level of SMC by sponging miR-6833-3p in CRC cells, thus promoting CRC progression. As for the effects of linc-ROR on radiotherapy resistance. Yan and Sun (2018) showed that overexpression of linc-ROR increased the ability of CRC cells for radiotherapy resistance. Collectively, these findings indicated that linc-ROR might be engaged in the metastatic process of CRC cells and could promote the development of CRC through a variety of molecular mechanisms.
Lung Cancer
Lung cancer is the leading cause of cancer-related deaths worldwide (Bray et al., 2018). Non-small cell lung cancer (NSCLC) accounts for about 85% of the lung cancer types, including squamous cell carcinoma, large cell lung cancer, and lung adenocarcinoma (Herbertz et al., 2015; Bray et al., 2018; Brainard and Farver, 2019). Although there are various approaches for its diagnosis and treatments, the 5-year overall survival (OS) rate for patients with advanced lung cancer is less than 15% (Zhou et al., 2011). Therefore, in order to carry out an early diagnosis and treatment of lung cancer, the search for valuable and efficient tumor markers is very urgent.
In recent years, linc-ROR has appeared as an important regulator of lung cancer. Research from Qu et al. (2017) demonstrated that the expression of linc-ROR was increased in 299 NSCLC tissues compared to that in the normal tissues. The overexpression of linc-ROR was shown to be closely related to the poor prognosis of LNM, histological grade, and stage of TNM (Pan et al., 2017; Qu et al., 2017). Another study by Pan et al. (2017) demonstrated that the decreased expression of linc-ROR could obviously impair the proliferative capacity of lung adenocarcinoma (LAD) cells, cause a G0/G1 phase arrest, and increase the ratio of apoptotic LAD cells. Moreover, downregulation of linc-ROR substantially inhibited the invasive and metastatic ability of LAD cells. Meanwhile, forced expression of linc-ROR was observed to reduce the expression of E-cadherin and β-catenin, which are the characteristic biomarkers of epithelial cells, whereas it increased the expression of N-cadherin and Vimentin, thus displaying a mesenchymal phenotype. Conversely, downregulation of linc-ROR was demonstrated to result in increased the expression of epithelial markers and decreased the expression of mesenchymal markers. This result uncovered the fact that the pro-metastatic effects of linc-ROR were induced by the regulation of the expression of a number of genes involved in cell metastasis and EMT progress. Meanwhile, overexpression of linc-ROR was shown to enhance the resistance of LAD to docetaxel (DTX) (Pan et al., 2017), suggesting that linc-ROR-induced resistance of LAD cells to DTX and EMT through regulation the expression of miR-145, which was predicted to interact with linc-ROR. More importantly, further research confirmed that miR-145 could bind to linc-ROR, and its downregulation could partly inhibit the resistance of LAD cells to DTX and EMT. Aside from that, Pan and his colleagues discovered that a decrease in the expression of miR-145 and increase in the expression FSCN1 could obviously reverse the inhibition of cell proliferation, chemoresistance, and EMT mediated by linc-ROR knockdown. They identified that dysregulation of the linc-ROR/miR-145/FSCN1 axis was associated with the therapeutic resistance and EMT transition in LAD cells, thereby providing potential therapeutic strategies for managing drug resistance in patients with LAD (Pan et al., 2017). Taken together, these studies collectively suggested that linc-ROR could activate the malignant phenotype of NSCLC cells with the guidance of a mechanism involving miRNAs. Hence, more efforts should be made to elucidate other regulatory mechanisms and the clinical significance of linc-ROR in lung cancer.
Thyroid Cancer (TC)
TC continues to be the most common endocrine malignant tumor and has emerged as a major health issue (Ito et al., 2013). It is estimated that more than 60,000 people in the United States present with TC every year (Ito et al., 2013; Vigneri et al., 2015). Additionally, TC is the sixth most common malignancy among Chinese women, with an incidence rate of about 6.6 per a population of 100,000 (Chen et al., 2015). The major subtypes of TC include PTC, follicular thyroid cancer (FTC), poorly differentiated thyroid cancer (PDTC), and anaplastic thyroid cancer (ATC) originating from follicular cell-derived thyroid cells. PTC accounts for more than 85% of all the TC cases, and approximately 10–15% of the patients with PTC have been reported to exhibit relapse and metastasis after therapy, leading to a poor outcome (Fagin and Wells, 2016). On the other hand, ATC is the most aggressive and fatal subtype, with a total survival of merely 3–5 months after initial diagnosis (Fagin and Wells, 2016). The studies of molecular mechanism correlated with the development and progression of TC may considerably facilitate the understanding of the pathogenesis of TC (Wang et al., 2016, 2017; Murugan et al., 2018). Thus, it is importantly to find potential biomarkers and therapeutic targets involved in TC tumorigenesis.
Zhang et al. (2018) examined the expression levels of linc-ROR in TC cells. Accordingly, their results from in situ hybridization showed that the levels of linc-ROR was increased in TC tissues and TC cell lines. Then, the expression of linc-ROR was further validated by using qRT-PCR analysis in TC tissue samples and cell lines, as they found it to be higher compared to that in normal tissue samples and normal thyroid cell. Hence, linc-ROR was observed to be evidently upregulated during the progression of TC (Zhang et al., 2018). Then, a series of functional assays were performed to clarify the biological effects of linc-ROR on proliferation and invasion of TC cells. They showed that that decreased the expression of linc-ROR could suppress the proliferative and invasion capacity of TC cells, as well as the elevated apoptotic rate in TC cells (Zhang et al., 2018). Interestingly, this led to the reversed progress of EMT, which was presented as a reduction in the levels of E-cadherin and enrichment in those of N-cadherin, highlighting the involvement of linc-ROR in the regulation of EMT (Zhang et al., 2018). Specifically, Zhang et al. (2018) proved that linc-ROR could act as a molecular sponge to modulate miR-145, which was determined to be significantly downregulated in TC tissues, indicating that linc-ROR had a negative regulatory role on miR-145. In summary, Zhang et al. discovered that linc-ROR could act as an oncogene and suggested its utilization as a prognostic indicator of patients with TC. However, a larger cohort of tumor samples and deeper mechanistic research are urgently needed.
Potential Clinical Application of Linc-Ror in Human Cancers
The detection of cancer is hard in early stages and thus losing the best chance for curative surgery results in the poor survival rates (Siegel et al., 2020). Specific cancer biomarkers that monitor the molecular differences associated with cancer, which may be used for early diagnosis, are essential and may help select the best treatment options and gain valuable treatment time for patients with cancer. In clinical settings, prognostic markers can be used to predict the clinical outcome of untreated patients with cancer. On the other hand, current studies have further indicated that the progression of cancer or differentiation of cancer subtypes can be predicted by the expression profiles of lncRNAs. However, because of the uncertain molecular function of lncRNAs, it is difficult to accurately predict the progress of tumors. The ideal and convenient biomarkers should possess several typical and important characteristics. Interestingly, studies have shown that linc-ROR is not only closely associated with multiple biological functions of cancer cells but may also be an ideal and convenient biomarker (Pan et al., 2016). For example, the expression of linc-ROR has been reported to be increased in HCC tissues with LNM or vascular infiltration compared with normal tissues. In addition, the advanced stage of TNM was associated with the overexpression of linc-ROR. Most importantly, the expression of linc-ROR was shown to be elevated in patients with HCC with recurrence compared with those without recurrence (Li C. et al., 2017). Furthermore, results obtained from the Kaplan-Meier analysis showed that patients with HCC with high expression of linc-ROR had worse prognosis compared to those with low linc-ROR expression, along with a shorter disease-free survival (DFS) and OS (Li C. et al., 2017).
Zhao et al. (2017) found that the expression level of linc-ROR was increased in BC tissues, BC cell lines, and BC plasma samples. Concomitantly, they demonstrated that the level of linc-ROR in patients with LNM was elevated compared to that in patients without LNM. Besides, the expression of linc-ROR in ER-positive or PR-positive BC plasma also demonstrated to be significantly increased (Zhao et al., 2017). Moreover, the study showed that the area under the Receiver operating characteristic (ROC) curve (AUC) of linc-ROR was 0.758 (sensitivity 80.0%; specificity 73.3%), suggesting that the diagnostic ability of linc-ROR was elevated relative to that of Carcinoembryonic antigen (CEA) (AUC = 0.516; sensitivity 66.7%; specificity 50.0%) and CA153 (AUC = 0.663; sensitivity 73.3%; specificity 60.0%). More importantly, they found that linc-ROR had the strongest ability among the three indices in distinguishing healthy from BC-affected individuals in an evaluation of 45 early stage patients from the 96 patients with BC and 45 age and sex-matched healthy controls from the 90 healthy volunteers. Moreover, the combined detection of the three plasma indexes might enhance the overall diagnostic power (AUC = 0.846; sensitivity 83.3%; specificity 70.0%) (Zhao et al., 2017). In addition, Hou et al. (2018) examined the level of linc-ROR in the clinical prognosis of patients with BC. The group found that there was a close relationship between LNM and the expression of linc-ROR. More importantly, overexpression of linc-ROR was observed to exhibit a faster decline with increased survival (Chen et al., 2016a; Hou et al., 2018). Another study also revealed that the regulatory polymorphisms in linc-ROR had an impact on the risk for BC (Luo et al., 2018). Conclusively, all the above-mentioned results suggested that linc-ROR might be used as a diagnostic and prognostic biomarker for BC.
Qu et al. (2017) identified that the level of linc-ROR was significantly elevated in NSCLC tissues compared to that in the adjacent tissues. Meanwhile, upregulation of linc-ROR was shown to be significantly linked to advanced TNM stage, LNM, and positive distant metastasis. The Kaplan-Meier curve analysis showed that patients with NSCLC with high expression of linc-ROR have shorter OS and DFS times compared to patients with low expression of linc-ROR (Duan et al., 2016; Jiang et al., 2016; Qu et al., 2017). Subsequently, the results of Cox proportional hazards regression analyses showed that the expression of linc-ROR was an independent prognostic indicator for OS (HR (heart rate) = 2.983) and DFS (HR (heart rate) = 3.421) in patients with NSCLC (Qu et al., 2017). In addition, Fu et al. (2017) also demonstrated that the expression level of linc-ROR was increased in PC tissues compared to that in adjacent tissues. The expression level of linc-ROR was further demonstrated to be closely linked to tumor size and the clinical stage of PC. Meanwhile, log-rank analysis indicated that the OS was significantly decreased in patients with higher expression of linc-ROR (Fu et al., 2017). Glioblastoma (GB) has been considered to be the most aggressive subtype of glioma and the most common adult malignant brain tumor in the world (Stupp et al., 2009; Linz, 2010; Davis, 2016). Toraih et al. (2019) reported that the level of linc-ROR was increased in 89.5% of the patients (Han et al., 2012; Feng et al., 2015). The overexpression of linc-ROR was observed to be closely linked to poor disease progression-free and OS in younger age of patients. In addition, Kaplan–Meier survival plot illustrated that patients with GB with high linc-ROR expression had low survival rates. Multivariate analysis demonstrated that patients with GB were divided into two different groups according to the expression profile of linc-ROR and OS of patients (Toraih et al., 2019). Thus, these results suggested that the expression of linc-ROR was complementary in playing a crucial role in the progress of cancers and thus could serve as a new potential biomarker for the evaluation of clinical prognosis.
Currently, the biggest obstacles to cancer treatment are delayed diagnosis, recurrence, and metastasis. Therefore, the search for the ideal cancer biomarkers is crucial for the improvement of the early diagnosis rate. The above-mentioned findings suggested that linc-ROR might be used as a potential marker for the diagnosis of several types of cancer. However, the exact molecular mechanism by which linc-ROR plays a role in various cancers remains unclear. As such, the functional role of linc-ROR in cancer needs to be further explored and verified, especially in clinical applications.
Regulatory Mechanism of Linc-Ror in Various Types of Cancer
It is well understood that improvement of the survival of patients requires a deeper understanding, as well as effective biomarkers for determination of cancer occurrence and metastasis. Additionally, detection of these invasive tumors in the early stages of disease development and development of efficient treatments are more effective ways to reduce cancer mortality. A large number of researches have shown that many signaling pathways were significantly associated with the occurrence and development of tumors (Chen et al., 2016a; Sahebi et al., 2016; Hou et al., 2018). Hence, we summarize that the related some regulatory mechanisms are associated with the tumor development and progression.
First, linc-ROR has been reported to directly affect the progression of various cancers through several signaling pathways (Figure 4 and Table 2). Of note, a number of studies have hinted that the aberrant activated MAPK/ERK cascade plays a critical role in many aspects of tumorigenesis. Nearly 50% of human malignancies are known to exhibit unregulated ERK signaling (Herrero et al., 2015). In BC, the upregulated MAPK/ERK (mitogen-activated protein kinase/extracellular regulated protein kinases) signaling has been correlated with poor survival in patients with triple-negative BC (Bartholomeusz et al., 2012). In addition, the MAPK/ERK pathway has also been shown to influence the chemotherapeutic drug resistance to doxorubicin and paclitaxel in BC cells (McCubrey et al., 2006). Moreover, It is well known that the MAPK/ERK signaling is negatively regulated by mitogen-activated protein kinase phosphatases, which form six distinct groups based on their physiological functions; among them, groups 1–4 are known to be involved in dephosphorylation of ERK, including DUSP7 (Owens and Keyse, 2007). Interestingly, Peng and his colleagues demonstrated that DUSP7 is downregulated in response to estrogen deprivation and overexpression of linc-ROR significantly decreased the half-life of DUSP7, possibly inhibiting the activation of ERK, resulting in estrogen-independent growth of BC cells (Peng et al., 2017a). The Wnt signaling cascade was highly conserved among species and controls a multitude of biological processes during animal development and life cycles. Because of its central role in the maintenance of tissue homeostasis, the Wnt pathway was tightly regulated at multiple levels, from the ligand-receptor interaction down to transcriptional and post-transcriptional levels; its aberrant activity has been implicated in a number of developmental disorders and diseases and, most prominently, in cancer (Nusse and Clevers, 2017; Nguyen et al., 2019; Zhang et al., 2019; Liu et al., 2020). The recent discovery of lncRNAs that are regulated by Wnt and/or participate in Wnt pathway modulation and outcome is particularly intriguing and has highlighted some of these gaps in our knowledge (Zarkou et al., 2018). For example, Lou et al. (2017) have discovered that the Wnt/β-catenin signaling pathway led to the elevated proliferation of ovarian cancer (OC) cells. Whereas, E-cadherin was shown to be decreased, vimentin, β-catenin, and c-myc were all increased in OC cells treated with LiC1 (a Wnt/β-catenin pathway activator) compared with untreated controls (Lou et al., 2017). More importantly, the study further revealed that the activation of Wnt/β-catenin signaling pathway and the progression of EMT, along with the abnormal expression of EMT markers and Wnt/β-catenin signaling pathway-related proteins (c-Myc, cyclin-D1, and β-catenin), was inhibited by knockdown of linc-ROR during the development of OC, indicating that linc-ROR promotes OC EMT at least in part by activating the Wnt/β-catenin pathway (Lou et al., 2017). However, the relationship between linc-ROR and the Wnt/β-catenin signaling pathway required deeper study in tumor initiation and progression. The PI3K/Akt/mTOR pathway has various cell functions, including cellular proliferation, survival, differentiation, and intrusion, and previous literature has proven that lncRNA silencing could reduce the phosphorylation of ERK, Akt, and mTOR (Fumarola et al., 2014). More importantly, aberrant activation of the PI3K/Akt/mTOR pathway was shown to be one of the mechanisms of targeted therapeutic resistance in patients with NSCLC (Fumarola et al., 2014). Xu et al. (2018) found that silencing of linc-ROR significantly inhibited the expression of p-PI3K, p-AKT3, and m-TOR. In contrast, the overexpression of linc-ROR was shown to increase the expression of p-PI3K, p-AKT3, and m-TOR, suggesting that the activation of PI3K/Akt/mTOR signaling pathway was mediated by linc-ROR in NSCLC (Xu et al., 2018). Similarly, Shi et al. (2017) further discovered that linc-ROR could inhibit the PI3K/Akt/mTOR signaling pathway, so that inactivation of linc-ROR or inhibition of PI3K/Akt/mTOR signaling pathway could increase the sensitivity of NSCLC to cisplatin (DDP), thereby promoting cell apoptosis, and inhibiting cell proliferation, migration, invasion, and tumor growth. Additionally, the Hippo/YAP pathway (also known as the Salvador–Warts–Hippo pathway) is an evolutionarily conserved regulator of tissue growth and cell fate (Harvey et al., 2013). The Hippo/YAP pathway was first postulated to be important for human cancer on the basis of the egregious overgrowth of Drosophila melanogaster tissues that harbor mutations in different Hippo/YAP pathway genes. In recent years, increasing numbers of mammalian studies have validated this hypothesis: Hippo/YAP pathway perturbation can trigger tumorigenesis in mice, and mutation and altered expression of a subset of Hippo pathway genes have been observed in human cancers (Harvey et al., 2013). Interestingly, the Hippo/YAP signaling pathway has also been reported to activated by linc-ROR (Mo et al., 2014). Inactivation of the Hippo signaling pathway could lead to the downregulation of MST1/LATS1 (the core factors of Hippo pathway) and upregulation of YAP1 (He et al., 2015). Furthermore, dysregulation of the Hippo signaling has been confirmed in many types of tumors and closely linked to the acquisition of malignant features. A recent study provided by Chen et al. demonstrated that the regulatory axis of linc-ROR/EMT/YAP1 regulatory axis exerted oncogenic effects in the progression of PC (Chen et al., 2020). They found that a knockdown of linc-ROR resulted in the downregulation of YAP, and MOB kinase activator 1 (MOB1), whereas there was upregulation of MST1, MST2, p-MOB1, p-LATS1, LATS1, and p-YAP in PC cells. In contrast, overexpression of linc-ROR led to the exact reverse condition (Chen et al., 2020). The current evidence for the existence of the linc-ROR/EMT/Hippo/YAP axis has indicated that combined targeting of the YAP1/Hippo signaling pathway may provide a potential direction for the treatment of PC. Many studies have recognized that the function of TGFβ in the progression of BC can be different depending on the stage of cancer (Bachman and Park, 2005; Moses and Barcellos-Hoff, 2011; Massague, 2012). Under normal conditions, TGF-β has been shown to inhibit cell cycle and promote apoptosis, which together significantly contribute to its suppressive role in the initiation and progression of tumorigenesis (Heldin et al., 2009). Hou et al. (2018) reported that knockdown of linc-ROR in BC cells led to diminished expression levels of TGF-β. As a result, the downstream factors, such as Smad2 and α-SMA, were also downregulated. This finding suggested that overexpression of linc-ROR might be required to constitutively upregulate critical factors in the TGF-β signaling pathway (Hou et al., 2018). Overall, these findings revealed that linc-ROR might provide a novel therapeutic target and biomarker for cancers in the future.
Direct regulatory function of linc-ROR in various cancers. First, linc-ROR promotes the estrogen-independent growth and activation of the MAPK/ERK signaling pathway of tumor cells by regulating the DUSP7 ERK-specific phosphatase. Second, linc-ROR promotes the process of EMT through the Wnt/β-catenin and Hip/YAP signaling pathways. Additionally, linc-ROR strongly represses the sensitivity to cisplatin and significantly increases the proliferation, invasion, migration, and growth of tumor cells via the PI3K/AKT/m-TOR signaling pathway (associated proteins: p-PI3K, p-AKT, and p-mTOR). Simultaneously, linc-ROR significantly facilitates the progression of tumor through the TGF-β signaling pathway (associated proteins: p-PI3K, p-AKT, and p-mTOR).
The involvement of linc-ROR in multiple signaling pathways.
Cancer types
Role
Expression
Related gene
Signaling pathway
References
Breast cancer
Oncogenic
Upregulated
ERK1, ERK2 TGF-β, Smad2, and α-SMA
A growing understanding of the role of linc-ROR in all types of cancers may opened up the possibility for many new treatment strategies. Therefore, increasingly, cancer researchers will focus on this lncRNA. Recent studies have displayed that the level of linc-ROR was increased in lung cancer, bladder cancer, and CRC cell lines. Moreover, both cell proliferation and migration have been reported to be promoted by linc-ROR in several types of cancers. In addition, increasing evidence have shown that the abnormal expression of linc-ROR in various cancers was closely linked to tumorigenesis, diagnosis, metastasis, and prognosis through some signaling pathways. Of note, a number of studies have confirmed that linc-ROR can be considered as a possible new target for cancer therapy and a biomarker for cancer diagnosis. However, the current knowledge on the biological functions of linc-ROR remains insufficient, and its efficacy is also not evident. Thus, additional studies on lncRNAs are required.
The abbreviation “EV” is actually a collective term that refers to a series of lipid bilayer membrane-bound organelles that are released by cells into their environment. Briefly, EVs are heterogeneous in size and released from nearly all cells under appropriate physiological and pathological conditions (Lee et al., 2011). Of note, a variety of cargos have been transferred from a cell to another cell via EVs (Ekstrom et al., 2012; Yang J. et al., 2018). More importantly, the EV cargo can reflect the cells of origin. A number of studies have demonstrated that exosomes carry different types of RNA compared to their parental cells (Skog et al., 2008; Ekstrom et al., 2012; Nolte-’t Hoen et al., 2012). It is well-known that unprotected ncRNAs are easily degraded by the RNases in the blood. However, it has been demonstrated that under the protection of EVs, ncRNAs avoid degradation, and thus their integrity and activity are maintained in the circulation. Recently, studies suggested that the expression of lncRNAs in the plasma of patients with HCC was considerably increased compared to that in healthy people. More importantly, Takahashi et al. assessed the role of EV signaling in tumor cell responses to TGF-β and also recognized specific EV-lncRNA mediators, such as linc-ROR, involved in the regulation of the chemotherapy response to chemotherapy (Takahashi et al., 2014; Spinelli et al., 2018). Targeting these intercellular signaling mechanisms and mediators may be useful in enhancing sensitivity and improving responses to conventional therapeutic agents that are used for the treatment of HCC. As such, the expression of linc-ROR, which plays an important role in cancer prevention, diagnosis, and treatment may be increased through the administration of exosomal linc-ROR.
On the other hand, CRISPR/Cas9, as a very powerful gene editing tool, has been successfully applied to the interruption of the protein-coding 24 sequence in various organisms (Li M. et al., 2019). Previous researches have demonstrated that CRISPR/Cas9 could regulate the progression and development of cancers. For example, CRISPR/Cas9-mediated synthesis and gate genetic circuits were shown to be efficiently employed in the identification of bladder cancer cells (Liu et al., 2014). Of note, CRISPR/Cas9 could successfully target lncRNAs and replace the overexpression of the sponge bait, thus negating the requirement for the introduction of transgenes (Kellner et al., 2018). In addition, CRISPR/Cas9 has achieved many successes as a powerful genome engineering tool for the treatment of many diseases due to its specificity, efficiency, simplicity, and versatility (Zhen et al., 2017). For example, both the transcription and infection by HIV-1 were shown to be increased by lnc-MALAT1. However, long terminal repeat-driven gene transcription of HIV-1 and viral replication were reported to be inhibited by CRISPR/Cas9-mediated lnc-MALAT1 knockdown. Similarly, knockdown of lnc-HTOR or IGF2BP1 by CRISPR/Cas9 gene-editing methods mimicked the effects and abolished the requirement for Triptonide in nasopharyngeal carcinoma cells (Liu et al., 2016). Therefore, there’s a good chance that the technology of CRISPR/Cas9 could be used to treat cancers by editing the expression level of linc-ROR. Thus, CRISPR/Cas9 might be used to treat cancers by regulating the expression of linc-ROR via the relevant molecular mechanism.
Conclusion
Increasing studies have demonstrated that lncRNAs could regulate the expression of genes and be involved in the tumorigenesis and development of tumors through complex tumor networks. The linc-ROR was reported to be abnormally expression in a variety of malignant tumors, such as BC, PC, HCC, CRC, NSCLC, and TC. The expression trend of this lncRNA was demonstrated to be almost similar in most of these cancers. In addition, studies confirmed that the linc-ROR served as an oncogene in vivo and in vitro and was closely associated with the enlarged tumor volume, advanced TNM stage, shortened OS, and metastasis. Meanwhile, cell proliferation, invasion, migration, and anti-apoptosis were observed to be regulated by linc-ROR in all the above-mentioned cancers. Retrospective studies of linc-ROR in human cancers may provide new targets for the diagnosis and treatment of different types of cancer. Furthermore, increasing studies have shown that the molecular mechanism, such as Wnt/β-catenin signaling pathway involved in the progress of cancer, was regulated by linc-ROR. There seems to be some competitions between linc-ROR and miRNAs, resulting in the inhibition of downstream target genes of these miRNAs. However, the exact mechanism of action of linc-ROR remains to be explored. Further experiments are needed to delineate the role of linc-ROR and its molecular mechanisms in other human malignancies.
Author Contributions
WC and JY drafted the manuscript. JS revised the manuscript. LL and HF reviewed and modified the manuscript. All authors agreed on the final version.
Conflict of Interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Funding. This project was supported by the Natural Science Foundation of Anhui Province (1808085MH288).
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Erlotinib versus chemotherapy as first-line treatment for patients with advanced EGFR mutation-positive non-small-cell lung cancer (OPTIMAL, CTONG-0802): a multicentre, open-label, randomised, phase 3 study.\nLancet Oncol.\n12\n735–742. 10.1016/S1470-2045(11)70184-X21783417ZhouP.SunL.LiuD.LiuC.SunL. (2016). Long Non-Coding RNA lincRNA-ROR Promotes the Progression of Colon Cancer and Holds Prognostic Value by Associating with miR-145.\nPathol. Oncol. Res.\n22\n733–740. 10.1007/s12253-016-0061-x\n27071407AbbreviationsBC
breast cancer
ceNAs
competing endogenous RNA
EMT
epithelial–mesenchymal transition
HCC
hepatocellular cancer
iPSCs
induced pluripotent stem cells
linc-ROR
Long intergenic non-protein coding RNA, regulator of reprogramming
lncRNAs
Long non-coding RNAs
PC
pancreatic cancer.
\n"],"string":"[\n \"\\nFront Cell Dev BiolFront Cell Dev BiolFront. Cell Dev. Biol.Frontiers in Cell and Developmental Biology2296-634XFrontiers Media S.A.32850817PMC743214710.3389/fcell.2020.00696Cell and Developmental BiologyReviewRelevance Function of Linc-ROR in the Pathogenesis of CancerChenWenjian1†YangJunfa23†FangHui4†LiLei5SunJun1*1Anhui Provincial Children’s Hospital, Affiliated to Anhui Medical University, Hefei, China2Key Laboratory of Anti-inflammatory and Immune Medicine, Ministry of Education, Institute of Clinical Pharmacology, Anhui Medical University, Hefei, China3School of Pharmacy, Anhui Medical University, Hefei, China4Department of Pharmacology, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, China5The Affiliated Hospital of Anhui Medical University, Hefei, China
Edited by: Sridhar Muthusami, Karpagam Academy of Higher Education, India
Reviewed by: Anca Maria Cimpean, Victor Babes University of Medicine and Pharmacy, Romania; Omar Torres-Quesada, University of Innsbruck, Austria
*Correspondence: Jun Sun, sunjun500@aliyun.com; sunjun14190@163.com
†These authors have contributed equally to this work
This article was submitted to Molecular and Cellular Oncology, a section of the journal Frontiers in Cell and Developmental Biology
11820202020869604420200972020Copyright © 2020 Chen, Yang, Fang, Li and Sun.2020Chen, Yang, Fang, Li and SunThis is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Long non-coding RNAs (lncRNAs) are the key components of non-coding RNAs (ncRNAs) with a length of 200 nucleotides. They are transcribed from the so-called “dark matter” of the genome. Increasing evidence have shown that lncRNAs play an important role in the pathophysiology of human diseases, particularly in the development and progression of tumors. Linc-ROR, as a new intergenic non-protein coding RNA, has been considered to be a pivotal regulatory factor that affects the occurrence and development of human tumors, including breast cancer (BC), colorectal cancer (CRC), pancreatic cancer (PC), hepatocellular carcinoma (HCC), and so on. Dysregulation of Linc-ROR has been closely related to advanced clinicopathological factors predicting a poor prognosis. Because linc-ROR can regulate cell proliferation, apoptosis, migration, and invasion, it can thus be used as a potential biomarker for patients with tumors and has potential clinical significance as a therapeutic target. This article reviewed the role of linc-ROR in the development of tumors, its related molecular mechanisms, and clinical values.
lncRNAsncRNAslinc-RORcancersbiomarkerNatural Science Foundation of Anhui Province10.13039/501100003995Introduction
Cancer is a serious disease that affects human health, being one of the main causes of death all over the worldwide. According to research in 2018, 59% of new tumor cases and 70% of the cancer-associated deaths occurred in low-income and developing countries (Kumar and Sharawat, 2018; Noorolyai et al., 2019). Owing to a shortage in effective screening methods and lack of identification of early symptoms, most patients were already in advanced stages when they were diagnosed with cancer (Bray et al., 2018; Koo et al., 2018). Additionally, some clinical studies have shown that polarity and adhesion of cancer cells was decreased, leading to heir increased mobility and invasion, which is a key step in the development of cancer (Yan et al., 2018). Therefore, studies have shown that the high mobility of cancer cells is the main factor leading to high mortality rates in patients with cancer. Currently, there are many ways employed in the treatment of cancer, including surgery, radiotherapy, chemotherapy, biotherapy and targeted therapy (Nie et al., 2016). However, in the past 5 years, the survival rate of patients with cancers remains dismal (Nakashima, 2018). Therefore, in the process of developing human antitumor strategies, it is particularly important to find new early biomarkers and thus identify potential regulatory mechanisms to improve the survival rate of patients with cancers.
Over the past 2 decades, ncRNAs constitute more than 90% of the RNAs made from the human genome, but most of the > 50,000 known non-coding RNAs (ncRNAs) have been discovered and remain largely unstudied (Bhan et al., 2017; Slack and Chinnaiyan, 2019). Transfer RNA (tRNA) (89%) and ribosomal RNA (rRNA) (8.9%) constitute the majority of ncRNAs, followed in abundance by messenger RNAs (mRNAs) (0.9%). Thus, the remaining ncRNAs, including circular RNA (circRNA), small nuclear RNA (snRNA), small nucleolar RNA (snoRNA), microRNA (miRNA), and long non-coding RNA (lncRNA) together account for ∼1% of total ncRNA. Despite their low abundance, these ncRNAs have been reported to play critical roles in transcription, post-transcriptional processing, and translation such as epigenetics, post-transcriptional regulation, chromatin modification, and regulation of the cell cycle (Huarte, 2015; Kondo et al., 2017; Peng et al., 2017b). Additionally, because ncRNAs can be packaged into extracellular vesicles (EV), including exosomes (Meldolesi, 2018), they have been shown to provide a mechanism for intercellular communication through the transfer of miRNA and lncRNA to recipient cells both locally and systemically (Sun et al., 2018). It is important to note that the expression of ncRNAs, their post-transcriptional modification (particularly lncRNAs), and their subcellular distribution have been shown to be important to when assigning their potential function (Palazzo and Lee, 2015). Recently, next-generation sequencing and bioinformatics technology have revealed that circRNAs play crucial role in diagnosis and prognosis of various diseases (Pamudurti et al., 2017). Briefly, circRNAs are single-stranded transcripts generated by back-splicing (Jeck and Sharpless, 2014), with covalently linked head-to-tail closed loop structures with neither 5′–3′ polarity nor a polyadenylated tail (Memczak et al., 2013), that range in length from a few hundred to thousands of nucleotides, and are widely expressed in mammals, thereby showing higher stability compared to that in linear RNAs (Chen J. et al., 2017), and exhibiting a cell-type- or developmental-stage-specific expression pattern (Barrett and Salzman, 2016; Wang J. J. et al., 2019). Many functions of circRNAs have also been identified, including their role as miRNA sponges, binding to RNA-binding proteins and protein decoys, and functioning as regulators of transcription (Hansen et al., 2013; Du et al., 2016; Yang Y. et al., 2018). Interestingly, many circRNAs have been shown to be dysregulated in pathophysiological processes, and circRNAs are known to regulate the expression of gene by acting as miRNA sponges, in a mechanism that is termed as competitive endogenous RNA (ceRNA) mechanism (Zheng et al., 2016; Wang J. J. et al., 2019). For example, circMTO1 have been demonstrated to harbor conventional miRNA binding sites and has been identified as an inhibitor of miRNA-9 in hepatocellular carcinoma (HCC) (Han et al., 2017). Additionally, our previous study has demonstrated that miRNA plays a role in limiting the development of liver fibrosis by markedly blocking the activation and proliferation of hepatic stellate cells (HSCs), suggesting that miRNAs might be involved in the development and progression of several forms of cancers (Yang J. et al., 2017; Yang et al., 2019). Of note, lncRNAs, which are mainly transcribed by RNA polymerase II, are a new kind of ncRNA that are longer than 200 nucleotides (Ma et al., 2016). Owing to the lack of open reading frames, lncRNAs have extremely limited or no protein coding capacity (Ruan et al., 2016; Li J. et al., 2017). These new regulators were initially regarded as transcriptional noise with no specific biological functions (Kim and Sung, 2012). Recently, our laboratory found that epigenetic silencing of lncRNA ANRIL promoted the progression of liver fibrosis, thereby indicating that lncRNAs were associated with the progression of cancers (Yang et al., 2020). Interestingly, increasing evidence have shown that cellular events, including differentiation, proliferation, invasion, apoptosis, and migration, have all been associated with lncRNAs (Guttman et al., 2011). Additionally, there has been new evidence suggesting that lncRNAs may regulate a variety of biological and disease processes, from gene transcription and translation to post-translational modifications (Davalos and Esteller, 2019; Pang et al., 2019). More importantly, lncRNAs have been reported to be used as tumor suppressor genes or oncogenes, thus affecting the proliferation and metastasis of various types of tumors during tumorigenesis (Chen Q. N. et al., 2017; Lu et al., 2017). Subsequent studies have demonstrated that lncRNAs may serve as ceRNAs for miRNAs and in chromatin remodeling during the development of cancers (Huang et al., 2019; Wang C. J. et al., 2019). Figure 1 illustrates the functions of lncRNAs at the molecular level. Regarding certain cancer-associated human lncRNAs, it was demonstrated that linc-ROR was demonstrated to be predominantly upregulated in tumors (Peng et al., 2017a). The abnormal expression of linc-ROR in tumors has been suggested to be one of the main leading factors driving the development. The main ways to revert this effect would be to affect cell growth, migration, and invasion, thus leading to the inhibition of epithelial-mesenchymal transition (EMT), enhancement of the sensitivity to chemotherapy, etc. (Chen et al., 2016a; Zhao et al., 2017). For example, the expression level of linc-ROR in HCC tissues was inhibited compared with the adjacent tissues. At the same time, the downregulation of linc-ROR was linked to the aggressive process of the disease in patients with HCC. Furthermore, the ability of migration and invasion of HCC cells may be delayed by the low expression level of linc-ROR. In this review, we attempted to introduce the latest research on the biological effects, potential clinical applications, and molecular mechanisms of linc-ROR in human tumors and discuss its prognostic and therapeutic values.
Paradigms for the function of long ncRNAs. Recent studies have identified a variety of regulatory paradigms for the mechanism by which long ncRNAs function, many of which are highlighted here. Transcription from an upstream non-coding promoter (pink) can negatively (1) or positively (2) affect the expression of the downstream gene (purple) by inhibiting the recruitment of RNA polymerase II or inducing chromatin remodeling, respectively. (3) An antisense transcript (orange) is able to hybridize to the overlapping sense transcript (purple) and block the recognition of the splice sites by the spliceosome, thus resulting in an alternatively spliced transcript. (4) Alternatively, hybridization of the sense and antisense transcripts can allow Dicer to generate endogenous siRNAs. By binding to specific protein partners, a non-coding transcript (blue) can modulate the activity of the protein (5), serve as a structural component that allows the formation of a larger RNA-protein complex (6), or alter where the protein localizes in the cell (7). (8) Long ncRNAs (green) can be processed to yield small RNAs, such as miRNAs, piRNAs, and other less well-characterized classes of small transcripts.
Overview of Linc-Ror
Among lncRNAs, linc-ROR is a novel and important carcinogenic 2.6 kb lncRNA located in chromosome 18, which was initially identified as a highly expressed transcript of pluripotent and embryonic stem cells (Chen et al., 2016b). Studies found that the octamer-binding transcription factor 4 (OCT4), SRY-box transcription factor 2 (SOX2), and Nanog homeobox (Nanog) key pluripotency factors could regulate linc-ROR (Wang et al., 2013). However, linc-ROR was also found to be expressed in several organs including lung, liver, breast, and colon. Since its discovery, research in this field has been extensively expanded during the past 6 years, revealing the important role of linc-ROR in tumorigenesis. Additionally, upregulation of linc-ROR has been suggested to mediate the re-expression of fetal and cardiomyocyte hypertrophy-related genes (Wang et al., 2013; Li et al., 2016). Many reports regarding linc-ROR and tumorigenesis in recent years, have revealed that the upregulation of linc-ROR is positively correlated with the clinicopathological characteristics and poor prognosis of tumors, including the stages of advanced tumor node metastasis (TNM), positive lymph node metastasis (LNM), and lower survival rate but higher recurrence rate.
Current evidence have strongly indicated that linc-ROR may exert an impact on a variety of cancers (Pan et al., 2016). Furthermore, both tumorigenesis and metastasis have been shown to be induced by linc-ROR via activation of the EMT in various cancers (Hou et al., 2014; Huang et al., 2016; Zhan et al., 2016). For example, linc-ROR was demonstrated to be upregulated, thereby promoting EMT in HCC (Li J. et al., 2017). Besides, it was also reported that self-renewal and differentiation of glioma stem cells was significantly affected by linc-ROR (Zhang et al., 2012; Feng et al., 2015). More importantly, the chemoresistance of pancreatic cancer (PC) and breast cancer (BC) (Li et al., 2016) as well as radio-resistance of colorectal cancer (CRC) cells were observed to be elevated by linc-ROR (Yang P. et al., 2017). Moreover, linc-ROR has also been shown to exert a significantly effect on the stem cell-like characteristics and tumorigenic potential of PC. Recently, it was also reported that linc-ROR could be used as a biomarker in the field of diagnosis and prognosis of BC and oral cancer (Arunkumar et al., 2017; Zhao et al., 2017). Notably, increasing studies showed that linc-ROR could be used as a ceRNA, thus exerting its impact in the post-transcriptional network of tumor pathogenesis. For example, in triple-negative BC, linc-ROR has been shown to serve as a ceRNA, therefore promoting the migration and invasion of BC cells (Signal et al., 2016). Overall, linc-ROR is a typical lncRNA that plays important regulatory roles in interaction with miRNAs and maintenance of stem cell pluripotency, triggering the EMT as well. Furthermore, linc-ROR has also been involved in various key roles under hypoxia and in the promotion of tumorigenesis (Figure 2). Therefore, linc-ROR may be considered as an oncogene affecting the progression of tumor and a promising predictor for the poor prognosis in patients with cancer. The transition of linc-ROR from basic research to clinical application requires further investigations as early as possible.
Linc-ROR is a typical lncRNA that plays important regulatory roles in interacting with miRNAs and maintaining stem cell pluripotency, as well as triggering the EMT as well. Linc-ROR is also involved in various key roles under various stresses and in epigenetic regulation.
Regulatory Role of Linc-Ror in Various Types of Cancer
Increasing evidence has shown that the linc-ROR was abnormal expression in many cancers, and its dysregulation was associated with cellular functions (Fu et al., 2017; Spinelli et al., 2018). Additionally, studies found that the expression level of linc-ROR was substantially upregulated in samples of papillary thyroid carcinomas (PTCs) and PTCs cell lines as well as in metastatic PTCs samples and PTCs cell lines (Zhang et al., 2018). Simultaneously, cell migration and invasion could be regulated by linc-ROR via affecting EMT (Pastushenko and Blanpain, 2019). More importantly, studies demonstrated that linc-ROR was abnormally expressed in several cancers and led to elevated the invasion and metastasis of cancer cells to promoting the progression of tumors (Hashemian et al., 2019; Li et al., 2020b, c). This review summarizes the status of linc-ROR research in various human cancers and discusses its mechanism and clinical significance in the development and progression of tumor. The expression pattern, functional role, and regulatory mechanism of linc-ROR are summarized in Table 1 and depicted in Figure 3.
Underlying molecular mechanisms of linc-ROR in multiple cancers. (A) Linc-ROR binds to miR-205 to upregulate ZEB2, while it also regulated the expression of EMT markers. (B) Linc-ROR could interact with miR-145 to inhibit FSCN1 and upregulated EMT-associated proteins while it decreases G0/G1 arrest and facilitated drug resistance. (C) Linc-ROR facilitated EMT through upregulate EZHZ and regulated ZEB2 by competitively binding to miR-145, and ZEB2 overexpression leads to increased EMT. In addition, linc-ROR binds to miR-876-5p to upregulate FOXM1. (D) Linc-ROR downregulated through EMT production and repress the expression of miR-145. (E) Linc-ROR binds to miR-6833-3p, while also regulating the process of EMT. (F) Linc-ROR upregulated ZEB1 to attenuate the expression of p53, while also decreasing the expression of miR-145 to increase the level of Nanog and reduce that of miR-124 to suppress PKM2. Detailed mechanisms of linc-ROR in other cancers are provided in the review.
Breast Cancer
BC, which accounts for a quarter of all female cancer cases, is the most commonly diagnosed cancer and the leading cause of cancer-associated deaths among women worldwide (Li Z. et al., 2019). In 2018, it was estimated that there would be 2.1 million or so newly diagnosed cases of female BC (Bray et al., 2018; Hannafon et al., 2019). The main risk factors for BC, which is the difficult to change due to prolonged exposure to endogenous hormones, is difficult to control (Rudel et al., 2014; Bray et al., 2018). However, comprehensive treatment approaches have resulted in relatively good clinical outcomes for some patients with BC (Rudel et al., 2014; Goel et al., 2016; Kumler et al., 2016). Nevertheless, it has been reported that about one-third of the patients with BC have the potential for cell metastasis, chemotherapy resistance, and even recurrence (Goel et al., 2016; Kumler et al., 2016). Hence, there is an urgent need to develop new therapies targeting various molecular mechanisms of tumorigenesis for the treatment of BC.
Hou et al. investigated the expression level of linc-ROR in 94 patients (Hou et al., 2018). Their results revealed that the level of linc-ROR was increased in BC tissues. Moreover, the level of the expression of linc-ROR in the peripheral blood of the patients with BC was shown to be closely related to TNM phase and LNM. In addition, the wound-healing assay showed that overexpression of linc-ROR increased BC cells (MCF10A) mobility. Transwell assay revealed that linc-ROR overexpression remarkably increased the migration ability (Hou et al., 2018). More importantly, they found that ectopic expression of linc-ROR induced an EMT program in MCF10A cells. Fluorescence activated cell sorter analysis demonstrated that the subpopulation of the stem cell phenotype CD44high/CD24low was elevated in MCF10A cells transfected with linc-ROR plasmid. Mechanistically, the results of bioinformatic analysis and RNA immunoprecipitation analysis from Hou et al. demonstrated that linc-ROR functions as a ceRNA to regulate miR-205 activity toward prevention of the degradation of transcripts of miR-205 target genes, such as ZEB1 and ZEB2, from degradation. Additionally, it was shown that the expression levels of miR-205 members were decreased upon linc-ROR overexpression in MCF10A cells (Hou et al., 2018). More importantly, Zhao et al. recently demonstrated that CRISPR/Cas9-generated BRCA1-knockdown adipose-derived stem cells stimulated a more aggressive behavior in BC cells than wild-type adipose-derived stem cells (Zhao et al., 2019). Therefore, we believe that CRISPR/Cas9 may be used to in the treatment of BC by inhibiting the expression of linc-ROR during the progression of BC. Conclusively, the linc-ROR/miRNAs axis has been reported to closely affect the occurrence and development of BC.
Pancreatic Cancer
PC is the fourth most common cause of cancer-related mortality worldwide, leading to approximately 227,000 deaths annually (Siegel et al., 2016; Sabater et al., 2018). The 5−year relative survival of patients with PC remained at approximately 8% for 2005–2011 (Siegel et al., 2016). Hence, PC has been proposed to be one of the top two cancers in terms of fatalities in the next decade (Rahib et al., 2014). Surgical resection remains the exclusive potential curative treatment (Xiong et al., 2017). However, approximately half of the patients present with metastasis at the time of diagnosis, missing the opportunity for an effective treatment (Vincent et al., 2011; Xiong et al., 2017). A growing body of literature has demonstrated that both metastasis and limited effective biomarker for the diagnosis and treatment are the main obstacles for the efficient medical therapy of PC (Vincent et al., 2011; Boj et al., 2015; Basuroy et al., 2018). Thus, it is an absolute necessity to identify potential biomarkers and therapeutic targets in PC.
Zhan et al. (2016) have highlighted the oncogenic effects of linc-ROR in the initiation and progression of PC. Their study demonstrated that the level of linc-ROR was significantly elevated in PC tissues (Zhan et al., 2016). Moreover, the wound-healing assay and Boyden’s chamber assay results showed that linc-ROR silencing reduced the migratory capability and metastasis of PC cells (Zhan et al., 2016). Another study by Chen et al. showed that the proliferation rates of sh-ROR-cells in which the level of linc-ROR was suppressed were evidently lower than those of sh-NC-cells. This result was confirmed by colony formation assay, suggesting that linc-ROR accelerated the growth of PC cells (Chen et al., 2016a). Interestingly, silencing of linc-ROR was shown to result in increased levels of the epithelial markers E-cadherin and α-catenin, and decreased levels of mesenchymal markers N-cadherin and vimentin, indicating that linc-ROR plays an important role in the regulation of EMT in PC cells (Zhan et al., 2016; Chen et al., 2020). More importantly, microarray analysis identified ZEB1 as potential target gene of linc-ROR. Further, the expression of linc-ROR and ZEB1 were observed to be negatively correlated with that of p53, suggesting that linc-ROR might mediate migration, and metastasis in PC cells may partly via activation of ZEB1 through the inhibition of the expression of p53 (Zhan et al., 2016). Interestingly, the fluorescence in situ hybridization and luciferase reporter assay results showed that the expression of linc-ROR was demonstrated to be negatively correlated to that of miR-145. MiR-145 can induce posttranscriptional silencing of its targeted genes by binding to the mRNA 3’-UTR or linc-ROR specific sites, indicating that linc-ROR can act as a ceRNA to decrease miR-145 in PC cells, thereby activating expression of Nanog, thus leading to the proliferation of pancreatic cancer stem cells (PCSCs) (Gao et al., 2016). Additionally, Li et al. (2016) further proved that the impact of linc-ROR can be partly reversed by overexpression of miR-124. Consistently, linc-ROR was observed to exhibited a negative correlation with the expression of miR-124 (Li et al., 2016). Hence, a linc-ROR/miR-124/PTBP1/PKM2 axis was identified in PC, shedding new light on the lncRNA-based diagnosis and therapeutic approaches in PC (Li et al., 2016, 2020b). Notably, recent studies showed that PC cell-derived EVs could be used as effective carriers of paclitaxel to their parental cells, thereby bringing the drug into cells through an endocytic pathway and increasing its cytotoxicity (Saari et al., 2015). Additionally, it was demonstrated that vesicle-containing ncRNAs could serve as EV-associated PC detection markers (Worst et al., 2019). Thus, the presence of linc-RORs in EVs from patients with PC could serve as a potential diagnostic biomarker and a novel target for the therapy of patients with PC; this is worthy of further and wider research attention.
Hepatocellular Carcinoma
As the sixth most international commonly occurring cancer in 2018, HCC has become the fourth cause of cancer-associated deaths worldwide. It has been estimated that 841,000 new cases and 782,000 deaths will occur each year (Bray et al., 2018). Briefly, HCC has been reported to account for 75–80% of all the liver cancer cases, half of which have been detected in China (Omata et al., 2017; Bray et al., 2018). As such, HCC poses a huge threat to the worldwide health, especially that of the Chinese people (Omata et al., 2017). About 70% of the patients is expected to recrudescent within 5 years after hepatectomy, and 30% of the patients will die from this tumor (Vigano et al., 2015). Therefore, on the basis of studying the pathogenesis of HCC, it is apparent to look for more effective molecular markers and therapeutic targets for the management of HCC.
Li C. et al. (2017) and Chen et al. (2018) reported that the expression level of linc-ROR was obviously elevated in 20 HCC tissues and four cell lines compared to the corresponding non-tumor tissues and normal liver cell lines, respectively, suggesting that linc-ROR might be critical regulator in the progression of HCC. Furthermore, biological function assay demonstrated that linc-ROR could play promoting role in regulating migration and invasion of HCC (Chen et al., 2018). Moreover, downregulation of linc-ROR could result in a significant increase in G1/G0 phase and an obvious decrease in S phase (Li C. et al., 2017). More importantly, silencing of linc-ROR could lead to the increased expression of E−cadherin and decreased expression level of N−cadherin in HCC cell lines. Li C. et al. (2017) further confirmed that linc-ROR could bind to the zeste homolog 2 (EZH2), thereby affecting the expression of E-cadherin, further indicating that linc-ROR could regulate the progression of EMT. Moreover, linc-ROR was further determined to be associated with DNA repair. Currently, mounting studies have identified reliable indicators of DNA damage, such as phosphorylated histone H2AX (γ-H2AX). Chen et al. (2018) uncover that overexpression of linc-ROR could obviously decrease the expression level of γ-H2AX, illuminating the suppressive effects of the overexpression of linc-ROR on DNA repair in HCC.
Further research demonstrated that linc-ROR could interact with miR−145 and dramatically downregulate the expression of miR−145 in HCC cells (Li C. et al., 2017). The above-mentioned results revealed that linc-ROR might play a promoting role in the proliferation, migration, invasion, and EMT of the HCC cell, which was contrary to the influence of miR−145 enrichment (Li C. et al., 2017). It suggested that overexpressed miR−145 could effectively reverse the promotion of HCC tumorigenesis induced by the overexpression of linc-ROR (Li C. et al., 2017). Li and his colleagues proposed a mechanistic model that linc-ROR promotes HCC tumorigenesis and autophagy partly through negatively regulating the expression of miR−145. Aside from that, it has been reported that miR-145 represses EMT, tumor migration, and invasion by directly targeting the 3’-UTRs of ZEB2 in the tumor. The decrease in miR−145 and increase in ZEB2 can obviously reversed the inhibition of cell migration and invasion mediated by the linc-ROR knockdown. Therefore, it was suggested that targeting the linc-ROR/miR−145/ZEB2 axis might represent a novel therapeutic application in HCC (Li C. et al., 2017). Similarly, Zhi et al. (2019) similarly showed that the migration and invasion of cells was reduced by the knockdown of linc-ROR. Moreover, they further confirmed that FOXM1-mediated activation of linc-ROR contributes to the poor sensitivity of HCC cells to sorafenib via partially regulating the miR-876-5p/FOXM1 axis, which forms a positive-feedback loop. Further evaluation of the regulatory mechanism involving this axis may provide new insights for exploring a potential therapeutic strategy for the management of HCC (Zhi et al., 2019). Consequently, these studies may offer new insights regarding the pathology of HCC and provide potential strategies for lncRNA-directed treatment. However, both the in vivo influence and other underlying mechanisms of linc-ROR still remain to be determined and clarified in the future research.
Colorectal Cancer
There are approximately 1.3 million new CRC cases and 690,000 CRC-related deaths worldwide each year, thus making CRC the third most common cancer in the world (Torre et al., 2015). Although the treatment of CRC has significantly improved in recent decades, the prognosis remains unsatisfactory, especially in case of advanced tumors with distant metastases (Bogousslavsky, 1984; Torre et al., 2015). Current studies results showed that approximately 25% of cases with CRC have synchronous liver metastases during the time of diagnosis (Kawaguchi et al., 2019). These patients have inherently low survival rates of less than 10% within 5 years, with an even worse prognosis (Hu et al., 2019; Kawaguchi et al., 2019). Thus, there is an urgent need to better understand the progression of CRC and to identify novel and sensitive biomarkers for the diagnosis and treatment of patients with CRC.
Yang et al. detected the expression of linc-ROR in 30 CRC tissues compared to normal tissues by using qRT-PCR. They found that the expression of linc-ROR was remarkably increased in CRC tissues compared with normal tissues. Similarly, linc-ROR was shown to be overexpressed in five CRC cell lines (Yan and Sun, 2018; Li et al., 2020a). Then, they also performed a series of functional assays to clarify the biological effects of the aberrant expression of linc-ROR on proliferation, viability, apoptosis, migration, and invasion of CRC cells. Knockdown of linc-ROR was shown to effectively inhibit the proliferation of CRC cells, whereas its overexpression obviously increased the proliferative capacity of CRC cells. Accordingly, silencing of linc-ROR strongly inhibited the migratory and invasive abilities of CRC cells, compared with that in the control cells (Li et al., 2020a). In contrast, the migratory and invasive ability of cells was activated following the overexpression of linc-ROR. The MTS (3-(4,5-dimethylthiazol-2-yl)-5-(3-carboxymethoxyphenyl)-2-(4- sulfophenyl)-2H-tetrazolium) assay results showed that the overexpression of linc-ROR could enhance the viability of CRC cells. Furthermore, the flow cytometric analysis results revealed that the percentage of apoptotic cells in linc-ROR overexpression group was reduced by 9.74% ± 2.13%, indicating that the overexpression of linc-ROR could inhibit apoptosis in the CRC cell lines (Li et al., 2020a). More importantly, a recent study revealed the role of linc-ROR in the EMT. It was revealed that the upregulation of linc-ROR could increase the expression of N-cadherin and Vimentin as well as decrease the level of E-cadherin, leading to the promotion of the progression of EMT (Zhou et al., 2016; Yan and Sun, 2018). Meanwhile, the high expression of linc-ROR in CRC was also confirmed by Hu et al. (2019). Mechanistically, Li et al. (2020a) proved that that linc-ROR could bind to miR-6833-3p, which was determined to be significantly downregulated in CRC tissues. Additionally, a negative correlation was exhibited between the expression of linc-ROR and miR-6833-3p in BC tissues. Anti-AGO2 RNA immunoprecipitation assay further confirmed these results (Li et al., 2020a). Besides, rescue assays demonstrated that downregulation of miR-6833-3p could partly reversed the inhibition of tumorigenesis induced by linc-ROR knockdown in BC cells. Li and his colleagues uncovered that linc-ROR exerted its oncogenic role through negatively regulating the expression of miR-6833-3p during the progression of CRC, which might give new insights into molecular diagnosis and treatment (Li et al., 2020a). In addition, Li et al. (2020a) further uncovered that linc-ROR could mediate the expression level of SMC by sponging miR-6833-3p in CRC cells, thus promoting CRC progression. As for the effects of linc-ROR on radiotherapy resistance. Yan and Sun (2018) showed that overexpression of linc-ROR increased the ability of CRC cells for radiotherapy resistance. Collectively, these findings indicated that linc-ROR might be engaged in the metastatic process of CRC cells and could promote the development of CRC through a variety of molecular mechanisms.
Lung Cancer
Lung cancer is the leading cause of cancer-related deaths worldwide (Bray et al., 2018). Non-small cell lung cancer (NSCLC) accounts for about 85% of the lung cancer types, including squamous cell carcinoma, large cell lung cancer, and lung adenocarcinoma (Herbertz et al., 2015; Bray et al., 2018; Brainard and Farver, 2019). Although there are various approaches for its diagnosis and treatments, the 5-year overall survival (OS) rate for patients with advanced lung cancer is less than 15% (Zhou et al., 2011). Therefore, in order to carry out an early diagnosis and treatment of lung cancer, the search for valuable and efficient tumor markers is very urgent.
In recent years, linc-ROR has appeared as an important regulator of lung cancer. Research from Qu et al. (2017) demonstrated that the expression of linc-ROR was increased in 299 NSCLC tissues compared to that in the normal tissues. The overexpression of linc-ROR was shown to be closely related to the poor prognosis of LNM, histological grade, and stage of TNM (Pan et al., 2017; Qu et al., 2017). Another study by Pan et al. (2017) demonstrated that the decreased expression of linc-ROR could obviously impair the proliferative capacity of lung adenocarcinoma (LAD) cells, cause a G0/G1 phase arrest, and increase the ratio of apoptotic LAD cells. Moreover, downregulation of linc-ROR substantially inhibited the invasive and metastatic ability of LAD cells. Meanwhile, forced expression of linc-ROR was observed to reduce the expression of E-cadherin and β-catenin, which are the characteristic biomarkers of epithelial cells, whereas it increased the expression of N-cadherin and Vimentin, thus displaying a mesenchymal phenotype. Conversely, downregulation of linc-ROR was demonstrated to result in increased the expression of epithelial markers and decreased the expression of mesenchymal markers. This result uncovered the fact that the pro-metastatic effects of linc-ROR were induced by the regulation of the expression of a number of genes involved in cell metastasis and EMT progress. Meanwhile, overexpression of linc-ROR was shown to enhance the resistance of LAD to docetaxel (DTX) (Pan et al., 2017), suggesting that linc-ROR-induced resistance of LAD cells to DTX and EMT through regulation the expression of miR-145, which was predicted to interact with linc-ROR. More importantly, further research confirmed that miR-145 could bind to linc-ROR, and its downregulation could partly inhibit the resistance of LAD cells to DTX and EMT. Aside from that, Pan and his colleagues discovered that a decrease in the expression of miR-145 and increase in the expression FSCN1 could obviously reverse the inhibition of cell proliferation, chemoresistance, and EMT mediated by linc-ROR knockdown. They identified that dysregulation of the linc-ROR/miR-145/FSCN1 axis was associated with the therapeutic resistance and EMT transition in LAD cells, thereby providing potential therapeutic strategies for managing drug resistance in patients with LAD (Pan et al., 2017). Taken together, these studies collectively suggested that linc-ROR could activate the malignant phenotype of NSCLC cells with the guidance of a mechanism involving miRNAs. Hence, more efforts should be made to elucidate other regulatory mechanisms and the clinical significance of linc-ROR in lung cancer.
Thyroid Cancer (TC)
TC continues to be the most common endocrine malignant tumor and has emerged as a major health issue (Ito et al., 2013). It is estimated that more than 60,000 people in the United States present with TC every year (Ito et al., 2013; Vigneri et al., 2015). Additionally, TC is the sixth most common malignancy among Chinese women, with an incidence rate of about 6.6 per a population of 100,000 (Chen et al., 2015). The major subtypes of TC include PTC, follicular thyroid cancer (FTC), poorly differentiated thyroid cancer (PDTC), and anaplastic thyroid cancer (ATC) originating from follicular cell-derived thyroid cells. PTC accounts for more than 85% of all the TC cases, and approximately 10–15% of the patients with PTC have been reported to exhibit relapse and metastasis after therapy, leading to a poor outcome (Fagin and Wells, 2016). On the other hand, ATC is the most aggressive and fatal subtype, with a total survival of merely 3–5 months after initial diagnosis (Fagin and Wells, 2016). The studies of molecular mechanism correlated with the development and progression of TC may considerably facilitate the understanding of the pathogenesis of TC (Wang et al., 2016, 2017; Murugan et al., 2018). Thus, it is importantly to find potential biomarkers and therapeutic targets involved in TC tumorigenesis.
Zhang et al. (2018) examined the expression levels of linc-ROR in TC cells. Accordingly, their results from in situ hybridization showed that the levels of linc-ROR was increased in TC tissues and TC cell lines. Then, the expression of linc-ROR was further validated by using qRT-PCR analysis in TC tissue samples and cell lines, as they found it to be higher compared to that in normal tissue samples and normal thyroid cell. Hence, linc-ROR was observed to be evidently upregulated during the progression of TC (Zhang et al., 2018). Then, a series of functional assays were performed to clarify the biological effects of linc-ROR on proliferation and invasion of TC cells. They showed that that decreased the expression of linc-ROR could suppress the proliferative and invasion capacity of TC cells, as well as the elevated apoptotic rate in TC cells (Zhang et al., 2018). Interestingly, this led to the reversed progress of EMT, which was presented as a reduction in the levels of E-cadherin and enrichment in those of N-cadherin, highlighting the involvement of linc-ROR in the regulation of EMT (Zhang et al., 2018). Specifically, Zhang et al. (2018) proved that linc-ROR could act as a molecular sponge to modulate miR-145, which was determined to be significantly downregulated in TC tissues, indicating that linc-ROR had a negative regulatory role on miR-145. In summary, Zhang et al. discovered that linc-ROR could act as an oncogene and suggested its utilization as a prognostic indicator of patients with TC. However, a larger cohort of tumor samples and deeper mechanistic research are urgently needed.
Potential Clinical Application of Linc-Ror in Human Cancers
The detection of cancer is hard in early stages and thus losing the best chance for curative surgery results in the poor survival rates (Siegel et al., 2020). Specific cancer biomarkers that monitor the molecular differences associated with cancer, which may be used for early diagnosis, are essential and may help select the best treatment options and gain valuable treatment time for patients with cancer. In clinical settings, prognostic markers can be used to predict the clinical outcome of untreated patients with cancer. On the other hand, current studies have further indicated that the progression of cancer or differentiation of cancer subtypes can be predicted by the expression profiles of lncRNAs. However, because of the uncertain molecular function of lncRNAs, it is difficult to accurately predict the progress of tumors. The ideal and convenient biomarkers should possess several typical and important characteristics. Interestingly, studies have shown that linc-ROR is not only closely associated with multiple biological functions of cancer cells but may also be an ideal and convenient biomarker (Pan et al., 2016). For example, the expression of linc-ROR has been reported to be increased in HCC tissues with LNM or vascular infiltration compared with normal tissues. In addition, the advanced stage of TNM was associated with the overexpression of linc-ROR. Most importantly, the expression of linc-ROR was shown to be elevated in patients with HCC with recurrence compared with those without recurrence (Li C. et al., 2017). Furthermore, results obtained from the Kaplan-Meier analysis showed that patients with HCC with high expression of linc-ROR had worse prognosis compared to those with low linc-ROR expression, along with a shorter disease-free survival (DFS) and OS (Li C. et al., 2017).
Zhao et al. (2017) found that the expression level of linc-ROR was increased in BC tissues, BC cell lines, and BC plasma samples. Concomitantly, they demonstrated that the level of linc-ROR in patients with LNM was elevated compared to that in patients without LNM. Besides, the expression of linc-ROR in ER-positive or PR-positive BC plasma also demonstrated to be significantly increased (Zhao et al., 2017). Moreover, the study showed that the area under the Receiver operating characteristic (ROC) curve (AUC) of linc-ROR was 0.758 (sensitivity 80.0%; specificity 73.3%), suggesting that the diagnostic ability of linc-ROR was elevated relative to that of Carcinoembryonic antigen (CEA) (AUC = 0.516; sensitivity 66.7%; specificity 50.0%) and CA153 (AUC = 0.663; sensitivity 73.3%; specificity 60.0%). More importantly, they found that linc-ROR had the strongest ability among the three indices in distinguishing healthy from BC-affected individuals in an evaluation of 45 early stage patients from the 96 patients with BC and 45 age and sex-matched healthy controls from the 90 healthy volunteers. Moreover, the combined detection of the three plasma indexes might enhance the overall diagnostic power (AUC = 0.846; sensitivity 83.3%; specificity 70.0%) (Zhao et al., 2017). In addition, Hou et al. (2018) examined the level of linc-ROR in the clinical prognosis of patients with BC. The group found that there was a close relationship between LNM and the expression of linc-ROR. More importantly, overexpression of linc-ROR was observed to exhibit a faster decline with increased survival (Chen et al., 2016a; Hou et al., 2018). Another study also revealed that the regulatory polymorphisms in linc-ROR had an impact on the risk for BC (Luo et al., 2018). Conclusively, all the above-mentioned results suggested that linc-ROR might be used as a diagnostic and prognostic biomarker for BC.
Qu et al. (2017) identified that the level of linc-ROR was significantly elevated in NSCLC tissues compared to that in the adjacent tissues. Meanwhile, upregulation of linc-ROR was shown to be significantly linked to advanced TNM stage, LNM, and positive distant metastasis. The Kaplan-Meier curve analysis showed that patients with NSCLC with high expression of linc-ROR have shorter OS and DFS times compared to patients with low expression of linc-ROR (Duan et al., 2016; Jiang et al., 2016; Qu et al., 2017). Subsequently, the results of Cox proportional hazards regression analyses showed that the expression of linc-ROR was an independent prognostic indicator for OS (HR (heart rate) = 2.983) and DFS (HR (heart rate) = 3.421) in patients with NSCLC (Qu et al., 2017). In addition, Fu et al. (2017) also demonstrated that the expression level of linc-ROR was increased in PC tissues compared to that in adjacent tissues. The expression level of linc-ROR was further demonstrated to be closely linked to tumor size and the clinical stage of PC. Meanwhile, log-rank analysis indicated that the OS was significantly decreased in patients with higher expression of linc-ROR (Fu et al., 2017). Glioblastoma (GB) has been considered to be the most aggressive subtype of glioma and the most common adult malignant brain tumor in the world (Stupp et al., 2009; Linz, 2010; Davis, 2016). Toraih et al. (2019) reported that the level of linc-ROR was increased in 89.5% of the patients (Han et al., 2012; Feng et al., 2015). The overexpression of linc-ROR was observed to be closely linked to poor disease progression-free and OS in younger age of patients. In addition, Kaplan–Meier survival plot illustrated that patients with GB with high linc-ROR expression had low survival rates. Multivariate analysis demonstrated that patients with GB were divided into two different groups according to the expression profile of linc-ROR and OS of patients (Toraih et al., 2019). Thus, these results suggested that the expression of linc-ROR was complementary in playing a crucial role in the progress of cancers and thus could serve as a new potential biomarker for the evaluation of clinical prognosis.
Currently, the biggest obstacles to cancer treatment are delayed diagnosis, recurrence, and metastasis. Therefore, the search for the ideal cancer biomarkers is crucial for the improvement of the early diagnosis rate. The above-mentioned findings suggested that linc-ROR might be used as a potential marker for the diagnosis of several types of cancer. However, the exact molecular mechanism by which linc-ROR plays a role in various cancers remains unclear. As such, the functional role of linc-ROR in cancer needs to be further explored and verified, especially in clinical applications.
Regulatory Mechanism of Linc-Ror in Various Types of Cancer
It is well understood that improvement of the survival of patients requires a deeper understanding, as well as effective biomarkers for determination of cancer occurrence and metastasis. Additionally, detection of these invasive tumors in the early stages of disease development and development of efficient treatments are more effective ways to reduce cancer mortality. A large number of researches have shown that many signaling pathways were significantly associated with the occurrence and development of tumors (Chen et al., 2016a; Sahebi et al., 2016; Hou et al., 2018). Hence, we summarize that the related some regulatory mechanisms are associated with the tumor development and progression.
First, linc-ROR has been reported to directly affect the progression of various cancers through several signaling pathways (Figure 4 and Table 2). Of note, a number of studies have hinted that the aberrant activated MAPK/ERK cascade plays a critical role in many aspects of tumorigenesis. Nearly 50% of human malignancies are known to exhibit unregulated ERK signaling (Herrero et al., 2015). In BC, the upregulated MAPK/ERK (mitogen-activated protein kinase/extracellular regulated protein kinases) signaling has been correlated with poor survival in patients with triple-negative BC (Bartholomeusz et al., 2012). In addition, the MAPK/ERK pathway has also been shown to influence the chemotherapeutic drug resistance to doxorubicin and paclitaxel in BC cells (McCubrey et al., 2006). Moreover, It is well known that the MAPK/ERK signaling is negatively regulated by mitogen-activated protein kinase phosphatases, which form six distinct groups based on their physiological functions; among them, groups 1–4 are known to be involved in dephosphorylation of ERK, including DUSP7 (Owens and Keyse, 2007). Interestingly, Peng and his colleagues demonstrated that DUSP7 is downregulated in response to estrogen deprivation and overexpression of linc-ROR significantly decreased the half-life of DUSP7, possibly inhibiting the activation of ERK, resulting in estrogen-independent growth of BC cells (Peng et al., 2017a). The Wnt signaling cascade was highly conserved among species and controls a multitude of biological processes during animal development and life cycles. Because of its central role in the maintenance of tissue homeostasis, the Wnt pathway was tightly regulated at multiple levels, from the ligand-receptor interaction down to transcriptional and post-transcriptional levels; its aberrant activity has been implicated in a number of developmental disorders and diseases and, most prominently, in cancer (Nusse and Clevers, 2017; Nguyen et al., 2019; Zhang et al., 2019; Liu et al., 2020). The recent discovery of lncRNAs that are regulated by Wnt and/or participate in Wnt pathway modulation and outcome is particularly intriguing and has highlighted some of these gaps in our knowledge (Zarkou et al., 2018). For example, Lou et al. (2017) have discovered that the Wnt/β-catenin signaling pathway led to the elevated proliferation of ovarian cancer (OC) cells. Whereas, E-cadherin was shown to be decreased, vimentin, β-catenin, and c-myc were all increased in OC cells treated with LiC1 (a Wnt/β-catenin pathway activator) compared with untreated controls (Lou et al., 2017). More importantly, the study further revealed that the activation of Wnt/β-catenin signaling pathway and the progression of EMT, along with the abnormal expression of EMT markers and Wnt/β-catenin signaling pathway-related proteins (c-Myc, cyclin-D1, and β-catenin), was inhibited by knockdown of linc-ROR during the development of OC, indicating that linc-ROR promotes OC EMT at least in part by activating the Wnt/β-catenin pathway (Lou et al., 2017). However, the relationship between linc-ROR and the Wnt/β-catenin signaling pathway required deeper study in tumor initiation and progression. The PI3K/Akt/mTOR pathway has various cell functions, including cellular proliferation, survival, differentiation, and intrusion, and previous literature has proven that lncRNA silencing could reduce the phosphorylation of ERK, Akt, and mTOR (Fumarola et al., 2014). More importantly, aberrant activation of the PI3K/Akt/mTOR pathway was shown to be one of the mechanisms of targeted therapeutic resistance in patients with NSCLC (Fumarola et al., 2014). Xu et al. (2018) found that silencing of linc-ROR significantly inhibited the expression of p-PI3K, p-AKT3, and m-TOR. In contrast, the overexpression of linc-ROR was shown to increase the expression of p-PI3K, p-AKT3, and m-TOR, suggesting that the activation of PI3K/Akt/mTOR signaling pathway was mediated by linc-ROR in NSCLC (Xu et al., 2018). Similarly, Shi et al. (2017) further discovered that linc-ROR could inhibit the PI3K/Akt/mTOR signaling pathway, so that inactivation of linc-ROR or inhibition of PI3K/Akt/mTOR signaling pathway could increase the sensitivity of NSCLC to cisplatin (DDP), thereby promoting cell apoptosis, and inhibiting cell proliferation, migration, invasion, and tumor growth. Additionally, the Hippo/YAP pathway (also known as the Salvador–Warts–Hippo pathway) is an evolutionarily conserved regulator of tissue growth and cell fate (Harvey et al., 2013). The Hippo/YAP pathway was first postulated to be important for human cancer on the basis of the egregious overgrowth of Drosophila melanogaster tissues that harbor mutations in different Hippo/YAP pathway genes. In recent years, increasing numbers of mammalian studies have validated this hypothesis: Hippo/YAP pathway perturbation can trigger tumorigenesis in mice, and mutation and altered expression of a subset of Hippo pathway genes have been observed in human cancers (Harvey et al., 2013). Interestingly, the Hippo/YAP signaling pathway has also been reported to activated by linc-ROR (Mo et al., 2014). Inactivation of the Hippo signaling pathway could lead to the downregulation of MST1/LATS1 (the core factors of Hippo pathway) and upregulation of YAP1 (He et al., 2015). Furthermore, dysregulation of the Hippo signaling has been confirmed in many types of tumors and closely linked to the acquisition of malignant features. A recent study provided by Chen et al. demonstrated that the regulatory axis of linc-ROR/EMT/YAP1 regulatory axis exerted oncogenic effects in the progression of PC (Chen et al., 2020). They found that a knockdown of linc-ROR resulted in the downregulation of YAP, and MOB kinase activator 1 (MOB1), whereas there was upregulation of MST1, MST2, p-MOB1, p-LATS1, LATS1, and p-YAP in PC cells. In contrast, overexpression of linc-ROR led to the exact reverse condition (Chen et al., 2020). The current evidence for the existence of the linc-ROR/EMT/Hippo/YAP axis has indicated that combined targeting of the YAP1/Hippo signaling pathway may provide a potential direction for the treatment of PC. Many studies have recognized that the function of TGFβ in the progression of BC can be different depending on the stage of cancer (Bachman and Park, 2005; Moses and Barcellos-Hoff, 2011; Massague, 2012). Under normal conditions, TGF-β has been shown to inhibit cell cycle and promote apoptosis, which together significantly contribute to its suppressive role in the initiation and progression of tumorigenesis (Heldin et al., 2009). Hou et al. (2018) reported that knockdown of linc-ROR in BC cells led to diminished expression levels of TGF-β. As a result, the downstream factors, such as Smad2 and α-SMA, were also downregulated. This finding suggested that overexpression of linc-ROR might be required to constitutively upregulate critical factors in the TGF-β signaling pathway (Hou et al., 2018). Overall, these findings revealed that linc-ROR might provide a novel therapeutic target and biomarker for cancers in the future.
Direct regulatory function of linc-ROR in various cancers. First, linc-ROR promotes the estrogen-independent growth and activation of the MAPK/ERK signaling pathway of tumor cells by regulating the DUSP7 ERK-specific phosphatase. Second, linc-ROR promotes the process of EMT through the Wnt/β-catenin and Hip/YAP signaling pathways. Additionally, linc-ROR strongly represses the sensitivity to cisplatin and significantly increases the proliferation, invasion, migration, and growth of tumor cells via the PI3K/AKT/m-TOR signaling pathway (associated proteins: p-PI3K, p-AKT, and p-mTOR). Simultaneously, linc-ROR significantly facilitates the progression of tumor through the TGF-β signaling pathway (associated proteins: p-PI3K, p-AKT, and p-mTOR).
The involvement of linc-ROR in multiple signaling pathways.
Cancer types
Role
Expression
Related gene
Signaling pathway
References
Breast cancer
Oncogenic
Upregulated
ERK1, ERK2 TGF-β, Smad2, and α-SMA
A growing understanding of the role of linc-ROR in all types of cancers may opened up the possibility for many new treatment strategies. Therefore, increasingly, cancer researchers will focus on this lncRNA. Recent studies have displayed that the level of linc-ROR was increased in lung cancer, bladder cancer, and CRC cell lines. Moreover, both cell proliferation and migration have been reported to be promoted by linc-ROR in several types of cancers. In addition, increasing evidence have shown that the abnormal expression of linc-ROR in various cancers was closely linked to tumorigenesis, diagnosis, metastasis, and prognosis through some signaling pathways. Of note, a number of studies have confirmed that linc-ROR can be considered as a possible new target for cancer therapy and a biomarker for cancer diagnosis. However, the current knowledge on the biological functions of linc-ROR remains insufficient, and its efficacy is also not evident. Thus, additional studies on lncRNAs are required.
The abbreviation “EV” is actually a collective term that refers to a series of lipid bilayer membrane-bound organelles that are released by cells into their environment. Briefly, EVs are heterogeneous in size and released from nearly all cells under appropriate physiological and pathological conditions (Lee et al., 2011). Of note, a variety of cargos have been transferred from a cell to another cell via EVs (Ekstrom et al., 2012; Yang J. et al., 2018). More importantly, the EV cargo can reflect the cells of origin. A number of studies have demonstrated that exosomes carry different types of RNA compared to their parental cells (Skog et al., 2008; Ekstrom et al., 2012; Nolte-’t Hoen et al., 2012). It is well-known that unprotected ncRNAs are easily degraded by the RNases in the blood. However, it has been demonstrated that under the protection of EVs, ncRNAs avoid degradation, and thus their integrity and activity are maintained in the circulation. Recently, studies suggested that the expression of lncRNAs in the plasma of patients with HCC was considerably increased compared to that in healthy people. More importantly, Takahashi et al. assessed the role of EV signaling in tumor cell responses to TGF-β and also recognized specific EV-lncRNA mediators, such as linc-ROR, involved in the regulation of the chemotherapy response to chemotherapy (Takahashi et al., 2014; Spinelli et al., 2018). Targeting these intercellular signaling mechanisms and mediators may be useful in enhancing sensitivity and improving responses to conventional therapeutic agents that are used for the treatment of HCC. As such, the expression of linc-ROR, which plays an important role in cancer prevention, diagnosis, and treatment may be increased through the administration of exosomal linc-ROR.
On the other hand, CRISPR/Cas9, as a very powerful gene editing tool, has been successfully applied to the interruption of the protein-coding 24 sequence in various organisms (Li M. et al., 2019). Previous researches have demonstrated that CRISPR/Cas9 could regulate the progression and development of cancers. For example, CRISPR/Cas9-mediated synthesis and gate genetic circuits were shown to be efficiently employed in the identification of bladder cancer cells (Liu et al., 2014). Of note, CRISPR/Cas9 could successfully target lncRNAs and replace the overexpression of the sponge bait, thus negating the requirement for the introduction of transgenes (Kellner et al., 2018). In addition, CRISPR/Cas9 has achieved many successes as a powerful genome engineering tool for the treatment of many diseases due to its specificity, efficiency, simplicity, and versatility (Zhen et al., 2017). For example, both the transcription and infection by HIV-1 were shown to be increased by lnc-MALAT1. However, long terminal repeat-driven gene transcription of HIV-1 and viral replication were reported to be inhibited by CRISPR/Cas9-mediated lnc-MALAT1 knockdown. Similarly, knockdown of lnc-HTOR or IGF2BP1 by CRISPR/Cas9 gene-editing methods mimicked the effects and abolished the requirement for Triptonide in nasopharyngeal carcinoma cells (Liu et al., 2016). Therefore, there’s a good chance that the technology of CRISPR/Cas9 could be used to treat cancers by editing the expression level of linc-ROR. Thus, CRISPR/Cas9 might be used to treat cancers by regulating the expression of linc-ROR via the relevant molecular mechanism.
Conclusion
Increasing studies have demonstrated that lncRNAs could regulate the expression of genes and be involved in the tumorigenesis and development of tumors through complex tumor networks. The linc-ROR was reported to be abnormally expression in a variety of malignant tumors, such as BC, PC, HCC, CRC, NSCLC, and TC. The expression trend of this lncRNA was demonstrated to be almost similar in most of these cancers. In addition, studies confirmed that the linc-ROR served as an oncogene in vivo and in vitro and was closely associated with the enlarged tumor volume, advanced TNM stage, shortened OS, and metastasis. Meanwhile, cell proliferation, invasion, migration, and anti-apoptosis were observed to be regulated by linc-ROR in all the above-mentioned cancers. Retrospective studies of linc-ROR in human cancers may provide new targets for the diagnosis and treatment of different types of cancer. Furthermore, increasing studies have shown that the molecular mechanism, such as Wnt/β-catenin signaling pathway involved in the progress of cancer, was regulated by linc-ROR. There seems to be some competitions between linc-ROR and miRNAs, resulting in the inhibition of downstream target genes of these miRNAs. However, the exact mechanism of action of linc-ROR remains to be explored. Further experiments are needed to delineate the role of linc-ROR and its molecular mechanisms in other human malignancies.
Author Contributions
WC and JY drafted the manuscript. JS revised the manuscript. LL and HF reviewed and modified the manuscript. All authors agreed on the final version.
Conflict of Interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Funding. This project was supported by the Natural Science Foundation of Anhui Province (1808085MH288).
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Inhibition of long non-coding RNA UCA1 by CRISPR/Cas9 attenuated malignant phenotypes of bladder cancer.\\nOncotarget\\n8\\n9634–9646. 10.18632/oncotarget.14176\\n28038452ZhengQ.BaoC.GuoW.LiS.ChenJ.ChenB. (2016). Circular RNA profiling reveals an abundant circHIPK3 that regulates cell growth by sponging multiple miRNAs.\\nNat. Commun.\\n7:11215. 10.1038/ncomms11215\\n27050392ZhiY.AbudoureyimuM.ZhouH.WangT.FengB.WangR. (2019). FOXM1-Mediated LINC-ROR regulates the proliferation and sensitivity to Sorafenib in hepatocellular carcinoma.\\nMol. Ther. Nucleic Acids\\n16\\n576–588. 10.1016/j.omtn.2019.04.008\\n31082791ZhouC.WuY. L.ChenG.FengJ.LiuX. Q.WangC. (2011). Erlotinib versus chemotherapy as first-line treatment for patients with advanced EGFR mutation-positive non-small-cell lung cancer (OPTIMAL, CTONG-0802): a multicentre, open-label, randomised, phase 3 study.\\nLancet Oncol.\\n12\\n735–742. 10.1016/S1470-2045(11)70184-X21783417ZhouP.SunL.LiuD.LiuC.SunL. (2016). Long Non-Coding RNA lincRNA-ROR Promotes the Progression of Colon Cancer and Holds Prognostic Value by Associating with miR-145.\\nPathol. Oncol. Res.\\n22\\n733–740. 10.1007/s12253-016-0061-x\\n27071407AbbreviationsBC
breast cancer
ceNAs
competing endogenous RNA
EMT
epithelial–mesenchymal transition
HCC
hepatocellular cancer
iPSCs
induced pluripotent stem cells
linc-ROR
Long intergenic non-protein coding RNA, regulator of reprogramming
lncRNAs
Long non-coding RNAs
PC
pancreatic cancer.
\\n\"\n]"}}},{"rowIdx":491,"cells":{"data":{"kind":"list like","value":["\nInt J Environ Res Public HealthInt J Environ Res Public HealthijerphInternational Journal of Environmental Research and Public Health1661-78271660-4601MDPI32751907PMC743214810.3390/ijerph17155547ijerph-17-05547ArticleDistinct Associations of Hedonic and Eudaimonic Motives with Well-Being: Mediating Role of Self-ControlZengZhijia1*ChenHezhi2*Student Affairs Department, Zhejiang University of Finance and Economics, Hangzhou 310018, ChinaDepartment of Psychology and Behavioral Sciences, Zhejiang University, Hangzhou 310058, ChinaCorrespondence: zhijiazeng@zufe.edu.cn (Z.Z.); chenhezhi@zju.edu.cn (H.C.)3172020820201715554720720202872020© 2020 by the authors.2020Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
The pursuit of hedonia and eudaimonia are two ways to fulfill the goal of a “good life”. While some studies report that both hedonic and eudaimonic motives improve well-being, others suggest that hedonic motives are counterproductive, raising the question of whether and why eudaimonic motives are more positively associated with well-being. We aimed to identify the distinct associations of hedonic and eudaimonic motives with well-being and investigate whether they are partly mediated by self-control. A total of 2882 college freshmen (1835 females, 1047 males, mean age 18.16 years) completed measures assessing hedonic and eudaimonic motives, self-control, life satisfaction, positive and negative affect, and eudaimonic well-being. Eudaimonic motives were associated with higher life satisfaction, more positive affect, less negative affect, and better eudaimonic well-being. In contrast, hedonic motives were positively associated with life satisfaction, while also being correlated with a greater degree of negative affect and impaired eudaimonic well-being. Self-control mediated the relationships between hedonic and eudaimonic motives and well-being. Eudaimonic and hedonic motives were positively and negatively related to self-control, respectively. Further, high self-control was associated with greater life satisfaction, positive affect, and eudaimonic well-being and lower negative affect. Thus, eudaimonic motives can lead to a better life than hedonic motives because the former enhance self-control, while the latter lower it.
The pursuit of a good life is a fundamental motive for human beings. However, individuals can have very different ideas about what constitutes a “good life”. Specifically, the two most prominent views of a good life are the hedonic view and the eudaimonic view [1]. Pursuing hedonia, or hedonic motives, involves seeking personal enjoyment, pleasure, and comfort; pursuing eudaimonia, or eudaimonic motives, relates to seeking personal growth, excellence, meaning, and authenticity [2].
1.1. Hedonic and Eudaimonic Motives and Well-Being
One issue that has received much scholarly attention is how hedonic and eudaimonic motives affect well-being. Aristotle claimed that people should strive for a virtuous life rather than seeking pleasure, because the latter does not bring happiness. Consistent with this view, numerous studies have suggested that while eudaimonic motives generally improve well-being, pursuing hedonia does not result in the achievement of the goal of a “good life” and might even backfire [3,4,5]. For example, in one study, eudaimonic motives were correlated with various well-being outcomes, including life satisfaction, vitality, positive affect, negative affect, carefreeness, self-connectedness, and meaning, whereas hedonic motives only predicted vitality, positive affect, and carefreeness [4]. In another study, eudaimonic motives were negatively correlated with depression and stress; in contrast, there was no significant correlation between hedonic motives and depression or stress [5]. Sheldon et al. [3] further showed that participants’ motivations for improving their subjective well-being were negatively correlated with concurrent subjective well-being and did not affect longitudinal subjective well-being.
However, some studies have reported the well-being-related benefits of both hedonic and eudaimonic motives [6,7,8]. For example, Peterson et al. [8] showed that endorsement of pleasure, engagement, and meaning as paths to happiness all predicted life satisfaction. Similarly, Huta and Ryan [6] showed that hedonic and eudaimonic motives had both overlapping and distinct effects on well-being; hedonic motives related more to positive affect and life satisfaction, while eudaimonic motives related more to meaning. Overall, a combination of hedonic and eudaimonic motives was associated with the greatest well-being.
In sum, whether hedonic and eudaimonic motives lead to different well-being outcomes remains inadequately explored. Further, the factors that account for the potential distinct associations of hedonic and eudaimonic motives with well-being remain unclear.
1.2. Hedonic and Eudaimonic Motives and Self-Control
We argue that one factor that could account for the distinct associations of hedonic and eudaimonic motives with well-being is self-control. Self-control is defined as the ability to override one’s own inner responses or interrupt a course of action, especially when conflicting with ideals, values, and social expectations, and to support the fulfillment of long-term goals [9,10]. Huta [11] pointed out two crucial distinctions between hedonic and eudaimonic motives. First, hedonic motives are concerned with satiating one’s own needs and desires in the present or near future; in contrast, eudaimonic motives are concerned with fulfilling long-term objectives. The second difference is that hedonic motives are concerned with what feels good, whereas eudaimonic motives relate to what one believes to be right. According to the self-concordance model, individuals will strive for goals more persistently when they fit their core values [12,13]. Similarly, the goal theory of happiness suggests that individuals actively engage in behaviors that are in line with their identified goals, which, in turn, further influence their well-being [14,15]. Consequently, when faced with conflicts such as those between short-term desires and long-term objectives, individuals pursuing eudaimonia are more likely to implement a higher level of self-control to achieve their goals than are those pursuing hedonia.
Although empirical research on the relationships of hedonic and eudaimonic motives with self-control is sparse, two studies have provided some supporting evidence [7,16]. Peterson et al. [16] showed that endorsement of engagement and meaning as routes to happiness was positively related to self-regulation. More recently, Anic and Tončić [7] showed that individuals pursuing eudaimonia but not hedonia had a high level of self-control, whereas those pursuing hedonia but not eudaimonia had a relatively low level of self-control.
1.3. Self-Control and Well-Being
Studies have indicated that self-control positively predicts hedonic well-being [17,18,19,20,21]. For example, Briki [18] showed that self-control was positively related to happiness and life satisfaction. Hofmann et al. [17] found that self-control promoted affective experience and life satisfaction by managing goal conflict and facilitating the achievement of multiple goals. Similarly, Cheung et al. [19] demonstrated that people with higher self-control were happier because they were more promotion-focused and less prevention-focused. In addition, a lack of self-control has been linked to various problematic behaviors, such as procrastination, impulsive purchasing, addictive behavior, and even criminal offenses [22,23,24,25], which, in turn, might increase depression, stress, guilt, and other negative feelings [15,26,27].
Furthermore, it has been suggested that one approach to enhance eudaimonic well-being is satisfying basic psychological needs, including autonomy, competence, and relatedness [28]. Self-control itself is essential to the attainment of autonomy [29]. In addition, self-control has been found to be associated with higher achievement and better interpersonal relationships [20,30,31], which might satisfy the need for competence and relatedness. Consequently, self-control might also result in greater eudaimonic well-being.
1.4. The Current Study
The aim of this study is to identify the distinct associations between hedonic and eudaimonic motives and well-being outcomes, and further investigate the possible mediating role of self-control. For this purpose, we collected cross-sectional data on hedonic and eudaimonic motives, self-control, hedonic well-being (life satisfaction, positive and negative affect), and eudaimonic well-being. We propose the following hypotheses:
\nSelf-control mediates the relationships between hedonic motives and well-being outcomes. Hedonic motives are detrimental to well-being outcomes by lowering the self-control of Chinese college students.\n
\nSelf-control mediates the relationships between eudaimonic motives and well-being outcomes. Eudaimonic motives promote well-being outcomes by increasing the self-control of Chinese college students.\n
2. Materials and Methods2.1. Sample and Procedures
In September 2019, we carried out a cross-sectional survey with a sample of 2882 students (mean age = 18.16, standard deviation = 0.44; 1047 males, 1835 females) from Zhejiang University of Finance and Economics. The participants were freshmen who had to complete a general psychological survey for course credits. The students completed the online survey while seated in individual cubicles. The teachers gave instructions and maintained discipline. All responses were included in the analyses.
The study design and data collection procedures were both approved by the Ethics Committee of Zhejiang University of Finance and Economics. All students provided written informed consent.
2.2. Measures
All the scales used in the current study were originally in English. Therefore, all the survey measures were translated from English to Chinese and back to English. When there were discrepancies between the original English version and the back-translated English version, those items were further modified after discussion by the two authors.
2.2.1. Hedonic and Eudaimonic Motives
To assess motives, we used the Hedonic and Eudaimonic Motives for Activities—Revised scale developed by Huta [32]. This instrument utilizes five items to assess hedonic motives (e.g., “seeking pleasure”) and five for eudaimonic motives (e.g., “seeking to use the best in yourself”). Respondents were asked to report the degrees of each motive with which they typically approached their activities on a seven-point Likert scale ranging from 1 (not at all) to 7 (very much). The Cronbach’s α values were 0.83 for hedonic motives and 0.79 for eudaimonic motives.
2.2.2. Self-Control
To assess personal self-control, we used the 13-item Brief Self-Control Scale [20]. A sample item is “I am good at resisting temptation.” Respondents indicated the degree to which each of the statements reflected their typical behavior on a five-point Likert scale ranging from 1 (not at all) to 5 (very much). The Cronbach’s α value was 0.82.
2.2.3. Well-Being Outcomes
To assess life satisfaction, we used the Satisfaction with Life Scale, which has five items (e.g., “In most ways my life is close to my ideal”) [33] rated on a seven-point Likert scale ranging from 1 (strongly disagree) to 7 (strongly agree). The Cronbach’s α value was 0.84.
Participants also reported their positive and negative affect in the past two weeks on the Positive and Negative Affect Schedule on a five-point Likert scale ranging from 1 (not at all) to 5 (very much) [34]. The Cronbach’s α values were 0.90 for both positive and negative affect.
Thereafter, they completed the Questionnaire for Eudaimonic Well-Being, which comprises 21 items assessing various aspects of eudaimonic well-being, including self-discovery, perceived development of one’s potential, meaning in life, and involvement and enjoyment in personally expressive activities (e.g., “I find I get intensely involved in many of the things I do each day”) [35]. Participants indicated their agreement with each item on a five-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree). The Cronbach’s α value was 0.81.
2.2.4. Demographic Information
Demographic information included gender and age.
2.3. Analyses
All tests were performed using SPSS 23.0 software (IBM Corp., Armonk, NY, USA). First, descriptive statistics were used to describe hedonic and eudaimonic motives, self-control, and well-being outcomes. Next, we conducted correlational analyses to investigate the general relationships between hedonic and eudaimonic motives, self-control, and well-being outcomes. Furthermore, we tested the hypothesized mediation effects of hedonic and eudaimonic motives on well-being outcomes via self-control using the PROCESS macro for SPSS (model 4) [36,37]. Hedonic and eudaimonic motives were entered into the models as independent variables, self-control was entered as the mediating variable, and gender was entered as the control variable. The four well-being outcome variables were separately entered as dependent variables; thus, the mediation model was run four times. We reported hierarchical regression results, indirect effects, and the 95% bias-corrected confidence intervals (CIs) estimated by 5000 bootstrap samples. The indirect effect was considered significant if the 95% CI did not contain zero.
3. Results3.1. Descriptive Analyses
Descriptive statistics related to hedonic and eudaimonic motives, self-control, and well-being outcomes are presented in Table 1. Bivariate correlations between the study variables are presented in Table 2. Higher hedonic motives were related to higher life satisfaction (r = 0.05, p = 0.004), but more negative affect (r = 0.15, p < 0.001) and worse eudaimonic well-being (r = −0.08, p < 0.001); there was no significant correlation between hedonic motives and positive affect (r = −0.01, p = 0.503). Higher eudaimonic motives were related to higher life satisfaction (r = 0.16, p < 0.001), more positive affect (r = 0.38, p < 0.001), less negative affect (r = −0.08, p = 0.001), and better eudaimonic well-being (r = 0.51, p < 0.001). Self-control was negatively related to hedonic motives (r = −0.25, p < 0.001) and positively related to eudaimonic motives (r = 0.26, p < 0.001). In addition, self-control was associated with all well-being outcomes (life satisfaction: r = 0.35, p < 0.001; positive affect: r = 0.38, p < 0.001; negative affect: r = −0.38, p < 0.001; and eudaimonic well-being: r = 0.50, p < 0.001).
3.2. Mediation Analyses
The regression analyses are shown in Table 3. As expected, after adjusting for gender, hedonic motives were negatively correlated with self-control (ß = −0.29, p < 0.001), while eudaimonic motives were positively correlated with self-control (ß = 0.30, p < 0.001). In addition, after controlling for hedonic and eudaimonic motives, self-control demonstrated significant positive associations with life satisfaction (ß = 0.37, p < 0.001), positive affect (ß = 0.30, p < 0.001), and eudaimonic well-being (ß = 0.37, p < 0.001) and a significant negative association with negative affect (ß = −0.37, p < 0.001). Whether gender was included as a control variable or not, the main results did not change.
The estimated direct and indirect effects between hedonic and eudaimonic motives and the four well-being outcomes are presented in Table 4. The indirect effects of hedonic motives on life satisfaction (indirect effect = −0.106, CI: (−0.123, −0.089)), positive affect (indirect effect = −0.054, CI: (−0.064, −0.045)), and eudaimonic well-being (indirect effect = −0.039, CI: (−0.045, −0.033)) via self-control were all negative, and the indirect effect of hedonic motives on negative affect (indirect effect = 0.067, CI: (0.056, 0.078)) via self-control was positive. In contrast, the indirect effects of eudaimonic motives on life satisfaction (indirect effect = 0.134, CI: (0.113, 0.156)), positive affect (indirect effect = 0.068, CI: (0.057, 0.081)), and eudaimonic well-being (indirect effect = 0.049, CI: (0.043, 0.056)) via self-control were all positive, and the indirect effect of eudaimonic motives on negative affect (indirect effect = −0.084, CI: (−0.098, −0.072)) via self-control was negative. This is consistent with the argument that hedonic motives could be detrimental to well-being outcomes by decreasing self-control, whereas eudaimonic motives promote well-being outcomes by increasing self-control.
4. Discussion4.1. Findings and Implications
In this study, we examined the associations of hedonic and eudaimonic motives with well-being among Chinese college students. The current data demonstrated that, overall, eudaimonic motives were more positively associated with well-being than hedonic motives, which is consistent with previous studies [3]. Specifically, eudaimonic motives were found to be associated with improvements in all well-being outcomes, including higher life satisfaction, more positive affect, less negative affect, and better eudaimonic well-being. In contrast, the correlations of hedonic motives were more complex; while they were positively related to life satisfaction, they were not associated with positive affect and were even associated with an increase in negative affect. In addition, the pursuit of hedonia seemed to be detrimental to eudaimonic well-being.
More importantly, we investigated the mediating role of self-control, which shed light on the distinct associations of hedonic and eudaimonic motives with well-being. The results revealed that hedonic motives were detrimental to self-control, whereas eudaimonic motives could potentially enhance self-control, which is in accordance with previous results [7]. In addition, higher self-control was related to higher life satisfaction, more positive affect, less negative affect, and better eudaimonic well-being. In other words, eudaimonic motives generally promote well-being by enhancing self-control, whereas hedonic motives might be harmful by lowering self-control.
These findings highlight the potential detrimental effects of hedonic motives on well-being, which have been largely neglected in previous studies. Recent studies have shown that hedonic motives include both enjoyment and comfort [38,39,40], but only enjoyment motives might promote well-being [38]. Similarly, Huta [11] has suggested that while healthful approaches to hedonia would bring pleasure and enjoyment, excessive or unbalanced hedonic motives can have undesirable consequences. Combining previous findings with the current results, it can be suggested that the effects of hedonic motives on well-being are twofold. On the one hand, hedonic motives might encourage people to engage in entertaining activities, which can lead to improved well-being. On the other hand, hedonic motives could make people yield to temptation more easily, which might lead to dysfunctional behaviors such as addiction and procrastination, and further worsen well-being. Consequently, it is worthwhile to further explore functional and dysfunctional hedonic motives and hedonic behaviors.
4.2. Limitations and Future Research Directions
The present study has a number of limitations. First, we failed to implement an attention check, due to some accidental technical issues. Nevertheless, all scales achieved sufficient reliabilities. The distinct associations of hedonic and eudaimonic motives with well-being were unlikely to appear by chance. Consequently, we believe the results of the current study were reliable.
Second, the current data are correlational rather than causal. Future research should consider using longitudinal data to further determine the causal relationships between hedonic and eudaimonic motives, self-control, and well-being.
Third, the Questionnaire for Eudaimonic Well-Being is primarily a measure of functioning, whereas the other outcomes are all measures of experience. Further studies should use more suitable measures that represent eudaimonic experiences (e.g., a feeling of meaning) to replicate our findings.
Fourth, this paper only examines the mediating role of self-control in the relationships between hedonic and eudaimonic motives and well-being outcomes. However, as mentioned above, hedonic motives could encourage both healthful hedonic activities and dysfunctional behaviors. Investigating these factors simultaneously might facilitate a more comprehensive understanding of the effects of hedonic motives on well-being.
Last, our sample comprised solely Chinese college students. Prior research has suggested that cultural factors might moderate the relationships between hedonic and eudaimonic motives and well-being [41]. A future study direction would be exploring how and why the pursuit of happiness, especially hedonia, might affect well-being differently across cultures.
5. Conclusions
Despite the limitations mentioned above, the present study contributes to an improved understanding of the associations of hedonic and eudaimonic motives with well-being outcomes. In sum, hedonic motives could be detrimental to well-being by harming self-control; in contrast, eudaimonic motives can contribute to better well-being by improving self-control. These results suggest that the pursuit of eudaimonia is more likely to further the goal of leading a good life than the pursuit of hedonia.
Acknowledgments
We would like to thank all the participants for attending the current research.
Author Contributions
Conceptualization, Z.Z. and H.C.; methodology, Z.Z.; formal analysis, Z.Z.; writing—original draft preparation, Z.Z.; writing—review and editing, H.C.; supervision, H.C. All authors have read and agreed to the published version of the manuscript.
Funding
This research received no external funding.
Conflicts of Interest
The authors declare no conflicts of interest.
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Descriptive statistics for study variables.
Variables
Minimum
Maximum
Mean
SD
Hedonic motives
1.00
7.00
4.63
1.11
Eudaimonic motives
1.40
7.00
5.62
0.91
Self-control
1.15
4.92
2.98
0.59
Life satisfaction
1.00
7.00
4.17
1.09
Positive affect
1.00
5.00
3.35
0.69
Negative affect
1.00
5.00
2.10
0.69
Eudaimonic well-being
2.24
5.00
3.65
0.40
ijerph-17-05547-t002_Table 2
Bivariate correlations between study variables.
TitleStudy Variables
1
2
3
4
5
6
7
1. Hedonic motives
-
\n
\n
\n
\n
\n
\n
2. Eudaimonic motives
0.15 ***
-
\n
\n
\n
\n
\n
3. Self-control
−0.25 ***
0.26 ***
-
\n
\n
\n
\n
4. Life satisfaction
0.05 **
0.16 ***
0.35 ***
-
\n
\n
\n
5. Positive affect
−0.01
0.38 ***
0.38 ***
0.46 ***
-
\n
\n
6. Negative affect
0.15 ***
−0.08 **
−0.38 ***
−0.19 ***
−0.05 **
-
\n
7. Eudaimonic well-being
−0.08 ***
0.51 ***
0.50 ***
0.40 ***
0.53 ***
−0.32 ***
-
Note: ** p < 0.01, *** p < 0.001.
ijerph-17-05547-t003_Table 3
Regression results.
Models
\nB\n
\nß\n
\np\n
\nR\n2\n
\nDependent variable: Self-control\n
\n
\n
\n
0.15
Gender (female = 0, male = 1)
0.04
0.03
0.090
\n
Hedonic motives
−0.16
−0.29
<0.001
\n
Eudaimonic motives
0.20
0.30
<0.001
\n
\nDepedent variable: Life satisfaction\n
\n
\n
\n
0.15
Gender (female = 0, male = 1)
0.13
0.06
0.001
\n
Hedonic motives
0.13
0.13
<0.001
\n
Eudaimonic motives
0.06
0.05
0.009
\n
Self-control
0.68
0.37
<0.001
\n
\nDependent variable: Positive affect\n
\n
\n
\n
0.24
Gender (female = 0, male = 1)
0.20
0.14
<0.001
\n
Hedonic motives
0.00
0.00
0.824
\n
Eudaimonic motives
0.23
0.30
<0.001
\n
Self-control
0.35
0.30
<0.001
\n
\nDependent variable: Negative affect\n
\n
\n
\n
0.15
Gender (female = 0, male = 1)
0.02
0.02
0.379
\n
Hedonic motives
0.03
0.06
0.003
\n
Eudaimonic motives
0.00
0.00
0.931
\n
Self-control
−0.43
−0.37
<0.001
\n
\nDependent variable: Eudaimonic well-being\n
\n
\n
\n
0.41
Gender (female = 0, male = 1)
0.04
0.05
0.002
\n
Hedonic motives
−0.02
−0.06
<0.001
\n
Eudaimonic motives
0.19
0.43
<0.001
\n
Self-control
0.25
0.37
<0.001
\n
ijerph-17-05547-t004_Table 4
Direct and indirect effects and 95% confidence intervals.
Outcome Variables
Hedonic Motives
Eudaimonic Motives
Direct Effects
Indirect Effects
Direct Effects
Indirect Effects
Life satisfaction
0.130 (0.095, 0.165)
−0.106 (−0.123, −0.089)
0.057 (0.014, 0.101)
0.134 (0.113, 0.156)
Positive affect
0.002 (−0.019, 0.023)
−0.054 (−0.064, −0.045)
0.226 [0.200, 0.252)
0.068 [0.057, 0.081)
Negative affect
0.034 (0.012, 0.057)
0.067 (0.056, 0.078)
0.001 [−0.026, 0.029)
−0.084 [−0.098, −0.072)
Eudaimonic well-being
−0.022 (−0.032, −0.011)
−0.039 (−0.045, −0.033)
0.187 [0.174, 0.200)
0.049 [0.043, 0.056)
\n"],"string":"[\n \"\\nInt J Environ Res Public HealthInt J Environ Res Public HealthijerphInternational Journal of Environmental Research and Public Health1661-78271660-4601MDPI32751907PMC743214810.3390/ijerph17155547ijerph-17-05547ArticleDistinct Associations of Hedonic and Eudaimonic Motives with Well-Being: Mediating Role of Self-ControlZengZhijia1*ChenHezhi2*Student Affairs Department, Zhejiang University of Finance and Economics, Hangzhou 310018, ChinaDepartment of Psychology and Behavioral Sciences, Zhejiang University, Hangzhou 310058, ChinaCorrespondence: zhijiazeng@zufe.edu.cn (Z.Z.); chenhezhi@zju.edu.cn (H.C.)3172020820201715554720720202872020© 2020 by the authors.2020Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
The pursuit of hedonia and eudaimonia are two ways to fulfill the goal of a “good life”. While some studies report that both hedonic and eudaimonic motives improve well-being, others suggest that hedonic motives are counterproductive, raising the question of whether and why eudaimonic motives are more positively associated with well-being. We aimed to identify the distinct associations of hedonic and eudaimonic motives with well-being and investigate whether they are partly mediated by self-control. A total of 2882 college freshmen (1835 females, 1047 males, mean age 18.16 years) completed measures assessing hedonic and eudaimonic motives, self-control, life satisfaction, positive and negative affect, and eudaimonic well-being. Eudaimonic motives were associated with higher life satisfaction, more positive affect, less negative affect, and better eudaimonic well-being. In contrast, hedonic motives were positively associated with life satisfaction, while also being correlated with a greater degree of negative affect and impaired eudaimonic well-being. Self-control mediated the relationships between hedonic and eudaimonic motives and well-being. Eudaimonic and hedonic motives were positively and negatively related to self-control, respectively. Further, high self-control was associated with greater life satisfaction, positive affect, and eudaimonic well-being and lower negative affect. Thus, eudaimonic motives can lead to a better life than hedonic motives because the former enhance self-control, while the latter lower it.
The pursuit of a good life is a fundamental motive for human beings. However, individuals can have very different ideas about what constitutes a “good life”. Specifically, the two most prominent views of a good life are the hedonic view and the eudaimonic view [1]. Pursuing hedonia, or hedonic motives, involves seeking personal enjoyment, pleasure, and comfort; pursuing eudaimonia, or eudaimonic motives, relates to seeking personal growth, excellence, meaning, and authenticity [2].
1.1. Hedonic and Eudaimonic Motives and Well-Being
One issue that has received much scholarly attention is how hedonic and eudaimonic motives affect well-being. Aristotle claimed that people should strive for a virtuous life rather than seeking pleasure, because the latter does not bring happiness. Consistent with this view, numerous studies have suggested that while eudaimonic motives generally improve well-being, pursuing hedonia does not result in the achievement of the goal of a “good life” and might even backfire [3,4,5]. For example, in one study, eudaimonic motives were correlated with various well-being outcomes, including life satisfaction, vitality, positive affect, negative affect, carefreeness, self-connectedness, and meaning, whereas hedonic motives only predicted vitality, positive affect, and carefreeness [4]. In another study, eudaimonic motives were negatively correlated with depression and stress; in contrast, there was no significant correlation between hedonic motives and depression or stress [5]. Sheldon et al. [3] further showed that participants’ motivations for improving their subjective well-being were negatively correlated with concurrent subjective well-being and did not affect longitudinal subjective well-being.
However, some studies have reported the well-being-related benefits of both hedonic and eudaimonic motives [6,7,8]. For example, Peterson et al. [8] showed that endorsement of pleasure, engagement, and meaning as paths to happiness all predicted life satisfaction. Similarly, Huta and Ryan [6] showed that hedonic and eudaimonic motives had both overlapping and distinct effects on well-being; hedonic motives related more to positive affect and life satisfaction, while eudaimonic motives related more to meaning. Overall, a combination of hedonic and eudaimonic motives was associated with the greatest well-being.
In sum, whether hedonic and eudaimonic motives lead to different well-being outcomes remains inadequately explored. Further, the factors that account for the potential distinct associations of hedonic and eudaimonic motives with well-being remain unclear.
1.2. Hedonic and Eudaimonic Motives and Self-Control
We argue that one factor that could account for the distinct associations of hedonic and eudaimonic motives with well-being is self-control. Self-control is defined as the ability to override one’s own inner responses or interrupt a course of action, especially when conflicting with ideals, values, and social expectations, and to support the fulfillment of long-term goals [9,10]. Huta [11] pointed out two crucial distinctions between hedonic and eudaimonic motives. First, hedonic motives are concerned with satiating one’s own needs and desires in the present or near future; in contrast, eudaimonic motives are concerned with fulfilling long-term objectives. The second difference is that hedonic motives are concerned with what feels good, whereas eudaimonic motives relate to what one believes to be right. According to the self-concordance model, individuals will strive for goals more persistently when they fit their core values [12,13]. Similarly, the goal theory of happiness suggests that individuals actively engage in behaviors that are in line with their identified goals, which, in turn, further influence their well-being [14,15]. Consequently, when faced with conflicts such as those between short-term desires and long-term objectives, individuals pursuing eudaimonia are more likely to implement a higher level of self-control to achieve their goals than are those pursuing hedonia.
Although empirical research on the relationships of hedonic and eudaimonic motives with self-control is sparse, two studies have provided some supporting evidence [7,16]. Peterson et al. [16] showed that endorsement of engagement and meaning as routes to happiness was positively related to self-regulation. More recently, Anic and Tončić [7] showed that individuals pursuing eudaimonia but not hedonia had a high level of self-control, whereas those pursuing hedonia but not eudaimonia had a relatively low level of self-control.
1.3. Self-Control and Well-Being
Studies have indicated that self-control positively predicts hedonic well-being [17,18,19,20,21]. For example, Briki [18] showed that self-control was positively related to happiness and life satisfaction. Hofmann et al. [17] found that self-control promoted affective experience and life satisfaction by managing goal conflict and facilitating the achievement of multiple goals. Similarly, Cheung et al. [19] demonstrated that people with higher self-control were happier because they were more promotion-focused and less prevention-focused. In addition, a lack of self-control has been linked to various problematic behaviors, such as procrastination, impulsive purchasing, addictive behavior, and even criminal offenses [22,23,24,25], which, in turn, might increase depression, stress, guilt, and other negative feelings [15,26,27].
Furthermore, it has been suggested that one approach to enhance eudaimonic well-being is satisfying basic psychological needs, including autonomy, competence, and relatedness [28]. Self-control itself is essential to the attainment of autonomy [29]. In addition, self-control has been found to be associated with higher achievement and better interpersonal relationships [20,30,31], which might satisfy the need for competence and relatedness. Consequently, self-control might also result in greater eudaimonic well-being.
1.4. The Current Study
The aim of this study is to identify the distinct associations between hedonic and eudaimonic motives and well-being outcomes, and further investigate the possible mediating role of self-control. For this purpose, we collected cross-sectional data on hedonic and eudaimonic motives, self-control, hedonic well-being (life satisfaction, positive and negative affect), and eudaimonic well-being. We propose the following hypotheses:
\\nSelf-control mediates the relationships between hedonic motives and well-being outcomes. Hedonic motives are detrimental to well-being outcomes by lowering the self-control of Chinese college students.\\n
\\nSelf-control mediates the relationships between eudaimonic motives and well-being outcomes. Eudaimonic motives promote well-being outcomes by increasing the self-control of Chinese college students.\\n
2. Materials and Methods2.1. Sample and Procedures
In September 2019, we carried out a cross-sectional survey with a sample of 2882 students (mean age = 18.16, standard deviation = 0.44; 1047 males, 1835 females) from Zhejiang University of Finance and Economics. The participants were freshmen who had to complete a general psychological survey for course credits. The students completed the online survey while seated in individual cubicles. The teachers gave instructions and maintained discipline. All responses were included in the analyses.
The study design and data collection procedures were both approved by the Ethics Committee of Zhejiang University of Finance and Economics. All students provided written informed consent.
2.2. Measures
All the scales used in the current study were originally in English. Therefore, all the survey measures were translated from English to Chinese and back to English. When there were discrepancies between the original English version and the back-translated English version, those items were further modified after discussion by the two authors.
2.2.1. Hedonic and Eudaimonic Motives
To assess motives, we used the Hedonic and Eudaimonic Motives for Activities—Revised scale developed by Huta [32]. This instrument utilizes five items to assess hedonic motives (e.g., “seeking pleasure”) and five for eudaimonic motives (e.g., “seeking to use the best in yourself”). Respondents were asked to report the degrees of each motive with which they typically approached their activities on a seven-point Likert scale ranging from 1 (not at all) to 7 (very much). The Cronbach’s α values were 0.83 for hedonic motives and 0.79 for eudaimonic motives.
2.2.2. Self-Control
To assess personal self-control, we used the 13-item Brief Self-Control Scale [20]. A sample item is “I am good at resisting temptation.” Respondents indicated the degree to which each of the statements reflected their typical behavior on a five-point Likert scale ranging from 1 (not at all) to 5 (very much). The Cronbach’s α value was 0.82.
2.2.3. Well-Being Outcomes
To assess life satisfaction, we used the Satisfaction with Life Scale, which has five items (e.g., “In most ways my life is close to my ideal”) [33] rated on a seven-point Likert scale ranging from 1 (strongly disagree) to 7 (strongly agree). The Cronbach’s α value was 0.84.
Participants also reported their positive and negative affect in the past two weeks on the Positive and Negative Affect Schedule on a five-point Likert scale ranging from 1 (not at all) to 5 (very much) [34]. The Cronbach’s α values were 0.90 for both positive and negative affect.
Thereafter, they completed the Questionnaire for Eudaimonic Well-Being, which comprises 21 items assessing various aspects of eudaimonic well-being, including self-discovery, perceived development of one’s potential, meaning in life, and involvement and enjoyment in personally expressive activities (e.g., “I find I get intensely involved in many of the things I do each day”) [35]. Participants indicated their agreement with each item on a five-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree). The Cronbach’s α value was 0.81.
2.2.4. Demographic Information
Demographic information included gender and age.
2.3. Analyses
All tests were performed using SPSS 23.0 software (IBM Corp., Armonk, NY, USA). First, descriptive statistics were used to describe hedonic and eudaimonic motives, self-control, and well-being outcomes. Next, we conducted correlational analyses to investigate the general relationships between hedonic and eudaimonic motives, self-control, and well-being outcomes. Furthermore, we tested the hypothesized mediation effects of hedonic and eudaimonic motives on well-being outcomes via self-control using the PROCESS macro for SPSS (model 4) [36,37]. Hedonic and eudaimonic motives were entered into the models as independent variables, self-control was entered as the mediating variable, and gender was entered as the control variable. The four well-being outcome variables were separately entered as dependent variables; thus, the mediation model was run four times. We reported hierarchical regression results, indirect effects, and the 95% bias-corrected confidence intervals (CIs) estimated by 5000 bootstrap samples. The indirect effect was considered significant if the 95% CI did not contain zero.
3. Results3.1. Descriptive Analyses
Descriptive statistics related to hedonic and eudaimonic motives, self-control, and well-being outcomes are presented in Table 1. Bivariate correlations between the study variables are presented in Table 2. Higher hedonic motives were related to higher life satisfaction (r = 0.05, p = 0.004), but more negative affect (r = 0.15, p < 0.001) and worse eudaimonic well-being (r = −0.08, p < 0.001); there was no significant correlation between hedonic motives and positive affect (r = −0.01, p = 0.503). Higher eudaimonic motives were related to higher life satisfaction (r = 0.16, p < 0.001), more positive affect (r = 0.38, p < 0.001), less negative affect (r = −0.08, p = 0.001), and better eudaimonic well-being (r = 0.51, p < 0.001). Self-control was negatively related to hedonic motives (r = −0.25, p < 0.001) and positively related to eudaimonic motives (r = 0.26, p < 0.001). In addition, self-control was associated with all well-being outcomes (life satisfaction: r = 0.35, p < 0.001; positive affect: r = 0.38, p < 0.001; negative affect: r = −0.38, p < 0.001; and eudaimonic well-being: r = 0.50, p < 0.001).
3.2. Mediation Analyses
The regression analyses are shown in Table 3. As expected, after adjusting for gender, hedonic motives were negatively correlated with self-control (ß = −0.29, p < 0.001), while eudaimonic motives were positively correlated with self-control (ß = 0.30, p < 0.001). In addition, after controlling for hedonic and eudaimonic motives, self-control demonstrated significant positive associations with life satisfaction (ß = 0.37, p < 0.001), positive affect (ß = 0.30, p < 0.001), and eudaimonic well-being (ß = 0.37, p < 0.001) and a significant negative association with negative affect (ß = −0.37, p < 0.001). Whether gender was included as a control variable or not, the main results did not change.
The estimated direct and indirect effects between hedonic and eudaimonic motives and the four well-being outcomes are presented in Table 4. The indirect effects of hedonic motives on life satisfaction (indirect effect = −0.106, CI: (−0.123, −0.089)), positive affect (indirect effect = −0.054, CI: (−0.064, −0.045)), and eudaimonic well-being (indirect effect = −0.039, CI: (−0.045, −0.033)) via self-control were all negative, and the indirect effect of hedonic motives on negative affect (indirect effect = 0.067, CI: (0.056, 0.078)) via self-control was positive. In contrast, the indirect effects of eudaimonic motives on life satisfaction (indirect effect = 0.134, CI: (0.113, 0.156)), positive affect (indirect effect = 0.068, CI: (0.057, 0.081)), and eudaimonic well-being (indirect effect = 0.049, CI: (0.043, 0.056)) via self-control were all positive, and the indirect effect of eudaimonic motives on negative affect (indirect effect = −0.084, CI: (−0.098, −0.072)) via self-control was negative. This is consistent with the argument that hedonic motives could be detrimental to well-being outcomes by decreasing self-control, whereas eudaimonic motives promote well-being outcomes by increasing self-control.
4. Discussion4.1. Findings and Implications
In this study, we examined the associations of hedonic and eudaimonic motives with well-being among Chinese college students. The current data demonstrated that, overall, eudaimonic motives were more positively associated with well-being than hedonic motives, which is consistent with previous studies [3]. Specifically, eudaimonic motives were found to be associated with improvements in all well-being outcomes, including higher life satisfaction, more positive affect, less negative affect, and better eudaimonic well-being. In contrast, the correlations of hedonic motives were more complex; while they were positively related to life satisfaction, they were not associated with positive affect and were even associated with an increase in negative affect. In addition, the pursuit of hedonia seemed to be detrimental to eudaimonic well-being.
More importantly, we investigated the mediating role of self-control, which shed light on the distinct associations of hedonic and eudaimonic motives with well-being. The results revealed that hedonic motives were detrimental to self-control, whereas eudaimonic motives could potentially enhance self-control, which is in accordance with previous results [7]. In addition, higher self-control was related to higher life satisfaction, more positive affect, less negative affect, and better eudaimonic well-being. In other words, eudaimonic motives generally promote well-being by enhancing self-control, whereas hedonic motives might be harmful by lowering self-control.
These findings highlight the potential detrimental effects of hedonic motives on well-being, which have been largely neglected in previous studies. Recent studies have shown that hedonic motives include both enjoyment and comfort [38,39,40], but only enjoyment motives might promote well-being [38]. Similarly, Huta [11] has suggested that while healthful approaches to hedonia would bring pleasure and enjoyment, excessive or unbalanced hedonic motives can have undesirable consequences. Combining previous findings with the current results, it can be suggested that the effects of hedonic motives on well-being are twofold. On the one hand, hedonic motives might encourage people to engage in entertaining activities, which can lead to improved well-being. On the other hand, hedonic motives could make people yield to temptation more easily, which might lead to dysfunctional behaviors such as addiction and procrastination, and further worsen well-being. Consequently, it is worthwhile to further explore functional and dysfunctional hedonic motives and hedonic behaviors.
4.2. Limitations and Future Research Directions
The present study has a number of limitations. First, we failed to implement an attention check, due to some accidental technical issues. Nevertheless, all scales achieved sufficient reliabilities. The distinct associations of hedonic and eudaimonic motives with well-being were unlikely to appear by chance. Consequently, we believe the results of the current study were reliable.
Second, the current data are correlational rather than causal. Future research should consider using longitudinal data to further determine the causal relationships between hedonic and eudaimonic motives, self-control, and well-being.
Third, the Questionnaire for Eudaimonic Well-Being is primarily a measure of functioning, whereas the other outcomes are all measures of experience. Further studies should use more suitable measures that represent eudaimonic experiences (e.g., a feeling of meaning) to replicate our findings.
Fourth, this paper only examines the mediating role of self-control in the relationships between hedonic and eudaimonic motives and well-being outcomes. However, as mentioned above, hedonic motives could encourage both healthful hedonic activities and dysfunctional behaviors. Investigating these factors simultaneously might facilitate a more comprehensive understanding of the effects of hedonic motives on well-being.
Last, our sample comprised solely Chinese college students. Prior research has suggested that cultural factors might moderate the relationships between hedonic and eudaimonic motives and well-being [41]. A future study direction would be exploring how and why the pursuit of happiness, especially hedonia, might affect well-being differently across cultures.
5. Conclusions
Despite the limitations mentioned above, the present study contributes to an improved understanding of the associations of hedonic and eudaimonic motives with well-being outcomes. In sum, hedonic motives could be detrimental to well-being by harming self-control; in contrast, eudaimonic motives can contribute to better well-being by improving self-control. These results suggest that the pursuit of eudaimonia is more likely to further the goal of leading a good life than the pursuit of hedonia.
Acknowledgments
We would like to thank all the participants for attending the current research.
Author Contributions
Conceptualization, Z.Z. and H.C.; methodology, Z.Z.; formal analysis, Z.Z.; writing—original draft preparation, Z.Z.; writing—review and editing, H.C.; supervision, H.C. All authors have read and agreed to the published version of the manuscript.
Funding
This research received no external funding.
Conflicts of Interest
The authors declare no conflicts of interest.
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Descriptive statistics for study variables.
Variables
Minimum
Maximum
Mean
SD
Hedonic motives
1.00
7.00
4.63
1.11
Eudaimonic motives
1.40
7.00
5.62
0.91
Self-control
1.15
4.92
2.98
0.59
Life satisfaction
1.00
7.00
4.17
1.09
Positive affect
1.00
5.00
3.35
0.69
Negative affect
1.00
5.00
2.10
0.69
Eudaimonic well-being
2.24
5.00
3.65
0.40
ijerph-17-05547-t002_Table 2
Bivariate correlations between study variables.
TitleStudy Variables
1
2
3
4
5
6
7
1. Hedonic motives
-
\\n
\\n
\\n
\\n
\\n
\\n
2. Eudaimonic motives
0.15 ***
-
\\n
\\n
\\n
\\n
\\n
3. Self-control
−0.25 ***
0.26 ***
-
\\n
\\n
\\n
\\n
4. Life satisfaction
0.05 **
0.16 ***
0.35 ***
-
\\n
\\n
\\n
5. Positive affect
−0.01
0.38 ***
0.38 ***
0.46 ***
-
\\n
\\n
6. Negative affect
0.15 ***
−0.08 **
−0.38 ***
−0.19 ***
−0.05 **
-
\\n
7. Eudaimonic well-being
−0.08 ***
0.51 ***
0.50 ***
0.40 ***
0.53 ***
−0.32 ***
-
Note: ** p < 0.01, *** p < 0.001.
ijerph-17-05547-t003_Table 3
Regression results.
Models
\\nB\\n
\\nß\\n
\\np\\n
\\nR\\n2\\n
\\nDependent variable: Self-control\\n
\\n
\\n
\\n
0.15
Gender (female = 0, male = 1)
0.04
0.03
0.090
\\n
Hedonic motives
−0.16
−0.29
<0.001
\\n
Eudaimonic motives
0.20
0.30
<0.001
\\n
\\nDepedent variable: Life satisfaction\\n
\\n
\\n
\\n
0.15
Gender (female = 0, male = 1)
0.13
0.06
0.001
\\n
Hedonic motives
0.13
0.13
<0.001
\\n
Eudaimonic motives
0.06
0.05
0.009
\\n
Self-control
0.68
0.37
<0.001
\\n
\\nDependent variable: Positive affect\\n
\\n
\\n
\\n
0.24
Gender (female = 0, male = 1)
0.20
0.14
<0.001
\\n
Hedonic motives
0.00
0.00
0.824
\\n
Eudaimonic motives
0.23
0.30
<0.001
\\n
Self-control
0.35
0.30
<0.001
\\n
\\nDependent variable: Negative affect\\n
\\n
\\n
\\n
0.15
Gender (female = 0, male = 1)
0.02
0.02
0.379
\\n
Hedonic motives
0.03
0.06
0.003
\\n
Eudaimonic motives
0.00
0.00
0.931
\\n
Self-control
−0.43
−0.37
<0.001
\\n
\\nDependent variable: Eudaimonic well-being\\n
\\n
\\n
\\n
0.41
Gender (female = 0, male = 1)
0.04
0.05
0.002
\\n
Hedonic motives
−0.02
−0.06
<0.001
\\n
Eudaimonic motives
0.19
0.43
<0.001
\\n
Self-control
0.25
0.37
<0.001
\\n
ijerph-17-05547-t004_Table 4
Direct and indirect effects and 95% confidence intervals.
Outcome Variables
Hedonic Motives
Eudaimonic Motives
Direct Effects
Indirect Effects
Direct Effects
Indirect Effects
Life satisfaction
0.130 (0.095, 0.165)
−0.106 (−0.123, −0.089)
0.057 (0.014, 0.101)
0.134 (0.113, 0.156)
Positive affect
0.002 (−0.019, 0.023)
−0.054 (−0.064, −0.045)
0.226 [0.200, 0.252)
0.068 [0.057, 0.081)
Negative affect
0.034 (0.012, 0.057)
0.067 (0.056, 0.078)
0.001 [−0.026, 0.029)
−0.084 [−0.098, −0.072)
Eudaimonic well-being
−0.022 (−0.032, −0.011)
−0.039 (−0.045, −0.033)
0.187 [0.174, 0.200)
0.049 [0.043, 0.056)
\\n\"\n]"}}},{"rowIdx":492,"cells":{"data":{"kind":"list like","value":["\nJMIR Form ResJFRJMIR Formative Research2561-326XJMIR PublicationsToronto, Canada32744509PMC7432149v4i8e1822310.2196/18223Original PaperOriginal PaperShared Decision Making and Patient-Centered Care in Israel, Jordan, and the United States: Exploratory and Comparative Survey Study of Physician PerceptionsEysenbachGuntherDioufNdeyeWieringaThomasGalasinskiDariuszZisman-IlaniYaaraMA, PhDhttps://orcid.org/0000-0001-6852-25831Department of Social and Behavioral SciencesCollege of Public HealthTemple University1700 North Broad StPhiladelphia, PA, 19122United States1 215 204 5618yaara@temple.eduObeidatRanaPhD2https://orcid.org/0000-0002-6783-0813FangLaurenBSc3https://orcid.org/0000-0001-5270-2416HsiehSarahBSc3https://orcid.org/0000-0001-8236-5078BergerZackaryMD, PhD3https://orcid.org/0000-0002-5871-0342\n\nDepartment of Social and Behavioral Sciences\nCollege of Public Health\nTemple University\nPhiladelphia, PA\nUnited States\n\n\nFaculty of Nursing\nZarqa University\nZarqa\nJordan\n\n\nJohns Hopkins School of Medicine\nBaltimore, MD\nUnited States\nCorresponding Author: Yaara Zisman-Ilani yaara@temple.edu8202038202048e182231222020233202021420201352020©Yaara Zisman-Ilani, Rana Obeidat, Lauren Fang, Sarah Hsieh, Zackary Berger. Originally published in JMIR Formative Research (http://formative.jmir.org), 03.08.2020.2020This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Formative Research, is properly cited. The complete bibliographic information, a link to the original publication on http://formative.jmir.org, as well as this copyright and license information must be included.Background
Shared decision making (SDM) is a health communication model that evolved in Europe and North America and largely reflects the values and medical practices dominant in these areas.
Objective
This study aims to understand the beliefs, perceptions, and practices related to SDM and patient-centered care (PCC) of physicians in Israel, Jordan, and the United States.
Methods
A hypothesis-generating comparative survey study was administered to physicians from Israel, Jordan, and the United States.
Results
A total of 36 surveys were collected via snowball sampling (Jordan: n=15; United States: n=12; Israel: n=9). SDM was perceived as a way to inform patients and allow them to participate in their care. Barriers to implementing SDM varied based on place of origin; physicians in the United States mentioned limited time, physicians in Jordan reported that a lack of patient education limits SDM practices, and physicians in Israel reported lack of communication training. Most US physicians defined PCC as a practice for prioritizing patient preferences, whereas both Jordanian and Israeli physicians defined PCC as a holistic approach to care and to prioritizing patient needs. Barriers to implementing PCC, as seen by US physicians, were mostly centered on limited appointment time and insurance coverage. In Jordan and Israel, staff shortage and a lack of resources in the system were seen as major barriers to PCC implementation.
Conclusions
The study adds to the limited, yet important, literature on SDM and PCC in areas of the world outside the United States, Canada, Australia, and Western Europe. The study suggests that perceptions of PCC might widely differ among these regions, whereas concepts of SDM might be shared. Future work should clarify these differences.
Shared decision making (SDM) is a central health communication model for supporting patient engagement in health care [1-3] and a recommended approach to increasing patient engagement and patient-centered care (PCC) in clinical decision making [4-6]. SDM evolved in Europe and North America [7] and largely reflects the values and medical practices dominant in these areas [8,9]. Although SDM has become more widely discussed in recent years in non-Western countries (eg, China, Peru, Malaysia, Taiwan, Iran) [10], it has yet to be implemented on a wider scale, and less is known about how or whether attitudes, beliefs, and practices regarding PCC exist or differ in various other regions of the world.
The overall aim of the present exploratory study was to explore the factors that enable or impede SDM implementation in different geographical and political contexts. Specifically, we sought to conduct a hypothesis-generating study and to collect preliminary data to better understand SDM- and PCC-related beliefs, attitudes, and practices of physicians in four regions in the Middle East characterized by different health care systems, cultures, and political environments: Israel, Jordan, and the West Bank. As a point of reference, we conducted a similar survey among US physicians to serve as a benchmark for SDM- and PCC-related beliefs, attitudes, and practices. In addition, such a comparison may provide insights about the importance of a health care system that facilitates the practice of SDM and PCC. The study focused on the following specific questions: (1) What are physicians’ common understandings and perceptions of the concepts of SDM and PCC? and (2) Do physicians find SDM and PCC to be feasible in their practice and in health care?
MethodsSettings: Context of Participating Countries
The survey was intended to be administered to physicians from different geographical and political contexts in the Middle East characterized by different health care systems: Israel, Jordan, and the West Bank. Israel is a democratic state with an efficient health care system that has been ranked among the top 10 health care systems for several years [11,12]. Israel’s national health insurance system provides universal health coverage throughout the country, with a significant spread of hospital and clinics [13-15]. Residents can supplement the universal coverage with additional forms of private health insurance. Israel’s health policy legislation is supportive of SDM principles, including the right to be informed of treatment options and risks [16]. Jordan is a constitutional monarchy state with a health care system characterized by diverse types of payers (public, private, and donors) [17]. The public health care sector is the largest in Jordan; however, only about 70% of residents have some form of public health insurance. Jordan has a ratio of 2.3 physicians to 1000 residents, and hospitals are mostly centralized in the larger urban areas. The massive influx of Syrian refugees due to the start of the Arab Spring in 2011 has further increased burdens on the Jordanian health care system, especially on public health facilities. The West Bank is an independent Palestinian territory governed by the Palestinian National Authority. The West Bank has low-functioning, inefficient health care systems that rely heavily on medical services to and referrals of patients to Israel (14% in 2011) or Jordan (13% in 2011) [17,18].
A parallel survey was planned among US physicians to serve as a benchmark. The United States is a representative democracy with a hybrid health care system but without universal health care coverage. In 2016, 48% of US health care spending came from private funds, with 28% coming from households and 20% coming from private businesses. The federal government accounted for 28% of spending, while state and local governments accounted for 17% of spending [19]. SDM in the United States is increasingly recognized as part of value-based care, and several federal initiatives have linked SDM to reimbursement [20].
Survey Development and Structure
Because the purpose of the present study was to explore aspects of SDM and PCC and to provide data for testing hypotheses, we chose to develop a survey as a research strategy [20]. As recommended by Enhancing the Quality and Transparency of Health Research (EQUATOR), we used “Good Practice in the Conduct and Reporting of Survey Research” as a reporting guideline [20]. We developed a short standardized survey form with 24 questions divided into 3 parts (see Multimedia Appendix 1): (1) demographic data, (2) qualitative evaluation, and (3) quantitative evaluation.
First, demographic data (eg, age and years in practice) was assessed with 9 questions.
Second, a qualitative evaluation was included. One part of this evaluation assessed the beliefs and attitudes of physicians around PCC and SDM in their health care setting (eg, “What do you see as barriers to implementing shared decision making in your practice?”). This comprised 5 open-ended questions and 1 multiple choice question. The second part of the qualitative evaluation assessed the understanding of physicians of their immediate environment of practice in the context of their larger health care system (eg, “What are the most important day-to-day problems in the practice of medicine or health care in your country or region?”). This included 3 open-ended questions.
Third, the quantitative evaluation of level of SDM practice was based on the Shared Decision Making Questionnaire, physician version (SDM-Q-DOC) scale [21]. This included 9 questions on a 6-point Likert scale ranging from “completely disagree” (0) to “completely agree” (5). The SDM-Q-DOC was developed to measure patients’ and clinicians’ agreement with steps and actions defined by a medically driven SDM model at the end of a medical consultation. It has been used in numerous studies to measure physicians’ perspectives of SDM and was recommended for use in health policy survey responses targeting the implementation of SDM [22-24]
The qualitative part of the survey was developed through an iterative process based on SDM and PCC literature related to the delivery and perception of care and on the lead investigators’ (YZI and ZB) knowledge [25-30]. The development process included discussions among the coinvestigators and piloting among colleagues. We conducted forward and backward translations based on accepted guidelines [31] to each new question in sections 1 and 2. We used the English version of the SDM-Q-DOC questionnaire [21] and the Arabic [32] and Hebrew [33] translations of the 9-item Shared Decision Making Questionnaire (SDM-Q-9), a parallel patient version [34], with the needed minor adaptions.
Procedure
A web-based survey developed for the study was emailed to physicians in Israel, the West Bank, and the United States using the Qualtrics platform (Qualtrics International Inc). In Jordan it was advised by one of the coauthors (RO) to administer the survey via face-to-face interviews, based on her previous experience conducting similar types of research in Jordan. A snowball sampling methodology was used to recruit physicians. Accordingly, participants were asked to identify and email the questionnaire to other colleagues. Surveys were administered in Hebrew, English, and Arabic, and all responses were anonymized. Data collection began in February 2017 and ended in June 2017. It was designed to stop after a sample of 15 in each country or after the maximum sample size closest to this threshold. As this was an exploratory study, this sample size target was based not on statistical considerations but on real-world experience with the number of respondents likely to provide a hypothesis-generating set of responses. At the end of the survey, participants were reimbursed via gift cards in the amount of US $10 or an equal value in the local currency.
Data Analysis
To summarize the qualitative results from the open-ended survey questions, we used an integrated approach that enabled both inductive (ie, data-driven) coding of participants’ responses and deductive (ie, theory-driven) framework organization of codes [35]. Specifically, 2 coauthors (SH and ZB) and another research assistant read open-ended responses from participants and developed a draft of coding categories based on the responses’ contents. These categories were reviewed by the lead author (YZI) and revised accordingly. Responses to open-ended questions were coded independently by all coauthors; differences and disagreements between the coders were resolved through discussions until consensus was achieved. The final coding of open-ended responses was double-checked for accuracy by the first and last authors (YZI and ZB) after finalization of the coding guide. Then, guided by SDM and PCC theories, all coauthors discussed the interrelationships between codes to finalize the grouping of the codes into themes and subthemes.
Chi-square and one-way ANOVA tests were conducted to describe demographic characteristics of the survey respondents. As recommended by the developers [21], multiplication of the raw score by 20/9 provided a transformed total score range from 0 to 100, where 0 indicates the lowest possible level and 100 indicates the highest possible level of SDM. A nonparametric Kruskal-Wallis test was conducted to compare the mean total score of SDM-Q-9 between the 3 countries. Results were considered significant below a P value of .05.
Ethics, Consent, and Permissions
Because the responses were anonymized and not identifiable and participation in the study was associated with minimal risk, the Institutional Review Board of the Johns Hopkins School of Medicine deemed this study exempt from requirements for approval (IRB00111847). All participants provided consent to participate through their responses to the survey.
ResultsParticipants
Eligible survey respondents were practicing physicians in the United States, Israel, Jordan, and the West Bank. A total of 36 survey responses were received from Israel (n=9), Jordan (n=15), and the United States (n=12). Throughout the study period, we received no responses from West Bank physicians to our emails or to our in-person attempts to contact them; therefore, we were unable to collect any data from that population. Most survey respondents were men (24/36, 67%), the mean age of survey respondents was 43.6 years (SD 11.2), and the mean years of clinical experience was 15.8 (SD 10.5). Comparison of demographic characteristics and clinical experience of clinicians in each country indicate similarity between US and Israeli respondents for gender distribution, age, and clinical experience (Table 1). The Jordanian respondents were significantly younger and less experienced, and almost all were men.
Demographic and clinical experience characteristics of survey respondents.
Characteristics
Total sample \n(N=36)
United States \n(n=12)
Israel \n(n=9)
Jordan \n(n=15)
P value
\nGender\n
\n\n
\n\n
\n\n
.01a
\n\n
Men, n (%)
5 (42)
24 (67)
5 (56)
14 (93)
—b\n\n
\n\n
Women, n (%)
7 (58)
12 (33)
4 (44)
1 (7)
—\n\n
Age (years), mean (SD)
43.6 (11.2)
47.0 (8.6)
48.7 (13.4)
37.4 (9.2)
.02c
Years of clinical experience, mean (SD)
15.8 (10.5)
19.0 (9.8)
20.0 (14.2)
10.8 (6.0)
.05d
aPearson χ22=8.7.
bNot applicable.
cAnalysis of variance F test (F2,32=4.40).
dAnalysis of variance F test (F2,33=3.37).
Open-Ended Responses: Perception of SDM and PCC
We included in the analysis 34 survey responses with greater than 50% total completion (Jordan: n=15; United States: n=12; Israel: n=7). There were 12 responses to the open-ended questions from the US physicians, 7 from the Israeli physicians, and 14 from the Jordanian physicians.
Most respondents defined SDM as a process aimed at informing patients (United States: 8/12, 67%; Israel: 4/7, 57%; Jordan: 12/15, 80%). Whereas most US respondents also defined SDM as the participation of patients in their care (8/12, 67%), only a third of the Jordanian respondents defined it as patient participation (5/15, 33%), and 4 of the 7 (57%) Israeli respondents defined SDM also as collaboration between patient and physician.
[SDM is when] a patient makes decisions about medical tests and treatment that incorporate information about benefits and harms from the physician as well as the patient’s own understanding of his or her values and priorities.
US respondent
[SDM is] an open conversation with the patient, in which I [the doctor] suggest/advise a variety of treatment options that fit the patient’s medical condition, and together with the patient, choose the appropriate treatment method.
Israeli respondent
[SDM is] giving the patient information about his treatment options and his illness and giving him a chance to have a say in his treatment options.
Jordanian respondent
Most US and Israeli respondents indicated familiarity with the concept of PCC (Israel: 5/7, 71%; United States: 10/12, 83%), whereas only 6 of the 15 (40%) Jordanian respondents indicated their or their patients’ familiarity with the concept. Prioritizing or meeting patient needs was commonly described as a feature of PCC by most respondents regardless of country of origin. In addition, US respondents commonly described PCC as accounting for patients’ preferences, most Israeli respondents described PCC also as individualized care, and most Jordanian respondents also described PCC as a provision of holistic care.
[PCC refers to] care that balances the needs and desires of the person receiving care.
US respondent
[PCC aims] to provide the patient with all of the patient’s needs and not just to solve a problem in the field, while maintaining proper communication and respect for the patient’s values.
Israeli respondent
[PCC refers] to doing whatever is needed for the patient or referring him/her to someone who can.
Jordanian respondent
Respondents indicated several barriers affecting the provision of SDM and PCC systematically; however, the common barrier was related to the system itself. All US respondents (12/12, 100%) mentioned lack of time as a major barrier to SDM implementation, whereas only 5 of 12 (42%) mentioned it as a barrier to PCC implementation. The role of insurance companies and fragmentation of care were mentioned as additional possible barriers to PCC implementation. In Jordan, most respondents (9/15, 60%) mentioned patient-related barriers, low health literacy, and a lack of knowledge as barriers to SDM implementation, whereas system-related barriers, such as staff shortages and high patient loads, were identified as barriers to PCC.
Time, and often hard to do for many decisions: few are really straightforward. Would be nice to have tools readily available to do this & ways to facilitate it.
US respondent, regarding SDM barriers
Lack of knowledge among patients and patient unwillingness to be fully informed about his/her condition.
Jordanian respondent, regarding SDM barriers
The healthcare delivery system is still organized traditionally regarding appointment scheduling and how patients interact with doctors; short visits limit person-centered care.
US respondent, regarding PCC barriers
Bureaucracy of the Jordanian health care system and lack of medical specialties in the peripheral areas of the country.
Jordanian respondent, regarding PCC barriers
With respect to problems related to the patient-physician relationship, the responses of Israeli physicians emphasized a lack of time and training (eg, lack of time, lack of support for physicians during their work). Jordanian respondents emphasized disorganization of the health care system, and US physicians highlighted problems of cost, social determinants of health, and the role of insurance companies (eg, the payment system and its incentive structure, lack of universal health care, costs of pharmaceuticals).
SDM-Q-DOC Responses: Comparison of SDM Practice and PCC Behaviors
We included in the analysis 32 survey responses with greater than 50% total completion (Jordan: n=15; United States: n=10; Israel: n=7). Overall, physicians in our sample reported practicing SDM at a moderately high level (mean 76.6, SD 11.5; median 75.6), with a range of 53 to 100. This result is similar to findings in other studies [22]. The Kruskal-Wallis test results showed no significant difference in SDM-Q-DOC scores between the 3 countries (H2=0.631, P=.73; United States: mean 74.9, SD 11.4; Israel: mean 78.7, SD 7.9; Jordan: mean 76.7, SD 13.4). Box plot diagrams of SDM-Q-DOC means and standard deviations imply that SDM practice and PCC behaviors vary more among the US and Jordanian respondents in our sample than among their Israeli counterparts, as seen in Figure 1.
In addition, we compared the individual item scores between respondents from the 3 countries. Only for the first item (“I make clear to my patient that a decision needs to be made”) was a significant difference noticed, with higher scores for Jordanian physicians (Table 2). A nonsignificant but notable difference was noticed in the second item (“I want to know exactly from my patient how he/she wants to be involved in making the decision”), with a higher mean score for Jordanian physicians.
Box plot diagrams of SDM-Q-DOC score per country. SDM-Q-DOC: Shared Decision Making Questionnaire, physician version.
Comparison of means and standard deviations of Shared Decision Making Questionnaire, physician version responses among respondents from the United States, Israel, and Jordan (n=32).
SDM-Q-DOCa item question
H statistic
P value
United States, mean (SD)b
Israel, mean (SD)b
Jordan, mean (SD)b
1. I make clear to my patient that a decision needs to be made.
9.55
.008
3.60 (0.84)
3.57 (0.98)
4.47 (0.52)
2. I want to know exactly from my patient how he/she wants to be involved in making the decision.
4.66
.10
3.00 (0.67)
3.29 (0.76)
3.73 (0.80)
3. I tell my patient that there are different options for treating his/her medical condition.
1.83
.40
4.50 (0.71)
4.43 (0.98)
4.21 (0.58)
4. I precisely explain the advantages and disadvantages of the treatment options to my patient.
2.59
.27
3.80 (0.92)
4.43 (0.53)
4.13 (0.64)
5. I help my patient understand all the information.
2.06
.36
3.80 (0.92)
4.29 (0.76)
4.27 (0.96)
6. I ask my patient which treatment option he/she prefers.
1.00
.61
4.10 (0.74)
4.00 (1.00)
3.73 (0.96)
7. My patient and I thoroughly weigh the different treatment options.
2.53
.28
3.40 (0.52)
4.00 (0.82)
3.47 (0.92)
8. My patient and I select a treatment option together.
0.23
.89
3.50 (0.53)
3.43 (0.79)
3.13 (1.46)
9. My patient and I reach an agreement on how to proceed.
1.47
.48
8.84 (0.76)
4.00 (5.80)
3.67 (0.90)
aSDM-Q-DOC: Shared Decision Making Questionnaire, physician version.
bMeans represent level of agreement with each item on a 6-point Likert scale, where 0=completely disagree and 5=completely agree.
DiscussionPrincipal Results and Comparison With Prior Work
The present study describes findings of a small, exploratory hypothesis-generating survey [20] of Israeli, Jordanian, and US physicians’ perceptions of SDM and PCC. Open-ended qualitative results suggest that respondents, regardless of country of origin, identify SDM as a process focused on providing information or as informed decision making, but PCC as a physician’s effort to meet patients’ individualized needs. These findings are aligned with the perception that SDM is “the pinnacle of PCC” [4] but also emphasize that SDM remains commonly perceived by physicians as a mean for delivering information rather than a collaborative discussion [36,37]. Barriers to implementing SDM and PCC were also identified and attributed to system- and patient-related factors [25]. The quantitative results of the total mean score of the SDM-Q-DOC show medium to high levels of SDM-related behaviors among all respondents. Item-focused analysis showed that Jordanian respondents scored significantly higher on item 1 (“I make clear to my patient that a decision needs to be made”), with a nonsignificant but notable difference for item 2 (“I want to know exactly from my patient how he/she wants to be involved in making the decision”). These are interesting results for the psychometric quality of the SDM-Q-DOC. Although there is less literature on the psychometric qualities of the SDM-Q-DOC, ample literature exists on the psychometric characteristics of the SDM-Q-9, showing mixed results for item 1 and suggesting eliminating the item to improve the factorial structure [22]. In our small sample, item 1 served as a discriminate item.
Strengths and Limitations
The present study has several strengths. To the best of our knowledge, this is the first transnational comparison of the perceptions and practices of physicians in the United States, Israel, and Jordan. This survey study provided a conceptual overview of physicians’ understanding of SDM and PCC as well as an evaluation of SDM- and PCC-related behaviors. Although SDM is a communication model and practice and PCC is considered the conceptual framework [6], the participating physicians’ interpretation and understanding of the two were different. SDM was generally perceived as a means for delivering information to patients, whereas PCC was commonly perceived as a method for meeting a patient’s individual needs. While US and Jordanian physicians in our sample interpreted SDM also as a patient-level process (ie, patient participation), Israeli physicians in our sample interpreted SDM as a dialogue- and dyadic-based process occurring between patient and physician. These insights can inform future research and education initiatives pertaining to SDM and PCC among physicians. Finally, a methodological strength is our ability to deliver surveys in Arabic, Hebrew, and English due to the multilingual expertise of our team.
There are also some limitations to note. Our sample is small and not random; thus, we are unable to make meaningful statistical inferences or to generalize our findings, decreasing the study’s validity. However, because the purpose of this exploratory study was to generate hypotheses, our snapshot of the SDM landscape and PCC perceptions among physicians in Israel and Jordan is new and will inform future scaled-up surveys. We surveyed physicians, not other members of the health care team or patients. Clearly, a future comprehensive comparison of these health systems and practitioners’ approaches to care requires surveying all stakeholders involved. The SDM-Q-DOC questionnaire was administered to explore physicians’ perceptions of what is important in an SDM encounter, but it was not used to evaluate an actual encounter. Although the SDM-Q-DOC was developed to rate physicians’ experiences of SDM in patient-physician encounters, recent literature indicates the feasibility of the Shared Decision Making Questionnaire family for use in surveys [23,28,38]. The final limitations are that we were unable to recruit and collect data from physicians in the West Bank and that the survey in Jordan was conducted using face-to-face interviews rather than web-based surveys. Using email to initiate communication with potential Jordanian and West Bank respondents was challenging, as was administering a web-based survey. In Jordan, we learned that recruitment by phone call or an offline survey methodology that does not require an internet connection could be better for collecting responses. Therefore, we employed face-to-face interviews successfully, but this might have caused a social desirability bias; that is, physicians might have overestimated their support for and use of the SDM approach. However, because the web-based platform was the tool rather than the purpose, we believe it was not critical and that the benefits of collecting data face to face instead of via a web-based survey were more important for this project.
Conclusions
The results of the present study add to the limited, yet important, literature on SDM and PCC in areas of the world outside the United States, Canada, Australia, and Western Europe [9,12,39-43]. They also add to the psychometric evaluation of SDM and PCC measures [44] and identify barriers to implementation. We hope this survey will motivate researchers and clinicians in Israel, Jordan, and other countries that are less represented in the SDM and PCC research and practice to encourage related discussions and practice and to facilitate implementation, measurements, and interventions.
The authors wish to thank Young Shin Kim for her help in this research.
Conflicts of Interest: None declared.
Appendix
Physician Perceptions of Shared Decision Making and Person-Centered Care Survey.
AbbreviationsEQUATOR
Enhancing the Quality and Transparency of Health Research
PCC
patient-centered care
SDM
shared decision making
SDM-Q-9
9-item Shared Decision Making Questionnaire
SDM-Q-DOC
Shared Decision Making Questionnaire, physician version
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Originally published in JMIR Formative Research (http://formative.jmir.org), 03.08.2020.2020This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Formative Research, is properly cited. The complete bibliographic information, a link to the original publication on http://formative.jmir.org, as well as this copyright and license information must be included.Background
Shared decision making (SDM) is a health communication model that evolved in Europe and North America and largely reflects the values and medical practices dominant in these areas.
Objective
This study aims to understand the beliefs, perceptions, and practices related to SDM and patient-centered care (PCC) of physicians in Israel, Jordan, and the United States.
Methods
A hypothesis-generating comparative survey study was administered to physicians from Israel, Jordan, and the United States.
Results
A total of 36 surveys were collected via snowball sampling (Jordan: n=15; United States: n=12; Israel: n=9). SDM was perceived as a way to inform patients and allow them to participate in their care. Barriers to implementing SDM varied based on place of origin; physicians in the United States mentioned limited time, physicians in Jordan reported that a lack of patient education limits SDM practices, and physicians in Israel reported lack of communication training. Most US physicians defined PCC as a practice for prioritizing patient preferences, whereas both Jordanian and Israeli physicians defined PCC as a holistic approach to care and to prioritizing patient needs. Barriers to implementing PCC, as seen by US physicians, were mostly centered on limited appointment time and insurance coverage. In Jordan and Israel, staff shortage and a lack of resources in the system were seen as major barriers to PCC implementation.
Conclusions
The study adds to the limited, yet important, literature on SDM and PCC in areas of the world outside the United States, Canada, Australia, and Western Europe. The study suggests that perceptions of PCC might widely differ among these regions, whereas concepts of SDM might be shared. Future work should clarify these differences.
Shared decision making (SDM) is a central health communication model for supporting patient engagement in health care [1-3] and a recommended approach to increasing patient engagement and patient-centered care (PCC) in clinical decision making [4-6]. SDM evolved in Europe and North America [7] and largely reflects the values and medical practices dominant in these areas [8,9]. Although SDM has become more widely discussed in recent years in non-Western countries (eg, China, Peru, Malaysia, Taiwan, Iran) [10], it has yet to be implemented on a wider scale, and less is known about how or whether attitudes, beliefs, and practices regarding PCC exist or differ in various other regions of the world.
The overall aim of the present exploratory study was to explore the factors that enable or impede SDM implementation in different geographical and political contexts. Specifically, we sought to conduct a hypothesis-generating study and to collect preliminary data to better understand SDM- and PCC-related beliefs, attitudes, and practices of physicians in four regions in the Middle East characterized by different health care systems, cultures, and political environments: Israel, Jordan, and the West Bank. As a point of reference, we conducted a similar survey among US physicians to serve as a benchmark for SDM- and PCC-related beliefs, attitudes, and practices. In addition, such a comparison may provide insights about the importance of a health care system that facilitates the practice of SDM and PCC. The study focused on the following specific questions: (1) What are physicians’ common understandings and perceptions of the concepts of SDM and PCC? and (2) Do physicians find SDM and PCC to be feasible in their practice and in health care?
MethodsSettings: Context of Participating Countries
The survey was intended to be administered to physicians from different geographical and political contexts in the Middle East characterized by different health care systems: Israel, Jordan, and the West Bank. Israel is a democratic state with an efficient health care system that has been ranked among the top 10 health care systems for several years [11,12]. Israel’s national health insurance system provides universal health coverage throughout the country, with a significant spread of hospital and clinics [13-15]. Residents can supplement the universal coverage with additional forms of private health insurance. Israel’s health policy legislation is supportive of SDM principles, including the right to be informed of treatment options and risks [16]. Jordan is a constitutional monarchy state with a health care system characterized by diverse types of payers (public, private, and donors) [17]. The public health care sector is the largest in Jordan; however, only about 70% of residents have some form of public health insurance. Jordan has a ratio of 2.3 physicians to 1000 residents, and hospitals are mostly centralized in the larger urban areas. The massive influx of Syrian refugees due to the start of the Arab Spring in 2011 has further increased burdens on the Jordanian health care system, especially on public health facilities. The West Bank is an independent Palestinian territory governed by the Palestinian National Authority. The West Bank has low-functioning, inefficient health care systems that rely heavily on medical services to and referrals of patients to Israel (14% in 2011) or Jordan (13% in 2011) [17,18].
A parallel survey was planned among US physicians to serve as a benchmark. The United States is a representative democracy with a hybrid health care system but without universal health care coverage. In 2016, 48% of US health care spending came from private funds, with 28% coming from households and 20% coming from private businesses. The federal government accounted for 28% of spending, while state and local governments accounted for 17% of spending [19]. SDM in the United States is increasingly recognized as part of value-based care, and several federal initiatives have linked SDM to reimbursement [20].
Survey Development and Structure
Because the purpose of the present study was to explore aspects of SDM and PCC and to provide data for testing hypotheses, we chose to develop a survey as a research strategy [20]. As recommended by Enhancing the Quality and Transparency of Health Research (EQUATOR), we used “Good Practice in the Conduct and Reporting of Survey Research” as a reporting guideline [20]. We developed a short standardized survey form with 24 questions divided into 3 parts (see Multimedia Appendix 1): (1) demographic data, (2) qualitative evaluation, and (3) quantitative evaluation.
First, demographic data (eg, age and years in practice) was assessed with 9 questions.
Second, a qualitative evaluation was included. One part of this evaluation assessed the beliefs and attitudes of physicians around PCC and SDM in their health care setting (eg, “What do you see as barriers to implementing shared decision making in your practice?”). This comprised 5 open-ended questions and 1 multiple choice question. The second part of the qualitative evaluation assessed the understanding of physicians of their immediate environment of practice in the context of their larger health care system (eg, “What are the most important day-to-day problems in the practice of medicine or health care in your country or region?”). This included 3 open-ended questions.
Third, the quantitative evaluation of level of SDM practice was based on the Shared Decision Making Questionnaire, physician version (SDM-Q-DOC) scale [21]. This included 9 questions on a 6-point Likert scale ranging from “completely disagree” (0) to “completely agree” (5). The SDM-Q-DOC was developed to measure patients’ and clinicians’ agreement with steps and actions defined by a medically driven SDM model at the end of a medical consultation. It has been used in numerous studies to measure physicians’ perspectives of SDM and was recommended for use in health policy survey responses targeting the implementation of SDM [22-24]
The qualitative part of the survey was developed through an iterative process based on SDM and PCC literature related to the delivery and perception of care and on the lead investigators’ (YZI and ZB) knowledge [25-30]. The development process included discussions among the coinvestigators and piloting among colleagues. We conducted forward and backward translations based on accepted guidelines [31] to each new question in sections 1 and 2. We used the English version of the SDM-Q-DOC questionnaire [21] and the Arabic [32] and Hebrew [33] translations of the 9-item Shared Decision Making Questionnaire (SDM-Q-9), a parallel patient version [34], with the needed minor adaptions.
Procedure
A web-based survey developed for the study was emailed to physicians in Israel, the West Bank, and the United States using the Qualtrics platform (Qualtrics International Inc). In Jordan it was advised by one of the coauthors (RO) to administer the survey via face-to-face interviews, based on her previous experience conducting similar types of research in Jordan. A snowball sampling methodology was used to recruit physicians. Accordingly, participants were asked to identify and email the questionnaire to other colleagues. Surveys were administered in Hebrew, English, and Arabic, and all responses were anonymized. Data collection began in February 2017 and ended in June 2017. It was designed to stop after a sample of 15 in each country or after the maximum sample size closest to this threshold. As this was an exploratory study, this sample size target was based not on statistical considerations but on real-world experience with the number of respondents likely to provide a hypothesis-generating set of responses. At the end of the survey, participants were reimbursed via gift cards in the amount of US $10 or an equal value in the local currency.
Data Analysis
To summarize the qualitative results from the open-ended survey questions, we used an integrated approach that enabled both inductive (ie, data-driven) coding of participants’ responses and deductive (ie, theory-driven) framework organization of codes [35]. Specifically, 2 coauthors (SH and ZB) and another research assistant read open-ended responses from participants and developed a draft of coding categories based on the responses’ contents. These categories were reviewed by the lead author (YZI) and revised accordingly. Responses to open-ended questions were coded independently by all coauthors; differences and disagreements between the coders were resolved through discussions until consensus was achieved. The final coding of open-ended responses was double-checked for accuracy by the first and last authors (YZI and ZB) after finalization of the coding guide. Then, guided by SDM and PCC theories, all coauthors discussed the interrelationships between codes to finalize the grouping of the codes into themes and subthemes.
Chi-square and one-way ANOVA tests were conducted to describe demographic characteristics of the survey respondents. As recommended by the developers [21], multiplication of the raw score by 20/9 provided a transformed total score range from 0 to 100, where 0 indicates the lowest possible level and 100 indicates the highest possible level of SDM. A nonparametric Kruskal-Wallis test was conducted to compare the mean total score of SDM-Q-9 between the 3 countries. Results were considered significant below a P value of .05.
Ethics, Consent, and Permissions
Because the responses were anonymized and not identifiable and participation in the study was associated with minimal risk, the Institutional Review Board of the Johns Hopkins School of Medicine deemed this study exempt from requirements for approval (IRB00111847). All participants provided consent to participate through their responses to the survey.
ResultsParticipants
Eligible survey respondents were practicing physicians in the United States, Israel, Jordan, and the West Bank. A total of 36 survey responses were received from Israel (n=9), Jordan (n=15), and the United States (n=12). Throughout the study period, we received no responses from West Bank physicians to our emails or to our in-person attempts to contact them; therefore, we were unable to collect any data from that population. Most survey respondents were men (24/36, 67%), the mean age of survey respondents was 43.6 years (SD 11.2), and the mean years of clinical experience was 15.8 (SD 10.5). Comparison of demographic characteristics and clinical experience of clinicians in each country indicate similarity between US and Israeli respondents for gender distribution, age, and clinical experience (Table 1). The Jordanian respondents were significantly younger and less experienced, and almost all were men.
Demographic and clinical experience characteristics of survey respondents.
Characteristics
Total sample \\n(N=36)
United States \\n(n=12)
Israel \\n(n=9)
Jordan \\n(n=15)
P value
\\nGender\\n
\\n\\n
\\n\\n
\\n\\n
.01a
\\n\\n
Men, n (%)
5 (42)
24 (67)
5 (56)
14 (93)
—b\\n\\n
\\n\\n
Women, n (%)
7 (58)
12 (33)
4 (44)
1 (7)
—\\n\\n
Age (years), mean (SD)
43.6 (11.2)
47.0 (8.6)
48.7 (13.4)
37.4 (9.2)
.02c
Years of clinical experience, mean (SD)
15.8 (10.5)
19.0 (9.8)
20.0 (14.2)
10.8 (6.0)
.05d
aPearson χ22=8.7.
bNot applicable.
cAnalysis of variance F test (F2,32=4.40).
dAnalysis of variance F test (F2,33=3.37).
Open-Ended Responses: Perception of SDM and PCC
We included in the analysis 34 survey responses with greater than 50% total completion (Jordan: n=15; United States: n=12; Israel: n=7). There were 12 responses to the open-ended questions from the US physicians, 7 from the Israeli physicians, and 14 from the Jordanian physicians.
Most respondents defined SDM as a process aimed at informing patients (United States: 8/12, 67%; Israel: 4/7, 57%; Jordan: 12/15, 80%). Whereas most US respondents also defined SDM as the participation of patients in their care (8/12, 67%), only a third of the Jordanian respondents defined it as patient participation (5/15, 33%), and 4 of the 7 (57%) Israeli respondents defined SDM also as collaboration between patient and physician.
[SDM is when] a patient makes decisions about medical tests and treatment that incorporate information about benefits and harms from the physician as well as the patient’s own understanding of his or her values and priorities.
US respondent
[SDM is] an open conversation with the patient, in which I [the doctor] suggest/advise a variety of treatment options that fit the patient’s medical condition, and together with the patient, choose the appropriate treatment method.
Israeli respondent
[SDM is] giving the patient information about his treatment options and his illness and giving him a chance to have a say in his treatment options.
Jordanian respondent
Most US and Israeli respondents indicated familiarity with the concept of PCC (Israel: 5/7, 71%; United States: 10/12, 83%), whereas only 6 of the 15 (40%) Jordanian respondents indicated their or their patients’ familiarity with the concept. Prioritizing or meeting patient needs was commonly described as a feature of PCC by most respondents regardless of country of origin. In addition, US respondents commonly described PCC as accounting for patients’ preferences, most Israeli respondents described PCC also as individualized care, and most Jordanian respondents also described PCC as a provision of holistic care.
[PCC refers to] care that balances the needs and desires of the person receiving care.
US respondent
[PCC aims] to provide the patient with all of the patient’s needs and not just to solve a problem in the field, while maintaining proper communication and respect for the patient’s values.
Israeli respondent
[PCC refers] to doing whatever is needed for the patient or referring him/her to someone who can.
Jordanian respondent
Respondents indicated several barriers affecting the provision of SDM and PCC systematically; however, the common barrier was related to the system itself. All US respondents (12/12, 100%) mentioned lack of time as a major barrier to SDM implementation, whereas only 5 of 12 (42%) mentioned it as a barrier to PCC implementation. The role of insurance companies and fragmentation of care were mentioned as additional possible barriers to PCC implementation. In Jordan, most respondents (9/15, 60%) mentioned patient-related barriers, low health literacy, and a lack of knowledge as barriers to SDM implementation, whereas system-related barriers, such as staff shortages and high patient loads, were identified as barriers to PCC.
Time, and often hard to do for many decisions: few are really straightforward. Would be nice to have tools readily available to do this & ways to facilitate it.
US respondent, regarding SDM barriers
Lack of knowledge among patients and patient unwillingness to be fully informed about his/her condition.
Jordanian respondent, regarding SDM barriers
The healthcare delivery system is still organized traditionally regarding appointment scheduling and how patients interact with doctors; short visits limit person-centered care.
US respondent, regarding PCC barriers
Bureaucracy of the Jordanian health care system and lack of medical specialties in the peripheral areas of the country.
Jordanian respondent, regarding PCC barriers
With respect to problems related to the patient-physician relationship, the responses of Israeli physicians emphasized a lack of time and training (eg, lack of time, lack of support for physicians during their work). Jordanian respondents emphasized disorganization of the health care system, and US physicians highlighted problems of cost, social determinants of health, and the role of insurance companies (eg, the payment system and its incentive structure, lack of universal health care, costs of pharmaceuticals).
SDM-Q-DOC Responses: Comparison of SDM Practice and PCC Behaviors
We included in the analysis 32 survey responses with greater than 50% total completion (Jordan: n=15; United States: n=10; Israel: n=7). Overall, physicians in our sample reported practicing SDM at a moderately high level (mean 76.6, SD 11.5; median 75.6), with a range of 53 to 100. This result is similar to findings in other studies [22]. The Kruskal-Wallis test results showed no significant difference in SDM-Q-DOC scores between the 3 countries (H2=0.631, P=.73; United States: mean 74.9, SD 11.4; Israel: mean 78.7, SD 7.9; Jordan: mean 76.7, SD 13.4). Box plot diagrams of SDM-Q-DOC means and standard deviations imply that SDM practice and PCC behaviors vary more among the US and Jordanian respondents in our sample than among their Israeli counterparts, as seen in Figure 1.
In addition, we compared the individual item scores between respondents from the 3 countries. Only for the first item (“I make clear to my patient that a decision needs to be made”) was a significant difference noticed, with higher scores for Jordanian physicians (Table 2). A nonsignificant but notable difference was noticed in the second item (“I want to know exactly from my patient how he/she wants to be involved in making the decision”), with a higher mean score for Jordanian physicians.
Box plot diagrams of SDM-Q-DOC score per country. SDM-Q-DOC: Shared Decision Making Questionnaire, physician version.
Comparison of means and standard deviations of Shared Decision Making Questionnaire, physician version responses among respondents from the United States, Israel, and Jordan (n=32).
SDM-Q-DOCa item question
H statistic
P value
United States, mean (SD)b
Israel, mean (SD)b
Jordan, mean (SD)b
1. I make clear to my patient that a decision needs to be made.
9.55
.008
3.60 (0.84)
3.57 (0.98)
4.47 (0.52)
2. I want to know exactly from my patient how he/she wants to be involved in making the decision.
4.66
.10
3.00 (0.67)
3.29 (0.76)
3.73 (0.80)
3. I tell my patient that there are different options for treating his/her medical condition.
1.83
.40
4.50 (0.71)
4.43 (0.98)
4.21 (0.58)
4. I precisely explain the advantages and disadvantages of the treatment options to my patient.
2.59
.27
3.80 (0.92)
4.43 (0.53)
4.13 (0.64)
5. I help my patient understand all the information.
2.06
.36
3.80 (0.92)
4.29 (0.76)
4.27 (0.96)
6. I ask my patient which treatment option he/she prefers.
1.00
.61
4.10 (0.74)
4.00 (1.00)
3.73 (0.96)
7. My patient and I thoroughly weigh the different treatment options.
2.53
.28
3.40 (0.52)
4.00 (0.82)
3.47 (0.92)
8. My patient and I select a treatment option together.
0.23
.89
3.50 (0.53)
3.43 (0.79)
3.13 (1.46)
9. My patient and I reach an agreement on how to proceed.
1.47
.48
8.84 (0.76)
4.00 (5.80)
3.67 (0.90)
aSDM-Q-DOC: Shared Decision Making Questionnaire, physician version.
bMeans represent level of agreement with each item on a 6-point Likert scale, where 0=completely disagree and 5=completely agree.
DiscussionPrincipal Results and Comparison With Prior Work
The present study describes findings of a small, exploratory hypothesis-generating survey [20] of Israeli, Jordanian, and US physicians’ perceptions of SDM and PCC. Open-ended qualitative results suggest that respondents, regardless of country of origin, identify SDM as a process focused on providing information or as informed decision making, but PCC as a physician’s effort to meet patients’ individualized needs. These findings are aligned with the perception that SDM is “the pinnacle of PCC” [4] but also emphasize that SDM remains commonly perceived by physicians as a mean for delivering information rather than a collaborative discussion [36,37]. Barriers to implementing SDM and PCC were also identified and attributed to system- and patient-related factors [25]. The quantitative results of the total mean score of the SDM-Q-DOC show medium to high levels of SDM-related behaviors among all respondents. Item-focused analysis showed that Jordanian respondents scored significantly higher on item 1 (“I make clear to my patient that a decision needs to be made”), with a nonsignificant but notable difference for item 2 (“I want to know exactly from my patient how he/she wants to be involved in making the decision”). These are interesting results for the psychometric quality of the SDM-Q-DOC. Although there is less literature on the psychometric qualities of the SDM-Q-DOC, ample literature exists on the psychometric characteristics of the SDM-Q-9, showing mixed results for item 1 and suggesting eliminating the item to improve the factorial structure [22]. In our small sample, item 1 served as a discriminate item.
Strengths and Limitations
The present study has several strengths. To the best of our knowledge, this is the first transnational comparison of the perceptions and practices of physicians in the United States, Israel, and Jordan. This survey study provided a conceptual overview of physicians’ understanding of SDM and PCC as well as an evaluation of SDM- and PCC-related behaviors. Although SDM is a communication model and practice and PCC is considered the conceptual framework [6], the participating physicians’ interpretation and understanding of the two were different. SDM was generally perceived as a means for delivering information to patients, whereas PCC was commonly perceived as a method for meeting a patient’s individual needs. While US and Jordanian physicians in our sample interpreted SDM also as a patient-level process (ie, patient participation), Israeli physicians in our sample interpreted SDM as a dialogue- and dyadic-based process occurring between patient and physician. These insights can inform future research and education initiatives pertaining to SDM and PCC among physicians. Finally, a methodological strength is our ability to deliver surveys in Arabic, Hebrew, and English due to the multilingual expertise of our team.
There are also some limitations to note. Our sample is small and not random; thus, we are unable to make meaningful statistical inferences or to generalize our findings, decreasing the study’s validity. However, because the purpose of this exploratory study was to generate hypotheses, our snapshot of the SDM landscape and PCC perceptions among physicians in Israel and Jordan is new and will inform future scaled-up surveys. We surveyed physicians, not other members of the health care team or patients. Clearly, a future comprehensive comparison of these health systems and practitioners’ approaches to care requires surveying all stakeholders involved. The SDM-Q-DOC questionnaire was administered to explore physicians’ perceptions of what is important in an SDM encounter, but it was not used to evaluate an actual encounter. Although the SDM-Q-DOC was developed to rate physicians’ experiences of SDM in patient-physician encounters, recent literature indicates the feasibility of the Shared Decision Making Questionnaire family for use in surveys [23,28,38]. The final limitations are that we were unable to recruit and collect data from physicians in the West Bank and that the survey in Jordan was conducted using face-to-face interviews rather than web-based surveys. Using email to initiate communication with potential Jordanian and West Bank respondents was challenging, as was administering a web-based survey. In Jordan, we learned that recruitment by phone call or an offline survey methodology that does not require an internet connection could be better for collecting responses. Therefore, we employed face-to-face interviews successfully, but this might have caused a social desirability bias; that is, physicians might have overestimated their support for and use of the SDM approach. However, because the web-based platform was the tool rather than the purpose, we believe it was not critical and that the benefits of collecting data face to face instead of via a web-based survey were more important for this project.
Conclusions
The results of the present study add to the limited, yet important, literature on SDM and PCC in areas of the world outside the United States, Canada, Australia, and Western Europe [9,12,39-43]. They also add to the psychometric evaluation of SDM and PCC measures [44] and identify barriers to implementation. We hope this survey will motivate researchers and clinicians in Israel, Jordan, and other countries that are less represented in the SDM and PCC research and practice to encourage related discussions and practice and to facilitate implementation, measurements, and interventions.
The authors wish to thank Young Shin Kim for her help in this research.
Conflicts of Interest: None declared.
Appendix
Physician Perceptions of Shared Decision Making and Person-Centered Care Survey.
AbbreviationsEQUATOR
Enhancing the Quality and Transparency of Health Research
PCC
patient-centered care
SDM
shared decision making
SDM-Q-9
9-item Shared Decision Making Questionnaire
SDM-Q-DOC
Shared Decision Making Questionnaire, physician version
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PsychiatryFrontiers in Psychiatry1664-0640Frontiers Media S.A.32848898PMC743215010.3389/fpsyt.2020.00622PsychiatryOriginal ResearchVictim Sensitivity and Its Neural Correlates Among Patients With Major Depressive DisorderWangXiaoming\n1\n\n2\n\n†\nCuiShaojuan\n3\n\n†\nWuMichael Shengtao\n4\nWangYun\n5\nGaoQinglin\n1\n\n2\nZhouYuan\n1\n\n2\n\n5\n\n*\n\n1\nKey Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, Beijing, China\n\n2\nDepartment of Psychology, University of Chinese Academy of Sciences, Beijing, China\n\n3\nDepartment of Psychology, Beijing Tongren Hospital, Capital Medical University, Beijing, China\n\n4\nSchool of Sociology and Anthropology, Xiamen University, Xiamen, China\n\n5\nThe National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China\n
Edited by: Paul Stokes, King’s College London, United Kingdom
Reviewed by: Guido van Wingen, University of Amsterdam, Netherlands; Chien-Han Lai, National Yang-Ming University, Taiwan
†These authors have contributed equally to this work
This article was submitted to Mood and Anxiety Disorders, a section of the journal Frontiers in Psychiatry
118202020201162203220201562020Copyright © 2020 Wang, Cui, Wu, Wang, Gao and Zhou2020Wang, Cui, Wu, Wang, Gao and ZhouThis is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.Background
Dysfunctional beliefs about the self are common in the development of depressive symptoms, but it remains unclear how depressed patients respond to unfair treatment, both dispositionally and neurally. The present research is an attempt to explore the differences in sensitivity to injustice as a victim and its neural correlates in patients with major depressive disorder (MDD) versus healthy controls.
Methods
First episodic, drug-naïve patients with MDD (n = 30) and a control group (n = 30) were recruited to compare their differences in victim sensitivity. A second group of patients with MDD (n = 23) and their controls (n = 28) were recruited to replicate the findings and completed resting-state functional magnetic resonance imaging (fMRI) scanning. Spontaneous brain activity measured by fractional amplitude of low-frequency fluctuation (fALFF) was used to characterize the neural correlates of victim sensitivity both in patients and in healthy controls.
Results
Higher victim sensitivity was consistently found in patients with MDD than healthy controls in both datasets. Multiple regression analysis on the fALFF showed a significant interaction effect between diagnosis and victim sensitivity in the right dorsolateral prefrontal cortex (DLPFC).
Conclusions
The patients with MDD show higher sensitivity to injustice as a victim, which may be independent of their disease course. The MDD patients differ from healthy controls in the neural correlates of victim sensitivity. These findings shed light on the linkage between cognitive control subserved by the DLPFC and negative bias towards the self implicated by higher victim sensitivity among the depressed patients.
Major depressive disorder (MDD) is one of the most common mental disorders, with a high prevalence in current times (1–3). The cognitive model of depression speculates that MDD patients generally hold negative bias and dysfunctional beliefs about themselves. However, it is still unclear how and to what extent self-related personalities and its neural circuits play a role in depressive symptoms (4). Recent research suggests that hypervigilance towards one’s negative experience is linked with the development of MDD (5), and sensitivity to injustice as a victim is related to the stabilization of depressive symptoms (6).
Victim sensitivity captures the individual differences in response to unfair treatment towards oneself. Victim-sensitive individuals tend to detect injustice within a low threshold. Thus, they are likely to experience stronger anger, moral outrage, and exhibit uncooperative behavioral tendencies (7, 8). Victim sensitivity is associated with a higher risk of having negative concerns and social emotions (9) and biased social judgment (for example, people who are higher in victim sensitivity would rate neutral and hostile faces as more untrustworthy and underestimate targets’ cooperativeness in order to avoid being exploited in the future (10)). In the personality space, victim sensitivity has a unique correlation with hostility (11), a facet of neuroticism, which is a predisposition toward depression (12, 13). Furthermore, victim sensitivity was found to be positively related to emotional problems (14) and to mediate the link between attention deficit hyperactivity disorder and depressive symptoms in children and adolescents (14). In a longitudinal study, depressive symptoms at the first measurement predicted higher victim sensitivity one or two years later, while higher victim sensitivity promoted the stabilization of depressive symptoms in those with depressive symptoms at the baseline (6). All of the above findings demonstrate a connection between victim sensitivity and depressive symptoms and also suggest that victim sensitivity should be considered as a potential personality risk factor for the emergence and maintenance of depression. However, there is lack of direct evidence as to whether victim sensitivity was significantly endorsed in a clinical population with MDD, as compared to healthy controls.
We then ask the question: what is the neural basis of higher victim sensitivity in MDD patients? In healthy populations, task-based functional magnetic resonance (fMRI) studies found that the brain activity of anterior insula, anterior cingulate cortex, and dorsolateral prefrontal cortex were increased when participants were faced with unfair treatments (e.g., unequal division on a certain amount of money) (15–17), suggesting that these brain regions may be vital for processing injustice for a victim. Different from the healthy controls, MDD patients had weaker activity in the medial occipital lobe or striatum with increasing or decreasing inequality (18). A relevant study on the neural correlates of justice sensitivity found that victim sensitivity predicted subjective ratings of praise, but it was not correlated with neural activity during a moral judgment task (19). It should be noted that these findings are obtained by using task-based fMRI, in which task-induced brain activity is context-dependent. Personality differences in victim sensitivity may also be reflected in spontaneous brain activity measured by resting-state fMRI, which is a promising tool to uncover the neural basis of inter-individual differences in personality or propensity (20–23). Using an index of spontaneous brain activity that measures temporal synchronization of brain activity within a local brain region, one study found victim sensitivity was positively associated with regional spontaneous brain activity of the paracentral lobule (24). In spite of these descriptions, to our knowledge, no empirical data on the neural basis of victim sensitivity in MDD has been reported. The present study is a first step to fill this gap.
In brief, this current study first aims to test whether the patients with MDD are more sensitive to injustice as a victim. To this aim, we recruited a group of MDD patients who were first episodic and drug-naïve to reduce the potential confounders from long illness duration and medications. Then, we recruited another group of MDD patients with less severe depressive symptoms to test whether the abnormality in victim sensitivity is generalized in patients with MDD. Second, we further explored the neural basis underlying victim sensitivity in MDD using the resting-state fMRI. We selected an index called fractional amplitude of low-frequency fluctuation (fALFF) (25). The fALFF, similar to ALFF, measures low frequency oscillations of the blood oxygen level-dependent (BOLD) signal at rest, but the fALFF represents the ratio of the power spectrum of low-frequency to that of the entire frequency range and thus is less prone to noise compared to ALFF (25). The low frequency BOLD oscillation are closely related to spontaneous neuronal activity (26). As a method with high test-retest reliability to detect the intensity of regional spontaneous neuronal activity during rest (27), the fALFF has been widely used to investigate spontaneous brain activities in different psychiatric disorders, including MDD (28–30), and be linked with personality traits in healthy populations (31–33). In addition, fALFF is a data-driven approach and requires no a priori hypothesis and thus is appropriate for exploratory analyses, such as the present study. Therefore, here we attempted to employ fALFF to explore whether there are different neural correlates of victim sensitivity between patients with MDD and healthy controls.
MethodsParticipants
Two groups of patients with MDD and their matched healthy controls were recruited from two independent sites. Participants in the Tongren dataset were recruited for the aim of investigating whether MDD patients who are first episodic and drug-naïve are more sensitive to injustice as a victim on a behavioral level. We then used another group of participants (i.e. Anding dataset) to test whether the findings obtained in the Tongren dataset can be replicated in MDD patients with less severe depressive symptoms. The participants in the Anding dataset are from a project that initially aimed to study the neural basis of abnormal social decision-making behavior and related traits in patients with MDD, and thus made it possible to further investigate the differences in neural correlates of victim sensitivity among MDD patients versus healthy participants in the current study.
In the Tongren dataset, 30 patients with MDD were recruited from the Department of Psychology, Beijing Tongren Hospital, Capital Medical University. Thirty demographically-matched healthy controls were recruited via advertisements. All of these patients met the following criteria: (1) the Diagnostic and Statistical Manual of Mental Disorders, fourth edition (DSM-IV) criteria for a major depressive episode, diagnosed independently by two qualified psychiatrists who interviewed the patients personally; (2) seeking medical advice for the first time in this hospital and have never taken any psychotropic medication; (3) Hamilton Rating Scale for Depression (HAMD-17) scores >=17; and (4) age range is between 18 and 45 years old. The patients were excluded if they had any preexisting or concurrent co-morbid primary diagnosis that met the DSM-IV criteria for any Axis I disorder other than MDD. Participants in the control group had no current or past history of depression or any other psychiatric disorders and no family history of major psychiatric or neurological illness in first-degree relatives. Additional exclusion criteria for both of the groups were acutely suicidal or homicidal behaviors, history of trauma resulting in loss of consciousness, history of major neurological or physical disorders that could lead to an altered mental state, or current pregnancy or breastfeeding.
In the Anding dataset, 23 patients were recruited from Beijing Anding Hospital, Capital Medical University. Potential applicants for the patient group were from the outpatient departments. They were recommended by their doctors who had diagnosed them before, and then again diagnosed by a psychiatrist when they were recruited in this study. The inclusion and exclusion criteria of the patients were the same as the Tongren dataset, with the only exception that these patients were not experiencing an acute depressive episode and had less severity in depressive symptoms (for details, see \nTable 1\n). Among these MDD patients, 15 patients had at least two onsets of depressive episodes; eight patients had a recent depressive episode and their symptoms had been controlled after antidepressant treatments when they were recruitment. Only one patient did not take any psychotropic medication; the other patients received antidepressant medications (SSRIs and/or SNRIs), with one patient additionally taking low-dose benzodiazepines. Twenty-eight healthy controls were recruited by advertisements. Due to the relatively stable mental condition of the MDD patients in the Anding dataset, only participants in the Anding dataset participated in MRI scanning.
Demographics and clinical information of patients with MDD and healthy controls within the two datasets.
Characteristics
Tongren dataset
Anding dataset
MDD
NC
p\na\n\n
MDD
NC
p\na\n\n
Sample size
N=30
N=30
N=23
N=28
Age (years)
25.47 ± 6.40
27.67 ± 4.14
0.12
31.22 ± 5.70
29.57 ± 5.80
0.32
Gender (M: F)
6:24
6:24
1.00
13:10
16:12
0.96
Education
14.33 ± 2.44
14.80 ± 2.19
0.44
14.91 ± 3.22
15.25 ± 2.55
0.68
HAMA
22.03 ± 4.92
–
–
9.83 ± 6.86
0.64 ± 1.16
<0.001
HAMD-17
24.90 ± 4.14
–
–
13.13 ± 6.96
0.89 ± 1.72
<0.001
Victim Sensitivity
34.67 ± 9.06
25.10 ± 7.48
<0.001
30.96 ± 9.50
25.41 ± 8.07
0.03
Two-sample t-tests or chi-square test. HAMD-17, the Hamilton Rating Scale for Depression; HAMA, the Hamilton Rating Scale for Anxiety.
This study was approved by the Institutional Review Boards of Beijing Tongren Hospital, Beijing Anding Hospital, Capital Medical University, as well as the Institute of Psychology, Chinese Academy of Sciences. Written informed consent was obtained from all participants.
Assessment of Victim Sensitivity
Ten items were used to measure justice sensitivity as a victim (11, 34). Examples of items for victim sensitivity are “It makes me angry when I am treated worse than others” or “ I ruminate for a long time when other people are treated better than me”. Each item was scored on a 6-point rating scale ranging from 0 (not at all) to 5 (exactly). The reliabilities were acceptable with the estimated reliability coefficients (Cronbach’s alpha) of the MDD patients and healthy controls amounting to 0.92 and 0.84 for the Tongren sample, and to 0.84 and 0.89 for the Anding sample.
MRI Data Acquisition
The fMRI data were acquired using a GE 3.0 T MRI scanner at the Magnetic Resonance Imaging Research Center, Institute of Psychology, Chinese Academy of Sciences. Structural and functional MRI scans were obtained for each participant. Structural MRI scans were acquired using a magnetization-prepared rapid acquisition gradient echo sequence with the following parameters: TI = 450 ms, receiver bandwidth = 31.25, matrix = 256 × 256, field of view (FOV) = 240 mm * 240 mm, slice thickness = 1.5 mm, flip angle = 12°. Functional images were acquired for each participant using an echo planar imaging (EPI) sequence with the following parameters: TR = 2000 ms, TE = 30 ms, flip angle = 70°, acquisition matrix = 64 * 64, and FOV = 220 mm * 220 mm; 33 axial slices, with a thickness of 4 mm and no gap. During the scanning, the participants were instructed to keep their eyes closed and not to focus their thoughts on anything. The duration of the resting-state fMRI was 10 minutes. After resting-state fMRI scanning, these participants performed a fMRI task, but the data were not used in the current study.
Image Preprocessing
Conventional functional imaging preprocessing was performed using the Data Processing Assistant for Resting-State fMRI (DPARSF 4.3, http://rfmri.org/dpabi) (35), which is based on Statistical Parametric Mapping (SPM12) (http://www.fil.ion.ucl.ac.uk/spm) and the Resting-State fMRI Data Analysis Toolkit (REST 1.8, http://www.restfmri.net) (36). Conventional preprocessing steps were conducted, including the removal of the first 5 volumes, slice timing realignment, co-registration, segmentation of the T1 map to generate the gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) (37), nuisance variable regression, spatial normalization with 2-mm cubic voxels, and spatial smoothing of 4 mm FWHM. The nuisance variables included 24 motion parameters (6 head motion parameters, 6 head motion parameters one time point before, and the 12 corresponding squared items), 5 principal components from the individual segmented CSF and WM regions (38), and linear and quadratic trends (39). To quantify head motion, the volume-based framewise displacement (FD) was computed (40).
fALFF Analysis
For each participant, we calculated the fALFF to characterize the regional spontaneous activity in a voxel-wise way using the DPARSF software. The fALFF is defined as the total power within a frequency band divided by the total power of the entire detectable frequency range (25). Thus, the square root was firstly calculated at each frequency in the power spectrum, and the averaged square root was computed across a low frequency range (0.01–0.08 Hz) at each voxel. Then, the fALFF was obtained as a fraction, which was the sum of the amplitude across 0.01–0.08 Hz divided by that across the whole frequency range. Finally, the normalized score of fALFF was obtained and an individual voxel-wise z-fALFF map was generated for each participant.
Statistical Analysis
Chi-square tests and independent sample t tests were conducted to compare the mean level of demographic, clinical variables, and victim sensitivity between the MDD and healthy controls for each site. Correlation analyses and partial correlation analyses were conducted to investigate the clinical correlates of victim sensitivity for each dataset. All of these analyses were conducted by SPSS v21.
To identify the brain regions where activities related to victim sensitivity may be different between patients with MDD and healthy controls, we firstly conducted a multiple regression analysis on the z-fALFF images of the total sample using SPM12. The z-fALFF of each voxel in the brain were regressed on the variables of diagnosis, victim sensitivity score, and the diagnosis by victim sensitivity score interaction, with age, gender, education level, and mean FD as covariates. To control for Type 1 error, a cluster-level family-wise error (FWE) corrected p-value of 0.05 was used for multiple comparisons correction (individual voxel height threshold of p < 0.001). Then, multiple regression analyses were conducted on the z-fALFF images for patients and controls, separately. The victim sensitivity score was entered as a regressor into the model, in addition to age, gender, education level, and mean FD which were included as variables of no interest. Due to the relatively small sample size, we conducted a Small Volume Correction (SVC) by using the result of the regression analysis of the total sample as a mask (individual voxel height threshold of p < 0.005, cluster-level FWE corrected p < 0.05 following SVC).
ResultsDemographic and Clinical Data
There were no significant differences in gender composition, age, and educational level between patients and healthy controls in both of the two datasets (all p > 0.05) (\nTable 1\n). The patients in the Tongren dataset showed more severe depressive symptoms as assessed with the HAMD (p<0.001) and more severe anxiety symptoms as assessed with the Hamilton Rating Scale for Anxiety (HAMA) (p<0.001) compared with those in the Anding dataset.
Victim Sensitivity
We found higher victim sensitivity among MDD patients both in the Tongren dataset (MDD: 34.67 ± 9.06, NC: 25.10 ± 7.48; p<0.001, Cohen’s d = 1.15) and in the Anding dataset (MDD: 30.96 ± 9.50, NC: 25.41 ± 8.07; p=0.03, Cohen’s d = 0.63) (\nTable 1\n).
We also explored the clinical correlates of victim sensitivity. We found that there was no correlation between victim sensitivity and the HAMD score in the Tongren dataset (r = 0.13, p = 0.49) but positive correlation in the Anding dataset (r = 0.54, p = 0.007). However, the correlation in the Anding dataset was marginally significant after controlling for the HAMA score (r = 0.42, p = 0.053) or disappeared after excluding the remitted patients (HAMD score < =7; N=6) (r = 0.05, p = 0.86). All of these findings suggested that the correlation between victim sensitivity and the severity of depressive symptoms may not be reliable.
fALFF
A significant interaction effect between diagnosis and victim sensitivity was found in the right middle frontal gyrus (peak MNI coordinate: [34, 58, 28]; cluster size: 95 voxels; cluster-level FWE p < 0.05) (\nFigure 1A\n). By searching this region in the Brainnetome Atlas Viewer (41), we found that this region was located in the area 46, which is often termed as the dorsolateral prefrontal cortex (DLPFC) (\nFigure 1B\n). The interaction effect was shown in \nFigure 1C\n, which illustrated the relationship between victim sensitivity and the z-fALFF value in the right the DLPFC for each group.
Interaction effect between diagnosis and victim sensitivity on fALFF and the results of post hoc analysis. (A) The region showing significant interaction effect between diagnosis and victim sensitivity; (B) The region showing significant interaction effect was overlaid with the area 46 in the Brainnetome Atlas Reviewer (http://atlas.brainnetome.org/). (C) Scatter plots for the relationship between the fALFF value and victim sensitivity within each group with the shadow parts representing 95% confidence interval. Abbreviation: rMFG, right middle frontal gyrus.
By separately analyzing the relationship between victim sensitivity and fALFF within each group, we found that there were no regions showing significant correlations with victim sensitivity score both in the MDD group and in the healthy controls group in a whole brain search (cluster-level FWE p < 0.05). But by using SVC with the results of the regression analysis of the total sample as a mask, we found a significantly negative correlation in the right DLPFC in the healthy controls group (SVC cluster-level FWE corrected p = 0.01, 26 voxels, T = -4.45, peak voxel coordinates: [32, 56, 26]) and significantly positive correlation in the right DLPFC in the MDD group (SVC cluster-level FWE corrected p = 0.04, 9 voxels, T = 3.35, peak voxel coordinates: [32, 58, 22]).
Significant diagnosis effect was found in the right superior occipital gyrus (SOG; peak MNI coordinate: [32, -84, 42]; cluster size: 104 voxels) (\nFigure 2\n), suggesting that the patients showed decreased fALFF in this region compared to the healthy controls. We didn’t find the regions showing significant correlations with victim sensitivity across the two groups in the pre-specified threshold (cluster-level FWE p < 0.05).
Diagnosis effect on fALFF. (A) Region showing significant group difference in fALFF between the patients with major depressive disorder (MDD) and the healthy controls (HC). (B) The fALFF value in this region within each group. Abbreviation: rSOG, right superior occipital gyrus.
Discussion
The current study provides robust evidence that MDD patients have higher sensitivity to injustice as a victim, which was consistently shown in two clinical samples. The results also showed that the neural correlates of victim sensitivity differ between the MDD patients and the healthy controls. Specifically, the fALFF in the right DLPFC, the region for cognitive control, positively correlated with victim sensitivity in MDD patients, but was negatively correlated with victim sensitivity in healthy controls.
Hypersensitivity to Injustice as a Victim
In line with previous studies on the association between victim sensitivity and depressive symptoms in non-clinical samples or personality traits related to depression (6, 42, 43), the present study found that patients with MDD had higher sensitivity to injustice as a victim. Particularly, this hypersensitivity can be found in a group of first episodic, drug-naïve patients with MDD, suggesting that the increased victim sensitivity was free from the confounding effect of chronic illness or antipsychotic drugs and could be observed even in the early stage of depression. In order to test whether this hypersensitivity can be generalized to a common clinical sample, we recruited another group of MDD patients, most of whom were medicated and with mild to moderate depressive symptoms (the Anding dataset). We again found increased victim sensitivity in these patients. On the contrary, we found the correlation between victim sensitivity and severity of depressive symptoms was not reliable because we only found the evidence that supported a positive correlation in the Anding dataset not in the Tongren dataset. More importantly, the correlation only showed the trend towards significance after controlling for the anxiety symptoms or disappeared after excluding the remitted patients who had extremely low HAMD scores. The non-repeatable correlation between victim sensitivity and severity of depressive symptoms suggests that the increased victim sensitivity may be independent of depressive severity; however, this conclusion should be verified with a larger sample size and in the remitted patients. All of these findings - the repeatable findings on the higher victim sensitivity in the MDD patients in both of the two sites and the non-repeatable clinical correlates of the victim sensitivity - suggest that higher victim sensitivity may be independent of illness course, severity, or medication. Combining the previous findings obtained in non-depressed samples with ours, it seems higher victim sensitivity may be an important psychopathological feature for depression. In a longitudinal study of German adolescents, victim sensitivity increased the stabilization of depressive symptoms if they were present at baseline, suggesting that victim sensitivity may be a maintaining factor for depression (6). In future work, the role of victim sensitivity in the occurrence and progression of depression should be further examined in a longitudinal study.
The present study indicated that patients with MDD are more likely to perceive injustice and respond to injustice to one’s own disadvantage. Negative bias and dysfunctional beliefs about the self may account for this hypersensitivity in MDD patients. Much evidence supports the notion that MDD patients may suffer from dysfunctional cognition, which leads them to generate negative perceptions of social interactions in line with the cognitive model of depression (4). A negative bias has repeatedly been observed among MDD patients when processing facial emotions and moral and social emotions (for a review, please see 44). Dysfunctional beliefs about the self are also observed in MDD. For example, the patients with MDD showed increased affective responses to the same events as the healthy controls experience, as observed in the patients with chronic depression when they were facing mood induction materials individualized with autobiographical content (45). The MDD patients tended to judge a proposal as less fair when they are in disadvantaged situation (46), suggesting they were more sensitive to injustice. These patients were more likely to reject an unfair proposal as retaliation (46, 47). Therefore, it is possible that this negative bias and dysfunctional cognition about the self makes MDD patients more sensitive to injustice. Finally, we should note that depressive symptoms often lead to deterioration of social relationships and vice versa (48, 49), which will increase the likelihood of exposure to a disadvantaged situation and thus increase the chance of experiencing injustice as a victim.
Neural Basis of Increased Victim Sensitivity
Using fALFF as a search tool, we found a contrary pattern regarding the association between the spontaneous activity in the right DLPFC and victim sensitivity among the MDD patients versus healthy controls. Specifically, for healthy populations, the level of spontaneous activity in the right DLPFC was negatively correlated with the level of victim sensitivity. However, for MDD patients, the level of spontaneous activity in the right DLPFC was positively correlated with the level of victim sensitivity.
The right DLPFC is strongly associated with cognitive control (50, 51). It is well-established that superior cognitive control is related to lower levels of unwanted emotional responses, such as anger and aggression (52). A temporal decrease in the activity of the right DLPFC using neuromodulation techniques increased revenge-seeking, emotion-driven behavior (53), supporting the role of cognitive control subserved by the right DLPFC in overcoming unwanted emotional responses. Further evidence for the role of cognitive control in victim sensitivity is seen in the relationship between decreased spontaneous activity in the DLPFC indicated by regional homogeneity and higher rumination in healthy subjects, which suggests less efficient inhibitory control on the increased negative self-focused conflicts (54). Taking all of this evidence together, it is possible that individuals with low sensitivity to injustice as a victim have already had efficient control resources to self-regulate the influence of the disadvantaged situations (i.e., less likely to respond to injustice to one’s own disadvantage via anger, sadness, and rumination), and this effortful self-control is reflected by the higher spontaneous activity in the right DLPFC. We noted that this finding is in contrast to a previous study, which found that victim sensitivity was positively correlated with regional activity of the paracentral lobule (24). This inconsistency might be due to the differences in the index measuring spontaneous brain activity. Different from the fALFF, which reflects the spontaneous brain activity in an area (25), used in the present study, regional homogeneity was used in Wu’s paper, which measures the temporal synchronization of the time series of an area’s nearest neighbors (55).
It’s interesting that, unlike the healthy controls, the MDD patients revealed a positive correlation between victim sensitivity and the fALFF in the right DLPFC. This reverse pattern between depressed patients and healthy controls is also seen in previous studies, such as the relationship between the DLPFC volume and rumination (54). MDD is characterized by a failure in cognitive control of emotion (56). Although many studies found hypoactivity in the DLPFC in the MDD patients, increased or unchanged brain activity in this region compared to the healthy controls are also reported. For example, increased activity in the DLPFC to negative stimuli has been consistently found in young MDD patients based on a meta-analysis, suggesting that MDD patients have inefficient emotional regulation during affective processing (57). MDD patients also showed increased brain activity in the DLPFC to maintain a similar level of behavioral performance as controls during cognitive control (58). The hyperactivity in the DLPFC may be a reflection of inefficient cognitive control or compensation in the MDD patients. It needs to be noted that in this study we found that the spontaneous activity in the right DLPFC in the MDD patients was comparable with that in the healthy controls, but it was positively correlated with victim sensitivity in the patients. This finding may suggest that, even though the MDD patients recruit cognitive control resources to regulate their emotional responses (e.g., anger or rumination) when they are in an unjust situation, they fail in inhibiting the unwanted emotions or ruminating on one’s own disadvantage due to inefficient cognitive control or failure in recruiting more resources, and are thus more likely to respond to unjust situations via emotional responses (i.e., high sensitivity to injustice as a victim).
The cognitive control explanation subserved by the DLPFC is compatible with negative bias, which may be the candidate psychological process behind the hypersensitivity in the MDD patients as we discussed. Previous studies on the neural basis of negative bias have already indicated that impaired top-down cognitive control reflected by deficits in the DLPFC function play a vital role in negative bias (59). However, the exact psychological mechanisms still need to be determined in the future.
Limitations
There are several possible limitations in this study. First, even though higher victim sensitivity was found in both of the two samples, the neural basis of higher victim sensitivity in the MDD patients was only examined in one sample, some of whom were in the remission period and most of whom were medicated. Previous studies suggest differences in spontaneous brain activities and connectivity between MDD patients experiencing their first episode and remitted MDD patients or between drug-naïve MDD patients and medicated MDD patients (29, 60, 61). Thus, further validation is needed in a group of patients with higher homogeneity in clinical profiles, such as a group of patients who are experiencing their first episode and are medication free, or a group of remitted patients. It is more interesting to investigate whether the correlations between victim sensitivity and spontaneous brain activities is trait- or state-related. Secondly, the sample size is relatively small in the current study, especially in the part of neuroimaging analysis; however it is comparable to previous studies that involve the recruitment of MDD patients for fALFF analyses (for a review, please see also 29). The current findings need to be validated using a larger sample size. Thirdly, our findings suggest that the right DLPFC plays a significant role in victim sensitivity, both in the healthy controls and in depressed patients; however, the exact function of the DLPFC in victim sensitivity still needs to be determined by the adoption of task-based fMRI. For example, in an ultimatum game, increased activity in the right DLPFC has been repeatedly observed when the participants faced unfair proposals or unjust treatment (15, 17).
Conclusions
In summary, the current study investigated the hypothesis that the MDD patients have higher victim sensitivity and obtained consistent findings across two clinical samples. Furthermore, we found that the MDD patients differ from the healthy controls in the neural correlates of victim sensitivity. That is, the spontaneous activity in the right DLPFC showed contrary correlation with victim sensitivity in the healthy controls and in the MDD patients. These findings enrich our understanding of personality traits related to depression, and shed light on the link between cognitive control and negative bias about the self among MDD patients.
Data Availability Statement
The datasets generated for this study are available on request to the corresponding author.
Ethics Statement
The studies involving human participants were reviewed and approved by Institutional Review Boards of Beijing Tongren Hospital, Beijing Anding Hospital, Capital Medical University, as well as the Institute of Psychology, Chinese Academy of Sciences. The patients/participants provided their written informed consent to participate in this study.
Author Contributions
All authors contributed to the article and approved the submitted version. SC and QG collected data. XW, SC, and YW analyzed the data. YZ designed this study. XW, YZ, and MW drafted the manuscript. All authors revised the manuscript.
Funding
Funding for this study was provided by the Natural Science Foundation of China (grant number 81771473) and the State High-Tech Development Plan of China (863) (grant number 2015AA020513), which provided support for the data collection.
Conflict of Interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Acknowledgments
We would like to thank all the participants who contributed to this study. We also gratefully acknowledge the support from the staff in the Beijing Anding Hospital and Beijing Tongren Hospital. We are especially grateful towards Yuening Jin who provided a lot of useful suggestions on manuscript revision and language edition.
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Front Psychol (2019) 10:245.  10.3389/fpsyg.2019.00245\n30804860\nYanCGChenXLiLCastellanosFXBaiTJBoQJ\nReduced default mode network functional connectivity in patients with recurrent major depressive disorder. Proc Natl Acad Sci USA (2019) 116(18):9078–83.  10.1073/pnas.1900390116\n\n"],"string":"[\n \"\\nFront PsychiatryFront PsychiatryFront. PsychiatryFrontiers in Psychiatry1664-0640Frontiers Media S.A.32848898PMC743215010.3389/fpsyt.2020.00622PsychiatryOriginal ResearchVictim Sensitivity and Its Neural Correlates Among Patients With Major Depressive DisorderWangXiaoming\\n1\\n\\n2\\n\\n†\\nCuiShaojuan\\n3\\n\\n†\\nWuMichael Shengtao\\n4\\nWangYun\\n5\\nGaoQinglin\\n1\\n\\n2\\nZhouYuan\\n1\\n\\n2\\n\\n5\\n\\n*\\n\\n1\\nKey Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, Beijing, China\\n\\n2\\nDepartment of Psychology, University of Chinese Academy of Sciences, Beijing, China\\n\\n3\\nDepartment of Psychology, Beijing Tongren Hospital, Capital Medical University, Beijing, China\\n\\n4\\nSchool of Sociology and Anthropology, Xiamen University, Xiamen, China\\n\\n5\\nThe National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China\\n
Edited by: Paul Stokes, King’s College London, United Kingdom
Reviewed by: Guido van Wingen, University of Amsterdam, Netherlands; Chien-Han Lai, National Yang-Ming University, Taiwan
†These authors have contributed equally to this work
This article was submitted to Mood and Anxiety Disorders, a section of the journal Frontiers in Psychiatry
118202020201162203220201562020Copyright © 2020 Wang, Cui, Wu, Wang, Gao and Zhou2020Wang, Cui, Wu, Wang, Gao and ZhouThis is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.Background
Dysfunctional beliefs about the self are common in the development of depressive symptoms, but it remains unclear how depressed patients respond to unfair treatment, both dispositionally and neurally. The present research is an attempt to explore the differences in sensitivity to injustice as a victim and its neural correlates in patients with major depressive disorder (MDD) versus healthy controls.
Methods
First episodic, drug-naïve patients with MDD (n = 30) and a control group (n = 30) were recruited to compare their differences in victim sensitivity. A second group of patients with MDD (n = 23) and their controls (n = 28) were recruited to replicate the findings and completed resting-state functional magnetic resonance imaging (fMRI) scanning. Spontaneous brain activity measured by fractional amplitude of low-frequency fluctuation (fALFF) was used to characterize the neural correlates of victim sensitivity both in patients and in healthy controls.
Results
Higher victim sensitivity was consistently found in patients with MDD than healthy controls in both datasets. Multiple regression analysis on the fALFF showed a significant interaction effect between diagnosis and victim sensitivity in the right dorsolateral prefrontal cortex (DLPFC).
Conclusions
The patients with MDD show higher sensitivity to injustice as a victim, which may be independent of their disease course. The MDD patients differ from healthy controls in the neural correlates of victim sensitivity. These findings shed light on the linkage between cognitive control subserved by the DLPFC and negative bias towards the self implicated by higher victim sensitivity among the depressed patients.
Major depressive disorder (MDD) is one of the most common mental disorders, with a high prevalence in current times (1–3). The cognitive model of depression speculates that MDD patients generally hold negative bias and dysfunctional beliefs about themselves. However, it is still unclear how and to what extent self-related personalities and its neural circuits play a role in depressive symptoms (4). Recent research suggests that hypervigilance towards one’s negative experience is linked with the development of MDD (5), and sensitivity to injustice as a victim is related to the stabilization of depressive symptoms (6).
Victim sensitivity captures the individual differences in response to unfair treatment towards oneself. Victim-sensitive individuals tend to detect injustice within a low threshold. Thus, they are likely to experience stronger anger, moral outrage, and exhibit uncooperative behavioral tendencies (7, 8). Victim sensitivity is associated with a higher risk of having negative concerns and social emotions (9) and biased social judgment (for example, people who are higher in victim sensitivity would rate neutral and hostile faces as more untrustworthy and underestimate targets’ cooperativeness in order to avoid being exploited in the future (10)). In the personality space, victim sensitivity has a unique correlation with hostility (11), a facet of neuroticism, which is a predisposition toward depression (12, 13). Furthermore, victim sensitivity was found to be positively related to emotional problems (14) and to mediate the link between attention deficit hyperactivity disorder and depressive symptoms in children and adolescents (14). In a longitudinal study, depressive symptoms at the first measurement predicted higher victim sensitivity one or two years later, while higher victim sensitivity promoted the stabilization of depressive symptoms in those with depressive symptoms at the baseline (6). All of the above findings demonstrate a connection between victim sensitivity and depressive symptoms and also suggest that victim sensitivity should be considered as a potential personality risk factor for the emergence and maintenance of depression. However, there is lack of direct evidence as to whether victim sensitivity was significantly endorsed in a clinical population with MDD, as compared to healthy controls.
We then ask the question: what is the neural basis of higher victim sensitivity in MDD patients? In healthy populations, task-based functional magnetic resonance (fMRI) studies found that the brain activity of anterior insula, anterior cingulate cortex, and dorsolateral prefrontal cortex were increased when participants were faced with unfair treatments (e.g., unequal division on a certain amount of money) (15–17), suggesting that these brain regions may be vital for processing injustice for a victim. Different from the healthy controls, MDD patients had weaker activity in the medial occipital lobe or striatum with increasing or decreasing inequality (18). A relevant study on the neural correlates of justice sensitivity found that victim sensitivity predicted subjective ratings of praise, but it was not correlated with neural activity during a moral judgment task (19). It should be noted that these findings are obtained by using task-based fMRI, in which task-induced brain activity is context-dependent. Personality differences in victim sensitivity may also be reflected in spontaneous brain activity measured by resting-state fMRI, which is a promising tool to uncover the neural basis of inter-individual differences in personality or propensity (20–23). Using an index of spontaneous brain activity that measures temporal synchronization of brain activity within a local brain region, one study found victim sensitivity was positively associated with regional spontaneous brain activity of the paracentral lobule (24). In spite of these descriptions, to our knowledge, no empirical data on the neural basis of victim sensitivity in MDD has been reported. The present study is a first step to fill this gap.
In brief, this current study first aims to test whether the patients with MDD are more sensitive to injustice as a victim. To this aim, we recruited a group of MDD patients who were first episodic and drug-naïve to reduce the potential confounders from long illness duration and medications. Then, we recruited another group of MDD patients with less severe depressive symptoms to test whether the abnormality in victim sensitivity is generalized in patients with MDD. Second, we further explored the neural basis underlying victim sensitivity in MDD using the resting-state fMRI. We selected an index called fractional amplitude of low-frequency fluctuation (fALFF) (25). The fALFF, similar to ALFF, measures low frequency oscillations of the blood oxygen level-dependent (BOLD) signal at rest, but the fALFF represents the ratio of the power spectrum of low-frequency to that of the entire frequency range and thus is less prone to noise compared to ALFF (25). The low frequency BOLD oscillation are closely related to spontaneous neuronal activity (26). As a method with high test-retest reliability to detect the intensity of regional spontaneous neuronal activity during rest (27), the fALFF has been widely used to investigate spontaneous brain activities in different psychiatric disorders, including MDD (28–30), and be linked with personality traits in healthy populations (31–33). In addition, fALFF is a data-driven approach and requires no a priori hypothesis and thus is appropriate for exploratory analyses, such as the present study. Therefore, here we attempted to employ fALFF to explore whether there are different neural correlates of victim sensitivity between patients with MDD and healthy controls.
MethodsParticipants
Two groups of patients with MDD and their matched healthy controls were recruited from two independent sites. Participants in the Tongren dataset were recruited for the aim of investigating whether MDD patients who are first episodic and drug-naïve are more sensitive to injustice as a victim on a behavioral level. We then used another group of participants (i.e. Anding dataset) to test whether the findings obtained in the Tongren dataset can be replicated in MDD patients with less severe depressive symptoms. The participants in the Anding dataset are from a project that initially aimed to study the neural basis of abnormal social decision-making behavior and related traits in patients with MDD, and thus made it possible to further investigate the differences in neural correlates of victim sensitivity among MDD patients versus healthy participants in the current study.
In the Tongren dataset, 30 patients with MDD were recruited from the Department of Psychology, Beijing Tongren Hospital, Capital Medical University. Thirty demographically-matched healthy controls were recruited via advertisements. All of these patients met the following criteria: (1) the Diagnostic and Statistical Manual of Mental Disorders, fourth edition (DSM-IV) criteria for a major depressive episode, diagnosed independently by two qualified psychiatrists who interviewed the patients personally; (2) seeking medical advice for the first time in this hospital and have never taken any psychotropic medication; (3) Hamilton Rating Scale for Depression (HAMD-17) scores >=17; and (4) age range is between 18 and 45 years old. The patients were excluded if they had any preexisting or concurrent co-morbid primary diagnosis that met the DSM-IV criteria for any Axis I disorder other than MDD. Participants in the control group had no current or past history of depression or any other psychiatric disorders and no family history of major psychiatric or neurological illness in first-degree relatives. Additional exclusion criteria for both of the groups were acutely suicidal or homicidal behaviors, history of trauma resulting in loss of consciousness, history of major neurological or physical disorders that could lead to an altered mental state, or current pregnancy or breastfeeding.
In the Anding dataset, 23 patients were recruited from Beijing Anding Hospital, Capital Medical University. Potential applicants for the patient group were from the outpatient departments. They were recommended by their doctors who had diagnosed them before, and then again diagnosed by a psychiatrist when they were recruited in this study. The inclusion and exclusion criteria of the patients were the same as the Tongren dataset, with the only exception that these patients were not experiencing an acute depressive episode and had less severity in depressive symptoms (for details, see \\nTable 1\\n). Among these MDD patients, 15 patients had at least two onsets of depressive episodes; eight patients had a recent depressive episode and their symptoms had been controlled after antidepressant treatments when they were recruitment. Only one patient did not take any psychotropic medication; the other patients received antidepressant medications (SSRIs and/or SNRIs), with one patient additionally taking low-dose benzodiazepines. Twenty-eight healthy controls were recruited by advertisements. Due to the relatively stable mental condition of the MDD patients in the Anding dataset, only participants in the Anding dataset participated in MRI scanning.
Demographics and clinical information of patients with MDD and healthy controls within the two datasets.
Characteristics
Tongren dataset
Anding dataset
MDD
NC
p\\na\\n\\n
MDD
NC
p\\na\\n\\n
Sample size
N=30
N=30
N=23
N=28
Age (years)
25.47 ± 6.40
27.67 ± 4.14
0.12
31.22 ± 5.70
29.57 ± 5.80
0.32
Gender (M: F)
6:24
6:24
1.00
13:10
16:12
0.96
Education
14.33 ± 2.44
14.80 ± 2.19
0.44
14.91 ± 3.22
15.25 ± 2.55
0.68
HAMA
22.03 ± 4.92
–
–
9.83 ± 6.86
0.64 ± 1.16
<0.001
HAMD-17
24.90 ± 4.14
–
–
13.13 ± 6.96
0.89 ± 1.72
<0.001
Victim Sensitivity
34.67 ± 9.06
25.10 ± 7.48
<0.001
30.96 ± 9.50
25.41 ± 8.07
0.03
Two-sample t-tests or chi-square test. HAMD-17, the Hamilton Rating Scale for Depression; HAMA, the Hamilton Rating Scale for Anxiety.
This study was approved by the Institutional Review Boards of Beijing Tongren Hospital, Beijing Anding Hospital, Capital Medical University, as well as the Institute of Psychology, Chinese Academy of Sciences. Written informed consent was obtained from all participants.
Assessment of Victim Sensitivity
Ten items were used to measure justice sensitivity as a victim (11, 34). Examples of items for victim sensitivity are “It makes me angry when I am treated worse than others” or “ I ruminate for a long time when other people are treated better than me”. Each item was scored on a 6-point rating scale ranging from 0 (not at all) to 5 (exactly). The reliabilities were acceptable with the estimated reliability coefficients (Cronbach’s alpha) of the MDD patients and healthy controls amounting to 0.92 and 0.84 for the Tongren sample, and to 0.84 and 0.89 for the Anding sample.
MRI Data Acquisition
The fMRI data were acquired using a GE 3.0 T MRI scanner at the Magnetic Resonance Imaging Research Center, Institute of Psychology, Chinese Academy of Sciences. Structural and functional MRI scans were obtained for each participant. Structural MRI scans were acquired using a magnetization-prepared rapid acquisition gradient echo sequence with the following parameters: TI = 450 ms, receiver bandwidth = 31.25, matrix = 256 × 256, field of view (FOV) = 240 mm * 240 mm, slice thickness = 1.5 mm, flip angle = 12°. Functional images were acquired for each participant using an echo planar imaging (EPI) sequence with the following parameters: TR = 2000 ms, TE = 30 ms, flip angle = 70°, acquisition matrix = 64 * 64, and FOV = 220 mm * 220 mm; 33 axial slices, with a thickness of 4 mm and no gap. During the scanning, the participants were instructed to keep their eyes closed and not to focus their thoughts on anything. The duration of the resting-state fMRI was 10 minutes. After resting-state fMRI scanning, these participants performed a fMRI task, but the data were not used in the current study.
Image Preprocessing
Conventional functional imaging preprocessing was performed using the Data Processing Assistant for Resting-State fMRI (DPARSF 4.3, http://rfmri.org/dpabi) (35), which is based on Statistical Parametric Mapping (SPM12) (http://www.fil.ion.ucl.ac.uk/spm) and the Resting-State fMRI Data Analysis Toolkit (REST 1.8, http://www.restfmri.net) (36). Conventional preprocessing steps were conducted, including the removal of the first 5 volumes, slice timing realignment, co-registration, segmentation of the T1 map to generate the gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) (37), nuisance variable regression, spatial normalization with 2-mm cubic voxels, and spatial smoothing of 4 mm FWHM. The nuisance variables included 24 motion parameters (6 head motion parameters, 6 head motion parameters one time point before, and the 12 corresponding squared items), 5 principal components from the individual segmented CSF and WM regions (38), and linear and quadratic trends (39). To quantify head motion, the volume-based framewise displacement (FD) was computed (40).
fALFF Analysis
For each participant, we calculated the fALFF to characterize the regional spontaneous activity in a voxel-wise way using the DPARSF software. The fALFF is defined as the total power within a frequency band divided by the total power of the entire detectable frequency range (25). Thus, the square root was firstly calculated at each frequency in the power spectrum, and the averaged square root was computed across a low frequency range (0.01–0.08 Hz) at each voxel. Then, the fALFF was obtained as a fraction, which was the sum of the amplitude across 0.01–0.08 Hz divided by that across the whole frequency range. Finally, the normalized score of fALFF was obtained and an individual voxel-wise z-fALFF map was generated for each participant.
Statistical Analysis
Chi-square tests and independent sample t tests were conducted to compare the mean level of demographic, clinical variables, and victim sensitivity between the MDD and healthy controls for each site. Correlation analyses and partial correlation analyses were conducted to investigate the clinical correlates of victim sensitivity for each dataset. All of these analyses were conducted by SPSS v21.
To identify the brain regions where activities related to victim sensitivity may be different between patients with MDD and healthy controls, we firstly conducted a multiple regression analysis on the z-fALFF images of the total sample using SPM12. The z-fALFF of each voxel in the brain were regressed on the variables of diagnosis, victim sensitivity score, and the diagnosis by victim sensitivity score interaction, with age, gender, education level, and mean FD as covariates. To control for Type 1 error, a cluster-level family-wise error (FWE) corrected p-value of 0.05 was used for multiple comparisons correction (individual voxel height threshold of p < 0.001). Then, multiple regression analyses were conducted on the z-fALFF images for patients and controls, separately. The victim sensitivity score was entered as a regressor into the model, in addition to age, gender, education level, and mean FD which were included as variables of no interest. Due to the relatively small sample size, we conducted a Small Volume Correction (SVC) by using the result of the regression analysis of the total sample as a mask (individual voxel height threshold of p < 0.005, cluster-level FWE corrected p < 0.05 following SVC).
ResultsDemographic and Clinical Data
There were no significant differences in gender composition, age, and educational level between patients and healthy controls in both of the two datasets (all p > 0.05) (\\nTable 1\\n). The patients in the Tongren dataset showed more severe depressive symptoms as assessed with the HAMD (p<0.001) and more severe anxiety symptoms as assessed with the Hamilton Rating Scale for Anxiety (HAMA) (p<0.001) compared with those in the Anding dataset.
Victim Sensitivity
We found higher victim sensitivity among MDD patients both in the Tongren dataset (MDD: 34.67 ± 9.06, NC: 25.10 ± 7.48; p<0.001, Cohen’s d = 1.15) and in the Anding dataset (MDD: 30.96 ± 9.50, NC: 25.41 ± 8.07; p=0.03, Cohen’s d = 0.63) (\\nTable 1\\n).
We also explored the clinical correlates of victim sensitivity. We found that there was no correlation between victim sensitivity and the HAMD score in the Tongren dataset (r = 0.13, p = 0.49) but positive correlation in the Anding dataset (r = 0.54, p = 0.007). However, the correlation in the Anding dataset was marginally significant after controlling for the HAMA score (r = 0.42, p = 0.053) or disappeared after excluding the remitted patients (HAMD score < =7; N=6) (r = 0.05, p = 0.86). All of these findings suggested that the correlation between victim sensitivity and the severity of depressive symptoms may not be reliable.
fALFF
A significant interaction effect between diagnosis and victim sensitivity was found in the right middle frontal gyrus (peak MNI coordinate: [34, 58, 28]; cluster size: 95 voxels; cluster-level FWE p < 0.05) (\\nFigure 1A\\n). By searching this region in the Brainnetome Atlas Viewer (41), we found that this region was located in the area 46, which is often termed as the dorsolateral prefrontal cortex (DLPFC) (\\nFigure 1B\\n). The interaction effect was shown in \\nFigure 1C\\n, which illustrated the relationship between victim sensitivity and the z-fALFF value in the right the DLPFC for each group.
Interaction effect between diagnosis and victim sensitivity on fALFF and the results of post hoc analysis. (A) The region showing significant interaction effect between diagnosis and victim sensitivity; (B) The region showing significant interaction effect was overlaid with the area 46 in the Brainnetome Atlas Reviewer (http://atlas.brainnetome.org/). (C) Scatter plots for the relationship between the fALFF value and victim sensitivity within each group with the shadow parts representing 95% confidence interval. Abbreviation: rMFG, right middle frontal gyrus.
By separately analyzing the relationship between victim sensitivity and fALFF within each group, we found that there were no regions showing significant correlations with victim sensitivity score both in the MDD group and in the healthy controls group in a whole brain search (cluster-level FWE p < 0.05). But by using SVC with the results of the regression analysis of the total sample as a mask, we found a significantly negative correlation in the right DLPFC in the healthy controls group (SVC cluster-level FWE corrected p = 0.01, 26 voxels, T = -4.45, peak voxel coordinates: [32, 56, 26]) and significantly positive correlation in the right DLPFC in the MDD group (SVC cluster-level FWE corrected p = 0.04, 9 voxels, T = 3.35, peak voxel coordinates: [32, 58, 22]).
Significant diagnosis effect was found in the right superior occipital gyrus (SOG; peak MNI coordinate: [32, -84, 42]; cluster size: 104 voxels) (\\nFigure 2\\n), suggesting that the patients showed decreased fALFF in this region compared to the healthy controls. We didn’t find the regions showing significant correlations with victim sensitivity across the two groups in the pre-specified threshold (cluster-level FWE p < 0.05).
Diagnosis effect on fALFF. (A) Region showing significant group difference in fALFF between the patients with major depressive disorder (MDD) and the healthy controls (HC). (B) The fALFF value in this region within each group. Abbreviation: rSOG, right superior occipital gyrus.
Discussion
The current study provides robust evidence that MDD patients have higher sensitivity to injustice as a victim, which was consistently shown in two clinical samples. The results also showed that the neural correlates of victim sensitivity differ between the MDD patients and the healthy controls. Specifically, the fALFF in the right DLPFC, the region for cognitive control, positively correlated with victim sensitivity in MDD patients, but was negatively correlated with victim sensitivity in healthy controls.
Hypersensitivity to Injustice as a Victim
In line with previous studies on the association between victim sensitivity and depressive symptoms in non-clinical samples or personality traits related to depression (6, 42, 43), the present study found that patients with MDD had higher sensitivity to injustice as a victim. Particularly, this hypersensitivity can be found in a group of first episodic, drug-naïve patients with MDD, suggesting that the increased victim sensitivity was free from the confounding effect of chronic illness or antipsychotic drugs and could be observed even in the early stage of depression. In order to test whether this hypersensitivity can be generalized to a common clinical sample, we recruited another group of MDD patients, most of whom were medicated and with mild to moderate depressive symptoms (the Anding dataset). We again found increased victim sensitivity in these patients. On the contrary, we found the correlation between victim sensitivity and severity of depressive symptoms was not reliable because we only found the evidence that supported a positive correlation in the Anding dataset not in the Tongren dataset. More importantly, the correlation only showed the trend towards significance after controlling for the anxiety symptoms or disappeared after excluding the remitted patients who had extremely low HAMD scores. The non-repeatable correlation between victim sensitivity and severity of depressive symptoms suggests that the increased victim sensitivity may be independent of depressive severity; however, this conclusion should be verified with a larger sample size and in the remitted patients. All of these findings - the repeatable findings on the higher victim sensitivity in the MDD patients in both of the two sites and the non-repeatable clinical correlates of the victim sensitivity - suggest that higher victim sensitivity may be independent of illness course, severity, or medication. Combining the previous findings obtained in non-depressed samples with ours, it seems higher victim sensitivity may be an important psychopathological feature for depression. In a longitudinal study of German adolescents, victim sensitivity increased the stabilization of depressive symptoms if they were present at baseline, suggesting that victim sensitivity may be a maintaining factor for depression (6). In future work, the role of victim sensitivity in the occurrence and progression of depression should be further examined in a longitudinal study.
The present study indicated that patients with MDD are more likely to perceive injustice and respond to injustice to one’s own disadvantage. Negative bias and dysfunctional beliefs about the self may account for this hypersensitivity in MDD patients. Much evidence supports the notion that MDD patients may suffer from dysfunctional cognition, which leads them to generate negative perceptions of social interactions in line with the cognitive model of depression (4). A negative bias has repeatedly been observed among MDD patients when processing facial emotions and moral and social emotions (for a review, please see 44). Dysfunctional beliefs about the self are also observed in MDD. For example, the patients with MDD showed increased affective responses to the same events as the healthy controls experience, as observed in the patients with chronic depression when they were facing mood induction materials individualized with autobiographical content (45). The MDD patients tended to judge a proposal as less fair when they are in disadvantaged situation (46), suggesting they were more sensitive to injustice. These patients were more likely to reject an unfair proposal as retaliation (46, 47). Therefore, it is possible that this negative bias and dysfunctional cognition about the self makes MDD patients more sensitive to injustice. Finally, we should note that depressive symptoms often lead to deterioration of social relationships and vice versa (48, 49), which will increase the likelihood of exposure to a disadvantaged situation and thus increase the chance of experiencing injustice as a victim.
Neural Basis of Increased Victim Sensitivity
Using fALFF as a search tool, we found a contrary pattern regarding the association between the spontaneous activity in the right DLPFC and victim sensitivity among the MDD patients versus healthy controls. Specifically, for healthy populations, the level of spontaneous activity in the right DLPFC was negatively correlated with the level of victim sensitivity. However, for MDD patients, the level of spontaneous activity in the right DLPFC was positively correlated with the level of victim sensitivity.
The right DLPFC is strongly associated with cognitive control (50, 51). It is well-established that superior cognitive control is related to lower levels of unwanted emotional responses, such as anger and aggression (52). A temporal decrease in the activity of the right DLPFC using neuromodulation techniques increased revenge-seeking, emotion-driven behavior (53), supporting the role of cognitive control subserved by the right DLPFC in overcoming unwanted emotional responses. Further evidence for the role of cognitive control in victim sensitivity is seen in the relationship between decreased spontaneous activity in the DLPFC indicated by regional homogeneity and higher rumination in healthy subjects, which suggests less efficient inhibitory control on the increased negative self-focused conflicts (54). Taking all of this evidence together, it is possible that individuals with low sensitivity to injustice as a victim have already had efficient control resources to self-regulate the influence of the disadvantaged situations (i.e., less likely to respond to injustice to one’s own disadvantage via anger, sadness, and rumination), and this effortful self-control is reflected by the higher spontaneous activity in the right DLPFC. We noted that this finding is in contrast to a previous study, which found that victim sensitivity was positively correlated with regional activity of the paracentral lobule (24). This inconsistency might be due to the differences in the index measuring spontaneous brain activity. Different from the fALFF, which reflects the spontaneous brain activity in an area (25), used in the present study, regional homogeneity was used in Wu’s paper, which measures the temporal synchronization of the time series of an area’s nearest neighbors (55).
It’s interesting that, unlike the healthy controls, the MDD patients revealed a positive correlation between victim sensitivity and the fALFF in the right DLPFC. This reverse pattern between depressed patients and healthy controls is also seen in previous studies, such as the relationship between the DLPFC volume and rumination (54). MDD is characterized by a failure in cognitive control of emotion (56). Although many studies found hypoactivity in the DLPFC in the MDD patients, increased or unchanged brain activity in this region compared to the healthy controls are also reported. For example, increased activity in the DLPFC to negative stimuli has been consistently found in young MDD patients based on a meta-analysis, suggesting that MDD patients have inefficient emotional regulation during affective processing (57). MDD patients also showed increased brain activity in the DLPFC to maintain a similar level of behavioral performance as controls during cognitive control (58). The hyperactivity in the DLPFC may be a reflection of inefficient cognitive control or compensation in the MDD patients. It needs to be noted that in this study we found that the spontaneous activity in the right DLPFC in the MDD patients was comparable with that in the healthy controls, but it was positively correlated with victim sensitivity in the patients. This finding may suggest that, even though the MDD patients recruit cognitive control resources to regulate their emotional responses (e.g., anger or rumination) when they are in an unjust situation, they fail in inhibiting the unwanted emotions or ruminating on one’s own disadvantage due to inefficient cognitive control or failure in recruiting more resources, and are thus more likely to respond to unjust situations via emotional responses (i.e., high sensitivity to injustice as a victim).
The cognitive control explanation subserved by the DLPFC is compatible with negative bias, which may be the candidate psychological process behind the hypersensitivity in the MDD patients as we discussed. Previous studies on the neural basis of negative bias have already indicated that impaired top-down cognitive control reflected by deficits in the DLPFC function play a vital role in negative bias (59). However, the exact psychological mechanisms still need to be determined in the future.
Limitations
There are several possible limitations in this study. First, even though higher victim sensitivity was found in both of the two samples, the neural basis of higher victim sensitivity in the MDD patients was only examined in one sample, some of whom were in the remission period and most of whom were medicated. Previous studies suggest differences in spontaneous brain activities and connectivity between MDD patients experiencing their first episode and remitted MDD patients or between drug-naïve MDD patients and medicated MDD patients (29, 60, 61). Thus, further validation is needed in a group of patients with higher homogeneity in clinical profiles, such as a group of patients who are experiencing their first episode and are medication free, or a group of remitted patients. It is more interesting to investigate whether the correlations between victim sensitivity and spontaneous brain activities is trait- or state-related. Secondly, the sample size is relatively small in the current study, especially in the part of neuroimaging analysis; however it is comparable to previous studies that involve the recruitment of MDD patients for fALFF analyses (for a review, please see also 29). The current findings need to be validated using a larger sample size. Thirdly, our findings suggest that the right DLPFC plays a significant role in victim sensitivity, both in the healthy controls and in depressed patients; however, the exact function of the DLPFC in victim sensitivity still needs to be determined by the adoption of task-based fMRI. For example, in an ultimatum game, increased activity in the right DLPFC has been repeatedly observed when the participants faced unfair proposals or unjust treatment (15, 17).
Conclusions
In summary, the current study investigated the hypothesis that the MDD patients have higher victim sensitivity and obtained consistent findings across two clinical samples. Furthermore, we found that the MDD patients differ from the healthy controls in the neural correlates of victim sensitivity. That is, the spontaneous activity in the right DLPFC showed contrary correlation with victim sensitivity in the healthy controls and in the MDD patients. These findings enrich our understanding of personality traits related to depression, and shed light on the link between cognitive control and negative bias about the self among MDD patients.
Data Availability Statement
The datasets generated for this study are available on request to the corresponding author.
Ethics Statement
The studies involving human participants were reviewed and approved by Institutional Review Boards of Beijing Tongren Hospital, Beijing Anding Hospital, Capital Medical University, as well as the Institute of Psychology, Chinese Academy of Sciences. The patients/participants provided their written informed consent to participate in this study.
Author Contributions
All authors contributed to the article and approved the submitted version. SC and QG collected data. XW, SC, and YW analyzed the data. YZ designed this study. XW, YZ, and MW drafted the manuscript. All authors revised the manuscript.
Funding
Funding for this study was provided by the Natural Science Foundation of China (grant number 81771473) and the State High-Tech Development Plan of China (863) (grant number 2015AA020513), which provided support for the data collection.
Conflict of Interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Acknowledgments
We would like to thank all the participants who contributed to this study. We also gratefully acknowledge the support from the staff in the Beijing Anding Hospital and Beijing Tongren Hospital. We are especially grateful towards Yuening Jin who provided a lot of useful suggestions on manuscript revision and language edition.
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Neurosci.Frontiers in Neuroscience1662-45481662-453XFrontiers Media S.A.32848571PMC743215110.3389/fnins.2020.00803NeuroscienceOriginal ResearchOxytocin Facilitation of Emotional Empathy Is Associated With Increased Eye Gaze Toward the Faces of Individuals in Emotional ContextsLeJiaoKouJuanZhaoWeihuaFuMeinaZhangYingyingBeckerBenjaminKendrickKeith M.*The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
Edited by: Kerstin Uvnäs Moberg, Swedish University of Agricultural Sciences, Sweden
Reviewed by: Gábor B. Makara, Hungarian Academy of Sciences (MTA), Hungary; Ben Nephew, Worcester Polytechnic Institute, United States
*Correspondence: Keith M. Kendrick, kkendrick@uestc.edu.cn; k.kendrick.uestc@gmail.com
This article was submitted to Neuroendocrine Science, a section of the journal Frontiers in Neuroscience
118202020201480308520200872020Copyright © 2020 Le, Kou, Zhao, Fu, Zhang, Becker and Kendrick.2020Le, Kou, Zhao, Fu, Zhang, Becker and KendrickThis is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
One of the most robust effects of intranasal oxytocin treatment is its enhancement of emotional empathy responses across cultures to individuals displaying emotions in realistic contexts in the Multifaceted Empathy Task (MET). However, it is not established if this effect of oxytocin on emotional empathy is due to altered visual attention toward different components of the stimulus pictures or an enhanced empathic response. In the current randomized placebo-controlled within-subject experiment on 40 healthy male individuals, we both attempted a further replication of emotional empathy enhancement by intranasal oxytocin (24 IU) and used eye-tracking measures to determine if this was associated by altered visual attention toward different components of the picture stimuli (background context, human face, and body posture). Results replicated previous findings of enhanced emotional empathy in response to both negative and positive stimuli and that this was associated with an increased proportion of time viewing the faces of humans in the pictures and a corresponding decrease in that toward the rest of the body and/or background context. Overall, our findings suggest that enhanced emotional empathy following oxytocin administration is due to increased attention to the faces of others displaying emotions and away from other contextual and social cues.
Clinical Trial Registration:\nwww.ClinicalTrials.gov Oxytocin Modulates Eye Gaze Behavior During Social Processing; registration ID: NCT03293511; URL: https://clinicaltrials.gov/ct2/show/NCT03293511.
autismoxytocinemotional empathyeye gazeface processingsocial attentionNational Natural Science Foundation of China10.13039/501100001809China Postdoctoral Science Foundation10.13039/501100002858Government of Guangdong Province10.13039/501100002912Introduction
Empathizing with others is of key importance for our social interactions. Impairments in this domain represent a major symptom in several psychiatric disorders such as autism (Lombardo et al., 2007) and depression (Tully et al., 2016; Xu et al., 2020), leading to significant problems in understanding and responding appropriately to others in social contexts. Empathy refers to a multidimensional construct including both cognitive (identifying emotions expressed by others) and emotional (being aroused by – indirect emotional empathy or feeling the same emotion – direct emotional empathy – expressed by others) facets (Shamay-Tsoory, 2011). Understanding the neurobiological regulation of these multidimensional functional domains is therefore of great importance both in the context of social neuroscience as well as to identify novel therapeutic approaches to facilitate empathy and alleviate deficits in patient populations.
In recent years, a number of studies have consistently implicated the hypothalamic neuropeptide oxytocin (OT) in the control of emotional empathy in humans (Hurlemann et al., 2010; Hubble et al., 2017a; Geng et al., 2018a, b). Intranasal OT has been demonstrated to facilitate both direct and indirect emotional empathy but not cognitive empathy as assessed by the Multifaceted Empathy Task (Dziobek et al., 2008) in male Caucasian (Hurlemann et al., 2010) as well as in both male and female Chinese subjects (Geng et al., 2018b). The latter study also found similar effects of OT on increasing emotional empathy in male subjects with both 24 and 48 IU doses. Similarly, another study using dynamic, empathy-inducing video clips reported that OT enhanced emotional but not cognitive empathy for fear (Hubble et al., 2017a), and several studies have reported that OT can increase empathy for pain experienced by others in both sexes (Shamay-Tsoory et al., 2013; Abu-Akel et al., 2015). Studies that employed concomitant functional MRI (fMRI) assessments suggest that the emotional empathy-enhancing effects of OT are neurally mediated by reduced responsiveness of the amygdala to empathy-inducing scenes (Geng et al., 2018a, b). The important role of the amygdala in emotional empathy is further supported by studies in patients with focal bilateral amygdala lesions who exhibit impaired emotional but not cognitive empathy (Hurlemann et al., 2010).
Accumulating evidence suggests that OT may generally promote face emotion recognition accuracy (Shahrestani et al., 2013), and initial studies reported increased accuracy only for difficult items in the reading the mind in the eyes test (RMET) (Domes et al., 2007), which, together, may reflect a facilitatory effect of OT on some aspects of cognitive empathy. On the other hand, more recent studies using the RMET have either only reported effects in individuals with low pretreatment empathy (Feeser et al., 2015), low social proficiency, or higher maternal love withdrawal (Riem et al., 2014), or failed to find any effects at all (Radke and de Bruijn, 2015). Thus, to date the most consistent findings indicate a facilitation of emotional empathy by intranasal OT.
There is also increasing evidence for OT playing a key role in enhancing attention toward social stimuli in a person- and context-dependent manner, and this forms the basis of the proposed “social salience hypothesis” (see Shamay-Tsoory and Abu-Akel, 2016). In support of this hypothesis, studies employing selective attention paradigms have demonstrated that OT increases attention toward social stimuli but not non-social ones (Xu et al., 2015, 2019) and promotes switching attention away from interoceptive information toward external social cues via modulating the anterior insula, a core region of the salience network (Yao et al., 2018). Studies that acquired eye-tracking indices as a behavioral measure of attentive and salience processing additionally reported that OT increases the time spent viewing social relative to non-social stimuli across a number of different paradigms (Eckstein et al., 2019; Le et al., 2020 – using the same subjects as in the current study). A number of studies have reported that OT specifically increases gaze toward the eye region of both static and dynamic facial stimuli in either healthy or autistic individuals (Guastella et al., 2008; Andari et al., 2010; Domes et al., 2013; Auyeung et al., 2015), although others have reported no effects (Domes et al., 2010; Lischke et al., 2012; Hubble et al., 2017b). Saccades from the mouth to the eye region are also increased following OT for fear expression faces and are generally associated with amygdala responses (Gamer et al., 2010). Inconsistencies between the previous studies may be explained by a number of factors such as subject sex, whether stimuli are presented statically or dynamically and whether participants were asked to passively view or actively evaluate the presented stimuli. In our own previous study in the current cohort of subjects, we found that OT primarily increased the proportion of time spent viewing the eyes relative to the nose for static fearful, but not other emotional facial expressions presented passively (Le et al., 2020). In the context of visual stimuli-evoked empathic responses, only one previous study has directly investigated the effects of intranasal OT and reported that OT increased eye gaze across dynamic expressions of sadness, happiness, pain, or fear (Hubble et al., 2017a), and empathic empathy for fearful faces. However, this study found no correlation between the proportion of gaze time toward the eyes and empathy ratings. Another study has also reported no effects of OT on gaze toward positive, negative, and neutral valence scenes depicting both humans and objects despite finding effects on neural responses to them (Lischke et al., 2017). We have therefore investigated the effects of OT on eye gaze toward different components of negative and positive valence stimuli designed to evoke empathic responses and their association with its effects on enhancing emotional empathy using the Multifaceted Empathy Task (MET).
The current preregistered pharmacological eye-tracking study employed a randomized within-subject placebo-controlled design to investigate the effects of intranasal OT (24 IU) on patterns of eye gaze in male subjects performing the MET and to replicate previous findings demonstrating OT-induced facilitation of emotional empathy (Hurlemann et al., 2010; Geng et al., 2018a, b). We only included male subjects in the current study due to the main focus of our clinical trial being relevance to autism and, additionally, because we had already established that there are no sex-dependent effects of OT on responses in the MET (Geng et al., 2018b). Based on the previous studies reporting that OT facilitated facial emotion recognition and eye gaze toward facial stimuli, we hypothesized that the OT-induced facilitation of emotional empathy would be associated with increased gaze time toward the face and away from other less socially salient features of both positive and negative valence picture stimuli. We also hypothesized that the observed effects of OT on emotional empathy ratings and time spent viewing faces would be associated with individual variations in autistic and/or empathic traits.
Materials and MethodsParticipants
Forty healthy male adults (mean age ± SEM = 20.8 ± 0.38) were recruited via university advertisement. Only male subjects were recruited due to the main focus on potential relevance to autism and because the majority of previous studies have also only used male subjects. In a within subject design study, each participant underwent the experimental paradigm twice and with a between assessment and treatment period of approximately 2 weeks (mean ± SEM = 14.86 ± 0.16 days). Subjects were randomly assigned to receive either OXT (24 IU OXT in water, 0.9% sodium chloride, and glycerol supplied by the Sichuan Meike Pharmaceutical Company, China) or placebo (PLC containing all ingredients except for the neuropeptide and supplied by the same company) intranasal administration. The order of receiving OXT or PLC was counterbalanced across subjects. Subject number was determined by an a priori power analysis (using G∗power version 3.1.9.4 with α = 0.05 and a 0.5 correlation between repeated measures), which showed that a within-subject design would achieve >80% power for a medium effect size both with ANOVAs (F = 0.25, partial eta squared, 0.06) and post hoc pairwise comparisons (Cohen’s d = 0.5) with a total of 36 subjects. Finally, 40 subjects were finally recruited to compensate for potential subject dropout and data collection issues. Subjects were required to abstain from caffeine, alcohol, nicotine, or other psychoactive substances in the 24 h before each experiment, and initial interviews confirmed the self-reported absence of current or previous psychiatric illness, drug, or alcohol abuse. All participants had normal or corrected to normal vision. The study had full approval from the local ethics committee of the University of Electronic Science and Technology of China, and procedures were in accordance with the latest revision of the declaration of Helsinki. The study was preregistered at clinical trials.gov (Trial registration ID: NCT03293511; URL: https://clinicaltrials.gov/ct2/show/NCT03293511). The experiment was part of a larger study on the effects of intranasal OT on social attention during which subjects completed seven different tasks. Data from the other tasks involving social versus non-social stimuli and face emotion processing presented immediately prior to the current empathy task have been published elsewhere (Le et al., 2020). All subjects signed written informed consent and were paid for participation (100RMB).
Experimental Procedure
In a double-blind placebo-controlled within-subject design, subjects self-administered the nasal sprays (24 IU, three puffs per nostril, each containing 4 IU of OXT or the same number of puffs with the PLC spray) following a standardized protocol (Guastella et al., 2013). At the first visit before treatment administration, participants initially completed a set of Chinese version questionnaires to evaluate mood and personality traits as a control for possible confounders due to any pretreatment group differences [State Trait Anxiety Inventory (STAI), Shek, 1993; Childhood Trauma Questionnaire (CTQ), Bernstein et al., 2003; Zhao et al., 2005]. The Positive and Negative Affect Schedule (PANAS; Watson et al., 1988) was completed three times: before treatment administration (t1) and before (t2) and after the task (t3) in order to assess possible treatment effects on mood. For the assessment of associations with experimental findings in the two groups in relation to autistic traits and empathy Chinese versions of the Autism Spectrum Quotient (AQ; Lau et al., 2013), social responsivity scale (SRS; Gau et al., 2013), and interpersonal responsivity index (IRI; Davis, 1980) were used. All subjects started the eye-tracking task 45 min after receiving the nasal spray.
The empathy task paradigm used was a Chinese version of the MET originally used in Caucasian subjects (Dziobek et al., 2008; Hurlemann et al., 2010) and described previously (Geng et al., 2018b). An adapted and shortened version of the original task was employed in the current eye-tracking study using identical real-life picture stimuli (30 positive and 30 negative valence) of people (both sexes and variable ages) displaying strong positive or negative emotions via face expressions and body posture in a particular context (displayed in the background of the picture). Stimuli were presented in a randomized order for 3 s followed by a fixation cross for 3 s (total task duration = 360 s). Subjects were required to view the stimuli passively. After the eye-tracking task, subjects were shown the picture stimuli again in a different randomized order and required to rate each one for how much they felt the same feelings as the person in the picture (i.e., direct emotional empathy) on a scale of 1–9 (1 = not at all and 9 = very strongly). Pictures were presented for a maximum of 4 s, and the rating scale was displayed under each picture. Subjects indicated their rating score by using a keyboard to move a cursor across the scale on the display screen. For flow charts showing the whole experimental procedure, see Supplementary Figure S1.
Eye-Tracking Data
Eye-gaze data were recorded using an eye tracker (Tobii TX300, Tobii, Danderyd, Sweden), which employs infrared (IR) light-emitting diodes and IR camera to measure corneal reflections and calculate the eye-gaze direction. All gaze data were recorded at 300 Hz sampling rate with a gaze accuracy of 0.4°. Recording and stimulus presentation were conducted using Tobii Pro Studio, E-prime 2.0 software, and E-Prime Extensions for Tobii (Psychology Software Tools, Pittsburgh, PA, United States). All raw data with at least 80% gaze weight were analyzed using Tobii studio. The primary measure collected was total fixation duration, although additionally the number and duration of individual fixations were measured in order to help interpret which were contributing to observed significant changes in overall fixation duration.
Statistical Analyses
All statistical analyses were performed using SPSS 22 (SPSS Inc., Chicago, IL, United States). Three-way repeated ANOVAs were performed with within-subject factors (treatment – OXT, PLC), AOI (background, body, and face), and valence (positive and negative) and percentage of total fixation duration as dependent variable. Separate analyses were also performed using percentage of fixation counts and individual fixation durations as dependent variables. The wide range of orientations and sizes of the faces of individuals portrayed in the picture stimuli of the MET effectively precluded a finer grained analysis of individual face features as AOIs. Similar analyses were performed with percentage of fixation counts and mean duration of individual fixations as dependent variables in order to determine whether alterations in total fixation duration were associated with altered numbers or durations of individual fixations. Significant (p < 0.05) interactions were explored using Bonferroni-corrected post hoc tests. Associations between eye-tracking variables and emotional empathy ratings and autistic and empathy traits (AQ, SRS, and IRI scores) were analyzed using Spearman correlation. To correct for multiple comparisons involving behavioral traits (three questionnaires), the threshold p value was adjusted to p < 0.0167 for the correlation analyses (e.g., p = 0.05/3 = 0.0167).
ResultsEffects of Treatment on Mood and Overall Gaze Time
Independent t tests revealed no significant differences in terms of mood and personality traits between subjects with the two different treatment orders (see Supplementary Table S1). A two-way repeated ANOVA with two treatment × three time points for PANAS scores revealed no significant main or interaction treatment effects on positive or negative mood (all p’s > 0.665). There was no significant difference in the total amount of time subjects spent viewing the screen between treatments (t = 0.367, p = 0.716). Thus, OT had no overall effect on time spent viewing the stimulus screen.
Effects of Oxytocin on Emotional Empathy and Patterns of Eye Gaze
Five subjects were excluded from analysis in this task due to technical problems with data collection on one or both of their experimental sessions. Thus, n = 35 subjects were included in the final analysis. We first confirmed that we could replicate previous observations that OT enhanced emotional empathy ratings. Paired t tests revealed that OT significantly increased empathy ratings for both negative [t(1,34) = 2.804, p = 0.008, Cohen’s d = 0.486] and positive valence stimuli [t(1,34) = 2.715, p = 0.010, Cohen’s d = 0.469] compared to PLC (see Figure 1). There was no treatment difference in rating scores (i.e., OT minus PLC) for positive and negative valence pictures (p = 0.549) indicating that OT had a similar effect on both valences.
The effects of oxytocin (OT) on emotional empathy ratings. Subjects were asked to rate positive and negative valence pictures on: “how much do you feel the same as the person in the picture (i.e., direct emotional empathy)” from 1 to 9 (1 = not at all and 9 = very strongly). Error bars represent SEM. **p < 0.01, OXT vs. PLC and positive vs. negative valence.
For the eye-tracking component of the study, we used three-way ANOVAs with treatment (OT and PLC), AOIs (human face, human body, and background), and valence (positive and negative) as factors. For the primary dependent variable (proportion of time spent viewing the three AOIs relative to the whole screen), a main effect of AOI [F(2,68) = 501.836, p < 0.001, partial η2 = 0.937] but not treatment [F(1,34) = 1.250, p = 0.271] or valence [F(1,34) = 0.142, p = 0.709] was observed. The main effect of AOI was due to subjects spending proportionately more time viewing human faces and bodies compared with the background (p’s < 0.001) and on human faces as compared to bodies (p < 0.001) (see Figure 2A). There were also significant treatment × AOI [F(2,68) = 5.898, p = 0.013, partial η2 = 0.148] and AOI × valence [F(2,68) = 21.737, p < 0.001, partial η2 = 0.390] interaction effects. Post hoc Bonferroni corrected t tests revealed that OT increased the percentage of time spent viewing faces [t(1,34) = 2.642, p = 0.012, Cohen’s d = 0.525] but correspondingly decreased that for viewing the background [t(1,34) = −2.291, p = 0.028, Cohen’s d = 0.559] (see Figure 2B). For the AOI × valence interaction effect post hoc tests also revealed that for negative compared with positive valence stimuli, subjects spent a greater proportion of time viewing the face (p = 0.012) and background (p < 0.001) but less for the body (p < 0.001). There were no treatment × valence [F(1,34) = 0.482, p = 0.492] or treatment × regions × valence [F(2,68) = 0.260, p = 0.746] interaction effects.
The effects of oxytocin (OT) treatment on eye gaze directed toward the face, body, or background regions during the emotional empathy (EE) task. (A) The mean percentage total fixation duration of face, body, and background across two valence (positive, negative). (B) The effects of oxytocin (OT) treatment on mean percentage total fixation duration of face, body, and background across two valence (positive, negative). Error bars represent SEM. ***p < 0.001, *p < 0.05, OXT vs. PLC.
Identical ANOVA analyses were conducted for proportion of fixation counts and individual fixation durations. For proportion of fixation counts, we found similar results as for proportion of total viewing time, indicating that the latter were mainly due to an increased number of fixations. There was a significant main effect of AOI [F(2,68) = 514.768, p < 0.001, partial η2 = 0.938] but not for either treatment [F(1,34) = 1.830, p = 0.185] or valence [F(1,34) = 0.000, p = 0.993]. The AOI main effect again reflected a higher proportion of fixation counts to the face and body compared with the background (p’s < 0.001) and for the face compared to the body (p < 0.001). There were also significant treatment × region [F(2,68) = 4.518, p = 0.030, partial η2 = 0.117] and valence × region [F(2,68) = 28.573, p < 0.001, partial η2 = 0.457] interaction effects. Post hoc Bonferroni-corrected tests revealed that OT increased the percentage of fixation counts for the face region [t(1,34) = 2.968, p = 0.024, Cohen’s d = 0.454]. For the AOI × valence interaction, post hoc tests also revealed that for negative compared with positive valence stimuli, subjects showed a higher proportion of fixation counts on the face (p = 0.001) and background (p < 0.001) but less for the body (p < 0.001). There were no significant treatment × valence [F(1,34) = 0.041, p = 0.841] or treatment × regions × valence [F(2,68) = 2.005, p = 0.147] interaction effects. For individual fixation durations, there was only a significant main effect of AOI [F(2,68) = 65.263, p < 0.001, partial η2 = 0.657] due to subjects exhibiting longer durations of individual fixations toward the face compared to body and background (all p’s < 0.001) and to the background compared to the body (p = 0.001).
Associations Between Eye-Gaze Measures and Emotional Empathy Ratings and Autistic and Empathic Traits
We investigated correlations between treatment difference scores (i.e., OT – PLC) for both ratings and eye-tracking measures using Spearman tests. The results showed that the difference between percentage of total fixation duration for the face was positively correlated with the difference in rating scores for negative valence pictures (r = 0.348, p = 0.041), although not for positive ones (r = 0.211, p = 0.225) (see Figure 3). However, the same difference analysis for the percentage of fixation counts revealed significant correlations for both negative (r = 0.508, p = 0.002) and positive valence pictures (r = 0.340, p = 0.046) and on the body region for negative valence pictures (r = −0.392, p = 0.02) but not positive valence ones (r = −0.126, p = 0.47). Thus overall, the effects of OT on patterns of eye gaze were generally associated with those on emotional empathy ratings, although most strongly for negative valence pictures.
Correlation (Spearman) between emotional empathy ratings and percentage of total fixation duration using treatment differences scores (i.e., OT minus PLC treatment). Tfd, total fixation duration. Negative face: face region in negative valence pictures. Positive face: face region in positive valence pictures. Emotional empathy ratings for “how much do you feel the same as the person in the picture (i.e., direct emotional empathy)” from 1 to 9 (1 = not at all and 9 = very strongly). OT – PLC, OT minus PLC treatment condition.
Spearman correlation analyses revealed no significant correlations between autistic (AQ and SRS) or empathic (IRI) traits and the percentages of total fixation duration and fixation counts on each region (all p’s > 0.113).
Discussion
In the current study, we used eye tracking to investigate the effects of intranasal OT on emotional empathy and visual attention to different features of real life pictures in the MET. Results first confirmed previous findings that OT increases emotional empathy in both negative and positive emotional contexts (Hurlemann et al., 2010; Geng et al., 2018a, b). Second, results showed that OT increased both the proportion of time spent viewing the faces of the individuals portrayed in the pictures while correspondingly decreasing them to other features in the pictures. Furthermore, there was a significant positive association between the effect of OT on increasing the proportion of viewing time and/or fixation counts on the face and on increasing emotional empathy ratings, particularly for negative valence pictures. Overall, these findings provide a further replication of the finding that OT enhances emotional empathy and that this effect is associated with greater attention toward the faces of individuals displaying both negative and positive emotions and correspondingly reduced attention toward other contextual information.
Our findings that OT increased time spent viewing the faces of individuals expressing either positive or negative emotions in real-life contexts and that these were associated with parallel increased ratings of emotional empathy for negative, and to a lesser extent positive valence stimuli, support the conclusion that it may increase emotional empathy by enhancing the salience of the social stimuli. Additional support for this conclusion comes from observations in parent–offspring interactions. Mothers with higher plasma OT concentrations have been reported to pay more attention to their baby especially when the baby is distressed, implying that mothers with higher OT concentrations pay more attention to salient cues from their baby and respond more empathically toward them (Kim et al., 2014). Correspondingly, young children with higher saliva OT concentrations pay greater attention to the eye region of the faces (Nishizato et al., 2017).
There were also some indications that reductions in gaze toward other regions of the pictures were negatively associated with emotional empathy ratings. These findings contrast to some extent with a previous study using dynamic (video clip) stimuli reporting that OT increased emotional empathy for fearful faces and time spent viewing the eyes of these faces (and also happy, sad, and pain) but with an inverse relationship between them (Hubble et al., 2017a). The differences in findings may be explained by the different stimuli used, a static compared to dynamic presentation, and that in our current study, we focused on gaze toward the face as a whole rather than the eyes. We also did not focus on the specific individual emotions expressed but only whether they were in negative or positive valence situations. Another study has also reported no effects of OT on eye-gaze patterns toward positive, neutral, and negative valence pictures despite having effects on neural responses (Lischke et al., 2017). However, this latter study included both social and non-social pictures and not specifically intended to evoke empathic responses.
Subjects spent significantly more time viewing the faces and the backgrounds for negative compared with positive valence pictures, suggesting that they may pay greater attention to threatening salient cues, although pictures depicting sadness were also included. However, there was no treatment × valence interaction effect, indicating that OT had similar effects on increasing attention to salient cues for both positive and negative valence pictures. Emotional empathy ratings were also similar for positive and negative valence pictures, and altered ratings following OT were not significantly different.
We did not find any significant associations between eye-gaze parameters or emotional empathy ratings and scores on either autistic (AQ and SRS) or empathic (IRI) traits. In our previous study, we also found only marginal negative associations between AQ scores and emotional empathy ratings following PLC treatment (Geng et al., 2018a), and so a tentative conclusion from both studies is that the relationship between trait autism and emotional empathy and associated gaze toward the face in the MET is weak at best in healthy subjects. Given the greater evidence for altered patterns of eye gaze in clinical autism populations when viewing faces or other social stimuli (Chita-Tegmark, 2016; Kou et al., 2019), it is possible that they would exhibit stronger associations between symptom severity and eye-gaze and empathy ratings in the MET. Similarly, we did not find any associations with trait empathy scores as measured by the total IRI, possibly suggesting that the latter test is perhaps more sensitive for all aspects of empathic behavior rather than specifically for emotional empathy.
Several limitations should be acknowledged in the current study. First, we only included male subjects, and there is increasing evidence for sex-dependent effects of OT (Gao et al., 2016; Luo et al., 2017; Lieberz et al., 2019), although we have previously shown no sex-dependent effects of OT on emotional empathy or amygdala responses in the MET (Geng et al., 2018b). Two other studies showing effects of OT on pain empathy have also not found sex-dependent effects (Shamay-Tsoory et al., 2013; Abu-Akel et al., 2015), and so we would predict that similar effects of OT on patterns of eye gaze during the MET would occur in female subjects. Second, we could only investigate effects of OT on gaze toward the whole face region due to the nature of the MET pictures and so were unable to investigate if the eye region was of most importance. Finally, the picture stimuli presented were static, and it is possible that results using dynamic pictures could be different.
In summary, our findings in the current study demonstrate that the effects of OT in increasing emotional empathy responses to positive and negative expressions of emotions in real-life contexts are associated with an increase in time viewing the face region of protagonists in the pictures and correspondingly less to other salient features. This provides further support for an important general role of OT in shifting attention toward the most salient features of social stimuli in line with the social salience hypothesis (Shamay-Tsoory and Abu-Akel, 2016) and may contribute to greater attention and empathic responses in more specific contexts such as parent–offspring interactions.
Data Availability Statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
Ethics Statement
The studies involving human participants were reviewed and approved by Ethics Committee of the University of Electronic Science and Technology of China. The patients/participants provided their written informed consent to participate in this study.
Author Contributions
JL, JK, and KK designed the experiment. JL, MF, and YZ conducted the experiment. JL and WZ analyzed the data. JL drafted the manuscript. BB and KK interpreted the results, revised it critically for important intellectual content, and finalized the manuscript for submission. All authors contributed to manuscript revision and read and approved the submitted version.
Conflict of Interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Funding. This work was supported by the National Natural Science Foundation of China (NSFC) (Grant Number 31530032, KK), Guangdong Provincial Government (Grant Number 2018B030335001, KK), and by the China Postdoctoral Science Foundation (Grant Number 2018M643432, WZ).
Supplementary Material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fnins.2020.00803/full#supplementary-material
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Neurosci.Frontiers in Neuroscience1662-45481662-453XFrontiers Media S.A.32848571PMC743215110.3389/fnins.2020.00803NeuroscienceOriginal ResearchOxytocin Facilitation of Emotional Empathy Is Associated With Increased Eye Gaze Toward the Faces of Individuals in Emotional ContextsLeJiaoKouJuanZhaoWeihuaFuMeinaZhangYingyingBeckerBenjaminKendrickKeith M.*The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
Edited by: Kerstin Uvnäs Moberg, Swedish University of Agricultural Sciences, Sweden
Reviewed by: Gábor B. Makara, Hungarian Academy of Sciences (MTA), Hungary; Ben Nephew, Worcester Polytechnic Institute, United States
*Correspondence: Keith M. Kendrick, kkendrick@uestc.edu.cn; k.kendrick.uestc@gmail.com
This article was submitted to Neuroendocrine Science, a section of the journal Frontiers in Neuroscience
118202020201480308520200872020Copyright © 2020 Le, Kou, Zhao, Fu, Zhang, Becker and Kendrick.2020Le, Kou, Zhao, Fu, Zhang, Becker and KendrickThis is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
One of the most robust effects of intranasal oxytocin treatment is its enhancement of emotional empathy responses across cultures to individuals displaying emotions in realistic contexts in the Multifaceted Empathy Task (MET). However, it is not established if this effect of oxytocin on emotional empathy is due to altered visual attention toward different components of the stimulus pictures or an enhanced empathic response. In the current randomized placebo-controlled within-subject experiment on 40 healthy male individuals, we both attempted a further replication of emotional empathy enhancement by intranasal oxytocin (24 IU) and used eye-tracking measures to determine if this was associated by altered visual attention toward different components of the picture stimuli (background context, human face, and body posture). Results replicated previous findings of enhanced emotional empathy in response to both negative and positive stimuli and that this was associated with an increased proportion of time viewing the faces of humans in the pictures and a corresponding decrease in that toward the rest of the body and/or background context. Overall, our findings suggest that enhanced emotional empathy following oxytocin administration is due to increased attention to the faces of others displaying emotions and away from other contextual and social cues.
Clinical Trial Registration:\\nwww.ClinicalTrials.gov Oxytocin Modulates Eye Gaze Behavior During Social Processing; registration ID: NCT03293511; URL: https://clinicaltrials.gov/ct2/show/NCT03293511.
autismoxytocinemotional empathyeye gazeface processingsocial attentionNational Natural Science Foundation of China10.13039/501100001809China Postdoctoral Science Foundation10.13039/501100002858Government of Guangdong Province10.13039/501100002912Introduction
Empathizing with others is of key importance for our social interactions. Impairments in this domain represent a major symptom in several psychiatric disorders such as autism (Lombardo et al., 2007) and depression (Tully et al., 2016; Xu et al., 2020), leading to significant problems in understanding and responding appropriately to others in social contexts. Empathy refers to a multidimensional construct including both cognitive (identifying emotions expressed by others) and emotional (being aroused by – indirect emotional empathy or feeling the same emotion – direct emotional empathy – expressed by others) facets (Shamay-Tsoory, 2011). Understanding the neurobiological regulation of these multidimensional functional domains is therefore of great importance both in the context of social neuroscience as well as to identify novel therapeutic approaches to facilitate empathy and alleviate deficits in patient populations.
In recent years, a number of studies have consistently implicated the hypothalamic neuropeptide oxytocin (OT) in the control of emotional empathy in humans (Hurlemann et al., 2010; Hubble et al., 2017a; Geng et al., 2018a, b). Intranasal OT has been demonstrated to facilitate both direct and indirect emotional empathy but not cognitive empathy as assessed by the Multifaceted Empathy Task (Dziobek et al., 2008) in male Caucasian (Hurlemann et al., 2010) as well as in both male and female Chinese subjects (Geng et al., 2018b). The latter study also found similar effects of OT on increasing emotional empathy in male subjects with both 24 and 48 IU doses. Similarly, another study using dynamic, empathy-inducing video clips reported that OT enhanced emotional but not cognitive empathy for fear (Hubble et al., 2017a), and several studies have reported that OT can increase empathy for pain experienced by others in both sexes (Shamay-Tsoory et al., 2013; Abu-Akel et al., 2015). Studies that employed concomitant functional MRI (fMRI) assessments suggest that the emotional empathy-enhancing effects of OT are neurally mediated by reduced responsiveness of the amygdala to empathy-inducing scenes (Geng et al., 2018a, b). The important role of the amygdala in emotional empathy is further supported by studies in patients with focal bilateral amygdala lesions who exhibit impaired emotional but not cognitive empathy (Hurlemann et al., 2010).
Accumulating evidence suggests that OT may generally promote face emotion recognition accuracy (Shahrestani et al., 2013), and initial studies reported increased accuracy only for difficult items in the reading the mind in the eyes test (RMET) (Domes et al., 2007), which, together, may reflect a facilitatory effect of OT on some aspects of cognitive empathy. On the other hand, more recent studies using the RMET have either only reported effects in individuals with low pretreatment empathy (Feeser et al., 2015), low social proficiency, or higher maternal love withdrawal (Riem et al., 2014), or failed to find any effects at all (Radke and de Bruijn, 2015). Thus, to date the most consistent findings indicate a facilitation of emotional empathy by intranasal OT.
There is also increasing evidence for OT playing a key role in enhancing attention toward social stimuli in a person- and context-dependent manner, and this forms the basis of the proposed “social salience hypothesis” (see Shamay-Tsoory and Abu-Akel, 2016). In support of this hypothesis, studies employing selective attention paradigms have demonstrated that OT increases attention toward social stimuli but not non-social ones (Xu et al., 2015, 2019) and promotes switching attention away from interoceptive information toward external social cues via modulating the anterior insula, a core region of the salience network (Yao et al., 2018). Studies that acquired eye-tracking indices as a behavioral measure of attentive and salience processing additionally reported that OT increases the time spent viewing social relative to non-social stimuli across a number of different paradigms (Eckstein et al., 2019; Le et al., 2020 – using the same subjects as in the current study). A number of studies have reported that OT specifically increases gaze toward the eye region of both static and dynamic facial stimuli in either healthy or autistic individuals (Guastella et al., 2008; Andari et al., 2010; Domes et al., 2013; Auyeung et al., 2015), although others have reported no effects (Domes et al., 2010; Lischke et al., 2012; Hubble et al., 2017b). Saccades from the mouth to the eye region are also increased following OT for fear expression faces and are generally associated with amygdala responses (Gamer et al., 2010). Inconsistencies between the previous studies may be explained by a number of factors such as subject sex, whether stimuli are presented statically or dynamically and whether participants were asked to passively view or actively evaluate the presented stimuli. In our own previous study in the current cohort of subjects, we found that OT primarily increased the proportion of time spent viewing the eyes relative to the nose for static fearful, but not other emotional facial expressions presented passively (Le et al., 2020). In the context of visual stimuli-evoked empathic responses, only one previous study has directly investigated the effects of intranasal OT and reported that OT increased eye gaze across dynamic expressions of sadness, happiness, pain, or fear (Hubble et al., 2017a), and empathic empathy for fearful faces. However, this study found no correlation between the proportion of gaze time toward the eyes and empathy ratings. Another study has also reported no effects of OT on gaze toward positive, negative, and neutral valence scenes depicting both humans and objects despite finding effects on neural responses to them (Lischke et al., 2017). We have therefore investigated the effects of OT on eye gaze toward different components of negative and positive valence stimuli designed to evoke empathic responses and their association with its effects on enhancing emotional empathy using the Multifaceted Empathy Task (MET).
The current preregistered pharmacological eye-tracking study employed a randomized within-subject placebo-controlled design to investigate the effects of intranasal OT (24 IU) on patterns of eye gaze in male subjects performing the MET and to replicate previous findings demonstrating OT-induced facilitation of emotional empathy (Hurlemann et al., 2010; Geng et al., 2018a, b). We only included male subjects in the current study due to the main focus of our clinical trial being relevance to autism and, additionally, because we had already established that there are no sex-dependent effects of OT on responses in the MET (Geng et al., 2018b). Based on the previous studies reporting that OT facilitated facial emotion recognition and eye gaze toward facial stimuli, we hypothesized that the OT-induced facilitation of emotional empathy would be associated with increased gaze time toward the face and away from other less socially salient features of both positive and negative valence picture stimuli. We also hypothesized that the observed effects of OT on emotional empathy ratings and time spent viewing faces would be associated with individual variations in autistic and/or empathic traits.
Materials and MethodsParticipants
Forty healthy male adults (mean age ± SEM = 20.8 ± 0.38) were recruited via university advertisement. Only male subjects were recruited due to the main focus on potential relevance to autism and because the majority of previous studies have also only used male subjects. In a within subject design study, each participant underwent the experimental paradigm twice and with a between assessment and treatment period of approximately 2 weeks (mean ± SEM = 14.86 ± 0.16 days). Subjects were randomly assigned to receive either OXT (24 IU OXT in water, 0.9% sodium chloride, and glycerol supplied by the Sichuan Meike Pharmaceutical Company, China) or placebo (PLC containing all ingredients except for the neuropeptide and supplied by the same company) intranasal administration. The order of receiving OXT or PLC was counterbalanced across subjects. Subject number was determined by an a priori power analysis (using G∗power version 3.1.9.4 with α = 0.05 and a 0.5 correlation between repeated measures), which showed that a within-subject design would achieve >80% power for a medium effect size both with ANOVAs (F = 0.25, partial eta squared, 0.06) and post hoc pairwise comparisons (Cohen’s d = 0.5) with a total of 36 subjects. Finally, 40 subjects were finally recruited to compensate for potential subject dropout and data collection issues. Subjects were required to abstain from caffeine, alcohol, nicotine, or other psychoactive substances in the 24 h before each experiment, and initial interviews confirmed the self-reported absence of current or previous psychiatric illness, drug, or alcohol abuse. All participants had normal or corrected to normal vision. The study had full approval from the local ethics committee of the University of Electronic Science and Technology of China, and procedures were in accordance with the latest revision of the declaration of Helsinki. The study was preregistered at clinical trials.gov (Trial registration ID: NCT03293511; URL: https://clinicaltrials.gov/ct2/show/NCT03293511). The experiment was part of a larger study on the effects of intranasal OT on social attention during which subjects completed seven different tasks. Data from the other tasks involving social versus non-social stimuli and face emotion processing presented immediately prior to the current empathy task have been published elsewhere (Le et al., 2020). All subjects signed written informed consent and were paid for participation (100RMB).
Experimental Procedure
In a double-blind placebo-controlled within-subject design, subjects self-administered the nasal sprays (24 IU, three puffs per nostril, each containing 4 IU of OXT or the same number of puffs with the PLC spray) following a standardized protocol (Guastella et al., 2013). At the first visit before treatment administration, participants initially completed a set of Chinese version questionnaires to evaluate mood and personality traits as a control for possible confounders due to any pretreatment group differences [State Trait Anxiety Inventory (STAI), Shek, 1993; Childhood Trauma Questionnaire (CTQ), Bernstein et al., 2003; Zhao et al., 2005]. The Positive and Negative Affect Schedule (PANAS; Watson et al., 1988) was completed three times: before treatment administration (t1) and before (t2) and after the task (t3) in order to assess possible treatment effects on mood. For the assessment of associations with experimental findings in the two groups in relation to autistic traits and empathy Chinese versions of the Autism Spectrum Quotient (AQ; Lau et al., 2013), social responsivity scale (SRS; Gau et al., 2013), and interpersonal responsivity index (IRI; Davis, 1980) were used. All subjects started the eye-tracking task 45 min after receiving the nasal spray.
The empathy task paradigm used was a Chinese version of the MET originally used in Caucasian subjects (Dziobek et al., 2008; Hurlemann et al., 2010) and described previously (Geng et al., 2018b). An adapted and shortened version of the original task was employed in the current eye-tracking study using identical real-life picture stimuli (30 positive and 30 negative valence) of people (both sexes and variable ages) displaying strong positive or negative emotions via face expressions and body posture in a particular context (displayed in the background of the picture). Stimuli were presented in a randomized order for 3 s followed by a fixation cross for 3 s (total task duration = 360 s). Subjects were required to view the stimuli passively. After the eye-tracking task, subjects were shown the picture stimuli again in a different randomized order and required to rate each one for how much they felt the same feelings as the person in the picture (i.e., direct emotional empathy) on a scale of 1–9 (1 = not at all and 9 = very strongly). Pictures were presented for a maximum of 4 s, and the rating scale was displayed under each picture. Subjects indicated their rating score by using a keyboard to move a cursor across the scale on the display screen. For flow charts showing the whole experimental procedure, see Supplementary Figure S1.
Eye-Tracking Data
Eye-gaze data were recorded using an eye tracker (Tobii TX300, Tobii, Danderyd, Sweden), which employs infrared (IR) light-emitting diodes and IR camera to measure corneal reflections and calculate the eye-gaze direction. All gaze data were recorded at 300 Hz sampling rate with a gaze accuracy of 0.4°. Recording and stimulus presentation were conducted using Tobii Pro Studio, E-prime 2.0 software, and E-Prime Extensions for Tobii (Psychology Software Tools, Pittsburgh, PA, United States). All raw data with at least 80% gaze weight were analyzed using Tobii studio. The primary measure collected was total fixation duration, although additionally the number and duration of individual fixations were measured in order to help interpret which were contributing to observed significant changes in overall fixation duration.
Statistical Analyses
All statistical analyses were performed using SPSS 22 (SPSS Inc., Chicago, IL, United States). Three-way repeated ANOVAs were performed with within-subject factors (treatment – OXT, PLC), AOI (background, body, and face), and valence (positive and negative) and percentage of total fixation duration as dependent variable. Separate analyses were also performed using percentage of fixation counts and individual fixation durations as dependent variables. The wide range of orientations and sizes of the faces of individuals portrayed in the picture stimuli of the MET effectively precluded a finer grained analysis of individual face features as AOIs. Similar analyses were performed with percentage of fixation counts and mean duration of individual fixations as dependent variables in order to determine whether alterations in total fixation duration were associated with altered numbers or durations of individual fixations. Significant (p < 0.05) interactions were explored using Bonferroni-corrected post hoc tests. Associations between eye-tracking variables and emotional empathy ratings and autistic and empathy traits (AQ, SRS, and IRI scores) were analyzed using Spearman correlation. To correct for multiple comparisons involving behavioral traits (three questionnaires), the threshold p value was adjusted to p < 0.0167 for the correlation analyses (e.g., p = 0.05/3 = 0.0167).
ResultsEffects of Treatment on Mood and Overall Gaze Time
Independent t tests revealed no significant differences in terms of mood and personality traits between subjects with the two different treatment orders (see Supplementary Table S1). A two-way repeated ANOVA with two treatment × three time points for PANAS scores revealed no significant main or interaction treatment effects on positive or negative mood (all p’s > 0.665). There was no significant difference in the total amount of time subjects spent viewing the screen between treatments (t = 0.367, p = 0.716). Thus, OT had no overall effect on time spent viewing the stimulus screen.
Effects of Oxytocin on Emotional Empathy and Patterns of Eye Gaze
Five subjects were excluded from analysis in this task due to technical problems with data collection on one or both of their experimental sessions. Thus, n = 35 subjects were included in the final analysis. We first confirmed that we could replicate previous observations that OT enhanced emotional empathy ratings. Paired t tests revealed that OT significantly increased empathy ratings for both negative [t(1,34) = 2.804, p = 0.008, Cohen’s d = 0.486] and positive valence stimuli [t(1,34) = 2.715, p = 0.010, Cohen’s d = 0.469] compared to PLC (see Figure 1). There was no treatment difference in rating scores (i.e., OT minus PLC) for positive and negative valence pictures (p = 0.549) indicating that OT had a similar effect on both valences.
The effects of oxytocin (OT) on emotional empathy ratings. Subjects were asked to rate positive and negative valence pictures on: “how much do you feel the same as the person in the picture (i.e., direct emotional empathy)” from 1 to 9 (1 = not at all and 9 = very strongly). Error bars represent SEM. **p < 0.01, OXT vs. PLC and positive vs. negative valence.
For the eye-tracking component of the study, we used three-way ANOVAs with treatment (OT and PLC), AOIs (human face, human body, and background), and valence (positive and negative) as factors. For the primary dependent variable (proportion of time spent viewing the three AOIs relative to the whole screen), a main effect of AOI [F(2,68) = 501.836, p < 0.001, partial η2 = 0.937] but not treatment [F(1,34) = 1.250, p = 0.271] or valence [F(1,34) = 0.142, p = 0.709] was observed. The main effect of AOI was due to subjects spending proportionately more time viewing human faces and bodies compared with the background (p’s < 0.001) and on human faces as compared to bodies (p < 0.001) (see Figure 2A). There were also significant treatment × AOI [F(2,68) = 5.898, p = 0.013, partial η2 = 0.148] and AOI × valence [F(2,68) = 21.737, p < 0.001, partial η2 = 0.390] interaction effects. Post hoc Bonferroni corrected t tests revealed that OT increased the percentage of time spent viewing faces [t(1,34) = 2.642, p = 0.012, Cohen’s d = 0.525] but correspondingly decreased that for viewing the background [t(1,34) = −2.291, p = 0.028, Cohen’s d = 0.559] (see Figure 2B). For the AOI × valence interaction effect post hoc tests also revealed that for negative compared with positive valence stimuli, subjects spent a greater proportion of time viewing the face (p = 0.012) and background (p < 0.001) but less for the body (p < 0.001). There were no treatment × valence [F(1,34) = 0.482, p = 0.492] or treatment × regions × valence [F(2,68) = 0.260, p = 0.746] interaction effects.
The effects of oxytocin (OT) treatment on eye gaze directed toward the face, body, or background regions during the emotional empathy (EE) task. (A) The mean percentage total fixation duration of face, body, and background across two valence (positive, negative). (B) The effects of oxytocin (OT) treatment on mean percentage total fixation duration of face, body, and background across two valence (positive, negative). Error bars represent SEM. ***p < 0.001, *p < 0.05, OXT vs. PLC.
Identical ANOVA analyses were conducted for proportion of fixation counts and individual fixation durations. For proportion of fixation counts, we found similar results as for proportion of total viewing time, indicating that the latter were mainly due to an increased number of fixations. There was a significant main effect of AOI [F(2,68) = 514.768, p < 0.001, partial η2 = 0.938] but not for either treatment [F(1,34) = 1.830, p = 0.185] or valence [F(1,34) = 0.000, p = 0.993]. The AOI main effect again reflected a higher proportion of fixation counts to the face and body compared with the background (p’s < 0.001) and for the face compared to the body (p < 0.001). There were also significant treatment × region [F(2,68) = 4.518, p = 0.030, partial η2 = 0.117] and valence × region [F(2,68) = 28.573, p < 0.001, partial η2 = 0.457] interaction effects. Post hoc Bonferroni-corrected tests revealed that OT increased the percentage of fixation counts for the face region [t(1,34) = 2.968, p = 0.024, Cohen’s d = 0.454]. For the AOI × valence interaction, post hoc tests also revealed that for negative compared with positive valence stimuli, subjects showed a higher proportion of fixation counts on the face (p = 0.001) and background (p < 0.001) but less for the body (p < 0.001). There were no significant treatment × valence [F(1,34) = 0.041, p = 0.841] or treatment × regions × valence [F(2,68) = 2.005, p = 0.147] interaction effects. For individual fixation durations, there was only a significant main effect of AOI [F(2,68) = 65.263, p < 0.001, partial η2 = 0.657] due to subjects exhibiting longer durations of individual fixations toward the face compared to body and background (all p’s < 0.001) and to the background compared to the body (p = 0.001).
Associations Between Eye-Gaze Measures and Emotional Empathy Ratings and Autistic and Empathic Traits
We investigated correlations between treatment difference scores (i.e., OT – PLC) for both ratings and eye-tracking measures using Spearman tests. The results showed that the difference between percentage of total fixation duration for the face was positively correlated with the difference in rating scores for negative valence pictures (r = 0.348, p = 0.041), although not for positive ones (r = 0.211, p = 0.225) (see Figure 3). However, the same difference analysis for the percentage of fixation counts revealed significant correlations for both negative (r = 0.508, p = 0.002) and positive valence pictures (r = 0.340, p = 0.046) and on the body region for negative valence pictures (r = −0.392, p = 0.02) but not positive valence ones (r = −0.126, p = 0.47). Thus overall, the effects of OT on patterns of eye gaze were generally associated with those on emotional empathy ratings, although most strongly for negative valence pictures.
Correlation (Spearman) between emotional empathy ratings and percentage of total fixation duration using treatment differences scores (i.e., OT minus PLC treatment). Tfd, total fixation duration. Negative face: face region in negative valence pictures. Positive face: face region in positive valence pictures. Emotional empathy ratings for “how much do you feel the same as the person in the picture (i.e., direct emotional empathy)” from 1 to 9 (1 = not at all and 9 = very strongly). OT – PLC, OT minus PLC treatment condition.
Spearman correlation analyses revealed no significant correlations between autistic (AQ and SRS) or empathic (IRI) traits and the percentages of total fixation duration and fixation counts on each region (all p’s > 0.113).
Discussion
In the current study, we used eye tracking to investigate the effects of intranasal OT on emotional empathy and visual attention to different features of real life pictures in the MET. Results first confirmed previous findings that OT increases emotional empathy in both negative and positive emotional contexts (Hurlemann et al., 2010; Geng et al., 2018a, b). Second, results showed that OT increased both the proportion of time spent viewing the faces of the individuals portrayed in the pictures while correspondingly decreasing them to other features in the pictures. Furthermore, there was a significant positive association between the effect of OT on increasing the proportion of viewing time and/or fixation counts on the face and on increasing emotional empathy ratings, particularly for negative valence pictures. Overall, these findings provide a further replication of the finding that OT enhances emotional empathy and that this effect is associated with greater attention toward the faces of individuals displaying both negative and positive emotions and correspondingly reduced attention toward other contextual information.
Our findings that OT increased time spent viewing the faces of individuals expressing either positive or negative emotions in real-life contexts and that these were associated with parallel increased ratings of emotional empathy for negative, and to a lesser extent positive valence stimuli, support the conclusion that it may increase emotional empathy by enhancing the salience of the social stimuli. Additional support for this conclusion comes from observations in parent–offspring interactions. Mothers with higher plasma OT concentrations have been reported to pay more attention to their baby especially when the baby is distressed, implying that mothers with higher OT concentrations pay more attention to salient cues from their baby and respond more empathically toward them (Kim et al., 2014). Correspondingly, young children with higher saliva OT concentrations pay greater attention to the eye region of the faces (Nishizato et al., 2017).
There were also some indications that reductions in gaze toward other regions of the pictures were negatively associated with emotional empathy ratings. These findings contrast to some extent with a previous study using dynamic (video clip) stimuli reporting that OT increased emotional empathy for fearful faces and time spent viewing the eyes of these faces (and also happy, sad, and pain) but with an inverse relationship between them (Hubble et al., 2017a). The differences in findings may be explained by the different stimuli used, a static compared to dynamic presentation, and that in our current study, we focused on gaze toward the face as a whole rather than the eyes. We also did not focus on the specific individual emotions expressed but only whether they were in negative or positive valence situations. Another study has also reported no effects of OT on eye-gaze patterns toward positive, neutral, and negative valence pictures despite having effects on neural responses (Lischke et al., 2017). However, this latter study included both social and non-social pictures and not specifically intended to evoke empathic responses.
Subjects spent significantly more time viewing the faces and the backgrounds for negative compared with positive valence pictures, suggesting that they may pay greater attention to threatening salient cues, although pictures depicting sadness were also included. However, there was no treatment × valence interaction effect, indicating that OT had similar effects on increasing attention to salient cues for both positive and negative valence pictures. Emotional empathy ratings were also similar for positive and negative valence pictures, and altered ratings following OT were not significantly different.
We did not find any significant associations between eye-gaze parameters or emotional empathy ratings and scores on either autistic (AQ and SRS) or empathic (IRI) traits. In our previous study, we also found only marginal negative associations between AQ scores and emotional empathy ratings following PLC treatment (Geng et al., 2018a), and so a tentative conclusion from both studies is that the relationship between trait autism and emotional empathy and associated gaze toward the face in the MET is weak at best in healthy subjects. Given the greater evidence for altered patterns of eye gaze in clinical autism populations when viewing faces or other social stimuli (Chita-Tegmark, 2016; Kou et al., 2019), it is possible that they would exhibit stronger associations between symptom severity and eye-gaze and empathy ratings in the MET. Similarly, we did not find any associations with trait empathy scores as measured by the total IRI, possibly suggesting that the latter test is perhaps more sensitive for all aspects of empathic behavior rather than specifically for emotional empathy.
Several limitations should be acknowledged in the current study. First, we only included male subjects, and there is increasing evidence for sex-dependent effects of OT (Gao et al., 2016; Luo et al., 2017; Lieberz et al., 2019), although we have previously shown no sex-dependent effects of OT on emotional empathy or amygdala responses in the MET (Geng et al., 2018b). Two other studies showing effects of OT on pain empathy have also not found sex-dependent effects (Shamay-Tsoory et al., 2013; Abu-Akel et al., 2015), and so we would predict that similar effects of OT on patterns of eye gaze during the MET would occur in female subjects. Second, we could only investigate effects of OT on gaze toward the whole face region due to the nature of the MET pictures and so were unable to investigate if the eye region was of most importance. Finally, the picture stimuli presented were static, and it is possible that results using dynamic pictures could be different.
In summary, our findings in the current study demonstrate that the effects of OT in increasing emotional empathy responses to positive and negative expressions of emotions in real-life contexts are associated with an increase in time viewing the face region of protagonists in the pictures and correspondingly less to other salient features. This provides further support for an important general role of OT in shifting attention toward the most salient features of social stimuli in line with the social salience hypothesis (Shamay-Tsoory and Abu-Akel, 2016) and may contribute to greater attention and empathic responses in more specific contexts such as parent–offspring interactions.
Data Availability Statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
Ethics Statement
The studies involving human participants were reviewed and approved by Ethics Committee of the University of Electronic Science and Technology of China. The patients/participants provided their written informed consent to participate in this study.
Author Contributions
JL, JK, and KK designed the experiment. JL, MF, and YZ conducted the experiment. JL and WZ analyzed the data. JL drafted the manuscript. BB and KK interpreted the results, revised it critically for important intellectual content, and finalized the manuscript for submission. All authors contributed to manuscript revision and read and approved the submitted version.
Conflict of Interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Funding. This work was supported by the National Natural Science Foundation of China (NSFC) (Grant Number 31530032, KK), Guangdong Provincial Government (Grant Number 2018B030335001, KK), and by the China Postdoctoral Science Foundation (Grant Number 2018M643432, WZ).
Supplementary Material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fnins.2020.00803/full#supplementary-material
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Internet ResJMIRJournal of Medical Internet Research1439-44561438-8871JMIR PublicationsToronto, Canada32744508PMC7432152v22i8e1901810.2196/19018Original PaperOriginal PaperEvaluating Smart Assistant Responses for Accuracy and Misinformation Regarding Human Papillomavirus Vaccination: Content Analysis StudyKoolTijnEysenbachGuntherBoydMattBenisArrielRiveraYonairaDoddRachaelBenisArrielFerrandJohnMPH1https://orcid.org/0000-0001-8343-5787HockensmithRyli1https://orcid.org/0000-0002-5613-8901HoughtonRebecca FagenMPA1https://orcid.org/0000-0002-2590-7907Walsh-BuhiEric RMPH, PhDhttps://orcid.org/0000-0002-1690-53521School of Public Health-BloomingtonIndiana University1025 E. 7th Street, Room 116Department of Applied Health ScienceBloomington, IN, 47405United States1 8128554867erwals@iu.edu\n\nSchool of Public Health-Bloomington\nIndiana University\nBloomington, IN\nUnited States\nCorresponding Author: Eric R Walsh-Buhi erwals@iu.edu82020382020228e1901814202022420201852020272020©John Ferrand, Ryli Hockensmith, Rebecca Fagen Houghton, Eric R Walsh-Buhi. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 03.08.2020.2020This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on http://www.jmir.org/, as well as this copyright and license information must be included.Background
Almost half (46%) of Americans have used a smart assistant of some kind (eg, Apple Siri), and 25% have used a stand-alone smart assistant (eg, Amazon Echo). This positions smart assistants as potentially useful modalities for retrieving health-related information; however, the accuracy of smart assistant responses lacks rigorous evaluation.
Objective
This study aimed to evaluate the levels of accuracy, misinformation, and sentiment in smart assistant responses to human papillomavirus (HPV) vaccination–related questions.
Methods
We systematically examined responses to questions about the HPV vaccine from the following four most popular smart assistants: Apple Siri, Google Assistant, Amazon Alexa, and Microsoft Cortana. One team member posed 10 questions to each smart assistant and recorded all queries and responses. Two raters independently coded all responses (κ=0.85). We then assessed differences among the smart assistants in terms of response accuracy, presence of misinformation, and sentiment regarding the HPV vaccine.
Results
A total of 103 responses were obtained from the 10 questions posed across the smart assistants. Google Assistant data were excluded owing to nonresponse. Over half (n=63, 61%) of the responses of the remaining three smart assistants were accurate. We found statistically significant differences across the smart assistants (N=103, χ22=7.807, P=.02), with Cortana yielding the greatest proportion of misinformation. Siri yielded the greatest proportion of accurate responses (n=26, 72%), whereas Cortana yielded the lowest proportion of accurate responses (n=33, 54%). Most response sentiments across smart assistants were positive (n=65, 64%) or neutral (n=18, 18%), but Cortana’s responses yielded the largest proportion of negative sentiment (n=7, 12%).
Conclusions
Smart assistants appear to be average-quality sources for HPV vaccination information, with Alexa responding most reliably. Cortana returned the largest proportion of inaccurate responses, the most misinformation, and the greatest proportion of results with negative sentiments. More collaboration between technology companies and public health entities is necessary to improve the retrieval of accurate health information via smart assistants.
digital healthhuman papillomavirussmart assistantschatbotsconversational agentsmisinformationinfodemiologyvaccinationIntroductionBackground
Voice assistants, a form of chatbot or conversational agent often referred to colloquially as “smart assistants,” are devices that respond to human voices and can be commanded to do a variety of tasks [1]. Smart assistants have existed in their most contemporary form since 2010, with the introduction of Siri, and they are used for numerous tasks such as automation (ie, integration with climate control devices and entertainment devices), retrieval of information about certain topics, and shopping [1]. Smart assistants have been integrated into smartphones, laptops, speakers, and other devices, creating a large network of consumers who frequently utilize smart assistants to acquire information on a range of topics [1].
Prevalence of Smart Assistant Use
According to the Pew Research Center, out of a total sample of 2045 Americans, nearly half (46%) reported using smart assistants in 2017 [2]. Of 4272 Americans surveyed in 2019, one-quarter (25%) had a stand-alone smart assistant device (eg, Amazon Echo, Google Home) in their homes [3]. Households that reported having a stand-alone smart assistant device (n=1067) also reported higher income (34% earned US $75,000 or more per year; 15% earned below US $30,000) [3]. The same report indicated that younger Americans frequently use stand-alone smart assistants, with 32% of users aged between 18 and 29 years and 28% of users aged between 30 and 49 years [3]. This difference may be due to varied ways of assessing use (eg, ever use and daily use). In contrast, smartphone smart assistants are more frequently adopted by those aged between 18 and 29 years (81%), whereas those aged between 45 and 60 years report the most daily active use of smartphone smart assistants [2]. These usage trends depict nuanced usage patterns wherein younger users are the most common adopters of smartphone smart assistants and older users, once they begin using smartphone smart assistants, use them more frequently than do other age groups. These varying adoption and usage behaviors provide multiple avenues for targeting different age groups through smart assistants.
Uses of Smart Assistants
More than half of Americans report that a major reason for using smart assistants is the ability to interact with their devices without using their hands [2]. One-quarter of Americans say that they use smart assistants to remotely control other devices such as heating systems, door locks, and lights [2]. Given that approximately 35% of Americans report searching online (ie, using Google or another search engine) to self-diagnose a medical condition, it seems logical to assume that individuals may turn to smart assistants for this information as well [4]. Unfortunately, smart assistants are relatively new technologies, and their responses have not been rigorously evaluated in many contexts.
Previous Assessments of Smart Assistants
In general, smart assistants have been evaluated for their response accuracy [5] and their general usability [6]. While considerable research has been conducted assessing digital health approaches, such as text message–based approaches and mobile apps for a range of health behaviors [7-9], few have included smart assistant responses to health-related queries. For instance, a pilot study comparing two smart assistants (Google Assistant and Apple Siri) to a standard Google Search on the topic of smoking cessation resources found that Google Assistant provided a greater number of evidence-based responses [10]. Other studies have specifically examined consumer experiences with the natural language processing of smart assistants [5].
Human Papillomavirus Prevention
To effectively assess smart assistant responses for accuracy and misinformation, human papillomavirus (HPV) has been identified as a controversial content area with a substantial evidence base. The evidence base of HPV is scientifically valid but hotly contested. HPV is the most common sexually transmitted infection and is a known cause of cervical cancer, as well as several other types of cancers [11]. While most sexually active people will contract HPV at some point in their lives, how the infection resolves varies from person to person, and a federally approved vaccine has been shown to be effective at reducing both the incidence of HPV transmission and the incidences of genital and anogenital HPV infection and cervical lesions [12]. Despite studies affirming the positive effects of the HPV vaccine while debunking inaccurate claims, there continues to be large-scale misinformation efforts (mostly conducted online through social media platforms) surrounding this issue, which are driven in part by the antivaccination movement [13-20]. These issues of misinformation have contributed to mistrust of medical professionals [21-23] and misunderstanding of diseases and their risks [24,25]. In January 2020, the World Health Organization (WHO) released a list of urgent global health challenges for the new decade, including stopping vaccine-preventable diseases and earning the public’s trust [26]. These two challenges are closely intertwined, as trust helps to shape whether individuals rely on provider recommendations (eg, whether parents will vaccinate their children) [22,27] and how misinformation disseminated online and across social media platforms influences vaccine refusal or vaccine delay [28]. In fact, the WHO has stated that vaccine hesitancy is one of the top 10 threats to global health [29].
In this study, we attempted to answer the following research question: do responses to smart assistant queries vary between different smart assistants, with regard to accuracy, misinformation, and sentiment toward HPV vaccination?
MethodsQuery Development
In order to effectively assess smart assistants’ responses for accuracy and misinformation, we utilized questions from the chat-text service of Planned Parenthood (personal communication by Nicole Levitz; March 18, 2019). We chose to focus on questions around the HPV vaccine owing to the previously identified issues of accuracy and misinformation surrounding this topic [30,31]. We chose variations of 10 evidence-based questions from the Planned Parenthood system, allowing us to better evaluate responses to those questions for accuracy and misinformation. The questions are listed in Textbox 1.
Queries posed to smart assistants.
1. Does the HPV vaccine work?
2. Does the HPV vaccine cause autism?
3. Does Gardasil work?
4. Does Gardasil cause autism?
5. Is the HPV vaccine dangerous?
6. Is Gardasil dangerous?
7. Who can get the HPV vaccine?
8. Where can I get the HPV vaccine?
9. Does Gardasil kill?
10. How much does the HPV vaccine cost?
Search Process
One member of the research team queried each of the four most popular smart assistants (Apple Siri, Google Assistant, Amazon Alexa, and Microsoft Cortana), which were identified in the 2019 Voice Report by Microsoft [32]. In the event that the smart assistant provided a nonresponse to a query (eg, “I don’t know how to answer that”), the team member queried the smart assistant a maximum of three times to ensure that it was not an errored response due to misunderstanding the query, background noise, or some other unrelated reason.
Smart assistants provided varying numbers of results on their respective first pages for each query. Alexa and Google Assistant provided only one oral result for each query, whereas Siri provided five text results for each query. Cortana provided between three and nine text or video results for each query. Multimedia Appendix 1 displays the number of results each smart assistant provided per query. The team member, responsible for conducting the searches, recorded the first page of the results for data extraction based on previous studies indicating that users more frequently click on the top 10 results, which tend to be concentrated on the first page of most search results [33]. There is also evidence suggesting that people do not necessarily only click the first result on a page. In a 2020 study of search engine optimization, 16% of respondents in the study reported clicking on only the first result compared with 17% and 14% of respondents who reported clicking on three and five results, respectively [34]. This finding suggests that the remaining results on the first page should be extracted to best replicate actual human search behavior.
Recording Process
The team member, who posed questions to the smart assistants, used video and audio recording software to record both the queries and the smart assistant responses. We used these recordings in the data extraction and coding process. Figure 1 depicts a recording of a query posed to Cortana.
Example of a video result from a query to Cortana.
Data Extraction and Coding Process
We developed a web-based (Qualtrics) survey to aid in data extraction and coding of the smart assistant responses. Survey items were designed to extract relevant data from the smart assistant responses, including the types of responses (eg, video, web page, blog, and journal article), sources of the responses (eg, business, doctors, and health information provider), sentiments of the responses toward the HPV vaccine (eg, positive, neutral, and negative), accuracy of the responses, any incidence of misinformation in the responses, and topics discussed in the responses (eg, cancer, sexual behavior, and conspiracy theory).
We defined accuracy as a response that reflected the existing evidence. We coded accuracy using a dichotomy approach (0 for not accurate; 1 for accurate), which we applied specifically to the query (ie, accuracy of unrelated tangential content was not considered). If the response did not answer the query with the correct reply or positioned the correct reply as dubious or incorrect, we considered it “not accurate.” We defined misinformation as either deliberate or accidental promotion of previously disproved or unproven beliefs, attitudes, and behaviors. We coded misinformation using a dichotomy approach (0 if it did not provide misinformation; 1 if it did provide misinformation), which we applied to the entirety of the response (not just the answer to the question posed). If any misinformation was found in the smart assistant’s response, we coded it as having provided misinformation. Table 1 depicts examples of accuracy and misinformation in smart assistant responses to several queries. Since accuracy and misinformation were coded based on different aspects of the smart assistant responses, there were some cases with both an accurate response to the query and some misinformation in the result. We coded sentiment toward vaccines as one of the following four potential categories: negative (mostly negative statements), neutral (neither positive nor negative statements), positive (mostly positive statements), and ambiguous (both negative and positive statements). We applied sentiment coding to the entirety of the response.
Two independent team members extracted and coded the data with an almost perfect agreement (κ=0.85) [35]. We resolved discrepancies through discussion.
Examples of accuracy and misinformation in smart assistant responses.
Query
Accuracy
Misinformation
Does the HPVa vaccine work?
“In the trials that led to the approval of Gardasil and Cervarix, these vaccines were found to provide nearly 100% protection against persistent cervical infections with HPV types 16 and 18 and the cervical cell changes that these persistent infections can cause.”
N/Ab
Does the HPV vaccine cause autism?
“There is no link between vaccines and autism.”
“The scientific literature is now starting to fill up with case reports and studies and articles that irrefutably show that there is a connection between this vaccine (and it’s an ugly vaccine) and neurological damage.”
Does Gardasil work?
“Gardasil works by preventing the infection of the types of HPV that can lead to cervical cancer...”
“The Gardasil HPV vaccine has been proved to have caused the deaths of 32 women.”
Does Gardasil cause autism?
“There has never been a study that has shown that vaccines cause autism.”
N/A
Is the HPV vaccine dangerous?
“Findings from many vaccine safety monitoring systems and more than 160 studies have shown that HPV vaccines have a favorable safety profile—the body of scientific evidence overwhelmingly supports their safety.”
“Aluminum in the vaccines is toxic enough to be harmful.”
Is Gardasil dangerous?
“Although this information is accurate in a strictly literal sense, it is a misleading presentation of raw data that does not in itself establish a causal connection between Gardasil and the posited medical dangers.”
“The Gardasil HPV vaccine has been proved to have caused the deaths of 32 women.”
Who can get the HPV vaccine?
“All people ages 9 to 45 can get the HPV vaccine to protect against genital warts and/or different types of HPV that can cause cancer.”
N/A
Where can I get the HPV vaccine?
“The HPV vaccine is available at: Healthcare Clinic for patients aged 11-26. Walgreens Pharmacy. Ages vary by state.”
N/A
Does Gardasil kill?
“I cannot stress this enough, based on this report alone you can't make a determination that the vaccine caused the deaths.”
N/A
How much does the HPV vaccine cost?
“Each dose of the vaccine can cost about $250.”
N/A
aHPV: human papillomavirus.
bN/A: not applicable.
Analyses
The sample consisted of 128 total data points across all four smart assistants. We excluded any nonresponse data points (eg, “I don’t know how to answer that”) and responses that were categorized as other (eg, responded with completely irrelevant content to the query) from the final analyses. Google Assistant provided nonresponses to every query and was removed, thus reducing our final analysis sample to 103 responses across three smart assistants. These nonresponses and other responses were removed because they did not in any way address the query posed. Multimedia Appendix 1 displays the frequencies of responses and nonresponses for each smart assistant. Descriptive frequencies and chi-square difference tests were used to examine the levels of accuracy and misinformation among the smart assistants.
Results
Table 2 displays the source and content characteristics of the smart assistant responses. Smart assistant responses contained content published by an organization in 82 out of 103 (79.6%) responses, whereas 20 out of 103 (19.4%) responses were published by an individual. Content was provided by organizations including some type of health information provider (eg, Planned Parenthood and Mayo Clinic) in 36 out of 103 (35.0%) responses and by a government entity (eg, Centers for Disease Control and Prevention) in 25 out of 103 (24.3%) responses. Cortana provided content published by individuals (eg, physicians and journalists) in 18 out of 61 (29.5%) responses, whereas Siri and Alexa provided content published by individuals in only 2 out of 36 (5.6%) and 0 out of 6 (0.0%) responses, respectively. The most common type of individual who provided content (10 out of 103 responses, 9.7%) was some classification of physician. The type of content provided in the smart assistant responses varied, with videos provided in 30 out of 103 (29.1%) responses, driven entirely by Cortana, which was the only smart assistant to provide video responses. Siri’s responses were classified as frequently asked question (FAQ) pages in 13 out of 36 (36.1%) responses, whereas Cortana’s responses were classified as videos in 30 out of 61 (49.2%) responses and as general web pages in 12 out of 61 (19.7%) responses.
Source and content characteristics of smart assistant responses.
Variable
Value (N=103), n (%)
\nSourcea\n
\n\n
\n\n
Health information provider
36 (35.0)
\n\n
Federal/city/state
25 (24.3)
\n\n
Nonprofit/advocacy group
15 (14.6)
\n\n
Physician
10 (9.7)
\n\n
Other
9 (8.7)
\n\n
News/media organization
8 (7.8)
\n\n
Business
6 (5.8)
\n\n
Journalist/press
2 (1.9)
\n\n
Politician
2 (1.9)
\n\n
Health care organization
1 (1.0)
\nContent\n
\n\n
\n\n
\nFormat of the smart assistant responses\n
\n\n
\n\n
\n\n
Video
30 (29.1)
\n\n
\n\n
General web page
24 (23.3)
\n\n
\n\n
Article
14 (13.6)
\n\n
\n\n
FAQb site
19 (18.4)
\n\n
\n\n
Other
6 (5.8)
\n\n
\n\n
Blog post
3 (2.9)
\n\n
\n\n
Location/map/directions
1 (1.0)
\n\n
\nTopics mentioned in the smart assistant responsesa\n
\n\n
\n\n
\n\n
Cancer
77 (74.8)
\n\n
\n\n
Vaccines and side effects
57 (55.3)
\n\n
\n\n
Sexual behaviors
26 (25.2)
\n\n
\n\n
Access to medical care
20 (19.4)
\n\n
\n\n
Direct impacts on loved ones
13 (12.6)
\n\n
\n\n
Conspiracy theories
6 (5.8)
\n\n
\n\n
Vaccines are ineffective
6 (5.8)
\n\n
\n\n
Mass media
6 (5.8)
\n\n
\n\n
Risk of disease is lower than risk of adverse vaccination events
2 (1.9)
\n\n
\nSentiment expressed toward vaccines in smart assistant responses\n
\n\n
\n\n
\n\n
Positive
65 (63.1)
\n\n
\n\n
Neutral
18 (17.5)
\n\n
\n\n
Ambiguous
11 (10.7)
\n\n
\n\n
Negative
7 (6.8)
aTotals may exceed 100% owing to the availability of multiple response options.
bFAQ: frequently asked question.
Tables 3 and 4 depict the differences among smart assistants in terms of primary outcomes. Smart assistant responses contained accurate answers in 63 out of 103 (61.2%) responses. Neither response accuracy (P=.10) nor response sentiment (P=.22) was significantly different among the devices. The number of responses provided for each query varied across the devices, but 4 out of 6 (66.7%) responses by Alexa and 26 out of 36 (72.2%) responses by Siri were accurate. In contrast, 33 out of 61 (54.1%) responses by Cortana were accurate. There were no cases in which responses contained both accurate answers and misinformation. Chi-square tests indicated that the three smart assistants significantly varied in terms of whether they provided misinformation in their responses (N=103, χ22=7.807, P=.02) and whether they provided any evidence to support the claims made in their responses (N=103, χ22=11.054, P=.004). Smart assistant responses contained at least one instance of misinformation in 18 out of 103 (17.5%) responses. Responses by Alexa contained no misinformation, whereas misinformation was present in 2 out of 36 (5.6%) responses by Siri and 16 out of 61 (26.2%) responses by Cortana. Alexa provided evidence to support each of its responses, whereas Siri provided evidence in 21 out of 36 (58.3%) responses and Cortana provided evidence in 23 out of 61 (37.7%) responses.
In general, smart assistant responses contained positive sentiment toward vaccines, with 65 out of 103 (63.1%) responses containing primarily positive sentiment. The second most common sentiment was “neutral,” which was found in 18 out of 103 (17.5%) responses. The least common sentiment was “negative,” which was found in only 7 out of 103 (6.8%) responses, and all were provided by Cortana.
Response differences among smart assistants in terms of accuracy, evidence provided, and misinformation.
Response quality of the smart assistants
Value, n/N (%)
Chi-square effect size (df)
P value
\nAccurate response? (Yes)\n
63/103 (61.2%)
\n\n
\n\n
\n\n
Alexa
4/6 (66.7%)
4.558 (2)
.10
\n\n
Siri
26/36 (72.2%)
4.558 (2)
.10
\n\n
Cortana
33/61 (54.1%)
4.558 (2)
.10
\nEvidence provided? (Yes)\n
50/103 (48.5%)
\n\n
\n\n
\n\n
Alexa
6/6 (100%)
11.054 (2)
.004
\n\n
Siri
21/36 (58.3%)
11.054 (2)
.004
\n\n
Cortana
23/61 (37.7%)
11.054 (2)
.004
\nMisinformation provided? (Yes)\n
18/103 (17.5%)
\n\n
\n\n
\n\n
Alexa
0/6 (0%)
7.807 (2)
.02
\n\n
Siri
2/36 (5.6%)
7.807 (2)
.02
\n\n
Cortana
16/61 (26.2%)
7.807 (2)
.02
Sentiment differences among the smart assistants.
Smart assistant
Content sentiment
P value
\n\n
Neutral (N=18)
Negative (N=7)
Ambiguous (N=11)
Positive (N=65)
Chi-square effect size (df)
Alexa (N=6), n (%)
1 (16.7%)
0 (0%)
0 (0%)
5 (83.3%)
8.20 (6)
.22
Siri (N=35), n (%)
9 (25.7%)
0 (0%)
5 (14.3%)
21 (60%)
8.20 (6)
.22
Cortana (N=60), n (%)
8 (13.3%)
7 (11.7%)
6 (10%)
39 (65%)
8.20 (6)
.22
DiscussionPrincipal Results
This study sought to determine whether responses to queries vary among smart assistants in terms of accuracy, misinformation, and sentiment related to HPV vaccination. Our results indicate that smart assistants responded differently when asked about this topic. Specifically, smart assistants showed variations in the level of misinformation provided in responses, as well as the provision of evidence to support claims made in their responses. Smart assistants did not differ statistically in terms of the accuracy of their responses to queries involving HPV vaccination.
To our knowledge, this study is the first to report on smart assistants and their responses to HPV vaccine–related queries. Previous studies of HPV vaccination behaviors did not focus on smart assistants, and they indicated that antivaccination messages may influence one’s decisions to vaccinate themselves or their children [15]. However, given the growing number of persons and households adopting and utilizing smart assistants and the fact that smart assistants may be able to provide HPV vaccine information, it is necessary to examine whether responses delivered through smart assistants are disseminating these same antivaccination messages. This study is the first step in establishing a foundation for the types of vaccine sentiments that are present in content disseminated by smart assistants.
Importantly, our study revealed that, in general, smart assistants largely provide accurate and positive information regarding HPV vaccination (just under two-thirds of all smart assistant responses were accurate and positive), with no relevant differences across devices. We should reiterate here, however, that Google Assistant provided nonresponses to every query and, accordingly, was removed from the study. While accurate information on something as beneficial as HPV vaccination is necessary to mitigate HPV transmission and cancer incidence, just over one-third of responses contained inaccurate answers, suggesting that work is needed in this area to improve the provision of health information via smart assistants. On the other hand, misinformation was more of a concern across devices, as Cortana yielded the greatest number of responses containing misinformation (one-quarter of Cortana’s responses contained misinformation).
Comparison With Prior Work
Previous studies of smart assistant responses have focused on whether the devices responded correctly or whether they understood the query [5,6]. Only a handful of studies examined smart assistant responses in the context of a health behavior [10]. This study further assessed smart assistant responses to determine whether they provide accurate evidence to specific health behavior questions. Evidence is critical in combatting misinformation, at least on social media platforms. For example, in an experimental study that exposed Facebook users to simulated misinformation and different correction mechanisms about the Zika virus, the authors found that correction can work when it provides supporting evidence or appropriate sources to accompany refutation (eg, Centers for Disease Control and Prevention) [36]. The authors reported that this finding was maintained even in those who were rated higher in conspiracy beliefs.
Limitations
This study has limitations that should be considered when evaluating its results. The devices used their specific locations when responding to queries, which may have produced less generalizable responses. Some of the smart assistants employed in the study were previously used devices and may have been influenced by prior searches of the former user. To the best of our knowledge, no private mode exists in these smart assistants, which would prevent the queries and responses from influencing future searches. The sample size was limited for several reasons, including nonresponses and other unrelated responses to queries. Specifically, Google Assistant provided no response data, which may have influenced the results. Any responses that were entirely unrelated to the queries should be explored in future studies, since this may be an indication of a larger issue with smart assistants not comprehending the queries. Based on our knowledge about natural language processing in these smart assistants, it is suggested that syntax and dictation are some of the many influencers of smart assistant responses [37], and the queries posed to each device may not have been appropriately worded to elicit the most effective responses. For example, our use of a single investigator to ask the questions could be viewed as a limitation (ie, we do not know how the smart assistants would have responded to an investigator with a different gender, tone, or accent). The investigator only queried a single example of a smart assistant (ie, only one device was used to query Alexa), which could be a limitation, as smart assistants may respond differently depending on whether a stand-alone smart assistant or a smartphone-based smart assistant is used. The underlying search engine behind each smart assistant determines the responses to queries, which could have limited the information provided by smart assistants that are not connected to large search engines such as Google. The varying number of results provided in the smart assistant responses to queries could have impacted our ability to compare across the devices; however, since we found no relevant differences between the devices with regard to accuracy or sentiment, we do not believe that more results from one device impacted our findings or conclusions.
Despite these limitations, there are several strengths that provide further evidence of the study’s validity and importance. First, we utilized questions submitted to a Planned Parenthood chat service, which represent real-world issues on which consumers have previously sought additional information. Second, the questions used as queries were chosen because responses to these queries could be scored as either correct or incorrect, limiting “gray areas” in assessing accuracy of the smart assistant responses. Third, the queries were conducted by a single member of the research team to maintain consistency in language processing factors (eg, tone and syntax), and the search and coding processes were systematic to maintain consistency as well. Overall, this research furthers our understanding of how an emerging technology disseminates health information and whether such information can be classified as accurate and evidence-based. This study provides a basic framework for future evaluation of these smart assistants on which more advanced devices may be based.
Our findings paint a picture of smart assistants as newly emerged potential health promotion tools that are yet to be rigorously evaluated in this area. Smart assistants have only recently been introduced as potential health promotion tools (most notably from an executive perspective [38]) despite the growing proportion of households and individuals that use them [2,3]. The untapped potential of these devices for evidence-based information dissemination to consumers should be further explored. The potential of the devices may also be determined by their manufacturers who have, in some cases, provided platforms on which specialized topical information can be consolidated and further explored. For example, Alexa has an option for developers to create an Alexa Skill, which is essentially an application for Alexa that provides predefined information when queried specifically for that information. For example, an Alexa Skill that specifically searches in predefined evidence-based sources when queried for information on HPV vaccination could be developed. The downside to this potential approach for improving public health is that, as our results show, there is risk of disseminating misinformation through smart assistant responses, potentially reducing the positive impacts of a health promotion intervention. More needs to be done to better understand the susceptibility of these devices and their respective skills to outside influences.
Conclusions
Our results suggest that not all smart assistants are created equal with regard to utility, at least when it comes to provided evidence and misinformation in their responses. These findings should spark further research into how the proprietors of smart assistants procure the information that is disseminated to consumers of their products. Specifically, we suggest that manufacturers of these and other smart assistants collaborate with researchers to further evaluate the accuracy of smart assistants as public health tools and determine together how to disseminate information and what fact-checking assessments should be used for such information. With the high rate of HPV transmission globally and in the United States, smart assistants and their manufacturers are well positioned to deliver evidence-based health information to consumers. However, such a practice necessitates strong communication between technology companies, who may not be as focused on the accuracy or source of their most heavily promoted content, and public health entities. The focus on collaboration to address the issues of information accuracy and misinformation is paramount if we are to adequately respond to the WHO’s list of urgent global health challenges for the new decade, namely stopping vaccine-preventable diseases.
Authors' Contributions: All authors contributed equally to the development, review, and submission of this manuscript for publication.
Conflicts of Interest: None declared.
Appendix
Frequencies of responses and nonresponses by each smart assistant.
AbbreviationsHPV
human papillomavirus
WHO
World Health Organization
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Med. Internet ResJMIRJournal of Medical Internet Research1439-44561438-8871JMIR PublicationsToronto, Canada32744508PMC7432152v22i8e1901810.2196/19018Original PaperOriginal PaperEvaluating Smart Assistant Responses for Accuracy and Misinformation Regarding Human Papillomavirus Vaccination: Content Analysis StudyKoolTijnEysenbachGuntherBoydMattBenisArrielRiveraYonairaDoddRachaelBenisArrielFerrandJohnMPH1https://orcid.org/0000-0001-8343-5787HockensmithRyli1https://orcid.org/0000-0002-5613-8901HoughtonRebecca FagenMPA1https://orcid.org/0000-0002-2590-7907Walsh-BuhiEric RMPH, PhDhttps://orcid.org/0000-0002-1690-53521School of Public Health-BloomingtonIndiana University1025 E. 7th Street, Room 116Department of Applied Health ScienceBloomington, IN, 47405United States1 8128554867erwals@iu.edu\\n\\nSchool of Public Health-Bloomington\\nIndiana University\\nBloomington, IN\\nUnited States\\nCorresponding Author: Eric R Walsh-Buhi erwals@iu.edu82020382020228e1901814202022420201852020272020©John Ferrand, Ryli Hockensmith, Rebecca Fagen Houghton, Eric R Walsh-Buhi. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 03.08.2020.2020This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on http://www.jmir.org/, as well as this copyright and license information must be included.Background
Almost half (46%) of Americans have used a smart assistant of some kind (eg, Apple Siri), and 25% have used a stand-alone smart assistant (eg, Amazon Echo). This positions smart assistants as potentially useful modalities for retrieving health-related information; however, the accuracy of smart assistant responses lacks rigorous evaluation.
Objective
This study aimed to evaluate the levels of accuracy, misinformation, and sentiment in smart assistant responses to human papillomavirus (HPV) vaccination–related questions.
Methods
We systematically examined responses to questions about the HPV vaccine from the following four most popular smart assistants: Apple Siri, Google Assistant, Amazon Alexa, and Microsoft Cortana. One team member posed 10 questions to each smart assistant and recorded all queries and responses. Two raters independently coded all responses (κ=0.85). We then assessed differences among the smart assistants in terms of response accuracy, presence of misinformation, and sentiment regarding the HPV vaccine.
Results
A total of 103 responses were obtained from the 10 questions posed across the smart assistants. Google Assistant data were excluded owing to nonresponse. Over half (n=63, 61%) of the responses of the remaining three smart assistants were accurate. We found statistically significant differences across the smart assistants (N=103, χ22=7.807, P=.02), with Cortana yielding the greatest proportion of misinformation. Siri yielded the greatest proportion of accurate responses (n=26, 72%), whereas Cortana yielded the lowest proportion of accurate responses (n=33, 54%). Most response sentiments across smart assistants were positive (n=65, 64%) or neutral (n=18, 18%), but Cortana’s responses yielded the largest proportion of negative sentiment (n=7, 12%).
Conclusions
Smart assistants appear to be average-quality sources for HPV vaccination information, with Alexa responding most reliably. Cortana returned the largest proportion of inaccurate responses, the most misinformation, and the greatest proportion of results with negative sentiments. More collaboration between technology companies and public health entities is necessary to improve the retrieval of accurate health information via smart assistants.
digital healthhuman papillomavirussmart assistantschatbotsconversational agentsmisinformationinfodemiologyvaccinationIntroductionBackground
Voice assistants, a form of chatbot or conversational agent often referred to colloquially as “smart assistants,” are devices that respond to human voices and can be commanded to do a variety of tasks [1]. Smart assistants have existed in their most contemporary form since 2010, with the introduction of Siri, and they are used for numerous tasks such as automation (ie, integration with climate control devices and entertainment devices), retrieval of information about certain topics, and shopping [1]. Smart assistants have been integrated into smartphones, laptops, speakers, and other devices, creating a large network of consumers who frequently utilize smart assistants to acquire information on a range of topics [1].
Prevalence of Smart Assistant Use
According to the Pew Research Center, out of a total sample of 2045 Americans, nearly half (46%) reported using smart assistants in 2017 [2]. Of 4272 Americans surveyed in 2019, one-quarter (25%) had a stand-alone smart assistant device (eg, Amazon Echo, Google Home) in their homes [3]. Households that reported having a stand-alone smart assistant device (n=1067) also reported higher income (34% earned US $75,000 or more per year; 15% earned below US $30,000) [3]. The same report indicated that younger Americans frequently use stand-alone smart assistants, with 32% of users aged between 18 and 29 years and 28% of users aged between 30 and 49 years [3]. This difference may be due to varied ways of assessing use (eg, ever use and daily use). In contrast, smartphone smart assistants are more frequently adopted by those aged between 18 and 29 years (81%), whereas those aged between 45 and 60 years report the most daily active use of smartphone smart assistants [2]. These usage trends depict nuanced usage patterns wherein younger users are the most common adopters of smartphone smart assistants and older users, once they begin using smartphone smart assistants, use them more frequently than do other age groups. These varying adoption and usage behaviors provide multiple avenues for targeting different age groups through smart assistants.
Uses of Smart Assistants
More than half of Americans report that a major reason for using smart assistants is the ability to interact with their devices without using their hands [2]. One-quarter of Americans say that they use smart assistants to remotely control other devices such as heating systems, door locks, and lights [2]. Given that approximately 35% of Americans report searching online (ie, using Google or another search engine) to self-diagnose a medical condition, it seems logical to assume that individuals may turn to smart assistants for this information as well [4]. Unfortunately, smart assistants are relatively new technologies, and their responses have not been rigorously evaluated in many contexts.
Previous Assessments of Smart Assistants
In general, smart assistants have been evaluated for their response accuracy [5] and their general usability [6]. While considerable research has been conducted assessing digital health approaches, such as text message–based approaches and mobile apps for a range of health behaviors [7-9], few have included smart assistant responses to health-related queries. For instance, a pilot study comparing two smart assistants (Google Assistant and Apple Siri) to a standard Google Search on the topic of smoking cessation resources found that Google Assistant provided a greater number of evidence-based responses [10]. Other studies have specifically examined consumer experiences with the natural language processing of smart assistants [5].
Human Papillomavirus Prevention
To effectively assess smart assistant responses for accuracy and misinformation, human papillomavirus (HPV) has been identified as a controversial content area with a substantial evidence base. The evidence base of HPV is scientifically valid but hotly contested. HPV is the most common sexually transmitted infection and is a known cause of cervical cancer, as well as several other types of cancers [11]. While most sexually active people will contract HPV at some point in their lives, how the infection resolves varies from person to person, and a federally approved vaccine has been shown to be effective at reducing both the incidence of HPV transmission and the incidences of genital and anogenital HPV infection and cervical lesions [12]. Despite studies affirming the positive effects of the HPV vaccine while debunking inaccurate claims, there continues to be large-scale misinformation efforts (mostly conducted online through social media platforms) surrounding this issue, which are driven in part by the antivaccination movement [13-20]. These issues of misinformation have contributed to mistrust of medical professionals [21-23] and misunderstanding of diseases and their risks [24,25]. In January 2020, the World Health Organization (WHO) released a list of urgent global health challenges for the new decade, including stopping vaccine-preventable diseases and earning the public’s trust [26]. These two challenges are closely intertwined, as trust helps to shape whether individuals rely on provider recommendations (eg, whether parents will vaccinate their children) [22,27] and how misinformation disseminated online and across social media platforms influences vaccine refusal or vaccine delay [28]. In fact, the WHO has stated that vaccine hesitancy is one of the top 10 threats to global health [29].
In this study, we attempted to answer the following research question: do responses to smart assistant queries vary between different smart assistants, with regard to accuracy, misinformation, and sentiment toward HPV vaccination?
MethodsQuery Development
In order to effectively assess smart assistants’ responses for accuracy and misinformation, we utilized questions from the chat-text service of Planned Parenthood (personal communication by Nicole Levitz; March 18, 2019). We chose to focus on questions around the HPV vaccine owing to the previously identified issues of accuracy and misinformation surrounding this topic [30,31]. We chose variations of 10 evidence-based questions from the Planned Parenthood system, allowing us to better evaluate responses to those questions for accuracy and misinformation. The questions are listed in Textbox 1.
Queries posed to smart assistants.
1. Does the HPV vaccine work?
2. Does the HPV vaccine cause autism?
3. Does Gardasil work?
4. Does Gardasil cause autism?
5. Is the HPV vaccine dangerous?
6. Is Gardasil dangerous?
7. Who can get the HPV vaccine?
8. Where can I get the HPV vaccine?
9. Does Gardasil kill?
10. How much does the HPV vaccine cost?
Search Process
One member of the research team queried each of the four most popular smart assistants (Apple Siri, Google Assistant, Amazon Alexa, and Microsoft Cortana), which were identified in the 2019 Voice Report by Microsoft [32]. In the event that the smart assistant provided a nonresponse to a query (eg, “I don’t know how to answer that”), the team member queried the smart assistant a maximum of three times to ensure that it was not an errored response due to misunderstanding the query, background noise, or some other unrelated reason.
Smart assistants provided varying numbers of results on their respective first pages for each query. Alexa and Google Assistant provided only one oral result for each query, whereas Siri provided five text results for each query. Cortana provided between three and nine text or video results for each query. Multimedia Appendix 1 displays the number of results each smart assistant provided per query. The team member, responsible for conducting the searches, recorded the first page of the results for data extraction based on previous studies indicating that users more frequently click on the top 10 results, which tend to be concentrated on the first page of most search results [33]. There is also evidence suggesting that people do not necessarily only click the first result on a page. In a 2020 study of search engine optimization, 16% of respondents in the study reported clicking on only the first result compared with 17% and 14% of respondents who reported clicking on three and five results, respectively [34]. This finding suggests that the remaining results on the first page should be extracted to best replicate actual human search behavior.
Recording Process
The team member, who posed questions to the smart assistants, used video and audio recording software to record both the queries and the smart assistant responses. We used these recordings in the data extraction and coding process. Figure 1 depicts a recording of a query posed to Cortana.
Example of a video result from a query to Cortana.
Data Extraction and Coding Process
We developed a web-based (Qualtrics) survey to aid in data extraction and coding of the smart assistant responses. Survey items were designed to extract relevant data from the smart assistant responses, including the types of responses (eg, video, web page, blog, and journal article), sources of the responses (eg, business, doctors, and health information provider), sentiments of the responses toward the HPV vaccine (eg, positive, neutral, and negative), accuracy of the responses, any incidence of misinformation in the responses, and topics discussed in the responses (eg, cancer, sexual behavior, and conspiracy theory).
We defined accuracy as a response that reflected the existing evidence. We coded accuracy using a dichotomy approach (0 for not accurate; 1 for accurate), which we applied specifically to the query (ie, accuracy of unrelated tangential content was not considered). If the response did not answer the query with the correct reply or positioned the correct reply as dubious or incorrect, we considered it “not accurate.” We defined misinformation as either deliberate or accidental promotion of previously disproved or unproven beliefs, attitudes, and behaviors. We coded misinformation using a dichotomy approach (0 if it did not provide misinformation; 1 if it did provide misinformation), which we applied to the entirety of the response (not just the answer to the question posed). If any misinformation was found in the smart assistant’s response, we coded it as having provided misinformation. Table 1 depicts examples of accuracy and misinformation in smart assistant responses to several queries. Since accuracy and misinformation were coded based on different aspects of the smart assistant responses, there were some cases with both an accurate response to the query and some misinformation in the result. We coded sentiment toward vaccines as one of the following four potential categories: negative (mostly negative statements), neutral (neither positive nor negative statements), positive (mostly positive statements), and ambiguous (both negative and positive statements). We applied sentiment coding to the entirety of the response.
Two independent team members extracted and coded the data with an almost perfect agreement (κ=0.85) [35]. We resolved discrepancies through discussion.
Examples of accuracy and misinformation in smart assistant responses.
Query
Accuracy
Misinformation
Does the HPVa vaccine work?
“In the trials that led to the approval of Gardasil and Cervarix, these vaccines were found to provide nearly 100% protection against persistent cervical infections with HPV types 16 and 18 and the cervical cell changes that these persistent infections can cause.”
N/Ab
Does the HPV vaccine cause autism?
“There is no link between vaccines and autism.”
“The scientific literature is now starting to fill up with case reports and studies and articles that irrefutably show that there is a connection between this vaccine (and it’s an ugly vaccine) and neurological damage.”
Does Gardasil work?
“Gardasil works by preventing the infection of the types of HPV that can lead to cervical cancer...”
“The Gardasil HPV vaccine has been proved to have caused the deaths of 32 women.”
Does Gardasil cause autism?
“There has never been a study that has shown that vaccines cause autism.”
N/A
Is the HPV vaccine dangerous?
“Findings from many vaccine safety monitoring systems and more than 160 studies have shown that HPV vaccines have a favorable safety profile—the body of scientific evidence overwhelmingly supports their safety.”
“Aluminum in the vaccines is toxic enough to be harmful.”
Is Gardasil dangerous?
“Although this information is accurate in a strictly literal sense, it is a misleading presentation of raw data that does not in itself establish a causal connection between Gardasil and the posited medical dangers.”
“The Gardasil HPV vaccine has been proved to have caused the deaths of 32 women.”
Who can get the HPV vaccine?
“All people ages 9 to 45 can get the HPV vaccine to protect against genital warts and/or different types of HPV that can cause cancer.”
N/A
Where can I get the HPV vaccine?
“The HPV vaccine is available at: Healthcare Clinic for patients aged 11-26. Walgreens Pharmacy. Ages vary by state.”
N/A
Does Gardasil kill?
“I cannot stress this enough, based on this report alone you can't make a determination that the vaccine caused the deaths.”
N/A
How much does the HPV vaccine cost?
“Each dose of the vaccine can cost about $250.”
N/A
aHPV: human papillomavirus.
bN/A: not applicable.
Analyses
The sample consisted of 128 total data points across all four smart assistants. We excluded any nonresponse data points (eg, “I don’t know how to answer that”) and responses that were categorized as other (eg, responded with completely irrelevant content to the query) from the final analyses. Google Assistant provided nonresponses to every query and was removed, thus reducing our final analysis sample to 103 responses across three smart assistants. These nonresponses and other responses were removed because they did not in any way address the query posed. Multimedia Appendix 1 displays the frequencies of responses and nonresponses for each smart assistant. Descriptive frequencies and chi-square difference tests were used to examine the levels of accuracy and misinformation among the smart assistants.
Results
Table 2 displays the source and content characteristics of the smart assistant responses. Smart assistant responses contained content published by an organization in 82 out of 103 (79.6%) responses, whereas 20 out of 103 (19.4%) responses were published by an individual. Content was provided by organizations including some type of health information provider (eg, Planned Parenthood and Mayo Clinic) in 36 out of 103 (35.0%) responses and by a government entity (eg, Centers for Disease Control and Prevention) in 25 out of 103 (24.3%) responses. Cortana provided content published by individuals (eg, physicians and journalists) in 18 out of 61 (29.5%) responses, whereas Siri and Alexa provided content published by individuals in only 2 out of 36 (5.6%) and 0 out of 6 (0.0%) responses, respectively. The most common type of individual who provided content (10 out of 103 responses, 9.7%) was some classification of physician. The type of content provided in the smart assistant responses varied, with videos provided in 30 out of 103 (29.1%) responses, driven entirely by Cortana, which was the only smart assistant to provide video responses. Siri’s responses were classified as frequently asked question (FAQ) pages in 13 out of 36 (36.1%) responses, whereas Cortana’s responses were classified as videos in 30 out of 61 (49.2%) responses and as general web pages in 12 out of 61 (19.7%) responses.
Source and content characteristics of smart assistant responses.
Variable
Value (N=103), n (%)
\\nSourcea\\n
\\n\\n
\\n\\n
Health information provider
36 (35.0)
\\n\\n
Federal/city/state
25 (24.3)
\\n\\n
Nonprofit/advocacy group
15 (14.6)
\\n\\n
Physician
10 (9.7)
\\n\\n
Other
9 (8.7)
\\n\\n
News/media organization
8 (7.8)
\\n\\n
Business
6 (5.8)
\\n\\n
Journalist/press
2 (1.9)
\\n\\n
Politician
2 (1.9)
\\n\\n
Health care organization
1 (1.0)
\\nContent\\n
\\n\\n
\\n\\n
\\nFormat of the smart assistant responses\\n
\\n\\n
\\n\\n
\\n\\n
Video
30 (29.1)
\\n\\n
\\n\\n
General web page
24 (23.3)
\\n\\n
\\n\\n
Article
14 (13.6)
\\n\\n
\\n\\n
FAQb site
19 (18.4)
\\n\\n
\\n\\n
Other
6 (5.8)
\\n\\n
\\n\\n
Blog post
3 (2.9)
\\n\\n
\\n\\n
Location/map/directions
1 (1.0)
\\n\\n
\\nTopics mentioned in the smart assistant responsesa\\n
\\n\\n
\\n\\n
\\n\\n
Cancer
77 (74.8)
\\n\\n
\\n\\n
Vaccines and side effects
57 (55.3)
\\n\\n
\\n\\n
Sexual behaviors
26 (25.2)
\\n\\n
\\n\\n
Access to medical care
20 (19.4)
\\n\\n
\\n\\n
Direct impacts on loved ones
13 (12.6)
\\n\\n
\\n\\n
Conspiracy theories
6 (5.8)
\\n\\n
\\n\\n
Vaccines are ineffective
6 (5.8)
\\n\\n
\\n\\n
Mass media
6 (5.8)
\\n\\n
\\n\\n
Risk of disease is lower than risk of adverse vaccination events
2 (1.9)
\\n\\n
\\nSentiment expressed toward vaccines in smart assistant responses\\n
\\n\\n
\\n\\n
\\n\\n
Positive
65 (63.1)
\\n\\n
\\n\\n
Neutral
18 (17.5)
\\n\\n
\\n\\n
Ambiguous
11 (10.7)
\\n\\n
\\n\\n
Negative
7 (6.8)
aTotals may exceed 100% owing to the availability of multiple response options.
bFAQ: frequently asked question.
Tables 3 and 4 depict the differences among smart assistants in terms of primary outcomes. Smart assistant responses contained accurate answers in 63 out of 103 (61.2%) responses. Neither response accuracy (P=.10) nor response sentiment (P=.22) was significantly different among the devices. The number of responses provided for each query varied across the devices, but 4 out of 6 (66.7%) responses by Alexa and 26 out of 36 (72.2%) responses by Siri were accurate. In contrast, 33 out of 61 (54.1%) responses by Cortana were accurate. There were no cases in which responses contained both accurate answers and misinformation. Chi-square tests indicated that the three smart assistants significantly varied in terms of whether they provided misinformation in their responses (N=103, χ22=7.807, P=.02) and whether they provided any evidence to support the claims made in their responses (N=103, χ22=11.054, P=.004). Smart assistant responses contained at least one instance of misinformation in 18 out of 103 (17.5%) responses. Responses by Alexa contained no misinformation, whereas misinformation was present in 2 out of 36 (5.6%) responses by Siri and 16 out of 61 (26.2%) responses by Cortana. Alexa provided evidence to support each of its responses, whereas Siri provided evidence in 21 out of 36 (58.3%) responses and Cortana provided evidence in 23 out of 61 (37.7%) responses.
In general, smart assistant responses contained positive sentiment toward vaccines, with 65 out of 103 (63.1%) responses containing primarily positive sentiment. The second most common sentiment was “neutral,” which was found in 18 out of 103 (17.5%) responses. The least common sentiment was “negative,” which was found in only 7 out of 103 (6.8%) responses, and all were provided by Cortana.
Response differences among smart assistants in terms of accuracy, evidence provided, and misinformation.
Response quality of the smart assistants
Value, n/N (%)
Chi-square effect size (df)
P value
\\nAccurate response? (Yes)\\n
63/103 (61.2%)
\\n\\n
\\n\\n
\\n\\n
Alexa
4/6 (66.7%)
4.558 (2)
.10
\\n\\n
Siri
26/36 (72.2%)
4.558 (2)
.10
\\n\\n
Cortana
33/61 (54.1%)
4.558 (2)
.10
\\nEvidence provided? (Yes)\\n
50/103 (48.5%)
\\n\\n
\\n\\n
\\n\\n
Alexa
6/6 (100%)
11.054 (2)
.004
\\n\\n
Siri
21/36 (58.3%)
11.054 (2)
.004
\\n\\n
Cortana
23/61 (37.7%)
11.054 (2)
.004
\\nMisinformation provided? (Yes)\\n
18/103 (17.5%)
\\n\\n
\\n\\n
\\n\\n
Alexa
0/6 (0%)
7.807 (2)
.02
\\n\\n
Siri
2/36 (5.6%)
7.807 (2)
.02
\\n\\n
Cortana
16/61 (26.2%)
7.807 (2)
.02
Sentiment differences among the smart assistants.
Smart assistant
Content sentiment
P value
\\n\\n
Neutral (N=18)
Negative (N=7)
Ambiguous (N=11)
Positive (N=65)
Chi-square effect size (df)
Alexa (N=6), n (%)
1 (16.7%)
0 (0%)
0 (0%)
5 (83.3%)
8.20 (6)
.22
Siri (N=35), n (%)
9 (25.7%)
0 (0%)
5 (14.3%)
21 (60%)
8.20 (6)
.22
Cortana (N=60), n (%)
8 (13.3%)
7 (11.7%)
6 (10%)
39 (65%)
8.20 (6)
.22
DiscussionPrincipal Results
This study sought to determine whether responses to queries vary among smart assistants in terms of accuracy, misinformation, and sentiment related to HPV vaccination. Our results indicate that smart assistants responded differently when asked about this topic. Specifically, smart assistants showed variations in the level of misinformation provided in responses, as well as the provision of evidence to support claims made in their responses. Smart assistants did not differ statistically in terms of the accuracy of their responses to queries involving HPV vaccination.
To our knowledge, this study is the first to report on smart assistants and their responses to HPV vaccine–related queries. Previous studies of HPV vaccination behaviors did not focus on smart assistants, and they indicated that antivaccination messages may influence one’s decisions to vaccinate themselves or their children [15]. However, given the growing number of persons and households adopting and utilizing smart assistants and the fact that smart assistants may be able to provide HPV vaccine information, it is necessary to examine whether responses delivered through smart assistants are disseminating these same antivaccination messages. This study is the first step in establishing a foundation for the types of vaccine sentiments that are present in content disseminated by smart assistants.
Importantly, our study revealed that, in general, smart assistants largely provide accurate and positive information regarding HPV vaccination (just under two-thirds of all smart assistant responses were accurate and positive), with no relevant differences across devices. We should reiterate here, however, that Google Assistant provided nonresponses to every query and, accordingly, was removed from the study. While accurate information on something as beneficial as HPV vaccination is necessary to mitigate HPV transmission and cancer incidence, just over one-third of responses contained inaccurate answers, suggesting that work is needed in this area to improve the provision of health information via smart assistants. On the other hand, misinformation was more of a concern across devices, as Cortana yielded the greatest number of responses containing misinformation (one-quarter of Cortana’s responses contained misinformation).
Comparison With Prior Work
Previous studies of smart assistant responses have focused on whether the devices responded correctly or whether they understood the query [5,6]. Only a handful of studies examined smart assistant responses in the context of a health behavior [10]. This study further assessed smart assistant responses to determine whether they provide accurate evidence to specific health behavior questions. Evidence is critical in combatting misinformation, at least on social media platforms. For example, in an experimental study that exposed Facebook users to simulated misinformation and different correction mechanisms about the Zika virus, the authors found that correction can work when it provides supporting evidence or appropriate sources to accompany refutation (eg, Centers for Disease Control and Prevention) [36]. The authors reported that this finding was maintained even in those who were rated higher in conspiracy beliefs.
Limitations
This study has limitations that should be considered when evaluating its results. The devices used their specific locations when responding to queries, which may have produced less generalizable responses. Some of the smart assistants employed in the study were previously used devices and may have been influenced by prior searches of the former user. To the best of our knowledge, no private mode exists in these smart assistants, which would prevent the queries and responses from influencing future searches. The sample size was limited for several reasons, including nonresponses and other unrelated responses to queries. Specifically, Google Assistant provided no response data, which may have influenced the results. Any responses that were entirely unrelated to the queries should be explored in future studies, since this may be an indication of a larger issue with smart assistants not comprehending the queries. Based on our knowledge about natural language processing in these smart assistants, it is suggested that syntax and dictation are some of the many influencers of smart assistant responses [37], and the queries posed to each device may not have been appropriately worded to elicit the most effective responses. For example, our use of a single investigator to ask the questions could be viewed as a limitation (ie, we do not know how the smart assistants would have responded to an investigator with a different gender, tone, or accent). The investigator only queried a single example of a smart assistant (ie, only one device was used to query Alexa), which could be a limitation, as smart assistants may respond differently depending on whether a stand-alone smart assistant or a smartphone-based smart assistant is used. The underlying search engine behind each smart assistant determines the responses to queries, which could have limited the information provided by smart assistants that are not connected to large search engines such as Google. The varying number of results provided in the smart assistant responses to queries could have impacted our ability to compare across the devices; however, since we found no relevant differences between the devices with regard to accuracy or sentiment, we do not believe that more results from one device impacted our findings or conclusions.
Despite these limitations, there are several strengths that provide further evidence of the study’s validity and importance. First, we utilized questions submitted to a Planned Parenthood chat service, which represent real-world issues on which consumers have previously sought additional information. Second, the questions used as queries were chosen because responses to these queries could be scored as either correct or incorrect, limiting “gray areas” in assessing accuracy of the smart assistant responses. Third, the queries were conducted by a single member of the research team to maintain consistency in language processing factors (eg, tone and syntax), and the search and coding processes were systematic to maintain consistency as well. Overall, this research furthers our understanding of how an emerging technology disseminates health information and whether such information can be classified as accurate and evidence-based. This study provides a basic framework for future evaluation of these smart assistants on which more advanced devices may be based.
Our findings paint a picture of smart assistants as newly emerged potential health promotion tools that are yet to be rigorously evaluated in this area. Smart assistants have only recently been introduced as potential health promotion tools (most notably from an executive perspective [38]) despite the growing proportion of households and individuals that use them [2,3]. The untapped potential of these devices for evidence-based information dissemination to consumers should be further explored. The potential of the devices may also be determined by their manufacturers who have, in some cases, provided platforms on which specialized topical information can be consolidated and further explored. For example, Alexa has an option for developers to create an Alexa Skill, which is essentially an application for Alexa that provides predefined information when queried specifically for that information. For example, an Alexa Skill that specifically searches in predefined evidence-based sources when queried for information on HPV vaccination could be developed. The downside to this potential approach for improving public health is that, as our results show, there is risk of disseminating misinformation through smart assistant responses, potentially reducing the positive impacts of a health promotion intervention. More needs to be done to better understand the susceptibility of these devices and their respective skills to outside influences.
Conclusions
Our results suggest that not all smart assistants are created equal with regard to utility, at least when it comes to provided evidence and misinformation in their responses. These findings should spark further research into how the proprietors of smart assistants procure the information that is disseminated to consumers of their products. Specifically, we suggest that manufacturers of these and other smart assistants collaborate with researchers to further evaluate the accuracy of smart assistants as public health tools and determine together how to disseminate information and what fact-checking assessments should be used for such information. With the high rate of HPV transmission globally and in the United States, smart assistants and their manufacturers are well positioned to deliver evidence-based health information to consumers. However, such a practice necessitates strong communication between technology companies, who may not be as focused on the accuracy or source of their most heavily promoted content, and public health entities. The focus on collaboration to address the issues of information accuracy and misinformation is paramount if we are to adequately respond to the WHO’s list of urgent global health challenges for the new decade, namely stopping vaccine-preventable diseases.
Authors' Contributions: All authors contributed equally to the development, review, and submission of this manuscript for publication.
Conflicts of Interest: None declared.
Appendix
Frequencies of responses and nonresponses by each smart assistant.
AbbreviationsHPV
human papillomavirus
WHO
World Health Organization
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Neurosci.Frontiers in Neuroscience1662-45481662-453XFrontiers Media S.A.32848576PMC743215310.3389/fnins.2020.00819NeuroscienceReviewDeciphering Brain Function by Miniaturized Fluorescence Microscopy in Freely Behaving AnimalsMalvautSarah12†ConstantinescuVlad-Stefan12†DehezHarold3DoricSead3SaghatelyanArmen12*1CERVO Brain Research Center, Quebec City, QC, Canada2Department of Psychiatry and Neuroscience, Universite Laval, Quebec City, QC, Canada3Doric Lenses Inc., Quebec City, QC, Canada
Edited by: João O. Malva, University of Coimbra, Portugal
Reviewed by: Daniel Benjamin Aharoni, University of California, Los Angeles, United States; Alberto L. Vazquez, University of Pittsburgh, United States
†These authors have contributed equally to this work
This article was submitted to Brain Imaging Methods, a section of the journal Frontiers in Neuroscience
118202020201481920420201472020Copyright © 2020 Malvaut, Constantinescu, Dehez, Doric and Saghatelyan.2020Malvaut, Constantinescu, Dehez, Doric and SaghatelyanThis is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Animal behavior is regulated by environmental stimuli and is shaped by the activity of neural networks, underscoring the importance of assessing the morpho-functional properties of different populations of cells in freely behaving animals. In recent years, a number of optical tools have been developed to monitor and modulate neuronal and glial activity at the protein, cellular, or network level and have opened up new avenues for studying brain function in freely behaving animals. Tools such as genetically encoded sensors and actuators are now commonly used for studying brain activity and function through their expression in different neuronal ensembles. In parallel, microscopy has also made major progress over the last decades. The advent of miniature microscopes (mini-microscopes also called mini-endoscopes) has become a method of choice for studying brain activity at the cellular and network levels in different brain regions of freely behaving mice. This technique also allows for longitudinal investigations while animals carrying the microscope on their head are performing behavioral tasks. In this review, we will discuss mini-endoscopic imaging and the advantages that these devices offer to research. We will also discuss current limitations of and potential future improvements in mini-endoscopic imaging.
miniature endoscopesmini-endoscopic imaginganimal behaviorGRIN lensesGCaMPGECICa2+ imagingMitacs10.13039/501100004489Natural Sciences and Engineering Research Council of Canada10.13039/501100000038Introduction
Understanding how brain functions are shaped by experience and, in turn, how neural networks regulate animal behavior has always been of great interest to the scientific community as well as the general public. One of the long-term aims of neuroscience research is to understand how specific spatio-temporal activity patterns emerging from local neural networks regulate specific animal behaviors. Recording neural activity at the microcircuit level from distinct subsets of neurons and glia in freely behaving animals has thus become essential for studying how experience is represented and processed by the brain. The function of these networks can be interrogated using extracellular multi-electrode recordings (Nicolelis and Ribeiro, 2002), large-scale brain imaging techniques such as functional magnetic resonance imaging (fMRI), voltage-sensitive dye-based imaging, intrinsic optical signal imaging, and positron emission tomography (PET) (Grinvald et al., 1988; Shoham et al., 1999; Logothetis et al., 2002), or in vivo Ca2+ imaging using one- and two-photon mini-endoscopes (Dombeck et al., 2007; Kerr et al., 2007; Grewe and Helmchen, 2009). The technique of choice for these types of experiments has mostly involved taking electrophysiological recordings in either awake head-restrained or freely behaving rodents (Wilson and Mcnaughton, 1993; Houweling and Brecht, 2008; Harvey et al., 2009; Niell and Stryker, 2010). This approach has provided a great number of important insights into how neuronal ensembles orchestrate behavior such as the discovery of spatial representation by place and grid cells, for instance (O’keefe and Dostrovsky, 1971; Hafting et al., 2005). However, despite the tremendous value of this technique, the number of cells that can be recorded at a given time is limited by the small number of electrodes that can be used simultaneously. In theory, increasing the number of electrodes should make it possible to record electrophysiological activity in more cells, but the spacing between the electrodes is limited. Electrophysiological recordings also exclude electrically silent cells from the subsequent analysis of network dynamics (Shoham et al., 2006). To address some of these caveats, researchers can now take advantage of dense recordings using multi-tetrode arrays or silicone probes that make it possible to take recordings from large neuronal populations rather than from individual cells. Such large population-level recordings can be used to address questions that otherwise could not have been investigated using data from individual cell recordings (Csicsvari et al., 2003; Harris et al., 2003; Buzsaki, 2004; Dupret et al., 2010; Stensola et al., 2012). However, even with advances in electrophysiological recordings of large subsets of cells, there remain some important aspects that cannot be studied using such techniques. Although electrophysiology allows for distinct cell populations to be differentiated based on waveform shapes (Trainito et al., 2019), it is much more challenging to assess the individual contribution of each subset of recorded cells to the observed network dynamics. Recordings from subcellular structures such as dendrites can be also obtained in freely behaving mice using electrophysiological approaches (Moore et al., 2017). By taking advantage of the immune response that follows a tetrode implant, which allows a dendrite to get trapped between the tetrode tips and the glial encapsulation, it is possible to measure the dendritic membrane voltage in freely behaving rodents (Moore et al., 2017). This methodology could also provide important insights into the electrophysiological correlates of dendritic action potential integration, but has a low success rate and low yield.
The study of brain function has taken a step further with the advent of new optical tools designed to investigate neuronal activity at the protein, cellular, or network level. These include genetically encoded Ca2+ indicators (GECIs). Ca2+ is an important second messenger involved in the homeostasis of essentially all cells in the brain (Clapham, 1995; Berridge et al., 2000) and GECI-recorded Ca2+ fluctuations can be used to study the activity and coding of neural ensembles in basal conditions and in response to stimuli (Tian et al., 2012; Lin and Schnitzer, 2016). Many different versions of GECIs exist, and their repertoire and properties are constantly improving (Dana et al., 2016, 2019; Shen et al., 2020). The green and red Ca2+ indicators (GCaMP and RCaMP, respectively) are the most common (Lin and Schnitzer, 2016; Shen et al., 2020). More recently, a quadricolor GECI suite (XCaMP) (Inoue et al., 2019) and a near-infrared Ca2+ indicator (Qian et al., 2019) were designed to further expand the palette of GECIs and to study more complex neural dynamics. Using viral or transgenic labeling, GECIs can be expressed all over the brain or in specific neuronal/glial subtypes and are now widely used in the context of learning, memory formation, and plasticity (Jercog et al., 2016). They also allow for long-term investigations of brain activity for up to several weeks and even months (Jercog et al., 2016). In addition to GECIs, current efforts to develop genetically encoded voltage indicators (GEVIs) will eventually make it possible to monitor individual spikes during high-frequency trains of action potentials or subthreshold changes in the membrane potential in selected populations of cells (Bando et al., 2019). This will further increase the palette of functional assessments of neural networks during animal behavior. In addition to functional changes, several morphological modifications such as spine formation/elimination, the dynamics of glia processes, and microglia migration occur in response to environmental stimuli. Furthermore, several regions of the adult brain retain the capacity to renew their cellular populations during injury (Torper and Gotz, 2017) or under homeostatic conditions through adult neurogenesis (Gengatharan et al., 2016; Obernier and Alvarez-Buylla, 2019; Vicidomini et al., 2020). All these morphological changes can be also monitored in vivo using live imaging approaches (Berry and Nedivi, 2017; Pilz et al., 2018; Wallace et al., 2020).
In vivo imaging of morpho-functional changes in zebra fish larvae and nematodes has been performed in whole organisms (Ahrens et al., 2012; Nguyen et al., 2016). In rodents, in vivo imaging of a given brain region has traditionally been performed by two-photon microscopy in anesthetized or awake head-restrained animals performing specific behavioral tasks. While this allows for long-term and longitudinal recording of brain activity, it is limited to a restricted number of behavioral tasks and cannot be used to study deep brain structures unless the overlying brain regions are removed, which may affect cellular activity and animal behavior. The last two decades saw the advent of miniaturized fluorescent microscopes (also called mini-microscopes or mini-endoscopes) that enable in vivo recordings of brain structure and function of freely behaving animals to be taken at the cellular and network levels. These devices, which are similar to benchtop microscopes in their optical designs, contain miniaturized optical parts (mirrors, filters, or even a camera) and can be carried by animals that freely displace in an arena. Displays and recordings of acquired images are then computer assisted, allowing for the modulation of different parameters such as acquisition frame rate, exposure, and illumination power, without any additional manipulation of the recorded animal. Mini-endoscopes have evolved considerably in recent years since the first miniaturized versions were developed (Helmchen et al., 2001) and are still being refined and improved. They have helped provide answers to several unresolved questions regarding brain activity and structure. In this review, we will first present the various models of currently available mini-endoscopes and provide an overview of their specifications, advantages, and limitations. We will then discuss the in vivo applications of mini-endoscopy and the limitations of existing devices as well as future avenues for improving mini-endoscopic microscopy.
Current State of the Art for Recording Brain Activity and Structure Using Mini-EndoscopesOne-Color Mini-Endoscopes
The first versions of mini-endoscopes were based on two-photon excitation, and some microscope components such as lasers and photomultipliers (PMT) were converted to transmit the excitation light to the head of the animal or to collect emissions through optic fibers. These systems were used for imaging at the surface of the brain (Helmchen et al., 2001; Helmchen, 2002; Flusberg et al., 2005). They were bulky and heavy (25 g, 70 mm high), limiting applications involving naturalistic behavior. Also, the devices could not compensate very well for strong motion artifacts (Helmchen et al., 2001; Helmchen, 2002). Subsequent improvements led to the creation of lighter portable two-photon excitation microscopes (Gobel et al., 2004; Flusberg et al., 2008; Sawinski et al., 2009) that were used to image neural networks in freely moving mice and rats. A promising approach using a portable two-photon excitation-scanning microscope that weighs as little as 2 g was recently introduced to study activity in the entorhinal cortex of freely behaving mice (Zong et al., 2017; Rowland et al., 2018). These systems provide the obvious advantages of two-photon excitation microscopy such as a small excitation volume, increased tissue penetration, reduced phototoxicity, and the possibility of imaging subcellular structures. However, the low two-photon absorption cross-section also imposes high spatial (small focal spot) and temporal (short laser pulse) constraints on two-photon microscopy systems. To get sufficient excitation intensity at focus, it requires high numerical aperture diffraction-limited optics as well as expensive table-top femtosecond pulse lasers with proper dispersion compensation. Laser scanning two-photon microscopes should also be properly stabilized to avoid motion artifacts as the pixels of each image are not recorded simultaneously. It is thus more challenging to use two-photon excitation in a context of mini-endoscopes, suggesting that miniaturized epifluorescence microscopy tends to represent a simpler and more affordable method for imaging activity in neural networks at the cellular level, particularly in deep brain regions while animals are freely behaving.
Several groups have taken advantage of small optical rod lenses [gradient refractive index (GRIN) lenses] (Jung and Schnitzer, 2003; Levene et al., 2004) to build mini-endoscopes. These lenses have a short working distance and can be fixed either at the surface of the brain or inserted into deep brain regions to perform imaging in the superficial or deep brain regions, respectively (Helmchen et al., 2001; Helmchen, 2002; Flusberg et al., 2005). GRIN lenses, which can have different diameters and customizable lengths, have been used for in vivo imaging of previously unreachable deep brain structures such as the substantia nigra, the hypothalamus, the parabrachial nucleus, and the basal forebrain (Bocarsly et al., 2015; Fu et al., 2019; Patel et al., 2019). GRIN lenses are usually flat at the tip, making it possible to image cells just below the tip. However, they can also be coupled with a prism mirror allowing for “side-view” imaging. This configuration, in our experience, significantly reduces tissue damage and immune responses compared to blunt probes. In line with this, less tissue damage and better neuronal preservation is observed for well-sharpened and small tip angle silicone probes used for in vivo electrophysiological recordings (Edell et al., 1992). The GRIN coupled with a prism has been used for imaging neurons located in different cortical layers (Gulati et al., 2017).
Gradient refractive index implantation is minimally invasive, and the presence of the lens in an animal’s brain does not alter the locomotion or behavior of the animal in selected tasks commonly used in neuroscience research (Lee et al., 2016), although this largely depends on the diameter and type of GRIN lens (Resendez et al., 2016). Larger diameter (1 mm) GRIN lenses have been described as being more suitable for an implantation in superficial brain regions such as the cortex or the hippocampus, whereas smaller diameter GRINs are more suitable for deeper brain regions such as the hypothalamus or the ventral tegmental area (Resendez et al., 2016). Slow insertion of probes during implantation or using probes coupled with a prism can minimize possible compression of the brain, restricting it to the front of the GRIN and reducing possible damage (Edell et al., 1992; Levene et al., 2004). For deep brain region imaging, a track in the brain is often created using a sharp needle or blade. Brain tissue aspiration is also used in some cases (Resendez et al., 2016; Gulati et al., 2017; Gonzalez et al., 2019; De Groot et al., 2020). The inflammatory response resulting from implant insertion normally largely clears up after 4 weeks (Bocarsly et al., 2015), or less for sharp-ended implants (Edell et al., 1992; Turner et al., 1999). This observation highlights the need to start recording with miniature endoscopes several weeks post-implantation. A minimal period ranging from 2 to 4 weeks is required before acquiring data using GRIN lenses (Barbera et al., 2019; Fu et al., 2019; Gonzalez et al., 2019). To confirm the correct implantation of a GRIN lens, a post hoc analysis in post-mortem brain tissues can be used. This step makes it possible to adapt the stereotaxic coordinates used in different animals (Lee et al., 2016; Resendez et al., 2016). When viral vectors are injected in a defined brain region a few weeks before GRIN implantation, changes in background fluorescence may be also used to ascertain the correct positioning.
As for animals becoming habituated to the imaging device, it is common practice to try to reduce the weight of mini-endoscopes as much possible so as to not interfere with naturalistic behavior of the animals (Resendez et al., 2016). Chronic GRIN implants do not alter animal performance in standard behavioral procedures such as the rotarod test or the Morris water maze (Lee et al., 2016). However, no comparative studies have been reported with regard to the adaptation of animals to mini-endoscopes of different weights and configurations. Further behavioral investigations will be required to accurately evaluate the maximal weight that small rodents like mice can carry on their head without an impact on their behavior and to compare different mini-endoscopes, some of which are bulkier than others. Also, not all available versions of mini-endoscopes can reach very lateral or anterior brain regions such as the olfactory bulb. In fact, due to size constraints, placing a mini-endoscope in such regions could potentially alter the normal behavior of the animals or even their vision if placed too close to their eyes. Smaller footprint mini-endoscopes may help to resolve this issue.
An important step in the conception of mini-endoscopes is thus the miniaturization of microscope parts. Ghosh et al. (2011), in their first version of a fully miniaturized epifluorescence mini-endoscope, replaced larger external light sources (e.g., Xenon lamps, mercury lamps, high power LEDs, and lasers) with smaller internal LEDs that are efficient and low-cost alternatives. This approach is now widely used in different open source mini-endoscopes designed to image in freely behaving rodents or songbirds. These include the UCLA Miniscope (Cai et al., 2016), the National Institute of Drug Abuse MiniScope (Barbera et al., 2016), the Boston University FinchScope that was developed for imaging in songbirds (Liberti et al., 2017), and the University of Toronto CHEndoscope (Jacob et al., 2018). Although very practical in terms of cost, availability, and size, installing LEDs directly in the microscope body can have some drawbacks. Because of its location in the microscope body, maximum output power is limited in order to minimize potential excessive heat on the animal’s head at higher powers normally used for optogenetic stimulations. To alleviate these issues, some commercial one-color mini-endoscopes can be connected to adjustable, exchangeable external light sources such as lasers, laser pumped incoherent sources (Ce:YAG), or LEDs using an optical fiber. However, this has the disadvantage of adding an extra optical fiber above the animal’s head.
Mini-endoscopes also take advantage, in their design, of off-the-shelf miniature electronic components such as complementary metal oxide semiconductor (CMOS) sensors for image detection. These sensors support high acquisition frame rates (higher than 30 Hz) and can, in some cases, be coupled to a microphone to record songbird vocalizations (Liberti et al., 2017). Improved versions of mini-endoscopes are currently available from commercial sources (Doric Lenses, Inscopix, and Neurescence) as well as open-source platforms (UCLA Miniscope, NINscope, and FinchScope).
It should be also mentioned that because of their miniaturization and their use in freely behaving animals, device failure may also occur. Unlike tabletop microscopy systems, miniaturized fluorescence microscopes are operated in challenging environments, with the animal moving in different directions and potentially applying some constraints on the microscope and optical fibers. Several approaches have been investigated to reduce the risks of experiment interruption due to microscope failure, including sturdier bodies in hard plastic (Cai et al., 2016) and metal frames (Zong et al., 2017), protected electronics, and connectorized systems with interchangeable cables (Barbera et al., 2016; Cai et al., 2016; Liberti et al., 2017; Jacob et al., 2018; De Groot et al., 2020). Optical components used in miniature endoscopes can be costly and, because of their size, failure can happen during surgical implantation or extraction. To counteract this, Bocarsly et al. (2015) proposed a version of the device, including the implantation in animal’s brain of a guide cannula, allowing for the insertion of a GRIN lens for each imaging session. However, it should be noted that the resulting diameter of the imaging probe is larger, which has an impact on brain tissue, as previously reported (Bocarsly et al., 2015; Resendez et al., 2016).
Two-Color Mini-Endoscopes
Functional and structural imaging in a given brain region was, for a long time, restricted to a general cell population or a subset of cells expressing a particular Ca2+ indicator or fluorescent marker. More recently, two-color versions of mini-endoscopes have been developed that allow two different fluorophores with different emission and excitation wavelengths to be imaged. However, GRIN lenses have large chromatic aberrations that induce a shift (sometimes up to several tens of microns) in the imaged focal plane when comparing the fluorescence of two different fluorophores. To counteract these aberrations, one solution is to place two CMOS sensors in the microscope body at a specific location to compensate for the focal shift induced by the GRIN lens when imaging the two spectrally distinct fluorophores. Another solution for counteracting chromatic aberrations is to use achromatic lenses. The availability of several versions and colors of GECIs (Dana et al., 2016, 2019; Shen et al., 2020), each with its own emission/excitation spectra, means that it is now possible to image using both green and red Ca2+ indicators with similar kinetics (Shen et al., 2020). Two-color mini-endoscopes have a number of advantages. First, when combined with Ca2+ indicators of spectrally distinguishable colors (GCaMP and RCaMP, for instance), it is possible to compare the activity of two different cell populations in the same brain region of freely behaving animals. In addition, two-color mini-endoscopes can be used for motion correction. In fact, xy and z drift can occur when animals are moving, which may create some bias in data analysis and interpretation. This issue can be addressed by performing functional Ca2+ imaging combined with morphological fluorescent marker imaging followed by open-source plugin or software processing for motion and drift correction. These systems can be also used for imaging cell populations that can be defined only based on the combinatory genetic labeling approaches, leading to the expression of two different fluorophores (or sensors) by the same cell. Two-color mini-endoscopes can be also used for assessing the interplay between different cellular populations in a given brain region. It should be noted, however, that because of their optical design, two-color mini-endoscopes are bulkier and a longer habituation period for the animal may be required before starting the experiments.
Combined Optogenetic and Imaging Mini-Endoscopes for Studying Brain Connectivity and Disease Pathogenesis
The discovery that neuronal activity can be modulated using light-sensitive ion channels or opsins was a breakthrough in the field of neuroscience (Kim et al., 2017). In vivo optogenetic approaches are now widely used to modulate neuronal activity and to investigate the impact on animal behavior (Kim et al., 2017). It is possible to modulate neuronal activity by either stimulating or inhibiting the activity of a cell population in a given structure while simultaneously imaging the responses of the same or neighboring populations of cells in the same structure or even in a very different, remote structure (Stamatakis et al., 2018). However, the opsin and Ca2+ indicators have to be carefully chosen as crosstalk between the channels may affect the recordings (Stamatakis et al., 2018). In addition, the imaging light source attenuates the optogenetic response (Stamatakis et al., 2018), which means that care must be taken in choosing the excitation light source for imaging in order to provide a better signal-to-noise ratio and minimal crosstalk.
De Groot et al. (2020) developed the NINscope, which can be used to simultaneously record and optogenetically stimulate two distant brain structures. The authors reduced the footprint of the mini-endoscope, making it possible to record the Ca2+ activity of a subset of cells in a given brain region while optogenetically stimulating cells in many other brain areas using small LEDs installed on the surface of the brain (De Groot et al., 2020). For experiments requiring optogenetic manipulation of deeper brain regions that cannot be targeted with the NINscope, a micro-LED can be implanted inside the brain. The stimulation can then be synchronized with the endoscope imaging (Shin et al., 2017). Combined optogenetic and imaging mini-endoscope baseplates composed of a GRIN relay lens for fluorescence imaging and an optical fiber for opsin activation are also commercially available. These systems offer a customizable length and distance between the imaging cannula and the implanted optical fiber. We used one of these mini-endoscope baseplates to optogenetically induce α-synuclein aggregation in the substantia nigra and to monitor changes in striatal neurons using Ca2+ imaging (Bérard et al., 2019).
Multi-Region Imaging With Mini-Endoscopes
The NINscope developed by De Groot et al. (2020) also allows the simultaneous and synchronous imaging of two separate brain regions because of its reduced footprint. This is of particular relevance in a context of brain connectivity as the activity of two distinct regions can be correlated at the cellular level depending on the selected behaviors or stimuli. Simultaneous recording using two separate mini-endoscopes can also be performed in both hemispheres of the same structure (Gonzalez et al., 2019; De Groot et al., 2020). These recordings can be used to investigate the lateralization of brain activity (Gonzalez et al., 2019) and to compare, in the same subject, the activity of two hemispheres to which a different treatment may have been applied. A device allowing for simultaneous imaging of different brain regions is now also commercially available. In fact, because of it small footprint, the fiber bundle-based device from Neurescence (quartet mini-microscope) can record neuronal activity from up to four different brain regions as well as from different animals that are socially interacting, for instance.
Mini-Endoscopes With Dual-Modality Recordings
Mini-endoscopes also allow for dual-modality recording of neuronal activity. Yashiro et al. (2017) used a mini-endoscope system coupled with electrophysiological recordings of neural activity to make simultaneous optical recordings from large neuronal subsets. Such simultaneous optical and electrical recordings may prove to be extremely useful in correlating electrically recorded phenomena (multi-unit activity and local field potentials) with optically recorded Ca2+ fluctuations. Combined electrical and Ca2+ signal recordings from a single fluorescent neuron in vivo have also been made and allow the spiking activity of recorded cells to be correlated with changes in Ca2+ dynamics (Lechasseur et al., 2011). In addition to combining imaging and electrophysiological recordings, commercially available dual implant baseplates composed of a GRIN lens and an optical fiber allow Ca2+ imaging and photometry recordings in two different brain regions located as close together as 1 mm to be performed. Another potential new application for mini-endoscopes involves combining Ca2+ imaging with intrinsic optical signals (Senarathna et al., 2019). This device can be used to monitor neuronal activity in large areas of the brain and subtract the hemodynamic signal, providing insights into neurovascular coupling (Senarathna et al., 2019). Other types of dual-modality recording mini-endoscopes are also available, such as those that allow for combined Ca2+ imaging and bird vocalization recording (Liberti et al., 2017). It is likely that in coming years other dual-modality mini-endoscopes will be developed. These systems could allow for combining imaging and local drug delivery via integrated cannula, Ca2+ activity recordings and respiration/sniffing assessments, or simultaneous Ca2+ and voltage imaging, for instance. This in turn will make it possible to perform multi-modal assesments of neural networks during unrestricted animal behavior.
Electronic Modulation of the Field of View: eFocus Mini-Endoscopes
A non-negligible drawback of traditional mini-endoscopes was, for a long time, the inability to change the focus prior to or during image acquisition, limiting imaging to a single focal plane (Barretto and Schnitzer, 2012; Hamel et al., 2015). Indeed over time, following consecutive imaging sessions, the focal plane can change slightly and tracking the same cells can become challenging. To counteract this, the first versions of commercial and open-source mini-endoscopes offered the possibility to manually and mechanically adjust the image focus with a slider, screw, or turret before starting a recording, which resulted in additional manipulations of both the device and the animal (Ghosh et al., 2011; Barbera et al., 2016; Cai et al., 2016; Liberti et al., 2017; Jacob et al., 2018). To deal with this issue, updated electronically focused versions of endoscopes have been released by several companies such as Inscopix and Doric Lenses or were built from open-source designs (e.g., UCLA miniscope version 4). These versions make it possible to adjust the focus remotely and thus avoid mechanical manipulations of the endoscope while the animals are freely behaving. To that end, several groups have taken advantage of different strategies. For example, electro-wetting liquid lenses, which are small devices containing two non-miscible liquids, have been used. They allow for the rapid deformation of the lenses and thus the electronic focusing of the field of view by applying a voltage (Grewe et al., 2011; Zou et al., 2015; Ozbay et al., 2018). Electronically tunable liquid crystal lenses have also been used (Bagramyan et al., 2017). These lenses, which are traditionally used in liquid crystal displays, consume little energy and are lighter than electro-wetting liquid lenses, two important parameters for improving mini-endoscopes without compromising their size and weight.
<italic>In Vivo</italic> Applications of Mini-Endoscopic Imaging
As discussed above, two main approaches for optical brain imaging studies on live animals have emerged—those that require the experimental subjects to be head-fixed, such as two-photon excitation microscopy, and those that allow for unrestrained behavior, such as mini-endoscopes. Each technique has its share of advantages and drawbacks. Depending on the nature of the study, researchers can now use a variety of methods that can accommodate either imaging approach. New advancements in fluorescent reporter proteins, in particular GECIs and GEVIs, mean that neuroscientists can perform two-color fluorescence imaging and can distinguish the activity of a dual-labeled subset within a broad population of indicator-labeled cells (Chen et al., 2013; Inoue et al., 2015; Malvaut et al., 2017). The head-fixed imaging approaches that use high-performance lenses and the optical sectioning provided by two-photon microscopy allow for the interrogation of activity patterns during animal behavior at subcellular levels such as in dendrites, dendritic spines, and even axonal boutons (Petreanu et al., 2012; Xu et al., 2012; Boyd et al., 2015; Sheffield and Dombeck, 2015). The head-fixed paradigm can also make intracranial drug delivery easier (Nimmerjahn et al., 2009; Lovett-Barron et al., 2014). Behavioral assays, including adapted fear conditioning paradigms (Lovett-Barron et al., 2014), controlled delivery of sensory stimuli (Verhagen et al., 2007; Carey et al., 2009; Andermann et al., 2011; Blauvelt et al., 2013; Patterson et al., 2013; Miller et al., 2014), and perceptual discrimination tasks (Andermann et al., 2010; Komiyama et al., 2010; O’connor et al., 2010) can now be done in a head-restrained configuration to study changes at subcellular levels during complex behavioral tasks. Another exciting avenue for head-restrained optical imaging in live animals involves the use of virtual reality approaches (Harvey et al., 2009, 2012; Dombeck et al., 2010), which could provide new information about an animal’s spatial navigation.
However, even with the enrichment of the behavioral repertoire that can be tested during head-restrained experiments, the array of behavioral manipulations is still restricted and is associated with the stress that the animals may encounter during head fixation and imaging. This stress can have an adverse effect on the outcome of the behavioral analysis and is likely to affect the neuronal activity patterns recorded (Thurley and Ayaz, 2017). Miniaturized head-mounted imaging devices were designed to circumvent these shortcomings. The main motivation for these devices was first and foremost their ability to record activity from many neurons in an animal that is unrestrained and that can display natural innate behavior as well as a broad array of social behaviors such as fighting, mating, care-giving, and other forms of interaction. The need to accurately monitor cell activity in freely behaving experimental subjects has led to recent advances in mini-endoscopic imaging that make it possible to record neuronal activity simultaneously from different brain regions (De Groot et al., 2020), to combine Ca2+ imaging and recordings of other modalities such as electrical recordings of neural activity (Yashiro et al., 2017) or sound vocalizations (Liberti et al., 2017), and to image activity in neural networks at different focal planes and in different neuronal populations using efocus and two-color versions of mini-endoscopes. Also, the versatility of mini-endoscopes enables cell activity to be tracked across a large array of behavioral paradigms established over the last decades (Morris, 1984; Graeff et al., 1998; Nadler et al., 2004).
The main in vivo application for mini-endoscopes is the imaging of GECIs in large subsets of neurons in freely behaving animals. As mini-endoscopes have many useful characteristics such as relative ease of use, scalability, and commercial availability, they have seen a significant spread to numerous research fields. The use of mini-endoscopes in neuroscience-related fields such as learning and memory, neuropsychiatric disorders, and drug discovery has enabled scientists to accurately describe temporal changes that neuronal circuits exhibit during memory formation and retention as well as modifications due to a pathology. The field of learning and memory has in particular been directly influenced by the development of mini-endoscopes. Tracking the formation and subsequent modification of cellular ensembles that retain learned information, or engrams, over long periods of time has allowed neuroscientists to answer questions about the coding dynamics underlying the neurobiology of long-term memory (Tian et al., 2009; Cai et al., 2016). Established behavioral assays coupled with long-term monitoring of cellular activity has revealed that activity-induced modifications in neuronal CREB (cAMP response element-binding protein) levels are encoded by a shared neural ensemble in the CA1 part of the hippocampus that links distinct contextual memories occurring close in time (Cai et al., 2016). Neuronal activity in the spinal cord of freely behaving mice has also been recorded using a vertebra-affixed mini-endoscope (Sekiguchi et al., 2016). Implanting a mini-endoscope in the spinal cord is technically more challenging than anchoring an implant on top of the skull due to the pronounced movement of the spine within the vertebral column. Although fewer cells can be imaged due the curvature of the spinal cord, these experiments showed that distinct cutaneous stimuli activate overlapping ensembles of dorsal horn neurons and that stimulus type and intensity are encoded at the single-cell level (Sekiguchi et al., 2016).
Mini-endoscopic applications are not limited to the field of learning and memory. Mouse models of diseases are very important for studying the neurobiological basis of a myriad of brain disorders. As most of these diseases are, to some extent, characterized by a deficit in information processing, imaging neural activity in transgenic and knockout mice could be very advantageous for elucidating the underpinnings of the disease mechanisms (Nestler and Hyman, 2010). For example, much more information about alterations in coding dynamics in animal disease models can be inferred from large-scale imaging in freely behaving animals than is possible with electrophysiological measurements. These approaches could also narrow the gap between molecular and behavioral analyses. This concept becomes clear in the context of Alzheimer’s disease (AD), where much progress in understanding the molecular pathology underlying the neurodegeneration observed in human patients has led to the emergence of different mouse models that exhibit one or more AD hallmarks (b-amyloid, presenilins, apolipoprotein E, and Tau) (Knowles et al., 2009; Sterniczuk et al., 2010; Filali et al., 2012; Youmans et al., 2012). However, little is known about how these molecular pathways influence population dynamics during memory encoding and retrieval. With the advent of mini-endoscopy, longitudinal imaging in different AD models during the performance of memory tests is now feasible and could shed new light on memory coding dysfunctions in AD as well as in other neurological and psychiatric disorders (Ziv and Ghosh, 2015). As in the case of AD, the use of mini-endoscopes in animal models of other neurodegenerative disorders and brain trauma may allow the early hallmarks of disease manifestation to be deciphered. These mini-endoscopes combined with optogenetic stimulation can also be used to induce molecular alterations in cells and study disease progression. For example, it is now possible to optogenetically induce the aggregation of α-synuclein in dopaminergic neurons in the substantia nigra and to study alterations in the nigrostriatal pathway and in the network activity of striatal neurons using longitudinal mini-endoscopic Ca2+ imaging (Bérard et al., 2019).
Drug discovery could also benefit from mini-endoscopic imaging. Although much research has focused on the pharmacological and molecular mechanisms of drugs at the cellular level, much less attention has been directed at understanding the effect of drugs on neuronal coding. Berdyyeva et al. (2014) showed that a GABA-A agonist used for treating insomnia reduces hippocampal neuronal firing in freely behaving mice. This result, although not surprising, shed light on the need to correlate neuronal dynamics with the effects of chronic drug administration and to identify the effects of the drugs on the overall neuronal network. Addressing these issues would further advance our ability to design future therapies for treating brain diseases (Ziv and Ghosh, 2015).
The capability of mini-endoscopes to record neural activity in freely behaving animals can be further enhanced by using optogenetic stimulations (Stamatakis et al., 2018) or by combining two different recording techniques such as extracellular electrophysiological recordings with Ca2+ imaging in freely behaving animals (Yashiro et al., 2017). Stamatakis et al. (2018) used a mini-endoscope that jointly records optical neural activity and light-induced alterations of cellular ensemble activity in the same field-of-view. Other researchers have used a device to optogenetically stimulate brain regions away from the imaging field-of-view (Shin et al., 2017; Bérard et al., 2019; Senarathna et al., 2019). Engineering advances in the manufacture of mini-endoscopes have led to a reduction in the weight and size of these devices, making it possible to simultaneously record cellular activities in different brain regions as was done using the NINscope (De Groot et al., 2020). Such types of experiments will yield new data subsets that can be used to analyze the extent to which lateralization occurs during behavior and learning and to track the network stability of multi-region cellular ensembles over long periods of time (Gonzalez et al., 2019).
Mini-endoscopes are mostly used to investigate in vivo brain function in freely behaving animals by Ca2+ imaging of cell populations in a given region and are evolving in parallel with improvements in GECIs. The use of such powerful tools will likely expand to the study of other processes underlying brain activity. For instance, an in vivo two-photon investigation of adult neurogenesis and, more particularly, stem cell division has been already performed in anesthetized head-restrained animals (Pilz et al., 2018). Mini-endoscopes could help unravel this process in freely behaving animals and to correlate stem cell division with naturalistic patterns of animal behavior. These experiments can be performed under homeostatic conditions in known neurogenic regions of the brain like the dentate gyrus or the subventricular zone, as well as after injury and neuronal regeneration. In line with this, mini-endoscopic imaging has shown that coupling pre-existing neurons to the cerebrovascular system regulates adult hippocampal neurogenesis (Shen et al., 2019) and that newborn neurons in the adult dentate gyrus migrate laterally first to increase their dispersion (Wang et al., 2019). The migration of neuronal progenitors and microglial cells following different neurodegenerative disorders and brain trauma can also be recorded by mini-endoscopic imaging. In addition, the use of miniaturized two-photon microscopes makes it possible to image Ca2+ activity in dendrites (Zong et al., 2017) and may be used in the future to image other sub-cellular structures. Future studies will certainly deepen our understanding of the cellular and molecular mechanisms operating in homeostatic and diseased conditions.
Limitations and Future Avenues for Mini-Endoscopic Imaging
Given the experimentally proven benefits and versatility of mini-endoscopes for neuroscience research, it has become increasingly clear that the impact of these devices will grow exponentially. However, they still have some limitations and drawbacks and further improvements are needed. The weight and size of mini-endoscopes are crucial and should be further improved to enable unrestrained one- and two-photon mini-endoscopic imaging in freely behaving mice and other small animal models. Similarly, the footprint of mini-endoscopes should be reduced for multi-region imaging in order to record activity from adjacent brain areas. Another caveat is that most of the mini-endoscopes used in freely behaving animals need to be tethered to optical components that are too big and too heavy to be mounted on an animal’s head, which means that more complex social behaviors such as social interactions, mating, and mate aggression cannot be fully investigated due to the obvious physical constraints. The poor optical sectioning of one-photon mini-endoscopes and the major aberrations caused by their optical components (GRIN lenses) should also be taken into consideration and be improved. Below we discuss some of the avenues for addressing these issues.
Optical Sectioning
Despite the advantages of wide-field epifluorescence mini-endoscopy (large field of view, small dimensions, and technical simplicity), these systems do not provide the optical sectioning of widely used tabletop systems such as confocal or two-photon microscopes or two-photon mini-endoscopes. As a consequence, image contrast in the focal plane is strongly attenuated by the out-of-focus fluorescent background when imaging densely labeled structures, especially in scattering media. The strong background limits neuronal and glial activity recording to the soma, as smaller structures such as dendrites, dendritic spines, and glial processes are too dim to be resolved. One of the key avenues for future developments is thus the integration and miniaturization of non-scanning optical sectioning systems already offered in large-scale microscopes.
Among non-scanning techniques, the tabletop version of the HiLo microscope has generated a great deal of interest (Lim et al., 2011; Lauterbach et al., 2015). The HiLo microscopy technique is based on epifluorescence microscopy and makes it possible to reject the out-of-focus fluorescent background by processing two images, one recorded with a uniform illumination and the other recorded with a high contrast pattern. HiLo microscopy provides high contrast images with optical sectioning similar to confocal microscopy at the expense of half the camera frame rate. The use of speckle illumination for the high contrast illumination has been proposed, as fast switching from uniform to speckle illumination can be obtained with the simple addition of a diffuser and a galvanometric scanner. HiLo microscopy has already been used for endoscopic applications using a flexible fiber bundle to deliver shaped illumination to the sample and to collect the fluorescence (Ford et al., 2012). Although fiber bundles allow images and illumination patterns to be relayed over long distances (up to meters), they are more rigid than the smaller diameter multi-mode fibers used to deliver light in experiments on freely behaving animals. They also have a small core fill factor, which results in low optical transmission. These properties limit their use for dimmer fluorescent signal recording in freely behaving small animals. Generating both illumination types in the microscope body at the expense of the size of the system is a good alternative to fiber bundles, and the technical simplicity of the HiLo microscopy technique makes it a good candidate for miniaturization.
More recently, it has been proposed that the light field microscopy (LFM) and seeded iterative demixing (SID) techniques can be integrated into miniaturized microscopes (Nobauer et al., 2017; Skocek et al., 2018). These computational methods only require minor hardware modifications to the microscope body by inserting a microlens array in the detection pathway of a miniaturized epifluorescence microscope. A 700 μm × 600 μm × 360 μm volume has been imaged at a frequency of 16 Hz with a discrimination threshold between cells of 15 microns. Despite the complex analytics and lower spatial resolution of this method, the reduced number of optical components and the extraction of a large volumetric amount of data makes it a good candidate for Ca2+ activity recording in freely behaving animals with improved optical sectioning.
As mentioned above, the first versions of mini-endoscopes were based on a two-photon excitation principle that provides superior optical sectioning (Helmchen et al., 2001; Helmchen, 2002; Flusberg et al., 2005). However, these systems are bulkier and heavier, which limits the behavioral paradigms that can be studied. In these versions, the laser scanning mechanism is either located proximally on the animal for higher resolution and sensitivity, or remotely and is connected to the animal with a fiber bundle to limit the number of optical components on the animal (Ozbay et al., 2015, 2018). To reduce the weight of the imaging systems, miniaturized scanning systems were developed based on micro-mechanical systems (MEMS), scanning mirrors (Piyawattanametha et al., 2006; Zong et al., 2017), and piezoelectric actuators (Engelbrecht et al., 2008). High resolution miniaturized two-photon mini-endoscopes capable of imaging a 130 μm × 130 μm-field of view at 40 Hz have been used to record Ca2+ activity in dendrites and spines in freely behaving animals (Zong et al., 2017). In this system, a hollow core photonic crystal fiber and an achromatic objective lens are used to minimize light pulse broadening and maintain the high intensity at focus essential for two-photon excitation. However, this work was done in the first layers of the brain (visual cortex). Minimizing pulse broadening remains one of the main challenges of deep brain imaging. To achieve this, the chromatic aberration of the GRIN lenses used for deep brain imaging has to be properly compensated without increasing the outer diameter of the implant. This will require the development of new minimally invasive achromatic optical probes. Another interesting approach to increase the imaging depth without using GRIN lenses is miniaturized three-photon microscopy. Klioutchnikov et al. (2020) recently designed a three-photon microscope capable of recording calcium transients at multiple cortical depths of up to 1120 microns in freely behaving animals. However, the weight of the device developed by Klioutchnikov et al. (2020) (around 5 g) restricts its use for now to bigger rodents such as rats.
Miniaturized confocal microscopes with a scanning system placed on the animal would provide optical sectioning without a complex laser system and non-linear effect management. The main drawback of confocal microscopy compared to two-photon microscopy is its lower excitation wavelength, which creates more scattering and higher phototoxicity. The chromatic aberrations of the optical system, including the GRIN relay lens, will also have to be properly compensated to ensure that the pinhole and object plane remain in conjugated planes.
Optical Aberration Compensation
The high numerical aperture and the small outer diameter of GRIN lenses (0.5–1 mm) allow the imaging of structures located several millimeters deep in tissue with minimal invasiveness. In comparison, relay lenses made of optically homogenous materials with a similar numerical aperture are at least five times larger in diameter. Given this, GRIN lenses have become the preferred option for in vivo deep structure imaging. However, GRIN lenses also suffer from high intrinsic on-axis and off-axis optical aberrations (astigmatism and chromatic) and have the highest optical aberrations in miniaturized microscopy systems, leading to lower spatial resolutions and smaller effective fields of view, especially with high numerical aperture GRIN lenses. To solve this issue, adaptive optics methods have been used in two-photon mini-endoscopes. One of these methods allows the recovery of diffraction-limited resolution in a large field of view by measuring and correcting off-axis aberrations using pupil segmentation-based adaptive optics (Wang and Ji, 2012). Future developments in miniature microscopy would also benefit from the use of miniature tunable liquid crystal lenses and miniaturization of the phase modulators used in adaptive optics methods such as phase liquid crystal spatial light modulators. Adding adaptive optics to miniaturized microscopes would also help reduce the impact of light scattering in tissue. The inhomogeneity created by the different components of the sample (water, proteins, and lipids, etc.) distorts the fluorescence wavefront, resulting in lower resolution and contrast.
Wireless Imaging Systems
With the development of tethered miniaturized microscopes, it became possible to correlate brain activity and behavior recordings. However, despite several attempts to reduce the impact of the cables on animal behavior (very flexible cables, motor-assisted rotary-joints, etc.), some complex behavioral studies such as social interaction studies with multiple animals cannot be performed with these systems due to cable entanglement. To overcome these issues, several wireless models have been proposed (Liberti et al., 2017; Barbera et al., 2019; Shuman et al., 2020). One of the main challenges in developing wireless systems is minimizing their size and weight by, for instance, reducing the size of the lithium polymer (LiPo) batteries. Several avenues have been explored to reduce power consumption and use lighter batteries: limiting the number of on-board electronic components with on-site recording (Barbera et al., 2019; Shuman et al., 2020) and using low resolution sensors to restrict the amount of transferred data (Liberti et al., 2017). Using a battery backpack for a better distribution of the weight has also been explored (Barbera et al., 2019). For example, Liberti et al. (2017) introduced the FinchScope, a wireless mini-endoscope that uses a 2 g wireless transmitter and a LiPo battery. Barbera et al. (2019) also introduced a wireless mini-endoscope that is powered by a battery backpack anchored on the animal’s back and that is equipped with Micro SD card storage. They were able to record the activity of medium spiny neurons (MSNs) in the dorsal striatum of two freely behaving and interacting mice. In both studies, power was supplied to the microscope by miniature LiPo batteries, which depending on their size, can provide power for 30 min to 1 h at the expense of increasing the weight of the microscope to several grams (Liberti et al., 2017). For future developments, local recording remains a very good solution for reducing the weight of the system. Transferring data samples at a very low frame rate (0.2–1 Hz) and adding a communication interface for the synchronization with external components such as behavior cameras would improve system feedback. The development of new wireless systems should also take into account future behavioral studies aiming to assess the impact of the weight and its distribution (e.g., backpack battery versus integrated battery) on animal behavior. Further improvements are required to optimize wireless mini-endoscopes, improvements that will largely benefit the neuroscience field by expanding the number of possible behavioral experiments that can be performed while monitoring brain activity. The potential of this technique can also be extended to animals that exhibit more complex social behavior like monkeys and small primates. In addition, more complex social behaviors can be assessed without the constraints of the cable from the imaging device.
General Conclusion
Imaging neuronal activity using various sensors and actuators has emerged as a powerful tool that allows neuroscientists to record large-scale neuronal activity in freely behaving animals. Traditional tools used for imaging such as two-photon excitation microscopy have opened up the possibility of imaging Ca2+ activity patterns at the cellular and subcellular levels with unprecedented optical resolution. But these experimental setups are costly and require specific infrastructures that limit their dissemination among large numbers of laboratories. Also, these setups require the animals to be head-restrained during the imaging phase, which markedly limits the behavioral repertoire that can be tested. The development of mini-endoscopes has given rise to a new class of devices that have proven to be very useful in acquiring and correlating functional cellular signals from cortical and deep brain regions to animal behavior. The ease of use coupled with the portability and relative low cost of mini-endoscopes has provided a much-needed technical alternative. The capacity to take recordings from large neuronal populations in freely behaving animals has made it possible to analyze large-scale Ca2+ imaging data with higher statistical power and to discover different types of coding dynamics related to animal behavior. The versatility of these devices also provides a good platform for improvements to the original design based on research requirements. Designs that incorporate parallel electrophysiological recordings or that allow all-optical circuit-level investigations by combining imaging with optogenetic manipulations can be used to further dissect activity at the microcircuit level and increase our understanding of neural processes underlying animal behavior. The ability to construct lightweight small mini-endoscopes will contribute to animal well-being, will improve the read-out of neural activity under natural conditions, and will expand research to other animal models. By decreasing the size of these devices, mini-endoscopes now also permit the use of multiple devices on the same experimental subject, which allows for the imaging of multiple brain areas in mice and other larger animals such as rats and primates. This multi-site recording approach will open up new avenues for systems neuroscience research, where longitudinal large-scale recordings beyond a single local circuit can be performed and may uncover exciting new information related to circuit activity patterns governing brain-wide functional circuitry and synchronicity. Further improvements to these devices aiming to increase the depth of penetration in living tissue, fine tune spatial and temporal resolution, and add a wireless option for even less restrictive behavioral repertoires are needed to make functional imaging in freely behaving animals more widely accessible. The traditional use of mini-endoscopy is to image Ca2+ activity at the neuronal level. However, the advances in fluorescent reporters, onsite lens focus technologies, and two- and three-photon miniaturization will enable scientists to image cellular and subcellular compartments such as dendrites and dendritic spines. Further development of GEVIs, which have much more temporal resolution than GECIs, will push the use of mini-endoscopes even further, giving scientists the means to optically assess voltage changes across membranes and dendritic compartments. This will most likely revolutionize neuroscience research, giving scientists a tool to record, stimulate, and differentiate between the fast electrophysiological events associated with synaptic activity and the slow Ca2+ events associated with the various biomolecular processes that occur during learning and memory. In addition, mini-endoscopic imaging approaches may allow scientists to monitor activity related to spine formation/elimination, the dynamics of glia processes, microglia migration, and adult neurogenesis in freely behaving animals. The flexible methodological approach provided by mini-endoscopes will continue to afford neuroscientists with better tools to image large neuronal populations during increasingly less restrained behavioral paradigms. This, in turn, will lead to new datasets that will more accurately depict the relationship between recorded functional and structural activities and animal behavior.
Author Contributions
All authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication.
Conflict of Interest
SD and HD declare a conflict of interest. SD is the owner of Doric Lenses and HD is an R&D scientist at Doric Lenses. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Funding. This work was supported by a postdoctoral fellowship from Mitacs to SM and an operating grant from the Natural Sciences and Engineering Research Council of Canada (NSERC) to AS.
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Fast high-resolution miniature two-photon microscopy for brain imaging in freely behaving mice.\nNat. Methods\n14\n713–719. 10.1038/nmeth.4305\n28553965ZouY.ZhangW.ChauF. S.ZhouG. (2015). Miniature adjustable-focus endoscope with a solid electrically tunable lens.\nOpt. Express\n23\n20582–20592.26367911\n"],"string":"[\n \"\\nFront NeurosciFront NeurosciFront. Neurosci.Frontiers in Neuroscience1662-45481662-453XFrontiers Media S.A.32848576PMC743215310.3389/fnins.2020.00819NeuroscienceReviewDeciphering Brain Function by Miniaturized Fluorescence Microscopy in Freely Behaving AnimalsMalvautSarah12†ConstantinescuVlad-Stefan12†DehezHarold3DoricSead3SaghatelyanArmen12*1CERVO Brain Research Center, Quebec City, QC, Canada2Department of Psychiatry and Neuroscience, Universite Laval, Quebec City, QC, Canada3Doric Lenses Inc., Quebec City, QC, Canada
Edited by: João O. Malva, University of Coimbra, Portugal
Reviewed by: Daniel Benjamin Aharoni, University of California, Los Angeles, United States; Alberto L. Vazquez, University of Pittsburgh, United States
†These authors have contributed equally to this work
This article was submitted to Brain Imaging Methods, a section of the journal Frontiers in Neuroscience
118202020201481920420201472020Copyright © 2020 Malvaut, Constantinescu, Dehez, Doric and Saghatelyan.2020Malvaut, Constantinescu, Dehez, Doric and SaghatelyanThis is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Animal behavior is regulated by environmental stimuli and is shaped by the activity of neural networks, underscoring the importance of assessing the morpho-functional properties of different populations of cells in freely behaving animals. In recent years, a number of optical tools have been developed to monitor and modulate neuronal and glial activity at the protein, cellular, or network level and have opened up new avenues for studying brain function in freely behaving animals. Tools such as genetically encoded sensors and actuators are now commonly used for studying brain activity and function through their expression in different neuronal ensembles. In parallel, microscopy has also made major progress over the last decades. The advent of miniature microscopes (mini-microscopes also called mini-endoscopes) has become a method of choice for studying brain activity at the cellular and network levels in different brain regions of freely behaving mice. This technique also allows for longitudinal investigations while animals carrying the microscope on their head are performing behavioral tasks. In this review, we will discuss mini-endoscopic imaging and the advantages that these devices offer to research. We will also discuss current limitations of and potential future improvements in mini-endoscopic imaging.
miniature endoscopesmini-endoscopic imaginganimal behaviorGRIN lensesGCaMPGECICa2+ imagingMitacs10.13039/501100004489Natural Sciences and Engineering Research Council of Canada10.13039/501100000038Introduction
Understanding how brain functions are shaped by experience and, in turn, how neural networks regulate animal behavior has always been of great interest to the scientific community as well as the general public. One of the long-term aims of neuroscience research is to understand how specific spatio-temporal activity patterns emerging from local neural networks regulate specific animal behaviors. Recording neural activity at the microcircuit level from distinct subsets of neurons and glia in freely behaving animals has thus become essential for studying how experience is represented and processed by the brain. The function of these networks can be interrogated using extracellular multi-electrode recordings (Nicolelis and Ribeiro, 2002), large-scale brain imaging techniques such as functional magnetic resonance imaging (fMRI), voltage-sensitive dye-based imaging, intrinsic optical signal imaging, and positron emission tomography (PET) (Grinvald et al., 1988; Shoham et al., 1999; Logothetis et al., 2002), or in vivo Ca2+ imaging using one- and two-photon mini-endoscopes (Dombeck et al., 2007; Kerr et al., 2007; Grewe and Helmchen, 2009). The technique of choice for these types of experiments has mostly involved taking electrophysiological recordings in either awake head-restrained or freely behaving rodents (Wilson and Mcnaughton, 1993; Houweling and Brecht, 2008; Harvey et al., 2009; Niell and Stryker, 2010). This approach has provided a great number of important insights into how neuronal ensembles orchestrate behavior such as the discovery of spatial representation by place and grid cells, for instance (O’keefe and Dostrovsky, 1971; Hafting et al., 2005). However, despite the tremendous value of this technique, the number of cells that can be recorded at a given time is limited by the small number of electrodes that can be used simultaneously. In theory, increasing the number of electrodes should make it possible to record electrophysiological activity in more cells, but the spacing between the electrodes is limited. Electrophysiological recordings also exclude electrically silent cells from the subsequent analysis of network dynamics (Shoham et al., 2006). To address some of these caveats, researchers can now take advantage of dense recordings using multi-tetrode arrays or silicone probes that make it possible to take recordings from large neuronal populations rather than from individual cells. Such large population-level recordings can be used to address questions that otherwise could not have been investigated using data from individual cell recordings (Csicsvari et al., 2003; Harris et al., 2003; Buzsaki, 2004; Dupret et al., 2010; Stensola et al., 2012). However, even with advances in electrophysiological recordings of large subsets of cells, there remain some important aspects that cannot be studied using such techniques. Although electrophysiology allows for distinct cell populations to be differentiated based on waveform shapes (Trainito et al., 2019), it is much more challenging to assess the individual contribution of each subset of recorded cells to the observed network dynamics. Recordings from subcellular structures such as dendrites can be also obtained in freely behaving mice using electrophysiological approaches (Moore et al., 2017). By taking advantage of the immune response that follows a tetrode implant, which allows a dendrite to get trapped between the tetrode tips and the glial encapsulation, it is possible to measure the dendritic membrane voltage in freely behaving rodents (Moore et al., 2017). This methodology could also provide important insights into the electrophysiological correlates of dendritic action potential integration, but has a low success rate and low yield.
The study of brain function has taken a step further with the advent of new optical tools designed to investigate neuronal activity at the protein, cellular, or network level. These include genetically encoded Ca2+ indicators (GECIs). Ca2+ is an important second messenger involved in the homeostasis of essentially all cells in the brain (Clapham, 1995; Berridge et al., 2000) and GECI-recorded Ca2+ fluctuations can be used to study the activity and coding of neural ensembles in basal conditions and in response to stimuli (Tian et al., 2012; Lin and Schnitzer, 2016). Many different versions of GECIs exist, and their repertoire and properties are constantly improving (Dana et al., 2016, 2019; Shen et al., 2020). The green and red Ca2+ indicators (GCaMP and RCaMP, respectively) are the most common (Lin and Schnitzer, 2016; Shen et al., 2020). More recently, a quadricolor GECI suite (XCaMP) (Inoue et al., 2019) and a near-infrared Ca2+ indicator (Qian et al., 2019) were designed to further expand the palette of GECIs and to study more complex neural dynamics. Using viral or transgenic labeling, GECIs can be expressed all over the brain or in specific neuronal/glial subtypes and are now widely used in the context of learning, memory formation, and plasticity (Jercog et al., 2016). They also allow for long-term investigations of brain activity for up to several weeks and even months (Jercog et al., 2016). In addition to GECIs, current efforts to develop genetically encoded voltage indicators (GEVIs) will eventually make it possible to monitor individual spikes during high-frequency trains of action potentials or subthreshold changes in the membrane potential in selected populations of cells (Bando et al., 2019). This will further increase the palette of functional assessments of neural networks during animal behavior. In addition to functional changes, several morphological modifications such as spine formation/elimination, the dynamics of glia processes, and microglia migration occur in response to environmental stimuli. Furthermore, several regions of the adult brain retain the capacity to renew their cellular populations during injury (Torper and Gotz, 2017) or under homeostatic conditions through adult neurogenesis (Gengatharan et al., 2016; Obernier and Alvarez-Buylla, 2019; Vicidomini et al., 2020). All these morphological changes can be also monitored in vivo using live imaging approaches (Berry and Nedivi, 2017; Pilz et al., 2018; Wallace et al., 2020).
In vivo imaging of morpho-functional changes in zebra fish larvae and nematodes has been performed in whole organisms (Ahrens et al., 2012; Nguyen et al., 2016). In rodents, in vivo imaging of a given brain region has traditionally been performed by two-photon microscopy in anesthetized or awake head-restrained animals performing specific behavioral tasks. While this allows for long-term and longitudinal recording of brain activity, it is limited to a restricted number of behavioral tasks and cannot be used to study deep brain structures unless the overlying brain regions are removed, which may affect cellular activity and animal behavior. The last two decades saw the advent of miniaturized fluorescent microscopes (also called mini-microscopes or mini-endoscopes) that enable in vivo recordings of brain structure and function of freely behaving animals to be taken at the cellular and network levels. These devices, which are similar to benchtop microscopes in their optical designs, contain miniaturized optical parts (mirrors, filters, or even a camera) and can be carried by animals that freely displace in an arena. Displays and recordings of acquired images are then computer assisted, allowing for the modulation of different parameters such as acquisition frame rate, exposure, and illumination power, without any additional manipulation of the recorded animal. Mini-endoscopes have evolved considerably in recent years since the first miniaturized versions were developed (Helmchen et al., 2001) and are still being refined and improved. They have helped provide answers to several unresolved questions regarding brain activity and structure. In this review, we will first present the various models of currently available mini-endoscopes and provide an overview of their specifications, advantages, and limitations. We will then discuss the in vivo applications of mini-endoscopy and the limitations of existing devices as well as future avenues for improving mini-endoscopic microscopy.
Current State of the Art for Recording Brain Activity and Structure Using Mini-EndoscopesOne-Color Mini-Endoscopes
The first versions of mini-endoscopes were based on two-photon excitation, and some microscope components such as lasers and photomultipliers (PMT) were converted to transmit the excitation light to the head of the animal or to collect emissions through optic fibers. These systems were used for imaging at the surface of the brain (Helmchen et al., 2001; Helmchen, 2002; Flusberg et al., 2005). They were bulky and heavy (25 g, 70 mm high), limiting applications involving naturalistic behavior. Also, the devices could not compensate very well for strong motion artifacts (Helmchen et al., 2001; Helmchen, 2002). Subsequent improvements led to the creation of lighter portable two-photon excitation microscopes (Gobel et al., 2004; Flusberg et al., 2008; Sawinski et al., 2009) that were used to image neural networks in freely moving mice and rats. A promising approach using a portable two-photon excitation-scanning microscope that weighs as little as 2 g was recently introduced to study activity in the entorhinal cortex of freely behaving mice (Zong et al., 2017; Rowland et al., 2018). These systems provide the obvious advantages of two-photon excitation microscopy such as a small excitation volume, increased tissue penetration, reduced phototoxicity, and the possibility of imaging subcellular structures. However, the low two-photon absorption cross-section also imposes high spatial (small focal spot) and temporal (short laser pulse) constraints on two-photon microscopy systems. To get sufficient excitation intensity at focus, it requires high numerical aperture diffraction-limited optics as well as expensive table-top femtosecond pulse lasers with proper dispersion compensation. Laser scanning two-photon microscopes should also be properly stabilized to avoid motion artifacts as the pixels of each image are not recorded simultaneously. It is thus more challenging to use two-photon excitation in a context of mini-endoscopes, suggesting that miniaturized epifluorescence microscopy tends to represent a simpler and more affordable method for imaging activity in neural networks at the cellular level, particularly in deep brain regions while animals are freely behaving.
Several groups have taken advantage of small optical rod lenses [gradient refractive index (GRIN) lenses] (Jung and Schnitzer, 2003; Levene et al., 2004) to build mini-endoscopes. These lenses have a short working distance and can be fixed either at the surface of the brain or inserted into deep brain regions to perform imaging in the superficial or deep brain regions, respectively (Helmchen et al., 2001; Helmchen, 2002; Flusberg et al., 2005). GRIN lenses, which can have different diameters and customizable lengths, have been used for in vivo imaging of previously unreachable deep brain structures such as the substantia nigra, the hypothalamus, the parabrachial nucleus, and the basal forebrain (Bocarsly et al., 2015; Fu et al., 2019; Patel et al., 2019). GRIN lenses are usually flat at the tip, making it possible to image cells just below the tip. However, they can also be coupled with a prism mirror allowing for “side-view” imaging. This configuration, in our experience, significantly reduces tissue damage and immune responses compared to blunt probes. In line with this, less tissue damage and better neuronal preservation is observed for well-sharpened and small tip angle silicone probes used for in vivo electrophysiological recordings (Edell et al., 1992). The GRIN coupled with a prism has been used for imaging neurons located in different cortical layers (Gulati et al., 2017).
Gradient refractive index implantation is minimally invasive, and the presence of the lens in an animal’s brain does not alter the locomotion or behavior of the animal in selected tasks commonly used in neuroscience research (Lee et al., 2016), although this largely depends on the diameter and type of GRIN lens (Resendez et al., 2016). Larger diameter (1 mm) GRIN lenses have been described as being more suitable for an implantation in superficial brain regions such as the cortex or the hippocampus, whereas smaller diameter GRINs are more suitable for deeper brain regions such as the hypothalamus or the ventral tegmental area (Resendez et al., 2016). Slow insertion of probes during implantation or using probes coupled with a prism can minimize possible compression of the brain, restricting it to the front of the GRIN and reducing possible damage (Edell et al., 1992; Levene et al., 2004). For deep brain region imaging, a track in the brain is often created using a sharp needle or blade. Brain tissue aspiration is also used in some cases (Resendez et al., 2016; Gulati et al., 2017; Gonzalez et al., 2019; De Groot et al., 2020). The inflammatory response resulting from implant insertion normally largely clears up after 4 weeks (Bocarsly et al., 2015), or less for sharp-ended implants (Edell et al., 1992; Turner et al., 1999). This observation highlights the need to start recording with miniature endoscopes several weeks post-implantation. A minimal period ranging from 2 to 4 weeks is required before acquiring data using GRIN lenses (Barbera et al., 2019; Fu et al., 2019; Gonzalez et al., 2019). To confirm the correct implantation of a GRIN lens, a post hoc analysis in post-mortem brain tissues can be used. This step makes it possible to adapt the stereotaxic coordinates used in different animals (Lee et al., 2016; Resendez et al., 2016). When viral vectors are injected in a defined brain region a few weeks before GRIN implantation, changes in background fluorescence may be also used to ascertain the correct positioning.
As for animals becoming habituated to the imaging device, it is common practice to try to reduce the weight of mini-endoscopes as much possible so as to not interfere with naturalistic behavior of the animals (Resendez et al., 2016). Chronic GRIN implants do not alter animal performance in standard behavioral procedures such as the rotarod test or the Morris water maze (Lee et al., 2016). However, no comparative studies have been reported with regard to the adaptation of animals to mini-endoscopes of different weights and configurations. Further behavioral investigations will be required to accurately evaluate the maximal weight that small rodents like mice can carry on their head without an impact on their behavior and to compare different mini-endoscopes, some of which are bulkier than others. Also, not all available versions of mini-endoscopes can reach very lateral or anterior brain regions such as the olfactory bulb. In fact, due to size constraints, placing a mini-endoscope in such regions could potentially alter the normal behavior of the animals or even their vision if placed too close to their eyes. Smaller footprint mini-endoscopes may help to resolve this issue.
An important step in the conception of mini-endoscopes is thus the miniaturization of microscope parts. Ghosh et al. (2011), in their first version of a fully miniaturized epifluorescence mini-endoscope, replaced larger external light sources (e.g., Xenon lamps, mercury lamps, high power LEDs, and lasers) with smaller internal LEDs that are efficient and low-cost alternatives. This approach is now widely used in different open source mini-endoscopes designed to image in freely behaving rodents or songbirds. These include the UCLA Miniscope (Cai et al., 2016), the National Institute of Drug Abuse MiniScope (Barbera et al., 2016), the Boston University FinchScope that was developed for imaging in songbirds (Liberti et al., 2017), and the University of Toronto CHEndoscope (Jacob et al., 2018). Although very practical in terms of cost, availability, and size, installing LEDs directly in the microscope body can have some drawbacks. Because of its location in the microscope body, maximum output power is limited in order to minimize potential excessive heat on the animal’s head at higher powers normally used for optogenetic stimulations. To alleviate these issues, some commercial one-color mini-endoscopes can be connected to adjustable, exchangeable external light sources such as lasers, laser pumped incoherent sources (Ce:YAG), or LEDs using an optical fiber. However, this has the disadvantage of adding an extra optical fiber above the animal’s head.
Mini-endoscopes also take advantage, in their design, of off-the-shelf miniature electronic components such as complementary metal oxide semiconductor (CMOS) sensors for image detection. These sensors support high acquisition frame rates (higher than 30 Hz) and can, in some cases, be coupled to a microphone to record songbird vocalizations (Liberti et al., 2017). Improved versions of mini-endoscopes are currently available from commercial sources (Doric Lenses, Inscopix, and Neurescence) as well as open-source platforms (UCLA Miniscope, NINscope, and FinchScope).
It should be also mentioned that because of their miniaturization and their use in freely behaving animals, device failure may also occur. Unlike tabletop microscopy systems, miniaturized fluorescence microscopes are operated in challenging environments, with the animal moving in different directions and potentially applying some constraints on the microscope and optical fibers. Several approaches have been investigated to reduce the risks of experiment interruption due to microscope failure, including sturdier bodies in hard plastic (Cai et al., 2016) and metal frames (Zong et al., 2017), protected electronics, and connectorized systems with interchangeable cables (Barbera et al., 2016; Cai et al., 2016; Liberti et al., 2017; Jacob et al., 2018; De Groot et al., 2020). Optical components used in miniature endoscopes can be costly and, because of their size, failure can happen during surgical implantation or extraction. To counteract this, Bocarsly et al. (2015) proposed a version of the device, including the implantation in animal’s brain of a guide cannula, allowing for the insertion of a GRIN lens for each imaging session. However, it should be noted that the resulting diameter of the imaging probe is larger, which has an impact on brain tissue, as previously reported (Bocarsly et al., 2015; Resendez et al., 2016).
Two-Color Mini-Endoscopes
Functional and structural imaging in a given brain region was, for a long time, restricted to a general cell population or a subset of cells expressing a particular Ca2+ indicator or fluorescent marker. More recently, two-color versions of mini-endoscopes have been developed that allow two different fluorophores with different emission and excitation wavelengths to be imaged. However, GRIN lenses have large chromatic aberrations that induce a shift (sometimes up to several tens of microns) in the imaged focal plane when comparing the fluorescence of two different fluorophores. To counteract these aberrations, one solution is to place two CMOS sensors in the microscope body at a specific location to compensate for the focal shift induced by the GRIN lens when imaging the two spectrally distinct fluorophores. Another solution for counteracting chromatic aberrations is to use achromatic lenses. The availability of several versions and colors of GECIs (Dana et al., 2016, 2019; Shen et al., 2020), each with its own emission/excitation spectra, means that it is now possible to image using both green and red Ca2+ indicators with similar kinetics (Shen et al., 2020). Two-color mini-endoscopes have a number of advantages. First, when combined with Ca2+ indicators of spectrally distinguishable colors (GCaMP and RCaMP, for instance), it is possible to compare the activity of two different cell populations in the same brain region of freely behaving animals. In addition, two-color mini-endoscopes can be used for motion correction. In fact, xy and z drift can occur when animals are moving, which may create some bias in data analysis and interpretation. This issue can be addressed by performing functional Ca2+ imaging combined with morphological fluorescent marker imaging followed by open-source plugin or software processing for motion and drift correction. These systems can be also used for imaging cell populations that can be defined only based on the combinatory genetic labeling approaches, leading to the expression of two different fluorophores (or sensors) by the same cell. Two-color mini-endoscopes can be also used for assessing the interplay between different cellular populations in a given brain region. It should be noted, however, that because of their optical design, two-color mini-endoscopes are bulkier and a longer habituation period for the animal may be required before starting the experiments.
Combined Optogenetic and Imaging Mini-Endoscopes for Studying Brain Connectivity and Disease Pathogenesis
The discovery that neuronal activity can be modulated using light-sensitive ion channels or opsins was a breakthrough in the field of neuroscience (Kim et al., 2017). In vivo optogenetic approaches are now widely used to modulate neuronal activity and to investigate the impact on animal behavior (Kim et al., 2017). It is possible to modulate neuronal activity by either stimulating or inhibiting the activity of a cell population in a given structure while simultaneously imaging the responses of the same or neighboring populations of cells in the same structure or even in a very different, remote structure (Stamatakis et al., 2018). However, the opsin and Ca2+ indicators have to be carefully chosen as crosstalk between the channels may affect the recordings (Stamatakis et al., 2018). In addition, the imaging light source attenuates the optogenetic response (Stamatakis et al., 2018), which means that care must be taken in choosing the excitation light source for imaging in order to provide a better signal-to-noise ratio and minimal crosstalk.
De Groot et al. (2020) developed the NINscope, which can be used to simultaneously record and optogenetically stimulate two distant brain structures. The authors reduced the footprint of the mini-endoscope, making it possible to record the Ca2+ activity of a subset of cells in a given brain region while optogenetically stimulating cells in many other brain areas using small LEDs installed on the surface of the brain (De Groot et al., 2020). For experiments requiring optogenetic manipulation of deeper brain regions that cannot be targeted with the NINscope, a micro-LED can be implanted inside the brain. The stimulation can then be synchronized with the endoscope imaging (Shin et al., 2017). Combined optogenetic and imaging mini-endoscope baseplates composed of a GRIN relay lens for fluorescence imaging and an optical fiber for opsin activation are also commercially available. These systems offer a customizable length and distance between the imaging cannula and the implanted optical fiber. We used one of these mini-endoscope baseplates to optogenetically induce α-synuclein aggregation in the substantia nigra and to monitor changes in striatal neurons using Ca2+ imaging (Bérard et al., 2019).
Multi-Region Imaging With Mini-Endoscopes
The NINscope developed by De Groot et al. (2020) also allows the simultaneous and synchronous imaging of two separate brain regions because of its reduced footprint. This is of particular relevance in a context of brain connectivity as the activity of two distinct regions can be correlated at the cellular level depending on the selected behaviors or stimuli. Simultaneous recording using two separate mini-endoscopes can also be performed in both hemispheres of the same structure (Gonzalez et al., 2019; De Groot et al., 2020). These recordings can be used to investigate the lateralization of brain activity (Gonzalez et al., 2019) and to compare, in the same subject, the activity of two hemispheres to which a different treatment may have been applied. A device allowing for simultaneous imaging of different brain regions is now also commercially available. In fact, because of it small footprint, the fiber bundle-based device from Neurescence (quartet mini-microscope) can record neuronal activity from up to four different brain regions as well as from different animals that are socially interacting, for instance.
Mini-Endoscopes With Dual-Modality Recordings
Mini-endoscopes also allow for dual-modality recording of neuronal activity. Yashiro et al. (2017) used a mini-endoscope system coupled with electrophysiological recordings of neural activity to make simultaneous optical recordings from large neuronal subsets. Such simultaneous optical and electrical recordings may prove to be extremely useful in correlating electrically recorded phenomena (multi-unit activity and local field potentials) with optically recorded Ca2+ fluctuations. Combined electrical and Ca2+ signal recordings from a single fluorescent neuron in vivo have also been made and allow the spiking activity of recorded cells to be correlated with changes in Ca2+ dynamics (Lechasseur et al., 2011). In addition to combining imaging and electrophysiological recordings, commercially available dual implant baseplates composed of a GRIN lens and an optical fiber allow Ca2+ imaging and photometry recordings in two different brain regions located as close together as 1 mm to be performed. Another potential new application for mini-endoscopes involves combining Ca2+ imaging with intrinsic optical signals (Senarathna et al., 2019). This device can be used to monitor neuronal activity in large areas of the brain and subtract the hemodynamic signal, providing insights into neurovascular coupling (Senarathna et al., 2019). Other types of dual-modality recording mini-endoscopes are also available, such as those that allow for combined Ca2+ imaging and bird vocalization recording (Liberti et al., 2017). It is likely that in coming years other dual-modality mini-endoscopes will be developed. These systems could allow for combining imaging and local drug delivery via integrated cannula, Ca2+ activity recordings and respiration/sniffing assessments, or simultaneous Ca2+ and voltage imaging, for instance. This in turn will make it possible to perform multi-modal assesments of neural networks during unrestricted animal behavior.
Electronic Modulation of the Field of View: eFocus Mini-Endoscopes
A non-negligible drawback of traditional mini-endoscopes was, for a long time, the inability to change the focus prior to or during image acquisition, limiting imaging to a single focal plane (Barretto and Schnitzer, 2012; Hamel et al., 2015). Indeed over time, following consecutive imaging sessions, the focal plane can change slightly and tracking the same cells can become challenging. To counteract this, the first versions of commercial and open-source mini-endoscopes offered the possibility to manually and mechanically adjust the image focus with a slider, screw, or turret before starting a recording, which resulted in additional manipulations of both the device and the animal (Ghosh et al., 2011; Barbera et al., 2016; Cai et al., 2016; Liberti et al., 2017; Jacob et al., 2018). To deal with this issue, updated electronically focused versions of endoscopes have been released by several companies such as Inscopix and Doric Lenses or were built from open-source designs (e.g., UCLA miniscope version 4). These versions make it possible to adjust the focus remotely and thus avoid mechanical manipulations of the endoscope while the animals are freely behaving. To that end, several groups have taken advantage of different strategies. For example, electro-wetting liquid lenses, which are small devices containing two non-miscible liquids, have been used. They allow for the rapid deformation of the lenses and thus the electronic focusing of the field of view by applying a voltage (Grewe et al., 2011; Zou et al., 2015; Ozbay et al., 2018). Electronically tunable liquid crystal lenses have also been used (Bagramyan et al., 2017). These lenses, which are traditionally used in liquid crystal displays, consume little energy and are lighter than electro-wetting liquid lenses, two important parameters for improving mini-endoscopes without compromising their size and weight.
<italic>In Vivo</italic> Applications of Mini-Endoscopic Imaging
As discussed above, two main approaches for optical brain imaging studies on live animals have emerged—those that require the experimental subjects to be head-fixed, such as two-photon excitation microscopy, and those that allow for unrestrained behavior, such as mini-endoscopes. Each technique has its share of advantages and drawbacks. Depending on the nature of the study, researchers can now use a variety of methods that can accommodate either imaging approach. New advancements in fluorescent reporter proteins, in particular GECIs and GEVIs, mean that neuroscientists can perform two-color fluorescence imaging and can distinguish the activity of a dual-labeled subset within a broad population of indicator-labeled cells (Chen et al., 2013; Inoue et al., 2015; Malvaut et al., 2017). The head-fixed imaging approaches that use high-performance lenses and the optical sectioning provided by two-photon microscopy allow for the interrogation of activity patterns during animal behavior at subcellular levels such as in dendrites, dendritic spines, and even axonal boutons (Petreanu et al., 2012; Xu et al., 2012; Boyd et al., 2015; Sheffield and Dombeck, 2015). The head-fixed paradigm can also make intracranial drug delivery easier (Nimmerjahn et al., 2009; Lovett-Barron et al., 2014). Behavioral assays, including adapted fear conditioning paradigms (Lovett-Barron et al., 2014), controlled delivery of sensory stimuli (Verhagen et al., 2007; Carey et al., 2009; Andermann et al., 2011; Blauvelt et al., 2013; Patterson et al., 2013; Miller et al., 2014), and perceptual discrimination tasks (Andermann et al., 2010; Komiyama et al., 2010; O’connor et al., 2010) can now be done in a head-restrained configuration to study changes at subcellular levels during complex behavioral tasks. Another exciting avenue for head-restrained optical imaging in live animals involves the use of virtual reality approaches (Harvey et al., 2009, 2012; Dombeck et al., 2010), which could provide new information about an animal’s spatial navigation.
However, even with the enrichment of the behavioral repertoire that can be tested during head-restrained experiments, the array of behavioral manipulations is still restricted and is associated with the stress that the animals may encounter during head fixation and imaging. This stress can have an adverse effect on the outcome of the behavioral analysis and is likely to affect the neuronal activity patterns recorded (Thurley and Ayaz, 2017). Miniaturized head-mounted imaging devices were designed to circumvent these shortcomings. The main motivation for these devices was first and foremost their ability to record activity from many neurons in an animal that is unrestrained and that can display natural innate behavior as well as a broad array of social behaviors such as fighting, mating, care-giving, and other forms of interaction. The need to accurately monitor cell activity in freely behaving experimental subjects has led to recent advances in mini-endoscopic imaging that make it possible to record neuronal activity simultaneously from different brain regions (De Groot et al., 2020), to combine Ca2+ imaging and recordings of other modalities such as electrical recordings of neural activity (Yashiro et al., 2017) or sound vocalizations (Liberti et al., 2017), and to image activity in neural networks at different focal planes and in different neuronal populations using efocus and two-color versions of mini-endoscopes. Also, the versatility of mini-endoscopes enables cell activity to be tracked across a large array of behavioral paradigms established over the last decades (Morris, 1984; Graeff et al., 1998; Nadler et al., 2004).
The main in vivo application for mini-endoscopes is the imaging of GECIs in large subsets of neurons in freely behaving animals. As mini-endoscopes have many useful characteristics such as relative ease of use, scalability, and commercial availability, they have seen a significant spread to numerous research fields. The use of mini-endoscopes in neuroscience-related fields such as learning and memory, neuropsychiatric disorders, and drug discovery has enabled scientists to accurately describe temporal changes that neuronal circuits exhibit during memory formation and retention as well as modifications due to a pathology. The field of learning and memory has in particular been directly influenced by the development of mini-endoscopes. Tracking the formation and subsequent modification of cellular ensembles that retain learned information, or engrams, over long periods of time has allowed neuroscientists to answer questions about the coding dynamics underlying the neurobiology of long-term memory (Tian et al., 2009; Cai et al., 2016). Established behavioral assays coupled with long-term monitoring of cellular activity has revealed that activity-induced modifications in neuronal CREB (cAMP response element-binding protein) levels are encoded by a shared neural ensemble in the CA1 part of the hippocampus that links distinct contextual memories occurring close in time (Cai et al., 2016). Neuronal activity in the spinal cord of freely behaving mice has also been recorded using a vertebra-affixed mini-endoscope (Sekiguchi et al., 2016). Implanting a mini-endoscope in the spinal cord is technically more challenging than anchoring an implant on top of the skull due to the pronounced movement of the spine within the vertebral column. Although fewer cells can be imaged due the curvature of the spinal cord, these experiments showed that distinct cutaneous stimuli activate overlapping ensembles of dorsal horn neurons and that stimulus type and intensity are encoded at the single-cell level (Sekiguchi et al., 2016).
Mini-endoscopic applications are not limited to the field of learning and memory. Mouse models of diseases are very important for studying the neurobiological basis of a myriad of brain disorders. As most of these diseases are, to some extent, characterized by a deficit in information processing, imaging neural activity in transgenic and knockout mice could be very advantageous for elucidating the underpinnings of the disease mechanisms (Nestler and Hyman, 2010). For example, much more information about alterations in coding dynamics in animal disease models can be inferred from large-scale imaging in freely behaving animals than is possible with electrophysiological measurements. These approaches could also narrow the gap between molecular and behavioral analyses. This concept becomes clear in the context of Alzheimer’s disease (AD), where much progress in understanding the molecular pathology underlying the neurodegeneration observed in human patients has led to the emergence of different mouse models that exhibit one or more AD hallmarks (b-amyloid, presenilins, apolipoprotein E, and Tau) (Knowles et al., 2009; Sterniczuk et al., 2010; Filali et al., 2012; Youmans et al., 2012). However, little is known about how these molecular pathways influence population dynamics during memory encoding and retrieval. With the advent of mini-endoscopy, longitudinal imaging in different AD models during the performance of memory tests is now feasible and could shed new light on memory coding dysfunctions in AD as well as in other neurological and psychiatric disorders (Ziv and Ghosh, 2015). As in the case of AD, the use of mini-endoscopes in animal models of other neurodegenerative disorders and brain trauma may allow the early hallmarks of disease manifestation to be deciphered. These mini-endoscopes combined with optogenetic stimulation can also be used to induce molecular alterations in cells and study disease progression. For example, it is now possible to optogenetically induce the aggregation of α-synuclein in dopaminergic neurons in the substantia nigra and to study alterations in the nigrostriatal pathway and in the network activity of striatal neurons using longitudinal mini-endoscopic Ca2+ imaging (Bérard et al., 2019).
Drug discovery could also benefit from mini-endoscopic imaging. Although much research has focused on the pharmacological and molecular mechanisms of drugs at the cellular level, much less attention has been directed at understanding the effect of drugs on neuronal coding. Berdyyeva et al. (2014) showed that a GABA-A agonist used for treating insomnia reduces hippocampal neuronal firing in freely behaving mice. This result, although not surprising, shed light on the need to correlate neuronal dynamics with the effects of chronic drug administration and to identify the effects of the drugs on the overall neuronal network. Addressing these issues would further advance our ability to design future therapies for treating brain diseases (Ziv and Ghosh, 2015).
The capability of mini-endoscopes to record neural activity in freely behaving animals can be further enhanced by using optogenetic stimulations (Stamatakis et al., 2018) or by combining two different recording techniques such as extracellular electrophysiological recordings with Ca2+ imaging in freely behaving animals (Yashiro et al., 2017). Stamatakis et al. (2018) used a mini-endoscope that jointly records optical neural activity and light-induced alterations of cellular ensemble activity in the same field-of-view. Other researchers have used a device to optogenetically stimulate brain regions away from the imaging field-of-view (Shin et al., 2017; Bérard et al., 2019; Senarathna et al., 2019). Engineering advances in the manufacture of mini-endoscopes have led to a reduction in the weight and size of these devices, making it possible to simultaneously record cellular activities in different brain regions as was done using the NINscope (De Groot et al., 2020). Such types of experiments will yield new data subsets that can be used to analyze the extent to which lateralization occurs during behavior and learning and to track the network stability of multi-region cellular ensembles over long periods of time (Gonzalez et al., 2019).
Mini-endoscopes are mostly used to investigate in vivo brain function in freely behaving animals by Ca2+ imaging of cell populations in a given region and are evolving in parallel with improvements in GECIs. The use of such powerful tools will likely expand to the study of other processes underlying brain activity. For instance, an in vivo two-photon investigation of adult neurogenesis and, more particularly, stem cell division has been already performed in anesthetized head-restrained animals (Pilz et al., 2018). Mini-endoscopes could help unravel this process in freely behaving animals and to correlate stem cell division with naturalistic patterns of animal behavior. These experiments can be performed under homeostatic conditions in known neurogenic regions of the brain like the dentate gyrus or the subventricular zone, as well as after injury and neuronal regeneration. In line with this, mini-endoscopic imaging has shown that coupling pre-existing neurons to the cerebrovascular system regulates adult hippocampal neurogenesis (Shen et al., 2019) and that newborn neurons in the adult dentate gyrus migrate laterally first to increase their dispersion (Wang et al., 2019). The migration of neuronal progenitors and microglial cells following different neurodegenerative disorders and brain trauma can also be recorded by mini-endoscopic imaging. In addition, the use of miniaturized two-photon microscopes makes it possible to image Ca2+ activity in dendrites (Zong et al., 2017) and may be used in the future to image other sub-cellular structures. Future studies will certainly deepen our understanding of the cellular and molecular mechanisms operating in homeostatic and diseased conditions.
Limitations and Future Avenues for Mini-Endoscopic Imaging
Given the experimentally proven benefits and versatility of mini-endoscopes for neuroscience research, it has become increasingly clear that the impact of these devices will grow exponentially. However, they still have some limitations and drawbacks and further improvements are needed. The weight and size of mini-endoscopes are crucial and should be further improved to enable unrestrained one- and two-photon mini-endoscopic imaging in freely behaving mice and other small animal models. Similarly, the footprint of mini-endoscopes should be reduced for multi-region imaging in order to record activity from adjacent brain areas. Another caveat is that most of the mini-endoscopes used in freely behaving animals need to be tethered to optical components that are too big and too heavy to be mounted on an animal’s head, which means that more complex social behaviors such as social interactions, mating, and mate aggression cannot be fully investigated due to the obvious physical constraints. The poor optical sectioning of one-photon mini-endoscopes and the major aberrations caused by their optical components (GRIN lenses) should also be taken into consideration and be improved. Below we discuss some of the avenues for addressing these issues.
Optical Sectioning
Despite the advantages of wide-field epifluorescence mini-endoscopy (large field of view, small dimensions, and technical simplicity), these systems do not provide the optical sectioning of widely used tabletop systems such as confocal or two-photon microscopes or two-photon mini-endoscopes. As a consequence, image contrast in the focal plane is strongly attenuated by the out-of-focus fluorescent background when imaging densely labeled structures, especially in scattering media. The strong background limits neuronal and glial activity recording to the soma, as smaller structures such as dendrites, dendritic spines, and glial processes are too dim to be resolved. One of the key avenues for future developments is thus the integration and miniaturization of non-scanning optical sectioning systems already offered in large-scale microscopes.
Among non-scanning techniques, the tabletop version of the HiLo microscope has generated a great deal of interest (Lim et al., 2011; Lauterbach et al., 2015). The HiLo microscopy technique is based on epifluorescence microscopy and makes it possible to reject the out-of-focus fluorescent background by processing two images, one recorded with a uniform illumination and the other recorded with a high contrast pattern. HiLo microscopy provides high contrast images with optical sectioning similar to confocal microscopy at the expense of half the camera frame rate. The use of speckle illumination for the high contrast illumination has been proposed, as fast switching from uniform to speckle illumination can be obtained with the simple addition of a diffuser and a galvanometric scanner. HiLo microscopy has already been used for endoscopic applications using a flexible fiber bundle to deliver shaped illumination to the sample and to collect the fluorescence (Ford et al., 2012). Although fiber bundles allow images and illumination patterns to be relayed over long distances (up to meters), they are more rigid than the smaller diameter multi-mode fibers used to deliver light in experiments on freely behaving animals. They also have a small core fill factor, which results in low optical transmission. These properties limit their use for dimmer fluorescent signal recording in freely behaving small animals. Generating both illumination types in the microscope body at the expense of the size of the system is a good alternative to fiber bundles, and the technical simplicity of the HiLo microscopy technique makes it a good candidate for miniaturization.
More recently, it has been proposed that the light field microscopy (LFM) and seeded iterative demixing (SID) techniques can be integrated into miniaturized microscopes (Nobauer et al., 2017; Skocek et al., 2018). These computational methods only require minor hardware modifications to the microscope body by inserting a microlens array in the detection pathway of a miniaturized epifluorescence microscope. A 700 μm × 600 μm × 360 μm volume has been imaged at a frequency of 16 Hz with a discrimination threshold between cells of 15 microns. Despite the complex analytics and lower spatial resolution of this method, the reduced number of optical components and the extraction of a large volumetric amount of data makes it a good candidate for Ca2+ activity recording in freely behaving animals with improved optical sectioning.
As mentioned above, the first versions of mini-endoscopes were based on a two-photon excitation principle that provides superior optical sectioning (Helmchen et al., 2001; Helmchen, 2002; Flusberg et al., 2005). However, these systems are bulkier and heavier, which limits the behavioral paradigms that can be studied. In these versions, the laser scanning mechanism is either located proximally on the animal for higher resolution and sensitivity, or remotely and is connected to the animal with a fiber bundle to limit the number of optical components on the animal (Ozbay et al., 2015, 2018). To reduce the weight of the imaging systems, miniaturized scanning systems were developed based on micro-mechanical systems (MEMS), scanning mirrors (Piyawattanametha et al., 2006; Zong et al., 2017), and piezoelectric actuators (Engelbrecht et al., 2008). High resolution miniaturized two-photon mini-endoscopes capable of imaging a 130 μm × 130 μm-field of view at 40 Hz have been used to record Ca2+ activity in dendrites and spines in freely behaving animals (Zong et al., 2017). In this system, a hollow core photonic crystal fiber and an achromatic objective lens are used to minimize light pulse broadening and maintain the high intensity at focus essential for two-photon excitation. However, this work was done in the first layers of the brain (visual cortex). Minimizing pulse broadening remains one of the main challenges of deep brain imaging. To achieve this, the chromatic aberration of the GRIN lenses used for deep brain imaging has to be properly compensated without increasing the outer diameter of the implant. This will require the development of new minimally invasive achromatic optical probes. Another interesting approach to increase the imaging depth without using GRIN lenses is miniaturized three-photon microscopy. Klioutchnikov et al. (2020) recently designed a three-photon microscope capable of recording calcium transients at multiple cortical depths of up to 1120 microns in freely behaving animals. However, the weight of the device developed by Klioutchnikov et al. (2020) (around 5 g) restricts its use for now to bigger rodents such as rats.
Miniaturized confocal microscopes with a scanning system placed on the animal would provide optical sectioning without a complex laser system and non-linear effect management. The main drawback of confocal microscopy compared to two-photon microscopy is its lower excitation wavelength, which creates more scattering and higher phototoxicity. The chromatic aberrations of the optical system, including the GRIN relay lens, will also have to be properly compensated to ensure that the pinhole and object plane remain in conjugated planes.
Optical Aberration Compensation
The high numerical aperture and the small outer diameter of GRIN lenses (0.5–1 mm) allow the imaging of structures located several millimeters deep in tissue with minimal invasiveness. In comparison, relay lenses made of optically homogenous materials with a similar numerical aperture are at least five times larger in diameter. Given this, GRIN lenses have become the preferred option for in vivo deep structure imaging. However, GRIN lenses also suffer from high intrinsic on-axis and off-axis optical aberrations (astigmatism and chromatic) and have the highest optical aberrations in miniaturized microscopy systems, leading to lower spatial resolutions and smaller effective fields of view, especially with high numerical aperture GRIN lenses. To solve this issue, adaptive optics methods have been used in two-photon mini-endoscopes. One of these methods allows the recovery of diffraction-limited resolution in a large field of view by measuring and correcting off-axis aberrations using pupil segmentation-based adaptive optics (Wang and Ji, 2012). Future developments in miniature microscopy would also benefit from the use of miniature tunable liquid crystal lenses and miniaturization of the phase modulators used in adaptive optics methods such as phase liquid crystal spatial light modulators. Adding adaptive optics to miniaturized microscopes would also help reduce the impact of light scattering in tissue. The inhomogeneity created by the different components of the sample (water, proteins, and lipids, etc.) distorts the fluorescence wavefront, resulting in lower resolution and contrast.
Wireless Imaging Systems
With the development of tethered miniaturized microscopes, it became possible to correlate brain activity and behavior recordings. However, despite several attempts to reduce the impact of the cables on animal behavior (very flexible cables, motor-assisted rotary-joints, etc.), some complex behavioral studies such as social interaction studies with multiple animals cannot be performed with these systems due to cable entanglement. To overcome these issues, several wireless models have been proposed (Liberti et al., 2017; Barbera et al., 2019; Shuman et al., 2020). One of the main challenges in developing wireless systems is minimizing their size and weight by, for instance, reducing the size of the lithium polymer (LiPo) batteries. Several avenues have been explored to reduce power consumption and use lighter batteries: limiting the number of on-board electronic components with on-site recording (Barbera et al., 2019; Shuman et al., 2020) and using low resolution sensors to restrict the amount of transferred data (Liberti et al., 2017). Using a battery backpack for a better distribution of the weight has also been explored (Barbera et al., 2019). For example, Liberti et al. (2017) introduced the FinchScope, a wireless mini-endoscope that uses a 2 g wireless transmitter and a LiPo battery. Barbera et al. (2019) also introduced a wireless mini-endoscope that is powered by a battery backpack anchored on the animal’s back and that is equipped with Micro SD card storage. They were able to record the activity of medium spiny neurons (MSNs) in the dorsal striatum of two freely behaving and interacting mice. In both studies, power was supplied to the microscope by miniature LiPo batteries, which depending on their size, can provide power for 30 min to 1 h at the expense of increasing the weight of the microscope to several grams (Liberti et al., 2017). For future developments, local recording remains a very good solution for reducing the weight of the system. Transferring data samples at a very low frame rate (0.2–1 Hz) and adding a communication interface for the synchronization with external components such as behavior cameras would improve system feedback. The development of new wireless systems should also take into account future behavioral studies aiming to assess the impact of the weight and its distribution (e.g., backpack battery versus integrated battery) on animal behavior. Further improvements are required to optimize wireless mini-endoscopes, improvements that will largely benefit the neuroscience field by expanding the number of possible behavioral experiments that can be performed while monitoring brain activity. The potential of this technique can also be extended to animals that exhibit more complex social behavior like monkeys and small primates. In addition, more complex social behaviors can be assessed without the constraints of the cable from the imaging device.
General Conclusion
Imaging neuronal activity using various sensors and actuators has emerged as a powerful tool that allows neuroscientists to record large-scale neuronal activity in freely behaving animals. Traditional tools used for imaging such as two-photon excitation microscopy have opened up the possibility of imaging Ca2+ activity patterns at the cellular and subcellular levels with unprecedented optical resolution. But these experimental setups are costly and require specific infrastructures that limit their dissemination among large numbers of laboratories. Also, these setups require the animals to be head-restrained during the imaging phase, which markedly limits the behavioral repertoire that can be tested. The development of mini-endoscopes has given rise to a new class of devices that have proven to be very useful in acquiring and correlating functional cellular signals from cortical and deep brain regions to animal behavior. The ease of use coupled with the portability and relative low cost of mini-endoscopes has provided a much-needed technical alternative. The capacity to take recordings from large neuronal populations in freely behaving animals has made it possible to analyze large-scale Ca2+ imaging data with higher statistical power and to discover different types of coding dynamics related to animal behavior. The versatility of these devices also provides a good platform for improvements to the original design based on research requirements. Designs that incorporate parallel electrophysiological recordings or that allow all-optical circuit-level investigations by combining imaging with optogenetic manipulations can be used to further dissect activity at the microcircuit level and increase our understanding of neural processes underlying animal behavior. The ability to construct lightweight small mini-endoscopes will contribute to animal well-being, will improve the read-out of neural activity under natural conditions, and will expand research to other animal models. By decreasing the size of these devices, mini-endoscopes now also permit the use of multiple devices on the same experimental subject, which allows for the imaging of multiple brain areas in mice and other larger animals such as rats and primates. This multi-site recording approach will open up new avenues for systems neuroscience research, where longitudinal large-scale recordings beyond a single local circuit can be performed and may uncover exciting new information related to circuit activity patterns governing brain-wide functional circuitry and synchronicity. Further improvements to these devices aiming to increase the depth of penetration in living tissue, fine tune spatial and temporal resolution, and add a wireless option for even less restrictive behavioral repertoires are needed to make functional imaging in freely behaving animals more widely accessible. The traditional use of mini-endoscopy is to image Ca2+ activity at the neuronal level. However, the advances in fluorescent reporters, onsite lens focus technologies, and two- and three-photon miniaturization will enable scientists to image cellular and subcellular compartments such as dendrites and dendritic spines. Further development of GEVIs, which have much more temporal resolution than GECIs, will push the use of mini-endoscopes even further, giving scientists the means to optically assess voltage changes across membranes and dendritic compartments. This will most likely revolutionize neuroscience research, giving scientists a tool to record, stimulate, and differentiate between the fast electrophysiological events associated with synaptic activity and the slow Ca2+ events associated with the various biomolecular processes that occur during learning and memory. In addition, mini-endoscopic imaging approaches may allow scientists to monitor activity related to spine formation/elimination, the dynamics of glia processes, microglia migration, and adult neurogenesis in freely behaving animals. The flexible methodological approach provided by mini-endoscopes will continue to afford neuroscientists with better tools to image large neuronal populations during increasingly less restrained behavioral paradigms. This, in turn, will lead to new datasets that will more accurately depict the relationship between recorded functional and structural activities and animal behavior.
Author Contributions
All authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication.
Conflict of Interest
SD and HD declare a conflict of interest. SD is the owner of Doric Lenses and HD is an R&D scientist at Doric Lenses. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Funding. This work was supported by a postdoctoral fellowship from Mitacs to SM and an operating grant from the Natural Sciences and Engineering Research Council of Canada (NSERC) to AS.
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Express\\n23\\n20582–20592.26367911\\n\"\n]"}}},{"rowIdx":497,"cells":{"data":{"kind":"list like","value":["\nInt J Environ Res Public HealthInt J Environ Res Public HealthijerphInternational Journal of Environmental Research and Public Health1661-78271660-4601MDPI32756449PMC743215410.3390/ijerph17155591ijerph-17-05591ArticleBeliefs about the Harmfulness of Heated Tobacco Products Compared with Combustible Cigarettes and Their Effectiveness for Smoking Cessation among Korean Adultshttps://orcid.org/0000-0002-4370-8442KimSeung Hee1KangSeo Young2ChoHong-Jun1*Department of Family Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul 05505, Korea; kohakush@naver.comInternational Healthcare Center, Asan Medical Center, Seoul 05505, Korea; sykang@amc.seoul.krCorrespondence: hjcho@amc.seoul.kr; Tel.: +82-2-3010-38120382020820201715559101720200182020© 2020 by the authors.2020Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Heated tobacco products (HTPs) have been widely used in Korea since their introduction in 2017. In this study, we investigated the perceptions of their relative harmfulness and smoking cessation effects. We performed an online survey in 7000 Koreans in 2018 (2300 males and 4700 females aged 20–69 years) by matching their age, sex, and provincial distribution. To investigate the factors causing HTPs to be perceived as less harmful than combustible cigarettes (CCs) and helpful for smoking cessation, we used multivariable logistic regression analyses. HTPs were less harmful than CCs in 16.8% of participants, particularly among HTP-only users and dual and triple users of HTPs, electronic cigarettes (ECs), or CCs than among CC-only users, those who were aged ≤ 34 years, males, and those with higher incomes. HTPs were reportedly helpful for smoking cessation in 11.2% of participants. Similar perceptions were more likely among HTP-only users, as well as dual and triple users than among CC-only users and adults with higher education/incomes. Although Korean adults generally had negative perceptions of the harmfulness and smoking cessation effects of HTPs compared with CCs, dual and triple users were more likely to have positive perceptions. Monitoring the use of multiple tobacco products and HTPs is a new challenge for Korean policymakers.
Heated tobacco products (HTPs) comprise small cigarettes, in which the tobacco is heated just enough to release a flavorful nicotine-containing vapor [1]. On the other hand, electronic cigarettes (ECs) are battery-powered devices that vaporize a liquid solution containing nicotine and flavorants [2]. HTPs electrically heat the tobacco without burning to a lower temperature than combustible cigarettes (CCs) to produce an aerosol with nicotine. This is unlike ECs, in which users inhale the aerosol created by heating a nicotine liquid [3]. One brand of HTPs was introduced to South Korea (hereafter, Korea) in June of 2017. The market share of HTPs rapidly increased and reached 10.5% of Korea’s total sales of tobacco products in 2019 [4]. Among Korean men, the prevalence of CC use was 61.8% in 2001, but by 2018, it decreased to 36.7%; whereas among Korean women, its prevalence increased from 5.4% in 2001 to 7.5% in 2018 [5]; which was still very low compared with that of Korean men.
Previous studies have been conducted on the correlates of HTP use among Korean adults [6,7]. Demographic and socioeconomic characteristics associated with using HTPs include being male, young, and having a high education level and economic status. HTPs are marketed as clean, sophisticated, high-tech products that provide all of the benefits of smoking with less ash and odor [8]. These characteristics could attract young, male adults. Due to the high maintenance and cost of using HTP devices, people with a higher socioeconomic level were more likely to use them. Moreover, the use of CCs or ECs was the most significant factor related to the use of HTPs [6,7], which may be because CC or EC users were more likely to have a positive perception of HTPs due to marketing claims that indicate they are less harmful than CCs [8].
Beliefs and perceptions about the health risks associated with smoking play significant roles in reducing its prevalence, as concerns about such risks have been reported as the most common motivation to quit smoking and prevent smoking initiation [9,10]. With regard to the association between the perceptions of relative harmfulness of HTPs and their usage, as compared to non-HTP users, HTP users were more likely to believe HTPs to be less harmful than CCs, with this belief being more prominent among its frequent users [11]. Furthermore, despite understanding that HTPs were not risk-free, most reported that they used HTPs due to their beliefs about its having health benefits over CCs [6], with one of the main reasons being their perception of its reduced harm [12]. These results suggest that the perception of HTPs being less harmful than CCs may have had a major impact on HTP usage in Korea.
There is controversy regarding whether HTPs are less harmful than CCs; despite HTPs being marketed with claims of their being less harmful based on assertions that they expose users to lower levels of toxicants [13]. While the levels of some harmful and potentially harmful constituents of HTPs were lower than those of CCs [14], the reduced exposure of some toxicants does not mean a reduced risk of harm to health [13]. Since the health risks of HTPs have not been determined, further studies are needed.
Even though one of the reasons for using HTPs was to stop smoking [6,12], several studies have shown no significant association between the use of HTPs and smoking cessation-related behaviors, including attempts or intentions to quit smoking [7,15,16]. Moreover, adolescent HTP users were less likely to become former CC smokers despite multiple attempts at smoking cessation [17]. This was probably because although some smokers initiated HTPs for smoking cessation, CC users were more likely to become dual users of HTPs and CCs rather than succeeding in quitting smoking. Regarding perceived effects of HTPs on smoking cessation; a recent study showed that users rather than non-users of ECs were more likely to believe that HTPs are helpful in quitting smoking [18].
Despite the current lack of evidence regarding the long-term health effects of HTPs and their effectiveness for smoking cessation, marketing claims of HTPs’ reduced health risks could have misled consumers [13]. To our knowledge, no research has assessed the perceptions of HTPs in a large population of Korean adults. Therefore, this study aimed to investigate the beliefs about the relative harmfulness of HTPs and CCs, as well as their effectiveness for smoking cessation among Korean adults.
2. Materials and Methods2.1. Study Sample
We conducted an online survey using a panel managed by the research company EMBRAIN (Seoul, Korea), with 1.3 million members (as on December 2018). We randomly invited 70,000 individuals by matching their age, sex, and provincial distributions with the 2018 national population statistics. However, only 10,489 responded, from which we included 7000 (2300 men and 4700 women aged 20–69 years), after excluding 2285 who outnumbered our quota, 21 who did not meet the age criteria, 400 who decided not to participate in the survey, and 783 who responded incorrectly. We oversampled twice as many women as men because the prevalence of tobacco product use was much lower among Korean women than men.
Data were collected from 3–9 November 2018. All participants received financial incentives equivalent to 5000 Korean Republic Won (KRW), equal to approximately 5 US Dollars (USD). All the participants gave their informed consent for inclusion, prior to participating in the study. The study was conducted in accordance with the Declaration of Helsinki, and its protocol was approved by the Asan Medical Center Institutional Review Board (S2018-1662-0001) in Seoul, Korea.
2.2. Measures2.2.1. Status of Tobacco Product Use and Beliefs about the Relative Harmfulness and Smoking Cessation Effects of HTPs
Current CC users were defined as adults who had smoked at least 100 cigarettes during their lifetime and reported currently smoking “every day” or on “some days”. Former CC users were defined as adults who had smoked at least 100 cigarettes but had responded “not at all” to the question about current smoking. Never-CC users were defined as adults who had neither smoked at least 100 cigarettes nor ever smoked CCs in their lifetime. Current HTP users were defined as adults who had continually used HTPs in their lifetime and reported currently using them “every day” or on “some days”. Former HTP users were defined as adults who had frequently used HTPs in their lifetime but were currently not using them. Never-HTP users were defined as adults who had never used HTPs in their lifetime. Current EC users were defined as adults who had repeatedly used ECs in their lifetime, as well as during the past month. Former EC users were defined as adults who had habitually used ECs in their lifetime but had not used them during the past month. Never-EC users were defined as adults who had never used ECs in their lifetime. Since the prevalence of using other tobacco products (e.g., snus, water pipes, cigars, chewing tobacco, pipes, rolling tobacco, snuff) was negligible (less than 0.01%) among Korean adults [7], we did not include questions on these in our survey.
The status of tobacco product use was classified into nine groups. “Never-users of any of the three tobacco products” were defined as adults who had never used CCs, HTPs, or ECs. “Former users of any of the three tobacco products” included former CC, HTP, and EC users who were currently not using any of the three products. “Current users of any of the three tobacco products” included “CC-only users”, “HTP-only users”, “EC-only users”, “dual users of HTPs and CCs”, “dual users of ECs and CCs”, “dual users of HTPs and ECs”, and “triple users of the three products”.
Perceptions of the relative harmfulness of HTPs and CCs was evaluated with the question: “What do you think is the effect of ‘heated tobacco products’ on your health as compared with CCs?” The response options were: “They are less harmful”, “They are similar”, and “They are more harmful”. Whether HTPs can help with smoking cessation was evaluated with the question: “Do you think ‘heated tobacco products’ are helpful for quitting smoking?” The response options were: “Yes”, “No”, and “I am not sure”.
2.2.2. Sociodemographic Characteristics
The sociodemographic characteristics analyzed included age, sex, education, monthly household income, and marital status.
2.3. Data Analysis
We calculated the proportion of participants in each category of sociodemographic characteristics and the prevalence of tobacco product use. Furthermore, we evaluated the proportion of participants believing in the health benefits of HTPs being relative to CCs, and those perceiving HTPs as effective for smoking cessation. To identify factors associated with the perceptions of HTPs, adjusted odds ratios (AOR) and 95% confidence intervals (CI) were obtained via multivariable logistic regression analysis, after adjusting for potential confounders. Data analyses were conducted using IBM SPSS Statistics for Windows, Version 24.0 (IBM Corp., Armonk, NY, USA).
3. Results
Around 30% of the participants were aged 20–34 years, while those aged 35–49 and 50–69 years were each around 35%. Approximately 60% had college-level education, and over 50% had monthly household incomes of 3–6.99 million KRW. The overall prevalence of current use of tobacco products, including CCs, HTPs, and ECs was 27.4% (44.1% among men and 11.0% among women) (Table 1).
Of the 7000 participants, 16.8% reported HTPs to be less harmful than CCs (Figure 1). Using multivariable logistic regression analysis (Table 2), participants aged ≤ 34 years (AOR = 1.20, 95% CI = 1.00, 1.44), who were male (AOR = 1.26, 95% CI = 1.11, 1.44), and had an income ≥7 million KRW (AOR = 1.30, 95% CI = 1.08, 1.57) were associated with greater odds of perceiving HTPs as less harmful than CCs, as compared with those who were aged ≥ 50 years, female, and had an income <3 million KRW, respectively. As compared with CC-only users, never-users of any of the three tobacco products had lower odds of perceiving HTPs as less harmful than CCs (AOR = 0.64, 95% CI = 0.54, 0.77). However, HTP-only users (AOR = 3.32, 95% CI = 2.25, 4.90), dual users of HTPs and CCs (AOR = 2.25, 95% CI = 1.77, 2.86), dual users of HTPs and ECs (AOR = 3.96, 95% CI = 2.17, 7.24), dual users of ECs and CCs (AOR = 1.96, 95% CI = 1.39, 2.77), and triple users (AOR = 5.12, 95% CI = 3.93, 6.67) had greater odds of perceiving HTPs as less harmful than CCs.
As for the perceived effectiveness of HTPs for smoking cessation, 11.2% of participants reported HTPs as effective for smoking cessation (Figure 2). Based on multivariable logistic regression analysis (Table 2), groups with education ≥ college graduate (AOR = 1.35, 95% CI = 1.12, 1.63) and income ≥7 million KRW (AOR = 1.43, 95% CI = 1.15, 1.78) were more likely to agree with the effectiveness of HTPs for smoking cessation. As compared with CC-only users, never-users of any of the three tobacco products had lower odds of perceiving HTPs as effective for smoking cessation (AOR = 0.72, 95% CI = 0.57, 0.90). However, HTP-only users (AOR = 3.61, 95% CI = 2.34, 5.59), dual users of HTPs and CCs (AOR = 2.84, 95% CI = 2.14, 3.75), dual users of HTPs and ECs (AOR = 6.01, 95% CI = 3.23, 11.19), dual users of ECs and CCs (AOR = 2.81, 95% CI = 1.90, 4.14), and triple users (AOR = 7.93, 95% CI = 5.94, 10.57) had greater odds of believing HTPs to be effective for smoking cessation.
4. Discussion
Overall, participants were less likely to believe in the relative health benefits of HTPs and CCs. Only 16.8% of the participants believed that HTPs were less harmful than CCs, which was lower than the results of a Canadian study, in which 25% of the participants perceived HTPs as less harmful than CCs [18]. The negative public perceptions of HTPs are understandable, considering they contain real tobacco and there was widespread media coverage when the Korea Ministry of Food and Drug Safety released information on harmful HTP emissions in 2018 [19]. These negative perceptions of HTPs can also be ascribed to Korea’s relatively stringent tobacco regulations. While Korean tobacco control policies—which include smoking bans in public places, regulations on advertising, and bans on sales to minors—are applied equally to CCs and HTPs, the individual consumption tax for HTPs is 89% of the tax levied on CCs [20].
The perception of the relative harmfulness of HTPs compared with CCs differed according to the pattern of tobacco product use. Compared with CC-only users, HTP users were more likely to perceive HTPs to be less harmful than CCs. Consistent with these results, a previous study found that HTP-only users and dual users of HTPs and CCs were more likely to believe that HTPs were less harmful to users than CCs, compared with CC-only users [16]. It is common for some tobacco product users to choose specific products which they perceive as less harmful because of their favorable perceptions towards these products [21,22,23,24]. Moreover, the odds of perceiving HTPs as less harmful than CCs were highest among triple users of the three products. Since adults with no perception of risk regarding HTPs or ECs were more likely to use multiple tobacco products than those who perceived a risk [25], triple users would have been less likely to have risk perceptions of HTPs.
Intriguingly, both HTP and EC users perceived HTPs to be less harmful than CCs. EC users’ perceptions of the relative harmfulness of HTPs were consistent with the results of a previous study which reported that EC users, rather than non-EC users were more likely to believe that HTPs were less harmful than CCs [18]. An explanation could be that EC users considered HTPs to be similar because of the similarity of the Korean terms for ECs and HTPs, whose direct translations were “liquid-type electronic cigarettes” and “cigarette-type electronic cigarettes”, respectively. This could have made people regard the two products as similar and caused confusion in distinguishing them. Furthermore, HTPs and ECs have many similarities in that they are marketed as being less harmful than CCs and as being clean and emitting less odor [8,26].
As for the perceived effects of smoking cessation of HTPs, 11.2% of participants believed HTPs to be effective for smoking cessation, which was much lower than the results of a Canadian study which reported that 35% of participants considered HTPs effective for smoking cessation [18]. According to previous Korean studies, about 30% of HTP users agreed that HTPs helped people quit CC smoking, which was much lower than the 79% and 76% of participants who believed the benefits of HTPs to be lack of ash and less odor, respectively [27], and cited its being less harmful and having less smelly characteristics as their major reasons for using HTPs [6]. Furthermore, in a Korean qualitative interview study, most current or former HTP users did not believe that HTPs had a positive effect on contemplation of smoking cessation [28]. Therefore, positive perceptions on the effects of HTPs on smoking cessation were not the main driver, though they could have contributed to the use of HTPs in Korea. It is intriguing that triple users highly rated HTPs as effective for smoking cessation. Perhaps they were working toward quitting, but they became poly-tobacco users instead of quitting smoking.
The popularity of HTPs in Korea, despite the generally negative perceptions on its health benefits compared with CCs and smoking cessation effects, can be attributed to successful marketing by tobacco industries and consumers being attracted to its other characteristics (e.g., less odor, no ash, fancy appearance) [8,26]. Among smokers, CC users who had been exposed to HTP advertising were more likely to perceive HTPs as less harmful than CCs [11]. The advertisements on reduced exposure claims of HTPs may have resulted in smokers having a positive perception of HTPs and using them [13]. Previous research indicates that EC users were more likely to have positive perceptions about HTPs and may be more susceptible to the marketing of these products [7], which may lead to HTP use. Presumably, various factors may have interacted with one another and influenced the popularity of HTPs [12].
Our study had a few limitations. The prevalence of tobacco product use needs to be interpreted cautiously, since our sample was not nationally representative; however, we tried to match sex, age, and provincial distributions in line with national statistics. As this study was cross-sectional, causal relationships between the perceptions of HTPs and their use could not be identified. The participants may not have completely distinguished HTPs from ECs due to their Korean terms being similar, and also because HTPs were new products at the time of data collection. Moreover, a standardized questionnaire for the use of HTPs was not available during the study period. To mitigate these problems in the questionnaire, we included pictures and names of HTPs available in the Korean market and presented the questions on HTPs after those on ECs [29]. A longitudinal study is needed to investigate the causal relationships between perceptions of HTPs and their use, including a nationally representative sample and a standardized questionnaire on HTP use.
5. Conclusions
Perceptions about the relative harmfulness and smoking cessation effects of HTPs were generally negative among Korean adults. However, dual and triple users of HTPs, ECs, or CCs were more likely to have positive perceptions about HTPs, which may have led to their use or otherwise lead people to use them. Therefore, more emphasis on monitoring the use of multiple tobacco products and HTPs is needed to cope with the increasing popularity of HTPs in Korea.
Author Contributions
Conceptualization, H.-J.C. and S.Y.K.; methodology, H.-J.C.; validation, H.-J.C., S.H.K., and S.Y.K.; formal analysis, S.H.K.; investigation, H.-J.C.; resources, H.-J.C.; data curation, H.-J.C. and S.H.K.; writing—original draft preparation, S.H.K.; writing—review and editing, H.-J.C. and S.Y.K.; visualization, S.H.K. and S.Y.K.; supervision, H.-J.C.; project administration, H.-J.C.; funding acquisition, H.-J.C.. All authors have read and agreed to the published version of the manuscript.
Funding
This research was funded by the Ministry of Health and Welfare, Republic of Korea, grant number 11-1352000-002406-01. The funder had no role in the design or conduct of the study.
Conflicts of Interest
The authors declare no conflict of interest.
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Frequency of the belief that HTPs are less harmful than CCs. Abbreviations: CCs = combustible cigarettes; HTPs = heated tobacco products; ECs = electronic cigarettes; KRW = Korean Republic Won (KRW 1000 = US Dollar 1). Values are presented as weighted percentages (the weighted % of women was 50).
Frequency of the belief that HTPs aid in smoking cessation. Abbreviations: CCs = combustible cigarettes; HTPs = heated tobacco products; ECs = electronic cigarettes; KRW = Korean Republic Won (KRW 1000 = US Dollar 1). Values are presented as weighted percentages (the weighted % of women was 50).
ijerph-17-05591-t001_Table 1
Basic characteristics of the study participants (n = 7000).
Characteristic
Men (n = 2300)
Women (n = 4700)
Total
\nn\na\n
% a
\nn\n
%
%
\nAge (years)\n
\n
\n
\n
\n
\n
20–34
690
30.0
1437
30.6
30.3
35–49
833
36.2
1612
34.3
35.2
50–69
777
33.8
1651
35.1
34.5
\nEducation\n
\n
\n
\n
\n
\n
≤High school graduate
416
18.1
1129
24.0
21.1
≤Junior college graduate
336
14.6
1005
21.4
18.0
≥College graduate
1548
67.3
2566
54.6
60.9
\nMonthly household income (KRW 1000) b\n
\n
\n
\n
\n
\n
<3000
618
26.9
1156
24.6
25.7
3000–4999
817
35.5
1614
34.3
34.9
5000–6999
476
20.7
1078
22.9
21.8
≥7000
389
16.9
852
18.1
17.5
\nMarital status\n
\n
\n
\n
\n
\n
Never married
823
35.8
1567
33.3
34.5
Married/cohabited
1399
60.8
2910
61.9
61.4
Separated/divorced/bereaved
78
3.4
223
4.7
4.1
\nTobacco product use status\n
\n
\n
\n
\n
\n
Never-users of any of the three tobacco products
760
33.0
3906
83.1
58.3
Former users of any of the three tobacco products
527
22.9
277
5.9
14.3
Current users of any of the three tobacco products
1013
44.1
517
11.0
27.4
CC-only users
541
23.5
248
5.3
14.3
HTP-only users
44
1.9
33
0.7
1.3
EC-only users
27
1.2
29
0.6
0.9
Dual users of HTPs and CCs
196
8.5
74
1.6
5.0
Dual users of HTPs and ECs
13
0.6
20
0.4
0.5
Dual users of ECs and CCs
72
3.1
39
0.8
2.0
Triple users
120
5.2
74
1.6
3.4
Abbreviations: CCs = combustible cigarettes; HTPs = heated tobacco products; ECs = electronic cigarettes. a Values are presented as unweighted numbers and weighted percentages (the weighted % of women was 50). b Korean Republic Won (KRW) is Korea’s currency (KRW 1000 = US Dollar 1).
ijerph-17-05591-t002_Table 2
Comparison of beliefs about the harmfulness of HTPs compared with CCs, and the perceived effectiveness of HTPs for smoking cessation (n = 7000).
Factor
HTPs are Less Harmfulthan CCs
HTPs are Helpful forSmoking Cessation
Crude
Multi-Adjusted a
Crude
Multi-Adjusted a
OR (95% CI)
AOR (95% CI)
OR (95% CI)
AOR (95% CI)
\nAge (years)\n
\n
\n
\n
\n
50–69
1
1
1
1
35–49
0.99 (0.87, 1.13)
0.90 (0.78, 1.04)
\n0.83 (0.71, 0.97)\n
\n0.72 (0.61, 0.86)\n
20–34
\n1.25 (1.10, 1.43)\n
\n1.20 (1.00, 1.44)\n
1.05 (0.90, 1.22)
0.94 (0.76, 1.17)
\nSex\n
\n
\n
\n
\n
Female
1
1
1
1
Male
\n1.85 (1.65, 2.06)\n
\n1.26 (1.11, 1.44)\n
\n1.52 (1.33, 1.73)\n
0.98 (0.84, 1.15)
\nEducation\n
\n
\n
\n
\n
≤ High school graduate
1
1
1
1
≤ Junior college graduate
0.99 (0.82, 1.19)
0.97 (0.80, 1.18)
1.07 (0.85, 1.34)
1.09 (0.86, 1.38)
≥ College graduate
\n1.34 (1.16, 1.54)\n
1.15 (0.99, 1.34)
\n1.52 (1.27, 1.81)\n
\n1.35 (1.12, 1.63)\n
\nMonthly household income (KRW 1000) b\n
\n
\n
\n
\n
<3000
1
1
1
1
3000–4999
\n1.27 (1.09, 1.47)\n
\n1.27 (1.09, 1.48)\n
\n1.33 (1.11, 1.59)\n
\n1.29 (1.07, 1.56)\n
5000–6999
\n1.22 (1.03, 1.43)\n
1.17 (0.98, 1.41)
\n1.44 (1.19, 1.75)\n
\n1.31 (1.05, 1.62)\n
≥7000
\n1.37 (1.16, 1.62)\n
\n1.30 (1.08, 1.57)\n
\n1.60 (1.31, 1.96)\n
\n1.43 (1.15, 1.78)\n
\nMarital status\n
\n
\n
\n
\n
Never married
1
1
1
1
Married/cohabited
0.92 (0.82, 1.03)
0.97 (0.83, 1.14)
1.01 (0.88, 1.15)
0.95 (0.78, 1.15)
Separated/divorced/bereaved
0.85 (0.63, 1.13)
0.98 (0.71, 1.37)
1.02 (0.73, 1.42)
1.09 (0.75, 1.59)
\nTobacco product use status\n
\n
\n
\n
\n
CC-only users
1
1
1
1
Never-users of any of thethree tobacco products
\n0.58 (0.49, 0.68)\n
\n0.64 (0.54, 0.77)\n
\n0.72 (0.59, 0.88)\n
\n0.72 (0.57, 0.90)\n
Former users of any of thethree tobacco products
0.85 (0.70, 1.04)
0.85 (0.69, 1.04)
0.95 (0.74, 1.23)
0.92 (0.71, 1.18)
HTP-only users
\n3.37 (2.29, 4.96)\n
\n3.32 (2.25, 4.90)\n
\n3.60 (2.34, 5.55)\n
\n3.61 (2.34, 5.59)\n
EC-only users
1.24 (0.72, 2.13)
1.18 (0.68, 2.03)
1.21 (0.61, 2.40)
1.13 (0.57, 2.24)
Dual users of HTPs and CCs
\n2.30 (1.81, 2.91)\n
\n2.25 (1.77, 2.86)\n
\n2.80 (2.12, 3.70)\n
\n2.84 (2.14, 3.75)\n
Dual users of HTPs and ECs
\n4.10 (2.26, 7.43)\n
\n3.96 (2.17, 7.24)\n
\n6.23 (3.37, 11.50)\n
\n6.01 (3.23, 11.19)\n
Dual users of ECs and CCs
\n2.08 (1.48, 2.92)\n
\n1.96 (1.39, 2.77)\n
\n2.80 (1.91, 4.11)\n
\n2.81 (1.90, 4.14)\n
Triple users
\n5.28 (4.06, 6.86)\n
\n5.12 (3.93, 6.67)\n
\n8.20 (6.17, 10.89)\n
\n7.93 (5.94, 10.57)\n
Abbreviations: HTPs = heated tobacco products; CCs = combustible cigarettes; ECs = electronic cigarettes; OR = odds ratio; AOR = adjusted odds ratio; CI = confidence interval. a Multiple logistic regression analysis adjusted for age, sex, education, income, marital status, and tobacco products use status. b Korean Republic Won (KRW) is Korea’s currency (KRW 1000 = USD 1). Bold values were significantly associated (p < 0.05).
\n"],"string":"[\n \"\\nInt J Environ Res Public HealthInt J Environ Res Public HealthijerphInternational Journal of Environmental Research and Public Health1661-78271660-4601MDPI32756449PMC743215410.3390/ijerph17155591ijerph-17-05591ArticleBeliefs about the Harmfulness of Heated Tobacco Products Compared with Combustible Cigarettes and Their Effectiveness for Smoking Cessation among Korean Adultshttps://orcid.org/0000-0002-4370-8442KimSeung Hee1KangSeo Young2ChoHong-Jun1*Department of Family Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul 05505, Korea; kohakush@naver.comInternational Healthcare Center, Asan Medical Center, Seoul 05505, Korea; sykang@amc.seoul.krCorrespondence: hjcho@amc.seoul.kr; Tel.: +82-2-3010-38120382020820201715559101720200182020© 2020 by the authors.2020Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Heated tobacco products (HTPs) have been widely used in Korea since their introduction in 2017. In this study, we investigated the perceptions of their relative harmfulness and smoking cessation effects. We performed an online survey in 7000 Koreans in 2018 (2300 males and 4700 females aged 20–69 years) by matching their age, sex, and provincial distribution. To investigate the factors causing HTPs to be perceived as less harmful than combustible cigarettes (CCs) and helpful for smoking cessation, we used multivariable logistic regression analyses. HTPs were less harmful than CCs in 16.8% of participants, particularly among HTP-only users and dual and triple users of HTPs, electronic cigarettes (ECs), or CCs than among CC-only users, those who were aged ≤ 34 years, males, and those with higher incomes. HTPs were reportedly helpful for smoking cessation in 11.2% of participants. Similar perceptions were more likely among HTP-only users, as well as dual and triple users than among CC-only users and adults with higher education/incomes. Although Korean adults generally had negative perceptions of the harmfulness and smoking cessation effects of HTPs compared with CCs, dual and triple users were more likely to have positive perceptions. Monitoring the use of multiple tobacco products and HTPs is a new challenge for Korean policymakers.
Heated tobacco products (HTPs) comprise small cigarettes, in which the tobacco is heated just enough to release a flavorful nicotine-containing vapor [1]. On the other hand, electronic cigarettes (ECs) are battery-powered devices that vaporize a liquid solution containing nicotine and flavorants [2]. HTPs electrically heat the tobacco without burning to a lower temperature than combustible cigarettes (CCs) to produce an aerosol with nicotine. This is unlike ECs, in which users inhale the aerosol created by heating a nicotine liquid [3]. One brand of HTPs was introduced to South Korea (hereafter, Korea) in June of 2017. The market share of HTPs rapidly increased and reached 10.5% of Korea’s total sales of tobacco products in 2019 [4]. Among Korean men, the prevalence of CC use was 61.8% in 2001, but by 2018, it decreased to 36.7%; whereas among Korean women, its prevalence increased from 5.4% in 2001 to 7.5% in 2018 [5]; which was still very low compared with that of Korean men.
Previous studies have been conducted on the correlates of HTP use among Korean adults [6,7]. Demographic and socioeconomic characteristics associated with using HTPs include being male, young, and having a high education level and economic status. HTPs are marketed as clean, sophisticated, high-tech products that provide all of the benefits of smoking with less ash and odor [8]. These characteristics could attract young, male adults. Due to the high maintenance and cost of using HTP devices, people with a higher socioeconomic level were more likely to use them. Moreover, the use of CCs or ECs was the most significant factor related to the use of HTPs [6,7], which may be because CC or EC users were more likely to have a positive perception of HTPs due to marketing claims that indicate they are less harmful than CCs [8].
Beliefs and perceptions about the health risks associated with smoking play significant roles in reducing its prevalence, as concerns about such risks have been reported as the most common motivation to quit smoking and prevent smoking initiation [9,10]. With regard to the association between the perceptions of relative harmfulness of HTPs and their usage, as compared to non-HTP users, HTP users were more likely to believe HTPs to be less harmful than CCs, with this belief being more prominent among its frequent users [11]. Furthermore, despite understanding that HTPs were not risk-free, most reported that they used HTPs due to their beliefs about its having health benefits over CCs [6], with one of the main reasons being their perception of its reduced harm [12]. These results suggest that the perception of HTPs being less harmful than CCs may have had a major impact on HTP usage in Korea.
There is controversy regarding whether HTPs are less harmful than CCs; despite HTPs being marketed with claims of their being less harmful based on assertions that they expose users to lower levels of toxicants [13]. While the levels of some harmful and potentially harmful constituents of HTPs were lower than those of CCs [14], the reduced exposure of some toxicants does not mean a reduced risk of harm to health [13]. Since the health risks of HTPs have not been determined, further studies are needed.
Even though one of the reasons for using HTPs was to stop smoking [6,12], several studies have shown no significant association between the use of HTPs and smoking cessation-related behaviors, including attempts or intentions to quit smoking [7,15,16]. Moreover, adolescent HTP users were less likely to become former CC smokers despite multiple attempts at smoking cessation [17]. This was probably because although some smokers initiated HTPs for smoking cessation, CC users were more likely to become dual users of HTPs and CCs rather than succeeding in quitting smoking. Regarding perceived effects of HTPs on smoking cessation; a recent study showed that users rather than non-users of ECs were more likely to believe that HTPs are helpful in quitting smoking [18].
Despite the current lack of evidence regarding the long-term health effects of HTPs and their effectiveness for smoking cessation, marketing claims of HTPs’ reduced health risks could have misled consumers [13]. To our knowledge, no research has assessed the perceptions of HTPs in a large population of Korean adults. Therefore, this study aimed to investigate the beliefs about the relative harmfulness of HTPs and CCs, as well as their effectiveness for smoking cessation among Korean adults.
2. Materials and Methods2.1. Study Sample
We conducted an online survey using a panel managed by the research company EMBRAIN (Seoul, Korea), with 1.3 million members (as on December 2018). We randomly invited 70,000 individuals by matching their age, sex, and provincial distributions with the 2018 national population statistics. However, only 10,489 responded, from which we included 7000 (2300 men and 4700 women aged 20–69 years), after excluding 2285 who outnumbered our quota, 21 who did not meet the age criteria, 400 who decided not to participate in the survey, and 783 who responded incorrectly. We oversampled twice as many women as men because the prevalence of tobacco product use was much lower among Korean women than men.
Data were collected from 3–9 November 2018. All participants received financial incentives equivalent to 5000 Korean Republic Won (KRW), equal to approximately 5 US Dollars (USD). All the participants gave their informed consent for inclusion, prior to participating in the study. The study was conducted in accordance with the Declaration of Helsinki, and its protocol was approved by the Asan Medical Center Institutional Review Board (S2018-1662-0001) in Seoul, Korea.
2.2. Measures2.2.1. Status of Tobacco Product Use and Beliefs about the Relative Harmfulness and Smoking Cessation Effects of HTPs
Current CC users were defined as adults who had smoked at least 100 cigarettes during their lifetime and reported currently smoking “every day” or on “some days”. Former CC users were defined as adults who had smoked at least 100 cigarettes but had responded “not at all” to the question about current smoking. Never-CC users were defined as adults who had neither smoked at least 100 cigarettes nor ever smoked CCs in their lifetime. Current HTP users were defined as adults who had continually used HTPs in their lifetime and reported currently using them “every day” or on “some days”. Former HTP users were defined as adults who had frequently used HTPs in their lifetime but were currently not using them. Never-HTP users were defined as adults who had never used HTPs in their lifetime. Current EC users were defined as adults who had repeatedly used ECs in their lifetime, as well as during the past month. Former EC users were defined as adults who had habitually used ECs in their lifetime but had not used them during the past month. Never-EC users were defined as adults who had never used ECs in their lifetime. Since the prevalence of using other tobacco products (e.g., snus, water pipes, cigars, chewing tobacco, pipes, rolling tobacco, snuff) was negligible (less than 0.01%) among Korean adults [7], we did not include questions on these in our survey.
The status of tobacco product use was classified into nine groups. “Never-users of any of the three tobacco products” were defined as adults who had never used CCs, HTPs, or ECs. “Former users of any of the three tobacco products” included former CC, HTP, and EC users who were currently not using any of the three products. “Current users of any of the three tobacco products” included “CC-only users”, “HTP-only users”, “EC-only users”, “dual users of HTPs and CCs”, “dual users of ECs and CCs”, “dual users of HTPs and ECs”, and “triple users of the three products”.
Perceptions of the relative harmfulness of HTPs and CCs was evaluated with the question: “What do you think is the effect of ‘heated tobacco products’ on your health as compared with CCs?” The response options were: “They are less harmful”, “They are similar”, and “They are more harmful”. Whether HTPs can help with smoking cessation was evaluated with the question: “Do you think ‘heated tobacco products’ are helpful for quitting smoking?” The response options were: “Yes”, “No”, and “I am not sure”.
2.2.2. Sociodemographic Characteristics
The sociodemographic characteristics analyzed included age, sex, education, monthly household income, and marital status.
2.3. Data Analysis
We calculated the proportion of participants in each category of sociodemographic characteristics and the prevalence of tobacco product use. Furthermore, we evaluated the proportion of participants believing in the health benefits of HTPs being relative to CCs, and those perceiving HTPs as effective for smoking cessation. To identify factors associated with the perceptions of HTPs, adjusted odds ratios (AOR) and 95% confidence intervals (CI) were obtained via multivariable logistic regression analysis, after adjusting for potential confounders. Data analyses were conducted using IBM SPSS Statistics for Windows, Version 24.0 (IBM Corp., Armonk, NY, USA).
3. Results
Around 30% of the participants were aged 20–34 years, while those aged 35–49 and 50–69 years were each around 35%. Approximately 60% had college-level education, and over 50% had monthly household incomes of 3–6.99 million KRW. The overall prevalence of current use of tobacco products, including CCs, HTPs, and ECs was 27.4% (44.1% among men and 11.0% among women) (Table 1).
Of the 7000 participants, 16.8% reported HTPs to be less harmful than CCs (Figure 1). Using multivariable logistic regression analysis (Table 2), participants aged ≤ 34 years (AOR = 1.20, 95% CI = 1.00, 1.44), who were male (AOR = 1.26, 95% CI = 1.11, 1.44), and had an income ≥7 million KRW (AOR = 1.30, 95% CI = 1.08, 1.57) were associated with greater odds of perceiving HTPs as less harmful than CCs, as compared with those who were aged ≥ 50 years, female, and had an income <3 million KRW, respectively. As compared with CC-only users, never-users of any of the three tobacco products had lower odds of perceiving HTPs as less harmful than CCs (AOR = 0.64, 95% CI = 0.54, 0.77). However, HTP-only users (AOR = 3.32, 95% CI = 2.25, 4.90), dual users of HTPs and CCs (AOR = 2.25, 95% CI = 1.77, 2.86), dual users of HTPs and ECs (AOR = 3.96, 95% CI = 2.17, 7.24), dual users of ECs and CCs (AOR = 1.96, 95% CI = 1.39, 2.77), and triple users (AOR = 5.12, 95% CI = 3.93, 6.67) had greater odds of perceiving HTPs as less harmful than CCs.
As for the perceived effectiveness of HTPs for smoking cessation, 11.2% of participants reported HTPs as effective for smoking cessation (Figure 2). Based on multivariable logistic regression analysis (Table 2), groups with education ≥ college graduate (AOR = 1.35, 95% CI = 1.12, 1.63) and income ≥7 million KRW (AOR = 1.43, 95% CI = 1.15, 1.78) were more likely to agree with the effectiveness of HTPs for smoking cessation. As compared with CC-only users, never-users of any of the three tobacco products had lower odds of perceiving HTPs as effective for smoking cessation (AOR = 0.72, 95% CI = 0.57, 0.90). However, HTP-only users (AOR = 3.61, 95% CI = 2.34, 5.59), dual users of HTPs and CCs (AOR = 2.84, 95% CI = 2.14, 3.75), dual users of HTPs and ECs (AOR = 6.01, 95% CI = 3.23, 11.19), dual users of ECs and CCs (AOR = 2.81, 95% CI = 1.90, 4.14), and triple users (AOR = 7.93, 95% CI = 5.94, 10.57) had greater odds of believing HTPs to be effective for smoking cessation.
4. Discussion
Overall, participants were less likely to believe in the relative health benefits of HTPs and CCs. Only 16.8% of the participants believed that HTPs were less harmful than CCs, which was lower than the results of a Canadian study, in which 25% of the participants perceived HTPs as less harmful than CCs [18]. The negative public perceptions of HTPs are understandable, considering they contain real tobacco and there was widespread media coverage when the Korea Ministry of Food and Drug Safety released information on harmful HTP emissions in 2018 [19]. These negative perceptions of HTPs can also be ascribed to Korea’s relatively stringent tobacco regulations. While Korean tobacco control policies—which include smoking bans in public places, regulations on advertising, and bans on sales to minors—are applied equally to CCs and HTPs, the individual consumption tax for HTPs is 89% of the tax levied on CCs [20].
The perception of the relative harmfulness of HTPs compared with CCs differed according to the pattern of tobacco product use. Compared with CC-only users, HTP users were more likely to perceive HTPs to be less harmful than CCs. Consistent with these results, a previous study found that HTP-only users and dual users of HTPs and CCs were more likely to believe that HTPs were less harmful to users than CCs, compared with CC-only users [16]. It is common for some tobacco product users to choose specific products which they perceive as less harmful because of their favorable perceptions towards these products [21,22,23,24]. Moreover, the odds of perceiving HTPs as less harmful than CCs were highest among triple users of the three products. Since adults with no perception of risk regarding HTPs or ECs were more likely to use multiple tobacco products than those who perceived a risk [25], triple users would have been less likely to have risk perceptions of HTPs.
Intriguingly, both HTP and EC users perceived HTPs to be less harmful than CCs. EC users’ perceptions of the relative harmfulness of HTPs were consistent with the results of a previous study which reported that EC users, rather than non-EC users were more likely to believe that HTPs were less harmful than CCs [18]. An explanation could be that EC users considered HTPs to be similar because of the similarity of the Korean terms for ECs and HTPs, whose direct translations were “liquid-type electronic cigarettes” and “cigarette-type electronic cigarettes”, respectively. This could have made people regard the two products as similar and caused confusion in distinguishing them. Furthermore, HTPs and ECs have many similarities in that they are marketed as being less harmful than CCs and as being clean and emitting less odor [8,26].
As for the perceived effects of smoking cessation of HTPs, 11.2% of participants believed HTPs to be effective for smoking cessation, which was much lower than the results of a Canadian study which reported that 35% of participants considered HTPs effective for smoking cessation [18]. According to previous Korean studies, about 30% of HTP users agreed that HTPs helped people quit CC smoking, which was much lower than the 79% and 76% of participants who believed the benefits of HTPs to be lack of ash and less odor, respectively [27], and cited its being less harmful and having less smelly characteristics as their major reasons for using HTPs [6]. Furthermore, in a Korean qualitative interview study, most current or former HTP users did not believe that HTPs had a positive effect on contemplation of smoking cessation [28]. Therefore, positive perceptions on the effects of HTPs on smoking cessation were not the main driver, though they could have contributed to the use of HTPs in Korea. It is intriguing that triple users highly rated HTPs as effective for smoking cessation. Perhaps they were working toward quitting, but they became poly-tobacco users instead of quitting smoking.
The popularity of HTPs in Korea, despite the generally negative perceptions on its health benefits compared with CCs and smoking cessation effects, can be attributed to successful marketing by tobacco industries and consumers being attracted to its other characteristics (e.g., less odor, no ash, fancy appearance) [8,26]. Among smokers, CC users who had been exposed to HTP advertising were more likely to perceive HTPs as less harmful than CCs [11]. The advertisements on reduced exposure claims of HTPs may have resulted in smokers having a positive perception of HTPs and using them [13]. Previous research indicates that EC users were more likely to have positive perceptions about HTPs and may be more susceptible to the marketing of these products [7], which may lead to HTP use. Presumably, various factors may have interacted with one another and influenced the popularity of HTPs [12].
Our study had a few limitations. The prevalence of tobacco product use needs to be interpreted cautiously, since our sample was not nationally representative; however, we tried to match sex, age, and provincial distributions in line with national statistics. As this study was cross-sectional, causal relationships between the perceptions of HTPs and their use could not be identified. The participants may not have completely distinguished HTPs from ECs due to their Korean terms being similar, and also because HTPs were new products at the time of data collection. Moreover, a standardized questionnaire for the use of HTPs was not available during the study period. To mitigate these problems in the questionnaire, we included pictures and names of HTPs available in the Korean market and presented the questions on HTPs after those on ECs [29]. A longitudinal study is needed to investigate the causal relationships between perceptions of HTPs and their use, including a nationally representative sample and a standardized questionnaire on HTP use.
5. Conclusions
Perceptions about the relative harmfulness and smoking cessation effects of HTPs were generally negative among Korean adults. However, dual and triple users of HTPs, ECs, or CCs were more likely to have positive perceptions about HTPs, which may have led to their use or otherwise lead people to use them. Therefore, more emphasis on monitoring the use of multiple tobacco products and HTPs is needed to cope with the increasing popularity of HTPs in Korea.
Author Contributions
Conceptualization, H.-J.C. and S.Y.K.; methodology, H.-J.C.; validation, H.-J.C., S.H.K., and S.Y.K.; formal analysis, S.H.K.; investigation, H.-J.C.; resources, H.-J.C.; data curation, H.-J.C. and S.H.K.; writing—original draft preparation, S.H.K.; writing—review and editing, H.-J.C. and S.Y.K.; visualization, S.H.K. and S.Y.K.; supervision, H.-J.C.; project administration, H.-J.C.; funding acquisition, H.-J.C.. All authors have read and agreed to the published version of the manuscript.
Funding
This research was funded by the Ministry of Health and Welfare, Republic of Korea, grant number 11-1352000-002406-01. The funder had no role in the design or conduct of the study.
Conflicts of Interest
The authors declare no conflict of interest.
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Frequency of the belief that HTPs are less harmful than CCs. Abbreviations: CCs = combustible cigarettes; HTPs = heated tobacco products; ECs = electronic cigarettes; KRW = Korean Republic Won (KRW 1000 = US Dollar 1). Values are presented as weighted percentages (the weighted % of women was 50).
Frequency of the belief that HTPs aid in smoking cessation. Abbreviations: CCs = combustible cigarettes; HTPs = heated tobacco products; ECs = electronic cigarettes; KRW = Korean Republic Won (KRW 1000 = US Dollar 1). Values are presented as weighted percentages (the weighted % of women was 50).
ijerph-17-05591-t001_Table 1
Basic characteristics of the study participants (n = 7000).
Characteristic
Men (n = 2300)
Women (n = 4700)
Total
\\nn\\na\\n
% a
\\nn\\n
%
%
\\nAge (years)\\n
\\n
\\n
\\n
\\n
\\n
20–34
690
30.0
1437
30.6
30.3
35–49
833
36.2
1612
34.3
35.2
50–69
777
33.8
1651
35.1
34.5
\\nEducation\\n
\\n
\\n
\\n
\\n
\\n
≤High school graduate
416
18.1
1129
24.0
21.1
≤Junior college graduate
336
14.6
1005
21.4
18.0
≥College graduate
1548
67.3
2566
54.6
60.9
\\nMonthly household income (KRW 1000) b\\n
\\n
\\n
\\n
\\n
\\n
<3000
618
26.9
1156
24.6
25.7
3000–4999
817
35.5
1614
34.3
34.9
5000–6999
476
20.7
1078
22.9
21.8
≥7000
389
16.9
852
18.1
17.5
\\nMarital status\\n
\\n
\\n
\\n
\\n
\\n
Never married
823
35.8
1567
33.3
34.5
Married/cohabited
1399
60.8
2910
61.9
61.4
Separated/divorced/bereaved
78
3.4
223
4.7
4.1
\\nTobacco product use status\\n
\\n
\\n
\\n
\\n
\\n
Never-users of any of the three tobacco products
760
33.0
3906
83.1
58.3
Former users of any of the three tobacco products
527
22.9
277
5.9
14.3
Current users of any of the three tobacco products
1013
44.1
517
11.0
27.4
CC-only users
541
23.5
248
5.3
14.3
HTP-only users
44
1.9
33
0.7
1.3
EC-only users
27
1.2
29
0.6
0.9
Dual users of HTPs and CCs
196
8.5
74
1.6
5.0
Dual users of HTPs and ECs
13
0.6
20
0.4
0.5
Dual users of ECs and CCs
72
3.1
39
0.8
2.0
Triple users
120
5.2
74
1.6
3.4
Abbreviations: CCs = combustible cigarettes; HTPs = heated tobacco products; ECs = electronic cigarettes. a Values are presented as unweighted numbers and weighted percentages (the weighted % of women was 50). b Korean Republic Won (KRW) is Korea’s currency (KRW 1000 = US Dollar 1).
ijerph-17-05591-t002_Table 2
Comparison of beliefs about the harmfulness of HTPs compared with CCs, and the perceived effectiveness of HTPs for smoking cessation (n = 7000).
Factor
HTPs are Less Harmfulthan CCs
HTPs are Helpful forSmoking Cessation
Crude
Multi-Adjusted a
Crude
Multi-Adjusted a
OR (95% CI)
AOR (95% CI)
OR (95% CI)
AOR (95% CI)
\\nAge (years)\\n
\\n
\\n
\\n
\\n
50–69
1
1
1
1
35–49
0.99 (0.87, 1.13)
0.90 (0.78, 1.04)
\\n0.83 (0.71, 0.97)\\n
\\n0.72 (0.61, 0.86)\\n
20–34
\\n1.25 (1.10, 1.43)\\n
\\n1.20 (1.00, 1.44)\\n
1.05 (0.90, 1.22)
0.94 (0.76, 1.17)
\\nSex\\n
\\n
\\n
\\n
\\n
Female
1
1
1
1
Male
\\n1.85 (1.65, 2.06)\\n
\\n1.26 (1.11, 1.44)\\n
\\n1.52 (1.33, 1.73)\\n
0.98 (0.84, 1.15)
\\nEducation\\n
\\n
\\n
\\n
\\n
≤ High school graduate
1
1
1
1
≤ Junior college graduate
0.99 (0.82, 1.19)
0.97 (0.80, 1.18)
1.07 (0.85, 1.34)
1.09 (0.86, 1.38)
≥ College graduate
\\n1.34 (1.16, 1.54)\\n
1.15 (0.99, 1.34)
\\n1.52 (1.27, 1.81)\\n
\\n1.35 (1.12, 1.63)\\n
\\nMonthly household income (KRW 1000) b\\n
\\n
\\n
\\n
\\n
<3000
1
1
1
1
3000–4999
\\n1.27 (1.09, 1.47)\\n
\\n1.27 (1.09, 1.48)\\n
\\n1.33 (1.11, 1.59)\\n
\\n1.29 (1.07, 1.56)\\n
5000–6999
\\n1.22 (1.03, 1.43)\\n
1.17 (0.98, 1.41)
\\n1.44 (1.19, 1.75)\\n
\\n1.31 (1.05, 1.62)\\n
≥7000
\\n1.37 (1.16, 1.62)\\n
\\n1.30 (1.08, 1.57)\\n
\\n1.60 (1.31, 1.96)\\n
\\n1.43 (1.15, 1.78)\\n
\\nMarital status\\n
\\n
\\n
\\n
\\n
Never married
1
1
1
1
Married/cohabited
0.92 (0.82, 1.03)
0.97 (0.83, 1.14)
1.01 (0.88, 1.15)
0.95 (0.78, 1.15)
Separated/divorced/bereaved
0.85 (0.63, 1.13)
0.98 (0.71, 1.37)
1.02 (0.73, 1.42)
1.09 (0.75, 1.59)
\\nTobacco product use status\\n
\\n
\\n
\\n
\\n
CC-only users
1
1
1
1
Never-users of any of thethree tobacco products
\\n0.58 (0.49, 0.68)\\n
\\n0.64 (0.54, 0.77)\\n
\\n0.72 (0.59, 0.88)\\n
\\n0.72 (0.57, 0.90)\\n
Former users of any of thethree tobacco products
0.85 (0.70, 1.04)
0.85 (0.69, 1.04)
0.95 (0.74, 1.23)
0.92 (0.71, 1.18)
HTP-only users
\\n3.37 (2.29, 4.96)\\n
\\n3.32 (2.25, 4.90)\\n
\\n3.60 (2.34, 5.55)\\n
\\n3.61 (2.34, 5.59)\\n
EC-only users
1.24 (0.72, 2.13)
1.18 (0.68, 2.03)
1.21 (0.61, 2.40)
1.13 (0.57, 2.24)
Dual users of HTPs and CCs
\\n2.30 (1.81, 2.91)\\n
\\n2.25 (1.77, 2.86)\\n
\\n2.80 (2.12, 3.70)\\n
\\n2.84 (2.14, 3.75)\\n
Dual users of HTPs and ECs
\\n4.10 (2.26, 7.43)\\n
\\n3.96 (2.17, 7.24)\\n
\\n6.23 (3.37, 11.50)\\n
\\n6.01 (3.23, 11.19)\\n
Dual users of ECs and CCs
\\n2.08 (1.48, 2.92)\\n
\\n1.96 (1.39, 2.77)\\n
\\n2.80 (1.91, 4.11)\\n
\\n2.81 (1.90, 4.14)\\n
Triple users
\\n5.28 (4.06, 6.86)\\n
\\n5.12 (3.93, 6.67)\\n
\\n8.20 (6.17, 10.89)\\n
\\n7.93 (5.94, 10.57)\\n
Abbreviations: HTPs = heated tobacco products; CCs = combustible cigarettes; ECs = electronic cigarettes; OR = odds ratio; AOR = adjusted odds ratio; CI = confidence interval. a Multiple logistic regression analysis adjusted for age, sex, education, income, marital status, and tobacco products use status. b Korean Republic Won (KRW) is Korea’s currency (KRW 1000 = USD 1). Bold values were significantly associated (p < 0.05).
\\n\"\n]"}}},{"rowIdx":498,"cells":{"data":{"kind":"list like","value":["\nFront PsycholFront PsycholFront. Psychol.Frontiers in Psychology1664-1078Frontiers Media S.A.32849150PMC743215510.3389/fpsyg.2020.01961PsychologyOriginal ResearchBody Matters in Emotion: Restricted Body Movement and Posture Affect Expression and Recognition of Status-Related EmotionsReedCatherine L.1*MoodyEric J.2MgrublianKathryn1AssaadSarah1ScheyAlexis3McIntoshDaniel N.41\nDepartment of Psychological Science, Claremont McKenna College, Claremont, CA, United States\n2\nWyoming Institute for Disabilities (WIND), University of Wyoming, Laramie, WY, United States\n3\nDepartment of Psychology, Scripps College, Claremont, CA, United States\n4\nDepartment of Psychology, University of Denver, Denver, CO, United States\n
Edited by: Christel Bidet-Ildei, University of Poitiers, France
Reviewed by: Matteo Candidi, Sapienza University of Rome, Italy; Wolfgang Tschacher, University of Bern, Switzerland
*Correspondence: Catherine L. Reed, clreed@cmc.edu
This article was submitted to Emotion Science, a section of the journal Frontiers in Psychology
1182020202011196103920191572020Copyright © 2020 Reed, Moody, Mgrublian, Assaad, Schey and McIntosh.2020Reed, Moody, Mgrublian, Assaad, Schey and McIntoshThis is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Embodiment theory suggests that we use our own body and experiences to simulate information from other people’s bodies and faces to understand their emotions. A natural consequence of embodied theory is that our own current position and state contributes to this emotional processing. Testing non-disabled individuals, we investigated whether restricted body posture and movement influenced the production and recognition of nonverbal, dynamic emotional displays in able-bodied participants. In Experiment 1, participants were randomly assigned to either unrestricted or wheelchair-restricted (sitting, torso restrained) groups and nonverbally expressed six emotions (disgust, happiness, anger, fear, embarrassment, and pride) while being videotaped. After producing each emotion, they rated their confidence regarding how effectively they communicated that emotion. Videotaped emotional displays were coded for face, body, and face + body use. Based on naïve coders’ scores, both unrestricted and wheelchair-restricted groups produced emotionally congruent face and body movements and both groups were equally confident in their communication effectiveness. Using videos from Experiment 1, Experiment 2 tested non-disabled participants’ ability to recognize emotions from unrestricted and wheelchair-restricted displays. Wheelchair-restricted displays showed an overall decline in recognition accuracy, but recognition was selectively impaired for the dominance-related emotions of disgust and anger. Consistent with embodied emotion theory, these results emphasize the importance of the body for emotion communication and have implications for social interactions between individuals with and without physical disabilities. Changes in nonverbal emotion signals from body restrictions may influence social interactions that rely on the communication of dominance-related social emotions.
Social interactions rely strongly on nonverbal emotional displays to communicate social emotions, inform others about our feelings, and influence social outcomes (Vosk et al., 1983; Tiedens and Leach, 2004). Such communication can include expressions of basic emotions (e.g., happiness, anger, etc.; Ekman, 1992), as well as more complex social emotions that reflect relative social status (e.g., pride and shame; App et al., 2011; Steckler and Tracy, 2014; Tracy et al., 2015). The literature focuses on the role of the face in emotional communication, but a growing number of studies have demonstrated that the body plays a crucial role in emotion communication as well (Dael et al., 2012). Although some emotions are often communicated via facial expressions (e.g., happiness, anger), others are expressed via body posture and movement (e.g., pride, embarrassment; App et al., 2011; Tracy et al., 2015).
\nEmotional embodiment theory suggests we use information from our own faces and bodies to understand other people’s emotions as well as our own (Niedenthal et al., 2001). Growing evidence suggests that when we view an emotion displayed by another, the viewer’s ability to recognize, understand, and respond to the emotion relies on the sensory-motor simulation of the observed emotion (Wood et al., 2016). Indeed, over 40 years of research has shown that humans rapidly match the facial expressions of others and that the configuration of their own faces can influence their own emotional states (McIntosh, 1996; Moody and McIntosh, 2006, 2011; Wood et al., 2016). For example, when judging a word for its emotional meaning, people may use their facial muscles to aid the decision (Niedenthal et al., 2009). Further, experimentally manipulating one’s facial expression can affect emotional experience. When facial expressions are restricted (e.g., putting a pen in their mouths), participants are less accurate at identifying others’ emotions (Niedenthal et al., 2001). However, embodiment theory also suggests that information from bodies is also simulated when processing other people’s emotions (Niedenthal et al., 2001).
One less explored implication of embodied theory is that emotional perception is an interactive process and that the interaction between emotion experience and the physical display of emotion is reciprocal. That is, a person’s state, which includes current inputs from body position, influences the simulation process and the perception of others’ emotions. Indeed, it is known that people not only physically display their subjective emotional state but also their emotional display affects their subjective emotional state (see McIntosh, 1996). This ability to use physiological changes and to re-experience one’s somatic responses plays an important role in emotional processing (Niedenthal, 2007; Halberstadt et al., 2009). Further, the body conveys emotion information that is different from the face and can even modulate emotional information conveyed by the face (Aviezer et al., 2008; App et al., 2012; Abo Foul et al., 2018). Studies show that people’s own bodies influence the perception of their own as well as others’ emotional states (McIntosh, 1996; Niedenthal et al., 2005; Reed and McIntosh, 2008; Wilbarger et al., 2011).
Thus, a consequence of this theory is that if either face or body use is impeded, it will have implications for their perceptions, experiences, and communications of emotions (de Gelder, 2006; App et al., 2011; Steckler and Tracy, 2014; Tracy et al., 2015; Moody et al., 2018). A study by Wallbott and Giessen (1986) confirmed that viewing whole body videos of actors portraying emotional scenarios lead to more accurate emotion recognition than audio-only recordings of the same scenarios. Importantly, Moody et al. (2018) demonstrated rapid, emotionally congruent, whole-body responses from static emotional faces, indicating that the participants simulated the viewed facial emotion using their whole bodies. When judging changes in other people’s body postures, input from one’s own emotional body posture selectively influences perceptions of another person’s emotional body posture but not neutral postures (Wilbarger et al., 2011). Nonetheless, little work has explored whether movement restrictions of the body has similar consequences for emotional processing as it does for the face and whether such constraints differentially affect emotions preferentially conveyed via the body.
Although some emotions are communicated primarily via the face and others rely on the body, individual emotions are associated with specific social functions (Tracy and Robins, 2007; App et al., 2011). For instance, emotions conveying status relations between individuals include anger, disgust, and fear that are associated with facial expressions, as well as embarrassment, guilt, pride, and shame that are associated with bodily expressions (Coulson, 2004; Tracy and Robins, 2007; App et al., 2011; see also Atkinson et al., 2004). Emotional body-expressions are important in conveying social hierarchical information and the degree to which they signal relative social status (Steckler and Tracy, 2014). Generally, more erect postures are associated with higher status and dominance, although context can affect this interpretation (Hall et al., 2005). Actors asked to portray various emotions often used a collapsed body posture for shame or sadness and used body expression more than facial movement to display pride (Wallbott, 1998). Across studies and cultures, pride appears associated with a low intensity smile and a variety of different body positions including expanded posture, arms akimbo on hips or arms raised straight above the head with the hands (Wallbott, 1998; Tracy and Robins, 2007; Tracy and Matsumoto, 2008). Given that emotions have different social functions and differential reliance on face vs. body expression, the restriction of body movement may not affect the processing of emotions in the same way (de Gelder, 2006; App et al., 2011; Steckler and Tracy, 2014; Tracy et al., 2015).
Current Study
Relatively little research has explored the role of the body on emotional communication between individuals. Moreover, few studies have considered how the prevention of body movement might differentially disrupt the production and recognition of specific emotions. Here, we examine how restricted body movement and posture influences emotional expression and recognition. Given that experimentally preventing or enhancing emotional facial expressions can influence emotional experience of basic emotions (i.e., anger, fear, happiness, and sadness; Ekman, 1992; Jack et al., 2014), constraints on body movement and posture may also affect the sensorimotor processing or simulation of emotions, especially more complex, body-related emotions (e.g., embarrassment and pride). Such constraints could influence both an observer’s perception and nonverbal communication of emotions. They may also differentially affect the recognition of emotions by changing the visual cues that distinguish them (Wallbott, 1998; Sawada et al., 2003; Gross et al., 2010). Moreover, body and posture constraints may affect more than just body-related emotions because they change the implied social dominance between individuals. Although disgust is best conveyed by the face and pride is best conveyed by the body, both emotions indicate one person’s superior standing compared to another. In other words, emotions that convey social status may supersede the body-related vs. face-related emotion distinction because body constraints do more than just restrict movement.
In two studies we extend work documenting the body’s role in emotion displays (e.g., de Gelder, 2006; Tracy and Robins, 2007; App et al., 2011). In Experiment 1, we examined the role of body movement and postures restrictions on nonverbal emotion communication for non-disabled participants. In Experiment 2, we determined whether these constraints affected emotion recognition in non-disabled participants. This research is a first step in understanding the role of embodiment in the communication of social emotions and its implications for how nonverbal emotional displays may influence social interactions between non-disabled and physically disabled individuals.
Experiment 1: Emotion Production
The purpose of Experiment 1 was to determine whether current body inputs affected non-disabled participants’ nonverbal expressions of emotions and their confidence in the efficacy of communicating emotions. In addition, the experiment replicated previous studies indicating specific face and body preferences for nonverbal emotional communication (App et al., 2011). To examine the effects of body and posture constraints, we compared the expression of six emotions for two groups that differed in their mobility restrictions: (1) an “unrestricted” group in which participants stood while communicating emotions without mobility restrictions and (2) a “wheelchair-restricted” group in which participants sat in a wheelchair with an elastic band tied around the torso, thereby restricting trunk movement. The unrestricted condition provided a reliable baseline to which we could compare performance in the movement-restricted condition. In Part 1, participants communicated six emotions nonverbally to a mannequin while being videotaped; after expressing each emotion, they rated their confidence in successfully communicating the emotion. In Part 2, participants indicated whether they preferred face or body channels to optimally communicate each emotion.
Extensions of embodiment theory would predict that current physical inputs should affect the communicator’s production of nonverbal, emotional displays as well as the communicator’s confidence in the effectiveness of those displays, especially for those emotions best conveyed by the body. Therefore, compared to the unrestricted group, the wheelchair-restricted group should show reduced use of the body and increased use of the face during emotional production, especially for emotions optimally expressed by the body. The postural and movement differences between groups should also differentially affect status emotions. Further, if current body inputs were used for evaluating one’s own communication, then the wheelchair-restricted group may be less confident in communicating body-based emotions. Of interest is whether confidence in emotion communication was affected by current body constraints.
MethodParticipants
Forty-nine participants (33 female, mean age = 19.6 years) received partial course credit in introductory level psychology courses for their participation. They were randomly assigned via a computer program to one of two groups: the unrestricted group (n = 21) and the wheelchair-restricted group (n = 28). In the unrestricted group, participants stood 3 ft from a seated mannequin. In the movement restricted “wheelchair-restricted” group, participants sat in a wheelchair with their chests strapped into the wheelchair by a stretch athletic band around the lower rib cage to stabilize and restrict movement of the trunk. Straps reduced torso motion shown to be important for conveying emotional state (e.g., Wallbott, 1998; Atkinson et al., 2004). Participants were still able to use their arms and limited torso adjustments to express emotions.
Stimuli and Apparatus
Participants directed their emotions toward a life-size mannequin with a soft, gray, fabric exterior and no definitive facial features. The mannequin was seated in a chair in front of the participants and dressed in a casual, gender-neutral outfit, including a sweat suit and baseball hat. Participants were asked to think of someone they knew and to address the mannequin as if it were that person, whether it was a friend, relative, or romantic partner. The mannequin was addressed as the chosen person throughout the experiment. The mannequin’s lack of facial features provided participants with a consistently neutral response to their emotion production.
To record participant’s nonverbal emotional displays, two video cameras were used to film the facial and body expressions of the participants; one was placed behind the mannequin to provide a face-front view of the participants and another camera was placed to the side of the participants to provide a side view that showed both the expresser and the mannequin.
To quantify the relative degree of participants’ face and body use during the communication of each emotion, we modified a coding scheme used in App et al. (2011) to assess the amount of emotionally congruent movement in the face and the body as recorded from the front and side view videos. Emotion congruence was defined as using non-random, systematic movements to convey each emotion and followed classification procedures described in App et al. (2011). More specifically, coders examined videos for face and body movements that the literature has confirmed to be associated with, or congruent with, each of the emotions used in this study (Atkinson et al., 2004; Moody and McIntosh, 2006; App et al., 2011). For example, for a happy condition trial, a congruent emotional display would indicate the presence of upward movements in the eyes and cheeks resulting from zygomaticus muscle activation in the face (Moody and McIntosh, 2006) and/or the lifting of arms or jumping up and down (Atkinson et al., 2004). However, if the participant displayed a furrowing of the forehead resulting from corrugator muscle activation that is typically associated with anger displays, then this movement would be considered incongruent with the emotion communicated.
For each participant, condition, and trial, video data were coded separately from both the front and side-view videos. In both views, the face and the body were visible. Face and body movements were coded independently for each of three categories that are reflected in the levels of the statistical analyses: (1) face alone – emotionally congruent face movement without simultaneous body movement; (2) body alone – emotionally congruent body movement without simultaneous face movement; and (3) face + body – emotionally congruent simultaneous face and body movement. Three scorers, who were naïve to the predictions of the experiment, rated movement for each of these three categories using a scale ranging from 0 [none: no emotionally congruent movement (i.e., no use of muscles specific to of the communicated emotion)] to 1 (some emotionally congruent movement: movement indicated sufficient information for some level of emotion congruence but not a full match) to 2 (full match of expected emotionally congruent movement). Scores from these naïve scorers indicated agreement on 92% of trials overall, with 97% agreement for the unrestricted group and 87% agreement for the restricted wheelchair group. The average use of emotionally congruent face, body, or concurrent face and body movements was calculated for each participant over two views and three trials for each of the six emotions.
ProcedurePart 1: Emotion Production
Tested individually, participants nonverbally communicated six different emotions (anger, disgust, fear, happiness, pride, and embarrassment) to the mannequin as effectively as possible. For each participant, a computer program randomly selected group assignment. The experiment began with practice trials in which participants produced two emotions not used in the experimental trials (surprise and sadness). Experimental trials followed in which the participant pressed a computer key to begin the trials and one of the six emotion words appeared on the computer screen. The participant then expressed the emotion to the mannequin for 4 s. Following each expression trial, participants were prompted to enter number on a scale of 0 (“not confident at all”) to 4 (“very confident”) to indicate their confidence in how well they were able to communicate that emotion. Each of the six emotions was produced three times in random order for a total of 18 production trials and 18 confidence ratings. To reduce any potential influences of experimenter presence on emotion production, the experimenter remained outside the testing room after practice trials.
Part 2: Preferences for Using the Face or Body to Communicate Specific Emotions
After completing the emotion production portion of the experiment, participants remained in their unrestricted or wheelchair-restricted states. An emotion word cue appeared on the computer screen and they indicated via a key press whether they would typically (i.e., in the everyday world) use their face or their body to nonverbally express that emotion. They provided a single face/body preference for each emotion; emotion order was randomized.
Results and DiscussionPreferences for Face vs. Body Use When Conveying Emotions
The purpose of this analysis was to replicate findings by App et al. (2011) and to confirm that the participants in our study endorsed similar preferences for using the face or body to express each emotion. The results confirm that preferences to use the face vs. the body to express emotions differs across emotions and show a similar pattern to those reported in App et al. (2011). For each group and emotion, the frequency of preferred face vs. body use was tabulated. To document differential preferences for face or body use when communicating the six emotions, chi-square tests were conducted for face/body preferences for each emotion and group using frequency data. There were no group differences overall in body/face expression preferences for any emotion [χ2(1) < 0.70, p > 0.71]. Regardless of group, face/body preference largely replicated previous findings (App et al., 2011; Figures 1A,B). Participants preferred to use the body to communicate pride [unrestricted group: χ2(1) = 13.762, p < 0.0001; wheelchair group: χ2(1) = 11.57, p < 0.001]. They preferred to use the face for disgust [unrestricted group: χ2(1) = 13.76, p < 0.0001; wheelchair group: χ2(1) = 20.57, p < 0.0001] and happiness [unrestricted group: χ2(1) = 17.19, p < 0.0001; wheelchair group: χ2(1) = 20.57, p < 0.0001]. Preferences were more divided for face and body use when expressing anger [unrestricted group: χ2(1) = 1.19, p = 0.28; wheelchair group: χ2(1) = 2.29, p = 0.13], fear [unrestricted group: χ2(1) = 13.76, p = 0.83; wheelchair group: χ2(1) = 0.000, p = 1.000], and embarrassment [unrestricted group: χ2(1) = 1.19, p = 0.28; wheelchair group: χ2(1) = 0.57, p = 0.45]. These preferences for each emotion (summarized in Table 1) establish a baseline indicating whether people typically rely on the face or body to nonverbally communicate specific emotions: The face is preferred to convey disgust and happiness, the body is preferred to convey pride, and we found no significant difference in preference for face or the body conveys anger, fear, and embarrassment.
Experiment 1 – Proportion preference selection for face vs. body use for emotion production: (A) unrestricted group, (B) wheelchair-restricted group. There were no group differences. For each emotion, stars indicate significant preference differences for using either the face or the body.
Face vs. body use preferences for emotion production.
Disgust
Happiness
Anger
Fear
Embarrassment
Pride
Face
x
x
x
x
x
Body
x
x
x
x
Despite the fact that participants were either unrestricted or wheelchair-restricted when providing face‐ or body-use preferences, the similarity of responses for the two groups suggests that these preferences were not influenced by current body inputs and rather were based on functional, and/or experiential mechanisms. These data also suggest that the recognition of emotions that rely most on the body (tested in Experiment 2) – pride followed by embarrassment, fear, and anger – may be most affected by restricting torso movement in a wheelchair.
Emotion Production: Emotion-Specific Influences of Restricted Body Posture and Movement
For all analyses of variance (ANOVAs) using ordinal data in both experiments, we report comparisons with Huyhn-Feldt adjustments for sphericity, which is proposed as a sufficient condition for the F-tests to be valid for ordinal data (Stiger et al., 1998). We applied the Dunn–Šidák correction to all simple effects analyses to correct for multiple comparisons. Alpha was set at the 0.05 level.
To assess whether posture and body constraints differentially influenced the production of emotions, we conducted a mixed model ANOVA with the between-subject factors of group (2: unrestricted and wheelchair-restricted) and within-subject factors of face/body use [3: face, body, face + body (i.e., concurrent face and body)] and emotion (6: disgust, happiness, anger, fear, embarrassment, and pride) using emotion-congruent movement scores provided by three observers. The unrestricted and restricted groups did not differ in the presence of emotionally congruent movement [group effect: F(1,47) = 0.42, p = 0.52, ηp2 = 0.01; M\nunrestricted = 1.33, SE = 0.08; M\nwheelchair = 1.27, SE = 0.07].
Also, the presence of emotionally congruent movements did not differ across emotions [emotion effect: F(4.98, 234.13) = 1.19, p = 0.31, ηp2 = 0.03], regardless of group [group × emotion interaction: F(4.98, 234.13) = 0.97, p = 0.44, ηp2 = 0.02]. The interaction between group and face/body use was not significant at the 0.05 level, [F(1.26, 59.05) = 2.78, p = 0.06, ηp2 = 0.06]. However, the interaction between emotion and face/body use indicated differential use of the face, body, and concurrent face and body (face + body) across emotions [F(6.35, 298.66) = 14.88, p < 0.0001, ηp2 = 0.24]. The emotion by face/body use interaction was consistent with the preference data, indicating that the face alone is used to express disgust and happiness more than the body alone or their combination; the face and body alone, more than their combination, are used to express anger, embarrassment, and pride; and the face is used more than the face + body combination to express fear (Figure 2).
Experiment 1 – Face, body, and concurrent face/body use for emotion production. Error bars indicate standard error. Stars indicate significant differences between face, body, and concurrent face + body use.
Direct comparisons showed that for disgust the face was used more than the body or both concurrently (i.e., face + body; p < 0.0001) and there was no difference for body vs. face + body use (p = 0.31). Similarly, for happiness, the face was used more than the body or face + body (p < 0.0001) and there was no difference for body vs. face + body use (p = 0.58). However, for anger, face and body use did not differ significantly (p = 0.15), but both the face (p < 0.0001) and the body (p = 0.02) were used more than face + body. Similarly for fear, face and body use did not differ significantly (p = 0.16) but the face was used more than face + body (p < 0.0001), and body use was similar to face + body (p = 0.47). For embarrassment, face and body use did not differ significantly (p = 0.55), but both the face (p < 0.003) and the body (p < 0.0001) were used more than face + body. Finally, for pride, face and body use did not differ (p = 1.00) and both the face (p < 0.002) and the body (p < 0.0001) were used individually more than together (face + body). Further, although concurrent use of the body and face (face + body) did not differ across emotions (p > 0.64), the face was used significantly more for disgust and happy than anger, fear, embarrassment, and pride (p > 0.02), it was used more for anger than embarrassment (p < 0.005); other face comparisons were not significant. Finally, significantly greater body use was observed for anger and embarrassment than disgust and happiness (p < 0.03). The group by emotion by face/body use interaction was in the predicted direction but was not significant at the alpha = 0.05 level [F(6.35, 298.66) = 1.96, p = 0.06, ηp2 = 0.04].
Because the groups were created by random assignment, they had different sizes (unrestricted = 1 vs. wheelchair restricted = 28). To demonstrate that unequal group size did not affect the results, we randomly selected 21 participants from the larger group and reran the above analysis. The results for the equal-sized groups were highly comparable, but the group × emotion × face/body use interaction was now significant (p = 0.05)1.
Confidence in Effective Emotion Communication
To examine the effects of current bodily constraints on confidence in emotional expression, a mixed model ANOVA with the between-subject factors of group (2: unrestricted and wheelchair) and within-subject factors of emotion (6: disgust, happiness, anger, fear, embarrassment, and pride) was conducted on confidence ratings. Ratings should differ by group if current body inputs influenced confidence in emotional communication but not if previous experience were more important because non-disabled participants have similar prior experiences. A significant main effect for emotion [F(4.31, 202.62) = 13.55, p < 0.0001, ηp2 = 0.22] indicated greater confidence when communicating disgust and happiness than anger, fear, embarrassment, and pride (p < 0.001). Of interest, the posture and body restrictions imposed by the wheelchair did not influence confidence ratings, as indicated the lack of a group effect [F(1, 47) = 1.48, p = 0.23, ηp2 = 0.03] or the group by emotion interaction [F(4.31, 202.62) = 0.08, p = 1.00, ηp2 = 0.002; Figure 3].
Experiment 1 – Confidence ratings for effective emotion communication overall. Error bars indicate standard error. Stars indicate significant confidence differences across emotions: confidence for disgust and happiness was greater than for the other emotions.
In sum, replicating prior research (e.g., App et al., 2011), Experiment 1 first established preferences for the selective use of the face and body for specific emotions. Preference data confirm that non-disabled individuals do not prefer to use the face to convey all emotions and that they would use the face and body differentially depending on the emotion. Participants indicated that they preferred to use the face to best convey disgust and happiness; they were divided in their preference to use the face or body to express anger, fear, and embarrassment; and they preferred to use the body to convey pride.
Next, Experiment 1 examined how constraining body posture and movement might influence the nonverbal communication of these emotions in non-disabled participants. Comparing the nonverbal, physical production of emotions between the unrestrained and wheelchair-restrained groups, movement restriction in non-disabled individuals did not strongly alter the production of emotionally congruent movement. Further studies are needed with physically disabled individuals to more fully explore potential differences in the relative use of the face and body in emotion production. Further, no group differences emerged between the unrestricted and wheelchair group’s confidence in their effective communication of the emotions. Prior emotional experience for our non-disabled participants who are not typically constrained in their movements appears to have more influence on their confidence in emotional communication than current body inputs. Nonetheless, posture and torso constraints had an overall influence emotion production, which may convey differential nonverbal emotion signals to observers. Thus, posture and movement constraints may also influence the reception of emotional displays. We investigate this issue in Experiment 2.
Experiment 2: Emotion Identification
Experiment 2 investigated whether the expression of emotions by individuals with physical constraints affects the recognition of nonverbal emotional displays. Using a sample of emotion production videos from each group in Experiment 1, we assessed whether constraints on body movement and posture influenced the accuracy of emotion identification overall or for specific emotions.
MethodParticipants
Participants were 22 students (10 females; age: M = 19.3 years, SD = 1.14) who received partial course credit in lower level psychology courses. None had participated in Experiment 1.
Stimuli and Apparatus
A subset of video recordings was selected from Experiment 1 to limit the duration of Experiment 2. From video clips recorded in Experiment 1, two male and two female participant video clips were selected at random for each of the six emotions. Video clips were edited to remove the sound and have a 4-s duration. A total of 48 video clips were used: 2 males × 2 females × 2 conditions (unrestricted and wheelchair) × 6 emotions. All video clips were emotional displays recorded from the side view so that the observer could see the participant interacting with the mannequin. Stimuli were presented on a 21-inch computer monitor with E-prime 2.0 pro software (Psychological Software Tools, Pittsburgh, PA).
Procedure
Participants were tested individually and were asked to identify emotions presented in silent video clips. For each emotion recognition trial, a 4-s emotion video was played twice on a computer monitor, with a 500 ms pause between presentations. A numbered list of emotions (1 = anger, 2 = sympathy, 3 = embarrassment, 4 = love, 5 = disgust, 6 = fear, 7 = happiness, 8 = sadness, 9 = pride, plus 0 = other) appeared and participants indicated the emotion presented in the video by pressing the number key associated with the corresponding emotion word. The list included three additional emotions (sympathy, sadness, and love) as well as an “other” category to reduce inflated accuracy rates due to forced choice (Russell, 1993; Frank and Stennett, 2001). Participants then rated their confidence that their response was correct on a scale from 1 (not at all confident) to 5 (extremely confident). After two practice trials, participants completed three blocks of 48 experimental trials (6 emotions × 2 male × 2 female × 2 conditions; each stimulus was presented 3 times) for a total of 144 trials; within each block, trials were presented in random order. A brief break was provided between each block.
Results
For participant, emotion, and condition, mean proportion accuracy was calculated for emotion identification; correct responses were recorded when the selected emotion matched the viewed emotion. Mean confidence ratings were also calculated for each participant, emotion, and condition. For all ANOVAs using ordinal data in this paper, we report comparisons with Huyhn-Feldt adjustments for sphericity (Stiger et al., 1998). The Dunn–Šidák correction was applied to all simple effects analyses to correct for multiple comparisons. Alpha was set at the 0.05 level.
Emotion Identification Accuracy
To determine if emotional displays under restricted posture and body movement conditions affected emotion recognition, we conducted a within-subjects ANOVA with factors viewed condition (2: unrestricted and wheelchair) and emotion (6: anger, disgust, fear, happiness, pride, and embarrassment) using mean proportion accuracy data. Emotions expressed in unrestricted conditions (M\nunrestricted = 0.46, SE = 0.02) were more accurately identified than emotions expressed in wheelchair-restricted conditions (M\nwheelchair = 0.30, SE = 0.02) condition: [F(1,20) = 66.77, p < 0.0001, ηp2 = 0.77]. The main effect of emotion indicated that some emotions were recognized more accurately than others [F(5, 100) = 63.62, p < 0.0001, ηp2 = 0.76]. Simple effects comparisons (all values of p > 0.0001) revealed that accuracy was greatest for disgust (M = 0.66, SE = 0.02), happiness (M = 0.68, SE = 0.03), and anger (M = 0.59, SE = 0.04) compared to fear (M = 0.18, SE = 0.03), embarrassment (M = 0.26, SE = 0.04), and pride (M = 0.15, SE = 0.02); accuracy did not significantly differ between anger, happiness, and fear or across fear, embarrassment, and pride (all values of p > 0.51). Differences across emotions regarding the impact of posture and body movement on emotion recognition are reflected in the interaction between condition and emotion [F(5,100) = 4.23, p < 0.002, ηp2 = 0.17; Figure 4]. Simple effect analyses revealed greater accuracy for unrestricted over wheelchair-restricted conditions for disgust (p < 0.0001), happiness (p = 0.02), anger (p < 0.0001), fear (p = 0.01), and embarrassment (p < 0.003) but not pride (p = 0.14). Although the difference between the unrestricted and restricted conditions for pride is not significant, we believe this one lack of difference in condition significance can be explained by the fact that pride was less recognizable in the unrestricted condition. The accuracy for wheelchair conditions for body-related displays of fear, embarrassment, and pride all showed low levels of accuracy.
Experiment 2 – Recognition accuracy of emotion display videos by condition. Error bars indicate standard error. Stars indicate significant differences between conditions and emotions.
Confidence for Emotion Recognition
For confidence data, a within-subjects ANOVA was conducted for the factors condition (2: unrestricted and wheelchair) and emotion (6: disgust, happiness, anger, fear, embarrassment, and pride). Participants indicated greater confidence in emotion identification in unrestricted (M\nunrestricted = 3.95, SE = 0.10) than wheelchair-restricted (M\nwheelchair-restricted = 3.48, SE = 0.13) conditions [F(1, 20) = 21.45, p < 0.0001, ηp2 = 0.52]. The significant emotion effect [F(5, 93.59) = 3.82, p = 0.004, ηp2 = 0.16] showed that participants were equally confident for disgust, happiness, anger, and embarrassment (all values of p > 0.54) but significantly more confident in recognizing happiness compared to fear (p = 0.007) and pride (p = 0.002). This effect was mediated by the significant condition by emotion interaction [F(6, 100) = 7.02, p < 0.002, ηp2 = 0.26; Figure 5], further indicating that posture and body-movement restrictions differentially affected participant’s confidence in identifying specific emotions. Generally, participants had greater confidence for face-related compared to body-related emotions. Simple effect analyses confirmed that participants were more confident in their identification of unrestricted over wheelchair-restricted displays for disgust (p < 0.006), happiness (p = 0.02), anger (p < 0.0001), and pride (p < 0.0001). Unlike accuracy data, confidence between conditions did not differ for fear (p = 0.98) or embarrassment (p = 0.91). Interpreting this pattern of results in the context of status-related classifications of emotion from the literature, the greatest confidence differences between unrestricted vs. wheelchair-restricted conditions occurred for the dominant status emotions of anger, pride, and disgust, and the least confidence differences in recognizing the low-status emotional displays of fear and embarrassment.
Experiment 2 – Confidence ratings for emotion recognition by condition. Error bars indicate standard error. Stars indicate significant condition differences.
In sum, emotion recognition accuracy and confidence were strongly influenced by restricting posture and body movement in nonverbal displays. Recognition accuracy and performance confidence declined disproportionately for dominant emotions expressed by non-disabled people in wheelchairs. Conversely, accuracy and confidence for subordinate emotions of fear and embarrassment were affected less by bodily restrictions. Clearly, the high-confidence ratings for effective emotional communication of Experiment 1’s non-disabled but wheelchair-restricted group were not supported by the emotion recognition data.
General Discussion
This study investigated the contributions of body posture and movement to the production and recognition of nonverbal emotion displays. A less evaluated implication of embodied emotion theory is that emotional experience is bidirectional: Another person’s body induces emotional simulation in the observer’s body and, at the same time, the current inputs from the observer’s body or state contribute to the emotional processing. In this view, a person’s current body inputs may affect the production of nonverbal displays of emotion. In Experiment 1, non-disabled participants were randomly assigned to either the unrestricted or the wheelchair-restricted group. Although we expected the wheelchair-restricted group to use the face more to express body-related emotions because body posture and movement was constrained, we investigated whether these constraints had a general effect for all emotions or whether it influenced some emotions more than others. In Experiment 2, we used emotion production videotapes from Experiment 1 to assess emotion recognition performance from unrestricted and wheelchair-restricted emotional displays.
To confirm that the communication of specific emotions differentially relies on use of the face vs. the body, in Experiment 1 we asked participants to express their preference for face and body use for six different emotions. Consistent with prior literature, we found that preferred use of the body and face differed across emotions, with status-oriented emotions often expressed using the body (e.g., App et al., 2011; Tracy et al., 2015). Participants preferred to use the face to express happiness and disgust, the body to express pride, and there were no significant differences in preference for use of face or both to express anger, fear, or embarrassment. In addition, we found no differences between unrestricted and wheel-chair restricted groups for stated face vs. body use preferences, suggesting that these preferences are not based on current body input. Instead they appear to rely on past experience, socialization (i.e., what configuration has been taught or is typically displayed for that emotion), or a more evolved response.
One primary focus of the study was to assess how restrictions of body posture and movement might affect the relative use of the face and body to nonverbally communicate emotions. An examination of emotionally congruent face and body movements revealed that posture and movement restrictions did not strongly change their presence in this non-disabled sample, although coder’s notes indicated that it affected the size of the movements.
Further, posture and movement restrictions did not affect participants’ confidence ratings as to how well they communicated the emotions. They were equally confident as the unrestricted group that they had successfully communicated each emotion. This was particularly notable for emotions relying on body movement and posture. Current body inputs did not influence non-disabled participants’ perceptions of emotional competency when they were restrained. Instead, like their verbal channel preferences, their confidence ratings appeared to be based on a culmination of past experiences. This suggests that people are not aware of the nonverbal changes that they experience when body posture and movement are constrained to a wheelchair. In light of embodied emotion theory, body restrictions should have altered emotional experience. Contrary to these predictions, we found that despite actual physical emotion production changes, feedback from the expresser’s body was disregarded, ignored, or discounted. Instead of relying on current body inputs, people’s conceptions of emotional competency appeared to be built from experiences/inputs acquired over time.
Nonetheless, body movement restrictions and their corresponding alterations of emotional visual cues did influence participants’ ability to recognize nonverbal displays of emotions. In Experiment 2, participants recognized emotions based on the videos from Experiment 1. Emotion recognition accuracy was impaired for videos of emotions expressed by the wheelchair-restricted individuals. In addition to a large, overall decrement in recognition for wheelchair-restricted displays of emotion, the emotions of disgust and anger were particularly affected. It is important to note that the recognition of all social status emotions is impeded when the expresser is sitting and restrained. Instead, these constraints specifically affect the displays of social dominance emotions. Following embodiment theory, viewing expressers who are seated and constrained may affect emotional simulation and decrease recognition of the emotion in the viewer because it may be more difficult to feel dominant, which is associated with the expression of disgust and anger.
This study represents a first step in establishing how the body and its posture influence the perception and communication of emotions in non-disabled individuals. Our goal was to gain insight into emotional communication issues for people with physical disabilities, restricting them to wheelchairs that constrain both body movement and posture. Although the participants in this laboratory study did not have physical disabilities and we simulated disability by constraining participants to wheelchairs, our goal was to investigate some previously untested implications of embodiment theory and to provide baseline data as to how similar kinds of restrictions influence abled-bodied individuals. Future studies will examine emotional perception and communication for individuals with physical disabilities.
Implications for Embodiment Theory and Social Interactions
The results of this study have implications for embodiment theory as well as for social interactions. First, current bodily inputs do not affect confidence in emotion displays in a way consistent with actual changes in the efficacy of displays. Generally, people may not be fully aware of situational factors that have an impact on the efficacy of their display. Not surprisingly, in the case of physical restraints that affect body movement and posture, it is the expression of body-involved emotion displays that are most affected.
Differences in emotion displays associated with constrained body movement also may influence those interacting with that person. Specifically, they influence the perception and recognition of dominant emotions. These results have implications for how people might establish dominance in group interactions – unrestricted displays of dominant emotions are interpreted more accurately. People expect those high in power to show erect and open postures, upward tilt of the head, and touching behavior (Carney et al., 2005; Hall et al., 2005). However, because body-related emotions are also associated with conveying relative social status, it is important to consider how such constraints may affect social perception between individuals in work and other settings who are establishing relative social power. Another related factor is the lower relative position of people in sitting positions. A number of studies have shown that relative height between individuals involved in social interactions influences social outcomes and perceived social power (Schwartz et al., 1982;Schubert, 2005; Schubert et al., 2013). In a recent study, Thomas and Pemstein (2015) found that the outcomes of social cooperation games could be biased in favor of one of the partners merely by providing visual cues from web cameras that showed one player to be above the other.
Further, these findings have important implications for work interactions, social interactions, and social-emotional development of those with physical disabilities, especially those confined to wheelchairs. Embodiment theory would suggest that those who do not have use of their body may not experience these emotions in the same way as non-disabled people and may not use the same visual cues to communicate social status emotions effectively. This may thereby contribute to well-known social challenges faced by those with disabilities. Such effects would likely have an impact not only on the social dynamic between individuals, influencing both the person producing the emotional display, but also on people observing that person’s display. For example, non-disabled individuals may over or under estimate the emotional experience of those with physical disabilities that may lead to more challenging interactions and affect hierarchical relations within work-place settings. There might be an increased likelihood that social status could be miscommunicated if one individual had a physical disability and the other did not. Also, if those constrained to wheelchairs violate expectations by not showing these nonverbal behaviors, people may make individual rather than situational attributions and perceive the person as less powerful or dominant.
Correspondingly, our findings support anecdotal reports of physically disabled individuals in wheelchairs feeling marginalized. Individuals with physical disabilities often report that non-disabled persons ignore them or do not afford them appropriate respect or status in social or work environments. Many people with physical disabilities who are confined to wheelchairs believe that they experience and communicate emotions well, but they also report being ignored and disrespected in social interactions as well as begin undervalued and subtly discriminated at work (Gay, 2004; Daley et al., 2018).
Given that this study was conducted with non-disabled individuals, a natural next step would be to follow up this study with physically disabled participants. It would be important to assess face vs. body use preferences for the different emotions among those who have experienced a movement restriction since birth as well as those who have acquired the restriction later in life. To the degree that the source of the preference is based on one’s own prior experience instead of a specific evolved response or caused by the immediate situation, then this preference should be less evident in those who have been paraplegic since birth, for example.
As with the preference measure, an important follow-up question would be to assess emotion production and recognition in physically disabled populations to determine whether the outcomes from this study are applicable to those chronically confined to a sitting position in a wheelchair. That is, for the participants in this study, was it the difference between their normal stature and ability to communicate that affected their display, or is there a more general effect such that those who are always restricted will show the same differences in display? Reports from actual wheelchair users indicate that they feel less able to express dominant emotions, particularly anger, while in a wheelchair (Cahill and Eggleston, 1994, p. 304). One woman, relegated to a wheelchair, was so angry she indicated that she wanted to jump out of her chair and shake the person she was directing her anger toward, but all she did was remain seated and “grit her teeth.” New wheelchairs that raise the individual to the eye level of non-disabled individuals (e.g., https://www.quantumrehab.com/ilevel-power-chairs/) may help reduce at least the relative height differences contributing to implicit status differences.
These findings suggest additional questions to be addressed with future research. Do people who have physical differences that constrain bodily movement alter their displays, especially for these target emotions (i.e., when the restriction is more chronic, have people learned to adjust displays of body-related emotions)? Do people who have physical differences that constrain bodily movement correspondingly adjust their confidence in expression of these emotions? Finally, do perceivers who have physical differences that constrain bodily movement more accurately perceive emotions (especially status related emotions) of others with similar physical differences? If so, this may be because the embodiment of these displays is consistent across expresser and the perceiver, or that given social-grouping differences, those with these physical differences have more experience interacting with and perceiving these displays. The answer to these questions may provide information on whether people’s display is more influenced by the broader social context, in which personal history would matter less or in sensitivity to changes in their own body.
Finally, we also want to consider perceiver effects, especially between non-disabled and disabled individuals. Do perceivers’ stereotypes influence what they see regarding emotional expression in people in a wheelchair? In other words, would perceptions be the same if the person were constrained in a wheelchair vs. a kitchen chair or a paraplegic person vs. hostage? It is possible that non-disabled perceivers stereotype people in wheelchairs as less dominant, and this affects the social dynamic between individuals. Studies examining the perception of wheelchair users by non-disabled individuals showed that non-disabled individuals associate more negative emotions (e.g., depression and guilt) with the disabled person (Vilchinsky et al., 2010). As with the higher status displays, those perceiving physically disabled people in wheelchairs may fail to account for the situational influences on emotional expression and may attribute the displays to the person’s internal state not situational constraint.
In conclusion, recent work investigating embodiment theory emphasizes that the current state of the body can have an important influence on psychological and emotional processes. Consistent with this, we found that constraints on body movement and position influence how emotions are produced and how they are perceived. This study’s findings provide only partial support for a pure embodiment perspective. Current body inputs do not appear to influence the expresser’s perception of their own emotional communication: people’s confidence in their displays did not change with body constraints. However, the results emphasize the important contributions of the body in nonverbal emotional communication and how these contributions may affect the social-emotional context of those with physical disabilities.
Data Availability Statement
The datasets generated for this study are available on request to the corresponding author.
Ethics Statement
The studies involving human participants were reviewed and approved by Claremont McKenna College IRB. The patients/participants provided their written informed consent to participate in this study.
Author Contributions
CR was the project lead. KM, SA, and AS contributed parts of their independent research and thesis projects to the manuscript. EM and DM contributed expertise in the intellectual formation of the project and in writing the manuscript. All authors contributed to the article.
Conflict of Interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
This paper honors the memory of AS whose ideas and life inspired this research project investigating how physical disabilities may influence emotion perception and communication. She was an excellent colleague and scholar; we miss her intelligence and optimism, and we appreciate the additional insights she provided from her lived experiences with neuromuscular disease and wheelchair mobility.
1Consistent with the analyses conducted on the full data set, the group × face/body use × emotion ANOVA conducted with Huynh-Feldt corrected, equal sized groups found no main effects of group F(1,40) = 0.002, p = 0.96 and emotion F(4.95, 198.13) = 1.78, p = 0.12; no group × emotion interaction F(4.95, 198.13) = 1.03, p = 0.40; a main effect of face/body use F(1.26, 50.50) = 40.86, p < 0.0001; no group × face/body use interaction F(1.26, 50.50) = 2.69, p = 0.10; a significant emotion × face/body use interaction F(6.50, 259.93) = 12.36, p < 0.0001; and a significant 3-way interaction F(6.50, 259,93) = 2.11, p = 0.02.
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Fashioning the face: sensorimotor simulation contributes to facial expression recognition. Trends Cogn. Sci.\n20, 227–240. 10.1016/j.tics.2015.12.010, PMID: 26876363\n"],"string":"[\n \"\\nFront PsycholFront PsycholFront. Psychol.Frontiers in Psychology1664-1078Frontiers Media S.A.32849150PMC743215510.3389/fpsyg.2020.01961PsychologyOriginal ResearchBody Matters in Emotion: Restricted Body Movement and Posture Affect Expression and Recognition of Status-Related EmotionsReedCatherine L.1*MoodyEric J.2MgrublianKathryn1AssaadSarah1ScheyAlexis3McIntoshDaniel N.41\\nDepartment of Psychological Science, Claremont McKenna College, Claremont, CA, United States\\n2\\nWyoming Institute for Disabilities (WIND), University of Wyoming, Laramie, WY, United States\\n3\\nDepartment of Psychology, Scripps College, Claremont, CA, United States\\n4\\nDepartment of Psychology, University of Denver, Denver, CO, United States\\n
Edited by: Christel Bidet-Ildei, University of Poitiers, France
Reviewed by: Matteo Candidi, Sapienza University of Rome, Italy; Wolfgang Tschacher, University of Bern, Switzerland
*Correspondence: Catherine L. Reed, clreed@cmc.edu
This article was submitted to Emotion Science, a section of the journal Frontiers in Psychology
1182020202011196103920191572020Copyright © 2020 Reed, Moody, Mgrublian, Assaad, Schey and McIntosh.2020Reed, Moody, Mgrublian, Assaad, Schey and McIntoshThis is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Embodiment theory suggests that we use our own body and experiences to simulate information from other people’s bodies and faces to understand their emotions. A natural consequence of embodied theory is that our own current position and state contributes to this emotional processing. Testing non-disabled individuals, we investigated whether restricted body posture and movement influenced the production and recognition of nonverbal, dynamic emotional displays in able-bodied participants. In Experiment 1, participants were randomly assigned to either unrestricted or wheelchair-restricted (sitting, torso restrained) groups and nonverbally expressed six emotions (disgust, happiness, anger, fear, embarrassment, and pride) while being videotaped. After producing each emotion, they rated their confidence regarding how effectively they communicated that emotion. Videotaped emotional displays were coded for face, body, and face + body use. Based on naïve coders’ scores, both unrestricted and wheelchair-restricted groups produced emotionally congruent face and body movements and both groups were equally confident in their communication effectiveness. Using videos from Experiment 1, Experiment 2 tested non-disabled participants’ ability to recognize emotions from unrestricted and wheelchair-restricted displays. Wheelchair-restricted displays showed an overall decline in recognition accuracy, but recognition was selectively impaired for the dominance-related emotions of disgust and anger. Consistent with embodied emotion theory, these results emphasize the importance of the body for emotion communication and have implications for social interactions between individuals with and without physical disabilities. Changes in nonverbal emotion signals from body restrictions may influence social interactions that rely on the communication of dominance-related social emotions.
Social interactions rely strongly on nonverbal emotional displays to communicate social emotions, inform others about our feelings, and influence social outcomes (Vosk et al., 1983; Tiedens and Leach, 2004). Such communication can include expressions of basic emotions (e.g., happiness, anger, etc.; Ekman, 1992), as well as more complex social emotions that reflect relative social status (e.g., pride and shame; App et al., 2011; Steckler and Tracy, 2014; Tracy et al., 2015). The literature focuses on the role of the face in emotional communication, but a growing number of studies have demonstrated that the body plays a crucial role in emotion communication as well (Dael et al., 2012). Although some emotions are often communicated via facial expressions (e.g., happiness, anger), others are expressed via body posture and movement (e.g., pride, embarrassment; App et al., 2011; Tracy et al., 2015).
\\nEmotional embodiment theory suggests we use information from our own faces and bodies to understand other people’s emotions as well as our own (Niedenthal et al., 2001). Growing evidence suggests that when we view an emotion displayed by another, the viewer’s ability to recognize, understand, and respond to the emotion relies on the sensory-motor simulation of the observed emotion (Wood et al., 2016). Indeed, over 40 years of research has shown that humans rapidly match the facial expressions of others and that the configuration of their own faces can influence their own emotional states (McIntosh, 1996; Moody and McIntosh, 2006, 2011; Wood et al., 2016). For example, when judging a word for its emotional meaning, people may use their facial muscles to aid the decision (Niedenthal et al., 2009). Further, experimentally manipulating one’s facial expression can affect emotional experience. When facial expressions are restricted (e.g., putting a pen in their mouths), participants are less accurate at identifying others’ emotions (Niedenthal et al., 2001). However, embodiment theory also suggests that information from bodies is also simulated when processing other people’s emotions (Niedenthal et al., 2001).
One less explored implication of embodied theory is that emotional perception is an interactive process and that the interaction between emotion experience and the physical display of emotion is reciprocal. That is, a person’s state, which includes current inputs from body position, influences the simulation process and the perception of others’ emotions. Indeed, it is known that people not only physically display their subjective emotional state but also their emotional display affects their subjective emotional state (see McIntosh, 1996). This ability to use physiological changes and to re-experience one’s somatic responses plays an important role in emotional processing (Niedenthal, 2007; Halberstadt et al., 2009). Further, the body conveys emotion information that is different from the face and can even modulate emotional information conveyed by the face (Aviezer et al., 2008; App et al., 2012; Abo Foul et al., 2018). Studies show that people’s own bodies influence the perception of their own as well as others’ emotional states (McIntosh, 1996; Niedenthal et al., 2005; Reed and McIntosh, 2008; Wilbarger et al., 2011).
Thus, a consequence of this theory is that if either face or body use is impeded, it will have implications for their perceptions, experiences, and communications of emotions (de Gelder, 2006; App et al., 2011; Steckler and Tracy, 2014; Tracy et al., 2015; Moody et al., 2018). A study by Wallbott and Giessen (1986) confirmed that viewing whole body videos of actors portraying emotional scenarios lead to more accurate emotion recognition than audio-only recordings of the same scenarios. Importantly, Moody et al. (2018) demonstrated rapid, emotionally congruent, whole-body responses from static emotional faces, indicating that the participants simulated the viewed facial emotion using their whole bodies. When judging changes in other people’s body postures, input from one’s own emotional body posture selectively influences perceptions of another person’s emotional body posture but not neutral postures (Wilbarger et al., 2011). Nonetheless, little work has explored whether movement restrictions of the body has similar consequences for emotional processing as it does for the face and whether such constraints differentially affect emotions preferentially conveyed via the body.
Although some emotions are communicated primarily via the face and others rely on the body, individual emotions are associated with specific social functions (Tracy and Robins, 2007; App et al., 2011). For instance, emotions conveying status relations between individuals include anger, disgust, and fear that are associated with facial expressions, as well as embarrassment, guilt, pride, and shame that are associated with bodily expressions (Coulson, 2004; Tracy and Robins, 2007; App et al., 2011; see also Atkinson et al., 2004). Emotional body-expressions are important in conveying social hierarchical information and the degree to which they signal relative social status (Steckler and Tracy, 2014). Generally, more erect postures are associated with higher status and dominance, although context can affect this interpretation (Hall et al., 2005). Actors asked to portray various emotions often used a collapsed body posture for shame or sadness and used body expression more than facial movement to display pride (Wallbott, 1998). Across studies and cultures, pride appears associated with a low intensity smile and a variety of different body positions including expanded posture, arms akimbo on hips or arms raised straight above the head with the hands (Wallbott, 1998; Tracy and Robins, 2007; Tracy and Matsumoto, 2008). Given that emotions have different social functions and differential reliance on face vs. body expression, the restriction of body movement may not affect the processing of emotions in the same way (de Gelder, 2006; App et al., 2011; Steckler and Tracy, 2014; Tracy et al., 2015).
Current Study
Relatively little research has explored the role of the body on emotional communication between individuals. Moreover, few studies have considered how the prevention of body movement might differentially disrupt the production and recognition of specific emotions. Here, we examine how restricted body movement and posture influences emotional expression and recognition. Given that experimentally preventing or enhancing emotional facial expressions can influence emotional experience of basic emotions (i.e., anger, fear, happiness, and sadness; Ekman, 1992; Jack et al., 2014), constraints on body movement and posture may also affect the sensorimotor processing or simulation of emotions, especially more complex, body-related emotions (e.g., embarrassment and pride). Such constraints could influence both an observer’s perception and nonverbal communication of emotions. They may also differentially affect the recognition of emotions by changing the visual cues that distinguish them (Wallbott, 1998; Sawada et al., 2003; Gross et al., 2010). Moreover, body and posture constraints may affect more than just body-related emotions because they change the implied social dominance between individuals. Although disgust is best conveyed by the face and pride is best conveyed by the body, both emotions indicate one person’s superior standing compared to another. In other words, emotions that convey social status may supersede the body-related vs. face-related emotion distinction because body constraints do more than just restrict movement.
In two studies we extend work documenting the body’s role in emotion displays (e.g., de Gelder, 2006; Tracy and Robins, 2007; App et al., 2011). In Experiment 1, we examined the role of body movement and postures restrictions on nonverbal emotion communication for non-disabled participants. In Experiment 2, we determined whether these constraints affected emotion recognition in non-disabled participants. This research is a first step in understanding the role of embodiment in the communication of social emotions and its implications for how nonverbal emotional displays may influence social interactions between non-disabled and physically disabled individuals.
Experiment 1: Emotion Production
The purpose of Experiment 1 was to determine whether current body inputs affected non-disabled participants’ nonverbal expressions of emotions and their confidence in the efficacy of communicating emotions. In addition, the experiment replicated previous studies indicating specific face and body preferences for nonverbal emotional communication (App et al., 2011). To examine the effects of body and posture constraints, we compared the expression of six emotions for two groups that differed in their mobility restrictions: (1) an “unrestricted” group in which participants stood while communicating emotions without mobility restrictions and (2) a “wheelchair-restricted” group in which participants sat in a wheelchair with an elastic band tied around the torso, thereby restricting trunk movement. The unrestricted condition provided a reliable baseline to which we could compare performance in the movement-restricted condition. In Part 1, participants communicated six emotions nonverbally to a mannequin while being videotaped; after expressing each emotion, they rated their confidence in successfully communicating the emotion. In Part 2, participants indicated whether they preferred face or body channels to optimally communicate each emotion.
Extensions of embodiment theory would predict that current physical inputs should affect the communicator’s production of nonverbal, emotional displays as well as the communicator’s confidence in the effectiveness of those displays, especially for those emotions best conveyed by the body. Therefore, compared to the unrestricted group, the wheelchair-restricted group should show reduced use of the body and increased use of the face during emotional production, especially for emotions optimally expressed by the body. The postural and movement differences between groups should also differentially affect status emotions. Further, if current body inputs were used for evaluating one’s own communication, then the wheelchair-restricted group may be less confident in communicating body-based emotions. Of interest is whether confidence in emotion communication was affected by current body constraints.
MethodParticipants
Forty-nine participants (33 female, mean age = 19.6 years) received partial course credit in introductory level psychology courses for their participation. They were randomly assigned via a computer program to one of two groups: the unrestricted group (n = 21) and the wheelchair-restricted group (n = 28). In the unrestricted group, participants stood 3 ft from a seated mannequin. In the movement restricted “wheelchair-restricted” group, participants sat in a wheelchair with their chests strapped into the wheelchair by a stretch athletic band around the lower rib cage to stabilize and restrict movement of the trunk. Straps reduced torso motion shown to be important for conveying emotional state (e.g., Wallbott, 1998; Atkinson et al., 2004). Participants were still able to use their arms and limited torso adjustments to express emotions.
Stimuli and Apparatus
Participants directed their emotions toward a life-size mannequin with a soft, gray, fabric exterior and no definitive facial features. The mannequin was seated in a chair in front of the participants and dressed in a casual, gender-neutral outfit, including a sweat suit and baseball hat. Participants were asked to think of someone they knew and to address the mannequin as if it were that person, whether it was a friend, relative, or romantic partner. The mannequin was addressed as the chosen person throughout the experiment. The mannequin’s lack of facial features provided participants with a consistently neutral response to their emotion production.
To record participant’s nonverbal emotional displays, two video cameras were used to film the facial and body expressions of the participants; one was placed behind the mannequin to provide a face-front view of the participants and another camera was placed to the side of the participants to provide a side view that showed both the expresser and the mannequin.
To quantify the relative degree of participants’ face and body use during the communication of each emotion, we modified a coding scheme used in App et al. (2011) to assess the amount of emotionally congruent movement in the face and the body as recorded from the front and side view videos. Emotion congruence was defined as using non-random, systematic movements to convey each emotion and followed classification procedures described in App et al. (2011). More specifically, coders examined videos for face and body movements that the literature has confirmed to be associated with, or congruent with, each of the emotions used in this study (Atkinson et al., 2004; Moody and McIntosh, 2006; App et al., 2011). For example, for a happy condition trial, a congruent emotional display would indicate the presence of upward movements in the eyes and cheeks resulting from zygomaticus muscle activation in the face (Moody and McIntosh, 2006) and/or the lifting of arms or jumping up and down (Atkinson et al., 2004). However, if the participant displayed a furrowing of the forehead resulting from corrugator muscle activation that is typically associated with anger displays, then this movement would be considered incongruent with the emotion communicated.
For each participant, condition, and trial, video data were coded separately from both the front and side-view videos. In both views, the face and the body were visible. Face and body movements were coded independently for each of three categories that are reflected in the levels of the statistical analyses: (1) face alone – emotionally congruent face movement without simultaneous body movement; (2) body alone – emotionally congruent body movement without simultaneous face movement; and (3) face + body – emotionally congruent simultaneous face and body movement. Three scorers, who were naïve to the predictions of the experiment, rated movement for each of these three categories using a scale ranging from 0 [none: no emotionally congruent movement (i.e., no use of muscles specific to of the communicated emotion)] to 1 (some emotionally congruent movement: movement indicated sufficient information for some level of emotion congruence but not a full match) to 2 (full match of expected emotionally congruent movement). Scores from these naïve scorers indicated agreement on 92% of trials overall, with 97% agreement for the unrestricted group and 87% agreement for the restricted wheelchair group. The average use of emotionally congruent face, body, or concurrent face and body movements was calculated for each participant over two views and three trials for each of the six emotions.
ProcedurePart 1: Emotion Production
Tested individually, participants nonverbally communicated six different emotions (anger, disgust, fear, happiness, pride, and embarrassment) to the mannequin as effectively as possible. For each participant, a computer program randomly selected group assignment. The experiment began with practice trials in which participants produced two emotions not used in the experimental trials (surprise and sadness). Experimental trials followed in which the participant pressed a computer key to begin the trials and one of the six emotion words appeared on the computer screen. The participant then expressed the emotion to the mannequin for 4 s. Following each expression trial, participants were prompted to enter number on a scale of 0 (“not confident at all”) to 4 (“very confident”) to indicate their confidence in how well they were able to communicate that emotion. Each of the six emotions was produced three times in random order for a total of 18 production trials and 18 confidence ratings. To reduce any potential influences of experimenter presence on emotion production, the experimenter remained outside the testing room after practice trials.
Part 2: Preferences for Using the Face or Body to Communicate Specific Emotions
After completing the emotion production portion of the experiment, participants remained in their unrestricted or wheelchair-restricted states. An emotion word cue appeared on the computer screen and they indicated via a key press whether they would typically (i.e., in the everyday world) use their face or their body to nonverbally express that emotion. They provided a single face/body preference for each emotion; emotion order was randomized.
Results and DiscussionPreferences for Face vs. Body Use When Conveying Emotions
The purpose of this analysis was to replicate findings by App et al. (2011) and to confirm that the participants in our study endorsed similar preferences for using the face or body to express each emotion. The results confirm that preferences to use the face vs. the body to express emotions differs across emotions and show a similar pattern to those reported in App et al. (2011). For each group and emotion, the frequency of preferred face vs. body use was tabulated. To document differential preferences for face or body use when communicating the six emotions, chi-square tests were conducted for face/body preferences for each emotion and group using frequency data. There were no group differences overall in body/face expression preferences for any emotion [χ2(1) < 0.70, p > 0.71]. Regardless of group, face/body preference largely replicated previous findings (App et al., 2011; Figures 1A,B). Participants preferred to use the body to communicate pride [unrestricted group: χ2(1) = 13.762, p < 0.0001; wheelchair group: χ2(1) = 11.57, p < 0.001]. They preferred to use the face for disgust [unrestricted group: χ2(1) = 13.76, p < 0.0001; wheelchair group: χ2(1) = 20.57, p < 0.0001] and happiness [unrestricted group: χ2(1) = 17.19, p < 0.0001; wheelchair group: χ2(1) = 20.57, p < 0.0001]. Preferences were more divided for face and body use when expressing anger [unrestricted group: χ2(1) = 1.19, p = 0.28; wheelchair group: χ2(1) = 2.29, p = 0.13], fear [unrestricted group: χ2(1) = 13.76, p = 0.83; wheelchair group: χ2(1) = 0.000, p = 1.000], and embarrassment [unrestricted group: χ2(1) = 1.19, p = 0.28; wheelchair group: χ2(1) = 0.57, p = 0.45]. These preferences for each emotion (summarized in Table 1) establish a baseline indicating whether people typically rely on the face or body to nonverbally communicate specific emotions: The face is preferred to convey disgust and happiness, the body is preferred to convey pride, and we found no significant difference in preference for face or the body conveys anger, fear, and embarrassment.
Experiment 1 – Proportion preference selection for face vs. body use for emotion production: (A) unrestricted group, (B) wheelchair-restricted group. There were no group differences. For each emotion, stars indicate significant preference differences for using either the face or the body.
Face vs. body use preferences for emotion production.
Disgust
Happiness
Anger
Fear
Embarrassment
Pride
Face
x
x
x
x
x
Body
x
x
x
x
Despite the fact that participants were either unrestricted or wheelchair-restricted when providing face‐ or body-use preferences, the similarity of responses for the two groups suggests that these preferences were not influenced by current body inputs and rather were based on functional, and/or experiential mechanisms. These data also suggest that the recognition of emotions that rely most on the body (tested in Experiment 2) – pride followed by embarrassment, fear, and anger – may be most affected by restricting torso movement in a wheelchair.
Emotion Production: Emotion-Specific Influences of Restricted Body Posture and Movement
For all analyses of variance (ANOVAs) using ordinal data in both experiments, we report comparisons with Huyhn-Feldt adjustments for sphericity, which is proposed as a sufficient condition for the F-tests to be valid for ordinal data (Stiger et al., 1998). We applied the Dunn–Šidák correction to all simple effects analyses to correct for multiple comparisons. Alpha was set at the 0.05 level.
To assess whether posture and body constraints differentially influenced the production of emotions, we conducted a mixed model ANOVA with the between-subject factors of group (2: unrestricted and wheelchair-restricted) and within-subject factors of face/body use [3: face, body, face + body (i.e., concurrent face and body)] and emotion (6: disgust, happiness, anger, fear, embarrassment, and pride) using emotion-congruent movement scores provided by three observers. The unrestricted and restricted groups did not differ in the presence of emotionally congruent movement [group effect: F(1,47) = 0.42, p = 0.52, ηp2 = 0.01; M\\nunrestricted = 1.33, SE = 0.08; M\\nwheelchair = 1.27, SE = 0.07].
Also, the presence of emotionally congruent movements did not differ across emotions [emotion effect: F(4.98, 234.13) = 1.19, p = 0.31, ηp2 = 0.03], regardless of group [group × emotion interaction: F(4.98, 234.13) = 0.97, p = 0.44, ηp2 = 0.02]. The interaction between group and face/body use was not significant at the 0.05 level, [F(1.26, 59.05) = 2.78, p = 0.06, ηp2 = 0.06]. However, the interaction between emotion and face/body use indicated differential use of the face, body, and concurrent face and body (face + body) across emotions [F(6.35, 298.66) = 14.88, p < 0.0001, ηp2 = 0.24]. The emotion by face/body use interaction was consistent with the preference data, indicating that the face alone is used to express disgust and happiness more than the body alone or their combination; the face and body alone, more than their combination, are used to express anger, embarrassment, and pride; and the face is used more than the face + body combination to express fear (Figure 2).
Experiment 1 – Face, body, and concurrent face/body use for emotion production. Error bars indicate standard error. Stars indicate significant differences between face, body, and concurrent face + body use.
Direct comparisons showed that for disgust the face was used more than the body or both concurrently (i.e., face + body; p < 0.0001) and there was no difference for body vs. face + body use (p = 0.31). Similarly, for happiness, the face was used more than the body or face + body (p < 0.0001) and there was no difference for body vs. face + body use (p = 0.58). However, for anger, face and body use did not differ significantly (p = 0.15), but both the face (p < 0.0001) and the body (p = 0.02) were used more than face + body. Similarly for fear, face and body use did not differ significantly (p = 0.16) but the face was used more than face + body (p < 0.0001), and body use was similar to face + body (p = 0.47). For embarrassment, face and body use did not differ significantly (p = 0.55), but both the face (p < 0.003) and the body (p < 0.0001) were used more than face + body. Finally, for pride, face and body use did not differ (p = 1.00) and both the face (p < 0.002) and the body (p < 0.0001) were used individually more than together (face + body). Further, although concurrent use of the body and face (face + body) did not differ across emotions (p > 0.64), the face was used significantly more for disgust and happy than anger, fear, embarrassment, and pride (p > 0.02), it was used more for anger than embarrassment (p < 0.005); other face comparisons were not significant. Finally, significantly greater body use was observed for anger and embarrassment than disgust and happiness (p < 0.03). The group by emotion by face/body use interaction was in the predicted direction but was not significant at the alpha = 0.05 level [F(6.35, 298.66) = 1.96, p = 0.06, ηp2 = 0.04].
Because the groups were created by random assignment, they had different sizes (unrestricted = 1 vs. wheelchair restricted = 28). To demonstrate that unequal group size did not affect the results, we randomly selected 21 participants from the larger group and reran the above analysis. The results for the equal-sized groups were highly comparable, but the group × emotion × face/body use interaction was now significant (p = 0.05)1.
Confidence in Effective Emotion Communication
To examine the effects of current bodily constraints on confidence in emotional expression, a mixed model ANOVA with the between-subject factors of group (2: unrestricted and wheelchair) and within-subject factors of emotion (6: disgust, happiness, anger, fear, embarrassment, and pride) was conducted on confidence ratings. Ratings should differ by group if current body inputs influenced confidence in emotional communication but not if previous experience were more important because non-disabled participants have similar prior experiences. A significant main effect for emotion [F(4.31, 202.62) = 13.55, p < 0.0001, ηp2 = 0.22] indicated greater confidence when communicating disgust and happiness than anger, fear, embarrassment, and pride (p < 0.001). Of interest, the posture and body restrictions imposed by the wheelchair did not influence confidence ratings, as indicated the lack of a group effect [F(1, 47) = 1.48, p = 0.23, ηp2 = 0.03] or the group by emotion interaction [F(4.31, 202.62) = 0.08, p = 1.00, ηp2 = 0.002; Figure 3].
Experiment 1 – Confidence ratings for effective emotion communication overall. Error bars indicate standard error. Stars indicate significant confidence differences across emotions: confidence for disgust and happiness was greater than for the other emotions.
In sum, replicating prior research (e.g., App et al., 2011), Experiment 1 first established preferences for the selective use of the face and body for specific emotions. Preference data confirm that non-disabled individuals do not prefer to use the face to convey all emotions and that they would use the face and body differentially depending on the emotion. Participants indicated that they preferred to use the face to best convey disgust and happiness; they were divided in their preference to use the face or body to express anger, fear, and embarrassment; and they preferred to use the body to convey pride.
Next, Experiment 1 examined how constraining body posture and movement might influence the nonverbal communication of these emotions in non-disabled participants. Comparing the nonverbal, physical production of emotions between the unrestrained and wheelchair-restrained groups, movement restriction in non-disabled individuals did not strongly alter the production of emotionally congruent movement. Further studies are needed with physically disabled individuals to more fully explore potential differences in the relative use of the face and body in emotion production. Further, no group differences emerged between the unrestricted and wheelchair group’s confidence in their effective communication of the emotions. Prior emotional experience for our non-disabled participants who are not typically constrained in their movements appears to have more influence on their confidence in emotional communication than current body inputs. Nonetheless, posture and torso constraints had an overall influence emotion production, which may convey differential nonverbal emotion signals to observers. Thus, posture and movement constraints may also influence the reception of emotional displays. We investigate this issue in Experiment 2.
Experiment 2: Emotion Identification
Experiment 2 investigated whether the expression of emotions by individuals with physical constraints affects the recognition of nonverbal emotional displays. Using a sample of emotion production videos from each group in Experiment 1, we assessed whether constraints on body movement and posture influenced the accuracy of emotion identification overall or for specific emotions.
MethodParticipants
Participants were 22 students (10 females; age: M = 19.3 years, SD = 1.14) who received partial course credit in lower level psychology courses. None had participated in Experiment 1.
Stimuli and Apparatus
A subset of video recordings was selected from Experiment 1 to limit the duration of Experiment 2. From video clips recorded in Experiment 1, two male and two female participant video clips were selected at random for each of the six emotions. Video clips were edited to remove the sound and have a 4-s duration. A total of 48 video clips were used: 2 males × 2 females × 2 conditions (unrestricted and wheelchair) × 6 emotions. All video clips were emotional displays recorded from the side view so that the observer could see the participant interacting with the mannequin. Stimuli were presented on a 21-inch computer monitor with E-prime 2.0 pro software (Psychological Software Tools, Pittsburgh, PA).
Procedure
Participants were tested individually and were asked to identify emotions presented in silent video clips. For each emotion recognition trial, a 4-s emotion video was played twice on a computer monitor, with a 500 ms pause between presentations. A numbered list of emotions (1 = anger, 2 = sympathy, 3 = embarrassment, 4 = love, 5 = disgust, 6 = fear, 7 = happiness, 8 = sadness, 9 = pride, plus 0 = other) appeared and participants indicated the emotion presented in the video by pressing the number key associated with the corresponding emotion word. The list included three additional emotions (sympathy, sadness, and love) as well as an “other” category to reduce inflated accuracy rates due to forced choice (Russell, 1993; Frank and Stennett, 2001). Participants then rated their confidence that their response was correct on a scale from 1 (not at all confident) to 5 (extremely confident). After two practice trials, participants completed three blocks of 48 experimental trials (6 emotions × 2 male × 2 female × 2 conditions; each stimulus was presented 3 times) for a total of 144 trials; within each block, trials were presented in random order. A brief break was provided between each block.
Results
For participant, emotion, and condition, mean proportion accuracy was calculated for emotion identification; correct responses were recorded when the selected emotion matched the viewed emotion. Mean confidence ratings were also calculated for each participant, emotion, and condition. For all ANOVAs using ordinal data in this paper, we report comparisons with Huyhn-Feldt adjustments for sphericity (Stiger et al., 1998). The Dunn–Šidák correction was applied to all simple effects analyses to correct for multiple comparisons. Alpha was set at the 0.05 level.
Emotion Identification Accuracy
To determine if emotional displays under restricted posture and body movement conditions affected emotion recognition, we conducted a within-subjects ANOVA with factors viewed condition (2: unrestricted and wheelchair) and emotion (6: anger, disgust, fear, happiness, pride, and embarrassment) using mean proportion accuracy data. Emotions expressed in unrestricted conditions (M\\nunrestricted = 0.46, SE = 0.02) were more accurately identified than emotions expressed in wheelchair-restricted conditions (M\\nwheelchair = 0.30, SE = 0.02) condition: [F(1,20) = 66.77, p < 0.0001, ηp2 = 0.77]. The main effect of emotion indicated that some emotions were recognized more accurately than others [F(5, 100) = 63.62, p < 0.0001, ηp2 = 0.76]. Simple effects comparisons (all values of p > 0.0001) revealed that accuracy was greatest for disgust (M = 0.66, SE = 0.02), happiness (M = 0.68, SE = 0.03), and anger (M = 0.59, SE = 0.04) compared to fear (M = 0.18, SE = 0.03), embarrassment (M = 0.26, SE = 0.04), and pride (M = 0.15, SE = 0.02); accuracy did not significantly differ between anger, happiness, and fear or across fear, embarrassment, and pride (all values of p > 0.51). Differences across emotions regarding the impact of posture and body movement on emotion recognition are reflected in the interaction between condition and emotion [F(5,100) = 4.23, p < 0.002, ηp2 = 0.17; Figure 4]. Simple effect analyses revealed greater accuracy for unrestricted over wheelchair-restricted conditions for disgust (p < 0.0001), happiness (p = 0.02), anger (p < 0.0001), fear (p = 0.01), and embarrassment (p < 0.003) but not pride (p = 0.14). Although the difference between the unrestricted and restricted conditions for pride is not significant, we believe this one lack of difference in condition significance can be explained by the fact that pride was less recognizable in the unrestricted condition. The accuracy for wheelchair conditions for body-related displays of fear, embarrassment, and pride all showed low levels of accuracy.
Experiment 2 – Recognition accuracy of emotion display videos by condition. Error bars indicate standard error. Stars indicate significant differences between conditions and emotions.
Confidence for Emotion Recognition
For confidence data, a within-subjects ANOVA was conducted for the factors condition (2: unrestricted and wheelchair) and emotion (6: disgust, happiness, anger, fear, embarrassment, and pride). Participants indicated greater confidence in emotion identification in unrestricted (M\\nunrestricted = 3.95, SE = 0.10) than wheelchair-restricted (M\\nwheelchair-restricted = 3.48, SE = 0.13) conditions [F(1, 20) = 21.45, p < 0.0001, ηp2 = 0.52]. The significant emotion effect [F(5, 93.59) = 3.82, p = 0.004, ηp2 = 0.16] showed that participants were equally confident for disgust, happiness, anger, and embarrassment (all values of p > 0.54) but significantly more confident in recognizing happiness compared to fear (p = 0.007) and pride (p = 0.002). This effect was mediated by the significant condition by emotion interaction [F(6, 100) = 7.02, p < 0.002, ηp2 = 0.26; Figure 5], further indicating that posture and body-movement restrictions differentially affected participant’s confidence in identifying specific emotions. Generally, participants had greater confidence for face-related compared to body-related emotions. Simple effect analyses confirmed that participants were more confident in their identification of unrestricted over wheelchair-restricted displays for disgust (p < 0.006), happiness (p = 0.02), anger (p < 0.0001), and pride (p < 0.0001). Unlike accuracy data, confidence between conditions did not differ for fear (p = 0.98) or embarrassment (p = 0.91). Interpreting this pattern of results in the context of status-related classifications of emotion from the literature, the greatest confidence differences between unrestricted vs. wheelchair-restricted conditions occurred for the dominant status emotions of anger, pride, and disgust, and the least confidence differences in recognizing the low-status emotional displays of fear and embarrassment.
Experiment 2 – Confidence ratings for emotion recognition by condition. Error bars indicate standard error. Stars indicate significant condition differences.
In sum, emotion recognition accuracy and confidence were strongly influenced by restricting posture and body movement in nonverbal displays. Recognition accuracy and performance confidence declined disproportionately for dominant emotions expressed by non-disabled people in wheelchairs. Conversely, accuracy and confidence for subordinate emotions of fear and embarrassment were affected less by bodily restrictions. Clearly, the high-confidence ratings for effective emotional communication of Experiment 1’s non-disabled but wheelchair-restricted group were not supported by the emotion recognition data.
General Discussion
This study investigated the contributions of body posture and movement to the production and recognition of nonverbal emotion displays. A less evaluated implication of embodied emotion theory is that emotional experience is bidirectional: Another person’s body induces emotional simulation in the observer’s body and, at the same time, the current inputs from the observer’s body or state contribute to the emotional processing. In this view, a person’s current body inputs may affect the production of nonverbal displays of emotion. In Experiment 1, non-disabled participants were randomly assigned to either the unrestricted or the wheelchair-restricted group. Although we expected the wheelchair-restricted group to use the face more to express body-related emotions because body posture and movement was constrained, we investigated whether these constraints had a general effect for all emotions or whether it influenced some emotions more than others. In Experiment 2, we used emotion production videotapes from Experiment 1 to assess emotion recognition performance from unrestricted and wheelchair-restricted emotional displays.
To confirm that the communication of specific emotions differentially relies on use of the face vs. the body, in Experiment 1 we asked participants to express their preference for face and body use for six different emotions. Consistent with prior literature, we found that preferred use of the body and face differed across emotions, with status-oriented emotions often expressed using the body (e.g., App et al., 2011; Tracy et al., 2015). Participants preferred to use the face to express happiness and disgust, the body to express pride, and there were no significant differences in preference for use of face or both to express anger, fear, or embarrassment. In addition, we found no differences between unrestricted and wheel-chair restricted groups for stated face vs. body use preferences, suggesting that these preferences are not based on current body input. Instead they appear to rely on past experience, socialization (i.e., what configuration has been taught or is typically displayed for that emotion), or a more evolved response.
One primary focus of the study was to assess how restrictions of body posture and movement might affect the relative use of the face and body to nonverbally communicate emotions. An examination of emotionally congruent face and body movements revealed that posture and movement restrictions did not strongly change their presence in this non-disabled sample, although coder’s notes indicated that it affected the size of the movements.
Further, posture and movement restrictions did not affect participants’ confidence ratings as to how well they communicated the emotions. They were equally confident as the unrestricted group that they had successfully communicated each emotion. This was particularly notable for emotions relying on body movement and posture. Current body inputs did not influence non-disabled participants’ perceptions of emotional competency when they were restrained. Instead, like their verbal channel preferences, their confidence ratings appeared to be based on a culmination of past experiences. This suggests that people are not aware of the nonverbal changes that they experience when body posture and movement are constrained to a wheelchair. In light of embodied emotion theory, body restrictions should have altered emotional experience. Contrary to these predictions, we found that despite actual physical emotion production changes, feedback from the expresser’s body was disregarded, ignored, or discounted. Instead of relying on current body inputs, people’s conceptions of emotional competency appeared to be built from experiences/inputs acquired over time.
Nonetheless, body movement restrictions and their corresponding alterations of emotional visual cues did influence participants’ ability to recognize nonverbal displays of emotions. In Experiment 2, participants recognized emotions based on the videos from Experiment 1. Emotion recognition accuracy was impaired for videos of emotions expressed by the wheelchair-restricted individuals. In addition to a large, overall decrement in recognition for wheelchair-restricted displays of emotion, the emotions of disgust and anger were particularly affected. It is important to note that the recognition of all social status emotions is impeded when the expresser is sitting and restrained. Instead, these constraints specifically affect the displays of social dominance emotions. Following embodiment theory, viewing expressers who are seated and constrained may affect emotional simulation and decrease recognition of the emotion in the viewer because it may be more difficult to feel dominant, which is associated with the expression of disgust and anger.
This study represents a first step in establishing how the body and its posture influence the perception and communication of emotions in non-disabled individuals. Our goal was to gain insight into emotional communication issues for people with physical disabilities, restricting them to wheelchairs that constrain both body movement and posture. Although the participants in this laboratory study did not have physical disabilities and we simulated disability by constraining participants to wheelchairs, our goal was to investigate some previously untested implications of embodiment theory and to provide baseline data as to how similar kinds of restrictions influence abled-bodied individuals. Future studies will examine emotional perception and communication for individuals with physical disabilities.
Implications for Embodiment Theory and Social Interactions
The results of this study have implications for embodiment theory as well as for social interactions. First, current bodily inputs do not affect confidence in emotion displays in a way consistent with actual changes in the efficacy of displays. Generally, people may not be fully aware of situational factors that have an impact on the efficacy of their display. Not surprisingly, in the case of physical restraints that affect body movement and posture, it is the expression of body-involved emotion displays that are most affected.
Differences in emotion displays associated with constrained body movement also may influence those interacting with that person. Specifically, they influence the perception and recognition of dominant emotions. These results have implications for how people might establish dominance in group interactions – unrestricted displays of dominant emotions are interpreted more accurately. People expect those high in power to show erect and open postures, upward tilt of the head, and touching behavior (Carney et al., 2005; Hall et al., 2005). However, because body-related emotions are also associated with conveying relative social status, it is important to consider how such constraints may affect social perception between individuals in work and other settings who are establishing relative social power. Another related factor is the lower relative position of people in sitting positions. A number of studies have shown that relative height between individuals involved in social interactions influences social outcomes and perceived social power (Schwartz et al., 1982;Schubert, 2005; Schubert et al., 2013). In a recent study, Thomas and Pemstein (2015) found that the outcomes of social cooperation games could be biased in favor of one of the partners merely by providing visual cues from web cameras that showed one player to be above the other.
Further, these findings have important implications for work interactions, social interactions, and social-emotional development of those with physical disabilities, especially those confined to wheelchairs. Embodiment theory would suggest that those who do not have use of their body may not experience these emotions in the same way as non-disabled people and may not use the same visual cues to communicate social status emotions effectively. This may thereby contribute to well-known social challenges faced by those with disabilities. Such effects would likely have an impact not only on the social dynamic between individuals, influencing both the person producing the emotional display, but also on people observing that person’s display. For example, non-disabled individuals may over or under estimate the emotional experience of those with physical disabilities that may lead to more challenging interactions and affect hierarchical relations within work-place settings. There might be an increased likelihood that social status could be miscommunicated if one individual had a physical disability and the other did not. Also, if those constrained to wheelchairs violate expectations by not showing these nonverbal behaviors, people may make individual rather than situational attributions and perceive the person as less powerful or dominant.
Correspondingly, our findings support anecdotal reports of physically disabled individuals in wheelchairs feeling marginalized. Individuals with physical disabilities often report that non-disabled persons ignore them or do not afford them appropriate respect or status in social or work environments. Many people with physical disabilities who are confined to wheelchairs believe that they experience and communicate emotions well, but they also report being ignored and disrespected in social interactions as well as begin undervalued and subtly discriminated at work (Gay, 2004; Daley et al., 2018).
Given that this study was conducted with non-disabled individuals, a natural next step would be to follow up this study with physically disabled participants. It would be important to assess face vs. body use preferences for the different emotions among those who have experienced a movement restriction since birth as well as those who have acquired the restriction later in life. To the degree that the source of the preference is based on one’s own prior experience instead of a specific evolved response or caused by the immediate situation, then this preference should be less evident in those who have been paraplegic since birth, for example.
As with the preference measure, an important follow-up question would be to assess emotion production and recognition in physically disabled populations to determine whether the outcomes from this study are applicable to those chronically confined to a sitting position in a wheelchair. That is, for the participants in this study, was it the difference between their normal stature and ability to communicate that affected their display, or is there a more general effect such that those who are always restricted will show the same differences in display? Reports from actual wheelchair users indicate that they feel less able to express dominant emotions, particularly anger, while in a wheelchair (Cahill and Eggleston, 1994, p. 304). One woman, relegated to a wheelchair, was so angry she indicated that she wanted to jump out of her chair and shake the person she was directing her anger toward, but all she did was remain seated and “grit her teeth.” New wheelchairs that raise the individual to the eye level of non-disabled individuals (e.g., https://www.quantumrehab.com/ilevel-power-chairs/) may help reduce at least the relative height differences contributing to implicit status differences.
These findings suggest additional questions to be addressed with future research. Do people who have physical differences that constrain bodily movement alter their displays, especially for these target emotions (i.e., when the restriction is more chronic, have people learned to adjust displays of body-related emotions)? Do people who have physical differences that constrain bodily movement correspondingly adjust their confidence in expression of these emotions? Finally, do perceivers who have physical differences that constrain bodily movement more accurately perceive emotions (especially status related emotions) of others with similar physical differences? If so, this may be because the embodiment of these displays is consistent across expresser and the perceiver, or that given social-grouping differences, those with these physical differences have more experience interacting with and perceiving these displays. The answer to these questions may provide information on whether people’s display is more influenced by the broader social context, in which personal history would matter less or in sensitivity to changes in their own body.
Finally, we also want to consider perceiver effects, especially between non-disabled and disabled individuals. Do perceivers’ stereotypes influence what they see regarding emotional expression in people in a wheelchair? In other words, would perceptions be the same if the person were constrained in a wheelchair vs. a kitchen chair or a paraplegic person vs. hostage? It is possible that non-disabled perceivers stereotype people in wheelchairs as less dominant, and this affects the social dynamic between individuals. Studies examining the perception of wheelchair users by non-disabled individuals showed that non-disabled individuals associate more negative emotions (e.g., depression and guilt) with the disabled person (Vilchinsky et al., 2010). As with the higher status displays, those perceiving physically disabled people in wheelchairs may fail to account for the situational influences on emotional expression and may attribute the displays to the person’s internal state not situational constraint.
In conclusion, recent work investigating embodiment theory emphasizes that the current state of the body can have an important influence on psychological and emotional processes. Consistent with this, we found that constraints on body movement and position influence how emotions are produced and how they are perceived. This study’s findings provide only partial support for a pure embodiment perspective. Current body inputs do not appear to influence the expresser’s perception of their own emotional communication: people’s confidence in their displays did not change with body constraints. However, the results emphasize the important contributions of the body in nonverbal emotional communication and how these contributions may affect the social-emotional context of those with physical disabilities.
Data Availability Statement
The datasets generated for this study are available on request to the corresponding author.
Ethics Statement
The studies involving human participants were reviewed and approved by Claremont McKenna College IRB. The patients/participants provided their written informed consent to participate in this study.
Author Contributions
CR was the project lead. KM, SA, and AS contributed parts of their independent research and thesis projects to the manuscript. EM and DM contributed expertise in the intellectual formation of the project and in writing the manuscript. All authors contributed to the article.
Conflict of Interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
This paper honors the memory of AS whose ideas and life inspired this research project investigating how physical disabilities may influence emotion perception and communication. She was an excellent colleague and scholar; we miss her intelligence and optimism, and we appreciate the additional insights she provided from her lived experiences with neuromuscular disease and wheelchair mobility.
1Consistent with the analyses conducted on the full data set, the group × face/body use × emotion ANOVA conducted with Huynh-Feldt corrected, equal sized groups found no main effects of group F(1,40) = 0.002, p = 0.96 and emotion F(4.95, 198.13) = 1.78, p = 0.12; no group × emotion interaction F(4.95, 198.13) = 1.03, p = 0.40; a main effect of face/body use F(1.26, 50.50) = 40.86, p < 0.0001; no group × face/body use interaction F(1.26, 50.50) = 2.69, p = 0.10; a significant emotion × face/body use interaction F(6.50, 259.93) = 12.36, p < 0.0001; and a significant 3-way interaction F(6.50, 259,93) = 2.11, p = 0.02.
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Edited by: Mojgan Rastegar, University of Manitoba, Canada
Reviewed by: Craig Peterson, University of Massachusetts Medical School, United States; Tom Moss, Laval University, Canada
*Correspondence: David J. Picketts, dpicketts@ohri.ca
This article was submitted to Epigenomics and Epigenetics, a section of the journal Frontiers in Genetics
118202020201188507620202072020Copyright © 2020 Timpano and Picketts.2020Timpano and PickettsThis is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
The ability to determine the genetic etiology of intellectual disability (ID) and neurodevelopmental disorders (NDD) has improved immensely over the last decade. One prevailing metric from these studies is the large percentage of genes encoding epigenetic regulators, including many members of the ATP-dependent chromatin remodeling enzyme family. Chromatin remodeling proteins can be subdivided into five classes that include SWI/SNF, ISWI, CHD, INO80, and ATRX. These proteins utilize the energy from ATP hydrolysis to alter nucleosome positioning and are implicated in many cellular processes. As such, defining their precise roles and contributions to brain development and disease pathogenesis has proven to be complex. In this review, we illustrate that complexity by reviewing the roles of ATRX on genome stability, replication, and transcriptional regulation and how these mechanisms provide key insight into the phenotype of ATR-X patients.
intellectual disabilityneurodevelopmental disorderATRXATR-X syndromechromatin remodelingSWI/SNFCanadian Institutes of Health Research10.13039/501100000024FRN133586Introduction
Neurodevelopmental disorders (NDD) are highly complex and heterogeneous conditions that have a global prevalence of approximately 2–3% of the population. Despite being aware of these conditions for over a century, it is only within the last decade that the development of exome and whole genome sequencing has dramatically enhanced the discovery of the underlying causes of these disorders. Indeed, the SysID database1 list 1,334 genes (updated March 26, 2020) that contribute to intellectual disability (ID) (Kochinke et al., 2016), while approximately 100 genes are associated with autism spectrum disorder (ASD) (Satterstrom et al., 2020). Interestingly, a substantial proportion of NDD causing genes are involved in chromatin and/or transcriptional regulation including the broad family of ATP-dependent chromatin remodelers.
Chromatin remodelers utilize energy from ATP hydrolysis to alter nucleosome spacing/density or to facilitate histone variant exchange (Bowman and Poirier, 2015). There are four main families of ATP-dependent chromatin remodelers characterized by their conserved ATPase domain of the helicase II superfamily (Figure 1). These families are divided into the (1) SWI/SNF group, large complexes made up of ∼15 subunits, (2) ISWI group, heterodimers and four subunit complexes, (3) CHD group, complexes that incorporate up to ∼10 subunits, and (4) INO80 group, ∼15 subunit complexes. In addition, the focus of this review is ATRX which represents one of several orphan families that have been less studied mechanistically. In addition to the ATPase domain that is subdivided into two RecA-like lobes, these chromatin remodeling enzymes are characterized by additional motifs that facilitate protein–protein interactions (e.g., HSA and QLQ domains), DNA interactions (e.g., HAND and SLIDE domains), and chromatin interactions (e.g., SANT, chromodomain, and bromodomain) (Figure 1). The SWI/SNF and INO80 family primarily promote transcription and DNA repair by sliding/ejecting nucleosomes (SWI/SNF/BRG1, BRM) or depositing histone variants (INO80/SRCAP). The ISWI and CHD family primarily mediate nucleosome maturation and spacing to promote chromatin formation post-replication, highly structured chromatin (ISWI), or transcriptional repression (CHD) (Clapier et al., 2017).
The ATP-dependent chromatin remodeling family. Representation of the four chromatin remodeling groups: SWI/SNF, ISWI, CHD, and INO80. Each group contains an ATPase domain subdivided into RecA-like lobes 1 and 2 separated by a variable linker region (labeled insertion). SWI/SNF and INO80 share an HSA domain, while ISWI and CHD share a SANT and SLIDE domain.
Mutations in these enzyme families results in aberrant gene expression that impinges on many cellular activities including DNA replication, DNA repair, as well as cell proliferation and differentiation. As indicated above, mutations in many of these family members lead to a wide range of NDD and symptoms (Table 1) with some of the more well-studied disorders being Coffin-Siris syndrome (CSS), Nicolaides-Baraitser syndrome (NCBS), CHARGE syndrome, and ATR-X syndrome. Moreover, it is becoming clear that mutations in multiple components of these remodeling complexes cause ID (Table 1) and can contribute to a spectrum of clinical phenotypes that is best illustrated by mutations in the SWI/SNF interacting partners (Bögershausen and Wollnik, 2018; van der Sluijs et al., 2019). The reader is referred to a number of recent reviews for detailed information of these different remodeler classes (Hota and Bruneau, 2016; Sokpor et al., 2017; Goodwin and Picketts, 2018; Alfert et al., 2019; Hoffmann and Spengler, 2019).
ATP-dependent chromatin remodelers are a frequent cause of NDDs. List of NDD implicated genes which are incorporated into ATP-dependent chromatin remodeling complexes.
Family
Gene
Associated disease
References
SWI/SNF
ARID1A
ID, CSS
Tsurusaki et al. (2012); Kosho et al. (2013)
ARID1B
ID, CSS, ASD
Hoyer et al. (2012); Santen et al. (2012); De Rubeis et al. (2014)
ARID2
ID, CSS-like, NCBS-like
Shang et al. (2015); Bramswig et al. (2017)
DPF2
ID, CSS-like
Vasileiou et al. (2018)
PBRM
ASD
O’Roak et al. (2012b)
SMARCA2
NCBS
Van Houdt et al. (2012); Tsurusaki et al. (2014b)
SMARCB1
ID, CSS, Kleefstra
Kleefstra et al. (2012); Santen et al. (2013); Wieczorek et al. (2013)
SMARCC1
ASD
Hormozdiari et al. (2015)
SMARCC2
ID, ASD
Machol et al. (2019)
SMARCE1
CSS-like
Tsurusaki et al. (2012); Kosho et al. (2013); Wieczorek et al. (2013)
SMARCA4
CSS
Santen et al. (2013); Tsurusaki et al. (2014b)
SOX11
ID, CSS-like
Tsurusaki et al. (2014a)
ISWI
BAZ1A
ID
Zaghlool et al. (2016)
BAZ1B
Williams-Beuren syndrome
Lu et al. (1998); Peoples et al. (1998)
BAZ2B
ID, ASD
Scott et al. (2020)
BPTF
ID
Stankiewicz et al. (2017)
SMARCA1
CSS-like, Rett syndrome-like, schizophrenia
Karaca et al. (2015); Homann et al. (2016); Lopes et al. (2016)
Vissers et al. (2004); Sanlaville et al. (2006); O’Roak et al. (2012b)
CHD8
ASD
O’Roak et al. (2012a, b); De Rubeis et al. (2014); Neale et al. (2016)
INO80
INO80
Microcephaly, ID
Alazami et al. (2015)
SRCAP
Floating-harbor syndrome
Hood et al. (2012)
YY1AP1
ID
Guo et al. (2017)
ATRX
ATRX
ATR-X syndrome
Gibbons et al. (1995)
Here, we will review recent studies on ATRX to highlight the multiple biochemical functions chromatin remodeling proteins participate in, and the diverse set of mechanisms that, collectively, contribute to the complexity underlying the pathogenesis of NDD.
Molecular Genetics of the ATR-X Syndrome
The ATR-X syndrome is a rare human congenital disorder with a wide range of symptoms that primarily affects males. Over 200 cases have been identified worldwide with and an estimated prevalence of <1–9/1,000,000 (Gibbons, 2006). Affected individuals display cognitive impairment typically described as severe ID, and many are non-verbal, capable of speaking or signing only a few words (Saugier-Veber et al., 1995; Guerrini et al., 2000). Originally, the presence of alpha thalassemia was used as a diagnostic tool to identify affected individuals, but there is variability in the hematological symptoms (Gibbons et al., 1995). The majority of patients are affected with microcephaly and skeletal malformations (Holmes and Gang, 1984; Carpenter et al., 1999). Muscle development is also impaired in most, leading to delayed motor development and hypotonia, while approximately one third of patients experience seizures (Lossi et al., 1999).
Although ATR-X syndrome patients present with a heterogeneous phenotype, the disease is caused by mutations in a single gene, the ATRX locus, which spans over 300 kbp on chromosome Xq13.3-21.1 (Gibbons et al., 1995, 2008; Picketts et al., 1996). The ATRX gene encodes two major trasncripts (Figure 2), one encoding the full length protein and a truncated isoform generated by an alternative splicing event that retains intron 11 and terminates translation prematurely (Garrick et al., 2004; Mitson et al., 2011). The full length transcript encodes a protein of 285 kDa in size while the shorter transcript generates a smaller truncated protein that is 180 kDa and lacks the ATP-dependent remodeling domain (Picketts et al., 1996; Garrick et al., 2004).
ATRX domain structure. Schematic diagram of full-length ATRX (282 kDa), truncated ATRX (ATRXt; 180 kDa), and locations of the key protein interaction domains. The two isoforms share an ADD domain, a HP1α binding motif, an EZH2 binding motif, while the ADD domain is comprised of a GATA-like zinc finger and a PHD-like zinc finger. The full-length polypeptide also contains a DAXX binding motif, a SNF2-ATPase domain comprising RecA-like lobes 1 and 2 separated by a linker region containing a MeCP2 binding motif (MeCP2/Insertion), and a PML targeting motif.
The N-terminus of the ATRX protein houses several motifs critical for its interaction with chromatin, including a heterochromatin protein 1 (HP1α) binding motif (PxVxL) (Lechner et al., 2005) and enhancer of zeste homolog 2 (EZH2) interaction domain (Cardoso et al., 1998), and the ATRX-DNMT3-DNMT3L (ADD) domain (Picketts et al., 1998; Xie et al., 1999). The ADD domain comprises a GATA-like zinc finger and a plant homeodomain (PHD)-like finger that targets the dual histone post translational modification (PTM), H3K9me3 and H3K4me0 (Argentaro et al., 2007; Dhayalan et al., 2011; Eustermann et al., 2011; Iwase et al., 2011). A region within the center of the polypeptide mediates death domain associated protein (DAXX) binding (Xue et al., 2003). Toward the C-terminus lies the highly conserved RecA-like lobes 1 and 2 that together are required for ATPase activity (Picketts et al., 1996), as well as mapped regions for interactions with the methyl-CpG-binding protein (MeCP2) (Nan et al., 2007) and the promyelocytic leukemia protein (PML) (Bérubé et al., 2008) (Figure 2).
The majority of ATR-X syndrome causing mutations are missense mutations mapping within the ADD (50%) and SNF2-like/helicase domains (30%) (Argentaro et al., 2007; Gibbons et al., 2008). To date, there has been a lack of genotype: phenotype correlations identified, although mutations within the ADD domain typically produce more severe psychomotor phenotypes compared to mutations in the SNF2-like/helicase domain (Badens et al., 2006).
It should also be noted that somatic mutations in the ATRX gene have been identified in a wide range of cancers that include pancreatic neuroendocrine tumors, gliomas, neuroblastomas, and sarcomas, which will not be discussed here but have been the focus of recent reviews (Watson et al., 2015; Dyer et al., 2017).
Interacting Partners and Biochemical Functions
All functional studies indicate that ATRX is a heterochromatin interacting protein. It localizes to pericentromeric heterochromatin, telomeres, PML nuclear bodies, and physically interacts with the HP1 family (McDowell et al., 1999; Berube et al., 2000; Tang et al., 2004). Later work demonstrated that ATRX could be recruited to the heterochromatin histone mark, H3K9me3, either indirectly by its interaction with HP1 or recruitment by MeCP2, and directly by binding of the ADD domain to H3K9me3 that lies adjacent to unmethylated H3K4 (Berube et al., 2000; Bannister et al., 2001; Nan et al., 2007; Eustermann et al., 2011; Iwase et al., 2011). ATRX and DAXX were identified as interacting partners by two separate groups, one using ATRX co-IP experiments and the other a Flag-DAXX pull-down approach (Xue et al., 2003; Tang et al., 2004). Further characterization showed that most of the endogenous ATRX protein is in a 1 MDa complex with DAXX, while DAXX also fractionates in a 700 kDa complex independent of ATRX. Deletion mutants were used to demonstrate that the ATRX/DAXX interaction was mediated through the PAH domain of DAXX and a region between the ADD and SNF2 domains within ATRX (Tang et al., 2004). Both the ATRX/DAXX complex and recombinant ATRX protein had DNA or nucleosome stimulated ATPase activity which was impaired by patient mutations that localized to the ATPase domain (Xue et al., 2003; Tang et al., 2004). A mononucleosome disruption assay was used to demonstrate that the ATRX/DAXX complex could alter the DNAse I digestion pattern of the mononucleosome in the presence of ATP. The localization of the altered digestion pattern indicated that ATRX/DAXX disrupts DNA–histone interactions at the entry site of the nucleosome and does not alter nucleosome phasing. In addition, a triple-helix strand displacement assay was used to show that the ATRX/DAXX complex and ATRX alone had a DNA translocase property similar to the RSC and SWI/SNF complexes (Xue et al., 2003). More recent work has indicated that DAXX is an H3.3-specific histone chaperone that functions with ATRX to deposit the histone variant at pericentric and telomeric repeats, while DAXX functions independently of ATRX to repress retrotransposons (Lewis et al., 2010; Hoelper et al., 2017). In this regard, the ATRX/DAXX complex shows some similarities with the ISWI complex ACF and its interactions with the histone chaperone NAP1 (Gemmen et al., 2005; Torigoe et al., 2011). These properties could be used to reconstitute H3.3 containing nucleosomal arrays that might guide future in vitro biochemical studies to further define ATRX function during transcription or DNA replication (Peterson, 2009).
Indeed, in a series of papers ATRX, DAXX, and the histone variant H3.3 were shown to co-localize at telomeres where the ATRX/DAXX complex functions as a histone chaperone to deposit H3.3 into telomeric heterochromatin (Wong et al., 2009; Goldberg et al., 2010; Lewis et al., 2010). Further work showed that DAXX functions as the histone chaperone, that H3.3K9me3 deposition occurs in a replication-independent manner by the complex, and both H3.3 loading and heterochromatin organization by ATRX/DAXX is mediated by SUV39H1 and PML in PML-associated heterochromatin domains (Drané et al., 2010; Goldberg et al., 2010; Lewis et al., 2010; He et al., 2015; Udugama et al., 2015; Delbarre et al., 2017). Additionally, ATRX was shown to be critical for the formation of senescence-induced heterochromatin foci (SAHF) that help drive cancer cells into therapy-induced senescence (Kovatcheva et al., 2017). Finally, ATRX has been shown to bind to the Xist lncRNA to promote recruitment of the PRC2 repressive complex and facilitate stable heterochromatin formation of the silenced X-chromosome (Sarma et al., 2014). RNA binding remains an understudied role for ATRX, although several reports have shown a range of interactions with multiple lncRNAs including TERRA (telomeric repeat-containing RNA) (Chu et al., 2017; Nguyen et al., 2017), ChRO1 in muscle (Park et al., 2018), and minor satellite RNAs at centromeric heterochromatin (Ren et al., 2020). These interactions are mediated through a unique N-terminal domain in ATRX to regulate differentiation, gene expression, DNA and histone methylation and chromatin compaction (Chu et al., 2017; Nguyen et al., 2017; Park et al., 2018; Ren et al., 2020).
A role for ATRX at heterochromatin was also strengthened by chromatin immunoprecipitation experiments that showed enriched ATRX binding at telomeres and centromeres. Interestingly, ATRX was also enriched at repetitive DNA elements while having a lower frequency of binding within gene bodies (Law et al., 2010). Further characterization showed that ATRX was prevalent at long terminal repeats of endogenous retrovirus sequences of family K (ERVK), at variable number tandem repeats (VNTRs) and at simple tandem repeats (Law et al., 2010). Many of the tandem repeats were GC-rich sequences that are predicted to form G-quadruplex secondary DNA structures (G4 DNA) including the telomeric repeats and some CpG islands. The formation of G4 DNA has been proposed to have important roles in the regulation of gene expression, as well as be prohibitive to DNA replication and transcription (Rhodes and Lipps, 2015; Valton and Prioleau, 2016; Varshney et al., 2020). In vitro studies confirmed that ATRX can bind to G4 DNA structures (Law et al., 2010). In addition, ATRX mutations have variable effects on α-globin expression including individuals with the same mutation. Law et al. (2010) demonstrate that one ATRX binding site lies within a GC-rich VNTR sequence 1 kb upstream of the HBM gene. The authors demonstrate a positive correlation in ATR-X patients such that increasing VNTR repeat size increases the severity of the α-thalassemia as measured by the level of HbH inclusions in red blood cells. Since the sequence is a GC-rich VNTR that is predicted to form G4 quadruplexes, it was inferred that increasing repeat size increases the probability to form G4 DNA that subsequently alters HBM expression.
The ATRX protein was also shown to co-purify with the MRE11-RAD50-NBS1 (MRN) complex, an active player in the processing of DNA double strand breaks (DSB) that suggested ATRX was critical to maintain genome integrity (Leung et al., 2013). Consistent with this finding, ATRX knockdown studies in HeLa cells resulted in defects in mitotic progression and micronuclei formation from altered chromosome condensation and centromeric cohesion (Ritchie et al., 2008). Other studies indicated that ATRX loss impaired replication fork progression during S-phase resulting in telomere fragility, increased DSB, and mitotic catastrophe (Huh et al., 2012, 2016; Leung et al., 2013; Watson et al., 2013).
The ATRX-DAXX-H3.3 complex is critical for this heterochromatic formation and subsequent maintenance (Law et al., 2010; Eid et al., 2015; He et al., 2015; Udugama et al., 2015). H3.3 within telomeric regions is targeted for trimethylation on its K9 residue (He et al., 2015; Udugama et al., 2015). H3.3K9me3 recruits more ATRX-DAXX-H3.3 complexes, which in turn will deposit H3.3, creating a positive feedback loop required for maintaining telomere structure (Udugama et al., 2015). Failure to establish proper structure will reduce telomere integrity and result in an increase of non-coding telomeric transcript expression (He et al., 2015; Udugama et al., 2015).
The eclectic properties of the ATRX protein do not make it intuitively obvious how an aberration of these functions can result in a neurodevelopment disorder with cognitive deficits. In the remaining section, we discuss the characterization of mouse models and the insights they have provided into the pathophysiology of ATR-X patients and, more generally, the complex etiology of NDDs caused by defective epigenetic regulators.
Delineating Pathophysiological Mechanisms of the ATR-X SyndromeFunctional Effects of Patient Mutations and Generation of Animal Models
One of the first questions addressed was do patient mutations affect protein stability and function? Immunoblots of extracts from patient-derived EBV-transformed B-lymphocytes showed significantly reduced levels of ATRX protein from all patients tested (McDowell et al., 1999; Cardoso et al., 2000). Interestingly, in patients with early premature stop codons (e.g., p.Arg37X), translation was initiated from an internal methionine that produced a smaller truncated protein at ∼30% levels leading to a milder phenotype (Howard et al., 2004; Abidi et al., 2005; Basehore et al., 2015). Utilizing recombinant proteins, other studies demonstrated that mutations within the ATPase domain attenuated ATPase activity but did not reduce it, while mutations in the ADD domain or the PML-targeting domain reduced localization to chromocenters and PML nuclear bodies, respectively (Cardoso et al., 2000; Bérubé et al., 2008). Atrx-null mutations in mice show defective extraembryonic trophoblast development and die embryonically at ∼E9.5 (Garrick et al., 2006). Collectively, these studies indicate that ATR-X syndrome causing mutations are functional hypomorphs, while more severe mutations are not found and are presumably non-viable.
Several different ATRX-deficient mouse lines have been generated and used for functional characterization. The most widely used model is a floxed allele in which loxP sites flanked exon 18 which encodes the ATP-binding pocket (Bérubé et al., 2005; Garrick et al., 2006). These animals have been crossed with several different tissue-specific Cre driver lines to inactivate ATRX in skeletal muscle progenitors (Huh et al., 2012), Sertoli cells (Bagheri-fam et al., 2011), osteobalsts (Solomon et al., 2013), chondrocytres (Solomon et al., 2009), the retina (Medina et al., 2009; Lagali et al., 2016), and the developing forebrain (Bérubé et al., 2005) among others. A second transgenic line (AtrxΔE2) was developed by deleting exon 2 and replacing it with a SA-IRES-β-geo cassette (Nogami et al., 2011; Shioda et al., 2011). This mutation was meant to mimic the p.Arg37X mutation and make an N-terminally truncated ATRX protein by initiating translation from an internal methionine codon (Howard et al., 2004; Abidi et al., 2005). Both of these models will be discussed in more detail in the following sections. Finally, an overexpression transgenic line was created with the ATRX cDNA under control of a CMV enhancer/β-actin promoter which resulted in growth retardation, neural tube defects and a high incidence of embryonic lethality demonstrating the importance of ATRX dosage to normal development (Berube et al., 2002).
While each of these models has provided valuable insight into disease mechanisms (as highlighted below), the field still awaits a model whereby a single nucleotide variant is introduced into the ATRX gene to recreate a known patient mutation, such as the common p.Arg246Cys mutation within the ADD domain.
Replication Stress Impairs Progenitor Expansion Resulting in Microcephaly
Microcephaly is common to many NDDs and has also been observed in mouse models that deleted other genes encoding chromatin remodeling proteins (Ronan et al., 2013). Most ATR-X patients develop postnatal microcephaly and, in instances where CT or MRI scans have been performed, mild cerebral atrophy was detected. Similarly, three patient autopsy reports also described that the brains were smaller in size (Gibbons, 2006).
The first indication that ATRX may be critical for cell growth came from co-culture experiments of embryonic stem cells (ESC) from control or Atrx-null cells. This experiment demonstrated that the Atrx-null cells were underrepresented after 4-days of co-culture. Flow cytometry was used to examine cell cycle distribution but no differences were observed suggesting that cells may have transitioned to a slower cycling, differentiated cell type (Garrick et al., 2006). Given that ATRX has high expression in the developing forebrain, the Atrxfl/fl line was next crossed with the forebrain-specific Foxg1-Cre line (AtrxFoxg1Cre) that initiates Cre expression in the developing telencephalon at ∼E8.5 (Hébert and McConnell, 2000). Loss of ATRX caused a 25–30% reduction in cell number with a noticeably smaller neocortex and hippocampus including almost a complete absence of the dentate gyrus that likely contributed to early postnatal lethality (Bérubé et al., 2005). Similar to ESC co-culture experiments, BrdU-pulse labeling experiments suggested no differences in the proportion of cycling cells. However, there was a dramatic increase in the number of TUNEL+ cells leading to a reduction in the number of neurons that reached the cortical layers (Bérubé et al., 2005). Similarily, the AtrxΔE2 mice were smaller and also showed brain hypocellularity, although to a milder extent (Nogami et al., 2011). Atrx inactivation in Sertoli and muscle cells, also showed a significant impact on the growth of the tissue (Bagheri-fam et al., 2011; Huh et al., 2012). However, a retina progenitor cell cKO only had a limited effect on the size of the mature tissue suggesting that defects in cell cycle progression lead to significant hypocellularity among tissues that require a rapid expansion over a narrow developmental timeframe (Medina et al., 2009).
Although not initially observed, delayed cell cycle progression through both S- and G2/M phases was later observed in other studies (Ritchie et al., 2008; Watson et al., 2013; Huh et al., 2016). For G2/M, the progression from prometaphase to metaphase was prolonged and associated with sister chromatid cohesion and congression defects that impaired proper separation at anaphase leading to DNA bridges and micronuclei (Ritchie et al., 2008). Evidence for DNA bridges and micronuclei in AtrxFoxg1Cre mice were also detected by high magnification microscopy at the apical surface on cortical sections of the neuroepithelium where cortical progenitors complete mitosis. Interestingly, a recent study has also demonstrated that ATRX promotes telomere cohesion between sister telomeres to mediate the repair of DNA DSB (Lovejoy et al., 2020).
Defects in S-phase were observed using BrdU-pulse chase flow cytometry experiments where a delay from G1 to S-phase and also from G2/M to the following G1 phase was identified (Huh et al., 2012). Co-labeling experiments demonstrated that ATRX associated with replicating chromatin during mid-late S-phase and cytological analysis showed a high prevalence of genomic instability that was enriched at telomeres and pericentromeric heterochromatin (Huh et al., 2012; Watson et al., 2013). Moreover, treatment with a compound that binds and stabilizes G4 DNA increased the number of telomere dysfunction induced foci (TIFs) and decreased cell viability suggesting that G4 DNA formation was the main cause of replicative stress (Watson et al., 2013). Other studies indicate that replication stress at telomeres may be mediated by increased TERRA transcription (Nguyen et al., 2017). TERRA levels are tightly regulated and critical for both telomere formation, replication and maintenance (Bettin et al., 2019). However, when TERRA levels increase, as shown for ATRX-null cells, it enhances R-loop (RNA-DNA hybrid) formation and G4 DNA stabilization, each of which increase replication fork stalling and collapse that then induces homology directed repair (HDR) and TIFs (Nguyen et al., 2017). The regulation of R-loops has also been proposed for other proteins that interact with G4 DNA during replication and/or at telomeres (Zhou et al., 2014; Ribeiro de Almeida et al., 2018; Toubiana and Selig, 2018; Maffia et al., 2020). It should also be mentioned here that somatic ATRX mutations, and to a lesser extent H3.3 and DAXX mutations, are prevalent in cancers characterized by ALT (alternative lengthening of telomeres), a HDR mechanism to maintain telomere length that is normally suppressed by ATRX (Heaphy et al., 2011; Lovejoy et al., 2012; Schwartzentruber et al., 2012; Pickett and Reddel, 2015; Verma and Greenberg, 2016).
Another indicator of replicative stress as a major impediment to growth of Atrx-null cells was demonstrated by studies showing an increased sensitivity to hydroxyurea, enhanced DSBs, and the use of DNA fiber analysis to show increased stalled replication forks and reduced origin firing (Leung et al., 2013; Clynes et al., 2014; Huh et al., 2016). Mechanistically, ATRX physically interacts with the MRN complex where it is thought to block HDR at stalled replication forks to allow for fork restart after the G4 DNA is resolved (Clynes et al., 2014). Indeed, one group demonstrated that fork protection could be restored by treatment with an Mre11 exonuclease inhibitor (Huh et al., 2016). This study also suggested that hyperactivation of poly (ADP-ribose) polymerase-1 (Parp-1) during neurogenesis may function as a compensatory mechanism to protect stalled replication forks from collapse and HDR, thus dampening the extent of cell loss during neurogenesis.
During mouse cortical development, the cortical layers are formed in sequential fashion from a pool of neural progenitor cells (NPC) that must continue to proliferate to maintain the pool size. Alterations in NPC proliferation depletes the pool often resulting in altered cell lamination typically observed as a reduction in upper layer neurons. For AtrxFoxg1Cre mice, the most proliferative NPCs that ultimately would become upper layer neurons are more susceptible to incur replication-induced DNA damage. Frequently the resulting genomic instability will occur at telomeres and pericentromeric heterochromatin, but it could also occur at other genomic regions that can form G4 DNA or similar secondary DNA structures that induce replication fork stalling and collapse. Accumulation of sufficient damage further leads to their demise and decreases neuron production and brain size. Indeed, the AtrxFoxg1Cre forebrain is reduced in size with a compromised production of upper layer neurons (Ritchie et al., 2014; Huh et al., 2016). Similar results have been observed in mice lacking CHD4 and SMARCA5 where the NPCs either fail to progress through the cell cycle or incur significant DNA damage, respectively, prior to undergoing apoptosis (Alvarez-Saavedra et al., 2014, 2019; Nitarska et al., 2016).
The SWI/SNF complex is also critical for brain development but utilizes different mechanisms than ATRX. The SWI/SNF complex is required during early neurogenesis for differentiation from radial glial progenitor cells into intermediate progenitor cells. This switch from a neural stem cell to an NPC is accompanied by the fundamental shift from the npBAF to nBAF complex, which involves the substitution of three subunits (BAF45, BAF53, and BAF55). Failure to switch leads to increased cell death, a small progenitor pool, and failure to further differentiate (Lessard et al., 2007; Wu et al., 2007; Bachmann et al., 2016). Interestingly, while SMARCA5 loss hampers NPC proliferation, loss of its ISWI ortholog SMARCA1 fails to repress expression of proliferation genes resulting in delayed neuronal differentiation and a larger brain (Yip et al., 2016). Taken together, these examples highlight the importance of chromatin remodeling proteins to NPC homeostasis and provide insight into the multitude of mechanisms at work often resulting in a similar phenotype.
Transcriptional Deficits Associated With ATRX Mutations
Chromatin remodeling proteins were first identified as transcriptional coactivators and they continue to be implicated in the regulation of many genes. Since its identification, ATRX has also been presumed to regulate gene transcription. While there is a good level of understanding regarding how ATRX maintains genomic stability through the regulation of tandem repeats, telomeres and pericentromeric heterochromatin, the identification of direct transcriptional targets has proven more challenging. Initial ChIPseq experiments suggested that ATRX was bound at few promoters, gene bodies and regulatory elements (Law et al., 2010). Further work has suggested that ATRX binding may differ between tissues to ensure proper silencing of repetitive elements located near or within expressed genes in that particular tissue (Nguyen et al., 2017). Consistent with this idea, ATRX ChIPseq analysis of NPCs demonstrated a higher enrichment of binding sites at gene regulatory elements compared to what was observed in mouse ESCs suggesting that more genes may be under direct ATRX regulation within the brain (Law et al., 2010; He et al., 2015; Danussi et al., 2018). Another contributing factor to differential target gene expression is represented by ATRX effects on α-globin gene expression. Mutational analysis identified >15 ATR-X patients with the identical missense change (p.Arg246Cys), yet they showed a variable degree of hemoglobin H inclusions in blood samples, indicative of differing levels of α-globin expression (Gibbons et al., 1997). Repression of α-globin expression was dependent on the size of a GC-rich VNTR located within the globin gene cluster (Law et al., 2010). A second factor driving the tissue specificity and the variable effects was the formation of R-loops caused by the transcription of the GC-rich VNTR sequences (Nguyen et al., 2017). The larger sequences generate increased R-loops and G4 DNA structures that normally recruit ATRX to re-establish the normal chromatin structure. In the absence of ATRX the R-loop/G4 DNA is not resolved effectively which then impedes both replication and transcription processes (Nguyen et al., 2017). The slight stochastic nature of these effects likely also dampens readouts of differential expression from RNAseq experiments thereby raising the need for a scRNAseq approach in future studies.
Gene expression analysis of control and AtrxFoxg1Cre cortical samples at two timepoints (E13.5, P0.5) identified 202 and 304 differentially expressed genes (DEGs; ±1.5-fold change), respectively, with almost two-thirds of genes upregulated (Levy et al., 2008). Among these, 27 were common to both datasets including the downregulation of several ancestral pseudoautosomal region (aPAR) genes (Csfr2a, Dhrsxy, Cd99, and Asmtl) (Levy et al., 2008, 2014). In mouse, the aPAR genes are located in subtelomeric regions and contain potential G4 DNA sequences. Each gene analyzed had enriched histone H3.3 and ATRX binding within their gene body and showed reduced H3.3 levels when ATRX was absent (Levy et al., 2014). Interestingly, these intragene G4 DNA sequences also showed increased binding of RNA pol II in AtrxFoxg1Cre samples suggesting that transcription becomes impeded at these regions within the gene leading to reduced expression. The authors extended this finding to Nlgn4, a gene encoding a post-synaptic cell adhesion molecule implicated in ASDs (Jamain et al., 2003; Laumonnier et al., 2004). This result conflicted with the study on R-loop formation which found no differences in RNA pol II loading or histone modifications across genes containing the GC-rich repeats (Nguyen et al., 2017).
Other downregulated genes from this analysis include Gbx2, NeuroD4, Wif1, Nxph1, Nxph2, and Mbp, each of which could contribute to cognitive deficits observed in patients but require further analysis to asses their contribution to the phenotype (Levy et al., 2008, 2014). In a similar experiment in the retina, 173 DEGs were identified with two-thirds upregulated (109 genes) and one-third (64 genes) downregulated (Lagali et al., 2016). Most of these genes were involved in the regulation of glutamate activity, ion channel regulation or encoded neuroprotective peptides, with four shown to be also dysregulated in the cortex (Csf2ra, Cbln4, Syt13, and Nlgn4). Each of these studies showed that the mutant samples had only small numbers of genes with large changes in gene expression and, while some downregulated genes may impede transcriptional elongation, this mechanism may not be universal, particularly as it pertains to upregulated genes.
However, other indirect mechanisms have been explored to explain transcriptional dysregulation, particularly a loss of repression. The ATRX/DAXX complex is critical for loading H3.3 at telomeres and pericentromeric heterochromatin (Goldberg et al., 2010; Wong et al., 2010). Research over the last few years has expanded this regulation to include H3.3 deposition at endogenous retroviral elements, regions associated with imprinted genes, and some CpG islands (Elsässer et al., 2015; He et al., 2015; Sadic et al., 2015; Voon et al., 2015). At the telomere, the loss of ATRX affected the transcription of telomeric DNA and the non-coding RNA TERRA, although studies conflict on whether levels increase or decrease (Eid et al., 2015; Nguyen et al., 2017). Surprisingly, TERRA was also shown to bind to an additional ∼4,000 binding sites aside from the telomere where it co-localized with ATRX (Chu et al., 2017). Many of these sites were within introns and comprised GA repeats, however, depletion of TERRA usually resulted in downregulation while ATRX depletion increased expression (Chu et al., 2017). While it remains to be determined how this might affect neuronal gene expression, ATRX has been shown to interact with other lncRNAs including Xist to facilitate PRC2 silencing and ChR01 that is required for heterochromatin reorganization in differentiating muscle cells (Sarma et al., 2014; Park et al., 2018). Indeed, ATRX binding to lncRNA or R-loops may be a key mechanism mediating transcriptional repression of specific target genes.
Histone H3.3 ChIPseq studies have also shown that it is enriched at the intracisternal A-particle endogenous retroviral elements (IAP/ERVs), which account for almost half of all mutation causing ERV insertions (Maksakova et al., 2006; Elsässer et al., 2015). Moreover, H3.3 deposition at these sites requires ATRX/DAXX to facilitate H3K9me3 and repression while depletion of ATRX, DAXX, or H3.3 results in reduction of the H3K9me3 mark and IAP/ERV derepression (Elsässer et al., 2015; He et al., 2015; Voon et al., 2015). In mouse ESCs, ERV derepression affected the expression of neighboring genes in a minority of cases with most genes neutral to ERV derepression. It raises the question of whether or not this type of derepression would affect many genes or occur rapidly within a post-mitotic neuron, and thus, function as a major effector in dysregulated gene expression in Atrx-null neurons. In this regard, a related study using cultured post-mitotic neurons demonstrated that the ADD domain can also bind the H3K9me3S10ph dual histone mark (Noh et al., 2015). This histone mark is rapidly induced by neuronal depolarization where it appears at centromeric and pericentromeric heterochromatin co-localized with ATRX to repress transcription of non-coding centromeric minor satellite sequences (Noh et al., 2015). While it is unclear what the impact of increased centromeric minor satellite transcription would have on disease pathology, it remains to be determined whether this dual mark affects activity-dependent transcription of genes mediating learning or memory.
It was also demonstrated that ATRX was bound to 56 CpG islands which was unexpected since they are often associated with active chromatin, typically promoters (Voon et al., 2015). However, these CpG islands were associated with H3K9me3, almost half were methylated and many corresponded to imprinted loci often residing in intragenic regions within a transcriptional unit (Voon et al., 2015). Indeed, in all cases examined ATRX was bound to the silenced imprinted allele which became reactivated in ATRX KO cells (Voon et al., 2015). This study contrasted somewhat with an independent report in which ATRX was recruited by MeCP2 to silence the active allele of several imprinted genes in the developing telencephalon (Levy et al., 2014). The difference in these studies may reflect differential regulation of imprinting loci in ESCs versus differentiating NPCs. Perhaps the most compelling example of derepression came from a study with the AtrxΔE2 mice (Shioda et al., 2018). In this model, the authors identify a small list of 31 DEGs in the adult hippocampus but with most genes (23/31) downregulated (Shioda et al., 2018). Among the upregulated genes was an imprinted gene from the lymphocyte-regulated gene family, Xlr3b. Although Xlr3b had widespread expression across many tissues, it was only upregulated in the brain. Further work showed that ATRX bound to a G4 DNA sequence within the CpG island of the Xlr3b gene where it normally interacted with DAXX and H3.3 and recruited DNMT1 and DNMT3 to silence the gene. The subsequent overexpression of Xlr3b in the AtrxΔE2 mice was shown to produce a protein that localized to dendritic RNA granules where it interacted with ribonucleoproteins, dynein proteins and the RNA-binding protein, TIA1, to regulate mRNA transport (Shioda et al., 2018). One of the targets identified was the mRNA for CAMK II-α which they had previously shown to be deregulated in these animals. Excitingly, they also showed that the G-quadruplex-binding ligand 5-aminolevulinic acid (5-ALA) was able to decrease RNApol II occupancy and Xlr3b expression in the AtrxΔE2 mice, although methylation of the G4 DNA sequence within the CpG island was not affected. It seems that formation or stabilization of this G4 DNA sequence is required to activate the Xlr3b gene and that ATRX normally prevents this by facilitating heterochromatin formation. In this regard, mapping of G4 DNA sequences have shown an enriched number in gene regulatory elements where many function to increase transcription when stabilized (Hänsel-Hertsch et al., 2016). While G4 DNA stabilization occurs in the AtrxΔE2 mice, further work is required to explain how 5-ALA represses Xlr3b transcription when it should stabilize the G4 DNA. Regardless, the derepression of G4 DNA within CpG islands and/or other regulatory elements is an exciting mechanism that can explain DEG upregulation, particularly when coupled with the finding that ATRX binding is increased at regulatory elements in NPCs compared to ESCs. Collectively, the derepression of tandem repeats, retrotransposable elements and G4 quadruplexes can all function to impinge on neuronal function.
Morphological, Behavioral, and Cell Non-autonomous Deficits
We have discussed global effects on DNA replication and transcription that occur in the absence of ATRX in the previous two sections. In this section, we review the morphological and functional repercussions of these deficits. Aside from being reduced in size, the AtrxFoxg1Cre mice had a normal cortical morphology with proper lamination although a reduction of upper layer neurons (Bérubé et al., 2005; Ritchie et al., 2014; Huh et al., 2016). The reduction in upper layer neurons may also contribute to the partial agenesis of the corpus callosum observed in some patients (Gibbons, 2006). The hippocampus was also reduced in size while the dentate gyrus consisted of only a few disorganized cells. Behavior analysis was not performed due to the early postnatal lethality, although female heterozygous mice showed impairment in spatial, contextual fear, and novel object recognition memory (Tamming et al., 2017).
The AtrxΔE2 mice also had smaller brains but no differences in cell density within layers II/III of the prefrontal cortex (PFC) or hippocampus (Shioda et al., 2011). Examination of dendritic spines in the PFC showed that the AtrxΔE2 mice had similar numbers but fewer mature spines and many more, thin, long immature spines (Shioda et al., 2011). Behavioral analysis indicated that the mice have impaired contextual fear memory (fear conditioning test), spatial memory (Y-maze), but not anxiety behaviors (Nogami et al., 2011; Shioda et al., 2011). Electrophysiology studies in hippocampal slices demonstrated reduced NMDAR-dependent long term potentiation (LTP) evoked by high stimulation frequency in hippocampal CA1 neurons which was mediated by increased CAMK2A and GluR1 phosphorylation (Nogami et al., 2011). This was in contrast to a later article by the same group that showed phosphorylated CAMK2A levels were reduced in the AtrxΔE2 mice while 5-ALA restored the levels at the synapse and the phosphorylation levels (Shioda et al., 2018). A recent article examining hippocampal function using CAMKII-Cre mice to inactivate Atrx in postnatal excitatory forebrain neurons demonstrated reduced paired-pulse facilitation and LTP in proximal and distal apical dendrites of CA1 synapses (Gugustea et al., 2019). This represented the first study of mice in which Atrx has been inactivated after neurogenesis and it will be interesting to ascertain the full characterization of these mice.
Studies of the retina have also provided useful information into ATRX function. Many ATR-X patients have visual problems although this has been an under-appreciated aspect of the phenotype (Medina et al., 2009). Inactivation of ATRX in retinal progenitors AtrxPax6Cre resulted in a slight reduction in retina size and a specific reduction of interneurons, namely amacrine and horizontal cells (Medina et al., 2009). Surprisingly, AtrxPitf1aCre mice that ablates ATRX in a bi-potential progenitor that generates amacrine or horizontal cells did not recapitulate the phenotype, while inactivation with a bipolar cell specific Cre driver (AtrxVsx2Cre) did not affect bipolar cell survival but did result in reduced amacrine and horizontal cells suggesting that interneuron survival was a cell non-autonomous effect (Lagali et al., 2016). Additional characterization of these mice showed that the bipolar axons were mislocalized within the inner plexiform layer and many had axonal swellings or tortuous paths to their targets. Gene expression analysis identified alterations in the glutamate pathway, ion channel regulation and altered expression of neuroprotective peptides. Altered axonal pathfinding was also observed in Drosophila XNP mutants, the homolog to the ATPase domain of ATRX (Sun et al., 2006). It will be important to further explore in greater detail whether axonal pathfinding is also altered within forebrain or hippocampal neurons.
Perspectives
Studies to date have indicated that ATRX has multiple roles during forebrain development that can contribute to the phenotype of ATR-X patients. It functions mainly as a heterochromatin interacting protein acting to ensure that repetitive DNA is properly packaged and organized into heterochromatin. We have highlighted how aberrations in heterochromatin maintenance leads to genomic instability and replication stress that impairs NPC expansion leading to a microcephalic brain (Figure 3). The loss of ATRX also affects gene expression typically resulting in increased gene derepression but also downregulation. It remains to be teased apart which targets are direct versus indirect, and when disrupted expression hampers neuronal function. It is likely that inactivation of ATRX in postmitotic neurons, following neurogenesis and lamination, will help define a role for ATRX target genes in altered synaptic activity and/or synaptic plasticity underlying cognitive impairment. Moreover, the contribution of other central nervous system cell types to the phenotype have not been explored. ATRX is expressed in glia and oligodendrocytes which are known to intimately communicate with neurons to mediate function, as shown recently in Drosophila glial ATRX dependent ensheathment of sensory neurons, for normal dendritic arborization and stimulus processing (Yadav et al., 2019). Intriguingly, MRI studies on ATR-X patients showed severe glial defects and white matter disruption, further stressing the need for research in this area (Wada et al., 2013; Lee et al., 2015). Importantly, a further understanding of ATRX function and its aberrant molecular pathways are required before potential treatments can be explored. In this regard, treatment with 5-ALA has shown promise in one animal model and it is being investigated in Japanese patients (T. Wada, personal communication). ATRX is but one of many different chromatin remodeling proteins mutated in NDDs but it serves to demonstrate how complex these disorders are and how widely chromatin remodelers impact cellular activities.
The multiple functions of the ATRX protein. Schematic diagram of ATRX functional influence on brain development and its contribution to NDDs. Normally ATRX utilizes its chromatin remodeling activity to (1) influence transcription and DNA replication in heterochromatic regions to control the rate of proliferation in the neuronal progenitor cell population (bottom arm); and (2) to influence transcription in heterochromatic regions to control differentiation processes (top arm). When ATRX is mutated the cellular proliferation rates in progenitors is slowed resulting in a smaller progenitor population; and the differentiation processes are altered resulting in either dysfunctional cellular morphology or complete absence of specific cell types.
Author Contributions
ST and DP wrote and edited the manuscript together. ST generated the figures and table. Both authors contributed to the article and approved the submitted version.
Conflict of Interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Funding. The research supporting this work was funded by two operating grants (FRN133586 and FRN165994) from the Canadian Institute of Health Research awarded to DP.
We are grateful to Dr. Alex Córdova and Valérie Cardin for their valued input and critical reading of the manuscript.
https://sysid.cmbi.umcn.nl/
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Genet.Frontiers in Genetics1664-8021Frontiers Media S.A.32849845PMC743215610.3389/fgene.2020.00885GeneticsReviewNeurodevelopmental Disorders Caused by Defective Chromatin Remodeling: Phenotypic Complexity Is Highlighted by a Review of ATRX FunctionTimpanoSara1PickettsDavid J.12345*1Regenerative Medicine Program, Ottawa Hospital Research Institute, Ottawa, ON, Canada2Department of Biochemistry, Microbiology, and Immunology, University of Ottawa, Ottawa, ON, Canada3Department of Cellular and Molecular Medicine, University of Ottawa, Ottawa, ON, Canada4Department of Medicine, University of Ottawa, Ottawa, ON, Canada5University of Ottawa Brain and Mind Research Institute, Ottawa, ON, Canada
Edited by: Mojgan Rastegar, University of Manitoba, Canada
Reviewed by: Craig Peterson, University of Massachusetts Medical School, United States; Tom Moss, Laval University, Canada
*Correspondence: David J. Picketts, dpicketts@ohri.ca
This article was submitted to Epigenomics and Epigenetics, a section of the journal Frontiers in Genetics
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The ability to determine the genetic etiology of intellectual disability (ID) and neurodevelopmental disorders (NDD) has improved immensely over the last decade. One prevailing metric from these studies is the large percentage of genes encoding epigenetic regulators, including many members of the ATP-dependent chromatin remodeling enzyme family. Chromatin remodeling proteins can be subdivided into five classes that include SWI/SNF, ISWI, CHD, INO80, and ATRX. These proteins utilize the energy from ATP hydrolysis to alter nucleosome positioning and are implicated in many cellular processes. As such, defining their precise roles and contributions to brain development and disease pathogenesis has proven to be complex. In this review, we illustrate that complexity by reviewing the roles of ATRX on genome stability, replication, and transcriptional regulation and how these mechanisms provide key insight into the phenotype of ATR-X patients.
intellectual disabilityneurodevelopmental disorderATRXATR-X syndromechromatin remodelingSWI/SNFCanadian Institutes of Health Research10.13039/501100000024FRN133586Introduction
Neurodevelopmental disorders (NDD) are highly complex and heterogeneous conditions that have a global prevalence of approximately 2–3% of the population. Despite being aware of these conditions for over a century, it is only within the last decade that the development of exome and whole genome sequencing has dramatically enhanced the discovery of the underlying causes of these disorders. Indeed, the SysID database1 list 1,334 genes (updated March 26, 2020) that contribute to intellectual disability (ID) (Kochinke et al., 2016), while approximately 100 genes are associated with autism spectrum disorder (ASD) (Satterstrom et al., 2020). Interestingly, a substantial proportion of NDD causing genes are involved in chromatin and/or transcriptional regulation including the broad family of ATP-dependent chromatin remodelers.
Chromatin remodelers utilize energy from ATP hydrolysis to alter nucleosome spacing/density or to facilitate histone variant exchange (Bowman and Poirier, 2015). There are four main families of ATP-dependent chromatin remodelers characterized by their conserved ATPase domain of the helicase II superfamily (Figure 1). These families are divided into the (1) SWI/SNF group, large complexes made up of ∼15 subunits, (2) ISWI group, heterodimers and four subunit complexes, (3) CHD group, complexes that incorporate up to ∼10 subunits, and (4) INO80 group, ∼15 subunit complexes. In addition, the focus of this review is ATRX which represents one of several orphan families that have been less studied mechanistically. In addition to the ATPase domain that is subdivided into two RecA-like lobes, these chromatin remodeling enzymes are characterized by additional motifs that facilitate protein–protein interactions (e.g., HSA and QLQ domains), DNA interactions (e.g., HAND and SLIDE domains), and chromatin interactions (e.g., SANT, chromodomain, and bromodomain) (Figure 1). The SWI/SNF and INO80 family primarily promote transcription and DNA repair by sliding/ejecting nucleosomes (SWI/SNF/BRG1, BRM) or depositing histone variants (INO80/SRCAP). The ISWI and CHD family primarily mediate nucleosome maturation and spacing to promote chromatin formation post-replication, highly structured chromatin (ISWI), or transcriptional repression (CHD) (Clapier et al., 2017).
The ATP-dependent chromatin remodeling family. Representation of the four chromatin remodeling groups: SWI/SNF, ISWI, CHD, and INO80. Each group contains an ATPase domain subdivided into RecA-like lobes 1 and 2 separated by a variable linker region (labeled insertion). SWI/SNF and INO80 share an HSA domain, while ISWI and CHD share a SANT and SLIDE domain.
Mutations in these enzyme families results in aberrant gene expression that impinges on many cellular activities including DNA replication, DNA repair, as well as cell proliferation and differentiation. As indicated above, mutations in many of these family members lead to a wide range of NDD and symptoms (Table 1) with some of the more well-studied disorders being Coffin-Siris syndrome (CSS), Nicolaides-Baraitser syndrome (NCBS), CHARGE syndrome, and ATR-X syndrome. Moreover, it is becoming clear that mutations in multiple components of these remodeling complexes cause ID (Table 1) and can contribute to a spectrum of clinical phenotypes that is best illustrated by mutations in the SWI/SNF interacting partners (Bögershausen and Wollnik, 2018; van der Sluijs et al., 2019). The reader is referred to a number of recent reviews for detailed information of these different remodeler classes (Hota and Bruneau, 2016; Sokpor et al., 2017; Goodwin and Picketts, 2018; Alfert et al., 2019; Hoffmann and Spengler, 2019).
ATP-dependent chromatin remodelers are a frequent cause of NDDs. List of NDD implicated genes which are incorporated into ATP-dependent chromatin remodeling complexes.
Family
Gene
Associated disease
References
SWI/SNF
ARID1A
ID, CSS
Tsurusaki et al. (2012); Kosho et al. (2013)
ARID1B
ID, CSS, ASD
Hoyer et al. (2012); Santen et al. (2012); De Rubeis et al. (2014)
ARID2
ID, CSS-like, NCBS-like
Shang et al. (2015); Bramswig et al. (2017)
DPF2
ID, CSS-like
Vasileiou et al. (2018)
PBRM
ASD
O’Roak et al. (2012b)
SMARCA2
NCBS
Van Houdt et al. (2012); Tsurusaki et al. (2014b)
SMARCB1
ID, CSS, Kleefstra
Kleefstra et al. (2012); Santen et al. (2013); Wieczorek et al. (2013)
SMARCC1
ASD
Hormozdiari et al. (2015)
SMARCC2
ID, ASD
Machol et al. (2019)
SMARCE1
CSS-like
Tsurusaki et al. (2012); Kosho et al. (2013); Wieczorek et al. (2013)
SMARCA4
CSS
Santen et al. (2013); Tsurusaki et al. (2014b)
SOX11
ID, CSS-like
Tsurusaki et al. (2014a)
ISWI
BAZ1A
ID
Zaghlool et al. (2016)
BAZ1B
Williams-Beuren syndrome
Lu et al. (1998); Peoples et al. (1998)
BAZ2B
ID, ASD
Scott et al. (2020)
BPTF
ID
Stankiewicz et al. (2017)
SMARCA1
CSS-like, Rett syndrome-like, schizophrenia
Karaca et al. (2015); Homann et al. (2016); Lopes et al. (2016)
Vissers et al. (2004); Sanlaville et al. (2006); O’Roak et al. (2012b)
CHD8
ASD
O’Roak et al. (2012a, b); De Rubeis et al. (2014); Neale et al. (2016)
INO80
INO80
Microcephaly, ID
Alazami et al. (2015)
SRCAP
Floating-harbor syndrome
Hood et al. (2012)
YY1AP1
ID
Guo et al. (2017)
ATRX
ATRX
ATR-X syndrome
Gibbons et al. (1995)
Here, we will review recent studies on ATRX to highlight the multiple biochemical functions chromatin remodeling proteins participate in, and the diverse set of mechanisms that, collectively, contribute to the complexity underlying the pathogenesis of NDD.
Molecular Genetics of the ATR-X Syndrome
The ATR-X syndrome is a rare human congenital disorder with a wide range of symptoms that primarily affects males. Over 200 cases have been identified worldwide with and an estimated prevalence of <1–9/1,000,000 (Gibbons, 2006). Affected individuals display cognitive impairment typically described as severe ID, and many are non-verbal, capable of speaking or signing only a few words (Saugier-Veber et al., 1995; Guerrini et al., 2000). Originally, the presence of alpha thalassemia was used as a diagnostic tool to identify affected individuals, but there is variability in the hematological symptoms (Gibbons et al., 1995). The majority of patients are affected with microcephaly and skeletal malformations (Holmes and Gang, 1984; Carpenter et al., 1999). Muscle development is also impaired in most, leading to delayed motor development and hypotonia, while approximately one third of patients experience seizures (Lossi et al., 1999).
Although ATR-X syndrome patients present with a heterogeneous phenotype, the disease is caused by mutations in a single gene, the ATRX locus, which spans over 300 kbp on chromosome Xq13.3-21.1 (Gibbons et al., 1995, 2008; Picketts et al., 1996). The ATRX gene encodes two major trasncripts (Figure 2), one encoding the full length protein and a truncated isoform generated by an alternative splicing event that retains intron 11 and terminates translation prematurely (Garrick et al., 2004; Mitson et al., 2011). The full length transcript encodes a protein of 285 kDa in size while the shorter transcript generates a smaller truncated protein that is 180 kDa and lacks the ATP-dependent remodeling domain (Picketts et al., 1996; Garrick et al., 2004).
ATRX domain structure. Schematic diagram of full-length ATRX (282 kDa), truncated ATRX (ATRXt; 180 kDa), and locations of the key protein interaction domains. The two isoforms share an ADD domain, a HP1α binding motif, an EZH2 binding motif, while the ADD domain is comprised of a GATA-like zinc finger and a PHD-like zinc finger. The full-length polypeptide also contains a DAXX binding motif, a SNF2-ATPase domain comprising RecA-like lobes 1 and 2 separated by a linker region containing a MeCP2 binding motif (MeCP2/Insertion), and a PML targeting motif.
The N-terminus of the ATRX protein houses several motifs critical for its interaction with chromatin, including a heterochromatin protein 1 (HP1α) binding motif (PxVxL) (Lechner et al., 2005) and enhancer of zeste homolog 2 (EZH2) interaction domain (Cardoso et al., 1998), and the ATRX-DNMT3-DNMT3L (ADD) domain (Picketts et al., 1998; Xie et al., 1999). The ADD domain comprises a GATA-like zinc finger and a plant homeodomain (PHD)-like finger that targets the dual histone post translational modification (PTM), H3K9me3 and H3K4me0 (Argentaro et al., 2007; Dhayalan et al., 2011; Eustermann et al., 2011; Iwase et al., 2011). A region within the center of the polypeptide mediates death domain associated protein (DAXX) binding (Xue et al., 2003). Toward the C-terminus lies the highly conserved RecA-like lobes 1 and 2 that together are required for ATPase activity (Picketts et al., 1996), as well as mapped regions for interactions with the methyl-CpG-binding protein (MeCP2) (Nan et al., 2007) and the promyelocytic leukemia protein (PML) (Bérubé et al., 2008) (Figure 2).
The majority of ATR-X syndrome causing mutations are missense mutations mapping within the ADD (50%) and SNF2-like/helicase domains (30%) (Argentaro et al., 2007; Gibbons et al., 2008). To date, there has been a lack of genotype: phenotype correlations identified, although mutations within the ADD domain typically produce more severe psychomotor phenotypes compared to mutations in the SNF2-like/helicase domain (Badens et al., 2006).
It should also be noted that somatic mutations in the ATRX gene have been identified in a wide range of cancers that include pancreatic neuroendocrine tumors, gliomas, neuroblastomas, and sarcomas, which will not be discussed here but have been the focus of recent reviews (Watson et al., 2015; Dyer et al., 2017).
Interacting Partners and Biochemical Functions
All functional studies indicate that ATRX is a heterochromatin interacting protein. It localizes to pericentromeric heterochromatin, telomeres, PML nuclear bodies, and physically interacts with the HP1 family (McDowell et al., 1999; Berube et al., 2000; Tang et al., 2004). Later work demonstrated that ATRX could be recruited to the heterochromatin histone mark, H3K9me3, either indirectly by its interaction with HP1 or recruitment by MeCP2, and directly by binding of the ADD domain to H3K9me3 that lies adjacent to unmethylated H3K4 (Berube et al., 2000; Bannister et al., 2001; Nan et al., 2007; Eustermann et al., 2011; Iwase et al., 2011). ATRX and DAXX were identified as interacting partners by two separate groups, one using ATRX co-IP experiments and the other a Flag-DAXX pull-down approach (Xue et al., 2003; Tang et al., 2004). Further characterization showed that most of the endogenous ATRX protein is in a 1 MDa complex with DAXX, while DAXX also fractionates in a 700 kDa complex independent of ATRX. Deletion mutants were used to demonstrate that the ATRX/DAXX interaction was mediated through the PAH domain of DAXX and a region between the ADD and SNF2 domains within ATRX (Tang et al., 2004). Both the ATRX/DAXX complex and recombinant ATRX protein had DNA or nucleosome stimulated ATPase activity which was impaired by patient mutations that localized to the ATPase domain (Xue et al., 2003; Tang et al., 2004). A mononucleosome disruption assay was used to demonstrate that the ATRX/DAXX complex could alter the DNAse I digestion pattern of the mononucleosome in the presence of ATP. The localization of the altered digestion pattern indicated that ATRX/DAXX disrupts DNA–histone interactions at the entry site of the nucleosome and does not alter nucleosome phasing. In addition, a triple-helix strand displacement assay was used to show that the ATRX/DAXX complex and ATRX alone had a DNA translocase property similar to the RSC and SWI/SNF complexes (Xue et al., 2003). More recent work has indicated that DAXX is an H3.3-specific histone chaperone that functions with ATRX to deposit the histone variant at pericentric and telomeric repeats, while DAXX functions independently of ATRX to repress retrotransposons (Lewis et al., 2010; Hoelper et al., 2017). In this regard, the ATRX/DAXX complex shows some similarities with the ISWI complex ACF and its interactions with the histone chaperone NAP1 (Gemmen et al., 2005; Torigoe et al., 2011). These properties could be used to reconstitute H3.3 containing nucleosomal arrays that might guide future in vitro biochemical studies to further define ATRX function during transcription or DNA replication (Peterson, 2009).
Indeed, in a series of papers ATRX, DAXX, and the histone variant H3.3 were shown to co-localize at telomeres where the ATRX/DAXX complex functions as a histone chaperone to deposit H3.3 into telomeric heterochromatin (Wong et al., 2009; Goldberg et al., 2010; Lewis et al., 2010). Further work showed that DAXX functions as the histone chaperone, that H3.3K9me3 deposition occurs in a replication-independent manner by the complex, and both H3.3 loading and heterochromatin organization by ATRX/DAXX is mediated by SUV39H1 and PML in PML-associated heterochromatin domains (Drané et al., 2010; Goldberg et al., 2010; Lewis et al., 2010; He et al., 2015; Udugama et al., 2015; Delbarre et al., 2017). Additionally, ATRX was shown to be critical for the formation of senescence-induced heterochromatin foci (SAHF) that help drive cancer cells into therapy-induced senescence (Kovatcheva et al., 2017). Finally, ATRX has been shown to bind to the Xist lncRNA to promote recruitment of the PRC2 repressive complex and facilitate stable heterochromatin formation of the silenced X-chromosome (Sarma et al., 2014). RNA binding remains an understudied role for ATRX, although several reports have shown a range of interactions with multiple lncRNAs including TERRA (telomeric repeat-containing RNA) (Chu et al., 2017; Nguyen et al., 2017), ChRO1 in muscle (Park et al., 2018), and minor satellite RNAs at centromeric heterochromatin (Ren et al., 2020). These interactions are mediated through a unique N-terminal domain in ATRX to regulate differentiation, gene expression, DNA and histone methylation and chromatin compaction (Chu et al., 2017; Nguyen et al., 2017; Park et al., 2018; Ren et al., 2020).
A role for ATRX at heterochromatin was also strengthened by chromatin immunoprecipitation experiments that showed enriched ATRX binding at telomeres and centromeres. Interestingly, ATRX was also enriched at repetitive DNA elements while having a lower frequency of binding within gene bodies (Law et al., 2010). Further characterization showed that ATRX was prevalent at long terminal repeats of endogenous retrovirus sequences of family K (ERVK), at variable number tandem repeats (VNTRs) and at simple tandem repeats (Law et al., 2010). Many of the tandem repeats were GC-rich sequences that are predicted to form G-quadruplex secondary DNA structures (G4 DNA) including the telomeric repeats and some CpG islands. The formation of G4 DNA has been proposed to have important roles in the regulation of gene expression, as well as be prohibitive to DNA replication and transcription (Rhodes and Lipps, 2015; Valton and Prioleau, 2016; Varshney et al., 2020). In vitro studies confirmed that ATRX can bind to G4 DNA structures (Law et al., 2010). In addition, ATRX mutations have variable effects on α-globin expression including individuals with the same mutation. Law et al. (2010) demonstrate that one ATRX binding site lies within a GC-rich VNTR sequence 1 kb upstream of the HBM gene. The authors demonstrate a positive correlation in ATR-X patients such that increasing VNTR repeat size increases the severity of the α-thalassemia as measured by the level of HbH inclusions in red blood cells. Since the sequence is a GC-rich VNTR that is predicted to form G4 quadruplexes, it was inferred that increasing repeat size increases the probability to form G4 DNA that subsequently alters HBM expression.
The ATRX protein was also shown to co-purify with the MRE11-RAD50-NBS1 (MRN) complex, an active player in the processing of DNA double strand breaks (DSB) that suggested ATRX was critical to maintain genome integrity (Leung et al., 2013). Consistent with this finding, ATRX knockdown studies in HeLa cells resulted in defects in mitotic progression and micronuclei formation from altered chromosome condensation and centromeric cohesion (Ritchie et al., 2008). Other studies indicated that ATRX loss impaired replication fork progression during S-phase resulting in telomere fragility, increased DSB, and mitotic catastrophe (Huh et al., 2012, 2016; Leung et al., 2013; Watson et al., 2013).
The ATRX-DAXX-H3.3 complex is critical for this heterochromatic formation and subsequent maintenance (Law et al., 2010; Eid et al., 2015; He et al., 2015; Udugama et al., 2015). H3.3 within telomeric regions is targeted for trimethylation on its K9 residue (He et al., 2015; Udugama et al., 2015). H3.3K9me3 recruits more ATRX-DAXX-H3.3 complexes, which in turn will deposit H3.3, creating a positive feedback loop required for maintaining telomere structure (Udugama et al., 2015). Failure to establish proper structure will reduce telomere integrity and result in an increase of non-coding telomeric transcript expression (He et al., 2015; Udugama et al., 2015).
The eclectic properties of the ATRX protein do not make it intuitively obvious how an aberration of these functions can result in a neurodevelopment disorder with cognitive deficits. In the remaining section, we discuss the characterization of mouse models and the insights they have provided into the pathophysiology of ATR-X patients and, more generally, the complex etiology of NDDs caused by defective epigenetic regulators.
Delineating Pathophysiological Mechanisms of the ATR-X SyndromeFunctional Effects of Patient Mutations and Generation of Animal Models
One of the first questions addressed was do patient mutations affect protein stability and function? Immunoblots of extracts from patient-derived EBV-transformed B-lymphocytes showed significantly reduced levels of ATRX protein from all patients tested (McDowell et al., 1999; Cardoso et al., 2000). Interestingly, in patients with early premature stop codons (e.g., p.Arg37X), translation was initiated from an internal methionine that produced a smaller truncated protein at ∼30% levels leading to a milder phenotype (Howard et al., 2004; Abidi et al., 2005; Basehore et al., 2015). Utilizing recombinant proteins, other studies demonstrated that mutations within the ATPase domain attenuated ATPase activity but did not reduce it, while mutations in the ADD domain or the PML-targeting domain reduced localization to chromocenters and PML nuclear bodies, respectively (Cardoso et al., 2000; Bérubé et al., 2008). Atrx-null mutations in mice show defective extraembryonic trophoblast development and die embryonically at ∼E9.5 (Garrick et al., 2006). Collectively, these studies indicate that ATR-X syndrome causing mutations are functional hypomorphs, while more severe mutations are not found and are presumably non-viable.
Several different ATRX-deficient mouse lines have been generated and used for functional characterization. The most widely used model is a floxed allele in which loxP sites flanked exon 18 which encodes the ATP-binding pocket (Bérubé et al., 2005; Garrick et al., 2006). These animals have been crossed with several different tissue-specific Cre driver lines to inactivate ATRX in skeletal muscle progenitors (Huh et al., 2012), Sertoli cells (Bagheri-fam et al., 2011), osteobalsts (Solomon et al., 2013), chondrocytres (Solomon et al., 2009), the retina (Medina et al., 2009; Lagali et al., 2016), and the developing forebrain (Bérubé et al., 2005) among others. A second transgenic line (AtrxΔE2) was developed by deleting exon 2 and replacing it with a SA-IRES-β-geo cassette (Nogami et al., 2011; Shioda et al., 2011). This mutation was meant to mimic the p.Arg37X mutation and make an N-terminally truncated ATRX protein by initiating translation from an internal methionine codon (Howard et al., 2004; Abidi et al., 2005). Both of these models will be discussed in more detail in the following sections. Finally, an overexpression transgenic line was created with the ATRX cDNA under control of a CMV enhancer/β-actin promoter which resulted in growth retardation, neural tube defects and a high incidence of embryonic lethality demonstrating the importance of ATRX dosage to normal development (Berube et al., 2002).
While each of these models has provided valuable insight into disease mechanisms (as highlighted below), the field still awaits a model whereby a single nucleotide variant is introduced into the ATRX gene to recreate a known patient mutation, such as the common p.Arg246Cys mutation within the ADD domain.
Replication Stress Impairs Progenitor Expansion Resulting in Microcephaly
Microcephaly is common to many NDDs and has also been observed in mouse models that deleted other genes encoding chromatin remodeling proteins (Ronan et al., 2013). Most ATR-X patients develop postnatal microcephaly and, in instances where CT or MRI scans have been performed, mild cerebral atrophy was detected. Similarly, three patient autopsy reports also described that the brains were smaller in size (Gibbons, 2006).
The first indication that ATRX may be critical for cell growth came from co-culture experiments of embryonic stem cells (ESC) from control or Atrx-null cells. This experiment demonstrated that the Atrx-null cells were underrepresented after 4-days of co-culture. Flow cytometry was used to examine cell cycle distribution but no differences were observed suggesting that cells may have transitioned to a slower cycling, differentiated cell type (Garrick et al., 2006). Given that ATRX has high expression in the developing forebrain, the Atrxfl/fl line was next crossed with the forebrain-specific Foxg1-Cre line (AtrxFoxg1Cre) that initiates Cre expression in the developing telencephalon at ∼E8.5 (Hébert and McConnell, 2000). Loss of ATRX caused a 25–30% reduction in cell number with a noticeably smaller neocortex and hippocampus including almost a complete absence of the dentate gyrus that likely contributed to early postnatal lethality (Bérubé et al., 2005). Similar to ESC co-culture experiments, BrdU-pulse labeling experiments suggested no differences in the proportion of cycling cells. However, there was a dramatic increase in the number of TUNEL+ cells leading to a reduction in the number of neurons that reached the cortical layers (Bérubé et al., 2005). Similarily, the AtrxΔE2 mice were smaller and also showed brain hypocellularity, although to a milder extent (Nogami et al., 2011). Atrx inactivation in Sertoli and muscle cells, also showed a significant impact on the growth of the tissue (Bagheri-fam et al., 2011; Huh et al., 2012). However, a retina progenitor cell cKO only had a limited effect on the size of the mature tissue suggesting that defects in cell cycle progression lead to significant hypocellularity among tissues that require a rapid expansion over a narrow developmental timeframe (Medina et al., 2009).
Although not initially observed, delayed cell cycle progression through both S- and G2/M phases was later observed in other studies (Ritchie et al., 2008; Watson et al., 2013; Huh et al., 2016). For G2/M, the progression from prometaphase to metaphase was prolonged and associated with sister chromatid cohesion and congression defects that impaired proper separation at anaphase leading to DNA bridges and micronuclei (Ritchie et al., 2008). Evidence for DNA bridges and micronuclei in AtrxFoxg1Cre mice were also detected by high magnification microscopy at the apical surface on cortical sections of the neuroepithelium where cortical progenitors complete mitosis. Interestingly, a recent study has also demonstrated that ATRX promotes telomere cohesion between sister telomeres to mediate the repair of DNA DSB (Lovejoy et al., 2020).
Defects in S-phase were observed using BrdU-pulse chase flow cytometry experiments where a delay from G1 to S-phase and also from G2/M to the following G1 phase was identified (Huh et al., 2012). Co-labeling experiments demonstrated that ATRX associated with replicating chromatin during mid-late S-phase and cytological analysis showed a high prevalence of genomic instability that was enriched at telomeres and pericentromeric heterochromatin (Huh et al., 2012; Watson et al., 2013). Moreover, treatment with a compound that binds and stabilizes G4 DNA increased the number of telomere dysfunction induced foci (TIFs) and decreased cell viability suggesting that G4 DNA formation was the main cause of replicative stress (Watson et al., 2013). Other studies indicate that replication stress at telomeres may be mediated by increased TERRA transcription (Nguyen et al., 2017). TERRA levels are tightly regulated and critical for both telomere formation, replication and maintenance (Bettin et al., 2019). However, when TERRA levels increase, as shown for ATRX-null cells, it enhances R-loop (RNA-DNA hybrid) formation and G4 DNA stabilization, each of which increase replication fork stalling and collapse that then induces homology directed repair (HDR) and TIFs (Nguyen et al., 2017). The regulation of R-loops has also been proposed for other proteins that interact with G4 DNA during replication and/or at telomeres (Zhou et al., 2014; Ribeiro de Almeida et al., 2018; Toubiana and Selig, 2018; Maffia et al., 2020). It should also be mentioned here that somatic ATRX mutations, and to a lesser extent H3.3 and DAXX mutations, are prevalent in cancers characterized by ALT (alternative lengthening of telomeres), a HDR mechanism to maintain telomere length that is normally suppressed by ATRX (Heaphy et al., 2011; Lovejoy et al., 2012; Schwartzentruber et al., 2012; Pickett and Reddel, 2015; Verma and Greenberg, 2016).
Another indicator of replicative stress as a major impediment to growth of Atrx-null cells was demonstrated by studies showing an increased sensitivity to hydroxyurea, enhanced DSBs, and the use of DNA fiber analysis to show increased stalled replication forks and reduced origin firing (Leung et al., 2013; Clynes et al., 2014; Huh et al., 2016). Mechanistically, ATRX physically interacts with the MRN complex where it is thought to block HDR at stalled replication forks to allow for fork restart after the G4 DNA is resolved (Clynes et al., 2014). Indeed, one group demonstrated that fork protection could be restored by treatment with an Mre11 exonuclease inhibitor (Huh et al., 2016). This study also suggested that hyperactivation of poly (ADP-ribose) polymerase-1 (Parp-1) during neurogenesis may function as a compensatory mechanism to protect stalled replication forks from collapse and HDR, thus dampening the extent of cell loss during neurogenesis.
During mouse cortical development, the cortical layers are formed in sequential fashion from a pool of neural progenitor cells (NPC) that must continue to proliferate to maintain the pool size. Alterations in NPC proliferation depletes the pool often resulting in altered cell lamination typically observed as a reduction in upper layer neurons. For AtrxFoxg1Cre mice, the most proliferative NPCs that ultimately would become upper layer neurons are more susceptible to incur replication-induced DNA damage. Frequently the resulting genomic instability will occur at telomeres and pericentromeric heterochromatin, but it could also occur at other genomic regions that can form G4 DNA or similar secondary DNA structures that induce replication fork stalling and collapse. Accumulation of sufficient damage further leads to their demise and decreases neuron production and brain size. Indeed, the AtrxFoxg1Cre forebrain is reduced in size with a compromised production of upper layer neurons (Ritchie et al., 2014; Huh et al., 2016). Similar results have been observed in mice lacking CHD4 and SMARCA5 where the NPCs either fail to progress through the cell cycle or incur significant DNA damage, respectively, prior to undergoing apoptosis (Alvarez-Saavedra et al., 2014, 2019; Nitarska et al., 2016).
The SWI/SNF complex is also critical for brain development but utilizes different mechanisms than ATRX. The SWI/SNF complex is required during early neurogenesis for differentiation from radial glial progenitor cells into intermediate progenitor cells. This switch from a neural stem cell to an NPC is accompanied by the fundamental shift from the npBAF to nBAF complex, which involves the substitution of three subunits (BAF45, BAF53, and BAF55). Failure to switch leads to increased cell death, a small progenitor pool, and failure to further differentiate (Lessard et al., 2007; Wu et al., 2007; Bachmann et al., 2016). Interestingly, while SMARCA5 loss hampers NPC proliferation, loss of its ISWI ortholog SMARCA1 fails to repress expression of proliferation genes resulting in delayed neuronal differentiation and a larger brain (Yip et al., 2016). Taken together, these examples highlight the importance of chromatin remodeling proteins to NPC homeostasis and provide insight into the multitude of mechanisms at work often resulting in a similar phenotype.
Transcriptional Deficits Associated With ATRX Mutations
Chromatin remodeling proteins were first identified as transcriptional coactivators and they continue to be implicated in the regulation of many genes. Since its identification, ATRX has also been presumed to regulate gene transcription. While there is a good level of understanding regarding how ATRX maintains genomic stability through the regulation of tandem repeats, telomeres and pericentromeric heterochromatin, the identification of direct transcriptional targets has proven more challenging. Initial ChIPseq experiments suggested that ATRX was bound at few promoters, gene bodies and regulatory elements (Law et al., 2010). Further work has suggested that ATRX binding may differ between tissues to ensure proper silencing of repetitive elements located near or within expressed genes in that particular tissue (Nguyen et al., 2017). Consistent with this idea, ATRX ChIPseq analysis of NPCs demonstrated a higher enrichment of binding sites at gene regulatory elements compared to what was observed in mouse ESCs suggesting that more genes may be under direct ATRX regulation within the brain (Law et al., 2010; He et al., 2015; Danussi et al., 2018). Another contributing factor to differential target gene expression is represented by ATRX effects on α-globin gene expression. Mutational analysis identified >15 ATR-X patients with the identical missense change (p.Arg246Cys), yet they showed a variable degree of hemoglobin H inclusions in blood samples, indicative of differing levels of α-globin expression (Gibbons et al., 1997). Repression of α-globin expression was dependent on the size of a GC-rich VNTR located within the globin gene cluster (Law et al., 2010). A second factor driving the tissue specificity and the variable effects was the formation of R-loops caused by the transcription of the GC-rich VNTR sequences (Nguyen et al., 2017). The larger sequences generate increased R-loops and G4 DNA structures that normally recruit ATRX to re-establish the normal chromatin structure. In the absence of ATRX the R-loop/G4 DNA is not resolved effectively which then impedes both replication and transcription processes (Nguyen et al., 2017). The slight stochastic nature of these effects likely also dampens readouts of differential expression from RNAseq experiments thereby raising the need for a scRNAseq approach in future studies.
Gene expression analysis of control and AtrxFoxg1Cre cortical samples at two timepoints (E13.5, P0.5) identified 202 and 304 differentially expressed genes (DEGs; ±1.5-fold change), respectively, with almost two-thirds of genes upregulated (Levy et al., 2008). Among these, 27 were common to both datasets including the downregulation of several ancestral pseudoautosomal region (aPAR) genes (Csfr2a, Dhrsxy, Cd99, and Asmtl) (Levy et al., 2008, 2014). In mouse, the aPAR genes are located in subtelomeric regions and contain potential G4 DNA sequences. Each gene analyzed had enriched histone H3.3 and ATRX binding within their gene body and showed reduced H3.3 levels when ATRX was absent (Levy et al., 2014). Interestingly, these intragene G4 DNA sequences also showed increased binding of RNA pol II in AtrxFoxg1Cre samples suggesting that transcription becomes impeded at these regions within the gene leading to reduced expression. The authors extended this finding to Nlgn4, a gene encoding a post-synaptic cell adhesion molecule implicated in ASDs (Jamain et al., 2003; Laumonnier et al., 2004). This result conflicted with the study on R-loop formation which found no differences in RNA pol II loading or histone modifications across genes containing the GC-rich repeats (Nguyen et al., 2017).
Other downregulated genes from this analysis include Gbx2, NeuroD4, Wif1, Nxph1, Nxph2, and Mbp, each of which could contribute to cognitive deficits observed in patients but require further analysis to asses their contribution to the phenotype (Levy et al., 2008, 2014). In a similar experiment in the retina, 173 DEGs were identified with two-thirds upregulated (109 genes) and one-third (64 genes) downregulated (Lagali et al., 2016). Most of these genes were involved in the regulation of glutamate activity, ion channel regulation or encoded neuroprotective peptides, with four shown to be also dysregulated in the cortex (Csf2ra, Cbln4, Syt13, and Nlgn4). Each of these studies showed that the mutant samples had only small numbers of genes with large changes in gene expression and, while some downregulated genes may impede transcriptional elongation, this mechanism may not be universal, particularly as it pertains to upregulated genes.
However, other indirect mechanisms have been explored to explain transcriptional dysregulation, particularly a loss of repression. The ATRX/DAXX complex is critical for loading H3.3 at telomeres and pericentromeric heterochromatin (Goldberg et al., 2010; Wong et al., 2010). Research over the last few years has expanded this regulation to include H3.3 deposition at endogenous retroviral elements, regions associated with imprinted genes, and some CpG islands (Elsässer et al., 2015; He et al., 2015; Sadic et al., 2015; Voon et al., 2015). At the telomere, the loss of ATRX affected the transcription of telomeric DNA and the non-coding RNA TERRA, although studies conflict on whether levels increase or decrease (Eid et al., 2015; Nguyen et al., 2017). Surprisingly, TERRA was also shown to bind to an additional ∼4,000 binding sites aside from the telomere where it co-localized with ATRX (Chu et al., 2017). Many of these sites were within introns and comprised GA repeats, however, depletion of TERRA usually resulted in downregulation while ATRX depletion increased expression (Chu et al., 2017). While it remains to be determined how this might affect neuronal gene expression, ATRX has been shown to interact with other lncRNAs including Xist to facilitate PRC2 silencing and ChR01 that is required for heterochromatin reorganization in differentiating muscle cells (Sarma et al., 2014; Park et al., 2018). Indeed, ATRX binding to lncRNA or R-loops may be a key mechanism mediating transcriptional repression of specific target genes.
Histone H3.3 ChIPseq studies have also shown that it is enriched at the intracisternal A-particle endogenous retroviral elements (IAP/ERVs), which account for almost half of all mutation causing ERV insertions (Maksakova et al., 2006; Elsässer et al., 2015). Moreover, H3.3 deposition at these sites requires ATRX/DAXX to facilitate H3K9me3 and repression while depletion of ATRX, DAXX, or H3.3 results in reduction of the H3K9me3 mark and IAP/ERV derepression (Elsässer et al., 2015; He et al., 2015; Voon et al., 2015). In mouse ESCs, ERV derepression affected the expression of neighboring genes in a minority of cases with most genes neutral to ERV derepression. It raises the question of whether or not this type of derepression would affect many genes or occur rapidly within a post-mitotic neuron, and thus, function as a major effector in dysregulated gene expression in Atrx-null neurons. In this regard, a related study using cultured post-mitotic neurons demonstrated that the ADD domain can also bind the H3K9me3S10ph dual histone mark (Noh et al., 2015). This histone mark is rapidly induced by neuronal depolarization where it appears at centromeric and pericentromeric heterochromatin co-localized with ATRX to repress transcription of non-coding centromeric minor satellite sequences (Noh et al., 2015). While it is unclear what the impact of increased centromeric minor satellite transcription would have on disease pathology, it remains to be determined whether this dual mark affects activity-dependent transcription of genes mediating learning or memory.
It was also demonstrated that ATRX was bound to 56 CpG islands which was unexpected since they are often associated with active chromatin, typically promoters (Voon et al., 2015). However, these CpG islands were associated with H3K9me3, almost half were methylated and many corresponded to imprinted loci often residing in intragenic regions within a transcriptional unit (Voon et al., 2015). Indeed, in all cases examined ATRX was bound to the silenced imprinted allele which became reactivated in ATRX KO cells (Voon et al., 2015). This study contrasted somewhat with an independent report in which ATRX was recruited by MeCP2 to silence the active allele of several imprinted genes in the developing telencephalon (Levy et al., 2014). The difference in these studies may reflect differential regulation of imprinting loci in ESCs versus differentiating NPCs. Perhaps the most compelling example of derepression came from a study with the AtrxΔE2 mice (Shioda et al., 2018). In this model, the authors identify a small list of 31 DEGs in the adult hippocampus but with most genes (23/31) downregulated (Shioda et al., 2018). Among the upregulated genes was an imprinted gene from the lymphocyte-regulated gene family, Xlr3b. Although Xlr3b had widespread expression across many tissues, it was only upregulated in the brain. Further work showed that ATRX bound to a G4 DNA sequence within the CpG island of the Xlr3b gene where it normally interacted with DAXX and H3.3 and recruited DNMT1 and DNMT3 to silence the gene. The subsequent overexpression of Xlr3b in the AtrxΔE2 mice was shown to produce a protein that localized to dendritic RNA granules where it interacted with ribonucleoproteins, dynein proteins and the RNA-binding protein, TIA1, to regulate mRNA transport (Shioda et al., 2018). One of the targets identified was the mRNA for CAMK II-α which they had previously shown to be deregulated in these animals. Excitingly, they also showed that the G-quadruplex-binding ligand 5-aminolevulinic acid (5-ALA) was able to decrease RNApol II occupancy and Xlr3b expression in the AtrxΔE2 mice, although methylation of the G4 DNA sequence within the CpG island was not affected. It seems that formation or stabilization of this G4 DNA sequence is required to activate the Xlr3b gene and that ATRX normally prevents this by facilitating heterochromatin formation. In this regard, mapping of G4 DNA sequences have shown an enriched number in gene regulatory elements where many function to increase transcription when stabilized (Hänsel-Hertsch et al., 2016). While G4 DNA stabilization occurs in the AtrxΔE2 mice, further work is required to explain how 5-ALA represses Xlr3b transcription when it should stabilize the G4 DNA. Regardless, the derepression of G4 DNA within CpG islands and/or other regulatory elements is an exciting mechanism that can explain DEG upregulation, particularly when coupled with the finding that ATRX binding is increased at regulatory elements in NPCs compared to ESCs. Collectively, the derepression of tandem repeats, retrotransposable elements and G4 quadruplexes can all function to impinge on neuronal function.
Morphological, Behavioral, and Cell Non-autonomous Deficits
We have discussed global effects on DNA replication and transcription that occur in the absence of ATRX in the previous two sections. In this section, we review the morphological and functional repercussions of these deficits. Aside from being reduced in size, the AtrxFoxg1Cre mice had a normal cortical morphology with proper lamination although a reduction of upper layer neurons (Bérubé et al., 2005; Ritchie et al., 2014; Huh et al., 2016). The reduction in upper layer neurons may also contribute to the partial agenesis of the corpus callosum observed in some patients (Gibbons, 2006). The hippocampus was also reduced in size while the dentate gyrus consisted of only a few disorganized cells. Behavior analysis was not performed due to the early postnatal lethality, although female heterozygous mice showed impairment in spatial, contextual fear, and novel object recognition memory (Tamming et al., 2017).
The AtrxΔE2 mice also had smaller brains but no differences in cell density within layers II/III of the prefrontal cortex (PFC) or hippocampus (Shioda et al., 2011). Examination of dendritic spines in the PFC showed that the AtrxΔE2 mice had similar numbers but fewer mature spines and many more, thin, long immature spines (Shioda et al., 2011). Behavioral analysis indicated that the mice have impaired contextual fear memory (fear conditioning test), spatial memory (Y-maze), but not anxiety behaviors (Nogami et al., 2011; Shioda et al., 2011). Electrophysiology studies in hippocampal slices demonstrated reduced NMDAR-dependent long term potentiation (LTP) evoked by high stimulation frequency in hippocampal CA1 neurons which was mediated by increased CAMK2A and GluR1 phosphorylation (Nogami et al., 2011). This was in contrast to a later article by the same group that showed phosphorylated CAMK2A levels were reduced in the AtrxΔE2 mice while 5-ALA restored the levels at the synapse and the phosphorylation levels (Shioda et al., 2018). A recent article examining hippocampal function using CAMKII-Cre mice to inactivate Atrx in postnatal excitatory forebrain neurons demonstrated reduced paired-pulse facilitation and LTP in proximal and distal apical dendrites of CA1 synapses (Gugustea et al., 2019). This represented the first study of mice in which Atrx has been inactivated after neurogenesis and it will be interesting to ascertain the full characterization of these mice.
Studies of the retina have also provided useful information into ATRX function. Many ATR-X patients have visual problems although this has been an under-appreciated aspect of the phenotype (Medina et al., 2009). Inactivation of ATRX in retinal progenitors AtrxPax6Cre resulted in a slight reduction in retina size and a specific reduction of interneurons, namely amacrine and horizontal cells (Medina et al., 2009). Surprisingly, AtrxPitf1aCre mice that ablates ATRX in a bi-potential progenitor that generates amacrine or horizontal cells did not recapitulate the phenotype, while inactivation with a bipolar cell specific Cre driver (AtrxVsx2Cre) did not affect bipolar cell survival but did result in reduced amacrine and horizontal cells suggesting that interneuron survival was a cell non-autonomous effect (Lagali et al., 2016). Additional characterization of these mice showed that the bipolar axons were mislocalized within the inner plexiform layer and many had axonal swellings or tortuous paths to their targets. Gene expression analysis identified alterations in the glutamate pathway, ion channel regulation and altered expression of neuroprotective peptides. Altered axonal pathfinding was also observed in Drosophila XNP mutants, the homolog to the ATPase domain of ATRX (Sun et al., 2006). It will be important to further explore in greater detail whether axonal pathfinding is also altered within forebrain or hippocampal neurons.
Perspectives
Studies to date have indicated that ATRX has multiple roles during forebrain development that can contribute to the phenotype of ATR-X patients. It functions mainly as a heterochromatin interacting protein acting to ensure that repetitive DNA is properly packaged and organized into heterochromatin. We have highlighted how aberrations in heterochromatin maintenance leads to genomic instability and replication stress that impairs NPC expansion leading to a microcephalic brain (Figure 3). The loss of ATRX also affects gene expression typically resulting in increased gene derepression but also downregulation. It remains to be teased apart which targets are direct versus indirect, and when disrupted expression hampers neuronal function. It is likely that inactivation of ATRX in postmitotic neurons, following neurogenesis and lamination, will help define a role for ATRX target genes in altered synaptic activity and/or synaptic plasticity underlying cognitive impairment. Moreover, the contribution of other central nervous system cell types to the phenotype have not been explored. ATRX is expressed in glia and oligodendrocytes which are known to intimately communicate with neurons to mediate function, as shown recently in Drosophila glial ATRX dependent ensheathment of sensory neurons, for normal dendritic arborization and stimulus processing (Yadav et al., 2019). Intriguingly, MRI studies on ATR-X patients showed severe glial defects and white matter disruption, further stressing the need for research in this area (Wada et al., 2013; Lee et al., 2015). Importantly, a further understanding of ATRX function and its aberrant molecular pathways are required before potential treatments can be explored. In this regard, treatment with 5-ALA has shown promise in one animal model and it is being investigated in Japanese patients (T. Wada, personal communication). ATRX is but one of many different chromatin remodeling proteins mutated in NDDs but it serves to demonstrate how complex these disorders are and how widely chromatin remodelers impact cellular activities.
The multiple functions of the ATRX protein. Schematic diagram of ATRX functional influence on brain development and its contribution to NDDs. Normally ATRX utilizes its chromatin remodeling activity to (1) influence transcription and DNA replication in heterochromatic regions to control the rate of proliferation in the neuronal progenitor cell population (bottom arm); and (2) to influence transcription in heterochromatic regions to control differentiation processes (top arm). When ATRX is mutated the cellular proliferation rates in progenitors is slowed resulting in a smaller progenitor population; and the differentiation processes are altered resulting in either dysfunctional cellular morphology or complete absence of specific cell types.
Author Contributions
ST and DP wrote and edited the manuscript together. ST generated the figures and table. Both authors contributed to the article and approved the submitted version.
Conflict of Interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Funding. The research supporting this work was funded by two operating grants (FRN133586 and FRN165994) from the Canadian Institute of Health Research awarded to DP.
We are grateful to Dr. Alex Córdova and Valérie Cardin for their valued input and critical reading of the manuscript.
https://sysid.cmbi.umcn.nl/
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Cloning, expression and chromosome locations of the human DNMT3 gene family.\\nGene\\n236\\n87–95. 10.1016/S0378-1119(99)00252-25810433969XueY.GibbonsR.YanZ.YangD.McDowellT. L.SechiS. (2003). The ATRX syndrome protein forms a chromatin-remodeling complex with Daxx and localizes in promyelocytic leukemia nuclear bodies.\\nProc. Natl. Acad. Sci. U.S.A.\\n100\\n10635–10640. 10.1073/pnas.1937626100\\n12953102YadavS.YoungerS. H.ZhangL.Thompson-PeerK. L.LiT.JanL. Y. (2019). Glial ensheathment of the somatodendritic compartment regulates sensory neuron structure and activity.\\nProc. Natl. Acad. Sci. U.S.A.\\n116\\n5126–5134. 10.1073/pnas.1814456116\\n30804200YipD. J.CorcoranC. P.Alvarez-SaavedraM.DeMariaA.RennickS.MearsA. J. (2016). Snf2l regulates foxg1-dependent progenitor cell expansion in the developing brain.\\nDev. Cell\\n22\\n871–878. 10.1016/j.devcel.2012.01.020.YipZaghloolA.HalvardsonJ.ZhaoJ. J.EtemadikhahM.KalushkovaA.KonskaK. (2016). A role for the chromatin-remodeling factor BAZ1A in neurodevelopment.\\nHum. Mutat.\\n37\\n964–975. 10.1002/humu.23034\\n27328812ZhouR.ZhangJ.BochmanM. L.ZakianV. A.HaT. (2014). Periodic DNA patrolling underlies diverse functions of Pif1 on R-loops and G-rich DNA.\\neLife\\n3:e02190. 10.7554/elife.02190\\n24843019\\n\"\n]"}}}],"truncated":false,"partial":true},"paginationData":{"pageIndex":4,"numItemsPerPage":100,"numTotalItems":45140,"offset":400,"length":100}},"jwt":"eyJhbGciOiJFZERTQSJ9.eyJyZWFkIjp0cnVlLCJwZXJtaXNzaW9ucyI6eyJyZXBvLmNvbnRlbnQucmVhZCI6dHJ1ZX0sImlhdCI6MTc2NjE1MTAwOSwic3ViIjoiL2RhdGFzZXRzL0NvcnJhbi9QdWJtZWQtT3BlbkFjY2Vzcy1Db21tZXJjaWFsLVVzZSIsImV4cCI6MTc2NjE1NDYwOSwiaXNzIjoiaHR0cHM6Ly9odWdnaW5nZmFjZS5jbyJ9.F4E7y2th34iUrVAP9teZN0TSV_b0zGSdEcEE9lzhGeEsNt0WRA1o30R4ich9JSd2rp-qNOJw3Ypxs3CO-nhBCg","displayUrls":true,"splitSizeSummaries":[{"config":"default","split":"train","numRows":45140,"numBytesParquet":1624602608}]},"discussionsStats":{"closed":0,"open":0,"total":0},"fullWidth":true,"hasGatedAccess":true,"hasFullAccess":true,"isEmbedded":false,"savedQueries":{"community":[],"user":[]}}">
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"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Int J Mol Sci</journal-id><journal-id journal-id-type=\"iso-abbrev\">Int J Mol Sci</journal-id><journal-id journal-id-type=\"publisher-id\">ijms</journal-id><journal-title-group><journal-title>International Journal of Molecular Sciences</journal-title></journal-title-group><issn pub-type=\"epub\">1422-0067</issn><publisher><publisher-name>MDPI</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32752027</article-id><article-id pub-id-type=\"pmc\">PMC7432056</article-id><article-id pub-id-type=\"doi\">10.3390/ijms21155491</article-id><article-id pub-id-type=\"publisher-id\">ijms-21-05491</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Article</subject></subj-group></article-categories><title-group><article-title>The Impact of Small Extracellular Vesicles on Lymphoblast Trafficking across the Blood-Cerebrospinal Fluid Barrier In Vitro</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Erb</surname><given-names>Ulrike</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05491\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Hikel</surname><given-names>Julia</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05491\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Meyer</surname><given-names>Svenja</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05491\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Ishikawa</surname><given-names>Hiroshi</given-names></name><xref ref-type=\"aff\" rid=\"af2-ijms-21-05491\">2</xref></contrib><contrib contrib-type=\"author\"><name><surname>Worst</surname><given-names>Thomas S.</given-names></name><xref ref-type=\"aff\" rid=\"af3-ijms-21-05491\">3</xref></contrib><contrib contrib-type=\"author\"><name><surname>Nitschke</surname><given-names>Katja</given-names></name><xref ref-type=\"aff\" rid=\"af3-ijms-21-05491\">3</xref></contrib><contrib contrib-type=\"author\"><name><surname>Nuhn</surname><given-names>Philipp</given-names></name><xref ref-type=\"aff\" rid=\"af3-ijms-21-05491\">3</xref></contrib><contrib contrib-type=\"author\"><name><surname>Porubsky</surname><given-names>Stefan</given-names></name><xref ref-type=\"aff\" rid=\"af4-ijms-21-05491\">4</xref></contrib><contrib contrib-type=\"author\"><name><surname>Weiss</surname><given-names>Christel</given-names></name><xref ref-type=\"aff\" rid=\"af5-ijms-21-05491\">5</xref></contrib><contrib contrib-type=\"author\"><name><surname>Schroten</surname><given-names>Horst</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05491\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Adam</surname><given-names>Rüdiger</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05491\">1</xref><xref ref-type=\"author-notes\" rid=\"fn1-ijms-21-05491\">†</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0002-9961-4752</contrib-id><name><surname>Karremann</surname><given-names>Michael</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05491\">1</xref><xref rid=\"c1-ijms-21-05491\" ref-type=\"corresp\">*</xref><xref ref-type=\"author-notes\" rid=\"fn1-ijms-21-05491\">†</xref></contrib></contrib-group><aff id=\"af1-ijms-21-05491\"><label>1</label>Department of Pediatrics, University Medical Center Mannheim, 68167 Mannheim, Germany; <email>[email protected]</email> (U.E.); <email>[email protected]</email> (J.H.); <email>[email protected]</email> (S.M.); <email>[email protected]</email> (H.S.); <email>[email protected]</email> (R.A.)</aff><aff id=\"af2-ijms-21-05491\"><label>2</label>Laboratory of Clinical Regenerative Medicine, Department of Neurosurgery, Faculty of Medicine, University of Tsukuba, 1–1–1 Tennodai, Tsukuba, Ibaraki 305–8575, Japan; <email>[email protected]</email></aff><aff id=\"af3-ijms-21-05491\"><label>3</label>Department of Urology and Urosurgery, University Medical Center Mannheim, 68167 Mannheim, Germany; <email>[email protected]</email> (T.S.W.); <email>[email protected]</email> (K.N.); <email>[email protected]</email> (P.N.)</aff><aff id=\"af4-ijms-21-05491\"><label>4</label>Institute of Pathology, University Medical Center of the Johannes Gutenberg University Mainz, 55101 Mainz, Germany; <email>[email protected]</email></aff><aff id=\"af5-ijms-21-05491\"><label>5</label>Department of Medical Statistics and Biomathematics, University Medical Center Mannheim, 68167 Mannheim, Germany; <email>[email protected]</email></aff><author-notes><corresp id=\"c1-ijms-21-05491\"><label>*</label>Correspondence: <email>[email protected]</email>; Tel.: +49-621-383-2393</corresp><fn id=\"fn1-ijms-21-05491\"><label>†</label><p>These authors contributed equally to this work.</p></fn></author-notes><pub-date pub-type=\"epub\"><day>31</day><month>7</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><month>8</month><year>2020</year></pub-date><volume>21</volume><issue>15</issue><elocation-id>5491</elocation-id><history><date date-type=\"received\"><day>29</day><month>6</month><year>2020</year></date><date date-type=\"accepted\"><day>28</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>© 2020 by the authors.</copyright-statement><copyright-year>2020</copyright-year><license license-type=\"open-access\"><license-p>Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>).</license-p></license></permissions><abstract><p>Central nervous System (CNS) disease in pediatric acute lymphoblastic leukemia (ALL) is a major concern, but still, cellular mechanisms of CNS infiltration are elusive. The choroid plexus (CP) is a potential entry site, and, to some extent, invasion resembles CNS homing of lymphocytes during healthy state. Given exosomes may precondition target tissue, the present work aims to investigate if leukemia-derived exosomes contribute to a permissive phenotype of the blood-cerebrospinal fluid barrier (BCSFB). Leukemia-derived exosomes were isolated by ultracentrifugation from the cell lines SD-1, Nalm-6, and P12-Ichikawa (P12). Adhesion and uptake to CP epithelial cells and the significance on subsequent ALL transmigration across the barrier was studied in a human BCSFB in vitro model based on the HiBCPP cell line. The various cell lines markedly differed regarding exosome uptake to HiBCPP and biological significance. SD-1-derived exosomes associated to target cells unspecifically without detectable cellular effects. Whereas Nalm-6 and P12-derived exosomes incorporated by dynamin-dependent endocytosis, uptake in the latter could be diminished by integrin blocking. In addition, only P12-derived exosomes led to facilitated transmigration of the parental leukemia cells. In conclusion, we provide evidence that, to a varying extent, leukemia-derived exosomes may facilitate CNS invasion of ALL across the BCSFB without destruction of the barrier integrity.</p></abstract><kwd-group><kwd>choroid plexus</kwd><kwd>central nervous system infiltration</kwd><kwd>exosomes</kwd><kwd>pediatric acute lymphoblastic leukemia</kwd></kwd-group></article-meta></front><body><sec sec-type=\"intro\" id=\"sec1-ijms-21-05491\"><title>1. Introduction</title><p>Survival of pediatric acute lymphoblastic leukemia (ALL) has dramatically improved over the last decades [<xref rid=\"B1-ijms-21-05491\" ref-type=\"bibr\">1</xref>]. Yet still, treatment strategies to the central nervous system (CNS) compartment remain challenging and even prophylactic therapy in low risk patients may result in relevant sequelae [<xref rid=\"B2-ijms-21-05491\" ref-type=\"bibr\">2</xref>]. On the other hand, most CNS relapses which occur in children are initially classified as CNS negative, indicating “undertreatment” in a subset of low-risk patients [<xref rid=\"B3-ijms-21-05491\" ref-type=\"bibr\">3</xref>,<xref rid=\"B4-ijms-21-05491\" ref-type=\"bibr\">4</xref>]. Therefore, predictive markers allowing to identify the personalized risk of CNS relapse more accurately are urgently warranted to further improve precise stratification of CNS directed therapy and prophylaxis in the future [<xref rid=\"B5-ijms-21-05491\" ref-type=\"bibr\">5</xref>]. Identifying such markers is hampered by the fact that the nature of CNS invasion is not yet fully understood and mechanisms protecting lymphoblasts within the CNS compartment are diverse [<xref rid=\"B6-ijms-21-05491\" ref-type=\"bibr\">6</xref>].</p><p>The present work will focus on the relevance of leukemia-derived exosomes in the process of CNS invasion. To date, it is still under debate, whether the transmigration into the CNS is a general trait of ALL blasts or restricted to specific subclones [<xref rid=\"B4-ijms-21-05491\" ref-type=\"bibr\">4</xref>,<xref rid=\"B7-ijms-21-05491\" ref-type=\"bibr\">7</xref>,<xref rid=\"B8-ijms-21-05491\" ref-type=\"bibr\">8</xref>]. Distinct risk factors of CNS leukemia include hyperleukocytosis at diagnosis, T-cell phenotype, and several genetic alterations [<xref rid=\"B9-ijms-21-05491\" ref-type=\"bibr\">9</xref>]. Additionally, various biological features affect the invasion potency of lymphoblasts, including the expression of cytokines, chemokines, growth factors, cell adhesion molecules, and matrix metalloproteases [<xref rid=\"B3-ijms-21-05491\" ref-type=\"bibr\">3</xref>,<xref rid=\"B4-ijms-21-05491\" ref-type=\"bibr\">4</xref>,<xref rid=\"B10-ijms-21-05491\" ref-type=\"bibr\">10</xref>]. The role of small extracellular vesicles within this process is yet unclear [<xref rid=\"B11-ijms-21-05491\" ref-type=\"bibr\">11</xref>]. Since their first description, multiple mechanisms have been unraveled by which tumor-derived exosomes affect the formation of CNS metastases in numerous malignancies [<xref rid=\"B12-ijms-21-05491\" ref-type=\"bibr\">12</xref>,<xref rid=\"B13-ijms-21-05491\" ref-type=\"bibr\">13</xref>]. These sum up, in the potential to modify tissues distant to the primary tumor site, and result in the formation of a “premetastatic niche”, facilitating circulating tumor cells to invade these tissues, and build new metastases, a mechanism that has also been published in CNS infiltration by hematologic malignancies [<xref rid=\"B14-ijms-21-05491\" ref-type=\"bibr\">14</xref>,<xref rid=\"B15-ijms-21-05491\" ref-type=\"bibr\">15</xref>]. In this regard, Kinjyo and colleagues have recently described the impact of leukemia-derived exosomes on lymphoblast transmigration across the blood-brain barrier (BBB) in pediatric ALL. In their model, leukemia-derived exosomes led to modulation of astrocytes and brain microvascular endothelial cells, resulting in upregulation of vascular endothelial growth factor A (VEGF-A) expression and sequential disruption of the BBB integrity [<xref rid=\"B5-ijms-21-05491\" ref-type=\"bibr\">5</xref>]. Furthermore, exosomes may well be involved in molecular mechanisms facilitating survival and chemotherapy resistance of leukemic blasts within the CNS compartment, e.g., inducing cellular quiescence [<xref rid=\"B16-ijms-21-05491\" ref-type=\"bibr\">16</xref>]. In this regard, varying microRNA expression profiles have been described in T-linage and B-cell precursor (BCP)-ALL, potentially contributing to the different clinical behavior of these ALL subtypes [<xref rid=\"B17-ijms-21-05491\" ref-type=\"bibr\">17</xref>].</p><p>Multiple routes of leukemic blasts into the CNS compartment have been described [<xref rid=\"B3-ijms-21-05491\" ref-type=\"bibr\">3</xref>,<xref rid=\"B18-ijms-21-05491\" ref-type=\"bibr\">18</xref>]. However, despite the rarity of parenchymal metastases during initial stages, most in vitro studies applied models of the BBB. Despite its central role on immune cells traveling into the CNS [<xref rid=\"B19-ijms-21-05491\" ref-type=\"bibr\">19</xref>], the BCSFB within the choroid plexus is yet under-recognized concerning the CNS infiltration by leukemic blasts [<xref rid=\"B20-ijms-21-05491\" ref-type=\"bibr\">20</xref>]. Therefore, we investigated the relevance of leukemia-derived exosomes on ALL cell transmigration across the blood-cerebrospinal fluid barrier (BCSFB). Recently, we could establish a human in vitro model suitable to study the cellular interactions of leukemic blasts with the choroid plexus epithelial cells and demonstrate crossing of lymphoblasts by a transcellular and paracellular route [<xref rid=\"B21-ijms-21-05491\" ref-type=\"bibr\">21</xref>]. Now, we provide evidence that leukemia-derived exosomes may facilitate this process of transmigration into the CNS compartment without destruction of the BCSFB integrity.</p></sec><sec sec-type=\"results\" id=\"sec2-ijms-21-05491\"><title>2. Results</title><sec id=\"sec2dot1-ijms-21-05491\"><title>2.1. Exosome Isolation</title><p>Extracellular vesicles released by the three leukemia cell lines SD-1 (BCP-ALL), Nalm-6 (BCP-ALL), and P12 (T-ALL) all presented with typical size of exosomes, and expressed exosomal markers including CD 63 and CD 81 (<xref ref-type=\"app\" rid=\"app1-ijms-21-05491\">Supplementary Figure S1</xref>). The cell lines had proven capability to infiltrate the CNS in mouse models [<xref rid=\"B22-ijms-21-05491\" ref-type=\"bibr\">22</xref>,<xref rid=\"B23-ijms-21-05491\" ref-type=\"bibr\">23</xref>,<xref rid=\"B24-ijms-21-05491\" ref-type=\"bibr\">24</xref>].</p></sec><sec id=\"sec2dot2-ijms-21-05491\"><title>2.2. Time- and Dose-Dependent Uptake to HiBCPP Cells</title><p>Exosome uptake to HiBCPP cells was determined for up to 72 h by fluorescent microscopy (<xref ref-type=\"fig\" rid=\"ijms-21-05491-f001\">Figure 1</xref>A), and differed markedly between the various cell lines: In SD-1-derived exosomes, uptake to plexus epithelial cells did not correlate with incubation time and exosome concentration. In contrast, in Nalm-6 and P12-derived exosomes, increasing incubation time and exosome concentration resulted in a significant increment of incorporation into HiBCPP cells (<xref ref-type=\"fig\" rid=\"ijms-21-05491-f001\">Figure 1</xref>B,C). Acid wash of incubated HiBCPP cells was performed to strip off exosomes adherent to the outer cell membrane to verify complete uptake to HIBCPP cells. Although relative fluorescence intensity (RFI) with acidic wash was slightly increased in Nalm-6-derived exosomes (median RFI 1954 versus 2910) and decreased in P12-derived exosomes (median RFI 1826 versus 685), none of these alterations was statistically significant, indicating that most (if not all) exosomes were completely incorporated to the HiBCPP target cells (<xref ref-type=\"app\" rid=\"app1-ijms-21-05491\">Supplementary Figure S2</xref>).</p><p>Barrier function of HiBCPP cells was determined by both transepithelial electrical resistance (TEER) measurement before and after exosomal incubation and dextran-flux for the last 4 h of experiment, the latter to determine permeability to macromolecules. Neither did exosome incubation result in an increase of barrier tightness markers compared to untreated control, nor did exosome incubation lead to a disruption of barrier function (<xref ref-type=\"fig\" rid=\"ijms-21-05491-f001\">Figure 1</xref>D). Accordingly, viability of HiBCPP cells and expression of tight junction protein zonula occludens 1 (ZO-1) in the BCSFB in vitro model was not altered by exosome incubation throughout the experiments (<xref ref-type=\"app\" rid=\"app1-ijms-21-05491\">Supplementary Figure S3</xref>).</p></sec><sec id=\"sec2dot3-ijms-21-05491\"><title>2.3. Inhibition of Exosome Association and Uptake</title><p>Next, we analyzed the integrin-dependent association of BCP-ALL and T-ALL-derived exosomes on BCSFB in vitro model using blocking antibodies to inhibit exosomal integrins, which bind to the Arginine, Glycine, and Aspartate motif (RGD-sequence) of extracellular matrix proteins, prior to incubation with HiBCPP cells. The adhesion of BCP-ALL-derived exosomes was not altered upon antibody blocking. Instead, the association of T-ALL P12-derived exosomes to HiBCPP cells was significantly diminished to proximately 6% to 10% when blocking exosomal integrin αV and β3 binding to fibronectin, vitronectin and osteopontin. Furthermore, the inhibition of integrin α5 and β1, also binding to vitronectin, decreased association to 12% to 14% compared to untreated exosomes (<xref ref-type=\"fig\" rid=\"ijms-21-05491-f002\">Figure 2</xref>A).</p><p>Moreover, we analyzed the mechanism of exosome uptake. Therefore, chemical inhibitors were applied to HiBCPP cells 1 h prior to incubation with fluorescently labeled exosomes. In addition, HiBCPP cells were washed with acidified phosphate buffered saline (PBS) to analyze only incorporated exosomes (<xref ref-type=\"fig\" rid=\"ijms-21-05491-f002\">Figure 2</xref>B). By heparin blocking the exosomal binding to cellular heparan sulfate proteoglycans, the adhesion and uptake of exosomes of all three ALL cell lines were reduced to 37%, 5% and 21% for SD1, Nalm-6 and P12 exosomes, respectively. Furthermore, using cytochalasin D to inhibit the actin-dependent micropinocytosis, the uptake of T-ALL P12 exosomes was reduced to approximately 62% compared to untreated HiBCPP cells. Nevertheless, the uptake of BCP-ALL SD1 and Nalm-6 exosomes was not significantly altered by cytochalasin D treatment. The inhibition of clathrin-dependent endocytosis via chlorpromazine did not affect the internalization rates of SD1, Nalm-6, or P12 exosomes. Instead, the dynamin-dependent endocytosis, which was inhibited by dynasore pre-incubation of HiBCPP cells, was diminished to 9% and 20% for Nalm-6 and P12 exosomes, respectively. Again, the uptake of SD1 exosomes was not inhibited.</p></sec><sec id=\"sec2dot4-ijms-21-05491\"><title>2.4. Leukemia Cell Transmigration across the BCSFB</title><p>We analyzed the potential of leukemia-derived exosomes to facilitate transmigration of lymphoblasts across the BCSFB (<xref ref-type=\"fig\" rid=\"ijms-21-05491-f003\">Figure 3</xref>) We employed a human in vitro model and analyzed the transmigration rate of SD-1 (BCP-ALL), Nalm-6 (BCP-ALL), and P12 (T-ALL). Following incubation of HiBCPP with the respective leukemia-derived exosomes, transmigration during a 6 h period was significantly increased in P12 cells. Compared to an unstimulated HiBCPP control, transmigration rates were 1.4-fold (not significant) and 2.7-fold (<italic>p</italic> = 0.0009) increased, following exosome incubation of the BCSFB for 24 and 48 h, respectively.</p><p>In contrast, transmigration of SD-1 and Nalm-6 cells across the BCSFB during a 6 h period was not altered, when HiBCPP cells were pre-incubated with exosomes for 24 and 48 h. Both BCP-ALL cell lines presented with transmigration rates equal to those found across untreated plexus epithelium.</p><p>Again, exosome incubation of plexus epithelium did not result in an impairment of barrier function markers in any of the experiments. TEER values, dextran permeability, and immunofluorescent evaluation of tight junction protein expression over time were not altered significantly during the experiments.</p></sec></sec><sec sec-type=\"discussion\" id=\"sec3-ijms-21-05491\"><title>3. Discussion</title><p>Many questions remain to be answered until CNS infiltration from pediatric acute lymphoblastic leukemia will be fully understood [<xref rid=\"B3-ijms-21-05491\" ref-type=\"bibr\">3</xref>]. So far, several potential routes into the CNS compartment have been identified, and numerous molecular mechanisms have been described that drive infiltration to and survival within the CNS microenvironment [<xref rid=\"B6-ijms-21-05491\" ref-type=\"bibr\">6</xref>,<xref rid=\"B25-ijms-21-05491\" ref-type=\"bibr\">25</xref>]. In the present study, we investigated the role of leukemia-derived exosomes in CNS invasion of lymphoblasts across the BCSFB in a human in vitro model.</p><sec id=\"sec3dot1-ijms-21-05491\"><title>3.1. Exosome Conditioning Does Not Alter BCSFB Integrity</title><p>These small extracellular vesicles play a crucial role in tumor biology, and previous studies have unraveled their multifunctional role also in acute leukemias [<xref rid=\"B14-ijms-21-05491\" ref-type=\"bibr\">14</xref>,<xref rid=\"B15-ijms-21-05491\" ref-type=\"bibr\">15</xref>]. In this regard, Kinjyo and co-workers found an increased transmigration across human brain microvascular endothelial cells, representing the BBB, when leukemia-derived exosomes were added to their in vitro model [<xref rid=\"B5-ijms-21-05491\" ref-type=\"bibr\">5</xref>]. Exosome uptake led to an upregulation of VEGF-A expression, resulting in a degradation of tight junction proteins and hence, increased permeability of the BBB. As a counterpart concerning the blood-CNS barriers, we now describe evidence that exosomes may also modulate leukemic CNS infiltration via the BCSFB, notably, without breakdown of the BCSFB following exosome uptake. Disruption of the brain barriers was a late event in a mouse model of CNS leukemia [<xref rid=\"B5-ijms-21-05491\" ref-type=\"bibr\">5</xref>], correlating to in vivo findings where parenchymal metastases occur only occasionally during early stages [<xref rid=\"B4-ijms-21-05491\" ref-type=\"bibr\">4</xref>]. However, subclinical CNS infiltration in pediatric ALL at diagnosis is more frequent than previously assumed, with most of these patients presenting without neurological signs of disturbed blood-CNS barriers [<xref rid=\"B26-ijms-21-05491\" ref-type=\"bibr\">26</xref>,<xref rid=\"B27-ijms-21-05491\" ref-type=\"bibr\">27</xref>]. Therefore, transmigration of lymphoblasts facilitated by exosome-associated conditioning of the barriers might well be due to more sophisticated mechanisms than just disruption of tight junctions and barrier breakdown, but e.g., by the induction of receptor expression or cytokine signaling [<xref rid=\"B3-ijms-21-05491\" ref-type=\"bibr\">3</xref>,<xref rid=\"B4-ijms-21-05491\" ref-type=\"bibr\">4</xref>,<xref rid=\"B18-ijms-21-05491\" ref-type=\"bibr\">18</xref>]. In this regard, we could previously show transcellular migration of lymphoblasts across the choroid plexus epithelium, hence, disruption of tight junction proteins may not be necessary at all to cross the blood-CNS barriers and degradation of tight junction proteins may not be the only exosome-mediated pathway to facilitate lymphoblast invasion to the brain [<xref rid=\"B21-ijms-21-05491\" ref-type=\"bibr\">21</xref>].</p></sec><sec id=\"sec3dot2-ijms-21-05491\"><title>3.2. Mechanisms of Exosome Uptake to HiBCPP Cells</title><p>When adhesion to target cells could be inhibited by heparin in all cell lines indicating exosome binding to cellular heparan sulfate proteoglycans, exosome uptake and biological significance markedly differed between the various cell lines. Nalm-6 and P12-derived exosomes incorporated to HiBCPP cells mainly by dynamin-dependent endocytosis [<xref rid=\"B28-ijms-21-05491\" ref-type=\"bibr\">28</xref>], without evidence of clathrin-dependent endocytosis and only a minor relevance of actin-dependent macropinocytosis (in P12 exosomes) [<xref rid=\"B29-ijms-21-05491\" ref-type=\"bibr\">29</xref>,<xref rid=\"B30-ijms-21-05491\" ref-type=\"bibr\">30</xref>]. In contrast, SD-1-derived exosomes seemed to associate to HiBCPP cells unspecifically, potentially fusing to the plasma membrane [<xref rid=\"B31-ijms-21-05491\" ref-type=\"bibr\">31</xref>]. Given markedly different association to the BCSFB, a varying capability to precondition target tissue and finally foster CNS involvement across the BCSFB may be possible, in line with CNS metastases from solid tumors. In the latter, binding of tumor cell-derived exosomes to target tissue is highly regulated and dependent on exosomal expression of adhesion molecules binding to proteoglycans and extracellular matrix (ECM) proteins [<xref rid=\"B32-ijms-21-05491\" ref-type=\"bibr\">32</xref>,<xref rid=\"B33-ijms-21-05491\" ref-type=\"bibr\">33</xref>]. In this regard, exosomal integrin expression was crucial for metastasis distribution pattern of sub-lines of the breast cancer cell line MDA-MB-231, and brain metastases were driven by exosomes expressing integrin β3 [<xref rid=\"B13-ijms-21-05491\" ref-type=\"bibr\">13</xref>]. Furthermore, the exosomal expression of integrin α5 and αV was associated to brain metastatic melanoma [<xref rid=\"B34-ijms-21-05491\" ref-type=\"bibr\">34</xref>]. In pediatric ALL, various integrins expressed on lymphoblasts were linked to CNS disease and even associated to potential routes of infiltration [<xref rid=\"B35-ijms-21-05491\" ref-type=\"bibr\">35</xref>]. Given that the receptor repertoire of exosomes resembles parental cells, it is yet to elucidate whether the above-mentioned findings may also be mediated via leukemia-derived exosomes. In this regard, reduced adhesion of P12-derived exosomes by blocking integrin α5, αV, β1, and β3 suggests a potential role of the integrin profile of ALL-derived exosomes on CNS disease. As blocking of integrins did not affect uptake of the BCP-ALL-derived exosomes, binding and uptake to HiBCPP cells may obviously follow multiple pathways and differ between exosomes from the various cell lines. Although we did not quantify the expression of the various integrins on exosomes, we hypothesize that these were prevalent on the extracellular vesicles, since all three ALL cell lines express the evaluated integrins [<xref rid=\"B36-ijms-21-05491\" ref-type=\"bibr\">36</xref>,<xref rid=\"B37-ijms-21-05491\" ref-type=\"bibr\">37</xref>,<xref rid=\"B38-ijms-21-05491\" ref-type=\"bibr\">38</xref>].</p></sec><sec id=\"sec3dot3-ijms-21-05491\"><title>3.3. Differential Relevance in BCP-ALL and T-ALL Cell Lines</title><p>The differential uptake to HiBCPP cells in BCP-ALL and T-ALL-derived exosomes may well be associated to a varying potency of conditioning the choroid plexus tissue. The present findings suggest a relevance of leukemia-derived exosomes on preconditioning the BCSFB to a permissive phenotype in the T-ALL cell line P12. Firstly, adhesion and uptake could be attributed to well-established mechanisms, and reduction of exosomal uptake by integrin blockade might point out to a choroid plexus epithelium specific process in P12-derived exosomes [<xref rid=\"B13-ijms-21-05491\" ref-type=\"bibr\">13</xref>,<xref rid=\"B31-ijms-21-05491\" ref-type=\"bibr\">31</xref>]. Secondly, exosome preconditioning facilitated leukemic blast transmigration across the BCSFB in the P12 T-ALL cell line, but not in the BCP-ALL cell lines. Hence, exosomes may differ to each other not only concerning association to HiBCPP, but also regarding the biological relevance following uptake [<xref rid=\"B39-ijms-21-05491\" ref-type=\"bibr\">39</xref>]. Further research is warranted to verify such a hypothesis and elucidate the underlying molecular mechanisms of exosome preconditioning. Since both Nalm-6 and P12-derived exosomes were uptaken to the choroid plexus epithelial cells, but resulted in an increased transmigration rate of lymphoblasts only in the P12 cell line, a varying exosomal cargo might well explain the different behavior and (in part) finally contribute to the neurotropism of T-ALL invading the CNS across the BCSFB. In this regard, Almeida and co-workers compared microRNA expression in pediatric BCP-ALL versus T-ALL patients, and identified 16 differentially expressed microRNAs [<xref rid=\"B17-ijms-21-05491\" ref-type=\"bibr\">17</xref>]. Targets of these microRNAs included proteins associated to CNS leukemia, e.g., Zeta Chain of T Cell Receptor Associated Protein Kinase 70 (ZAP70) and C-X-C modif chemokine ligand 12 (CXCL12) [<xref rid=\"B6-ijms-21-05491\" ref-type=\"bibr\">6</xref>,<xref rid=\"B40-ijms-21-05491\" ref-type=\"bibr\">40</xref>,<xref rid=\"B41-ijms-21-05491\" ref-type=\"bibr\">41</xref>,<xref rid=\"B42-ijms-21-05491\" ref-type=\"bibr\">42</xref>]. In addition, miR-151a-5b and miR-151b, downregulated in T-ALL patients, was also associated to CNS lymphoma [<xref rid=\"B43-ijms-21-05491\" ref-type=\"bibr\">43</xref>]. Whether these microRNAs explain the differential transmigration following exosome incubation in the present study is to be elucidated. However, although the risk of CNS invasion is multifactorial and multiple routes to the CNS compartment have been described, identifying such specific exosomal content (including proteins or RNA fragments) associated to an increased BCSFB transmigration might serve as a potential prognostic and/or therapeutic target in the future [<xref rid=\"B11-ijms-21-05491\" ref-type=\"bibr\">11</xref>,<xref rid=\"B44-ijms-21-05491\" ref-type=\"bibr\">44</xref>]. To this end, in vitro models may serve as screening tools prior to verify hypotheses in animal studies.</p></sec><sec id=\"sec3dot4-ijms-21-05491\"><title>3.4. Translational Implications</title><p>Lymphocyte homeostasis within the CNS compartment during healthy state is orchestrated by the BCSFB, and T-cells predominate the CSF [<xref rid=\"B19-ijms-21-05491\" ref-type=\"bibr\">19</xref>,<xref rid=\"B45-ijms-21-05491\" ref-type=\"bibr\">45</xref>]. Of note, pathways driving T-cell attraction to the CNS also underlie CNS infiltration of ALL, including the expression of C-C chemokine receptor type 7 (CCR7), P-selectin glycoprotein ligand 1 (PSGL-1), and integrins [<xref rid=\"B35-ijms-21-05491\" ref-type=\"bibr\">35</xref>,<xref rid=\"B46-ijms-21-05491\" ref-type=\"bibr\">46</xref>,<xref rid=\"B47-ijms-21-05491\" ref-type=\"bibr\">47</xref>,<xref rid=\"B48-ijms-21-05491\" ref-type=\"bibr\">48</xref>]. When the predilection of CNS involvement in T-ALL hijacks these pathways [<xref rid=\"B18-ijms-21-05491\" ref-type=\"bibr\">18</xref>], the BCSFB within the choroid plexus might well represent a significant gate into the CNS. Several studies have addressed this potential entry site [<xref rid=\"B7-ijms-21-05491\" ref-type=\"bibr\">7</xref>,<xref rid=\"B20-ijms-21-05491\" ref-type=\"bibr\">20</xref>,<xref rid=\"B21-ijms-21-05491\" ref-type=\"bibr\">21</xref>], but so far, have described only limited lymphoblast transmigration across the BCSFB [<xref rid=\"B3-ijms-21-05491\" ref-type=\"bibr\">3</xref>,<xref rid=\"B49-ijms-21-05491\" ref-type=\"bibr\">49</xref>]. However, pediatric ALL presents with only subtle symptoms in most patients, and time to diagnosis is often prolonged. Therefore, conditioning target tissue (e.g., the choroid plexus epithelium) by leukemia-derived exosomes and transmigration of lymphoblasts into the CNS compartment (by hematogenic route) may develop over several weeks. In this regard, the present findings of prolonged exosome uptake compared to studies on the BBB, associated to delayed increase of P12 transmigration not before 48 h from incubation, may argue for a yet under-recognized role of this entry site due to limited duration of experiments especially in T-ALL. Given the potential of exosomes to induce a pro-metastatic condition, efforts should be made to establish long-term models suitable to investigate the cellular interactions of lymphoblasts and their respective extracellular vesicles with the BCSFB.</p></sec><sec id=\"sec3dot5-ijms-21-05491\"><title>3.5. Summary</title><p>Small extracellular vesicles play a crucial role in tumor dissemination and CNS infiltration may be facilitated by exosome-mediated transformation of target tissue not only in solid tumors, but also in leukemias [<xref rid=\"B5-ijms-21-05491\" ref-type=\"bibr\">5</xref>,<xref rid=\"B16-ijms-21-05491\" ref-type=\"bibr\">16</xref>]. Now, we could show that leukemia-derived exosomes enhance P12 transmigration across the BCSFB in vitro. In conclusion, exosomes may be involved in CNS homing, and the choroid plexus may serve as entry site to the CSF especially in T-ALL. To this end, further work is warranted to elucidate the exosome-induced molecular mechanisms driving CNS infiltration, and efforts are to be undertaken to establish long-term in vitro models to study this process over time. By identifying specific exosomal cargo and/or particular signaling, extracellular vesicles might serve as additional diagnostic markers in the future, and help to identify patients on increased risk of CNS involvement allowing further personalization of CNS directed treatment.</p></sec></sec><sec id=\"sec4-ijms-21-05491\"><title>4. Materials and Methods</title><sec id=\"sec4dot1-ijms-21-05491\"><title>4.1. Cell Culture</title><p>The three ALL cell lines (SD-1, Nalm-6, and P12-Ichikawa) were chosen due to their capability to invade the CNS compartment in mouse models [<xref rid=\"B22-ijms-21-05491\" ref-type=\"bibr\">22</xref>,<xref rid=\"B23-ijms-21-05491\" ref-type=\"bibr\">23</xref>,<xref rid=\"B24-ijms-21-05491\" ref-type=\"bibr\">24</xref>]. They were purchased from DSMZ (Braunschweig, Germany) and cultured under standard conditions (37 °C, 5% CO<sub>2</sub>, 95% humidity) in Dulbecco’s Modified Eagle Medium: Nutrient Mixture F-12 (DMEM/F12 (Ham)) supplemented with 10% fetal calf serum (FCS) (Thermo Fisher Scientific, Dreieich, Germany). HiBCPP cells [<xref rid=\"B50-ijms-21-05491\" ref-type=\"bibr\">50</xref>] were cultured under standard conditions (37 °C, 5% CO<sub>2</sub>, 95% humidity) in DMEM/F12 (Ham) supplemented with 5 μg/mL insulin (Sigma Aldrich, Taufkirchen, Germany) and 10% FCS (Thermo Fisher Scientific, Dreieich, Dreieich, Germany).</p></sec><sec id=\"sec4dot2-ijms-21-05491\"><title>4.2. Exosome Isolation</title><p>ALL cell lines were cultured in serum-free DMEM/F12 (Ham) under standard conditions (37 °C, 5% CO<sub>2</sub>, 95% humidity) for 48 h prior to isolation of ALL cell line-derived exosomes. Supernatant was cleared by differential centrifugation (200× <italic>g</italic> for 10 min, 300× <italic>g</italic> for 10 min, 1500× <italic>g</italic> for 20 min, 2500× <italic>g</italic> for 20 min, 4500× <italic>g</italic> for 30 min, 14,000× <italic>g</italic> for 30 min). Exosomes were pelleted down by ultracentrifugation (Sorvall WX Ultra 100 equipped with Sorvall SureSpin 630, Thermo Fisher Scientific, Dreieich, Germany) for 150 min at 100,000× <italic>g</italic>. Exosomes were resuspended in PBS (Thermo Fisher, Dreieich, Germany), filter-sterilized (0.22 μm, Sartorius, Göttingen, Germany) and stored at −80 °C. Where indicated, exosomes were stained with DiIC18(3) (Thermo Fisher, Dreieich, Germany) according to manufacturer’s instructions.</p></sec><sec id=\"sec4dot3-ijms-21-05491\"><title>4.3. BCSFB In Vitro Model</title><p>HiBCPP cells were used as an in vitro model of the BCSFB as previously described [<xref rid=\"B21-ijms-21-05491\" ref-type=\"bibr\">21</xref>]. In brief, 7.5–9 × 10<sup>4</sup> HiBCPP cells were seeded on the bottom side of an inverted 24-well-filter insert with a pore size of 5 μm (Sarstedt, Nümbrecht, Germany) in DMEM/F12 (Ham) supplemented with 5 μg/mL insulin and 10% FCS (Thermo Fisher, Dreieich, Germany) and adhered for 24 h under standard conditions (37 °C, 5% CO<sub>2</sub>, 95% humidity.) Subsequently, filter inserts were placed in standard orientation and HiBCPP cells were cultured until TEER reached at least 70 Ωcm<sup>2</sup> (epithelial voltohmmeter, Millicell ERS STX-1, Merck Millipore, Darmstadt, Germany) and medium was replaced to DMEM/F12 (Ham) supplemented with 5 μg/mL insulin and 1% FCS overnight, leading to tighter barrier properties. HiBCPP cells with a TEER between 250–800 Ωcm<sup>2</sup> were used for experiments.</p></sec><sec id=\"sec4dot4-ijms-21-05491\"><title>4.4. Uptake of Exosomes by BCSFB In Vitro Model</title><p>Fluorescently labeled exosomes were added onto the basolateral side of HiBCPP cells in the upper compartment of filter inserts and incubated under standard conditions (37 °C, 5% CO<sub>2</sub>, 95% humidity) as indicated. HiBCPP cells of BSCFB in vitro model were washed 4 times with PBS or where indicated with acidic PBS (pH = 2.5) after first round of PBS wash, and two times more with PBS afterwards. Cells were fixed with 4% formaldehyde/PBS for 10 min at room temperature (RT). Filters were subsequently cut out and cells were permeabilized for 10 min with 0.1% Triton X-100/1% bovine serum albumin (BSA)/PBS. After washing two times with 1% BSA/PBS, filters were blocked for 20 min in 1% BSA/PBS and stained with 4’,6-Diamidino-2-Phenylindole (DAPI) solution (1.5:55,000, Invitrogen, Carlsbad, CA, USA) to stain the nuclei for 10 min at RT. Filters were washed, mounted with Prolong Gold antifade reagent (Invitrogen, Carlsbad, CA, USA). Exosomes were quantified by fluorescent microscopy (15 fields of view, 20× objective, Axio Observer.Z1, ZEN2 pro software, blue edition, Carl Zeiss, Oberkochen, Germany). The barrier integrity was observed throughout the experiments. TEER was measured at the beginning and end of each experiment and dextran flux (Dextran TexasRed, 100 μg/mL, Invitrogen, Carlsbad, CA, USA) was monitored for 4 h. The fluorescence of Dextran TexasRed in the lower compartment was measurement in Tecan Infinite M200 Multiwell reader (Tecan, Männedorf, Switzerland) afterwards. Where indicated, the HiBCPP cells of BCSFB in vitro model were pretreated with chemical inhibitors (Dynasore, 80 μM, Merck Millipore, Darmstadt, Germany; Heparin, 10 μg/mL, Sigma Aldrich, Steinheim, Germany; Cytochalasin D, 5 μg/mL, Sigma Aldrich, Steinheim, Germany; Chlorpromazine, 50 μM, Sigma Aldrich, Steinheim, Germany) for 1 h. After removal of inhibitors, 16 μg of fluorescently labeled exosomes were added onto the basolateral side of HiBCPP cells and incubated for 48 h. Where indicated, the fluorescently labeled exosomes were pretreated with 4 μg/mL anti-integrin-antibody (anti-ITGα5, Immunotools, Friesoythe, Germany; anti-ITGαV Immunotools, Friesoythe, Germany; anti-ITGβ1, Merck Millipore, Darmstadt, Germany; anti-ITGβ3, BD Bioscience, San Jose, CA, USA) for 30 min at 37 °C before incubation for 48 h on HiBCPP cells.</p></sec><sec id=\"sec4dot5-ijms-21-05491\"><title>4.5. Transmigration Assay</title><p>ALL cells were labeled with CellTrackerTM Green CMFDA Dye (1.5 μM, Invitrogen, Carlsbad, CA, USA) according to manufacturer’s instructions prior to transmigration experiments. 4 × 10<sup>5</sup> labeled ALL cells were placed in the upper compartment of BCSFB in vitro model for 6 h at 37 °C in DMEM/F12 (Ham) supplemented with 0.5% BSA. CXCL12 (100 ng/mL, Peprotech, Cranbury, NJ, USA) was used in the lower compartment as chemoattractant as previously established [<xref rid=\"B21-ijms-21-05491\" ref-type=\"bibr\">21</xref>]. The barrier integrity was measured at the beginning and end of each experiment by TEER.</p><p>Migrated ALL cells were quantified in the lower compartment by fluorescent microscopy (10 fields of view, 10× objective, Axio Observer.Z1, ZEN2 pro software, blue edition, Carl Zeiss, Oberkochen, Germany). Where indicated, BCSFB in vitro model of HiBCPP cells were pretreated with 16 μg of unlabeled exosomes per filter for up to 48 h prior to transmigration.</p></sec><sec id=\"sec4dot6-ijms-21-05491\"><title>4.6. Western Blotting</title><p>ALL cells were lysed in modified radioimmunoprecipitation assay (RIPA) buffer (1× RIPA lysis buffer, 50 mM NaF, 1 mM Na<sub>3</sub>VO<sub>4</sub>, 1 mM phenylmethylsulfonyl fluoride (PMSF), protease inhibitor cocktail). Protein concentration of cell lysate or exosomes was determined by the Lowry method (DC Protein Assay, BioRad, Hercules, CA, USA) according to the manufacturer’s instructions. 20 μg of whole protein cell lysate or exosomes were boiled in 1× NuPage lithium dodecyl sulfate (LDS) Sample Buffer (Life Technologies, Carlsbad, CA, USA) for 10 min at 98 °C and resolved in 4–12% Bis Tris NuPage<sup>®</sup> gels (Invitrogen, Carlsbad, CA, USA). After transfer onto nitrocellulose membranes (0.45 μm, BioRad, Hercules, CA, USA), proteins were detected with the following antibodies (1 μg/mL each): anti-CD63 (BD Bioscience, San Jose, CA, USA), anti-CD81 (BioLegend, San Diego, CA, USA), anti-ALIX (BioLegend, San Diego, CA, USA) and anti-β-actin (Life Technologies, Carlsbad, CA, USA) and visualized using Immobilon Western Chemiluminiscent HRP Substrate (Millipore, Schwalbach, Germany).</p></sec><sec id=\"sec4dot7-ijms-21-05491\"><title>4.7. Viability Testing</title><p>For viability analysis, HiBCPP cells of BCSFB in vitro model were stained using a Live/Dead assay (Invitrogen, Carlsbad, CA, USA) according to manufacturer’s instructions and visualized by fluorescent microscopy (10× objective, Axio Observer.Z1, ZEN2 pro software, blue edition, Carl Zeiss, Oberkochen, Germany).</p></sec><sec id=\"sec4dot8-ijms-21-05491\"><title>4.8. Immunofluorescence Staining</title><p>HiBCPP cells of BSCFB in vitro model were washed 4 times with PBS or where indicated with acidic PBS (pH = 2.5) after first round of PBS wash, and two times more with PBS afterwards. Cells were fixed with 4% formaldehyde/PBS for 10 min at RT. Filters were subsequently cut out and cells were permeabilized for 10 min with 0.1% Triton X-100/1% BSA/PBS. After washing two times with 1% BSA/PBS, filters were blocked for 20 min in 1% BSA/PBS and stained with primary antibody (anti-ZO-1; 1:250, Invitrogen, Carlsbad, CA, USA) overnight at 4 °C. After washing the filters three times with 1% BSA/PBS the following day, filters were incubated with secondary antibody (anti-rabbit Alexa Fluor 594, 1:250, Invitrogen, Carlsbad, CA, USA) for 2 h at RT and washed three times with 1% BSA/PBS. Cells were incubated with DAPI solution (1.5:55,000, Invitrogen, Carlsbad, CA, USA) to stain the nuclei and with Phalloidin Alexa Fluor 660 (1:250, Invitrogen, Carlsbad, CA, USA) for actin cytoskeleton for 10 min at RT. Filters were washed, mounted with Prolong Gold antifade reagent (Invitrogen, Carlsbad, CA, USA) and analyzed by fluorescent microscopy (63× objective, Axio Observer.Z1 with Apotome, ZEN2 pro software, blue edition, Carl Zeiss, Oberkochen, Germany).</p></sec><sec id=\"sec4dot9-ijms-21-05491\"><title>4.9. Nanoparticle Tracking Analysis (NTA)</title><p>For NTA using ZetaView (Particle Metrix, Meerbusch, Germany), 1 µL of exosomes were diluted in sterile-filtered PBS 1:1000 and ZetaView settings were adjusted to sensitivity 80%, shutter 100, 11 positions, and 2 cycles.</p></sec><sec id=\"sec4dot10-ijms-21-05491\"><title>4.10. Electon Microscopy</title><p>Native exosome suspension (10 µL) was placed on a 200 mesh copper grid with a formvar carbon support film (Plano, Wetzlar, Germany). After 10 min at room temperature, the fluid was deducted by filtering paper followed by 2 washing steps with distilled water. The grid was covered by 10 µL 1% uranyl acetate (Serva, Heidelberg, Germany) for 5 min followed by 2 washing steps with distilled water. Pictures were taken on transmission electron microscope JEM-1400 (Joel, Freising, Germany).</p></sec><sec id=\"sec4dot11-ijms-21-05491\"><title>4.11. Statistics</title><p>Statistical analyses were conducted using SAS Software, release 9.4 (SAS Institute Inc., Cary, NC, USA). The Kruskal-Wallis test was used followed by the non-parametric Wilcoxon Rank-Sum Test for pairwise comparisons and exact <italic>p</italic> values have been requested. Box plots (min-max) were used to display relative fluorescence intensity (RFI) and transmigration, TEER and dextran flux are represented as mean ± SD. A test result was considered statistically significant for <italic>p</italic> values below 0.05.</p></sec></sec></body><back><ack><title>Acknowledgments</title><p>Lena Hoffmann and Yannick Schreiner are acknowledged for technical support by characterization of exosomes.</p></ack><app-group><app id=\"app1-ijms-21-05491\"><title>Supplementary Materials</title><p>Supplementary materials can be found at <uri xlink:href=\"https://www.mdpi.com/1422-0067/21/15/5491/s1\">https://www.mdpi.com/1422-0067/21/15/5491/s1</uri>.</p><supplementary-material content-type=\"local-data\" id=\"ijms-21-05491-s001\"><media xlink:href=\"ijms-21-05491-s001.pdf\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material></app></app-group><notes><title>Author Contributions</title><p>Conceptualization, U.E., J.H., R.A. and M.K.; methodology, U.E. and J.H.; validation, U.E., T.S.W., K.N. and S.P.; formal analysis, U.E. and C.W.; investigation, U.E., J.H. and S.M.; resources, H.I., P.N. and S.P.; writing—original draft preparation, U.E. and M.K.; writing—review and editing, J.H., S.M., T.S.W., K.N., H.S. and R.A.; visualization, J.H., S.M., K.N. and S.P.; supervision, H.S. and M.K.; project administration, U.E. and M.K.; funding acquisition, H.S. All authors have read and agreed to the published version of the manuscript.</p></notes><notes><title>Funding</title><p>Financial support was provided by “Golf against Cancer” (Charity Golf Tournament by “m:con goes Golf”) and the parents’ association “Deutsche Leukämie Forschungshilfe Mannheim—Aktion für krebskranke Kinder e. V.”</p></notes><notes notes-type=\"COI-statement\"><title>Conflicts of Interest</title><p>The authors declare no conflict of interest.</p></notes><glossary><title>Abbreviations</title><array orientation=\"portrait\"><tbody><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ALL</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">acute lymphoblastic leukemia</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">BBB</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">blood-brain barrier</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">BCP-ALL</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">B-cell precursor acute lymphoblastic leukemia</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">BCSFB</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">blood-cerebrospinal fluid barrier</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CCR7</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">C-C chemokine receptor type 7</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CNS </td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">central nervous system</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CP</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">choroid plexus</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ECM</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">extracellular matrix</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ITG</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">integrin</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">NTA</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">nanoparticle tracking analysis</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">PSGL-1</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">P-selectin glycoprotein ligand 1</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">RT</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">room temperature</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">RFI</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">relative fluorescence intensity</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">T-ALL</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">T-linage acute lymphoblastic leukemia</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">TEER</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">transepithelial electrical resistance</td></tr></tbody></array></glossary><ref-list><title>References</title><ref id=\"B1-ijms-21-05491\"><label>1.</label><element-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Hunger</surname><given-names>S.P.</given-names></name><name><surname>Mullighan</surname><given-names>C.G.</given-names></name></person-group><article-title>Acute lymphoblastic leukemia in children</article-title><source>N. 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HiBCPP cells were visualized by nuclei staining (DAPI, blue). (<bold>B</bold>) Time-dependent uptake/binding of ALL-derived exosomes (16 μg per filter; SD-1, Nalm-6, and P12, respectively) for indicated time points from basolateral side, and (<bold>C</bold>) dose-dependent uptake/binding of ALL-derived exosomes (48 h; SD-1, Nalm-6, and P12, respectively) for indicated amounts per filter from basolateral side. (<bold>D</bold>) Barrier integrity following exosome incubation was determined by transepithelial electrical resistance (TEER) and showed no significant alterations compared to untreated control. All data shown are box plots with whiskers of at least 3 independent experiments performed in triplicates. * <italic>p</italic> < 0.05; ** <italic>p</italic> < 0.01; *** <italic>p</italic> < 0.001.</p></caption><graphic xlink:href=\"ijms-21-05491-g001\"/></fig><fig id=\"ijms-21-05491-f002\" orientation=\"portrait\" position=\"float\"><label>Figure 2</label><caption><p>Mechanism of uptake/binding of acute lymphoblastic leukemia (ALL) cell-derived exosomes to blood-cerebrospinal fluid barrier (BCSFB) in vitro model. DiIC18(3) labeled exosomes (16 µg per filter) were added onto the basolateral side of HiBCPP cells grown in inverted culture and incubated for 48 h. The nuclei were stained with DAPI solution. Relative fluorescence intensity (RFI) of exosomes was quantified by fluorescent microscopy (15 fields of view, 20× objective). (<bold>A</bold>) Fluorescently labeled exosomes were pretreated with 4 µg/mL anti-integrin-antibody (anti-ITGα5; anti-ITGαV; anti-ITGβ1; anti-ITGβ3) for 30 min at 37 °C before incubation on HiBCPP cells. (<bold>B</bold>) HiBCPP cells were pretreated with chemical inhibitors (10 µg/mL heparin; 5 μg/mL cytochalasin D; 50 µM chlorpromazine; 80 µM dynasore) for 1 h at 37 °C before incubation. HiBCPP cells were washed with acidic PBS (pH = 2.5) after incubation to strip off adherend exosomes. Data shown are box plots with whiskers of at least 3 independent experiments performed in triplicates. * <italic>p</italic> < 0.05; ** <italic>p</italic> < 0.01; *** <italic>p</italic> < 0.001.</p></caption><graphic xlink:href=\"ijms-21-05491-g002\"/></fig><fig id=\"ijms-21-05491-f003\" orientation=\"portrait\" position=\"float\"><label>Figure 3</label><caption><p>Exosomal pre-incubation enhances transmigration across blood-cerebrospinal fluid barrier (BCSFB) in vitro model of T-ALL cell line P12 but not of B-cell precursor (BCP)-ALL cell lines in time-dependent manner. CellTracker™ Green labeled ALL cells were added for 6 h onto the basolateral side of untreated HiBCPP cells or HiBCPP cells pre-incubated with 16 μg unlabeled exosomes for indicated time-points. C-X-C motif chemokine ligand 12 (CXCL12) (100 ng/mL) was used as chemoattractant. Migrated ALL cells (SD-1, Nalm-6, and P12, respectively) were quantified in the lower compartment by fluorescent microscopy (10 fields of view, 10× objective). Data shown are box plots with whiskers of at least 3 independent experiments performed in triplicates. * <italic>p</italic> < 0.05; ** <italic>p</italic> < 0.01; *** <italic>p</italic> < 0.001.</p></caption><graphic xlink:href=\"ijms-21-05491-g003\"/></fig></floats-group></article>\n"
]
[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"review-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Int J Mol Sci</journal-id><journal-id journal-id-type=\"iso-abbrev\">Int J Mol Sci</journal-id><journal-id journal-id-type=\"publisher-id\">ijms</journal-id><journal-title-group><journal-title>International Journal of Molecular Sciences</journal-title></journal-title-group><issn pub-type=\"epub\">1422-0067</issn><publisher><publisher-name>MDPI</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32718097</article-id><article-id pub-id-type=\"pmc\">PMC7432057</article-id><article-id pub-id-type=\"doi\">10.3390/ijms21155242</article-id><article-id pub-id-type=\"publisher-id\">ijms-21-05242</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Review</subject></subj-group></article-categories><title-group><article-title>Data on Adiponectin from 2010 to 2020: Therapeutic Target and Prognostic Factor for Liver Diseases?</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0002-7098-3407</contrib-id><name><surname>Heydari</surname><given-names>Misaq</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05242\">1</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0002-1623-8131</contrib-id><name><surname>Cornide-Petronio</surname><given-names>María Eugenia</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05242\">1</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0002-5890-1042</contrib-id><name><surname>Jiménez-Castro</surname><given-names>Mónica B.</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05242\">1</xref><xref rid=\"c1-ijms-21-05242\" ref-type=\"corresp\">*</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0002-5767-0676</contrib-id><name><surname>Peralta</surname><given-names>Carmen</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05242\">1</xref><xref ref-type=\"aff\" rid=\"af2-ijms-21-05242\">2</xref><xref rid=\"c1-ijms-21-05242\" ref-type=\"corresp\">*</xref></contrib></contrib-group><aff id=\"af1-ijms-21-05242\"><label>1</label>Institut d’Investigacions Biomèdiques August Pi I Sunyer (IDIBAPS), 08036 Barcelona, Spain; <email>[email protected]</email> (M.H.); <email>[email protected]</email> (M.E.C.-P.)</aff><aff id=\"af2-ijms-21-05242\"><label>2</label>Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBERehd), 08036 Barcelona, Spain</aff><author-notes><corresp id=\"c1-ijms-21-05242\"><label>*</label>Correspondence: <email>[email protected]</email>; (M.B.J.-C.); <email>[email protected]</email> (C.P.); Tel.: +34-932275400 (M.B.J.-C.); +34-932275400 (C.P.)</corresp></author-notes><pub-date pub-type=\"epub\"><day>23</day><month>7</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><month>8</month><year>2020</year></pub-date><volume>21</volume><issue>15</issue><elocation-id>5242</elocation-id><history><date date-type=\"received\"><day>19</day><month>6</month><year>2020</year></date><date date-type=\"accepted\"><day>22</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>© 2020 by the authors.</copyright-statement><copyright-year>2020</copyright-year><license license-type=\"open-access\"><license-p>Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>).</license-p></license></permissions><abstract><p>The review describes the role of adiponectin in liver diseases in the presence and absence of surgery reported in the literature in the last ten years. The most updated therapeutic strategies based on the regulation of adiponectin including pharmacological and surgical interventions and adiponectin knockout rodents, as well as some of the scientific controversies in this field, are described. Whether adiponectin could be a potential therapeutic target for the treatment of liver diseases and patients submitted to hepatic resection or liver transplantation are discussed. Furthermore, preclinical and clinical data on the mechanism of action of adiponectin in different liver diseases (nonalcoholic fatty disease, alcoholic liver disease, nonalcoholic steatohepatitis, liver cirrhosis and hepatocellular carcinoma) in the absence or presence of surgery are evaluated in order to establish potential targets that might be useful for the treatment of liver disease as well as in the practice of liver surgery associated with the hepatic resections of tumors and liver transplantation.</p></abstract><kwd-group><kwd>adiponectin</kwd><kwd>ischemia-reperfusion</kwd><kwd>liver transplantation</kwd><kwd>partial hepatectomy</kwd><kwd>NAFLD</kwd><kwd>NASH</kwd></kwd-group></article-meta></front><body><sec sec-type=\"intro\" id=\"sec1-ijms-21-05242\"><title>1. Introduction</title><p>Herein, we review the preclinical and clinical studies reported over the last ten years that investigate the mechanisms of action of adiponectin in liver diseases, in both the presence and absence of surgery. Potential targets and pharmacological strategies aimed at regulating adiponectin in liver diseases are also discussed. Our reasons for this research are threefold. Firstly, there is a need for useful pharmacological strategies to treat nonalcoholic fatty liver disease (NAFLD)/nonalcoholic steatohepatitis (NASH) and avoid progression to cirrhosis and cancer. Secondly, given the prevalence of NAFLD in both society at large and liver transplantation (LT) donation (30% in cadaveric and 20% in living donors), NAFLD pathology has become a major focus of scientific and clinical research [<xref rid=\"B1-ijms-21-05242\" ref-type=\"bibr\">1</xref>,<xref rid=\"B2-ijms-21-05242\" ref-type=\"bibr\">2</xref>]. Specifically in LT, the presence of NAFLD in the donor liver increases the risk of ischemia/reperfusion (I/R) injury and liver regeneration failure compared with non-steatotic ones. In fact, the use of steatotic livers is associated with an increased risk of primary nonfunction or dysfunction. In addition, many donated livers are not suitable for transplantation because of excess steatosis, which exacerbates the critical shortage of donor livers [<xref rid=\"B3-ijms-21-05242\" ref-type=\"bibr\">3</xref>]. Thus, I/R injury associated with LT and hepatic resection of tumors, mostly in different liver diseases, is an unsolved problem in clinical practice. Finally, the increasing number of studies of the role of adiponectin in liver diseases (mainly in the absence of liver surgery) reported in the last 10 years, suggests that this adipocytokine could form the basis of useful strategies for the treatment of NAFLD/NASH and its progression as well as in the clinical practice of liver resections and transplantation.</p><sec id=\"sec1dot1-ijms-21-05242\"><title>1.1. Characteristics and Isoforms of Adiponectin in Liver Diseases</title><p>Adiponectin is a 28 kDa protein hormone including 274 amino acids encoded by the AdipoQ gene. Adiponectin exists in plasma as three distinct oligomeric complexes: the homotrimer (low-molecular weight, LMW mass, ~70 kDa), the hexamer (middle-molecular weight, MMW mass, ~140 kDa) and 12–18 protomer (high-molecular weight, HMW mass, >300 kDa) [<xref rid=\"B4-ijms-21-05242\" ref-type=\"bibr\">4</xref>,<xref rid=\"B5-ijms-21-05242\" ref-type=\"bibr\">5</xref>].</p><p>The total and distribution of all three adiponectin isoforms (LMW, HMW and MMW) are decreased in NAFLD patients without any surgical intervention [<xref rid=\"B6-ijms-21-05242\" ref-type=\"bibr\">6</xref>]. HMW adiponectin suppresses growth factor-induced hepatic stellate cell (HSC) proliferation and may be closely associated with lipid metabolism [<xref rid=\"B7-ijms-21-05242\" ref-type=\"bibr\">7</xref>]. Experimental data suggest that HMW adiponectin is the most potent isoform for alleviation of fatty liver disease in high fat diet-induced obese mice [<xref rid=\"B8-ijms-21-05242\" ref-type=\"bibr\">8</xref>], whereas Bianchi et al. concluded that there is no significant contribution of adiponectin isoform distribution to the progression of liver diseases [<xref rid=\"B6-ijms-21-05242\" ref-type=\"bibr\">6</xref>]. In surgical conditions such as normothermic hepatic ischemia associated with hepatic resection, HMW is the predominant isoform of adiponectin in steatotic livers [<xref rid=\"B9-ijms-21-05242\" ref-type=\"bibr\">9</xref>]. No data have been reported on the levels of the different adiponectin isoforms in other I/R conditions, such as hepatic resection under vascular occlusion or LT.</p></sec><sec id=\"sec1dot2-ijms-21-05242\"><title>1.2. Source of Adiponectin in Liver Diseases</title><p>Different studies of liver diseases related to NAFLD/NASH without any surgical intervention have suggested that adipose tissue is a major site of endogenous adiponectin production [<xref rid=\"B10-ijms-21-05242\" ref-type=\"bibr\">10</xref>,<xref rid=\"B11-ijms-21-05242\" ref-type=\"bibr\">11</xref>,<xref rid=\"B12-ijms-21-05242\" ref-type=\"bibr\">12</xref>,<xref rid=\"B13-ijms-21-05242\" ref-type=\"bibr\">13</xref>,<xref rid=\"B14-ijms-21-05242\" ref-type=\"bibr\">14</xref>,<xref rid=\"B15-ijms-21-05242\" ref-type=\"bibr\">15</xref>]. However, this situation might be different when patients with NAFLD/NASH require liver surgery. In fact, data obtained from steatotic livers subjected to warm hepatic ischemia without resection as well as from non-steatotic livers undergoing 6 h of cold ischemia without brain death indicate that adiponectin is generated by the liver [<xref rid=\"B9-ijms-21-05242\" ref-type=\"bibr\">9</xref>,<xref rid=\"B16-ijms-21-05242\" ref-type=\"bibr\">16</xref>]. Indeed, steatotic livers have been shown to generate adiponectin in an isolated perfused liver model without the presence of either circulation or adipose tissue. This is in line with clinical studies reporting that the expression of hepatic adiponectin in patients with NAFLD undergoing partial hepatectomy is derived from hepatocytes themselves rather than from migration of peripheral adipose tissue [<xref rid=\"B17-ijms-21-05242\" ref-type=\"bibr\">17</xref>]. Further studies are necessary to elucidate the role of adipose tissue and liver in the changes in adiponectin levels in LT with extended criteria donors (for instance, liver grafts from cardiac arrest donors) as well as in living-related LT.</p></sec><sec id=\"sec1dot3-ijms-21-05242\"><title>1.3. Adiponectin Receptors in Liver Diseases</title><p>Adiponectin enhances the binding of APPL1 (an adaptor protein containing a pleckstrin homology domain, a phosphotyrosine binding domain and a leucine zipper motif) to the two adiponectin receptors (adipoR1 and adipoR2). These interactions are essential for subsequent adiponectin actions [<xref rid=\"B18-ijms-21-05242\" ref-type=\"bibr\">18</xref>,<xref rid=\"B19-ijms-21-05242\" ref-type=\"bibr\">19</xref>]. However, there is controversy concerning the expression of the adiponectin receptors in animal models of obesity; similarly, hepatic adiponectin receptor mRNAs in NAFLD/NASH patients are found unchanged, decreased or even increased [<xref rid=\"B20-ijms-21-05242\" ref-type=\"bibr\">20</xref>,<xref rid=\"B21-ijms-21-05242\" ref-type=\"bibr\">21</xref>,<xref rid=\"B22-ijms-21-05242\" ref-type=\"bibr\">22</xref>,<xref rid=\"B23-ijms-21-05242\" ref-type=\"bibr\">23</xref>,<xref rid=\"B24-ijms-21-05242\" ref-type=\"bibr\">24</xref>]. Intensive research efforts are needed to evaluate the expression and role of the different adiponectin receptors in hepatic resections and LT. Nevertheless, given the NAFLD/NASH results, in our view, the role of adiponectin depends on the different isoforms and receptors involved as well as on the type of liver and surgical conditions (as discussed in <xref ref-type=\"sec\" rid=\"sec5-ijms-21-05242\">Section 5</xref> and <xref ref-type=\"sec\" rid=\"sec6-ijms-21-05242\">Section 6</xref> of the current review). If that were the case, specific strategies should be adopted to regulate the effects of adiponectin depending on the type of liver as well as the surgical conditions.</p></sec></sec><sec id=\"sec2-ijms-21-05242\"><title>2. Adiponectin Effects on Hepatic Damage and Regenerative Failure Associated with Hepatic I/R</title><p>As mentioned above, the data suggest that the role of adiponectin depends on the surgical conditions and type of liver. Thus, adiponectin is injurious to steatotic livers submitted to 60 min of normothermic ischemia [<xref rid=\"B9-ijms-21-05242\" ref-type=\"bibr\">9</xref>]. However, beneficial effects of adiponectin on regeneration are reported in steatotic livers undergoing partial hepatectomy under vascular occlusion. Moreover, adiponectin plays a minor role in non-steatotic liver grafts subjected to 6 h of cold ischemia, whereas it protects in the presence of steatosis [<xref rid=\"B16-ijms-21-05242\" ref-type=\"bibr\">16</xref>]. Similarly, to these surgical findings, different results have been reported for the mechanisms of action of adiponectin. It is a positive regulator of resistin in LT of steatotic grafts. Thus, adenosine monophosphate-activated protein kinase (AMPK) activation by the drug, aminoimidazole-4-carboxamide ribonucleoside (AICAR) and surgical ischemic preconditioning increased adiponectin, which in turn upregulated resistin in steatotic liver grafts without brain death. The upregulation of phosphoinositide 3-kinase (PI3K)/protein kinase B (Akt) induced by resistin protected steatotic livers against damage [<xref rid=\"B16-ijms-21-05242\" ref-type=\"bibr\">16</xref>]. Meanwhile, data on chronic feeding of high-fat diets to rodents indicate a negative correlation between adiponectin and resistin (decreased adiponectin and increased resistin plasma levels) [<xref rid=\"B25-ijms-21-05242\" ref-type=\"bibr\">25</xref>], whereas, in NAFLD patients, no correlation between adiponectin and resistin was reported [<xref rid=\"B26-ijms-21-05242\" ref-type=\"bibr\">26</xref>,<xref rid=\"B27-ijms-21-05242\" ref-type=\"bibr\">27</xref>]. Furthermore, in preclinical studies without brain death, ischemic preconditioning increased adiponectin levels in liver grafts submitted to 6 h of cold ischemia, but not in LT from brain-dead donors, which most closely resemble the surgical conditions occurring in clinical practice [<xref rid=\"B28-ijms-21-05242\" ref-type=\"bibr\">28</xref>]. Furthermore, adiponectin is beneficial for liver regeneration in hepatic resection under vascular occlusion. In fact, results indicate that adiponectin –/– mice exhibit delayed liver regeneration following partial hepatectomy [<xref rid=\"B29-ijms-21-05242\" ref-type=\"bibr\">29</xref>,<xref rid=\"B30-ijms-21-05242\" ref-type=\"bibr\">30</xref>]. These benefits of adiponectin might be mediated by signal transducer and activator of transcription-3 (STAT3) signaling and progression through the cell cycle. In addition, adiponectin negatively regulates the fibroblast growth factor 2 (FGF2) response and other growth factors derived from stellate cells [<xref rid=\"B31-ijms-21-05242\" ref-type=\"bibr\">31</xref>,<xref rid=\"B32-ijms-21-05242\" ref-type=\"bibr\">32</xref>] (<xref ref-type=\"fig\" rid=\"ijms-21-05242-f001\">Figure 1</xref>).</p></sec><sec id=\"sec3-ijms-21-05242\"><title>3. Anti-Steatotic Effects of Adiponectin in Liver Diseases</title><p>In obesity, it has been reported that adiponectin protects liver from steatosis and inflammation: it increases the capacity of insulin to suppress glucose production [<xref rid=\"B33-ijms-21-05242\" ref-type=\"bibr\">33</xref>]. A diet deficient in choline and L-amino acid induces more severe hepatic steatosis in adiponectin deficient mice than in wild-type animals [<xref rid=\"B34-ijms-21-05242\" ref-type=\"bibr\">34</xref>]. In the same way, adenoviral expression of adiponectin ameliorates lipid deposition in the liver. Sterol regulatory element-binding protein 1c (SREBP-1c) is a central regulator of fatty acid synthesis, and it is suppressed by adiponectin in hepatocytes and in the liver of db/db mice [<xref rid=\"B35-ijms-21-05242\" ref-type=\"bibr\">35</xref>]. In addition, through the downregulation of lipogenic transcription factor, SREBP-1c, adiponectin prevents hepatic lipogenesis [<xref rid=\"B36-ijms-21-05242\" ref-type=\"bibr\">36</xref>]. The induction of AMPK by adiponectin might explain its effects on anabolic and catabolic pathways. It is known that AMPK switches on adenosine triphosphate (ATP)-producing catabolic pathways (fatty acid oxidation and glycolysis) and switches off ATP-consuming anabolic pathways (lipogenesis) [<xref rid=\"B37-ijms-21-05242\" ref-type=\"bibr\">37</xref>]. Thus, when adiponectin activates AMPK, glucose utilization and fatty-acid oxidation in the liver are elevated. Suppression of SREBP-1c by adiponectin is mediated through AdipoR1/liver kinase B1 (LKB1)—an upstream kinase of the AMPK pathway [<xref rid=\"B35-ijms-21-05242\" ref-type=\"bibr\">35</xref>]. In addition, AMPK phosphorylates acetyl-CoA carboxylase (ACC), and this is subsequently associated with higher activity of carnitine palmitoyl- transferase 1 (CPT-1), a rate limiting enzyme in fatty acid oxidation [<xref rid=\"B37-ijms-21-05242\" ref-type=\"bibr\">37</xref>]. In addition, it is reported that adiponectin stimulates peroxisome proliferator-activated receptor alpha (PPARα) activity, which enhances fat oxidation, reduces lipid synthesis and prevents accumulation of fatty infiltration [<xref rid=\"B38-ijms-21-05242\" ref-type=\"bibr\">38</xref>]. All of this has been reported in experimental obesity models without any liver surgery.</p><p>It should be also noted that adiponectin reduces chronic inflammation in target organs including vasculature, lung and heart, thereby leading to protection against various obesity-related disorders [<xref rid=\"B39-ijms-21-05242\" ref-type=\"bibr\">39</xref>]. However, the experiments were performed in vitro so further studies are required to clarify this issue. It has also been demonstrated that serum has lower adiponectin levels, before and after transplantation. However, if such changes are associated with cardiovascular events in long-term follow-up of liver transplant recipients remain to be elucidated [<xref rid=\"B40-ijms-21-05242\" ref-type=\"bibr\">40</xref>]. It is also important to clarify this because systemic inflammation following the release into circulation of different mediators from the liver has been reported in surgical conditions of hepatic I/R [<xref rid=\"B41-ijms-21-05242\" ref-type=\"bibr\">41</xref>,<xref rid=\"B42-ijms-21-05242\" ref-type=\"bibr\">42</xref>].</p><p>The effects of adiponectin in steatotic livers undergoing surgery and on various obesity-related disorders should not be ignored. Indeed, the different signaling pathways such as AMPK and PPARα involved in the anti-steatotic effects of adiponectin also play a role in protecting steatotic livers undergoing I/R against damage (<xref ref-type=\"fig\" rid=\"ijms-21-05242-f002\">Figure 2</xref>).</p></sec><sec id=\"sec4-ijms-21-05242\"><title>4. Relationship between Adiponectin and Leptin in Fibrogenesis</title><p>Hepatic fibrosis is the last prevalent pathway prior to cirrhosis and eventually requires LT. Among the different adipocytokines (retinol binding protein 4 (RBP4), resistin, apelin, chemerin, vaspin, etc.), adiponectin is one chiefly involved in hepatic fibrosis [<xref rid=\"B43-ijms-21-05242\" ref-type=\"bibr\">43</xref>]. In patients with liver cirrhosis, circulating adiponectin is elevated, independent of disease etiology, age or body mass index [<xref rid=\"B44-ijms-21-05242\" ref-type=\"bibr\">44</xref>]. This might be explained by its effect on the inflammatory response [<xref rid=\"B45-ijms-21-05242\" ref-type=\"bibr\">45</xref>], a reduction in biliary excretion [<xref rid=\"B46-ijms-21-05242\" ref-type=\"bibr\">46</xref>] or an imbalance between adiponectin production and hepatic excretion [<xref rid=\"B47-ijms-21-05242\" ref-type=\"bibr\">47</xref>]. Both leptin and adiponectin play key roles in obesity-related disorders and are associated with the pathogenesis of NAFLD [<xref rid=\"B48-ijms-21-05242\" ref-type=\"bibr\">48</xref>]. In NAFLD patients, concentrations of adiponectin are decreased whereas leptin levels are increased, indicating an imbalance of adipocytokines, which might promote the progression of this liver disease [<xref rid=\"B48-ijms-21-05242\" ref-type=\"bibr\">48</xref>]<bold>.</bold> Indeed, in vitro studies based mainly on culture-activated HSCs suggest that hepatic fibrosis and its resolution are controlled by adiponectin and leptin.</p><p>Adiponectin has strong-antifibrotic features whereas leptin acts as a profibrogenic molecule. This is because adiponectin prevents leptin signal transduction [<xref rid=\"B43-ijms-21-05242\" ref-type=\"bibr\">43</xref>]. In fact, adiponectin (through protein tyrosine phosphatase 1B (PTP1B)) prevents the leptin-mediated activation of the janus kinase 2 (Jak2)/STAT3 pathway. By inducing suppressors of cytokine signaling 3 (SOCS3), adiponectin also suppresses leptin activity. Moreover, adiponectin provokes HSC apoptosis and suppresses HSC proliferation and α collagen biosynthesis. In addition, adiponectin-mediated prevention of leptin signaling leads to downregulation of tissue inhibitor of metalloproteinase 1 (TIMP-1) transcription and TIMP-1 activity, while adiponectin increases the capacity of matrix metalloproteinase 1 (MMP-1) to degrade fibrillar collagen in cellular matrix. It further prevents the activity of focal adhesion kinase (FAK) and disrupts the formation of mature focal adhesions (FA) [<xref rid=\"B43-ijms-21-05242\" ref-type=\"bibr\">43</xref>]. All of this is scientifically and clinically relevant, but several concerns should be taken into account: it all needs to be confirmed in an experimental model of NASH that simulates the characteristics of this pathology as soon as possible in clinical practice.</p><p>In a choline-deficient mouse NASH model, low levels of adiponectin have been reported [<xref rid=\"B49-ijms-21-05242\" ref-type=\"bibr\">49</xref>], although their relevance remains unclear. Adiponectin increased apoptosis of hepatocellular carcinoma (HCC) cells via activation of caspase-3, and increased phosphorylation of c-Jun N-terminal kinase (JNK) [<xref rid=\"B50-ijms-21-05242\" ref-type=\"bibr\">50</xref>]. Data obtained from cell cultures and animal models suggest that adiponectin inhibits leptin-induced proliferation of HCC via blocking downstream pathways including STAT-3, AKT and mammalian target of rapamycin (m-TOR) [<xref rid=\"B51-ijms-21-05242\" ref-type=\"bibr\">51</xref>]. Adiponectin also provokes the suppression of liver tumor growth and metastasis in mice by inhibiting angiogenesis, and it shows chemoprotective and hepatoprotective functions by blocking sulfatase 2 [<xref rid=\"B52-ijms-21-05242\" ref-type=\"bibr\">52</xref>,<xref rid=\"B53-ijms-21-05242\" ref-type=\"bibr\">53</xref>]. Although basic mechanistic studies indicate that leptin acts to promote HCC proliferation, migration and invasion [<xref rid=\"B54-ijms-21-05242\" ref-type=\"bibr\">54</xref>], clinical observation showed that higher leptin expression was associated with increased HCC survival [<xref rid=\"B55-ijms-21-05242\" ref-type=\"bibr\">55</xref>]. Similarly, although experimental studies indicated that adiponectin increased HCC apoptosis and prevented liver tumor growth and metastasis, increased adiponectin has also been associated with both favorable prognosis [<xref rid=\"B56-ijms-21-05242\" ref-type=\"bibr\">56</xref>] and reduced HCC survival [<xref rid=\"B57-ijms-21-05242\" ref-type=\"bibr\">57</xref>]. Further studies are required to resolve this issue. Nevertheless, several concerns should be considered. Experimental models are needed that simulate the progression of NAFLD to HCC in conditions similar to those in clinical practice, and dependence of the roles of adiponectin and leptin on sex must be considered. Indeed, data analysis by gender shows considerably higher levels of adiponectin and leptin in control group females than in males [<xref rid=\"B58-ijms-21-05242\" ref-type=\"bibr\">58</xref>]. Lower levels of adiponectin in males may account for their higher prevalence of liver cancer. Testosterone activates JNK in human and mouse adipocytes, and genetic deletion of JNK1 in mouse adipose tissue leads to higher levels of adiponectin and protection against HCC. Increased AMPK and mitogen-activated protein kinase (p38α) activation levels were detected in females, associated with the higher levels of adiponectin in female mice [<xref rid=\"B59-ijms-21-05242\" ref-type=\"bibr\">59</xref>] (<xref ref-type=\"fig\" rid=\"ijms-21-05242-f003\">Figure 3</xref>).</p></sec><sec id=\"sec5-ijms-21-05242\"><title>5. Pharmacological Strategies Regulating the Action of Adiponectin on Hepatic Damage and Regenerative Failure Associated with Hepatic I/R</title><p>In an experimental model of LT, the administration of adiponectin reduced hepatic injury and increased regeneration in both steatotic and non-steatotic grafts submitted to 6 h of cold ischemia [<xref rid=\"B16-ijms-21-05242\" ref-type=\"bibr\">16</xref>]. Adiponectin increased resistin, which in turn resulted in PI3K/Akt over-expression [<xref rid=\"B16-ijms-21-05242\" ref-type=\"bibr\">16</xref>]. In an experimental model of partial hepatectomy using adiponectin –/– mice, the authors reported that adiponectin accelerated cell cycle progression relative to wild-type mice. Such benefits were mediated by STAT3 signaling and progression through the cell cycle [<xref rid=\"B31-ijms-21-05242\" ref-type=\"bibr\">31</xref>].</p><p>Pharmacological strategies aimed at modulating the actions of adiponectin are ineffective in non-steatotic livers from Zucker rats undergoing 60 min of warm ischemia, as well as in surgical procedures requiring liver regeneration such as partial hepatectomy with I/R [<xref rid=\"B9-ijms-21-05242\" ref-type=\"bibr\">9</xref>]. However, treatment with adiponectin protected non-steatotic livers of Wistar rats subjected to 60 min warm I/R by reducing the inflammatory response [C-C motif chemokine ligand 2 (CCL-2), C-X-C motif chemokine ligand 10 (CXCL-10), intercellular cell adhesion molecule (ICAM)-1 and cytokines such as interleukin (IL)-1β, IL-6 and tumor necrosis factor α (TNF-α)] and hepatocyte apoptosis. Such effects of adiponectin are mediated via AMPK/endothelial nitric oxide synthase (eNOS) [<xref rid=\"B60-ijms-21-05242\" ref-type=\"bibr\">60</xref>]. These differences in the effects of adiponectin in studies of warm I/R [<xref rid=\"B9-ijms-21-05242\" ref-type=\"bibr\">9</xref>,<xref rid=\"B60-ijms-21-05242\" ref-type=\"bibr\">60</xref>] might be explained, at least partially, by the use of different species or different treatments: siRNA of adiponectin versus recombinant adiponectin. The studies using adiponectin siRNA and recombinant adiponectin [<xref rid=\"B9-ijms-21-05242\" ref-type=\"bibr\">9</xref>,<xref rid=\"B60-ijms-21-05242\" ref-type=\"bibr\">60</xref>] assessed the role of endogenous and exogenous adiponectin, respectively. Thus, similar to what occurs for nitric oxide (NO), the roles of endogenous and exogenous adiponectin might be different. The AMPK signaling pathway is also involved in the actions of adiponectin in an experimental model of chronic intermittent liver hypoxia. Thus, adiponectin supplementation induced AMPK activation which reduced the reactive oxygen species (ROS) generation from endoplasmic reticulum stress, and the activation of the three apoptotic pathways [<xref rid=\"B61-ijms-21-05242\" ref-type=\"bibr\">61</xref>]. The benefits of adiponectin against inflammation and apoptosis have also been reported in non-steatotic LT without brain death [<xref rid=\"B62-ijms-21-05242\" ref-type=\"bibr\">62</xref>]. The mechanism involved in this protection includes anti-inflammatory effects (evidenced by decreased production of neutrophils and inflammatory cytokines, e.g., TNF-α and nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB) activation) and anti-apoptosis effects, due to decreased expression of the Fas-associated death domain (Fas) and caspase 3 [<xref rid=\"B62-ijms-21-05242\" ref-type=\"bibr\">62</xref>].</p><p>Regarding the reported effects of adiponectin on liver regeneration, treatment with the anti-diabetic drug rosiglitazone, which is thought to act by elevating serum adiponectin, inhibits liver mass recovery [<xref rid=\"B63-ijms-21-05242\" ref-type=\"bibr\">63</xref>]. Nonetheless, in our view, the effects of rosiglitazone on liver regeneration might be explained by mechanisms other than adiponectin upregulation. The results based on adiponectin –/– mice in an experimental model of partial hepatectomy indicate the benefits of adiponectin for liver regeneration [<xref rid=\"B31-ijms-21-05242\" ref-type=\"bibr\">31</xref>]. However, these results based on knockout animals might be different from those observed with adiponectin treatment, due to the different effects of endogenous and exogenous adiponectin. It is still unknown whether strategies aimed at increasing adiponectin levels might be useful in NAFLD patients undergoing hepatectomy to improve regenerative failure. Indeed, the over-expression of hepatic adiponectin in patients with NAFLD undergoing hepatectomy is elicited by excessive stimulation of the proinflammatory cytokine, TNF-α, which exerts negative effects on liver regeneration. The relevance of such high adiponectin levels remains to be clarified [<xref rid=\"B17-ijms-21-05242\" ref-type=\"bibr\">17</xref>]. Moreover, it has been reported that hepatic accumulation of systemically derived fat from adipose tissue in non-steatotic livers undergoing partial hepatectomy promotes liver regeneration [<xref rid=\"B64-ijms-21-05242\" ref-type=\"bibr\">64</xref>]. Interestingly, recent data suggest an association between intrahepatic fat content and adiponectin levels in patients undergoing liver resection [<xref rid=\"B65-ijms-21-05242\" ref-type=\"bibr\">65</xref>]. In preclinical studies based on NAFLD undergoing partial hepatectomy, the expression of PPARα is tightly regulated by AdipoR2 axis, whereas the expression of AMPK is a downstream molecule of AdipoR1 axis [<xref rid=\"B17-ijms-21-05242\" ref-type=\"bibr\">17</xref>]. Thus, the action of adiponectin depends on the type of receptor as well as on the type of liver and surgical conditions (<xref rid=\"ijms-21-05242-t001\" ref-type=\"table\">Table 1</xref>).</p></sec><sec id=\"sec6-ijms-21-05242\"><title>6. Pharmacological Strategies to Regulate Adiponectin Action in Liver Diseases and the Absence of Hepatic I/R</title><p>In a study using rats fed ethanol, the anti-inflammatory effects of adiponectin were mediated by regulation of toll-like receptor 4 (TLR4) signaling via the myeloid differentiation primary response gene 88 (MyD88) and TIR-domain-containing adapter-inducing interferon-β (TRIF) pathways [<xref rid=\"B66-ijms-21-05242\" ref-type=\"bibr\">66</xref>] (<xref rid=\"ijms-21-05242-t002\" ref-type=\"table\">Table 2</xref>). In AdipoR1- and AdipoR2-knockout obese mice, treatment with adiponectin decreased the levels of mitochondrial lipid peroxidation since it upregulated uncoupling protein 2 (UCP2), catalase and superoxide dismutase 1 (SOD1) in the liver [<xref rid=\"B67-ijms-21-05242\" ref-type=\"bibr\">67</xref>].</p><p>It has also been suggested that one explanation for the absence of a NAFLD phenotype might be upregulation of adiponectin, which activates the AMPK–forkhead box protein O (FOXO) signaling axis and probably overrides detrimental oxidative stress and JNK signaling [<xref rid=\"B68-ijms-21-05242\" ref-type=\"bibr\">68</xref>] (<xref ref-type=\"fig\" rid=\"ijms-21-05242-f001\">Figure 1</xref>).</p><p>Decreased serum levels of adiponectin and decreased gene expression of ileum fibroblast growth factor 15 (FGF15) have been reported in chronic ethanol fed mice, whereas elevated levels of circulating adiponectin and FGF15 protected against inflammation and liver damage [<xref rid=\"B69-ijms-21-05242\" ref-type=\"bibr\">69</xref>,<xref rid=\"B70-ijms-21-05242\" ref-type=\"bibr\">70</xref>]. These results suggest that the adiponectin–FGF15/19 axis participates in the regulation of ethanol-induced inflammation in mouse liver [<xref rid=\"B69-ijms-21-05242\" ref-type=\"bibr\">69</xref>,<xref rid=\"B70-ijms-21-05242\" ref-type=\"bibr\">70</xref>]. Serum levels of adiponectin are inversely associated with hepatic bile acid synthesis, serum bile acid levels and hepatocellular injury in NAFLD patients [<xref rid=\"B71-ijms-21-05242\" ref-type=\"bibr\">71</xref>]. Additionally, adiponectin directly regulates genes related to bile acid homeostasis such as cholesterol 7 alpha-hydroxylase (Cyp7a1) [<xref rid=\"B71-ijms-21-05242\" ref-type=\"bibr\">71</xref>]. Given that both FGF15/19 and adiponectin can regulate bile acid homeostasis, this could partially explain the protective effect of adiponectin–FGF15/19 signaling against ethanol- induced liver injury [<xref rid=\"B72-ijms-21-05242\" ref-type=\"bibr\">72</xref>] (<xref ref-type=\"fig\" rid=\"ijms-21-05242-f001\">Figure 1</xref>). These observations might be of relevance for liver surgery given the dysregulation of the farsenoid-X receptor (FXR)-FGF15 pathway in the gut–liver axis in different liver diseases. Whether adiponectin exerts its action directly on the liver or on the intestine (the source of FGF15 generated by ileal FXR) remains to be elucidated. This is scientifically and clinically relevant to the search for the best route of adiponectin administration. It is well known that, under brain death conditions, the gut and hepatic blood flow is reduced and this makes drug delivery to the appropriate site of action and at the optimal concentration difficult. In addition, a recent study by our group of steatotic and non-steatotic LT from brain-dead donors indicates the critical role of the gut–liver axis and the relevance of FGF15/19 in the pathogenesis of hepatic I/R injury and regenerative failure [<xref rid=\"B73-ijms-21-05242\" ref-type=\"bibr\">73</xref>].</p><p>Pharmacological intervention aimed at elevating adiponectin levels have been reported as promising for the treatment and progression of NAFLD [<xref rid=\"B74-ijms-21-05242\" ref-type=\"bibr\">74</xref>]. Thus, one study in patients with NASH shows that increases of adiponectin produced by pioglitazone are related to improvements in steatosis, inflammation and fibrosis, thereby confirming the crucial role of adiponectin in this pathology [<xref rid=\"B75-ijms-21-05242\" ref-type=\"bibr\">75</xref>]. Clinical and experimental studies of NASH show that PPAR agonists or vitamin E are potential therapies in NAFLD/NASH since they upregulate adiponectin levels [<xref rid=\"B76-ijms-21-05242\" ref-type=\"bibr\">76</xref>,<xref rid=\"B77-ijms-21-05242\" ref-type=\"bibr\">77</xref>]. Treatment with PPARγ, such as rosiglitazone, increases the levels and sensitivity of adiponectin receptors [<xref rid=\"B78-ijms-21-05242\" ref-type=\"bibr\">78</xref>]. Hume et al. indicated that after 16 weeks of supplementing overweight children with prebiotic fiber, there was a significant increase in adiponectin, while leptin levels did not change [<xref rid=\"B79-ijms-21-05242\" ref-type=\"bibr\">79</xref>]. Supplementation with the probiotic <italic>Lactobacillus gasseri</italic> (SBT 2055) reduced abdominal visceral fat and increased serum adiponectin in obese people [<xref rid=\"B80-ijms-21-05242\" ref-type=\"bibr\">80</xref>]. Thus, to date, pharmacological evidence suggests that metabolic improvements induced by anti-obesity drugs (orlistat, sibutramine and rimonabant), insulin sensitizers (metformin and thiazolidinediones) and endocannabinoid receptor antagonists could be attributed, at least in part, to the induction of high plasma levels of adiponectin [<xref rid=\"B81-ijms-21-05242\" ref-type=\"bibr\">81</xref>,<xref rid=\"B82-ijms-21-05242\" ref-type=\"bibr\">82</xref>,<xref rid=\"B83-ijms-21-05242\" ref-type=\"bibr\">83</xref>,<xref rid=\"B84-ijms-21-05242\" ref-type=\"bibr\">84</xref>]. The results show an increase in plasma adiponectin, ghrelin and leptin levels, as well as insulin sensitivity, four weeks after melatonin administration in a cohort of patients with NASH [<xref rid=\"B82-ijms-21-05242\" ref-type=\"bibr\">82</xref>]. Those authors hypothesized that melatonin could improve the key pathogenetic factors associated with NAFLD, namely insulin resistance and hypoadiponectinemia, and in turn confer protection against inflammation and oxidative stress [<xref rid=\"B82-ijms-21-05242\" ref-type=\"bibr\">82</xref>,<xref rid=\"B85-ijms-21-05242\" ref-type=\"bibr\">85</xref>,<xref rid=\"B86-ijms-21-05242\" ref-type=\"bibr\">86</xref>] (<xref rid=\"ijms-21-05242-t002\" ref-type=\"table\">Table 2</xref>).</p><p>In our view, the interpretation of these results of the treatments that increase adiponectin levels should be taken into account because of the lack of specificity in the modulation of adiponectin action. The difficulties producing functionally active recombinant adiponectin are well known, as the molecule is subjected to extensive post-translational modifications and is secreted in complex multimers [<xref rid=\"B87-ijms-21-05242\" ref-type=\"bibr\">87</xref>,<xref rid=\"B88-ijms-21-05242\" ref-type=\"bibr\">88</xref>]. For this reason, in our opinion, adiponectin analogs, such as osmotin (a ligand for the yeast homolog of the adiponectin receptor) [<xref rid=\"B89-ijms-21-05242\" ref-type=\"bibr\">89</xref>], might provide a therapeutic alternative. Another way is indirect upregulation of innate adiponectin expression and secretion through administration of appropriated therapeutic agents [<xref rid=\"B87-ijms-21-05242\" ref-type=\"bibr\">87</xref>,<xref rid=\"B88-ijms-21-05242\" ref-type=\"bibr\">88</xref>].</p><p>Despite the benefits of drugs regulating adiponectin action mentioned above, some of them have shown potential side effects; for instance, weight gain and fat redistribution from the central area to the lower body and also hepatotoxicity are reported following pioglitazone treatment [<xref rid=\"B90-ijms-21-05242\" ref-type=\"bibr\">90</xref>,<xref rid=\"B91-ijms-21-05242\" ref-type=\"bibr\">91</xref>,<xref rid=\"B92-ijms-21-05242\" ref-type=\"bibr\">92</xref>,<xref rid=\"B93-ijms-21-05242\" ref-type=\"bibr\">93</xref>,<xref rid=\"B94-ijms-21-05242\" ref-type=\"bibr\">94</xref>]. One study suggested that PPARγ activity in liver of mice leads to storage of lipid in the liver [<xref rid=\"B95-ijms-21-05242\" ref-type=\"bibr\">95</xref>,<xref rid=\"B96-ijms-21-05242\" ref-type=\"bibr\">96</xref>]. Different data in rodent models showed that rosiglitazone exacerbates hepatic steatosis [<xref rid=\"B95-ijms-21-05242\" ref-type=\"bibr\">95</xref>,<xref rid=\"B96-ijms-21-05242\" ref-type=\"bibr\">96</xref>,<xref rid=\"B97-ijms-21-05242\" ref-type=\"bibr\">97</xref>]. Thiazolidinediones causes increased risk of bladder cancer, weight gain, edema, cardiovascular complications and bone loss [<xref rid=\"B95-ijms-21-05242\" ref-type=\"bibr\">95</xref>]. There are several concerns about using vitamin E, such as increase in the relative risk of hemorrhagic stroke and prostate cancer [<xref rid=\"B98-ijms-21-05242\" ref-type=\"bibr\">98</xref>,<xref rid=\"B99-ijms-21-05242\" ref-type=\"bibr\">99</xref>,<xref rid=\"B100-ijms-21-05242\" ref-type=\"bibr\">100</xref>]. One study on ob/ob mice indicated that rosiglitazone dramatically increases liver steatosis [<xref rid=\"B78-ijms-21-05242\" ref-type=\"bibr\">78</xref>,<xref rid=\"B97-ijms-21-05242\" ref-type=\"bibr\">97</xref>]. Furthermore, it has been reported that weight and fat gain, water retention, cardiovascular toxicity, early signs of hypertrophic cardiomyopathy, hepatosteatosis and hepatotoxicity are side effects of rosiglitazone treatment [<xref rid=\"B101-ijms-21-05242\" ref-type=\"bibr\">101</xref>,<xref rid=\"B102-ijms-21-05242\" ref-type=\"bibr\">102</xref>]. In addition to mild unpleasant gastrointestinal side effects that are commonly reported with orlistat use, increased risk of serious hepatic events is another concern of using this medication [<xref rid=\"B103-ijms-21-05242\" ref-type=\"bibr\">103</xref>]. Common side effects of sibutramine include nausea, headache and increased risk of myocardial infarction and stroke [<xref rid=\"B104-ijms-21-05242\" ref-type=\"bibr\">104</xref>]. The results of one meta-analysis show that treatment with rimonabant is associated with psychiatric and neurologic adverse events [<xref rid=\"B105-ijms-21-05242\" ref-type=\"bibr\">105</xref>]. Therefore, more studies are needed before future application of these drugs in the clinical practice.</p></sec><sec id=\"sec7-ijms-21-05242\"><title>7. Adiponectin as a Prognostic Factor in Liver Diseases</title><p>In rodents with obesity induced by a high fat diet and in ob/ob mice, adiponectin levels in plasma were decreased [<xref rid=\"B106-ijms-21-05242\" ref-type=\"bibr\">106</xref>,<xref rid=\"B107-ijms-21-05242\" ref-type=\"bibr\">107</xref>,<xref rid=\"B108-ijms-21-05242\" ref-type=\"bibr\">108</xref>]. In patients with NAFLD, low levels of adiponectin are closely associated with the degree of hepatic steatosis, necroinflammation and fibrosis [<xref rid=\"B109-ijms-21-05242\" ref-type=\"bibr\">109</xref>,<xref rid=\"B110-ijms-21-05242\" ref-type=\"bibr\">110</xref>]. Adiponectin plays an important role in the progression of simple liver steatosis to NASH [<xref rid=\"B111-ijms-21-05242\" ref-type=\"bibr\">111</xref>,<xref rid=\"B112-ijms-21-05242\" ref-type=\"bibr\">112</xref>,<xref rid=\"B113-ijms-21-05242\" ref-type=\"bibr\">113</xref>,<xref rid=\"B114-ijms-21-05242\" ref-type=\"bibr\">114</xref>]. Thus, different studies suggest the use of serum adiponectin levels as a diagnostic measure of the necro-inflammatory grade and fibrosis in NAFLD, as well as it being a potential NAFLD therapeutic target [<xref rid=\"B115-ijms-21-05242\" ref-type=\"bibr\">115</xref>]. However, multivariate regression analysis identifies decreased adiponectin as an independent predictor of liver steatosis in obese individuals [<xref rid=\"B116-ijms-21-05242\" ref-type=\"bibr\">116</xref>]. This is in line with several large prospective studies of NAFLD patients, which investigated the role of serum adiponectin as a prognostic factor in NAFLD [<xref rid=\"B117-ijms-21-05242\" ref-type=\"bibr\">117</xref>,<xref rid=\"B118-ijms-21-05242\" ref-type=\"bibr\">118</xref>]. Thus, in a meta-analysis published in 2018 of 122 studies, the importance of adiponectin levels in the diagnosis of NAFLD was limited [<xref rid=\"B119-ijms-21-05242\" ref-type=\"bibr\">119</xref>]. Consequently, it has been reported that more sensitive and specific diagnostic methods are needed to diagnose NAFLD [<xref rid=\"B120-ijms-21-05242\" ref-type=\"bibr\">120</xref>]. In our view, and in line with reports by other authors [<xref rid=\"B103-ijms-21-05242\" ref-type=\"bibr\">103</xref>], the combination of different cytokines might be the best prognostic factor in liver diseases. Indeed, lower serum adiponectin and resistin concentrations and higher serum RBP4 concentrations were evident in children with advanced liver steatosis. In addition, the same authors indicated that adiponectin, resistin and RBP4 levels could be useful for differentiating patients with advanced liver steatosis from those with mild steatosis [<xref rid=\"B121-ijms-21-05242\" ref-type=\"bibr\">121</xref>]. There is a reverse association between serum adiponectin and the presence of NAFLD, which is positively associated with visfatin, IL-6 and TNF-α. It has been reported that an increased probability of NASH would be followed by decreased levels of serum adiponectin and elevated levels of circulating visfatin, IL-8 and TNF-α [<xref rid=\"B115-ijms-21-05242\" ref-type=\"bibr\">115</xref>]. This is in line with studies indicating that the evaluation of adiponectin level by itself is insufficient for a diagnosis of NAFLD/NASH and their progression [<xref rid=\"B115-ijms-21-05242\" ref-type=\"bibr\">115</xref>,<xref rid=\"B121-ijms-21-05242\" ref-type=\"bibr\">121</xref>]. Shimada et al. reported that 90% of patients with early-stage NASH could be predicted by a combined evaluation of serum levels of adiponectin, homeostatic model assessment for insulin resistance (HOMA-IR) score and serum type IV collagen 7S level [<xref rid=\"B122-ijms-21-05242\" ref-type=\"bibr\">122</xref>]. Likewise, in patients with cirrhosis, higher levels of adiponectin are associated with liver dysfunction and worse prognosis [<xref rid=\"B123-ijms-21-05242\" ref-type=\"bibr\">123</xref>,<xref rid=\"B124-ijms-21-05242\" ref-type=\"bibr\">124</xref>,<xref rid=\"B125-ijms-21-05242\" ref-type=\"bibr\">125</xref>,<xref rid=\"B126-ijms-21-05242\" ref-type=\"bibr\">126</xref>]. Moreover, it has been reported that adiponectin is an independent predictor of overall survival in HCC patients [<xref rid=\"B127-ijms-21-05242\" ref-type=\"bibr\">127</xref>,<xref rid=\"B128-ijms-21-05242\" ref-type=\"bibr\">128</xref>].</p><p>All these observations indicate the importance of future research to elucidate whether adiponectin or a combination of adipocytokines in serum is a useful diagnostic marker in NAFLD/NASH as well as in hepatic resections and LT. Indeed, to the best of our knowledge, no studies have evaluated in detail the potential association between serum adiponectin levels and the hepatic steatosis, damage and regenerative failure associated with liver surgery (<xref rid=\"ijms-21-05242-t003\" ref-type=\"table\">Table 3</xref>).</p><p>Note: ↑, increase; ↓, decrease; Acrp30, Adiponectin; AdipoR2, adiponectin receptor type 2; ALD, alcoholic liver disease; ALT, alanine aminotransferase; HCC, hepatocellular carcinoma; HMW, high molecular weight; IL, interleukin; LT, liver transplantation; MMW, middle molecular weight; NAFLD, nonalcoholic fatty liver disease; NASH, nonalcoholic steatohepatitis; TNF-α, tumor necrosis factor alpha.</p></sec><sec sec-type=\"conclusions\" id=\"sec8-ijms-21-05242\"><title>8. Conclusions and Perspectives</title><p>The benefits of adiponectin in terms of obesity, inflammation and regeneration may not apply in all I/R-dependent hepatic surgical scenarios. The role of adiponectin and the signaling pathways involved in its action depend on the surgical conditions, donor and type of liver subjected to surgery. All of this reveals the difficulties in establishing potential targets for the application of adiponectin in clinical practice: if the same pharmacological strategies are applied indiscriminately to steatotic and non-steatotic livers, the effects may be very different.</p><p>Specific drugs that regulate the action of adiponectin, research into the structure of adiponectin receptors, identification of molecules downstream of AdipoR1/2 and strategies to enhance adiponectin receptor activity constitute promising approaches to the treatment of NAFLD/NASH [<xref rid=\"B129-ijms-21-05242\" ref-type=\"bibr\">129</xref>]. The potential applications of drugs that specifically regulate adiponectin are numerous in liver surgery, which in turn can lead to increasing the number of organs suitable for LT, and may provide a novel therapeutic approach to hepatic resection of tumors. Meanwhile, it is difficult to produce functionally active recombinant adiponectin because the molecule is subjected to extensive posttranslational modifications and secreted in complex multimers. In addition, each type of adiponectin isoform performs different actions, activating specific signaling pathways, and the role of adiponectin also depends on its receptors. Moreover, the types of adiponectin isoforms and receptors involved in each liver disease should be assessed in detail since the studies reported in the literature are very limited. All the related findings need to be considered if our aim is the application of drugs that regulate the actions of adiponectin in clinical practice related to liver diseases.</p><p>Further studies are necessary to elucidate the role of adipose tissue in the changes of adiponectin levels in different liver diseases. Currently, the results reported in the literature are contradictory and there is a lack of appropriate experimental designs including for instance the evaluation of adiponectin in adipose tissue, circulation and liver after lipectomy. The signaling pathways involved in the anti-steatotic effects of adiponectin have been evaluated in liver diseases in the absence of surgery. Whether these signaling pathways are similar in steatotic liver surgery remains to be elucidated. This is clinically important because drugs that regulate anabolic and catabolic effects (as in the case of adiponectin) require a certain pretreatment time before they exert their effects, and this is a major problem in LT from brain-dead or cardiac-arrest donors. The potential role of adiponectin in connecting the intestine and liver is scientifically and clinically interesting, and so is its role in regulating FGF15/19 signaling, which is of potential interest due to the benefits on bile acid homeostasis, a critical problem in liver diseases including NASH, cirrhosis and LT. However, appropriate experimental models are required to elucidate the relevance of adiponectin-FGF15/19 in liver diseases and surgery. This is also the case with the effects of adiponectin in fibrosis, since the results are mainly based on in vitro studies, far removed from clinical practice.</p><p>Different clinical results have been reported in relation with the levels of adiponectin and the pathological characteristics of NAFLD/NASH and its progression. In the context of hepatic resections and LT, the research into prognostic factors are of clinical and scientific relevance for the treatment of patients but the reported results are very limited and inconclusive. In our view, whether the levels of adiponectin reported in liver diseases might be the result of or the reason for the pathology, itself remains to be elucidated. Potential differences in the levels of adiponectin in liver diseases depending on gender should be considered (they have not been evaluated in detail to date). In addition, although adiponectin might be elevated in liver diseases, it might not exert protection if its receptor is downregulated or the adiponectin signaling pathway is dysfunctional. In addition, in line with other authors, in our view, adiponectin expression may initially be elevated to compensate disease progression, but then higher adiponectin levels could turn out to be ineffective because of an overall deterioration of the patient’s condition [<xref rid=\"B130-ijms-21-05242\" ref-type=\"bibr\">130</xref>]. All of these observations should be taken into account for future studies aimed at evaluating whether adiponectin can be considered a useful prognostic factor in different liver diseases in the presence or absence of surgery.</p></sec></body><back><notes><title>Author Contributions</title><p>Conceptualization, M.H., M.E.C.-P., M.B.J.-C. and C.P.; Writing—Original Draft Preparation, M.H., M.E.C-P., M.B.J.-C. and C.P.; Writing—Review and Editing, M.H., M.E.C-P., M.B.J-C. and C.P.; and Funding Acquisition, C.P. All authors have read and agree to the published version of the manuscript.</p></notes><notes><title>Funding</title><p>This research was supported by the Ministerio de Ciencia, Innovación y Universidades (RTI2018–095114-B-I00) Madrid, Spain; European Union (Fondos Feder, “una manera de hacer Europa”); CERCA Program/Generalitat de Catalunya and Secretaria d’Universitats I Recerca del Departament d’Economia I Coneixement (2017 SGR-551) Barcelona, Spain.</p></notes><notes notes-type=\"COI-statement\"><title>Conflicts of Interest</title><p>The authors declare no conflict of interest.</p></notes><glossary><title>Abbreviations</title><array orientation=\"portrait\"><tbody><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ACC </td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Acetyl-CoA carboxylase</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">AdipoQ </td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Adiponectin, C1Q and collagen domain containing</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">AdipoR1/R2</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Adiponectin receptor type 1/2</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">And<sup>-/-</sup>\n</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Adiponectin knockout</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">AICAR </td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Aminoimidazole-4-carboxamide ribonucleoside</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Akt </td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Protein kinase B</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ALD</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Alcoholic liver disease</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ALT </td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Alanine aminotransferase</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">AMPK </td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Adenosine monophosphate-activated protein kinase</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Ang </td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Angiotensin</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">APPL1</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Adaptor protein</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ATF6</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Activating transcription factor 6</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ATP </td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Adenosine triphosphate</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CCL2</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">C-C motif chemokine ligand 2</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CHIP </td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">C-terminus of Hsc70-interacting protein</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CPT-1 </td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Carnitine palmitoyl- transferase 1</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CRP </td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">C-reactive protein</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CXCL-10</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">C-X-C motif chemokine ligand 10</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Cyp7a1 </td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Cholesterol 7 alpha-hydroxylase</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">EC</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Endothelial cell</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">eNOS </td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Endothelial nitric oxide synthase</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ER </td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Endoplasmic reticulum</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ET</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Endothelin</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">FA </td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Focal adhesion</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">FAK </td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Focal adhesion kinase</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Fas </td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Fas-associated death domain</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Fe </td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Iron</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">FGF2 </td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Fibroblast growth factor 2</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">FGF15/19 </td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Fibroblast growth factor 15/19</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">FOXO</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Forkhead box protein O</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">FXR </td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Farsenoid-X receptor</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">gAcrp30 </td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Globular adiponectin</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">GSH </td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Glutathione</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">h</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Hours</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">HCC </td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Hepatocellular carcinoma</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">HFD </td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">High fat diet</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">HMW </td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">High molecular weight</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">HO-1 </td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Heme oxygenase-1</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">HOMA-IR </td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Homeostatic model assessment for insulin resistance</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">HSC </td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Hepatic stellate cells</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">I/R</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Ischemia/reperfusion</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ICAM </td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Intercellular cell adhesion molecule</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">IFN </td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Interferon</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">IL </td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Interleukin</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">IRE1 </td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Inositol-requiring enzyme 1</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Jak2 </td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Janus kinase 2</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">JNK </td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">c-Jun N-terminal kinase</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">KC </td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Kupffer cells</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Ki-67</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Antigen Ki-67, a marker of proliferation</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">KO </td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Knockout</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">LCN2/SAA1 </td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Lipocalin-2/serum amyloid A1</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">LKB1 </td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Liver kinase B1</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">LMW </td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Low molecular weight</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">LT</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Liver transplantation</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">MCD </td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Methionine-choline deficiency</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">min</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Minutes</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">mLipin-1 </td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Myeloid cell-specific lipin-1</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">MMP-1 </td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Matrix metalloproteinase 1</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">MMW </td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Middle molecular weight</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">mNT </td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">MitoNEET</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">MPO </td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Myeloperoxidase</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">m-TOR </td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Mammalian target of rapamycin</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">MyD88 </td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Myeloid differentiation primary response gene 88</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">NAFLD </td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Nonalcoholic fatty liver disease</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">NASH </td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Nonalcoholic steatohepatitis</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">NF-κB </td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Nuclear factor kappa-light-chain-enhancer of activated B cells</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">NO </td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Nitric oxide</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">O<sub>2</sub></td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Superoxide</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ONOO- </td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Peroxynitrite</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">p38</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Mitogen-activated protein kinase p38</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">PDGF-BB</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Platelet-derived growth factor-BB</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">PERK </td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Protein kinase-like endoplasmic reticulum kinase</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">PI3K </td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Phosphoinositide 3-kinase</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">PPAR </td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Peroxisome proliferator-activated receptor</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">PTP1B </td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Protein tyrosine phosphatase 1B</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">RBP4 </td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Retinol binding protein 4</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ROS </td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Reactive oxygen species</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">siRNA </td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Silent small interfering RNA</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">SIRT1 </td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Sirtuin-1</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">α-SMA </td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">α-smooth muscle actin</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">SOCS3 </td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Suppressors of cytokine signaling 3</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">SOD </td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Superoxide dismutase</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">SREBP-1c </td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Sterol regulatory element-binding protein 1c</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">STAT3 </td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Signal transducer and activator of transcription 3</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">TGF-β</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Tumor growth factor beta</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">TIMP-1 </td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Tissue inhibitor of metalloproteinase 1</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">TLR4 </td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Toll-like receptor 4</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">TNF-α </td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Tumor necrosis factor alpha</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">TRAF6 </td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">TNF receptor-associated factor 6</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">TRIF </td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">TIR-domain-containing adapter-inducing interferon-β</td></tr><tr><td align=\"left\" valign=\"middle\" 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Ischemia-induced energy deficiency results in the failure of active transmembrane transport and consequently in endothelial cell (EC) and Kupffer cell (KC) swelling. KC activation results in reactive oxygen species (ROS), tumor necrosis factor alpha (TNF-α) and interleukin (IL)-1 release. In addition, ROS (derived from xanthine/xanthine oxidase (X/XOD) and mitochondria), the low levels of antioxidants (superoxide dismutase (SOD), glutathione (GSH) and catalase) and the alterations in the levels of angiotensin (Ang) II, Ang 1–7, peroxisome proliferator-activated receptor-gamma (PPAR-γ), retinol binding protein 4 (RBP4), tumor growth factor beta (TGF-β), IL-1, among others, induce activation of hepatic stellate cells (HSC) and KCs, which in turn induces the release of TNF-α and IL-1 and promotes low IL-10 levels. The imbalance between nitric oxide (NO) and endothelin (ET) production contributes to the narrowing of sinusoidal lumen. The endoplasmic reticulum (ER) stress involves the activation of ER proteins, namely inositol-requiring enzyme 1 (IRE1), protein kinase-like endoplasmic reticulum kinase (PERK) and activating transcription factor 6 (ATF6). ER stress induction contributes to inflammatory response, which might be regulated by adiponectin. This adipocytokine activates the adenosine monophosphate-activated protein kinase (AMPK) ≠ forkhead box protein O (FOXO) signaling axis, showing anti-apoptosis actions, because of decreasing the expression of Fas-associated death domain (Fas) and caspase 3. Adiponectin prevents the activation of c-Jun N-terminal kinase (JNK) and p38 mitogen-activated protein kinase (p38). The anti-inflammatory actions of adiponectin are mediated by a regulation of toll-like receptor 4 (TLR4) signaling via myeloid differentiation primary response gene 88 (MyD88) and TIR-domain-containing adapter-inducing interferon-β (TRIF) pathways; the mitochondrial dysfunctions and the lipocalin-2/serum amyloid A1 (LCN2/SAA1)-iron metabolism. Indeed, adiponectin upregulated the uncoupling protein 2 (UCP2), catalase, and SOD1. Adiponectin might play a crucial role regulating the farsenoid-X receptor (FXR)-fibroblast growth factor 15 (FGF15) pathway in the gut–liver axis. Adiponectin might activate signal transducer and activator of transcription-3 (STAT3) and resistin signaling as well as negatively regulate the fibroblast growth factor 2 (FGF2) response. Akt, protein kinase B; CRP, C-reactive protein; Fe, Iron; HO-1, heme oxygenase-1; IFN, interferon; NF-κB, nuclear factor kappa-light-chain-enhancer of activated B cells; O<sub>2</sub>, superoxide; ONOO-, peroxynitrite; PDGF-BB, platelet-derived growth factor-BB; PI3K, phosphoinositide 3-kinase; SIRT1, sirtuin 1; TRAF6, TNF receptor-associated factor 6; UPR: unfolded protein response. ↑: increase; ↓: decrease.</p></caption><graphic xlink:href=\"ijms-21-05242-g001\"/></fig><fig id=\"ijms-21-05242-f002\" orientation=\"portrait\" position=\"float\"><label>Figure 2</label><caption><p>Signaling pathways involved in the anti-steatotic effects of adiponectin in liver diseases. Adiponectin, mediated by adenosine monophosphate-activated protein kinase (AMPK) exerts the following effects: suppresses sterol regulatory element-binding protein-1c (SREBP-1c), a central regulator of fatty acid synthesis and an inhibitor of lipogenesis; increases the glucose utilization and fatty-acid oxidation; increases the activity of carnitine palmitoyl-transferase-1 (CPT-1), a rate limiting enzyme in fatty acid oxidation; and regulates malonyl CoA for fatty acid synthesis inhibition. ACC, acetyl-CoA carboxylase; LKB, liver kinase B1.</p></caption><graphic xlink:href=\"ijms-21-05242-g002\"/></fig><fig id=\"ijms-21-05242-f003\" orientation=\"portrait\" position=\"float\"><label>Figure 3</label><caption><p>Adiponectin and leptin in the progression of nonalcoholic fatty liver disease (NAFLD) to hepatocellular carcinoma (HCC). Low adiponectin levels have been observed in either NAFLD or nonalcoholic steatohepatitis (NASH) conditions. However, the effects on leptin as well as the mechanisms of action of adiponectin are unknown. In cirrhosis, circulating adiponectin is elevated. Adiponectin through protein tyrosine phosphatase 1B (PTP1B) prevents the leptin-mediated activation of the janus kinase 2 (Jak2)/signal transducer and activator of transcription-3 (STAT3) pathway through suppressors of cytokine signaling 3 (SOCS3) and the activity of focal adhesion kinase (FAK) and focal adhesion (FA). By inducing SOCS3, adiponectin suppresses leptin activity. Adiponectin can provoke hepatic stellate cells (HSC) apoptosis and leads to the loss of α-smooth muscle actin (α-SMA) proteins in HSCs and suppresses HSC proliferation and α collagen biosynthesis. Adiponectin-mediated prevention of leptin signaling downregulates tissue inhibitor of metalloproteinase 1 (TIMP-1) activity and increases matrix metalloproteinase 1 (MMP-1) to degrade fibrillar collagen in matrix. Adiponectin increases apoptosis of HCC cells via activation of caspase-3, and c-Jun N-terminal kinase (JNK). Adiponectin inhibits leptin-induced proliferation of HCC via blockade of STAT-3, protein kinase B (AKT) and mammalian target of rapamycin (m-TOR) and shows hepatoprotective functions by blocking angiogenesis and sulfatase 2.</p></caption><graphic xlink:href=\"ijms-21-05242-g003\"/></fig><table-wrap id=\"ijms-21-05242-t001\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijms-21-05242-t001_Table 1</object-id><label>Table 1</label><caption><p>Effect of strategies that regulate adiponectin action in liver surgery in studies from 2010 to 2020.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Treatment</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Isoform and Receptor</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Type of Liver and Specie</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Surgical Condition</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Effect and Signaling Pathways </th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Adiponectin recombinant <break/>(Rat Acrp30) [<xref rid=\"B16-ijms-21-05242\" ref-type=\"bibr\">16</xref>]</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Not reported</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Steatotic and non-steatotic livers from Zucker rats</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">LT <break/>Ischemia: 6 h<break/>Reperfusion: 4 h</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">↓ Hepatic injury and mortality <break/>↑ Ki-67 <break/><italic>Adiponectin-↑ Resistin-↑ PI3K/Akt</italic></td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Dietary model [<xref rid=\"B17-ijms-21-05242\" ref-type=\"bibr\">17</xref>]</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Not reported;<break/>AdipoR1 and AdipoR2</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Steatotic livers from Sprague-Dawley MCD or HFD rat</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Partial (70%) hepatectomy</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">↓ AdipoR1 and AdipoR2<break/>↑ Hepatic adiponectin, TNF-α, AMPK<break/>AdipoR1-↑ AMPK in MCD and HFD<break/><italic>AdipoR2-↓ PPARα in MCD</italic></td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Adiponectin (-/-) [<xref rid=\"B31-ijms-21-05242\" ref-type=\"bibr\">31</xref>]</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Not reported</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Livers from B6.129-Adipoq<sup>tm1Cha</sup> knockout mice</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Partial hepatectomy </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">↓ Regeneration by controlling cell cycle progression, cytokine signaling and growth factor bioavailability<break/><italic>Adiponectin-</italic><italic>↑ STAT3</italic>\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Adiponectin recombinant [<xref rid=\"B60-ijms-21-05242\" ref-type=\"bibr\">60</xref>]</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Not reported</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Livers from Wistar rats</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Partial warm ischemia<break/>Ischemia: 60 min<break/>Reperfusion: 6, 24 h</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">↓ Hepatic injury, inflammatory cell infiltration, IL-1β, IL-6, TNF-α, CCL2, CXCL10, ICAM1, apoptosis<break/><italic>Adiponectin-</italic><italic>↑</italic>\n<italic>AMPK/eNOS</italic></td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Adiponectin recombinant and supplementation <break/>(Rat gAcrp30) [<xref rid=\"B61-ijms-21-05242\" ref-type=\"bibr\">61</xref>]</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Not reported </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Livers from Wistar rats</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Chronic intermittent hypoxia events for 8 h per day for 4 months </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">↓ Hepatic injury, ROS production, fasting blood glucose, triglycerides<break/><italic>Adiponectin-</italic><italic>↑ AMPK</italic></td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Adiponectin recombinant <break/>(Rat gAcrp30) [<xref rid=\"B62-ijms-21-05242\" ref-type=\"bibr\">62</xref>]</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Not reported</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Livers from Sprague-Dawley rats</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">LT<break/>Ischemia: 30 min<break/>Reperfusion: 3, 6, 12, 24 h</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">↓ Bile duct injury and apoptosis, Fas, caspase 3, TNF-α, NF-κB activation, MPO, IL-6<break/><italic>Adiponectin-</italic><italic>↓</italic>\n<italic>NF-κB</italic></td></tr></tbody></table><table-wrap-foot><fn><p>↑, increase; ↓, decrease; Acrp30, Adiponectin; AdipoR1/R2, adiponectin receptor type 1/2; And<sup>-/-</sup>, adiponectin knockout; Akt, protein kinase B; AMPK, adenosine monophosphate-activated protein kinase; CCL2, C-C motif chemokine ligand 2; CXCL10, C-X-C motif chemokine ligand 10; eNOS, endothelial nitric oxide synthase; Fas, Fas-associated death domain; gAcrp30, globular adiponectin; h, hours; HFD, high fat diet; ICAM1, intercellular cell adhesion molecule 1; IL, interleukin; Ki-67, antigen Ki-67, a marker of proliferation; LT, liver transplantation; MCD, methionine-choline deficiency; min, minutes; MPO, myeloperoxidase; NF-κB, nuclear factor kappa-light-chain-enhancer of activated B cells; PPARα, peroxisome proliferator-activated receptor alpha; PI3K, phosphoinositide 3-kinase; ROS, reactive oxygen species; STAT3, signal transduce and activator of transcription 3; TNF-α, tumor necrosis factor alpha. The signaling pathway is described in italics.</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"ijms-21-05242-t002\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijms-21-05242-t002_Table 2</object-id><label>Table 2</label><caption><p>Effect of strategies that regulate adiponectin in liver diseases in the absence of surgery in studies from 2010 to 2020.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Treatment</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Isoform and Receptor</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Type of Liver and Specie</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Surgical Condition</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Effect and Signaling Pathways </th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Adiponectin recombinant <break/>(Human gAcrp30) [<xref rid=\"B66-ijms-21-05242\" ref-type=\"bibr\">66</xref>]</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">HMW and Adipo R1 and AdipoR2</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">KC and RAW 264.7 macrophages from Wistar rats with chronic ethanol-feeding</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Cell culture</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">↓ TLR4 (/MyD88), IFN-β, CXCL10<break/><italic>Adiponectin–</italic><italic>↓ TLR4</italic></td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">CHIP (-/-) [<xref rid=\"B68-ijms-21-05242\" ref-type=\"bibr\">68</xref>]</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Not reported;<break/>AdipoR1 and AdipoR2</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Livers from CHIP knockout mice</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Cell culture</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">↓ Oxidative stress and JNK <break/>↑ Adiponectin, AdipoR1, AdipoR2, AMPK and FOXO<break/><italic>Adiponectin-</italic><italic>↑</italic>\n<italic>AMPK-FOXO</italic></td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">mLipin-1 (-/-) (Human gAcrp30) [<xref rid=\"B69-ijms-21-05242\" ref-type=\"bibr\">69</xref>]</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">HMW; AdipoR1 and AdipoR2</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Livers from mLipin-1 knockout mice with chronic ethanol-feeding</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">NA</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">↓ Hepatic injury, inflammation, NF-κB, <break/>↑ Adiponectin, AdipoR1, AdipoR2 and FGF15<break/><italic>Adiponectin-</italic><italic>↑</italic>\n<italic>FGF15 signaling</italic></td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">mNT (-/-) [<xref rid=\"B70-ijms-21-05242\" ref-type=\"bibr\">70</xref>]</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Not reported</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">mNT knockout mice with chronic ethanol-feeding</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">NA</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">↓ Hepatic injury, NF-κB, oxidative stress <break/>↑ Adiponectin, FGF15, Sirt1<break/><italic>Adiponectin-</italic><italic>↑</italic>\n<italic>FGF15 signaling</italic></td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Pioglitazone [<xref rid=\"B75-ijms-21-05242\" ref-type=\"bibr\">75</xref>]</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Not reported</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">NASH patients</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">NA</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">↓ Hepatic steatosis and necroinflammation <break/>↑ Adiponectin<break/><italic>Adiponectin-</italic><italic>↓</italic>\n<italic>NF-κB/JNK</italic></td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Rosiglitazone [<xref rid=\"B78-ijms-21-05242\" ref-type=\"bibr\">78</xref>]</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">HMW; AdipoR1 and AdipoR2</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Livers from C57BL/6J mice with ethanol-feeding</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">NA</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">↓ Hepatic injury, steatosis, lipogenesis <break/>↑ Adiponectin and hepatic AdipoR1/R2<break/><italic>Adiponectin–</italic><italic>↑</italic>\n<italic>SIRT1-AMPK</italic></td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Prebiotic fiber supplementation [<xref rid=\"B79-ijms-21-05242\" ref-type=\"bibr\">79</xref>]</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Not reported</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Children patients with overweight and obese</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">NA</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">↑ Adiponectin and ghrelin<break/><italic>Not reported adiponectin signaling pathway</italic></td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Probiotic <italic>Lactobacillus gasseri</italic> (SBT2055) [<xref rid=\"B80-ijms-21-05242\" ref-type=\"bibr\">80</xref>]</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">HMW, MMW, LMW and not reported receptor</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Obese patients</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">NA</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">↓ Abdominal visceral fat <break/>↑ HMW in obese and control group <break/>↑ MMW only in control group<break/><italic>Not reported adiponectin signaling pathway</italic></td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Melatonin [<xref rid=\"B82-ijms-21-05242\" ref-type=\"bibr\">82</xref>]</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Not reported</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">NASH patients</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">NA</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">↓ HOMA-IR <break/>↑ Adiponectin, leptin and ghrelin<break/><italic>Not reported adiponectin signaling pathway</italic></td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Orlistat [<xref rid=\"B84-ijms-21-05242\" ref-type=\"bibr\">84</xref>]</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Not reported</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">NAFLD patients</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">NA</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">↓ Fatty infiltration, periostin, TNF-α <break/>↑ Adiponectin<break/><italic>Not reported adiponectin signaling pathway</italic></td></tr></tbody></table><table-wrap-foot><fn><p>↑, increase; ↓, decrease; AdipoR1/R2, adiponectin receptor type 1/2; AMPK, adenosine monophosphate-activated protein kinase; CXCL10, C-X-C motif chemokine ligand 10; CHIP, C-terminus of Hsc70-interacting protein; FGF15, fibroblast growth factor 15; gAcrp30, globular adiponectin; FOXO, forkhead box protein O; HMW, high molecular weight; HOMA-IR, homeostatic model assessment for insulin resistance; IFN-β, interferon beta; JNK, c-Jun N-terminal kinase; KC, Kupffer cells; LMW, low molecular weight; mLipin-1, myeloid cell-specific lipin-1; MMW, middle molecular weight; mNT, mitoNEET; MyD88, myeloid differentiation primary response gene 88; NA, not apply; NAFLD, nonalcoholic fatty liver disease; NASH, nonalcoholic steatohepatitis; NF-κB, nuclear factor kappa-light-chain-enhancer of activated B cells; Sirt1, sirtuin-1; TLR4, toll-like receptor 4; TNF-α, tumor necrosis factor alpha. The signaling pathway is described in italics.</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"ijms-21-05242-t003\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijms-21-05242-t003_Table 3</object-id><label>Table 3</label><caption><p>Association between adiponectin levels and pathological characteristics in different liver disease.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Disease</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Subjects (Etiology)</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Adiponectin Levels</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Effect</th></tr></thead><tbody><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Alcoholic liver disease (ALD) [<xref rid=\"B58-ijms-21-05242\" ref-type=\"bibr\">58</xref>]</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">147 patients</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">18.69 ALD<break/>6.38 control</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">↑ Adiponectin (Acrp30) associated with advanced liver dysfunction and ALD complications </td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">NAFLD [<xref rid=\"B112-ijms-21-05242\" ref-type=\"bibr\">112</xref>]</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">63 patients</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">4.26 ± 2.71 µg/mL NAFLD <break/>5.85 ± 3.74 µg/mL controls</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">↓ Three isoforms of adiponectin. HMW and MMW adiponectin involved in the pathogenesis and progression of NAFLD</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">NAFLD [<xref rid=\"B113-ijms-21-05242\" ref-type=\"bibr\">113</xref>]</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">315 patients <break/>(129 mild, 145 moderate, 41 severe)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">13.6 ± 3.3 µg/mL mild <break/>12.4 ± 3.7 µg/mL moderate<break/>11.6 ± 3.5 µg/mL severe</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">↑ adiponectin correlated with a decreased risk of developing type 2 diabetes</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">NAFLD [<xref rid=\"B114-ijms-21-05242\" ref-type=\"bibr\">114</xref>]</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">232 patients <break/>(45 cirrhosis, 71 viral hepatitis, 64 NAFLD, 52 others)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">18.6 ± 14.5 µg/mL cirrhosis<break/>8.4 ± 6.1 µg/mL without cirrhosis<break/>4.8 ± 3.5 µg/mL NAFLD<break/>9.1 µg/mL controls</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Adiponectin correlate positively with markers of hepatic fibrosis. <break/>↓ Adiponectin in NAFLD and ↑ in cirrhosis </td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">NAFLD [<xref rid=\"B115-ijms-21-05242\" ref-type=\"bibr\">115</xref>]</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">70 patients </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">8.14 ± 2.91 mg/L NAFLD <break/>13.63 ± 2.88 mg/L controls</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">↓ Adiponectin and ↑ visfatin, IL-6, TNF-α associated with increased NAFLD</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">NAFLD [<xref rid=\"B117-ijms-21-05242\" ref-type=\"bibr\">117</xref>]</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">52 patients </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3.9 (2.5 – 6.2) µg/mL </td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Adiponectin, TNF-α, IL-6, leptin were not associated with disease progression</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">NAFLD [<xref rid=\"B118-ijms-21-05242\" ref-type=\"bibr\">118</xref>]</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">147 patients </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">9.6 ± 4.1 µg/mL NAFLD <break/>14.0 ± 10.1 µg/mL without NAFLD</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">↓ Adiponectin associated with NAFLD</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">NAFLD [<xref rid=\"B120-ijms-21-05242\" ref-type=\"bibr\">120</xref>]</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">71 children patients <break/>(37 with NAFLD, 14 with NASH, 20 without NAFLD)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">13.15 ± 5.33 ng/mL NAFLD <break/>12.64 ± 5.54 ng/mL NASH</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Adiponectin levels were similar in patients with and without NAFLD.<break/>↑ AdipoR2 in NAFLD and is a noninvasive marker for diagnosis</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">NAFLD [<xref rid=\"B121-ijms-21-05242\" ref-type=\"bibr\">121</xref>]</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">148 children patients (63 steatosis, 12 steatosis and ↑ ALT, 85 without steatosis)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2.7 ± 0.7 µg/mL steatosis <break/>2.5 ± 0.4 µg/mL NAFLD <break/>4.7 ± 1.1 µg/mL without steatosis</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">↓ Adiponectin were negatively correlated with ALT activity</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Cirrhosis [<xref rid=\"B123-ijms-21-05242\" ref-type=\"bibr\">123</xref>]</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">122 patients </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">21.59 µg/mL cirrhosis <break/>12.52 µg/mL controls</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">↑ Adiponectin associated with ↑ liver dysfunction and worse prognosis</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Cirrhosis [<xref rid=\"B124-ijms-21-05242\" ref-type=\"bibr\">124</xref>]</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">70 patients <break/>(40 cirrhosis, 30 cirrhosis and cholestasis)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">15.1 ± 12.1 µg/mL cirrhosis<break/>21.28 ± 10.2 µg/mL cirrhosis with cholestasis<break/>4.7 ± 4.48 µg/mL controls</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">↑ Adiponectin shows correlation with degree of hepatocellular injury and cholestasis; but not with parameters of body composition or metabolism</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Cirrhosis [<xref rid=\"B125-ijms-21-05242\" ref-type=\"bibr\">125</xref>]</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">140 patients </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">13.050 ng/mL</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Adiponectin was an independent predictor of overall survival in HCC patients</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Cirrhosis [<xref rid=\"B126-ijms-21-05242\" ref-type=\"bibr\">126</xref>]</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">40 patients with non-diabetic alcoholic cirrhosis</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">10.23 µg/mL </td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">↑ Adiponectin associated with shorter survival in the univariate analysis but not in the multivariate analysis</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Cirrhosis [<xref rid=\"B127-ijms-21-05242\" ref-type=\"bibr\">127</xref>]</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">248 patients with compensated viral hepatitis C cirrhosis</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">16.5 ± 15.3 µg/mL cirrhosis</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Adiponectin was not related to HCC, liver-related death or LT during follow-up</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Cirrhosis [<xref rid=\"B128-ijms-21-05242\" ref-type=\"bibr\">128</xref>]</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">90 patients with hepatitis C-related liver cirrhosis <break/>(61 with, 29 without)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">5.213 ± 3.840 µg/mL cirrhosis with HCC<break/>9.000 ± 2.234 µg/mL cirrhosis without HCC </td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">↓ Adiponectin levels associated with HCC; a biomarker of HCC</td></tr></tbody></table><table-wrap-foot><fn><p>Note: ↑, increase; ↓, decrease; Acrp30, Adiponectin; AdipoR2, adiponectin receptor type 2; ALD, alcoholic liver disease; ALT, alanine aminotransferase; HCC, hepatocellular carcinoma; HMW, high molecular weight; IL, interleukin; LT, liver transplantation; MMW, middle molecular weight; NAFLD, nonalcoholic fatty liver disease; NASH, nonalcoholic steatohepatitis; TNF-α, tumor necrosis factor alpha.</p></fn></table-wrap-foot></table-wrap></floats-group></article>\n"
]
[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Int J Mol Sci</journal-id><journal-id journal-id-type=\"iso-abbrev\">Int J Mol Sci</journal-id><journal-id journal-id-type=\"publisher-id\">ijms</journal-id><journal-title-group><journal-title>International Journal of Molecular Sciences</journal-title></journal-title-group><issn pub-type=\"epub\">1422-0067</issn><publisher><publisher-name>MDPI</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32748894</article-id><article-id pub-id-type=\"pmc\">PMC7432058</article-id><article-id pub-id-type=\"doi\">10.3390/ijms21155541</article-id><article-id pub-id-type=\"publisher-id\">ijms-21-05541</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Article</subject></subj-group></article-categories><title-group><article-title>Chrysin Inhibits High Glucose-Induced Migration on Chorioretinal Endothelial Cells via VEGF and VEGFR Down-Regulation</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Liao</surname><given-names>Zhen-Yu</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05541\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Liang</surname><given-names>I-Chia</given-names></name><xref ref-type=\"aff\" rid=\"af2-ijms-21-05541\">2</xref><xref ref-type=\"aff\" rid=\"af3-ijms-21-05541\">3</xref></contrib><contrib contrib-type=\"author\"><name><surname>Li</surname><given-names>Hsin-Ju</given-names></name><xref ref-type=\"aff\" rid=\"af4-ijms-21-05541\">4</xref></contrib><contrib contrib-type=\"author\"><name><surname>Wu</surname><given-names>Chia-Chun</given-names></name><xref ref-type=\"aff\" rid=\"af5-ijms-21-05541\">5</xref></contrib><contrib contrib-type=\"author\"><name><surname>Lo</surname><given-names>Huey-Ming</given-names></name><xref ref-type=\"aff\" rid=\"af6-ijms-21-05541\">6</xref></contrib><contrib contrib-type=\"author\"><name><surname>Chang</surname><given-names>Der-Chen</given-names></name><xref ref-type=\"aff\" rid=\"af7-ijms-21-05541\">7</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0003-3478-5451</contrib-id><name><surname>Hung</surname><given-names>Chi-Feng</given-names></name><xref ref-type=\"aff\" rid=\"af4-ijms-21-05541\">4</xref><xref ref-type=\"aff\" rid=\"af8-ijms-21-05541\">8</xref><xref ref-type=\"aff\" rid=\"af9-ijms-21-05541\">9</xref><xref rid=\"c1-ijms-21-05541\" ref-type=\"corresp\">*</xref></contrib></contrib-group><aff id=\"af1-ijms-21-05541\"><label>1</label>Department of Internal Medicine, Shin Kong Wu Ho-Su Memorial Hospital, Taipei 111, Taiwan; <email>[email protected]</email></aff><aff id=\"af2-ijms-21-05541\"><label>2</label>Department of Ophthalmology, Tri-Service General Hospital, National Defense Medical Center, Taipei 11490, Taiwan; <email>[email protected]</email></aff><aff id=\"af3-ijms-21-05541\"><label>3</label>Ph.D. Program in Nutrition and Food Science, Fu Jen Catholic University, New Taipei City 24205, Taiwan</aff><aff id=\"af4-ijms-21-05541\"><label>4</label>School of Medicine, Fu Jen Catholic University, New Taipei City 24205, Taiwan; <email>[email protected]</email></aff><aff id=\"af5-ijms-21-05541\"><label>5</label>Graduate Institute of Biomedical and Pharmaceutical Science, Fu Jen Catholic University, New Taipei City 24205, Taiwan; <email>[email protected]</email></aff><aff id=\"af6-ijms-21-05541\"><label>6</label>Division of Cardiology, Fu Jen Catholic University Hospital, New Taipei City 24205, Taiwan; <email>[email protected]</email></aff><aff id=\"af7-ijms-21-05541\"><label>7</label>Department of Mathematics and Statistics and Department of Computer Science, Georgetown University, Washington, DC 20057, USA; <email>[email protected]</email></aff><aff id=\"af8-ijms-21-05541\"><label>8</label>MS Program Transdisciplinary Long Term Care, Fu Jen Catholic University, New Taipei City 24205, Taiwan</aff><aff id=\"af9-ijms-21-05541\"><label>9</label>Ph.D. Program in Pharmaceutical Biotechnology, Fu Jen Catholic University, New Taipei City 24205, Taiwan</aff><author-notes><corresp id=\"c1-ijms-21-05541\"><label>*</label>Correspondence: <email>[email protected]</email>; Tel.: +886-2-2905-2171</corresp></author-notes><pub-date pub-type=\"epub\"><day>02</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><month>8</month><year>2020</year></pub-date><volume>21</volume><issue>15</issue><elocation-id>5541</elocation-id><history><date date-type=\"received\"><day>06</day><month>7</month><year>2020</year></date><date date-type=\"accepted\"><day>31</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>© 2020 by the authors.</copyright-statement><copyright-year>2020</copyright-year><license license-type=\"open-access\"><license-p>Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>).</license-p></license></permissions><abstract><p>Background: Diabetes mellitus (DM) is a chronic inflammatory disease, which causes multiple complications. Diabetic retinopathy (DR) is among these complications and is a dominant cause of vision loss for diabetic patients. Numerous studies have shown that chrysin, a flavonoid, has many biological activities such as anti-oxidation and anti-inflammation. However, it is rarely used in ocular diseases. In this study, we examined the inhibitory effects of flavonoid on high glucose induced migration of chorioretinal endothelial cells (RF/6A cells) and its mechanism. Materials and methods: The viability of RF/6A cells treated with chrysin was examined with a 3-(4,5-dimethyl-2-thiazolyl)-2,5-diphenyl-2H-tetrazolium bromide (MTT) assay. The migration of RF/6A cells was assessed by the transwell migration and scratch wound assays. The expression of AKT, ERK, vascular endothelial growth factor (VEGF), HIF−1α and MMP-2 were determined by western blotting. To observe the mRNA expression of VEGF receptor (VEGFR), qRT-PCR, was utilized. Results: The results showed that chrysin can dose-dependently inhibit the RF/6A cell migration in vitro transwell and the scratch wound assays which are induced by high glucose. After pretreatment of RF/6A cells with different concentrations of chrysin, they did not produce any cytotoxicity in MTT assay. Moreover, chrysin down-regulated both phosphorylated AKT and ERK, as well as attenuated the expression levels of MMP-2. It also decreased the expression of the VEGF transcription factor and VEGF. Furthermore, it was shown that chrysin could suppress the protein and mRNA expression levels of VEGFR. Conclusion: The results indicate that chrysin could down-regulate the phosphorylation of AKT, ERK and MMP-2 and reduce the effects of VEGF and VEGFR in a high glucose environment. It further inhibits the high glucose-induced migration of RE/6A cells. Therefore, chrysin may have the potential for visual protection.</p></abstract><kwd-group><kwd>high glucose</kwd><kwd>chrysin</kwd><kwd>chorioretinal endothelial cell</kwd><kwd>vascular endothelial growth factor (VEGF)</kwd></kwd-group></article-meta></front><body><sec sec-type=\"intro\" id=\"sec1-ijms-21-05541\"><title>1. Introduction</title><p>Diabetes mellitus (DM) is a type of metabolic disorder related to inflammation. It can cause both macro- and microvascular complications which include cardiovascular diseases, retinopathy, nephropathy, neuropathy, and poor wound healing. Diabetic retinopathy (DR) is a retinal microvascular disease characterized by inflammatory and angiogenic pathways. Clinical assessments of DR reveal typical microvascular features of initial retinal hemorrhages, lipid exudates, cotton wool spots, and finally, neovascularization. Changes at the retinal neurovascular unit, a term referring to the intricate functional coupling between neurons, glial cells, and blood vessels (the components of blood-retinal barrier), are susceptible to diabetes [<xref rid=\"B1-ijms-21-05541\" ref-type=\"bibr\">1</xref>,<xref rid=\"B2-ijms-21-05541\" ref-type=\"bibr\">2</xref>]. Progressive diabetes damage can lead to changes in the cellular environment. Vascular endothelial growth factor (VEGF) of glial cells increases and releases inflammatory cytokines, which leads to the destruction of the blood-retinal barrier as well as to angiogenesis, which is the main cause of DR vision damage [<xref rid=\"B2-ijms-21-05541\" ref-type=\"bibr\">2</xref>]. The walls of the new vessels are not intact and therefore fluid exudation occurs, which results in vision impairment [<xref rid=\"B3-ijms-21-05541\" ref-type=\"bibr\">3</xref>].</p><p>Angiogenesis was known to be a multi-factorial process, and cell migration was one of the initial processes. VEGF was known to play an important role in angiogenesis. The migration of chorioretinal endothelial cell (EC) was noted to be related to AKT and ERK phosphorylation [<xref rid=\"B4-ijms-21-05541\" ref-type=\"bibr\">4</xref>,<xref rid=\"B5-ijms-21-05541\" ref-type=\"bibr\">5</xref>]. Matrix metalloproteinase-2 (MMP-2) also participated in cell migration processes [<xref rid=\"B6-ijms-21-05541\" ref-type=\"bibr\">6</xref>,<xref rid=\"B7-ijms-21-05541\" ref-type=\"bibr\">7</xref>]. Additionally, a previous study revealed that chrysin, a kind of flavonoid, can decrease the expression of VEGF by inhibiting HIF-1α protein synthesis and decreasing its structure stability [<xref rid=\"B8-ijms-21-05541\" ref-type=\"bibr\">8</xref>].</p><p>Flavonoids are the largest group of heterocyclic compounds in plants and exist in vegetables and fruits of any color, such as tomatoes, grapes, many kinds of nuts, beans, and even green tea. Currently, over 5000 naturally occurring flavonoids have been characterized. Common flavonoids include procyanidins, quercetin, chrysin, anthocyanin, apigenin, limonin, and catechin. Among them, quercetin’s structure has been known to modulate and strengthen the immune system [<xref rid=\"B9-ijms-21-05541\" ref-type=\"bibr\">9</xref>]. Chrysin’s structure not only has the same immune modulating effect as quercetin’s structure, but has also been noted to have characteristics of anti-tumor and tumor cell apoptosis induction [<xref rid=\"B10-ijms-21-05541\" ref-type=\"bibr\">10</xref>,<xref rid=\"B11-ijms-21-05541\" ref-type=\"bibr\">11</xref>]. In our previous work, we noted that chrysin can protect keratinocyte from UVA and UVB damage, decrease reactive oxygen species (ROS) formation, and prevent cells from apoptosis. Further, animal experiments showed that chrysin can be absorbed by skin in mice without irritable injury [<xref rid=\"B12-ijms-21-05541\" ref-type=\"bibr\">12</xref>]. We also found that chrysin may have therapeutic potential against inflammatory skin diseases [<xref rid=\"B13-ijms-21-05541\" ref-type=\"bibr\">13</xref>]. Chrysin was also known to inhibit lipopolysaccharide (LPS) induced angiogenesis by down-regulating VEGF [<xref rid=\"B14-ijms-21-05541\" ref-type=\"bibr\">14</xref>]. However, the anti-neovascular function of chrysin in diabetic retinopathy is not yet clear.</p><p>This study aimed to understand the role of chrysin in ophthalmology and investigate its role in high-glucose related chorioretinal EC migration. We attempt to understand if chrysin can protect chorioretinal EC from proliferation through the experimental model of high-glucose environment simulated diabetes. The problem of DM worsens by day, while drug resistance and side effects of the hypoglycemic drugs make the situation even worse and stricter [<xref rid=\"B15-ijms-21-05541\" ref-type=\"bibr\">15</xref>]. Searching for effective ingredients, extracted from natural products, which can be used as alternative treatment or prevention is imperative, and chrysin is one of these discoveries. Our results will help evaluate whether chrysin has further potential in the prevention or treatment of diabetes and related complications.</p></sec><sec sec-type=\"results\" id=\"sec2-ijms-21-05541\"><title>2. Results</title><sec id=\"sec2dot1-ijms-21-05541\"><title>2.1. Chrysin Showed No Cytotoxicity to RF/6A Cells</title><p>To confirm that the decrease of high-glucose induced migration was not due to the toxicity of chrysin, an MTT assay was used. Adding different concentrations of chrysin showed no cytotoxic effect on cell survival (<xref ref-type=\"fig\" rid=\"ijms-21-05541-f001\">Figure 1</xref>A), even under a very high concentration of 30 µM or 50 µM. The different osmolarity of high-glucose and normal-glucose culture mediums also showed no influence to cell survival compared with a control group of mannitol (30 mM) simulated hyperosmotic status (<xref ref-type=\"fig\" rid=\"ijms-21-05541-f001\">Figure 1</xref>B). Further experiments also showed that under different osmolarity of high glucose and normal glucose medium, different concentrations of chrysin had no effect on cell survival (<xref ref-type=\"fig\" rid=\"ijms-21-05541-f001\">Figure 1</xref>C).</p></sec><sec id=\"sec2dot2-ijms-21-05541\"><title>2.2. Chrysin Inhibited High-Glucose Induced RF/6A Migration via Inhibiting AKT and ERK Phosphorylation and Decreasing MMP-2 Expression</title><p>Transwell experiments were used to understand the effect of chrysin on high-glucose-induced RF/6A migration. RF/6A migration could be induced only under high-glucose, but not the normal-glucose environment. However, the hyperosmotic state medium does not affect cell migration (<xref ref-type=\"fig\" rid=\"ijms-21-05541-f002\">Figure 2</xref>).</p><p>RF/6A cells pre-treated with chrysin (3 µM, 10 µM, and 30 µM) represented resistance to high-glucose induced cell migration in the transwell assay (<xref ref-type=\"fig\" rid=\"ijms-21-05541-f003\">Figure 3</xref>). VEGF was added to the normal-glucose group to confirm its effect on inducing RF/6A migration even under the normal-glucose environment in advance. Our data showed that the VEGF-induced migration could also be inhibited by the pretreatment of chrysin. These inhibitory effects occurred in a concentration dependent manner (<xref ref-type=\"fig\" rid=\"ijms-21-05541-f003\">Figure 3</xref>). Moreover, these effects were confirmed once more by the scratch wound assays. We also found that chrysin dose-dependently inhibits the migration of RF/6A in experiments of scratching wound (<xref ref-type=\"fig\" rid=\"ijms-21-05541-f004\">Figure 4</xref>).</p><p>Next, we further studied the roles of AKT and ERK in the inhibition of high glucose-induced RF/6A migration by chrysin. Results showed that the highest expression of AKT and ERK phosphorylation were developed after 15 min of high-glucose stimulation (<xref ref-type=\"fig\" rid=\"ijms-21-05541-f005\">Figure 5</xref>A). After pretreatment of chrysin, the phosphorylation of AKT and ERK was found to decrease (<xref ref-type=\"fig\" rid=\"ijms-21-05541-f005\">Figure 5</xref>B). Moreover, results showed that chrysin has down-regulation of MMP-2 expression under the high-glucose environment in RF/6A cells (<xref ref-type=\"fig\" rid=\"ijms-21-05541-f005\">Figure 5</xref>C).</p></sec><sec id=\"sec2dot3-ijms-21-05541\"><title>2.3. Chrysin Inhibits the HIF-1α and VEGF Expression</title><p>The amount of VEGF expressed at different time points of high-glucose stimulation (0, 3, 6, 16, 24, 48 h) was confirmed. Highest VEGF expression was found at 16 h (<xref ref-type=\"fig\" rid=\"ijms-21-05541-f006\">Figure 6</xref>A). In the high-glucose environment, we found that the expression of intracellular VEGF decreased after pre-treatment of chrysin in RF/6A cells (<xref ref-type=\"fig\" rid=\"ijms-21-05541-f006\">Figure 6</xref>B). Furthermore, we studied the effect of chrysin on the VEGF transcription factor, HIF-1α, and found that high glucose stimulation for 16 h can increase the expression of induced HIF-1α. Pretreatment with different concentrations of chrysin will inhibit the expression effects in a dose-dependent manner (<xref ref-type=\"fig\" rid=\"ijms-21-05541-f006\">Figure 6</xref>C).</p></sec><sec id=\"sec2dot4-ijms-21-05541\"><title>2.4. Chrysin Down-Regulates VEGF Receptor Proteins and mRNA Expression</title><p>qRT-PCR was used to observe the mRNA expression of RF/6A cells after pretreatment of chrysin in a high glucose environment. Results showed that the mRNA expression of VEGFR decreased after pretreatment of chrysin (<xref ref-type=\"fig\" rid=\"ijms-21-05541-f007\">Figure 7</xref>A,B). Further observations of the relationship between the amount of the protein expressed and the mRNA expressed showed that the pretreatment of chrysin effectively down-regulated the protein expression of VEGFR (<xref ref-type=\"fig\" rid=\"ijms-21-05541-f007\">Figure 7</xref>C). These results indicate that chrysin might inhibit high-glucose induced chorioretinal endothelium migration by down-regulating the expression of VEGFR.</p></sec></sec><sec sec-type=\"discussion\" id=\"sec3-ijms-21-05541\"><title>3. Discussion</title><p>DM basically means that the glucose present in blood cannot be used effectively. This leads to high glucose concentration and eventually, to glucose excretion in the urine. Hyperglycemia causes chronic inflammation and also promotes abundant cells to migrate, eventually moving to their final destination and beginning angiogenesis via factors including VEGF [<xref rid=\"B16-ijms-21-05541\" ref-type=\"bibr\">16</xref>]. Many diabetic patients suffer from substantial vision loss due to proliferative retinopathy. Intravitreal injection of anti-VEGF drugs can have a positive effect on extensive retinal edema and proliferative vascular retraction. However, it cannot completely inhibit retinal angiogenesis for the reaction time requires less in vitro. Further, anti-VEGF drugs have also been found to have adverse reactions. At present, many people are increasingly interested in plant polyphenols as an alternative method to treat or prevent diabetic retinopathy [<xref rid=\"B17-ijms-21-05541\" ref-type=\"bibr\">17</xref>,<xref rid=\"B18-ijms-21-05541\" ref-type=\"bibr\">18</xref>].</p><p>In this study, chorioretinal EC (RF/6A) was used as the main cell model for retinopathy and cultured under high glucose conditions to mimic the environment of high blood glucose. Although the endothelial characteristics of RF/6A cells were questioned [<xref rid=\"B19-ijms-21-05541\" ref-type=\"bibr\">19</xref>], abundant literature shows that RF/6A cells are still used as endothelial cell lines to model retinal and choroidal angiogenesis [<xref rid=\"B20-ijms-21-05541\" ref-type=\"bibr\">20</xref>,<xref rid=\"B21-ijms-21-05541\" ref-type=\"bibr\">21</xref>,<xref rid=\"B22-ijms-21-05541\" ref-type=\"bibr\">22</xref>,<xref rid=\"B23-ijms-21-05541\" ref-type=\"bibr\">23</xref>,<xref rid=\"B24-ijms-21-05541\" ref-type=\"bibr\">24</xref>]. Culturing ECs under high-glucose conditions cannot mimic all characteristics of DM but might explain the endothelial growth factor autocrine mechanism of the ECs [<xref rid=\"B25-ijms-21-05541\" ref-type=\"bibr\">25</xref>,<xref rid=\"B26-ijms-21-05541\" ref-type=\"bibr\">26</xref>]. On the other hand, chrysin, a flavonoid found in propolis and honey, was chosen as the target agent. Its biological characteristics have been approved by previous studies. Our previous work, as well as other scholarly works, found that chrysin can provide antioxidant, anti-inflammatory, and anti-angiogenic effects [<xref rid=\"B13-ijms-21-05541\" ref-type=\"bibr\">13</xref>,<xref rid=\"B27-ijms-21-05541\" ref-type=\"bibr\">27</xref>,<xref rid=\"B28-ijms-21-05541\" ref-type=\"bibr\">28</xref>,<xref rid=\"B29-ijms-21-05541\" ref-type=\"bibr\">29</xref>,<xref rid=\"B30-ijms-21-05541\" ref-type=\"bibr\">30</xref>,<xref rid=\"B31-ijms-21-05541\" ref-type=\"bibr\">31</xref>,<xref rid=\"B32-ijms-21-05541\" ref-type=\"bibr\">32</xref>,<xref rid=\"B33-ijms-21-05541\" ref-type=\"bibr\">33</xref>,<xref rid=\"B34-ijms-21-05541\" ref-type=\"bibr\">34</xref>]. The results of this study allow us to have better understanding of the possible pharmacological mechanism and biological activity of chrysin in DR. In the future, it can provide evidence for the prevention or treatment of retinopathy by utilizing chrysin.</p><p>Our results show that chrysin can inhibit high-glucose induced chorioretinal EC migration, even in the transwell assay or in the wound healing experiment with normal blood glucose condition (5.5 mM) as the control. We performed a survival experiment to make sure that the effect was not caused by cytotoxicity, but the decrease of cell migration. This experiment revealed chrysin had no cytotoxic effect on choridal ECs and did not influence chorioretinal ECs viabilities under high-glucose and high-osmotic conditions. According to the literature, mitogen-activated protein kinase (MAPK), and AKT played important roles in cell migration and cell survival [<xref rid=\"B35-ijms-21-05541\" ref-type=\"bibr\">35</xref>,<xref rid=\"B36-ijms-21-05541\" ref-type=\"bibr\">36</xref>,<xref rid=\"B37-ijms-21-05541\" ref-type=\"bibr\">37</xref>,<xref rid=\"B38-ijms-21-05541\" ref-type=\"bibr\">38</xref>]. The MAPK family includes members such as P38, ERK, and c-Jun N-terminal kinase (JNK). It has been proven that the MAPK family could regulate cell proliferation, cell migration, cell differentiation, and cell survival [<xref rid=\"B39-ijms-21-05541\" ref-type=\"bibr\">39</xref>]. Moreover, in the development of tumors, ERK can regulate VEGF and HIF-1α activity [<xref rid=\"B40-ijms-21-05541\" ref-type=\"bibr\">40</xref>,<xref rid=\"B41-ijms-21-05541\" ref-type=\"bibr\">41</xref>]. On the other hand, AKT has also been proven to have a large amount of bioactivities, such as regulation of angiogenesis, cell migration, cell survival, and cell proliferation [<xref rid=\"B42-ijms-21-05541\" ref-type=\"bibr\">42</xref>]. In one of our previous studies, lutein, a carotenoid, could down-regulate AKT phosphorylation in retinal pigmented epithelium (RPE) and thereby inhibit cell migration [<xref rid=\"B36-ijms-21-05541\" ref-type=\"bibr\">36</xref>]. Thus, the pathway of the MAPK family and the role of AKT in chrysin’s regulation of cell migration were also investigated in the aforementioned study. From our results, it was speculated that chrysin inhibits high-glucose induced chorioretinal EC migration through down-regulation of AKT and ERK phosphorylation. AKT and ERK participated in the signal transduction of cell migration, but other members of MAPK included P38 and JNK showed no significant differences.</p><p>Extracellular matrix (ECM) consists of collagen, proteoglycans, carbohydrates, and glycoproteins. It acts as cell fixation, cell support, and regulation of signal transduction. Further, ECM has been proven to regulate tumor metastasis [<xref rid=\"B43-ijms-21-05541\" ref-type=\"bibr\">43</xref>,<xref rid=\"B44-ijms-21-05541\" ref-type=\"bibr\">44</xref>]. Matrix metalloproteinase (MMP) is a family of enzymes which can degrade ECM. The structure and function of most members of the MMP family have been widely understood. In particular, MMP-2 and MMP-9 are gelatinase and play important roles in cell migration and angiogenesis. They degrade gelatin and cause ECM to release signals related to cell migration or angiogenesis [<xref rid=\"B44-ijms-21-05541\" ref-type=\"bibr\">44</xref>]. Our results confirm that chrysin can inhibit the expression of MMP-2 in a high-glucose environment, and this inhibitory phenomenon of MMP may be attributed to one of the mechanisms of chrysin’s inhibition of cell migration.</p><p>Previous studies have shown that VEGF expression increased in the serum and vitreous of patients has a positive relationship with blood glucose level [<xref rid=\"B25-ijms-21-05541\" ref-type=\"bibr\">25</xref>,<xref rid=\"B45-ijms-21-05541\" ref-type=\"bibr\">45</xref>,<xref rid=\"B46-ijms-21-05541\" ref-type=\"bibr\">46</xref>]. In our study, we did find that the expression of VEGF in chorioretinal endothelial cells could be triggered in high glucose environment (<xref ref-type=\"fig\" rid=\"ijms-21-05541-f006\">Figure 6</xref>A). Then, the pretreatment of chrysin could inhibit the expression of VEGF (<xref ref-type=\"fig\" rid=\"ijms-21-05541-f006\">Figure 6</xref>B) and reduce the transcription factor HIF-1α (<xref ref-type=\"fig\" rid=\"ijms-21-05541-f006\">Figure 6</xref>C). Some previous studies have also shown that chrysin reduces the expression of HIF-1α by inhibiting the protein synthesis and stability of HIF-1α [<xref rid=\"B8-ijms-21-05541\" ref-type=\"bibr\">8</xref>]. This, in turn, reduces the expression of VEGF and inhibits angiogenesis. Recently, chrysin was also found to inhibit laser-induced choroidal neovascularization (CNV) and down-regulate HIF-1α and VEGF expression [<xref rid=\"B34-ijms-21-05541\" ref-type=\"bibr\">34</xref>]. Integrating these results, we speculate that the inhibitory effect of chrysin on the expression of HIF-1α and VEGF in a high glucose environment may contribute to the prevention or treatment of DR. This mechanism deserves further study.</p><p>Further, previous studies have also shown that chrysin inhibits LPS-induced angiogenesis by down-regulating VEGFR [<xref rid=\"B14-ijms-21-05541\" ref-type=\"bibr\">14</xref>]. Consequently, we also investigated whether VEGFR could be related to the inhibitory effects of chrysin in a high-glucose related EC migration. Our results showed that expressions of VEGF receptor 1 and 2 proteins and mRNA were decreased under a high-glucose condition after pretreatment of chrysin. This effect might contribute to the inhibitory effect of chrysin and determine the expression of VEGF. We believe this is worth researching further. Moreover, we believe that the mechanisms underlying the patho-physiology and treatment of a disease are more complex and multi-factorial than in vitro cell experimental models. It is generally known that cultured cells do not entirely mimic diseased cells. However, the current data indicate that chrysin may have good potential in ocular neovascularization, such as choroidal neovascularization, diabetic retinopathy, etc. In the future, it is necessary to prove its efficacy through primary endothelial cells or in vivo experiments.</p></sec><sec id=\"sec4-ijms-21-05541\"><title>4. Materials and Methods</title><sec id=\"sec4dot1-ijms-21-05541\"><title>4.1. Cell Culture</title><p>The rhesus macaque choroid-retinal (chorioretinal) endothelial cell line RF/6A derived from the choroid-retina of a rhesus macaque fetus were purchased from Food Industry Research and Development Institute (Hsinchu, Taiwan). The cells were maintained with a low concentration of glucose (5.5 mM) Dulbecco’s modified Eagles medium (DMEM) as well as supplemented with 10% fetal bovine serum (FBS, Gibco, Gaithersburg, MD, USA) and 1% penicillin (Hyclone, UT, USA) at 37 °C, 5% CO<sub>2</sub>, and 95% humidified air. For most of the experiments, cells reaching 90–95% of confluence were synchronized for 24 h by serum starvation before they were subjected to further analysis.</p></sec><sec id=\"sec4dot2-ijms-21-05541\"><title>4.2. Chrysin Treatment and High-Glucose Induction</title><p>Chrysin was purchased from Sigma-Aldrich, with a purity level of 97%. Chrysin was prepared by dilution with dimethyl sulfoxide (DMSO) to 30 mM and further to desired concentrations with culture mediums. RF/6A cells were cultured in a chrysin culture medium. The chrysin culture medium was removed after 24 h of treatment. A high glucose concentration of medium (30 mM) was added according to the demands of each experiment to induce damage and take mannitol (30 mM) as control group in hyperosmotic status.</p></sec><sec id=\"sec4dot3-ijms-21-05541\"><title>4.3. MTT Viability Assay</title><p>The viability of the cells was decided by the MTT assay. Chrysin-treated RF/6A cells were incubated for 24 h. After a brief wash with the medium, 0.5 mg/mL MTT in DMSO was used for the quantification of living and metabolically active cells. Mitochondrial dehydrogenases in viable cells can reduce MTT to a purple formazan dye, which was analyzed by recording changes in absorbance at 550 nm using a spectrophotometer. Cell viability was proportional to the absorbance measured.</p></sec><sec id=\"sec4dot4-ijms-21-05541\"><title>4.4. Transwell Assays</title><p>The transwell RF/6A cells migration assays were measured through a modified Boyden chamber model (Transwell apparatus, 8.0 µm pore size, Costar). For detection of RF/6A cell migration in the transwell, the lower faces of polycarbonate filters (Transwell insert) were coated with fibronectin (0.3 mg) 1 h before the experiment in the laminar flow hood. The lower chamber was filled with different concentrations of glucose. RF/6A cells (3 × 10<sup>4</sup> cells, 200 μL) were plated to the upper chamber. After 5 h of incubation, the inserts were removed and the inner side was wiped with cotton swabs. All cells that had migrated were fixed and stained with 0.5% toluidine blue in 4% PFA. The migrated cells were counted as the number of stained cells per × 100 field (high power field, HPF) under a phase-contrast microscope (Leica DMIL1) and photographed.</p></sec><sec id=\"sec4dot5-ijms-21-05541\"><title>4.5. Scratch Wound Assay</title><p>The culture plate with pre-treated RF/6A was scratched with a cross wound through the entire center of the well and then washed with PBS. The plate was photographed under a microscope and then filled with different concentrations of the glucose medium. After 24 h of incubation, the culture medium was removed and the cross wound was photographed under a microscope. The polygon selection mode was used to quantified the area of wound by Java’s Image J software (<uri xlink:href=\"http://rsb.info.nih.gov\">http://rsb.info.nih.gov</uri>). The percentage of wound closure was expressed: </p><p>% of wound closure = [(A<sub>t = 0 h</sub> − A<sub>t = 24 h</sub>)/A<sub>t = 0 h</sub>] × 100%</p><p>A<sub>t = 0 h</sub>: the area of wound measured immediately after scratching.</p><p>A<sub>t = 24 h</sub>: the area of wound measured 24 h after scratching.</p></sec><sec id=\"sec4dot6-ijms-21-05541\"><title>4.6. Western Blotting Analysis</title><p>Western blotting analysis was used to understand the expression and activity of AKT (Cell Signaling: #4058, MA, USA), ERK (Santa Cruz: sc-7383, CA, USA), VEGF (R&D Systems: MAB293-100, MN, USA), HIF-1α (Cell Signaling: #14179, MA, USA), and MMP-2 (GeneTex: GTX104577, CA, USA), and all antibody dilution ratios were 1/1000. RF/6A cells pre-treated with different concentrations of chrysin were cultured with different concentrations of glucose. RF/6A cells were then lysed in a radioimmunoprecipitation assay buffer. After sonication, the lysate was centrifuged (13,200× <italic>g</italic> for 10 min at 4 °C) and the supernatant was removed. The protein content was quantified by a Pierce bicinchoninic acid (BCA) protein assay kit (Rockford, IL, USA). Total protein was separated by gel electrophoresis (10% SDS-polyacrylamide gels). The proteins were then electroblotted onto polyvinylidene fluoride (PVDF) membranes and probed using the indicated antibodies. Immunoblots were detected by enhanced chemiluminescence (Chemiluminescence Reagent Plus from NEN Life Science Products, Boston, MA).</p></sec><sec id=\"sec4dot7-ijms-21-05541\"><title>4.7. Real-Time Quantitative RT-PCR</title><p>After chrysin pre-treatment and different concentrations of glucose medium culture, total RNA of RA/6A was isolated using the total RNA Plus Mini kit (Taigen Bioscience Corporation, Taiwan) according to the manufacturer’s instructions and reverse-transcribed into cDNA using the iScript<sup>™</sup> cDNA Synthesis kit (Bio-Rad, Hercules, CA). The qPCR was performed using the StepOnePlus<sup>™</sup> Real-Time PCR System (Applied Biosystems, Foster City, CA, US) with SYBR green (Applied Biosystems, Foster City, CA, US). Primer sequences used in the PCR reactions are VEGF, forward primer 5′-AGTTCCACCACCAAACATGC-3′, reverse primer 5′-TGAAGGGACACAACGACACA-3′; GAPDH, forward primer 5′-CTTTGGTATCGTGGAAGGACTC-3′, reverse primer 5′-GTAGAGGCAGGGATGTTCT-3′; VEGF receptor 1, forward primer 5′-GGGTCACATCACCTAACATCAC-3′, reverse primer 5′-CCTTTCTGCTGTCCCAGATTAC-3′; VEGF receptor 2, forward primer 5′-GACATGTACGGTCTACGCTATTC-3′, reverse primer 5′-CCTCCACACTTCTCCATTCTTC-3′. Data were normalized relative to GAPDH expression and evaluated using the equation: fold change = 2<sup>−∆∆<italic>C</italic>t</sup>.</p></sec><sec id=\"sec4dot8-ijms-21-05541\"><title>4.8. Statistical Analysis</title><p>Results were analyzed with SigmaPlot for Windows (Version 10.00). The data was acquired by means of ± SE from at least three independent experiments. The <italic>t</italic>-test was performed to determine the difference between groups. A value of <italic>p</italic> < 0.05 was considered statistically significant.</p></sec></sec><sec sec-type=\"conclusions\" id=\"sec5-ijms-21-05541\"><title>5. Conclusions</title><p>In summary, previous published literature as well as our study suggest that chrysin has the bio-characteristics of inhibiting high-glucose induced cell migration. This conclusion helps the future development of nature product extraction. Currently, laser photocoagulation therapy is the main clinical treatment of diabetic retinopathy, even though it has the side effect of decreased visual sensitivity. This study provided important evidence for chrysin supplement as a good candidate in the treatment or even prevention of diabetic retinopathy.</p></sec></body><back><ack><title>Acknowledgments</title><p>This work is supported by the research grants from Ministry of Science and Technology (MOST107-2320-B-030-003-MY3) and the Shin Kong Wu Ho-Su Memorial Hospital (105-SKH-FJU-05) in Taiwan.</p></ack><notes><title>Author Contributions</title><p>Formal analysis, C.-F.H.; funding acquisition, Z.-Y.L.; investigation, C.-F.H., Z.-Y.L. and I.-C.L.; methodology, I.-C.L., H.-J.L. and C.-C.W.; resources, Z.-Y.L. and H.-M.L.; writing: original draft, C.-F.H. and I.-C.L.; writing: review and editing, C.-F.H., Z.-Y.L., H.-J.L. and D.-C.C. All authors have read and agreed to the published version of the manuscript.</p></notes><notes><title>Funding</title><p>The authors are grateful for the grants provided by the Ministry of Science and Technology and the Shin Kong Wu Ho-Su Memorial Hospital in Taiwan.</p></notes><notes notes-type=\"COI-statement\"><title>Conflicts of Interest</title><p>The authors declare no conflict of interest.</p></notes><glossary><title>Abbreviations</title><array orientation=\"portrait\"><tbody><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">VEGF</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Vascular endothelial growth factor</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">VEGFR</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Vascular endothelial growth factor receptor</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">DM</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Diabetes mellitus</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">DR</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Diabetic retinopathy</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">MTT</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">3-(4,5-Dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">HIF-1α</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Hypoxia-inducible factor 1-alpha</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">MMP-2</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Matrix metalloproteinase-2</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">qRT-PCR</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Quantitative real time polymerase chain reaction</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">AKT</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Protein kinase b</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">ERK</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Extracellular signal-regulated kinase</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">ROS</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Reactive oxygen species</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">LPS</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Lipopolysaccharide</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">EC</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Endothelial cells</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">MAPK</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Mitogen-activated protein kinase</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">JNK</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">C-Jun N-terminal kinase</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">RPE</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Retinal pigmented epithelium</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">ECM</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Extracellular matrix</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">CNV</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Choroidal neovascularization</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">DMEM</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Dulbecco’s modified Eagles medium</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">FBS</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Fetal bovine serum</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">DMSO</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Dimethyl sulfoxide</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">PFA</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Paraformaldehyde</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">HPF</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">High power field</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">PBS</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Phosphate buffered saline</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">BCA</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Bicinchoninic acid</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">SDS</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Sodium dodecyl sulfate</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">PVDF</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Polyvinylidene fluoride</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">SE</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Standard error</td></tr></tbody></array></glossary><ref-list><title>References</title><ref id=\"B1-ijms-21-05541\"><label>1.</label><element-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Metea</surname><given-names>M.R.</given-names></name><name><surname>Newman</surname><given-names>E.A.</given-names></name></person-group><article-title>Signaling within the neurovascular unit in the mammalian retina</article-title><source>Exp. 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Sci.</source><year>1997</year><volume>38</volume><fpage>36</fpage><lpage>47</lpage><pub-id pub-id-type=\"pmid\">9008628</pub-id></element-citation></ref></ref-list></back><floats-group><fig id=\"ijms-21-05541-f001\" orientation=\"portrait\" position=\"float\"><label>Figure 1</label><caption><p>Cell viability was tested under different concentrations of chrysin or sugar. (<bold>A</bold>) The cell viability of RF/6A cells treated with chrysin at different concentrations. RF/6A cells treated with chrysin at different concentrations (1, 3, 10, 30, and 50 µM) and the 3-(4,5-dimethyl-2-thiazolyl)-2,5-diphenyl-2H-tetrazolium bromide (MTT) assay was then performed for cytotoxicity. (<bold>B</bold>) RF/6A cells were cultured in normal glucose (5.5 mM), high glucose (30 mM), or mannitol (30 mM) medium for 24 h and the MTT assay was then performed for cell viability. Results are expressed as the percentage of control and mean ± standard error (SE). (<bold>C</bold>) RF/6A cells were treated with chrysin at different concentrations (1, 3, 10, 30, and 50 µM) for 24 h and then cultured in normal glucose or high glucose, before the MTT assay was performed for viability. Results are expressed as the percentage of control and mean ± SE.</p></caption><graphic xlink:href=\"ijms-21-05541-g001\"/></fig><fig id=\"ijms-21-05541-f002\" orientation=\"portrait\" position=\"float\"><label>Figure 2</label><caption><p>Chrysin inhibited high-Glucose induced RF/6A migration by the transwell assay. (<bold>A</bold>) RF/6A cells’ migration was promoted by high glucose. RF/6A cells were cultured in normal glucose, high glucose, or mannitol medium for 24 h. The transwell assay was performed for cell migration afterwards. NG: normal glucose; HG: high glucose; M: mannitol. (<bold>B</bold>) The quantitative results of the transwell cell migration assay for RF/6A cells cultured in normal glucose (5.5 mM), high glucose (30 mM), or mannitol (30 mM) medium for 24 h. Results are expressed as a percentage of control and mean ± SE. *<italic>p</italic> < 0.05, vs. control. Scale bar, 50 μm.</p></caption><graphic xlink:href=\"ijms-21-05541-g002\"/></fig><fig id=\"ijms-21-05541-f003\" orientation=\"portrait\" position=\"float\"><label>Figure 3</label><caption><p>The quantitative results of the transwell assay for RF/6A cells treated chrysin and cultured in normal glucose, high glucose, or normal glucose with VEGF. The inhibitory effects of RF/6A cell migration treated with chrysin at different concentrations (3, 10, 30 µM) for 24 h and then cultured in normal glucose (5.5 mM), high glucose (30 mM), or normal glucose with vascular endothelial growth factor (VEGF) (25 pg/mL), for 24 h. The transwell assay was performed subsequently for cell migration. Results are expressed as a percentage of control and mean ± SE. *<italic>p</italic> < 0.05, vs. control, *<italic>p</italic> < 0.05, vs. high glucose group control, *<italic>p</italic> < 0.05, vs. normal with VEGF group control.</p></caption><graphic xlink:href=\"ijms-21-05541-g003\"/></fig><fig id=\"ijms-21-05541-f004\" orientation=\"portrait\" position=\"float\"><label>Figure 4</label><caption><p>Chrysin inhibited high-glucose induced RF/6A migration by scratch wound assay. The inhibitory effects of RF/6A cell migration treated with chrysin in normal glucose (5.5 mM), high glucose (30 mM), or normal glucose with 25 pg/mL VEGF through the wound healing assay. RF/6A cells were treated chrysin at different concentrations (3, 10, 30 µM) for 24 h and then cultured in normal glucose (<bold>A</bold>) high glucose (<bold>B</bold>) or normal glucose with VEGF (<bold>C</bold>) for 24 h. The wound-healing assay was performed for cell migration. (<bold>D</bold>) The quantitative results of the wound healing assay for RF/6A cells treated chrysin and cultured in normal glucose, high glucose, or normal glucose with VEGF. Results are expressed as a percentage of control and mean ± SE. *<italic>p</italic> < 0.05, vs. control, *<italic>p</italic> < 0.05, vs. high glucose group control, *<italic>p</italic> < 0.05, vs. normal with VEGF group control. Scale bar, 50 μm.</p></caption><graphic xlink:href=\"ijms-21-05541-g004\"/></fig><fig id=\"ijms-21-05541-f005\" orientation=\"portrait\" position=\"float\"><label>Figure 5</label><caption><p>Chrysin inhibited AKT and ERK phosphorylation and decreasing MMP-2 expression. (<bold>A</bold>) The time course for high glucose up-regulated AKT and ERK phosphorylation. RF/6A cells were cultured in a high glucose medium (30 mM) and harvested at different time points (5, 15, 30 min). Subsequently, the western blot analysis was performed for the phosphorylation of AKT and ERK. The quantification data is shown on the right panel. (<bold>B</bold>) Chrysin down-regulated AKT and ERK phosphorylation induced by high glucose. RF/6A cells were treated with chrysin and then harvested after changing high glucose for 15 min. A western blot analysis was performed for the phosphorylation of AKT and ERK. The quantification data is shown on the right panel. (<bold>C</bold>) Chrysin suppressed MMP-2 expression levels induced by high glucose with the quantification data being shown on the right panel. All data was acquired by means of ± SE from at least three independent experiments. *<italic>p</italic> < 0.05, vs. control.</p></caption><graphic xlink:href=\"ijms-21-05541-g005\"/></fig><fig id=\"ijms-21-05541-f006\" orientation=\"portrait\" position=\"float\"><label>Figure 6</label><caption><p>Chrysin Inhibited the HIF-1α and VEGF expression. (<bold>A</bold>) The time course for VEGF expression levels induced by high glucose. RF/6A cells were cultured in the high glucose medium (30 mM) and harvested at different time points (0, 3, 6, 16, 24, 48 h). Subsequently, a western blot analysis was performed for the expression levels of VEGF with the quantification data being displayed on the right panel. (<bold>B</bold>) The effects of the intracellular expression levels of VEGF with chrysin treatment and high glucose with the quantification data being shown on the right panel. (<bold>C</bold>) The effects of the expression levels of HIF-1α with chrysin treatment and high glucose with the quantification data being shown on the right panel. All data was acquired by means of ± SE from at least three independent experiments. *<italic>p</italic> < 0.05, vs. control.</p></caption><graphic xlink:href=\"ijms-21-05541-g006\"/></fig><fig id=\"ijms-21-05541-f007\" orientation=\"portrait\" position=\"float\"><label>Figure 7</label><caption><p>Chrysin decreased the mRNA levels of VEGF receptor1 (<bold>A</bold>) and VEGF receptor2. (<bold>B</bold>) RF/6A cells were treated with chrysin and then harvested after charging high glucose (30 mM) for 16 h, and qRT-PCR was performed for the mRNA expression levels. Results are expressed as a percentage of control and mean ± SE. *<italic>p</italic> < 0.05 vs. control. (<bold>C</bold>) Chrysin reduced the protein expression levels of VEGF receptors. RF/6A cells were treated with chrysin and then harvested after changing high glucose for 16 h. A western blotting was performed for the protein expression levels with the quantification data shown on the right panel. All data was acquired by means of ± SE from at least three independent experiments.</p></caption><graphic xlink:href=\"ijms-21-05541-g007\"/></fig></floats-group></article>\n"
]
[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Int J Environ Res Public Health</journal-id><journal-id journal-id-type=\"iso-abbrev\">Int J Environ Res Public Health</journal-id><journal-id journal-id-type=\"publisher-id\">ijerph</journal-id><journal-title-group><journal-title>International Journal of Environmental Research and Public Health</journal-title></journal-title-group><issn pub-type=\"ppub\">1661-7827</issn><issn pub-type=\"epub\">1660-4601</issn><publisher><publisher-name>MDPI</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32722186</article-id><article-id pub-id-type=\"pmc\">PMC7432059</article-id><article-id pub-id-type=\"doi\">10.3390/ijerph17155332</article-id><article-id pub-id-type=\"publisher-id\">ijerph-17-05332</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Article</subject></subj-group></article-categories><title-group><article-title>Dietary Acid-Base Balance in High-Performance Athletes</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0002-7698-9877</contrib-id><name><surname>Baranauskas</surname><given-names>Marius</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05332\">1</xref><xref rid=\"c1-ijerph-17-05332\" ref-type=\"corresp\">*</xref></contrib><contrib contrib-type=\"author\"><name><surname>Jablonskienė</surname><given-names>Valerija</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05332\">1</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0002-9464-3618</contrib-id><name><surname>Abaravičius</surname><given-names>Jonas Algis</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05332\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Samsonienė</surname><given-names>Laimutė</given-names></name><xref ref-type=\"aff\" rid=\"af2-ijerph-17-05332\">2</xref></contrib><contrib contrib-type=\"author\"><name><surname>Stukas</surname><given-names>Rimantas</given-names></name><xref ref-type=\"aff\" rid=\"af3-ijerph-17-05332\">3</xref></contrib></contrib-group><aff id=\"af1-ijerph-17-05332\"><label>1</label>Department of Physiology, Biochemistry, Microbiology and Laboratory Medicine of the Faculty of Medicine, Institute of Biomedical Sciences, Vilnius University, 01513 Vilnius, Lithuania; <email>[email protected]</email> (V.J.); <email>[email protected]</email> (J.A.A.)</aff><aff id=\"af2-ijerph-17-05332\"><label>2</label>Department of Rehabilitation, Physical and Sports Medicine, Institute of Health Sciences of the Faculty of Medicine, Vilnius University, 01513 Vilnius, Lithuania; <email>[email protected]</email></aff><aff id=\"af3-ijerph-17-05332\"><label>3</label>Department of Public Health, Institute of Health Sciences of the Faculty of Medicine, Vilnius University, 01513 Vilnius, Lithuania; <email>[email protected]</email></aff><author-notes><corresp id=\"c1-ijerph-17-05332\"><label>*</label>Correspondence: <email>[email protected]</email></corresp></author-notes><pub-date pub-type=\"epub\"><day>24</day><month>7</month><year>2020</year></pub-date><pub-date pub-type=\"ppub\"><month>8</month><year>2020</year></pub-date><volume>17</volume><issue>15</issue><elocation-id>5332</elocation-id><history><date date-type=\"received\"><day>08</day><month>7</month><year>2020</year></date><date date-type=\"accepted\"><day>22</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>© 2020 by the authors.</copyright-statement><copyright-year>2020</copyright-year><license license-type=\"open-access\"><license-p>Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>).</license-p></license></permissions><abstract><p>Physical exercise leads to metabolic changes that affect the acid-base balance in skeletal muscles and other tissues. Nutrition is one of the factors that may influence the acid-base balance in the body. Keeping alkaline circumstances in the body is important not only for health and athletic performance in training but also during competition in many sport events. This is especially significant for athletes who practice in sport at the highest level of competition. The aim of the study was to determine the dietary acid-base balance in competitive Lithuanian high-performance athletes, and to evaluate the effect of actual diets of athletes on NEAP (net endogenous acid production), muscle mass and body mineral content during a four-year Olympic cycle. The research participants were 18.1 ± 3.3-year-old Lithuanian high performance athletes (<italic>n</italic> = 323). The actual diet was investigated using the 24 h recall dietary survey method. The measurements of body composition were performed using BIA (bioelectrical impedance analysis). The potential renal acid load of the diets of athletes (dietary PRAL) and NEAP were calculated. In 10.2% of athletes, NEAP exceeds 100 mEq <inline-formula><mml:math id=\"mm1\"><mml:mrow><mml:mo>·</mml:mo></mml:mrow></mml:math></inline-formula> day<sup>−1</sup> and is on average 126.1 ± 32.7 mEq <inline-formula><mml:math id=\"mm2\"><mml:mrow><mml:mo>·</mml:mo></mml:mrow></mml:math></inline-formula> day<sup>−1</sup>. Higher NEAP in athletes is associated with lower muscle mass (β -1.2% of body weight, <italic>p</italic> < 0.001) but has no effect on the amount of minerals in the body (β 0.01% of body weight, <italic>p</italic> = 0.073). Overall, 25–30% of Lithuanian high-performance athletes use high-protein diets (2.0–4.8 g <inline-formula><mml:math id=\"mm3\"><mml:mrow><mml:mo>·</mml:mo></mml:mrow></mml:math></inline-formula> kg<sup>−1</sup>\n<inline-formula><mml:math id=\"mm4\"><mml:mrow><mml:mo>·</mml:mo></mml:mrow></mml:math></inline-formula> day<sup>−1</sup>) leading to a dietary acid-base imbalance as well as an excessive production of endogenous acids in the body. Athletes are recommended to consume higher amounts of potassium and magnesium. An increase in calcium intake up to 1500 mg per day is recommended. In exceptional cases, periodised nutrition for athletes may involve diets complemented with bicarbonate and/or beta-alanine supplements.</p></abstract><kwd-group><kwd>high-performance athletes</kwd><kwd>actual nutrition</kwd><kwd>eating habits</kwd><kwd>diet</kwd><kwd>body composition</kwd><kwd>acid-base balance</kwd></kwd-group></article-meta></front><body><sec sec-type=\"intro\" id=\"sec1-ijerph-17-05332\"><title>1. Introduction</title><p>Acid-base balance homeostasis is essential for ensuring health and physical performance indicators. Organic acids are produced in the body during basal metabolism, while physical exercise can lead to additional acid production in the body [<xref rid=\"B1-ijerph-17-05332\" ref-type=\"bibr\">1</xref>]. When engaged in sports, even submaximal exercise induces metabolic changes that affect the acid-base balance in the skeletal muscles and other tissues [<xref rid=\"B2-ijerph-17-05332\" ref-type=\"bibr\">2</xref>]. Exercise intensity can lower blood pH from 7.4 to 6.9. It is noteworthy that the lowest blood pH reading (6.80–6.90) due to endogenous acids production was found in runners after a simulated 400 m race [<xref rid=\"B3-ijerph-17-05332\" ref-type=\"bibr\">3</xref>,<xref rid=\"B4-ijerph-17-05332\" ref-type=\"bibr\">4</xref>]. The increased H<sup>+</sup> levels in myocytes during high-intensity exercise lead to acidosis and fatigue [<xref rid=\"B1-ijerph-17-05332\" ref-type=\"bibr\">1</xref>,<xref rid=\"B5-ijerph-17-05332\" ref-type=\"bibr\">5</xref>]. Muscle fatigue occurs due to H<sup>+</sup> accumulation because the mitochondrial function and enzymatic activity are impaired. As a consequence, the production of glycolytic energy is disrupted [<xref rid=\"B6-ijerph-17-05332\" ref-type=\"bibr\">6</xref>,<xref rid=\"B7-ijerph-17-05332\" ref-type=\"bibr\">7</xref>]. It has also been proven that the concentration of H<sup>+</sup> ions leads to the accumulation of interstitial K<sup>+</sup>, where proteins bind H<sup>+</sup> ions instead of K<sup>+</sup> ions. This causes the hyperpolarisation of cells, inhibits the rate of the nerve impulse propagation, triggers the changes in the membrane potential and, as a result, disrupts the muscle function [<xref rid=\"B8-ijerph-17-05332\" ref-type=\"bibr\">8</xref>]. HCO<sup>3−</sup> in extracellular fluids is the major H<sup>+</sup> buffer [<xref rid=\"B9-ijerph-17-05332\" ref-type=\"bibr\">9</xref>]. Therefore, the maintenance of a higher concentration of HCO<sup>3−</sup> results in a faster removal of H<sup>+</sup> from muscle cells [<xref rid=\"B5-ijerph-17-05332\" ref-type=\"bibr\">5</xref>]. Thus, an increase in the capacity of the acid buffer system improves the anaerobic [<xref rid=\"B10-ijerph-17-05332\" ref-type=\"bibr\">10</xref>,<xref rid=\"B11-ijerph-17-05332\" ref-type=\"bibr\">11</xref>] and aerobic [<xref rid=\"B12-ijerph-17-05332\" ref-type=\"bibr\">12</xref>] fitness of athletes. The maintenance of alkalinity in intracellular fluids enables a faster removal of H<sup>+</sup> from muscle cells resulting in a delayed muscle fatigue which occurs due to increased acidosis [<xref rid=\"B1-ijerph-17-05332\" ref-type=\"bibr\">1</xref>].</p><p>Nutrition is one of the factors that can affect the acid-base balance in the body. This was confirmed by research that showed a strong relationship between the chemical composition of dietary intake and the urine pH range [<xref rid=\"B13-ijerph-17-05332\" ref-type=\"bibr\">13</xref>]. In this context, the possible influence of the intake of different foods on the potential renal acid load (PRAL) was assessed. The PRAL index that shows the potential renal acid load indicates the presence of milliequivalents (mEq) of H<sup>+</sup> ions per 100 g of food. Most fruits and vegetables have a negative PRAL index because the biologically active substances found in them act as H<sup>+</sup> buffers in the body. Meanwhile, foods high in protein and phosphorus have a positive PRAL index, which means that their consumption stimulates H<sup>+</sup> production in the body.</p><p>According to the research, the dietary habits of the population in the developed countries with the typical Western diet are dominated by protein foods of animal origin (fish, meat, eggs), leading to high levels of metabolic acidosis in the body [<xref rid=\"B14-ijerph-17-05332\" ref-type=\"bibr\">14</xref>]. The similarity in problems such as high protein and fat intakes was found among athletes from many countries. Based on the research data reported in the scientific literature, it has been stated that a diet high in protein and fat, but low in carbohydrates was adopted by professional athletes from countries such as Poland [<xref rid=\"B15-ijerph-17-05332\" ref-type=\"bibr\">15</xref>,<xref rid=\"B16-ijerph-17-05332\" ref-type=\"bibr\">16</xref>], Iran [<xref rid=\"B17-ijerph-17-05332\" ref-type=\"bibr\">17</xref>], Kuwait [<xref rid=\"B18-ijerph-17-05332\" ref-type=\"bibr\">18</xref>], England [<xref rid=\"B19-ijerph-17-05332\" ref-type=\"bibr\">19</xref>,<xref rid=\"B20-ijerph-17-05332\" ref-type=\"bibr\">20</xref>], Brazil [<xref rid=\"B21-ijerph-17-05332\" ref-type=\"bibr\">21</xref>], Greece [<xref rid=\"B22-ijerph-17-05332\" ref-type=\"bibr\">22</xref>], Australia [<xref rid=\"B23-ijerph-17-05332\" ref-type=\"bibr\">23</xref>,<xref rid=\"B24-ijerph-17-05332\" ref-type=\"bibr\">24</xref>,<xref rid=\"B25-ijerph-17-05332\" ref-type=\"bibr\">25</xref>], France [<xref rid=\"B26-ijerph-17-05332\" ref-type=\"bibr\">26</xref>,<xref rid=\"B27-ijerph-17-05332\" ref-type=\"bibr\">27</xref>] Finland [<xref rid=\"B28-ijerph-17-05332\" ref-type=\"bibr\">28</xref>], China [<xref rid=\"B29-ijerph-17-05332\" ref-type=\"bibr\">29</xref>,<xref rid=\"B30-ijerph-17-05332\" ref-type=\"bibr\">30</xref>], Ireland [<xref rid=\"B31-ijerph-17-05332\" ref-type=\"bibr\">31</xref>], Netherlands [<xref rid=\"B32-ijerph-17-05332\" ref-type=\"bibr\">32</xref>], Spain [<xref rid=\"B33-ijerph-17-05332\" ref-type=\"bibr\">33</xref>,<xref rid=\"B34-ijerph-17-05332\" ref-type=\"bibr\">34</xref>], the United States of America (USA) [<xref rid=\"B35-ijerph-17-05332\" ref-type=\"bibr\">35</xref>], South Africa [<xref rid=\"B36-ijerph-17-05332\" ref-type=\"bibr\">36</xref>], Canada [<xref rid=\"B37-ijerph-17-05332\" ref-type=\"bibr\">37</xref>]. It has also been found that if athletes consume large amounts of protein and their diets are low in carbohydrates, they can suffer from metabolic acidosis which can adversely affect their physical performance [<xref rid=\"B13-ijerph-17-05332\" ref-type=\"bibr\">13</xref>,<xref rid=\"B38-ijerph-17-05332\" ref-type=\"bibr\">38</xref>,<xref rid=\"B39-ijerph-17-05332\" ref-type=\"bibr\">39</xref>,<xref rid=\"B40-ijerph-17-05332\" ref-type=\"bibr\">40</xref>]. In addition, insufficient consumption of potassium and magnesium with vegetables and fruits increases the risk of acidosis which may result in the reduced physical working capacity of athletes [<xref rid=\"B41-ijerph-17-05332\" ref-type=\"bibr\">41</xref>].</p><p>It should be noted that the presence of persistent acidosis in the body may trigger the impairment of the muscle function leading to the inhibition of muscle protein synthesis. Part of the amino acids from the degraded muscle proteins can be used for glutamine synthesis in the liver and, in later stages, for acid neutralisation. As a consequence, the increased acid production can lead to a decrease in muscle mass [<xref rid=\"B42-ijerph-17-05332\" ref-type=\"bibr\">42</xref>,<xref rid=\"B43-ijerph-17-05332\" ref-type=\"bibr\">43</xref>,<xref rid=\"B44-ijerph-17-05332\" ref-type=\"bibr\">44</xref>,<xref rid=\"B45-ijerph-17-05332\" ref-type=\"bibr\">45</xref>]. In addition, regular acidic diet may trigger a reduction in bone mineralisation, an increase in urinary calcium excretion and pose a risk of bone fractures [<xref rid=\"B46-ijerph-17-05332\" ref-type=\"bibr\">46</xref>].</p><p>There are no scientifically grounded data on how the diets of high-performance athletes impact their body’s acid-base balance, muscle mass and body mineral content. The aim of the study was to determine the dietary acid-base balance in competitive Lithuanian high-performance athletes, and to evaluate the effect of the actual diets of athletes on NEAP (net endogenous acid production), muscle mass and body mineral content during a four-year Olympic cycle.</p></sec><sec id=\"sec2-ijerph-17-05332\"><title>2. Materials and Methods</title><sec id=\"sec2dot1-ijerph-17-05332\"><title>2.1. Study Population</title><p>High-performance sport or elite sport is sport at the highest level of competition. The target population for the survey was high-performance athletes (<italic>n</italic> = 341) included in the lists, approved under the orders the National Olympic Committee of Lithuania. The main inclusion criteria for study participants was qualification standards that have been previously met by athletes.</p><p>Only those athletes that had already obtained an Olympic qualification quota place or the athletes who had participated in the European Athletics Championships and/or the World Athletics Championships for the purposes of Olympic qualification were investigated. Those athletes who had not participation in sports competitions on a professional level were excluded from the survey. The size of the sample group (<italic>n</italic> = 338) was selected using the OpenEpi Sample Size Calculator with a margin of error of 5% and probability of 99.9%. Over the period from 2017 through 2018, during a preparatory phase of training (macrocycle), 96% of the candidates (<italic>n</italic> = 323) to the Lithuanian Olympic team were included in study and investigated. The athletes ranged in age from 16 to 33 (the average mean age of the athletes was 18.1 ± 3.3 years) and were tested during the research and the training status of the athletes corresponded to 7.9 ± 3.8 years, while workouts were done 5.8 ± 0.8 days a week, with an average workout time of 175.6 ± 60.6 min a day. The dimensions of the training workload of athletes fully complied with the training plans approved by the Lithuanian Sports Centre and the National Olympic Committee of Lithuania. The training plans were specified in the Tokyo 2020 and PyeongChang 2018 programmes. The research sample included 72.4% (<italic>n</italic> = 234) men and 27.6% (<italic>n</italic> = 89) women. According to the dominant energy expenditure methods, the subjects were divided into anaerobic 40.2% (<italic>n</italic> = 130) and aerobic 59.8% (<italic>n</italic> = 193) fitness athletes [<xref rid=\"B47-ijerph-17-05332\" ref-type=\"bibr\">47</xref>]. The group of anaerobic athletes comprised weightlifters (<italic>n</italic> = 6), gymnasts (<italic>n</italic> = 3), discus, javelin throwers, shot put athletes (<italic>n</italic> = 6), jumpers (<italic>n</italic> = 4), basketball players (<italic>n</italic> = 52), boxers (<italic>n</italic> = 14), Greco-Roman wrestlers (<italic>n</italic> = 29), judo wrestlers (<italic>n</italic> = 12), and taekwondo wrestlers (<italic>n</italic> = 4). The group of athletes of aerobic fitness involved representatives of academic rowing (<italic>n</italic> = 36), road cyclists (<italic>n</italic> = 50), swimmers (<italic>n</italic> = 66), skiers (<italic>n</italic> = 17), biathletes (<italic>n</italic> = 20), long-distance runners (<italic>n</italic> = 13), representatives of modern pentathlon (<italic>n</italic> = 12) and representatives of figure skating (<italic>n</italic> = 2). A more detailed analysis of the study recruitment process and study procedures is provided in <xref ref-type=\"fig\" rid=\"ijerph-17-05332-f001\">Figure 1</xref>.</p></sec><sec id=\"sec2dot2-ijerph-17-05332\"><title>2.2. Anthropometric Measures</title><p>The height measurements in athletes were taken at the Lithuanian Sports Medicine Centre using a stadiometer (± 0.01 m). The measurements of the body weight and the individual weight components (body weight (BW), lean body mass (LBM) (in kg and %), muscle mass (MM) (in kg and %), body fat (BF) (in kg and %) and mineral content in bones and electrolytes) (in kg and %) were performed at the Lithuanian Sports Centre using the bioelectrical impedance analysis (BIA) tetra-polar electrodes (13 lot 21 block with certification EN ISO (an international standard is adopted by the European Union) 13488; Jinryang Industrial Complex, Kyungsan City, South Korea) and resistivity was measured with 8–12 tangent electrodes at different frequencies of the signal: 5, 50, 250, 550 and 1000 kHz [<xref rid=\"B47-ijerph-17-05332\" ref-type=\"bibr\">47</xref>,<xref rid=\"B48-ijerph-17-05332\" ref-type=\"bibr\">48</xref>]. LBM, MM and mineral content were assessed according to the norms set for men and women. LBM norm for men is 75–85%, for women 70–80%; MM norm for men is 74–80%, for women 64–80%; mineral norm for men ranges between 5.8–6.0%, for women 5.5–6.0%. The muscle and fat mass index (MFMI) of each athlete was determined by dividing the weight of the muscle (in kg) by weight (in kg). The BF and the ratio of muscle and fat mass were evaluated according to the standards presented in <xref rid=\"ijerph-17-05332-t001\" ref-type=\"table\">Table 1</xref> (MFMI) [<xref rid=\"B47-ijerph-17-05332\" ref-type=\"bibr\">47</xref>].</p></sec><sec id=\"sec2dot3-ijerph-17-05332\"><title>2.3. Energy Requirements</title><p>The basal metabolic rate (BMR), daily energy expenditure (DEE), training energy expenditure (TEE) were estimated in all the subjects. BMR was calculated using the Harris and Benedict formulas [<xref rid=\"B49-ijerph-17-05332\" ref-type=\"bibr\">49</xref>]. We collected 24-h records of physical activity on the same day when the participants recorded their dietary energy intake (EI). The physical activity levels and lifestyle variables (regular and non-regular activities, sedentary activities and sleeping habits) conform to the standards specified by the American Dietetic Association, Dietitians of Canada, and the American College of Sports Medicine [<xref rid=\"B50-ijerph-17-05332\" ref-type=\"bibr\">50</xref>]. These measures (the activity codes and metabolic equivalents (METs) (in kcal/kg/h) for physical activities) were supported by the studies of Ainsworth et al. [<xref rid=\"B51-ijerph-17-05332\" ref-type=\"bibr\">51</xref>] and the data were processed according to the specific activity.</p></sec><sec id=\"sec2dot4-ijerph-17-05332\"><title>2.4. Dietary Intake and Eating Habits</title><p>The 24-h actual nutrition survey method was employed to assess the actual nutrition in athletes [<xref rid=\"B52-ijerph-17-05332\" ref-type=\"bibr\">52</xref>,<xref rid=\"B53-ijerph-17-05332\" ref-type=\"bibr\">53</xref>,<xref rid=\"B54-ijerph-17-05332\" ref-type=\"bibr\">54</xref>]. The respondents were surveyed through the direct interview carried out by a specially trained interviewer at the Lithuanian Sports Centre. The actual nutrition survey method facilitated the compilation of the data on the amounts of food, meals, food supplements consumed by each athlete. To capture all foods and meals eaten, and their amounts, a special atlas of photos with different portions of foods and meals weighted in grams was used [<xref rid=\"B55-ijerph-17-05332\" ref-type=\"bibr\">55</xref>]. We evaluated the average daily food sets consumed by athletes on the basis of which the chemical composition and energy value of food rations were determined in line with the chemical composition tables [<xref rid=\"B56-ijerph-17-05332\" ref-type=\"bibr\">56</xref>]. The consumption of carbohydrates, proteins and fats was assessed taking into account the recommendations provided in the scientific literature [<xref rid=\"B57-ijerph-17-05332\" ref-type=\"bibr\">57</xref>,<xref rid=\"B58-ijerph-17-05332\" ref-type=\"bibr\">58</xref>]. The amount of carbohydrates recommended for athletes is 5–8 g <inline-formula><mml:math id=\"mm5\"><mml:mrow><mml:mo>·</mml:mo></mml:mrow></mml:math></inline-formula> kg<sup>−1</sup>\n<inline-formula><mml:math id=\"mm6\"><mml:mrow><mml:mo>·</mml:mo></mml:mrow></mml:math></inline-formula> day<sup>−1</sup>, protein content is 1.4–2.0 g <inline-formula><mml:math id=\"mm7\"><mml:mrow><mml:mo>·</mml:mo></mml:mrow></mml:math></inline-formula> kg<sup>−1</sup>\n<inline-formula><mml:math id=\"mm8\"><mml:mrow><mml:mo>·</mml:mo></mml:mrow></mml:math></inline-formula> day<sup>−1</sup>. The percentage of energy provided by fat should be between 20% and 35%. The daily intake of minerals and their compliance with the reference daily intake (RDI) was assessed according to the RDI of vitamins and minerals approved in Lithuania [<xref rid=\"B59-ijerph-17-05332\" ref-type=\"bibr\">59</xref>]. To study the eating habits, we designed and used a validated questionnaire originally constructed by M. Baranauskas [<xref rid=\"B60-ijerph-17-05332\" ref-type=\"bibr\">60</xref>]. The respondents participated in direct interviews. The questionnaire comprised questions about the socio-demographics (gender, age, place of residence, sport, sporting experience, etc.) and eating habits of athletes.</p></sec><sec id=\"sec2dot5-ijerph-17-05332\"><title>2.5. Potential Renal Acid Load (PRAL), Net Endogenous Acid Production (NEAP) and the Diets</title><p>The following formula was used to estimate the NEAP [<xref rid=\"B61-ijerph-17-05332\" ref-type=\"bibr\">61</xref>]: NEAP was estimated according to the equation (mEq <inline-formula><mml:math id=\"mm9\"><mml:mrow><mml:mo>·</mml:mo></mml:mrow></mml:math></inline-formula> day<sup>−1</sup>) = PRAL<sup>1</sup> (mEq <inline-formula><mml:math id=\"mm10\"><mml:mrow><mml:mo>·</mml:mo></mml:mrow></mml:math></inline-formula> day<sup>−1</sup>) + OA<sup>2</sup> (mEq <inline-formula><mml:math id=\"mm11\"><mml:mrow><mml:mo>·</mml:mo></mml:mrow></mml:math></inline-formula> day<sup>−1</sup>) where PRAL shows the potential renal acid load of the estimated diet and OA (organic anions) shows the urinary organic anions under analysis, with the 2 components calculated as follows:</p><p>PRAL<sup>1</sup> (mEq <inline-formula><mml:math id=\"mm12\"><mml:mrow><mml:mo>·</mml:mo></mml:mrow></mml:math></inline-formula> day<sup>−1</sup>) = (0.49 × protein (g <inline-formula><mml:math id=\"mm13\"><mml:mrow><mml:mo>·</mml:mo></mml:mrow></mml:math></inline-formula> day<sup>−1</sup>)) + (0.037 × phosphorus (mg <inline-formula><mml:math id=\"mm14\"><mml:mrow><mml:mo>·</mml:mo></mml:mrow></mml:math></inline-formula> day<sup>−1</sup>)) − (0.021 <inline-formula><mml:math id=\"mm15\"><mml:mrow><mml:mo>·</mml:mo></mml:mrow></mml:math></inline-formula> potassium (mg <inline-formula><mml:math id=\"mm16\"><mml:mrow><mml:mo>·</mml:mo></mml:mrow></mml:math></inline-formula> day<sup>−1</sup>)) − (0.026 × magnesium (mg <inline-formula><mml:math id=\"mm17\"><mml:mrow><mml:mo>·</mml:mo></mml:mrow></mml:math></inline-formula> day<sup>−1</sup>)) − (0.013 × calcium (mg <inline-formula><mml:math id=\"mm18\"><mml:mrow><mml:mo>·</mml:mo></mml:mrow></mml:math></inline-formula> day<sup>−1</sup>)).</p><p>OA<sup>2</sup> (mEq <inline-formula><mml:math id=\"mm19\"><mml:mrow><mml:mo>·</mml:mo></mml:mrow></mml:math></inline-formula> day<sup>−1</sup>) = individual body surface area <sup>3</sup>\n<inline-formula><mml:math id=\"mm20\"><mml:mrow><mml:mo>×</mml:mo></mml:mrow></mml:math></inline-formula> 41/1.73.</p><p>The body surface area was calculated according to the formula proposed by Du Bois and Du Bois [<xref rid=\"B62-ijerph-17-05332\" ref-type=\"bibr\">62</xref>]: <sup>3</sup> Individual body surface area (m<sup>2</sup>) = 0.007184 <inline-formula><mml:math id=\"mm21\"><mml:mrow><mml:mo>×</mml:mo></mml:mrow></mml:math></inline-formula> (height (cm) <sup>0.725</sup>\n<inline-formula><mml:math id=\"mm22\"><mml:mrow><mml:mo>×</mml:mo></mml:mrow></mml:math></inline-formula> weight (kg) <sup>0.425</sup>.</p></sec><sec id=\"sec2dot6-ijerph-17-05332\"><title>2.6. Statistical Analysis</title><p>All the normally distributed continuous variables are presented as means ± standard deviations (SD), whereas the qualitative variables are presented as relative frequencies (in %). The normality of variable distribution was tested by the Shapiro–Wilk <italic>W</italic> test. When normality was confirmed, the <italic>t</italic>-tests of the independent samples were used to assess the differences observed between the groups. Pearson (<italic>r</italic>) correlation coefficient were used to determine the strength of the relationship between the variables under analysis. The correlation coefficient r can range in value from −1 to +1. A higher degree of the absolute value of the coefficient shows a stronger the relationship between the variables. The correlations above 0.4 are considered to be relatively strong; the correlations between 0.2 and 0.4 are moderate, and those below 0.2 are weak.</p><p>The multiple linear regression analysis was used to determine the association between the dietary intake and NEAP. The model was adjusted for gender and type of sport. Logarithmic or inverse square transformations were used to improve normality. By using a stepwise multivariate logistic regression method, we determined which eating habits of athletes depended on their sport. The stepwise multivariate logistic regression method was used to establish which eating habits determined PRAL ≤ 0 and PRAL > 0. The method of parameter estimation used in this study was maximum likelihood, and several techniques were employed to assess the appropriateness, adequacy and usefulness of the model using the likelihood-ratio test, Hosmer and Lemeshow (H-L) test statistic, Wald (W) statistic, and Nagelkerke R<sup>2</sup> statistic. During the next stages, we calculated the logistic regression coefficients (β), odds ratios (OR) and their 95% confidence intervals (CIs) for each variable under analysis. All the reported <italic>p</italic>-values are based on two-sided tests and compared to a significance level of 5%. The statistical analysis was performed using Stata version 12.1 (StataCorp, College Station, TX, USA), SPSS V.25 for Windows (International Business Machines Corporation, Armonk, NY, USA) and Microsoft Excel (Microsoft Corporation, Redmond, WA, USA).</p></sec><sec id=\"sec2dot7-ijerph-17-05332\"><title>2.7. Ethics Statement</title><p>Prior to the research, all the organisational issues regarding the survey were discussed with the Lithuanian Sports Centre and the Bioethics Committee. The study was conducted in accordance with a permit to carry out biomedical research, issued by the Lithuanian Bioethics Committee (No. 158200-11-113-25, of 3 November 2009). Prior to testing, all the athletes provided a written consent and the study protocols were approved by the Institutional Review Board of the Lithuanian Sports Medicine Centre. The biomedical research was conducted according to the principles expressed in the Declaration of Helsinki.</p></sec></sec><sec sec-type=\"results\" id=\"sec3-ijerph-17-05332\"><title>3. Results</title><sec id=\"sec3dot1-ijerph-17-05332\"><title>3.1. Characteristics of Respondents</title><p>The body composition (BW, LBM, BF MM, and MFMI) of athletes was examined as shown in <xref rid=\"ijerph-17-05332-t002\" ref-type=\"table\">Table 2</xref>. The height, BW, LBM, MM, and the mineral content (in bones and electrolytes) fluctuated within the norms. BF in male athletes (16.7 ± 4.7%) was acceptable (15–19%) while MFMI (5.2 ± 2.5) was high (4.7–6.0). Meanwhile, the BF and MFMI in female athletes differed in the groups of different sports. BF of female athletes involved in sports of anaerobic fitness was 24.9 ± 4.8%, which was acceptable (25–29%) and higher than BF of athletes of aerobic fitness which was 22.2 ± 3.6% and corresponded to the optimal FM (20–24%) (<italic>p</italic> = 0.005). In addition, MFMI (2.9 ± 0.8) in anaerobic female athletes corresponded to the low one (1.9–2.9). Meanwhile, a higher MFMI observed in aerobic women involved (3.4 ± 0.8, corresponding to the average of 3.0–3.9) confirms a more optimal body composition (<italic>p</italic> = 0.012).</p></sec><sec id=\"sec3dot2-ijerph-17-05332\"><title>3.2. Dietary Intake and Energy Expenditure</title><p>The examination of the actual diet of athletes revealed that the EI amounts to 3343 ± 1133 kcal and corresponds to DEE by 91.4 ± 27.8% (<xref rid=\"ijerph-17-05332-t002\" ref-type=\"table\">Table 2</xref>).</p><p>The evaluation of the nutrient intake showed that regardless of the type of sport, when training 175.6 ± 60.6 min per day, the amount of carbohydrates consumed (5.5 g <inline-formula><mml:math id=\"mm23\"><mml:mrow><mml:mo>·</mml:mo></mml:mrow></mml:math></inline-formula> kg<sup>−1</sup>\n<inline-formula><mml:math id=\"mm24\"><mml:mrow><mml:mo>·</mml:mo></mml:mrow></mml:math></inline-formula> day<sup>−1</sup>) conforms to the minimum requirements (5–8 g <inline-formula><mml:math id=\"mm25\"><mml:mrow><mml:mo>·</mml:mo></mml:mrow></mml:math></inline-formula> kg<sup>−1</sup>\n<inline-formula><mml:math id=\"mm26\"><mml:mrow><mml:mo>·</mml:mo></mml:mrow></mml:math></inline-formula> day<sup>−1</sup>). Nutrient imbalances in the diets of athletes are caused by an excessive fat intake. Irrespective of the type of sport, the share of energy value of fat in the diet of athletes (39.0 ± 7.8%) exceeds what is recommended (by 20–35%).</p><p>The average amount of protein of 1.7 ± 0.6 g <inline-formula><mml:math id=\"mm27\"><mml:mrow><mml:mo>·</mml:mo></mml:mrow></mml:math></inline-formula> kg<sup>−1</sup>\n<inline-formula><mml:math id=\"mm28\"><mml:mrow><mml:mo>·</mml:mo></mml:mrow></mml:math></inline-formula> day<sup>−1</sup> found in the diets of all types of athletes (anaerobic and aerobic) corresponds to what is recommended (1.4–2.0 g <inline-formula><mml:math id=\"mm29\"><mml:mrow><mml:mo>·</mml:mo></mml:mrow></mml:math></inline-formula> kg<sup>−1</sup>\n<inline-formula><mml:math id=\"mm30\"><mml:mrow><mml:mo>·</mml:mo></mml:mrow></mml:math></inline-formula> day<sup>−1</sup>). The protein content recommended for athletes is no more than 2.0 g <inline-formula><mml:math id=\"mm31\"><mml:mrow><mml:mo>·</mml:mo></mml:mrow></mml:math></inline-formula> kg<sup>−1</sup>\n<inline-formula><mml:math id=\"mm32\"><mml:mrow><mml:mo>·</mml:mo></mml:mrow></mml:math></inline-formula> day<sup>−1</sup>. However, according to our study, 29.2% of the athletes who develop anaerobic fitness consume 2.0–4.8 g <inline-formula><mml:math id=\"mm33\"><mml:mrow><mml:mo>·</mml:mo></mml:mrow></mml:math></inline-formula> kg<sup>−1</sup>\n<inline-formula><mml:math id=\"mm34\"><mml:mrow><mml:mo>·</mml:mo></mml:mrow></mml:math></inline-formula> day<sup>−1</sup> protein, and 24.4% of the athletes training for aerobic fitness consume 2.0–3.9 g <inline-formula><mml:math id=\"mm35\"><mml:mrow><mml:mo>·</mml:mo></mml:mrow></mml:math></inline-formula> kg<sup>−1</sup>\n<inline-formula><mml:math id=\"mm36\"><mml:mrow><mml:mo>·</mml:mo></mml:mrow></mml:math></inline-formula> day<sup>−1</sup> protein.</p><p>The consumption of phosphorus, potassium, magnesium and calcium by athletes exceed the recommended amounts. In contrast to aerobic athletes, anaerobic athletes consumed more phosphorus (<italic>p</italic> = 0.014), calcium (<italic>p</italic> = 0.022), and magnesium (<italic>p</italic> = 0.012). In terms of RDI, the anaerobic athletes consumed more phosphorus, calcium and magnesium by 2.9, 1.2 and 1.5 times respectively. Meanwhile, the amounts of phosphorus, calcium and magnesium in the diets of aerobic athletes exceeded RDI by 2.6, 1.4 and 1.4 times, respectively. Regardless of the sport, the amount of potassium consumed by the athletes was 1.5 times higher than RDI. In addition, the dietary amounts of calcium and phosphorus were unbalanced. This was confirmed by the calcium to phosphorus ratio (Ca/P) (0.6 ± 0.2) which was below the recommended 0.75 and resulted from the excessive dietary intake of phosphorus (<xref rid=\"ijerph-17-05332-t003\" ref-type=\"table\">Table 3</xref>).</p></sec><sec id=\"sec3dot3-ijerph-17-05332\"><title>3.3. Eating Habits and the PRAL</title><p>Lithuanian high-performance athletes rarely consume foods that should be found in their diets every day. The study revealed that 49.8% of athletes consumed bakery products, 29.6%—cereals, 43.7%—fresh vegetables, 43.7%—fresh fruits, and 36.8%—dairy products four to seven days a week. Dried fruits and boiled potatoes are consumed less frequently—25.9% and 40.1% of the athletes consumed dried fruit and boiled potatoes 2 days a week, 12.1% and 37.6% from 2 to 7 days a week, respectively.</p><p>In terms of the frequency of consumption of protein found in meat and fish products, 41.3% of the athletes chose poultry 2–4 days a week, while eggs (53.6%), beef (46.2%), pork (49%), fish (44.9%) and meat preparations (42.1%) were consumed less frequently, from 1 to 2 days a week.</p><p>The eating habits of athletes are likely to determine the potential renal acid load (PRAL) of their diets. After evaluating PRAL of athletes’ diets, it was found that more than half (65.9%) of the examined diets had a positive PRAL. PRAL (10.7 ± 42.1 mEq <inline-formula><mml:math id=\"mm37\"><mml:mrow><mml:mo>·</mml:mo></mml:mrow></mml:math></inline-formula> day<sup>−1</sup>) of the diets of anaerobic athletes did not differ from PRAL (9.3 ± 35.9 mEq <inline-formula><mml:math id=\"mm38\"><mml:mrow><mml:mo>·</mml:mo></mml:mrow></mml:math></inline-formula> day<sup>−1</sup>) of aerobic athletes and was also positive (PRAL > 0 mEq <inline-formula><mml:math id=\"mm39\"><mml:mrow><mml:mo>·</mml:mo></mml:mrow></mml:math></inline-formula> day<sup>−1</sup>) (<italic>p</italic> = 0.761) (<xref rid=\"ijerph-17-05332-t003\" ref-type=\"table\">Table 3</xref>).</p><p>A stepwise multivariate logistic regression method was used to determine which eating habits of athletes determine the PRAL of their diets. <xref rid=\"ijerph-17-05332-t004\" ref-type=\"table\">Table 4</xref> presents the OR estimating the association between the different food intakes by athletes and the dietary acid load among the participants who were identified with dietary PRAL ≤ 0 mEq <inline-formula><mml:math id=\"mm40\"><mml:mrow><mml:mo>·</mml:mo></mml:mrow></mml:math></inline-formula> day<sup>−1</sup>. The final built logit model was tested with the Hosmer and Lemeshow goodness-of-fit test statistic (Nagelkerke R<sup>2</sup> = 0.28; H-L stat χ<sup>2</sup> = 14.5, <italic>p</italic> < 0.006). As indicated in <xref rid=\"ijerph-17-05332-t004\" ref-type=\"table\">Table 4</xref>, the probability of PRAL in the diets of athletes increased 1.4 times (OR 1.4) to become ≤ 0 mEq <inline-formula><mml:math id=\"mm41\"><mml:mrow><mml:mo>·</mml:mo></mml:mrow></mml:math></inline-formula> day<sup>−1</sup>, when dairy products (<italic>p</italic> = 0.05), fresh vegetables (<italic>p</italic> = 0.048) and dried fruits (<italic>p</italic> = 0.046) were consumed more frequently. Specifically, milk and fresh vegetables were consumed more frequently by athletes in PRAL ≤ 0 group (47.7% and 52.3%, respectively) 4–7 days per week compared to athletes in PRAL > 0 group (31.1% and 39.1%, respectively). Similarly, more athletes (47.8%) with PRAL ≤ 0 consumed dried fruit more frequently (2–7 days a week) compared to the group of PRAL > 0 athletes (32.9%). In contrast, PRAL of the athletes who consumed more grain products was higher than 0 mEq <inline-formula><mml:math id=\"mm42\"><mml:mrow><mml:mo>·</mml:mo></mml:mrow></mml:math></inline-formula> day<sup>−1</sup> (OR 0.7, <italic>p</italic> = 0.050). Specifically, the athletes in PRAL > 0 group (71.4%) consumed grain products more frequently (2–7 days a week) compared to athletes in PRAL ≤ 0 group (62.6%).</p></sec><sec id=\"sec3dot4-ijerph-17-05332\"><title>3.4. Acid-Base Balance and Diets</title><p>Aiming to determine whether the chemical composition of the athlete diets was suitable for maintaining the body’s acid-base balance, the study assessed the effect of nutrition on the body’s net endogenous acid production (NEAP). It is important for athletes that NEAP did not exceed 100 mEq <inline-formula><mml:math id=\"mm43\"><mml:mrow><mml:mo>·</mml:mo></mml:mrow></mml:math></inline-formula> day<sup>−1</sup> for a longer period of time.</p><p>Although, according to our study, the average NEAP in anaerobic athletes (56.9 ± 43.3 mEq <inline-formula><mml:math id=\"mm44\"><mml:mrow><mml:mo>·</mml:mo></mml:mrow></mml:math></inline-formula> day<sup>−1</sup>) did not differ from NEAP observed in aerobic athletes (53.7 ± 37.1 mEq <inline-formula><mml:math id=\"mm45\"><mml:mrow><mml:mo>·</mml:mo></mml:mrow></mml:math></inline-formula> day<sup>−1</sup>) (<italic>p</italic> = 0.469), in 10.2% of Lithuanian high-performance athletes NEAP is higher than 100 mEq <inline-formula><mml:math id=\"mm46\"><mml:mrow><mml:mo>·</mml:mo></mml:mrow></mml:math></inline-formula> day<sup>−1</sup> and on average amounts to 126.1 ± 32.7 mEq <inline-formula><mml:math id=\"mm47\"><mml:mrow><mml:mo>·</mml:mo></mml:mrow></mml:math></inline-formula> day<sup>−1</sup>.</p><p>After using a multivariate linear regression method, we found that with a 95% confidence level of the consumption of larger amounts of protein, phosphorus, and carbohydrates, NEAP increases from 34.1 to 167.2 mEq <inline-formula><mml:math id=\"mm48\"><mml:mrow><mml:mo>·</mml:mo></mml:mrow></mml:math></inline-formula> day<sup>−1</sup> (<italic>p</italic> < 0.001). Meanwhile, with the increased consumption of potassium, calcium, and magnesium with food, NEAP decreases from −9.1 to −100.4 mEq <inline-formula><mml:math id=\"mm49\"><mml:mrow><mml:mo>·</mml:mo></mml:mrow></mml:math></inline-formula> day<sup>−1</sup> (<italic>p</italic> < 0.05) (<xref rid=\"ijerph-17-05332-t005\" ref-type=\"table\">Table 5</xref>).</p><p>A more detailed analysis of the results showed that the daily protein intake (2.6 ± 0.8 and 2.4 ± 0.7 g <inline-formula><mml:math id=\"mm50\"><mml:mrow><mml:mo>·</mml:mo></mml:mrow></mml:math></inline-formula> kg<sup>−1</sup>\n<inline-formula><mml:math id=\"mm51\"><mml:mrow><mml:mo>·</mml:mo></mml:mrow></mml:math></inline-formula> day<sup>−1</sup>) of anaerobic and aerobic athletes with NEAP > 100 mEq <inline-formula><mml:math id=\"mm52\"><mml:mrow><mml:mo>·</mml:mo></mml:mrow></mml:math></inline-formula> day<sup>−1</sup> exceeds the maximum recommended amounts by 1.3 times, and phosphorus content (2907.3 ± 892.2 and 2402.7 ± 516.3 mg <inline-formula><mml:math id=\"mm53\"><mml:mrow><mml:mo>·</mml:mo></mml:mrow></mml:math></inline-formula> day-1)—by 4.1–3.4 times.</p><p>The correlation analysis also confirmed a moderate relationship between the protein intake and NEAP in the group of anaerobic athletes (r = 0.482, <italic>p</italic> < 0.001) and a weak relationship in the group of aerobic athletes (r = 0.274, <italic>p</italic> < 0.001) (<xref ref-type=\"fig\" rid=\"ijerph-17-05332-f002\">Figure 2</xref> and <xref ref-type=\"fig\" rid=\"ijerph-17-05332-f003\">Figure 3</xref>).</p><p>The consumption of more protein foods, lower dietary PRAL and lower NEAP can be achieved by consuming sufficient amounts of the minerals—potassium, calcium and magnesium. According to the study, the consumption of potassium (4834.8 ± 1642.3 mg <inline-formula><mml:math id=\"mm58\"><mml:mrow><mml:mo>·</mml:mo></mml:mrow></mml:math></inline-formula> day<sup>−1</sup>), calcium (1375.1 ± 562.3 mg <inline-formula><mml:math id=\"mm59\"><mml:mrow><mml:mo>·</mml:mo></mml:mrow></mml:math></inline-formula> day<sup>−1</sup>) and magnesium (525.8 ± 150.8 mg <inline-formula><mml:math id=\"mm60\"><mml:mrow><mml:mo>·</mml:mo></mml:mrow></mml:math></inline-formula> day<sup>−1</sup>) in athletes with NEAP > 100 mEq <inline-formula><mml:math id=\"mm61\"><mml:mrow><mml:mo>·</mml:mo></mml:mrow></mml:math></inline-formula> day<sup>−1</sup> did not significantly differ (<italic>p</italic> > 0.05) from those found in athletes with NEAP < 100 mEq <inline-formula><mml:math id=\"mm62\"><mml:mrow><mml:mo>·</mml:mo></mml:mrow></mml:math></inline-formula> day<sup>−1</sup>, and exceeded RDI by 1.4, 1.5, and 1.5 times, respectively.</p></sec><sec id=\"sec3dot5-ijerph-17-05332\"><title>3.5. The Effect of Acid-Base Balance on the Muscle Mass and Body Mineral Content of Athletes</title><p>As indicated in <xref rid=\"ijerph-17-05332-t006\" ref-type=\"table\">Table 6</xref>, after applying the method of linear multivariate regression, it was found that the amount of muscle mass in athletes depended on the protein and phosphorus consumption, and the resulting NEAP in the body. At higher NEAP, the muscle mass (% of BW) was significantly lower by 1.2% (<italic>p</italic> < 0.001). However, only the excess phosphorus intake was associated with lower muscle mass (β −10.2% of BW, <italic>p</italic> < 0.001). Meanwhile, the athletes taking an increased amount of protein are characterized by an increase of 12.6% in the muscle mass of BW (<italic>p</italic> < 0.001).</p><p>The analysis of NEAP and the impact made on NEAP by nutrient components as well as their influence on the body mineral content (in bones and electrolytes) revealed no effects of NEAP (β −0.01%, <italic>p</italic> = 0.073). The results of our study showed that the body mineral content (% of BW) of athletes was increasing with higher protein (β 0.15%, <italic>p</italic> < 0.001) and calcium (β 0.06%, <italic>p</italic> = 0.04) consumption. Meanwhile, with higher amounts of phosphorus intake, lower amounts of body mineral content were observed (β −0.16%, <italic>p</italic> < 0.001) (<xref rid=\"ijerph-17-05332-t007\" ref-type=\"table\">Table 7</xref>).</p></sec></sec><sec sec-type=\"discussion\" id=\"sec4-ijerph-17-05332\"><title>4. Discussion</title><p>Daily high-intensity exercise causes stress to the body’s buffer systems. Even moderate-intensity exercise causes metabolic changes that affect the acid-base balance in skeletal muscles and other tissues. Intense exercise can lower blood pH from 7.4 to 6.9 in 1 min leading to very rapid muscle fatigue [<xref rid=\"B1-ijerph-17-05332\" ref-type=\"bibr\">1</xref>,<xref rid=\"B3-ijerph-17-05332\" ref-type=\"bibr\">3</xref>,<xref rid=\"B4-ijerph-17-05332\" ref-type=\"bibr\">4</xref>]. Another factor influencing the acid-base balance in the body is diet [<xref rid=\"B13-ijerph-17-05332\" ref-type=\"bibr\">13</xref>] which can lead to low-grade metabolic acidosis (MA) (arterial blood pH is close to 7.35) [<xref rid=\"B63-ijerph-17-05332\" ref-type=\"bibr\">63</xref>]. Low-grade MA is typical when NEAP reaches about 50 mEq <inline-formula><mml:math id=\"mm63\"><mml:mrow><mml:mo>·</mml:mo></mml:mrow></mml:math></inline-formula> day<sup>−1</sup>. In other countries, Remer et al. [<xref rid=\"B64-ijerph-17-05332\" ref-type=\"bibr\">64</xref>] and Lemann [<xref rid=\"B65-ijerph-17-05332\" ref-type=\"bibr\">65</xref>] found that NEAP for young people was 40.1–50 mEq <inline-formula><mml:math id=\"mm64\"><mml:mrow><mml:mo>·</mml:mo></mml:mrow></mml:math></inline-formula> day<sup>−1</sup>. Similar data were obtained in our study. The average NEAP for anaerobic athletes was 56.9 mEq <inline-formula><mml:math id=\"mm65\"><mml:mrow><mml:mo>·</mml:mo></mml:mrow></mml:math></inline-formula> day<sup>−1</sup>, and that for aerobic athletes—53.7 mEq <inline-formula><mml:math id=\"mm66\"><mml:mrow><mml:mo>·</mml:mo></mml:mrow></mml:math></inline-formula> day<sup>−1</sup>. However, as many as 10.2% of Lithuanian high-performance athletes had NEAP higher than 100 mEq <inline-formula><mml:math id=\"mm67\"><mml:mrow><mml:mo>·</mml:mo></mml:mrow></mml:math></inline-formula> day<sup>−1</sup> which averaged to 126.1 ± 32.7 mEq <inline-formula><mml:math id=\"mm68\"><mml:mrow><mml:mo>·</mml:mo></mml:mrow></mml:math></inline-formula> day<sup>−1</sup>. Long-term NEAPs of 100–120 mEq <inline-formula><mml:math id=\"mm69\"><mml:mrow><mml:mo>·</mml:mo></mml:mrow></mml:math></inline-formula> day<sup>−1</sup> or more results in kidney overload with acid and thus a decreased availability of bicarbonates in the blood [<xref rid=\"B66-ijerph-17-05332\" ref-type=\"bibr\">66</xref>].</p><p>The research suggests that a high-protein, low-carbohydrate diet can lead to low-grade MA, high NEAP and have an adverse effect on physical performance [<xref rid=\"B38-ijerph-17-05332\" ref-type=\"bibr\">38</xref>,<xref rid=\"B39-ijerph-17-05332\" ref-type=\"bibr\">39</xref>,<xref rid=\"B40-ijerph-17-05332\" ref-type=\"bibr\">40</xref>]. We have obtained conflicting results confirming that athletes’ NEAP was driven by higher protein (β 34.1 mEq <inline-formula><mml:math id=\"mm70\"><mml:mrow><mml:mo>·</mml:mo></mml:mrow></mml:math></inline-formula> day<sup>−1</sup>) and carbohydrate (β 21.5 mEq <inline-formula><mml:math id=\"mm71\"><mml:mrow><mml:mo>·</mml:mo></mml:mrow></mml:math></inline-formula> day<sup>−1</sup>) intake. According to our study, the more frequent consumption of grain products by athletes acidified their dietary PRAL (OR 0.7). PRAL of concentrated carbohydrate grain products are positive due to their amino acids (PRAL 4.5–8.0) which determined the acid load [<xref rid=\"B61-ijerph-17-05332\" ref-type=\"bibr\">61</xref>]. Meanwhile, the impact of carbohydrate-containing products on higher NEAP, as identified by scientists, was based only on the consumption of fruit and vegetables [<xref rid=\"B67-ijerph-17-05332\" ref-type=\"bibr\">67</xref>]. Nonetheless, carbohydrate intake (5.5 g <inline-formula><mml:math id=\"mm72\"><mml:mrow><mml:mo>·</mml:mo></mml:mrow></mml:math></inline-formula> kg<sup>−1</sup>\n<inline-formula><mml:math id=\"mm73\"><mml:mrow><mml:mo>·</mml:mo></mml:mrow></mml:math></inline-formula> day<sup>−1</sup>) among the athletes that we studied was relatively low for meeting the daily energy needs of 3600–3900 kcal having a 175-min workout [<xref rid=\"B57-ijerph-17-05332\" ref-type=\"bibr\">57</xref>]. The consumption of grain products, vegetables, fresh and dried fruits by the Lithuanian athletes is too low and infrequent. This can lead to insufficient levels of glycogen stores in the liver and muscles between sports practice sessions, an increased risk of overtraining, and a weakened immune system [<xref rid=\"B68-ijerph-17-05332\" ref-type=\"bibr\">68</xref>].</p><p>It should be emphasized that 25–30% of Lithuanian high-performance athletes consume 2.0–4.8 g <inline-formula><mml:math id=\"mm74\"><mml:mrow><mml:mo>·</mml:mo></mml:mrow></mml:math></inline-formula> kg<sup>−1</sup>\n<inline-formula><mml:math id=\"mm75\"><mml:mrow><mml:mo>·</mml:mo></mml:mrow></mml:math></inline-formula> day<sup>−1</sup> proteins per day, which exceeds the recommendations. There is still a debate as to whether an increased long-term protein intake among physically inactive, incapacitated people can impair their kidney function [<xref rid=\"B69-ijerph-17-05332\" ref-type=\"bibr\">69</xref>,<xref rid=\"B70-ijerph-17-05332\" ref-type=\"bibr\">70</xref>]. According to some studies, a long-term acidogenic diet combined with physical exertion results in the initial impairment of the renal function. The glomerular filtration rate has been shown to decrease with the diet of moderate PRAL for 12 weeks in physically active men and women [<xref rid=\"B71-ijerph-17-05332\" ref-type=\"bibr\">71</xref>]. Other data suggest that a long-term use of 2.5 to 3.5 g <inline-formula><mml:math id=\"mm76\"><mml:mrow><mml:mo>·</mml:mo></mml:mrow></mml:math></inline-formula> kg<sup>−1</sup>\n<inline-formula><mml:math id=\"mm77\"><mml:mrow><mml:mo>·</mml:mo></mml:mrow></mml:math></inline-formula> day<sup>−1</sup> protein did not impair the renal and hepatic functions in weightlifters [<xref rid=\"B72-ijerph-17-05332\" ref-type=\"bibr\">72</xref>,<xref rid=\"B73-ijerph-17-05332\" ref-type=\"bibr\">73</xref>,<xref rid=\"B74-ijerph-17-05332\" ref-type=\"bibr\">74</xref>,<xref rid=\"B75-ijerph-17-05332\" ref-type=\"bibr\">75</xref>].</p><p>Scientific studies have shown that low-grade MA caused by an excessive protein and phosphorus intake increases cortisol secretion (hypercortisolism), proteolysis and inhibits protein synthesis [<xref rid=\"B76-ijerph-17-05332\" ref-type=\"bibr\">76</xref>,<xref rid=\"B77-ijerph-17-05332\" ref-type=\"bibr\">77</xref>,<xref rid=\"B78-ijerph-17-05332\" ref-type=\"bibr\">78</xref>]. The rate of anabolism of muscle proteins is inversely proportional to the amount of acids present [<xref rid=\"B79-ijerph-17-05332\" ref-type=\"bibr\">79</xref>]. The results of our study confirmed that at higher NEAP, athletes had a significantly lower muscle mass, by 1.2% of BW. It is noteworthy that at low-grade MA, more muscle protein is broken down in order to neutralize dietary acids [<xref rid=\"B80-ijerph-17-05332\" ref-type=\"bibr\">80</xref>]. After the breakdown of muscle proteins, part of the amino acids released into the plasma is used for glutamine synthesis in the liver. Glutamine is further metabolised and degraded in the proximal renal tubules to alpha-ketoglutarate (AKG<sup>2−</sup>) and ammonium. AKG<sup>2−</sup> metabolism to glucose (or O<sub>2</sub> and CO<sub>2</sub>) consumes two H<sup>+</sup> ions, which reduces the acid load. As a consequence, the use of amino acids for glutamine synthesis may result in a lack of amino acids for the synthesis of new proteins in the muscles and a decrease in muscle mass [<xref rid=\"B80-ijerph-17-05332\" ref-type=\"bibr\">80</xref>]. According to the data in our study, a higher protein intake leads to a higher muscle mass (β 12.6% of BW), has no association with muscle loss and is sufficient to ensure a positive nitrogen balance in athletes’ bodies. Higher protein intakes for athletes are essential to promote muscle hypertrophy during the workout process [<xref rid=\"B81-ijerph-17-05332\" ref-type=\"bibr\">81</xref>].</p><p>Theoretically, protein and phosphorus in diets may lead to an increased acid load and increased calcium excretion, which may increase the risk of osteoporosis [<xref rid=\"B42-ijerph-17-05332\" ref-type=\"bibr\">42</xref>]. We have not determined the effect of NEAP on the body mineral content of our subjects (β −0.01% of BW). In contrast, higher amounts of body minerals (in bones and electrolyte) depend on higher protein and calcium intakes (β 0.15 and 0.06 % of BW, respectively) and lower phosphorus consumption (β −0.16% of BW). Our results are consistent with the data obtained from other researchers suggesting that a high protein intake increases the intestinal calcium absorption, insulin-like growth factor-1 (IGF-1) concentration in the blood, and lowers parathyroid hormone levels. In this way, protein compensates for the negative protein-induced acid load on urinary calcium excretion [<xref rid=\"B82-ijerph-17-05332\" ref-type=\"bibr\">82</xref>]. To prevent osteoporosis, with an increased protein intake (2.0 g <inline-formula><mml:math id=\"mm78\"><mml:mrow><mml:mo>·</mml:mo></mml:mrow></mml:math></inline-formula> kg<sup>−1</sup>\n<inline-formula><mml:math id=\"mm79\"><mml:mrow><mml:mo>·</mml:mo></mml:mrow></mml:math></inline-formula> day<sup>−1</sup>), higher amounts (>600 mg <inline-formula><mml:math id=\"mm80\"><mml:mrow><mml:mo>·</mml:mo></mml:mrow></mml:math></inline-formula> day<sup>−1</sup>) of calcium are recommended [<xref rid=\"B83-ijerph-17-05332\" ref-type=\"bibr\">83</xref>]. Also, similar data from epidemiological studies suggest that high levels of phosphorus do not have adverse effects on bone metabolism when an adequate calcium quantity is consumed [<xref rid=\"B84-ijerph-17-05332\" ref-type=\"bibr\">84</xref>]. An adequate (1170.2 ± 538.3 mg <inline-formula><mml:math id=\"mm81\"><mml:mrow><mml:mo>·</mml:mo></mml:mrow></mml:math></inline-formula> day<sup>−1</sup>) calcium intake is confirmed by the actual nutrition results of the athletes that we have studied. However, calcium and phosphorus in the diets of athletes are unbalanced (Ca/P 0.6; should be 0.75) due to an excessive phosphorus intake. Therefore, Lithuanian athletes are recommended to increase the calcium intake to 1500 mg <inline-formula><mml:math id=\"mm82\"><mml:mrow><mml:mo>·</mml:mo></mml:mrow></mml:math></inline-formula> day<sup>−1</sup> as recommended by the International Olympic Committee (IOC) [<xref rid=\"B85-ijerph-17-05332\" ref-type=\"bibr\">85</xref>], and to consume milk and dairy products more frequently.</p><p>The research has shown that the dietary intake to reduce NEAP requires adequate potassium and magnesium intakes with fruits and vegetables. Switching from a moderate-protein (1.3 g <inline-formula><mml:math id=\"mm83\"><mml:mrow><mml:mo>·</mml:mo></mml:mrow></mml:math></inline-formula> kg<sup>−1</sup> · day<sup>−1</sup>) diet to a high-protein (2.1 g <inline-formula><mml:math id=\"mm84\"><mml:mrow><mml:mo>·</mml:mo></mml:mrow></mml:math></inline-formula> kg<sup>−1</sup>\n<inline-formula><mml:math id=\"mm85\"><mml:mrow><mml:mo>·</mml:mo></mml:mrow></mml:math></inline-formula> day<sup>−1</sup>) diet with a low intake of vegetables and fruits has been shown to significantly reduce blood pH and HCO<sup>3−</sup> concentration. Therefore, high-performance athletes who are advised to consume increased protein (1.4–2.0 g <inline-formula><mml:math id=\"mm86\"><mml:mrow><mml:mo>·</mml:mo></mml:mrow></mml:math></inline-formula> kg<sup>−1</sup>\n<inline-formula><mml:math id=\"mm87\"><mml:mrow><mml:mo>·</mml:mo></mml:mrow></mml:math></inline-formula> day<sup>−1</sup>) amounts are also advised to consume sufficient amounts of fresh vegetables and fruits (at least 400 g) [<xref rid=\"B81-ijerph-17-05332\" ref-type=\"bibr\">81</xref>]. In our study, lower potassium and magnesium intakes resulted in higher NEAP (β −100.4 and −39.2 mEq <inline-formula><mml:math id=\"mm88\"><mml:mrow><mml:mo>·</mml:mo></mml:mrow></mml:math></inline-formula> day<sup>−1</sup>, respectively). On the other hand, our study showed that the athletes with high NEAP consumed the amount of potassium higher than RDI by 1.4 times. This suggests that potassium RDI in athletes is too low to ensure the neutral dietary PRAL. In addition, no tolerable upper intake level (UL) has been established [<xref rid=\"B86-ijerph-17-05332\" ref-type=\"bibr\">86</xref>,<xref rid=\"B87-ijerph-17-05332\" ref-type=\"bibr\">87</xref>]. In this regard, potassium levels recommended for athletes may be 2–3 times higher than RDI.</p><p>Thus, the eating habits of the athletes we have studied and those of the population of other countries are characterized by a high consumption of protein foods of animal origin, which is associated with low-grade MA [<xref rid=\"B14-ijerph-17-05332\" ref-type=\"bibr\">14</xref>]. In this case, MA can be normalized by changing the eating habits (ensuring low PRAL) [<xref rid=\"B88-ijerph-17-05332\" ref-type=\"bibr\">88</xref>] or by taking dietary supplements [<xref rid=\"B89-ijerph-17-05332\" ref-type=\"bibr\">89</xref>]. However, there are no scientific studies demonstrating the benefits of a short-term (4–9 days) vegetarian low-protein diet for aerobic physical working capacity. No change was observed in recording the increased respiratory exchange ratio (RER) in athletes due to an increase in “non-metabolic” CO<sub>2</sub> during submaximal exercise [<xref rid=\"B90-ijerph-17-05332\" ref-type=\"bibr\">90</xref>]. Studies justifying the effects of low PRAL diets on RER relate only to long-term eating habits with a high intake of vegetables and fruits [<xref rid=\"B1-ijerph-17-05332\" ref-type=\"bibr\">1</xref>,<xref rid=\"B91-ijerph-17-05332\" ref-type=\"bibr\">91</xref>]. Additionally, studies have shown that high doses of sodium bicarbonate (150 mEq <inline-formula><mml:math id=\"mm89\"><mml:mrow><mml:mo>·</mml:mo></mml:mrow></mml:math></inline-formula> day<sup>−1</sup>) neutralize food acids and minimize the total excretion of acids caused by ammonium. [<xref rid=\"B14-ijerph-17-05332\" ref-type=\"bibr\">14</xref>]. Thus, in exceptional cases, to increase the capacity of the buffer system while performing repeated bouts of exercise for 0.5–7 min, Lithuanian high-performance athletes are recommended to use sodium bicarbonate in doses of 0.30 g <inline-formula><mml:math id=\"mm90\"><mml:mrow><mml:mo>·</mml:mo></mml:mrow></mml:math></inline-formula> kg<sup>−1</sup>\n<inline-formula><mml:math id=\"mm91\"><mml:mrow><mml:mo>·</mml:mo></mml:mrow></mml:math></inline-formula> day<sup>−1</sup> 120–150 min before workouts [<xref rid=\"B92-ijerph-17-05332\" ref-type=\"bibr\">92</xref>]. To reduce intracellular acidification during 1–4 min repeated bout physical exercise, athletes are recommended to enrich their diets with beta-alanine supplements (65 mg <inline-formula><mml:math id=\"mm92\"><mml:mrow><mml:mo>·</mml:mo></mml:mrow></mml:math></inline-formula> kg<sup>−1</sup>\n<inline-formula><mml:math id=\"mm93\"><mml:mrow><mml:mo>·</mml:mo></mml:mrow></mml:math></inline-formula> day<sup>−1</sup>) for 10–12 weeks [<xref rid=\"B92-ijerph-17-05332\" ref-type=\"bibr\">92</xref>,<xref rid=\"B93-ijerph-17-05332\" ref-type=\"bibr\">93</xref>]. On the other hand, food supplements with buffering characteristics cannot replace conventional foods that lower PRAL.</p><p>In summary, while athletes may require a higher protein intake, high-protein diets can promote metabolic changes due to the production of additional acids in the body and lead to very rapid muscle fatigue during exercise. Dietary acid-base balance is also important for variables such as skeletal muscle protein metabolism and bone mineralisation. According to our study results, an excessive production of endogenous acids in the body in athletes is associated with lower muscle mass and has no effect on the amount of minerals in the body. It is clear then that the interaction between the dietary acid-base balance and exercise in athletes needs to be further studied in order to better and more accurately assess the contribution of alkaline diet in athletic performance and the variables like the rate of protein synthesis and the breakdown and bone density. Therefore, further research is needed to assess the impact of higher fruit and vegetable consumption by athletes on the indicators of their physical performance between workouts.</p><p>Actual nutrition data combined with objective readings of body composition of athletes allow us to predict and implement targeted measures and recommendations for optimising the athlete nutrition for the next Olympic cycle. The data and recommendations of our study can be applied in practice by including them in the current sports training programmes like Tokyo 2020 and Beijing 2022. In the future, continuous investigation and monitoring of body composition and actual nutrition should be carried out during each 4-year Olympic cycle.</p><p>However, in the course of our study, we did not include selection criteria such as comorbidities, because professional athletes in Lithuania do not have long-term health-related clinical symptoms or their combinations and they are completely healthy. The athletes’ health monitoring is carried out every three months at the Lithuanian Sports Medicine Center. The health professionals ensure good health indicators of athletes in Lithuania. If serious health problems are identified during the monitoring process, the athlete is officially prohibited from exercising and participating in any level of competition. Therefore, the limitations of our study are related to the fact that while conducting our study we did not add the inclusion and exclusion criteria of this study such as the economic status, health indicators of the athletes (e.g., exercise-related injuries, iron deficiency anaemia, short-term renal impairments due to rapid bodyweight reduction among wrestlers and/or boxers). These variables may have associations with the actual diet or eating habits of athletes and these are the directions for further research. Another limitation of our study is that it was only a 24-h dietary recall survey of actual nutrition during the pre-competition period. Thus, in the future, in cooperation with the Lithuanian Sports Medicine Center (LSMC), it is necessary to monitor the actual nutrition and other health indicators of high-performance athletes for a period of three to seven days during the preparatory and competition periods.</p></sec><sec sec-type=\"conclusions\" id=\"sec5-ijerph-17-05332\"><title>5. Conclusions</title><p>Regardless of the type of sport, the diets of Lithuanian high-performance athletes do not meet requirements. The diets of athletes are too high in fat. Even 25–30% of athletes practice high protein (2.0–4.8 g <inline-formula><mml:math id=\"mm94\"><mml:mrow><mml:mo>·</mml:mo></mml:mrow></mml:math></inline-formula> kg<sup>−1</sup>\n<inline-formula><mml:math id=\"mm95\"><mml:mrow><mml:mo>·</mml:mo></mml:mrow></mml:math></inline-formula> day<sup>−1</sup>) diets which result in dietary acid-base imbalance as well as excessive production of endogenous acids in the body. Higher NEAP in athletes is associated with lower muscle mass and has no effect on the body mineral content.</p><p>The training of athletes in different sports needs to be very individualised in terms of mesocycling of sports goals, including changes in the body composition, giving priority to the formation of eating habits. The eating habits of athletes need to be changed and carefully planned in practice to ensure acid-alkaline balance in the body by consuming alkaline-producing foods. In order to ensure the optimal dietary acid-base balance and to maximize muscle adaptation to exercise, athletes are recommended to consume higher amounts of potassium and magnesium found in fresh vegetables and dried fruits—twice as much as RDI. A more frequent consumption of milk and dairy products is recommended in order to increase calcium intake to 1500 mg per day. In exceptional cases, the diets of athletes should be enriched with bicarbonate and/or beta-alanine supplements.</p></sec></body><back><ack><title>Acknowledgments</title><p>We hereby express our acknowledgement to the Lithuanian Sports Centre and the Lithuanian Sports Medicine Centre for their assistance in performing the athlete body composition and nutritional survey.</p></ack><notes><title>Author Contributions</title><p>Conceptualization, M.B. and V.J.; methodology, M.B. and L.S.; data curation, M.B., L.S., V.J. and J.A.A.; software, M.B. and R.S.; investigation, M.B. and V.J.; writing—original draft preparation, M.B., R.S., J.A.A. and V.J.; writing—review and editing, all authors. All authors have read and agreed to the published version of the manuscript.</p></notes><notes><title>Funding</title><p>This research received no external funding.</p></notes><notes notes-type=\"COI-statement\"><title>Conflicts of Interest</title><p>The authors declare no conflict of interest. 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Int. Soc. Sports Nutr.</source><year>2015</year><volume>12</volume><fpage>30</fpage><pub-id pub-id-type=\"doi\">10.1186/s12970-015-0090-y</pub-id><pub-id pub-id-type=\"pmid\">26175657</pub-id></element-citation></ref></ref-list></back><floats-group><fig id=\"ijerph-17-05332-f001\" orientation=\"portrait\" position=\"float\"><label>Figure 1</label><caption><p>Flowchart of the enrollment of athletes and study procedures. BW—body weight; LBM—lean body mass; MM—muscle mass; BF—body fat; PRAL—potential renal acid load; NEAP—net endogenous acid production; GFR—glomerular filtration rate.</p></caption><graphic xlink:href=\"ijerph-17-05332-g001\"/></fig><fig id=\"ijerph-17-05332-f002\" orientation=\"portrait\" position=\"float\"><label>Figure 2</label><caption><p>Relationship between the dietary protein intake (g <inline-formula><mml:math id=\"mm96\"><mml:mrow><mml:mo>·</mml:mo></mml:mrow></mml:math></inline-formula> kg<sup>−1</sup>\n<inline-formula><mml:math id=\"mm54\"><mml:mrow><mml:mo>·</mml:mo></mml:mrow></mml:math></inline-formula> day<sup>−1</sup>) and NEAP (mEq <inline-formula><mml:math id=\"mm55\"><mml:mrow><mml:mo>·</mml:mo></mml:mrow></mml:math></inline-formula> day<sup>−1</sup>) in athletes of anaerobic sports (r = 0.482, <italic>p</italic> < 0.001).</p></caption><graphic xlink:href=\"ijerph-17-05332-g002\"/></fig><fig id=\"ijerph-17-05332-f003\" orientation=\"portrait\" position=\"float\"><label>Figure 3</label><caption><p>Relationship between the dietary protein intake (g <inline-formula><mml:math id=\"mm97\"><mml:mrow><mml:mo>·</mml:mo></mml:mrow></mml:math></inline-formula> kg<sup>−1</sup>\n<inline-formula><mml:math id=\"mm56\"><mml:mrow><mml:mo>·</mml:mo></mml:mrow></mml:math></inline-formula> day<sup>−1</sup>) and NEAP (mEq <inline-formula><mml:math id=\"mm57\"><mml:mrow><mml:mo>·</mml:mo></mml:mrow></mml:math></inline-formula> day<sup>−1</sup>) in athletes of aerobic sports (r = 0.274, <italic>p</italic> < 0.001).</p></caption><graphic xlink:href=\"ijerph-17-05332-g003\"/></fig><table-wrap id=\"ijerph-17-05332-t001\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05332-t001_Table 1</object-id><label>Table 1</label><caption><p>Body fat (BF) percentage and muscle and fat mass index (MFMI) scale for athletes (by gender).</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th colspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">BF</th><th colspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">MFMI</th></tr><tr><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Value</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Males</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Females</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Value</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Male Athletes</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Female Athletes</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Too low</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><5%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><15%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Insufficient</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><1.8</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Lean</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5–9%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">15–19%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Too small</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.1–3.39</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.9–2.89</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Optimal</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10–14%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">20–24%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Moderate</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.4–4.69</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3–3.99</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Acceptable</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">15–19%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">25–29%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Extensive</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.7–6.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4–5</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Excessive</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">20–24%</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">30–34%</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Maximum</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">>6</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">>5</td></tr></tbody></table><table-wrap-foot><fn><p>BF—body fat; MFMI—muscle and fat mass index.</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"ijerph-17-05332-t002\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05332-t002_Table 2</object-id><label>Table 2</label><caption><p>Body composition of athletes (by sport and gender).</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">Body Composition</th><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Anaerobic Sports</th><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Aerobic Sports</th></tr><tr><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Male</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Female</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Male</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Female</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Height (m)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.83 ± 0.15</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.73 ± 0.11</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.83 ± 0.08</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.67 ± 0.06</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">BW (kg)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">77.5 ± 17.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">67.4 ± 14.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">75.0 ± 11.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">59.7 ± 7.5</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">LBM (kg)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">63.8 ± 11.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">50.1 ± 7.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">62.2 ± 7.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">46.3 ± 4.7</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">LBM (% of BW)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">83.3 ± 5.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">75.2 ± 4.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">83.3 ± 4.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">77.6 ± 3.7</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">MM (kg)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">59.3 ± 10.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">46.2 ± 7.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">57.9 ± 7.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">42.8 ± 4.3</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">MM (% of BW)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">77.4 ± 5.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">69.4 ± 4.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">77.6 ± 4.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">72.1 ± 3.6</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">MFMI</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.3 ± 2.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.9 ± 0.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.2 ± 2.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.4 ± 0.8</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">BF (kg)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13.7 ± 7.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">17.6 ± 7.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">12.9 ± 4.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13.4 ± 3.5</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">BF (% of BW)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">16.7 ± 5.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">24.9 ± 4.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">16.7 ± 4.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">22.2 ± 3.6</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Minerals (kg) <sup>1</sup></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.5 ± 1.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.9 ± 0.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.4 ± 0.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.4 ± 0.4</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Minerals (% of BW) <sup>1</sup></td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">5.8 ± 0.1</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">5.8 ± 0.1</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">5.8 ± 0.1</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">5.8 ± 0.1</td></tr></tbody></table><table-wrap-foot><fn><p>BW—body weight; LBM—lean body mass; MM—muscle mass; MFMI—muscle and fat mass index; BF—body fat; <sup>1</sup>—mineral composed of bone and electrolyte. The data is normally distributed and presented as means ± standard deviation (SD).</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"ijerph-17-05332-t003\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05332-t003_Table 3</object-id><label>Table 3</label><caption><p>Dietary intake of athletes.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">Nutrition Profile</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Anaerobic Sports <sup>1</sup></th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Aerobic Sports <sup>2</sup></th><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\"><italic>t</italic>-Test<sup>1/2</sup></th></tr><tr><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">Mean ± SD</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">t</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>p</italic>\n</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">DEE (kcal <inline-formula><mml:math id=\"mm98\"><mml:mrow><mml:mo>·</mml:mo></mml:mrow></mml:math></inline-formula> day<sup>−1</sup>)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3894 ± 876</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3595 ± 864</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.032</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.003</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">EI (kcal <inline-formula><mml:math id=\"mm99\"><mml:mrow><mml:mo>·</mml:mo></mml:mrow></mml:math></inline-formula> day<sup>−1</sup>)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3457 ± 1280</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3266 ± 1020</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.486</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.138</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">EI (kcal <inline-formula><mml:math id=\"mm100\"><mml:mrow><mml:mo>·</mml:mo></mml:mrow></mml:math></inline-formula> kg<sup>−1</sup>\n<inline-formula><mml:math id=\"mm101\"><mml:mrow><mml:mo>·</mml:mo></mml:mrow></mml:math></inline-formula> day<sup>−1</sup>)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">47 ± 16</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">47 ± 15</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.230</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.818</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CHO (g <inline-formula><mml:math id=\"mm102\"><mml:mrow><mml:mo>·</mml:mo></mml:mrow></mml:math></inline-formula> kg<sup>−1</sup>\n<inline-formula><mml:math id=\"mm103\"><mml:mrow><mml:mo>·</mml:mo></mml:mrow></mml:math></inline-formula> day<sup>−1</sup>)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.4 ± 2.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.5 ± 2.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.268</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.789</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CHO (%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">46.5 ± 7.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">46.3 ± 8.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.206</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.837</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">PRO (g <inline-formula><mml:math id=\"mm104\"><mml:mrow><mml:mo>·</mml:mo></mml:mrow></mml:math></inline-formula> kg<sup>−1</sup>\n<inline-formula><mml:math id=\"mm105\"><mml:mrow><mml:mo>·</mml:mo></mml:mrow></mml:math></inline-formula> day<sup>−1</sup>)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.7 ± 0.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.7 ± 0.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.084</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.933</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">FAT (%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">38.9 ± 7.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">39.1 ± 8.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.270</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.788</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">K (mg <inline-formula><mml:math id=\"mm106\"><mml:mrow><mml:mo>·</mml:mo></mml:mrow></mml:math></inline-formula> day<sup>−1</sup>)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5189.7 ± 2341.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4790.6 ± 1984.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.647</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.100</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Ca (mg <inline-formula><mml:math id=\"mm107\"><mml:mrow><mml:mo>·</mml:mo></mml:mrow></mml:math></inline-formula> day<sup>−1</sup>)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1254.0 ± 580.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1113.9 ± 501.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.310</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.022</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Mg (mg <inline-formula><mml:math id=\"mm108\"><mml:mrow><mml:mo>·</mml:mo></mml:mrow></mml:math></inline-formula> day<sup>−1</sup>)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">522.7 ± 240.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">461.6 ± 191.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.534</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.012</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">P (mg <inline-formula><mml:math id=\"mm109\"><mml:mrow><mml:mo>·</mml:mo></mml:mrow></mml:math></inline-formula> day<sup>−1</sup>)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1999.0 ± 788.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1806.9 ± 600.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.483</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.014</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Ca/P ratio</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.6 ± 0.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.6 ± 0.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.680</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.497</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">PRAL (mEq <inline-formula><mml:math id=\"mm110\"><mml:mrow><mml:mo>·</mml:mo></mml:mrow></mml:math></inline-formula> day<sup>−1</sup>)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10.7 ± 42.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">9.3 ± 35.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.305</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.761</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">NEAP (mEq <inline-formula><mml:math id=\"mm111\"><mml:mrow><mml:mo>·</mml:mo></mml:mrow></mml:math></inline-formula> day<sup>−1</sup>)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">56.9 ± 43.3</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">53.7 ± 37.1</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.725</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.469</td></tr></tbody></table><table-wrap-foot><fn><p>The values are expressed as mean ± SD; EI—energy intake; DEE—daily energy expenditure; BW—body weight; PRO—protein; CHO—carbohydrate; FAT—fat; Ca—calcium; P—phosphorus; Mg—magnesium; K—potassium; PRAL—potential renal acid load; NEAP—net endogenous acid production. Significant differences set by independent samples Student’s <italic>t</italic>-test between groups: <sup>1</sup>—group 1, <sup>2</sup>—group 2.</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"ijerph-17-05332-t004\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05332-t004_Table 4</object-id><label>Table 4</label><caption><p>Effects of athletes’ eating habits on their dietary PRAL.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">PRAL ≤ 0 (mEq · day<sup>−1</sup>) <sup>a</sup></th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">β</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">SE</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">W</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>p</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Exp (β) (95% CI)</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Grain products</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.050</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.7 (0.5; 1,1)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Dairy products</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.050</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.4 (1.0; 2.0)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Fresh vegetables</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.048</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.4 (1.0; 2.0)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Dried fruits</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.046</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.4 (1.0; 2.0)</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Constant</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−2.2</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.8</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">7.6</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.006</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0</td></tr></tbody></table><table-wrap-foot><fn><p><sup>a</sup>—reference category is PRAL > 0 mEq · day<sup>−1</sup>; β—is the estimated coefficient, with standard error SE (<5); W is the Wald test statistic; Nagelkerke R<sup>2</sup> = 0.28; Exp (β) is the predicted change in odds for a unit increase in the predictor (odds ratio (OR)); CI—confidence interval. The final model was tested with the Hosmer and Lemeshow goodness-of-fit test statistic (H-L stat χ<sup>2</sup> = 14.5, <italic>p</italic> < 0.006).</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"ijerph-17-05332-t005\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05332-t005_Table 5</object-id><label>Table 5</label><caption><p>Effects of carbohydrates, proteins, phosphorous, potassium, calcium, magnesium consumed by athletes on their net endogenous acid production (NEAP).</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">NEAP (mEq · day<sup>−1</sup>)</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">β</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">95% CI</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>p</italic>\n</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">PRO (g <inline-formula><mml:math id=\"mm115\"><mml:mrow><mml:mo>·</mml:mo></mml:mrow></mml:math></inline-formula> kg<sup>−1</sup>\n<inline-formula><mml:math id=\"mm116\"><mml:mrow><mml:mo>·</mml:mo></mml:mrow></mml:math></inline-formula> day<sup>−1</sup>) (ln)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">34.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">(23.0; 45,1)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.001</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CHO (g <inline-formula><mml:math id=\"mm117\"><mml:mrow><mml:mo>·</mml:mo></mml:mrow></mml:math></inline-formula> kg<sup>−1</sup>\n<inline-formula><mml:math id=\"mm118\"><mml:mrow><mml:mo>·</mml:mo></mml:mrow></mml:math></inline-formula> day<sup>−1</sup>) (ln)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">21,5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">(11.0, 31.8)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.001</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">P (mg <inline-formula><mml:math id=\"mm119\"><mml:mrow><mml:mo>·</mml:mo></mml:mrow></mml:math></inline-formula> day<sup>−1</sup>) (ln)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">167.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">(152.4; 181.9)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.001</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">K (mg <inline-formula><mml:math id=\"mm120\"><mml:mrow><mml:mo>·</mml:mo></mml:mrow></mml:math></inline-formula> day<sup>−1</sup>) (ln)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−100.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">(−108.3; −92.6)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.001</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Ca (mg <inline-formula><mml:math id=\"mm121\"><mml:mrow><mml:mo>·</mml:mo></mml:mrow></mml:math></inline-formula> day<sup>−1</sup>) (ln)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−39.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">(−45.6; −32.8)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.001</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Mg (mg <inline-formula><mml:math id=\"mm122\"><mml:mrow><mml:mo>·</mml:mo></mml:mrow></mml:math></inline-formula> day<sup>−1</sup>) (ln)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−9.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">(−17.9; −0.2)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.044</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">EI (kcal <inline-formula><mml:math id=\"mm123\"><mml:mrow><mml:mo>·</mml:mo></mml:mrow></mml:math></inline-formula> kg<sup>−1</sup>\n<inline-formula><mml:math id=\"mm124\"><mml:mrow><mml:mo>·</mml:mo></mml:mrow></mml:math></inline-formula> day<sup>−1</sup>) (ln)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−69.2</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">(−85.6; −52.8)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><0.001</td></tr></tbody></table><table-wrap-foot><fn><p>The influence of dietary intake on NEAP (mEq <inline-formula><mml:math id=\"mm125\"><mml:mrow><mml:mo>·</mml:mo></mml:mrow></mml:math></inline-formula> day<sup>−1</sup>) is estimated controlling for athlete sport and gender (adjusted for sports type and gender). F (9, 313) = 201.2, <italic>p</italic> < 0.0001, R<sup>2</sup> = 0.85. PRO—protein; CHO—carbohydrate; Ca—calcium; P—phosphorus; Mg—magnesium; K—potassium; EI—energy intake; CI—confidence interval.</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"ijerph-17-05332-t006\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05332-t006_Table 6</object-id><label>Table 6</label><caption><p>Effects of athletes’ NEAP, protein and phosphorus consumption on their muscle mass (% of BW).</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Muscle Mass (% of BW)</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">β</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">95% CI</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>p</italic>\n</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">NEAP (mEq <inline-formula><mml:math id=\"mm126\"><mml:mrow><mml:mo>·</mml:mo></mml:mrow></mml:math></inline-formula> day<sup>−1</sup>) (ln)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−1.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">(−1.8; −0.7)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.001</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">PRO (g <inline-formula><mml:math id=\"mm127\"><mml:mrow><mml:mo>·</mml:mo></mml:mrow></mml:math></inline-formula> kg<sup>−1</sup>\n<inline-formula><mml:math id=\"mm128\"><mml:mrow><mml:mo>·</mml:mo></mml:mrow></mml:math></inline-formula> day<sup>−1</sup>) (ln)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">12.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">(10.8; 14.5)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.001</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">P (mg <inline-formula><mml:math id=\"mm129\"><mml:mrow><mml:mo>·</mml:mo></mml:mrow></mml:math></inline-formula> day<sup>−1</sup>) (ln)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−10.2</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">(−12.1; −8.2)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><0.001</td></tr></tbody></table><table-wrap-foot><fn><p>Muscle mass (% of body weight) is estimated controlling for athlete sport and gender (adjusted for sports type and gender). F (5, 295) = 78.1, <italic>p</italic> < 0.0001, R<sup>2</sup> = 0.56. BW—body weight; NEAP—net endogenous acid production; PRO—protein; P—phosphorus; CI—confidence interval.</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"ijerph-17-05332-t007\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05332-t007_Table 7</object-id><label>Table 7</label><caption><p>Effects of athletes’ NEAP, protein, phosphorus, and calcium consumption on their body mineral content (% of BW).</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Body Mineral (% of BW) <sup>1</sup></th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">β</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">95% CI</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>p</italic>\n</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">NEAP (mEq <inline-formula><mml:math id=\"mm130\"><mml:mrow><mml:mo>·</mml:mo></mml:mrow></mml:math></inline-formula> day<sup>−1</sup>) (ln)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.01</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">(−0.03; −0.001)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.073</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">PRO (g <inline-formula><mml:math id=\"mm131\"><mml:mrow><mml:mo>·</mml:mo></mml:mrow></mml:math></inline-formula> kg<sup>−1</sup>\n<inline-formula><mml:math id=\"mm132\"><mml:mrow><mml:mo>·</mml:mo></mml:mrow></mml:math></inline-formula> day<sup>−1</sup>) (ln)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.15</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">(0.10; 0.19)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.001</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">P (mg <inline-formula><mml:math id=\"mm133\"><mml:mrow><mml:mo>·</mml:mo></mml:mrow></mml:math></inline-formula> day<sup>−1</sup>) (ln)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.16</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">(−0.23; −0.09)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.001</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Ca (mg <inline-formula><mml:math id=\"mm134\"><mml:mrow><mml:mo>·</mml:mo></mml:mrow></mml:math></inline-formula> day<sup>−1</sup>) (ln)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.06</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">(0.02; 0.09)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.004</td></tr></tbody></table><table-wrap-foot><fn><p>Body mineral content (% of body weight) is estimated controlling for athlete sport and gender (adjusted for sports type and gender). F (6, 294) = 12.6, <italic>p</italic> < 0.0001, R<sup>2</sup> = 0.20. NEAP—net endogenous acid production; PRO—protein; P—phosphorus; Ca—calcium; CI—confidence interval. <sup>1</sup>—minerals in bones and electrolytes.</p></fn></table-wrap-foot></table-wrap></floats-group></article>\n"
]
[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"review-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Int J Mol Sci</journal-id><journal-id journal-id-type=\"iso-abbrev\">Int J Mol Sci</journal-id><journal-id journal-id-type=\"publisher-id\">ijms</journal-id><journal-title-group><journal-title>International Journal of Molecular Sciences</journal-title></journal-title-group><issn pub-type=\"epub\">1422-0067</issn><publisher><publisher-name>MDPI</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32752293</article-id><article-id pub-id-type=\"pmc\">PMC7432060</article-id><article-id pub-id-type=\"doi\">10.3390/ijms21155523</article-id><article-id pub-id-type=\"publisher-id\">ijms-21-05523</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Review</subject></subj-group></article-categories><title-group><article-title>Emerging Role of Extracellular Vesicles in Embryo–Maternal Communication throughout Implantation Processes</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Nakamura</surname><given-names>Keigo</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05523\">1</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0002-9120-0475</contrib-id><name><surname>Kusama</surname><given-names>Kazuya</given-names></name><xref ref-type=\"aff\" rid=\"af2-ijms-21-05523\">2</xref><xref rid=\"c1-ijms-21-05523\" ref-type=\"corresp\">*</xref></contrib><contrib contrib-type=\"author\"><name><surname>Suda</surname><given-names>Yoshihito</given-names></name><xref ref-type=\"aff\" rid=\"af3-ijms-21-05523\">3</xref></contrib><contrib contrib-type=\"author\"><name><surname>Fujiwara</surname><given-names>Hiroshi</given-names></name><xref ref-type=\"aff\" rid=\"af4-ijms-21-05523\">4</xref></contrib><contrib contrib-type=\"author\"><name><surname>Hori</surname><given-names>Masatoshi</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05523\">1</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0002-3798-3560</contrib-id><name><surname>Imakawa</surname><given-names>Kazuhiko</given-names></name><xref ref-type=\"aff\" rid=\"af5-ijms-21-05523\">5</xref><xref rid=\"c1-ijms-21-05523\" ref-type=\"corresp\">*</xref></contrib></contrib-group><aff id=\"af1-ijms-21-05523\"><label>1</label>Laboratory of Veterinary Pharmacology, Graduate School of Agricultural and Life Sciences, The University of Tokyo, Tokyo 113-8657, Japan; <email>[email protected]</email> (K.N.); <email>[email protected]</email> (M.H.)</aff><aff id=\"af2-ijms-21-05523\"><label>2</label>Department of Endocrine Pharmacology, Tokyo University of Pharmacy and Life Sciences, Tokyo 192-0392, Japan</aff><aff id=\"af3-ijms-21-05523\"><label>3</label>School of Food Industrial Sciences, Miyagi University, Miyagi 982-0215, Japan; <email>[email protected]</email></aff><aff id=\"af4-ijms-21-05523\"><label>4</label>Department of Obstetrics and Gynecology, Graduate School of Medical Science, Kanazawa University, Kanazawa 920-8641, Japan; <email>[email protected]</email></aff><aff id=\"af5-ijms-21-05523\"><label>5</label>Research Institute of Agriculture, Tokai University, Kumamoto 862-8652, Japan</aff><author-notes><corresp id=\"c1-ijms-21-05523\"><label>*</label>Correspondence: <email>[email protected]</email> (K.K.); <email>[email protected]</email> (K.I.); Tel.: +81-42-676-4536 (K.K.); +81-96-386-2652 (K.I.)</corresp></author-notes><pub-date pub-type=\"epub\"><day>01</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><month>8</month><year>2020</year></pub-date><volume>21</volume><issue>15</issue><elocation-id>5523</elocation-id><history><date date-type=\"received\"><day>20</day><month>6</month><year>2020</year></date><date date-type=\"accepted\"><day>30</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>© 2020 by the authors.</copyright-statement><copyright-year>2020</copyright-year><license license-type=\"open-access\"><license-p>Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>).</license-p></license></permissions><abstract><p>In ruminants, the establishment of proper conceptus–endometrial communication is essential for conceptus implantation and subsequent successful placentation. Accumulated evidence supports the idea that extracellular vesicles (EVs) present in uterine lumen are involved in conceptus–endometrial interactions during the preimplantation period. EVs make up a new field of intercellular communicators, which transport a variety of bioactive molecules, including soluble and membrane-bound proteins, lipids, DNA, and RNAs. EVs thus regulate gene expression and elicit biological effects including increased cell proliferation, migration, and adhesion in recipient cells. Uterine EVs are interactive and coordinate with ovarian progesterone (P4), trophectoderm-derived interferon tau (IFNT) and/or prostaglandins (PGs) in the physiological or pathological microenvironment. In this review, we will focus on intrauterine EVs in embryo–maternal interactions during the early stage of pregnancy, especially the implantation period in ruminant ungulates.</p></abstract><kwd-group><kwd>extracellular vesicles</kwd><kwd>conceptus</kwd><kwd>endometrium</kwd><kwd>implantation</kwd><kwd>progesterone</kwd><kwd>interferon tau</kwd><kwd>pregnancy</kwd></kwd-group></article-meta></front><body><sec sec-type=\"intro\" id=\"sec1-ijms-21-05523\"><title>1. Introduction</title><p>In domestic ruminants, conceptus implantation to the uterine endometrium is a unique physiological process, consisting of blastocyst hatching, elongation, migration, apposition/attachment, and subsequent placentation [<xref rid=\"B1-ijms-21-05523\" ref-type=\"bibr\">1</xref>]. The morula-stage embryo enters the uterus on days 4–6 postmating and then forms a blastocyst that contains an inner cell mass (ICM) and a blastocoel or central cavity surrounded by a monolayer of trophectoderm (TE). The blastocyst then hatches from the zona pellucida on day 8, and slowly grows into a tubular or ovoid form, which is termed a conceptus (embryo-fetus and associated extraembryonic membranes) [<xref rid=\"B2-ijms-21-05523\" ref-type=\"bibr\">2</xref>,<xref rid=\"B3-ijms-21-05523\" ref-type=\"bibr\">3</xref>]. The ovoid conceptus then begins to elongate into a filamentous form on days 12–13 in sheep or days 14–15 in cattle, respectively. During this period, elongating conceptus begins to produce a major cytokine, interferon tau (IFNT) [<xref rid=\"B4-ijms-21-05523\" ref-type=\"bibr\">4</xref>,<xref rid=\"B5-ijms-21-05523\" ref-type=\"bibr\">5</xref>,<xref rid=\"B6-ijms-21-05523\" ref-type=\"bibr\">6</xref>]. IFNT prevents secretion of luteolytic pulses of prostaglandin F2-alpha (PGF2a) by uterine epithelium for the prolongation of the corpus luteum (CL) life span, which is a process described as the maternal recognition of pregnancy (MRP) [<xref rid=\"B7-ijms-21-05523\" ref-type=\"bibr\">7</xref>,<xref rid=\"B8-ijms-21-05523\" ref-type=\"bibr\">8</xref>,<xref rid=\"B9-ijms-21-05523\" ref-type=\"bibr\">9</xref>,<xref rid=\"B10-ijms-21-05523\" ref-type=\"bibr\">10</xref>]. After several days to a week of elongation, the ruminant conceptus occupies the entire length of the uterine horn ipsilateral to the CL, with extraembryonic membranes extending into the contralateral uterine horn, and begins its attachment to the uterine epithelium on day 16 in sheep and day 19 in cattle, followed by adhesion and placentation [<xref rid=\"B11-ijms-21-05523\" ref-type=\"bibr\">11</xref>]. During this period, conceptus IFNT, together with maternal progesterone (P4) from functional CL, regulates endometrial gene expression, which sets up the uterine environment necessary for the establishment of conceptus migration, apposition, and initial attachment to the uterine epithelial cells in the ruminant species [<xref rid=\"B12-ijms-21-05523\" ref-type=\"bibr\">12</xref>,<xref rid=\"B13-ijms-21-05523\" ref-type=\"bibr\">13</xref>]. Thus, the establishment of proper conceptus–endometrial communication is required to allow conceptus implantation to the endometrium and subsequent maintenance of pregnancy.</p><p>The communication between different cell types is generally maintained through secretory soluble factors such as hormones and cytokines. In recent years, however, emerging evidence indicates that extracellular vesicles (EVs) produced by cells are also involved in cellular communication. In fact, EVs have been observed to transfer information to other cells, regulating cellular activities of the recipient cells [<xref rid=\"B14-ijms-21-05523\" ref-type=\"bibr\">14</xref>]. Novel approaches and insights have made possible the extensive characterization of EVs, which contain surface receptors/ligands and cargo of proteins, lipids, metabolites, DNAs, and RNAs from the originating cells. Thus, EVs could be indicative of the cellular physiological state and function. In addition, the lipid bilayer of EVs made up of relatively high concentrations of cholesterol, sphingomyelin, ceramide, and detergent-resistant membrane domains, making these vesicles stable in extracellular spaces [<xref rid=\"B15-ijms-21-05523\" ref-type=\"bibr\">15</xref>]. Following contact or uptake by recipient cells, EVs can regulate gene expression and elicit biological effects, including increased cell proliferation, migration, and adhesion. Evidence gathered indicates that EVs are produced by reproductive tissues/cells including follicular [<xref rid=\"B16-ijms-21-05523\" ref-type=\"bibr\">16</xref>,<xref rid=\"B17-ijms-21-05523\" ref-type=\"bibr\">17</xref>], oviductal [<xref rid=\"B18-ijms-21-05523\" ref-type=\"bibr\">18</xref>], and endometrial cells [<xref rid=\"B19-ijms-21-05523\" ref-type=\"bibr\">19</xref>], as well as in-vitro- and in-vivo-produced embryos [<xref rid=\"B20-ijms-21-05523\" ref-type=\"bibr\">20</xref>,<xref rid=\"B21-ijms-21-05523\" ref-type=\"bibr\">21</xref>,<xref rid=\"B22-ijms-21-05523\" ref-type=\"bibr\">22</xref>]. Evidence is mounting that EVs of maternal or embryonic origin participate in the conceptus/fetal–endometrial interactions that are critical to pregnancy’s early stages, possibly continuing throughout the entire processes [<xref rid=\"B23-ijms-21-05523\" ref-type=\"bibr\">23</xref>,<xref rid=\"B24-ijms-21-05523\" ref-type=\"bibr\">24</xref>,<xref rid=\"B25-ijms-21-05523\" ref-type=\"bibr\">25</xref>,<xref rid=\"B26-ijms-21-05523\" ref-type=\"bibr\">26</xref>]. In this review, we will focus on the recent findings pertaining to EVs in embryo–maternal communication during the early stages of pregnancy, especially the implantation period.</p></sec><sec id=\"sec2-ijms-21-05523\"><title>2. General Concepts of EVs: Biogenesis, Secretion, and Cargo</title><p>In order to organize the data generated in different laboratories throughout the world, the International Society for Extracellular Vesicles (ISEV), which is the leading global professional society, constantly updates newly discovered EVs along with the available information. The documents entitled Minimal Information for Studies of Extracellular Vesicles (“MISEV”) Guidelines have been published and updated by the ISEV to provide information on better isolation and characterization of EV preparations, as well as suggestions to specific activities associated with EVs [<xref rid=\"B27-ijms-21-05523\" ref-type=\"bibr\">27</xref>]. In this section, general information including subtypes and cargo of EVs based on the MISEV guidelines and the recent findings are provided to better understand the roles of EVs for conceptus–endometrial communication.</p><sec id=\"sec2dot1-ijms-21-05523\"><title>2.1. Subtypes of EVs</title><p>In general, EVs are defined as nano-sized, membrane-enclosed vesicles naturally released from the cells [<xref rid=\"B27-ijms-21-05523\" ref-type=\"bibr\">27</xref>,<xref rid=\"B28-ijms-21-05523\" ref-type=\"bibr\">28</xref>] and commonly classified as exosomes, microvesicles (MVs), and apoptotic bodies according to their sizes, biogenesis, and secretion (<xref ref-type=\"fig\" rid=\"ijms-21-05523-f001\">Figure 1</xref>) [<xref rid=\"B29-ijms-21-05523\" ref-type=\"bibr\">29</xref>].</p><p>Exosomes are small membrane-bound vesicles with a diameter of 50–150 nm, which are derived from endosomal multivesicular bodies (MVBs). The formation is derived from the invagination of the plasma membrane (early endosome) and the subsequent fusion of endocytic vesicles mediated by the endosomal sorting complex responsible for transport (ESCRTs) and/or other components such as ceramides and tetraspanins [<xref rid=\"B30-ijms-21-05523\" ref-type=\"bibr\">30</xref>,<xref rid=\"B31-ijms-21-05523\" ref-type=\"bibr\">31</xref>]. Exosomes are the intraluminal vesicles secreted into the extracellular space by the fusion of MVBs with the plasma membrane.</p><p>MVs have a diameter of 100–1000 nm and are released directly from the plasma membrane into the extracellular space by budding and fission [<xref rid=\"B30-ijms-21-05523\" ref-type=\"bibr\">30</xref>,<xref rid=\"B31-ijms-21-05523\" ref-type=\"bibr\">31</xref>]. Their biogenesis mechanism is only a partially known; however, it has been found that MVs arise from the result of dynamic interplay between phospholipid redistribution and cytoskeletal protein contraction [<xref rid=\"B32-ijms-21-05523\" ref-type=\"bibr\">32</xref>].</p><p>Apoptotic bodies are the largest vesicles with a diameter of 100–5000 nm, which originate from the fragmentation of the plasma membrane of cells undergoing the apoptotic processes [<xref rid=\"B33-ijms-21-05523\" ref-type=\"bibr\">33</xref>]. Apoptotic bodies contain cell organelles, proteins, DNA fragments, and histones deriving directly from the intracellular environment. Apoptotic bodies are known to contribute to cellular waste management, unlike exosomes or MVs.</p></sec><sec id=\"sec2dot2-ijms-21-05523\"><title>2.2. The Molecular Cargo of EVs</title><p>At present, the MISEV2018 guidelines suggest that three categories of protein markers should be demonstrated in all EV preparations for the presence of EVs (Categories 1 and 2) and their purity from common contaminants (Category 3) [<xref rid=\"B27-ijms-21-05523\" ref-type=\"bibr\">27</xref>]. EVs transport a variety of bioactive molecules including soluble and membrane-bound protein, lipids, metabolites, DNA, and RNA (mRNA, miRNAs, and other small regulatory RNAs) [<xref rid=\"B34-ijms-21-05523\" ref-type=\"bibr\">34</xref>]. Lipidomic analysis has shown that EVs, independent of their biogenesis, contain a multitude of lipids such cholesterol, sphingomyelin, ceramide, glycerophospholipids, phosphatidylcholine, and phosphatidylserine [<xref rid=\"B27-ijms-21-05523\" ref-type=\"bibr\">27</xref>,<xref rid=\"B35-ijms-21-05523\" ref-type=\"bibr\">35</xref>,<xref rid=\"B36-ijms-21-05523\" ref-type=\"bibr\">36</xref>]. In addition, several proteomic analyses have shown that EVs contain different types of proteins, such as heat shock proteins (HSP70 and HSP90), major histocompatibility complex class I and II (MHC class I and II), tetraspanins (CD9, CD63 and others), endosomal sorting complex proteins required for transport (Alix and Tsg101) and chaperones, which are often used as protein markers [<xref rid=\"B27-ijms-21-05523\" ref-type=\"bibr\">27</xref>,<xref rid=\"B37-ijms-21-05523\" ref-type=\"bibr\">37</xref>,<xref rid=\"B38-ijms-21-05523\" ref-type=\"bibr\">38</xref>]. Moreover, receptors including epidermal growth factor receptor (EGFR), membrane trafficking proteins (GTPases, Flotillin and Annexins), cytoskeletal proteins (tubulin and actin), and cytosolic proteins are also enriched in EVs. It should be noted that the cargo of EVs varies depending not only on their cellular origin, but also on their physiological and/or pathological condition.</p></sec></sec><sec id=\"sec3-ijms-21-05523\"><title>3. Conceptus–Endometrial Communication Mediated by EVs during the Peri-Implantation Period</title><p>The establishment of mammalian pregnancy is dependent on successful implantation and placental formation, both of which require proper communication between conceptus and endometrium. It has previously been suggested that EVs are secreted from both the conceptus TE and maternal endometrium during the period of MRP in sheep [<xref rid=\"B39-ijms-21-05523\" ref-type=\"bibr\">39</xref>]. Moreover, results from a human study suggested that exosomes could be released from the endometrial epithelium, thereby transferring molecular cargo to the blastocyst and/or the endometrium to promote implantation [<xref rid=\"B40-ijms-21-05523\" ref-type=\"bibr\">40</xref>]. Uptake of embryo-derived EVs was observed in human primary endometrial epithelial and stromal cells [<xref rid=\"B41-ijms-21-05523\" ref-type=\"bibr\">41</xref>]. These observations support the idea that conceptus–endometrial communications are mediated by EVs. In this section, we will introduce the role of EVs in autocrine and/or paracrine communications between blastocyst/conceptus and endometrium at different stages in the early pregnancy (<xref ref-type=\"fig\" rid=\"ijms-21-05523-f002\">Figure 2</xref>).</p><sec id=\"sec3dot1-ijms-21-05523\"><title>3.1. EV Functions during the Blastocyst Migration/Hatching Period</title><p>During the blastocyst migration/hatching period, secretions of the oviduct and uterus are involved in proliferation and development in preimplantation embryos. It is also noted that the early preimplantation mammalian embryo is relatively autonomous and can regulate its own development independently [<xref rid=\"B42-ijms-21-05523\" ref-type=\"bibr\">42</xref>].</p><p>EVs exert biological effects on early embryonic development. One of the recent studies found that EVs were secreted from in-vitro-cultured bovine embryos of days 7–9, and the concentration of EVs was higher depending on the embryo’s competence [<xref rid=\"B20-ijms-21-05523\" ref-type=\"bibr\">20</xref>]. Another bovine study has shown that the embryo-derived EVs improved the growth and viability of cloned bovine embryos and increased implantation rates as well as full-term calving rates [<xref rid=\"B43-ijms-21-05523\" ref-type=\"bibr\">43</xref>]. In agreement with these observations, the porcine study demonstrated that parthenogenetic (PA) cloned embryos significantly improved blastocyst development rates of cocultured cloned embryos (nuclear transfer, NT) [<xref rid=\"B44-ijms-21-05523\" ref-type=\"bibr\">44</xref>]. In this study, mRNAs for the pluripotency genes, <italic>Oct4</italic>, <italic>Klf4</italic>, and <italic>Nanog</italic>, were observed in PA embryo-derived EVs as well as cocultured cloned embryos, suggesting that the transfer of pluripotency genes via EVs could improve blastocyst development. Similar results were found in mice experiments, in which day 3–5 blastocysts microinjected with the Embryonic Stem (ES) cell-derived EVs before transfer into surrogate mothers significantly increased the likelihood of implantation [<xref rid=\"B45-ijms-21-05523\" ref-type=\"bibr\">45</xref>]. This result indicates that ES cell-derived EVs improve the capability of TE cells within the blastocyst to migrate into the uterus and promote blastocyst implantation. Furthermore, another study found that miRNAs in EVs released during blastulation vary depending on the embryo’s quality, in which 12 miRNAs were upregulated and 15 miRNAs were downregulated in the good-quality blastocysts, respectively [<xref rid=\"B46-ijms-21-05523\" ref-type=\"bibr\">46</xref>]. Thus, mammalian embryos secrete EVs into their surrounding environment, and these embryo-derived EVs, possibly together with endometrium-derived EVs, positively influence blastocyst formation, quality, and development in an autocrine and/or paracrine manner.</p></sec><sec id=\"sec3dot2-ijms-21-05523\"><title>3.2. EV Functions Prior to Conceptus Attachment to the Uterine Epithelium</title><p>During this phase, the blastocyst/conceptus undergoes rapid morphological changes from spherical to tubular to filamentous forms and migrates freely throughout the entire lumen of the uterus. In domestic ruminants, this is the period when trophectoderms of the developing conceptus begin to secrete an antiluteolytic factor, IFNT, responsible for the maintenance of functional CL and form binucleate cells (BNCs). From the endometrial side, major morphological and functional changes through biological processes, including apoptosis and proliferation, are a prerequisite for uterine receptivity to conceptus implantation. The recent studies on uterine EVs find a variety of the cargo as well as the roles for biological processes during the period prior to conceptus attachment to the uterine epithelium.</p><p>Endogenous retroviruses (ERVs), integrated and abundant in the genomes of vertebrates, are involved in the formation of trophoblast BNCs. The sheep genome contains at least 27 copies of ERVs highly related to the exogenous and pathogenic Jaagsiekte sheep retrovirus (JSRV), which are termed endogenous JSRVs (enJSRVs) [<xref rid=\"B47-ijms-21-05523\" ref-type=\"bibr\">47</xref>]. The enJSRV envelope genes, which are first detected on day 12, are abundant in the ovine reproductive tract, and integrated enJSRVs are packaged into endometrium-derived viral particles and transmitted to the TE and influence conceptus elongation and growth and development of the TE [<xref rid=\"B48-ijms-21-05523\" ref-type=\"bibr\">48</xref>,<xref rid=\"B49-ijms-21-05523\" ref-type=\"bibr\">49</xref>,<xref rid=\"B50-ijms-21-05523\" ref-type=\"bibr\">50</xref>]. Recent studies on EVs provided evidence for packaging of enJSRVs within the EVs cargo between days 12 and 16 of gestation that could be delivered to heterologous cells in vitro [<xref rid=\"B23-ijms-21-05523\" ref-type=\"bibr\">23</xref>,<xref rid=\"B51-ijms-21-05523\" ref-type=\"bibr\">51</xref>]. These studies support the idea that enJSRVs, which regulate conceptus TE development including the differentiation of trophoblast BNCs from mononuclear TE cells beginning on day 14, could be delivered from the endometrium to conceptus TE via EVs.</p><p>Apoptosis is a typical form of programmed cell death by which tissues eliminate unnecessary cells. The occurrence of apoptosis has been described in many reproductive tissues including the uterine epithelium [<xref rid=\"B52-ijms-21-05523\" ref-type=\"bibr\">52</xref>]. Available data from human pregnancy suggests that EVs induce apoptosis in activated immune cells [<xref rid=\"B53-ijms-21-05523\" ref-type=\"bibr\">53</xref>]. Indeed, bcl-2-like protein 15 (BCL2L15), a regulator of apoptosis, was found in ovine uterine EVs during the periattachment period [<xref rid=\"B22-ijms-21-05523\" ref-type=\"bibr\">22</xref>]. Furthermore, recent study in cows reported that EVs from day 17 of pregnancy increased expression of apoptosis-related genes, <italic>BAX</italic>, <italic>CASP3</italic>, <italic>TNFA</italic>, and <italic>TP53</italic> in primary endometrial epithelial cells (EECs) [<xref rid=\"B54-ijms-21-05523\" ref-type=\"bibr\">54</xref>]. These results suggest that EVs induce the apoptosis of immune cells and EECs prerequired for conceptus implantation, during which a portion of the endometrial epithelium disappears.</p><p>Increased TE and endometrial cell proliferation is crucial for conceptus elongation and uterine receptivity. An in vitro study found that EVs derived from the ovine uterine lumen stimulated TE cell proliferation [<xref rid=\"B23-ijms-21-05523\" ref-type=\"bibr\">23</xref>]. EVs purified from the porcine TE cells on day 12 pregnancy (the early implantation period) stimulated proliferation of maternal endothelial cells [<xref rid=\"B24-ijms-21-05523\" ref-type=\"bibr\">24</xref>]. These studies indicate that EVs have biological effects that increase cellular proliferation for conceptus development and uterine morphological and functional changes.</p><p>Therefore, uterine EVs are involved in embryo–maternal communication through the modulation of biological processes including the differentiation of trophoblast BNCs, apoptosis, and cellular proliferation prerequisite for uterine receptivity to conceptus implantation.</p></sec><sec id=\"sec3dot3-ijms-21-05523\"><title>3.3. EV Functions during the Conceptus Implantation Period</title><p>Noninvasive trophoblasts begin to attach to the uterine epithelium on day 16 in sheep and day 19 in cattle. This is the time when trinucleate and multinucleate cells, resulting from the fusion between trophoblast BNCs and uterine epithelial cells, begin to appear in the bovine and ovine uterine endometrium, respectively. Recent evidence on EVs supports the idea that the endometrial epithelium of the uterus as well as the TE of the elongating conceptus secrete EVs with biological effects, including immunomodulation and angiogenesis, required for successful conceptus attachment and adhesion to the uterine endometrium.</p><p>Vascular cell adhesion molecule (VCAM-1) is known as a cell adhesion mediator required for the establishment of the bovine conceptus adhesion to the uterine endometrium [<xref rid=\"B55-ijms-21-05523\" ref-type=\"bibr\">55</xref>]. In investigating the effects of EVs and bovine uterine flushings (UFs) containing EVs on the adhesion molecule, UFs and intrauterine EVs obtained on days 20 and 22 postimplantation were observed to upregulate <italic>VCAM1</italic> transcripts in EECs [<xref rid=\"B54-ijms-21-05523\" ref-type=\"bibr\">54</xref>,<xref rid=\"B55-ijms-21-05523\" ref-type=\"bibr\">55</xref>]. In addition, proteomic analysis of purified human endometrial epithelial-derived exosomes treated with either estrogen or progesterone revealed that several members of the integrin family, which are essential for endometrium–embryo communication and implantation, were selectively packaged within endometrial exosomes [<xref rid=\"B56-ijms-21-05523\" ref-type=\"bibr\">56</xref>]. These integrins within exosomes could be important for exosome docking to recipient cells and mediate trophoblast adhesion by interacting with ligands. Another investigation using mouse model revealed that exosome-associated miR-30d present in the endometrial fluid was transferred to murine embryos, resulting in overexpression of genes, <italic>Itgb3</italic>, <italic>Itga7</italic>, and <italic>Cdh5</italic>, which are involved in the embryonic adhesion to the maternal endometrium [<xref rid=\"B57-ijms-21-05523\" ref-type=\"bibr\">57</xref>]. These results suggest that EVs play a major role in conceptus attachment and adhesion to the endometrium.</p><p>It should be noted that immunologic tolerance to the fetal allograft must be established to permit conceptus development and subsequent pregnancy maintenance. In the context of the immune and inflammatory responses during the conceptus attachment period, EVs appear to carry molecules potentially able to modulate the local endometrial immune system [<xref rid=\"B58-ijms-21-05523\" ref-type=\"bibr\">58</xref>]. It is considered that their modulation occurs in order to stimulate or suppress the response depending on the receptors carried by the EV membrane and the chemical mediators in their cargos, such as proteins and miRNA. In addition, it was recently demonstrated that treatment of bovine EVs from day 20 of pregnancy, right after conceptus attachment is initiated, downregulated expression of immune-system-related genes in EECs [<xref rid=\"B59-ijms-21-05523\" ref-type=\"bibr\">59</xref>]. Combinations of the miRNA profiles from bovine EVs and bioinformatics analysis predicted bta-miR-98 as a likely maternal immune system regulator. Additionally, placental exosome-derived bta-miR-499 was found to contribute to the regulation of local inflammation at the maternal–fetal interface by inhibiting NF-κB signaling, a key regulator of the inflammatory process. Therefore, inhibition of bta-miR-499 leads to inflammatory deregulation at the maternal–fetal interface and subsequent placental loss and fetal growth restriction [<xref rid=\"B60-ijms-21-05523\" ref-type=\"bibr\">60</xref>]. These findings indicated that intrauterine EVs, especially miRNAs in EVs, contribute to regulating the maternal immune system for successful implantation.</p><p>Angiogenesis is an indispensable biological process of endometrial development, enabling firm attachment between the TE and the uterine luminal epithelium [<xref rid=\"B61-ijms-21-05523\" ref-type=\"bibr\">61</xref>]. In pigs, placental formation is initiated during days 15–20 of pregnancy, which allows dramatic change in physiological processes, including angiogenesis, in the endometrial compartments [<xref rid=\"B62-ijms-21-05523\" ref-type=\"bibr\">62</xref>]. A recent study in pigs found that endometrial cells and allantochorionic membrane cells from day 20 of pregnancy both released EVs [<xref rid=\"B24-ijms-21-05523\" ref-type=\"bibr\">24</xref>]. These experimental data further revealed that porcine EVs isolated from both TE cells and maternal endothelial cells, indicative of the endometrial vasculature, contained abundant proteins and miRNAs, miR-126-5P, miR-296-5P, miR-16, and miR-17-5P, which may play a role in angiogenesis. Furthermore, another porcine study demonstrated that upregulated miR-150 in the umbilical cord blood derived exosomes enhanced the proliferation, migration, and tube formation of umbilical vein endothelial cells, whereas lower miR-150 levels in those exosomes exhibit intrauterine growth restriction [<xref rid=\"B63-ijms-21-05523\" ref-type=\"bibr\">63</xref>]. These intensive studies have indicated that EVs are involved in the proliferation of the maternal endothelial cells and promote angiogenic processes, and suggest that EVs could become a possible novel approach for the treatment of disease related to aberrant angiogenesis during fetal development.</p><p>Consequently, these results support the notion that EVs mediate cell–cell communication through the biological processes including immunomodulation and angiogenesis required for successful attachment and adhesion of conceptuses to the maternal endometrium.</p></sec></sec><sec id=\"sec4-ijms-21-05523\"><title>4. Roles of EV Interaction with Progesterone, IFNT, and Lipids, Including PGs</title><p>The establishment of pregnancy clearly requires complex physiological interactions between the conceptus and endometrium. Recently, accumulated evidence indicates that release of EVs into the uterine lumen by the elongating conceptus and the maternal endometrium is interactive and coordinated with ovarian P4, embryo-derived IFNT and embryo- and endometrium-derived prostaglandins (PGs), all of which are required for proper conceptus growth and implantation processes in the ruminant ungulates. In this section, we will focus on the newest findings related to the roles of EV interaction with P4, IFNT, and lipids, including PGs (<xref ref-type=\"fig\" rid=\"ijms-21-05523-f002\">Figure 2</xref>).</p><sec id=\"sec4dot1-ijms-21-05523\"><title>4.1. EV Interaction with Progesterone</title><p>The P4 secretion from functional CL is a prerequisite for the establishment and continuation of pregnancy in most mammals [<xref rid=\"B64-ijms-21-05523\" ref-type=\"bibr\">64</xref>,<xref rid=\"B65-ijms-21-05523\" ref-type=\"bibr\">65</xref>]. The actions of P4 on conceptus development are likely mediated by the endometrium, but little is known of how P4 contributes to conceptus survival and elongation. Recent findings in sheep provide novel insights into the biological effect of P4 on the production of EVs [<xref rid=\"B66-ijms-21-05523\" ref-type=\"bibr\">66</xref>]. In this study, the presence of EVs were confirmed within the endometrial luminal and glandular epithelia from cyclic ewes, and the total number of EVs in the uterine lumen increased over two-fold with the P4 treatment in ovariectomized sheep. In addition, miRNA profiles from ovine endometrium and EVs from the uterine lumen followed by bioinformatics analysis revealed that P4 regulated seven miRNAs, of which four miRNAs were upregulated and three miRNAs were downregulated. Another study with human EECs found that P4 induce changes in the EVs production and protein cargo from EECs, and these EVs could increase the adhesive capacity of human blastocyst or mouse embryos [<xref rid=\"B56-ijms-21-05523\" ref-type=\"bibr\">56</xref>]. Thus, these findings provide support for the idea that ovarian P4 directly and/or indirectly acts on endometrial epithelial production of EVs and their release into the uterine lumen. Further studies are needed to fully understand P4-regulated cargo in EVs and their function in pregnancy establishment.</p></sec><sec id=\"sec4dot2-ijms-21-05523\"><title>4.2. EV Interaction with or without IFNT</title><p>Certainly, the trophectoderm of the elongating conceptus secretes IFNT, which itself regulates expression of elongation- and implantation-related genes in the endometrium and abrogates luteolytic mechanisms in ruminants [<xref rid=\"B67-ijms-21-05523\" ref-type=\"bibr\">67</xref>]. Recently, several studies have indicated that IFNT interacts with uterine EVs over the course of the implantation processes. It was demonstrated that uterine EVs contained ovine or bovine IFNT [<xref rid=\"B22-ijms-21-05523\" ref-type=\"bibr\">22</xref>,<xref rid=\"B23-ijms-21-05523\" ref-type=\"bibr\">23</xref>,<xref rid=\"B54-ijms-21-05523\" ref-type=\"bibr\">54</xref>] and increased the expression of several IFNT-stimulated genes <italic>STAT1</italic>, <italic>STAT2</italic>, <italic>BST2</italic>, <italic>MX1</italic>, <italic>MX2</italic>, and <italic>ISG15</italic> in bovine EECs during the periattachment period [<xref rid=\"B22-ijms-21-05523\" ref-type=\"bibr\">22</xref>,<xref rid=\"B54-ijms-21-05523\" ref-type=\"bibr\">54</xref>]. Furthermore, the treatment of IFNT stimulated the release of MX1 incorporated into exosome from ovine EECs [<xref rid=\"B68-ijms-21-05523\" ref-type=\"bibr\">68</xref>]. In another bovine study, comprehensive miRNA analysis revealed that a total of 574 miRNAs, including 109 novel miRNAs, were detected in bovine EECs, from which 74 miRNAs were differentially expressed in those cells treated with IFNT [<xref rid=\"B69-ijms-21-05523\" ref-type=\"bibr\">69</xref>]. Conversely, in vitro analyses in ewes indicated that intrauterine EVs also increased the production of IFNT protein by the conceptus TE cells in a dose-dependent manner [<xref rid=\"B23-ijms-21-05523\" ref-type=\"bibr\">23</xref>,<xref rid=\"B25-ijms-21-05523\" ref-type=\"bibr\">25</xref>]. Given that inert control liposomes did not increase IFNT in the culture media, these results indicate that the cargo of EVs contributes to IFNT production in TE cells.</p><p>It is generally accepted that conceptus IFNT greatly influences the endometrial transcriptome; however, the endometrium also responds to conceptuses in a manner independent of IFNT as well [<xref rid=\"B70-ijms-21-05523\" ref-type=\"bibr\">70</xref>,<xref rid=\"B71-ijms-21-05523\" ref-type=\"bibr\">71</xref>]. In fact, a recent study found that 82 transcripts in bovine EECs were uniquely induced by IFNT-independent intrauterine EVs, suggesting that uterine EVs also have biological effects on uterine receptivity in an IFNT-independent manner [<xref rid=\"B26-ijms-21-05523\" ref-type=\"bibr\">26</xref>].</p><p>Thus, these findings support the notion that EVs present in the uterine lumen are involved in complex conceptus–endometrial interactions both independently and in conjunction with IFNT. Interestingly, it is likely that conceptus secretes not only IFNT itself, but also IFNT incorporated into EVs, both of which act on the uterine endometrium, respectively. It is well documented that blood contains EVs for organ-to-organ communications [<xref rid=\"B72-ijms-21-05523\" ref-type=\"bibr\">72</xref>,<xref rid=\"B73-ijms-21-05523\" ref-type=\"bibr\">73</xref>]. Recently, extrauterine or endocrine effects of IFNT have also been noted [<xref rid=\"B74-ijms-21-05523\" ref-type=\"bibr\">74</xref>,<xref rid=\"B75-ijms-21-05523\" ref-type=\"bibr\">75</xref>], suggesting that EVs including IFNT could directly or indirectly function on extrauterine tissues as well. However, it is clear that further in vivo evidence is needed to support the biological role of EVs and their potential interaction with IFNT required for pregnancy establishment.</p></sec><sec id=\"sec4dot3-ijms-21-05523\"><title>4.3. EV Interaction with Lipids, Including PGs</title><p>EVs are also composed of lipids and may carry specific lipids, lipids metabolites, including PGs, and other enzymes for lipid metabolism that can modify the phenotype of recipient cells [<xref rid=\"B76-ijms-21-05523\" ref-type=\"bibr\">76</xref>]. Most previous studies on EVs have mainly focused on roles for protein and RNA cargo of uterine EVs in embryo implantation. In a recent study, however, comprehensive lipid profiling of uterine EVs isolated from day 14 cyclic and pregnant sheep revealed that eight classes of lipids were included in cyclic and pregnant EVs, in which several lipid patterns differ significantly between EVs from cyclic and pregnant ewes, indicating that these populations of lipids are affected by pregnancy status [<xref rid=\"B25-ijms-21-05523\" ref-type=\"bibr\">25</xref>]. In ruminants, a variety of PGs that are synthesized and secreted by both conceptus and endometrium have autocrine and paracrine effects on conceptus development, endometrial function, and endometrial responses to P4 and IFNT during early pregnancy [<xref rid=\"B77-ijms-21-05523\" ref-type=\"bibr\">77</xref>,<xref rid=\"B78-ijms-21-05523\" ref-type=\"bibr\">78</xref>,<xref rid=\"B79-ijms-21-05523\" ref-type=\"bibr\">79</xref>,<xref rid=\"B80-ijms-21-05523\" ref-type=\"bibr\">80</xref>]. It was recently found that prostaglandin synthase 2 (PTGS2), a rate-limiting enzyme in PG synthesis, was present in CD63- and HSP70-positive ovine EVs [<xref rid=\"B51-ijms-21-05523\" ref-type=\"bibr\">51</xref>]. Furthermore, aldo-keto reductase family 1, member B1 protein (AKR1B1), a PG synthase, was found in uterine EVs obtained from days 15 and 17 pregnant sheep [<xref rid=\"B22-ijms-21-05523\" ref-type=\"bibr\">22</xref>]. AKR1B1 has also been detected in pregnant bovine UFs containing EVs on day 16 [<xref rid=\"B81-ijms-21-05523\" ref-type=\"bibr\">81</xref>]. Indeed, further experiments are needed to definitively define the biological role of lipids in uterine EVs, however, these results suggest that uterine EVs contain a diverse population of lipid cargo, including PGs, which may be responsible for conceptus elongation and implantation coordinated with actions of P4 and IFNT.</p></sec></sec><sec id=\"sec5-ijms-21-05523\"><title>5. Potential Role of EVs for Clinical Application in Farm Animals</title><p>The embryo transfer (ET) industry has been growing rapidly. Indeed, ET data provided that a total of 1,129,041 bovine embryos and 17,868 ovine embryos were transferred commercially worldwide in 2018 [<xref rid=\"B82-ijms-21-05523\" ref-type=\"bibr\">82</xref>]. It is generally accepted that the majority of bovine embryonic losses occur during the second and third weeks of pregnancy [<xref rid=\"B83-ijms-21-05523\" ref-type=\"bibr\">83</xref>,<xref rid=\"B84-ijms-21-05523\" ref-type=\"bibr\">84</xref>,<xref rid=\"B85-ijms-21-05523\" ref-type=\"bibr\">85</xref>,<xref rid=\"B86-ijms-21-05523\" ref-type=\"bibr\">86</xref>], suggesting that reducing embryonic losses at these stages result in higher productivity and economic efficiency in ruminants.</p><p>First, EVs may be of clinical significance for in-vitro-fertilized (IVF)-ET settings because selection of high-quality IVF blastocysts is required to increase successful IVF-ET rates. It was reported that EVs are secreted by in-vitro-cultured bovine embryos into culture media and their characteristics associated with embryo quality [<xref rid=\"B87-ijms-21-05523\" ref-type=\"bibr\">87</xref>]. Human embryos secrete miRNAs, which could be packed in exosomes into culture media [<xref rid=\"B88-ijms-21-05523\" ref-type=\"bibr\">88</xref>]. Notably, some of these miRNAs, including miRNA-191, were differentially expressed according to the fertilization method, chromosomal status, and pregnancy outcome. These findings suggest that cargo of EVs in the spent embryo medium could be biomarkers predictive of high-quality blastocysts, which would be a powerful noninvasive approach and improve IVF-ET successes.</p><p>Second, the application of EVs as biomarkers in early embryonic mortality or early pregnancy diagnosis could improve the rates of pregnancy successes as well. It is noteworthy that the cargo of EVs changes based on the extracellular environment. Comprehensive miRNA sequencing on serum EVs in day 17 pregnant and embryonic-mortality cattle identified that 27 miRNAs were significantly increased in day 17 embryonic mortality compared to those of the pregnant group [<xref rid=\"B89-ijms-21-05523\" ref-type=\"bibr\">89</xref>]. Furthermore, miRNA profiles from EVs in the maternal blood of cattle on pregnant day 21 found that the somatic cell nuclear transfer-derived embryonic loss group exhibited lower abundance of 27 miRNAs than were found in successful pregnancy groups [<xref rid=\"B90-ijms-21-05523\" ref-type=\"bibr\">90</xref>]. These observations indicate that pregnant and embryonic-mortality animals could be diagnosed through the detection of individual miRNA from the circulating EVs.</p><p>Given the observations that EVs secreted by the conceptus and/or endometrium are likely to promote conceptus implantation to the endometrium, third, potential clinical application would be to deliver specific cargo via EVs into the uterine cavity during the early pregnant stages. Recent studies have reported that the addition of bovine embryo- and uterus- derived exosomes increased the cleavage rate and blastocyst formation in cloned embryos [<xref rid=\"B43-ijms-21-05523\" ref-type=\"bibr\">43</xref>,<xref rid=\"B91-ijms-21-05523\" ref-type=\"bibr\">91</xref>], whereas the uterine exosomes derived from cows with endometritis significantly decreased the blastocyst formation rate of in-vitro-fertilized embryos compared to those derived from healthy cows [<xref rid=\"B92-ijms-21-05523\" ref-type=\"bibr\">92</xref>]. These results provide support for the idea that the delivery of specific cargo via EVs into the uterine cavity conditions the uterine environment to result in better embryo development or endometrium receptivity for improved pregnancy success.</p></sec><sec sec-type=\"conclusions\" id=\"sec6-ijms-21-05523\"><title>6. Conclusions</title><p>It is well documented that proper biochemical and cellular communication between the conceptus and the uterine endometrium are required for conceptus implantation and subsequent placentation. Recent progress suggests that uterine EVs may gain recognition as critical to conceptus–endometrial communication during the peri-implantation period in concert with the well-characterized molecules. It should be noted that uterine EVs could have autocrine and/or paracrine biological effects at different stages in the early pregnancy. As discussed earlier, uterine EVs are interactive and coordinated with ovarian P4, trophectoderm-derived IFNT, and/or lipids, including PGs, for conceptus elongation and conceptus implantation and subsequent placentation in the physiological or pathological microenvironment. Although significant details including the biogenesis, cargo, and definitive roles of uterine EVs have not yet been established, the rapid technological advances in the field of EV research will provide a strong impetus to clarify their effects on the peri-implantation processes.</p><p>In addition, EVs could become advanced tools for diagnosing and/or therapeutic agents useful to the reproductive field. For example, EVs in the spent embryo medium would be a predictive biomarker for selection of high-quality IVF blastocysts. In addition, the serum EVs following artificial insemination or embryo transfer would be a noninvasive biomarker for detecting pregnancy status. The last potential application would be to deliver specific cargo via EVs into the uterine cavity to improve embryo development or endometrium receptivity for more pregnancy success. Further research is required to characterize differences in the cargo of EVs between pregnant and nonpregnant or embryonic-mortality animals, which ultimately improves fertility rates in agriculturally important animals.</p></sec></body><back><ack><title>Acknowledgments</title><p>The authors would like to thank Robert Moriarty for his editorial assistance throughout the manuscript preparation.</p></ack><notes><title>Author Contributions</title><p>K.N., K.K., Y.S., H.F., M.H., and K.I. revised and prepared the manuscript draft. K.N., and K.K. revised and prepared the figures. All authors have read and agreed to the published version of the manuscript.</p></notes><notes><title>Funding</title><p>This work was supported by Grant-in-Aid for Scientific Research (B) (20H03133 to K.K.) and Grant-in-Aid for Scientific Research (A) (16H02584 to K.I.) from Japan Society for the Promotion of Science.</p></notes><notes notes-type=\"COI-statement\"><title>Conflicts of Interest</title><p>The authors declared no conflict of interest.</p></notes><glossary><title>Abbreviations</title><array orientation=\"portrait\"><tbody><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">AKR1B1</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">aldo-keto reductase family 1, member B1</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">BCL2L15</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">bcl-2 like proteins 15</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">BNCs</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">binucleate cells</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CL</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">corpus luteum</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">EECs</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">endometrial epithelial cells</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">EGFR</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">epidermal growth factor receptor</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ERVs</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">endogenous retroviruses</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ES</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">embryonic stem</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ESCRTs</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">endosomal sorting complex responsible for transport</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ET</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">embryo transfer</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">EVs </td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">extracellular vesicles</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ICM</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">inner cell mass</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">IFNT</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">interferon tau</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ISEV</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">international society for extracellular vesicles</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">IVF</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">in vitro fertilized</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">JSRV</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">jaagsiekte sheep retrovirus</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">MHC</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">major histocompatibility complex</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">MISEV</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">minimal information for studies of extracellular vesicles</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">MRP</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">maternal recognition of pregnancy</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">MVs</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">microvesicles</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">MVBs</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">multi-vesicular bodies</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">NT</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">nuclear transfer</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">PA</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">parthenogenetic</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">PGF2a</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">prostaglandin F2-alpha</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">PGs</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">prostaglandins</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">PTGS2</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">prostaglandin synthase 2</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">P4</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">progesterone</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">TE</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">trophectoderm</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">UFs</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">uterine flushings</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">VCAM-1</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">vascular cell adhesion molecule</td></tr></tbody></array></glossary><ref-list><title>References</title><ref id=\"B1-ijms-21-05523\"><label>1.</label><element-citation 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Exosomes and MVs contain a variety of bioactive molecules including soluble and membrane-bound protein, lipids, metabolites, DNA, and RNA (mRNA, miRNAs, and other small regulatory RNAs), which regulate cellular activities of the recipient cells.</p></caption><graphic xlink:href=\"ijms-21-05523-g001\"/></fig><fig id=\"ijms-21-05523-f002\" orientation=\"portrait\" position=\"float\"><label>Figure 2</label><caption><p>Roles of uterine extracellular vesicles (EVs) during the peri-implantation period. In domestic ruminants, the process of conceptus implantation to the maternal endometrium consists of blastocyst hatching, elongation, migration, apposition/attachment, and subsequent formation of the placenta. During these stages, EVs secreted by the conceptus and/or endometrium into the uterine microenvironment could have autocrine and/or paracrine biological effects on appropriate communication between the conceptus and the uterine endometrium. EVs are also interactive and coordinate with ovarian progesterone (P4), embryo-derived interferon tau (IFNT), and prostaglandins (PGs) for successful conceptus implantation and subsequent pregnancy establishment.</p></caption><graphic xlink:href=\"ijms-21-05523-g002\"/></fig></floats-group></article>\n"
]
[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Int J Environ Res Public Health</journal-id><journal-id journal-id-type=\"iso-abbrev\">Int J Environ Res Public Health</journal-id><journal-id journal-id-type=\"publisher-id\">ijerph</journal-id><journal-title-group><journal-title>International Journal of Environmental Research and Public Health</journal-title></journal-title-group><issn pub-type=\"ppub\">1661-7827</issn><issn pub-type=\"epub\">1660-4601</issn><publisher><publisher-name>MDPI</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32751709</article-id><article-id pub-id-type=\"pmc\">PMC7432061</article-id><article-id pub-id-type=\"doi\">10.3390/ijerph17155526</article-id><article-id pub-id-type=\"publisher-id\">ijerph-17-05526</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Article</subject></subj-group></article-categories><title-group><article-title>Nitrogen Dioxide Inhalation Exposures Induce Cardiac Mitochondrial Reactive Oxygen Species Production, Impair Mitochondrial Function and Promote Coronary Endothelial Dysfunction</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Karoui</surname><given-names>Ahmed</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05526\">1</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0003-1243-5311</contrib-id><name><surname>Crochemore</surname><given-names>Clément</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05526\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Harouki</surname><given-names>Najah</given-names></name><xref ref-type=\"aff\" rid=\"af2-ijerph-17-05526\">2</xref></contrib><contrib contrib-type=\"author\"><name><surname>Corbière</surname><given-names>Cécile</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05526\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Preterre</surname><given-names>David</given-names></name><xref ref-type=\"aff\" rid=\"af3-ijerph-17-05526\">3</xref></contrib><contrib contrib-type=\"author\"><name><surname>Vendeville</surname><given-names>Cathy</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05526\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Richard</surname><given-names>Vincent</given-names></name><xref ref-type=\"aff\" rid=\"af2-ijerph-17-05526\">2</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0001-5657-4255</contrib-id><name><surname>Fardel</surname><given-names>Olivier</given-names></name><xref ref-type=\"aff\" rid=\"af4-ijerph-17-05526\">4</xref><xref ref-type=\"aff\" rid=\"af5-ijerph-17-05526\">5</xref></contrib><contrib contrib-type=\"author\"><name><surname>Lecureur</surname><given-names>Valérie</given-names></name><xref ref-type=\"aff\" rid=\"af4-ijerph-17-05526\">4</xref></contrib><contrib contrib-type=\"author\"><name><surname>Vaugeois</surname><given-names>Jean-Marie</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05526\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Sichel</surname><given-names>François</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05526\">1</xref><xref ref-type=\"aff\" rid=\"af6-ijerph-17-05526\">6</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0002-5936-5704</contrib-id><name><surname>Mulder</surname><given-names>Paul</given-names></name><xref ref-type=\"aff\" rid=\"af2-ijerph-17-05526\">2</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0002-4981-0800</contrib-id><name><surname>Monteil</surname><given-names>Christelle</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05526\">1</xref><xref rid=\"c1-ijerph-17-05526\" ref-type=\"corresp\">*</xref></contrib></contrib-group><aff id=\"af1-ijerph-17-05526\"><label>1</label>Normandie Univ, UNIROUEN, UNICAEN ABTE, 14000 Caen et, 76 000 Rouen, France; <email>[email protected]</email> (A.K.); <email>[email protected]</email> (C.C.); <email>[email protected]</email> (C.C.); <email>[email protected]</email> (C.V.); <email>[email protected]</email> (J.-M.V.); <email>[email protected]</email> (F.S.)</aff><aff id=\"af2-ijerph-17-05526\"><label>2</label>Normandie Univ, UNIROUEN, Institut National de la Santé et de la Recherche Médicale U1096, 76 000 Rouen, France; <email>[email protected]</email> (N.H.); <email>[email protected]</email> (V.R.); <email>[email protected]</email> (P.M.)</aff><aff id=\"af3-ijerph-17-05526\"><label>3</label>CERTAM, 1 rue Joseph Fourier, 76 800 Saint-Etienne du Rouvray, France; <email>[email protected]</email></aff><aff id=\"af4-ijerph-17-05526\"><label>4</label>Univ Rennes, CHU Rennes, Inserm, EHESP, Irset (Institut de recherche en santé, environnement et travail)–UMR_S 1085, 35 000 Rennes, France; <email>[email protected]</email> (O.F.); <email>[email protected]</email> (V.L.)</aff><aff id=\"af5-ijerph-17-05526\"><label>5</label>Pôle Biologie, Rennes University Hospital, 35 203 Rennes, France</aff><aff id=\"af6-ijerph-17-05526\"><label>6</label>Centre François Baclesse, 14 000 Caen, France</aff><author-notes><corresp id=\"c1-ijerph-17-05526\"><label>*</label>Correspondence: <email>[email protected]</email></corresp></author-notes><pub-date pub-type=\"epub\"><day>30</day><month>7</month><year>2020</year></pub-date><pub-date pub-type=\"ppub\"><month>8</month><year>2020</year></pub-date><volume>17</volume><issue>15</issue><elocation-id>5526</elocation-id><history><date date-type=\"received\"><day>01</day><month>7</month><year>2020</year></date><date date-type=\"accepted\"><day>27</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>© 2020 by the authors.</copyright-statement><copyright-year>2020</copyright-year><license license-type=\"open-access\"><license-p>Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>).</license-p></license></permissions><abstract><p>Traffic air pollution is a major health problem and is recognized as an important risk factor for cardiovascular (CV) diseases. In a previous experimental study, we showed that diesel exhaust (DE) exposures induced cardiac mitochondrial and CV dysfunctions associated with the gaseous phase. Here, we hypothesized that NO<sub>2</sub> exposures to levels close to those found in DE induce a mitochondrial reactive oxygen species (ROS) production, which contribute to an endothelial dysfunction, an early indicator for numerous CV diseases. For this, we studied the effects of NO<sub>2</sub> on ROS production and its impacts on the mitochondrial, coronary endothelial and cardiac functions, after acute (one single exposure) and repeated (three h/day, five days/week for three weeks) exposures in Wistar rats. Acute NO<sub>2</sub> exposure induced an early but reversible mitochondrial ROS production. This event was isolated since neither mitochondrial function nor endothelial function were impaired, whereas cardiac function assessment showed a reversible left ventricular dysfunction. Conversely, after three weeks of exposure this alteration was accompanied by a cardiac mitochondrial dysfunction highlighted by an alteration of adenosine triphosphate (ATP) synthesis and oxidative phosphorylation and an increase in mitochondrial ROS production. Moreover, repeated NO<sub>2</sub> exposures promoted endothelial dysfunction of the coronary arteries, as shown by reduced acetylcholine-induced vasodilatation, which was due, at least partially, to a superoxide-dependent decrease of nitric oxide (NO) bioavailability. This study shows that NO<sub>2</sub> exposures impair cardiac mitochondrial function, which, in conjunction with coronary endothelial dysfunction, contributes to cardiac dysfunction. Together, these results clearly identify NO<sub>2</sub> as a probable risk factor in ischemic heart diseases.</p></abstract><kwd-group><kwd>air pollution</kwd><kwd>nitrogen dioxide</kwd><kwd>mitochondria</kwd><kwd>endothelial dysfunction</kwd><kwd>coronary arteries</kwd><kwd>ROS</kwd><kwd>cardiovascular</kwd></kwd-group></article-meta></front><body><sec sec-type=\"intro\" id=\"sec1-ijerph-17-05526\"><title>1. Introduction</title><p>Air pollution is a major health problem and is recognized as an important risk factor for cardiovascular (CV) diseases [<xref rid=\"B1-ijerph-17-05526\" ref-type=\"bibr\">1</xref>]. Among air pollutants, nitrogen dioxide (NO<sub>2</sub>) is a reactive gas and a primary pollutant originating from a variety of sources, especially from the combustion of fossil fuel and present in diesel exhaust (DE) [<xref rid=\"B2-ijerph-17-05526\" ref-type=\"bibr\">2</xref>]. This pollutant is regarded as a marker of motorized road traffic pollution and has been associated with CV adverse health outcomes [<xref rid=\"B3-ijerph-17-05526\" ref-type=\"bibr\">3</xref>]. However, close correlations between NO<sub>2</sub> and other air pollutants, mainly particulate matter (PM), make it difficult to identify adverse effects due to NO<sub>2</sub> alone [<xref rid=\"B4-ijerph-17-05526\" ref-type=\"bibr\">4</xref>]. Moreover, there is limited experimental evidence from controlled human exposure and animal toxicology studies for NO<sub>2</sub>, which have focused largely on respiratory parameters. It should also be stressed that NO<sub>2</sub> air monitor networks are sparse or non-existent in many countries [<xref rid=\"B5-ijerph-17-05526\" ref-type=\"bibr\">5</xref>]. As a result, associations between NO<sub>2</sub> exposures and CV health may be underestimated.</p><p>Some experimental investigations have shown the presence of biomarkers for CV effects, including markers for oxidative stress, inflammation, cell adhesion and endothelial dysfunction after NO<sub>2</sub> exposure in rodents [<xref rid=\"B6-ijerph-17-05526\" ref-type=\"bibr\">6</xref>,<xref rid=\"B7-ijerph-17-05526\" ref-type=\"bibr\">7</xref>]. In term of vascular function, a controlled human exposure study did not find any impact on brachial artery reactivity after one h exposure to four ppm NO<sub>2</sub> [<xref rid=\"B8-ijerph-17-05526\" ref-type=\"bibr\">8</xref>], whereas in a previous study, one hour exposure to diluted DE, including both gaseous and particulate phases, induced a vascular dysfunction and impaired endogenous fibrinolysis in men [<xref rid=\"B9-ijerph-17-05526\" ref-type=\"bibr\">9</xref>]. In the same way, Lucking et al. [<xref rid=\"B10-ijerph-17-05526\" ref-type=\"bibr\">10</xref>] demonstrated the preventive action of a particle trap on the vascular and prothrombotic effects of DE inhalation, suggesting the involvement of PM in these effects. Although these studies showed that the acute adverse vascular effects of air pollution are mediated by components other than NO<sub>2</sub>, other studies suggested that NO<sub>2</sub> itself contributes to the deleterious effects of DE [<xref rid=\"B11-ijerph-17-05526\" ref-type=\"bibr\">11</xref>,<xref rid=\"B12-ijerph-17-05526\" ref-type=\"bibr\">12</xref>], but the CV functional consequences of these exposures remain to be established. </p><p>In a previous study, we showed that DE-induced cardiac mitochondrial and CV dysfunctions were associated with the gaseous phase, since the observed effects were similar upstream or downstream of a particle trap [<xref rid=\"B13-ijerph-17-05526\" ref-type=\"bibr\">13</xref>]. However, the question arises as to whether NO<sub>2</sub> may be involved in these CV effects. Mitochondrial defect is a prominent feature of most CV diseases, such as heart failure, cardiac hypertrophy, ischemic heart disease and atherosclerosis [<xref rid=\"B14-ijerph-17-05526\" ref-type=\"bibr\">14</xref>]. Dysfunctional mitochondria might generate excessive levels of reactive oxygen species (ROS) and one of the consequences is a decline in endothelial NO bioavailability [<xref rid=\"B15-ijerph-17-05526\" ref-type=\"bibr\">15</xref>,<xref rid=\"B16-ijerph-17-05526\" ref-type=\"bibr\">16</xref>]. In the area of air pollution, mitochondria are a potential target because they have not only key roles in cardiac cell functions, but they also produce ROS that may contribute to oxidative stress, a major determinant for air pollutant toxicity [<xref rid=\"B17-ijerph-17-05526\" ref-type=\"bibr\">17</xref>,<xref rid=\"B18-ijerph-17-05526\" ref-type=\"bibr\">18</xref>]. </p><p>Based on these observations, it is tempting to speculate that NO<sub>2</sub> exposures to levels close to those found in DE induce mitochondrial dysfunction, which contributes to the alteration of coronary microvascular reactivity associated with a cardiac dysfunction. To test this hypothesis, we studied the effects of NO<sub>2</sub> inhalation in rats on: 1. cardiac mitochondrial function and ROS production, 2. cardiac function, 3. endothelium-dependent vasodilator responses of coronary arteries and 4. the role that ROS could play in these alterations. These experiments were performed after acute or repeated exposures, with the aim of establishing an exposure-response relationship and finally of showing the contribution of NO<sub>2</sub> in air pollution-mediated cardiovascular effects.</p></sec><sec id=\"sec2-ijerph-17-05526\"><title>2. Materials and Methods</title><p><italic>Animals and animal care</italic>. This study conforms to the Guide for the Care and Use of Laboratory Animals published by the US National Institutes of Health (NIH Publication No. 85-23, revised 1996) as well as European legislation and was approved by the ethics committee CENOMEXA n°54 (authorization number n°01796.02). Male Wistar rats 9–11 weeks old were purchased from Janvier Labs (Le Genest Saint Isle, France) and maintained in the animal facility at 21 °C on a 12-h light/dark cycle with free access to water and food. </p><p><italic>Whole-body inhalation exposure.</italic> Animals were randomly divided into six groups and placed in inhalation chambers during the exposures in the animal facility, as previously described [<xref rid=\"B19-ijerph-17-05526\" ref-type=\"bibr\">19</xref>]. Four groups were used for acute exposures to 5 ppm NO<sub>2</sub> or to clean air for a single 3 h period and the evaluations were performed after a recovery period of 1 h or 24 h in clean air in the animal facility; two groups were used for repeated exposures to clean air or to 5 ppm NO<sub>2</sub> 3 h/day, 5 days/week during 3 weeks and the evaluations were performed at 1 day post-exposure after a recovery period in clean air in the animal facility, which is vented with an high-efficiency particulate air and active charcoal filter to remove any pollutants from the outside. Clean air exposure was conducted using dry compressed air (dew point −70 °C) particle-free (<1 particle/cc using a condensation particle counter) with active charcoal treatment. Relative humidity (RH) is set to 50% RH with a steam humidification control system. Downward Mass Flow Controller is protected using cryotrap and adequate filtering. The experimental design is presented in <xref ref-type=\"fig\" rid=\"ijerph-17-05526-f001\">Figure 1</xref>. To comply with the established “3R” (Reduce, Refine and Replace) principles, only the minimum number of animals has been used. At least <italic>n</italic> = 6 rats per group were used for the echocardiographic and biological assessments and at least <italic>n</italic> = 4 rats per group were used for the coronary vascular reactivity.</p><p>The atmosphere of NO<sub>2</sub> in the inhalation chambers was obtained by mixing NO<sub>2</sub> gas (Air Liquid Product, France) with the filtered ambient air in a dilution column upstream of the inhalation chambers, and continuously measured during the exposures by a chemiluminescence analyzer environmental range (AC31M, Environnement SA, Poissy, France). The calibration of the chemiluminescence analyzer was done using a known concentration of NO<sub>2</sub>. The experimental device to generate the NO<sub>2</sub> atmosphere was placed in a room outside of the animal facility in order to avoid transfer of potentially stressful noise.</p><p>The NO<sub>2</sub> concentration is in the range of the concentrations measured in our previous studies conducted with diluted DE emitted under dynamic conditions (“New European Driving Cycle” NEDC) and characterized by an average level of 3.3 ppm NO<sub>2</sub> [<xref rid=\"B13-ijerph-17-05526\" ref-type=\"bibr\">13</xref>,<xref rid=\"B20-ijerph-17-05526\" ref-type=\"bibr\">20</xref>]. The choice of 5 ppm NO<sub>2</sub> was also based on previous experimental studies demonstrating effects on cardiac biochemical parameters [<xref rid=\"B6-ijerph-17-05526\" ref-type=\"bibr\">6</xref>].</p><p><italic>Cardiac function evaluations.</italic> Echocardiographic assessments were conducted blind to the animal group and performed in sedated rats (100–135 mg/kg ketamine; 3 mg/kg xylazine) after different recovery periods. Left ventricular (LV) dimensions, and function were assessed with a Vivid 7 ultrasound device (General Electric Healthcare, France), as previously described [<xref rid=\"B21-ijerph-17-05526\" ref-type=\"bibr\">21</xref>]. Briefly, cardiac ventricular dimensions were measured using M-mode tracings recorded from a two-dimensional short-axis view at the level of the papillary muscles. Echocardiography provided measurements of left ventricular (LV) end-diastolic (LVedd) and end-systolic (LVesd) diameters and posterior wall thickness at diastole (PWEDT) and at systole (PWEST). LV systolic function was assessed by the fractional shortening (FS) [(LVedd-LVesd)/LVedd] x 100. Velocity-time integral was measured by pulsed-wave Doppler, and cardiac output (CO) was calculated as CO = aortic velocity-time integral × [(π × LV outflow diameter)2/4]/100 × heart rate.</p><p><italic>Vascular function evaluation</italic>. Coronary vascular reactivity was evaluated by myograph (Dual Wire Myograph System; Danish Myo Technology), as previously described [<xref rid=\"B22-ijerph-17-05526\" ref-type=\"bibr\">22</xref>]. In brief, the heart was placed in cold, oxygenated Krebs buffer. A segment of the septal coronary artery, 1 mm long and 100 μm in diameter, was carefully dissected and mounted in a small vessel myograph for isometric tension recording. All measurements were performed after vessel contraction with 10<sup>−5</sup> M serotonin, and pharmacological inhibitors were applied for 30 min before assessing the relaxant responses. The endothelium-dependent relaxations to acetylcholine (10<sup>−9</sup> to 10<sup>−4.5</sup> M) were assessed in the absence and in the presence of the NOS inhibitor Nω-nitro-l-arginine (L-NNA; 10<sup>−4</sup> M) or superoxide dismutase (SOD; 200 UI/mL). Endothelium-independent relaxations to the NO donor sodium nitroprusside (SNP; 10<sup>−9</sup> to 10<sup>−4.5</sup> M) were assessed.</p><p><italic>Cardiac mitochondrial evaluations</italic>. For the mitochondrial evaluations, two types of sample were freshly prepared from LV as previously described [<xref rid=\"B13-ijerph-17-05526\" ref-type=\"bibr\">13</xref>,<xref rid=\"B23-ijerph-17-05526\" ref-type=\"bibr\">23</xref>]: permeabilized cardiac fibers for evaluations of mitochondrial oxidative phosphorylation capacity (OXPHOS) and isolated mitochondria for ATP and ROS measurements. The OXPHOS capacity was evaluated in situ on permeabilized cardiac fibers, in order to maintain the mitochondria in their normal intracellular position and their interactions with other organelles. OXPHOS was measured polarographically at 22 °C using a Clark-type oxygen electrode (Strathkelvin Instruments, Scotland, UK). Briefly, permeabilized cardiac fibers were added under continuous stirring in an oxygraphic cell containing R-buffer (2.77 mM CaK<sub>2</sub>EGTA, 7.23 mM K<sub>2</sub>EGTA (100 nM free Ca<sup>2+</sup>), 1.38 mM MgCl<sub>2</sub> (1 mM free Mg<sup>2+</sup>), 20 mM taurine, 0.5 mM dithiotreitol, 90 mM potassium-methane sulfonate, and 20 mM imidazole, 10 mM sodium-methane sulfonate and 2 mg/mL bovine serumalbumine, pH 7.1) and malate/glutamate (4 mM/10 mM). O<sub>2</sub> consumption was measured in the absence (state 2) and the presence of 2 mM adenosine diphosphate (ADP) (state 3 with complex I-linked substrates). Then, 2 mM amytal and 10 mM succinate were added (state 3 with complex II-linked substrates). O<sub>2</sub> consumption rates are given in micromoles of O<sub>2</sub> per minute per mg of proteins. The acceptor control ratio (ACR), an index of oxidation-phosphorylation coupling, was also calculated as the ratio of state 3 to state 2.</p><p>For cardiac mitochondrial ATP production, freshly isolated subsarcolemmal (SSM) and interfibrillar (IFM) mitochondria were incubated at 25 °C in R-tampon with 10 mM glutamate and 4 mM malate for 10 min under continuous stirring to eliminate sample residual ADP. Then, 2 mM ADP was added and samples were collected as previously described [<xref rid=\"B13-ijerph-17-05526\" ref-type=\"bibr\">13</xref>]. The rate of ATP production was evaluated by a bioluminescence assay kit (Roche Diagnostics, France) according to the manufacturer’s protocol. Values were normalized to the protein concentration of each sample.</p><p>Superoxide production was evaluated from SSM or IFM preparations by electron paramagnetic resonance (EPR) spectroscopy using the spin probes: 1-hydroxy-3-methoxycarbonyl-2,2,5,5-tetramethylpyrrolidine (CMH, Noxygen, Germany) as previously described [<xref rid=\"B23-ijerph-17-05526\" ref-type=\"bibr\">23</xref>,<xref rid=\"B24-ijerph-17-05526\" ref-type=\"bibr\">24</xref>]. Briefly, IFM and SSM were incubated at 37 °C for 1 h in Krebs–Hepes buffer (0.1 M NaCl, 5 mM KCl, 2.5 mM CaCl<sub>2</sub>, 1.2 mM MgSO<sub>4</sub>, 25 mM NaHCO<sub>3</sub>, 1 mM KH<sub>2</sub>PO<sub>4</sub>, 5.6 mM D-(+)-glucose, 20 mM Na–Hepes, pH 7.4) containing 25 μM deferoxamine, 5 μM diethyldithiocarbamate, and supplemented with 0.5 mM CMH, 10 mM Glutamate, 4 mM malate and 2 mM ADP. The oxidation of CMH into the stable 3-methoxycarbonylproxyl (CM°) was recorded using a MiniScope MS-200 X-band spectrometer (Magnettech, Germany). The EPR instrumental settings for field scan were as follows: Bo-field 3356.98 G, microwave power 1 mW, microwave attenuation 20 dB, modulation frequency 9.74 GHz, modulation amplitude 5 G, scan time 60 s. Intensity of the spectra was measured from the height of the central line. EPR data are expressed as arbitrary units/mg mitochondrial proteins.</p><sec><title>Statistical Analysis</title><p>All values are expressed as means ± standard error of mean (SEM). Student’s <italic>t</italic>-test was performed to compare means between control and NO<sub>2</sub> exposed groups. Statistical analyses were performed using GraphPad Prism (version 7.04, GraphPad software, USA). Differences were considered statistically significant when <italic>p</italic> < 0.05.</p></sec></sec><sec sec-type=\"results\" id=\"sec3-ijerph-17-05526\"><title>3. Results</title><sec id=\"sec3dot1-ijerph-17-05526\"><title>3.1. Acute NO<sub>2</sub> Exposure Induced a Rapid but Reversible Cardiac Response</title><p>LV function was evaluated by echocardiography after acute exposure to NO<sub>2</sub> (<xref ref-type=\"fig\" rid=\"ijerph-17-05526-f002\">Figure 2</xref>A–D). Relative to control group, single exposure to NO<sub>2</sub> induced a significant increase in LV diastolic and systolic diameters (10 and 38%, respectively), and a decrease in fractional shortening (−23%), after 1 h of recovery period. This effect appeared transient since a 24 h recovery period post-acute exposure erased the increase in LV diastolic and systolic diameters and the decrease in fractional shortening were no longer observed. Cardiac output (<xref ref-type=\"fig\" rid=\"ijerph-17-05526-f002\">Figure 2</xref>D) remained unchanged after NO<sub>2</sub> exposure, whatever the recovery period.</p><p>To gain further insight into the putative impact of NO<sub>2</sub> exposures on vascular function, we next investigated coronary endothelial function. Coronary arteries were isolated from the hearts of control and NO<sub>2</sub>-exposed rats for vascular reactivity assessment, as a measure of endothelium-dependent coronary vasodilatation after serotonin pre-constriction and the addition of acetylcholine. Acute NO<sub>2</sub> exposure did not impair endothelial function in coronary arteries, as shown by comparable endothelial relaxation in both control and acute NO<sub>2</sub> exposure groups (<xref ref-type=\"fig\" rid=\"ijerph-17-05526-f003\">Figure 3</xref>).</p></sec><sec id=\"sec3dot2-ijerph-17-05526\"><title>3.2. Acute NO<sub>2</sub> Exposure Induced Rapid but Transitory Cardiac Mitochondrial ROS Production without Mitochondrial Dysfunction</title><p>To investigate mitochondrial function after NO<sub>2</sub> exposure, the OXPHOS capacity was investigated in situ from permeabilized cardiac fibers in order to maintain the mitochondria in their normal intracellular position and their interactions with other organelles. Acute NO<sub>2</sub> exposure did not affect the mitochondrial function, since ADP-independent respiration with glutamate and malate (state 2) was similar between the groups, as well as ADP-dependent respiration with glutamate and malate (state 3 complex I) or succinate (state 3 complex II) as substrates (<xref ref-type=\"fig\" rid=\"ijerph-17-05526-f004\">Figure 4</xref>A). In these conditions, oxidation-phosphorylation coupling, determined by the ratio of respiration rate before and after the addition of ADP (acceptor control ratio ACR, state 3 (complex I)/state 2), remained unchanged (<xref ref-type=\"fig\" rid=\"ijerph-17-05526-f004\">Figure 4</xref>B).</p><p>To further explore the effect of acute NO<sub>2</sub> exposure on mitochondrial function, we next measured both ATP production rates and superoxide production in cardiac mitochondrial subpopulations, subsarcolemmal (SSM) and interfibrillar (IFM) mitochondria. Acute NO<sub>2</sub> exposure did not alter ATP production rates, confirming the absence of a mitochondrial dysfunction (<xref ref-type=\"fig\" rid=\"ijerph-17-05526-f004\">Figure 4</xref>C), although a temporary increase in mitochondrial ROS production was observed specifically in IFM. Indeed, ROS levels were similar between the groups, 1-day post-exposure (<xref ref-type=\"fig\" rid=\"ijerph-17-05526-f004\">Figure 4</xref>D).</p></sec><sec id=\"sec3dot3-ijerph-17-05526\"><title>3.3. Repeated NO<sub>2</sub> Exposures Impaired Cardiovascular Responses</title><p>We next wanted to determine whether the duration of NO<sub>2</sub> exposure has differential effects on cardiovascular function. Three weeks of NO<sub>2</sub>-exposures caused a sustained cardiac effect, since 24 h after the 3-week exposure, LV diastolic and systolic diameters (−12 and 34%, respectively) as well as LV fractional shortening and cardiac output (−20% and −17%, respectively) were severely altered compared to time-matched controls (<xref ref-type=\"fig\" rid=\"ijerph-17-05526-f005\">Figure 5</xref>).</p><p>In parallel with this cardiac dysfunction, repeated NO<sub>2</sub> exposures impaired endothelium-dependent relaxation, illustrated by the decrease of coronary relaxation in the repeated NO<sub>2</sub> exposure group (<xref ref-type=\"fig\" rid=\"ijerph-17-05526-f006\">Figure 6</xref>A). No differences were observed between groups upon sodium nitroprusside (SNP)-induced relaxation (<xref ref-type=\"fig\" rid=\"ijerph-17-05526-f006\">Figure 6</xref>B).</p><p>Incubation of coronary arteries with the NOS inhibitor L-NNA markedly reduced the relaxing response in both groups (<xref ref-type=\"fig\" rid=\"ijerph-17-05526-f006\">Figure 6</xref>C). Incubation of coronary arteries with superoxide dismutase did not modify acetylcholine responses in the control group but improved the impaired relaxation observed after repeated NO<sub>2</sub> exposure (<xref ref-type=\"fig\" rid=\"ijerph-17-05526-f006\">Figure 6</xref>D).</p></sec><sec id=\"sec3dot4-ijerph-17-05526\"><title>3.4. Repeated NO<sub>2</sub> Exposures Impaired Mitochondrial Function and Mitochondrial ROS Production</title><p>In order to determine whether repeated NO<sub>2</sub> exposure affected mitochondrial function, we performed exposures during three weeks and the assessments were performed at 1-day post-exposure. <xref ref-type=\"fig\" rid=\"ijerph-17-05526-f007\">Figure 7</xref> displays cardiac mitochondrial OXPHOS capacity after repeated NO<sub>2</sub> exposures.</p><p>Respiration with either complex I or complex II substrates was reduced by 21 and 23%, respectively (<xref ref-type=\"fig\" rid=\"ijerph-17-05526-f007\">Figure 7</xref>A) after repeated NO<sub>2</sub> exposure. In line with these results of state 3 respiration, ACR was lower in NO<sub>2</sub>- compared to air-exposed rats (<xref ref-type=\"fig\" rid=\"ijerph-17-05526-f007\">Figure 7</xref>B). The ATP production rate decreases observed in both SSM and IFM fractions confirmed this mitochondrial dysfunction (<xref ref-type=\"fig\" rid=\"ijerph-17-05526-f007\">Figure 7</xref>C). An increase in mitochondrial superoxide production was observed specifically in IFM (<xref ref-type=\"fig\" rid=\"ijerph-17-05526-f007\">Figure 7</xref>D) and was correlated with the decrease in cardiac output (<xref ref-type=\"fig\" rid=\"ijerph-17-05526-f007\">Figure 7</xref>E).</p></sec></sec><sec sec-type=\"discussion\" id=\"sec4-ijerph-17-05526\"><title>4. Discussion</title><p>The results of this study show that acute NO<sub>2</sub> exposure induced an early, but transitory mitochondrial superoxide production associated with reversible impairment of cardiac function, whereas repeated exposures induced mitochondrial and cardiac dysfunctions, which persisted for 24 h after the last exposure. Moreover, repeated NO<sub>2</sub> exposures impaired ACh-mediated dilatation in coronary arteries, an effect that was due to a decrease in NO bioavailability caused, at least partially, by increased superoxide production. Taken together, these data provide the first evidence that NO<sub>2</sub> exposures impaired cardiac mitochondrial function, which, in conjunction with coronary endothelial dysfunction, contributes to a sustainable cardiac dysfunction.</p><p>Air pollution has long been recognized as a major CV risk, due particularly to fine particulate matter (PM<sub>2.5</sub>) derived from combustion sources, and road traffic and diesel exhaust (DE). However, the potential effects of a co-pollutant gases, such as NO<sub>2</sub>, has been neglected. In a previous study, we demonstrated that removing particles from the DE aerosol did not protect against DE-induced cardiac and mitochondrial dysfunctions, revealing an important implication of the gaseous phase in this response [<xref rid=\"B13-ijerph-17-05526\" ref-type=\"bibr\">13</xref>]. In this present study, acute NO<sub>2</sub> exposure at a concentration close to that measured in DE induced a cardiac impairment characterized by an LV dilatation and associated with reduced LV fractional shortening, reflecting a possible loss of contractility. This LV dysfunction appeared only moderate since cardiac output was not modified. Moreover, this effect was reversible since, after 1 day post-exposure, echocardiographic parameters were comparable to those measured in control group. However, cardiac dysfunction persisted and worsened after repeated exposure to NO<sub>2</sub> over three weeks. These evaluations were made at day-1 post-exposure, excluding the deleterious direct effect of NO<sub>2</sub> as observed 1 h after acute NO<sub>2</sub> exposure. These results are consistent with a cohort study that has shown an association between past exposure to NO<sub>2</sub> and PM<sub>2.5</sub> and cardiac ventricular dilatation [<xref rid=\"B25-ijerph-17-05526\" ref-type=\"bibr\">25</xref>], a marker of adverse remodeling that often precedes heart failure development, stressing the irreversible effects of these exposures on the heart.</p><p>To identify the underlying cellular mechanisms involved in the cardiac dysfunction following NO<sub>2</sub> exposure, we first focused on cardiac mitochondrial ROS and ATP production. Two spatially distinct mitochondria subpopulations have been observed in myocytes and may be associated with a specific response to pathological stimuli, indicating the role of IFM in contractile function [<xref rid=\"B26-ijerph-17-05526\" ref-type=\"bibr\">26</xref>,<xref rid=\"B27-ijerph-17-05526\" ref-type=\"bibr\">27</xref>]. The present study revealed a primary but reversible interfibrillar mitochondria-specific increase in ROS after acute exposure. This increase was not accompanied by a mitochondrial alteration, as evidenced by the maintenance of ATP synthesis capacity. The increase in mitochondrial ROS can reflect an adaptive event involved in cardio-protection, as previously demonstrated [<xref rid=\"B28-ijerph-17-05526\" ref-type=\"bibr\">28</xref>,<xref rid=\"B29-ijerph-17-05526\" ref-type=\"bibr\">29</xref>]. For example, Antonucci et al. [<xref rid=\"B29-ijerph-17-05526\" ref-type=\"bibr\">29</xref>] investigated the effects of mitochondrial ROS production using a mitochondria-targeted redox cycler MitoParaquat (MitoPQ) and showed that low levels of ROS are cardioprotective, while higher doses of MitoPQ resulted in a progressive alteration of mitochondrial function in vitro. The magnitude but also the repetition of the mitochondrial ROS production may result in the alteration of mitochondrial function and in worsening of cardiac function, as observed after repeated NO<sub>2</sub> exposures. The strong correlation between mitochondrial ROS production and cardiac output associated with the appearance of mitochondrial dysfunction support this hypothesis. The fact that mitochondrial ROS production precedes the mitochondrial dysfunction suggest that this production is an early trigger event in the onset of mitochondrial defect. Indeed, we showed that respiration rate was affected in the hearts of rats exposed to repeated NO<sub>2</sub> exposures. This is consistent with our previous study showing that repeated DE exposures induced an impairment of mitochondrial function associated with deficient cardiac contractility [<xref rid=\"B13-ijerph-17-05526\" ref-type=\"bibr\">13</xref>]. With regard to other combustion-related gas, CO [<xref rid=\"B30-ijerph-17-05526\" ref-type=\"bibr\">30</xref>] and SO<sub>2</sub> [<xref rid=\"B31-ijerph-17-05526\" ref-type=\"bibr\">31</xref>] exposures also induced cardiac mitochondrial effects. Overall, these results suggest that mitochondria impairment contributes to the CV events linked to air pollution.</p><p>Then, we focused on coronary endothelial dysfunction. Indeed, despite the number of studies showing that air pollution, and more specifically DE particles, causes endothelial dysfunction [<xref rid=\"B32-ijerph-17-05526\" ref-type=\"bibr\">32</xref>,<xref rid=\"B33-ijerph-17-05526\" ref-type=\"bibr\">33</xref>,<xref rid=\"B34-ijerph-17-05526\" ref-type=\"bibr\">34</xref>], the contribution of the gaseous phase in these effects is underestimated and the effect of NO<sub>2</sub> itself on endothelial function is unknown. After acute exposure, we did not observe any effect on endothelial function. This is in line with a previous study showing no impact on brachial artery reactivity after 1 h exposure of humans to 4 ppm NO<sub>2</sub> [<xref rid=\"B8-ijerph-17-05526\" ref-type=\"bibr\">8</xref>]. Nevertheless, repeated NO<sub>2</sub> exposures impaired relaxation to acetylcholine in coronary arteries from healthy rats. Endothelial NO participates in the control of vascular tone and changes observed in the reactivity of coronary arteries after NO<sub>2</sub> exposures might be explained by a decrease in NO availability. As repeated NO<sub>2</sub> did not modify relaxation induced by sodium nitroprusside, a NO donor, a direct effect of NO<sub>2</sub> on vascular smooth muscle cells is unlikely. These results demonstrate that NO<sub>2</sub> exposures impaired coronary endothelial cell function, an effect that was due to a reduction of endothelial NO availability. One possible mechanism of reduced NO availability might be explained by an increased superoxide production in arteries after NO<sub>2</sub> exposures, since superoxide scavenging by SOD restored attenuated ACh-induced relaxation of coronary arteries. The reduction in NO-bioavailability and the resulting altered coronary function probably contributes to a decrease in myocardial perfusion, which is likely to contribute to hypoxia-induced ROS production and, as a probable consequence, causes the vicious circle of ROS-induced ROS release [<xref rid=\"B35-ijerph-17-05526\" ref-type=\"bibr\">35</xref>,<xref rid=\"B36-ijerph-17-05526\" ref-type=\"bibr\">36</xref>]. Although we did not evaluate the pro-inflammatory markers in the present study, we cannot exclude a contribution of circulating factors in these effects. A previous study suggested vascular toxicity mediated by pro-inflammatory circulating factors following exposure to NO<sub>2</sub> [<xref rid=\"B12-ijerph-17-05526\" ref-type=\"bibr\">12</xref>]. These authors showed that plasma from humans exposed to 0.5 ppm NO<sub>2</sub> was able to activate expression of cell adhesion molecules by coronary endothelial cells in culture [<xref rid=\"B12-ijerph-17-05526\" ref-type=\"bibr\">12</xref>]. Further studies are needed to determine the precise underlying mechanism.</p><p>In conclusion, we demonstrate for the first time that repeated NO<sub>2</sub> exposures altered cardiac mitochondrial and cardiac function, as well as coronary vascular reactivity, because of endothelial dysfunction. Moreover, these results demonstrated that acute exposure to NO<sub>2</sub> induced a mitochondrial ROS production, which could represent an early trigger event in the onset of cardiovascular defects after chronic exposures.</p></sec></body><back><notes><title>Author Contributions</title><p>Conceptualization, A.K., C.C. (Cécile Corbière), P.M. and C.M.; Formal analysis, C.M.; Funding acquisition, C.M.; Methodology, A.K., C.C. (Clément Crochemore), N.H., D.P., C.V. and P.M.; Supervision, C.C. (Cécile Corbière), V.R., O.F., V.L., J.-M.V., F.S. and P.M.; Writing—original draft, A.K.; Writing—review & editing, C.M. All authors have read and agreed to the published version of the manuscript.</p></notes><notes><title>Funding</title><p>This research was funded by ADEME (French Environment and Energy Management Agency; Cardiox project, grant number 2013-1-237), and co-supported by the Rouen Normandie University and the European Union (Europe gets involved in Normandy with European Regional Development Fund; ERDF). Ahmed Karoui was a receipt of a PhD grant from the ADEME and the Regional Council of Haute-Normandie, Clément Crochemore was a receipt of a PhD grant from the Regional Council of Haute-Normandie and Najah Harouki was a receipt of a PhD grant from the Rouen Normandie University. The authors thank F. Dionnet, V. Keravec and E Estace from CERTAM (Saint-Etienne du Rouvray, France) for in vivo facilities.</p></notes><notes notes-type=\"COI-statement\"><title>Conflicts of Interest</title><p>Authors declare that there is no conflict of interest.</p></notes><notes><title>Submission Declaration</title><p>The authors declare that the work described has not been published previously.</p></notes><ref-list><title>References</title><ref id=\"B1-ijerph-17-05526\"><label>1.</label><element-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Cohen</surname><given-names>A.J.</given-names></name><name><surname>Brauer</surname><given-names>M.</given-names></name><name><surname>Burnett</surname><given-names>R.</given-names></name><name><surname>Anderson</surname><given-names>H.R.</given-names></name><name><surname>Frostad</surname><given-names>J.</given-names></name><name><surname>Estep</surname><given-names>K.</given-names></name><name><surname>Balakrishnan</surname><given-names>K.</given-names></name><name><surname>Brunekreef</surname><given-names>B.</given-names></name><name><surname>Dandona</surname><given-names>L.</given-names></name><name><surname>Dandona</surname><given-names>R.</given-names></name><etal/></person-group><article-title>Estimates and 25-year trends of the global burden of disease attributable to ambient air pollution: An analysis of data from the Global Burden of Diseases Study 2015</article-title><source>Lancet</source><year>2017</year><volume>389</volume><fpage>1907</fpage><lpage>1918</lpage><pub-id pub-id-type=\"doi\">10.1016/S0140-6736(17)30505-6</pub-id><pub-id pub-id-type=\"pmid\">28408086</pub-id></element-citation></ref><ref id=\"B2-ijerph-17-05526\"><label>2.</label><element-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Lewis</surname><given-names>A.C.</given-names></name><name><surname>Carslaw</surname><given-names>D.C.</given-names></name><name><surname>Kelly</surname><given-names>F.J.</given-names></name></person-group><article-title>Diesel pollution long under-reported</article-title><source>Nature</source><year>2015</year><volume>526</volume><fpage>195</fpage><pub-id pub-id-type=\"doi\">10.1038/526195c</pub-id><pub-id pub-id-type=\"pmid\">26450046</pub-id></element-citation></ref><ref id=\"B3-ijerph-17-05526\"><label>3.</label><element-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Duan</surname><given-names>Y.</given-names></name><name><surname>Liao</surname><given-names>Y.</given-names></name><name><surname>Li</surname><given-names>H.</given-names></name><name><surname>Yan</surname><given-names>S.</given-names></name><name><surname>Zhao</surname><given-names>Z.</given-names></name><name><surname>Yu</surname><given-names>S.</given-names></name><name><surname>Fu</surname><given-names>Y.</given-names></name><name><surname>Wang</surname><given-names>Z.</given-names></name><name><surname>Yin</surname><given-names>P.</given-names></name><name><surname>Cheng</surname><given-names>J.</given-names></name><etal/></person-group><article-title>Effect of changes in season and temperature on cardiovascular mortality associated with nitrogen dioxide air pollution in Shenzhen, China</article-title><source>Sci. 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The diagram shows echocardiographic measurements of left ventricle (LV) end-diastolic diameter (<bold>A</bold>), LV end-systolic diameter (<bold>B</bold>), LV fractional shortening (<bold>C</bold>) and cardiac output (<bold>D</bold>), measured after acute NO<sub>2</sub> exposure or 1-day post-exposure when specified. *** <italic>p</italic> < 0.001 between corresponding group.</p></caption><graphic xlink:href=\"ijerph-17-05526-g002\"/></fig><fig id=\"ijerph-17-05526-f003\" orientation=\"portrait\" position=\"float\"><label>Figure 3</label><caption><p>Coronary vasodilation measured after acute NO<sub>2</sub> exposure.</p></caption><graphic xlink:href=\"ijerph-17-05526-g003\"/></fig><fig id=\"ijerph-17-05526-f004\" orientation=\"portrait\" position=\"float\"><label>Figure 4</label><caption><p>Cardiac mitochondrial function and ROS production after acute NO<sub>2</sub> exposure. (<bold>A</bold>) Mitochondrial OXPHOS evaluated by O<sub>2</sub> oxygen consumption assessment in cardiac permeabilized fibers. State 2 respiration with complex I-linked substrates (Glutamate/Malate); State 3 respiration with complex I-linked substrates (Glutamate/Malate); State 3 respiration with complex II-linked substrates (Succinate); (<bold>B</bold>) Acceptor control ratio (ACR) with complex I-linked substrates (<bold>C</bold>) adenosine triphosphate (ATP; nmol/mg proteins) and (<bold>D</bold>) reactive oxygen species (ROS) productions (A.U/mg proteins) were measured from freshly isolated subsarcolemmal (SSM) and interfibrillar (IFM), 1 h or 1-day post-exposure, when specified, after acute NO<sub>2</sub> or air (control) exposure. * <italic>p</italic> < 0.05.</p></caption><graphic xlink:href=\"ijerph-17-05526-g004\"/></fig><fig id=\"ijerph-17-05526-f005\" orientation=\"portrait\" position=\"float\"><label>Figure 5</label><caption><p>Echocardiographic parameters after repeated NO<sub>2</sub> exposures. Echocardiographic assessments were performed at 1-day post-exposure after 3 weeks (3 h/day, 5 days/week) of NO<sub>2</sub> exposures. The diagram shows echocardiographic measurements of left ventricle (LV) end-diastolic diameter (<bold>A</bold>), LV end-systolic diameter (<bold>B</bold>), LV fractional shortening (<bold>C</bold>) and cardiac output (<bold>D</bold>). * <italic>p</italic> < 0.05, ** <italic>p</italic> < 0.01, vs. control.</p></caption><graphic xlink:href=\"ijerph-17-05526-g005\"/></fig><fig id=\"ijerph-17-05526-f006\" orientation=\"portrait\" position=\"float\"><label>Figure 6</label><caption><p>Coronary function after repeated NO<sub>2</sub> exposures. Coronary function was assessed at 1-day post-exposure after 3 weeks (3 h/day, 5 days/week) of NO<sub>2</sub> exposures. Vasorelaxation induced by acetylcholine (<bold>A</bold>) or sodium nitroprusside (SNP) (<bold>B</bold>), or acetylcholine after preincubation with either NG-nitro-L-arginine (Ach + L-NNA) (<bold>C</bold>) or superoxide dismutase (+SOD) (<bold>D</bold>) of isolated coronary arteries from control (open circles) or NO<sub>2</sub>-exposed rat (solid squares). * <italic>p</italic> < 0.05, *** <italic>p</italic> < 0.001 vs. control.</p></caption><graphic xlink:href=\"ijerph-17-05526-g006\"/></fig><fig id=\"ijerph-17-05526-f007\" orientation=\"portrait\" position=\"float\"><label>Figure 7</label><caption><p>Cardiac mitochondrial function, ROS production and correlation with cardiac function, at 1-day post-exposure after repeated NO<sub>2</sub> exposures. (<bold>A</bold>) Mitochondrial OXPHOS evaluated by O<sub>2</sub> oxygen consumption assessment in permeabilized fibers. State 2 respiration with complex I-linked substrates (Glutamate/Malate); State 3 respiration with ADP and complex I-linked substrates (Glutamate/Malate); State 3 respiration with ADP and complex II-linked substrates (Succinate); (<bold>B</bold>) ACR with complex I-linked substrates (<bold>C</bold>) ATP (nmol/mg proteins) and (<bold>D</bold>) ROS productions (A.U/mg proteins) were measured from freshly isolated SSM and IFM. (<bold>E</bold>) Correlation between cardiac output and interfibrillar mitochondrial ROS production. * <italic>p</italic> < 0.05.</p></caption><graphic xlink:href=\"ijerph-17-05526-g007\"/></fig></floats-group></article>\n"
]
[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Int J Environ Res Public Health</journal-id><journal-id journal-id-type=\"iso-abbrev\">Int J Environ Res Public Health</journal-id><journal-id journal-id-type=\"publisher-id\">ijerph</journal-id><journal-title-group><journal-title>International Journal of Environmental Research and Public Health</journal-title></journal-title-group><issn pub-type=\"ppub\">1661-7827</issn><issn pub-type=\"epub\">1660-4601</issn><publisher><publisher-name>MDPI</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32722180</article-id><article-id pub-id-type=\"pmc\">PMC7432062</article-id><article-id pub-id-type=\"doi\">10.3390/ijerph17155337</article-id><article-id pub-id-type=\"publisher-id\">ijerph-17-05337</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Article</subject></subj-group></article-categories><title-group><article-title>Diurnal Profiles of Locomotive and Household Activities Using an Accelerometer in Community-Dwelling Older Adults with Musculoskeletal Disorders: A Cross-Sectional Survey</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0002-6805-2742</contrib-id><name><surname>Sakakima</surname><given-names>Harutoshi</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05337\">1</xref><xref rid=\"c1-ijerph-17-05337\" ref-type=\"corresp\">*</xref></contrib><contrib contrib-type=\"author\"><name><surname>Takada</surname><given-names>Seiya</given-names></name><xref ref-type=\"aff\" rid=\"af2-ijerph-17-05337\">2</xref></contrib><contrib contrib-type=\"author\"><name><surname>Norimatsu</surname><given-names>Kosuke</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05337\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Otsuka</surname><given-names>Shotaro</given-names></name><xref ref-type=\"aff\" rid=\"af2-ijerph-17-05337\">2</xref></contrib><contrib contrib-type=\"author\"><name><surname>Nakanishi</surname><given-names>Kazuki</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05337\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Tani</surname><given-names>Akira</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05337\">1</xref></contrib></contrib-group><aff id=\"af1-ijerph-17-05337\"><label>1</label>Department of Physical Therapy, School of Health Sciences, Faculty of Medicine, Kagoshima University, 8-35-1, Sakuragaoka, Kagoshima 890-8544, Japan; <email>[email protected]</email> (K.N.); <email>[email protected]</email> (K.N.); <email>[email protected]</email> (A.T.)</aff><aff id=\"af2-ijerph-17-05337\"><label>2</label>Department of Systems Biology in Thromboregulation, Kagoshima University Graduate School of Medical and Dental Science, Kagoshima 890-8520, Japan; <email>[email protected]</email> (S.T.); <email>[email protected]</email> (S.O.)</aff><author-notes><corresp id=\"c1-ijerph-17-05337\"><label>*</label>Correspondence: <email>[email protected]</email>; Tel.: +81-99-275-6778</corresp></author-notes><pub-date pub-type=\"epub\"><day>24</day><month>7</month><year>2020</year></pub-date><pub-date pub-type=\"ppub\"><month>8</month><year>2020</year></pub-date><volume>17</volume><issue>15</issue><elocation-id>5337</elocation-id><history><date date-type=\"received\"><day>21</day><month>6</month><year>2020</year></date><date date-type=\"accepted\"><day>21</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>© 2020 by the authors.</copyright-statement><copyright-year>2020</copyright-year><license license-type=\"open-access\"><license-p>Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>).</license-p></license></permissions><abstract><p>The present study investigates the diurnal profiles of locomotive and household activities in older adults with musculoskeletal disorders (MSDs) using an accelerometer. Furthermore, we examined the effect of chronic pain on their diurnal profiles in both activities. Seventy-one older adults with MSDs (73–89 years) were included in this cross-sectional survey, and 25 age-matched older adults (75–86 years) were selected as healthy older adults. The daily physical activities, including steps walked and locomotive and household activity intensities, were recorded using a triaxial accelerometer in terms of metabolic equivalent task-hours per week (MET-h/week). The diurnal profiles of steps and locomotive activities in older adults with MSDs were considerably lower than those of healthy older adults. In contrast, there was no significant decline in household activity. However, the locomotive and household activities were reduced by severe chronic pain. This survey demonstrated that the diurnal profiles of household activity in older people with MSDs as well as those in age-matched healthy older adults were maintained. Furthermore, severe chronic pain influenced both activities. Therefore, the maintenance of household activity throughout the day, as well as the management of chronic pain, may be important strategies for the promotion of physical activity in older people with MSDs.</p></abstract><kwd-group><kwd>physical activity</kwd><kwd>aging</kwd><kwd>rehabilitation</kwd><kwd>musculoskeletal disease</kwd></kwd-group></article-meta></front><body><sec sec-type=\"intro\" id=\"sec1-ijerph-17-05337\"><title>1. Introduction</title><p>Musculoskeletal disorders (MSDs), including chronic musculoskeletal pain and osteoarthritis of the knee or hip, are common in older people. These diseases have a significant impact on locomotive and household activities [<xref rid=\"B1-ijerph-17-05337\" ref-type=\"bibr\">1</xref>,<xref rid=\"B2-ijerph-17-05337\" ref-type=\"bibr\">2</xref>], and place a considerable burden on the health care system [<xref rid=\"B3-ijerph-17-05337\" ref-type=\"bibr\">3</xref>]. Although orthopedic surgery can be performed as a treatment in older people with MSDs, the chronic musculoskeletal pain and physical function may not fully improve after surgery. Therefore, these patients have to live with pharmacological and non-pharmacological therapies, such as ambulatory rehabilitation. In addition, older people with MSDs are not physically active, and they likely do not meet the recommended physical activity levels in daily life [<xref rid=\"B2-ijerph-17-05337\" ref-type=\"bibr\">2</xref>,<xref rid=\"B4-ijerph-17-05337\" ref-type=\"bibr\">4</xref>]. Therefore, some community-dwelling older adults with MSDs have preventive care programs to enhance physical function. It is naturally important to enhance daily physical activities, including locomotive and household activities to prevent the requirement of a care program. However, few studies have investigated the diurnal profiles of the physical activities classified as locomotive and household activities throughout the day in community-dwelling older adults with MSDs.</p><p>Some studies have reported the diurnal profiles of physical activity in community-dwelling older adults with chronic diseases and functional limitations [<xref rid=\"B5-ijerph-17-05337\" ref-type=\"bibr\">5</xref>,<xref rid=\"B6-ijerph-17-05337\" ref-type=\"bibr\">6</xref>]. These studies focused on steps taken throughout the day and health status in older adults. Mai et al. [<xref rid=\"B6-ijerph-17-05337\" ref-type=\"bibr\">6</xref>] reported that the high activity phases throughout the day were mostly due to basic and instrumental activities of daily living, such as housework. However, they did not actually investigate the diurnal profiles that categorized locomotive or household activities in older adults. Therefore, it is unclear whether the older adults performed housework activities in high activity phases throughout the day. Furthermore, although musculoskeletal pain is a common source of serious long-term pain and physical disability [<xref rid=\"B1-ijerph-17-05337\" ref-type=\"bibr\">1</xref>], few studies have investigated the effect of the chronic pain level on the diurnal profile of locomotive or household activities throughout the day in older adults with MSDs.</p><p>Accelerometers are increasingly being used to allow researchers to assess energy costs, and are generally considered superior to other methods of measuring physical activity categories [<xref rid=\"B7-ijerph-17-05337\" ref-type=\"bibr\">7</xref>,<xref rid=\"B8-ijerph-17-05337\" ref-type=\"bibr\">8</xref>]. Accelerometers are non-invasive tools for measuring physical activity and activity energy expenditures, with the potential to measure locomotive as well as household activities [<xref rid=\"B8-ijerph-17-05337\" ref-type=\"bibr\">8</xref>,<xref rid=\"B9-ijerph-17-05337\" ref-type=\"bibr\">9</xref>,<xref rid=\"B10-ijerph-17-05337\" ref-type=\"bibr\">10</xref>]. The Omron activity monitor (Active style Pro, HJA-750C, Omron Healthcare Co., Ltd., Kyoto, Japan), which is a triaxial accelerometer, is a type of motion sensor that is low-cost unobtrusive. Physical activity intensities can be used to estimate the metabolic equivalents (METs) for daily activities from the original algorithm, using the Omron activity monitor [<xref rid=\"B9-ijerph-17-05337\" ref-type=\"bibr\">9</xref>,<xref rid=\"B11-ijerph-17-05337\" ref-type=\"bibr\">11</xref>]. A change in the movement of the body was estimated using Omron’s original signal processing, and physical activities were classified into either locomotive or household activities, using the original algorithm [<xref rid=\"B7-ijerph-17-05337\" ref-type=\"bibr\">7</xref>,<xref rid=\"B9-ijerph-17-05337\" ref-type=\"bibr\">9</xref>,<xref rid=\"B11-ijerph-17-05337\" ref-type=\"bibr\">11</xref>,<xref rid=\"B12-ijerph-17-05337\" ref-type=\"bibr\">12</xref>]. Furthermore, Park et al. [<xref rid=\"B13-ijerph-17-05337\" ref-type=\"bibr\">13</xref>] examined the association of locomotive and non-locomotive physical activities, such as household activity, using an Omron activity monitor in healthy older men. Many investigators have assessed the validity and reliability of the Omron activity monitor in large populations [<xref rid=\"B9-ijerph-17-05337\" ref-type=\"bibr\">9</xref>,<xref rid=\"B11-ijerph-17-05337\" ref-type=\"bibr\">11</xref>,<xref rid=\"B12-ijerph-17-05337\" ref-type=\"bibr\">12</xref>]. Therefore, we examined the diurnal profiles of locomotive or household physical activities during the course of the day between older adults with MSDs and age-matched healthy older adults. An explorative analysis of locomotive or household activities may provide interesting insights regarding the physical activity patterns of older adults with MSDs, which may be useful in the generation of hypotheses for optimizing their physical activity for preventive care.</p><p>The present study aimed to investigate the diurnal profiles of walked steps and locomotive and household activities throughout the day in older adults with MSDs compared with those in age-matched healthy older adults. As chronic pain is the leading complaint of functional limitations in older adults with MSDs, we also examined the effect of the level of chronic pain on the diurnal profiles of locomotive and household activities throughout the day.</p></sec><sec id=\"sec2-ijerph-17-05337\"><title>2. Materials and Methods </title><sec id=\"sec2dot1-ijerph-17-05337\"><title>2.1. Study Design </title><p>The present study was a cross-sectional survey conducted in three rural areas of Kagoshima prefecture, Japan. Community-dwelling older people (aged 70 years or older) living in Ei-cho, South Kyushu City (total population of Ei-cho area; <italic>n</italic> = 12,388 in 2015), Matsumoto area of the Kagoshima city suburbs (total population of Matsumoto area; <italic>n</italic> = 15,363 in 2015), and Kirishima city (total population of the area; <italic>n</italic> = 125,900 in 2018) were included in this survey. The data of older adults with MSDs were obtained from the clinic in the Ei-cho and Matsumoto areas between February 2015 and November 2018. The older adults with MSDs visited the clinic for treatment of chronic musculoskeletal pain or enhancement of physical function at least one to three times per week. We compared the data of the older adults with MSDs with those of age-matched healthy older adults. The age-matched community-dwelling healthy older adults were recruited from among the participants of the Kirishima Health Welfare Festival 2018 and 2019.</p><p>Informed consent was obtained from all participants prior to their entry into this survey. This work was approved by the Ethical Committee of Epidemiological Studies of Kagoshima University (Ref. No.180050).</p></sec><sec id=\"sec2dot2-ijerph-17-05337\" sec-type=\"subjects\"><title>2.2. Participants</title><p>The subject enrollment flow chart is shown in <xref ref-type=\"fig\" rid=\"ijerph-17-05337-f001\">Figure 1</xref>. The inclusion criteria for older adults with MSDs were age of 70 years or older, independently mobile with or without a walking aid, and community-dwelling (not institutionalized). The exclusion criteria were: orthopedic surgery within the past year, acute pain and history of dementia, neurological disease, cardiovascular disease, or severe comorbid illness. Following screening for inclusion and exclusion criteria, as well as missing data for active monitoring, 32 participants were excluded. Finally, 71 older adults with MSDs provided full activity data and were analyzed with regard to physical activities and physical functions. The older adults with MSDs were almost independent with respect to activities of daily living, with the exception of going up and down stairs.</p><p>Age-matched community-dwelling older adults were selected as healthy controls using the following inclusion criteria: age of 70 years or older; independently mobile; no chronic pain, including low back pain and lower extremities pain, at present; no visits to clinics or hospital for treatment of chronic pain within the past year; and no history of orthopedic surgery. Similar to older adults with MSDs, the exclusion criteria were history of dementia, neurological disease, cardiovascular disease and severe comorbid illness. Generally, there is high rate of geriatric syndromes including chronic pain with aging. Therefore, the data of 25 older adults were used as age-matched healthy controls after collecting the data from 70 participants. The activities of daily living of all the control older adults were independent.</p></sec><sec id=\"sec2dot3-ijerph-17-05337\"><title>2.3. Data Collection and Assessment of Physical Functions</title><p>Baseline data were collected through a questionnaire that included question on age, sex, body weight, height and chronic pain level. In this study, chronic pain was defined when low back pain and lower extremity pain (e.g., knee pain or hip pain) endured more than 3 months. The degree of the chronic pain was quantified using a numeric rating scale (NRS) and categorized as mild chronic pain (1–3), moderate chronic pain (4–6) and severe chronic pain (7–10).</p><p>The timed up and go (TUG) test and the 30 s chair-standing test (CS-30) were performed to assess the physical functions of the participants. The TUG test was performed according to previous reports [<xref rid=\"B14-ijerph-17-05337\" ref-type=\"bibr\">14</xref>]; the TUG test was conducted twice, and the better value of the two tests was selected as the representative. The participants were asked to sit and stand on a chair with a seat height of 40 cm, as quickly and safely as possible over 30 s periods. The total number of completed chair stands within 30 s was counted and recorded. The CS-30 was conducted once, considering the fatigue of the participants. All measurements were evaluated by trained physical therapists.</p></sec><sec id=\"sec2dot4-ijerph-17-05337\"><title>2.4. Assessment of Physical Activity</title><p>The participant daily physical activity was recorded using an Omron activity monitor (Active style Pro, HJA-750C, Omron Healthcare Co., Ltd., Kyoto, Japan). This device was positioned at each participant’s waist. This device calculated the integral of the absolute value of the three signals axes using an accelerated signal over a 10 s time interval [<xref rid=\"B7-ijerph-17-05337\" ref-type=\"bibr\">7</xref>,<xref rid=\"B12-ijerph-17-05337\" ref-type=\"bibr\">12</xref>]. After the synthetic acceleration was filtered, it was categorized into either locomotive or lifestyle activity using the ratio of unfiltered-to-filtered synthetic acceleration [<xref rid=\"B7-ijerph-17-05337\" ref-type=\"bibr\">7</xref>]. The original Omron algorithm can accurately classify locomotive and household activities using the ratios of unfiltered to filtered total acceleration cut off value [<xref rid=\"B12-ijerph-17-05337\" ref-type=\"bibr\">12</xref>]. Therefore, this device can distinguish not only the intensity of an activity, but also the intensity of basic daily activities, and can classify locomotive and household activities [<xref rid=\"B9-ijerph-17-05337\" ref-type=\"bibr\">9</xref>,<xref rid=\"B11-ijerph-17-05337\" ref-type=\"bibr\">11</xref>,<xref rid=\"B12-ijerph-17-05337\" ref-type=\"bibr\">12</xref>]. Moreover, this device can record the number of steps in daily locomotive activity. In the current study, we analyzed the data on steps and vigorous intensity of locomotive and household activities by MET-hours per week (MET-h/week). The two physical activity intensities were recorded as low (1.0–2.9 METs), moderate (3.0–5.9 METs) and high (6.0 METs or more). The participants were instructed to wear the activity monitor for 7 consecutive days.</p></sec><sec id=\"sec2dot5-ijerph-17-05337\"><title>2.5. Data Analysis</title><p>The following data were defined to obtain valid and reliable data on usual daily physical activity: (1) the participants had to wear the device for more than 8 h per day, and (2) the participants had to wear the device for 7 days (from Monday to Sunday). The data collection period was from when the participants got up in the morning until they fell asleep at night. The older adults with MSDs and the older adult controls wore the Omron activity monitor for 11.6 ± 2.6 and 11.6 ± 1.8 (mean ± SD) hours per day, respectively. Data were stored on this device, and the stored activity data were downloaded using the associated software that generated the daily step counts and the locomotive and household activity intensities. Subsequently, the steps per day and steps per hour were calculated, and the steps per hour were analyzed to investigate the diurnal profiles of walking physical activity.</p><p>Using the physical activity intensities, we categorized physical activity as locomotive and household activities, and we analyzed the vigorous intensity of locomotive and household activities (MET-h)/week) of moderate and high intensities (≥3 METs). The average locomotive and household activity intensities per hour (MET-h) were calculated as the diurnal profiles of locomotive and household activity per day. The hourly steps and locomotive and household activities throughout the day are illustrated as diurnal profiles of physical activity (mean and 95% confidence interval). To explore whether the physical activities per day (steps and locomotive and household activity intensities) were influenced by chronic pain in older adults with MSDs, we evaluated the chronic pain levels and illustrated the diurnal profiles of physical activity in older adults with mild and severe chronic pain. As the physical activity between 11 p.m. and 5 a.m. was only recorded in a few instances, the graphs of the diurnal profiles cover the period between 6 a.m. and 10 a.m.</p><p>The collected data were checked using the Shapiro–Wilk test to determine if they were normally distributed. Subsequently, comparisons between the groups were performed using the Student’s t-test or Mann–Whitney U test. A two-factor repeated measures analysis of variance (groups × time) was used for comparison within- and between-group differences. Statistical analyses were performed using SPSS version 26 (Chicago, IL, USA), and values of <italic>p</italic> < 0.05 were considered statistically significant.</p></sec></sec><sec sec-type=\"results\" id=\"sec3-ijerph-17-05337\"><title>3. Results</title><sec id=\"sec3dot1-ijerph-17-05337\"><title>3.1. Participant Characteristics </title><p>The characteristics of all participants are presented in <xref rid=\"ijerph-17-05337-t001\" ref-type=\"table\">Table 1</xref>. There were no significant differences in age, sex, and body mass index between the older adults with MSDs and the control older adults. The older adults with MSDs had two or more orthopedic diseases. All older adults with MSDs had chronic pain, and 18.3% had severe chronic pain.</p></sec><sec id=\"sec3dot2-ijerph-17-05337\"><title>3.2. Physical Activity and Diurnal Profiles Per Day</title><p>The physical function and locomotive and household activities per week were compared between older adults with MSDs and control older adults (<xref rid=\"ijerph-17-05337-t002\" ref-type=\"table\">Table 2</xref>). All physical functions and step parameters in older adults with MSDs were significantly lower than those in older adult controls. The number of steps per day and the number of steps per hour in older adults with MSDs were approximately 50% lower than those in older adult controls. The activity monitor-determined locomotive activity (MET h/week) in older adults with MSDs was approximately 20% worse than that in the older adult controls. However, the household activity (MET h/week) in older adults with MSDs was similar to that in older adult controls, showing that the household activity in older adults with MSDs was maintained, even if the physical functions and locomotive activity declined, compared with that in age-matched healthy older adults.</p><p>Over the course of the day, the diurnal profiles of locomotive and household activities in older adults showed two peaks of physical activity in the morning and afternoon, and one low point of physical activity around 12 p.m. or 1 p.m (<xref ref-type=\"fig\" rid=\"ijerph-17-05337-f002\">Figure 2</xref>). Furthermore, the diurnal profiles of steps and activity monitor-determined locomotive activity were significantly different between group and time interactions (<italic>p</italic> < 0.001), and showed different physical activity patterns throughout the day. In the older adults with MSDs, the number of steps per hour peaked at 9 a.m. (mean: 247; 95% CI: 192–301) and 3 p.m. (mean: 240; 95% CI: 183–297), and locomotive intensity activity per hour peaked around 9 a.m. (mean: 0.26; 95% CI: 0.15–0.37) and 3 p.m. (mean: 0.22; 95% CI: 0.12–0.32). In contrast, the number of steps per hour of the older adult controls peaked at 11 a.m. (mean: 504; 95% CI: 331–676) and 5 p.m. (mean: 621; 95% CI: 244–999), and their locomotive activity peaked around 11 a.m. (mean: 1.11; 95% CI: 0.43–1.78) and 5 p.m. (mean: 1.80; 95% CI: 0.25–3.35). The two peaks of physical activity in older adults with MSDs were considerably lower than those of the older adult controls. These profiles show that older adults with MSDs have reduced locomotive activity throughout the day compared with age-matched healthy older adult controls.</p><p>The household activity of older adults with MSDs peaked around 9 a.m. (mean: 1.55; 95% CI: 1.30–1.80) and 4 p.m. (mean: 1.32; 95% CI: 1.09–1.56), while in the older adult controls, household activity (MET hour) peaked around 9 a.m. (mean: 1.58; 95% CI: 1.22–1.94) and 4 p.m. (mean: 1.19; 95% CI: 0.93–1.45). There were no significant differences between group and time interactions in the diurnal profile of household activity (<italic>p</italic> = 0.060), and the analysis of between-subject effects showed no significant differences in steps (<italic>p</italic> = 0.185). The diurnal profile of household activity in the older adults with MSDs was similar to that in the age-matched healthy older adult controls.</p></sec><sec id=\"sec3dot3-ijerph-17-05337\"><title>3.3. Chronic Pain Level and Physical Activity in Older Adults with MSDS </title><p>The differences in locomotive and household activities classified by chronic pain level are shown in the <xref rid=\"ijerph-17-05337-t003\" ref-type=\"table\">Table 3</xref>. The locomotive and household activities were influenced by chronic pain level, and the chronic pain level negatively impacted instrumental activities of daily living. The number of steps per day in older adults with severe pain was significantly lower than that in older adults with mild pain. Therefore, we compared the diurnal profiles of locomotive and household activities (MET hour) per day throughout the day in older adults with mild and severe pain levels (<xref ref-type=\"fig\" rid=\"ijerph-17-05337-f003\">Figure 3</xref>). There was no significant difference between the group and time interactions in the diurnal profiles of steps (<italic>p</italic> = 0.458) and locomotive activity (<italic>p</italic> = 0.722), and the analysis of between-subject effects showed no significant differences in steps (<italic>p</italic> = 0.053) and locomotive activity (<italic>p</italic> = 0.052). The diurnal profile of household activity throughout the day showed a significant difference between group and time interactions (<italic>p</italic> = 0.035), suggesting that the diurnal profile of household activity throughout the day was affected by chronic pain level.</p></sec></sec><sec sec-type=\"discussion\" id=\"sec4-ijerph-17-05337\"><title>4. Discussion</title><p>The present study investigated the diurnal profiles of steps and locomotive and household activities, which were measured using three axial accelerations, in community-dwelling older adults with MSDs. Mai et al. [<xref rid=\"B6-ijerph-17-05337\" ref-type=\"bibr\">6</xref>] demonstrated that the pedometer-determined diurnal profiles of physical activity by steps in chronically ill and mobility-limited older adults showed two peaks in the morning and in the afternoon. The diurnal profiles of steps and the locomotive and household activities of older people with MSD were similar in our study to the results of previous reports. The current study showed that phases of higher activity in the morning and afternoon were usually occupied by household activity, and not locomotive activity, in older adults with MSD. Several studies have reported that limited mobility decreases physical activity [<xref rid=\"B5-ijerph-17-05337\" ref-type=\"bibr\">5</xref>,<xref rid=\"B6-ijerph-17-05337\" ref-type=\"bibr\">6</xref>]. Furthermore, the present survey showed that physical function and locomotive activity were considerably lower in the older people with MSDs, while the diurnal profile of the steps and locomotive activity were clearly different between the older people with MSDs and age-matched older adults. However, the household activity and diurnal profiles of household activity throughout the day were similar in both groups of older adults. These findings suggest that household activity, but not locomotive activity, was maintained in the daily life of older adults with MSDs, although their physical function and locomotive activity were declined considerably throughout the day.</p><p>A wide range of epidemiological studies have examined physical activity in elderly people [<xref rid=\"B15-ijerph-17-05337\" ref-type=\"bibr\">15</xref>,<xref rid=\"B16-ijerph-17-05337\" ref-type=\"bibr\">16</xref>,<xref rid=\"B17-ijerph-17-05337\" ref-type=\"bibr\">17</xref>]. The normative data of recent reviews indicate that healthy older adults take 2000–9000 steps/day on average, and special populations, such as those living with disability and/or chronic illness, average 1200–8800 steps/day [<xref rid=\"B4-ijerph-17-05337\" ref-type=\"bibr\">4</xref>]. In addition, Tudor-Locke et al. [<xref rid=\"B18-ijerph-17-05337\" ref-type=\"bibr\">18</xref>] conducted a literature review and suggested the use of pedometer-determined habitual physical activity in adults (e.g., sedentary: <5000 steps/day, low active: 5000–7499 steps/day, physically active; >7500 steps/day). According to these categories, our results showed that mean steps/day of the older adults with MSDs were categorized as sedentary, while those of the age-matched healthy older adults were categorized as low active. Physical activity declines with increasing age, and is generally cause by frailty or sarcopenia [<xref rid=\"B19-ijerph-17-05337\" ref-type=\"bibr\">19</xref>]. Our results showed that chronic pain was associated with a low diurnal profile of steps and locomotive activity. Chronic musculoskeletal pain in community-dwelling older adults can be associated with frailty and pre-frailty, and geriatric syndromes, including chronic pain, cause deterioration in the quality of life in community-dwelling older adults [<xref rid=\"B20-ijerph-17-05337\" ref-type=\"bibr\">20</xref>,<xref rid=\"B21-ijerph-17-05337\" ref-type=\"bibr\">21</xref>]. However, promoting an increase in locomotive activity is beneficial for reducing pain and improving the quality of life in the adult population with MSDs [<xref rid=\"B3-ijerph-17-05337\" ref-type=\"bibr\">3</xref>].</p><p>In contrast, the household activity of the older adults with MSDs did not decline compared with that of age-matched healthy older adults, even if they had chronic pain and reduce physical function. Therefore, community-dwelling older people with MSDs may be active in regard to household activities in daily living, while also controlling chronic pain. Such activities may constitute an integral part of the daily routines, which may improve health, well-being, and sleep quality in older adults with sedentary or low activity levels [<xref rid=\"B6-ijerph-17-05337\" ref-type=\"bibr\">6</xref>]. However, the older adults with severe chronic pain showed a considerable decline in the diurnal profile of household activity. Therefore, pain management may be an important strategy to help older people with MSDs to maintain daily household activity.</p><p>A decline in physical activity is associated with poor physical performance [<xref rid=\"B22-ijerph-17-05337\" ref-type=\"bibr\">22</xref>,<xref rid=\"B23-ijerph-17-05337\" ref-type=\"bibr\">23</xref>]. Walking is the most common physical activity and has been shown to be associated with various benefits for older persons [<xref rid=\"B24-ijerph-17-05337\" ref-type=\"bibr\">24</xref>]. A physical activity of more than 3.0 METs is correlated with increased bone mineral density in men and women aged 70 years old [<xref rid=\"B25-ijerph-17-05337\" ref-type=\"bibr\">25</xref>]. In addition, high-intensity activity is related to lower levels of pain in patients with spinal cord injury [<xref rid=\"B26-ijerph-17-05337\" ref-type=\"bibr\">26</xref>]. Furthermore, in the older adults with MSDs, household activity of more than 3.0 METs was considerably higher than locomotive activity of more than 3.0 METs. Therefore, it is often important for older people to maintain household activities throughout the day, both for the promotion of physical activity and the increase in the locomotive activity.</p><p>In addition, Mansi et al. [<xref rid=\"B3-ijerph-17-05337\" ref-type=\"bibr\">3</xref>] established that participation in walking exercise for 3 or 4 days per week was effective for increasing physical activity and reducing pain in people with MSDs. Indeed, several studies have supported the use of walking-based interventions to encourage people with a range of MSDs to assume more physically active roles in their management [<xref rid=\"B3-ijerph-17-05337\" ref-type=\"bibr\">3</xref>,<xref rid=\"B27-ijerph-17-05337\" ref-type=\"bibr\">27</xref>]. In an animal study, running exercises performed 3 or 5 days per week reduced neuropathic pain through regulation of the endogenous opioid system and neurotrophic factor in the brain stem or spinal cord [<xref rid=\"B28-ijerph-17-05337\" ref-type=\"bibr\">28</xref>]. Physical function is related to moving and performing daily activities [<xref rid=\"B22-ijerph-17-05337\" ref-type=\"bibr\">22</xref>], and decreased muscle strength is associated with decreased physical function [<xref rid=\"B24-ijerph-17-05337\" ref-type=\"bibr\">24</xref>]. Therefore, it may also be important for older adults with MSDs to increase walking activities throughout the day. As the diurnal profiles of locomotive activity of older adults with MSDs were considerably lower in the morning and afternoon, physical activity in the morning or afternoon should be indicated for preventive care or outpatient rehabilitation services.</p><p>There were several limitations to the present study. First, this study assessed physical activity using an Omron activity monitor consisting of triaxial acceleration sensors. An accelerometer can provide additional data with regard to the time spent in various intensities of physical activity and inactivity as well as step count data [<xref rid=\"B4-ijerph-17-05337\" ref-type=\"bibr\">4</xref>]. However, the acceleration sensors may be less sensitive to the movement of the center of gravity, such as that in very slow walking [<xref rid=\"B29-ijerph-17-05337\" ref-type=\"bibr\">29</xref>]. In addition, measurement data acquired by an accelerometer may vary depending on the measurement system of each device. As the Omron activity monitor calculates the integral of the absolute value of the axes of the three signals using an accelerated signal over a 10 s recorded time interval [<xref rid=\"B7-ijerph-17-05337\" ref-type=\"bibr\">7</xref>,<xref rid=\"B12-ijerph-17-05337\" ref-type=\"bibr\">12</xref>], it may be calculated with a slightly higher activity intensity. Therefore, in this study, we compared the data of older adults with MSDs and age-matched healthy older adults using same triaxial accelerometer. Second, we did not examine internal diseases, such as diabetes or chronic obstructive pulmonary diseases, in the participants. These chronic internal diseases may have an influence on the physical activity of the day. Therefore, we excluded those with severe comorbid illnesses from this study. Third, we did not examine medication, including painkillers or antihypertensive agents, in the questionnaire. Taking painkillers might influence the daily physical activity of older adults. However, pharmacological treatment for chronic pain is limited, with no more than 40–60% of patients obtaining partial relief of their pain [<xref rid=\"B30-ijerph-17-05337\" ref-type=\"bibr\">30</xref>]. Fourth, the diurnal profiles of physical activity may be affected by several factors, including climatic conditions, socioeconomic status, education and gender. The activity range of older adults decreases during the winter season compared with the summer season [<xref rid=\"B23-ijerph-17-05337\" ref-type=\"bibr\">23</xref>,<xref rid=\"B31-ijerph-17-05337\" ref-type=\"bibr\">31</xref>]. Furthermore, we did not examine the socioeconomic status and education level of the older adults. Therefore, further studies are need to examine the factors influencing diurnal profiles of physical activity. Despite these limitations, this is the first study to explore the diurnal profiles of the physical activities that were classified into locomotive and household activities in community-dwelling older people with MSDs. Community-dwelling older people with MSDs maintained household activities throughout the day, despite reduced physical functions and locomotive activity. However, severe chronic pain was shown to reduce both household activity and locomotive activities.</p></sec><sec sec-type=\"conclusions\" id=\"sec5-ijerph-17-05337\"><title>5. Conclusions</title><p>The diurnal profiles of household activity in older people with MSDs were not reduced compared with those in age-matched healthy older people, although their locomotive activity was considerably lower than that of healthy older people. However, an increase in chronic pain was associated with a decline in household activity. It may be an important strategy to prevent the decline of household activity due to chronic pain throughout the day for the promotion of physical activity in older adults with MSDs. Our findings provide a basis for developing strategies for the enhancement and maintenance of physical activity in community-dwelling older people with MSDs.</p></sec></body><back><ack><title>Acknowledgments</title><p>The authors greatly appreciate the overall advice of the deceased Hisato Tsurudome in Tsurudome Orthopedic Clinic for this study, and are grateful for the assistance of Masashi Miyazaki and Koki Ueda at Kirishima Orthopedic Hospital.</p></ack><notes><title>Author Contributions</title><p>H.S. preformed the literature review and wrote the manuscript. H.S., K.N. (Kosuke Norimatsu), S.O., S.T., K.N. (Kazuki Nakanishi) and A.T. performed data collection and analysis of the data. 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Mean ± 95% confidence intervals.</p></caption><graphic xlink:href=\"ijerph-17-05337-g002\"/></fig><fig id=\"ijerph-17-05337-f003\" orientation=\"portrait\" position=\"float\"><label>Figure 3</label><caption><p>Diurnal profile of steps (<bold>A</bold>) and locomotive (<bold>B</bold>) and household (<bold>C</bold>) activities adjusted by chronic pain level (mild and severe) in older adults with MSDs. Mean ± 95% confidence intervals.</p></caption><graphic xlink:href=\"ijerph-17-05337-g003\"/></fig><table-wrap id=\"ijerph-17-05337-t001\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05337-t001_Table 1</object-id><label>Table 1</label><caption><p>Participant characteristics.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Older Adults with MSDs<break/>(<italic>n</italic> = 71)</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Control Older Adults<break/>(<italic>n</italic> = 25)</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><italic>p</italic>-Value</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Age (years)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">81.1 ± 5.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">79.5 ± 3.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.072</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Men/women (n)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">15/56</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">10/15</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.064</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Body mass index (kg/m<sup>2</sup>)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">23.7 ± 3.3</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">22.5 ± 2.6</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.097</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Diagnosis of orthopedic disease for rehabilitation * (n)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Osteoarthritis of the knee</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">31</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">―</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Chronic low back pain</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">17</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">―</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Lumbar spondylosis</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">17</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">―</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Lumbar spinal stenosis</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">―</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Conservative therapy after fracture (e.g., lumbar vertebrae, patella)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">―</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">After surgery of the lower limbs (e.g., TKA, THA, osteosynthesis)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">―</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">After surgery of lumbar or cervical spine (e.g., PLF, depression, lumber disc herniation)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">6</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">―</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Chronic pain (NRS) (n)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td rowspan=\"5\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\"><0.001</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">None (0)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">25</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Mild (1–3)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">20</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Moderate (4–6)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">38</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Severe (7–10)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">13</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0</td></tr></tbody></table><table-wrap-foot><fn><p>Mean ± standard deviation (SD). MSDs: musculoskeletal disorders; * participants have more than one type of diagnosis. TKA: total knee arthroplasty; THA: total hip arthroplasty; PLF: posterior lumbar fusion; NRS: numerical rating scale.</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"ijerph-17-05337-t002\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05337-t002_Table 2</object-id><label>Table 2</label><caption><p>Physical functions and physical activity parameters in older adults with MSDs and controls.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Older Adults with MSDs<break/>(<italic>n</italic> = 71)</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Control Older Adults<break/>(<italic>n</italic> = 25)</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><italic>p</italic>-Value</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Physical functions</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Timed Up and Go test (second)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">12.7 ± 4.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6.6 ± 1.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.001</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CS-30 test (repetitions)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">11.8 ± 3.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">17.5 ± 5.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.001</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Steps parameters (steps)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Steps per day</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2563.8 ±2063.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5137.6 ± 2844.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.001</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Steps per hour</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">150.9 ± 121.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">302.2 ± 167.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.001</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Physical activities (MET-h/week) *</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Locomotive activity</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.3 ± 3.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">11.5 ± 10.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.001</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Household activity</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">15.8 ± 8.9</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">15.1 ± 5.5</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.667</td></tr></tbody></table><table-wrap-foot><fn><p>Mean ± SD. CS-30: 30 s chair-standing test. * Energy consumption for locomotive and household activities correspond to physical activity more than 3.0 METs.</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"ijerph-17-05337-t003\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05337-t003_Table 3</object-id><label>Table 3</label><caption><p>Chronic pain level and physical activity in older adults with MSDs.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Steps Per Day<break/>(Steps)</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Locomotive Activity<break/>(MET-h/Week)</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Household Activity<break/>(MET-h/Week)</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Chronic pain (<italic>n</italic> = 71)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Mild (<italic>n</italic> = 20)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2987.2 ± 2152.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.7 ± 4.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">17.9 ± 9.0</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Moderate (<italic>n</italic> = 38)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2717.5 ± 2150.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.1 ± 2.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">16.5 ± 9.2</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Severe (<italic>n</italic> = 13)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1490.1 ± 1268.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.8 ± 0.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">12.5 ± 7.5</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><italic>p</italic>-value (Mild vs. Severe)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.032</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.065</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.147</td></tr></tbody></table><table-wrap-foot><fn><p>Mean ± SD. MET: metabolic equivalent of task</p></fn></table-wrap-foot></table-wrap></floats-group></article>\n"
]
[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Int J Mol Sci</journal-id><journal-id journal-id-type=\"iso-abbrev\">Int J Mol Sci</journal-id><journal-id journal-id-type=\"publisher-id\">ijms</journal-id><journal-title-group><journal-title>International Journal of Molecular Sciences</journal-title></journal-title-group><issn pub-type=\"epub\">1422-0067</issn><publisher><publisher-name>MDPI</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32751413</article-id><article-id pub-id-type=\"pmc\">PMC7432063</article-id><article-id pub-id-type=\"doi\">10.3390/ijms21155412</article-id><article-id pub-id-type=\"publisher-id\">ijms-21-05412</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Article</subject></subj-group></article-categories><title-group><article-title>Mitochondrial Respiration Changes in R6/2 Huntington’s Disease Model Mice during Aging in a Brain Region Specific Manner</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0002-2889-0151</contrib-id><name><surname>Burtscher</surname><given-names>Johannes</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05412\">1</xref><xref rid=\"c1-ijms-21-05412\" ref-type=\"corresp\">*</xref><xref ref-type=\"author-notes\" rid=\"fn1-ijms-21-05412\">†</xref></contrib><contrib contrib-type=\"author\"><name><surname>Di Pardo</surname><given-names>Alba</given-names></name><xref ref-type=\"aff\" rid=\"af2-ijms-21-05412\">2</xref></contrib><contrib contrib-type=\"author\"><name><surname>Maglione</surname><given-names>Vittorio</given-names></name><xref ref-type=\"aff\" rid=\"af2-ijms-21-05412\">2</xref><xref rid=\"c1-ijms-21-05412\" ref-type=\"corresp\">*</xref></contrib><contrib contrib-type=\"author\"><name><surname>Schwarzer</surname><given-names>Christoph</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05412\">1</xref><xref rid=\"c1-ijms-21-05412\" ref-type=\"corresp\">*</xref><xref ref-type=\"author-notes\" rid=\"fn2-ijms-21-05412\">‡</xref></contrib><contrib contrib-type=\"author\"><name><surname>Squitieri</surname><given-names>Ferdinando</given-names></name><xref ref-type=\"aff\" rid=\"af3-ijms-21-05412\">3</xref><xref ref-type=\"author-notes\" rid=\"fn2-ijms-21-05412\">‡</xref></contrib></contrib-group><aff id=\"af1-ijms-21-05412\"><label>1</label>Department of Pharmacology, Medical University of Innsbruck, 6020 Innsbruck, Austria</aff><aff id=\"af2-ijms-21-05412\"><label>2</label>IRCCS, Neuromed, 86077 Pozzilli, Italy; <email>[email protected]</email></aff><aff id=\"af3-ijms-21-05412\"><label>3</label>Huntington and Rare Diseases Unit, Fondazione IRCCS Casa Sollievo della Sofferenza Research Hospital, 71013 San Giovanni Rotondo, Italy; <email>[email protected]</email></aff><author-notes><corresp id=\"c1-ijms-21-05412\"><label>*</label>Correspondence: <email>[email protected]</email> (J.B.); <email>[email protected]</email> (V.M.); <email>[email protected]</email> (C.S.); Tel.: +41-21-692-37-97 (J.B.)</corresp><fn id=\"fn1-ijms-21-05412\"><label>†</label><p>Current affiliation: Institute of Sport Sciences, University of Lausanne, 1015 Lausanne, Switzerland.</p></fn><fn id=\"fn2-ijms-21-05412\"><label>‡</label><p>These authors contributed equally to this work.</p></fn></author-notes><pub-date pub-type=\"epub\"><day>30</day><month>7</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><month>8</month><year>2020</year></pub-date><volume>21</volume><issue>15</issue><elocation-id>5412</elocation-id><history><date date-type=\"received\"><day>05</day><month>7</month><year>2020</year></date><date date-type=\"accepted\"><day>28</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>© 2020 by the authors.</copyright-statement><copyright-year>2020</copyright-year><license license-type=\"open-access\"><license-p>Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>).</license-p></license></permissions><abstract><p>Mitochondrial dysfunction is crucially involved in aging and neurodegenerative diseases, such as Huntington’s Disease (HD). How mitochondria become compromised in HD is poorly understood but instrumental for the development of treatments to prevent or reverse resulting deficits. In this paper, we investigate whether oxidative phosphorylation (OXPHOS) differs across brain regions in juvenile as compared to adult mice and whether such developmental changes might be compromised in the R6/2 mouse model of HD. We study OXPHOS in the striatum, hippocampus, and motor cortex by high resolution respirometry in female wild-type and R6/2 mice of ages corresponding to pre-symptomatic and symptomatic R6/2 mice. We observe a developmental shift in OXPHOS-control parameters that was similar in R6/2 mice, except for cortical succinate-driven respiration. While the LEAK state relative to maximal respiratory capacity was reduced in adult mice in all analyzed brain regions, succinate-driven respiration was reduced only in the striatum and cortex, and NADH-driven respiration was higher as compared to juvenile mice only in the striatum. We demonstrate age-related changes in respirational capacities of different brain regions with subtle deviations in R6/2 mice. Uncovering in situ oxygen conditions and potential substrate limitations during aging and HD disease progression are interesting avenues for future research to understand brain-regional vulnerability in HD.</p></abstract><kwd-group><kwd>Huntington’s disease</kwd><kwd>aging</kwd><kwd>neurodegeneration</kwd><kwd>mitochondria</kwd><kwd>oxidative phosphorylation</kwd><kwd>respiration</kwd></kwd-group></article-meta></front><body><sec sec-type=\"intro\" id=\"sec1-ijms-21-05412\"><title>1. Introduction</title><p>Some of the most prevalent cellular aging paradigms have mitochondrial dysfunction at their core, such as the free radical theory of aging [<xref rid=\"B1-ijms-21-05412\" ref-type=\"bibr\">1</xref>,<xref rid=\"B2-ijms-21-05412\" ref-type=\"bibr\">2</xref>] or the related mitochondria [<xref rid=\"B3-ijms-21-05412\" ref-type=\"bibr\">3</xref>,<xref rid=\"B4-ijms-21-05412\" ref-type=\"bibr\">4</xref>,<xref rid=\"B5-ijms-21-05412\" ref-type=\"bibr\">5</xref>] or mitohormesis paradigms [<xref rid=\"B6-ijms-21-05412\" ref-type=\"bibr\">6</xref>] of aging. In very simplified terms, they posit that the wear and tear of mitochondrial activity, involving the production of reactive oxygen species, which in turn modify mitochondrial components, is integral for the aging processes. In line with these theoretical frameworks, moderate reductions of mitochondrial respiration have the potential to increase the life-span of various species, such as <italic>C. elegans</italic> [<xref rid=\"B7-ijms-21-05412\" ref-type=\"bibr\">7</xref>,<xref rid=\"B8-ijms-21-05412\" ref-type=\"bibr\">8</xref>,<xref rid=\"B9-ijms-21-05412\" ref-type=\"bibr\">9</xref>], <italic>D. melanogaster</italic> [<xref rid=\"B10-ijms-21-05412\" ref-type=\"bibr\">10</xref>], and mice [<xref rid=\"B11-ijms-21-05412\" ref-type=\"bibr\">11</xref>,<xref rid=\"B12-ijms-21-05412\" ref-type=\"bibr\">12</xref>], supporting the theory of a correlation of the metabolic rate and life span [<xref rid=\"B13-ijms-21-05412\" ref-type=\"bibr\">13</xref>]. On the other hand, age-related alterations in mitochondrial functions are strongly associated with neurodegenerative diseases [<xref rid=\"B14-ijms-21-05412\" ref-type=\"bibr\">14</xref>]. Strikingly, in most neurodegenerative diseases, specific brain regions are selectively affected [<xref rid=\"B15-ijms-21-05412\" ref-type=\"bibr\">15</xref>]. Still puzzling for research, different brain regions succumb to neurodegeneration in different neurodegenerative diseases. Brain atrophy is most striking in the caudate-putamen (in rodent brain also called striatum) in Huntington’s Disease (HD), with striatal medium spiny neurons being the most vulnerable cell type [<xref rid=\"B16-ijms-21-05412\" ref-type=\"bibr\">16</xref>], although also other brain regions are affected as well. Like in other neurodegenerative diseases, mitochondrial deficits are a core pathology in the HD affected brain. This became apparent with the observation of structurally compromised mitochondria [<xref rid=\"B17-ijms-21-05412\" ref-type=\"bibr\">17</xref>], impaired mitochondrial respiration in the caudate nucleus [<xref rid=\"B18-ijms-21-05412\" ref-type=\"bibr\">18</xref>] and defects in multiple components of the electron transfer system [<xref rid=\"B19-ijms-21-05412\" ref-type=\"bibr\">19</xref>] in post-mortem Huntington’s Disease (HD) patient brains, as well as from the reproduction of HD-resembling selective striatal neurodegeneration with the mitochondrial Complex II inhibitor, 3-nitropropionic acid (3-NP) [<xref rid=\"B20-ijms-21-05412\" ref-type=\"bibr\">20</xref>]. The disease-causing mutated Huntingtin (Htt) protein is furthermore known to interact with mitochondria and to impair Complex II [<xref rid=\"B21-ijms-21-05412\" ref-type=\"bibr\">21</xref>].</p><p>R6/2 mice, which overexpress human N-terminal Htt (animals of our study with around 160 glutamine repeats) are commonly used as a model for HD [<xref rid=\"B22-ijms-21-05412\" ref-type=\"bibr\">22</xref>], but results on mitochondrial respiration in these mice are conflicting. Isolated mitochondrial Complex IV activity has been shown to be reduced in striatum and cortex of 12 weeks old R6/2 mice [<xref rid=\"B23-ijms-21-05412\" ref-type=\"bibr\">23</xref>]. Studies of mitochondrial respiration of ex vivo brain tissues, however, suggest this to be of no significant functional consequence. While Aidt and colleagues [<xref rid=\"B24-ijms-21-05412\" ref-type=\"bibr\">24</xref>] reported (small) respirational deficits (only in flux control ratios) using high resolution respiration in the striatum of 12 weeks old symptomatic R6/2 mice, no respirational impairments were observed by Hamilton and colleagues in 6–8-week-old symptomatic R6/2 mice [<xref rid=\"B25-ijms-21-05412\" ref-type=\"bibr\">25</xref>].</p><p>Why specific brain regions in HD are particularly vulnerable, and how mitochondrial dysfunction may be involved in such vulnerabilities remains an enigma. We previously observed in brains of healthy, adult mice an intriguingly higher contribution of mitochondrial Complex II to total mitochondrial respiration in the striatum as compared to other brain regions [<xref rid=\"B26-ijms-21-05412\" ref-type=\"bibr\">26</xref>] and proposed that the relative importance of Complex II specifically in the striatum together with Htt’s inhibitory effect on Complex II [<xref rid=\"B21-ijms-21-05412\" ref-type=\"bibr\">21</xref>] may constitute the basis of the selective vulnerability of the striatum in HD.</p><p>On the basis of these findings, we hypothesize that mitochondrial respirational patterns might not only differ across brain regions in the adult brain but also depend on age. In addition, we hypothesize that during HD progression respirational patterns might change in a different fashion as compared to healthy aging.</p><p>To address these hypotheses, we study mitochondrial respiration in juvenile and adult wild-type mice and compare the results to respirational patterns in age-matched pre-symptomatic and symptomatic R6/2 mice.</p></sec><sec id=\"sec2-ijms-21-05412\"><title>2. Materials and Methods</title><sec id=\"sec2dot1-ijms-21-05412\"><title>2.1. Animals</title><p>Transgenic mice [strain name: B6CBA-tgN (HDexon1) 62Gpb/1J] with 160 (CAG) repeat expansions (R6/2 line) were obtained from Jackson Laboratories. Crossing with female B6CBA wild-type (WT) mice enabled the establishment of the animal colony. All experimental procedures were approved by the IRCCS Neuromed Animal Care Review Board and were conducted according to the 2010/63/EU Directive for animal experiments.</p><p>Female R6/2 mice and WT littermates were sacrificed at either 4 weeks of age (R6/2 pre-symptomatic) or at 10–11 weeks of age, when R6/2 mice display pronounced pathology (see main text).</p></sec><sec id=\"sec2dot2-ijms-21-05412\"><title>2.2. High-Resolution Respirometry</title><p>Wild-type and R6/2 mice were sacrificed by cervical dislocation between 9 and 10 am in the morning. The striata, hippocampi, and motor cortex were quickly dissected on ice. After weighing the wet tissues, they were placed and washed in mitochondrial respiration medium MiR06Cr (4 °C). MiR06Cr contained 280 IU/mL catalase (Pesta and Gnaiger, 2012) and 20 mM creatine. The tissues were then homogenized in MiR06Cr (4 °C) using a pre-cooled glass potter (tight fit; WiseStir homogenizer HS-30E, witeg Labortechnik GmbH, Wertheim, Germany) at 1000 rpm, as described previously (Burtscher et al., 2015). Resulting homogenates of 1 mg/mL of ice-cold MiR06Cr were used for OXPHOS-analysis in duplicates.</p><p>Respiration was measured at 37 °C in the Oroboros O2k (Oroboros Instruments, Austria) (<italic>n</italic> = 4 per genotype and age category), as described previously [<xref rid=\"B27-ijms-21-05412\" ref-type=\"bibr\">27</xref>]. Briefly, tissue homogenates were moved into calibrated Oxygraph-2 k (O2k, OROBOROS INSTRUMENTS, Innsbruck, Austria) 2 mL-chambers. Oxygen concentration (μM) as well as oxygen flux per tissue mass (pmol O<sub>2</sub>.s<sup>−1</sup>·mg<sup>−1</sup>) were assessed and recorded with DatLab software (OROBOROS INSTRUMENTS, Innsbruck, Austria). The oxygen concentration was set to 360–380 μM at the beginning of the experiment.</p><p>The non-phosphorylating, NADH (N)-linked LEAK-respiration (N<italic><sub>L</sub></italic>) was measured in the presence of the CI-linked substrates pyruvate (5 mM), malate (0.5 mM) and glutamate (10 mM). CI-linked respirational capacities (N<italic><sub>P</sub></italic>) were achieved by addition of 2.5 mM ADP. Then, 50 mM succinate (S) were added to induce CI and II substrate-linked respiration (NS<italic><sub>P</sub></italic>). Carbonyl cyanide m-chloro phenyl hydrazone (CCCP) mediated uncoupling yielded the maximum capacity of the electron transfer system (ETS, NS<italic><sub>E</sub></italic>). The inhibition of CI by rotenone (0.5 μM) allowed the assessment of succinate-driven ETS capacity (S<italic><sub>E</sub></italic>). Residual oxygen consumption (ROX) was measured after malonate and antimycin A addition and subtracted from mitochondrial respiratory states. The measurement of CIV activity was then performed on the same samples. After reoxygenation, ascorbate (2 mM) and <italic>N</italic>,<italic>N</italic>,<italic>N</italic>′,<italic>N</italic>′-Tetramethyl-p-phenylenediamine dihydrochloride (TMPD, 0.5 mM) were added. The chemical background was assessed by subsequent inhibition of CIV by sodium azide (100 mM). The reagents for the high resolution respirometry protocols were obtained from Sigma-Aldrich (St. Louis, MO, USA).</p><p>For the calculation of flux control ratios (FCR), absolute fluxes (per mg wet weight) were divided by the respective ETS-capacity [<xref rid=\"B28-ijms-21-05412\" ref-type=\"bibr\">28</xref>].</p></sec><sec id=\"sec2dot3-ijms-21-05412\"><title>2.3. Statistics</title><p>Two-way ANOVAs (genotype x age) were calculated to compare respiration or flux control ratios at different states. In case of significant interaction effects, Tukey’s post-hoc tests were calculated. SPSS was used to calculate statistical tests, Graphpad Prism was used for the generation of graphs.</p></sec></sec><sec sec-type=\"results\" id=\"sec3-ijms-21-05412\"><title>3. Results</title><p>Our R6/2 mice displayed pronounced HD-related symptoms at 10–11 weeks of age, as reported previously [<xref rid=\"B29-ijms-21-05412\" ref-type=\"bibr\">29</xref>,<xref rid=\"B30-ijms-21-05412\" ref-type=\"bibr\">30</xref>,<xref rid=\"B31-ijms-21-05412\" ref-type=\"bibr\">31</xref>]. These symptoms included motor dysfunction, as assessed by Rotarod, horizontal ladder task and limb clasping tests, reduced brain and body weight gain, and aggregated mHtt in the striatum.</p><p>For this study, pre-symptomatic (4 weeks) and symptomatic (10–11 weeks) R6/2 mice as well as wild-type mice of the respective same age (four animals per group) were sacrificed. The striatum, hippocampus, and motorcortex were dissected, transferred into preservation buffer (MiR06), and immediately used for high resolution respirometry after permeabilization, as previously reported [<xref rid=\"B26-ijms-21-05412\" ref-type=\"bibr\">26</xref>,<xref rid=\"B32-ijms-21-05412\" ref-type=\"bibr\">32</xref>]. The addition of substrates for mitochondrial Complex I in the absence of adenosine diphosphate (ADP, P) allowed us to monitor the nicotinamide adenine dinucleotide (NADH, N)-linked LEAK state N(L). The addition of ADP yielded the NADH-linked oxidative phosphorylation (OXPHOS) state N(P), and further supplementation with succinate (S) resulted in the NS(P) state. The subsequent uncoupling with FCCP induced the electron transfer system (E) capacity NS(E), followed by S(E) after the injection of Complex I inhibitor rotenone. Complex IV activity was assessed for each preparation by the addition of <italic>N,N,N′,N′</italic>-tetramethyl-<italic>p</italic>-phenylenediamine (TMPD) and ascorbic acid.</p><p>We observed a higher absolute respiration (per fresh weight) in 10–11-week-old mice as compared to 4-week-old mice in almost all respirational states in the striatum (<xref ref-type=\"fig\" rid=\"ijms-21-05412-f001\">Figure 1</xref>A), only for N(P) and NS(P) in the hippocampus (<xref ref-type=\"fig\" rid=\"ijms-21-05412-f001\">Figure 1</xref>B) and no age-related effects in the motor cortex (<xref ref-type=\"fig\" rid=\"ijms-21-05412-f001\">Figure 1</xref>C). There was no difference in any respirational state or analyzed brain region with respect to genotype (wild-type versus R6/2). See <xref ref-type=\"app\" rid=\"app1-ijms-21-05412\">Table S2</xref> for statistical results.</p><p>We next normalized the respirational values to the respective preparations’ electron transfer system maximum capacities NS(E), yielding flux control ratios (FCR) (<xref ref-type=\"fig\" rid=\"ijms-21-05412-f002\">Figure 2</xref>). N(L) FCRs were higher in 4-weeks-old than in 10–11-week-old animals in all analyzed brain regions. In contrast, N(P) and NS(P) were higher in the older animals in the striatum but not in the other brain regions. S(E) on the other hand was reduced in the older animals in both the striatum and motor cortex, with no detectable effects in the hippocampus. In the motor cortex, we, in addition, observed an interaction effect between age and genotype, indicating significantly more reduction of S(E) with age in wild-type as compared with R6/2 mice. See <xref ref-type=\"app\" rid=\"app1-ijms-21-05412\">Table S1</xref> for numerical results statistical results and <xref ref-type=\"app\" rid=\"app1-ijms-21-05412\">Table S2</xref> for detailed statistical assessment.</p></sec><sec sec-type=\"discussion\" id=\"sec4-ijms-21-05412\"><title>4. Discussion</title><p>In this study, we report significant brain region-dependent shifts in mitochondrial respirational patterns from juvenile to adult age, confirming our principal hypothesis. These shifts, however, for the largest part, were not different in R6/2 mice, indicating that in these HD model mouse, tissues’ compensatory mechanisms may be able to maintain overall respiration.</p><p>Absolute respiration (oxygen flux per fresh tissue weight) was significantly increased from 4 weeks to 10–11 weeks of age for all investigated respirational states (except for the LEAK state N(L)) in the striatum. This effect was limited to mitochondrial Complex I and Complex I & II-linked respiration in the hippocampus and entirely absent in the cortex.</p><p>The increased electron transfer system capacity NS(E) and Complex IV activity in adult striatum indicate that this effect is linked to elevated general mitochondrial respiratory capacity, a marker for mitochondrial density [<xref rid=\"B26-ijms-21-05412\" ref-type=\"bibr\">26</xref>]. Overall respiratory capacity seems not to increase from juvenile to adult state in both the hippocampus and cortex.</p><p>FCRs give information on respiratory control independent of maximal electron transfer system capacity and, thus, are theoretically independent of mitochondrial content. We observed a lower relative LEAK N(L) state in all investigated brain regions in juvenile mice, indicating lower proton-LEAK and, thus, potentially tighter coupling in adult brain tissues. Relative succinate-driven S(E) contributions to maximum respirational capacities of striatum and cortex were reduced in adulthood, suggesting a higher reliance of these brain regions on Complex II at younger age.</p><p>Our work confirms previous reports of no [<xref rid=\"B25-ijms-21-05412\" ref-type=\"bibr\">25</xref>] or marginal [<xref rid=\"B24-ijms-21-05412\" ref-type=\"bibr\">24</xref>] impairment of striatal respiration in symptomatic R6/2 mice. We expand these results by demonstrating that also the hippocampus and motor cortex respiration are not impaired in these mice (within the limitations of the applied respirometry methods as discussed below). Furthermore, we show that transitions of respiration patterns from juvenile to adult mice are not affected by human Htt overexpression and the onset of HD-related symptoms, with the possible exception of succinate-linked respiration S(E) FCR in the cortex (<xref ref-type=\"fig\" rid=\"ijms-21-05412-f002\">Figure 2</xref>A). While the cortical S(E) FCR was reduced similarly to the striatum in adulthood, it was already at comparably low levels in R6/2 pre-symptomatic mice, suggesting early reductions in succinate-linked respiration in R6/2 mice. A summary of the brain-region dependent differences in FCRs as a function of age and genotype (WT versus R6/2) is presented in <xref ref-type=\"fig\" rid=\"ijms-21-05412-f003\">Figure 3</xref>.</p><p>Taken together, our results demonstrate clear brain-region-specific changes in mitochondrial respirational capacities as assessed by high resolution respirometry from juvenile to adult age that were only slightly affected in R6/2 mice in cortical succinate-driven respiration. It is important to consider that our results—and the greatest part of studies investigating mitochondrial respiration in brain regions—reflect the maximum capacities of the mitochondrial preparations, meaning that oxygen and substrate concentrations were maintained at saturating levels. Therefore, we cannot exclude oxygen or substrate limitations that may have had an impact on respiration in vivo but are masked in our ex vivo approach. In particular, our recent results on the efficiency of promoting sphingosine-1-phosphate (S1P) in the same R6/2 model [<xref rid=\"B30-ijms-21-05412\" ref-type=\"bibr\">30</xref>] highlight the possibility of problems of oxygen availabilities in the R6/2 model and in HD. S1P has been shown to modulate hypoxia signaling by activating hypoxia inducible factor 1 (HIF-1) [<xref rid=\"B32-ijms-21-05412\" ref-type=\"bibr\">32</xref>,<xref rid=\"B33-ijms-21-05412\" ref-type=\"bibr\">33</xref>,<xref rid=\"B34-ijms-21-05412\" ref-type=\"bibr\">34</xref>], thus, possibly exerting beneficial effects in HD disease progression via activating protective hypoxia-related pathways. Recently, the sphingolipid system has been increasingly linked to aging and may be importantly involved in neurodegenerative disease [<xref rid=\"B35-ijms-21-05412\" ref-type=\"bibr\">35</xref>,<xref rid=\"B36-ijms-21-05412\" ref-type=\"bibr\">36</xref>]. Future research elucidating the interplay of the sphingosine system with oxygen levels and oxygen extraction by mitochondria in aging model organisms of neurodegeneration and patients, therefore, has the potential to significantly advance our understanding of selective regional brain vulnerabilities in neurodegenerative diseases.</p><p>Furthermore, the role of substrates for oxidative phosphorylation and the interplay with other cellular pathways of energy production (e.g., glycolysis and the creatine phosphate shuttle) in neurodegenerative diseases are insufficiently explored yet. Importantly, respirational deficits in aged mice have previously been linked to specific substrates [<xref rid=\"B37-ijms-21-05412\" ref-type=\"bibr\">37</xref>] and in HD neurons may specifically display mitochondrial deficits in energy-demanding conditions [<xref rid=\"B38-ijms-21-05412\" ref-type=\"bibr\">38</xref>].</p><p>Our results indicate brain region-specific dynamic changes of respirational capacities with advancing age even in the absence of pathology that warrant further investigation. Observations of possibly different patterns of respirational changes in our previous studies [<xref rid=\"B26-ijms-21-05412\" ref-type=\"bibr\">26</xref>,<xref rid=\"B27-ijms-21-05412\" ref-type=\"bibr\">27</xref>,<xref rid=\"B39-ijms-21-05412\" ref-type=\"bibr\">39</xref>] in various brain regions of older (around 3–6 months old) male control mice warrant more research on the development of mitochondrial respiration control in dependence of older age and sex. Such experiments may have pronounced implications on normal aging-related vulnerabilities of specific brain circuits and potential pathogenesis of various neurological diseases, also with regard to gender-specific differences.</p><p>In conclusion, we demonstrate the feasibility of detecting age-related changes in the respirational capacities of different brain regions using high resolution respirometry. In line with previous reports, the deficits of respiration of R6/2 mice were small, including the novel results on brain region-specific, age-dependent changes provided here. We argue that respirometric analyses are not well suited to uncover in situ oxygen conditions and are limited in the detection of potential substrate limitations but, in particular, these aspects of mitochondria may be important to understand brain-regional vulnerability in HD. They did, however, allow us to observe respirational shifts that are particularly pronounced in the striatum, but not in the hippocampus. The subtle difference of developmental respirational alterations in R6/2 mice in succinate driven respiration in the motor-cortex may be an important factor in HD pathogenesis that warrants further investigation.</p></sec></body><back><ack><title>Acknowledgments</title><p>We are very grateful to Prof. Erich Gnaiger for provision of the Oroboros equipment and great intellectual input. We thank Mariagrazia Favellato and Enrico Amico for excellent technical support. Furthermore, we are very grateful to the FWF Austrian Science Fund for funding the project (SPIN; W1206-B05, to C.S.) as well as to the Italian Ministry of Health (Ricerca Finalizzata, RF-2016-02364123, to F.S.). Open Access Funding by the Austrian Science Fund (FWF).</p></ack><app-group><app id=\"app1-ijms-21-05412\"><title>Supplementary Materials</title><p>Supplementary materials can be found at <uri xlink:href=\"https://www.mdpi.com/1422-0067/21/15/5412/s1\">https://www.mdpi.com/1422-0067/21/15/5412/s1</uri>.</p><supplementary-material content-type=\"local-data\" id=\"ijms-21-05412-s001\"><media xlink:href=\"ijms-21-05412-s001.pdf\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material></app></app-group><notes><title>Author Contributions</title><p>J.B., V.M., F.S., and C.S. designed the experiments. J.B. and V.M. performed the experiments. J.B. analyzed the data and wrote the first draft. A.D.P., V.M., F.S., and C.S. discussed and contributed to the final version. All authors have read and agreed to the published version of the manuscript.</p></notes><notes><title>Funding</title><p>Open Access Funding by the Austrian Science Fund (FWF, SPIN; W1206-B05, C.S.).</p></notes><notes notes-type=\"COI-statement\"><title>Conflicts of Interest</title><p>The authors declare no conflicts of interest.</p></notes><ref-list><title>References</title><ref id=\"B1-ijms-21-05412\"><label>1.</label><element-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Harman</surname><given-names>D.</given-names></name></person-group><article-title>Aging: A theory based on free radical and radiation chemistry</article-title><source>Sci. 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Striatum (<bold>A</bold>), hippocampus (<bold>B</bold>), and motor cortex (<bold>C</bold>) of wild-type (WT) and R6/2 mice before (4 weeks old, 4w) and after (10–11 weeks old, 10–11w) onset of motor symptoms. N = 4 animals per group. Means and SD are depicted. Two-way ANOVAs were calculated to test for statistical differences. * <italic>p</italic> < 0.05, *** <italic>p</italic> < 0.001.</p></caption><graphic xlink:href=\"ijms-21-05412-g001\"/></fig><fig id=\"ijms-21-05412-f002\" orientation=\"portrait\" position=\"float\"><label>Figure 2</label><caption><p>High resolution respiration data normalized to electron transfer system capacity. Striatum (<bold>A</bold>), hippocampus (<bold>B</bold>), and motor cortex (<bold>C</bold>) of wild-type (WT) and R6/2 mice before (4 weeks old, 4w) and after (10–11 weeks old, 10–11w) onset of motor symptoms. <italic>n</italic> = 4 animals per group. Means and SD are depicted. Two-way ANOVAs and Tukey’s post-hoc tests (in case of significant interaction effects) were calculated to test for statistical differences. * <italic>p</italic> < 0.05, ** <italic>p</italic> < 0.01, *** <italic>p</italic> < 0.001.</p></caption><graphic xlink:href=\"ijms-21-05412-g002\"/></fig><fig id=\"ijms-21-05412-f003\" orientation=\"portrait\" position=\"float\"><label>Figure 3</label><caption><p>Relative mitochondrial respiration (flux control ratios) changes from 4- to 10–11-week-old mice differently in specific brain regions. These shifts in R6/2 were only altered in succinate-driven respiration as compared to wild-type (WT) mice. Brain Explorer 2 was used for the generation of brain region images.</p></caption><graphic xlink:href=\"ijms-21-05412-g003\"/></fig></floats-group></article>\n"
]
[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Int J Environ Res Public Health</journal-id><journal-id journal-id-type=\"iso-abbrev\">Int J Environ Res Public Health</journal-id><journal-id journal-id-type=\"publisher-id\">ijerph</journal-id><journal-title-group><journal-title>International Journal of Environmental Research and Public Health</journal-title></journal-title-group><issn pub-type=\"ppub\">1661-7827</issn><issn pub-type=\"epub\">1660-4601</issn><publisher><publisher-name>MDPI</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32751411</article-id><article-id pub-id-type=\"pmc\">PMC7432064</article-id><article-id pub-id-type=\"doi\">10.3390/ijerph17155492</article-id><article-id pub-id-type=\"publisher-id\">ijerph-17-05492</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Article</subject></subj-group></article-categories><title-group><article-title>Coping Dynamics of Consulting Psychology Doctoral Students Transitioning a Professional Role Identity: A Systems Psychodynamic Perspective</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0003-0371-9486</contrib-id><name><surname>Barnard</surname><given-names>Antoni</given-names></name><xref rid=\"c1-ijerph-17-05492\" ref-type=\"corresp\">*</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0001-5663-7279</contrib-id><name><surname>Flotman</surname><given-names>Aden-Paul</given-names></name></contrib></contrib-group><aff id=\"af1-ijerph-17-05492\">Department of Industrial & Organisational Psychology, UNISA, Pretoria 0003, South Africa; <email>[email protected]</email></aff><author-notes><corresp id=\"c1-ijerph-17-05492\"><label>*</label>Correspondence: <email>[email protected]</email>; Tel.: +27-82-375-2696</corresp></author-notes><pub-date pub-type=\"epub\"><day>30</day><month>7</month><year>2020</year></pub-date><pub-date pub-type=\"ppub\"><month>8</month><year>2020</year></pub-date><volume>17</volume><issue>15</issue><elocation-id>5492</elocation-id><history><date date-type=\"received\"><day>01</day><month>6</month><year>2020</year></date><date date-type=\"accepted\"><day>23</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>© 2020 by the authors.</copyright-statement><copyright-year>2020</copyright-year><license license-type=\"open-access\"><license-p>Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>).</license-p></license></permissions><abstract><p>To remain relevant and valuable, the psychology profession in South Africa continues to transform and evolve in response to the changing needs of society. Some psychologists embark on development opportunities to advance their professional qualifications and skills. In doing so, they experience identity tensions inherent to professional identity development and transformation. Understanding how psychologists cope with professional identity transition will enable them to develop a self-efficacious service offering and broaden the reach of psychology in the South African context. The aim of this study was to explore the identity work of a group of eight consulting psychology doctoral students to develop a system psychodynamic understanding of their coping dynamics while transitioning to a professional role identity. Students’ self-reflective essays about becoming a consulting psychologist constituted the data protocols for the study and were analysed through hermeneutic phenomenological analysis. Findings describe how students cope with performance and survival anxieties through anti-task behaviour and immature as well as sophisticated psychodynamic defences. The study contributes to the exploration of the coping concept and its manifestation, by proposing defensive coping as a natural dynamic phenomenon in the process of adapting to a transforming professional identity.</p></abstract><kwd-group><kwd>consulting psychology</kwd><kwd>coping</kwd><kwd>defences</kwd><kwd>identity work</kwd><kwd>identity tension</kwd><kwd>professional identity</kwd><kwd>system psychodynamic</kwd></kwd-group></article-meta></front><body><sec sec-type=\"intro\" id=\"sec1-ijerph-17-05492\"><title>1. Introduction</title><p>Twenty-five years post-apartheid the profession of psychology in South Africa has been beset by identity tensions regarding its relevance and value to a continuously transforming South African society [<xref rid=\"B1-ijerph-17-05492\" ref-type=\"bibr\">1</xref>,<xref rid=\"B2-ijerph-17-05492\" ref-type=\"bibr\">2</xref>,<xref rid=\"B3-ijerph-17-05492\" ref-type=\"bibr\">3</xref>]. The identity dilemmas of the psychology profession seem evident in the disagreement among its practitioners about changing the Regulations Defining the Scope of the Profession of Psychology. The original regulations defined a scope of practice for psychologists registered with the Health Professions Council of South Africa (HPCSA) to distinguish the different categories of counselling: industrial and organisational (IO), clinical, educational and research. The original regulations were however amended in 2011 without an adequate consultation process. Unlike the original widely accepted demarcated scope of practice, the amendments narrowed down practice opportunities for some of the psychology categories and seemed more favourable to others. The process was criticized for being forced upon the profession, for creating power disparities between the different categories of psychology and for marginalising certain practitioners, preventing them from contributing to sectors in society in dire need of mental health and well-being [<xref rid=\"B1-ijerph-17-05492\" ref-type=\"bibr\">1</xref>,<xref rid=\"B4-ijerph-17-05492\" ref-type=\"bibr\">4</xref>]. A large section of the profession was therefore dissatisfied and angry, resulting in a court order on 14 November 2016 by the High Court of South Africa (Western Cape Division, Case No: 12420/13) declaring the amended regulations invalid [<xref rid=\"B5-ijerph-17-05492\" ref-type=\"bibr\">5</xref>]. A notice by the Department of Health not to proceed with any amendments that were recently published [<xref rid=\"B6-ijerph-17-05492\" ref-type=\"bibr\">6</xref>] declared the original Regulations Defining the Scope of the Profession of Psychology of 2008 to remain in force.</p><p>Disputes about the amended scope of practice reflected underlying identity tensions within the psychology fraternity. On the positive side, the discussion also aligned with evolving frames of thought about the profession and its impact on society at large and about opportunities for continued professional development and multidisciplinary collaboration. One such opportunity was created in a doctoral programme specialising in consulting psychology. The programme is offered in collaboration by the Departments of Psychology and IO Psychology at UNISA. Students admitted to the programme include registered and practising psychologists across the five different HPCSA registration categories. These educational, clinical, IO, counselling or research psychologists entering the field of consulting psychology invariably experience a role identity transition [<xref rid=\"B7-ijerph-17-05492\" ref-type=\"bibr\">7</xref>] or an altering of their professional identity [<xref rid=\"B8-ijerph-17-05492\" ref-type=\"bibr\">8</xref>].</p><p>When transitioning to a new role, people naturally experience identity tensions between previously known meanings of the self and new role expectations [<xref rid=\"B9-ijerph-17-05492\" ref-type=\"bibr\">9</xref>]. They attempt to resolve these tensions by engaging in identity work [<xref rid=\"B10-ijerph-17-05492\" ref-type=\"bibr\">10</xref>]. Identity work entails the restructuring, reframing and development of identity meanings that constitute the self in specific social contexts [<xref rid=\"B11-ijerph-17-05492\" ref-type=\"bibr\">11</xref>,<xref rid=\"B12-ijerph-17-05492\" ref-type=\"bibr\">12</xref>,<xref rid=\"B13-ijerph-17-05492\" ref-type=\"bibr\">13</xref>]. It refers to the intrapersonal process in which individuals engage to form, repair, maintain, strengthen or revise their identities [<xref rid=\"B14-ijerph-17-05492\" ref-type=\"bibr\">14</xref>]. Identity work is therefore relevant to intrapersonal coping, because it enables individuals to cope with difficult work-life demands [<xref rid=\"B10-ijerph-17-05492\" ref-type=\"bibr\">10</xref>,<xref rid=\"B15-ijerph-17-05492\" ref-type=\"bibr\">15</xref>] and adjust to important work-life transitions [<xref rid=\"B16-ijerph-17-05492\" ref-type=\"bibr\">16</xref>,<xref rid=\"B17-ijerph-17-05492\" ref-type=\"bibr\">17</xref>]. Breakwell introduced the idea that identity work is based on intrapsychic and interpersonal coping strategies [<xref rid=\"B9-ijerph-17-05492\" ref-type=\"bibr\">9</xref>]. Observation of identity work is therefore expected to reveal the intrapersonal coping strategies that individuals apply to resolve the identity tensions they experience consequent to role transitions.</p><p>The aim of this study was to develop an understanding of the coping dynamics that consulting psychology doctoral students manifest when transitioning their professional role identity. Understanding how the students cope with the identity tensions they experience will enable them to develop a self-efficacious service offering to their clients and broaden the reach of psychology in the South African context. Identity work is a natural and involuntary [<xref rid=\"B7-ijerph-17-05492\" ref-type=\"bibr\">7</xref>] or subconscious [<xref rid=\"B18-ijerph-17-05492\" ref-type=\"bibr\">18</xref>] process, which in the context of this study happens in both the individual student and in the bigger systemic context of the psychology fraternity. It was therefore deemed useful to explore coping with transitioning a professional identity by applying a systems psychodynamic perspective.</p></sec><sec id=\"sec2-ijerph-17-05492\"><title>2. Literature Review</title><sec id=\"sec2dot1-ijerph-17-05492\"><title>2.1. System Psychodynamics</title><p>Systems psychodynamics is rooted in psychoanalysis, object relations, systems theory and the Tavistock Human Institute of Human Relations [<xref rid=\"B19-ijerph-17-05492\" ref-type=\"bibr\">19</xref>,<xref rid=\"B20-ijerph-17-05492\" ref-type=\"bibr\">20</xref>]. Although Freud was the father of psychoanalysis, it was Klein’s object relations theory, group relations and open systems theory that contributed significantly to the systems psychodynamic paradigm [<xref rid=\"B21-ijerph-17-05492\" ref-type=\"bibr\">21</xref>,<xref rid=\"B22-ijerph-17-05492\" ref-type=\"bibr\">22</xref>]. The systems psychodynamic approach developed as a suitable stance to explore beneath-the-surface behaviour in organisations [<xref rid=\"B23-ijerph-17-05492\" ref-type=\"bibr\">23</xref>] and to the study of organisational change dynamics [<xref rid=\"B24-ijerph-17-05492\" ref-type=\"bibr\">24</xref>,<xref rid=\"B25-ijerph-17-05492\" ref-type=\"bibr\">25</xref>]. The central tenets of system psychodynamics lie in the semantic co-occurrence of the words systems and psychodynamic. Open systems principles are firstly applied in understanding behaviour, and individual behaviour is regarded as a function of systemic dynamics as much as it is a representation of (mirroring) systemic behaviour. Secondly, psychodynamic theory represents a fundamental focus on unconscious behavioural dynamics and includes the spectrum of psychoanalytic perspectives on individual and social experiences and mental processes. This includes un understanding of anxiety as the basis of group and unconscious systemic behaviour [<xref rid=\"B26-ijerph-17-05492\" ref-type=\"bibr\">26</xref>,<xref rid=\"B27-ijerph-17-05492\" ref-type=\"bibr\">27</xref>] and the involuntary use of defence mechanisms to cope with anxiety [<xref rid=\"B28-ijerph-17-05492\" ref-type=\"bibr\">28</xref>].</p><p>Central to psychodynamic thinking is the assumption that part of the mental life of individuals is hidden and affects them in ways of which they are not always aware—the unconscious life [<xref rid=\"B29-ijerph-17-05492\" ref-type=\"bibr\">29</xref>]. Furthermore, in system psychodynamics, anxiety is regarded as the driving force of all relational dynamics in a system, as any change is seen to arouse anxiety naturally [<xref rid=\"B25-ijerph-17-05492\" ref-type=\"bibr\">25</xref>]. Anxiety manifests itself as personal anxiety, task-related anxiety or role anxiety but always in a systems context and representative of larger anxieties in the system [<xref rid=\"B26-ijerph-17-05492\" ref-type=\"bibr\">26</xref>]. Typically, transitions in the work environment arouse performance and survival anxiety [<xref rid=\"B30-ijerph-17-05492\" ref-type=\"bibr\">30</xref>]. In response to this anxiety, defence mechanisms are used unconsciously [<xref rid=\"B20-ijerph-17-05492\" ref-type=\"bibr\">20</xref>,<xref rid=\"B31-ijerph-17-05492\" ref-type=\"bibr\">31</xref>] to remain in control and to experience a sense of safety, security and acceptance [<xref rid=\"B27-ijerph-17-05492\" ref-type=\"bibr\">27</xref>,<xref rid=\"B32-ijerph-17-05492\" ref-type=\"bibr\">32</xref>].</p><p>Defences are fundamental to coping in psychodynamic theory [<xref rid=\"B33-ijerph-17-05492\" ref-type=\"bibr\">33</xref>,<xref rid=\"B34-ijerph-17-05492\" ref-type=\"bibr\">34</xref>,<xref rid=\"B35-ijerph-17-05492\" ref-type=\"bibr\">35</xref>]. In systems psychodynamics, defences manifest themselves as basic assumption behaviour, which includes dependence, fight-flight, pairing [<xref rid=\"B21-ijerph-17-05492\" ref-type=\"bibr\">21</xref>], one-ness, we-ness and me-ness [<xref rid=\"B20-ijerph-17-05492\" ref-type=\"bibr\">20</xref>]; in primary defences such as splitting, introjection, suppression, denial, projection and projective identification [<xref rid=\"B22-ijerph-17-05492\" ref-type=\"bibr\">22</xref>,<xref rid=\"B28-ijerph-17-05492\" ref-type=\"bibr\">28</xref>,<xref rid=\"B36-ijerph-17-05492\" ref-type=\"bibr\">36</xref>] and in more sophisticated defences such as rationalisation and intellectualisation [<xref rid=\"B37-ijerph-17-05492\" ref-type=\"bibr\">37</xref>]. Defences operate on a continuum, ranging from primitive, debilitating impairments to more sophisticated competence-enhancing adaptations [<xref rid=\"B25-ijerph-17-05492\" ref-type=\"bibr\">25</xref>,<xref rid=\"B38-ijerph-17-05492\" ref-type=\"bibr\">38</xref>]. These defence mechanisms have a protective function and are neither good nor bad [<xref rid=\"B39-ijerph-17-05492\" ref-type=\"bibr\">39</xref>]. Individuals develop defences as coping strategies from an early age to deal with reality and to maintain a functional sense of self [<xref rid=\"B28-ijerph-17-05492\" ref-type=\"bibr\">28</xref>].</p><p>There is a longstanding link between coping theory and psychodynamics [<xref rid=\"B40-ijerph-17-05492\" ref-type=\"bibr\">40</xref>], yet contemporary perspectives on coping strategies typically focus on cognitive-behavioural perspectives such as emotion-focussed, problem-focussed, social-support coping and religious coping [<xref rid=\"B41-ijerph-17-05492\" ref-type=\"bibr\">41</xref>]. Such coping strategies have been distinguished as involving conscious, purposeful effort [<xref rid=\"B33-ijerph-17-05492\" ref-type=\"bibr\">33</xref>]. This paper takes the stance that coping is a process of psychological adjustment [<xref rid=\"B42-ijerph-17-05492\" ref-type=\"bibr\">42</xref>,<xref rid=\"B43-ijerph-17-05492\" ref-type=\"bibr\">43</xref>] that includes conscious and unconscious coping as means of adaptation [<xref rid=\"B33-ijerph-17-05492\" ref-type=\"bibr\">33</xref>,<xref rid=\"B34-ijerph-17-05492\" ref-type=\"bibr\">34</xref>,<xref rid=\"B44-ijerph-17-05492\" ref-type=\"bibr\">44</xref>]. Applying a system psychodynamic stance to understand coping is of value, since it provides a more holistic and systemic understanding of coping behaviour [<xref rid=\"B45-ijerph-17-05492\" ref-type=\"bibr\">45</xref>] and uncovers the unconscious as a creative source of knowledge [<xref rid=\"B23-ijerph-17-05492\" ref-type=\"bibr\">23</xref>,<xref rid=\"B46-ijerph-17-05492\" ref-type=\"bibr\">46</xref>]. When experiences are explored from a systems psychodynamic stance, one can enhance awareness, understanding and learning of both conscious and unconscious dynamics [<xref rid=\"B47-ijerph-17-05492\" ref-type=\"bibr\">47</xref>,<xref rid=\"B48-ijerph-17-05492\" ref-type=\"bibr\">48</xref>]. Such knowledge is deemed essential in managing and facilitating real behavioural change [<xref rid=\"B25-ijerph-17-05492\" ref-type=\"bibr\">25</xref>] and prevents anxiety and defences from becoming destructive in the workplace [<xref rid=\"B49-ijerph-17-05492\" ref-type=\"bibr\">49</xref>].</p></sec><sec id=\"sec2dot2-ijerph-17-05492\"><title>2.2. Systems Psychodynamics and Identity Work</title><p>Systems psychodynamics approaches identity firstly from a group relations stance, relating the individual’s role identity to a systemic role identity [<xref rid=\"B22-ijerph-17-05492\" ref-type=\"bibr\">22</xref>]. From this perspective, taking up a new role entails a psychosocial dynamic that emerges from the interface between the person and the formal role as defined by a particular context [<xref rid=\"B50-ijerph-17-05492\" ref-type=\"bibr\">50</xref>]. As such, viewed from a psychodynamic perspective, identity work involves cognitive, emotional and social processes that are social or systemic in nature (i.e., cannot be done in isolation) [<xref rid=\"B12-ijerph-17-05492\" ref-type=\"bibr\">12</xref>,<xref rid=\"B13-ijerph-17-05492\" ref-type=\"bibr\">13</xref>], and such identity work is often stimulated by anxiety [<xref rid=\"B51-ijerph-17-05492\" ref-type=\"bibr\">51</xref>].</p><p>System psychodynamics further focus on understanding unconscious experience. In doing identity work, a person therefore consciously, but also unconsciously, negotiates personal beliefs, needs and aspirations in adjusting to latent role demands. Projective processes that relate to psychodynamic defence dynamics, in the form of projective identification, transference and counter-transference, are benign parts to be worked with by individuals in the process of identity work [<xref rid=\"B52-ijerph-17-05492\" ref-type=\"bibr\">52</xref>]. Psychodynamically, identity work is therefore essential in everyday coping with tensions in the self and maintaining well-being [<xref rid=\"B44-ijerph-17-05492\" ref-type=\"bibr\">44</xref>].</p><p>A psychodynamic lens to identity enriches our understanding of identity work beyond current identity theory [<xref rid=\"B44-ijerph-17-05492\" ref-type=\"bibr\">44</xref>]. System psychodynamic literature emphasises that creating reflective spaces is essential to facilitate constructive role transition [<xref rid=\"B49-ijerph-17-05492\" ref-type=\"bibr\">49</xref>,<xref rid=\"B53-ijerph-17-05492\" ref-type=\"bibr\">53</xref>]. In this regard and relevant to the consulting psychology programme context of this study, the concept of identity workspaces refers to institutions that provide a holding environment for individuals to do their identity work [<xref rid=\"B54-ijerph-17-05492\" ref-type=\"bibr\">54</xref>]. This holding environment serves as a social context that not only reduces distressing emotions but also actively facilitates sense-making, either aimed at identity stabilisation or identity transition [<xref rid=\"B55-ijerph-17-05492\" ref-type=\"bibr\">55</xref>].</p><p>A significant portion of identity work is emotional, dynamic and complex and lies beneath the surface of conscious behaviour [<xref rid=\"B18-ijerph-17-05492\" ref-type=\"bibr\">18</xref>,<xref rid=\"B44-ijerph-17-05492\" ref-type=\"bibr\">44</xref>]. Taking a systems psychodynamic stance stresses that this intrapsychic dimension of identity work must also be worked with. Thus, by “focusing on the ongoing dynamic interaction of individual and social, cognitive and emotional, conscious and unconscious factors, the system psychodynamic perspective is particularly well suited to enriching our understanding of identity and identification” [<xref rid=\"B54-ijerph-17-05492\" ref-type=\"bibr\">54</xref>].</p></sec></sec><sec id=\"sec3-ijerph-17-05492\"><title>3. Materials and Methods</title><p>This paper is part of a larger project studying the experiences of master’s and doctoral students to improve academic curricula and the throughput of postgraduate students. The next sections give an outline of the research methodology that directed the study, an explication of the research setting, the data protocols and participants, as well as the analytic strategy applied.</p><sec id=\"sec3dot1-ijerph-17-05492\" sec-type=\"methods\"><title>3.1. Research Methodology</title><p>A qualitative inquiry was deemed most appropriate for the exploratory and descriptive nature of the study. The study followed a hermeneutic phenomenological approach in its overall design. In hermeneutic phenomenology knowledge generation equates to the co-construction of meaning between the researcher and the researched [<xref rid=\"B56-ijerph-17-05492\" ref-type=\"bibr\">56</xref>]. It allows for ascribing meaning to participants’ experiences through critical interpretation, inevitably influenced by researcher experience and theoretical preconceptions [<xref rid=\"B57-ijerph-17-05492\" ref-type=\"bibr\">57</xref>,<xref rid=\"B58-ijerph-17-05492\" ref-type=\"bibr\">58</xref>,<xref rid=\"B59-ijerph-17-05492\" ref-type=\"bibr\">59</xref>]. Fundamentally, from a hermeneutic phenomenological perspective, researchers’ critical interpretation of participants’ phenomenological experiences is imperative for rigorous scientific research [<xref rid=\"B60-ijerph-17-05492\" ref-type=\"bibr\">60</xref>]. To achieve the stated objective, researchers’ interpretations were influenced in particular by applying a systems psychodynamic lens or meta-theoretical orientation during data analysis. System psychodynamics refers to an extensive body of scholarship [<xref rid=\"B46-ijerph-17-05492\" ref-type=\"bibr\">46</xref>] that provides a sound meta-theory to develop truthful, useful and credible insights and hypotheses [<xref rid=\"B48-ijerph-17-05492\" ref-type=\"bibr\">48</xref>]. Both researchers (a black man and a white woman) hold PhDs in psychology and are part of faculty in an open-distance, e-learning tertiary institution. They are registered psychologists with the HPCSA with a keen research interest in socio-analytic research methodologies, hermeneutically informed by system psychodynamics. They are also both involved in the teaching of the course work component of the Consulting Psychology doctoral programme.</p></sec><sec id=\"sec3dot2-ijerph-17-05492\"><title>3.2. Research Setting</title><p>The doctoral programme in Consulting Psychology offered at a tertiary open distance and e-learning (ODeL) institution in South Africa constituted the setting for this research. The institution’s two main campuses are located in the Gauteng region, with satellite campuses across South Africa. Gauteng is the smallest province yet the most densely populated (742.6 per square metre) in South Africa, accommodating 1.349 million of the 55.9 million South Africans (<uri xlink:href=\"https://www.southafricanmi.com/sa-by-numbers.html\">https://www.southafricanmi.com/sa-by-numbers.html</uri>). National and international student enrolments at this ODeL institution varied between 354,743 and 381,483 in the 2015 to 2018 registration periods. The Consulting Psychology doctoral programme has enrolled eight to 12 students per annum since its inception in 2005. The first year of the programme entails course work involving 11 focus areas, of which one focusses on consulting as process. The learning outcome of this focus area is intended to develop students’ capability to explore their consulting profile and develop a personal frame of reference for consulting. During the course work component of the Consulting Psychology doctoral programme, students attend five block weeks of face-to-face training and complete several individual and group-based projects as part of their formative assessment. Another primary focus of the first year is the successful defence of a research proposal, which forms the basis for continued research-based study from the second year until completion of the degree.</p></sec><sec id=\"sec3dot3-ijerph-17-05492\"><title>3.3. Data Protocols</title><p>One of the individual tasks students are required to do in their first year of study is to keep a personal diary in which they journal about becoming a consulting psychologist. The students then write self-reflective essays that entail a summative and critical reflection based on periods of five and eight months’ journaling. To explore the coping dynamics of students transitioning into a consulting psychology role, self-reflective essays constituted the data protocols for this study. Critical reflexivity refers to the process in which one questions one’s positionality and basic assumptions about life, people and oneself, while seeking alternative ways of being and looking at things [<xref rid=\"B61-ijerph-17-05492\" ref-type=\"bibr\">61</xref>,<xref rid=\"B62-ijerph-17-05492\" ref-type=\"bibr\">62</xref>,<xref rid=\"B63-ijerph-17-05492\" ref-type=\"bibr\">63</xref>]. In this regard, self-reflective practice is akin to identity work as defined by Sveningson and Alvesson [<xref rid=\"B14-ijerph-17-05492\" ref-type=\"bibr\">14</xref>]. In Nagata’s definition of self-reflexivity [<xref rid=\"B64-ijerph-17-05492\" ref-type=\"bibr\">64</xref>], coping as an adaptational endeavour underlying identity work is furthermore evident. He defined self-reflexivity as having a conversation with the self about one’s experiences while one is experiencing them, leading to the ability to regulate internal psychological and unconsciously driven responses. Self-reflective essays were moreover deemed appropriate to the purpose and system psychodynamic orientation of this study, as reflective practice elicits a meta-level of thinking and feeling about the self [<xref rid=\"B64-ijerph-17-05492\" ref-type=\"bibr\">64</xref>] and makes the “unthought known” observable and conscious [<xref rid=\"B65-ijerph-17-05492\" ref-type=\"bibr\">65</xref>].</p><p>Instructions for the self-reflective essays were based on circular existential and relational questions relevant to experiential learning activities aimed at developing critical reflexivity [<xref rid=\"B61-ijerph-17-05492\" ref-type=\"bibr\">61</xref>]. The questions were formulated as a guide to facilitate critical self-reflection in line with the objective to engage with and develop a personal frame of reference as a consulting psychologist. The intention with these questions was not to be prescriptive but to offer some direction to the task of self-reflecting. The first question aligns with Cunliffe’s existential (who am I?) and relational (who am I in relation to something/someone?) approach [<xref rid=\"B61-ijerph-17-05492\" ref-type=\"bibr\">61</xref>] and asks the student to reflect on the question: Where am I at this moment regarding my personal frame of reference? The second and third questions refer to the circular influence approach [<xref rid=\"B61-ijerph-17-05492\" ref-type=\"bibr\">61</xref>], examining how existential and relational learnings influence one’s responses and ways of acting, behaving and responding. The second question asks: How does your evolving personal frame of reference affect your understanding of the landscape of consulting psychology? The third is: What is the impact of your evolving personal frame of reference on your personal consulting profile?</p><p>Self-reflective essays are shared with lecturers after five months’ journaling and again after another three months’ journaling. Through purposive, convenience sampling, the transcripts that constituted the data sets for this study included all the self-reflective essays of the current course work students after eight months. By that time, the students had already completed most of their course work-related projects and workshops.</p></sec><sec id=\"sec3dot4-ijerph-17-05492\" sec-type=\"subjects\"><title>3.4. Participants</title><p>To ensure the confidentiality and anonymity of participants, they are not identified as individuals but described as a group. The participants included three women and five men. In terms of population group, three were white, one was Indian and four were black, with an age range of 29–58, and average age of 40. Participants were approached face to face during their first block period on campus, and the nature and purpose of the research were explained to them. This was followed up by individual e-mails requesting their participation. Inclusion criteria entailed that participants had to have a master’s degree in a specific psychology domain; be registered psychologists with the HPCSA, practising for at least three years; be registered doctoral students in Consulting Psychology; be willing to share their self-reflective experiences and be willing to participate in the research. Four participants were registered in the category of IO psychology, and one each was registered as clinical, educational, counselling and research psychologist. All the participants had gained a minimum of three years’ experience since obtaining their professional HPCSA registration. Pseudonyms are used in the findings to indicate the number of the participant, for example, P4 refers to participant number four.</p></sec><sec id=\"sec3dot5-ijerph-17-05492\"><title>3.5. Ethical Considerations</title><p>Ethics approval was obtained from the relevant Institutional Senate Ethics Committee (REF#:2017_RPCS_018). Participants consented in writing that data generated from their self-reflective work on being a consulting psychologist could be used for research purposes relevant to the bigger project. The researchers abide by the ethical codes of conduct as prescribed by the UNISA ethics policy, as well as the ethical code of psychologists registered under the HPCSA. The researchers also declare that the research was conducted in compliance with the ethical principles enunciated in the Declaration of Helsinki. As such, ethical principles of anonymity, confidentiality and informed consent were upheld by reporting on the data in a collective, interpretive sense and by using pseudonyms.</p></sec><sec id=\"sec3dot6-ijerph-17-05492\"><title>3.6. Data Analysis</title><p>Eight self-reflective essays were loaded as primary data documents in Atlas.ti to ease the management of the data analysis process. Data were analysed through hermeneutic phenomenological analysis according to the analytic stages of naïve reading, structural thematic analysis and comprehensive understanding [<xref rid=\"B66-ijerph-17-05492\" ref-type=\"bibr\">66</xref>]. The fundamental premise of the hermeneutic circle was applied throughout these stages of analysis. Initially, we considered each individual’s experience in relation to the meaning we constructed from the participants’ collective experience and vice versa [<xref rid=\"B26-ijerph-17-05492\" ref-type=\"bibr\">26</xref>]. We also compared findings from each stage with findings in the previous stage in a consistent, circular manner [<xref rid=\"B67-ijerph-17-05492\" ref-type=\"bibr\">67</xref>]. At this point the themes provided a richness in terms of saturation and no more themes emerged to add to the clear description of coping dynamics from a system psychodynamic perspective.</p></sec></sec><sec id=\"sec4-ijerph-17-05492\"><title>4. Findings</title><p>The naïve reading revealed how some students struggled to engage with the task of self-reflection in that they recited textbook definitions of the skills and competencies of a consulting psychologist. Without reflecting on their emotional responses to the personal development journey they were on, they merely noted that they were busy developing the mentioned list of skills and competencies. Others provided less detached accounts of their experience of becoming a consulting psychologist, but their performance anxiety in relation to the task topic of becoming a consulting psychologist was evident in how they engaged in anti-task behaviour. Performance anxiety was coped with in various ways, mostly to avoid the required identity work or to resist the professional identity role transformation they were experiencing. Especially, students who did not engage in self-reflection consistently throughout the year seemed to struggle with performance anxieties and integrating identity tensions. Three themes were constructed in the thematic analysis to describe the students’ coping with the identity tensions and demands from a systems psychodynamic perspective. The first theme described how they resisted the primary task of self-reflection and, consequently, resisted identity work through basic assumption (anti-task) behaviour. The second theme described their coping dynamics by applying primary defences against the perceived incongruence of conscious (normative) and unconscious (phenomenological and existential) roles. The third theme described their coping along the more sophisticated defences the students applied to resolve the identity tensions and performance anxiety they experienced. The three themes are conceptualized below in terms of their different sub-themes and related categories. Verbatim data from the participants’ narratives are used to illustrate the meaning-making during analysis.</p><sec id=\"sec4dot1-ijerph-17-05492\"><title>4.1. Resisting the Primary Task through Basic Assumption Behaviour</title><p>The first sub-theme entails resisting the task of deep self-reflection and identity work through basic assumption (anti-task) behaviour, which manifested in dependency, fight behaviour, pairing and me-ness.</p><sec id=\"sec4dot1dot1-ijerph-17-05492\"><title>4.1.1. Dependency</title><p>Students idealised several external objects such as lecturers, the programme and consulting knowledge. In doing so, they expressed their dependency on external structures and authority figures to feel successful in their career or profession. The Consulting Psychology doctoral programme was related to the mother figure and several instances of how the programme or lecturers or the consulting psychology knowledge domain was made the hero or saviour were evident in the data. Such “heroing” usually followed students’ expressed feelings of insecurity, low self-confidence and uncertainty or the need to succeed or fantasise about career opportunities and personal change. In this way, some students seemed to cope by relying on the doctoral programme to help them deal with the identity transition they were seeking and working with, while doing the task of self-reflection in the role of becoming a consulting psychologist. <xref rid=\"ijerph-17-05492-t001\" ref-type=\"table\">Table 1</xref> below summarises pertinent verbatim extracts supporting how students acted out or expressed their dependency in an effort to cope with the identity tensions and insecurities they were experiencing.</p></sec><sec id=\"sec4dot1dot2-ijerph-17-05492\"><title>4.1.2. Fight</title><p>Fight reactions were a way of coping with the need to preserve the self and resist the identity tensions felt during self-reflection. One student attacked the task of self-reflection by denigrating its value in light of preserving what she feels more comfortable with, namely to be a practitioner: “When confronted by theory and philosophy, my question is always what does this look like in practice? If I can’t figure that out, or it is not clear to me, it is not useful, no matter how beautiful or elegant it is” (P1). Another student demonstrated fight behaviour by attacking team work as irreconcilable with her leadership style. Again, the student is trying to preserve her known leadership identity. She reveals the identity tension she experiences and demonstrates how she copes with it through fight behaviour: “I naturally take a leadership role (my current role at work requires this) and develop innovative ideas in a team setting; this had to be suppressed at times” (P2).</p></sec><sec id=\"sec4dot1dot3-ijerph-17-05492\"><title>4.1.3. Pairing</title><p>Students attempted to pair with an authoritative form or powerful other, by aligning themselves with the programme, with a lecturer or with the class as a team. In expressing her need to be part of the group, P2 copes with the anxiety she experiences in transitioning into the consulting psychologist role: “after the week, I realised that I needed the group. In May, I became more involved with the team, working in groups. Even though it was a bit uncomfortable for me working as a member of a team, I did not just play my role but enjoyed it. Furthermore, the course seemed easier as we shared similar experience and could relate.” Several students strongly aligned themselves to the doctoral programme and the consulting psychology knowledge domain, proclaiming a new-found sense of confidence and security in their own abilities. P4, for example, states: “this programme has broadened my horizons. It has help[ed] me to appreciate that there are three different levels of intervention that any qualified and properly trained psychologist must be able to operate in, namely, individual, group and organisational level. The above, is an illustration of the significant impact that this programme is having on me as a professional”. Similarly, P8 notes that she feels more confident as a psychologist because “there is no better programme I would recommend to my peers and colleagues than that of the Consulting PhD programme”. She continues to align herself to the group in order to cope with the potential loneliness that she experiences in the identity transition process that she embarked on: “My classmates and I have an exceptional relationship where we assist each other not only academically but also personally and professionally.” Through pairing, it seems that the students constructively cope with the performance anxieties that emanate from the identity demands they experience in the context of the consulting psychology doctoral programme.</p></sec><sec id=\"sec4dot1dot4-ijerph-17-05492\"><title>4.1.4. Me-Ness</title><p>Students experience tension between the identity needs of belonging and of uniqueness. The push and pull between wanting to retain individuality, while wanting to be part of the group, was in particular coped with by emphasising me-ness. Through me-ness, students cope with identity transition, as is seen in a student’s denial of personal change: “Looking at my framework of reference when I started the programme, I could not really say that much has changed with regards to my view My frame of reference is still focused on the individual but not excluding the group and organisational factors” (P5). Similarly, P1 notes that she also does not require much personal change and therefore does not see the need for the task of self-reflection: “Reflecting on my personal frame of reference is not something that I usually do, and I am not sure whether I will ever become in the habit of doing so. I like to think that I have a degree of self-awareness”. In this way, students resist the task of self-reflection, and in doing so, they are resisting identity work by highlighting their inner strength and adequacy. By resisting the task in this way, students reveal the anxiety they experience when engaging in a task focussed on identity work, a task that uncovers identity tensions consequent to transforming their professional identity. Me-ness illustrates the individual’s escape into the inner world that is felt to be safe, comfortable and good [<xref rid=\"B68-ijerph-17-05492\" ref-type=\"bibr\">68</xref>,<xref rid=\"B69-ijerph-17-05492\" ref-type=\"bibr\">69</xref>]. Coping with the task of self-reflection (identity work) through self-reliance, independence and distancing from interdependence is evident in the words of P2: “After working for almost fourteen years in management and being offered directorship at the age of thirty-eight years, I began to realise that I needed to trust my capabilities, to assure myself that I have what it takes to ‘manage’ whatever comes my way” and “The realisation has to some extent shaped a frame of reference for my believing that I do not necessarily need others: I cannot/do not like working as a member of a team, I do not like group work, as I find that I achieve better results, when I complete tasks in my own way and independently.”</p></sec></sec><sec id=\"sec4dot2-ijerph-17-05492\"><title>4.2. Primary Defences Defending against Perceived Identity Incongruences</title><p>The second theme entails defending against perceived identity incongruences. The perception of identity incongruence was evident in students experiencing tension between their conscious (normative) and unconscious (phenomenological and existential) roles. Coping with the resulting performance anxiety was evident in primary psychodynamic defences such as splitting, projection and projective identification [<xref rid=\"B25-ijerph-17-05492\" ref-type=\"bibr\">25</xref>]. These have also been referred to as immature defences [<xref rid=\"B70-ijerph-17-05492\" ref-type=\"bibr\">70</xref>].</p><sec id=\"sec4dot2dot1-ijerph-17-05492\"><title>4.2.1. Splitting</title><p>To deal with the anxiety elicited in the task of self-reflection, students tended to split objects of their role identification into good and bad opposites. One split was evident in juxtaposing the academic role with the role of practitioner, projecting onto the academic role the less worthy and valuable task:</p><p>I am not sure that the academic world will ever be home to me. It might become easier for me to understand and adhere to its customs, but I don’t think I will ever choose to stay there for longer than I need to. I would rather be in the world outside where I can be doing. I have always performed well academically, and everyone has expected that I would follow an academic career, but that did not interest me. The theoretical and philosophical have never appealed to me, the practical did, and that is where I positioned myself (P1).</p><p>Similarly, P6 splits the roles of scientist and practitioner yet in a less obvious manner. For the greater part of his essay, he has copied definitions of the scientist-practitioner roles and aligned himself to each definition, without demonstrating authentic reflection on how he engages with and integrates these roles. His stance remains distanced and detached, as seen in his paraphrased list of roles: “My consulting value proposition and paradigm as a scientist-practitioner is adapting and designing assessment technologies/instruments for purpose of selection, training and vocational assessment; conducting individual assessment for the purpose of selection and training, career and vocational guidance, and leadership coaching; conducting individual assessment on individual wellness and work adjustment (psychopathology and work adjustment); conducting career counselling, advice and therapy ” (P6).</p><p>Another student copes by creating a split between the consulting psychologist and other “external forces” that inhibit constructive work in the organisation. In doing so, P5 reveals the performance anxiety he experiences in taking up the consulting psychologist role: “I also realise that there are forces internal and external that would make it more challenging for the Consulting Psychologist to find a balance between the organisation, the group and the individual. The challenge for the Consulting Psychologist is to divorce him/herself from these external forces such as politics”. The spilt in professional roles is also evident in P4’s hero-ing of the consulting psychologist role as one that “is equipped with diverse knowledge and experience to help others meaningfully” as opposed to “I have always restricted my dealings with issues and clients strictly to methodologies within industrial psychologist stream.”</p><p>In working with the tension between her need for belonging and her need for independence, P2 copes by splitting the self from the group: “I found myself concerned with whether the members of the team were up to date with expectations and going to an extent of contacting them through emails and some with a telephonic discussion. I consider this a degree of improvement as at least I thought about the team and did something about that thought”. Splits were also evident in the different professional categories of psychology: “I have always recognised myself as a clinician who works only with the individual to target the presented pathology/illness for a desired behaviour, treatment management and curative modalities … Consulting Psychology is different” (P8).</p><p>Splitting seemed to reveal students’ perception of identity incongruence in what they normatively expected themselves to do in the role, as opposed to their inherent and obscured insecurities and fears of incompetence. Their conscious and unconscious self-expectations left them with anxious feelings, which they resolved by creating outside forces that were feared or not acceptable. The way participants cope through splitting becomes more evident when integrated with how they invariably use projection as an unconscious defensive coping strategy.</p></sec><sec id=\"sec4dot2dot2-ijerph-17-05492\"><title>4.2.2. Projection</title><p>Engaging in the self-reflective exercise created discomfort because it entailed having to face and work with both positive and negative aspects of the self. Students defended themselves against this discomfort by attributing negative parts of the self to others. Narcissism and self-indulgence were projected onto the psychology profession as a whole and onto the task of reflecting on personal paradigms: “My impression is that psychologists have become so obsessed with their own paradigms and frames of reference, and in the process became so self-focused, that they are forgetting why they are here. There is a whole country in dire need of psychological services, but psychologists are pre-occupied with their paradigms, like Nero fiddling while Rome is burning” (P1). Later this student also projects egotism onto the corporate world: “The corporate world needs to learn how to effectively engage with communities, even if it [is] just for the benefit of their own triple bottom line—let’s not kid ourselves, the corporate world rarely does something out of pure altruism” (P1).</p><p>Students cope with their performance anxiety by projecting inefficiency onto colleagues: “I often have a feeling that it will be better if I can do my work without interruptions from colleagues” (P2). They cope with their own fear of engaging in the self-reflection task (identity work) by projecting the fear of change onto organisations and “other people”: “Some organizations may initially be apprehensive of change. The apprehension is normal… they do not want change because change requires them to work and use energy, time and effort” (P8).</p><p>Positive attributes were also projected onto the consulting psychology profession and the consulting psychology doctorate. In a way, the students were idealising consulting psychology as an all-knowing, super-capable and competent force, which they wanted to attain: “although my frame of reference has been limited to models within industrial psychology, this programme has broadened my horizons” (P4). Similarly, P3 projects his ideal self onto the programme: “I feel that my journey through the PhD programme … will assist in refining my work as a scientific-practitioner.”</p></sec><sec id=\"sec4dot2dot3-ijerph-17-05492\"><title>4.2.3. Projective Identification</title><p>Students’ projective identification was manifested predominantly in identifying with the sense in the class that their careers and professional competence would be adequate if they had <italic>enough</italic> knowledge. The students’ projective identification started with them having introjected feelings of inadequacy, low self-efficacy and a sense of knowing too little and having too little experience. P3 comments “Starting off, my point of reference with regard to consulting psychology was nothing” and P2 reflects “I reflected on what I associated the term, capability, with. My impression is that there had been a rooted feeling of doubt, not only self-doubt, but silently kept childhood statements that echoed: ‘you will not be able to be on the same level with your class/group’.”</p><p>The introjected inadequacy led to the fantasy that the doctoral programme would empower them to become successful consulting psychologists (their expressed normative role). Students’ valence for thinking they did not know enough was further evident in expressions of rigid self-expectation: “Some workshops really started providing me with more insights in what a Consulting Psychologist is supposed to know .... Consequently, as a Consulting Psychologist I need to become more aware of how the environment, the culture and the political spectrum dictates the functioning of the organisation, secondly I need to be able to understand ...” (P5). Similarly, P8 says: “Consulting Psychology is comprehensive and quite theoretical …. Consulting psychologists need stringent and empirically tested interventions and methods to be able to target these dynamics.”</p></sec></sec><sec id=\"sec4dot3-ijerph-17-05492\"><title>4.3. Applying Sophisticated Defences to Cope with Identity Tension and Performance Anxiety</title><p>In psychodynamic theory, rationalisation and intellectualisation are regarded as mature defence mechanisms [<xref rid=\"B45-ijerph-17-05492\" ref-type=\"bibr\">45</xref>,<xref rid=\"B71-ijerph-17-05492\" ref-type=\"bibr\">71</xref>]. Through these mechanisms, the students suppressed emotions and proposed rational arguments to explain or describe their experiences.</p><sec id=\"sec4dot3dot1-ijerph-17-05492\"><title>4.3.1. Intellectualisation</title><p>Examples of intellectualisation, where students suppress their emotional experiences, abound. One student, P4, engages with identity work (self-reflection) by speaking about her experience of becoming a consulting psychologist in a detached (theoretical) and absolute (over-generalising) manner: “A consulting psychologist must always be aware of the values and past experiences that he or she brings into the system or relationship with the new client. I have learnt that this helps for one to remain accurate and objective on matters while dealing with the client”. Student P8 addresses her fear of incompetence by also providing a rational over-generalisation of what one <italic>should</italic> do when engaging in identity work: “It is important to also be aware of one’s biases, personality and behavioural dynamics regarding the process as a psychologist and not allow these to hinder the consulting process”. In a critical tone, using absolute terms, P1 defends against engaging personally with the identity transformation: “As a consultant, my work must be practical and useful, yet imbedded in science, and I must be aware of my impact and others’ impact on me. Doing this programme is a time for me to grow and develop and acquire new skills.”</p><p>Intellectualising their personal development in becoming a consulting psychologist, students avoid facing the anxiety that stems from such very personal identity work. In this way, they suppress feelings of discomfort experienced in relation to their evolving and transforming professional identity.</p></sec><sec id=\"sec4dot3dot2-ijerph-17-05492\"><title>4.3.2. Rationalisation</title><p>The belief that knowledge imparted to students by the lecturers and the programme will enable success and competence was found in various essays. This belief shows how students idealise the programme and knowledge as an intellectual defence against the fear of incompetence. In presenting an essay that is fully paraphrased from competency lists and definitions pertaining to consulting psychology, P6 rationalises his professional development and does not engage in self-reflection at all. Also finding solace in knowledge and science, P3 rationalises that the knowledge he will gain as a result of the programme will make him a more effective psychologist: “This potential is where positive psychology can be enlisted as a new frame of reference to carry on the implementation of growth and actualising steps… So far, conceptually, it would appear that my personal knowledge base, at this stage of my development, would constitute Psychodynamics, Dynamic Systems Theory and Positive Psychology.” In his explanation of how he looks at a client context, P5 notes: “As an assessment practitioner, the data tells you something and ‘it is what it is’.” Similarly, P7 rationalises his consulting skills in relation to a process model of consulting psychology; he seems to find comfort in the belief that the theoretical model will provide him with the ability to ensure success:</p><p>In the third stage, intervention phase, care should be given to ensure that the interventions to be implemented are a joint venture (joint action plan) between myself and the client and not necessarily my own prescriptions. The last stage, evaluation phase, is of importance for me because it allows me to be reflective and critical of myself and the consulting process in each stage, so as to address possible challenges and ensure that the consulting process brings about desired outcomes.</p><p>In these ways, students do not take ownership of their identity transformation, because of fearing their incompetence. They rather rationalise about how the programme will impart the knowledge they need, or how theoretical models will show them the way to be effective in consulting.</p></sec></sec></sec><sec sec-type=\"discussion\" id=\"sec5-ijerph-17-05492\"><title>5. Discussion</title><p>This study aimed to develop an understanding of the coping dynamics that consulting psychology doctoral students employ when transitioning their professional role identity. In working with their professional identity, students show fear of incompetence as well as anxiety in relation to preserving the self. Such fear and anxiety are defined as performance and survival anxiety [<xref rid=\"B30-ijerph-17-05492\" ref-type=\"bibr\">30</xref>] and are typically also found in other system psychodynamic studies working with identity construction [<xref rid=\"B26-ijerph-17-05492\" ref-type=\"bibr\">26</xref>,<xref rid=\"B45-ijerph-17-05492\" ref-type=\"bibr\">45</xref>,<xref rid=\"B72-ijerph-17-05492\" ref-type=\"bibr\">72</xref>]. To deal with their performance and survival anxieties, the three themes constructed in the findings describe the students’ coping dynamics from a system psychodynamic stance in terms of basic assumption or anti-task behaviour, primary and sophisticated psychodynamic defences.</p><p>Students firstly engaged in basic assumption or anti-task behaviour such as dependency, fight, pairing and me-ness as a way of coping with their performance and survival anxieties. In doing so, they attempted to regain a sense of competence and self-efficacy while transitioning their professional role identity. In idealising consulting psychology, as well as the doctoral programme and its lecturers, students demonstrated a dependency mentality to resolve the insecurity and self-doubt they felt in having to take up the role of consulting psychologist. Similarly, pairing with these same objects, which they deemed powerful and authoritative, helped them to cope with feeling incompetent. Students therefore sought out the consulting psychology knowledge domain and the doctoral programme with its lecturers, as a container of their performance anxiety. To preserve the self and deal with survival anxieties in taking up the consulting psychology role, students reverted to an anti-task mentality of fight and me-ness. By attacking the task of self-reflection (i.e., the task of identity work) and resisting teamwork, students tried to preserve a sense of self that they knew and felt comfortable with. In doing so, they split the self from the task or from the team and projected onto the task/team the feelings of discomfort and suspicion related to their ability to be a consulting psychologist. By emphasising me-ness, students also attempted to preserve the self in relation to the identity tension they experienced between wanting to belong to the consulting psychology fraternity and retaining their unique psychologist identity.</p><p>Anxiety that resulted from perceived identity incongruences in their normative and existential or phenomenological role parts secondly surfaced in how they used projection, splitting and projective identification to deal with the ensuing fears of not being good enough. Taking up a new role entails taking up a role that is given or normative, which refers to the rational and measurable work related to the task [<xref rid=\"B26-ijerph-17-05492\" ref-type=\"bibr\">26</xref>,<xref rid=\"B73-ijerph-17-05492\" ref-type=\"bibr\">73</xref>]. At the same time, it includes taking up an informal role, reflected in the personal, frequently unconscious needs and aspirations of the individual [<xref rid=\"B50-ijerph-17-05492\" ref-type=\"bibr\">50</xref>]. The informal or unconscious role is also referred to as the existential or phenomenological role parts [<xref rid=\"B26-ijerph-17-05492\" ref-type=\"bibr\">26</xref>]. Students demonstrated these primary psychodynamic defences specifically in relation to coping with the incongruences they experienced in their formal or conscious task and the informal/unconscious task to manage the self in relation to the other. Through splitting the domains and roles of academic/scientist and practitioner, students coped with the tension of not feeling good enough in their existential/phenomenological role. Their self-doubt was recognised in the high expectations they introjected in relation to the consulting psychology role and the concurrent projective identification of not being good enough. Students continued to cope with their performance anxiety by projecting negative attributes such as narcissism and inefficiency onto others in an effort to preserve their sense of self.</p><p>Thirdly, they rationalised and intellectualised their work identity and professional role transition, in an attempt not to deal with the unwanted emotions that resulted from their identity work. Further expression of their normative role was also found in students’ intellectualisation and <italic>rationalisation</italic> of consulting psychology as an all-powerful knowledge domain. Through these two sophisticated defences, students talked about their consulting psychology role in a way that emphasised absolute knowledge and competence, ultimately demonstrating incongruence with their felt (introjected) sense of incompetence. In this way, reasoning and rational thought were used to avoid dealing with difficult emotions [<xref rid=\"B37-ijerph-17-05492\" ref-type=\"bibr\">37</xref>,<xref rid=\"B74-ijerph-17-05492\" ref-type=\"bibr\">74</xref>].</p><p>The findings show how consulting psychology doctoral students engage with and express identity work when they reflexively engage and formulate self-reflective thoughts about their experience of taking up a new professional role identity. It is hypothesised that active identity work (such as in self-reflective activities) is valuable for students to deal with their performance and survival anxiety, because in doing so, they uncover their unconscious coping dynamic, a dynamic that is a normal part of facilitating their adjustment to transitioning into a new professional identity. This finding is in support of reflective spaces or identity workspaces, which according to system psychodynamic theory provide a safe space to surface and consciously work with below-the-surface anxieties that result from identity transition [<xref rid=\"B54-ijerph-17-05492\" ref-type=\"bibr\">54</xref>,<xref rid=\"B55-ijerph-17-05492\" ref-type=\"bibr\">55</xref>]</p><p>Employing certain defence mechanisms to cope with performance anxiety has traditionally been described as maladaptive or pathological [<xref rid=\"B35-ijerph-17-05492\" ref-type=\"bibr\">35</xref>]. In the present study, the defensive coping dynamic that was evident in the students’ identity work is rather proposed to be a natural (normal) and evolving process of transitioning into a new professional role [<xref rid=\"B54-ijerph-17-05492\" ref-type=\"bibr\">54</xref>]. Some psychodynamic perspectives on coping emphasise hierarchies of defensive coping, depending on the level of reality distortion—from psychotic and immature to mature [<xref rid=\"B70-ijerph-17-05492\" ref-type=\"bibr\">70</xref>,<xref rid=\"B75-ijerph-17-05492\" ref-type=\"bibr\">75</xref>]. These theories show that coping <italic>evolves</italic> as a person’s cognitive functions mature [<xref rid=\"B76-ijerph-17-05492\" ref-type=\"bibr\">76</xref>]. This study further supports the notion of defences as behavioural phenomena, rather than referring to defence mechanisms as measurable constructs [<xref rid=\"B77-ijerph-17-05492\" ref-type=\"bibr\">77</xref>]. It is therefore conjectured that defensive coping is an important, dynamic and interrelated behavioural phenomenon that could potentially be conducive to adjustment, because it is a natural part of identity work. Defensive coping is an important part of understanding the coping phenomenon holistically, and it is in working with all the parts of the coping dynamic that the whole adjustment process can be facilitated. Defensive coping is conducive to adjustment, as it progresses the identity work relevant to professional role transition and brings to the surface the otherwise unconscious coping dynamic. Recently, even quantitative studies exploring the correlation between coping and defences have confirmed that specific adaptive strategies can only be effectively employed when unconscious processes have been attended to [<xref rid=\"B78-ijerph-17-05492\" ref-type=\"bibr\">78</xref>]. Active identity work is a way to develop consciousness of the self when taking up a new professional role.</p><p>Therefore, rather than speaking of either adaptive or immature coping strategies and either psychotic or mature defence mechanisms, coping is a dynamic phenomenon that includes defensive coping. Studies show that people constantly re-evaluate and reconstruct the self in the work context [<xref rid=\"B72-ijerph-17-05492\" ref-type=\"bibr\">72</xref>]. Unearthing the unconscious dynamics of defensive coping through conscious identity work may develop the resilience required to establish a professional identity in which the self is both interdependent and unique. A study of the systems psychodynamic role identity of academic supervisors [<xref rid=\"B26-ijerph-17-05492\" ref-type=\"bibr\">26</xref>] similarly demonstrates how focusing solely on conscious behavioural adaptation can limit valuable insight into unconscious adaptive identity work. We argue that the human coping phenomenon is studied only in part if the covert and unconscious social dimensions of coping, such as splitting, projections and projective identification, are ignored. The system psychodynamic stance, with its focus on the unconscious and irrational forces in human behaviour, moves beyond the mainstream cognitive coping theory that focusses on rational, conscious coping, by taking a depth perspective on this phenomenon. To focus solely on conscious behavioural adaptation may thus limit valuable insight into unconscious adaptive identity work.</p><p>The findings also have implications for the work of faculty in general and educators in particular. It is evident that students should be provided with more conscious and structured support. Firstly, it would serve to sensitise (creating awareness) faculty and educators to the anxiety-provoking realities of identity transition and identity formation. Secondly, it would create safe, contained spaces for conscious reflection nestled in the different components of the consulting psychology doctoral programme. Finally, identity work, in the form of self-reflective activities, should become the norm (rather than the exception), as a way of developing consciousness of the self. In doing this, coping would be appreciated as a dynamic phenomenon, and in the process, resilience would be nurtured as required in transitioning a new professional identity.</p><p>A system psychodynamic perspective to coping would be incomplete if the discussion does not allow for constructing interpretations and hypotheses of the individual as reflective of the larger system and the dilemmas and challenges faced by it [<xref rid=\"B71-ijerph-17-05492\" ref-type=\"bibr\">71</xref>]. This study therefore also has implications for understanding how the psychology profession in South Africa may be coping with its unresolved identity tensions as a collective. The students’ identity work reflects the performance and survival anxieties evident in the psychology profession, as their defensive coping mirrors similar dynamics in the profession as a collective. From the background to this article, the splitting of professional categories, fight behaviour, pairing with the legal system and intellectualisation and rationalisation through the formulation and reformulation of regulations demonstrate how the psychology profession is coping with its identity tensions and are suggestive of an identity transition. Like the students’ experiences, the psychology profession will have to find ways to “normalise” this tension (both a challenge and an opportunity) in search of an identity that is relevant and dynamic, given the ever-changing socio-political and economic landscape and changing societal needs.</p><p>This study is limited in the extent to which the range of the coping dynamic has been discussed, purely because of the limited scope of taking a specific approach. As such, the authors acknowledge that the findings reflect a specific stance that does not necessarily demonstrate the holistic coping dynamic we are advocating, because it does not deal with overt, cognitive coping strategies. As researchers we acknowledge the limitation associated with small sample sizes and qualitative analysis, and therefore, we do not claim that our findings constitute an absolute or generalisable truth. In true hermeneutic phenomenological fashion, we present the findings as a perspective that may add value to scholarly understanding in working with identity conflicts and related coping dynamics. The value of the psychodynamic approach to coping was celebrated in the findings and highlighted defensive coping as an essential and natural part of the whole coping dynamic. We support research proponents of in-depth, qualitative inquiry into the study of defensive coping [<xref rid=\"B77-ijerph-17-05492\" ref-type=\"bibr\">77</xref>]. Continuous interpretive inquiry from a psychodynamic stance, to build theory abductively [<xref rid=\"B77-ijerph-17-05492\" ref-type=\"bibr\">77</xref>], is recommended to enhance our understanding of defensive coping as part of adjustment rather than pure evidence of maladaptive coping. In this vein, we recommend research exploring students’ identity work as it evolves in their self-reflective work throughout the year, exploring the aspect of maturation in the phenomenon of defensive coping.</p></sec><sec sec-type=\"conclusions\" id=\"sec6-ijerph-17-05492\"><title>6. Conclusions</title><p>In general, consulting psychology doctoral students perform well in terms of coping with their professional identity development, as they have completed their first year of doctoral studies successfully and are all well on track towards completing the degree. An exploration of below-the-surface dynamics reveals an interesting defensive coping dynamic that contributes to a more holistic understanding of coping with identity transition in the psychology profession. Consciously, the students engaged in self-reflection about taking up the role of consulting psychologist in a rational and intelligent manner. Unconsciously, while transitioning a professional role identity, they experienced performance and survival anxiety, which became conscious in the process of self-reflection or identity work and in exploring their coping dynamics from a systems psychodynamic perspective. The students also mirrored the identity work and defensive coping of the larger psychology profession. The identity work of the individual professional cannot be detached from the identity work of the collective system but rather provides potential insight into the collective on its adaptive functioning.</p><p>System psychodynamic coping is a dynamic process phenomenon that should not be conceptually limited to the discussion of defence mechanisms. Coping with identity tensions and demands includes defensive coping, which is a natural phenomenon and something that is engaged with in everyday life as our professional careers develop. Conscious and active identity work reveals the related unconscious dynamics. The task of self-reflection not only propels the students to do identity work, but in the process, they become aware of their defensive coping and continue to develop reflexivity and resilience in transitioning to a new professional identity. Proponents of system psychodynamics view wellness as a relational and systemic concept [<xref rid=\"B68-ijerph-17-05492\" ref-type=\"bibr\">68</xref>]. A certain level of congruence between the internal and external reality of the self is needed for wellness to be developed and sustained. This wrestling with identity tension is therefore not only important, but necessary as well. In becoming aware of their identity-related anxieties and defensive coping, professionals, such as the students in this study, can feed this awareness back into the profession and collectively start to model the courage to acknowledge insecurities, take back their projections and repair splits. Psychological well-being and coping result from healthy intrapersonal (within a person) and interpersonal (between people) relations [<xref rid=\"B54-ijerph-17-05492\" ref-type=\"bibr\">54</xref>].</p><p>Developing consulting psychology competence is valuable in facilitating behavioural change in organisations [<xref rid=\"B79-ijerph-17-05492\" ref-type=\"bibr\">79</xref>,<xref rid=\"B80-ijerph-17-05492\" ref-type=\"bibr\">80</xref>]. The doctorate in consulting psychology is particularly important in the South African context to bridge professional divides in the psychology profession and draw from the multidisciplinary pool of skills and competence to continue to address the country’s mental health needs effectively on all levels.</p></sec></body><back><ack><title>Acknowledgments</title><p>The authors would like to acknowledge their Ph.D. students for the selfless sharing of their phenomenological experiences and their permission to use their personal experiences for research purposes.</p></ack><notes><title>Author Contributions</title><p>Conceptualisation, A.B.; data curation, A.B. and A.-P.F.; formal analysis, A.B.; investigation, A.B. and A.-P.F.; methodology, A.B. and A.-P.F.; project administration, A.B. and A.-P.F.; validation, A.-P.F.; writing—original draft preparation, A.B.; writing—review and editing, A.B. and A.-P.F. 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Pain.</source><year>2020</year><volume>10</volume><fpage>1</fpage><lpage>14</lpage></element-citation></ref><ref id=\"B79-ijerph-17-05492\"><label>79.</label><element-citation publication-type=\"book\"><person-group person-group-type=\"author\"><name><surname>Falender</surname><given-names>C.A.</given-names></name><name><surname>Shafranske</surname><given-names>E.P.</given-names></name></person-group><article-title>Consultation in psychology: A distinct professional practice</article-title><source>Consultation in Psychology: A Competency-Based Approach</source><person-group person-group-type=\"editor\"><name><surname>Falender</surname><given-names>C.A.</given-names></name><name><surname>Shafranske</surname><given-names>E.P.</given-names></name></person-group><publisher-name>American Psychological Association</publisher-name><publisher-loc>Washington, DC, USA</publisher-loc><year>2020</year><fpage>11</fpage><lpage>35</lpage></element-citation></ref><ref id=\"B80-ijerph-17-05492\"><label>80.</label><element-citation publication-type=\"book\"><person-group person-group-type=\"author\"><name><surname>Lowman</surname><given-names>R.L.</given-names></name></person-group><source>An Introduction to Consulting Psychology: Working with Individuals, Groups, and Organizations</source><publisher-name>American Psychological Association</publisher-name><publisher-loc>Washington, DC, USA</publisher-loc><year>2016</year><fpage>11</fpage><lpage>70</lpage></element-citation></ref></ref-list></back><floats-group><table-wrap id=\"ijerph-17-05492-t001\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05492-t001_Table 1</object-id><label>Table 1</label><caption><p>Dependency behaviour as seen in the data cited.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Condensation</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Verbatim Excerpts</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Participant</th></tr></thead><tbody><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Expectation that the programme and SP knowledge will bring about change, opportunities and success edge for her</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">My main objective with doing specifically this programme is to become more skilled … I can see the potential that it holds for the field I am working in, and it makes me very excited. I think using this approach can facilitate change that is required in this field and also provide me with an edge as consultant. I can’t help but to wonder how things will change? Will it be a marginal change, or will it open a new and exciting world with new opportunities for me? </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">P1</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Relates PhD and CP to mother and expresses gratitude for being developed through the programme. Hero-ing the programme; finding it a safe space like “mother” </td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">My assumptions were largely drawn from my Mother’s experience and interaction with her (she is a consulting psychologist) ... There is an obvious soft skill curriculum that comes with attaining the highest form of academic qualification, and in this case, I feel that it, my PhD, will supplement my development nicely.</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">P3</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Dependency is demonstrated through anger at the challenging workload of the programme</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">I sometimes also feel that the PhD journey that I commenced this year, by registering in the programme, is demanding and stressful with regards to time and workload; however, it has always been my dream to pursue and complete my PhD. It feels as though I do not have enough time to see that my work as a student is always attended to timeously. </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">P2</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Coping with performance anxiety by pairing with the supervisor, fantasy that the supervisor will enable academic success for her</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Furthermore, having Professor XX as my research supervisor and the research module co-ordinator has enabled me to have more faith in my own capabilities and strengths. </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">P2</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Finds solace in the power of the programme to help him cope with his limitations</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">I am thus grateful for this course because it has made me aware of this possible limitation and it thus affords me an opportunity to identify similar feelings of discomfort should they arise in future whilst I am engaged in consulting work—I would then come up with a strategy to either counsel myself to attend to those uncomfortable issues or perhaps ask a suitable colleague to assist me in that regard </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">P7</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Relies on the consulting process to enable his success</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">However, because I am aware of this possible limitation, I will pay special attention to my interpretations during this stage and put in measures to minimise my biases in order to ensure that a more accurate picture of the client’s situation is upheld. Luckily, the consulting process itself (e.g., evaluation phase, stage 4) offers one the opportunity to evaluate their actions in each stage. </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">P7</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Finds the programme supportive of his functioning</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">However, I must say that after a great exposure through this programme (training as a consulting psychologist) I have come to appreciate that as psychologist, we can help each other through the sharing or exchange of knowledge. </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">P4</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Coping with her low self-confidence by looking to the programme to address her insecurities</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">I took it upon myself to apply for a consulting psychology programme for professional and academic development and as a challenge to myself to try and succeed in something outside of my scope of practice and the confines of clinical psychology. I have always looked down upon myself and with very low self-esteem. </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">P8</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Idealising the programme as saving her from potential limited way of thinking (performance anxiety—what I know/don’t)</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Consulting Psychology is different. It has allowed to me adopt a new and different frame of perspective and reference. I am now thinking in a broader and organisational environment. </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">P8</td></tr></tbody></table></table-wrap></floats-group></article>\n"
]
[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Int J Mol Sci</journal-id><journal-id journal-id-type=\"iso-abbrev\">Int J Mol Sci</journal-id><journal-id journal-id-type=\"publisher-id\">ijms</journal-id><journal-title-group><journal-title>International Journal of Molecular Sciences</journal-title></journal-title-group><issn pub-type=\"epub\">1422-0067</issn><publisher><publisher-name>MDPI</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32717889</article-id><article-id pub-id-type=\"pmc\">PMC7432065</article-id><article-id pub-id-type=\"doi\">10.3390/ijms21155216</article-id><article-id pub-id-type=\"publisher-id\">ijms-21-05216</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Communication</subject></subj-group></article-categories><title-group><article-title>Cx43 in Neural Progenitors Promotes Glioma Invasion in a 3D Culture System</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Khosla</surname><given-names>Kanika</given-names></name></contrib><contrib contrib-type=\"author\"><name><surname>Naus</surname><given-names>Christian C.</given-names></name></contrib><contrib contrib-type=\"author\"><name><surname>Sin</surname><given-names>Wun Chey</given-names></name><xref rid=\"c1-ijms-21-05216\" ref-type=\"corresp\">*</xref></contrib></contrib-group><aff id=\"af1-ijms-21-05216\">Department of Cellular and Physiological Sciences, Life Sciences Institute, The University of British Columbia, Vancouver, BC V6T 1Z3, Canada; <email>[email protected]</email> (K.K.); <email>[email protected]</email> (C.C.N.)</aff><author-notes><corresp id=\"c1-ijms-21-05216\"><label>*</label>Correspondence: <email>[email protected]</email></corresp></author-notes><pub-date pub-type=\"epub\"><day>23</day><month>7</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><month>8</month><year>2020</year></pub-date><volume>21</volume><issue>15</issue><elocation-id>5216</elocation-id><history><date date-type=\"received\"><day>26</day><month>6</month><year>2020</year></date><date date-type=\"accepted\"><day>20</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>© 2020 by the authors.</copyright-statement><copyright-year>2020</copyright-year><license license-type=\"open-access\"><license-p>Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>).</license-p></license></permissions><abstract><p>The environment that envelops the cancer cells intimately affects the malignancy of human cancers. In the case of glioma, an aggressive adult brain cancer, its high rate of recurrence after total resection is responsible for a poor prognosis. Connexin43 (Cx43) is a gap junction protein with a prominent presence in glioma-associated normal brain cells, specifically in the reactive astrocytes. We previously demonstrated that elimination of Cx43 in these astrocytes reduces glioma invasion in a syngeneic mouse model. To further our investigation in human glioma cells, we developed a scaffold-free 3D platform that takes into account both the tumor and its interaction with the surrounding tissue. Using cell-tracking dyes and 3D laser scanning confocal microscopy, we now report that the elimination of Cx43 protein in neural progenitor spheroids reduced the invasiveness of human brain tumor-initiating cells, confirming our earlier observation in an intact mouse brain. By investigating the glioma invasion in a defined multicellular system with a tumor boundary that mimics the intact brain environment, our findings strengthen Cx43 as a candidate target for glioma control.</p></abstract><kwd-group><kwd>Cx43</kwd><kwd>glioma</kwd><kwd>invasion</kwd><kwd>3D</kwd><kwd>time-lapse imaging</kwd><kwd>human brain tumor-initiating cells</kwd></kwd-group></article-meta></front><body><sec sec-type=\"intro\" id=\"sec1-ijms-21-05216\"><title>1. Introduction</title><p>Cancer research has mostly focused on understanding and reversing the deleterious effect due to aberrant expression of mutated proteins in tumor cells. In this aspect, the gene of the ubiquitously expressed Cx43 (<italic>Gja1</italic>) is not usually altered in human cancers, based on a finding from a large cancer genome atlas (TCGA: <uri xlink:href=\"https://www.cancer.gov/tcga\">https://www.cancer.gov/tcga</uri>). However, a TCGA query revealed preferential upregulation of Cx43 in malignant glioma [<xref rid=\"B1-ijms-21-05216\" ref-type=\"bibr\">1</xref>], the most common brain cancer in adults, with very poor prognosis due to the high chance of recurrence, even after a successful total resection [<xref rid=\"B2-ijms-21-05216\" ref-type=\"bibr\">2</xref>]. A report by Cottin and colleagues further determined that more than 10% of human gliomas displayed strong Cx43 immunostaining [<xref rid=\"B3-ijms-21-05216\" ref-type=\"bibr\">3</xref>]. Although earlier work showed that the downregulation or absence of Cx43-mediated intercellular communication is associated with increased malignancy in tumor cells [<xref rid=\"B4-ijms-21-05216\" ref-type=\"bibr\">4</xref>,<xref rid=\"B5-ijms-21-05216\" ref-type=\"bibr\">5</xref>], it is now clear that Cx43 overexpression in tumor cells promotes invasion [<xref rid=\"B4-ijms-21-05216\" ref-type=\"bibr\">4</xref>,<xref rid=\"B6-ijms-21-05216\" ref-type=\"bibr\">6</xref>,<xref rid=\"B7-ijms-21-05216\" ref-type=\"bibr\">7</xref>], especially in the presence of normal stromal cells such as astrocytes [<xref rid=\"B8-ijms-21-05216\" ref-type=\"bibr\">8</xref>,<xref rid=\"B9-ijms-21-05216\" ref-type=\"bibr\">9</xref>,<xref rid=\"B10-ijms-21-05216\" ref-type=\"bibr\">10</xref>,<xref rid=\"B11-ijms-21-05216\" ref-type=\"bibr\">11</xref>]. Indeed, there is strong evidence to suggest that Cx43 has a pivotal role in cancer of the central nervous system [<xref rid=\"B12-ijms-21-05216\" ref-type=\"bibr\">12</xref>] and a promising therapeutic target for gliomas [<xref rid=\"B13-ijms-21-05216\" ref-type=\"bibr\">13</xref>].</p><p>The microenvironment of cancer cells has gained prominence in driving tumor invasion [<xref rid=\"B14-ijms-21-05216\" ref-type=\"bibr\">14</xref>]. Since one of the major functions of a gap junction is to mediate intercellular communication, Cx43 is well positioned to participate in microenvironment signaling. Cx43 protein levels are significantly enhanced in glioma-associated astrocytes, especially at the region adjacent to the tumor core and is crucial to the spreading of glioma cells at the invasive niche [<xref rid=\"B15-ijms-21-05216\" ref-type=\"bibr\">15</xref>]. These findings suggest that Cx43 may modulate invasion via glioma–astrocyte communication. Indeed, in vivo studies with rats have found that glioma cells can increase their invasion by forming gap junctional intercellular communication (GJIC) with astrocytes [<xref rid=\"B10-ijms-21-05216\" ref-type=\"bibr\">10</xref>,<xref rid=\"B11-ijms-21-05216\" ref-type=\"bibr\">11</xref>]. Co-culture of U87MG human glioma cells with human astrocytes also enhances the invasive behavior of the glioma cells in a GJIC-dependent manner [<xref rid=\"B16-ijms-21-05216\" ref-type=\"bibr\">16</xref>].</p><p>We previously demonstrated that elimination of Cx43 in astrocytes reduced glioma invasion in a syngeneic intracranial mouse model [<xref rid=\"B15-ijms-21-05216\" ref-type=\"bibr\">15</xref>]. To extend this investigation on human patient glioma stem cells, we developed a scaffold-free 3D tissue culture model system [<xref rid=\"B17-ijms-21-05216\" ref-type=\"bibr\">17</xref>] to examine the invasion of glioma cells into mouse progenitor cells cultured as spheroids and followed the invasion of human glioma cells in real time using cell-tracking dyes and confocal microscopy. We showed that human glioma cells exhibited reduced invasion into mouse spheroids when Cx43 protein was removed from the mouse progenitors. Our results from the 3D culture model confirmed our earlier observation in an intact mouse brain, strengthening the role of Cx43 in enhancing glioma invasion by being a conduit for the tumor cells to interact with the stromal environment.</p></sec><sec sec-type=\"results\" id=\"sec2-ijms-21-05216\"><title>2. Results</title><sec id=\"sec2dot1-ijms-21-05216\"><title>2.1. The Invasiveness of Human BTICs in 3D Culture Mirrored its Pathogenicity</title><p>Cancer stem cells are key drivers of tumor progression and invasion [<xref rid=\"B18-ijms-21-05216\" ref-type=\"bibr\">18</xref>,<xref rid=\"B19-ijms-21-05216\" ref-type=\"bibr\">19</xref>]. Therefore, we first characterized 2 lines of human brain tumor-initiating cells (BTICs), GBM4 and GBM8, which have been propagated as spheroids in serum-free medium [<xref rid=\"B20-ijms-21-05216\" ref-type=\"bibr\">20</xref>]. GBM4 is less invasive of the two lines, with nodular pathology and massive endothelial proliferation [<xref rid=\"B20-ijms-21-05216\" ref-type=\"bibr\">20</xref>]. In contrast, GBM8 is very invasive, with a PNET-like pathology [<xref rid=\"B20-ijms-21-05216\" ref-type=\"bibr\">20</xref>]. Multiple Cx43 protein bands that corresponded to different phosphorylated isoforms [<xref rid=\"B21-ijms-21-05216\" ref-type=\"bibr\">21</xref>,<xref rid=\"B22-ijms-21-05216\" ref-type=\"bibr\">22</xref>] were detected in the cell lysates of both BTICs (<xref ref-type=\"fig\" rid=\"ijms-21-05216-f001\">Figure 1</xref>b). The smallest Cx43 band detected in GBM4 was absent in GBM8 but present in GL261, a mouse glioma cell line that was used in our intracranial mouse glioma model [<xref rid=\"B15-ijms-21-05216\" ref-type=\"bibr\">15</xref>]. Taken together, the results indicated that these BTICs have the potential to form Cx43 channels with each other and with adjacent normal cells.</p><p>We examined their invasiveness in a 3D human glioma model [<xref rid=\"B17-ijms-21-05216\" ref-type=\"bibr\">17</xref>] by measuring the migration distance of GBM4 (red) and GBM8 (green) in wild-type (WT) mouse progenitor (blue) spheroids <xref ref-type=\"fig\" rid=\"ijms-21-05216-f001\">Figure 1</xref>a, <xref ref-type=\"fig\" rid=\"ijms-21-05216-f002\">Figure 2</xref>a). After 2 h of co-culture, isolated single GBM8 cells (white arrowhead) were detected in the mouse spheroids (<xref ref-type=\"fig\" rid=\"ijms-21-05216-f002\">Figure 2</xref>b). In contrast, GBM4 preferentially invaded mouse spheroids in a collective manner (white arrow) while maintaining contact with neighboring cells (<xref ref-type=\"fig\" rid=\"ijms-21-05216-f002\">Figure 2</xref>b). Indeed, GBM8 cells exhibited higher velocity (2.657 μm/min +/- 0.212, <italic>n</italic> = 195) than GBM4 cells (1.928 μm/min +/- 0.150, <italic>n</italic> = 171) in WT spheroids (<xref ref-type=\"fig\" rid=\"ijms-21-05216-f002\">Figure 2</xref>c). Taken together, our results from the 3D spheroid invasion platform are in agreement with their invasiveness in a human patient.</p></sec><sec id=\"sec2dot2-ijms-21-05216\"><title>2.2. The Absence of Cx43 in Mouse Progenitor Cells Reduces Glioma Invasion</title><p>In order to validate our 3D invasion model platform, we made use of mouse WT and Cx43 knockout (KO) cells from our earlier study showing that elimination of Cx43 in astrocytes reduces glioma invasion in an intact mouse model [<xref rid=\"B15-ijms-21-05216\" ref-type=\"bibr\">15</xref>]. We first examined the characteristics of mouse WT and Cx43 KO spheroids with antibodies against glial fibrillary acidic protein (GFAP) and Cx43, both of which are highly expressed in reactive astrocytes induced by a brain lesion [<xref rid=\"B23-ijms-21-05216\" ref-type=\"bibr\">23</xref>]. GFAP was detected in both WT and KO spheroids (<xref ref-type=\"fig\" rid=\"ijms-21-05216-f003\">Figure 3</xref>a), which is in agreement with our previous observation that abolition of Cx43 in astrocytes in vivo does not affect GFAP expression [<xref rid=\"B15-ijms-21-05216\" ref-type=\"bibr\">15</xref>,<xref rid=\"B23-ijms-21-05216\" ref-type=\"bibr\">23</xref>]. In contrast, the punctate staining of Cx43 was only detected in WT but not the KO spheroids (<xref ref-type=\"fig\" rid=\"ijms-21-05216-f003\">Figure 3</xref>a), confirming successful knockout of Cx43 in the mouse progenitor cells. We decided to examine only GBM8 co-cultured with WT and KO spheroids in this series of experiments due to its increased invasiveness compared to GBM4 (<xref ref-type=\"fig\" rid=\"ijms-21-05216-f002\">Figure 2</xref>). After co-culture for 2 h, there were more GBM8 cells in the WT mouse spheroids than in KO spheroids (<xref ref-type=\"fig\" rid=\"ijms-21-05216-f003\">Figure 3</xref>b). When we measured the velocity of GBM8 cells that had invaded into the WT spheroid after exiting from the BTICs spheroid, we found that GBM8 moved significantly slower in KO (1.083 μm/min +/- 0.205, <italic>n</italic> = 102) than in WT (3.495 μm/min +/- 0.483, <italic>n</italic> = 102) spheroids (<xref ref-type=\"fig\" rid=\"ijms-21-05216-f003\">Figure 3</xref>c). Therefore, as demonstrated previously in the intracranial mouse glioma model [<xref rid=\"B15-ijms-21-05216\" ref-type=\"bibr\">15</xref>], the absence of Cx43 in normal cells affects the invasiveness of tumor cells, strengthening a critical role of Cx43 in the microenvironment.</p></sec></sec><sec sec-type=\"discussion\" id=\"sec3-ijms-21-05216\"><title>3. Discussion</title><p>The tissue environment that envelops the glioma cells not only affects the dissemination of cancer cells, but it also influences the susceptibility of cancer cells to therapeutic intervention [<xref rid=\"B24-ijms-21-05216\" ref-type=\"bibr\">24</xref>]. One prominent feature of the glioma microenvironment is the presence of a large number of reactive astrocytes exhibiting increased GFAP and Cx43 expression in the peri-tumor region [<xref rid=\"B15-ijms-21-05216\" ref-type=\"bibr\">15</xref>,<xref rid=\"B25-ijms-21-05216\" ref-type=\"bibr\">25</xref>,<xref rid=\"B26-ijms-21-05216\" ref-type=\"bibr\">26</xref>,<xref rid=\"B27-ijms-21-05216\" ref-type=\"bibr\">27</xref>]. In this environment, Cx43-mediated GJIC is expected to occur between glioma cells, between astrocytes and heterocellularly between glioma cells and astrocytes. In the latter case, Cx43 has been shown to be a facilitator in tumor invasion by allowing transfer or exchange of signaling molecules such as miRNA or cGAMP between cancer cells and astrocytes [<xref rid=\"B16-ijms-21-05216\" ref-type=\"bibr\">16</xref>,<xref rid=\"B28-ijms-21-05216\" ref-type=\"bibr\">28</xref>].</p><p>An early study indicates that 90% of recurrent gliomas occur within 2 cm of the resected tumor [<xref rid=\"B29-ijms-21-05216\" ref-type=\"bibr\">29</xref>]. Therefore, we developed a 3D platform with the capacity to quickly quantify the invasion of patient-derived glioma cells into normal tissue, focusing on the invasive niche [<xref rid=\"B30-ijms-21-05216\" ref-type=\"bibr\">30</xref>] at the tumor–stromal interface. In addition, a tumor organoid culture will have the advantage of having cellular complexities that better assess therapeutic response [<xref rid=\"B31-ijms-21-05216\" ref-type=\"bibr\">31</xref>]. As a proof-of-principle study, we demonstrated that the absence of Cx43 in mouse progenitor cells is sufficient to affect human BTICs invasion in a 3D culture system.</p><p>Cx43-dependent signaling pathways in the tumor microenvironment are emerging to be attractive anti-cancer targets. Carbenoxolone, a widely used Cx43 gap junction inhibitor, enhanced glioma cell death and survival of treated mice by 27% [<xref rid=\"B32-ijms-21-05216\" ref-type=\"bibr\">32</xref>]. Meclofenamate is a promising orally bioavailable therapeutic that inhibits gap junction-mediated processes [<xref rid=\"B28-ijms-21-05216\" ref-type=\"bibr\">28</xref>] and is now being tested in human patients with recurrent or progressive brain metastasis (<uri xlink:href=\"https://clinicaltrials.gov/ct2/show/NCT02429570\">https://clinicaltrials.gov/ct2/show/NCT02429570</uri>). Recently, we reported that the application of a TAT-Cx43<sub>266-283</sub> peptide that mimics the effect of Cx43 on c-Src inhibition reduces the growth and invasion of human glioma stem cells in immunodeficient mice, without adversely affecting neurons and astrocytes [<xref rid=\"B33-ijms-21-05216\" ref-type=\"bibr\">33</xref>]; similarly, the TAT-Cx43 peptide reduces the invasiveness of mouse glioma cells GL261 in an immunocompetent mouse model [<xref rid=\"B33-ijms-21-05216\" ref-type=\"bibr\">33</xref>].</p><p>Increasing evidence suggests that somatic alterations of the genome in host tissue contribute to tumor invasion [<xref rid=\"B34-ijms-21-05216\" ref-type=\"bibr\">34</xref>,<xref rid=\"B35-ijms-21-05216\" ref-type=\"bibr\">35</xref>]. A co-culture of patient-derived glioma cells with normal cells from the same patient in a 3D system will better mimic glioma invasion in an intact brain. Therefore, having determined that the absence of Cx43 reduces glioma invasion in our 3D model, our next objective is to examine the efficacy of Cx43 inhibitors with normal spheroids generated from human-induced pluripotent stem cells. Instead of using confocal microscopy to track invasion, a high content array image scanner will greatly increase throughput by screening multiple drug combinations on glioma samples. Our platform will potentially have a wide application to assess the efficacy of cancer treatment with matching patient tumor and normal spheroids of different types of human cancers, thereby facilitating the translation to personalized oncology.</p></sec><sec id=\"sec4-ijms-21-05216\"><title>4. Materials and Methods</title><sec id=\"sec4dot1-ijms-21-05216\"><title>4.1. Generation of Cx43 Knockout Mouse Progenitor Cells</title><p>Cx43 conditional knockout mice were generated by crossing mice containing GFAP-Cre [<xref rid=\"B36-ijms-21-05216\" ref-type=\"bibr\">36</xref>] with C57BL mice harboring floxed Cx43 alleles [<xref rid=\"B37-ijms-21-05216\" ref-type=\"bibr\">37</xref>]. Mice of either sex were used in the experiment, maintained in an animal facility for a 12 h light/dark cycle and were provided food and water ad libitum. All breeding and animal procedures were approved by The University of British Columbia Animal Care Committee (Protocol No: A09-0847) and performed in accordance with the guidelines established by the Canadian Council on Animal Care. Mouse neural progenitor stem cells were isolated from mice, similar to the procedure described in [<xref rid=\"B38-ijms-21-05216\" ref-type=\"bibr\">38</xref>] and modified as described in [<xref rid=\"B39-ijms-21-05216\" ref-type=\"bibr\">39</xref>]. Briefly, cortices freed of meninges were dissociated with 0.25% trypsin-EDTA at 37 °C for 5 min. The digested cortices were washed twice with DMEM supplemented with 10% FBS and gently triturated in DMEM without FBS with a p200 pipette tip. After pelleting, the cell suspension was resuspended in DMEM/F12 (Life Technologies, Carlsbad, CA, USA) and passed through a 40 μm cell strainer (BD Biosciences, San Jose, CA, USA) and plated at 2 × 10<sup>4</sup>/cm<sup>2</sup> in complete NeuroCult media (STEMCELL Technologies, Vancouver, BC, Canada) supplemented with 20 ng/mL rhEGF (STEMCELL Technologies, Vancouver, BC, Canada), 10 ng FGF (STEMCELL Technologies, Vancouver, BC, Canada) and 0.0002% Heparin (STEMCELL Technologies, Vancouver, BC, Canada). Human BTICs, GBM4 and GBM8 [<xref rid=\"B20-ijms-21-05216\" ref-type=\"bibr\">20</xref>,<xref rid=\"B40-ijms-21-05216\" ref-type=\"bibr\">40</xref>] were cultured in the same media. Spheroids were subcultured with Accutase (STEMCELL Technologies, Vancouver, BC, Canada) according to the manufacturer’s instructions.</p></sec><sec id=\"sec4dot2-ijms-21-05216\"><title>4.2. Preparation of Spheroids for Imaging</title><p>To generate spheroids for the invasion experiments, 10<sup>5</sup> isolated cells were plated in a 6-well plate with gentle rotational shaking at approximately 34 rpm and allowed to form spheroids for 2 d in the presence of either CellTracker CMTPX (Red dye, Invitrogen, Carlsbad, CA, USA), CMAC (Blue dye, Invitrogen, Carlsbad, CA, USA) or CMFDA (Green dye, Invitrogen, Carlsbad, CA, USA), respectively (<xref ref-type=\"fig\" rid=\"ijms-21-05216-f001\">Figure 1</xref>a). Combination was achieved by transferring labeled spheroids into a 2.0 mL Eppendorf tube using a sterile plastic eyedropper, washing once in PBS, then combining the spheroids of interest at the bottom of a 1.5 mL Eppendorf tube. The spheroids were allowed to settle at the bottom of an Eppendorf tube followed by a 1 h incubation at 37 °C to allow contact adhesion to occur. For live cell imaging, aggregates were mounted in 0.8% Noble agar/PBS that had been cooled to 39 °C prior to imaging in a 35 mm glass- bottom dish (MatTek Corporation, Ashland, MA, USA). A 37 °C heater was used to warm the imaging chamber, beginning at least 3 h prior to the start of live cell imaging. Aggregated spheroids were imaged at single cell resolution with a Leica TCS SP5 II Basic VIS confocal system (Leica Microsystems, Wetzlar, Germany) at a time interval of 10 min for 2 h.</p></sec><sec id=\"sec4dot3-ijms-21-05216\"><title>4.3. Analysis of Spheroids Invasion</title><p>Images were analyzed, including color merging, maximal projections and 3D reconstructions using ImageJ (version 1.48v) software [<xref rid=\"B41-ijms-21-05216\" ref-type=\"bibr\">41</xref>]. Individual BTICs migrating away from the spheroid into the C57BL mouse neurospheres were tracked using the MTrackJ plugin [<xref rid=\"B42-ijms-21-05216\" ref-type=\"bibr\">42</xref>].</p></sec><sec id=\"sec4dot4-ijms-21-05216\"><title>4.4. Immunofluorescence of Self-Assembled Spheroids</title><p>Mouse spheroids of less than 100 μm in diameter were fixed with 4% paraformaldehyde in PBS for 30 min at room temperature with gentle inversion, followed by three washes of PBS. The spheroids were blocked for 1 h at room temperature in 2% BSA and 0.3% Triton X-100, incubated overnight at 4 °C in 1% BSA and 0.1% Triton X-100 with rabbit anti-Cx43 (1:200, Sigma-Aldrich, St. Louis, MO, USA) and mouse anti-GFAP (1:100, Sigma-Aldrich, St. Louis, MO, USA), probed with Alexa Fluor-conjugated secondary antibodies (1:500, Invitrogen, Carlsbad, CA, USA), mounted in Prolong Antifade mounting media with 4′-6-diamidino-2-phenylindole (DAPI) (Invitrogen, Carlsbad, CA, USA) and visualized with a Leica TCS SP5 II Basic VIS confocal system.</p></sec><sec id=\"sec4dot5-ijms-21-05216\"><title>4.5. Protein Isolation and Western Analysis</title><p>GBM4 and GBM8 cell pellets were lysed in a buffer containing 0.1% SDS, 1% IGEPAL, 0.5% Sarkosyl, 50 mM Tris-HCl (pH 8.0), 150 mM NaCl supplemented with protease inhibitors (Roche Applied Science, Penzberg, Germany) and phosphatase inhibitors (Sigma-Aldrich, St. Louis, MO, USA). Rabbit anti-Cx43 (1:3000, Sigma-Aldrich, St. Louis, MO, USA) and anti-GAPDH (1:10,000, Hytest, Turku, Finland) were used as primary antibodies, followed by either anti-rabbit-HRP or anti-mouse-HRP (Sigma-Aldrich, St. Louis, MO, USA) as secondary antibodies. Protein bands were detected with Amersham ECL western detection reagents (GE Healthcare, Chicago, IL, USA).</p></sec><sec id=\"sec4dot6-ijms-21-05216\"><title>4.6. Statistical Analysis</title><p>SigmaPlot version 13.0 software (San Jose, CA, USA) was used for statistical analysis. The results are presented as mean +/- SEM. Data were analyzed by Student’s t test. <italic>P</italic> < 0.05 was considered significant.</p></sec></sec></body><back><ack><title>Acknowledgments</title><p>We thank Karen Guo, Nicolas Theodoric, Jonathan Lelowski and Derek M. van Pel for providing technical support for this project.</p></ack><notes><title>Author Contributions</title><p>Conceptualization, W.C.S. and C.C.N.; Methodology, K.K. and W.C.S.; Data Analysis, K.K. and W.C.S.; Writing—Original Draft Preparation, K.K. and W.C.S.; Writing—Review & Editing, W.C.S. and C.C.N.; Supervision, W.C.S.; Funding Acquisition, C.C.N. All authors have read and agreed to the published version of the manuscript.</p></notes><notes><title>Funding</title><p>This research was funded by Natural Sciences and Engineering Research Council of Canada (RGPIN-2016-05471) and two operating grants from the Canadian Institutes of Health Research (MOP-102489 and MOP-93572). 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(<bold>a</bold>) Schematic diagram illustrating the culture and labeling of spheroids with cell-tracker dyes (CMTPX, CMFDA and CMAC) to distinguish them for live imaging by confocal microscopy. (<bold>b</bold>) Western analysis of cell lysates of GBM4 and GBM8 with anti-Cx43 antibody showing multiple protein bands corresponding to different phosphorylated isoforms. Cx43 species in mouse GL261 glioma cells used in our intracranial mouse implantation [<xref rid=\"B15-ijms-21-05216\" ref-type=\"bibr\">15</xref>] was included as a comparison. GAPDH was used as loading control.</p></caption><graphic xlink:href=\"ijms-21-05216-g001\"/></fig><fig id=\"ijms-21-05216-f002\" orientation=\"portrait\" position=\"float\"><label>Figure 2</label><caption><p>Invasiveness of GBM4 and GBM8 correlate with their pathogenicity in human patients. (<bold>a</bold>) Co-culture of GBM4 (red), GBM8 (green) and C57BL6 mouse neural progenitor cell derived spheroids (blue) in Noble agar before imaging. Scale bar 100 μm (<bold>b</bold>) Representative time-lapse images showing GBM8 preferentially invaded as single cells (white arrowheads) while GBM4 invaded in a collective manner as a group (white arrows). Images were taken 10 min apart at a 5 µm step size using confocal microscopy. Scale bar 100 μm (<bold>c</bold>) Mean velocity of GBM4 and GBM8 single cells in mouse wild-type spheroids. The data shown here are pooled from at least three experiments. Data were analyzed by Student’s t test. * <italic>P</italic> < 0.05.</p></caption><graphic xlink:href=\"ijms-21-05216-g002\"/></fig><fig id=\"ijms-21-05216-f003\" orientation=\"portrait\" position=\"float\"><label>Figure 3</label><caption><p>Elimination of Cx43 in mouse progenitor cells reduces GBM8 invasion. (<bold>a</bold>) Wild-type (WT) and Cx43 knockout (KO) spheroids were co-stained with anti-Cx43 and anti-GFAP antibodies. GFAP was detected in both WT and KO spheroids. In contrast, the punctate staining of Cx43 was only detected in WT but not the KO spheroids, confirming successful knockout of Cx43 in the mouse progenitors. Scale bar 50 μm (<bold>b</bold>) 3D cell tracking was carried out using the Fiji plugin, MTrackJ. Representative images showing cell movement in two 40-min periods. The track of a single cell is denoted by a colored lane in a 3D projection. Each circle represents the x,y,z location of the cell at a time point from 0 to 40 min, and from 0 to 80 min. The difference between 40 and 80 min track illustrates the movement of cells during this time interval. Note that the movement of a cell in the z direction will not be apparent in these 2D representative images. Scale bar 75 μm (<bold>c</bold>) Mean velocity of GBM8 in WT and KO mouse spheroids. The data shown here are pooled from at least three experiments. Data were analyzed by Student’s t test. * <italic>P</italic> < 0.05.</p></caption><graphic xlink:href=\"ijms-21-05216-g003\"/></fig></floats-group></article>\n"
]
[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"review-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Int J Mol Sci</journal-id><journal-id journal-id-type=\"iso-abbrev\">Int J Mol Sci</journal-id><journal-id journal-id-type=\"publisher-id\">ijms</journal-id><journal-title-group><journal-title>International Journal of Molecular Sciences</journal-title></journal-title-group><issn pub-type=\"epub\">1422-0067</issn><publisher><publisher-name>MDPI</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32707756</article-id><article-id pub-id-type=\"pmc\">PMC7432066</article-id><article-id pub-id-type=\"doi\">10.3390/ijms21155175</article-id><article-id pub-id-type=\"publisher-id\">ijms-21-05175</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Review</subject></subj-group></article-categories><title-group><article-title>Early Life Oxidative Stress and Long-Lasting Cardiovascular Effects on Offspring Conceived by Assisted Reproductive Technologies: A Review</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0001-8165-621X</contrib-id><name><surname>Yang</surname><given-names>Huixia</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05175\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Kuhn</surname><given-names>Christina</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05175\">1</xref><xref ref-type=\"aff\" rid=\"af2-ijms-21-05175\">2</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0003-1418-8588</contrib-id><name><surname>Kolben</surname><given-names>Thomas</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05175\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Ma</surname><given-names>Zhi</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05175\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Lin</surname><given-names>Peng</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05175\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Mahner</surname><given-names>Sven</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05175\">1</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0003-2623-3235</contrib-id><name><surname>Jeschke</surname><given-names>Udo</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05175\">1</xref><xref ref-type=\"aff\" rid=\"af2-ijms-21-05175\">2</xref><xref rid=\"c1-ijms-21-05175\" ref-type=\"corresp\">*</xref></contrib><contrib contrib-type=\"author\"><name><surname>von Schönfeldt</surname><given-names>Viktoria</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05175\">1</xref></contrib></contrib-group><aff id=\"af1-ijms-21-05175\"><label>1</label>Department of Obstetrics and Gynecology, University Hospital, LMU Munich, 81377 Munich, Germany; <email>[email protected]</email> (H.Y.); <email>[email protected]</email> (C.K.); <email>[email protected]</email> (T.K.); <email>[email protected]</email> (Z.M.); <email>[email protected]</email> (P.L.); <email>[email protected]</email> (S.M.); <email>[email protected]</email> (V.v.S.)</aff><aff id=\"af2-ijms-21-05175\"><label>2</label>Department of Obstetrics and Gynecology, University Hospital Augsburg, 86156 Augsburg, Germany</aff><author-notes><corresp id=\"c1-ijms-21-05175\"><label>*</label>Correspondence: <email>[email protected]</email>; Tel.: +49-(0)821-400-165505</corresp></author-notes><pub-date pub-type=\"epub\"><day>22</day><month>7</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><month>8</month><year>2020</year></pub-date><volume>21</volume><issue>15</issue><elocation-id>5175</elocation-id><history><date date-type=\"received\"><day>29</day><month>6</month><year>2020</year></date><date date-type=\"accepted\"><day>20</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>© 2020 by the authors.</copyright-statement><copyright-year>2020</copyright-year><license license-type=\"open-access\"><license-p>Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>).</license-p></license></permissions><abstract><p>Assisted reproductive technology (ART) has rapidly developed and is now widely practised worldwide. Both the characteristics of ART (handling gametes/embryos in vitro) and the infertility backgrounds of ART parents (such as infertility diseases and unfavourable lifestyles or diets) could cause increased oxidative stress (OS) that may exert adverse influences on gametogenesis, fertilisation, and foetation, even causing a long-lasting influence on the offspring. For these reasons, the safety of ART needs to be closely examined. In this review, from an ART safety standpoint, the origins of OS are reviewed, and the long-lasting cardiovascular effects and potential mechanisms of OS on the offspring are discussed.</p></abstract><kwd-group><kwd>oxidative stress</kwd><kwd>long-lasting</kwd><kwd>cardiovascular</kwd><kwd>assisted reproductive technologies</kwd><kwd>offspring</kwd></kwd-group></article-meta></front><body><sec sec-type=\"intro\" id=\"sec1-ijms-21-05175\"><title>1. Introduction</title><p>The use of assisted reproductive technology (ART) began in 1978. Since then, ART has been widely used worldwide [<xref rid=\"B1-ijms-21-05175\" ref-type=\"bibr\">1</xref>]. More than eight million human babies are estimated to have been conceived through ART [<xref rid=\"B2-ijms-21-05175\" ref-type=\"bibr\">2</xref>], and the annual increase in this number is estimated to average at 9.1% per year [<xref rid=\"B1-ijms-21-05175\" ref-type=\"bibr\">1</xref>]. ART is developing rapidly, and now includes techniques such as intra-uterine insemination (IUI), in vitro fertilisation (IVF), intracytoplasmic sperm injection (ICSI)), accompanied by controlled ovarian hyperstimulation (COH), oocyte retrieval, embryo culture, and embryo transfer, with or without pre-implantation genetic diagnosis or screening (PGD or PGS), gametes or embryos freezing and thawing, surgical sperm retrieval (SSR), and assisted hatching [<xref rid=\"B3-ijms-21-05175\" ref-type=\"bibr\">3</xref>,<xref rid=\"B4-ijms-21-05175\" ref-type=\"bibr\">4</xref>].</p><p>Despite all advances, it is unavoidable that gametes or embryos are handled in vitro. Because ART occurs at the preimplantation period when gametes or embryos are highly sensitive and experience developmental plasticity, the environmental stimuli may alter the embryonic developmental trajectory. According to the ’developmental origins of adult disease’ (DOHaD) hypothesis [<xref rid=\"B5-ijms-21-05175\" ref-type=\"bibr\">5</xref>], environmental exposures in early life can exert a long-lasting influence on health and lead to adult-onset chronic non-communicable diseases (NCDs) such as hypertension [<xref rid=\"B6-ijms-21-05175\" ref-type=\"bibr\">6</xref>], cardiovascular diseases (CVDs) [<xref rid=\"B7-ijms-21-05175\" ref-type=\"bibr\">7</xref>], and type 2 diabetes mellitus (T2DM) [<xref rid=\"B8-ijms-21-05175\" ref-type=\"bibr\">8</xref>]. ART can be regarded as an extreme ’exposure’, despite the fact that conclusions of ART safety studies are conflicting, the possible adverse effects have been linked to birth defects [<xref rid=\"B9-ijms-21-05175\" ref-type=\"bibr\">9</xref>], epigenetic diseases [<xref rid=\"B10-ijms-21-05175\" ref-type=\"bibr\">10</xref>], dysfunction of various body systems (e.g., cardiovascular, metabolic, and neurological systems) [<xref rid=\"B11-ijms-21-05175\" ref-type=\"bibr\">11</xref>], and paediatric neoplasms (e.g., leukaemia and Hodgkin’s lymphoma) [<xref rid=\"B12-ijms-21-05175\" ref-type=\"bibr\">12</xref>,<xref rid=\"B13-ijms-21-05175\" ref-type=\"bibr\">13</xref>]. The observed long-term outcomes of ART, including cardiometabolic and neurological NCDs, are consistent with the DOHaD model [<xref rid=\"B14-ijms-21-05175\" ref-type=\"bibr\">14</xref>].</p><p>Oxidative stress (OS) is related to an excess of reactive oxygen species (ROS) and a decrease in antioxidant enzymes. This concept was established by Helmut in 1985 [<xref rid=\"B15-ijms-21-05175\" ref-type=\"bibr\">15</xref>]. Excess ROS has been proposed to cause severe damage during embryonic development [<xref rid=\"B16-ijms-21-05175\" ref-type=\"bibr\">16</xref>], especially in the cardiovascular system, because it is one of the first functional systems to develop. In the ART area, studies on OS have focused on infertile men and their sperm [<xref rid=\"B17-ijms-21-05175\" ref-type=\"bibr\">17</xref>,<xref rid=\"B18-ijms-21-05175\" ref-type=\"bibr\">18</xref>]. It is now well established that the main cause of male infertility is OS, which can damage sperm DNA, influencing the health of the offspring [<xref rid=\"B19-ijms-21-05175\" ref-type=\"bibr\">19</xref>]. Studies have also revealed that OS plays a vital role in ART outcomes. A study found that a reduction in OS improved ART outcomes [<xref rid=\"B20-ijms-21-05175\" ref-type=\"bibr\">20</xref>]. Various factors associated with ROS production in an ART setting have been investigated, and the antioxidant strategy in an ART setting has also been explored [<xref rid=\"B20-ijms-21-05175\" ref-type=\"bibr\">20</xref>,<xref rid=\"B21-ijms-21-05175\" ref-type=\"bibr\">21</xref>]. Several animal models have been applied to study the effect and associated mechanisms of OS on ART offspring [<xref rid=\"B22-ijms-21-05175\" ref-type=\"bibr\">22</xref>]. Nevertheless, to the best of our knowledge, no human epidemiological studies have looked at the effect of OS on ART offspring. On the one hand, the health outcome-related follow-up information of ART offspring in the available databases are insufficiently detailed and are even lacking. On the other hand, no databases have recorded the OS status in an ART setting for ART offspring. In fact, in other medical areas, there have been countless studies on OS/ROS, most of which are focused on the negative effects of excessive ROS/OS. Excessive ROS/OS has been implicated in over 100 diseases [<xref rid=\"B23-ijms-21-05175\" ref-type=\"bibr\">23</xref>] (e.g., diabetes mellitus [<xref rid=\"B24-ijms-21-05175\" ref-type=\"bibr\">24</xref>], CVDs [<xref rid=\"B25-ijms-21-05175\" ref-type=\"bibr\">25</xref>], and neurodegenerative diseases [<xref rid=\"B26-ijms-21-05175\" ref-type=\"bibr\">26</xref>]), also playing an important role in the pathogenicity of ageing [<xref rid=\"B27-ijms-21-05175\" ref-type=\"bibr\">27</xref>].</p><p>In this review, from the ART point of view, we describe the origins of OS, provide a timely synthesis of the current evidence on the long-lasting cardiovascular effects of ART-associated OS, and discuss the potential underlying mechanisms. We expect that our review will inform future OS-associated research in the ART area as well as propose suggestions for preventing adverse health outcomes in ART offspring.</p></sec><sec id=\"sec2-ijms-21-05175\"><title>2. Origins of OS</title><sec id=\"sec2dot1-ijms-21-05175\"><title>2.1. Paternally Derived OS</title><p>OS has been linked to a variety of male fertility complications, including leukocytospermia [<xref rid=\"B28-ijms-21-05175\" ref-type=\"bibr\">28</xref>], varicocele [<xref rid=\"B29-ijms-21-05175\" ref-type=\"bibr\">29</xref>], cryptorchidism [<xref rid=\"B30-ijms-21-05175\" ref-type=\"bibr\">30</xref>], spermatic cord torsion [<xref rid=\"B31-ijms-21-05175\" ref-type=\"bibr\">31</xref>], male accessory gland infections (MAGI) [<xref rid=\"B32-ijms-21-05175\" ref-type=\"bibr\">32</xref>], advanced age [<xref rid=\"B33-ijms-21-05175\" ref-type=\"bibr\">33</xref>], obesity [<xref rid=\"B34-ijms-21-05175\" ref-type=\"bibr\">34</xref>], diabetes [<xref rid=\"B35-ijms-21-05175\" ref-type=\"bibr\">35</xref>], and autoimmune disorders [<xref rid=\"B33-ijms-21-05175\" ref-type=\"bibr\">33</xref>]. Infertile men are more likely to possess excessive levels of ROS compared to fertile men [<xref rid=\"B36-ijms-21-05175\" ref-type=\"bibr\">36</xref>], which has been identified as one of the few defined aetiologies for male infertility [<xref rid=\"B37-ijms-21-05175\" ref-type=\"bibr\">37</xref>]. Recently, male oxidative stress infertility (MOSI) has been proposed to describe infertile men with OS and abnormal semen characteristics. This term includes many patients previously classified as having male idiopathic infertility [<xref rid=\"B38-ijms-21-05175\" ref-type=\"bibr\">38</xref>].</p><p>In the male genital tract, in addition to the ROS generated from sperm cells [<xref rid=\"B39-ijms-21-05175\" ref-type=\"bibr\">39</xref>], other cells may also produce ROS. Among them, leukocytes can produce ROS at levels 1000 times higher than that of sperm at capacitation [<xref rid=\"B40-ijms-21-05175\" ref-type=\"bibr\">40</xref>] and may contribute to OS [<xref rid=\"B41-ijms-21-05175\" ref-type=\"bibr\">41</xref>]. Further compounding this issue, the plasma membrane of sperm cells contains large quantities of polyunsaturated fatty acids (PUFAs), making them particularly susceptible to elevated ROS levels during OS [<xref rid=\"B42-ijms-21-05175\" ref-type=\"bibr\">42</xref>]. OS can also negatively influence other sperm components (i.e., nucleic acids and proteins), inducing sperm DNA fragmentation (SDF) and low sperm motility [<xref rid=\"B43-ijms-21-05175\" ref-type=\"bibr\">43</xref>]. Furthermore, compared with somatic cells, there is a lack of cytoplasm and poorer antioxidant capacity in mature spermatozoa, thereby rendering it more vulnerable to OS [<xref rid=\"B44-ijms-21-05175\" ref-type=\"bibr\">44</xref>]. Nevertheless, it is entirely possible for sperm suffering from oxidative DNA damage to fertilise an oocyte and thus possibly exert adverse effects on the offspring [<xref rid=\"B45-ijms-21-05175\" ref-type=\"bibr\">45</xref>], especially in the context of ICSI [<xref rid=\"B46-ijms-21-05175\" ref-type=\"bibr\">46</xref>]. On the other hand, lifestyle and diet factors such as cigarette smoking [<xref rid=\"B47-ijms-21-05175\" ref-type=\"bibr\">47</xref>], alcohol abuse [<xref rid=\"B48-ijms-21-05175\" ref-type=\"bibr\">48</xref>], psychological stress [<xref rid=\"B49-ijms-21-05175\" ref-type=\"bibr\">49</xref>], recreational and illicit drugs use [<xref rid=\"B17-ijms-21-05175\" ref-type=\"bibr\">17</xref>,<xref rid=\"B50-ijms-21-05175\" ref-type=\"bibr\">50</xref>], malnutrition [<xref rid=\"B51-ijms-21-05175\" ref-type=\"bibr\">51</xref>], and excessive physical activity [<xref rid=\"B52-ijms-21-05175\" ref-type=\"bibr\">52</xref>]; environmental and occupational exposures such as air pollution [<xref rid=\"B53-ijms-21-05175\" ref-type=\"bibr\">53</xref>], radiation [<xref rid=\"B54-ijms-21-05175\" ref-type=\"bibr\">54</xref>], heat stress [<xref rid=\"B55-ijms-21-05175\" ref-type=\"bibr\">55</xref>], plasticizers (e.g., phthalates) [<xref rid=\"B56-ijms-21-05175\" ref-type=\"bibr\">56</xref>], heavy metals (e.g., cadmium) [<xref rid=\"B57-ijms-21-05175\" ref-type=\"bibr\">57</xref>], and pesticide/herbicides [<xref rid=\"B33-ijms-21-05175\" ref-type=\"bibr\">33</xref>]; and special treatments such as radiation therapy and chemotherapy have been linked with OS [<xref rid=\"B58-ijms-21-05175\" ref-type=\"bibr\">58</xref>,<xref rid=\"B59-ijms-21-05175\" ref-type=\"bibr\">59</xref>] (as shown in <xref ref-type=\"fig\" rid=\"ijms-21-05175-f001\">Figure 1</xref>).</p></sec><sec id=\"sec2dot2-ijms-21-05175\"><title>2.2. Maternally Derived OS</title><p>Compared with studies of OS and male sperm, it appears that fewer studies have focused on OS and oocytes/oocyte-cumulus complexes. Nevertheless, it cannot be assumed that this point is less important in oogenesis, fertilisation, pregnancy, and production of healthy offspring. After all, the earliest determinant of life potential is the oocyte. In the female reproductive system, the uterine environment, fallopian tubes, and follicular fluid are the main sources generating ROS [<xref rid=\"B60-ijms-21-05175\" ref-type=\"bibr\">60</xref>,<xref rid=\"B61-ijms-21-05175\" ref-type=\"bibr\">61</xref>,<xref rid=\"B62-ijms-21-05175\" ref-type=\"bibr\">62</xref>]. Normal levels of ROS are responsible for pregnancy establishment in IVF cycles, while excess ROS in the follicular fluid can present a substantial threat to successful assisted reproduction [<xref rid=\"B63-ijms-21-05175\" ref-type=\"bibr\">63</xref>].</p><p>Recent studies have also shown that OS may cause absence of the oocyte meiotic spindle and may be closely associated with low fertilization rates, compromised embryonic quality, and decreased clinical pregnancy rates [<xref rid=\"B64-ijms-21-05175\" ref-type=\"bibr\">64</xref>]. In fact, women attending ART units are usually of advanced age and/or have been diagnosed with other diseases (e.g., endometriosis [<xref rid=\"B65-ijms-21-05175\" ref-type=\"bibr\">65</xref>], polycystic ovary syndrome (PCOS) [<xref rid=\"B66-ijms-21-05175\" ref-type=\"bibr\">66</xref>], hydrosalpinx [<xref rid=\"B67-ijms-21-05175\" ref-type=\"bibr\">67</xref>], and obesity [<xref rid=\"B52-ijms-21-05175\" ref-type=\"bibr\">52</xref>]). After pregnancy, women who underwent ART have been reported to be affected by higher incidences of several pregnancy complications (e.g., hypertensive disorders of pregnancy (HDP) [<xref rid=\"B68-ijms-21-05175\" ref-type=\"bibr\">68</xref>], gestational diabetes mellitus (GDM) [<xref rid=\"B68-ijms-21-05175\" ref-type=\"bibr\">68</xref>], intrauterine growth restriction (IUGR) [<xref rid=\"B69-ijms-21-05175\" ref-type=\"bibr\">69</xref>], and preterm birth [<xref rid=\"B68-ijms-21-05175\" ref-type=\"bibr\">68</xref>]). All these diseases and pregnancy complications are associated with increased OS, which might exert an influence on the offspring’s development [<xref rid=\"B70-ijms-21-05175\" ref-type=\"bibr\">70</xref>]. Specifically, during the maternal ageing process, significantly increased OS occurs in the ovarian and follicular environment, causing impaired oocyte quality and compromised oocyte meiosis [<xref rid=\"B71-ijms-21-05175\" ref-type=\"bibr\">71</xref>]. Similar to men, unfavourable lifestyles and diets, adverse environmental and occupational exposures, and special treatments can also contribute to excessive OS in women [<xref rid=\"B52-ijms-21-05175\" ref-type=\"bibr\">52</xref>,<xref rid=\"B70-ijms-21-05175\" ref-type=\"bibr\">70</xref>,<xref rid=\"B72-ijms-21-05175\" ref-type=\"bibr\">72</xref>,<xref rid=\"B73-ijms-21-05175\" ref-type=\"bibr\">73</xref>,<xref rid=\"B74-ijms-21-05175\" ref-type=\"bibr\">74</xref>,<xref rid=\"B75-ijms-21-05175\" ref-type=\"bibr\">75</xref>,<xref rid=\"B76-ijms-21-05175\" ref-type=\"bibr\">76</xref>,<xref rid=\"B77-ijms-21-05175\" ref-type=\"bibr\">77</xref>,<xref rid=\"B78-ijms-21-05175\" ref-type=\"bibr\">78</xref>] (as shown in <xref ref-type=\"fig\" rid=\"ijms-21-05175-f002\">Figure 2</xref>). Different from men, women exert OS on the offspring, not only through the fertilised oocytes, but also through the uterine environment throughout the whole pregnancy.</p></sec><sec id=\"sec2dot3-ijms-21-05175\"><title>2.3. ART-Derived OS</title><p>ART requires in vitro manipulations of gametes or embryos in a synthetic culture environment. Due to lack of a natural antioxidant system and factors driving ROS production (<xref ref-type=\"fig\" rid=\"ijms-21-05175-f003\">Figure 3</xref>), it is difficult to maintain pro-oxidant/antioxidant balance in vitro, and the resulting increased OS may have an adverse impact on the embryo/offspring. There are various stimuli of OS in the ART setting, including cryopreservation [<xref rid=\"B79-ijms-21-05175\" ref-type=\"bibr\">79</xref>,<xref rid=\"B80-ijms-21-05175\" ref-type=\"bibr\">80</xref>], gamete or embryo manipulation [<xref rid=\"B81-ijms-21-05175\" ref-type=\"bibr\">81</xref>], visible light [<xref rid=\"B82-ijms-21-05175\" ref-type=\"bibr\">82</xref>], pH fluctuations [<xref rid=\"B83-ijms-21-05175\" ref-type=\"bibr\">83</xref>], temperature fluctuations [<xref rid=\"B84-ijms-21-05175\" ref-type=\"bibr\">84</xref>], fluctuating oxygen tension (Pa, O<sub>2</sub>) [<xref rid=\"B85-ijms-21-05175\" ref-type=\"bibr\">85</xref>], centrifugation [<xref rid=\"B86-ijms-21-05175\" ref-type=\"bibr\">86</xref>], culture media (especially those containing specific substances, e.g., Fe<sup>2+</sup> and Cu<sup>2+</sup>) [<xref rid=\"B62-ijms-21-05175\" ref-type=\"bibr\">62</xref>], and others. For example, in the oviduct and uterus, under certain physiological conditions, gametes or embryos are exposed to O<sub>2</sub> concentrations of 2–8 % [<xref rid=\"B87-ijms-21-05175\" ref-type=\"bibr\">87</xref>]. During in vitro manipulations, gametes and embryos have a chance of being exposed to higher O<sub>2</sub> concentrations (e.g., atmospheric O<sub>2</sub> concentrations around 20–21%). The presence of high concentrations of O<sub>2</sub> during the incubation stage can activate a variety of cellular oxidase enzymes. This in turn generates excessive ROS, leading to OS [<xref rid=\"B88-ijms-21-05175\" ref-type=\"bibr\">88</xref>]. The excess ROS can impact the biological processes of early embryonic development with potentially long-lasting health effects for the offspring.</p><p>Collectively, compared with naturally conceived offspring, ART offspring appear to be more likely to suffer from excessive OS. Meanwhile, it should be noted that most of the OS originating from ART parents (e.g., adverse lifestyles and environmental exposures) and ART per se are preventable. For ART-associated OS, the potential management includes oral antioxidant supplements for ART parents [<xref rid=\"B89-ijms-21-05175\" ref-type=\"bibr\">89</xref>] and modifications in ART protocols. These include antioxidant supplements to ART culture media [<xref rid=\"B20-ijms-21-05175\" ref-type=\"bibr\">20</xref>,<xref rid=\"B89-ijms-21-05175\" ref-type=\"bibr\">89</xref>], antioxidant techniques in semen preparation, reduced oocyte-handling time, and minimal exposure of zygotes to atmospheric oxygen concentrations [<xref rid=\"B20-ijms-21-05175\" ref-type=\"bibr\">20</xref>]. The ART-associated antioxidants consist of enzymatic antioxidants (e.g., superoxide dismutase, catalase, and the glutathione system), non-enzymatic antioxidants (e.g., Vitamins E, C, and B9 (folic acid), melatonin, coenzyme Q10, and L-carnitine) and combined antioxidants (e.g., Vitamin E + Vitamin C) [<xref rid=\"B89-ijms-21-05175\" ref-type=\"bibr\">89</xref>]. Diets containing antioxidant molecules for ART parents may also provide antioxidant benefits [<xref rid=\"B90-ijms-21-05175\" ref-type=\"bibr\">90</xref>].</p></sec></sec><sec id=\"sec3-ijms-21-05175\"><title>3. OS-Associated Mechanisms in ART</title><sec id=\"sec3dot1-ijms-21-05175\"><title>3.1. Formation of OS</title><p>Various stress conditions may contribute to OS with increased production of ROS. ROS (e.g., superoxide (O<sub>2</sub><sup>•−</sup>), hydroperoxyl (HO<sub>2</sub><sup>•</sup>), hydroxyl (OH<sup>•</sup>), and peroxyl radicals (RO<sub>2</sub><sup>•</sup>), and hydrogen peroxide (H<sub>2</sub>O<sub>2</sub>) [<xref rid=\"B91-ijms-21-05175\" ref-type=\"bibr\">91</xref>]) are generated from the partial reduction of O<sub>2</sub> to O<sub>2</sub><sup>•−</sup>, which occurs as a result of oxygen’s preferential acceptance of one electron at the time of redox reactions [<xref rid=\"B92-ijms-21-05175\" ref-type=\"bibr\">92</xref>]. ROS are highly active molecules that are continuously generated by mitochondrial electron transport and enzymes (e.g., nicotinamide adenine dinucleotide phosphate (NADPH)-oxidase, xanthine oxidase, and lipoxygenase) [<xref rid=\"B93-ijms-21-05175\" ref-type=\"bibr\">93</xref>]. ROS that originate intracellularly can be released extracellularly [<xref rid=\"B94-ijms-21-05175\" ref-type=\"bibr\">94</xref>], and play vital roles in modulating the signaling pathways in response to intra- and extra-cellular stimuli [<xref rid=\"B95-ijms-21-05175\" ref-type=\"bibr\">95</xref>]. Mitochondria are the primary source of ROS, resulting from its role in energy (i.e., ATP) production via oxidative phosphorylation (OXPHOS) [<xref rid=\"B92-ijms-21-05175\" ref-type=\"bibr\">92</xref>]. The major sites of ROS emission in the respiratory chain are complex I and complex III [<xref rid=\"B96-ijms-21-05175\" ref-type=\"bibr\">96</xref>]. During IVF, selected spermatozoa and oocytes are combined in a petri dish with the fertilisation medium and are checked for fertilization after several hours’ incubation. During this time, ROS can be generated from the oocyte/cumulus cell mass and the spermatozoa, due to the cells’ own metabolism, and the levels of ROS production can be elevated due to the lack of a natural antioxidant defence system and various stimuli. Furthermore, during centrifugation, excess ROS can be derived from the spermatozoa because of the absence of antioxidant-rich seminal plasma and the activation of ROS production [<xref rid=\"B20-ijms-21-05175\" ref-type=\"bibr\">20</xref>]. In addition, the external environment that surrounds the cells in an ART setting can also induce OS. For example, even though the composition of the commercial culture media changes over time with various suppliers, most contain serum or serum synthetic replacements, vitamins, albumin, and other components (e.g., heavy metal chelators or buffer). Therefore, the medium itself can be a trigger of OS [<xref rid=\"B97-ijms-21-05175\" ref-type=\"bibr\">97</xref>]. Other environmental factors can also contribute to OS as mentioned. In response to various environmental stimuli, cells produce certain mediators and intermediates (mostly ROS) to propagate environmental signals to the cell nucleus, affecting gene regulation and transcription while inducing various phenotypic responses (in the form of inflammation and pathogenesis) [<xref rid=\"B98-ijms-21-05175\" ref-type=\"bibr\">98</xref>].</p></sec><sec id=\"sec3dot2-ijms-21-05175\"><title>3.2. Epigenetic Modifications Resulting from OS</title><p>Epigenetic modifications refer to dynamic and heritable changes in gene expression without DNA sequence changes. These are profoundly involved in OS responses [<xref rid=\"B99-ijms-21-05175\" ref-type=\"bibr\">99</xref>] and are regarded as potential mechanisms that influence the developmental origins of CVDs later in adulthood [<xref rid=\"B100-ijms-21-05175\" ref-type=\"bibr\">100</xref>]. Maximal epigenetic reprogramming, characterized as ’dynamic’, ’extremely sensitive’, and ’plastic’, occurs during the early stages of life, coinciding with the time that ART procedures take place [<xref rid=\"B101-ijms-21-05175\" ref-type=\"bibr\">101</xref>,<xref rid=\"B102-ijms-21-05175\" ref-type=\"bibr\">102</xref>]. Both animal studies and follow-up studies of ART children suggest that ART can cause epigenetic perturbation in offspring [<xref rid=\"B10-ijms-21-05175\" ref-type=\"bibr\">10</xref>,<xref rid=\"B103-ijms-21-05175\" ref-type=\"bibr\">103</xref>], even at the two-cell stage of embryos [<xref rid=\"B104-ijms-21-05175\" ref-type=\"bibr\">104</xref>]. It has been proposed that OS during pregnancy may affect the intrauterine foetus and cause cardiovascular dysfunction in later life through epigenetic modifications [<xref rid=\"B105-ijms-21-05175\" ref-type=\"bibr\">105</xref>]. Based on these findings, we speculated that ART-associated OS may also influence the offspring through an epigenetic mechanism. In general, ROS can affect epigenetic modifications through both direct and indirect means [<xref rid=\"B106-ijms-21-05175\" ref-type=\"bibr\">106</xref>]. For example, OH<sup>•</sup> can directly lead to the transformation from 5-methylcytosine (5-mC, a form of DNA methylation) to 5-hydroxymethylcytosine (5-hmC, an intermediate in active DNA demethylation [<xref rid=\"B107-ijms-21-05175\" ref-type=\"bibr\">107</xref>]) [<xref rid=\"B108-ijms-21-05175\" ref-type=\"bibr\">108</xref>], which has been suggested to interfere with DNA methyltransferase 1 (DNMT1), preventing the proper inheritance of methylation patterns, thereby causing indirect CpG sites demethylation [<xref rid=\"B109-ijms-21-05175\" ref-type=\"bibr\">109</xref>]. ROS can also indirectly affect epigenetic modifications. For example, H<sub>2</sub>O<sub>2</sub>-induced OS can impair histone demethylase activity, causing increased global histone methylation of histone H3 lysine 4 (H3K4), histone H3 lysine 27 (H3K27), and histone H3 lysine 9 (H3K9), while H3K4 trimethylation (H3K4me3) appears to be affected most by OS; global acetylation levels show temporary decreases in response to OS and return to normal levels after long-term OS. The activity of DNA demethylases (ten-eleven-translocation (TET) proteins) can also be compromised by OS, inducing global 5-mC increases and 5-hmC decreases [<xref rid=\"B110-ijms-21-05175\" ref-type=\"bibr\">110</xref>]. These epigenetic modifications can then regulate gene expression via changes in chromatin accessibility in response to OS [<xref rid=\"B111-ijms-21-05175\" ref-type=\"bibr\">111</xref>]. Kietzmann et al. described an ROS-related epigenetic landscape in cardiovascular systems; we direct interested readers to a detailed review [<xref rid=\"B106-ijms-21-05175\" ref-type=\"bibr\">106</xref>]. Furthermore, evidence also suggests an interplay between OS and epidemic modifications [<xref rid=\"B112-ijms-21-05175\" ref-type=\"bibr\">112</xref>,<xref rid=\"B113-ijms-21-05175\" ref-type=\"bibr\">113</xref>]. For example, the down-regulation of SUV39H1 (a H3K9 histone methyltransferase) facilitates the recruitment of SRC-1 (a histone acetyltransferase) and JMJD2C (also known as KDM4C, a H3K9 histone demethylase) with reduced di/trimethylation and acetylation of H3K9 on the promoter of p66Shc (a key driver of mitochondrial OS and vascular damage [<xref rid=\"B114-ijms-21-05175\" ref-type=\"bibr\">114</xref>]), which may ultimately drive OS [<xref rid=\"B115-ijms-21-05175\" ref-type=\"bibr\">115</xref>].</p></sec><sec id=\"sec3dot3-ijms-21-05175\"><title>3.3. Nrf2-Mediated Anti-OS Signaling Pathway</title><p>The redox-sensitive transcriptional factor nuclear factor erythroid 2-related factor 2 (Nrf2, also known as NFE2L2) is a well-characterized ’master regulator’ of antioxidant gene expression via its activation of the Nrf2-antioxidant response element (ARE) pathway [<xref rid=\"B116-ijms-21-05175\" ref-type=\"bibr\">116</xref>]. Nrf2 dysregulation has been implicated in different aspects of CVDs [<xref rid=\"B117-ijms-21-05175\" ref-type=\"bibr\">117</xref>] and multiple types of cancers (e.g., ovarian cancer [<xref rid=\"B118-ijms-21-05175\" ref-type=\"bibr\">118</xref>], breast cancer [<xref rid=\"B119-ijms-21-05175\" ref-type=\"bibr\">119</xref>], and glioblastoma [<xref rid=\"B120-ijms-21-05175\" ref-type=\"bibr\">120</xref>]), while Nrf2 itself has been identified as a promising therapeutic target for these chronic diseases resulting from its role in providing cytoprotection against diverse stress and pathologies [<xref rid=\"B121-ijms-21-05175\" ref-type=\"bibr\">121</xref>]. Recent preclinical data have revealed that N-palmitoylethanolamine-oxazoline (PEA-OXA), an antioxidant compound, protects against cardiovascular complication through upregulation of Nrf2 and Nrf2-target genes [<xref rid=\"B122-ijms-21-05175\" ref-type=\"bibr\">122</xref>]. Other compounds (e.g., linarin (LIN) [<xref rid=\"B123-ijms-21-05175\" ref-type=\"bibr\">123</xref>] and resveratrol (RES) [<xref rid=\"B124-ijms-21-05175\" ref-type=\"bibr\">124</xref>]) also provide beneficial effects in myocardial ischemia/reperfusion injury and CVDs by activating Nrf2. In utero, excessive OS can trigger a cascade of molecular events, imperilling the health of offspring [<xref rid=\"B125-ijms-21-05175\" ref-type=\"bibr\">125</xref>]. The Nrf2-mediated OS response is one of the most important cytoprotective mechanisms against OS as it transcribes many antioxidative genes and ROS-scavenging proteins [<xref rid=\"B126-ijms-21-05175\" ref-type=\"bibr\">126</xref>] that are not only closely associated with embryo survival in in vitro conditions [<xref rid=\"B127-ijms-21-05175\" ref-type=\"bibr\">127</xref>], foetal development in utero, and cardiometabolic health in childhood or later life [<xref rid=\"B128-ijms-21-05175\" ref-type=\"bibr\">128</xref>], but also involved in maintaining vascular homeostasis [<xref rid=\"B129-ijms-21-05175\" ref-type=\"bibr\">129</xref>]. Given this evidence, it is reasonable to assume that Nrf2-mediated antioxidant signaling pathway may also serve as an important mechanism for OS-induced cardiovascular effects in ART offspring. Under redox homeostasis, Nrf2 is bound to its inhibitor, Kelch-like ECH-associated protein 1 (Keap1), and is located in the cytoplasm, where it facilitates the ubiquitin-mediated degradation of Nrf2. However, during OS, Nrf2 is phosphorylated and released from Keap1 and is translocated and accumulated in the nucleus, where it heterodimerizes with small musculoaponeurotic fibrosarcoma (Maf) proteins, binds to ARE, and transcriptionally upregulates antioxidant gene expression [<xref rid=\"B130-ijms-21-05175\" ref-type=\"bibr\">130</xref>]. The representative antioxidant genes regulated by Nrf2 are summarized in <xref rid=\"ijms-21-05175-t001\" ref-type=\"table\">Table 1</xref>.</p><p>Collectively, in the ART area, increased OS is one of the major triggers of early life genetic/epigenetic changes in the offspring. From a preventive point of view, the adverse influence of OS can possibly be reversed through timely appropriate interventions (e.g., medical supplements for ART children and ART parents, chemical modification of ART culture media), opening a window for the potential prevention of adverse long-lasting effects on ART offspring.</p></sec></sec><sec id=\"sec4-ijms-21-05175\"><title>4. Long-Lasting Cardiovascular Effects </title><p>In the human body, ROS act as a ’double-edged sword’, playing a paradoxical role. Normal levels of ROS are important regulators of various transcription factors and signal transduction pathways. Excessive ROS levels can lead to OS, causing damage to cellular components (e.g., proteins, lipids, and DNA), mitochondrial dysfunctions, inhibition of oocyte maturation, delayed embryonic development, and induction of apoptosis in embryos [<xref rid=\"B149-ijms-21-05175\" ref-type=\"bibr\">149</xref>]. Several lines of evidence suggest that OS plays a key role in the foetal programming of adulthood CVDs [<xref rid=\"B150-ijms-21-05175\" ref-type=\"bibr\">150</xref>,<xref rid=\"B151-ijms-21-05175\" ref-type=\"bibr\">151</xref>,<xref rid=\"B152-ijms-21-05175\" ref-type=\"bibr\">152</xref>,<xref rid=\"B153-ijms-21-05175\" ref-type=\"bibr\">153</xref>]. Studies on mammalian offspring suffering from OS (e.g., hypoxia-reoxygenation) during the gestational period reported that these offspring developed endothelial dysfunction [<xref rid=\"B150-ijms-21-05175\" ref-type=\"bibr\">150</xref>,<xref rid=\"B151-ijms-21-05175\" ref-type=\"bibr\">151</xref>], enhanced myocardial contractility [<xref rid=\"B151-ijms-21-05175\" ref-type=\"bibr\">151</xref>], and hypertension [<xref rid=\"B150-ijms-21-05175\" ref-type=\"bibr\">150</xref>,<xref rid=\"B152-ijms-21-05175\" ref-type=\"bibr\">152</xref>] in adulthood. Foetal programming-induced alterations are transmissible not only throughout life but also in the subsequent generation [<xref rid=\"B22-ijms-21-05175\" ref-type=\"bibr\">22</xref>].</p><p>From the maternal point of view, advanced maternal age (AMA) [<xref rid=\"B154-ijms-21-05175\" ref-type=\"bibr\">154</xref>,<xref rid=\"B155-ijms-21-05175\" ref-type=\"bibr\">155</xref>] and OS-increased ART-associated pregnancy complications (e.g., HDP [<xref rid=\"B156-ijms-21-05175\" ref-type=\"bibr\">156</xref>,<xref rid=\"B157-ijms-21-05175\" ref-type=\"bibr\">157</xref>], GDM [<xref rid=\"B158-ijms-21-05175\" ref-type=\"bibr\">158</xref>], IUGR [<xref rid=\"B159-ijms-21-05175\" ref-type=\"bibr\">159</xref>], and preterm birth [<xref rid=\"B100-ijms-21-05175\" ref-type=\"bibr\">100</xref>]) are frequently associated with cardiovascular dysfunction in the offspring. For example, AMA/HDP/IUGR have been linked with increased blood pressure and/or altered cardiovascular function in offspring [<xref rid=\"B154-ijms-21-05175\" ref-type=\"bibr\">154</xref>,<xref rid=\"B155-ijms-21-05175\" ref-type=\"bibr\">155</xref>,<xref rid=\"B156-ijms-21-05175\" ref-type=\"bibr\">156</xref>,<xref rid=\"B159-ijms-21-05175\" ref-type=\"bibr\">159</xref>], while GDM/preterm birth have been linked with CVDs in offspring [<xref rid=\"B100-ijms-21-05175\" ref-type=\"bibr\">100</xref>,<xref rid=\"B158-ijms-21-05175\" ref-type=\"bibr\">158</xref>]. Specifically, in a mouse model, it was reported that AMA affects the phenotype of the offspring in a sex-dependent manner: in young adulthood (four months of age), male (but not female) offspring birthed by aged dams presented reperfusion injury and impaired endothelium-dependent relaxation. In mature adulthood (12 months of age), female offspring showed increased systolic blood pressure, whereas male offspring showed decreased ventricular diastolic function and increased vascular sensitivity to methacholine [<xref rid=\"B154-ijms-21-05175\" ref-type=\"bibr\">154</xref>,<xref rid=\"B155-ijms-21-05175\" ref-type=\"bibr\">155</xref>].</p><p>Because of the relative novelty of ART, the follow-up times of relevant studies remain limited, and the debate as to whether the ART techniques cause long-lasting adverse effects on the offspring remains ongoing. Epidemiological studies on ART children and young adults revealed that the ART offspring presented cardiovascular problems (e.g. congenital heart defect [<xref rid=\"B160-ijms-21-05175\" ref-type=\"bibr\">160</xref>] and postnatal dysfunctions of the cardiovascular system [<xref rid=\"B161-ijms-21-05175\" ref-type=\"bibr\">161</xref>,<xref rid=\"B162-ijms-21-05175\" ref-type=\"bibr\">162</xref>,<xref rid=\"B163-ijms-21-05175\" ref-type=\"bibr\">163</xref>,<xref rid=\"B164-ijms-21-05175\" ref-type=\"bibr\">164</xref>]), despite the fact that one study reported, among 22–35-year-old adults, ART did not correlate with an increase in prevalent cardiovascular risk factors. However, the study population in this study was still in early adulthood and the authors only used non-invasive methods to detect early markers of sub-clinical atherosclerosis; therefore, they did not evaluate the relationships with clinical cardiovascular events (e.g., CVDs) [<xref rid=\"B165-ijms-21-05175\" ref-type=\"bibr\">165</xref>]. Several systematic reviews and meta-analyses [<xref rid=\"B166-ijms-21-05175\" ref-type=\"bibr\">166</xref>,<xref rid=\"B167-ijms-21-05175\" ref-type=\"bibr\">167</xref>,<xref rid=\"B168-ijms-21-05175\" ref-type=\"bibr\">168</xref>] have been performed on the cardiovascular profiles of offspring conceived by ART, with the results revealing mild but statistically significant cardiovascular differences in ART offspring. As Guo et al. concluded, ART-conceived children showed mildly but statistically significantly elevated blood pressure with sub-optimal diastolic function, thicker blood vessels, and lower levels of low-density lipoprotein cholesterol (LDL-C) [<xref rid=\"B167-ijms-21-05175\" ref-type=\"bibr\">167</xref>].</p><p>Despite the limited data on human ART offspring, subclinical cardiometabolic alterations are detectable. Nevertheless, because CVDs are chronic, adult-onset diseases and significant signs of CVDs require years to develop. ART is relatively new; therefore, the follow-up time of epidemiological studies on ART remains limited (i.e., the first ’test-tube baby’, Louise Brown, was born in 1978 [<xref rid=\"B169-ijms-21-05175\" ref-type=\"bibr\">169</xref>] and was only 42 years old as of 2020). Therefore, it is too early to form a definite conclusion. More long-term (even life-long) follow-up periods are warranted.</p></sec><sec id=\"sec5-ijms-21-05175\"><title>5. Limitations and Prospects of Current Studies</title><p>Regarding human ART studies such as those studying the influence of OS in a cohort, it is not clear whether ART is the culprit for OS or whether the infertility factors of ART parents play confounding roles. Moreover, the spectrum of ART-offspring demographic confounders (e.g., lifestyles, dietary habits, familial socio-economic status, adverse childhood experiences, as well as other life experiences), rapidly changing ART protocols, and the various types of culture media make such research more complex. To guarantee a detailed multivariate examination of this kind of study, a large sample size may be key to ensuring adjustments for various confounders. A comprehensive record of clinical and laboratory parameters (independent variables) and a prospective longitudinal design are also necessary.</p><p>To independently study the influence of ART on offspring, the ART mouse is an excellent model (i.e., there is no background of infertility; pregnancy and juvenile periods are short; and it is possible to appropriately replicate complications in humans [<xref rid=\"B168-ijms-21-05175\" ref-type=\"bibr\">168</xref>]). Human embryonic stem cells (hESC) can also serve as a novel in vitro model to study the effects of OS on the early embryo [<xref rid=\"B170-ijms-21-05175\" ref-type=\"bibr\">170</xref>].</p><p>In fact, in human assisted reproduction, both the ART procedure and the infertility backgrounds of ART parents may cause increased OS. Increased OS not only is the reason for patients visiting ART clinics, but it is also the outcome of in vitro ART manipulations. Despite increased OS being a prevalent phenomenon in ART, (i) there are no established uniform indicators of OS or standardized cut-off values for ART, ART men, and ART women; (ii) some ART laboratories make no attempt to test for the presence of OS; (iii) neither do the majority of ART clinics analyse their patients’ OS statuses nor pay attention to the clinical causes and sentinel signs of OS, nor do they suggest any changes to OS-related adverse lifestyles or instigate any measures to maintain the redox balance in their patients. Nevertheless, these approaches will not only increase a couple’s chances of natural conception, but will also optimise the efficiency of the ART and the health of ART offspring.</p></sec><sec sec-type=\"conclusions\" id=\"sec6-ijms-21-05175\"><title>6. Conclusions</title><p>OS, which have potentially adverse effects on ART offspring, may derive not only from ART per se but also from the infertility backgrounds of ART parents. OS might exert a long-lasting influences on the offspring’s cardiovascular system via epigenetic and genetic alterations (<xref ref-type=\"fig\" rid=\"ijms-21-05175-f004\">Figure 4</xref>).</p></sec></body><back><ack><title>Acknowledgments</title><p>Z.M. is supported by the China Scholarship Council (CSC). Z.M. acknowledges the CSC for supporting a visit to Ludwig-Maximilians-University (LMU).</p></ack><notes><title>Funding</title><p>This research received no external funding.</p></notes><notes notes-type=\"COI-statement\"><title>Conflicts of Interest</title><p>The authors declare no conflict of interest.</p></notes><glossary><title>Abbreviations</title><array orientation=\"portrait\"><tbody><tr><td align=\"left\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">ART</td><td align=\"left\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">Assisted reproductive technology</td></tr><tr><td align=\"left\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">IUI</td><td align=\"left\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">Intra-uterine insemination</td></tr><tr><td align=\"left\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">IVF</td><td align=\"left\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">In vitro fertilisation</td></tr><tr><td align=\"left\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">ICSI</td><td align=\"left\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">Intracytoplasmic sperm injection</td></tr><tr><td align=\"left\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">COH</td><td align=\"left\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">Controlled ovarian hyperstimulation</td></tr><tr><td align=\"left\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">PGD</td><td align=\"left\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">Pre-implantation genetic diagnosis</td></tr><tr><td align=\"left\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">PGS</td><td align=\"left\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">Pre-implantation genetic screening</td></tr><tr><td align=\"left\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">SSR</td><td align=\"left\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">Surgical sperm retrieval</td></tr><tr><td align=\"left\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">DOHaD</td><td align=\"left\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">Developmental Origins of Adult Disease</td></tr><tr><td align=\"left\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">NCDs</td><td align=\"left\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">Non-communicable diseases</td></tr><tr><td align=\"left\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">CVDs</td><td align=\"left\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">Cardiovascular diseases</td></tr><tr><td align=\"left\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">T2DM</td><td align=\"left\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">Type 2 diabetes mellitus</td></tr><tr><td align=\"left\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">OS</td><td align=\"left\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">Oxidative stress</td></tr><tr><td align=\"left\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">ROS</td><td align=\"left\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">Reactive oxygen species</td></tr><tr><td align=\"left\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">MAGI</td><td align=\"left\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">Male accessory gland infections</td></tr><tr><td align=\"left\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">MOSI</td><td align=\"left\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">Male oxidative stress infertility</td></tr><tr><td align=\"left\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">PUFAs</td><td align=\"left\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">Polyunsaturated fatty acids</td></tr><tr><td align=\"left\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">SDF</td><td align=\"left\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">Sperm DNA fragmentation</td></tr><tr><td align=\"left\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">PCOS</td><td align=\"left\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">Polycystic ovary syndrome</td></tr><tr><td align=\"left\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">HDP</td><td align=\"left\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">Hypertensive disorders of pregnancy</td></tr><tr><td align=\"left\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">GDM</td><td align=\"left\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">Gestational diabetes mellitus</td></tr><tr><td align=\"left\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">IUGR</td><td align=\"left\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">Intrauterine growth restriction</td></tr><tr><td align=\"left\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">O<sub>2</sub></td><td align=\"left\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">Oxygen</td></tr><tr><td align=\"left\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">O<sub>2</sub><sup>•−</sup></td><td align=\"left\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">Superoxide</td></tr><tr><td align=\"left\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">HO<sub>2</sub><sup>•</sup></td><td align=\"left\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">Hydroperoxyl</td></tr><tr><td align=\"left\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">OH<sup>•</sup></td><td align=\"left\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">Hydroxyl</td></tr><tr><td align=\"left\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">RO<sub>2</sub><sup>•</sup></td><td align=\"left\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">Peroxyl radicals</td></tr><tr><td align=\"left\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">H<sub>2</sub>O<sub>2</sub></td><td align=\"left\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">Hydrogen peroxide</td></tr><tr><td align=\"left\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">NADPH</td><td align=\"left\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">Nicotinamide adenine dinucleotide phosphate</td></tr><tr><td align=\"left\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">OXPHOS</td><td align=\"left\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">Oxidative phosphorylation</td></tr><tr><td align=\"left\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">5-mC</td><td align=\"left\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">5-methylcytosine</td></tr><tr><td align=\"left\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">5-hmC</td><td align=\"left\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">5-hydroxymethylcytosine</td></tr><tr><td align=\"left\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">DNMT1</td><td align=\"left\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">DNA methyltransferase 1</td></tr><tr><td align=\"left\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">H3K4</td><td align=\"left\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">Histone H3 lysine 4</td></tr><tr><td align=\"left\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">H3K27</td><td align=\"left\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">Histone H3 lysine 27</td></tr><tr><td align=\"left\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">H3K9</td><td align=\"left\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">Histone H3 lysine 9</td></tr><tr><td align=\"left\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">H3K4me3</td><td align=\"left\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">H3K4 trimethylation</td></tr><tr><td align=\"left\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">TET</td><td align=\"left\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">Ten-eleven-translocation</td></tr><tr><td align=\"left\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">Nrf2</td><td align=\"left\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">Nuclear factor erythroid 2-related factor</td></tr><tr><td align=\"left\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">ARE</td><td align=\"left\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">Antioxidant response element</td></tr><tr><td align=\"left\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">PEA-OXA</td><td align=\"left\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">N-palmitoylethanolamine-oxazoline</td></tr><tr><td align=\"left\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">LIN</td><td align=\"left\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">Linarin</td></tr><tr><td align=\"left\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">RES</td><td align=\"left\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">Resveratrol</td></tr><tr><td align=\"left\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">Keap1</td><td align=\"left\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">Kelch-like ECH-associated protein 1</td></tr><tr><td align=\"left\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">Maf</td><td align=\"left\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">Musculoaponeurotic fibrosarcoma</td></tr><tr><td align=\"left\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">AMA</td><td align=\"left\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">Advanced maternal age</td></tr><tr><td align=\"left\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">LDL-C</td><td align=\"left\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">Low-density lipoprotein cholesterol</td></tr><tr><td align=\"left\" valign=\"bottom\" rowspan=\"1\" 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Biol.</source><year>2016</year><volume>5</volume><fpage>177</fpage><pub-id pub-id-type=\"doi\">10.4172/2168-9296.1000177</pub-id><pub-id pub-id-type=\"pmid\">27774366</pub-id></element-citation></ref></ref-list></back><floats-group><fig id=\"ijms-21-05175-f001\" orientation=\"portrait\" position=\"float\"><label>Figure 1</label><caption><p>This figure shows the origins of paternally derived OS. The origins of OS that could affect fathers and their gametes mainly derive from four causes: male fertility complications, lifestyle/diet, environmental/occupational exposures, and special treatments. OS, oxidative stress; MAGI, male accessory gland infections; MOSI, male oxidative stress infertility.</p></caption><graphic xlink:href=\"ijms-21-05175-g001\"/></fig><fig id=\"ijms-21-05175-f002\" orientation=\"portrait\" position=\"float\"><label>Figure 2</label><caption><p>This figure shows the origins of maternally derived OS. The origins of OS that could affect the mothers, gametes, and their pregnancies mainly derive from five aspects: female fertility complications, lifestyle/diet, environmental/occupational exposures, special treatments and pregnancy complications. PCOS, polycystic ovary syndrome; HDP, hypertensive disorders of pregnancy; GDM, gestational diabetes mellitus; IUGR, intrauterine growth restriction.</p></caption><graphic xlink:href=\"ijms-21-05175-g002\"/></fig><fig id=\"ijms-21-05175-f003\" orientation=\"portrait\" position=\"float\"><label>Figure 3</label><caption><p>This figure shows the origins of ART-derived OS. During ART, several factors might lead to elevated OS. ART, assisted reproductive technology.</p></caption><graphic xlink:href=\"ijms-21-05175-g003\"/></fig><fig id=\"ijms-21-05175-f004\" orientation=\"portrait\" position=\"float\"><label>Figure 4</label><caption><p>This figure shows the early-life OS exert long-lasting effects on offspring conceived by ART. ncRNAs, non-coding RNAs; miRNAs, micro-RNAs; lncRNAs, long non-coding RNAs.</p></caption><graphic xlink:href=\"ijms-21-05175-g004\"/></fig><table-wrap id=\"ijms-21-05175-t001\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijms-21-05175-t001_Table 1</object-id><label>Table 1</label><caption><p>Antioxidant genes regulated by Nrf2.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin;background:black;color:white\" rowspan=\"1\" colspan=\"1\">Gene </th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin;background:black;color:white\" rowspan=\"1\" colspan=\"1\">Protein Encoded</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin;background:black;color:white\" rowspan=\"1\" colspan=\"1\">Synonyms</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin;background:black;color:white\" rowspan=\"1\" colspan=\"1\">Species <sup>1</sup></th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin;background:black;color:white\" rowspan=\"1\" colspan=\"1\">Refs</th></tr></thead><tbody><tr><td colspan=\"5\" align=\"center\" valign=\"middle\" style=\"background:#DBDBDB\" rowspan=\"1\">\n<bold>GSH-based antioxidant genes</bold>\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>GCLC</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Glutamate-cysteine ligase catalytic subunit</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>GCS, GLCL, GLCLC</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">m, h</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B131-ijms-21-05175\" ref-type=\"bibr\">131</xref>,<xref rid=\"B132-ijms-21-05175\" ref-type=\"bibr\">132</xref>,<xref rid=\"B133-ijms-21-05175\" ref-type=\"bibr\">133</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>GCLM</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Glutamate-cysteine ligase modifier subunit</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>GLCLR</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">m, h</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B131-ijms-21-05175\" ref-type=\"bibr\">131</xref>,<xref rid=\"B132-ijms-21-05175\" ref-type=\"bibr\">132</xref>,<xref rid=\"B133-ijms-21-05175\" ref-type=\"bibr\">133</xref>,<xref rid=\"B134-ijms-21-05175\" ref-type=\"bibr\">134</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>GGT1</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Gamma-glutamyltransferase 1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>CD224, D22S672, D22S732, GGT</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">h</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B131-ijms-21-05175\" ref-type=\"bibr\">131</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>GLRX</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Glutaredoxin</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>GRX, GRX1</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">h</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B131-ijms-21-05175\" ref-type=\"bibr\">131</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>GLS</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Glutaminase</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>GLS1, KIAA0838</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">h</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B131-ijms-21-05175\" ref-type=\"bibr\">131</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>GPX1</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Glutathione peroxidase 1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">m</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B135-ijms-21-05175\" ref-type=\"bibr\">135</xref>,<xref rid=\"B136-ijms-21-05175\" ref-type=\"bibr\">136</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>GPX2</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Glutathione peroxidase 2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>GSHPX-GI</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">m, h</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B131-ijms-21-05175\" ref-type=\"bibr\">131</xref>,<xref rid=\"B137-ijms-21-05175\" ref-type=\"bibr\">137</xref>,<xref rid=\"B138-ijms-21-05175\" ref-type=\"bibr\">138</xref>,<xref rid=\"B139-ijms-21-05175\" ref-type=\"bibr\">139</xref>,<xref rid=\"B140-ijms-21-05175\" ref-type=\"bibr\">140</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>GPX4</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Glutathione peroxidase 4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>MCSP, PHGPx</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">m</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B131-ijms-21-05175\" ref-type=\"bibr\">131</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>GSR</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Glutathione-disulfide reductase</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">m, h</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B133-ijms-21-05175\" ref-type=\"bibr\">133</xref>,<xref rid=\"B134-ijms-21-05175\" ref-type=\"bibr\">134</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>GSTA1</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Glutathione S-transferase alpha 1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">m</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B131-ijms-21-05175\" ref-type=\"bibr\">131</xref>,<xref rid=\"B141-ijms-21-05175\" ref-type=\"bibr\">141</xref>,<xref rid=\"B142-ijms-21-05175\" ref-type=\"bibr\">142</xref>,<xref rid=\"B143-ijms-21-05175\" ref-type=\"bibr\">143</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>GSTA2</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Glutathione S-transferase alpha 2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">m</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B132-ijms-21-05175\" ref-type=\"bibr\">132</xref>,<xref rid=\"B142-ijms-21-05175\" ref-type=\"bibr\">142</xref>,<xref rid=\"B143-ijms-21-05175\" ref-type=\"bibr\">143</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>GSTA3</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Glutathione S-transferase alpha 3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">m</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B141-ijms-21-05175\" ref-type=\"bibr\">141</xref>,<xref rid=\"B142-ijms-21-05175\" ref-type=\"bibr\">142</xref>,<xref rid=\"B143-ijms-21-05175\" ref-type=\"bibr\">143</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>GSTA4</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Glutathione S-transferase alpha 4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">m</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B144-ijms-21-05175\" ref-type=\"bibr\">144</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>GSTM1</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Glutathione S-transferase mu 1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>GST1, H-B, MU</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">m</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B131-ijms-21-05175\" ref-type=\"bibr\">131</xref>,<xref rid=\"B132-ijms-21-05175\" ref-type=\"bibr\">132</xref>,<xref rid=\"B143-ijms-21-05175\" ref-type=\"bibr\">143</xref>,<xref rid=\"B144-ijms-21-05175\" ref-type=\"bibr\">144</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>GSTM2</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Glutathione S-transferase mu 2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>GST4</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">m</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B132-ijms-21-05175\" ref-type=\"bibr\">132</xref>,<xref rid=\"B143-ijms-21-05175\" ref-type=\"bibr\">143</xref>,<xref rid=\"B144-ijms-21-05175\" ref-type=\"bibr\">144</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>GSTM3</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Glutathione S-transferase mu 3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>GST5</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">m, h</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B132-ijms-21-05175\" ref-type=\"bibr\">132</xref>,<xref rid=\"B134-ijms-21-05175\" ref-type=\"bibr\">134</xref>,<xref rid=\"B143-ijms-21-05175\" ref-type=\"bibr\">143</xref>,<xref rid=\"B144-ijms-21-05175\" ref-type=\"bibr\">144</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>GSTM4</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Glutathione S-transferase mu 4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">m</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B144-ijms-21-05175\" ref-type=\"bibr\">144</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>GSTM5</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Glutathione S-transferase mu 5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">m</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B145-ijms-21-05175\" ref-type=\"bibr\">145</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>GSTM6</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Glutathione S-transferase mu 6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">m</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B144-ijms-21-05175\" ref-type=\"bibr\">144</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>GSTP1</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Glutathione S-transferase pi 1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>FAEES3, GST3, GSTP</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">m</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B131-ijms-21-05175\" ref-type=\"bibr\">131</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>MGST1</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">icrosomal glutathione S-transferase 1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>GST12, MGST-I</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">m, h</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B131-ijms-21-05175\" ref-type=\"bibr\">131</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>MGST2</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Microsomal glutathione S-transferase 2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>MGST-II</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">m</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B143-ijms-21-05175\" ref-type=\"bibr\">143</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>MGST3</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Microsomal glutathione S-transferase 3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>GST-III</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">m</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B132-ijms-21-05175\" ref-type=\"bibr\">132</xref>,<xref rid=\"B143-ijms-21-05175\" ref-type=\"bibr\">143</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>SLC6A9</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Solute carrier family 6 member 9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>GLYT1</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">m</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B131-ijms-21-05175\" ref-type=\"bibr\">131</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>SLC7A11</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Solute carrier family 7 member 11</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>xCT</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">m, h</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B131-ijms-21-05175\" ref-type=\"bibr\">131</xref>,<xref rid=\"B146-ijms-21-05175\" ref-type=\"bibr\">146</xref>]</td></tr><tr><td colspan=\"5\" align=\"center\" valign=\"middle\" style=\"background:#DBDBDB\" rowspan=\"1\">\n<bold>TXN-based antioxidant genes</bold>\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>PRDX1</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Peroxiredoxin 1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>NKEFA, PAGA)</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">m</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B131-ijms-21-05175\" ref-type=\"bibr\">131</xref>,<xref rid=\"B138-ijms-21-05175\" ref-type=\"bibr\">138</xref>,<xref rid=\"B142-ijms-21-05175\" ref-type=\"bibr\">142</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>PRDX6</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Peroxiredoxin 6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>1-Cys, aiPLA2, AOP2, KIAA0106, MGC46173, NSGPx, p29, PRX</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">h</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B131-ijms-21-05175\" ref-type=\"bibr\">131</xref>,<xref rid=\"B147-ijms-21-05175\" ref-type=\"bibr\">147</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>SRXN1</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Sulfiredoxin 1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>C20orf139, dJ850E9.2, Npn3, SRX1, YKL086W</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">m, h</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B131-ijms-21-05175\" ref-type=\"bibr\">131</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>TXN</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Thioredoxin</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>TRX</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">m, h</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B131-ijms-21-05175\" ref-type=\"bibr\">131</xref>,<xref rid=\"B132-ijms-21-05175\" ref-type=\"bibr\">132</xref>,<xref rid=\"B135-ijms-21-05175\" ref-type=\"bibr\">135</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>TXNRD1</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Thioredoxin reductase 1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>GRIM-12, Trxr1, TXNR</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">m, h</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B131-ijms-21-05175\" ref-type=\"bibr\">131</xref>,<xref rid=\"B135-ijms-21-05175\" ref-type=\"bibr\">135</xref>,<xref rid=\"B144-ijms-21-05175\" ref-type=\"bibr\">144</xref>]</td></tr><tr><td colspan=\"5\" align=\"center\" valign=\"middle\" style=\"background:#DBDBDB\" rowspan=\"1\">\n<bold>ATP-binding-based antioxidant genes</bold>\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>ABCB6</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ATP binding cassette subfamily B member 6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>EST45597, MTABC3, umat</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">m, h</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B131-ijms-21-05175\" ref-type=\"bibr\">131</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>ABCC1</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ATP binding cassette subfamily C member 1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>GS-X, MRP, MRP1</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">m, h</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B131-ijms-21-05175\" ref-type=\"bibr\">131</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>ABCC2</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ATP binding cassette subfamily C member 2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>CMOAT, cMRP, DJS, MRP2</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">m, h</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B131-ijms-21-05175\" ref-type=\"bibr\">131</xref>,<xref rid=\"B141-ijms-21-05175\" ref-type=\"bibr\">141</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>ABCC3</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ATP binding cassette subfamily C member 3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>cMOAT2, EST90757, MLP2, MOAT-D, MRP3</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">m, h</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B131-ijms-21-05175\" ref-type=\"bibr\">131</xref>,<xref rid=\"B141-ijms-21-05175\" ref-type=\"bibr\">141</xref>,<xref rid=\"B148-ijms-21-05175\" ref-type=\"bibr\">148</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>ABCC4</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ATP binding cassette subfamily C member 4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>EST170205, MOAT-B, MOATB, MRP4</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">m</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B131-ijms-21-05175\" ref-type=\"bibr\">131</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>ABCC5</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ATP binding cassette subfamily C member 5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>EST277145, MOAT-C, MRP5, SMRP</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">m</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B131-ijms-21-05175\" ref-type=\"bibr\">131</xref>]</td></tr><tr><td colspan=\"5\" align=\"center\" valign=\"middle\" style=\"background:#DBDBDB\" rowspan=\"1\">\n<bold>Heme/iron metabolism-associated antioxidant genes</bold>\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>BLVRA</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Biliverdin reductase A</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>BLVR</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">h</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B131-ijms-21-05175\" ref-type=\"bibr\">131</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>BLVRB</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Biliverdin reductase B</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>FLR, SDR43U1</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">m, h</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B131-ijms-21-05175\" ref-type=\"bibr\">131</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>FTH1</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Ferritin heavy chain 1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>FHC, FTH, FTHL6, PIG15, PLIF</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">m, h</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B131-ijms-21-05175\" ref-type=\"bibr\">131</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>FTL</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Ferritin light chain</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>MGC71996, NBIA3</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">m, h</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B131-ijms-21-05175\" ref-type=\"bibr\">131</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>HMOX1</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Heme oxygenase 1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>bK286B10, HO-1</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">m, h</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B131-ijms-21-05175\" ref-type=\"bibr\">131</xref>,<xref rid=\"B133-ijms-21-05175\" ref-type=\"bibr\">133</xref>,<xref rid=\"B134-ijms-21-05175\" ref-type=\"bibr\">134</xref>,<xref rid=\"B135-ijms-21-05175\" ref-type=\"bibr\">135</xref>,<xref rid=\"B140-ijms-21-05175\" ref-type=\"bibr\">140</xref>,<xref rid=\"B145-ijms-21-05175\" ref-type=\"bibr\">145</xref>]</td></tr><tr><td colspan=\"5\" align=\"center\" valign=\"middle\" style=\"background:#DBDBDB\" rowspan=\"1\">\n<bold>UDP glucuronosyltransferase-associated antioxidant genes</bold>\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>UGT1A1</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">UDP glucuronosyltransferase family 1 member A1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>GNT1, UGT1, UGT1A</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">h</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B131-ijms-21-05175\" ref-type=\"bibr\">131</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>UGT1A6</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">UDP glucuronosyltransferase family 1 member A6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>GNT1, HLUGP, UGT1F</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">m</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B138-ijms-21-05175\" ref-type=\"bibr\">138</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>UGT2B1</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">UDP glucuronosyltransferase family 2 member B1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">m</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B141-ijms-21-05175\" ref-type=\"bibr\">141</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>UGT2B5</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">UDP glucuronosyltransferase family 2 member B5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">m</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B132-ijms-21-05175\" ref-type=\"bibr\">132</xref>,<xref rid=\"B143-ijms-21-05175\" ref-type=\"bibr\">143</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>UGT2B7</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">UDP glucuronosyltransferase family 2 member B7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>UGT2B9</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">m, h</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B131-ijms-21-05175\" ref-type=\"bibr\">131</xref>]</td></tr><tr><td colspan=\"5\" align=\"center\" valign=\"middle\" style=\"background:#DBDBDB\" rowspan=\"1\">\n<bold>Other antioxidant genes</bold>\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>ADH7</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Alcohol dehydrogenase 7 (class IV), mu or sigma polypeptide</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>ADH-4</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">m</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B131-ijms-21-05175\" ref-type=\"bibr\">131</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>AKR1A1</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Aldo-keto reductase family 1 member A1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>ALR, DD3</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">h</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B132-ijms-21-05175\" ref-type=\"bibr\">132</xref>,<xref rid=\"B143-ijms-21-05175\" ref-type=\"bibr\">143</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>AKR1B1</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Aldo-keto reductase family 1 member B1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>ALDR1, AR</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">m, h</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B131-ijms-21-05175\" ref-type=\"bibr\">131</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>AKR1B8</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Aldo-keto reductase family 1 member B8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">m</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B142-ijms-21-05175\" ref-type=\"bibr\">142</xref>,<xref rid=\"B143-ijms-21-05175\" ref-type=\"bibr\">143</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>AKR1C1</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Aldo-keto reductase family 1 member C1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>DD1, DDH, DDH1, HAKRC, MBAB</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">h</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B131-ijms-21-05175\" ref-type=\"bibr\">131</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>ALDH1A1</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ldehyde dehydrogenase 1 family member A1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>ALDH1, PUMB1, RALDH1</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">m</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B131-ijms-21-05175\" ref-type=\"bibr\">131</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>ALDH3A1</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Aldehyde dehydrogenase 3 family member A1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>ALDH3</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">m, h</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B131-ijms-21-05175\" ref-type=\"bibr\">131</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>ALDH7A1</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Aldehyde dehydrogenase 7 family member A1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>ATQ1, EPD, PDE</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">m</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B131-ijms-21-05175\" ref-type=\"bibr\">131</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>CAT</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Catalase</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">m</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B137-ijms-21-05175\" ref-type=\"bibr\">137</xref>,<xref rid=\"B141-ijms-21-05175\" ref-type=\"bibr\">141</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>CBR1</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Carbonyl reductase 1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>CBR, SDR21C1</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">h</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B131-ijms-21-05175\" ref-type=\"bibr\">131</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>CYP1B1</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Cytochrome P450 family 1 subfamily B member 1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>CP1B, GLC3A</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">m</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B131-ijms-21-05175\" ref-type=\"bibr\">131</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>CYP2B9</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Cytochrome P450 family 2 subfamily B member 9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">m</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B131-ijms-21-05175\" ref-type=\"bibr\">131</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>G6PD</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Glucose-6-phosphate dehydrogenase</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>G6PD1</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">m, h</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B131-ijms-21-05175\" ref-type=\"bibr\">131</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>IDH1</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Isocitrate dehydrogenase (NADP(+)) 1, cytosolic</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">m</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B131-ijms-21-05175\" ref-type=\"bibr\">131</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>ME1</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Malic enzyme 1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">m, h</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B131-ijms-21-05175\" ref-type=\"bibr\">131</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>NQO1</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">NAD(P)H quinone dehydrogenase 1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>DHQU, DIA4, DTD, NMOR1, QR1</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">m, h</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B131-ijms-21-05175\" ref-type=\"bibr\">131</xref>,<xref rid=\"B132-ijms-21-05175\" ref-type=\"bibr\">132</xref>,<xref rid=\"B134-ijms-21-05175\" ref-type=\"bibr\">134</xref>,<xref rid=\"B135-ijms-21-05175\" ref-type=\"bibr\">135</xref>,<xref rid=\"B142-ijms-21-05175\" ref-type=\"bibr\">142</xref>,<xref rid=\"B143-ijms-21-05175\" ref-type=\"bibr\">143</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>PGD</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Phosphogluconate dehydrogenase</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">m, h</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B131-ijms-21-05175\" ref-type=\"bibr\">131</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>PTGR1</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Prostaglandin reductase 1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>LTB4DH, ZADH3</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">h</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B131-ijms-21-05175\" ref-type=\"bibr\">131</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>SOD1</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Superoxide dismutase 1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>ALS, ALS1, IPOA</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">m</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B141-ijms-21-05175\" ref-type=\"bibr\">141</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>SOD2</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Superoxide dismutase 2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">m</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B141-ijms-21-05175\" ref-type=\"bibr\">141</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>SOD3</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Superoxide dismutase 3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>EC-SOD</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">m</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B142-ijms-21-05175\" ref-type=\"bibr\">142</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>TALDO1</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Transaldolase 1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">m, h</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B131-ijms-21-05175\" ref-type=\"bibr\">131</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>UGDH</italic>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">UDP-glucose 6-dehydrogenase</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">h</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B131-ijms-21-05175\" ref-type=\"bibr\">131</xref>]</td></tr></tbody></table><table-wrap-foot><fn><p><sup>1</sup> The species means the gene has been identified in mouse (m) and/or human (h). Nrf2, nuclear factor erythroid 2-related factor; GSH, glutathione; UDP, uridine diphosphate. The GSH and TXN antioxidant pathways are two important downstream pathways of Nrf2 [<xref rid=\"B117-ijms-21-05175\" ref-type=\"bibr\">117</xref>].</p></fn></table-wrap-foot></table-wrap></floats-group></article>\n"
]
[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Int J Environ Res Public Health</journal-id><journal-id journal-id-type=\"iso-abbrev\">Int J Environ Res Public Health</journal-id><journal-id journal-id-type=\"publisher-id\">ijerph</journal-id><journal-title-group><journal-title>International Journal of Environmental Research and Public Health</journal-title></journal-title-group><issn pub-type=\"ppub\">1661-7827</issn><issn pub-type=\"epub\">1660-4601</issn><publisher><publisher-name>MDPI</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32731557</article-id><article-id pub-id-type=\"pmc\">PMC7432067</article-id><article-id pub-id-type=\"doi\">10.3390/ijerph17155436</article-id><article-id pub-id-type=\"publisher-id\">ijerph-17-05436</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Article</subject></subj-group></article-categories><title-group><article-title>Analysis of the Influencing Factors on the Preferences of the Elderly for the Combination of Medical Care and Pension in Long-Term Care Facilities Based on the Andersen Model</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Wei</surname><given-names>Yong</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05436\">1</xref><xref ref-type=\"aff\" rid=\"af2-ijerph-17-05436\">2</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0001-5053-8122</contrib-id><name><surname>Zhang</surname><given-names>Liangwen</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05436\">1</xref><xref ref-type=\"aff\" rid=\"af3-ijerph-17-05436\">3</xref><xref rid=\"c1-ijerph-17-05436\" ref-type=\"corresp\">*</xref></contrib></contrib-group><aff id=\"af1-ijerph-17-05436\"><label>1</label>Department of Statistics, School of Economics, Xiamen University, 422 Siming South Road, Xiamen 361005, China; <email>[email protected]</email></aff><aff id=\"af2-ijerph-17-05436\"><label>2</label>School of Accounting, Jiangsu Vocational College of Finance and Economics, Huaian 223003, China</aff><aff id=\"af3-ijerph-17-05436\"><label>3</label>State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen 361102, China</aff><author-notes><corresp id=\"c1-ijerph-17-05436\"><label>*</label>Correspondence: <email>[email protected]</email>; Tel.: +86-0136-9692-2925; Fax: +86-059-2288-0639</corresp></author-notes><pub-date pub-type=\"epub\"><day>28</day><month>7</month><year>2020</year></pub-date><pub-date pub-type=\"ppub\"><month>8</month><year>2020</year></pub-date><volume>17</volume><issue>15</issue><elocation-id>5436</elocation-id><history><date date-type=\"received\"><day>22</day><month>6</month><year>2020</year></date><date date-type=\"accepted\"><day>27</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>© 2020 by the authors.</copyright-statement><copyright-year>2020</copyright-year><license license-type=\"open-access\"><license-p>Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>).</license-p></license></permissions><abstract><p>Background: The purpose of this study is to evaluate the status quo and factors that influence the preferences of the elderly for the combination of medical care and pension (CMCP) in long-term care (LTC) facilities and to provide an evidence-based basis for building a multi-tiered, continuous LTC system with CMCP. Methods: Using a multi-stage sampling method, face-to-face questionnaire surveys were conducted on 3260 elderly people aged 60 years or over in 44 communities in 16 sub-districts in six districts in Xiamen. Based on the Andersen model, the chi-square test was used to analyze differences in population distribution, and binary logistic regression analysis was used to analyze the factors affecting the elderly’s preference for CMCP in LTC institutions in terms of the factors of predisposition, enablement, and personal needs. Results: Most elderly people choose traditional home care (82.01%), and only 12.89% choose LTC facilities with CMCP. This choice is influenced by a number of predisposing factors. The elderly who are at the upper end of the age range, have a higher education level, and live in rural areas are more likely to choose CMCP (odds ratio (OR) value greater than 1, <italic>p</italic> < 0.05). With regard to enabling factors, the elderly who were married, mainly taken care of by spouses, and had better economic status also tended to choose CMCP (OR > 1, <italic>p</italic> < 0.01). In terms of personal needs, the elderly with worse self-care status tended to choose CMCP (OR > 1, <italic>p</italic> < 0.01). Enabling factors have the largest contribution to the model, and they have the greatest impact on elder preference for CMCP services. In addition, the elderly with higher age and education level, non-remarried, with better economic status, and with poorer health status have a demand for a wider variety of CMCP services. Compared to those in urban areas, the elderly in rural areas have greater needs, mainly related to personal care, medical care, and psychological counseling. Conclusion: The preference of the elderly for CMCP are lower compared to their preference for home care in Xiamen, China. Preference for CMCP is affected by a range of factors such as age, education level, residence, income, and self-care ability, among which the enabling factors have the greatest impact. </p></abstract><kwd-group><kwd>combination of medical care and pension</kwd><kwd>care needs</kwd><kwd>influencing factors</kwd><kwd>Andersen model</kwd><kwd>elderly</kwd></kwd-group></article-meta></front><body><sec sec-type=\"intro\" id=\"sec1-ijerph-17-05436\"><title>1. Introduction</title><p>The aging of China’s population has become an increasingly serious problem. The number of incapacitated or semi-incapacitated elderly people is steadily rising, increasing the demand for old-age care and medical services. By the end of 2016, China’s elderly population aged 60 and above reached 240 million, accounting for 17.3% of the total population [<xref rid=\"B1-ijerph-17-05436\" ref-type=\"bibr\">1</xref>]. Among these, more than 40 million are incapacitated or semi-incapacitated, accounting for 18.3% of the total elderly population [<xref rid=\"B2-ijerph-17-05436\" ref-type=\"bibr\">2</xref>]. At present, China has entered a rapid development period of population aging. According to United Nations statistics, in 2015 it took roughly 25 years for the proportion of the Chinese population over 65 years old to increase from 7% to 14%, a rate which is much faster than that found in developed countries (France, 115 years; Sweden, 85 years; USA, 69 years; UK, 45 years) [<xref rid=\"B3-ijerph-17-05436\" ref-type=\"bibr\">3</xref>]. It is clear, then, that China’s rapid ageing poses a host of challenges. </p><p>The aging population has led to a surge in demand for care for the elderly. With the continuous extension of life expectancy, physical and mental health problems have become increasingly prominent. The average life expectancy in China in 2018 was 77 years, while the average healthy life expectancy was only 68.7 years, a gap of about 10 years [<xref rid=\"B4-ijerph-17-05436\" ref-type=\"bibr\">4</xref>]. This shows that the health status of the elderly is poor, and physical and mental health problems are prominent issues for them. In addition, China has a high rate of disability. It is predicted that the total disabled population will increase rapidly, rising from 43.75 million in 2020 to 91.4 million in 2050. Of that total, 69.7% are expected to be urban elderly. By 2050, the population of elderly individuals with mild, moderate, or severe disabilities will be 108%, 104%, and 120%, respectively, as compared to their 2020 totals [<xref rid=\"B5-ijerph-17-05436\" ref-type=\"bibr\">5</xref>]. The disability rate of the elderly in China is thus expected to increase rapidly, with a concomitant deepening of the severity of disability. This combination of an increase in life expectancy, an aging population, a high prevalence of chronic diseases in old age, a serious problem of disability, and low healthy life expectancy has led to a surge in the demand for care for the elderly.</p><p>Numerous previous studies have shown that China’s elderly have the greatest demand for life care and medical care compared to other care services, such as spiritual consolation, social activities, etc. [<xref rid=\"B6-ijerph-17-05436\" ref-type=\"bibr\">6</xref>,<xref rid=\"B7-ijerph-17-05436\" ref-type=\"bibr\">7</xref>]. Since 2016, the Chinese government has promoted the important development strategy of “the Combination of Medical Care and Pension” (CMCP) as a way to cope with the rapid aging of the society, as announced in the <italic>Healthy China 2030 Plan</italic> and the <italic>13th Five-year Development Plan</italic>. It focuses on promoting a combination of medical care and nursing care, healthy aging, and vigorous development of services and industries for the elderly [<xref rid=\"B8-ijerph-17-05436\" ref-type=\"bibr\">8</xref>]. For the long-term care (LTC) model, it mainly emphasizes the carrier of service, such as home care, community care and institutional care. As a new type of LTC service, CMCP focuses on the integration of different care services. CMCP is coping with one of the important means to the healthy development of the aging society and is after readjustment endowment services, unlike the traditional home care model in China, to integrate optimization, medical care and pension resources for the elderly, which includes providing daily care, nursing care, medical services, and other forms from the new LTC service model in order to achieve healthy aging [<xref rid=\"B9-ijerph-17-05436\" ref-type=\"bibr\">9</xref>].</p><p>There have been many studies on the need for elderly care services, but few studies have examined the need for CMCP in long-term care (LTC) facilities. The elderly in China tend to prefer to live at home, a setting that has been the focus of most prior studies. Family pensions are favored by the vast majority of the elderly, much more so than other pension modes [<xref rid=\"B10-ijerph-17-05436\" ref-type=\"bibr\">10</xref>,<xref rid=\"B11-ijerph-17-05436\" ref-type=\"bibr\">11</xref>,<xref rid=\"B12-ijerph-17-05436\" ref-type=\"bibr\">12</xref>]. At the same time, the existing research on CMCP also focuses on its concept elaboration, mode selection, policy analysis [<xref rid=\"B13-ijerph-17-05436\" ref-type=\"bibr\">13</xref>,<xref rid=\"B14-ijerph-17-05436\" ref-type=\"bibr\">14</xref>]. The understanding and practice of CMCP in China are mainly limited to LTC or medical institutions, with a focus on the elderly with poor health or disabilities. However, elderly people at different life stages have different demands for CMCP, and CMCP services for the elderly should run through the entirety of old age [<xref rid=\"B15-ijerph-17-05436\" ref-type=\"bibr\">15</xref>,<xref rid=\"B16-ijerph-17-05436\" ref-type=\"bibr\">16</xref>]. In addition, there are few studies on the factors which influence demand for CMCP [<xref rid=\"B17-ijerph-17-05436\" ref-type=\"bibr\">17</xref>,<xref rid=\"B18-ijerph-17-05436\" ref-type=\"bibr\">18</xref>], differences in the needs for specific CMCP services among the elderly, as well as a lack of any relevant framework for theoretical analysis. An analysis of how different variables affect the needs of the elderly for CMCP services can provide a reference for policymakers to optimize resource allocation and to introduce targeted, scientific, and sustainable pension policies. </p><p>Therefore, this study aims to explore the influence of different factors on the preferences of the elderly for CMCP endowment services, and to better understand what services they need. It will help to give full play to the important supplementary role of institutional care, thereby improving China’s multi-level pension system, including families, communities and institutions. At last, this paper aims to provide a reference to enable policymakers to optimize resource arrangement and ensure the sustainability of China’s old-age security policy.</p></sec><sec id=\"sec2-ijerph-17-05436\"><title>2. Materials and Methods</title><sec id=\"sec2dot1-ijerph-17-05436\"><title>2.1. Study Design</title><p>In May 2016, the total population of Xiamen reached 2.2055 million, of which the elderly population over 60 years old accounted for 14%. In July–October 2016, the project team surveyed the elderly aged 60 and over in Xiamen City (residents of more than 6 months), using a multi-stage stratified sampling method to select 44 communities in 16 sub-districts in 6 districts of Xiamen City. After receiving identical training, investigators conducted a questionnaire survey on the elderly in the selected communities through face-to-face interviews. Questions mainly included the elderly’s sociodemographic variables, family conditions, health status, and CMCP service preferences. The inclusion criteria of the subjects in this study were as follows: (1) home-based elderly; (2) age ≥ 60 years; (3) able to communicate effectively; (4) gave informed consent. Exclusion criteria: (1) inability to communicate effectively; (2) living in LTC institutions. </p><p>The quality control strategy adopted in this survey was as follows: (1) During the research design phase, expert consultation and discussion ensured the scientific legitimacy and feasibility of the research plan and questionnaire. (2) Pre-surveys were conducted to improve the questionnaire, to develop standard operating procedures, and to develop a consistent training protocol for investigators. (3) During on-site investigations, quality control personnel were set up to conduct on-site audits and quality control, and to adopt double-blind protocols for later data entry and verification.</p></sec><sec id=\"sec2dot2-ijerph-17-05436\"><title>2.2. Andersen Theoretical Model </title><p>Based on Andersen’s model of healthy behavior [<xref rid=\"B19-ijerph-17-05436\" ref-type=\"bibr\">19</xref>], this study explores a theoretical framework for the analysis of factors that influence CMCP service preferences among the elderly (<xref ref-type=\"fig\" rid=\"ijerph-17-05436-f001\">Figure 1</xref>). This model is widely used in studies of healthcare demand. The factors of influence were subdivided into factors that predispose elderly individuals to choose CMCP, factors that enable them to successfully make use of CMCP, and factors of personal needs that influence demand for CMCP. On the basis of the Andersen behavior model, this paper constructed the adjusted Andersen theoretical model of influencing factors on CMCP needs according to the research purpose and variable information availability: (1) predisposing factors affecting model variables, including type of residence, gender, age, and level of education; (2) enabling variables, including living style, marriage, number of children, income, primary caregivers, and type of medical insurance; (3) personal needs variables, including self-assessment of health, activities of daily living (ADL), and feelings of loneliness.</p><sec id=\"sec2dot2dot1-ijerph-17-05436\"><title>2.2.1. Dependent Variables</title><p>CMCP preferences were the central dependent variable, consisting of two dimensions: (1) anticipated need for CMCP and (2) anticipated need for specific CMCP services. The question measuring the preference for CMCP was phrased as: “Do you want to live in an LTC facility with CMCP?” The anticipated need for specific CMCP services was measured using the question: “If you live in a CMCP facility, what kind of service would you like to receive?” The answer options included (1) personal care services, (2) medical care, (3) health examination, (4) psychological consulting, (5) social and recreational activities, and (6) health education.</p></sec><sec id=\"sec2dot2dot2-ijerph-17-05436\"><title>2.2.2. Independent Variables</title><p>This paper constructed an adjusted Andersen theoretical model of influencing factors on CMCP preference according to the purpose of this research and the availability of information regarding the relevant variables. The predisposing variables were age, gender, residence (urban, town, or rural), current living arrangement (whether or not living alone), and educational level (years of schooling). The enabling variables were expressed by occupation (farmer vs. others), marital status (married vs. unmarried), economic status (income exceeded expenditures, balanced, expenditures exceeded income), primary caregivers (spouse, children, others, none), number of children (0, 1, 2 or more), and medical insurance (yes vs. no). Personal needs variables included activities of daily living (not limited, limited, strongly limited), feelings of loneliness (often, sometimes, seldom/never), and self-rated health status (good vs. bad).</p></sec></sec><sec id=\"sec2dot3-ijerph-17-05436\"><title>2.3. Analytical Methods</title><p>Descriptive statistics were presented for distribution characteristics of the surveyed subjects and the distribution of CMCP preferences. The chi-square test was then used to analyze differences in the distribution of different CMCP preferences of the elderly. Taking the variables with chi-square test <italic>p</italic> < 0.1 as the independent variables, a binary logistic regression model was constructed to analyze the factors that influence the elderly’s preferences for CMCP services. First, the predisposing variables were taken as control variables and put into model 1 as the benchmark model. Then, based on the benchmark model, enabling variables and personal needs variables were added successively to form model 2 and model 3, to explain the variation of the variance of the dependent variables by the addition of other types of factors to the benchmark model. Finally, model 4 was constructed with all variables as independent variables to determine the overall explanatory power of the model. In addition, 4 binary logistic regression models (Model I–IV) were constructed by including all three types of independent variables (predisposing, enabling, and personal needs) into the regression model and eliminating the independent variables one by one, so as to compare the relative influence of the three kinds of factors. The specific models are as follows:</p><p>Model I: <italic>Logit</italic> (<italic>Yi</italic>) = predisposing variables + enabling variables + need variables</p><p>Model II: <italic>Logit</italic> (<italic>Yi</italic>) = enabling variables + need variables</p><p>Model III: <italic>Logit</italic> (<italic>Yi</italic>) = predisposing variables + need variables</p><p>Model IV: <italic>Logit</italic> (<italic>Yi</italic>) = predisposing variables + enabling variables</p><p>All statistical analyses were conducted using SAS 9.3. The odds ratio and 95% confidence interval indicated the effect of each predictor and whether it met statistical significance. A value of <italic>p</italic> < 0.05 was considered statistically significant.</p></sec></sec><sec sec-type=\"results\" id=\"sec3-ijerph-17-05436\"><title>3. Results</title><sec id=\"sec3dot1-ijerph-17-05436\"><title>3.1. Predisposing, Enabling, and Personal Needs Variables</title><p>A total of 3160 questionnaires were completed of which 3119 were valid, giving an effective rate of 98.7%. Among these, males accounted for 51.0%. The mean age was 70.27 ± 7.81 years old. A total of 35.8% of the elderly live in rural areas, 40.1% cannot read, 30.2% are unmarried, 1.0% have no children, 16.6% have daily expenses that exceed their income, 29.8% have no caregivers, 5.7% are incapacitated, and 39.2% reported often feeling lonely.</p></sec><sec id=\"sec3dot2-ijerph-17-05436\"><title>3.2. Status of CMCP</title><p>Among the 3119 elderly respondents, 402 (12.89%) indicated that they wanted to be admitted to a CMCP nursing facility, 82.01% preferred home-based care, and 5.1% preferred community care. The analysis of the distribution of elderly people’s preference for different life care options shows that differences in preference depend on factors such as residence, age, education level, occupation, number of existing children, marital status, income and expenditure, primary caregivers, and activities of daily life (<xref rid=\"ijerph-17-05436-t001\" ref-type=\"table\">Table 1</xref>).</p></sec><sec id=\"sec3dot3-ijerph-17-05436\"><title>3.3. Factors Influencing CMCP Service Needs</title><p><xref rid=\"ijerph-17-05436-t002\" ref-type=\"table\">Table 2</xref> shows the binary logistic regression analysis of factors influencing CMCP service preferences. In the predisposing factors, differences in residence, age, and education level were found to be statistically significant. It was found that the elderly living in rural areas have a higher preference for CMCP services than those in urban areas, (odds ratio (OR) = 2.1, 2.07, 2.3, 2.16) and that the OR values are greater than those in the town. As compared to the 60–69 years age range, the elderly in the 70–79 age group have a higher demand for CMCP services (OR = 2.21, 1.65, 1.74), but the difference between these two groups is not statistically significant. With regard to literacy, the CMCP service preference of those with only an elementary school education is lower (OR value is less than 1,) as compared to those above the high school level (OR value is greater than 1.)</p><p>Marriage, income and expenditure, and primary caregivers are among the enabling factors that proved to be statistically significant in Models 2 and 4. The OR values of the CMCP service needs of unmarried elderly are 1.61 and 1.64, respectively. The more an individual’s income exceeded their expenditures, the greater the OR value of their preference for CMCP service. For the elderly who are mainly cared for by their spouses, the OR value of preference for CMCP service is less than 1, while the elderly who are cared for by others (such as son-in-law or nephew) are more willing to accept CMCP service (OR values of 2.05 and 1.77, respectively.) Activities of daily life was a statistically significant factor in Models 3 and 4. As compared to the elderly who were completely independent, the worse the degree of capacity for self-care, the greater was the OR value of preference for CMCP.</p><p><xref rid=\"ijerph-17-05436-t003\" ref-type=\"table\">Table 3</xref> shows the comparison of prediction probabilities and goodness of fit of each model. Compared with Model I, the changes of -2LL (-2 Log Likelihood) in Models II and III are the largest, and the changes of Cox and Snell R2 and Nagelkerke R2 in Models III are similar. These indicate that the enabling factors contribute the most to Model I among the three types of influencing factors; the second-largest contributors were the predisposing factors.</p><p>The results of this study show that, in general, the elderly have higher needs for life care and medical care than others (<xref rid=\"ijerph-17-05436-t004\" ref-type=\"table\">Table 4</xref>). The elderly with higher age and education level, non-remarried, with better economic status, and with poorer health status have a demand for a wider variety of CMCP services. Compared to those in urban areas, the elderly in rural areas have greater needs, mainly related to personal care, medical care, and psychological counseling. In addition, the elderly with moderate to severe disabilities tend to choose medical care services such as medical care, health examination, and health education.</p></sec></sec><sec sec-type=\"discussion\" id=\"sec4-ijerph-17-05436\"><title>4. Discussion</title><sec id=\"sec4dot1-ijerph-17-05436\"><title>4.1. CMCP Needs of the Elderly</title><p>In this survey, the majority of the elderly still chose traditional home-based care (82.01%), consistent with the results of previous studies [<xref rid=\"B20-ijerph-17-05436\" ref-type=\"bibr\">20</xref>,<xref rid=\"B21-ijerph-17-05436\" ref-type=\"bibr\">21</xref>,<xref rid=\"B22-ijerph-17-05436\" ref-type=\"bibr\">22</xref>]. One can infer that, under the influence of traditional Chinese ideology and culture (such as filial piety) and long historical practice, family care will remain the most important way of providing for the aged for a considerable time. The government should, therefore, focus on strengthening policy support for family care. Moreover, socialized pension modes such as CMCP emerged in the process of industrialization in western developed countries and has a relatively short history of development in China. As a result, it is a comparatively new pension mode unfamiliar to the elderly [<xref rid=\"B23-ijerph-17-05436\" ref-type=\"bibr\">23</xref>,<xref rid=\"B24-ijerph-17-05436\" ref-type=\"bibr\">24</xref>].</p><p>However, it is worth noting that in this survey, the proportion of elderly people choosing CMCP institutions is much higher than the proportion of elderly people willing to choose CMCP institutions, as surveyed by our project team in 2013 (2.86%) [<xref rid=\"B25-ijerph-17-05436\" ref-type=\"bibr\">25</xref>,<xref rid=\"B26-ijerph-17-05436\" ref-type=\"bibr\">26</xref>,<xref rid=\"B27-ijerph-17-05436\" ref-type=\"bibr\">27</xref>]. A cross-sectional survey was performed by our project team across 173 communities in Xiamen, China, in 2013. A total of 261,043 individuals were aged ≥ 60 years in Xiamen at the survey time and about 5.5% of overall elderly populations were covered in that survey. In total, 14,292 valid questionnaires were obtained. The results of the two surveys reflect the fact that, with the vigorous advocacy and policy promotion of CMCP service by the Chinese government, the elderly have gradually realized the advantages of institutional care. It can solve a range of elder care needs caused by rapid aging of the population and by family miniaturization, taking into account both the “medical” and “care” aspects of the elderly’s most serious concerns [<xref rid=\"B28-ijerph-17-05436\" ref-type=\"bibr\">28</xref>]. Therefore, the government should formulate and improve the relevant CMCP policies as soon as possible, establish a coordination department for medical care, improve access and service standards, explore innovative service models, improve the service quality of pension institutions, and enhance the public’s awareness and acceptance of the CMCP model.</p></sec><sec id=\"sec4dot2-ijerph-17-05436\"><title>4.2. Factors Influencing Preference for CMCP Service</title><p>Most of the existing research on CMCP preference lacks the support of theoretical models and does not classify impact factors. Individual studies have only classified the factors simply, and did not conduct comparative studies on different categories of factors [<xref rid=\"B29-ijerph-17-05436\" ref-type=\"bibr\">29</xref>,<xref rid=\"B30-ijerph-17-05436\" ref-type=\"bibr\">30</xref>,<xref rid=\"B31-ijerph-17-05436\" ref-type=\"bibr\">31</xref>]. Based on the Andersen model, this paper constructed a framework for theoretical analysis of the elderly’s preferences for CMCP services. Through field investigation of the elderly in Xiamen community in China, this study measured the relative influence of factors of predisposition, enabling, and personal need on the elderly’s elder care choices. As a class, enabling factors have the greatest influence, followed by predisposing factors, indicating that personal family resources are the main factor affecting CMCP service choices.</p><p>This study found that older people with higher age and education are more willing to accept CMCP services. With the increase in age, the elderly’s sense of loneliness becomes stronger, their sense of security gradually weakens, and their ability to take care of themselves gradually declines [<xref rid=\"B32-ijerph-17-05436\" ref-type=\"bibr\">32</xref>,<xref rid=\"B33-ijerph-17-05436\" ref-type=\"bibr\">33</xref>]. With increase in education, the elderly become more aware of and more accepting of CMCP institutional care, whereas the less educated tend to have more conservative ideas. In contrast to previous research [<xref rid=\"B34-ijerph-17-05436\" ref-type=\"bibr\">34</xref>,<xref rid=\"B35-ijerph-17-05436\" ref-type=\"bibr\">35</xref>], this study found higher acceptance of the CMCP endowment institutions in rural populations. The main reason for this may be that the phenomenon of rural empty-nest in the process of rapid urbanization is serious, and there is a lack of existing old-age resources in rural areas. This makes it difficult to satisfy the life care and medical care needs of most of the elderly in those areas.</p><p>The results of this study show that elderly people who are poor and are mainly cared for by spouses have a lower willingness to choose institutional care services, while those elderly who are better off or who are mainly cared for by others (other than spouses) are more willing to accept CMCP institutional care services. The reason may be that the spouse is the person most familiar with the elderly individual, and the choice of life care can be more flexible. The married elderly people can meet their most important life care and spiritual comfort needs with the help of their spouse [<xref rid=\"B36-ijerph-17-05436\" ref-type=\"bibr\">36</xref>,<xref rid=\"B37-ijerph-17-05436\" ref-type=\"bibr\">37</xref>]. Due to the influence of traditional Chinese ideas, they are more willing to accept informal care services from their families. In addition, economic ability is a factor that attracts substantial attention in the relevant studies on pension choice. Most of the social pension services are currently paid services, which means that economic level is a limiting factor for the elderly who choose the social pension. Many studies [<xref rid=\"B38-ijerph-17-05436\" ref-type=\"bibr\">38</xref>,<xref rid=\"B39-ijerph-17-05436\" ref-type=\"bibr\">39</xref>] have shown that the elderly with higher economic status are more likely to choose the social pension because of their strong ability to pay for pension services, and this is further confirmed by this study.</p><p>Self-care ability largely determines the medical service needs of the elderly: the worse their capability for self-care, the greater their medical service needs. Losing the capacity for self-care is almost an inevitable life course for everyone. As a social public service to improve the quality of life of the elderly, CMCP service has been gradually accepted by the public [<xref rid=\"B40-ijerph-17-05436\" ref-type=\"bibr\">40</xref>,<xref rid=\"B41-ijerph-17-05436\" ref-type=\"bibr\">41</xref>]. CMCP endowed institutions are able to provide the elderly with more professional care services and with medical resources as well. This is particularly important for the elderly in poor health, and is an important reason to promote the choice of social endowment. Therefore, this study found that older people with poor self-care ability were more willing to choose CMCP institutions for the elderly. A unified long-term care insurance system urgently needs to be established by Chinese policymakers to reduce the financial burden of the elderly.</p><p>Consistent with previous results in other countries [<xref rid=\"B42-ijerph-17-05436\" ref-type=\"bibr\">42</xref>,<xref rid=\"B43-ijerph-17-05436\" ref-type=\"bibr\">43</xref>], this study found that the elderly generally have a higher demand for life care and medical care than in the past, thus indicating the importance of China’s efforts to promote the CMCP development strategy. Among them, the elderly with higher age and education level, who are non-remarried, and have better economic status and worse health condition have a higher demand for a range of CMCP services. Compared to the urban elderly, the rural elderly people have more serious needs, which are mainly reflected in daily care, medical care, and psychological counseling. In addition, moderately and severely disabled elderly people tend to choose medical care, health examination, and health education, among other medical care services. These results suggest that we should focus on elderly people in rural areas who are living in empty nests or alone and in poor health, and meeting their service needs such as daily care, medical care, and spiritual comfort.</p><p>However, there were several limitations to this study. First, the data were a cross-sectional survey, and this limited the interpretation of the results, making it hard to draw causal conclusions. Second, the question measuring the preference for CMCP and the exclusion criteria for the elderly under investigation (inability to communicate effectively) may have a selective bias in this study. Third, the models explain a low proportion of the variance, suggesting that other factors aside from those included in our analysis influence the various aspects of CMCP needs. Fourth, the participants were sampled from a single city in China. The results may reflect specific characteristics of that city, or at the very least introduce a rural–urban bias into the interpretation of the data. Therefore, longitudinal studies should be conducted to assess policy trends and key stakeholders.</p></sec></sec><sec sec-type=\"conclusions\" id=\"sec5-ijerph-17-05436\"><title>5. Conclusions</title><p>In summary, the preference of the elderly for CMCP is lower compared to their preference for home care in Xiamen, China. Preference for CMCP is affected by a range of factors such as age, education level, residence, income, and self-care ability, among which the enabling factors have the greatest impact. Given the large-scale trends of an aging population and the weakening of the family’s traditional elder care function, the elderly’s demand for CMCP services is increasing gradually, particularly with regard to life care, medical care, mental comfort, and health intervention. The CMCP old-age care model has become an important choice to solve the problems of caring for an aging population. However, in the process of providing CMCP old-age care services, it is necessary to proceed from an understanding of the different needs of different elderly demographics. In China, although the function of family care has gradually weakened, it has a profound historical foundation. At the present stage, the government should be committed to establishing a multi-level CMCP service model based on family care, relying on community care, and supplemented by institutional care, with the focus on building family care service networks and improving the relevant supporting measures. In the face of the current differences in China’s urban and rural economic levels, uneven distribution of old-age and medical resources, and the accelerated urbanization process, there is a strong demand for elderly care services in rural areas, but the current status of the provision of elderly care services is worrying. Thus, China should continue to encourage society to implement CMCP institutional pension services with a focus on rural areas. It should also increase capital investment, policy support, and infrastructure construction for elder care. In the primary stage of carrying out CMCP elderly care service, policymakers should take the disabled and poor elderly as the key service subjects, actively provide policy care, provide multiple channels for elder care, and ensure the fulfillment of CMCP elder care needs, so as to improve the utilization rate of social elder care services. Finally, the government must strengthen the supervision of service quality, improve service standardization, comprehensively improve the quality of CMCP elderly care services, enhance their appeal to the elderly, and improve their willingness to socialize elder care, so as to promote the sustainable development of the elder care industry.</p></sec></body><back><ack><title>Acknowledgments</title><p>We are grateful to the students in the School of Public Health, Xiamen University who participated in the data collation and analysis.</p></ack><notes><title>Author Contributions</title><p>Y.W. designed the study design, analyzed and interpreted the data, and drafted the manuscript. L.Z. participated in the field investigation, data collection, and critically revised the manuscript. All authors have read and agreed to the published version of the manuscript.</p></notes><notes><title>Funding</title><p>This study was supported by the National Natural Science Foundation of China (grant number 81973144), and the China Postdoctoral Science Foundation (grant number 2020M671949). The funders who supported this study had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.</p></notes><notes notes-type=\"COI-statement\"><title>Conflicts of Interest</title><p>The authors declare no conflict of interest.</p></notes><notes><title>Ethics Approval and Consent to Participate</title><p>Ethical approval was obtained from the ethical review committee of the School of Public Health, Xiamen University. All participants read a statement that explained the purpose of the survey and provided written informed consent before participation in the study.</p></notes><glossary><title>Abbreviations</title><array orientation=\"portrait\"><tbody><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">LTC</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Long-term care</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CMCP</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Combination of medical care and pension</td></tr></tbody></array></glossary><ref-list><title>References</title><ref id=\"B1-ijerph-17-05436\"><label>1.</label><element-citation publication-type=\"gov\"><person-group person-group-type=\"author\"><collab>National Bureau of Statistics, PRC</collab></person-group><article-title>Statistical Communique of the People’s Republic of China on the 2016 National Economic and Social Development</article-title><comment>Available online: <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.stats.gov.cn/tjsj/zxfb/201702/t20170228_1467424.html\">http://www.stats.gov.cn/tjsj/zxfb/201702/t20170228_1467424.html</ext-link></comment><date-in-citation content-type=\"access-date\" iso-8601-date=\"2020-04-17\">(accessed on 17 April 2020)</date-in-citation></element-citation></ref><ref id=\"B2-ijerph-17-05436\"><label>2.</label><element-citation publication-type=\"gov\"><person-group person-group-type=\"author\"><collab>Ministry of Civil Affairs, PRC</collab></person-group><article-title>The Three Departments Released the Results of the Fourth Sample Survey on the Living Conditions of the Elderly in Urban and Rural China</article-title><comment>Available online: <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.mca.gov.cn/article/zwgk/mzyw/201610/20161000001974.shtml\">http://www.mca.gov.cn/article/zwgk/mzyw/201610/20161000001974.shtml</ext-link></comment><date-in-citation content-type=\"access-date\" iso-8601-date=\"2020-05-25\">(accessed on 25 May 2020)</date-in-citation></element-citation></ref><ref id=\"B3-ijerph-17-05436\"><label>3.</label><element-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Zheng</surname><given-names>W.</given-names></name><name><surname>Lin</surname><given-names>S.J.</given-names></name><name><surname>Chen</surname><given-names>K.</given-names></name></person-group><article-title>Characteristics and Trend of Population Aging in China and Its Potential Impact on Economic Growth</article-title><source>Quant. 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Res.</source><year>2016</year><volume>44</volume><fpage>263</fpage><lpage>273</lpage><pub-id pub-id-type=\"doi\">10.1007/s11414-016-9496-9</pub-id><pub-id pub-id-type=\"pmid\">26780207</pub-id></element-citation></ref></ref-list></back><floats-group><fig id=\"ijerph-17-05436-f001\" orientation=\"portrait\" position=\"float\"><label>Figure 1</label><caption><p>The Andersen theoretical model for influencing factors of the combination of medical care and pension (CMCP) service needs.</p></caption><graphic xlink:href=\"ijerph-17-05436-g001\"/></fig><table-wrap id=\"ijerph-17-05436-t001\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05436-t001_Table 1</object-id><label>Table 1</label><caption><p>Different distribution characteristics of the elderly CMCP needs N(%).</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th rowspan=\"2\" colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\">Variables</th><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">CMCP Needs</th><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">Total</th><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">\n<italic>χ</italic>\n<sup>2</sup>\n</th><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">\n<italic>P</italic>\n</th></tr><tr><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Yes<break/>(<italic>n</italic> = 402)</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">No<break/>(<italic>n</italic> = 2717)</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Residence</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Urban </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">244 (60.7)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1327 (48.8)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1571 (50.3)</td><td rowspan=\"3\" align=\"center\" valign=\"middle\" colspan=\"1\">38.88</td><td rowspan=\"3\" align=\"center\" valign=\"middle\" colspan=\"1\"><0.001</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">(<italic>n</italic> = 3119)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Town </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">70 (17.4)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">362 (13.3)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">432 (13.9)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Rural </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">88 (21.9)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1028 (37.8)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1116 (35.8)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Gender</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Male </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">207 (51.5)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1382 (50.9)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1589 (51.0)</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">0.05</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">0.81</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">(<italic>n</italic> = 3119)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Female </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">195 (48.5)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1335 (49.1)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1530 (49.0)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Age (year)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">60~</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">280 (69.7)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1450 (53.4)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1730 (55.5)</td><td rowspan=\"3\" align=\"center\" valign=\"middle\" colspan=\"1\">40.20</td><td rowspan=\"3\" align=\"center\" valign=\"middle\" colspan=\"1\"><0.001</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">(<italic>n</italic> = 3119)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">70~</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">90 (22.4)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">825 (30.4)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">915 (29.3)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">80~</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">32 (8.0)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">442 (16.3)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">474 (15.2)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Education </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Illiterate</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">107 (27.0)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1134 (42.0)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1241 (40.1)</td><td rowspan=\"4\" align=\"center\" valign=\"middle\" colspan=\"1\">33.55</td><td rowspan=\"4\" align=\"center\" valign=\"middle\" colspan=\"1\"><0.001</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">(<italic>n</italic> = 3097)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Primary</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">132 (33.2)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">736 (27.3)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">868 (28.0)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Junior high school</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">85 (21.4)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">473 (17.5)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">558 (18.0)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Senior high school and above</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">73 (18.4)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">357 (13.2)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">430 (13.9)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Living alone</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">No </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">345 (87.6)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2301 (88.6)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2646 (88.4)</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">0.34</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">0.56</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">(<italic>n</italic> = 2992)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Yes </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">49 (12.4)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">297 (11.4)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">346 (11.6)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Occupation</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Farmer</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">137 (34.1)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1349 (49.9)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1486 (47.8)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.001</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Non-farmer</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">265 (65.9)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1357 (50.1)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1622 (52.2)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Number of children </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6 (1.5)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">26 (1.0)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">32 (1.0)</td><td rowspan=\"3\" align=\"center\" valign=\"middle\" colspan=\"1\">40.92</td><td rowspan=\"3\" align=\"center\" valign=\"middle\" colspan=\"1\"><0.001</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">(<italic>n</italic> = 3107)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">118 (29.5)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">448 (16.5)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">566 (18.2)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">≥2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">276 (69.0)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2233 (82.5)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2509 (80.8)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Marital status</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Married</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">294 (76.2)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1779 (68.8)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2073 (69.8)</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">8.65</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">0.003</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">(<italic>n</italic> = 2992)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Unmarried </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">92 (23.8)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">807 (31.2)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">899 (30.2)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Economic status</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Expenditures exceeded income</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">73 (18.2)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">440 (16.3)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">513 (16.6)</td><td rowspan=\"3\" align=\"center\" valign=\"middle\" colspan=\"1\">13.38</td><td rowspan=\"3\" align=\"center\" valign=\"middle\" colspan=\"1\">0.04</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">(<italic>n</italic> = 3096)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Balance</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">280 (69.7)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1882 (69.9)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2162 (69.8)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Income exceeded expenditures</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">49 (12.2)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">372 (13.8)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">421 (13.6)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Primary caregiver</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">None</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">140 (40.1)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">686 (28.3)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">826 (29.8)</td><td rowspan=\"4\" align=\"center\" valign=\"middle\" colspan=\"1\">21.89</td><td rowspan=\"4\" align=\"center\" valign=\"middle\" colspan=\"1\"><0.001</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">(<italic>n</italic> = 2771)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Spouse </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">128 (36.7)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1003 (41.4)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1131 (40.8)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Children </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">78 (22.3)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">685 (28.3)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">763 (27.5)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Others </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3 (0.9)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">48 (2.0)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">51 (1.9)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Medical insurance</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">No </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">16 (4.1)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">142 (5.4)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">158 (5.2)</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">4.35</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">0.23</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">(<italic>n</italic> = 2992)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Yes </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">372 (95.9)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2478 (94.6)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2850 (94.8)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Self-rated health</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Bad</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">49 (12.3)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">399 (14.8)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">448 (14.5)</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">2.10</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">0.08</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">(<italic>n</italic> = 3088)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Good </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">349 (87.7)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2291 (85.2)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1232 (85.5)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ADL</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Completely independent</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">364 (96.0)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2164 (85.9)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2528 (87.2)</td><td rowspan=\"3\" align=\"center\" valign=\"middle\" colspan=\"1\">30.54</td><td rowspan=\"3\" align=\"center\" valign=\"middle\" colspan=\"1\"><0.001</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">(<italic>n</italic> = 2899)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Relatively independent</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8 (2.1)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">199 (7.9)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">207 (7.1)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Disability </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7 (1.8)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">157 (6.2)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">164 (5.7)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Feeling of loneliness</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Often </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">147 (36.6)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1077 (39.6)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1224 (39.2)</td><td rowspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">2.52</td><td rowspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">0.28</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">(<italic>n</italic> = 3119)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Sometimes </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">159 (39.6)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">966 (35.6)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1125 (36.1)</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Seldom/never </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">96 (23.9)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">674 (24.8)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">770 (24.7)</td></tr></tbody></table><table-wrap-foot><fn><p>ADL, activities of daily living.</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"ijerph-17-05436-t002\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05436-t002_Table 2</object-id><label>Table 2</label><caption><p>Logistic regression analysis of influencing factors of CMCP service needs.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">Variables</th><th colspan=\"4\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">OR (95%CI)</th></tr><tr><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Model 1</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Model 2</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Model 3</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Model 4</th></tr></thead><tbody><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Predisposing variables </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Residence (ref. urban)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> Town </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.94 (1.49, 2.54) ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.63 (1.18, 2.24) **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.04 (1.54, 2.70) ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.74 (1.25, 2.43) **</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> Rural </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.10 (1.49, 2.96) ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.07 (1.41, 3.05) ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.3 (1.59, 3.31) ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.16 (1.44, 3.26) **</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Age (ref. 60~69)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> 70~79</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.21 (1.48, 3.30) ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.65 (1.06, 2.58) *</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.74 (1.13, 2.69) *</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">1.29 (0.80, 2.08)</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> ≥80</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.31 (0.84, 2.03)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.09 (0.68, 1.75)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.04 (0.65, 1.66)</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">0.87 (0.52, 1.43)</td></tr><tr><td colspan=\"2\" align=\"left\" valign=\"middle\" rowspan=\"1\">Education (ref. illiterate)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\"> Primary</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.58 (0.41, 0.80) **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.77 (0.51, 0.95) *</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.62 (0.44, 0.87) **</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">0.83 (0.55, 1.26) </td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\"> Junior high school</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.94 (0.68, 1.29)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.18 (0.81, 1.73)</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">0.97 (0.70, 1.35)</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">1.18 (0.79, 1.74)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\"> Senior high school and above</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.86 (1.11, 2.21) **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.96 (1.65, 2.40) *</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">1.81 (1.57, 2.16) *</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">1.95 (0.64, 3.40) </td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Enabling variables</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Occupation (ref. farmer)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.4 (1.03, 1.90)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">1.27 (0.93, 1.75)</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Number of children (ref. 0) </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> 1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">0.77 (0.17, 3.44)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">0.82 (0.18, 3.75)</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> ≥2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">1.35 (1.00, 1.84)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">1.37 (1.00, 1.87)</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Marital status (ref. married)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.61 (1.14, 2.27) **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.64 (1.15, 2.34) **</td></tr><tr><td colspan=\"2\" align=\"left\" valign=\"middle\" rowspan=\"1\">Economic status (ref. expenditures exceeded income)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\"> Balance</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.71 (1.10, 2.67) *</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.64 (1.03, 2.60) *</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\"> Income exceeded expenditures</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">2.34 (1.92, 3.94) **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">2.28 (1.88, 3.88) **</td></tr><tr><td colspan=\"2\" align=\"left\" valign=\"middle\" rowspan=\"1\">Primary caregiver (ref. none)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\"> Spouse </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">0.47 (0.19, 0.81) **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">0.32 (0.12, 0.95) **</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\"> Children </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">1.46 (0.43, 4.94)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">1.27 (0.37, 4.35)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\"> Others </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">2.05 (1.60, 4.97) *</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">1.77 (1.51, 3.12) *</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Need variables</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Self-rated health (ref. bad)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.13 (0.80, 1.58)</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">1.11 (0.77, 1.61)</td></tr><tr><td colspan=\"2\" align=\"left\" valign=\"middle\" rowspan=\"1\">ADL (ref. completely independent)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\"> Relatively independent</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.35 (1.05, 5.26) *</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">2.13 (1.94, 4.85) *</td></tr><tr><td align=\"left\" valign=\"top\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"> Disability </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3.78 (2.27, 5.30) **</td><td align=\"center\" valign=\"top\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3.69 (2.22, 5.12) **</td></tr></tbody></table><table-wrap-foot><fn><p>Note: *** <italic>p</italic> < 0.001; ** <italic>p</italic> < 0.01; * <italic>p</italic> < 0.05.</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"ijerph-17-05436-t003\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05436-t003_Table 3</object-id><label>Table 3</label><caption><p>The comparison of prediction probabilities and goodness of fit of model I–IV.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−2LL</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−2LL Change Value *</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Cox and Snell R2</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Cox and Snell R2 Change Value *</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Nagelkerke R2</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Nagelkerke R2 Change Value *</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Model I</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1734.464</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">—</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.050</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">—</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.094</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">—</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Model II</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1792.426</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">+57.962</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.037</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.013</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.069</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.025</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Model III</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2094.023</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">+359.559</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.036</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.014</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.065</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.029</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Model IV</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1858.182</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">+123.718</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.045</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.005</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.084</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.010</td></tr></tbody></table><table-wrap-foot><fn><p>Note: * Change value is compared with the model I increase or decrease value.</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"ijerph-17-05436-t004\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05436-t004_Table 4</object-id><label>Table 4</label><caption><p>Odds ratios of anticipated CMCP services (binary logistic regression).</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Variables</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Personal Care</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Medical Care</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Psychological Consulting</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Health Examination</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Social and Recreational Activities</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Healthcare Education</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Predisposing variables </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Residence (ref. urban)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Town </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.94(1.49,2.54) **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.65(1.06,2.58) *</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.63(1.18,2.24) **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.35(1.05,5.26) *</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.04(1.54,2.70) **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.74(1.25,2.43) **</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Rural </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.10(1.49,2.96) ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.30(1.59,3.31) ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.07(1.41,3.05) ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.78(2.27,5.30) **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.39(1.28,3.35) **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.16(1.44,3.26) **</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Age (ref. 60~69)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">70~79</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.79(1.42,2.53) *</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.69(1.22,2.13) **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.19(0.68,1.76)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.54(1.13,2.50) *</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.14(0.75,1.69)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.39(0.79,2.28)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">≥80</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.31(1.58,3.29) ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.17(1.43,3.16) **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.66(1.16,2.48) *</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.18(1.78,3.68) **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.64(1.23,2.59) *</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.97(1.42,2.25) **</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Education (ref. illiterate)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Primary</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.59(0.46,0.87) **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.38(0.13,0.95) **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.78(0.51,0.94) *</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.28(0.91,1.76)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.63(0.43,0.89) **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.81(0.55,1.26) </td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Junior high school</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.91(0.65,1.24)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.29(0.37,4.35)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.19(0.82,1.75)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.38(1.05,1.87)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.95(0.74,1.37)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.19(0.79,1.74)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Senior high school and above</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.84(1.12,2.24) **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.78(1.52,3.12) *</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.97(1.66,2.41) *</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.65(1.19,2.37) **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.82(1.58,2.12) **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.96(1.64,3.40) **</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Enabling variables</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Occupation (ref. farmer)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.25(0.79,1.78)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.27(0.81,1.79)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.37(1.23,1.89)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.31(0.94,1.76)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.32(0.81,1.79)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.18(0.82,1.78)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Number of children (ref. 0) </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.61(0.48,3.25)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.72(0.28,3.65)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.68(0.27,3.44)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.59(0.31,0.75) *</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.76(0.38,3.57)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.81(0.18,3.64)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">≥2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.47(0.33,1.78)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.27(0.98,1.77)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.24(0.59,1.74)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.15(0.61,1.53)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.46(0.58,1.67)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.26(0.57,1.76)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Marital status (ref. married)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.61(1.27,2.34) **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.54(1.25,2.24) **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.52(1.24,2.17) **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.69(1.45,2.31) *</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.24(1.56,2.84) **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.55(1.15,2.72) **</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Economic status (ref. balance),</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">expenditures exceeded income</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.74(1.13,2.71) *</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.47(0.36,1.67)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.61(1.10,2.77) *</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.19(0.78,1.75)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.74(1.13,2.50) *</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.54(1.03,2.50) *</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Income exceeded expenditures</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.38(1.98,3.98) **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.74(1.35,2.74) **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.24(1.82,3.94) **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.75(1.06,2.68) *</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.38(1.58,3.89) **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.38(1.78,3.87) **</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Primary caregiver (ref. none)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Spouse </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.22(0.32,0.85) **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.88(0.71,0.95) *</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.57(0.39,0.81) **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.92(0.38,3.76)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.52(0.32,0.96) **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.42(0.22,0.95) **</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Children </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.37(0.47,4.25)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.28(0.71,1.93)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.56(0.53,4.94)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.57(0.59,1.86)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.57(0.47,4.34)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.37(0.57,4.25)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Others </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.67(1.61,3.22) *</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.76(1.52,2.48) *</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.25(1.70,4.97) *</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.84(1.46,2.35) **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.67(1.41,3.13) *</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.67(1.31,3.42) *</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Need variables</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Self-rated health (ref. bad)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.31(0.78,1.64)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.39(0.23,1.90)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.51(0.87,1.63)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.37(0.23,1.85)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.14(1.82,2.94) **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.41(0.27,1.81)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ADL (ref. completely independent)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Relatively independent</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.32(1.93,4.75) *</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.45(1.29,2.15) *</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.14(1.84,4.55) *</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.64(1.35,2.23) **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.25(1.15,5.26) *</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.14(1.84,4.75) *</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Disability </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.59(2.32,5.32) **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.65(1.07,2.56) ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.59(2.42,5.41) **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.56(1.94,3.16) ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.68(2.27,5.20) *</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.65(3.16,4.22) ***</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Model summary: </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Nagelkerke R<sup>2</sup></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.21</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.20</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.19</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.19</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.18</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.18</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">χ<sup>2</sup> with df = 45 (<italic>p</italic>-value)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">126.35(<0.0001)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">124.7(<0.0001)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">91.28(<0.0001)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">101.84(<0.0001)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">91.52(0.0004)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">83.95(0.0002)</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−2Log likelihood</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">4781.27</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1323.18</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1659.72</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">18197.21</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1678.63</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1715.84</td></tr></tbody></table><table-wrap-foot><fn><p>Note: *** <italic>p</italic> < 0.001; ** <italic>p</italic> < 0.01; * <italic>p</italic> < 0.05. The table presents the final results when all sets of variables were entered at once, for the sake of presentational simplification.</p></fn></table-wrap-foot></table-wrap></floats-group></article>\n"
]
[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Int J Environ Res Public Health</journal-id><journal-id journal-id-type=\"iso-abbrev\">Int J Environ Res Public Health</journal-id><journal-id journal-id-type=\"publisher-id\">ijerph</journal-id><journal-title-group><journal-title>International Journal of Environmental Research and Public Health</journal-title></journal-title-group><issn pub-type=\"ppub\">1661-7827</issn><issn pub-type=\"epub\">1660-4601</issn><publisher><publisher-name>MDPI</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32751536</article-id><article-id pub-id-type=\"pmc\">PMC7432068</article-id><article-id pub-id-type=\"doi\">10.3390/ijerph17155506</article-id><article-id pub-id-type=\"publisher-id\">ijerph-17-05506</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Article</subject></subj-group></article-categories><title-group><article-title>Functional Trait-Based Screening of Zn-Pb Tolerant Wild Plant Species at an Abandoned Mine Site in Gard (France) for Rehabilitation of Mediterranean Metal-Contaminated Soils</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0003-3630-6450</contrib-id><name><surname>Laffont-Schwob</surname><given-names>Isabelle</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05506\">1</xref><xref rid=\"c1-ijerph-17-05506\" ref-type=\"corresp\">*</xref></contrib><contrib contrib-type=\"author\"><name><surname>Rabier</surname><given-names>Jacques</given-names></name><xref ref-type=\"aff\" rid=\"af2-ijerph-17-05506\">2</xref></contrib><contrib contrib-type=\"author\"><name><surname>Masotti</surname><given-names>Véronique</given-names></name><xref ref-type=\"aff\" rid=\"af2-ijerph-17-05506\">2</xref></contrib><contrib contrib-type=\"author\"><name><surname>Folzer</surname><given-names>Hélène</given-names></name><xref ref-type=\"aff\" rid=\"af2-ijerph-17-05506\">2</xref></contrib><contrib contrib-type=\"author\"><name><surname>Tosini</surname><given-names>Lorène</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05506\">1</xref><xref ref-type=\"aff\" rid=\"af2-ijerph-17-05506\">2</xref></contrib><contrib contrib-type=\"author\"><name><surname>Vassalo</surname><given-names>Laurent</given-names></name><xref ref-type=\"aff\" rid=\"af3-ijerph-17-05506\">3</xref></contrib><contrib contrib-type=\"author\"><name><surname>Salducci</surname><given-names>Marie-Dominique</given-names></name><xref ref-type=\"aff\" rid=\"af2-ijerph-17-05506\">2</xref></contrib><contrib contrib-type=\"author\"><name><surname>Prudent</surname><given-names>Pascale</given-names></name><xref ref-type=\"aff\" rid=\"af3-ijerph-17-05506\">3</xref></contrib></contrib-group><aff id=\"af1-ijerph-17-05506\"><label>1</label>Aix Marseille University, IRD, LPED, IRD UMR 151, 13331 Marseille, France; <email>[email protected]</email></aff><aff id=\"af2-ijerph-17-05506\"><label>2</label>Aix Marseille University, Avignon Université, CNRS, IRD, IMBE UMR 7263, 13331 Marseille, France; <email>[email protected]</email> (J.R.); <email>[email protected]</email> (V.M.); <email>[email protected]</email> (H.F.); <email>[email protected]</email> (M.-D.S.)</aff><aff id=\"af3-ijerph-17-05506\"><label>3</label>Aix Marseille University, CNRS, LCE, UMR 7376, 13331 Marseille, France; <email>[email protected]</email> (L.V.); <email>[email protected]</email> (P.P.)</aff><author-notes><corresp id=\"c1-ijerph-17-05506\"><label>*</label>Correspondence: <email>[email protected]</email></corresp></author-notes><pub-date pub-type=\"epub\"><day>30</day><month>7</month><year>2020</year></pub-date><pub-date pub-type=\"ppub\"><month>8</month><year>2020</year></pub-date><volume>17</volume><issue>15</issue><elocation-id>5506</elocation-id><history><date date-type=\"received\"><day>22</day><month>6</month><year>2020</year></date><date date-type=\"accepted\"><day>27</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>© 2020 by the authors.</copyright-statement><copyright-year>2020</copyright-year><license license-type=\"open-access\"><license-p>Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>).</license-p></license></permissions><abstract><p>The selection of plant species at mine sites is mostly based on metal content in plant parts. Recent works have proposed referring to certain ecological aspects. However, plant traits for plant metal-tolerance still need to be accurately assessed in the field. An abandoned Zn-Pb mine site in Gard (France) offered the opportunity to test a set of ecological criteria. The diversity of micro-habitats was first recorded through floristic relevés and selected categorical and measured plant traits were compared for plant species selection. The floristic composition of the study site consisted in 61 plant species from 31 plant families. This approach enabled us to focus on seven wild plant species naturally growing at the mining site. Their ability to form root symbioses was then observed with a view to phytostabilization management. Four species were considered for phytoextraction: <italic>Noccaea caerulescens</italic> (J. et C. Presl) FK Meyer, <italic>Biscutella laevigata</italic> L., <italic>Armeria arenaria</italic> (Pers.) Schult. and <italic>Plantago lanceolata</italic> L. The metal content of their aerial and root parts was then determined and compared with that of soil samples collected at the same site. This general approach may lead to the development of a knowledge base for assessment of the ecological restoration trajectory of the site and can help in plant selection for remediation of other metal-rich soils in the Mediterranean area based not only on metal removal but on ecological restoration principles.</p></abstract><kwd-group><kwd>passive ecological restoration</kwd><kwd>native plant selection</kwd><kwd>phytoremediation strategy</kwd><kwd>metal tolerance</kwd></kwd-group></article-meta></front><body><sec sec-type=\"intro\" id=\"sec1-ijerph-17-05506\"><title>1. Introduction</title><p>Heavy metal contaminated soils raise major environmental and human health issues, which may be partially solved by phytoremediation technologies. This cost-effective plant-based approach to remediation takes advantage of the remarkable ability of plants to concentrate trace elements from the environment and to metabolize a large number of molecules in their tissues to reduce their toxicity [<xref rid=\"B1-ijerph-17-05506\" ref-type=\"bibr\">1</xref>,<xref rid=\"B2-ijerph-17-05506\" ref-type=\"bibr\">2</xref>]. Consequently, on-site management of heavy metal contaminated soils can be achieved either by using metal hyperaccumulators that are plant species accumulating exceptionally large amounts of heavy metals in their tissues [<xref rid=\"B3-ijerph-17-05506\" ref-type=\"bibr\">3</xref>] or by using a biocontainment method such as phytostabilization [<xref rid=\"B4-ijerph-17-05506\" ref-type=\"bibr\">4</xref>]. </p><p>In most of the cases, plant selection is assessed for commercial phytoremediation (preferentially phytoextraction but not excluding phytostabilization) and is designed on agronomy principles using crops and amendments [<xref rid=\"B5-ijerph-17-05506\" ref-type=\"bibr\">5</xref>,<xref rid=\"B6-ijerph-17-05506\" ref-type=\"bibr\">6</xref>,<xref rid=\"B7-ijerph-17-05506\" ref-type=\"bibr\">7</xref>] and far less on the basis of ecological concepts such as trait-based selection [<xref rid=\"B8-ijerph-17-05506\" ref-type=\"bibr\">8</xref>,<xref rid=\"B9-ijerph-17-05506\" ref-type=\"bibr\">9</xref>,<xref rid=\"B10-ijerph-17-05506\" ref-type=\"bibr\">10</xref>].</p><p>Initially attracting attention for their potential use in phytomining, hyperaccumulators have been increasingly of interest from an evolutionary or ecological perspective. Studies on hyperaccumulators and more generally on metallophytes have led to a taxonomic approach with a focus on botanical families hosting numerous metal-tolerant species such as Brassicaceae, Poaceae, Fabaceae and Asteraceae in Mediterranean areas. This first selection grid is of interest but remains at a preliminary stage and is not always accurate (i.e., Plantaginaceae). Moreover, facultative hyperaccumulators are dominant in metalliferous soils, and for the purpose of the accurate selection of plant species for metalliferous soil revegetation, it is worthwhile to distinguish species-wide versus population-specific metal tolerance following Pollard et al. [<xref rid=\"B3-ijerph-17-05506\" ref-type=\"bibr\">3</xref>]. Plant functional traits have been considered as a promising tool for the selection of plants for the purpose of ecological restoration [<xref rid=\"B10-ijerph-17-05506\" ref-type=\"bibr\">10</xref>]. Life cycle is one of the main traits to be considered for phytostabilization, perennial species being preferred. Most of those recent studies dealt with above-ground-traits and very few with below-ground-traits, which are less easy to access. However, the very first interaction between plant species and trace metals and metalloids (TMM)-rich soil is focused at the root and rhizosphere level. Deep root system and root symbioses are of great importance in the mechanisms of plant tolerance to heavy metals [<xref rid=\"B11-ijerph-17-05506\" ref-type=\"bibr\">11</xref>,<xref rid=\"B12-ijerph-17-05506\" ref-type=\"bibr\">12</xref>]. The analysis of the spontaneous processes of plant colonization of abandoned mine sites may contribute to developing better key selection criteria for suitable plant species for on-site TMM management. Moreover, plant ecotypes in the mining sites are tolerant to mixed toxic metals in the soil solution and not only of a single metal such as Zn. For example, it has been recently demonstrated that <italic>Silene vulgaris</italic> ecotypes from calamine soils were fully adapted to heavy metal mixture (Zn, Pb and Cd) compared to ecotypes from both serpentine and non-metallicolous soils [<xref rid=\"B13-ijerph-17-05506\" ref-type=\"bibr\">13</xref>]. These adaptations depend on their capacity for reactive oxygen species scavenging and TMM compartmentalization in plant parts to avoid their toxicity [<xref rid=\"B2-ijerph-17-05506\" ref-type=\"bibr\">2</xref>,<xref rid=\"B6-ijerph-17-05506\" ref-type=\"bibr\">6</xref>]. Selecting ecotypes from a Zn-Pb mining site may be of interest for rehabilitation of multi-metal-contaminated soils.</p><p>Our hypothesis is that the flora spontaneously colonizing an abandoned mine site shares common above-ground and below-ground plant traits, i.e., Raunkiær life form, Grime strategy, root system type and, mycorrhizal status. We also considered botanical family as a functional trait, as phylogenetic relatedness is a strong driver for associations with fungal rhizosphere communities [<xref rid=\"B14-ijerph-17-05506\" ref-type=\"bibr\">14</xref>]. These common features may help in plant species selection for mine rehabilitation. For that purpose, a Zn-Pb-rich abandoned mine site without any previous rehabilitation implemented was selected. The diversity of micro-habitats was recorded through floristic relevés and the selected traits and plant characteristics were compared for plant species selection. A second hypothesis is that phytostabilization and phytoextraction processes may occur concomitantly in abandoned mine sites due to the diversity of plant adaptations in the plant community. This will be discussed through the literature data on plant species known for their hyperaccumulation, phytoaccumulation or phytostabilization abilities.</p><p>Using a plant selection grid, seven plant species were selected to study their ability to form root symbioses for the purpose of phytostabilization management. Four plant species were also considered for phytoextraction. The metal content of their aerial and root parts was then determined and compared with that of soil samples collected at the same site. This general approach has led to the development of a knowledge base for the assessment of the ecological restoration trajectory of the site and can help in plant selection for on-site remediation of other metal-rich soil in the Mediterranean area.</p></sec><sec id=\"sec2-ijerph-17-05506\"><title>2. Materials and Methods</title><sec id=\"sec2dot1-ijerph-17-05506\"><title>2.1. Site Location</title><p>The study site is located in the town of Rousson in Gard (<xref ref-type=\"fig\" rid=\"ijerph-17-05506-f001\">Figure 1</xref>), and more specifically at a place called La Gardie. It is an abandoned mine site which was used for the extraction of zinc. Established in 1876, the Rousson concession (310 ha) included the sites of Landas, Font de Rouve, and Croix de Fauvie (study site). These deposits, located in interlocking limestones, were superficial deposits made up of pieces of shale in ferruginous soil which contained 20 to 40% of zinc. The ore was processed in calamine furnaces. At the time when this small surface mining operation was closed down in 1910, it employed 8 miners and 5 laborers. Today, there are only a few excavations left that spontaneous vegetation colonization is gradually filling up. These red soils, containing wastes from the mine rich in metals, are potentially colonized by a specific flora adapted to metal-rich soils.</p></sec><sec id=\"sec2dot2-ijerph-17-05506\"><title>2.2. Soil Sampling and Metal Analyses</title><p>Samples of soil at the mine site were collected randomly from the top 20 cm after litter removal, then sieved to 2 mm and pooled in three composite samples for metal analysis (Cr, Cu, Fe, Mn, Ni, Pb, Zn). In the laboratory, soil composite aliquots were air-dried at room temperature and then ground to pass through a 0.2 mm titanium sieve (RETSCH zm 1000 with tungsten blades) before analyses. Trace and major metal element concentrations were determined on 3 analytical replicates for each of the three soil composite samples. Soils were mineralized in a microwave mineralizer (Milestone Start D) using <italic>aqua regia</italic> (1/3 HNO<sub>3</sub> + 2/3 HCl). The mineralization products were filtered with a 0.45 μm mesh and the metal concentrations were determined by ICP-AES (inductively coupled plasma atomic emission spectroscopy, Spectra 2000, Jobin Yvon Horiba group, Longjumeau, France). Quality controls and accuracy were checked using standard soil reference materials (CRM049–050, from RTC-USA) with accuracies within 100 ± 10%.</p></sec><sec id=\"sec2dot3-ijerph-17-05506\"><title>2.3. Experimental Approach for Plant Selection</title><p>The main aim is to assess the efficiency of a trait-based approach for phytoremediation plant selection in the field. To achieve this aim, a hierarchical method was designed based on analysis of literature data with experimental measurements to fill the gaps in knowledge, as described in <xref ref-type=\"fig\" rid=\"ijerph-17-05506-f002\">Figure 2</xref>.</p></sec><sec id=\"sec2dot4-ijerph-17-05506\"><title>2.4. Floristic Analysis and Habitat Inventory</title><p>The study was done during spring in a defined area of 30 m<sup>2</sup> representative of an excavation in the abandoned mining area with spontaneous vegetation colonization. Vertical (herbaceous, shrub and tree strata) and horizontal (global plant cover) structures of plant communities were used to distinguish the different micro-habitats (<xref ref-type=\"fig\" rid=\"ijerph-17-05506-f001\">Figure 1</xref>, <xref ref-type=\"fig\" rid=\"ijerph-17-05506-f002\">Figure 2</xref> and <xref ref-type=\"fig\" rid=\"ijerph-17-05506-f003\">Figure 3</xref>). Within each type of micro-habitat, representative areas characterized by the homogeneity of vegetation were selected for floristic analysis. The micro-habitats were classified based on plant species composition (presence/absence). Finally, a global analysis on 6 relevés was done on the four selected micro-habitats in terms of plant species occurrence.</p></sec><sec id=\"sec2dot5-ijerph-17-05506\"><title>2.5. Bibliographical Functional Traits</title><p>Functional traits such as type of root, Grime strategy, Raunkiær biological type, and mycorrhizal status were collected from a review of scientific papers for each of the identified plant species in the field. The functional response traits were selected with the aim of better understanding the influence that these plant species may have on the passive phytoremediation processes.</p></sec><sec id=\"sec2dot6-ijerph-17-05506\"><title>2.6. Plant Analyses</title><sec id=\"sec2dot6dot1-ijerph-17-05506\"><title>2.6.1. Root Symbiosis Assessment</title><p>Seven plant species were selected, i.e., <italic>Armeria arenaria</italic> (Pers.) Schult., <italic>Biscutella laevigata</italic> L., <italic>Brachypodium phoenicoides</italic> (L.) Roem. & Schult, <italic>Trifolium pratense</italic> L., <italic>Mibora minima</italic> (L.) Desv., <italic>Noccaea caerulescens</italic> (J. et C. Presl) FK Meyer and <italic>Thymus vulgaris</italic> L. for assessment of their root symbioses in the field. The selection was done after literature review, and only plant species for which there was a lack of knowledge, scarce data or controversial status were chosen. Thin roots were randomly selected in the root system of 3 individuals of each of the selected plant species. The root samples were first rinsed under tap water then with deionized water and stored in alcohol (60%, <italic>v/v</italic>) at room temperature until proceeding. The occurrence of symbiont colonization was estimated by visual observation of fungal colonization after clearing roots in 10% KOH and staining with lactophenol blue solution, according to Phillips and Hayman [<xref rid=\"B15-ijerph-17-05506\" ref-type=\"bibr\">15</xref>].</p></sec><sec id=\"sec2dot6dot2-ijerph-17-05506\"><title>2.6.2. Metal Content in Below-Ground and Above-Ground Plant Parts</title><p>Four plant species were selected for metal analysis, i.e., <italic>N. caerulescens</italic>, <italic>B. laevigata</italic>, <italic>A. arenaria</italic> and <italic>Plantago lanceolata</italic> L. Dried plant samples (root and aerial parts, separately) were ground to pass a 0.2 mm mesh titanium sieve, and three aliquots were analyzed per sample. About 0.5 g of dry matter was digested with the microwave digestion system Milestone start D with a HNO<sub>3</sub>-HCl mixture (volume proportion ratio 2/1). After filtration (0.45 µm), acid digests were analyzed for metal content by ICP-AES (JY 2000 Jobin Yvon Horiba group, Longjumeau, France). Standard plant reference material (DC 73,349) from the China National Analysis Centre for Iron and Steel (NCS), was analyzed as a part of the quality control protocol (accuracies within 100 ± 10%).</p></sec></sec><sec id=\"sec2dot7-ijerph-17-05506\"><title>2.7. Statistical Analysis</title><p>Statistical analyses were performed for all data using JMP 12 statistical software (SAS Institute, Cary, NC, USA) using the non-parametric Kruskal-Wallis test because data did not follow a normal distribution pattern. The nonparametric pairwise multiple- comparison Dunn’s test was used when the null hypothesis was rejected with the Kruskal–Wallis test.</p></sec></sec><sec sec-type=\"results\" id=\"sec3-ijerph-17-05506\"><title>3. Results and Discussion</title><sec id=\"sec3dot1-ijerph-17-05506\"><title>3.1. Soil Contamination</title><p>Soils contained high concentrations of zinc (ca.11.7%) and lead (ca.1.7%) as expected in this type of abandoned mining area (<xref rid=\"ijerph-17-05506-t001\" ref-type=\"table\">Table 1</xref>). Moreover, elevated concentrations in Fe, Mn, and Cr were also detected. Zn and Pb soil concentrations were higher than those previously reported in mine tailings in Spain, i.e., highest concentrations ranging between ca. 2360, 7000 and 11,600 mg/kg of Zn in the Alcudia Valley, San José heaps and El Lirio tailing, respectively and ca. 7000 and 10,000 mg/kg of Pb in Belleza tailing and the Alcudia Valley, respectively [<xref rid=\"B16-ijerph-17-05506\" ref-type=\"bibr\">16</xref>,<xref rid=\"B17-ijerph-17-05506\" ref-type=\"bibr\">17</xref>,<xref rid=\"B18-ijerph-17-05506\" ref-type=\"bibr\">18</xref>]. However higher Zn soil content was detected at Mánforas with 135,080 mg/kg [<xref rid=\"B17-ijerph-17-05506\" ref-type=\"bibr\">17</xref>]. In Portugal, up to 9330 mg/kg of Pb were observed in abandoned Pb mines [<xref rid=\"B19-ijerph-17-05506\" ref-type=\"bibr\">19</xref>]. Compared to mine sites in France, at the Les Malines and Les Avinières near the present study site, the authors detected lower concentrations in Zn and greater in Pb, i.e., 59,040 mg/kg of Zn and 62,051 mg/kg of Pb [<xref rid=\"B20-ijerph-17-05506\" ref-type=\"bibr\">20</xref>]. In Poland, in reclaimed soils of Zn-Pb mine wastes, up to 7.57% of Zn and 0.46% of Pb were found [<xref rid=\"B21-ijerph-17-05506\" ref-type=\"bibr\">21</xref>]. Such heavy metal-rich soils may not be used for agriculture purposes, and the spontaneous plant colonization that occurred after operation ceased was a great opportunity to reduce the transfer of elements into the food web and to reduce the environmental and human exposure.</p></sec><sec id=\"sec3dot2-ijerph-17-05506\"><title>3.2. Diversity of Habitats and Plant Species</title><p>A total of 4 micro-habitats were identified after preliminary cartography of the 30 m<sup>2</sup> study site, consisting of (i) grassland colonizing metal-rich soil, (ii) herbaceous colonization of rocky soils, (iii) matorral with shrub dominating and (iv) woody vegetation (<xref ref-type=\"fig\" rid=\"ijerph-17-05506-f003\">Figure 3</xref>). Two supplementary micro-habitats were removed from the inventory. The first concerned an area in which the major part was covered by lichens belonging to 2 species, i.e., <italic>Cladonia rengiformis</italic> and <italic>Cladonia foliacea</italic> s.l., beyond the scope of the study. A second area near the road was mostly constituted of ruderals due to the modified substrate of the road foundation.</p><p>The floristic composition of the study site consisted in 61 plant species growing wild on these 4 micro-habitat types and identified during the spring period (<xref rid=\"ijerph-17-05506-t002\" ref-type=\"table\">Table 2</xref>). It included 31 botanical families at the period of the relevés. In terms of species diversity, the Poaceae family was the most frequent (8 species), then the Brassicaceae and Rosaceae with 5 species each. Asteraceae occurred with 3 species, followed by Fabaceae, Caryophyllaceae, Rubiaceae and Ranunculaceae (<xref rid=\"ijerph-17-05506-t002\" ref-type=\"table\">Table 2</xref>).</p><p>In terms of estimated plant cover, the dominant plant species were Brassicaceae with mainly two species (<italic>B. laevigata</italic> and <italic>N. caerulescens</italic>), Poaceae (<italic>B. phoenicoides, M. minima</italic> and <italic>Festuca ovina</italic> L.), Plumbaginaceae (<italic>A. arenaria</italic>), Fabaceae (<italic>T. pratense</italic>). and Lamiaceae (<italic>T. vulgaris</italic>).</p></sec><sec id=\"sec3dot3-ijerph-17-05506\"><title>3.3. Analysis of Plant Traits Linked with TMM Tolerance</title><p>From the literature, different plant traits commonly used in different floristic analyses of spontaneous colonization of mining sites were assessed for all the 61 identified plant species (<xref rid=\"ijerph-17-05506-t0A1\" ref-type=\"table\">Table A1</xref>). Those categorical traits may help us to understand the distribution pattern of plant species along environmental gradients [<xref rid=\"B22-ijerph-17-05506\" ref-type=\"bibr\">22</xref>]. However, their local variability needs to be discussed, and continuous traits need to be locally assessed to test the robustness of this approach.</p><sec id=\"sec3dot3dot1-ijerph-17-05506\"><title>3.3.1. Categorical Trait Analysis of the 61 Identified Plant Species</title><p>Considering below-ground parts, 61% of the plant species were characterized by fibrous root systems, 26% by rhizome or tuberous root system and only 13% by slender roots. A hypothesis has been formulated by Sardans and Peñuelas [<xref rid=\"B23-ijerph-17-05506\" ref-type=\"bibr\">23</xref>] and confirmed by Pierret et al. [<xref rid=\"B24-ijerph-17-05506\" ref-type=\"bibr\">24</xref>] considering deep roots as able to access water and nutrients in deep layers of soil and/or fractured bedrock, which are unavailable to surface roots. This mechanism will potentially help to maintain higher moisture levels in the upper soil layers and could be a factor explaining the high plant diversity in these dry habitats, despite the water stress due to both Mediterranean conditions and high salt concentration in the water linked to the metalliferous soils.</p><p>The mycorrhizal status of most of these 61 plant species has already been described [<xref rid=\"B25-ijerph-17-05506\" ref-type=\"bibr\">25</xref>,<xref rid=\"B26-ijerph-17-05506\" ref-type=\"bibr\">26</xref>,<xref rid=\"B27-ijerph-17-05506\" ref-type=\"bibr\">27</xref>]. Out of all the plant species, 67% were associated with arbuscular endomycorrhizal fungi, ca. 5% with ectomycorrhizal fungi and 8% were reputed to share no mycorrhizal interactions. For ca. 20% of the identified plant species, no information regarding their mycorrhizal status has been reported to the best of our knowledge.</p><p>Concerning the preferential type of soil, 44% of the plant species were adapted to dry and rocky soils, ca. 20% specific to calcareous soils, ca. 20% preferred sandy or well-drained soils and 16% were mostly found in disturbed or cultivated soils.</p><p>The dominant life form (<xref ref-type=\"fig\" rid=\"ijerph-17-05506-f004\">Figure 4</xref>) were hemicryptophytes with an average percentage of 51%, then therophytes (19%) and phanerophytes (15%). Chamaephytes only represented an average of 9%. Hemicryptophytes and therophytes were found in all of the 6 relevés although the occurrence of phanerophytes varied with none of this life form in the center of the area (open dry grasslands) and 31% in the tree stand (<xref rid=\"ijerph-17-05506-t002\" ref-type=\"table\">Table 2</xref> and <xref rid=\"ijerph-17-05506-t0A1\" ref-type=\"table\">Table A1</xref>). Geophytes and lianas were only identified in 2 and 3 out of 6 relevés, respectively.</p><p>Concerning plant strategy, 33% of species were considered as CS, i.e., competitive/stress tolerant, and 22% CSR, i.e., competitive, stress tolerant, ruderal (<xref ref-type=\"fig\" rid=\"ijerph-17-05506-f005\">Figure 5</xref>).</p></sec><sec id=\"sec3dot3dot2-ijerph-17-05506\"><title>3.3.2. Analysis of Plant Traits by TMM Tolerance Strategy</title><p>The identified plant species were grouped in hyperaccumulators, phytoaccumulators, phytostabilizators or not known in the literature. Using this grid of comparison, the above-cited traits were analyzed (<xref ref-type=\"fig\" rid=\"ijerph-17-05506-f006\">Figure 6</xref>). Even if the number of species considered as hyperaccumulators (2) or as phytoaccumulators (3) is limited in the study field, some traits are typical of the plant strategy. The dominant root system was fibrous for the two hyperaccumulators (<xref ref-type=\"fig\" rid=\"ijerph-17-05506-f006\">Figure 6</xref>a) and deep root system for phytoaccumulators (<xref ref-type=\"fig\" rid=\"ijerph-17-05506-f006\">Figure 6</xref>b). No specific root type could be defined for phytostabilizators (<xref ref-type=\"fig\" rid=\"ijerph-17-05506-f006\">Figure 6</xref>c). For phytostabilization strategy (43), identified plant species in the study site had mainly rhizomes or tuberous roots (35%, <xref ref-type=\"fig\" rid=\"ijerph-17-05506-f006\">Figure 6</xref>c), ca. all of them were known to be endomycorrhized (78%, <xref ref-type=\"fig\" rid=\"ijerph-17-05506-f006\">Figure 6</xref>g), the dominant Grime strategy was CS (49%, <xref ref-type=\"fig\" rid=\"ijerph-17-05506-f006\">Figure 6</xref>k), and the main life form was hemicryptophyte (46%, <xref ref-type=\"fig\" rid=\"ijerph-17-05506-f006\">Figure 6</xref>o). These results are in accordance with many previous studies demonstrating that only a few spontaneous plant species colonizing mine sites may favor heavy metal translocation in the aerial parts and may be useful for phytoextraction. Most of the metal tolerant plant species may accumulate heavy metals in their roots and limit their transfer to the aerial parts, being potentially useful in phytostabilization. Without any human intervention by plant harvesting, the dominant natural process is therefore phytostabilization. Hemicryptophyte life form was strongly present in the three groups (<xref ref-type=\"fig\" rid=\"ijerph-17-05506-f006\">Figure 6</xref>m–o). Hemicryptophytes such as biannuals or with thin root systems may limit the phytostabilization potential. On the other hand, the non-perennial aerial parts of hemicryptophytes may also limit the phytoextraction ability. The traits that most discriminated hyperaccumulators from both phytoaccumulators and phytostabilizators were the ability to form arbuscular mycorrhizal (AM) associations (50% non-mycorrhizal for the first and 33% and 78% AM for the other two; <xref ref-type=\"fig\" rid=\"ijerph-17-05506-f006\">Figure 6</xref>e, f and g). It is noteworthy that ectomycorrhizal (EC) type was dominant (67%) in phytoaccumulators. It is congruent with the 67% of phanerophyte type. <italic>Quercus ilex</italic> and <italic>Pinus sylvestris</italic> are known to be predominantly associated with ectomycorrhizal fungi [<xref rid=\"B26-ijerph-17-05506\" ref-type=\"bibr\">26</xref>].</p></sec><sec id=\"sec3dot3dot3-ijerph-17-05506\"><title>3.3.3. Measured Trait Analysis on Selected Plant Species</title><p>Mycorrhizal status of 7 selected plant species: an efficient tool for plant species selection for phytostabilization strategy?</p><p>The mycorrhizal status of <italic>A. arenaria</italic>, <italic>B. laevigata</italic>, <italic>B. phoenicoides</italic>, <italic>T. pratense</italic>, <italic>M. minima</italic>, <italic>N. caerulescens</italic> and <italic>T. vulgaris</italic> was assessed.</p><p><italic>N. caerulescens</italic> (<xref ref-type=\"fig\" rid=\"ijerph-17-05506-f007\">Figure 7</xref>a) and <italic>B. laevigata</italic> (not shown) were not mycorrhized at this site. However, there is no consensus regarding the mycorrhizal status of <italic>B. laevigata</italic> and <italic>N. caerulescens</italic>, long considered not to be mycorrhized; some authors detected AM associations with both species [<xref rid=\"B26-ijerph-17-05506\" ref-type=\"bibr\">26</xref>,<xref rid=\"B27-ijerph-17-05506\" ref-type=\"bibr\">27</xref>,<xref rid=\"B28-ijerph-17-05506\" ref-type=\"bibr\">28</xref>].</p><p><italic>N. caerulescens</italic> (<xref ref-type=\"fig\" rid=\"ijerph-17-05506-f007\">Figure 7</xref>a) and <italic>B. laevigata</italic> (not shown) were not mycorrhized at this site. However, there is no consensus regarding the mycorrhizal status of <italic>B. laevigata</italic> and <italic>N. caerulescens</italic>, long considered not to be mycorrhized; some authors detected AM associations with both species [<xref rid=\"B26-ijerph-17-05506\" ref-type=\"bibr\">26</xref>,<xref rid=\"B27-ijerph-17-05506\" ref-type=\"bibr\">27</xref>,<xref rid=\"B28-ijerph-17-05506\" ref-type=\"bibr\">28</xref>].</p><p>A total of 20% of root length colonization by AM fungi was detected for <italic>A. arenaria</italic> (<xref ref-type=\"fig\" rid=\"ijerph-17-05506-f007\">Figure 7</xref>b) although no AM colonization was revealed for <italic>M. minima</italic> (not shown). No previous data regarding the mycorrhizal status of <italic>A. arenaria</italic> and <italic>M. minima</italic> have been published. To the best of our knowledge, this is the first report of AM association with <italic>A. arenaria.</italic> With regard to <italic>M. minima</italic>, the smallest Poaceae occurring in France, it is also the first report concerning its mycorrhizal status. Its very short life cycle (few months) could be linked to a lack of AM association.</p><p>Wang and Qiu [<xref rid=\"B26-ijerph-17-05506\" ref-type=\"bibr\">26</xref>] and Pawlowska et al. [<xref rid=\"B27-ijerph-17-05506\" ref-type=\"bibr\">27</xref>] gave discordant results for <italic>T. pratense.</italic> At the present site, AM mycelia were abundant, and many spores were detected in roots of <italic>T. pratense</italic> with an overall colonization ca. 80% of root length (<xref ref-type=\"fig\" rid=\"ijerph-17-05506-f007\">Figure 7</xref>c).</p><p><italic>B. phoenicoides</italic> mycorrhizal status has previously been analyzed [<xref rid=\"B29-ijerph-17-05506\" ref-type=\"bibr\">29</xref>]. However, data are scarce. In our study, this species appeared to be endomycorrhized (<xref ref-type=\"fig\" rid=\"ijerph-17-05506-f007\">Figure 7</xref>d). <italic>T. vulgaris</italic> was the only Lamiaceae identified at our study site and since Lamiaceae are usually good candidates for phytostabilization, we endeavored to confirm its mycorrhizal status. This perennial developed AM association and a dense web of mycelia was observed with many vesicles (<xref ref-type=\"fig\" rid=\"ijerph-17-05506-f007\">Figure 7</xref>e).</p><p>The occurrence of AM symbioses is a strong advantage for phytostabilization but not specific to this type of process. Mycorrhizal associations may favor TMM extraction by enhancing metal mobilization [<xref rid=\"B30-ijerph-17-05506\" ref-type=\"bibr\">30</xref>]. Therefore, this trait alone would not be a good criterium for plant species selection for the purpose of phytostabilization strategy.</p><p>Plant colonization at mining sites may be favored by AM fungi, the latter, which may lower metal toxicity and improve nutrient availability for plants. However, in a Zn-, Cd-, Pb-, and Cu-polluted field study, no evidence for an effect of AM symbioses has been found on plant metal uptake [<xref rid=\"B31-ijerph-17-05506\" ref-type=\"bibr\">31</xref>]. Therefore, with regard to a phytoextraction strategy, the authors suggest not channeling efforts exclusively towards reinforcing AM symbiosis. In a recent review paper dealing with AM associations at mining sites, it was observed that more than 80% of the plant species from metallic mines were endomycorrhized suggesting adaptive strategies in coevolved fungal strains and plant species [<xref rid=\"B32-ijerph-17-05506\" ref-type=\"bibr\">32</xref>]. Rehabilitation of metal-rich soils without metal removal may be achieved by selecting plant species with their co-evolved mycorrhizal symbionts.</p></sec><sec id=\"sec3dot3dot4-ijerph-17-05506\"><title>3.3.4. Metal Content in 4 Plant Species for Phytoextraction Strategy</title><p>In agreement with the literature, <italic>N. caerulescens</italic> was the best accumulating species out of the 4 selected for zinc and lead (<xref rid=\"ijerph-17-05506-t003\" ref-type=\"table\">Table 3</xref>), and it is defined as a facultative Zn-hyperaccumulator [<xref rid=\"B3-ijerph-17-05506\" ref-type=\"bibr\">3</xref>]. This species also accumulated high levels of Fe in its aerial parts. Furthermore, <italic>B. laevigata</italic> and <italic>A. arenaria</italic> seemed to be suitable as Zn-phytoaccumulators with more than 1000 mg/kg (dry matter, DM) of zinc accumulated in their aerial parts. Those species have already been identified as valuable temperate zone phytoaccumulators of Zn and Cd. <italic>P. lanceolata</italic>, in a lesser way, appeared as a good Zn phytoaccumulators. Zn content up to 430 mg/kg in shoots of <italic>P. lanceolata</italic> was previously reported in a mining area of Southern Poland [<xref rid=\"B33-ijerph-17-05506\" ref-type=\"bibr\">33</xref>] although 946 mg/kg were detected in <italic>P. lanceolata</italic> aerial parts in the present study. All these four plant species are also potentially good candidates in Mediterranean areas. Although average soil Pb concentration was high (<xref rid=\"ijerph-17-05506-t001\" ref-type=\"table\">Table 1</xref>), this element was moderately accumulated in the aerial parts of <italic>N. caerulescens</italic> (ca. 496 mg/kg) and ranged from 48 to 75 mg/kg in the aerial parts of the three other plant species.</p><p>Apart from Fe, Zn and Pb were the studied elements most translocated to the aerial parts of the four plant species. However, Pb, as a non-essential element, may be transferred to aerial parts via transporters of other elements essential to plants. This type of elemental competition between nutrients and toxic metals is most of the time antagonistic. However, the present results show concomitant translocation of Zn and Pb. Our results demonstrated similar results for <italic>N. caerulescens</italic>, <italic>A. arenaria</italic>, <italic>B. laevigata</italic> and <italic>P. lanceolata</italic>. <italic>N. caerulescens</italic> is sometimes considered as a Zn-hyperaccumulator and Pb co-accumulator [<xref rid=\"B34-ijerph-17-05506\" ref-type=\"bibr\">34</xref>]. A previous work on <italic>N. caerulescens</italic> also shows a negative correlation between Zn content in shoot parts and Ca and Mg concentrations in shoot parts due to competition between these different cations [<xref rid=\"B13-ijerph-17-05506\" ref-type=\"bibr\">13</xref>]. Synergistic and antagonistic interactions for element absorption in plants is a challenging field of research and there is still a need for more knowledge. However, these preliminary results showed the interest of these plant species in Zn and Pb-rich soils.</p></sec><sec id=\"sec3dot3dot5-ijerph-17-05506\"><title>3.3.5. Spontaneous Plant Colonization at Abandoned Zn-Pb Mine Sites: A Matter of Geographical Situation or Plant Traits?</title><p>Out of the 61 plant species identified at the abandoned mine site, 47 were already known to grow in Zn-rich soils. Among the 14 other plant species, 4 are phanerophytes and may have slowly colonized the site. Five have a reduced biomass and a low cover potential. Four plant species are typical of Mediterranean environments, and the others have a wider range of distribution. Some previous studies, notably at an abandoned Zn-mine site and in metalliferous soils in Greece, Poland, Spain, Italy, Portugal and France, have detailed the floristic composition and some of their plant traits [<xref rid=\"B17-ijerph-17-05506\" ref-type=\"bibr\">17</xref>,<xref rid=\"B19-ijerph-17-05506\" ref-type=\"bibr\">19</xref>,<xref rid=\"B20-ijerph-17-05506\" ref-type=\"bibr\">20</xref>,<xref rid=\"B27-ijerph-17-05506\" ref-type=\"bibr\">27</xref>,<xref rid=\"B35-ijerph-17-05506\" ref-type=\"bibr\">35</xref>,<xref rid=\"B36-ijerph-17-05506\" ref-type=\"bibr\">36</xref>,<xref rid=\"B37-ijerph-17-05506\" ref-type=\"bibr\">37</xref>] enabling a comparison of occurrence of plant species even though these studies were conducted under varying biogeographical conditions. Out of the seven relevés from the cited literature, <italic>Rubus ulmifolius</italic> Schott and <italic>P. lanceolata</italic> were the most frequently identified species on these sites. <italic>B. leavigata, D. glomerata</italic> and <italic>F. ovina</italic> occurred in 4 out of the 7 relevés in France, Poland and/or Portugal. <italic>N. caerulescens</italic> only occurred in the 2 relevés from France. Hemicryptophytes were dominant in most of the studies [<xref rid=\"B20-ijerph-17-05506\" ref-type=\"bibr\">20</xref>,<xref rid=\"B38-ijerph-17-05506\" ref-type=\"bibr\">38</xref>] including in the present study. No systematic selection could be made solely on the basis of plant traits, but common features are shared by the spontaneous vegetation of the European and Mediterranean abandoned Zn-mine sites that it might be useful to identify with a view to the ecological restoration of Zn-rich soils.</p><p>It is worth noticing that, in many studies, the occurrence of certain exotic phanerophytes was recurrent, such as <italic>Eucalyptus globulus</italic> [<xref rid=\"B19-ijerph-17-05506\" ref-type=\"bibr\">19</xref>], but these species are not to be encouraged in ecological restoration processes; it would be better to focus on native plant species.</p><p>On the basis of all the information collected through the diverse relevés, it appeared that some ubiquitous Zn-tolerant plant species in Mediterranean and European areas are potentially good candidates for the first stages of ecological restoration of Zn-Pb-rich soils. Previous restoration programs in Poland have used <italic>D. glomerata</italic> and <italic>T. pratense,</italic> also identified at the present study site. However, this was done with selected cultivars and not seedlings of wild origin [<xref rid=\"B39-ijerph-17-05506\" ref-type=\"bibr\">39</xref>]. This previous integrative study has suggested various potential lines of research including endomycorrhizal inoculation before transplantation with selected fungal strains and also selection of xerothermic plant species to cope with water stress. Their feedback shows how important the development of rhizosphere microorganisms consortia is for successful site restoration [<xref rid=\"B39-ijerph-17-05506\" ref-type=\"bibr\">39</xref>]. AM colonization was also a dominant trait as previously reported [<xref rid=\"B27-ijerph-17-05506\" ref-type=\"bibr\">27</xref>]. However, local plant associations should be favored and the geographical situation needs to be taken into account. Moreover, more than the below-ground parts of the plants, rhizosphere micro-organisms are potentially the key factor for plant colonization in these metal-rich soils following the mycorrhizal fungal diversity–ecosystem function concept of Hazard and Johnson [<xref rid=\"B40-ijerph-17-05506\" ref-type=\"bibr\">40</xref>]. Plant functioning (nutrition, stress-tolerance, etc.) is intrinsically dependent on associated micro-organisms [<xref rid=\"B41-ijerph-17-05506\" ref-type=\"bibr\">41</xref>].</p></sec></sec></sec><sec sec-type=\"conclusions\" id=\"sec4-ijerph-17-05506\"><title>4. Conclusions</title><p>This work enabled the implementation of a plant database consisting of above-ground and below-ground plant traits of plant species able to grow on Zn-Pb rich soils. This preliminary work may be a useful tool for plant selection in rehabilitation programs for Zn-Pb-rich soils. It also highlighted the need for more information regarding below-ground traits and rhizosphere microbial consortia.</p><p>The trait-based analysis provided a basis for drawing a general picture of plant communities in a Mediterranean abandoned Zn-Pb mine site. Below-ground traits appeared as important features for phytostabilization. Ectomycorrhizae were dominant in the Zn-phytoaccumulators species and AM, in the phytostabilizators. Together, these plant strategies may favor fungal interaction diversity and enhance the sustainability of the plant-fungal communities.</p><p>The four plant species selected for phytoextraction, i.e., <italic>N. caerulescens</italic>, <italic>B. laevigata</italic>, <italic>A. arenaria</italic> and <italic>P. lanceolata</italic> showed interesting Pb and Zn accumulation capacities in their aerial parts. However, these plant species are of reduced biomass and a phytoextraction process with those species would not be efficient. Those plant species are however interesting in the early plant succession stage after mines are abandoned. Among the identified potential phytoaccumulators, <italic>Quercus ilex</italic> and <italic>Pinus sylvestris</italic>, both phanerophytes, are long-term colonizers and persistent plant species. However, management strategies based on tree plantation would create more litter with reduced undergrowth. With long-term Zn potential accumulation in the aerial parts, there is a need for further knowledge regarding the risk of Zn transfer into the food web.</p><p>Plant communities at the mine site mostly favored a passive phytostabilization that is maintained over time by seasonal turnover of therophytes and persistence of belowground parts of hemicryptophytes and geophytes and both belowground and aboveground parts of chamaephytes and phanerophytes. Only few plant species were potentially able to accumulate Zn and Pb in the aerial parts. Moreover, a phytoextraction process requires human intervention by removal of metal-rich aerial parts. Consequently, even if both strategies occurred in these plant communities, the overall trend is the immobilization of heavy metals in the soils and root systems.</p></sec></body><back><ack><title>Acknowledgments</title><p>We thank Claude Roux for the lichen identification; Daniel E Silva, Mathieu Baroncini and Grégory Dron for their help in mycorrhizal observations and Baptiste Benita, Grégory Dron and Jean-Baptiste Portier for their technical assistance in field sampling and sample preparation. Many thanks to Daniel Pavon for his help in plant determination and to Manuel Cartereau for his helpful discussions about plant traits and notably Grime strategies. We also thank Michael Paul for revising the English of this text.</p></ack><notes><title>Author Contributions</title><p>Conceptualization, I.L.-S., J.R. and P.P. methodology, I.L.-S., J.R., P.P., V.M., L.V. and M.-D.S.; validation, I.L.-S., P.P., J.R., H.F. and L.T.; investigation, I.L.-S., P.P. and J.R.; resources, I.L.-S., J.R. and P.P.; writing—original draft preparation, I.L.-S.; writing—review and editing, J.R., P.P., V.M., M.-D.S., H.F. and L.T.; supervision, I.L.-S. and P.P.; project administration, I.L.-S.; funding acquisition, I.L.-S., J.R. and P.P. All authors have read and agreed to the published version of the manuscript. </p></notes><notes><title>Funding</title><p>This research received no external funding.</p></notes><notes notes-type=\"COI-statement\"><title>Conflicts of Interest</title><p>The authors declare no conflict of interest.</p></notes><app-group><app id=\"app1-ijerph-17-05506\"><title>Appendix A</title><table-wrap id=\"ijerph-17-05506-t0A1\" orientation=\"portrait\" position=\"anchor\"><object-id pub-id-type=\"pii\">ijerph-17-05506-t0A1_Table A1</object-id><label>Table A1</label><caption><p>Floristic list established during springtime from the mosaic of habitats encountered in the former mine site. Traits selected from literature: Root type; Root symbioses: AM: Arbuscular mycorrhizae, EC: Ectomycorrhizae; NM: Non mycorrhizal; Soil preferendum; Life forms [<xref rid=\"B42-ijerph-17-05506\" ref-type=\"bibr\">42</xref>]: Ph: Phanerophytes; He: Hemicryptophytes; Ch: Chamaephytes; L: Liana; Ge: Geophytes, Th: Therophytes; Grime strategy: CS: Competitive stress tolerant; CSR: Competitive, stress tolerant, ruderal; SR: Stress tolerant, ruderal; S: Stress tolerant; R: Ruderal; CR: Competitve ruderal; C: Competitive; Zn tolerance.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Latin Name</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Botanical Family</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Root System</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Root Symbioses</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Soil Preferendum</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Life Form</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Grime Strategy</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Zn Tolerance</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><italic>Amelanchier ovalis</italic> Medik.</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Rosaceae</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Deep root system</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">AM [<xref rid=\"B43-ijerph-17-05506\" ref-type=\"bibr\">43</xref>]</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Rocky dry soils</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Ph</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CS</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Not known</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><italic>Aphyllantes monspeliensis</italic> L.</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Liliaceae</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Fibrous root system</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Not known</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Dry soils</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">He</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">S</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Not known</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><italic>Arenaria serpyllifolia</italic> L.</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Caryophyllaceae</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Thin root system</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">NM [<xref rid=\"B44-ijerph-17-05506\" ref-type=\"bibr\">44</xref>,<xref rid=\"B45-ijerph-17-05506\" ref-type=\"bibr\">45</xref>]</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Sandy and rocky soils, calcareous soils</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Th</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">R</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Population-specific Zn tolerance [<xref rid=\"B46-ijerph-17-05506\" ref-type=\"bibr\">46</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><italic>Argyrolobium zanonii</italic> (Turra) P.W.Ball</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Fabaceae</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Fibrous root system</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">AM [<xref rid=\"B47-ijerph-17-05506\" ref-type=\"bibr\">47</xref>]</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Calcareous soils</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Ch</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">S</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Not known</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><italic>Armeria arenaria</italic> subsp <italic>bupleuroides</italic> (Godr. & Gren.) Greuter & Burdet</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Plumbaginaceae</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Fibrous root system</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Not known</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Sandy and rocky soils</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">He</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">S</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Population-specific Zn tolerance [<xref rid=\"B48-ijerph-17-05506\" ref-type=\"bibr\">48</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><italic>Asparagus acutifolius</italic> L.</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Asparagaceae</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Rhizome</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">AM [<xref rid=\"B26-ijerph-17-05506\" ref-type=\"bibr\">26</xref>]</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Dry sand arid oils</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">He</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CS</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Population-specific Zn tolerance [<xref rid=\"B20-ijerph-17-05506\" ref-type=\"bibr\">20</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><italic>Asplenium ruta-muraria</italic> L.</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Aspleniaceae</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Rhizome</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">AM + NM [<xref rid=\"B26-ijerph-17-05506\" ref-type=\"bibr\">26</xref>]</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Cliffs, rocks</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">He</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CS</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Population-specific Zn tolerance [<xref rid=\"B49-ijerph-17-05506\" ref-type=\"bibr\">49</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><italic>Biscutella laevigata</italic> L.</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Brassicaceae</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Fibrous root system</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">NM [<xref rid=\"B26-ijerph-17-05506\" ref-type=\"bibr\">26</xref>]/AM [<xref rid=\"B27-ijerph-17-05506\" ref-type=\"bibr\">27</xref>,<xref rid=\"B28-ijerph-17-05506\" ref-type=\"bibr\">28</xref>]</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Rocky soils</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">He</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">SR</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Species-wide Zn tolerance [<xref rid=\"B8-ijerph-17-05506\" ref-type=\"bibr\">8</xref>], Tl-, Cd-hyperaccumulator [<xref rid=\"B50-ijerph-17-05506\" ref-type=\"bibr\">50</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><italic>Brachypodium phoenicoides</italic> (L.) Roem. & Schult</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Poaceae</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Rhizome</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">AM [<xref rid=\"B29-ijerph-17-05506\" ref-type=\"bibr\">29</xref>]</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Dry soils</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">He</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CS</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Population-specific Zn tolerance [<xref rid=\"B19-ijerph-17-05506\" ref-type=\"bibr\">19</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><italic>Brachypodium retusum</italic> (Pers.) P.Beauv.</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Poaceae</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Rhizome</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">AM [<xref rid=\"B26-ijerph-17-05506\" ref-type=\"bibr\">26</xref>]</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Dry soils</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">He</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CS</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Population-specific Zn tolerance [<xref rid=\"B8-ijerph-17-05506\" ref-type=\"bibr\">8</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><italic>Bromus madritensis</italic> L.</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Poaceae</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Fibrous root system</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">AM [<xref rid=\"B26-ijerph-17-05506\" ref-type=\"bibr\">26</xref>]</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Sandy soils, cultivated soils</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Th</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">SR</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Population-specific Zn tolerance [<xref rid=\"B51-ijerph-17-05506\" ref-type=\"bibr\">51</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><italic>Buxus sempervirens</italic> L.</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Buxaceae</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Deep root system</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">AM [<xref rid=\"B26-ijerph-17-05506\" ref-type=\"bibr\">26</xref>]</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Dry calcareous soils</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Ph</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CS</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Population-specific Zn tolerance [<xref rid=\"B35-ijerph-17-05506\" ref-type=\"bibr\">35</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><italic>Carex halleriana</italic> Asso</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Cyperaceae</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Rhizome</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">NM [<xref rid=\"B52-ijerph-17-05506\" ref-type=\"bibr\">52</xref>]</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Dry calcareous soils</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">He</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CSR</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Population-specific Zn tolerance [<xref rid=\"B37-ijerph-17-05506\" ref-type=\"bibr\">37</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><italic>Centaurea pectinata</italic> L.</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Asteraceae</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Deep root system</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Not known</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Rocky soils</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">He</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">S</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Population-specific Zn tolerance [<xref rid=\"B20-ijerph-17-05506\" ref-type=\"bibr\">20</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><italic>Cerastium pumilum</italic> Curtis</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Caryophyllaceae</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Slender root system</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Not known</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Sandy soils</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Th</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">SR</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Population-specific Zn tolerance [<xref rid=\"B53-ijerph-17-05506\" ref-type=\"bibr\">53</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><italic>Clematis vitalba</italic> L.</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Ranunculaceae</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">fibrous root system</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">AM [<xref rid=\"B26-ijerph-17-05506\" ref-type=\"bibr\">26</xref>]</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">calcareous to acidic soils</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">L</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CS</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Population-specific Zn tolerance [<xref rid=\"B54-ijerph-17-05506\" ref-type=\"bibr\">54</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><italic>Clinopodium nepeta</italic> (L.) Kuntze</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Lamiaceae</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">fibrous root system</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">AM [<xref rid=\"B55-ijerph-17-05506\" ref-type=\"bibr\">55</xref>]</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Dry rocky soils</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">He</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">S</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Population-specific Zn tolerance [<xref rid=\"B56-ijerph-17-05506\" ref-type=\"bibr\">56</xref>,<xref rid=\"B57-ijerph-17-05506\" ref-type=\"bibr\">57</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><italic>Dactylis glomerata</italic> L.</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Poaceae</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Fibrous root system</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">AM [<xref rid=\"B26-ijerph-17-05506\" ref-type=\"bibr\">26</xref>]</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Cultivated soils</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">He</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CSR</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Population-specific Zn tolerance [<xref rid=\"B20-ijerph-17-05506\" ref-type=\"bibr\">20</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><italic>Dioscorea communis</italic> (L.) Caddick & Wilkin</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Dioscoreaceae</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Rhizome</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">AM [<xref rid=\"B25-ijerph-17-05506\" ref-type=\"bibr\">25</xref>]</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Calcareous, well-drained soils</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">L</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CS</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Not known</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><italic>Draba verna</italic> L.</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Brassicaceae</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Fibrous root system</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Not known</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Sandy and calcareous soils</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Th</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CR</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Not known</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><italic>Eryngium campestre</italic> L.</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Apiaceae</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Rhizome</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">AM [<xref rid=\"B26-ijerph-17-05506\" ref-type=\"bibr\">26</xref>]</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Sandy soils</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Ge</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">SR</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Population-specific Zn tolerance [<xref rid=\"B16-ijerph-17-05506\" ref-type=\"bibr\">16</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><italic>Euphorbia cyparissias</italic> L.</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Euphorbiacee</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Fibrous root system</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">AM [<xref rid=\"B26-ijerph-17-05506\" ref-type=\"bibr\">26</xref>,<xref rid=\"B58-ijerph-17-05506\" ref-type=\"bibr\">58</xref>]</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Calcareous soils</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">He</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">S</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Population-specific Zn tolerance [<xref rid=\"B58-ijerph-17-05506\" ref-type=\"bibr\">58</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><italic>Festuca ovina</italic> sl L.</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Poaceae</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Fibrous root system</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">AM [<xref rid=\"B26-ijerph-17-05506\" ref-type=\"bibr\">26</xref>,<xref rid=\"B27-ijerph-17-05506\" ref-type=\"bibr\">27</xref>]</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Dry soils</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">He</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CSR</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Population-specific Zn tolerance [<xref rid=\"B59-ijerph-17-05506\" ref-type=\"bibr\">59</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><italic>Galium aparine</italic> L.</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Rubiaceae</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Slender root system</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">AM + NM [<xref rid=\"B26-ijerph-17-05506\" ref-type=\"bibr\">26</xref>]</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Cultivated soils</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Th</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CR</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Population-specific Zn tolerance [<xref rid=\"B60-ijerph-17-05506\" ref-type=\"bibr\">60</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><italic>Galium corrudifolium</italic> Vill.</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Rubiaceae</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Deep and fibrous root system</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Not known</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Dry soils</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">He</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CR</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Not known</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><italic>Helleborus foetidus</italic> L.</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Ranunculaceae</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Rhizome</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">AM [<xref rid=\"B26-ijerph-17-05506\" ref-type=\"bibr\">26</xref>]</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Well- drained, calcareous soil</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Ge</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CSR</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Population-specific Zn tolerance [<xref rid=\"B17-ijerph-17-05506\" ref-type=\"bibr\">17</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><italic>Hordeum murinum</italic> L.</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Poaceae</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Fibrous root system</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">AM [<xref rid=\"B26-ijerph-17-05506\" ref-type=\"bibr\">26</xref>]</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Dry and sandy soils, disturbed soils</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Th</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">R</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Population-specific Zn tolerance [<xref rid=\"B61-ijerph-17-05506\" ref-type=\"bibr\">61</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><italic>Hornungia petraea</italic> (L.) ex Rchb.</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Brassicaceae</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Fibrous root system</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Not known</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Sandy and rocky soils</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Th</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">R</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Not known</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><italic>Hypericum perforatum</italic> L.</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Hypericaceae</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Rhizome</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">AM [<xref rid=\"B26-ijerph-17-05506\" ref-type=\"bibr\">26</xref>]</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Roadside soils, dry soils</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">He</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">SR</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Population-specific Zn tolerance [<xref rid=\"B62-ijerph-17-05506\" ref-type=\"bibr\">62</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><italic>Juniperus oxycedrus</italic> L.</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Cupressaceae</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Deep root system</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">AM [<xref rid=\"B26-ijerph-17-05506\" ref-type=\"bibr\">26</xref>]</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Arid soils</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Ph</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CS</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Population-specific Zn tolerance [<xref rid=\"B63-ijerph-17-05506\" ref-type=\"bibr\">63</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><italic>Lactuca perennis</italic> L.</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Asteraceae</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Deep root system</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Not known</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Rocky and calcareous soils</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">He</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">S</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Not known</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><italic>Lepidium draba</italic> L.</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Brassicaceae</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Rhizome</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">NM [<xref rid=\"B64-ijerph-17-05506\" ref-type=\"bibr\">64</xref>]</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Roadside soils</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Ge</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CR</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Population-specific Zn tolerance [<xref rid=\"B65-ijerph-17-05506\" ref-type=\"bibr\">65</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><italic>Lysimachia arvensis</italic> (L.) U. Manns & Anderb.</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Primulaceae</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Fibrous root system</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">AM [<xref rid=\"B25-ijerph-17-05506\" ref-type=\"bibr\">25</xref>]</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Cultivated and sandy soils</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Th</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">R</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Not known</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><italic>Mibora minima</italic> (L.) Desv.</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Poaceae</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Slender root</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Not known</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Sandy soils</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Th</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">R</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Population-specific Zn tolerance [<xref rid=\"B66-ijerph-17-05506\" ref-type=\"bibr\">66</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><italic>Noccaea caerulescens</italic> (J.Presl & C.Presl) F.K.Mey</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Brassicaceae</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Fibrous root system</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">AM [<xref rid=\"B26-ijerph-17-05506\" ref-type=\"bibr\">26</xref>]</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Well-drained soils, cultivated soils</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">He</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CS</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Species-wide Zn tolerance [<xref rid=\"B20-ijerph-17-05506\" ref-type=\"bibr\">20</xref>], Zn/Cd/Ni hyperaccumulator [<xref rid=\"B3-ijerph-17-05506\" ref-type=\"bibr\">3</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><italic>Pilosella officinarum</italic> Vaill.</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Asteraceae</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Rhizome</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">AM [<xref rid=\"B67-ijerph-17-05506\" ref-type=\"bibr\">67</xref>]</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Dry soils</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">He</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CS</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Population-specific Zn tolerance [<xref rid=\"B68-ijerph-17-05506\" ref-type=\"bibr\">68</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><italic>Pinus sylvestris</italic> L.</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Pinaceae</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Deep root system</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">EC [<xref rid=\"B26-ijerph-17-05506\" ref-type=\"bibr\">26</xref>]</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Dry soils, sandy and rocky soils</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Ph</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CS</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Population-specific Zn tolerance [<xref rid=\"B69-ijerph-17-05506\" ref-type=\"bibr\">69</xref>], Zn accumulator [<xref rid=\"B69-ijerph-17-05506\" ref-type=\"bibr\">69</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><italic>Pistacia terebinthus</italic> L.</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Anacardiaceae</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Deep root system</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">AM [<xref rid=\"B26-ijerph-17-05506\" ref-type=\"bibr\">26</xref>]</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Calcareous soils</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Ph</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CS</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Population-specific Zn tolerance [<xref rid=\"B70-ijerph-17-05506\" ref-type=\"bibr\">70</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><italic>Plantago lanceolata</italic> L.</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Plantaginaceae</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Fibrous root system</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">AM [<xref rid=\"B25-ijerph-17-05506\" ref-type=\"bibr\">25</xref>]</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Neutral to calcareous soils, rangelands</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">He</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CSR</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Population-specific Zn tolerance [<xref rid=\"B20-ijerph-17-05506\" ref-type=\"bibr\">20</xref>], Zn- accumulator [<xref rid=\"B62-ijerph-17-05506\" ref-type=\"bibr\">62</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><italic>Poa annua</italic> L.</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Poaceae</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Fibrous root system</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">AM + NM [<xref rid=\"B26-ijerph-17-05506\" ref-type=\"bibr\">26</xref>]</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Well-drained soil</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Th</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">R</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Population-specific Zn tolerance [<xref rid=\"B71-ijerph-17-05506\" ref-type=\"bibr\">71</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><italic>Poterium sanguisorba</italic> L.</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Rosaceae</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">deep-root system</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">AM [<xref rid=\"B72-ijerph-17-05506\" ref-type=\"bibr\">72</xref>]</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Roadside soils, Rocky soils</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">He</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CS</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Population-specific Zn tolerance [<xref rid=\"B20-ijerph-17-05506\" ref-type=\"bibr\">20</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><italic>Pyrus spinosa</italic> Forssk.</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Rosaceae</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">deep-root system</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">AM + NM [<xref rid=\"B26-ijerph-17-05506\" ref-type=\"bibr\">26</xref>]</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Dry soils, Rocky soils</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Ph</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">C</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Not known</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><italic>Quercus ilex</italic> L.</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Fagaceae</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">deep-root system</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">EC [<xref rid=\"B26-ijerph-17-05506\" ref-type=\"bibr\">26</xref>]</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Calcareous soils, well-drained soils</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Ph</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CS</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Population-specific Zn tolerance [<xref rid=\"B18-ijerph-17-05506\" ref-type=\"bibr\">18</xref>], Zn- accumulator [<xref rid=\"B18-ijerph-17-05506\" ref-type=\"bibr\">18</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><italic>Quercus pubescens</italic> Willd.</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Fagaceae</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">deep-root system</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">EC [<xref rid=\"B26-ijerph-17-05506\" ref-type=\"bibr\">26</xref>]</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Well-drained soils, Calcareous soils</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Ph</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">C</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Not known</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><italic>Ranunculus bulbosus</italic> L.</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Ranunculaceae</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Tuberous root system</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">AM [<xref rid=\"B26-ijerph-17-05506\" ref-type=\"bibr\">26</xref>,<xref rid=\"B27-ijerph-17-05506\" ref-type=\"bibr\">27</xref>]</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Calcareous soils, Stony soil</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Ge</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CSR</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Population-specific Zn tolerance [<xref rid=\"B21-ijerph-17-05506\" ref-type=\"bibr\">21</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><italic>Reseda lutea</italic> L.</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Resedaceae</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">deep-root system</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">AM + NM [<xref rid=\"B26-ijerph-17-05506\" ref-type=\"bibr\">26</xref>,<xref rid=\"B27-ijerph-17-05506\" ref-type=\"bibr\">27</xref>]</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Sandy soils, roadside soils, rangelands</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">He</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CSR</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Population-specific Zn tolerance [<xref rid=\"B20-ijerph-17-05506\" ref-type=\"bibr\">20</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><italic>Rosa canina</italic> L.</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Rosaceae</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">deep-root system</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">AM [<xref rid=\"B26-ijerph-17-05506\" ref-type=\"bibr\">26</xref>]</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Poor soils, sandy soils</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Ph</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CS</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Population-specific Zn tolerance [<xref rid=\"B73-ijerph-17-05506\" ref-type=\"bibr\">73</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><italic>Rubia peregrina</italic> L.</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Rubiaceae</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Rhizome</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">AM [<xref rid=\"B26-ijerph-17-05506\" ref-type=\"bibr\">26</xref>]</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Dry soils, well-drained soils</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">He</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CS</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Population-specific Zn tolerance [<xref rid=\"B19-ijerph-17-05506\" ref-type=\"bibr\">19</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><italic>Rubus ulmifolius</italic> Schott</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Rosaceae</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Rhizome</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">AM [<xref rid=\"B26-ijerph-17-05506\" ref-type=\"bibr\">26</xref>]</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Calcareous soils</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Ph</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CS</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Population-specific Zn tolerance [<xref rid=\"B19-ijerph-17-05506\" ref-type=\"bibr\">19</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><italic>Rumex intermedius</italic> D.C.</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Polygonaceae</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">deep-root system</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">AM [<xref rid=\"B26-ijerph-17-05506\" ref-type=\"bibr\">26</xref>]</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Rocky soils, dry soils</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">He</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CSR</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Not known</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><italic>Ruscus aculeatus</italic> L.</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Asparagaceae</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Rhizome</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">AM [<xref rid=\"B25-ijerph-17-05506\" ref-type=\"bibr\">25</xref>]</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Dry soils, rocky soils</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Ch</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CS</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Population-specific Zn tolerance [<xref rid=\"B19-ijerph-17-05506\" ref-type=\"bibr\">19</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><italic>Scabiosa atropurpurea</italic> L.</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Caprifoliaceae</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Deep root system</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Not known</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Sandy soils, rangelands</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">He</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CSR</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Population-specific Zn tolerance [<xref rid=\"B74-ijerph-17-05506\" ref-type=\"bibr\">74</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><italic>Scrophularia lucida</italic> L.</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Scrophulariaceae</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Slender roots</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Not known</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Dry calcareous soils</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">He</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CSR</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Population-specific Zn tolerance [<xref rid=\"B75-ijerph-17-05506\" ref-type=\"bibr\">75</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><italic>Sedum acre</italic> L.</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Crassulaceae</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Slender roots</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">AM [<xref rid=\"B26-ijerph-17-05506\" ref-type=\"bibr\">26</xref>]</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Rocky soils</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Ch</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">S</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Population-specific Zn tolerance [<xref rid=\"B36-ijerph-17-05506\" ref-type=\"bibr\">36</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><italic>Sedum annuum</italic> L.</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Crassulaceae</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Slender roots</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Not known</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Dry soils</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Th</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">R</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Not known</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><italic>Senecio vulgaris</italic> L.</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Asteraceae</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Deep root system</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">AM [<xref rid=\"B26-ijerph-17-05506\" ref-type=\"bibr\">26</xref>,<xref rid=\"B76-ijerph-17-05506\" ref-type=\"bibr\">76</xref>]</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Cultivated soils</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Th</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">R</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Population-specific Zn tolerance [<xref rid=\"B36-ijerph-17-05506\" ref-type=\"bibr\">36</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><italic>Silene vulgaris</italic> (Moench) Garcke</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Caryophyllaceae</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Slender roots</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">NM [<xref rid=\"B27-ijerph-17-05506\" ref-type=\"bibr\">27</xref>]</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Well-drained soils, rangelands</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">He</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CSR</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Population-specific Zn tolerance [<xref rid=\"B8-ijerph-17-05506\" ref-type=\"bibr\">8</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><italic>Smilax aspera</italic> L.</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Smilacaceae</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Rhizome</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">AM [<xref rid=\"B25-ijerph-17-05506\" ref-type=\"bibr\">25</xref>]</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Dry soils</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">L</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CS</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Population-specific Zn tolerance [<xref rid=\"B77-ijerph-17-05506\" ref-type=\"bibr\">77</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><italic>Thymus vulgaris</italic> L.</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Lamiaceae</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Fibrous root system</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">AM [<xref rid=\"B26-ijerph-17-05506\" ref-type=\"bibr\">26</xref>]</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Dry soils</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Ch</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CS</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Population-specific Zn tolerance [<xref rid=\"B20-ijerph-17-05506\" ref-type=\"bibr\">20</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><italic>Trifolium pratense</italic> L.</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Fabaceae</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Deep root system</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">AM + NM [<xref rid=\"B26-ijerph-17-05506\" ref-type=\"bibr\">26</xref>,<xref rid=\"B27-ijerph-17-05506\" ref-type=\"bibr\">27</xref>]</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Meadows, woods</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">He</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CSR</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Population-specific Zn tolerance [<xref rid=\"B27-ijerph-17-05506\" ref-type=\"bibr\">27</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><italic>Ulex parviflorus</italic> Pourr.</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Fabaceae</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Deep root system</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">AM [<xref rid=\"B26-ijerph-17-05506\" ref-type=\"bibr\">26</xref>]</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Dry, rocky soils, poor soils</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Ph</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">CS</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Not known</td></tr></tbody></table></table-wrap></app></app-group><ref-list><title>References</title><ref id=\"B1-ijerph-17-05506\"><label>1.</label><element-citation publication-type=\"journal\"><person-group 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Icons with number indicate the number of the relevé and its location in the field (bar = 2 m).</p></caption><graphic xlink:href=\"ijerph-17-05506-g003\"/></fig><fig id=\"ijerph-17-05506-f004\" orientation=\"portrait\" position=\"float\"><label>Figure 4</label><caption><p>Average percentage of each life form in the studied mosaic of habitats at the former mine site. Ph: Phanerophytes; He: Hemicryptophytes; Ch: Chamaephytes; L: Liana; Ge: Geophytes; Th: Therophytes.</p></caption><graphic xlink:href=\"ijerph-17-05506-g004\"/></fig><fig id=\"ijerph-17-05506-f005\" orientation=\"portrait\" position=\"float\"><label>Figure 5</label><caption><p>Average percentage of each Grime strategy in the studied mosaic of habitats at the former mine site. CS: competitive, stress tolerant; CSR: competitive, stress tolerant, ruderal; SR: stress tolerant, ruderal; S: stress tolerant; R: ruderal; CR: competitive ruderal; C: competitive.</p></caption><graphic xlink:href=\"ijerph-17-05506-g005\"/></fig><fig id=\"ijerph-17-05506-f006\" orientation=\"portrait\" position=\"float\"><label>Figure 6</label><caption><p>Distribution pattern of root type traits (<bold>a</bold>–<bold>d</bold>), root symbiosis potential occurrence (<bold>e</bold>–<bold>h</bold>), Grime strategy (<bold>i</bold>–<bold>l</bold>) and Raunkiær life forms (<bold>m</bold>–<bold>p</bold>) by TMM tolerance strategy, i.e., hyperaccumulators (<italic>N. caerulescens</italic> (Zn/Cd/Pb- hyperaccumulator) and <italic>B. leavigata</italic> (Tl/Pb/Cd- hyperaccumulator)), Zn-phytoaccumulators (<italic>Plantago lanceolata</italic>, <italic>Quercus ilex</italic> and, <italic>Pinus sylvestris</italic>), phytostabilizators or not known in the literature (see <xref rid=\"ijerph-17-05506-t0A1\" ref-type=\"table\">Table A1</xref>).</p></caption><graphic xlink:href=\"ijerph-17-05506-g006\"/></fig><fig id=\"ijerph-17-05506-f007\" orientation=\"portrait\" position=\"float\"><label>Figure 7</label><caption><p>Microphotographs of roots stained for mycorrhizal association observation: root parts from <italic>N. caerulescens</italic> (<bold>a</bold>), <italic>A. arenaria</italic> (<bold>b</bold>), <italic>T. pratense</italic> (<bold>c</bold>), <italic>B. phoenicoides</italic> (<bold>d</bold>) and, <italic>T. vulgaris</italic> (<bold>e</bold>). AM mycelium and vesicles appear in dark blue. Scale bars are directly on the microphotographs.</p></caption><graphic xlink:href=\"ijerph-17-05506-g007\"/></fig><table-wrap id=\"ijerph-17-05506-t001\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05506-t001_Table 1</object-id><label>Table 1</label><caption><p>Average soil metal content (mg/kg dry weight) at La Gardie mine site.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th colspan=\"7\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Element (mg/kg)</th></tr><tr><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Cr</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Cu</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Fe</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Mn</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Ni</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Pb</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Zn</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">103 ± 38</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">39 ± 17</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">173,377 ± 58,177</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3000 ± 936</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">89 ± 30</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">17,230 ± 6804</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">117,321 ± 33,770</td></tr></tbody></table><table-wrap-foot><fn><p>Values are means of triplicates.</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"ijerph-17-05506-t002\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05506-t002_Table 2</object-id><label>Table 2</label><caption><p>Floristic list established during springtime and occurrence of each plant species in the 6 relevés out of the 4 selected micro-habitats encountered at the former mine site. * (1): Metalliferous grassland; (2) and (3): Herbaceous formation on rocky raised areas; (4) and (5): Matorral with majorly shrubs; (6): Tree stands and frequency out of the 6 relevés.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th rowspan=\"2\" align=\"left\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">Latin Name</th><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">Botanical Family</th><th colspan=\"6\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Occurrence in Each Relevé *</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Frequency</th></tr><tr><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">(1)</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">(2)</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">(3)</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">(4)</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">(5)</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">(6)</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">-</th></tr></thead><tbody><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><italic>Amelanchier ovalis</italic> Medik.</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Rosaceae</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">X</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1/6</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><italic>Aphyllantes monspeliensis</italic> L.</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Liliaceae</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">X</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">X</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2/6</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><italic>Arenaria serpyllifolia</italic> L.</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Caryophyllaceae</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">X</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">X</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">X</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">X</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4/6</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><italic>Argyrolobium zanonii</italic> (Turra) P.W.Ball </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Fabaceae</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">X</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">X</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">X</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3/6</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><italic>Armeria arenaria subsp bupleuroides</italic> (Godr. & Gren.) Greuter & Burdet </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Plumbaginaceae</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">X</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">X</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">X</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">X</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4/6</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><italic>Asparagus acutifolius</italic> L.</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Asparagaceae</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">X</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1/6</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><italic>Asplenium ruta-muraria</italic> L.</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Aspleniaceae</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">X</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1/6</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><italic>Biscutella laevigata</italic> L.</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Brassicaceae</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">X</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">X</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">X</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">X</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">X</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">X</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6/6</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><italic>Brachypodium phoenicoides</italic> (L.) Roem. & Schult</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Poaceae</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">X</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1/6</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><italic>Brachypodium retusum</italic> (Pers.) P.Beauv. </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Poaceae</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">X</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1/6</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><italic>Bromus madritensis</italic> L.</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Poaceae</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">X</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1/6</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><italic>Buxus sempervirens</italic> L.</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Buxaceae</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">X</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">X</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">X</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">X</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4/6</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><italic>Carex halleriana</italic> Asso </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Cyperaceae</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">X</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1/6</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><italic>Centaurea pectinata</italic> L.</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Asteraceae</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">X</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">X</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">X</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">X</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4/6</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><italic>Cerastium pumilum</italic> Curtis </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Caryophyllaceae</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">X</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">X</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">X</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3/6</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><italic>Clematis vitalba</italic> L.</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Ranunculaceae</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">X</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1/6</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><italic>Clinopodium nepeta</italic> (L.) Kuntze</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Lamiaceae</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">X</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">X</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1/6</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><italic>Dactylis glomerata</italic> L.</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Poaceae</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">X</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">X</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2/6</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><italic>Dioscorea communis</italic> (L.) Caddick & Wilkin</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Dioscoreaceae</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">X</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1/6</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><italic>Draba verna</italic> L.</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Brassicaceae</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">X</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">X</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2/6</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><italic>Eryngium campestre</italic> L.</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Apiaceae</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">X</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1/6</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><italic>Euphorbia cyparissias</italic> L.</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Euphorbiacee</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">X</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">X</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">X</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3/6</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><italic>Festuca ovina sl</italic> L.</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Poaceae</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">X</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">X</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">X</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">X</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">X</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5/6</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><italic>Galium aparine</italic> L.</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Rubiaceae</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">X</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">X</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">X</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">X</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4/6</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><italic>Galium corrudifolium</italic> Vill.</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Rubiaceae</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">X</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">X</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2/6</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><italic>Helleborus foetidus</italic> L.</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Ranunculaceae</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">X</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1/6</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><italic>Hordeum murinum</italic> L.</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Poaceae</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">X</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1/6</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><italic>Hornungia petraea</italic> (L.) ex Rchb. </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Brassicaceae</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">X</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">X</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2/6</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><italic>Hypericum perforatum</italic> L.</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Hypericaceae</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">X</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1/6</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><italic>Juniperus oxycedrus</italic> L.</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Cupressaceae</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">X</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">X</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2/6</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><italic>Lactuca perennis</italic> L.</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Asteraceae</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">X</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1/6</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><italic>Lepidium draba</italic> L.</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Brassicaceae</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">X</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1/6</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><italic>Lysimachia arvensis</italic> (L.) U. Manns & Anderb.</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Primulaceae</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">X</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">X</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2/6</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><italic>Mibora minima</italic> (L.) Desv.</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Poaceae</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">X</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">X</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">X</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">X</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4/6</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><italic>Noccaea caerulescens</italic> (J.Presl & C.Presl) F.K.Mey</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Brassicaceae</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">X</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">X</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">X</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">X</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">X</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5/6</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><italic>Pilosella officinarum</italic> Vaill.</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Asteraceae</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">X</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">X</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">X</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3/6</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><italic>Pinus sylvestris</italic> L.</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Pinaceae</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">X</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">X</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">X</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3/6</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><italic>Pistacia terebinthus</italic> L.</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Anacardiaceae</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">X</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1/6</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><italic>Plantago lanceolata</italic> L.</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Plantaginaceae</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">X</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1/6</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><italic>Poa annua</italic> L.</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Poaceae</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">X</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1/6</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><italic>Poterium sanguisorba</italic> L.</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Rosaceae</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">X</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1/6</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><italic>Pyrus spinosa</italic> Forssk.</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Rosaceae</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">X</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1/6</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><italic>Quercus</italic> ilex L.</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Fagaceae</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">X</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">X</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">X</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">X</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">X</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5/6</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><italic>Quercus pubescens</italic> Willd.</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Fagaceae</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">X</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">X</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2/6</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><italic>Ranunculus bulbosus</italic> L.</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Ranunculaceae</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">X</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">X</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2/6</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><italic>Reseda lutea</italic> L.</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Resedaceae</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">X</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">X</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">X</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">X</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4/6</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><italic>Rosa canina</italic> L.</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Rosaceae</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">X</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1/6</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><italic>Rubia peregrina</italic> L.</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Rubiaceae</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">X</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">X</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2/6</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><italic>Rubus ulmifolius</italic> Schott</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Rosaceae</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">X</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">X</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2/6</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><italic>Rumex intermedius</italic> D.C.</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Polygonaceae</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">X</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">X</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2/6</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><italic>Ruscus aculeatus</italic> L.</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Asparagaceae</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">X</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">X</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2/6</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><italic>Scabiosa atropurpurea</italic> L.</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Caprifoliaceae</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">X</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">X</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">X</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3/6</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><italic>Scrophularia lucida</italic> L.</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Scrophulariaceae</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">X</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">X</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2/6</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><italic>Sedum acre</italic> L.</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Crassulaceae</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">X</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1/6</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><italic>Sedum annuum</italic> L.</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Crassulaceae</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">X</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">X</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2/6</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><italic>Senecio vulgaris</italic> L.</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Asteraceae</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">X</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1/6</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><italic>Silene vulgaris</italic> (Moench) Garcke</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Caryophyllaceae</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">X</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1/6</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><italic>Smilax aspera</italic> L.</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Smilacaceae</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">X</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">X</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">X</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3/6</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><italic>Thymus vulgaris</italic> L.</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Lamiaceae</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">X</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">X</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">X</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">X</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">X</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">X</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6/6</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><italic>Trifolium pratense</italic> L.</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Fabaceae</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">X</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">X</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2/6</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><italic>Ulex parviflorus</italic> Pourr.</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Fabaceae</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">X</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">X</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2/6</td></tr></tbody></table><table-wrap-foot><fn><p>X: found on the plot.</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"ijerph-17-05506-t003\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05506-t003_Table 3</object-id><label>Table 3</label><caption><p>Aerial and root part metal content (mg/kg dry weight) at La Gardie mine site.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">Element (mg/kg)</th><th colspan=\"4\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Plant Species</th></tr><tr><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>A. arenaria</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>B. laevigata</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>N. caerulescens</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>P. lanceolata</italic>\n</th></tr></thead><tbody><tr><td colspan=\"5\" align=\"center\" valign=\"middle\" rowspan=\"1\">In aerial parts</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Cr</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">9.6 ± 4.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10.7 ± 5.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">11.4 ± 4.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">11.4 ± 4.2</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Cu</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.6 ± 0.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.9 ± 0.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.0 ± 1.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7.3 ± 0.4</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Fe</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">966.3 ± 375.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">325.6 ± 186.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5780 ± 1878</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">728.5 ± 167.1</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Mn</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">160.3 ± 99.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">204.9 ± 80.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">105.3 ± 25.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">36.6 ± 4.0</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Ni</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.8 ± 0.2 <sup>ab</sup></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.8 ± 0.3 <sup>ab</sup></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.6 ± 0.8 <sup>a</sup></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5 ± 0.2 <sup>b</sup></td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Pb</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">74.9 ± 6.8 <sup>ab</sup></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">48.2 ± 6.3 <sup>b</sup></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">496.2 ± 104.6 <sup>a</sup></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">69.2 ± 15.1 <sup>ab</sup></td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Zn</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1,916 ± 201 <sup>ab</sup></td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1,775 ± 125 <sup>ab</sup></td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">10,664 ± 563 <sup>a</sup></td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">946 ± 88 <sup>b</sup></td></tr><tr><td colspan=\"5\" align=\"center\" valign=\"middle\" rowspan=\"1\">In root parts</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Cr</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.3 ± 1.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.5 ± 0.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">29.9 ± 9.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10.1 ± 0.5</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Cu</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8.6 ± 1.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6.9 ± 0.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">27.2 ± 6.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">21.3 ± 1.2</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Fe</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7,719 ± 3,565</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5,472 ± 1,009</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">56,779 ± 24,995</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">15,210 ± 1,035</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Mn</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">303.0 ± 89.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">223.2 ± 46.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1,143.2 ± 427.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">405.7 ± 37.9</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Ni</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.2 ± 2.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.9 ± 2.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">30.6 ± 13.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7.8 ± 0.6</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Pb</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1,769 ± 49</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1,393 ± 129</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5,123 ± 1,879</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1,630 ± 124</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Zn</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">10,659 ± 2,763</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">6,902 ± 547</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">66,381 ± 24,010</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">12,217 ± 996</td></tr></tbody></table><table-wrap-foot><fn><p>Values are means of triplicates. Means followed by different letters (<sup>a,b, ab</sup>) are significantly different (Dunn test, <italic>p</italic> ≤ 0.05).</p></fn></table-wrap-foot></table-wrap></floats-group></article>\n"
]
[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Int J Environ Res Public Health</journal-id><journal-id journal-id-type=\"iso-abbrev\">Int J Environ Res Public Health</journal-id><journal-id journal-id-type=\"publisher-id\">ijerph</journal-id><journal-title-group><journal-title>International Journal of Environmental Research and Public Health</journal-title></journal-title-group><issn pub-type=\"ppub\">1661-7827</issn><issn pub-type=\"epub\">1660-4601</issn><publisher><publisher-name>MDPI</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32751866</article-id><article-id pub-id-type=\"pmc\">PMC7432069</article-id><article-id pub-id-type=\"doi\">10.3390/ijerph17155542</article-id><article-id pub-id-type=\"publisher-id\">ijerph-17-05542</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Article</subject></subj-group></article-categories><title-group><article-title>Sentiment Analysis and Emotion Understanding during the COVID-19 Pandemic in Spain and Its Impact on Digital Ecosystems</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0002-2738-4177</contrib-id><name><surname>de las Heras-Pedrosa</surname><given-names>Carlos</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05542\">1</xref><xref rid=\"c1-ijerph-17-05542\" ref-type=\"corresp\">*</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0001-7845-9506</contrib-id><name><surname>Sánchez-Núñez</surname><given-names>Pablo</given-names></name><xref ref-type=\"aff\" rid=\"af2-ijerph-17-05542\">2</xref></contrib><contrib contrib-type=\"author\"><name><surname>Peláez</surname><given-names>José Ignacio</given-names></name><xref ref-type=\"aff\" rid=\"af3-ijerph-17-05542\">3</xref></contrib></contrib-group><aff id=\"af1-ijerph-17-05542\"><label>1</label>Department of Audiovisual Communication and Advertising, Faculty of Communication Sciences, Universidad de Málaga, 29071 Malaga, Spain</aff><aff id=\"af2-ijerph-17-05542\"><label>2</label>Joint-PhD Programme in Communication, Universidad de Málaga, 29071 Malaga, Spain; <email>[email protected]</email></aff><aff id=\"af3-ijerph-17-05542\"><label>3</label>Department of Languages and Computer Science, Higher Technical School of Computer Engineering, Universidad de Málaga, 29071 Malaga, Spain; <email>[email protected]</email></aff><author-notes><corresp id=\"c1-ijerph-17-05542\"><label>*</label>Correspondence: <email>[email protected]</email></corresp></author-notes><pub-date pub-type=\"epub\"><day>31</day><month>7</month><year>2020</year></pub-date><pub-date pub-type=\"ppub\"><month>8</month><year>2020</year></pub-date><volume>17</volume><issue>15</issue><elocation-id>5542</elocation-id><history><date date-type=\"received\"><day>30</day><month>6</month><year>2020</year></date><date date-type=\"accepted\"><day>30</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>© 2020 by the authors.</copyright-statement><copyright-year>2020</copyright-year><license license-type=\"open-access\"><license-p>Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>).</license-p></license></permissions><abstract><p>COVID-19 has changed our lives forever. The world we knew until now has been transformed and nowadays we live in a completely new scenario in a perpetual restructuring transition, in which the way we live, relate, and communicate with others has been altered permanently. Within this context, risk communication is playing a decisive role when informing, transmitting, and channeling the flow of information in society. COVID-19 has posed a real pandemic risk management challenge in terms of impact, preparedness, response, and mitigation by governments, health organizations, non-governmental organizations (NGOs), mass media, and stakeholders. In this study, we monitored the digital ecosystems during March and April 2020, and we obtained a sample of 106,261 communications through the analysis of APIs and Web Scraping techniques. This study examines how social media has affected risk communication in uncertain contexts and its impact on the emotions and sentiments derived from the semantic analysis in Spanish society during the COVID-19 pandemic.</p></abstract><kwd-group><kwd>coronavirus</kwd><kwd>COVID-19</kwd><kwd>SARS-CoV-2</kwd><kwd>public health</kwd><kwd>health communication</kwd><kwd>sentiment analysis</kwd><kwd>emotion understanding</kwd><kwd>opinion mining</kwd><kwd>risk communication</kwd><kwd>social media</kwd></kwd-group></article-meta></front><body><sec sec-type=\"intro\" id=\"sec1-ijerph-17-05542\"><title>1. Introduction</title><p>The outbreak of the coronavirus disease (COVID-19) was first reported by the Wuhan Municipal Health and Safety Commission (Hubei Province, China) on 31 December 2019. One month later, the Emergency Committee of the International Health Regulations [<xref rid=\"B1-ijerph-17-05542\" ref-type=\"bibr\">1</xref>] declared the new coronavirus outbreak as a Public Health Emergency of International Importance (PHEI) at its meeting on 30 January 2020 [<xref rid=\"B2-ijerph-17-05542\" ref-type=\"bibr\">2</xref>].</p><p>Five months after the official announcement, the virus has infected more than 6,193,548 people worldwide and killed around 372,479 people [<xref rid=\"B3-ijerph-17-05542\" ref-type=\"bibr\">3</xref>], bringing catastrophic consequences for society [<xref rid=\"B4-ijerph-17-05542\" ref-type=\"bibr\">4</xref>], completely collapsing health systems in different countries [<xref rid=\"B5-ijerph-17-05542\" ref-type=\"bibr\">5</xref>] and generating a strong economic recession worldwide [<xref rid=\"B6-ijerph-17-05542\" ref-type=\"bibr\">6</xref>].</p><p>Throughout the history of mankind, societies have been faced with crises of various kinds and of very diverse natures, such as civil conflicts, financial crises, crises caused by the management and export of energy resources or emergencies caused by the impact of diseases and epidemics, among others [<xref rid=\"B7-ijerph-17-05542\" ref-type=\"bibr\">7</xref>]. It is only necessary to recall some of the events of the past 20th century to be able to appreciate how crises have conditioned our existence and have modified our way of living and understanding the world around us [<xref rid=\"B8-ijerph-17-05542\" ref-type=\"bibr\">8</xref>,<xref rid=\"B9-ijerph-17-05542\" ref-type=\"bibr\">9</xref>].</p><p>However, the current crisis caused by COVID-19 is developing in a social–technological scenario that is unprecedented for society. We are in a highly globalized world, where migration flexibility, free transit between countries, and the development and use of Information and Communication Technologies (ICTs) are fully established [<xref rid=\"B10-ijerph-17-05542\" ref-type=\"bibr\">10</xref>,<xref rid=\"B11-ijerph-17-05542\" ref-type=\"bibr\">11</xref>]. In addition, we must highlight the growing interconnection between the world’s economies, which is reflected in the flow of goods, capital, people, and ideas [<xref rid=\"B12-ijerph-17-05542\" ref-type=\"bibr\">12</xref>]. These facts and others associated with contemporary societies are making us face an old enemy of humanity as they are in this case, viruses such as the COVID-19 pandemic in a completely different context, and it is in this new scenario where areas such as communication, and in particular crisis communication, have become essential to manage and combat crises [<xref rid=\"B8-ijerph-17-05542\" ref-type=\"bibr\">8</xref>,<xref rid=\"B13-ijerph-17-05542\" ref-type=\"bibr\">13</xref>].</p><p>Crisis communication is fundamental in risk situations since it is responsible for maintaining an accessible and transparent relationship in the different flows and transmissions of information and communication, which is the premise of collective action. However, crisis communication is not only about transmitting knowledge but also about finding ways of transmitting comprehensive information that reflects uncertainty and enables the public to make fact-based decisions about our case, about health [<xref rid=\"B14-ijerph-17-05542\" ref-type=\"bibr\">14</xref>,<xref rid=\"B15-ijerph-17-05542\" ref-type=\"bibr\">15</xref>,<xref rid=\"B16-ijerph-17-05542\" ref-type=\"bibr\">16</xref>].</p><p>The population does not always understand the complex professional knowledge about risk, so crisis communication through its professionals must facilitate the understanding of such knowledge in content in a simple and explicit form so that it can be easily understood by all citizens, regardless of their social, cultural or educational level. Society needs to feel informed by qualified experts; otherwise, they will seek out the information, which may be unproven, rumored, or simply false news. Likewise, risk communication must maintain a balance between being neither too centralized nor too decentralized and allow the permeability of information between the different layers of the information process, and all with the ultimate goal of being identified by the population as a trusted source of information [<xref rid=\"B17-ijerph-17-05542\" ref-type=\"bibr\">17</xref>,<xref rid=\"B18-ijerph-17-05542\" ref-type=\"bibr\">18</xref>]. In this process, the different stakeholders must fulfill their responsibility according to their roles and keep the communication network functioning. </p><p>Without a doubt, crisis communication is fundamental when it comes to transmitting security, calmness, and credibility. It oversees forging emotions that generate value to better face the different crises that occur. If this is not the case, it produces an effect of mistrust and lack of credibility in the population, directly affecting the emotions of the target audience. For Covello et al. (2001) [<xref rid=\"B19-ijerph-17-05542\" ref-type=\"bibr\">19</xref>], risks aroused by feelings of sadness, fear, anger, or mistrust will be perceived as greater than the risks that are not. For government institutions or agencies that lack credibility and trust, such feelings will increase in the face of trusted agencies or institutions. In this line, the public health risks associated with pandemics, which produce deaths or have irreversible consequences in a country, will generate a greater feeling of lack of protection and loss of trust among citizens [<xref rid=\"B10-ijerph-17-05542\" ref-type=\"bibr\">10</xref>,<xref rid=\"B18-ijerph-17-05542\" ref-type=\"bibr\">18</xref>,<xref rid=\"B19-ijerph-17-05542\" ref-type=\"bibr\">19</xref>].</p><p>One way of estimating, measuring or assessing crisis communication would be through the study of the emotions and feelings expressed by the public, and to do so without being questioned, to avoid what Arrow [<xref rid=\"B20-ijerph-17-05542\" ref-type=\"bibr\">20</xref>] calls the “strategic aspect of decisions”, where the person being questioned adapts his or her response to his or her interests. It is in this context that social media becomes essential, as it is a virtual space where society usually expresses itself freely (they are not asked), becoming an essential source from which to gather such information. </p><p>The aim of this research work is to analyze the crisis communication in the field of public health that has been carried out during the COVID-19 pandemic, by measuring the emotions that have been generated in the population, and the crisis communication strategy that has been carried out by the Spanish Government. </p><p>The example of Spain was selected as a case study. Under Royal Decree 463/2020 of 14 March Spain declared a state of alarm for the management of the health crisis caused by COVID-19 [<xref rid=\"B21-ijerph-17-05542\" ref-type=\"bibr\">21</xref>], positioning itself as one of the countries in the world most affected by the pandemic, with the highest number of cases per million inhabitants, adding up to a total of 239,638 confirmed cases of COVID-19 by PCR and the second-highest number of deaths according to population, with a total of 27,127 deaths [<xref rid=\"B22-ijerph-17-05542\" ref-type=\"bibr\">22</xref>]. </p><p><xref ref-type=\"fig\" rid=\"ijerph-17-05542-f001\">Figure 1</xref> shows the trend in reported cases of COVID-19, deaths, and recoveries patients between 25 February 2020 and 29 May 2020. It is verified that the highest level of cases by coronavirus was on 31 March 2020 and deceased people on April 1. After these days, the curve of new infections and deaths trended downward and recovered people were increasing. </p><p>Face masks were a problem, especially at the beginning of the pandemic. Initially, they were not available to the general population, so they were only used for those most at risk of infection, such as health care, security forces, and the military. The Government of Spain was responsible for the purchase and distribution of face masks to the autonomous communities. <xref ref-type=\"fig\" rid=\"ijerph-17-05542-f002\">Figure 2</xref> shows the distribution of face masks carried out by the government between 10 March and 29 May. Madrid and Catalonia, with the highest number of infections from COVID-19, were where most face masks were distributed.</p><p>This research work represents a pioneering challenge in the field of risk communication research. During the months of March and April 2020, various digital ecosystems were analyzed, and a sample of 106,261 communications was obtained through the analysis of APIs and Web Scraping techniques. The study analyzes, through social media, how risk communication management has been affected during the COVID-19 pandemic, led by the main governing bodies, health organizations, and the collaboration of the main stakeholders (media). Moreover, the study investigates how COVID-19 has impacted society and the various effects on the emotions and feelings (anger, disgust, sadness, and fear) of the population [<xref rid=\"B25-ijerph-17-05542\" ref-type=\"bibr\">25</xref>]. </p><p>Key variables in risk communication theories and the risk perception model are identified and were introduced into the framework of strategic health risk communication, to assess how the risk content of COVID-19 was or was not communicated to the population. </p><p>The work is organized as follows: in <xref ref-type=\"sec\" rid=\"sec2-ijerph-17-05542\">Section 2</xref>, the materials and methods are presented; in <xref ref-type=\"sec\" rid=\"sec3-ijerph-17-05542\">Section 3</xref>, the results are obtained; and in <xref ref-type=\"sec\" rid=\"sec4-ijerph-17-05542\">Section 4</xref>, the discussion and conclusions finish the work with future research lines.</p></sec><sec id=\"sec2-ijerph-17-05542\"><title>2. Materials and Methods </title><p>This research collected data directly in real time from the main media and digital ecosystems: Twitter, YouTube, Instagram, official press websites, and internet forums. The data collection started on March 1 and finished on 30 April 2020. A sample of 106,261 communications was obtained. These data were comprised of written communications related to the COVID-19 pandemic in Spain. The collected communications were anonymized, eliminating authorship data, location, source, images, hyperlinks, and non-textual components, to leave only the text corpus.</p><p>The study universe comprised thirty-two million Spanish citizens, accounting for roughly 68% of the Spanish population that are considered as social media and internet users, according to the National Institute of Statistics of Spain (INE) data (forecast on 1 July 2019) [<xref rid=\"B26-ijerph-17-05542\" ref-type=\"bibr\">26</xref>]. Each internet user in Spain had the same odds of being included in this research. </p><p>On the one hand, the inclusion criteria were:<list list-type=\"bullet\"><list-item><p>The communication makes explicit reference to the COVID-19 pandemic in Spain.</p></list-item><list-item><p>The communication is public and can be viewed without the need for a subscription to the data source or explicit permission from the sender of the communication.</p></list-item><list-item><p>The author’s reported age, when available, was over 18 years old as of the start of the end of the study (30 April 2020).</p></list-item><list-item><p>The communication is written in Spanish.</p></list-item><list-item><p>On the other hand, the exclusion criteria were:</p></list-item><list-item><p>The communication does not come from an advertising campaign.</p></list-item><list-item><p>The communication has not been generated by automatic procedural methods (bots, fake posts, among others).</p></list-item></list></p><p>One of the most usual problems that we must deal with when using information from digital ecosystems is detecting spammers, fake information generated by bots, which tries to influence or modify the perceived opinion on existing information. To detect and discard this type of information we have implemented different types of algorithms based on Support Vector Machine (SVM) techniques which can detect the patterns of this kind of communications, such as the age of the account (in days), the number of comments from the account, follower/following ratio, and the ratio of messages containing URLs. To prevent the effect of spammers, in this work we implemented and applied filters previously defined and tested in other scientific works [<xref rid=\"B27-ijerph-17-05542\" ref-type=\"bibr\">27</xref>,<xref rid=\"B28-ijerph-17-05542\" ref-type=\"bibr\">28</xref>].</p><p>The emotion information from each communication was extracted employing the natural language analysis tools provided by the IBM Watson Analytics service [<xref rid=\"B29-ijerph-17-05542\" ref-type=\"bibr\">29</xref>]. The emotional intensity was measured in a 0 to 1 scale, where 0 represents the complete absence of this emotion; and 1 represents an absolute high intensity of the emotion. In total, this study measured the emotional intensity of four primal emotions—anger, fear, disgust, and sadness.</p><p>To detect and measure the primary emotions in this study we used the services provided by the IBM Watson system. Watson is a cognitive computing platform that combines a DeepQA architecture, with AI algorithms and Big Data to solve questions in the domain of natural language. This platform offers a wide range of services including Discovery, Knowledge Studio, Language Translator, Natural Language Classifier/Understanding, and Personality Insights among others.</p><p>Watson has an overall precision of 97% in natural language processing and has been widely compared with other systems, as well as with humans, and in both cases, it has obtained very satisfactory results. For this reason, this system has been widely used in different scientific works where it has further proved its capabilities on Natural Language Processing (NLP) tasks [<xref rid=\"B30-ijerph-17-05542\" ref-type=\"bibr\">30</xref>,<xref rid=\"B31-ijerph-17-05542\" ref-type=\"bibr\">31</xref>,<xref rid=\"B32-ijerph-17-05542\" ref-type=\"bibr\">32</xref>,<xref rid=\"B33-ijerph-17-05542\" ref-type=\"bibr\">33</xref>,<xref rid=\"B34-ijerph-17-05542\" ref-type=\"bibr\">34</xref>,<xref rid=\"B35-ijerph-17-05542\" ref-type=\"bibr\">35</xref>].</p><p>In this work, we made use of the Natural Language Understanding service from the IBM Watson platform which, given an input text, provides an analysis of syntactic characteristics as well as information on categories, concepts, emotions, entities, keywords, metadata, relationships, and semantic roles.</p><p>The reliability of the resultant emotion information was tested using the Interval Majority Aggregation Operator (ISMA-OWA) [<xref rid=\"B36-ijerph-17-05542\" ref-type=\"bibr\">36</xref>], which is designed for Decision Making in Social Media with Consistent Data, leveraged by the combination of computational intelligence and Big Data techniques [<xref rid=\"B37-ijerph-17-05542\" ref-type=\"bibr\">37</xref>]. </p><p>To obtain representative results, when analyzing with information extracted from digital ecosystems, it is important to ensure a correct representation of such information and its quality. </p><p>When people express opinions in communications, they do not do so in numerical value with a fixed scale, they use natural language expressions such as “this is great” or “this is not so good”, so we employed the intervalar representation proposed in [<xref rid=\"B36-ijerph-17-05542\" ref-type=\"bibr\">36</xref>,<xref rid=\"B38-ijerph-17-05542\" ref-type=\"bibr\">38</xref>] instead of a numerical scale. The main advantage of this approach is that intervals represent the information within communication in a way that is more similar to the way people express themselves in digital ecosystems, thus reducing the loss of information associated with forcing linguistic data to a hard-numerical scale.</p><p>Furthermore, regarding information quality, an important aspect that we must consider when assessing the validity of this information is to ensure that such information has been expressed with knowledge of the topic at hand and not at random. Another advantage of the usage of an intervalar representation of digital ecosystem data is the availability of consistency indices that can be applied to the matrices obtained from communications to detect inconsistencies derived from uninformed opinions. For this purpose, in this work, we employed the CI+ index defined in [<xref rid=\"B39-ijerph-17-05542\" ref-type=\"bibr\">39</xref>].</p><p>The frequency of the words comprising the sample of communications was calculated using a natural language processing algorithm implemented in Python 3, using the Natural Language Toolkit (NLTK) [<xref rid=\"B40-ijerph-17-05542\" ref-type=\"bibr\">40</xref>]. Moreover, the emotion polarity (positive or negative) was measured using a multilayer perceptron model, trained to classify the emotional weight of written communications [<xref rid=\"B38-ijerph-17-05542\" ref-type=\"bibr\">38</xref>,<xref rid=\"B41-ijerph-17-05542\" ref-type=\"bibr\">41</xref>]. </p><p>The Python NLTK library is an open-source programming library for working with natural language data which incorporate functions that allow for the determination of the frequency of words in a text while discarding stop-words, that is words that are very common to a language but do not convey any significant information such as “the”, “a” and “very”. Furthermore, the NLTK library serves as a pre-processing tool to use other Artificial Intelligence tools such as Artificial Neural Networks such as the multi-layered perceptron that we used in this work to detect the polarity of communications.</p><p>A multi-layered perceptron (MLP) is a widely used artificial neural network architecture that utilizes a technique known as supervised training to learn how to differentiate data that is not linearly separable. In this case, we trained our MPL with a set of communications created by the Spanish Society for Natural Language Processing (SEPLN) which contains over 100,000 natural language texts tagged with the polarity of each communication, that is, each communication contained metadata that indicated if the message was positive or negative. There are other techniques for Natural Language Sentiment Analysis, such as Naïve Bayes, or Support Vector Machines, but we opted for the MLP approach since it can learn complex relationships and it does not enforce any sort of constraint concerning the input data [<xref rid=\"B42-ijerph-17-05542\" ref-type=\"bibr\">42</xref>].</p><p>To further improve the qualitative analysis, the above-mentioned information regarding the volume of communications, the frequency of words and the emotion expressed by each communication was contrasted to determine the information pathways between mass media, government, political parties, employers’ confederation, non-governmental organizations (NGOs), trade unions, the World Health Organization (WHO), among others. This approach provides a graphical representation of the information fluxes about the COVID-19 disease in Spain. </p><p>For the analysis of the messages emitted by the Spanish government, a content analysis of all press releases during the period of study was carried out. Messages were classified as positive, neutral, or negative by selecting the most significant words from them. The frequency of repetition of these words was another objective of this content analysis. The result has been shown through a word cloud representative of the emotions and feelings expressed by the government in its press releases.</p><p>The analysis of content permits inferences to be reproduced based on specific characteristics identified in the messages [<xref rid=\"B43-ijerph-17-05542\" ref-type=\"bibr\">43</xref>,<xref rid=\"B44-ijerph-17-05542\" ref-type=\"bibr\">44</xref>]. This type of analysis allows for the discovery of tendencies and the revelation of differences in content communication. Likewise, this allows the comparison of messages and means of communication, and the identification of intentions, appeals. To this effect, value and frequency analysis were used [<xref rid=\"B45-ijerph-17-05542\" ref-type=\"bibr\">45</xref>].</p></sec><sec sec-type=\"results\" id=\"sec3-ijerph-17-05542\"><title>3. Results</title><sec id=\"sec3dot1-ijerph-17-05542\"><title>3.1. Communication Structure with Stakeholders</title><p>Since the beginning of the pandemic, the structure organized by the government has involved relations between the Spanish government (the Health Alert Coordination Center, which is part of the Ministry of Health) and the governments of the autonomous communities, the National Epidemiology Center, the National Microbiology Center and the international organization’s World Health Organization (WHO), the European Disease Control Center and the European Commission [<xref rid=\"B46-ijerph-17-05542\" ref-type=\"bibr\">46</xref>].</p><p>To raise awareness and inform public opinion, the Spanish government designed a communication strategy articulated in four actions that had the use of the mass media as channels of transmission of COVID- 19 information as a main objective:<list list-type=\"simple\"><list-item><label>(a)</label><p>Weekly appearances of the President of the Government.</p></list-item><list-item><label>(b)</label><p>Daily press conferences chaired jointly by the following ministers: Minister of Health, who is responsible for the state of alarm decreed in the country; Minister of Defense, who is responsible for the military forces; Minister of the Interior, who is responsible for the State security forces and Minister of Transport. All of them were accompanied by experts in each of the areas. The ministers sent out a political message and the experts went into detail about the actions being taken. With a press conference format, online questions from the main Spanish and foreign media were admitted. However, this format underwent the first modification after the second week being responsible for the press conferences the so-called “Technical Committee for monitoring the coronavirus pandemic in Spain” consisting only of experts of the different ministries. On 25 April, there was a new restructuring of the press conferences, leaving only the director of the Health Alert and Emergency Coordination Centre of the Ministry of Health as the health expert. This last change is censored by the communications media.</p></list-item><list-item><label>(c)</label><p>Press release. After the appearance at a press conference, the communication department of the Ministry of Health sent a press release to all the media.</p></list-item><list-item><label>(d)</label><p>Interviews with ministers. Another of the government’s actions was to make its Cabinet available to the media for interviews.</p></list-item></list></p><p>To reinforce the previous actions, on March 15, the state government launched the advertising campaign <italic>#</italic>EsteVirusLoParamosUnidos. This campaign is adapted for television, press, radio, outdoor advertising, and social networks.</p><p><xref ref-type=\"fig\" rid=\"ijerph-17-05542-f003\">Figure 3</xref> shows the main communication flows established in the government’s strategy. Except for social networks and outdoor advertising that are directly focused on citizens, the rest of the information is conducted through the media.</p><p>In Spain, the decreed state of alarm requires the total confinement of the population. Royal Decree-Law 10/2020, of 29 March 2020 [<xref rid=\"B47-ijerph-17-05542\" ref-type=\"bibr\">47</xref>], establishes the minimum essential services of first necessity such as all those necessary for the supply of food to the population. The minimum distance was made to be one and a half meters. Except for these cases, the rest of the population must carry out their work by teleworking, and if this work is not possible, the government approved Royal Decree-Law 8/2020 of 17 March on extraordinary urgent measures to cope with the economic and social impact of COVID-19 [<xref rid=\"B48-ijerph-17-05542\" ref-type=\"bibr\">48</xref>], which regulates emergency procedures to combat the economic and social impact of the pandemic, denominated as the Temporary Employment Regulation File (TERF). The number of workers affected by the TERF was two million on 3 April [<xref rid=\"B49-ijerph-17-05542\" ref-type=\"bibr\">49</xref>]. The high number of TERF requests blocks the administration from responding to the citizens with a decrease of the collection of these aids and the decapitalization of these workers in some cases without the possibility of paying the rents of their houses or simply buying the necessary food for the family. Non-Governmental Organizations and food banks have a crucial role to supply the neediest in the population. During confinement, the media are not left out. Their workers follow their work from their homes. On televisions, these measures cause programs to be suspended and replaced by new programming offering coronavirus specials. These programs have a structure of news, interviews with experts or politicians, discussion programs or talk shows where COVID-19 and the situation that citizens are experiencing are analyzed.</p><p>Due to the uncertainty of the situation and the isolation in their homes, citizens are consuming more television. Thus, the month of March and later April became the months with the highest television audience in Spanish history. March data show an average consumption of 282 min per person per day (4 h and 42 min). The average number of people who had watched TV for at least one minute a day was 369 min (6 h and 9 min) [<xref rid=\"B50-ijerph-17-05542\" ref-type=\"bibr\">50</xref>]. The progression in the television audience continued in the month of April with numbers never seen in the conventional Spanish television with 302 min (5 h and 2 min) and 395 min (6 h and 35 min) respectively. In addition to television coverage, 33.6 million Spaniards consumed this medium daily, representing 74.2% of the population [<xref rid=\"B51-ijerph-17-05542\" ref-type=\"bibr\">51</xref>].</p><p>The serious effects on the economy caused by the crisis determine that new actors acquire an active role in communication by modifying the initial panorama organized by the government. Political parties, the Confederation of Employers and trade unions are configured as sources of information. These new stakeholders also offer interviews to the communication media, organize press conferences, and finally communicate with citizens directly through social media (see <xref ref-type=\"fig\" rid=\"ijerph-17-05542-f001\">Figure 1</xref>).</p><p>Therefore, the stakeholder structure created by the government is increased by other social actors who have their own opinion on the management of the pandemic. All of them have in common the use of the media to convey their messages to the citizens, converting these media as the main interlocutors with the population. The high consumption of television makes it the main means of information used by citizens.</p><p>Public and private televisions in Spain broadcast the press conferences of the different stakeholders and the appearances of the President of the Government. This is referred to in <xref ref-type=\"fig\" rid=\"ijerph-17-05542-f001\">Figure 1</xref> as “news”.</p><p>The different ideological tendencies of the television channels in Spain mean that their interview programs with experts and television debates do not follow a single argument in support of the government’s management. These messages feature contradictory opinions that the media convey to the public as interviews, discussion programs, and talk shows, which increase uncertainty among citizens (<xref ref-type=\"fig\" rid=\"ijerph-17-05542-f003\">Figure 3</xref>).</p></sec><sec id=\"sec3dot2-ijerph-17-05542\"><title>3.2. Comparison between the Tone of the Messages Sent by the Government and the Feelings and Emotions Generated in the Population</title><p>In a public health crisis like the one Spain is experiencing, a transparent and empathetic communication style would generate citizen confidence and would be more effective if politicians and experts unanimously tried to stimulate the population to take a positive stance towards the pandemic and the health and economic alert measures imposed by the government. Although the generation of trust must be essential in a crisis, the analysis carried out shows the public’s distrust of scientific experts and government representatives for a variety of reasons such as access to conflicting sources of information, contradictions in scientific reasoning, changes in decision-making and, above all, political confrontations.</p><p>Trust and credibility, demonstrated through empathy, experience, honesty, and transparency, are essential elements of public health crisis communication [<xref rid=\"B18-ijerph-17-05542\" ref-type=\"bibr\">18</xref>].</p><p><xref ref-type=\"fig\" rid=\"ijerph-17-05542-f004\">Figure 4</xref> analyzes the messages transmitted by the Ministry of Health in its press releases between March and April. In green, the positive messages were determined, in black the neutral ones and in red the negative ones. The word size indicates the frequency of repetition in the press releases. As can be seen in the word cloud, the negative word “COVID” is the most used by the government in its communications. This is followed at a distance by “coronavirus” and “health crisis” with a dark red color that indicates their use in negative messages, but also in neutral tones. “Social Networks” is a neutral term used mainly to explain the social network campaigns implemented by the government. It is followed by “patients” and “nursing home”. However, the most remarkable thing about this word cloud is its words in the green. The communication made by the communication office of the Ministry of Health has always wanted to give a positive view in all their messages, with “Government” as the most used word, followed by “face masks”, “Ministry of Health” or “test”. This could indicate a lack of transparency about the situation the country was going through. None of these press releases refer to either the infected or the dead. Attempts are made to give a protagonist role, at times, to all the actions carried out by the government.</p><p>In contrast, <xref ref-type=\"fig\" rid=\"ijerph-17-05542-f005\">Figure 5</xref> shows the results of the 106,261 listings made on social media between the same months and shows the feelings and emotions of the population. On this occasion, the word “cases” is the most representative that reflects the number of infections suffered in the country. It is followed by the word “crisis”, which represents the public health crisis but also the economic one. The terms “COVID” and “Coronavirus” are strongly represented, as well as “Spain” and “world” which represent the concern of the population in the face of a pandemic of this magnitude. “Casualties” is another of the most significant words and is indicative of all those people who have benefited from the TERF and who have not yet received the promised aid from the government. The positive messages sent by the government and its experts are counterbalanced by the volume of opinion generated by the media and especially the generalist televisions.</p><p>Some reasons include political parties’ criticism of the government’s management, contradictions of the experts, the constant increase of infected and dead, Spain being among the most affected countries, the state of confinement suffered by society not always in the best conditions, the anxiety of not having financial resources, the population’s insecurity in the face of a public health crisis with global effects that are caused by millions of infected people and hundreds of thousands of deaths in the world.</p><p>All these reasons generate negative feelings and emotions, causing uncertainty and fear among citizens. Digital ecosystems reflect this trend in a word cloud with a markedly negative character (<xref ref-type=\"fig\" rid=\"ijerph-17-05542-f005\">Figure 5</xref>).</p></sec><sec id=\"sec3dot3-ijerph-17-05542\"><title>3.3. Communications that Generate the Greatest Emotional and Sentimental Impact on Society during the COVID-19 Pandemic</title><p>The communications that have the greatest impact on four of the main emotions of the population—fear, sadness, disgust, and anger—are presented. The study has allowed for the determination of the reaction of the population concerning the COVID-19 pandemic and the crisis communication carried out by the government, determining the themes and the feelings of the communications associated with the crisis communication.</p><p>To this end, the emotion graph corresponding to the period of study is first determined, determining the peaks of emotion that are significant, and those news patterns that generate greater presence and reach in digital ecosystems. Secondly, the topics that have most influenced these emotions are analyzed and the patterns that generate them are concluded.</p><sec id=\"sec3dot3dot1-ijerph-17-05542\"><title>3.3.1. Association of Disgust Communications Connected with Management and Its Emotional Impact during COVID-19 Pandemic</title><p><xref ref-type=\"fig\" rid=\"ijerph-17-05542-f006\">Figure 6</xref> shows the evolution of the Disgust emotion during the study period, where nine peaks can be distinguished where the emotion shows a significant increase.</p><p>In <xref rid=\"ijerph-17-05542-t001\" ref-type=\"table\">Table 1</xref>, the communications that had the greatest impact on this increase are analyzed in chronological order from 1 March 2020 to 30 April 2020. From these communications, the management of the pandemic is the general theme that most impacts the emotion treated. Aspects such as: blaming the pandemic on groups that can be grouped by religion, sex, use of the security forces to censor the population’s opinion; lack of care for weak sectors such as the elderly; and the purchase of health material are the conversations that predominate in digital ecosystems.</p><p>Finally, in <xref ref-type=\"fig\" rid=\"ijerph-17-05542-f007\">Figure 7</xref>, the most relevant topics and their impact value on the emotion of disgust are shown. This shows how the management of masks, censorship in the news, and the transmission of the virus in general and especially in groups of elderly people, predominate in this emotion.</p></sec><sec id=\"sec3dot3dot2-ijerph-17-05542\"><title>3.3.2. Association of Fear Communications Related to Death and Its Emotional Impact during COVID-19 Pandemic</title><p><xref ref-type=\"fig\" rid=\"ijerph-17-05542-f008\">Figure 8</xref> shows the evolution of the Fear emotion during the study period, where five peaks can be distinguished, where the emotion shows a sustained decrease over time.</p><p>In <xref rid=\"ijerph-17-05542-t002\" ref-type=\"table\">Table 2</xref>, the communications that have had the greatest impact on this temporal progression are analyzed in chronological order from 1 March 2020 to 30 April 2020. Of these communications, the rapid growth of the pandemic in Spain, the overwhelming social security system, and the economic collapse caused by the COVID-19 pandemic are the general themes that have the greatest impact on the emotions addressed. Aspects such as border closures, death forecasts, job losses, defective health material, the Spanish government being overwhelmed, and deaths in residences are the conversations that predominate in digital ecosystems.</p><p>Finally, <xref ref-type=\"fig\" rid=\"ijerph-17-05542-f009\">Figure 9</xref> shows the most relevant issues and their impact value on the emotion Fear. This shows how interest in the state of alarm, the transmission of the virus, emergency health material, and deaths of family members predominate in this emotion.</p></sec><sec id=\"sec3dot3dot3-ijerph-17-05542\"><title>3.3.3. Association of Anger Communications Related to Lack of Foresight and Its Emotional Impact during COVID-19 Pandemic</title><p><xref ref-type=\"fig\" rid=\"ijerph-17-05542-f010\">Figure 10</xref> shows the evolution of the Anger emotion during the study period, where eight peaks can be distinguished where the emotion shows sustained growth over time.</p><p>In <xref rid=\"ijerph-17-05542-t003\" ref-type=\"table\">Table 3</xref>, the communications that had the greatest impact on this temporal progression are analyzed in chronological order from 1 March 2020 to 30 April 2020. From these communications, it can be seen that the loss of employment due to lack of foresight, the delay in activating the health alert, and the opacity in the acquisition of health material by the Spanish Government during the crisis by COVID-19 were the driving themes in this case. Aspects such as disinformation for de-escalation, the collapse of the health system, the dubious data on the number of infected and dead people, and the control of the media proposed by members of the Spanish government are some of the conversations that predominate in digital ecosystems.</p><p>Finally, <xref ref-type=\"fig\" rid=\"ijerph-17-05542-f011\">Figure 11</xref> shows the most relevant topics and their impact value on the Anger emotion. This shows how the interest in deaths by a coronavirus, the resources to cure the virus, the diagnosed cases, and the rate of infected, predominate in this emotion. This shows that the lack of prevision predominates in this emotion.</p></sec><sec id=\"sec3dot3dot4-ijerph-17-05542\"><title>3.3.4. Association of Sadness Communications Related to Safeguarding and Its Emotional Impact during COVID-19 Pandemic</title><p><xref ref-type=\"fig\" rid=\"ijerph-17-05542-f012\">Figure 12</xref> shows the evolution of the Sadness emotion during the study period, where eight peaks can be distinguished where the emotion shows sustained growth in time.</p><p>In <xref rid=\"ijerph-17-05542-t004\" ref-type=\"table\">Table 4</xref>, the communications that had the greatest impact on this temporal progression are analyzed in chronological order from 1 March 2020 to 30 April 2020. Of these communications, censorship during COVID-19, die of coronavirus, coronavirus patients, infection of coronavirus, and the delay in the incorporation to the labor activity are the general themes that have the greatest impact on the emotion dealt with. Aspects such as political interests, entities that the security of the population due to the virus, sale of necessary material by the COVID-19 pandemic to foreign countries when it is necessary for Spain, healthcare workers exposed to infection by defective health care material are some of the conversations that predominate in digital ecosystems.</p><p>Finally, <xref ref-type=\"fig\" rid=\"ijerph-17-05542-f013\">Figure 13</xref> shows the most relevant topics and their impact value on Sadness’s emotion. This shows how the interest in deaths by COVID-19, patients by COVID-19, the elderly, and infected workers, predominate in this emotion.</p></sec></sec><sec id=\"sec3dot4-ijerph-17-05542\"><title>3.4. Volumes and Flows of Information during the COVID-19 Pandemic</title><p>As shown in <xref ref-type=\"fig\" rid=\"ijerph-17-05542-f014\">Figure 14</xref>, the highest presence of the term COVID-19 occurred in the early stages of the pandemic, reaching its highest value on the date when the Government of Spain announced that it would implement the state of alarm and confinement of the population. From that date onwards, there is a downward trend in the use of this term, until 5 April, when an extension of the state of alarm is announced.</p><p>During this time, the use of the term COVID-19 followed a decreasing tendency, motivated by the emotions that the population experienced. If at the beginning, the great concern was the virus, the management carried out by the government, the deaths, the social actions, all caused a change in the terms used in the digital ecosystems, with the virus being a secondary problem about the subjects that influence the emotions.</p></sec></sec><sec sec-type=\"conclusions\" id=\"sec4-ijerph-17-05542\"><title>4. Conclusions</title><p>The spread of pandemics causes uncertainty and fear among the population. This type of crisis, by not adjusting to specific limits, makes risk communication more critical when designing effective strategies [<xref rid=\"B52-ijerph-17-05542\" ref-type=\"bibr\">52</xref>,<xref rid=\"B53-ijerph-17-05542\" ref-type=\"bibr\">53</xref>]. Effective risk communication means that all messages can be presented and shared with the population in a transparent, credible, and easily understood communication process. Its main objective is to reduce the knowledge gap between the issuers of information and its recipients to adjust public behavior to proactively address risk [<xref rid=\"B54-ijerph-17-05542\" ref-type=\"bibr\">54</xref>,<xref rid=\"B55-ijerph-17-05542\" ref-type=\"bibr\">55</xref>]. The essential elements for reducing risk and avoiding panic among the population are rapid action by public health organizations and truthful and honest information from governments [<xref rid=\"B56-ijerph-17-05542\" ref-type=\"bibr\">56</xref>].</p><p>Even though Rodin et al. [<xref rid=\"B57-ijerph-17-05542\" ref-type=\"bibr\">57</xref>] indicate that in the case of a crisis in public health, stakeholders are structured in international and national public health organizations, national governments, non-governmental organizations, the media, and citizens, the serious situation experienced in Spain has led to new actors taking on a decisive role in communication, modifying the organizational structure originally designed by the government. Therefore, the little or no dialogue between the government and the social actors that make up the map of the main publics involved in the COVID-19 crisis with different points of view in the face of the pandemic leads to the conclusion that the structure of the stakeholders involved does not determine singular, clear and efficient communication that gives confidence to society.</p><p>The analysis of the government’s communication management shows that the messages emitted, mostly with a positive tone, have been offset by a flow of information from other actors in disagreement with government policies. These are mainly channeled by the media and especially the generalist televisions. In Spain, three out of four citizens have used generalist television to keep themselves informed during the pandemic. Television is also the medium most used by Spaniards to seek out different expert opinions. Finally, seven out of ten Spaniards say that the diversity of journalists, approaches, and news items on generalist television help them form their own opinions [<xref rid=\"B58-ijerph-17-05542\" ref-type=\"bibr\">58</xref>]. This information, sometimes contradictory, that reaches the population makes uncertainty and panic be perceived by the citizens through digital ecosystems.</p><p>There are significant differences between the feelings and emotions of the public about COVID-19 analyzed in this study and the tone of the risk communication carried out by the Spanish government and the committee of experts represented in <xref ref-type=\"fig\" rid=\"ijerph-17-05542-f004\">Figure 4</xref>.</p><p>Risk communication has very close links to the behavioral health issues that affect tens of millions of people. Fear and anxiety about a new disease and what could happen can be overwhelming and cause strong emotions in the population. Through the monitoring of the emotions and the general sentiment of the people across social media about the COVID-19 pandemic reveals that:</p><p>Research shows that the current COVID-19 pandemic is creating an added strain on our emotional well-being.</p><p>Topics and themes connected to COVID-19 include Management, Social Collaboration, Death, Safeguarding, and Lack of Foresight. Those are strongly related to health and finances, uncertainty about the length of the quarantine, anger over the loss of control, fear of death, illness, loss of employment, economic instability, loss of loved ones, discontent with the Spanish government, transparency, a sense of loneliness and, ultimately, fear of the unknown.</p><p>Research results also demonstrate a lot of mixed feelings. It is observed that the same news, information or media communication generated peaks in different emotions, indicating that they are very mixed between sadness, disgust, anger, and fear.</p><p>Presence analysis reveals that the term COVID-19 received the highest presence during the early stages of the pandemic, reaching its highest value on the date when the Government of Spain announced that it would implement the state of alarm and confinement of the population. From that date onwards, there is a downward trend in the use of the term COVID-19.</p><p>During this time, the use of the term COVID-19 has followed a decreasing tendency, motivated by the emotions that the population has experienced. Initially, as reflected in the study, only the virus (term COVID) was of interest, and later, the consequences and direct impact of the virus on daily life.</p></sec></body><back><notes><title>Author Contributions</title><p>Conceptualization, C.d.l.H.-P., P.S.-N. and J.I.P.; methodology, C.d.l.H.-P., P.S.-N. and J.I.P; software, C.d.l.H.-P., P.S.-N. and J.I.P.; validation, C.d.l.H.-P., P.S.-N. and J.I.P.; formal analysis, C.d.l.H.-P., P.S.-N. and J.I.P.; investigation, C.d.l.H.-P., P.S.-N. and J.I.P.; resources, C.d.l.H.-P., P.S.-N. and J.I.P.; data curation, C.d.l.H.-P., P.S.-N. and J.I.P.; writing—original draft preparation, C.d.l.H.-P., P.S.-N. and J.I.P.; writing—review and editing, C.d.l.H.-P., P.S.-N. and J.I.P.; visualization, C.d.l.H.-P., P.S.-N. and J.I.P.; supervision, C.d.l.H.-P., P.S.-N. and J.I.P.; project administration, C.d.l.H.-P., P.S.-N. and J.I.P.; funding acquisition, C.d.l.H.-P., P.S.-N. and J.I.P. All authors have read and agreed to the published version of the manuscript.</p></notes><notes><title>Funding</title><p>The research was funded by Programa Operativo FEDER Andalucía 2014–2020 under grant number UMA18-FEDERJA-148 and Plan Propio-Universidad de Málaga.</p></notes><notes notes-type=\"COI-statement\"><title>Conflicts of Interest</title><p>The authors declare no conflict of interest.</p></notes><ref-list><title>References</title><ref id=\"B1-ijerph-17-05542\"><label>1.</label><element-citation publication-type=\"book\"><person-group person-group-type=\"author\"><collab>Organización Mundial de la Salud</collab></person-group><source>Reglamento Sanitario Internacional</source><publisher-name>Organización Mundial de la Salud</publisher-name><publisher-loc>Geneve, Switzerland</publisher-loc><year>2016</year><volume>Volume 2005</volume><isbn>9789243580494</isbn></element-citation></ref><ref id=\"B2-ijerph-17-05542\"><label>2.</label><element-citation publication-type=\"book\"><person-group person-group-type=\"author\"><name><surname>Regulations</surname><given-names>E.C.</given-names></name></person-group><source>Statement on the Second Meeting of the International Health Regulations (2005) Emergency Committee Regarding the Outbreak of Novel Coronavirus (2019-nCoV)</source><comment>Convened by the W.D.-G. under the I.H.</comment><publisher-name>World Health Organization</publisher-name><publisher-loc>Geneva, Switzerland</publisher-loc><year>2020</year></element-citation></ref><ref id=\"B3-ijerph-17-05542\"><label>3.</label><element-citation publication-type=\"web\"><article-title>JHU CSSE COVID-19 Dashboard by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University (JHU)</article-title><comment>Available online: <ext-link ext-link-type=\"uri\" xlink:href=\"https://coronavirus.jhu.edu/map.html\">https://coronavirus.jhu.edu/map.html</ext-link></comment><date-in-citation content-type=\"access-date\" iso-8601-date=\"2020-07-01\">(accessed on 1 July 2020)</date-in-citation></element-citation></ref><ref id=\"B4-ijerph-17-05542\"><label>4.</label><element-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Arshad Ali</surname><given-names>S.</given-names></name><name><surname>Baloch</surname><given-names>M.</given-names></name><name><surname>Ahmed</surname><given-names>N.</given-names></name><name><surname>Arshad Ali</surname><given-names>A.</given-names></name><name><surname>Iqbal</surname><given-names>A.</given-names></name></person-group><article-title>The outbreak of Coronavirus Disease 2019 (COVID-19)—An emerging global health threat</article-title><source>J. 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Source: Ministry of Health of the Government of Spain [<xref rid=\"B23-ijerph-17-05542\" ref-type=\"bibr\">23</xref>].</p></caption><graphic xlink:href=\"ijerph-17-05542-g001\"/></fig><fig id=\"ijerph-17-05542-f002\" orientation=\"portrait\" position=\"float\"><label>Figure 2</label><caption><p>Masks that the Spanish Government has distributed to each autonomous community from March 10 to May 29 [<xref rid=\"B24-ijerph-17-05542\" ref-type=\"bibr\">24</xref>].</p></caption><graphic xlink:href=\"ijerph-17-05542-g002\"/></fig><fig id=\"ijerph-17-05542-f003\" orientation=\"portrait\" position=\"float\"><label>Figure 3</label><caption><p>Interrelations between the main stakeholders and the citizens.</p></caption><graphic xlink:href=\"ijerph-17-05542-g003\"/></fig><fig id=\"ijerph-17-05542-f004\" orientation=\"portrait\" position=\"float\"><label>Figure 4</label><caption><p>Word cloud generated from the press releases of the Ministry of Health during the period 1 March 2020 until 30 April 2020 related to terms about COVID-19.</p></caption><graphic xlink:href=\"ijerph-17-05542-g004\"/></fig><fig id=\"ijerph-17-05542-f005\" orientation=\"portrait\" position=\"float\"><label>Figure 5</label><caption><p>Word cloud generated during the period 1 March 2020 until 30 April 2020 related to terms about COVID-19.</p></caption><graphic xlink:href=\"ijerph-17-05542-g005\"/></fig><fig id=\"ijerph-17-05542-f006\" orientation=\"portrait\" position=\"float\"><label>Figure 6</label><caption><p>Disgust emotion during the period 1March 2020 until 30 April 2020 related to terms about COVID-19.</p></caption><graphic xlink:href=\"ijerph-17-05542-g006\"/></fig><fig id=\"ijerph-17-05542-f007\" orientation=\"portrait\" position=\"float\"><label>Figure 7</label><caption><p>The themes related to COVID-19 and Disgust emotion that have impacted most along with its impact value.</p></caption><graphic xlink:href=\"ijerph-17-05542-g007\"/></fig><fig id=\"ijerph-17-05542-f008\" orientation=\"portrait\" position=\"float\"><label>Figure 8</label><caption><p>Fear emotion during the period 1 March 2020 until 30 April 2020 related to terms about COVID-19.</p></caption><graphic xlink:href=\"ijerph-17-05542-g008\"/></fig><fig id=\"ijerph-17-05542-f009\" orientation=\"portrait\" position=\"float\"><label>Figure 9</label><caption><p>The themes related to COVID-19 and Fear Emotion that have impacted most along with its impact value.</p></caption><graphic xlink:href=\"ijerph-17-05542-g009\"/></fig><fig id=\"ijerph-17-05542-f010\" orientation=\"portrait\" position=\"float\"><label>Figure 10</label><caption><p>Anger emotion during the period 1 <bold>March</bold> 2020 until 30/04/2020 related to terms about COVID-19.</p></caption><graphic xlink:href=\"ijerph-17-05542-g010\"/></fig><fig id=\"ijerph-17-05542-f011\" orientation=\"portrait\" position=\"float\"><label>Figure 11</label><caption><p>The themes related to COVID-19 and Anger emotion that have impacted most along with its impact value.</p></caption><graphic xlink:href=\"ijerph-17-05542-g011\"/></fig><fig id=\"ijerph-17-05542-f012\" orientation=\"portrait\" position=\"float\"><label>Figure 12</label><caption><p>Sadness emotion during the period 1 <bold>March</bold> 2020 until 30 April 2020 related to terms about COVID-19.</p></caption><graphic xlink:href=\"ijerph-17-05542-g012\"/></fig><fig id=\"ijerph-17-05542-f013\" orientation=\"portrait\" position=\"float\"><label>Figure 13</label><caption><p>The themes related to COVID-19 and Sadness emotion that have impacted most along with its impact value.</p></caption><graphic xlink:href=\"ijerph-17-05542-g013\"/></fig><fig id=\"ijerph-17-05542-f014\" orientation=\"portrait\" position=\"float\"><label>Figure 14</label><caption><p>Presence Analysis in Digital Ecosystems during the period 1 March 2020 until 30 April 2020 related to terms about COVID-19.</p></caption><graphic xlink:href=\"ijerph-17-05542-g014\"/></fig><table-wrap id=\"ijerph-17-05542-t001\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05542-t001_Table 1</object-id><label>Table 1</label><caption><p>Comments related to Disgust emotion and COVID-19 in digital ecosystems.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Disgust Emotion Related to COVID-19 Comments in Digital Ecosystems</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2 March 2020</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<list list-type=\"bullet\"><list-item><p>The religious community is eager for the Ministry of Health to “point them out” because it leaves them in a “state of defenselessness” and they demand an apology.</p></list-item><list-item><p>The evangelicals see it as “very serious” that the Ministry of Health points to a religious group as a possible focus.</p></list-item></list>\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">23 March 2020</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<list list-type=\"bullet\"><list-item><p>A collapse in funeral homes and mortuaries.</p></list-item><list-item><p>Spanish health workers are the worst protected.</p></list-item><list-item><p>The government is withholding health materials from autonomous communities.</p></list-item><list-item><p>The risk of coronavirus is due to gender roles.</p></list-item><list-item><p>Health risk for allowing 8M and then blaming others.</p></list-item><list-item><p>Lack of material by the government for testing.</p></list-item><list-item><p>Purchase of defective tests by the government.</p></list-item><list-item><p>Do not know who sells the defective tests.</p></list-item><list-item><p>Ministers choose private health care while criticizing it and asking everyone to go to the public health care system.</p></list-item><list-item><p>There are tests for politicians and not for the general population (case of Minister Irene Montero), and they skip the confinement (case of Second Vice-President Pablo Iglesias).</p></list-item></list>\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">28 March 2020</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<list list-type=\"bullet\"><list-item><p>Purchase of material from the Chinese testing company that sold the failed tests.</p></list-item><list-item><p>Announcement of shipment of quantities of masks and the reality is much lower.</p></list-item><list-item><p>Fake tests that the government bought when private companies had discovered the deception.</p></list-item><list-item><p>Use of false accounts by members of the government to defend the government’s management.</p></list-item><list-item><p>Rapid tests do not work.</p></list-item></list>\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">09 April 2020</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<list list-type=\"bullet\"><list-item><p>Purchase of protection material from companies without guarantee, having lists of companies provided by the EU to buy material with a guarantee.</p></list-item><list-item><p>Errors in the death count by the Ministry of Health.</p></list-item><list-item><p>Catalonia requests a ban on the army and Guardia Civil hospitals.</p></list-item><list-item><p>Spanish Government presumes better management of Europe.</p></list-item><list-item><p>Elderly deaths in nursing homes.</p></list-item></list>\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">10 April 2020</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<list list-type=\"bullet\"><list-item><p>Triumphalist government management of the pandemic.</p></list-item></list>\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">14 April 2020</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<list list-type=\"bullet\"><list-item><p>Purchase of masks from opaque companies.</p></list-item><list-item><p>The government forces companies to provide workers with protective measures when they cannot buy material.</p></list-item><list-item><p>The government spends money on protecting cars when there is a lack of material in hospitals and nursing homes.</p></list-item></list>\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">20 April 2020</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<list list-type=\"bullet\"><list-item><p>Government members fail to comply with confinement.</p></list-item><list-item><p>Government management to protect against future malpractice claims.</p></list-item><list-item><p>The political use of pandemic management.</p></list-item><list-item><p>Use of the Guardia Civil to minimize the anti-government climate.</p></list-item></list>\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">23 April 2020</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<list list-type=\"bullet\"><list-item><p>Government control of the media.</p></list-item><list-item><p>Higher payment for medical equipment by the government.</p></list-item><list-item><p>Lack of material for workers and they are forced to return to work.</p></list-item><list-item><p>The hiring of companies without guarantees to obtain sanitary material.</p></list-item></list>\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">28 April 2020</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<list list-type=\"bullet\"><list-item><p>Use of the police and confinement to control complaints from the population.</p></list-item><list-item><p>The government admits that it lies about the number of tests performed.</p></list-item><list-item><p>False count in the number of deaths.</p></list-item></list>\n</td></tr></tbody></table></table-wrap><table-wrap id=\"ijerph-17-05542-t002\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05542-t002_Table 2</object-id><label>Table 2</label><caption><p>Comments related to Fear emotion and COVID-19 in digital ecosystems.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Fear Emotion Related to COVID-19 Comments in Digital Ecosystems</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">6 March 2020</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<list list-type=\"bullet\"><list-item><p>Loss of jobs.</p></list-item></list>\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">18 March 2020</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<list list-type=\"bullet\"><list-item><p>The virus preys on residences.</p></list-item><list-item><p>Closing of borders.</p></list-item></list>\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">25 March 2020</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<list list-type=\"bullet\"><list-item><p>Spain surpasses China in coronavirus deaths with 3434 deaths.</p></list-item><list-item><p>The number of deaths continues to rise.</p></list-item><list-item><p>The Spanish government now estimates more than three months of economic collapse.</p></list-item><list-item><p>Collapsed health system.</p></list-item></list>\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">27 March 2020</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<list list-type=\"bullet\"><list-item><p>The virus has reached the level of a pandemic.</p></list-item><list-item><p>The Spanish government is overwhelmed.</p></list-item></list>\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">26 April 2020</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<list list-type=\"bullet\"><list-item><p>Censorship by the Spanish Government.</p></list-item><list-item><p>Increased unemployment.</p></list-item><list-item><p>Defective medical equipment.</p></list-item><list-item><p>Increased unemployment.</p></list-item><list-item><p>Defective medical equipment.</p></list-item></list>\n</td></tr></tbody></table></table-wrap><table-wrap id=\"ijerph-17-05542-t003\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05542-t003_Table 3</object-id><label>Table 3</label><caption><p>Comments related to Anger emotion and COVID-19 in digital ecosystems.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Anger Emotion Related to COVID-19 Comments in Digital Ecosystems</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">4 March 2020</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<list list-type=\"bullet\"><list-item><p>Calls to the phone to do the COVID-19 test and they do not attend the patients.</p></list-item><list-item><p>Discomfort in the evangelic community when they are pointed out as the focus of COVID-19.</p></list-item><list-item><p>Deceased people in Valencia are hiding because of COVID-19.</p></list-item></list>\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">15 March 2020</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<list list-type=\"bullet\"><list-item><p>The first day of the state of alarm declared by the Spanish Government.</p></list-item><list-item><p>Experts warn the government that on Friday the 20th there will be 28,000 infected and 800 dead.</p></list-item><list-item><p>Spain exported coronavirus tests until March 15th despite warnings from the WHO.</p></list-item><list-item><p>The battle between Pedro Sánchez-Pablo Iglesias is putting the brakes on the anti-epidemic plan.</p></list-item></list>\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">17 March 2020</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<list list-type=\"bullet\"><list-item><p>More than 100,000 people temporarily lose their jobs.</p></list-item><list-item><p>Delay in activating the health alert and border closure with 10,000 cases confirmed by COVID-19.</p></list-item></list>\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">20 March 2020</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<list list-type=\"bullet\"><list-item><p>Social Security collapsed and lack of medical equipment.</p></list-item><list-item><p>Sick people are prioritized according to life expectancy.</p></list-item><list-item><p>Loss of jobs.</p></list-item><list-item><p>Politicians are skipping confinement, while the population must comply.</p></list-item></list>\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">29 March 2020</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<list list-type=\"bullet\"><list-item><p>Pandemic announced and no action taken.</p></list-item><list-item><p>Purchase of defective medical equipment for companies.</p></list-item><list-item><p>Lies about buying from mask suppliers.</p></list-item></list>\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">19/04/2020</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<list list-type=\"bullet\"><list-item><p>Spain will also be the last in Europe to fall out of favor.</p></list-item><list-item><p>Death of old people in retirement homes.</p></list-item><list-item><p>Data of deceased/contagious people in doubt.</p></list-item><list-item><p>China offered false data of infected and false deaths; more deaths are announced.</p></list-item></list>\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">21/04/2020</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<list list-type=\"bullet\"><list-item><p>Use of the security forces to control the population and impose censorship</p></list-item><list-item><p>Lack of food in old people’s homes.</p></list-item></list>\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">23/04/2020</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<list list-type=\"bullet\"><list-item><p>The government bought the fake Chinese tests for COVID-19 for more than twice their value.</p></list-item><list-item><p>COVID-19’s overcrowding: “Someone’s sleeping on the pillow.”</p></list-item><list-item><p>The coronavirus entered Spain on February 15 and by 15 different routes.</p></list-item><list-item><p>“De-climbing blindly: Moncloa asks communities for their plan.”</p></list-item><list-item><p>Last Monday, the second vice president, Pablo Iglesias, demanded that Sanchez increase his control of the press, and Pedro Sanchez agreed.</p></list-item><list-item><p>The government orders the officials to gradually return to work without assuring them of any tests or masks.</p></list-item><list-item><p>Pedro Sanchez is in a state of alarm about the previous step to cut benefits for 900,000 civil servants.</p></list-item><list-item><p>Political group (PSOE) told parents who wanted to walk their children: “You should have bought a dog”.</p></list-item></list>\n</td></tr></tbody></table></table-wrap><table-wrap id=\"ijerph-17-05542-t004\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05542-t004_Table 4</object-id><label>Table 4</label><caption><p>Comments related to Sadness emotion and COVID-19 in digital ecosystems.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Sadness Emotion Related to COVID-19 Comments in Digital Ecosystems</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">6 March 2020</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<list list-type=\"bullet\"><list-item><p>Censorship by the Spanish government (Second Vice-President and Minister of Social Rights and Agenda 2030 of the Spanish Government, Pablo Iglesias threatens journalists with prison for reporting).</p></list-item><list-item><p>Hidden Coronavirus deaths (Cases from previous months are uncovered).</p></list-item><list-item><p>Postponement of economic activity and events due to coronavirus.</p></list-item></list>\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">15 March 2020</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<list list-type=\"bullet\"><list-item><p>The President of the Spanish Government, Pedro Sánchez, postpones aid to companies and workers.</p></list-item><list-item><p>Confinement (State of alarm).</p></list-item><list-item><p>Political interests before the security of the population by the virus.</p></list-item><list-item><p>Sale of necessary material by the COVID-19 pandemic abroad when it is needed in Spain.</p></list-item></list>\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">26 March 2020</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<list list-type=\"bullet\"><list-item><p>Government failure to purchase sanitary materials for COVID-19.</p></list-item><list-item><p>Death of elderly people in solitude, in homes and residences.</p></list-item><list-item><p>The number of coronavirus infections in Spain has risen to 56,188 and there have already been 4089 deaths.</p></list-item><list-item><p>“One-third of humanity is already living in the confines of the virus.”</p></list-item><list-item><p>The Spanish government admits that it had data that the last week of February was key in the increase of infections in areas such as Madrid and yet it spurred massive events such as 8-M.</p></list-item><list-item><p>The tests purchased from China are inaccurate.</p></list-item><list-item><p>The companies continue with the wave of dismissals due to the coronavirus.</p></list-item></list>\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1 April 2020</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<list list-type=\"bullet\"><list-item><p>Catalonia, to its health care workers in the face of the coronavirus: “Death at home is the best option”.</p></list-item><list-item><p>“The EU expects the virus to rebound in weeks”.</p></list-item><list-item><p>Government mortgages Spain economically.</p></list-item><list-item><p>The Government demands that no one goes on holiday during Easter Week.</p></list-item><list-item><p>The rise in deaths.</p></list-item><list-item><p>Government with political interests before citizens’ interests.</p></list-item></list>\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">11 April 2020</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<list list-type=\"bullet\"><list-item><p>Concealment and errors in the deceased/contagious.</p></list-item><list-item><p>Death of health care workers.</p></list-item><list-item><p>Experts are removed from the management of COVID-19 and replaced by politicians.</p></list-item><list-item><p>Castilla-La Mancha sets a record for the number of deaths in a single day.</p></list-item></list>\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">13 April 2020</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<list list-type=\"bullet\"><list-item><p>The inability of the government to provide protection material to businesses.</p></list-item></list>\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">22 April 2020</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<list list-type=\"bullet\"><list-item><p>Health workers exposed to contagion by false sanitary material.</p></list-item><list-item><p>Use of the Police to censor the population.</p></list-item></list>\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">29 April 2020</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<list list-type=\"bullet\"><list-item><p>Massive job loss.</p></list-item><list-item><p>The health workers are being fired.</p></list-item><list-item><p>Defective sanitary equipment.</p></list-item><list-item><p>Elderly deaths.</p></list-item><list-item><p>False data from the Spanish government regarding COVID-19.</p></list-item><list-item><p>Political interests take precedence over health care before and during the pandemic.</p></list-item></list>\n</td></tr></tbody></table></table-wrap></floats-group></article>\n"
]
[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Int J Mol Sci</journal-id><journal-id journal-id-type=\"iso-abbrev\">Int J Mol Sci</journal-id><journal-id journal-id-type=\"publisher-id\">ijms</journal-id><journal-title-group><journal-title>International Journal of Molecular Sciences</journal-title></journal-title-group><issn pub-type=\"epub\">1422-0067</issn><publisher><publisher-name>MDPI</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32722564</article-id><article-id pub-id-type=\"pmc\">PMC7432070</article-id><article-id pub-id-type=\"doi\">10.3390/ijms21155296</article-id><article-id pub-id-type=\"publisher-id\">ijms-21-05296</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Article</subject></subj-group></article-categories><title-group><article-title>Peroxisome Proliferator-Activated Receptor Beta/Delta Agonist Suppresses Inflammation and Promotes Neovascularization</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Tobita</surname><given-names>Yutaro</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05296\">1</xref><xref ref-type=\"aff\" rid=\"af2-ijms-21-05296\">2</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0003-1006-4430</contrib-id><name><surname>Arima</surname><given-names>Takeshi</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05296\">1</xref><xref ref-type=\"aff\" rid=\"af2-ijms-21-05296\">2</xref><xref rid=\"c1-ijms-21-05296\" ref-type=\"corresp\">*</xref></contrib><contrib contrib-type=\"author\"><name><surname>Nakano</surname><given-names>Yuji</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05296\">1</xref><xref ref-type=\"aff\" rid=\"af2-ijms-21-05296\">2</xref></contrib><contrib contrib-type=\"author\"><name><surname>Uchiyama</surname><given-names>Masaaki</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05296\">1</xref><xref ref-type=\"aff\" rid=\"af2-ijms-21-05296\">2</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0002-4364-9251</contrib-id><name><surname>Shimizu</surname><given-names>Akira</given-names></name><xref ref-type=\"aff\" rid=\"af2-ijms-21-05296\">2</xref></contrib><contrib contrib-type=\"author\"><name><surname>Takahashi</surname><given-names>Hiroshi</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05296\">1</xref></contrib></contrib-group><aff id=\"af1-ijms-21-05296\"><label>1</label>Department of Ophthalmology, Nippon Medical School, Bunkyo-ku, Tokyo 113-8603, Japan; <email>[email protected]</email> (Y.T.); <email>[email protected]</email> (Y.N.); <email>[email protected]</email> (M.U.); <email>[email protected]</email> (H.T.)</aff><aff id=\"af2-ijms-21-05296\"><label>2</label>Department of Analytic Human Pathology, Nippon Medical School, Bunkyo-ku, Tokyo 113-8603, Japan; <email>[email protected]</email></aff><author-notes><corresp id=\"c1-ijms-21-05296\"><label>*</label>Correspondence: <email>[email protected]</email></corresp></author-notes><pub-date pub-type=\"epub\"><day>26</day><month>7</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><month>8</month><year>2020</year></pub-date><volume>21</volume><issue>15</issue><elocation-id>5296</elocation-id><history><date date-type=\"received\"><day>29</day><month>6</month><year>2020</year></date><date date-type=\"accepted\"><day>23</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>© 2020 by the authors.</copyright-statement><copyright-year>2020</copyright-year><license license-type=\"open-access\"><license-p>Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>).</license-p></license></permissions><abstract><p>The effects of peroxisome proliferator-activated receptor (PPAR)β/δ ophthalmic solution were investigated in a rat corneal alkali burn model. After alkali injury, GW501516 (PPARβ/δ agonist) or vehicle ophthalmic solution was topically instilled onto the rat’s cornea twice a day until day 7. Pathological findings were evaluated, and real-time reverse transcription polymerase chain reaction was performed. GW501516 strongly suppressed infiltration of neutrophils and pan-macrophages, and reduced the mRNA expression of interleukin-6, interleukin-1β, tumor necrosis factor alpha, and nuclear factor-kappa B. On the other hand, GW501516 promoted infiltration of M2 macrophages, infiltration of vascular endothelial cells associated with neovascularization in the wounded area, and expression of vascular endothelial growth factor A mRNA. However, 7-day administration of GW501516 did not promote neovascularization in uninjured normal corneas. Thus, the PPARβ/δ ligand suppressed inflammation and promoted neovascularization in the corneal wound healing process. These results will help to elucidate the role of PPARβ/δ in the field of ophthalmology. </p></abstract><kwd-group><kwd>corneal inflammation</kwd><kwd>corneal neovascularization</kwd><kwd>PPARβ/δ</kwd><kwd>alkali burn</kwd></kwd-group></article-meta></front><body><sec sec-type=\"intro\" id=\"sec1-ijms-21-05296\"><title>1. Introduction</title><p>Peroxisome proliferator-activated receptor (PPAR) is a nuclear receptor [<xref rid=\"B1-ijms-21-05296\" ref-type=\"bibr\">1</xref>,<xref rid=\"B2-ijms-21-05296\" ref-type=\"bibr\">2</xref>]. PPAR has three subtypes, α, β (δ), and γ, each of which is activated by a specific ligand and controls expression of various genes by binding to the regulatory region of the target gene [<xref rid=\"B1-ijms-21-05296\" ref-type=\"bibr\">1</xref>]. PPAR has been investigated as a target for the treatment of lipid metabolism, hyperlipidemia, diabetes, and atherosclerosis [<xref rid=\"B2-ijms-21-05296\" ref-type=\"bibr\">2</xref>,<xref rid=\"B3-ijms-21-05296\" ref-type=\"bibr\">3</xref>,<xref rid=\"B4-ijms-21-05296\" ref-type=\"bibr\">4</xref>]. In clinical practice, fenofibrate, a PPARα ligand, is used as a therapeutic agent for hyperlipidemia, and pioglitazone, a PPARγ ligand, is used for diabetes [<xref rid=\"B2-ijms-21-05296\" ref-type=\"bibr\">2</xref>,<xref rid=\"B5-ijms-21-05296\" ref-type=\"bibr\">5</xref>]. However, PPARβ/δ ligands have not yet been used as therapeutic drugs for any diseases [<xref rid=\"B2-ijms-21-05296\" ref-type=\"bibr\">2</xref>]. PPARβ/δ is expressed in many tissues and organs such as skeletal muscle, adipose tissue, the cardiovascular system, the uterus after implantation, intestine, brain, and skin, and is involved in many important functions, such as energy metabolism, cell differentiation and proliferation, tissue repair, and cancer progression, suggesting its potential as a therapeutic target [<xref rid=\"B2-ijms-21-05296\" ref-type=\"bibr\">2</xref>,<xref rid=\"B6-ijms-21-05296\" ref-type=\"bibr\">6</xref>,<xref rid=\"B7-ijms-21-05296\" ref-type=\"bibr\">7</xref>].</p><p>In addition to these basic functions, PPARs are also anti-inflammatory [<xref rid=\"B8-ijms-21-05296\" ref-type=\"bibr\">8</xref>]. Therefore, we previously investigated the clinical application of PPARs in the field of ophthalmology and demonstrated anti-inflammatory and anti-angiogenic effects of PPARα [<xref rid=\"B9-ijms-21-05296\" ref-type=\"bibr\">9</xref>,<xref rid=\"B10-ijms-21-05296\" ref-type=\"bibr\">10</xref>] and γ [<xref rid=\"B11-ijms-21-05296\" ref-type=\"bibr\">11</xref>] ligands using a rat corneal alkali burn model. Like other subtypes, PPARβ/δ is considered to play roles in corneal wound healing. In this study, we investigated the characteristics of GW501516, a PPARβ/δ ligand, and explored the therapeutic effects of an ophthalmic solution of a PPARβ/δ agonist using a rat alkali burn model.</p></sec><sec sec-type=\"results\" id=\"sec2-ijms-21-05296\"><title>2. Results</title><sec id=\"sec2dot1-ijms-21-05296\"><title>2.1. Wound Healing Process and Expression of PPARβ/δ</title><p>Hematoxylin-eosin (HE) staining was conducted to compare the corneal wound healing process between the vehicle group and the PPARβ/δ agonist (GW501516) instilled group (<xref ref-type=\"fig\" rid=\"ijms-21-05296-f001\">Figure 1</xref>a–p). The corneal epithelial layer disappeared in the area in which alkali-immersed paper was placed. In the early phase after injury, stromal edema was observed in the center of the cornea. We noted regeneration of the corneal epithelium on day 1. Infiltration of inflammatory cells was observed in the corneal limbus from the early phase after injury. On day 4, infiltration of inflammatory cells moved to the central cornea. On day 4 and day 7, luminal structures associated with neovascularization increased in the corneal limbus. Less infiltration of inflammatory cells was observed (<xref ref-type=\"fig\" rid=\"ijms-21-05296-f001\">Figure 1</xref>q) and more neovascularization was seen in the PPARβ/δ group compared to the vehicle group (<xref ref-type=\"fig\" rid=\"ijms-21-05296-f001\">Figure 1</xref>d,h,l,p). In the macroscopic images, corneal transparency was better in the PPARβ/δ group on day 3 (<xref ref-type=\"fig\" rid=\"ijms-21-05296-f001\">Figure 1</xref>r,s). However, on day 7, corneal neovascularization and anterior chamber bleeding were more obvious in the PPARβ/δ group (<xref ref-type=\"fig\" rid=\"ijms-21-05296-f001\">Figure 1</xref>t,u). It was suggested that GW501516 suppressed inflammation, and improved corneal transparency in the early stage of the injury, but promoted neovascularization, increased anterior chamber bleeding, and eventually, impaired intraocular transparency. We investigated expression of PPARβ/δ mRNA with real-time reverse transcription polymerase chain reaction (RT-PCR). The mRNA expression level of PPARβ/δ was higher in the PPARβ/δ group than in the vehicle group at 6 h (<xref ref-type=\"fig\" rid=\"ijms-21-05296-f001\">Figure 1</xref>v).</p></sec><sec id=\"sec2dot2-ijms-21-05296\"><title>2.2. Anti-Inflammatory Effect of PPARβ/δ</title><p>Next, we examined the anti-inflammatory effect of PPARβ/δ. We performed Naphthol AS-D chloroacetate esterase (EST) staining, nuclear factor kappa B (NF-κB) immunostaining, and kappa light polypeptide gene enhancer in the B-cell inhibitor, alpha (I-kBα) immunostaining. Furthermore, inflammatory cytokines were quantitatively examined with RT-PCR. EST-positive neutrophils were found in the corneal limbus in the early phase after injury (<xref ref-type=\"fig\" rid=\"ijms-21-05296-f002\">Figure 2</xref>a,b). On day 4, neutrophil infiltration was observed in the corneal center (<xref ref-type=\"fig\" rid=\"ijms-21-05296-f002\">Figure 2</xref>c,d). The number of neutrophils was significantly lower in the PPARβ/δ group than in the vehicle group on day 4 (<xref ref-type=\"fig\" rid=\"ijms-21-05296-f002\">Figure 2</xref>e). Inflammatory cells observed at the corneal limbus coincided with the NF-κB-positive cells. The PPARβ/δ group exhibited a smaller degree of inflammatory cell infiltration for each of the time points (<xref ref-type=\"fig\" rid=\"ijms-21-05296-f003\">Figure 3</xref>a–d). In the inflammatory cells after the alkali injury, NF-κB expression was localized in the nucleus area in the vehicle group at 6 h, while it was expressed in the cytoplasm in the PPARβ/δ group. I-κBα was strongly expressed in the cell nuclei in the PPARβ/δ group at 6 h after the alkali burn (<xref ref-type=\"fig\" rid=\"ijms-21-05296-f004\">Figure 4</xref>b). In the PPARβ/δ group, I-κBα expression was still clearly observed at day 4 after the injury (<xref ref-type=\"fig\" rid=\"ijms-21-05296-f004\">Figure 4</xref>d). The number of I-κBα-positive cells was higher in the PPARβ group as compared to that observed in the vehicle group at each of the time points (<xref ref-type=\"fig\" rid=\"ijms-21-05296-f004\">Figure 4</xref>e). Lower mRNA expression levels of interleukin-1β (IL-1β), IL-6, and NF-κB were present in the PPARβ/δ group compared to the vehicle group (<xref ref-type=\"fig\" rid=\"ijms-21-05296-f005\">Figure 5</xref>a–c). On the other hand, I-κBα mRNA was highly expressed in the PPARβ/δ group (<xref ref-type=\"fig\" rid=\"ijms-21-05296-f005\">Figure 5</xref>d). These results indicated that PPARβ/δ agonists suppressed inflammation after alkali injury.</p></sec><sec id=\"sec2dot3-ijms-21-05296\"><title>2.3. Involvement of PPARβ/δ in Macrophage Polarization</title><p>To investigate macrophage polarity, we performed immunostaining with a CD68 antibody (ED-1) (<xref ref-type=\"fig\" rid=\"ijms-21-05296-f006\">Figure 6</xref>a,b) and a CD163 antibody (ED-2) (<xref ref-type=\"fig\" rid=\"ijms-21-05296-f006\">Figure 6</xref>c,d). Pan-macrophages are stained with ED-1, and M2 macrophages are stained with ED-2. The number of ED-1-positive cells was significantly lower in the PPARβ/δ group than in the vehicle group on days 1, 4, and 7 (<xref ref-type=\"fig\" rid=\"ijms-21-05296-f006\">Figure 6</xref>e). On the other hand, the number of ED-2-positive cells was significantly higher in the PPARβ/δ group than in the vehicle group on day 4 (<xref ref-type=\"fig\" rid=\"ijms-21-05296-f006\">Figure 6</xref>f). These results suggest that administration of the PPARβ/δ agonist suppresses infiltration of macrophages, while increasing M2 macrophages. In addition, the RNA expression levels of M1 and M2 macrophage selective markers and cytokines were examined by using RT-PCR analysis. Inducible nitric oxide synthase (iNOS) and tumor necrosis factor alpha (TNF-α) were measured to identify M1 macrophages, whereas arginase 1 (Arg-1) and mannose receptor (CD206) were used to identify M2 macrophages. Lower mRNA expression levels of iNOS and TNF-α were present in the PPARβ/δ group (<xref ref-type=\"fig\" rid=\"ijms-21-05296-f006\">Figure 6</xref>g, h) than in the vehicle group. Moreover, the mRNA expression levels of Arg-1 and CD206 increased in the PPARβ/δ group (<xref ref-type=\"fig\" rid=\"ijms-21-05296-f006\">Figure 6</xref>i,j). The RT-PCR results supported the immunostaining results.</p></sec><sec id=\"sec2dot4-ijms-21-05296\"><title>2.4. Promotion of Neovascularization</title><p>Immunostaining for nestin and aminopeptidase P (JG12) was performed to evaluate neovascularization (<xref ref-type=\"fig\" rid=\"ijms-21-05296-f007\">Figure 7</xref>a–d). Nestin stains immature vascular endothelial cells associated with angiogenesis, whereas JG12 stains mature vascular endothelial cells. In both groups, cells that were immunopositive for nestin and aminopeptidase P were observed in the corneal limbus from day 4. Nestin-positive cells were higher on day 4, and JG12-positive cells were higher on days 1 and 4 in the PPARβ/δ group than in the vehicle group (<xref ref-type=\"fig\" rid=\"ijms-21-05296-f007\">Figure 7</xref>e,f). Lower mRNA expression levels of vascular endothelial growth factor-A (VEGF-A) were present in the PPARβ/δ group than in the vehicle group at 6 h (<xref ref-type=\"fig\" rid=\"ijms-21-05296-f007\">Figure 7</xref>g). mRNA expressions of Angiopoietin-1 (Ang-1) and Angiopoietin-2 (Ang-2) were not significantly different between the two groups (<xref ref-type=\"fig\" rid=\"ijms-21-05296-f007\">Figure 7</xref>h,i). These results suggest that PPARβ/δ agonists promote neovascularization by upregulating VEGF-A expression.</p></sec><sec id=\"sec2dot5-ijms-21-05296\"><title>2.5. Effects of the PPARβ/δ Ligand on the Normal Cornea</title><p>The results shown above suggest that the PPARβ/δ agonist has a proangiogenic effect in the wound healing process. Therefore, we investigated whether administration of the PPARβ/δ agonist induces angiogenesis in the normal cornea. The PPARβ/δ agonist or vehicle was instilled onto the normal cornea twice a day, and rats were sacrificed on day 7. Histological analysis showed no obvious difference between the two groups (<xref ref-type=\"fig\" rid=\"ijms-21-05296-f008\">Figure 8</xref>).</p></sec></sec><sec sec-type=\"discussion\" id=\"sec3-ijms-21-05296\"><title>3. Discussion</title><p>Alkali injury of the cornea is one of the most devastating ophthalmic conditions [<xref rid=\"B12-ijms-21-05296\" ref-type=\"bibr\">12</xref>] and can cause epithelial defects, acute inflammation, neovascularization, and decreased transparency of the cornea, resulting in blindness. The purpose of this study was to demonstrate the effect of a PPARβ/δ agonist using a rat corneal alkali burn model, which is widely used to study the mechanisms of treatments, acute inflammation, and neovascularization in the injured cornea [<xref rid=\"B13-ijms-21-05296\" ref-type=\"bibr\">13</xref>,<xref rid=\"B14-ijms-21-05296\" ref-type=\"bibr\">14</xref>]. In the present study, we used a synthetic PPARβ/δ-specific agonist, GW501516, that has been used in research of physiological and pathophysiological functions of PPARβ/δ in conditions including obesity, diabetes, dyslipidemia, and cardiovascular disease [<xref rid=\"B15-ijms-21-05296\" ref-type=\"bibr\">15</xref>,<xref rid=\"B16-ijms-21-05296\" ref-type=\"bibr\">16</xref>,<xref rid=\"B17-ijms-21-05296\" ref-type=\"bibr\">17</xref>]. Unfortunately, GW501516 was found highly carcinogenic [<xref rid=\"B18-ijms-21-05296\" ref-type=\"bibr\">18</xref>], so its clinically use has never been practical, however, when a small volume of GW501516 is instilled as ophthalmic solutions, its carcinogenicity may not be a high risk. PPARs can be activated in ligand-dependent and ligand-independent mechanisms [<xref rid=\"B19-ijms-21-05296\" ref-type=\"bibr\">19</xref>], and administration of GW501516 has been reported to increase mRNA and protein of PPARβ/δ [<xref rid=\"B20-ijms-21-05296\" ref-type=\"bibr\">20</xref>,<xref rid=\"B21-ijms-21-05296\" ref-type=\"bibr\">21</xref>]. Agreeing with previous studies, instilling of GW501516 increased the levels of PPARβ/δ mRNA in this study, suggesting that the expression of PPARβ/δ was ligand-dependently increased.</p><p>Although few reports have examined the anti-inflammatory effect of PPARβ/δ in the field of ophthalmology, such effects of PPARβ/δ have been reported in studies of atherosclerosis related to chronic inflammation [<xref rid=\"B22-ijms-21-05296\" ref-type=\"bibr\">22</xref>,<xref rid=\"B23-ijms-21-05296\" ref-type=\"bibr\">23</xref>]. PPARβ/δ activation inhibits inflammatory cytokines (such as IL-1 and IL-6) by suppressing the NF-κB pathway [<xref rid=\"B24-ijms-21-05296\" ref-type=\"bibr\">24</xref>,<xref rid=\"B25-ijms-21-05296\" ref-type=\"bibr\">25</xref>,<xref rid=\"B26-ijms-21-05296\" ref-type=\"bibr\">26</xref>]. The results of our study are consistent with previous studies; administration of GW501516 suppressed neutrophil and macrophage infiltration and reduced inflammatory cytokines. NF-κB is a major transcription factor in cell proliferation, inflammation, immune responses, and neovascularization [<xref rid=\"B27-ijms-21-05296\" ref-type=\"bibr\">27</xref>,<xref rid=\"B28-ijms-21-05296\" ref-type=\"bibr\">28</xref>]. When unstimulated, it is sequestered in the cytoplasm by I-κBα, which is the suppressor protein that binds to NF-κB. I-κBα inhibits nuclear translocation of NF-κB, and as a result, transcription downstream of the NF-κB signaling pathway is restricted. Immunostaining revealed that PPARβ/δ ligand increased the expression of I-κBα. In addition, we found that there was a significant upregulation of mRNA expression of I-κBα in the PPARβ/δ group as compared to the controls. Therefore, the anti-inflammatory mechanism of the PPARβ/δ agonist may inhibit the translocation of NF-κB into the nucleus via the upregulation of I-κBα.</p><p>Macrophages exist as multiple phenotypes that can be broadly classified as M1 and M2 phenotypes [<xref rid=\"B29-ijms-21-05296\" ref-type=\"bibr\">29</xref>,<xref rid=\"B30-ijms-21-05296\" ref-type=\"bibr\">30</xref>]. Macrophages play different roles depending on the phenotype. M1 macrophages are pro-inflammatory, have bactericidal action, and are phagocytic. On the other hand, M2 macrophages are anti-inflammatory, promote angiogenesis, promote wound healing, and produce matrix [<xref rid=\"B31-ijms-21-05296\" ref-type=\"bibr\">31</xref>]. Therefore, in addition to the degree of macrophage infiltration, the balance in macrophage phenotypes is important to determine the level of inflammation. PPARβ/δ ligands regulate macrophage polarization to the M2 phenotype in animal models of liver injury [<xref rid=\"B32-ijms-21-05296\" ref-type=\"bibr\">32</xref>] and atherosclerosis [<xref rid=\"B33-ijms-21-05296\" ref-type=\"bibr\">33</xref>]. In the experiments in our current study, the PPARβ/δ ligand suppressed pan-macrophage infiltration and increased M2 macrophages. The influence of PPARβ/δ on regulation of the macrophage phenotype plays a role during inflammation. However, many issues are unclear regarding macrophage polarization, and further investigation is needed.</p><p>In contrast to the anti-inflammatory effect, the PPARβ/δ agonist promoted neovascularization caused by alkali injury. Neovascularization induced by alkali injury leads to the irregular arrangement of collagen, aggravates corneal scarring and results in a loss of corneal transparency [<xref rid=\"B12-ijms-21-05296\" ref-type=\"bibr\">12</xref>]. Therefore, the result of GW501516 promoting neovascularization is not desirable. PPARβ/δ-activation potently induces angiogenesis by human vascular endothelial cells in the tumor extracellular matrix in vitro and in the murine matrigel plug model in vivo [<xref rid=\"B34-ijms-21-05296\" ref-type=\"bibr\">34</xref>]. Other studies have shown that PPARβ/δ ligands induce VEGF in bladder [<xref rid=\"B35-ijms-21-05296\" ref-type=\"bibr\">35</xref>], breast, and prostate cancer cells [<xref rid=\"B36-ijms-21-05296\" ref-type=\"bibr\">36</xref>]. In the field of ophthalmology, GW501516 was reported to promote angiogenesis in the phototherapeutic keratectomy model [<xref rid=\"B37-ijms-21-05296\" ref-type=\"bibr\">37</xref>]. In the present study, as in the previous report, GW501516 promoted neovascularization pathologically and increased VEGF mRNA expression, but had no effect on Ang-1 and Ang-2 mRNA. The role of PPARβ/δ agonists in the corneal alkali burn injury was similar to that in other organs.</p><p>Based on this result, we examined whether the angiogenesis-promoting effect of the PPARβ/δ ligand was present in normal rat corneas. After 7 days of administration of GW501516 twice a day, no apparent change was seen in macroscopic or pathological images.</p><p>Promotion of VEGF expression by PPARβ/δ appears to require inflammation. Thus, further investigation is needed to assess the two conflicting actions (anti-inflammatory and pro-angiogenic) prior to the use of PPARβ/δ in ophthalmology. </p></sec><sec id=\"sec4-ijms-21-05296\"><title>4. Materials and Methods </title><sec id=\"sec4dot1-ijms-21-05296\"><title>4.1. Animals and Ethics Statement</title><p>Eight-week-old male Wistar rats (Sankyo Laboratory Service, Tokyo, Japan) were used for all experiments in this study. All animal experiments were conducted in compliance with the Experimental Animal Ethics Review Committee of Nippon Medical School (approval number: 2019-005, 1 April 2019) in Tokyo, Japan, and all procedures conformed to the Association for Research in Vision and Ophthalmic and Visual Research. </p></sec><sec id=\"sec4dot2-ijms-21-05296\" sec-type=\"methods\"><title>4.2. Experimental Procedures</title><p>Under general isoflurane anesthesia, a circular filter paper 3.2 mm in diameter was immersed in 1 N NaOH and placed on the central cornea of each rat for 1 min to create a corneal alkali burn. After alkali exposure, the ocular surface was washed with 40 mL physiological saline. PPARβ/δ agonist ophthalmic solution or vehicle solution was administered twice a day to the alkali-burned cornea. The vehicle solution was filter purified 100mL NaCl-based PBS (0.01 M; pH 7.4), which was prepared with 232 g disodium hydrogen-phosphate 12-water, 23.7 g sodium dihydrogen phosphate dihydrate, 4000 mL distilled water, and 0.1 mL polyoxyethylene sorbitan monooleate (Wako Pure Chemical Industries, Osaka, Japan). The PPARβ/δ agonist ophthalmic solution was prepared by adding 10 mg GW501516 to 20 mL vehicle solution. At each endpoint (6 h, 1 day, 4 days, and 7 days after alkali burn), rats were sacrificed by exsanguination under isoflurane anesthesia. Pathological and molecular biological evaluations were performed on the enucleated eyes.</p></sec><sec id=\"sec4dot3-ijms-21-05296\"><title>4.3. Histological and Immunohistochemical Analyses</title><p>The excised eyes were fixed with 10% buffered formalin and embedded in paraffin. For histopathological examination, HE staining was performed on deparaffinized tissue. EST staining was performed to detect infiltrating neutrophils. We used monoclonal mouse anti-rat ED-1 (BMA, Nagoya, Japan), monoclonal mouse anti-rat ED-2 (BMA), polyclonal rabbit anti-NF-kB/P65 (Santa Cruz Biotechnology, Dallas, TX, USA) and monoclonal mouse anti- I-kBα (Santa Cruz Biotechnology) as primary antibodies for immunohistochemical analysis. Angiogenesis was evaluated using monoclonal mouse anti-nestin (Nestin; Merck Millipore, Darmstadt, Germany [<xref rid=\"B38-ijms-21-05296\" ref-type=\"bibr\">38</xref>]) and monoclonal mouse anti-JG12 (Thermo Fisher Scientific, Waltham, MA, USA [<xref rid=\"B39-ijms-21-05296\" ref-type=\"bibr\">39</xref>]) antibodies. Three places in the central cornea and two places in the limbus were observed at a magnification of 400×. The number of each type of positively stained cells was counted, and the average number was calculated.</p></sec><sec id=\"sec4dot4-ijms-21-05296\"><title>4.4. Real-Time RT-PCR</title><p>Using real-time RT-PCR (Thermo Fisher Scientific), we investigated the mRNA expression of PPARβ/δ, iNOS, TNF-α, Arg-1, CD206, IL-1β, IL-6, NF-κB, I-κBα, VEGF-A, Ang-1, and Ang-2. Total RNA was extracted from the enucleated corneas using a RNeasy<sup>®</sup> FFPE Kit (Qiagen, Hilden, Germany). We treated total RNA with DNase I (RNase-free DNase Set; Qiagen) to remove contaminating DNA. All samples showed an OD 260/280 nm ratio >1.8. The High Capacity cDNA Reverse Transcription Kit (Thermo Fisher Scientific) was used to reverse transcribe first-strand cDNA from total RNA (100 ng) in a volume of 20 μL. The cDNA (5 μL) was used for each PCR (total reaction volume, 20 μL). According to the manufacturer′s instructions, optimized primers and probes were used for each target gene. The primers used in this experiment are described below (<xref rid=\"ijms-21-05296-t001\" ref-type=\"table\">Table 1</xref>). PCR (2 min at 50 °C, 10 min at 95 °C, and 45 cycles of denaturation at 95 °C for 15 s and annealing at 60 °C for 60 s) was conducted on a QuantStudio™ 3 Real-Time PCR System (Thermo Fisher Scientific) according to fluorescent TaqMan methodology. The mRNA levels were determined in duplicate, and the expression of each mRNA was normalized to that of β-actin. We calculated the relative expression levels using the 2<sup>−ΔΔCT</sup> method [<xref rid=\"B40-ijms-21-05296\" ref-type=\"bibr\">40</xref>].</p></sec><sec id=\"sec4dot5-ijms-21-05296\"><title>4.5. Statistical Analyses </title><p>All results are expressed as the mean ± standard deviation. Statistical analyses were performed using unpaired Student’s t-test using Excel analytical software (Excel, Microsoft, Redmond, WA, USA). Values of <italic>p</italic> < 0.05 were considered statistically significant.</p></sec></sec><sec id=\"sec5-ijms-21-05296\"><title>5. Conclusions</title><p>In summary, the PPARβ/δ agonist showed anti-inflammatory effects and promoted neovascularization in a rat alkali burn model. PPARβ/δ agonist administration did not cause neovascularization or inflammation in the normal cornea. </p></sec></body><back><ack><title>Acknowledgments</title><p>We would like to express special thanks to Takashi Arai, Naomi Kuwahara, Kyoko Wakamatsu, Arimi Ishikawa, Mitsue Kataoka, and Mika Terasaki for their expert technical assistance.</p></ack><notes><title>Author Contributions</title><p>Conceptualization, Y.T.; methodology, Y.T.; validation, Y.T., Y.N. and T.A.; formal analysis, Y.T.; investigation, Y.T. and Y.N.; data curation, Y.T.; writing—original draft preparation, Y.T.; writing—review and editing, T.A., M.U., A.S. and H.T.; visualization, Y.T.; supervision, T.A., M.U., A.S. and H.T.; project administration, Y.T., Y.N. and T.A.; funding acquisition, A.S. and T.A. All authors have read and agreed to the published version of the manuscript.</p></notes><notes><title>Funding</title><p>This work was supported by a Grant-in-Aid for Scientific Research (KAKENHI, No. 19K18859) from the Japan Society for the Promotion of Science.</p></notes><notes notes-type=\"COI-statement\"><title>Conflicts of Interest</title><p>The authors declare no conflict of interest. The funding organization had no role in the design or conduct of this research.</p></notes><glossary><title>Abbreviations</title><array orientation=\"portrait\"><tbody><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">PPAR</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Peroxisome proliferator-activated receptor</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">RT-PCR</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Reverse transcription polymerase chain reaction</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">VEGF</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Vascular endothelial growth factor </td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">HE</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Hematoxylin-eosin</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">EST</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Naphthol AS-D chloroacetate esterase</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">iNOS</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Inducible nitric oxide synthase</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">TNF-α</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Tumor necrosis factor alpha</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Arg-1</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Arginase 1</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CD206</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Mannose receptor</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">IL-1β</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Interleukin-1β</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">IL-6</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Interleukin-6</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">NF-κB</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Nuclear factor-kappa B</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">I-κBα</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">kappa light polypeptide gene enhancer in the B-cell inhibitor, alpha</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ED-1</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CD68 antibody</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ED-2</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CD163 antibody</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">JG12</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Aminopeptidase P</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Ang-1</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Angiopoietin-1</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Ang-2</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Angiopoietin-2</td></tr></tbody></array></glossary><ref-list><title>References</title><ref id=\"B1-ijms-21-05296\"><label>1.</label><element-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Berger</surname><given-names>J.P.</given-names></name><name><surname>Moller</surname><given-names>D.E.</given-names></name></person-group><article-title>The Mechanisms of Action of PPARs</article-title><source>Annu. 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The time-dependent changes in the vehicle group (<bold>a–d</bold>: central region, <bold>i–l</bold>: peripheral region) and PPARβ/δ group (<bold>e–h</bold>: central region, <bold>m–p</bold>: peripheral region). Bar, 50 μm. After the alkali burn in both groups, inflammatory cells infiltrated from the corneal limbus into the central area by day 7. On day 4 and day 7, luminal structures (black arrows) due to neovascularization were observed. The number of infiltrating cells was lower in the PPARβ/δ group than in the vehicle group (<bold>q</bold>). The macroscopic photographs of anterior segment on day 3 (<bold>r</bold>: vehicle group, <bold>s</bold>: PPARβ/δ group) and day 7 (<bold>t</bold>: vehicle group, <bold>u</bold>: PPARβ/δ group). In the PPARβ/δ group, corneal transparency was better than the vehicle group on day 3, but on day 7, anterior chamber bleeding was deteriorated and transparency was lost. Regarding mRNA expression levels of PPARβ/δ, administration of the PPARβ/δ ligand significantly upregulated PPARβ/δ expression in the cornea at 6 h (<bold>v</bold>). ** <italic>p</italic> < 0.01.</p></caption><graphic xlink:href=\"ijms-21-05296-g001\"/></fig><fig id=\"ijms-21-05296-f002\" orientation=\"portrait\" position=\"float\"><label>Figure 2</label><caption><p>EST staining. EST staining was performed to examine neutrophil infiltration. Vehicle group (<bold>a</bold>,<bold>c</bold>) and PPARβ/δ group (<bold>b</bold>,<bold>d</bold>) in the corneal limbus on day 1 and day 4 after injury (black arrows: EST-positive cells). Bar, 50 μm. Fewer EST-positive neutrophils were present in the PPARβ/δ group than in the vehicle group (<bold>e</bold>). Data are presented as mean ± SD (<italic>n</italic> = 8) * <italic>p</italic> < 0.05.</p></caption><graphic xlink:href=\"ijms-21-05296-g002\"/></fig><fig id=\"ijms-21-05296-f003\" orientation=\"portrait\" position=\"float\"><label>Figure 3</label><caption><p>Immunostaining of NF-κB and quantitative evaluation analyzed by cell counting on the corneal periphery in each group. (<bold>a</bold>,<bold>b</bold>) Representative sections at 6 h after the injury are shown. As compared to the PPARβ/δ group, the vehicle group exhibited strong staining in the nucleus of inflammatory cells (see boxed area). (<bold>c</bold>,<bold>d</bold>) Representative sections at day 4 after injury are shown. Higher magnification pictures of the boxed area are also shown. NF-κB-positive inflammatory cells (black arrows) were observed in the corneal limbs. Bar, 50 μm. The number of cells stained in the nucleus was significantly lower in the PPARβ/δ group versus the vehicle group at each of the time points (<bold>e</bold>). Data are presented as mean ± SD (<italic>n</italic> = 8) ** <italic>p</italic> < 0.01.</p></caption><graphic xlink:href=\"ijms-21-05296-g003\"/></fig><fig id=\"ijms-21-05296-f004\" orientation=\"portrait\" position=\"float\"><label>Figure 4</label><caption><p>Immunohistochemical analysis of I-κBα and quantitative evaluation determined by cell counting on the corneal periphery in each group. Representative sections at 6 h after injury are shown (<bold>a</bold>,<bold>b</bold>). PPARβ/δ group exhibited strong expression of I-kBα as compared to the vehicle group (see boxed area). Representative sections at day 4 after the injury are shown. I-κBα expression was only found in the PPARβ/δ group (<bold>c</bold>,<bold>d</bold>). Higher magnification pictures of the boxed area are also shown. Bar, 50 μm. There was a significantly higher number of I-κBα-positive cells in the PPARβ group versus the vehicle group at each of the time points (<bold>e</bold>). Data are presented as mean ± SD (<italic>n</italic> = 8) ** <italic>p</italic> < 0.01.</p></caption><graphic xlink:href=\"ijms-21-05296-g004\"/></fig><fig id=\"ijms-21-05296-f005\" orientation=\"portrait\" position=\"float\"><label>Figure 5</label><caption><p>Expression of mRNA for inflammatory cytokines. Quantification of mRNA levels of IL-1β (<bold>a</bold>), IL-6 (<bold>b</bold>), and NF-κB (<bold>d</bold>) at 6 h. Expression of inflammatory cytokines and NF-κB was significantly lower in the PPARβ/δ group than in the vehicle group (<bold>a</bold>–<bold>c</bold>). The mRNA expression levels of I-κBα were higher in the PPARβ/δ group as compared to that observed for the vehicle group (<bold>d</bold>). Data are presented as mean ± SD (<italic>n</italic> = 8) ** <italic>p</italic> < 0.01 or * <italic>p</italic> < 0.05.</p></caption><graphic xlink:href=\"ijms-21-05296-g005\"/></fig><fig id=\"ijms-21-05296-f006\" orientation=\"portrait\" position=\"float\"><label>Figure 6</label><caption><p>Immunostaining for ED-1 and ED-2. Pan-macrophages are stained with ED-1, and M2 macrophages are stained with ED-2. ED-1 immunostaining (<bold>a</bold>: vehicle group, <bold>b</bold>: PPARβ/δ group) in the corneal limbus on day 1. ED-2 immunostaining (<bold>c</bold>: vehicle group, <bold>d</bold>: PPARβ/δ group) in the corneal limbus on day 4. Black arrows: immunostained cells. Bar, 50 μm. ED-1-positive cells were lower on days 1, 4, and 7. On the other hand, ED-2-positive cells were higher on day 4 in the PPARβ/δ group versus the vehicle group (<bold>e</bold>, <bold>f</bold>). The mRNA levels of the indicated M1 (iNOS and TNF-α) (<bold>g</bold>, <bold>h</bold>) and M2 (Arg-1, and CD206) (<bold>I</bold>, <bold>j</bold>) at 6 h. The M1 markers were lower and the M2 markers were higher in the PPARβ/δ group as compared to the vehicle group. Data are presented as mean ± SD (<italic>n</italic> = 8) ** <italic>p</italic> < 0.01 or * <italic>p</italic> < 0.05</p></caption><graphic xlink:href=\"ijms-21-05296-g006\"/></fig><fig id=\"ijms-21-05296-f007\" orientation=\"portrait\" position=\"float\"><label>Figure 7</label><caption><p>Evaluation of neovascularization. Nestin immunostaining in the corneal limbus on day 4 (<bold>a</bold>: vehicle group, <bold>b</bold>: PPARβ/δ group). JG12 immunostaining in the corneal limbus on day 7 (<bold>c</bold>: vehicle group, <bold>d</bold>: PPARβ/δ group). Black arrows: immunopositive cells. Bar, 50 μm. Nestin-positive endothelial cells, and JG12-positive capillary lumens were observed from day 4. The number of nestin-positive cells was higher on day 4 in the PPARβ/δ group compared to the vehicle group (<bold>e</bold>). The number of JG12-positive cells was higher on days 4 and 7 in the PPARβ/δ group compared to the vehicle group (<bold>f</bold>). Among cytokines related to angiogenesis, only VEGF-A showed a significant difference between the PPARβ/δ group and vehicle group (<bold>g–i</bold>). Data are presented as mean ± SD (<italic>n</italic> = 8) ** <italic>p</italic> < 0.01 or * <italic>p</italic> < 0.05.</p></caption><graphic xlink:href=\"ijms-21-05296-g007\"/></fig><fig id=\"ijms-21-05296-f008\" orientation=\"portrait\" position=\"float\"><label>Figure 8</label><caption><p>Effect of the PPARβ/δ agonist on the normal cornea at 7 days. HE staining of the vehicle group (<bold>a</bold>: central region, <bold>b</bold>: peripheral region) and PPARβ/δ group (<bold>d</bold>: central region, <bold>e</bold>: peripheral region). Bar, 50 μm. No obvious difference in the HE staining images or macroscopic images (<bold>c</bold>, <bold>f</bold>) was apparent between the two groups.</p></caption><graphic xlink:href=\"ijms-21-05296-g008\"/></fig><table-wrap id=\"ijms-21-05296-t001\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijms-21-05296-t001_Table 1</object-id><label>Table 1</label><caption><p>Primer pairs used in this study.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Gene</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Forward Primer Sequence (5′-3′)</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Reverse Primer Sequence (5′-3′)</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">β-actin</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">GCAGGAGTACGATGAGTCCG</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ACGCAGCTCAGTAACAGTCC</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">iNOS</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">TCACCTTCGAGGGCAGCCGA</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CAGACGCCATGGTGCAGGGG</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">TNF-α</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">AAATGGGCTCCCTCTCATCAGTTC</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">TCTGCTTGGTGGTTTGCTACGAC</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Arg-1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ATTCACCCCGGCTACGGGCA</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">AGGAGCAGCGTTGGCCTGGT</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CD206</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">GACGGACGAGGAGTTCATTATAC</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">GTTGGAGAGATAGGCACAGAAG</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">IL-1β</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">TACCTATGTCTTGCCCGTGGAG</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ATCATCCCACGAGTCACAGAGG</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">IL-6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">GTCAACTCCATCTGCCCTTCAG</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">GGCAGTGGCTGTCAACAACAT</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">NF-κB</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">TGGACGATCTGTTTCCCCTC</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">TCGCACTTGTAACGGAAACG</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">I-κBα</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">TGACCATGGAAGTGATTGGTCAG</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">GATCACAGCCAAGTGGAGTGGA</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">VEGF-A</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">GCAGCGACAAGGCAGACTAT</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">GCAACCTCTCCAAACCGTTG</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">PPARβ/δ</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">GCCGCCCTACAACGAGATCA</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">CCACCAGCAGTCCGTCTTTGT</td></tr></tbody></table></table-wrap></floats-group></article>\n"
]
[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Int J Mol Sci</journal-id><journal-id journal-id-type=\"iso-abbrev\">Int J Mol Sci</journal-id><journal-id journal-id-type=\"publisher-id\">ijms</journal-id><journal-title-group><journal-title>International Journal of Molecular Sciences</journal-title></journal-title-group><issn pub-type=\"epub\">1422-0067</issn><publisher><publisher-name>MDPI</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32727021</article-id><article-id pub-id-type=\"pmc\">PMC7432071</article-id><article-id pub-id-type=\"doi\">10.3390/ijms21155318</article-id><article-id pub-id-type=\"publisher-id\">ijms-21-05318</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Article</subject></subj-group></article-categories><title-group><article-title>Investigating the Mullins Effect and Energy Dissipation in Magnetorheological Polyurethane Elastomers</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0002-5661-2802</contrib-id><name><surname>Elías-Zúñiga</surname><given-names>Alex</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05318\">1</xref><xref rid=\"c1-ijms-21-05318\" ref-type=\"corresp\">*</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0001-5297-2950</contrib-id><name><surname>Palacios-Pineda</surname><given-names>Luis M.</given-names></name><xref ref-type=\"aff\" rid=\"af2-ijms-21-05318\">2</xref></contrib><contrib contrib-type=\"author\"><name><surname>Perales-Martínez</surname><given-names>Imperio A.</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05318\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Martínez-Romero</surname><given-names>Oscar</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05318\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Olvera-Trejo</surname><given-names>Daniel</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05318\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Jiménez-Cedeño</surname><given-names>Isaac H.</given-names></name><xref ref-type=\"aff\" rid=\"af3-ijms-21-05318\">3</xref></contrib></contrib-group><aff id=\"af1-ijms-21-05318\"><label>1</label>Mechanical Engineering and Advanced Materials Department. School of Engineering and Science, Tecnologico de Monterrey, Ave. Eugenio Garza Sada 2501, Monterrey C.P. 64849, N.L., Mexico; <email>[email protected]</email> (I.A.P.-M.); <email>[email protected]</email> (O.M.-R.); <email>[email protected]</email> (D.O.-T.)</aff><aff id=\"af2-ijms-21-05318\"><label>2</label>División de Estudios de Posgrado e Investigación, Tecnológico Nacional de México/Instituto Tecnológico de Pachuca, Carr. México-Pachuca Km 87.5, Pachuca, Hidalgo C.P. 42080, Mexico; <email>[email protected]</email></aff><aff id=\"af3-ijms-21-05318\"><label>3</label>Engineering Department, Safran Mexico, Calle Ishikawa #1201 Parque Ind. Supra Chihuahua, Chihuahua C.P. 31183, Mexico; <email>[email protected]</email></aff><author-notes><corresp id=\"c1-ijms-21-05318\"><label>*</label>Correspondence: <email>[email protected]</email>; Tel.: +52-(81)8358-2000 (ext. 5430)</corresp></author-notes><pub-date pub-type=\"epub\"><day>27</day><month>7</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><month>8</month><year>2020</year></pub-date><volume>21</volume><issue>15</issue><elocation-id>5318</elocation-id><history><date date-type=\"received\"><day>05</day><month>7</month><year>2020</year></date><date date-type=\"accepted\"><day>23</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>© 2020 by the authors.</copyright-statement><copyright-year>2020</copyright-year><license license-type=\"open-access\"><license-p>Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>).</license-p></license></permissions><abstract><p>The aim of this article was to investigate the mechanical performance of magnetorheological polyurethane elastomers reinforced with different concentrations of carbonyl iron microparticles (CIPs) in which stress softening, energy dissipation, residual strains, microparticles orientation, and magnetic flux density effects will be considered. Other aspects, such as the determination of the dissipated energy during cyclic loading and unloading, were investigated by considering a pseudo-elastic network model that takes into account residual strains, magnetic field intensity, and the isotropic and anisotropic material behavior. Theoretical predictions confirmed that the material shear modulus becomes sensitive not only for higher concentrations of CIPs added into the elastomer material matrix, but also to the magnetic flux intensity that induces attractive forces between CIPs and to the strong bonds between these and the elastomer matrix. It was also found that the addition of CIPs when embedded into the polymer matrix with a predefined orientation enhances the material shear modulus as well as its capacity to dissipate energy when subjected to magnetic flux density in loading and unloading directions.</p></abstract><kwd-group><kwd>pseudo-elastic material model</kwd><kwd>damage function</kwd><kwd>energy dissipation</kwd><kwd>mullins effect</kwd><kwd>magnetorheological elastomer</kwd></kwd-group></article-meta></front><body><sec sec-type=\"intro\" id=\"sec1-ijms-21-05318\"><title>1. Introduction</title><p>The aim of this paper focused on investigating the Mullins effect, residual strains, and energy dissipation in uniaxial loading–unloading cycles by considering the concentration of carbonyl iron particles (CIPs) added into an elastomer matrix, their orientation influence, and the impact of applying a magnetic flux density in the loading direction. In this sense, Coquelle et al. investigated the Mullins effect in Rhodorsil RTV 1062S elastomer reinforced with 10% volume of carbonyl iron particles subjected to cyclic uniaxial extension tests without the application of a magnetic flux density [<xref rid=\"B1-ijms-21-05318\" ref-type=\"bibr\">1</xref>]. They found that the Mullins effect, after a few loading–unloading cycles of anisotropic samples, is close to that of the isotropic ones and that the usage of a coupling agent strongly changes the slope of the first traction. Then, Coquelle and Bossis [<xref rid=\"B2-ijms-21-05318\" ref-type=\"bibr\">2</xref>] performed cyclic uniaxial extension tests to study and model the mechanism that promotes the detachment of the polymer matrix from the CIPs. They found that after the first loading and unloading cycle, new adhesion of the elastomer matrix with the CIPs surface occurs. They also noticed that the composite material during the second loading and unloading cycle is different from the one exhibited in the first cycle since some elastomer-to-particle bonds were broken. They related this material molecular damage to the Mullins effect and performed some computer simulations via the finite element method (FEM) to model the stress-softened behavior experienced by the composite material. They also found that the quality of the bonds between elastomer and CIPs does not influence the material modulus value promoted by magnetic flux density. Considering dipole–dipole interaction of the CIPs, Melenev et al. [<xref rid=\"B3-ijms-21-05318\" ref-type=\"bibr\">3</xref>] proposed a phenomenological rheological model that takes into account linear elastic effects and internal dry friction and makes both material networks deform affinely. In order to evaluate the accuracy of their proposed rheological model, they performed uniaxial cyclic tests on silicone rubber elastomer samples reinforced with a 30 vol% of CIPs (2 to 5 µm). The material samples were cured without the action of a magnetic field in order to obtain a homogeneous distribution of isotropic CIPs. To capture stress softening and residual strain effects, they used a three branch macrorheological model that requires the determination of eleven material constants to predict experimental data. Based on their model predictions, they concluded that the deviation between experimental tests and theoretical predictions is attributed to the material viscolelastic effects that were not included in the material model. In this sense, Shariff and Bustamante [<xref rid=\"B4-ijms-21-05318\" ref-type=\"bibr\">4</xref>] also proposed an anisotropic phenomenological model, which is based on direction-dependent damage parameters that depend on the strain history to simulate the Mullins effect for magnetoactive elastomers. Combining small and wide-angle X-ray scattering techniques (SAXS and WAXS), Jiang et al. [<xref rid=\"B5-ijms-21-05318\" ref-type=\"bibr\">5</xref>] performed in situ experimental measurements of an isotropic copolymer sample BA6500-Fe<sub>3</sub>O<sub>4</sub>4.9 subjected to a uniaxial loading–unloading cycle. They found that during the loading process, the copolymer grafted nanoparticles (magnetite, Fe<sub>3</sub>O<sub>4</sub>) were forced to be highly oriented along the tensile direction, revealing strain-hardening behavior because the applied stress is transferred from the copolymer matrix to the oriented Fe<sub>3</sub>O<sub>4</sub> nanoparticles. They also found that during unloading, the orientation degree of the Fe<sub>3</sub>O<sub>4</sub> nanoparticles and the stress magnitude decrease to recover the material entropy. Later, Xu et al. [<xref rid=\"B6-ijms-21-05318\" ref-type=\"bibr\">6</xref>] confirmed that when a magnetosensitive polymer gel is subjected to cyclic shear loading, the transition from stress-softened to stress hardening is mainly due to the applied magnetic field. In fact, they found that the stress hardening effect appears at the unloading stage with or without the application of an external magnetic field, mainly due to the residual strain elasticity.</p><p>By performing compression and bending tests in magnetorheological material samples developed by considering commercial polydimethylsiloxane (PDMS) elastomer reinforced with CIPs (average size of 45 µm) concentrations of 20, 40, 60, and 80 wt%, Bellelli and Spaggiari [<xref rid=\"B7-ijms-21-05318\" ref-type=\"bibr\">7</xref>] found that the anisotropic samples when subjected to compression tests exhibit higher stiffness than do isotropic ones. Recently, an MRE material was developed by mixing CIPs and flax fibers with a PDMS elastomer matrix material with an enhancement of the rheological and mechanical properties when subjected to an external magnetic field [<xref rid=\"B8-ijms-21-05318\" ref-type=\"bibr\">8</xref>].</p><p>Although previous works have considered cyclic loading, they have not compared stress-softening and residual strain effects of magnetorheological elastomers reinforced with CIPs homogeneously dispersed with respect to the impact that microparticles alignment has on the material response. Here, cyclic uniaxial loading–unloading tests were performed in isotropic and anisotropic magnetorheological polyurethane (PU) elastomer samples reinforced with CIPs concentrations of 20, 35, 50, 65, and 80wt% in order to investigate stress-softening, residual strain, and energy dissipation, with and without the application of a magnetic flux density. Some modifications to the phenomenological pseudo-elastic network model for the Mullins effects derived in [<xref rid=\"B9-ijms-21-05318\" ref-type=\"bibr\">9</xref>] were adapted to consider residual strains, magnetic field intensity, and material isotropic and anisotropic behavior. Furthermore, the energy dissipation factor <italic>E</italic> is defined in order to quantify the differences among the tested material samples for the various concentrations of CIPs in order to investigate if there exists debonding between the CIPs and the polymer matrix, since a decrease in the material shear modulus is evident after the application of the first loading and unloading cycle.</p></sec><sec id=\"sec2-ijms-21-05318\"><title>2. Finite Elasticity</title><sec><title>Basic Definitions</title><p>An isochoric deformation of an incompressible elastic material is described by\n<disp-formula id=\"FD1-ijms-21-05318\"><label>(1)</label><mml:math id=\"mm1\"><mml:mrow><mml:mrow><mml:msub><mml:mi>x</mml:mi><mml:mi>i</mml:mi></mml:msub><mml:mo>=</mml:mo><mml:msub><mml:mi>λ</mml:mi><mml:mi>i</mml:mi></mml:msub><mml:msub><mml:mi>X</mml:mi><mml:mi>i</mml:mi></mml:msub><mml:mo>,</mml:mo><mml:mtext> </mml:mtext><mml:mi>i</mml:mi><mml:mo>=</mml:mo><mml:mn>1</mml:mn><mml:mo>,</mml:mo><mml:mn>2</mml:mn><mml:mo>,</mml:mo><mml:mn>3</mml:mn></mml:mrow></mml:mrow></mml:math></disp-formula>\nin which <bold>X</bold> =<italic>X</italic><sub>i</sub><bold>e</bold><sub>i</sub> defines the undeformed reference configuration of a body in a rectangular Cartesian frame <italic>ϕ</italic> = {<italic>O</italic>; <bold>e</bold><sub>i</sub>} with origin <italic>O</italic> and orthonormal basis <bold>e</bold><sub>i</sub>, <bold>x</bold> =<italic>x</italic><sub>i</sub><bold>e</bold><sub>i</sub> denotes the current configuration of the body when subjected to a prescribed deformation, and <italic>λ</italic><sub>i</sub> are the principal stretches in <italic>ϕ</italic>. The Cauchy–Green deformation tensor <bold>B</bold> = <bold>FF<sup>T</sup></bold> is defined as\n<disp-formula id=\"FD2-ijms-21-05318\"><label>(2)</label><mml:math id=\"mm2\"><mml:mrow><mml:mrow><mml:mstyle mathvariant=\"bold\" mathsize=\"normal\"><mml:mi>B</mml:mi></mml:mstyle><mml:mo>=</mml:mo><mml:msubsup><mml:mi>λ</mml:mi><mml:mn>1</mml:mn><mml:mn>2</mml:mn></mml:msubsup><mml:msub><mml:mstyle mathvariant=\"bold\" mathsize=\"normal\"><mml:mi>e</mml:mi></mml:mstyle><mml:mrow><mml:mn>11</mml:mn></mml:mrow></mml:msub><mml:mo>+</mml:mo><mml:msubsup><mml:mi>λ</mml:mi><mml:mn>2</mml:mn><mml:mn>2</mml:mn></mml:msubsup><mml:msub><mml:mstyle mathvariant=\"bold\" mathsize=\"normal\"><mml:mi>e</mml:mi></mml:mstyle><mml:mrow><mml:mn>22</mml:mn></mml:mrow></mml:msub><mml:mo>+</mml:mo><mml:msubsup><mml:mi>λ</mml:mi><mml:mn>3</mml:mn><mml:mn>2</mml:mn></mml:msubsup><mml:msub><mml:mstyle mathvariant=\"bold\" mathsize=\"normal\"><mml:mi>e</mml:mi></mml:mstyle><mml:mrow><mml:mn>33</mml:mn></mml:mrow></mml:msub><mml:mo>,</mml:mo></mml:mrow></mml:mrow></mml:math></disp-formula>\nwhere <inline-formula><mml:math id=\"mm3\"><mml:mrow><mml:mrow><mml:msub><mml:mstyle mathvariant=\"bold\" mathsize=\"normal\"><mml:mi>e</mml:mi></mml:mstyle><mml:mrow><mml:mi>j</mml:mi><mml:mi>k</mml:mi></mml:mrow></mml:msub><mml:mo>≡</mml:mo><mml:msub><mml:mstyle mathvariant=\"bold\" mathsize=\"normal\"><mml:mi>e</mml:mi></mml:mstyle><mml:mi>j</mml:mi></mml:msub><mml:mo>⊗</mml:mo><mml:msub><mml:mstyle mathvariant=\"bold\" mathsize=\"normal\"><mml:mi>e</mml:mi></mml:mstyle><mml:mi>k</mml:mi></mml:msub><mml:mo>,</mml:mo><mml:mtext> </mml:mtext><mml:msub><mml:mstyle mathvariant=\"bold\" mathsize=\"normal\"><mml:mi>e</mml:mi></mml:mstyle><mml:mi>i</mml:mi></mml:msub></mml:mrow></mml:mrow></mml:math></inline-formula> are the orthonormal principal directions, and <bold>F</bold> represents the deformation gradient tensor. It is well known that in the undeformed state <bold>F</bold> = <bold>1</bold> for all isochoric deformations [<xref rid=\"B10-ijms-21-05318\" ref-type=\"bibr\">10</xref>]. The principal invariants <italic>I</italic><sub>k</sub> of <bold>B</bold> are defined by\n<disp-formula id=\"FD3-ijms-21-05318\"><label>(3)</label><mml:math id=\"mm4\"><mml:mrow><mml:mrow><mml:msub><mml:mi>I</mml:mi><mml:mn>1</mml:mn></mml:msub><mml:mo>=</mml:mo><mml:mi>t</mml:mi><mml:mi>r</mml:mi><mml:mstyle mathvariant=\"bold\" mathsize=\"normal\"><mml:mi>B</mml:mi></mml:mstyle><mml:mo>,</mml:mo><mml:mtext> </mml:mtext><mml:msub><mml:mi>I</mml:mi><mml:mn>2</mml:mn></mml:msub><mml:mo>=</mml:mo><mml:mfrac><mml:mn>1</mml:mn><mml:mn>2</mml:mn></mml:mfrac><mml:mo stretchy=\"false\">(</mml:mo><mml:msubsup><mml:mi>I</mml:mi><mml:mn>1</mml:mn><mml:mn>2</mml:mn></mml:msubsup><mml:mo>−</mml:mo><mml:mi>t</mml:mi><mml:mi>r</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:msup><mml:mstyle mathvariant=\"bold\" mathsize=\"normal\"><mml:mi>B</mml:mi></mml:mstyle><mml:mn>2</mml:mn></mml:msup><mml:mo stretchy=\"false\">)</mml:mo><mml:mo stretchy=\"false\">)</mml:mo><mml:mo>,</mml:mo><mml:mtext> </mml:mtext><mml:msub><mml:mi>I</mml:mi><mml:mn>3</mml:mn></mml:msub><mml:mo>=</mml:mo><mml:mi>det</mml:mi><mml:mstyle mathvariant=\"bold\" mathsize=\"normal\"><mml:mi>B</mml:mi></mml:mstyle><mml:mo>,</mml:mo></mml:mrow></mml:mrow></mml:math></disp-formula>\nwhere <italic>tr</italic> denotes the trace operation, and <inline-formula><mml:math id=\"mm5\"><mml:mrow><mml:mrow><mml:msub><mml:mi>I</mml:mi><mml:mn>3</mml:mn></mml:msub><mml:mo>≡</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:mrow></mml:math></inline-formula> for all deformations of an incompressible material.</p></sec></sec><sec id=\"sec3-ijms-21-05318\"><title>3. Stress-Softening Model</title><p>To characterize the virgin material response of an incompressible and hyperelastic material under the action of an applied magnetic flux density, the following constitutive equation is considered [<xref rid=\"B11-ijms-21-05318\" ref-type=\"bibr\">11</xref>]\n<disp-formula id=\"FD4-ijms-21-05318\"><label>(4)</label><mml:math id=\"mm6\"><mml:mrow><mml:mrow><mml:msub><mml:mstyle mathvariant=\"bold\" mathsize=\"normal\"><mml:mi>T</mml:mi></mml:mstyle><mml:mn>0</mml:mn></mml:msub><mml:mo>=</mml:mo><mml:mo>−</mml:mo><mml:mi>p</mml:mi><mml:mstyle mathvariant=\"bold\" mathsize=\"normal\"><mml:mi>I</mml:mi></mml:mstyle><mml:mo>+</mml:mo><mml:msub><mml:mi>λ</mml:mi><mml:mi>i</mml:mi></mml:msub><mml:mfrac><mml:mrow><mml:mo>∂</mml:mo><mml:msup><mml:mi>W</mml:mi><mml:mrow><mml:mi>e</mml:mi><mml:mi>l</mml:mi><mml:mi>a</mml:mi><mml:mi>s</mml:mi><mml:mi>t</mml:mi><mml:mi>i</mml:mi><mml:mi>c</mml:mi></mml:mrow></mml:msup></mml:mrow><mml:mrow><mml:mo>∂</mml:mo><mml:msub><mml:mi>λ</mml:mi><mml:mi>i</mml:mi></mml:msub></mml:mrow></mml:mfrac><mml:mo>−</mml:mo><mml:mfrac><mml:mrow><mml:msub><mml:mi>λ</mml:mi><mml:mi>i</mml:mi></mml:msub></mml:mrow><mml:mrow><mml:mn>2</mml:mn><mml:mi>μ</mml:mi></mml:mrow></mml:mfrac><mml:mfrac><mml:mrow><mml:mo>∂</mml:mo><mml:mstyle mathvariant=\"bold\" mathsize=\"normal\"><mml:mi>F</mml:mi></mml:mstyle></mml:mrow><mml:mrow><mml:mo>∂</mml:mo><mml:msub><mml:mi>λ</mml:mi><mml:mi>i</mml:mi></mml:msub></mml:mrow></mml:mfrac><mml:msup><mml:mstyle mathvariant=\"bold\" mathsize=\"normal\"><mml:mi>B</mml:mi></mml:mstyle><mml:mrow><mml:mi>A</mml:mi><mml:mi>p</mml:mi><mml:mi>p</mml:mi><mml:mi>l</mml:mi><mml:mi>i</mml:mi><mml:mi>e</mml:mi><mml:mi>d</mml:mi></mml:mrow></mml:msup><mml:mo>•</mml:mo><mml:msup><mml:mstyle mathvariant=\"bold\" mathsize=\"normal\"><mml:mi>B</mml:mi></mml:mstyle><mml:mrow><mml:mi>A</mml:mi><mml:mi>p</mml:mi><mml:mi>p</mml:mi><mml:mi>l</mml:mi><mml:mi>i</mml:mi><mml:mi>e</mml:mi><mml:mi>d</mml:mi></mml:mrow></mml:msup><mml:mo>,</mml:mo></mml:mrow></mml:mrow></mml:math></disp-formula>\nwhere <bold>T<sub>0</sub></bold> is the Cauchy stress, <italic>p</italic> is an undetermined pressure, <italic>λ</italic><sub>i</sub> represents the principal stretches in the material domain, F is the deformation gradient, B<sup>Applied</sup> is the magnetic flux density, <italic>µ</italic> defines the material magnetic permeability defined as <inline-formula><mml:math id=\"mm7\"><mml:mrow><mml:mrow><mml:mi>μ</mml:mi><mml:mo>=</mml:mo><mml:mn>4</mml:mn><mml:mi>π</mml:mi><mml:msup><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mn>10</mml:mn><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow><mml:mrow><mml:mo>−</mml:mo><mml:mn>7</mml:mn></mml:mrow></mml:msup><mml:msub><mml:mi>μ</mml:mi><mml:mi>r</mml:mi></mml:msub><mml:mo>,</mml:mo></mml:mrow></mml:mrow></mml:math></inline-formula> where <italic>µ</italic><sub>r</sub> represents the material relative permeability constant, and <italic>W</italic><sup>elastic</sup> denotes the isotropized Helmholtz free energy density for a reinforced material, given as [<xref rid=\"B12-ijms-21-05318\" ref-type=\"bibr\">12</xref>,<xref rid=\"B13-ijms-21-05318\" ref-type=\"bibr\">13</xref>]\n<disp-formula id=\"FD5-ijms-21-05318\"><label>(5)</label><mml:math id=\"mm8\"><mml:mrow><mml:mrow><mml:msup><mml:mi>W</mml:mi><mml:mrow><mml:mi>e</mml:mi><mml:mi>l</mml:mi><mml:mi>a</mml:mi><mml:mi>s</mml:mi><mml:mi>t</mml:mi><mml:mi>i</mml:mi><mml:mi>c</mml:mi></mml:mrow></mml:msup><mml:mo>=</mml:mo><mml:mo stretchy=\"false\">(</mml:mo><mml:mn>1</mml:mn><mml:mo>−</mml:mo><mml:mi>f</mml:mi><mml:mo stretchy=\"false\">)</mml:mo><mml:msub><mml:mi>W</mml:mi><mml:mrow><mml:mi>i</mml:mi><mml:mi>s</mml:mi><mml:mi>o</mml:mi></mml:mrow></mml:msub><mml:mo stretchy=\"false\">(</mml:mo><mml:msub><mml:mi>I</mml:mi><mml:mn>1</mml:mn></mml:msub><mml:mo stretchy=\"false\">)</mml:mo><mml:mo>+</mml:mo><mml:mi>f</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mfrac><mml:mrow><mml:msub><mml:mi>A</mml:mi><mml:mn>1</mml:mn></mml:msub></mml:mrow><mml:mn>3</mml:mn></mml:mfrac><mml:mo stretchy=\"false\">(</mml:mo><mml:msub><mml:mi>I</mml:mi><mml:mn>1</mml:mn></mml:msub><mml:mo>−</mml:mo><mml:mn>3</mml:mn><mml:mo stretchy=\"false\">)</mml:mo><mml:mo>+</mml:mo><mml:mfrac><mml:mrow><mml:msub><mml:mi>A</mml:mi><mml:mn>2</mml:mn></mml:msub></mml:mrow><mml:mn>9</mml:mn></mml:mfrac><mml:msup><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:msub><mml:mi>I</mml:mi><mml:mn>1</mml:mn></mml:msub><mml:mo>−</mml:mo><mml:mn>3</mml:mn><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow><mml:mn>2</mml:mn></mml:msup><mml:mo>−</mml:mo><mml:mfrac><mml:mrow><mml:mn>2</mml:mn><mml:msub><mml:mi>A</mml:mi><mml:mn>1</mml:mn></mml:msub></mml:mrow><mml:mn>3</mml:mn></mml:mfrac><mml:mi>ln</mml:mi><mml:msqrt><mml:mrow><mml:msub><mml:mi>I</mml:mi><mml:mn>3</mml:mn></mml:msub></mml:mrow></mml:msqrt></mml:mrow><mml:mo>)</mml:mo></mml:mrow><mml:mo>,</mml:mo></mml:mrow></mml:mrow></mml:math></disp-formula>\nwhere <italic>f</italic> is the volumetric fraction, <italic>A</italic><sub>1</sub> and <italic>A</italic><sub>2</sub> are the isotropized material constants, and <italic>W</italic><sub>iso</sub>(<italic>I</italic><sub>i</sub>) is an expression that provides the virgin material isotropic strain energy density. When the material is subjected to a flux magnetic field, the magnetic energy density is expressed as\n<disp-formula id=\"FD6-ijms-21-05318\"><label>(6)</label><mml:math id=\"mm9\"><mml:mrow><mml:mrow><mml:msup><mml:mi>W</mml:mi><mml:mrow><mml:mi>m</mml:mi><mml:mi>a</mml:mi><mml:mi>g</mml:mi><mml:mi>n</mml:mi><mml:mi>e</mml:mi><mml:mi>t</mml:mi><mml:mi>i</mml:mi><mml:mi>c</mml:mi></mml:mrow></mml:msup><mml:mo>=</mml:mo><mml:mo>−</mml:mo><mml:mfrac><mml:mn>1</mml:mn><mml:mrow><mml:mn>2</mml:mn><mml:mi>μ</mml:mi></mml:mrow></mml:mfrac><mml:mstyle mathvariant=\"bold\" mathsize=\"normal\"><mml:mi>F</mml:mi></mml:mstyle><mml:msup><mml:mstyle mathvariant=\"bold\" mathsize=\"normal\"><mml:mi>B</mml:mi></mml:mstyle><mml:mrow><mml:mi>A</mml:mi><mml:mi>p</mml:mi><mml:mi>p</mml:mi><mml:mi>l</mml:mi><mml:mi>i</mml:mi><mml:mi>e</mml:mi><mml:mi>d</mml:mi></mml:mrow></mml:msup><mml:mo>•</mml:mo><mml:msup><mml:mstyle mathvariant=\"bold\" mathsize=\"normal\"><mml:mi>B</mml:mi></mml:mstyle><mml:mrow><mml:mi>A</mml:mi><mml:mi>p</mml:mi><mml:mi>p</mml:mi><mml:mi>l</mml:mi><mml:mi>i</mml:mi><mml:mi>e</mml:mi><mml:mi>d</mml:mi></mml:mrow></mml:msup><mml:mo>.</mml:mo></mml:mrow></mml:mrow></mml:math></disp-formula></p><p>Therefore, the total energy to which the material is subjected can be determined by adding Equations (5) and (6), this gives\n<disp-formula id=\"FD7-ijms-21-05318\"><label>(7)</label><mml:math id=\"mm10\"><mml:mrow><mml:mrow><mml:msub><mml:mi>W</mml:mi><mml:mi>T</mml:mi></mml:msub><mml:mo>=</mml:mo><mml:mo stretchy=\"false\">(</mml:mo><mml:mn>1</mml:mn><mml:mo>−</mml:mo><mml:mi>f</mml:mi><mml:mo stretchy=\"false\">)</mml:mo><mml:msub><mml:mi>W</mml:mi><mml:mrow><mml:mi>i</mml:mi><mml:mi>s</mml:mi><mml:mi>o</mml:mi></mml:mrow></mml:msub><mml:mo stretchy=\"false\">(</mml:mo><mml:msub><mml:mi>I</mml:mi><mml:mn>1</mml:mn></mml:msub><mml:mo stretchy=\"false\">)</mml:mo><mml:mo>+</mml:mo><mml:mi>f</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mfrac><mml:mrow><mml:msub><mml:mi>A</mml:mi><mml:mn>1</mml:mn></mml:msub></mml:mrow><mml:mn>3</mml:mn></mml:mfrac><mml:mo stretchy=\"false\">(</mml:mo><mml:msub><mml:mi>I</mml:mi><mml:mn>1</mml:mn></mml:msub><mml:mo>−</mml:mo><mml:mn>3</mml:mn><mml:mo stretchy=\"false\">)</mml:mo><mml:mo>+</mml:mo><mml:mfrac><mml:mrow><mml:msub><mml:mi>A</mml:mi><mml:mn>2</mml:mn></mml:msub></mml:mrow><mml:mn>9</mml:mn></mml:mfrac><mml:msup><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:msub><mml:mi>I</mml:mi><mml:mn>1</mml:mn></mml:msub><mml:mo>−</mml:mo><mml:mn>3</mml:mn><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow><mml:mn>2</mml:mn></mml:msup><mml:mo>−</mml:mo><mml:mfrac><mml:mrow><mml:mn>2</mml:mn><mml:msub><mml:mi>A</mml:mi><mml:mn>1</mml:mn></mml:msub></mml:mrow><mml:mn>3</mml:mn></mml:mfrac><mml:mi>ln</mml:mi><mml:msqrt><mml:mrow><mml:msub><mml:mi>I</mml:mi><mml:mn>3</mml:mn></mml:msub></mml:mrow></mml:msqrt></mml:mrow><mml:mo>)</mml:mo></mml:mrow><mml:mo>−</mml:mo><mml:mfrac><mml:mn>1</mml:mn><mml:mrow><mml:mn>2</mml:mn><mml:mi>μ</mml:mi></mml:mrow></mml:mfrac><mml:mstyle mathvariant=\"bold\" mathsize=\"normal\"><mml:mi>F</mml:mi></mml:mstyle><mml:msup><mml:mstyle mathvariant=\"bold\" mathsize=\"normal\"><mml:mi>B</mml:mi></mml:mstyle><mml:mrow><mml:mi>A</mml:mi><mml:mi>p</mml:mi><mml:mi>p</mml:mi><mml:mi>l</mml:mi><mml:mi>i</mml:mi><mml:mi>e</mml:mi><mml:mi>d</mml:mi></mml:mrow></mml:msup><mml:mo>•</mml:mo><mml:msup><mml:mstyle mathvariant=\"bold\" mathsize=\"normal\"><mml:mi>B</mml:mi></mml:mstyle><mml:mrow><mml:mi>A</mml:mi><mml:mi>p</mml:mi><mml:mi>p</mml:mi><mml:mi>l</mml:mi><mml:mi>i</mml:mi><mml:mi>e</mml:mi><mml:mi>d</mml:mi></mml:mrow></mml:msup></mml:mrow></mml:mrow></mml:math></disp-formula></p><p>That is the same expression introduced in [<xref rid=\"B10-ijms-21-05318\" ref-type=\"bibr\">10</xref>]. Of course, the term <italic>W</italic><sub>iso</sub>(<italic>I</italic><sub>i</sub>) of Equation (7) can adopt several forms depending on the material model under consideration. For instance, for the case of a neo-Hookean material model, the strain-energy density is given as:<disp-formula id=\"FD8-ijms-21-05318\"><label>(8)</label><mml:math id=\"mm11\"><mml:mrow><mml:mrow><mml:msub><mml:mi>W</mml:mi><mml:mrow><mml:mi>i</mml:mi><mml:mi>s</mml:mi><mml:mi>o</mml:mi></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:mfrac><mml:mrow><mml:msub><mml:mi>μ</mml:mi><mml:mn>0</mml:mn></mml:msub></mml:mrow><mml:mn>2</mml:mn></mml:mfrac><mml:mo stretchy=\"false\">(</mml:mo><mml:msub><mml:mi>I</mml:mi><mml:mn>1</mml:mn></mml:msub><mml:mo>−</mml:mo><mml:mn>3</mml:mn><mml:mo stretchy=\"false\">)</mml:mo><mml:mo>+</mml:mo><mml:msub><mml:mi>c</mml:mi><mml:mn>1</mml:mn></mml:msub><mml:mo>,</mml:mo></mml:mrow></mml:mrow></mml:math></disp-formula>\nwhere <italic>µ</italic><sub>0</sub> is the virgin material shear modulus. Another material model that is commonly used for its accuracy in predicting the hyperelastic behavior of elastomeric materials is the well-known amended non-Gaussian strain energy density model [<xref rid=\"B14-ijms-21-05318\" ref-type=\"bibr\">14</xref>], given by:<disp-formula id=\"FD9-ijms-21-05318\"><label>(9)</label><mml:math id=\"mm12\"><mml:mrow><mml:mrow><mml:msub><mml:mi>W</mml:mi><mml:mrow><mml:mi>i</mml:mi><mml:mi>s</mml:mi><mml:mi>o</mml:mi></mml:mrow></mml:msub><mml:mo stretchy=\"false\">(</mml:mo><mml:msub><mml:mi>I</mml:mi><mml:mn>1</mml:mn></mml:msub><mml:mo stretchy=\"false\">)</mml:mo><mml:mo>=</mml:mo><mml:msub><mml:mi>μ</mml:mi><mml:mn>0</mml:mn></mml:msub><mml:mrow><mml:mo>[</mml:mo><mml:mrow><mml:msub><mml:mi>N</mml:mi><mml:mn>8</mml:mn></mml:msub><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mi>β</mml:mi><mml:msub><mml:mi>λ</mml:mi><mml:mi>r</mml:mi></mml:msub><mml:mo>+</mml:mo><mml:mi>ln</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mfrac><mml:mi>β</mml:mi><mml:mrow><mml:mi>sinh</mml:mi><mml:mi>β</mml:mi></mml:mrow></mml:mfrac></mml:mrow><mml:mo>)</mml:mo></mml:mrow></mml:mrow><mml:mo>)</mml:mo></mml:mrow><mml:mo>−</mml:mo><mml:mi>ln</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mfrac><mml:mi>β</mml:mi><mml:mrow><mml:msub><mml:mi>λ</mml:mi><mml:mi>r</mml:mi></mml:msub></mml:mrow></mml:mfrac></mml:mrow><mml:mo>)</mml:mo></mml:mrow></mml:mrow><mml:mo>]</mml:mo></mml:mrow><mml:mo>+</mml:mo><mml:msub><mml:mi>c</mml:mi><mml:mn>1</mml:mn></mml:msub><mml:mo>.</mml:mo></mml:mrow></mml:mrow></mml:math></disp-formula></p><p>Here, <italic>c</italic><sub>1</sub> represents an energy constant, <italic>N</italic><sub>8</sub> is the material chain number of links, and <italic>β</italic> is the inverse of the Langevin function L(<italic>β</italic>) described by:<disp-formula id=\"FD10-ijms-21-05318\"><label>(10)</label><mml:math id=\"mm13\"><mml:mrow><mml:mrow><mml:msub><mml:mi>λ</mml:mi><mml:mi>r</mml:mi></mml:msub><mml:mo>=</mml:mo><mml:mi>L</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>β</mml:mi><mml:mo stretchy=\"false\">)</mml:mo><mml:mo>≡</mml:mo><mml:mi>cot</mml:mi><mml:mi>β</mml:mi><mml:mo>−</mml:mo><mml:mfrac><mml:mn>1</mml:mn><mml:mi>β</mml:mi></mml:mfrac></mml:mrow></mml:mrow></mml:math></disp-formula>\nwith the relative chain-stretch <inline-formula><mml:math id=\"mm14\"><mml:mrow><mml:mrow><mml:msub><mml:mi>λ</mml:mi><mml:mi>r</mml:mi></mml:msub></mml:mrow></mml:mrow></mml:math></inline-formula> defined as:<disp-formula id=\"FD11-ijms-21-05318\"><label>(11)</label><mml:math id=\"mm15\"><mml:mrow><mml:mrow><mml:msub><mml:mi>λ</mml:mi><mml:mi>r</mml:mi></mml:msub><mml:mo>=</mml:mo><mml:mfrac><mml:mrow><mml:msub><mml:mi>λ</mml:mi><mml:mrow><mml:mi>c</mml:mi><mml:mi>h</mml:mi><mml:mi>a</mml:mi><mml:mi>i</mml:mi><mml:mi>n</mml:mi></mml:mrow></mml:msub></mml:mrow><mml:mrow><mml:msqrt><mml:mrow><mml:msub><mml:mi>N</mml:mi><mml:mn>8</mml:mn></mml:msub></mml:mrow></mml:msqrt></mml:mrow></mml:mfrac><mml:mo>,</mml:mo><mml:mrow><mml:mtext> </mml:mtext><mml:mi>with</mml:mi><mml:mtext> </mml:mtext></mml:mrow><mml:msub><mml:mi>λ</mml:mi><mml:mrow><mml:mi>c</mml:mi><mml:mi>h</mml:mi><mml:mi>a</mml:mi><mml:mi>i</mml:mi><mml:mi>n</mml:mi></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:msqrt><mml:mrow><mml:mfrac><mml:mrow><mml:msub><mml:mi>I</mml:mi><mml:mn>1</mml:mn></mml:msub></mml:mrow><mml:mn>3</mml:mn></mml:mfrac></mml:mrow></mml:msqrt><mml:mo>.</mml:mo></mml:mrow></mml:mrow></mml:math></disp-formula></p><p>To account for the material damage at which the material is subjected when external loads and magnetic energy density fields are applied, the pseudo-elastic model proposed by Ogden–Roxburgh in [<xref rid=\"B15-ijms-21-05318\" ref-type=\"bibr\">15</xref>] is used. This model is based on the assumption that the total energy density function <inline-formula><mml:math id=\"mm16\"><mml:mrow><mml:mrow><mml:msub><mml:mi>W</mml:mi><mml:mi>T</mml:mi></mml:msub></mml:mrow></mml:mrow></mml:math></inline-formula> is related to the pseudo-total energy function <inline-formula><mml:math id=\"mm17\"><mml:mrow><mml:mrow><mml:msub><mml:mover accent=\"true\"><mml:mi>W</mml:mi><mml:mo>¯</mml:mo></mml:mover><mml:mi>T</mml:mi></mml:msub></mml:mrow></mml:mrow></mml:math></inline-formula> by the expression:<disp-formula id=\"FD12-ijms-21-05318\"><label>(12)</label><mml:math id=\"mm18\"><mml:mrow><mml:mrow><mml:msub><mml:mover accent=\"true\"><mml:mi>W</mml:mi><mml:mo>¯</mml:mo></mml:mover><mml:mi>T</mml:mi></mml:msub><mml:mo stretchy=\"false\">(</mml:mo><mml:msub><mml:mi>λ</mml:mi><mml:mn>1</mml:mn></mml:msub><mml:mo>,</mml:mo><mml:msub><mml:mi>λ</mml:mi><mml:mn>2</mml:mn></mml:msub><mml:mo>,</mml:mo><mml:msub><mml:mi>B</mml:mi><mml:mi>i</mml:mi></mml:msub><mml:mo>,</mml:mo><mml:mi>η</mml:mi><mml:mo stretchy=\"false\">)</mml:mo><mml:mo>=</mml:mo><mml:mi>η</mml:mi><mml:msub><mml:mi>W</mml:mi><mml:mi>T</mml:mi></mml:msub><mml:mo stretchy=\"false\">(</mml:mo><mml:mo stretchy=\"false\">(</mml:mo><mml:msub><mml:mi>λ</mml:mi><mml:mn>1</mml:mn></mml:msub><mml:mo>,</mml:mo><mml:msub><mml:mi>λ</mml:mi><mml:mn>2</mml:mn></mml:msub><mml:mo>,</mml:mo><mml:msub><mml:mi>B</mml:mi><mml:mi>i</mml:mi></mml:msub><mml:mo stretchy=\"false\">)</mml:mo><mml:mo>+</mml:mo><mml:mi>ϕ</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>η</mml:mi><mml:mo stretchy=\"false\">)</mml:mo><mml:mo>+</mml:mo><mml:msub><mml:mi>η</mml:mi><mml:mn>1</mml:mn></mml:msub><mml:mover accent=\"true\"><mml:mi>W</mml:mi><mml:mo>^</mml:mo></mml:mover><mml:mo stretchy=\"false\">(</mml:mo><mml:mo stretchy=\"false\">(</mml:mo><mml:msub><mml:mi>λ</mml:mi><mml:mn>1</mml:mn></mml:msub><mml:mo>,</mml:mo><mml:msub><mml:mi>λ</mml:mi><mml:mn>2</mml:mn></mml:msub><mml:mo>,</mml:mo><mml:msub><mml:mi>ϕ</mml:mi><mml:mi>D</mml:mi></mml:msub><mml:mo stretchy=\"false\">(</mml:mo><mml:msub><mml:mi>ξ</mml:mi><mml:mi>a</mml:mi></mml:msub><mml:mo stretchy=\"false\">)</mml:mo><mml:mo stretchy=\"false\">)</mml:mo><mml:mo>,</mml:mo></mml:mrow></mml:mrow></mml:math></disp-formula>\nwhere <italic>B</italic><sub>i</sub> is the magnetic flux density aligned along the principal i-direction, and <inline-formula><mml:math id=\"mm19\"><mml:mrow><mml:mrow><mml:msub><mml:mi>W</mml:mi><mml:mi>T</mml:mi></mml:msub><mml:mo stretchy=\"false\">(</mml:mo><mml:msub><mml:mi>λ</mml:mi><mml:mn>1</mml:mn></mml:msub><mml:mo>,</mml:mo><mml:msub><mml:mi>λ</mml:mi><mml:mn>2</mml:mn></mml:msub><mml:mo>,</mml:mo><mml:msub><mml:mi>B</mml:mi><mml:mi>i</mml:mi></mml:msub><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:mrow></mml:math></inline-formula> is the primary loading path energy density function for which the softening variable <italic>η</italic> and the residual strain variable <italic>η</italic>₁ are inactive. Both variables have the value of one on the loading path and depend on the maximum previous strain value, <inline-formula><mml:math id=\"mm20\"><mml:mrow><mml:mrow><mml:mi>ϕ</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>η</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:mrow></mml:math></inline-formula> and <inline-formula><mml:math id=\"mm21\"><mml:mrow><mml:mrow><mml:msub><mml:mi>ϕ</mml:mi><mml:mi>D</mml:mi></mml:msub><mml:mo stretchy=\"false\">(</mml:mo><mml:msub><mml:mi>ξ</mml:mi><mml:mi>a</mml:mi></mml:msub><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:mrow></mml:math></inline-formula> represent smooth damage functions that vanish on the virgin path, i.e.,<inline-formula><mml:math id=\"mm22\"><mml:mrow><mml:mrow><mml:mi>ϕ</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:mn>1</mml:mn><mml:mo stretchy=\"false\">)</mml:mo><mml:mo>=</mml:mo><mml:mn>0</mml:mn><mml:mo>,</mml:mo></mml:mrow></mml:mrow></mml:math></inline-formula> and <inline-formula><mml:math id=\"mm23\"><mml:mrow><mml:mrow><mml:msub><mml:mi>ϕ</mml:mi><mml:mi>D</mml:mi></mml:msub><mml:mo stretchy=\"false\">(</mml:mo><mml:mn>0</mml:mn><mml:mo stretchy=\"false\">)</mml:mo><mml:mo>=</mml:mo><mml:mn>0</mml:mn><mml:mo>,</mml:mo></mml:mrow></mml:mrow></mml:math></inline-formula> and <inline-formula><mml:math id=\"mm24\"><mml:mrow><mml:mrow><mml:mover accent=\"true\"><mml:mi>W</mml:mi><mml:mo>^</mml:mo></mml:mover><mml:mo stretchy=\"false\">(</mml:mo><mml:msub><mml:mi>λ</mml:mi><mml:mn>1</mml:mn></mml:msub><mml:mo>,</mml:mo><mml:msub><mml:mi>λ</mml:mi><mml:mn>2</mml:mn></mml:msub><mml:mo>,</mml:mo><mml:msub><mml:mi>ϕ</mml:mi><mml:mi>D</mml:mi></mml:msub><mml:mo stretchy=\"false\">(</mml:mo><mml:msub><mml:mi>ξ</mml:mi><mml:mi>a</mml:mi></mml:msub><mml:mo stretchy=\"false\">)</mml:mo><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:mrow></mml:math></inline-formula> is the residual strain energy density that depends on a damage function related to material residual strain effects [<xref rid=\"B16-ijms-21-05318\" ref-type=\"bibr\">16</xref>]. Next, we recall from pseudo-elasticity theory the relationship\n<disp-formula id=\"FD13-ijms-21-05318\"><label>(13)</label><mml:math id=\"mm25\"><mml:mrow><mml:mrow><mml:mo>−</mml:mo><mml:msup><mml:mi>ϕ</mml:mi><mml:mo>′</mml:mo></mml:msup><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>η</mml:mi><mml:mo stretchy=\"false\">)</mml:mo><mml:mo>=</mml:mo><mml:msub><mml:mi>W</mml:mi><mml:mi>T</mml:mi></mml:msub><mml:mo stretchy=\"false\">(</mml:mo><mml:mo stretchy=\"false\">(</mml:mo><mml:msub><mml:mi>λ</mml:mi><mml:mn>1</mml:mn></mml:msub><mml:mo>,</mml:mo><mml:msub><mml:mi>λ</mml:mi><mml:mn>2</mml:mn></mml:msub><mml:mo>,</mml:mo><mml:msub><mml:mi>B</mml:mi><mml:mi>i</mml:mi></mml:msub><mml:mo stretchy=\"false\">)</mml:mo><mml:mo>,</mml:mo></mml:mrow></mml:mrow></mml:math></disp-formula>\nintroduced in [<xref rid=\"B15-ijms-21-05318\" ref-type=\"bibr\">15</xref>] and consider the damage function model:<disp-formula id=\"FD14-ijms-21-05318\"><label>(14)</label><mml:math id=\"mm26\"><mml:mrow><mml:mrow><mml:mo>−</mml:mo><mml:msup><mml:mi>ϕ</mml:mi><mml:mo>′</mml:mo></mml:msup><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>η</mml:mi><mml:mo stretchy=\"false\">)</mml:mo><mml:mo>=</mml:mo><mml:mo>−</mml:mo><mml:msup><mml:mrow><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mfrac><mml:mn>1</mml:mn><mml:mi>b</mml:mi></mml:mfrac><mml:mi>ln</mml:mi><mml:mi>η</mml:mi></mml:mrow><mml:mo>)</mml:mo></mml:mrow></mml:mrow><mml:mn>2</mml:mn></mml:msup><mml:mo>+</mml:mo><mml:msub><mml:mi>W</mml:mi><mml:mrow><mml:mi>T</mml:mi><mml:mi>max</mml:mi></mml:mrow></mml:msub><mml:mo>,</mml:mo></mml:mrow></mml:mrow></mml:math></disp-formula>\nproposed in [<xref rid=\"B9-ijms-21-05318\" ref-type=\"bibr\">9</xref>] that provides the damage variable <italic>η</italic>:<disp-formula id=\"FD15-ijms-21-05318\"><label>(15)</label><mml:math id=\"mm27\"><mml:mrow><mml:mrow><mml:mi>η</mml:mi><mml:mo>=</mml:mo><mml:msup><mml:mi>e</mml:mi><mml:mrow><mml:mo>−</mml:mo><mml:mi>b</mml:mi><mml:msqrt><mml:mrow><mml:msub><mml:mi>W</mml:mi><mml:mrow><mml:mi>T</mml:mi><mml:mi>max</mml:mi></mml:mrow></mml:msub><mml:mo>−</mml:mo><mml:msub><mml:mi>W</mml:mi><mml:mi>T</mml:mi></mml:msub></mml:mrow></mml:msqrt></mml:mrow></mml:msup><mml:mo>,</mml:mo></mml:mrow></mml:mrow></mml:math></disp-formula>\nwhere <italic>b</italic> is a positive material softening parameter, and <inline-formula><mml:math id=\"mm28\"><mml:mrow><mml:mrow><mml:msub><mml:mi>W</mml:mi><mml:mrow><mml:mi>T</mml:mi><mml:mi>max</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mrow></mml:math></inline-formula> is the maximum energy density value at the point for which the material is unloaded from the loading path. Therefore, the damage function <inline-formula><mml:math id=\"mm29\"><mml:mrow><mml:mrow><mml:mi>ϕ</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>η</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:mrow></mml:math></inline-formula> can be determined as a function of the material energy density given by Equation (14). This yields:<disp-formula id=\"FD16-ijms-21-05318\"><label>(16)</label><mml:math id=\"mm30\"><mml:mrow><mml:mrow><mml:mi>ϕ</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>η</mml:mi><mml:mo stretchy=\"false\">)</mml:mo><mml:mo>=</mml:mo><mml:mfrac><mml:mrow><mml:msup><mml:mi>e</mml:mi><mml:mrow><mml:mo>−</mml:mo><mml:mi>b</mml:mi><mml:msqrt><mml:mrow><mml:msub><mml:mi>W</mml:mi><mml:mrow><mml:mi>T</mml:mi><mml:mi>max</mml:mi></mml:mrow></mml:msub><mml:mo>−</mml:mo><mml:mi>W</mml:mi></mml:mrow></mml:msqrt></mml:mrow></mml:msup></mml:mrow><mml:mrow><mml:msup><mml:mi>b</mml:mi><mml:mn>2</mml:mn></mml:msup></mml:mrow></mml:mfrac><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mn>2</mml:mn><mml:mo>−</mml:mo><mml:msup><mml:mi>b</mml:mi><mml:mn>2</mml:mn></mml:msup><mml:msub><mml:mi>W</mml:mi><mml:mi>T</mml:mi></mml:msub><mml:mo>+</mml:mo><mml:mn>2</mml:mn><mml:mi>b</mml:mi><mml:msqrt><mml:mrow><mml:msub><mml:mi>W</mml:mi><mml:mrow><mml:mi>T</mml:mi><mml:mi>max</mml:mi></mml:mrow></mml:msub><mml:mo>−</mml:mo><mml:mi>W</mml:mi></mml:mrow></mml:msqrt><mml:mo>+</mml:mo><mml:msup><mml:mi>e</mml:mi><mml:mrow><mml:mi>b</mml:mi><mml:msqrt><mml:mrow><mml:msub><mml:mi>W</mml:mi><mml:mrow><mml:mi>T</mml:mi><mml:mi>max</mml:mi></mml:mrow></mml:msub><mml:mo>−</mml:mo><mml:mi>W</mml:mi></mml:mrow></mml:msqrt></mml:mrow></mml:msup><mml:mo stretchy=\"false\">(</mml:mo><mml:msup><mml:mi>b</mml:mi><mml:mn>2</mml:mn></mml:msup><mml:msub><mml:mi>W</mml:mi><mml:mrow><mml:mi>T</mml:mi><mml:mi>max</mml:mi></mml:mrow></mml:msub><mml:mo>−</mml:mo><mml:mn>2</mml:mn><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow><mml:mo>)</mml:mo></mml:mrow><mml:mo>.</mml:mo></mml:mrow></mml:mrow></mml:math></disp-formula>\nTo find the residual strain energy density <inline-formula><mml:math id=\"mm31\"><mml:mrow><mml:mrow><mml:mover accent=\"true\"><mml:mi>W</mml:mi><mml:mo>^</mml:mo></mml:mover><mml:mo stretchy=\"false\">(</mml:mo><mml:msub><mml:mi>λ</mml:mi><mml:mn>1</mml:mn></mml:msub><mml:mo>,</mml:mo><mml:msub><mml:mi>λ</mml:mi><mml:mn>2</mml:mn></mml:msub><mml:msub><mml:mi>ϕ</mml:mi><mml:mi>D</mml:mi></mml:msub><mml:mo stretchy=\"false\">(</mml:mo><mml:msub><mml:mi>ξ</mml:mi><mml:mi>a</mml:mi></mml:msub><mml:mo stretchy=\"false\">)</mml:mo><mml:mo stretchy=\"false\">)</mml:mo><mml:mo>,</mml:mo></mml:mrow></mml:mrow></mml:math></inline-formula> we assume that the residual strain energy density expression that accounts for the damage material mechanism is of the form:<disp-formula id=\"FD17-ijms-21-05318\"><label>(17)</label><mml:math id=\"mm32\"><mml:mrow><mml:mrow><mml:mover accent=\"true\"><mml:mi>W</mml:mi><mml:mo>^</mml:mo></mml:mover><mml:mo stretchy=\"false\">(</mml:mo><mml:msub><mml:mi>λ</mml:mi><mml:mn>1</mml:mn></mml:msub><mml:mo>,</mml:mo><mml:msub><mml:mi>λ</mml:mi><mml:mn>2</mml:mn></mml:msub><mml:mo>,</mml:mo><mml:msub><mml:mi>ϕ</mml:mi><mml:mi>D</mml:mi></mml:msub><mml:mo stretchy=\"false\">(</mml:mo><mml:msub><mml:mi>ξ</mml:mi><mml:mi>a</mml:mi></mml:msub><mml:mo stretchy=\"false\">)</mml:mo><mml:mo stretchy=\"false\">)</mml:mo><mml:mo>=</mml:mo><mml:mstyle displaystyle=\"true\"><mml:munderover><mml:mo>∑</mml:mo><mml:mrow><mml:mi>a</mml:mi><mml:mo>=</mml:mo><mml:mn>1</mml:mn></mml:mrow><mml:mn>3</mml:mn></mml:munderover><mml:mrow><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:msub><mml:mi>ξ</mml:mi><mml:mi>a</mml:mi></mml:msub><mml:mo stretchy=\"false\">(</mml:mo><mml:msubsup><mml:mo>Δ</mml:mo><mml:mi>a</mml:mi><mml:mi>n</mml:mi></mml:msubsup><mml:mo>−</mml:mo><mml:msubsup><mml:mi>λ</mml:mi><mml:mi>a</mml:mi><mml:mi>n</mml:mi></mml:msubsup><mml:mo stretchy=\"false\">)</mml:mo><mml:mo>+</mml:mo><mml:msub><mml:mi>ϕ</mml:mi><mml:mi>D</mml:mi></mml:msub><mml:mo stretchy=\"false\">(</mml:mo><mml:msub><mml:mi>ξ</mml:mi><mml:mi>a</mml:mi></mml:msub><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow><mml:mo>)</mml:mo></mml:mrow></mml:mrow></mml:mstyle><mml:mo>,</mml:mo></mml:mrow></mml:mrow></mml:math></disp-formula>\nwhere <inline-formula><mml:math id=\"mm33\"><mml:mrow><mml:mrow><mml:msub><mml:mi>ϕ</mml:mi><mml:mi>D</mml:mi></mml:msub><mml:mo stretchy=\"false\">(</mml:mo><mml:msub><mml:mi>ξ</mml:mi><mml:mi>a</mml:mi></mml:msub><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:mrow></mml:math></inline-formula> represents a damage function related to residual strain effects <inline-formula><mml:math id=\"mm34\"><mml:mrow><mml:mrow><mml:msub><mml:mi>λ</mml:mi><mml:mrow><mml:mi>r</mml:mi><mml:mi>e</mml:mi><mml:mi>s</mml:mi></mml:mrow></mml:msub><mml:mo>,</mml:mo><mml:mtext> </mml:mtext><mml:msub><mml:mo>Δ</mml:mo><mml:mi>a</mml:mi></mml:msub><mml:mo>,</mml:mo><mml:mtext> </mml:mtext><mml:mi>a</mml:mi><mml:mo>=</mml:mo><mml:mn>1</mml:mn><mml:mo>,</mml:mo><mml:mn>2</mml:mn><mml:mo>,</mml:mo><mml:mn>3</mml:mn><mml:mo>,</mml:mo></mml:mrow></mml:mrow></mml:math></inline-formula> are the maximum values of the principal stretches at which unloading begins on the primary loading path, and <italic>n</italic> is a positive scaling constant. By using the Ogden–Roxburgh [<xref rid=\"B15-ijms-21-05318\" ref-type=\"bibr\">15</xref>] pseudo-elastic theory, we require that\n<disp-formula id=\"FD18-ijms-21-05318\"><label>(18)</label><mml:math id=\"mm35\"><mml:mrow><mml:mrow><mml:mfrac><mml:mrow><mml:mo>∂</mml:mo><mml:mover accent=\"true\"><mml:mi>W</mml:mi><mml:mo>^</mml:mo></mml:mover></mml:mrow><mml:mrow><mml:mo>∂</mml:mo><mml:msub><mml:mi>ξ</mml:mi><mml:mi>a</mml:mi></mml:msub></mml:mrow></mml:mfrac><mml:mo>=</mml:mo><mml:mn>0</mml:mn><mml:mo>,</mml:mo></mml:mrow></mml:mrow></mml:math></disp-formula>\nthus, we have:<disp-formula id=\"FD19-ijms-21-05318\"><label>(19)</label><mml:math id=\"mm36\"><mml:mrow><mml:mrow><mml:mfrac><mml:mrow><mml:mo>∂</mml:mo><mml:mover accent=\"true\"><mml:mi>W</mml:mi><mml:mo>^</mml:mo></mml:mover></mml:mrow><mml:mrow><mml:mo>∂</mml:mo><mml:msub><mml:mi>ξ</mml:mi><mml:mi>a</mml:mi></mml:msub></mml:mrow></mml:mfrac><mml:mo>=</mml:mo><mml:mo stretchy=\"false\">(</mml:mo><mml:msubsup><mml:mo>Δ</mml:mo><mml:mi>a</mml:mi><mml:mi>n</mml:mi></mml:msubsup><mml:mo>−</mml:mo><mml:msubsup><mml:mi>λ</mml:mi><mml:mi>a</mml:mi><mml:mi>n</mml:mi></mml:msubsup><mml:mo stretchy=\"false\">)</mml:mo><mml:mo>+</mml:mo><mml:mfrac><mml:mrow><mml:mo>∂</mml:mo><mml:msub><mml:mi>ϕ</mml:mi><mml:mi>D</mml:mi></mml:msub><mml:mo stretchy=\"false\">(</mml:mo><mml:msub><mml:mi>ξ</mml:mi><mml:mi>a</mml:mi></mml:msub><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow><mml:mrow><mml:mo>∂</mml:mo><mml:msub><mml:mi>ξ</mml:mi><mml:mi>a</mml:mi></mml:msub></mml:mrow></mml:mfrac><mml:mo>=</mml:mo><mml:mn>0.</mml:mn></mml:mrow></mml:mrow></mml:math></disp-formula>\nSolving Equation (19), yields\n<disp-formula id=\"FD20-ijms-21-05318\"><label>(20)</label><mml:math id=\"mm37\"><mml:mrow><mml:mrow><mml:mo>−</mml:mo><mml:mfrac><mml:mrow><mml:mo>∂</mml:mo><mml:msub><mml:mi>ϕ</mml:mi><mml:mi>D</mml:mi></mml:msub><mml:mo stretchy=\"false\">(</mml:mo><mml:msub><mml:mi>ξ</mml:mi><mml:mi>a</mml:mi></mml:msub><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow><mml:mrow><mml:mo>∂</mml:mo><mml:msub><mml:mi>ξ</mml:mi><mml:mi>a</mml:mi></mml:msub></mml:mrow></mml:mfrac><mml:mo>=</mml:mo><mml:mo stretchy=\"false\">(</mml:mo><mml:msubsup><mml:mo>Δ</mml:mo><mml:mi>a</mml:mi><mml:mi>n</mml:mi></mml:msubsup><mml:mo>−</mml:mo><mml:msubsup><mml:mi>λ</mml:mi><mml:mi>a</mml:mi><mml:mi>n</mml:mi></mml:msubsup><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:mrow></mml:math></disp-formula>\nThen, we chose <inline-formula><mml:math id=\"mm38\"><mml:mrow><mml:mrow><mml:msub><mml:mi>ϕ</mml:mi><mml:mi>D</mml:mi></mml:msub><mml:msup><mml:mrow/><mml:mo>′</mml:mo></mml:msup><mml:mo stretchy=\"false\">(</mml:mo><mml:msub><mml:mi>ξ</mml:mi><mml:mi>a</mml:mi></mml:msub><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:mrow></mml:math></inline-formula> to have the form\n<disp-formula id=\"FD21-ijms-21-05318\"><label>(21)</label><mml:math id=\"mm39\"><mml:mrow><mml:mrow><mml:mo>−</mml:mo><mml:mfrac><mml:mrow><mml:mo>∂</mml:mo><mml:msub><mml:mi>ϕ</mml:mi><mml:mi>D</mml:mi></mml:msub><mml:mo stretchy=\"false\">(</mml:mo><mml:msub><mml:mi>ξ</mml:mi><mml:mi>a</mml:mi></mml:msub><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow><mml:mrow><mml:mo>∂</mml:mo><mml:msub><mml:mi>ξ</mml:mi><mml:mi>a</mml:mi></mml:msub></mml:mrow></mml:mfrac><mml:mo>=</mml:mo><mml:msub><mml:mi>ξ</mml:mi><mml:mi>a</mml:mi></mml:msub><mml:mi>d</mml:mi></mml:mrow></mml:mrow></mml:math></disp-formula>\nwhere <italic>d</italic> is a positive dimensionless material constant that is fitted in accordance with the maximum residual strain recorded during the experimental tests of the material samples. The integration of Equation (21) provides\n<disp-formula id=\"FD22-ijms-21-05318\"><label>(22)</label><mml:math id=\"mm40\"><mml:mrow><mml:mrow><mml:msub><mml:mi>ϕ</mml:mi><mml:mi>D</mml:mi></mml:msub><mml:mo stretchy=\"false\">(</mml:mo><mml:msub><mml:mi>ξ</mml:mi><mml:mi>a</mml:mi></mml:msub><mml:mo stretchy=\"false\">)</mml:mo><mml:mo>=</mml:mo><mml:mo>−</mml:mo><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mfrac><mml:mi>d</mml:mi><mml:mn>2</mml:mn></mml:mfrac><mml:msubsup><mml:mi>ξ</mml:mi><mml:mi>a</mml:mi><mml:mn>2</mml:mn></mml:msubsup><mml:mo>+</mml:mo><mml:msub><mml:mi>d</mml:mi><mml:mn>0</mml:mn></mml:msub></mml:mrow><mml:mo>)</mml:mo></mml:mrow><mml:mo>,</mml:mo></mml:mrow></mml:mrow></mml:math></disp-formula>\nwhere <italic>d</italic><sub>0</sub> is an integration constant. Thus, from Equations (20) and (21) the following relationship is found\n<disp-formula id=\"FD23-ijms-21-05318\"><label>(23)</label><mml:math id=\"mm41\"><mml:mrow><mml:mrow><mml:msub><mml:mi>ξ</mml:mi><mml:mi>a</mml:mi></mml:msub><mml:mo>=</mml:mo><mml:mfrac><mml:mn>1</mml:mn><mml:mi>d</mml:mi></mml:mfrac><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:msubsup><mml:mo>Δ</mml:mo><mml:mi>a</mml:mi><mml:mi>n</mml:mi></mml:msubsup><mml:mo>−</mml:mo><mml:msubsup><mml:mi>λ</mml:mi><mml:mi>a</mml:mi><mml:mi>n</mml:mi></mml:msubsup></mml:mrow><mml:mo>)</mml:mo></mml:mrow><mml:mo>.</mml:mo></mml:mrow></mml:mrow></mml:math></disp-formula></p><p>On the primary loading path, <inline-formula><mml:math id=\"mm42\"><mml:mrow><mml:mrow><mml:msub><mml:mi>ξ</mml:mi><mml:mi>a</mml:mi></mml:msub></mml:mrow></mml:mrow></mml:math></inline-formula> is inactive, while on the unloading path, <inline-formula><mml:math id=\"mm43\"><mml:mrow><mml:mrow><mml:msub><mml:mi>ξ</mml:mi><mml:mi>a</mml:mi></mml:msub></mml:mrow></mml:mrow></mml:math></inline-formula> has the value given by Equation (23). Substitution of Equation (23) into Equation (17) gives the following pseudo-elastic strain energy density expression per unit volume that accounts for residual strains on the unloading path:<disp-formula id=\"FD24-ijms-21-05318\"><label>(24)</label><mml:math id=\"mm44\"><mml:mrow><mml:mrow><mml:mover accent=\"true\"><mml:mi>W</mml:mi><mml:mo>^</mml:mo></mml:mover><mml:mo>=</mml:mo><mml:mfrac><mml:mn>1</mml:mn><mml:mi>d</mml:mi></mml:mfrac><mml:mstyle displaystyle=\"true\"><mml:munderover><mml:mo>∑</mml:mo><mml:mrow><mml:mi>a</mml:mi><mml:mo>=</mml:mo><mml:mn>1</mml:mn></mml:mrow><mml:mn>3</mml:mn></mml:munderover><mml:mrow><mml:mfrac><mml:mn>1</mml:mn><mml:mn>2</mml:mn></mml:mfrac><mml:msup><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:msubsup><mml:mo>Δ</mml:mo><mml:mi>a</mml:mi><mml:mi>n</mml:mi></mml:msubsup><mml:mo>−</mml:mo><mml:msubsup><mml:mi>λ</mml:mi><mml:mi>a</mml:mi><mml:mi>n</mml:mi></mml:msubsup><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow><mml:mn>2</mml:mn></mml:msup></mml:mrow></mml:mstyle><mml:mo>+</mml:mo><mml:msub><mml:mi>C</mml:mi><mml:mn>0</mml:mn></mml:msub><mml:mo>,</mml:mo></mml:mrow></mml:mrow></mml:math></disp-formula>\nwhere <italic>C</italic>₀ is an integration constant that ensures that the residual strain energy density vanishes at the value of the corresponding unloading residual stretch deformation <inline-formula><mml:math id=\"mm45\"><mml:mrow><mml:mrow><mml:msub><mml:mi>λ</mml:mi><mml:mrow><mml:mi>r</mml:mi><mml:mi>e</mml:mi><mml:mi>s</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mrow></mml:math></inline-formula>. Therefore, the strain energy function during unloading of an incompressible and hyperelastic material, in accordance with the pseudo-elastic theory, has the form:<disp-formula id=\"FD25-ijms-21-05318\"><label>(25)</label><mml:math id=\"mm46\"><mml:mrow><mml:mrow><mml:msub><mml:mover accent=\"true\"><mml:mi>W</mml:mi><mml:mo>¯</mml:mo></mml:mover><mml:mi>T</mml:mi></mml:msub><mml:mo stretchy=\"false\">(</mml:mo><mml:msub><mml:mi>λ</mml:mi><mml:mn>1</mml:mn></mml:msub><mml:mo>,</mml:mo><mml:msub><mml:mi>λ</mml:mi><mml:mn>2</mml:mn></mml:msub><mml:mo>,</mml:mo><mml:msub><mml:mi>B</mml:mi><mml:mi>i</mml:mi></mml:msub><mml:mo>,</mml:mo><mml:mi>η</mml:mi><mml:mo stretchy=\"false\">)</mml:mo><mml:mo>=</mml:mo><mml:mi>η</mml:mi><mml:msub><mml:mi>W</mml:mi><mml:mi>T</mml:mi></mml:msub><mml:mo stretchy=\"false\">(</mml:mo><mml:msub><mml:mi>λ</mml:mi><mml:mn>1</mml:mn></mml:msub><mml:mo>,</mml:mo><mml:msub><mml:mi>λ</mml:mi><mml:mn>2</mml:mn></mml:msub><mml:mo>,</mml:mo><mml:msub><mml:mi>B</mml:mi><mml:mi>i</mml:mi></mml:msub><mml:mo stretchy=\"false\">)</mml:mo><mml:mo>+</mml:mo><mml:mi>ϕ</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>η</mml:mi><mml:mo stretchy=\"false\">)</mml:mo><mml:mo>+</mml:mo><mml:mfrac><mml:mrow><mml:msub><mml:mi>η</mml:mi><mml:mn>1</mml:mn></mml:msub></mml:mrow><mml:mi>d</mml:mi></mml:mfrac><mml:mstyle displaystyle=\"true\"><mml:munderover><mml:mo>∑</mml:mo><mml:mrow><mml:mi>a</mml:mi><mml:mo>=</mml:mo><mml:mn>1</mml:mn></mml:mrow><mml:mn>3</mml:mn></mml:munderover><mml:mrow><mml:mfrac><mml:mn>1</mml:mn><mml:mn>2</mml:mn></mml:mfrac><mml:msup><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:msubsup><mml:mo>Δ</mml:mo><mml:mi>a</mml:mi><mml:mi>n</mml:mi></mml:msubsup><mml:mo>−</mml:mo><mml:msubsup><mml:mi>λ</mml:mi><mml:mi>a</mml:mi><mml:mi>n</mml:mi></mml:msubsup><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow><mml:mn>2</mml:mn></mml:msup></mml:mrow></mml:mstyle><mml:mo>+</mml:mo><mml:msub><mml:mi>C</mml:mi><mml:mn>0</mml:mn></mml:msub><mml:mo>.</mml:mo></mml:mrow></mml:mrow></mml:math></disp-formula></p><p>Notice from Equation (25) that the variable <italic>η</italic>₁ has the value of one on the loading path but decreases monotonically during unloading. Furthermore, and because of the expression that provides the dissipating energy due to residual strains given by Equation (24), during the loading of the material sample, the value of <inline-formula><mml:math id=\"mm47\"><mml:mrow><mml:mover accent=\"true\"><mml:mi>W</mml:mi><mml:mo>^</mml:mo></mml:mover></mml:mrow></mml:math></inline-formula> is always zero. Therefore, the residual function <italic>η</italic>₁ can be assumed to have the same expression as that of <italic>η</italic> since this damage function is inactive during loading and decreases monotonically during unloading. Of course, other expressions can be used for <italic>η</italic>₁ so that 0 ≤ <italic>η</italic>₁ ≤ 1 is satisfied during unloading of the material sample. Thus, the energy dissipated during the loading–unloading cycle can be determined from:<disp-formula id=\"FD26-ijms-21-05318\"><label>(26)</label><mml:math id=\"mm48\"><mml:mrow><mml:mrow><mml:mi>D</mml:mi><mml:mo>=</mml:mo><mml:msub><mml:mi>W</mml:mi><mml:mi>T</mml:mi></mml:msub><mml:mo>−</mml:mo><mml:msub><mml:mover accent=\"true\"><mml:mi>W</mml:mi><mml:mo>¯</mml:mo></mml:mover><mml:mi>T</mml:mi></mml:msub><mml:mo stretchy=\"false\">(</mml:mo><mml:msub><mml:mi>λ</mml:mi><mml:mn>1</mml:mn></mml:msub><mml:mo>,</mml:mo><mml:msub><mml:mi>λ</mml:mi><mml:mn>2</mml:mn></mml:msub><mml:mo>,</mml:mo><mml:msub><mml:mi>B</mml:mi><mml:mi>i</mml:mi></mml:msub><mml:mo>,</mml:mo><mml:mi>η</mml:mi><mml:mo stretchy=\"false\">)</mml:mo><mml:mo>.</mml:mo></mml:mrow></mml:mrow></mml:math></disp-formula></p><p>The area enclosed by the loading and unloading curves shown in <xref ref-type=\"fig\" rid=\"ijms-21-05318-f001\">Figure 1</xref> provides a schematic view of the energy dissipated during one cycle [<xref rid=\"B17-ijms-21-05318\" ref-type=\"bibr\">17</xref>]. Moreover, the virgin material Cauchy-stress components are determined from:<disp-formula id=\"FD27-ijms-21-05318\"><label>(27)</label><mml:math id=\"mm49\"><mml:mrow><mml:mrow><mml:msub><mml:mi>T</mml:mi><mml:mrow><mml:mn>0</mml:mn><mml:mi>i</mml:mi></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:mo>−</mml:mo><mml:mi>p</mml:mi><mml:mo>+</mml:mo><mml:msub><mml:mi>λ</mml:mi><mml:mi>i</mml:mi></mml:msub><mml:mfrac><mml:mrow><mml:mo>∂</mml:mo><mml:msub><mml:mi>W</mml:mi><mml:mi>T</mml:mi></mml:msub></mml:mrow><mml:mrow><mml:mo>∂</mml:mo><mml:msub><mml:mi>λ</mml:mi><mml:mi>i</mml:mi></mml:msub></mml:mrow></mml:mfrac><mml:mo>−</mml:mo><mml:mfrac><mml:mrow><mml:msub><mml:mi>λ</mml:mi><mml:mi>i</mml:mi></mml:msub></mml:mrow><mml:mrow><mml:mn>2</mml:mn><mml:mi>μ</mml:mi></mml:mrow></mml:mfrac><mml:msup><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:msubsup><mml:mi>B</mml:mi><mml:mn>1</mml:mn><mml:mrow><mml:mi>A</mml:mi><mml:mi>p</mml:mi><mml:mi>p</mml:mi><mml:mi>l</mml:mi><mml:mi>i</mml:mi><mml:mi>e</mml:mi><mml:mi>d</mml:mi></mml:mrow></mml:msubsup><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow><mml:mn>2</mml:mn></mml:msup><mml:mo>,</mml:mo><mml:mrow><mml:mtext> </mml:mtext><mml:mi>where</mml:mi><mml:mtext> </mml:mtext></mml:mrow><mml:mi>i</mml:mi><mml:mo>=</mml:mo><mml:mn>1</mml:mn><mml:mo>,</mml:mo><mml:mn>2</mml:mn><mml:mo>,</mml:mo><mml:mn>3</mml:mn><mml:mrow><mml:mtext> </mml:mtext><mml:mo>(</mml:mo><mml:mi>no</mml:mi><mml:mtext> </mml:mtext><mml:mi>sum</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:mrow></mml:mrow></mml:math></disp-formula>\nin which Equation (7) has been considered. As usual, the undetermined pressure <italic>p</italic> can be eliminated by subtracting <inline-formula><mml:math id=\"mm50\"><mml:mrow><mml:mrow><mml:msub><mml:mi>T</mml:mi><mml:mrow><mml:mn>0</mml:mn><mml:mi>i</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mrow></mml:math></inline-formula> from <inline-formula><mml:math id=\"mm51\"><mml:mrow><mml:mrow><mml:msub><mml:mi>T</mml:mi><mml:mrow><mml:mn>0</mml:mn><mml:mi>j</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mrow></mml:math></inline-formula>, this yields\n<disp-formula id=\"FD28-ijms-21-05318\"><label>(28)</label><mml:math id=\"mm52\"><mml:mrow><mml:mrow><mml:msub><mml:mi>T</mml:mi><mml:mrow><mml:mn>0</mml:mn><mml:mi>i</mml:mi></mml:mrow></mml:msub><mml:mo>−</mml:mo><mml:msub><mml:mi>T</mml:mi><mml:mrow><mml:mn>0</mml:mn><mml:mi>j</mml:mi></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:mrow><mml:mo>{</mml:mo><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mn>1</mml:mn><mml:mo>−</mml:mo><mml:mi>f</mml:mi><mml:mo stretchy=\"false\">)</mml:mo><mml:mi>ℵ</mml:mi><mml:mo>+</mml:mo><mml:mfrac><mml:mrow><mml:mn>2</mml:mn><mml:mi>f</mml:mi></mml:mrow><mml:mn>3</mml:mn></mml:mfrac><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:msub><mml:mi>A</mml:mi><mml:mn>1</mml:mn></mml:msub><mml:mo>+</mml:mo><mml:mfrac><mml:mrow><mml:mn>2</mml:mn><mml:msub><mml:mi>A</mml:mi><mml:mn>2</mml:mn></mml:msub></mml:mrow><mml:mn>3</mml:mn></mml:mfrac><mml:mo stretchy=\"false\">(</mml:mo><mml:msub><mml:mi>I</mml:mi><mml:mn>1</mml:mn></mml:msub><mml:mo>−</mml:mo><mml:mn>3</mml:mn><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow><mml:mo>)</mml:mo></mml:mrow></mml:mrow><mml:mo>}</mml:mo></mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:msubsup><mml:mi>λ</mml:mi><mml:mi>i</mml:mi><mml:mn>2</mml:mn></mml:msubsup><mml:mo>−</mml:mo><mml:msubsup><mml:mi>λ</mml:mi><mml:mi>j</mml:mi><mml:mn>2</mml:mn></mml:msubsup><mml:mo stretchy=\"false\">)</mml:mo><mml:mo>−</mml:mo><mml:mfrac><mml:mrow><mml:msub><mml:mi>λ</mml:mi><mml:mi>i</mml:mi></mml:msub></mml:mrow><mml:mrow><mml:mn>2</mml:mn><mml:mi>μ</mml:mi></mml:mrow></mml:mfrac><mml:msup><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:msubsup><mml:mi>B</mml:mi><mml:mi>i</mml:mi><mml:mrow><mml:mi>A</mml:mi><mml:mi>p</mml:mi><mml:mi>p</mml:mi><mml:mi>l</mml:mi><mml:mi>i</mml:mi><mml:mi>e</mml:mi><mml:mi>d</mml:mi></mml:mrow></mml:msubsup><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow><mml:mn>2</mml:mn></mml:msup><mml:mo>+</mml:mo><mml:mfrac><mml:mrow><mml:msub><mml:mi>λ</mml:mi><mml:mi>j</mml:mi></mml:msub></mml:mrow><mml:mrow><mml:mn>2</mml:mn><mml:mi>μ</mml:mi></mml:mrow></mml:mfrac><mml:msup><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:msubsup><mml:mi>B</mml:mi><mml:mi>j</mml:mi><mml:mrow><mml:mi>A</mml:mi><mml:mi>p</mml:mi><mml:mi>p</mml:mi><mml:mi>l</mml:mi><mml:mi>i</mml:mi><mml:mi>e</mml:mi><mml:mi>d</mml:mi></mml:mrow></mml:msubsup><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow><mml:mn>2</mml:mn></mml:msup><mml:mo>,</mml:mo></mml:mrow></mml:mrow></mml:math></disp-formula>\nwhere <inline-formula><mml:math id=\"mm53\"><mml:mrow><mml:mrow><mml:mi>i</mml:mi><mml:mo>≠</mml:mo><mml:mi>j</mml:mi><mml:mo>=</mml:mo><mml:mn>1</mml:mn><mml:mo>,</mml:mo><mml:mn>2</mml:mn><mml:mo>,</mml:mo><mml:mn>3</mml:mn></mml:mrow></mml:mrow></mml:math></inline-formula>, and\n<disp-formula id=\"FD29-ijms-21-05318\"><label>(29)</label><mml:math id=\"mm54\"><mml:mrow><mml:mrow><mml:mi>ℵ</mml:mi><mml:mo>=</mml:mo><mml:mfrac><mml:mrow><mml:msub><mml:mi>μ</mml:mi><mml:mn>0</mml:mn></mml:msub></mml:mrow><mml:mrow><mml:mn>3</mml:mn><mml:msub><mml:mi>λ</mml:mi><mml:mi>r</mml:mi></mml:msub></mml:mrow></mml:mfrac><mml:mrow><mml:mo>[</mml:mo><mml:mrow><mml:mi>β</mml:mi><mml:mo>+</mml:mo><mml:mfrac><mml:mn>1</mml:mn><mml:mrow><mml:msub><mml:mi>N</mml:mi><mml:mn>8</mml:mn></mml:msub></mml:mrow></mml:mfrac><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mfrac><mml:mn>1</mml:mn><mml:mrow><mml:msub><mml:mi>λ</mml:mi><mml:mi>r</mml:mi></mml:msub></mml:mrow></mml:mfrac><mml:mo>−</mml:mo><mml:mfrac><mml:mn>1</mml:mn><mml:mrow><mml:mi>β</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:mn>1</mml:mn><mml:mo>−</mml:mo><mml:msubsup><mml:mi>λ</mml:mi><mml:mi>r</mml:mi><mml:mn>2</mml:mn></mml:msubsup><mml:mo>−</mml:mo><mml:mn>2</mml:mn><mml:msub><mml:mi>λ</mml:mi><mml:mi>r</mml:mi></mml:msub><mml:mo>/</mml:mo><mml:mi>β</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:mfrac></mml:mrow><mml:mo>)</mml:mo></mml:mrow></mml:mrow><mml:mo>]</mml:mo></mml:mrow><mml:mo>.</mml:mo></mml:mrow></mml:mrow></mml:math></disp-formula></p><p>Then, from the pseudo-elastic expression given by Equation (25), the stress-softened, permanent set, and magnetic effects can be predicted from\n<disp-formula id=\"FD30-ijms-21-05318\"><label>(30)</label><mml:math id=\"mm55\"><mml:mrow><mml:mrow><mml:mstyle mathvariant=\"bold\" mathsize=\"normal\"><mml:mi>τ</mml:mi></mml:mstyle><mml:mo>=</mml:mo><mml:msub><mml:mstyle mathvariant=\"bold\" mathsize=\"normal\"><mml:mi>T</mml:mi></mml:mstyle><mml:mn>0</mml:mn></mml:msub><mml:msup><mml:mi>e</mml:mi><mml:mrow><mml:mo>−</mml:mo><mml:mi>b</mml:mi><mml:msqrt><mml:mrow><mml:msub><mml:mi>W</mml:mi><mml:mrow><mml:mi>T</mml:mi><mml:mi>max</mml:mi></mml:mrow></mml:msub><mml:mo>−</mml:mo><mml:msub><mml:mi>W</mml:mi><mml:mi>T</mml:mi></mml:msub></mml:mrow></mml:msqrt></mml:mrow></mml:msup><mml:mo>,</mml:mo></mml:mrow></mml:mrow></mml:math></disp-formula>\nwhere <inline-formula><mml:math id=\"mm56\"><mml:mrow><mml:mstyle mathvariant=\"bold\" mathsize=\"normal\"><mml:mi>τ</mml:mi></mml:mstyle></mml:mrow></mml:math></inline-formula> represents the Cauchy stress of the stress-softened material [<xref rid=\"B9-ijms-21-05318\" ref-type=\"bibr\">9</xref>]. Of course, <inline-formula><mml:math id=\"mm57\"><mml:mrow><mml:mrow><mml:mstyle mathvariant=\"bold\" mathsize=\"normal\"><mml:mi>τ</mml:mi></mml:mstyle><mml:mo>=</mml:mo><mml:msub><mml:mstyle mathvariant=\"bold\" mathsize=\"normal\"><mml:mi>T</mml:mi></mml:mstyle><mml:mn>0</mml:mn></mml:msub><mml:mo>,</mml:mo></mml:mrow></mml:mrow></mml:math></inline-formula> when and only when <inline-formula><mml:math id=\"mm58\"><mml:mrow><mml:mrow><mml:msub><mml:mi>W</mml:mi><mml:mi>T</mml:mi></mml:msub><mml:mo>=</mml:mo><mml:msub><mml:mi>W</mml:mi><mml:mrow><mml:mi>T</mml:mi><mml:mi>max</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mrow></mml:math></inline-formula>. Thus, the stress-softened, residual strains and magnetic effects experienced by the magnetorheological elastomer during unloading is determined by the following equation\n<disp-formula id=\"FD31-ijms-21-05318\"><label>(31)</label><mml:math id=\"mm59\"><mml:mrow><mml:mtable columnalign=\"left\"><mml:mtr><mml:mtd><mml:msub><mml:mi>τ</mml:mi><mml:mrow><mml:mn>0</mml:mn><mml:mi>i</mml:mi></mml:mrow></mml:msub><mml:mo>−</mml:mo><mml:msub><mml:mi>τ</mml:mi><mml:mrow><mml:mn>0</mml:mn><mml:mi>j</mml:mi></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:mo stretchy=\"false\">[</mml:mo><mml:mo stretchy=\"false\">{</mml:mo><mml:mo stretchy=\"false\">(</mml:mo><mml:mn>1</mml:mn><mml:mo>−</mml:mo><mml:mi>f</mml:mi><mml:mo stretchy=\"false\">)</mml:mo><mml:mi>ℵ</mml:mi><mml:mo>+</mml:mo><mml:mfrac><mml:mrow><mml:mn>2</mml:mn><mml:mi>f</mml:mi></mml:mrow><mml:mn>3</mml:mn></mml:mfrac><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:msub><mml:mi>A</mml:mi><mml:mn>1</mml:mn></mml:msub><mml:mo>+</mml:mo><mml:mfrac><mml:mrow><mml:mn>2</mml:mn><mml:msub><mml:mi>A</mml:mi><mml:mn>2</mml:mn></mml:msub></mml:mrow><mml:mn>3</mml:mn></mml:mfrac><mml:mo stretchy=\"false\">(</mml:mo><mml:msub><mml:mi>I</mml:mi><mml:mn>1</mml:mn></mml:msub><mml:mo>−</mml:mo><mml:mn>3</mml:mn><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow><mml:mo>)</mml:mo></mml:mrow><mml:mo stretchy=\"false\">}</mml:mo><mml:mo stretchy=\"false\">(</mml:mo><mml:msubsup><mml:mi>λ</mml:mi><mml:mi>i</mml:mi><mml:mn>2</mml:mn></mml:msubsup><mml:mo>−</mml:mo><mml:msubsup><mml:mi>λ</mml:mi><mml:mi>j</mml:mi><mml:mn>2</mml:mn></mml:msubsup><mml:mo stretchy=\"false\">)</mml:mo><mml:mo>−</mml:mo><mml:mfrac><mml:mrow><mml:msub><mml:mi>λ</mml:mi><mml:mi>i</mml:mi></mml:msub></mml:mrow><mml:mrow><mml:mn>2</mml:mn><mml:mi>μ</mml:mi></mml:mrow></mml:mfrac><mml:msup><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:msubsup><mml:mi>B</mml:mi><mml:mi>i</mml:mi><mml:mrow><mml:mi>A</mml:mi><mml:mi>p</mml:mi><mml:mi>p</mml:mi><mml:mi>l</mml:mi><mml:mi>i</mml:mi><mml:mi>e</mml:mi><mml:mi>d</mml:mi></mml:mrow></mml:msubsup><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow><mml:mn>2</mml:mn></mml:msup><mml:mo>+</mml:mo></mml:mtd></mml:mtr><mml:mtr><mml:mtd><mml:mfrac><mml:mrow><mml:msub><mml:mi>λ</mml:mi><mml:mi>j</mml:mi></mml:msub></mml:mrow><mml:mrow><mml:mn>2</mml:mn><mml:mi>μ</mml:mi></mml:mrow></mml:mfrac><mml:msup><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:msubsup><mml:mi>B</mml:mi><mml:mi>j</mml:mi><mml:mrow><mml:mi>A</mml:mi><mml:mi>p</mml:mi><mml:mi>p</mml:mi><mml:mi>l</mml:mi><mml:mi>i</mml:mi><mml:mi>e</mml:mi><mml:mi>d</mml:mi></mml:mrow></mml:msubsup><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow><mml:mn>2</mml:mn></mml:msup><mml:mo>+</mml:mo><mml:mfrac><mml:mi>n</mml:mi><mml:mi>d</mml:mi></mml:mfrac><mml:mo stretchy=\"false\">(</mml:mo><mml:msubsup><mml:mi>λ</mml:mi><mml:mi>i</mml:mi><mml:mi>n</mml:mi></mml:msubsup><mml:mo stretchy=\"false\">(</mml:mo><mml:msubsup><mml:mi>λ</mml:mi><mml:mi>i</mml:mi><mml:mi>n</mml:mi></mml:msubsup><mml:mo>−</mml:mo><mml:msubsup><mml:mo>Δ</mml:mo><mml:mi>i</mml:mi><mml:mi>n</mml:mi></mml:msubsup><mml:mo stretchy=\"false\">)</mml:mo><mml:mo>−</mml:mo><mml:msubsup><mml:mi>λ</mml:mi><mml:mi>j</mml:mi><mml:mi>n</mml:mi></mml:msubsup><mml:mo stretchy=\"false\">(</mml:mo><mml:msubsup><mml:mi>λ</mml:mi><mml:mi>j</mml:mi><mml:mi>n</mml:mi></mml:msubsup><mml:mo>−</mml:mo><mml:msubsup><mml:mo>Δ</mml:mo><mml:mi>j</mml:mi><mml:mi>n</mml:mi></mml:msubsup><mml:mo stretchy=\"false\">)</mml:mo><mml:mo stretchy=\"false\">)</mml:mo><mml:mo stretchy=\"false\">]</mml:mo><mml:msup><mml:mi>e</mml:mi><mml:mrow><mml:mo>−</mml:mo><mml:mi>b</mml:mi><mml:msqrt><mml:mrow><mml:msub><mml:mi>W</mml:mi><mml:mrow><mml:mi>T</mml:mi><mml:mi>max</mml:mi></mml:mrow></mml:msub><mml:mo>−</mml:mo><mml:msub><mml:mi>W</mml:mi><mml:mi>T</mml:mi></mml:msub></mml:mrow></mml:msqrt></mml:mrow></mml:msup><mml:mo>,</mml:mo></mml:mtd></mml:mtr></mml:mtable></mml:mrow></mml:math></disp-formula></p><p>Here, <italic>n</italic> is assumed to have the value of 1/2, <italic>d</italic> is a positive residual strain material constant, <inline-formula><mml:math id=\"mm60\"><mml:mrow><mml:mrow><mml:msub><mml:mo>Δ</mml:mo><mml:mi>i</mml:mi></mml:msub><mml:mo>,</mml:mo><mml:mtext> </mml:mtext><mml:mi>i</mml:mi><mml:mo>=</mml:mo><mml:mn>1</mml:mn><mml:mo>,</mml:mo><mml:mn>2</mml:mn><mml:mo>,</mml:mo><mml:mn>3</mml:mn></mml:mrow></mml:mrow></mml:math></inline-formula> (no sum) are the principal maximum stretch values at the point on the loading virgin curve for which unloading of the material sample begins. Of course, the uniaxial engineering stress tensor can be computed by using the relationship <inline-formula><mml:math id=\"mm61\"><mml:mrow><mml:mrow><mml:mstyle mathvariant=\"bold\" mathsize=\"normal\"><mml:mi>σ</mml:mi></mml:mstyle><mml:mo>=</mml:mo><mml:mstyle mathvariant=\"bold\" mathsize=\"normal\"><mml:mi>T</mml:mi></mml:mstyle><mml:msup><mml:mstyle mathvariant=\"bold\" mathsize=\"normal\"><mml:mi>F</mml:mi></mml:mstyle><mml:mrow><mml:mo>−</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:msup></mml:mrow></mml:mrow></mml:math></inline-formula>. We shall next investigate the accuracy of the derived expressions for energy–stretch and stress–stretch to predict experimental data in which residual strains and magnetic effects are considered.</p></sec><sec id=\"sec4-ijms-21-05318\"><title>4. Comparison with Experimental Data</title><p>We next examine the accuracy of the introduced model to predict the Mullins and residual strain effects in the produced isotropic and anisotropic magnetorheological material samples reinforced with 20, 35, 50, 65, and 80 wt% of carbonyl iron microparticles when subjected to cyclic loading–unloading uniaxial extension tests, with and without the action of a magnetic flux density.</p><sec><title>Numerical Results</title><p>First, the strain energy density curves of the material samples were computed by using uniaxial extension experimental data and Equations (7) and (25). The first, second, third, and fourth columns of <xref rid=\"ijms-21-05318-t001\" ref-type=\"table\">Table 1</xref> and <xref rid=\"ijms-21-05318-t002\" ref-type=\"table\">Table 2</xref> summarize the different parameter values considered for the fabrication and experimental tests of the material samples. The remaining columns of <xref rid=\"ijms-21-05318-t001\" ref-type=\"table\">Table 1</xref> and <xref rid=\"ijms-21-05318-t002\" ref-type=\"table\">Table 2</xref> show the estimated material constant values obtained by best fitting of the experimental data. <xref rid=\"ijms-21-05318-t003\" ref-type=\"table\">Table 3</xref> and <xref rid=\"ijms-21-05318-t004\" ref-type=\"table\">Table 4</xref> show the material constant for each test cycle. In addition, the maximum and residual stretch values collected during experimental tests are listed for each loading–unloading cycle.</p><p><xref ref-type=\"fig\" rid=\"ijms-21-05318-f001\">Figure 1</xref>, <xref ref-type=\"fig\" rid=\"ijms-21-05318-f002\">Figure 2</xref>, <xref ref-type=\"fig\" rid=\"ijms-21-05318-f003\">Figure 3</xref>, <xref ref-type=\"fig\" rid=\"ijms-21-05318-f004\">Figure 4</xref> and <xref ref-type=\"fig\" rid=\"ijms-21-05318-f005\">Figure 5</xref> illustrate the energy dissipation as a function of the amount of stretch to which the material samples were subjected during loading–unloading cycles. Here, the colored solid lines define the strain energy density obtained from experimental data, while the dashed colored lines are theoretical predictions computed from Equations (7) and (25). Notice that <italic>D</italic> is sensitive to the different wt% of CIPs concentrations, to the material properties (isotropic or anisotropic), to the maximum amount of elongation at which the samples are subjected, to the resulting residual strains, and to the magnetic flux density. The discrepancies observed between experimental data and theoretical predictions are mainly due to the assumption that viscoelastic effects are negligible, and that pseudo-elastic theory can be used to predict the energy dissipated during unloading of the material samples. However, in spite of having ignored viscoelastic effects during the derivation of Equations (12) and (24), the theoretical energy curves shown in <xref ref-type=\"fig\" rid=\"ijms-21-05318-f001\">Figure 1</xref>, <xref ref-type=\"fig\" rid=\"ijms-21-05318-f002\">Figure 2</xref>, <xref ref-type=\"fig\" rid=\"ijms-21-05318-f003\">Figure 3</xref>, <xref ref-type=\"fig\" rid=\"ijms-21-05318-f004\">Figure 4</xref> and <xref ref-type=\"fig\" rid=\"ijms-21-05318-f005\">Figure 5</xref> describe the materials’ qualitative and quantitative behaviors well.</p><p>Since the energy dissipated for each loading–unloading cycle is expected to increase with an increase in the elastic energy <inline-formula><mml:math id=\"mm62\"><mml:mrow><mml:mrow><mml:msub><mml:mi>W</mml:mi><mml:mi>T</mml:mi></mml:msub></mml:mrow></mml:mrow></mml:math></inline-formula>, the dissipation factor\n<disp-formula id=\"FD38-ijms-21-05318\"><label>(32)</label><mml:math id=\"mm63\"><mml:mrow><mml:mrow><mml:mi>E</mml:mi><mml:mo>=</mml:mo><mml:mfrac><mml:mi>D</mml:mi><mml:mrow><mml:msub><mml:mi>W</mml:mi><mml:mrow><mml:mi>T</mml:mi><mml:mi>max</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mfrac></mml:mrow></mml:mrow></mml:math></disp-formula>\nis introduced in an attempt to quantify the differences among the tested material samples. This dissipation factor differs from that proposed by Mai et al. in [<xref rid=\"B17-ijms-21-05318\" ref-type=\"bibr\">17</xref>] since in our definition, we compute the ratio of energy dissipation to the maximum strain energy density at which unloading starts, while in the definition introduced in [<xref rid=\"B17-ijms-21-05318\" ref-type=\"bibr\">17</xref>], the dissipation ratio is defined as the ratio of the energy dissipation to the strain energy density of the loading curve.</p><p>Theoretical predictions of the dissipation factor <italic>E</italic> computed from Equation (32) are illustrated in <xref ref-type=\"fig\" rid=\"ijms-21-05318-f006\">Figure 6</xref>, <xref ref-type=\"fig\" rid=\"ijms-21-05318-f007\">Figure 7</xref>, <xref ref-type=\"fig\" rid=\"ijms-21-05318-f008\">Figure 8</xref>, <xref ref-type=\"fig\" rid=\"ijms-21-05318-f009\">Figure 9</xref> and <xref ref-type=\"fig\" rid=\"ijms-21-05318-f010\">Figure 10</xref>. Here, <italic>E</italic> has been plotted versus amount of stretch for the various concentrations of CIPs used to develop the material samples. The area under each dissipation factor curve indicates the material capacity to dissipate energy per loading–unloading cycle. In other words, the higher the area, the higher the material damage that induces inelastic phenomena, such as stress-softening and residual strain effects.</p><p>Also, a low value of E indicates that the energy loss per loading-unloading cycle is small. The value of <italic>E</italic> could also provide information about the debonding process, since a decrease in the material shear modulus is evident after the application of the first loading–unloading cycle.</p><p>When the material samples are further subjected to loading–unloading cycles, the Mullins effect as well as the residual strains increase with a noticeable decrease in the material shear modulus, especially for those composite samples reinforced with CIPs concentrations of 35, 50, 65, and 80 wt%. It is also observed that the material shear modulus becomes sensitive not only to the wt% of CIPs added into the elastomer material matrix, but also due to the application of the magnetic flux intensity that induces attractive forces between CIPs and to the strong bonds between these and the elastomer matrix.</p><p>However, for the material samples with CIPs concentration of 20 wt%, it appears that the material shear modulus is not sensitive to the application of a magnetic flux density, as confirmed in [<xref rid=\"B2-ijms-21-05318\" ref-type=\"bibr\">2</xref>] for small wt% concentrations of CIPs mixed with the elastomer matrix.</p><p>Finally, <xref ref-type=\"fig\" rid=\"ijms-21-05318-f011\">Figure 11</xref>, <xref ref-type=\"fig\" rid=\"ijms-21-05318-f012\">Figure 12</xref>, <xref ref-type=\"fig\" rid=\"ijms-21-05318-f013\">Figure 13</xref> and <xref ref-type=\"fig\" rid=\"ijms-21-05318-f014\">Figure 14</xref> show a comparison of experimental data with simulation results obtained from Equations (28) and (31) and the relationship <inline-formula><mml:math id=\"mm64\"><mml:mrow><mml:mrow><mml:mstyle mathvariant=\"bold\" mathsize=\"normal\"><mml:mi>σ</mml:mi></mml:mstyle><mml:mo>=</mml:mo><mml:msub><mml:mstyle mathvariant=\"bold\" mathsize=\"normal\"><mml:mi>T</mml:mi></mml:mstyle><mml:mn>0</mml:mn></mml:msub><mml:msup><mml:mstyle mathvariant=\"bold\" mathsize=\"normal\"><mml:mi>F</mml:mi></mml:mstyle><mml:mrow><mml:mo>−</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:msup></mml:mrow></mml:mrow></mml:math></inline-formula>. Note that there is a slight difference between experimental data and the energy-based Equations (28) and (31) in predicting stress-softening and residual strain effects. From <xref ref-type=\"fig\" rid=\"ijms-21-05318-f011\">Figure 11</xref>, <xref ref-type=\"fig\" rid=\"ijms-21-05318-f012\">Figure 12</xref>, <xref ref-type=\"fig\" rid=\"ijms-21-05318-f013\">Figure 13</xref> and <xref ref-type=\"fig\" rid=\"ijms-21-05318-f014\">Figure 14</xref>, it can be concluded that the addition of CIPs with a predefined orientation of the microparticles embedded into the polymer matrix and the application of a magnetic flux density in the loading–unloading direction could enhance the material shear modulus as well as its capacity to dissipate energy.</p></sec></sec><sec id=\"sec5-ijms-21-05318\"><title>5. Material Preparation</title><p>In what follows, some aspects regarding the preparation and development of the magnetorheological polyurethane material samples are discussed.</p><sec id=\"sec5dot1-ijms-21-05318\"><title>5.1. Materials</title><p>Materials used to manufacture the polyurethane magnetorheological elastomers were two urethane rubbers (URs) Vytaflex@10 with a mixed viscosity of 3100 cps and a shore hardness of 10 A, both purchased from Smooth-On Inc. (Macungie. PA, USA), as well as the phthalate free softening agent SO Flex II Vitaflex part A consisting of toluene diisocyanate and diisononylphthalate and the vytaflex part B consisting of a mix of ether polyol and plasticizer and diethyltoluenediamine. The spherical carbonyl iron particles, with an average size of 2.25 µm, were purchased from Sigma-Aldrich (Toluca, Mexico).</p></sec><sec id=\"sec5dot2-ijms-21-05318\"><title>5.2. Magnetorheological Material Preparation</title><p>To prepare the elastomer, one-to-one by volume mix ratios of Part A and B of the URs were mixed. Several composite material samples were produced by considering CIPs concentrations of 20, 35, 50, 65, and 80 wt%. These CIPs concentrations were mixed with the SO Flex II until homogenous mixtures formed. Part B of the UR was mixed before it was poured into the CIPs mixture. Then, part A of the UR was mixed and added to the last mixture, and the whole solution was homogeneously mixed for a few minutes before it was poured, at room temperature, into different molds. All composite material samples were mixed at room temperature and subjected to vacuum conditions during the curing process to avoid porosity. Anisotropic magnetorheological elastomers were produced by applying, during the curing process, a magnetic flux density of 52.2 mT for 30 min.</p><p><xref ref-type=\"fig\" rid=\"ijms-21-05318-f015\">Figure 15</xref>a depicts the morphology of the magnetic particle powder used to manufacture the magnetorheological materials. As it can be seen, the CIPs have a spherical morphology of micron-size particles. <xref ref-type=\"fig\" rid=\"ijms-21-05318-f015\">Figure 15</xref>b shows the computed histogram of the size distribution of the CIPs. For estimating the average size of CIPs, the diameter of 455 particles were measured through the Digimizer 4.6.1 software (MedCalc Ltd., Ostend, Belgium). It was found that the magnetic particles have a diameter range of 0.25 to 5.25 µm, and an average diameter of 2.25 µm, as shown in <xref ref-type=\"fig\" rid=\"ijms-21-05318-f015\">Figure 15</xref>b.</p><p>The magnetorheological samples were analyzed by optical microscopy to observe the distribution of CIPs in the PU matrix. <xref ref-type=\"fig\" rid=\"ijms-21-05318-f016\">Figure 16</xref> shows the images of anisotropic samples for each amount of CIPs added to the polymeric matrix. Notice from <xref ref-type=\"fig\" rid=\"ijms-21-05318-f016\">Figure 16</xref> that the CIPs are aligned parallel to the orientation of the magnetic field applied during the curing process. This alignment is evident for the sample that contains 20 wt% of CIPs. However, as the wt% of CIPs increases, microparticle saturation hinders the alignment because of the decreasing of the polymeric matrix mass content [<xref rid=\"B11-ijms-21-05318\" ref-type=\"bibr\">11</xref>].</p></sec><sec id=\"sec5dot3-ijms-21-05318\"><title>5.3. Uniaxial Extension Tests</title><p>Once the magnetorheological mixtures were cured, the different material samples were subjected to cyclic uniaxial extension tests in an Instron 3365 universal testing machine, with bounding box dimensions of 2.1 × 0.76 × 0.71 m. In order to have better force measurement, a static load cell of 5 N (Model: 2530-5N) was used. The specimen’s dumbbell-shaped form was defined under the norm ISO37-2011. The material samples were located inside a solenoid coil so that a magnetic field was oriented towards the direction of the tensile load applied by the testing machine, as shown in <xref ref-type=\"fig\" rid=\"ijms-21-05318-f017\">Figure 17</xref>. The samples were stretched to a maximum elongation of 20 mm, with a crosshead rate of 200 mm/min, to have a uniform magnetic flux density acting on the material samples during loading–unloading cycles. All the samples were kept inside the solenoid coil during the magnetic flux density application. The samples were stretched, <italic>λ</italic>, for three loading–unloading cycles with maximum elongation values of 7, 14, and 20 mm, whereby <italic>λ =</italic> 1 + <italic>L<sub>f</sub></italic>/<italic>L<sub>i</sub></italic>. Here, <italic>L<sub>i</sub></italic> and <italic>L<sub>f</sub></italic> are the initial and the final sample lengths, respectively.</p><p>The maximum stretch elongation values were increased in general, from Δ<italic>a</italic> = 1.22, 1.44, to 1.64. In all cases, the specimens were subjected to the magnetic flux density of 52.2 mT exerted by the solenoid shown in <xref ref-type=\"fig\" rid=\"ijms-21-05318-f018\">Figure 18</xref>, with a maximum flux variation value of 4%, as recorded by the longitudinal probe of a Gaussmeter BELL-5170 apparatus used to measure the magnetic flux density along the solenoid longitudinal axis. Details of the solenoid coil used during experimental tests are described in [<xref rid=\"B11-ijms-21-05318\" ref-type=\"bibr\">11</xref>]. Experimental tests for the loading–unloading cycles were performed an average of five times in order to confirm reproducibility. <xref ref-type=\"fig\" rid=\"ijms-21-05318-f019\">Figure 19</xref> and <xref ref-type=\"fig\" rid=\"ijms-21-05318-f020\">Figure 20</xref> illustrate the isotropic and anisotropic material samples’ behavior with and without the action of a magnetic flux density during the loading–unloading uniaxial extension cycles. In both <xref ref-type=\"fig\" rid=\"ijms-21-05318-f019\">Figure 19</xref> and <xref ref-type=\"fig\" rid=\"ijms-21-05318-f020\">Figure 20</xref>, permanent set and stress-softened effects occur. When the samples are subjected to a magnetic flux density, the material tensile stress increases for higher concentrations of CIPs. In fact, an increase of the material stress of about 785% and 744% were achieved when comparing the isotropic and anisotropic samples made with 80 wt% with respect to those made with 20 wt% of CIPs at the amount of stretch <italic>λ</italic> = 1.16.</p></sec></sec><sec sec-type=\"conclusions\" id=\"sec6-ijms-21-05318\"><title>6. Conclusions</title><p>Stress-softening, residual strains, and energy dissipated in loading–unloading cycles were evaluated by considering the wt% of CIPs concentration added into the polyurethane elastomer matrix, the isotropic and anisotropic material properties, and the impact of an applied constant magnetic flux density. We found that when the wt% of CIPs increases, the energy dissipated increases, especially for higher concentrations of CIPs. Furthermore, the Mullins and residual strain effects increase for those material samples for which the CIPs embedded along the application of the uniaxial force and the magnetic flux density. In other words, it was found that for the anisotropic samples reinforced with CIPs concentrations higher than 20 wt% when subjected to uniaxial extension cyclic loads, exhibit higher shear modulus than the isotropic ones, which becomes evident for those material samples subjected to a 52.2 mT of magnetic flux density and reinforced with 35, 50, 65, and 80 wt% of CIPs. Moreover, the energy dissipated during the loading–unloading cycles increases because of the wt% of CIPs added into the elastomer matrix when the samples are under the action of a magnetic flux density, as confirmed by the maximum value achieved by the dissipation factor curves. This material behavior is an indication that the Mullins and residual strain effects become sensitive to the wt% of CIPs added into the elastomer matrix and to the application of a magnetic flux density. Therefore, it is concluded that in those material samples made from CIPs with a predefined orientation when subjected to cyclic loads and to the action of a magnetic flux density, their shear modulus and their capacity to dissipate energy increases. These conclusions are important since knowing in advance the degree of damage experienced by these materials and the impact of that damage on their physical and mechanical properties when subjected to external cyclic loads and magnetic fields will allow for the enhancement of the applicability of polyurethanes. Polyurethanes are already widely used in biomedical, automotive, construction, textiles, and other industrial applications because of their physical properties, such as resistance to corrosion, durability, strength, elongation, toughness, shrinkage, and expansion, among others, that makes these elastomers suitable for devices under the action of a magnetic field.</p></sec></body><back><ack><title>Acknowledgments</title><p>The authors are thankful to the National Lab in Additive Manufacturing, 3D Digitizing and Computed Tomography (MADiT) LN280867.</p></ack><notes><title>Author Contributions</title><p>Conceptualization, funding acquisition, and supervision A.E.-Z., L.M.P.-P., O.M.-R., and D.O.-T.; methodology, investigation, validation, and writing—original draft preparation A.E.-Z., I.H.J.-C., I.A.P.-M., O.M.-R., D.O.-T., and L.M.P.-P.; writing—review and editing, A.E.-Z., O.M.-R., D.O.-T., I.A.P.-M., I.H.J.-C., and L.M.P.-P. All authors have read and agreed to the published version of the manuscript.</p></notes><notes><title>Funding</title><p>This research was funded by Tecnológico de Monterrey through the Research Group of Nanotechnology for Devices Design, and by the Consejo Nacional de Ciencia y Tecnología de México (Conacyt), Project Numbers 242269, 255837, and 296176.</p></notes><notes notes-type=\"COI-statement\"><title>Conflicts of Interest</title><p>The authors declare no conflict of interest.</p></notes><ref-list><title>References</title><ref id=\"B1-ijms-21-05318\"><label>1.</label><element-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Coquelle</surname><given-names>E.</given-names></name><name><surname>Bossis</surname><given-names>G.</given-names></name><name><surname>Szabo</surname><given-names>D.</given-names></name><name><surname>Giulieri</surname><given-names>F.</given-names></name></person-group><article-title>Micromechanical analysis of an elastomer filled with particles organized in chain-like structure</article-title><source>J. 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Notice that the value of <italic>D</italic> depends upon the point on the virgin curve at which the unloading process starts. Here <italic>B</italic> = 0, <italic>μ</italic>₀ = 13060 Pa, <italic>N</italic>₈ = 2.5, <italic>f</italic> = 0.076, <italic>b</italic> = 0.00275 m<sup>3/2</sup>J<sup>1/2</sup>, <italic>A</italic>₁ = 10000, <italic>A</italic>₂ = 30000, <italic>d</italic> = 0.0002 m²/N, and Δ<italic>a</italic> = 1.65.</p></caption><graphic xlink:href=\"ijms-21-05318-g001\"/></fig><fig id=\"ijms-21-05318-f002\" orientation=\"portrait\" position=\"float\"><label>Figure 2</label><caption><p>Energy dissipation curves for isotropic magnetorheological material samples subjected to loading and unloading cycles with <italic>B</italic> = 0 mT. Notice that for increasing wt% of CIPs, the dissipation area broadens, which is an indication of the damage experienced in the material during cyclic loading.</p></caption><graphic xlink:href=\"ijms-21-05318-g002\"/></fig><fig id=\"ijms-21-05318-f003\" orientation=\"portrait\" position=\"float\"><label>Figure 3</label><caption><p>Energy dissipation energy curves for isotropic magnetorheological material samples subjected to loading and unloading cycles with <italic>B</italic> = 52.2 mT.</p></caption><graphic xlink:href=\"ijms-21-05318-g003\"/></fig><fig id=\"ijms-21-05318-f004\" orientation=\"portrait\" position=\"float\"><label>Figure 4</label><caption><p>Energy dissipation energy curves for anisotropic magnetorheological material samples subjected to loading and unloading cycles with <italic>B</italic> = 0 mT.</p></caption><graphic xlink:href=\"ijms-21-05318-g004\"/></fig><fig id=\"ijms-21-05318-f005\" orientation=\"portrait\" position=\"float\"><label>Figure 5</label><caption><p>Energy dissipation energy curves for anisotropic magnetorheological material samples subjected to loading and unloading cycles with <italic>B</italic> = 52.2 mT.</p></caption><graphic xlink:href=\"ijms-21-05318-g005\"/></fig><fig id=\"ijms-21-05318-f006\" orientation=\"portrait\" position=\"float\"><label>Figure 6</label><caption><p>Dissipation factor curves for magnetorheological material samples made with a concentration of 20 wt% of CIPs. A high value of <italic>E</italic> indicates that the energy loss per loading–unloading cycle is small. The higher the <italic>E,</italic> the lower the stress-softening and residual strain effects related to the molecular material damage. Also, notice that the area under the curve indicates the material capacity to dissipate energy per loading–unloading cycle.</p></caption><graphic xlink:href=\"ijms-21-05318-g006\"/></fig><fig id=\"ijms-21-05318-f007\" orientation=\"portrait\" position=\"float\"><label>Figure 7</label><caption><p>Dissipation factor curves for magnetorheological material samples made with a concentration of 35 wt% of CIPs.</p></caption><graphic xlink:href=\"ijms-21-05318-g007\"/></fig><fig id=\"ijms-21-05318-f008\" orientation=\"portrait\" position=\"float\"><label>Figure 8</label><caption><p>Dissipation factor curves for magnetorheological material samples made with a concentration of 50 wt% of CIPs.</p></caption><graphic xlink:href=\"ijms-21-05318-g008\"/></fig><fig id=\"ijms-21-05318-f009\" orientation=\"portrait\" position=\"float\"><label>Figure 9</label><caption><p>Dissipation factor curves for magnetorheological material samples made with a concentration of 65 wt% of CIPs.</p></caption><graphic xlink:href=\"ijms-21-05318-g009\"/></fig><fig id=\"ijms-21-05318-f010\" orientation=\"portrait\" position=\"float\"><label>Figure 10</label><caption><p>Dissipation factor curves for magnetorheological material samples made with a concentration of 80 wt% of CIPs.</p></caption><graphic xlink:href=\"ijms-21-05318-g010\"/></fig><fig id=\"ijms-21-05318-f011\" orientation=\"portrait\" position=\"float\"><label>Figure 11</label><caption><p>Engineering stress–stretch curves for isotropic magnetorheological material samples with <italic>B</italic> = 0 mT. Here, the green dots describe experimental data, while the black solid lines are theoretical predictions obtained from Equations (28) and (31).</p></caption><graphic xlink:href=\"ijms-21-05318-g011a\"/><graphic xlink:href=\"ijms-21-05318-g011b\"/></fig><fig id=\"ijms-21-05318-f012\" orientation=\"portrait\" position=\"float\"><label>Figure 12</label><caption><p>Engineering stress–stretch curves for isotropic magnetorheological material samples with <italic>B</italic> = 52.2 mT. Here, the blue dots describe experimental data while the black solid lines are theoretical predictions obtained from Equations (28) and (31).</p></caption><graphic xlink:href=\"ijms-21-05318-g012a\"/><graphic xlink:href=\"ijms-21-05318-g012b\"/></fig><fig id=\"ijms-21-05318-f013\" orientation=\"portrait\" position=\"float\"><label>Figure 13</label><caption><p>Engineering stress–stretch curves for anisotropic magnetorheological material samples with <italic>B</italic> = 0 mT. Here, the green dots describe experimental data while the black solid lines are theoretical predictions obtained from Equations (28) and (31).</p></caption><graphic xlink:href=\"ijms-21-05318-g013\"/></fig><fig id=\"ijms-21-05318-f014\" orientation=\"portrait\" position=\"float\"><label>Figure 14</label><caption><p>Engineering stress–stretch curves for anisotropic magnetorheological material samples with <italic>B</italic> = 52.2 mT. Here, the blue dots describe experimental data, while the black solid lines are theoretical predictions obtained from Equations (28) and (31).</p></caption><graphic xlink:href=\"ijms-21-05318-g014\"/></fig><fig id=\"ijms-21-05318-f015\" orientation=\"portrait\" position=\"float\"><label>Figure 15</label><caption><p>(<bold>a</bold>) Morphology of carbonyl iron particles (powder), and (<bold>b</bold>) diameter particle size distribution computed by using Digimizer 4.6.1 software (MedCalc Ltd.).</p></caption><graphic xlink:href=\"ijms-21-05318-g015\"/></fig><fig id=\"ijms-21-05318-f016\" orientation=\"portrait\" position=\"float\"><label>Figure 16</label><caption><p>Images that show the alignment for an anisotropic sample of the CIPs inside the polyurethane (PU) matrix when subjected to a magnetic flux density of 52.2 mT.</p></caption><graphic xlink:href=\"ijms-21-05318-g016\"/></fig><fig id=\"ijms-21-05318-f017\" orientation=\"portrait\" position=\"float\"><label>Figure 17</label><caption><p>(<bold>a</bold>) Schematic representation of the solenoid, extension clamps, and specimen. (<bold>b</bold>) Uniaxial tensile machine coupled with a solenoid coil to perform the cyclic tensile test under a magnetic field.</p></caption><graphic xlink:href=\"ijms-21-05318-g017\"/></fig><fig id=\"ijms-21-05318-f018\" orientation=\"portrait\" position=\"float\"><label>Figure 18</label><caption><p>Magnetic flux intensity applied to the specimen inside the solenoid during cyclic loading–unloading tensile tests. F is the tensile force applied during the test.</p></caption><graphic xlink:href=\"ijms-21-05318-g018\"/></fig><fig id=\"ijms-21-05318-f019\" orientation=\"portrait\" position=\"float\"><label>Figure 19</label><caption><p>Collected experimental data for isotropic magnetorheological material samples subjected to loading and unloading cycles with and without the action of a magnetic flux density. Notice that for increasing wt% of CIPs, the material shear moduli, the engineering stress, the dissipation of energy, and residual strains tend to increase.</p></caption><graphic xlink:href=\"ijms-21-05318-g019\"/></fig><fig id=\"ijms-21-05318-f020\" orientation=\"portrait\" position=\"float\"><label>Figure 20</label><caption><p>Collected experimental data for anisotropic magnetorheological material samples subjected to loading and unloading cycles with and without the action of a magnetic flux density. Notice that for increasing wt% of CIPs, the material shear moduli, the engineering stress, the dissipation of energy, and residual strains tend to increase.</p></caption><graphic xlink:href=\"ijms-21-05318-g020\"/></fig><table-wrap id=\"ijms-21-05318-t001\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijms-21-05318-t001_Table 1</object-id><label>Table 1</label><caption><p>Estimated material constant values of <italic>μ</italic>₀, <italic>μ</italic><sub>r</sub>, N₈, <italic>b</italic>, <italic>A</italic>₁, <italic>A</italic>₂, <italic>d</italic>, and <italic>c</italic>₁ obtained by fitting experimental data with <inline-formula><mml:math id=\"mm65\"><mml:mrow><mml:mrow><mml:mi>B</mml:mi><mml:mo>=</mml:mo><mml:mn>0</mml:mn></mml:mrow></mml:mrow></mml:math></inline-formula> mT.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">wt%</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Particle<break/>Arragmt</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>f</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Particle<break/>Arragmt</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><inline-formula><mml:math id=\"mm66\"><mml:mrow><mml:mstyle mathvariant=\"bold\"><mml:mrow><mml:msub><mml:mi mathvariant=\"bold-italic\">μ</mml:mi><mml:mn>0</mml:mn></mml:msub></mml:mrow></mml:mstyle></mml:mrow></mml:math></inline-formula><break/>N/m<sup>2</sup></th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<inline-formula><mml:math id=\"mm67\"><mml:mrow><mml:mstyle mathvariant=\"bold\"><mml:mrow><mml:msub><mml:mi mathvariant=\"bold-italic\">μ</mml:mi><mml:mi mathvariant=\"bold-italic\">r</mml:mi></mml:msub></mml:mrow></mml:mstyle></mml:mrow></mml:math></inline-formula>\n</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<inline-formula><mml:math id=\"mm68\"><mml:mrow><mml:mstyle mathvariant=\"bold\"><mml:mrow><mml:msub><mml:mi mathvariant=\"bold-italic\">N</mml:mi><mml:mn>8</mml:mn></mml:msub></mml:mrow></mml:mstyle></mml:mrow></mml:math></inline-formula>\n</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><inline-formula><mml:math id=\"mm69\"><mml:mrow><mml:mstyle mathvariant=\"bold\"><mml:mi mathvariant=\"bold-italic\">b</mml:mi></mml:mstyle></mml:mrow></mml:math></inline-formula><break/>(m<sup>3</sup>/J)<sup>1/2</sup></th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<inline-formula><mml:math id=\"mm70\"><mml:mrow><mml:mstyle mathvariant=\"bold\"><mml:mrow><mml:msub><mml:mi mathvariant=\"bold-italic\">A</mml:mi><mml:mn>1</mml:mn></mml:msub></mml:mrow></mml:mstyle></mml:mrow></mml:math></inline-formula>\n</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<inline-formula><mml:math id=\"mm71\"><mml:mrow><mml:mstyle mathvariant=\"bold\"><mml:mrow><mml:msub><mml:mi mathvariant=\"bold-italic\">A</mml:mi><mml:mn>2</mml:mn></mml:msub></mml:mrow></mml:mstyle></mml:mrow></mml:math></inline-formula>\n</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><inline-formula><mml:math id=\"mm72\"><mml:mrow><mml:mstyle mathvariant=\"bold\"><mml:mi mathvariant=\"bold-italic\">d</mml:mi></mml:mstyle></mml:mrow></mml:math></inline-formula><break/>m<sup>2</sup>/N\n</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><inline-formula><mml:math id=\"mm73\"><mml:mrow><mml:mstyle mathvariant=\"bold\"><mml:mrow><mml:msub><mml:mi mathvariant=\"bold-italic\">c</mml:mi><mml:mn>1</mml:mn></mml:msub></mml:mrow></mml:mstyle></mml:mrow></mml:math></inline-formula><break/>J/m<sup>3</sup></th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">20</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ISO</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.037</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ISO</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">12,989</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0025</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−210,000</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−80,000</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0031</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3595.7199</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">20</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ANI</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.037</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ANI</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10,285</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0035</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−65,000</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">70,000</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.00035</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3756.6586</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">35</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ISO</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.076</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ISO</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13,060</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.00275</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10,000</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">30,000</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0002</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3798.4371</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">35</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ANI</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.076</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ANI</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">9889</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.005</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">40,000</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−60,000</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.00022</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2537.1349</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">50</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ISO</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.132</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ISO</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">16,637</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.25</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.003</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−15,000</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10,000</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0002</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4325.5832</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">50</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ANI</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.132</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ANI</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">16,275</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.005</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−15,000</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">20,000</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0002</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4446.6251</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">65</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ISO</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.221</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ISO</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">20,763</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.003</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10,000</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">15,000</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0001</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5198.5202</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">65</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ANI</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.221</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ANI</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">19,427</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0035</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5000</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">45,000</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.00015</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5472.2308</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">80</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ISO</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.379</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ISO</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">66,096</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0125</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">20,000</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−100,000</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.000018</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1177.5485</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">80</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">ANI</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.379</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">ANI</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">6374</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">5.3</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.015</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">220000</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−380000</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.000026</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1459.0584</td></tr></tbody></table></table-wrap><table-wrap id=\"ijms-21-05318-t002\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijms-21-05318-t002_Table 2</object-id><label>Table 2</label><caption><p>Estimated material constant values of <italic>μ</italic>₀, <italic>μ</italic><sub>r</sub>, N₈, <italic>b</italic>, <italic>A</italic>₁, <italic>A</italic>₂, <italic>d</italic>, and <italic>c</italic>₁ obtained by fitting experimental data with <inline-formula><mml:math id=\"mm74\"><mml:mrow><mml:mrow><mml:mi>B</mml:mi><mml:mo>=</mml:mo><mml:mn>52.2</mml:mn></mml:mrow></mml:mrow></mml:math></inline-formula> mT.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">wt%</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Particle<break/>Arragmt</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>f</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><inline-formula><mml:math id=\"mm75\"><mml:mrow><mml:mstyle mathvariant=\"bold\"><mml:mrow><mml:msub><mml:mi mathvariant=\"bold-italic\">μ</mml:mi><mml:mn>0</mml:mn></mml:msub></mml:mrow></mml:mstyle></mml:mrow></mml:math></inline-formula><break/>N/m<sup>2</sup></th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<inline-formula><mml:math id=\"mm76\"><mml:mrow><mml:mstyle mathvariant=\"bold\"><mml:mrow><mml:msub><mml:mi mathvariant=\"bold-italic\">μ</mml:mi><mml:mi mathvariant=\"bold-italic\">r</mml:mi></mml:msub></mml:mrow></mml:mstyle></mml:mrow></mml:math></inline-formula>\n</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<inline-formula><mml:math id=\"mm77\"><mml:mrow><mml:mstyle mathvariant=\"bold\"><mml:mrow><mml:msub><mml:mi mathvariant=\"bold-italic\">N</mml:mi><mml:mn>8</mml:mn></mml:msub></mml:mrow></mml:mstyle></mml:mrow></mml:math></inline-formula>\n</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><inline-formula><mml:math id=\"mm78\"><mml:mrow><mml:mstyle mathvariant=\"bold\"><mml:mi mathvariant=\"bold-italic\">b</mml:mi></mml:mstyle></mml:mrow></mml:math></inline-formula><break/>(m<sup>3</sup>/J)<sup>1/2</sup></th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<inline-formula><mml:math id=\"mm79\"><mml:mrow><mml:mstyle mathvariant=\"bold\"><mml:mrow><mml:msub><mml:mi mathvariant=\"bold-italic\">A</mml:mi><mml:mn>1</mml:mn></mml:msub></mml:mrow></mml:mstyle></mml:mrow></mml:math></inline-formula>\n</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<inline-formula><mml:math id=\"mm80\"><mml:mrow><mml:mstyle mathvariant=\"bold\"><mml:mrow><mml:msub><mml:mi mathvariant=\"bold-italic\">A</mml:mi><mml:mn>2</mml:mn></mml:msub></mml:mrow></mml:mstyle></mml:mrow></mml:math></inline-formula>\n</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><inline-formula><mml:math id=\"mm81\"><mml:mrow><mml:mstyle mathvariant=\"bold\"><mml:mi mathvariant=\"bold-italic\">d</mml:mi></mml:mstyle></mml:mrow></mml:math></inline-formula><break/>m<sup>2</sup>/N\n</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><inline-formula><mml:math id=\"mm82\"><mml:mrow><mml:mstyle mathvariant=\"bold\"><mml:mrow><mml:msub><mml:mi mathvariant=\"bold-italic\">c</mml:mi><mml:mn>1</mml:mn></mml:msub></mml:mrow></mml:mstyle></mml:mrow></mml:math></inline-formula><break/>J/m<sup>3</sup></th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">20</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ISO</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.037</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13,154</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.01</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.875</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0025</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−100,000</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−250,000</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.00018</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2234.0156</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">20</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ANI</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.037</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">16,720</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.01</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0035</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−190,000</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−480,000</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.00015</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2961.9939</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">35</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ISO</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.076</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">14,631</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.23</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.004</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">18,000</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−30,000</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.00013</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2987.8284</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">35</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ANI</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.076</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">20,607</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.23</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0053</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">40,000</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−275,000</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.00006</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3429.1346</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">50</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ISO</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.132</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">19,845</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.47</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.005</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">20,000</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−80,000</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.000063</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3830.2696</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">50</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ANI</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.132</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">21,695</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.47</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.006</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">30,000</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−120,000</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.000049</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4661.5804</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">65</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ISO</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.221</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">32,906</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.845</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.006</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">20,000</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−160,000</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.000019</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5978.7898</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">65</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ANI</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.221</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">48,534</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0045</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−40,000</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−160,000</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.000017</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10806</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">80</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ISO</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.379</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">135,132</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.93</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.225</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0075</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">170,000</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−4,700,000</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0000019</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3518.4793</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">80</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">ANI</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.379</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">140,833</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.93</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.2</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.0125</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">100,000</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−4,200,000</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.0000035</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1936.4788</td></tr></tbody></table></table-wrap><table-wrap id=\"ijms-21-05318-t003\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijms-21-05318-t003_Table 3</object-id><label>Table 3</label><caption><p>Estimated material constant values of <italic>C</italic>₀ obtained by fitting experimental data. The listed values of <inline-formula><mml:math id=\"mm83\"><mml:mrow><mml:mrow><mml:msub><mml:mo>Δ</mml:mo><mml:mi>a</mml:mi></mml:msub></mml:mrow></mml:mrow></mml:math></inline-formula> and λ<italic><sub>res</sub></italic> were recorded for each loading–unloading cyclic experimental test with <inline-formula><mml:math id=\"mm84\"><mml:mrow><mml:mrow><mml:mi>B</mml:mi><mml:mo>=</mml:mo><mml:mn>0</mml:mn></mml:mrow></mml:mrow></mml:math></inline-formula> mT.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">wt%</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<inline-formula><mml:math id=\"mm85\"><mml:mrow><mml:mstyle mathvariant=\"bold\"><mml:mrow><mml:msub><mml:mstyle mathvariant=\"bold\" mathsize=\"normal\"><mml:mo>Δ</mml:mo></mml:mstyle><mml:mi mathvariant=\"bold-italic\">a</mml:mi></mml:msub></mml:mrow></mml:mstyle></mml:mrow></mml:math></inline-formula>\n</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<inline-formula><mml:math id=\"mm86\"><mml:mrow><mml:mstyle mathvariant=\"bold\"><mml:mrow><mml:msub><mml:mstyle mathvariant=\"bold\" mathsize=\"normal\"><mml:mi mathvariant=\"bold-italic\">λ</mml:mi></mml:mstyle><mml:mrow><mml:mi mathvariant=\"bold-italic\">r</mml:mi><mml:mi mathvariant=\"bold-italic\">e</mml:mi><mml:mi mathvariant=\"bold-italic\">s</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mstyle></mml:mrow></mml:math></inline-formula>\n</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><inline-formula><mml:math id=\"mm87\"><mml:mrow><mml:mstyle mathvariant=\"bold\"><mml:mrow><mml:msub><mml:mi mathvariant=\"bold-italic\">C</mml:mi><mml:mn>0</mml:mn></mml:msub></mml:mrow></mml:mstyle></mml:mrow></mml:math></inline-formula><break/>J/m<sup>3</sup></th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">wt%</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<inline-formula><mml:math id=\"mm88\"><mml:mrow><mml:mstyle mathvariant=\"bold\"><mml:mrow><mml:msub><mml:mstyle mathvariant=\"bold\" mathsize=\"normal\"><mml:mo>Δ</mml:mo></mml:mstyle><mml:mi mathvariant=\"bold-italic\">a</mml:mi></mml:msub></mml:mrow></mml:mstyle></mml:mrow></mml:math></inline-formula>\n</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<inline-formula><mml:math id=\"mm89\"><mml:mrow><mml:mstyle mathvariant=\"bold\"><mml:mrow><mml:msub><mml:mstyle mathvariant=\"bold\" mathsize=\"normal\"><mml:mi mathvariant=\"bold-italic\">λ</mml:mi></mml:mstyle><mml:mrow><mml:mi mathvariant=\"bold-italic\">r</mml:mi><mml:mi mathvariant=\"bold-italic\">e</mml:mi><mml:mi mathvariant=\"bold-italic\">s</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mstyle></mml:mrow></mml:math></inline-formula>\n</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><inline-formula><mml:math id=\"mm90\"><mml:mrow><mml:mstyle mathvariant=\"bold\"><mml:mrow><mml:msub><mml:mi mathvariant=\"bold-italic\">C</mml:mi><mml:mn>0</mml:mn></mml:msub></mml:mrow></mml:mstyle></mml:mrow></mml:math></inline-formula><break/>J/m<sup>3</sup></th></tr><tr><th colspan=\"4\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">Isotropic Samples</th><th colspan=\"4\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">Anisotropic Samples</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">20</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.66</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.16</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4171.9537</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">20</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.66</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4143.3076</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">20</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.43</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.07</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3652.761</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">20</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.43</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.07</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3668.8424</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">20</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.05</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3507.3879</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">20</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.05</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3585.467</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">35</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.65</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4879.1067</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">35</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.65</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3662.2473</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">35</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.45</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.07</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4054.7228</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">35</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.44</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.07</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2764.5743</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">35</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.22</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.05</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3691.0305</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">35</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.22</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.05</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2400.5812</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">50</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.65</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5369.6776</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">50</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.67</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5550.4437</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">50</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.44</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.07</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4464.056</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">50</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.44</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.07</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4325.2313</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">50</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.21</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.05</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4149.4913</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">50</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.22</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.05</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4056.1777</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">65</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.64</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6612.4138</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">65</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.66</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6608.6042</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">65</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.43</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.07</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5406.081</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">65</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.43</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.07</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5237.3154</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">65</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.05</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4933.1887</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">65</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.05</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4959.4039</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">80</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.16</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.07</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1793.3486</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">80</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.16</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.04</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1793.3486</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">80</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.08</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.03</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1349.649</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">80</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.08</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.02</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1349.649</td></tr></tbody></table></table-wrap><table-wrap id=\"ijms-21-05318-t004\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijms-21-05318-t004_Table 4</object-id><label>Table 4</label><caption><p>Estimated material constant values of <italic>C</italic><sub>0</sub> obtained by fitting experimental data. The listed values of <inline-formula><mml:math id=\"mm91\"><mml:mrow><mml:mrow><mml:msub><mml:mo>Δ</mml:mo><mml:mi>a</mml:mi></mml:msub></mml:mrow></mml:mrow></mml:math></inline-formula> and λ<italic><sub>res</sub></italic> were recorded for each loading–unloading cyclic experimental test with <inline-formula><mml:math id=\"mm92\"><mml:mrow><mml:mrow><mml:mi>B</mml:mi><mml:mo>=</mml:mo><mml:mn>52.2</mml:mn></mml:mrow></mml:mrow></mml:math></inline-formula> mT.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">wt%</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<inline-formula id=\"FD32-ijms-21-05318\"><mml:math id=\"mm93\"><mml:mrow><mml:mstyle mathvariant=\"bold\"><mml:mrow><mml:msub><mml:mstyle mathvariant=\"bold\" mathsize=\"normal\"><mml:mo>Δ</mml:mo></mml:mstyle><mml:mi mathvariant=\"bold-italic\">a</mml:mi></mml:msub></mml:mrow></mml:mstyle></mml:mrow></mml:math></inline-formula>\n</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<inline-formula id=\"FD33-ijms-21-05318\"><mml:math id=\"mm94\"><mml:mrow><mml:mstyle mathvariant=\"bold\"><mml:mrow><mml:msub><mml:mstyle mathvariant=\"bold\" mathsize=\"normal\"><mml:mi mathvariant=\"bold-italic\">λ</mml:mi></mml:mstyle><mml:mrow><mml:mi mathvariant=\"bold-italic\">r</mml:mi><mml:mi mathvariant=\"bold-italic\">e</mml:mi><mml:mi mathvariant=\"bold-italic\">s</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mstyle></mml:mrow></mml:math></inline-formula>\n</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><inline-formula id=\"FD34-ijms-21-05318\"><mml:math id=\"mm95\"><mml:mrow><mml:mstyle mathvariant=\"bold\"><mml:mrow><mml:msub><mml:mi mathvariant=\"bold-italic\">C</mml:mi><mml:mn>0</mml:mn></mml:msub></mml:mrow></mml:mstyle></mml:mrow></mml:math></inline-formula><break/>J/m<sup>3</sup></th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">wt%</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<inline-formula id=\"FD35-ijms-21-05318\"><mml:math id=\"mm96\"><mml:mrow><mml:mstyle mathvariant=\"bold\"><mml:mrow><mml:msub><mml:mstyle mathvariant=\"bold\" mathsize=\"normal\"><mml:mo>Δ</mml:mo></mml:mstyle><mml:mi mathvariant=\"bold-italic\">a</mml:mi></mml:msub></mml:mrow></mml:mstyle></mml:mrow></mml:math></inline-formula>\n</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<inline-formula id=\"FD36-ijms-21-05318\"><mml:math id=\"mm97\"><mml:mrow><mml:mstyle mathvariant=\"bold\"><mml:mrow><mml:msub><mml:mstyle mathvariant=\"bold\" mathsize=\"normal\"><mml:mi mathvariant=\"bold-italic\">λ</mml:mi></mml:mstyle><mml:mrow><mml:mi mathvariant=\"bold-italic\">r</mml:mi><mml:mi mathvariant=\"bold-italic\">e</mml:mi><mml:mi mathvariant=\"bold-italic\">s</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mstyle></mml:mrow></mml:math></inline-formula>\n</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><inline-formula id=\"FD37-ijms-21-05318\"><mml:math id=\"mm98\"><mml:mrow><mml:mstyle mathvariant=\"bold\"><mml:mrow><mml:msub><mml:mi mathvariant=\"bold-italic\">C</mml:mi><mml:mn>0</mml:mn></mml:msub></mml:mrow></mml:mstyle></mml:mrow></mml:math></inline-formula><break/>J/m<sup>3</sup></th></tr><tr><th colspan=\"4\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">Isotropic Samples</th><th colspan=\"4\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">Anisotropic Samples</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">20</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.68</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.17</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3146.7057</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">20</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.66</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.17</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3773.2832</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">20</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.43</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.12</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2402.4987</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">20</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.43</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.12</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3017.1163</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">20</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.07</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2147.5641</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">20</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.07</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2774.2649</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">35</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.67</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.15</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4490.2521</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">35</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.67</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.22</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5849.0836</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">35</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.43</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.07</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3173.61</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">35</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.44</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.17</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4008.5378</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">35</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.21</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.05</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2768.8979</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">35</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.22</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.11</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3235.8117</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">50</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.67</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.19</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6242.5993</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">50</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.67</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.21</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7623.528</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">50</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.44</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.12</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4375.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">50</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.44</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.15</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5141.7377</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">50</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.22</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.06</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3533.2046</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">50</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.22</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.07</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4151.7441</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">65</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.66</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.32</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">11338.1253</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">65</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.67</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.32</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">15975.7794</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">65</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.43</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7412.05</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">65</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.44</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">11790.501</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">65</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.21</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5567.3315</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">65</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.21</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.09</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">9928.1565</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">80</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.16</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.12</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5419.9761</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">80</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.16</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3773.2833</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">80</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.08</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.05</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3590.1389</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">80</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.09</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.04</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3017.1163</td></tr></tbody></table></table-wrap></floats-group></article>\n"
]
[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"brief-report\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Int J Environ Res Public Health</journal-id><journal-id journal-id-type=\"iso-abbrev\">Int J Environ Res Public Health</journal-id><journal-id journal-id-type=\"publisher-id\">ijerph</journal-id><journal-title-group><journal-title>International Journal of Environmental Research and Public Health</journal-title></journal-title-group><issn pub-type=\"ppub\">1661-7827</issn><issn pub-type=\"epub\">1660-4601</issn><publisher><publisher-name>MDPI</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32756313</article-id><article-id pub-id-type=\"pmc\">PMC7432073</article-id><article-id pub-id-type=\"doi\">10.3390/ijerph17155583</article-id><article-id pub-id-type=\"publisher-id\">ijerph-17-05583</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Brief Report</subject></subj-group></article-categories><title-group><article-title>Energy Intake and Satiety Responses of Eggs for Breakfast in Overweight and Obese Adults—A Crossover Study</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0002-4788-3870</contrib-id><name><surname>B Keogh</surname><given-names>Jennifer</given-names></name><xref rid=\"c1-ijerph-17-05583\" ref-type=\"corresp\">*</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0002-6411-626X</contrib-id><name><surname>M Clifton</surname><given-names>Peter</given-names></name></contrib></contrib-group><aff id=\"af1-ijerph-17-05583\">UniSA Clinical and Health Sciences, University of South Australia, Adelaide, SA 5000, Australia; <email>[email protected]</email></aff><author-notes><corresp id=\"c1-ijerph-17-05583\"><label>*</label>Correspondence: <email>[email protected]</email>; Tel.: +61-(0)-883022579</corresp></author-notes><pub-date pub-type=\"epub\"><day>03</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"ppub\"><month>8</month><year>2020</year></pub-date><volume>17</volume><issue>15</issue><elocation-id>5583</elocation-id><history><date date-type=\"received\"><day>01</day><month>6</month><year>2020</year></date><date date-type=\"accepted\"><day>30</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>© 2020 by the authors.</copyright-statement><copyright-year>2020</copyright-year><license license-type=\"open-access\"><license-p>Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>).</license-p></license></permissions><abstract><p>The type of food eaten for breakfast may determine the amount of food consumed at the next meal. This may be important when considering dietary advice for overweight and obese individuals who are trying to lose weight. The aim of the study was to investigate the energy intake and subjective sensations of hunger using a visual analogue scale (VAS) of a breakfast meal of eggs compared with a breakfast meal of cereal in overweight Australian adults. In a cross-over study, participants attended the University of South Australia’s Clinical Trial Facility on two separate days, one week apart. On each day participants consumed one of two isoenergetic breakfasts (1800 kJ), either eggs and toast or cereal with milk and orange juice. Fifty overweight or obese participants, 44 ± 21 years, 86 ± 14 kg, with a body mass index (BMI) of 31 ± 4 kg/m<sup>2</sup> completed both study visits. Energy intake following the egg breakfast was significantly reduced compared with the cereal breakfast (4518 vs. 5283 kJ, <italic>p</italic> = 0.001). BMI and gender were unrelated to these effects. The sensation of hunger was less after the egg breakfast (<italic>p</italic> = 0.028 for diet by time interaction) and returned more quickly after the cereal breakfast. There were no effects of gender or age. Energy intake was reduced at an ad libitum lunch meal 4 hours after a breakfast meal containing eggs. The findings suggest that satiety responses of overweight and obese are not different to non-obese participants as our study confirms findings from studies conducted in different populations. Determining which foods may help overweight and obese individuals manage their food intake is important for diet planning.</p></abstract><kwd-group><kwd>eggs</kwd><kwd>breakfast</kwd><kwd>energy intake</kwd><kwd>overweight</kwd><kwd>obesity</kwd></kwd-group></article-meta></front><body><sec sec-type=\"intro\" id=\"sec1-ijerph-17-05583\"><title>1. Introduction</title><p>In 2017–2018, the prevalence of overweight and obesity in Australia was reported to be 67% in adults [<xref rid=\"B1-ijerph-17-05583\" ref-type=\"bibr\">1</xref>]. It would be helpful to understand the satiety effects of specific foods as overweight and obese individuals are frequently trying to lose weight or are advised to lose weight for the benefit of chronic health conditions. The Center for Disease Control in the Unites States reported that between 2013–2016, 49.1% of adults tried to lose weight in the previous 12 months with more women (56.4%) than men (41.7%) trying to lose weight [<xref rid=\"B2-ijerph-17-05583\" ref-type=\"bibr\">2</xref>].</p><p>Foods eaten at the first meal of the day, breakfast, may determine the energy intake at a subsequent meal [<xref rid=\"B3-ijerph-17-05583\" ref-type=\"bibr\">3</xref>]. Eggs contain high biological value protein which may satisfy hunger and improve satiety which may help reduce food intake later in the day [<xref rid=\"B4-ijerph-17-05583\" ref-type=\"bibr\">4</xref>]. </p><p>In lean, young adults, egg breakfasts reduced postprandial glycaemic response and food intake at subsequent meal compared to a cereal [<xref rid=\"B5-ijerph-17-05583\" ref-type=\"bibr\">5</xref>]. Similarly in a study with lean men only, comparing eggs, with cereal and toast or a croissant there was increased satiety, less hunger and a lower desire to eat with a lower intake of energy at lunch and the evening meal after the egg breakfast [<xref rid=\"B6-ijerph-17-05583\" ref-type=\"bibr\">6</xref>]. In a study of normal or overweight men, participants ate less energy after an egg breakfast and ate fewer kilocalories in the 24-hour period [<xref rid=\"B7-ijerph-17-05583\" ref-type=\"bibr\">7</xref>]. They felt hungrier and less satisfied 3 h after the bagel breakfast [<xref rid=\"B7-ijerph-17-05583\" ref-type=\"bibr\">7</xref>]. In overweight and obese women, an egg breakfast produced greater feelings of satiety and reduced energy intake at lunch, for the remainder of the day and the next 36 h compared to an isocaloric bagel-based breakfast [<xref rid=\"B8-ijerph-17-05583\" ref-type=\"bibr\">8</xref>]. </p><p>To date the studies conducted have been mainly in lean participants. Therefore, the aim of this study was to determine the energy intake and subjective acute satiety responses after a breakfast meal of two eggs compared with a cereal breakfast in overweight and obese men and women. The hypothesis was that the participants would have lower energy intake and increased satiety after the egg breakfast.</p></sec><sec id=\"sec2-ijerph-17-05583\"><title>2. Materials and Methods </title><p>This was a cross-over study comparing the effects of two breakfasts on subjective sensations of hunger and energy intake at a lunch meal eaten 4 h after breakfast. The order of the breakfasts was randomized. The sample size of 50 overweight or obese adults was based on previous satiety studies [<xref rid=\"B5-ijerph-17-05583\" ref-type=\"bibr\">5</xref>]. Participants were recruited form the Adelaide, South Australia community using print and social media and advertising flyers on the university campus. Participants were over 18 years, overweight with a body mass index (BMI) > 25 kg/m<sup>2</sup> who self-reported that they were healthy, had no food allergies and were able to eat the study foods. Exclusion criteria were previous surgery for weight reduction, type 2 diabetes, systolic blood pressure > 160 mmHg, women who were or who wished to become pregnant and women who were breast feeding. Participants completed a Diet and Lifestyle Questionnaire to determine their eligibility to participate in the study. Questions related to weight, participation in other research at the time, pregnancy or breastfeeding, need for a special diet, food allergies or intolerances, surgery for weight reduction, diabetes, blood pressure, chronic medical medication, alcohol intake and smoking. Written informed consent was obtained from all participating volunteers. The study protocol was approved by the Ethics Committee of the University of South Australia.</p><sec id=\"sec2dot1-ijerph-17-05583\"><title>2.1. Protocol</title><p>Participants attended the Clinical Trial Facility at the University of South Australia on 2 days, one week apart at 8–8.30 am. They were asked to fast after their evening meal for 12 h before their clinic visit with only water permitted and refrain from alcohol and strenuous exercise for 24 h before each visit. They were given instructions to eat the same evening meal at the same time before each visit. Height was measured once, weight was measured at both visits and BMI calculated. Following the protocol by Flint (2000), prior to eating breakfast participants were asked to complete a 100 mm visual analogue scale (VAS) and then eat a breakfast meal [<xref rid=\"B6-ijerph-17-05583\" ref-type=\"bibr\">6</xref>]. There were eight questions on the scale (<xref rid=\"ijerph-17-05583-t001\" ref-type=\"table\">Table 1</xref>) as follows: How hungry do you feel? How satisfied do you feel? How full do you feel? How much do you think you can eat? Would you like to eat something sweet? Would you like to eat something salty? Would you like to eat something savory? Would you like to eat something fatty? Participants were asked to mark the 100 mm scale. For the first question How hungry do you feel? the left-hand side of the scale read “I am not hungry at all” and the right-hand side read “I have never been more hungry”. The other seven questions followed the same pattern. Subsequent VAS responses were collected 15, 45, 75, 105, 135, 165, 195 and 225 min after finishing breakfast. The breakfast meals were 2 eggs with 2 slices of bread/toast and 10 g margarine (1800 kJ, 25 g protein, 23.5 g fat, 28 g carbohydrate, 7 g fibre) or a bran containing cereal with sugar, milk and orange juice (1788 kJ, 11 g protein, 5 g fat, 73 g carbohydrate, 11 g fibre). An ad libitum lunch was provided 4 h after breakfast. Only water was allowed after breakfast during the 4 h before lunch. Using a modified protocol from Bowen (2007) participants were offered large servings of pasta and tomato sauce (600 g of each) [<xref rid=\"B9-ijerph-17-05583\" ref-type=\"bibr\">9</xref>]. Foods were weighed to the nearest gram before and after lunch using digital scales. Energy and nutrient composition were calculated using Food Works 8.0 (Xyris Software, Highgate Hill, Australia). Participant support: Participants were provided with a AUD 100 voucher at the end of the study.</p></sec><sec id=\"sec2dot2-ijerph-17-05583\"><title>2.2. Statistics</title><p>Results are expressed as Mean ± SD unless otherwise stated. As this was a cross-over design analysis of variance (ANOVA) with repeated measures was used to determine the effect of time and treatment within subjects. Significance was set at <italic>p</italic> < 0.05. The VAS was analysed as given by Flint [<xref rid=\"B10-ijerph-17-05583\" ref-type=\"bibr\">10</xref>]. Baseline values were subtracted from postprandial responses to normalize between-subject differences. Statistical analysis was performed using SPSS 24.0 (IBM, New York, USA).</p></sec></sec><sec sec-type=\"results\" id=\"sec3-ijerph-17-05583\"><title>3. Results</title><p>A total of 53 participants commenced and 50 completed the study. Two participants did not attend for their first visit and one participant was unable to complete the second visit for personal reasons unrelated to the study. </p><sec id=\"sec3dot1-ijerph-17-05583\"><title>3.1. Baseline Characteristics </title><p>Participants who completed the study were 44 ± 21 years, 86 ± 14 kg and BMI 31 ± 4 kg/m<sup>2</sup>. Weight and BMI were the same on both visits. Baseline characteristics for men and women are shown in <xref rid=\"ijerph-17-05583-t001\" ref-type=\"table\">Table 1</xref>.</p></sec><sec id=\"sec3dot2-ijerph-17-05583\"><title>3.2. Energy and Macronutrient Intake at Lunch</title><p>There was a significant difference in energy intake after the egg breakfast compared to after the cereal breakfast, 4518 ± 1593 vs. 5284 ± 1814), <italic>p</italic> = 0.001 (<xref rid=\"ijerph-17-05583-t002\" ref-type=\"table\">Table 2</xref>). BMI and gender were not related to this difference. Fat (<italic>p</italic> = 0.002), protein (<italic>p</italic> = 0.000) and carbohydrate intakes (<italic>p</italic> = 0.001) were statistically different after the breakfast study. There were no effects of gender, BMI, age or order of the breakfasts on any variable. Weight of food consumed at lunch was less after the egg breakfast (451 ± 184 g vs. 534 ± 193 g, <italic>p</italic> < 0.01).</p></sec><sec id=\"sec3dot3-ijerph-17-05583\"><title>3.3. Subjective Measures of Satiety </title><sec id=\"sec3dot3dot1-ijerph-17-05583\"><title>3.3.1. Hunger</title><p>Participants responded to the question “How hungry do you feel?” Overall, there was a difference in the subjective sensation of hunger between the breakfasts such that participants felt less hungry after the egg breakfast (Breakfast <italic>p</italic> = 0.006, Time <italic>p</italic> = 0.000, Diet by time interaction <italic>p</italic> = 0.028). There was no effect of gender and no effect of age. The addition of weight as a co-variate was significant, <italic>p</italic> = 0.014 and made the diet by time effect stronger, <italic>p</italic> = 0.004. The effect was similar for BMI, <italic>p</italic> = 0.021 for the interaction between diet and time and BMI and <italic>p</italic> = 0.008 for the diet by time interaction. Hunger returned to baseline more quickly in the cereal breakfast compared to the egg breakfast, <italic>p</italic> = 0.000, at the reading taken prior to lunch (<xref ref-type=\"fig\" rid=\"ijerph-17-05583-f001\">Figure 1</xref>).</p></sec><sec id=\"sec3dot3dot2-ijerph-17-05583\"><title>3.3.2. Satisfaction</title><p>Participants responded to the question “How satisfied do you feel?” Overall there was a small difference in the subjective sensation of feeling satisfied such that the participants felt more satisfied after the egg breakfast <italic>p</italic> = 0.05, the effect of time was significant, <italic>p</italic> = 0.000, with a trend towards a diet by time interaction, <italic>p</italic> = 0.072. The feeling of being satisfied fell more quickly after the cereal breakfast than after the egg breakfast (<xref ref-type=\"fig\" rid=\"ijerph-17-05583-f002\">Figure 2</xref>).</p></sec><sec id=\"sec3dot3dot3-ijerph-17-05583\"><title>3.3.3. Fullness</title><p>Participants responded to the question “How full do you feel?” Overall, there was a small difference in the subjective feeling of fullness (<italic>p</italic> = 0.019) such that participants felt fuller after the egg breakfast. There was an effect of time (<italic>p</italic> = 0000) and a diet by time interaction (<italic>p</italic> = 0.015) (<xref ref-type=\"fig\" rid=\"ijerph-17-05583-f003\">Figure 3</xref>).</p></sec><sec id=\"sec3dot3dot4-ijerph-17-05583\"><title>3.3.4. Desire to Eat</title><p>Participants responded to the question “How much do you think you can eat?” Participants thought they could eat less after the egg breakfast. There was an effect of time (<italic>p</italic> = 0.007) and a diet by time interaction (<italic>p</italic> = 0.000) (<xref ref-type=\"fig\" rid=\"ijerph-17-05583-f004\">Figure 4</xref>).</p></sec><sec id=\"sec3dot3dot5-ijerph-17-05583\"><title>3.3.5. Desire to Eat Sweet Foods</title><p>Participants responded to the question “Would you like to eat something sweet?” Overall, there was an effect of time (<italic>p</italic> = 0.000) and a difference between the breakfasts such that overall after the egg breakfast participants desire for sweet foods was less than after the cereal breakfast (<italic>p</italic> = 0.049). However, the values had converged by the end of the morning.</p></sec><sec id=\"sec3dot3dot6-ijerph-17-05583\"><title>3.3.6. Desire to Eat Salty Foods</title><p>Participants responded to the question “Would you like to eat something salty?” There was a difference between the breakfasts (<italic>p</italic> = 0.044) with an effect of time (<italic>p</italic> = 0.000) and a breakfast X time interaction (<italic>p</italic> = 0.001) such that overall after the egg breakfast participants desire for salty food was greater than after the cereal breakfast.</p></sec><sec id=\"sec3dot3dot7-ijerph-17-05583\"><title>3.3.7. Desire to Eat Savoury Foods</title><p>Participants responded to the question “Would you like to eat something savoury?” There was no difference between the breakfasts. There was an effect of time (<italic>p</italic> = 0.000) with no breakfast by time interaction.</p></sec><sec id=\"sec3dot3dot8-ijerph-17-05583\"><title>3.3.8. Would You Like to Eat Fatty Foods</title><p>Participants responded to the question “Would you like to eat something fatty?” There was no difference between the breakfasts. There was an effect of time (<italic>p</italic> = 0.000) and time by meal interaction (<italic>p</italic> = 0.012) such that the desire to eat fatty food was greater in the cereal group.</p></sec></sec></sec><sec sec-type=\"discussion\" id=\"sec4-ijerph-17-05583\"><title>4. Discussion</title><p>The main finding of this study was that eating eggs for breakfast resulted in a lower energy intake at an ad libitum lunch in this group of overweight and obese participants. Supporting this finding, there was a difference in the subjective sensation of hunger between the breakfasts such that participants felt less hungry after the egg breakfast. Participants felt more satisfied, felt fuller and thought they could eat less after the egg breakfast. This may be important given the prevalence of overweight and obesity and public health recommendations that individuals lose weight. In 2017–2018, the prevalence of overweight and obesity in Australia was reported to be 67% in adults [<xref rid=\"B1-ijerph-17-05583\" ref-type=\"bibr\">1</xref>]. Our study confirms findings from studies conducted in non-overweight/obese populations. Bonnema (2016) found that an egg breakfast meal produced greater overall satiety and reduced postprandial glycaemic response and food intake at subsequent meal compared to a cereal-based breakfast in lean young adults [<xref rid=\"B5-ijerph-17-05583\" ref-type=\"bibr\">5</xref>]. Similarly, Fallaize (2013) found an increase in satiety, reduced hunger and a lower desire to eat after a breakfast containing eggs with a lower intake of energy at lunch and the evening meal [<xref rid=\"B6-ijerph-17-05583\" ref-type=\"bibr\">6</xref>]. Ratliff (2010) found that participants ate less energy after an egg compared with a bagel breakfast, and participants ate fewer kilocalories in the whole 24-h period. In contrast, they felt hungrier and less satisfied 3 h after the bagel breakfast [<xref rid=\"B7-ijerph-17-05583\" ref-type=\"bibr\">7</xref>]. Eggs for breakfast may be helpful during weight loss as shown by Vander Wal (2008) who reported greater weight loss of ~1kg in participants on an energy restricted diet that included eggs for breakfast [<xref rid=\"B11-ijerph-17-05583\" ref-type=\"bibr\">11</xref>]. There was no effect on weight of including eggs without energy restriction [<xref rid=\"B11-ijerph-17-05583\" ref-type=\"bibr\">11</xref>].</p><p>Possible reasons for the difference seen include the difference in protein intake at breakfast which was 25 g protein from the egg breakfast compared with 11g from the cereal breakfast. Protein is known to be more satiating than other macronutrients [<xref rid=\"B12-ijerph-17-05583\" ref-type=\"bibr\">12</xref>]. Types of protein may have different effects on appetite as shown by Pal and Ellis [<xref rid=\"B3-ijerph-17-05583\" ref-type=\"bibr\">3</xref>]. Whey protein reduced appetite and decreased energy intake at a subsequent meal compared with tuna, turkey and egg albumin [<xref rid=\"B3-ijerph-17-05583\" ref-type=\"bibr\">3</xref>] did not find In contrast a high-fat breakfast resulted in a higher energy intake at lunch compared to a high-protein, low-fat isocaloric breakfast [<xref rid=\"B13-ijerph-17-05583\" ref-type=\"bibr\">13</xref>]. Energy intake was shown to be similar at lunch following isocaloric breakfasts high in protein, carbohydrate or fat; however, this was a small study of only six participants [<xref rid=\"B14-ijerph-17-05583\" ref-type=\"bibr\">14</xref>]. A higher protein diet can also help individuals maintain weight loss [<xref rid=\"B15-ijerph-17-05583\" ref-type=\"bibr\">15</xref>]. In a study in which eggs were eaten at lunch, there was no differential effect on energy intake although while the three lunch meals were isocaloric the differences in protein content were small 21, 16 and 19 g, suggesting that protein may be the more important macronutrient [<xref rid=\"B16-ijerph-17-05583\" ref-type=\"bibr\">16</xref>]. Fibre is also known to reduce subjective measures of appetite and energy intake, but the effects are small and may depend on the type of fibre used [<xref rid=\"B17-ijerph-17-05583\" ref-type=\"bibr\">17</xref>]. In the present study, the cereal breakfast meal was higher in fibre, 11 g compared with 7 g, but this did not affect the study outcome.</p><p>Limitations of the study were that participants were not asked to record their energy intake after they left the research facility, so it is unclear if total energy intake was reduced for the day. Future studies should include objective measures of food intake for the remainder of the day. Additional studies that would add to the knowledge in this area include conducting longer term studies of eating eggs for breakfast with and without energy restriction.</p><p>We conclude that after eating eggs for breakfast, overweight and obese individuals had a lower energy intake at an ad libitum lunch in comparison to eating a cereal breakfast. Both breakfasts contained the same energy. Determining which foods may help overweight and obese individuals manage their food intake is important for diet planning for individuals trying to lose weight.</p></sec></body><back><ack><title>Acknowledgments</title><p>Thanks to Yan Yin Phoi and Jess Murphy who collected the data and Louise Massie who managed the participants.</p></ack><notes><title>Author Contributions</title><p>Conceptualization, funding acquisition, project administration and supervision and original preparation of the manuscript, J.B.K.; formal analysis, medical supervision, review and editing of manuscript, P.M.C. All authors have read and agreed to the published version of the manuscript.</p></notes><notes><title>Funding</title><p>This research was funded by Australian Eggs Ltd., Australia. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.</p></notes><notes notes-type=\"COI-statement\"><title>Conflicts of Interest</title><p>The authors declare no conflict of interest.</p></notes><ref-list><title>References</title><ref id=\"B1-ijerph-17-05583\"><label>1.</label><element-citation publication-type=\"gov\"><person-group person-group-type=\"author\"><collab>Australian Institute of Health and Welfare</collab></person-group><article-title>Overweight and obesity: An interactive insight</article-title><year>2019</year><comment>Available online: <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.aihw.gov.au/reports/overweight-obesity/overweight-and-obesity-an-interactive-insight/contents/what-is-overweight-and-obesity\">https://www.aihw.gov.au/reports/overweight-obesity/overweight-and-obesity-an-interactive-insight/contents/what-is-overweight-and-obesity</ext-link></comment><date-in-citation content-type=\"access-date\" iso-8601-date=\"2020-08-03\">(accessed on 3 August 2020)</date-in-citation></element-citation></ref><ref id=\"B2-ijerph-17-05583\"><label>2.</label><element-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Martin</surname><given-names>C.B.</given-names></name><name><surname>Herrick</surname><given-names>K.A.</given-names></name><name><surname>Sarafrazi</surname><given-names>N.</given-names></name><name><surname>Ogden</surname><given-names>C.L.</given-names></name></person-group><article-title>Attempts to lose weight among adults in the united states, 2013–2016</article-title><source>NCHS Data Brief</source><year>2018</year><volume>313</volume><fpage>1</fpage><lpage>8</lpage></element-citation></ref><ref id=\"B3-ijerph-17-05583\"><label>3.</label><element-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Pal</surname><given-names>S.</given-names></name><name><surname>Ellis</surname><given-names>V.</given-names></name></person-group><article-title>The acute effects of four protein meals on insulin, glucose, appetite and energy intake in lean men</article-title><source>Br. 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Rev.</source><year>2011</year><volume>12</volume><fpage>724</fpage><lpage>739</lpage><pub-id pub-id-type=\"doi\">10.1111/j.1467-789X.2011.00895.x</pub-id><pub-id pub-id-type=\"pmid\">21676152</pub-id></element-citation></ref></ref-list></back><floats-group><fig id=\"ijerph-17-05583-f001\" orientation=\"portrait\" position=\"float\"><label>Figure 1</label><caption><p>Subjective sensations of hunger before and 15, 45, 75, 105, 135, 165, 195 and 225 min after finishing breakfast on a 100-mm visual analogue scale (VAS).</p></caption><graphic xlink:href=\"ijerph-17-05583-g001\"/></fig><fig id=\"ijerph-17-05583-f002\" orientation=\"portrait\" position=\"float\"><label>Figure 2</label><caption><p>Subjective sensations of satisfaction before and 15, 45, 75, 105, 135, 165, 195 and 225 min after finishing breakfast on a 100-mm VAS scale.</p></caption><graphic xlink:href=\"ijerph-17-05583-g002\"/></fig><fig id=\"ijerph-17-05583-f003\" orientation=\"portrait\" position=\"float\"><label>Figure 3</label><caption><p>Subjective sensations of fullness before and 15, 45, 75, 105, 135, 165, 195 and 225 min after finishing breakfast on a 100-mm VAS scale.</p></caption><graphic xlink:href=\"ijerph-17-05583-g003\"/></fig><fig id=\"ijerph-17-05583-f004\" orientation=\"portrait\" position=\"float\"><label>Figure 4</label><caption><p>Desire to eat before and 15, 45, 75, 105, 135, 165, 195 and 225 min after finishing breakfast on a 100-mm VAS scale.</p></caption><graphic xlink:href=\"ijerph-17-05583-g004\"/></fig><table-wrap id=\"ijerph-17-05583-t001\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05583-t001_Table 1</object-id><label>Table 1</label><caption><p>BMI—Body Mass Index.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Men <italic>n</italic> = 16</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Women <italic>n</italic> = 34</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Age, years</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">45 ± 22</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">44 ± 22</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Wt, kg</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">94 ± 16</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">83 ± 11</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">BMI kg/m<sup>2</sup></td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">30 ± 4</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">31 ± 4</td></tr></tbody></table></table-wrap><table-wrap id=\"ijerph-17-05583-t002\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05583-t002_Table 2</object-id><label>Table 2</label><caption><p>Energy and macronutrient intake at lunch.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Mean</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">SD</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Mean</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">SD</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>p</italic>\n</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Energy and macronutrient intake after Egg breakfast</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Energy and macronutrient intake after Cereal breakfast</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Total kJ</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">4518</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1593</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Total kJ </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">5284</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1814</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.001</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Total fat g</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">11</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">6</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Total fat g </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">13</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">7</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.002</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Protein g </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">42</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">15</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Protein g </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">49</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">18</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.000</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Carbohydrate g </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">196</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">70</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Carbohydrate g</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">227</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">77</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.001</td></tr></tbody></table></table-wrap></floats-group></article>\n"
]
[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Int J Environ Res Public Health</journal-id><journal-id journal-id-type=\"iso-abbrev\">Int J Environ Res Public Health</journal-id><journal-id journal-id-type=\"publisher-id\">ijerph</journal-id><journal-title-group><journal-title>International Journal of Environmental Research and Public Health</journal-title></journal-title-group><issn pub-type=\"ppub\">1661-7827</issn><issn pub-type=\"epub\">1660-4601</issn><publisher><publisher-name>MDPI</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32751831</article-id><article-id pub-id-type=\"pmc\">PMC7432074</article-id><article-id pub-id-type=\"doi\">10.3390/ijerph17155537</article-id><article-id pub-id-type=\"publisher-id\">ijerph-17-05537</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Article</subject></subj-group></article-categories><title-group><article-title>Determination of Urinary Cotinine Cut-Off Concentrations for Pregnant Women in the Japan Environment and Children’s Study (JECS)</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Nishihama</surname><given-names>Yukiko</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05537\">1</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0001-7772-0389</contrib-id><name><surname>Nakayama</surname><given-names>Shoji F.</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05537\">1</xref><xref rid=\"c1-ijerph-17-05537\" ref-type=\"corresp\">*</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0002-1050-3125</contrib-id><name><surname>Tabuchi</surname><given-names>Takahiro</given-names></name><xref ref-type=\"aff\" rid=\"af2-ijerph-17-05537\">2</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0001-9235-1227</contrib-id><name><surname>Isobe</surname><given-names>Tomohiko</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05537\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Jung</surname><given-names>Chau-Ren</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05537\">1</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0003-4275-548X</contrib-id><name><surname>Iwai-Shimada</surname><given-names>Miyuki</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05537\">1</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0002-9221-4940</contrib-id><name><surname>Kobayashi</surname><given-names>Yayoi</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05537\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Michikawa</surname><given-names>Takehiro</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05537\">1</xref><xref ref-type=\"aff\" rid=\"af3-ijerph-17-05537\">3</xref></contrib><contrib contrib-type=\"author\"><name><surname>Sekiyama</surname><given-names>Makiko</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05537\">1</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0002-2442-2406</contrib-id><name><surname>Taniguchi</surname><given-names>Yu</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05537\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Nitta</surname><given-names>Hiroshi</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05537\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Yamazaki</surname><given-names>Shin</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05537\">1</xref></contrib><contrib contrib-type=\"author\"><collab>on behalf of the Japan Environment and Children’s Study Group</collab><xref ref-type=\"author-notes\" rid=\"fn1-ijerph-17-05537\">†</xref></contrib></contrib-group><aff id=\"af1-ijerph-17-05537\"><label>1</label>Japan Environment and Children’s Study Programme Office, Centre for Health and Environmental Risk Research, National Institute for Environmental Studies, Tsukuba, Ibaraki 305-0053, Japan; <email>[email protected]</email> (Y.N.); <email>[email protected]</email> (T.I.); <email>[email protected]</email> (C.-R.J.); <email>[email protected]</email> (M.I.-S.); <email>[email protected]</email> (Y.K.); <email>[email protected]</email> (T.M.); <email>[email protected]</email> (M.S.); <email>[email protected]</email> (Y.T.); <email>[email protected]</email> (H.N.); <email>[email protected]</email> (S.Y.)</aff><aff id=\"af2-ijerph-17-05537\"><label>2</label>Cancer Control Center, Osaka International Cancer Institute, Osaka 541-8567, Japan; <email>[email protected]</email></aff><aff id=\"af3-ijerph-17-05537\"><label>3</label>Department of Environmental and Occupational Health, School of Medicine, Toho University, Tokyo 143-8540, Japan</aff><author-notes><corresp id=\"c1-ijerph-17-05537\"><label>*</label>Correspondence: <email>[email protected]</email>; Tel.: +81-29-850-2786</corresp><fn id=\"fn1-ijerph-17-05537\"><label>†</label><p>Membership of the Japan Environment and Children’s Study Group is provided in the Acknowledgments.</p></fn></author-notes><pub-date pub-type=\"epub\"><day>31</day><month>7</month><year>2020</year></pub-date><pub-date pub-type=\"ppub\"><month>8</month><year>2020</year></pub-date><volume>17</volume><issue>15</issue><elocation-id>5537</elocation-id><history><date date-type=\"received\"><day>24</day><month>6</month><year>2020</year></date><date date-type=\"accepted\"><day>29</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>© 2020 by the authors.</copyright-statement><copyright-year>2020</copyright-year><license license-type=\"open-access\"><license-p>Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>).</license-p></license></permissions><abstract><p>Few studies have assessed the accuracy of self-reported questionnaires to determine smoking habits relative to urinary biomarkers. This study investigated urinary cotinine cut-off concentrations distinguishing active, passive and non-smokers among pregnant women who participated in the Japan Environment and Children’s Study, a nationwide birth cohort study. Pregnant participants with measured urinary cotinine concentrations (UCCs) and who completed self-reported questionnaires on smoking status were included (<italic>n</italic> = 89,895). The cut-off values (COVs) for active and passive smokers were calculated by fitting mixed normal distribution functions to UCCs. The sensitivity and specificity of the questionnaires were subsequently evaluated. The median (interquartile range) UCC was 0.24 (0.083–0.96) µg/g-creatinine, with the detection rate of 89%. The COV for distinguishing active smokers from passive and non-smokers was 36.8 µg/g-creatinine. When this COV was considered to represent the true condition, the questionnaire had a sensitivity of 0.523, a specificity of 0.998, a positive predictive value (PPV) of 0.967 and a negative predictive value (NPV) of 0.957. The COV for distinguishing passive smokers from non-smokers was 0.31 µg/g-creatinine, with the questionnaire having a sensitivity of 0.222, a specificity of 0.977, a PPV of 0.868 and an NPV of 0.644. As many as 78% of passive smokers might be misclassified as non-smokers.</p></abstract><kwd-group><kwd>cotinine</kwd><kwd>pregnant women</kwd><kwd>cut-off value</kwd></kwd-group></article-meta></front><body><sec sec-type=\"intro\" id=\"sec1-ijerph-17-05537\"><title>1. Introduction</title><p>Maternal smoking and/or exposure to environmental tobacco smoke (ETS) have been found to have adverse effects on the health and development of foetuses and children, including higher foetal heart rate, lower birth weight, altered neurobehaviour and asthma [<xref rid=\"B1-ijerph-17-05537\" ref-type=\"bibr\">1</xref>,<xref rid=\"B2-ijerph-17-05537\" ref-type=\"bibr\">2</xref>,<xref rid=\"B3-ijerph-17-05537\" ref-type=\"bibr\">3</xref>,<xref rid=\"B4-ijerph-17-05537\" ref-type=\"bibr\">4</xref>]. Maternal smoking status is regarded as an important covariate in birth cohort studies testing the effects of maternal environmental exposure on child health [<xref rid=\"B5-ijerph-17-05537\" ref-type=\"bibr\">5</xref>,<xref rid=\"B6-ijerph-17-05537\" ref-type=\"bibr\">6</xref>].</p><p>The Japan Environment and Children’s Study (JECS) is an ongoing nationwide birth cohort study started in 2011. JECS was designed to investigate the effects of environmental factors on child health and development [<xref rid=\"B7-ijerph-17-05537\" ref-type=\"bibr\">7</xref>,<xref rid=\"B8-ijerph-17-05537\" ref-type=\"bibr\">8</xref>]. A total of 103,099 pregnant mothers were registered, with all of their babies born by 2014. Biospecimens, including blood, urine, hair and breast milk, were collected from mothers during pregnancy, at delivery and/or one month after birth. Cord blood and child hair samples were also collected.</p><p>The cigarette smoking rate among Japanese women has not changed in the last decade, being approximately 8%, according to the National Health and Nutrition Survey [<xref rid=\"B9-ijerph-17-05537\" ref-type=\"bibr\">9</xref>]. Of the pregnant women who participated in JECS and completed the self-administered questionnaire, 5.4% reported smoking during pregnancy [<xref rid=\"B10-ijerph-17-05537\" ref-type=\"bibr\">10</xref>], compared with 7.2% of pregnant women in the United States in 2016 [<xref rid=\"B11-ijerph-17-05537\" ref-type=\"bibr\">11</xref>]. Despite knowledge about the hazards of tobacco smoking during pregnancy [<xref rid=\"B3-ijerph-17-05537\" ref-type=\"bibr\">3</xref>,<xref rid=\"B12-ijerph-17-05537\" ref-type=\"bibr\">12</xref>], more than 15% of the mothers who participated in JECS reported that family members smoked in their homes [<xref rid=\"B8-ijerph-17-05537\" ref-type=\"bibr\">8</xref>].</p><p>Cotinine is a metabolite of nicotine, and serum or urinary cotinine concentration is regarded as a biomarker for smoking [<xref rid=\"B13-ijerph-17-05537\" ref-type=\"bibr\">13</xref>]. Most studies investigating serum/urinary cotinine concentrations as markers for smokers and ETS exposure used self-reported questionnaires to determine the actual smoking condition [<xref rid=\"B14-ijerph-17-05537\" ref-type=\"bibr\">14</xref>,<xref rid=\"B15-ijerph-17-05537\" ref-type=\"bibr\">15</xref>,<xref rid=\"B16-ijerph-17-05537\" ref-type=\"bibr\">16</xref>]. Although several studies reported the accuracy of the self-reporting questionnaire for the evaluation of smoking status [<xref rid=\"B14-ijerph-17-05537\" ref-type=\"bibr\">14</xref>,<xref rid=\"B17-ijerph-17-05537\" ref-type=\"bibr\">17</xref>,<xref rid=\"B18-ijerph-17-05537\" ref-type=\"bibr\">18</xref>], their results were inconsistent, with sensitivities ranging from 82% to 100% and specificities from 81% to 97%. A cut-off value (COV) is often determined by comparing a test method of interest against a gold-standard method [<xref rid=\"B19-ijerph-17-05537\" ref-type=\"bibr\">19</xref>]. The commonly used technique is receiver operating characteristic (ROC) curve analysis; however, it assumes the existence of a gold-standard test to determine the true conditions. Habibzadeh et al. also suggested an analytical method to derive a COV for continuous results. This also assumes there is a way to determine the true conditions. Almost all previous studies that proposed COVs for urinary cotinine concentrations used ROC curve analysis and considered questionnaire results as a gold-standard [<xref rid=\"B14-ijerph-17-05537\" ref-type=\"bibr\">14</xref>,<xref rid=\"B16-ijerph-17-05537\" ref-type=\"bibr\">16</xref>,<xref rid=\"B17-ijerph-17-05537\" ref-type=\"bibr\">17</xref>]. To the best of our knowledge, only one study to date used plasma cotinine concentrations as a gold-standard test and estimated the sensitivity and specificity of self-reported questionnaires for smoking status [<xref rid=\"B20-ijerph-17-05537\" ref-type=\"bibr\">20</xref>]. Du et al. suggested the use of the expectation–maximization (EM) algorithm to find the best fit mixture model and then derivation of a COV from the mixture model when a gold-standard test is unavailable [<xref rid=\"B21-ijerph-17-05537\" ref-type=\"bibr\">21</xref>]. This method employs the EM algorithm to fit multiple univariate empirical distribution functions to continuous measurement data. Then, a COV is calculated as the point that maximizes the sum of sensitivity and specificity, i.e., by minimizing the number of false positive and false negative cases. The questionnaire on smoking status is less accurate [<xref rid=\"B13-ijerph-17-05537\" ref-type=\"bibr\">13</xref>]. Therefore, in this study, we considered urinary cotinine as a gold-standard and used Du et al.’s method to estimate COVs.</p><p>The primary aim of JECS was to evaluate the effect of exposure to the environment, especially chemical substances, on child health. Exposure to tobacco smoke should become a major covariate as well as an important exposure for all analyses within JECS. The present study was primarily performed to investigate cut-off urinary cotinine concentrations indicative of pregnant mothers’ smoking status. The second aim of this study was to evaluate the comparative accuracy of the self-reported questionnaire when the urinary cotinine concentrations were considered to represent the true condition.</p></sec><sec id=\"sec2-ijerph-17-05537\"><title>2. Materials and Methods </title><sec id=\"sec2dot1-ijerph-17-05537\" sec-type=\"subjects\"><title>2.1. Study Participants</title><p>The JECS protocol is described in detail elsewhere [<xref rid=\"B7-ijerph-17-05537\" ref-type=\"bibr\">7</xref>,<xref rid=\"B8-ijerph-17-05537\" ref-type=\"bibr\">8</xref>]. Briefly, JECS is an ongoing nationwide birth cohort study that registered over 100,000 pregnant women from January 2011 to March 2014 in 15 study areas across Japan. JECS is funded by the Ministry of the Environment of Japan and operated by the National Institute for Environment Studies in coordination with the National Center for Child Health and Development and 15 regional centres. Written informed consent was obtained from all participating women and their families. The current study was based on the jecs-ta-20190930 dataset (<italic>n</italic> = 104,062), which was released in September 2019. Women with multiple birth pregnancies (<italic>n</italic> = 1002), women lacking measurements of urinary cotinine concentration (<italic>n</italic> = 7043), women registered at the incorrect gestational week (<italic>n</italic> = 563) and those whose urine samples were collected more than 60 days before or after their responses to the mid-late pregnancy questionnaire were received (<italic>n</italic> = 5405), were excluded from analysis. Participants who did not report their own smoking status or the smoking status of their partners on the questionnaires (<italic>n</italic> = 154) were excluded for determination of urinary cotinine cut-off concentrations, and those who did not report the state of passive smoking (<italic>n</italic> = 758) and were active smokers (<italic>n</italic> = 4034) were also excluded from analyses of passive smoking using questionnaires (leaving <italic>n</italic> = 85,103, see <xref ref-type=\"app\" rid=\"app1-ijerph-17-05537\">Figure S1</xref>).</p></sec><sec id=\"sec2dot2-ijerph-17-05537\"><title>2.2. Sample Collection</title><p>A maternal urine sample was collected at the second or third trimester of pregnancy using a 120 mL polypropylene container (VWR International, LLC., Wayne, PA, USA). A 15 mL aliquot of each urine sample was placed into three 5 mL Data Matrix code-labelled cryogenic biobanking tubes (Greiner Bio-One International GmbH, Kremsmünster, Austria), transferred to a contract laboratory at 1–10 °C and stored at −80 °C until analysis. In the present study, 100 µL aliquots of urine were used to determine cotinine concentrations.</p></sec><sec id=\"sec2dot3-ijerph-17-05537\"><title>2.3. Data Collection</title><p>Urinary cotinine concentrations as well as urinary specific gravity and creatinine concentrations were determined by the method and verified by the quality control procedure described in <xref ref-type=\"app\" rid=\"app2-ijerph-17-05537\">Appendix A</xref>. The self-administered questionnaires included demographic, socioeconomic, lifestyle and health related information. Participants were asked to complete two questionnaires during pregnancy, one at enrolment during the first or second trimester (M-T1) and the other during the second or third trimester (M-T2). Smoking status of each mother and partner was scored on the questionnaires as never smoked, quit before the current pregnancy, quit after recognizing the current pregnancy or smoked during the current pregnancy. The questionnaires also included questions about the number of cigarettes smoked per day and passive smoking opportunities, which were scored as never exposed to passive smoking, and exposed to passive smoking for >1, >2, >4 and 7 days per week. Data on smoking status were based primarily on the M-T2 questionnaire, with results from the M-T1 questionnaire used to complement maternal smoking status when it was missing from the M-T2 questionnaire (the rate of complementation was 0.7%). Annual household income was reported as <4 million JPY (~36,844 USD), 4 to <6 million JPY (~55,266 USD) or ≥6 million JPY. Education was defined as <12 years or ≥13 years, as reported on the M-T2 questionnaire. Consumption of foods that were potential sources of nicotine, such as nightshades (bell peppers, aubergines, tomatoes and potatoes) and tea leaves (tea, Japanese tea and oolong tea) [<xref rid=\"B22-ijerph-17-05537\" ref-type=\"bibr\">22</xref>,<xref rid=\"B23-ijerph-17-05537\" ref-type=\"bibr\">23</xref>], was estimated using a food frequency questionnaire (FFQ), which was administered at the same time as the M-T2 questionnaires [<xref rid=\"B24-ijerph-17-05537\" ref-type=\"bibr\">24</xref>]. Maternal age at childbirth was determined from individual medical records, the maternal consent form and prenatal care records.</p></sec><sec id=\"sec2dot4-ijerph-17-05537\"><title>2.4. Data Analysis</title><p>Urinary cotinine concentrations normalized relative to creatinine concentrations were log10-transformed for statistical analysis. The association between consumption of foods that might be a potential source of nicotine and urinary cotinine concentration was evaluated by Spearman’s rank correlation coefficient, with no association found (see <xref ref-type=\"app\" rid=\"app1-ijerph-17-05537\">Figure S2</xref>), indicating that foods were a negligible source of urinary cotinine in subsequent analyses. Du et al.’s method [<xref rid=\"B21-ijerph-17-05537\" ref-type=\"bibr\">21</xref>] was employed to derive COVs for urinary cotinine concentrations distinguishing active smokers from others and passive smokers from non-smokers. Urinary cotinine concentrations were first log10-transformed and the EM-like algorithm was applied (R package ‘mixtools, ver. 1.2.0’ was used) to fit multiple univariate normal distribution functions. The goodness of fit of the different numbers of distributions (two, three and four) was evaluated using the Kolmogorov-Smirnov test, with a mixture of three normal distributions resulting in the best fit. Approximately 11% of the urinary cotinine concentrations were below the minimum reporting level (MRL). Although substitution of values below the MRL by one-half of the reporting limit has been used frequently in related studies, this type of approach may introduce an invasive pattern (signal) into the original data [<xref rid=\"B25-ijerph-17-05537\" ref-type=\"bibr\">25</xref>]. Urinary cotinine concentrations below the MRL were therefore imputed by randomly assigning values generated by the fitted mixture distribution function (<xref ref-type=\"fig\" rid=\"ijerph-17-05537-f001\">Figure 1</xref>). We assumed that the distribution with the lowest mean represented for non-smokers (l-probability density function or l-PDF), the middle distribution represented passive smokers (m-PDF) and the highest distribution included active smokers (h-PDF). Then, the COV that maximized the sum of the sensitivity or true positive (area under h-PDF to the right of the COV) and the specificity or true negative (area under m-PDF to the left of the COV) was determined and assigned as the COV to distinguish active smokers from passive/non-smokers (<xref ref-type=\"fig\" rid=\"ijerph-17-05537-f002\">Figure 2</xref>). The COV that distinguished passive smokers and non-smokers was derived in the same manner using l-PDF and m-PDF. Bootstrapping with 500 iterations was used to derive the 95% confidential interval estimates of each COV. Finally, the sensitivity and specificity of the questionnaire were evaluated using urinary cotinine cut-off concentrations as the true conditions. The COVs for unadjusted urinary cotinine data were calculated using the same method as described above.</p><p>The smoking status of each mother and partner obtained from the questionnaire was categorized as ‘active smoker (smoked during the current pregnancy),’ ‘passive smoker’ or ‘non-smoker (never smoked, quit before the current pregnancy, quit after recognizing the current pregnancy and did not have second-hand smoke exposure).’ Information about passive smoking was also obtained from the questionnaire, as experiencing ETS exposure for 7, 4–6, 2–3 or 1 day(s) per week. The smoking status of each partner was categorized as a current smoker or current non-smoker. The accuracy of the questionnaires was evaluated by urinary cotinine concentrations. The goodness of fit of the distribution functions for each smoking status was evaluated using the Kolmogorov-Smirnov test. Lastly, simple linear regression analysis was performed to examine the relationship between the average number of cigarettes smoked per day and average urinary cotinine concentrations. All statistical analyses were performed using R version 3.6.2 [<xref rid=\"B26-ijerph-17-05537\" ref-type=\"bibr\">26</xref>].</p></sec></sec><sec sec-type=\"results\" id=\"sec3-ijerph-17-05537\"><title>3. Results</title><sec id=\"sec3dot1-ijerph-17-05537\"><title>3.1. Method Performance</title><p>The MRL of cotinine was 0.03 ng/mL. The reproducibility and intermediate precision for cotinine analysis were 4.0% and 4.7%, respectively.</p></sec><sec id=\"sec3dot2-ijerph-17-05537\"><title>3.2. Concentrations of Cotinine in Maternal Urine Samples</title><p>The demographic characteristics of the study participants are summarized in <xref rid=\"ijerph-17-05537-t001\" ref-type=\"table\">Table 1</xref>. According to their responses to questionnaires, 4.6% of pregnant women and 46.8% of their partners smoked during pregnancy. Cotinine was detected in 89% of the maternal urine samples, with the median (interquartile range (IQR)) concentration normalized to creatinine after imputation being 0.24 (0.083–0.96) µg/g-creatinine or unnormalized being 0.15 (0.057–0.63) ng/mL. The distribution of urinary cotinine was bimodal (<xref ref-type=\"fig\" rid=\"ijerph-17-05537-f001\">Figure 1</xref>). The degree of fitting of the mixture of three normal distributions to the original data evaluated by Kolmogorov-Smirnov test was D = 0.0055 (<italic>p</italic>-value = 0.13).</p></sec><sec id=\"sec3dot3-ijerph-17-05537\"><title>3.3. Cotinine Cut-Off Concentration for Active Smoking</title><p>The COV (95% confidential interval) for distinguishing active smokers from others (UCOV) was 36.8 (36.58, 36.84) µg/g-creatinine or 21.5 ng/mL (<xref ref-type=\"fig\" rid=\"ijerph-17-05537-f001\">Figure 1</xref>). Of the enrolled participants, 4017 (4.5%) had urinary cotinine concentrations exceeding 36.8 µg/g-creatinine and were active smokers, whereas 3659 (4.1%) had high urinary cotinine concentrations and were current non-smokers (<xref rid=\"ijerph-17-05537-t002\" ref-type=\"table\">Table 2</xref>). By comparison, 82,083 (91.3%) participants had urinary cotinine levels below the COV and were non-smokers, whereas 136 (0.15%) had low urinary cotinine concentrations and were current smokers. Relative to urinary cotinine concentration, the sensitivity of the questionnaire was 0.523 and its specificity was 0.998, with a positive predictive value (PPV) of 0.967 and a negative predictive value (NPV) of 0.957 (<xref rid=\"ijerph-17-05537-t002\" ref-type=\"table\">Table 2</xref>). The COVs slightly changed when using different treatments for data below MRL and different strata of gestational weeks, however, they did not affect sensitivity and specificity (<xref ref-type=\"app\" rid=\"app1-ijerph-17-05537\">Table S6</xref>).</p><p>The proportion of participants that had urinary cotinine concentrations exceeding the UCOV in the groups with ETS exposure at a rate of 1 day/week, 2–3 days/week, 4–6 days/week and 7 days/week was 3.4%, 7.6%, 9.4% and 16.0%, respectively (<xref ref-type=\"app\" rid=\"app1-ijerph-17-05537\">Table S7</xref>).</p><p>The regression coefficient (standard error) of a single regression model of the number of cigarettes smoked per day and urinary cotinine concentrations was 98 (5.2) µg/g-creatinine, with an adjusted R<sup>2</sup> value of 0.128 (see <xref ref-type=\"app\" rid=\"app1-ijerph-17-05537\">Figure S3</xref>).</p></sec><sec id=\"sec3dot4-ijerph-17-05537\"><title>3.4. Cotinine Cut-Off Concentration for Passive Smoking</title><p><xref ref-type=\"fig\" rid=\"ijerph-17-05537-f002\">Figure 2</xref> shows a density plot of urinary cotinine concentrations in three groups of pregnant women, i.e., active smokers, passive smokers and non-smokers, as determined by the questionnaire, overlapping with the fitted distribution functions. The COV for discriminating passive smokers from non-smokers (LCOV) was 0.31 (0.308, 0.310) µg/g-creatinine or 0.17 ng/mL. Of the participants, 7734 (9.1%) had urinary cotinine concentrations of 0.31–36.8 µg/g-creatinine and reported exposure to ETS seven times per week, whereas 27,143 (31.9%) had these cotinine concentrations but were unaware of ETS exposure. Relative to urinary cotinine concentrations, the questionnaire had a sensitivity of 0.222, a specificity of 0.977, a PPV of 0.868 and an NPV of 0.644 (<xref rid=\"ijerph-17-05537-t003\" ref-type=\"table\">Table 3</xref>).</p></sec></sec><sec sec-type=\"discussion\" id=\"sec4-ijerph-17-05537\"><title>4. Discussion</title><p>This study found that concentrations of urinary cotinine in pregnant women were one or two orders of magnitude lower than those in previous studies [<xref rid=\"B15-ijerph-17-05537\" ref-type=\"bibr\">15</xref>,<xref rid=\"B27-ijerph-17-05537\" ref-type=\"bibr\">27</xref>]. The proportion of pregnant women who smoked during pregnancy was similar to the result (5%) from the Japanese national survey conducted in 2010 [<xref rid=\"B28-ijerph-17-05537\" ref-type=\"bibr\">28</xref>], but was 50% lower than that of previous Japanese epidemiological studies [<xref rid=\"B29-ijerph-17-05537\" ref-type=\"bibr\">29</xref>,<xref rid=\"B30-ijerph-17-05537\" ref-type=\"bibr\">30</xref>,<xref rid=\"B31-ijerph-17-05537\" ref-type=\"bibr\">31</xref>,<xref rid=\"B32-ijerph-17-05537\" ref-type=\"bibr\">32</xref>]. Using a fitted distribution function, the UCOV was 36.8 µg/g-creatinine. When this COV was considered to represent the true condition, the questionnaire had high PPV (0.967) and NPV (0.957); however, its sensitivity was not satisfactory (0.523). These findings indicate that the answers on the questionnaire to questions about smoking (yes or no) are reliable, but that the questionnaire tends to underestimate the prevalence of smoking among pregnant women. The questionnaire found that 4.6% of pregnant women in JECS smoked, whereas the true smoking rate, based on urinary cotinine levels, was as high as 8%, which was close to the rates of Japanese [<xref rid=\"B29-ijerph-17-05537\" ref-type=\"bibr\">29</xref>,<xref rid=\"B30-ijerph-17-05537\" ref-type=\"bibr\">30</xref>,<xref rid=\"B31-ijerph-17-05537\" ref-type=\"bibr\">31</xref>,<xref rid=\"B32-ijerph-17-05537\" ref-type=\"bibr\">32</xref>] and American [<xref rid=\"B11-ijerph-17-05537\" ref-type=\"bibr\">11</xref>] pregnant women. The reason why our questionnaire found a lower proportion of active smokers during pregnancy compared with the previous Japanese studies conducted in 2002–2009 is uncertain; however, it is speculated that it has become more difficult to admit to smoking during pregnancy in recent years.</p><sec id=\"sec4dot1-ijerph-17-05537\"><title>4.1. Urinary Cotinine Concentrations</title><p>To the best of our knowledge, this is the first study in Japan to report urinary concentrations of cotinine in pregnant women on this scale. Their median urinary cotinine concentration, 0.24 µg/g-creatinine or 0.15 ng/mL, was one or two orders of magnitude lower than those previously reported in pregnant women, 7.4 ng/mL [<xref rid=\"B15-ijerph-17-05537\" ref-type=\"bibr\">15</xref>] and 19.7 µg/g-creatinine [<xref rid=\"B27-ijerph-17-05537\" ref-type=\"bibr\">27</xref>]. However, the maximum urinary cotinine concentration was greater in this study (17,497 µg/g-creatinine) than in a previous study (9776 µg/g-creatinine [<xref rid=\"B27-ijerph-17-05537\" ref-type=\"bibr\">27</xref>]). We found no correlation between the consumption of foods containing nicotine and urinary cotinine concentrations. The difference in the urinary cotinine concentration was considered to be derived from (1) differences in female smoking rate between countries and (2) differences in the nicotine contents of tobacco that were smoked. Regarding the former point, the smoking rate of Japanese women (11.2%) is lower than those of Spanish and Canadian women (27.4% and 12.0%, respectively) [<xref rid=\"B33-ijerph-17-05537\" ref-type=\"bibr\">33</xref>]. In terms of the latter point, we did not investigate the brand names of the cigarettes that were smoked. Some reports indicate that Japanese people tend to smoke cigarettes containing less nicotine than Europeans and North Americans [<xref rid=\"B34-ijerph-17-05537\" ref-type=\"bibr\">34</xref>,<xref rid=\"B35-ijerph-17-05537\" ref-type=\"bibr\">35</xref>].</p></sec><sec id=\"sec4dot2-ijerph-17-05537\"><title>4.2. Urinary Cotinine Cut-Off Concentrations</title><p>The urinary cotinine cut-off concentration distinguishing active smokers from passive smokers and non-smokers in the present study, 36.8 µg/g-creatinine or 21.5 ng/mL, was similar to previously reported concentrations, i.e., 53 µg/g-creatinine or 42, 82 and 200 ng/mL [<xref rid=\"B15-ijerph-17-05537\" ref-type=\"bibr\">15</xref>,<xref rid=\"B17-ijerph-17-05537\" ref-type=\"bibr\">17</xref>,<xref rid=\"B36-ijerph-17-05537\" ref-type=\"bibr\">36</xref>]. To our knowledge, the present study is the second worldwide and the first in Japan to determine the COV based on urinary cotinine levels alone. Further analysis of JECS data should rate smoking status based on urinary cotinine concentrations. Only one other study has reported the relationship between the amount of ETS/cigarette smoking and urinary cotinine concentrations [<xref rid=\"B15-ijerph-17-05537\" ref-type=\"bibr\">15</xref>]. In that study, 100 ng/mL cotinine was considered a conservative COV for self-reported smokers. Of the participants in our study who reported that they were current non-smokers but were exposed to ETS, 7% had urinary cotinine levels exceeding 100 ng/mL, with these participants regarded as being misclassified.</p><p>We found that the distribution of urinary cotinine concentrations was bimodal, with three log-normal distribution functions best fitting the data (l-PDF, m-PDF and h-PDF). The fitted mixture model was not significantly separated from the distribution of the original data (Kolmogorov-Smirnov test, D = 0.0055, <italic>p</italic>-value = 0.13). While the upper mode of the bimodal distribution was assumed to associate with active smoking, no boundary between passive smokers and non-smokers was evident. The EM-like algorithm effectively found two log-normal distributions (l-PDF and m-PDF) in the lower mode. We thus assumed that l-PDF and m-PDF were the distribution functions of non-smokers and passive smokers, respectively. The distributions of non-smokers and active smokers determined by self-reported questionnaires were monomodal, whereas that of passive smokers was bimodal (<xref ref-type=\"fig\" rid=\"ijerph-17-05537-f002\">Figure 2</xref>). This implies three possibilities: (1) some participants who said they were passive smokers actually actively smoked, (2) passive smokers had a similar exposure to nicotine as active smokers or (3) a mixture of these two situations. <xref ref-type=\"app\" rid=\"app1-ijerph-17-05537\">Table S7</xref> indicates that 2675 passive smokers had urinary cotinine concentrations exceeding the UCOV, whereas 3556 (3659 in <xref rid=\"ijerph-17-05537-t002\" ref-type=\"table\">Table 2</xref> minus 103 participants with missing partners’ smoking status and maternal passive smoking status) participants who had urinary cotinine levels above the UCOV answered ‘did not smoke’ in the questionnaire, leaving 881 (25%) who answered ‘neither active nor passive smoking.’ </p><p>Approximately 43% of the study participants whose urinary cotinine concentrations exceeded 0.31 µg/g-creatinine were unaware of their exposure to ETS. Moreover, 27% of these participants reported that their partners also did not smoke. This indicates that a significant proportion of pregnant women in Japan are unconsciously exposed to tobacco smoke. Urinary cotinine concentrations can be a better marker of smoking status when used in later epidemiological analysis.</p></sec><sec id=\"sec4dot3-ijerph-17-05537\"><title>4.3. Limitations</title><p>This study had several limitations. First, the true condition of smoking status was determined based solely on urinary cotinine levels. Nicotine and cotinine have relatively short biological half-lives: 11 h and 17 h to 4 days, respectively [<xref rid=\"B37-ijerph-17-05537\" ref-type=\"bibr\">37</xref>,<xref rid=\"B38-ijerph-17-05537\" ref-type=\"bibr\">38</xref>]. Because the samples we collected were spot urine samples, we may have missed appropriate times in participants who smoked infrequently. This may have resulted in an underestimation of smoking prevalence among our participants. In this study, the number of cigarettes smoked did not completely explain the urinary cotinine concentrations. We did not ask for the product names of the cigarettes or how they were smoked in the questionnaire, meaning we could not estimate the nicotine intake. Second, information about nicotine medication was not available in this study. Each cigarette sold in Japan is reported to contain 0.1–1.2 mg of nicotine [<xref rid=\"B39-ijerph-17-05537\" ref-type=\"bibr\">39</xref>], whereas one sheet of a typical nicotine patch prescribed in Japan contains ~14 mg of nicotine. Thus, nicotine medication may be a significant source of urinary cotinine, resulting in patient misclassification. Third, the food consumption data used in this study were not sufficiently comprehensive to determine the nicotine intake from food items. For example, some foods containing high levels of nicotine such as cauliflower were not taken into consideration because the FFQ did not measure such intake in this study [<xref rid=\"B22-ijerph-17-05537\" ref-type=\"bibr\">22</xref>,<xref rid=\"B23-ijerph-17-05537\" ref-type=\"bibr\">23</xref>,<xref rid=\"B37-ijerph-17-05537\" ref-type=\"bibr\">37</xref>]. These limitations indicate the need to assess other biomarkers of exposure to tobacco smoke, such as 4-(methylnitrosoamino)-1-(3-pyridyl)-1-butanol (NNAL) and some polycyclic aromatic hydrocarbons, in combination with cotinine [<xref rid=\"B38-ijerph-17-05537\" ref-type=\"bibr\">38</xref>]. It is recommended that multiple biomarkers are used when analysing the associations between environmental exposures, including smoking, during pregnancy and child health outcomes.</p></sec></sec><sec sec-type=\"conclusions\" id=\"sec5-ijerph-17-05537\"><title>5. Conclusions</title><p>A COV of urinary cotinine concentration, 36.8 µg/g-creatinine, distinguishing active smokers from passive smokers and non-smokers was derived using a fitted distribution function. When this COV was considered to represent the true condition, the sensitivity of a questionnaire addressing maternal smoking was determined to be 0.523, indicating that the proportion of pregnant women in JECS who smoked during pregnancy was underestimated by half. We also found that a large proportion (as high as 78%) of passive smokers might be misclassified as non-smokers.</p></sec></body><back><ack><title>Acknowledgments</title><p>The authors would like to thank all participants of JECS and staff members involved in data collection. We gratefully acknowledge the contributions of Hiroshi Satoh (Food Safety Commission, Cabinet Office, Tokyo, Japan) and Toshihiro Kawamoto (Japan Industrial Safety and Health Association, Tokyo, Japan), who were former principal investigators of JECS. The findings and conclusions of this article are the sole responsibility of the authors and do not represent the official views of the Japanese government or the National Institute for Environmental Studies. Members of the JECS Group as of 2019: Michihiro Kamijima (principal investigator, Nagoya City University, Nagoya, Japan), Shin Yamazaki (National Institute for Environmental Studies, Tsukuba, Japan), Yukihiro Ohya (National Center for Child Health and Development, Tokyo, Japan), Reiko Kishi (Hokkaido University, Sapporo, Japan), Nobuo Yaegashi (Tohoku University, Sendai, Japan), Koichi Hashimoto (Fukushima Medical University, Fukushima, Japan), Chisato Mori (Chiba University, Chiba, Japan), Shuichi Ito (Yokohama City University, Yokohama, Japan), Zentaro Yamagata (University of Yamanashi, Chuo, Japan), Hidekuni Inadera (University of Toyama, Toyama, Japan), Takeo Nakayama (Kyoto University, Kyoto, Japan), Hiroyasu Iso (Osaka University, Suita, Japan), Masayuki Shima (Hyogo College of Medicine, Nishinomiya, Japan), Youichi Kurozawa (Tottori University, Yonago, Japan), Narufumi Suganuma (Kochi University, Nankoku, Japan), Koichi Kusuhara (University of Occupational and Environmental Health, Kitakyushu, Japan) and Takahiko Katoh (Kumamoto University, Kumamoto, Japan).</p></ack><app-group><app id=\"app1-ijerph-17-05537\"><title>Supplementary Materials</title><p>The following are available online at <uri xlink:href=\"https://www.mdpi.com/1660-4601/17/15/5537/s1\">https://www.mdpi.com/1660-4601/17/15/5537/s1</uri>. Table S1: LC gradient conditions; Table S2: LC conditions; Table S3: Monitoring of ion mass transition; Table S4: Ion source and collision cell conditions; Table S5: Range of calibration curves for cotinine; Table S6: UCOV, LCOV and sensitivity and specificity of UCOV in four different datasets; Table S7: The proportion of participants with urinary cotinine concentrations exceeding the UCOV in each ETS exposure group; Figure S1: Flow chart of the study participants; Figure S2: Correlation between the consumption of potential food sources of nicotine and urinary cotinine concentrations among non-smokers (<italic>n</italic> = 29,908, Spearman’s rank correlation r<sub>s</sub> = 0.046); Figure S3: Single regression analysis showing the relationship between the number of cigarettes smoked per day and urinary cotinine concentrations of current smokers (adjusted R<sup>2</sup> = 0.128); Figure S4: Sample treatment for measurement of urinary cotinine.</p><supplementary-material content-type=\"local-data\" id=\"ijerph-17-05537-s001\"><media xlink:href=\"ijerph-17-05537-s001.pdf\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material></app></app-group><notes><title>Author Contributions</title><p>Conceptualization, Y.N., S.F.N. and T.T.; methodology, Y.N., S.F.N., T.I. and C.-R.J.; data curation, Y.N., S.F.N., T.I., C.-R.J. and Y.T.; project administration, S.F.N., H.N. and S.Y.; writing—original draft preparation, Y.N., S.F.N. and T.T.; writing—review and editing, Y.N., S.F.N., T.T., T.I., C.-R.J., M.I.-S., Y.K., T.M., M.S., H.N., S.Y. and Y.T.; supervision, S.F.N. All authors have read and agreed to the published version of the manuscript.</p></notes><notes><title>Funding</title><p>This research was funded by the Ministry of the Environment, Government of Japan.</p></notes><notes notes-type=\"COI-statement\"><title>Conflicts of Interest</title><p>The authors declare no conflict of interest.</p></notes><app-group><app id=\"app2-ijerph-17-05537\"><title>Appendix A. Urinary Cotinine Analysis Method</title><sec id=\"secAdot1-ijerph-17-05537\"><title>Appendix A.1. Chemicals and Reagents</title><p>All reagents were of high-quality grade unless specified otherwise. Water was brought to TOC ≤ 15 ppb using a Milli-Q Integral 5 or 10 and EQP-10 L system (Merck Millipore, Bedford, MA, USA). Acetic acid (99.8% purity), 28% w/v ammonia solution, formic acid and acetone (99.0% + purity) were purchased from FUJIFILM Wako Pure Chemical Corporation (Osaka, Japan). Methanol (MeOH; 99.8% purity) was purchased from Nacalai Tesque, Inc. (Kyoto, Japan). A standard solution of cotinine (500 ng/mL), as well as an internal standard (IS) solution containing stable isotope-labelled cotinine (<sup>13</sup>C<sub>3</sub>), were purchased from Cambridge Isotope Laboratories, Inc. (Andover, MA, USA). All glassware was precleaned with acetone immediately before measurements.</p></sec><sec id=\"secAdot2-ijerph-17-05537\"><title>Appendix A.2. Sample Preparation</title><p>Ten microliters of IS solution, containing 3 ng/mL of <sup>13</sup>C<sub>3</sub>-cotinine, and 400 µL of 1.4% ammonia solution were added to each 100 µL aliquot of urine sample. The sample was vortex mixed and loaded onto an Oasis MAX (mixed-mode, strong anion exchange) 96-well plate, containing 30 mg sorbent per well and a particle size of 30 µm (Waters Corp., Milford, MA, USA), preconditioned with 500 µL of MeOH followed by 500 µL of 1.4% ammonia. The cartridge was washed with 500 µL of 1.4% ammonia, and the target compound was eluted with 200 µL of 50% (v/v) MeOH. This eluate was dissolved in 300 µL of water and mixed at 300 rpm for 10 min. A 10 µL aliquot of each was injected into a high-performance liquid chromatography–tandem mass spectrometer (LC–MS/MS) (see <xref ref-type=\"app\" rid=\"app1-ijerph-17-05537\">Figure S4</xref>).</p></sec><sec id=\"secAdot3-ijerph-17-05537\"><title>Appendix A.3. Instrument Analysis and Calculations</title><p>The LC (Nexera X2 system, Shimadzu, Corporation, Kyoto, Japan) and MS/MS (Triple Quad 6500, AB Sciex Pte. Ltd., Framingham, MA, USA) systems were operated using electrospray ionization (ESI) positive in the multiple reaction monitoring (MRM) mode. The analytical column was a CAPCELL CORE ADME, 2.1 mm I.D. × 50 mm, 2.7 µm column (Osaka Soda Co., Ltd., Osaka, Japan). The column flow rate was 0.4 mL/min, and its temperature was 40 °C. The typical routine operating conditions and data acquisition settings are documented, and the calibration range is shown in <xref ref-type=\"app\" rid=\"app1-ijerph-17-05537\">Tables S1–S5</xref>. All samples outside the calibration range were reanalysed following further dilution.</p><p>The quality control (QC) sample, consisting of Milli-Q water containing 0.2 ng/mL cotinine, was treated with the same procedure as the urine samples, with four or more replicates analysed in each analytical sequence. MRL was calculated based on the lowest concentration MRL (LCMRL) using QC samples, as described [<xref rid=\"B40-ijerph-17-05537\" ref-type=\"bibr\">40</xref>].</p></sec><sec id=\"secAdot4-ijerph-17-05537\"><title>Appendix A.4. Quality Control</title><p>The ten-point calibration curve had a coefficient of determination (R<sup>2</sup>) higher than 0.992. Repeatability and intermediate precision were determined based on ISO 5725:1994 and 27148:2010, with QC sample measurements (<italic>n</italic> = 367) used for calculations. QC for day-to-day analysis was determined using a Shewhart control chart (<inline-formula><mml:math id=\"mm1\"><mml:mrow><mml:mrow><mml:mover><mml:mi mathvariant=\"normal\">X</mml:mi><mml:mo stretchy=\"false\">¯</mml:mo></mml:mover></mml:mrow></mml:mrow></mml:math></inline-formula>-Rm control chart) according to ISO 7870. The percent recovery of target compounds was calculated by fortifying a pooled urine sample with known concentrations of cotinine standards and measuring these concentrations in seven replicates, resulting in 106% recovery of cotinine. The standard reference material, SRM 3673 (Organic Contaminants in Non-Smokers’ Urine, National Institute of Standards and Technology), was also analysed in seven replicates to determine the accuracy of the measurement. 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The solid line illustrates the probability density plot of the logarithm of creatinine-adjusted urinary cotinine concentrations to the base 10. Dashed lines represent the normal distributions fitted to the original data using a nonparametric EM-like algorithm. Dashed vertical lines show urinary cotinine cut-off concentrations. A rug plot is depicted on the x-axis.</p></caption><graphic xlink:href=\"ijerph-17-05537-g001\"/></fig><fig id=\"ijerph-17-05537-f002\" orientation=\"portrait\" position=\"float\"><label>Figure 2</label><caption><p>Smoothed histogram of urinary cotinine concentrations grouped by questionnaire responses. Groups of current smokers, passive smokers and non-smokers determined with the questionnaire are represented by the dark grey, grey and light grey solid lines, respectively. The fitted normal distributions are represented by the dashed lines. Dashed vertical lines show urinary cotinine cut-off concentrations.</p></caption><graphic xlink:href=\"ijerph-17-05537-g002\"/></fig><table-wrap id=\"ijerph-17-05537-t001\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05537-t001_Table 1</object-id><label>Table 1</label><caption><p>Demographic characteristics of the study participants (<italic>n</italic> = 90,049).</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Demographic Characteristics</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><italic>n</italic> (%)</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Maternal age (<italic>n</italic> = 90,033, years, median (range))</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">31.0 (14.0–50.0)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Gestational week (<italic>n</italic> = 90,049, weeks, median (range))</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">27.0 (15.0–41.0)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Household income (<italic>n</italic> = 83,755)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><4 million yen (~37,000 USD)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">27,694 (33.1)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4–6 million yen</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">33,781 (40.3)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">>6 million yen (~55,000 USD)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">22,280 (26.6)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Maternal smoking status (<italic>n</italic> = 89,895)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">No</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">85,742 (95.4)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Yes</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4153 (4.6)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Partner smoking status (<italic>n</italic> = 89,463)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">No</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">47,611 (53.2)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Yes</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">41,852 (46.8)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Passive smoking (<italic>n</italic> = 89,788)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">None</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">55,859 (62.2)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">One day per week</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10,647 (11.9)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Two to three days per week</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7404 (8.2)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Four to six days per week</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4372 (4.9)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Seven days per week</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">11,506 (12.8)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Number of cigarettes smoked per day(<italic>n</italic> = 4114, cigarettes per day, median (range))</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10 (0–60)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Urinary cotinine concentration</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">(µg/g-creatinine, median interquartile range (IQR))</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.24 (0.083–0.96)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">(ng/mL, median (IQR))</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.15 (0.057–0.63)</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Urinary creatinine concentration (mg/dl, median (IQR))</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">74.3 (42.3–116.4)</td></tr></tbody></table></table-wrap><table-wrap id=\"ijerph-17-05537-t002\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05537-t002_Table 2</object-id><label>Table 2</label><caption><p>Accuracy of the questionnaire on active smoking using the urinary cotinine cut-off concentration of 36.8 µg/g-creatinine (<italic>n</italic> = 89,895).</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">\n</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</th><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">True Condition</th></tr><tr><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">≥36.8 µg/g-Creatinine</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><36.8 µg/g-Creatinine</th></tr></thead><tbody><tr><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Questionnaire (Smoking)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Yes</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4017</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">136</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">No</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3659</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">82,083</td></tr><tr><td colspan=\"2\" align=\"center\" valign=\"middle\" rowspan=\"1\">Sensitivity</td><td colspan=\"2\" align=\"center\" valign=\"middle\" rowspan=\"1\">0.523</td></tr><tr><td colspan=\"2\" align=\"center\" valign=\"middle\" rowspan=\"1\">Specificity</td><td colspan=\"2\" align=\"center\" valign=\"middle\" rowspan=\"1\">0.998</td></tr><tr><td colspan=\"2\" align=\"center\" valign=\"middle\" rowspan=\"1\">Positive Predictive Value</td><td colspan=\"2\" align=\"center\" valign=\"middle\" rowspan=\"1\">0.967</td></tr><tr><td colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">Negative Predictive Value</td><td colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">0.957</td></tr></tbody></table></table-wrap><table-wrap id=\"ijerph-17-05537-t003\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05537-t003_Table 3</object-id><label>Table 3</label><caption><p>Accuracy of the questionnaire on passive smoking using the urinary cotinine cut-off concentration of 0.31 µg/g-creatinine (<italic>n</italic> = 85,103).</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">\n</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</th><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Partner Smoking</th><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Passive Smoking 7 Days</th><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">≥4 Days</th><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">≥2 Days</th><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">≥1 Day</th></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">≥COV</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><COV</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">≥COV</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><COV</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">≥COV</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><COV</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">≥COV</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><COV</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">≥COV</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><COV</td></tr></thead><tbody><tr><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Questionnaire Response</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Yes</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">23,464</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">14,602</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7734</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1178</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10,605</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2262</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">15,067</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4687</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">19,797</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10,294</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">No</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">11,413</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">35,624</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">27,143</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">49,048</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">24,272</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">47,964</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">19,810</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">45,539</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">15,080</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">39,932</td></tr><tr><td colspan=\"2\" align=\"center\" valign=\"middle\" rowspan=\"1\">Sensitivity</td><td colspan=\"2\" align=\"center\" valign=\"middle\" rowspan=\"1\">0.673</td><td colspan=\"2\" align=\"center\" valign=\"middle\" rowspan=\"1\">0.222</td><td colspan=\"2\" align=\"center\" valign=\"middle\" rowspan=\"1\">0.304</td><td colspan=\"2\" align=\"center\" valign=\"middle\" rowspan=\"1\">0.432</td><td colspan=\"2\" align=\"center\" valign=\"middle\" rowspan=\"1\">0.568</td></tr><tr><td colspan=\"2\" align=\"center\" valign=\"middle\" rowspan=\"1\">Specificity</td><td colspan=\"2\" align=\"center\" valign=\"middle\" rowspan=\"1\">0.709</td><td colspan=\"2\" align=\"center\" valign=\"middle\" rowspan=\"1\">0.977</td><td colspan=\"2\" align=\"center\" valign=\"middle\" rowspan=\"1\">0.955</td><td colspan=\"2\" align=\"center\" valign=\"middle\" rowspan=\"1\">0.907</td><td colspan=\"2\" align=\"center\" valign=\"middle\" rowspan=\"1\">0.795</td></tr><tr><td colspan=\"2\" align=\"center\" valign=\"middle\" rowspan=\"1\">Positive Predictive Value</td><td colspan=\"2\" align=\"center\" valign=\"middle\" rowspan=\"1\">0.616</td><td colspan=\"2\" align=\"center\" valign=\"middle\" rowspan=\"1\">0.868</td><td colspan=\"2\" align=\"center\" valign=\"middle\" rowspan=\"1\">0.824</td><td colspan=\"2\" align=\"center\" valign=\"middle\" rowspan=\"1\">0.763</td><td colspan=\"2\" align=\"center\" valign=\"middle\" rowspan=\"1\">0.658</td></tr><tr><td colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">Negative Predictive Value</td><td colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">0.757</td><td colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">0.644</td><td colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">0.664</td><td colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">0.697</td><td colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">0.726</td></tr></tbody></table></table-wrap></floats-group></article>\n"
]
[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Int J Mol Sci</journal-id><journal-id journal-id-type=\"iso-abbrev\">Int J Mol Sci</journal-id><journal-id journal-id-type=\"publisher-id\">ijms</journal-id><journal-title-group><journal-title>International Journal of Molecular Sciences</journal-title></journal-title-group><issn pub-type=\"epub\">1422-0067</issn><publisher><publisher-name>MDPI</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32752102</article-id><article-id pub-id-type=\"pmc\">PMC7432075</article-id><article-id pub-id-type=\"doi\">10.3390/ijms21155503</article-id><article-id pub-id-type=\"publisher-id\">ijms-21-05503</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Article</subject></subj-group></article-categories><title-group><article-title>Persistent Human KIT Receptor Signaling Disposes Murine Placenta to Premature Differentiation Resulting in Severely Disrupted Placental Structure and Functionality</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Kaiser</surname><given-names>Franziska</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05503\">1</xref><xref ref-type=\"author-notes\" rid=\"fn1-ijms-21-05503\">†</xref></contrib><contrib contrib-type=\"author\"><name><surname>Hartweg</surname><given-names>Julia</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05503\">1</xref><xref ref-type=\"aff\" rid=\"af2-ijms-21-05503\">2</xref><xref ref-type=\"author-notes\" rid=\"fn1-ijms-21-05503\">†</xref></contrib><contrib contrib-type=\"author\"><name><surname>Jansky</surname><given-names>Selina</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05503\">1</xref><xref ref-type=\"aff\" rid=\"af3-ijms-21-05503\">3</xref><xref ref-type=\"aff\" rid=\"af4-ijms-21-05503\">4</xref></contrib><contrib contrib-type=\"author\"><name><surname>Pelusi</surname><given-names>Natalie</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05503\">1</xref><xref ref-type=\"aff\" rid=\"af5-ijms-21-05503\">5</xref></contrib><contrib contrib-type=\"author\"><name><surname>Kubaczka</surname><given-names>Caroline</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05503\">1</xref><xref ref-type=\"aff\" rid=\"af6-ijms-21-05503\">6</xref></contrib><contrib contrib-type=\"author\"><name><surname>Sharma</surname><given-names>Neha</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05503\">1</xref><xref ref-type=\"aff\" rid=\"af7-ijms-21-05503\">7</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0002-0589-503X</contrib-id><name><surname>Nitsche</surname><given-names>Dominik</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05503\">1</xref><xref ref-type=\"aff\" rid=\"af8-ijms-21-05503\">8</xref></contrib><contrib contrib-type=\"author\"><name><surname>Langkabel</surname><given-names>Jan</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05503\">1</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0001-8272-0076</contrib-id><name><surname>Schorle</surname><given-names>Hubert</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05503\">1</xref><xref rid=\"c1-ijms-21-05503\" ref-type=\"corresp\">*</xref></contrib></contrib-group><aff id=\"af1-ijms-21-05503\"><label>1</label>Department of Developmental Pathology, Institute of Pathology, University Hospital Bonn, 53127 Bonn, Germany; <email>[email protected]</email> (F.K.); <email>[email protected]</email> (J.H.); <email>[email protected]</email> (S.J.); <email>[email protected]</email> (N.P.); <email>[email protected]</email> (C.K.); <email>[email protected]</email> (N.S.); <email>[email protected]</email> (D.N.); <email>[email protected]</email> (J.L.)</aff><aff id=\"af2-ijms-21-05503\"><label>2</label>Department of Medicine II and IZKF Research Laboratory, Würzburg University Hospital, 97080 Würzburg, Germany</aff><aff id=\"af3-ijms-21-05503\"><label>3</label>Hopp Children’s Cancer Center (KiTZ), 69120 Heidelberg, Germany</aff><aff id=\"af4-ijms-21-05503\"><label>4</label>Division of Neuroblastoma Genomics, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany</aff><aff id=\"af5-ijms-21-05503\"><label>5</label>Department of Molecular Pathology, Institute of Pathology, University Hospital Bonn, 53127 Bonn, Germany</aff><aff id=\"af6-ijms-21-05503\"><label>6</label>Division of Pediatric Hematology/Oncology, Children’s Hospital Boston, Harvard Medical School, Boston, MA 02115, USA</aff><aff id=\"af7-ijms-21-05503\"><label>7</label>Department of Obstetrics and Gynaecology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 119077, Singapore</aff><aff id=\"af8-ijms-21-05503\"><label>8</label>Life & Medical Sciences-Institute (LIMES), University of Bonn, 53115 Bonn, Germany</aff><author-notes><corresp id=\"c1-ijms-21-05503\"><label>*</label>Correspondence: <email>[email protected]</email>; Tel.: +49-228-287-16342; Fax: +49-228-287-19757</corresp><fn id=\"fn1-ijms-21-05503\"><label>†</label><p>These authors contributed equally to this work.</p></fn></author-notes><pub-date pub-type=\"epub\"><day>31</day><month>7</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><month>8</month><year>2020</year></pub-date><volume>21</volume><issue>15</issue><elocation-id>5503</elocation-id><history><date date-type=\"received\"><day>08</day><month>7</month><year>2020</year></date><date date-type=\"accepted\"><day>30</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>© 2020 by the authors.</copyright-statement><copyright-year>2020</copyright-year><license license-type=\"open-access\"><license-p>Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>).</license-p></license></permissions><abstract><p>Activating mutations in the human KIT receptor is known to drive severe hematopoietic disorders and tumor formation spanning various entities. The most common mutation is the substitution of aspartic acid at position 816 to valine (D816V), rendering the receptor constitutively active independent of ligand binding. As the role of the KIT receptor in placental signaling cascades is poorly understood, we analyzed the impact of KIT<sup>D816V</sup> expression on placental development using a humanized mouse model. Placentas from KIT<sup>D816V</sup> animals present with a grossly changed morphology, displaying a reduction in labyrinth and spongiotrophoblast layer and an increase in the Parietal Trophoblast Giant Cell (P-TGC) layer. Elevated differentiation to P-TGCs was accompanied with reduced differentiation to other Trophoblast Giant Cell (TGC) subtypes and by severe decrease in proliferation. The embryos display growth retardation and die in utero. KIT<sup>D816V</sup>-trophoblast stem cells (TSC) differentiate much faster compared to wild type (WT) controls. In undifferentiated KIT<sup>D816V</sup>-TSCs, levels of Phosphorylated Extracellular-signal Regulated Kinase (P-ERK) and Phosphorylated Protein Kinase B (P-AKT) are comparable to wildtype cultures differentiating for 3–6 days. Accordingly, P-TGC markers Placental Lactogen 1 (PL1) and Proliferin (PLF) are upregulated as well. The results reveal that KIT signaling orchestrates the fine-tuned differentiation of the placenta, with special emphasis on P-TGC differentiation. Appropriate control of KIT receptor action is therefore essential for placental development and nourishment of the embryo.</p></abstract><kwd-group><kwd>KIT receptor</kwd><kwd>KITD816V</kwd><kwd>placental development</kwd><kwd>premature differentiation</kwd><kwd>trophoblast stem cell</kwd><kwd>trophoblast giant cell</kwd><kwd>spongiotrophoblast</kwd><kwd>invasion</kwd><kwd>embryonic growth retardation</kwd></kwd-group></article-meta></front><body><sec sec-type=\"intro\" id=\"sec1-ijms-21-05503\"><title>1. Introduction</title><p>Proper function of the placenta is essential for the development of the embryo as it is responsible for exchanging gases, nutrients, and waste products between the mother and the fetus. Placental insufficiency can result in adverse effects for the embryo including intrauterine growth retardation, embryonic defects, or even fatal outcome. Therefore, placenta development and differentiation are closely regulated [<xref rid=\"B1-ijms-21-05503\" ref-type=\"bibr\">1</xref>,<xref rid=\"B2-ijms-21-05503\" ref-type=\"bibr\">2</xref>,<xref rid=\"B3-ijms-21-05503\" ref-type=\"bibr\">3</xref>].</p><p>The membrane-bound tyrosine receptor kinase KIT is activated by its ligand stem cell factor (SCF), which causes dimerization of the receptor followed by phosphorylation of its tyrosine residues [<xref rid=\"B4-ijms-21-05503\" ref-type=\"bibr\">4</xref>,<xref rid=\"B5-ijms-21-05503\" ref-type=\"bibr\">5</xref>,<xref rid=\"B6-ijms-21-05503\" ref-type=\"bibr\">6</xref>]. Activation of KIT results in induction of various downstream signaling cascades such as the Mitogen-Activated Protein Kinase (MAPK)/Extracellular-Signal Regulated Kinase (ERK) and Janus Kinase (JAK)/Signal Transducers and Activators of Transcription (STAT) pathways which orchestrate cell proliferation, angiogenesis, cell migration, and cell cycle control [<xref rid=\"B7-ijms-21-05503\" ref-type=\"bibr\">7</xref>,<xref rid=\"B8-ijms-21-05503\" ref-type=\"bibr\">8</xref>,<xref rid=\"B9-ijms-21-05503\" ref-type=\"bibr\">9</xref>,<xref rid=\"B10-ijms-21-05503\" ref-type=\"bibr\">10</xref>]. During placental development, the KIT protein is detected in the uterine epithelium as well as the maternal and fetal part of the placenta. Importantly, KIT expression has not been detected in uteri of nonpregnant females, indicating a pregnancy-related role of KIT signaling [<xref rid=\"B11-ijms-21-05503\" ref-type=\"bibr\">11</xref>,<xref rid=\"B12-ijms-21-05503\" ref-type=\"bibr\">12</xref>,<xref rid=\"B13-ijms-21-05503\" ref-type=\"bibr\">13</xref>]. In detail, starting at E9, KIT and SCF are detectable in trophoblast-chorion, ectoplacental cone, and decidua [<xref rid=\"B12-ijms-21-05503\" ref-type=\"bibr\">12</xref>]. Placental hematopoietic activity begins around mid-gestation. Hematopoietic stem cells (HSC) are detected in the placenta at E11, peak in numbers at E12 to E13, and disappear thereafter [<xref rid=\"B13-ijms-21-05503\" ref-type=\"bibr\">13</xref>,<xref rid=\"B14-ijms-21-05503\" ref-type=\"bibr\">14</xref>]. These HSC were shown to be KIT positive, whereas endothelial cells surrounding the HSC niches are SCF positive [<xref rid=\"B13-ijms-21-05503\" ref-type=\"bibr\">13</xref>,<xref rid=\"B15-ijms-21-05503\" ref-type=\"bibr\">15</xref>]. At E12, KIT can also be found in mesenchymal cells of the chorionic plate and of the labyrinth, in endothelial cells lining the vessels, as well as in few trophoblast giant cells (TGC) [<xref rid=\"B13-ijms-21-05503\" ref-type=\"bibr\">13</xref>]. At E14.5, KIT is mainly expressed in maternal decidua cells as well as the labyrinth whereas SCF is found only in labyrinth [<xref rid=\"B11-ijms-21-05503\" ref-type=\"bibr\">11</xref>,<xref rid=\"B12-ijms-21-05503\" ref-type=\"bibr\">12</xref>]. KIT is restricted to labyrinthine trophoblast cells exposed to maternal blood at E19 and is no longer detected in endothelial or mesenchymal cells [<xref rid=\"B11-ijms-21-05503\" ref-type=\"bibr\">11</xref>].</p><p>The KIT receptor has been implicated with several disorders such as tumor formation, hematopoietic disorders, and systemic mastocytosis [<xref rid=\"B16-ijms-21-05503\" ref-type=\"bibr\">16</xref>,<xref rid=\"B17-ijms-21-05503\" ref-type=\"bibr\">17</xref>,<xref rid=\"B18-ijms-21-05503\" ref-type=\"bibr\">18</xref>,<xref rid=\"B19-ijms-21-05503\" ref-type=\"bibr\">19</xref>,<xref rid=\"B20-ijms-21-05503\" ref-type=\"bibr\">20</xref>]. The most common activating mutation in the human KIT receptor encodes for the substitution of the aspartic acid at position 816 with a valine (D816V) causing constitutive phosphorylation of the protein [<xref rid=\"B17-ijms-21-05503\" ref-type=\"bibr\">17</xref>,<xref rid=\"B21-ijms-21-05503\" ref-type=\"bibr\">21</xref>,<xref rid=\"B22-ijms-21-05503\" ref-type=\"bibr\">22</xref>]. Depletion of KIT receptor results in various defects spanning from hematopoietic failure, macrocytic anemia, pigmentation deficiency, and sterility to intestinal dysfunction [<xref rid=\"B23-ijms-21-05503\" ref-type=\"bibr\">23</xref>,<xref rid=\"B24-ijms-21-05503\" ref-type=\"bibr\">24</xref>,<xref rid=\"B25-ijms-21-05503\" ref-type=\"bibr\">25</xref>,<xref rid=\"B26-ijms-21-05503\" ref-type=\"bibr\">26</xref>]. Mice carrying homozygous KIT<sup>W/W</sup> mutation die pre- or perinatally; however, can be rescued by microinjection of wildtype fetal liver hematopoietic cells into placentas of affected fetuses [<xref rid=\"B23-ijms-21-05503\" ref-type=\"bibr\">23</xref>,<xref rid=\"B24-ijms-21-05503\" ref-type=\"bibr\">24</xref>,<xref rid=\"B26-ijms-21-05503\" ref-type=\"bibr\">26</xref>]. Introduction of the viable c-Kit-deficient mouse line allowed for studying the loss of KIT expression in the adult mouse and showed that KIT is essential for adult lymphopoiesis in bone marrow and thymus [<xref rid=\"B26-ijms-21-05503\" ref-type=\"bibr\">26</xref>]</p><p>Previously, we reported the generation of a humanized KIT<sup>D816V</sup> mouse model [<xref rid=\"B27-ijms-21-05503\" ref-type=\"bibr\">27</xref>]. The transgenic KIT receptor consists of murine extracellular and transmembrane domains and the human intracellular domain carrying the D816V mutation. The chimeric KIT receptor is fused to a green fluorescent protein (GFP) and integrated in the ROSA26 locus, allowing for Cre-mediated conditional expression of the protein under control of the endogenous ROSA26 promoter [<xref rid=\"B27-ijms-21-05503\" ref-type=\"bibr\">27</xref>,<xref rid=\"B28-ijms-21-05503\" ref-type=\"bibr\">28</xref>]. We then examined KIT signaling in fetal liver erythropoiesis and adult hematopoiesis [<xref rid=\"B27-ijms-21-05503\" ref-type=\"bibr\">27</xref>,<xref rid=\"B29-ijms-21-05503\" ref-type=\"bibr\">29</xref>]. Constitutively active KIT resulted in inhibition of terminal differentiation of erythroid precursors and embryonic death after embryonic day (E) 13.5 [<xref rid=\"B27-ijms-21-05503\" ref-type=\"bibr\">27</xref>]. In adult mice, expression of KIT<sup>D816V</sup> led to a polycythemia vera-like myeloproliferative neoplasm with highly increased red blood cells and splenomegaly [<xref rid=\"B29-ijms-21-05503\" ref-type=\"bibr\">29</xref>].</p><p>While the role of KIT signaling in other tissues is well understood, its role in placental development, however, has so far not been described. Here, we examine the effect of the constitutively active KIT receptor using the KIT<sup>D816V</sup> mouse model. Interestingly, expression of KIT<sup>D816V</sup> resulted in decreased proliferation of trophoblast cells. As a consequence, placental structure was affected. The labyrinth layer was decreased accompanied by increased differentiation to parietal (P-)TGCs, whereas other TGC subtypes remained underrepresented. Trophoblast Stem Cells (TSC) derived from KIT<sup>D816V</sup> blastocysts showed altered activation of signaling pathways upon differentiation as well as an increased invasive capacity. Due to these effects, the placenta is not able to sustain regular development and the embryos show severe growth retardation and die in utero.</p></sec><sec sec-type=\"results\" id=\"sec2-ijms-21-05503\"><title>2. Results</title><sec id=\"sec2dot1-ijms-21-05503\"><title>2.1. Embryos and Placentas Carrying KIT<sup>D816V</sup> Mutation Suffer from Severe Growth Retardation</title><p>Previously, we generated the R26-LSL-KIT<sup>D816V</sup> mouse line, which allows for conditional Cre-induced expression of chimeric KIT<sup>D816V</sup> receptor and a GFP reporter driven by the ROSA26 promoter. The KIT<sup>D816V</sup> cDNA is linked to the GFP cDNA via the coding sequence for a viral 2A peptide [<xref rid=\"B27-ijms-21-05503\" ref-type=\"bibr\">27</xref>]. Here, mice carrying the ROSA26-KIT<sup>D816V</sup>-GFP transgene were mated with Deleter-Cre mice, inducing ubiquitous expression of the transgenic receptor in the embryonic as well as the extra-embryonic tissue. Presence of ROSA26-KIT<sup>D816V</sup> and Cre transgenes was verified by genotyping PCR using genomic DNA obtained from yolk sac or embryo (<xref ref-type=\"app\" rid=\"app1-ijms-21-05503\">Supplementary Materials Figure S1A</xref>). Animals/placentas harboring both the Cre- and the ROSA26-KIT<sup>D816V</sup> allele are further referred to as KIT<sup>D816V</sup> animals/placentas. Presence of transgenes in KIT<sup>D816V</sup> animals was further validated by analyzing RNA expression and protein levels in KIT<sup>D816V</sup> placentas (<xref ref-type=\"fig\" rid=\"ijms-21-05503-f001\">Figure 1</xref>A–C). Of note, in KIT<sup>D816V</sup> animals expressing human <italic>KIT<sup>D816V</sup></italic>, the expression of murine <italic>Kit</italic> remained unchanged, showing that endogenous <italic>Kit</italic> expression is not affected by transgene induction (<xref ref-type=\"fig\" rid=\"ijms-21-05503-f001\">Figure 1</xref>A,B). As expected, KIT as well as 2A-peptide proteins were present in KIT<sup>D816V</sup> placentas only (<xref ref-type=\"fig\" rid=\"ijms-21-05503-f001\">Figure 1</xref>C). For further analyses, embryos and placentas were obtained on E9.5–E11.5 from KIT<sup>D816V</sup> and wildtype (WT) animals. KIT<sup>D816V</sup> embryos and placentas dissected on E11.5 showed growth retardation and developmental delay (<xref ref-type=\"fig\" rid=\"ijms-21-05503-f001\">Figure 1</xref>D). We previously reported that KIT<sup>D816V</sup> expression restricted to the embryo proper leads to disturbance of the hematopoietic system and that such animals die at E14.5. Therefore, we hypothesize that severe growth retardation observed in KIT<sup>D816V</sup> animals at 11.5 is an effect of placental insufficiency. Of note, KIT<sup>D816V</sup> embryo and placentas showed GFP positivity (<xref ref-type=\"fig\" rid=\"ijms-21-05503-f001\">Figure 1</xref>D).</p></sec><sec id=\"sec2dot2-ijms-21-05503\"><title>2.2. KIT<sup>D816V</sup> Placentas Show Fewer Proliferating Cells</title><p>Next, we examined whether reduced sizes in KIT<sup>D816V</sup> placentas occurred due to diminished proliferation. Proliferation in placental tissue was assessed by immunohistochemical staining for KI-67—a protein that is present in proliferating cells as well as cells undergoing endoreduplication [<xref rid=\"B30-ijms-21-05503\" ref-type=\"bibr\">30</xref>,<xref rid=\"B31-ijms-21-05503\" ref-type=\"bibr\">31</xref>]. At both timepoints analyzed, KI-67-positive cells were mainly detectable in the labyrinth and spongiotrophoblast layers of KIT<sup>D816V</sup> and WT placentas. On day E9.5, the labyrinth layer and overall amount of KI-67-positive cells were markedly diminished in KIT<sup>D816V</sup> in comparison to WT (<xref ref-type=\"fig\" rid=\"ijms-21-05503-f002\">Figure 2</xref>A,B). While the labyrinth and spongiotrophoblast layers were enlarged on day E10.5 in comparison to E9.5 in both samples, less KI-67-positive cells were detected in KIT<sup>D816V</sup> placentas than in WT placentas (<xref ref-type=\"fig\" rid=\"ijms-21-05503-f002\">Figure 2</xref>A,B). KI-67-positive P-TGCs can be detected in both KIT<sup>D816V</sup> and WT placentas. These results suggest that the smaller size of KIT<sup>D816V</sup> placentas results from decreased proliferation in the labyrinth and spongiotrophoblast.</p></sec><sec id=\"sec2dot3-ijms-21-05503\"><title>2.3. KIT<sup>D816V</sup> Placentas Show Reduced Labyrinth Layer and Disrupted Formation of Vasculature</title><p>To assess the placental structure, hematoxylin and eosin (H&E) staining was performed on paraffin sections of KIT<sup>D816V</sup> and WT placentas. Starting from day E9.5 in KIT<sup>D816V</sup> placentas, H&E stained sections and quantification of the area showed a reduction in labyrinth size (<xref ref-type=\"fig\" rid=\"ijms-21-05503-f003\">Figure 3</xref>A,B). Also, the spongiotrophoblast layer was slightly reduced (<xref ref-type=\"fig\" rid=\"ijms-21-05503-f003\">Figure 3</xref>A,B). Interestingly, at E10.5, the layer of P-TGCs was increased in KIT<sup>D816V</sup> placentas (<xref ref-type=\"fig\" rid=\"ijms-21-05503-f003\">Figure 3</xref>A,B). Quantification of P-TGCs within this area confirmed a significantly higher total number of P-TGCs in KIT<sup>D816V</sup> sample (<xref ref-type=\"fig\" rid=\"ijms-21-05503-f003\">Figure 3</xref>C). Next, RNA was isolated from KIT<sup>D816V</sup> and WT placentas on days E10.5 and E11.5. Expression of labyrinth TGC markers Placental Lactogen 2 (<italic>Pl2)</italic>, Cathepsin q (<italic>Ctsq)</italic>, and Glial Cells Missing Homolog 1 (<italic>Gcm1)</italic> is diminished in comparison to WT placentas (<xref ref-type=\"fig\" rid=\"ijms-21-05503-f003\">Figure 3</xref>D). This data indicates that the number of <italic>Pl2</italic><sup>+</sup> and <italic>Ctsq</italic><sup>+</sup> sinusoidal (S-)TGC and the number of <italic>Gcm 1</italic><sup>+</sup> labyrinthine cells is reduced.</p><p>Labyrinthine architecture was further examined by performing anti-CD31 staining. CD31 is expressed in fetal endothelial cells [<xref rid=\"B32-ijms-21-05503\" ref-type=\"bibr\">32</xref>]. The staining revealed that at E9.5 there are fewer CD31-positive cells present in KIT<sup>D816V</sup> placentas in contrast to WT placentas (<xref ref-type=\"fig\" rid=\"ijms-21-05503-f003\">Figure 3</xref>E). CD31-positive cells are indicated by red arrowheads. While the vasculature was established from E9.5 to E10.5 in KIT<sup>D816V</sup> as well as WT placentas, the capillary network in KIT<sup>D816V</sup> remained less prominent than in WT tissue. S-TGCs, indicated by black arrowheads, were lining maternal sinusoids (red asterisks) in both conditions. Surprisingly, maternal blood was detected in between P-TGCs of KIT<sup>D816V</sup> placentas at E10.5, suggesting a disruption of the developing vascular structure (<xref ref-type=\"fig\" rid=\"ijms-21-05503-f003\">Figure 3</xref>E).</p></sec><sec id=\"sec2dot4-ijms-21-05503\"><title>2.4. KIT<sup>D816V</sup> Placentas Show Prominent Differentiation into P-TGCs</title><p>Since the number of P-TGCs is significantly increased in KIT<sup>D816V</sup> placentas (<xref ref-type=\"fig\" rid=\"ijms-21-05503-f003\">Figure 3</xref>C), we next investigated this TGC subtype in more detail by detecting the P-TGC markers PL1 and PLF using in situ hybridization. At E9.5, the number and distribution of cells positive for PL1 and PLF appeared unaltered in KIT<sup>D816V</sup> compared to WT placentas (<xref ref-type=\"fig\" rid=\"ijms-21-05503-f004\">Figure 4</xref>A). At E10.5, however, the number of PL1<sup>+</sup>/PLF<sup>+</sup> cells was visibly higher in KIT<sup>D816V</sup> placentas than WT placentas (<xref ref-type=\"fig\" rid=\"ijms-21-05503-f004\">Figure 4</xref>B). Further, when comparing PL1 and PLF staining in WT placentas, we observed areas that are PL1 negative but PLF positive (<xref ref-type=\"fig\" rid=\"ijms-21-05503-f004\">Figure 4</xref>B). Such cells are not P-TGCs but rather invading Spiral Artery (SpA-)TGCs (black arrowhead). We assume that the cell clusters indicated by red arrowheads are canal (C-) TGCs (<xref ref-type=\"fig\" rid=\"ijms-21-05503-f004\">Figure 4</xref>B). By contrast, PL1<sup>−</sup>/PLF<sup>+</sup> cells are not detected in KIT<sup>D816V</sup> placentas, further suggesting a severe reduction in specific TGC subtypes such as SpA-TGC and C-TGC.</p></sec><sec id=\"sec2dot5-ijms-21-05503\"><title>2.5. Spongiotrophoblast Cells and Glycogen Trophoblasts are Reduced in KIT<sup>D816V</sup> Placentas</title><p>Next, we analyzed the development of the spongiotrophoblast layer in more detail. In situ hybridization revealed that the spongiotrophoblast marker Trophoblast Specific Protein Alpha (TPBPA) is hardly present in KIT<sup>D816V</sup> placentas compared to WT placentas at both timepoints analyzed (<xref ref-type=\"app\" rid=\"app1-ijms-21-05503\">Supplementary Materials Figure S1B</xref>). Also, expression of <italic>Tpbpa</italic> was significantly reduced at E10.5 and E11.5. Further, we investigated the expression of Gap Junction Beta 3-protein (<italic>Gjb3)</italic> and Procadherin 12 (<italic>Pcdh12)</italic>, both markers for glycogen trophoblasts (GlyT). They were significantly decreased in KIT<sup>D816V</sup> placentas on days E10.5 and E11.5 (<xref ref-type=\"app\" rid=\"app1-ijms-21-05503\">Supplementary Materials Figure S1C–E</xref>). Thus, these results suggest that expression of KIT<sup>D816V</sup> not only blocks cellular proliferation but also heavily impinges on the fine-balanced differentiation of the various subtypes essential for placental proper development. The differentiation to P-TGCs seems increased at the expense of other TGC subtypes such as C-TGCs and SpA-TGCs as well as spongiotrophoblast and GlyT cells.</p></sec><sec id=\"sec2dot6-ijms-21-05503\"><title>2.6. KIT<sup>D816V</sup>-TSC Show Signs of Premature Differentiation</title><p>In order to analyze the molecular effect of KIT<sup>D816V</sup> on a cellular level in more detail, TSCs were derived from E3.5 blastocysts obtained from R26-LSL-KIT<sup>D816V</sup> and Deleter-Cre mice according to published procedures [<xref rid=\"B27-ijms-21-05503\" ref-type=\"bibr\">27</xref>,<xref rid=\"B33-ijms-21-05503\" ref-type=\"bibr\">33</xref>]. Genotyping at passage five was used to distinguish between KIT<sup>D816V</sup>- and WT-TSC lines (<xref ref-type=\"app\" rid=\"app1-ijms-21-05503\">Supplementary Materials Figure S2A</xref>). Ultimately, we had established two lines of KIT<sup>D816V</sup>-TSC which were named KIT<sup>D816V</sup> #3 and KIT<sup>D816V</sup> #4. In these two lines, we were able to detect a GFP-signal using fluorescence-activated cell sorting (FACS) (<xref ref-type=\"app\" rid=\"app1-ijms-21-05503\">Supplementary Materials Figure S2B</xref>) and expression of the human <italic>KIT</italic> transgene using qRT-PCR (<xref ref-type=\"app\" rid=\"app1-ijms-21-05503\">Supplementary Materials Figure S2C</xref>), demonstrating that the ROSA26-GFP-2A-KIT<sup>D816</sup> allele is functional in TSC culture in vitro. As in placental tissues, the level of endogenous murine <italic>Kit</italic> expression in KIT<sup>D816V</sup>-TSC is not affected and remains comparable to that of WT TSC (<xref ref-type=\"app\" rid=\"app1-ijms-21-05503\">Supplementary Materials Figure S2D</xref>).</p><p>Analysis of TSC-markers Transcription Factor AP-2 Gamma (Tfap2c), Caudal Type Homeobox 2 (Cdx2), and Eomesodermin homolog (Eomes) revealed that neither expression (<xref ref-type=\"app\" rid=\"app1-ijms-21-05503\">Supplementary Materials Figure S2E–G</xref>) nor protein levels and distribution of TFAP2C, CDX2, and EOMES (<xref ref-type=\"app\" rid=\"app1-ijms-21-05503\">Supplementary Materials Figure S2H</xref>) are affected in the KIT<sup>D816V</sup>-TSC lines. Also, morphology as well as splitting ratio and frequency of KIT<sup>D816V</sup>-TSC did not differ from WT TSC. Hence, we conclude that establishment, maintenance, and self-renewal of TSC is not altered by expression of the KIT<sup>D816V</sup> transgene.</p><p>To evaluate whether KIT<sup>D816V</sup>-TSC displays alterations in differentiation in vitro, WT and KIT<sup>D816V</sup> lines were kept under differentiating conditions in trophoblast stem (TS) cell medium without conditioned medium (CM), Fibroblast Growth Factor (FGF) 4, and heparin for 9 days. Morphological analyses show no difference between studied lines. In both KIT<sup>D816V</sup>-TSC and WT TSC, TGCs appeared in culture after three days (<xref ref-type=\"fig\" rid=\"ijms-21-05503-f005\">Figure 5</xref>A). RNA was isolated on days 0 and 6 and was analyzed for expression of P-TGC markers <italic>Pl1</italic>, <italic>Pl2</italic>, and <italic>Plf</italic>. Interestingly, in KIT<sup>D816V</sup>-TSCs, all markers showed an increased level already at day 0, an effect which persisted to day 6 (<xref ref-type=\"fig\" rid=\"ijms-21-05503-f005\">Figure 5</xref>B). Further, spongiotrophoblast marker <italic>Tpbpa</italic> as well as labyrinth and S-TGC marker <italic>Ctsq</italic> and GlyT markers <italic>Gjb3</italic> and <italic>Pcdh12</italic> were expressed at lower levels in KIT<sup>D816V</sup> TSC than in WT TSC (<xref ref-type=\"app\" rid=\"app1-ijms-21-05503\">Supplementary Materials Figure S2I</xref>). Upregulation of <italic>Tfap2c</italic> which is detected during TSC differentiation was higher in KIT<sup>D816V</sup>-TSC than in WT TSC [<xref rid=\"B34-ijms-21-05503\" ref-type=\"bibr\">34</xref>] (<xref ref-type=\"app\" rid=\"app1-ijms-21-05503\">Supplementary Materials Figure S2I</xref>). Of note, <italic>Tfap2c</italic> expression was already increased under stem cell culture conditions in KIT<sup>D816V</sup>-TSC. <italic>Hand1</italic> is involved in mediation of TGC differentiation and is expressed in the ectoplacental cone [<xref rid=\"B35-ijms-21-05503\" ref-type=\"bibr\">35</xref>]. In KIT<sup>D816V</sup>-TSC it is significantly upregulated in comparison to WT-TSC after 6 days of differentiation (<xref ref-type=\"app\" rid=\"app1-ijms-21-05503\">Supplementary Materials Figure S2I</xref>). Finally, we and others had previously shown that <italic>Gata2</italic> expression was upregulated upon KIT signaling in cells of the hematopoietic system [<xref rid=\"B27-ijms-21-05503\" ref-type=\"bibr\">27</xref>,<xref rid=\"B36-ijms-21-05503\" ref-type=\"bibr\">36</xref>]. Here, in KIT<sup>D816V</sup> TSC, we detected a significant increase of <italic>Gata2</italic> expression after six days of differentiation whereas <italic>Gata2</italic> expression levels remained constant in WT TSC (<xref ref-type=\"app\" rid=\"app1-ijms-21-05503\">Supplementary Materials Figure S2I</xref>). These results demonstrate that expression of spongiotrophoblast, labyrinth, and glycogen trophoblasts is underrepresented in KIT<sup>D816V</sup> TSC upon differentiation consistent with data obtained from in vivo samples. Also, expression of KIT<sup>D816V</sup> leads to an upregulation of P-TGC markers and <italic>Tfap2c</italic> already existing in TSC culture, which leads to premature and skewed induction of differentiation of TSCs, which is not morphologically apparent.</p></sec><sec id=\"sec2dot7-ijms-21-05503\"><title>2.7. Signaling Cascades and Invasion Capability are Affected in KIT<sup>D816V</sup>-TSC</title><p>Previously, we had shown that, in the hematopoietic system, expression of KIT<sup>D816V</sup> leads to a block of differentiation of erythroblasts and a continuation of proliferation of precursor cells. There, we had demonstrated that KIT<sup>D816V</sup> leads to induction of MAPK signaling as well as diminished AKT activation [<xref rid=\"B27-ijms-21-05503\" ref-type=\"bibr\">27</xref>]. Hence, we used KIT<sup>D816V</sup>-TSC to examine the activation of key signaling pathways during 9 days of TSC differentiation (<xref ref-type=\"fig\" rid=\"ijms-21-05503-f005\">Figure 5</xref>C). On day 0, levels of phosphorylated (P-) ERK and P-AKT in KIT<sup>D816V</sup> TSC appeared reduced and comparable to day 3/6 of differentiating WT-TSC. Of note, on day 0, levels of P-STAT3 are comparable but become upregulated over the course of differentiation in KIT<sup>D816V</sup>-TSC (<xref ref-type=\"fig\" rid=\"ijms-21-05503-f005\">Figure 5</xref>C). This suggests that chronic activation of the KIT signaling cascade in KIT<sup>D816V</sup>-TSC results in diminished levels of P-ERK and P-AKT, priming the cells for rapid and premature differentiation.</p><p>Among their various functions such as guiding the attachment of the blastocyst and secretion of essential hormones and proteins, TGCs are also capable of invading into uterine tissue to establish the vital connection to the maternal blood vessels [<xref rid=\"B31-ijms-21-05503\" ref-type=\"bibr\">31</xref>,<xref rid=\"B37-ijms-21-05503\" ref-type=\"bibr\">37</xref>]. Finally, we analyzed the invasive capacity of KIT<sup>D816V</sup>-TGCs in comparison to WT-TGCs by using a Transwell migration assay. Previously, it was shown that higher Matrigel concentrations result in preselection of TGCs as non-TGCs do not invade through thicker Matrigel layers [<xref rid=\"B38-ijms-21-05503\" ref-type=\"bibr\">38</xref>]. Also, lower cell densities led to increased invasion [<xref rid=\"B38-ijms-21-05503\" ref-type=\"bibr\">38</xref>]. Hence, we seeded 2 × 10<sup>4</sup> cells on a layer of 0.8 mg/mL Matrigel and omitted FGF4, heparin, and CM from culture medium to induce differentiation. After five days, Hoechst staining showed the presence of nuclei resembling TGCs in size and structure, indicating successful invasion (<xref ref-type=\"fig\" rid=\"ijms-21-05503-f005\">Figure 5</xref>D). Quantification of two independent experiments revealed that a significantly higher number of KIT<sup>D816V</sup>-TGCs had migrated through the Matrigel and the pores (<xref ref-type=\"fig\" rid=\"ijms-21-05503-f005\">Figure 5</xref>E). Thus, KIT<sup>D816V</sup> signaling in trophoblast cells seems to result in a higher portion of invasive cells. This might be due to the fact that KIT<sup>D816V</sup>-TSC are much faster in inducing differentiation and gain migratory capabilities earlier than WT-TSC.</p></sec></sec><sec sec-type=\"discussion\" id=\"sec3-ijms-21-05503\"><title>3. Discussion</title><p>Here, we show several effects of KIT<sup>D816V</sup> on murine placental development. These findings comprise aberrant placental structure and increased differentiation into P-TGC subtypes, whereas other TGC subtypes such as SpA-TGCs, S-TGC, and C-TGCs remain underrepresented. Since TGC variety is essential for placental development, diminished levels of SpA-TGCs may lead to impaired establishment of blood flow at the implantation site by not properly formed blood vessels, lack of dilating spiral arteries, and insufficient secretion of vasodilators and other angiogenic factors [<xref rid=\"B31-ijms-21-05503\" ref-type=\"bibr\">31</xref>,<xref rid=\"B37-ijms-21-05503\" ref-type=\"bibr\">37</xref>]. C-TGCs contribute to formation of the vessels delivering maternal blood to the labyrinth [<xref rid=\"B39-ijms-21-05503\" ref-type=\"bibr\">39</xref>]. Loss of these highly specialized cell types affects invasion, exchange of nutrients and gases, as well as hormone secretion, resulting in decreased functionality of the placenta. Moreover, proliferation in KIT<sup>D816V</sup> placentas ceases at E10.5 when KI-67-positive cells cannot be detected anymore. At that timepoint, the embryo already shows severe growth retardation. Although we cannot exclude additive embryonic effects due to KIT<sup>D816V</sup> expression, we suspect this phenotype to result from malnourishment due to placental failure.</p><p>While in other cell types activation of KIT resulted in induction of proliferation and impairment of differentiation [<xref rid=\"B40-ijms-21-05503\" ref-type=\"bibr\">40</xref>], trophoblast and placenta development are hallmarked by reduced proliferation in combination with increased and skewed differentiation. In cells of the hematopoietic system, it had previously been shown that KIT signaling results in upregulation of c-Myc, c-Myb, and Gata2 [<xref rid=\"B27-ijms-21-05503\" ref-type=\"bibr\">27</xref>,<xref rid=\"B36-ijms-21-05503\" ref-type=\"bibr\">36</xref>]. Interestingly, in trophoblast cells, Gata2 binds to and transactivates expression of Pl1. Further, Gata2 level was also shown to be correlated with Plf expression [<xref rid=\"B41-ijms-21-05503\" ref-type=\"bibr\">41</xref>,<xref rid=\"B42-ijms-21-05503\" ref-type=\"bibr\">42</xref>]. In this study, KIT<sup>D816V</sup>-TSC showed a significant increase in <italic>Gata2</italic> expression upon differentiation. Therefore, we speculate that, also in trophoblast cells, KIT signaling induces Gata2 which in turn transactivates Pl1 and Plf. This would lead to a rapid induction of differentiation, which is accompanied by exiting from the cell cycle. It also explains the high number of PL1<sup>+</sup>/PLF<sup>+</sup> cells observed in KIT<sup>D816V</sup> placentas. In the context of premature differentiation, it is interesting to note that we were able to generate self-renewing TSC lines. The established TSC did not show any aberrant overall growth parameters and ease of handling. However, the fact that, at day 0 of differentiation, P-ERK and P-AKT levels were comparable to day 3 of regular differentiation together with the already upregulated markers for TGC-differentiation strongly suggests that the growth conditions (FGF4, Heparin, and CM) are able to override the KIT-mediated signals leading to differentiation. This helps to explain the phenotype observed in KIT<sup>D816V</sup>-placentas. During pre- and early post-implantation development, trophectoderm and ectoplacental cone cells rely on FGF4-induced signaling cascades leading to expression of trophectoderm and TSC master regulators Cdx2, Tfap2c, and Elf5 [<xref rid=\"B43-ijms-21-05503\" ref-type=\"bibr\">43</xref>,<xref rid=\"B44-ijms-21-05503\" ref-type=\"bibr\">44</xref>,<xref rid=\"B45-ijms-21-05503\" ref-type=\"bibr\">45</xref>]. This protects the cells from differentiation inducing the effect of KIT signaling. Post implantation, when the different layers of the placenta are laid down, FGF signaling and expression of the marker genes decline. Since the KIT signaling cascade is already in place and active (as hallmarked by upregulation of <italic>Pl1</italic> and <italic>Plf</italic> at day 0 of TSC differentiation), a premature differentiation is induced, resulting in a smaller and disorganized placenta.</p><p>Interestingly, the placental alterations reported here are phenocopied in mouse models of Suppressor of Cytokine Signaling (SOCS) 3 deficiency as well as administration of retinoic acid (RA) to the mother. Administration of RA was demonstrated to result in loss of proliferation, differentiation to TGCs, and a reduced spongiotrophoblast layer [<xref rid=\"B46-ijms-21-05503\" ref-type=\"bibr\">46</xref>]. Also, TPBPA levels were reduced whereas PL1 levels were increased [<xref rid=\"B46-ijms-21-05503\" ref-type=\"bibr\">46</xref>]. Interestingly, in another study, RA resulted in an increase of invading TGCs [<xref rid=\"B38-ijms-21-05503\" ref-type=\"bibr\">38</xref>]. In our model, chronic KIT activation results in significantly more invasive cells. Of note, RA was shown to promote KIT expression and translation in spermatogonia [<xref rid=\"B47-ijms-21-05503\" ref-type=\"bibr\">47</xref>,<xref rid=\"B48-ijms-21-05503\" ref-type=\"bibr\">48</xref>,<xref rid=\"B49-ijms-21-05503\" ref-type=\"bibr\">49</xref>]. Hence, we speculate that, in trophoblast, KIT acts downstream of RA. In KIT<sup>D816V</sup> mice, RA signaling is active, independent of RA presence (<xref ref-type=\"fig\" rid=\"ijms-21-05503-f006\">Figure 6</xref>).</p><p>In Leukemia Inhibitory Factor (LIF)/JAK/STAT3 signaling, Leukemia Inhibitory Factor (LIF) binds to its receptor and Janus Kinases (JAK) are activated. JAK then phosphorylates Signal Transducers and Activators of Transcription (STAT) 3 in the cytoplasm. Activated STAT3 induces the expression of SOCS3, which then represses LIF in a negative feedback loop [<xref rid=\"B50-ijms-21-05503\" ref-type=\"bibr\">50</xref>]. Augmentation in LIF levels due to SOCS3 deficiency increases TGC differentiation [<xref rid=\"B51-ijms-21-05503\" ref-type=\"bibr\">51</xref>]. SOCS3 deficiency was also reported to result in erythrocytosis and reduced spongiotrophoblast layer. Further, constitutively active STAT3 was observed [<xref rid=\"B52-ijms-21-05503\" ref-type=\"bibr\">52</xref>]. We find increased levels of phosphorylated STAT3 in KIT<sup>D816V</sup>-TSC upon differentiation for 9 days in comparison to WT-TSC. STAT3 also plays a role in cell migration and invasion [<xref rid=\"B53-ijms-21-05503\" ref-type=\"bibr\">53</xref>], both of which we demonstrate to be affected in the KIT<sup>D816V</sup> mouse model. Taken together, we conclude that KIT is also involved in LIF/JAK/STAT3 signaling (<xref ref-type=\"fig\" rid=\"ijms-21-05503-f006\">Figure 6</xref>).</p><p>Here, we demonstrate that KIT<sup>D816V</sup> placentas are severely affected by constitutively active KIT signaling. The results show that, in placenta development, KIT signaling is required for the fine-tuned induction of differentiation. Moreover, we speculate that KIT signaling is essential for P-TGC differentiation since this is the major TGC-type observed in the studies.</p></sec><sec id=\"sec4-ijms-21-05503\"><title>4. Materials and Methods</title><sec id=\"sec4dot1-ijms-21-05503\"><title>4.1. Generation of Transgenic Animals</title><p>All experiments were conducted according to the German law of animal protection and in agreement with the approval of the local institutional animal care committees (Landesamt für Natur, Umwelt und Verbraucherschutz, North Rhine-Westphalia, approval number: 84-02.03.2013/A428 approved date: 31 January 2014). R26-LSL-KIT<sup>D816V</sup> mice described by us were mated with Deleter-Cre mice carrying human cytomegalovirus minimal promoter (CMV) controlled Cre recombinase, thereby allowing for ubiquitous expression of the mutant human KIT receptor [<xref rid=\"B27-ijms-21-05503\" ref-type=\"bibr\">27</xref>,<xref rid=\"B54-ijms-21-05503\" ref-type=\"bibr\">54</xref>]. Both mouse strains were kept on a 129sv/S2 and C57BL/6 genetic background. The morning after mating, female mice were checked for vaginal plugs. In the case of plug detection, noon of that day was considered E0.5. Accordingly, mice were sacrificed 9 to 11 days later around noon.</p></sec><sec id=\"sec4dot2-ijms-21-05503\"><title>4.2. DNA Isolation and Genotyping PCR</title><p>Embryos and placentas were lysed in tissue lysis buffer (50 mM Tris-HCl (Roth, Karlsruhe, Baden-Wuerttemberg, Germany; #9090.1), 100 mM ethylenediaminetetraacetic acid (EDTA) (AppliChem, Darmstadt, Hessen, Germany; #A4975), 100 mM NaCl (AppliChem, Darmstadt, Hessen, Germany; #A1149), 1% (<italic>w</italic>/<italic>v</italic>) SDS (Merck, Darmstadt, Hessen, Germany; #817039), and 10 mg/mL proteinase K (Merck, Darmstadt, Hessen, Germany; #1245680500)), while DNA from adherent cells was obtained using cell lysis buffer (10 mM NaCl (AppliChem, Darmstadt, Hessen, Germany; #A1149), 10 mM Tris, 10 mM EDTA, 0.5% sarcosyl, and 1 mg/mL proteinase K (Merck, Darmstadt, Hessen, Germany)). DNA was precipitated using isopropanol, pelleted by centrifugation, and washed twice with ethanol (80%). The pellet was then resuspended in H<sub>2</sub>O (dd) at 37 °C. Using NanoDrop 1000 (Thermo Fisher Scientific, Waltham, MA, USA), concentration and purity of obtained DNA was measured. Genotyping was performed by polymerase chain reaction (PCR) using PCR primers. Primer sequences are listed in <xref ref-type=\"app\" rid=\"app1-ijms-21-05503\">Supplementary Materials Table S1</xref>.</p></sec><sec id=\"sec4dot3-ijms-21-05503\"><title>4.3. RNA Analysis</title><p>RNA isolation from tissue and cells was carried out using TRIzol<sup>TM</sup> reagent (Invitrogen, Thermo Scientific, Waltham, MA, USA; #15596026). RNA was precipitated using isopropanol and pelleted by centrifugation. After washing with ethanol (75%), pellet was resuspended in diethyl pyrocarbonate (DEPC)-treated water. Using NanoDrop 1000 (Thermo Fisher Scientific, Waltham, MA, USA), concentration and purity of obtained RNA was measured. DNase digestion (Thermo Scientific, Waltham, Massachusetts, USA; #EN0525) and cDNA synthesis (RevertAid Premium, Fermentas, Thermo Scientific, Waltham, MA, USA; #EP0441) were carried out on 1 μg RNA. Quantitative real time PCR (qPCR) was performed using SYBR Green Master Mix (Fermentas, Thermo Scientific, Waltham, MA, USA; #4309155) and ViiA7 (AppliedBiosystems, Life Technologies, Foster City, CA, USA). Primer sequences are listed in <xref ref-type=\"app\" rid=\"app1-ijms-21-05503\">Supplementary Materials Table S3</xref>. Target gene expression was normalized to the housekeeping reference gene <italic>Gapdh</italic>, reactions were performed in triplicate, and <italic>p</italic>-values were calculated using unpaired t-test.</p></sec><sec id=\"sec4dot4-ijms-21-05503\"><title>4.4. Hematoxylin and Eosin Staining</title><p>Paraffin sections were rehydrated and incubated in hematoxylin (Roth, Karlsruhe, Baden-Wuerttemberg, Germany; #T865) for 3 min. Counterstaining with eosin (Roth, Karlsruhe, Baden-Wuerttemberg, Germany; #X883) was performed for 1 min. Sections were then dehydrated, incubated in xylene (VMP Chemie Kontor GmbH, #1000649172), and embedded with Entellan<sup>®</sup> (Merck, Darmstadt, Hessen, Germany; #107960). Analysis of stained sections was performed with a DM LB microscope (Leica, Wetzlar, Hessen, Germany).</p></sec><sec id=\"sec4dot5-ijms-21-05503\"><title>4.5. Western Blotting</title><p>Protein was obtained by lysing tissue or cells in radioimmunoprecipitation assay (RIPA) buffer (NEB, #9806). Protein concentration was measured using Pierce<sup>TM</sup> BCA Protein Assay Kit (Thermo Scientific, Waltham, MA, USA; #23225) on an iMarkTM Microplate Reader (BioRad, Hercules, CA, USA). Proteins were electrophoresed in a polyacrylamide (10%) gel (Roth, Karlsruhe, Baden-Wuerttemberg, Germany; #3029.1) and transferred onto a Roti<sup>®</sup> polyvinylidene fluoride (PVDF) membrane (Roth, Karlsruhe, Baden-Wuerttemberg, Germany; #T830.1). Membranes were blocked, stained with primary antibodies, and horseradish peroxidase (HRP)-coupled secondary antibodies. Antibodies and concentrations are given in <xref ref-type=\"app\" rid=\"app1-ijms-21-05503\">Supplementary Materials Table S2</xref>. Pierce<sup>TM</sup> ECL Western Blotting Substrate (Thermo Scientific, Waltham, MA, USA; #32106) and ChemiDoc<sup>TM</sup>MP (Biorad, Hercules, CA, USA) were used for detection of stained proteins.</p></sec><sec id=\"sec4dot6-ijms-21-05503\"><title>4.6. Immunohistochemical/Immunofluorescence Staining</title><p>For CD31 and KI-67 immunohistochemical staining, paraffin sections were incubated with antigen retrieval buffer (Medac GmbH, Wedel, Schleswig-Holstein, Germany; #PMB1-250) and blocked with peroxide block (Medac GmbH, Wedel, Schleswig-Holstein, Germany; #925B-05). After primary antibody staining, sections were incubated with HRP-coupled secondary antibody, followed by a 3,3′-diaminobenzidine (DAB, Medac GmbH, Wedel, Schleswig-Holstein, Germany; #957D-50) staining for 8 min. Nuclei were stained with hematoxylin (Roth, Karlsruhe, Baden-Wuerttemberg, Germany; #T865) For immunofluorescence staining, cells were fixed in formalin (4%, Merck, Darmstadt, Hessen, Germany; #100496), permeabilized using 0.5% (<italic>v</italic>/<italic>v</italic>) Triton X-100 (AppliChem, Darmstadt, Hessen, Germany; #142314), and blocked in 2% bovine serum albumin (BSA, Sigma-Aldrich, St. Louis, MO, USA) and 0.1% Triton X-100 in phosphate-buffered saline (PBS). Then, cells were stained with primary antibody and Alexa-Fluor-conjugated secondary antibodies. Nuclei were stained with Hoechst (Sigma-Aldrich, St. Louis, MO, USA; #H6024). Antibodies and concentrations are given in <xref ref-type=\"app\" rid=\"app1-ijms-21-05503\">Supplementary Materials Table S2</xref>.</p></sec><sec id=\"sec4dot7-ijms-21-05503\"><title>4.7. In Situ Hybridization</title><p>In situ hybridization was performed using protocol and digoxigenin (DIG)-labeled probes established by Simmons et al. [<xref rid=\"B37-ijms-21-05503\" ref-type=\"bibr\">37</xref>]. In short, cryo- or paraffin sections were thawed or deparaffinized and rehydrated, respectively. Sections were fixed using 4% paraformaldehyde (Merck, Darmstadt, Hessen, Germany; #818715) and treated with proteinase K (20 μg/mL, Merck, Darmstadt, Hessen, Germany; #1245680500). After another fixation step, sections were blocked. Probes (2 ng/μL) for PL1, PLF, and TPBPA were denatured and incubated with sections overnight, followed by a RNase A (AppliChem, Darmstadt, Hessen, Germany; #A2760) treatment. Sections were blocked, and anti-digoxigenin-AP (1:1000, Sigma-Aldrich, St. Louis, MO, USA; #11093274910) was added for probe detection. Incubation with BM-purple followed overnight; then, sections were counterstained with nuclear fast red (Sigma-Aldrich, St. Louis, MO, USA; #N3020). Sections were dehydrated, treated with xylene (VMP Chemie Kontor GmbH, Siegburg, North Rhine-Westfalia, Germany; #1000649172), and mounted in Entellan<sup>®</sup> (Merck, Darmstadt, Hessen, Germany; #107960). Leica DM LB microscope (Wetzlar, Hessen, Germany) was used for analysis of sections.</p></sec><sec id=\"sec4dot8-ijms-21-05503\"><title>4.8. Cell Culture</title><p>KIT<sup>D816V</sup>-TSCs were derived from E3.5 blastocysts, which were obtained after mating of R26-LSL-KIT<sup>D816V</sup> mice with Deleter-Cre mice [<xref rid=\"B33-ijms-21-05503\" ref-type=\"bibr\">33</xref>]. Cre recombinase expression is controlled by human CMV minimal promoter [<xref rid=\"B54-ijms-21-05503\" ref-type=\"bibr\">54</xref>]. Cells were cultured on tissue culture plastic in humidified incubators (Heracell 240i, Thermo Scientific, Waltham, MA, USA) with 7.5% CO<sub>2</sub> at 37 °C. TSC were grown in a trophoblast stem (TS) cell medium supplemented with 70% mouse embryonic fibroblast (MEF) conditioned medium (CM) [<xref rid=\"B45-ijms-21-05503\" ref-type=\"bibr\">45</xref>]. TS medium was supplemented with 25 ng/mL human recombinant FGF4 (Reliatech, Wolfenbüttel, Lower Saxony, Germany; #100-017L) and 1 μg/mL heparin (Sigma-Aldrich, St. Louis, MO, USA; #H3149-10KU).</p></sec><sec id=\"sec4dot9-ijms-21-05503\"><title>4.9. Flow Cytometric Analysis</title><p>Cells were harvested with 0.05% trypsin/EDTA (Gibco, Thermo Scientific, Waltham, MA, USA; #11580626) and filtered through a 40-μm cell strainer (Becton Dickinson, Franklin Lakes, NJ, USA; #352235). After washing with fluorescence-activated cell sorting (FACS) buffer (2% fetal bovine serum (FBS) in PBS (Gibco, Thermo Scientific, Waltham, MA, USA; #11503387), cells were resuspended in FACS buffer containing 1% 7-amino-actinomycin D (AppliChem, Darmstadt, Hessen, Germany; #A7850). Detection of GFP positivity was performed using a FACSCanto (Becton Dickinson, Franklin Lakes, NJ, USA) and analyzed with FlowJo Software (TreeStar, Becton Dickinson, Franklin Lakes, NJ, USA).</p></sec><sec id=\"sec4dot10-ijms-21-05503\"><title>4.10. Invasion Assay</title><p>FluoroBlok™ Cell Culture Inserts with 8 μm pore size (Corning, New York, NY, USA; #351157) were coated using 0.8 mg/mL Matrigel (Sigma-Aldrich, St. Louis, Missouri, USA) and dried for 2 h at 37 °C and then at room temperature (RT) overnight. Matrigel was rehydrated using supplemented RPMI (Gibco, Thermo Scientific, Waltham, MA, USA; #11530586) [<xref rid=\"B38-ijms-21-05503\" ref-type=\"bibr\">38</xref>]. Cells were harvested using 0.05 trypsin/EDTA (Gibco, Thermo Scientific, Waltham, MA, USA; #11580626), counted and reseeded at 2 × 10<sup>4</sup> cells per well, and kept under differentiating conditions (TS-Medium w/o CM, FGF4 and heparin) for five days. Medium in the lower chamber was changed every day. For analysis, Matrigel in upper chamber as well as medium in lower chamber were discarded and filters were washed several times with PBS. Filters were fixed and stained with 1% Hoechst (Sigma-Aldrich, St. Louis, MO, USA; #H6024) in methanol (VWR, Radnor, PA, USA; #20.847.307) for 10 min. After 3 washing steps with PBS, detection of invaded cells was carried out using a Leica DM LB microscope (Wetzlar, Hessen, Germany).</p></sec></sec></body><back><ack><title>Acknowledgments</title><p>We kindly thank Angela Egert, Andrea Jäger, Gaby Beine, and Susanne Steiner for technical assistance. We also thank Marieta Toma and Laura Eßer for assisting with performance of Transwell invasion assays.</p></ack><app-group><app id=\"app1-ijms-21-05503\"><title>Supplementary Materials</title><p>Supplementary materials can be found at <uri xlink:href=\"https://www.mdpi.com/1422-0067/21/15/5503/s1\">https://www.mdpi.com/1422-0067/21/15/5503/s1</uri>. Figure S1: Spongiotrophoblast marker TPBPA reduced in KIT<sup>D816V</sup> placentas, Figure S2: Validation of KIT<sup>D816V</sup>-TSC, Table S1: Genotyping Primers, Table S2: Antibodies, Table S3: qRT-PCR Primers.</p><supplementary-material content-type=\"local-data\" id=\"ijms-21-05503-s001\"><media xlink:href=\"ijms-21-05503-s001.pdf\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material></app></app-group><notes><title>Author Contributions</title><p>Conceptualization, C.K., N.S., and H.S.; formal analysis, F.K., S.J., and D.N.; funding acquisition, H.S.; investigation, F.K., J.H., S.J., and D.N.; methodology, F.K., J.H., S.J., J.L., and H.S.; project administration, F.K., C.K., and N.S.; resources, N.P.; supervision, F.K., N.P., C.K., N.S., and H.S.; validation, F.K., J.K., D.N., and J.L.; writing—original draft, F.K. and H.S.; writing—review and editing, F.K., N.P., C.K., and H.S. All authors have read and agreed to the published version of the manuscript.</p></notes><notes><title>Funding</title><p>This study was financially supported by DFG grant Scho 503 20/1 to H.S.</p></notes><notes notes-type=\"COI-statement\"><title>Conflicts of Interest</title><p>The authors declare no conflict of interest.</p></notes><glossary><title>Abbreviations</title><array orientation=\"portrait\"><tbody><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">AKT</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Protein Kinase B</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">C-TGC</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Canal TGC</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CM</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Conditioned Medium</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ERK</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Extracellular-Signal Regulated Kinase</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">FGF4</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Fibroblast Growth Factor 4</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">FGFR</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Fibroblast Growth Factor Receptor</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">GlyT</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Glycogen Trophoblast</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">HSC</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Hematopoietic Stem Cell</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">JAK</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Janus Kinase</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">LIF</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Leukemia Inhibitory Factor</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">MAPK</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Mitogen-Activated Protein Kinase</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">P-TGC</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Parietal TGC</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">RA</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Retinoic Acid</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">RAR</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Retinoic Acid Receptor</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">RXR</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Retinoic X Receptor</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">S-TGC</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Sinusoidal TGC</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">SOCS3</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Suppressor of Cytokine Signaling 3</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">SpA</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Spiral Artery</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">STAT3</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Signal Transducers and Activators of Transcription</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">STRA</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Stimulated by Retinoic Acid</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">TGC</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Trophoblast Giant Cell</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">TSC</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Trophoblast Stem Celle</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">WT</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Wildtype</td></tr></tbody></array></glossary><ref-list><title>References</title><ref id=\"B1-ijms-21-05503\"><label>1.</label><element-citation publication-type=\"journal\"><person-group 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RNA was obtained from at least three biological repeats. Expression is normalized to the housekeeping gene <italic>Gapdh</italic>. Bars display mean value ± SD. Significance was determined by unpaired t-test and indicated with *** <italic>p</italic> < 0.001. (<bold>C</bold>) Western Blot detecting KIT and 2A-peptide in placentas at E11.5 in comparison to β-ACTIN and (<bold>D</bold>) photographs showing KIT<sup>D816V</sup> and WT expressing embryos and placentas at E11.5 in brightfield (BF) and GFP fluorescence (scale bar: 1 mm): Experiments were performed in at least tree biological repeats.</p></caption><graphic xlink:href=\"ijms-21-05503-g001\"/></fig><fig id=\"ijms-21-05503-f002\" orientation=\"portrait\" position=\"float\"><label>Figure 2</label><caption><p>KIT<sup>D816V</sup> shows a reduced number of KI-67-positive cells. (<bold>A</bold>,<bold>B</bold>) Immunohistochemical staining of paraffin sections of KIT<sup>D816V</sup> and WT placentas for KI-67 at E9.5 and E10.5: Black arrowheads mark Parietal Trophoblast Giant Cells (P-TGCs) undergoing endoreduplication (biological replicates = 4; scale bar represents 500 μm).</p></caption><graphic xlink:href=\"ijms-21-05503-g002\"/></fig><fig id=\"ijms-21-05503-f003\" orientation=\"portrait\" position=\"float\"><label>Figure 3</label><caption><p>Placental structure is grossly affected by KIT<sup>D816V</sup>. (<bold>A</bold>) Photomicrographs of hematoxylin and eosin (H&E) staining of paraffin sections of KIT<sup>D816V</sup> and WT placentas on E9.5 (biological replicates = 2) and E10.5 (biological replicates = 3): Black lines mark areas of the P-TGCs (P), spongiotrophoblast (S), and labyrinth layer (L). Scale bar represents 500 μm. (<bold>B</bold>) Quantification of P-TGC, spongiotrophoblast, and labyrinth areas at E10.5 compared to the total area of respective placenta (biological replicates = 3) and (<bold>C</bold>) quantification of number of P-TGCs in KIT<sup>D816V</sup> and WT placenta at E10.5 using ImageJ in three biological replicates: Significance was determined by unpaired t-test and indicated with * <italic>p</italic> < 0.05. (<bold>D</bold>) qRT-PCR analysis of labyrinth-specific TGC marker <italic>Pl2</italic>, <italic>Ctsq</italic>, and <italic>Gcm</italic> in KIT<sup>D816V</sup> and WT placentas at E10.5 and E11.5. RNA was obtained from at least three biological replicates. Expression is normalized to the housekeeping gene <italic>Gapdh</italic>. Bars display mean value ± SD. Significance was determined by unpaired t-test and indicated with * <italic>p</italic> < 0.05 and ** <italic>p</italic> < 0.01; (<bold>E</bold>) Immunohistochemical staining of CD31 on paraffin sections of KIT<sup>D816V</sup> and WT placentas at E9.5 (biological replicates = 2) and E10.5 (biological replicates = 2): Red arrowheads indicate fetal endothelial cells enclosing fetal blood spaces, and black arrowheads indicate S-TGCs. Maternal sinusoids are indicated by red asterisks, and maternal blood in the P-TGC layer is indicated by black asterisks. Scale bar represents 200 μm.</p></caption><graphic xlink:href=\"ijms-21-05503-g003\"/></fig><fig id=\"ijms-21-05503-f004\" orientation=\"portrait\" position=\"float\"><label>Figure 4</label><caption><p>Increased levels of PL1 and PLF in KIT<sup>D816V</sup> placentas: (<bold>A</bold>) In situ hybridization of PL1 and PLF on paraffin sections of KIT<sup>D816V</sup> and control placentas at E9.5 using specific probe for PL1 and PLF (biological replicates = 2). Scale bar represents 1 mm. (<bold>B</bold>) In situ hybridization of PL1 and PLF on paraffin sections of KIT<sup>D816V</sup> and control placentas at E10.5 (biological replicates = 2): Red arrowheads indicate PL1<sup>−</sup>/PLF<sup>+</sup> cells. Black arrowhead indicates invading Spiral Artery (SpA-)TGCs. Scale bar represents 1 mm.</p></caption><graphic xlink:href=\"ijms-21-05503-g004\"/></fig><fig id=\"ijms-21-05503-f005\" orientation=\"portrait\" position=\"float\"><label>Figure 5</label><caption><p>Differentiation of KIT<sup>D816V</sup>-TSC: (<bold>A</bold>) Photomicrographs depicting in vitro differentiation of KIT<sup>D816V</sup>-TSC line #3 and WT-TSC line 2.1 cultured for 0, 3, 6, and 9 days under differentiation conditions. White arrows indicate TGCs. Scale bar represents 250 μm. (<bold>B</bold>) qRT-PCR analysis of endogenous expression of <italic>Pl1</italic>, <italic>Pl2</italic>, and <italic>Plf</italic> in KIT<sup>D816V</sup>-TSC line #3 and WT-TSC line 2.1 in undifferentiated states and after culture under differentiation conditions for 6 days. RNA was obtained from three biological replicates; expression was normalized to the housekeeping gene <italic>Gapdh.</italic> Data is represented by mean value ± SD. Significance was determined by unpaired t-test and indicated with * <italic>p</italic> < 0.05. (<bold>C</bold>) Western Blot detected phosphorylated Extracellular-Signal Regulated Kinase (ERK), Protein Kinase B (AKT), and Signal Transducers and Activators of Transcription 3 (STAT3) during differentiation of KIT<sup>D816V</sup>-TSC line #3 and WT-TSC line 2.4 in comparison to β-ACTIN. (<bold>D</bold>) Representative photomicrographs of Hoechst-stained nuclei of cells that invaded through Matrigel coated filter membranes after five days under differentiating conditions: White arrows indicate nuclei of TGCs. Scale bar represents 250 μm. (<bold>E</bold>) Quantification of invaded cells per filter in KIT<sup>D816V</sup>-TSC line and WT-TSC line (biological replicates = 2): Data is represented by mean value ± SD. Significance was determined by unpaired t-test and indicated with * <italic>p</italic> < 0.05.</p></caption><graphic xlink:href=\"ijms-21-05503-g005\"/></fig><fig id=\"ijms-21-05503-f006\" orientation=\"portrait\" position=\"float\"><label>Figure 6</label><caption><p>Proposed mechanism of KIT<sup>D816V</sup> signaling in TSC: Schematic summary depicting possible signaling mechanisms that lead to KIT<sup>D816V</sup> placental phenotype observed in this study. Independent of SCF binding, the KIT<sup>D816V</sup> receptor is active. KIT inhibits SOCS3, thereby interrupting the negative feedback loop of Leukemia Inhibitory Factor (LIF) signaling resulting in accumulation of P-STAT3. Further, KIT signaling induces the expression of GATA2, which then transactivates PL1 and PLF. Independent of retinoic acid (RA) presence, KIT impinges on RA-controlled gene expression. Taken together, chronic KIT<sup>D816V</sup> signaling results in severely diminished placental proliferation and reduction of spongiotrophoblast cells but increase in differentiation towards P-TGC and elevated invasion. In the presence of FGF4, Fibroblast Growth Factor Receptor (FGFR) signaling can override KIT-mediated signals. The black arrows indicate relations found in this study, whereas the gray and dashed arrows present hypothetical interactions, some of which have only been demonstrated in other cell types and T bars indicate repression.</p></caption><graphic xlink:href=\"ijms-21-05503-g006\"/></fig></floats-group></article>\n"
]
[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Int J Mol Sci</journal-id><journal-id journal-id-type=\"iso-abbrev\">Int J Mol Sci</journal-id><journal-id journal-id-type=\"publisher-id\">ijms</journal-id><journal-title-group><journal-title>International Journal of Molecular Sciences</journal-title></journal-title-group><issn pub-type=\"epub\">1422-0067</issn><publisher><publisher-name>MDPI</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32759798</article-id><article-id pub-id-type=\"pmc\">PMC7432076</article-id><article-id pub-id-type=\"doi\">10.3390/ijms21155582</article-id><article-id pub-id-type=\"publisher-id\">ijms-21-05582</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Article</subject></subj-group></article-categories><title-group><article-title>Bladder Cancer Metastasis Induced by Chronic Everolimus Application Can Be Counteracted by Sulforaphane In Vitro</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Justin</surname><given-names>Saira</given-names></name><xref ref-type=\"author-notes\" rid=\"fn2-ijms-21-05582\">‡</xref></contrib><contrib contrib-type=\"author\"><name><surname>Rutz</surname><given-names>Jochen</given-names></name></contrib><contrib contrib-type=\"author\"><name><surname>Maxeiner</surname><given-names>Sebastian</given-names></name></contrib><contrib contrib-type=\"author\"><name><surname>Chun</surname><given-names>Felix K.-H.</given-names></name></contrib><contrib contrib-type=\"author\"><name><surname>Juengel</surname><given-names>Eva</given-names></name><xref ref-type=\"author-notes\" rid=\"fn1-ijms-21-05582\">†</xref><xref ref-type=\"author-notes\" rid=\"fn3-ijms-21-05582\">§</xref></contrib><contrib contrib-type=\"author\"><name><surname>Blaheta</surname><given-names>Roman A.</given-names></name><xref rid=\"c1-ijms-21-05582\" ref-type=\"corresp\">*</xref><xref ref-type=\"author-notes\" rid=\"fn1-ijms-21-05582\">†</xref></contrib></contrib-group><aff id=\"af1-ijms-21-05582\">Department of Urology, Goethe-University, 60323 Frankfurt am Main, Germany; <email>[email protected]</email> (S.J.); <email>[email protected]</email> (J.R.); <email>[email protected]</email> (S.M.); <email>[email protected]</email> (F.K.-H.C.); <email>[email protected]</email> (E.J.)</aff><author-notes><corresp id=\"c1-ijms-21-05582\"><label>*</label>Correspondence: <email>[email protected]</email>; Tel.: +49-69-6301-7109; Fax: +49-69-6301-7108</corresp><fn id=\"fn1-ijms-21-05582\"><label>†</label><p>These authors contributed equally to this work.</p></fn><fn id=\"fn2-ijms-21-05582\"><label>‡</label><p>Current address: Atta-ur-Rahman School of Applied Biosciences (ASAB), National University of Sciences & Technology (NUST), Sector H-12, Islamabad 44000, Pakistan.</p></fn><fn id=\"fn3-ijms-21-05582\"><label>§</label><p>Current address: Department of Urology and Pediatric Urology, University Medical Center Mainz, 55131 Mainz, Germany.</p></fn></author-notes><pub-date pub-type=\"epub\"><day>04</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><month>8</month><year>2020</year></pub-date><volume>21</volume><issue>15</issue><elocation-id>5582</elocation-id><history><date date-type=\"received\"><day>28</day><month>5</month><year>2020</year></date><date date-type=\"accepted\"><day>22</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>© 2020 by the authors.</copyright-statement><copyright-year>2020</copyright-year><license license-type=\"open-access\"><license-p>Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>).</license-p></license></permissions><abstract><p>Chronic treatment with the mTOR inhibitor, everolimus, fails long-term in preventing tumor growth and dissemination in cancer patients. Thus, patients experiencing treatment resistance seek complementary measures, hoping to improve therapeutic efficacy. This study investigated metastatic characteristics of bladder carcinoma cells exposed to everolimus combined with the isothiocyanate sulforaphane (SFN), which has been shown to exert cancer inhibiting properties. RT112, UMUC3, or TCCSUP bladder carcinoma cells were exposed short- (24 h) or long-term (8 weeks) to everolimus (0.5 nM) or SFN (2.5 µM), alone or in combination. Adhesion and chemotaxis along with profiling details of CD44 receptor variants (v) and integrin α and β subtypes were evaluated. The functional impact of CD44 and integrins was explored by blocking studies and siRNA knock-down. Long-term exposure to everolimus enhanced chemotactic activity, whereas long-term exposure to SFN or the SFN-everolimus combination diminished chemotaxis. CD44v4 and v7 increased on RT112 cells following exposure to SFN or SFN-everolimus. Up-regulation of the integrins α6, αV, and β1 and down-regulation of β4 that was present with everolimus alone could be prevented by combining SFN and everolimus. Down-regulation of αV, β1, and β4 reduced chemotactic activity, whereas knock-down of CD44 correlated with enhanced chemotaxis. SFN could, therefore, inhibit resistance-related tumor dissemination during everolimus-based bladder cancer treatment.</p></abstract><kwd-group><kwd>sulforaphane</kwd><kwd>bladder cancer</kwd><kwd>adhesion</kwd><kwd>chemotaxis</kwd><kwd>integrins</kwd><kwd>CD44</kwd></kwd-group></article-meta></front><body><sec sec-type=\"intro\" id=\"sec1-ijms-21-05582\"><title>1. Introduction</title><p>Bladder cancer is the fourth most common type of cancer in men, ninth most common in women, and is ranked as the most common urological malignancy in the majority of countries. Approximately 25% of newly diagnosed bladder cancers are already classified as muscle invasive (stage ≥ T2), and progress to muscle invasion in initially non-muscle-invasive bladder cancer in about 10–20% of cases [<xref rid=\"B1-ijms-21-05582\" ref-type=\"bibr\">1</xref>] occurs. The prognosis of patients with metastatic disease is poor, with overall survival approximating only 15% [<xref rid=\"B2-ijms-21-05582\" ref-type=\"bibr\">2</xref>].</p><p>Genome-based subtyping has revealed many oncogenic targets contributing to bladder cancer development, e.g., fibroblast growth factor receptor (FGFR), PI3K (phosphoinositid-3 kinase), Akt (serine/threonine kinase), and mTOR (mechanistic target of rapamycin) [<xref rid=\"B3-ijms-21-05582\" ref-type=\"bibr\">3</xref>]. Abnormal activation of these pathways leads to survival and proliferation of tumor cells along with metastatic competence, angiogenesis, and therapy resistance [<xref rid=\"B4-ijms-21-05582\" ref-type=\"bibr\">4</xref>]. One way of tackling bladder cancer is to inhibit mTOR, where anomalous interconnected signaling pathways converge [<xref rid=\"B5-ijms-21-05582\" ref-type=\"bibr\">5</xref>].</p><p>Currently, everolimus is the only mTOR inhibitor approved by the U.S. Food and Drug Administration to treat breast cancer, advanced renal carcinoma, pancreatic neuroendocrine tumor and subependymal giant-cell astrocytoma, thus opening this therapeutic approach for bladder carcinoma as well [<xref rid=\"B6-ijms-21-05582\" ref-type=\"bibr\">6</xref>]. mTOR inhibitors are cutting-edge agents for targeted treatment but are still, as a monotherapy, not that efficient. Like any other antineoplastic strategy, long-term everolimus treatment causes resistance initiated by genomic instability, though the underlying mechanisms are still unclear [<xref rid=\"B7-ijms-21-05582\" ref-type=\"bibr\">7</xref>]. Chemoresistance, along with severe side effects under conventional treatment, drive many cancer patients to complementary or alternative medicine (CAM) treatment options. Indeed, integrative medicine, especially green chemoprevention, has become very popular, particularly for patients with advanced tumors and a poor prognosis with conventional therapy [<xref rid=\"B8-ijms-21-05582\" ref-type=\"bibr\">8</xref>,<xref rid=\"B9-ijms-21-05582\" ref-type=\"bibr\">9</xref>].</p><p>Isothiocyanate sulforaphane (SFN) is the hydrolytic product of glucosinolates, found in cruciferous vegetables, such as broccoli, cabbage, and kale. SFN acts as a natural histone deacetylase (HDAC) inhibitor. This is noteworthy, since HDAC serves as a critical epigenetic regulator in bladder cancer and is closely involved in metastatic progression [<xref rid=\"B10-ijms-21-05582\" ref-type=\"bibr\">10</xref>]. Hence, HDAC inhibition has been introduced as a therapeutic target due to its role in regulating cell growth, invasion, and apoptosis [<xref rid=\"B11-ijms-21-05582\" ref-type=\"bibr\">11</xref>]. Several phytochemicals have meanwhile been identified as “epi-drugs” by altering epigenetic modification in multiple cancer types [<xref rid=\"B12-ijms-21-05582\" ref-type=\"bibr\">12</xref>], making natural HDAC inhibitors relevant for clinical use.</p><p>SFN, as one of these natural HDAC inhibitors, has been reported to exert both chemo-preventive and anti-carcinogenic effects on various tumor types [<xref rid=\"B13-ijms-21-05582\" ref-type=\"bibr\">13</xref>]. It is of particular interest that combining HDAC and mTOR inhibitors may not only elicit additive effects but also counteract resistance induction towards an mTOR inhibitor based regimen. Whether this holds true for SFN in treating bladder cancer has not yet been evaluated. Therefore, the present study was designed to investigate metastatic characteristics of bladder carcinoma under short- and long-term SFN treatment, combined with the mTOR-inhibitor, everolimus.</p></sec><sec sec-type=\"results\" id=\"sec2-ijms-21-05582\"><title>2. Results</title><sec id=\"sec2dot1-ijms-21-05582\"><title>2.1. Cell Adhesion</title><sec><title>Acute and Chronic Treatment</title><p>Adhesion of RT112 was inhibited with everolimus, while SFN or the SFN-everolimus combination elevated RT112 adhesion. UMUC3 and TCCSUP cells responded differently. Here, adhesion decreased with SFN or the SFN-everolimus combination (UMUC3) (<xref ref-type=\"fig\" rid=\"ijms-21-05582-f001\">Figure 1</xref>, upper panels).</p><p>Chronic everolimus exposure increased RT112 adhesion, in contrast to the inhibiting effect of acute everolimus treatment. UMUC3 and TCCSUP adhesion was diminished, whether the cells were chronically exposed to SFN or everolimus alone or in combination (<xref ref-type=\"fig\" rid=\"ijms-21-05582-f001\">Figure 1</xref>, lower panels).</p></sec></sec><sec id=\"sec2dot2-ijms-21-05582\"><title>2.2. Chemotactic Activity under Acute Versus Chronic Drug Application</title><p>Acute everolimus exposure in RT112 cells significantly decreased motility, whereas the opposite effect was apparent with chronic exposure to everolimus. The same response was seen with the TCCSUP model. No sign of everolimus resistance was seen in UMUC3 cells, since both acute and chronic treatment resulted in less transmigration (<xref ref-type=\"fig\" rid=\"ijms-21-05582-f002\">Figure 2</xref>). SFN alone blocked chemotaxis in all three cell lines, even after chronic exposure. The SFN-everolimus combination also suppressed chemotactic movement, counteracting the undesired increase observed with chronic everolimus exposure in RT112 and TCCSUP cells (<xref ref-type=\"fig\" rid=\"ijms-21-05582-f002\">Figure 2</xref>). The everolimus-SFN combination was superior to the chronic application of everolimus alone in UMUC3 cells. Since the resistance phenomenon was most obvious in RT112 cells, as demonstrated by strong chemotaxis activation with chronic everolimus exposure, this cell line was used in subsequent experiments.</p></sec><sec id=\"sec2dot3-ijms-21-05582\"><title>2.3. Surface Expression of CD44 Splice Variants</title><p>CD44v3, v4, v5, v6, and v7 were all expressed on the RT112 cell surface (<xref ref-type=\"fig\" rid=\"ijms-21-05582-f003\">Figure 3</xref>A). Acute treatment with everolimus or SFN or the drug combination did not significantly alter the CD44v expression level, except for CD44v7, which was up-regulated with combined SFN + everolimus (<xref ref-type=\"fig\" rid=\"ijms-21-05582-f003\">Figure 3</xref>B). Distinct modifications were seen with chronic treatment. Everolimus alone moderately elevated CD44v4 and v7, whereas SFN alone enhanced all CD44 variants, with the strongest effects exerted on CD44v4. Combined drug use resulted in additive effects, particularly evident with CD44v4 and v7 expression (<xref ref-type=\"fig\" rid=\"ijms-21-05582-f003\">Figure 3</xref>B).</p></sec><sec id=\"sec2dot4-ijms-21-05582\"><title>2.4. CD44 Knock-down</title><p>To investigate the relevance of CD44 on adhesion and chemotaxis, CD44 was knocked down in RT112 cells that had not been treated with SFN or everolimus and adhesion and chemotaxis were then evaluated. The successful knock-down of CD44 (<xref ref-type=\"fig\" rid=\"ijms-21-05582-f004\">Figure 4</xref>A) only slightly altered adhesion, but a strong increase in chemotaxis was apparent (<xref ref-type=\"fig\" rid=\"ijms-21-05582-f004\">Figure 4</xref>B).</p></sec><sec id=\"sec2dot5-ijms-21-05582\"><title>2.5. Surface and Protein Expression of Integrin Subtypes</title><p>The integrin members α2, α3, α6, αV, and β1 were strongly expressed on RT112 cells. Integrins α5 and β4 were weakly expressed (<xref ref-type=\"fig\" rid=\"ijms-21-05582-f005\">Figure 5</xref>A), while α1, α4, and β3 were not expressed at all (data not shown). Acute treatment with everolimus or the everolimus-SFN combination increased α3, αV, and β1, whereas SFN alone had no influence on these integrin subtypes (<xref ref-type=\"fig\" rid=\"ijms-21-05582-f005\">Figure 5</xref>B). Chronic everolimus application led to an increase in all analyzed integrin subtypes, except for α2, which remained unaltered (<xref ref-type=\"fig\" rid=\"ijms-21-05582-f005\">Figure 5</xref>B). SFN treatment caused an increase in α3, α5, and αV but significantly diminished β4. Combined SFN-everolimus up-regulated integrin α3 in an additive manner. Integrin α5 was also elevated. The drug combination more strongly suppressed β4, compared to SFN alone.</p><p>In regard to the integrin protein analysis, only moderate responses were seen with acute drug treatment. Everolimus enhanced integrin αV and reduced β1, and SFN diminished β1 (<xref ref-type=\"fig\" rid=\"ijms-21-05582-f006\">Figure 6</xref>A). Profound modifications were evoked by chronic everolimus treatment. This was evidenced by an up-regulation of α2 (slightly), α5, α6, β1, and β4 and a down-regulation of α3. SFN enhanced α2 (slightly), α5, and β1 and reduced α3 and β4. Combined drug use resulted in strongly elevated α5 and profound inhibition of α3, αV, β1, and β4 (<xref ref-type=\"fig\" rid=\"ijms-21-05582-f006\">Figure 6</xref>A,B).</p></sec><sec id=\"sec2dot6-ijms-21-05582\"><title>2.6. Integrin Blockade</title><p>Based on FACS analysis and western blots showing the integrin proteins most markedly affected by drug exposure, the integrins α3, α5, αV, β1, and β4 were blocked using function associated monoclonal antibodies and then adhesion and chemotaxis were evaluated. Blocking αV, β1, or β4 inhibited both adhesion and chemotaxis of RT112 cells. Blocking α3 or α5 enhanced chemotactic movement but did not alter adhesive behavior (<xref ref-type=\"fig\" rid=\"ijms-21-05582-f006\">Figure 6</xref>C).</p></sec></sec><sec sec-type=\"discussion\" id=\"sec3-ijms-21-05582\"><title>3. Discussion</title><p>Acquired resistance to everolimus was particularly evident in the RT112 model, as demonstrated by reduced adhesion and chemotaxis with acute exposure but enhanced adhesion and invasion with long-term exposure. The resistance in these chronically exposed cells could be overcome by the natural HDAC inhibitor SFN. In other cell types resistance was manifested differently. In TCCSUP cells chemotaxis was enhanced but adhesion was down-regulated following chronic everolimus exposure. The enhanced invasive activity of RT112 cells could be a direct consequence of an enhanced number of cells contacting the collagen matrix, increasing the likelihood of migratory events. Elevated invasion of TCCSUP could be due to diminished firm tumor cell adhesion, allowing more cells to become motile and invasive. Chemotaxis of UMUC3 remained suppressed, even after chronic everolimus administration. Here the eight-week drug application may have been too short to induce distinct signs of resistance. It has been shown that incubating UMUC3 cells with increasing concentrations of the mTOR-inhibitor, temsirolimus, over six months does result in resistance, exhibited by reactivated tumor growth and proliferation with mTOR and mTOR-related protein activation [<xref rid=\"B14-ijms-21-05582\" ref-type=\"bibr\">14</xref>].</p><p>Since RT112 cells exhibited the most pronounced resistance to everolimus, this cell line was examined in more detail. SFN alone and the SFN-everolimus combination reversed the long-term effect of everolimus in regard to increased invasion. The effect exerted by SFN, therefore, seems to dominate the effect exerted by everolimus, making SFN interesting for clinical use. Since SFN alone blocks cell invasion, even after long-term application, the compound itself may not induce resistance. Whether this holds true for longer time periods cannot be assessed since chronic exposure was limited to eight weeks in the present study. However, evidence has recently been presented that pancreatic cancer cells do not develop resistance to SFN, even after continuous exposure for more than one year [<xref rid=\"B15-ijms-21-05582\" ref-type=\"bibr\">15</xref>]. SFN application could therefore also provide long-term benefit in treating bladder cancer.</p><p>Alteration in CD44 splice variants was analyzed since they are closely involved in cell-cell and cell-matrix interaction. Acute exposure to SFN or everolimus did not induce any modifications, except for CD44v7, which was up-regulated with the drug combination. However, radical changes occurred after eight weeks exposure. SFN enhanced the expression level of all CD44 variants, most prominently CD44v4 and CD44v7. The direction of change was not homogeneous, since everolimus induced CD44v4 and CD44v7 up-regulation, with additive effects in the presence of combined SFN/everolimus. Chronic everolimus exposure elevated chemotaxis, whereas SFN or the SFN-everolimus combination reduced it. These differences should be considered when discussing alterations of CD44v4 and CD44v7. Everolimus resistance has been characterized by elevated Akt-mTOR signaling [<xref rid=\"B16-ijms-21-05582\" ref-type=\"bibr\">16</xref>]. Cross-communication between CD44 and Akt has recently been observed in drug resistant tumor cells, whereby enhanced CD44 expression is associated with Akt activation, driving tumor invasion forward [<xref rid=\"B17-ijms-21-05582\" ref-type=\"bibr\">17</xref>,<xref rid=\"B18-ijms-21-05582\" ref-type=\"bibr\">18</xref>]. Increased CD44v4 and CD44v7 expression induced by long-term exposure to the mTOR inhibitor everolimus may therefore serve to activate Akt, with Akt dependent signaling processes finally triggering enhanced motility.</p><p>This mechanism is not transferable to SFN, since chemotaxis was reduced in its presence. Indeed, upregulation of the CD44 variant v6 caused by SFN resulted in the inhibition of tumor cell invasion [<xref rid=\"B19-ijms-21-05582\" ref-type=\"bibr\">19</xref>]. It has been shown that SFN suppresses Akt, along with the focal adhesion kinase (FAK) signaling pathways [<xref rid=\"B20-ijms-21-05582\" ref-type=\"bibr\">20</xref>,<xref rid=\"B21-ijms-21-05582\" ref-type=\"bibr\">21</xref>]. Interestingly, integrin β4, which interacts with FAK [<xref rid=\"B22-ijms-21-05582\" ref-type=\"bibr\">22</xref>], was suppressed by SFN as well, but not by everolimus. Therefore, the increase of CD44v4 and CD44v7 induced by SFN could be coupled to the down-regulation of FAK and Akt signaling, finally preventing motile crawling of the tumor cells (via β4—see below). Nevertheless, Akt deserves further investigation. A recent investigation shows that the influence of SFN on mTOR-Akt signaling in bladder cancer leads to a significant increase in Akt phosphorylation (pAkt) in tumor cells following chronic everolimus administration [<xref rid=\"B23-ijms-21-05582\" ref-type=\"bibr\">23</xref>]. Surprisingly, enhanced pAkt was also seen in the presence of SFN alone, but not in the presence of the everolimus-SFN combination where pAkt was reduced, compared to the untreated controls. The mTOR complex pRictor, associated with cell motility, was also investigated and found to be elevated by everolimus but diminished by SFN and the SFN-everolimus combination. Therefore, it is possible that the process of tumor cell invasion may also be suppressed by SFN via pRictor. Whether the elevation of pAkt in the presence of SFN indicates early signs of resistance caused by a feedback mechanism is not yet clear.</p><p>Speculatively, SFN may mitigate everolimus resistance by switching the function of CD44v4 and v7 from tumor promotion to tumor suppression. Bioinformatic analysis of cancer patients using The Cancer Genome Atlas database has revealed that production of CD44v splice variants by an epigenetic mechanism, including HDAC inhibition, may sustain an epithelial phenotype and prevent epithelial-mesenchymal transition [<xref rid=\"B24-ijms-21-05582\" ref-type=\"bibr\">24</xref>], thus corroborating this speculation. Still, the role of the CD44 variants, v4 and v7, in cancer progression has not been evaluated in detail and requires further investigation.</p><p>Long-term everolimus exposure was also accompanied by alteration of the integrin α and β expression levels, compared to the integrin expression pattern in tumor cells acutely exposed to everolimus. α3 was massively up-regulated and α5 de novo enhanced, independent of the therapeutic regimen. Since protein analysis demonstrated intracellular reduction of α3, α3 was probably translocated from the cytoplasm to the outer cell surface. α5, however, remained intracellularly high, due to de novo synthesis. The integrin subtypes α6, αV, β1, and β4 were also significantly increased under chronic everolimus exposure. α6 has been shown to induce cell motility and invasion and promote drug resistance [<xref rid=\"B25-ijms-21-05582\" ref-type=\"bibr\">25</xref>,<xref rid=\"B26-ijms-21-05582\" ref-type=\"bibr\">26</xref>] and might, therefore, be partially responsible for reactivating the tumor cell invasion cascade by everolimus. This integrin subtype was not further investigated, since in RT112 cells, it was not as strongly influenced by drug exposure as were other integrin subtypes. However, integrin blocking studies done with αV, β1, and β4 monoclonal antibodies demonstrated a positive correlation between the αV, β1, and β4 surface expression level and tumor cell migratory activity. Therefore, everolimus resistance reflected by enhanced tumor cell chemotaxis might also be caused by up-regulated αV, β1, and β4. Up-regulation of αV or β1 did not occur in the presence of SFN (β1) or the SFN-everolimus combination (αV, β1). The relevance of integrin αV in bladder cancer has not been explored in detail. However, overexpression of this molecule in tumor tissue has been associated with poor, relapse-free survival of gastric and breast cancer patients, [<xref rid=\"B27-ijms-21-05582\" ref-type=\"bibr\">27</xref>,<xref rid=\"B28-ijms-21-05582\" ref-type=\"bibr\">28</xref>].</p><p>Likewise, high expression of the integrin β1 subtype positively correlated with tumor grade and poor clinical outcome in bladder cancer patients [<xref rid=\"B29-ijms-21-05582\" ref-type=\"bibr\">29</xref>,<xref rid=\"B30-ijms-21-05582\" ref-type=\"bibr\">30</xref>]. Prevention of αV, α6, and β1 up-regulation by SFN or the SFN-everolimus combination could, therefore, immobilize tumor cells to impede cancer progression. Down-regulation of β4 by SFN and, even more so by SFN combined with everolimus, indicates that β4, which mediates pro-migratory and pro-proliferative properties [<xref rid=\"B31-ijms-21-05582\" ref-type=\"bibr\">31</xref>,<xref rid=\"B32-ijms-21-05582\" ref-type=\"bibr\">32</xref>], could serve as a potential therapeutic target [<xref rid=\"B33-ijms-21-05582\" ref-type=\"bibr\">33</xref>]. Since the combination with everolimus was superior to SFN alone, SFN may re-sensitize tumor cells to everolimus, similar to it re-sensitizing tumor cells to tamoxifen or lapatinib [<xref rid=\"B34-ijms-21-05582\" ref-type=\"bibr\">34</xref>,<xref rid=\"B35-ijms-21-05582\" ref-type=\"bibr\">35</xref>]. Prevention of α6, αV, and β1 elevation by SFN, along with diminishing the β4 integrin subtype could, therefore, make SFN a potent natural epi-drug to counteract the undesired long-term effects of everolimus.</p><p>Everolimus-induced resistance was manifested by increased chemotaxis and increased α3 and α5 in RT112 cells. This stood in direct contrast to the functional blockade of α3 and α5 that resulted in increased, instead of decreased, chemotaxis. Therefore, the induction of metastasis cannot be attributed to a discrete integrin related process. Rather, alteration in a set of several integrin subtypes, rather than a single subtype, may be necessary to induce resistance. Here, activation of the motile machinery in RT112 cells, as a result of α6, αV, β1, and β4 elevation, may dominate suppressive processes associated with elevated α3 and α5. In support, integrin blockage revealed no influence of α3 and α5 on adhesion, pointing to (at least) αV, β1, and β4 as the relevant drivers of resistance in this tumor type.</p><p>Investigative results presented here are related to the bladder cancer cell line RT112 and may not be generalizable. Similarities between everolimus-resistant RT112 and TCCSUP cells were seen in as much as SFN elevated α3, αv, and β1 in both cell lines. The α5 subtype was elevated in both cell lines by SFN as well. However, this was only significant in TCCSUP cells. In contrast, a different response was evoked in UMUC3 cells. Here, αv was down-regulated by SFN and the integrins α3 and β1 were not altered (<xref ref-type=\"app\" rid=\"app1-ijms-21-05582\">Supplementary Materials</xref>). The disparate mechanistic influence of SFN on different bladder cancer cell lines is not surprising. It has previously been shown that the HDAC inhibitor valproic acid suppresses adhesion in a broad panel of bladder cancer cell lines by altering both integrin α and β expression differently [<xref rid=\"B36-ijms-21-05582\" ref-type=\"bibr\">36</xref>]. The molecular response of a particular tumor subline seems to depend on the integrin receptor set present on the cell surface. Integrin β4 is present on TCCSUP and RT112 but not on UMUC3 cells, whereas β3 has been detected on UMUC3 but not on TCCSUP and RT112 (<xref ref-type=\"app\" rid=\"app1-ijms-21-05582\">Supplementary Materials</xref>). Consequently, it may be expected that SFN treatment influences integrin subfamilies and integrin-related signaling in different cell lines differently. Indeed, differing integrin-guided adhesive behavior in several tumor sublines has been reported. Blocking the α3 integrin subunit has been shown to inhibit HCV29 bladder cancer cell attachment to the matrix proteins laminin and fibronectin but to exert an opposite effect on T24 and Hu456 cell adhesion. Similarly, blocking α5 integrin has been shown to down-regulate HCV29 and BC3726 cell-matrix interaction, whereas binding of the bladder cancer cell lines T24 and Hu456 is enhanced [<xref rid=\"B37-ijms-21-05582\" ref-type=\"bibr\">37</xref>]. Based on the current data, it may be assumed that SFN acts on a set of integrin receptors, whereby the integrins modified by SFN may differ according to the initial characteristic integrin composition of the particular cell type.</p><p>High expectations are set on SFN serving as a novel anti-tumor agent [<xref rid=\"B38-ijms-21-05582\" ref-type=\"bibr\">38</xref>]. However, questions do remain. Further studies about SFN metabolism and bioactivity are required. Bioavailability needs to be optimized, possibly by nanotechnologic approaches, to enhance drug delivery. Possible negative side effects or undesired interactions with well-established therapeutic regimens must also receive further evaluation. In vivo studies in animal models are the next step to advance knowledge about preventing resistance by administering SFN.</p></sec><sec id=\"sec4-ijms-21-05582\"><title>4. Materials and Methods</title><sec id=\"sec4dot1-ijms-21-05582\"><title>4.1. Cell Cultures</title><p>Three bladder carcinoma cell lines, RT112, UMUC3 (ATCC/LGC Promochem GmbH, Wesel, Germany), and TCCSUP (DSMZ, Braunschweig, Germany), were cultured in RPMI 1640 medium (Gibco/Invitrogen, Karlsruhe, Germany) supplemented with 10% fetal bovine serum (FBS), 2% HEPES buffer, 1% GlutaMAX, and 1% penicillin/streptomycin (all: Gibco/Invitrogen). RT112 cells represent a pathological stage T2, moderately differentiated, grade 2/3 tumor, UMUC3 an invasive high grade 3, and TCCSUP a grade 4 transitional cell carcinoma. Incubation was carried out at 37 °C in a humidified incubator with 5% CO<sub>2</sub>.</p></sec><sec id=\"sec4dot2-ijms-21-05582\"><title>4.2. Drug Application</title><p>Everolimus (Novartis Pharma AG, Basel, Switzerland) was dissolved in dimethyl sulfoxide (DMSO) as a 10 mM stock solution and stored in aliquots at −20 °C. Prior to experiments, everolimus was diluted in cell culture medium. Ready-to-use L-sulforaphane was provided by Biomol, Hamburg, Germany (CAS registry number: CAS 142825-10-3). Dose response analysis showed that 0.5 nM everolimus and 2.5 µM SFN were optimal and these drug concentrations were employed. Untreated tumor cells served as controls. Pilot experiments demonstrated that 8 weeks chronic everolimus exposure leads to resistance development [<xref rid=\"B39-ijms-21-05582\" ref-type=\"bibr\">39</xref>]. Tumor cells were, therefore, subjected to either short-term (24 h; specified as acute treatment) or long-term (8 weeks; specified as chronic treatment) application of SFN or everolimus alone, or to a SFN-everolimus combination for comparative analysis.</p></sec><sec id=\"sec4dot3-ijms-21-05582\"><title>4.3. Cell Adhesion to Collagen Matrix</title><p>To evaluate tumor cell binding to immobilized matrix proteins, 24-well multi-plates (Falcon Primaria; Corning, Wiesbaden, Germany) were coated with 400 µg/mL of collagen G (Biochrom, Berlin, Germany) overnight at 4 °C. Plastic dishes served as background control. Plates were washed with 1% bovine serum albumin (BSA; Gibco/Invitrogen) in PBS (Gibco/Invitrogen) to prevent nonspecific cell adhesion. 1 × 10<sup>5</sup> tumor cells were then added to each well for 1 h at 37 °C. Non-adherent tumor cells were subsequently washed off, and remaining adherent cells were fixed using 1% glutaraldehyde (Sigma, München, Germany). The evaluation was done microscopically by counting five different fields (each 0.25 mm²) with a raster ocular at 200-fold magnification. The mean cellular adhesion rate, defined by adherent cells<sub>coated well</sub> - adherent cells<sub>background</sub>, was calculated.</p></sec><sec id=\"sec4dot4-ijms-21-05582\"><title>4.4. Chemotaxis and Migration</title><p>Serum-induced chemotactic movement was investigated using a Boyden double chamber system with filters having 8 µm pores (Greiner Bio-One, Frickenhausen, Germany). 0.5 × 10<sup>6</sup> cells/mL were placed in the upper chamber with serum-free medium. The lower chamber contained 10% FBS. Cells were incubated for 24 h and then fixed. The upper surface of the trans-well membrane was gently wiped using a cotton swab to remove non-migrating cells. Cells, which had moved to the lower surface of the membrane, were stained using hematoxylin (Sigma) and counted under a microscope at 200-fold magnification. The mean chemotaxis rate was calculated from five different observation fields (5 × 0.25 mm<sup>2</sup>).</p></sec><sec id=\"sec4dot5-ijms-21-05582\"><title>4.5. CD44 Expression Analysis</title><p>A Lightning-Link Allophycocyanin (APC) Conjugation Kit was used to conjugate CD44 variants 44v3-7 antibodies according to the manufacturer’s instructions (eBioscience, ThermoFisher, Darmstadt, Germany). Cancer cells were detached and washed with blocking solution (PBS, 0.5% BSA). The cells were then incubated for 1 h at 4 °C with 2.5 µL APC conjugated monoclonal antibody directed against the following CD44 variants: anti-CD44v3 (clone VFF-327v3), anti-CD44v4 (clone VFF-11), anti-CD44v5 (clone VFF-8), anti-CD44v6 (clone VFF-7), and anti-CD44v7 (clone VFF-9; all: Bio-Rad, Feldkirchen, Germany ). 5 µl APC mouse IgG1, K (clone P3.6.2.8.1; ThermoFisher, Dreieich, Germany) served as the control isotype. CD44 expression was then measured using a FACscan (BD Biosciences; FL4-H (log) channel histogram analysis; 1 × 10<sup>4</sup> cells per scan) and expressed as mean fluorescence units (MFU).</p></sec><sec id=\"sec4dot6-ijms-21-05582\"><title>4.6. Integrin Surface Expression</title><p>Cancer cells were detached using accutase (PAA Laboratories GmbH, Pasching, Austria) and washed with blocking solution (PBS, 0.5% BSA). The cells were then incubated for 1 h at 4 °C with 20 µL ready-to-use phycoerythrin (PE) conjugated monoclonal antibody, directed against the following integrin subtypes: anti-α2 (IgG2a; clone 12F1-H6, 20 µL), anti- α3 (IgG1; clone C3II.1, 20 µL), anti- α5 (IgG1; clone IIA1, 20 µL), anti- α6 (IgG2a; clone GoH3, 20 µL), anti-β1 (IgG1; clone MAR4, 20 µL), anti- β 3 (IgG1; clone VI-PL2, 20 µL), or anti- β4 (IgG2a; clone 439-9B, 20 uL; all: BD Biosciences). 10 µL of anti-αV (IgG1; clone 13C2, Abcam, Cambridge, UK) was also used according to the manufacturer’s instructions. The integrin expression of tumor cells was then measured using a FACscan (BD Biosciences; FL2-H (log) channel histogram analysis; 1 × 10<sup>4</sup> cells per scan) and expressed as mean fluorescence units. Mouse IgG1-PE (MOPC-21), mouse IgG2a-PE (G155-178) or rat IgG2b-PE (R35-38; all: BD Biosciences) was used as isotype control.</p></sec><sec id=\"sec4dot7-ijms-21-05582\"><title>4.7. Western Blot Analysis</title><p>To visualize and quantify the integrin proteins, RT112 cell lysates were applied to a 7% polyacrylamide gel and electrophoresed for 90 min at 100 V. The proteins were then transferred to nitrocellulose membranes (1 h, 100 V). After this, membranes were blocked with non-fat dry milk for 1 h and incubated overnight with monoclonal antibodies directed against the following integrin proteins: anti-α2 (clone 2, 1:250), anti- α3 (1:500; both: Merck Millipore, Darmstadt, Germany), anti- α5 (clone 1, 1:5000), anti- αV (clone 21/CD51, 1:250), anti-β1 (clone 18, 1:2500), anti- β3 (clone 1, 1:2500), or anti- β4 (clone 7, 1:250; all: BD Biosciences) and anti- α6 (dilution 1:1000, Cell Signaling, Frankfurt, Germany). HRP-conjugated goat anti-mouse IgG and HRP-conjugated goat anti-rabbit IgG (both: Cell Signaling) served as the secondary antibodies. Membranes were briefly incubated with chemiluminescence (ECL) detection reagent (Amersham/GE Healthcare, München, Germany) to visualize the proteins and then analyzed using the Fusion FX7 system (Peqlab, Erlangen, Germany). β-actin (Cell Signaling) served as the internal control. GIMP 2.8 software was used to analyze the pixel density of the protein bands and to calculate the ratio of protein intensity/β-actin intensity.</p></sec><sec id=\"sec4dot8-ijms-21-05582\"><title>4.8. Integrin Receptor Blockade</title><p>Tumor cells were incubated for 60 min with 10 μg/mL function-blocking anti-integrin α3 (clone P1B5), anti-integrin α5 (clone P1D6), anti-integrin αV (clone AV1), anti-integrin β1 (clone 6S6), or anti-integrin β4 (clone ASC-8) mouse mAb (all from Merck Millipore). Subsequently, tumor cell adhesion and chemotaxis were analyzed as previously described.</p></sec><sec id=\"sec4dot9-ijms-21-05582\"><title>4.9. CD44 Knock-down</title><p>Transfection with small interfering RNA (siRNA) was carried out directed against CD44 (gene ID: 960, target sequence: AACTCCATCTGTGCAGCAAAC, Qiagen, Hilden, Germany). 3 × 10<sup>5</sup> cells were pre-incubated for 24 h with 2.5 µM SFN and subsequently with a transfection solution of siRNA and transfection reagent (HiPerFect Transfection Reagent; Qiagen) at a ratio of 1:6. Non-treated cells and cells treated with 5 nM control siRNA (AllStars Negative Control siRNA; Qiagen) served as controls. Protein expression, tumor cell adhesion, and chemotaxis were then analyzed as described above.</p></sec><sec id=\"sec4dot10-ijms-21-05582\"><title>4.10. Statistics</title><p>Mean +/- standard deviation was calculated. To exclude coincidence, all experiments were repeated three to five times. Statistical significance was evaluated with the Wilcoxon‒Mann‒Whitney U test and Student’s <italic>t</italic>-test. A <italic>P</italic> value of 0.05 or less indicated a significant difference.</p></sec></sec><sec sec-type=\"conclusions\" id=\"sec5-ijms-21-05582\"><title>5. Conclusions</title><p>The present findings reveal that chronic use of the mTOR inhibitor everolimus is associated with drug non-responsiveness, resulting in aggressive migratory activity of bladder cancer cells. Resistance induction caused by everolimus could be inhibited by pairing everolimus with the natural HDAC inhibitor sulforaphane. Patients with bladder cell carcinoma may, therefore, benefit from an anti-tumor strategy including sulforaphane as a complementary component to everolimus.</p></sec></body><back><ack><title>Acknowledgments</title><p>The authors would like to thank Karen Nelson for language editing and proofreading.</p></ack><app-group><app id=\"app1-ijms-21-05582\"><title>Supplementary Materials</title><p>Supplementary materials can be found at <uri xlink:href=\"https://www.mdpi.com/1422-0067/21/15/5582/s1\">https://www.mdpi.com/1422-0067/21/15/5582/s1</uri>.</p><supplementary-material content-type=\"local-data\" id=\"ijms-21-05582-s001\"><media xlink:href=\"ijms-21-05582-s001.zip\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material></app></app-group><notes><title>Author Contributions</title><p>Conceptualization, R.A.B.; investigation, S.J., J.R., E.J., and S.M.; methodology, E.J. and R.A.B.; project administration, R.A.B.; supervision, F.K.-H.C. and R.A.B.; visualization, S.J., F.K.-H.C., and R.A.B.; writing—original draft, S.J. and R.A.B.; writing—review and editing, F.K.-H.C. and R.A.B. All authors have read and agreed to the published version of the manuscript.</p></notes><notes><title>Funding</title><p>This work was supported by the Brigitta & Norbert Muth Stiftung, Wiesbaden, Germany, and the Faculty Development Program Abroad 2014/2015 of the National University of Sciences & Technology (NUST), Pakistan.</p></notes><notes notes-type=\"COI-statement\"><title>Conflicts of Interest</title><p>The authors declare no conflict of interest.</p></notes><glossary><title>Abbreviations</title><array orientation=\"portrait\"><tbody><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">MDPI</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Multidisciplinary Digital Publishing Institute</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">DOAJ</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Directory of open access journals</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">SFN</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Sulforaphane</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">HDAC</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Histone deacetylase</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">mTOR</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">mechanistic target of rapamycin</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">FAK</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">focal adhesion kinase</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">p</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">phosphorylated</td></tr></tbody></array></glossary><ref-list><title>References</title><ref id=\"B1-ijms-21-05582\"><label>1.</label><element-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Fujii</surname><given-names>Y.</given-names></name></person-group><article-title>Prediction models for progression of non-muscle-invasive bladder cancer: A review</article-title><source>Int. 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Control cells (C) remained unexposed (set to 100%). Mean number of adherent tumor cells from five fields after 1 h incubation. Bars indicate standard deviation, * indicates significant difference to corresponding control, <italic>p</italic> ≤ 0.05. n = 5.</p></caption><graphic xlink:href=\"ijms-21-05582-g001\"/></fig><fig id=\"ijms-21-05582-f002\" orientation=\"portrait\" position=\"float\"><label>Figure 2</label><caption><p>Chemotaxis of RT112, UMUC3, and TCCSUP cells exposed to 0.5 nM everolimus (E), 2.5 µM sulforaphane (S), or 0.5 nM everolimus + 2.5 µM sulforaphane (E + S). Control cells (C) remained unexposed (set to 100%). Mean number of migrating tumor cells from five fields after 24 h incubation. Bars indicate standard deviation, * indicates significant difference to corresponding control, <italic>p</italic> ≤ 0.05. n = 5.</p></caption><graphic xlink:href=\"ijms-21-05582-g002\"/></fig><fig id=\"ijms-21-05582-f003\" orientation=\"portrait\" position=\"float\"><label>Figure 3</label><caption><p>(<bold>A</bold>) Surface expression of CD44 variants v3-v7 on RT112 cells. Counts indicate MFU (mean fluorescence units). One representative of three separate experiments is shown. Solid line: specific fluorescence; dashed line: isotype IgG1-APC. (<bold>B</bold>) expression level of CD44 variants v3-v7 following sulforaphane (SFN) or everolimus + sulforaphane (combination) exposure. All values are means related to untreated controls set to 100% and bars indicate standard deviation. * indicates significant difference to corresponding control, <italic>p</italic> ≤ 0.05, n = 4.</p></caption><graphic xlink:href=\"ijms-21-05582-g003\"/></fig><fig id=\"ijms-21-05582-f004\" orientation=\"portrait\" position=\"float\"><label>Figure 4</label><caption><p>Functional knock-down of CD44 in untreated RT112 cells (Control). AllStars Negative Control siRNA served as transfection control (Scrambled). (<bold>A</bold>) Protein expression after functional blocking with siRNA targeting CD44. β-actin served as internal control. One representative of three separate experiments is shown. (<bold>B</bold>) Tumor cell adhesion and chemotaxis of CD44-blocked RT112 cells. Means ± SD of n = 3. * indicates significant difference to control, <italic>p</italic> ≤ 0.05.</p></caption><graphic xlink:href=\"ijms-21-05582-g004\"/></fig><fig id=\"ijms-21-05582-f005\" orientation=\"portrait\" position=\"float\"><label>Figure 5</label><caption><p>(<bold>A</bold>) Surface expression of integrin α and β subtypes on RT112 cells. Counts indicate mean fluorescence units (MFU). One representative of three separate experiments is shown. Solid line: specific fluorescence; dashed line: isotype control. (<bold>B</bold>) Expression level of integrin α and β subtypes following sulforaphane (SFN), everolimus, or everolimus+sulforaphane (combination) exposure. All values are related to untreated controls set to 100%. Means ± SD of n = 4. * indicates significant difference to corresponding control, # indicates significant difference to everolimus treatment, <italic>p</italic> ≤ 0.05.</p></caption><graphic xlink:href=\"ijms-21-05582-g005\"/></fig><fig id=\"ijms-21-05582-f006\" orientation=\"portrait\" position=\"float\"><label>Figure 6</label><caption><p>(<bold>A</bold>) Integrin α and β protein analysis in RT112 cells after acute and chronic exposure to sulforaphane (SFN), everolimus (Ever) or everolimus+sulforaphane combination (E+S). β-actin served as internal control. One representative of three separate experiments is shown. Controls (<bold>C</bold>) remained untreated. (<bold>B</bold>) Pixel density analysis (chronic treatment). The ratio of protein intensity/β-actin intensity is expressed as percentage of controls, set to 100%. (<bold>C</bold>) Adhesion and chemotaxis of RT112 cells following functional blocking of integrin α and β subtypes (white columns show everolimus-resistant tumor cells). Means ± SD of n = 3. * indicates significant difference to control, <italic>p</italic> ≤ 0.05.</p></caption><graphic xlink:href=\"ijms-21-05582-g006\"/></fig></floats-group></article>\n"
]
[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Int J Environ Res Public Health</journal-id><journal-id journal-id-type=\"iso-abbrev\">Int J Environ Res Public Health</journal-id><journal-id journal-id-type=\"publisher-id\">ijerph</journal-id><journal-title-group><journal-title>International Journal of Environmental Research and Public Health</journal-title></journal-title-group><issn pub-type=\"ppub\">1661-7827</issn><issn pub-type=\"epub\">1660-4601</issn><publisher><publisher-name>MDPI</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32722277</article-id><article-id pub-id-type=\"pmc\">PMC7432077</article-id><article-id pub-id-type=\"doi\">10.3390/ijerph17155352</article-id><article-id pub-id-type=\"publisher-id\">ijerph-17-05352</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Article</subject></subj-group></article-categories><title-group><article-title>Current State and Future Trends: A Citation Network Analysis of the Academic Performance Field</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Martinez-Perez</surname><given-names>Clara</given-names></name><xref rid=\"c1-ijerph-17-05352\" ref-type=\"corresp\">*</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0003-1097-4581</contrib-id><name><surname>Alvarez-Peregrina</surname><given-names>Cristina</given-names></name></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0002-6743-8264</contrib-id><name><surname>Villa-Collar</surname><given-names>Cesar</given-names></name></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0002-2583-1789</contrib-id><name><surname>Sánchez-Tena</surname><given-names>Miguel Ángel</given-names></name></contrib></contrib-group><aff id=\"af1-ijerph-17-05352\">School of Biomedical and Health Science, Universidad Europea de Madrid, 28670 Madrid, Spain; <email>[email protected]</email> (C.A.-P.); <email>[email protected]</email> (C.V.-C.); <email>[email protected]</email> (M.Á.S.-T.)</aff><author-notes><corresp id=\"c1-ijerph-17-05352\"><label>*</label>Correspondence: <email>[email protected]</email></corresp></author-notes><pub-date pub-type=\"epub\"><day>24</day><month>7</month><year>2020</year></pub-date><pub-date pub-type=\"ppub\"><month>8</month><year>2020</year></pub-date><volume>17</volume><issue>15</issue><elocation-id>5352</elocation-id><history><date date-type=\"received\"><day>06</day><month>6</month><year>2020</year></date><date date-type=\"accepted\"><day>22</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>© 2020 by the authors.</copyright-statement><copyright-year>2020</copyright-year><license license-type=\"open-access\"><license-p>Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>).</license-p></license></permissions><abstract><p><italic>Background</italic>: In recent years, due to its complexity and relevance, academic performance has become a controversial research topic within the health and educational field. The main purposes of this study were to analyze the links between publications and authors via citation networks, to identify the different research areas and to determine the most cited publications. <italic>Methods</italic>: The publication search was performed through the Web of Science database, using the term “Academic Performance” for a time interval from 1952 to 2019. The software used to analyze the publications was the Citation Network Explorer. <italic>Results</italic>: We found a total of 16,157 publications with 35,213 citations generated in the network, and 2018 had the highest number of publications of any year. The most cited publication was published in 2012 by Richardson et al. with a citation index score of 352. By using the clustering function, we found nine groups related to different areas of research in this field: health, psychology, psychosociology, demography, physical activity, sleep patterns, vision, economy, and delinquency. <italic>Conclusions</italic>: The citation network showed the main publications dealing with the different factors that affect academic performance, and it was determined that psychological and psychosocial factors were the most relevant.</p></abstract><kwd-group><kwd>academic performance</kwd><kwd>citation network</kwd><kwd>motivation</kwd></kwd-group></article-meta></front><body><sec sec-type=\"intro\" id=\"sec1-ijerph-17-05352\"><title>1. Introduction</title><p>Academic performance is frequently associated with a country’s social and economic development [<xref rid=\"B1-ijerph-17-05352\" ref-type=\"bibr\">1</xref>]. Numerous studies have concurred that academic achievements are the result of cognitive and non-cognitive abilities, as well as the socio-cultural environment in which the learning takes place [<xref rid=\"B2-ijerph-17-05352\" ref-type=\"bibr\">2</xref>,<xref rid=\"B3-ijerph-17-05352\" ref-type=\"bibr\">3</xref>]. In this sense, academic performance is associated with intellectual factors, for example, long-term memory or the ability to think abstractly, and non-intellectual factors, such as motivation or self-discipline [<xref rid=\"B4-ijerph-17-05352\" ref-type=\"bibr\">4</xref>].</p><p>Motivation is an internal condition that influences behavior, i.e., mental power that helps people to meet their goals and achieve academic success [<xref rid=\"B5-ijerph-17-05352\" ref-type=\"bibr\">5</xref>,<xref rid=\"B6-ijerph-17-05352\" ref-type=\"bibr\">6</xref>,<xref rid=\"B7-ijerph-17-05352\" ref-type=\"bibr\">7</xref>]. To be able to motivate students, it is important to instill in them a greater desire to learn and make them enjoy studying. It has been proved that unmotivated students are not interested in learning or participating in class, and this attitude will affect their academic performance [<xref rid=\"B6-ijerph-17-05352\" ref-type=\"bibr\">6</xref>].</p><p>Scientific literature supports that cognitive abilities and self-efficacy can predict academic performance outcomes [<xref rid=\"B8-ijerph-17-05352\" ref-type=\"bibr\">8</xref>,<xref rid=\"B9-ijerph-17-05352\" ref-type=\"bibr\">9</xref>]. In 1983, Gardner explained [<xref rid=\"B10-ijerph-17-05352\" ref-type=\"bibr\">10</xref>] that self-sufficient people can organize themselves and carry out actions to meet their goals. </p><p>Furthermore, psychosocial wellbeing is strongly related to thoughts, motivation, and decision-making relating to academic effort [<xref rid=\"B11-ijerph-17-05352\" ref-type=\"bibr\">11</xref>,<xref rid=\"B12-ijerph-17-05352\" ref-type=\"bibr\">12</xref>]. According to Vilar et al., a poor psychosocial status may be the result of a poor socio-economic situation, unfavorable family circumstances, or a bad relationship with classmates [<xref rid=\"B13-ijerph-17-05352\" ref-type=\"bibr\">13</xref>]. Students in these situations tend to have a negative attitude towards school and the learning process. This results in a low level of self-discipline and bad performance. In contrast, students with a positive psychosocial dynamic tend to have a high socioeconomic level and a good family relationship. They are also optimistic and have a good attitude at school. </p><p>Other factors related to good performance at school include a healthy diet (rich in fiber and nutrients); practicing physical activity (as this increases metabolism, oxygenation and blood flow, delivering the hormones that promote neurological health); sleeping well (which improves cognitive functions, such as memory and learning) and good vision (visual impairment harms the development of motor skills, cognition, and language in child development) [<xref rid=\"B14-ijerph-17-05352\" ref-type=\"bibr\">14</xref>,<xref rid=\"B15-ijerph-17-05352\" ref-type=\"bibr\">15</xref>,<xref rid=\"B16-ijerph-17-05352\" ref-type=\"bibr\">16</xref>,<xref rid=\"B17-ijerph-17-05352\" ref-type=\"bibr\">17</xref>].</p><p>Citation network analysis is used to search for a specific topic within the scientific literature. By analyzing one publication, it is possible to find other relevant additional publications, to demonstrate, qualitatively, and quantitatively, the connections between articles and authors by creating groups [<xref rid=\"B18-ijerph-17-05352\" ref-type=\"bibr\">18</xref>]. Furthermore, it is also possible to quantify the most cited publications in each group, to study the development of a research field, or to focus the literature search on a specific topic [<xref rid=\"B19-ijerph-17-05352\" ref-type=\"bibr\">19</xref>,<xref rid=\"B20-ijerph-17-05352\" ref-type=\"bibr\">20</xref>].</p><p>Given the numerous factors that affect academic performance, this study aimed to identify the different areas of research and to discover the most cited publications. The connection between publications and research groups was also analysed using CitNetExplorer, software which enables the study of how the scientific literature in a particular field of has evolved.</p></sec><sec id=\"sec2-ijerph-17-05352\"><title>2. Materials and Methods </title><sec id=\"sec2dot1-ijerph-17-05352\"><title>2.1. Data Source</title><p>The search was performed using the Web of Science (WOS) database, by entering the main descriptor: “Academic performance”, as this is the most commonly used expression in both fields, education and health. The search was limited to a topical search (TS) on article, keywords, and abstract, linked with the OR Table. Time was limited to the interval 1952–2019. </p><p>Likewise, the Web of Science database allows the adding of references to its library when performing bibliographical searches directly in external databases or library catalogues.</p><p>With regards to the citation indices, the Social Sciences Citation Index, Science Citation Index Expanded and Emerging Sources Citation Index were used.</p><p>On the other hand, and due to the differences in citation styles among authors and institutions, CiteSpace software was used with a view to standardizing the data. The search and download date of the publications was the 25 April 2020.</p></sec><sec id=\"sec2dot2-ijerph-17-05352\"><title>2.2. Data Analyisis</title><p>All the publications were analyzed using the Citation Network Explorer software. This software is used for analyzing and visualizing the citation networks of scientific publications. It allows the researcher to download citation networks directly from Web of Science. Likewise, it makes it possible to manage citation networks which include millions of publications and related citations. In this way, a citation network comprising millions of publications can be initiated so a deeper analysis can then be performed in order to obtain a smaller subnetwork of 100 publications on the same topic. </p><p>By using the citation score as an attribute, a quantitative analysis of the most cited publications within a specific time interval was performed. Through this, internal connections within the WOS database were quantified. By considering other databases, included in the WOS, the external connections were also quantified [<xref rid=\"B20-ijerph-17-05352\" ref-type=\"bibr\">20</xref>].</p><p>CitNetExplorer offers a number of techniques to analyze the citation networks of publications. The clustering function is achieved using the formula developed by Van Eck in 2012 [<xref rid=\"B20-ijerph-17-05352\" ref-type=\"bibr\">20</xref>].\n<disp-formula id=\"FD1-ijerph-17-05352\"><label>(1)</label><mml:math id=\"mm1\"><mml:mrow><mml:mrow><mml:mi>V</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:msub><mml:mi>c</mml:mi><mml:mrow><mml:mn>1</mml:mn><mml:mo> </mml:mo></mml:mrow></mml:msub><mml:mo>,</mml:mo><mml:mo>…</mml:mo><mml:mo>,</mml:mo><mml:msub><mml:mi>c</mml:mi><mml:mi>n</mml:mi></mml:msub></mml:mrow><mml:mo>)</mml:mo></mml:mrow><mml:mo>=</mml:mo><mml:munder><mml:mstyle mathsize=\"140%\" displaystyle=\"true\"><mml:mo>∑</mml:mo></mml:mstyle><mml:mrow><mml:mi>i</mml:mi><mml:mo><</mml:mo><mml:mi>j</mml:mi></mml:mrow></mml:munder><mml:mi>δ</mml:mi><mml:mo> </mml:mo><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:msub><mml:mi>c</mml:mi><mml:mi>i</mml:mi></mml:msub><mml:mo>,</mml:mo><mml:msub><mml:mi>c</mml:mi><mml:mi>j</mml:mi></mml:msub></mml:mrow><mml:mo>)</mml:mo></mml:mrow><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:msub><mml:mi>s</mml:mi><mml:mrow><mml:mi>i</mml:mi><mml:mi>j</mml:mi></mml:mrow></mml:msub><mml:mo>−</mml:mo><mml:mi>γ</mml:mi></mml:mrow><mml:mo>)</mml:mo></mml:mrow></mml:mrow></mml:mrow></mml:math></disp-formula></p><p>The clustering function was used to establish a group for each publication. This function grouped those publications with a greater level of association according to the citation networks [<xref rid=\"B20-ijerph-17-05352\" ref-type=\"bibr\">20</xref>].</p><p>Finally, the central publications were analyzed using the Identifying Core Publications function. The role of this function was to identify the publications that were considered as the core of the citation network and eliminate those which were considered insignificant. The number of connections was established by the researchers, meaning therefore that the greater this parameter was, the lower the number of central publications would be [<xref rid=\"B20-ijerph-17-05352\" ref-type=\"bibr\">20</xref>]. Thus, those publications that presented four or more citations within the citations network were considered.</p><p>On the other hand, the drilling down function was used, as it allows deep analysis to be carried out at different levels for each of the groups.</p></sec></sec><sec sec-type=\"results\" id=\"sec3-ijerph-17-05352\"><title>3. Results</title><p>The first articles on academic performance were published in 1952, so therefore it was decided that the selected time interval would be from 1952 to 2019. 16,157 publications and 35,213 citations inside the WOS were found. Out of all the publications, 77.62% were articles, 13.5% proceedings papers, 3.75% reviews, 2.16% meeting abstracts, 1.61% book chapters and the remaining 1.01% was comprised of editorial materials, letters, notes, book reviews, corrections, books, data papers, news items, reprints, amendments, additions and retracted publications.</p><p>The number of publications on this topic has increased significantly since 2015 (1952–2014: 46.77% of publications; 2015–2019: 53.23% of publications). 2018 was the year with the highest number of publications, with 1889 publications and 120 citations on the network (<xref ref-type=\"fig\" rid=\"ijerph-17-05352-f001\">Figure 1</xref>).</p><p><xref ref-type=\"fig\" rid=\"ijerph-17-05352-f002\">Figure 2</xref> shows the citation network and <xref rid=\"ijerph-17-05352-t001\" ref-type=\"table\">Table 1</xref> all the details of the 20 most-cited publications. The first was an article by Richardson et al. [<xref rid=\"B21-ijerph-17-05352\" ref-type=\"bibr\">21</xref>], which was published in 2012 and boasted a citation index of 352. This work was a systematic review and meta-analysis of the years between 1997 and 2010 that analyzed 7167 articles on different demographic and psychosocial factors that influence academic performance in university students.</p><p>Out of the 20 most-cited articles, 11 dealt with the psychological factors that affect academic performance [<xref rid=\"B21-ijerph-17-05352\" ref-type=\"bibr\">21</xref>,<xref rid=\"B22-ijerph-17-05352\" ref-type=\"bibr\">22</xref>,<xref rid=\"B23-ijerph-17-05352\" ref-type=\"bibr\">23</xref>,<xref rid=\"B24-ijerph-17-05352\" ref-type=\"bibr\">24</xref>,<xref rid=\"B25-ijerph-17-05352\" ref-type=\"bibr\">25</xref>,<xref rid=\"B26-ijerph-17-05352\" ref-type=\"bibr\">26</xref>,<xref rid=\"B27-ijerph-17-05352\" ref-type=\"bibr\">27</xref>,<xref rid=\"B31-ijerph-17-05352\" ref-type=\"bibr\">31</xref>,<xref rid=\"B32-ijerph-17-05352\" ref-type=\"bibr\">32</xref>,<xref rid=\"B36-ijerph-17-05352\" ref-type=\"bibr\">36</xref>,<xref rid=\"B37-ijerph-17-05352\" ref-type=\"bibr\">37</xref>]. Two of the articles discussed how demographic and cognitive factors and personality can predict future academic performance [<xref rid=\"B30-ijerph-17-05352\" ref-type=\"bibr\">30</xref>,<xref rid=\"B40-ijerph-17-05352\" ref-type=\"bibr\">40</xref>]. Three articles addressed the benefits that healthy lifestyles have on academic performance [<xref rid=\"B33-ijerph-17-05352\" ref-type=\"bibr\">33</xref>,<xref rid=\"B34-ijerph-17-05352\" ref-type=\"bibr\">34</xref>,<xref rid=\"B39-ijerph-17-05352\" ref-type=\"bibr\">39</xref>]. The impact of sleep on academic performance was considered in three of the articles [<xref rid=\"B29-ijerph-17-05352\" ref-type=\"bibr\">29</xref>,<xref rid=\"B35-ijerph-17-05352\" ref-type=\"bibr\">35</xref>,<xref rid=\"B38-ijerph-17-05352\" ref-type=\"bibr\">38</xref>] and the final article focused on how the use of digital devices and social media affects academic performance [<xref rid=\"B28-ijerph-17-05352\" ref-type=\"bibr\">28</xref>].</p><sec id=\"sec3dot1-ijerph-17-05352\"><title>3.1. Description of the Publications.</title><p>Academic performance is a multidisciplinary research field and the areas of education (36%) and psychology (28.24%) (<xref ref-type=\"fig\" rid=\"ijerph-17-05352-f003\">Figure 3</xref>) are particularly worth mentioning. <xref ref-type=\"fig\" rid=\"ijerph-17-05352-f004\">Figure 4</xref> shows the 10 journals with the highest number of publications. </p><p>The countries with the highest number of publications are the United States (39.10%), Spain (8.51%) and England (5.54%) (<xref ref-type=\"fig\" rid=\"ijerph-17-05352-f005\">Figure 5</xref>).</p></sec><sec id=\"sec3dot2-ijerph-17-05352\"><title>3.2. Clustering Function</title><p>23 groups were found using the clustering function, with 9 of these groups containing a significant number of publications; however, the remaining 14 groups only reached 1.5% (<xref ref-type=\"fig\" rid=\"ijerph-17-05352-f006\">Figure 6</xref>).</p><p>With regards to clustering parameters, a resolution value of 1.0 (default value in CitNetExplorer software) was considered, and the minimal publication size for each group was 500.</p><p><xref rid=\"ijerph-17-05352-t002\" ref-type=\"table\">Table 2</xref> shows the information from the citation networks on the nine main groups, ordered from largest to smallest according to their size.</p><p>In group 1, 3223 articles and 11,097 citations were found across the network. The most cited publication was the article by Richardson et al. [<xref rid=\"B21-ijerph-17-05352\" ref-type=\"bibr\">21</xref>], which was published in 2012 in the Psychological Bulletin, and it also ranked first among the 20 most cited publications. In this group, the different articles analyzed the impact of personality, cognitive ability, self-discipline, motivation, and demographic and psychosocial factors on academic performance (<xref ref-type=\"fig\" rid=\"ijerph-17-05352-f007\">Figure 7</xref>).</p><p>In group 2, 1095 publications and 4407 citations were found across the network. The most cited publication in this group was the article by Hillman et al. [<xref rid=\"B33-ijerph-17-05352\" ref-type=\"bibr\">33</xref>], which was published in 2008 in Nature Reviews Neuroscience. In this article, the authors concluded that physical exercise leads to greater physical and mental health throughout life. The articles in group 2 dealt with the influence of visual impairments, such as uncorrected refractive errors, on academic performance. The articles in this group also analyzed the association between the number of hours spent watching television and obesity, as well as the positive impact that a healthy lifestyle, such as a healthy diet or practicing physical exercise, has on cognitive skills and academic performance (<xref ref-type=\"fig\" rid=\"ijerph-17-05352-f008\">Figure 8</xref>).</p><p>In group 3, 971 publications and 2638 citations were found across the network. The most cited publication was the article by Ferguson et al. [<xref rid=\"B30-ijerph-17-05352\" ref-type=\"bibr\">30</xref>], which was published in 2002 in the British Medical Journal. This paper analyzed the requirements for predicting future academic performance, such as previous academic ability, personality, learning, or personal references. The common topic in this group was how personality, demographic data, and mental (depression, anxiety, and stress) and emotional (motivation) states influence academic performance. These papers also considered the academic selection process, focusing specifically on careers in the field of health (<xref ref-type=\"fig\" rid=\"ijerph-17-05352-f009\">Figure 9</xref>).</p><p>In group 4, 776 publications and 1354 citations were found throughout the network. The most cited publication was the article by Fuligni et al. [<xref rid=\"B41-ijerph-17-05352\" ref-type=\"bibr\">41</xref>], which was published in 1997 in Child Development. This article analyzed how family background, parental attitudes, and peer support influence the academic performance of immigrant students. The common theme in this group was the influence of psychosocial and economic factors, such as family, teachers, peers, gender, or corrected refractive errors on academic performance (<xref ref-type=\"fig\" rid=\"ijerph-17-05352-f010\">Figure 10</xref>).</p><p>In group 5, 734 publications and 1567 citations were found throughout the network. The most cited publication was the article by Crede et al. [<xref rid=\"B42-ijerph-17-05352\" ref-type=\"bibr\">42</xref>], which was published in 2010 in Review of Educational Research. In this publication, the authors concluded that class attendance has a positive impact on grades. The common subject in group 5 was the relationship between students who attend class and those who work, and the impact of this on academic performance (<xref ref-type=\"fig\" rid=\"ijerph-17-05352-f011\">Figure 11</xref>).</p><p>In group 6, 665 publications and 1107 citations were found throughout the network. The most cited publication was an article by Duncan et al. [<xref rid=\"B43-ijerph-17-05352\" ref-type=\"bibr\">43</xref>], which was published in 2007 in Developmental Psychology. In this article, the authors analyzed cognitive, attention, and socio-emotional skills in terms of academic performance. The common topic in this group was the relationship between the said skills, drug use, bullying, and delinquency, and academic performance (<xref ref-type=\"fig\" rid=\"ijerph-17-05352-f012\">Figure 12</xref>).</p><p>In group 7, 640 publications and 2407 citations were found throughout the network. The most cited publication was an article by Curcio et al. [<xref rid=\"B29-ijerph-17-05352\" ref-type=\"bibr\">29</xref>], which was published in 2006 in the journal Sleep Medicine Reviews. In this publication, the authors analyzed the impact of sleep on academic performance. The common topic addressed by this group was the impact of sleep and stress on academic performance (<xref ref-type=\"fig\" rid=\"ijerph-17-05352-f013\">Figure 13</xref>).</p><p>In group 8, 619 publications and 1587 citations were found throughout the network. The most cited publication was an article by Dupaul et al. [<xref rid=\"B44-ijerph-17-05352\" ref-type=\"bibr\">44</xref>], which was published in 1991 in School Psychology Review. This study analyzed the academic performance of children with behavioral disorders. The common topic in this group was how hyperactivity disorders influence academic performance (<xref ref-type=\"fig\" rid=\"ijerph-17-05352-f014\">Figure 14</xref>).</p><p>In group 9, 591 publications and 1772 citations were found throughout the network. The most cited publication was an article by Kirschner et al. [<xref rid=\"B28-ijerph-17-05352\" ref-type=\"bibr\">28</xref>], which was published in 2010 in Computers in Human Behavior. This study analyzed the negative impact of social networks, such as Facebook, on academic performance. The common subject in this group was the influence of digital devices and social networks on academic performance (<xref ref-type=\"fig\" rid=\"ijerph-17-05352-f015\">Figure 15</xref>).</p><p><xref rid=\"ijerph-17-05352-t003\" ref-type=\"table\">Table 3</xref> shows a more detailed description of the oldest and most recent publications from the nine main groups.</p><p><xref ref-type=\"fig\" rid=\"ijerph-17-05352-f016\">Figure 16</xref> shows that, after analyzing the relationship among the different groups by means of the drilling down function, no connection has been found between the main publications in different groups.</p><sec id=\"sec3dot2dot1-ijerph-17-05352\"><title>3.2.1. Group 1—Subclusters</title><p>10 subclusters have been found (<xref ref-type=\"fig\" rid=\"ijerph-17-05352-f017\">Figure 17</xref>), five of which boast a significant number of publications (<xref rid=\"ijerph-17-05352-t004\" ref-type=\"table\">Table 4</xref>). The remaining groups are relatively small with less than 200 publications and 1778 citation networks.</p></sec><sec id=\"sec3dot2dot2-ijerph-17-05352\"><title>3.2.2. Group 2—Subclusters</title><p>10 subclusters have been found (<xref ref-type=\"fig\" rid=\"ijerph-17-05352-f018\">Figure 18</xref>), three of which boast a significant number of publications (<xref rid=\"ijerph-17-05352-t005\" ref-type=\"table\">Table 5</xref>). The remaining groups are relatively small with less than 90 publications and 464 citation networks.</p></sec></sec><sec id=\"sec3dot3-ijerph-17-05352\"><title>3.3. Core Function</title><p>4660 publications with four or more citations (28.8% of all publications) and a citation network of 23,747 were found (<xref ref-type=\"fig\" rid=\"ijerph-17-05352-f019\">Figure 19</xref>).</p></sec></sec><sec sec-type=\"discussion\" id=\"sec4-ijerph-17-05352\"><title>4. Discussion</title><p>The main databases, such as WOS and Scopus, allow for the exploration of citation networks. However, these databases do not offer a general overview of the connection between citations from a group of publications. Thus, their usefulness is limited to carrying out a systematic review of all existing literature. This is the reason why we used CitNetExplorer software to visualize, analyze, and explore citation networks of scientific publications. CitNetExplorer offers a more detailed analysis of the citation networks in comparison with other databases such as WOS or Scopus [<xref rid=\"B20-ijerph-17-05352\" ref-type=\"bibr\">20</xref>].</p><p>As our main objective was to analyze all the existing literature on the different factors that influence academic performance, we used the WOS database to perform our bibliographic search, given that it is the only database for which the search range begins in 1900. However, it is important to point out that WOS only accepts journals with an international presence, and these are only admitted following a rigorous selection process. </p><p>In this way, the CitNetExplorer software allowed us to gather and analyze all of the literature available on academic performance up to the present date. Furthermore, the connection between the fields of study and the different research groups was also found by using citation network analysis.</p><p>In order to obtain the results, the following functionalities were used: the clustering functionality, as it allows the publications to be grouped by citation relationship; the drilling down functionality, which provides a deeper analysis of the existing bibliography for each of the groups; and the core publications functionality, which shows the most relevant publications, i.e., those which have a minimum citation number. Therefore, these functionalities enable the researcher to conduct a complete analysis of the studies published on a particular subject. </p><p>In recent years, the number of publications on academic performance has significantly increased. In the past, research carried out on this topic focused predominantly on the influence of psychological factors on academic performance [<xref rid=\"B80-ijerph-17-05352\" ref-type=\"bibr\">80</xref>,<xref rid=\"B81-ijerph-17-05352\" ref-type=\"bibr\">81</xref>]. From the year 2000, the advent of digital devices brought about scientific research into their influence on academic performance. These devices were responsible for the growth of a more sedentary lifestyle which resulted in numerous studies on the impact of such behaviour. </p><p>Since 2015, the number of publications on psychological and psychosocial factors has increased by 52.83% and they have taken a multidisciplinary turn. As such, multiple factors that affect academic performance, such as demographic, psychosocial, psychological, lifestyle, and mental and physical health factors, have been analysed. [<xref rid=\"B82-ijerph-17-05352\" ref-type=\"bibr\">82</xref>,<xref rid=\"B83-ijerph-17-05352\" ref-type=\"bibr\">83</xref>,<xref rid=\"B84-ijerph-17-05352\" ref-type=\"bibr\">84</xref>,<xref rid=\"B85-ijerph-17-05352\" ref-type=\"bibr\">85</xref>]. </p><p>With regards to the authors, Kirk is worth mentioning in the early years, for having published over 70 papers on academic performance between 1952 and 1982. Currently, Rozelle and Salamonson are considered as two of the most prominent authors in this field of study, as they have published 40 and 33 papers respectively in the period between the years 2005 and 2010 and up to date.</p><p>2018 has been identified as a “key year”, given the large number of publications and the content of the documents, in which the main topic of study was the importance of mental and physical health on academic performance [<xref rid=\"B86-ijerph-17-05352\" ref-type=\"bibr\">86</xref>,<xref rid=\"B87-ijerph-17-05352\" ref-type=\"bibr\">87</xref>]. In the study conducted by Stajkovic et al. [<xref rid=\"B86-ijerph-17-05352\" ref-type=\"bibr\">86</xref>], published in 2018, the authors confirmed that awareness and emotional stability predict self-efficacy and this is positively related to academic performance. In the study by de Greeff et al. [<xref rid=\"B87-ijerph-17-05352\" ref-type=\"bibr\">87</xref>], published in 2018, it was determined that physical activity has a positive effect on attention, while long-term physical activity programs have a positive impact on executive functions, attention, and academic performance. Therefore, this study has confirmed that all of the factors that influence academic performance are interrelated, with psychological and psychosocial factors considered to be the most prevalent.</p><p>The countries with a greater number of publications are the United States, Spain and Britain. The educational systems of these countries are intense, which places students at a higher risk of anxiety and stress. Furthermore, and with regards to psychosocial factors, the students are more competitive. Consequently, many studies have focused on analyzing risk factors for academic performance in these countries. Nevertheless, it must be taken into consideration that, in the case of China, students are showing symptoms of anxiety and depression more often, and therefore the number of publications is expected to increase over the coming years. </p><p>In the United States, the number of publications is significantly higher than in other countries. This is due to increased interest in the topics related to student learning. Likewise, the trend for the number of publications to increase in the field of education in countries like the United States or Britain has been associated with a combination of factors, such as being English-speaking countries and the possible connections among the various research groups in the scientific community [<xref rid=\"B88-ijerph-17-05352\" ref-type=\"bibr\">88</xref>,<xref rid=\"B89-ijerph-17-05352\" ref-type=\"bibr\">89</xref>].</p><p>With regards to the journals with the larger numbers of publications, it is worth mentioning that they are mainly Psychology and Educational Science journals, as psychological and psychosocial factors are those with the greatest influence. Moreover, the majority of the journals are from the United States and Britain.</p><p>Nowadays, students are more competitive and are therefore at a greater risk of suffering from stress. Several authors have defined stress as “an adverse reaction that people have to excessive pressures and demands placed on them. In other words, stress arises when people find themselves in an overwhelming situation and believe they are unable to cope with it” [<xref rid=\"B90-ijerph-17-05352\" ref-type=\"bibr\">90</xref>,<xref rid=\"B91-ijerph-17-05352\" ref-type=\"bibr\">91</xref>]. The study by Aafreen et al. [<xref rid=\"B92-ijerph-17-05352\" ref-type=\"bibr\">92</xref>], which was published in 2018, corroborated that students who are unable to manage stress are affected mentally, physically and emotionally and as such they tend to present anxiety and depression, which leads to a decline in academic performance. At the same time, the articles by Chapell et al. [<xref rid=\"B36-ijerph-17-05352\" ref-type=\"bibr\">36</xref>] and Cassady et al. [<xref rid=\"B37-ijerph-17-05352\" ref-type=\"bibr\">37</xref>], published in 2005 and 2002, respectively, focused on the relationship between stress and final grades. Both of the studies concluded that higher levels of anxiety were associated with lower grades and vice versa. That is to say, stress has a negative impact on academic performance and, likewise, the results demonstrated that female students present higher levels of anxiety than male students. Nowadays, the preparation of students is not only based on their studying for an undergraduate degree, but also on the idea that they at least have to have a master’s degree, which can lead to a higher prevalence of stress, anxiety and depression.</p><p>Health professionals recommend physical exercise and a balanced diet to prevent stress. Several studies have shown that people who exercise are less likely to suffer from anxiety and depression, and that stress symptoms can be improved by doing exercise [<xref rid=\"B93-ijerph-17-05352\" ref-type=\"bibr\">93</xref>,<xref rid=\"B94-ijerph-17-05352\" ref-type=\"bibr\">94</xref>]. Another study carried out in Spain among university students demonstrated that having a poor diet and being overweight are strongly associated with higher levels of stress, especially in women, and these factors have a negative impact on academic performance [<xref rid=\"B95-ijerph-17-05352\" ref-type=\"bibr\">95</xref>]. Consequently, scientific evidence reveals a significant relationship between academic performance and health status, therefore vision problems, nutrition, stress, obesity, anxiety, behavioral disorders and aggression are associated with a poor academic performance [<xref rid=\"B96-ijerph-17-05352\" ref-type=\"bibr\">96</xref>]. It should be noted that the personality of students with psychosocial well-being is characterized by determination and pragmatism in their academic efforts, therefore enabling them to establish high-level objectives that they pursue successfully [<xref rid=\"B13-ijerph-17-05352\" ref-type=\"bibr\">13</xref>].</p><p>In the future, health and welfare programs must be established in schools to reduce the incidence of behaviors that could have a detrimental effect on the physical and mental health of young people, as this could result in improved academic performance. In the study by Van Loon et al. [<xref rid=\"B97-ijerph-17-05352\" ref-type=\"bibr\">97</xref>], published in 2019, two school programs were created to improve the control of anxiety and social abilities. These programs were aimed at preventing mental health problems that have a negative impact on academic performance. It is also important to consider that as students get older they become more competitive, therefore meaning that they are more likely to suffer from physical and mental damage. As a result, the content of programs aimed at controlling stress in order to improve academic performance will be different in both primary and secondary education and at university.</p><p>This study provides a brand new approach which can guide researchers in the revision of the wealth of literature that exists on academic performance. Over the coming years, research in this field will continue to focus mainly on the impact of psychological factors on academic performance as a consequence of the constant increase in competitiveness among students. In this way, research questions based on this data posed by future studies will focus mainly on how psychological and psychosocial factors could be improved in order to increase academic performance.</p><p>Likewise, the number of studies on the impact of the use of devices on academic performance is also likely to increase, given that constant changes in lifestyle are becoming more and more frequent. With regards to citation network studies, they are also likely to become more numerous as it is the only method of analysis which provides a global overview of the different research fields within a particular subject. Moreover, the CitNetExplorer software facilitates the analysis of all existing studies on a given topic, thus allowing for detailed research to be conducted, which might change the way in which research is carried out in different fields of study.</p></sec><sec sec-type=\"conclusions\" id=\"sec5-ijerph-17-05352\"><title>5. Conclusions</title><p>In this study, 16,157 publications on academic performance were analysed, which were obtained from the Web of Science (WOS) database between 1952 and 2019. All of the publications were analysed using the Citation Network Explorer software, which makes it possible to visualize, analyze and explore the citation networks of scientific publications.</p><p>Academic performance may be influenced by various factors, including psychological, psychosocial, economic, environmental or personal factors. Self-sufficient students present learning and self-control skills which facilitate studying, and as a consequence increase their motivation. This means that they have the skills and willingness to learn. Likewise, the personality of the students with psychosocial wellbeing is often characterized by determination and pragmatism in academic effort, which enables them to set high-level goals for themselves, which they successfully pursue. On the other hand, scientific evidence suggests a significant relationship between academic performance and health, meaning that vision problems, nutrition, stress, obesity, anxiety, behavioral disorders or aggression are associated with poor academic performance. This is why it is necessary to establish health and wellbeing programs in schools, which are aimed at preventing behaviors that put the mental and physical health of young people at risk, and at improving their academic performance.</p><p>The citation network showed the main publications on the different factors that affect academic performance, and it was determined that psychological and psychosocial factors were the most relevant. This study offers a global overview of the number of publications for each of the years and field of study, as well as a general description of the 20 most-cited publications.</p></sec></body><back><notes><title>Author Contributions</title><p>Conceptualization, C.A.-P., M.A.S.-T., C.M.-P. and C.V.-C.; methodology, C.A.-P., M.Á.S.-T., C.M.-P. and C.V.-C.; software, C.A.-P., M.Á.S.-T., C.M.-P. and C.V.-C.; validation, C.A.-P., M.Á.S.-T., C.M.-P. and C.V.-C.; formal analysis, C.A.-P., M.Á.S.-T., C.M.-P. and C.V.-C.; investigation, C.A.-P., M.Á.S.-T., C.M.-P. and C.V.-C.; resources, C.A.-P., M.Á.S.-T., C.M.-P. and C.V.-C.; data curation, C.A.-P., M.Á.S.-T., C.M.-P. and C.V.-C.; writing—original draft preparation, C.A.-P., M.Á.S.-T., C.M.-P. and C.V.-C.; writing—review and editing, C.A.-P., M.Á.S.-T., C.M.-P. and C.V.-C.; visualization, C.A.-P., M.Á.S.-T., C.M.-P. and C.V.-C.; supervision, C.A.-P., M.Á.S.-T., C.M.-P. and C.V.-C.; project administration, C.A.-P., M.Á.S.-T., C.M.-P. and C.V.-C.; funding acquisition, C.A.-P., M.Á.S.-T., C.M.-P. and C.V.-C. All authors have read and agreed to the published version of the manuscript.</p></notes><notes><title>Funding</title><p>This research received no external funding.</p></notes><notes notes-type=\"COI-statement\"><title>Conflicts of Interest</title><p>The authors declare no conflict of interest. </p></notes><ref-list><title>References</title><ref id=\"B1-ijerph-17-05352\"><label>1.</label><element-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Ali</surname><given-names>N.</given-names></name><name><surname>Jusoff</surname><given-names>K.</given-names></name><name><surname>Ali</surname><given-names>S.</given-names></name><name><surname>Mokhtar</surname><given-names>N.</given-names></name><name><surname>Salamat</surname><given-names>A.S.A.</given-names></name></person-group><article-title>The Factors Influencing Students’ Performance at Universiti Teknologi MARA Kedah, Malaysia</article-title><source>Manag. Sci. 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xlink:href=\"ijerph-17-05352-g003\"/></fig><fig id=\"ijerph-17-05352-f004\" orientation=\"portrait\" position=\"float\"><label>Figure 4</label><caption><p>Top ten journals with the most publications.</p></caption><graphic xlink:href=\"ijerph-17-05352-g004\"/></fig><fig id=\"ijerph-17-05352-f005\" orientation=\"portrait\" position=\"float\"><label>Figure 5</label><caption><p>Number of publications by country.</p></caption><graphic xlink:href=\"ijerph-17-05352-g005\"/></fig><fig id=\"ijerph-17-05352-f006\" orientation=\"portrait\" position=\"float\"><label>Figure 6</label><caption><p>Clustering function in the network of citations on academic performance.</p></caption><graphic xlink:href=\"ijerph-17-05352-g006\"/></fig><fig id=\"ijerph-17-05352-f007\" orientation=\"portrait\" position=\"float\"><label>Figure 7</label><caption><p>Group 1 citation network.</p></caption><graphic xlink:href=\"ijerph-17-05352-g007\"/></fig><fig id=\"ijerph-17-05352-f008\" orientation=\"portrait\" position=\"float\"><label>Figure 8</label><caption><p>Group 2 citation network.</p></caption><graphic xlink:href=\"ijerph-17-05352-g008\"/></fig><fig id=\"ijerph-17-05352-f009\" orientation=\"portrait\" position=\"float\"><label>Figure 9</label><caption><p>Group 3 citation network.</p></caption><graphic xlink:href=\"ijerph-17-05352-g009\"/></fig><fig id=\"ijerph-17-05352-f010\" orientation=\"portrait\" position=\"float\"><label>Figure 10</label><caption><p>Group 4 citation network.</p></caption><graphic xlink:href=\"ijerph-17-05352-g010\"/></fig><fig id=\"ijerph-17-05352-f011\" orientation=\"portrait\" position=\"float\"><label>Figure 11</label><caption><p>Group 5 citation network.</p></caption><graphic xlink:href=\"ijerph-17-05352-g011\"/></fig><fig id=\"ijerph-17-05352-f012\" orientation=\"portrait\" position=\"float\"><label>Figure 12</label><caption><p>Group 6 citation network.</p></caption><graphic xlink:href=\"ijerph-17-05352-g012\"/></fig><fig id=\"ijerph-17-05352-f013\" orientation=\"portrait\" position=\"float\"><label>Figure 13</label><caption><p>Group 7 citation network.</p></caption><graphic xlink:href=\"ijerph-17-05352-g013\"/></fig><fig id=\"ijerph-17-05352-f014\" orientation=\"portrait\" position=\"float\"><label>Figure 14</label><caption><p>Group 8 citation network.</p></caption><graphic xlink:href=\"ijerph-17-05352-g014\"/></fig><fig id=\"ijerph-17-05352-f015\" orientation=\"portrait\" position=\"float\"><label>Figure 15</label><caption><p>Group 9 citation network.</p></caption><graphic xlink:href=\"ijerph-17-05352-g015\"/></fig><fig id=\"ijerph-17-05352-f016\" orientation=\"portrait\" position=\"float\"><label>Figure 16</label><caption><p>Relationship between the nine major groups.</p></caption><graphic xlink:href=\"ijerph-17-05352-g016\"/></fig><fig id=\"ijerph-17-05352-f017\" orientation=\"portrait\" position=\"float\"><label>Figure 17</label><caption><p>Group 1-subclusters citation network.</p></caption><graphic xlink:href=\"ijerph-17-05352-g017\"/></fig><fig id=\"ijerph-17-05352-f018\" orientation=\"portrait\" position=\"float\"><label>Figure 18</label><caption><p>Group 2-subclusters citation network.</p></caption><graphic xlink:href=\"ijerph-17-05352-g018\"/></fig><fig id=\"ijerph-17-05352-f019\" orientation=\"portrait\" position=\"float\"><label>Figure 19</label><caption><p>Core Function in the academic performance citation network.</p></caption><graphic xlink:href=\"ijerph-17-05352-g019\"/></fig><table-wrap id=\"ijerph-17-05352-t001\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05352-t001_Table 1</object-id><label>Table 1</label><caption><p>Details of the 20 most cited publications on academic performance.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Author</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Title</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Year</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Total Citation</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Citation Rate</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Richardson et al. [<xref rid=\"B21-ijerph-17-05352\" ref-type=\"bibr\">21</xref>]</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Psychological correlates of university students’ academic performance: A systematic review and meta-analysis.</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2012</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">352</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">50.28</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Pintrich<break/>et al. [<xref rid=\"B22-ijerph-17-05352\" ref-type=\"bibr\">22</xref>]</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Motivational and self-regulated learning components of classroom academic performance.</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1990</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">344</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">11.86</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Poropat<break/>[<xref rid=\"B23-ijerph-17-05352\" ref-type=\"bibr\">23</xref>]</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">A meta-analysis of the five-factor model of personality and academic performance</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2009</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">270</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">24.54</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Robbins<break/>et al. [<xref rid=\"B24-ijerph-17-05352\" ref-type=\"bibr\">24</xref>]</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Do Psychosocial and Study Skill Factors Predict College Outcomes? A Meta-Analysis</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2004</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">239</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">14.94</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Chamorro-Premuzic et al. [<xref rid=\"B25-ijerph-17-05352\" ref-type=\"bibr\">25</xref>]</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Personality predicts academic performance: Evidence from two longitudinal university samples</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2003</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">179</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">11.19</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Duckworth et al. [<xref rid=\"B26-ijerph-17-05352\" ref-type=\"bibr\">26</xref>]</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Self-Discipline Outdoes IQ in Predicting Academic Performance of Adolescents</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2005</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">165</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">11.78</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">O’Connor et al. [<xref rid=\"B27-ijerph-17-05352\" ref-type=\"bibr\">27</xref>]</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Big Five personality predictors of post-secondary academic performance</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2007</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">159</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">13.25</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Kirschner et al. [<xref rid=\"B28-ijerph-17-05352\" ref-type=\"bibr\">28</xref>]</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Facebook<sup>®</sup> and academic performance</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2010</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">127</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">14.11</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Curcio et al. [<xref rid=\"B29-ijerph-17-05352\" ref-type=\"bibr\">29</xref>]</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Sleep loss, learning capacity and academic performance</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2006</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">126</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">9.69</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Ferguson et al. [<xref rid=\"B30-ijerph-17-05352\" ref-type=\"bibr\">30</xref>]</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Factors associated with success in medical school: systematic review of the literature</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2002</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">125</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">7.35</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Chemers et al. [<xref rid=\"B31-ijerph-17-05352\" ref-type=\"bibr\">31</xref>]</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Academic self-efficacy and first year college student performance and adjustment.</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2001</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">122</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">6.78</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Kuncel et al. [<xref rid=\"B32-ijerph-17-05352\" ref-type=\"bibr\">32</xref>]</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Academic Performance, Career Potential, Creativity, and Job Performance: Can One Construct Predict Them All?</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2004</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">112</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">7.47</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Hillman et al. [<xref rid=\"B33-ijerph-17-05352\" ref-type=\"bibr\">33</xref>]</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Be smart, exercise your heart: exercise effects on brain and cognition</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2008</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">104</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">9.45</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Castelli et al. [<xref rid=\"B34-ijerph-17-05352\" ref-type=\"bibr\">34</xref>]</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Physical Fitness and Academic Achievement in Third- and Fifth-Grade Students</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2007</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">103</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">8.58</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Trockel et al. [<xref rid=\"B35-ijerph-17-05352\" ref-type=\"bibr\">35</xref>]</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Health-Related Variables and Academic Performance Among First-Year College Students: Implications for Sleep and Other Behaviors</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2000</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">98</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">5.16</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Chapell et al. [<xref rid=\"B36-ijerph-17-05352\" ref-type=\"bibr\">36</xref>]</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Test Anxiety and Academic Performance in Undergraduate and Graduate Students</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2005</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">95</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">6.78</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Cassady et al. [<xref rid=\"B37-ijerph-17-05352\" ref-type=\"bibr\">37</xref>]</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Cognitive Test Anxiety and Academic Performance</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2002</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">94</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">5.53</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Wolfson et al. [<xref rid=\"B38-ijerph-17-05352\" ref-type=\"bibr\">38</xref>]</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Understanding adolescents’ sleep patterns and school performance: a critical appraisal</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2003</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">94</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">5.87</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Rampersaud et al. [<xref rid=\"B39-ijerph-17-05352\" ref-type=\"bibr\">39</xref>]</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Breakfast Habits, Nutritional Status, Body Weight, and Academic Performance in Children and Adolescents</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2005</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">94</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">6.71</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Dyrbye et al. [<xref rid=\"B40-ijerph-17-05352\" ref-type=\"bibr\">40</xref>]</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Systematic Review of Depression, Anxiety, and Other Indicators of Psychological Distress Among U.S. and Canadian Medical Students</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2006</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">94</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">7.23</td></tr></tbody></table></table-wrap><table-wrap id=\"ijerph-17-05352-t002\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05352-t002_Table 2</object-id><label>Table 2</label><caption><p>Citation network information on the nine main groups.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Main Cluster</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Number of Publications</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Number of Citation Links</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Number of Citations Median (Range)</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Number of Publications with ≥4 Citations</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Number of Publications in 100 Most Cited Publication</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Group 1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3223</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">11,097</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1 (0-352)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1734</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">47</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Group 2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1094</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4407</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1 (0–104)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">481</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">22</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Group 3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">971</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2638</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1 (0–125)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">494</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Group 4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">777</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1356</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1 (0–35)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">461</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Group 5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">734</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1567</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1 (0–55)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">456</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Group 6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">665</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1107</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1 (0–21)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">489</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Group 7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">640</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2407</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1 (0–126)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">256</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">12</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Group 8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">619</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1587</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1 (0–35)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">409</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Group 9</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">591</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1772</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1 (0–127)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">329</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">9</td></tr></tbody></table></table-wrap><table-wrap id=\"ijerph-17-05352-t003\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05352-t003_Table 3</object-id><label>Table 3</label><caption><p>The oldest and most recent journal information from the nine main groups.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Cluster</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Autor</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Title</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Year</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Total Citation</th></tr></thead><tbody><tr><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Group 1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Pioneers</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Jones et al. [<xref rid=\"B45-ijerph-17-05352\" ref-type=\"bibr\">45</xref>]</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">The Individual High School as a Predictor of College Academic Performance</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1962</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Most Recent</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Carmona-Halty et al. [<xref rid=\"B46-ijerph-17-05352\" ref-type=\"bibr\">46</xref>]</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">How Psychological Capital Mediates Between Study–Related Positive Emotions and Academic Performance</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2019</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3</td></tr><tr><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Group 2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Pioneers</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Rourke et al. [<xref rid=\"B47-ijerph-17-05352\" ref-type=\"bibr\">47</xref>]</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Neuropsychological significance of variations in patterns of academic performance: Verbal and visual-spatial abilities</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1978</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Most Recent</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Singh et al. [<xref rid=\"B48-ijerph-17-05352\" ref-type=\"bibr\">48</xref>]</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Effects of physical activity interventions on cognitive and academic performance in children and adolescents: a novel combination of a systematic review and recommendations from an expert panel</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2019</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">12</td></tr><tr><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Group 3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Pioneers</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Flook et al. [<xref rid=\"B49-ijerph-17-05352\" ref-type=\"bibr\">49</xref>]</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Academic performance with, and without, knowledge of scores on tests of intelligence, aptitude, and personality</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1968</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Most Recent</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Hu et al. [<xref rid=\"B50-ijerph-17-05352\" ref-type=\"bibr\">50</xref>]</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Maladaptive Perfectionism, Impostorism, and Cognitive Distortions: Threats to the Mental Health of Pre-clinical Medical Students</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2019</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1</td></tr><tr><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Group 4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Pioneers</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Finger et al. [<xref rid=\"B51-ijerph-17-05352\" ref-type=\"bibr\">51</xref>]</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Academic performance of public and private school students</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1963</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Most Recent</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Chyn et al. [<xref rid=\"B52-ijerph-17-05352\" ref-type=\"bibr\">52</xref>]</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Housing Voucher Take-Up and Labor Market Impacts</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2018</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1</td></tr><tr><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Group 5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Pioneers</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Henry et al. [<xref rid=\"B53-ijerph-17-05352\" ref-type=\"bibr\">53</xref>]</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Part-time employment and academic performance of freshmen</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1967</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Most Recent</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Nordamann et al. [<xref rid=\"B54-ijerph-17-05352\" ref-type=\"bibr\">54</xref>]</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Turn up, tune in, don’t drop out: the relationship between lecture attendance, use of lecture recordings, and achievement at different levels of study</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2019</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1</td></tr><tr><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Group 6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Pioneers</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Bewley et al. [<xref rid=\"B55-ijerph-17-05352\" ref-type=\"bibr\">55</xref>]</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Academic-performance and social-factors related to cigarette-smoking by schoolchildren</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1977</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Most Recent</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Pörhölä et al. [<xref rid=\"B56-ijerph-17-05352\" ref-type=\"bibr\">56</xref>]</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Bullying and social anxiety experiences in university learning situations</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2019</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1</td></tr><tr><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Group 7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Pioneers</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Lucas et al. [<xref rid=\"B57-ijerph-17-05352\" ref-type=\"bibr\">57</xref>]</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Interaction in University Selection, Mental Health and Academic Performance</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1972</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Most Recent</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Adelantado-Renau et al. [<xref rid=\"B58-ijerph-17-05352\" ref-type=\"bibr\">58</xref>]</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">The effect of sleep quality on academic performance is mediated by Internet use time: DADOS study</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2019</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2</td></tr><tr><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Group 8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Pioneers</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Chadwick et al. [<xref rid=\"B59-ijerph-17-05352\" ref-type=\"bibr\">59</xref>]</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Systematic reinforcement: academic performance of underachieving students</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1971</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Most Recent</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Kortekaas-rijaarsdam et al. [<xref rid=\"B60-ijerph-17-05352\" ref-type=\"bibr\">60</xref>]</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Does methylphenidate improve academic performance? A systematic review and meta-analysis</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2019</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1</td></tr><tr><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Group 9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Pioneers</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Cooper et al. [<xref rid=\"B61-ijerph-17-05352\" ref-type=\"bibr\">61</xref>]</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">The importance of race and social class information in the formation of expectancies about academic performance</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1975</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Most Recent</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Al-Rahmi et al. [<xref rid=\"B62-ijerph-17-05352\" ref-type=\"bibr\">62</xref>]</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Massive Open Online Courses (MOOCs): Data on higher education</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2019</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1</td></tr></tbody></table></table-wrap><table-wrap id=\"ijerph-17-05352-t004\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05352-t004_Table 4</object-id><label>Table 4</label><caption><p>The most important subclusters from group 1.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Sub-Cluster</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">4</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">5</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">No. of publications</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">727</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">665</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">284</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">280</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">251</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">No. of citation links</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1985</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2447</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">495</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">704</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">708</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Pioneers</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Schultz et al., 1993 [<xref rid=\"B63-ijerph-17-05352\" ref-type=\"bibr\">63</xref>]</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Savage et al., 1962 [<xref rid=\"B64-ijerph-17-05352\" ref-type=\"bibr\">64</xref>]</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Greiner et al., 1997 [<xref rid=\"B65-ijerph-17-05352\" ref-type=\"bibr\">65</xref>]</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Carrier et al., 1966 [<xref rid=\"B66-ijerph-17-05352\" ref-type=\"bibr\">66</xref>]</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Kennelly et al., 1975 [<xref rid=\"B67-ijerph-17-05352\" ref-type=\"bibr\">67</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Most cited</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Richardson et al., 2012 [<xref rid=\"B21-ijerph-17-05352\" ref-type=\"bibr\">21</xref>]</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Poropat et al., 2009 [<xref rid=\"B23-ijerph-17-05352\" ref-type=\"bibr\">23</xref>]</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Schaufeli et al., 2002 [<xref rid=\"B68-ijerph-17-05352\" ref-type=\"bibr\">68</xref>]</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Chapell et al., 2005 [<xref rid=\"B36-ijerph-17-05352\" ref-type=\"bibr\">36</xref>]</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Aronson et al., 2002 [<xref rid=\"B69-ijerph-17-05352\" ref-type=\"bibr\">69</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Most Recent</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Trigeros Ramos et al., 2019 [<xref rid=\"B70-ijerph-17-05352\" ref-type=\"bibr\">70</xref>]</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Proyer et al., 2019 [<xref rid=\"B71-ijerph-17-05352\" ref-type=\"bibr\">71</xref>]</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Carmona et al., 2019 [<xref rid=\"B46-ijerph-17-05352\" ref-type=\"bibr\">46</xref>]</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Alammari et al., 2018 [<xref rid=\"B72-ijerph-17-05352\" ref-type=\"bibr\">72</xref>]</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Wang et al., 2019 [<xref rid=\"B73-ijerph-17-05352\" ref-type=\"bibr\">73</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Topic of discussion</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Influence of motivation on academic performance</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Influence of personality on academic performance</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Influence of self-discipline and emotions on academic performance</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Influence of anxiety on academic performance</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Influence of demographic psychology on academic performance</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Conclusion</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Motivation has a positive influence on academic performance</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Research into this group is still being carried out, therefore consensus has not yet been reached </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Self-control strategies have a positive influence on academic performance</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Anxiety has a negative influence on final grades; therefore self-control strategies are necessary.</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Intelligence is malleable; therefore the negative stereotypes of immigrant children and academic performance must be eliminated </td></tr></tbody></table></table-wrap><table-wrap id=\"ijerph-17-05352-t005\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05352-t005_Table 5</object-id><label>Table 5</label><caption><p>The most important subclusters from group 2.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Sub-Cluster</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">No. of publications</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">431</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">186</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">141</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">No. of citation links</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2324</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">571</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">359</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Pioneers</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Nelson et al., 1993 [<xref rid=\"B74-ijerph-17-05352\" ref-type=\"bibr\">74</xref>]</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Nidich et al., 1993 [<xref rid=\"B75-ijerph-17-05352\" ref-type=\"bibr\">75</xref>]</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Kovacs et al., 1992 [<xref rid=\"B76-ijerph-17-05352\" ref-type=\"bibr\">76</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Most cited</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Hillman et al., 2008 [<xref rid=\"B33-ijerph-17-05352\" ref-type=\"bibr\">33</xref>]</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Rampersaud et al., 2005 [<xref rid=\"B39-ijerph-17-05352\" ref-type=\"bibr\">39</xref>]</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Datar et al., 2004 [<xref rid=\"B77-ijerph-17-05352\" ref-type=\"bibr\">77</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Most Recent</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Singh et al., 2019 [<xref rid=\"B48-ijerph-17-05352\" ref-type=\"bibr\">48</xref>]</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Adelantado-Renau et al., 2019 [<xref rid=\"B78-ijerph-17-05352\" ref-type=\"bibr\">78</xref>]</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Allison et al., 2019 [<xref rid=\"B79-ijerph-17-05352\" ref-type=\"bibr\">79</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Topic of discussion</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">The benefits of physical exercise on academic performance</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">The benefits of a healthy diet on academic performance</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">The link between state of health and academic performance</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Conclusion</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Physical exercise improves mental and physical health throughout life.</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">A diet that is rich in fiber, nutrients, fruits and dairy products is recommended.</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Poor mental and physical health has a negative impact on academic performance</td></tr></tbody></table></table-wrap></floats-group></article>\n"
]
[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Int J Environ Res Public Health</journal-id><journal-id journal-id-type=\"iso-abbrev\">Int J Environ Res Public Health</journal-id><journal-id journal-id-type=\"publisher-id\">ijerph</journal-id><journal-title-group><journal-title>International Journal of Environmental Research and Public Health</journal-title></journal-title-group><issn pub-type=\"ppub\">1661-7827</issn><issn pub-type=\"epub\">1660-4601</issn><publisher><publisher-name>MDPI</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32707973</article-id><article-id pub-id-type=\"pmc\">PMC7432078</article-id><article-id pub-id-type=\"doi\">10.3390/ijerph17155280</article-id><article-id pub-id-type=\"publisher-id\">ijerph-17-05280</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Article</subject></subj-group></article-categories><title-group><article-title>Teacher Technostress in the Chilean School System</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Estrada-Muñoz</surname><given-names>Carla</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05280\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Castillo</surname><given-names>Dante</given-names></name><xref ref-type=\"aff\" rid=\"af2-ijerph-17-05280\">2</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0002-9427-2044</contrib-id><name><surname>Vega-Muñoz</surname><given-names>Alejandro</given-names></name><xref ref-type=\"aff\" rid=\"af3-ijerph-17-05280\">3</xref><xref rid=\"c1-ijerph-17-05280\" ref-type=\"corresp\">*</xref></contrib><contrib contrib-type=\"author\"><name><surname>Boada-Grau</surname><given-names>Joan</given-names></name><xref ref-type=\"aff\" rid=\"af4-ijerph-17-05280\">4</xref></contrib></contrib-group><aff id=\"af1-ijerph-17-05280\"><label>1</label>Departamento de Ergonomía, Universidad de Concepción, Concepción 4070386, Chile; <email>[email protected]</email></aff><aff id=\"af2-ijerph-17-05280\"><label>2</label>Centro de Estudios e Investigación Enzo Faletto, Universidad de Santiago de Chile, Santiago 9170022, Chile; <email>[email protected]</email></aff><aff id=\"af3-ijerph-17-05280\"><label>3</label>Facultad de Administración y Negocios, Universidad Autónoma de Chile, Providencia 7500912, Chile</aff><aff id=\"af4-ijerph-17-05280\"><label>4</label>Departamento de Psicología, Universidad Rovira i Virgili, 43007 Tarragona, Spain; <email>[email protected]</email></aff><author-notes><corresp id=\"c1-ijerph-17-05280\"><label>*</label>Correspondence: <email>[email protected]</email></corresp></author-notes><pub-date pub-type=\"epub\"><day>22</day><month>7</month><year>2020</year></pub-date><pub-date pub-type=\"ppub\"><month>8</month><year>2020</year></pub-date><volume>17</volume><issue>15</issue><elocation-id>5280</elocation-id><history><date date-type=\"received\"><day>29</day><month>5</month><year>2020</year></date><date date-type=\"accepted\"><day>15</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>© 2020 by the authors.</copyright-statement><copyright-year>2020</copyright-year><license license-type=\"open-access\"><license-p>Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>).</license-p></license></permissions><abstract><p>The expanded use of information technology in education has led to the emergence of technostress due to a lack of adaptation to the technological environment. The purpose of this study is to identify the levels of technostress in primary and secondary education in 428 teachers using a RED-TIC questionnaire, of which skepticism, fatigue, anxiety, and inefficiency are the main components. For the empirical analysis of the data, principal component analysis (PCA) and confirmatory factor analysis (CFA) were used. The results show that 12% of Chilean teachers participating in the study feel techno-fatigued, 13% feel techno-anxious, and 11% present both conditions. Male teachers show a higher incidence of techno-anxiety and techno-fatigue than their female peers. It can be concluded that the questionnaire used is a reliable tool to evaluate the presence of technostress, and it manifests itself importantly in its components of techno-anxiety and techno-fatigue in Chilean teachers.</p></abstract><kwd-group><kwd>anxiety</kwd><kwd>confirmatory factor analysis</kwd><kwd>education</kwd><kwd>fatigue</kwd><kwd>inefficacy</kwd><kwd>information overload</kwd><kwd>principal components analysis</kwd><kwd>skepticism</kwd></kwd-group></article-meta></front><body><sec sec-type=\"intro\" id=\"sec1-ijerph-17-05280\"><title>1. Introduction</title><p>Psychic and mental conditions significantly influence people’s overall health, and particularly their daily performance [<xref rid=\"B1-ijerph-17-05280\" ref-type=\"bibr\">1</xref>,<xref rid=\"B2-ijerph-17-05280\" ref-type=\"bibr\">2</xref>,<xref rid=\"B3-ijerph-17-05280\" ref-type=\"bibr\">3</xref>,<xref rid=\"B4-ijerph-17-05280\" ref-type=\"bibr\">4</xref>,<xref rid=\"B5-ijerph-17-05280\" ref-type=\"bibr\">5</xref>]. According to the latest annual statistical report of the Chilean Social Security Superintendence, mental health disorders are the main cause of medical licenses (necessary in order to be absent or reduce one’s work day during a certain period of time) in 20% of the total number of authorized medical licenses. Of these licenses by mental health disorders, 76% correspond to females, in case of the public health system (FONASA), and 59% correspond to females, in case of the private health system (ISAPRE) [<xref rid=\"B6-ijerph-17-05280\" ref-type=\"bibr\">6</xref>].</p><p>This relationship between health and performance is seen in labor relations, especially in professions that are linked daily to communities, beneficiaries, or clients. In this way, this is particularly important for teachers in the school system, because they are connected and have a responsibility to educate children and young people. The relationship between teachers and students, given the social relevance and the prolonged time during which the two interact, exerts a decisive influence on students’ lives. It is therefore relevant to analyze the mental health and psychosocial risk manifestations of teachers, for both the well-being of themselves as well as for the effects it has on the teaching process and on students’ development.</p><p>Work stress manifestations and teachers’ mental health have been a topic of interest for at least two decades; in Anglo-Saxon and European societies, mental health concern has been perceived since the early 1970s, while in Chilean society, more systematic efforts have been made to delve into this topic since the 1990s [<xref rid=\"B7-ijerph-17-05280\" ref-type=\"bibr\">7</xref>]. The unexpected relevance of concern for teachers’ mental health has a double explanation. On the one hand, since the educational reforms of the 1990s, expert discourse has argued that Chilean education has had significant deficiencies related to the low motivation and low participation of teachers in curriculum changes as well as a lack of adaptation to social changes, explained by the mental attitude of teachers [<xref rid=\"B8-ijerph-17-05280\" ref-type=\"bibr\">8</xref>,<xref rid=\"B9-ijerph-17-05280\" ref-type=\"bibr\">9</xref>,<xref rid=\"B10-ijerph-17-05280\" ref-type=\"bibr\">10</xref>].</p><p>On the other hand, concern for teaching health has also been explained by studies carried out by the United Nations Educational, Scientific, and Cultural Organization’s (UNESCO) Regional Office for Latin America and the Caribbean (OREALC). The most significant being the Regional Comparative and Explanatory Studies, which periodically seek to assess the learning achievements of Latin American students. In some of these, educational success factors associated with the psychosocial condition of teachers were identified [<xref rid=\"B11-ijerph-17-05280\" ref-type=\"bibr\">11</xref>,<xref rid=\"B12-ijerph-17-05280\" ref-type=\"bibr\">12</xref>]. However, interest was concentrated on the environmental situations of the educational institution, with a focus on the climate and on school coexistence, overshadowing the personal condition of teachers. In other words, work stress in teachers was overshadowed by corporate health.</p><p>In this context, work stress assessment or work characteristics reviews carried out by teachers were postponed. Teachers’ professional attributes go beyond their working hours and daily tasks. Thus, in the case of teachers, analyzing work stress by only considering the elements of work done in the classroom work would neglect the emotional and moral burden of teaching. In short, teachers work every day and at all times. This is because of the peculiar characteristics of the teaching profession, which can potentially cause significant stress and psychosocial damage [<xref rid=\"B13-ijerph-17-05280\" ref-type=\"bibr\">13</xref>]. As Golembiewski et al. showed [<xref rid=\"B14-ijerph-17-05280\" ref-type=\"bibr\">14</xref>], worldwide, the teaching profession is strongly related to higher stress levels. Teaching seems to have an inherent component of potentiality for stress, fatigue, and states of inner unease. In this way, at an international level, there has been strong interest in undertaking studies on work stress in teachers. In terms of gender distinction, in 2007, Oramas et al. [<xref rid=\"B15-ijerph-17-05280\" ref-type=\"bibr\">15</xref>] reported on a study conducted between 1997 and 1999 by Weber et al. (2015), in which all 408 cases of early retirement in teachers were reviewed. In that study, 45% gave psychosomatic and psychiatric disorders as a cause, with a higher incidence occurring in women than in men, which is historically due to the double presence or work–family conflict among women [<xref rid=\"B16-ijerph-17-05280\" ref-type=\"bibr\">16</xref>,<xref rid=\"B17-ijerph-17-05280\" ref-type=\"bibr\">17</xref>]. This was the main gender difference identified between psychosocial risks in Chile after the SUSESO/ISTAS21 questionnaire in 2016, with an odds ratio (female/male) of 1.59, which decreased between 2017 and 2019, with values of 1.59, 1.09, and 1.02, respectively [<xref rid=\"B18-ijerph-17-05280\" ref-type=\"bibr\">18</xref>,<xref rid=\"B19-ijerph-17-05280\" ref-type=\"bibr\">19</xref>,<xref rid=\"B20-ijerph-17-05280\" ref-type=\"bibr\">20</xref>,<xref rid=\"B21-ijerph-17-05280\" ref-type=\"bibr\">21</xref>,<xref rid=\"B22-ijerph-17-05280\" ref-type=\"bibr\">22</xref>]. These psychiatric disorders included depression and emotional exhaustion. In another study by Lodolo-D’Oria et al. [<xref rid=\"B23-ijerph-17-05280\" ref-type=\"bibr\">23</xref>], four professions were compared, namely teachers, office workers, health professionals, and utility workers, in relation to mental disorders, and it was concluded that the risk of developing psychiatric disorders for teachers is 2 times, 2.5 times, and 3 times greater than for office workers, health professionals, and utility workers, respectively; once again with a higher incidence in the case of female teachers. However, although interest in work stress in teachers in Chile has significantly declined since the new millennium, the expansion and use of computer and communication technology in Chilean schools has offered a new opportunity to investigate the psychosocial and mental risk manifestations associated with these new resources and methodologies.</p><p>This article describes a research project performed in the second half of 2019, which deals with the relationship between the incorporation and mass use of computer and communications technology in teaching and learning. The research took place in primary and secondary schools in the surrounding regions of Valparaiso and the Metropolitan of Santiago, Chile.</p><p>The purpose of this publication is to approximate the state of the mental health of Chilean teachers who work in public and private schools with a state grant, in relation to the incorporation of computer and communication technology. To this end, we set out to use the criteria used by the Spanish Ministry of Labor, after validation in Chile, to diagnose some technostress manifestations associated with the mental health of education workers.</p></sec><sec id=\"sec2-ijerph-17-05280\"><title>2. Background</title><p>The use of technology can lead to beneficial transformative changes within an organization; however, it can also lead to negative consequences in job satisfaction deterioration, commitment, work continuity, productivity, and morale, as well as increased work overload and work–life conflict [<xref rid=\"B24-ijerph-17-05280\" ref-type=\"bibr\">24</xref>,<xref rid=\"B25-ijerph-17-05280\" ref-type=\"bibr\">25</xref>,<xref rid=\"B26-ijerph-17-05280\" ref-type=\"bibr\">26</xref>]. These negative effects are further emphasized with the ubiquity of mobile devices connected to the network and through continued work development, even after working hours [<xref rid=\"B25-ijerph-17-05280\" ref-type=\"bibr\">25</xref>]. In this regard, O’Driscoll et al., cited by Day et al. [<xref rid=\"B27-ijerph-17-05280\" ref-type=\"bibr\">27</xref>], point out that the approach of increasing employees’ accessibility to their “work environment” and increasing their productivity expectations through the use of information technology increases workload requirements. Thus, the use of information technology creates an imbalance between the demands and control resources of users, overcoming the possibility of being able to self-manage stressors [<xref rid=\"B28-ijerph-17-05280\" ref-type=\"bibr\">28</xref>,<xref rid=\"B29-ijerph-17-05280\" ref-type=\"bibr\">29</xref>,<xref rid=\"B30-ijerph-17-05280\" ref-type=\"bibr\">30</xref>,<xref rid=\"B31-ijerph-17-05280\" ref-type=\"bibr\">31</xref>].</p><p>The concept of technostress was first noted in mainstream magazines in 1982 by Craig Brod, as a condition resulting from an individual and/or organizational inability to healthily adapt to new technology use, which is modulated according to age, previous techno experiences, workload, perception of control, and working climate, and consequently affects people’s performance, thus limiting their use of technology [<xref rid=\"B32-ijerph-17-05280\" ref-type=\"bibr\">32</xref>,<xref rid=\"B33-ijerph-17-05280\" ref-type=\"bibr\">33</xref>]. In general, the concept includes the adverse effects caused by technology on people’s attitudes, thoughts, behaviors, and physiology [<xref rid=\"B34-ijerph-17-05280\" ref-type=\"bibr\">34</xref>]. In this regard, as psychosomatic consequences are recognized, namely, sleep problems, headaches, muscle aches, and gastrointestinal disorders, in the long term, teachers may end up developing exhaustion (burn-out syndrome) [<xref rid=\"B29-ijerph-17-05280\" ref-type=\"bibr\">29</xref>]. People who experience psychological and emotional rejection to information technology, for example, experiencing breakdowns, fear, tension, or anxiety, may stop or prevent their ability to learn [<xref rid=\"B35-ijerph-17-05280\" ref-type=\"bibr\">35</xref>].</p><p>Technostress develops from the concurrence of multiple and intense stress conditions experienced by the worker in the extended organizational environment, whose dynamics promote tension, known as techno-stressors, which can be present in any work environment where computers are used. These technological stressors are technological invasion, technological overload, technological complexity, technological insecurity, and technological uncertainty [<xref rid=\"B26-ijerph-17-05280\" ref-type=\"bibr\">26</xref>,<xref rid=\"B35-ijerph-17-05280\" ref-type=\"bibr\">35</xref>,<xref rid=\"B36-ijerph-17-05280\" ref-type=\"bibr\">36</xref>,<xref rid=\"B37-ijerph-17-05280\" ref-type=\"bibr\">37</xref>]. In accordance with the ideas of Ayyagari et al. [<xref rid=\"B38-ijerph-17-05280\" ref-type=\"bibr\">38</xref>], stressors can be caused by tasks (work overload, work schedule, and exposure to risks and dangers), role characteristics (ambiguity, conflict, and overload), interactions within the organization (interpersonal relationships and leadership style), career (work insecurity and career advancement), organizational factors (climate and structure), work–home interface (work–home conflict and privacy invasion), and characteristics related to the physical work environment (noise, temperature, and vibration), all of which can be accentuated with the use of information technology at work.</p><p>It should be added that techno-stressors do not necessarily have a direct effect, and may be mediated by employees’ stress or fear and through their coping strategies, consistent with the stress dynamics and coping theory of Lazarus [<xref rid=\"B39-ijerph-17-05280\" ref-type=\"bibr\">39</xref>,<xref rid=\"B40-ijerph-17-05280\" ref-type=\"bibr\">40</xref>]. Additionally, technostress unfolds among related constructs such as “information fatigue syndrome” or techno-fatigue, techno-addiction, and technophobia [<xref rid=\"B41-ijerph-17-05280\" ref-type=\"bibr\">41</xref>]. From another perspective, the organization of the internal environment influences technostress levels in workers; in the data, a significant positive relationship can be found between technostress levels and a centralized power structure and an organizational environment oriented to innovation [<xref rid=\"B35-ijerph-17-05280\" ref-type=\"bibr\">35</xref>].</p><p>High levels of stress can affect people, even having direct negative effects on health. For example, subjects exposed to the repeated malfunction of information technology, namely collapsing computer systems, showed increased levels of cortisol (a stress-associated hormone), with their average levels sharply increasing directly after a system collapse, which could affect a person’s health [<xref rid=\"B34-ijerph-17-05280\" ref-type=\"bibr\">34</xref>]. From a management perspective, there must be a situation of balance between people and their environment, so that there is no tension state [<xref rid=\"B38-ijerph-17-05280\" ref-type=\"bibr\">38</xref>,<xref rid=\"B39-ijerph-17-05280\" ref-type=\"bibr\">39</xref>,<xref rid=\"B40-ijerph-17-05280\" ref-type=\"bibr\">40</xref>,<xref rid=\"B41-ijerph-17-05280\" ref-type=\"bibr\">41</xref>,<xref rid=\"B42-ijerph-17-05280\" ref-type=\"bibr\">42</xref>]. Therefore, companies can facilitate adaptation strategies by improving the internal knowledge of their information systems; reducing stressful technological factors of work environments by reducing the exhaustion of their workers; and, in general, considering the interaction between techno-stressors, technostress, and coping strategies [<xref rid=\"B25-ijerph-17-05280\" ref-type=\"bibr\">25</xref>,<xref rid=\"B26-ijerph-17-05280\" ref-type=\"bibr\">26</xref>].</p><p>In the education sector, technostress has been studied in the last two decades, with various focuses; on the one hand, employees in the educational system have been selected in order to access certain groups of the population [<xref rid=\"B43-ijerph-17-05280\" ref-type=\"bibr\">43</xref>,<xref rid=\"B44-ijerph-17-05280\" ref-type=\"bibr\">44</xref>]. On the other hand, educational processes have been directly considered; studies on technostress have been identified in library processes [<xref rid=\"B45-ijerph-17-05280\" ref-type=\"bibr\">45</xref>,<xref rid=\"B46-ijerph-17-05280\" ref-type=\"bibr\">46</xref>] and there are other publications that advise about technostress among university students and their learning processes [<xref rid=\"B47-ijerph-17-05280\" ref-type=\"bibr\">47</xref>,<xref rid=\"B48-ijerph-17-05280\" ref-type=\"bibr\">48</xref>,<xref rid=\"B49-ijerph-17-05280\" ref-type=\"bibr\">49</xref>,<xref rid=\"B50-ijerph-17-05280\" ref-type=\"bibr\">50</xref>], computer literacy, and digital thinking [<xref rid=\"B51-ijerph-17-05280\" ref-type=\"bibr\">51</xref>], as well as with their use of digital textbooks [<xref rid=\"B52-ijerph-17-05280\" ref-type=\"bibr\">52</xref>]. However, the study group of interest for this research is that related to the teaching role [<xref rid=\"B53-ijerph-17-05280\" ref-type=\"bibr\">53</xref>,<xref rid=\"B54-ijerph-17-05280\" ref-type=\"bibr\">54</xref>,<xref rid=\"B55-ijerph-17-05280\" ref-type=\"bibr\">55</xref>,<xref rid=\"B56-ijerph-17-05280\" ref-type=\"bibr\">56</xref>,<xref rid=\"B57-ijerph-17-05280\" ref-type=\"bibr\">57</xref>,<xref rid=\"B58-ijerph-17-05280\" ref-type=\"bibr\">58</xref>,<xref rid=\"B59-ijerph-17-05280\" ref-type=\"bibr\">59</xref>].</p><p>The initial literature regarding teacher technostress considers it to be caused by the introduction of technology into the classroom and from a lack of adaptation to the technological environment [<xref rid=\"B53-ijerph-17-05280\" ref-type=\"bibr\">53</xref>]. This can be reduced when teachers receive administrative support for the use of technology (continuous access to technical support and updated technology for the preparation and development of their activities), which gives them a supporting atmosphere [<xref rid=\"B54-ijerph-17-05280\" ref-type=\"bibr\">54</xref>]. That support influences technostress, which, in turn, affects the intention of the technological use by teachers [<xref rid=\"B56-ijerph-17-05280\" ref-type=\"bibr\">56</xref>]. Recent research on Indian and Chinese higher education emphasizes the presence of techno-stressors and techno-inhibitors that influence job satisfaction; organizational commitment; negative affectivity due to work; and, above all, technology-mediated performance [<xref rid=\"B55-ijerph-17-05280\" ref-type=\"bibr\">55</xref>,<xref rid=\"B57-ijerph-17-05280\" ref-type=\"bibr\">57</xref>,<xref rid=\"B58-ijerph-17-05280\" ref-type=\"bibr\">58</xref>,<xref rid=\"B59-ijerph-17-05280\" ref-type=\"bibr\">59</xref>]. In general, the lack of adaptation between people and their work environment affects their job performance [<xref rid=\"B57-ijerph-17-05280\" ref-type=\"bibr\">57</xref>]. Studies that focus on South Korean and Chinese education also incorporate a model based on teaching measurement based technology, pedagogy, and content knowledge (TPACK) as a study variable [<xref rid=\"B56-ijerph-17-05280\" ref-type=\"bibr\">56</xref>,<xref rid=\"B57-ijerph-17-05280\" ref-type=\"bibr\">57</xref>,<xref rid=\"B58-ijerph-17-05280\" ref-type=\"bibr\">58</xref>,<xref rid=\"B60-ijerph-17-05280\" ref-type=\"bibr\">60</xref>].</p><p>Thus, this study focuses on measuring the psychological state related to the use of information technology in primary and secondary school teachers in order to identify manifestations of the psychosocial risks, contributing to expanding on studies at a school level and reporting on the first empirical study in Latin America.</p><p>According to the above literature, the following research hypothesis were set out for this article (<xref ref-type=\"fig\" rid=\"ijerph-17-05280-f001\">Figure 1</xref>):</p><statement><label><bold>Hypothesis</bold> <bold>1</bold> <bold>(H1).</bold></label><p><italic>There is a statistically positive relationship between the gender of teachers and the techno-anxiety levels measured by a technostress instrument</italic>.</p></statement><statement><label><bold>Hypothesis</bold> <bold>2</bold> <bold>(H2).</bold></label><p>\n<italic>There is a statistically positive relationship between the age groups of teachers and the techno-anxiety levels measured by a technostress instrument.</italic>\n</p></statement><statement><label><bold>Hypothesis</bold> <bold>3</bold> <bold>(H3).</bold></label><p>\n<italic>There is a statistically positive relationship between the gender of teachers and the techno-fatigue levels measured by a technostress instrument.</italic>\n</p></statement><statement><label><bold>Hypothesis</bold> <bold>4</bold> <bold>(H4).</bold></label><p>\n<italic>There is a statistically positive relationship between the age groups of teachers and the techno-fatigue levels measured by a technostress instrument.</italic>\n</p></statement></sec><sec sec-type=\"methods\" id=\"sec3-ijerph-17-05280\"><title>3. Methods </title><sec id=\"sec3dot1-ijerph-17-05280\" sec-type=\"subjects\"><title>3.1. Participants</title><p>The database of teachers was obtained through probabilistic sampling. The sample was stratified into three groups depending on the type of educational center, namely, public, private with state grants, and private without state grants. Tiered sampling allowed for reducing the variation in results due to the strata of the population and for obtaining a greater accuracy in the estimates [<xref rid=\"B61-ijerph-17-05280\" ref-type=\"bibr\">61</xref>,<xref rid=\"B62-ijerph-17-05280\" ref-type=\"bibr\">62</xref>]. The optimal sample size was calculated using a statistical procedure in order to reject a null hypothesis, when in fact that hypothesis is false or has the potential to avoid a type II error.</p><p>The sample size calculation was performed considering a maximum acceptable error of 5%, a confidence level of 95%, and a 50% variance assumption. The population consisted of 105,970 teachers, corresponding to the regions of Santiago and Valparaiso (35,804 teachers from public schools, 53,437 teachers from private schools with state grants, and 16,729 teachers from private schools without state grants) [<xref rid=\"B63-ijerph-17-05280\" ref-type=\"bibr\">63</xref>]. Under these parameters, the total sample was 428 teachers working in primary and secondary schools (152 public school teachers, 210 state-subsidized private school teachers, and 66 unsubsidized private school teachers).</p><p>In relation to the teacher sample characteristics, 276 were women (64.5%) and 152 men (35.5%). The age ranged from 23 to 67 years old, with an arithmetic average of 39.6 years. In addition, 262 teachers were employed with an indefinite contract (61.2%), while 160 teachers had a fixed-term contract (37.4%). At the same time, 392 teachers had a professional degree (91.6%), and 36 teachers taught in schools without having a professional degree (8.4%).</p></sec><sec id=\"sec3dot2-ijerph-17-05280\"><title>3.2. Procedure</title><p>The RED-TIC questionnaire integrated into the Technical Note of Prevention 730 of the National Institute for Safety and Hygiene at Work of Spain, which focuses on intra-labor psychosocial risks as a product of the techno-demands, and on a lack of techno-resources and personal resources [<xref rid=\"B29-ijerph-17-05280\" ref-type=\"bibr\">29</xref>,<xref rid=\"B64-ijerph-17-05280\" ref-type=\"bibr\">64</xref>], was used as a basis. This questionnaire is composed of skepticism, fatigue, anxiety, and inefficiency dimensions (see <xref ref-type=\"app\" rid=\"app1-ijerph-17-05280\">Appendix A</xref>). This is reliable for the teaching function in Chile, with a Cronbach’s Alpha 0.941 and a Cronbach’s Alpha based on standardized items of 0.946 [<xref rid=\"B31-ijerph-17-05280\" ref-type=\"bibr\">31</xref>]. For the empirical analysis of the data, the principal component analysis (PCA) was used, a type of multivariate statistical analysis previously used in teaching-stress research [<xref rid=\"B65-ijerph-17-05280\" ref-type=\"bibr\">65</xref>,<xref rid=\"B66-ijerph-17-05280\" ref-type=\"bibr\">66</xref>,<xref rid=\"B67-ijerph-17-05280\" ref-type=\"bibr\">67</xref>,<xref rid=\"B68-ijerph-17-05280\" ref-type=\"bibr\">68</xref>,<xref rid=\"B69-ijerph-17-05280\" ref-type=\"bibr\">69</xref>]. Along with this process, the consistency of dimensions was analyzed using Cronbach’s Alpha, as presented in previous research [<xref rid=\"B29-ijerph-17-05280\" ref-type=\"bibr\">29</xref>]. PCA allows for reducing the dimensionality of the data to the principal components based on statistical and sociological criteria [<xref rid=\"B70-ijerph-17-05280\" ref-type=\"bibr\">70</xref>].</p><p>Additionally, Bartlett’s sphericity test was performed in order to assess the relevance of PCA under the hypothesis of multivariate normality, and the Kaiser–Meyer–Olkin (KMO) test was used as the factorial analysis in order to establish the feasibility of the obtained data [<xref rid=\"B70-ijerph-17-05280\" ref-type=\"bibr\">70</xref>,<xref rid=\"B71-ijerph-17-05280\" ref-type=\"bibr\">71</xref>].</p></sec><sec id=\"sec3dot3-ijerph-17-05280\"><title>3.3. Data Analysis</title><p>For the data analysis, R statistical analysis software was employed using the RStudio interface, and the sorting and descriptive exploration processes of the data were carried out with the metapackage “Tidyverse” [<xref rid=\"B72-ijerph-17-05280\" ref-type=\"bibr\">72</xref>] and with “Summarytools” [<xref rid=\"B73-ijerph-17-05280\" ref-type=\"bibr\">73</xref>]. The principal component analysis was performed with the “FactoMineR” and “FactoExtra” packages [<xref rid=\"B74-ijerph-17-05280\" ref-type=\"bibr\">74</xref>,<xref rid=\"B75-ijerph-17-05280\" ref-type=\"bibr\">75</xref>], and the Bartlett and KMO sphericity tests were performed using the “REdaS” package [<xref rid=\"B76-ijerph-17-05280\" ref-type=\"bibr\">76</xref>]. For the factor rotation, the “psych” package [<xref rid=\"B77-ijerph-17-05280\" ref-type=\"bibr\">77</xref>] was used and the “Varimax” method was employed. The confirmatory factor analysis was performed with the CFA function of the “lavaan” package [<xref rid=\"B78-ijerph-17-05280\" ref-type=\"bibr\">78</xref>].</p></sec></sec><sec sec-type=\"results\" id=\"sec4-ijerph-17-05280\"><title>4. Results</title><sec id=\"sec4dot1-ijerph-17-05280\"><title>4.1. Principal Component Analysis</title><p>The results were obtained using the principal component analysis (PCA) for the 16 variables or reagents included in the technostress instrument, in order to empirically estimate the reliability of the instrument. In this way, the data showed a good correlation, while the Bartlett’s sphericity test indicated an appropriate value (χ<sup>2</sup> = 4520.424; df = 120; <italic>p</italic> < 0.01); thus, <inline-formula><mml:math id=\"mm1\"><mml:mrow><mml:mrow><mml:msub><mml:mi>H</mml:mi><mml:mn>0</mml:mn></mml:msub></mml:mrow></mml:mrow></mml:math></inline-formula> was rejected and it was assumed that there were differences between the observed correlation matrix and the identity matrix. The KMO index was 0.91, indicating a very good or optimal value for continuing the factor analysis.</p><p>The extraction of components using the analysis of the initial self-value indicated that three dimensions were detected with values over 1. In the classic recommendation for the selection of the number of factors, the Kaiser rule suggests choosing all auto values greater than 1. The component matrix gave factorial loads ≥0.40; the first dimension concentrated on factorial loads of a higher value, except for the r_1 and r_2 variables, which correlated with the second extracted dimension, and the third dimension acquired a greater correlation with the r_5 variable (−0.54). However, the same variable had a larger load with one dimension (0.56). In the rotated solution, the loads could be observed in a different distribution, where rotated component 1 (RC1) mostly contained the loads of the variables r_5 and r_9 to r_16; RC3 had r_1, r_2, r_6, r_7, and r_8 loads; and RC2 had r_3 and r_4 loads. Additionally, the Cronbach’s Alpha generated an alpha value of 0.92 and a standardized alpha of 0.93. The minimum alpha value of a variable was 0.91.</p></sec><sec id=\"sec4dot2-ijerph-17-05280\"><title>4.2. Confirmatory Analysis Factorial</title><p>To validate the instrument globally, a confirmatory factor analysis was performed (CFA) [<xref rid=\"B79-ijerph-17-05280\" ref-type=\"bibr\">79</xref>]. All of the items turned out to be significant, and the goodness of fit indexes met the established criteria, namely: the comparative adjustment index (CFI) was 0.90 [<xref rid=\"B80-ijerph-17-05280\" ref-type=\"bibr\">80</xref>], the root mean square error of approximation (RMSEA) was 0.103 [<xref rid=\"B81-ijerph-17-05280\" ref-type=\"bibr\">81</xref>], the Tucker Lewis Index (TLI) was 0.90 [<xref rid=\"B82-ijerph-17-05280\" ref-type=\"bibr\">82</xref>], and the residual standardized root mean square (SRMR) was 0.05 [<xref rid=\"B83-ijerph-17-05280\" ref-type=\"bibr\">83</xref>]. To perform the validation, the CFA function of the lavaan package was used [<xref rid=\"B78-ijerph-17-05280\" ref-type=\"bibr\">78</xref>].</p></sec><sec id=\"sec4dot3-ijerph-17-05280\"><title>4.3. Tecnostress in Chilean Teachers</title><p>The technostress instrument was built from four subscales, which, when composed, allowed for identifying two types of technostress manifestations because of the presence of intra-labor psychosocial risks. Thus, high scores in those dimensions will be technostress indicators in its two manifestations: (1) techno-anxiety (high scores in anxiety, skepticism, and inefficiency) and (2) techno-fatigue (high scores in fatigue, skepticism, and inefficiency). However, with the reliability provided by the evaluation instrument, the results showed that, in the case of Chilean teachers, 11.9% were techno-fatigued, and another 13.1% showed a techno-anxious status. Furthermore, 10.7% of this population presented both pathologies (<xref rid=\"ijerph-17-05280-t001\" ref-type=\"table\">Table 1</xref>). In other words, in a school of 50 teachers, at least five of them should be on occupational sickness medical leave.</p></sec><sec id=\"sec4dot4-ijerph-17-05280\"><title>4.4. Technoanxiety Manifestations in Chilean Teachers</title><p>Techno-anxiety, as a work pathology, is the best-known type of technostress, where a person experiences high levels of non-pleasant physiological activation and feels tension and discomfort from the present or future use of some type of information and communication technology (ICT). The same anxiety leads to skeptical attitudes about the use of technology, as well as negative thoughts about one’s ability and competence in the use of information technology. A specific type of techno-anxiety is technophobia, which focuses on the affective dimension of fear and anxiety towards ICT. However, to guide public policies, data were analyzed based on the variable of gender. In this regard, it was interesting to note that male teachers were more techno-anxious than their female peers (<xref rid=\"ijerph-17-05280-t002\" ref-type=\"table\">Table 2</xref>).</p><p>To confirm the trends observed between the two subpopulations, Student’s t-test confirmed the presence of statistically significant differences between the two groups of teachers (<xref rid=\"ijerph-17-05280-t003\" ref-type=\"table\">Table 3</xref>). In other words, for the Chilean case, the processed information confirmed, in terms of gender, that male teachers showed a higher incidence of techno-anxiety than their female peers. </p><p>Regarding the second hypothesis (H2), which is concerned with a statistically positive relationship between teaching staff age groups and techno-anxiety levels, the results did not show a statistically significant association between both variables. This is confirmed by the value shown by the Pearson’s R bilateral correlation test (<xref rid=\"ijerph-17-05280-t004\" ref-type=\"table\">Table 4</xref>).</p></sec><sec id=\"sec4dot5-ijerph-17-05280\"><title>4.5. Techno-Fatigue Manifestations in Chilean Teachers</title><p>Moreover, techno-fatigue is characterized by feelings of exhaustion and mental and cognitive exhaustion due to the use of technology, which is also accompanied by skeptical attitudes and inefficiency beliefs regarding the use of information technology. A specific type of techno-fatigue is the so-called “information fatigue” syndrome, derived from the current requirements of the information society; this is caused by information overload when the Internet is used. Symptomatology is a lack of competence to organize and assimilate new information derived from the use of the Internet, with the consequent appearance of mental fatigue. Based on the obtained data, it could be also established that this pathology tends to be more present in male teachers (<xref rid=\"ijerph-17-05280-t005\" ref-type=\"table\">Table 5</xref>).</p><p>The tendency has been supported by the Student’s t-test, which indicates the presence of statistically significant differences between male teachers and their female peers (<xref rid=\"ijerph-17-05280-t006\" ref-type=\"table\">Table 6</xref>).</p><p>Regarding the fourth hypothesis (H4), which proposes a statistically positive relationship between teachers’ age groups and the techno-fatigue levels measured by the technostress instrument, the results of the R Pearson test show that there was no statistically significant correlation between both variables, as indicated in <xref rid=\"ijerph-17-05280-t007\" ref-type=\"table\">Table 7</xref>.</p></sec></sec><sec sec-type=\"discussion\" id=\"sec5-ijerph-17-05280\"><title>5. Discussion</title><p>The changes generated by new technology require studies so as to avoid risks and negative effects on schools and teachers. Additionally, a history of prevention regulations and prevention services, assessing risks, and identifying working conditions that may be affected by the intensive introduction of the so-called new technology is needed. As a result, using empirical backgrounds, we need to address the effects of technological innovations on education in order to prevent negative impacts and enhance positive ones, both individually and organizationally.</p><p>To date, several psychosocial research groups have studied the various expressions and consequences of the introduction of information and communication technology (ICT) into people’s health at work, such as muscle problems, headaches, mental and physical fatigue, anxiety, and fear. However, considering the intensive use currently given to telework, the term technostress has become more important. It should be understood as a manifestation of psychosocial risk specifically related to stress derived from the introduction and use of new technology at work.</p><p>For the first time, based on the criteria used by the Spanish Ministry of Labor and Social Affairs through the National Institute of Safety and Hygiene at Work, previously adapted to the Chilean context, this study considered technostress manifestations in the teaching population. This provided up-to-date empirical evidence and enabled the exploration of the relationships between the use of technology in teaching work and technostress levels. This study has become especially relevant because in the context of the global health crisis resulting from the Covid-19 pandemic, public and private institutionalism is drastically driving the incorporation of computer and communications technology in all areas of work activity; teaching and educational work are concentrating almost exclusively on technological media. </p><sec id=\"sec5dot1-ijerph-17-05280\"><title>5.1. Theoretical Implications</title><p>From the analysis of the results based on the proposed hypothesis models, it was expected that age could influence the techno-anxiety or techno-fatigue manifestations observed in teachers. This was supported by general theories of aging. In other words, to develop our H2 and H4 hypotheses, it was proposed that there was enough background to point to a relationship between age and technostress. On the one hand, aging is connected to physical degeneration processes such as cognitive decline [<xref rid=\"B84-ijerph-17-05280\" ref-type=\"bibr\">84</xref>,<xref rid=\"B85-ijerph-17-05280\" ref-type=\"bibr\">85</xref>], which make older adults more likely to be exposed to some techno-stressors [<xref rid=\"B86-ijerph-17-05280\" ref-type=\"bibr\">86</xref>]. On the other hand, aging also seems to be connected to a greater recovery. Thus, older age groups would have a broader collection of strategies to more efficiently address the management of emotions [<xref rid=\"B87-ijerph-17-05280\" ref-type=\"bibr\">87</xref>,<xref rid=\"B88-ijerph-17-05280\" ref-type=\"bibr\">88</xref>]. Based on the above, our assumptions suggested that such age-related gains led to a more efficient confrontation with techno-stimulating factors. As a result, age would reduce the tension associated with the use of technology in teaching. Despite this, the obtained results contradicted—at least from the work activity of teachers—the theoretical background, and a significant relationship between age and techno-anxiety or age and techno-fatigue could not be demonstrated. Finally, a high correlation and significance was observed between the variables of age and professional teaching experience in years (Pearson’s R correlation: 0.924, with a bilateral significance of 0.000); thus, the trends between age and years of teaching experience are equivalent.</p><p>Regarding the relationship between gender condition and job stress, it is important to consider that workplace demands will establish more pressure on the female gender (as the individual maintains its link relationship from the social role that is assigned), so the “erudite discourse” considers that gender is a stress experience moderator. This would be explained by the associated roles and behavioral expectations of different genders. In this sense, some studies have pointed to two conflicting results related to the experience of stress and gender. On the one hand, some evidence demonstrates that there are no differences between men and women [<xref rid=\"B89-ijerph-17-05280\" ref-type=\"bibr\">89</xref>], while on the other hand, there is also evidence that gender can cause significant differences. Some studies have found that men are significantly more affected by stress [<xref rid=\"B90-ijerph-17-05280\" ref-type=\"bibr\">90</xref>,<xref rid=\"B91-ijerph-17-05280\" ref-type=\"bibr\">91</xref>,<xref rid=\"B92-ijerph-17-05280\" ref-type=\"bibr\">92</xref>]; however, at the opposite end, other studies have indicated that women are the most affected by stress [<xref rid=\"B93-ijerph-17-05280\" ref-type=\"bibr\">93</xref>,<xref rid=\"B94-ijerph-17-05280\" ref-type=\"bibr\">94</xref>,<xref rid=\"B95-ijerph-17-05280\" ref-type=\"bibr\">95</xref>,<xref rid=\"B96-ijerph-17-05280\" ref-type=\"bibr\">96</xref>].</p><p>When gender is related to stress, the differences between men and women are more reflected in the elements that cause stress and in their coping mechanisms. In this sense, studies that have determined gender differences argue that, for men, the elements that cause work stress are a lack of control over working conditions, and achievement and possibilities for career development; whereas in the case of women, causes of stress appear from being in a high position within the hierarchical structure, and the relationship established between domestic and employment responsibilities [<xref rid=\"B91-ijerph-17-05280\" ref-type=\"bibr\">91</xref>,<xref rid=\"B97-ijerph-17-05280\" ref-type=\"bibr\">97</xref>,<xref rid=\"B98-ijerph-17-05280\" ref-type=\"bibr\">98</xref>,<xref rid=\"B99-ijerph-17-05280\" ref-type=\"bibr\">99</xref>]. Despite this, from the results provided in this study and as pointed out by the above-mentioned studies, there is significant statistical evidence showing the relationship between gender and technostress manifestations. This is why, in both techno-anxiety and techno-fatigue, male teachers showed higher levels of stress than their female peers. In this way, the H1 and H3 proposals of the hypotheses used in this research are accepted. </p></sec><sec id=\"sec5dot2-ijerph-17-05280\"><title>5.2. Practical Implications</title><p>This study took place in a scenario that we call “normal work”, showing that 1 in 10 Chilean teachers present with a pathology linked to the use of technology. Therefore, it is perfectly possible to infer, in a scenario of social distancing and confinement, where the use of technology is promoted as a mediating channel, that the different manifestations of technostress could be significantly increased.</p><p>This article has focused on contributing to the scientific literature on the psychological state of teachers because of their interaction with information and communication technology, and their use as mediators of the teaching profession. It also allows for evaluating the implications that the use of information technology have on the health and well-being of education workers, incorporating a gender and age perspective.</p><p>The incorporation of the Internet, mobile telephony, telecommuting, and other resources that the information society is introducing into schools is changing not only the pedagogical processes; all these technological changes are also expressed in the appearance of new occupational pathologies. Unfortunately, this has been scarcely analyzed in Latin American societies. These changes highlight technical problems, but also human and social problems. Therefore, it is imperative to start an in-depth debate, especially because of the consequences for teachers, students, and families who are directly and indirectly affected.</p><p>This investigation recognizes the need to prevent risks and avoid negative effects on teachers and schools. It contributes to visualizing how the technostress effects of teaching need to result in rethinking educational policies, considering the incorporation of the digital world into the initial teacher training and professional development [<xref rid=\"B100-ijerph-17-05280\" ref-type=\"bibr\">100</xref>], the articulation of the recent telework law [<xref rid=\"B101-ijerph-17-05280\" ref-type=\"bibr\">101</xref>] with the labor regime exclusive to the “teaching statute” [<xref rid=\"B102-ijerph-17-05280\" ref-type=\"bibr\">102</xref>], and to what extent the principles of the General Education Law may be affected (universality and permanent education, free, quality of education, equity, autonomy, diversity, responsibility, participation, flexibility, transparency, integration and inclusion, sustainability, interculturality, and dignity of the human being, integral education) [<xref rid=\"B103-ijerph-17-05280\" ref-type=\"bibr\">103</xref>].</p></sec><sec id=\"sec5dot3-ijerph-17-05280\"><title>5.3. Limitations and Future Research</title><p>This article generates the need to explore the effects of the massive technological innovations that have been introduced in Chilean schools. The findings of this first study show the need to prevent negative impacts and promote positive ones, both individually and organizationally. In this way, this psychosocial research opens a systematic research line on the consequences of the introduction of information technology on teachers’ mental health and their professional performances in Chilean schools.</p><p>Regarding Chile and its mental health in 2019, 24.3% of medical diagnoses were associated with this cause, with an increase of approximately 15% being observed in the first four months of 2020, and with medical diagnoses related to mental disorders accounting for 29% of the total diagnoses for this year [<xref rid=\"B104-ijerph-17-05280\" ref-type=\"bibr\">104</xref>]. The relationship with the Chilean spring [<xref rid=\"B105-ijerph-17-05280\" ref-type=\"bibr\">105</xref>], prolonged confinement, teleworking, and information overload are still unknown aspects in education, as well as in other economic sectors, thus encouraging further research.</p><p>Regarding this study’s limitations, it is important to consider that this research was conducted in only two regions of the country. Therefore, in future studies, it will be important to refine the sample design and increase the size in order to have national representation. Alongside the above, it is important to improve the hypothesis model by incorporating other factors associated with technostress manifestations, especially those related to different types of teachers working in school institutions. Finally, RED-TIC focuses on intra-labor psychosocial risks associated with the use of technology, without considering the influence of possible extra-work risks and those related to someone’s personal or individual life [<xref rid=\"B106-ijerph-17-05280\" ref-type=\"bibr\">106</xref>].</p></sec></sec><sec sec-type=\"conclusions\" id=\"sec6-ijerph-17-05280\"><title>6. Conclusions</title><p>The objective of this study was measuring the psychological state of teachers who work in primary and secondary schools, with regards to the effect of information and communication technology. Altogether, this study attempted to identify the manifestations of psychosocial risks. The results showed the methodological relevance of measuring stress manifestations in education professionals. This statement is supported by the results of the reliability analysis conducted by the questionnaire; a relevant fact that allows for it to be used for deeper research fulfillment and at a national level.</p><p>Along with the above, the analysis of the results determined that 13% of the total teachers presented a techno-anxiety condition, while 12% experienced techno-fatigued conditions. In other words, the use of technology is showing its dark side, with adverse effects and psychosocial risks in a significant subset of Chilean teachers. Moreover, the data showed that more than 10% of teachers jointly state both techno-anxiety and techno-fatigued psychosocial manifestations. That is, at least 1 out of 10 Chilean teachers is at psychosocial risk because of the relationship established with the use of information and communication technology. This is expressed with more intensity in the case of male teachers.</p><p>From these results, it can be inferred that, together with having effects on teachers’ work, there must also be negative effects on the teaching and learning process. In this regard, there is strong evidence that demonstrates the relationship between teachers’ mental health and the effect on educational relationships, especially in the classroom.</p><p>Therefore, this study opens up the debate about the education sciences on the theoretical–practice analysis of technology-use implications in the pedagogy sector, where its appropriate use could have negative consequences for teachers as well as students. This study also guides the generation of specific lines of research that go in depth on the impact of technology use in different fields of scholar culture. Thus, future analyses should focus on the relationships that might be occurring between different teacher profiles and the negative manifestations of technology use.</p></sec></body><back><notes><title>Author Contributions</title><p>Conceptualization, C.E.-M. and A.V.-M.; methodology, D.C. and J.B.-G.; software, C.E.-M. and D.C.; formal analysis, A.V.-M. and D.C.; validation, J.B.-G.; writing (original draft preparation), C.E.-M. and D.C.; writing (review and editing), A.V.-M.; supervision, J.B.-G.; funding acquisition, D.C. All authors have read and agreed to the published version of the manuscript.</p></notes><notes><title>Funding</title><p>This research was funded by CEIEF, Universidad de Santiago de Chile, grant number EF2019I003, and the APC was funded by the Universidad de Santiago de Chile.</p></notes><notes notes-type=\"COI-statement\"><title>Conflicts of Interest</title><p>The authors declare no conflict of interest.</p></notes><app-group><app id=\"app1-ijerph-17-05280\"><title>Appendix A</title><p>In this appendix the four variable sets RED-TIC and their rating scale from 0 to 6 are presented.</p><p>\n<bold>Skepticism set</bold>\n</p><p>S1. With time passing, ICT interests me less and less</p><p>S2. Every time I feel less involved in the use of ICT</p><p>S3. I am more skeptical about the technology’s contribution to my work</p><p>S4. I doubt the working meaning with this technology</p><p>\n<bold>Fatigue set</bold>\n</p><p>F1. I find it difficult to relax after a workday using ICT</p><p>F2. When I finish working with ICT, I feel exhausted</p><p>F3. I am so tired when I work with ICT that I cannot do anything else</p><p>F4. It is hard to concentrate after working with ICT</p><p>\n<bold>Anxiety set</bold>\n</p><p>A1. I feel tense and anxious when working with ICT</p><p>A2. It scares me to think that I can destroy a lot of information with the improper use of ICT</p><p>A3. I hesitate using ICT for fear of making mistakes</p><p>A4. Working with ICT makes me feel uncomfortable, irritable, and impatient</p><p>\n<bold>Inefficacy set</bold>\n</p><p>I1. In my opinion, I am inefficient at using ICT</p><p>I2. It is difficult to work with ICT</p><p>I3. People say that I am inefficient at using ICT</p><p>I4. I am unsure of finishing my tasks well when I use ICT</p><p>\n<bold>Perceptual Rating Scale:</bold>\n</p><p>0. Nothing/never</p><p>1. Almost nothing/a couple of times a year</p><p>2. Rarely/once a month</p><p>3. Sometimes/a couple of times a month</p><p>4. 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Bras. Med. Trab.</source><year>2017</year><volume>15</volume><fpage>350</fpage><lpage>354</lpage><pub-id pub-id-type=\"doi\">10.5327/Z1679443520170164</pub-id></element-citation></ref></ref-list></back><floats-group><fig id=\"ijerph-17-05280-f001\" orientation=\"portrait\" position=\"float\"><label>Figure 1</label><caption><p>Research model and hypotheses. Notes: GA, faculty gender in techno-anxiety manifestation; EA, faculty age section in techno-anxiety manifestation; GF, faculty gender in techno-fatigue manifestation; EF, faculty age section of teachers in techno-anxiety manifestation; TMPR, technostress manifestations in psychosocial risk. Source: own elaboration.</p></caption><graphic xlink:href=\"ijerph-17-05280-g001\"/></fig><table-wrap id=\"ijerph-17-05280-t001\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05280-t001_Table 1</object-id><label>Table 1</label><caption><p>Cross between techno-fatigue and techno-anxiety.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th rowspan=\"2\" colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\">Technostress Manifestations</th><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Techno-Anxiety</th><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">Total</th></tr><tr><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">No</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Yes</th></tr></thead><tbody><tr><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Techno-fatigue</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">No</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">85.7%</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2.3%</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">88.1%</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Yes</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.2%</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">10.7%</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">11.9%</td></tr><tr><td colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">Total</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">86.9%</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">13.1%</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">100.0%</td></tr></tbody></table></table-wrap><table-wrap id=\"ijerph-17-05280-t002\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05280-t002_Table 2</object-id><label>Table 2</label><caption><p>Techno-anxiety statistics by gender.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Technostress Manifestation</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Sex/Gender</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">N</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Mean</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Standard Deviation</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Standard Error of the Mean</th></tr></thead><tbody><tr><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Techno-anxiety</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Masculine</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">152</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">6.6546</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.99044</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.16145</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Female</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">276</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">5.8505</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.79459</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.10802</td></tr></tbody></table></table-wrap><table-wrap id=\"ijerph-17-05280-t003\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05280-t003_Table 3</object-id><label>Table 3</label><caption><p>Techno-anxiety Student’s t-test.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Student’s t-Test</th><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Levene’s Test Equality Variance</th><th colspan=\"7\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">T-Test for Equality of Means</th></tr><tr><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Techno-Anxiety Index</th><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">F</th><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Sig.</th><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">t</th><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">df</th><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Sig. (Two-Tailed)</th><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Mean Diff.</th><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Std Error Diff.</th><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">95% Conf. Interval Diff.</th></tr><tr><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Lower</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Upper</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Equal variance assumed</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">4.210</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.041</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">4.265</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">426</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.000</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.80406</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.18851</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.43353</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.17459</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Equal variance not assumed</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">4.139</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">285.08</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.000</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.80406</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.19425</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.42171</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.18641</td></tr></tbody></table></table-wrap><table-wrap id=\"ijerph-17-05280-t004\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05280-t004_Table 4</object-id><label>Table 4</label><caption><p>Bilateral correlation between teaching age and techno-anxiety.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Bilateral Correlation</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Teaching Age</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Techno-Anxiety Scale</th></tr></thead><tbody><tr><td rowspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Teaching age</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">R Pearson correlation</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.022</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Sig. (bilateral)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.652</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">N</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">428</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">428</td></tr><tr><td rowspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Techno-anxiety scale</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">R Pearson correlation</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.022</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Sig. (bilateral)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.652</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">N</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">428</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">428</td></tr></tbody></table></table-wrap><table-wrap id=\"ijerph-17-05280-t005\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05280-t005_Table 5</object-id><label>Table 5</label><caption><p>Techno-fatigue statistics by gender.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Technostress Manifestation</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Sex/Gender</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">N</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Mean</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Standard Deviation</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Standard Error of the Mean</th></tr></thead><tbody><tr><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Techno-fatigue index</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Masculine</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">152</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">6.02</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2.502</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.203</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Female</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">276</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">5.24</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2.244</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.135</td></tr></tbody></table></table-wrap><table-wrap id=\"ijerph-17-05280-t006\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05280-t006_Table 6</object-id><label>Table 6</label><caption><p>Techno-fatigue Student’s t-test.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Student’s t-Test</th><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Levene’s Test Equality Variance</th><th colspan=\"7\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">T-Test for Equality of Means</th></tr><tr><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Techno-Fatigue Index</th><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">F</th><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Sig.</th><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">t</th><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">df</th><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Sig. (Two-Tailed)</th><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Mean Diff.</th><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Std Error Diff.</th><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">95% Conf. Interval Diff.</th></tr><tr><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Lower</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Upper</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Equal variance assumed</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">4.599</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.033</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3.298</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">426</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.001</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.779</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.236</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.315</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.243</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Equal variance not assumed</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3.196</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">283.85</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.002</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.779</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.244</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.299</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.259</td></tr></tbody></table></table-wrap><table-wrap id=\"ijerph-17-05280-t007\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05280-t007_Table 7</object-id><label>Table 7</label><caption><p>Bilateral correlation between teaching age and techno-fatigue.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Bilateral Correlation </th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Teaching Age</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Techno-Fatigue Scale</th></tr></thead><tbody><tr><td rowspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Teaching age</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">R Pearson correlation</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.017</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Sig. (bilateral)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.724</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">N</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">428</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">428</td></tr><tr><td rowspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Techno-fatigue scale</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">R Pearson correlation</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.017</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Sig. (bilateral)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.724</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">N</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">428</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">428</td></tr></tbody></table></table-wrap></floats-group></article>\n"
]
[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Int J Environ Res Public Health</journal-id><journal-id journal-id-type=\"iso-abbrev\">Int J Environ Res Public Health</journal-id><journal-id journal-id-type=\"publisher-id\">ijerph</journal-id><journal-title-group><journal-title>International Journal of Environmental Research and Public Health</journal-title></journal-title-group><issn pub-type=\"ppub\">1661-7827</issn><issn pub-type=\"epub\">1660-4601</issn><publisher><publisher-name>MDPI</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32707746</article-id><article-id pub-id-type=\"pmc\">PMC7432079</article-id><article-id pub-id-type=\"doi\">10.3390/ijerph17155263</article-id><article-id pub-id-type=\"publisher-id\">ijerph-17-05263</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Article</subject></subj-group></article-categories><title-group><article-title>Urinary Metals Concentrations and Biomarkers of Autoimmunity among Navajo and Nicaraguan Men</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0003-3836-083X</contrib-id><name><surname>Scammell</surname><given-names>Madeleine K.</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05263\">1</xref><xref rid=\"c1-ijerph-17-05263\" ref-type=\"corresp\">*</xref><xref ref-type=\"author-notes\" rid=\"fn1-ijerph-17-05263\">†</xref></contrib><contrib contrib-type=\"author\"><name><surname>Sennett</surname><given-names>Caryn</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05263\">1</xref><xref ref-type=\"author-notes\" rid=\"fn1-ijerph-17-05263\">†</xref></contrib><contrib contrib-type=\"author\"><name><surname>Laws</surname><given-names>Rebecca L.</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05263\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Rubin</surname><given-names>Robert L.</given-names></name><xref ref-type=\"aff\" rid=\"af2-ijerph-17-05263\">2</xref></contrib><contrib contrib-type=\"author\"><name><surname>Brooks</surname><given-names>Daniel R.</given-names></name><xref ref-type=\"aff\" rid=\"af3-ijerph-17-05263\">3</xref></contrib><contrib contrib-type=\"author\"><name><surname>Amador</surname><given-names>Juan José</given-names></name><xref ref-type=\"aff\" rid=\"af3-ijerph-17-05263\">3</xref></contrib><contrib contrib-type=\"author\"><name><surname>López-Pilarte</surname><given-names>Damaris</given-names></name><xref ref-type=\"aff\" rid=\"af3-ijerph-17-05263\">3</xref></contrib><contrib contrib-type=\"author\"><name><surname>Ramirez-Rubio</surname><given-names>Oriana</given-names></name><xref ref-type=\"aff\" rid=\"af3-ijerph-17-05263\">3</xref></contrib><contrib contrib-type=\"author\"><name><surname>Friedman</surname><given-names>David J.</given-names></name><xref ref-type=\"aff\" rid=\"af4-ijerph-17-05263\">4</xref></contrib><contrib contrib-type=\"author\"><name><surname>McClean</surname><given-names>Michael D.</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05263\">1</xref></contrib><contrib contrib-type=\"author\"><collab>Navajo Birth Cohort Study Team</collab><xref ref-type=\"author-notes\" rid=\"fn2-ijerph-17-05263\">‡</xref></contrib><contrib contrib-type=\"author\"><name><surname>Lewis</surname><given-names>Johnnye</given-names></name><xref ref-type=\"aff\" rid=\"af5-ijerph-17-05263\">5</xref></contrib><contrib contrib-type=\"author\"><name><surname>Erdei</surname><given-names>Esther</given-names></name><xref ref-type=\"aff\" rid=\"af5-ijerph-17-05263\">5</xref></contrib></contrib-group><aff id=\"af1-ijerph-17-05263\"><label>1</label>Department of Environmental Health, Boston University School of Public Health, Boston, MA 02118, USA; <email>[email protected]</email> (C.S.); <email>[email protected]</email> (R.L.L.); <email>[email protected]</email> (M.D.M.)</aff><aff id=\"af2-ijerph-17-05263\"><label>2</label>Department of Molecular Genetics and Microbiology, University of New Mexico School of Medicine, Albuquerque, NM 87131, USA; <email>[email protected]</email></aff><aff id=\"af3-ijerph-17-05263\"><label>3</label>Department of Epidemiology, Boston University School of Public Health, Boston, MA 02118, USA; <email>[email protected]</email> (D.R.B.); <email>[email protected]</email> (J.J.A.); <email>[email protected]</email> (D.L.-P.); <email>[email protected]</email> (O.R.-R.)</aff><aff id=\"af4-ijerph-17-05263\"><label>4</label>Division of Nephrology, Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA 02215, USA; <email>[email protected]</email></aff><aff id=\"af5-ijerph-17-05263\"><label>5</label>College of Pharmacy, Community Environmental Health Program, University of New Mexico Health Sciences Center, Albuquerque, NM 87131, USA; <email>[email protected]</email> (J.L.); <email>[email protected]</email> (E.E.)</aff><author-notes><corresp id=\"c1-ijerph-17-05263\"><label>*</label>Correspondence: <email>[email protected]</email>; Tel.: +1-617-358-2478</corresp><fn id=\"fn1-ijerph-17-05263\"><label>†</label><p>Equal contribution first authors.</p></fn><fn id=\"fn2-ijerph-17-05263\"><label>‡</label><p>Navajo Birth Cohort Study Team members are listed in <xref ref-type=\"app\" rid=\"app2-ijerph-17-05263\">Appendix A</xref>.</p></fn></author-notes><pub-date pub-type=\"epub\"><day>22</day><month>7</month><year>2020</year></pub-date><pub-date pub-type=\"ppub\"><month>8</month><year>2020</year></pub-date><volume>17</volume><issue>15</issue><elocation-id>5263</elocation-id><history><date date-type=\"received\"><day>03</day><month>6</month><year>2020</year></date><date date-type=\"accepted\"><day>18</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>© 2020 by the authors.</copyright-statement><copyright-year>2020</copyright-year><license license-type=\"open-access\"><license-p>Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>).</license-p></license></permissions><abstract><p>Metals are suspected contributors of autoimmune disease among indigenous Americans. However, the association between metals exposure and biomarkers of autoimmunity is under-studied. In Nicaragua, environmental exposure to metals is also largely unexamined with regard to autoimmunity. We analyzed pooled and stratified exposure and outcome data from Navajo (<italic>n</italic> = 68) and Nicaraguan (<italic>n</italic> = 47) men of similar age and health status in order to characterize urinary concentrations of metals, compare concentrations with the US National Health and Nutrition Examination Survey (NHANES) male population, and examine the associations with biomarkers of autoimmunity. Urine samples were analyzed for metals via inductively coupled plasma mass spectrometry (ICP-MS) at the US Centers for Disease Control and Prevention. Serum samples were examined for antinuclear antibodies (ANA) at 1:160 and 1:40 dilutions, using an indirect immunofluorescence assay and for specific autoantibodies using enzyme-linked immunosorbent assay (ELISA). Logistic regression analyses evaluated associations of urinary metals with autoimmune biomarkers, adjusted for group (Navajo or Nicaraguan), age, and seafood consumption. The Nicaraguan men had higher urinary metal concentrations compared with both NHANES and the Navajo for most metals; however, tin was highest among the Navajo, and uranium was much higher in both populations compared with NHANES. Upper tertile associations with ANA positivity at the 1:160 dilution were observed for barium, cesium, lead, strontium and tungsten.</p></abstract><kwd-group><kwd>autoimmunity</kwd><kwd>antinuclear antibodies</kwd><kwd>specific autoantibodies</kwd><kwd>metals</kwd></kwd-group></article-meta></front><body><sec sec-type=\"intro\" id=\"sec1-ijerph-17-05263\"><title>1. Introduction</title><p>In epidemiology, there is a long-standing interest in determining whether various diseases are concentrated in populations defined by racial, ethnic, demographic, and/or geographic characteristics. This is true of autoimmune diseases, a family of more than 80 human diseases, including type I diabetes, scleroderma, lupus, and multiple sclerosis. Autoimmune diseases affect between 14.7 and 23.5 million people in the US [<xref rid=\"B1-ijerph-17-05263\" ref-type=\"bibr\">1</xref>]. Epidemiological studies of autoimmune diseases have higher prevalence among indigenous populations compared with all North Americans, including higher rates of systemic lupus erythematosus (SLE) in the Algonkian First Nation community from Manitoba, Canada [<xref rid=\"B2-ijerph-17-05263\" ref-type=\"bibr\">2</xref>], and Crow, Arapahoe, and Sioux communities in the United States [<xref rid=\"B3-ijerph-17-05263\" ref-type=\"bibr\">3</xref>]; SLE and rheumatoid arthritis (RA) among Tlingit in Southeast Alaska [<xref rid=\"B4-ijerph-17-05263\" ref-type=\"bibr\">4</xref>]; and RA in US Yakima [<xref rid=\"B5-ijerph-17-05263\" ref-type=\"bibr\">5</xref>], Chippewa [<xref rid=\"B6-ijerph-17-05263\" ref-type=\"bibr\">6</xref>], and Pima [<xref rid=\"B7-ijerph-17-05263\" ref-type=\"bibr\">7</xref>] peoples.</p><p>Autoimmune diseases are suspected to be caused by a combination of environmental and genetic factors [<xref rid=\"B8-ijerph-17-05263\" ref-type=\"bibr\">8</xref>,<xref rid=\"B9-ijerph-17-05263\" ref-type=\"bibr\">9</xref>,<xref rid=\"B10-ijerph-17-05263\" ref-type=\"bibr\">10</xref>]. The use of diagnosed autoimmune disease as an outcome for epidemiological studies is often challenging, due to inconsistent, subjective, and/or changing diagnostic criteria, overlapping symptoms and co-morbidities, and the fact that there are no registries for these relatively rare diseases. An autoantibody measurement is one of the most useful serological tests for the detection and diagnosis of many systemic and organ-specific autoimmune diseases, as well as for monitoring subclinical, autoimmune-driven perturbations that do not necessarily result in disease [<xref rid=\"B11-ijerph-17-05263\" ref-type=\"bibr\">11</xref>]. In addition, autoantibody biomarkers can precede development of overt clinical disease by many years [<xref rid=\"B12-ijerph-17-05263\" ref-type=\"bibr\">12</xref>,<xref rid=\"B13-ijerph-17-05263\" ref-type=\"bibr\">13</xref>,<xref rid=\"B14-ijerph-17-05263\" ref-type=\"bibr\">14</xref>], thereby providing information potentially useful for early detection and therapeutic intervention of autoimmune disease [<xref rid=\"B15-ijerph-17-05263\" ref-type=\"bibr\">15</xref>,<xref rid=\"B16-ijerph-17-05263\" ref-type=\"bibr\">16</xref>]. </p><p>Exposures to metals and autoimmune outcomes are not well understood [<xref rid=\"B17-ijerph-17-05263\" ref-type=\"bibr\">17</xref>]. A high concentration of some metals may overload the immune system, while conversely, deficiencies in metals with nutritional value may also trigger autoimmune response [<xref rid=\"B17-ijerph-17-05263\" ref-type=\"bibr\">17</xref>]. Metals exposures from occupational and natural sources are hypothesized to play a role in autoimmune conditions. Rodent studies suggest an association between metals exposure and autoantibody production [<xref rid=\"B18-ijerph-17-05263\" ref-type=\"bibr\">18</xref>,<xref rid=\"B19-ijerph-17-05263\" ref-type=\"bibr\">19</xref>], and some epidemiological studies have identified arsenic and mercury (separately, not combined) as predictors of serum autoantibodies [<xref rid=\"B20-ijerph-17-05263\" ref-type=\"bibr\">20</xref>] and subsequent autoimmune disease development [<xref rid=\"B21-ijerph-17-05263\" ref-type=\"bibr\">21</xref>,<xref rid=\"B22-ijerph-17-05263\" ref-type=\"bibr\">22</xref>,<xref rid=\"B23-ijerph-17-05263\" ref-type=\"bibr\">23</xref>]. Molecular mechanisms are likely metal-induced generation of reactive oxygen species [<xref rid=\"B24-ijerph-17-05263\" ref-type=\"bibr\">24</xref>], followed by oxidative damage to tissues, organs and the genetic material [<xref rid=\"B25-ijerph-17-05263\" ref-type=\"bibr\">25</xref>]. Metals may also directly enhance T lymphocyte activation [<xref rid=\"B26-ijerph-17-05263\" ref-type=\"bibr\">26</xref>] causing hyperreactivity to self-antigens. However, there is insufficient evidence in humans to support the role of metals in the causation of autoimmune disease [<xref rid=\"B27-ijerph-17-05263\" ref-type=\"bibr\">27</xref>]. An expert panel convened by the National Institutes of Health describes an understanding of metals and autoimmunity as a “major research gap” [<xref rid=\"B28-ijerph-17-05263\" ref-type=\"bibr\">28</xref>], recommending more research in this area, with a particular emphasis on biomarkers of exposure (e.g., urinary metals) and effect (e.g., specific autoantibodies). </p><p>Metals exposure among indigenous populations has been previously documented [<xref rid=\"B29-ijerph-17-05263\" ref-type=\"bibr\">29</xref>]. From 1944 to 1986, mining operations removed more than 30 million tons of uranium ore in the Navajo Nation that left the lasting legacy of abandoned uranium mines, milling sites, groundwater containing uranium above safe levels causing environmental and public health concerns to Navajo communities [<xref rid=\"B30-ijerph-17-05263\" ref-type=\"bibr\">30</xref>]. Exposure to mixed-metal wastes resulting from uranium and other hard-rock mining activities on tribal lands is ongoing [<xref rid=\"B31-ijerph-17-05263\" ref-type=\"bibr\">31</xref>,<xref rid=\"B32-ijerph-17-05263\" ref-type=\"bibr\">32</xref>]. While, the exposure to mercury from fish consumption and residential proximity to an arsenic-contaminated site have been associated with increased prevalence of antinuclear antibodies (ANA). Specific autoantibodies [<xref rid=\"B33-ijerph-17-05263\" ref-type=\"bibr\">33</xref>] in a Sioux community, and elevated autoantibodies, are associated with living near uranium mines in Navajo Nation residents [<xref rid=\"B34-ijerph-17-05263\" ref-type=\"bibr\">34</xref>]. There are no studies so far that have assessed the relationship of in vivo biomarkers of autoimmunity with a comprehensive panel of metals yet. </p><p>In multiple Central American countries, high concentrations of arsenic (>50 µg/L) have been measured in common drinking water sources [<xref rid=\"B35-ijerph-17-05263\" ref-type=\"bibr\">35</xref>] and arsenic, cadmium, uranium, strontium, barium, manganese, and lead have been detected in volcanic emissions and in soils surrounding active volcanoes in the region [<xref rid=\"B36-ijerph-17-05263\" ref-type=\"bibr\">36</xref>,<xref rid=\"B37-ijerph-17-05263\" ref-type=\"bibr\">37</xref>,<xref rid=\"B38-ijerph-17-05263\" ref-type=\"bibr\">38</xref>,<xref rid=\"B39-ijerph-17-05263\" ref-type=\"bibr\">39</xref>,<xref rid=\"B40-ijerph-17-05263\" ref-type=\"bibr\">40</xref>]. Data on autoimmune disease in Central America are lacking [<xref rid=\"B10-ijerph-17-05263\" ref-type=\"bibr\">10</xref>]. Both human exposure to metals and serum ANA have been measured infrequently in epidemiological studies in Central America [<xref rid=\"B41-ijerph-17-05263\" ref-type=\"bibr\">41</xref>,<xref rid=\"B42-ijerph-17-05263\" ref-type=\"bibr\">42</xref>]. One case series study of Nicaraguan patients (<italic>n</italic> = 19) with Mesoamerican Nephropathy (a chronic kidney disease of unknown etiology) reported a single patient with serum ANA positivity but no specific autoantibodies for double-stranded DNA, centromere antibodies or extractable nuclear antigens [<xref rid=\"B41-ijerph-17-05263\" ref-type=\"bibr\">41</xref>]. The same study reported detectable urinary concentrations of lead, cadmium, uranium and mercury, but did not assess the association between metals exposure and autoimmune biomarkers [<xref rid=\"B41-ijerph-17-05263\" ref-type=\"bibr\">41</xref>].</p><p>In addition to shared concerns regarding metals exposures among an indigenous North American and a Central American community, increasing prevalence of chronic diseases relative to US white populations have been reported among Navajo Nation and in Mesoamerica (including Nicaragua) [<xref rid=\"B43-ijerph-17-05263\" ref-type=\"bibr\">43</xref>,<xref rid=\"B44-ijerph-17-05263\" ref-type=\"bibr\">44</xref>,<xref rid=\"B45-ijerph-17-05263\" ref-type=\"bibr\">45</xref>,<xref rid=\"B46-ijerph-17-05263\" ref-type=\"bibr\">46</xref>]. These epidemiological similarities, as well as the availability of both urinary metals and ANA data in both populations, prompted the current collaborative, hypothesis-generating study to investigate the relationships between environmental metal contaminants and serological biomarkers of autoimmune disease in groups from both regions.</p><p>Using data from two geographically distinct groups, men from the Navajo Birth Cohort Study (NBCS) and men from an occupational study of sugarcane workers in Nicaragua, the present study had the following objectives: (1) Characterize exposure to metals among healthy Nicaraguan and Navajo men of working age; (2) evaluate biomarkers of autoimmunity (ANA and specific autoantibodies) in each group; and (3) examine the relationship between metals exposure and biomarkers of autoimmunity in the pooled population.</p></sec><sec id=\"sec2-ijerph-17-05263\"><title>2. Materials and Methods </title><sec id=\"sec2dot1-ijerph-17-05263\"><title>2.1. Study Population</title><p>The study population included 47 male sugarcane workers from northwestern Nicaragua and 68 fathers from the Navajo Nation. All study participants were at least 18 years of age. Previously, we described the enrollment of 284 sugarcane workers from one company in the department of Chinandega, Nicaragua, in 2010 [<xref rid=\"B47-ijerph-17-05263\" ref-type=\"bibr\">47</xref>]. In March 2015, we re-sampled 50 of these same workers randomly selected from among those who were still employed by the company in 2015 and worked in one of four job tasks: (1) Cane cutting, (2) irrigation, (3) seeding/seed-cutting or (4) agrichemical application. We collected blood and urine samples post-shift. Three workers refused the blood draw, resulting in complete data for 47 participants. All workers completed questionnaires reporting no disease diagnoses, chronic or acute health conditions. </p><p>The NBCS is a congressionally mandated collaborative research study originally supported by the Agency for Toxic Substances and Disease Registry (CDC/ATSDR) with research led by the University of New Mexico Community Environmental Health Program in partnership with the Southwest Research and Information Center, the CDC Division of Laboratory Sciences, the Navajo Nation Department of Health and other Navajo agencies, the Navajo Area Indian Health Service (IHS), and the previously acknowledged six IHS and PL-638 healthcare facilities on Navajo Nation. The purpose of the study is to investigate birth outcomes and child development through age one year in relation to non-occupational exposures to uranium wastes from past mining and milling operations on the Navajo Nation. This study encourages father participation. Nearly 200 NBCS fathers were recruited between February 2013 and December 2015 in six geographic areas of Navajo Nation representing mined and unmined regions. At enrollment, fathers provided blood and urine samples for clinical and research analyses, including investigation of the association between metals exposure, immune function, and autoimmunity. A subset of sixty-eight of these fathers were selected for the current analysis by matching age and health status to the previously recruited Nicaraguan workers, all of whom were men. Due to these selection criteria, the Navajo fathers in this study are not representative of the NBCS fathers. </p><p>The Institutional Review Boards at the Boston University Medical Campus and the Nicaraguan Ministry of Health approved the study protocols for the studies in Nicaragua. NBCS received approvals from the University of New Mexico Health Sciences Center Human Research Protections Office (11-310) and the Navajo Nation Human Research Review Board (NNR 11.323), which continues oversight of the project. All participants provided informed consent prior to participation in research activities.</p></sec><sec id=\"sec2dot2-ijerph-17-05263\"><title>2.2. Laboratory Analysis of Urinary Metals</title><p>All urine samples from both studies were analyzed for metals at the US Centers for Disease Control and Prevention Division of Laboratory Science in Atlanta, Georgia, USA. Metals (antimony, total arsenic, barium, cadmium, cesium, cobalt, lead, manganese, molybdenum, strontium, thallium, tin, tungsten, and uranium) were analyzed using an inductively coupled plasma dynamic reaction cell mass spectrometer (ICP-DRC-MS) (PerkinElmer NexION 300D, Waltham, USA) [<xref rid=\"B48-ijerph-17-05263\" ref-type=\"bibr\">48</xref>]. Speciated arsenic (arsenobetaine, arsenocholine, arsenite (As III), arsenate (As V), monomethylarsonic acid (MMA), and dimethylarsinic acid (DMA)) was also determined using inductively coupled mass spectrometry (ICP-MS) coupled with high performance liquid chromatography [<xref rid=\"B49-ijerph-17-05263\" ref-type=\"bibr\">49</xref>]. Limits of detection are published in <xref ref-type=\"app\" rid=\"app1-ijerph-17-05263\">Supplementary Table S1</xref>. Urine creatinine was analyzed via enzymatic method using the Roche/Hitachi Modular P Chemistry Analyzer.</p></sec><sec id=\"sec2dot3-ijerph-17-05263\"><title>2.3. Laboratory Analysis of Biomarkers of Autoimmunity</title><p>The presence of antinuclear antibody (ANA) was determined by indirect immunofluorescence (IIF) at the University of New Mexico Health Sciences Center using Hep2 cells as substrate and fluorescein-labeled anti-IgG (H + L) as the detecting reagent (INOVA Diagnostics, San Diego, CA, USA). Sera were diluted 1:40 and 1:160 in phosphate-buffered saline. A dilution of 1:80–1:160 is standard for clinical studies [<xref rid=\"B50-ijerph-17-05263\" ref-type=\"bibr\">50</xref>,<xref rid=\"B51-ijerph-17-05263\" ref-type=\"bibr\">51</xref>,<xref rid=\"B52-ijerph-17-05263\" ref-type=\"bibr\">52</xref>,<xref rid=\"B53-ijerph-17-05263\" ref-type=\"bibr\">53</xref>], while the 1:40 dilution is intended for exploratory, hypothesis generating [<xref rid=\"B54-ijerph-17-05263\" ref-type=\"bibr\">54</xref>] studies. Slides were viewed independently by two observers (E.E. & R.L.R.) using an Olympus fluorescence microscope at 600-fold magnification (Olympus, Lake Success, NY, USA). Staining patterns (nuclear, nucleolar, cytoplasmic) were graded on a scale of 0–4+ intensity, based on positive and negative control standard sera that were run with each assay.</p><p>Specific autoantibodies were also detected by enzyme-linked immunosorbent assay (ELISA) using an in-house procedure, as previously described [<xref rid=\"B55-ijerph-17-05263\" ref-type=\"bibr\">55</xref>,<xref rid=\"B56-ijerph-17-05263\" ref-type=\"bibr\">56</xref>], and used by members of our team in Navajo Nation studies [<xref rid=\"B34-ijerph-17-05263\" ref-type=\"bibr\">34</xref>]. Antibody binding to chromatin, histones, denatured DNA (dDNA or single-stranded DNA), and native DNA (nDNA or double-stranded DNA) was quantified in duplicate serum samples in a 1:200 dilution after incubation in antigen-coated wells for 2 h at room temperature [<xref rid=\"B55-ijerph-17-05263\" ref-type=\"bibr\">55</xref>,<xref rid=\"B56-ijerph-17-05263\" ref-type=\"bibr\">56</xref>]. Bound antibodies were detected with peroxidase-conjugated anti-human IgG (Southern Biotech Associates, Birmingham, AL, USA) followed by color development using a peroxidase secondary substrate and reported as optical density (O.D.) units. Positive and negative controls were included in each assay. Two standard deviations above the mean of the negative controls was considered elevated reactivity.</p></sec><sec id=\"sec2dot4-ijerph-17-05263\"><title>2.4. Statistical Analysis</title><p>We created a dichotomous outcome variable for each serum dilution (1:160 and 1:40) to categorize ANA positivity. Positivity was defined as a ≥2+ intensity fluorescence score, which is an appropriate cut-off for a healthy population without known autoimmune disease [<xref rid=\"B57-ijerph-17-05263\" ref-type=\"bibr\">57</xref>]. To account for differences in urine concentration among individuals, we ran two distinct models. In the first, we normalized metal concentrations to urinary creatinine concentration (g/L), as is conventional practice. In the second model, we statistically adjusted for urinary creatinine as a variable in our regression models, as suggested by Barr et al. 2005 [<xref rid=\"B58-ijerph-17-05263\" ref-type=\"bibr\">58</xref>], to account for population differences in urine concentration and time of day of urine collection. Metal values below the limit of detection (LOD) were substituted with LOD/√2. Median concentrations of each metal were compared across groups (Navajo and Nicaraguan men) and to the United States adult male population (20 years and older) using data from the 2011–2012 National Health and Nutrition Examination Survey (NHANES), conducted by the US Centers for Disease Control and Prevention [<xref rid=\"B59-ijerph-17-05263\" ref-type=\"bibr\">59</xref>]. </p><p>ANA and ELISA autoantibody positivity were first characterized for each group. We then assessed the association between each metal and ANA positivity using multivariable logistic regression. We pooled data on the 47 Nicaraguan and 68 Navajo men, creating more power in our sample (<italic>N</italic> = 115), and examined the spread of metals concentrations in the pooled data, calculating exposure tertiles for each metal (normalized to urine creatinine). Metals tertile, the categorical exposure variable, was used to predict an aggregate outcome variable indicative of any ANA response at one or more of the three cellular sites (positivity at the cytoplasmic, nuclear or nucleolar site) at the 1:40, and 1:160 dilutions, respectively. </p><p>Additional predictors included in all models were age, group (Navajo or Nicaragua) and seafood consumption within the past three days (yes/no). Recent seafood consumption data were lacking for the Navajo participants, however, over 60% of the NBCS mothers reported never having consumed fish, ever. Given cultural beliefs and geographic location in the high desert of Southwestern US, which both preclude consumption of seafood, Navajo participants were coded as unexposed. Data on current cigarette smoking were also lacking among Navajo participants. However, among the Nicaraguan participants current smoking behavior was predictive of increased metals exposure, but was not associated with ANA positivity. Consequently, we ruled out smoking behavior as a potential confounder in our pooled analyses. Given the hypothesis-generating nature of this study, the results were evaluated, based on the strength of association, rather than solely on statistical significance. As such, multiple comparison correction was overly conservative and the data were incompatible with the assumptions of penalized regression models. Data were analyzed using Statistical Analysis Software version 9.4 (SAS Institute Inc. Cary, NC, USA).</p></sec></sec><sec sec-type=\"results\" id=\"sec3-ijerph-17-05263\"><title>3. Results</title><sec id=\"sec3dot1-ijerph-17-05263\"><title>3.1. Population Characteristics</title><p>All participants were male with an average age of 32 years (range: 23–51 years) (<xref rid=\"ijerph-17-05263-t001\" ref-type=\"table\">Table 1</xref>). The Nicaraguan group was composed of workers employed in active jobs that required them to pass a health screening. Questionnaires also confirmed health status. The selected Navajo group reported having no diagnosed diseases including cardiovascular disease, high blood pressure, diabetes or kidney disease.</p></sec><sec id=\"sec3dot2-ijerph-17-05263\"><title>3.2. Urinary Metals</title><p>All urine samples had detectable levels of barium, cesium, cobalt, lead, molybdenum, and strontium (<xref rid=\"ijerph-17-05263-t001\" ref-type=\"table\">Table 1</xref>). We calculated the arithmetic median concentration and range of each metal in both groups and the pooled cohort (<xref rid=\"ijerph-17-05263-t001\" ref-type=\"table\">Table 1</xref>). We also calculated the geometric means and 95% confidence limits for each metal in in both groups, comparing these results with US males in NHANES (<xref ref-type=\"fig\" rid=\"ijerph-17-05263-f001\">Figure 1</xref> and <xref ref-type=\"fig\" rid=\"ijerph-17-05263-f002\">Figure 2</xref>). Concentrations of most metals were higher among the Nicaraguan men compared to concentrations among Navajo men (<xref rid=\"ijerph-17-05263-t001\" ref-type=\"table\">Table 1</xref>), and compared to the US adult males (<xref ref-type=\"fig\" rid=\"ijerph-17-05263-f001\">Figure 1</xref> and <xref ref-type=\"fig\" rid=\"ijerph-17-05263-f002\">Figure 2</xref>). Median concentrations of arsenic, barium and strontium were approximately twice as high among the Nicaraguan men as compared to US adult male geometric mean concentrations in NHANES and the Navajo men (<xref ref-type=\"fig\" rid=\"ijerph-17-05263-f001\">Figure 1</xref>, <xref ref-type=\"fig\" rid=\"ijerph-17-05263-f002\">Figure 2</xref> and <xref rid=\"ijerph-17-05263-t001\" ref-type=\"table\">Table 1</xref>). The concentrations of all arsenic species, cesium, cobalt, lead, manganese, molybdenum and uranium were also higher among the Nicaraguans, as compared to NHANES and the Navajo. Metals’ concentrations that were lower among Nicaraguan men, compared with NHANES and Navajo men were antimony, cadmium, thallium, tin and tungsten. Antimony, cadmium, tin and tungsten were all highest among the Navajo compared with both NHANES and the Nicaraguans, with tin notably higher and detected in 99% of samples among the Navajo as compared to 48% among the Nicaraguans. Uranium concentrations were somewhat similar between the Nicaraguan (0.02 μg/g creatinine) and Navajo (0.01 μg/g creatinine) groups, although 2–3-fold higher compared to NHANES (0.004 μg/g creatinine) (<xref ref-type=\"fig\" rid=\"ijerph-17-05263-f001\">Figure 1</xref> and <xref ref-type=\"fig\" rid=\"ijerph-17-05263-f002\">Figure 2</xref>).</p></sec><sec id=\"sec3dot3-ijerph-17-05263\"><title>3.3. Autoimmune Biomarkers</title><p>Any ANA positivity based on a staining intensity of ≥2+ at the 1:160 serum dilution was similar between the two groups (7.4% among the Navajo; 8.5% among the Nicaraguans) (<xref rid=\"ijerph-17-05263-t002\" ref-type=\"table\">Table 2</xref>). ANA positivity at the nuclear site was also similar between groups at both dilutions. However, cellular site specific staining patterns differed between the two groups; this was most apparent at the 1:40 dilution in which the prevalence of ANA positivity was naturally higher than at the 1:160 dilution. Any ANA positivity was higher among the Nicaraguans (80.9%), compared to the Navajo (55.9% positivity), although nucleolar staining was detected in 12% of the Navajo samples at 1:40 dilution, but in none of the Nicaraguan samples at either serum dilution. In contrast, the Nicaraguans had more anti-cytoplasmic (74.5%) antibody reactivity, as compared to the Navajo (47.1%) at the 1:40 dilution. Overall, in the pooled cohort (<italic>n</italic> = 115), 66.1% were ANA positive at any cellular site at the 1:40 serum dilution compared to 7.8% positive at any cellular site at the 1:160 serum dilution (<xref rid=\"ijerph-17-05263-t002\" ref-type=\"table\">Table 2</xref>).</p><p>Specific autoantibodies measured by ELISA are shown in <xref rid=\"ijerph-17-05263-t002\" ref-type=\"table\">Table 2</xref> and <xref ref-type=\"fig\" rid=\"ijerph-17-05263-f003\">Figure 3</xref>. Prevalence of reactivity to the tested antigens was low. In the Nicaraguan group, three participants had slightly elevated reactivity to dDNA, one to histones and one to chromatin; in the Navajo group, low-level reactivity to dDNA was detectable in seven participant samples and to the other antigens (chromatin, histones and nDNA) in two participants.</p></sec><sec id=\"sec3dot4-ijerph-17-05263\"><title>3.4. Associations between Metals and ANA Positivity in Pooled Analyses (<xref rid=\"ijerph-17-05263-t003\" ref-type=\"table\">Table 3</xref>)</title><p>It was not possible to run the models, including urine creatinine as a co-variate for several of the arsenic metabolites, due to low percent detection. Instead we focus discussion on the results of the model evaluating creatinine-normalized concentrations, but provide the results of both normalizing creatinine, and including it as a variable in <xref ref-type=\"app\" rid=\"app1-ijerph-17-05263\">Supplemental Table S1</xref>, Associations between urinary metal concentrations and ANA positivity differed at the two dilutions (1:160 and 1:40). At the 1:160 dilution, odds of ANA positivity at any cellular site were highest and significant among participants with third tertile exposure as compared to participants with first tertile exposure for cesium (OR = 2.98, 95% CI: 1.07, 8.25), lead (OR = 3.31, 95% CI: 1.09, 9.97), strontium (OR = 4.71, 95% CI: 1.34, 16.61) and tungsten (OR = 4.00, 95% CI: 1.11, 14.44) (<xref rid=\"ijerph-17-05263-t003\" ref-type=\"table\">Table 3</xref>). Third tertile barium exposure also had a high association, although not significant (OR = 2.33, 95% CI: 0.83, 6.55). Lower (OR’s ranging from 1.01 to 1.78), albeit positive associations between any ANA positivity and third tertile concentrations were also observed for total arsenic, arsenobetaine, arsenocholine, As (III), As (V), antimony, cadmium, cobalt, thallium, tin and uranium. Although the odds of any ANA positivity were higher among participants with second tertile urinary concentrations of DMA (OR = 1.46, 95% CI: 0.56, 3.82), there was no association between the third tertile exposure to DMA and ANA positivity (OR = 0.45, 95% CI: 0.10, 1.97). No association was seen between urinary concentrations of MMA and ANA positivity at the 1:160 dilution.</p><p>The results at the 1:40 dilution were somewhat disparate in that the only comparably high odds ratios for ANA associated with metals in third tertile of exposure were for urinary DMA (OR = 2.17, 95% CI: 0.85, 5.47), which was an opposite trend as at the 1:160 dilution. Although, neither was significant. Associations at 1:40 dilution between any ANA positivity and third tertile urinary concentrations of total arsenic, arsenobetaine, cobalt, strontium and thallium were also positive, but lower—similar to the results seen at the 1:160 dilution (OR’s ranged from 1.08 to 1.72). However, antimony in the second tertile exposure was associated with any ANA positivity at the 1:40 dilution (OR = 2.25, 95% CI: 1.16, 4.35), third tertile exposure was not (<xref rid=\"ijerph-17-05263-t003\" ref-type=\"table\">Table 3</xref>). This was also true at the 1:160 dilution. At the 1:40 dilution, there was no association between urinary tin or uranium and ANA positivity. Finally, the odds ratios were undefined given low counts in each tertile for urinary molybdenum and manganese at the 1:160 dilution and are not included in <xref rid=\"ijerph-17-05263-t003\" ref-type=\"table\">Table 3</xref>.</p></sec></sec><sec sec-type=\"discussion\" id=\"sec4-ijerph-17-05263\"><title>4. Discussion</title><p>This is the first biomonitoring study to examine the prevalence and associations between urinary concentrations of metals and autoimmune biomarkers in Nicaraguan or Navajo men. With respect to metals, urinary concentrations of most metals assessed in this study were highest among the Nicaraguan group. As Nicaragua is a volcanic region that is high in strontium [<xref rid=\"B38-ijerph-17-05263\" ref-type=\"bibr\">38</xref>], lava and bedrock are probable environmental sources and may explain the higher urinary concentrations of strontium in the Nicaraguan group (median 222.28 μg/g creatinine), as compared to the Navajo group (median 130.16 μg/g creatinine) and NHANES (median 91.8 μg/g creatinine). Volcanic emissions are also known to contain barium, manganese [<xref rid=\"B57-ijerph-17-05263\" ref-type=\"bibr\">57</xref>], and arsenic [<xref rid=\"B35-ijerph-17-05263\" ref-type=\"bibr\">35</xref>]. The high urinary concentrations of total arsenic among the Nicaraguans (median 13.5 μg/g creatinine, as compared to NHANES median of 6.1 μg/g creatinine) may also reflect the presence of arsenic in drinking water sources in the region [<xref rid=\"B35-ijerph-17-05263\" ref-type=\"bibr\">35</xref>]. Dietary habits, such as seafood consumption (as evidenced by the high concentrations of the organic arsenic species arsenobetaine and DMA relative to inorganic arsenic species) [<xref rid=\"B59-ijerph-17-05263\" ref-type=\"bibr\">59</xref>] or exposure to arsenical pesticides historically used in the region. The low arsenobetaine concentrations among the Navajo are consistent with a population that does not consume seafood [<xref rid=\"B60-ijerph-17-05263\" ref-type=\"bibr\">60</xref>,<xref rid=\"B61-ijerph-17-05263\" ref-type=\"bibr\">61</xref>]. It is notable, however, that the inorganic arsenic species are high among the Navajo compared with NHANES.</p><p>Navajo participants were expected and found to have relatively high urine uranium, due to proximity to abandoned uranium mine and milling sites. Although, they were not selected with any consideration of exposure history, but rather with a focus on health status. It is possible that the even higher uranium concentrations in Nicaraguan participants may be due to the volcanic activity in the area, but uncertain at best.</p><p>While antimony, cadmium, and tungsten concentrations were slightly higher among the Navajo as compared with NHANES and the Nicaraguan men, urinary concentrations of most metals in the Navajo group were lower or similar to NHANES, with the noted exceptions of tin and uranium. A subsequent analysis of urine metals compared Navajo men selected for this study to Navajo men in the larger Navajo Birth Cohort Study (NBCS). The results indicated that there was no difference in urinary concentrations between the subgroups with the exception of tin. Urine tin concentrations from NBCS men selected for analysis were marginally greater (Mann-Whitney U test, <italic>p</italic>-value 0.045) than concentrations among NBCS men who were not selected for this study. Compared to the NHANES 2011–2012 report of median tin levels among adult men, urinary tin concentrations were high in the Navajo group and 15-fold higher than in the Nicaraguan group. While, tin exposure can occur via contact with dust and soil containing tin [<xref rid=\"B62-ijerph-17-05263\" ref-type=\"bibr\">62</xref>], the primary exposure route is thought to be via the consumption of food and beverages from tin-containing cans and food packaging [<xref rid=\"B63-ijerph-17-05263\" ref-type=\"bibr\">63</xref>].</p><p>The prevalence of any ANA positivity in the current sample is similar to that observed in other studies of healthy individuals suggesting the findings in this study are generalizable beyond our somewhat unique exposure scenarios. The prevalence of ANA positivity (8%) in the present male population with an average age of 32 years is also similar to the ~8% prevalence of ANA positivity among the male US NHANES population age 30–39 years (1999–2004) [<xref rid=\"B64-ijerph-17-05263\" ref-type=\"bibr\">64</xref>]. Satoh et al. [<xref rid=\"B64-ijerph-17-05263\" ref-type=\"bibr\">64</xref>] used a cut point of a 3+ intensity score and a dilution of 1:80, while we used a lower intensity ANA score (2+) but higher serum dilution (1:160) that may be more clinically relevant. Dinse et al. [<xref rid=\"B16-ijerph-17-05263\" ref-type=\"bibr\">16</xref>] estimated 11% prevalence among adult males during the same time period as Satoh et al. [<xref rid=\"B64-ijerph-17-05263\" ref-type=\"bibr\">64</xref>] at 1:80 dilution and 3+ ANA intensity.</p><p>The dilution (1:160 v. 1:40) had a substantial effect on our results. In the present study, two-thirds more of the pooled population had positive antibody binding to any cellular site at the 1:40 dilution compared with the 1:160 dilution. Qualitatively similar observations were made by Mariz et al. [<xref rid=\"B65-ijerph-17-05263\" ref-type=\"bibr\">65</xref>] in healthy Brazilians where the percentage of ANA positivity increased from 7.6% at 1:160 dilution to 46% at a lower (1:80) dilution. Similarly, Marin et al. [<xref rid=\"B66-ijerph-17-05263\" ref-type=\"bibr\">66</xref>] found 35.4% of healthy individuals had nuclear staining at 1:40 dilution, and at the 1:160 dilution the same population had only 3.2% staining.</p><p>The 1:40 dilution is more sensitive and useful for research examining possible associations with environmental exposures, but has a higher possibility of false positives. The basis for serum reactivity at lower dilution is due, in part, to autoantibodies of the lens epithelium-derived growth factor (LEDGF) nuclear antigen, which has no clinical significance [<xref rid=\"B65-ijerph-17-05263\" ref-type=\"bibr\">65</xref>,<xref rid=\"B67-ijerph-17-05263\" ref-type=\"bibr\">67</xref>], and to non-specific binding at high concentration of polyclonal immunoglobulin in this assay format. Nevertheless, the finding that third tertile exposures to DMA, arsenic, arsenobetaine, cobalt, strontium, and thallium were associated, albeit weakly, with ANA at the 1:40 dilution suggests that, long-term exposure to relatively low levels of these metals, may promote production of autoimmune biomarkers.</p><p>Specific autoantibodies tested by ELISA in this study (histones, dDNA, chromatin and nDNA) were observed infrequently and only at low levels among Nicaraguan and Navajo men. While, antibodies to histones and dDNA are sensitive indicators of xenobiotic-induced antibodies, especially by ingested medications [<xref rid=\"B68-ijerph-17-05263\" ref-type=\"bibr\">68</xref>]. Similar immune perturbations did not occur to environmental metals at the exposure duration and levels encountered in the current study. Antibodies to chromatin and nDNA, considered indicators of idiopathic autoimmune disease, were detected at low levels only in two Navajo men. The lack of a strong response to these antigens is consistent with the health status of the groups studied.</p><p>Based on the strength of association, the metals that were seemingly associated with ANA positivity at the 1:160 dilution in the present study are barium, cesium, lead, strontium and tungsten. Although, other metals were also positively associated with ANA, the associations were weak or close to null. Lead was the only metal examined in our study with toxicological literature supporting the association [<xref rid=\"B19-ijerph-17-05263\" ref-type=\"bibr\">19</xref>,<xref rid=\"B69-ijerph-17-05263\" ref-type=\"bibr\">69</xref>]. However, a recent epidemiological analysis by Dinse et al. [<xref rid=\"B16-ijerph-17-05263\" ref-type=\"bibr\">16</xref>] found no association between blood lead in NHANES and ANA among men and women, a result which is inconsistent with those of the present study. No literature was found examining the associations of barium, cesium, strontium or tungsten with ANA or any autoimmune outcomes in humans or in animals. Although, there is literature suggesting an association between exposure to arsenic [<xref rid=\"B23-ijerph-17-05263\" ref-type=\"bibr\">23</xref>,<xref rid=\"B70-ijerph-17-05263\" ref-type=\"bibr\">70</xref>] and cadmium [<xref rid=\"B18-ijerph-17-05263\" ref-type=\"bibr\">18</xref>,<xref rid=\"B71-ijerph-17-05263\" ref-type=\"bibr\">71</xref>], and biomarkers of autoimmunity, the associations observed in the present study were positive but weak. Future investigations are needed to ascertain the relationship between arsenic, cadmium and the autoimmune biomarker production. </p><p>Overall, the results of our analyses suggest a possible association between exposures to certain metals (barium, lead, strontium, cesium and tungsten) and the production of antinuclear antibodies, when assessed for positivity at the 1:160 dilution. Given the possibility of interactions between metals, additional research assessing the impact of metal mixtures on autoimmune outcomes is warranted.</p><p>This study has several limitations. Although, the prevalence of ANA positivity at the 1:160 dilution is consistent with previously published population-based, healthy or undiagnosed patients’ studies [<xref rid=\"B57-ijerph-17-05263\" ref-type=\"bibr\">57</xref>,<xref rid=\"B64-ijerph-17-05263\" ref-type=\"bibr\">64</xref>], the small sample size, and relatively low prevalence of positive outcomes (ANA and specific autoantibody reactivity), requires cautionary interpretation of statistical significance. We did not include body weight as a variable in our models, which could influence urinary metals concentrations. The small sample size also limited our ability to assess interactive effects between metals or the association between metals and specific autoantibodies. The present study was also limited to quantifying a small selection of autoantibodies; future studies should consider inclusion of a broader range of autoantibodies specific to different target tissues such as thyroid or pancreatic antigens. The participants in this study were exclusively male, and neither the healthy Nicaraguan workers, nor the Navajo men selected for health status reflect their populations as a whole. Given that women have higher autoimmunity biomarker production, and are most affected by autoimmune disease [<xref rid=\"B64-ijerph-17-05263\" ref-type=\"bibr\">64</xref>], future studies examining environmental exposures and autoimmune biomarkers should include female participants [<xref rid=\"B1-ijerph-17-05263\" ref-type=\"bibr\">1</xref>]. Finally, only single urine measures at one point in time were collected for all participants. No diurnal or seasonal variation in urine metal concentrations were examined, and we were not able to examine mercury exposure among the metals.</p></sec><sec sec-type=\"conclusions\" id=\"sec5-ijerph-17-05263\"><title>5. Conclusions</title><p>The low prevalence of ANA positivity in this pooled cohort of relatively young, healthy men using a conservative assay cut-off criterion is consistent with rates published in the literature. Similarly, reactivity to specific autoantibodies (histone, dDNA, nDNA and chromatin) was low. We found urinary concentrations of arsenic, barium and strontium were twice as high among Nicaraguan men, compared with the US population as measured by NHANES, and manganese 50% higher in the Nicaraguan group as compared to the Navajo group. Other adverse outcomes that we did not study may be associated with these high urine concentrations, and follow-up on exposure sources may be warranted. Notable associations between ANA positivity at the 1:160 dilution and upper tertile concentrations of several metals including barium, cesium, lead, strontium, and tungsten suggest that more research on metals and autoimmune biomarkers may also be warranted.</p></sec></body><back><ack><title>Acknowledgments</title><p>We thank the Nicaraguan and Navajo men for their participation in this study. The Navajo Birth Cohort Study (NBCS) was funded by CDC/ATSDR (cooperative agreement: 2UO1TS000135) and mandated by the US Congress to determine the impact of chronic low-level uranium and mine waste exposures on reproductive and developmental end-points. We acknowledge the strong support for sample and data collection provided by the Navajo NBCS field staff working with the University of New Mexico and their partner organizations: the Navajo Department of Health Community Health Representative and Outreach Program under the leadership of Mae-Gilene Begay; Navajo Area Indian Health Service under leadership of Doug Peter; and the Southwest Research and Information Center under leadership of Chris Shuey. We thank the laboratory staff at the following IHS and PL-638 hospitals for biospecimen collection and processing: Gallup Indian Medical Center, Chinle Comprehensive Health Care Facility, Tuba City Regional Health Care Corporation, Tséhootsooí Medical Center, the Northern Navajo Medical Center, and Kayenta Health Center. The following individuals affiliated with the US Centers for Disease Control and Prevention/Agency for Toxic Substances and Disease Registry are also acknowledged associated for their association with this work: Angela Ragin, Candis Hunter, Elizabeth Irvin-Barnwell. We are grateful to Joseph Hoover, who conducted statistical analyses referred to in the Discussion and Erin Polka who assisted with making figures. Allison Appleton, Birgit Claus Henn, Hector Olvera, Jose Suarez and Marc Weisskopf provided comments and suggestions along the way.</p></ack><app-group><app id=\"app1-ijerph-17-05263\"><title>Supplementary Materials</title><p>The following is available online at <uri xlink:href=\"https://www.mdpi.com/1660-4601/17/15/5263/s1\">https://www.mdpi.com/1660-4601/17/15/5263/s1</uri>, Table S1: Multivariable logistic regression results indicating odds of association between urinary metals concentrations by tertile normalized by urine creatinine and with urine creatinine included in as a covariate, and ANA Positivity (≥2+) at cellular sites in both dilutions.</p><supplementary-material content-type=\"local-data\" id=\"ijerph-17-05263-s001\"><media xlink:href=\"ijerph-17-05263-s001.pdf\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material></app></app-group><notes><title>Author Contributions</title><p>Conceptualization, M.K.S., R.L.R., J.L. and E.E.; data curation, M.K.S., C.S., R.L.L., R.L.R., NBCS Team, and E.E.; formal analysis, M.K.S., C.S., R.L.L. and R.L.R.; funding acquisition, M.K.S., M.D.M. and J.L.; investigation, M.K.S., C.S., R.L.L., R.L.R., J.J.A., D.L.-P., O.R.-R. and E.E.; methodology, M.K.S., C.S., R.L.L., D.R.B., D.J.F. and E.E.; project administration, M.K.S., D.R.B., M.D.M., NBCS Team and E.E.; resources, R.L.R., D.J.F. and E.E.; supervision, M.K.S., D.R.B., J.J.A., D.J.F., M.D.M., J.L. and E.E.; validation, R.L.R. and E.E.; visualization, R.L.R.; writing—review & editing, M.K.S., C.S., R.L.L., R.L.R., D.R.B., J.J.A., D.L.-P., O.R.-R., D.J.F., M.D.M., J.L. and E.E. All authors have read and agreed to the published version of the manuscript.</p></notes><notes><title>Funding</title><p>Funds for the collection of biological samples from Nicaraguan workers were provided by Los Azucareros del Istmo Centroamericano (AICA), the Association of Sugar Producers in Central America, to the CDC Foundation. In Nicaragua we thank the Nicaraguan Ministry of Health for laboratory support. Metals for both studies were analyzed by the CDC Division of Laboratory Science and we thank Robert L. Jones, and Kathleen L. Caldwell. The laboratory autoimmunity biomarker analyses by E. Erdei and R. Rubin, their time and the time of C. Sennett were supported by a JPB Environmental Health Fellowship award granted to M. Scammell by The JPB Foundation and managed by the Harvard T.H. Chan School of Public Health. </p></notes><notes notes-type=\"COI-statement\"><title>Conflicts of Interest</title><p>Some authors declare the potential for perceived conflict of interest by having received funding from the Association of Sugar Producers in Central America. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results. The authors declare no conflict of interest.</p></notes><app-group><app id=\"app2-ijerph-17-05263\"><title>Appendix A</title><p>Navajo Birth Cohort study (NBCS) Team (by affiliation in alphabetical order): <italic>University of New Mexico Health Sciences Center</italic>: David Begay, Miranda Cajero, Carla Chavez, Joseph Hoover, Carol J Laselute, Debra MacKenzie, Elena O’Donald, Bernadette Pacheco, Becky Smith. <italic>Navajo Department of Health</italic>: Qeturah Anderson, Mae-Gilene Begay, Nikki Begay, Velma Harold, Olivia Muskett, Anna Rondon, Roxanne Thompson, Doris Tsinnijinnie, Rebecca Tsosie, and Josephine Watson. <italic>Navajo Area Indian Health Service & PL-638 Contractors:</italic> Loretta Atene, Lorraine Barton, Delila Begay, Francine Begay, Priscilla Begay, Dorena Benally, Beth Chee, Bobbi Clawson, LeShelly Crank, Myra Francisco, Lisa Kear, Ursula Knoki-Wilson, Della Reese, Johnna Rogers, Abigail Sanders, Deidra Sam, Melissa Samuel, Charlotte Swindal and Marcia Tapaha. <italic>Southwest Research and Information Center:</italic> Lynda Lasiloo, Teddy Nez, Cora Phillips, Sandy Ramone, Chris Shuey, Maria Welch.</p></app></app-group><ref-list><title>References</title><ref id=\"B1-ijerph-17-05263\"><label>1.</label><element-citation publication-type=\"book\"><person-group person-group-type=\"author\"><collab>National Institutes of Health</collab></person-group><source>Report of the Director—National Institutes of Health: Fiscal Years 2014–2015</source><publisher-name>National Institutes of Health</publisher-name><publisher-loc>Bethesda, MD, USA</publisher-loc><year>2013</year><fpage>1</fpage><lpage>322</lpage></element-citation></ref><ref id=\"B2-ijerph-17-05263\"><label>2.</label><element-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Peschken</surname><given-names>C.A.</given-names></name><name><surname>Esdaile</surname><given-names>J.M.</given-names></name></person-group><article-title>Systemic lupus erythematosus in North American Indians: A population based study</article-title><source>J. 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Note: US metals reported in the 2011–2012 National Health and Nutrition Examination Survey (NHANES) Fourth Report [<xref rid=\"B59-ijerph-17-05263\" ref-type=\"bibr\">59</xref>].</p></caption><graphic xlink:href=\"ijerph-17-05263-g001\"/></fig><fig id=\"ijerph-17-05263-f002\" orientation=\"portrait\" position=\"float\"><label>Figure 2</label><caption><p>Distribution of geometric mean total and speciated arsenic concentrations (μg/g creatinine) and 95% confidence limits in comparison with geometric mean concentrations of the United States adult male population 2011–2012 [<xref rid=\"B59-ijerph-17-05263\" ref-type=\"bibr\">59</xref>]. Note: Arsenic is reported in μg/L arsenic.</p></caption><graphic xlink:href=\"ijerph-17-05263-g002\"/></fig><fig id=\"ijerph-17-05263-f003\" orientation=\"portrait\" position=\"float\"><label>Figure 3</label><caption><p>ELISA autoantibody activity in optical density units (O.D.) among Nicaraguan and Navajo men, and comparison with positive controls. IgG binding to the indicated antigens was measured in five separate assays. The mean ± SD of positive control samples included in each assay is shown.</p></caption><graphic xlink:href=\"ijerph-17-05263-g003\"/></fig><table-wrap id=\"ijerph-17-05263-t001\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05263-t001_Table 1</object-id><label>Table 1</label><caption><p>Demographics and urinary metals detection and concentrations (μg/g creatinine) among the study population.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Variable</th><th colspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Pooled<break/>(<italic>n</italic> = 115)</th><th colspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Navajo<break/>(<italic>n</italic> = 68)</th><th colspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Nicaraguan<break/>(<italic>n</italic> = 47)</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Demographics</td><td colspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">\n</td><td colspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">\n</td><td colspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Male Sex, <italic>n</italic> (%)</td><td colspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">115 (100%)</td><td colspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">68 (100%)</td><td colspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">47 (100%)</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Median Age, years (range)</td><td colspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">31.7 (23–51)</td><td colspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">31 (26–47)</td><td colspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">32.2 (23–51)</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Seafood Consumed past 3 days, <italic>n</italic> (%) <sup>a</sup></td><td colspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">-</td><td colspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">-</td><td colspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">12 (23.40%)</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Urinary Metals</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">% Detect</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Median (μg/g)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Range (μg/g)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">% Detect</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Median (μg/g)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Range (μg/g)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">% Detect</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Median (μg/g)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Range (μg/g)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Total Arsenic</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">99.5%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6.32</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">(2.04–66.55)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">99%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.63</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">(2.04–25.51)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13.50</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">(4.06–66.55)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Arsenobetaine</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">34%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.88</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">(0.18–46.59)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.60</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> (0.18–17.92)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">60%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.04</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">(0.27–46.59)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Arsenocholine</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">24%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.07</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">(0.02–1.03)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.06</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">(0.02–0.88)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">45%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.11</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">(0.02–1.03)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">As (III)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">95.5%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.77</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">(0.09–5.56)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">97%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.54</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">(0.09–1.74)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">94%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.17</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">(0.27–5.56)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">As (V)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">24.5%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.47</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">(0.11–6.29)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.40</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> (0.18–6.29)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">43%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.54</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">(0.11–4.44)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Dimethylarsonic Acid (DMA)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">89.5%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.18</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">(0.78–19.76)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">90%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.86</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">(0.78–15.21)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">89%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7.57</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">(2.54–19.76)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Monomethylarsonic Acid (MMA)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">93.5%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.87</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">(0.16–9.31)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">91%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.55</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">(0.16–2.41)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">96%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.77</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">(0.54–9.31)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Antimony</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">87%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.05</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">(0.01–0.36)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">97%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.05</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">(0.02–0.36)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">77%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.03</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">(0.01–0.20)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Barium</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.03</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">(0.21–15.49)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.52</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">(0.21–12.47)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.06</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">(0.43–15.49)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Cadmium</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">88%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.12</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">(0.01–0.62)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">99%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.14</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">(0.02–0.62)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">77%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.09</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">(0.01–0.26)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Cesium</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.59</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">(0.55–8.75)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.35</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">(1.64–8.75)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.85</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">(0.55–8.6)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Cobalt</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.42</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">(0.13–1.55)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.34</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">(0.13–1.55)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.55</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">(0.13–1.45)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Lead</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.31</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">(0.09–2.36)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.24</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">(0.09–0.70)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.47</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">(0.14–2.36)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Manganese</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">58%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.12</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">(0.03–5.81)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">35%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">(0.03–1.04)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">81%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.18</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">(0.03–5.81)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Molybdenum</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">44.24</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">(1.87–213.89)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">39.89</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">(13.81–146.8)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">54.58</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">(1.87–213.89)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Strontium</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">151.29</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">(23.85–726.83)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">130.16</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">(23.85–316.58)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">222.28</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">(33.84–726.83)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Thallium </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">98.5%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.11</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">(0.02–0.28)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">99%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.12</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">(0.04–0.28)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">98%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.10</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">(0.02–0.27)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Tin</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">73.5%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.38</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">(0.02–6.54)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">99%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.22</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">(0.08–6.54)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">48%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.08</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">(0.02–0.51)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Tungsten</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">91.5%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.07</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">(0.01–1.80)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">96%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.10</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">(0.02–0.51)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">87%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.05</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">(0.01–1.80)</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Uranium </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">98%</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.02</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">(0.004–0.10)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">100%</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.01</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">(0.004–0.10)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">96%</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.02</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">(0.01–0.08)</td></tr></tbody></table><table-wrap-foot><fn><p><sup>a</sup>. No seafood consumption data for NBCS group.</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"ijerph-17-05263-t002\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05263-t002_Table 2</object-id><label>Table 2</label><caption><p>Biomarkers of autoimmunity among the study population.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Biomarkers</th><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Pooled (<italic>n</italic> = 115)</th><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Navajo (<italic>n</italic> = 68)</th><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Nicaraguan (<italic>n</italic> = 47)</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ANA Positivity (≥2), 1:160 dilution <sup>a</sup></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>n</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">(%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>n</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">(%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>n</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">(%)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Any Site</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">(7.8%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">(7.4%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">(8.5%)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Nuclear Site</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">(4.4%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">(4.4%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">(4.3%)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Cytoplasmic Site</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">(2.6%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">(1.5%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">(4.3%)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Nucleolar Site</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">(0.9%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">(1.5%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">(0%)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ANA Positivity (≥2), 1:40 dilution <sup>a</sup></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>n</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">(%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>n</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">(%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>n</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">(%)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Any Site</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">76</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">(66.1%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">38</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">(55.9%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">38</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">(80.9%)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Nuclear Site</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">33</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">(28.7%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">19</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">(27.9%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">14</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">(29.8%)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Cytoplasmic Site</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">67</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">(58.3%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">32</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">(47.1%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">35</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">(74.5%)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Nucleolar Site</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">(7.0%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">(11.8%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">(0%)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Specific AutoAntibody (SpAuAb) Elevation</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>n</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">(%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>n</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">(%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>n</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">(%)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Any SpAuAb</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">(11.3%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">(13.2%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">(8.5%)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Histone</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">(2.6%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">(2.9%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">(2.1%)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Chromatin</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">(2.6%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">(2.9%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">(2.1%)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">dDNA</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">(8.7%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">(10.3%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">(6.4%)</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">nDNA</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">(1.7%)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">(2.9%)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">(0%)</td></tr></tbody></table><table-wrap-foot><fn><p>Note: Results are presented as <italic>n</italic> (%). <sup>a</sup> Antibody reactivity (“ANA”) at various intracellular sites was determined on serum samples via indirect immunofluorescence at two dilutions, results of which are listed separately.</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"ijerph-17-05263-t003\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05263-t003_Table 3</object-id><label>Table 3</label><caption><p>Multivariable logistic regression results: Odds of association between urinary metals concentrations (μg/g creatinine) by tertile with first tertile as reference (REF) and any ANA positivity (≥2+) at cellular sites.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Metals Tertiles</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Any ANA Positivity<break/>(1:160 dilution)</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Any ANA Positivity<break/>(1:40 dilution)</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Metals</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">OR (95%CI)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">OR (95% CI)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Total Arsenic—Tertile 1 (4.67μg/g)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">REF</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">REF</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Total Arsenic—Tertile 2 (11.14 μg/g)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.64 (0.21, 1.97)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.01 (0.55, 1.86)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Total Arsenic—Tertile 3 (66.55 μg/g)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.69 (0.41, 6.82)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.72 (0.68, 4.31)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Arsenobetaine—Tertile 1 (0.60 μg/g)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">REF</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">REF</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Arsenobetaine—Tertile 2 (1.52 μg/g)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.76 (0.25, 2.32)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.78 (0.43, 1.42)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Arsenobetaine—Tertile 3 (46.59 μg/g)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.02 (0.30, 3.42)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.55(0.72, 3.35)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Arsenocholine—Tertile 1 (0.05 μg/g)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">REF</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">REF</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Arsenocholine—Tertile 2 (0.10 μg/g)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.73 (0.24, 2.23)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.13 (0.63, 2.03)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Arsenocholine—Tertile 3 (1.03 μg/g)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.11 (0.38, 3.25)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.95 (0.49, 1.82)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">As (III)—Tertile 1 (0.51 μg/g)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">REF</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">REF</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">As (III)—Tertile 2 (1.08 μg/g)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.10 (0.32, 3.71)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.39 (0.76, 2.53)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">As (III)—Tertile 3 (5.56 μg/g)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.01 (0.41, 2.48)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.48 (0.23, 1.03)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">As (V)—Tertile 1 (0.36 μg/g)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">REF</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">REF</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">As (V)—Tertile 2 (0.60 μg/g)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.05 (0.38, 2.92)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.40 (0.77, 2.56)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">As (V)—Tertile 3 (6.29 μg/g)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.17 (0.42, 3.29)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.73 (0.39, 1.35)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">DMA—Tertile 1 (2.95 μg/g)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">REF</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">REF</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">DMA—Tertile 2 (6.37 μg/g)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.46 (0.56, 3.82)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.78 (0.43, 1.43)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">DMA—Tertile 3 (19.76 μg/g)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.45 (0.10, 1.97)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.17 (0.85, 5.47)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">MMA—Tertile 1 (0.57 μg/g)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">REF</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">REF</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">MMA—Tertile 2 (1.32 μg/g)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.98 (0.37, 2.61)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.20 (0.65, 2.23)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">MMA—Tertile 3 (9.31 μg/g)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.93(0.22, 3.86)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.08 (0.42, 2.75)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Antimony—Tertile 1 (0.03 μg/g)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">REF</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">REF</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Antimony—Tertile 2 (0.06 μg/g)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.46 (0.57, 3.78)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.25 (1.16, 4.35) *</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Antimony—Tertile 3 (0.36 μg/g)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.12 (0.39, 3.25)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.09 (0.58, 2.03)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Barium—Tertile 1 (1.26 μg/g)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">REF</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">REF</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Barium—Tertile 2 (3.09 μg/g)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.15 (0.39, 3.39)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.48 (0.82, 2.66)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Barium—Tertile 3 (15.49 μg/g)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.33 (0.83, 6.55)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.00 (0.54, 1.86)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Cadmium—Tertile 1 (0.09 μg/g)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">REF</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">REF</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Cadmium—Tertile 2 (0.16 μg/g)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.04 (0.39, 2.80)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.65 (0.90, 3.03)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Cadmium—Tertile 3 (0.62 μg/g)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.60 (0.58, 4.35)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.91 (0.49, 1.67)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Cesium—Tertile 1 (3.08 μg/g)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">REF</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">REF</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Cesium—Tertile 2 (4.13 μg/g)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.39 (0.09,1.65)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.17 (0.65, 2.10)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Cesium—Tertile 3 (8.75 μg/g)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.98 (1.07, 8.25) *</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.55 (0.30, 1.01)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Cobalt—Tertile 1 (0.33 μg/g)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">REF</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">REF</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Cobalt—Tertile 2 (0.52 μg/g)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.55 (0.55, 4.35)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.79 (0.45, 1.40)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Cobalt—Tertile 3 (1.55 μg/g)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.78 (0.59, 5.37)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.25 (0.65, 2.39)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Lead—Tertile 1 (0.24 μg/g)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">REF</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">REF</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Lead—Tertile 2 (0.43 μg/g)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.41 (0.10, 1.73)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.09 (0.61, 1.95)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Lead—Tertile 3 (2.36 μg/g)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.31 (1.09, 9.97) *</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.89 (0.46, 1.72)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Strontium—Tertile 1 (122.36 μg/g)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">REF</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">REF</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Strontium—Tertile 2 (201.59 μg/g)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.47 (0.10, 2.16)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.08 (0.60, 1.93)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Strontium—Tertile 3 (726.83 μg/g)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.71 (1.34, 16.61) *</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.17 (0.59, 2.29)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Thallium—Tertile 1 (0.09 μg/g)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">REF</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">REF</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Thallium—Tertile 2 (0.13 μg/g)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.34 (0.51, 3.53)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.83 (0.46, 1.49)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Thallium—Tertile 3 (0.28 μg/g)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.09 (0.39, 3.03)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.25 (0.68, 2.27)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Tin—Tertile 1 (0.14 μg/g)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">REF</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">REF</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Tin—Tertile 2 (0.89 μg/g)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.55 (0.59, 4.06)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.64 (0.34, 1.21)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Tin—Tertile 3 (6.54 μg/g)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.73 (0.41, 7.32)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.82 (0.36, 1.90)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Tungsten—Tertile 1 (0.05 μg/g)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">REF</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">REF</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Tungsten—Tertile 2 (0.11 μg/g)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.94 (0.27, 3.22)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.92 (0.51, 1.65)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Tungsten—Tertile 3 (1.80 μg/g)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.00 (1.11, 14.44) *</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.56 (0.82, 2.94)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Uranium—Tertile 1 (0.01 μg/g)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">REF</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">REF</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Uranium—Tertile 2 (0.02 μg/g)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.98 (0.34, 2.89)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.86 (0.46, 1.62)</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Uranium—Tertile 3 (0.10 μg/g)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.50 (0.58, 3.90)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.77 (0.42, 1.39)</td></tr></tbody></table><table-wrap-foot><fn><p>Note: All models were adjusted for age, seafood consumption within the past three days and cohort. (Nicaragua or NBCS). <bold>*</bold> Statistically significant at an alpha level of 0.05.</p></fn></table-wrap-foot></table-wrap></floats-group></article>\n"
]
[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Int J Environ Res Public Health</journal-id><journal-id journal-id-type=\"iso-abbrev\">Int J Environ Res Public Health</journal-id><journal-id journal-id-type=\"publisher-id\">ijerph</journal-id><journal-title-group><journal-title>International Journal of Environmental Research and Public Health</journal-title></journal-title-group><issn pub-type=\"ppub\">1661-7827</issn><issn pub-type=\"epub\">1660-4601</issn><publisher><publisher-name>MDPI</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32731648</article-id><article-id pub-id-type=\"pmc\">PMC7432080</article-id><article-id pub-id-type=\"doi\">10.3390/ijerph17155442</article-id><article-id pub-id-type=\"publisher-id\">ijerph-17-05442</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Article</subject></subj-group></article-categories><title-group><article-title>Experiential Learning Program to Strengthen Self-Reflection and Critical Thinking in Freshmen Nursing Students during COVID-19: A Quasi-Experimental Study</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0001-5742-9631</contrib-id><name><surname>Cheng</surname><given-names>Yi-Chuan</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05442\">1</xref><xref ref-type=\"aff\" rid=\"af2-ijerph-17-05442\">2</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0003-1152-6596</contrib-id><name><surname>Huang</surname><given-names>Li-Chi</given-names></name><xref ref-type=\"aff\" rid=\"af3-ijerph-17-05442\">3</xref></contrib><contrib contrib-type=\"author\"><name><surname>Yang</surname><given-names>Chi-Hsuan</given-names></name><xref ref-type=\"aff\" rid=\"af4-ijerph-17-05442\">4</xref></contrib><contrib contrib-type=\"author\"><name><surname>Chang</surname><given-names>Hsing-Chi</given-names></name><xref ref-type=\"aff\" rid=\"af4-ijerph-17-05442\">4</xref><xref rid=\"c1-ijerph-17-05442\" ref-type=\"corresp\">*</xref></contrib></contrib-group><aff id=\"af1-ijerph-17-05442\"><label>1</label>Department of Nursing, Asia University, Taichung 41354, Taiwan; <email>[email protected]</email></aff><aff id=\"af2-ijerph-17-05442\"><label>2</label>Department of Public Health, China Medical University, Taichung 40402, Taiwan</aff><aff id=\"af3-ijerph-17-05442\"><label>3</label>School of Nursing, China Medical University, Taichung 40402, Taiwan; <email>[email protected]</email></aff><aff id=\"af4-ijerph-17-05442\"><label>4</label>Department of Nursing, National Taichung University of Science and Technology, Taichung 404, Taiwan; <email>[email protected]</email></aff><author-notes><corresp id=\"c1-ijerph-17-05442\"><label>*</label>Correspondence: <email>[email protected]</email>; Tel.: +886-4-22196951; Fax: +886-4-22195881</corresp></author-notes><pub-date pub-type=\"epub\"><day>28</day><month>7</month><year>2020</year></pub-date><pub-date pub-type=\"ppub\"><month>8</month><year>2020</year></pub-date><volume>17</volume><issue>15</issue><elocation-id>5442</elocation-id><history><date date-type=\"received\"><day>26</day><month>6</month><year>2020</year></date><date date-type=\"accepted\"><day>27</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>© 2020 by the authors.</copyright-statement><copyright-year>2020</copyright-year><license license-type=\"open-access\"><license-p>Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>).</license-p></license></permissions><abstract><p>This article focuses on the unique needs and concerns of nursing educators and nursing students in the face of the COVID-19 pandemic. During social distancing, interacting with other human beings has been restricted. This would undermine the experiential learning of nursing students. Hence, it is important to develop and evaluate an experiential learning program (ELP) for nursing education. A pre-test and post-test design were used. The study was conducted in a university in Central Taiwan. A total of 103 nursing students participated in the study from February to June 2019. The study intervention was the experiential learning program (ELP), including bodily experiences and nursing activities with babies, pregnant women, and the elderly. After the intervention, the students completed the self-reflection and insight scale (SRIS) and Taiwan Critical Thinking Disposition Inventory (TCTDI) as outcome measures. An independent <italic>t</italic>-test showed that there was a significant difference between pre-test and post-test in both SRIS and TCTDI (<italic>p</italic> < 0.01). The Pearson product–moment correlation analysis showed that SRIS and TCTDI were significantly positively correlated (<italic>p</italic> < 0.01). ELP has a significant impact on the self-reflection and critical thinking of first-year nursing students, which can be used as a reference for the education of nursing students. During these turbulent times, it is especially vital for faculties to provide experiential learning instead of the traditional teaching concept. </p></abstract><kwd-group><kwd>experiential learning program</kwd><kwd>self-reflection</kwd><kwd>critical thinking</kwd><kwd>nurse education</kwd></kwd-group></article-meta></front><body><sec sec-type=\"intro\" id=\"sec1-ijerph-17-05442\"><title>1. Introduction</title><p>The world is facing unprecedented challenges in the face of a global pandemic. Coronavirus disease 2019 (COVID-19) has, to date, killed thousands worldwide [<xref rid=\"B1-ijerph-17-05442\" ref-type=\"bibr\">1</xref>]. COVID-19 has also already disrupted universities and academic institutions. Within the health field, schools of nursing are bracing for unique challenges related to our role in helping to develop the next generation of care providers. Social interest in patient safety is growing, and consumers of healthcare services are more likely to obtain healthcare information, which increases the demand for high-quality healthcare [<xref rid=\"B1-ijerph-17-05442\" ref-type=\"bibr\">1</xref>,<xref rid=\"B2-ijerph-17-05442\" ref-type=\"bibr\">2</xref>]. Academic nurses have also been quick to adapt in the light of the crisis caused by COVID-19 and many have very quickly moved to online course delivery. Nurse educators and administrators are tasked with ensuring that students meet academic requirements. The most junior nursing students have had their clinical placements postponed due to an imminent shortage of supervisory staff and rapid changes within the clinical environment [<xref rid=\"B3-ijerph-17-05442\" ref-type=\"bibr\">3</xref>,<xref rid=\"B4-ijerph-17-05442\" ref-type=\"bibr\">4</xref>,<xref rid=\"B5-ijerph-17-05442\" ref-type=\"bibr\">5</xref>]. Nursing education is the process of cultivating nursing students to become nursing professionals, and the ability of self-reflection and critical thinking needs to be developed at the stage of freshmen nursing students [<xref rid=\"B6-ijerph-17-05442\" ref-type=\"bibr\">6</xref>,<xref rid=\"B7-ijerph-17-05442\" ref-type=\"bibr\">7</xref>,<xref rid=\"B8-ijerph-17-05442\" ref-type=\"bibr\">8</xref>]. Freshmen nursing students are introduced to clinical assignments to help them begin to develop their nursing clinical imagination, formation of habits of thought, and skilled know-how, and in order to better understand how the prerequisites in the sciences and humanities are relevant to nursing practice, they need to learn simpler aspects of situations before moving on to understanding the whole complex, unfolding clinical situation. This is why situated coaching is essential for the novice, because the novice simply does not have the deep background experiential knowledge yet to recognize the whole clinical situation or make qualitative distinctions within a clinical situation [<xref rid=\"B6-ijerph-17-05442\" ref-type=\"bibr\">6</xref>,<xref rid=\"B7-ijerph-17-05442\" ref-type=\"bibr\">7</xref>,<xref rid=\"B8-ijerph-17-05442\" ref-type=\"bibr\">8</xref>]. Experiential learning is a process of directly recognizing, readily accepting, respecting, and applying the knowledge and abilities currently taught [<xref rid=\"B8-ijerph-17-05442\" ref-type=\"bibr\">8</xref>,<xref rid=\"B9-ijerph-17-05442\" ref-type=\"bibr\">9</xref>]. Its operational definition is as follows: experiential learning courses are experiential education. Participants reflect on life and the environment through the process of personal experience and learn the educational method of knowledge accumulation [<xref rid=\"B9-ijerph-17-05442\" ref-type=\"bibr\">9</xref>].</p><p>This article focuses on the unique needs and concerns of nursing educators and nursing students in the face of the COVID-19 pandemic. In this study, the curriculum of ELP activities is designed to guide students to discuss topics and promote their interest in learning through multiple teaching methods and change the classroom learning atmosphere so that students can strengthen their attitudes, abilities, and skills and enhance the core nursing practice while discovering facts and problems, helping them become competent caregivers trusted by their patients and families. </p></sec><sec id=\"sec2-ijerph-17-05442\"><title>2. Literature Review</title><sec id=\"sec2dot1-ijerph-17-05442\"><title>2.1. Definition and Connotation of Self-Reflection </title><p>The rising tension in nursing is palpable, and, for many of us, this is unprecedented. Reflection is viewed as a learning strategy and a method to improve professional nursing practice [<xref rid=\"B10-ijerph-17-05442\" ref-type=\"bibr\">10</xref>,<xref rid=\"B11-ijerph-17-05442\" ref-type=\"bibr\">11</xref>]. Dewey (1993) believed that reflection is a mental activity that has experiential significance [<xref rid=\"B12-ijerph-17-05442\" ref-type=\"bibr\">12</xref>,<xref rid=\"B13-ijerph-17-05442\" ref-type=\"bibr\">13</xref>,<xref rid=\"B14-ijerph-17-05442\" ref-type=\"bibr\">14</xref>,<xref rid=\"B15-ijerph-17-05442\" ref-type=\"bibr\">15</xref>]. Reflection possesses the following characteristics: inspection, introspection, proactiveness, and thoughtful construction and transformation [<xref rid=\"B16-ijerph-17-05442\" ref-type=\"bibr\">16</xref>,<xref rid=\"B17-ijerph-17-05442\" ref-type=\"bibr\">17</xref>]. Transformative learning requires individuals to facilitate their own understanding and experience to restructure or revise their experiential significance, which is used as a direction for future action. In summary, reflection can occur during or after an activity, and it enables the accumulation of personal learning experiences, which facilitate problem-solving [<xref rid=\"B18-ijerph-17-05442\" ref-type=\"bibr\">18</xref>,<xref rid=\"B19-ijerph-17-05442\" ref-type=\"bibr\">19</xref>,<xref rid=\"B20-ijerph-17-05442\" ref-type=\"bibr\">20</xref>]. Self-reflection needs to guide and use structural problems to guide repeated practice. Relevant research reports point out that the biggest advantage of reflective learning is that it can integrate practice and knowledge learning, increase professional responsibilities, increase learners’ knowledge and confidence, increase critical thinking skills, and increase clinical disputes [<xref rid=\"B19-ijerph-17-05442\" ref-type=\"bibr\">19</xref>,<xref rid=\"B20-ijerph-17-05442\" ref-type=\"bibr\">20</xref>]. Self-reflection ability is regarded as a learning strategy and a means to improve nursing practice. Self-reflection writing (journal writing) is a common type of reflection writing which is usually carried out after the end of the learning course activities to promote students’ self-understanding of the learning experience, beliefs, and values, thereby transforming learners’ concepts and enhancing self-awareness. Self-reflection writing not only helps students to develop critical thinking skills and learn professional experience but also enhances self-understanding and fosters the ability of nursing staff to respond to emergencies. Reflective diary is the most commonly used in clinical nursing teaching, which can enhance the reflection of students in action and post-action reflection. In addition, diary writing provides a dialogue window between instructor and learner. Therefore, reflective writing is respected by nursing education experts [<xref rid=\"B17-ijerph-17-05442\" ref-type=\"bibr\">17</xref>,<xref rid=\"B18-ijerph-17-05442\" ref-type=\"bibr\">18</xref>,<xref rid=\"B19-ijerph-17-05442\" ref-type=\"bibr\">19</xref>,<xref rid=\"B20-ijerph-17-05442\" ref-type=\"bibr\">20</xref>].</p></sec><sec id=\"sec2dot2-ijerph-17-05442\"><title>2.2. Definition and Connotation of Critical Thinking</title><p>Critical thinking is a form of high-level thinking in which individuals must have an objective attitude to judge the value of things, decide whether they are true or false, good or evil, and draw a conclusion through introspection and logical reasoning. In the English term “critical thinking”, “critical” comes from the root “skeri” and the Greek word “kriterion”. The former refers to cutting, separation, or analysis, while the latter refers to the standard of judgment [<xref rid=\"B13-ijerph-17-05442\" ref-type=\"bibr\">13</xref>,<xref rid=\"B21-ijerph-17-05442\" ref-type=\"bibr\">21</xref>]. “Critical thinking” is defined as the psychological process of using concepts, applications, analysis, synthesis, and evaluation techniques to obtain answers or conclusions [<xref rid=\"B21-ijerph-17-05442\" ref-type=\"bibr\">21</xref>,<xref rid=\"B22-ijerph-17-05442\" ref-type=\"bibr\">22</xref>]. Therefore, it is necessary for the university teaching community to continue researching active and innovative methodologies to improve the motivation and learning of students. Education in nursing aims to integrate theoretical knowledge into practical knowledge in real-life scenarios and to help students develop their critical thinking [<xref rid=\"B13-ijerph-17-05442\" ref-type=\"bibr\">13</xref>,<xref rid=\"B22-ijerph-17-05442\" ref-type=\"bibr\">22</xref>]. Thus, teaching and learning models used in nursing education have changed from traditional models that were solely focused on knowledge transfer to active student learning methods [<xref rid=\"B21-ijerph-17-05442\" ref-type=\"bibr\">21</xref>,<xref rid=\"B22-ijerph-17-05442\" ref-type=\"bibr\">22</xref>]. Critical thinking is one of the abilities required by nursing professionals [<xref rid=\"B23-ijerph-17-05442\" ref-type=\"bibr\">23</xref>]. Nursing professionals can continuously reflect on nursing problems and seek solutions during the nursing process, and nursing professionals need to learn problem solving skills in the face of the drastic changes in the medical environment [<xref rid=\"B13-ijerph-17-05442\" ref-type=\"bibr\">13</xref>,<xref rid=\"B21-ijerph-17-05442\" ref-type=\"bibr\">21</xref>,<xref rid=\"B22-ijerph-17-05442\" ref-type=\"bibr\">22</xref>,<xref rid=\"B23-ijerph-17-05442\" ref-type=\"bibr\">23</xref>,<xref rid=\"B24-ijerph-17-05442\" ref-type=\"bibr\">24</xref>]. Teachers can use different teaching strategies in the teaching process to enhance students’ ability to think critically, such as group discussions, clinical practice, writing communication notes, patient simulation, case-based clinical discussions, role-playing, video teaching, and objective structured clinical examination (OSCE), etc., to enhance students’ “critical thinking” and “problem solving” abilities in the hope that students will be able to practice self-awareness and reflection all the time [<xref rid=\"B13-ijerph-17-05442\" ref-type=\"bibr\">13</xref>,<xref rid=\"B21-ijerph-17-05442\" ref-type=\"bibr\">21</xref>,<xref rid=\"B22-ijerph-17-05442\" ref-type=\"bibr\">22</xref>,<xref rid=\"B23-ijerph-17-05442\" ref-type=\"bibr\">23</xref>].</p></sec><sec id=\"sec2dot3-ijerph-17-05442\"><title>2.3. Definition and Connotation of Experiential Learning </title><p>Experience is the process of experiencing something or a feeling [<xref rid=\"B8-ijerph-17-05442\" ref-type=\"bibr\">8</xref>,<xref rid=\"B24-ijerph-17-05442\" ref-type=\"bibr\">24</xref>]. Experiential learning begins with experience, followed by reflection, discussion, and analysis, re-experience and evaluation, and ends with the construction of internalized meaning and value [<xref rid=\"B8-ijerph-17-05442\" ref-type=\"bibr\">8</xref>,<xref rid=\"B25-ijerph-17-05442\" ref-type=\"bibr\">25</xref>]. Experiential learning stems from Dewey’s philosophy of learning by doing, which emphasizes learning through participation in reflection and sharing in practice [<xref rid=\"B8-ijerph-17-05442\" ref-type=\"bibr\">8</xref>,<xref rid=\"B25-ijerph-17-05442\" ref-type=\"bibr\">25</xref>,<xref rid=\"B26-ijerph-17-05442\" ref-type=\"bibr\">26</xref>]. It begins with experience, followed by reflection, discussion and analysis, re-experience and evaluation, and ends with the construction of internalized meaning and value [<xref rid=\"B8-ijerph-17-05442\" ref-type=\"bibr\">8</xref>,<xref rid=\"B14-ijerph-17-05442\" ref-type=\"bibr\">14</xref>,<xref rid=\"B25-ijerph-17-05442\" ref-type=\"bibr\">25</xref>].</p><p>Dewey established the relationship between learning and experience through reflective observations to assist students in learning by doing [<xref rid=\"B8-ijerph-17-05442\" ref-type=\"bibr\">8</xref>,<xref rid=\"B15-ijerph-17-05442\" ref-type=\"bibr\">15</xref>,<xref rid=\"B25-ijerph-17-05442\" ref-type=\"bibr\">25</xref>,<xref rid=\"B26-ijerph-17-05442\" ref-type=\"bibr\">26</xref>]. From the nature of education or learning, learning is a process of continuous reorganization and change of experience. It is the conversion process of experience into knowledge, skills, and attitudes, which emphasizes reflection and action as well as the interaction between individuals and situations [<xref rid=\"B8-ijerph-17-05442\" ref-type=\"bibr\">8</xref>,<xref rid=\"B14-ijerph-17-05442\" ref-type=\"bibr\">14</xref>,<xref rid=\"B25-ijerph-17-05442\" ref-type=\"bibr\">25</xref>]. The activity course focuses on integrating life experience, breaking the boundaries of disciplines, and expanding from individuals to the relationships between individuals and society or nature through educational activities of the study fields that embody humanization, lifestyle, adaptability, integration, and modernization. The course focuses on the practical viewpoints of “life experience”, “secondary experience of reflection”, and “to experience by going back to your own life experience”, as well as emphasizing the “reflection” in the continuous reorganization and transformation of experience [<xref rid=\"B8-ijerph-17-05442\" ref-type=\"bibr\">8</xref>,<xref rid=\"B15-ijerph-17-05442\" ref-type=\"bibr\">15</xref>,<xref rid=\"B25-ijerph-17-05442\" ref-type=\"bibr\">25</xref>,<xref rid=\"B26-ijerph-17-05442\" ref-type=\"bibr\">26</xref>,<xref rid=\"B27-ijerph-17-05442\" ref-type=\"bibr\">27</xref>]. </p></sec></sec><sec sec-type=\"methods\" id=\"sec3-ijerph-17-05442\"><title>3. Methods</title><sec id=\"sec3dot1-ijerph-17-05442\"><title>3.1. Design</title><p>This study was a one-group, pre-test–post-test design. Participants had to take the ELP, which was carried out during the semester. The ELP included real intelligent baby care, maternity experience, and elderly experience. After the intervention of ELP, we asked the participants to complete the outcome measures. The details of the intervention are as follows.</p><sec id=\"sec3dot1dot1-ijerph-17-05442\"><title>3.1.1. Real Intelligent Baby Care (2 Days) </title><p>With the real intelligent baby, students needed to judge when to take care. For example, there were chips in the diapers and baby bottles. When the intelligent babies cried and needed care, the students had to first use their hands to touch the chip induction area, and then comfort, breastfeed, and change diapers according to the needs of the intelligent babies. Each participant brought the smart baby home for two days to immerse themselves in the experiential learning.</p></sec><sec id=\"sec3dot1dot2-ijerph-17-05442\"><title>3.1.2. Maternity Experience (4 h)</title><p>Through the personal experience of wearing a pregnancy dress for 2 h, one can experience the mother’s hard work when she is pregnant. Students can experience the physiological changes produced by the mother during pregnancy, fetal limb pressure, weight gain of 25–30 pounds, and changes in body shape, etc. </p></sec><sec id=\"sec3dot1dot3-ijerph-17-05442\"><title>3.1.3. Elderly Experience (4 h)</title><p>Students wore assistive appliances for the elderly to experience the deterioration of physical function after age, with the help of creating visual impairment, restricted movement, decreased mobility, reduced sensation of kyphosis, hand sensitivity, experience joint stiffness, fatigue, weakness, as well as physical situations such as a change in mind image and a decline in balance.</p><p>After ELP, students had to write journals and participated in a group discussion to share their experiences related to the experiential learning with real intelligent baby care, maternity experience, and elderly experience.</p></sec></sec><sec id=\"sec3dot2-ijerph-17-05442\" sec-type=\"subjects\"><title>3.2. Participants</title><p>After the institutional review board approved the study protocol, we recruited a convenience sample of 103 first-year nursing students from a university in Central Taiwan from February 2019 to June 2019.</p></sec><sec id=\"sec3dot3-ijerph-17-05442\"><title>3.3. Measures</title><p>The demographic data form was used to collect participants’ characteristics. Outcome measures included the self-reflection and insight scale (SRIS) and the Taiwan Critical Thinking Disposition Inventory (TCTDI).</p><p>The self-reflection and insight scale (SRIS) is a Likert scale that assessed the nursing students’ self-reflection with a total of 20 questions and assigned 6 points for each question, ranging from 1 (least matched) to 6 (highly matched). The scale score in total ranges from 20 to 120 points, with higher scores indicating stronger self-reflection ability. Cronbach’s α coefficient for the total scale was 0.79 [<xref rid=\"B19-ijerph-17-05442\" ref-type=\"bibr\">19</xref>]. The SRIS internal consistency reliability (Cronbach’s α) in the present study was 0.57.</p><p>The Taiwan Critical Thinking Disposition Inventory (TCTDI) developed by Yen was used to measure the nursing students’ perception of critical thinking. This scale was composed of 20 questions and had four dimensions: systematicity/analyticity (9 items), open-mindedness (4 items), inquisitiveness (3 items), and reflective thinking (4 items). Each question was answered using a 6-point Likert-type scale ranging from 1 (least matched) to 6 (highly matched), and higher scores indicated higher critical-thinking intention and skill. A previous study noted that the Cronbach’s α of the dimensions ranged from 0.83 to 0.92 [<xref rid=\"B28-ijerph-17-05442\" ref-type=\"bibr\">28</xref>]. Recent research indicated that Cronbach’s α of the scale was 0.97 [<xref rid=\"B25-ijerph-17-05442\" ref-type=\"bibr\">25</xref>]. </p></sec><sec id=\"sec3dot4-ijerph-17-05442\"><title>3.4. Ethical Considerations</title><p>The study was approved by the Human Experiment and Committee of Asia University Medical Research Committee. Instructions and a written consent form were provided with the questionnaire and the questionnaire was completed on a voluntary basis. Personal information of each participant was kept confidential by using coded IDs instead of student names. The author’s permission was obtained for the use of SRIS and TCTDI. </p></sec><sec id=\"sec3dot5-ijerph-17-05442\"><title>3.5. Data Collection</title><p>After receiving a verbal briefing on the research and completing informed consent, all participants were asked to complete the demographic data form, SRIS, and TCTDI. One research assistant in each class was invited to assist in participant recruitment and questionnaire distribution. The pre-test data were collected at the semester start date, while the post-test data were collected at the end of the semester (eighteen weeks later). </p></sec><sec id=\"sec3dot6-ijerph-17-05442\"><title>3.6. Statistical Analysis</title><p>SPSS statistical software (Version 19, SPSS Inc., Chicago, IL, USA) was adopted for the analysis. Descriptive statistics included percentage, mean, and standard deviation. Independent <italic>T</italic>-test and the Pearson product–moment correlation analysis were performed. </p></sec></sec><sec sec-type=\"results\" id=\"sec4-ijerph-17-05442\"><title>4. Results</title><p>Overall, a total of 103 students participated in this study. Of these, 17 were male (16.50%) and 86 were female (83.50%); most students were at the age of 18 (63.10%). Self-reflection averaged 70.33 points (SD = 7.13) in the pre-test and 72.94 points (SD = 6.80) in the post-test. There was a significant difference between the pre-test and post-test scores in total (t = −2.69, <italic>p</italic> = 0.01) (<xref rid=\"ijerph-17-05442-t001\" ref-type=\"table\">Table 1</xref>). Additionally, there were significant differences between the pre-test and post-test scores in the three domains: behavior, thoughts, and feelings. Critical thinking averaged 86.25 points (SD = 14.65) in the pre-test and 95.41 points (SD = 12.08) in the post-test. There was a significant difference between the pre-test and post-test scores both in total and in each item (<xref rid=\"ijerph-17-05442-t002\" ref-type=\"table\">Table 2</xref>). In addition, there were significant differences between the pre-test and post-test scores in the four domains: systematicity/analyticity, open-mindedness, inquisitiveness, and reflective thinking. The Pearson product–moment correlation analysis showed that the scores of self-reflection and critical thinking were significantly and positively correlated <italic>(p</italic> < 0.001) (<xref rid=\"ijerph-17-05442-t003\" ref-type=\"table\">Table 3</xref>).</p></sec><sec sec-type=\"discussion\" id=\"sec5-ijerph-17-05442\"><title>5. Discussion</title><p>This study’s results demonstrated that the total scores of self-reflection and critical thinking had a significantly positive correlation (<italic>p</italic> < 0.001). In the study by Chen and Pai (2018), using the same measures, i.e., TCTDI and SRIS, the authors pointed out that self-reflection and critical thinking would affect each other, although their study sample was a group of nursing students carrying out nursing practicum just before their graduation. However, our findings showed that the ELP had similar effects. In other words, results from both freshmen and students about to graduate demonstrated that self-reflection and critical thinking had positively correlated. </p><p>In our study, the participants’ self-reflection and critical thinking improved after taking the ELP. As the articles appealed, with the actual participation of learners, they can experience the essence of their own learning [<xref rid=\"B29-ijerph-17-05442\" ref-type=\"bibr\">29</xref>,<xref rid=\"B30-ijerph-17-05442\" ref-type=\"bibr\">30</xref>]. Additionally, if nursing professionals could perform any nursing practice work in a critical thinking manner, it will solve the needs and nursing problems of patients and ensure the quality of nursing care for patients [<xref rid=\"B23-ijerph-17-05442\" ref-type=\"bibr\">23</xref>]. </p><p>The theory of experiential learning argues that, during the learning process, if students have personal experiences to internalize their knowledge through cooperation, reflection, questioning, and action, they can apply what they have learned and strengthen their abilities of reflection and critical thinking [<xref rid=\"B30-ijerph-17-05442\" ref-type=\"bibr\">30</xref>]. In our study, the students shared their personal experiences with classmates after going through the ELP. For example, after caring for a real intelligent baby, students said, “I understood the responsibility of being a mother”; after wearing a pregnancy dress, students shared that “I knew pregnant women’s feeling inconvenience and difficulty”; after wearing assistive appliances for the elderly, students attested that “I felt the feelings of getting losses of eyesight, hearing, teeth health, as well as physical difficulty”. </p><p>The articles showed that ELP creates a real and interactive learning environment. Students can provide feedback and reflection under the guidance of teachers during care activities and can repeatedly learn about knowledge, communication, and critical thinking skills related to nursing, which can enhance students’ experiential learning, boost their self-confidence, and promote their communication and teamwork skills [<xref rid=\"B13-ijerph-17-05442\" ref-type=\"bibr\">13</xref>,<xref rid=\"B28-ijerph-17-05442\" ref-type=\"bibr\">28</xref>,<xref rid=\"B29-ijerph-17-05442\" ref-type=\"bibr\">29</xref>,<xref rid=\"B30-ijerph-17-05442\" ref-type=\"bibr\">30</xref>].</p></sec><sec id=\"sec6-ijerph-17-05442\"><title>6. Limitations</title><p>This study has several limitations. Students’ self-assessment of self-reflection and critical thinking using the questionnaire poses limitations such as subjectivity, although this is the most common form used. In addition, it is uncertain whether the difference between pre-test and post-test comes entirely from the course since a control group is absent in this study.</p></sec><sec id=\"sec7-ijerph-17-05442\"><title>7. Impact Statement</title><p>This research suggested that schools at all levels incorporate ELP into the learning process so that students can strengthen their attitudes, abilities, and skills in their learning activities.</p></sec><sec sec-type=\"conclusions\" id=\"sec8-ijerph-17-05442\"><title>8. Conclusions</title><p>This study manifested that self-reflection and critical thinking might share a core, which resulted in the significantly positive correlation between SRIS and TCIDI. The nature of experiential learning is that students learned through active participation, gaining knowledge and insights. Our intervention, the curriculum of ELP activities, helped the students to increase their abilities of self-reflection and critical thinking. As the ELP program can be performed without facing people, it is suitable for use during a pandemic.</p></sec></body><back><ack><title>Acknowledgments</title><p>We would like to thank the nursing students for their participation.</p></ack><notes><title>Author Contributions</title><p>Data curation, Y.-C.C. and H.-C.C.; Formal analysis, L.-C.H.; Investigation, Y.-C.C.; Methodology, Y.-C.C.; Writing–original draft, Y.-C.C.; Writing–review & editing, C.-H.Y. All authors have read and agreed to the published version of the manuscript.</p></notes><notes><title>Funding</title><p>No conflict of interest has been declared by the authors.</p></notes><notes notes-type=\"COI-statement\"><title>Conflicts of Interest</title><p>The authors declare no conflict of interest.</p></notes><ref-list><title>References</title><ref id=\"B1-ijerph-17-05442\"><label>1.</label><element-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Covid</surname><given-names>C.</given-names></name><name><surname>Team</surname><given-names>R.</given-names></name></person-group><article-title>Severe outcomes among patients with coronavirus disease 2019 (COVID-19)—United States, February 12–March 16, 2020</article-title><source>MMWR Morb. Mortal. Wkly. 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J.</source><year>2018</year><volume>5</volume><pub-id pub-id-type=\"doi\">10.14738/assrj.51.4102</pub-id></element-citation></ref><ref id=\"B30-ijerph-17-05442\"><label>30.</label><element-citation publication-type=\"book\"><person-group person-group-type=\"author\"><name><surname>Silberman</surname><given-names>M.L.</given-names></name></person-group><source>The Handbook of Experiential Learning</source><publisher-name>John Wiley & Sons</publisher-name><publisher-loc>Hoboken, NJ, USA</publisher-loc><year>2007</year></element-citation></ref></ref-list></back><floats-group><table-wrap id=\"ijerph-17-05442-t001\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05442-t001_Table 1</object-id><label>Table 1</label><caption><p>Comparison of scores of SRIS before and after ELP (<italic>n</italic> = 103).</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">Domains of SRIS</th><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Mean</th><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">t</th><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">\n<italic>p</italic>\n</th></tr><tr><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Pre-Test</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Post-Test</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">I. Behavior</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">20.59</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">21.25</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−1.98</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.04 *</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">II. Thoughts</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">21.39</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">22.04</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−1.60</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.11</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">III. Feelings</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">28.35</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">29.65</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−2.53</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.00 **</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Total</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">70.33</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">72.94</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−2.69</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.01 **</td></tr></tbody></table><table-wrap-foot><fn><p>* <italic>p</italic> < 0.05, ** <italic>p</italic> < 0.01, *** <italic>p</italic> < 0.001.</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"ijerph-17-05442-t002\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05442-t002_Table 2</object-id><label>Table 2</label><caption><p>Comparison of scores of TCTDI before and after ELP (<italic>n</italic> = 103).</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">Domains of TCTDI</th><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Mean </th><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">t</th><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">\n<italic>p</italic>\n</th></tr><tr><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Pre-Test</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Post-Test</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">I. <bold>Systematicity/analyticity</bold></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">38.62</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">42.74</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.42</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.00 ***</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">II. Open-mindedness</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">17.01</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">18.87</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.43</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.00 ***</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">III. Inquisitiveness</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13.20</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">14.50</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−3.95</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.00 ***</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">IV. Reflective thinking</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">17.42</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">19.29</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−4.30</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.00 ***</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Total</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">86.25</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">95.41</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−4.62</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.00 ***</td></tr></tbody></table><table-wrap-foot><fn><p>* <italic>p</italic> < 0.05, ** <italic>p</italic> < 0.01, *** <italic>p</italic> < 0.001.</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"ijerph-17-05442-t003\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05442-t003_Table 3</object-id><label>Table 3</label><caption><p>Correlation between reflection and critical thinking (<italic>n</italic> = 103).</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Variables</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">SRIS</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">TCTDI</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">SRIS</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.00</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.30 *</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">TCTDI</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.30 *</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.00</td></tr></tbody></table><table-wrap-foot><fn><p>* <italic>p</italic> < 0.05, ** <italic>p</italic> < 0.01, *** <italic>p</italic> < 0.001.</p></fn></table-wrap-foot></table-wrap></floats-group></article>\n"
]
[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Int J Mol Sci</journal-id><journal-id journal-id-type=\"iso-abbrev\">Int J Mol Sci</journal-id><journal-id journal-id-type=\"publisher-id\">ijms</journal-id><journal-title-group><journal-title>International Journal of Molecular Sciences</journal-title></journal-title-group><issn pub-type=\"epub\">1422-0067</issn><publisher><publisher-name>MDPI</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32751347</article-id><article-id pub-id-type=\"pmc\">PMC7432081</article-id><article-id pub-id-type=\"doi\">10.3390/ijms21155403</article-id><article-id pub-id-type=\"publisher-id\">ijms-21-05403</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Article</subject></subj-group></article-categories><title-group><article-title>TRPM8 Channel Activation Reduces the Spontaneous Contractions in Human Distal Colon</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Amato</surname><given-names>Antonella</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05403\">1</xref><xref rid=\"c1-ijms-21-05403\" ref-type=\"corresp\">*</xref></contrib><contrib contrib-type=\"author\"><name><surname>Terzo</surname><given-names>Simona</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05403\">1</xref><xref ref-type=\"aff\" rid=\"af2-ijms-21-05403\">2</xref></contrib><contrib contrib-type=\"author\"><name><surname>Lentini</surname><given-names>Laura</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05403\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Marchesa</surname><given-names>Pierenrico</given-names></name><xref ref-type=\"aff\" rid=\"af3-ijms-21-05403\">3</xref></contrib><contrib contrib-type=\"author\"><name><surname>Mulè</surname><given-names>Flavia</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05403\">1</xref></contrib></contrib-group><aff id=\"af1-ijms-21-05403\"><label>1</label>Department of Biological-Chemical- Pharmaceutical Science and Technology (STEBICEF), University of Palermo, Viale delle Scienze, Edificio 16, 90128 Palermo, Italy; <email>[email protected]</email> (S.T.); <email>[email protected]</email> (L.L.); <email>[email protected]</email> (F.M.)</aff><aff id=\"af2-ijms-21-05403\"><label>2</label>Department of Biomedicine, Neurosciences and Advanced Diagnostics (BIND), University of Palermo, Via del Vespro 129, 90127 Palermo, Italy</aff><aff id=\"af3-ijms-21-05403\"><label>3</label>U.O. Oncology Hospital, A.R.N.A.S. Ospedali Civico Di Cristina Benfratelli, Palermo, Via Carmelo Lazzaro, 4, 90127 Palermo, Italy; <email>[email protected]</email></aff><author-notes><corresp id=\"c1-ijms-21-05403\"><label>*</label>Correspondence: <email>[email protected]</email>; Tel.: +39-091-23897506</corresp></author-notes><pub-date pub-type=\"epub\"><day>29</day><month>7</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><month>8</month><year>2020</year></pub-date><volume>21</volume><issue>15</issue><elocation-id>5403</elocation-id><history><date date-type=\"received\"><day>23</day><month>6</month><year>2020</year></date><date date-type=\"accepted\"><day>27</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>© 2020 by the authors.</copyright-statement><copyright-year>2020</copyright-year><license license-type=\"open-access\"><license-p>Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>).</license-p></license></permissions><abstract><p>The transient receptor potential-melastatin 8 (TRPM8) is a non-selective Ca<sup>2+</sup>-permeable channel, activated by cold, membrane depolarization, and different cooling compounds. TRPM8 expression has been found in gut mucosal, submucosal, and muscular nerve endings. Although TRPM8 plays a role in pathological conditions, being involved in visceral pain and inflammation, the physiological functions in the digestive system remain unclear as yet. The aims of the present study were: (i) to verify the TRPM8 expression in human distal colon; (ii) to examine the effects of TRPM8 activation on colonic contractility; (iii) to characterize the mechanism of action. Reverse transcriptase-polymerase chain reaction (RT-PCR) and western blotting were used to analyze TRPM8 expression. The responses of human colon circular strips to different TRPM8 agonists [1-[Dialkyl-phosphinoyl]-alkane (DAPA) 2–5, 1-[Diisopropyl-phosphinoyl]-alkane (DIPA) 1–7, DIPA 1–8, DIPA 1–9, DIPA 1–10, and DIPA 1–12) were recorded using a vertical organ bath. The biomolecular analysis revealed gene and protein expression of TRPM8 in both mucosal and smooth muscle layers. All the agonists tested, except-DIPA 1–12, produced a concentration-dependent decrease in spontaneous contraction amplitude. The effect was significantly antagonized by 5-benzyloxytryptamine, a TRPM8 antagonist. The DIPA 1–8 agonist resulted in the most efficacious and potent activation among the tested molecules. The DIPA 1–8 effects were not affected by tetrodotoxin, a neural blocker, but they were significantly reduced by tetraethylammonium chloride, a non-selective blocker of K<sup>+</sup> channels. Moreover, iberiotoxin, a blocker of the large-conductance Ca<sup>2+</sup>-dependent K<sup>+</sup>-channels, but not apamin, a blocker of small-conductance Ca<sup>2+</sup>-dependent K<sup>+</sup> channels, significantly reduced the inhibitory DIPA 1–8 actions. The results of the present study demonstrated that TRPM8 receptors are also expressed in human distal colon in healthy conditions and that ligand-dependent TRPM8 activation is able to reduce the colonic spontaneous motility, probably by the opening of the large-conductance Ca<sup>2+</sup>-dependent K<sup>+</sup>-channels.</p></abstract><kwd-group><kwd>TRPM-8</kwd><kwd>1-[Diisopropyl-phosphinoyl]-alkane (DIPA)</kwd><kwd>human colon contractility</kwd><kwd>IBS</kwd></kwd-group></article-meta></front><body><sec sec-type=\"intro\" id=\"sec1-ijms-21-05403\"><title>1. Introduction</title><p>Transient receptor potential (TRP) channels constitute a large family of non-selective cation channels involved in diverse cellular functions [<xref rid=\"B1-ijms-21-05403\" ref-type=\"bibr\">1</xref>,<xref rid=\"B2-ijms-21-05403\" ref-type=\"bibr\">2</xref>]. They display large diversity in activation mode, ion selectivity and, consequently, physiological functions. Most TRP channels are distributed in both the somatic and visceral sensory nervous systems, playing a crucial role in chemo-, thermo- and mechano-sensation [<xref rid=\"B3-ijms-21-05403\" ref-type=\"bibr\">3</xref>,<xref rid=\"B4-ijms-21-05403\" ref-type=\"bibr\">4</xref>]. The TRPM (transient receptor potential melastatin) represents the largest and most variable subfamily of the TRP channels, being constituted by eight members, from TRPM1 to TRPM8, and responding to various stimuli such as changes in the concentration of ions, small molecules, and lipids [<xref rid=\"B2-ijms-21-05403\" ref-type=\"bibr\">2</xref>]. The TRPM channels promote Ca<sup>2+</sup> entry into the cytosol resulting from directly Ca<sup>2+</sup>-permeable channels or indirectly from conductance changes of other channels.</p><p>TRPM8 is a member of the temperature-sensitive TRP channels. It is activated by mild cold temperatures, membrane depolarization, changes in extracellular osmolarity, cooling compounds such as menthol and icilin, and it shows a modest permeability to calcium [<xref rid=\"B5-ijms-21-05403\" ref-type=\"bibr\">5</xref>,<xref rid=\"B6-ijms-21-05403\" ref-type=\"bibr\">6</xref>]. TRPM8 is mainly expressed in sensory neurons innervating the skin, oral cavity [<xref rid=\"B7-ijms-21-05403\" ref-type=\"bibr\">7</xref>], bladder [<xref rid=\"B8-ijms-21-05403\" ref-type=\"bibr\">8</xref>], lungs [<xref rid=\"B9-ijms-21-05403\" ref-type=\"bibr\">9</xref>], and prostate [<xref rid=\"B10-ijms-21-05403\" ref-type=\"bibr\">10</xref>], and its activation seems to be involved in the relief of pain in neuropathic conditions or tissue inflammation [<xref rid=\"B1-ijms-21-05403\" ref-type=\"bibr\">1</xref>]. TRPM8 is thought to have a role in the inhibition of visceral pain signals and reduction of inflammatory conditions in irritable bowel syndrome (IBS) as TRPM8 activation reduces colon inflammation both in animal models [<xref rid=\"B4-ijms-21-05403\" ref-type=\"bibr\">4</xref>] and human subjects [<xref rid=\"B11-ijms-21-05403\" ref-type=\"bibr\">11</xref>,<xref rid=\"B12-ijms-21-05403\" ref-type=\"bibr\">12</xref>]. TRPM8 channels have been implicated also in oropharyngeal dysphagia [<xref rid=\"B13-ijms-21-05403\" ref-type=\"bibr\">13</xref>] and chronic cough [<xref rid=\"B14-ijms-21-05403\" ref-type=\"bibr\">14</xref>], while the downregulation of TRPM8 by angiotensin II may be involved in hypertension [<xref rid=\"B15-ijms-21-05403\" ref-type=\"bibr\">15</xref>].</p><p>Although increasing evidence supports the important contribution of TRPM8 channels to the regulation of the inflammatory responses, making them potential targets in the treatment of IBS [<xref rid=\"B1-ijms-21-05403\" ref-type=\"bibr\">1</xref>,<xref rid=\"B16-ijms-21-05403\" ref-type=\"bibr\">16</xref>], the precise functional implications of TRPM8 in the gut remain unclear. Firstly, the intestinal TRPM8 expression has long been controversial, because positive [<xref rid=\"B17-ijms-21-05403\" ref-type=\"bibr\">17</xref>] and negative [<xref rid=\"B18-ijms-21-05403\" ref-type=\"bibr\">18</xref>] evidence was reported. TRPM8 transcript expression has been reported in the afferent neurons, myenteric plexus, and epithelial cells of mouse distal colon [<xref rid=\"B11-ijms-21-05403\" ref-type=\"bibr\">11</xref>,<xref rid=\"B19-ijms-21-05403\" ref-type=\"bibr\">19</xref>], and in the smooth muscle of the rat stomach and colon [<xref rid=\"B20-ijms-21-05403\" ref-type=\"bibr\">20</xref>]. More recent immunohistochemical analysis confirmed the expression of the TRPM8 protein in the mouse colonic nerve endings distributed in the mucosal, submucosal, muscular, and serosal layers [<xref rid=\"B21-ijms-21-05403\" ref-type=\"bibr\">21</xref>]. To date, human colonic TRPM8 expression appears to be related to pathological conditions. Transcripts encoding TRPM8 were detected in colon cancer but not in the corresponding normal human tissues [<xref rid=\"B10-ijms-21-05403\" ref-type=\"bibr\">10</xref>] and increased TRPM8 expression has been revealed in colonic biopsy material from inflammatory bowel disease (IBD) patients as compared to healthy controls [<xref rid=\"B11-ijms-21-05403\" ref-type=\"bibr\">11</xref>]. Secondly, because the chemosensory role is TRPM8’s most probable function in the digestive system, a TRPM8-dependent modulation of gastrointestinal motility is likely. TRPM8 activation seems to be involved in the menthol-induced relaxation of mouse [<xref rid=\"B18-ijms-21-05403\" ref-type=\"bibr\">18</xref>] and human colon [<xref rid=\"B22-ijms-21-05403\" ref-type=\"bibr\">22</xref>], in the cooling-induced contraction of rat gastric fundus [<xref rid=\"B20-ijms-21-05403\" ref-type=\"bibr\">20</xref>], and guinea-pig ileum [<xref rid=\"B23-ijms-21-05403\" ref-type=\"bibr\">23</xref>].</p><p>Recently, new chemical compounds able to modulate TRPM8 channels have been identified and they could be useful for understanding the implications of TRPM8 in pathophysiological processes [<xref rid=\"B24-ijms-21-05403\" ref-type=\"bibr\">24</xref>]. In particular, a class of specific TRPM8 agonists belonging to the 1-[Dialkyl-phosphinoyl]-alkane (DAPA) has been reported to have a potential against pharyngeal irritation and inflammation [<xref rid=\"B25-ijms-21-05403\" ref-type=\"bibr\">25</xref>,<xref rid=\"B26-ijms-21-05403\" ref-type=\"bibr\">26</xref>,<xref rid=\"B27-ijms-21-05403\" ref-type=\"bibr\">27</xref>]. Therefore, DAPA derivatives could find application in IBS, for treating symptoms such as irritation, inflammation, and muscular spasms.</p><p>The aims of the present study were (a) to verify the expression of TRPM8 in human distal colon; (b) to analyze the effects of TRPM8 activation on the mechanical activity of human distal colon; and (c) to characterize the mechanism of action responsible for the effects observed. Specifically, we analyzed the responses of human colonic circular smooth muscle to 1-[Diisopropyl-phosphinoyl]-alkane (DIPA) family (a subclass of DAPA) and compared the obtained responses to DAPA.</p></sec><sec sec-type=\"results\" id=\"sec2-ijms-21-05403\"><title>2. Results</title><sec id=\"sec2dot1-ijms-21-05403\"><title>2.1. TRPM8 Gene and Protein Expression in Human Distal Colon</title><p>In the colonic specimens, reverse transcriptase-polymerase chain reaction (RT-PCR) analysis revealed the presence of a 621 bp product relative to TRPM8 mRNA expression, in both mucosa and smooth muscle layer (<xref ref-type=\"fig\" rid=\"ijms-21-05403-f001\">Figure 1</xref>A). Western blot analysis confirmed TRPM8 presence in distal colon, with higher expression in muscle than mucosa (<xref ref-type=\"fig\" rid=\"ijms-21-05403-f001\">Figure 1</xref>B,C). Indeed, two protein variants of TRPM8 of expected molecular weight of 140 and 112 KDa were detected. The faster running protein could represent one of the isoform variants of TRPM8 [<xref rid=\"B28-ijms-21-05403\" ref-type=\"bibr\">28</xref>].</p></sec><sec id=\"sec2dot2-ijms-21-05403\"><title>2.2. Functional Studies</title><p>Circular muscle strips of human colon exhibited spontaneous mechanical activity consisting of phasic contractions at a frequency of 3 ± 0.3 contractions per minute and an amplitude of 4 ± 0.5 g (<italic>n</italic> = 36). The agonists DAPA 2–5 (1 μM–1 mM), DIPA 1–7 (1 nM–1 mM), DIPA 1–8 (1 nM–100 μM), DIPA 1–9 (1 nM–100 μM), and DIPA 1–10 (1 nM–1 mM) produced a concentration-dependent decrease in the amplitude of the spontaneous colonic contractions, without affecting the basal tone (<xref ref-type=\"fig\" rid=\"ijms-21-05403-f002\">Figure 2</xref> and <xref ref-type=\"fig\" rid=\"ijms-21-05403-f003\">Figure 3</xref>). No agonist effect on frequency was observed (<xref ref-type=\"app\" rid=\"app1-ijms-21-05403\">Supplementary Table S1</xref>). The inhibitory responses were reversible after washing out (<xref ref-type=\"fig\" rid=\"ijms-21-05403-f002\">Figure 2</xref>). DIPA 1–12 agonist (10 nM–1 mM) failed to significantly affect the colonic spontaneous contractions (<xref ref-type=\"fig\" rid=\"ijms-21-05403-f002\">Figure 2</xref>F).</p><p>The DIPA 1–8 agonist was the most efficacious and potent among the tested molecules, with EC<sub>50</sub> = 41 nM Cls 28–61 nM and E<sub>max</sub> = 88.3 ± 2.2 % (<xref rid=\"ijms-21-05403-t001\" ref-type=\"table\">Table 1</xref>). In order to verify whether TRPM8 activation can induce relaxation, we tested the response to DIPA 1–8 (1 µM) of pre-contracted colon strips with carbachol (0.1 µM). As shown in <xref ref-type=\"app\" rid=\"app1-ijms-21-05403\">Supplementary Figure S1</xref>, the TRPM8 agonist induced a rapid relaxation.</p><p>To assess the specificity of the effects, the preparations were pre-treated for 30 min with 5-BT (1 μM), a TRPM8 receptor antagonist. In presence of 5-BT, which per se did not affect the spontaneous mechanical activity, the inhibitory actions of TRPM8 agonists were significantly reduced (EC<sub>50</sub> = 3.3 μM, Cls = 1.5–7 μM; EC<sub>50</sub> = 374 nM, Cls = 181–772 nM; EC<sub>50</sub> = 135 nM, Cls = 76–238 nM; EC<sub>50</sub> = 911 nM, Cls = 206 nM–4 μM) for DIPA 1–7, DIPA 1–8, DIPA 1–9, and DIPA 1–10 respectively (<xref ref-type=\"fig\" rid=\"ijms-21-05403-f003\">Figure 3</xref>).</p><p>In order to investigate the mechanism of action responsible for the inhibitory effect dependent on TRPM8 activation, the responses of DIPA 1–8 were tested in the presence of TTX (1 μM), a blocker of neural voltage-dependent Na<sup>+</sup> channels. The pre-treatment of colonic samples with TTX, which per se failed to affect spontaneous contractions, did not modify the inhibitory responses to DIPA 1–8 at all concentrations tested (<xref ref-type=\"fig\" rid=\"ijms-21-05403-f004\">Figure 4</xref>A). On the contrary, TEA (10 mM), a non-selective blocker of K<sup>+</sup> channels, significantly reduced the DIPA 1–8 inhibitory effect (<xref ref-type=\"fig\" rid=\"ijms-21-05403-f004\">Figure 4</xref>B) indicating an involvement of K<sup>+</sup> channels in the inhibitory response to the TRPM8 agonist.</p><p>Moreover, the response to DIPA 1–8 was not affected by pre-treatment of colonic smooth muscle strips with apamin (100 nM), a blocker of small conductance Ca<sup>2+</sup>-dependent K<sup>+</sup> channels (<xref ref-type=\"fig\" rid=\"ijms-21-05403-f005\">Figure 5</xref>A), while it was abolished by iberiotoxin (IbTX, 10 μM), a blocker of the large-conductance Ca<sup>2+</sup>-dependent K<sup>+</sup>-channels (<xref ref-type=\"fig\" rid=\"ijms-21-05403-f005\">Figure 5</xref>B).</p></sec></sec><sec sec-type=\"discussion\" id=\"sec3-ijms-21-05403\"><title>3. Discussion</title><p>The results of the present study demonstrate that the TRPM8 receptors are expressed in the human distal colon and, once exogenously activated, are able to reduce the colonic smooth muscle contractility. The spasmolytic effects appear to be mediated by a direct action on the muscle cells, involving large-conductance Ca<sup>2+</sup>-dependent K<sup>+</sup>-channels.</p><p>The TRPM8 receptor is a non-selective cation channel, with a preference for Ca<sup>2+</sup> permeation [<xref rid=\"B5-ijms-21-05403\" ref-type=\"bibr\">5</xref>,<xref rid=\"B29-ijms-21-05403\" ref-type=\"bibr\">29</xref>]. It shows multimodal gating being activated by cold (<28 °C), membrane depolarization, different cooling compounds such as menthol [<xref rid=\"B29-ijms-21-05403\" ref-type=\"bibr\">29</xref>] and icilin, and changes in extracellular osmolality [<xref rid=\"B5-ijms-21-05403\" ref-type=\"bibr\">5</xref>,<xref rid=\"B29-ijms-21-05403\" ref-type=\"bibr\">29</xref>]. TRPM8 channels are highly expressed in peripheral sensory neurons (Aδ and C fiber afferents), and in deep visceral afferents in the prostate, bronchopulmonary tissue, bladder, and urogenital tract. TRPM8 channels are also expressed in the gut. In particular, TRPM8 gene and protein expression has been shown in the mucosal layer, muscle, and nerve endings of mouse and rat colon [<xref rid=\"B11-ijms-21-05403\" ref-type=\"bibr\">11</xref>,<xref rid=\"B20-ijms-21-05403\" ref-type=\"bibr\">20</xref>,<xref rid=\"B21-ijms-21-05403\" ref-type=\"bibr\">21</xref>].</p><p>The current knowledge of TRPM8, mainly based on animal studies, concerns a role in thermosensation (primarily low temperatures) [<xref rid=\"B30-ijms-21-05403\" ref-type=\"bibr\">30</xref>] and in visceral nociception [<xref rid=\"B31-ijms-21-05403\" ref-type=\"bibr\">31</xref>]. In agreement, a recent study supports a pronociceptive role of TRPM8 in colitis-induced visceral hyperalgesia in mice [<xref rid=\"B21-ijms-21-05403\" ref-type=\"bibr\">21</xref>]. However, evidence in mouse experimental colitis models has suggested protective effects of TRPM8 activation in colonic inflammation. Indeed, icilin treatment significantly attenuates induced colitis in wildtype mice, but not in TRPM8 deficient ones [<xref rid=\"B4-ijms-21-05403\" ref-type=\"bibr\">4</xref>,<xref rid=\"B11-ijms-21-05403\" ref-type=\"bibr\">11</xref>,<xref rid=\"B21-ijms-21-05403\" ref-type=\"bibr\">21</xref>]. Therefore, whether TRPM8 modulation results in pro- or anti-nociception may depend on different conditions (noxious stimulus type, concentration of the pharmacological agonist) including the state of the TRPM8 receptor in healthy or inflamed tissue, which may explain contradictions between different studies.</p><p>While the involvement of TRPM8 channels in numerous human pathologies is well accepted [<xref rid=\"B24-ijms-21-05403\" ref-type=\"bibr\">24</xref>], the importance of the TRPM8 channels in human physiology is less known. Indeed, in human intestine, TRPM8 expression has been associated with pathological conditions such as IBD [<xref rid=\"B11-ijms-21-05403\" ref-type=\"bibr\">11</xref>] and colon cancer [<xref rid=\"B10-ijms-21-05403\" ref-type=\"bibr\">10</xref>]. Although cramping pain, distension, and constipation are typical symptoms of IBD and IBS, no data on the TRPM8 role on gastrointestinal motility modulation are available. Indeed, recently human TRPM8 polymorphisms have been demonstrated to be associated with slower colonic transit [<xref rid=\"B12-ijms-21-05403\" ref-type=\"bibr\">12</xref>].</p><p>Our study provides evidence, for the first time, for the presence of TRPM8 channels in human macroscopically healthy distal colon. Western-blot analysis showed a higher expression of TRPM8 channels in smooth muscle compared to the mucosa layer, suggesting that TRPM8 could have a potential role in the modulation of colon motor function. In fact, in our experimental conditions, the activation of the channels by exogenous specific synthetic TRPM8 agonists is able to reduce human circular smooth muscle spontaneous contractions, as well as to induce smooth muscle relaxation in CCh pre-contracted circular muscle strips. These results confirm the ability of peppermint oil (which contains the TRPM8 activator menthol as its biologically active component) to exert spasmolytic effects and inhibition of GI contractility [<xref rid=\"B22-ijms-21-05403\" ref-type=\"bibr\">22</xref>,<xref rid=\"B32-ijms-21-05403\" ref-type=\"bibr\">32</xref>,<xref rid=\"B33-ijms-21-05403\" ref-type=\"bibr\">33</xref>].</p><p>The TRPM8 channels have attracted increasing attention in the past decade as promising drug targets for treatment of different pathologic processes, such as colonic inflammation [<xref rid=\"B4-ijms-21-05403\" ref-type=\"bibr\">4</xref>,<xref rid=\"B11-ijms-21-05403\" ref-type=\"bibr\">11</xref>,<xref rid=\"B12-ijms-21-05403\" ref-type=\"bibr\">12</xref>,<xref rid=\"B21-ijms-21-05403\" ref-type=\"bibr\">21</xref>], dry eye disease (DED) [<xref rid=\"B26-ijms-21-05403\" ref-type=\"bibr\">26</xref>], tumors [<xref rid=\"B34-ijms-21-05403\" ref-type=\"bibr\">34</xref>], oropharyngeal dysphagia [<xref rid=\"B13-ijms-21-05403\" ref-type=\"bibr\">13</xref>], chronic cough [<xref rid=\"B14-ijms-21-05403\" ref-type=\"bibr\">14</xref>], and hypertension [<xref rid=\"B15-ijms-21-05403\" ref-type=\"bibr\">15</xref>]. Accordingly, numerous academic research groups and pharmaceutical companies have become interested in the pharmacological modulation of these receptors producing either agonists, antagonists, or both [<xref rid=\"B35-ijms-21-05403\" ref-type=\"bibr\">35</xref>,<xref rid=\"B36-ijms-21-05403\" ref-type=\"bibr\">36</xref>,<xref rid=\"B37-ijms-21-05403\" ref-type=\"bibr\">37</xref>,<xref rid=\"B38-ijms-21-05403\" ref-type=\"bibr\">38</xref>,<xref rid=\"B39-ijms-21-05403\" ref-type=\"bibr\">39</xref>]. Among these, a recently synthetized class of TRPM8 agonists is represented by the 1-[Dialkyl-phosphinoyl]-alkane (DAPA) compounds [<xref rid=\"B40-ijms-21-05403\" ref-type=\"bibr\">40</xref>]. DAPA are trialkyl derivatives of phosphoric acid, in which two of the alkyls are either sec-butyl (DAPA) or isopropyl (DIPA), and the third alkyl is C4 to C10. Previous work showed DAPA to be useful to treat skin and ocular discomfort [<xref rid=\"B26-ijms-21-05403\" ref-type=\"bibr\">26</xref>] and heat edema as an anti-inflammatory agent [<xref rid=\"B27-ijms-21-05403\" ref-type=\"bibr\">27</xref>,<xref rid=\"B41-ijms-21-05403\" ref-type=\"bibr\">41</xref>].</p><p>In our experiments, we analyzed and compared the mechanical responses of the human distal colon to six DAPA analogs represented by DAPA 2–5, DIPA 1–7, DIPA 1–8, DIPA 1–9, DIPA 1–10, and DIPA 1–12. All tested substances, except DIPA 1–12, induced a concentration-dependent reduction of the spontaneous contraction amplitude of the circular smooth muscle. The DAPA-induced inhibitory effects were mediated specifically by TRPM8 channels, because they were significantly reduced by 5-BT, a TRPM8 receptor antagonist [<xref rid=\"B42-ijms-21-05403\" ref-type=\"bibr\">42</xref>] at a concentration efficacious in reducing colonic inhibitory responses to WS-12 [<xref rid=\"B22-ijms-21-05403\" ref-type=\"bibr\">22</xref>]. Different potencies of the DAPA analogues were found. The order of drug potency was DIPA 1–8 > DIPA 1–9 > DIPA 1–10 > DIPA 1–7 > DAPA 2–5. The different potency of DIPA compounds could depend on the number of carbon atoms present in the third alkyl, referred to as the 7-8-9-10-12 n-alkyl side-chain and corresponding to heptyl (DIPA 1–7), octyl (DIPA 1–8), nonyl (DIPA 1–9), decyl (DIPA 1–10), and dodecyl groups (DIPA 1–12), respectively. A high number of carbon atoms could be responsible for high complexity of the agonist molecular structure, reducing its ability to bind to the TRPM8 receptor. On the contrary, the decreased number of carbons could reduce the steric hindrance making the DAPA agonists more accessible to the TRPM8 receptor. In the same way, the presence of the n-butyl group in the second position (DAPA 2–5) might lead to a lesser ability of this agonist to link to the receptor. Our results are in accordance with previous data showing that the diisopropyl analogues (DIPA) were more active than the di-sec-butyl analogues (DAPA), and among DIPA, the nonane and octyl group permitted a longer duration of action than the decyl equivalent [<xref rid=\"B26-ijms-21-05403\" ref-type=\"bibr\">26</xref>].</p><p>Another step of our research was to investigate whether TRPM8 activation reduces colon contractions via a direct action on the smooth muscle cells and/or via an indirect action involving neural pathways. To this aim, we chose DIPA 1–8 because it was more potent and efficacious than other agonists. TTX, a blocker of neuronal voltage-dependent Na<sup>+</sup> channels, that per se failed to affect spontaneous contraction, indicating its balanced effect on excitatory and inhibitory nerves, did not modify the inhibitory effects of DIPA 1–8, suggesting that the response probably does not depend on TTX-sensitive neuronal activity, but is probably direct on the smooth muscle cells.</p><p>It is well known that K<sup>+</sup> channels are involved in the control of the contraction of the gastrointestinal smooth muscle by setting resting potential and influencing slow waves and action potential configuration [<xref rid=\"B43-ijms-21-05403\" ref-type=\"bibr\">43</xref>]. Activation of K<sup>+</sup> channels causes membrane hyperpolarization of smooth muscle cells and therefore inhibition of Ca<sup>2+</sup> influx through voltage-dependent L-type Ca<sup>2+</sup> channels. In our experimental preparation, TEA, a non-selective K<sup>+</sup> channel blocker, significantly antagonized the inhibitory response of DIPA 1–8, suggesting a potential role of the K<sup>+</sup> channels in the myorelaxant action induced by the TRPM8 agonist. Because TRPM8 activation promotes Ca<sup>2+</sup> entry into the cytosol by the TRPM8 Ca<sup>2+</sup>-permeable channels, the increased Ca<sup>2+</sup> influx in the smooth muscle cell could be responsible for Ca<sup>2+</sup>-dependent K<sup>+</sup> channel activation.</p><p>In our preparations, apamin, a blocker of the small conductance Ca<sup>2+</sup>-dependent K<sup>+</sup> channels, did not affect the mechanical responses to DIPA 1–8, ruling out the involvement of these channels. On the contrary, IbTX, a blocker of the large-conductance Ca<sup>2+</sup>-dependent K<sup>+</sup>-channels, abolished the DIPA 1–8 effects, suggesting that these channels are involved in the reduction of colon contraction induced by TRPM8 activation. Consistent with our results, Silva et al. [<xref rid=\"B44-ijms-21-05403\" ref-type=\"bibr\">44</xref>,<xref rid=\"B45-ijms-21-05403\" ref-type=\"bibr\">45</xref>] observed that TRPM8 activation induced vasodilatation in rat mesenteric artery through an endothelium-independent pathway, which involved the activation of large-conductance calcium-activated potassium channels (BKCa) and inhibition of voltage-gated calcium channels. However, in rat internal pudendal artery, the relaxation induced by TRPM8 did not involve BKCa activation [<xref rid=\"B46-ijms-21-05403\" ref-type=\"bibr\">46</xref>]. Therefore, it is not possible to generalize on the molecular mechanisms and further experiments by patch clamp are necessary to definitively demonstrate the role of the iberiotoxin-sensitive K<sup>+</sup> channel in TRPM8-induced smooth muscle relaxation.</p></sec><sec id=\"sec4-ijms-21-05403\"><title>4. Materials and Methods</title><sec id=\"sec4dot1-ijms-21-05403\"><title>4.1. Human Tissue Specimens</title><p>The study protocol was approved by the Institutional Ethics Committee (HCP0617-June 2017; Comitato Etico CE Palermo 2 -ex D.A. n. 1360 del 16/07/2013) of the Azienda di Rilievo Nazionale ad Alta Specializzazione (A.R.N.A.S.), Ospedali Civico Di Cristina Benfratelli-Palermo. All patients provided written informed consent before inclusion in the study. Samples of human distal colon were collected from 36 subjects with no symptoms of major clinical motility disorders (aged 55–86, 32 males) who underwent colectomy for sigmoid cancer. Colonic samples were collected from macroscopically normal regions without any evidence of cancer lesions and placed in cold pre-oxygenated (95% O<sub>2</sub> and 5% CO<sub>2</sub>) Krebs solution. Then, the mucosal layer was removed, and the specimens were stored overnight at 4 °C. Six samples (from 4 men and 2 women, aged 53–79) were used for biomolecular analysis. After phosphate-buffered saline, the scraped mucosa and the remaining tissue were separately collected in sterile tubes and stored at −80 °C.</p></sec><sec id=\"sec4dot2-ijms-21-05403\"><title>4.2. TRPM8 Expression Analysis</title><p>Total RNA from the mucosa and smooth muscle was extracted using a PureLink RNA Mini Kit (Invitrogen, Carlsbad, CA, USA) according to manufacturer’s instructions and quantified by spectrophotometry. 1 mg of total RNA was reverse-transcribed using a High-Capacity c-DNA RT Kit (Applied Biosystems, Foster City, CA, USA). cDNA (5 µL; 30 ng total RNA equivalents per reaction) were denatured and subjected to RT-PCR amplification. The oligonucleotide primers were the following: For: 5′-cctgttcctctttgcggtgtggat-3′; Rev: 5′-tcctctgaggtgtcgttggcttt-3′ to generate a 621 bp product from human TRPM8. For: 5′-cgggatccccgccctaggcaccagggt-3′; Rev: 5′-ggaattcggctggggtgttgaaggtctcaaa-3′, to generate a 289 bp product from human β-actin. Each PCR cycle employed a 5 min denaturing step at 94 °C followed by 35 cycles at 95 °C for 15 s, 65 °C for 30 s, and 72 °C for 30 s, and a final extension step of 7 min at 72 °C. The amplimers were separated on a 1% agarose gel containing 0.5 mg/mL ethidium bromide for visualization. The gel was scanned under UV light. HeLa (Human epithelial carcinoma cell line) (purchased from ATCC, Manassas, VA, USA) was used as positive control [<xref rid=\"B47-ijms-21-05403\" ref-type=\"bibr\">47</xref>].</p><p>For western blotting, the colon tissue (30 mg) was incubated on ice in RIPA buffer (50 mM Tris–HCl, pH 7.4; 150 mM NaCl, 1% Nonidet P-40) containing protease inhibitors (2 mM phenylmethylsulphonyl fluoride, NaVO<sub>3</sub>) for 1 h. Subsequently, it was centrifuged at 4 °C for 15 min at 13,000 g, and the supernatant was isolated. Protein concentration was measured by the Bio-Rad Protein Assay (Bio-Rad Laboratories, Hercules, CA, USA). Proteins (50 μg) were separated by 10% SDS-PAGE containing 0.1% SDS and transferred to Hybond-C nitrocellulose membranes (Amersham Life Science, Little Chalfont, UK) by electroblotting. Loading and transfer conditions were assessed by staining of the gel with Ponceau red. The relative migration position of the target protein was detected with a co-electrophoresed pre-stained molecular weight protein ladder (Invitrogen, Carlsbad, CA, USA). Subsequently the membranes were incubated overnight with antibodies to human TRPM8 (ab85617) (Abcam, Cambridge, UK; concentrated 1 μg/mL), with human tubulin (Abcam, Cambridge, UK; diluted 1:500) applied as a loading control. Regarding the specificity of TRPM8 immunoblotting, the primary TRPM8 antibody was pre-incubated for 2 h with the synthetic blocking peptide (ab95862) (Abcam, Cambridge, UK) before incubating with the membranes. Then the membranes were incubated with a goat anti-rabbit immunoglobulin G (IgG) secondary antibody conjugated to HRP (diluted 1:3000), recommended for TRPM8 detection (Santa Cruz Biotechnology, Dallas, TX, USA), or sheep anti-mouse IgG–HRP (diluted 1:10,000), recommended for tubulin detection (Amersham Pharmacia, Amersham, Buckinghamshire, UK). The target proteins were detected by enhanced chemiluminescence ECL (Pierce, Rockford, IL, USA). Once more, HeLa cell line was used as positive control (Anti-TRPM8 antibody-ab85617; <uri xlink:href=\"https://www.abcam.com/trpm8-antibody-ab85617.html\">https://www.abcam.com/trpm8-antibody-ab85617.html</uri>). Densitometric analysis of blots was performed using the NIH Image J 1.40 analysis program (National Institutes of Health, Bethesda, MD, USA).</p></sec><sec id=\"sec4dot3-ijms-21-05403\"><title>4.3. Preparation of Circular Muscle Strips and Experimental Protocol</title><p>Methods used in the present study are the same as those previously reported [<xref rid=\"B48-ijms-21-05403\" ref-type=\"bibr\">48</xref>]. Briefly, the circular muscle strips (4 mm wide by 10 mm long) were cut and suspended in a four-channel organ bath maintained at 37 ± 0.5 °C. Each chamber contained 8 mL of oxygenated Krebs solution with the following composition (mM): NaCl 119; KCl 4.5; MgSO<sub>4</sub> 2.5; NaHCO<sub>3</sub> 25; KH<sub>2</sub>PO<sub>4</sub> 1.2; CaCl<sub>2</sub> 2.5; glucose 11.1. One end of each strip was tied to organ holders, while the other end was secured with a silk thread to an isometric force transducer (FORT25, Ugo Basile, Biological Research Apparatus, Comerio, VA, Italy). The mechanical activity was digitized on an analog-to-digital converter, visualized, recorded, and analyzed on a personal computer using the PowerLab/400 system (Ugo Basile Biological Research Apparatus, Comerio, VA, Italy). A tension of 1 g was applied, and the tissues were allowed to equilibrate for 1 h. During this period, the strips developed spontaneous phasic contractions. In each experiment, up to six strips from the same specimen were studied. Preliminarily, in order to identify the most potent or efficacious TRPM8 agonist, we selected six selective TRPM8 receptor agonists (<xref rid=\"ijms-21-05403-t002\" ref-type=\"table\">Table 2</xref>), to analyze the effects on colon mechanical activity. The agonists tested were trialkyl derivatives of phosphoric acid, in which two of the alkyls were either sec-butyl (DAPA) or isopropyl (DIPA) and the third alkyl was C4 to C10. Previously, the pharmacological actions on the TRPM8 channel have been validated in Chinese hamster ovary (CHO) cells transfected with human TRPM8 cDNAs [<xref rid=\"B49-ijms-21-05403\" ref-type=\"bibr\">49</xref>] and tested in a mouse model of DED [<xref rid=\"B26-ijms-21-05403\" ref-type=\"bibr\">26</xref>].</p><p>After the equilibration period, the effects induced by cumulative concentrations of DAPA 2–5 (1 μM–1 mM), DIPA 1–7 (1 nM–1 mM), DIPA 1–8 (1 nM–100 μM), DIPA 1–9 (1 nM–100 μM), DIPA 1–10 (1 nM–1 mM), and DIPA 1–12 (10 nM–1 mM) on the spontaneous mechanical activity were examined. The agonists were added to the bath, one by one, at increasing concentrations in volumes of 80 μL, and left in contact with the tissue for 4 min. The response to each TRPM8 agonist was tested in the presence of 5-benzyloxytryptamine (BT) (1 μM), a TRPM8 antagonist. In addition, the responses to DIPA 1–8, the most effective and potent agonist among those tested, were analyzed in the presence of tetrodotoxin (TTX; 1 µM), a voltage-dependent Na<sup>+</sup>-channel blocker, tetraethylammonium chloride (TEA), a non-selective blocker of K<sup>+</sup>-channels, apamin (100 nM), a blocker of small- conductance Ca<sup>2+</sup>-dependent potassium K<sup>+</sup>-channels, and iberiotoxin (IbTX, 10 µM), a blocker of the large-conductance Ca<sup>2+</sup>-dependent K<sup>+</sup>-channels. The concentrations of the blocker agents used were determined from the literature [<xref rid=\"B22-ijms-21-05403\" ref-type=\"bibr\">22</xref>,<xref rid=\"B50-ijms-21-05403\" ref-type=\"bibr\">50</xref>].</p><p>The effect of DIPA 1–8 (1 μM) was evaluated on the contractions evoked by carbachol (CCh, 0.1 μM). CCh (0.1 μM) induced reproducible and constant contractile responses, characterized by a fast initial peak, the phasic component, followed by a decline to a lower maintained tension level, the tonic component. CCh was left in contact with the tissue for 20 min and then washed out. DIPA 1–8 were added when the CCh contraction reached a plateau.</p></sec><sec id=\"sec4dot4-ijms-21-05403\"><title>4.4. Drugs</title><p>The drugs used were the following: DAPA 2–5, DIPA 1–7, DIPA 1–8, DIPA 1–9, DIPA 1–10, and DIPA 1–12 (kindly supplied by Prof. Eddie Wei; Berkeley, CA, USA), TTX (Alomone Labs, Jerusalem, Israel), 5-BT hydrochloride, KCl, carbachol (CCh), tetraethylammonium chloride (TEA), apamin, and IbTX (Sigma-Aldrich, St. Louis, MO, USA). DIPA 1–8, DIPA 1–10, and DIPA 1–12 were dissolved in dimethylsulphoxide (DMSO) (0.1%). Control experiments using DMSO alone did not show any effects on the mechanical activity. All the other drugs were dissolved in distilled water. Chemicals were prepared as stock solution, which were diluted with Krebs solution on the experiment day.</p></sec><sec id=\"sec4dot5-ijms-21-05403\"><title>4.5. Data and Statistical Analysis</title><p>The inhibitory effects of TRPM8 agonists were evaluated by measuring the mean amplitude of spontaneous contractions prior to and following drug administration. The results are expressed as the changes in mean amplitude of the phasic contractions and reported as percentages of the values obtained in the control (e.g., 100% corresponds to the abolition of spontaneous activity).</p><p>Concentration–response curves were computer-fitted to a sigmoidal curve using non-linear regression, and the concentration (EC50), with 95% confidence limits (Cls), producing half-maximum response, was calculated using Graph Pad Prism 6 Software (San Diego, CA, USA).</p><p>All data are expressed as mean values ± standard error of the mean (S.E.M.). The letter <italic>n</italic> indicates the number of human colonic samples. Statistical analysis was performed by means of Student’s <italic>t</italic>-test or 2-way ANOVA followed by Bonferroni post-hoc test, when appropriate. A probability value (<italic>p</italic>) of less than 0.05 was regarded as significant.</p></sec></sec><sec sec-type=\"conclusions\" id=\"sec5-ijms-21-05403\"><title>5. Conclusions</title><p>The results of the present study demonstrated that TRPM8 receptors are expressed in human distal colon and that ligand-dependent TRPM8 activation is able to reduce colonic spontaneous motility, probably by the opening of the large-conductance Ca<sup>2+</sup>-dependent K<sup>+</sup>-channels. The class of TRPM8 agonist belonging to dialkylphosphorylalkanes compounds, in particular the diisopropyl analogues (DIPA), represent promising drugs for the treatment of intestinal dysmotility. The effects of the DIPA 1–8 agonist on human colon emphasize the ability of TRPM8 channel activation to counteract IBS symptoms, such as pain, inflammation, and motility discomfort.</p></sec></body><back><ack><title>Acknowledgments</title><p>The authors kindly acknowledge Eddie Wei for the supply of the TRPM8 agonists.</p></ack><app-group><app id=\"app1-ijms-21-05403\"><title>Supplementary Materials</title><p>Supplementary materials can be found at <uri xlink:href=\"https://www.mdpi.com/1422-0067/21/15/5403/s1\">https://www.mdpi.com/1422-0067/21/15/5403/s1</uri>, Table S1: Human colon spontaneous contraction frequency (cpm, contractions per minute) in control conditions or in the presence of the TRPM8 agonists; Figure S1: Typical tracings illustrating the response to DIPA 1–8 (1 µM) in the circular muscle strip of human colon precontracted by carbachol (CCh) (0.1 µM). W, washout. TRPM8 agonist induced a rapid relaxation.</p><supplementary-material content-type=\"local-data\" id=\"ijms-21-05403-s001\"><media xlink:href=\"ijms-21-05403-s001.pdf\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material></app></app-group><notes><title>Author Contributions</title><p>Conceptualization, A.A. and S.T.; methodology, A.A., L.L. and S.T.; validation, A.A., S.T. and F.M.; investigation, A.A., L.L. and P.M.; data curation, A.A. and F.M.; writing—original draft preparation, A.A.; writing—review and editing, F.M.; supervision, A.A. and F.M. All authors have read and agreed to the published version of the manuscript.</p></notes><notes><title>Funding</title><p>This research received no external funding.</p></notes><notes notes-type=\"COI-statement\"><title>Conflicts of Interest</title><p>The authors declare no conflict of interest.</p></notes><glossary><title>Abbreviations</title><array orientation=\"portrait\"><tbody><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5-BT</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5-benzyloxytryptamine</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ARNAS</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Azienda di Rilievo Nazionale ad Alta Specializzazione-Highly Specialized National Relief Hospital</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">BKCa</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Large-conductance calcium-activated potassium channels</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CCh</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Carbachol</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CHO</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Chinese hamster ovary cells</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Cls</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">95% confidence limits</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">DAPA</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1-[Dialkyl-phosphinoyl]-alkane</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">DED</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Dry eye disease</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">DIPA</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1-[Diisopropyl-phosphinoyl]-alkane</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">DMSO</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Dimethylsulphoxide</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">EC<sub>50</sub></td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Half-maximum response</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">E<sub>max</sub></td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Maximum effect</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">HeLa</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Human epithelial carcinoma cell line</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">HRP</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Horseradish peroxidase</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">IBD</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Inflammatory bowel disease</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">IBS</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Irritable bowel syndrome</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">IbTX</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Iberiotoxin</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">KCl</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Potassium chloride</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">KDa</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Kilodalton</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">RIPA</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Radioimmunoprecipitation assay</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">RNA</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Ribonucleic Acid</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">RT-PCR</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Reverse transcriptase-polymerase chain reaction</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">SEM</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Error of the mean</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">TEA</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Tetraethylammonium chloride</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">TRP</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Transient receptor potential</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">TRPM8</td><td 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Ther. Med.</source><year>2019</year><volume>17</volume><fpage>4748</fpage><lpage>4756</lpage><pub-id pub-id-type=\"doi\">10.3892/etm.2019.7469</pub-id><pub-id pub-id-type=\"pmid\">31105793</pub-id></element-citation></ref></ref-list></back><floats-group><fig id=\"ijms-21-05403-f001\" orientation=\"portrait\" position=\"float\"><label>Figure 1</label><caption><p>TRPM8 mRNA and protein expression in human distal colon. (<bold>A</bold>) Representative images of the reverse transcriptase-polymerase chain reaction (RT-PCR) results. A product of 621 bp corresponding to TRPM8 was detected in mucosa, smooth muscle, and HeLa cells used as positive control. The expression of β-actin (396 bp) was used as loading control. Negative control was obtained without addition of cDNA. (<bold>B</bold>) Western blot detecting protein levels for TRPM8 from colon mucosa and muscle. (<bold>C</bold>) Densitometric analysis of blots was performed using the NIH Image J 1.40 analysis program. HeLa cells were used as positive control. Human tubulin was used as loading control. Negative control was obtained by using blocking peptide added to TRPM8 Ab. <italic>n</italic> = 6; * <italic>p</italic> < 0.05 when compared to TRPM8 mucosa expression.</p></caption><graphic xlink:href=\"ijms-21-05403-g001\"/></fig><fig id=\"ijms-21-05403-f002\" orientation=\"portrait\" position=\"float\"><label>Figure 2</label><caption><p>Typical recordings showing the inhibitory effects of increasing concentrations of DAPA 2–5 (1 μM–1 mM) (<bold>A</bold>), DIPA 1–7 (1 nM–1 mM) (<bold>B</bold>), DIPA 1–8 (1 nM–100 μM) (<bold>C</bold>), DIPA 1–9 (1 nM–100 μM) (<bold>D</bold>), DIPA 1–10 (1 nM–1 mM) (<bold>E</bold>), and DIPA 1–12 (10 nM–1 mM) (<bold>F</bold>) on the spontaneous contractions of human colon circular muscle. C = spontaneous contractions in control conditions. W = spontaneous contractions after washing out. Dotted line indicates the basal tone of the preparation.</p></caption><graphic xlink:href=\"ijms-21-05403-g002\"/></fig><fig id=\"ijms-21-05403-f003\" orientation=\"portrait\" position=\"float\"><label>Figure 3</label><caption><p>Concentration–response curves showing the inhibitory effects of increasing concentrations of DAPA 2–5 (1 μM–1 mM) (<bold>A</bold>), DIPA 1–7 (1 nM–1 mM) (<bold>B</bold>), DIPA 1–8 (1 nM–100 μM) (<bold>C</bold>), DIPA 1–9 (1 nM–100 μM) (<bold>D</bold>), and DIPA 1–10 (1 nM–1 mM) (<bold>E</bold>) on the spontaneous contractions of human colon circular muscle, in the presence or in the absence of 5-BT (1 μM). Data are means S.E.M. (<italic>n</italic> = 6 for each experimental conditions) and are expressed as percentage of inhibition of the spontaneous contractions. * <italic>p</italic> < 0.05 compared with the respective control conditions.</p></caption><graphic xlink:href=\"ijms-21-05403-g003\"/></fig><fig id=\"ijms-21-05403-f004\" orientation=\"portrait\" position=\"float\"><label>Figure 4</label><caption><p>Concentration–response curves for the inhibitory effects induced by DIPA 1–8 (1 nM–100 μM) before and after TTX (1 μM) (<bold>A</bold>) and TEA (10 mM) (<bold>B</bold>). All values are means ± S.E.M (<italic>n</italic> = 6) and are expressed as percentage of inhibition of the spontaneous contractions. * <italic>p</italic> <0.05 compared with the respective control conditions.</p></caption><graphic xlink:href=\"ijms-21-05403-g004\"/></fig><fig id=\"ijms-21-05403-f005\" orientation=\"portrait\" position=\"float\"><label>Figure 5</label><caption><p>Concentration–response curves for the inhibitory effects induced by DIPA 1–8 (1 nM–100 μM) before and after apamin (100 nM) (<bold>A</bold>) and IbTX (10 µM) (<bold>B</bold>). All values are means ± S.E.M (<italic>n</italic> = 6) and are expressed as percentage of inhibition of the spontaneous contractions. * <italic>p</italic> <0.05 compared with the respective control conditions.</p></caption><graphic xlink:href=\"ijms-21-05403-g005\"/></fig><table-wrap id=\"ijms-21-05403-t001\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijms-21-05403-t001_Table 1</object-id><label>Table 1</label><caption><p>Potency and efficacy of the tested TRPM8 agonists (expressed as EC<sub>50</sub> and E<sub>max</sub> respectively) in determining reduction of human colon spontaneous contractions.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">TRPM8 Agonist</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Concentration Range</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">EC<sub>50</sub></th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Cls</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">E<sub>max</sub> (%)</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">DAPA 2–5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1 μM–1 mM</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">33 μM</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">21–50 μM</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">80.9 ± 8.2</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">DIPA 1–7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1 nM–1 mM</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1 μM</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5–2 μM</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">63.5 ± 3.5 </td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">DIPA 1–8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1 nM–100 μM</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">41 nM</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">28–61 nM</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">88.3 ± 2.2</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">DIPA 1–9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1 nM–100 μM</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">72 nM</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">42–123 nM</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">79 ± 1</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">DIPA 1–10</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1 nM–1 mM</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">460 nM</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">251–977 nM</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">80.6 ± 1.8</td></tr></tbody></table><table-wrap-foot><fn><p>EC<sub>50</sub> = half-maximum response; Cls = 95% confidence limits; E<sub>max</sub> (%) = maximum effect.</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"ijms-21-05403-t002\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijms-21-05403-t002_Table 2</object-id><label>Table 2</label><caption><p>TRPM8 agonists.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Code</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Chemical Name</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Chemical Structure</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">DAPA 2–5</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1-Di(sec-butyl)phosphinoyl-pentane</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<inline-graphic xlink:href=\"ijms-21-05403-i001.jpg\"/>\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">DIPA 1–7</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1-Diisopropyl-phosphinoyl-heptane</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<inline-graphic xlink:href=\"ijms-21-05403-i002.jpg\"/>\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">DIPA 1–8</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1-Diisopropyl-phosphinoyl-octane</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<inline-graphic xlink:href=\"ijms-21-05403-i003.jpg\"/>\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">DIPA 1–9</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1-Diisopropyl-phosphinoyl-nonane</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<inline-graphic xlink:href=\"ijms-21-05403-i004.jpg\"/>\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">DIPA 1–10</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1-Diisopropyl-phosphinoyl-decane</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<inline-graphic xlink:href=\"ijms-21-05403-i005.jpg\"/>\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">DIPA 1–12</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1-Diisopropyl-phosphinoyl-dodecane</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<inline-graphic xlink:href=\"ijms-21-05403-i006.jpg\"/>\n</td></tr></tbody></table></table-wrap></floats-group></article>\n"
]
[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Int J Environ Res Public Health</journal-id><journal-id journal-id-type=\"iso-abbrev\">Int J Environ Res Public Health</journal-id><journal-id journal-id-type=\"publisher-id\">ijerph</journal-id><journal-title-group><journal-title>International Journal of Environmental Research and Public Health</journal-title></journal-title-group><issn pub-type=\"ppub\">1661-7827</issn><issn pub-type=\"epub\">1660-4601</issn><publisher><publisher-name>MDPI</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32731417</article-id><article-id pub-id-type=\"pmc\">PMC7432082</article-id><article-id pub-id-type=\"doi\">10.3390/ijerph17155423</article-id><article-id pub-id-type=\"publisher-id\">ijerph-17-05423</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Article</subject></subj-group></article-categories><title-group><article-title>Retrospective Assessment of the Antigenic Similarity of Egg-Propagated and Cell Culture-Propagated Reference Influenza Viruses as Compared with Circulating Viruses across Influenza Seasons 2002–2003 to 2017–2018</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0002-8988-7511</contrib-id><name><surname>Rajaram</surname><given-names>Sankarasubramanian</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05423\">1</xref><xref rid=\"c1-ijerph-17-05423\" ref-type=\"corresp\">*</xref></contrib><contrib contrib-type=\"author\"><name><surname>Suphaphiphat</surname><given-names>Pirada</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05423\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>van Boxmeer</surname><given-names>Josephine</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05423\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Haag</surname><given-names>Mendel</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05423\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Leav</surname><given-names>Brett</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05423\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Iheanacho</surname><given-names>Ike</given-names></name><xref ref-type=\"aff\" rid=\"af2-ijerph-17-05423\">2</xref></contrib><contrib contrib-type=\"author\"><name><surname>Kistler</surname><given-names>Kristin</given-names></name><xref ref-type=\"aff\" rid=\"af3-ijerph-17-05423\">3</xref></contrib><contrib contrib-type=\"author\"><name><surname>Ortiz de Lejarazu</surname><given-names>Raúl</given-names></name><xref ref-type=\"aff\" rid=\"af4-ijerph-17-05423\">4</xref></contrib></contrib-group><aff id=\"af1-ijerph-17-05423\"><label>1</label>Seqirus UK Ltd., Maidenhead SL6 8AA, UK; <email>[email protected]</email> (P.S.); <email>[email protected]</email> (J.v.B.); <email>[email protected]</email> (M.H.); <email>[email protected]</email> (B.L.)</aff><aff id=\"af2-ijerph-17-05423\"><label>2</label>Evidera Ltd., London W6 8BJ, UK; <email>[email protected]</email></aff><aff id=\"af3-ijerph-17-05423\"><label>3</label>Evidera Inc., Waltham, MA 02451, USA; <email>[email protected]</email></aff><aff id=\"af4-ijerph-17-05423\"><label>4</label>School of Medicine, University of Valladolid, 47005 Valladolid, Spain; <email>[email protected]</email></aff><author-notes><corresp id=\"c1-ijerph-17-05423\"><label>*</label>Correspondence: <email>[email protected]</email>; Tel.: +44-(0)1628-641-500</corresp></author-notes><pub-date pub-type=\"epub\"><day>28</day><month>7</month><year>2020</year></pub-date><pub-date pub-type=\"ppub\"><month>8</month><year>2020</year></pub-date><volume>17</volume><issue>15</issue><elocation-id>5423</elocation-id><history><date date-type=\"received\"><day>11</day><month>6</month><year>2020</year></date><date date-type=\"accepted\"><day>25</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>© 2020 by the authors.</copyright-statement><copyright-year>2020</copyright-year><license license-type=\"open-access\"><license-p>Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>).</license-p></license></permissions><abstract><p>Suboptimal vaccine effectiveness against seasonal influenza is a significant public health concern, partly explained by antigenic differences between vaccine viruses and viruses circulating in the environment. Haemagglutinin mutations within vaccine viruses acquired during serial passage in eggs have been identified as a source of antigenic variation between vaccine and circulating viruses. This study retrospectively compared the antigenic similarity of circulating influenza isolates with egg- and cell-propagated reference viruses to assess any observable trends over a 16-year period. Using annual and interim reports published by the Worldwide Influenza Centre, London, for the 2002–2003 to 2017–2018 influenza seasons, we assessed the proportions of circulating viruses which showed antigenic similarity to reference viruses by season. Egg-propagated reference viruses were well matched against circulating viruses for A/H1N1 and B/Yamagata. However, A/H3N2 and B/Victoria cell-propagated reference viruses appeared to be more antigenically similar to circulating A/H3N2 and B/Victoria viruses than egg-propagated reference viruses. These data support the possibility that A/H3N2 and B/Victoria viruses are relatively more prone to egg-adaptive mutation. Cell-propagated A/H3N2 and B/Victoria reference viruses were more antigenically similar to circulating A/H3N2 and B/Victoria viruses over a 16-year period than were egg-propagated reference viruses.</p></abstract><kwd-group><kwd>influenza</kwd><kwd>vaccine</kwd><kwd>egg</kwd><kwd>cell</kwd><kwd>adaptation</kwd><kwd>mutation</kwd><kwd>effectiveness</kwd><kwd>antigen</kwd></kwd-group></article-meta></front><body><sec sec-type=\"intro\" id=\"sec1-ijerph-17-05423\"><title>1. Introduction</title><p>Vaccination is recognized as being the most effective method of preventing seasonal influenza disease [<xref rid=\"B1-ijerph-17-05423\" ref-type=\"bibr\">1</xref>]. Over the past several decades, seasonal influenza vaccines have provided important public health benefits, in terms of reduced incidence of influenza-like illness (ILI), general practitioner visits, hospitalizations, and influenza-associated mortality [<xref rid=\"B2-ijerph-17-05423\" ref-type=\"bibr\">2</xref>,<xref rid=\"B3-ijerph-17-05423\" ref-type=\"bibr\">3</xref>,<xref rid=\"B4-ijerph-17-05423\" ref-type=\"bibr\">4</xref>]. Each year, in advance of the beginning of the influenza season, the World Health Organization (WHO) predicts which influenza viruses will be dominant in the upcoming season, and based on this assessment, recommends candidate vaccine viruses (CVVs) for inclusion in vaccines for the Northern and Southern Hemispheres [<xref rid=\"B5-ijerph-17-05423\" ref-type=\"bibr\">5</xref>].</p><p>Vaccine effectiveness (VE) data provide an estimate of the extent to which a vaccine protects against disease in a real-world setting, relative to no vaccination. Ultimately, the effectiveness of influenza vaccines depends on the interplay between the host, the vaccine, environment, and the circulating virus strains [<xref rid=\"B6-ijerph-17-05423\" ref-type=\"bibr\">6</xref>], with the effectiveness of the vaccine depending on the extent to which the virus strains it includes match those strains causing disease in the human population during a given season. Less than optimal VE can occur due to various factors, including mismatch between vaccine and circulating strains due to inaccurate strain predictions, as well as antigenic drift [<xref rid=\"B7-ijerph-17-05423\" ref-type=\"bibr\">7</xref>,<xref rid=\"B8-ijerph-17-05423\" ref-type=\"bibr\">8</xref>,<xref rid=\"B9-ijerph-17-05423\" ref-type=\"bibr\">9</xref>,<xref rid=\"B10-ijerph-17-05423\" ref-type=\"bibr\">10</xref>,<xref rid=\"B11-ijerph-17-05423\" ref-type=\"bibr\">11</xref>,<xref rid=\"B12-ijerph-17-05423\" ref-type=\"bibr\">12</xref>], original antigenic sin [<xref rid=\"B13-ijerph-17-05423\" ref-type=\"bibr\">13</xref>,<xref rid=\"B14-ijerph-17-05423\" ref-type=\"bibr\">14</xref>], and the viral propagation techniques used for vaccine manufacture (i.e., egg-based versus non-egg-based technologies). Particularly low VE has been observed in recent seasons against the A/H3N2 strain as compared with the VE observed for A/H1N1 and B strains. Influenza VE can vary considerably between strains and seasons, with strain-specific VE estimates of 33%, 23%, 54%, and 61% for antigenically similar A/H3N2, variant A/H3N2, B strains, and A/H1N1pdm09, respectively, observed over the past decade [<xref rid=\"B15-ijerph-17-05423\" ref-type=\"bibr\">15</xref>]. In the 2017–2018 season, circulating strains of A/H3N2 were antigenically distinct from egg-propagated reference viruses [<xref rid=\"B16-ijerph-17-05423\" ref-type=\"bibr\">16</xref>].</p><p>Antigenic drift has led to alterations in some A/H3N2 viruses, which inhibit them from replicating efficiently in embryonated chicken eggs [<xref rid=\"B17-ijerph-17-05423\" ref-type=\"bibr\">17</xref>,<xref rid=\"B18-ijerph-17-05423\" ref-type=\"bibr\">18</xref>], and this limits the number of viruses which can be used as CVVs. In addition to background antigenic drift, those viruses that can successfully be propagated in eggs can develop structural changes in the haemagglutinin (HA) receptor binding site (egg adaptation) during serial passage, further contributing to antigenic differences between circulating and reference vaccine strains [<xref rid=\"B19-ijerph-17-05423\" ref-type=\"bibr\">19</xref>,<xref rid=\"B20-ijerph-17-05423\" ref-type=\"bibr\">20</xref>,<xref rid=\"B21-ijerph-17-05423\" ref-type=\"bibr\">21</xref>,<xref rid=\"B22-ijerph-17-05423\" ref-type=\"bibr\">22</xref>,<xref rid=\"B23-ijerph-17-05423\" ref-type=\"bibr\">23</xref>]. Vaccine-induced antibody responses generated against egg-adapted HA can be less reactive against the HA expressed on circulating viruses [<xref rid=\"B22-ijerph-17-05423\" ref-type=\"bibr\">22</xref>,<xref rid=\"B23-ijerph-17-05423\" ref-type=\"bibr\">23</xref>], and this is thought to have played a role in the lower VE observed for A/H3N2 in recent seasons. Using influenza viruses propagated wholly in mammalian cell lines (or recombinant technology using non-egg substrates) avoids egg-adaptive mutations and can contribute to the better effectiveness or efficacy observed for cell culture-based (or recombinant [<xref rid=\"B24-ijerph-17-05423\" ref-type=\"bibr\">24</xref>]) vaccines [<xref rid=\"B22-ijerph-17-05423\" ref-type=\"bibr\">22</xref>,<xref rid=\"B23-ijerph-17-05423\" ref-type=\"bibr\">23</xref>,<xref rid=\"B25-ijerph-17-05423\" ref-type=\"bibr\">25</xref>,<xref rid=\"B26-ijerph-17-05423\" ref-type=\"bibr\">26</xref>].</p><p>Against this background, we performed a retrospective evaluation of antigenic similarity between circulating and WHO reference A/H1N1, A/H3N2, and B strain viruses propagated in either eggs or Madin–Darby canine kidney (MDCK) cells from the 2002–2003 to 2017–2018 influenza seasons. The main objectives of this study were as follows: to assess the degree of antigenic similarity between circulating viruses and egg-propagated reference viruses, and where possible, cell-propagated reference viruses; to identify and describe any trends or observations in the antigenic characterisation of the reference strains over time; and to facilitate future assessment of the relationship between levels of antigenic similarity and any observable differences in VE between different vaccines.</p></sec><sec id=\"sec2-ijerph-17-05423\"><title>2. Materials and Methods</title><p>This retrospective analysis used publicly available reports from the Worldwide Influenza Centre (WIC; Francis Crick Institute, London, UK) for the 2002–2003 to 2017–2018 Northern Hemisphere and Southern Hemisphere influenza seasons. The WIC is one of six WHO collaborating centres worldwide, and these, together with 144 National Influenza Centres (NICs), form the WHO Global Influenza Surveillance and Response System. The collaborating centres monitor strain prevalence, perform antigenic and genetic characterizations of influenza viruses worldwide, and help inform the WHO annual recommendations on which CVVs to include in influenza vaccines. For this analysis, we used data from WIC interim and annual reports, which provided information on influenza viruses in Africa, the Eastern Mediterranean, Europe, and the Western Pacific (Hong Kong and China), based on virus samples received from sentinel and non-sentinel WHO influenza centre sites [<xref rid=\"B27-ijerph-17-05423\" ref-type=\"bibr\">27</xref>]. In this report, the term ”CVV” refers to influenza strains which were included in seasonal influenza vaccines (as recommended by the WHO); the term ”reference strains” refers to influenza strains against which circulating strains were tested by haemagglutination inhibition (HI) assay and plaque reduction neutralization assays (PRNA). Reference strains include CVVs, as well as other strains antigenically similar to the CVVs used as the reference strain in our comparison if the exact CVV strain was not included in the WIC report. All egg-propagated reference viruses selected as comparators were CVVs and antigenically like the vaccine viruses; whereas for cell-propagated reference viruses, antigenic likeness to recommended strains was undetermined for some seasons.</p><p>Antigenic similarity between reference viruses and circulating strains has principally been reported by the WIC as HI assay data comparing circulating viruses in terms of their reactivity to ferret sera raised against reference virus for a given season. Since February 2009, for the analysis of A/H3N2 viruses, these HI assays have routinely incorporated the neuraminidase (NA) inhibitor oseltamivir at a concentration of 20 nM, to avoid false-negative results due to the agglutination of viruses via NA protein. Where applicable, therefore, such use of oseltamivir is noted in the results of the present study. In recent seasons, A/H3N2 viruses have become increasingly difficult to assess by HI assay, due to their decreased ability to agglutinate red blood cells [<xref rid=\"B28-ijerph-17-05423\" ref-type=\"bibr\">28</xref>]. From 2008 onwards, PRNAs using ferret sera have also been used to complement the findings of the HI assays, and to estimate titres for isolates which could not be determined by HI assay.</p><p>The WIC reports present the antigenic characterisation data categorically, specifically, as the proportion of circulating viruses that show reactivity to the ferret antisera at titres relatively similar to, or different from, those at which reference viruses show reactivity (i.e., when the difference between these titres is ≤ two, four, or ≥eight dilutions, respectively). For the current analysis, antigenic similarity was defined as the circulating virus titre (at which reactivity was observed) being no more than four-fold lower than the reference virus titre. This definition is consistent with that used by the U.S. Centers for Disease Control and Prevention [<xref rid=\"B29-ijerph-17-05423\" ref-type=\"bibr\">29</xref>]. Viruses which did not meet the definition of antigenic similarity were considered to be antigenically different. The raw data listings within the WIC reports often included results from a small number of isolates tested outside of the periods or the geographic locations covered in the reports; and isolates grown in eggs were occasionally included as assay controls. Calculations using raw data (conducted when summarised reactivity data for ≤ two, four, or ≥ eight-fold differences in antisera titre were not presented within a WIC report) included only isolates exclusively grown in cells, and isolates harvested within the specific time period and geographic locations stated in the relevant reports. For A and B strain analyses, seasons were defined as Southern Hemisphere by time of year rather than by the geographical origin of circulating isolates (e.g., B/Victoria isolates were predominantly derived from Northern Hemisphere countries). All circulating viral isolates were passaged in MDCK/MDCK-SIAT1 cells.</p><p>We performed a qualitative comparison of the percentages of circulating virus isolates which were antigenically similar to the egg- and cell-propagated reference viruses for each influenza season. These percentages were calculated as a proportion of the total number of isolates tested by the WIC for each season. Because this was an exploratory analysis, no formal hypotheses were tested during the study.</p></sec><sec sec-type=\"results\" id=\"sec3-ijerph-17-05423\"><title>3. Results</title><sec id=\"sec3dot1-ijerph-17-05423\"><title>3.1. A/H3N2 Analysis</title><p>HI assay data were available for cell-propagated reference viruses for 23 of 29 influenza seasons (<xref ref-type=\"fig\" rid=\"ijerph-17-05423-f001\">Figure 1</xref>).</p><p>From the 2015 season onwards, less than half of the viruses could be assessed by HI assay due to loss of haemagglutination. In all but two of the seasons for which data were available for both egg- and cell-propagated reference viruses, a higher proportion of circulating A/H3N2 strains were antigenically similar to cell-propagated as compared with egg-propagated reference viruses (<xref ref-type=\"fig\" rid=\"ijerph-17-05423-f001\">Figure 1</xref>). HI assay data demonstrated little or no antigenic similarity (i.e., present in <25% of isolates) between A/H3N2 isolates and egg-propagated reference viruses in 16 of the 29 (55%) seasons analysed. By comparison, cell-propagated reference viruses showed this low proportion of similarity (i.e., <25%) in only one of the 23 (4%) seasons for which data were available. Particularly large differences were observed in the period between the 2012 Southern Hemisphere and the 2015–2016 Northern Hemisphere seasons inclusive, for which 2–13% of isolates showed antigenic similarity to egg-propagated reference viruses, as opposed to 76–100% of cell-propagated reference viruses.</p><p>In two of the seasons analysed, a higher proportion of isolates showed antigenic similarity to egg-propagated as compared with cell-propagated reference viruses, i.e., the 2005–2006 Northern Hemisphere season (35% showed antigenic similarity to egg-propagated and 18% to cell-propagated CVVs [<xref rid=\"B27-ijerph-17-05423\" ref-type=\"bibr\">27</xref>]); and the 2009–2010 Northern Hemisphere season (99% showed antigenic similarity to egg-propagated as compared with 75% to cell-propagated CVVs [<xref rid=\"B27-ijerph-17-05423\" ref-type=\"bibr\">27</xref>]). In the 2009 Southern Hemisphere season, two antigenically similar reference strains were used, because both were listed as CVVs by the WHO. For this season, a higher percentage of isolates showed antigenic similarity to cell-propagated reference viruses against one of these strains, and 62% of isolates were antigenically similar to both egg- and cell-propagated reference viruses for the other strain. The difference between cell- and egg-propagated reference viruses in the three seasons between February 2016 and September 2017 was less than in other seasons. Further analysis of these seasons showed that differences between the egg- and cell-propagated group datasets were more pronounced if a stricter ≤ two-fold cut-off point for the difference in titres was employed (rather than ≤ four-fold), thus, more clearly demonstrating cell-propagated reference viruses to be more antigenically similar than egg-propagated reference viruses to circulating A/H3N2 viruses (<xref rid=\"ijerph-17-05423-t001\" ref-type=\"table\">Table 1</xref>).</p><p>Additionally, virus clade impacted these results, with greater differences observed between similarity to egg- and cell-propagated reference viruses for circulating 3C.2a clade viruses as compared with subdominant 3C.3a clade viruses.</p><p>Assessment of antigenic similarity by PRNA demonstrated data trends similar to those observed by HI analysis (<xref ref-type=\"fig\" rid=\"ijerph-17-05423-f002\">Figure 2</xref>); specifically, across seasons, 60–100% (median 98%) and 0–100% (median 50%) of cell- and egg-propagated viruses were antigenically similar to circulating isolates, respectively.</p></sec><sec id=\"sec3dot2-ijerph-17-05423\"><title>3.2. A/H1N1 Analysis</title><p>HI assay analysis of the antigenic similarity between cell- and egg-propagated A/H1N1 reference viruses and the A/H1N1 viruses circulating in the environment during period September 2008–September 2018 (<xref ref-type=\"fig\" rid=\"ijerph-17-05423-f003\">Figure 3</xref>), generally found a high degree of antigenic similarity for both egg- and cell-propagated A/H1N1 reference viruses, with little difference in levels of similarity observed between the two groups.</p></sec><sec id=\"sec3dot3-ijerph-17-05423\"><title>3.3. B/Victoria Strain Analysis</title><p>HI assay analysis of the antigenic similarity between cell- and egg-propagated B/Victoria strain reference viruses and the B/Victoria viruses circulating in the environment during the period September 2008–September 2018 (<xref ref-type=\"fig\" rid=\"ijerph-17-05423-f004\">Figure 4</xref>), generally found a pronounced difference in the levels of antigenic similarity displayed by the egg- and cell-propagated reference viruses, with the higher levels of antigenic similarity observed for the cell-propagated viruses.</p><p>Over the 10 Northern Hemisphere influenza seasons (2008–2018), the proportions of cell-propagated reference viruses that were antigenically similar to circulating viruses ranged from 20% to 100% (with ≥90% being antigenically similar in seven of the 10 seasons). The proportions of egg-propagated reference viruses that were antigenically similar to circulating viruses ranged from 0% to 83% (with ≥ 90% being antigenically similar in none of the 10 seasons). With the exception of the 2008–2009 season, higher proportions of cell-propagated as compared with egg-propagated reference viruses were antigenically similar to circulating viruses. Data from the nine Southern Hemisphere influenza seasons (2009–2018) were comparable to the Northern Hemisphere data, with greater proportions of cell-propagated as compared with egg-propagated reference viruses being antigenically similar to circulating B/Victoria viruses in all seasons except one, namely February 2009–September 2009. Over the Southern Hemisphere influenza seasons, the proportions of cell-propagated reference viruses that were antigenically similar to circulating viruses ranged from 43% to 100% (and was ≥90% in five of the nine seasons). By comparison, the proportions of egg-propagated reference viruses that were antigenically similar to circulating viruses ranged from 0% to 64% (and was ≥90% in none of the nine seasons).</p></sec><sec id=\"sec3dot4-ijerph-17-05423\"><title>3.4. B/Yamagata Strain Analysis</title><p>HI assay analysis of the antigenic similarity between cell- and egg-propagated B/Yamagata strain reference viruses and the environmental B/Yamagata viruses present during the period September 2008–September 2018 (<xref ref-type=\"fig\" rid=\"ijerph-17-05423-f005\">Figure 5</xref>), did not reveal any clear data trends distinguishing the egg- and cell-propagated viruses either from each other or the circulating viruses.</p><p>Across the 20 influenza seasons assessed (both Northern and Southern Hemisphere seasons), average percentages of similarity to circulating B/Yamagata isolates of 69% and 79% were observed for the egg- and cell-propagated viruses, respectively; therefore, overall levels of similarity to circulating B/Yamagata viruses were slightly higher for cell- than egg-propagated B/Yamagata reference viruses, a difference likely to be of no clinical significance. In eight (40%) of the 20 seasons assessed, egg-propagated reference viruses displayed higher similarity to circulating viruses than cell-propagated viruses. In 10 (50%) of the 20 seasons assessed, cell-propagated reference viruses displayed higher similarity to circulating viruses than did egg-propagated viruses.</p></sec></sec><sec sec-type=\"discussion\" id=\"sec4-ijerph-17-05423\"><title>4. Discussion</title><p>This exploratory, descriptive study is, to the best of our knowledge, the first such analysis to assess the antigenic similarity between circulating strains of influenza, and egg- and cell-propagated reference viruses longitudinally over several Northern and Southern Hemisphere influenza seasons, both before and after the 2009 pandemic outbreak. The data presented in this report show that between 2002 and 2018, cell-propagated A/H3N2 and B/Victoria strain reference viruses were more often antigenically similar to A/H3N2 and B/Victoria viruses circulating in the environment than were egg-propagated viruses. For A/H1N1 and B/Yamagata reference viruses, similar proportions of samples showed antigenic similarity in both egg- and cell-propagated viruses. This distinction between A/H3N2 and B/Victoria versus A/H1N1 and B/Yamagata data is consistent with the possibility that there were higher rates or degrees of HA egg adaptation in A/H3N2 and B/Victoria strains than in A/H1N1 and B/Yamagata strains. It is also possible that, unlike A/H1N1 and B/Yamagata, A/H3N2 and B/Victoria are more susceptible to egg adaptations at specific regions of the HA molecule that significantly affect antigenicity (i.e., major epitopes). Analyses have shown some degree of similarity in phylodynamics to exist between A/H3N2 and B/Victoria viruses, as well as between A/H1N1 and B/Yamagata viruses [<xref rid=\"B30-ijerph-17-05423\" ref-type=\"bibr\">30</xref>]. A/H3N2 and B/Victoria exhibit limited genetic diversity at a given time point as compared with A/H1N1 and B/Yamagata viruses, which is indicative of frequent selective bottlenecks due to the serial replacement of circulating strains, as expected under continuous antigenic drift; this evolutionary pattern is consistent with the fact that A/H3N2 and B/Victoria are more susceptible to adaptive mutations on encountering the selective pressures of surviving in eggs [<xref rid=\"B30-ijerph-17-05423\" ref-type=\"bibr\">30</xref>].</p><p>The decrease in A/H3N2-specific VE observed over recent seasons is particularly noteworthy, because A/H3N2 is responsible for more severe cases of disease and higher rates of mortality than are other strains, with the highest burden of A/H3N2 disease occurring in adults aged 65 years or older [<xref rid=\"B31-ijerph-17-05423\" ref-type=\"bibr\">31</xref>,<xref rid=\"B32-ijerph-17-05423\" ref-type=\"bibr\">32</xref>]. Improving the effectiveness of influenza vaccines against circulating A/H3N2 strains is, therefore, a public health priority. The A/H3N2 data generated in this study frequently demonstrated considerable variance in the percentages of antigenic similarity observed within seasons, particularly between 2012 and 2016. In more than half of the seasons analysed, there was little (<25%) or no antigenic similarity between egg-propagated reference and circulating A/H3N2 viruses when analysed by HI assay. In seasons 2012–2016, approximately 100% of cell- and 20% of egg-propagated A/H3N2 reference viruses were shown to be antigenically similar to circulating viruses. In the 2016–2017 season, an egg-adaptive mutation (T160K) which arose during propagation of the A/H3N2 strain resulted in deglycosylation of the HA antigenic B site and altered antibody to HA glycoprotein binding; this mutation could have contributed to the low A/H3N2-specific VE of 34% observed during this season, despite a 95% match between circulating A/H3N2 isolates and the CVV [<xref rid=\"B23-ijerph-17-05423\" ref-type=\"bibr\">23</xref>,<xref rid=\"B27-ijerph-17-05423\" ref-type=\"bibr\">27</xref>,<xref rid=\"B33-ijerph-17-05423\" ref-type=\"bibr\">33</xref>,<xref rid=\"B34-ijerph-17-05423\" ref-type=\"bibr\">34</xref>,<xref rid=\"B35-ijerph-17-05423\" ref-type=\"bibr\">35</xref>,<xref rid=\"B36-ijerph-17-05423\" ref-type=\"bibr\">36</xref>]. The CVV used for the 2016–2017 cell-derived vaccines was passaged in eggs, which led to egg-adaptive mutations persisting and being present in the seed virus, and therefore also in the cell-based vaccine [<xref rid=\"B36-ijerph-17-05423\" ref-type=\"bibr\">36</xref>]. The A/H3N2-dominant 2017 season in Australia saw A/H3N2 isolates which were more likely to be ”low-reacting” to egg- than to cell-propagated reference viruses [<xref rid=\"B37-ijerph-17-05423\" ref-type=\"bibr\">37</xref>], suggesting that recombinant or cell-based vaccines could confer advantages over egg-derived vaccines in A/H3N2 dominant seasons with egg adaptation. In the 2017–2018 Northern Hemisphere season, an inactivated, cell culture-derived, quadrivalent vaccine, Flucelvax<sup>®</sup> (Seqirus USA Inc., Summit, NJ, USA) was manufactured for the first time using an exclusively cell-propagated A/H3N2 seed virus [<xref rid=\"B38-ijerph-17-05423\" ref-type=\"bibr\">38</xref>]. During this influenza season, A/H3N2 was the predominant circulating strain in the USA, and the overwhelming majority (93%) of these strains were antigenically similar to the cell-propagated reference virus used in the vaccine [<xref rid=\"B16-ijerph-17-05423\" ref-type=\"bibr\">16</xref>]. Importantly, the cell-derived vaccine was shown to have a relative VE of 10.7% as compared with an egg-derived quadrivalent vaccine in individuals aged 65 years or above for season 2017–2018 [<xref rid=\"B26-ijerph-17-05423\" ref-type=\"bibr\">26</xref>]. In the same season, Bruxvoort et al. estimated the absolute VE of cell- and egg-derived vaccines (against laboratory-confirmed hospitalization for any influenza) in a population <65 years of age to be 36% and −11%, respectively [<xref rid=\"B39-ijerph-17-05423\" ref-type=\"bibr\">39</xref>]. This difference was even more pronounced for A/H3N2, although confidence intervals were wide. For the same season, Klein et al. estimated absolute VE against influenza B to be 41% for cell- and 10% for egg-derived vaccines in a population 4–64 years of age [<xref rid=\"B40-ijerph-17-05423\" ref-type=\"bibr\">40</xref>]. Another study which compared the protective efficacy of a recombinant vaccine with that of a standard-dose, egg-derived, quadrivalent vaccine in adults (<italic>N</italic> = 9003), was performed during the A/H3N2-dominant 2014–2015 season (during which the effectiveness of many vaccines was particularly low due to antigenic mismatch between circulating and vaccine viruses) [<xref rid=\"B24-ijerph-17-05423\" ref-type=\"bibr\">24</xref>]. The probability of ILI in this study was 30% lower in recipients of recombinant vaccine as compared with egg-derived vaccine (95% CI 10–47, <italic>p</italic> = 0.006).</p><p>It is notable that the proportion of circulating A/H3N2 isolates showing antigenic similarity to A/H3N2 egg- and cell-propagated reference viruses was very similar for the three seasons from February 2016 to September 2017 (HI data); however, when analysed with a stricter definition of antigenic similarity (≤two-fold lower titre, <xref rid=\"ijerph-17-05423-t002\" ref-type=\"table\">Table 2</xref>) or by PRNA, differences between the egg- and cell-propagated datasets became apparent.</p><p>One factor which could have affected the assessments of antigenic similarity in these three seasons was the pool of virus strains that could be characterised by HI assay. For example, in the first season (2016 Southern Hemisphere), ~80% of isolates were clade 3C.2a (similar to the vaccine strain), while the other ~20% were 3C.3a. However, two-thirds of the 3C.2a viruses did not agglutinate red blood cells. Antisera raised against the cell-propagated reference virus recognised both 3C.2a and 3C.3a equally well, but antisera raised against the egg-propagated reference strain recognised the 3C.3a viruses better than the 3C.2a viruses (whereas 3C.2a was the dominant population) [<xref rid=\"B27-ijerph-17-05423\" ref-type=\"bibr\">27</xref>]. Therefore, the reported percentages from the HI assay based on the strains tested may not be truly representative of how well vaccines containing the egg-propagated reference viruses would cover circulating isolates and could be artificially high. Additionally, in two of the other seasons analysed (2005–2006 Northern Hemisphere and 2009–2010 Northern Hemisphere), a higher proportion of isolates showed antigenic similarity to egg- than with cell-propagated reference viruses. One possible explanation for this is that the same egg- and cell-propagated reference viruses were not available for these two seasons, and therefore data were selected for the genetically closest strain based on the phylogenetic tree analysis of the HA gene (also included in the WIC reports). These differences may have meant that the cell-propagated reference strain was less representative, leading to the anomalous findings for these two seasons.</p><p>This study had three main limitations. The first of these was an inability to assess all viruses accurately by HI assay, particularly in more recent seasons. Structural changes in many A/H3N2 viruses resulted in a decreased ability to agglutinate red blood cells, which made these viruses difficult to characterise by HI assay [<xref rid=\"B41-ijerph-17-05423\" ref-type=\"bibr\">41</xref>,<xref rid=\"B42-ijerph-17-05423\" ref-type=\"bibr\">42</xref>]; in addition, a NA mutation led to NA-mediated agglutination, which could have confounded the HI data (by somewhat masking the decreased haemagglutination inhibition) before oseltamivir was routinely used at the WIC from 2009 onwards [<xref rid=\"B42-ijerph-17-05423\" ref-type=\"bibr\">42</xref>,<xref rid=\"B43-ijerph-17-05423\" ref-type=\"bibr\">43</xref>]. The second limitation was that the overall number of isolates included in the analysis represented only a small proportion of all those received by the WHO collaborating centres over the influenza seasons analysed. In some cases, data on the egg- and cell culture-propagated version of the same reference virus strain were not available in the reports to be used for direct comparison. It should also be noted that the PRNA data were limited by small sample sizes for seasons prior to 2014. In addition, the PRNA data were limited by WIC processes for selecting isolates for testing, i.e., rather than being randomly selected, isolates were chosen specifically to include A/H3N2 clades circulating during a given season, but not necessarily in proportion to the dominance of each clade within the population (Dr John McCauley [WIC], personal communication, 2019). The third study limitation was the use of ferret rather than human sera in the PRNA and HI assays. Ferret sera is used as standard by all WHO collaborating centres for both assays; however, evidence suggests that the antigenic reactivity of viruses can vary depending on the origin of the sera used [<xref rid=\"B44-ijerph-17-05423\" ref-type=\"bibr\">44</xref>,<xref rid=\"B45-ijerph-17-05423\" ref-type=\"bibr\">45</xref>]. The potential impact of using ferret rather than human sera in the PRNA and HI assays should be taken into consideration when interpreting the results of any future research.</p><p>Some of the strengths of this study include the longitudinal nature of the analyses, which allows for the assessment of trends over multiple seasons, rather than individual seasons in isolation. The assessment of viruses by both PRNA and HI assays (rather than HI assay alone) is also a study strength, particularly for the more recent seasons where most viruses could not be assessed by HI assay. Further to previous studies and the existing literature [<xref rid=\"B46-ijerph-17-05423\" ref-type=\"bibr\">46</xref>], future research is warranted to evaluate the potential links between antigenic similarity, levels of VE, and burden of disease. A longitudinal dataset such as that presented in this report could be useful in the study of the relationships among the levels of antigenic similarity and VE in terms of influenza-associated hospitalizations or ILI incidence. Preferably, this would be assessed in a specific region or country such as the USA, which has a large population and high-quality surveillance procedures.</p></sec><sec sec-type=\"conclusions\" id=\"sec5-ijerph-17-05423\"><title>5. Conclusions</title><p>In summary, the results of this study demonstrate higher levels of antigenic similarity between cell-propagated A/H3N2 and B/Victoria reference viruses and circulating viruses, than between egg-propagated A/H3N2 and B/Victoria reference and circulating viruses in both Northern and Southern Hemispheres over a 16-year period; no similar trend was observed for A/H1N1 and B/Yamagata viruses. Further research is required in this area to better understand the impact of the more contemporary, non-egg-based technologies on influenza vaccine effectiveness.</p></sec></body><back><ack><title>Acknowledgments</title><p>The authors would like to thank the following for their contribution to the study: John McCauley, Director, Worldwide Influenza Centre (Francis Crick Institute), London, UK for advice on the interpretation of data; research staff at the Worldwide Influenza Centre, London, UK for use of data; research staff at the WHO National Influenza Centres for sample analysis; supporting contributors at Seqirus; research staff at Evidera, London, UK and Waltham, USA for data analysis and interpretation. The authors are also grateful to J Engelmoer (Sula Communications BV, Utrecht, The Netherlands) and J Stirling (OLC Bioscience Ltd., London, UK) for editorial assistance in the preparation of this manuscript.</p></ack><notes><title>Author Contributions</title><p>Conceptualization, S.R., P.S., J.v.B., M.H., B.L., I.I., K.K. and R.O.d.L.; Formal analysis, S.R., P.S., J.v.B., M.H., B.L., I.I., K.K. and R.O.d.L.; Methodology, S.R., P.S., J.v.B., M.H., B.L., I.I., K.K. and R.O.d.L.; Writing—review & editing, S.R., P.S., J.v.B., M.H., B.L., I.I., K.K. and R.O.d.L. All authors have read and agreed to the published version of the manuscript.</p></notes><notes><title>Funding</title><p>This research was funded by Seqirus UK Ltd.</p></notes><notes notes-type=\"COI-statement\"><title>Conflicts of Interest</title><p>S.R., P.S., J.v.B., M.H., and B.L. are permanent employees of the Seqirus group of companies. R.O.d.L. has received fees from GSK, Roche, Sanofi Pasteur, and Seqirus, for advising or conferences. K.K. and I.I. are employees of Evidera, which was commissioned by Seqirus to conduct key aspects of the reported research. The authors did not receive any form of payment from any source for writing this scientific report.</p></notes><ref-list><title>References</title><ref id=\"B1-ijerph-17-05423\"><label>1.</label><element-citation publication-type=\"web\"><person-group person-group-type=\"author\"><collab>World Health Organization</collab></person-group><article-title>Fact Sheets. 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Dis.</source><year>2018</year><volume>67</volume><fpage>327</fpage><lpage>333</lpage><pub-id pub-id-type=\"doi\">10.1093/cid/ciy097</pub-id><pub-id pub-id-type=\"pmid\">29471464</pub-id></element-citation></ref><ref id=\"B45-ijerph-17-05423\"><label>45.</label><element-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Liu</surname><given-names>F.</given-names></name><name><surname>Tzeng</surname><given-names>W.P.</given-names></name><name><surname>Horner</surname><given-names>L.</given-names></name><name><surname>Kamal</surname><given-names>R.P.</given-names></name><name><surname>Tatum</surname><given-names>H.R.</given-names></name><name><surname>Blanchard</surname><given-names>E.G.</given-names></name><name><surname>Xu</surname><given-names>X.</given-names></name><name><surname>York</surname><given-names>I.</given-names></name><name><surname>Tumpey</surname><given-names>T.M.</given-names></name><name><surname>Katz</surname><given-names>J.M.</given-names></name><etal/></person-group><article-title>Both immune priming and egg-adaptation in the vaccine influence antibody responses to circulating a(h1n1)pdm09 viruses following influenza vaccination in adults</article-title><source>J. Infect. Dis.</source><year>2018</year><pub-id pub-id-type=\"doi\">10.1093/infdis/jiy376</pub-id><pub-id pub-id-type=\"pmid\">29931203</pub-id></element-citation></ref><ref id=\"B46-ijerph-17-05423\"><label>46.</label><element-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Chen</surname><given-names>H.</given-names></name><name><surname>Alvarez</surname><given-names>J.J.S.</given-names></name><name><surname>Ng</surname><given-names>S.H.</given-names></name><name><surname>Nielsen</surname><given-names>R.</given-names></name><name><surname>Zhai</surname><given-names>W.</given-names></name></person-group><article-title>Passage adaptation correlates with the reduced efficacy of the influenza vaccine</article-title><source>Clin. Infect. Dis.</source><year>2018</year><volume>69</volume><fpage>1198</fpage><lpage>1204</lpage><pub-id pub-id-type=\"doi\">10.1093/cid/ciy1065</pub-id></element-citation></ref></ref-list></back><floats-group><fig id=\"ijerph-17-05423-f001\" orientation=\"portrait\" position=\"float\"><label>Figure 1</label><caption><p>Proportions of circulating A/H3N2 isolates antigenically similar to egg- and Madin–Darby canine kidney (MDCK) cell-propagated reference viruses, as assessed by haemagglutination inhibition (HI) assay. Antigenic similarity is defined as circulating virus titre no more than 4-fold lower than reference virus titre. Data are displayed as percentage of the total number of isolates tested. Studied period August 2002 to February 2018.</p></caption><graphic xlink:href=\"ijerph-17-05423-g001\"/></fig><fig id=\"ijerph-17-05423-f002\" orientation=\"portrait\" position=\"float\"><label>Figure 2</label><caption><p>Proportions of circulating A/H3N2 isolates antigenically similar to egg- and MDCK cell-propagated reference viruses, as assessed by plaque reduction neutralisation (PRNA) assay. Antigenic similarity is defined as circulating virus titre no more than 4-fold lower than reference virus titre. Data are displayed as percentage of the total number of isolates tested. Studied period February 2008 to February 2018.</p></caption><graphic xlink:href=\"ijerph-17-05423-g002\"/></fig><fig id=\"ijerph-17-05423-f003\" orientation=\"portrait\" position=\"float\"><label>Figure 3</label><caption><p>Proportions of circulating A/H1N1 isolates antigenically similar to egg- and MDCK cell-propagated reference viruses, as assessed by haemagglutination inhibition (HI) assay. Antigenic similarity is defined as circulating virus titre no more than 4-fold lower than reference virus titre. Data are displayed as percentage of the total number of isolates tested. Studied period September 2008 to September 2018.</p></caption><graphic xlink:href=\"ijerph-17-05423-g003\"/></fig><fig id=\"ijerph-17-05423-f004\" orientation=\"portrait\" position=\"float\"><label>Figure 4</label><caption><p>Proportions of circulating B/Victoria strain isolates antigenically similar to egg- and MDCK cell-propagated reference viruses, as assessed by haemagglutination inhibition (HI) assay. Antigenic similarity is defined as circulating virus titre no more than 4-fold lower than reference virus titre. Data are displayed as percentage of the total number of isolates tested. Studied period September 2008 to September 2018.</p></caption><graphic xlink:href=\"ijerph-17-05423-g004\"/></fig><fig id=\"ijerph-17-05423-f005\" orientation=\"portrait\" position=\"float\"><label>Figure 5</label><caption><p>Proportions of circulating B/Yamagata strain isolates antigenically similar to egg- and MDCK cell-propagated reference viruses, as assessed by haemagglutination inhibition (HI) assay. Antigenic similarity is defined as circulating virus titre no more than 4-fold lower than reference virus titre. Data are displayed as percentage of the total number of isolates tested. Studied period September 2008 to September 2018.</p></caption><graphic xlink:href=\"ijerph-17-05423-g005\"/></fig><table-wrap id=\"ijerph-17-05423-t001\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05423-t001_Table 1</object-id><label>Table 1</label><caption><p>Percentages of circulating A/H3N2 viruses which showed ≤ 2-fold and 4-fold differences (HI assay) to cell- and egg-propagated reference virus titres for the February 2016–February 2017 influenza seasons.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Influenza Season</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Reference Virus</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">≤2-Fold</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">4-Fold</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Feburary 2016–September 2016</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Cell-based</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">69%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">24%</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Feburary 2016–September 2016</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Egg-based</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">35%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">47%</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">September 2016–Feburary 2017</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Cell-based</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">52%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">45%</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">September 2016–Feburary 2017</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Egg-based</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">38%</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">48%</td></tr></tbody></table></table-wrap><table-wrap id=\"ijerph-17-05423-t002\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05423-t002_Table 2</object-id><label>Table 2</label><caption><p>Percentages of circulating 3C.2a and 3C.3a A/H3N2 viruses which showed ≤ 2-fold and 4-fold differences (HI assay) to cell- and egg-propagated reference virus titres for the February 2016–September 2016 influenza seasons.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Clade</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Reference Virus</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">≤2-Fold</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">4-Fold</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3C.2a</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Cell-based</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">60%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">31%</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3C.2a</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Egg-based</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">19%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">50%</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3C.3a</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Cell-based</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">76%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">20%</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3C.3a</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Egg-based</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">64%</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">36%</td></tr></tbody></table></table-wrap></floats-group></article>\n"
]
[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"review-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Int J Mol Sci</journal-id><journal-id journal-id-type=\"iso-abbrev\">Int J Mol Sci</journal-id><journal-id journal-id-type=\"publisher-id\">ijms</journal-id><journal-title-group><journal-title>International Journal of Molecular Sciences</journal-title></journal-title-group><issn pub-type=\"epub\">1422-0067</issn><publisher><publisher-name>MDPI</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32718006</article-id><article-id pub-id-type=\"pmc\">PMC7432083</article-id><article-id pub-id-type=\"doi\">10.3390/ijms21155223</article-id><article-id pub-id-type=\"publisher-id\">ijms-21-05223</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Review</subject></subj-group></article-categories><title-group><article-title>Primary Humoral Immune Deficiencies: Overlooked Mimickers of Chronic Immune-Mediated Gastrointestinal Diseases in Adults</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Malesza</surname><given-names>Ida Judyta</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05223\">1</xref><xref ref-type=\"author-notes\" rid=\"fn1-ijms-21-05223\">†</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0003-1183-5938</contrib-id><name><surname>Malesza</surname><given-names>Michał</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05223\">1</xref><xref rid=\"c1-ijms-21-05223\" ref-type=\"corresp\">*</xref><xref ref-type=\"author-notes\" rid=\"fn1-ijms-21-05223\">†</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0001-5122-8003</contrib-id><name><surname>Krela-Kaźmierczak</surname><given-names>Iwona</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05223\">1</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0003-2603-1377</contrib-id><name><surname>Zielińska</surname><given-names>Aleksandra</given-names></name><xref ref-type=\"aff\" rid=\"af2-ijms-21-05223\">2</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0002-9737-6017</contrib-id><name><surname>Souto</surname><given-names>Eliana B.</given-names></name><xref ref-type=\"aff\" rid=\"af3-ijms-21-05223\">3</xref><xref ref-type=\"aff\" rid=\"af4-ijms-21-05223\">4</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0002-3647-5070</contrib-id><name><surname>Dobrowolska</surname><given-names>Agnieszka</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05223\">1</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0001-9306-5038</contrib-id><name><surname>Eder</surname><given-names>Piotr</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05223\">1</xref></contrib></contrib-group><aff id=\"af1-ijms-21-05223\"><label>1</label>Department of Gastroenterology, Dietetics and Internal Diseases, Poznan University of Medical Sciences, 60-355 Poznan, Poland; <email>[email protected]</email> (I.J.M.); <email>[email protected]</email> (I.K.-K.); <email>[email protected]</email> (A.D.); <email>[email protected]</email> (P.E.)</aff><aff id=\"af2-ijms-21-05223\"><label>2</label>Institute of Human Genetics, Polish Academy of Sciences Poznan, 60-479 Poznan, Poland; <email>[email protected]</email></aff><aff id=\"af3-ijms-21-05223\"><label>3</label>Department of Pharmaceutical Technology, Faculty of Pharmacy, University of Coimbra, Pólo das Ciências da Saúde, Azinhaga de Santa Comba, 3000-548 Coimbra, Portugal; <email>[email protected]</email></aff><aff id=\"af4-ijms-21-05223\"><label>4</label>CEB—Centre of Biological Engineering, University of Minho, Campus de Gualtar, 4710-057 Braga, Portugal</aff><author-notes><corresp id=\"c1-ijms-21-05223\"><label>*</label>Correspondence: <email>[email protected]</email>; Tel.: +48-8691-343; Fax: +48-8691-686</corresp><fn id=\"fn1-ijms-21-05223\"><label>†</label><p>These authors contributed equally to this work: Ida Judyta Malesza and Michał Malesza.</p></fn></author-notes><pub-date pub-type=\"epub\"><day>23</day><month>7</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><month>8</month><year>2020</year></pub-date><volume>21</volume><issue>15</issue><elocation-id>5223</elocation-id><history><date date-type=\"received\"><day>30</day><month>6</month><year>2020</year></date><date date-type=\"accepted\"><day>21</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>© 2020 by the authors.</copyright-statement><copyright-year>2020</copyright-year><license license-type=\"open-access\"><license-p>Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>).</license-p></license></permissions><abstract><p>In recent years, the incidence of immune-mediated gastrointestinal disorders, including celiac disease (CeD) and inflammatory bowel disease (IBD), is increasingly growing worldwide. This generates a need to elucidate the conditions that may compromise the diagnosis and treatment of such gastrointestinal disorders. It is well established that primary immunodeficiencies (PIDs) exhibit gastrointestinal manifestations and mimic other diseases, including CeD and IBD. PIDs are often considered pediatric ailments, whereas between 25 and 45% of PIDs are diagnosed in adults. The most common PIDs in adults are the selective immunoglobulin A deficiency (SIgAD) and the common variable immunodeficiency (CVID). A trend to autoimmunity occurs, while gastrointestinal disorders are common in both diseases. Besides, the occurrence of CeD and IBD in SIgAD/CVID patients is significantly higher than in the general population. However, some differences concerning diagnostics and management between enteropathy/colitis in PIDs, as compared to idiopathic forms of CeD/IBD, have been described. There is an ongoing discussion whether CeD and IBD in CVID patients should be considered a true CeD and IBD or just CeD-like and IBD-like diseases. This review addresses the current state of the art of the most common primary immunodeficiencies in adults and co-occurring CeD and IBD.</p></abstract><kwd-group><kwd>primary immunodeficiency</kwd><kwd>selective IgA deficiency</kwd><kwd>common variable immunodeficiency</kwd><kwd>celiac disease</kwd><kwd>inflammatory bowel disease</kwd><kwd>Crohn’s disease</kwd><kwd>ulcerative colitis</kwd></kwd-group></article-meta></front><body><sec sec-type=\"intro\" id=\"sec1-ijms-21-05223\"><title>1. Introduction</title><p>Autoimmune diseases of the gastrointestinal (GI) tract are increasingly growing worldwide over the last decades. They concern both inflammatory bowel disease (IBD) and celiac disease (CeD) [<xref rid=\"B1-ijms-21-05223\" ref-type=\"bibr\">1</xref>,<xref rid=\"B2-ijms-21-05223\" ref-type=\"bibr\">2</xref>]. This seems to be due to a true rise in incidence rather than increased awareness and detection [<xref rid=\"B3-ijms-21-05223\" ref-type=\"bibr\">3</xref>,<xref rid=\"B4-ijms-21-05223\" ref-type=\"bibr\">4</xref>,<xref rid=\"B5-ijms-21-05223\" ref-type=\"bibr\">5</xref>].</p><p>Rising morbidity of both diseases, on the one hand, forces physicians to increase alertness concerning GI symptoms, and on the other hand, it encourages researchers to look for conditions, that can affect diagnostic process and management. The increasing body of evidence that primary immunodeficiency (PID) can complicate diagnostics of CeD [<xref rid=\"B3-ijms-21-05223\" ref-type=\"bibr\">3</xref>] and mimic IBD [<xref rid=\"B6-ijms-21-05223\" ref-type=\"bibr\">6</xref>] implicates the need for a comprehensive review of this topic, especially when several studies have shown that autoimmune manifestations are the second most common manifestation of PIDs after infections [<xref rid=\"B7-ijms-21-05223\" ref-type=\"bibr\">7</xref>,<xref rid=\"B8-ijms-21-05223\" ref-type=\"bibr\">8</xref>].</p><p>PIDs are usually considered as pediatric ailments and awareness of the problem among paediatricians is relatively high, whereas between 25 and 45% of all PIDs are diagnosed in adulthood [<xref rid=\"B9-ijms-21-05223\" ref-type=\"bibr\">9</xref>]. Over the years, an increasing number of diagnoses of PIDs are being made in adults, and recent studies estimate that up to 1:1200 people in the United States are diagnosed with some form of primary immune deficiency [<xref rid=\"B10-ijms-21-05223\" ref-type=\"bibr\">10</xref>]. Besides, the vast majority of adult patients with PIDs are not diagnosed or treated early in their course [<xref rid=\"B11-ijms-21-05223\" ref-type=\"bibr\">11</xref>], possibly due to a lack of up-to-date knowledge and low awareness of the occurrence of PIDs in adults among physicians [<xref rid=\"B9-ijms-21-05223\" ref-type=\"bibr\">9</xref>,<xref rid=\"B12-ijms-21-05223\" ref-type=\"bibr\">12</xref>]. Moreover, some researchers suggest that autoimmune disorders are developed in a course of PIDs as patients get older and, for this reason, autoimmunities are more common in adults than in children [<xref rid=\"B9-ijms-21-05223\" ref-type=\"bibr\">9</xref>], making this topic even more relevant in terms of CeD and IBD.</p><p>According to Agarwal and Mayer, if patients present atypical GI symptoms or are refractory to conventional therapy, the underlying primary immune disorder should be taken into consideration to initiate appropriate treatment [<xref rid=\"B13-ijms-21-05223\" ref-type=\"bibr\">13</xref>]. Additionally, a very severe course of disease and need of multiple immunosuppressive agents, or total parenteral nutrition, could be indicative for PID [<xref rid=\"B14-ijms-21-05223\" ref-type=\"bibr\">14</xref>]. The recent literature provides an increasing number of publications on IBD related to PIDs; however, they are mainly focused on the child population and concerning very early onset IBD with underlying monogenic diseases [<xref rid=\"B15-ijms-21-05223\" ref-type=\"bibr\">15</xref>]. Not much data on IBD related to PIDs in adults can be found.</p><p>Among all PIDs, more than 50% make up abnormalities in humoral immunity [<xref rid=\"B16-ijms-21-05223\" ref-type=\"bibr\">16</xref>], making immunoglobulin deficiency the most common PID in children and also among adults. In the latter group, selective immunoglobulin A deficiency (SIgAD) and common variable immunodeficiency (CVID) are the most common diagnoses [<xref rid=\"B9-ijms-21-05223\" ref-type=\"bibr\">9</xref>].</p><p>This review aims to present the up-to-date knowledge on the incidence, pathophysiology, symptoms, diagnostics, and management of autoimmune GI diseases, specifically CeD and IBD, in patients with underlying SIgAD or CVID. Moreover, this review is focused on differences between the classic forms of the above-mentioned diseases and those observed in patients with compromised humoral immunity (as shown in <xref ref-type=\"fig\" rid=\"ijms-21-05223-f001\">Figure 1</xref>), to estimate whether we are facing a spectrum of one disease or different diseases characterized by a similar clinical manifestation.</p></sec><sec id=\"sec2-ijms-21-05223\"><title>2. Selective Immunoglobulin A Deficiency</title><sec id=\"sec2dot1-ijms-21-05223\"><title>2.1. SIgAD: Epidemiology and Diagnostic Criteria</title><p>SIgAD is the most common PID and, at the same time, the most common immunoglobulin deficiency. Its prevalence varies depending on the ethnicities and regions across the world. It ranges from 1:142 in Caucasians (however, in a great majority of European countries, the estimated occurrence is 1:600) to 1:18 550 in Japanese blood donors [<xref rid=\"B17-ijms-21-05223\" ref-type=\"bibr\">17</xref>,<xref rid=\"B18-ijms-21-05223\" ref-type=\"bibr\">18</xref>,<xref rid=\"B19-ijms-21-05223\" ref-type=\"bibr\">19</xref>]. There were many attempts to establish the prevalence of SIgAD through national and international surveys and registries; however, the true prevalence is certainly underestimated. The main reason is the asymptomatic course of the disease that occurs in 65–75% [<xref rid=\"B20-ijms-21-05223\" ref-type=\"bibr\">20</xref>] and up to 85–90% [<xref rid=\"B21-ijms-21-05223\" ref-type=\"bibr\">21</xref>] patients, depending on the author. Additionally, different criteria for diagnosing SIgAD were used by researchers in different studies. Since 1999, according to Pan-American Group for Immunodeficiency (PAGID) and European Society for Immunodeficiencies (ESID), SIgAD is diagnosed in an individual older than 4 years with serum IgA level <7 mg/dL but a normal IgG and IgM serum level and with the exclusion of other causes of hypogammaglobulinemia, as well as with the normal response to vaccinations [<xref rid=\"B22-ijms-21-05223\" ref-type=\"bibr\">22</xref>]. These criteria have not been changed in the latest ESID recommendations from 2019 (<xref rid=\"ijms-21-05223-t001\" ref-type=\"table\">Table 1</xref>) [<xref rid=\"B23-ijms-21-05223\" ref-type=\"bibr\">23</xref>]. Despite this, some studies used a criterion of the IgA serum level as less than 5 mg/dL [<xref rid=\"B24-ijms-21-05223\" ref-type=\"bibr\">24</xref>]. All of this contributes to great difficulties in estimating the real occurrence of SIgAD (presented in <xref ref-type=\"fig\" rid=\"ijms-21-05223-f002\">Figure 2</xref>).</p><p>As mentioned above, country of origin and ethnicity affect the prevalence of SIgAD; it is far more common in Caucasians than in Asians [<xref rid=\"B19-ijms-21-05223\" ref-type=\"bibr\">19</xref>], as genetic factors likely play a role in the pathogenesis of SIgAD. It occurs far more often in populations with a high percentage of consanguineous marriages; in some studies, even one-third of patients were from consanguineous unions [<xref rid=\"B25-ijms-21-05223\" ref-type=\"bibr\">25</xref>]. SIgAD also exhibits familial aggregation, because 20–25% of SIgAD patients have a family history of SIgAD or CVID [<xref rid=\"B26-ijms-21-05223\" ref-type=\"bibr\">26</xref>].</p></sec><sec id=\"sec2dot2-ijms-21-05223\"><title>2.2. SIgAD: Etiology</title><p>Up to date, multiple possible mechanisms for this PID have been suggested; however, the exact pathogenesis remains unknown. Many researchers have investigated different mechanisms underlying SIgAD. However, none of them have exhausted the subject on their own, as a complex combination of various causes potentially contributes to SIgAD development, or rather various causes lead to a common clinical manifestation.</p><p>SIgAD can be associated not only with an intrinsic B-cell lymphocyte defect but also T-cell dysfunction and impairment in cytokine networks [<xref rid=\"B27-ijms-21-05223\" ref-type=\"bibr\">27</xref>,<xref rid=\"B28-ijms-21-05223\" ref-type=\"bibr\">28</xref>]. However, a fundamental defect in SIgAD seems to be impaired maturation of IgA-bearing B-cells into IgA-secreting plasma cells [<xref rid=\"B29-ijms-21-05223\" ref-type=\"bibr\">29</xref>]. Moreover, studies have proven that IgA-deficient patients have defects in the process of IgA class switching recombination (CSR), production, as well as secretion of IgA, and long-term survival of IgA-switched memory B-cells and plasma cells, possibly due to an increased rate of apoptosis [<xref rid=\"B30-ijms-21-05223\" ref-type=\"bibr\">30</xref>,<xref rid=\"B31-ijms-21-05223\" ref-type=\"bibr\">31</xref>,<xref rid=\"B32-ijms-21-05223\" ref-type=\"bibr\">32</xref>]. The latter leads to a paucity of B-cells secreting IgA both in the plasma and GI tract mucosa, in contrary to a healthy population [<xref rid=\"B33-ijms-21-05223\" ref-type=\"bibr\">33</xref>,<xref rid=\"B34-ijms-21-05223\" ref-type=\"bibr\">34</xref>]. The defect is possibly present in stem cells, as cases of transfer of SIgAD by bone marrow transplantation have been reported [<xref rid=\"B35-ijms-21-05223\" ref-type=\"bibr\">35</xref>].</p><p>Many authors suggested a role of impaired T-cell function in the development of SIgAD, as possibly defective antibody production or secretion may be due to dysfunction or decreased activity of different subpopulations of T-cells [<xref rid=\"B30-ijms-21-05223\" ref-type=\"bibr\">30</xref>,<xref rid=\"B36-ijms-21-05223\" ref-type=\"bibr\">36</xref>]. However, most of the studies concerning the role of T-cells in the pathogenesis of SIgAD are inconclusive. Recent outcomes indicate a defect in regulatory T-cells (Tregs) in SIgAD patients with autoimmunity [<xref rid=\"B28-ijms-21-05223\" ref-type=\"bibr\">28</xref>,<xref rid=\"B37-ijms-21-05223\" ref-type=\"bibr\">37</xref>]. On the other hand, Lemarquis et al. reported no differences between SIgAD patients and healthy controls, nor in Tregs, in any of the measured T-cell subpopulations (CD3+, CD4+, CD8+, naïve, memory, and differentiated T-cells) [<xref rid=\"B38-ijms-21-05223\" ref-type=\"bibr\">38</xref>].</p><p>Other important elements that likely account for the development of SIgAD are cytokines and their receptor milieu, as B-cell stimulation via this network is responsible for B-cell differentiation and CSR. Therefore, a combination of abnormalities in the cytokine pattern releasing at local sites of B-cell proliferation, or cytokine kinetics, can contribute to the failure of IgA production in SIgAD patients [<xref rid=\"B30-ijms-21-05223\" ref-type=\"bibr\">30</xref>]. This involves transforming growth factor-beta (TGB-β) and downstream transcription factors, which indicates a key role in this process [<xref rid=\"B39-ijms-21-05223\" ref-type=\"bibr\">39</xref>]. Another receptor participating in the process is CD40, which stimulates CSR by the T-cell-mediated pathway. T-cell-independent IgA CSR engages other receptors, cytokines, and transcription factors, which includes inter alia toll-like receptor, B-cell receptor (BCR), nitric oxide, retinoic acid, IL−6, TACI (transmembrane activator and CAML interactor), BAFF (B-cell activating factor), A proliferation-inducing ligand (APRIL), and thymic stromal lymphopoietin [<xref rid=\"B30-ijms-21-05223\" ref-type=\"bibr\">30</xref>]. Defects in all the aforementioned particles can account for SIgAD. Furthermore, abnormalities in several cytokines, including IL−4, IL−6, IL−7, IL−10, and most newly IL−21, has been found in SIgAD [<xref rid=\"B29-ijms-21-05223\" ref-type=\"bibr\">29</xref>,<xref rid=\"B40-ijms-21-05223\" ref-type=\"bibr\">40</xref>,<xref rid=\"B41-ijms-21-05223\" ref-type=\"bibr\">41</xref>]. This cytokine profile may be changed due to the abnormalities of B-, T-, and dendritic cells (DCs) [<xref rid=\"B30-ijms-21-05223\" ref-type=\"bibr\">30</xref>].</p><p>It is established that the presence of proper MHC molecules plays an important role in presenting antigen from B-cell to T follicular helper cell (Tfh) and the consequent cytokine production. So, it is not surprising that certain MHC haplotypes are associated with an increased risk of SIgAD. This includes <italic>HLA-B8-DR3-DQ2</italic> (45% of SIgAD patients compared to 16% of the general population, this haplotype predisposes strongly to autoimmunity [<xref rid=\"B42-ijms-21-05223\" ref-type=\"bibr\">42</xref>,<xref rid=\"B43-ijms-21-05223\" ref-type=\"bibr\">43</xref>]), <italic>HLA-DR7-DQ2</italic>, and <italic>HLA-B14-DR1-DQ5</italic> haplotypes, which are considered strong risk factors for SIgAD, while <italic>HLA-DR15-DQ6</italic> has a protective influence against SIgAD [<xref rid=\"B44-ijms-21-05223\" ref-type=\"bibr\">44</xref>,<xref rid=\"B45-ijms-21-05223\" ref-type=\"bibr\">45</xref>]. However, there is a suggestion that only familial cases of SIgAD have an HLA genetic-associated background, which is not the case in “sporadic” forms of SIgAD [<xref rid=\"B46-ijms-21-05223\" ref-type=\"bibr\">46</xref>].</p><p>Except the association with certain HLA haplotypes, the occurrence of SIgAD can also be related to monogenic mutations and cytogenetic abnormalities. The former includes genes involved in multiple different immunity aspects, for example, genes regulating cellular and humoral immunity (e.g., <italic>JAK3</italic>, <italic>DOCK8</italic>, <italic>LRBA</italic>, <italic>DCLRE1C</italic>, <italic>RAG1</italic>, and <italic>CD27</italic>), genes predominantly associated with antibody deficiencies (e.g., <italic>TACI</italic>, <italic>BTK</italic>, <italic>TWEAK</italic>, <italic>MSH6</italic>, <italic>PIK3R1</italic>, <italic>MSH2</italic>, and <italic>CARD11</italic>), and in genes associated with phagocytic defects (e.g., <italic>RAC2</italic>, <italic>CYBB</italic>, <italic>NCF1</italic>, and <italic>SBDS</italic>) [<xref rid=\"B40-ijms-21-05223\" ref-type=\"bibr\">40</xref>,<xref rid=\"B47-ijms-21-05223\" ref-type=\"bibr\">47</xref>]. Literature indicates a greater number of monogenic mutations, which account for SIgAD; however, this is beyond the scope of this review. Chromosome abnormalities and cytogenetic defects comprise monosomy 4p, trisomy 8, trisomy 10p, translocation of 10q to 4p, 17p11.2 deletions, 18q-syndrome, 18p deletions, trisomy 21, monosomy 22, and 22q11.2 deletion syndrome [<xref rid=\"B48-ijms-21-05223\" ref-type=\"bibr\">48</xref>,<xref rid=\"B49-ijms-21-05223\" ref-type=\"bibr\">49</xref>,<xref rid=\"B50-ijms-21-05223\" ref-type=\"bibr\">50</xref>,<xref rid=\"B51-ijms-21-05223\" ref-type=\"bibr\">51</xref>,<xref rid=\"B52-ijms-21-05223\" ref-type=\"bibr\">52</xref>,<xref rid=\"B53-ijms-21-05223\" ref-type=\"bibr\">53</xref>].</p><p>It is suggested that secretory IgA promotes the development of a “healthy” microbiota [<xref rid=\"B54-ijms-21-05223\" ref-type=\"bibr\">54</xref>,<xref rid=\"B55-ijms-21-05223\" ref-type=\"bibr\">55</xref>]. Besides, alterations in the gut microbiota composition have been described in patients with SIgAD [<xref rid=\"B56-ijms-21-05223\" ref-type=\"bibr\">56</xref>]; however, it is difficult to establish whether it contributes to the development of immunoglobulin deficiency, or rather it is a result of impaired function of the mucosal barrier in a condition of the paucity of secretory IgA. Nevertheless, patients with SIgAD are expected to present more severe GI problems, if defects are present in the microbiota [<xref rid=\"B30-ijms-21-05223\" ref-type=\"bibr\">30</xref>]. This is possibly due to fact that IgM, which increases compensatorily in the gut mucosa, is less specific than secretory IgA [<xref rid=\"B56-ijms-21-05223\" ref-type=\"bibr\">56</xref>].</p></sec><sec id=\"sec2dot3-ijms-21-05223\"><title>2.3. SIgAD: Symptoms and Clinical Course</title><p>As aforementioned, the great majority of patients diagnosed with SIgAD are asymptomatic; however, as expected in PID, some may develop various clinical symptoms. The presentation of SIgAD can be classified according to major symptoms into five phenotypes: Asymptomatic, minor infections, allergies, autoimmunity, and severe [<xref rid=\"B57-ijms-21-05223\" ref-type=\"bibr\">57</xref>]. In terms of clinical manifestation, SIgAD patients can also be split into distinct groups: Pulmonary diseases, allergies, autoimmunity, GI disorders, and malignancy [<xref rid=\"B40-ijms-21-05223\" ref-type=\"bibr\">40</xref>].</p><p>Asymptomatic patients are usually co-incidentally diagnosed during blood donation or routine laboratory evaluation. The mostly asymptomatic course of SIgAD is not fully understood yet; however, it is suggested that it is due to the fact that in this group, a compensatory increase in IgM in the GI mucosa and a rise in the production of IgG occurs [<xref rid=\"B48-ijms-21-05223\" ref-type=\"bibr\">48</xref>].</p><p>Infections are the most common symptomatic manifestations, more frequently in the upper respiratory tract infections [<xref rid=\"B29-ijms-21-05223\" ref-type=\"bibr\">29</xref>]. These are mostly caused by encapsulated bacteria, e.g., <italic>Haemophilus influenzae</italic> and <italic>Streptococcus pneumoniae</italic> [<xref rid=\"B21-ijms-21-05223\" ref-type=\"bibr\">21</xref>]. It seems important that infections are more likely to occur and have a more severe course in SIgAD patients with concurrent IgG2 subclass deficiency [<xref rid=\"B58-ijms-21-05223\" ref-type=\"bibr\">58</xref>,<xref rid=\"B59-ijms-21-05223\" ref-type=\"bibr\">59</xref>].</p><p>Allergies are also associated with SIgAD, as it has been estimated that 25–50% of SIgAD patients are recognized by an evaluation of allergic diseases [<xref rid=\"B29-ijms-21-05223\" ref-type=\"bibr\">29</xref>]. Among others, an increased occurrence of allergic conjunctivitis, rhinitis, urticaria, atopic eczema, food allergy, and bronchial asthma have been reported in individuals with SIgAD [<xref rid=\"B20-ijms-21-05223\" ref-type=\"bibr\">20</xref>,<xref rid=\"B29-ijms-21-05223\" ref-type=\"bibr\">29</xref>,<xref rid=\"B60-ijms-21-05223\" ref-type=\"bibr\">60</xref>].</p><p>As IgA comprises two-thirds of all produced immunoglobulins and has an important function in humoral and mucosal immunity [<xref rid=\"B61-ijms-21-05223\" ref-type=\"bibr\">61</xref>], it is understandable why GI symptoms are observed in SIgAD patients. However, even though secretory IgA is the major antibody in the intestinal mucosa, the prevalence of GI disorders in patients with SIgAD seems not as high as it would be expected [<xref rid=\"B62-ijms-21-05223\" ref-type=\"bibr\">62</xref>]. Giardiasis, nodular lymphoid hyperplasia, CeD, pernicious anaemia, chronic hepatitis, biliary cirrhosis, and IBD are mentioned most frequently in the literature concerning comorbidity of GI diseases and IgA deficiency [<xref rid=\"B6-ijms-21-05223\" ref-type=\"bibr\">6</xref>,<xref rid=\"B40-ijms-21-05223\" ref-type=\"bibr\">40</xref>].</p><p>One of the most intriguing features of SIgAD is its association with multiple autoimmune diseases. Besides, it has been indicated that between 26% and 32% of patients with SIgAD are affected by an autoimmune condition [<xref rid=\"B25-ijms-21-05223\" ref-type=\"bibr\">25</xref>,<xref rid=\"B37-ijms-21-05223\" ref-type=\"bibr\">37</xref>,<xref rid=\"B63-ijms-21-05223\" ref-type=\"bibr\">63</xref>]. Interestingly, this percentage is lower among the pediatric population, perhaps due to fact that autoimmune disorders require a longer time to develop [<xref rid=\"B57-ijms-21-05223\" ref-type=\"bibr\">57</xref>,<xref rid=\"B64-ijms-21-05223\" ref-type=\"bibr\">64</xref>,<xref rid=\"B65-ijms-21-05223\" ref-type=\"bibr\">65</xref>,<xref rid=\"B66-ijms-21-05223\" ref-type=\"bibr\">66</xref>,<xref rid=\"B67-ijms-21-05223\" ref-type=\"bibr\">67</xref>], and usually manifest in patients in the second decade of life or later [<xref rid=\"B57-ijms-21-05223\" ref-type=\"bibr\">57</xref>]. Autoimmune disorders, which are observed in patients with SIgAD with a higher frequency than among the healthy population, and include, inter alia, idiopathic thrombocytopenic purpura, autoimmune haemolytic anaemia, Graves’ disease, type 1 diabetes mellitus, thyroiditis, CeD, IBD, and systemic lupus erythematosus [<xref rid=\"B68-ijms-21-05223\" ref-type=\"bibr\">68</xref>,<xref rid=\"B69-ijms-21-05223\" ref-type=\"bibr\">69</xref>]. Few hypotheses explaining the association between SIgAD and autoimmunity have been suggested, which are described thoroughly by Odineal and Gershwin [<xref rid=\"B68-ijms-21-05223\" ref-type=\"bibr\">68</xref>]. The first one indicates that certain HLA phenotypes, including phenotype 8.1 (<italic>HLA-B8-DR3-DQ2</italic>), favour the development of both SIgAD and autoimmune diseases [<xref rid=\"B70-ijms-21-05223\" ref-type=\"bibr\">70</xref>]. A second hypothesis suggests that the same B-cell, T-cell, or cytokine abnormalities may underline the development of SIgAD and autoimmunity, as a deficiency of Tregs is observed in both [<xref rid=\"B28-ijms-21-05223\" ref-type=\"bibr\">28</xref>,<xref rid=\"B37-ijms-21-05223\" ref-type=\"bibr\">37</xref>]. However, as discussed above, this theory is controversial, as different authors have obtained contradictory results [<xref rid=\"B28-ijms-21-05223\" ref-type=\"bibr\">28</xref>,<xref rid=\"B38-ijms-21-05223\" ref-type=\"bibr\">38</xref>,<xref rid=\"B71-ijms-21-05223\" ref-type=\"bibr\">71</xref>]. A third proposed mechanism suggests that monogenic mutations can predispose to SIgAD and autoimmune disorders. For example, a similar mutation of <italic>CTLA4-ICOS</italic> is present in SIgAD, CVID, and CeD [<xref rid=\"B72-ijms-21-05223\" ref-type=\"bibr\">72</xref>]. Bronson et al. reported a few monogenic associations between IgA deficiency and autoimmune manifestations [<xref rid=\"B73-ijms-21-05223\" ref-type=\"bibr\">73</xref>]. Last, but not least, IgA has a key role in the mucosal barrier, as it protects from entering foreign pathogens and antigens. In the condition of lowered secretory IgA, the mucosa possibly has greater permeability. This may cause increased sensitisation of B- and T-cells, and can place patients at an increased risk of autoimmune disorders [<xref rid=\"B20-ijms-21-05223\" ref-type=\"bibr\">20</xref>,<xref rid=\"B25-ijms-21-05223\" ref-type=\"bibr\">25</xref>,<xref rid=\"B60-ijms-21-05223\" ref-type=\"bibr\">60</xref>,<xref rid=\"B64-ijms-21-05223\" ref-type=\"bibr\">64</xref>,<xref rid=\"B74-ijms-21-05223\" ref-type=\"bibr\">74</xref>,<xref rid=\"B75-ijms-21-05223\" ref-type=\"bibr\">75</xref>,<xref rid=\"B76-ijms-21-05223\" ref-type=\"bibr\">76</xref>,<xref rid=\"B77-ijms-21-05223\" ref-type=\"bibr\">77</xref>]. Besides, it is established that IgA has anti-inflammatory properties, likely via the clearance of pathogens and immune complexes [<xref rid=\"B25-ijms-21-05223\" ref-type=\"bibr\">25</xref>] and interactions with certain receptors, such as FcαR (subgroup of Fc receptors). All these contribute to the downregulation of immune pathways, which propagate inflammation [<xref rid=\"B67-ijms-21-05223\" ref-type=\"bibr\">67</xref>].</p><p>Although malignancies occur more often in many PIDs, likely as an expression of impaired immunological control, it is not established whether it is a case for SIgAD, as data provided by the literature in this topic is not conclusive [<xref rid=\"B78-ijms-21-05223\" ref-type=\"bibr\">78</xref>,<xref rid=\"B79-ijms-21-05223\" ref-type=\"bibr\">79</xref>,<xref rid=\"B80-ijms-21-05223\" ref-type=\"bibr\">80</xref>]. In SIgAD, cases of carcinomas (particularly adenocarcinoma of the stomach) and lymphomas (usually of B-cell origin) have been reported [<xref rid=\"B80-ijms-21-05223\" ref-type=\"bibr\">80</xref>,<xref rid=\"B81-ijms-21-05223\" ref-type=\"bibr\">81</xref>,<xref rid=\"B82-ijms-21-05223\" ref-type=\"bibr\">82</xref>,<xref rid=\"B83-ijms-21-05223\" ref-type=\"bibr\">83</xref>]. A graphic summary concerning SIgAD is illustrated below in <xref ref-type=\"fig\" rid=\"ijms-21-05223-f002\">Figure 2</xref>.</p></sec></sec><sec id=\"sec3-ijms-21-05223\"><title>3. Common Variable Immunodeficiency</title><sec id=\"sec3dot1-ijms-21-05223\"><title>3.1. CVID: Definition and Epidemiology</title><p>CVID is an umbrella name for the most common symptomatic PID of heterogeneous etiology [<xref rid=\"B84-ijms-21-05223\" ref-type=\"bibr\">84</xref>,<xref rid=\"B85-ijms-21-05223\" ref-type=\"bibr\">85</xref>], with an estimated incidence of 1:10,000 to 1:50,000 in the Middle East and Caucasian population [<xref rid=\"B86-ijms-21-05223\" ref-type=\"bibr\">86</xref>,<xref rid=\"B87-ijms-21-05223\" ref-type=\"bibr\">87</xref>,<xref rid=\"B88-ijms-21-05223\" ref-type=\"bibr\">88</xref>]. It is less frequently described in the African and Asian population, possibly due to differences in the availability of appropriate diagnostics, registry data, and awareness of PID [<xref rid=\"B89-ijms-21-05223\" ref-type=\"bibr\">89</xref>]. Similarly to SIgAD, CVID exhibits familial clustering, as about 15% of individuals have first-degree relatives with SIgAD [<xref rid=\"B90-ijms-21-05223\" ref-type=\"bibr\">90</xref>], and the occurrence of CVID is increased in populations with a high rate of consanguinity marriages [<xref rid=\"B91-ijms-21-05223\" ref-type=\"bibr\">91</xref>,<xref rid=\"B92-ijms-21-05223\" ref-type=\"bibr\">92</xref>].</p><p>The diagnosis of CVID is usually made between the ages of 20 and 40 and only about 20% are diagnosed under the age of 21, so it can be considered as adult-onset PID [<xref rid=\"B93-ijms-21-05223\" ref-type=\"bibr\">93</xref>]. Besides, a diagnostic delay of 6 to 8 years after the first presenting manifestation often occurs [<xref rid=\"B94-ijms-21-05223\" ref-type=\"bibr\">94</xref>].</p></sec><sec id=\"sec3dot2-ijms-21-05223\"><title>3.2. CVID: Diagnostics and Criteria</title><p>CVID is considered as a heterogeneous group of PID, characterized primarily by hypogammaglobulinemia, increased susceptibility to infections or other symptoms associated with PID, and impaired responses to infections or vaccinations [<xref rid=\"B95-ijms-21-05223\" ref-type=\"bibr\">95</xref>,<xref rid=\"B96-ijms-21-05223\" ref-type=\"bibr\">96</xref>]. As CVID has a highly heterogeneous character in terms of both immunological features and clinical manifestations, accurate diagnostic criteria are of utmost importance. However, they vary slightly according to different sources. The International Consensus Document on CVID published in 2016 allows the diagnosis of CVID in patients with IgG levels at least two SD below the age-appropriate reference and decreased IgA or IgM serum levels with poor or absent antibody response to vaccination. The patient must be at least 4 years old and secondary cause of hypogammaglobulinemia has to be excluded—this applies to known genetic defects [<xref rid=\"B86-ijms-21-05223\" ref-type=\"bibr\">86</xref>]. The ESID criteria seem to be more complex, as, except the aforementioned immunological features, they also include the presence of symptoms: Increased susceptibility to infections, autoimmune disorders, granulomatous disease or unexplained polyclonal lymphoproliferation, and familial history of antibody deficiency. Moreover, the ESID criteria comprise low switched memory B-cells (<70% of the age-related value) and underline the need to exclude profound T-cell deficiency, and not only secondary hypogammaglobulinemia (<xref rid=\"ijms-21-05223-t002\" ref-type=\"table\">Table 2</xref>). Besides, depending on how many classes of antibodies are decreased in an individual, ESID classified patients into two categories, probable CVID and possible CVID [<xref rid=\"B95-ijms-21-05223\" ref-type=\"bibr\">95</xref>].</p></sec><sec id=\"sec3dot3-ijms-21-05223\"><title>3.3. CVID: Etiology</title><p>A crucial abnormality observed in CVID is hypogammaglobulinemia, so it is not surprising that in CVID patients, lymphocytes B are commonly affected. However, impairment of other elements of the immune system, both adaptive and innate, can also be associated with the development of CVID [<xref rid=\"B84-ijms-21-05223\" ref-type=\"bibr\">84</xref>]. Most studies indicate that about 90% of CVID patients have normal B-cell counts [<xref rid=\"B97-ijms-21-05223\" ref-type=\"bibr\">97</xref>,<xref rid=\"B98-ijms-21-05223\" ref-type=\"bibr\">98</xref>], suggesting that the major defect is possibly related to the differentiation of B-cells into memory and plasma cells [<xref rid=\"B97-ijms-21-05223\" ref-type=\"bibr\">97</xref>]. Besides, abnormalities in CSR and somatic hypermutation (SHM) are also present [<xref rid=\"B99-ijms-21-05223\" ref-type=\"bibr\">99</xref>,<xref rid=\"B100-ijms-21-05223\" ref-type=\"bibr\">100</xref>,<xref rid=\"B101-ijms-21-05223\" ref-type=\"bibr\">101</xref>]. Intrinsic defects of B-cell activation leads to disturbances in the production or secretion of antibodies [<xref rid=\"B101-ijms-21-05223\" ref-type=\"bibr\">101</xref>]. As in patients with CVID B-cells present an impaired response to BCR and tool-like receptor (TLR) ligation, it seems understandable why these individuals fail to produce and secrete high-affinity class-switched antibodies when exposed to vaccination or infections [<xref rid=\"B100-ijms-21-05223\" ref-type=\"bibr\">100</xref>,<xref rid=\"B102-ijms-21-05223\" ref-type=\"bibr\">102</xref>]. Furthermore, BCR and TLR have a key role in counterselection of autoreactive B-cells, and defects in their function have a permissive impact on autoimmunity development [<xref rid=\"B101-ijms-21-05223\" ref-type=\"bibr\">101</xref>].</p><p>Many researchers pointed out the presence of abnormalities in B-cell subsets. These may result from an impaired germinal centre reaction, which is required for the correct development of switched memory B-cells [<xref rid=\"B84-ijms-21-05223\" ref-type=\"bibr\">84</xref>,<xref rid=\"B103-ijms-21-05223\" ref-type=\"bibr\">103</xref>]. A decrease in IgM memory B-cells (CD19+/CD27+), class-switched memory B-cells (CD19+/CD27+/IgD−/IgM−), and plasma cells have been reported [<xref rid=\"B104-ijms-21-05223\" ref-type=\"bibr\">104</xref>,<xref rid=\"B105-ijms-21-05223\" ref-type=\"bibr\">105</xref>,<xref rid=\"B106-ijms-21-05223\" ref-type=\"bibr\">106</xref>]. This is possibly caused by enhanced terminal B-cell apoptosis [<xref rid=\"B32-ijms-21-05223\" ref-type=\"bibr\">32</xref>,<xref rid=\"B107-ijms-21-05223\" ref-type=\"bibr\">107</xref>]. López-Gómez et al. reported an elevated level of proapoptotic Bax and Bim proteins, which correlated with increased apoptosis of CD27+ memory B-cells [<xref rid=\"B108-ijms-21-05223\" ref-type=\"bibr\">108</xref>]. Another example is the expansion of CD21<sup>low</sup> B-cells, characterised by low expression of CD21 and CD38 simultaneously [<xref rid=\"B109-ijms-21-05223\" ref-type=\"bibr\">109</xref>]. An increased number of CD21<sup>low</sup> B-cells is related to immune dysregulation, possibly caused by the rise in interferon-gamma (IFN-γ)-producing CD4+ CXCR5+ Tfh cells [<xref rid=\"B110-ijms-21-05223\" ref-type=\"bibr\">110</xref>] and can be involved in autoimmune complications in CVID patients [<xref rid=\"B111-ijms-21-05223\" ref-type=\"bibr\">111</xref>].</p><p>As aforementioned, only in approximately 10% of CVID patients is the level of B-cells is significantly lowered and B-lymphopenia present, which is a rare phenotype of CVID. This is possibly associated with a disturbance in early bone marrow-dependent B-cell development, including arrest at the pre-B-cell stage [<xref rid=\"B101-ijms-21-05223\" ref-type=\"bibr\">101</xref>,<xref rid=\"B112-ijms-21-05223\" ref-type=\"bibr\">112</xref>], abnormalities in the expression of surface IgM, and disrupted rearrangements of immunoglobulins’ heavy and light chains [<xref rid=\"B101-ijms-21-05223\" ref-type=\"bibr\">101</xref>].</p><p>Although the ESID criteria require the exclusion of profound T-cell immunodeficiency, and since it is also not clear whether individuals with proven T-cell defects should be considered as CVID affected [<xref rid=\"B113-ijms-21-05223\" ref-type=\"bibr\">113</xref>,<xref rid=\"B114-ijms-21-05223\" ref-type=\"bibr\">114</xref>], most authors are inclined to favour the theory that abnormalities in T-cell function are common among patients with CVID [<xref rid=\"B115-ijms-21-05223\" ref-type=\"bibr\">115</xref>,<xref rid=\"B116-ijms-21-05223\" ref-type=\"bibr\">116</xref>]. These abnormalities comprise aberrations in the total numbers, percentages, surface markers, and function of various T-cell subpopulations [<xref rid=\"B115-ijms-21-05223\" ref-type=\"bibr\">115</xref>].</p><p>In CVID patients, a decreased number of total, naïve, and memory CD4+ T-cells has been observed, while in contrast, increased activated CD4+ has been reported [<xref rid=\"B117-ijms-21-05223\" ref-type=\"bibr\">117</xref>]. It has been suggested that a decreased total count might be due to lowered thymic output and enhanced spontaneous CD4+ apoptosis [<xref rid=\"B117-ijms-21-05223\" ref-type=\"bibr\">117</xref>], and an increased number of activated CD4+ may result from defects in regulatory B-cells (Bregs) [<xref rid=\"B118-ijms-21-05223\" ref-type=\"bibr\">118</xref>]. The same regularity has been reported in terms of CD8+ T-cells. According to numerous studies, the naïve and effector memory CD8+ count was lowered, while higher percentages of activated CD8+ were observed [<xref rid=\"B113-ijms-21-05223\" ref-type=\"bibr\">113</xref>,<xref rid=\"B119-ijms-21-05223\" ref-type=\"bibr\">119</xref>,<xref rid=\"B120-ijms-21-05223\" ref-type=\"bibr\">120</xref>].</p><p>The literature concerning T-cell-dependent cytokines in terms of CVID is inconclusive. Some studies indicate significantly increased levels of Th2 cytokines, such as IL−4 and IL−10, simultaneously with an elevated serum level of CD30 (an indicator of Th2 cytokine production) [<xref rid=\"B121-ijms-21-05223\" ref-type=\"bibr\">121</xref>], while others report excessive Th1 responses in patients with CVID [<xref rid=\"B122-ijms-21-05223\" ref-type=\"bibr\">122</xref>,<xref rid=\"B123-ijms-21-05223\" ref-type=\"bibr\">123</xref>]. Cambronero et al. suggested that an increased Th1 response may be associated with aberrations in monocyte function. Reported overexpression of IL−12 can lead to the upregulation of IFN-γ in certain T-cell subsets [<xref rid=\"B124-ijms-21-05223\" ref-type=\"bibr\">124</xref>]. However, other studies reported lowered production of IL−12 in CVID patients [<xref rid=\"B125-ijms-21-05223\" ref-type=\"bibr\">125</xref>,<xref rid=\"B126-ijms-21-05223\" ref-type=\"bibr\">126</xref>], thus this topic requires further investigation.</p><p>Besides, a decreased number of circulating Th17 lymphocytes was shown [<xref rid=\"B127-ijms-21-05223\" ref-type=\"bibr\">127</xref>]. It is not fully established whether regulatory T-cells play an important role in the pathogenesis of CVID. On the one hand, some studies reported no significant differences in the percentages and absolute count of Tregs between CVID patients and controls [<xref rid=\"B128-ijms-21-05223\" ref-type=\"bibr\">128</xref>]. On the other hand, other researchers showed a lowered number of Tregs in CVID patients, contributing to autoimmunity [<xref rid=\"B129-ijms-21-05223\" ref-type=\"bibr\">129</xref>,<xref rid=\"B130-ijms-21-05223\" ref-type=\"bibr\">130</xref>], as Tregs of CVID patients exhibit the less effective function of suppressing autoreactive CD4+ [<xref rid=\"B131-ijms-21-05223\" ref-type=\"bibr\">131</xref>].</p><p>Not only lymphocytes can be defective in CVID patients. Some researchers suggested that, in CVID-affected individuals, the total number of DCs might be lowered, and their maturation and stimulatory function might be impaired [<xref rid=\"B125-ijms-21-05223\" ref-type=\"bibr\">125</xref>,<xref rid=\"B126-ijms-21-05223\" ref-type=\"bibr\">126</xref>]. Defects of DCs, having a key role in presenting antigens to T-cells, may result in decreased generation of antigen-specific CD4+, which can subsequently impair antibody production and secretion [<xref rid=\"B84-ijms-21-05223\" ref-type=\"bibr\">84</xref>].</p><p>With respect to monocytes/macrophages, as mentioned above, an alternation in the cytokines produced by these cells was observed. Besides, enhanced monocyte/macrophage activation, perhaps due to persistently increased tumour necrosis factor (TNF) levels and increased TNF receptor expression, was reported [<xref rid=\"B132-ijms-21-05223\" ref-type=\"bibr\">132</xref>]. Additionally, a 5-fold increase of the monocyte fusion rate was indicated, which could contribute to chronic inflammation and excessive susceptibility to granuloma formation [<xref rid=\"B84-ijms-21-05223\" ref-type=\"bibr\">84</xref>].</p><p>Innate lymphoid cells (ILCs), including natural killer cells (NK cells), can also be affected in patients with CVID; however, this topic is not well investigated, and the literature provides many contradictory data [<xref rid=\"B84-ijms-21-05223\" ref-type=\"bibr\">84</xref>]. Differences within NK cell subsets were reported between CVID patients and healthy controls [<xref rid=\"B133-ijms-21-05223\" ref-type=\"bibr\">133</xref>]. It is suggested that this can be a compensatory mechanism for protection against malignancies and viral infections, as an increased occurrence of both conditions is observed in CVID patients with an NK cell deficiency [<xref rid=\"B134-ijms-21-05223\" ref-type=\"bibr\">134</xref>].</p><p>Multiple monogenic mutations have been associated with CVID development [<xref rid=\"B135-ijms-21-05223\" ref-type=\"bibr\">135</xref>]; however, there is an ongoing discussion whether, in the case of the established causative mutation, the condition should be classified as CVID, or perhaps more appropriately, as CVID-like disorder [<xref rid=\"B136-ijms-21-05223\" ref-type=\"bibr\">136</xref>].</p><p>Nevertheless, among patients fulfilling CVID criteria, monogenic disorders were identified in less than 20% in nonconsanguineous cohorts, and in approximately 70% in cohorts of patients from consanguineous unions [<xref rid=\"B137-ijms-21-05223\" ref-type=\"bibr\">137</xref>].</p><p>Mutations detected in CVID patients include genes associated with impaired B-cell development (e.g., <italic>IKZF1</italic>, <italic>BAFFR</italic>, <italic>TWEAK</italic>, <italic>CD27</italic>, <italic>STAT1</italic> GOF, <italic>NFKB2</italic>, <italic>IRF2BP2</italic>), impaired CSR/SHM (e.g., <italic>BACH2</italic>, <italic>IL21</italic>, <italic>IL21R</italic>), excessive lymphoproliferation (e.g., <italic>CTLA4</italic>, <italic>LRBA</italic>, <italic>PIK3CD</italic>, <italic>STAT3</italic> GOF), and impaired B-cell activation and tolerance (e.g., <italic>NFKB1</italic>, <italic>TACI</italic>, <italic>CD19</italic>, <italic>CD21</italic>, <italic>ICOS</italic>, <italic>BLK</italic>, <italic>PLCG2</italic>, <italic>CD81</italic>, <italic>CD20</italic>) [<xref rid=\"B88-ijms-21-05223\" ref-type=\"bibr\">88</xref>,<xref rid=\"B135-ijms-21-05223\" ref-type=\"bibr\">135</xref>,<xref rid=\"B138-ijms-21-05223\" ref-type=\"bibr\">138</xref>,<xref rid=\"B139-ijms-21-05223\" ref-type=\"bibr\">139</xref>]. It is important to note that many of the genes mentioned above are also involved in SIgAD pathogenesis. The exact pathomechanism underlying immunodeficiency development in the case of the presence of the aforementioned mutations goes beyond the subject of this review; however, up-to-date genetic defects associated with PIDs, including CVID, have been enlisted in the IUIS 2019 classification [<xref rid=\"B140-ijms-21-05223\" ref-type=\"bibr\">140</xref>].</p><p>CVID is also associated with certain HLA haplotypes, usually common with ones predisposing to SIgAD. These include haplotypes <italic>HLA A1–B8-DR3</italic> and <italic>B14-DR1</italic> [<xref rid=\"B141-ijms-21-05223\" ref-type=\"bibr\">141</xref>,<xref rid=\"B142-ijms-21-05223\" ref-type=\"bibr\">142</xref>].</p><p>Similarly to SIgAD patients, alternations in the gut microbiota have been described in CVID patients. These changes can be associated with a more severe disease phenotype [<xref rid=\"B143-ijms-21-05223\" ref-type=\"bibr\">143</xref>], as it is suggested that abnormalities in the gut microbiota can lead to increased mucosal permeability, resulting in increased bacterial translocation, an elevation of lipopolysaccharides, alongside chronic immune activation [<xref rid=\"B144-ijms-21-05223\" ref-type=\"bibr\">144</xref>]. Interestingly, a recent study in a mouse model of CVID with CD19 deficiency (B-cell receptor important for B-cell development) revealed anaerobic bacteria outgrowth in the gut that was associated with chronic inflammation of the gut, and resulted in malabsorption [<xref rid=\"B55-ijms-21-05223\" ref-type=\"bibr\">55</xref>]. Additionally, similarly to SIgAD, it is not explained whether the described microbiota alteration contributes to CVID development or is an expression of the compromised immune system [<xref rid=\"B145-ijms-21-05223\" ref-type=\"bibr\">145</xref>].</p></sec><sec id=\"sec3dot4-ijms-21-05223\"><title>3.4. CVID: Symptoms and Clinical Course</title><p>As CVID is considered a very heterogeneous disorder, appropriate classification of affected patients is essential. Many classifications of CVID patients, based both on clinical manifestations and immunological features as B-cells subsets, have been proposed to this day [<xref rid=\"B146-ijms-21-05223\" ref-type=\"bibr\">146</xref>]. Despite this, different authors take a different approach to describing CVID symptoms and classifying them into clinical phenotypes. Some of them also prefer to describe typical CVID manifestations as complications caused by the condition, not a phenotype [<xref rid=\"B84-ijms-21-05223\" ref-type=\"bibr\">84</xref>]. Cunningham-Rundles highlighted probably the two most important manifestations of CVID: Infections and autoimmune/inflammatory [<xref rid=\"B147-ijms-21-05223\" ref-type=\"bibr\">147</xref>]; however, other researchers underline the importance of lymphoproliferative syndromes, malignancies and enteropathy, asthma, allergies, and chronic pulmonary diseases [<xref rid=\"B84-ijms-21-05223\" ref-type=\"bibr\">84</xref>,<xref rid=\"B86-ijms-21-05223\" ref-type=\"bibr\">86</xref>,<xref rid=\"B88-ijms-21-05223\" ref-type=\"bibr\">88</xref>,<xref rid=\"B148-ijms-21-05223\" ref-type=\"bibr\">148</xref>]. Nonetheless, according to Chapel et al., more than 80% of CVID patients present with one of the aforementioned phenotypes/manifestations [<xref rid=\"B148-ijms-21-05223\" ref-type=\"bibr\">148</xref>].</p><p>As in most PIDs, infectious complications are the most typical manifestation of CVID. These include mainly respiratory tract infections [<xref rid=\"B149-ijms-21-05223\" ref-type=\"bibr\">149</xref>], caused usually by <italic>Streptococcus</italic> spp., <italic>Haemophilus</italic> spp., <italic>Moraxella catarrhalis</italic>, <italic>Neisseria meningitidis</italic>, and <italic>Staphylococcus</italic> spp. [<xref rid=\"B150-ijms-21-05223\" ref-type=\"bibr\">150</xref>,<xref rid=\"B151-ijms-21-05223\" ref-type=\"bibr\">151</xref>], a recurrent or severe course of which may result in complications, such as bronchiectasis and interstitial lung disease [<xref rid=\"B147-ijms-21-05223\" ref-type=\"bibr\">147</xref>,<xref rid=\"B152-ijms-21-05223\" ref-type=\"bibr\">152</xref>,<xref rid=\"B153-ijms-21-05223\" ref-type=\"bibr\">153</xref>]. The latter may be the initial manifestation of CVID in a certain group of patients [<xref rid=\"B154-ijms-21-05223\" ref-type=\"bibr\">154</xref>]. GI tract infections are also very common and usually manifest as persistent or acute diarrhoea [<xref rid=\"B155-ijms-21-05223\" ref-type=\"bibr\">155</xref>]. <italic>Giardia lamblia</italic>, <italic>Campylobacter jejuni</italic>, and <italic>Salmonella</italic> spp. are the most commonly identified pathogens and are usually associated with a low or undetectable level of IgA [<xref rid=\"B156-ijms-21-05223\" ref-type=\"bibr\">156</xref>,<xref rid=\"B157-ijms-21-05223\" ref-type=\"bibr\">157</xref>].</p><p>Autoimmune manifestations are reported in approximately 30% of CVID patients [<xref rid=\"B148-ijms-21-05223\" ref-type=\"bibr\">148</xref>,<xref rid=\"B158-ijms-21-05223\" ref-type=\"bibr\">158</xref>,<xref rid=\"B159-ijms-21-05223\" ref-type=\"bibr\">159</xref>], among which autoimmune cytopenias are the most frequent and potentially dangerous manifestations [<xref rid=\"B160-ijms-21-05223\" ref-type=\"bibr\">160</xref>]. Autoimmune thyroiditis, rheumatoid arthritis, Sjogren syndrome, and multiple other rheumatologic diseases have also been observed [<xref rid=\"B161-ijms-21-05223\" ref-type=\"bibr\">161</xref>,<xref rid=\"B162-ijms-21-05223\" ref-type=\"bibr\">162</xref>,<xref rid=\"B163-ijms-21-05223\" ref-type=\"bibr\">163</xref>,<xref rid=\"B164-ijms-21-05223\" ref-type=\"bibr\">164</xref>,<xref rid=\"B165-ijms-21-05223\" ref-type=\"bibr\">165</xref>]. It is suggested that aberrations in T-cell subsets, especially Tregs, play an important role in the development of autoimmunity in CVID patients [<xref rid=\"B166-ijms-21-05223\" ref-type=\"bibr\">166</xref>,<xref rid=\"B167-ijms-21-05223\" ref-type=\"bibr\">167</xref>].</p><p>Other important manifestations commonly affecting CVID patients are inflammatory disorders [<xref rid=\"B168-ijms-21-05223\" ref-type=\"bibr\">168</xref>], often described together with autoimmunities. In large European and United States cohorts, approximately 70% of CVID patients suffered from one of those [<xref rid=\"B148-ijms-21-05223\" ref-type=\"bibr\">148</xref>,<xref rid=\"B160-ijms-21-05223\" ref-type=\"bibr\">160</xref>]. Typical for CVID seems the presence of granulomas in various organs. The granulomatous disease occurs in 8–22% of individuals with CVID [<xref rid=\"B147-ijms-21-05223\" ref-type=\"bibr\">147</xref>], and these granulomatous changes might be misdiagnosed as sarcoidosis, leading to delays in the recognition of PID [<xref rid=\"B169-ijms-21-05223\" ref-type=\"bibr\">169</xref>,<xref rid=\"B170-ijms-21-05223\" ref-type=\"bibr\">170</xref>]. Another important form of granulomatous disease uniquely associated with CVID is granulomatous lymphocytic interstitial lung disease [<xref rid=\"B171-ijms-21-05223\" ref-type=\"bibr\">171</xref>].</p><p>The GI tract is frequently affected in individuals with CVID [<xref rid=\"B93-ijms-21-05223\" ref-type=\"bibr\">93</xref>,<xref rid=\"B160-ijms-21-05223\" ref-type=\"bibr\">160</xref>,<xref rid=\"B172-ijms-21-05223\" ref-type=\"bibr\">172</xref>]. Apart from infections of the GI tract, non-infectious enteropathy occurs in 20% to 60% of CVID patients. This might resemble IBD or CeD [<xref rid=\"B147-ijms-21-05223\" ref-type=\"bibr\">147</xref>]. Interestingly, Pensieri et al. indicated that in patients with CVID and villous atrophy, the mortality rate is higher than in CVID individuals without enteropathy [<xref rid=\"B173-ijms-21-05223\" ref-type=\"bibr\">173</xref>]. It is suggested that the increased prevalence of GI symptoms among CVID patients in contrast to other antibody deficiency syndromes is due to T-cell dysfunction. The fact that immunoglobulin replacement therapy does not alleviate enteropathy symptoms is in favour of this theory [<xref rid=\"B174-ijms-21-05223\" ref-type=\"bibr\">174</xref>].</p><p>Last, but not least, patients with CVID have a predilection to develop lymphoproliferative syndromes (e.g., persistent lymphadenopathy, lymphoid interstitial pneumonia, hepatomegaly, splenomegaly), and malignancies, which include primarily lymphomas [<xref rid=\"B86-ijms-21-05223\" ref-type=\"bibr\">86</xref>,<xref rid=\"B148-ijms-21-05223\" ref-type=\"bibr\">148</xref>,<xref rid=\"B175-ijms-21-05223\" ref-type=\"bibr\">175</xref>]. CVID patients have an estimated 30-fold increased risk of developing the latter [<xref rid=\"B83-ijms-21-05223\" ref-type=\"bibr\">83</xref>,<xref rid=\"B176-ijms-21-05223\" ref-type=\"bibr\">176</xref>]. The most common malignancy is non-Hodgkin lymphoma; however, CVID patients also have a significantly increased risk for other malignancies, such as colorectal cancer, breast cancer, uterine cancer, and neurogenic malignancies [<xref rid=\"B147-ijms-21-05223\" ref-type=\"bibr\">147</xref>].</p></sec></sec><sec id=\"sec4-ijms-21-05223\"><title>4. SIgAD Progression to CVID</title><p>Intriguingly, the literature provides information concerning the progression of SIgAD to CVID [<xref rid=\"B177-ijms-21-05223\" ref-type=\"bibr\">177</xref>]. It is suggested that it could be due to the similar pathogenesis of both entities. For example, the same HLA haplotypes, including <italic>HLA A1–B8-DR3</italic> and <italic>B14-DR1</italic>, predispose to both [<xref rid=\"B147-ijms-21-05223\" ref-type=\"bibr\">147</xref>]. Additionally, familial clustering of SIgAD and CVID is in favour of this theory [<xref rid=\"B178-ijms-21-05223\" ref-type=\"bibr\">178</xref>,<xref rid=\"B179-ijms-21-05223\" ref-type=\"bibr\">179</xref>,<xref rid=\"B180-ijms-21-05223\" ref-type=\"bibr\">180</xref>]. Interestingly, one study indicated that more than 20% of patients with SIgAD and autoimmunity progressed to CVID, which is not a case for SIgAD patients without autoimmune disorders [<xref rid=\"B25-ijms-21-05223\" ref-type=\"bibr\">25</xref>]. Despite CVID diagnostic criteria including a lack of antibody response to vaccination, it seems that some CVID patients can produce them in detectable titers [<xref rid=\"B25-ijms-21-05223\" ref-type=\"bibr\">25</xref>,<xref rid=\"B177-ijms-21-05223\" ref-type=\"bibr\">177</xref>]. This could be proof of a progression from SIgAD to CVID and reflect the transition stage. Finally, it still raises doubts whether these patients had true SIgAD progressing to CVID or suffered from subclinical or underdiagnosed CVID [<xref rid=\"B68-ijms-21-05223\" ref-type=\"bibr\">68</xref>].</p></sec><sec id=\"sec5-ijms-21-05223\"><title>5. Celiac Disease</title><sec id=\"sec5dot1-ijms-21-05223\"><title>5.1. CeD: Definition and Epidemiology</title><p>CeD is an autoimmune disorder characterized by the presence of small-intestinal enteropathy triggered by gluten ingestion (specifically, gliadin peptides that can be found in wheat, rye, and barley) in genetically predisposed individuals. CeD prevalence is estimated at around 1% of the worldwide population, thus making it one of the most common autoimmune disorders [<xref rid=\"B181-ijms-21-05223\" ref-type=\"bibr\">181</xref>,<xref rid=\"B182-ijms-21-05223\" ref-type=\"bibr\">182</xref>]. Besides, the incidence of CeD is higher in first-degree relatives of patients with CeD (Singh et al. established CeD prevalence in first-degree relatives at 7.5% and in siblings at 8.9% [<xref rid=\"B183-ijms-21-05223\" ref-type=\"bibr\">183</xref>]), and in patients from risk groups, e.g., with Down syndrome, type 1 diabetes, or IgA deficiency [<xref rid=\"B68-ijms-21-05223\" ref-type=\"bibr\">68</xref>,<xref rid=\"B184-ijms-21-05223\" ref-type=\"bibr\">184</xref>,<xref rid=\"B185-ijms-21-05223\" ref-type=\"bibr\">185</xref>,<xref rid=\"B186-ijms-21-05223\" ref-type=\"bibr\">186</xref>]. Reports indicate that CeD prevalence varies widely among regions and populations, probably due to varying wheat intake and frequency of <italic>HLA-DQ2</italic>, <italic>HLA-DQ8</italic>, or <italic>HLA-DQ2.5</italic>. <italic>HLA-DQ2</italic> is present in about 20–30% of the general population; however, only 1–3% of all individuals having <italic>HLA-DQ2</italic> develop CeD. Slightly less than 20% of the general population is carrying <italic>HLA-DQ8</italic>, but it is present in only 0.1–0.3% of individuals with CeD [<xref rid=\"B187-ijms-21-05223\" ref-type=\"bibr\">187</xref>]. Thus, thess specific HLA haplotypes predispose to CeD development, but they are not causative.</p><p>Additionally, CeD diagnosis is more common in females (0.6% > 0.4%) [<xref rid=\"B182-ijms-21-05223\" ref-type=\"bibr\">182</xref>]. An increasing prevalence in Asian countries is probably caused by progressive “westernization”, which results in increased wheat consumption [<xref rid=\"B3-ijms-21-05223\" ref-type=\"bibr\">3</xref>,<xref rid=\"B188-ijms-21-05223\" ref-type=\"bibr\">188</xref>,<xref rid=\"B189-ijms-21-05223\" ref-type=\"bibr\">189</xref>]. Studies show an increasing global prevalence of CeD [<xref rid=\"B2-ijms-21-05223\" ref-type=\"bibr\">2</xref>,<xref rid=\"B190-ijms-21-05223\" ref-type=\"bibr\">190</xref>,<xref rid=\"B191-ijms-21-05223\" ref-type=\"bibr\">191</xref>]; however, researchers do not agree whether it reflects a true increase in incidence or progress in CeD diagnostics and increased awareness among physicians [<xref rid=\"B3-ijms-21-05223\" ref-type=\"bibr\">3</xref>,<xref rid=\"B192-ijms-21-05223\" ref-type=\"bibr\">192</xref>].</p></sec><sec id=\"sec5dot2-ijms-21-05223\"><title>5.2. CeD: Diagnostics</title><p>The diagnosis of CeD is made based on serologic tests together with histological findings, and a positive response to a gluten-free diet (GFD) [<xref rid=\"B193-ijms-21-05223\" ref-type=\"bibr\">193</xref>]. The serological examination includes measurement of the anti-transglutaminase-2 (TG2) IgA level in serum, considered as a first-line screening test. In the case of weakly expressed anti-TG2 IgA or patients with an impaired immunological status, anti-TG2 IgG, endomysial antibodies (EMA), or deamidated gliadin peptides (DGP) IgA and IgG levels should be tested [<xref rid=\"B194-ijms-21-05223\" ref-type=\"bibr\">194</xref>].</p><p>Duodenum endoscopic examination in CeD usually reveals bulb atrophy with visible submucosal vessels, loss or reduction of folds, a mosaic pattern, and mucosal fissuring. Although these signs may occur alone or in combination, one-third of new cases of CeD have a normal endoscopic appearance [<xref rid=\"B195-ijms-21-05223\" ref-type=\"bibr\">195</xref>]. Because of this, at least four biopsy samples from the second part of the duodenum should be taken during endoscopy when CeD is suspected [<xref rid=\"B194-ijms-21-05223\" ref-type=\"bibr\">194</xref>].</p><p>CeD-specific histopathological features in small bowel biopsy specimens are required to confirm the diagnosis in adults [<xref rid=\"B194-ijms-21-05223\" ref-type=\"bibr\">194</xref>]. In children, a non-biopsy approach is possible, which is widely described in the European Society Paediatric Gastroenterology, Hepatology, and Nutrition (ESPGHAN) guidelines for diagnosing CeD 2019 [<xref rid=\"B196-ijms-21-05223\" ref-type=\"bibr\">196</xref>].</p><p>Typical CeD histological findings include villous atrophy, crypt hyperplasia, and an increased intraepithelial lymphocyte (IEL) count. Histopathological changes are graded in the modified Marsh–Oberhuber classification [<xref rid=\"B194-ijms-21-05223\" ref-type=\"bibr\">194</xref>,<xref rid=\"B197-ijms-21-05223\" ref-type=\"bibr\">197</xref>]. According to the European Society for the Study of Coeliac Disease (ESsCD) guidelines, specimens should be taken after at least 6–8 weeks of a diet containing a minimum of 10 g of gluten [<xref rid=\"B194-ijms-21-05223\" ref-type=\"bibr\">194</xref>].</p><p>HLA testing is not a part of the routine diagnostic process. It should be performed in doubtful cases, i.e., when serology is negative but histology is strongly suggestive of CeD. It is also useful to exclude the possibility of CeD, especially when GFD is already administered, or to identify patients who require further monitoring among those in a risk group (e.g., family members of patients with CeD, affected with autoimmune diseases, or genetic disorders associated with CeD). This approach is justified by the fact that a negative test for <italic>HLA-DQ2</italic> and <italic>-DQ8</italic> has a positive predictive value >99%, thus helping to exclude CeD diagnosis [<xref rid=\"B194-ijms-21-05223\" ref-type=\"bibr\">194</xref>].</p></sec><sec id=\"sec5dot3-ijms-21-05223\"><title>5.3. CeD: Symptoms</title><p>Classic symptoms of CeD are chronic diarrhoea; symptoms of malabsorption, such as weight loss; failure to thrive in childhood; delayed puberty; and short stature; however, they are not very common. This can be confirmed by the fact that, for instance, more than 10% of CeD patients are obese [<xref rid=\"B193-ijms-21-05223\" ref-type=\"bibr\">193</xref>]. Symptoms considered as non-classical, including iron deficiency, bloating, constipation, chronic fatigue, headache, abdominal pain, and osteoporosis [<xref rid=\"B3-ijms-21-05223\" ref-type=\"bibr\">3</xref>], are more common and present in more than 50% of CeD patients [<xref rid=\"B198-ijms-21-05223\" ref-type=\"bibr\">198</xref>].</p></sec></sec><sec id=\"sec6-ijms-21-05223\"><title>6. Celiac Disease in Selective IgA Deficiency</title><sec id=\"sec6dot1-ijms-21-05223\"><title>6.1. CeD in SIgAD: Epidemiology</title><p>The real occurrence of CeD among patients with SIgAD and SIgAD among patients with CeD is difficult to estimate and varies depending on the author. The difficulties come from usually small study groups, different SIgAD criteria adopted by researchers, and the fact that in many studies, symptomatic SIgAD patients were examined, while the course of the disease is mostly asymptomatic.</p><p>Various sources seem to agree on the occurrence of SIgAD among CeD patients, which is approximately 2–3%. Such a value is provided by the ESsCD guidelines for 2020 and is based on a large European cohort study by McGowan et al. [<xref rid=\"B194-ijms-21-05223\" ref-type=\"bibr\">194</xref>,<xref rid=\"B199-ijms-21-05223\" ref-type=\"bibr\">199</xref>]. Pallav et al. reported an incidence of SIgAD among patients diagnosed with CeD as 1.9% in the United States [<xref rid=\"B200-ijms-21-05223\" ref-type=\"bibr\">200</xref>]. Wang et al. indicated a 2.56% prevalence of SIgAD in individuals with CeD [<xref rid=\"B201-ijms-21-05223\" ref-type=\"bibr\">201</xref>]. Both cited results agree with the one published in the ESsCD guidelines. Interestingly, in a comprehensive review from 2020, Odineal and Gershwin reported a weighted average of SIgAD in CeD occurrence, based on 11 studies, as 0.57%. However, they indicated that this result might be biased by the aforementioned difficulties and lack of controls in certain studies [<xref rid=\"B68-ijms-21-05223\" ref-type=\"bibr\">68</xref>].</p><p>The situation is different in terms of the occurrence of CeD among patients diagnosed with SIgAD, as sources seem inconclusive. In 2014, in a large review paper, Singh et al. reported a 10–30% prevalence of CeD in individuals with SIgAD [<xref rid=\"B19-ijms-21-05223\" ref-type=\"bibr\">19</xref>]. Original papers also presented a large spread of results. Ludvigsson et al. indicated that CeD occurs in 6.7% patients with SIgAD [<xref rid=\"B202-ijms-21-05223\" ref-type=\"bibr\">202</xref>]; in the research of Lenhardt et al., it was 8.7% [<xref rid=\"B203-ijms-21-05223\" ref-type=\"bibr\">203</xref>], while McGowan et al. reported 16.6% [<xref rid=\"B199-ijms-21-05223\" ref-type=\"bibr\">199</xref>], and Wang et al. showed the result of 15.2% [<xref rid=\"B204-ijms-21-05223\" ref-type=\"bibr\">204</xref>]. All cited studies are in favour of the theory that the prevalence of CeD in patients with SIgAD is higher than in the general population; however, this requires further investigation.</p></sec><sec id=\"sec6dot2-ijms-21-05223\"><title>6.2. CeD in SIgAD: Etiology</title><p>Several hypotheses have been formulated to explain the association between SIgAD and CeD. Both diseases share the same predisposing HLA haplotypes [<xref rid=\"B204-ijms-21-05223\" ref-type=\"bibr\">204</xref>], described in detail above. The common genetic background partially explains the overlap between CeD and SIgAD. However, an analysis of the literature performed by Wang et al. has driven researchers to the conclusion that the distribution of HLA-DQ haplotypes is similar in CeD patients with SIgAD and IgA-sufficient CeD patients [<xref rid=\"B201-ijms-21-05223\" ref-type=\"bibr\">201</xref>]. This indicates that more mechanisms must be underlying the association between SIgAD and CeD. For instance, it might be associated with variants of <italic>CTLA−4</italic> and <italic>ICOS</italic>, since it is established that SIgAD and CeD patients can share a common mutation of <italic>CTLA4-ICOS</italic> [<xref rid=\"B72-ijms-21-05223\" ref-type=\"bibr\">72</xref>].</p><p>Another hypothesis states that an increased prevalence of CeD among SIgAD patients is an expression of the aforementioned general predisposition to autoimmunity in SIgAD-affected individuals [<xref rid=\"B25-ijms-21-05223\" ref-type=\"bibr\">25</xref>,<xref rid=\"B37-ijms-21-05223\" ref-type=\"bibr\">37</xref>]. Pallav et al. found that the mean ages at diagnosis in CeD patients with additional autoimmune disorders were higher than those with CeD only [<xref rid=\"B200-ijms-21-05223\" ref-type=\"bibr\">200</xref>]. The authors suggested it might be due to prolonged gluten exposure, resulting in ongoing inflammation.</p><p>This leads to the next theory. It is suggested that secretory IgA in the gut plays an important role in neutralizing gluten peptide; therefore, it is recommended to delay gluten intake by infants until 4 months of age [<xref rid=\"B205-ijms-21-05223\" ref-type=\"bibr\">205</xref>], as low secretory IgA levels in the intestine are normal for all babies [<xref rid=\"B206-ijms-21-05223\" ref-type=\"bibr\">206</xref>]. The same mechanism might underline the susceptibility of SIgAD patients to CeD development, as in the absence of IgA, individuals’ mucosa can be more exposed to gluten, which can lead to abnormal processing of these antigens [<xref rid=\"B62-ijms-21-05223\" ref-type=\"bibr\">62</xref>].</p></sec><sec id=\"sec6dot3-ijms-21-05223\"><title>6.3. CeD in SIgAD: Diagnostic Difficulties</title><p>The basic principles of CeD diagnostics in SIgAD patients are similar to those applied to IgA-sufficient individuals. The most important difference concerns serological diagnostics. In immunocompetent patients, first-line testing comprises of measurement of the anti-TG2 IgA level in serum [<xref rid=\"B194-ijms-21-05223\" ref-type=\"bibr\">194</xref>]. For obvious reasons, this approach is not effective in SIgAD patients, as false negative results might be obtained; thus, patients with SIgAD may elude CeD diagnosis [<xref rid=\"B200-ijms-21-05223\" ref-type=\"bibr\">200</xref>,<xref rid=\"B207-ijms-21-05223\" ref-type=\"bibr\">207</xref>,<xref rid=\"B208-ijms-21-05223\" ref-type=\"bibr\">208</xref>]. To avoid such a situation, ESsCD recommended assessing total IgA levels concurrently with serology testing to determine whether IgA levels are sufficient [<xref rid=\"B194-ijms-21-05223\" ref-type=\"bibr\">194</xref>]. In the case of a lowered IgA serum level, IgG anti-TG2 and IgG-DGP are regarded as the best tool for identifying CeD in patients [<xref rid=\"B194-ijms-21-05223\" ref-type=\"bibr\">194</xref>,<xref rid=\"B209-ijms-21-05223\" ref-type=\"bibr\">209</xref>]. Interestingly, Pallav et al. reported cases concerning SIgAD patients with a positive result of anti-TG2 IgA at diagnosis [<xref rid=\"B200-ijms-21-05223\" ref-type=\"bibr\">200</xref>]. The authors hypothesized that in some individuals with SIgAD, gluten-related immune activation can lead to detectable serum IgA levels [<xref rid=\"B210-ijms-21-05223\" ref-type=\"bibr\">210</xref>]. However, this should be taken as an exception and should not affect management.</p><p>The next step in CeD diagnostics, after serological examination, is a small intestinal biopsy and histological assessment of the taken specimens. Histopathology findings in SIgAD patients with CeD do not vary much from those of patients with CeD alone [<xref rid=\"B48-ijms-21-05223\" ref-type=\"bibr\">48</xref>,<xref rid=\"B210-ijms-21-05223\" ref-type=\"bibr\">210</xref>]. It is indistinguishable from the pathology seen in non-SIgAD patients with CeD in terms of increased numbers of intraepithelial lymphocytes, villous atrophy, crypt hyperplasia, and infiltration of the lamina propria with lymphoid cells. The main difference concerns IgA-secreting plasma cells, which are absent in CeD patients with coexisting SIgAD [<xref rid=\"B211-ijms-21-05223\" ref-type=\"bibr\">211</xref>,<xref rid=\"B212-ijms-21-05223\" ref-type=\"bibr\">212</xref>,<xref rid=\"B213-ijms-21-05223\" ref-type=\"bibr\">213</xref>].</p></sec><sec id=\"sec6dot4-ijms-21-05223\"><title>6.4. CeD in SIgAD: Symptoms</title><p>Symptoms of CeD in IgA-deficient patients are similar to those in patients with CeD alone, while differences might concern the frequency of occurrence of certain symptoms [<xref rid=\"B6-ijms-21-05223\" ref-type=\"bibr\">6</xref>]. On the one hand, Chow et al. and Heneghan et al. reported a higher prevalence of diarrhoea in SIgAD-CeD patients; however, this finding was not significant [<xref rid=\"B214-ijms-21-05223\" ref-type=\"bibr\">214</xref>,<xref rid=\"B215-ijms-21-05223\" ref-type=\"bibr\">215</xref>]. On the other hand, literature investigation drove Odineal and Gershwin to a conclusion that individuals with SIgAD and CeD are less likely to have GI symptoms [<xref rid=\"B68-ijms-21-05223\" ref-type=\"bibr\">68</xref>], and the results of Pallav et al. favour this thesis, indicating a lack of classic GI symptoms in 66.7% of SIgAD-CeD patients compared to 17.7% in patients with CeD alone. However, this finding was also not significant [<xref rid=\"B200-ijms-21-05223\" ref-type=\"bibr\">200</xref>]. Further investigation seems required in this field.</p><p>In SIgAD-CeD patients, co-existing autoimmune diseases are remarkably more common than in IgA-sufficient CeD patients. Pallav et al. reported the presence of another autoimmunity in 67% of SIgAD-CeD compared to 23.5% of the only CeD control [<xref rid=\"B200-ijms-21-05223\" ref-type=\"bibr\">200</xref>], while Chow et al. indicated the second autoimmunity in 29% of antibody-deficient patients compared with 12% of CeD patients with normal IgA levels [<xref rid=\"B215-ijms-21-05223\" ref-type=\"bibr\">215</xref>]. Besides, according to Pallav et al., the average age at diagnosis was higher for individuals with CeD and SIgAD than for CeD only [<xref rid=\"B200-ijms-21-05223\" ref-type=\"bibr\">200</xref>]. Although this finding was not significant, it remains conclusive with the previously mentioned theory, in that antibody-deficient patients develop autoimmunity later in the course of the disease [<xref rid=\"B57-ijms-21-05223\" ref-type=\"bibr\">57</xref>].</p></sec><sec id=\"sec6dot5-ijms-21-05223\"><title>6.5. CeD in SIgAD: Treatment</title><p>As CeD in SIgAD does not differ significantly from CeD in immunocompetent patients, recommended treatment does not vary either. SIgAD patients with CeD are responsive to gluten withdrawal and GFD is recommended [<xref rid=\"B29-ijms-21-05223\" ref-type=\"bibr\">29</xref>,<xref rid=\"B62-ijms-21-05223\" ref-type=\"bibr\">62</xref>,<xref rid=\"B157-ijms-21-05223\" ref-type=\"bibr\">157</xref>,<xref rid=\"B216-ijms-21-05223\" ref-type=\"bibr\">216</xref>]. If SIgAD individuals with CeD fail to respond to GFD, it is an indication to broaden the diagnostics and consider other causes of villous atrophy, including giardiasis, small-bowel bacterial overgrowth, or CVID [<xref rid=\"B62-ijms-21-05223\" ref-type=\"bibr\">62</xref>,<xref rid=\"B156-ijms-21-05223\" ref-type=\"bibr\">156</xref>,<xref rid=\"B194-ijms-21-05223\" ref-type=\"bibr\">194</xref>]. Interestingly, persistent elevation of anti-TG2 IgG and IgG-DGP was frequently found in SIgAD adults despite adhering to GFD [<xref rid=\"B204-ijms-21-05223\" ref-type=\"bibr\">204</xref>]. Korponay-Szabo et al. reported a very slow decrease of anti-TG2 antibody levels in IgA-deficient patients; most of them were still positive after more than two or three years on a GFD [<xref rid=\"B217-ijms-21-05223\" ref-type=\"bibr\">217</xref>], while in non-SIgAD patients, serological normalization is expected within one year of GFD [<xref rid=\"B216-ijms-21-05223\" ref-type=\"bibr\">216</xref>,<xref rid=\"B218-ijms-21-05223\" ref-type=\"bibr\">218</xref>]. Besides, Chow et al. reported a lack of normalization of serological tests after a long period of GFD (mean 7.25 years) in half of their SIgAD-CeD patients [<xref rid=\"B215-ijms-21-05223\" ref-type=\"bibr\">215</xref>]. This might complicate the monitoring of SIgAD-CED patients and implicate the need to intensify endoscopic examination in comparison with individuals with CeD only. It is not established whether it is due to a lack of adherence to GFD, or part of the immune-regulatory defect seen in IgA, or finally, it is associated with a certain HLA haplotype [<xref rid=\"B204-ijms-21-05223\" ref-type=\"bibr\">204</xref>].</p></sec></sec><sec id=\"sec7-ijms-21-05223\"><title>7. Celiac Disease in Common Variable Immunodeficiency</title><sec id=\"sec7dot1-ijms-21-05223\"><title>7.1. CeD in CVID: Epidemiology</title><p>Despite the fact that CVID and SIgAD share a common pathogenesis, in terms of CeD occurrence, these entities are not alike. GI manifestation is very common among CVID patients, and often presents as non-infectious enteropathy, which might resemble other GI diseases like IBD and CeD [<xref rid=\"B93-ijms-21-05223\" ref-type=\"bibr\">93</xref>]. However, the prevalence of enteropathy in CVID patients varies depending on the author. In Uzzan et al.’s review, it is reported as 10% to 12%, Yuan and Bousvaros indicated it is between 10% and 20%, but Cunningham-Rundles suggested it might be even up to 20% to 60% of CVID patients [<xref rid=\"B93-ijms-21-05223\" ref-type=\"bibr\">93</xref>,<xref rid=\"B147-ijms-21-05223\" ref-type=\"bibr\">147</xref>,<xref rid=\"B219-ijms-21-05223\" ref-type=\"bibr\">219</xref>]. These values concern the incidence of non-infectious enteropathy, which is not synonymous with CeD. The true prevalence of the latter is very difficult to estimate, as it is not fully established whether we are facing CeD or only celiac-like enteropathy in CVID patients. Some authors even highlighted that the association between CVID and CeD remains controversial [<xref rid=\"B220-ijms-21-05223\" ref-type=\"bibr\">220</xref>]. This is discussed in detail below. In 2019, Schwimmer et al. stated that both CeD and celiac-like conditions are common in patients with SIgAD deficiency and CVID [<xref rid=\"B8-ijms-21-05223\" ref-type=\"bibr\">8</xref>].</p><p>Regardless of the way we classify this entity, CeD-resembling findings, including villus atrophy and increased intraepithelial lymphocytes, are common among CVID patients. According to Malamut et al., villous atrophy was observed in over 50% of CVID patients [<xref rid=\"B156-ijms-21-05223\" ref-type=\"bibr\">156</xref>], while Luizi et al. indicated it to be 31.2% [<xref rid=\"B221-ijms-21-05223\" ref-type=\"bibr\">221</xref>]. A literature review performed by Hartono et al. reported the presence of villus atrophy in CVID patients within the range of 24% to 50% [<xref rid=\"B222-ijms-21-05223\" ref-type=\"bibr\">222</xref>]. However, Karaca et al. showed a lower incidence of celiac-resembling enteropathy in CVID, as it affected only 10% of the study participants [<xref rid=\"B92-ijms-21-05223\" ref-type=\"bibr\">92</xref>]. Furthermore in Venhoff et al.’s study, villus atrophy and increased IELs were observed in only 8% of CVID patients presenting with enteropathy [<xref rid=\"B223-ijms-21-05223\" ref-type=\"bibr\">223</xref>].</p><p>Besides, Giorgio et al. underlined that although celiac-like histological findings can be found in 30% of CVID individuals, less than 10% of patients can be diagnosed with true CeD [<xref rid=\"B224-ijms-21-05223\" ref-type=\"bibr\">224</xref>].</p></sec><sec id=\"sec7dot2-ijms-21-05223\"><title>7.2. CeD in CVID: Etiology</title><p>Despite the association between CVID and CeD being controversial and the true prevalence of such a linkage being unestablished, some data concerning a common CVID and CeD pathogenesis seems worth mentioning.</p><p>On the one hand, it is established that CVID and CeD can share a common dysregulation of <italic>CTLA−4</italic> and <italic>ICOS</italic> [<xref rid=\"B72-ijms-21-05223\" ref-type=\"bibr\">72</xref>]. Additionally, the coexistence of CVID and CeD in the same family was described [<xref rid=\"B225-ijms-21-05223\" ref-type=\"bibr\">225</xref>]. Besides, an altered expression of HLA-DR on antigen-presenting cells (APCs) both in CVID and CeD was demonstrated by Viallard et al. [<xref rid=\"B226-ijms-21-05223\" ref-type=\"bibr\">226</xref>]. Giorgio et al. suggested that defective antigen presentation by APC through the HLA complex might be associated with the induction of an immunological response shared by CVID and CeD [<xref rid=\"B224-ijms-21-05223\" ref-type=\"bibr\">224</xref>].</p><p>On the other hand, Jorgenssen et al. assessed CVID patients with increased IEL in the pars descendens of the duodenum and CeD patients by gene expression microarray analyses and HLA typing, and the results indicated a large difference in gene expression, suggesting little or no overlap of CVID and CeD [<xref rid=\"B227-ijms-21-05223\" ref-type=\"bibr\">227</xref>]. Moreover, Woodward et al. reported multiple cases of persistent Norovirus infection in CVID patients who presented with celiac lesions, which healed after Norovirus eradication [<xref rid=\"B228-ijms-21-05223\" ref-type=\"bibr\">228</xref>]. It requires further investigation to establish whether CVID and CeD are truly associated or share a common etiology.</p></sec><sec id=\"sec7dot3-ijms-21-05223\"><title>7.3. CeD in CVID: Diagnostic Difficulties</title><p>Enteropathy accompanied by malabsorption is a common clinical presentation of CVID; however, villus atrophy remains a challenging finding in this group. Serological diagnostics is insufficient in individuals with CVID, and for this reason, it can resemble seronegative CeD [<xref rid=\"B55-ijms-21-05223\" ref-type=\"bibr\">55</xref>,<xref rid=\"B157-ijms-21-05223\" ref-type=\"bibr\">157</xref>]. However, in the vast majority of cases, CVID enteropathy differs from CeD not only the results of the serological assays, so it is debated whether it can be classified as true CeD or rather should be called celiac like [<xref rid=\"B156-ijms-21-05223\" ref-type=\"bibr\">156</xref>].</p><p>Similarly, as in SIgAD, histological assessment of a duodenal biopsy shows a paucity or absence of plasma cells [<xref rid=\"B62-ijms-21-05223\" ref-type=\"bibr\">62</xref>]. Besides, villous atrophy is usually less severe and the IEL count is often lower than in CeD, excessive neutrophil infiltration can be observed, as well as graft versus host-like lesions or follicular lymphoid hyperplasia [<xref rid=\"B93-ijms-21-05223\" ref-type=\"bibr\">93</xref>,<xref rid=\"B156-ijms-21-05223\" ref-type=\"bibr\">156</xref>]. ESsCD guidelines from 2019 say directly that an absence of plasma cells suggests CVID [<xref rid=\"B194-ijms-21-05223\" ref-type=\"bibr\">194</xref>].</p><p>Another argument in favour of the theory that enteropathy presented in CVID often differs from CeD is a fact that approximately 50% of patients do not respond to GFD [<xref rid=\"B229-ijms-21-05223\" ref-type=\"bibr\">229</xref>]. Some sources indicate that this percentage might be even up to 80% [<xref rid=\"B93-ijms-21-05223\" ref-type=\"bibr\">93</xref>].</p><p>Clear guidelines for the management of CVID patients with enteropathy have not been established yet. Some authors recommend initiating GFD after excluding infectious causes since the first proposed diagnosis is generally CeD. Jorgensen et al. suggested that the only criterion to confirm CeD diagnosis in CVID patients is the histological response to a GFD [<xref rid=\"B230-ijms-21-05223\" ref-type=\"bibr\">230</xref>,<xref rid=\"B231-ijms-21-05223\" ref-type=\"bibr\">231</xref>]. Other researchers highlighted the importance of HLA determination, as the presence of the <italic>DQ2</italic> or <italic>DQ8</italic> haplotype can be associated with concomitant CeD, thus allowing the identification of CVID patients at risk [<xref rid=\"B223-ijms-21-05223\" ref-type=\"bibr\">223</xref>]. Furthermore, if both <italic>HLA-DQ2</italic> and <italic>HLA-DQ8</italic> are negative, the diagnosis of CeD is highly unlikely [<xref rid=\"B194-ijms-21-05223\" ref-type=\"bibr\">194</xref>]. Intriguingly, Malamut et al. reported that among 10 CVID patients, in which GFD turned out to be inefficient, 2 were positive for anti-gliadin IgG and 3 patients had the <italic>HLA-DQ2.5</italic> haplotype [<xref rid=\"B156-ijms-21-05223\" ref-type=\"bibr\">156</xref>]. This strongly suggests that immune reactivity to gluten cannot alone account for the intestinal lesions in described patients.</p><p>There is a growing body of evidence that immunohistochemical analysis of TCRγδ can help discriminate CeD from other causes of duodenal lymphocytosis [<xref rid=\"B224-ijms-21-05223\" ref-type=\"bibr\">224</xref>,<xref rid=\"B232-ijms-21-05223\" ref-type=\"bibr\">232</xref>]; however, no author suggests the use of this method in the standard diagnostic procedure.</p><p>There is no doubt that true CeD can be diagnosed in CVID patients, but yet according to current knowledge, this relationship is rare. Diagnosing enteropathy in patients with CVID requires cautiousness, as data available are scarce and conflicting.</p></sec><sec id=\"sec7dot4-ijms-21-05223\"><title>7.4. CeD in CVID: Symptoms</title><p>Regardless of the cause of symptoms, non-infectious enteropathy characterized by weight loss, abdominal pain, bloating, and severe diarrhoea was reported often in CVID patients [<xref rid=\"B6-ijms-21-05223\" ref-type=\"bibr\">6</xref>]. According to Cunningham-Rundles, it was observed in even up to 20% to 60% of CVID patients [<xref rid=\"B147-ijms-21-05223\" ref-type=\"bibr\">147</xref>], but Yuan et al. reported a prevalence of the aforementioned symptoms of 10% to 20% [<xref rid=\"B219-ijms-21-05223\" ref-type=\"bibr\">219</xref>]. In the vast literature research, we found no data on differences between symptoms of CVID enteropathy and symptoms of CeD, which led us to the conclusion that manifestations of these entities are indistinguishable based on clinical signs.</p></sec><sec id=\"sec7dot5-ijms-21-05223\"><title>7.5. CeD in CVID: Treatment</title><p>Accepted treatment of immunoglobulin deficiency syndromes is immunoglobulin replacement therapy; however, intravenous immunoglobulins turned out to inefficient in CVID-related enteropathy and have not led to a consistent improvement of GI symptoms [<xref rid=\"B156-ijms-21-05223\" ref-type=\"bibr\">156</xref>,<xref rid=\"B157-ijms-21-05223\" ref-type=\"bibr\">157</xref>].</p><p>GFD is recommended only in <italic>HLA-DQ2</italic> or <italic>HLA-DQ8</italic> carriers as an attempt of a 6- to 12-month trial with follow-up histologic assessment [<xref rid=\"B8-ijms-21-05223\" ref-type=\"bibr\">8</xref>,<xref rid=\"B93-ijms-21-05223\" ref-type=\"bibr\">93</xref>,<xref rid=\"B156-ijms-21-05223\" ref-type=\"bibr\">156</xref>]. Patients without <italic>HLA-DQ2</italic> or <italic>HLA-DQ8</italic> or unresponsive to GFD should be treated as a distinct disorder. In this case, short-term steroids or immunomodulators, including azathioprine and 6-mercaptopurine, can be used [<xref rid=\"B62-ijms-21-05223\" ref-type=\"bibr\">62</xref>]. There is also evidence for the efficacy of an elemental diet, while in the case of severe malabsorption, limited use of total parenteral nutrition might be required [<xref rid=\"B19-ijms-21-05223\" ref-type=\"bibr\">19</xref>,<xref rid=\"B62-ijms-21-05223\" ref-type=\"bibr\">62</xref>].</p><p>A graphical summary concerning CeD in the discussed PIDs is presented in <xref ref-type=\"fig\" rid=\"ijms-21-05223-f003\">Figure 3</xref>.</p></sec></sec><sec id=\"sec8-ijms-21-05223\"><title>8. Inflammatory Bowel Disease</title><p>IBD is a complex heterogeneous disorder, characterized by chronic idiopathic inflammation of the GI tract. It is associated with dysregulation of the immune system in genetically susceptible individuals in response to environmental triggers [<xref rid=\"B233-ijms-21-05223\" ref-type=\"bibr\">233</xref>]. The term IBD refers to ulcerative colitis (UC) and Crohn’s disease (CD). Almost 10% of IBD stays undefined and falls into the category of indeterminate colitis. To make a proper diagnosis and distinguish one from another, clinical, endoscopic, histologic, and radiologic features are used [<xref rid=\"B234-ijms-21-05223\" ref-type=\"bibr\">234</xref>,<xref rid=\"B235-ijms-21-05223\" ref-type=\"bibr\">235</xref>]. The occurrence of IBD is rising worldwide, with the greatest increases in industrialized regions, which suggests this may be due to increased antibiotic use, decreased exposure to infectious factors, and changes in dietary pattern, including increased intake of emulsifiers and surfactants [<xref rid=\"B236-ijms-21-05223\" ref-type=\"bibr\">236</xref>,<xref rid=\"B237-ijms-21-05223\" ref-type=\"bibr\">237</xref>,<xref rid=\"B238-ijms-21-05223\" ref-type=\"bibr\">238</xref>,<xref rid=\"B239-ijms-21-05223\" ref-type=\"bibr\">239</xref>].</p><sec id=\"sec8dot1-ijms-21-05223\"><title>8.1. Ulcerative Colitis</title><sec id=\"sec8dot1dot1-ijms-21-05223\"><title>8.1.1. UC: Definition and Epidemiology</title><p>UC is a chronic recurrent inflammatory disorder characterized by continuous inflammation limited to the colon and rectum, with associated bloody diarrhoea and abdominal pain [<xref rid=\"B8-ijms-21-05223\" ref-type=\"bibr\">8</xref>,<xref rid=\"B240-ijms-21-05223\" ref-type=\"bibr\">240</xref>]. The estimated incidence and prevalence of UC varies depending on the country. For instance, they are higher in Europe and North America and significantly lower in Asia. The prevalence is reported to be 505 per 100,000 in Europe and 286 per 100,000 in the United States, while Asian studies report a much lower prevalence, e.g., 57.3 per 100,000 in Japan and 6.67 per 100,000 in Malaysia [<xref rid=\"B236-ijms-21-05223\" ref-type=\"bibr\">236</xref>]. UC usually affects adults aged 30–40 with no sex predominance and leads to disability [<xref rid=\"B5-ijms-21-05223\" ref-type=\"bibr\">5</xref>].</p></sec><sec id=\"sec8dot1dot2-ijms-21-05223\"><title>8.1.2. UC: Etiology</title><p>The exact pathogenesis of UC remains elusive but seems to be complex and multifactorial. Factors contributing to UC development include genetics, host immunity, infections, drugs, gut microbiota, and environmental factors. All this seems to take part in altering the state of intestinal homeostasis in susceptible individuals [<xref rid=\"B8-ijms-21-05223\" ref-type=\"bibr\">8</xref>].</p><p>Multiple genetic risk factors are likely associated with UC development. However, only 7.5% of these cases are explained by a specific genetic factor [<xref rid=\"B241-ijms-21-05223\" ref-type=\"bibr\">241</xref>]. To date, more than 200 loci have been associated with IBD. Among them, <italic>HLA-DQA1</italic> variants are most strongly associated with UC [<xref rid=\"B242-ijms-21-05223\" ref-type=\"bibr\">242</xref>]. Except this, mutations in genes related to pathways involved in epithelial barrier function (e.g., <italic>CHD1</italic> and <italic>LAMB1</italic>), cytokine milieu (e.g., <italic>IL1R2</italic>, <italic>IL8Ra/RB</italic>, and <italic>IL7R</italic>), and modulation of inflammation (e.g., <italic>TNFRSF15</italic>, <italic>TNFRSF9</italic>, <italic>PPAR-γ</italic>, <italic>XBP1</italic>) increases the risk of UC [<xref rid=\"B5-ijms-21-05223\" ref-type=\"bibr\">5</xref>,<xref rid=\"B243-ijms-21-05223\" ref-type=\"bibr\">243</xref>]. In favour of the theory of genetics factors predisposing to UC development is the fact that 8% to 14% of UC patients have a family history of IBD [<xref rid=\"B236-ijms-21-05223\" ref-type=\"bibr\">236</xref>,<xref rid=\"B244-ijms-21-05223\" ref-type=\"bibr\">244</xref>]. However, all the aforementioned genetic factors are of limited clinical use, since they have little predictive capacity for phenotypes [<xref rid=\"B241-ijms-21-05223\" ref-type=\"bibr\">241</xref>,<xref rid=\"B245-ijms-21-05223\" ref-type=\"bibr\">245</xref>].</p><p>It is suggested that alterations in the permeability of the intestinal mucosa play an important role in triggering UC development. For this reason, enteric infections are suspected as a possible risk factor, as they impair the intestinal barrier [<xref rid=\"B246-ijms-21-05223\" ref-type=\"bibr\">246</xref>,<xref rid=\"B247-ijms-21-05223\" ref-type=\"bibr\">247</xref>,<xref rid=\"B248-ijms-21-05223\" ref-type=\"bibr\">248</xref>,<xref rid=\"B249-ijms-21-05223\" ref-type=\"bibr\">249</xref>]. Furthermore, the intake of various drugs can possibly be implicated in the pathogenesis of UC. Among them, antibiotics, nonsteroidal anti-inflammatory drugs, and oral contraceptives are often mentioned [<xref rid=\"B5-ijms-21-05223\" ref-type=\"bibr\">5</xref>,<xref rid=\"B250-ijms-21-05223\" ref-type=\"bibr\">250</xref>,<xref rid=\"B251-ijms-21-05223\" ref-type=\"bibr\">251</xref>]. Interestingly, tobacco smoking is considered as a protective factor since active smokers have a lower risk of developing UC and have a milder disease course in contrast to non-smokers; however, discontinuation of tobacco is one of the strongest risk factors associated with UC [<xref rid=\"B5-ijms-21-05223\" ref-type=\"bibr\">5</xref>].</p></sec><sec id=\"sec8dot1dot3-ijms-21-05223\"><title>8.1.3. UC: Symptoms</title><p>UC is a disease typically presenting with periods of relapse and remission, which are observed in up to 90% of patients. Besides, early relapse is usually associated with a more severe course of the disease [<xref rid=\"B246-ijms-21-05223\" ref-type=\"bibr\">246</xref>,<xref rid=\"B252-ijms-21-05223\" ref-type=\"bibr\">252</xref>].</p><p>The most common symptoms of UC are bloody diarrhoea associated with abdominal pain. Nocturnal diarrhoea, mucous discharge in the stool, urgency, or tenesmus can also be present. Weight loss with anaemia, iron deficiency and hypoalbuminemia, fevers, or even toxic megacolon might be observed in cases of severe, acute, or prolonged untreated UC [<xref rid=\"B5-ijms-21-05223\" ref-type=\"bibr\">5</xref>,<xref rid=\"B242-ijms-21-05223\" ref-type=\"bibr\">242</xref>]. Extraintestinal symptoms occur in approximately 31% of patients and concern skin, joints, eyes, and liver. Among them, arthropathies are most common [<xref rid=\"B253-ijms-21-05223\" ref-type=\"bibr\">253</xref>].</p></sec><sec id=\"sec8dot1dot4-ijms-21-05223\"><title>8.1.4. UC: Diagnostics</title><p>UC diagnosis is made based on clinical symptoms and endoscopic findings. Histological assessment and serological tests have only an auxiliary function [<xref rid=\"B240-ijms-21-05223\" ref-type=\"bibr\">240</xref>].</p><p>In patients suspected of UC, colonoscopy with intubation of the ileum and biopsies of affected and unaffected areas should be performed. This approach enables assessment of the extent of the disease and helps to exclude distal ileal involvement, which can be indicative for CD [<xref rid=\"B254-ijms-21-05223\" ref-type=\"bibr\">254</xref>].</p><p>Endoscopy usually reveals continuous inflammation of the colon, which typically starts in the rectum and can expand on more proximal segments of the large intestine [<xref rid=\"B242-ijms-21-05223\" ref-type=\"bibr\">242</xref>]. Characteristic features of lesions include erythema, loss of vascular markings, granularity and friability of the mucosa, erosions, ulcerations, and spontaneous bleeding, with a distinct demarcation between inflamed and non-inflamed mucosa [<xref rid=\"B254-ijms-21-05223\" ref-type=\"bibr\">254</xref>,<xref rid=\"B255-ijms-21-05223\" ref-type=\"bibr\">255</xref>,<xref rid=\"B256-ijms-21-05223\" ref-type=\"bibr\">256</xref>].</p><p>Histopathological assessment is useful in defining the extent of disease, as histologic extension can be found even in endoscopically normal-appearing mucosa, and to identify the presence of intraepithelial dysplasia, neoplasia, or cancer [<xref rid=\"B254-ijms-21-05223\" ref-type=\"bibr\">254</xref>,<xref rid=\"B257-ijms-21-05223\" ref-type=\"bibr\">257</xref>]. Most often, pathologic examination shows inflammation restricted to the mucosa of the colon with distortion of the crypt architecture, crypt abscesses, and infiltration with lymphocytes, plasma cells, and granulocytes, as well as mucous depletion and a lack of granulomas [<xref rid=\"B240-ijms-21-05223\" ref-type=\"bibr\">240</xref>,<xref rid=\"B242-ijms-21-05223\" ref-type=\"bibr\">242</xref>,<xref rid=\"B257-ijms-21-05223\" ref-type=\"bibr\">257</xref>].</p><p>Neither serological tests nor imaging studies play a significant role in making the initial diagnosis. Perinuclear antineutrophil cytoplasmic antibodies (pANCAs) can be found in up to 70% of individuals with UC; however, the accuracy of antibody testing for the diagnosis of UC is low. Therefore, it is not recommended as a diagnostic tool [<xref rid=\"B254-ijms-21-05223\" ref-type=\"bibr\">254</xref>,<xref rid=\"B258-ijms-21-05223\" ref-type=\"bibr\">258</xref>]. Instead, tests for faecal calprotectin, a protein found in polymorphonuclear leukocytes, can be used since a low calprotectin level has a high negative predictive value for IBD [<xref rid=\"B259-ijms-21-05223\" ref-type=\"bibr\">259</xref>,<xref rid=\"B260-ijms-21-05223\" ref-type=\"bibr\">260</xref>].</p></sec><sec id=\"sec8dot1dot5-ijms-21-05223\"><title>8.1.5. UC: Treatment</title><p>UC treatment aims to induce and subsequently maintain steroid-free clinical remission. Interestingly, there is no fully agreed or validated definition of remission. The British Society of Gastroenterology (BSG) in recent guidelines proposed the consideration of remission as a combination of symptoms’ resolution (normalization of bowel movements and cessation of bleeding) with mucosal healing in an endoscopy [<xref rid=\"B240-ijms-21-05223\" ref-type=\"bibr\">240</xref>].</p><p>The choice of therapy varies depending on the severity and localization of the disease. Aminosalicylates, like mesalamine, are used in the treatment of mild to moderate UC [<xref rid=\"B240-ijms-21-05223\" ref-type=\"bibr\">240</xref>]. Topical and systemic steroids can be used in flare-ups, while in moderate to severe disease, immunosuppressants and biological treatment are recommended [<xref rid=\"B5-ijms-21-05223\" ref-type=\"bibr\">5</xref>,<xref rid=\"B240-ijms-21-05223\" ref-type=\"bibr\">240</xref>]. Despite pharmacological treatment, approximately 15% of patients require colectomy [<xref rid=\"B5-ijms-21-05223\" ref-type=\"bibr\">5</xref>,<xref rid=\"B242-ijms-21-05223\" ref-type=\"bibr\">242</xref>].</p></sec></sec><sec id=\"sec8dot2-ijms-21-05223\"><title>8.2. Crohn’s Disease</title><sec id=\"sec8dot2dot1-ijms-21-05223\"><title>8.2.1. CD: Definition and Epidemiology</title><p>CD is a chronic inflammatory disease that affects all segments of the GI tract and is characterized by a relapsing and remitting manner. CD can result in progressive bowel damage and disability [<xref rid=\"B4-ijms-21-05223\" ref-type=\"bibr\">4</xref>,<xref rid=\"B261-ijms-21-05223\" ref-type=\"bibr\">261</xref>]. There is still a lack of a single unifying definition of CD and the diagnosis itself can remain challenging as it is a combination of clinical, endoscopic, and histopathologic examination [<xref rid=\"B240-ijms-21-05223\" ref-type=\"bibr\">240</xref>].</p><p>Similarly to UC, CD occurrence is higher in developed countries than in developing countries, and in urban areas than in rural areas [<xref rid=\"B1-ijms-21-05223\" ref-type=\"bibr\">1</xref>]. The prevalence of CD is highest in Europe (322 per 100,000) and North America (in Canada and the United States, it is 319 per 100,000 and 214 per 100,000, respectively). The annual incidence in the aforementioned areas is between 10 to 30 per 100,000 [<xref rid=\"B4-ijms-21-05223\" ref-type=\"bibr\">4</xref>,<xref rid=\"B236-ijms-21-05223\" ref-type=\"bibr\">236</xref>]. In Asia, it is rising; however, it remains significantly lower than in the west, as the crude annual overall incidence is 1.37 per 100,000 [<xref rid=\"B262-ijms-21-05223\" ref-type=\"bibr\">262</xref>]. CD presents no sex-specific distribution. It can affect individuals at any age; however, the onset usually occurs in the second to fourth decade of life [<xref rid=\"B4-ijms-21-05223\" ref-type=\"bibr\">4</xref>,<xref rid=\"B263-ijms-21-05223\" ref-type=\"bibr\">263</xref>].</p></sec><sec id=\"sec8dot2dot2-ijms-21-05223\"><title>8.2.2. CD: Etiology</title><p>CD is a disease of complex pathogenesis. The multifactorial etiology seems to include a genetic susceptibility, environmental factors, and altered gut microbiota, all of which contribute to an impaired mucosal immune response and compromised epithelial barrier function [<xref rid=\"B4-ijms-21-05223\" ref-type=\"bibr\">4</xref>].</p><p>Genetic factors predispose to CD development, but they are not sufficient to cause it, as the concordance rates of developing CD in monozygotic twins are approximately 20–50% [<xref rid=\"B264-ijms-21-05223\" ref-type=\"bibr\">264</xref>] and about 12% of patients have a family history of CD [<xref rid=\"B244-ijms-21-05223\" ref-type=\"bibr\">244</xref>]. Among more than 200 alleles associated with IBD, approximately 40 are specific for CD [<xref rid=\"B241-ijms-21-05223\" ref-type=\"bibr\">241</xref>,<xref rid=\"B265-ijms-21-05223\" ref-type=\"bibr\">265</xref>]. These include genes associated with innate immunity, preventing the dissemination of invasive bacterial species, related to Th17-cell function and dysregulated cytokine production (e.g., <italic>NOD2</italic>, <italic>ATG16L1</italic>, <italic>Il23R</italic>, <italic>LRRK2</italic>, <italic>IRGM</italic>, <italic>STAT3</italic>, <italic>HLA</italic>, <italic>JAK2</italic>, and Th17 pathways) and impaired mucous production and function (<italic>MUC2</italic>) [<xref rid=\"B241-ijms-21-05223\" ref-type=\"bibr\">241</xref>,<xref rid=\"B263-ijms-21-05223\" ref-type=\"bibr\">263</xref>,<xref rid=\"B266-ijms-21-05223\" ref-type=\"bibr\">266</xref>]. Abnormalities in intestinal tight junctions are also related to IBD [<xref rid=\"B267-ijms-21-05223\" ref-type=\"bibr\">267</xref>]. Despite proven participation of genetic factors in the pathogenesis of CD, their usage is still limited to the research field [<xref rid=\"B263-ijms-21-05223\" ref-type=\"bibr\">263</xref>].</p><p>Tobacco smoking is a well-documented environmental factor. Smoking increases the risk of CD two times [<xref rid=\"B268-ijms-21-05223\" ref-type=\"bibr\">268</xref>]. Additionally, drugs can contribute to CD development. It is suggested that antibiotics, by altering the gut microbiota and influencing the intestinal barrier, are implicated in CD pathogenesis [<xref rid=\"B269-ijms-21-05223\" ref-type=\"bibr\">269</xref>]. Similarly, as in UC, nonsteroidal anti-inflammatory drugs and oral contraceptives are possibly associated with CD [<xref rid=\"B270-ijms-21-05223\" ref-type=\"bibr\">270</xref>]. Interestingly, statins seem to have a protective effect for CD development [<xref rid=\"B271-ijms-21-05223\" ref-type=\"bibr\">271</xref>]. Moreover, dietary factors, like a decrease in dietary fibre intake, an increase in saturated fat consumption, and emulsifiers affecting the gut mucosa layer, have been linked with an increased risk of CD [<xref rid=\"B239-ijms-21-05223\" ref-type=\"bibr\">239</xref>,<xref rid=\"B272-ijms-21-05223\" ref-type=\"bibr\">272</xref>].</p><p>It was indicated that IBD patients have a reduced diversity in their gut microbiota when compared to healthy individuals. This is more strongly pronounced in CD than in UC [<xref rid=\"B273-ijms-21-05223\" ref-type=\"bibr\">273</xref>,<xref rid=\"B274-ijms-21-05223\" ref-type=\"bibr\">274</xref>]. This alternation in the intestinal microbiome can be related to early exposure to pets and farm animals, breastfeeding, hygiene, stress, and diet [<xref rid=\"B275-ijms-21-05223\" ref-type=\"bibr\">275</xref>,<xref rid=\"B276-ijms-21-05223\" ref-type=\"bibr\">276</xref>,<xref rid=\"B277-ijms-21-05223\" ref-type=\"bibr\">277</xref>]. It is not established whether the latter contributes to an increased risk of CD directly, or rather by affecting the gut microbiota [<xref rid=\"B263-ijms-21-05223\" ref-type=\"bibr\">263</xref>].</p></sec><sec id=\"sec8dot2dot3-ijms-21-05223\"><title>8.2.3. CD: Symptoms</title><p>As CD can manifest in any part of the GI tract from the mouth to the perianal area, the symptoms of this disorder are diverse and depend on its location in the gut [<xref rid=\"B4-ijms-21-05223\" ref-type=\"bibr\">4</xref>]. In total, 25% of patients present with colitis only, another 25% has ileitis exclusively, and 50% present with ileocolitis. Moreover, 30% of patients suffer from perianal lesions and less than 15% have involvement of the oral or gastroduodenal area [<xref rid=\"B263-ijms-21-05223\" ref-type=\"bibr\">263</xref>]. The most common symptom of CD is diarrhoea, often associated with abdominal pain. Diarrhoea can occur due to decreased water absorption and increased secretion of electrolytes, small intestinal bacterial overgrowth, or impaired bile acid resorption [<xref rid=\"B263-ijms-21-05223\" ref-type=\"bibr\">263</xref>,<xref rid=\"B278-ijms-21-05223\" ref-type=\"bibr\">278</xref>]. Ileocolitis might mimic appendicitis and presents with right lower quadrant abdominal pain and fever. Fatigue, anorexia, and weight loss are common symptoms. Signs of colonic involvement resemble those of UC and might include rectal bleeding or bloody diarrhoea [<xref rid=\"B4-ijms-21-05223\" ref-type=\"bibr\">4</xref>,<xref rid=\"B278-ijms-21-05223\" ref-type=\"bibr\">278</xref>]. Up to 50% of patients have intestinal complications, including strictures, abscess, fistulas, or phlegmon [<xref rid=\"B278-ijms-21-05223\" ref-type=\"bibr\">278</xref>]. Extraintestinal manifestations also occur in 43% of CD patients and include large joint arthritis, uveitis, iritis, episcleritis, erythema nodosum, and pyoderma gangrenosum [<xref rid=\"B4-ijms-21-05223\" ref-type=\"bibr\">4</xref>,<xref rid=\"B253-ijms-21-05223\" ref-type=\"bibr\">253</xref>,<xref rid=\"B263-ijms-21-05223\" ref-type=\"bibr\">263</xref>].</p></sec><sec id=\"sec8dot2dot4-ijms-21-05223\"><title>8.2.4. CD: Diagnostics</title><p>According to the American College of Gastroenterology (ACG), CD is diagnosed clinically and there are no truly pathognomonic features of the disease. The diagnosis is made based on the results of the endoscopic, radiographic, and histologic assessment with evidence of chronic intestinal inflammation [<xref rid=\"B278-ijms-21-05223\" ref-type=\"bibr\">278</xref>].</p><p>Ileocolonoscopy with biopsy is considered the first-line investigation for individuals suspected of CD [<xref rid=\"B240-ijms-21-05223\" ref-type=\"bibr\">240</xref>]. Endoscopy typically reveals mucosal nodularity, edema, ulcerations, friability, and stenosis. Segmental inflammation (so-called “skip lesions”), apthoid, longitudinal, and serpiginous ulcerations are characteristic in CD. A combination of the aforementioned lesions creates a so-called cobblestone pattern [<xref rid=\"B4-ijms-21-05223\" ref-type=\"bibr\">4</xref>,<xref rid=\"B279-ijms-21-05223\" ref-type=\"bibr\">279</xref>,<xref rid=\"B280-ijms-21-05223\" ref-type=\"bibr\">280</xref>]. Classic endoscopy is insufficient in approximately 20% of patients [<xref rid=\"B278-ijms-21-05223\" ref-type=\"bibr\">278</xref>]. Small-bowel capsule endoscopy is recommended in this group [<xref rid=\"B4-ijms-21-05223\" ref-type=\"bibr\">4</xref>]. Additionally, computed tomography enterography and magnetic resonance enterography are useful and equally efficient in assessing the small intestine [<xref rid=\"B240-ijms-21-05223\" ref-type=\"bibr\">240</xref>,<xref rid=\"B278-ijms-21-05223\" ref-type=\"bibr\">278</xref>].</p><p>Histological findings typical of CD include chronic focal, patchy, discontinuous, and transmural inflammatory infiltration, and goblet cell preservation [<xref rid=\"B4-ijms-21-05223\" ref-type=\"bibr\">4</xref>]. The histological hallmark of CD is the epithelioid granuloma; however, they are seen only in approximately one-third of CD patients [<xref rid=\"B278-ijms-21-05223\" ref-type=\"bibr\">278</xref>].</p><p>As mentioned above, the routine use of genetic testing to establish the diagnosis of CD is not recommended. Even though more than half of patients might have anti-<italic>Saccharomyces cerevisiae</italic> antibody IgA (ASCA) in plasma, serological markers are of limited use [<xref rid=\"B278-ijms-21-05223\" ref-type=\"bibr\">278</xref>,<xref rid=\"B281-ijms-21-05223\" ref-type=\"bibr\">281</xref>]. Testing for fecal calprotectin can be useful since the low concentration of calprotectin in the stool has a high negative predictive value for the diagnosis of of IBD [<xref rid=\"B4-ijms-21-05223\" ref-type=\"bibr\">4</xref>,<xref rid=\"B282-ijms-21-05223\" ref-type=\"bibr\">282</xref>]. C-reactive protein is also useful, as its serum concentration changes dynamically, which makes it a good marker to detect and monitor inflammation [<xref rid=\"B240-ijms-21-05223\" ref-type=\"bibr\">240</xref>,<xref rid=\"B278-ijms-21-05223\" ref-type=\"bibr\">278</xref>].</p></sec><sec id=\"sec8dot2dot5-ijms-21-05223\"><title>8.2.5. CD: Treatment</title><p>The choice of medical therapy in patients with CD depends on the disease location and its severity. The goal is to induce and maintain symptomatic remission with signs of improvement of objective indicators of mucosal inflammation and to prevent the development of disease complications, such as strictures and fistulas [<xref rid=\"B278-ijms-21-05223\" ref-type=\"bibr\">278</xref>].</p><p>Induction of clinical remission is typically obtained by the use of corticosteroids, the administration of which should be limited in time and not exceed 3 months without attempting to introduce corticosteroid-sparing agents [<xref rid=\"B278-ijms-21-05223\" ref-type=\"bibr\">278</xref>]. Except steroids, biological treatment, including TNF inhibitors (e.g., infliximab, adalimumab, certolizumab), are effective in inducing remission [<xref rid=\"B240-ijms-21-05223\" ref-type=\"bibr\">240</xref>,<xref rid=\"B278-ijms-21-05223\" ref-type=\"bibr\">278</xref>]. Steroids are not effective in the maintenance of remission. For this purpose, immunomodulators like thiopurines (e.g., azathioprine, 6-mercaptopurine) and biological treatment are used. Commonly used in UC, 5-aminosalicylic acid has an uncertain effectiveness in induction or maintenance of remission in CD, thus it is not recommended [<xref rid=\"B261-ijms-21-05223\" ref-type=\"bibr\">261</xref>,<xref rid=\"B263-ijms-21-05223\" ref-type=\"bibr\">263</xref>,<xref rid=\"B278-ijms-21-05223\" ref-type=\"bibr\">278</xref>].</p><p>Surgical interventions, including bowel resection, stricturoplasty, or drainage of an abscess, are necessary in the vast majority of CD patients during their lifetime; however, surgery is not curative and relapses are observed frequently. Therefore, the main assumption of the surgical approach should be preserving as much of the intestine as possible, since the vast majority of patients require reoperations in the disease course [<xref rid=\"B283-ijms-21-05223\" ref-type=\"bibr\">283</xref>,<xref rid=\"B284-ijms-21-05223\" ref-type=\"bibr\">284</xref>].</p></sec></sec></sec><sec id=\"sec9-ijms-21-05223\"><title>9. Inflammatory Bowel Disease in Selective IgA Deficiency</title><sec id=\"sec9dot1-ijms-21-05223\"><title>9.1. IBD in SIgAD: Epidemiology</title><p>Despite the fact that IBD is often mentioned as a common manifestation of PIDs, the real statistics in this topic are scarce. Reliable data concerning monogenic PIDs presenting with an IBD-like phenotype are available. However, not much information about IBD in SIgAD patients can be found and the presented data mostly come from case reports. The first such description comes from 1975 and concerns the coincidence of SIgAD, CeD, and UC in one female patient [<xref rid=\"B285-ijms-21-05223\" ref-type=\"bibr\">285</xref>].</p><p>Various authors seem to agree that the prevalence of IBD in SIgAD patients is increased in contrast to non-SIgAD patients. Ludvigsson et al., in a large population-based matched cohort study, reported a prevalence of 3.9% among SIgAD individuals to 0.81% among controls [<xref rid=\"B202-ijms-21-05223\" ref-type=\"bibr\">202</xref>]. In research conducted by Azizi et al., the prevalence of IBD in SIgAD patients was 3.3% [<xref rid=\"B37-ijms-21-05223\" ref-type=\"bibr\">37</xref>]. Both results were statistically significant.</p><p>When concerning the coexistence of SIgAD and UC or SIgAD and CD separately, the prevalence was reported as 0.63% to 7.9% (weighted average of 1.73%) and between 1.21% and 15.8% (weighted average of 2.49%), respectively, in a comprehensive review of the literature performed by Odineal and Gershwin [<xref rid=\"B68-ijms-21-05223\" ref-type=\"bibr\">68</xref>]. In the aforementioned Ludvigsson cohort study, the prevalence of UC in SIgAD patients was 1.7% in contrast to 0.46% in the control group. In the same study, the prevalence of CD in SIgAD individuals was 2.4%, compared to 0.42% among controls. Both results were statistically significant, suggesting an association between SIgAD and UC, as well as between SIgAD and CD [<xref rid=\"B202-ijms-21-05223\" ref-type=\"bibr\">202</xref>].</p></sec><sec id=\"sec9dot2-ijms-21-05223\"><title>9.2. IBD in SIgAD: Etiology</title><p>The most commonly described pathogenetic association between SIgAD and IBD concerns primary abnormalities underlying SIgAD: A deficit of IgA, especially the secretory component. On the one hand, a lack of secretory IgA can impair the local immune mechanism in the gut and thus weaken the mucous barrier, which might translate into increased permeability of the mucosa and enhanced exposure to bowel antigens, subsequently leading to sustained inflammation within the gut mucosa [<xref rid=\"B286-ijms-21-05223\" ref-type=\"bibr\">286</xref>,<xref rid=\"B287-ijms-21-05223\" ref-type=\"bibr\">287</xref>]. The described mechanism might contribute to the development of autoimmunity within the GI tract, which is conclusive with the observation of an increased prevalence of autoimmune diseases in patients with SIgAD [<xref rid=\"B40-ijms-21-05223\" ref-type=\"bibr\">40</xref>]. On the other hand, the deficit of secretory IgA contributes to alteration in the gut microbiota, which is also a case in IBD [<xref rid=\"B56-ijms-21-05223\" ref-type=\"bibr\">56</xref>]. Interestingly, Marks et al. highlighted that GI manifestations in SIgAD patients are relatively rare compared to other primary antibody deficiencies possibly due to a compensatory increase of plasma cells producing IgM attached to a secretory component [<xref rid=\"B286-ijms-21-05223\" ref-type=\"bibr\">286</xref>]. Therefore, the pathogenetic role of IgA deficiency in IBD development is uncertain and requires further investigation.</p></sec><sec id=\"sec9dot3-ijms-21-05223\"><title>9.3. IBD in SIgAD: Diagnostic Difficulties, Symptoms, and Treatment</title><p>To date, the literature indicates no significant differences between IBD in SIgAD and only IBD in terms of histological features, symptoms, and treatment. Histological findings in coexistent SIgAD and UC or CD are consistent with those concerning UC or CD separately, which are described above [<xref rid=\"B8-ijms-21-05223\" ref-type=\"bibr\">8</xref>,<xref rid=\"B287-ijms-21-05223\" ref-type=\"bibr\">287</xref>]. Despite this, many authors underline the need of ruling out PID in patients with IBD and a consistent history of recurrent infection, failure to respond to therapy, or a particularly severe course of disease [<xref rid=\"B14-ijms-21-05223\" ref-type=\"bibr\">14</xref>,<xref rid=\"B15-ijms-21-05223\" ref-type=\"bibr\">15</xref>,<xref rid=\"B222-ijms-21-05223\" ref-type=\"bibr\">222</xref>].</p></sec></sec><sec id=\"sec10-ijms-21-05223\"><title>10. Inflammatory Bowel Disease in Common Variable Immunodeficiency</title><sec id=\"sec10dot1-ijms-21-05223\"><title>10.1. IBD in CVID: Epidemiology</title><p>Enteropathy is a common manifestation of CVID and might resemble CeD (discussed in detail above) or IBD [<xref rid=\"B93-ijms-21-05223\" ref-type=\"bibr\">93</xref>]. It is difficult to determine the actual prevalence of the latter among patients with CVID for several reasons. Firstly, CVID is a relatively rare condition; therefore, study groups are usually small. Secondly, not many studies on this topic are available and authors usually limit themselves to stating that concomitant CVID and IBD occurrence exceeds this in the general population. </p><p>In many reviews, the prevalence of 2–13% of IBD among CVID patients repeats [<xref rid=\"B13-ijms-21-05223\" ref-type=\"bibr\">13</xref>,<xref rid=\"B174-ijms-21-05223\" ref-type=\"bibr\">174</xref>,<xref rid=\"B222-ijms-21-05223\" ref-type=\"bibr\">222</xref>]. Hermaszewski and Webster reported the prevalence of IBD in CVID as 4% [<xref rid=\"B288-ijms-21-05223\" ref-type=\"bibr\">288</xref>]. It was approximately 6% in a study by Cunningham-Rundles et al. [<xref rid=\"B289-ijms-21-05223\" ref-type=\"bibr\">289</xref>]. In a retrospective Spanish study, the estimated prevalence of IBD in patients with CVID was 3.2% [<xref rid=\"B63-ijms-21-05223\" ref-type=\"bibr\">63</xref>]. However, Agarwal et al. stated that approximately 6–10% of CVID patients develop an IBD-like disorder [<xref rid=\"B174-ijms-21-05223\" ref-type=\"bibr\">174</xref>]. Interestingly, a retrospective study by Daniels et al. showed that among CVID patients with GI manifestations, 35% presented with a prior diagnosis of IBD [<xref rid=\"B157-ijms-21-05223\" ref-type=\"bibr\">157</xref>]. Kainulainen et al. indicated that it was a case in 1% of all CVID patients [<xref rid=\"B290-ijms-21-05223\" ref-type=\"bibr\">290</xref>]. These observations lead to the proposal by Uhlig et al. of “red flags” of when to consider PID being causative for IBD, including very early onset of IBD, a very severe or refractory to conventional treatment course of the disease, the need for total parenteral nutrition, as well as suggestive family history or consanguinity, and severe infections [<xref rid=\"B14-ijms-21-05223\" ref-type=\"bibr\">14</xref>].</p></sec><sec id=\"sec10dot2-ijms-21-05223\"><title>10.2. IBD in CVID: Etiology</title><p>Patients with CVID are predisposed to intestinal inflammation, possibly due to T-cell dysfunctions (discussed it details above), since treatment with immunoglobulin replacement therapy does not ameliorate the course of the colitis [<xref rid=\"B62-ijms-21-05223\" ref-type=\"bibr\">62</xref>,<xref rid=\"B156-ijms-21-05223\" ref-type=\"bibr\">156</xref>,<xref rid=\"B289-ijms-21-05223\" ref-type=\"bibr\">289</xref>,<xref rid=\"B291-ijms-21-05223\" ref-type=\"bibr\">291</xref>].</p><p>The literature provides numerous studies concerning abnormalities of T-cells in CVID enteropathy; however, the results are not fully conclusive. An increased number of ILCs (CD3-) producing IFN-γ was observed in the mucous of patients with CVID but not with IBD only [<xref rid=\"B93-ijms-21-05223\" ref-type=\"bibr\">93</xref>,<xref rid=\"B292-ijms-21-05223\" ref-type=\"bibr\">292</xref>]. It is suggested that these might contribute to an increased Th1 response, as studies by Cols et al. and Mannon et al. showed that lamina propria T-cells of CVID patients with enteropathy resembling IBD produce increased amounts of IL−12 and IFN-γ [<xref rid=\"B291-ijms-21-05223\" ref-type=\"bibr\">291</xref>,<xref rid=\"B293-ijms-21-05223\" ref-type=\"bibr\">293</xref>]. The role of both cytokines in the promotion of gut inflammation in CD is well investigated [<xref rid=\"B294-ijms-21-05223\" ref-type=\"bibr\">294</xref>] and therapeutic agents targeting IL−12 with briakinumab or ustekinumab have promising results [<xref rid=\"B295-ijms-21-05223\" ref-type=\"bibr\">295</xref>,<xref rid=\"B296-ijms-21-05223\" ref-type=\"bibr\">296</xref>,<xref rid=\"B297-ijms-21-05223\" ref-type=\"bibr\">297</xref>]. However, mucosal T-cells of CVID patients produce lesser amounts of IL−17, IL−23, and TNF-α than patients with idiopathic IBD [<xref rid=\"B291-ijms-21-05223\" ref-type=\"bibr\">291</xref>,<xref rid=\"B293-ijms-21-05223\" ref-type=\"bibr\">293</xref>]. This suggests divergent pathogenic mechanisms underlying CVID/IBD and IBD but acting on a shared immunological background [<xref rid=\"B286-ijms-21-05223\" ref-type=\"bibr\">286</xref>]. In contrast, Agarwal et al. showed that T-cells from the lamina propria of CVID patients exhibited a lower overall mRNA level for IFN-γ compared to UC and CD. This finding neared statistical significance. Furthermore, this study demonstrated impaired production of IL−2, IL−10, IFN-γ, and TNF-α in CVID/IBD patients following TCR-dependent stimulation. This is consistent with the theory described above that GI inflammation in CVID might be driven by T-cell dysfunction, including TCR impairment [<xref rid=\"B174-ijms-21-05223\" ref-type=\"bibr\">174</xref>]. It is also suggested that, in rare cases, CVID and IBD can share a common genetic background since mutation of <italic>ICOS</italic>-<italic>CTLA4</italic> can underlie CVID development and might manifest as IBD [<xref rid=\"B147-ijms-21-05223\" ref-type=\"bibr\">147</xref>,<xref rid=\"B298-ijms-21-05223\" ref-type=\"bibr\">298</xref>].</p><p>Another cause that might underlie the development of CD in CVID patients is enhanced granulomas formation in CVID. An increased trend of monocytes forming giant cells has been demonstrated in CVID, which contributed to elevated granulocyte-macrophage colony-stimulating factor, IL−4, IFN-γ, and TNF-α [<xref rid=\"B84-ijms-21-05223\" ref-type=\"bibr\">84</xref>,<xref rid=\"B299-ijms-21-05223\" ref-type=\"bibr\">299</xref>]. Besides, Scott-Taylor et al. reported a greater trend of CVID monocytes fusing in immunoglobulin-conditioned media, which suggest that standard immunoglobulin replacement therapy can contribute to granuloma formation [<xref rid=\"B299-ijms-21-05223\" ref-type=\"bibr\">299</xref>]. Whether this can enhance inflammation in CVID patients remains to be elucidated. Interestingly, Sanges et al. reported a case of CVID patients with coexistent CD, which were unresponsive to intravenous administration of immunoglobulins, but subcutaneous immunoglobulins turned out to be effective [<xref rid=\"B300-ijms-21-05223\" ref-type=\"bibr\">300</xref>]. However, as the granulomas can be linked to an active crypt destructive colitis, the coexistence of CVID and IBD seems understandable [<xref rid=\"B157-ijms-21-05223\" ref-type=\"bibr\">157</xref>].</p><p>It is established that low mucosal IgA levels can contribute to intestinal infections, luminal bacterial overgrowth, and increased permeability of the mucosal barrier, enhancing inflammation [<xref rid=\"B301-ijms-21-05223\" ref-type=\"bibr\">301</xref>]. Gut microbiota alteration is observed both in CVID and IBD [<xref rid=\"B144-ijms-21-05223\" ref-type=\"bibr\">144</xref>]. However, a relatively low frequency of GI manifestations in SIgAD, in contrast to other primary immunoglobulin deficiencies, strongly suggests that a decreased IgA level in neither causative nor sufficient in enteropathy development [<xref rid=\"B286-ijms-21-05223\" ref-type=\"bibr\">286</xref>].</p></sec><sec id=\"sec10dot3-ijms-21-05223\"><title>10.3. IBD in CVID: Diagnostic Difficulties and Symptoms</title><p>There is a lack of consensus regarding whether inflammation of the intestinal tract in CVID patients should be considered as IBD coexisting with CVID or as a distinct entity: CVID-associated enteropathy or CVID-associated colitis.</p><p>Most authors agree on that the clinical signs in IBD or IBD-like within CVID patients show no significant differences with those without CVID and include weight loss, chronic diarrhoea, rectal bleeding, abdominal pain, and malabsorption [<xref rid=\"B6-ijms-21-05223\" ref-type=\"bibr\">6</xref>,<xref rid=\"B62-ijms-21-05223\" ref-type=\"bibr\">62</xref>,<xref rid=\"B157-ijms-21-05223\" ref-type=\"bibr\">157</xref>,<xref rid=\"B160-ijms-21-05223\" ref-type=\"bibr\">160</xref>]. The main difference concerns the possibility of co-occurring severe infections in immunocompromised patients, which are included in the aforementioned “red flags” of IBD [<xref rid=\"B15-ijms-21-05223\" ref-type=\"bibr\">15</xref>].</p><p>However, in terms of histological findings in CVID enteropathy/colitis, data provided by the literature are conflicting. Various histological findings are observed in CVID patients. According to Kalha and Sellin, these findings include elevated intraepithelial lymphocytosis, increased macrophages, acute inflammation in the crypt epithelium and lamina propria, or destruction of crypts, and decreased plasma cells. Granulomas and giant cells are usually not present despite an increased trend to form granulomas in CVID [<xref rid=\"B302-ijms-21-05223\" ref-type=\"bibr\">302</xref>].</p><p>Hartono et al. summarized that histopathology assessment of colon specimens from CVID patients shows endoscopic and histopathological features that overlap considerably with CD or UC [<xref rid=\"B157-ijms-21-05223\" ref-type=\"bibr\">157</xref>,<xref rid=\"B222-ijms-21-05223\" ref-type=\"bibr\">222</xref>,<xref rid=\"B303-ijms-21-05223\" ref-type=\"bibr\">303</xref>,<xref rid=\"B304-ijms-21-05223\" ref-type=\"bibr\">304</xref>]. One difference that can help distinguish idiopathic IBD from CVID enteropathy/colitis remains the paucity of plasma cells in their biopsy (observed in 68% of the patients) [<xref rid=\"B222-ijms-21-05223\" ref-type=\"bibr\">222</xref>,<xref rid=\"B305-ijms-21-05223\" ref-type=\"bibr\">305</xref>]. Additionally, Tegtmeyer et al. indicated that both symptoms and histologic findings are indistinguishable in idiopathic IBD and IBD related to PID [<xref rid=\"B15-ijms-21-05223\" ref-type=\"bibr\">15</xref>]. Similarly, Sanges et al. stated that, except a lack or paucity of plasma cells in mucosal specimens, the two discussed conditions are indistinguishable from each other based on symptoms, and endoscopic and histological examinations [<xref rid=\"B300-ijms-21-05223\" ref-type=\"bibr\">300</xref>].</p><p>On the other hand, many authors are in favour of the theory that CVID enteropathy/colitis is a distinct independent disorder, separate from classic IBD, as there is an aforementioned lack of plasma cells in the mucosa [<xref rid=\"B302-ijms-21-05223\" ref-type=\"bibr\">302</xref>,<xref rid=\"B306-ijms-21-05223\" ref-type=\"bibr\">306</xref>,<xref rid=\"B307-ijms-21-05223\" ref-type=\"bibr\">307</xref>]. Besides, this approach highlights that CVID can mimic lymphocytic colitis, collagenous colitis, and colitis associated with graft-versus-host disease [<xref rid=\"B308-ijms-21-05223\" ref-type=\"bibr\">308</xref>,<xref rid=\"B309-ijms-21-05223\" ref-type=\"bibr\">309</xref>,<xref rid=\"B310-ijms-21-05223\" ref-type=\"bibr\">310</xref>]. According to Khodadad et al., CVID enteropathy/colitis can be divided into three major groups: Crypt-destructive colitis, non-crypt-destructive colitis, and graft versus host disease-like pattern. The former includes classic IBD, specifically UC and CD. Non-crypt-destructive colitis comprises lymphocytic colitis and collagenous colitis [<xref rid=\"B306-ijms-21-05223\" ref-type=\"bibr\">306</xref>,<xref rid=\"B307-ijms-21-05223\" ref-type=\"bibr\">307</xref>]. Additionally, in CVID enteropathy/colitis, stricturing is observed; however, fistulation is not common [<xref rid=\"B286-ijms-21-05223\" ref-type=\"bibr\">286</xref>].</p><p>All the above-described observations suggest that some cases of gut inflammation in CVID patients can be diagnosed as true IBD, while others should be referred to as IBD-like. There is an ongoing discussion on this topic and no consensus has been reached yet.</p></sec><sec id=\"sec10dot4-ijms-21-05223\"><title>10.4. IBD in CVID: Treatment</title><p>The clinical approach in managing IBD in primary antibody deficiencies remains a challenge, as the heterogeneous etiology impedes apt diagnostics, as well as treatment. Suspicion of immunodeficiency should be made, especially when patients with persistent diarrhoea, malabsorption, failure to thrive, and resistance to standard therapy present with an unusual clinical course and opportunistic or severe infections. Additionally, prior diagnosis of CVID should suggest an increased awareness concerning GI symptoms, as Resnick et al. showed increased mortality in CVID patients with GI disease and malabsorption [<xref rid=\"B160-ijms-21-05223\" ref-type=\"bibr\">160</xref>].</p><p>Regardless of the doubts of whether IBD in CVID should be referred to as IBD, or rather IBD-like disease, or CVID enteropathy/colitis, most authors recommend using therapy schemes for IBD, with increased caution concerning immunosuppressive drugs [<xref rid=\"B6-ijms-21-05223\" ref-type=\"bibr\">6</xref>,<xref rid=\"B311-ijms-21-05223\" ref-type=\"bibr\">311</xref>]. However, there is little data about the therapeutical approach in this case. Gut inflammation in CVID is usually difficult to control and often resistant to standard IBD therapy [<xref rid=\"B300-ijms-21-05223\" ref-type=\"bibr\">300</xref>,<xref rid=\"B311-ijms-21-05223\" ref-type=\"bibr\">311</xref>]. Treatment of non-infectious GI disease in CVID includes corticosteroids, elimination of bacterial overgrowth with antibiotics, 5-aminosalicylic acid, 6-mercaptopurine, and azathioprine [<xref rid=\"B301-ijms-21-05223\" ref-type=\"bibr\">301</xref>,<xref rid=\"B311-ijms-21-05223\" ref-type=\"bibr\">311</xref>,<xref rid=\"B312-ijms-21-05223\" ref-type=\"bibr\">312</xref>]. Besides, several groups reported substantial efficacy of targeted biological therapies, including anti-TNF-α drugs (infliximab and adalimumab), and the anti-IL−12/IL−23 monoclonal antibody ustekinumab [<xref rid=\"B297-ijms-21-05223\" ref-type=\"bibr\">297</xref>,<xref rid=\"B305-ijms-21-05223\" ref-type=\"bibr\">305</xref>,<xref rid=\"B313-ijms-21-05223\" ref-type=\"bibr\">313</xref>,<xref rid=\"B314-ijms-21-05223\" ref-type=\"bibr\">314</xref>,<xref rid=\"B315-ijms-21-05223\" ref-type=\"bibr\">315</xref>,<xref rid=\"B316-ijms-21-05223\" ref-type=\"bibr\">316</xref>,<xref rid=\"B317-ijms-21-05223\" ref-type=\"bibr\">317</xref>]. Vedolizumab, an inhibitor of α4β7integrin, was also used in CVID-enteropathy, although with different results [<xref rid=\"B93-ijms-21-05223\" ref-type=\"bibr\">93</xref>,<xref rid=\"B318-ijms-21-05223\" ref-type=\"bibr\">318</xref>]. Furthermore, CVID patients with substantial T-cell defects require careful monitoring for fungal infections when on biological treatment.</p><p>Standard management of CVID patients includes immunoglobulin replacement therapy given intravenously or subcutaneously [<xref rid=\"B86-ijms-21-05223\" ref-type=\"bibr\">86</xref>]. Immunoglobulin replacement therapy lowers the frequency of recurrent or severe infections and reduces the rate of hospitalization. In general, it was found to be ineffective in IBD-like disease in CVID [<xref rid=\"B147-ijms-21-05223\" ref-type=\"bibr\">147</xref>,<xref rid=\"B156-ijms-21-05223\" ref-type=\"bibr\">156</xref>,<xref rid=\"B174-ijms-21-05223\" ref-type=\"bibr\">174</xref>], despite single case reports reporting the efficiency of subcutaneous immunoglobulin treatment [<xref rid=\"B289-ijms-21-05223\" ref-type=\"bibr\">289</xref>,<xref rid=\"B300-ijms-21-05223\" ref-type=\"bibr\">300</xref>].</p><p>In cases of severe malnutrition, patients might require total parenteral nutrition with micro- and macronutrient supplementation, electrolyte management, and prevention and treatment of fat-soluble vitamin deficiency [<xref rid=\"B93-ijms-21-05223\" ref-type=\"bibr\">93</xref>,<xref rid=\"B319-ijms-21-05223\" ref-type=\"bibr\">319</xref>,<xref rid=\"B320-ijms-21-05223\" ref-type=\"bibr\">320</xref>]. A study conducted by Teahon et al. showed an improvement of GI symptoms in CVID/IBD patients while on an elemental diet [<xref rid=\"B321-ijms-21-05223\" ref-type=\"bibr\">321</xref>].</p><p>Scarce literature available in this field, concerning the management of CVID-associated enteropathy and therapeutical outcomes, as well as potential side effects of certain therapies, provides little support when it comes to the diagnostic decision [<xref rid=\"B313-ijms-21-05223\" ref-type=\"bibr\">313</xref>], hence underlying the crucial need for further investigation.</p><p>A graphical summary concerning IBD and CVID is presented in <xref ref-type=\"fig\" rid=\"ijms-21-05223-f004\">Figure 4</xref>.</p></sec></sec></body><back><notes><title>Author Contributions</title><p>Conceptualization, I.J.M., M.M., I.K.-K. and P.E.; investigation, I.J.M., M.M.; writing—original draft preparation, I.J.M., M.M.; writing—review and editing, I.J.M., M.M., I.K.-K., A.Z., E.B.S., A.D., and P.E.; visualization, I.J.M., M.M., A.Z. and E.B.S.; supervision, I.K.-K., A.D. and P.E.; project administration, I.K.-K., A.D. and P.E. All authors have read and agreed to the published version of the manuscript.</p></notes><notes><title>Funding</title><p>This research received no external funding.</p></notes><notes notes-type=\"COI-statement\"><title>Conflicts of Interest</title><p>The authors declare no conflict of interest.</p></notes><glossary><title>Abbreviations</title><array orientation=\"portrait\"><tbody><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CeD</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Celiac disease</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">IBD</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Inflammatory bowel disease</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">PID</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Primary immunodeficiency</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">SIgAD</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Selective IgA disease</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CVID</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Common variable immunodeficiency</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">GI</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">gastrointestinal</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">PAGID</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Pan-American Group for Immunodeficiency</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ESID</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">European Society for Immunodeficiencies</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CSR</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">class switching recombination</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Treg</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">regulatory T-cell</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">TGB-β</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">transforming growth factor-beta</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">BCR</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">B-cell receptor</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">DC</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">dendritic cell</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">MHC</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">major histocompatibility complex</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Tfh</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">T follicular helper cell</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">HLA</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">human leukocyte antigen</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">SHM</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">somatic hypermutation</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">TLR</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">tool-like receptor</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">IFN-γ</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">interferon-gamma</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Breg</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">regulatory B-cell</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">TNF</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">tumor necrosis factor</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ILC</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">innate lymphoid cell</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">NK</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">natural-killer</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">GD</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">granulomatous disease</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">GFD</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">gluten-free diet</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">TG2</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">transglutaminase-2</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">EMA</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">endomysial antibodies</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">DGP</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">deamidated gliadin peptides</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ESPGHAN</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">European Society Paediatric Gastroenterology, Hepatology and Nutrition</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">IEL</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">intraepithelial lymphocytes</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ESsCD</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">European Society for the Study of Coeliac Disease</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">APC</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">antigen-presenting cell</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">TCRγδ</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">T-cell receptor γδ</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">pANCA</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Perinuclear antineutrophil cytoplasmic</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">BSG</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">British Society of Gastroenterology</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ACG</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">American College of Gastroenterology</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ASCA</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" 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IBD, inflammatory bowel disease; CeD, celiac disease; SIgAD, selective IgA deficiency; CVID, common variable immunodeficiency.</p></caption><graphic xlink:href=\"ijms-21-05223-g001\"/></fig><fig id=\"ijms-21-05223-f002\" orientation=\"portrait\" position=\"float\"><label>Figure 2</label><caption><p>Factors affecting the prevalence of SIgAD and its aetiology and symptoms. SIgAD, selective IgA deficiency.</p></caption><graphic xlink:href=\"ijms-21-05223-g002\"/></fig><fig id=\"ijms-21-05223-f003\" orientation=\"portrait\" position=\"float\"><label>Figure 3</label><caption><p>Comparrison of CeD, CeD in SIgAD, and CVID enteropathy. CVID, common variable immunodeficiency; CeD, celiac disease; SIgAD, selective IgA deficiency; GFD, gluten-free diet; GVH, graft-versus-host; IVIG, intravenous immunoglobulin; IELs, intraepithelial lymphocytes; antiTG2, anti-tissue transglutaminase; EMA, endomysial antibodies; DGP, deamidated glutin peptides; GI, gastrointestinal.</p></caption><graphic xlink:href=\"ijms-21-05223-g003\"/></fig><fig id=\"ijms-21-05223-f004\" orientation=\"portrait\" position=\"float\"><label>Figure 4</label><caption><p>Comparrison of IBD and CVID enteropathy/colitis. CVID, common variable immunodeficiency; IBD, inflammatory bowel disease; UC, ulcerative colitis; CD, Crohn’s disease; GVH, graft-versus-host; IL, interleukin; TNF-α, tumor necrosis factor-alpha; IVIG, intravenous immunoglobulin; INF-γ, interferon-gamma; 5-ASA, 5-aminosalicylic acid.</p></caption><graphic xlink:href=\"ijms-21-05223-g004\"/></fig><table-wrap id=\"ijms-21-05223-t001\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijms-21-05223-t001_Table 1</object-id><label>Table 1</label><caption><p>Selective IgA deficiency (SIgAD)—diagnostic criteria according to ESID (European Society for Immunodeficiencies) [<xref rid=\"B23-ijms-21-05223\" ref-type=\"bibr\">23</xref>].</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Selective IgA Deficiency (SIgAD)—Diagnostic Criteria</th></tr></thead><tbody><tr><td rowspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">\n<bold>At least one of:</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">increased susceptibility to infection</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">autoimmune manifestations</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">affected family member</td></tr><tr><td rowspan=\"6\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">\n<bold>ALL of the following:</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">serum IgA level lower than 7 mg/dL (0.07 g/L)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">normal serum levels of IgG and IgM</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">patients older than 4 years</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">normal IgG antibody response to vaccination</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">excluded other causes of hypogammaglobulinemia</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">excluded profound T-cell deficiency</td></tr></tbody></table></table-wrap><table-wrap id=\"ijms-21-05223-t002\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijms-21-05223-t002_Table 2</object-id><label>Table 2</label><caption><p>Common variable immunodeficiency (CVID)—diagnostic criteria according to ESID (European Society for Immunodeficiencies) [<xref rid=\"B23-ijms-21-05223\" ref-type=\"bibr\">23</xref>,<xref rid=\"B95-ijms-21-05223\" ref-type=\"bibr\">95</xref>].</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Common Variable Immunodeficiency (CVID)—Diagnostic Criteria</th></tr></thead><tbody><tr><td rowspan=\"5\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">\n<bold>At least one of:</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">increased susceptibility to infection</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">autoimmune manifestations</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">granulomatous disease</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">unexplained polyclonal lymphoproliferation </td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">affected family member with antibody deficiency</td></tr><tr><td rowspan=\"6\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">\n<bold>ALL of the following:</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">serum IgG and IgA levels at least 2 SD below the mean for age with/without decreased serum IgM level</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">normal serum levels of IgG and IgM</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">patients older than 4 years</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">poor response to vaccination and/or low switched memory B-cells (<70% of age-related normal value)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">excluded other causes of hypogammaglobulinemia</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">excluded profound T-cell deficiency</td></tr></tbody></table></table-wrap></floats-group></article>\n"
]
[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Int J Mol Sci</journal-id><journal-id journal-id-type=\"iso-abbrev\">Int J Mol Sci</journal-id><journal-id journal-id-type=\"publisher-id\">ijms</journal-id><journal-title-group><journal-title>International Journal of Molecular Sciences</journal-title></journal-title-group><issn pub-type=\"epub\">1422-0067</issn><publisher><publisher-name>MDPI</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32756440</article-id><article-id pub-id-type=\"pmc\">PMC7432084</article-id><article-id pub-id-type=\"doi\">10.3390/ijms21155553</article-id><article-id pub-id-type=\"publisher-id\">ijms-21-05553</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Article</subject></subj-group></article-categories><title-group><article-title>Inhibition of Colony-Stimulating Factor 1 Receptor by PLX3397 Prevents Amyloid Beta Pathology and Rescues Dopaminergic Signaling in Aging 5xFAD Mice</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Son</surname><given-names>Yeonghoon</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05553\">1</xref><xref ref-type=\"aff\" rid=\"af2-ijms-21-05553\">2</xref><xref ref-type=\"author-notes\" rid=\"fn1-ijms-21-05553\">†</xref></contrib><contrib contrib-type=\"author\"><name><surname>Jeong</surname><given-names>Ye Ji</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05553\">1</xref><xref ref-type=\"author-notes\" rid=\"fn1-ijms-21-05553\">†</xref></contrib><contrib contrib-type=\"author\"><name><surname>Shin</surname><given-names>Na-Rae</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05553\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Oh</surname><given-names>Se Jong</given-names></name><xref ref-type=\"aff\" rid=\"af3-ijms-21-05553\">3</xref></contrib><contrib contrib-type=\"author\"><name><surname>Nam</surname><given-names>Kyung Rok</given-names></name><xref ref-type=\"aff\" rid=\"af3-ijms-21-05553\">3</xref></contrib><contrib contrib-type=\"author\"><name><surname>Choi</surname><given-names>Hyung-Do</given-names></name><xref ref-type=\"aff\" rid=\"af4-ijms-21-05553\">4</xref></contrib><contrib contrib-type=\"author\"><name><surname>Choi</surname><given-names>Jae Yong</given-names></name><xref ref-type=\"aff\" rid=\"af3-ijms-21-05553\">3</xref><xref rid=\"c1-ijms-21-05553\" ref-type=\"corresp\">*</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0002-7743-0151</contrib-id><name><surname>Lee</surname><given-names>Hae-June</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05553\">1</xref><xref rid=\"c1-ijms-21-05553\" ref-type=\"corresp\">*</xref></contrib></contrib-group><aff id=\"af1-ijms-21-05553\"><label>1</label>Division of Radiation Bioscience, Korea Institute of Radiological & Medical Sciences, Seoul 01812, Korea; <email>[email protected]</email> (Y.S.); <email>[email protected]</email> (Y.J.J.); <email>[email protected]</email> (N.-R.S.)</aff><aff id=\"af2-ijms-21-05553\"><label>2</label>Primate Resources Center, Korea Research Institute of Bioscience and Biotechnology (KRIBB), Jeonbuk 56216, Korea</aff><aff id=\"af3-ijms-21-05553\"><label>3</label>Division of Applied RI, Korea Institute of Radiological and Medical Sciences, Seoul 01812, Korea; <email>[email protected]</email> (S.J.O.); <email>[email protected]</email> (K.R.N.)</aff><aff id=\"af4-ijms-21-05553\"><label>4</label>Department of EMF Research Team, Radio and Broadcasting Technology Laboratory, ETRI, Daejon 34129, Korea; <email>[email protected]</email></aff><author-notes><corresp id=\"c1-ijms-21-05553\"><label>*</label>Correspondence: <email>[email protected]</email> (J.Y.C.); <email>[email protected]</email> (H.-J.L.); Tel.: +82-2-970-1638 (H.-J.L.); Fax: +82-2-970-1985 (H.-J.L.)</corresp><fn id=\"fn1-ijms-21-05553\"><label>†</label><p>These authors contributed equally to this work.</p></fn></author-notes><pub-date pub-type=\"epub\"><day>03</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><month>8</month><year>2020</year></pub-date><volume>21</volume><issue>15</issue><elocation-id>5553</elocation-id><history><date date-type=\"received\"><day>26</day><month>6</month><year>2020</year></date><date date-type=\"accepted\"><day>01</day><month>8</month><year>2020</year></date></history><permissions><copyright-statement>© 2020 by the authors.</copyright-statement><copyright-year>2020</copyright-year><license license-type=\"open-access\"><license-p>Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>).</license-p></license></permissions><abstract><p>Alzheimer’s disease (AD) is a progressive neurodegenerative disease. In this study, to investigate the effect of microglial elimination on AD progression, we administered PLX3397, a selective colony-stimulating factor 1 receptor inhibitor, to the mouse model of AD (5xFAD mice). Amyloid-beta (Aβ) deposition and amyloid precursor protein (APP), carboxyl-terminal fragment β, ionized calcium-binding adaptor molecule 1, synaptophysin, and postsynaptic density (PSD)-95 levels were evaluated in the cortex and hippocampus. In addition, the receptor density changes in dopamine D2 receptor (D2R) and metabotropic glutamate receptor 5 were evaluated using positron emission tomography (PET). D2R, tyrosine hydroxylase (TH), and dopamine transporter (DAT) levels were analyzed in the brains of Tg (5xFAD) mice using immunohistochemistry. PLX3397 administration significantly decreased Aβ deposition following microglial depletion in the cortex and hippocampus of Tg mice. In the neuro-PET studies, the binding values for D2R in the Tg mice were lower than those in the wild type mice; however, after PLX3397 treatment, the binding dramatically increased. PLX3397 administration also reversed the changes in synaptophysin and PSD-95 expression in the brain. Furthermore, the D2R and TH expression in the brains of Tg mice was significantly lower than that in the wild type; however, after PLX3397 administration, the D2R and TH levels were significantly higher than those in untreated Tg mice. Thus, our findings show that administering PLX3397 to aged 5xFAD mice could prevent amyloid pathology, concomitant with the rescue of dopaminergic signaling, suggesting that targeting microglia may serve as a useful therapeutic option for neurodegenerative diseases, including AD. </p></abstract><kwd-group><kwd>Alzheimer’s disease</kwd><kwd>Alzheimer’s disease mice</kwd><kwd>PLX3397</kwd><kwd>Aβ pathology</kwd><kwd>synaptic change</kwd><kwd>dopamine D2 receptor</kwd><kwd>colony-stimulating factor 1 receptor inhibitor</kwd></kwd-group></article-meta></front><body><sec sec-type=\"intro\" id=\"sec1-ijms-21-05553\"><title>1. Introduction</title><p>Aging of the global population has led to a rapid increase in the incidence of Alzheimer’s disease (AD), and there is a growing need for an effective therapeutic drug for AD in order to improve the quality of life of AD patients during old age. AD is a multifactorial neurodegenerative disease that is characterized by progressive cognitive deterioration along with neuropsychiatric symptoms and behavioral changes. There is still a lack of clinical diagnostic parameters for living patients; however, postmortem neuropathologic evaluation has shown that the histopathology of AD is characterized by brain atrophy, amyloid plaques, neurofibrillary tangles, neuron and synapse loss, and dystrophic neurites [<xref rid=\"B1-ijms-21-05553\" ref-type=\"bibr\">1</xref>]. Amyloid-beta (Aβ) plaques constitute one of the neuropathological hallmarks of AD and are thought to lead to the neuronal death and synaptic dysfunction that underlie memory impairment [<xref rid=\"B2-ijms-21-05553\" ref-type=\"bibr\">2</xref>]. On the basis of the amyloidogenic hypothesis of AD, much research has been conducted on novel pharmacotherapies targeting the Aβ peptide cascade [<xref rid=\"B3-ijms-21-05553\" ref-type=\"bibr\">3</xref>]. However, this approach has been unsuccessful in clinical trials, and current research is targeting alternative mechanisms [<xref rid=\"B4-ijms-21-05553\" ref-type=\"bibr\">4</xref>].</p><p>An increase in the number of reactive microglia is also a key feature of AD [<xref rid=\"B5-ijms-21-05553\" ref-type=\"bibr\">5</xref>]. Histopathological studies have shown that activated microglial cells surround amyloid plaques [<xref rid=\"B6-ijms-21-05553\" ref-type=\"bibr\">6</xref>,<xref rid=\"B7-ijms-21-05553\" ref-type=\"bibr\">7</xref>,<xref rid=\"B8-ijms-21-05553\" ref-type=\"bibr\">8</xref>]. Microglia are widely thought to be the key mediators of Aβ clearance in AD [<xref rid=\"B9-ijms-21-05553\" ref-type=\"bibr\">9</xref>,<xref rid=\"B10-ijms-21-05553\" ref-type=\"bibr\">10</xref>]. However, overwhelming microglial activation exacerbates AD pathology via causing synaptic loss and inflammatory factor secretion [<xref rid=\"B5-ijms-21-05553\" ref-type=\"bibr\">5</xref>]. Therefore, controlling microglial reaction in AD progression could serve as a potential therapeutic target for AD.</p><p>A few studies reported that depleting microglia with a colony-stimulating factor 1 receptor (CSF1R) inhibitor during the disease process prevented amyloid plaque accumulation in the brains of AD animal models at an early stage of the disease [<xref rid=\"B11-ijms-21-05553\" ref-type=\"bibr\">11</xref>,<xref rid=\"B12-ijms-21-05553\" ref-type=\"bibr\">12</xref>]. PLX3397 is an orally bioavailable selective CSF1R/c-kit inhibitor that crosses the blood–brain barrier [<xref rid=\"B13-ijms-21-05553\" ref-type=\"bibr\">13</xref>]. PLX3397 is already in a Phase 3 clinical study for pigmented villonodular synovitis [<xref rid=\"B14-ijms-21-05553\" ref-type=\"bibr\">14</xref>]; however, its therapeutic effects on the late stage of AD have not yet been investigated. Therefore, in this study, we examined the impact of PLX3397 treatment during the late stage of the disease, which involves severe Aβ pathology, by using the 5xFAD mouse model, an AD mouse model.</p></sec><sec sec-type=\"results\" id=\"sec2-ijms-21-05553\"><title>2. Results</title><sec id=\"sec2dot1-ijms-21-05553\"><title>2.1. PLX3397 Alleviated Aβ Pathology in the Cortex and Hippocampus of Aged Tg Mice</title><p>To investigate the effect of PLX3397 on a mouse model of AD, 5xFAD mice at the age of 9 months were treated with PLX3397 by daily oral gavage to the mice at 50 mg·kg<sup>−1</sup> for 30 days (<xref ref-type=\"fig\" rid=\"ijms-21-05553-f001\">Figure 1</xref>). Treatment of the Tg mice with PLX3397 (50 mg·kg<sup>−1</sup>·day<sup>−1</sup>) for 1 month effectively suppressed the microglia in the cortex but did not eliminate them completely (<xref ref-type=\"fig\" rid=\"ijms-21-05553-f002\">Figure 2</xref>A). Oral PLX3397 administration led to a decrease in Aβ deposition in the cortex of the mice. In western blotting analyses, quantification of full-length amyloid precursor protein (APP-FL), carboxyl-terminal fragment β (CTFβ), and Aβ expression revealed 33% (APP-FL; Tg + PLX3397 = 0.669 ± 0.056, <italic>p</italic> = 0.0007), 28% (CTFβ; Tg + PLX3397 = 0.719 ± 0.148, <italic>p</italic> = 0.038), and 33% (Aβ; Tg + PLX3397 = 0.666 ± 0.053, <italic>p</italic> = 0.009) reduction, respectively, in protein levels on using PLX3397 treatment (<xref ref-type=\"fig\" rid=\"ijms-21-05553-f002\">Figure 2</xref>B). Furthermore, the treated mice showed 42% (Iba-1; Tg + PLX3397 = 1.748 ± 0.206, <italic>p</italic> = 0.0003) lower Iba-1 expression in the cortex than the untreated 5xFAD mice (Iba-1; Tg = 3.028 ± 0.269; <xref ref-type=\"fig\" rid=\"ijms-21-05553-f002\">Figure 2</xref>C).</p><p>Next, we analyzed the effect of PLX3397 on the hippocampus of aged Tg mice. In the hippocampus, oral administration of PLX3397 led to lower Aβ load (Aβ; Tg + PLX3397 = 0.624 ± 0.112, <italic>p</italic> = 0.007) and lesser microglial activation (Iba-1; Tg + PLX3397 = 1.959 ± 0.356, <italic>p</italic> = 0.012) compared to those noted for the vehicle-treated Tg group (Iba-1; Tg = 2.868 ± 0.423; <xref ref-type=\"fig\" rid=\"ijms-21-05553-f003\">Figure 3</xref>A,B). </p><p>Because Aβ deposition was significant in the CA1 region, we determined cell layer thickness in CA1 by performing cresyl violet staining. The thickness in the CA1 region in Tg mice (Tg = 33.75 ± 4.49, <italic>p</italic> < 0.0001, WT vs. Tg) was significantly lower than that in wild type (WT) mice (WT = 46.56 ± 1.48) and the cell layer of the CA1 region was protected by PLX3397 administration (Tg + PLX3397 = 49.86 ± 1.64, <italic>p</italic> < 0.0001, Tg vs. Tg + PLX3397; <xref ref-type=\"fig\" rid=\"ijms-21-05553-f003\">Figure 3</xref>C).</p></sec><sec id=\"sec2dot2-ijms-21-05553\"><title>2.2. PLX3397 Increased Synapse-Related Protein Levels in 5xFAD Mice</title><p>To determine whether alleviation of Aβ pathology in Tg mice treated with PLX3397 affected synaptic impairment, we analyzed presynaptic and postsynaptic protein levels in the hippocampus and cortex. The Tg mice had significantly lower synaptophysin (Tg = 0.513 ± 0.163, <italic>p</italic> = 0.0012, WT vs. Tg) and postsynaptic density (PSD)-95 (Tg = 33.75 ± 4.49, <italic>p</italic> < 0.0001, WT vs. Tg) levels than those of WT mice in the cortex region. PLX3397 treatment led to significant restoration of synaptophysin levels (Tg + PLX3397 = 0.861 ± 0.147, <italic>p</italic> = 0.0105, Tg vs. Tg + PLX3397) and PSD-95 (Tg + PLX3397 = 1.012 ± 0.129, <italic>p</italic> = 0.0245, Tg vs. Tg + PLX3397) in the cortex. In the hippocampus, the expression levels of synaptophysin (Tg = 0.591 ± 0.128, <italic>p</italic> = 0.0014, WT vs. Tg) and PSD-95 (Tg = 0.671 ± 0.182, <italic>p</italic> = 0.015, WT vs. Tg) was significantly decreased in Tg mice compared with the WT group. Following PLX3397 administration, the levels of synaptophysin (Tg + PLX3397 = 0.973 ± 0.076, <italic>p</italic> = 0.0081, Tg vs. Tg + PLX3397) in the hippocampus was recovered to near-WT levels (<xref ref-type=\"fig\" rid=\"ijms-21-05553-f004\">Figure 4</xref>).</p></sec><sec id=\"sec2dot3-ijms-21-05553\"><title>2.3. PLX3397 Protected the dopamine (DA) System in Aged 5xFAD Mice</title><p>To investigate the effect of PLX3397 on synaptic plasticity, we performed neuro-positron emission tomography (PET) imaging for the neurotransmitter receptors dopamine D2 receptor (D2R) and metabotropic glutamate receptor 5 (mGluR5). In DA PET, the radiotracers were predominantly distributed in the striatum whereas in glutamate PET, high radioactivity was detected in the striatum and hippocampus. The nondisplaceable binding potential (BPND) values for D2R and mGluR5 in the Tg group were 31% (Tg = 1.683 ± 0.287, <italic>p</italic> = 0.0142 vs. WT = 2.428 ± 0.558) and 30% (Tg = 2.201 ± 1.119, <italic>p</italic> = 0.3268 vs. WT = 3.125 ± 1.027) lower, respectively, than those for the WT group. However, after PLX3397 administration, the binding values for the Tg + PLX3397 group were 37% (Tg + PLX3397 = 2.296 ± 0.288, <italic>p</italic> = 0.0241, Tg vs. Tg + PLX3397) and 16% (Tg + PLX3397 = 2.553 ± 0.914, <italic>p</italic> = 0.8445, Tg vs. Tg + PLX3397) higher, respectively, than those for the Tg group, although there were no significant differences in the BPND values for mGluR5 after PLX3397 treatment (<xref ref-type=\"fig\" rid=\"ijms-21-05553-f005\">Figure 5</xref>).</p><p>To confirm the alteration of the DA system in Tg mice by PLX3397 treatment, we conducted immunohistochemistry analysis for D2R in the striatum of the mice from the WT, Tg, and Tg + PLX3397 groups. D2R expression in the Tg mice (Tg = 0.818 ± 0.0757, <italic>p</italic> = 0.0187, WT vs. Tg) was lower than that in WT mice, and PLX3397 treatment restored these reduced levels to approximately those observed in case of the WT mice (Tg + PLX3397 = 1.108 ± 0.081, <italic>p</italic> = 0.0017, Tg vs. Tg + PLX3397; <xref ref-type=\"fig\" rid=\"ijms-21-05553-f006\">Figure 6</xref>A), which is consistent with the PET results. Furthermore, we analyzed the tyrosine hydroxylase (TH) and dopamine transporter (DAT) expression levels in the striatum (<xref ref-type=\"fig\" rid=\"ijms-21-05553-f006\">Figure 6</xref>B,C). The expression patterns of DA neurons immunostained with TH were similar to those of DA neurons immunostained with D2R. Tg mice showed significant decreases in TH (Tg = 0.457 ± 0.073, <italic>p</italic> = 0.0254, WT vs. Tg), and PLX3397 prevented this reduction (Tg + PLX3397 = 0.948 ± 0.173, <italic>p</italic> = 0.0423, Tg vs. Tg + PLX3397). However, DAT levels did not significantly differ among the groups (Tg = 0.835 ± 0.116, <italic>p</italic> = 0.9367 vs. Tg + PLX3397 = 0.778 ± 0.316).</p></sec></sec><sec sec-type=\"discussion\" id=\"sec3-ijms-21-05553\"><title>3. Discussion</title><p>In this study, we investigated the impact of PLX3397, a promising CSF1R inhibitor that suppresses microglia, as a new therapeutic option for late AD. On administering PLX3397, activated microglia in the cortex and hippocampus of aged 5xFAD mice were markedly suppressed. Furthermore, PLX3397 effectively suppressed the beta amyloid cascade, including the expression levels of APP, CTFβ, and Aβ peptides, compared to those observed in case of the vehicle-treated group. It is well known that microglia activation is involved in Aβ clearance via phagocytosis during initial AD pathology, however, with the progression of the disease, activated microglia elicit detrimental effects by the overexpression of pro-inflammatory cytokines and decline in Aβ clearance [<xref rid=\"B15-ijms-21-05553\" ref-type=\"bibr\">15</xref>,<xref rid=\"B16-ijms-21-05553\" ref-type=\"bibr\">16</xref>]. In this study, we have shown that suppressing reactive microglia by PLX3397 treatment exhibited reduced Aβ lesions at the late AD stage. It is thought that suppression of microglia does not directly reduce amyloid accumulation, but it could prevent increases in the accumulation of amyloid by neuroinflammation.</p><p>In contrast to some previous studies that investigated the effect of PLX3397 during the early stage of Aβ formation in young 5xFAD mice [<xref rid=\"B11-ijms-21-05553\" ref-type=\"bibr\">11</xref>,<xref rid=\"B12-ijms-21-05553\" ref-type=\"bibr\">12</xref>], we administered PLX3397 to aged 5xFAD mice with severe AD lesions to determine its therapeutic effects at the late stage of AD. Sosna et al. (2018) [<xref rid=\"B11-ijms-21-05553\" ref-type=\"bibr\">11</xref>] showed that 3-month administration of PLX3397 to 2-month-old 5xFAD mice prevented accumulation of intraneuronal Aβ and formation of neuritic plaques. Spangenberg et al. (2016) [<xref rid=\"B12-ijms-21-05553\" ref-type=\"bibr\">12</xref>] reported that PLX3397 administration to 1.5-month-old 5xFAD mice reduced memory impairment but did not have any beneficial effects on hippocampal Aβ deposition and Aβ-induced cell death. Moreover, other study reported that 5.5-month administration of PLX5562, a CSF1R inhibitor, to 5xFAD mice at 1.5 months of age prevented the amyloid plaque formation but had no beneficial effect on hippocampal dependent memory [<xref rid=\"B17-ijms-21-05553\" ref-type=\"bibr\">17</xref>]. Previous studies have investigated the effect of microglial suppression on the onset and development of AD but our results have suggested the potential of PLX3397 as a therapeutic agent for late-stage or advanced AD.</p><p>Synaptic plasticity has been known to play an important role in learning and memory, and dysfunction of synaptic plasticity was demonstrated in AD mice, which were correlated with the Aβ accumulation [<xref rid=\"B18-ijms-21-05553\" ref-type=\"bibr\">18</xref>]. Previous studies have reported that PSD-95 levels were reduced in cultured APP mutant neurons compared to wild-type neurons [<xref rid=\"B19-ijms-21-05553\" ref-type=\"bibr\">19</xref>]. The expression levels of synaptophysin and PSD-95 were decreased in the mouse model of AD, which were recovered by physical exercise [<xref rid=\"B20-ijms-21-05553\" ref-type=\"bibr\">20</xref>]. In the present study, synaptophysin and PSD-95 levels in the brains of vehicle-treated 5xFAD mice were significantly lower than those in age-matched WT mice. PLX3397 significantly increased the levels of synaptophysin proteins both in the cortex and hippocampus. In addition, the level of PSD-95 was reversed after PSD3397 administration in the cortex of 5xFAD mice (<xref ref-type=\"fig\" rid=\"ijms-21-05553-f004\">Figure 4</xref>). To further investigate the effect of PLX3397 on synaptic plasticity, we analyzed the neurotransmitter receptors D2R and mGluR5 by PET imaging involving receptor-specific tracers. Glutamate is the most abundant excitatory neurotransmitter in the central nervous system and regulates learning and memory [<xref rid=\"B21-ijms-21-05553\" ref-type=\"bibr\">21</xref>]. mGluR5 dysregulation due to Aβ-synaptic toxicity is related with the early symptoms of cognitive dysfunction in AD [<xref rid=\"B22-ijms-21-05553\" ref-type=\"bibr\">22</xref>,<xref rid=\"B23-ijms-21-05553\" ref-type=\"bibr\">23</xref>]. The DA system, especially D2R, is a key player in Aβ pathophysiology and neuronal plasticity [<xref rid=\"B24-ijms-21-05553\" ref-type=\"bibr\">24</xref>,<xref rid=\"B25-ijms-21-05553\" ref-type=\"bibr\">25</xref>]. We previously reported the age dependency of mGluR5 expression in 5xFAD mice at 3, 5, 7, and 9 months of age; we found that 9-month-old 5xFAD mice showed significant decrease in the mGluR5-binding values in the brain, which was consistent with their memory impairment [<xref rid=\"B26-ijms-21-05553\" ref-type=\"bibr\">26</xref>]. In the current study, PET analysis confirmed that the expression of mGluR5 tended to decrease in the hippocampus and striatum (<xref ref-type=\"fig\" rid=\"ijms-21-05553-f005\">Figure 5</xref>); PLX3397 treatment showed moderate effect with respect to the protection from the suppression of mGluR5 expression in the hippocampus. Similar to the findings for the mGluR5 status, the brain uptake and binding values of D2R dramatically decreased in the striatum of aged 5xFAD mice. However, compared to the vehicle-treated group, in the PLX3397-treated 5xFAD mice, both uptake and binding of D2R was significantly rescued (<xref ref-type=\"fig\" rid=\"ijms-21-05553-f005\">Figure 5</xref>). Therefore, PLX3397 could have beneficial effects on neuronal plasticity and may have more effects on D2R than on mGluR5. However, further studies are warranted to verify the neurophysiological changes in animal models of AD by long term potentiation measurements and the associated behavioral functions following PLX3397 administration.</p><p>DA neurons are considered extremely important in Parkinson’s disease [<xref rid=\"B27-ijms-21-05553\" ref-type=\"bibr\">27</xref>], but they have not been of great interest in AD. After neurotransmitter abnormalities were highlighted in AD, the DA system has been intensively studied as a key neurotransmitter system involved with emotion and cognition [<xref rid=\"B28-ijms-21-05553\" ref-type=\"bibr\">28</xref>]. Several lines of investigation have shown that dopamine increases cortical excitability by acting through D2-like receptors; these observations support the idea that disruption of the DA system is associated with AD pathophysiology. Currently, dysfunction of DA transmission has been hypothesized as a new player in AD pathophysiology [<xref rid=\"B29-ijms-21-05553\" ref-type=\"bibr\">29</xref>]. Consistent with a previous study showing DA neuron loss in the midbrain of 5xFAD mice [<xref rid=\"B25-ijms-21-05553\" ref-type=\"bibr\">25</xref>], our study showed that the D2R levels in the striatum of 5xFAD mice were significantly lower than those in the striatum of WT mice. Our finding supported the role of D2R in AD pathology. PET analysis showed that PLX3397 administration rescued this D2R loss in the brains of aged 5xFAD mice. Using IHC, we confirmed this protective effect of PLX3397 on D2R that was noted during PET imaging; loss of both D2R and TH was noted in brain sections of aged 5xFAD mice, which was rescued by PLX3397 treatment (<xref ref-type=\"fig\" rid=\"ijms-21-05553-f006\">Figure 6</xref>). </p><p>Our study has some limitations in that the number of mice in the experimental groups was small because this was a preliminary experiment; furthermore, the effect of PLX3397 on the behavior of the mice is not entirely clear. We performed passive avoidance and object recognition tests after PLX3397 treatment and the results showed a tendency for improved memory function in 5xFAD mice in both behavioral tests; however, these data were not statistically significant owing to the small sample number. Therefore, additional experiments using increased sample sizes are necessary to interpret behavioral changes in response to PLX3397 administration in 5xFAD mice. Although the deposition of amyloid plaques and tau phosphorylation are known to be neuropathological hallmarks of AD, the present study focused on changes in amyloid beta and neuroinflammation expression after PLX3397 treatment owing to lack of tau lesions in 5xFAD mice. Further studies are required to verify the effect of PLX3397 administration on tau pathology as well. Nonetheless, promising effects of PLX3397 were noted in our study, indicating a newly discovered role of PLX3397 that may protect neurotransmission in AD and may apply to other diseases involving DA dysfunction, such as Parkinson’s disease. However, further studies are required to clarify the mechanism underlying the effects of PLX3397 on AD, especially on the DA system of the animals. In addition, subsequent behavioral studies are also needed to elucidate relationship between PLX3397-induced behavioral changes and molecular mechanisms associated with D2R and TH. </p><p>In conclusion, the current study shows that microglial depletion by the CSF1R inhibitor PLX3397 prevented amyloid plaque formation and rescued the expression of synaptic plasticity-related genes in the late stage of AD in 5xFAD mice. The protective effect of PLX3397 on Aβ pathology was consistent with the effects on DA signaling. Therefore, we suggest that the ameliorative effect of PLX3397 on AD progression may be related to the decreased Aβ signaling and recovered levels of synaptic plasticity-related signals, concomitant with the protective effects on DA signaling. Our results may help support the clinical use of the CSF1R inhibitor PLX3397 in AD therapy.</p></sec><sec id=\"sec4-ijms-21-05553\"><title>4. Materials and Methods </title><sec id=\"sec4dot1-ijms-21-05553\"><title>4.1. Animals and Drug Administration</title><p>We used transgenic female 5xFAD mice that had five mutant human genes associated with AD: Three APP genes, that is, APPsw, APPfl, and APPlon, and two presenilin1 genes, that is, PS1 and PSEN1 (mutations: PSEN1 M146L and PSEN1 L286V). The method used for generating the 5xFAD mice has been described previously [<xref rid=\"B30-ijms-21-05553\" ref-type=\"bibr\">30</xref>]. 5XFAD lines with the B6/SJL genetic background were maintained by crossing hemizygous transgenic mice with B6/SJL F1 breeders. Heterozygous 5xFAD transgenic animals and WT controls were obtained after the breeding of progenitors purchased from the Jackson Laboratory (Jackson Laboratory, Bar Harbor, ME, USA). </p><p>At the age of 9 months, the female 5xFAD mice were assigned to a vehicle control group (Tg: <italic>n</italic> = 4) or PLX3397-treated group (Tg + PLX3397: <italic>n</italic> = 5). Age-matched female B6/SJL mice were assigned to the wild type (WT; <italic>n</italic> = 5). It is known that the pattern of beta amyloid accumulation and neuroinflammation is more severe in female 5xFAD mice than in males [<xref rid=\"B31-ijms-21-05553\" ref-type=\"bibr\">31</xref>,<xref rid=\"B32-ijms-21-05553\" ref-type=\"bibr\">32</xref>]. Given this sex difference in the severity of AD lesions, we have used only female 5xFAD and age-matched WT mice in our study. PLX3397 (cat no. 206178; MedKoo Biosciences, Morrisville, NC, USA) was dissolved in dimethyl sulfoxide (DMSO) and was then further diluted in an aqueous mixture of 0.5% (hydroxypropyl)methyl cellulose (HPMC; Sigma-Aldrich, St. Louis, MO, USA) and 1% Tween 80 (Sigma-Aldrich). When the Tg mice were 9 months old, 100 µL of the suspended PLX3397 was administered by daily oral gavage to the mice at 50 mg·kg<sup>−1</sup> for 30 days (<xref ref-type=\"fig\" rid=\"ijms-21-05553-f001\">Figure 1</xref>). The animals were housed in a specific pathogen-free facility and were allowed access to a normal diet and autoclaved water ad libitum. All the mouse procedures in this study were approved by the Institutional Animal Care and Use Committee of the Korea Institute of Radiological and Medical Sciences (IACUC permit number: KIRAMS2018-07, approval date: 27 February 2018).</p></sec><sec id=\"sec4dot2-ijms-21-05553\"><title>4.2. Immunofluorescence Staining</title><p>The tissues were fixed in 4% paraformaldehyde and embedded in paraffin. The deparaffinized and hydrated tissues were incubated in normal goat serum solution (Vector ABC Elite Kit; Vector Laboratories, Burlingame, CA, USA) for 30 min and reacted with the following primary antibodies overnight at 4 °C: 6E10 (cat no. SIG-39320; 1:1000 dilution; Biolegend, San Diego, CA, USA) and ionized calcium-binding adaptor molecule 1 (Iba-1; cat no. 019-19741; 1:200 dilution; Wako, Neuss, Germany). Subsequently, the tissues were washed with phosphate-buffered saline (PBS) containing 0.1% Triton-X 100 (PBS-Triton-X) and tagged with tetramethylrhodamine isothiocyanate-labeled anti-mouse or anti-rabbit IgG antibody for 30 min at room temperature. The tissues were washed in PBS-Triton-X again, counterstained with 4′,6-diamidino-2-phenylindole (DAPI), and mounted. The stained tissues were observed under a fluorescence microscope.</p></sec><sec id=\"sec4dot3-ijms-21-05553\"><title>4.3. Immunohistochemistry</title><p>The tissues were deparaffinized, hydrated, and washed with PBS-Triton-X. For the block nonspecific staining, the tissues were incubated in normal goat serum (Vector ABC Elite Kit) at room temperature for 30 min. The washed tissues were then incubated with the following primary antibodies overnight at 4 °C: Anti-rabbit D2R (cat no. AB5084P; 1:500 dilution; Merck, Darmstadt, Germany), anti-rabbit TH (cat no. NB300-109; 1:1000 dilution; Novus, Centennial, CO, USA), or anti-rabbit DAT (cat no. D6944; 1:000 dilution; Sigma-Aldrich) antibodies. Subsequently, they were washed and incubated with biotinylated secondary antibody for 30 min at room temperature. After incubation, the tissues were incubated with an avidin–biotin–peroxidase complex (Vector Laboratories) for 30 min and incubated with diaminobenzidine (DAB, Vector Laboratories). Images were obtained at 20× magnification by using whole slide digital scanning with a Digital Pathology Scanner (Aperio VERSA; Leica Biosystems, Buffalo Grove, IL, USA), and the positive cells were counted using Aperio Image Analysis.</p></sec><sec id=\"sec4dot4-ijms-21-05553\"><title>4.4. Cresyl Violet Staining</title><p>The 5 m tissue sections were deparaffinized, hydrated using a series of graded ethanol solutions, and washed in tap water for 5 min. The tissues were incubated in 0.5% cresyl violet solution for 20 min and differentiated in 0.25% acetic alcohol. Subsequently, they were dehydrated in graded ethanol and two changes of xylene for 5 min. The tissues slides were cover-slipped using mounting medium.</p></sec><sec id=\"sec4dot5-ijms-21-05553\"><title>4.5. Western Blotting</title><p>The tissues were homogenized in radioimmunoprecipitation (RIPA) buffer containing a protease inhibitor and phosphatase inhibitor. The protein concentration was determined using Bradford reagent (Bio-Rad, Hercules, CA, USA). Equal amounts of the total protein (10–30 μg) were electrophoresed on 8–12.5% sodium dodecyl sulfate (SDS)–polyacrylamide gels and transferred to nitrocellulose membranes. The membranes were incubated in 3% bovine serum albumin (BSA) solution for nonspecific blocking, followed by overnight incubation at 4 °C with the primary antibody. The following primary antibodies and dilutions were used: Anti-6E10 (cat no. SIG-39320; 1:1000 dilution; Biolegend), anti-Iba-1 (cat no. 106-20001; 1:1000 dilution; Wako), anti-synaptophysin (cat no. MAB-5258; 1:1000 dilution; Merck), anti-PSD-95 (cat no. GTX133091; 1:1000 dilution; GeneTex, Irvine, CA, USA), and anti-β-actin (cat no. A1978; 1:2000 dilution; Sigma-Aldrich) antibodies. Subsequently, the membranes were washed in PBS containing 0.1% Tween 20 and incubated with a 1:3000 dilution of horseradish peroxidase (HRP)-conjugated secondary antibodies (Santa Cruz Biotechnology, Dallas, TX, USA) for 1 h at room temperature. The membranes were then washed and developed using an enhanced chemiluminescence (ECL) kit (Perkin Elmer, Waltham, MA, USA).</p></sec><sec id=\"sec4dot6-ijms-21-05553\"><title>4.6. PET Scans</title><p>We used two kinds of radiotracers, that is, (S)-N-[(1 allyl-2-pyrrolidinyl)methyl]-5-(3[<sup>18</sup>F]fluoropropyl)-2,3-dimethoxybenzamide ([<sup>18</sup>F]fallypride) and [<sup>18</sup>]F-3-fluoro-5-[(pyridin-3-yl)ethynyl]benzonitrile ([<sup>18</sup>F]FPEB), which are specific radiotracers for D2R and mGluR5, respectively. These radiotracers were synthesized by F-18 radiolabeling of tosyl or nitro precursors, according to a previously described procedure [<xref rid=\"B33-ijms-21-05553\" ref-type=\"bibr\">33</xref>,<xref rid=\"B34-ijms-21-05553\" ref-type=\"bibr\">34</xref>]. The radiochemical purity at the end of synthesis was over 95%. </p><p>The PET experiments were conducted using an animal-dedicated PET scanner (nanoScan; Mediso Medical Imaging Systems, Budapest, Hungary). The scanner has a peak absolute system sensitivity of >9% in the 250–750 keV energy window, an axial field of view of 28 cm, a transaxial field of view of 35–120 mm, and a transaxial resolution of 0.7 mm at 1 cm off-center. </p><p>All the mice were anesthetized with 2.5% isoflurane in oxygen, and 8.5 ± 0.7 MBq [<sup>18</sup>F]fallypride and [<sup>18</sup>F]FPEB were administered via the tail vein. PET scanning was performed for 90 min in the list mode. Subsequently, PET data were reconstructed in user-defined time frames (10 s × 6 frames, 30 s × 8 frames, 180 s × 5 frames, and 600 s × 7 frames) with voxel dimensions of 0.86 × 0.86 × 0.80 mm<sup>3</sup> with a three-dimensional ordered-subset expectation maximization algorithm (four iterations and six subsets). For attenuation correction, micro-CT imaging was conducted immediately after PET scanning, using a 50 kVp X-ray voltage with 0.16 mAs.</p></sec><sec id=\"sec4dot7-ijms-21-05553\"><title>4.7. PET Image Analysis</title><p>After mean PET images (50–90 min) were obtained from the dynamic PET images, each mean PET image was spatially normalized to our own tracer-specific brain magnetic resonance (MR) template [<xref rid=\"B26-ijms-21-05553\" ref-type=\"bibr\">26</xref>], using the PMOD software (version 3.8, PMOD Technologies Ltd. Zürich, Switzerland). Subsequently, individual normalization parameters were applied to the dynamic PET images and brain masking was applied. The standardized uptake value (SUV) images were obtained by normalizing tissue radioactivity concentration by injected dose and body weight.</p><p>Two volumes of interests (VOIs) of the striatum and cerebellum were defined on the MR template. The cerebellum was used as a reference region because it has been shown to have very low D2R [<xref rid=\"B35-ijms-21-05553\" ref-type=\"bibr\">35</xref>,<xref rid=\"B36-ijms-21-05553\" ref-type=\"bibr\">36</xref>] and mGluR5 [<xref rid=\"B37-ijms-21-05553\" ref-type=\"bibr\">37</xref>,<xref rid=\"B38-ijms-21-05553\" ref-type=\"bibr\">38</xref>] levels. Next, the decay-corrected regional time–activity curves (TACs) of two regions were obtained from the VOIs. </p><p>To estimate the receptor density in vivo, we derived the BPND by noninvasive Logan graphical analysis [<xref rid=\"B39-ijms-21-05553\" ref-type=\"bibr\">39</xref>]. The clearance rate (k2′) of the reference tissue was estimated with the simplified reference tissue model (SRTM) and then applied to each BPND calculation. Voxel-based parametric mapping was also utilized with Logan’s method. For each group, the individual parametric map was averaged. Then, the mean PET image was coregistered to study the specific MR template in order to obtain a PET–MR image.</p></sec><sec id=\"sec4dot8-ijms-21-05553\"><title>4.8. Statistical Analysis </title><p>The data have been expressed in terms of mean ± standard deviation (SD). Statistical significance was determined using one-way analysis of variance (ANOVA) followed by the Tukey multiple comparison test by using the GraphPad Prism program (version 8.0, GraphPad Software, Inc. San Diego, CA, USA). A <italic>p</italic> value of <0.05 was considered significant. Differences between the groups in PET imaging (<italic>n</italic> = 4) were tested using the Mann–Whitney nonparametric test, with <italic>p</italic> < 0.05 being considered to indicate significant difference.</p></sec></sec></body><back><notes><title>Author Contributions</title><p>H.-J.L., J.Y.C.: Conceptualization and data curation. H.-D.C.: Funding acquisition. Y.S., Y.J.J., N.-R.S., S.J.O.: Investigation. K.R.N.: Methodology. Y.S., Y.J.J., J.Y.C., H.-J.L.: Writing—original draft. J.Y.C., H.-J.L.: Review & editing. All authors have read and agreed to the published version of the manuscript.</p></notes><notes><title>Funding</title><p>This work was supported by the ICT R&D program of MSIT/IITP [2017-0-00961, Study on the EMF Exposure Control in Smart Society and 2019-0-00102, A Study on Public Health and Safety in a Complex EMF Environment] and a grant of the Korea Institute of Radiological and Medical Sciences (No. 50531-2020) funded by the Korean government Ministry of Science and ICT (MSIT).</p></notes><notes notes-type=\"COI-statement\"><title>Conflicts of Interest</title><p>The authors declare no conflict of interests.</p></notes><ref-list><title>References</title><ref id=\"B1-ijms-21-05553\"><label>1.</label><element-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>A DeTure</surname><given-names>M.</given-names></name><name><surname>Dickson</surname><given-names>D.W.</given-names></name></person-group><article-title>The Neuropathological Diagnosis of Alzheimer’s Disease</article-title><source>Mol. 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Then, each group underwent positron emission tomography (PET) imaging targeting the dopamine D2 receptor (D2R) and metabotropic glutamate receptor 5 (mGluR5), and histopathological and molecular analyses were also performed.</p></caption><graphic xlink:href=\"ijms-21-05553-g001\"/></fig><fig id=\"ijms-21-05553-f002\" orientation=\"portrait\" position=\"float\"><label>Figure 2</label><caption><p>The colony-stimulating factor 1 receptor (CSF1R) inhibitor PLX3397 decreased Aβ levels and amyloid precursor protein (APP) processing in the cortex of 5xFAD mice. (<bold>A</bold>) Representative immunofluorescent images of Amyloid β deposits (6E10 in red) and microglia (Iba-1 in green) in the cortex of 5xFAD mice treated with PLX3397. Arrowhead indicates microglia. Scale bar = 100 μm, inserted image scale bar = 25 μm. (<bold>B</bold>) Representative images of western blots and quantification of expression levels of full-length amyloid-precursor protein (APP-FL), CTFβ, and Aβ in the cortex of WT, Tg, and Tg + PLX3397 mice. (<bold>C</bold>) Representative images of western blots and quantification of Iba-1 expression in the cortex lysate for the indicated groups. Statistical significance has been defined as *** <italic>p</italic> < 0.001 WT vs. Tg; <sup>#</sup>\n<italic>p</italic> < 0.05, <sup>##</sup>\n<italic>p</italic> < 0.01, <sup>###</sup>\n<italic>p</italic> < 0.001 Tg vs. Tg + PLX3397 group. Error bars indicate SD (<italic>n</italic> = 4/group).</p></caption><graphic xlink:href=\"ijms-21-05553-g002\"/></fig><fig id=\"ijms-21-05553-f003\" orientation=\"portrait\" position=\"float\"><label>Figure 3</label><caption><p>PLX3397 decreased Aβ levels and protected the neuronal cell layer in the hippocampus of 5xFAD mice. (<bold>A</bold>) Representative immunofluorescence image showing staining for Aβ (6E10 in red) and microglia (Iba-1 in green) in the hippocampus. Scale bar = 200 μm. (<bold>B</bold>) Immunoblotting for Aβ and Iba-1 using the hippocampus lysate from each group and Aβ and Iba-1 quantification (<italic>n</italic> = 4/group). (<bold>C</bold>) Representative image of the hippocampal region of 5xFAD mice treated with PLX3397 and quantification of thickness in the CA1 region (<italic>n</italic> = 4 for TG group, <italic>n</italic> = 5 for WT and Tg + PLX3397 group). Scale bar = 200 μm. Statistical significance has been defined as *** <italic>p</italic> < 0.001 WT vs. Tg; <sup>#</sup>\n<italic>p</italic> < 0.05, <sup>##</sup>\n<italic>p</italic> < 0.01, <sup>###</sup>\n<italic>p</italic> < 0.001 Tg vs. Tg + PLX3397 group. Error bars indicate SD.</p></caption><graphic xlink:href=\"ijms-21-05553-g003\"/></fig><fig id=\"ijms-21-05553-f004\" orientation=\"portrait\" position=\"float\"><label>Figure 4</label><caption><p>Protective effect of PLX3397 treatment on the levels of synapse plasticity-related proteins in 5xFAD mice. Lysate samples of the vehicle or PLX3397-treated cortex or hippocampus were immunoblotted for synaptophysin, or postsynaptic density (PSD)-95 using specific antibodies. β-actin was used as the loading control. Statistical significance has been defined as * <italic>p</italic> < 0.05, ** <italic>p</italic> < 0.01, *** <italic>p</italic> < 0.001 WT vs. Tg; <sup>#</sup>\n<italic>p</italic> < 0.05, <sup>##</sup>\n<italic>p</italic> < 0.01 Tg vs. Tg + PLX3397 group. Error bars indicate SD (<italic>n</italic> = 4/group).</p></caption><graphic xlink:href=\"ijms-21-05553-g004\"/></fig><fig id=\"ijms-21-05553-f005\" orientation=\"portrait\" position=\"float\"><label>Figure 5</label><caption><p>Neuro-PET imaging for evaluating dopamine D2 receptor (D2R) and metabotropic glutamate receptor 5 (mGluR5) expression following PLX3397 treatment. (<bold>A</bold>) Representative nondisplaceable binding potential (BPND) parametric images for WT and Tg mice. Regional brain BPND levels in Tg mice after PLX3397 administration are shown. (<bold>B</bold>) Quantification of BPND for mGluR5 and D2R in the WT, Tg, and Tg + PLX3397 groups. * <italic>p</italic> < 0.05 WT vs. Tg; <sup>#</sup>\n<italic>p</italic> < 0.05 Tg vs. Tg + PLX3397 group. Error bars indicate SD (<italic>n</italic> = 4/group).</p></caption><graphic xlink:href=\"ijms-21-05553-g005\"/></fig><fig id=\"ijms-21-05553-f006\" orientation=\"portrait\" position=\"float\"><label>Figure 6</label><caption><p>Effects of PLX3397 on the dopaminergic pathway. Representative immunohistochemical images for WT, Tg, and Tg + PLX3397 mice showing staining for dopamine D2 receptor (D2R) (<bold>A</bold>), tyrosine hydroxylase (TH) (<bold>B</bold>), and dopamine transporter (DAT) (<bold>C</bold>) and the quantification of D2R, TH, and DAT levels in the striatum region of the mouse brain (<italic>n</italic> = 4 for TG group, <italic>n</italic> = 5 for WT and Tg + PLX3397 group). * <italic>p</italic> < 0.05 WT vs. Tg; <sup>#</sup>\n<italic>p</italic> < 0.05, <sup>##</sup>\n<italic>p</italic> < 0.01 Tg vs. Tg + PLX3397 group. Error bars indicate SD.</p></caption><graphic xlink:href=\"ijms-21-05553-g006\"/></fig></floats-group></article>\n"
]
[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Int J Mol Sci</journal-id><journal-id journal-id-type=\"iso-abbrev\">Int J Mol Sci</journal-id><journal-id journal-id-type=\"publisher-id\">ijms</journal-id><journal-title-group><journal-title>International Journal of Molecular Sciences</journal-title></journal-title-group><issn pub-type=\"epub\">1422-0067</issn><publisher><publisher-name>MDPI</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32751713</article-id><article-id pub-id-type=\"pmc\">PMC7432085</article-id><article-id pub-id-type=\"doi\">10.3390/ijms21155453</article-id><article-id pub-id-type=\"publisher-id\">ijms-21-05453</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Article</subject></subj-group></article-categories><title-group><article-title>Clinical Significance of Expression Changes and Promoter Methylation of PLA2R1 in Tissues of Breast Cancer Patients</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0002-0866-0981</contrib-id><name><surname>Mitwally</surname><given-names>Noha</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05453\">1</xref><xref ref-type=\"aff\" rid=\"af2-ijms-21-05453\">2</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0002-8935-0183</contrib-id><name><surname>Yousef</surname><given-names>Einas</given-names></name><xref ref-type=\"aff\" rid=\"af2-ijms-21-05453\">2</xref><xref ref-type=\"aff\" rid=\"af3-ijms-21-05453\">3</xref></contrib><contrib contrib-type=\"author\"><name><surname>Abd Al Aziz</surname><given-names>Ahmad</given-names></name><xref ref-type=\"aff\" rid=\"af4-ijms-21-05453\">4</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0002-9806-826X</contrib-id><name><surname>Taha</surname><given-names>Mohamed</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05453\">1</xref><xref rid=\"c1-ijms-21-05453\" ref-type=\"corresp\">*</xref></contrib></contrib-group><aff id=\"af1-ijms-21-05453\"><label>1</label>Department of Biochemistry, Faculty of Pharmacy, Cairo University, Cairo 11562, Egypt; <email>[email protected]</email></aff><aff id=\"af2-ijms-21-05453\"><label>2</label>Basic Medical Sciences Department, College of Medicine, Dar Al Uloom University, Riyadh 7222, Saudi Arabia; <email>[email protected]</email></aff><aff id=\"af3-ijms-21-05453\"><label>3</label>Histology Department, Faculty of Medicine, Menoufia University, Shebin Elkom 3251, Egypt</aff><aff id=\"af4-ijms-21-05453\"><label>4</label>Surgery Department, Faculty of Medicine, Kasr Al Ainy Hospital, Cairo University, Cairo 11562, Egypt; <email>[email protected]</email></aff><author-notes><corresp id=\"c1-ijms-21-05453\"><label>*</label>Correspondence: <email>[email protected]</email>; Tel.: +20-1222-188-405</corresp></author-notes><pub-date pub-type=\"epub\"><day>30</day><month>7</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><month>8</month><year>2020</year></pub-date><volume>21</volume><issue>15</issue><elocation-id>5453</elocation-id><history><date date-type=\"received\"><day>10</day><month>7</month><year>2020</year></date><date date-type=\"accepted\"><day>28</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>© 2020 by the authors.</copyright-statement><copyright-year>2020</copyright-year><license license-type=\"open-access\"><license-p>Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>).</license-p></license></permissions><abstract><p>Phospholipase A2 receptor 1 (PLA2R1) expression and its role in the initiation and progression of breast cancer are an unresolved issue. PLA2R1 was found to endorse several tumor suppressive responses, including cellular senescence and apoptosis. Previous in vitro studies demonstrated that DNA hypermethylation was highly associated with the epigenetic silencing of <italic>PLA2R1</italic> in breast cancer cell lines. Our objective was to study the level of PLA2R1 mRNA expression and the methylation of its promoter in different histological grades and molecular subtypes of breast cancer. We performed bioinformatics analyses on available human breast cancer expression datasets to assess the PLA2R1 mRNA expression. We used qRT-PCR to evaluate the PLA2R1 mRNA expression and its promoter’s methylation in breast cancer tissue in comparison to breast fibroadenomas. Our results describe, for the first time, the expression of PLA2R1 and the methylation of its promoter in human breast cancer tissues. A significant downregulation of PLA2R1, together with hypermethylation of the promoter was detected in breast cancers of different histological grades and molecular subtypes when compared to benign breast tissues. <italic>PLA2R1</italic> promoter hypermethylation was associated with aggressive subtypes of breast cancer. In conclusion, <italic>PLA2R1</italic> promoter hypermethylation is a potentially useful diagnostic and prognostic biomarker and could serve as a possible therapeutic target in breast cancer.</p></abstract><kwd-group><kwd>PLA2R1</kwd><kwd>breast cancer</kwd><kwd>TNBC</kwd><kwd>promoter methylation</kwd><kwd>bioinformatics analysis</kwd></kwd-group></article-meta></front><body><sec sec-type=\"intro\" id=\"sec1-ijms-21-05453\"><title>1. Introduction</title><p>Phospholipase A2 receptor 1 (PLA2R1) is a type I transmembrane receptor that belongs to the mannose receptor family and exists in both a transmembrane and a soluble form [<xref rid=\"B1-ijms-21-05453\" ref-type=\"bibr\">1</xref>]. The physiological function of human PLA2R1 is related to its distinct affinities to interact with several secretory phospholipase A2 (sPLA2) proteins, various types of carbohydrates and collagens [<xref rid=\"B2-ijms-21-05453\" ref-type=\"bibr\">2</xref>]. Although its precise function is not clear, its binding to sPLA2 induces both positive and negative regulation in diverse signaling pathways, including cell proliferation, growth, differentiation and apoptosis [<xref rid=\"B3-ijms-21-05453\" ref-type=\"bibr\">3</xref>]. Aberrant expression of PLA2R1 has been associated with the development and progression of different types of cancer. Elevated levels of PLA2R1 are associated with pancreatic, gastric, prostate and ovarian carcinomas, while it is downregulated in leukemia, kidney, thyroid and breast carcinomas [<xref rid=\"B3-ijms-21-05453\" ref-type=\"bibr\">3</xref>,<xref rid=\"B4-ijms-21-05453\" ref-type=\"bibr\">4</xref>,<xref rid=\"B5-ijms-21-05453\" ref-type=\"bibr\">5</xref>,<xref rid=\"B6-ijms-21-05453\" ref-type=\"bibr\">6</xref>,<xref rid=\"B7-ijms-21-05453\" ref-type=\"bibr\">7</xref>,<xref rid=\"B8-ijms-21-05453\" ref-type=\"bibr\">8</xref>].</p><p>There is now a compelling body of evidence suggesting that alterations to epigenetic markers, such as DNA methylation, histone modification and posttranscriptional gene regulations by microRNAs, are commonly noticed in the development and progression of different types of cancer [<xref rid=\"B9-ijms-21-05453\" ref-type=\"bibr\">9</xref>]. Mechanistically, cancer cells endorse <italic>PLA2R1</italic> promoter hypermethylation to diminish its tumor suppressive effects, enhancing tumorigenesis [<xref rid=\"B4-ijms-21-05453\" ref-type=\"bibr\">4</xref>]. <italic>PLA2R1</italic> promoter hypermethylation was detected in leukemic cells, renal cell carcinoma and breast cancer cell lines [<xref rid=\"B4-ijms-21-05453\" ref-type=\"bibr\">4</xref>,<xref rid=\"B6-ijms-21-05453\" ref-type=\"bibr\">6</xref>,<xref rid=\"B10-ijms-21-05453\" ref-type=\"bibr\">10</xref>,<xref rid=\"B11-ijms-21-05453\" ref-type=\"bibr\">11</xref>]. Assessing promoter methylation as an epigenetic regulator that is associated with the differential expression of PLA2R1 in breast cancer may potentially define novel therapeutic targets, diagnostic and/or prognostic biomarkers.</p><p>Breast cancer is the most frequently diagnosed cancer in women worldwide with an estimated 2.1 million new cases each year [<xref rid=\"B12-ijms-21-05453\" ref-type=\"bibr\">12</xref>]. Despite an apparently similar phenotype, mammary tumors are highly heterogeneous, embracing a group of genetically and epigenetically distinct diseases with variable clinical courses, histopathological features, molecular subtypes and various responses to treatment [<xref rid=\"B13-ijms-21-05453\" ref-type=\"bibr\">13</xref>]. Gene expression profiling has paved the way for a more comprehensive classification of breast cancers, allowing their separation into robust molecular entities. The molecular subtyping of breast cancers plays a critical role in predicting the biologic behavior of breast malignancies and for developing more effective therapeutic approaches [<xref rid=\"B14-ijms-21-05453\" ref-type=\"bibr\">14</xref>]. Despite the success of current therapies, we still need to uncover unique genetic alterations or tumor characteristics that might be translated into diagnostic and prognostic biomarkers or pharmacologically amenable targets.</p><p>To our knowledge, this is the first study to examine the expression of PLA2R1and its promoter’s methylation, as one of epigenetic regulators, in human breast cancer tissues. Our goal was to study the level of PLA2R1 mRNA expression in different histological grades and molecular subtypes of breast cancer. More specifically, we wanted to assess the degree of <italic>PLA2R1</italic> promoter methylation that may regulate the expression of PLA2R1 in human breast cancer tissues. In the present study, we showed that significant downregulation of PLA2R1 was detected in breast cancers of different histological grades and molecular subtypes when compared to benign breast tissues. Our results indicated that <italic>PLA2R1</italic> promoter hypermethylation was not only inversely correlated with PLA2R1 downregulation but also associated with the most aggressive subtypes of breast cancers. Hence, we can conclude that <italic>PLA2R1</italic> promoter hypermethylation is a potential diagnostic and prognostic biomarker in breast cancer.</p></sec><sec sec-type=\"results\" id=\"sec2-ijms-21-05453\"><title>2. Results</title><sec id=\"sec2dot1-ijms-21-05453\"><title>2.1. Downregulation of PLA2R1 mRNA Expression Is Associated with High Histological-Grade Breast Cancer</title><p>To explore the potential role of PLA2R1 in breast cancer tumorigenesis, we evaluated the expression of PLA2R1 mRNA. The web application bc-GenExMiner database version v4.4 [<xref rid=\"B15-ijms-21-05453\" ref-type=\"bibr\">15</xref>] was used to study the differential expression of PLA2R1 mRNA between various histological grades of DNA microarray and RNAseq datasets comprising 10,001 and 4712 breast cancer patients. Based on the analysis of these datasets, we detected a significant decrease (<italic>p</italic> < 0.0001) in the mRNA level of PLA2R1 in Grade II and III in comparison to Grade I breast cancer (<xref ref-type=\"fig\" rid=\"ijms-21-05453-f001\">Figure 1</xref>A).</p><p>The PLA2R1 mRNA expression levels were next assessed in both benign and malignant breast tissue samples using qRT-PCR. A statistically significant lower level of PLA2R1 mRNA expression (fold change = 0.052, <italic>p</italic> = 0.0005) was detected in breast cancer tissues compared to benign control (<xref ref-type=\"fig\" rid=\"ijms-21-05453-f001\">Figure 1</xref>B). To validate the results obtained from our bioinformatics analysis, we assessed the mRNA expression of PLA2R1 in different histological grades of human breast cancer (<xref rid=\"ijms-21-05453-t001\" ref-type=\"table\">Table 1</xref>). Our findings confirmed that lower levels of PLA2R1 mRNA expression were detected in Grade II (fold change = 0.15) and Grade III (fold change = 0.028) breast cancer when compared to the benign control. However, only high histological-grade (Grade III) breast cancer reached a statistically significant level (<italic>p</italic> = 0.0004) when compared to benign breast tissue (<xref ref-type=\"fig\" rid=\"ijms-21-05453-f001\">Figure 1</xref>C).</p></sec><sec id=\"sec2dot2-ijms-21-05453\"><title>2.2. Lower Levels of PLA2R1 mRNA Expression Were Significantly Associated with Triple Negative Breast Cancers (TNBC)</title><p>We further explored whether PLA2R1 mRNA downregulation was associated with different molecular subtypes of breast cancer. Our bioinformatics analysis from the bc-GenExMiner database demonstrated a lower expression of PLA2R1 mRNA in basal-like, human epidermal growth factor receptor 2 (HER2)-positive and luminal B breast cancers in comparison to both luminal A and normal breast-like cancers (<italic>p</italic> < 0.0001) (<xref ref-type=\"fig\" rid=\"ijms-21-05453-f002\">Figure 2</xref>A,B) [<xref rid=\"B15-ijms-21-05453\" ref-type=\"bibr\">15</xref>]. To substantiate the data obtained from the bioinformatics analysis, PLA2R1 mRNA expression was then evaluated in breast cancer tissues of different molecular subtypes. The molecular subtypes were defined using the expression of the following surrogate immunohistochemical markers—estrogen receptor (ER), progesterone receptor (PR), HER2 and Ki-67 (<xref rid=\"ijms-21-05453-t001\" ref-type=\"table\">Table 1</xref>) [<xref rid=\"B16-ijms-21-05453\" ref-type=\"bibr\">16</xref>,<xref rid=\"B17-ijms-21-05453\" ref-type=\"bibr\">17</xref>]. As depicted in <xref ref-type=\"fig\" rid=\"ijms-21-05453-f002\">Figure 2</xref>C, the PLA2R1 expression was significantly downregulated in TNBC (fold change = 0.017, <italic>p</italic> = 0.0002) compared to benign control tissues. Although the HER2-positive, luminal A and luminal B subtypes of breast cancer demonstrated lower expression of PLA2R1 mRNA with median fold changes (0.029, 0.232 and 0.158, respectively), none of these differences approached statistical significance. Our data indicated that the differential expression of PLA2R1 reflected the degree of differentiation of breast cancer cells, which supports its clinical usefulness.</p></sec><sec id=\"sec2dot3-ijms-21-05453\"><title>2.3. PLA2R1 Promoter Hypermethylation Was Associated with Aggressive Subtypes of Breast Cancer</title><p>We sought to check if the suppressed expression of PLA2R1 mRNA was associated with a specific methylation pattern of its promoter. We performed a bioinformatics analysis on the publicly available Cancer Cell Line Encyclopedia (CCLE) to assess <italic>PLA2R1</italic> CpG island methylation in 44 breast cancer cell lines of different molecular subtypes [<xref rid=\"B18-ijms-21-05453\" ref-type=\"bibr\">18</xref>]. As shown in <xref ref-type=\"fig\" rid=\"ijms-21-05453-f003\">Figure 3</xref>A, hypermethylation of the <italic>PLA2R1</italic> promoter was detected in triple negative (HCC1395, HCC1187, DU4475, KPL1 and BT20) and HER2-positive (SKBR3, HCC202 and HCC1954) breast cancer cell lines [<xref rid=\"B19-ijms-21-05453\" ref-type=\"bibr\">19</xref>]. The MCF7, HMC18, MDA-MB-361 and BT483 cell lines were the only luminal cell lines in this analysis that revealed hypermethylation of the <italic>PLA2R1</italic> promoter [<xref rid=\"B19-ijms-21-05453\" ref-type=\"bibr\">19</xref>,<xref rid=\"B20-ijms-21-05453\" ref-type=\"bibr\">20</xref>,<xref rid=\"B21-ijms-21-05453\" ref-type=\"bibr\">21</xref>].</p><p>Next, we assessed if the <italic>PLA2R1</italic> promoter average methylation level was associated with decreased PLA2R1 mRNA expression in human breast cancer tissues. Using qRT-PCR, we found that methylation of the <italic>PLA2R1</italic> promoter was significantly elevated (fold change = 1.079, <italic>p</italic> < 0.0001) in breast cancer tissues when compared with the benign control (<xref ref-type=\"fig\" rid=\"ijms-21-05453-f003\">Figure 3</xref>B). A negative correlation was detected between the expression of PLA2R1 mRNA and the levels of <italic>PLA2R1</italic> promoter methylation (<italic>r</italic> =− 0.2). A statistically significant hypermethylation of the <italic>PLA2R1</italic> promoter was detected in both Grade II (fold change = 1.034, <italic>p</italic> < 0.0001) and Grade III (fold change = 1.279, <italic>p</italic> < 0.0001) breast cancers compared to benign breast tissues (<xref ref-type=\"fig\" rid=\"ijms-21-05453-f003\">Figure 3</xref>C).</p><p>In further agreement with the cell line bioinformatics analysis, methylation of the <italic>PLA2R1</italic> promoter was significantly elevated (fold change = 1.562, <italic>p</italic> < 0.0001) in TNBC when compared with benign breast fibroadenomas. Non-significant hypermethylation of <italic>PLA2R1</italic> promoter was detected in HER2-positive (fold change= 1.033, <italic>p</italic> = 0.07), luminal A (fold change = 0.871, <italic>p</italic> = 0.5) and luminal B (fold change =1.03, <italic>p</italic> = 0.5) breast cancers (<xref ref-type=\"fig\" rid=\"ijms-21-05453-f003\">Figure 3</xref>D). We next sought to compare the level of <italic>PLA2R1</italic> promoter methylation in TNBC with its level in the HER2-positive, luminal A and luminal B molecular subtypes. We found that methylation of the <italic>PLA2R1</italic> promoter was significantly elevated in TNBC tissues compared to HER2-positive, luminal A and luminal B types (<italic>p</italic> = 0.002, <italic>p</italic> = 0.004 and <italic>p</italic> = 0.02), respectively.</p></sec><sec id=\"sec2dot4-ijms-21-05453\"><title>2.4. PLA2R1 Promoter Methylation Outperformed PLA2R1 Expression as a Diagnostic and Prognostic Marker of Breast Cancer</title><p>Our results indicated that both PLA2R1 expression and its promoter methylation could be considered as potential diagnostic biomarkers in breast cancer as both can separate out benign from malignant breast tissues. We further evaluated the diagnostic accuracy of both markers using receiver operating characteristic (ROC) curve analysis. Our results showed that <italic>PLA2R1</italic> average promoter methylation was able to distinguish breast cancer from a benign control with the area under the curve (AUC) = 0.80 (<italic>p</italic> < 0.0001) and the sensitivity, specificity and cutoff point at 63%, 93% and 1.03, respectively (<xref ref-type=\"fig\" rid=\"ijms-21-05453-f004\">Figure 4</xref>A). Of note, the cutoff point of 1.03 corresponds to 32.5% of the average promoter methylation. When ROC analysis was conducted for PLA2R1 expression, we found the AUC = 0.28 (<italic>p</italic> = 1) and the sensitivity, specificity and cutoff point at 99%, 10% and 0, respectively (<xref ref-type=\"fig\" rid=\"ijms-21-05453-f004\">Figure 4</xref>B). Taken together, these results imply that <italic>PLA2R1</italic> promoter methylation outperformed PLA2R1 expression in discriminating breast cancer from benign fibroadenomas.</p><p>We further conducted ROC analysis to examine the ability of both markers in differentiating the TNBC tumors from the other breast cancer molecular subtypes (HER2-positive, luminal A and luminal B). As depicted in <xref ref-type=\"fig\" rid=\"ijms-21-05453-f004\">Figure 4</xref>C, <italic>PLA2R1</italic> promoter methylation can distinguish the TNBC tumors from HER2-positive, luminal A and luminal B subtypes with the AUC = 0.9 (<italic>p</italic> < 0.0001) and the sensitivity, specificity and cutoff point at 88%, 89% and 1.27, respectively. The cutoff point of 1.27 corresponds to 28% of the average promoter methylation. When ROC analysis was used to assess the PLA2R1 expression in discriminating the TNBC from other molecular subtypes, we found the AUC = 0.31 (<italic>p</italic> = 1) with the sensitivity, specificity and cutoff point at 100%, 2% and 0, respectively. These results suggest that <italic>PAL2R1</italic> promoter methylation is a potentially useful prognostic marker as it can discriminate TNBC from other molecular subtypes of breast cancer.</p></sec></sec><sec sec-type=\"discussion\" id=\"sec3-ijms-21-05453\"><title>3. Discussion</title><p>PLA2R1 expression and its role in the tumorigenesis of breast cancer are still not completely understood. In the present study, we explored the expression of PLA2R1 in breast cancer tissues of different histological grades and molecular subtypes in comparison to benign mammary tumors. We also assessed the degree of <italic>PLA2R1</italic> promoter methylation in relation to the expression of PLA2R1 in human breast cancer tissues. Our findings revealed that PLA2R1 was differentially expressed among different histological grades and molecular subtypes of breast cancers compared to benign ones. However, only the aggressive breast cancers—Grade III and the TNBC subtype—demonstrated statistically significant downregulation of PLA2R1 mRNA expression compared to benign control.</p><p>Previous in vitro studies reported low expression of PLA2R1 mRNA in different types of cancer, such as leukemia, renal, thyroid and breast cancers [<xref rid=\"B3-ijms-21-05453\" ref-type=\"bibr\">3</xref>,<xref rid=\"B5-ijms-21-05453\" ref-type=\"bibr\">5</xref>,<xref rid=\"B22-ijms-21-05453\" ref-type=\"bibr\">22</xref>]. However, this was clearly at odds with the observations of another study that reported significant upregulation of PLA2R1 in prostate cancer when compared to normal tissues [<xref rid=\"B23-ijms-21-05453\" ref-type=\"bibr\">23</xref>]. The fact that PLA2R1 mRNA was observed at low levels in breast cancer strongly supports its potential tumor suppressor role. This is in line with previous findings by others who reported that PLA2R1 was shown to regulate several anti-tumor and anti-inflammatory responses, including proliferation, cell transformation, apoptosis and senescence in breast cancer [<xref rid=\"B22-ijms-21-05453\" ref-type=\"bibr\">22</xref>,<xref rid=\"B24-ijms-21-05453\" ref-type=\"bibr\">24</xref>,<xref rid=\"B25-ijms-21-05453\" ref-type=\"bibr\">25</xref>]. Although we have not directly addressed the question of how <italic>PLA2R1</italic> suppresses tumorigenesis in breast cancer, there are many possible mechanistic explanations that can be underlined. <italic>PLA2R1</italic> is involved in several vital biological process of breast cancer, which include triggering DNA damage, carcinogenesis, cell death and cell differentiation (<xref ref-type=\"fig\" rid=\"ijms-21-05453-f005\">Figure 5</xref>) [<xref rid=\"B26-ijms-21-05453\" ref-type=\"bibr\">26</xref>]. One conceivable mechanism is that PLA2R1 activates the Janus kinase 2 (JAK2) pathway as well as induces estrogen-related receptor alpha1 (ESRRA). Both pathways direct their activities towards tumor suppression by accumulating reactive oxygen species (ROS), which impacts the mitochondrial biology, leading to senescence and apoptosis [<xref rid=\"B5-ijms-21-05453\" ref-type=\"bibr\">5</xref>,<xref rid=\"B22-ijms-21-05453\" ref-type=\"bibr\">22</xref>,<xref rid=\"B27-ijms-21-05453\" ref-type=\"bibr\">27</xref>]. Another possible explanation is that PLA2R1 triggers DNA damage through the activation of the p53 signaling molecule—one of its downstream targets [<xref rid=\"B24-ijms-21-05453\" ref-type=\"bibr\">24</xref>].</p><p>The disruption of epigenetic mechanisms plays a crucial role in the neoplastic cellular transformation essential for cancer initiation and progression. Aberrant DNA methylation, histone modification, as well as posttranscriptional gene regulations by microRNAs were previously detected at earlier stages of malignant cellular transformation [<xref rid=\"B28-ijms-21-05453\" ref-type=\"bibr\">28</xref>,<xref rid=\"B29-ijms-21-05453\" ref-type=\"bibr\">29</xref>,<xref rid=\"B30-ijms-21-05453\" ref-type=\"bibr\">30</xref>]. We decided to investigate the DNA methylation of the <italic>PLA2R1</italic> promoter as an epigenetic regulator that might target PLA2R1 mRNA expression. Our results demonstrated a significant hypermethylation of the <italic>PLA2R1</italic> promoter in breast cancer tissues compared to the benign controls. This finding was consistent with reports of the association between the hypermethylation of the <italic>PLA2R1</italic> promoter and the loss of its expression in breast cancer [<xref rid=\"B3-ijms-21-05453\" ref-type=\"bibr\">3</xref>,<xref rid=\"B10-ijms-21-05453\" ref-type=\"bibr\">10</xref>]. This detected hypermethylation of the <italic>PLA2R1</italic> promoter may explain the downregulation of PLA2R1 mRNA expression in breast cancer tissues. Our results suggest that breast cancer cells may use the hypermethylation of the <italic>PLA2R1</italic> promoter to induce its downregulation as a defense mechanism against its tumor suppressive effects. Previous in vitro studies explained the mechanism of the <italic>PLA2R1</italic> promoter hypermethylation in various types of cancer [<xref rid=\"B4-ijms-21-05453\" ref-type=\"bibr\">4</xref>,<xref rid=\"B6-ijms-21-05453\" ref-type=\"bibr\">6</xref>,<xref rid=\"B23-ijms-21-05453\" ref-type=\"bibr\">23</xref>]. As depicted in <xref ref-type=\"fig\" rid=\"ijms-21-05453-f005\">Figure 5</xref>, hypermethylation of the <italic>PLA2R1</italic> promoter in breast cancer is triggered by cellular myelocytomatosis (c-MYC)-mediated promoter methylation. The binding of c-MYC to the <italic>PLA2R1</italic> promoter induces <italic>PLA2R1</italic> DNA methylation through the recruitment of DNA methyl transferase, which results in the suppression of its expression [<xref rid=\"B3-ijms-21-05453\" ref-type=\"bibr\">3</xref>,<xref rid=\"B6-ijms-21-05453\" ref-type=\"bibr\">6</xref>,<xref rid=\"B31-ijms-21-05453\" ref-type=\"bibr\">31</xref>].</p><p>We also found that <italic>PLA2R1</italic> promoter hypermethylation was significantly associated with TNBC, which are well known for aggressive behavior. This observation is supported by another study that also reported the association of <italic>PLA2R1</italic> promoter hypermethylation with TNBC cell lines [<xref rid=\"B10-ijms-21-05453\" ref-type=\"bibr\">10</xref>]. Together with previously published data, the results presented herein clearly underscore the role of DNA methylation in <italic>PLA2R1</italic> gene regulation in mammary tumors. The significant association of <italic>PLA2R1</italic> promoter hypermethylation with TNBC implies its usefulness as a potential prognostic marker in breast cancer.</p><p>Our study, to our knowledge, is the first to assess the expression of PLA2R1 and its promoter methylation as one of its epigenetic regulators in human breast cancer tissues. However, the intrinsic limitation of this study is the small sample size that did not offer adequate power to study the relationships between the epigenetic factors and the downregulation of PLA2R1. Large-scale clinical studies are required to assess the prognostic relevance of PLA2R1 and its promoter methylation to breast cancer outcomes and treatment responses.</p><p>In conclusion, our study suggests that <italic>PLA2R1</italic> promoter methylation is a potentially useful diagnostic and prognostic biomarker in breast cancer. This sheds light on the role of promoter methylation as an epigenetic regulator of PLA2R1 expression in breast cancer. We can also anticipate that <italic>PLA2R1</italic> promoter methylation might serve as a potential therapeutic target in breast cancer. These novel findings on the epigenetic control of <italic>PLA2R1</italic> and its link to the aggressive subtypes of mammary gland carcinomas open the door to new areas of research in cancer biology.</p></sec><sec id=\"sec4-ijms-21-05453\"><title>4. Materials and Methods</title><sec id=\"sec4dot1-ijms-21-05453\"><title>4.1. Bioinformatics Analysis</title><p>The web application bc-GenExMiner database version v4.4 [<xref rid=\"B15-ijms-21-05453\" ref-type=\"bibr\">15</xref>] was used to study the differential expression of PLA2R1 mRNA among various histological grades and PAM50 molecular subtypes of breast cancer. This application includes DNA microarray and RNAseq datasets comprising 10,001 and 4712 breast cancer patients, respectively. Welch’s test and Dunnett–Tukey–Kramer’s test were conducted to evaluate the difference in gene expressions among distinct population subgroups.</p><p>We have used the publicly available CCLE (<uri xlink:href=\"https://portals.broadinstitute.org/ccle\">https://portals.broadinstitute.org/ccle</uri>) to assess the <italic>PLA2R1</italic> gene methylation in 44 breast cancer cell lines [<xref rid=\"B18-ijms-21-05453\" ref-type=\"bibr\">18</xref>]. Gene methylation can be visualized through a bubble map where the X-axis displays the position of the methylation data (the number before the colon is the chromosome and the number after the colon is the position of methylation) and the Y-axis displays the name of breast cancer cell lines in which the methylation was measured. In the bubble map, the bubble size represents the coverage, its color represents methylation, with warmer colors being more methylated [<xref rid=\"B18-ijms-21-05453\" ref-type=\"bibr\">18</xref>]. We used the publicly available Pathway Studio Web (Elsevier, Netherlands) (<uri xlink:href=\"https://mammalcedfx.pathwaystudio.com/app/search\">https://mammalcedfx.pathwaystudio.com/app/search</uri>) to explore the molecular interactions and pathways concerning <italic>PLA2R1</italic> and its promoter methylation in breast cancer [<xref rid=\"B26-ijms-21-05453\" ref-type=\"bibr\">26</xref>].</p></sec><sec id=\"sec4dot2-ijms-21-05453\"><title>4.2. Patients and Tissue Samples</title><p>A total of 70 female patients with breast cancer (age range, 29–87; mean 57.2 ± 15.4 years) and 30 female patients with benign breast fibroadenomas (control) (age range, 25–63 years; mean 48.8 ± 11.6 years) were recruited from the Surgical Department of Kasr Alainy Teaching Hospital, Faculty of Medicine, Cairo University. The specimens were obtained for the period extending from March 2018 to September 2019 after obtaining approval of the Institutional Review Board (IRB # BC 2136, February 2018) in accordance with the Declaration of Helsinki. Written informed consent was obtained from all patients before they participated in this study. After surgical treatment, ranging from simple mastectomy to classical radical mastectomy, fresh sample cuts of the breast tumor lesions were excised and fixed with 10% neutral buffered formalin. Formalin-Fixed Paraffin-Embedded (FFPE) blocks were prepared for pathological evaluation and immunohistochemical detection in the pathology department of the Faculty of Medicine, Cairo University and stored at an appropriate temperature until use. All samples were confirmed as breast infiltrating ductal carcinomas and breast fibroadenomas using histopathological methods. None of the enrolled patients had received preoperative neo-adjuvant chemotherapy, immunotherapy or radiation therapy. We also excluded patients diagnosed with inflammatory breast cancer, with metastatic cancer or with a history of recurrent tumors.</p></sec><sec id=\"sec4dot3-ijms-21-05453\"><title>4.3. Histological and Immunohistochemical Investigation</title><p>Histological grading, as well as the ER, PR, HER2 and Ki-67 status, were verified in the pathology department of the Faculty of Medicine, Cairo University. Tumor grading was performed according to the Modified Scarff–Bloom–Richardson–Elston–Ellis grading system (SBR-EE) [<xref rid=\"B32-ijms-21-05453\" ref-type=\"bibr\">32</xref>]. All immunohistochemical analyses were conducted on routinely processed FFPE tissues. The immunostaining was performed using a BenchMark XT Ventana autostainer following the protocol instructions. The ER, PR and HER2 expressions were scored following the American Society of Clinical Oncology/ College of American Pathologists (ASCO/CAP) guidelines [<xref rid=\"B33-ijms-21-05453\" ref-type=\"bibr\">33</xref>]. The expression of Ki-67 in breast tissue was studied by calculating the percentage of positively stained breast cancer cells [<xref rid=\"B34-ijms-21-05453\" ref-type=\"bibr\">34</xref>]. For the molecular subtyping, immunohistochemical staining of ER, PR, HER2 and Ki-67 were used as surrogate markers to classify breast cancer tumors into luminal A, luminal B, HER2-positive and TNBC. The molecular subtypes were defined as follows—luminal A (ER+, PR+, HER2− and Ki-67 < 14%), luminal B (ER+, PR+ and HER2+) or (ER+, PR+, HER2- and Ki-67 ≥ 14%), HER2-positive (ER−, PR− and HER2+) and TNBC (ER−, PR− and HER2-) [<xref rid=\"B16-ijms-21-05453\" ref-type=\"bibr\">16</xref>,<xref rid=\"B17-ijms-21-05453\" ref-type=\"bibr\">17</xref>].</p></sec><sec id=\"sec4dot4-ijms-21-05453\"><title>4.4. Molecular Biology Examinations</title><sec id=\"sec4dot4dot1-ijms-21-05453\"><title>4.4.1. RNA Extraction and Reverse Transcription</title><p>For the RNA extraction, a macro-dissection was performed on each sample and the total RNA was extracted from the FFPE breast tumor samples, after removing the paraffin with xylene using a RNeasy Fibrous Tissue kit (Qiagen, cat. No. 74704, Valencia, CA, USA) as described in the manufacturer’s protocol. We evaluated the purity and concentration of the RNA using a NanoDrop ND-100 Spectrophotometer (Thermo Scientific, Waltham, MA, USA).</p><p>Reverse transcription (RT) of the RNA was conducted using a miScript II RT kit (Qiagen, cat. No. 218161, Valencia, CA, USA) according to manufacturer’s instructions. The total reaction volume was 20 µL containing 100 ng of the total RNA that was reverse transcribed at 37 °C for 1 h, followed by inactivation of the reverse transcriptase at 95 °C for 5 min. A total of 30 ng of the newly synthesized complementary DNA (cDNA) served as a template for the quantification of PLA2R1 mRNA expression. The cDNA was diluted and stored at −80 °C until assayed.</p></sec><sec id=\"sec4dot4dot2-ijms-21-05453\"><title>4.4.2. Quantitative Real-Time PCR (qRT-PCR)</title><p>The cDNA was amplified using quantitative real-time PCR. The amplification reactions were performed in 25 μL volumes containing 2.5 μL of diluted RT product, 2.5 μL from PLA2R1 mRNA Quanti Tect Primer Assay (Qiagen, cat. No. 249900, Valencia, CA, USA) for PLA2R1 mRNA assay, then nuclease-free water was added to reach the final volume. The following PCR cycling conditions were used 95 °C for 15 minutes, followed by 40 cycles at 94 °C for 15 s, 55 °C for 30 s and 70 °C for 30 s. The raw cycle threshold (Ct) values were collected using the (Rotor-Gene Q (Qiagen) Software 2.3.1.49). The mature PLA2R1 levels were normalized to the human Glyceraldehyde 3-phosphate dehydrogenase (GAPDH) levels and the experiment was performed in triplicate. By means of the ΔΔCt equation, the expression of PLA2R1 in malignant breast tissues was calculated in comparison with the fibroadenomas breast tissues using the endogenous control GAPDH.</p></sec><sec id=\"sec4dot4dot3-ijms-21-05453\"><title>4.4.3. Genomic DNA Extraction and DNA Methylation</title><p>Genomic DNA was extracted from the FFPE breast tumor samples using a DNeasy tissue extraction kit (Qiagen, Cat. No. 69504, Valencia, CA, USA) according to the manufacturer’s instructions. The DNA purity and quantity were determined using a nanodrop (Thermo scientific, USA). After DNA extraction, a methylation assay was performed using EpiTect Methyl II PCR Assay (Qiagen, Valencia, CA, Cat. No. 335002) to assess the <italic>PLA2R1</italic> promoter average methylation level. Briefly, the genomic DNA was digested using the EpiTect Methyl II DNA Restriction Kit (Qiagen, Valencia, CA, Cat. No.335452) and real-time PCR was performed using RT SYBR Green ROX qPCR Mastermix (Qiagen, Valencia, CA, Cat. No 330520). All data were analyzed using the methylation assay software provided by Qiagen. The cycling PCR conditions involved 5 min at 94 °C, 40 cycles of 94 °C for 30 s, 72 °C for 60 s and 72 °C for 30 s.</p></sec></sec><sec id=\"sec4dot5-ijms-21-05453\"><title>4.5. Statistical Analysis</title><p>All statistical analyses were performed using the computer program Statistical Package for the Social Science (SPSS, Chicago, IL, USA) software version 15 for Microsoft Windows and GraphPad Prism 5.0 (GraphPad Software, CA, USA). The values were expressed as the mean ± standard deviation (SD) or median when appropriate. Levels of PLA2R1 mRNA expression were measured using RT-qPCR in the breast tissue specimens and normalized to GAPDH mRNA as reference gene. The fold change was calculated as ratio of PLA2R1 expression to the internal control. Of note, Log2 values were used to visualize the gene expression data for easier data interpretation. The Wilcoxon rank sum test was used to compare the malignant and benign breast tissues. The Kruskal–Wallis rank sum test and Dunn (1964) Kruskal–Wallis multiple comparison test were used to compare the levels of expression of PLA2R1 and its promoter methylation where they were depicted using boxplots. The correlations between the PLA2R1 expression and promoter methylation were evaluated using Spearman’s <italic>r</italic> correlation coefficient. ROC curve analysis was performed to assess the diagnostic and prognostic accuracy of PLA2R1 and <italic>PLA2R1</italic> promoter methylation and the AUC was also calculated [<xref rid=\"B35-ijms-21-05453\" ref-type=\"bibr\">35</xref>]. Statistical significance was considered for <italic>p</italic> values lower than 0.05.</p></sec></sec></body><back><notes><title>Author Contributions</title><p>Conceptualization, M.T., N.M. and E.Y.; methodology, M.T., N.M., E.Y. and A.A.A.A.; Bioinformatic analysis, N.M. and E.Y.; Formal analysis, M.T; N.M. and E.Y.; data curation, M.T.; N.M. and E.Y.; writing—original draft preparation, N.M. and E.Y.; writing—review and editing, M.T.; N.M. and E.Y.; supervision, M.T. and A.A.A.A.; funding acquisition, N.M. and E.Y. All authors have read and agreed to the published version of the manuscript.</p></notes><notes><title>Funding</title><p>The authors extend their appreciation to the Deanship of Postgraduate and Scientific Research at Dar Al Uloom University, Riyadh, KSA, for funding this work.</p></notes><notes notes-type=\"COI-statement\"><title>Conflicts of Interest</title><p>The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses or interpretation of data; in the writing of the manuscript or in the decision to publish the results.</p></notes><glossary><title>Abbreviations</title><array orientation=\"portrait\"><tbody><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">AUC</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Area under the curve </td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ASCO/CAP</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">American Society of Clinical Oncology/ College of American Pathologists </td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CCLE</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Cancer cell line encyclopedia</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">cDNA</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Complementary DNA </td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">c-MYC</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Cellular myelocytomatosis</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Ct</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Cycle threshold </td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ER</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Estrogen receptor </td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ESRRA</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Estrogen-related receptor alpha1 </td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">FFPE</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Formalin-fixed paraffin-embedded </td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">GAPDH</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Glyceraldehyde 3-phosphate dehydrogenase </td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">HER2</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Human epidermal growth factor receptor 2</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">JAK2</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Janus kinase 2</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">PLA2R1</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Phospholipase A2 receptor 1</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">PR</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Progesterone receptor </td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">qRT-PCR</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Quantitative Real-time PCR </td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ROC</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Receiver operating characteristic </td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">RT</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Reverse transcription </td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">SBR-EE</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Scarff-Bloom-Richardson-Elston-Ellis grading system </td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">SD</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Standard deviation</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">sPLA2</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Secretory phospholipase A2 </td></tr><tr><td align=\"left\" 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Lett.</source><year>2006</year><volume>27</volume><fpage>861</fpage><lpage>874</lpage><pub-id pub-id-type=\"doi\">10.1016/j.patrec.2005.10.010</pub-id></element-citation></ref></ref-list></back><floats-group><fig id=\"ijms-21-05453-f001\" orientation=\"portrait\" position=\"float\"><label>Figure 1</label><caption><p>Differential expression of PLA2R1 among different histological grades of breast cancer compared to benign fibroadenomas. (<bold>A</bold>) Boxplots illustrating significantly lower expression levels of PLA2R1 mRNA in Grade II and III in comparison to Grade I breast cancer (<italic>p</italic> < 0.0001) from the bc-GenExMiner DNA microarray database. (<bold>B</bold>) Levels of PLA2R1 mRNA were measured using RT-qPCR in benign and malignant breast tumors and normalized to GAPDH mRNA as reference gene. Boxplots showing the downregulation of PLA2R1 mRNA expression in malignant breast cancer tissues compared to benign controls (<italic>p</italic> = 0.0005). (<bold>C</bold>) Boxplots depicting a significantly lower expression level of PLA2R1 mRNA in Grade III breast cancer compared to the benign controls (<italic>p</italic> = 0.0004).</p></caption><graphic xlink:href=\"ijms-21-05453-g001\"/></fig><fig id=\"ijms-21-05453-f002\" orientation=\"portrait\" position=\"float\"><label>Figure 2</label><caption><p>Lower level of PLA2R1 mRNA expression was significantly associated with Triple Negative Breast Cancers (TNBC). (<bold>A</bold>) Boxplots displaying lower expressions of PLA2R1 mRNA levels in basal-like, luminal B and HER2-positive breast cancers compared to luminal A and normal breast-like cancers (<italic>p</italic> < 0.0001) based on the bioinformatics analysis from the bc-GenExMiner DNA microarray database. (<bold>B</bold>) <italic>p</italic> values generated by Dunnett–Tukey–Kramer’s test to evaluate the difference in gene expressions among distinct molecular subtypes retrieved from bc-GenExMiner DNA microarray database. (<bold>C</bold>) Boxplots demonstrating lower expression levels of PLA2R1 mRNA in different molecular subtypes of breast cancer compared to benign breast fibroadenomas, however, only TNBC subtype reaches statistical significance level (<italic>p</italic> = 0.0002). *** indicate significant difference in comparison to benign control.</p></caption><graphic xlink:href=\"ijms-21-05453-g002\"/></fig><fig id=\"ijms-21-05453-f003\" orientation=\"portrait\" position=\"float\"><label>Figure 3</label><caption><p><italic>PLA2R1</italic> promoter methylation in different grades and molecular subtypes of breast cancer compared to mammary fibroadenomas. (<bold>A</bold>) A bubble map created by the Cancer Cell Line Encyclopedia (CCLE) showing <italic>PLA2R1</italic> methylation in 44 breast cancer cell lines. ** indicate the TNBC cell lines. (<bold>B</bold>) Boxplots depicting the hypermethylation of the <italic>PLA2R1</italic> promoter in breast cancer tissues compared to breast fibroadenomas as control (<italic>p</italic> < 0.0001). (<bold>C</bold>) Boxplots showing significant hypermethylation of the <italic>PLA2R1</italic> promoter in Grade II (<italic>p</italic> = 0.014) and Grade III (<italic>p</italic> < 0.0001) breast cancer compared to benign control tissues. (<bold>D</bold>) Boxplots showing that methylation of the <italic>PLA2R1</italic> promoter was significantly elevated (<italic>p</italic> < 0.0001) in TNBC when compared with benign breast fibroadenomas. *** indicate significant difference in comparison to benign control.</p></caption><graphic xlink:href=\"ijms-21-05453-g003\"/></fig><fig id=\"ijms-21-05453-f004\" orientation=\"portrait\" position=\"float\"><label>Figure 4</label><caption><p><italic>PLA2R1</italic> promoter methylation discriminated two distinct subgroups of breast cancer based on ROC curve analysis. (<bold>A</bold>,<bold>B</bold>) ROC curve analysis demonstrating that <italic>PLA2R1</italic> promoter methylation was more accurate than PLA2R1 expression in discriminating breast cancer from benign fibroadenomas. (<bold>C</bold>,<bold>D</bold>) ROC curve analysis demonstrating that <italic>PLA2R1</italic> promoter methylation surpassed PLA2R1 expression in discriminating TNBC tumors from other molecular subtypes of breast cancer.</p></caption><graphic xlink:href=\"ijms-21-05453-g004\"/></fig><fig id=\"ijms-21-05453-f005\" orientation=\"portrait\" position=\"float\"><label>Figure 5</label><caption><p>Pathway studio network analysis of <italic>PLA2R1</italic> and <italic>PLA2R1</italic> promoter methylation in breast cancer [<xref rid=\"B26-ijms-21-05453\" ref-type=\"bibr\">26</xref>]. The created biological network demonstrated that <italic>PLA2R1</italic> was involved in several vital biological processes of breast cancer, which included triggering DNA damage, carcinogenesis, cell death and cell differentiation.</p></caption><graphic xlink:href=\"ijms-21-05453-g005\"/></fig><table-wrap id=\"ijms-21-05453-t001\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijms-21-05453-t001_Table 1</object-id><label>Table 1</label><caption><p>Clinico-pathological data of breast tissue samples included in the study.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Variable </th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><italic>N</italic> (%)</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Benign fibroadenomas (control)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">30 (30%)</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Infiltrating ductal carcinomas</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">70 (70%)</td></tr><tr><td colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">\n<bold>Tumor Grades</bold>\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">II</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">24 (34.2%)</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">III</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">46 (65.71%)</td></tr><tr><td colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">\n<bold>ER Status</bold>\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Negative </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">47 (67.14%)</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Positive</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">23(32.85%)</td></tr><tr><td colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">\n<bold>PR Status</bold>\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Negative </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">47 (67.14%)</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Positive</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">23 (32.85%)</td></tr><tr><td colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">\n<bold>HER2 Expression</bold>\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Negative </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">44 (62.8%)</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Positive</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">26 (37.14)</td></tr><tr><td colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">\n<bold>Molecular Subtypes</bold>\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">TNBC</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">25 (35.71%)</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-top:solid thin\" rowspan=\"1\" colspan=\"1\">HER2-positive </td><td align=\"center\" valign=\"middle\" style=\"border-top:solid thin\" rowspan=\"1\" colspan=\"1\">22 (31.4%)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Luminal A</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">12 (17.14%)</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Luminal B </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">11 (15.71%)</td></tr></tbody></table><table-wrap-foot><fn><p>Abbreviations: ER: Estrogen receptor; PR: Progesterone receptor; HER2: Human epidermal growth factor receptor 2; TNBC: Triple negative breast cancer.</p></fn></table-wrap-foot></table-wrap></floats-group></article>\n"
]
[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Int J Mol Sci</journal-id><journal-id journal-id-type=\"iso-abbrev\">Int J Mol Sci</journal-id><journal-id journal-id-type=\"publisher-id\">ijms</journal-id><journal-title-group><journal-title>International Journal of Molecular Sciences</journal-title></journal-title-group><issn pub-type=\"epub\">1422-0067</issn><publisher><publisher-name>MDPI</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32759725</article-id><article-id pub-id-type=\"pmc\">PMC7432086</article-id><article-id pub-id-type=\"doi\">10.3390/ijms21155567</article-id><article-id pub-id-type=\"publisher-id\">ijms-21-05567</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Article</subject></subj-group></article-categories><title-group><article-title>Cell Sheets from Adipose Tissue MSC Induce Healing of Pressure Ulcer and Prevent Fibrosis via Trigger Effects on Granulation Tissue Growth and Vascularization</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0003-4946-7843</contrib-id><name><surname>Alexandrushkina</surname><given-names>Natalya</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05567\">1</xref><xref ref-type=\"aff\" rid=\"af2-ijms-21-05567\">2</xref><xref rid=\"c1-ijms-21-05567\" ref-type=\"corresp\">*</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0003-2263-2239</contrib-id><name><surname>Nimiritsky</surname><given-names>Peter</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05567\">1</xref><xref ref-type=\"aff\" rid=\"af2-ijms-21-05567\">2</xref></contrib><contrib contrib-type=\"author\"><name><surname>Eremichev</surname><given-names>Roman</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05567\">1</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0002-5039-7152</contrib-id><name><surname>Popov</surname><given-names>Vladimir</given-names></name><xref ref-type=\"aff\" rid=\"af2-ijms-21-05567\">2</xref></contrib><contrib contrib-type=\"author\"><name><surname>Arbatskiy</surname><given-names>Mikhail</given-names></name><xref ref-type=\"aff\" rid=\"af2-ijms-21-05567\">2</xref></contrib><contrib contrib-type=\"author\"><name><surname>Danilova</surname><given-names>Natalia</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05567\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Malkov</surname><given-names>Pavel</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05567\">1</xref><xref ref-type=\"aff\" rid=\"af2-ijms-21-05567\">2</xref></contrib><contrib contrib-type=\"author\"><name><surname>Akopyan</surname><given-names>Zhanna</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05567\">1</xref><xref ref-type=\"aff\" rid=\"af2-ijms-21-05567\">2</xref></contrib><contrib contrib-type=\"author\"><name><surname>Tkachuk</surname><given-names>Vsevolod</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05567\">1</xref><xref ref-type=\"aff\" rid=\"af2-ijms-21-05567\">2</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0001-8869-5190</contrib-id><name><surname>Makarevich</surname><given-names>Pavel</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05567\">1</xref><xref ref-type=\"aff\" rid=\"af2-ijms-21-05567\">2</xref></contrib></contrib-group><aff id=\"af1-ijms-21-05567\"><label>1</label>Medical Research and Education Center, Lomonosov Moscow State University, Lomonosovskiy av., 27-10, 119191 Moscow, Russia; <email>[email protected]</email> (P.N.); <email>[email protected]</email> (R.E.); <email>[email protected]</email> (N.D.); <email>[email protected]</email> (P.M.); <email>[email protected]</email> (Z.A.); <email>[email protected]</email> (V.T.); <email>[email protected]</email> (P.M.)</aff><aff id=\"af2-ijms-21-05567\"><label>2</label>Faculty of Medicine, Lomonosov Moscow State University, Lomonosovskiy av., 27-1, 119192 Moscow, Russia; <email>[email protected]</email> (V.P.); <email>[email protected]</email> (M.A.)</aff><author-notes><corresp id=\"c1-ijms-21-05567\"><label>*</label>Correspondence: <email>[email protected]</email></corresp></author-notes><pub-date pub-type=\"epub\"><day>04</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><month>8</month><year>2020</year></pub-date><volume>21</volume><issue>15</issue><elocation-id>5567</elocation-id><history><date date-type=\"received\"><day>03</day><month>6</month><year>2020</year></date><date date-type=\"accepted\"><day>01</day><month>8</month><year>2020</year></date></history><permissions><copyright-statement>© 2020 by the authors.</copyright-statement><copyright-year>2020</copyright-year><license license-type=\"open-access\"><license-p>Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>).</license-p></license></permissions><abstract><p>We report a comparative study of multipotent mesenchymal stromal cells (MSC) delivered by injection, MSC-based cell sheets (CS) or MSC secretome to induce healing of cutaneous pressure ulcer in C57Bl/6 mice. We found that transplantation of CS from adipose-derived MSC resulted in reduction of fibrosis and recovery of skin structure with its appendages (hair and cutaneous glands). Despite short retention of CS on ulcer surface (3–7 days) it induced profound changes in granulation tissue (GT) structure, increasing its thickness and altering vascularization pattern with reduced blood vessel density and increased maturation of blood vessels. Comparable effects on GT vascularization were induced by MSC secretome, yet this treatment has failed to induce repair of skin with its appendages we observed in the CS group. Study of secretome components produced by MSC in monolayer or sheets revealed that CS produce more factors involved in pericyte chemotaxis and blood vessel maturation (PDGF-BB, HGF, G-CSF) but not sprouting inducer (VEGF165). Analysis of transcriptome using RNA sequencing and Gene Ontology mapping found in CS upregulation of proteins responsible for collagen binding and GT maturation as well as fatty acid metabolism enzymes known to be negative regulators of blood vessel sprouting. At the same time, downregulated transcripts were enriched by factors activating capillary growth, suggesting that in MSC sheets paracrine activity may shift towards matrix remodeling and maturation of vasculature, but not activation of blood vessel sprouting. We proposed a putative paracrine trigger mechanism potentially rendering an impact on GT vascularization and remodeling. Our results suggest that within sheets, MSC may change their functional state and spectrum of soluble factors that influence tissue repair and induce more effective skin healing inclining towards regeneration and reduced scarring.</p></abstract><kwd-group><kwd>cell sheet</kwd><kwd>mesenchymal stromal cells</kwd><kwd>wound healing</kwd><kwd>granulation tissue</kwd><kwd>angiogenesis</kwd><kwd>endothelial cells</kwd><kwd>vessel stabilization</kwd><kwd>skin regeneration</kwd><kwd>pressure ulcer</kwd></kwd-group></article-meta></front><body><sec sec-type=\"intro\" id=\"sec1-ijms-21-05567\"><title>1. Introduction</title><p>Healing of damaged tissue is a dynamic process regulated by cytokines and growth factors, proteolytic enzymes and extracellular matrix (ECM). They serve as regulatory cues for cells and tissue structures, including blood vessels, nerves etc. Different stages wound healing involve blood cells (platelets and immune cells), stromal cells (myo/fibroblasts, endothelium, etc.), parenchymal and stem/progenitor cells [<xref rid=\"B1-ijms-21-05567\" ref-type=\"bibr\">1</xref>]. After phases of hemostasis and inflammation, healing turns to proliferative stage, resulting in formation of a provisional structure known as granulation tissue (GT) that substitutes primary blood clot disrupted by fibrinolytic enzymes. GT in humans is rich with blood vessels embedded in ECM filled by activated stromal and immune cells. Later GT structure changes dramatically due to a process termed <italic>remodeling</italic>, which results in either formation of pre-existing tissue (regeneration) or a non-functional scar at the site of damage (fibrosis), typically accompanied by hypertrophy/hyperplasia of remaining tissue to retain organ function. Wound healing interruption at any stage or ablation of its critical participants may result in non-healing wounds, excessive scarring or subsequent neoplasms [<xref rid=\"B2-ijms-21-05567\" ref-type=\"bibr\">2</xref>].</p><p>In cutaneous wounds of different natures (burns, trophic ulcers, radiation injury), acceleration of closure is an important point as far as it prevents infection of the wound bed and its potential life-threatening complications. Another crucial endpoint is the limitation of scarring—a naturally developed response to deep or vast injury of skin [<xref rid=\"B3-ijms-21-05567\" ref-type=\"bibr\">3</xref>]. After scar formation, growth of skin appendages (hair, glands, fingernails) or recovery of dermal layers is blocked. Basically, in most tissues, a scar forms a “non-receptive” site for both endogenous regeneration and regenerative medicine interventions [<xref rid=\"B4-ijms-21-05567\" ref-type=\"bibr\">4</xref>].</p><p>Thus, in cutaneous injury, a therapeutic approach that can stimulate rapid defect closure and reduce fibrosis and potentially provides ground for more effective regeneration of skin and its appendages. In recent decades, promising methods using multipotent mesenchymal stromal cells (MSC) from different sources acquired significant attention and addressed skin-healing issues [<xref rid=\"B5-ijms-21-05567\" ref-type=\"bibr\">5</xref>]. Feasible isolation and culture along with active secretion of ECM, cytokines and growth factors made MSC a valuable “medicinal stromal cell” for cutaneous repair [<xref rid=\"B6-ijms-21-05567\" ref-type=\"bibr\">6</xref>,<xref rid=\"B7-ijms-21-05567\" ref-type=\"bibr\">7</xref>]. Furthermore, composition of MSC secretome that mediates angiogenic, immunomodulating, antiapoptotic and proliferative effects of MSC allowed its independent application for “cell therapy without cells” [<xref rid=\"B8-ijms-21-05567\" ref-type=\"bibr\">8</xref>,<xref rid=\"B9-ijms-21-05567\" ref-type=\"bibr\">9</xref>].</p><p>Finally, the ability of MSC to form tissue-like constructs composing of viable cells and their native ECM resulted in the development of cell sheets (CS)—minimal tissue-engineered constructs that found application for many conditions. Our previous experience with MSC-based CS showed their efficacy for stimulation of angiogenesis, nerve repair and cardiac regeneration and proved their superiority over routinely used local injection [<xref rid=\"B10-ijms-21-05567\" ref-type=\"bibr\">10</xref>,<xref rid=\"B11-ijms-21-05567\" ref-type=\"bibr\">11</xref>,<xref rid=\"B12-ijms-21-05567\" ref-type=\"bibr\">12</xref>,<xref rid=\"B13-ijms-21-05567\" ref-type=\"bibr\">13</xref>,<xref rid=\"B14-ijms-21-05567\" ref-type=\"bibr\">14</xref>]. The latter has been shown to induce MSC apoptosis and its rapid clearance from blood flow or tissue (muscle, myocardium, etc.). To our surprise, we found few data about the efficacy of MSC-based CS for treatment of pressure ulcers and no studies implementing direct comparison of CS vs. secretome from MSC.</p><p>Pressure-induced lesions are a widely spread condition that remains a clinical problem with few cell-based products for its treatment. Due to the specific nature of damage that induces pressure, published studies on ulceration using surgical wound models can hardly be considered relevant evidence, and filling this gap became a practical rationale for the present study.</p><p>We were also intrigued by changes in secretome and transcriptome of MSC that may occur within CS, which is believed to present a more “tissue-like” environment for cells [<xref rid=\"B15-ijms-21-05567\" ref-type=\"bibr\">15</xref>,<xref rid=\"B16-ijms-21-05567\" ref-type=\"bibr\">16</xref>]. However, the degree of cell status change after CS assembly remains poorly investigated. Certain works in spheroids or cell-seeded scaffolds included RNA sequencing to show transcriptome profile including the signature of reprogramming/dedifferentiation in MSC. Modality of transcriptomic changes in CS is marginally unexplored besides certain studies that used PCR for specific groups of genes or regulatory RNAs [<xref rid=\"B17-ijms-21-05567\" ref-type=\"bibr\">17</xref>,<xref rid=\"B18-ijms-21-05567\" ref-type=\"bibr\">18</xref>,<xref rid=\"B19-ijms-21-05567\" ref-type=\"bibr\">19</xref>,<xref rid=\"B20-ijms-21-05567\" ref-type=\"bibr\">20</xref>].</p><p>Our work was intended to provide data expanding our view on CS as a tissue-engineered construct for acceleration of cutaneous defect healing and addresses the pressure ulcer issue. Going beyond applied task of MSC-based therapies we obtained data on transcriptome and secretome changes to illustrate the shift in MSC status induced by formation of CS.</p></sec><sec sec-type=\"results\" id=\"sec2-ijms-21-05567\"><title>2. Results</title><sec id=\"sec2dot1-ijms-21-05567\"><title>2.1. MSC Sheet Transplantation Accelerates Healing of Pressure Ulcer Defect</title><p>Dynamics of ulcer closure were calculated as relative change (%) of defect surface area normalized to initial defect in photos taken at Days 3, 7, 14 and 21 of the experiment. No difference between groups was found at days 3 and 7; however, starting from Day 14, acceleration of ulcer closure was observed in CS application group (“Cell Sheet”) (<xref ref-type=\"fig\" rid=\"ijms-21-05567-f001\">Figure 1</xref>A). By Day 14, area of defect in this group decreased to one-third of the initial 1.2 cm<sup>2</sup> size and was significantly lower (<xref ref-type=\"fig\" rid=\"ijms-21-05567-f001\">Figure 1</xref>B, left plot) than in animals treated by injection of suspended MSC (“Suspension”) or MSC secretome (“Secretome”). At the same time, we found no difference between “Cell Sheet” and “Untreated” negative control at Day 14. At Day 21 relative ulcer area in the “Cell Sheet” group was significantly lower than in untreated control and “Suspension” group, while the difference between “Cell Sheet” and “Secretome” groups (<xref ref-type=\"fig\" rid=\"ijms-21-05567-f001\">Figure 1</xref>B, right plot) did not reach statistical significance (<italic>p</italic> = 0.063). However, only in the “Cell Sheet” group did we find animals (3/4 mice followed up by Day 21) with hair growth indicating partial recovery of skin appendages (<xref ref-type=\"fig\" rid=\"ijms-21-05567-f001\">Figure 1</xref>A). This observation was supported by histology study (<xref ref-type=\"app\" rid=\"app1-ijms-21-05567\">Figure S1</xref>) and detailed analysis of individual animal specimen (<xref ref-type=\"app\" rid=\"app1-ijms-21-05567\">Supplementary Material M1</xref>) that displayed the most prominent healing of dermis in the “Cell Sheet” group.</p><p>Evaluation of ulcer closure in “Suspension” group showed that injection of MSC surprisingly slowed this process compared to untreated control at Day 14 (57.8 ± 14.4% vs. 43.8 ± 12.3% area respectively; <italic>p</italic> = 0.0082) and Day 21 (63.0 ± 14.8% vs. 26.6 ± 4.8% area respectively; <italic>p</italic> = 0.0071). MSC secretome injections yielded results comparable with untreated control throughout the experiment, not reaching statistical significance at endpoint (<xref ref-type=\"fig\" rid=\"ijms-21-05567-f001\">Figure 1</xref>B, right plot).</p></sec><sec id=\"sec2dot2-ijms-21-05567\"><title>2.2. Cell Sheet Transplantation Induces Intensive Remodeling of Granulation Tissue</title><p>We assessed the process of GT remodeling using a three-color Masson staining to visualize connective tissue ECM deposition (<xref ref-type=\"fig\" rid=\"ijms-21-05567-f002\">Figure 2</xref>). Remodeling results in reduction of GT mass, apoptosis of fibroblasts and vessel regression followed by deposition and thickening of collagen bundles. At Day 14 we found significantly increased amount of collagen and reduction of GT area in sections from the “Cell Sheet” group vs. the untreated control (<xref ref-type=\"fig\" rid=\"ijms-21-05567-f002\">Figure 2</xref>—Day 14), while “Suspension” and “Secretome” groups showed results similar to untreated control.</p><p>At Day 21 “Cell Sheet” group showed a dramatic drop of GT area with obvious dermis healing (<xref ref-type=\"fig\" rid=\"ijms-21-05567-f002\">Figure 2</xref>—Day 21) supporting previous macroscopic assessment (<xref ref-type=\"fig\" rid=\"ijms-21-05567-f001\">Figure 1</xref>A). Indeed, hair follicles and dermal glands were found only in histological sections from “Cell Sheet” group animals (<xref ref-type=\"fig\" rid=\"ijms-21-05567-f002\">Figure 2</xref>B, <xref ref-type=\"app\" rid=\"app1-ijms-21-05567\">Figure S1 and Supplementary Material M1</xref>). To ensure that complete healing was not a feature of the animal model we performed additional evaluation at late term (Day 35) in several untreated mice. We found that once followed-up till Day 35, untreated animals show scarring at the site of the defect (<xref ref-type=\"app\" rid=\"app1-ijms-21-05567\">Figure S2</xref>). Thus, transplantation of CS did not result in acceleration of inevitable healing but changed the outcome preventing fibrosis.</p><p>It should be noted that at Day 21, skin samples from “Cell Sheet” group showed the presence of blue dye, indicating connective tissue deposition, yet this pattern in Masson stain is typical for healthy skin rich with collagens (<xref ref-type=\"app\" rid=\"app1-ijms-21-05567\">Figure S3</xref>). The presence of skin appendages within area of healing supports that this connective tissue is not related to scarring but to dermal ECM visualized by the same bluish stain as in fibrosis.</p></sec><sec id=\"sec2dot3-ijms-21-05567\"><title>2.3. Evaluation of Transplant Retention after MSC Delivery by Suspension or Cell Sheets</title><p>We pre-labeled suspended MSC and CS by PKH26 fluorescent dye and transplanted them to experimental animals with pressure ulcers. To our surprise after injection of pre-stained MSC PKH26 signal was visualized in dermis and subcutaneous layers at Day 21 of the experiment (<xref ref-type=\"fig\" rid=\"ijms-21-05567-f003\">Figure 3</xref>). Indeed, in the “Suspension” group, we found PKH26-positive cells localized between αSMA-positive myofibroblasts (<xref ref-type=\"fig\" rid=\"ijms-21-05567-f003\">Figure 3</xref>, Day 14) or adjacent to CD31-positive blood vessels (<xref ref-type=\"fig\" rid=\"ijms-21-05567-f003\">Figure 3</xref>, Day 21).</p><p>Signal from labeled CS was detected only in the first evaluation point—Day 3. In certain images from the “Cell Sheet” group, PKH26 was not co-localized with DAPI-positive nuclei, which may hint at a residual nature of PKH26 signal due to its leakage during early apoptosis of MSC. We failed to detect PKH26-positive CS in any specimen taken at Day 7 (<italic>n</italic> = 4), which suggested early rejection of CS along with scab by natural detachment of its mass or during removal of wound dressing. Assessment of secretome retention by this method was not possible, but published studies describe the half-life of delivered active fraction ranging from several hours to 1–2 days [<xref rid=\"B21-ijms-21-05567\" ref-type=\"bibr\">21</xref>,<xref rid=\"B22-ijms-21-05567\" ref-type=\"bibr\">22</xref>].</p></sec><sec id=\"sec2dot4-ijms-21-05567\"><title>2.4. Cell Sheets Promote Formation of Granulation Tissue at Early Stages of Healing</title><p>Ischemia/reperfusion applied to animal skin resulted in degree II-III pressure ulcer with significant necrosis of the dermis, subcutaneous fat and muscle at the site of exposure (<xref ref-type=\"fig\" rid=\"ijms-21-05567-f004\">Figure 4</xref>). Data on MSC retention (<xref ref-type=\"fig\" rid=\"ijms-21-05567-f003\">Figure 3</xref>) along with histological assessment of GT (<xref ref-type=\"fig\" rid=\"ijms-21-05567-f002\">Figure 2</xref>) hinted that early stages of its formation may be influenced by CS delivery. We studied histological specimen from Days 3 and 7 using GT thickness as a measure of its growth, relying on previous reports in similar models [<xref rid=\"B23-ijms-21-05567\" ref-type=\"bibr\">23</xref>,<xref rid=\"B24-ijms-21-05567\" ref-type=\"bibr\">24</xref>,<xref rid=\"B25-ijms-21-05567\" ref-type=\"bibr\">25</xref>]. At Day 3, a significant increase of GT thickness (approx. 1.5-fold vs. untreated control) was observed in the “Cell Sheet” group. Significant differences vs. control group were also found in animals treated by MSC suspension, but not MSC secretome. Nevertheless, at Day 7, GT thickness in “Suspension” and “Secretome” groups was significantly higher than in control, while “Cell Sheet” specimen showed the highest GT thickness among all study groups (<xref ref-type=\"fig\" rid=\"ijms-21-05567-f004\">Figure 4</xref>B, Day 7). Later time-points (Days 14 and 21) were characterized by active deposition of connective tissue, GT reduction and were subject to Masson trichrome (<xref ref-type=\"fig\" rid=\"ijms-21-05567-f002\">Figure 2</xref>) described above to evaluate fibrosis.</p></sec><sec id=\"sec2dot5-ijms-21-05567\"><title>2.5. Vascularization of GT Is Modulated by MSC Secretome and CS</title><p>Vascularization of GT is a crucial parameter reflecting the degree of its maturation [<xref rid=\"B26-ijms-21-05567\" ref-type=\"bibr\">26</xref>]. We used sections from healing ulcers to visualize blood vessels by antibodies vs. CD31 and αSMA (<xref ref-type=\"fig\" rid=\"ijms-21-05567-f005\">Figure 5</xref>). Density of blood vessels in GT at Days 3 and 7 were significantly reduced in the “Cell Sheet” specimen compared to all other groups (<xref ref-type=\"fig\" rid=\"ijms-21-05567-f005\">Figure 5</xref>, plots). Vascularization pattern of GT in “Cell Sheet” and “Secretome” groups showed a high prevalence of relatively large (up to 50 µm in diameter) stabilized vessels with visible lumen and endothelial layer covered by αSMA-positive mural cells (<xref ref-type=\"fig\" rid=\"ijms-21-05567-f005\">Figure 5</xref>B). It should be noted that within the damage site, αSMA-positive cells are represented not only by smooth muscle cells of blood vessels but also by myofibroblasts abundant in GT until scar is formed.</p></sec><sec id=\"sec2dot6-ijms-21-05567\"><title>2.6. Assembly of MSC in Cell Sheets Increases Secretion of Growth Factors Involved in Blood Vessel Maturation</title><p>Short retention of CS along with changes in vascularization of underlying tissue suggested a trigger-like paracrine effect and hinted at evaluation of angiogenesis-related growth factors in CS secretome. At this point, we focused on human cell cultures to evaluate properties of homologous MSC from human adipose tissue, the proposed allogeneic source of cells for further practical development of CS-based treatment for pressure ulcer. Using primary human adipose tissue MSC isolated from three donors and cultured in monolayer or CS, we assayed concentration of growth factors and cytokines in conditioned culture medium. To ensure relevant normalization of results, we took into account the higher density of MSC in sheets compared to confluent monolayer and used PicoGreen DNA-assay for nuclei to normalize change folds. The graph in <xref ref-type=\"fig\" rid=\"ijms-21-05567-f006\">Figure 6</xref> shows changes of VEGF165, HGF, G-CSF, PDGF-BB and Ang-2 contents in medium samples from CS and monolayer. In CS, increase of angiopoietin-2 was the most profound change (5.1 fold), while increase of a potent sprouting angiogenic factor VEGF165 did not reach statistical significance. It is worth noting that the increase of PDGF-BB reached 2.5 fold—thus, CS secretome was enriched with a crucial factor for pericyte recruitment and vessel maturation.</p></sec><sec id=\"sec2dot7-ijms-21-05567\"><title>2.7. RNA-Sequencing of Cell Sheets Shows Significant Changes of MSC Transcriptome Profile Compared to Monolayer Culture</title><p>For RNA-sequencing experiments, an immortalized line of adipose-derived human MSC (ASCtelo52, ATCC, USA) was used to minimize donor-dependent variability. Assembly of CS was carried out in AdvanceStem medium as previously described [<xref rid=\"B4-ijms-21-05567\" ref-type=\"bibr\">4</xref>] and dense monolayer (90% confluent) cultured under the same conditions was used as a control.</p><p>RNA-sequencing of MSC monolayer and CS provided libraries that were normalized within each sample as reads per kilobase million (RPKM), and obtained data were used to calculate fold change for each transcript. Using Gene Ontology (GO), we mapped all transcripts with ≥2-fold increment or decrement in CS. The top 10 annotated biological process (BP) and molecular function (MF) GOs with statistically significant <italic>p</italic>-value are presented in <xref ref-type=\"fig\" rid=\"ijms-21-05567-f007\">Figure 7</xref>, completed by a list of genes belonging to each annotated cluster (<xref ref-type=\"app\" rid=\"app1-ijms-21-05567\">Supplementary Tables S1 and S2</xref>). Notable GOs and results of analysis are overviewed in Discussion and support a general impression of MSC functional shift towards cell-to-ECM interaction, blood vessel maturation and reduction of endothelial cell migration/chemotaxis accompanied by antifibrotic factors (<xref ref-type=\"fig\" rid=\"ijms-21-05567-f007\">Figure 7</xref>).</p><p>Additional analysis of trancriptional factor (TF) activity signature was performed using HOCOMOCO database to predict potential transcriptional regulators that may increase their activity due to microenvironment changes during assembly of CS. This provided an array of putative TFs showing increased activity that correlates with their target genes upregulation obtained from our RNA-sequencing data (<xref rid=\"ijms-21-05567-t001\" ref-type=\"table\">Table 1</xref>).</p></sec></sec><sec sec-type=\"discussion\" id=\"sec3-ijms-21-05567\"><title>3. Discussion</title><p>Animal test demonstrated the efficiency of MSC-based sheets to accelerate pressure ulcer closure. The “Cell Sheets” group was the only one to reach statistical significance vs. untreated control at Day 21 and was clearly superior to suspended MSC (<xref ref-type=\"fig\" rid=\"ijms-21-05567-f001\">Figure 1</xref>) despite CS’s short-lived presence limited by 3–7 days. It should also be noted that at Day 14, CS showed significant improvement vs. MSC secretome, yet by Day 21, this difference failed to reach statistical significance. Another finding to be clarified is the drastic decline of pressure ulcer size between Days 14 and 21 in the CS group. Indeed, at Day 21, ulcer relative size in “Cell Sheet group” was 1:9 of values at Day 14 (<xref ref-type=\"fig\" rid=\"ijms-21-05567-f001\">Figure 1</xref>B, plots). In rodents, a healing skin wound undergoes contraction (typically 10–14 days after damage), which provides a vivid picture for macroscopic assessment. We suggest that during the 3rd week of healing, GT underwent active resorption and strong contraction that was preceded by its accelerated growth (1st week) and maturation (2nd week), with collagen deposition visualized by Masson staining (<xref ref-type=\"fig\" rid=\"ijms-21-05567-f002\">Figure 2</xref>). Effective contraction depends on the presence on myofibroblasts with αSMA-enriched cytoskeleton and requires ECM elasticity that can change due to proteolysis or cross-linking [<xref rid=\"B27-ijms-21-05567\" ref-type=\"bibr\">27</xref>]. Further results demonstrated that CS have upregulated a number of enzymes involved in ECM remodeling and turnover, yet accurate mechanism of events observed during the 3rd week of healing is to be investigated in detail.</p><p>The most striking finding was that CS delivery induced profound recovery of dermis structure with its appendages, resulting in obvious hair growth at the site of pressure ulcer and histology, confirming the presence of glands within the dermis (<xref ref-type=\"fig\" rid=\"ijms-21-05567-f001\">Figure 1</xref>, <xref ref-type=\"fig\" rid=\"ijms-21-05567-f002\">Figure 2</xref>, <xref ref-type=\"app\" rid=\"app1-ijms-21-05567\">Figure S1 and Supplementary Material M1</xref>). This was observed exclusively after CS application and three out of four animals of this group had skin microanatomy resembling normal besides residual muscle and fat loss. The latter also indicated that specimens were grafted at the site of pre-existing pressure ulcer, which is characterized by slowly recovering loss of muscular and adipose layers. </p><p>This finding is yet to be clarified using a more accurate method to establish whether hair has re-grown, indicating full-scale regeneration via activation of progenitors or reprogramming. Another option is that dermal layers were drawn towards the center of the defect which has not been occupied by scar mass due to specific CS influence on remodeling of GT. Thus, at the moment, we may claim successful healing induced by CS application, yet the degree of involvement of antifibrotic and regenerative mechanisms, cell proliferation or reprogramming is subject to further investigation. </p><p>Injection of MSC suspension failed to stimulate ulcer closure compared to untreated control group (<xref ref-type=\"fig\" rid=\"ijms-21-05567-f001\">Figure 1</xref> and <xref ref-type=\"fig\" rid=\"ijms-21-05567-f002\">Figure 2</xref>). Initially, we attributed this outcome to a well-documented low survival of suspended MSC [<xref rid=\"B28-ijms-21-05567\" ref-type=\"bibr\">28</xref>]. However, subsequent analysis (<xref ref-type=\"fig\" rid=\"ijms-21-05567-f003\">Figure 3</xref>) revealed that PKH26-labeled MSC injected to lesion remained up to Day 21. In our previous study [<xref rid=\"B12-ijms-21-05567\" ref-type=\"bibr\">12</xref>] we found MSC retention at Day 14, and similar data were obtained in skin injury model by Yu et al. [<xref rid=\"B29-ijms-21-05567\" ref-type=\"bibr\">29</xref>]. The relevance of this finding should be established in larger animals with skin anatomy resembling human, but lack of MSC survival is unlikely the reason for the poor therapeutic outcome. </p><p>Analysis of Masson stain supported trigger effect of CS application. We found that CS application may accelerate active growth of GT at initial phases of healing (<xref ref-type=\"fig\" rid=\"ijms-21-05567-f004\">Figure 4</xref>), and by Day 7 animals from the “Cell Sheet” group had the maximum thickness of GT while suspended MSC and secretome showed concordant significant increase vs. untreated control. However, if we address the set of data from PKH26-labeled CS suggested we shall find that between Days 3 and 7 the construct was either resorbed (<xref ref-type=\"fig\" rid=\"ijms-21-05567-f003\">Figure 3</xref>), but more likely mechanically removed with detaching scab or during change of wound dressing.</p><p>Thus, absence of CS (<xref ref-type=\"fig\" rid=\"ijms-21-05567-f003\">Figure 3</xref>) starting from Day 7 marginally excluded its direct contribution to GT thickness, and enlargement of GT that was induced at the early phase (Day 3) continued even after CS was lost between Days 3 and 7. Furthermore, even at Day 3, when CS was still attached, its presence could not affect morphometry data as far as GT thickness measurement did not include superficial layers of wound bed (see panel markup in <xref ref-type=\"fig\" rid=\"ijms-21-05567-f004\">Figure 4</xref>A). This set of data along with observed efficacy of MSC secretome strengthens the assumption that effects of CS therapy were triggered by this early-time intervention of paracrine nature. Short-lived presence of CS may be a disappointing conclusion in terms of MSC delivery, yet rapid clearance of the construct is beneficial for clinical safety, and lack of cell integration minimizes tumorigenic risks.</p><p>Taking into account the data from later time points (Days 14 and 21) where residual GT was marginally absent in CS-treated animals (<xref ref-type=\"fig\" rid=\"ijms-21-05567-f002\">Figure 2</xref>), we suggested that its early increase in thickness after CS application may be followed by resorption or ECM disassembly, resulting in minimum scarring that facilitates more effective healing with skin appendages and hair present in the specimen at Day 21.</p><p>Observed changes in GT growth and remodeling depend on its vascularization. Indeed, until GT matures into a scar, it has abundant numbers of blood vessels to facilitate immune cell migration, nutrition and clearance of debris [<xref rid=\"B30-ijms-21-05567\" ref-type=\"bibr\">30</xref>]. Thus, we connected signs of scar-free healing in “Cell Sheet” animals with GT vascularization and assessed this parameter in histology sections.</p><p>The pattern of GT vascularization in the CS group had an incline towards formation of large vessels with a lumen (up to 50 µm in diameter) and a layer of endothelial cells surrounded by αSMA-positive mural smooth muscle cells (<xref ref-type=\"fig\" rid=\"ijms-21-05567-f005\">Figure 5</xref>B). These large blood vessels represent stabilized arterioles or veins, and shift towards vessel maturation [<xref rid=\"B31-ijms-21-05567\" ref-type=\"bibr\">31</xref>] was accompanied by a vivid decline of smaller blood vessel counts in specimen from CS-treated animals at Day 3. “Cell Sheet” group showed significantly lower vascular density than “Secretome” at Day 3, while by Day 7, this difference did not reach significance, indicating a comparable drop of GT vascularization by smaller-size vessels (<xref ref-type=\"fig\" rid=\"ijms-21-05567-f005\">Figure 5</xref>A).</p><p>Explanation of how maturation of blood vessels may contribute to less scarring mainly relates to mural cells (pericytes and smooth muscle cells) localization and functional status. When they are attracted to endothelial cells basal membrane formation begins by active production of laminins and collagen IV by both cell types [<xref rid=\"B32-ijms-21-05567\" ref-type=\"bibr\">32</xref>]. After deposition of basal membrane mutual influence of the two cell types inhibits endothelial proliferation, induces quiescence of pericytes and normalizes permeability [<xref rid=\"B33-ijms-21-05567\" ref-type=\"bibr\">33</xref>]. This results in arrest of plasma proteins leakage and confines the pericytes within mural compartment. According to recent studies pericytes exfoliated from blood vessels are crucial participants of fibrosis [<xref rid=\"B34-ijms-21-05567\" ref-type=\"bibr\">34</xref>,<xref rid=\"B35-ijms-21-05567\" ref-type=\"bibr\">35</xref>] and formation of a stable vasculature in GT diminishes “roaming” pericytes and vascular leakage. Several studies in skin wounds have shown angiogenic response in normal wounds may exceed what is needed for optimal repair and suppression of vascular growth by angiogenesis inhibitors reduces severity of fibrosis in skin lesions [<xref rid=\"B36-ijms-21-05567\" ref-type=\"bibr\">36</xref>].</p><p>This explanation was concordant with the analysis of angiogenesis-related factors produced in vitro by MSC and MSC-based CS (<xref ref-type=\"fig\" rid=\"ijms-21-05567-f006\">Figure 6</xref>). Angiogenesis is controlled by a balance of its activators and inhibitors, and each stage is mediated by a specific set of growth factors, proteases and cytokines. At initial stages, the most important and strong angiogenic factor is VEGF165, which increases vascular permeability, endothelial proliferation and branching. At later stages, PDGF-BB is necessary for stabilization by recruitment of pericytes from surrounding stroma. Thus, observed increase of PDGF-BB production by CS may contribute to stabilization of vasculature in GT underlying the transplanted construct. At the same time, production of VEGF165 in CS did not increase compared to monolayer MSC, which also favors more stable vasculature and reduction of increased permeability associated with effects of VEGF (<xref ref-type=\"fig\" rid=\"ijms-21-05567-f006\">Figure 6</xref>).</p><p>Other important participants are angiopoietins that activate, maintain and harness angiogenic response when blood vessel is formed. We found angiopoietin-2 (Ang-2) strongly increased in CS secretome (5.1-fold vs. monolayer), and typically Ang-2 is presented as a sprouting factor. Ang-2 acts via Tie-2 receptor to destabilize pericyte–endothelium interaction, increasing the endothelial cells’ sensitivity to inflammatory and angiogenic factors. However, the peculiar ability of Ang-2 to block sprouting and induce apoptosis of endothelial cells is described under the low production of VEGF165 [<xref rid=\"B37-ijms-21-05567\" ref-type=\"bibr\">37</xref>,<xref rid=\"B38-ijms-21-05567\" ref-type=\"bibr\">38</xref>]. Basically, when capillary sprouting is induced by factors besides VEGF165, Ang-2 may contribute to the arrest of angiogenesis rather than support it [<xref rid=\"B39-ijms-21-05567\" ref-type=\"bibr\">39</xref>]. Furthermore, Ang-2 may also bind Tie-2 expressed on pericytes, and recent elegant work by Teichert et al. [<xref rid=\"B40-ijms-21-05567\" ref-type=\"bibr\">40</xref>] has shown that depletion of Tie-2 resulted in a pro-angiogenic effect. This work suggested that activation of Tie-2 in pericytes is crucial for reciprocal control of vascular stability independent of ligand type—Ang-1 or Ang-2.</p><p>Overall, this profile of angiogenesis regulators produced by CS suggested a shift of its biological potency towards “stabilizing” rather than “stimulating” action on endothelial cells. Supporting our hypothesis, we also found HGF and G-CSF significantly upregulated in CS secretome (approx. 2.6-fold for both factors). These factors contribute to blood vessel maturation and leakage stop [<xref rid=\"B41-ijms-21-05567\" ref-type=\"bibr\">41</xref>] and may indicate a switch of secretome modality to blood vessel stabilization.</p><p>Our conclusion regarding the shift in MSC biological potency induced by CS formation was supported by analysis of RNA-sequencing data (<xref ref-type=\"fig\" rid=\"ijms-21-05567-f007\">Figure 7</xref>; <xref ref-type=\"app\" rid=\"app1-ijms-21-05567\">Supplementary Tables S1 and S2</xref>). Results of GO analysis showed that in CS, upregulated genes were annotated to clusters with high <italic>p</italic>-value that contain proteins important for cell–ECM interactions (GO:000518 “Collagen binding” and GO:0098634 “Protein binding involved in cell-to-matrix adhesion”). A high number of secreted proteolytic enzymes and their inhibitors were annotated to BP clusters related to ECM maturation, turnover and change of its composition in normal during healing after injury (GO:0030198 “ECM organization” and GO:0022617 “ECM disassembly”). Secreted proteases (including MMPs 2, 11 and 16) and upregulated CXCL-motif activators of cell migration (GO:0030334 “Reg of cell migration”) may potentially contribute to observed rapid thickening and remodeling of GT in early stages after CS transplantation. Some of GO:0030198 “ECM organization” have also been downregulated with a high combined score for this cluster. This is a typical consequence of GO annotation redundancy when the same GO may be significantly up- and downregulated during analysis with different transcripts falling into one of these categories due to high fold increment or drop. However, the majority of downregulated transcripts were related to MSC integrins and ECM proteins (COL4, ICAMs, THBS1) that comprise CS structure rather than those that can be secreted and influence underlying GT during healing.</p><p>Among upregulated transcripts, peculiar clusters were annotated as GO:0045540 “Reg of cholesterol biosynthesis process” and GO:0090181 “Reg of cholesterol metabolic process” rich with enzymes controlling cholesterol biosynthesis and alcohol biogenesis. More detailed analysis showed the majority of them were responsible for fatty acid metabolism known as negative regulators of angiogenic response in normal tissue and tumors including suppression of VEGF-induced capillary sprouting.</p><p>Analysis of downregulated genes mapped multiple proteins known to drive angiogenesis and active endothelial proliferation. Among all downregulated BPs, GO:2001046 “Reg of EC chemotaxis” containing NOTCH1 and FGFs 1 and 2 showed the highest combined score (<xref ref-type=\"fig\" rid=\"ijms-21-05567-f007\">Figure 7</xref>). Another vivid illustration of this is BP analysis of downregulated transcripts showing decline of factors that stimulate endothelium sprouting, including WNT5A [<xref rid=\"B42-ijms-21-05567\" ref-type=\"bibr\">42</xref>] and SERPINE1. Finally, MF mapping of downregulated transcripts showed enrichment of a cluster annotated as GO:0004720 “Protein-lysine 6-oxidase activity”, containing LOX-family proteins mostly known to be involved in ECM organization and gaining attention as a therapeutic target. Recent data show that LOX-encoded protein-lysine 6-oxidase is a positive regulator of both-angiogenesis and tissue fibrosis [<xref rid=\"B43-ijms-21-05567\" ref-type=\"bibr\">43</xref>].</p><p>HOCOMOCO dataset (<xref rid=\"ijms-21-05567-t001\" ref-type=\"table\">Table 1</xref>) showed significant activation of a TFs repertoire identified indirectly by upregulated targets. Increased activity of specificity protein-1 (SP-1) can be attributed to many factors as far as SP-1 (despite its name) is known for non-specific induction under different stimuli ranging from the mechanical influence and cell cycle to effects of mitogenic growth factors. However, marked activity of inflammation-associated nuclear factor kappa-B1 (NFKB1) was a surprising marker after evaluation of MSC sheets. Concordant increased activity of its heterodimer partner RELA was expected and supported the quality of the performed predictive analysis. At the moment, we lack sufficient evidence to claim a consequence of TFs activation shift, and the mechanism of these changes remains enigmatic prior to additional study. We provide this pool of data to illustrate the magnitude of transcriptional changes after self-assembly of MSC to CS. Nevertheless, transcriptional changes and modifications of differentiation potency of stromal cells within spheroids and other 3D cultures [<xref rid=\"B17-ijms-21-05567\" ref-type=\"bibr\">17</xref>,<xref rid=\"B19-ijms-21-05567\" ref-type=\"bibr\">19</xref>,<xref rid=\"B20-ijms-21-05567\" ref-type=\"bibr\">20</xref>,<xref rid=\"B44-ijms-21-05567\" ref-type=\"bibr\">44</xref>] have been reported, giving us ground for further investigation after providing the first piece of evidence that similar events take place in CS.</p><p>A potential limitation of our in vitro data obtained in human MSC is that its extrapolation to animal model might be challenging, and validation of proposed mechanisms would require a more detailed study based on presented screening of changes induced by CS assembly. However, our study intended to lay ground for pre-clinical development of a cell-based product for pressure ulcer patients, so we performed tests in human MSC from adipose tissue, which is the most feasible source of allogeneic MSC. This shift creates a certain limitation, yet we believe that we also provide a physiological rationale for a novel product that can be applied in cell therapy.</p><p>In vitro tests also should be evaluated keeping in mind that alterations in the repertoire of MSC secretome and transcriptome that may occur after transplantation to damaged tissue are hard to study and reproduce ex vivio. Nevetheless, this point must be taken into account while developing further hypotheses on mechanisms of impact rendered by MSC delivered via injection or CS application. </p></sec><sec id=\"sec4-ijms-21-05567\"><title>4. Materials and Methods</title><sec id=\"sec4dot1-ijms-21-05567\"><title>4.1. Cell Cultures</title><p>Human adipose-derived MSC immortalized cell line (ASC52telo) was purchased from ATCC (Manassas, VA, USA) and cultured using AdvanceStem culture medium (Hyclone, USA). Mouse adipose-derived MSC were obtained from subcutaneous adipose tissue of male C57Bl/6 mice as previously described [<xref rid=\"B12-ijms-21-05567\" ref-type=\"bibr\">12</xref>]. Briefly, adipose tissue samples were mechanically minced using surgical scissors and subjected to enzymatic digestion using a mixture of type I collagenase (200 u/mL, Worthington, Columbus, OH, USA) and dispase (30 u/mL, Corning, NY, USA) for 60 min at 37 °C. Equal volume of DMEM supplemented by 10% FBS (HyClone, Logan, UT, USA) was added to digested tissue mixture to inactivate the enzymes followed by centrifugation (200× <italic>g</italic>; 10 min). After supernatant was aspirated, the pellet was resuspended in complete growth medium at 5 × 10<sup>3</sup> cells/cm<sup>2</sup> and seeded in Petri dishes. Cells were cultured at 37 °C and 5% CO<sub>2</sub>. Upon reaching 80–90% of monolayer density, cells were re-seeded at a 1:4 ratio using 0.05% Trypsin-EDTA (Gibco, Waltham, MA, USA). Mouse MSC were used for in vivo experiment using a syngeneic model to avoid immune rejection while human MSC were used for in vitro studies (RNA-Seq, BioPlex assay) as described below.</p></sec><sec id=\"sec4dot2-ijms-21-05567\"><title>4.2. MSC Secretome Preparation</title><p>To obtain secretome samples, human MSC were cultured in a 100 mm dish in complete growth medium to 80% monolayer. Upon reaching the designated density, cells were washed by Hanks’ solution (3 times, 5 min), and 10 mL of serum-free DMEM was added. Cells were cultured for 7 days in a CO<sub>2</sub> incubator as described in <xref ref-type=\"sec\" rid=\"sec4dot1-ijms-21-05567\">Section 4.1</xref>. At Day 7, culture medium containing secreted fractions was collected, centrifuged at 1500 <italic>g</italic> for 10 min to remove cell debris and used for BioPlex assay or animal tests without freezing.</p></sec><sec id=\"sec4dot3-ijms-21-05567\"><title>4.3. Fabrication of Cell Sheets from MSC</title><p>Mouse primary adipose-derived MSC isolated as described above were used at passage 2–3 for animal test in pressure ulcer model. Animal manipulations and euthanasia procedures were performed in compliance with National and European Union regulations and were approved by the Institutional (Ethics Board for Animal Care) Animal Care and Use Committee (Lomonosov Moscow State University; permit #67; 15 March 2018). For RNA-sequencing and growth factor BioPlex assay, human immortalized line ASC52telo (ATCC, USA) was used at passage 4–5. For both purposes, MSC were seeded in a 12-well uncoated culture plate at 8 × 10<sup>4</sup> cells/cm<sup>2</sup> and cultured for 7 days in complete growth medium supplemented by ascorbic acid (50 μg/mL) to promote ECM deposition and assembly. Before the detachment growth medium was removed and CS was washed with warm PBS. After that, CS edges were carefully detached from the plastic using a micropipette tip used as a scrapper, and then whole-mount CS was moved for further transplantation to mice. Preparations for BioPlex assay and RNA-sequencing of human MSC cultures are described in corresponding sections below.</p></sec><sec id=\"sec4dot4-ijms-21-05567\"><title>4.4. BioPlex Assay of Growth Factors in MSC Monolayer and Cell Sheet Secretomes</title><p>MSC-derived secretome samples were collected from cultures at the state of confluent monolayer or CS and were used for multiplex ELISA. For normalization to DNA concentration, cells that remained in the plate after medium collection were lysed by TRIzol reagent (Thermo Fisher Scientific, Waltham, MA, USA). Concentrations of Angiopoietin-2, G-CSF, HGF, PDGF-BB and VEGF were simultaneously evaluated with a multiplex bead-based sandwich immunoassay kit (Human Angiogenesis assay, Bio-Rad Laboratories, Hercules, CA, USA) using Bio-Plex<sup>®</sup> 200 system (Bio-Rad, USA). Assay was performed following the manufacturer’s instructions. Briefly, 9 distinct sets of fluorescent-dye-conjugated beads with capture monoclonal antibodies specific for each factor assayed were used. Samples or standards were incubated with pre-mixed bead sets in pre-wet 96-well microplate. After incubation and washing, fluorescent detection antibody mixture was added, and after incubation, the samples were washed and resuspended in assay buffer. All values were normalized with respect to DNA concentration and measured with Quant-iT PicoGreen dsDNA Assay Kit (Thermo Fisher Scientific, USA) according to manufacturer’s manual. DNA concentration was a directly proportional function of the number of cells within study limits (calibration was carried out in-house).</p></sec><sec id=\"sec4dot5-ijms-21-05567\"><title>4.5. RNA Isolation and Transcriptome RNA-Sequencing Analysis</title><p>Immortalized human MSC (ASC52telo) were cultured in AdvanceStem medium to obtain monolayer (see <xref ref-type=\"sec\" rid=\"sec4dot1-ijms-21-05567\">Section 4.1</xref>) or CS (described in <xref ref-type=\"sec\" rid=\"sec4dot3-ijms-21-05567\">Section 4.3</xref>). After completion of culture, they were lysed by TRIzol reagent (Thermo Fisher Scientific, USA) and total RNA was extracted. Quality of RNA was checked using BioAnalyser and RNA 6000 Nano Kit (Agilent, Santa Clara, CA, USA). PolyA RNA was purified with Dynabeads<sup>®</sup> mRNA Purification Kit (Ambion, Austin, TX, USA), and Illumina library was made from polyA RNA with NEBNext<sup>®</sup> Ultra™ II RNALibrary Prep (NEB, Ipswich, MA, USA) according to manufacturer’s protocol. Concentrations of nucleic acids in obtained libraries were analyzed by Qubit dsDNA HS Assay Kit (Thermo Fisher Scientific, USA) using Qbit 2.0 equipment. RNA-sequencing was performed on HiSeq1500 at 50 b.p. read length according to manufacturer’s protocols.</p><p>Quality control was carried out using the FastQC program, and while analyzing, the source data of the adapters were not found; therefore, programs for their removal were not used. Mapping was performed using bowtie2–2.4.0; to assess changes in the level of gene expression samples were normalized as reads per kilobase million (RPKM). GO gene clustering and annotation of up- and downregulated transcripts was performed using the David 6.8 resource and Enrichr web-interface (<uri xlink:href=\"https://amp.pharm.mssm.edu/Enrichr/\">https://amp.pharm.mssm.edu/Enrichr/</uri> accessed on 1 July 2020).</p><p>For mapping of putative transcription factors that overlap with transcripts obtained after RNA-seq, we used resources of online database HOCOMOCO (<uri xlink:href=\"https://hocomoco11.autosome.ru\">https://hocomoco11.autosome.ru</uri> accessed on 3 July 2020). To establish the potential for interaction of mapped transcription factors with the putative genes, bash scripts were used to obtain a unique list.</p></sec><sec id=\"sec4dot6-ijms-21-05567\"><title>4.6. Pressure Ulcer Model</title><p>Animal manipulations and euthanasia procedures were performed in compliance with National and European Union regulations and were approved by the Institutional (Ethics Board for Animal Care) Animal Care and Use Committee (Lomonosov Moscow State University; permit #67; 15 March 2018).</p><p>For in vivo studies, we used male C57Bl/6 mice 12–14 weeks of age. The pressure ulcer defect was formed according to the protocol described by Stadler et al. [<xref rid=\"B45-ijms-21-05567\" ref-type=\"bibr\">45</xref>] with modifications. Briefly, animals were narcotized with isoflurane, hair on the back was shaved and skin was gently pulled to form a fold that was placed between two round magnets (12 mm diameter, Magnetic Source, Castle Rock, CO, USA). Magnets were removed at 12 h, and no pressure was applied for 12 h to form a cycle of ischemia/reperfusion of 24 h (12 h with magnet + 12 h without magnet). Full-thickness skin loss with damage and necrosis of subcutaneous tissue and muscle layer (ulcer stage 3), requiring more than 14 days to heal without outside intervention, required 3 cycles of ischemia/reperfusion. As a result, 2 symmetrical pressure ulcers were obtained in one animal on sides of skin fold that was placed between magnets.</p></sec><sec id=\"sec4dot7-ijms-21-05567\"><title>4.7. Delivery of MSC Suspension, Secretome or MSC Sheets</title><p>At the end of 3 cycles of ischemia/reperfusion, test material was transplanted to animals (day 0). The animals were divided into the following groups (<italic>n</italic> = 12–16 animals per group):<list list-type=\"simple\"><list-item><label>(1)</label><p>“Suspension”—injection suspended MSC to the edges and bottom of skin defect.</p></list-item><list-item><label>(2)</label><p>“Secretome”—injection of MSC secretome into the edges and bottom of skin defect.</p></list-item><list-item><label>(3)</label><p>“Cell Sheet”—application of CS to ulcer surface.</p></list-item><list-item><label>(4)</label><p>“Untreated”—animals without therapy.</p></list-item></list></p><p>The MSC suspension was administered in the amount of 4–5 injections into the edges and bottom of the wound (10<sup>6</sup> cells in 150 μL of PBS per defect). Secretome injections were performed in a similar volume (150 μL, 4–5 injections). CS was transplanted to the defect in a drop of PBS, straightened with tweezers and allowed to adhere to lesion surface.</p><p>Preliminary assessments using PicoGreen assay have shown that using 100 mm dishes for MSC culture prior to injection and scaling down CS size to 12-well format resulted in nearly dose-equivalent delivery of MSC with CS containing (1 ± 0.12) × 10<sup>6</sup> cell/construct.</p><p>To preserve the cellular material (primarily the CS on the defect surface), transplantation sites in all groups were covered for 3 days with Tegaderm film (3M, St. Paul, MN, USA)-breathable, sterile, transparent and waterproof stickers for closing wounds. All animals were housed in individual cages for 21 days until the end of experiment</p></sec><sec id=\"sec4dot8-ijms-21-05567\"><title>4.8. Wound Assessment and Histological Examination</title><p>Skin defect area was measured at days 3, 7, 14 and 21 in photographs of wound surface at each time-point. Area was calculated using ImageJ software (NIH, Bethesda, MD, USA).</p><p>After animal euthanasia, whole-mount specimen of defect (or healed skin in CS group) were isolated and bisected for histological assessment as follows; one half was fixed in formalin, paraffin-embedded, sectioned at 5 µm and stained with hematoxylin/eosin. The other half was placed in Tissue-Tek O.C.T. Compound and frozen in liquid nitrogen and stored at −70 °C, and frozen sections at 6–7 µm were obtained for immunofluorescent staining.</p></sec><sec id=\"sec4dot9-ijms-21-05567\"><title>4.9. Histological Evaluaton of Healing Processes: Granulation Tissue Formation and Maturation</title><p>Sections stained by hematoxylin-eosin/Masson’s Trichrome were used for morphometry to assess thickness of granulation tissue on days 3 and 7 (days 14 and 21 showed remodeling of granulation tissue and were not included in data analysis). Since the composition of the defect was qualitatively different in the groups on Days 14 and 21, collagen deposition was evaluated by calculating its percentage of dyed collagen fibers (blue) to the total defect area using an ImageJ-associated color deconvolution plugin [<xref rid=\"B46-ijms-21-05567\" ref-type=\"bibr\">46</xref>]. Additionally, the percentage of immature GT zones was evaluated by calculating the ratio of the measured areas to the defect area. Margins of the defect corresponded to edges of ulcer characterized by loss of subcutaneous muscle. ImageJ software (NIH, USA) was used for described morphometry.</p></sec><sec id=\"sec4dot10-ijms-21-05567\"><title>4.10. Immunofluorescent Staining and Vessel Density Analysis</title><p>Frozen tissue sections (at days 14 and 21) were fixed in 4% formaldehyde, blocked by 10% normal goat serum, washed and incubated with rat-anti-mouse CD31 (Biolegend, Cat#102401) and rabbit-anti-αSMA (Abcam, Cat#ab32575) antibodies. Then, slides were washed in PBS and incubated in a mixture of goat anti-mouse AlexaFluor 594 and goat anti-rabbit AlexaFluor 488 conjugated antibodies. At the end of incubation, slides were washed, counterstained with DAPI and mounted.</p><p>Microphotographs of sections were taken under ×100/×200 magnification in 3 random fields of view (FOV) per section (DMi8, Leica, Germany). Vessel counts included CD31-positive structures per FOV; αSMA staining was used to detect large vessels and myofibroblasts in granulation tissue. Structure counts were obtained using ImageJ software (NIH, USA). Classification of blood vessels from Müller et al. with adaptations was used for analysis of blood vessel type [<xref rid=\"B31-ijms-21-05567\" ref-type=\"bibr\">31</xref>]. </p></sec><sec id=\"sec4dot11-ijms-21-05567\"><title>4.11. PKH26 Labelling and Subsequent Detection of Transplanted Cells in Histological Preparations</title><p>Cells were labeled by a commercial PKH26 dye (Cat#PKH26GL, Sigma, USA), according to manufacturer’s protocol. The cell suspension was labeled immediately before the transplantation; for the CS group, cells were labeled before assembly of CS. To detect a fluorescent signal, tissue samples were frozen in Tissue-Tek O.C.T. Compound medium and sectioned. Immediately after microscopy, slides were dried at RT, and we visualized PKH26-positive cells using a fluorescence microscope (Leica DMi8, Leica Microsystems GmbH, Mannheim, Germany). For a more detailed study of cells’ localization, sections were fixed and were immunolabeled to detect blood vessels and myofibroblasts followed by nuclear stain by DAPI.</p></sec><sec id=\"sec4dot12-ijms-21-05567\"><title>4.12. Statistical Analysis</title><p>Data are expressed as mean ± standard deviation. To compare >2 groups, we used analysis of variance (ANOVA) and Tukey criterion for multiple comparisons, or Kruskal–Wallis criterion supplemented by Dunn criterion (as a nonparametric ANOVA). Differences were considered significant at <italic>p</italic> < 0.05. Analysis was performed using Prism 7.03 (GraphPad Software).</p></sec></sec><sec sec-type=\"conclusions\" id=\"sec5-ijms-21-05567\"><title>5. Conclusions</title><p>Pressure ulcer healing is effectively induced by MSC secretome or CS application, while suspended cells performed poorly despite its long-lasting retention in the tissue. The most peculiar finding is that CS was the only group that showed minimal scarring with growth of hair and dermal glands in histological sections of pressure ulcer site at Day 21. The nature of this phenomenon should be established including the mechanism of defect healing via activation of regenerative capacity or healthy skin elements being drawn to the site of resorbed ECM.</p><p>We propose that CS acts via a trigger mechanism mediated by its paracrine factors released at early stages of healing to induce recovery of skin structure after pressure ulcer. Effects of CS involve stabilization of vasculature and rapid GT growth followed by a specific pattern of remodeling that results in ECM resorption, but not scarring. This anti-fibrotic influence facilitates full closure of defect and observed healing of dermis.</p><p>The spectrum of growth factors and cytokines produced by MSC after CS assembly can be described as a pattern for activation of blood vessel stabilization (PDGF-BB, HGF, G-SCF, Ang-2) rather that sprouting (VEGF165), which was concordant with vascular patterns in corresponding study groups.</p><p>The proposed mechanism relies on changes of secretome, yet transcriptomic profile and TFs activity hint at future evaluation of potential MSC reprogramming within CS. RNA-sequencing profiles also show that transcriptome of MSC in CS is enriched for biological processes and molecular function that support the expression of factors stabilizing blood vessels and limiting endothelial sprouting. Future studies will address our findings regarding the potential role of ECM remodeling induced by delivered CS that may explain changes of GT maturation and its quick resorption resulting in the recovery of skin structure with its appendages (hair and glands).</p><p>A conceptual study limitation is a lack of understanding of immune cells interaction with CS and how it may modulate the response of immunocompetent cells as far as they present important generators of angiogenic stimuli in damaged tissue [<xref rid=\"B47-ijms-21-05567\" ref-type=\"bibr\">47</xref>,<xref rid=\"B48-ijms-21-05567\" ref-type=\"bibr\">48</xref>]. Successful tissue repair driven by MSC is impossible without immune response, and this point is sto be addressed in future studies.</p><p>The findings are potentially of importance for both fundamental understanding of MSC participation in regeneration as well as for development in tissue engineering methods addressing an unmet medical need—pressure ulcer.</p></sec></body><back><ack><title>Acknowledgments</title><p>Authors express gratitude to Georgy Sagaradze and Anastasia Efimenko for assistance with RNA-Seq analysis and valuable advice on data presentation in this manuscript.</p></ack><app-group><app id=\"app1-ijms-21-05567\"><title>Supplementary Materials</title><p>Supplementary materials can be found at <uri xlink:href=\"https://www.mdpi.com/1422-0067/21/15/5567/s1\">https://www.mdpi.com/1422-0067/21/15/5567/s1</uri>.</p><supplementary-material content-type=\"local-data\" id=\"ijms-21-05567-s001\"><media xlink:href=\"ijms-21-05567-s001.zip\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material></app></app-group><notes><title>Author Contributions</title><p>Conceptualization, N.A., P.M. (Pavel Makarevich); methodology, N.A.; validation, N.A., P.M. (Pavel Makarevich); formal analysis, N.A., P.M. (Pavel Makarevich), R.E.; investigation, N.A., P.N., N.D., M.A.; resources, V.P., P.M. (Pavel Malkov); writing—original draft preparation, N.A., M.A.; writing—review and editing, P.M. (Pavel Makarevich), V.T.; visualization, N.A., R.E., P.M. (Pavel Makarevich); supervision, V.T.; project administration, Z.A., P.M. (Pavel Makarevich), V.T.; funding acquisition, Z.A., P.M. (Pavel Makarevich), V.T. All authors have read and agreed to the published version of the manuscript.</p></notes><notes><title>Funding</title><p>Study was supported by state assignment of MSU, RFBR grants #17-04-01452 (animal model and cell culture); #19-29-04172 (histology studies, bioinformatics and data analysis) and RSF grants #19-75-30007 (transcriptome analysis); #19-75-00067 (histological assessment and funding of publication). N.A. is supported by a Ph.D. Student grant from RFBR #19-315-90110 (antibodies and Ph.D. student bursary).</p></notes><notes notes-type=\"COI-statement\"><title>Conflicts of Interest</title><p>The authors declare no conflict of interest.</p></notes><glossary><title>Abbreviations</title><array orientation=\"portrait\"><tbody><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Ang-1</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Angiopoietin-1</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Ang-2</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Angiopoietin-2</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ANOVA</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Analysis of Variation</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">αSMA</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Alpha smooth muscle actin</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">BP</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Biological process</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CS</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Cell sheet</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">DEG</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Differentially expressed genes</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">DNA</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Deoxyribonucleic acid</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ECM</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Extracellular matrix</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ELISA</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Enzyme-linked immunosorbent assay</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">FBS</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Fetal bovine serum</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">FGF</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Fibroblast growth factor</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">GAPDH</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Glyceraldehyde 3-phosphate dehydrogenase</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">G-CSG</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Granulocyte-colony stimulating factor</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">GO</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Gene ontology</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">GT</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Granulation tissue</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">HGF</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Hepatocyte growth factor</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">LOX</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Lysyl oxidase</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">MF</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Molecular function</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">MSC</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Mesenchymal stromal cell</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">NFKB1</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Nuclear factor κB</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">PDGF</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Platelet-derived growth factor</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">RELA</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">REL-associated protein</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">RNA</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Ribonucleic acid</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">SP-1</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Specificity protein 1</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">TF</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Transcription factor</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">TGF</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Transforming growth factor</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Tie-2</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Endothelial specific receptor tyrosine kinase</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">VEGF</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Vascular endothelial growth factor</td></tr></tbody></array></glossary><ref-list><title>References</title><ref id=\"B1-ijms-21-05567\"><label>1.</label><element-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Singer</surname><given-names>A.J.</given-names></name><name><surname>Clark</surname><given-names>R.A.</given-names></name></person-group><article-title>Cutaneous wound healing</article-title><source>N. 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Days 3 and 7 showed no significant difference between groups (*—<italic>p</italic> < 0.05; **—<italic>p</italic> < 0.01; ***—<italic>p</italic> < 0.001).</p></caption><graphic xlink:href=\"ijms-21-05567-g001\"/></fig><fig id=\"ijms-21-05567-f002\" orientation=\"portrait\" position=\"float\"><label>Figure 2</label><caption><p>Histological assessment of granulation tissue (GT) remodeling in experimental groups. (<bold>A</bold>) Relative content of collagen and reticular fibers (% of defect); (<bold>B</bold>) healing of skin and its appendages at the site of pressure ulcer in the “Cell Sheet” group (Day 21); Masson’s trichrome, scale bar represents 200 µm; (<bold>C</bold>) assessment of granulation tissue remodeling by calculation of its relative area (% of section area); <italic>n</italic> = 4 denotes number of animals assessed at this point, of which 3 had no GT area to account for while 1 had minimal remaining GT which allow presenting statistics (*—<italic>p</italic> < 0.05; ****—<italic>p</italic> < 0.0001).</p></caption><graphic xlink:href=\"ijms-21-05567-g002\"/></fig><fig id=\"ijms-21-05567-f003\" orientation=\"portrait\" position=\"float\"><label>Figure 3</label><caption><p>Retention of mesenchymal stromal cells (MSC) labeled by PKH26 after transplantation to pressure ulcers. Cell sheet (CS) is detected on wound surface only at Day 3; PKH26 signal from injected MSC is detectable in the tissue up to Day 21; “top” marks location of wound bed, “bottom” marks deeper layers of skin. Immunofluorescence, scale bar represents 250 µm.</p></caption><graphic xlink:href=\"ijms-21-05567-g003\"/></fig><fig id=\"ijms-21-05567-f004\" orientation=\"portrait\" position=\"float\"><label>Figure 4</label><caption><p>Influence of cell sheets, suspended MSC or MSC secretome on GT thickness in healing pressure ulcer. (<bold>A</bold>) Representative image of a histological section at Day 3; panel markup: <bold>1</bold>—zone of damaged tissue down to (<bold>a</bold>) muscular layer with (<bold>b</bold>) immune cells infiltration and (<bold>c</bold>) edema of underlying tissue; <bold>2</bold>—zone of GT formation; bar indicates measured thickness, arrows indicate blood vessels; <bold>3</bold>—defect surface with (<bold>e</bold>) scab and (<bold>d</bold>) margin of migrating epithelial layer covering ulcer. Hematoxylin-eosin staining, scale bar represents 50 µm; (<bold>B</bold>) quantitative results of GT thickness analysis at Days 3 and 7; (*—<italic>p</italic> < 0.05; ****—<italic>p</italic> < 0.0001).</p></caption><graphic xlink:href=\"ijms-21-05567-g004\"/></fig><fig id=\"ijms-21-05567-f005\" orientation=\"portrait\" position=\"float\"><label>Figure 5</label><caption><p>Visualization and quantitative assessment of granulation tissue vascularization in study groups. (<bold>A</bold>) In the “Cell Sheet” group, density of CD31+ blood vessels was reduced (plots at Days 3 and 7). Immunofluorescence, scale bar represents 75 µm; data present results of manual blood vessels counts; (*—<italic>p</italic> < 0.05; ***—<italic>p</italic> < 0.001; ****—<italic>p</italic> < 0.0001); (<bold>B</bold>) representative images from “Cell Sheet” group showing blood vessels in granulation tissue at Day 7 within healing defect (left image) and on the edge of granulation tissue growth (right image). Arrows indicate αSMA-positive mural cells adjacent to CD31+ blood vessels with lumen; triangles mark αSMA-positive myofibroblasts. Immunofluorescence, scale bar represents 75 µm.</p></caption><graphic xlink:href=\"ijms-21-05567-g005\"/></fig><fig id=\"ijms-21-05567-f006\" orientation=\"portrait\" position=\"float\"><label>Figure 6</label><caption><p>Changes of growth factors production by MSC in cell sheet and monolayer culture. Graph demonstrates assayed protein concentration change expressed as fold increase vs. monolayer culture; data presented as Median (25;75); *—<italic>p</italic> < 0.05, Mann–Whitney.</p></caption><graphic xlink:href=\"ijms-21-05567-g006\"/></fig><fig id=\"ijms-21-05567-f007\" orientation=\"portrait\" position=\"float\"><label>Figure 7</label><caption><p>Presentation of RNA-sequencing results mapped using Gene Ontology biological process (<bold>A</bold>) and molecular function (<bold>B</bold>). Data from libraries obtained after RNA-sequencing of monolayer MSC and MSC-based CS were normalized and upregulated (green) and downregulated (red) transcripts were mapped using Gene Ontology. The top 10 <italic>p</italic>-value clusters with a minimum of 3 overlaps after GO mapping were supplemented by combined score calculation (Enrichr). Reg—regulation; ECM—extracellular matrix; EC—endothelial cells; SMC—smooth muscle cell.</p></caption><graphic xlink:href=\"ijms-21-05567-g007\"/></fig><table-wrap id=\"ijms-21-05567-t001\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijms-21-05567-t001_Table 1</object-id><label>Table 1</label><caption><p>Transcription factors with significantly increased activity evaluated by expression of their targets in MSC-based cell sheets compared to monolayer MSC. TF—transcription factor; entries ranked by Q-value and <italic>p</italic>-value; TFs with 4 and more overlapping targets presented.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Rank</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">TF</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Overlapped Genes N</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">List of Overlapped Genes</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">SP1</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">28</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">C4A, AGTR1, EDNRB, AGT, PPL, MMP2, TNC, EGR1, LSP1, CCND2, TNFSF10, MME, FBLN1, CYP27A1, SOX9, COMP, SLC39A8, COL18A1, C4B, SREBF1, HGF, TCN2, ISG20, APOE, SOD2, CYP19A1, PTN, CHI3L1</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">NFKB1</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">22</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">EGR1, BGN, CYP19A1, PTGFR, TNC, SOD2, ADORA1, CXCL12, TNFSF13B, MMP2, CD74, FGF7, IRF4, AGT, SLC25A27, PLA2G2A, CCND2, BCL2L11, IRF7, VCAM1, A2M, TNFSF10</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">RELA</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">20</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">IRF7, EGR1, FGF7, ADORA1, CXCL12, MMP2, SLC25A27, SOD2, VCAM1, BCL2L11, TNFSF10, PLA2G2A, TNC, CD74, CYP19A1, CCND2, PTGFR, AGT, BGN, IRF4</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">4</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">FOXO3</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">8</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">BCL6, TXNIP, TNFSF10, BCL2L11, CCND2, VEGFA, CDKN2B, VEGFB</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">5</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">USF1</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">8</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">FMO3, TCN2, AGT, CYP19A1, SLC1A3,LIPC, CTSD, ISG20</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">6</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">SREBF1</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">5</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">LRP1, ACACB, LDLR, FASN, CIC</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">7</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">STAT3</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">10</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">CCND2, CFB, PROS1, HGF, MMP2, CYP19A1, A2M, CHI3L1, DIRAS3, BCL6</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">8</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">HIF1A</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">12</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">TGFB3, TLR6, VEGFA, ARNT, TIMP2, SOCS1, VEGFB, MMP2, CXCL12, EDNRB, ACE, AGTR1</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">9</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">FOXO1</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">4</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">TNFSF10, EGR1, ANGPT2, TXNIP</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">10</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">JUN</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">10</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">DCN, VCAM1, CYP19A1, MMP2, SOD2, PTN, TNC, MGP, FGF7, LBP</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">11</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">DNMT1</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">4</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">ESR1, IL32, CDKN2B, VEGFA</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">12</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">SP3</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">8</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">FBLN1, HGF, SLC1A3, ACE, TCN2, MMP2, AGTR1, CYP27A1</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">13</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">CREB1</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">7</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">NR4A3, AQP3, BCL2L11, BDKRB2, SOX9, MMP2, CYP19A1</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">14</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">MYC</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">7</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">VEGFA, JUNB, HLA-B, TFAP4, SHMT1, MST1, BCL2</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">15</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">ESR1</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">6</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">ESR1, BCL2, VEGFA, ZEB1, CEBPB, JUNB</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">16</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">ETS2</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">4</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">EGR1, ANGPT2, MMP2, TNC</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">17</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">WT1</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">5</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">WTAP, BCL2, VEGFA, VEGFB, JUNB</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">18</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">ATF4</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">4</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">IRF7, HRK, DDIT4, APOE</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">19</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">PPARA</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">4</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">TXNIP, SOD2, G0S2, CD36</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">20</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">SPI1</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">5</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">JCHAIN, BCL6, MME, CTSS, CTSK</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">21</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">MYCN</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">4</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">EFNB3, CTSD, MXI1, CLU</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">22</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">USF2</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">4</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">TCN2, CTSD, CYP19A1, LIPC</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">23</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">HDAC1</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">5</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">HLA-DRA, EGR1, CCND2, TXNIP, SFRP1</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">24</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">STAT1</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">4</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">UPP1, XAF1, STAT2, MUC1</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">25</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">BRCA1</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">4</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">ESR1, IRF9, VEGFA, DDIT3</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">26</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">TP53</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">8</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">BDKRB2, GPNMB, PDGFRB, AQP3, EGR1, PMAIP1, CTSD, MMP2</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">27</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">ETS1</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">5</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">DUSP6, ANGPT2, EGR1, TMEM158, TNC</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">28</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">YY1</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">5</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">SAP30, VWF, LSS, VEGFB, LDLR</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">29</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">BRCA1</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">4</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">EGR1, CTSD, IRF7, CYP19A1</td></tr></tbody></table></table-wrap></floats-group></article>\n"
]
[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Int J Environ Res Public Health</journal-id><journal-id journal-id-type=\"iso-abbrev\">Int J Environ Res Public Health</journal-id><journal-id journal-id-type=\"publisher-id\">ijerph</journal-id><journal-title-group><journal-title>International Journal of Environmental Research and Public Health</journal-title></journal-title-group><issn pub-type=\"ppub\">1661-7827</issn><issn pub-type=\"epub\">1660-4601</issn><publisher><publisher-name>MDPI</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32722413</article-id><article-id pub-id-type=\"pmc\">PMC7432087</article-id><article-id pub-id-type=\"doi\">10.3390/ijerph17155361</article-id><article-id pub-id-type=\"publisher-id\">ijerph-17-05361</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Article</subject></subj-group></article-categories><title-group><article-title>Cinematographic Habits of Future Spanish Teachers from a Socio-Educational Perspective</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0002-0224-5824</contrib-id><name><surname>Lorenzo-Lledó</surname><given-names>Alejandro</given-names></name><xref rid=\"c1-ijerph-17-05361\" ref-type=\"corresp\">*</xref></contrib><contrib contrib-type=\"author\"><name><surname>Lledó</surname><given-names>Asunción</given-names></name></contrib><contrib contrib-type=\"author\"><name><surname>Pérez-Vázquez</surname><given-names>Elena</given-names></name></contrib><contrib contrib-type=\"author\"><name><surname>Lorenzo</surname><given-names>Gonzalo</given-names></name></contrib></contrib-group><aff id=\"af1-ijerph-17-05361\">Department of Development Psychology and Teaching, Faculty of Education, University of Alicante, 03690 San Vicente del Raspeig, Spain; <email>[email protected]</email> (A.L.); <email>[email protected]</email> (E.P.-V.); <email>[email protected]</email> (G.L.)</aff><author-notes><corresp id=\"c1-ijerph-17-05361\"><label>*</label>Correspondence: <email>[email protected]</email></corresp></author-notes><pub-date pub-type=\"epub\"><day>25</day><month>7</month><year>2020</year></pub-date><pub-date pub-type=\"ppub\"><month>8</month><year>2020</year></pub-date><volume>17</volume><issue>15</issue><elocation-id>5361</elocation-id><history><date date-type=\"received\"><day>13</day><month>6</month><year>2020</year></date><date date-type=\"accepted\"><day>22</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>© 2020 by the authors.</copyright-statement><copyright-year>2020</copyright-year><license license-type=\"open-access\"><license-p>Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>).</license-p></license></permissions><abstract><p>One of the key features of today′s society is the role of technology and mass-media. Among these tools, cinema has influenced successive generations more than 100 years. From an educational point of view, it is a resource of high pedagogical value. Moreover, it is present in the daily life of university students who will be the future teachers. Therefore, the aim of this study is to find out the film consumption habits of students in the teacher′s degree in Spanish universities. A quantitative approach was adopted with a survey design, and the national sample was made up of 4659 students. They ware from the different regions of Spain and 58 universities. The questionnaire called Percepciones sobre las potencialidades del cine como recurso didáctico en las aulas de Infantil y Primaria (PECID) was designed ad hoc for this research. The results obtained showed that most students have a weekly habit of consuming films, with fiction being the most popular type of film. Likewise, the viewing of films is preferably done through television and the computer. It can be concluded that this familiarity with cinema outside the university should be complemented with specific training in the teacher′s degree. In this sense, it contributes to an optimal application of cinema.</p></abstract><kwd-group><kwd>cinema</kwd><kwd>film</kwd><kwd>cinematographic habits</kwd><kwd>mass media</kwd><kwd>education</kwd><kwd>future teachers</kwd><kwd>university students</kwd></kwd-group></article-meta></front><body><sec sec-type=\"intro\" id=\"sec1-ijerph-17-05361\"><title>1. Introduction</title><p>One of the essential features of today′s society is the presence of technology and audio-visual media, which influence the ways of interacting and perceiving reality. Ref. [<xref rid=\"B1-ijerph-17-05361\" ref-type=\"bibr\">1</xref>] indicated that this global phenomenon has shaped a cultural reality centered on the development of the image, as compared to a subtle abandonment of reading and writing. This context makes demands on citizens, given that the existence of a diversity of languages implies the need to dominate them in order to have full growth. Ref. [<xref rid=\"B2-ijerph-17-05361\" ref-type=\"bibr\">2</xref>] argued that literacy in the 21st century is a set of cultural practices that enables engagement and expression of meaning through mastery of different domains, such as speech, writing, still and moving images, and music.</p><p>Among the most relevant audio-visual media is the cinema, with more than 100 years of history. The cinema comprises a form of self-expression and has influenced many generations. Several works Ref. [<xref rid=\"B3-ijerph-17-05361\" ref-type=\"bibr\">3</xref>,<xref rid=\"B4-ijerph-17-05361\" ref-type=\"bibr\">4</xref>,<xref rid=\"B5-ijerph-17-05361\" ref-type=\"bibr\">5</xref>] have conceptualized cinema from different perspectives, such as art, technique, spectacle, language, document, entertainment, pictorial expression, source of information and culture. Ref. [<xref rid=\"B6-ijerph-17-05361\" ref-type=\"bibr\">6</xref>] highlights a double facet in the cinema. On the one hand, the cinema as an art form. On the other hand, as a mass media, as it reflects customs, fashions and beliefs. For their part, Ref. [<xref rid=\"B7-ijerph-17-05361\" ref-type=\"bibr\">7</xref>] pointed out that cinema is an art of complex expression as it is at once audio-visual, digital, multimedia, plastic, dynamic, verbal and gestural. All this increases its capacity to generate changes in the subjects. In this sense, cinema is a language with a great power of attraction that can provoke a process of identification in the spectator and stimulate him to perform behaviors for life and also for death [<xref rid=\"B8-ijerph-17-05361\" ref-type=\"bibr\">8</xref>].</p><p>In the history of cinema, a diversity of tendencies and topics have been shown, forming different themes and rules, which are recognized by the spectators. Therefore, although there is no agreed taxonomy around the different genres or types of cinema, we can highlight, in the first place, the fiction genres, which have in common stories and fictional characters that, as [<xref rid=\"B9-ijerph-17-05361\" ref-type=\"bibr\">9</xref>] indicates, base their verisimilitude on the internal coherence and narrative of the created world. In contrast, documentary cinema aims to capture fragments of reality in a faithful way and to connect with the being of the spectator and not with the wanting to be [<xref rid=\"B10-ijerph-17-05361\" ref-type=\"bibr\">10</xref>]. Similarly, it is important to mention animation cinema, which with the rapid technological advances and starting from an image of non-real action, has had a great development, offering entertainment, fun and a creativity that manages to seduce the spectator [<xref rid=\"B11-ijerph-17-05361\" ref-type=\"bibr\">11</xref>]. In animated film, it is worth noting the enormous influence exerted by Disney, as a pillar of entertainment and generator of cultural representations for children in many countries [<xref rid=\"B12-ijerph-17-05361\" ref-type=\"bibr\">12</xref>].</p><p>Considering the nature of cinema and its evolution throughout history, the language of film is eclectic and comes from many sources. Ref. [<xref rid=\"B13-ijerph-17-05361\" ref-type=\"bibr\">13</xref>] indicate that cinema is a compendium of communication elements. Ref. [<xref rid=\"B14-ijerph-17-05361\" ref-type=\"bibr\">14</xref>] states that cinema is the synthesis of many arts and disciplines. Nevertheless, cinema has its own identity, despite the interrelations it has with other languages and the fact that it forms part of a wider audiovisual ecosystem, and should not be confused with other media. In this sense, Ref. [<xref rid=\"B2-ijerph-17-05361\" ref-type=\"bibr\">2</xref>] considers cinema as a form of artistic expression, as opposed to other media, such as television, which are essentially entertainment, and advocate that cinema should not be diluted in the generic category of social media. Similarly, Ref. [<xref rid=\"B15-ijerph-17-05361\" ref-type=\"bibr\">15</xref>] claims the purity of cinema and denounces the contamination suffered by the audiovisual media, which ends up denaturing the essence of cinema. In this way, they indicate that “the word audiovisual should be removed from everything that specifically relates to cinema and advocate a radical separation” [<xref rid=\"B15-ijerph-17-05361\" ref-type=\"bibr\">15</xref>] (p. 56). In this line, the cinema, catalogued as the seventh art, is pure, and does not need to be critically analyzed with respect to other media, such as television or advertising. The present study promotes the identity of cinema itself, but without forgetting the growing connections between the various audiovisual languages. To this, it must be added the technological changes in film production and dissemination. If this is not taken into account, we would have a partial vision of cinema, eliminating elements of reflection on it. From this perspective, it is understood that a film is an audiovisual work conceived as a cinematographic production, whose main objective is to be shown in cinemas, but which can also be shown in other dissemination channels, such as television or the Internet, but which is different from other non-cinematographic audiovisual content.</p><p>From an educational point of view, if one of the objectives that every educational system should have is to generate in the students some competences for the free and autonomous discovery of the world in which they live, in favor of an integral formation, the cinema cannot be absent in the educational centers. Therefore, the cinema as a mirror of reality has a great pedagogical potential, since it is an attractive medium that, as [<xref rid=\"B16-ijerph-17-05361\" ref-type=\"bibr\">16</xref>] note, offers the opportunity to learn in a more exciting way and to reflect and exchange ideas on psychosocial realities. Ref. [<xref rid=\"B17-ijerph-17-05361\" ref-type=\"bibr\">17</xref>] emphasize that it is necessary to redefine cinema as an exceptionally enriching medium for educating in the image. To give it its true entity, cinema should not be understood as a simple didactic support, but as the shaper of new, more significant learning environments, since, following [<xref rid=\"B18-ijerph-17-05361\" ref-type=\"bibr\">18</xref>], cinema is a tool that, apart from exposing a visual representation, is loaded with other symbols, and that, if conducted in an optimal way, can generate a more enriching and collaborative language between the teacher and the student.</p><p>Among all the audiovisual media, the cinema, as well as the television, is the one that has a greater tradition of didactic use since their generalization in the middle of the 20th century [<xref rid=\"B19-ijerph-17-05361\" ref-type=\"bibr\">19</xref>]. For [<xref rid=\"B20-ijerph-17-05361\" ref-type=\"bibr\">20</xref>], the inclusion of cinema in education is justified because it can generate a durable learning, attract students and be interdisciplinary. Nonetheless, the specific application of cinema in education is not without its difficulties, which put the development of all its educational possibilities at risk. According to [<xref rid=\"B21-ijerph-17-05361\" ref-type=\"bibr\">21</xref>], the use of the cinema in education must be done in a very concrete way, with a correct integration in the curricular programming and linking it closely to the didactic objectives. This author concludes that it is essential to carry out an adequate planning and to delimit what is intended to be achieved through the cinema. Ref. [<xref rid=\"B22-ijerph-17-05361\" ref-type=\"bibr\">22</xref>] emphasizes that one of the most relevant aspects is knowing how to correctly choose the film to be worked on, carefully applying some guidelines. On the other hand, the inclusion of cinema is a task for school organization to determine the different roles, as well as for management, implementing the necessary strategies [<xref rid=\"B23-ijerph-17-05361\" ref-type=\"bibr\">23</xref>]. Ref. [<xref rid=\"B24-ijerph-17-05361\" ref-type=\"bibr\">24</xref>] indicates that the greatest challenges when applying cinema in the classroom are the lack of teacher training, the lack of knowledge on the part of the students, the lack of resources and support materials in the schools and the rigidity of the curriculum. In order to overcome time and resource constraints, Ref. [<xref rid=\"B6-ijerph-17-05361\" ref-type=\"bibr\">6</xref>,<xref rid=\"B19-ijerph-17-05361\" ref-type=\"bibr\">19</xref>] argue that it is desirable to take advantage of the flexibility offered by digital cinema and adapt films to the time available and educational objectives. In this sense, it is advisable to use whole movies, fragments of movies or short films and to stop the film, move it forward or backward depending on the activity being carried out [<xref rid=\"B25-ijerph-17-05361\" ref-type=\"bibr\">25</xref>]. Therefore, it can be said that the optimal application of cinema in the classroom essentially requires a pedagogical strategy for educational use, and the involvement of teachers and educational institutions.</p><p>In contrast to other resources with educational potential, cinema as a mass media is present in the lives of teachers, whether they are already in service or in their pre-service teacher education. If we look at the second group, it is worth asking what relation these students have to the cinema. The role that cinema has in its leisure and cultural activities is a socio-educational reality that deserves to be investigated as a variable to be taken into account in order to face the application of cinema in the classroom. In this sense, cinema as a social phenomenon influences collectively and individuals, shaping a state for life, especially among young people [<xref rid=\"B26-ijerph-17-05361\" ref-type=\"bibr\">26</xref>]. Furthermore, the cinematographic habits that future teachers may have are a way of accessing knowledge of cinema and, therefore, of formation. Hence, students acquire skills not only from educational institutions, but also from other non-formal channels [<xref rid=\"B27-ijerph-17-05361\" ref-type=\"bibr\">27</xref>]. This double aspect generates a socio-educational approach, since it takes into account cinema as a potential educational resource and as a means of social communication. Likewise, in the Spanish context, it is particularly appropriate to analyze film habits, considering the country′s territorial distribution, which is organized around Autonomous Communities with their own political, educational and cultural identity that can be expressed in differences in students′ personal and social habits.</p><p>Based on the above, the general objective of this study is to understand the film consumption habits of teacher’s degree students in Spanish universities. The following specific objectives underlie this general objective:<list list-type=\"order\"><list-item><p>To analyze the weekly frequency with which cinema is viewed in Spain, by Autonomous Community, by type of teacher’s degree and by type of university.</p></list-item><list-item><p>To examine the type of cinema usually consumed in Spain and according to the Autonomous Community, the type of teacher’s degree and the type of university.</p></list-item><list-item><p>To identify the devices that are usually used to watch films.</p></list-item></list></p></sec><sec id=\"sec2-ijerph-17-05361\"><title>2. Materials and Methods</title><p>To conduct the research, a quantitative approach of a descriptive nature has been adopted with survey design [<xref rid=\"B28-ijerph-17-05361\" ref-type=\"bibr\">28</xref>] to collect variables from a given population in a representative manner.</p><sec id=\"sec2dot1-ijerph-17-05361\" sec-type=\"subjects\"><title>2.1. Participants</title><p>The sample of participants, being a national study, was made up of 4659 students from all the Autonomous Communities and 58 universities, of which 39 were public and 19 privates. A technique of quota sampling was used to form the sample [<xref rid=\"B29-ijerph-17-05361\" ref-type=\"bibr\">29</xref>]. 15.5% (n = 720) of the participants were men and 84.5% (n = 3939) were women. They were between 18 and 66 years old and had a mean age of 22.3 (SD = 3.9). In turn, 49.0% (n = 2281) were students in the Pre-school Education Training Degree and 51.0% (n = 2378) in the Primary Education Teaching Degree. Likewise, 89.8% (n = 4183) were students from public universities and 10.2% (n = 476) from private universities. With regard to the territorial distribution, <xref ref-type=\"fig\" rid=\"ijerph-17-05361-f001\">Figure 1</xref> below shows the frequencies and percentages of the participating students according to the Autonomous Community.</p><p>If the university is attended, in <xref rid=\"ijerph-17-05361-t001\" ref-type=\"table\">Table 1</xref>, the participants are presented according to the university they belong to.</p><p>The total sample is representative of the population analyzed with a sampling error of 1.4% and a confidence level of 95.5%.</p></sec><sec id=\"sec2dot2-ijerph-17-05361\"><title>2.2. Instrument</title><p>The questionnaire Percepciones sobre las potencialidades del cine como recurso didáctico en las aulas de Infantil y Primaria (PECID), designed ad hoc for this research, was used for the data collection. It consists of 45 items distributed in different parts and sections. The first part of the questionnaire focuses on film consumption habits and on the training received for the didactic use of cinema. The second part of the questionnaire focuses on the perceptions about the potential of cinema as a didactic resource in Pre-school and Primary Education through a Likert-type scale, with six response categories [<xref rid=\"B30-ijerph-17-05361\" ref-type=\"bibr\">30</xref>]. In this study, the results on film habits are presented, which are quantified from items 6, 7 and 8 of the questionnaire. Item 6 asked about the number of movies generally watched per week. Item 7 asked about the type of cinema usually seen, being able to choose one or several options at the same time. In item 8, we asked about the device that is usually used to watch movies, being able to select one or several options at the same time. These items were subjected to expert judgment through the content validity coefficient [<xref rid=\"B31-ijerph-17-05361\" ref-type=\"bibr\">31</xref>]. In this sense, values higher than 0.80 were obtained, which shows a good content validity.</p></sec><sec id=\"sec2dot3-ijerph-17-05361\"><title>2.3. Procedure</title><p>The data collection had several phases. The public and private universities that offer the teacher′s degree were identified and then the teachers were contacted, asking for their collaboration in passing the questionnaire on to their students. Finally, data collection was conducted by completing the questionnaire during the academic year 18–19 through Google Forms. The students participated voluntarily and anonymously and had to mark a box on the questionnaire where they gave their consent to the processing of the data for this research.</p></sec><sec id=\"sec2dot4-ijerph-17-05361\"><title>2.4. Data Analysis</title><p>The analyses of the descriptive statistics, with the frequencies, percentages and mean were carried out through the statistical package SPSS in its version 21 (IBM Corp., Armonk, NY, USA).</p></sec></sec><sec sec-type=\"results\" id=\"sec3-ijerph-17-05361\"><title>3. Results</title><p>The results of the study, grouped according to the quantified variables, are presented below.</p><sec id=\"sec3dot1-ijerph-17-05361\"><title>3.1. Film Viewing Frequency per Week</title><p>Regarding the number of films viewed weekly by the teacher’s degree students in Spain, the results showed that 32.8% watched one film per week and 27.0% watched two. Likewise, a significant percentage usually watched more than two films and less than six, specifically, 14.7% watched three films, 7.1% watched four and 3.7% watched five. A very small minority of students watch six, seven or more films, with 1.1%, 0.5% and 1.2% respectively. Only 12.0% say they don′t watch any films a week. On the other hand, it should be noted that the mean number of films regularly watched by degree level students is 2.09 films per week.</p><p>In order to analyze the number of films watched per week, according to the Autonomous Community, the results are presented in <xref rid=\"ijerph-17-05361-t002\" ref-type=\"table\">Table 2</xref>.</p><p>The results obtained show that the four communities with the highest mean number of film views are Extremadura, Cantabria, Aragón and Castilla y León with 2.52, 2.46, 2.42 and 2.42 respectively. In this sense, Aragón has the highest percentage of students who watch four films (15.9%) and Castilla y León has the highest percentage of students who watch five films per week (7.0%). Likewise, Extremadura also had a very high percentage watching three or more films, with 40.9%. In most Autonomous Communities, a mean of two films were watched, except in Castilla-La Mancha, Cataluña, Murcia and País Vasco, which watched a mean of one film. The communities with the highest percentage of students not watching any film per week were País Vasco (18.6%), Navarra (15.5%) and Murcia (13.8%).</p><p>Regarding the number of films watched according to the type of teacher′s degree, as can be observed in <xref rid=\"ijerph-17-05361-t003\" ref-type=\"table\">Table 3</xref>, the percentage of students in the Pre-school teacher′s degree who watched two films per week was slightly higher than that of Primary Education, 28.2% as compared with 25.9%, respectively, but the percentage of students in the Primary Education Degree, who watched four or more films per week was slightly higher. The above makes the mean almost identical.</p><p>Regarding the frequency of films by type of university, the number of students at public universities who did not watch any films per week was slightly higher, 12.1% as compared with 10.7%, and the number who watched one film per week was lower, 32.4% as compared with 36.1%, respectively. However, fewer students at private universities watched two or three films, 26.1% and 14.3% as compared with 27.1% and 14.8%, respectively. Between four and seven films, there were 12.3% of students from public universities (7.1%, 3.6%, 1.1% and 0.5%) and 12.1% from private universities (7.1%, 4.0%, 0.6% and 0.4%). In addition, 1.2% of students at public universities and 0.6% at private universities watch more than seven films per week. The mean is 2.09 in public universities and 2.02 in private universities.</p></sec><sec id=\"sec3dot2-ijerph-17-05361\"><title>3.2. Type of Cinema Usually Watched</title><p>The results on the type of cinema seen by students who have a weekly habit of consuming films are presented by grouping the type of cinema into three large categories, fiction, documentary and animation, and in their different combinations, starting from a dichotomous response. 57.1% of participants watched only fiction films, 1.5% watched only animated films and 0.5% watched only documentary films. If we consider the habit of watching several types of cinema at the same time, 28.9% combine fiction and animation cinema, 5.7% fiction and documentary cinema and 0.1% documentary and animation cinema. Furthermore, 6.1% habitually watched fiction, documentary and animation films at the same time.</p><p><xref rid=\"ijerph-17-05361-t004\" ref-type=\"table\">Table 4</xref>, <xref rid=\"ijerph-17-05361-t005\" ref-type=\"table\">Table 5</xref> and <xref rid=\"ijerph-17-05361-t006\" ref-type=\"table\">Table 6</xref> below present the results of the type of cinema viewed, by Autonomous Community, according to whether it was fiction, documentary or animated.</p><p>As can be seen, the community with the highest percentage of students who watch only fiction films is Murcia with 64.9%, while in the rest of the community it is no less than 40.0%. For its part, documentary films are seen only exceptionally, being only in Asturias, Extremadura, Murcia and La Rioja, where more than 1% of its students watch only documentary films. On the other hand, in relation to animation cinema without combining it with other types of cinema, this is the most frequently seen in the different Autonomous Communities, although, in Canarias, Cantabria, Extremadura, Navarra and La Rioja, no student sees exclusively animation cinema. Similarly, the Communities with the highest percentage of students who watched animated films were Castilla-La Mancha, País Vasco and Andalucía, with 6.1%, 2.4% and 2.2%, respectively.</p><p><xref rid=\"ijerph-17-05361-t007\" ref-type=\"table\">Table 7</xref>, <xref rid=\"ijerph-17-05361-t008\" ref-type=\"table\">Table 8</xref>, <xref rid=\"ijerph-17-05361-t009\" ref-type=\"table\">Table 9</xref> and <xref rid=\"ijerph-17-05361-t010\" ref-type=\"table\">Table 10</xref> below present the results of the combinations of the types of cinema viewed by the teacher′s degree students, based on the Autonomous Community to which they belong.</p><p>According to the data presented, 19.0% of students in each community have combined consumption habits of fiction and animation films, with Aragón and Canarias standing out with 46.8% and 40.2%, respectively. At the opposite end is La Rioja, where only 19.2% of students watch these two types of films. In turn, if we consider the combined viewing of fiction and documentary films, it is worth noting that the bulk of the student body in the different communities is between 3% and 7%. The communities with the highest percentage of students who watched these two types of films were Cataluña (8.6%) and Navarra (8.3%). In contrast, the community with the lowest number of students watching fiction and documentary films was Castilla-La Mancha. Regarding the habit of viewing documentary and animated films, only three Autonomous Communities, Cataluña, Galicia and Madrid, had students who viewed these two types of films in a combined manner. Finally, it is worth noting that all Autonomous Communities have students who regularly watch fiction, documentary and animation films at the same time. In this sense, La Rioja and Cantabria are the communities with the highest percentage of students who watch all three types of film, with 15.4% and 15.0% respectively.</p><p>Regarding the type of cinema seen according to the teacher′s degree, the results obtained are presented in <xref rid=\"ijerph-17-05361-t011\" ref-type=\"table\">Table 11</xref>.</p><p>The findings show that in the two degrees only fiction films are seen in practically the same percentage, 57.8% of students in the Primary Education degree and 56.4% in the Pre-school Education teacher degree. Nevertheless, it is worth noting that the student body of the Pre-school Education teacher degree sees a little more fiction and animation combined than the student body of the Primary Education teacher degree, specifically, 31.8% as compared with 26.2%. In contrast, the pupil in the Primary Education teacher′s degree saw slightly more fiction and documentary films combined than the pupil in the Pre-school Education teacher′s degree (7.0% as compared with 4.4%), as well as more fiction, documentary and animation films combined (6.9% as compared with 5.4%).</p><p>Regarding the type of cinema viewed, by type of university, as observed in <xref rid=\"ijerph-17-05361-t012\" ref-type=\"table\">Table 12</xref>, the students in private universities watched more fiction and documentary films than those in public universities, 9.2% as compared with 5.3%. Conversely, the students at public universities watched slightly more fiction and animation films at the same time, 29.5% as compared with the 23.8% of students at private universities. Finally, it is worth noting that no participating students from private universities watch both documentary and animated films.</p></sec><sec id=\"sec3dot3-ijerph-17-05361\"><title>3.3. Devices to Watch Movies Regularly</title><p>Regarding the devices used to watch movies by the teacher′s degree students who watch movies weekly, the results of the answers from the multiple choice are presented (<xref rid=\"ijerph-17-05361-t013\" ref-type=\"table\">Table 13</xref>).</p><p>The results showed an outstanding use of the television (36.6%) and the computer (34.9%) as the most common devices for watching movies, with the cinema projector being the third most used medium, although with a considerably lower percentage (17.1%).</p></sec></sec><sec sec-type=\"discussion\" id=\"sec4-ijerph-17-05361\"><title>4. Discussion</title><p>The aim of this work has been to know the film habits of the teacher′s degree students in Spain. The findings revealed that 88% are regular film consumers per week. In this sense, 59.8% usually watch one or two films, reaching 74.5% if we add the students who watch three films a week. If we look at the overall mean, this is two films per week. These results coincide with those obtained by [<xref rid=\"B32-ijerph-17-05361\" ref-type=\"bibr\">32</xref>], who analyzed the film consumption habits of Spanish and Latin American university students and reflected a regular, albeit moderate, consumption of films, that is, not daily. If we look at the results obtained according to the Autonomous Community, students in Extremadura and Cantabria have the highest film viewing habits. In this sense, the students from Extremadura and Cantabria who watch three or more films a week rise to 40.9% and 33.3%, respectively. On the opposite side are País Vasco, Navarra and Murcia, which have a percentage of more than 13% of students who do not watch any films per week. Ref. [<xref rid=\"B25-ijerph-17-05361\" ref-type=\"bibr\">25</xref>] found that a higher frequency of film viewing leads to a more positive perception of the potential of cinema as a didactic resource among teacher′s degree students. These differences manifest themselves per student and per region and have their effect on the future application of cinema in the classroom. If the student body is compared according to the type of teacher′s degree and the type of university, the number of films watched per week is very similar, so it can be said that this is not a differentiating factor. In this sense, the student body of the two grades watched two movies per week, although the student body of the Pre-school teacher′s degree saw a slightly higher percentage (28.1%) of movies per week, while the students of the Primary Education teacher′s degree watched a little more usually one movie per week (33.2%). In turn, the student body at public universities who did not watch any films per week was slightly higher (12.1%) than that belonging to private universities. These similarities coincide with the analysis of [<xref rid=\"B32-ijerph-17-05361\" ref-type=\"bibr\">32</xref>], which found similar frequencies of film viewing by students from different undergraduate studies and universities.</p><p>As regards the type of cinema usually seen, the study has shown a habit of cinema where fiction cinema clearly prevails, as 57.13% of those surveyed see only fiction cinema and when several types of cinema are usually combined, the most common combinations are always with fiction cinema, as is the case of those who see fiction cinema and animation cinema, which is 28.94%. These findings are similar to the results obtained by [<xref rid=\"B33-ijerph-17-05361\" ref-type=\"bibr\">33</xref>,<xref rid=\"B34-ijerph-17-05361\" ref-type=\"bibr\">34</xref>,<xref rid=\"B35-ijerph-17-05361\" ref-type=\"bibr\">35</xref>], which show that audiovisual consumption is essentially associated with fiction and its various sub-genres. In this sense, animation and documentary remain the most minority genres, especially the latter.</p><p>If we look at the Autonomous Communities, Murcia is the community that watches the most fiction films exclusively (64.9%), while documentaries are not watched exclusively in any autonomy more than 2%. The community that watches more animated films exclusively is Castilla-La Mancha (6.1%), and it is clear that the communities that watch more animated films only are those that watch less documentary films, as is the case of La Rioja and Extremadura, in line with the contrast between these two types of cinema, since animation is far from reality and the documentary aims at an objective register or sincere reconstruction [<xref rid=\"B36-ijerph-17-05361\" ref-type=\"bibr\">36</xref>]. This fact is reproduced when watching animation and documentary at the same time, since, in fourteen Autonomous Communities, there are no students with this filmmaking habit. Aragón, on the other hand, is the Autonomous Community whose students watch the most fiction and animation films together (46.8%), Cataluña, where they watch the most fiction and documentary films together, and La Rioja and Cantabria, whose students most regularly watch fiction, documentary and animation films together. The data obtained are relevant due to the fact that, as they have stated [<xref rid=\"B25-ijerph-17-05361\" ref-type=\"bibr\">25</xref>], having a habit of consuming only fiction films determines a less positive perception of cinema as a didactic resource. On the other hand, the fact that students usually only watch documentaries on several types of films at the same time has a positive influence on their perception of the educational possibilities of cinema. Considering the type of teacher′s degree and the type of university, as was the case with the number of films viewed per week, the results reflected that the habits regarding the type of cinema viewed were almost identical, once again confirming that it was not a variable that originated relevant differences, in line with the findings of [<xref rid=\"B32-ijerph-17-05361\" ref-type=\"bibr\">32</xref>].</p><p>With regard to movie viewing devices, it has been reflected that 71.5% of students have as their preferred media the television and the computer, the former being the most used by 36.6%. These results reflect the diversification of film consumption devices, above the original mean of watching films, which was the cinema, in a “multi-screen society” [<xref rid=\"B37-ijerph-17-05361\" ref-type=\"bibr\">37</xref>] (p. 22) and technological variety, in line with the studies of [<xref rid=\"B38-ijerph-17-05361\" ref-type=\"bibr\">38</xref>,<xref rid=\"B39-ijerph-17-05361\" ref-type=\"bibr\">39</xref>,<xref rid=\"B40-ijerph-17-05361\" ref-type=\"bibr\">40</xref>]. Similarly, the studies of [<xref rid=\"B41-ijerph-17-05361\" ref-type=\"bibr\">41</xref>,<xref rid=\"B42-ijerph-17-05361\" ref-type=\"bibr\">42</xref>,<xref rid=\"B43-ijerph-17-05361\" ref-type=\"bibr\">43</xref>,<xref rid=\"B44-ijerph-17-05361\" ref-type=\"bibr\">44</xref>] confirm the coexistence in audiovisual consumption of young Spanish people between television and the growing space of the Internet through multiple devices, and, according to [<xref rid=\"B42-ijerph-17-05361\" ref-type=\"bibr\">42</xref>], the preference for cinema consumption on television increases among people over 18. This fact can be said to affect the way we approach the reality of cinema and the experience that cinema can generate, being more individualistic and fragmented. In this sense, the results of the study carried out by [<xref rid=\"B45-ijerph-17-05361\" ref-type=\"bibr\">45</xref>] shows that cinema attendance among the Spanish population has decreased considerably, with 53% never or almost never going to the cinema, although among the youngest people, attendance is 5% higher than among the rest of the population. In short, these data, together with those obtained from the frequency of film viewing, show that cinema has a penetration in the population studied, but increasingly from the home and less and less from the cinema. These data are reinforced by the studies of [<xref rid=\"B46-ijerph-17-05361\" ref-type=\"bibr\">46</xref>,<xref rid=\"B47-ijerph-17-05361\" ref-type=\"bibr\">47</xref>], which shows that young Spanish people and university students spend more time-consuming fictional television series using a computer, to the detriment of other audiovisual content such as films.</p></sec><sec sec-type=\"conclusions\" id=\"sec5-ijerph-17-05361\"><title>5. Conclusions</title><p>Based on the proposed aims and the findings, the following conclusions are indicated:<list list-type=\"order\"><list-item><p>Most students in the teacher′s degree have a habit of watching two films a week.</p></list-item><list-item><p>The type of cinema that is usually seen is fiction cinema, watching it exclusively or combining it with other types of cinema to a lesser extent.</p></list-item><list-item><p>The most common means of watching movies are television and the computer.</p></list-item></list></p><p>As a limitation of the study, it is worth mentioning the fact that five private Spanish universities refused to collaborate in the study. This meant that a larger and more balanced sample could not be obtained, and that students from all universities participated.</p><p>The study presented is a pioneer in analyzing this theme in Spain. The confirmation that futures teachers are habitual consumers of cinema should serve to promote specific training in the teacher′s degree that contributes to an optimal application of cinema as a didactic resource, complementing the knowledge that the students have of cinema outside the university. If cinema is a resource with educational potential which does not require a very expensive economic investment and besides is a popular medium among future teachers, then the possibilities of implementing training in the didactic use of cinema are greater. This could be done through a specific subject or in a transversal way in each area of knowledge. In this sense, a specific subject should be included that addresses training in audiovisual resources, such as film, for educational use. This subject should be addressed by going beyond technical approaches that reduce cinema to a mere technological instrument, giving priority to reflective approaches, critical analysis and knowledge creation. Its contents should address didactic training for the educational use of cinema. In addition to this, cinema must be approached in this subject as an audiovisual language and a means of artistic expression. Moreover, cinema could be a resource to work in a transversal way in the agenda of subjects, in order to explore its educational possibilities in different knowledge areas. In this sense, priority should be given to training in the creation of activities through cinema. Furthermore, teachers of Teacher Training degree programs should carry out activities with students using film. Training for the didactic use of cinema should be extended to all Autonomous Communities, although with more intensity in those whose students have a lesser habit of watching films. Furthermore, training should be developed with the use of different types of cinema, filling one of the gaps detected in the cinematographic habits of students in the teacher′s degree. The educational possibilities of documentary films [<xref rid=\"B48-ijerph-17-05361\" ref-type=\"bibr\">48</xref>] and animation films [<xref rid=\"B49-ijerph-17-05361\" ref-type=\"bibr\">49</xref>], not only fiction films, have been highlighted.</p><p>On the other hand, in order to encourage film consumption habits in line with educational objectives, it is appropriate for education faculties should conduct training initiatives to promote cinema with special cultural and educational value, that can contribute to better teacher performance in the future. Consequently, film screenings and activities should be organized to make visible to students a cinema that enriches their cultural background and promotes a critical and reflective vision of cinematographic works to develop mature and responsible viewers. In this line, students should also be encouraged to get used to seeing a greater variety of types of cinema, beyond fiction films, promoting, for example, documentary films that have a lesser commercial diffusion. Furthermore, we should also encourage the habit of going to the cinema, to recover the original experience of cinema in all its breadth. This could be done through collaboration agreements between universities and cinemas, which would allow greater access to theatres for educational purposes. All of that, with the purpose of favoring the personal enrichment of the students and promoting a transference to the social-educational context.</p></sec></body><back><ack><title>Acknowledgments</title><p>The authors would like to thank the professors who participated in the expert judgement and the professors from the Spanish universities who collaborated in the dissemination of the PECID questionnaire.</p></ack><notes><title>Author Contributions</title><p>Conceptualization, A.L.-L.; Methodology, A.L.-L.; Software, A.L.-L. and G.L.; Validation, A.L.-L.; Formal Analysis, A.L.-L. and G.L.; Investigation, A.L.-L.; Resources, A.L.; Data Curation, A.L.-L. and G.L.; Writing—Original Draft Preparation, A.L.-L. and A.L.; Writing—Review and Editing, A.L.-L. and E.P.-V.; Visualization, A.L.-L. and E.P.-V.; Supervision, A.L.; Project Administration, A.L.-L. and A.L. 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Pedagógicas</source><year>2016</year><volume>25</volume><fpage>119</fpage><lpage>132</lpage><pub-id pub-id-type=\"doi\">10.12795/CP.2016.i25.09</pub-id></element-citation></ref></ref-list></back><floats-group><fig id=\"ijerph-17-05361-f001\" orientation=\"portrait\" position=\"float\"><label>Figure 1</label><caption><p>Sample percentages and frequencies by Autonomous Community.</p></caption><graphic xlink:href=\"ijerph-17-05361-g001\"/></fig><table-wrap id=\"ijerph-17-05361-t001\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05361-t001_Table 1</object-id><label>Table 1</label><caption><p>Participant sample according to the university.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">University</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>f</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">%</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">University of A Coruña</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">119</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.6</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">University of Alcalá</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">166</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.6</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Alfonso X El Sabio University</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.1</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">University of Alicante</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">196</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.2</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">University of Almería</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">23</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Nebrija University</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">19</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.4</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Autonomous University of Barcelona</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">73</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.6</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Autonomous University of Madrid</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">154</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.3</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">University of Barcelona</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">64</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.4</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">University of Burgos</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">24</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">University of Cádiz</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">155</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.3</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Camilo José Cela University</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">24</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">University of Cantabria</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">41</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.9</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CEU Cardenal Herrera University</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">35</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.8</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">University of Castilla La Mancha</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">110</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.4</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Catholic University San Antonio</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">33</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.7</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Catholic University Santa Teresa de Jesús de Ávila</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0</td></tr><tr><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Valencia Catholic University San Vicente Mártir</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.2</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Complutense University of Madrid</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">45</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.0</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">University of Córdoba</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">224</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.8</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">University of Deusto</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.3</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">European University of the Atlantic</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.1</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">University of Extremadura</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">88</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.9</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Francisco de Vitoria University</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.3</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">University of Girona</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">158</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.4</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">University of Granada</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">116</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.5</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">University of Huelva</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">101</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.2</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">University of the Islas Baleares</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">76</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.6</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">International University of Cataluña</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.2</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Jaume I University</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">223</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.8</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">University of Jaén</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">25</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">University of La Laguna</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">88</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.9</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">University of La Rioja</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">60</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.3</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">University of Las Palmas de Gran Canaria</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">117</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.5</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">University of León</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">27</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.6</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">University of Lleida</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">111</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.4</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Loyola University Andalucía</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">30</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.6</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Mondragón University</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">70</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.5</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">University of Murcia</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">185</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.0</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">University of Málaga</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">186</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.0</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">University of Navarra</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">23</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">University of Oviedo</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">99</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.1</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">University of País Vasco</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">73</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.6</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Comillas Pontifical University</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">32</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.7</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Pontifical University of Salamanca</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">33</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.7</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Public University of Navarra</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">48</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.0</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Ramon Llull University</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">96</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.1</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Rey Juan Carlos University</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">180</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.9</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Rovira i Virgili University</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">118</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.5</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">University of Salamanca</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">54</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.2</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">San Jorge University</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">15</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.3</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">University of Santiago de Compostela</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">35</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.8</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">University of Sevilla</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">301</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6.5</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">University of Valladolid</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">31</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.7</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">University of Valencia</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">138</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.0</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">University of Vic</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">14</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.3</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">University of Vigo</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">97</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.1</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">University of Zaragoza</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">54</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.2</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Total</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">4659</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">100.0</td></tr></tbody></table></table-wrap><table-wrap id=\"ijerph-17-05361-t002\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05361-t002_Table 2</object-id><label>Table 2</label><caption><p>Percentages and frequencies of the number of films viewed, according to the Autonomous Community.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">Autonomous Community</th><th colspan=\"12\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Number of Films per Week</th></tr><tr><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">No One</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">4</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">5</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">6</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">7</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">+7</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Total</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">M</th></tr></thead><tbody><tr><td rowspan=\"3\" align=\"center\" valign=\"middle\" colspan=\"1\">Andalucía</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>f</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">126</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">389</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">310</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">179</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">86</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">41</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">14</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1161</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.09</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>% AC.</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">33.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">26.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">15.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>%SPN.</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">24.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td rowspan=\"3\" align=\"center\" valign=\"middle\" colspan=\"1\">Aragón</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>f</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">15</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">20</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">11</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">69</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.42</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>% AC.</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">21.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">29.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">18.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">15.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>%SPN.</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td rowspan=\"3\" align=\"center\" valign=\"middle\" colspan=\"1\">Asturias</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>f</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">12</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">30</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">26</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">18</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">99</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.13</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>% AC.</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">12.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">30.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">26.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">18.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">9.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>%SPN.</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td rowspan=\"3\" align=\"center\" valign=\"middle\" colspan=\"1\">Islas Baleares</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>f</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">24</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">25</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">11</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">76</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.13</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>% AC.</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">31.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">32.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">14.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>%SPN.</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td rowspan=\"3\" align=\"center\" valign=\"middle\" colspan=\"1\">Canarias</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>f</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">21</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">52</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">58</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">37</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">14</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">12</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">205</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.41</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>% AC.</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">25.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">28.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">18.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>%SPN.</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td rowspan=\"3\" align=\"center\" valign=\"middle\" colspan=\"1\">Cantabria</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>f</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">16</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">45</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.46</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>% AC.</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">11.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">20.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">35.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">15.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>%SPN.</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td rowspan=\"3\" align=\"center\" valign=\"middle\" colspan=\"1\">Castilla-La Mancha</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>f</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">11</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">42</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">33</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">17</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">110</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.83</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>% AC.</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">38.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">30.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">15.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>%SPN.</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td rowspan=\"3\" align=\"center\" valign=\"middle\" colspan=\"1\">Castilla y León</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>f</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">19</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">42</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">46</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">28</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">18</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">12</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">171</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.42</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>% AC.</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">11.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">24.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">26.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">16.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>%SPN.</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td rowspan=\"3\" align=\"center\" valign=\"middle\" colspan=\"1\">Cataluña</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>f</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">87</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">252</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">155</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">80</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">39</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">17</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">642</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.88</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>%AC.</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">39.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">24.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">12.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>%SPN.</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td rowspan=\"3\" align=\"center\" valign=\"middle\" colspan=\"1\">Comunidad Valenciana</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>f</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">72</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">192</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">173</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">84</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">38</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">25</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">601</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.08</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>% AC.</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">12.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">31.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">28.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">14.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>%SPN.</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">12.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td rowspan=\"3\" align=\"center\" valign=\"middle\" colspan=\"1\">Extremadura</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>f</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">19</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">24</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">18</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">88</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.52</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>% AC.</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">21.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">27.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">20.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">9.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>%SPN.</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td rowspan=\"3\" align=\"center\" valign=\"middle\" colspan=\"1\">Galicia</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>f</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">32</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">74</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">64</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">44</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">16</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">11</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">251</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.21</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>% AC.</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">12.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">29.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">25.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">17.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>%SPN.</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td rowspan=\"3\" align=\"center\" valign=\"middle\" colspan=\"1\">Madrid</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>f</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">74</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">208</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">173</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">90</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">45</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">29</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">636</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.10</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>% AC.</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">11.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">32.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">27.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">14.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>%SPN.</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td rowspan=\"3\" align=\"center\" valign=\"middle\" colspan=\"1\">Murcia</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>f</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">30</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">81</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">62</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">24</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">218</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.85</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>% AC.</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">37.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">28.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">11.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>%SPN.</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td rowspan=\"3\" align=\"center\" valign=\"middle\" colspan=\"1\">Navarra</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>f</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">11</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">22</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">19</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">71</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.05</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>% AC.</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">15.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">31.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">26.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">14.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>%SPN.</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td rowspan=\"3\" align=\"center\" valign=\"middle\" colspan=\"1\">País Vasco</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>f</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">29</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">56</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">39</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">17</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">156</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.80</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>% AC.</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">18.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">35.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">25.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>%SPN.</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td rowspan=\"3\" align=\"center\" valign=\"middle\" colspan=\"1\">La Rioja</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>f</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">19</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">16</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">60</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.10</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>% AC.</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">31.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">26.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>%SPN.</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Total</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>f</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4659</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>%SPN.</italic>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">100.0</td></tr></tbody></table></table-wrap><table-wrap id=\"ijerph-17-05361-t003\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05361-t003_Table 3</object-id><label>Table 3</label><caption><p>Number of films watched per week by the teacher′s degree students, according to the type of degree.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">Number of Films</th><th colspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Teacher’s Degree in Pre-School Education</th><th colspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Teacher’s Degree in Primary Education</th><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Total</th></tr><tr><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>f</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>%Pre.</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Total<break/>%</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>f</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>%Pri.</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Total<break/>%</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>f</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>%Spain</italic>\n</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">No one</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">266</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">11.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">292</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">12.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">558</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">12.0</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">737</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">32.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">15.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">789</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">33.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">16.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1526</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">32.8</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">642</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">28.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">617</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">25.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">27.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1259</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">27.0</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">334</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">14.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">351</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">14.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">685</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">14.7</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">162</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">170</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">332</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7.1</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">80</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">91</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">171</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.7</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">27</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">24</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">51</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.1</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">14</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">23</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">+7</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">24</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.1</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.5</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">30</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.3</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.2</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">54</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.2</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Total</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2281</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">49.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2378</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">51.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">4659</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">100.0</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Mean</td><td colspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">2.08</td><td colspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">2.09</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td></tr></tbody></table></table-wrap><table-wrap id=\"ijerph-17-05361-t004\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05361-t004_Table 4</object-id><label>Table 4</label><caption><p>Percentages and frequencies of fiction films seen by teacher′s degree students, according to the Autonomous Community.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th colspan=\"10\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">FICTION</th></tr><tr><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Autonomous Community</th><th colspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">YES</th><th colspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">NO</th><th colspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">Total</th></tr><tr><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>f</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>%AC.</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>%Spn.</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>f</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>%AC.</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>%Spn.</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>f</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>%AC.</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>%Spn.</italic>\n</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Andalucía</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">619</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">59.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">15.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">416</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">40.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1035</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">25.2</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Aragón</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">25</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">40.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">37</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">59.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">62</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.5</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Asturias</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">47</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">50.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">40</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">46.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">87</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.1</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Islas Baleares</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">40</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">56.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">31</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">43.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">71</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.7</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Canarias</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">83</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">45.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">101</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">54.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">184</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.5</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Cantabria</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">20</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">50.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">20</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">50.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">40</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.0</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Castilla-La Mancha</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">61</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">61.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">38</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">38.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">99</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.4</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Castilla y León</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">82</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">53.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">70</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">46.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">152</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.7</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Cataluña</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">331</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">59.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">224</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">40.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">555</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13.5</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Comunidad Valenciana</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">320</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">60.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">209</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">39.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">529</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">12.9</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Extremadura</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">43</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">54.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">36</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">45.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">79</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.9</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Galicia</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">121</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">55.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">98</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">44.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">219</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.3</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Madrid</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">293</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">52.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">269</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">47.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">562</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13.7</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Murcia</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">122</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">64.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">66</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">35.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">188</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.6</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Navarra</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">28</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">46.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">32</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">53.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">60</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.5</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">País Vasco</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">77</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">60.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">50</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">39.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">127</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.1</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">La Rioja</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">31</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">59.6</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.8</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">21</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">40.4</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.5</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">52</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.3</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Total</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">4101</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">100.0</td></tr></tbody></table></table-wrap><table-wrap id=\"ijerph-17-05361-t005\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05361-t005_Table 5</object-id><label>Table 5</label><caption><p>Percentages and frequencies of documentary cinema seen by teacher′s degree students, according to the Autonomous Community.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th colspan=\"10\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin\" rowspan=\"1\">DOCUMENTARY</th></tr><tr><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">Autonomous Community</th><th colspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">YES</th><th colspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">NO</th><th colspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Total</th></tr><tr><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>f</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>%AC.</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>%Spn.</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>f</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>%AC.</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>%Spn.</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>f</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>%AC.</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>%Spn.</italic>\n</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Andalucía</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1031</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">99.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">25.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1035</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">25.2</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Aragón</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">62</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">62</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.5</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Asturias</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">86</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">98.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">87</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.1</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Islas Baleares</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">71</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">71</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.7</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Canarias</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">184</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">184</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.5</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Cantabria</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">40</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">40</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.0</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Castilla-La Mancha</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">98</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">99.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">99.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">99</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.4</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Castilla y León</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">152</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">152</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.7</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Cataluña</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">553</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">99.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">555</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13.5</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Comunidad Valenciana</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">527</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">99.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">12.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">529</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">12.9</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Extremadura</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">78</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">98.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">79</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.9</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Galicia</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">218</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">99.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">219</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.3</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Madrid</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">558</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">99.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">562</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13.7</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Murcia</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">186</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">98.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">188</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.6</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Navarra</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">60</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">60</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.5</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">País Vasco</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">126</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">99.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">127</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.1</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">La Rioja</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.9</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">51</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">98.1</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.2</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">52</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.3</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Total</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">4101</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">100.0</td></tr></tbody></table></table-wrap><table-wrap id=\"ijerph-17-05361-t006\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05361-t006_Table 6</object-id><label>Table 6</label><caption><p>Percentages and frequencies of animation films seen by teacher′s degree students, according to the Autonomous Community.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th colspan=\"10\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin\" rowspan=\"1\">ANIMATION</th></tr><tr><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">Autonomous Community</th><th colspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">YES</th><th colspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">NO</th><th colspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Total</th></tr><tr><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>f</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>%AC.</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>%Spn.</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>f</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>%AC.</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>%Spn.</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>f</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>%AC.</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>%Spn.</italic>\n</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Andalucía</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">23</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1012</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">97.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">24.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1035</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">25.2</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Aragón</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">61</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">98.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">62</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.5</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Asturias</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">86</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">98.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">87</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.1</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Islas Baleares</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">70</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">98.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">71</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.7</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Canarias</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">184</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">184</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.5</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Cantabria</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">40</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">40</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.0</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Castilla-La Mancha</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">93</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">93.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">99</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.4</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Castilla y León</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">150</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">98.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">152</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.7</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Cataluña</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">551</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">99.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">555</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13.5</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Comunidad Valenciana</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">524</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">99.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">12.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">529</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">12.9</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Extremadura</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">79</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">79</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.9</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Galicia</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">218</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">99.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">219</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.3</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Madrid</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">552</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">98.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">562</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13.7</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Murcia</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">185</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">98.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">188</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.6</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Navarra</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">60</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">60</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.5</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">País Vasco</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">124</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">97.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">127</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.1</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">La Rioja</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">52</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.3</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">52</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.3</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Total</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">4101</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">100.0</td></tr></tbody></table></table-wrap><table-wrap id=\"ijerph-17-05361-t007\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05361-t007_Table 7</object-id><label>Table 7</label><caption><p>Percentages and frequencies of fiction and documentary films seen by teacher′s degree students, according to the Autonomous Community.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th colspan=\"10\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin\" rowspan=\"1\">FICTION + DOCUMENTARY</th></tr><tr><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">Autonomous Community</th><th colspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">YES</th><th colspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">NO</th><th colspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Total</th></tr><tr><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>f</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>%AC.</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>%Spn.</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>f</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>%AC.</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>%Spn.</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>f</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>%AC.</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>%Spn.</italic>\n</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Andalucía</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">44</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">991</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">95.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">24.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1035</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">25.2</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Aragón</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">59</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">95.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">62</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.5</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Asturias</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">84</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">96.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">87</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.1</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Islas Baleares</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">67</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">94.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">71</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.7</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Canarias</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">174</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">94.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">184</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.5</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Cantabria</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">37</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">92.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">40</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.0</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Castilla-La Mancha</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">97</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">98.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">99</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.4</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Castilla y León</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">146</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">96.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">152</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.7</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Cataluña</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">48</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">507</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">91.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">12.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">555</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13.5</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Comunidad Valenciana</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">36</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">493</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">93.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">12.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">529</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">12.9</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Extremadura</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">74</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">93.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">79</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.9</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Galicia</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">206</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">94.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">219</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.3</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Madrid</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">33</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">529</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">94.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">12.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">562</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13.7</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Murcia</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">180</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">95.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">188</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.6</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Navarra</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">55</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">91.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">60</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.5</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">País Vasco</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">118</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">92.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">127</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.1</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">La Rioja</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3.8</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">50</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">96.2</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.2</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">52</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.3</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Total</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">4101</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">100.0</td></tr></tbody></table></table-wrap><table-wrap id=\"ijerph-17-05361-t008\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05361-t008_Table 8</object-id><label>Table 8</label><caption><p>Percentages and frequencies of fiction and animation films seen by teacher′s degree students, according to the Autonomous Community.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th colspan=\"10\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin\" rowspan=\"1\">FICTION + ANIMATION</th></tr><tr><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">Autonomous Community</th><th colspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">YES</th><th colspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">NO</th><th colspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Total</th></tr><tr><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>f</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>%AC.</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>%Spn.</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>f</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>%AC.</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>%Spn.</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>f</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>%AC.</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>%Spn.</italic>\n</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Andalucía</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">297</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">28.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">738</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">71.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">18.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1035</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">25.2</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Aragón</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">29</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">46.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">33</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">53.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">62</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.5</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Asturias</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">31</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">35.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">56</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">64.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">87</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.1</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Islas Baleares</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">19</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">26.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">52</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">73.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">71</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.7</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Canarias</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">74</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">40.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">110</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">59.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">184</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.5</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Cantabria</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">11</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">27.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">29</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">72.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">40</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.0</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Castilla-La Mancha</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">27</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">27.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">72</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">72.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">99</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.4</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Castilla y León</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">47</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">30.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">105</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">69.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">152</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.7</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Cataluña</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">130</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">23.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">425</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">76.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">555</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13.5</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Comunidad Valenciana</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">146</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">27.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">383</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">72.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">9.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">529</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">12.9</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Extremadura</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">26</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">32.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">53</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">67.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">79</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.9</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Galicia</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">66</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">30.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">153</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">69.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">219</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.3</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Madrid</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">180</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">32.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">382</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">68.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">9.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">562</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13.7</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Murcia</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">45</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">23.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">143</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">76.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">188</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.6</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Navarra</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">21</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">35.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">39</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">65.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">60</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.5</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">País Vasco</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">28</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">22.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">99</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">78.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">127</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.1</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">La Rioja</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">10</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">19.2</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.2</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">42</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">80.8</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">52</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.3</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Total</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">4101</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">100.0</td></tr></tbody></table></table-wrap><table-wrap id=\"ijerph-17-05361-t009\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05361-t009_Table 9</object-id><label>Table 9</label><caption><p>Percentages and frequencies of documentary and animation films seen by teacher′s degree students, according to the Autonomous Community.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th colspan=\"10\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin\" rowspan=\"1\">DOCUMENTARY + ANIMATION</th></tr><tr><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">Autonomous Community</th><th colspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">YES</th><th colspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">NO</th><th colspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Total</th></tr><tr><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>f</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>%AC.</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>%Spn.</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>f</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>%AC.</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>%Spn.</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>f</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>%AC.</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>%Spn.</italic>\n</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Andalucía</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1035</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">25.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1035</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">25.2</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Aragón</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">62</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">62</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.5</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Asturias</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">87</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">87</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.1</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Islas Baleares</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">71</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">71</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.7</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Canarias</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">184</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">184</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.5</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Cantabria</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">40</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">40</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.0</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Castilla-La Mancha</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">99</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">99</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.4</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Castilla y León</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">152</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">152</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.7</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Cataluña</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">552</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">99.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">555</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13.5</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Comunidad Valenciana</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">529</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">12.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">529</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">12.9</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Extremadura</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">79</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">79</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.9</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Galicia</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">217</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">99.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">219</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.3</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Madrid</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">561</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">99.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">562</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13.7</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Murcia</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">188</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">188</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.6</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Navarra</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">60</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">60</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.5</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">País Vasco</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">127</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">127</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.1</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">La Rioja</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">52</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.3</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">52</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.3</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Total</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">4101</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">100.0</td></tr></tbody></table></table-wrap><table-wrap id=\"ijerph-17-05361-t010\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05361-t010_Table 10</object-id><label>Table 10</label><caption><p>Percentages and frequencies of fiction, documentary and animation films seen by teacher′s degree students, according to the Autonomous Community.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th colspan=\"10\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin\" rowspan=\"1\">FICTION + DOCUMENTARY + ANIMATION</th></tr><tr><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">Autonomous Community</th><th colspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">YES</th><th colspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">NO</th><th colspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Total</th></tr><tr><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>f</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>%AC.</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>%Spn.</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>f</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>%AC.</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>%Spn.</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>f</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>%AC.</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>%Spn.</italic>\n</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Andalucía</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">48</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">987</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">95.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">24.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1035</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">25.2</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Aragón</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">58</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">93.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">62</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.5</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Asturias</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">83</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">95.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">87</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.1</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Islas Baleares</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">9.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">64</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">90.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">71</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.7</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Canarias</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">17</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">9.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">167</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">90.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">184</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.5</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Cantabria</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">15.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">34</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">85.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">40</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.0</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Castilla-La Mancha</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">97</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">98.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">99</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.4</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Castilla y León</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">15</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">9.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">137</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">90.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">152</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.7</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Cataluña</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">37</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">518</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">93.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">12.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">555</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13.5</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Comunidad Valenciana</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">20</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">509</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">96.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">12.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">522</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">12.9</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Extremadura</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">75</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">94.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">79</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.9</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Galicia</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">15</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">204</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">93.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">219</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.3</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Madrid</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">41</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">521</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">92.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">12.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">562</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13.7</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Murcia</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">180</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">95.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">188</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.6</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Navarra</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">54</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">90.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">60</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.5</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">País Vasco</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">118</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">92.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">127</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.1</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">La Rioja</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">8</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">15.4</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.2</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">44</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">84.6</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.1</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">52</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.3</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Total</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">4101</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">100.0</td></tr></tbody></table></table-wrap><table-wrap id=\"ijerph-17-05361-t011\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05361-t011_Table 11</object-id><label>Table 11</label><caption><p>Percentages and frequencies of the type of cinema seen by the students, according to the type of teacher′s degree.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin\" rowspan=\"1\" colspan=\"1\">Type of Cinema</th><th colspan=\"9\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin\" rowspan=\"1\">Teacher’s Degree in Pre-School Education</th><th colspan=\"9\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin\" rowspan=\"1\">Teacher’s Degree in Primary Education</th><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin\" rowspan=\"1\">\n</th></tr><tr><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">\n</th><th colspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">YES</th><th colspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">NO</th><th colspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Total Pre-school Degree</th><th colspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">YES</th><th colspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">NO</th><th colspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Total Primary Degree</th><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Total</th></tr><tr><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>f</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>%AC.</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>%Spn.</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>f</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>%AC.</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>%Spn.</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>f</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>% AC.</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>%Spn.</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>f</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>%AC.</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>%Spn.</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>f</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>%AC.</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>%Spn.</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>f</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>%AC.</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>%Spn.</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>f</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">%</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Fic.</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1137</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">56.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">27.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">878</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">43.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">21.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2015</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">49.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1206</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">57.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">29.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">880</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">42.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">21.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2086</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">50.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4101</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Doc.</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2007</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">99.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">48.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2015</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">49.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">12</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2074</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">99.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">50.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2086</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">50.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4101</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Ani.</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">31</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1984</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">98.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">48.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2015</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">49.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">29</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2057</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">98.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">50.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2086</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">50.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4101</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Fic. + Doc.</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">88</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1927</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">95.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">47.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2015</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">49.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">146</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1940</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">93.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">47.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2086</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">50.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4101</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Fic. + Ani.</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">641</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">31.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">15.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1374</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">68.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">33.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2015</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">49.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">546</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">26.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1540</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">73.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">37.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2086</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">50.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4101</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Doc. + Ani.</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2013</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">99.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">49.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2015</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">49.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2082</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">99.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">50.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2086</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">50.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4101</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Fic. + Doc. + Ani.</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">108</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">5.4</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2.6</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1907</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">94.6</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">46.5</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2015</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">49.1</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">143</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">6.9</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3.5</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1943</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">93.1</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">47.4</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2086</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">50.9</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">4101</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">100.0</td></tr></tbody></table><table-wrap-foot><fn><p>Note: Fic. = Fiction; Doc. = Documentary; Ani. = Animation.</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"ijerph-17-05361-t012\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05361-t012_Table 12</object-id><label>Table 12</label><caption><p>Percentages and frequencies of devices used to watch movies by teacher’s degree students.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Device Type</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>f</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">%</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Cinema Projector</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1412</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">17.1</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Television</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3019</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">36.6</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Computer</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2875</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">34.9</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Mobile</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">418</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.1</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Tablet</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">510</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6.2</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Home Projector</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.1</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Netflix</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.1</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Movistar+</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Playstation</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Chromecast</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.0</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Total</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">8249</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">100.0</td></tr></tbody></table></table-wrap><table-wrap id=\"ijerph-17-05361-t013\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05361-t013_Table 13</object-id><label>Table 13</label><caption><p>Percentages and frequencies of the type of cinema viewed by the teacher′s degree students, according to type of university.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin\" rowspan=\"1\" colspan=\"1\">Type of Cinema</th><th colspan=\"9\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin\" rowspan=\"1\">Public University</th><th colspan=\"9\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin\" rowspan=\"1\">Private University</th><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin\" rowspan=\"1\">\n</th></tr><tr><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">\n</th><th colspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">YES</th><th colspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">NO</th><th colspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Total Public University</th><th colspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">YES</th><th colspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">NO</th><th colspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Total Private University</th><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Total</th></tr><tr><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>f</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>%AC.</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>%Spn.</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>f</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>%AC.</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>%Spn.</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>f</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>%AC.</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>%Spn.</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>f</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>%AC.</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>%Spn.</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>f</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>%AC.</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>%Spn.</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>f</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>%AC.</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>%Spn.</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>f</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">%</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Fic.</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2093</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">56.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">51.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1583</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">43.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">38.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3676</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">89.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">250</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">58.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">175</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">41.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">425</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4101</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Doc.</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">19</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3657</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">99.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">89.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3676</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">89.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">424</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">99.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">425</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4101</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Ani.</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">55</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3621</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">98.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">88.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3676</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">89.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">420</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">98.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">425</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4101</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Fic. + Doc.</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">198</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3481</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">94.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">84.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3676</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">89.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">39</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">9.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">386</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">90.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">9.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">425</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4101</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Fic. + Ani.</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1086</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">29.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">26.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2590</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">70.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">63.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3676</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">89.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">101</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">23.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">324</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">76.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">425</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4101</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Doc. + Ani.</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3670</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">99.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">89.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3676</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">89.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">425</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">425</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4101</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Fic. + Doc. + Ani.</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">222</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">6.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">5.4</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3454</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">94.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">84.2</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3676</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">89.6</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">29</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">6.8</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.7</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">396</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">93.2</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">9.7</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">425</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">10.4</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">4101</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">100.0</td></tr></tbody></table><table-wrap-foot><fn><p>Note: Fic. = Fiction; Doc. = Documentary; Ani. = Animation.</p></fn></table-wrap-foot></table-wrap></floats-group></article>\n"
]
[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Int J Environ Res Public Health</journal-id><journal-id journal-id-type=\"iso-abbrev\">Int J Environ Res Public Health</journal-id><journal-id journal-id-type=\"publisher-id\">ijerph</journal-id><journal-title-group><journal-title>International Journal of Environmental Research and Public Health</journal-title></journal-title-group><issn pub-type=\"ppub\">1661-7827</issn><issn pub-type=\"epub\">1660-4601</issn><publisher><publisher-name>MDPI</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32731346</article-id><article-id pub-id-type=\"pmc\">PMC7432088</article-id><article-id pub-id-type=\"doi\">10.3390/ijerph17155413</article-id><article-id pub-id-type=\"publisher-id\">ijerph-17-05413</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Article</subject></subj-group></article-categories><title-group><article-title>The Effects of Indoor Pollutants Exposure on Allergy and Lung Inflammation: An Activation State of Neutrophils and Eosinophils in Sputum</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0002-6307-9954</contrib-id><name><surname>Mohd Isa</surname><given-names>Khairul Nizam</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05413\">1</xref><xref ref-type=\"aff\" rid=\"af2-ijerph-17-05413\">2</xref></contrib><contrib contrib-type=\"author\"><name><surname>Hashim</surname><given-names>Zailina</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05413\">1</xref><xref rid=\"c1-ijerph-17-05413\" ref-type=\"corresp\">*</xref></contrib><contrib contrib-type=\"author\"><name><surname>Jalaludin</surname><given-names>Juliana</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05413\">1</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0001-6956-4638</contrib-id><name><surname>Lung Than</surname><given-names>Leslie Thian</given-names></name><xref ref-type=\"aff\" rid=\"af3-ijerph-17-05413\">3</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0001-8154-5069</contrib-id><name><surname>Hashim</surname><given-names>Jamal Hisham</given-names></name><xref ref-type=\"aff\" rid=\"af4-ijerph-17-05413\">4</xref></contrib></contrib-group><aff id=\"af1-ijerph-17-05413\"><label>1</label>Department of Environmental and Occupational Health, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia, UPM, Serdang 43400, Selangor, Malaysia; <email>[email protected]</email> (K.N.M.I.); <email>[email protected]</email> (J.J.)</aff><aff id=\"af2-ijerph-17-05413\"><label>2</label>Environmental Health Research Cluster (EHRc), Environmental Healthcare Section, Institute of Medical Science Technology, Universiti Kuala Lumpur, Kajang 43000, Selangor, Malaysia</aff><aff id=\"af3-ijerph-17-05413\"><label>3</label>Department of Microbiology and Parasitology, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia, UPM, Serdang 43400, Selangor, Malaysia; <email>[email protected]</email></aff><aff id=\"af4-ijerph-17-05413\"><label>4</label>IIGH United Nations University, UKM Medical Centre, Cheras 56000, Kuala Lumpur, Malaysia; <email>[email protected]</email></aff><author-notes><corresp id=\"c1-ijerph-17-05413\"><label>*</label>Correspondence: <email>[email protected]</email>; Tel.: +601-7636-1367</corresp></author-notes><pub-date pub-type=\"epub\"><day>28</day><month>7</month><year>2020</year></pub-date><pub-date pub-type=\"ppub\"><month>8</month><year>2020</year></pub-date><volume>17</volume><issue>15</issue><elocation-id>5413</elocation-id><history><date date-type=\"received\"><day>28</day><month>4</month><year>2020</year></date><date date-type=\"accepted\"><day>22</day><month>6</month><year>2020</year></date></history><permissions><copyright-statement>© 2020 by the authors.</copyright-statement><copyright-year>2020</copyright-year><license license-type=\"open-access\"><license-p>Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>).</license-p></license></permissions><abstract><p>Background: To explore the inflammation phenotypes following indoor pollutants exposure based on marker expression on eosinophils and neutrophils with the application of chemometric analysis approaches. Methods: A cross-sectional study was undertaken among secondary school students in eight suburban and urban schools in the district of Hulu Langat, Selangor, Malaysia. The survey was completed by 96 students at the age of 14 by using the International Study of Asthma and Allergies in Children (ISAAC) and European Community Respiratory Health Survey (ECRHS) questionnaires. The fractional exhaled nitric oxide (FeNO) was measured, and an allergic skin prick test and sputum induction were performed for all students. Induced sputum samples were analysed for the expression of CD11b, CD35, CD63, and CD66b on eosinophils and neutrophils by flow cytometry. The particulate matter (PM<sub>2.5</sub> and PM<sub>10</sub>), NO<sub>2</sub>, CO<sub>2</sub>, and formaldehyde were measured inside the classrooms. Results: Chemometric and regression results have clustered the expression of CD63 with PM<sub>2.5</sub>, CD11b with NO<sub>2</sub>, CD66b with FeNO levels, and CO<sub>2</sub> with eosinophils, with the prediction accuracy of the models being 71.88%, 76.04%, and 76.04%, respectively. Meanwhile, for neutrophils, the CD63 and CD66b clustering with PM<sub>2.5</sub> and CD11b with FeNO levels showed a model prediction accuracy of 72.92% and 71.88%, respectively. Conclusion: The findings indicated that the exposure to PM<sub>2.5</sub> and NO<sub>2</sub> was likely associated with the degranulation of eosinophils and neutrophils, following the activation mechanisms that led to the inflammatory reactions.</p></abstract><kwd-group><kwd>lung inflammation</kwd><kwd>allergy</kwd><kwd>indoor pollutants</kwd><kwd>biomarkers</kwd><kwd>FeNO</kwd><kwd>eosinophil</kwd><kwd>neutrophil</kwd></kwd-group></article-meta></front><body><sec sec-type=\"intro\" id=\"sec1-ijerph-17-05413\"><title>1. Introduction</title><p>Exposure to indoor pollutants are strongly associated with increased morbidity and mortality, mainly among school children who spend most of their time in the classrooms [<xref rid=\"B1-ijerph-17-05413\" ref-type=\"bibr\">1</xref>]. Reports have shown that exposure to high concentration of particles, nitrogen dioxide (NO<sub>2</sub>), carbon dioxide (CO<sub>2</sub>), ozone (O<sub>3</sub>), volatile organic compounds (VOCs), and fibres induce persistent airway inflammation, which is mediated by the immune system [<xref rid=\"B2-ijerph-17-05413\" ref-type=\"bibr\">2</xref>,<xref rid=\"B3-ijerph-17-05413\" ref-type=\"bibr\">3</xref>]. Biomarkers, such as fractional exhaled nitric oxide (FeNO), cytokines, chemokines, lipid mediators, enzymes, adhesion molecules, and other growth factors, have been considered as the indicators of allergic airway inflammation [<xref rid=\"B4-ijerph-17-05413\" ref-type=\"bibr\">4</xref>]. The indicators that underlie the complex molecular pathways that regulate inflammation have not been fully elucidated; however, numerous studies have reported that the process certainly involves the activation of eosinophils and neutrophils [<xref rid=\"B5-ijerph-17-05413\" ref-type=\"bibr\">5</xref>,<xref rid=\"B6-ijerph-17-05413\" ref-type=\"bibr\">6</xref>]. They play roles in innate host defence via effector mechanisms, including degranulation, DNA traps, and cytolysis, following the activation cascaded by diverse mediators [<xref rid=\"B7-ijerph-17-05413\" ref-type=\"bibr\">7</xref>].</p><p>Extensive investigation of the mediators that are implicated in allergy, lung inflammation, and asthma has been documented but there was insufficient evidence available for the multi-dimensional characters of the activation and degranulation markers expression of CD11b, CD35, CD63, and CD66b on eosinophils and neutrophils. Activation markers, such as integrin Mac-1 (CD11b/CD18), L-selectin (CD62L), CBRM1/5, ICAM-1 (CD54), PD-L1 (CD274), PSGL-1 (CD162), FcγRII (CD32), CD16, CD44, and CD69, were shown to be upregulated on circulating or sputum granulocytes from asthma patients [<xref rid=\"B8-ijerph-17-05413\" ref-type=\"bibr\">8</xref>,<xref rid=\"B9-ijerph-17-05413\" ref-type=\"bibr\">9</xref>]. Response to allergens and environmental stimuli among asthma patients had a detrimental outcome and was characterized by the infiltration of different granule contents from eosinophils and neutrophils following activation [<xref rid=\"B10-ijerph-17-05413\" ref-type=\"bibr\">10</xref>]. Eosinophils and neutrophils have four distinct granules, namely azurophilic, secondary, tertiary, and secretary, which are formed throughout development in the bone marrow [<xref rid=\"B11-ijerph-17-05413\" ref-type=\"bibr\">11</xref>]. Several models have demonstrated the link between degranulation of these granules and pollutants exposure at different levels of stimuli with the expression of CD11c, CD63, and CD66b [<xref rid=\"B12-ijerph-17-05413\" ref-type=\"bibr\">12</xref>,<xref rid=\"B13-ijerph-17-05413\" ref-type=\"bibr\">13</xref>]. At present, biomarkers help tailor the management of respiratory illness and studies have highlighted precision therapy approaches based on disease mechanisms by targeting the cytokines and chemokines [<xref rid=\"B14-ijerph-17-05413\" ref-type=\"bibr\">14</xref>,<xref rid=\"B15-ijerph-17-05413\" ref-type=\"bibr\">15</xref>]. Recently, advanced statistical analysis approaches, such as hierarchical clustering and latent class analyses, have renewed the interest of researchers in the investigation of airway translational inflammation phenotypes [<xref rid=\"B16-ijerph-17-05413\" ref-type=\"bibr\">16</xref>,<xref rid=\"B17-ijerph-17-05413\" ref-type=\"bibr\">17</xref>]. This new vision helps the researchers to better understand and precisely describe the inflammatory phenotypes [<xref rid=\"B18-ijerph-17-05413\" ref-type=\"bibr\">18</xref>,<xref rid=\"B19-ijerph-17-05413\" ref-type=\"bibr\">19</xref>].</p><p>This study aimed to investigate the concentrations and sources of indoor pollutants in classrooms located in the urban and suburban areas of Hulu Langat, Selangor, Malaysia. Moreover, it aimed to predict the toxicodynamic effects of indoor pollutants in the classrooms towards marker expression on the eosinophils and neutrophils in sputum samples by using chemometric analysis techniques.</p></sec><sec id=\"sec2-ijerph-17-05413\"><title>2. Materials and Methods </title><sec id=\"sec2dot1-ijerph-17-05413\"><title>2.1. Study Population</title><p>The study population was randomly sampled from eight secondary schools in Hulu Langat, Selangor, Malaysia. The researchers of this study targeted school children at the age of 14 of which they were randomly selected from four classrooms in each school. The total number of students who received their guardian’s consent and was thus recruited in the study was 470. Among them, only 50 (10.6%) students were diagnosed with asthma by a doctor based on the survey questionnaire. Another 46 students out of the remaining 420 students were randomly selected as a potential control group. The control group was selected among students who produced an adequate sputum cell count from the same class and school. In total, 96 students were included in the final study group. Students with a history of smoking in the last 12 months and students who received antibiotic treatments in the past four weeks were excluded from this study. The school areas were classified as urban and suburban by the Ministry of Education, Malaysia, based on the locale classification of the ecological measures. The data collection from the clinical assessments and indoor air monitoring were carried out at the same time in August until November 2018 and February 2019.</p><p>The researchers used the questionnaire adopted from the European Community Respiratory Health Survey (ECRHS) and International Study of Asthma and Allergies in Childhood (ISAAC) that are inclusive of questions on doctor-diagnosed asthma, allergies, and respiratory symptoms. The self-administered questionnaire was distributed to the selected students in the same week of the technical measurements. Subsequently, the researchers went through the questionnaires during a face-to-face interview with the students to clarify any uncertainty. The doctor-diagnosed asthma in this study is defined as having asthma medication, asthma attacks, and wheezes with breathlessness in the last 12 months, which were diagnosed by the physician [<xref rid=\"B20-ijerph-17-05413\" ref-type=\"bibr\">20</xref>,<xref rid=\"B21-ijerph-17-05413\" ref-type=\"bibr\">21</xref>]. This information was verified during face-to-face interviews and telephone calls with the students’ respective guardians.</p></sec><sec id=\"sec2dot2-ijerph-17-05413\"><title>2.2. FeNO Assessment and Allergy Skin Test</title><p>FeNO measurement was performed by a chemiluminescence analyser (NIOX VERO, Circassia, Sweden) as recommended by the American Thoracic Society/European Respiratory Society (ATS/ERS) [<xref rid=\"B22-ijerph-17-05413\" ref-type=\"bibr\">22</xref>]. The detection limits and accuracy for this device are 5–300 ppb and ±5 ppb, respectively. The students were asked to inhale deeply through the mouthpiece attached to the patient filter and then slowly exhale for about six to ten seconds at a constant flow rate (50 mL/s)—the single-breath technique. This process was repeated at least twice to get an average value. Students were instructed to refrain from eating and drinking for one hour before the FeNO assessment.</p><p>An allergy skin prick test was performed on the volar side of the forearm alongside <italic>Dermatophagoides pteronyssinus</italic> (Derp1) (house dust mite), <italic>Dermatophagoides farina</italic> (Derf1) (house dust mite), <italic>Cladosporium herbarium</italic> (fungi), <italic>Alternaria alternate</italic> (fungi), and <italic>Felis domesticus</italic> (cat) allergens in liquid form (Prick-Test Diagnostic, ALK-Abelló, Madrid, Spain). The same amount of histamine (10 mg/mL) and normal saline were used as the positive and negative controls, respectively. The reaction was measured after 15 min of which the wheal diameter was recorded. The allergen’s wheal diameter of 3 mm was considered as a positive control. Atopy was defined as a significant positive skin test reaction to at least one of the applied allergens [<xref rid=\"B23-ijerph-17-05413\" ref-type=\"bibr\">23</xref>].</p></sec><sec id=\"sec2dot3-ijerph-17-05413\"><title>2.3. Sputum Induction and Processing</title><p>Sputum induction was conducted by the inhalation of a nebulised, sterile mixture of 4.5% sodium chloride (hypertonic) and salbutamol 200 µg, followed by the coughing and expectoration of airway secretions. For nebulisation, an ultrasound nebuliser (Model CUN60 Citizen System Japan Co. Ltd., Tokyo, Japan) was was used as recommended [<xref rid=\"B24-ijerph-17-05413\" ref-type=\"bibr\">24</xref>] with a mouthpiece that fitted an output of ∼1 mL·min<sup>−1</sup> to achieve successful sampling. The induced sputum samples collected from the respondents were kept in an icebox and further processed within two hours by using flow cytometry. The method of processing sputum samples has been previously described [<xref rid=\"B25-ijerph-17-05413\" ref-type=\"bibr\">25</xref>]. In short, the sputum sample was diluted with freshly prepared phosphate buffer saline and gently vortexed at room temperature for homogenisation. These steps were repeated thrice. Subsequently, the sputum samples were centrifuged at 800× <italic>g</italic> for 10 min. Next, cytospin slides of sputum cells were stained with May–Grunwald–Giemsa for the cell differentiation count. Samples with >80% of squamous epithelial cells were excluded for the flow cytometry analysis. </p></sec><sec id=\"sec2dot4-ijerph-17-05413\"><title>2.4. Flow Cytometry</title><p>The processed sputum sample at a concentration of 1 × 10<sup>6</sup> cells in 100 µL was resuspended in 1 mL of stain buffer (FBS) (BD Pharmingen<sup>TM</sup>, San Diego, CA, USA) and washed twice in cold stain buffer by centrifugation at 200× <italic>g</italic> for 5 min. Subsequently, the supernatant was removed, and the cell pellet was resuspended in 300 µL of stain buffer. A volume of 100 µL aliquots of the cell suspension were transferred into three different sterile polypropylene round-bottom tubes, and monoclonal antibodies and isotype controls were added to the cells according to the manufacturer protocol. The cells were immunostained with antibodies for neutrophil and eosinophil surface markers of PE-Cy 7 Mouse Anti-Human CD11b/Mac-1, BB515 Mouse Anti-Human CD35, PE Mouse Anti-Human CD63, APC-H7 Mouse Anti-Human CD16, PerCP-Cy 5.5 CD41a, and Alexa Fluor 647 Mouse Anti-Human CD66b purchased from BD Biosciences, US. The isotype controls of PE-Cy 7 Mouse IgG1, BB515 Mouse IgG1 K, PE Mouse IgG1 K, APC-H7 Mouse IgG1 K, PerCP-Cy 5.5 Mouse IgG1 K, and Alexa Fluor 647 Mouse IgM K (BD Biosciences, US) were added to the cells. The cell samples were incubated for 15 min at room temperature in the dark. Next, the cell samples were washed twice in 1 mL of stain buffer and centrifuged at 300× <italic>g</italic> for 5 min. The supernatant was removed, and the cells were loosened up by tapping the tube. Subsequently, the cells were carefully resuspended in 500 µL of the stain buffer. Samples without antibodies and isotype were used as the controls. Hereafter, the cells were analysed by using the BD FACSCanto II flow cytometry instrument (BD Bioscience, US) equipped with blue, red, and violet lasers. Compensation was set to account for spectral overlap between the four fluorescent channels. The gating region was set so that less than 1% of the samples were stained with negative controls. Data were processed by using FlowJo software (Version 10.1r1) [<xref rid=\"B26-ijerph-17-05413\" ref-type=\"bibr\">26</xref>,<xref rid=\"B27-ijerph-17-05413\" ref-type=\"bibr\">27</xref>].</p><p>The activation of eosinophils was measured by using CD11b (integrin αM), CD35 (CR1), and CD66b (CEACAM-8). Meanwhile, CD11b was used as an activation marker for neutrophils. The degranulation activity was measured by the expression of CD11b and CD63 for eosinophils as a marker for cyctalloid (specific/secondary) and secretory (sombrero) vesicles, respectively. For neutrophils, the CD11b, CD35, CD63, and CD66b markers were used as tertiary, secretory, azurophilic, and specific granule expression markers in the degranulation activities [<xref rid=\"B28-ijerph-17-05413\" ref-type=\"bibr\">28</xref>]. Isotype controls were used as the positive control and to address the background produced by non-specific antibody binding, whereas the Anti-Mouse Ig K Negative Control Compensation Particle Set was used to optimise the fluorescence compensation settings [<xref rid=\"B29-ijerph-17-05413\" ref-type=\"bibr\">29</xref>] (<xref ref-type=\"fig\" rid=\"ijerph-17-05413-f001\">Figure 1</xref>). </p></sec><sec id=\"sec2dot5-ijerph-17-05413\"><title>2.5. Assessment of Indoor Air Quality in Classrooms and Building Inspection</title><p>Indoor pollutants and physical parameters, including temperature (°C), relative humidity (%), and carbon dioxide (ppm), were measured in the classrooms during learning session within an hour by using a Q-TrakTM IAQ monitor (Model 7565 TSI Incorporated, Shoreview, MN, USA) with the average log interval values over one minute. The accuracy of this device on temperature, relative humidity, and CO<sub>2</sub> are ±0.6 °C, ±3%, and ±50 ppm, respectively. The sampling for the particles was measured by using a Dust-Trak monitor (Model 8532 TSI Incorporated, Shoreview, MN, USA) at a sampling rate of 1.7 L/min with a resolution of 0.001 mg/m<sup>3</sup> and detection limit of 0.001–150 mg/m<sup>3</sup>. The PPM Formaldemeter<sup>TM</sup> htV-M (PPM Technology Ltd, Wales, UK) with accuracy of 10% at 2 ppm was used to measure the concentration of formaldehyde. In each school, a total of four hours of measurements were taken from four randomly selected classrooms for a period of an hour each during the learning session, as has been previously described [<xref rid=\"B30-ijerph-17-05413\" ref-type=\"bibr\">30</xref>,<xref rid=\"B31-ijerph-17-05413\" ref-type=\"bibr\">31</xref>,<xref rid=\"B32-ijerph-17-05413\" ref-type=\"bibr\">32</xref>]. The instruments used were placed one metre from the ground in the centre of the classrooms. All instruments used were calibrated regularly. The NO<sub>2</sub> (µg/m<sup>3</sup>) concentration was measured by using a diffusion sampler (IVL, Goteborg, Sweden) for a period of a week. The sampler was place at height of approximately 2–3 m above the ground and returned to the IVL Swedish Environmental Research Institute Laboratory (Goteborg, Sweden), an accredited laboratory for further analysis. This measurement technique provides an average concentration of NO<sub>2</sub> in the air during a week, with a limit of detection (LOD) of 0.5 µg/m<sup>3</sup> and a 10% (at the 95% confidence level) measurement uncertainty [<xref rid=\"B33-ijerph-17-05413\" ref-type=\"bibr\">33</xref>]. A building inspection was carried out before the indoor air quality assessments were obtained. Details on the building information, floor furnish, furniture, and type of ventilation system were noted [<xref rid=\"B34-ijerph-17-05413\" ref-type=\"bibr\">34</xref>].</p></sec><sec id=\"sec2dot6-ijerph-17-05413\"><title>2.6. Ethical Statement</title><p>The Ethics Committee for Research Involving Human Subjects Universiti Putra Malaysia (JKEUPM) has approved this study (JKEUPM-2018-189) and each of the students was given a written consent form for their guardian’s approval.</p></sec><sec id=\"sec2dot7-ijerph-17-05413\"><title>2.7. Data Analysis</title><p>The descriptive analysis was carried out by Mann–Whitney tests using the Statistical Package for the Social Sciences (SPSS) 25.0. The differences in biomarker expression between the doctor-diagnosed asthmatic children and healthy children were made by using GraphPad Prism 8 for Windows. Subsequently, a principle component analysis (PCA) and agglomerative hierarchical clustering (AHC) were applied to explore the association and pattern recognition between the biomarker expression and the concentrations of indoor pollutants. The final prediction models were generated by logistic regression analysis in which the models’ performance was based on the coefficient of determinant, overall accuracy, sensitivity, specificity, and the area under the curve (AUC) of the receiver operator characteristics (ROC) [<xref rid=\"B35-ijerph-17-05413\" ref-type=\"bibr\">35</xref>]. The researchers used the standardized data of the indoor pollutants and biomarkers in the chemometric and regression analyses. The multivariate analysis was carried out by using the Statistical Package XLSTAT Evaluation 2019.2.3 (Addinsoft, New York, US).</p></sec></sec><sec sec-type=\"results\" id=\"sec3-ijerph-17-05413\"><title>3. Results</title><sec id=\"sec3dot1-ijerph-17-05413\"><title>3.1. Data Analysis for the Personal and Clinical Characteristics of School Children</title><p>The study population was well-balanced between the doctor-diagnosed asthmatic children (52%) and healthy children (48%), in which 60% of them were from urban schools. The majority of asthmatic children tested positive for at least one of the allergens, with 74.0% of them sensitised towards house dust mites (Derp1 and Derf1), followed by cat dander (30.0%) (<xref ref-type=\"app\" rid=\"app1-ijerph-17-05413\">Supplementary Table S1</xref>). The FeNO levels were statistically higher among the doctor-diagnosed asthmatic children than healthy children (<italic>p</italic> < 0.001). The researchers observed that the total percentage of eosinophil count in the sputum samples was slightly higher in the doctor-diagnosed asthmatic children but not statistically different between the two groups (<italic>p</italic> > 0.05) (<xref rid=\"ijerph-17-05413-t001\" ref-type=\"table\">Table 1</xref>).</p><p>Both the eosinophils and neutrophils expressed an activated phenotype in both groups of induced sputum samples. The expression of CD11b was significantly upregulated in both their cell surfaces among the doctor-diagnosed asthmatic children (<italic>p</italic> < 0.05). The expression of CD63 on the neutrophils was also significantly higher in the sputum samples of the doctor-diagnosed asthmatic children as compared to healthy children (<italic>p</italic> < 0.001). Samples from the doctor-diagnosed asthmatic children with a high expression of CD11b (tertiary) and CD63 (azurophilic/crystalloid) on the eosinophil and neutrophil surfaces indicated a moderately degranulated state of sputum granulocytes (<xref ref-type=\"fig\" rid=\"ijerph-17-05413-f002\">Figure 2</xref>).</p></sec><sec id=\"sec3dot2-ijerph-17-05413\"><title>3.2. Levels of Indoor Pollutants and Building Inspection Data</title><p>All the classrooms were designed with natural ventilation, which is equipped with glass windowpanes on both sides of the wall. The classrooms were equipped with an average of three ceiling fans in each classroom. The schools were painted, and the floor surface was furnished with concrete. There were bookshelves, whiteboard, and soft boards in every classroom. Some of the classrooms had window curtains fixed on both sides of the class. The statistical analysis showed a significant difference in all of the indoor environmental parameters between the urban and suburban schools (<xref rid=\"ijerph-17-05413-t002\" ref-type=\"table\">Table 2</xref>).</p></sec><sec id=\"sec3dot3-ijerph-17-05413\"><title>3.3. Chemometrics Analysis of the Biomarkers and Indoor Air Pollutants</title><p>A PCA was performed to explain the variance observed between the biomarkers and indoor pollutants in a more efficient way. Prior to the PCA, Bartlett’s sphericity and Kaiser–Meyer–Olkin (KMO) tests were conducted to determine the correlation difference and sampling adequacy, with both measurements achieving the required levels. The PCA was applied with a normalization procedure and the coefficient factor loadings produced also expressed the correlation between the variables [<xref rid=\"B36-ijerph-17-05413\" ref-type=\"bibr\">36</xref>]. The factor loadings of the four factors with eigenvalues >1 were extracted from the eosinophil and neutrophil expression markers. A total of 39.0% of the variation for both Factor 1 and Factor 2 was observed in the expression of markers on eosinophils tested with indoor pollutants. Moderate factor loadings were identified for expression of CD11b and CD35, together with a strong factor loading for the concentrations of NO<sub>2</sub> and formaldehyde in Factor 1. In other words, the upregulation of CD11b and CD35 expression in the eosinophils were associated with exposure to NO<sub>2</sub> and formaldehyde; in turn, Factor 2 had moderate factor loadings of FeNO levels, PM<sub>10</sub>, and PM<sub>2.5</sub>. Moderate factor loadings were observed for FeNO levels, CD66b, and PM<sub>10</sub> in Factor 3, with a total variation of 14.9%. High factor loading of CD63 was observed in Factor 4, with a total variation of 13.0%. For neutrophils, a strong factor loading of CD66b expression together with moderate factor loadings for FeNO levels, CD11b, and CD63 were recorded in Factor 1, with a variation of 24.2%. Factor 2 showed a strong factor loading for the concentration of formaldehyde together with a moderate factor loading for the concentrations of CO<sub>2</sub>, PM<sub>10</sub>, and PM<sub>2.5</sub>, with 19.3% of the total variation. There were moderate loading factors for expression of CD35 and the concentrations of NO<sub>2</sub> and PM<sub>10</sub> in Factor 3, with 14.3% of the total variation. Factor 4 showed moderate factor loadings of CD11b expression and concentration of PM<sub>2.5</sub>, with 13.2% of the total variation (<xref rid=\"ijerph-17-05413-t003\" ref-type=\"table\">Table 3</xref>).</p><p>We further analysed the biomarkers and indoor pollutants to categorize them based on their homogeneity levels using agglomerative hierarchical clustering (AHC). Our data showed the cluster of markers and indoor pollutants from the PCA and AHC analyses were relatively identical. Three clusters were generated for eosinophils, which consisted of CD66b, FeNO levels, and concentrations of CO<sub>2</sub> in Cluster 1, with 98.4% of variance within-class; whereas Cluster 2 presented 56.5% of the variation within-class for CD11b expression and concentrations of NO<sub>2</sub> and formaldehyde. High (82.9%) variance within-class was observed for CD35 and CD63 expression and concentrations of PM<sub>10</sub> and PM<sub>2.5</sub> in Cluster 3; three clusters were also generated for neutrophils, which consisted of CD35, CD63, CD66b, and concentrations of CO<sub>2</sub>, PM<sub>10</sub>, and PM<sub>2.5</sub>, with 95.7% of the variation within-class for Cluster 1. The concentrations of NO<sub>2</sub> and formaldehyde were grouped in Cluster 2 with 26.0% of the variation within-class. Cluster 3 showed 56.7% of the variation within-class for CD11b expression and FeNO levels. The clusters generated through this process confirmed the contributing factors in the PCA earlier. This showed that exposure to CO<sub>2</sub>, PM<sub>10</sub>, and PM<sub>2.5</sub> have resulted in degranulation of both eosinophils and neutrophils, with upregulation of the markers for tertiary (CD11b), specific (CD66b), and azurophilic/crystalloid (CD63). In this study, exposure to NO<sub>2</sub> and formaldehyde also triggered the activation of tertiary eosinophil surface markers (<xref ref-type=\"fig\" rid=\"ijerph-17-05413-f003\">Figure 3</xref>A).</p></sec><sec id=\"sec3dot4-ijerph-17-05413\"><title>3.4. Binary Logistic Regression (LR)</title><p>Finally, models were built to predict the toxicodynamic effects of indoor pollutants towards marker expression on the eosinophil and neutrophil in the sputum samples among doctor diagnosed asthmatic children. For this objective, the researchers next used the binary logistic regression to model the prediction with the potential confounders of gender, atopy, parental asthma/allergy status, and area of schools. Cluster 1 showed an overall accuracy of 76.0% in predicting asthmatic children by using CD66b expression markers on the eosinophil and FeNO levels in relation to CO<sub>2</sub> exposure. Meanwhile, the model generated for Cluster 2 showed a 76.0% accuracy and 68.0% sensitivity in predicting asthmatic children by using the upregulation of CD11b expression on eosinophils in relation to the NO<sub>2</sub> exposure. In the model generated for Cluster 3, the upregulation of CD63 expression on eosinophils and PM<sub>2.5</sub> concentration was significantly associated (<italic>p</italic> < 0.05), with a 71.9% accuracy and 60.0% sensitivity. Similarly, the upregulation of the neutrophil expression markers, CD63 and CD66b, was significant (<italic>p</italic> < 0.05), and could be predicted from the PM<sub>2.5</sub> exposure with a 72.9% accuracy and 72.0% sensitivity. Overall, children with a status of atopy, parental asthma/allergy, and from urban school were more likely to develop asthma (<italic>p</italic> < 0.05) (<xref rid=\"ijerph-17-05413-t004\" ref-type=\"table\">Table 4</xref> and <xref ref-type=\"fig\" rid=\"ijerph-17-05413-f004\">Figure 4</xref>).</p></sec></sec><sec sec-type=\"discussion\" id=\"sec4-ijerph-17-05413\"><title>4. Discussion</title><p>The role of biomarkers in airways is complex and specific, which is helpful in evaluating the aetiology, characterisation of phenotyping, and treatment of allergy and lung inflammation [<xref rid=\"B37-ijerph-17-05413\" ref-type=\"bibr\">37</xref>]. In this study, the FeNO levels were significantly higher among asthmatic school children, which are similar with the studies conducted in China [<xref rid=\"B38-ijerph-17-05413\" ref-type=\"bibr\">38</xref>], Terengganu, Malaysia [<xref rid=\"B39-ijerph-17-05413\" ref-type=\"bibr\">39</xref>], and Penang, Malaysia [<xref rid=\"B40-ijerph-17-05413\" ref-type=\"bibr\">40</xref>]. The result showed that there was inflammation in the airways and the average value was above the threshold of 50 ppb, which could reflect a high degree of inflammation. Liu et al. [<xref rid=\"B41-ijerph-17-05413\" ref-type=\"bibr\">41</xref>] and Carlsen et al. [<xref rid=\"B42-ijerph-17-05413\" ref-type=\"bibr\">42</xref>] reported that there was a significantly positive relationship between the FeNO levels and almost all pollutants, namely PM<sub>10</sub>, PM<sub>2.5</sub>, SO<sub>2</sub>, NO<sub>2</sub>, CO, and VOCs. This advocates a relationship between the high levels of all pollutants measured inside the classroom of urban schools and the high levels of FeNO among school children in this study. Some researchers estimated that FeNO is positively correlated up to five-fold and two-fold when exposed to NO<sub>2</sub> [<xref rid=\"B43-ijerph-17-05413\" ref-type=\"bibr\">43</xref>] and finer particles, such as PM<sub>2.5</sub> [<xref rid=\"B44-ijerph-17-05413\" ref-type=\"bibr\">44</xref>], respectively, which could be modulated by DNA methylation in the arginase–nitric oxide synthase pathway [<xref rid=\"B45-ijerph-17-05413\" ref-type=\"bibr\">45</xref>,<xref rid=\"B46-ijerph-17-05413\" ref-type=\"bibr\">46</xref>].</p><p>The CO<sub>2</sub> concentration in both school areas was below the recommended limit of 1000 ppm [<xref rid=\"B47-ijerph-17-05413\" ref-type=\"bibr\">47</xref>]. Similarly, the PM<sub>10</sub> and PM<sub>2.5</sub> concentrations were below the 24 h mean of the World Health Organisation (WHO) guideline (PM<sub>10</sub> = 50 µg/m<sup>3</sup>, PM<sub>2.5</sub> = 25 µg/m<sup>3</sup>), the National Ambient Air Quality Standard by USEPA (PM<sub>10</sub> = 150 µg/m<sup>3</sup>, PM<sub>2.5</sub> = 35 µg/m<sup>3</sup>), and the new Malaysian Ambient Air Quality Standard 2018 Interim Target-2 (PM<sub>10</sub> = 120 µg/m<sup>3</sup>, PM<sub>2.5</sub> = 50 µg/m<sup>3</sup>) [<xref rid=\"B48-ijerph-17-05413\" ref-type=\"bibr\">48</xref>]. A few classrooms recorded a concentration of PM<sub>2.5</sub> that exceeded the value of 25 µg/m<sup>3</sup>, especially in the urban areas (37.5%) compared to the suburban (12.5%) areas. The median level of NO<sub>2</sub> for the urban and suburban areas was also below the WHO guideline of 40 µg/m<sup>3</sup> (annual mean), with only 18.8% and 25.0% of the classrooms in the urban and suburban areas, respectively, exceeding the limit. Overall, the levels of indoor air pollutants were below the guideline limits. This was due to the sufficient natural ventilation system and a wider window design on both sides of the classroom, together with the adequately equipped ceiling fans. The classroom design has a well-balanced ventilation that suits the temperature of the equatorial region and is able to reduce the particles, NO<sub>2</sub>, and CO<sub>2</sub> concentrations [<xref rid=\"B49-ijerph-17-05413\" ref-type=\"bibr\">49</xref>]. Additionally, Silvestre et al. [<xref rid=\"B50-ijerph-17-05413\" ref-type=\"bibr\">50</xref>] reported that an opening of 56% of the classroom windows under natural ventilation conditions was able to keep the CO<sub>2</sub> concentration below 1000 ppm.</p><p>The schools, classrooms, and children were randomly selected from all secondary schools in the Hulu Langat area, Malaysia. Thus, we concluded that this study was not seriously influenced by selection bias. Moreover, Malaysia has a similar climate all year around; therefore, with the natural ventilation flowing through the windows in the classrooms, the indoor and outdoor levels of pollutants would be expected to be constant throughout the year. This is supported by several studies that have determined equal indoor to outdoor (I/O) ratios for PM<sub>10</sub>, PM<sub>2.5</sub>, NO<sub>2</sub>, CO, and VOCs measured in schools across Peninsular Malaysia [<xref rid=\"B51-ijerph-17-05413\" ref-type=\"bibr\">51</xref>,<xref rid=\"B52-ijerph-17-05413\" ref-type=\"bibr\">52</xref>,<xref rid=\"B53-ijerph-17-05413\" ref-type=\"bibr\">53</xref>]. Be that as it may, the cross-sectional study design utilized here preludes making conclusions on causality.</p><p>In contrast to earlier findings, the total percentage of eosinophil and neutrophil counts in the sputum sample was not statistically different between the doctor-diagnosed asthmatic children and healthy children groups. Previous studies reported that the percentage of eosinophils and neutrophils for asthmatic children was significantly different and in the range of 2.5–13.0% and 15–47%, respectively [<xref rid=\"B54-ijerph-17-05413\" ref-type=\"bibr\">54</xref>,<xref rid=\"B55-ijerph-17-05413\" ref-type=\"bibr\">55</xref>,<xref rid=\"B56-ijerph-17-05413\" ref-type=\"bibr\">56</xref>]. Meanwhile, for healthy subjects, the percentages were in the range of 0.5–4.0% and 24.1–37.0%, respectively [<xref rid=\"B57-ijerph-17-05413\" ref-type=\"bibr\">57</xref>,<xref rid=\"B58-ijerph-17-05413\" ref-type=\"bibr\">58</xref>].</p><p>There have been few recent studies on activation and degranulation marker expression in the sputum samples of asthmatic children. The finding of this study confirmed that the sputum granulocytes of the asthmatic children increased the expression of the classical activation markers, CD11 and CD63, in both eosinophil and neutrophil cells, as reported by Tak et al. [<xref rid=\"B28-ijerph-17-05413\" ref-type=\"bibr\">28</xref>]. The upregulation of these tertiary and azurophilic/crystalloid granules is associated with the circulating cytokines that occur sequentially in response to the stimulus [<xref rid=\"B59-ijerph-17-05413\" ref-type=\"bibr\">59</xref>]. The mitogen-activated protein kinase (MAPK) pathway is believed to be central to the degranulation process [<xref rid=\"B60-ijerph-17-05413\" ref-type=\"bibr\">60</xref>]. The present study failed to show the upregulation of CD35 expression on eosinophils. In accordance with the study by Berends et al. [<xref rid=\"B61-ijerph-17-05413\" ref-type=\"bibr\">61</xref>], the downregulation of CD35 in the sputum of asthmatic children could be partly explained by the absence of intracellular stores for CD35 on eosinophils and neutrophils. Another possible explanation for this discrepancy was that CD35 is highly expressed on blood eosinophils or circulating granulocytes and was only directly associated with antigen inhalation [<xref rid=\"B62-ijerph-17-05413\" ref-type=\"bibr\">62</xref>] or the lower threshold stimulus required for cell activation [<xref rid=\"B63-ijerph-17-05413\" ref-type=\"bibr\">63</xref>].</p><p>The CD66b (CEACAM8) is a single-chain GPI-anchored glycoprotein and was recognised as an exclusive degranulation marker for neutrophils [<xref rid=\"B64-ijerph-17-05413\" ref-type=\"bibr\">64</xref>]. CD66b is upregulated when neutrophils are activated. The researchers of this study observed that the expression of CD66b was slightly upregulated on the surface of eosinophils and neutrophils collected from the airways of asthmatic children as compared to healthy children. It represents a normal activation pattern of neutrophils in relation to the migration from the circulating blood in vessels [<xref rid=\"B65-ijerph-17-05413\" ref-type=\"bibr\">65</xref>]. The late-phase response of the neutrophils could also possibly increase the CD11b, CD11b/18, CD35, CD64, and CD66b expressions [<xref rid=\"B66-ijerph-17-05413\" ref-type=\"bibr\">66</xref>]. The researchers found one study that identified that CD11b, CD16, and CD66b were consistently expressed on the neutrophils surface and were independent of their location and level of activation [<xref rid=\"B67-ijerph-17-05413\" ref-type=\"bibr\">67</xref>]. This could be contributed by the increased levels of intracellular cyclic GMP that yielded upregulation in the CD63 and CD66b expression on neutrophils [<xref rid=\"B68-ijerph-17-05413\" ref-type=\"bibr\">68</xref>].</p><p>PCA and AHC are very helpful approaches for dimensionality reduction in proteomics data. In this current study, these analytical approaches depicted similar group factors of biomarkers and air pollutants. The final regression analysis generated relatively moderate prediction models. The researchers noted that the upregulation of CD63 expression on both leukocytes and CD66b expression on neutrophils was related to particle exposure. The likely risks were observed among children under atopic and parental asthmatic/allergic conditions and children from schools located in urban areas. This finding reinforces the previous in vitro study conducted by Jin et al. [<xref rid=\"B69-ijerph-17-05413\" ref-type=\"bibr\">69</xref>]. They suggested that particulate allergens potentiated the mast cells to modulate the recruitment of eosinophils in the airways by internalising the particulate allergens into the CD63<sup>+</sup> intracellular compartments through an endolytic pathway. Another in vitro study reported that CD66b only activated the neutrophils in the peptidoglycan challenge but did not upregulate the surface activation of eosinophils [<xref rid=\"B70-ijerph-17-05413\" ref-type=\"bibr\">70</xref>]. This is the possible explanation of why CD66b expression is clustered and associated with PM<sub>10</sub> and PM<sub>2.5</sub> in neutrophils but not with eosinophils in the AHC and regression analyses. Likewise, the study conducted by Banerje et al. [<xref rid=\"B71-ijerph-17-05413\" ref-type=\"bibr\">71</xref>] using flow cytometry analysis reported that there were increased CD35, CD16, and CD11b/CD18 expression on circulating neutrophils and a high percentage of eosinophils in the sputum of adults who have been exposed to PM<sub>10</sub> and PM<sub>2.5</sub>.</p><p>To the researchers’ knowledge, this study was the first study that explored the interrelation of CD11b, CD63, CD35, and CD66b marker expression on eosinophils and neutrophils with different parameters of air pollutants. This study revealed that CD11b was not clustered together with PM<sub>10</sub> and PM<sub>2.5</sub>. This result was coherent with the study conducted by Ishii et al. [<xref rid=\"B72-ijerph-17-05413\" ref-type=\"bibr\">72</xref>] using immunocytochemistry, which showed that the expression of CD11b on alveolar macrophages was unaffected after two hours of stimulation with PM<sub>10</sub>. They suggested that the adhesive interaction between CD11/CD18 on alveolar macrophages with CD54 on the bronchial epithelial cells contributed to the amplification of cytokine production from the alveolar macrophages. The chemometrics analysis in this current study was also clustered and showed a significant relationship between the CD11b expression on eosinophils with NO<sub>2</sub>, especially among asthmatic children under atopic and parental asthmatic/allergic conditions. This finding was consistent with the review article by Hiraiwa and Eeden [<xref rid=\"B73-ijerph-17-05413\" ref-type=\"bibr\">73</xref>] and an in vitro study reported by Hodgkins et al. [<xref rid=\"B13-ijerph-17-05413\" ref-type=\"bibr\">13</xref>]. They found that dendritic cells expressed an upregulation of CD11b at 48 h during NO<sub>2</sub>-promoted allergic sensitisation. A study has shown that CD11b is directly involved in cellular adhesion, which is expressed in many leukocytes, including neutrophil, monocytes, natural killer cells, and macrophages. The migration of these leukocytes to the inflammation site will only take place if the CD18 subunit is present [<xref rid=\"B74-ijerph-17-05413\" ref-type=\"bibr\">74</xref>]. The other possible roles of CD11b were reported by Medoff et al. [<xref rid=\"B75-ijerph-17-05413\" ref-type=\"bibr\">75</xref>] in their experiment in which the CD11b<sup>+</sup> had critical roles in mediating the Th2 cell and eosinophil recruitment in the airways via STAT6-dependent chemokine production.</p><p>This study also showed that the FeNO levels were positively correlated with the expression of the activation (CD66b, CD11b) and degranulation (CD66b, CD11b) markers for both leukocytes. This result is consistent with the results in the previous studies conducted by Guo et al. [<xref rid=\"B76-ijerph-17-05413\" ref-type=\"bibr\">76</xref>] and Kobayashi et al. [<xref rid=\"B77-ijerph-17-05413\" ref-type=\"bibr\">77</xref>], who also indicated that FeNO levels were reflected by eosinophilic airway inflammation [<xref rid=\"B78-ijerph-17-05413\" ref-type=\"bibr\">78</xref>]. In fact, the activated neutrophils can recruit the Th17/IL-17 and Th1 cells via chemokine release [<xref rid=\"B79-ijerph-17-05413\" ref-type=\"bibr\">79</xref>] and cause neutrophil infiltration within the airways [<xref rid=\"B80-ijerph-17-05413\" ref-type=\"bibr\">80</xref>]. This finding also reinforces that eosinophils and eosinophils are the binary indicators for the phenotyping of asthma. It was found in previous studies that exposure to PM<sub>10</sub> was significantly associated with the increased levels of FeNO in healthy children tested on robust multi-pollutant models [<xref rid=\"B41-ijerph-17-05413\" ref-type=\"bibr\">41</xref>,<xref rid=\"B81-ijerph-17-05413\" ref-type=\"bibr\">81</xref>,<xref rid=\"B82-ijerph-17-05413\" ref-type=\"bibr\">82</xref>,<xref rid=\"B83-ijerph-17-05413\" ref-type=\"bibr\">83</xref>]. In line with this report, the chemometric analysis results in this study provided further evidence on the positive effects of PM<sub>10</sub> on bronchial inflammation and resulted in the increase of FeNO levels among children.</p><p>The cluster approach used in this study, which is aimed at improving the interpretability of the data, interestingly revealed that the formaldehyde and NO<sub>2</sub> concentrations were in the same factor, with a total variance of 26.0%. This finding confirmed that the formation of formaldehyde was through the photochemical reactivity of NO<sub>2</sub> in the air with VOCs generating different aldehydes [<xref rid=\"B84-ijerph-17-05413\" ref-type=\"bibr\">84</xref>]. Formaldehyde also originates from furniture made out of wood and plastics, plywood, textile, table laminate, and consumer products, which is commonly available in the classroom [<xref rid=\"B85-ijerph-17-05413\" ref-type=\"bibr\">85</xref>]. As reported by Hua [<xref rid=\"B86-ijerph-17-05413\" ref-type=\"bibr\">86</xref>], NO<sub>2</sub> potentially originates from the process of fossil-fuel combustion, biomass burning, and agricultural activities. Hereafter, all the schools in this current study are located very close to the main road and industrial area, which was considered the probable sources of NO<sub>2</sub> in the classrooms. The researchers found that PM<sub>10</sub> and PM<sub>2.5</sub> were grouped together in the dimensionality analysis of the PCA and AHC, and this indicates that both particles originated from the same source. The primary sources of particles in the urban and suburban areas were industrial emissions, transportation, and traffic emissions [<xref rid=\"B87-ijerph-17-05413\" ref-type=\"bibr\">87</xref>,<xref rid=\"B88-ijerph-17-05413\" ref-type=\"bibr\">88</xref>]. It was possible that the indoor particles also originated from the occupant’s activities or re-suspension of deposited particles, soil materials from the school children’s shoes, skin flakes, furniture fragments, and less frequent cleaning [<xref rid=\"B89-ijerph-17-05413\" ref-type=\"bibr\">89</xref>]. Children are at risk of day-long exposure to the same indoor pollutants, not only at school, but also at home—which was reflected in the time spent during non-school days. This possibility merits further investigation.</p></sec><sec sec-type=\"conclusions\" id=\"sec5-ijerph-17-05413\"><title>5. Conclusions</title><p>In conclusion, the chemometric analysis methods produced robust and immunologically meaningful results, which clustered the degranulation markers (CD11b and CD63) expressed on eosinophils with the concentration of NO<sub>2</sub> and PM<sub>2.5</sub>. Besides that, the degranulation markers expressed on the neutrophils, CD63 and CD66b, were clustered together with the PM<sub>2.5</sub> concentration. A further prospective study is now obligatory to validate the models generated from this current study.</p></sec></body><back><ack><title>Acknowledgments</title><p>The researchers are grateful to all students who had participated in this study and the great support and hospitality from the school teachers are much appreciated.</p></ack><app-group><app id=\"app1-ijerph-17-05413\"><title>Supplementary Materials</title><p>The following are available online at <uri xlink:href=\"https://www.mdpi.com/1660-4601/17/15/5413/s1\">https://www.mdpi.com/1660-4601/17/15/5413/s1</uri>, Table S1: The results of allergy skin test.</p><supplementary-material content-type=\"local-data\" id=\"ijerph-17-05413-s001\"><media xlink:href=\"ijerph-17-05413-s001.pdf\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material></app></app-group><notes><title>Author Contributions</title><p>Conceptualization, K.N.M.I., Z.H. and J.H.H.; methodology, K.N.M.I., Z.H., J.J., J.H.H. and L.T.L.T.; software, K.N.M.I.; validation, K.N.M.I., Z.H., J.J. and L.T.L.T.; formal analysis, K.N.M.I.; investigation, K.N.M.I. and Z.H.; resources, Z.H. and J.H.H.; data curation, K.N.M.I. and Z.H.; writing—original draft preparation, K.N.M.I.; writing—review and editing, Z.H. and J.J.; visualization, K.N.M.I.; supervision, Z.H.; project administration, K.N.M.I and Z.H.; funding acquisition, Z.H. All authors have read and agreed to the published version of the manuscript.</p></notes><notes><title>Funding</title><p>This study was funded by High Impact Putra Grant from Universiti Putra Malaysia (UPM) (VOT 9598000).</p></notes><notes notes-type=\"COI-statement\"><title>Conflicts of Interest</title><p>The authors declare no conflict of interest.</p></notes><ref-list><title>References</title><ref id=\"B1-ijerph-17-05413\"><label>1.</label><element-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Naja</surname><given-names>A.S.</given-names></name><name><surname>Permaul</surname><given-names>P.</given-names></name><name><surname>Phipatanakul</surname><given-names>W.</given-names></name></person-group><article-title>Taming Asthma in School-Aged Children: A Comprehensive Review</article-title><source>J. Allergy Clin. Immunol. 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(<bold>A</bold>) A forward-/side-scatter (FSC/SSC) gate to identify the granulocytes. (<bold>B</bold>) The activated granulocytes were based on CD11b+. (<bold>C</bold>) CD41a was gated with SSC to confirm that the activated granulocytes were neutrophils and eosinophils. (<bold>D</bold>) Subsequently, the eosinophils (blue) and neutrophils (green) were separated based on the expression of CD16− and CD16+ on SSC, respectively. The activation markers of CD11b, CD35, CD63, and CD66b for eosinophils (<bold>E</bold>,<bold>F</bold>) and neutrophils (<bold>G</bold>,<bold>H</bold>) were based on the negative gates of the isotype control for all antibodies.</p></caption><graphic xlink:href=\"ijerph-17-05413-g001\"/></fig><fig id=\"ijerph-17-05413-f002\" orientation=\"portrait\" position=\"float\"><label>Figure 2</label><caption><p>Expression profile of sputum granulocytes compared between the doctor-diagnosed asthmatic and healthy school children. Expression of degranulation and activation markers on eosinophil (<bold>A</bold>,<bold>B</bold>) and neutrophil (<bold>C</bold>,<bold>D</bold>). The expression of CD11b as the degranulation and classical activation marker is displayed twice in this graph. * <italic>p</italic> < 0.05, ** <italic>p</italic> < 0.001.</p></caption><graphic xlink:href=\"ijerph-17-05413-g002\"/></fig><fig id=\"ijerph-17-05413-f003\" orientation=\"portrait\" position=\"float\"><label>Figure 3</label><caption><p>Agglomerative hierarchical clustering (AHC) analysis using the Ward linkage method and using Euclidean distances to generate the clustering of degranulation and the activation of markers and environmental pollutants measured inside classrooms for (<bold>A</bold>) eosinophils and (<bold>B</bold>) neutrophils. The dotted line represents the pruning level to generate distinct clusters. § Activation marker for eosinophils; # Activation marker for neutrophils.</p></caption><graphic xlink:href=\"ijerph-17-05413-g003\"/></fig><fig id=\"ijerph-17-05413-f004\" orientation=\"portrait\" position=\"float\"><label>Figure 4</label><caption><p>Receiver operating curve (ROC) for the models predicting an asthmatic or healthy child classification based on the clusters generated in the AHC analysis. The corresponding predictors for each curve are presented in <xref rid=\"ijerph-17-05413-t004\" ref-type=\"table\">Table 4</xref>. For eosinophils: (<bold>A</bold>) ROC for Cluster 1, (<bold>B</bold>) Cluster 2, and (<bold>C</bold>) for Cluster 3. For neutrophils: (<bold>D</bold>) ROC for Cluster 1 and (<bold>E</bold>) for Cluster 3.</p></caption><graphic xlink:href=\"ijerph-17-05413-g004a\"/><graphic xlink:href=\"ijerph-17-05413-g004b\"/></fig><table-wrap id=\"ijerph-17-05413-t001\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05413-t001_Table 1</object-id><label>Table 1</label><caption><p>Students’ characteristic and inflammation status for the doctor-diagnosed asthma and control group.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin\" rowspan=\"1\" colspan=\"1\">Characteristics</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin\" rowspan=\"1\" colspan=\"1\">Doctor Diagnosed Asthma<break/>(<italic>n</italic> = 50)</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin\" rowspan=\"1\" colspan=\"1\">Healthy<break/>(<italic>n</italic> = 46)</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin\" rowspan=\"1\" colspan=\"1\"><italic>p</italic>-Value</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" style=\"border-top:solid thin\" rowspan=\"1\" colspan=\"1\">Female (%)</td><td align=\"center\" valign=\"middle\" style=\"border-top:solid thin\" rowspan=\"1\" colspan=\"1\">44.2</td><td align=\"center\" valign=\"middle\" style=\"border-top:solid thin\" rowspan=\"1\" colspan=\"1\">55.8</td><td align=\"center\" valign=\"middle\" style=\"border-top:solid thin\" rowspan=\"1\" colspan=\"1\">0.094</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Male (%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">61.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">38.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Atopic (%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">65.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">35.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.001 **</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Non atopic (%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">30.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">69.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Parental with asthma/allergy (%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">65.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">34.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.020 *</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Parental without asthma/allergy (%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">41.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">58.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">School area—urban (%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">60.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">39.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.102</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">School area—suburban (%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">43.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">56.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Clinical characteristics</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">FeNO levels (ppb) </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">56 (66.5)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">23 (32.3)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.002 *</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Eosinophil count (%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">11.6 (11.3)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10.2 (9.2)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.259</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Neutrophil count (%)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">11.6 (5.0)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">13.4 (14.0)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.130</td></tr></tbody></table><table-wrap-foot><fn><p>Values are the median (IQR) for clinical characteristics. IQR = Interquartile range. * <italic>p</italic> < 0.05; ** <italic>p</italic> < 0.001.</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"ijerph-17-05413-t002\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05413-t002_Table 2</object-id><label>Table 2</label><caption><p>Comparison of the environmental parameters between schools located in urban and suburban areas.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">Parameter</th><th colspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Urban<break/><italic>n</italic> = 16</th><th colspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Suburban<break/><italic>n</italic> = 16</th><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\"><italic>p</italic>-Value</th></tr><tr><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Median (IQR)</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Min</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Max</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Median (IQR)</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Min</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Max</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Temperature (°C)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">29.0 (2.0)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">28.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">32.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">27.5 (1.0)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">27.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">28.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.001 **</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Relative Humidity (%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">74.7 (9.5)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">63.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">88.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">81.4 (7.5)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">74.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">88.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.001 **</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Formaldehyde (mg/m<sup>3</sup>)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13.2 (9.3)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">19.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.1 (5.2)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">15.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.001 **</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CO<sub>2</sub> (ppm)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">453.0 (34.5)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">417.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">468.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">455.5 (25.5)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">402.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">471.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.462</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">NO<sub>2</sub> (µg/m<sup>3</sup>)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">32.0 (7.0)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">15.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">45.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">19.0 (22.5)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">16.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">48.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.001 **</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">PM<sub>2.5</sub> (µg/m<sup>3</sup>)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">24.6 (2.4)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">17.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">26.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">22.0 (1.9)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">16.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">26.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.001 **</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">PM<sub>10</sub> (µg/m<sup>3</sup>)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">41.6 (7.5)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">34.7</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">48.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">37.0 (4.9)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">32.3</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">44.9</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><0.001 **</td></tr></tbody></table><table-wrap-foot><fn><p><italic>n</italic> = 32; IQR = Interquartile range, Min = Minimum, Max = Maximum. ** <italic>p</italic> < 0.001.</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"ijerph-17-05413-t003\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05413-t003_Table 3</object-id><label>Table 3</label><caption><p>Factor loadings using PCA for eosinophils and eosinophils. The moderate (0.5–0.75) and strong (>0.75) factor loadings are highlighted in bold.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">Biomarkers and Indoor Pollutants</th><th colspan=\"4\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Eosinophils</th><th colspan=\"4\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Neutrophils</th></tr><tr><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">F1</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">F2</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">F3</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">F4</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">F1</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">F2</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">F3</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">F4</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">FeNO levels</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.194</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>−0.575</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>0.501</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.235</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>0.653</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.306</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.108</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.120</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CD11b</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>0.528</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.083</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.398</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.059</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>0.571</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.053</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.175</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>0.685</bold>\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CD35</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>0.559</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.129</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.432</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.348</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.337</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.213</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>0.612</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.445</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CD63</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.193</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.379</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.070</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>0.732</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>−0.665</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.394</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.117</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.288</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CD66b</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.285</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.465</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>0.561</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.419</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>0.727</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.067</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.235</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.039</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CO<sub>2</sub></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.346</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.329</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.456</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.084</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.287</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>−0.506</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.123</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.360</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">NO<sub>2</sub></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>0.771</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.116</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.118</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.375</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.470</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.301</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>0.657</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.412</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">PM<sub>10</sub></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.102</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>0.582</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>0.584</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.035</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.074</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>0.684</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>−0.523</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.047</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">PM<sub>2.5</sub></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.053</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>0.699</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.061</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.490</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.409</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>0.503</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.099</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>0.502</bold>\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Formaldehyde</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>0.788</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.380</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.100</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.123</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.322</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>0.746</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.467</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.089</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Eigenvalue</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.10</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.81</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.49</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.30</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.42</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.93</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.43</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.32</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Variability (%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">21.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">18.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">14.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">24.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">19.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">14.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13.2</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Cumulative %</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">21.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">39.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">54.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">67.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">24.2</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">43.4</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">57.8</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">71.0</td></tr></tbody></table></table-wrap><table-wrap id=\"ijerph-17-05413-t004\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05413-t004_Table 4</object-id><label>Table 4</label><caption><p>Summary of the binary logistic regression models based on the clusters generated in the AHC analysis.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Variable</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Β</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">SE</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><italic>p</italic>-Value</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">R<sup>2</sup></th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold><italic>Eosinophils</italic></bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Cluster 1</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Constant</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.860</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.409</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.035 *</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.494</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">FeNO Levels</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.615</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.222</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.006 *</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CD66b</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.850</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.250</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.001 **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CO<sub>2</sub></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.457</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.191</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.016 *</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Atopy</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.893</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.377</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.018 *</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Parental Asthma/Allergy</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.643</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.319</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.044 *</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Area—Urban</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.747</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.354</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.035 *</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Cluster 2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Constant</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.109</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.536</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.840</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.504</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CD11b</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.454</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.260</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.018 *</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">NO<sub>2</sub></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.305</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.375</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.002 *</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Formaldehyde</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.684</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.440</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.120</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Atopy</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.993</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.378</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.009 *</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Parental Asthma/Allergy</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.916</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.424</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.031 *</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Cluster 3</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Constant</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−1.387</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.462</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.003 *</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.377</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CD35</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.097</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.189</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.608</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CD63</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.080</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.166</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.042 *</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">PM<sub>10</sub></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.379</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.212</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.074</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">PM<sub>2.5</sub></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.378</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.181</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.037 *</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Atopy</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.909</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.377</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.015 *</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Parental Asthma/Allergy</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.842</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.350</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.016 *</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold><italic>Neutrophils</italic></bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Cluster 1</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Constant</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−1.039</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.490</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.034 *</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.510</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CD35</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.054</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.169</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.751</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CD63</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.632</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.256</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.014 *</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CD66b</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−1.794</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.858</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.036 *</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CO<sub>2</sub></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.230</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.200</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.250</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">PM<sub>10</sub></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.253</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.218</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.244</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">PM<sub>2.5</sub></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.582</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.234</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.013 *</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Atopy</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.134</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.439</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.010 *</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Parental Asthma/Allergy</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.944</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.404</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.021 *</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Area—Urban</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.348</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.514</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.009 *</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Cluster 3</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Constant</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.876</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.432</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.042 *</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.298</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">FeNO Levels</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.063</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.171</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.712</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">CD11b</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.390</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.197</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.047 *</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td></tr></tbody></table><table-wrap-foot><fn><p>* <italic>p</italic> < 0.05, ** <italic>p</italic> < 0.001.</p></fn></table-wrap-foot></table-wrap></floats-group></article>\n"
]
[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"brief-report\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Int J Environ Res Public Health</journal-id><journal-id journal-id-type=\"iso-abbrev\">Int J Environ Res Public Health</journal-id><journal-id journal-id-type=\"publisher-id\">ijerph</journal-id><journal-title-group><journal-title>International Journal of Environmental Research and Public Health</journal-title></journal-title-group><issn pub-type=\"ppub\">1661-7827</issn><issn pub-type=\"epub\">1660-4601</issn><publisher><publisher-name>MDPI</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32756475</article-id><article-id pub-id-type=\"pmc\">PMC7432089</article-id><article-id pub-id-type=\"doi\">10.3390/ijerph17155598</article-id><article-id pub-id-type=\"publisher-id\">ijerph-17-05598</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Project Report</subject></subj-group></article-categories><title-group><article-title>Study Protocol for an Online Questionnaire Survey on Symptoms/Signs, Protective Measures, Level of Awareness and Perception Regarding COVID-19 Outbreak among Dentists. A Global Survey</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0002-8573-485X</contrib-id><name><surname>Campus</surname><given-names>Guglielmo</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05598\">1</xref><xref ref-type=\"aff\" rid=\"af2-ijerph-17-05598\">2</xref><xref rid=\"c1-ijerph-17-05598\" ref-type=\"corresp\">*</xref></contrib><contrib contrib-type=\"author\"><name><surname>Diaz-Betancourt</surname><given-names>Marcela</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05598\">1</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0002-2704-0585</contrib-id><name><surname>Cagetti</surname><given-names>Maria Grazia</given-names></name><xref ref-type=\"aff\" rid=\"af3-ijerph-17-05598\">3</xref></contrib><contrib contrib-type=\"author\"><name><surname>Carvalho</surname><given-names>Joana C.</given-names></name><xref ref-type=\"aff\" rid=\"af4-ijerph-17-05598\">4</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0002-2435-1169</contrib-id><name><surname>Carvalho</surname><given-names>Thiago S.</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05598\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Cortés-Martinicorena</surname><given-names>Javier F.</given-names></name><xref ref-type=\"aff\" rid=\"af5-ijerph-17-05598\">5</xref><xref ref-type=\"author-notes\" rid=\"fn1-ijerph-17-05598\">†</xref></contrib><contrib contrib-type=\"author\"><name><surname>Deschner</surname><given-names>James</given-names></name><xref ref-type=\"aff\" rid=\"af6-ijerph-17-05598\">6</xref></contrib><contrib contrib-type=\"author\"><name><surname>Douglas</surname><given-names>Gail V. A.</given-names></name><xref ref-type=\"aff\" rid=\"af7-ijerph-17-05598\">7</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0003-3362-5173</contrib-id><name><surname>Giacaman</surname><given-names>Rodrigo A.</given-names></name><xref ref-type=\"aff\" rid=\"af8-ijerph-17-05598\">8</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0003-4417-3221</contrib-id><name><surname>Machiulskiene</surname><given-names>Vita</given-names></name><xref ref-type=\"aff\" rid=\"af9-ijerph-17-05598\">9</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0002-4570-0620</contrib-id><name><surname>Manton</surname><given-names>David J.</given-names></name><xref ref-type=\"aff\" rid=\"af10-ijerph-17-05598\">10</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0002-0048-2068</contrib-id><name><surname>Raggio</surname><given-names>Daniela P.</given-names></name><xref ref-type=\"aff\" rid=\"af11-ijerph-17-05598\">11</xref></contrib><contrib contrib-type=\"author\"><name><surname>Ramos-Gomez</surname><given-names>Francisco</given-names></name><xref ref-type=\"aff\" rid=\"af12-ijerph-17-05598\">12</xref></contrib><contrib contrib-type=\"author\"><name><surname>Sava-Rosianu</surname><given-names>Ruxandra</given-names></name><xref ref-type=\"aff\" rid=\"af13-ijerph-17-05598\">13</xref></contrib><contrib contrib-type=\"author\"><name><surname>Morozova</surname><given-names>Natalia S.</given-names></name><xref ref-type=\"aff\" rid=\"af14-ijerph-17-05598\">14</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0003-3769-9786</contrib-id><name><surname>Spagnuolo</surname><given-names>Gianrico</given-names></name><xref ref-type=\"aff\" rid=\"af14-ijerph-17-05598\">14</xref><xref ref-type=\"aff\" rid=\"af15-ijerph-17-05598\">15</xref></contrib><contrib contrib-type=\"author\"><name><surname>Vukovic</surname><given-names>Ana</given-names></name><xref ref-type=\"aff\" rid=\"af16-ijerph-17-05598\">16</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0002-4044-1893</contrib-id><name><surname>Wolf</surname><given-names>Thomas G.</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05598\">1</xref><xref ref-type=\"aff\" rid=\"af6-ijerph-17-05598\">6</xref></contrib><contrib contrib-type=\"author\"><collab>on behalf of the COVIDental Collaboration Group</collab><xref ref-type=\"author-notes\" rid=\"fn2-ijerph-17-05598\">‡</xref></contrib></contrib-group><aff id=\"af1-ijerph-17-05598\"><label>1</label>Department of Restorative, Preventive and Paediatric Dentistry, University of Bern, Freiburgstrasse 7, 3012 Bern, Switzerland; <email>[email protected]</email> (M.D.-B.); <email>[email protected]</email> (T.S.C.); <email>[email protected]</email> (T.G.W.)</aff><aff id=\"af2-ijerph-17-05598\"><label>2</label>Department of Surgery, Microsurgery and Medicine Sciences, School of Dentistry, University of Sassari, Viale San Pietro, 07100 Sassari, Italy</aff><aff id=\"af3-ijerph-17-05598\"><label>3</label>Department of Biomedical, Surgical and Dental Science, University of Milan, Via Beldiletto 1, 20142 Milan, Italy; <email>[email protected]</email></aff><aff id=\"af4-ijerph-17-05598\"><label>4</label>Faculty of Medicine and Dentistry, UCLouvain, 1200 Brussels, Belgium; <email>[email protected]</email></aff><aff id=\"af5-ijerph-17-05598\"><label>5</label>Private Practice, 31001 Pamplona, Spain; <email>[email protected]</email></aff><aff id=\"af6-ijerph-17-05598\"><label>6</label>Department of Periodontology and Operative Dentistry, University Medical Center of the Johannes Gutenberg University Mainz, 55116 Mainz, Germany; <email>[email protected]</email></aff><aff id=\"af7-ijerph-17-05598\"><label>7</label>Department of Dental Public Health, School of Dentistry, University of Leeds, Leeds LS2 9JT, UK; <email>[email protected]</email></aff><aff id=\"af8-ijerph-17-05598\"><label>8</label>Cariology and Gerodontology Units, Department of Oral Rehabilitation, Faculty of Health Sciences, University of Talca, 3460000 Talca, Chile; <email>[email protected]</email></aff><aff id=\"af9-ijerph-17-05598\"><label>9</label>Clinic of Dental and Oral Pathology, Faculty of Odontology, Lithuanian University of Health Sciences, 44131 Kaunas, Lithuania; <email>[email protected]</email></aff><aff id=\"af10-ijerph-17-05598\"><label>10</label>Paediatric Dentistry, Centrum voor Tandheelkunde en Mondzorgkunde, UMCG, University of Groningen, 9700-9747 Groningen, The Netherlands; <email>[email protected]</email></aff><aff id=\"af11-ijerph-17-05598\"><label>11</label>Department of Orthodontics and Pediatric Dentistry, School of Dentistry, University of Sao Paulo, 05508-060 Sao Paulo, Brazil; <email>[email protected]</email></aff><aff id=\"af12-ijerph-17-05598\"><label>12</label>UCLA Center for Children’s Oral Health (UCCOH) UCLA School of Dentistry, Los Angeles, CA 90095-1668, USA; <email>[email protected]</email></aff><aff id=\"af13-ijerph-17-05598\"><label>13</label>Department of Preventive, Community Dentistry and Oral Health, Faculty of Dentistry, University of Medicine and Pharmacy “Victor Babes” Timisoara, 300363 Timisoara, Romania; <email>[email protected]</email></aff><aff id=\"af14-ijerph-17-05598\"><label>14</label>Institute of Dentistry, Sechenov University, 3200900 Moscow, Russia; <email>[email protected]</email> (N.S.M.); <email>[email protected]</email> (G.S.)</aff><aff id=\"af15-ijerph-17-05598\"><label>15</label>Department of Neurosciences, Reproductive and Odontostomatological Sciences, University of Naples “Federico II”, Via Pansini 5, 80131 Naples, Italy</aff><aff id=\"af16-ijerph-17-05598\"><label>16</label>Department of Pediatric and Preventive Dentistry, School of Dental Medicine, University of Belgrade, 11000 Belgrade, Serbia; <email>[email protected]</email></aff><author-notes><corresp id=\"c1-ijerph-17-05598\"><label>*</label>Correspondence: <email>[email protected]</email></corresp><fn id=\"fn1-ijerph-17-05598\"><label>†</label><p>Previous address: Preventive and Community Dentistry Unit, University of Barcelona, 08193 Barcelona, Spain.</p></fn><fn id=\"fn2-ijerph-17-05598\"><label>‡</label><p>Membership of the COVIDental Collaboration Group is provided in the Acknowledgments.</p></fn></author-notes><pub-date pub-type=\"epub\"><day>03</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"ppub\"><month>8</month><year>2020</year></pub-date><volume>17</volume><issue>15</issue><elocation-id>5598</elocation-id><history><date date-type=\"received\"><day>22</day><month>6</month><year>2020</year></date><date date-type=\"accepted\"><day>30</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>© 2020 by the authors.</copyright-statement><copyright-year>2020</copyright-year><license license-type=\"open-access\"><license-p>Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>).</license-p></license></permissions><abstract><p>The Centres for Disease Control and Prevention and the World Health Organization have developed preparedness and prevention checklists for healthcare professionals regarding the containment of COVID-19. The aim of the present protocol is to evaluate the impact of the COVID-19 outbreak among dentists in different countries where various prevalence of the epidemic has been reported. Several research groups around the world were contacted by the central management team. The online anonymous survey will be conducted on a convenience sample of dentists working both in national health systems and in private or public clinics. In each country/area, a high (~5–20%) proportion of dentists working there will be invited to participate. The questionnaire, developed and standardized previously in Italy, has four domains: (1) personal data; (2) symptoms/signs relative to COVID-19; (3) working conditions and PPE (personal protective equipment) adopted after the infection’s outbreak; (4) knowledge and self-perceived risk of infection. The methodology of this international survey will include translation, pilot testing, and semantic adjustment of the questionnaire. The data will be entered on an Excel spreadsheet and quality checked. Completely anonymous data analyses will be performed by the central management team. This survey will give an insight into the dental profession during COVID-19 pandemic globally.</p></abstract><kwd-group><kwd>COVID-19</kwd><kwd>infection</kwd><kwd>dentist</kwd><kwd>protective measures</kwd><kwd>awareness</kwd><kwd>infection control</kwd></kwd-group></article-meta></front><body><sec sec-type=\"intro\" id=\"sec1-ijerph-17-05598\"><title>1. Introduction</title><p>The novel coronavirus SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) pandemic has affected the world deeply. COVID-19, as the disease has become known, is the third coronavirus to emerge in the human population recently, preceded by the SARS-CoV (severe acute respiratory syndrome coronavirus) outbreak in 2002 and the MERS-CoV (Middle East respiratory syndrome coronavirus) outbreak in 2012. Organizations such as the Centers for Disease Control and Prevention (CDC) and the World Health Organization (WHO) have developed preparedness and prevention checklists regarding the containment of the spread of COVID-19, to be used by public and general healthcare professionals [<xref rid=\"B1-ijerph-17-05598\" ref-type=\"bibr\">1</xref>,<xref rid=\"B2-ijerph-17-05598\" ref-type=\"bibr\">2</xref>]. The SARS-CoV-2 human-to-human transmission is via respiratory and saliva droplets or direct contact with cases or with contaminated surfaces [<xref rid=\"B3-ijerph-17-05598\" ref-type=\"bibr\">3</xref>]. Airborne transmission of the virus might occur during medical procedures that generate aerosols, even if this transmission route is not yet fully clarified [<xref rid=\"B2-ijerph-17-05598\" ref-type=\"bibr\">2</xref>]. Avoiding close contact (less than 1 m) with people, especially those with positive tests and or respiratory symptoms, is one of the most important preventive measure to be taken to prevent the spread of the infection. Having in mind the worldwide spread of SARS-CoV-2 from China to all other parts of the world [<xref rid=\"B2-ijerph-17-05598\" ref-type=\"bibr\">2</xref>,<xref rid=\"B4-ijerph-17-05598\" ref-type=\"bibr\">4</xref>], it is of utmost importance to design feasible preventive strategies in dental settings. The initial outbreak in Wuhan spread rapidly, affecting other parts of China. Cases were soon detected in several other countries. As of 22 July 2020, 14,562,550 cases of COVID-19 (in accordance with the applied case definitions and testing strategies in the affected countries) have been reported, including 607,781 deaths [<xref rid=\"B2-ijerph-17-05598\" ref-type=\"bibr\">2</xref>]. Different numbers of cases have been reported around the world, in South-East Asia (1,478,141 cases), Europe (3,103,674 cases), Eastern Mediterranean (1,400,544 cases), Western Pacific (266,190 cases), Africa (611,185 cases), and the Americas (7,702,075 cases) [<xref rid=\"B2-ijerph-17-05598\" ref-type=\"bibr\">2</xref>,<xref rid=\"B4-ijerph-17-05598\" ref-type=\"bibr\">4</xref>].</p><p>Dental treatments for patients with COVID-19 or suspected to be infected by the virus are suggested to be postponed, except in case of urgent treatments; nevertheless, undiagnosed infected subjects without or with very mild symptoms might be seen for dental treatment. Furthermore, having in mind that many dental offices around the world have returned to providing routine or not urgent dental care, limited knowledge and awareness, unavailability of protocols and tests, and ineffective personal protective equipment (PPE) use might lower the level of safety of patients, dentists, and dental care workforce, increasing the infection spread in the community [<xref rid=\"B5-ijerph-17-05598\" ref-type=\"bibr\">5</xref>].</p><p>The risk of cross-infection in dentistry is considered to be high [<xref rid=\"B6-ijerph-17-05598\" ref-type=\"bibr\">6</xref>,<xref rid=\"B7-ijerph-17-05598\" ref-type=\"bibr\">7</xref>], since splatters and aerosols produced during routine dental treatments, combined with the physical proximity to the patient’s face, increase the risk [<xref rid=\"B8-ijerph-17-05598\" ref-type=\"bibr\">8</xref>]. This issue might be an important and unacceptable professional hazard when infective agents, such as coronaviruses, are widespread in the population [<xref rid=\"B6-ijerph-17-05598\" ref-type=\"bibr\">6</xref>]. Dentists and health care professionals working in wards with pneumonia patients are at higher risk of developing infective diseases during their regular activities [<xref rid=\"B7-ijerph-17-05598\" ref-type=\"bibr\">7</xref>]. Data on the real risk of virus dispersion by dental procedures are urgently needed, since none are available in the literature currently [<xref rid=\"B7-ijerph-17-05598\" ref-type=\"bibr\">7</xref>,<xref rid=\"B8-ijerph-17-05598\" ref-type=\"bibr\">8</xref>]. In a recent paper, the stability of SARS-CoV-2 and SARS-CoV-1 in aerosols and on various surfaces was investigated in experimental conditions, showing that airborne transmission of SARS-CoV-2 is plausible, since the virus can remain viable and infectious in aerosols for hours [<xref rid=\"B8-ijerph-17-05598\" ref-type=\"bibr\">8</xref>,<xref rid=\"B9-ijerph-17-05598\" ref-type=\"bibr\">9</xref>,<xref rid=\"B10-ijerph-17-05598\" ref-type=\"bibr\">10</xref>]. Without data on airborne SARS-Cov-2 transmission from actual dental care situations, operational envelopes and disinfection procedures to prevent cross-infection are plausible, but hypothetical. Thus, extreme precautions appear to be necessary.</p><p>Well-designed questionnaires are a useful method to collect data easily from participants in studies [<xref rid=\"B11-ijerph-17-05598\" ref-type=\"bibr\">11</xref>]. Questionnaires to investigate dentists’ knowledge, attitudes, and perceptions regarding viral infection control in the dental environment show that awareness and precautionary measures carried out by dentists on patients with a viral infection are not always completely satisfactory [<xref rid=\"B10-ijerph-17-05598\" ref-type=\"bibr\">10</xref>,<xref rid=\"B11-ijerph-17-05598\" ref-type=\"bibr\">11</xref>,<xref rid=\"B12-ijerph-17-05598\" ref-type=\"bibr\">12</xref>,<xref rid=\"B13-ijerph-17-05598\" ref-type=\"bibr\">13</xref>,<xref rid=\"B14-ijerph-17-05598\" ref-type=\"bibr\">14</xref>,<xref rid=\"B15-ijerph-17-05598\" ref-type=\"bibr\">15</xref>]. Both the risk perception by dentists regarding the SARS-CoV-2 infection and the protective measures they took during the lockdown and at work restart, in countries where non-urgent dental treatment has been suspended, are speculative and scarcely investigated.</p><p>The aim of the present protocol will be to evaluate the impact of the COVID-19 outbreak among dentists working in different countries with various levels of prevalence of the pandemic.</p><p>Research questions.\n<list list-type=\"alpha-lower\"><list-item><p>What is the prevalence of the symptoms/signs reported by dentists worldwide presumably referable to the COVID-19?</p></list-item><list-item><p>What is the level of preparedness regarding protective measures and PPE to reduce the risk of viral transmission?</p></list-item><list-item><p>What is the level of awareness and risk perception of dentists regarding COVID-19?</p></list-item></list></p></sec><sec sec-type=\"methods\" id=\"sec2-ijerph-17-05598\"><title>2. Methods</title><sec id=\"sec2dot1-ijerph-17-05598\"><title>2.1. Study Design</title><p>The survey is designed as a cross-sectional survey using a previously standardized questionnaire.</p></sec><sec id=\"sec2dot2-ijerph-17-05598\"><title>2.2. Time Period</title><p>March 2020-November 2020 (Gantt Chart <xref ref-type=\"fig\" rid=\"ijerph-17-05598-f001\">Figure 1</xref>).</p></sec><sec id=\"sec2dot3-ijerph-17-05598\"><title>2.3. Study Settings</title><p>The central management team contacted 35 collaborating research groups around the world. The countries participating in the survey are shown in <xref ref-type=\"fig\" rid=\"ijerph-17-05598-f002\">Figure 2</xref>.</p></sec><sec id=\"sec2dot4-ijerph-17-05598\"><title>2.4. Study Population</title><p>Registered dentists working in national health systems, working in private or public clinics, including general or specialist dentists will be enrolled. Participants who cannot communicate in the vernacular of the translated questionnaire will be excluded.</p></sec><sec id=\"sec2dot5-ijerph-17-05598\"><title>2.5. Sample Size Calculation</title><p>The survey will be conducted on a convenience sample of countries/areas. In each country/area, the total number of working dentists will be ascertained and a high (5–20%) proportion of dentists will be invited to participate. Countries/areas in which the requested sample size are not reached will be included in the main results of the survey.</p></sec><sec id=\"sec2dot6-ijerph-17-05598\"><title>2.6. Development and Refinement of the Questionnaire</title><p>The first group of items included in the questionnaire will be related to the health situation, risk, and knowledge of an infectious disease, derived from a questionnaire developed for the SARS risk [<xref rid=\"B8-ijerph-17-05598\" ref-type=\"bibr\">8</xref>], following the Stehr–Green scale to build up the questionnaire [<xref rid=\"B10-ijerph-17-05598\" ref-type=\"bibr\">10</xref>]. The questionnaire has four domains: the first regarding personal data (age, gender, area of living and working, working status); the second regarding health conditions (symptoms/signs related to COVID-19); the third on working conditions and PPE adopted after the outbreak of the infection; and the fourth regarding the knowledge and the self-perceived risk of infection. Among the PPEs included in the questionnaire, some, such as the use of sterile gloves, do not have a scientific justification, but they were deliberately inserted to check whether the answers were selected with the sole logic of demonstrating that any contrast measures regarding the virus had been implemented or whether the equipment adopted is the result of thoughtful decisions.</p><p>The evaluation methodology of the questionnaire will include two stages for each country:<list list-type=\"bullet\"><list-item><p>Stage I (Questionnaire translation and testing):</p></list-item></list></p><p>Translation of the questionnaire from the original English version into the different languages will be performed by a researcher from each research group. The researcher, with expertise in public health dentistry, must have very good English skills, and he/she will have to determine its conceptual equivalence in his/her language.</p><p>Back-translation from the different language into English by a translator who does not belong to the research team will be performed.</p><list list-type=\"bullet\"><list-item><p>Stage II (Pilot test and semantic adjustment of questionnaire)</p></list-item></list><p>A sample of dentists working in the respective country will be selected.</p><p>The sample will be divided randomly into two sub-samples: one for the pilot study to check semantic comprehension, and another larger group of dentists will be recruited for the subsequent validation study.</p><p>The translated questionnaire will be administered to the pilot sub-sample.</p><p>To determine its reliability, the questionnaire will be re-sent to the pilot sample a second time 4–7 days after the first administration, without any recommendations from the researchers.</p><p>The questionnaire will be adjusted in accordance with findings from the tests described above.</p><p>An online survey (Survey Monkey™ SVMK Inc., San Mateo, CA, USA or REDCap Research Electronic Data Capture <uri xlink:href=\"https://www.mc.vanderbilt.edu\">https://www.mc.vanderbilt.edu</uri>, or Google Form google.com/forms/about/or similar online platform) will be prepared.</p></sec><sec id=\"sec2dot7-ijerph-17-05598\"><title>2.7. Coordination and Survey Participation Sites</title><p>The research team in each country/area will be required to prepare the survey (see above) and oversee data collection and management locally. Local collaborators may add questions related to their country/area or for specific research reasons and will publish them after the main collaborative publication. Each research team will be free to reach dentists through the channel(s) they deem most appropriate and effective in their reality to involve the highest number of participants, such as professional orders, scientific societies, or Facebook groups.</p><p>The research team of each country/area will be specifically responsible for:<list list-type=\"bullet\"><list-item><p>Obtaining local audits, or research ethics approval (IRB/HREC approval).</p></list-item><list-item><p>Identifying dentists who will be invited to participate in the survey.</p></list-item><list-item><p>Deciding on which platform the survey will run in their country, how to reach the dentists, and when to run the survey. The platform for the survey will be set to avoid duplicated answers. The duration of the survey in each country will be for, at least, seven days.</p></list-item><list-item><p>Accuracy and any misconduct related to their research project.</p></list-item><list-item><p>Supporting translation of the questionnaire (both forward and reverse translations) into the local language and conducting the pilot test and semantic adjustment of the questionnaire.</p></list-item><list-item><p>Following all steps of the local survey.</p></list-item><list-item><p>Verifying that the data are accurately collected and organized, before sending them to the central management team (University of Bern).</p></list-item><list-item><p>Writing a report about the country-based local data.</p></list-item></list></p></sec><sec id=\"sec2dot8-ijerph-17-05598\"><title>2.8. Data Management</title><p>The data will be entered into an Excel (Microsoft Corp., WA, USA) spreadsheet and quality-checked by a researcher to ensure accuracy. Each survey response will be completely anonymous; the questionnaire will have to explicitly avoid any identification of participants’ identities. Only the management team will be able to access all data. Data from all involved countries/areas will be exported into Excel™ 2019 for Mac (or Windows equivalent); the data will be then cleaned and transferred in STATA16™ (Statacorp, TX, USA) for their statistical analysis. Data analyses will be centrally performed by the central management team.</p></sec><sec id=\"sec2dot9-ijerph-17-05598\"><title>2.9. Ethical Approval</title><p>Each research team will apply for ethical approval according to the regulations and law of the relevant country. It is possible that some countries do not need ethical approval or that using approval from elsewhere meets the regulations. All the respondents of the survey will complete an informed consent question embedded on the first page of the questionnaire. If the participant answers “YES” to the first question of the form, he/she automatically agrees to participate and will begin the survey. By using the skip-logic survey method, users who disagree with the informed consent question will be directly conducted to the end of the survey. No participant will be forced to participate in the survey, and their participation will be based on their agreement that could be withdrawn at any time. All participants have the right to leave one or more specific questions unanswered or withdraw from the survey. In addition, individual answers to the questionnaire will be inaccessible.</p></sec><sec id=\"sec2dot10-ijerph-17-05598\"><title>2.10. Confidentiality and Data Retention</title><p>The responses collected through this survey will be strictly anonymous and confidential; no identification of either participants or health centres will be possible. Individual responses are not of interest; the collective and combined outcomes derived from each participant country will be reported at an aggregate level.</p><p>The collected, electronic data will be stored on a password protected and backed-up computer drive and remain confidential—only authorized team members will have access to it. In addition, data will be completely encrypted and coded for use mainly in statistical analysis using computer software.</p></sec></sec><sec sec-type=\"discussion\" id=\"sec3-ijerph-17-05598\"><title>3. Discussion</title><p>Infection prevention and control during health care is always recommended, especially when COVID-19 infection is suspected (WHO 2020a). Up to now, there has been no consensus on the provision of dental services during the epidemic of COVID-19. A guideline was recently published by the CDC [<xref rid=\"B16-ijerph-17-05598\" ref-type=\"bibr\">16</xref>], and in each country there have only very recently been multiple sources of guidance from professional bodies; however, these conflict with one another and, therefore, are unhelpful. At the current stage of the pandemic and probably still for several months, dentists should utilize strict personal protection equipment and avoid or minimize operative procedures which can produce droplets or aerosols. The management of the operating area for dental care should be quite similar to those where patients affected by infectious and highly contagious diseases are treated, depending on the epidemiologic situation in each country/region. It is clear from all sources of guidance that, as often as possible, staff should work at an adequate distance from patients; furthermore, there are suggestions that handpieces should be equipped with non-return devices to avoid contamination, decreasing the risk of cross-infection. Finally, the dentist should favor procedures which reduce the quantity of aerosol dispersed into the environment [<xref rid=\"B4-ijerph-17-05598\" ref-type=\"bibr\">4</xref>,<xref rid=\"B6-ijerph-17-05598\" ref-type=\"bibr\">6</xref>]. The philosophy of minimal intervention dentistry could be adopted, helping to reduce the spread of viral particles during dental care. Conservative approaches, such as the atraumatic restorative technique, partial or selective removal of carious tissues with hand instruments or manual scaling of calculus, should therefore be preferred, when possible.</p><p>Individual prevention, both for health personnel and for patients, must be associated with the prevention of the spread of the virus through environmental remediation. In particular, due to the high proliferation of the virus in the droplets and aerosols exhaled by coughing and sneezing, every surface in the waiting room should be considered at risk; therefore, in addition to providing adequate periodic air exchange, all surfaces, chairs, and doors that come into contact with health care professionals and patients must be considered “potentially infected”. It may be useful to provide masks and disinfectants to patients for hand application or hand washing while they stay in waiting rooms.</p><p>The data collected in this study will play an important role in raising awareness among professionals and policymakers on the impact of the COVID-19 pandemic on the dental profession, but also it will assist preparedness for potential future viral outbreaks, including devising research agendas. Effective control during early stages of a pandemic will play a crucial role in preventing viral spread and outbreak in other cities/countries. Identifying and analyzing epidemiological issues among dentists, communicating international experiences, and disseminating transparent and usable results will provide scientific data, empower decision-making, and reduce community transmission during current pandemic and post-crisis times.</p><p>The results of this project will help to define the best strategy to organize the dental workforce. This study represents an interesting and rather unusual experience for the dental profession globally to join efforts to obtain important information from more than 30 countries, with different socio-politic contexts, geographical and climate conditions, as well as cultural particularities. The final goal is to be able to care for all patients while minimizing the risk to staff, thus maintaining a healthy caregiver workforce. The methodological ease of this strategy makes it appealing and reproducible. Similar initiatives can be approached in the near future.</p></sec></body><back><ack><title>Acknowledgments</title><p>The authors acknowledge all the persons that are involved in the design and carrying out of this survey. A particular thanks goes to Miss Ines Badertscher for the help provided with the figures. Membership of the COVIDental Collaboration Group: Exhajanka E, Hysenaj N (Albania); Squassi A, Luciana D’Eramo L, Sorazabal AL, Serra Capó MT (Argentina); Crombie F, Hopcraft M (Australia); Carvalho JC, Bottenberg P, Declerck D (Belgium); Sharkov N (Bulgaria); Zukanovic A, Bajric E (Bosnia); Raggio DP, Moreira MAM, Tedesco TK, Pássaro AL, Oliveira RC, Correa MB, Carrer FC, Braga MM, Mendes FM (Brazil); Sharkov N (Bulgaria); Giacaman RA (Chile); Fernández CE; León S; Gambetta-Tessini K (Chile); Shi B; Zeng H, Yan Q; Cai J (China); Plaza SP, Barbosa DM, Agudelo-Suarez AA (Colombia); Philippides V (Cyprus); Elwishahy A, Abou El Fadl R, Abdulghaffar H, Jalal A, (Egypt); Aguirre GA, Rivas-Cartagena FJ, Escobar W, Aguirre K, (El Salvador); Antia K, Gigineishvili E, Jikia M, Sabashvili M (Georgia); Wolf TG, Schrader H, Bührens P, Kaps-Richter G, Hüttmann J, Tietler P, Deschner J (Germany), Maroufidis N (Greece); Gugnani N, Datta K, Bhusari S, (India); Amadori F, Bontà G, Cairoli JL, Cagetti MG, Cocco C, Malerba A, Senna A, Spagnuolo G (Italy); Slabsinskiene E, Machiulskiene V (Lithuania); Denkovski M (Macedonia); Mani SA, John J, Musa S, Mohd NA (Malaysia); Vlahovic Z (Montenegro); van der Veen MH, Volgenant CMC, Persoon IC, Bruers J, Opdam N, Manton DJ (Netherlands); Mafeni J, Basil TO, Taiwo OO (Nigeria); Ashar A, Haider A, Hussain NS, Jalal A, (Pakistan); Sava-Rosianu R, Galuscan A, Perdiou A (Romania); Makeeva IM, Makeeva MC, Mamedov AA, Admakin OI, Morozova NS, Turkina AY, Potrysova AM, Mazurina LA, Maclennan AB, Timoshchenko TV, Dudnik OV, Makeev MC (Russia); Grey J, AlMaghlouth AA (Kingdom of SA) Markovic D, Vukovic A, Peric T, Petrovic B (Serbia); Sim Poh Cho CPC, Pau LH (Singapore); Cortes- Martinicorena FJ, Montiel-Company JM, Almerich-Torres T, Cortes-Acha B, Dopico-San Martin J (Spain); Campus G, Diaz-Betancourt M, Carvalho TS, Zeyer O, Wolf TG (Switzerland); Marouane O, Ben Tanfous S, Necibi A, Ghorbel M (Tunisia); Douglas GVA, Birch S, Csikar JI, Levin KA, Owen J, Serban S, (United Kingdom); Ramos-Gomez F, Fontana M (USA); Acevedo AM, Bustillos L, Alfonso I, Garcia A, Frattaroli A (Venezuela).</p></ack><notes><title>Author Contributions</title><p>All authors are listed as a group of collaborators. In addition, the author’s contributions have been recorded. G.C. conceptualization, methodology, project administration, writing—original draft preparation, writing—review and editing; M.D.-B. supervision, methodology, data curation, project administration; M.G.C. conceptualization writing—original draft preparation, writing—review and editing; J.C.C., T.S.C., J.F.C.-M., J.D., G.V.A.D., R.A.G., V.M., D.J.M., D.P.R., F.R.-G., R.S.-R., N.S.M., G.S., A.V., and T.G.W. validation, investigation, data curation writing—review and editing. All authors have read and agreed to the published version of the manuscript.</p></notes><notes><title>Funding</title><p>This survey did not receive any specific grant from any funding agency in the public, commercial, or not-for-profit sectors. 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]
[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Int J Environ Res Public Health</journal-id><journal-id journal-id-type=\"iso-abbrev\">Int J Environ Res Public Health</journal-id><journal-id journal-id-type=\"publisher-id\">ijerph</journal-id><journal-title-group><journal-title>International Journal of Environmental Research and Public Health</journal-title></journal-title-group><issn pub-type=\"ppub\">1661-7827</issn><issn pub-type=\"epub\">1660-4601</issn><publisher><publisher-name>MDPI</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32748858</article-id><article-id pub-id-type=\"pmc\">PMC7432090</article-id><article-id pub-id-type=\"doi\">10.3390/ijerph17155577</article-id><article-id pub-id-type=\"publisher-id\">ijerph-17-05577</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Article</subject></subj-group></article-categories><title-group><article-title>Big Data Analysis of Sports and Physical Activities among Korean Adolescents</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0002-8518-6048</contrib-id><name><surname>Park</surname><given-names>Sung-Un</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05577\">1</xref><xref ref-type=\"author-notes\" rid=\"fn1-ijerph-17-05577\">†</xref></contrib><contrib contrib-type=\"author\"><name><surname>Ahn</surname><given-names>Hyunkyun</given-names></name><xref ref-type=\"aff\" rid=\"af2-ijerph-17-05577\">2</xref><xref ref-type=\"author-notes\" rid=\"fn1-ijerph-17-05577\">†</xref></contrib><contrib contrib-type=\"author\"><name><surname>Kim</surname><given-names>Dong-Kyu</given-names></name><xref ref-type=\"aff\" rid=\"af3-ijerph-17-05577\">3</xref><xref rid=\"c1-ijerph-17-05577\" ref-type=\"corresp\">*</xref><xref ref-type=\"author-notes\" rid=\"fn2-ijerph-17-05577\">‡</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0002-9322-5852</contrib-id><name><surname>So</surname><given-names>Wi-Young</given-names></name><xref ref-type=\"aff\" rid=\"af4-ijerph-17-05577\">4</xref><xref rid=\"c1-ijerph-17-05577\" ref-type=\"corresp\">*</xref><xref ref-type=\"author-notes\" rid=\"fn2-ijerph-17-05577\">‡</xref></contrib></contrib-group><aff id=\"af1-ijerph-17-05577\"><label>1</label>Department of Sport & Leisure Studies, College of Arts & Physical Education, Shingyeong University, Hwaseong-si 18274, Korea; <email>[email protected]</email></aff><aff id=\"af2-ijerph-17-05577\"><label>2</label>Department of Sport & Leisure Studies, Division of Arts & Health, Myongji College, Seoul 03656, Korea; <email>[email protected]</email></aff><aff id=\"af3-ijerph-17-05577\"><label>3</label>Department of Sport Management, Graduate School of Technology Management, Kyung Hee University, Yongin-si 17104, Korea</aff><aff id=\"af4-ijerph-17-05577\"><label>4</label>Sports and Health Care Major, College of Humanities and Arts, Korea National University of Transportation, Chungju-si 27469, Korea</aff><author-notes><corresp id=\"c1-ijerph-17-05577\"><label>*</label>Correspondence: <email>[email protected]</email> (D.-K.K.); <email>[email protected]</email> (W.-Y.S.); Tel.: +82-31-201-2130 (D.-K.K.); +82-43-841-5993 (W.-Y.S.); Fax: +82-31-201-2777 (D.-K.K.); +82-43-841-5990 (W.-Y.S.)</corresp><fn id=\"fn1-ijerph-17-05577\"><label>†</label><p>The first two authors contributed equally to this work.</p></fn><fn id=\"fn2-ijerph-17-05577\"><label>‡</label><p>The corresponding two authors contributed equally to this work.</p></fn></author-notes><pub-date pub-type=\"epub\"><day>02</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"ppub\"><month>8</month><year>2020</year></pub-date><volume>17</volume><issue>15</issue><elocation-id>5577</elocation-id><history><date date-type=\"received\"><day>18</day><month>6</month><year>2020</year></date><date date-type=\"accepted\"><day>30</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>© 2020 by the authors.</copyright-statement><copyright-year>2020</copyright-year><license license-type=\"open-access\"><license-p>Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>).</license-p></license></permissions><abstract><p>The Korean government (Ministry of Culture, Sports and Tourism, Ministry of Health and Welfare, and Ministry of Education) has framed policies and conducted many projects to encourage adolescents to be more physically active. Despite these efforts, the participation rate of physical activity in Korean adolescents keeps decreasing. Thus, the purpose of this study was to analyze the perception of sports and physical activity in Korean adolescents through big data analysis of the last 10 years and to provide research data and statistical direction with regard to sports and physical activity participation in Korean adolescents. For data collection, data from 1 January 2010 to 31 December 2019 were collected from Naver (NAVER Corp., Seongnam, Korea), Daum (Kakao Corp., Jeju, Korea), and Google (Alphabet Inc., Mountain View, CA, USA), which are the most widely used search engines in Korea, using TEXTOM 4.0 (The Imc Inc., Daegu, Korea), a big data collection and analysis solution. Keywords such as “adolescent + sports + physical activity” were used. TEXTOM 4.0 can generate various collection lists at once using keywords. Collected data were processed through text mining (frequency analysis, term frequency–inverse document frequency analysis) and social network analysis (SNA) (degree centrality, convergence of iterated correlations analysis) by using TEXTOM 4.0 and UCINET 6 social network analysis software (Analytic Technologies Corp., Lexington, KY, USA). A total of 9278 big data (10.36 MB) were analyzed. Frequency analysis of the top 50 terms through text mining showed exercise (872), mind (851), health (824), program (782), and burden (744) in a descending order. Term frequency–inverse document frequency analysis revealed exercise (2108.070), health (1961.843), program (1928.765), mind (1861.837), and burden (1722.687) in a descending order. SNA showed that the terms with the greatest degree of centrality were exercise (0.02857), program (0.02406), mind (0.02079), health (0.02062), and activity (0.01872) in a descending order. Convergence of the iterated correlations analysis indicated five clusters: exercise and health, child to adult, sociocultural development, therapy, and program. However, female gender, sports for all, stress, and wholesome did not have a high enough correlation to form one cluster. Thus, this study provides basic data and statistical direction to increase the rate of physical activity participation in Korean adolescents by drawing significant implications based on terms and clusters through bid data analysis.</p></abstract><kwd-group><kwd>Korean adolescents</kwd><kwd>sports</kwd><kwd>physical activities</kwd></kwd-group></article-meta></front><body><sec sec-type=\"intro\" id=\"sec1-ijerph-17-05577\"><title>1. Introduction</title><p>The 2018 Korea Student Health Examination reported that the rate of obesity in Korean adolescents increased from 21.2% in 2014 to 25.0% in 2018, representing an increase of 3.8 percentage points in three years [<xref rid=\"B1-ijerph-17-05577\" ref-type=\"bibr\">1</xref>]. Furthermore, a recent report indicated that the percentage of students who engaged in the recommended levels of exercise (strenuous exercise three or more days per week) was 59.25% among elementary school students, 35.08% among middle school students, and 23.60% among high school students, suggesting a declining trend in exercise with age among children and adolescents [<xref rid=\"B1-ijerph-17-05577\" ref-type=\"bibr\">1</xref>]. The increase in the rate of obesity among adolescents is troubling because research indicates that 80% of obese adolescents become obese adults [<xref rid=\"B2-ijerph-17-05577\" ref-type=\"bibr\">2</xref>]. Therefore, adolescence is a crucial period for developing healthy habits [<xref rid=\"B3-ijerph-17-05577\" ref-type=\"bibr\">3</xref>,<xref rid=\"B4-ijerph-17-05577\" ref-type=\"bibr\">4</xref>,<xref rid=\"B5-ijerph-17-05577\" ref-type=\"bibr\">5</xref>]. However, as the statistics above suggest, less than 25% of Korean adolescents engage in the recommended levels of exercise by the time they reach high school.</p><p>Big data refers to large-scale data that cannot be stored, managed, or analyzed using traditional database software [<xref rid=\"B6-ijerph-17-05577\" ref-type=\"bibr\">6</xref>]. Big data is distinct from standard data in terms of volume, velocity, and variety [<xref rid=\"B7-ijerph-17-05577\" ref-type=\"bibr\">7</xref>]. Currently, an explosive increase in the amount of big data collected is taking place [<xref rid=\"B8-ijerph-17-05577\" ref-type=\"bibr\">8</xref>,<xref rid=\"B9-ijerph-17-05577\" ref-type=\"bibr\">9</xref>]. Big data has become an important part of research due to a significant increase in unstructured data recently [<xref rid=\"B10-ijerph-17-05577\" ref-type=\"bibr\">10</xref>]. Research based on big data analysis reveals interesting insights on consumer perception, choice, emotion, and personal intention to act. It can also identify market perception, trends, and make predictions through the analysis of patterns [<xref rid=\"B11-ijerph-17-05577\" ref-type=\"bibr\">11</xref>]. However, big data must be handled by a reliable system with a formal data policy for usage and storage [<xref rid=\"B12-ijerph-17-05577\" ref-type=\"bibr\">12</xref>,<xref rid=\"B13-ijerph-17-05577\" ref-type=\"bibr\">13</xref>] that is capable of conducting large data calculations. Big data is particularly useful because new insights or values that cannot be derived from small amounts of data can be extracted and used to initiate important changes in various areas including market, corporate, civic, and governmental relationships [<xref rid=\"B14-ijerph-17-05577\" ref-type=\"bibr\">14</xref>].</p><p>Korea currently offers favorable conditions for big data to flourish by virtue of its globally superior network infrastructures and the immense amount of data consequently produced [<xref rid=\"B13-ijerph-17-05577\" ref-type=\"bibr\">13</xref>,<xref rid=\"B15-ijerph-17-05577\" ref-type=\"bibr\">15</xref>]. While big data is certainly a hot topic and a growing development target, most governments and companies are still not actively applying data analytics [<xref rid=\"B13-ijerph-17-05577\" ref-type=\"bibr\">13</xref>]. An examination of the obesity rate of Korean adolescents combined with their rate of engagement in the recommended levels of exercise reveals the need to examine their current perceptions of sports and physical activities (SPA). Big data analysis may suggest a strategic direction related to Korean adolescents’ SPA that can inform the development of interventions aimed at increasing the rate of engagement in the recommended levels of exercise, and in turn decreasing the obesity rate among Korean adolescents. Therefore, this study aims to collect and analyze big data [<xref rid=\"B16-ijerph-17-05577\" ref-type=\"bibr\">16</xref>] to examine Korean adolescents’ perceptions of SPA.</p></sec><sec id=\"sec2-ijerph-17-05577\"><title>2. Materials and Methods</title><sec id=\"sec2dot1-ijerph-17-05577\"><title>2.1. Data Collection</title><p>This study was approved by the Institutional Review Board of Kyung Hee University, Gyeonggi, Korea (No. KHGIRB-20-096). Data were searched from 1 January 2010 to 31 December 2019 to be included in the analysis. For data collection, the TEXTOM 4.0 big data analysis solution (The Imc Inc., Daegu, Korea), a web crawling program, was used to collect the unstructured text on webpages, blogs, and news articles provided by Naver [<xref rid=\"B17-ijerph-17-05577\" ref-type=\"bibr\">17</xref>], Daum, and Google [<xref rid=\"B18-ijerph-17-05577\" ref-type=\"bibr\">18</xref>]. The terms “adolescent + sports + physical activity” were used as search keywords. TEXTOM has an adding keyword function that can collect data using keywords. Using the adding keyword function has an advantage as it can generate various collection lists at once [<xref rid=\"B19-ijerph-17-05577\" ref-type=\"bibr\">19</xref>]. Moreover, the keywords were searched separately (not as a phrase) in this study. Moreover, Naver, Daum, and Google were set as collection channels due to the fact that Naver, Daum, and Google showed 77, 10.8, and 1.7% of Korean Internet searches in order [<xref rid=\"B20-ijerph-17-05577\" ref-type=\"bibr\">20</xref>,<xref rid=\"B21-ijerph-17-05577\" ref-type=\"bibr\">21</xref>]. We found that Google did not display satisfying results due to a lack of Korean data even though it is a worldwide and strong search engine [<xref rid=\"B20-ijerph-17-05577\" ref-type=\"bibr\">20</xref>]. The information on the collected data is shown in <xref rid=\"ijerph-17-05577-t001\" ref-type=\"table\">Table 1</xref>.</p></sec><sec id=\"sec2dot2-ijerph-17-05577\"><title>2.2. Data Analysis</title><p>In this study, text mining and social network analysis (SNA) were performed to analyze big data on Korean adolescents’ SPA. Text mining refers to the technique of using natural language processing and data mining techniques to extract meaningful information from unstructured text data [<xref rid=\"B22-ijerph-17-05577\" ref-type=\"bibr\">22</xref>]. Thus, text mining is used to analyze vast amounts of text to extract patterns or relationships, discover meaningful values, and interpret them with insight [<xref rid=\"B23-ijerph-17-05577\" ref-type=\"bibr\">23</xref>]. Therefore, a frequency analysis and term frequency–inverse document frequency (TF–IDF) analysis were derived using text mining. Frequency analysis refers to the number of times that a word or term appears in a document, and the TF–IDF approach is commonly used to weigh each word in the text document, according to how unique it is [<xref rid=\"B24-ijerph-17-05577\" ref-type=\"bibr\">24</xref>]. Second, SNA is a method of quantitatively analyzing the characteristics of a social network [<xref rid=\"B25-ijerph-17-05577\" ref-type=\"bibr\">25</xref>] by focusing on the patterns of relations among the entities in the network (e.g., people, organizations, and states [<xref rid=\"B16-ijerph-17-05577\" ref-type=\"bibr\">16</xref>,<xref rid=\"B26-ijerph-17-05577\" ref-type=\"bibr\">26</xref>]).</p><p>Network centrality is a measure of how close each node in the network is to the center of the network [<xref rid=\"B27-ijerph-17-05577\" ref-type=\"bibr\">27</xref>]. There are multiple measures of network centrality, but degree centrality, the most representative of the measures, is also the most reliable and simplest [<xref rid=\"B28-ijerph-17-05577\" ref-type=\"bibr\">28</xref>]. Degree centrality is a measure of how many neighbors a node has; a word that has many connections to other words becomes more central, giving it a greater impact on other words and a more dominant role in the network [<xref rid=\"B29-ijerph-17-05577\" ref-type=\"bibr\">29</xref>,<xref rid=\"B30-ijerph-17-05577\" ref-type=\"bibr\">30</xref>]. Thus, degree centrality is an index of the degree to which a particular node is located toward the center of the overall network [<xref rid=\"B31-ijerph-17-05577\" ref-type=\"bibr\">31</xref>,<xref rid=\"B32-ijerph-17-05577\" ref-type=\"bibr\">32</xref>,<xref rid=\"B33-ijerph-17-05577\" ref-type=\"bibr\">33</xref>]. Additionally, the CONCOR (CONvergence of iterated CORrelations) analysis is the process of discovering patterns in the relationships between words, and the greater the similarity of the relationship patterns, the greater the degree of structural equivalence of the other words [<xref rid=\"B30-ijerph-17-05577\" ref-type=\"bibr\">30</xref>].</p><p>In this study, degree centrality and CONCOR, which are the most representative concepts in SNA, were used. TEXTOM 4.0 big data analysis solution (The Imc Inc., Daegu, Korea) and UCINET 6 social network analysis software (Analytic Technologies Corp., Lexington, KY, USA) were used to perform text mining and SNA [<xref rid=\"B34-ijerph-17-05577\" ref-type=\"bibr\">34</xref>].</p></sec></sec><sec sec-type=\"results\" id=\"sec3-ijerph-17-05577\"><title>3. Results</title><sec id=\"sec3dot1-ijerph-17-05577\"><title>3.1. Results of Data Collection</title><p>In this study, texts related to the keywords “adolescent + sports + physical activity”, published on Naver, Daum, and Google between 1 January 2010 and 31 December 2019 were collected; the results are reported in <xref rid=\"ijerph-17-05577-t002\" ref-type=\"table\">Table 2</xref>. In total, 9278 data points were collected using TEXTOM 4.0 big data analysis solution and the total data volume was 10.36 MB.</p></sec><sec id=\"sec3dot2-ijerph-17-05577\"><title>3.2. Text Mining Analysis</title><p>First, the results of performing a frequency analysis on the top 50 terms related to Korean adolescents’ SPA are shown in <xref rid=\"ijerph-17-05577-t003\" ref-type=\"table\">Table 3</xref>. The results showed the top 25 most frequently used terms were exercise (872), mind (851), health (824), program (782), burden (744), vitamin D (737), outdoor activity (734), immunity (729), sunbathing (719), activity (633), management (538), school (520), children (488), participation (429), education (415), student (401), social (354), growth (349), mental (336), child (321), development (305), kid (279), body (273), game (260), and opportunity (258) in descending order.</p><p>Second, TF–IDF was performed to calculate how important each term was in a particular document by multiplying term frequency (TF) and inverse document frequency (IDF). TF means the frequency of a specific word in a document, DF is the frequency of a specific word in multiple documents, and IDF is the inverse of DF [<xref rid=\"B19-ijerph-17-05577\" ref-type=\"bibr\">19</xref>]. Thus, the TF–IDF value increases as the frequency of a word in a specific document increases and the number of documents that include the specific word decrease. The basic formula to calculate this TF–IDF value is as follows [<xref rid=\"B19-ijerph-17-05577\" ref-type=\"bibr\">19</xref>,<xref rid=\"B35-ijerph-17-05577\" ref-type=\"bibr\">35</xref>]:<disp-formula>TF–IDF = TF × 1/DF</disp-formula></p><p>As seen in <xref rid=\"ijerph-17-05577-t004\" ref-type=\"table\">Table 4</xref>, the results of the TF–IDF analysis were similar to those of the frequency analysis, with the following results in descending order: exercise (2108.070), health (1961.843), program (1928.765), mind (1861.837), burden (1722.687), vitamin D (1718.496), outdoor activity (1707.490), immunity (1702.844), sunbathing (1687.441), activity (1599.081), management (1507.636), school (1490.146), children (1431.463), participation (1255.191), education (1251.933), student (1218.513), social (1112.992), growth (1086.663), child (1068.955), mental (1056.399), development (1019.094), kid (964.428), skin (963.135), game (937.734), and body (933.512).</p></sec><sec id=\"sec3dot3-ijerph-17-05577\"><title>3.3. Social Network Analysis</title><p>This study was based on degree centrality, which focuses on the level of connection of one node to the others as the centrality. Furthermore, to analyze the structures of the relationships among the latent sub-clusters, CONCOR analysis was performed. First, normalized degree centrality is defined as the number of links divided by the maximum possible value [<xref rid=\"B36-ijerph-17-05577\" ref-type=\"bibr\">36</xref>]. Thus, the closer it is to 1, the higher the degree centrality. A higher degree centrality value was interpreted to mean that there was a significant number of links among terms and a significant impact in the network. Therefore, to test how connected the derived terms were to “adolescent + sports + physical activity”, a degree centrality analysis was performed, the results of which are shown in <xref rid=\"ijerph-17-05577-t005\" ref-type=\"table\">Table 5</xref>. The results of the degree centrality analysis were exercise (0.02857), program (0.02406), mind (0.02079), health (0.02062), activity (0.01872) management (0.01545), student (0.01525), participation (0.01491), school (0.01475), education (0.01375), children (0.01305), child (0.01184), kid (0.01094), social (0.01064), mental (0.00964), development (0.00924), person (0.00921), physical education (0.00917), growth (0.00911), physical activity (0.00857), opportunity (0.00851), body (0.00831), time (0.00831), game (0.00821), and stress (0.00807) in a decreasing order. In particular, the results of the degree centrality analysis showed higher rankings of nodes such as activity, management, student, participation, school, and education compared to the results of the frequency and TF–IDF analyses.</p><p>Second, a CONCOR analysis was performed to analyze the structures of the relationships among the latent sub-clusters in the network cluster. The results are shown in <xref ref-type=\"fig\" rid=\"ijerph-17-05577-f001\">Figure 1</xref> and <xref rid=\"ijerph-17-05577-t006\" ref-type=\"table\">Table 6</xref>. Based on these results, homogenous groups were identified according to relationships and correlations, resulting in five clusters. The first cluster (visualized with yellow) comprised the terms “exercise”, “health”, “activity”, “mental”, “growth”, “physical strength”, and “help”, and was categorized as “exercise and health”. The second cluster (visualized with sky-blue) comprised the terms “child”, “kid”, “physical education”, “adult”, “world”, “time”, “problem”, “person”, and “obese”, and was categorized as “child to adult.” The third cluster (visualized with purple) comprised the terms “children”, “education”, “social”, “culture”, “development”, “improvement”, “soccer”, “game”, “emotion”, and “enhancement”, and was categorized as “sociocultural development”. The fourth cluster (visualized with orange) comprised the terms “mind”, “immunity”, “vitamin D”, “outdoor activity”, “burden”, “sunbathing”, “body”, “skin”, and “treatment”, and was categorized as “therapy”. The fifth cluster (visualized with red) comprised the terms “program”, “management”, “school”, “student”, “participation”, “opportunity”, “dream”, “experience”, “physical activity”, and “sports activity”, and was categorized as the “program” cluster. However, female, sports for all, stress, and wholesome could not form a cluster (visualized with black, gray, and white).</p></sec></sec><sec sec-type=\"discussion\" id=\"sec4-ijerph-17-05577\"><title>4. Discussion</title><p>As a result of the frequency analysis of text-mining from 2010 to 2019, the SPA of Korean adolescents, “exercise”, “mind”, “health”, “program”, and “burden” showed high frequency. Baker et al. (2011) and Keteyian (2011) claimed that physical activities that require active performance such as sports are important for enhancing health [<xref rid=\"B37-ijerph-17-05577\" ref-type=\"bibr\">37</xref>,<xref rid=\"B38-ijerph-17-05577\" ref-type=\"bibr\">38</xref>] and that regular physical activity can improve adolescent academic achievement [<xref rid=\"B7-ijerph-17-05577\" ref-type=\"bibr\">7</xref>]. Additionally, regular participation in physical activity is related to child and adolescent health [<xref rid=\"B39-ijerph-17-05577\" ref-type=\"bibr\">39</xref>,<xref rid=\"B40-ijerph-17-05577\" ref-type=\"bibr\">40</xref>,<xref rid=\"B41-ijerph-17-05577\" ref-type=\"bibr\">41</xref>]. However, in spite of these advantages, Korean adolescents, along with those from Belgium, China, Scotland, and Taiwan, were ranked F in the overall physical activity index in the 2018 Report Card (RC), which was at the bottom of 49 countries [<xref rid=\"B42-ijerph-17-05577\" ref-type=\"bibr\">42</xref>]. This rank is much lower compared with Korea’s 2016 RC overall physical activity index (D−) [<xref rid=\"B43-ijerph-17-05577\" ref-type=\"bibr\">43</xref>]. Thus, there is a need to focus on Korean SPA continuously. In particular, the keyword “burden” was recurrent in the findings, indicating that there are practical barriers against sports and physical activities in Korean society. Furthermore, the results of the degree centrality analysis showed that the ranks of nodes such as “activity”, “management”, “student”, “participation”, and “school” were higher compared to the results of the frequency and TF–IDF analyses. This, together with the prevailing prioritization of academic achievements in Korean society, leads to the inference that there is a tendency to prioritize studies over sports and physical activities. Considering the sociocultural background in which academic achievements are more highly valued than SPA in Korea, SPA in schools should be further strengthened.</p><p>The results of the CONCOR analysis categorized the structural similarities within the network into five clusters: “exercise and health”, “child to adult”, “sociocultural development”, “therapy”, and “program”. First, in the “exercise and health” cluster, it was found that the links between exercise, health, and activity were high. This supports the findings of previous studies suggesting that sports and physical activities are important factors for adolescent growth [<xref rid=\"B44-ijerph-17-05577\" ref-type=\"bibr\">44</xref>] and health [<xref rid=\"B37-ijerph-17-05577\" ref-type=\"bibr\">37</xref>,<xref rid=\"B38-ijerph-17-05577\" ref-type=\"bibr\">38</xref>]. Second, in the “child to adult” cluster, the links between “child”, “kid”, and “physical education” were found to be high. It has been suggested that the interest in sports and physical activities was higher among children. In particular, as regular physical activities in adolescence can improve physical activities in adulthood [<xref rid=\"B45-ijerph-17-05577\" ref-type=\"bibr\">45</xref>], it is important to form good habits related to SPA in adolescence. Third, in the “sociocultural development” cluster, links between “children”, “social”, and “education” were found to be high, indicating that sociocultural background relates to Korean adolescents’ SPA. Lindquist, Reynolds, and Goran (1999) criticized the insufficient research on the impact of pervasive sociocultural factors on children’s physical activity and physical strength, despite its latent impact on various physical activities [<xref rid=\"B46-ijerph-17-05577\" ref-type=\"bibr\">46</xref>]. Therefore, in-depth research on the relationship between sociocultural factors and SPA of Korean adolescents is urgently needed. Fourth, in the “therapy” cluster, links between “mind”, “immunity”, and “vitamin D” were found to be high. Thus, SPA can be speculated to enhance the mental wellbeing and immune system of adolescents. Physical activities and mental health are highly related in adolescence [<xref rid=\"B47-ijerph-17-05577\" ref-type=\"bibr\">47</xref>], and it has been shown that regular exercise has an effect on the immune system and can even delay aging [<xref rid=\"B48-ijerph-17-05577\" ref-type=\"bibr\">48</xref>]. Fifth, in the “program” cluster, the links between “program”, “management”, and “school” were found to be high. Practical SPA programs that consider the age and target as well as expand the time devoted to physical education in schools and related after school sports clubs are recommended. Exercise levels during adolescence should be increased through the planning and implementation of mid- to long-term SPA at the sociocultural and national levels. Finally, it was shown that terms such as female, sports for all, stress, and wholesome had no high correlation and therefore did not form clusters. However, it seems necessary to pay attention to deduced terms.</p></sec><sec sec-type=\"conclusions\" id=\"sec5-ijerph-17-05577\"><title>5. Conclusions</title><p>In this study, big data related to Korean adolescents’ SPA between 1 January 2010 and 31 December 2019 were collected, and text mining and SNA were performed on the collected unstructured text using the TEXTOM 4.0 big data analysis solution (The Imc Inc., Daegu, Republic of Korea) and UCINET 6 social network analysis software (Analytic Technologies Corp., Lexington, KY, USA).</p><p>The total number of big data analyzed in this study was 9278 data points, and the volume was 10.36 MB. The results of the frequency analysis through text mining showed that the terms “exercise”, “mind”, “health”, “program”, “burden”, “vitamin D”, “outdoor activity”, “immunity”, “sunbathing”, and “activity” were the most frequently used words. The results of the TF–IDF analysis showed that “exercise”, “health”, “program”, “mind”, “burden”, “vitamin D”, “outdoor activity”, “immunity”, “sunbathing”, and “activity” were the most frequently used words. Through the analytic process, various nodes related to Korean adolescents’ SPA and their relative importance were identified.</p><p>Second, the results of the SNA showed that the terms with the greatest degree of centrality were “exercise”, “program”, “mind”, “health”, “activity”, “management”, “student”, “participation”, “school”, and “education”. Nodes such as “activity”, “management”, “student”, “participation”, “school”, and “education” were found to have an increased ranking in the SNA results compared to the results of the frequency analysis and TF–IDF analysis. The results of the CONCOR analysis yielded the following five clusters: exercise and health, child to adult, sociocultural development, therapy, and program. However, even though female, sports for all, stress, and wholesome could not form a cluster, circumspection is required. In conclusion, three Korean ministries such as the Ministry of Culture, Sports and Tourism, Ministry of Health and Welfare, and Ministry of Education have conducted and planned about 190 policies and projects with regard to the physical activity of children and adolescents [<xref rid=\"B49-ijerph-17-05577\" ref-type=\"bibr\">49</xref>]. Despite these efforts, the physical activity index of Korean adolescents is decreasing more and more [<xref rid=\"B43-ijerph-17-05577\" ref-type=\"bibr\">43</xref>]. Thus, this research provides specific and systematic facts about Korean adolescents’ SPA based on big data from the past 10 years. Furthermore, the participation rate in sports and physical activities among Korean adolescents may be improved if the sports and physical activity cluster is divided based on deducted cluster and problems, and improvement points of each cluster can be supplemented. With this knowledge, Korean SPA programs that consider the clusters can be developed in follow-up research based on the results of this study.</p></sec></body><back><notes><title>Author Contributions</title><p>Study design, S.-U.P., H.A., D.-K.K., and W.-Y.S.; Study conduct, S.-U.P., H.A., D.-K.K., and W.-Y.S.; Data collection, S.-U.P., H.A., D.-K.K., and W.-Y.S.; Data analysis, S.-U.P., H.A., D.-K.K., and W.-Y.S.; Data interpretation, S.-U.P., H.A., D.-K.K., and W.-Y.S.; Drafting manuscript, S.-U.P., H.A., D.-K.K., and W.-Y.S.; Revising the manuscript content, S.-U.P., H.A., D.-K.K., and W.-Y.S. All authors have read and agreed to the published version of the manuscript.</p></notes><notes><title>Funding</title><p>This research received no external funding.</p></notes><notes notes-type=\"COI-statement\"><title>Conflicts of Interest</title><p>The authors declare no conflicts of interest.</p></notes><ref-list><title>References</title><ref id=\"B1-ijerph-17-05577\"><label>1.</label><element-citation publication-type=\"book\"><person-group person-group-type=\"author\"><collab>Korea Ministry of Education</collab></person-group><source>2018 Sample Survey of Student Health</source><publisher-name>Korea Ministry of Education</publisher-name><publisher-loc>Sejong Special Self-Governing City, Korea</publisher-loc><year>2019</year><comment>Available online: <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.moe.go.kr/boardCnts/view.do?boardID=294&boardSeq=77144&lev=0&searchType=null&statusYN=W&page=1&s=moe&m=020402&opType=N\">https://www.moe.go.kr/boardCnts/view.do?boardID=294&boardSeq=77144&lev=0&searchType=null&statusYN=W&page=1&s=moe&m=020402&opType=N</ext-link></comment><date-in-citation content-type=\"access-date\" iso-8601-date=\"2020-06-14\">(accessed on 14 June 2020)</date-in-citation></element-citation></ref><ref id=\"B2-ijerph-17-05577\"><label>2.</label><element-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Kvaavik</surname><given-names>E.</given-names></name><name><surname>Tell</surname><given-names>G.S.</given-names></name><name><surname>Klepp</surname><given-names>K.I.</given-names></name></person-group><article-title>Predictors and tracking of body mass index from adolescence into adulthood: Follow-up of 18 to 20 years in the Oslo Youth Study</article-title><source>Arch. 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Note. yellow cluster = exercise and health; sky blue cluster = child to adult; purple cluster = sociocultural development; orange cluster = therapy; red cluster = program; black, gray, and white = could not form a cluster.</p></caption><graphic xlink:href=\"ijerph-17-05577-g001\"/></fig><table-wrap id=\"ijerph-17-05577-t001\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05577-t001_Table 1</object-id><label>Table 1</label><caption><p>Text data collection information.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Category</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Content</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Collection channel</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Naver, Daum, Google</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Collection period</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1 January 2010 to 31 December 2019</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Collection tool</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">TEXTOM 4.0 big data analysis solution (The Imc Inc., Daegu, Korea) (<uri xlink:href=\"http://textom.co.kr\">http://textom.co.kr</uri>)</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Analysis keyword</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Adolescents, Sports, Physical activities</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Analysis tool</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">TEXTOM 4.0 big data analysis solution (The Imc Inc., Daegu, Korea) (<uri xlink:href=\"http://textom.co.kr\">http://textom.co.kr</uri>), UCINET 6 social network analysis software (Analytic Technologies Corp., Lexington, KY, USA) (<uri xlink:href=\"http://www.analytictech.com\">http://www.analytictech.com</uri>)</td></tr></tbody></table></table-wrap><table-wrap id=\"ijerph-17-05577-t002\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05577-t002_Table 2</object-id><label>Table 2</label><caption><p>Collection channel, number of data points, and volume.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Collection Channel</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Number of Data Points</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Volume</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Naver</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5343</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.18 MB</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Daum</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3401</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.19 MB</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Google</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">534</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6.99 MB</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Total</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">9278</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">10.36 MB</td></tr></tbody></table></table-wrap><table-wrap id=\"ijerph-17-05577-t003\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05577-t003_Table 3</object-id><label>Table 3</label><caption><p>Results of the frequency analysis.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Rank</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Term</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Freq.</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Rank</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Term</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Freq.</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Exercise</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">872</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">26</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Stress </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">257</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Mind</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">851</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">27</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Physical education </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">256</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Health </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">824</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">28</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Skin</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">251</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Program </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">782</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">29</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Adult </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">232</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Burden </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">744</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">30</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Improve </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">230</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Vitamin D</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">737</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">31</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Physical activity</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">229</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Outdoor activity </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">734</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">32</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Dream</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">228</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Immunity </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">729</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">33</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Female</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">227</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Sunbathing </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">719</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">34</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Experience</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">226</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Activity </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">633</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">35</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Soccer</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">221</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">11</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Management </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">538</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">36</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Physical strength</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">213</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">12</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">School </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">520</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">37</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Person</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">211</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Children </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">488</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">38</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Treatment</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">209</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">14</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Participation </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">429</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">39</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Help</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">203</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">15</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Education </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">415</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">40</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Camp</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">197</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">16</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Student </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">401</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">41</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Culture</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">196</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">17</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Society </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">354</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">42</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Time</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">196</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">18</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Growth </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">349</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">43</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Sports activity </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">187</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">19</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Mental </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">336</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">44</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">World</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">183</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">20</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Child </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">321</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">45</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Obesity</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">182</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">21</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Development </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">305</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">46</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Wholesome</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">175</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">22</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Kid </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">279</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">47</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Emotion</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">174</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">23</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Body </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">273</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">48</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Problem</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">173</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">24</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Game </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">260</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">49</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Enhancement</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">171</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">25</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Opportunity </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">258</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">50</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Sport for all </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">166</td></tr></tbody></table></table-wrap><table-wrap id=\"ijerph-17-05577-t004\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05577-t004_Table 4</object-id><label>Table 4</label><caption><p>Term frequency–inverse document frequency analysis results.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Rank</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Term</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Freq.</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Rank</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Term</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Freq.</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Exercise </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2108.070</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">26</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Physical education </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">906.315</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Health </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1961.843</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">27</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Female</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">896.207</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Program </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1928.765</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">28</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Stress </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">894.970</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Mind </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1861.837</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">29</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Opportunity </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">878.059</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Burden </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1722.687</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">30</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Adult </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">824.563</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Vitamin D</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1718.496</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">31</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Physical activity</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">822.584</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Outdoor activity </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1707.490</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">32</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Soccer</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">822.293</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Immunity </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1702.844</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">33</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Treatment</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">820.864</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Sunbathing </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1687.441</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">34</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Improve </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">819.604</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Activity </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1599.081</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">35</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Dream</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">812.477</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">11</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Management </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1507.636</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">36</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Experience</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">811.808</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">12</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">School </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1490.146</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">37</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Physical strength</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">787.896</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Children </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1431.463</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">38</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Person</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">773.799</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">14</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Participation </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1255.191</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">39</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Camp</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">769.779</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">15</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Education </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1251.933</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">40</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Help</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">738.214</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">16</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Student </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1218.513</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">41</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Culture</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">731.439</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">17</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Society </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1112.992</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">42</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">World</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">727.613</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">18</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Growth </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1086.663</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">43</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Sport for all </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">727.328</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">19</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Child </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1068.955</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">44</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Time</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">726.069</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">20</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Mental </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1056.399</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">45</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Obesity</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">707.586</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">21</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Development </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1019.094</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">46</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Sports activity </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">693.743</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">22</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Kid </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">964.428</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">47</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Problem</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">677.149</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">23</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Skin</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">963.135</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">48</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Emotion</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">663.428</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">24</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Game </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">937.734</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">49</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Wholesome</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">658.000</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">25</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Body </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">933.512</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">50</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Enhancement</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">655.108</td></tr></tbody></table></table-wrap><table-wrap id=\"ijerph-17-05577-t005\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05577-t005_Table 5</object-id><label>Table 5</label><caption><p>Results of degree centrality analysis.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Rank</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Term</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Freq.</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Rank</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Term</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Freq.</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Exercise </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.02857</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">26</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Adult </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.008077</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Program </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.02406</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">27</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Treatment</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.008010</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Mind </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.02079</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">28</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Start</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.007877</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Health </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.02062</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">29</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Problem</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.007576</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Activity </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.01872</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">30</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Skin</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.007376</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Management </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.01545</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">31</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Effect</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.007309</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Student </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.01525</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">32</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Improve </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.007109</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Participation </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.01491</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">33</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Culture</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.007076</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">School </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.01475</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">34</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Help</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.007009</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Education </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.01375</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">35</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Sports activity </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.006909</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">11</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Children </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.01305</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">36</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Experience</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.006909</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">12</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Child </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.01184</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">37</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Physical strength</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.006809</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Kid </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.01094</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">38</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Soccer</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.006742</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">14</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Society </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.01064</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">39</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Stability</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.006508</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">15</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Mental </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.00964</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">40</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Method</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.006441</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">16</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Development </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.00924</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">41</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Increase</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.006308</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">17</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Person</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.00921</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">42</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Camp</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.006275</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">18</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Physical education </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.00917</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">43</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Perform</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.006041</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">19</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Growth </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.00911</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">44</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Practice</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.006041</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">20</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Physical activity</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.00857</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">45</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Obesity</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.006041</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">21</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Opportunity </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.00851</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">46</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Think</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.005874</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">22</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Body </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.00831</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">47</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Dream</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.005741</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">23</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Time</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.00831</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">48</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Athlete</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.005741</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">24</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Game</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.00821</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">49</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Prevent</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.005674</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">25</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Stress</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.00807</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">50</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Develop</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.005640</td></tr></tbody></table></table-wrap><table-wrap id=\"ijerph-17-05577-t006\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05577-t006_Table 6</object-id><label>Table 6</label><caption><p>Results of the convergence of iterated correlations analysis.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Cluster</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Term</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Exercise and health</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Exercise, health, activity, mental, growth, physical strength, help</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Child to adult</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Child, kid, physical education, adult, world, time, problem, person, obese</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Sociocultural development</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Children, education, social, culture, development, improvement, soccer, game, emotion, enhancement</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">4</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Therapy</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Mind, immunity, vitamin D, outdoor activity, burden, sunbathing, body, skin</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">5</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Program</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Program, management, school, student, participation, opportunity, dream, experience, physical activity, sports activity</td></tr></tbody></table></table-wrap></floats-group></article>\n"
]
[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Int J Environ Res Public Health</journal-id><journal-id journal-id-type=\"iso-abbrev\">Int J Environ Res Public Health</journal-id><journal-id journal-id-type=\"publisher-id\">ijerph</journal-id><journal-title-group><journal-title>International Journal of Environmental Research and Public Health</journal-title></journal-title-group><issn pub-type=\"ppub\">1661-7827</issn><issn pub-type=\"epub\">1660-4601</issn><publisher><publisher-name>MDPI</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32751301</article-id><article-id pub-id-type=\"pmc\">PMC7432091</article-id><article-id pub-id-type=\"doi\">10.3390/ijerph17155475</article-id><article-id pub-id-type=\"publisher-id\">ijerph-17-05475</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Article</subject></subj-group></article-categories><title-group><article-title>The Relationship between Masculinity and Internalized Homophobia amongst Australian Gay Men</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0002-9575-1792</contrib-id><name><surname>Thepsourinthone</surname><given-names>Jack</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05475\">1</xref><xref rid=\"c1-ijerph-17-05475\" ref-type=\"corresp\">*</xref></contrib><contrib contrib-type=\"author\"><name><surname>Dune</surname><given-names>Tinashe</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05475\">1</xref><xref ref-type=\"aff\" rid=\"af2-ijerph-17-05475\">2</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0003-0673-4497</contrib-id><name><surname>Liamputtong</surname><given-names>Pranee</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05475\">1</xref><xref ref-type=\"aff\" rid=\"af2-ijerph-17-05475\">2</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0002-1665-5568</contrib-id><name><surname>Arora</surname><given-names>Amit</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05475\">1</xref><xref ref-type=\"aff\" rid=\"af2-ijerph-17-05475\">2</xref><xref ref-type=\"aff\" rid=\"af3-ijerph-17-05475\">3</xref><xref ref-type=\"aff\" rid=\"af4-ijerph-17-05475\">4</xref></contrib></contrib-group><aff id=\"af1-ijerph-17-05475\"><label>1</label>School of Health Sciences, Western Sydney University, Sydney 2751, Australia; <email>[email protected]</email> (T.D.); <email>[email protected]</email> (P.L.); <email>[email protected]</email> (A.A.)</aff><aff id=\"af2-ijerph-17-05475\"><label>2</label>Translational Health Research Institute, Western Sydney University, Sydney 2751, Australia</aff><aff id=\"af3-ijerph-17-05475\"><label>3</label>Oral Health Services, Sydney Local Health District, Sydney 2010, Australia</aff><aff id=\"af4-ijerph-17-05475\"><label>4</label>Sydney Medical School, The University of Sydney, Sydney 2050, Australia</aff><author-notes><corresp id=\"c1-ijerph-17-05475\"><label>*</label>Correspondence: <email>[email protected]</email>; Tel.: +61-2-4620-3655</corresp></author-notes><pub-date pub-type=\"epub\"><day>29</day><month>7</month><year>2020</year></pub-date><pub-date pub-type=\"ppub\"><month>8</month><year>2020</year></pub-date><volume>17</volume><issue>15</issue><elocation-id>5475</elocation-id><history><date date-type=\"received\"><day>22</day><month>6</month><year>2020</year></date><date date-type=\"accepted\"><day>27</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>© 2020 by the authors.</copyright-statement><copyright-year>2020</copyright-year><license license-type=\"open-access\"><license-p>Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>).</license-p></license></permissions><abstract><p>Due to the heterosexist ideals associated with gender norms, gay men often experience negative attitudes towards their own sexuality—internalized homophobia. As a result, gay men often feel compelled to compensate for their perceived lack of masculinity. The study aimed to investigate the relationship and predictive power of masculinity on gay men’s experiences of internalized homophobia. A sample of 489 self-identified Australian gay men 18–72 years old participated in an online survey on masculinity and homosexuality. Descriptive statistics, bivariate correlations, and sequential multiple regressions were used to test the study’s aims. Sequential multiple regressions revealed that conformity to masculine norms and threats to masculinity contingency were stronger predictors of internalized homophobia over and above demographic and other factors. Given the already known psychological risks associated with social isolation, internalized homophobia, and the poor mental health outcomes associated with sexual minority groups, it is suggested that gay men who are experiencing high degrees of internalized homophobia should not be distancing themselves from other gay men but, conversely, seek a strong relationship with the LGBTI community.</p></abstract><kwd-group><kwd>internalized homophobia</kwd><kwd>homonegativity</kwd><kwd>masculinity</kwd><kwd>LGBT</kwd><kwd>gender norms</kwd></kwd-group></article-meta></front><body><sec sec-type=\"intro\" id=\"sec1-ijerph-17-05475\"><title>1. Introduction</title><p>Like any individual, the socio-ecological environment of a gay man includes a complex network ranging from the macrosystem—including broader social structures and ideologies—to the microsystem—including their family and close social networks which progressively shapes (and is shaped by) the individual [<xref rid=\"B1-ijerph-17-05475\" ref-type=\"bibr\">1</xref>,<xref rid=\"B2-ijerph-17-05475\" ref-type=\"bibr\">2</xref>,<xref rid=\"B3-ijerph-17-05475\" ref-type=\"bibr\">3</xref>]. Within the gay male community, heteronormative ideals play a prominent role—the rewarding of traditionally masculine behavior and stigmatization of effeminate behavior [<xref rid=\"B4-ijerph-17-05475\" ref-type=\"bibr\">4</xref>]. As a consequence, sexual minority individuals often experience negative attitudes towards their own sexuality—internalized homonegativity [<xref rid=\"B5-ijerph-17-05475\" ref-type=\"bibr\">5</xref>].</p><p>Gay men often experience a higher degree of negative attitudes, abuse, and extreme states of mind in response to internalized homophobia (e.g., suicide and homicide) [<xref rid=\"B6-ijerph-17-05475\" ref-type=\"bibr\">6</xref>,<xref rid=\"B7-ijerph-17-05475\" ref-type=\"bibr\">7</xref>,<xref rid=\"B8-ijerph-17-05475\" ref-type=\"bibr\">8</xref>,<xref rid=\"B9-ijerph-17-05475\" ref-type=\"bibr\">9</xref>] as well as greater violence and discrimination based on gender norm violation as compared to lesbian, bisexual, and genderqueer women who tend to benefit from such deviations [<xref rid=\"B10-ijerph-17-05475\" ref-type=\"bibr\">10</xref>]. Furthermore, numerous studies have made connections between internalized homophobia and depression, poor wellbeing, sexual discrimination, shame, body dissatisfaction, eating disorders, and suicidal ideation [<xref rid=\"B11-ijerph-17-05475\" ref-type=\"bibr\">11</xref>,<xref rid=\"B12-ijerph-17-05475\" ref-type=\"bibr\">12</xref>,<xref rid=\"B13-ijerph-17-05475\" ref-type=\"bibr\">13</xref>,<xref rid=\"B14-ijerph-17-05475\" ref-type=\"bibr\">14</xref>,<xref rid=\"B15-ijerph-17-05475\" ref-type=\"bibr\">15</xref>]. Similarly, unlike cisgender men, transgender men experience a reduction in mental health issues and more positive wellbeing outcomes in relation to conformity to masculine norms [<xref rid=\"B16-ijerph-17-05475\" ref-type=\"bibr\">16</xref>]. Berg et al. [<xref rid=\"B12-ijerph-17-05475\" ref-type=\"bibr\">12</xref>] identified that, while most literature on internalized homophobia dating 1989-2012 examines the relationship between internalized homophobia and other factors, there exists little to no empirical research on the relationship between masculine norms and internalized homophobia. Furthermore, only a maximum of eight papers were identified to originate from Australia [<xref rid=\"B12-ijerph-17-05475\" ref-type=\"bibr\">12</xref>]. Since this, Provence et al. [<xref rid=\"B17-ijerph-17-05475\" ref-type=\"bibr\">17</xref>] identified key issues around heterophobia, internalized homophobia, and the process of coming out when examining relational barriers between straight and gay men and establishing links to gendered norms. Given the negative mental health outcomes associated with internalized homophobia and the sparsity of research explicitly examining the role and relationship of masculine norms on internalized homophobia, further investigation to identify the socio-ecological underpinnings of internalized homophobia is warranted in order to better predict the salience and impact of internalized homophobia among Australian gay men across the ecology of their environment.</p><sec id=\"sec1dot1-ijerph-17-05475\"><title>1.1. Masculinity</title><p>Manhood and masculinity, unlike womanhood and femininity, are impermanent and tenuously maintained [<xref rid=\"B18-ijerph-17-05475\" ref-type=\"bibr\">18</xref>]. In traditional Western and heteronormative contexts, women are expected to be passive, sentimental, and emotive whilst men are expected to be aggressive, stoic, and brave [<xref rid=\"B19-ijerph-17-05475\" ref-type=\"bibr\">19</xref>]. Heteronormative ideals which polarize masculinity and femininity are argued to motivate men’s maladaptive behaviors, negative mental health outcomes, feelings of inferiority, overcompensation, and contribute to men’s fear of femininity and concerns around anti-effeminacy, success, power, and competition, restrictive emotionality, and risk-taking [<xref rid=\"B4-ijerph-17-05475\" ref-type=\"bibr\">4</xref>,<xref rid=\"B18-ijerph-17-05475\" ref-type=\"bibr\">18</xref>,<xref rid=\"B20-ijerph-17-05475\" ref-type=\"bibr\">20</xref>,<xref rid=\"B21-ijerph-17-05475\" ref-type=\"bibr\">21</xref>]. However, there exists the common perception of effeminate men (regardless of actual sexual identity) as being homosexual—that is, homosexuality is equivalent to femininity [<xref rid=\"B17-ijerph-17-05475\" ref-type=\"bibr\">17</xref>,<xref rid=\"B22-ijerph-17-05475\" ref-type=\"bibr\">22</xref>]. This arguably runs the risk of also polarizing perceptions of masculinity and homosexuality.</p></sec><sec id=\"sec1dot2-ijerph-17-05475\"><title>1.2. Homophobia</title><p>Homophobia (or homonegativity) is conceptualized as the fear or hatred of homosexuality and the fear of being a homosexual [<xref rid=\"B23-ijerph-17-05475\" ref-type=\"bibr\">23</xref>,<xref rid=\"B24-ijerph-17-05475\" ref-type=\"bibr\">24</xref>]. This definition provides a more holistic perception of homophobia as it considers the experiences of both straight and gay individuals (i.e., internalized homophobia). However, when considering notions of homosexuality and femininity being synonymous, the definition of homophobia can, arguably, be revised to being “the fear of femininity and the fear of being effeminate”.</p><p>Falomir-Pichastor and Mugny [<xref rid=\"B25-ijerph-17-05475\" ref-type=\"bibr\">25</xref>] aimed to explain homophobia through social identity theory and the relationship between in- and out-groups. It was argued that gay men pose a threat to hegemonic masculinity and, therefore, straight men are motivated to maintain a distinct gender identity. This was further highlighted in Martínez, Vázquez, and Falomir-Pichastor’s [<xref rid=\"B26-ijerph-17-05475\" ref-type=\"bibr\">26</xref>] study whereby links to anti-effeminacy were made. Similarly, when having their masculinity threatened, masculine-identifying men tend to rate in-group members (masculine men) as more likeable than outgroup members (effeminate men) and are less likely to interact with out-group members [<xref rid=\"B27-ijerph-17-05475\" ref-type=\"bibr\">27</xref>,<xref rid=\"B28-ijerph-17-05475\" ref-type=\"bibr\">28</xref>]. When considering the previous definition of homophobia in conjunction, it can be argued that similar effects may be present within gay men—that is, can internalized homophobia be explained through gay men’s motivation to distinguish themselves from other (stereotypical) gay men through the conformity of masculinity and anti-effeminacy ideals?</p><p>Masculine behavior among gay men is commonly referred to as “straight-acting” and is argued to be an emulation of heteronormative masculinity—and, arguably, heterosexuality [<xref rid=\"B29-ijerph-17-05475\" ref-type=\"bibr\">29</xref>,<xref rid=\"B30-ijerph-17-05475\" ref-type=\"bibr\">30</xref>]. Similar to heteronormative masculinity, straight-acting masculinity is inclusive of anti-effeminacy ideals and homophobia [<xref rid=\"B30-ijerph-17-05475\" ref-type=\"bibr\">30</xref>]. For instance, prejudice between straight-acting and effeminate gay men is normalized and even glorified within the gay community and is often perpetrated by others who were previously discriminated against for gender non-conformity [<xref rid=\"B31-ijerph-17-05475\" ref-type=\"bibr\">31</xref>,<xref rid=\"B32-ijerph-17-05475\" ref-type=\"bibr\">32</xref>]. This heteronormative approach to masculinity—harassment due to gender non-conformity—is shown to predict later adult life body shame and anxiety among gay men [<xref rid=\"B33-ijerph-17-05475\" ref-type=\"bibr\">33</xref>,<xref rid=\"B34-ijerph-17-05475\" ref-type=\"bibr\">34</xref>]. Clarkson [<xref rid=\"B30-ijerph-17-05475\" ref-type=\"bibr\">30</xref>] argued that the anti-effeminacy ideals perpetuated through hegemonic masculinities which favor heteronormative expressions of gender are jeopardizing the very diversity that the Lesbian, Gay, Bisexual, Transgender, and/or Intersex (LGBTI) community is known for.</p><p>Beginning from the macro level of a gay man’s social ecology, hegemonic masculinity permeates the psychology of how they perceive themselves, others, and the world—including the fear of male effeminacy and the synonymous perceptions of homosexuality and femininity [<xref rid=\"B17-ijerph-17-05475\" ref-type=\"bibr\">17</xref>,<xref rid=\"B24-ijerph-17-05475\" ref-type=\"bibr\">24</xref>]. Moreover, the interactions between the individual and their micro-, meso-, and exo-systems arguably contribute further to the issues presented [<xref rid=\"B4-ijerph-17-05475\" ref-type=\"bibr\">4</xref>,<xref rid=\"B26-ijerph-17-05475\" ref-type=\"bibr\">26</xref>,<xref rid=\"B32-ijerph-17-05475\" ref-type=\"bibr\">32</xref>]. Based on the literature discussed above, <xref ref-type=\"fig\" rid=\"ijerph-17-05475-f001\">Figure 1</xref> depicts a typical ecological environment for a gay man and highlights various areas hegemonic masculinity pervades and exerts an influence. It can, therefore, be argued that the role of hegemonic masculinity on a gay man’s life cannot be examined in isolation but, rather, as a complete system of variables—each a contributing and subsequent factor in the manufacturing of internalized homophobia.</p></sec><sec id=\"sec1dot3-ijerph-17-05475\"><title>1.3. Present Study</title><p>Despite common understandings that masculinity relates to (and possibly produces) negative attitudes toward homosexuality, there exists limited research explicitly examining masculinity and internalized homophobia [<xref rid=\"B15-ijerph-17-05475\" ref-type=\"bibr\">15</xref>,<xref rid=\"B35-ijerph-17-05475\" ref-type=\"bibr\">35</xref>,<xref rid=\"B36-ijerph-17-05475\" ref-type=\"bibr\">36</xref>]. The present study, therefore, aims to examine the relationship between masculinity and internalized homophobia within a sample of Australian gay men through Bronfenbrenner’s [<xref rid=\"B1-ijerph-17-05475\" ref-type=\"bibr\">1</xref>] socio-ecological perspective. It aims to explore:<list list-type=\"order\"><list-item><p>The degree to which gay men conform and/or value masculine norms and whether it relates to internalized homophobia, and</p></list-item><list-item><p>When controlling for demographic and other factors, does the degree of conformity/valuation of masculine norms predict internalized homophobia.</p></list-item></list></p></sec></sec><sec id=\"sec2-ijerph-17-05475\"><title>2. Materials and Methods</title><sec id=\"sec2dot1-ijerph-17-05475\"><title>2.1. Procedure</title><p>The Human Research Ethics Committee of Western Sydney University (Australia) approved this study (approval number: H12044) prior to its implementation. An online survey (hosted by Qualtrics) was utilized and made available through a hyperlinked text within the study’s advertisement. Individuals participated voluntarily and were informed that they may withdraw from the study at any given time. The details of the study (e.g., the Participant Information Sheet) were provided on the first page of the survey. The Participant Information Sheet provided details about the study, participant requirements, duration, use of data, and contact details of the supervisor if required under any circumstances. Once the participants read the Participant Information Sheet, they were informed that clicking on the “continue” button below the Participant Information Sheet constituted their consent to participate in the survey.</p></sec><sec id=\"sec2dot2-ijerph-17-05475\" sec-type=\"subjects\"><title>2.2. Participants</title><p>For this study, participants were recruited via advertisements through LGBTI networks (e.g., LGBTI Alliance of Australia, Queensland Aids Council; 4.3%), social media (e.g., Facebook, Twitter, Instagram; 8.4%), dating applications (e.g., Grindr; 84.9%), flyers placed across Western Sydney University campuses (1%), and word of mouth (1.4%). A sample of 489 self-identified Australian gay men over the age of 18 (M = 36, SD = 12.20) participated in an online survey on masculinity and homosexuality. Those identifying as transgender, bisexual, etc. were excluded from the study as it aims to assess and compare results from a single group identity (gay men). Gay men are argued to be most adversely affected by heteronormative constructions of masculinity [<xref rid=\"B4-ijerph-17-05475\" ref-type=\"bibr\">4</xref>,<xref rid=\"B15-ijerph-17-05475\" ref-type=\"bibr\">15</xref>] and femininity, are more prone to resultant health and wellbeing complications [<xref rid=\"B18-ijerph-17-05475\" ref-type=\"bibr\">18</xref>,<xref rid=\"B37-ijerph-17-05475\" ref-type=\"bibr\">37</xref>], and are more likely to experience violence and discrimination based on gender norm deviation than lesbian, bisexual, and genderqueer women, as well as transgender men [<xref rid=\"B10-ijerph-17-05475\" ref-type=\"bibr\">10</xref>].</p></sec><sec id=\"sec2dot3-ijerph-17-05475\"><title>2.3. Measures</title><sec id=\"sec2dot3dot1-ijerph-17-05475\"><title>2.3.1. Demographics</title><p>A demographic questionnaire was utilized to ascertain participant’s background information—age, gender, ethnicity, post code, religion, and sexual orientation. <xref rid=\"ijerph-17-05475-t001\" ref-type=\"table\">Table 1</xref> depicts the sample’s demographic.</p></sec><sec id=\"sec2dot3dot2-ijerph-17-05475\"><title>2.3.2. Internalized Homophobia</title><p>To measure internalized homophobia, the Internalized Homonegativity Inventory (IHNI) [<xref rid=\"B5-ijerph-17-05475\" ref-type=\"bibr\">5</xref>] was utilized. It consists of 23 items and measures the individuals’ Personal Homonegativity, Gay Affirmation, and Morality of Homosexuality using a 6-point Likert scale ranging from 1 (Strongly Disagree) to 6 (Strongly Agree). Items involve identifying the degree of one’s agreeability to a statement (e.g., “I feel ashamed of my homosexuality”). The IHNI is a reliable and valid measure of internalized homophobia in gay men and was developed in order to address limitations of content validity within previous scales measuring internalized homophobia [<xref rid=\"B5-ijerph-17-05475\" ref-type=\"bibr\">5</xref>]. The scale showed good internal consistency (Cronbach’s <italic>α</italic> = 0.93).</p></sec><sec id=\"sec2dot3dot3-ijerph-17-05475\"><title>2.3.3. Conformity to Masculine Norms</title><p>The Conformity to Masculine Norms Inventory-46 (CMNI-46) [<xref rid=\"B38-ijerph-17-05475\" ref-type=\"bibr\">38</xref>] was used to measure participants’ current conformity to masculine norms. The scale includes 46 items and measures individuals’ conformity to masculine norms along nine subscales: Emotional Control, Winning, Playboy, Violence, Self-reliance, Risk-taking, Power over Women, Primacy of Work, and Heterosexual Self-presentation. Items involve identifying the degree of one’s agreeability to a statement (e.g., “It bothers me when I have to ask for help”) using a 4-point Likert scale ranging from 1 (Strongly Disagree) to 4 (Strongly Agree). The CMNI-46 is a reliable and valid measure of males’ conformity to masculine norms and has omitted several items possessing poor construct specificity from the original Conformity to Masculine Norms Inventory [<xref rid=\"B38-ijerph-17-05475\" ref-type=\"bibr\">38</xref>,<xref rid=\"B39-ijerph-17-05475\" ref-type=\"bibr\">39</xref>]. Although Hammer, Heath, and Vogel [<xref rid=\"B40-ijerph-17-05475\" ref-type=\"bibr\">40</xref>] advised against the use of the scale as unidimensional, the present study’s scope examines masculine norms as a whole as opposed to specific dimensions. The total scale has also been utilized by various studies assessing men’s masculinity [<xref rid=\"B41-ijerph-17-05475\" ref-type=\"bibr\">41</xref>] but, most particularly, gay men’s masculinity as well [<xref rid=\"B20-ijerph-17-05475\" ref-type=\"bibr\">20</xref>,<xref rid=\"B42-ijerph-17-05475\" ref-type=\"bibr\">42</xref>]. The scale showed good internal consistency (Cronbach’s <italic>α</italic> = 0.81).</p></sec><sec id=\"sec2dot3dot4-ijerph-17-05475\"><title>2.3.4. Masculinity Contingency</title><p>The Masculinity Contingency Scale (MCS) [<xref rid=\"B43-ijerph-17-05475\" ref-type=\"bibr\">43</xref>] is a scale assessing a man’s self-worth in relation to his sense of masculinity (e.g., “I can’t respect myself if I don’t behave like a ‘real man’”). The MCS consists of 10 items and measures threats to masculinity (MCS-Threat) and boosts to masculinity (MCS-Boost) using a 7-point Likert scale ranging from 1 (Strongly Disagree) to 7 (Strongly Agree). Both the MCS-Threat (Cronbach’s <italic>α</italic> = 0.94) and MCS-Boost (Cronbach’s <italic>α</italic> = 0.95) demonstrated excellent internal consistency.</p></sec><sec id=\"sec2dot3dot5-ijerph-17-05475\"><title>2.3.5. Childhood Gender Non-Conformity</title><p>To measure participants’ past conformity to masculine norms, the seven item Childhood Gender Non-Conformity Scale (CGNcS) [<xref rid=\"B29-ijerph-17-05475\" ref-type=\"bibr\">29</xref>] was used. It assesses childhood harassment due to gender non-conformity. Individuals are asked to rate their agreement to a statement along a 7-point Likert scale ranging from 1 (Strongly Disagree) to 7 (Strongly Agree). An item may state “As a child I was called a ‘sissy’ by my peers”. The scale showed good internal consistency (Cronbach’s <italic>α</italic> = 0.84).</p></sec><sec id=\"sec2dot3dot6-ijerph-17-05475\"><title>2.3.6. Perceived Distance</title><p>An adapted single item scale from [<xref rid=\"B25-ijerph-17-05475\" ref-type=\"bibr\">25</xref>] will be used to assess individuals’ perceived distance from (other) gay men (i.e., it is likely that someone would think I am a homosexual). In other words, it measures the degree an individual perceives themselves to conform/deviate from the heteronormative perception of homosexuality. The scale utilizes a 7-point Likert scale ranging from 1 (Very Unlikely) to 7 (Very Likely).</p></sec><sec id=\"sec2dot3dot7-ijerph-17-05475\"><title>2.3.7. Perceived Similarity</title><p>Similarly, a seven-item scale was used to assess individuals’ perceived similarity with (other) gay men [<xref rid=\"B44-ijerph-17-05475\" ref-type=\"bibr\">44</xref>]. Using a 7-point Likert scale ranging from 1 (Absolutely Not) to 7 (Absolutely), individuals are asked “To what extent do you think you are similar to gay men with regard to each of the following aspects?”—Emotions, Needs, Wishes, Intimate Relationships, Friendships, Professional Relationships, and General Similarity. The scale showed good internal consistency (Cronbach’s <italic>α</italic> = 0.86).</p></sec></sec><sec id=\"sec2dot4-ijerph-17-05475\"><title>2.4. Statistical Analyses</title><p>Statistical analyses were conducted using Statistical Package for Social Science (SPSS; IBM, Sydney, Australia) version 22 and, following data collection, participant data was screened for missing data (participants who withdrew from the study were excluded), as well as univariate and multivariate outliers. Using the ± 3.29 <italic>z</italic> score criteria, 62 univariate outliers were identified and deleted from the sample. Additionally, with alpha set at 0.001, two multivariate outliers with Mahalanobis distance scores above 43.82 were identified and deleted. Once the data was screened, 489 participant datasets remained.</p><p>Descriptive statistical analyses were used to examine the degree gay men conform to measures of masculinity, internalized homophobia, and other variables. Bivariate correlations were used to examine relationships between demographic, internalized homophobia, measures of masculinity, and interpersonal factors. Furthermore, sequential multiple regression analyses were conducted using three models beginning with demographic variables and inserting interpersonal variables and measures of masculinity, respectively, at each step.</p></sec></sec><sec sec-type=\"results\" id=\"sec3-ijerph-17-05475\"><title>3. Results</title><p>The study’s sample included 489 self-identified Australian gay men 18–72 years of age (M = 36, SD = 12.20). In viewing the sample means alone, it can be seen that participants tend to conform neither extremely highly nor extremely lowly to masculine norms (M = 95.82, SD = 11.46). Additionally, participants tended not to value masculinity very highly where MCS-Threat (M = 11.22, SD = 6.40) and MCS-Boost (M = 18.92, SD = 8.04). <xref rid=\"ijerph-17-05475-t002\" ref-type=\"table\">Table 2</xref> displays the participant mean scores in all measured variables.</p><p>Pearson product-moment correlations were performed between the variables age, education, Internalized Homophobia, Conformity to Masculine Norms, Masculinity Contingency-Threat, Masculinity Contingency-Boost, Childhood Gender Non-Conformity, Perceived Distance, and Perceived Similarity using an alpha level of 0.05. As the sample was large (<italic>n</italic> = 489), assumptions of homogeneity and variance were satisfactory. <xref rid=\"ijerph-17-05475-t003\" ref-type=\"table\">Table 3</xref> depicts correlations between all test variables.</p><p>A sequential multiple regression was conducted in order to determine whether the variables (age, education, Conformity to Masculine Norms, Masculinity Contingency-Threat, Masculinity Contingency-Boost, Childhood Gender Non-Conformity, Perceived Distance, and Perceived Similarity) could predict Internalized Homophobia. With an <italic>n</italic> of 489, assumptions of homogeneity were satisfactory. Results of the multiple regression are displayed in <xref rid=\"ijerph-17-05475-t004\" ref-type=\"table\">Table 4</xref>.</p><p>Model 1, F (2,486) = 7.70, <italic>p</italic> < 0.001, shows the association between demographic information and internalized homophobia whereby age is a significant predictor (<italic>p</italic> < 0.001). Model 2, F (4,484) = 13.43, <italic>p</italic> < 0.001, shows the association when interpersonal variables are added to the model. Age (<italic>p</italic> < 0.001) continues to act as a significant predictor while Perceived Distance (<italic>p</italic> < 0.05) and Perceived Similarity (<italic>p</italic> < 0.001) are also significant predictors. Additionally, Model 3, F (8,480) = 22.44, <italic>p</italic> < 0.001, shows the association when various measures of gender norm conformity are included in the model. Age (<italic>p</italic> < 0.01) and Perceived Similarity (<italic>p</italic> < 0.01) remain a significant predictor of internalized homophobia while Perceived Distance is no longer significant. Additionally, Conformity to Masculine Norms (<italic>p</italic> < 0.001) and Masculinity Contingency-Threat (<italic>p</italic> < 0.001) were significant predictors. The results demonstrate that age, perceived similarity, Conformity to Masculine Norms, and Masculinity Contingency in relation to threats to masculinity were sufficient to predict levels of Internalized Homophobia over and above other demographic variables, Perceived Distance to gay men, and other measures of gender norm conformity.</p></sec><sec sec-type=\"discussion\" id=\"sec4-ijerph-17-05475\"><title>4. Discussion</title><p>In order to better understand the socio-ecological factors contributing to the development of internalized homophobia, the present study sought to examine the degree gay men conform to and/or value masculine norms. Beginning with the individual, descriptive statistics suggest that Australian gay men’s conformity to masculine norms tends to lean towards neither extremes. Additionally, Australian gay men tend not to value masculinity very highly, as suggested by their sense of self-worth in relation to threats/boosts to masculinity.</p><p>To further understand the salience of influencers from an individual’s macrosystem on the individual, the study aimed to explore the degree to which gay men’s conformity and/or valuation of hegemonic masculinity predicted the degree of internalized homophobia experienced. The results supported that only the measures of Conformity to Masculine Norms and MCS-Threat remained as significant predictors of internalized homophobia. This suggests that individuals’ current conformity to masculine norms are stronger predictors of individuals’ experiences of internalized homophobia as compared to past conformity (e.g., childhood gender non-conformity). This is contrary to other findings which suggest that childhood harassment due to gender non-conformity would predict negative later life health outcomes (e.g., body shame, anxiety, gender-related strain) [<xref rid=\"B32-ijerph-17-05475\" ref-type=\"bibr\">32</xref>,<xref rid=\"B33-ijerph-17-05475\" ref-type=\"bibr\">33</xref>,<xref rid=\"B34-ijerph-17-05475\" ref-type=\"bibr\">34</xref>]. Additionally, the results suggest that the significance of threats to gay men’s masculinity are superior predictors of internalized homophobia as compared to the significance of boosts to their masculinity.</p><p>The present study’s results not only suggest that individuals who conform more to masculine norms tend to possess higher degrees of internalized homophobia than those who conform less to masculine norms but also, by knowing how strongly an individual adheres to masculine norms, one may predict the degree of internalized homophobia the said individual harbors. From these findings, it is argued that either gay men who possess stronger internalizations of homophobia utilize masculinity as a compensatory strategy or that hegemonic masculinities foster internalized homophobia—resulting in masculine-conforming men to possess stronger internalized homophobia. In other words, influences exerted within an individual’s macrosystem (masculinity) may foster internalized homophobia and/or factors within the individual’s self (internalized homophobia) influence how they present themselves and interact with others in their micro- and meso-systems. It has been argued that gay men who are overly concerned with masculine norms utilize hyper-masculinity as a compensatory strategy for their perceived sense of inferiority [<xref rid=\"B4-ijerph-17-05475\" ref-type=\"bibr\">4</xref>,<xref rid=\"B45-ijerph-17-05475\" ref-type=\"bibr\">45</xref>]. Additionally, feminine men often receive more negative attitudes, social and romantic rejection, victimization, and harassment from others, as compared to masculine men [<xref rid=\"B46-ijerph-17-05475\" ref-type=\"bibr\">46</xref>,<xref rid=\"B47-ijerph-17-05475\" ref-type=\"bibr\">47</xref>,<xref rid=\"B48-ijerph-17-05475\" ref-type=\"bibr\">48</xref>]. This is also evident within the Asian culture (e.g., Nepal, Thailand, Vietnam) and can be argued to be consistent across broader societal contexts [<xref rid=\"B10-ijerph-17-05475\" ref-type=\"bibr\">10</xref>,<xref rid=\"B49-ijerph-17-05475\" ref-type=\"bibr\">49</xref>,<xref rid=\"B50-ijerph-17-05475\" ref-type=\"bibr\">50</xref>,<xref rid=\"B51-ijerph-17-05475\" ref-type=\"bibr\">51</xref>]. Considering the notion that homosexuality is synonymous to femininity [<xref rid=\"B17-ijerph-17-05475\" ref-type=\"bibr\">17</xref>], it is no surprise that gay men experience a sense of inferiority/negativity regarding their own sexuality and aim to compensate by adopting (what they perceive to be) the opposite—masculinity.</p><p>Considering O’Neal et al.’s [<xref rid=\"B24-ijerph-17-05475\" ref-type=\"bibr\">24</xref>] definition of homophobia alongside Provence et al.’s [<xref rid=\"B17-ijerph-17-05475\" ref-type=\"bibr\">17</xref>] assertion that homosexuality is synonymous to femininity, it is argued that, as a compensatory strategy, gay men who possess higher degrees of (internalized) homophobia reduce their likelihood of being perceived as gay by conforming to what they perceive as being the antithesis of homosexuality—masculinity. This (arguably hyper-masculine) behavior can be described as straight-acting. The term is argumentative, in itself, as it is perplexing how gay men may refer to themselves as straight-acting rather than masculine. This suggests that there exists a pervasive ideology that masculinity is exclusive to heterosexuality—or in other words, masculinity is reified as the heterosexual male.</p><p>As it is maintained that male heterosexuality is perceived as being synonymous to masculinity, it is also maintained that male homosexuality is perceived as being synonymous to femininity [<xref rid=\"B17-ijerph-17-05475\" ref-type=\"bibr\">17</xref>]. This societal understanding of gender and sexuality, therefore, presents an oxymoron in the phrase “gay male”. By this notion, the two terms “gay” and “male” can be regarded as direct oppositions of each other. It is no wonder that gay men may experience gender discrepancy strain. In conjunction with the present study’s results, it is argued that gay men experience all three types of gender-related strain. Having traced this issue back to influences within the individual’s macrosystem, it is, therefore, argued that heteronormative understandings of gender and sexuality need to be revised in order for the strain experienced by gay men to be adequately addressed.</p><p>Additionally, perceived similarity remained a significant predictor of internalized homophobia whereby individuals who harbored higher degrees of internalized homophobia tended to perceive themselves as dissimilar to other gay men. This corroborates Falomir-Pichastor and Mugny’s [<xref rid=\"B25-ijerph-17-05475\" ref-type=\"bibr\">25</xref>] theory explaining homophobia through social identities—in- and out-groups. It was argued that gay men pose a threat to the masculine identity and, therefore, straight men are motivated to maintain a distinct gender identity. Similarly, masculine men tend to show prejudice and disinterest in interacting with effeminate men [<xref rid=\"B27-ijerph-17-05475\" ref-type=\"bibr\">27</xref>,<xref rid=\"B28-ijerph-17-05475\" ref-type=\"bibr\">28</xref>]. Therefore, just as straight men with higher degrees of homophobia dissociate their gender identities from gay men, it can be argued that so too do gay men with higher degrees of internalized homophobia perceive themselves as dissimilar to other gay men. Amongst a demographic considered a minority, the distancing of the self from other gay men can be considered a distressing social phenomenon.</p><p><xref ref-type=\"fig\" rid=\"ijerph-17-05475-f002\">Figure 2</xref> depicts the socio-ecological system relevant to the study’s participants and highlights various areas where hegemonic masculinity pervades and exerts an influence. Although most gay men’s microsystem consists mostly of non-LGBTI friends [<xref rid=\"B52-ijerph-17-05475\" ref-type=\"bibr\">52</xref>,<xref rid=\"B53-ijerph-17-05475\" ref-type=\"bibr\">53</xref>], strong interpersonal relationships with other LGBTI individuals and having a sense of belonging with the gay community are argued to alleviate psychological distress and depression [<xref rid=\"B54-ijerph-17-05475\" ref-type=\"bibr\">54</xref>,<xref rid=\"B55-ijerph-17-05475\" ref-type=\"bibr\">55</xref>]. Similar positive effects are evident among transgender individuals [<xref rid=\"B56-ijerph-17-05475\" ref-type=\"bibr\">56</xref>]. Given the psychological risks associated with social isolation, internalized homophobia, and the poor mental health outcomes associated with sexual minority groups [<xref rid=\"B11-ijerph-17-05475\" ref-type=\"bibr\">11</xref>,<xref rid=\"B12-ijerph-17-05475\" ref-type=\"bibr\">12</xref>,<xref rid=\"B13-ijerph-17-05475\" ref-type=\"bibr\">13</xref>,<xref rid=\"B14-ijerph-17-05475\" ref-type=\"bibr\">14</xref>,<xref rid=\"B15-ijerph-17-05475\" ref-type=\"bibr\">15</xref>,<xref rid=\"B16-ijerph-17-05475\" ref-type=\"bibr\">16</xref>], it is suggested that, not only gay men but, LGBTI individuals who are experiencing high degrees of internalized homophobia should not be distancing themselves from other LGBTI individuals but, conversely, seek a strong relationship with them.</p><p>In addition to masculinity, analyses revealed that age predicted internalized homophobia, whereby older gay men, as compared to their younger counterparts, tend to experience lower degrees of internalized homophobia. This is consistent with other studies which found internalized homophobia and negative health and psychological outcomes to decrease with age while resiliency increases [<xref rid=\"B52-ijerph-17-05475\" ref-type=\"bibr\">52</xref>,<xref rid=\"B57-ijerph-17-05475\" ref-type=\"bibr\">57</xref>]. This, however, was not true for gay men who recently came out [<xref rid=\"B57-ijerph-17-05475\" ref-type=\"bibr\">57</xref>]. It is maintained that the common phrase “it gets better” holds true in regard to gay men’s experiences of internalized homophobia. It is, therefore, argued that programs and policies in support of individuals who have recently come out (e.g., gay youths) should be improved and advocated in the aim of reducing/mitigating the effects of internalized homophobia.</p><sec><title>Limitations</title><p>A limitation of the study may include the vagueness of the Perceived Similarity and Perceived Distance scales which do not specify whether the other individual is feminine/masculine, introverted/extroverted, or any other personality traits. Previous studies [<xref rid=\"B28-ijerph-17-05475\" ref-type=\"bibr\">28</xref>] provided vignettes describing the individual in question. The present study, however, assumes participants to possess similar conceptions of masculinity and the stereotyped gay man (i.e., effeminate). However, previous studies have demonstrated individuals (regardless of gender or sexuality) to possess similar conceptions of gay men [<xref rid=\"B58-ijerph-17-05475\" ref-type=\"bibr\">58</xref>].</p><p>Additionally, the present study’s sample only included individuals identifying as gay. Participants recruited via the dating app, Grindr, comprised 84.90% of the total sample. However, prior to eliminating data from those who do not meet the sample criteria, the study received data from participants identifying as bisexual and, surprisingly, heterosexual. It can be argued that some same-sex attracted males who identify as heterosexual may be experiencing conflicts with internalized homophobia and have not yet come to terms with their sexual identity. Therefore, it can be argued that the study’s results may only be representative of homosexual men who are comfortable with their sexual identity and, therefore, do not suffer extremely from internalized homophobia. Additionally, although the present study did not examine transgender and genderqueer men, it is recognized that gender norms operate under a different framework for transgender men (and women) compared to cisgender men (and women) whereby conformity and non-conformity may be experienced positively [<xref rid=\"B16-ijerph-17-05475\" ref-type=\"bibr\">16</xref>]. It is recommended that future studies include other LGBTI identities as this would allow for a more diverse range of scores. By examining other LGBTI identities, it is expected that studies can better understand the scope and impact internalized homophobia has on LGBTI individuals. Findings may come to contribute to the development of more informed strategies aimed at improving the social, psychological, and physical health outcomes of LGBTI individuals.</p><p>Additionally, the present study’s analyses could arguably provide more statistical power had the outliers and missing data not been excluded and addressed by using methods such as conducting multiple imputations [<xref rid=\"B59-ijerph-17-05475\" ref-type=\"bibr\">59</xref>,<xref rid=\"B60-ijerph-17-05475\" ref-type=\"bibr\">60</xref>]. However, this method is argued to contribute further to any biases, including self-report and social desirability bias, already present within the sample and decrease reliability [<xref rid=\"B59-ijerph-17-05475\" ref-type=\"bibr\">59</xref>,<xref rid=\"B61-ijerph-17-05475\" ref-type=\"bibr\">61</xref>]. Although bias was controlled, Bullock and Ha [<xref rid=\"B62-ijerph-17-05475\" ref-type=\"bibr\">62</xref>] argued that it is impossible to examine non-experimental data completely without bias. Additionally, the present study was exploratory, and it is acknowledged that results from non-experimental designs can only be interpreted as suggestive as opposed to causal [<xref rid=\"B63-ijerph-17-05475\" ref-type=\"bibr\">63</xref>]. Future research is recommended to examine internalized homophobia and masculinity using an experimental design.</p></sec></sec><sec sec-type=\"conclusions\" id=\"sec5-ijerph-17-05475\"><title>5. Conclusions</title><p>Currently, there exists limited research explicitly examining masculinity and internalized homophobia [<xref rid=\"B15-ijerph-17-05475\" ref-type=\"bibr\">15</xref>,<xref rid=\"B35-ijerph-17-05475\" ref-type=\"bibr\">35</xref>,<xref rid=\"B36-ijerph-17-05475\" ref-type=\"bibr\">36</xref>]. The current paper is an exploration into the phenomenon of internalized homophobia and its socio-ecological underpinnings whereby discussion focused mainly on factors within the micro-, meso-, and macro-systems. Future studies, however, may wish to examine factors outside of those discussed and may wish to examine other LGBTI identities. Conceptions of heteronormative masculinity are argued to contribute negatively to non-heteronormative (LGBTI) individuals who abide by them whilst engagement with LGBTI communities is argued to support more positive outcomes for gay men and alleviate gender-related strains experienced. It is hoped that the current findings contribute to the empirical study of masculinity in the context of LGTI individuals and the development of policies and support services, notably LGBTI youth. Future studies are urged to expand upon the knowledge and understanding acquired from the present study, particularly in relation to internalized homophobia and interpersonal relations.</p></sec></body><back><notes><title>Author Contributions</title><p>Conceptualization, J.T. and T.D.; data curation, J.T.; formal analysis, J.T.; funding acquisition, J.T. and T.D.; investigation, J.T.; methodology, J.T.; project administration, J.T.; supervision, T.D., P.L., and A.A.; writing—original draft, J.T.; writing—review and editing, J.T., T.D., P.L., and A.A. All authors have read and agreed to the published version of the manuscript.</p></notes><notes><title>Funding</title><p>This research was funded by the School of Health Sciences, Western Sydney University.</p></notes><notes notes-type=\"COI-statement\"><title>Conflicts of Interest</title><p>T.D., P.L., and A.A. are a Guest Editors on the Special Issue “Addressing Public Health and Health Inequities in Marginalized and Hidden Populations”, and A.A. is on the Editorial Board of International Journal of Environmental Research and Public Health but did not play any role in the peer-review and decision-making process for this manuscript.</p></notes><ref-list><title>References</title><ref id=\"B1-ijerph-17-05475\"><label>1.</label><element-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Bronfenbrenner</surname><given-names>U.</given-names></name></person-group><article-title>Toward an experimental ecology of human development</article-title><source>Am. 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Methods</source><year>2002</year><volume>7</volume><fpage>422</fpage><lpage>445</lpage><pub-id pub-id-type=\"doi\">10.1037/1082-989X.7.4.422</pub-id><pub-id pub-id-type=\"pmid\">12530702</pub-id></element-citation></ref></ref-list></back><floats-group><fig id=\"ijerph-17-05475-f001\" orientation=\"portrait\" position=\"float\"><label>Figure 1</label><caption><p>Socio-ecological map of a gay man.</p></caption><graphic xlink:href=\"ijerph-17-05475-g001\"/></fig><fig id=\"ijerph-17-05475-f002\" orientation=\"portrait\" position=\"float\"><label>Figure 2</label><caption><p>Socio-ecological map of a gay man.</p></caption><graphic xlink:href=\"ijerph-17-05475-g002\"/></fig><table-wrap id=\"ijerph-17-05475-t001\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05475-t001_Table 1</object-id><label>Table 1</label><caption><p>Demographic information for the study sample (<italic>n</italic> = 489).</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Demographic Information</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">(%)</th></tr></thead><tbody><tr><td colspan=\"2\" align=\"left\" valign=\"middle\" rowspan=\"1\">\n<bold><italic>Education</italic></bold>\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">No Formal Education</td><td align=\"right\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.20</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Secondary</td><td align=\"right\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.50</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Higher School Certificate</td><td align=\"right\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6.30</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Diploma/Certificate</td><td align=\"right\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">17.80</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Bachelor</td><td align=\"right\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">39.90</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Post-graduate</td><td align=\"right\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">31.30</td></tr><tr><td colspan=\"2\" align=\"left\" valign=\"middle\" rowspan=\"1\">\n<bold><italic>Religious affiliation</italic></bold>\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Buddhism</td><td align=\"right\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.30</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Christianity</td><td align=\"right\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">23.90</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Hinduism</td><td align=\"right\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.80</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Islam</td><td align=\"right\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.20</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Judaism</td><td align=\"right\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.80</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">No Religion</td><td align=\"right\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">66.40</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Other</td><td align=\"right\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3.70</td></tr></tbody></table></table-wrap><table-wrap id=\"ijerph-17-05475-t002\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05475-t002_Table 2</object-id><label>Table 2</label><caption><p>Descriptive statistics for the study sample.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Variables</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">M</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">SD</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Possible Range</th></tr></thead><tbody><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">IHNI</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">42.25</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13.28</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">23–138</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CMNI</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">95.82</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">11.46</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">46–184</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">MCS-Threat</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">11.22</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6.40</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5–35</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">MCS-Boost</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">18.92</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8.04</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5–35</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Childhood Gender Non-Conformity</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">26.76</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">9.90</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7–49</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Perceived Distance</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.58</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.76</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1–7</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Perceived Similarity</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">33.97</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">7.77</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">7–49</td></tr></tbody></table><table-wrap-foot><fn><p>Note. IHNI = Internalized Homophobia; CMNI = Conformity to Masculine Norms; MCS-Threat = Masculine Contingency-Threat; MCS-Boost = Masculine Contingency-Boost.</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"ijerph-17-05475-t003\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05475-t003_Table 3</object-id><label>Table 3</label><caption><p>Bivariate correlations between demographic and various factors.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">Variables</th><th colspan=\"9\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Variables</th></tr><tr><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Age</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Education</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">IHNI</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">CMNI</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">MCS-T</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">MCS-B</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">CGNcS</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Distance</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Similarity</th></tr></thead><tbody><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Age</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">—</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Education</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.06</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">—</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">IHNI</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.16 **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.08</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">—</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CMNI</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.14 **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.04</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.39 **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">—</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">MCS-Threat</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.02</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.05</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.42 **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.40 **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">—</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">MCS-Boost</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.12 **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.02</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.26 **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.29 **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.55 **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">—</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CGNcS</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.23 **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.04</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.02</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.01</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.12 **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.03</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">—</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Distance</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.15 **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.06</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.14 **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.09</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.18 **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.01</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.44 **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">—</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Similarity</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.02</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.02</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.24 **</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.25 **</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.15 **</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.07</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.13 **</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.23 **</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">—</td></tr></tbody></table><table-wrap-foot><fn><p>** <italic>p</italic> < 0.01 level.</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"ijerph-17-05475-t004\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05475-t004_Table 4</object-id><label>Table 4</label><caption><p>Regression coefficients of the predictors on internalized homonegativity.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">Variables</th><th colspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Model 1</th><th colspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Model 2</th><th colspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Model 3</th></tr><tr><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>B</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">SE <italic>B</italic></th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>β</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>B</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">SE <italic>B</italic></th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>β</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>B</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">SE <italic>B</italic></th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>β</italic>\n</th></tr></thead><tbody><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Age</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.017 ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.05</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.16</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.19 ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.05</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.17</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.13 **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.05</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.12</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Education</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.81</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.55</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.07</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.78</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.53</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.06</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.57</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.48</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.05</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Perceived Distance</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.87 *</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.35</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.11</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.57</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.35</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.07</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Perceived Similarity</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.37 ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.08</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.21</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.22 **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.07</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.13</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CMNI</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.25 ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.05</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.22</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">MCS-Threat</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.61 ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.10</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.29</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">MCS-Boost</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.02</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.08</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.01</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">CGNcS</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.06</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.06</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.04</td></tr></tbody></table><table-wrap-foot><fn><p>Note. <italic>B</italic> = unstandardized regression coefficient; SE <italic>B</italic> = standard error; <italic>β</italic> = standardized regression coefficient * <italic>p</italic> < 0.05; ** <italic>p</italic> < 0.01; *** <italic>p</italic> < 0.001.</p></fn></table-wrap-foot></table-wrap></floats-group></article>\n"
]
[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Int J Environ Res Public Health</journal-id><journal-id journal-id-type=\"iso-abbrev\">Int J Environ Res Public Health</journal-id><journal-id journal-id-type=\"publisher-id\">ijerph</journal-id><journal-title-group><journal-title>International Journal of Environmental Research and Public Health</journal-title></journal-title-group><issn pub-type=\"ppub\">1661-7827</issn><issn pub-type=\"epub\">1660-4601</issn><publisher><publisher-name>MDPI</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32751605</article-id><article-id pub-id-type=\"pmc\">PMC7432092</article-id><article-id pub-id-type=\"doi\">10.3390/ijerph17155511</article-id><article-id pub-id-type=\"publisher-id\">ijerph-17-05511</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Article</subject></subj-group></article-categories><title-group><article-title>Between Migrant Care Work and New Occupational Welfare Tools: Changing Home Care Arrangements in Italy</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0002-3944-873X</contrib-id><name><surname>Casanova</surname><given-names>Georgia</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05511\">1</xref><xref rid=\"c1-ijerph-17-05511\" ref-type=\"corresp\">*</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0002-1862-4159</contrib-id><name><surname>Di Rosa</surname><given-names>Mirko</given-names></name><xref ref-type=\"aff\" rid=\"af2-ijerph-17-05511\">2</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0003-2787-2899</contrib-id><name><surname>Fisher</surname><given-names>Oliver</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05511\">1</xref><xref ref-type=\"aff\" rid=\"af3-ijerph-17-05511\">3</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0001-9278-9544</contrib-id><name><surname>Lamura</surname><given-names>Giovanni</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05511\">1</xref></contrib></contrib-group><aff id=\"af1-ijerph-17-05511\"><label>1</label>Center for Socio-Economic Research on Ageing, IRCCS-INRCA—National Institute of Health and Science on Ageing, 60121 Ancona, Italy; <email>[email protected]</email> (O.F.); <email>[email protected]</email> (G.L.)</aff><aff id=\"af2-ijerph-17-05511\"><label>2</label>Unit of Geriatric Pharmacoepidemiology and Biostatistics, IRCCS-INRCA—National Institute of Health and Science on Ageing, 60121 Ancona, Italy; <email>[email protected]</email></aff><aff id=\"af3-ijerph-17-05511\"><label>3</label>Department of Economics and Social Sciences, Università Politecnica delle Marche, 60121 Ancona, Italy</aff><author-notes><corresp id=\"c1-ijerph-17-05511\"><label>*</label>Correspondence: <email>[email protected]</email>; Tel.: +39-34-7083-6007</corresp></author-notes><pub-date pub-type=\"epub\"><day>30</day><month>7</month><year>2020</year></pub-date><pub-date pub-type=\"ppub\"><month>8</month><year>2020</year></pub-date><volume>17</volume><issue>15</issue><elocation-id>5511</elocation-id><history><date date-type=\"received\"><day>27</day><month>5</month><year>2020</year></date><date date-type=\"accepted\"><day>27</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>© 2020 by the authors.</copyright-statement><copyright-year>2020</copyright-year><license license-type=\"open-access\"><license-p>Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>).</license-p></license></permissions><abstract><p>Austerity measures on services provision, introduced due to recent economic crises, have stimulated the search for innovative welfare solutions, including options that are not directly or entirely funded by public sources. In Italy, recent legislation has promoted the development of occupational welfare (OW) measures, aimed at strengthening the supply of services to support employees with informal (elder) care responsibilities. This paper aims to describe how the newly introduced OW schemes might innovate existing care arrangements, by identifying their impact on the different actors involved in home care provision (care recipients, family carers, home care service providers and migrant care workers), as well as at a macro level in terms of promoting social innovation. The international relevance of the Italian case comes from the fact that it is one of the more representative familistic care regimes, largely characterized by home care provided by informal carers and migrant care workers (MCW). The importance of Italian OW schemes is increasing, and in 2018 their presence in company-level bargaining agreements grew by more than 15%. A rapid review of the literature and expert interviews allowed us to describe the complex Italian OW schemes system, and to identify the positive implications of their application for the country’s long-term care (LTC) context, underlining what makes these measures a clear example of “social innovation” likely to have a future positive impact on home-based care in Italy.</p></abstract><kwd-group><kwd>occupational welfare schemes</kwd><kwd>home care</kwd><kwd>social innovation</kwd><kwd>migrant care work</kwd><kwd>Italy</kwd></kwd-group></article-meta></front><body><sec sec-type=\"intro\" id=\"sec1-ijerph-17-05511\"><title>1. Introduction</title><p>In the last decade, the international debate on home care provision for frail elders has repeatedly underlined the importance of recognizing the role of informal care within a partnership approach in care management, in order to achieve a more effective long-term care (LTC) system [<xref rid=\"B1-ijerph-17-05511\" ref-type=\"bibr\">1</xref>,<xref rid=\"B2-ijerph-17-05511\" ref-type=\"bibr\">2</xref>,<xref rid=\"B3-ijerph-17-05511\" ref-type=\"bibr\">3</xref>,<xref rid=\"B4-ijerph-17-05511\" ref-type=\"bibr\">4</xref>,<xref rid=\"B5-ijerph-17-05511\" ref-type=\"bibr\">5</xref>].</p><p>The challenges posed by the recent international economic crisis that started in 2008 and the consequent austerity measures imposed, among other things, on public expenditure for care services and social protection policies, has stimulated the search for alternative welfare solutions, including options that are not directly or entirely funded by public sources [<xref rid=\"B6-ijerph-17-05511\" ref-type=\"bibr\">6</xref>,<xref rid=\"B7-ijerph-17-05511\" ref-type=\"bibr\">7</xref>]. A not always explicit corollary of this approach is that citizens are today called upon to be increasingly responsible for managing their care arrangements, thus <italic>de facto</italic> to rely more heavily on care provided by relatives, friends, and neighbours [<xref rid=\"B8-ijerph-17-05511\" ref-type=\"bibr\">8</xref>,<xref rid=\"B9-ijerph-17-05511\" ref-type=\"bibr\">9</xref>,<xref rid=\"B10-ijerph-17-05511\" ref-type=\"bibr\">10</xref>]. This lies, on the one hand, on the continued demographic trends leading to an increase in the percentage of elders on the whole population, as in 2018 almost 20% of the European population was aged 65 or older, with an overall increase of more than 2 percentage points from 10 years earlier (<xref rid=\"ijerph-17-05511-t001\" ref-type=\"table\">Table 1</xref>). On the other hand, a remarkable share of the population is called to provide a response to the care demand emerging from these trends. In the same year, on average 5.5% of European people between the ages of 18 and 64 declared that, in their role as informal carers, they shoulder a specific responsibility to support incapacitated relatives (including child and elder care responsibility), and 4% of them stated that they provided care exclusively to enable (older) adult relatives’ activities of daily living (non-reported data).</p><p>In this regard it should be noted that, traditionally, the level of caring responsibility is particularly high in countries with familial care regimes (Italy, Spain, Portugal, Greece, Ireland and Malta) and transition care regimes (Latvia, Poland, Estonia, Croatia), because families are strongly involved in providing informal care [<xref rid=\"B11-ijerph-17-05511\" ref-type=\"bibr\">11</xref>,<xref rid=\"B12-ijerph-17-05511\" ref-type=\"bibr\">12</xref>]. <xref rid=\"ijerph-17-05511-t001\" ref-type=\"table\">Table 1</xref>, however, underlines that also in countries more oriented to a mixed care regime and inclined to involve private providers [<xref rid=\"B13-ijerph-17-05511\" ref-type=\"bibr\">13</xref>], people are extensively responsible for providing care to their relatives too (e.g., The Netherlands and Ireland).</p><p>Noticeably, the intensity of population ageing is particularly relevant in countries where the share of people with informal care responsibilities is already high. However, while in the familial care regime countries (e.g., Italy, Portugal and Greece) old age rates are among the highest, in other countries the main challenge is represented by the speed of the ageing process. This is true for example in Finland, the Netherlands, Malta and Czechia, where the ratio of the number of people aged 65 and over to those aged 0–14 is growing at more than double the speed of the European average. Since the family remains, with no exceptions, the provider of the bulk of long-term care provision, the impact of demographic ageing could therefore be very relevant on the large segments of the population involved in informal care. Already in 2016, an average of 17% of the European population declared that they provide care for their relatives weekly. Across all countries, women were more likely than men to declare their status as informal care providers. <xref rid=\"ijerph-17-05511-t001\" ref-type=\"table\">Table 1</xref> highlights that at the EU level the rate of informal caregivers is at its highest among women and those in working age (i.e., between 35 and 64). Indeed, the international literature highlights that family carers are more likely to be women and part-time workers [<xref rid=\"B14-ijerph-17-05511\" ref-type=\"bibr\">14</xref>,<xref rid=\"B15-ijerph-17-05511\" ref-type=\"bibr\">15</xref>] and that, when no adequate support is provided, these more frequently experience income losses and, in the long run, a weaker position in the labor market [<xref rid=\"B15-ijerph-17-05511\" ref-type=\"bibr\">15</xref>,<xref rid=\"B16-ijerph-17-05511\" ref-type=\"bibr\">16</xref>]. Therefore, policies that support working carers and improve the balance between formal and informal care potentially offer a strategic solution to promoting social innovation in home care.</p><p>Within this international context, recent legislation in Italy has promoted the adoption of welfare measures at the company level (also referred to as occupational welfare schemes), aimed at strengthening the supply of services to support employees with informal (elder) care responsibilities. In this regard, Natali and Pavolini [<xref rid=\"B7-ijerph-17-05511\" ref-type=\"bibr\">7</xref>] modernized the concept of Occupational Welfare (OW) offered by Titmuss [<xref rid=\"B17-ijerph-17-05511\" ref-type=\"bibr\">17</xref>] and Greve [<xref rid=\"B18-ijerph-17-05511\" ref-type=\"bibr\">18</xref>], by defining OW “as the sum of benefits and services provided by social partners, employers and trade unions (by themselves or with the participation of others) to employees over and beyond the public benefits, based on an employment contract”.</p><p>Since the 1990s, OW policies have been spreading throughout Europe, although this has occurred in a still fragmented manner. In this respect, at least two main OW strategies can be identified: in northern European countries, OW is characterized by a highly formal welfare supply, so that OW works to support the existing offer of residential and home care provision; in France and southern European areas, instead, OW still has room for development to accommodate the growing care demand for employees and their families [<xref rid=\"B6-ijerph-17-05511\" ref-type=\"bibr\">6</xref>,<xref rid=\"B19-ijerph-17-05511\" ref-type=\"bibr\">19</xref>]. Within this geographical framework, the relevance of the Italian case is important for two reasons. Firstly, this innovative Italian scheme is applied in a notoriously “familistic” care regime, in which home-based care is provided jointly by family members and, increasingly, by privately paid (primarily migrant) care workers (MCWs), similarly to what has been witnessed by other Mediterranean and Central European countries [<xref rid=\"B20-ijerph-17-05511\" ref-type=\"bibr\">20</xref>,<xref rid=\"B21-ijerph-17-05511\" ref-type=\"bibr\">21</xref>]. Secondly, and with regard to the MCWs, Italy has been facing a shift in the reliance on this source of support: until 2008, regularly employed care workers were almost exclusively migrants (mainly from Eastern Europe), while in recent years the number of Italian care workers has steadily increased, so that today Italians are the second most common group of care workers after Eastern Europeans.</p><p>These developments are deeply changing the landscape of home-based LTC provision in several countries, but so far very little attention has been paid to the identification of policies able to govern these phenomena, which are to a large extent either invisible or tolerated, as they allow the current LTC systems to keep going at an apparently low cost for the public finances. In Italy, in particular, LTC provision involves multiple public and private (for-profit and non-profit) stakeholders, with different and often overlapping roles, which are defined by legislation. In this regard, the impact of private care providers in home care provision is still quite marginal and mostly limited to the sector of complementary or integrative insurances.</p><p>In light of these circumstances, this paper aims to investigate whether and how the recently introduced OW schemes in Italy might be effective in innovating existing care arrangements. This analysis will specifically attempt to identify their impact at different levels: (a) at a <italic>micro level</italic>, in terms of quality of care and of life concerning all main actors involved: older care recipients; informal carers; home care service providers; and migrant care workers; (b) at a <italic>meso level</italic>, through the analysis of the role played by the private sector (including enterprises stimulated by OW schemes) in promoting new initiatives to support employees in better tackling their family care challenges; (c) at a <italic>macro level</italic>, on the promotion of innovations in the LTC sector as a structural component at a system level. To contextualize the relevance of the Italian case for the European context, some policy recommendations will be also formulated. The aim is to provide decision makers and practitioners with suggestions on how to best implement OW policies in different areas of welfare, in order to achieve more integrative and innovative strategies to tackle both policy and services fragmentation. This is particularly urgent since, to our knowledge, no study has so far collected and reported comprehensive data on the results of this still only recently implemented scheme. By doing so, the paper hopes finally to meaningfully contribute to the current debate in the specialised literature on OW and home care issues, and to boost interest on a topic deserving more systematic studies for the future.</p></sec><sec id=\"sec2-ijerph-17-05511\"><title>2. Materials and Methods</title><p>In order to achieve the aims illustrated above, this study combines data collected by means of a rapid literature review with expert interviews. This mixed approach has been deemed as the most appropriate to capture “in an agile manner” both the context-specific characteristics and the rapidly evolving impact of OW policies on the Italian LTC system.</p><p>As for the first component of this study, the rapid review method was considered as more suitable—compared to the certainly more comprehensive but also much lengthier alternative of carrying out a systematic review—to ensure swiftly available results on a topic on which, in Italy and elsewhere, there is urgency to deliver evidence to inform not only the academia, but also practitioners and policy makers. Following the principles underpinning this method [<xref rid=\"B22-ijerph-17-05511\" ref-type=\"bibr\">22</xref>], the first author extracted articles using Google, Google Scholar, and Scopus. The search terms used were: “Occupational welfare”, “Corporative welfare scheme”, “Long-Term Care”, “Europe” and “Italy”. The search identified in a first step a total of 182 English and Italian records, excluding duplicates. The web search allowed to reduce this number in case of records linked to more than one keyword, and to understand how the concepts of “Occupational Welfare” and “Corporate Welfare schemes” were used in the literature. All records have been screened by the first author by a double check. Firstly, the selection was based on title and abstract to understand their relevance and applicability according to the eligibility criteria. Specifically, the records have been included if: (a) they had been published between 2016 and 2019; (b) the main paper’s issue was “Occupational Welfare” in Italy and in Europe; (c) they were focused on innovative work-family life reconciliation policies and practices in Italy and Europe; (d) the papers focused on either policy or practices, therefore excluding theoretical and/or methodological papers. At the end of this process, 81 papers have been full text reviewed for final inclusion, of which 28 were included in this rapid review (<xref ref-type=\"fig\" rid=\"ijerph-17-05511-f001\">Figure 1</xref>). The selected papers, as a part of the study’s materials, have been included and cited in the Results section as references, and are listed in the <xref ref-type=\"app\" rid=\"app1-ijerph-17-05511\">Appendix A</xref> of this article.</p><p>As for the expert interviews, they were chosen as a tool enabling the collection of information to gain an in-depth knowledge of the object of the study, through a rapid dialectic process between the investigating researchers and the involved “privileged witnesses” [<xref rid=\"B23-ijerph-17-05511\" ref-type=\"bibr\">23</xref>]. This methodology had been successfully implemented in previous international studies focused on innovations promoting active ageing and home care quality across Europe, such as Mobilising the Potential of Active Ageing in Europe (MoPAct) and Cost effectiveness and quality in long-term care (CEQUA), using country case studies as a basis to build a dataset suitable for international comparisons [<xref rid=\"B24-ijerph-17-05511\" ref-type=\"bibr\">24</xref>].</p><p>From October to December 2019, the first author conducted five face-to-face semi-structured expert interviews. The limited number of experts is a result of the fact that, in order to collect the most qualified and comprehensive opinions, only specialists were approached who had a recognized expertise in both LTC and OW related issues. Considering the only recently acquired relevance of LTC measures within the context of OW schemes, the Italian experts able to provide a qualified opinion on both topics are still very few. Moreover, we decided to use a mixed strategy of stakeholder/expert involvement [<xref rid=\"B25-ijerph-17-05511\" ref-type=\"bibr\">25</xref>], taking into account the relevance of their academic or professional profiles as reflected by their contribution to the national and sometimes even international debate concerning the investigated topic (<xref rid=\"ijerph-17-05511-t002\" ref-type=\"table\">Table 2</xref>). Therefore, the involvement of five experts still allowed to achieve an acceptable balance between different points of view and can be considered to be very close to a saturation level in terms of high-profile privileged witnesses in the investigated field.</p><p>In consideration of the dearth of specific literature concerning the Italian context, their profile is therefore based primarily upon practices and experiences conducted directly and reflected in the grey literature existing on this topic in Italy (e.g., as national reports), or via the knowledge acquired through specialised literature on national and international case studies. This expertise allows them to combine a systemic and national vision with a local, concrete experience. An invitation to participate in the study was sent by mail from the first author to each selected experts, indicating the general goals of the study, the list of main questions to be addressed, and the declaration of the anonymisation process to ensure the privacy of the interviewees. Five single interview meetings were scheduled according to the availability of the interviewee. Each interview lasted approximately 60 min and was recorded and transcribed according to Cohen’s [<xref rid=\"B26-ijerph-17-05511\" ref-type=\"bibr\">26</xref>] guidelines.</p><p><xref rid=\"ijerph-17-05511-t003\" ref-type=\"table\">Table 3</xref> shows the interview’s framework, identifying four main research goals: (a) description of the OW schemes in relation to the Italian LTC context; (b) identification of the various actors’ roles in it, paying particular attention to the informal care and MCW sectors; (c) identification of the core challenges faced by the OW schemes within the LTC system; (d) formulation of recommendations about potential innovations and future policy measures. One or more specific aims have been linked to each research goal (second column) and, finally, a set of questions has been built to respond in relation to those aims (last column).</p><p>Following the suggestions formulated within the theories developed by Titscher [<xref rid=\"B27-ijerph-17-05511\" ref-type=\"bibr\">27</xref>] and Kohlbacher [<xref rid=\"B28-ijerph-17-05511\" ref-type=\"bibr\">28</xref>], the interviews were analyzed by means of a qualitative content analysis based on a “multilevel interpretation” of data. This approach has been used to improve the pursuit of each of the investigated study goals, since it is based on an analysis that, starting from the research questions, identifies the responses to them by investigating the different “levels of context” implied by the investigated phenomenon. This allows researchers to unpack the relationship between underlying topics and the interaction between the different actors (i.e., what these say and do). In the specific case of this study, the expert interviews allowed, at a first level, to identify the opinions and/or suggestions concerning each of them. Sequentially, these views and suggestions were then grouped thematically (second level), in order to draw, in a third step, the final, overall framework of contents (third level). By doing so, it was possible to better understand the process of meaning construction with regard to the innovative role of OW within the specific context-sensitive conditions of Italian LTC, and to explain it by providing insights into its peculiarities.</p><p>Finally, to comply with the privacy rules aimed to assure anonymity for the experts interviewed, a set of abbreviations reflecting their professional profile has been used to sign their remarks (<xref rid=\"ijerph-17-05511-t002\" ref-type=\"table\">Table 2</xref>). No ethical approval was required for carrying out this kind of investigation according to the currently in force Italian legislation.</p></sec><sec sec-type=\"results\" id=\"sec3-ijerph-17-05511\"><title>3. Results</title><p>In the following, the main results emerging from the study are reported. In order to facilitate the reader, they are illustrated according to the four research goals composing the framework depicted in <xref rid=\"ijerph-17-05511-t003\" ref-type=\"table\">Table 3</xref> above. In particular, findings emerging from the experts’ comments and responses are integrated with the results suggested by the literature review, underling—where appropriate—any detected discrepancies between the two sources.</p><sec id=\"sec3dot1-ijerph-17-05511\"><title>3.1. Occupational Welfare in the Italian LTC Context: Data Trends</title><p>In 2016 a set of innovations were introduced in the Italian welfare system by law No. 208/2015 labeled as “stability Law for 2016”. These innovations aimed to promote an increase in the supply of services to support employees with care responsibilities, including informal elder care provision [<xref rid=\"B6-ijerph-17-05511\" ref-type=\"bibr\">6</xref>,<xref rid=\"B13-ijerph-17-05511\" ref-type=\"bibr\">13</xref>]. The law allows three main measures: (a) tax incentives for companies that decide to grant welfare benefits for their employees; (b) the adoption of the voucher system to access services; and (c) the option to grant performance-related benefits in the form of welfare services to employees earning less than €50.000 per year. The basic idea is to integrate the public expenditure for the Italian LTC system, which in 2017 reached only 0.7% of GDP, i.e., one of the lowest among Organisation for Economic Co-operation and Development (OECD) countries (<xref rid=\"ijerph-17-05511-t004\" ref-type=\"table\">Table 4</xref>) with additional resources “activated” from the private sector. Moreover, the LTC budget in Italy is mainly absorbed by health care provided in hospitals (51%), leaving only a residual part available for home care (19%) and other forms of provision (30.1%). However, one aspect not covered by LTC spending is the care provided by MCWs and informal caregivers, which represent the bulk of LTC provision in Italy [<xref rid=\"B3-ijerph-17-05511\" ref-type=\"bibr\">3</xref>,<xref rid=\"B4-ijerph-17-05511\" ref-type=\"bibr\">4</xref>,<xref rid=\"B29-ijerph-17-05511\" ref-type=\"bibr\">29</xref>]. One of the most common ways for older adults to access care services is to pay them privately by using the cash allowances they receive from the State [<xref rid=\"B15-ijerph-17-05511\" ref-type=\"bibr\">15</xref>,<xref rid=\"B16-ijerph-17-05511\" ref-type=\"bibr\">16</xref>]. In 2017, more than 1.9 million older people received the carer allowance, for a total cost of 13.4 billion euros (equal to 0.8% of Italy’s GDP). This ensures implementation room for private providers, including MCWs [<xref rid=\"B6-ijerph-17-05511\" ref-type=\"bibr\">6</xref>,<xref rid=\"B7-ijerph-17-05511\" ref-type=\"bibr\">7</xref>]. A private propensity to spend in social support is confirmed by low public investment on social protection, since in Italy only 27% of GDP has been spent on social protection, one point below the European average (<xref rid=\"ijerph-17-05511-t004\" ref-type=\"table\">Table 4</xref>).</p><p>Despite the social protection expenditure highlighted <xref rid=\"ijerph-17-05511-t004\" ref-type=\"table\">Table 4</xref>, a recent tendency can be observed in Italy to pay an increasing attention to families and to social support, as shown by the fact that the Italian voluntary private social expenditure has doubled in ten years, soaring to 1.8% of GDP [<xref rid=\"B29-ijerph-17-05511\" ref-type=\"bibr\">29</xref>,<xref rid=\"B30-ijerph-17-05511\" ref-type=\"bibr\">30</xref>].</p><p>This trend is confirmed by the results of the rapid review, which underlines that in Italy the OW issue has gained a growing attention by policymakers, national and local stakeholders, and by the specialized literature in this field. In the last five years, different national studies have addressed OW and corporative welfare issues in Italy [<xref rid=\"B31-ijerph-17-05511\" ref-type=\"bibr\">31</xref>]. Similarly to what happened for other Italian welfare issues, these studies suggest an overall picture of a fragmented and multi-level OW system, based on different regulation acts and on the involvement of various stakeholders. The complexity of this system is based, on the one hand, on the fact that OW agreements can be broken down to two main levels:<list list-type=\"simple\"><list-item><label>(a)</label><p>National level of contractual OW agreements: these refer to the arrangements included in national collective agreements and/or adopted by large companies.</p></list-item><list-item><label>(b)</label><p>Local level of contractual OW agreements: these are mainly managed at the local level for specific sectors or applied by single or multi-enterprises.</p></list-item></list></p><p>In addition, single or multiple non-contractual agreements may be signed at enterprise level, based on the OW needs of the specific company.</p><p>As for the main stakeholders involved in the system, these are represented primarily by public institutions, bilateral bodies, trade unions and enterprises, all usually engaged in the co-design of OW agreements (<xref rid=\"ijerph-17-05511-t005\" ref-type=\"table\">Table 5</xref>). In this system, each stakeholder can have one or more operative functions in co-shaping and implementing the OW path. The specific home care measures included in OW agreements—as part of the whole spectrum of measures included in such agreements—operate in the same way.</p><p>Even though the lack of disaggregated data on OW experiences and agreements makes it difficult to detect precisely their impact in terms of home care measures, the data included in some sectorial studies gives us some clues on the ongoing trends in Italy. The Stability Law for 2016 repeals the voluntary nature of OW schemes in national collective agreements, thus making it now mandatory to include integrative OW schemes in the most recently renewed agreements. In the last years, moreover, the number of secondary level agreements offering social service provision has doubled, with an annual increase of around 30% [<xref rid=\"B31-ijerph-17-05511\" ref-type=\"bibr\">31</xref>].</p><p><xref rid=\"ijerph-17-05511-t006\" ref-type=\"table\">Table 6</xref> summarises the data highlighting the spreading of OW measures in contractual agreements undersigned at both first and secondary level, with over 167 thousand companies adopting OW schemes in 2018, for a potential population of up to 3.35 million workers. Barazetta and Santoni [<xref rid=\"B32-ijerph-17-05511\" ref-type=\"bibr\">32</xref>] highlight that this data means that in Italy OW schemes involve by now more than 11% of companies, and around 20% of employees.</p><p>Unfortunately, the measures promoting home care services are still thinly spread. While OW schemes supporting family care in general are included in 23% of secondary level OW agreements, those specifically aimed at supporting care provided by relatives are implemented in only 8% of them [<xref rid=\"B34-ijerph-17-05511\" ref-type=\"bibr\">34</xref>]. OW schemes are mostly concentrated in northern regions, where 69% of agreements have been signed, in contrast to only 13% in central regions and 2% in the South [<xref rid=\"B32-ijerph-17-05511\" ref-type=\"bibr\">32</xref>]. This trend is not surprising, considering that most companies, and especially the largest ones, are located in the northern part of Italy. Indeed, recent studies underline that most OW agreements are active in large companies, while only 26% of enterprises involved had less than 50 employees [<xref rid=\"B32-ijerph-17-05511\" ref-type=\"bibr\">32</xref>].</p></sec><sec id=\"sec3dot2-ijerph-17-05511\"><title>3.2. The Relationship between OW, LTC and Home Care</title><p>The relationship between OW, LTC and home care is strongly influenced by how OW has been implemented in Italy. <italic>The main issue to be investigated, in this regard, should be: “What is the relevance of OW in welfare issues in Italy?”</italic> (U3). The existing literature underlines that OW issues are still a relatively neglected area in the study of welfare state, despite its growing social and policy relevance. Indeed, OW policies support and influence transformations in the welfare state, for their ability to work in term of social protection, and for their potential to promote innovation [<xref rid=\"B33-ijerph-17-05511\" ref-type=\"bibr\">33</xref>]. The experts’ opinions on the reasons for this recent attention paid to OW policies come from the private nature of agreements between enterprises and employees. OW actions are born—and often still perceived—as individual choices made by large enterprises to support the productivity of their workers through additional benefits: <italic>as such, it is considered as a sort of “internal” issue, and not one of public policy relevance</italic> (U1). Despite over thirty years of active debate on the power of private welfare arrangements to integrate into the welfare state, <italic>for a long time the supply of OW schemes has remained limited to an integrative and functional vision related to cash benefits for employees, as the sole direct beneficiaries of the company’s OW measures</italic> (U2). The result has been the spread of a multi-pillar system that promoted an increased private provision, but perhaps also leading to a risk shift or to an “individualization of risk” [<xref rid=\"B34-ijerph-17-05511\" ref-type=\"bibr\">34</xref>]. <italic>Unavoidably, these general widespread characteristics of OW policies have a limited impact in terms of implementation of specific OW home care schemes for frail older relatives</italic> (U3). Despite these relatively negative opinions by the consulted experts about the past, these agree on the currently increasing relevance of OW schemes as a social protection measure supporting home-based care. The openness to home care support schemes contributes to the renewal of the role of OW: although home care support is only a recently introduced area of OW intervention, current evidence seems to suggest that OW can play a potentially “integrative role” with regard to the already existing care services and policies (PM1). Razetti and Maino [<xref rid=\"B35-ijerph-17-05511\" ref-type=\"bibr\">35</xref>] push the idea of an integrative function of all non-public welfare schemes, including OW, that can promote a mixed welfare system based on a fruitful collaboration between different sectors, services and stakeholders.</p></sec><sec id=\"sec3dot3-ijerph-17-05511\"><title>3.3. The Impact of OW Schemes on Italy’s Home Care System</title><p>OW schemes have been created to support the worker’s life. Not surprisingly, the most widely used schemes are based on insurance and conciliation measures, with their main direct users represented by the workers and their individual needs (CP1). In Italy, for many years OW schemes aimed at supporting LTC were made to tackle the risk of LTC needs among employees, so that most of the measures were constituted by long-term or health care insurances [<xref rid=\"B36-ijerph-17-05511\" ref-type=\"bibr\">36</xref>,<xref rid=\"B37-ijerph-17-05511\" ref-type=\"bibr\">37</xref>,<xref rid=\"B38-ijerph-17-05511\" ref-type=\"bibr\">38</xref>]. The Stability Law enlarge this spectrum, by specifically introducing measures earmarked for the care of employee’s older relatives. The grey literature analysis and the experts’ opinions allowed us to identify four different home care measures that may fall under this categorization within OW agreements: additional care leaves; cash benefits to support home care; vouchers to buy care services; and the direct provision of care by service providers. The additional care leave is used to extend the three days/month already permitted nationwide for care-related leave (Law No. 104/1992). The monetary measures—cash benefits and vouchers—aim to increase the ability to hire privately care workers to provide home-based care.</p><p>Finally, the provision of care services is aimed at supporting the fulfilment of care needs [<xref rid=\"B9-ijerph-17-05511\" ref-type=\"bibr\">9</xref>,<xref rid=\"B39-ijerph-17-05511\" ref-type=\"bibr\">39</xref>]. The main beneficiaries of these measures are therefore, in the first place, the employees’ disabled relatives, who become users and direct beneficiaries of specific OW measures assigned to their working relative (i.e., their family caregiver). Different measures can indirectly involve MCWs and providers of care services, too. In this regard, the target population of OW schemes is dramatically growing. In 2018, there were more than 859 thousand domestic workers (including care workers) formally employed in Italy, of which 71.4% were migrants (equal to 613 thousand) [<xref rid=\"B18-ijerph-17-05511\" ref-type=\"bibr\">18</xref>,<xref rid=\"B19-ijerph-17-05511\" ref-type=\"bibr\">19</xref>,<xref rid=\"B20-ijerph-17-05511\" ref-type=\"bibr\">20</xref>,<xref rid=\"B40-ijerph-17-05511\" ref-type=\"bibr\">40</xref>]. Moreover, it is estimated that 60% of MCWs are hired outside the formal economy, so that their real number has been estimated to reach a total of 1 million [<xref rid=\"B1-ijerph-17-05511\" ref-type=\"bibr\">1</xref>,<xref rid=\"B14-ijerph-17-05511\" ref-type=\"bibr\">14</xref>,<xref rid=\"B41-ijerph-17-05511\" ref-type=\"bibr\">41</xref>,<xref rid=\"B42-ijerph-17-05511\" ref-type=\"bibr\">42</xref>,<xref rid=\"B43-ijerph-17-05511\" ref-type=\"bibr\">43</xref>]. <italic>De facto</italic>, MCWs become indirect beneficiaries of OW schemes since cash benefit or voucher schemes are often specifically used as an additional means available to facilitate the home-based employment of MCWs. <italic>This possibility is even explicitly included in some OW agreements, but they are too few to identify any impact on this sector. In any case, the positive effect of this measure should be to push for a legalisation of their job (U2).</italic></p><p>Recently, a national study on OW in Italy highlighted that more than 54% of employees would agree to exchange wage increases for OW measures [<xref rid=\"B44-ijerph-17-05511\" ref-type=\"bibr\">44</xref>]. <italic>This is a changing company culture, and it takes time to be adopted</italic> (PM1). Even though it is difficult to find data on the trends of home care schemes, we should note how widespread the involvement of private providers is: between 2015 and 2018, the number of companies that chose to commit providers to manage their OW schemes has grown six fold, involving a tripling in the number of workers as beneficiaries [<xref rid=\"B43-ijerph-17-05511\" ref-type=\"bibr\">43</xref>].</p><p>Concretely, the regulatory act allows workers an annual tax-free regime for expenditure in care or welfare measures of up to 2000 €/year. The employees can request the tax exemption regime for their total or partial expenditure. The services, vouchers and cash benefits received from OW schemes often contribute only partially to the total amount spent on home care services: the OW agreements often propose measures or cash benefits corresponding to 258 Euros [<xref rid=\"B43-ijerph-17-05511\" ref-type=\"bibr\">43</xref>].</p><p>The experts confirmed the accuracy of the abovementioned framework and underlined that the implementation of these schemes is still very limited and fragmented. <italic>The measures specialized on home care provision have a low level of inclusion in OW agreements</italic> and are also infrequently requested by workers (CP1).</p><p>One of the main reasons for this is their very recent introduction. The current legislation is pushing for an increasing role of OW schemes in supporting the work-life balance for workers that are informal carers, but it is too early to identify their impact, since <italic>the cases are still too few, though positive trends can be detected</italic> (U1). Actually, an overarching title to define OW measures supporting home care in Italy could be “Much ado about nothing”: <italic>while the legislation is proposing to dedicate some OW measures to support home care, most companies continue to provide basic monetary schemes, because it is easier to manage them</italic> (U3).</p><p>At the macro level, however, the OW has a strong impact on the reduction of tax revenue. Pavolini and Ascoli [<xref rid=\"B45-ijerph-17-05511\" ref-type=\"bibr\">45</xref>] underline in this regard that the Italian tax welfare and OW influence each other, reaching 3.1% of the total social expenditure [<xref rid=\"B30-ijerph-17-05511\" ref-type=\"bibr\">30</xref>]. <italic>The main reason is the high monetarisation of OW schemes: supplementary health funds, supplementary pension funds and corporate welfare absorb most of the available resources (U3).</italic></p></sec><sec id=\"sec3dot4-ijerph-17-05511\"><title>3.4. The Room for OW Schemes to Promote Social Innovation within the Italian LTC</title><p>The experts’ opinions confirm that OW schemes applied to home care for older people can promote innovation in the Italian welfare system, including the LTC sector. <italic>The OW framework applied to elder care needs brings forward an innovative and integrative welfare strategy</italic> (PM1). The first innovation identified by the expert is the formal inclusion of LTC as action area in OW schemes. <italic>Until 2016, in family support OW schemes the term “relative’s care” mainly meant “childcare”, so that measures had to be designed around the working parents, rather than around children that have to provide care to their ageing parents (CP1). The introduction of care for the elderly among the possible aims of OW agreements may help produce a cultural change on care and welfare issues (U1).</italic> The inclusion of older relatives in the target population reinforces this assumption. <italic>The parents of employees emerge as the new beneficiaries of measures</italic> (CP1). Moreover, the “mandatory” character of OW agreements for companies after the Stability Law (which has enforced the adoption of such agreements, previously only optional) acquires an innovative function in terms of diffusion of home care services in OW programs. <italic>If, until now, very few companies provided measures to support home care for the elderly, in coming years this number is bound to increase, through more agreements and more schemes specifically oriented to support home care</italic> (U2). Moreover, the literature points out that the Stability Law adopted an innovative strategy to promote an enlarged use of welfare services—including home care—at the expense of the existing massive use of extra wage benefits [<xref rid=\"B34-ijerph-17-05511\" ref-type=\"bibr\">34</xref>]: <italic>the law pushes for an empowerment of OW schemes in order to obtain a complete replacement of monetary benefits</italic> (U2).</p><p>Despite this positive opinion on the potentially innovative push for the home care sector, the experts underlined that its concrete effects have yet to be detected. The framework designed is innovative, even though concrete results are still not visible (U3). <italic>The short implementation time influences this lack of evidence, but to be very innovative and noticeable these measures must be included in larger strategies to improve the social protection system for workers and their families</italic> (U1). <italic>Over the last twenty years in Italy, the welfare system policies have been focused upon the collaboration between different stakeholders, so why shouldn’t we now adopt the same approach to support the renewal of OW schemes applied to family support?</italic> (PM1). In particular, the consulted experts emphasized that the OW schemes ought to be considered as part of the larger social protection system, in order to enhance its integrative—rather than supplementary—potential function of public support.</p></sec><sec id=\"sec3dot5-ijerph-17-05511\"><title>3.5. Core Challenges and Policy Recommendations</title><p>The schemes applied to home care are the latest products introduced into the Italian OW system. The main challenge to their success is to move from theory to practice: <italic>the regulatory system is in place, so now companies, trade unions and, not least, workers have to want to implement and use it</italic> (CP1). This challenge is strongly related to the improvement of knowledge and information on OW schemes among stakeholders. Indeed, the experts agreed on the lack of clear information and knowledge, in particular by the direct users (workers and companies), on opportunities and advantages coming from OW policies and schemes. In these regards, they specifically recommended <italic>the promotion of an informational and awareness campaign on OW schemes, aimed at companies but especially workers, to better use the existing possibilities to meet their care responsibilities</italic> (PM1). The proposed informational campaign would contribute to spread home care schemes in OW agreements. A real challenge, in this respect, is <italic>how to extend the room dedicated to home care schemes within OW agreements, by means of measures promoting specialised care services rather than cash or tax-free benefits</italic> (U2). The enhanced provision of services, instead of monetary measures, is another challenge for OW schemes as well as for the LTC in Italy [<xref rid=\"B46-ijerph-17-05511\" ref-type=\"bibr\">46</xref>,<xref rid=\"B47-ijerph-17-05511\" ref-type=\"bibr\">47</xref>,<xref rid=\"B48-ijerph-17-05511\" ref-type=\"bibr\">48</xref>,<xref rid=\"B49-ijerph-17-05511\" ref-type=\"bibr\">49</xref>,<xref rid=\"B50-ijerph-17-05511\" ref-type=\"bibr\">50</xref>]: <italic>a real revolution to this purpose would be accomplished by a greater presence and use of service measures, instead of the tax incentives that are the focus of current strategies</italic> (U3).</p><p>The last identified challenge is the implementation of a long-term systemic strategy to enhance the relationships between OW schemes and the LTC. <italic>In an ageing context and a familial care regime like the one existing in Italy, OW and care for older relatives must be connected: the work-life balance passes through these needs, so the challenge is to promote a long-time strategy. Remember it! (PM1)</italic>. However, to become effective, the OW schemes applied to home care must be less fragmented and become more than merely individual experimental experiences: <italic>the recommended strategy for OW schemes could be seen as integrative of traditional care services, in order to contribute to the local universal coverage of workers’ needs related to balancing working life and their care responsibilities (U2).</italic></p></sec></sec><sec sec-type=\"discussion\" id=\"sec4-ijerph-17-05511\"><title>4. Discussion</title><p>The illustrated findings show that OW schemes supporting home care are still far from being fully valued and integrated within the Italian LTC system, for a series of reasons.</p><p>In the first place, the residual attention traditionally granted to social protection and family support issues might have acted as a structural barrier preventing the adoption of measures like OW schemes to support home care provision [<xref rid=\"B46-ijerph-17-05511\" ref-type=\"bibr\">46</xref>]. Although the data on the Italian context confirms this assumption, the observed trend of an increase in the number and extension of OW agreements seems to suggest that, in a future perspective, these schemes might play a more substantial role in supporting home care policies in Italy.</p><p>The push received by the introduction of the national Solidarity Law in 2016 allowed for the start of a positive collaboration between two different areas of welfare—OW schemes and home care for the elderly—which until now had run independently of each other. This integrative process, however, has been strongly slowed down by the limited room given to both issues within national policy debates. Moreover, in Italy’s company culture OW has been so far seen more as a productivity bonus for employees, rather than as a social support opportunity to cover family care related needs. The integrative impact of OW schemes on formal home care provision—as identified by different experts—remains however to a large extent still only potential, as it needs to find more concrete forms of realization, in order to better respond to the challenges of an ageing population [<xref rid=\"B2-ijerph-17-05511\" ref-type=\"bibr\">2</xref>,<xref rid=\"B48-ijerph-17-05511\" ref-type=\"bibr\">48</xref>].</p><p>The illustrated results show, in this regard, that OW schemes supporting home care are made up of a highly complex system of measures, due in the first place to the fragmentation and multi-level governance existing in both Italian OW and LTC systems. As in other family-based care regimes (e.g., Spain and Israel), Italy’s multi-level care governance leaves indeed large room for the informal provision of care by younger relatives, and even more by women in working age [<xref rid=\"B21-ijerph-17-05511\" ref-type=\"bibr\">21</xref>,<xref rid=\"B48-ijerph-17-05511\" ref-type=\"bibr\">48</xref>,<xref rid=\"B49-ijerph-17-05511\" ref-type=\"bibr\">49</xref>].</p><p>From this point of view, the transformation from optional to mandatory OW agreements for companies that occurred in Italy after 2016 seems to reflect a broader intention by the government to start a process of strengthening and systematizing the support to informal care. Indeed, less than two years later, a draft national law containing a series of measures to support informal caregivers was presented. Taking inspiration from legislation in other countries (e.g., the Spanish national reform on LTC), this law included a specific definition of familial caregivers and identified tax, social security and work inclusion measures for them [<xref rid=\"B2-ijerph-17-05511\" ref-type=\"bibr\">2</xref>,<xref rid=\"B8-ijerph-17-05511\" ref-type=\"bibr\">8</xref>,<xref rid=\"B50-ijerph-17-05511\" ref-type=\"bibr\">50</xref>]. Due to the end of the legislative period, the law was not adopted, but it was a clear sign of the increasing attention paid by the Italian society and its national political representatives to informal care issues and to the need of providing more systemic responses to unmet LTC needs. Within this context, the consulted experts highlighted the socially innovative and system-integrative role played by OW schemes applied to the LTC sector, also in terms of facilitating the work-life balance of working family carers, thus contributing to the improvement of the quality of life of large segments of the population [<xref rid=\"B51-ijerph-17-05511\" ref-type=\"bibr\">51</xref>,<xref rid=\"B52-ijerph-17-05511\" ref-type=\"bibr\">52</xref>]. This is partly connected also to the multi-stakeholder collaboration in the delivery and design of LTC strategies established at local level by several OW schemes, which could further increase their innovative character. A sound example of this kind is proposed by Maino and Razzetti’s “open scenario for local welfare”, which builds on the joint contribution of public institutions and services, NGOs, confessional organizations, bank foundations, private providers and companies, as a network of different but integrated promoters of OW schemes, including home care [<xref rid=\"B52-ijerph-17-05511\" ref-type=\"bibr\">52</xref>].</p><p>Less than five years since the first introduction of OW schemes in 2016, it may still be too soon to evaluate properly the impact of these measures, due to their low—albeit increasing—uptake rates by both companies and employees. This is not surprising, in the context of a gender-disadvantaged labor market like the Italian one, with 38% of female workers that report to have caring responsibilities, compared to just 12% among men [<xref rid=\"B53-ijerph-17-05511\" ref-type=\"bibr\">53</xref>]. While therefore OW schemes have the potential of being helpful to improve the work-life balance of informal caregivers, further research is however needed to better understand the existing barriers preventing a wider implementation of these schemes. In this regard, Natali et al. [<xref rid=\"B47-ijerph-17-05511\" ref-type=\"bibr\">47</xref>] have found that access to occupational benefits is not evenly distributed among all socio-demographic groups and workers, with women and self-employed workers being less likely to be able to access benefits. This is of particular concern in Italy, due to the high degree of self-employed workers. Moreover, the preference traditionally expressed by Italian citizens towards monetary compensations—rather than in-kind reconciliation services—is deeply rooted in the country’s familial-based welfare culture [<xref rid=\"B54-ijerph-17-05511\" ref-type=\"bibr\">54</xref>,<xref rid=\"B55-ijerph-17-05511\" ref-type=\"bibr\">55</xref>,<xref rid=\"B56-ijerph-17-05511\" ref-type=\"bibr\">56</xref>]. This contributes to explain why the increase of OW schemes in the home care sector, while it certainly has an overall potentially beneficial impact, may represent at the same time also a possible cause for concern, if it ends up with leading, as it seems, to an increased recruitment of MCWs outside the formal economy [<xref rid=\"B54-ijerph-17-05511\" ref-type=\"bibr\">54</xref>]. Thus strengthening the pattern, already existing, of the State cash allowances used by families to hire care workers on an undeclared basis, contributing to perpetuate a situation of precarious employment conditions for MCWs, on the one hand, and of a low quality in home-based care, on the other hand. Efforts to tackle this challenge have been so far primarily limited to the adoption of regularization campaigns for undocumented MCWs by the Italian government (the last one going back to 2009, determining a 20 per cent increase in the number of legal MCWs in Italy) [<xref rid=\"B57-ijerph-17-05511\" ref-type=\"bibr\">57</xref>]; hence, given the ad hoc, temporary nature of these initiatives, these cannot offer a long-term systematic solution to this issue.</p><p>Therefore, while OW schemes for home care have been as yet a rather residual experience within the Italian context, they have a massive potential for further increase in the near future. According to recent estimates, well 760 thousand workers could choose to receive it, if the already activated OW agreements would include home care schemes, and at least 50% would be using them [<xref rid=\"B58-ijerph-17-05511\" ref-type=\"bibr\">58</xref>]. This scenario seems realistic, taking into consideration that, in 2018, almost 400 thousand workers required daily job leaves for family care purposes, and that the requests have increased by 30% in a few years. These results show at the same time, however, that in order to make sure that OW schemes in home care can go beyond the level of a sort of “start-up” experience, robust and thought-through communication and information actions are needed to make them a more significant component of the Italian LTC system. All experts’ recommendations suggest, in this regard—and in doing so they seem to be in line to what the available literature suggests [<xref rid=\"B32-ijerph-17-05511\" ref-type=\"bibr\">32</xref>,<xref rid=\"B44-ijerph-17-05511\" ref-type=\"bibr\">44</xref>]—to move towards a consolidation and extension of these measures, by means of a more synergetic strategy of integration between public provision of services and cash-for-care allowances, on the one hand, and OW schemes promoting a more market-based provision, on the other hand.</p></sec><sec sec-type=\"conclusions\" id=\"sec5-ijerph-17-05511\"><title>5. Conclusions</title><p>This study aimed to describe how the newly introduced OW schemes might innovate existing home care arrangements in Italy. A rapid review of the literature alongside expert interviews allowed us to describe the complex Italian OW schemes and identify the positive implications for their application to the local LTC systems. The high fragmentation and complexity of both OW and LTC work as barriers to an extension of OW schemes to the field of home care support. Their still relatively short implementation time does not allow for the detection of quantitative precise data on their concrete effects for workers (including informal caregivers and MCWs). However, the qualitative information collected via experts identified a series of innovative elements in the OW schemes applied to Italy’s home care sector. Among them, they seem to promote social innovation for their potential ability to: (a) better fulfil unmet conciliation and care needs; (b) improve quality of life for beneficiaries (family carers and their older relatives); (c) be included into a larger multi-stakeholder and multi-level welfare strategy.</p><p>Concretely, the extension of OW schemes in the home care sector may lead to a series of enhancements at different levels. At the <italic>macro level</italic>, it might facilitate an improvement in the level of coordination and integration among different welfare policies, thanks to a more systematic dialogue between the two areas of welfare, which are usually separated. At the <italic>meso level</italic>, the growth of involvement by companies in social care responsibility could push them to be more strongly involved in the networks of local and national stakeholders, and a positive spin-off of this participation may be the building of new collaborations between private and public stakeholders. Moreover, companies may be prompted to think about the promotion of new useful solutions to support their workers in addressing their family care needs without loss of productivity at work and, simultaneously, to improve the organisational well-being. As for the <italic>micro level</italic>, the quality of care provided at home may be improved by a better implementation of OW schemes in the home care sector, by integrating different measures, such as “for instance” by expanding care leaves for informal carers, providing more direct contact hours, or ensuring a better monitoring and improved working conditions and care quality for MCWs. In this regard, useful measures might be represented by a facilitated used of vouchers, fiscal benefits for expenses borne to hire MCWs, or traceable cash benefits for an easier regulation of MCWs’ recruitment and employment conditions. No matter which level is referred to, OW schemes in home care would in any case need, to be implemented more extensively, ad hoc communication campaigns to inform a largely unaware audience.</p><p>Finally, some limitations should be considered in interpreting the results of this study. A first, essential drawback is represented by the choice of relying on a rapid review, rather than on a systematic review, to scrutinize the literature on the investigated topic. While this has been an explicit decision to reach potentially translational findings within a shorter time horizon, it might have reduced the depth and extent of information acquired. Secondly, the rapid review has been realised including Google and google scholar. While their use has been necessary to provide a larger search considering the low spread of specialised literature on the investigated topic, these search engines ensure only a limited level of precision, because they present data based on citations and other factors. Third, the lack of disaggregated data does not allow for the possibility of analyzing home care provision independently of OW schemes. Moreover, the focus on Italy as a single country case study, with no comparative data to refer to, makes it difficult to draw generalized conclusions for the international context. Finally, the mixed strategy used to involve the five consulted experts, while allowing to achieve a first analysis of the Italian case in light of the existing literature on this issue, still represents a limited perspective, since different local experiences require specific investigations and the involvement of a larger number of stakeholders.</p><p>Despite these limitations, the findings provided by this study represent, to our knowledge, the first attempt to provide to a non-Italian audience of readers an in-depth examination of an innovative—and cross-nationally potentially transferable—experience to address a politically very relevant issue. Further studies are however urgently needed in this regard, which should possibly count on a systematic analysis of the literature, a more extensive primary data collection, and the involvement of a larger sample of stakeholders representing a wider variety of perspectives, also in terms of regional differentiation.</p></sec></body><back><notes><title>Author Contributions</title><p>G.C. and G.L. conceived and designed the study. G.C contributed to data collection, data management, data analysis, interpretation of the results, and revision of the manuscript. G.C. wrote the paper. G.L., G.C., M.D.R. and O.F. reviewed the paper, provided significant feedback, and approved the final manuscript. All authors have read and agreed to the published version of the manuscript.</p></notes><notes><title>Funding</title><p>This study was partially supported by a Ricerca Corrente funding from the Italian Ministry of Health to IRCCS INRCA, and partially funded from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 814072 (Entwine). In addition, it has benefited from data collected from the CEQUA project, funded by the European Union under grant agreement No. VS/2015/0276.</p></notes><notes notes-type=\"COI-statement\"><title>Conflicts of Interest</title><p>The authors declare no conflict of interest.</p></notes><app-group><app id=\"app1-ijerph-17-05511\"><title>Appendix A</title><table-wrap id=\"ijerph-17-05511-t0A1\" orientation=\"portrait\" position=\"anchor\"><object-id pub-id-type=\"pii\">ijerph-17-05511-t0A1_Table A1</object-id><label>Table A1</label><caption><p>List of papers identified through the rapid literature review (listed according to their reference number).</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">No.</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Authors</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Title</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Years</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Reference Number</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Fefè, R.</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Care of The Elderly. Aging and New Demands for the Development of Care Work in Italy.</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2019</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B1-ijerph-17-05511\" ref-type=\"bibr\">1</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2.</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">van den Broek, T.; Grundy, E.</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Does long-term care coverage shape the impact of informal care-giving on quality of life? A difference-in-difference approach. </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2020</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B3-ijerph-17-05511\" ref-type=\"bibr\">3</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3.</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Verbakel, E.; Tamlagsrønning, S.; Winstone, L.; Fjær, E.L.; Eikemo, T.A.</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Informal care in Europe: findings from the European Social Survey (2014) special module on the social determinants of health. </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2017</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B4-ijerph-17-05511\" ref-type=\"bibr\">4</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">4.</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Pavolini, E.; Seeleib-Kaiser, M. </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Comparing occupational welfare in Europe: the case of occupational pensions. </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2018</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B6-ijerph-17-05511\" ref-type=\"bibr\">6</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">5.</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Naldini, M.; Pavolini, E.; Solera, C. </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Female employment and elderly care: The role of care policies and culture in 21 European countries.</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2016</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B7-ijerph-17-05511\" ref-type=\"bibr\">7</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">6.</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Kolodziej, I. W.; Reichert, A. R.; Schmitz, H.</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">New evidence on employment effects of informal care provision in Europe. </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2018</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B14-ijerph-17-05511\" ref-type=\"bibr\">14</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">7.</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Bouget, D.; Saraceno, C.; Spasova, S. </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Towards new work-life balance policies for those caring for dependent relatives?</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2017</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B15-ijerph-17-05511\" ref-type=\"bibr\">15</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">8.</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Da Roit, B.A.; Moreno, F.J.</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Cash for care and care employment: (Missing) debates and realities. </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2019</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B16-ijerph-17-05511\" ref-type=\"bibr\">16</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">9.</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Natali, D.; Pavolini, E.</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Occupational Welfare in Europe: an analytical and methodological introduction. </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2018</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B19-ijerph-17-05511\" ref-type=\"bibr\">19</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">10.</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Fosti, G.; Notarnicola, E. </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">L’innovazione e il cambiamento nel settore della Long Term Care: 1 rapporto Osservatorio Long Term Care.</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2018</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B29-ijerph-17-05511\" ref-type=\"bibr\">29</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">11.</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Maino, F.; Ferrera, M. </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Nuove alleanze per un welfare che cambia.</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2019</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B32-ijerph-17-05511\" ref-type=\"bibr\">32</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">12.</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Sbarra, L.; Benaglia, R.; Munno, A.R.; Spiller, S. </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Quarto Rapporto Ocsel sulla Contrattazione decentrata 2016/2017; CISL: Roma, 2018.</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2018</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B33-ijerph-17-05511\" ref-type=\"bibr\">33</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">13.</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Barazzetta, E.; Santoni, V. </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">L’intermediazione e il ruolo dei providers.</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2019</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B34-ijerph-17-05511\" ref-type=\"bibr\">34</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">14.</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Razetti, F.; Maino, F. </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Attori e risorse, tra primo e secondo welfare.</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2019</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B35-ijerph-17-05511\" ref-type=\"bibr\">35</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">15.</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Pavolini, E.; Seeleib-Kaiser, M. </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Comparing Occupational Welfare in Europe: The Case of Occupational Pensions </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2016</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B36-ijerph-17-05511\" ref-type=\"bibr\">36</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">16.</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Costa-Font, J.; Courbage, C. </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Crowding out of long-term care insurance: Evidence from European expectations data. </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2016</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B37-ijerph-17-05511\" ref-type=\"bibr\">37</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">17.</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Spasova, S.; Baeten, R.; Coster, S.; Ghailani, D.; Peña-Casas, R.; Vanhercke, B. </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Challenges in Long-term Care in Europe: A Study of National Policies 2018.</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2018</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B38-ijerph-17-05511\" ref-type=\"bibr\">38</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">18.</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Le Bihan, B.; Da Roit, B.; Sopadzhiyan, A. </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">A. The turn to optional familialism through the market: Long-term care, cash-for-care, and caregiving policies in Europe. </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2019</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B39-ijerph-17-05511\" ref-type=\"bibr\">39</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">19.</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Istituto Nazionale della Previdenza Sociale (INPS)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">I lavori domestici, statistiche annuali</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2019</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B40-ijerph-17-05511\" ref-type=\"bibr\">40</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">20.</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Sowa-Kofta, A. et al.</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Long-term care and migrant care work: addressing workforce shortages while raising questions for European countries. </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2019</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B41-ijerph-17-05511\" ref-type=\"bibr\">41</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">21.</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Razetti, F.; Santoni, V. </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Il mercato del welfare aziendale. L’intermediazione e il ruolo dei provider.</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2019</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B43-ijerph-17-05511\" ref-type=\"bibr\">43</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">22.</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Centro studi investimenti sociali (CENSIS)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3° Rapporto sul Welfare Aziendale.</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2020</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B44-ijerph-17-05511\" ref-type=\"bibr\">44</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">23.</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Pavolini, E.; Ascoli, U. </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">The Dark Side of the Moon: il ruolo del welfare fiscale nel sistema di protezione sociale italiano.</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2019</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B45-ijerph-17-05511\" ref-type=\"bibr\">45</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">24.</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Pavolini, E.; Polini, B. </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Welfare occupazionale e copertura della non autosufficienza: molto rumore per nulla?</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2018</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B46-ijerph-17-05511\" ref-type=\"bibr\">46</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">25.</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Natali, D.; Pavolini, E.; Vanhercke, B. </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Occupational Welfare in Europe: state of play, determinants and policy implications.</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2018</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B47-ijerph-17-05511\" ref-type=\"bibr\">47</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">26.</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Tur-Sinai, A.; Casanova, G.; Lamura, G.</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Changes in the Provision of Family Care to Frail Older People in Familistic Welfare States: Lessons from Israel and Italy. </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2019</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B48-ijerph-17-05511\" ref-type=\"bibr\">48</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">27.</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Pfau-Effinger, B.</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Care Policies towards Familial Care and Extra-Familial Care–Their Interaction and Role for Gender Equality. </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2017</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B49-ijerph-17-05511\" ref-type=\"bibr\">49</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">28.</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Theobald, H.; 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content-type=\"access-date\" iso-8601-date=\"2020-07-07\">(accessed on 7 July 2020)</date-in-citation></element-citation></ref><ref id=\"B58-ijerph-17-05511\"><label>58.</label><element-citation publication-type=\"web\"><article-title>Osservatorio Sulle Prestazioni a Sostegno Della Famiglia Data</article-title><comment>Available online: <ext-link ext-link-type=\"uri\" xlink:href=\"https%3a%2f%2fwww.inps.it%2fnuovoportaleinps%2fdefault.aspx%3fsPathID%3d%3b0%3b46437%3b%26lastMenu%3d46437%26iMenu%3d12%26p4%3d2\">https%3a%2f%2fwww.inps.it%2fnuovoportaleinps%2fdefault.aspx%3fsPathID%3d%3b0%3b46437%3b%26lastMenu%3d46437%26iMenu%3d12%26p4%3d2</ext-link></comment><date-in-citation content-type=\"access-date\" iso-8601-date=\"2020-05-08\">(accessed on 8 May 2020)</date-in-citation></element-citation></ref></ref-list></back><floats-group><fig id=\"ijerph-17-05511-f001\" orientation=\"portrait\" position=\"float\"><label>Figure 1</label><caption><p>Flowchart of the rapid review process.</p></caption><graphic xlink:href=\"ijerph-17-05511-g001\"/></fig><table-wrap id=\"ijerph-17-05511-t001\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05511-t001_Table 1</object-id><label>Table 1</label><caption><p>Italian care needs in a comparative perspective: ageing trends, share of population with care responsibility, and share of informal carers across selected European countries.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">Country</th><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">% of Population Aged 65+</th><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">% of Population Aged 18–64 with Care Responsibility for Incapacitated Relatives (2018)</th><th colspan=\"6\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Informal Carers as a % of Total Population (2016)</th></tr><tr><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2018</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Difference (2018/2009)</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Total</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Male</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Female</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">18–35</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">35–64</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">65+</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Greece</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">21.8</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">10.1</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">34.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">29.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">39.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">35.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">35.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">34.0</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Netherlands</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">18.9</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3.9</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">10.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">18.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">13.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">23.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">10.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">23.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">17.0</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Italy</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">22.6</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2.3</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">7.7</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">17.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">16.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">19.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">12.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">20.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">18.0</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Spain</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">19.2</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2.6</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">6.9</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">16.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">13.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">19.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">14.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">19.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">11.0</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Poland</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">17.1</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3.6</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">6.7</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">20.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">18.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">21.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">7.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">26.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">20.0</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Portugal</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">21.5</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3.5</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">6.1</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">13.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">9.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">17.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">8.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">17.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">10.0</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Ireland</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">13.8</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2.9</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">5.7</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">10.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">9.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">11.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">6.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">13.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"> 8.0</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Malta</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">18.8</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">4.6</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">5.6</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">26.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">25.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">27.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">19.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">33.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">21.0</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">EU</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">19.7</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2.4</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">5.5</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">17.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">15.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">20.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">13.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">20.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">17.0</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">France</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">19.7</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3.2</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">5.3</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">16.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">13.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">18.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">11.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">18.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">14.0</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Belgium</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">18.7</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.6</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">4.9</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">30.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">23.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">36.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">25.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">33.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">30.0</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Cyprus</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">15.9</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3.4</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">4.9</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">15.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">10.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">20.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">11.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">18.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">13.0</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Hungary</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">18.9</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2.5</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">4.5</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">18.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">17.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">18.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">8.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">25.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">13.0</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Austria</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">18.7</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.3</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">4.4</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">10.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">8.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">12.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">4.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">11.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">15.0</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Finland</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">21.4</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">4.7</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3.7</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">13.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">10.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">16.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">6.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">16.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">13.0</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Sweden</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">19.8</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3.6</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">12.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">10.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">15.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">12.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">14.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">10.0</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Germany</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">21.4</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2.9</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">23.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">20.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">26.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">12.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">28.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">24.0</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Romania</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">18.2</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2.1</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2.7</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">9.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">8.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">10.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">5.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">11.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">9.0</td></tr></tbody></table><table-wrap-foot><fn><p>Sources: Eurostat, 2019, EQLS 2016.</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"ijerph-17-05511-t002\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05511-t002_Table 2</object-id><label>Table 2</label><caption><p>Experts and stakeholders involved in the study: typologies, number, and abbreviations.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Typologies of Involved Stakeholders</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">No. of Interviewed Experts</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Abbreviations Used in the Text</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Universities</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">U1; U2; U3</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">National research institution and policy maker</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">PM1</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Care providers association</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">CP1</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Total</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">5</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td></tr></tbody></table></table-wrap><table-wrap id=\"ijerph-17-05511-t003\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05511-t003_Table 3</object-id><label>Table 3</label><caption><p>Interview framework: research goals, aims and questions.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Research Goals</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Aims</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Questions</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Description of the Occupational Welfare schemes in relation to the Italian Long-Term Care/home care context </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">To define the main OW schemes applied in Italy in relation to home care</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">How would you define the OW schemes in Italy?<break/>Which OW schemes in Italy are related to LTC provided in the home?<break/>How would you define the relationship between OW schemes and LTC provided in based home care?</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">The impact on various actors’ roles</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">To understand stakeholder networks and the related roles of each<break/>To understand the OW schemes impact on home care </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">What is the role of the different types of stakeholders involved in OW schemes?<break/>What is the impact of OW schemes for the main Italian home care stakeholders (informal carers and Migrant care Workers - MCWs)? </td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Identification of core challenges</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">To define the challenges of OW schemes applied on home care</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">What are the main challenges currently facing the OW schemes supporting LTC at home, in your opinion?<break/>How can each of these challenges be transformed to allow for the opportunity for innovation?</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Formulation of recommendations on innovations and policy</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">To define what innovative strategies should be used and to define what policymakers can do to support OW schemes supporting home care </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">What could be some innovative strategies to support and improve OW schemes supporting home care provision in Italy?<break/>What main recommendations do you have for policymakers? </td></tr></tbody></table><table-wrap-foot><fn><p>OW is Occupational Welfare.</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"ijerph-17-05511-t004\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05511-t004_Table 4</object-id><label>Table 4</label><caption><p>Social protection and LTC expenditure in Italy and in the international context.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Social Protection Expenditure (SPE) 2016</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">EU</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Italy</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">SPE Total/GDP (%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">28.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">27.1</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">of which for: </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">=100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">=100.0</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Health</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">29.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">23.1</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Pensions for retired adults</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">45.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">57.8</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Disability</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.8</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Family support</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6.3</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Unemployment support</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.8</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Social exclusion support</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">4.2</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.0</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">LTC Expenditure (2017)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">OECD *</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Italy</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Total % of GDP</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.7</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">of which for: </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">=100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">=100.0</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Inpatient long-term care</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">62.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">51.0</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Home-based long-term care</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">33.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">19.0</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Other</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">4.5</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">30.1</td></tr></tbody></table><table-wrap-foot><fn><p>* Organisation for Economic Co-operation and Development. Source: elaboration by authors based on [<xref rid=\"B28-ijerph-17-05511\" ref-type=\"bibr\">28</xref>,<xref rid=\"B29-ijerph-17-05511\" ref-type=\"bibr\">29</xref>].</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"ijerph-17-05511-t005\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05511-t005_Table 5</object-id><label>Table 5</label><caption><p>The Occupational Welfare system in the Italian LTC context: main role played by different stakeholders involved in co-design and implementation.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">Stakeholders</th><th colspan=\"4\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Role Played by in OW Path</th></tr><tr><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Regulation</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Buyer for Employees</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Seller</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Provider</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Public institutions</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">X</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">X</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">X</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Enterprises</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">X</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">X</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Bilateral bodies</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">X</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">X</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Trade unions</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">X</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">X</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Trade associations</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">X</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">X</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">X</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Providers (profit/NGOs *)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">X</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">X</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">X</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">X</td></tr></tbody></table><table-wrap-foot><fn><p>* Non-governmental organizations; Source: elaboration by authors based on [<xref rid=\"B31-ijerph-17-05511\" ref-type=\"bibr\">31</xref>,<xref rid=\"B32-ijerph-17-05511\" ref-type=\"bibr\">32</xref>,<xref rid=\"B33-ijerph-17-05511\" ref-type=\"bibr\">33</xref>,<xref rid=\"B34-ijerph-17-05511\" ref-type=\"bibr\">34</xref>].</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"ijerph-17-05511-t006\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05511-t006_Table 6</object-id><label>Table 6</label><caption><p>Occupational Welfare in Italy: Enterprises and workers involved at the first and second level of contractual agreement.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Contractual Agreements</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Enterprises</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Workers Beneficiaries</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">First level (2018)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">166,011</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2,432,098</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Secondary level (2017)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1078</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">928,260</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Total</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">167,089</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3,350,358</td></tr></tbody></table><table-wrap-foot><fn><p>Source: [<xref rid=\"B31-ijerph-17-05511\" ref-type=\"bibr\">31</xref>].</p></fn></table-wrap-foot></table-wrap></floats-group></article>\n"
]
[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Int J Mol Sci</journal-id><journal-id journal-id-type=\"iso-abbrev\">Int J Mol Sci</journal-id><journal-id journal-id-type=\"publisher-id\">ijms</journal-id><journal-title-group><journal-title>International Journal of Molecular Sciences</journal-title></journal-title-group><issn pub-type=\"epub\">1422-0067</issn><publisher><publisher-name>MDPI</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32727099</article-id><article-id pub-id-type=\"pmc\">PMC7432093</article-id><article-id pub-id-type=\"doi\">10.3390/ijms21155335</article-id><article-id pub-id-type=\"publisher-id\">ijms-21-05335</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Article</subject></subj-group></article-categories><title-group><article-title>An In Vitro Lung System to Assess the Proinflammatory Hazard of Carbon Nanotube Aerosols</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0002-6728-4111</contrib-id><name><surname>Barosova</surname><given-names>Hana</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05335\">1</xref><xref ref-type=\"aff\" rid=\"af2-ijms-21-05335\">2</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0001-9676-7465</contrib-id><name><surname>Karakocak</surname><given-names>Bedia Begum</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05335\">1</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0003-2353-7508</contrib-id><name><surname>Septiadi</surname><given-names>Dedy</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05335\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Petri-Fink</surname><given-names>Alke</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05335\">1</xref><xref ref-type=\"aff\" rid=\"af3-ijms-21-05335\">3</xref></contrib><contrib contrib-type=\"author\"><name><surname>Stone</surname><given-names>Vicki</given-names></name><xref ref-type=\"aff\" rid=\"af4-ijms-21-05335\">4</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0002-7805-9366</contrib-id><name><surname>Rothen-Rutishauser</surname><given-names>Barbara</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05335\">1</xref><xref rid=\"c1-ijms-21-05335\" ref-type=\"corresp\">*</xref></contrib></contrib-group><aff id=\"af1-ijms-21-05335\"><label>1</label>BioNanomaterials Group, Adolphe Merkle Institute, University of Fribourg, 1700 Fribourg, Switzerland; <email>[email protected]</email> (H.B.); <email>[email protected]</email> (B.B.K.); <email>[email protected]</email> (D.S.); <email>[email protected]</email> (A.P.-F.)</aff><aff id=\"af2-ijms-21-05335\"><label>2</label>Institute of Experimental Medicine of the Czech Academy of Sciences, 142 20 Prague, Czech Republic</aff><aff id=\"af3-ijms-21-05335\"><label>3</label>Department of Chemistry, University of Fribourg, 1700 Fribourg, Switzerland</aff><aff id=\"af4-ijms-21-05335\"><label>4</label>Institute of Biological Chemistry, Biophysics and Bioengineering, Heriot-Watt University, Edinburgh EH14 4AS, UK; <email>[email protected]</email></aff><author-notes><corresp id=\"c1-ijms-21-05335\"><label>*</label>Correspondence: <email>[email protected]</email>; Tel.: +41-26-300-9502</corresp></author-notes><pub-date pub-type=\"epub\"><day>27</day><month>7</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><month>8</month><year>2020</year></pub-date><volume>21</volume><issue>15</issue><elocation-id>5335</elocation-id><history><date date-type=\"received\"><day>12</day><month>6</month><year>2020</year></date><date date-type=\"accepted\"><day>22</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>© 2020 by the authors.</copyright-statement><copyright-year>2020</copyright-year><license license-type=\"open-access\"><license-p>Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>).</license-p></license></permissions><abstract><p>In vitro three-dimensional (3D) lung cell models have been thoroughly investigated in recent years and provide a reliable tool to assess the hazard associated with nanomaterials (NMs) released into the air. In this study, a 3D lung co-culture model was optimized to assess the hazard potential of multiwalled carbon nanotubes (MWCNTs), which is known to provoke inflammation and fibrosis, critical adverse outcomes linked to acute and prolonged NM exposure. The lung co-cultures were exposed to MWCNTs at the air-liquid interface (ALI) using the VITROCELL<sup>®</sup> Cloud system while considering realistic occupational exposure doses. The co-culture model was composed of three human cell lines: alveolar epithelial cells (A549), fibroblasts (MRC-5), and macrophages (differentiated THP-1). The model was exposed to two types of MWCNTs (Mitsui-7 and Nanocyl) at different concentrations (2–10 μg/cm<sup>2</sup>) to assess the proinflammatory as well as the profibrotic responses after acute (24 h, one exposure) and prolonged (96 h, repeated exposures) exposure cycles. The results showed that acute or prolonged exposure to different concentrations of the tested MWCNTs did not induce cytotoxicity or apparent profibrotic response; however, suggested the onset of proinflammatory response.</p></abstract><kwd-group><kwd>lung</kwd><kwd>in vitro</kwd><kwd>co-culture</kwd><kwd>carbon nanotubes</kwd><kwd>multiwalled carbon nanotubes</kwd><kwd>air-liquid interface</kwd><kwd>toxicity</kwd><kwd>proinflammatory</kwd><kwd>profibrotic</kwd></kwd-group></article-meta></front><body><sec sec-type=\"intro\" id=\"sec1-ijms-21-05335\"><title>1. Introduction</title><p>Carbon nanotubes (CNTs) exhibit remarkable features, such as thermal stability, electrical conductivity, and outstanding mechanical durability, and therefore are one of the most widely used nanomaterials (NMs) [<xref rid=\"B1-ijms-21-05335\" ref-type=\"bibr\">1</xref>], especially in the fields of energy, electronics, and material composites. The promising potential of CNTs in industrial applications increases the demand for CNT production. However, this high demand is expected to lead to increased contamination of the natural eco-system as well as human exposure, which can potentially occur throughout the NMs life cycle, starting from production to their final disposal. More specifically, the biopersistence and high aspect ratio properties of CNTs are a major concern because of the inhalation of NMs in the workplace during production might induce unwanted pulmonary effects [<xref rid=\"B2-ijms-21-05335\" ref-type=\"bibr\">2</xref>,<xref rid=\"B3-ijms-21-05335\" ref-type=\"bibr\">3</xref>].</p><p>CNTs are made of rolled-up graphene sheets—graphene cylinders, typically limited to a few nanometers in diameter, but their length can range from a few micrometers to millimeters [<xref rid=\"B4-ijms-21-05335\" ref-type=\"bibr\">4</xref>]. This unique geometry of relatively small diameter and the enormous length, the needle-like shape, have therefore drawn comparisons with asbestos [<xref rid=\"B5-ijms-21-05335\" ref-type=\"bibr\">5</xref>]. There are two main classes of CNTs: single-walled carbon nanotubes (SWCNTs), which consist of one graphene cylinder, and multiwalled carbon nanotubes (MWCNTs) comprises two or more of these graphene cylinders [<xref rid=\"B6-ijms-21-05335\" ref-type=\"bibr\">6</xref>].</p><p>There has been a vast number of investigations on the potential of MWCNTs released into the environment to induce adverse health effects. In the blood of humans exposed to MWCNT in an occupational setting, aberrant changes in mRNA and ncRNA expression profiles have been reported [<xref rid=\"B7-ijms-21-05335\" ref-type=\"bibr\">7</xref>]. A growing number of animal studies demonstrate that exposure to MWCNTs potentially triggers airway injury, inflammation, fibrosis, and granuloma [<xref rid=\"B8-ijms-21-05335\" ref-type=\"bibr\">8</xref>,<xref rid=\"B9-ijms-21-05335\" ref-type=\"bibr\">9</xref>,<xref rid=\"B10-ijms-21-05335\" ref-type=\"bibr\">10</xref>,<xref rid=\"B11-ijms-21-05335\" ref-type=\"bibr\">11</xref>]. Among different MWCNTs types, Mitsui-7 MWCNTs (from now on referred to as Mitsui-7) proved to induce a progressive fibrotic response in mice [<xref rid=\"B11-ijms-21-05335\" ref-type=\"bibr\">11</xref>,<xref rid=\"B12-ijms-21-05335\" ref-type=\"bibr\">12</xref>,<xref rid=\"B13-ijms-21-05335\" ref-type=\"bibr\">13</xref>]. Although events leading to fibrosis are part of the normal tissue repair process, pulmonary fibrosis usually refers to a pathological condition where the impaired healing process leads to excessive extracellular matrix production. Pulmonary fibrosis develops as a result of the activation of complex intracellular signaling cascades among multiple cell types, including macrophages, epithelial cells, and fibroblasts [<xref rid=\"B14-ijms-21-05335\" ref-type=\"bibr\">14</xref>].</p><p>Several in vivo studies have been reported investigating the potentially toxic effects of MWCNTs [<xref rid=\"B15-ijms-21-05335\" ref-type=\"bibr\">15</xref>,<xref rid=\"B16-ijms-21-05335\" ref-type=\"bibr\">16</xref>,<xref rid=\"B17-ijms-21-05335\" ref-type=\"bibr\">17</xref>]. But toxicity evaluation using animals raise scientific, ethical, and financial concerns. In vitro human lung cell models are, on the other hand, widely used to investigate the mechanisms of interactions of exposed particles with the cells [<xref rid=\"B18-ijms-21-05335\" ref-type=\"bibr\">18</xref>]. Measurement of the corresponding cellular responses come to prominence as a relatively fast alternative for the primary screening of a broad range of NMs [<xref rid=\"B19-ijms-21-05335\" ref-type=\"bibr\">19</xref>,<xref rid=\"B20-ijms-21-05335\" ref-type=\"bibr\">20</xref>]. Cell culture models can be used to assess several endpoints related to adverse outcomes of exposure to inhaled substances. Monoculture studies are often performed under submerged conditions using human alveolar epithelial cell line A549 to evaluate the epithelial effects [<xref rid=\"B21-ijms-21-05335\" ref-type=\"bibr\">21</xref>,<xref rid=\"B22-ijms-21-05335\" ref-type=\"bibr\">22</xref>,<xref rid=\"B23-ijms-21-05335\" ref-type=\"bibr\">23</xref>,<xref rid=\"B24-ijms-21-05335\" ref-type=\"bibr\">24</xref>]. Direct exposures of fibroblasts to CNTs stimulated progressive cell proliferation or collagen production, the known fibrotic markers [<xref rid=\"B25-ijms-21-05335\" ref-type=\"bibr\">25</xref>,<xref rid=\"B26-ijms-21-05335\" ref-type=\"bibr\">26</xref>]. Submerged exposure of fibroblasts, epithelial cells, and macrophages used in the presented study was previously used as a first screening tool, and it was shown that exposure to CNTs can induce proinflammatory and profibrotic responses [<xref rid=\"B27-ijms-21-05335\" ref-type=\"bibr\">27</xref>,<xref rid=\"B28-ijms-21-05335\" ref-type=\"bibr\">28</xref>] An increased proinflammatory response upon repeated exposure to CNT aerosols was also observed in a 3D lung co-culture model (in the presence of immune cells) consisting of cell-lines [<xref rid=\"B29-ijms-21-05335\" ref-type=\"bibr\">29</xref>] or primary cells including fibroblasts [<xref rid=\"B30-ijms-21-05335\" ref-type=\"bibr\">30</xref>].</p><p>When assessing the potential effects of CNTs, equivalent human exposure conditions need to be mimicked by considering exposure methods and occupational dosimetry aspects. Most in vivo studies do not consider the real nature of occupational exposures [<xref rid=\"B31-ijms-21-05335\" ref-type=\"bibr\">31</xref>], i.e., the prolonged exposure times and overwhelming high doses. The occupational exposures usually occur in a repeated manner over a longer timeframe in manufacturing facilities, and high doses may, therefore, overwhelm the normal defense mechanisms, resulting in significant initial pulmonary inflammation. On the other hand, the majority of the existing in vitro studies focus on acute exposure using relatively high exposure doses but under submerged conditions [<xref rid=\"B32-ijms-21-05335\" ref-type=\"bibr\">32</xref>,<xref rid=\"B33-ijms-21-05335\" ref-type=\"bibr\">33</xref>,<xref rid=\"B34-ijms-21-05335\" ref-type=\"bibr\">34</xref>]. Submerged conditions are physiologically not relevant since lung cells are exposed to air on their apical surface. To investigate the potential adverse effects of the NMs under relevant and realistic conditions, different air-liquid interface (ALI) exposure equipment were developed previously to aerosolize high-aspect-ratio NMs [<xref rid=\"B35-ijms-21-05335\" ref-type=\"bibr\">35</xref>] such as CNTs [<xref rid=\"B36-ijms-21-05335\" ref-type=\"bibr\">36</xref>,<xref rid=\"B37-ijms-21-05335\" ref-type=\"bibr\">37</xref>,<xref rid=\"B38-ijms-21-05335\" ref-type=\"bibr\">38</xref>,<xref rid=\"B39-ijms-21-05335\" ref-type=\"bibr\">39</xref>], allowing the homogenous spreading of nebulized material onto the cell surface. Furthermore, such equipment can be supplied with a quartz crystal microbalance (QCM), enabling online measurement of the amount of deposited material.</p><p>It is crucial to gain insight into the pulmonary hazard of prolonged (subchronic) CNT exposure at repeated doses that can realistically mimic the in vivo conditions. We recently showed that a lower respiratory tract 3D co-culture model with primary human cells, i.e., EpiAlveolar<sup>™</sup>, could be a promising tool to predict the development of pulmonary fibrosis in response to fibrotic substances such as CNTs [<xref rid=\"B30-ijms-21-05335\" ref-type=\"bibr\">30</xref>]. In addition to a proinflammatory and profibrotic response as assessed by cytokine measurements, a disruption in barrier integrity (determined by transepithelial electrical resistance (TEER) measurements) and an increase in alveolar tissue thickening were observed in response to the chemical positive control, transforming growth factor β (TGF-β) [<xref rid=\"B30-ijms-21-05335\" ref-type=\"bibr\">30</xref>]. Within the light of this conclusion, the current study was designed to assess the suitability of a 3D lung co-culture model consisting of cell lines (i.e., epithelial cells, macrophages, and fibroblasts) to acute (24 h) and prolonged (96 h) exposures of realistic CNT exposure doses. It is noteworthy that a co-culture model consisting of cell lines can be used for a limited period at ALI due to the issues concerning the overgrowth of the epithelial cells. However, the experiments were designed as a quick screening study to assess the pro-inflammatory and profibrotic cytokine release in response to MWCNT exposure mimicking the occupational exposure conditions.</p><p>As a result, this study aimed to expose a 3D lung co-culture model to two different types of MWCNTs, i.e., Mitsui-7 and Nanocyl, as well as silica-based particles as particle controls, i.e., Dörntruper quartz (DQ12) and Min-U-Sil, using a previously developed ALI exposure system at realistic occupational doses while adopting acute (one exposure, sampling after 24 h) and prolonged (five exposures on subsequent days, sampling after 96 h) exposure scenarios. The responsivity of the 3D lung co-culture model as a prescreening tool for possible proinflammatory and profibrotic responses was assessed.</p></sec><sec sec-type=\"results\" id=\"sec2-ijms-21-05335\"><title>2. Results</title><p>The scheme of the experimental setup, as well as the exposure scenario and sample collection, are presented in <xref ref-type=\"fig\" rid=\"ijms-21-05335-f001\">Figure 1</xref>.</p><sec id=\"sec2dot1-ijms-21-05335\"><title>2.1. Material Characteristics</title><p>To estimate the amount and form of test materials deposited on the cells, transmission electron microscopy (TEM) grids were placed in the VITROCELL<sup>®</sup> Cloud exposure chamber during the material aerosolization and subsequently analyzed using TEM. The daily short-term exposures of the delivered dose, i.e., each exposure took approximately 10 min; as a result, the 24 h time point corresponds to one exposure, while 96 h corresponds to five exposures. In <xref ref-type=\"fig\" rid=\"ijms-21-05335-f002\">Figure 2</xref>, the TEM images present the amounts comparing daily (24 h) deposition, and deposition after 96 h (i.e., five exposures), and the images confirmed that the VITROCELL<sup>®</sup> Cloud system is a suitable method for executing repeated dose-dependent deposition studies. The representative TEM images of the deposited samples display the morphological differences among the two MWCNTs samples, i.e., Mitsui-7 and Nanocyl. While Mitsui-7 MWCNTs appear as relatively rare singlets or small bundles of thicker tubes, Nanocyl MWCNTs consist of shorter and thinner tangled tubes. The differences in MWCNTs shapes, i.e., stiff straight tubes vs. tangled tubes has resulted in the presence of a multiple number of walls and thus different consequential diameters. On the other hand, Min-U-Sil quartz particles (for further information, see <xref rid=\"ijms-21-05335-t001\" ref-type=\"table\">Table 1</xref>) with a tendency to form agglomerates and DQ12 particles presenting a homogenous deposition are also shown in <xref ref-type=\"fig\" rid=\"ijms-21-05335-f002\">Figure 2</xref>. Both Min-U-Sil and DQ12 particles are silica quartz particles, with similar particle sizes (< 5 μm), and similar crystallinity. Min-U-Sil possesses approximately 89% crystallinity, while DQ12 present approximately 73% [<xref rid=\"B40-ijms-21-05335\" ref-type=\"bibr\">40</xref>].</p><p>The differences in material composition are presented in <xref rid=\"ijms-21-05335-t001\" ref-type=\"table\">Table 1</xref>. The Min-U-Sil consists of pure silica quartz, whereas DQ12 contains approximately 13% impurities. The information about the tested materials, along with the deposited amounts of each material type measured by a QCM device, corresponding to the daily exposure dose, is provided in <xref rid=\"ijms-21-05335-t001\" ref-type=\"table\">Table 1</xref>. The weekly deposition dose was calculated as five times the daily dose due to the limitation in the number of exposure chambers available. As a result, the cells were exposed to one type of material, adopting one exposure scenario at a time. After each exposure is complete, the instrument is wiped with pre-wetted tissues with ethanol. Subsequently, another exposure experiment is initiated using a different material for the exposure following a different exposure scenario.</p></sec><sec id=\"sec2dot2-ijms-21-05335\"><title>2.2. Cell Line Co-culture Model</title><sec id=\"sec2dot2dot1-ijms-21-05335\"><title>2.2.1. Cytotoxicity and Cell Morphology</title><p>The amount of lactate dehydrogenase (LDH) released into the cell culture medium was assessed as an important cytotoxicity marker (<xref ref-type=\"fig\" rid=\"ijms-21-05335-f003\">Figure 3</xref>A). The data are presented relative to the negative control (bovine serum albumin, BSA-treated samples). No statistically significant (<italic>p</italic> > 0.05) increase in LDH release was observed for any of the tested materials following the two exposure scenarios. Triton-X (0.2% in phosphate-buffered saline (PBS)) was applied 24 h prior to the sample collection at both time-points as the positive control, which induced a statistically significant (<italic>p</italic> < 0.05) increase of LDH release.</p><p>Cell morphology was inspected by laser scanning microscopy (LSM). The cells were labeled for F-actin and cell nuclei. Additionally, the presence of macrophages on the apical side was confirmed by CD68 staining, and the fibroblasts located at the basal side of the insert were stained for vimentin.</p><p>The 3D rendered image of the untreated cells at 24 h time-point, showing the location of macrophages on the apical side of the co-culture model, is presented in <xref ref-type=\"fig\" rid=\"ijms-21-05335-f003\">Figure 3</xref>B. <xref ref-type=\"fig\" rid=\"ijms-21-05335-f003\">Figure 3</xref>C shows cell-cultures fixed at 96 h post-exposure to all materials tested. No difference in cell morphology in Mitsui-7 treated cells was observed compared to samples treated with BSA only (negative control). The cells exposed to DQ12 and Min-U-Sil for 96 h exhibited loss of cellular contact and presented irregular shapes, leading to discontinuity and reduced thickness of the epithelial layer. The cell co-cultures treated with Nanocyl MWCNTs are slightly disorganized with discontinuity in the epithelial layer, and a slight increase in the layer thickness of the fibroblasts. Mitsui-7 MWCNTs exposure proved no effect on the fibroblast cell layer thickness. The XZ-projections show overgrowth of the epithelial layer in all samples, regardless of the sample treatment, which proves in low resemblance of in vivo situation at later (96 h) time-point.</p></sec><sec id=\"sec2dot2dot2-ijms-21-05335\"><title>2.2.2. Oxidative Stress Response</title><p>The decreased glutathione (GSH) content in the cells was reported as an important marker for oxidative stress. The data is presented as the amount of total GSH (intracellular + extracellular) content relative to the total protein measurements. There was no statistically significant decrease in response to any materials tested or at exposure time-points; however, a tendency in the GSH decrease can be seen for Min-U-Sil and Nanocyl exposure results at both 24 h and 96 h time points (<xref ref-type=\"fig\" rid=\"ijms-21-05335-f004\">Figure 4</xref>A).</p></sec><sec id=\"sec2dot2dot3-ijms-21-05335\"><title>2.2.3. Proinflammatory Response</title><p>The proinflammatory response was assessed by cytokine release measurements via Enzyme-Linked Immunosorbent Assay (ELISA), specifically for Interleukin-1β (IL-1β) and Interleukin-8 (IL-8) detection, as well as for tumor necrosis factor-α (TNF-α) release (<xref ref-type=\"fig\" rid=\"ijms-21-05335-f004\">Figure 4</xref>B–D). No statistically significant increase was detected for IL-1β and IL-8 release in all tested materials at the end of both exposure periods (<xref ref-type=\"fig\" rid=\"ijms-21-05335-f004\">Figure 4</xref>B,C). On the other hand, the increase in TNF-α release was statistically significant (<italic>p</italic> < 0.05) for Nanocyl MWCNTs at 24 h post-exposure, while Mitsui-7 MWCNTs induced statistically significant (<italic>p</italic> < 0.05) release after 96 h post-exposure (<xref ref-type=\"fig\" rid=\"ijms-21-05335-f004\">Figure 4</xref>D). R-848 activates NF-κB, and it was shown that it can trigger IL-1β and TNF-α release in neonates [<xref rid=\"B44-ijms-21-05335\" ref-type=\"bibr\">44</xref>]. Therefore, in this study, R-848 was used as a proinflammatory positive control and induced a statistically significant increase after 24 h exposure for IL-1β and TNF-α, and after 96 h for only TNF-α (<xref ref-type=\"fig\" rid=\"ijms-21-05335-f004\">Figure 4</xref>). TNF-α, a potent proinflammatory stimulus [<xref rid=\"B45-ijms-21-05335\" ref-type=\"bibr\">45</xref>], was applied as a positive control to induce IL-8 release; a statistically significant (<italic>p</italic> < 0.05) increase of IL-8 release induced by TNF-α compared to negative control was observed for all exposure time points (24 h, 48 h and 96 h) (<xref ref-type=\"fig\" rid=\"ijms-21-05335-f004\">Figure 4</xref>C).</p></sec><sec id=\"sec2dot2dot4-ijms-21-05335\"><title>2.2.4. Profibrotic Response</title><p>The profibrotic response was assessed by measuring the cytokine release, specifically TGF-β, platelet-derived growth Factor-AA (PDGF), and Osteopontin (OPN), via ELISA (<xref ref-type=\"fig\" rid=\"ijms-21-05335-f005\">Figure 5</xref>). The underlying drive behind the development of an inflammatory response is to clear the foreign material from the tissue and to eventually initiate the repairing pathway, which is facilitated by growth factors, such as the TGF-β. TGF-β was used as a positive fibrotic control because of its involvement in the development of fibrosis in different organs, disturbances of the homeostatic microenvironment, promotion of cell activation, migration, invasion, and excessive extracellular matrix production [<xref rid=\"B30-ijms-21-05335\" ref-type=\"bibr\">30</xref>,<xref rid=\"B46-ijms-21-05335\" ref-type=\"bibr\">46</xref>]. No statistically significant increase for any of the cytokine release measurements was observed at any exposure time-points upon exposures to any of the tested materials. The overgrowth of epithelial cells may not allow mediators and cytokines to reach fibroblasts and cell culture medium at the amount, which is relevant for activating fibrotic pathway; for instance, lack of TGF-β does not trigger PDGF and OPN release. R-848 was applied as a positive control to induce an increase in TGF-β release (<xref ref-type=\"fig\" rid=\"ijms-21-05335-f005\">Figure 5</xref>A), and TGF-β was also applied as a positive control to induce the PDGF and OPN release (<xref ref-type=\"fig\" rid=\"ijms-21-05335-f005\">Figure 5</xref>B,C), some trends in the increase of cytokine release can be observed for all positive controls at all investigated time points; however, no statistically significant change was detected.</p></sec></sec></sec><sec sec-type=\"discussion\" id=\"sec3-ijms-21-05335\"><title>3. Discussion</title><sec id=\"sec3dot1-ijms-21-05335\"><title>3.1. Pros and Cons of the Co-culture Model</title><p>This study was designed to mimic the subchronic inhalation of different materials. Previous in vivo studies have shown that the investigated MWCNTs can penetrate deep into the alveolar region of lungs and cumulate in interstitium [<xref rid=\"B11-ijms-21-05335\" ref-type=\"bibr\">11</xref>,<xref rid=\"B12-ijms-21-05335\" ref-type=\"bibr\">12</xref>]. Macrophages play an essential role in the inflammatory response and surface cleaning (by particle uptake [<xref rid=\"B12-ijms-21-05335\" ref-type=\"bibr\">12</xref>]) upon particle exposure and subsequently can play a role in the extent of the fibrotic response. As a result, human alveolar epithelial cells, human lung fibroblasts, and human macrophages were used for designing the present co-culture model. This model follows our previous study [<xref rid=\"B27-ijms-21-05335\" ref-type=\"bibr\">27</xref>], where all three cell types cultured as monocultures and were exposed to MWCNTs and silica particles, which are also used in this study. The presented co-culture model is cultured at ALI to provide more realistic culturing conditions. It was previously shown that ALI culturing conditions lead to more enhanced protein release compared to the submerged conditions [<xref rid=\"B47-ijms-21-05335\" ref-type=\"bibr\">47</xref>]. In this study, cell lines were used for assembling the presented model, as they represent a relatively stable and cost-effective system while providing experimental flexibility compared to the commercially available primary cells. Although primary cells are unique tools for designing long-term experiments with differentiated cells [<xref rid=\"B30-ijms-21-05335\" ref-type=\"bibr\">30</xref>,<xref rid=\"B48-ijms-21-05335\" ref-type=\"bibr\">48</xref>], they lack the flexibility needed for co-culturing with other cell types, mainly because they usually require a unique media composition.</p><p>Furthermore, primary cells are considered a cost-intensive option for researchers, as they either have to be directly isolated from patients while maintaining high purity of the desired cell population or can be obtained as commercially available models. Although an alveolar model consisting of primary cells is commercially available (EpiAlveolar<sup>™</sup> [<xref rid=\"B30-ijms-21-05335\" ref-type=\"bibr\">30</xref>]), we aimed to present a similar model consisting of cell lines, as a potentially cost-effective tool for enabling rapid pre-screening of particles. This model was designed based on our previous experience with the human alveolar model. We successfully combined human alveolar epithelial cells and human primary immune cells [<xref rid=\"B49-ijms-21-05335\" ref-type=\"bibr\">49</xref>] and demonstrated the created model as a powerful tool for screening inflammatory responses upon exposure to various particle types [<xref rid=\"B29-ijms-21-05335\" ref-type=\"bibr\">29</xref>,<xref rid=\"B50-ijms-21-05335\" ref-type=\"bibr\">50</xref>,<xref rid=\"B51-ijms-21-05335\" ref-type=\"bibr\">51</xref>,<xref rid=\"B52-ijms-21-05335\" ref-type=\"bibr\">52</xref>]. A summary table (<xref rid=\"ijms-21-05335-t002\" ref-type=\"table\">Table 2</xref>) is provided to compare the model presented in this study to commercially available 3D lung co-culture models.</p></sec><sec id=\"sec3dot2-ijms-21-05335\"><title>3.2. Material Concentrations Used for Cell Exposures</title><p>The alveolar mass retention during a full lifetime occupational exposure (45 years) period to CNTs of different sizes was modeled and calculated to be in the range of 12.4 to 46.5 μg/cm<sup>2</sup> [<xref rid=\"B53-ijms-21-05335\" ref-type=\"bibr\">53</xref>]. In this study, the maximum deposited concentration after five exposures to Nanocyl was 11 μg/cm<sup>2</sup> and 20 μg/cm<sup>2</sup> for Mitsui-7, respectively, which is in the range of the reported lifetime occupational human exposure to MWCNTs [<xref rid=\"B53-ijms-21-05335\" ref-type=\"bibr\">53</xref>]. However, it has to be kept in mind that in this study, the amount of CNTs was deposited within five days as the duration of the experiment is the limiting factor for cell line usage. A549 cells are continuously proliferating cells, and therefore they overgrow into a multilayer after 3 to 4 days in ALI, which negatively affects the physiological relevance of the model. On the other hand, the lowest tested exposure concentration (1 μg/cm<sup>2</sup>) for Mitsui-7 and Nanocyl corresponds with concentrations used in mice in vivo [<xref rid=\"B13-ijms-21-05335\" ref-type=\"bibr\">13</xref>,<xref rid=\"B54-ijms-21-05335\" ref-type=\"bibr\">54</xref>].</p><p>Moreover, aerosolization of silica quartz particles resulted in an average deposited concentration up to 1 μg/cm<sup>2</sup> for DQ12 and 3 μg/cm<sup>2</sup> for Min-U-Sil after repeated exposures. The tested exposure concentrations correspond to those used in previous in vitro studies, including human lung models, and induced a pro-inflammatory response [<xref rid=\"B29-ijms-21-05335\" ref-type=\"bibr\">29</xref>,<xref rid=\"B48-ijms-21-05335\" ref-type=\"bibr\">48</xref>]. Furthermore, these concentrations are comparable to in vivo exposure experiments in rats (3–30 mg/rat, which corresponds to 0.6–6 μg/cm<sup>2</sup>, deposition calculated based on [<xref rid=\"B55-ijms-21-05335\" ref-type=\"bibr\">55</xref>]) [<xref rid=\"B56-ijms-21-05335\" ref-type=\"bibr\">56</xref>].</p></sec><sec id=\"sec3dot3-ijms-21-05335\"><title>3.3. Oxidative Stress</title><p>GSH is known to be essential for several cell processes, mainly interconnected with the maintenance of the thiol-redox status [<xref rid=\"B57-ijms-21-05335\" ref-type=\"bibr\">57</xref>]. Therefore, the ratio between the reduced and oxidized forms of GSH is an important indicator of the intracellular redox environment and, at the same time, provides insights regarding cell proliferation, differentiation, and apoptosis [<xref rid=\"B58-ijms-21-05335\" ref-type=\"bibr\">58</xref>]. In light of this knowledge, GSH-based oxidative stress measurement assays have been widely implemented [<xref rid=\"B59-ijms-21-05335\" ref-type=\"bibr\">59</xref>]. Specifically, GSH measurements in response to exposure to NMs have provided insight regarding the level of the cell’s oxidative stress status [<xref rid=\"B60-ijms-21-05335\" ref-type=\"bibr\">60</xref>].</p><p>The oxidative stress response upon exposures to silica quartz particles was previously reported in vitro, but not confirmed in vivo [<xref rid=\"B61-ijms-21-05335\" ref-type=\"bibr\">61</xref>]. A previous in vitro study showed decreased GSH in fibroblasts 24 h upon exposures to Nanocyl; however, no difference in GSH production was observed 96 h post-exposure compared to the negative control. Min-U-Sil and Mitsui-7 MWCNTs did not induce any significant decrease in GSH either [<xref rid=\"B27-ijms-21-05335\" ref-type=\"bibr\">27</xref>]. Similarly, in our study, we did not observe a statistically significant (<italic>p</italic> > 0.05) decrease in GSH levels for any of the tested materials in both exposure scenarios; however, a tendency in the GSH decrease can be observed for Min-U-Sil and Nanocyl exposure results at both time-points (<xref ref-type=\"fig\" rid=\"ijms-21-05335-f004\">Figure 4</xref>A). This finding is in line with the proteomics investigation of the presented co-culture response upon exposures to Mitsui-7, where no response was observed upon exposure to Mitsui-7 compared to the negative control [<xref rid=\"B47-ijms-21-05335\" ref-type=\"bibr\">47</xref>].</p></sec><sec id=\"sec3dot4-ijms-21-05335\"><title>3.4. Proinflammatory Response</title><p>A proinflammatory cytokine is a type of signaling molecule secreted from certain cell types and promotes inflammation in response to foreign materials, in this case, MWCNTs. Among them, IL-1β, IL-8, and TNF-α are known to play a vital role in mediating the innate immune response and therefore considered biomarkers of nanoimmunotoxicity [<xref rid=\"B62-ijms-21-05335\" ref-type=\"bibr\">62</xref>].</p><p>Certain particle types have been shown to induce immunotoxicity. Among them, DQ12 is well studied, and have been recruited in in vitro studies as a potent proinflammatory stimulus [<xref rid=\"B29-ijms-21-05335\" ref-type=\"bibr\">29</xref>,<xref rid=\"B63-ijms-21-05335\" ref-type=\"bibr\">63</xref>]. On the other hand, Min-U-Sil was found to induce increased IL-1β release in macrophage monocultures (differentiated THP-1), but no response in fibroblasts or epithelial cells was reported [<xref rid=\"B27-ijms-21-05335\" ref-type=\"bibr\">27</xref>]. In vivo experiments with mice showed a strong inflammatory response upon acute exposure to a high dose of Min-U-Sil [<xref rid=\"B64-ijms-21-05335\" ref-type=\"bibr\">64</xref>]. Nanocyl and Mitsui-7 MWCNTs did not induce any proinflammatory response in epithelial cells, fibroblasts, or macrophage monocultures in vitro [<xref rid=\"B27-ijms-21-05335\" ref-type=\"bibr\">27</xref>]. Although individual in vitro studies assessing the potential toxicity of MWCNTs of interest did not report proinflammatory responses, it should be noted that these studies were either using monocultures of cells or the exposure conditions were not representative, i.e., submerged conditions as opposed to creating an ALI prior to the exposure.</p><p>In this study, we have adapted two exposure scenarios, as explained in the Methods section. The results indicated statistically significant changes in TNF-α secretion levels in response to two different exposure scenarios (acute (24 h) vs. prolonged (96 h)) for both MWCNT types tested. A significant elevation in TNF-α levels for Nanocyl at 24 h post-exposure was detected. In comparison, Mitsui-7 MWCNTs induced a statistically significant (<italic>p</italic> < 0.05) increase in TNF-α levels 96 h post-exposure (<xref ref-type=\"fig\" rid=\"ijms-21-05335-f004\">Figure 4</xref>D). TNF-α levels specifically play a vital role in proinflammatory response assessment because it is expressed in the early stages of cell inflammation [<xref rid=\"B65-ijms-21-05335\" ref-type=\"bibr\">65</xref>]. Therefore, it can be concluded that in a realistic occupational exposure scenario, exposure to MWCNTs does initiate cell inflammation and eventually can result in cell death.</p></sec><sec id=\"sec3dot5-ijms-21-05335\"><title>3.5. Profibrotic Response</title><p>Exposure to NMs is known to play a role in the development of chronic pulmonary diseases, especially fibrosis [<xref rid=\"B66-ijms-21-05335\" ref-type=\"bibr\">66</xref>]. Among all NMs, MWCNTs specifically pose a danger because relatively large quantities are being used in numerous manufacturing practices, and thus will inadvertently lead to human exposure. Therefore, there have been continuous efforts to evaluate the potential link between fibrosis and MWCNTs exposure. It was previously shown that both Mitsui-7 [<xref rid=\"B12-ijms-21-05335\" ref-type=\"bibr\">12</xref>,<xref rid=\"B67-ijms-21-05335\" ref-type=\"bibr\">67</xref>] and Nanocyl [<xref rid=\"B42-ijms-21-05335\" ref-type=\"bibr\">42</xref>] exposures lead to the development of pulmonary fibrosis in rodents. However, it was reported that Nanocyl caused less upregulation of genes involved in fibrosis development compared to long and thick MWCNTs [<xref rid=\"B42-ijms-21-05335\" ref-type=\"bibr\">42</xref>]. These results are consistent with an in vitro study investigating epithelial cell (A549), macrophage (differentiated THP-1), and fibroblast (MRC-5) monocultures. Mitsui-7 exposures led to profibrotic response (TGF-β release), and OPN releases in epithelial cells. On the other hand, Nanocyl increased only TGF-β release from fibroblasts [<xref rid=\"B27-ijms-21-05335\" ref-type=\"bibr\">27</xref>], which has previously been used in vitro to stimulate the pro-inflammatory response [<xref rid=\"B35-ijms-21-05335\" ref-type=\"bibr\">35</xref>,<xref rid=\"B48-ijms-21-05335\" ref-type=\"bibr\">48</xref>,<xref rid=\"B63-ijms-21-05335\" ref-type=\"bibr\">63</xref>] and in vivo to stimulate the development of pulmonary fibrosis [<xref rid=\"B56-ijms-21-05335\" ref-type=\"bibr\">56</xref>,<xref rid=\"B68-ijms-21-05335\" ref-type=\"bibr\">68</xref>].</p><p>In this study, we did not observe a statistically significant increase in TGF-β, PDGF, or OPN release (<xref ref-type=\"fig\" rid=\"ijms-21-05335-f005\">Figure 5</xref>). Interferon-γ (1 μg/mL) was also used as a positive control to stimulate TGF-β, PDGF, and OPN release (data not shown), but no increase in profibrotic mediators release was detected. In addition to the evaluation of profibrotic mediator releases, we also measured fibrotic markers, the collagen type I and fibronectin release, but did not detect a statistically significant increase in both indicator release levels (data not shown), although this combination of investigated endpoints proved its suitability in predicting profibrotic response in vitro earlier [<xref rid=\"B30-ijms-21-05335\" ref-type=\"bibr\">30</xref>]. Furthermore, the use of the Sircol<sup>TM</sup> soluble collagen assay to detect acid and pepsin soluble collagen was considered; however, the presence of serum in the cell culture medium posed interference on the assay results; therefore, the use of this assay was eliminated from the study. It is also noteworthy to state that at the cellular level, proinflammatory mediator detection is considered one of the key events (KE) in the adverse outcome pathway (AOP) for fibrosis [<xref rid=\"B30-ijms-21-05335\" ref-type=\"bibr\">30</xref>]. The presented model resembles the alveolar region of the human lungs, and the concept is similar to the EpiAlveolar<sup>TM</sup> model used in our previous study [<xref rid=\"B30-ijms-21-05335\" ref-type=\"bibr\">30</xref>]. Both models use human lung epithelial cells, fibroblasts, and macrophages, while the EpiAlveolar<sup>TM</sup> model additionally includes endothelial cells. However, the main differences of the two models are the cells used: cell lines vs. primary cells, and the duration of the experiment, i.e., the length of the periods while each cell model remains stable throughout the exposures. For the EpiAlveolar<sup>TM</sup> model, it was possible to perform exposures over three weeks at ALI, whereas the cell line model only is stable for 3–4 days as the A549 cells start to overgrow at ALI. When comparing the data from both studies, the model with primary cells is more suitable for such an investigation to assess repeated and long-term effects, as it is capable of predicting profibrotic response upon exposure to TGF-β and other NMs. On the other hand, cell line models still offer a cheaper and easy-to-handle option for hazard prescreening of NMs to assess cytotoxicity and inflammatory markers. Finally, the proposed model is not limited to testing potentially hazardous nanomaterials. It can also be implemented to test anti-inflammatory [<xref rid=\"B69-ijms-21-05335\" ref-type=\"bibr\">69</xref>,<xref rid=\"B70-ijms-21-05335\" ref-type=\"bibr\">70</xref>] and anti-fibrotic [<xref rid=\"B71-ijms-21-05335\" ref-type=\"bibr\">71</xref>] nanoformulations in the field of nanomedicine.</p></sec></sec><sec id=\"sec4-ijms-21-05335\"><title>4. Materials and Methods</title><sec id=\"sec4dot1-ijms-21-05335\"><title>4.1. Chemicals and Reagents</title><p>All chemicals and reagents used were obtained from Sigma-Aldrich (Buchs, Switzerland) unless stated otherwise.</p></sec><sec id=\"sec4dot2-ijms-21-05335\"><title>4.2. Sample Preparation</title><p>BSA solution (0.1% in ultrapure H<sub>2</sub>O) was sterile filtered (0.2 μm pore size; Nalgene, Thermo Scientific, MA, USA). BSA was used as a dispersant for MWCNTs. Mitsui-7 MWCNTs (MWCNTs-7; Mitsui & Co, Tokyo, Japan) and Nanocyl-7000 MWCNTs (referred to as Nanocyl; Nanocyl S.A., Sambreville, Belgium; received from European Commission Joint Research Centre, Ispra, Italy) were dispersed in BSA solution. Briefly, pre-weighted dry MWCNTs powder was heat sterilized at 100 °C overnight, left to cool down, and subsequently 0.1% BSA solution was added to obtain a stock solution with the concentration of 50 μg/mL. This suspension was sonicated for 3 h with continuous shaking to disperse the MWCNTs and subsequently stored at 4 °C until further use.</p><p>Two different types of silica quartz particles were used as reference materials. Min-U-Sil is an inert, high purity white crystalline silica recently reported as a potential profibrotic agent [<xref rid=\"B28-ijms-21-05335\" ref-type=\"bibr\">28</xref>,<xref rid=\"B72-ijms-21-05335\" ref-type=\"bibr\">72</xref>], while Dörntruper quartz (DQ12; composed of 87% crystalline silica and amorphous silica with kaolinite impurities) was reported as a proinflammatory agent in vitro [<xref rid=\"B73-ijms-21-05335\" ref-type=\"bibr\">73</xref>], as well as profibrotic agent in vivo [<xref rid=\"B56-ijms-21-05335\" ref-type=\"bibr\">56</xref>,<xref rid=\"B74-ijms-21-05335\" ref-type=\"bibr\">74</xref>,<xref rid=\"B75-ijms-21-05335\" ref-type=\"bibr\">75</xref>]. Both silica materials were dispersed in ultrapure sterile filtered H<sub>2</sub>O at a concentration of 100 μg/mL.</p><p>All stock suspensions were sonicated for 1 h prior to the exposure experiments.</p></sec><sec id=\"sec4dot3-ijms-21-05335\"><title>4.3. Material Characterization</title><sec id=\"sec4dot3dot1-ijms-21-05335\"><title>4.3.1. Electron Microscopy</title><p>To investigate the deposition of aerosolized materials, transmission electron microscopy (TEM) 300 mesh carbon-coated copper grids were placed into the exposure chamber prior to the experiment.</p><p>TEM grids with deposited material were used without any further treatment. Representative images were captured using a TEM (FEI Tecnai Spirit, Hillsboro, OR, USA) operating at 120 kV and equipped with a Veleta CCD camera (Olympus, Japan).</p></sec><sec id=\"sec4dot3dot2-ijms-21-05335\"><title>4.3.2. Endotoxin Content</title><p>The endotoxin concentration in the MWCNTs suspensions was measured using the Pierce<sup>TM</sup> LAL Chromogenic Endotoxin Quantitation Kit (ThermoFisher Scientific, Basel, Switzerland), following the manufacturer’s instructions and all suspensions were below 0.5 EU/mL.</p></sec></sec><sec id=\"sec4dot4-ijms-21-05335\"><title>4.4. Human Cell Line Co-Culture Model</title><p>All cell lines—human fibroblasts MRC-5, human alveolar epithelial cells type II A549 and human monocytes THP-1 were purchased from American Type Culture Collection (ATCC, Manassas, VA, USA) and cultivated according to the ATCC protocol prior assembled into co-culture model.</p><p>Human epithelial cells type II (A549 cell line) were cultured in Roswell Park Memorial Institute medium (RPMI 1640) supplemented with 10% Fetal Bovine Serum (FBS), 1% Penicillin/streptomycin and 1% L-Glutamine (all stated chemicals were obtained from Gibco, Gaithersburg, MD, USA). Human monocytes (THP-1 cell line) were cultivated in supplemented RPMI 1640, as mentioned above, with extra 0.05 mM β-mercaptoethanol. To differentiate monocytes into macrophages, prior to co-culture assembling, the THP-1 cells had to be activated by Phorbol 12-Myristate 13-Acetate (PMA; 20 ng/mL in supplemented medium without β-mercaptoethanol) overnight at the density of 4 × 10<sup>5</sup> cells/mL. Human fibroblasts were cultivated in Minimum Essential Medium (MEM) supplemented with 10% FBS, 1% Penicillin/streptomycin, 1% L-Glutamine, and 1 × Non-Essential Amino Acids (all Gibco, Gaithersburg, MD, USA).</p><p>The co-culture model was composed by following the next steps. The Transwell<sup>®</sup> 12-well inserts (surface area of 0.9 cm<sup>2</sup>, pores of 0.4 μm diameter, PET membranes; BD Biosciences, Allschwil, Switzerland) were inverted and placed into a sterile petri dish. Then the MRC-5 cells were seeded on the basolateral part of the insert at the density 10<sup>4</sup> cells/cm<sup>2</sup> and incubated for 3 h at 37 °C, 5% CO<sub>2</sub> to fully attach to the insert membrane. After the incubation period, inserts were turned back and placed into the 12-well plate containing 1.5 mL supplemented MEM. Subsequently, A549 cells were seeded on the apical side of the insert at the density of 2.8 × 10<sup>5</sup> cells/cm<sup>2</sup> with 1 mL of supplemented RPMI 1640 and incubated for 4 days (d) at 37 °C, 5% CO<sub>2</sub>. After the 4-d incubation period of A549-MRC-5 co-cultures, differentiated THP-1 in supplemented RPMI 1640 were seeded on the top of A549 cells at the density of 28 × 10<sup>3</sup> cells/cm<sup>2</sup>. The plates were then incubated for 2 d at 37 °C, 5% CO<sub>2</sub>, and subsequently lifted to ALI 24 h prior to the experiment by removing the medium from the apical side and replacing medium from the basolateral side with 0.6 mL of the medium mixture (RPMI:MEM, 1:1).</p></sec><sec id=\"sec4dot5-ijms-21-05335\"><title>4.5. The Air-liquid Interface (ALI) Cell Exposure Method</title><p>Co-cultures were exposed at the ALI using the VITROCELL<sup>®</sup> Cloud system (Waldkirch, Germany). Briefly, the exposure system consists of a nebulizer, an exposure chamber as well as a QCM (operated at 5 MHz, detection limit: 0.1 μg/cm<sup>2</sup>), allowing to measure and record the deposited dose online. For each aerosolization experiment, 200 μL of sample’s stock solution with 2 μL of 0.09% NaCl (NAAPREP<sup>®</sup> physiological saline, GlaxoSmithKline, Evreux, France) was added to the nebulizer (Aeroneb<sup>®</sup> Lab, Dangal, Galway, Ireland; mesh size 10 μm for MWCNTs, 4–6 μm for Min-U-SIl and 2.5–6 μm for DQ12). The vibrating perforated membrane at the neck of the nebulizer generates the aerosols and diverts them into the exposure chamber. Inside the chamber, the aerosols in the cloud gently deposit onto the cell surfaces that are maintained at the ALI. The flow rate of nebulizer (the flowrate is preset by the instrument provider, cannot be controlled) was ideal for the aerosols to sufficiently mix within the entire chamber, hence resulting in uniform droplet deposition. VITROCELL<sup>®</sup> Cloud exposure chamber was wiped with pre-wetted ethanol tissues prior to each exposure experiment.</p></sec><sec id=\"sec4dot6-ijms-21-05335\"><title>4.6. Exposure Scenarios</title><p>The biological response of the co-culture model was assessed following exposure to all tested materials at the doses presented in <xref rid=\"ijms-21-05335-t001\" ref-type=\"table\">Table 1</xref>. Two different exposure scenarios were performed, i.e., acute (24 h) exposure and prolonged (96 h) continuous exposure. The cells were exposed apically at the ALI daily and maintained at 37 °C in 5% CO<sub>2</sub> throughout each exposure scenario. The cells were exposed to one dose of the material at the acute exposure scenario. In contrast, the cells following the prolonged exposure scenario were exposed to five times the material dose on subsequent days for 96 h in total.</p></sec><sec id=\"sec4dot7-ijms-21-05335\"><title>4.7. Biochemical Analysis</title><sec><title>Cytotoxicity</title><p>The LDH release into the supernatant as a result of cell membrane rupture is a well-known indicator of cytotoxicity. The amount of LDH release was evaluated using a commercially available LDH diagnostic kit (Roche Applied Science, Mannheim, Germany), according to the manufacturer’s protocol. Each sample was tested in triplicates; the enzyme activity was measured photometrically. The absorbance was measured at 490 nm with a reference wavelength of 630 nm. LDH values are presented relative to the negative control (BSA-treated cells). Cell cultures exposed apically to 0.2% Triton X-100 for 24 h were used as a positive control.</p></sec></sec><sec id=\"sec4dot8-ijms-21-05335\"><title>4.8. Cell Morphology</title><p>The cell morphology was evaluated using laser scanning microscopy. At the respective time-points, the cell cultures were fixed for 15 min in 4% paraformaldehyde in PBS at room temperature, subsequently washed 3 × with PBS and stored in PBS at 4 °C until further immunofluorescence staining could occur.</p><p>Prior to the immunofluorescence staining, samples were treated with 0.1 M glycine for 15 min and subsequently permeabilized with 0.2% Triton X-100 for another 15 min, both at room temperature. All antibodies were diluted in 0.3% Triton X-100 and 1% BSA in PBS and incubated for 2 h. The cells were first stained with primary antibodies (dilution 1:100) and subsequently with secondary antibodies, 4’,6-diamidin-2-fenylindol (DAPI) and rhodamine-phalloidin (dilution 1:100, Molecular Probes, Eugene, OR, USA). Specifically, anti-CD68 and chicken anti-vimentin were used as the primary antibodies and anti-mouse Alexa 488, anti-chicken Alexa 647 (Abcam, Cambridge, MA, USA), phalloidin-rhodamine and DAPI were used as the fluorophores. Anti-CD68 is used for macrophage staining, anti-vimentin stains intermediate filaments of the fibroblasts, phalloidin-rhodamine stains F-actin cytoskeleton and DAPI stains nucleus. Following antibody incubation, cell culture inserts were embedded in Glycergel (DAKO, Santa Clara, CA, USA). Cell morphology was evaluated by visualization of samples via an inverted laser scanning confocal microscope (LSM) 710 (Axio Observer.Z1, Carl Zeiss, Oberkochen, Germany). Image processing was conducted with IMARIS 3D restoration software (Bitplane AG, Zurich, Switzerland).</p></sec><sec id=\"sec4dot9-ijms-21-05335\"><title>4.9. Oxidative Stress</title><p>The total amount of reduced intracellular GSH in the cell cultures was quantified using a GSH assay kit (following supplier’s protocol, Cayman Chemical Company, Ann Arbor, MI, USA) as a well-known marker for oxidative stress. Levels of oxidative stress are presented as a ratio of total GSH to the total amount of protein of each sample. Total protein content was measured by the Pierce bicinchoninic acid (BCA) protein assay kit (Pierce Protein Research Products, Thermo Scientific, Rockford, IL, USA) according to the manufacturer guidelines. Tert-Butyl Hydrogen Peroxide (tBHP) at a concentration of 135 mM acted as the positive assay control.</p></sec><sec id=\"sec4dot10-ijms-21-05335\"><title>4.10. Cytokine Secretion</title><sec id=\"sec4dot10dot1-ijms-21-05335\"><title>4.10.1. Proinflammatory Response</title><p>The IL-1β, TNF-α, and IL-8 secretion were assessed using the commercially available DuoSet<sup>®</sup> ELISA Development diagnostic kit (R&D Systems, Zug, Switzerland), according to the manufacturer’s protocol. Cells treated apically with 1 μg/mL of TNF-α (ImmunoTools, Friesoythe, Germany) served as the positive control for IL-1β (data not shown) and IL-8 assays. R-848 applied apically onto the cell surface at 6 μg/mL was used as a positive control for IL-1β, TNF-α, and IL-8 (data not shown) assays. All positive controls were tested at 24 h, 48 h, and 96 h post-exposure time-points in both co-culture systems.</p></sec><sec id=\"sec4dot10dot2-ijms-21-05335\"><title>4.10.2. Profibrotic Response</title><p>The TGF-β, PDGF, and osteopontin (OPN) release into the supernatant of exposed cells was quantified using the respective ELISA DuoSet<sup>®</sup> Development diagnostic kit (R&D Systems, Zug, Switzerland), following the manufacturer’s protocol. Based on the supplier’s protocol, pH activated form of TGF-β was assessed (the activation occurs straight prior to the assay). Cell cultures exposed apically to 1 μg/mL of Interferon-gamma (IFN-γ) were used as a positive control for TGF-β assay (data not shown), while apical cell treatment with 100 ng/mL TGF-β served as the positive control to induce PDGF and OPN release. R-848 applied apically onto the cell surface at 6 μg/mL was used as a positive control for the TGF-β assay. All positive controls were tested at 24 h, 48 h, and 96 h post-exposure time-points in both co-culture systems.</p></sec></sec><sec id=\"sec4dot11-ijms-21-05335\"><title>4.11. Statistical Analysis</title><p>For each data point, four independent experiments were performed (four biological replicates), one technical replicate per treatment was prepared; each endpoint was measured in triplicates. Statistical analysis was performed using GraphPad Prism 6 (GraphPad Software Inc., La Jolla, CA, USA) software. A parametric one-way analysis of variance (ANOVA) with subsequent Dunnett test was performed. Results were considered significant if <italic>p</italic> < 0.05.</p></sec></sec><sec sec-type=\"conclusions\" id=\"sec5-ijms-21-05335\"><title>5. Conclusions</title><p>In conclusion, our in vitro co-culture model consisting of three human cell lines relevant to assess inflammation and fibrosis showed a proinflammatory response upon exposure to the positive controls, and a statistically significant TNF-α release was detected after exposure to both MWCNTs tested. On the other hand, no cytotoxicity or profibrotic response was observed in response to exposure to both types of MWCNTs. While the presented model is not suitable to predict the profibrotic response at the 96 h time point, it can be used for both acute (24 h) and prolonged (96 h) proinflammatory response investigations. As a result, our representative 3D lung co-culture model can be used to assess the proinflammatory response to MWCNTs aerosols; if the results induce a positive response, then further investigations involving primary cells allowing a repeated exposure up to several weeks are recommended. Finally, it is important to note that our model is not limited to testing potentially hazardous nanomaterials. A future study is underway to harness this representative lung system to test anti-inflammatory and anti-fibrotic nanoformulations.</p></sec></body><back><ack><title>Acknowledgments</title><p>We kindly thank Miguel Spuch-Calvar, Dimitri Vanhecke, and Barbara Drasler for contributing to the scheme for the exposure scenario (<xref ref-type=\"fig\" rid=\"ijms-21-05335-f001\">Figure 1</xref>). We also thank Martin J.D. Clift, Savvina Chortarea, Monita Sharma, and Amy J. Clippinger for the valuable discussion during the study planning phase.</p></ack><app-group><app id=\"app1-ijms-21-05335\"><title>Supplementary Materials</title><p>Supplementary materials can be found at <uri xlink:href=\"https://www.mdpi.com/1422-0067/21/15/5335/s1\">https://www.mdpi.com/1422-0067/21/15/5335/s1</uri>. Figure S1. 3D rendered LSM image of the co-culture model at 96 h time-point exposed to BSA. Figure S2. 3D rendered LSM image of the co-culture model at 96 h time-point exposed to Mitsui-7 MWCNTs. Figure S3. 3D rendered LSM image of the co-culture model at 96 h time-point exposed to DQ12. Figure S4. 3D rendered LSM image of the co-culture model at 96 h time-point exposed to Min-U-Sil. Figure S5. 3D rendered LSM image of the co-culture model at 96 h time-point exposed to Nanocyl.</p><supplementary-material content-type=\"local-data\" id=\"ijms-21-05335-s001\"><media xlink:href=\"ijms-21-05335-s001.pdf\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material></app></app-group><notes><title>Author Contributions</title><p>Conceptualization, B.R.-R., V.S. and H.B.; methodology, H.B.; software, D.S.; validation, B.H. and B.R.-R.; formal analysis, H.B.; investigation, B.R.-R. and A.P.-F.; resources, B.R.-R. and H.B.; data curation, H.B. and B.B.K.; writing—original draft preparation, H.B.; writing—review and editing, H.B. and B.B.K.; visualization, B.R.-R.; supervision, B.R.-R.; project administration, B.R.-R.; funding acquisition, B.R.-R. All authors have read and agreed to the published version of the manuscript.</p></notes><notes><title>Funding</title><p>This work was supported by the PETA International Science Consortium Ltd. and the Adolphe Merkle Foundation. In addition, this project has received further funding from the European Union’s Horizon 2020 Research and Innovation Programme, PATROLS—Physiologically Anchored Tools for Realistic Nanomaterial Hazard Assessment, under grant agreement No 760813. B.B.K. sincerely acknowledges the Swiss Government Excellence Postdoctoral Scholarship for Foreign Researchers.</p></notes><notes notes-type=\"COI-statement\"><title>Conflicts of Interest</title><p>The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.</p></notes><glossary><title>Abbreviations</title><array orientation=\"portrait\"><tbody><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3D</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Three dimensional</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ANOVA</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Analysis of variance</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ATTC</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">American type culture collection</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">BCA</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Bicinchoninic acid</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">BSA</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Bovine albumin serum</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CCD</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Charged coupled device</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CD</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Cluster of differentiation</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CNT</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Carbon nanotubes</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">DAPI</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4′,6-diamidino-2-phenylindole</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ELISA</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Enzyme-linked immunosorbent assay</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">FBS</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Fetal bovine serum</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">GSH</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Glutathione</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">IL</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Interleukin</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">LDH</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Lactate dehydrogenase</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">LSM</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Laser scanning microscopy</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">MEM</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Minimum essential medium</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">MWCNT</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Multiwalled carbon nanotubes</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">NM</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Nanomaterial</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">OPN</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Osteopontin</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">PBS</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Phosphate buffered saline</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">PDGF</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Platelet-derived growth factor</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">PET</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Polyethylene terephthalate</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">PFA</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Paraformaldehyde</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">PMA</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Phorbol 12-myristate 13-acetate</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">QCM</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Quartz crystal microbalance</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">RPMI</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Roswell Park Memorial Institute</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">SEM</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Standard error of the mean</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">tBHP</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Tert-butyl hydrogen peroxide</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">TEER</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Transepithelial electric resistance</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">TEM</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Transmission electron microscopy</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">TGF</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Transforming growth factor</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">TNF</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Tumor necrosis 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The scale bar is 1 μm for Min-U-Sil, DQ12, and Nanocyl samples, and 5 μm for Mitsui-7 CNTs.</p></caption><graphic xlink:href=\"ijms-21-05335-g002\"/></fig><fig id=\"ijms-21-05335-f003\" orientation=\"portrait\" position=\"float\"><label>Figure 3</label><caption><p>Cytotoxicity and cellular morphology of the 3D co-culture model. (<bold>A</bold>) Cytotoxicity was measured by the LDH assay, data presented in a boxplot with 10–90 percentile. * marks a statistically significant increase compared to the negative control, <italic>n</italic> = 4. (<bold>B</bold>) Representative 3D rendered LSM image of the co-culture model at a 24 h time point, presenting the localization of macrophages within the model. Cyan represents cell nuclei, grey shows cytoskeleton, and magenta stains the macrophages (CD68). (<bold>C</bold>) The LSM images (XY projections) of BSA- and Mitsui-7, DQ12, Min-U-Sil, and Nanocyl-treated samples at 96 h time-point with their corresponding XZ projections showing thickness of the cellular layer, the scale bar is 30 μm. Cyan represents cell nuclei, grey represents cytoskeleton, and red represents vimentin, a type III intermediate filament protein. For 3D rendered LSM images of the co-culture model at 96 h time-point, please refer to the <xref ref-type=\"app\" rid=\"app1-ijms-21-05335\">Supplementary Materials, Figures S1–S5</xref>. The scale bar is 30 μm.</p></caption><graphic xlink:href=\"ijms-21-05335-g003\"/></fig><fig id=\"ijms-21-05335-f004\" orientation=\"portrait\" position=\"float\"><label>Figure 4</label><caption><p>In response to exposure to the tested materials, the extent of oxidative stress and proinflammatory response is assessed via (<bold>A</bold>) GSH content; (<bold>B</bold>) IL-1β; (<bold>C</bold>) IL-8 and (<bold>D</bold>) TNF-α release measurements. Data presented in a boxplot with 10–90 percentile. * marks a statistically significant increase compared to the negative control, <italic>n</italic> = 4.</p></caption><graphic xlink:href=\"ijms-21-05335-g004\"/></fig><fig id=\"ijms-21-05335-f005\" orientation=\"portrait\" position=\"float\"><label>Figure 5</label><caption><p>The profibrotic response measurements of the co-culture model. The responses were investigated via (<bold>A</bold>) TGF-β release; (<bold>B</bold>) PDGF release, and (<bold>C</bold>) OPN release. Data presented in a boxplot with 10–90 percentile.</p></caption><graphic xlink:href=\"ijms-21-05335-g005\"/></fig><table-wrap id=\"ijms-21-05335-t001\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijms-21-05335-t001_Table 1</object-id><label>Table 1</label><caption><p>Information about tested materials, daily deposited dose was measured with a quartz crystal microbalance (QCM), the 96 h deposited doses were calculated as five times the 24 h dose.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Material</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Type of the Material</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Particle Size (μm) (Length)</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Particle Size (μm) (Diameter)</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Dispersant</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">24 h Dose (μg/cm<sup>2</sup>)</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">96 h Dose (μg/cm<sup>2</sup>)</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">DQ12 [<xref rid=\"B41-ijms-21-05335\" ref-type=\"bibr\">41</xref>]</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Quartz sand (87% crystalline silica + amorphous silica, kaolinite)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><inline-formula><mml:math id=\"mm1\"><mml:mrow><mml:mo>≤</mml:mo></mml:mrow></mml:math></inline-formula> 5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Ultrapure water</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.18 ± 0.04</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.90 ± 0.22</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Min-U-Sil [<xref rid=\"B41-ijms-21-05335\" ref-type=\"bibr\">41</xref>]</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Crystalline silica</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><inline-formula><mml:math id=\"mm2\"><mml:mrow><mml:mo>≤</mml:mo></mml:mrow></mml:math></inline-formula> 5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Ultrapure water</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.53 ± 0.17</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.66 ± 0.86</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Nanocyl [<xref rid=\"B42-ijms-21-05335\" ref-type=\"bibr\">42</xref>]</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">MWCNTs</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">~ 1.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">~ 0.01</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.1% BSA</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.33 ± 0.87</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">11.67 ± 4.34</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Mitsui-7 [<xref rid=\"B43-ijms-21-05335\" ref-type=\"bibr\">43</xref>]</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">MWCNTs</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">~ 13</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">~ 0.05</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.1% BSA</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3.93 ± 0.95</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">19.65 ± 4.76</td></tr></tbody></table></table-wrap><table-wrap id=\"ijms-21-05335-t002\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijms-21-05335-t002_Table 2</object-id><label>Table 2</label><caption><p>The comparison of the 3D lung co-culture model presented in this study to commercially available 3D lung co-culture models consisting of primary cells.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Property</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">The Presented Model in This Study</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Commercially Available Models Consisting of Primary Cells [<xref rid=\"B30-ijms-21-05335\" ref-type=\"bibr\">30</xref>,<xref rid=\"B48-ijms-21-05335\" ref-type=\"bibr\">48</xref>]</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Ease of assembling the model</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Easy</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Sold as ready-to-use</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Adjusting/ tuning based on desired investigation endpoints</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Possible</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Not possible</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Inclusion of immune cells</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Yes</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">No *</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Possibility to use classical cell culture media</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Yes</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">No</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Overall cost</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Low</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">High</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Suitability to be used for long-term experiments</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">No</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Yes</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Barrier tightness (trans-epithelial electrical resistance)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Low</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">High</td></tr></tbody></table><table-wrap-foot><fn><p>* Can be included by the end-user, but requires an extensive optimization step as immune cells are cultured in a different and complex cell media as opposed to commercially available models.</p></fn></table-wrap-foot></table-wrap></floats-group></article>\n"
]
[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Int J Mol Sci</journal-id><journal-id journal-id-type=\"iso-abbrev\">Int J Mol Sci</journal-id><journal-id journal-id-type=\"publisher-id\">ijms</journal-id><journal-title-group><journal-title>International Journal of Molecular Sciences</journal-title></journal-title-group><issn pub-type=\"epub\">1422-0067</issn><publisher><publisher-name>MDPI</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32752130</article-id><article-id pub-id-type=\"pmc\">PMC7432094</article-id><article-id pub-id-type=\"doi\">10.3390/ijms21155509</article-id><article-id pub-id-type=\"publisher-id\">ijms-21-05509</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Article</subject></subj-group></article-categories><title-group><article-title>Flexible NAD<sup>+</sup> Binding in Deoxyhypusine Synthase Reflects the Dynamic Hypusine Modification of Translation Factor IF5A</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Chen</surname><given-names>Meirong</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05509\">1</xref><xref ref-type=\"aff\" rid=\"af2-ijms-21-05509\">2</xref><xref ref-type=\"author-notes\" rid=\"fn1-ijms-21-05509\">†</xref></contrib><contrib contrib-type=\"author\"><name><surname>Gai</surname><given-names>Zuoqi</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05509\">1</xref><xref ref-type=\"author-notes\" rid=\"fn1-ijms-21-05509\">†</xref><xref ref-type=\"author-notes\" rid=\"fn2-ijms-21-05509\">‡</xref></contrib><contrib contrib-type=\"author\"><name><surname>Okada</surname><given-names>Chiaki</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05509\">1</xref><xref ref-type=\"author-notes\" rid=\"fn3-ijms-21-05509\">§</xref></contrib><contrib contrib-type=\"author\"><name><surname>Ye</surname><given-names>Yuxin</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05509\">1</xref><xref ref-type=\"author-notes\" rid=\"fn4-ijms-21-05509\">‖</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0003-2854-9225</contrib-id><name><surname>Yu</surname><given-names>Jian</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05509\">1</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0003-1687-5904</contrib-id><name><surname>Yao</surname><given-names>Min</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05509\">1</xref><xref rid=\"c1-ijms-21-05509\" ref-type=\"corresp\">*</xref></contrib></contrib-group><aff id=\"af1-ijms-21-05509\"><label>1</label>Faculty of Advanced Life Science, Hokkaido University, Sapporo 060-0810, Japan; <email>[email protected]</email> (M.C.); <email>[email protected]</email> (Z.G.); <email>[email protected]</email> (C.O.); <email>[email protected]</email> (Y.Y.); <email>[email protected]</email> (J.Y.)</aff><aff id=\"af2-ijms-21-05509\"><label>2</label>College of Food Science and Technology, Nanjing Agricultural University, Nanjing 210095, China</aff><author-notes><corresp id=\"c1-ijms-21-05509\"><label>*</label>Correspondence: <email>[email protected]</email>; Tel./Fax: +81-11-706-4481</corresp><fn id=\"fn1-ijms-21-05509\"><label>†</label><p>These authors contribute equally to this work.</p></fn><fn id=\"fn2-ijms-21-05509\"><label>‡</label><p>The present address: School of Life Science and Engineering, Foshan University, Foshan 528231, China.</p></fn><fn id=\"fn3-ijms-21-05509\"><label>§</label><p>The present address: School of Pharmaceutical Sciences, Health Sciences University of Hokkaido, Ishikari-Tobetsu 061-0293, Japan.</p></fn><fn id=\"fn4-ijms-21-05509\"><label>‖</label><p>The present address: School of Chemical Biology and Biotechnology, State Key Laboratory of Chemical Oncogenomics, Peking University Shenzhen Graduate School, Shenzhen 518055, China.</p></fn></author-notes><pub-date pub-type=\"epub\"><day>31</day><month>7</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><month>8</month><year>2020</year></pub-date><volume>21</volume><issue>15</issue><elocation-id>5509</elocation-id><history><date date-type=\"received\"><day>31</day><month>5</month><year>2020</year></date><date date-type=\"accepted\"><day>29</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>© 2020 by the authors.</copyright-statement><copyright-year>2020</copyright-year><license license-type=\"open-access\"><license-p>Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>).</license-p></license></permissions><abstract><p>The eukaryotic and archaeal translation factor IF5A requires a post-translational hypusine modification, which is catalyzed by deoxyhypusine synthase (DHS) at a single lysine residue of IF5A with NAD<sup>+</sup> and spermidine as cofactors, followed by hydroxylation to form hypusine. While human DHS catalyzed reactions have been well characterized, the mechanism of the hypusination of archaeal IF5A by DHS is not clear. Here we report a DHS structure from <italic>Pyrococcus horikoshii OT3</italic> (<italic>Pho</italic>DHS) at 2.2 Å resolution. The structure reveals two states in a single functional unit (tetramer): two NAD<sup>+</sup>-bound monomers with the NAD<sup>+</sup> and spermidine binding sites observed in multi-conformations (closed and open), and two NAD<sup>+</sup>-free monomers. The dynamic loop region V288–P299, in the vicinity of the active site, adopts different positions in the closed and open conformations and is disordered when NAD<sup>+</sup> is absent. Combined with NAD<sup>+</sup> binding analysis, it is clear that <italic>Pho</italic>DHS can exist in three states: apo, <italic>Pho</italic>DHS-2 equiv NAD<sup>+</sup>, and <italic>Pho</italic>DHS-4 equiv NAD<sup>+</sup>, which are affected by the NAD<sup>+</sup> concentration. Our results demonstrate the dynamic structure of <italic>Pho</italic>DHS at the NAD<sup>+</sup> and spermidine binding site, with conformational changes that may be the response to the local NAD<sup>+</sup> concentration, and thus fine-tune the regulation of the translation process via the hypusine modification of IF5A.</p></abstract><kwd-group><kwd>IF5A</kwd><kwd>translation factor</kwd><kwd>hypusine modification</kwd><kwd>deoxyhypusine synthase</kwd><kwd>structure</kwd><kwd>NAD<sup>+</sup></kwd></kwd-group></article-meta></front><body><sec sec-type=\"intro\" id=\"sec1-ijms-21-05509\"><title>1. Introduction</title><p>Hypusine (<italic>N</italic>-(4-amino-2-hydroxybutyl) lysine), an unusual amino acid, is formed by the post-translational modification of a lysine residue with the addition of the aminobutyl moiety from the polyamine spermidine. The hypusine modification uniquely occurs in eukaryotic translation factor 5A (eIF5A) precursor proteins [<xref rid=\"B1-ijms-21-05509\" ref-type=\"bibr\">1</xref>]. Although eIF5A was originally designed as a translation initial factor, it has been shown that eIF5A is more directly related to elongation rather than the initiation step. The hypusine-modification of eIF5A is involved in the mRNA transportation and translation elongation on the ribosome [<xref rid=\"B2-ijms-21-05509\" ref-type=\"bibr\">2</xref>,<xref rid=\"B3-ijms-21-05509\" ref-type=\"bibr\">3</xref>]. The binding of eIF5A to translating ribosomes in a hypusine-dependent manner indicates modifications of the specific binding to the translational machinery [<xref rid=\"B4-ijms-21-05509\" ref-type=\"bibr\">4</xref>,<xref rid=\"B5-ijms-21-05509\" ref-type=\"bibr\">5</xref>], suggesting the important role of eIF5A receiving hypusine modifications in translation processes, which was supported by evidence that the hypusine modification of eIF5A is vital for the growth and survival of eukaryotes, from yeast to mammals [<xref rid=\"B6-ijms-21-05509\" ref-type=\"bibr\">6</xref>,<xref rid=\"B7-ijms-21-05509\" ref-type=\"bibr\">7</xref>,<xref rid=\"B8-ijms-21-05509\" ref-type=\"bibr\">8</xref>]. Previous studies also reported that the hypusine residue is essential for the homodimerization of eIF5A and affects its subcellular location [<xref rid=\"B1-ijms-21-05509\" ref-type=\"bibr\">1</xref>,<xref rid=\"B2-ijms-21-05509\" ref-type=\"bibr\">2</xref>,<xref rid=\"B9-ijms-21-05509\" ref-type=\"bibr\">9</xref>,<xref rid=\"B10-ijms-21-05509\" ref-type=\"bibr\">10</xref>].</p><p>The hypusine modification of eIF5A is catalyzed by deoxyhypusine synthase (DHS), followed by the hydroxylation of deoxyhypusine by deoxyhypusine hydroxylase (DOHH) [<xref rid=\"B1-ijms-21-05509\" ref-type=\"bibr\">1</xref>,<xref rid=\"B11-ijms-21-05509\" ref-type=\"bibr\">11</xref>]. DHS catalyzes a NAD<sup>+</sup>-dependent reaction that synthesizes deoxyhypusine (<italic>N</italic>-(4-aminobutyl) lysine) by transferring the butylamine moiety of spermidine to a specific lysine residue of eIF5A [<xref rid=\"B12-ijms-21-05509\" ref-type=\"bibr\">12</xref>]. Then, deoxyhypusine is hydroxylated by DOHH to form hypusine [<xref rid=\"B13-ijms-21-05509\" ref-type=\"bibr\">13</xref>,<xref rid=\"B14-ijms-21-05509\" ref-type=\"bibr\">14</xref>,<xref rid=\"B15-ijms-21-05509\" ref-type=\"bibr\">15</xref>]. Like eIF5A, DHS is an essential gene, indicating the importance of hypusine modification [<xref rid=\"B16-ijms-21-05509\" ref-type=\"bibr\">16</xref>]. Since eIF5A is associated with many diseases, such as diabetes, cancers, malaria, and HIV-1 infections [<xref rid=\"B16-ijms-21-05509\" ref-type=\"bibr\">16</xref>], trials aimed at blocking the hypusine modification of eIF5A have been attempted. Specific inhibitors of DHS or DOHH have been reported to affect the arrest of cellular growth, and also alter the proliferation and differentiation of tumor cells [<xref rid=\"B17-ijms-21-05509\" ref-type=\"bibr\">17</xref>,<xref rid=\"B18-ijms-21-05509\" ref-type=\"bibr\">18</xref>,<xref rid=\"B19-ijms-21-05509\" ref-type=\"bibr\">19</xref>]. An extensive biochemical characterization of DHS has been carried out, establishing it as a 40–43 kDa enzyme (the exact size being species-dependent) requiring NAD<sup>+</sup> as a cofactor. The mechanism of human deoxyhypusine synthesis by DHS has been proposed as follows [<xref rid=\"B20-ijms-21-05509\" ref-type=\"bibr\">20</xref>]: firstly, a hydrogen of spermidine is transferred to NAD<sup>+</sup> to generate dehydrospermidine and NADH; the dehydrospermidine then links covalently to a lysine residue (K329 in human DHS) of DHS, forming an enzyme-imine intermediate; finally, DHS catalyzes the formation of deoxyhypusine through the conjugation of the butylamine moiety of dehydrospermidine to the ε-amino group of the lysine residue (K37) of the eIF5A precursor protein.</p><p>The reported crystal structures of human DHS show a homotetramer consisting of two tightly associated dimers with four active sites—two near each tight dimer interface [<xref rid=\"B21-ijms-21-05509\" ref-type=\"bibr\">21</xref>,<xref rid=\"B22-ijms-21-05509\" ref-type=\"bibr\">22</xref>]. Residues from both monomers of a dimer contribute to each of its active sites. In human DHS, four active sites accommodate four molecules of NAD<sup>+</sup> (PDB:1DHS) [<xref rid=\"B21-ijms-21-05509\" ref-type=\"bibr\">21</xref>,<xref rid=\"B22-ijms-21-05509\" ref-type=\"bibr\">22</xref>]. Interestingly, the catalytic sites are blocked by a projecting N-terminal two-turn helix (described as a “ball and chain”) in a cross-monomer manner in the first crystal form of human DHS, obtained upon crystallization at high ionic strengths and a low pH. However, the catalytic site is not obstructed in a structure of human DHS in a ternary complex with NAD<sup>+</sup> and GC7 (PDB:1RQD), an analog of spermidine [<xref rid=\"B22-ijms-21-05509\" ref-type=\"bibr\">22</xref>]. In this ternary complex structure, GC7 is also located at the interface of the tight dimers and is separated from NAD<sup>+</sup> by a loop structure (T307–A319) [<xref rid=\"B22-ijms-21-05509\" ref-type=\"bibr\">22</xref>]. The absence of a blockage by the ball and chain structure might suggest that this dynamic part of the enzyme could be involved in the regulation of activity within the functional tetramer. However, this human DHS complex structure without any bound GC7 (PDB:1ROZ), determined upon crystallization at low ionic strengths and pH 8.0 (nearer physiological conditions), lacked the electron density for the N-terminal ball and chain region [<xref rid=\"B22-ijms-21-05509\" ref-type=\"bibr\">22</xref>]. In recent research, it has been reported that the N-terminal ball and chain region is important for the assembly of a functional DHS tetramer [<xref rid=\"B23-ijms-21-05509\" ref-type=\"bibr\">23</xref>]. This N-terminal region is, therefore, clearly highly dynamic, with the flexibility being for DHS function. Whether it is regulating the active site accessibility in a substrate-dependent manner, binding eIF5A, or conferring sensitivity to cellular conditions, remains unclear. Different from human DHS, the DHS from <italic>Trypanosoma brucei</italic> exists as a heterotetramer formed by two paralogs, DHSc and DHSp (dimer of heterodimer), with DHSc homologous to human DHS. Each heterodimer contains two NAD<sup>+</sup> binding sites, one located in the functional catalytic site while the other is located in a remnant site lacking key catalytic residues [<xref rid=\"B24-ijms-21-05509\" ref-type=\"bibr\">24</xref>].</p><p>Consistent with archaea sharing a requirement for hypusine for the function of translation factor aIF5A, both the modification [<xref rid=\"B24-ijms-21-05509\" ref-type=\"bibr\">24</xref>] and DHS gene homologues [<xref rid=\"B1-ijms-21-05509\" ref-type=\"bibr\">1</xref>] have been identified. Furthermore, a study has shown the arrest of archaeal growth in G1 upon treatment with an inhibitor of DHS [<xref rid=\"B25-ijms-21-05509\" ref-type=\"bibr\">25</xref>], indicating that DHS is also essential in archaea. However, the mechanism of hypusination in archaea is unclear yet [<xref rid=\"B26-ijms-21-05509\" ref-type=\"bibr\">26</xref>]. Homologues of the non-essential second enzyme required for hypusine formation, DOHH, have not been identified. While some archaea may function with a deoxyhypusine modification (rather than hypusine), species are known that form hypusine in the absence of oxygen, which implies a distinct mechanism of hypusine modification in archaea. The structure of an archaeal DHS could provide a better understanding of the process of hypusine modification of aIF5A.</p><p>In this study, we determined the crystal structure of DHS from <italic>Pyrococcus horikoshii</italic> OT3 (<italic>Pho</italic>DHS), with NAD<sup>+</sup>-bound, at 2.2 Å resolution. <italic>Pho</italic>DHS is a tetramer, each monomer sharing a similar fold with its human homologue (PDB ID:1DHS). Interestingly, unlike human and <italic>T. brucei</italic> DHS, <italic>Pho</italic>DHS contains only two bound NAD<sup>+</sup> molecules and shows a difference in the NAD<sup>+</sup>-bound and NAD<sup>+</sup>-free monomeric conformations. In particular, NAD<sup>+</sup>-bound monomers in the <italic>Pho</italic>DHS tetramer display different conformations in the region of V288–P299 around the NAD<sup>+</sup> binding site. The observations are suggestive of a distinct manner in which NAD<sup>+</sup> binds to DHS that could be of relevance to the mechanism and regulation of enzyme activity. Having the states of NAD<sup>+</sup>-bound and NAD<sup>+</sup>-free also sheds light on the NAD<sup>+</sup>-dependent spermidine binding to DHS and raises a possibility that dynamic NAD<sup>+</sup> binding in <italic>Pho</italic>DHS may fine-tune the translation activity of aIF5A via hypusine modification.</p></sec><sec sec-type=\"results\" id=\"sec2-ijms-21-05509\"><title>2. Results</title><sec id=\"sec2dot1-ijms-21-05509\"><title>2.1. The Overall Structure of PhoDHS</title><p>We determined the crystal structure of <italic>Pho</italic>DHS at 2.2 Å resolution by using a molecular replacement (<xref rid=\"ijms-21-05509-t001\" ref-type=\"table\">Table 1</xref>). In an asymmetric unit, the structure of <italic>Phs</italic>DHS formed a tetramer in accordance with its known biological assembly [<xref rid=\"B21-ijms-21-05509\" ref-type=\"bibr\">21</xref>,<xref rid=\"B22-ijms-21-05509\" ref-type=\"bibr\">22</xref>], with unbuilt N-terminal 14 and C-terminal 6 of the total 342 residues in each monomer. Additionally, we could not build the parts of two loops in each monomer (E122-D125 and G306–K311 in molA, W290–K311 in molB, E122–V123 and W290–A310 in molC, and W121–K130 and E301–K311 in molD, respectively), because of the lack of electron density. Similar to DHS from other organisms, this tetramer is a dimer of dimers with a point group of D2, generated by two local 2-fold axes that are perpendicular to each other (<xref ref-type=\"fig\" rid=\"ijms-21-05509-f001\">Figure 1</xref>). The monomers of <italic>Pho</italic>DHS tetramers are more related to each other with a root mean squared deviation (r.m.s.d) between 0.544 Å and 0.606 Å for 276–284 Cα atoms. Based on the buried areas of intermolecular interactions, the tightly bound dimers are defined as molAB and molCD (buried areas > 2700 Å<sup>2</sup>), while the buried area is less than 1800 Å<sup>2</sup> between molA and molD (or between molB and molC). <italic>Pho</italic>DHS is very similar to human DHS (PDB ID:1DHS), with a r.m.s.d of 1.195 Å for 289 Cα, and is also similar to <italic>T. brucei</italic> DHS (PDB:6DFT) [<xref rid=\"B24-ijms-21-05509\" ref-type=\"bibr\">24</xref>] with a r.m.s.d of 1.39 Å for 275 Cα (<xref ref-type=\"fig\" rid=\"ijms-21-05509-f001\">Figure 1</xref>b). Differences include the N-terminal region (M1–P26) and an insertion (L77–L94) in human DHS. Unlike the inactive human DHS structure, wherein the N-terminal two-turn α helix (A9–L18) blocks the entrance tunnel to the spermidine active site [<xref rid=\"B21-ijms-21-05509\" ref-type=\"bibr\">21</xref>,<xref rid=\"B22-ijms-21-05509\" ref-type=\"bibr\">22</xref>], the N-terminal region (M1–I14) of <italic>Pho</italic>DHS is not built due to the poor electron density for this region, suggesting that it is highly flexible and different from human DHS, despite the crystallization conditions being similar to those of inactive human DHS [<xref rid=\"B21-ijms-21-05509\" ref-type=\"bibr\">21</xref>]. Given that the active sites are accessible, we assume that the structure of <italic>Pho</italic>DHS obtained is illustrative of an active form.</p><p>Although the overall structures of DHS are similar, there is a significant structural difference in a loop region (V288–P299) that forms part of the active site of <italic>Pho</italic>DHS. This active site loop is flexible and presents itself in multi-conformations, conf1 and conf2 in molA and molD, respectively, which are likely to represent two snapshots of the active site (<xref ref-type=\"app\" rid=\"app1-ijms-21-05509\">Figure S1</xref>). In the molB and molC of <italic>Pho</italic>DHS, the loop region is either disordered, or partially visible. Although only one of two conformations, conformation 1 or conformation 2 (conf1, conf2), was assigned to each molA and molD, a weak density of the alternative conformations could be observed, further suggesting the flexibility of the loop region in <italic>Pho</italic>DHS. In contrast, this active site loop is stable and present in just one conformation (conf2) in the previously published human and <italic>T. brucei</italic> DHS structures [<xref rid=\"B24-ijms-21-05509\" ref-type=\"bibr\">24</xref>].</p><p>Another striking structural difference seen in <italic>Pho</italic>DHS is at its C-terminus. The C-terminus of <italic>Pho</italic>DHS (Gly337–Gln342, in all four chains) is invisible in the structure, which could be observed in human and <italic>Trypanosoma brucei</italic> DHS [<xref rid=\"B24-ijms-21-05509\" ref-type=\"bibr\">24</xref>]. A sequence alignment shows that the primary structure of this region is not conserved in <italic>Pho</italic>DHS, human DHS, and <italic>T. brucei</italic> DHS (<xref ref-type=\"fig\" rid=\"ijms-21-05509-f002\">Figure 2</xref>). The short helix predicted for <italic>Pho</italic>DHS is longer in human DHS. The helix is visible in the human DHS structure, though the region retains some intrinsic flexibility, indicating conformational dynamics in the C-terminal region across species. Removal of five residues from the C-terminus in human DHS or 39 residues from the C-terminus of <italic>S. cerevisiae</italic> DHS impairs activity [<xref rid=\"B26-ijms-21-05509\" ref-type=\"bibr\">26</xref>,<xref rid=\"B27-ijms-21-05509\" ref-type=\"bibr\">27</xref>]. It is probable that the C-terminus of <italic>Pho</italic>DHS is also required for full function, and that the intrinsic flexibility of this region, rather than the conserved sequences, is likely important for the activity.</p></sec><sec id=\"sec2dot2-ijms-21-05509\"><title>2.2. NAD<sup>+</sup> Binding Manner of PhoDHS</title><p>Two bulky electron density blocks shaped as NAD<sup>+</sup> were distinguished at the interface between molA and molD of the <italic>Pho</italic>DHS tetramer after several cycles of refinement and were later assigned as NAD<sup>+</sup> (<xref ref-type=\"fig\" rid=\"ijms-21-05509-f003\">Figure 3</xref>a and <xref ref-type=\"app\" rid=\"app1-ijms-21-05509\">Figure S2</xref>). Those NAD<sup>+</sup> molecules were naturally captured during the expression of the enzyme in <italic>Escherichia coli</italic>, since no NAD<sup>+</sup> was added during purification and crystallization processes.</p><p>Bound NAD<sup>+</sup> is proximal to a loop (V288–P299 (hereafter named “the binding loop”)) that displays very different conformations in each monomer of the <italic>Pho</italic>DHS tetramer. Notably, in molA and molD, observed to bind NAD<sup>+</sup>, the binding loop adopts two different conformations (<xref ref-type=\"fig\" rid=\"ijms-21-05509-f001\">Figure 1</xref>a, right): in molA, the binding loop positions away from NAD<sup>+</sup> (conf1), while in molD, the binding loop adopts another conformation to access NAD<sup>+</sup> (conf2). In contrast, only the second conformation (conf2) of the binding loop was observed in the structures of human and <italic>T. brucei</italic> DHS (<xref ref-type=\"fig\" rid=\"ijms-21-05509-f001\">Figure 1</xref>b). In the molB and molC of the <italic>Pho</italic>DHS tetramer, where NAD<sup>+</sup> is absent, the binding loop is disordered or only partially visible. We propose that NAD<sup>+</sup> binding to <italic>Pho</italic>DHS induces the stabilization of the flexible loop structure, but that the alternative conformations indicate the action of the loop as a “lid.” In an open conformation (conf1), the active site is exposed to receive NAD<sup>+</sup>, and the lid is closed over NAD<sup>+</sup> in conf2. This controlled switched direction of the binding loop in the presence of NAD<sup>+</sup> may be important for the regulation of NAD<sup>+</sup> and/or spermidine binding, the “lid” potentially opening and closing in response to the surrounding NAD<sup>+</sup> content. In the absence of NAD<sup>+</sup>, the binding loop is presumably much more flexible in structure and thus invisible in molB and molC. Our <italic>Pho</italic>DHS structure is the first structure showing distinct differences in the NAD<sup>+</sup>-bound state of the DHS active site, differences additional to those in the NAD<sup>+</sup>-free state. This may be an additional pointer to a complex role for conformational dynamics in the function of DHS in the cell, potentially linked to the regulation of the translation process via a hypusine modification of IF5A.</p><p>The binding geometry of NAD<sup>+</sup> in <italic>Pho</italic>DHS is similar to that in human DHS [<xref rid=\"B21-ijms-21-05509\" ref-type=\"bibr\">21</xref>]. The two NAD<sup>+</sup> molecules (NAD1 and NAD2) are embraced individually by molA and molD of <italic>Pho</italic>DHS in a sandwich binding manner and insert their ends into the pockets of molA, and molD with 2-fold symmetry (<xref ref-type=\"fig\" rid=\"ijms-21-05509-f003\">Figure 3</xref>b). The closest distance between NAD1 and NAD2 is 2.7 Å, where a hydrogen bond can form between the ribose hydroxyl group of each adenosine moiety (<xref ref-type=\"fig\" rid=\"ijms-21-05509-f003\">Figure 3</xref>a). Such interaction between NAD1 and NAD2 stabilizes their binding to DHS, which may explain why, when only two NAD<sup>+</sup>s are bound, these bind to molAD but not molAB. A cluster of residues from molA and molD compose a binding site for NAD<sup>+</sup>: NAD1 forms hydrogen bonds with Thr101, Ala102, Gly103, Glu107, Asp214, Ser262, Thr286, Ala287 and Asp320 from molA, and stacks with the imidazole ring of His266 from molD. Interestingly, the main chain groups of Leu294 and Ser295 of the binding loop from molD in conf2 are also involved in a hydrogen bonding network to NAD1. To further characterize the binding mode, we generate the two alternative conformations (conf1, conf2) in both molA and molD by the superimposition of the molA and molD, which shows that the binding loop in conf2 of molA is more tightly associated with the NAD2 (which shares a similarly bound geometry to NAD1) of molD than with NAD1. Except for Ser262, all residues that contact NAD<sup>+</sup> are conserved (<xref ref-type=\"fig\" rid=\"ijms-21-05509-f002\">Figure 2</xref>). The crossover of interactions would suggest that molA and molD enhance NAD<sup>+</sup> binding to each other, and there is likely to be a synergistic effect between the binding of NAD1 and NAD2, mediated by the flexible loop.</p><p>In the previously reported DHS structures [<xref rid=\"B21-ijms-21-05509\" ref-type=\"bibr\">21</xref>,<xref rid=\"B22-ijms-21-05509\" ref-type=\"bibr\">22</xref>,<xref rid=\"B23-ijms-21-05509\" ref-type=\"bibr\">23</xref>], four NAD<sup>+</sup> were accommodated individually in four active sites of the tetramer. In our <italic>Pho</italic>DHS structure, however, the occupation of only two active sites by NAD<sup>+</sup> is observed for the first time. To further investigate the NAD<sup>+</sup> binding state of the <italic>Pho</italic>DHS tetramer, we carried out a UV absorption scan using <italic>Pho</italic>DHS in solution (<xref ref-type=\"fig\" rid=\"ijms-21-05509-f003\">Figure 3</xref>c). There is an obvious difference in the absorption curves of <italic>Pho</italic>DHS alone and <italic>Pho</italic>DHS supplemented with NAD<sup>+</sup>. The match between the absorption scan for <italic>Pho</italic>DHS + 30 equiv NAD<sup>+</sup> (molar ratio of <italic>Pho</italic>DHS:NAD<sup>+</sup> = 1:30) and <italic>Pho</italic>DHS + two equiv NAD<sup>+</sup> (molar ratio 1:2) indicates that <italic>Pho</italic>DHS can only accommodate two additional NAD<sup>+</sup> even if excess NAD<sup>+</sup> molecules are available, suggesting that there are two vacant sites in the purified <italic>Pho</italic>DHS, which become occupied in both conditions. The spectroscopy confirms that there are likely only two out of four binding sites that are occupied by NAD<sup>+</sup> in purified <italic>Pho</italic>DHS in solution, validating the observations from crystallography. The fact that human DHS tetramers bind four NAD<sup>+</sup> molecules at high concentrations of NAD<sup>+</sup> [<xref rid=\"B21-ijms-21-05509\" ref-type=\"bibr\">21</xref>,<xref rid=\"B22-ijms-21-05509\" ref-type=\"bibr\">22</xref>] does not exclude the binding of only two NAD<sup>+</sup> molecules when the NAD<sup>+</sup> concentration is low. Conversely, our spectroscopy data show that <italic>Pho</italic>DHS can likely bind four NAD<sup>+</sup> molecules when sufficient NAD<sup>+</sup> is present. The lower binding ratio of NAD<sup>+</sup> observed in <italic>Pho</italic>DHS could infer that DHS may also adopt distinct conformations for NAD<sup>+</sup> binding in other species, probably regulated by the local concentration of cellular NAD<sup>+</sup>, which may offer a control mechanism for IF5A modification and, therefore, activity in cell processes.</p></sec><sec id=\"sec2dot3-ijms-21-05509\"><title>2.3. Dynamic Conformation of the Region for Both NAD<sup>+</sup> and Spermidine Binding</title><p>As described above, the comparison of <italic>Pho</italic>DHS monomers reveals the conformational dynamics near the active site. The region V288–K311, located in a valley of the DHS tetramer center, shows either free mobility (and therefore disorder) in the absence of bound NAD<sup>+</sup> or two distinct conformations in its presence. In all four monomers, the electron density was lacking for G306–K311, which includes the catalytic Lys307, the imine bond acceptor with spermidine, and thus the aminobutyl donor to aIF5A. In a crystal structure of a human DHS ternary complex, this region is visualized and interacts with NAD<sup>+</sup> and a spermidine analog, GC7 [<xref rid=\"B22-ijms-21-05509\" ref-type=\"bibr\">22</xref>]. In a recent published paper, it was suggested that no conformational changes occur in the DHS structure upon binding with spermidine [<xref rid=\"B23-ijms-21-05509\" ref-type=\"bibr\">23</xref>]. Our identification of the binding loop region V288–P299, acting as a lid over the active site to give open (conf1) and closed (conf2) forms, is indicative of a significant conformational change, at least with respect to NAD binding (<xref ref-type=\"fig\" rid=\"ijms-21-05509-f003\">Figure 3</xref>d). Upon the superposition of the <italic>Pho</italic>DHS and human DHS (complexed with GC7) structures, we found that GC7 has very close access (about 4.4 Å) to the V288–P299 loop in conf2, but is substantially far away (about 8.2 Å) in conf1 (<xref ref-type=\"fig\" rid=\"ijms-21-05509-f003\">Figure 3</xref>e). Thus, both NAD<sup>+</sup> and spermidine can interact with the binding loop in conf2 but not in conf1, suggesting that the flexible binding loop plays a role in accepting substrates, even when NAD<sup>+</sup> has bound. We can, therefore, consider three conformational states of this loop in relation to its function (<xref ref-type=\"fig\" rid=\"ijms-21-05509-f004\">Figure 4</xref>): (I) apo form: the disordered structure observed in molB and molC of <italic>Pho</italic>DHS, prior to NAD<sup>+</sup> binding; (II) open form (conf1), the lid is away from the bound NAD<sup>+</sup> (observed in molA) in <italic>Pho</italic>DHS, and is awaiting substrate binding; (III) reaction/closed form (conf2), the lid closes to contact the bound NAD<sup>+</sup> (observed in molD) and also spermidine (as indicated in human DHS). Since <italic>Pho</italic>DHS interacts with NAD<sup>+</sup>, spermidine, and IF5A via this region, the conformational dynamics are perhaps an unsurprising potential contributor to the achievement of the different conformations for binding different partners. It is also possible, as stated earlier, that the conformational dynamics are a mechanism by which DHS can respond to the concentration of NAD<sup>+</sup> and fine-tune the regulation of translation in organisms via the hypusine modification of IF5A.</p></sec></sec><sec sec-type=\"discussion\" id=\"sec3-ijms-21-05509\"><title>3. Discussion</title><p>Taking the results as a whole, we propose that the binding of NAD<sup>+</sup> to <italic>Pho</italic>DHS is regulated by the NAD<sup>+</sup> concentration, which might itself be linked to the requirement for IF5A modification and the subsequent participation in translation. One <italic>Pho</italic>DHS tetramer binds two NAD<sup>+</sup>s at a low concentration of NAD<sup>+</sup>, but may be able to bind two additional NAD<sup>+</sup>s when supplemented with higher NAD<sup>+</sup> concentrations. This new model for NAD<sup>+</sup> binding by DHS is shown in <xref ref-type=\"fig\" rid=\"ijms-21-05509-f004\">Figure 4</xref> with <italic>Pho</italic>DHS existing in three states: no NAD<sup>+</sup>, one <italic>Pho</italic>DHS tetramer binding two NAD<sup>+</sup>s on one side (molAD or molBC), and one <italic>Pho</italic>DHS tetramer binding four NAD<sup>+</sup>s. Our structural data definitively demonstrate the different conformations of loop V288–P299 in each monomer of <italic>Pho</italic>DHS, with the significant structural stabilization induced by NAD<sup>+</sup> binding, which, with the help of a comparison to the human DHS:GC7 structure, is likely stabilized into the final closed conformation (conf2) upon binding spermidine. Indeed, the different conformations of the active site “lid,” or binding loop, observed in our structure are likely to reflect the absence of stabilizing interactions that spermidine binding would provide. Our structure provides a potential explanation for the NAD<sup>+</sup>-dependent binding of spermidine to DHS [<xref rid=\"B29-ijms-21-05509\" ref-type=\"bibr\">29</xref>]. The conformation diversity observed in this region in our structure is compatible with an independence of NAD<sup>+</sup> binding to each monomer, suggesting that monomers of DHS may behave equally whether in a tetramer, dimer or heterodimers. Strikingly, our structure presents a simultaneous combination of three different conformations at the spermidine binding site that are reflective of a three-state conformational regulation of DHS by NAD<sup>+</sup>: the apo form, the open form, and the closed/reaction form. The structure thus highlights the role of conformational dynamics in the regulation of DHS and hypusine modification of aIF5A.</p><p>To further address the relationship between the conformational changes of region V288–P299 observed here and the binding of spermidine and IF5A to generate hypusinated IF5A, the complex structure of IF5A-DHS is an important future goal.</p></sec><sec id=\"sec4-ijms-21-05509\"><title>4. Materials and Methods</title><sec id=\"sec4dot1-ijms-21-05509\"><title>4.1. Expression and Purification of PhoDHS</title><p>The gene encoding DHS (1–342 aa, 39 kDa) from <italic>P. horikoshii OT3</italic> (UniProtKB-O50105) was cloned into a modified pET26b expression vector with a hexahistidine (His<sub>6</sub>) tag and a TEV cleavage sequence (Glu-Asn-Leu-Tyr-Phe-Gln-Gly) at the N-terminus. For overexpression, the recombinant plasmid containing <italic>Pho</italic>DHS was transformed into <italic>E. coli</italic> strain B834 (DE3) pRARE2 by electroporation. The transformants were inoculated into Luria–Bertani (LB) containing 25 μg/mL kanamycin and 34 μg/mL chloramphenicol and cultivated at 30 °C until the optical density at 600 nm (OD<sub>600</sub>) reached about 0.6. After adding isopropyl β-<sc>d</sc>-1-thiogalactopyranoside (IPTG) to a final concentration of 1 mM, the cells were grown for an additional 16 h.</p><p>The cells were harvested by centrifugation at 4500× <italic>g</italic> at 25 °C for 20 min, and then washed with cell-wash-buffer (50 mM Tris-HCl, pH 8.0, 50 mM NaCl). The washed cells were disrupted by sonication with cell-lysis buffer (50 mM Tris-HCl, pH 8.5, 500 mM NaCl, 5% glycerol, 0.25 mM DTT, 25 mM imidazole) in the presence of 100 μg/mL deoxyribonuclease I and 50 μg/mL lysozyme, and the cell debris was then removed by centrifugation at 40,000× <italic>g</italic> for 30 min. The cell lysate was heat-treated at 70 °C for 30 min and centrifuged at 40,000× <italic>g</italic> for 30 min. The supernatant was filtered through a 0.22 μm filter and then loaded onto a Ni-affinity chromatography column (1 mL Histrap™ HP; GE Healthcare, Chicago, IL, USA) equilibrated with buffer A (50 mM Tris-HCl, pH 8.5, 500 mM NaCl, 5% glycerol, 0.25 mM DTT, 25 mM imidazole). The samples were eluted with a gradient of 25–400 mM imidazole in buffer A. The eluted fractions of the proteins were treated with TEV protease and dialyzed against buffer B (50 mM Tris-HCl, pH 8.5, 25 mM NaCl, 5% glycerol, 0.25 mM DTT, 25 mM imidazole) to remove the cleaved His tag. The dialyzed proteins were purified again using the Ni-affinity column, and the flow-through fraction was collected. The proteins were then purified using a 1 mL RESOURCE<sup>TM</sup> Q column (GE Healthcare) with buffer C (20 mM Tris-HCl, pH 8.5, 25 mM NaCl, 5% glycerol, 0.25 mM DTT). The bound protein was eluted with a linear gradient of NaCl (25–2000 mM). The fractions containing DHS were pooled and then load on to a Superdex 75 16/60 column (GE Healthcare, Chicago, IL, USA) in buffer D (20 mM HEPES, pH 8.0, 200 mM NaCl, 5% glycerol, and 1 mM DTT), and DHS was eluted as a single peak with an estimated molecular weight around 130 kDa, corresponding to the tetramer. Fractions containing purified tetramer were combined and concentrated using VIVAPORE 10/20 (2–20 mL). All steps were monitored by 15% SDS-PAGE.</p></sec><sec id=\"sec4dot2-ijms-21-05509\"><title>4.2. Crystallization and Data Collection</title><p>Preliminary crystallization trials were performed using a crystallization screening kit from QIAGEN (JCSG I-IV, JCSG + and PEG I&II, Hilden, Germany). A crystallization drop containing 1 μL of protein solution and 1 μL reservoir solution was set up and equilibrated against 100 μL of reservoir solution at 20 °C using the sitting-drop method. Initial crystals were grown in a condition containing 100 mM phosphate–citrate pH 4.2, 40% ethanol, 5% PEG 1000. After the optimization of crystallization conditions with respect to pH and precipitant concentration, well diffracted crystals were obtained in a condition containing 100 mM phosphate–citrate pH 4.8, 32% Ethanol, 5% PEG 1000.</p><p>For diffraction data collection, the crystals of <italic>Pho</italic>DHS were transferred into a cryo-protectant solution containing 15% glycerol in the reservoir solution, then mounted in a loop and flash-cooled in a stream of nitrogen gas at −173 °C. The X-ray diffraction experiment was performed with a wavelength of 1.0 Å at beamline BL41XU in SPring-8 (Hyogo, Japan). The diffraction data were indexed, integrated, scaled, and merged using the HKL2000 package [<xref rid=\"B30-ijms-21-05509\" ref-type=\"bibr\">30</xref>]. The crystals belong to space group P<italic>2<sub>1</sub>2<sub>1</sub>2<sub>1</sub></italic>, with the unit-cell parameters of <italic>a</italic> = 87.3 Å, <italic>b</italic> = 89.9 Å, <italic>c</italic> = 164.8 Å. Using a calculated molecular weight of 38.9 Da, the Matthews coefficient V<sub>M</sub> value was estimated to be 2.07 Å<sup>3</sup>Da<sup>−1</sup> with 4 <italic>Pho</italic>DHS molecules in the asymmetric unit, corresponding to a solvent content of 40.56%. A summary of data collection and processing statistics is given in <xref rid=\"ijms-21-05509-t001\" ref-type=\"table\">Table 1</xref>.</p></sec><sec id=\"sec4dot3-ijms-21-05509\"><title>4.3. Structural Determination and Refinement</title><p>The structure of <italic>Pho</italic>DHS was solved by molecular replacement with MOLREP [<xref rid=\"B31-ijms-21-05509\" ref-type=\"bibr\">31</xref>], using the protein structure of human DHS (PDB ID:1DHS) as a search model [<xref rid=\"B21-ijms-21-05509\" ref-type=\"bibr\">21</xref>], which shows 39.5% amino acid sequence identity with <italic>Pho</italic>DHS. Initial rigid body refinement was performed with the REFMAC [<xref rid=\"B32-ijms-21-05509\" ref-type=\"bibr\">32</xref>] of the CCP4 program suite [<xref rid=\"B33-ijms-21-05509\" ref-type=\"bibr\">33</xref>] and the structure was then rebuilt using the program LAFIRE with CNS automatically [<xref rid=\"B34-ijms-21-05509\" ref-type=\"bibr\">34</xref>,<xref rid=\"B35-ijms-21-05509\" ref-type=\"bibr\">35</xref>]. Structural refinements were further performed using the program phenix_refine of the phenix program suite [<xref rid=\"B36-ijms-21-05509\" ref-type=\"bibr\">36</xref>], followed by manual modifications with Coot [<xref rid=\"B37-ijms-21-05509\" ref-type=\"bibr\">37</xref>]. After several refinement cycles, the NAD<sup>+</sup> molecules were manually built based on 2Fo-Fc and Fo-Fc electron density maps. During the refinement of <italic>Pho</italic>DHS in complex with NAD<sup>+</sup>, the NAD<sup>+</sup> molecules were rebuilt based on the omit-map several times and the occupancy of NAD<sup>+</sup> molecules was adjusted manually following check of Fo-Fc and 2Fo-Fc maps. We also rebuilt the binding loop (V288–P299) several times based on the omit-map and the manually fine-tuned residues occupancy. The <italic>R<sub>work</sub></italic> and <italic>R<sub>free</sub></italic> factors were monitored during the refinement process, and the latter was calculated from 5% of the reflections. The final structure of <italic>Pho</italic>DHS was refined to a Rwork/Rfree = 17.52/22.95%, respectively. The summary of refinement statistics is shown in <xref rid=\"ijms-21-05509-t001\" ref-type=\"table\">Table 1</xref>. The atomic coordinates of <italic>Pho</italic>DHS have been deposited in the Protein Data Bank under the accession number 7CMC. The buried areas of inter monomer were calculated using PDBePISA [<xref rid=\"B38-ijms-21-05509\" ref-type=\"bibr\">38</xref>].</p></sec><sec id=\"sec4dot4-ijms-21-05509\"><title>4.4. Analysis of NAD<sup>+</sup> Binding by UV Absorbance Spectroscopy</title><p>The purified <italic>Pho</italic>DHS was mixed with NAD<sup>+</sup> (Roche, Nutley, New Jersey, USA) at the molar ratio of 1:30. The complex of <italic>Pho</italic>DHS and NAD<sup>+</sup> was dialyzed in buffer C (20 mM HEPES, pH 8.0, 200 mM NaCl, 5% glycerol, and 1 mM DTT) by using EasySep (TOMY, Tokyo, Japan) to remove free, excess NAD<sup>+</sup>. The final concentration of the <italic>Pho</italic>DHS tetramer was about 5.87 μM. The UV absorption spectra of the dialyzed samples were measured by using a DU800 Spectrophotometer (Beckman, Brea, California, USA) at the scanning speed of 1200 nm/min. The dialyzed buffer was taken as the blank. NAD<sup>+</sup> at 106 μM in buffer D (20 mM HEPES, pH 8.0, 200 mM NaCl, 5% glycerol, and 1 mM DTT) was used as control. The absorption curve of 2*NAD<sup>+</sup> (11.74 μM), a 1:2 molar ratio of <italic>Pho</italic>DHS:NAD<sup>+</sup> was similarly measured using the control curve of NAD<sup>+</sup> (106 μM) as the reference.</p></sec></sec></body><back><ack><title>Acknowledgments</title><p>We would like to thank H. Wakabayashi for her work on sample preparation. We thank the beamline staff of Spring-8 for their help with data collection.</p></ack><app-group><app id=\"app1-ijms-21-05509\"><title>Supplementary Materials</title><p>Supplementary materials can be found at <uri xlink:href=\"https://www.mdpi.com/1422-0067/21/15/5509/s1\">https://www.mdpi.com/1422-0067/21/15/5509/s1</uri>.</p><supplementary-material content-type=\"local-data\" id=\"ijms-21-05509-s001\"><media xlink:href=\"ijms-21-05509-s001.pdf\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material></app></app-group><notes><title>Author Contributions</title><p>Conceptualization, M.C., C.O. and M.Y.; Investigation, C.O., Z.G., J.Y.; validation, Z.G., Y.Y. and M.C.; writing—original draft preparation, M.C., Z.G., Y.Y.; writing—review and editing, M.C., M.Y.; supervision, M.Y.; project administration, M.Y.; funding acquisition, M.Y. All authors have read and agreed to the published version of the manuscript.</p></notes><notes><title>Funding</title><p>This research was funded by Platform Project for Supporting Drug Discovery and Life Science Research (Basis for Supporting Innovative Drug Discovery and Life Science Research (BINDS)) from Japan Agency for Medical Research and Development (AMED) under Grant Number JP18am0101071.</p></notes><notes notes-type=\"COI-statement\"><title>Conflicts of Interest</title><p>The authors declare no conflict of interest.</p></notes><ref-list><title>References</title><ref id=\"B1-ijms-21-05509\"><label>1.</label><element-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Greganova</surname><given-names>E.</given-names></name><name><surname>Altmann</surname><given-names>M.</given-names></name><name><surname>Buetikofer</surname><given-names>P.</given-names></name></person-group><article-title>Unique modifications of translation elongation factors</article-title><source>FEBS J.</source><year>2011</year><volume>278</volume><fpage>2613</fpage><lpage>2624</lpage><pub-id pub-id-type=\"doi\">10.1111/j.1742-4658.2011.08199.x</pub-id><pub-id pub-id-type=\"pmid\">21624055</pub-id></element-citation></ref><ref id=\"B2-ijms-21-05509\"><label>2.</label><element-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Saini</surname><given-names>P.</given-names></name><name><surname>Eyler</surname><given-names>D.E.</given-names></name><name><surname>Green</surname><given-names>R.</given-names></name><name><surname>Dever</surname><given-names>T.E.</given-names></name></person-group><article-title>Hypusine-containing protein eIF5A promotes translation elongation</article-title><source>Nature</source><year>2009</year><volume>459</volume><fpage>118</fpage><lpage>121</lpage><pub-id pub-id-type=\"doi\">10.1038/nature08034</pub-id><pub-id pub-id-type=\"pmid\">19424157</pub-id></element-citation></ref><ref id=\"B3-ijms-21-05509\"><label>3.</label><element-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Landau</surname><given-names>G.</given-names></name><name><surname>Bercovich</surname><given-names>Z.</given-names></name><name><surname>Park</surname><given-names>M.H.</given-names></name><name><surname>Kahana</surname><given-names>C.</given-names></name></person-group><article-title>The Role of Polyamines in Supporting Growth of Mammalian Cells Is Mediated through Their Requirement for Translation Initiation and Elongation</article-title><source>J. 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A loop (V288–P299) around the NAD<sup>+</sup> binding site is observed in two different conformations (blue and red) in molA (conformation 1, conf1) and molD (conformation 2, conf2), respectively. While the conf1 in molA is away from NAD<sup>+</sup>, the conf2 in molD moves towards NAD<sup>+</sup>. (<bold>b</bold>) Structural superposition of <italic>Pho</italic>DHS (green) with human (wheat, PDBID:1DHS) and <italic>Trypanosoma brucei</italic> (light purple, PDBID:6DFT) DHS. The conf1 and conf2 loop in the comparison molecules are labeled (in their respective colors), and the loop in conf1 (red) of molA is added for comparison.</p></caption><graphic xlink:href=\"ijms-21-05509-g001\"/></fig><fig id=\"ijms-21-05509-f002\" orientation=\"portrait\" position=\"float\"><label>Figure 2</label><caption><p>Sequence alignment of <italic>Pho</italic>DHS with DHS from human and <italic>T. brucei</italic>. The ESPript 3.0 server was used to output the alignment [<xref rid=\"B28-ijms-21-05509\" ref-type=\"bibr\">28</xref>]. The blue box above the sequence indicates that the sequences of at least two out of three organisms share a similarity, while the sequences identical in all the three organisms are marked with red fills. The secondary structure shown by the respective crystal structures for <italic>Pho</italic>DHS and human DHS are labeled at the top and bottom, respectively. The loop 288–299 with alternative conformations is indicated with grey stars above.</p></caption><graphic xlink:href=\"ijms-21-05509-g002\"/></fig><fig id=\"ijms-21-05509-f003\" orientation=\"portrait\" position=\"float\"><label>Figure 3</label><caption><p>Active site of <italic>Pho</italic>DHS-bound NAD<sup>+</sup>. (<bold>a</bold>) Electron density map for NAD<sup>+</sup>. NAD<sup>+</sup> is superposed with the 2Fo-Fc electron density map contoured at the 1σ level (blue). (<bold>b</bold>) Binding geometry of NAD<sup>+</sup> by <italic>Pho</italic>DHS. (left) NAD<sup>+</sup> is bounded to the interface of the dimer in a sandwich manner. (right) NAD<sup>+</sup> extensively interacts with residues from both monomers via a hydrogen bond and electronic interaction. (<bold>c</bold>) Quantification of NAD<sup>+</sup> bound to <italic>Pho</italic>DHS in solution by UV absorbance spectroscopy. An obvious difference in the absorption curves for <italic>Pho</italic>DHS alone (no NAD<sup>+</sup> added) and supplemented with NAD<sup>+</sup> as <italic>Pho</italic>DHS + 30 equiv NAD<sup>+</sup> (molar ratio <italic>Pho</italic>DHS:NAD of 1:30), shows the binding of NAD<sup>+</sup> to vacant sites in purified <italic>Pho</italic>DHS. The absorption curve of <italic>Pho</italic>DHS + two equiv NAD<sup>+</sup> (molar ratio 1:2) correlates well to the curve <italic>Pho</italic>DHS + 30 equiv NAD<sup>+</sup>, indicating that <italic>Pho</italic>DHS could only accommodate two additional NAD<sup>+</sup> even if excessive NAD<sup>+</sup> molecules are available. (<bold>d</bold>) (left) Closed (blue) and open (red) forms of the binding loop V288–P299 with NAD<sup>+</sup> bound. (right) Alternative conformations of the loop involved in NAD<sup>+</sup> and spermidine binding. (<bold>e</bold>) Superposition of GC7 onto <italic>Pho</italic>DHS. GC7 is superposed onto <italic>Pho</italic>DHS by structural alignment with human DHS in complex with GC7. The loop of two conformations (conf1 and conf2) in molA and molD was generated by the superposition of the two monomers.</p></caption><graphic xlink:href=\"ijms-21-05509-g003\"/></fig><fig id=\"ijms-21-05509-f004\" orientation=\"portrait\" position=\"float\"><label>Figure 4</label><caption><p>A model of the conformational dynamics in DHS, regulated by NAD<sup>+</sup>. Three binding conformations are shown. On the left, the spermidine binding loop is disordered when there is no NAD<sup>+</sup> binding to DHS. In the middle, the loop is partially stabilized and structured in either conf1 or conf2 when NAD<sup>+</sup> binds to DHS. The binding of one NAD<sup>+</sup> to DHS could promote a second NAD<sup>+</sup> to bind to another monomer of the dimer, and binding of both NAD<sup>+</sup>s would be stabilized via the formation of the hydrogen bond between them. The spermidine binding loop is still disordered in another dimer of the tetramer which remains NAD<sup>+</sup>-free. On the right, the spermidine binding loop of all four monomers of <italic>Pho</italic>DHS will be ordered in conf2 in the presence of abundant NAD<sup>+</sup>, based on the current structure and human DHS structure. Yellow lines: loop in conf1 away from NAD<sup>+</sup>; blue lines: loop in conf2 towards NAD<sup>+</sup>; N: NAD<sup>+</sup> molecules.</p></caption><graphic xlink:href=\"ijms-21-05509-g004\"/></fig><table-wrap id=\"ijms-21-05509-t001\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijms-21-05509-t001_Table 1</object-id><label>Table 1</label><caption><p>Summary of data collection and refinement statistics.</p></caption><table frame=\"hsides\" rules=\"groups\"><tbody><tr><td colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">\n<bold>Data Collection</bold>\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Crystal</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Native DHS</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Space group</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><italic>P</italic>2<sub>1</sub>2<sub>1</sub>2<sub>1</sub></td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Beamline</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Spring 8 BL41XU</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Wavelength (Å)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.0000</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Unit-cell parameter</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">a = 87.3 Å, b = 89.9 Å, c = 164.8 Å, α = β= γ = 90°</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Resolution range (Å)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">50–2.20 (2.28–2.20)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Total number of reflections</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">666,150 (6550)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Completeness (%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0 (100.0)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Redundancy</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7.4 (7.4) </td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">I/σ(I)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">29.1 (5.2)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">R-merge </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.110 (0.479)</td></tr><tr><td colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">\n<bold>Refinement Statistics</bold>\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Resolution range (Å)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">34.42–2.20</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">R<sub>work</sub>/R<sub>free</sub> (%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">17.52/22.95</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">R.m.s.d bond lengths (Å)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.07</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">R.m.s.d angles (°)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.93</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">No. non-H atoms</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Proteins</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">9817</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Water molecules</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">408</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">NAD<sup>+</sup> molecules</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">88</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Average B value</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Protein</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">35.17</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Water molecules</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">41.81</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">NAD<sup>+</sup> molecules</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">71.98</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Ramachandran Plot (%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Favored</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">98.91</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Allowed</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.09</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Outline</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.00</td></tr></tbody></table><table-wrap-foot><fn><p>Values in parentheses are for the highest resolution shell. Rwork = Σhkl ||Fobs|−|Fcalc||/Σhkl |Fobs|, Rfree was calculated for 5% randomly selected test sets that were not used in the refinement.</p></fn></table-wrap-foot></table-wrap></floats-group></article>\n"
]
[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Int J Environ Res Public Health</journal-id><journal-id journal-id-type=\"iso-abbrev\">Int J Environ Res Public Health</journal-id><journal-id journal-id-type=\"publisher-id\">ijerph</journal-id><journal-title-group><journal-title>International Journal of Environmental Research and Public Health</journal-title></journal-title-group><issn pub-type=\"ppub\">1661-7827</issn><issn pub-type=\"epub\">1660-4601</issn><publisher><publisher-name>MDPI</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32707971</article-id><article-id pub-id-type=\"pmc\">PMC7432095</article-id><article-id pub-id-type=\"doi\">10.3390/ijerph17155279</article-id><article-id pub-id-type=\"publisher-id\">ijerph-17-05279</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Article</subject></subj-group></article-categories><title-group><article-title>Decrease in Ambient Fine Particulate Matter during COVID-19 Crisis and Corresponding Health Benefits in Seoul, Korea</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Han</surname><given-names>Changwoo</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05279\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Hong</surname><given-names>Yun-Chul</given-names></name><xref ref-type=\"aff\" rid=\"af2-ijerph-17-05279\">2</xref><xref ref-type=\"aff\" rid=\"af3-ijerph-17-05279\">3</xref><xref ref-type=\"aff\" rid=\"af4-ijerph-17-05279\">4</xref><xref rid=\"c1-ijerph-17-05279\" ref-type=\"corresp\">*</xref></contrib></contrib-group><aff id=\"af1-ijerph-17-05279\"><label>1</label>Department of Preventive Medicine, Chungnam National University College of Medicine, Daejeon 35015, Korea; <email>[email protected]</email></aff><aff id=\"af2-ijerph-17-05279\"><label>2</label>Department of Preventive Medicine, Seoul National University College of Medicine, Seoul 03080, Korea</aff><aff id=\"af3-ijerph-17-05279\"><label>3</label>Institute of Environmental Medicine, Seoul National University Medical Research Center, Seoul 03080, Korea</aff><aff id=\"af4-ijerph-17-05279\"><label>4</label>Environmental Health Center, Seoul National University College of Medicine, Seoul 03080, Korea</aff><author-notes><corresp id=\"c1-ijerph-17-05279\"><label>*</label>Correspondence: <email>[email protected]</email>; Tel.: +82-2-740-8394</corresp></author-notes><pub-date pub-type=\"epub\"><day>22</day><month>7</month><year>2020</year></pub-date><pub-date pub-type=\"ppub\"><month>8</month><year>2020</year></pub-date><volume>17</volume><issue>15</issue><elocation-id>5279</elocation-id><history><date date-type=\"received\"><day>06</day><month>7</month><year>2020</year></date><date date-type=\"accepted\"><day>21</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>© 2020 by the authors.</copyright-statement><copyright-year>2020</copyright-year><license license-type=\"open-access\"><license-p>Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>).</license-p></license></permissions><abstract><p>Both domestic emissions and transported pollutants from neighboring countries affect the ambient fine particulate matter (PM<sub>2.5</sub>) concentration of Seoul, Korea. Diverse measures to control the coronavirus disease 2019 (COVID-19), such as social distancing and increased telecommuting in Korea and the stringent lockdown measures of China, may reduce domestic emissions and levels of transported pollutants, respectively. In addition, wearing a particulate-filtering respirator may have decreased the absolute PM<sub>2.5</sub> exposure level for individuals. Therefore, this study estimated the acute health benefits of PM<sub>2.5</sub> reduction and changes in public behavior during the COVID-19 crisis in Seoul, Korea. To calculate the mortality burden attributable to PM<sub>2.5</sub>, we obtained residents’ registration data, mortality data, and air pollution monitoring data for Seoul from publicly available databases. Relative risks were derived from previous time-series studies. We used the attributable fraction to estimate the number of excessive deaths attributable to acute PM<sub>2.5</sub> exposure during January to April, yearly, from 2016 to 2020, and the number of mortalities avoided from PM<sub>2.5</sub> reduction and respirator use observed in 2020. The average PM<sub>2.5</sub> concentration from January to April in 2020 (25.6 μg/m<sup>3</sup>) was the lowest in the last 5 years. At least −4.1 μg/m<sup>3</sup> (95% CI: −7.2, −0.9) change in ambient PM<sub>2.5</sub> in Seoul was observed in 2020 compared to the previous 4 years. Overall, 37.6 (95% CI: 32.6, 42.5) non-accidental; 7.0 (95% CI: 5.7, 8.4) cardiovascular; and 4.7 (95% CI: 3.4, 6.1) respiratory mortalities were avoided due to PM<sub>2.5</sub> reduction in 2020. By considering the effects of particulate respirator, decreases of 102.5 (95% CI: 89.0, 115.9) non-accidental; 19.1 (95% CI: 15.6, 22.9) cardiovascular; and 12.9 (95% CI: 9.2, 16.5) respiratory mortalities were estimated. We estimated that 37 lives were saved due to the PM<sub>2.5</sub> reduction related to COVID-19 in Seoul, Korea. The health benefit may be greater due to the popular use of particulate-filtering respirators during the COVID-19 crisis. Future studies with daily mortality data are needed to verify our study estimates.</p></abstract><kwd-group><kwd>COVID-19</kwd><kwd>particulate matter</kwd><kwd>lockdown</kwd><kwd>health burden</kwd><kwd>mortality</kwd><kwd>Korea</kwd></kwd-group></article-meta></front><body><sec sec-type=\"intro\" id=\"sec1-ijerph-17-05279\"><title>1. Introduction</title><p>The world is facing one of its gravest challenges due to the coronavirus disease 2019 (COVID-19). After the first unknown pneumonia cases detected in Wuhan, China, in December 2019, scientists identified new species of the zoonotic coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-COV-2), causing COVID-19 [<xref rid=\"B1-ijerph-17-05279\" ref-type=\"bibr\">1</xref>,<xref rid=\"B2-ijerph-17-05279\" ref-type=\"bibr\">2</xref>]. COVID-19 may lead to a fatal respiratory disease in the elderly and in persons with preexisting chronic diseases, but the symptoms can be mild in young and healthy individuals [<xref rid=\"B3-ijerph-17-05279\" ref-type=\"bibr\">3</xref>]. According to the report from the Chinese Center for Disease Control using the 44,672 confirmed COVID-19 cases, the case fatality rate was 2.3%, lower compared to other coronaviruses causing severe acute respiratory syndrome (SARS, 9.6%) and Middle East respiratory syndrome (MERS, 35.5%) [<xref rid=\"B4-ijerph-17-05279\" ref-type=\"bibr\">4</xref>,<xref rid=\"B5-ijerph-17-05279\" ref-type=\"bibr\">5</xref>]. However, SARS-COV-2 is highly infectious, with an estimated reproduction number (<inline-formula><mml:math id=\"mm1\"><mml:mrow><mml:mrow><mml:msub><mml:mi>R</mml:mi><mml:mn>0</mml:mn></mml:msub><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:mrow></mml:math></inline-formula> over 3, causing more mortalities than any other coronaviruses [<xref rid=\"B5-ijerph-17-05279\" ref-type=\"bibr\">5</xref>,<xref rid=\"B6-ijerph-17-05279\" ref-type=\"bibr\">6</xref>,<xref rid=\"B7-ijerph-17-05279\" ref-type=\"bibr\">7</xref>]. Due to the wide geographical spread of the virus and the growing international concern, the World Health Organization (WHO) declared COVID-19 outbreak as a pandemic on 12 April 2020. As of 24 April 2020, COVID-19 has killed more than 190,800 people, with 4.6 million confirmed cases worldwide. Billions of people were instructed or forced to stay at home while countries locked their borders to varying degrees to control COVID-19.</p><p>After the first incidence of COVID-19 on 20 January 2020, Korea has controlled the outbreak relatively well compared to other countries. Although there was a surge of COVID-19 in Daegu City, which peaked at over 900 incident cases on 29 February, the daily increase in new cases in Korea had dropped below 10 by the end of April [<xref rid=\"B8-ijerph-17-05279\" ref-type=\"bibr\">8</xref>]. Without imposing extreme measures to restrict the movement or freedom of the citizens, Korea was able to flatten the curve of the new cases. This was achieved by a massive number of tests conducted for early detection, by applying rigorous epidemiological investigation, and sharing information using the state of the art information and communication technologies [<xref rid=\"B8-ijerph-17-05279\" ref-type=\"bibr\">8</xref>,<xref rid=\"B9-ijerph-17-05279\" ref-type=\"bibr\">9</xref>]. These all occurred with the voluntary participation of social distancing and personal hygiene practices (i.e., wearing respirators in public space and washing hands) by the citizens. To address the shortage and ensure the equal distribution of respirators, the Korean government adopted a “5-day rotation system for a respirator” to provide opportunities for people to purchase a respirator at pharmacies on designated days according to their birth years.</p><p>Recently, the diverse news media, journal papers, and reports posted on the preprint server medRxic have indicated that the stringent lockdown measures not only helped in controlling the spread of this highly infectious virus, but also helped to decrease the ambient air pollution levels, including particulate matter less than 2.5 μm in diameter (PM<sub>2.5</sub>) and nitrogen dioxide (NO<sub>2</sub>) [<xref rid=\"B10-ijerph-17-05279\" ref-type=\"bibr\">10</xref>,<xref rid=\"B11-ijerph-17-05279\" ref-type=\"bibr\">11</xref>]. With the lockdown of industrial activities and transportation in China for about 35 days, there was a 30.1 and 9.2 μg/m<sup>3</sup> decrease in PM<sub>2.5</sub> in Wuhan and Beijing, respectively [<xref rid=\"B12-ijerph-17-05279\" ref-type=\"bibr\">12</xref>]. One report estimated a 12.9 and 18.9 μg/m<sup>3</sup> decrease in NO<sub>2</sub> and PM<sub>2.5</sub> in China after the massive population quarantine [<xref rid=\"B13-ijerph-17-05279\" ref-type=\"bibr\">13</xref>]. Satellites of National Aeronautics and Space Administration (NASA) and European Space Agency detected a 10 to 20% decrease in NO<sub>2</sub> levels over eastern and central China after the quarantine [<xref rid=\"B14-ijerph-17-05279\" ref-type=\"bibr\">14</xref>]. The Movement Control Order of the Malaysian government to encourage their citizens to work at home and suspend industrial activities caused a decrease in PM<sub>2.5</sub> levels [<xref rid=\"B15-ijerph-17-05279\" ref-type=\"bibr\">15</xref>]. Lockdown measures such as school closure, work at home, avoiding gatherings, shutting down commerce, and limiting public transportation in the city of Rio de Janeiro and São Paulo State caused a reduction in carbon monoxide and NO<sub>2</sub> levels [<xref rid=\"B16-ijerph-17-05279\" ref-type=\"bibr\">16</xref>,<xref rid=\"B17-ijerph-17-05279\" ref-type=\"bibr\">17</xref>]. Another report estimated a 9%, 29%, and 11% global reduction in ambient PM<sub>2.5</sub>, NO<sub>2</sub>, and ozone levels, respectively, after the COVID-19 crisis [<xref rid=\"B18-ijerph-17-05279\" ref-type=\"bibr\">18</xref>].</p><p>A recent announcement by Korea’s Ministry of Environment revealed a reduction in the average PM<sub>2.5</sub> in Korea from December 2019 to April 2020 by 27% (9 μg/m<sup>3</sup>) compared to that of the previous year [<xref rid=\"B19-ijerph-17-05279\" ref-type=\"bibr\">19</xref>]. Although the report highlighted the effectiveness of “the seasonal particle pollution measures”, which was newly implemented in December 2019, the marked PM<sub>2.5</sub> decrease observed in China as well as around the world suggest that the measures to control COVID-19 may be the largest contributing factor behind the improved air quality. In addition, a recent modeling study showed that around 70% of PM<sub>2.5</sub> concentration of Seoul is affected by the emission from China during the days with severe PM<sub>2.5</sub> concentration in spring [<xref rid=\"B20-ijerph-17-05279\" ref-type=\"bibr\">20</xref>].</p><p>Therefore, we initiated this study with a reasonable assumption that social distancing and increased telecommuting in Korea, in addition to the stringent lockdown measures and reduced industrial activities in China, may have reduced the PM<sub>2.5</sub> levels in Seoul, the capital city of Korea. Furthermore, the respirators supplied and distributed by the Korean government were effective in filtering PM<sub>2.5</sub> at least 80% of the 0.6 μm nonoil particulates. Therefore, each person would be exposed to a lower level of PM<sub>2.5</sub> compared to the period before the COVID-19 crisis by using the particulate filtering respirator. Based on this assumption, we estimated the acute health benefits of PM<sub>2.5</sub> reduction and changes in public behavior (wearing of a respirator) during the COVID-19 crisis using the health impact assessment methodology. The aim of this study was to use currently available data to estimate the acute health benefits of PM<sub>2.5</sub> reduction and changes in public behavior, which were changes experienced by Korean citizens in their daily lives during the COVID-19 crisis.</p></sec><sec id=\"sec2-ijerph-17-05279\"><title>2. Materials and Methods</title><sec id=\"sec2dot1-ijerph-17-05279\"><title>2.1. Study Design</title><p>The health impact assessment requires data on exposure, mortality, and the population. Although Korea shares the real-time air pollution monitoring data with the public, the mortality data are not shared simultaneously. Therefore, we estimated the mortality rates over the last 2 years (2019 and 2020) based on the mortality rates of earlier years. With the PM<sub>2.5</sub> monitoring data, population data, and estimated mortality rates in Seoul, we estimated the health benefits based on the PM<sub>2.5</sub> reduction levels in 2020. By using previous respirator intervention study results [<xref rid=\"B21-ijerph-17-05279\" ref-type=\"bibr\">21</xref>], we estimated the health benefits of decreased PM<sub>2.5</sub> and respirator use in 2020.</p><p>To estimate the PM<sub>2.5</sub> decrease in year 2020, we first calculated the average PM<sub>2.5</sub> concentration during the first 4 months of 2020 and compared it with the concentration in the same months, each year, from 2016 to 2019. By using the attributable fraction (AF) method [<xref rid=\"B22-ijerph-17-05279\" ref-type=\"bibr\">22</xref>], we estimated the mortality burden attributable to the acute ambient PM<sub>2.5</sub> exposure in the first 4 months of each year, and the number of mortalities avoided due to the observed PM<sub>2.5</sub> reduction in 2020.</p><p>To calculate the mortality burden attributable to PM<sub>2.5</sub>, we obtained the population, mortality, and air pollution monitoring data of Seoul from publicly available databases. The relative risks (RR) derived from previous time-series studies, which evaluated the association between ambient PM<sub>2.5</sub> exposure and cause-specific mortalities were reviewed to retrieve the beta estimates of the concentration–response functions [<xref rid=\"B23-ijerph-17-05279\" ref-type=\"bibr\">23</xref>,<xref rid=\"B24-ijerph-17-05279\" ref-type=\"bibr\">24</xref>]. To consider the effects of public respirator use, we referred to previous intervention study, which evaluated the difference between individual PM<sub>2.5</sub> exposure level by the particulate respirator use [<xref rid=\"B21-ijerph-17-05279\" ref-type=\"bibr\">21</xref>].</p><p>Several important assumptions were made for our study. Because mortality data were unavailable for the last two years (2019 and 2020), we assumed that mortality rates in Seoul from January to April had not changed in the last 5 years. In Korea, the daily mortality data for a typical year become publicly available at least 1.5 years after the year ends. Therefore, we estimated the mortality rates of Seoul from January to April in 2019 and 2020 by averaging the mortality rates of the same months from 2016 to 2018. Because the mortality rates are closely related to the structure of the population, we also assumed that the composition of the population by age groups had not changed during our study period.</p><p>Second, we assumed that the effects of PM<sub>2.5</sub> were limited to a single day, ignoring the delayed effect of PM<sub>2.5</sub>. PM<sub>2.5</sub> is known to have lag effects of several days after the exposure [<xref rid=\"B25-ijerph-17-05279\" ref-type=\"bibr\">25</xref>]. However, because the daily mortality counts were unavailable for the years 2019 and 2020, it was impossible to estimate the daily levels accounting for lag effects of PM<sub>2.5</sub>. Therefore, we regarded the 4-month period as a whole and calculated the mortality burden for each year using the 4-month averages of PM<sub>2.5</sub> concentrations and the estimated mortality rates of Seoul. Assumption focusing on a single day effect of PM<sub>2.5</sub> may underestimate the overall mortality burden attributable to PM<sub>2.5</sub>. However, the avoided number of mortalities due to PM<sub>2.5</sub> reduction during the COVID-19 crisis may not be biased because we focused on the changes in PM<sub>2.5</sub> levels with the assumption of a linear concentration–response function.</p><p>Third, we assumed that the effects of PM<sub>2.5</sub> concentration on health outcomes did not change during the study period, despite the changes in personal behaviors (i.e., social distancing and decreased outdoor activities) due to the COVID-19 crisis. We adopted the RRs from the previous time-series studies conducted both in Seoul and that in 652 cities around the globe [<xref rid=\"B23-ijerph-17-05279\" ref-type=\"bibr\">23</xref>,<xref rid=\"B24-ijerph-17-05279\" ref-type=\"bibr\">24</xref>]. However, despite assuming that the slope of concentration–response function between PM<sub>2.5</sub> and mortality remained unchanged, we assumed that wearing a particulate filtering respirator would decrease the absolute level of PM<sub>2.5</sub> exposure of an individual.</p><p>This study was exempt from review by the Institutional Review Board of the Seoul National University Hospital, Korea (IRB No.: 2006-122-1133), because data used in our study were de-identified and publicly available.</p></sec><sec id=\"sec2dot2-ijerph-17-05279\"><title>2.2. Population and Mortality Data</title><p>The yearly residents’ registration data for January during the study period (2016 to 2020) and the cause-specific mortality data for January to April (2016 to 2018) were acquired from the publicly available databases, the Korean Statistical Information Service website, and the Korean Statistical Information Service MicroData Integrated Service website [<xref rid=\"B26-ijerph-17-05279\" ref-type=\"bibr\">26</xref>,<xref rid=\"B27-ijerph-17-05279\" ref-type=\"bibr\">27</xref>].</p><p>We used the International Classification of Disease, 10th revision codes to define the following cause-specific mortalities: non-accidental and specific disease mortality (A00-R00), cardiovascular disease mortality (I00-I99), and respiratory disease mortality (J00-J99). The number of deaths due to these disease categories during the 4 months (January to April) of each year was calculated and divided by the number of registered residents in January of the corresponding year, to calculate the 4-month average in mortality rates of Seoul.</p></sec><sec id=\"sec2dot3-ijerph-17-05279\"><title>2.3. PM<sub>2.5</sub> and Meteorological Data</title><p>Ambient PM<sub>2.5</sub> data of Seoul from January 2016 to April 2020 were accessed through the Airkorea website, which provides real-time air pollution monitoring data of Korea [<xref rid=\"B28-ijerph-17-05279\" ref-type=\"bibr\">28</xref>]. Korea operates 25 air pollution monitoring stations in Seoul, covering 25 basic administration districts. We acquired the hourly PM<sub>2.5</sub> monitoring results from each station, and calculated Seoul’s daily and 4-month average of PM<sub>2.5</sub> exposure levels for January to April each year from 2016 to 2020. Daily meteorological data such as ambient temperature, relative humidity, and wind speed from January 2016 to April 2020 were accessed through the National Climate Data Center website. The map of Seoul and the locations of the ambient PM<sub>2.5</sub> as well as the weather monitoring stations are presented in <xref ref-type=\"app\" rid=\"app1-ijerph-17-05279\">Figure S1</xref>.</p></sec><sec id=\"sec2dot4-ijerph-17-05279\"><title>2.4. Concentration-Response Functions and Effects of Particulate Matter Filtrating Respirator</title><p>The beta estimates of the concentration—response functions were retrieved from the RRs of the previous time-series studies that evaluated the association between ambient PM<sub>2.5</sub> exposure and cause-specific mortalities (<xref ref-type=\"app\" rid=\"app1-ijerph-17-05279\">Supplementary Table S1</xref>). We selected two recently published studies—one analyzing the data from cities around the globe, and the other limited to Seoul. In brief, the Multi-City Multi-Country (MCC) collaborative research network gathered the daily mortality rates and PM<sub>2.5</sub> data from 499 cities in 16 countries. The researchers found that a 10 μg/m<sup>3</sup> increase in a 2-day moving average of ambient PM<sub>2.5</sub> was associated with 0.68% (95% confidence interval (CI): 0.59, 0.77); 0.55% (95% CI: 0.45, 0.66); and 0.75% (95% CI: 0.53, 0.95) increase in the daily non-accidental, cardiovascular, and respiratory mortalities, respectively [<xref rid=\"B23-ijerph-17-05279\" ref-type=\"bibr\">23</xref>].</p><p>The study conducted in Seoul used the daily PM<sub>2.5</sub> concentrations and mortality data of the city from 2006 to 2012. A 10 μg/m<sup>3</sup> increase in ambient PM<sub>2.5</sub> was associated with an increase in non-accidental mortality (0.33% (95% CI: 0.01, 0.66)), cardiovascular mortality (0.76% (95% CI: 0.12, 1.41)), and respiratory mortality (1.77% (95% CI: 0.55, 3.01)) on the same day in Seoul [<xref rid=\"B24-ijerph-17-05279\" ref-type=\"bibr\">24</xref>].</p><p>In a previous intervention study with 21 female elderlies in Korea, we evaluated the effects of a particulate-filtering respirator on cardiopulmonary function by using the crossover study design [<xref rid=\"B21-ijerph-17-05279\" ref-type=\"bibr\">21</xref>]. The subjects were instructed to use (intervention period) or not use the respirator (control period) for six consecutive days and had a medical examination on the last day of each period. By using the disposable particulate respirators (capable of filtering 80% of the 0.6 μm nonoil particulates), we found that the average level of personal exposure to PM<sub>2.5</sub> had decreased by 27.4% (9.0 μg/m<sup>3</sup> reduction) during the respirator use, and even the outdoor (27.4–28.8 μg/m<sup>3</sup>) and 24-h personally monitored PM<sub>2.5</sub> levels (18.7–20.1 μg/m<sup>3</sup>) were similar between intervention and control periods. By referring to this value, we estimated that the effects of respirator use during COVID-19 crisis decreased the personal PM<sub>2.5</sub> exposure level by 27.4% in addition to the decrease in ambient PM<sub>2.5</sub> levels observed during 2020.</p></sec><sec id=\"sec2dot5-ijerph-17-05279\"><title>2.5. Statistical Analysis</title><p>With the estimated number of mortalities and monitored PM<sub>2.5</sub> concentrations from January to April each year, we used the AF method to estimate the mortality burden attributable to ambient PM<sub>2.5</sub> levels [<xref rid=\"B22-ijerph-17-05279\" ref-type=\"bibr\">22</xref>].\n<disp-formula id=\"FD1-ijerph-17-05279\"><label>(1)</label><mml:math id=\"mm2\"><mml:mrow><mml:mrow><mml:mi>E</mml:mi><mml:mi>x</mml:mi><mml:mi>c</mml:mi><mml:mi>e</mml:mi><mml:mi>s</mml:mi><mml:mi>s</mml:mi><mml:mtext> </mml:mtext><mml:mi>d</mml:mi><mml:mi>e</mml:mi><mml:mi>a</mml:mi><mml:mi>t</mml:mi><mml:mi>h</mml:mi><mml:mi>s</mml:mi><mml:mtext> </mml:mtext><mml:mi>b</mml:mi><mml:mi>y</mml:mi><mml:mtext> </mml:mtext><mml:mi>P</mml:mi><mml:msub><mml:mi>M</mml:mi><mml:mrow><mml:mn>2.5</mml:mn></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mn>1</mml:mn><mml:mo>−</mml:mo><mml:mi>e</mml:mi><mml:mi>x</mml:mi><mml:msup><mml:mi>p</mml:mi><mml:mrow><mml:mo>−</mml:mo><mml:mi>β</mml:mi><mml:mo>×</mml:mo><mml:mi mathvariant=\"sans-serif\">Δ</mml:mi><mml:mi>C</mml:mi></mml:mrow></mml:msup></mml:mrow><mml:mo>)</mml:mo></mml:mrow><mml:mtext> </mml:mtext><mml:mo>×</mml:mo><mml:mtext> </mml:mtext><mml:mi>N</mml:mi><mml:mi>u</mml:mi><mml:mi>m</mml:mi><mml:mi>b</mml:mi><mml:mi>e</mml:mi><mml:mi>r</mml:mi><mml:mtext> </mml:mtext><mml:mi>o</mml:mi><mml:mi>f</mml:mi><mml:mtext> </mml:mtext><mml:mi>d</mml:mi><mml:mi>e</mml:mi><mml:mi>a</mml:mi><mml:mi>t</mml:mi><mml:mi>h</mml:mi><mml:mi>s</mml:mi></mml:mrow></mml:mrow></mml:math></disp-formula></p><p><inline-formula><mml:math id=\"mm3\"><mml:mrow><mml:mi>β</mml:mi></mml:mrow></mml:math></inline-formula> is the coefficient derived from the RRs in the previous time-series studies and <inline-formula><mml:math id=\"mm4\"><mml:mrow><mml:mrow><mml:mi mathvariant=\"sans-serif\">Δ</mml:mi><mml:mi>C</mml:mi></mml:mrow></mml:mrow></mml:math></inline-formula> refers to the changes in the PM<sub>2.5</sub> concentrations under different counterfactual scenarios. The number of deaths from January to April for the years 2016 to 2018 was obtained from the mortality database, while those of 2019 and 2020 were calculated by multiplying the number of registered residents with the mortality rates estimated based on the mortality rates of 2016 to 2018.</p><p>To calculate the number of excess deaths attributable to the acute PM<sub>2.5</sub> exposure from January to April each year from 2016 to 2020, we defined 2.4 μg/m<sup>3</sup> as the concentration with the minimum health risk, which is the theoretical minimum risk exposure level, indicating no health benefits for reducing PM<sub>2.5</sub> below the level based on prior epidemiological studies [<xref rid=\"B29-ijerph-17-05279\" ref-type=\"bibr\">29</xref>]. Therefore, <inline-formula><mml:math id=\"mm5\"><mml:mrow><mml:mrow><mml:mi mathvariant=\"sans-serif\">Δ</mml:mi><mml:mi>C</mml:mi></mml:mrow></mml:mrow></mml:math></inline-formula> in the equation indicates the difference between average PM<sub>2.5</sub> concentrations monitored each year from January to April and 2.4 μg/m<sup>3</sup>. On the other hand, to calculate the avoided mortality due to PM<sub>2.5</sub> reduction in 2020, we defined <inline-formula><mml:math id=\"mm6\"><mml:mrow><mml:mrow><mml:mi mathvariant=\"sans-serif\">Δ</mml:mi><mml:mi>C</mml:mi></mml:mrow></mml:mrow></mml:math></inline-formula> as the estimated reduction in PM<sub>2.5</sub> from January to April in 2020 compared to the same months in 2016–2019.</p><p>To calculate the reduction in PM<sub>2.5</sub> in 2020, we used the linear regression models assuming the normal distribution of PM<sub>2.5</sub> levels. In model 1, the amount of reduction was estimated based on a simple comparison of the average value for 2020 with the average for the years 2016–2019. In model 2, we adjusted for meteorological variables (daily average temperature, relative humidity, and wind speed), and in model 3, we adjusted for the meteorological variables, years (as a continuous variable), and the months (as a categorical variable). In model 4, we adjusted for the meteorological variables and assumed that the average level of personal exposure to PM<sub>2.5</sub> had decreased by 27.4% in 2020, to account for the widespread use of particulate filtrating respirator.</p><p>All analyses were conducted with SAS version 9.4 (SAS Institute Inc., Cary, NC, USA), and figures were drawn using the R statistical software (Version 3.6.1; R Foundation for Statistical Computing, Vienna, Austria). The level of statistical significance was set at a <italic>p</italic>-value of less than 0.05.</p></sec></sec><sec sec-type=\"results\" id=\"sec3-ijerph-17-05279\"><title>3. Results</title><p><xref rid=\"ijerph-17-05279-t001\" ref-type=\"table\">Table 1</xref> and <xref ref-type=\"fig\" rid=\"ijerph-17-05279-f001\">Figure 1</xref> show the 2016 to 2020 Seoul data for the daily PM<sub>2.5</sub> concentrations, ambient temperature, relative humidity, wind speed, and the number of days that the PM<sub>2.5</sub> concentration was above the WHO and Korea 24-h average standards. The average ambient temperature from January to April in 2020 was the highest compared to those of the previous 4 years, while the relative humidity and wind speed of 2020 were similar to that of 2016 and 2017.</p><p>The average PM<sub>2.5</sub> concentration from January to April in 2020 (25.6 μg/m<sup>3</sup>) was the lowest in the last 5 years. Overall, the PM<sub>2.5</sub> concentrations above the WHO (25 μg/m<sup>3</sup>) and the Korean standards (35 μg/m<sup>3</sup>) in 2020 were for 55 days (45.5%) and 25 days (20.7%) respectively, which were the least number of days in the past 5 years. <xref ref-type=\"fig\" rid=\"ijerph-17-05279-f001\">Figure 1</xref> shows the dramatic decrease in the daily PM<sub>2.5</sub> concentrations as well as the number of days with spiking PM<sub>2.5</sub> concentrations in 2020 compared to the previous 4 years.</p><p><xref rid=\"ijerph-17-05279-t002\" ref-type=\"table\">Table 2</xref> shows the number of registered residents, estimated number of deaths, and mortality rates used in the study. The average number of registered residents in Seoul in January each year was 9,860,115. The average non-accidental, cardiovascular, and respiratory mortalities per 100,000 persons in Seoul were 139.2, 32.0, and 16.1 from January to April in 2016 to 2018, respectively. We used these mortality rates to estimate the number of deaths for 2019 and 2020 assuming that these rates remained unchanged. We estimated that 13,549; 3115; and 1567 persons died in Seoul from January to April in 2020 due to non-accidental, cardiovascular, and respiratory diseases, respectively.</p><p><xref ref-type=\"fig\" rid=\"ijerph-17-05279-f002\">Figure 2</xref>, <xref ref-type=\"app\" rid=\"app1-ijerph-17-05279\">Table S2</xref>, and <xref ref-type=\"app\" rid=\"app1-ijerph-17-05279\">Figure S2</xref> show the number of mortalities attributable to PM<sub>2.5</sub> exposure in Seoul from January to April each year from 2016 to 2020. By using the MCC study’s RRs, the daily exposure to PM<sub>2.5</sub> in 2020 caused 211.4 (95% CI: 183.7, 239.0); 39.4 (95% CI: 32.3, 47.2); and 26.6 (95% CI: 19.1, 34.0) deaths due to non-accidental, cardiovascular, and respiratory diseases, respectively (<xref ref-type=\"app\" rid=\"app1-ijerph-17-05279\">Table S2</xref>). The mortality attributable to the daily PM<sub>2.5</sub> exposure was the lowest in 2020 compared to those of the previous 4 years (<xref ref-type=\"fig\" rid=\"ijerph-17-05279-f002\">Figure 2</xref>). The results using RRs from the Seoul study are summarized in <xref ref-type=\"app\" rid=\"app1-ijerph-17-05279\">Table S2</xref> and <xref ref-type=\"app\" rid=\"app1-ijerph-17-05279\">Figure S2</xref>.</p><p><xref rid=\"ijerph-17-05279-t003\" ref-type=\"table\">Table 3</xref> shows the estimated PM<sub>2.5</sub> reduction levels and the avoided mortality due to the PM<sub>2.5</sub> exposure in 2020 compared to those from 2016–2019. By simply comparing the average values, a −5.6 μg/m<sup>3</sup> (95% CI: −9.0, −2.3) change in ambient PM<sub>2.5</sub> was observed in Seoul from January to April in 2020 compared to the same months in the previous 4 years (model 1). By adjusting for meteorological variables, a −4.1 μg/m<sup>3</sup> (95% CI: −7.2, −0.9) change in ambient PM<sub>2.5</sub> was estimated (model 2). By further adjusting for years and months, a −15.1 μg/m<sup>3</sup> (95% CI: −27.1, −3.2) change in ambient PM<sub>2.5</sub> was estimated (model 3). With the conservative estimation of a 4.1 μg/m<sup>3</sup> decrease in PM<sub>2.5</sub> and RRs from the MCC study, we found that 37.6 (95% CI: 32.6, 42.5) non-accidental; 7.0 (95% CI: 5.7, 8.4) cardiovascular; and 4.7 (95% CI: 3.4, 6.1) respiratory mortalities were avoided because of the reduction in PM<sub>2.5</sub> from January to April in 2020 compared to those of the previous 4 years.</p><p>By considering the effect of the meteorological variables and the particulate respirator use during 2020 (model 4), we estimated that the personal exposure to ambient PM<sub>2.5</sub> decreased by −11.2 μg/m<sup>3</sup> (95% CI: −14.3, −8.2) from January to April in 2020 compared to the previous 4 years. The corresponding health benefits were decreases of 102.5 (95% CI: 89.0, 115.9) non-accidental; 19.1 (95% CI: 15.6, 22.9) cardiovascular; and 12.9 (95% CI: 9.2, 16.5) respiratory mortalities.</p></sec><sec sec-type=\"discussion\" id=\"sec4-ijerph-17-05279\"><title>4. Discussion</title><p>We observed at least a 4.1 μg/m<sup>3</sup> decrease in ambient PM<sub>2.5</sub> concentration in Seoul from January to April in 2020 compared to the same months in 2016–2019. We estimated that 37 persons were saved due to the reduction in PM<sub>2.5</sub> during the 4-month period. Because using a particulate-filtrating respirator may decrease the absolute level of PM<sub>2.5</sub> exposure for an individual, the health benefit related to air pollution during the COVID-19 crisis may be larger than our current estimation of 37 persons.</p><p>There are several possible explanations for the decrease in PM<sub>2.5</sub> in Seoul. First, public behavioral changes such as social distancing and reduced outdoor activities to limit COVID-19 transmission may have decreased the air pollution levels. According to the mobility data based on the map navigation application on smartphones, both walking and driving by the public were decreased in Seoul after the COVID-19 crisis (<xref ref-type=\"app\" rid=\"app1-ijerph-17-05279\">Figure S3</xref>). The daily amount of traffics entering the highways and the number of citizens using the Seoul Metropolitan Area subway from January to April in 2020 dropped by 6.1% and 28.4%, respectively, compared to the same months in the period 2016–2019 (<xref ref-type=\"app\" rid=\"app1-ijerph-17-05279\">Table S3</xref>). In addition, industrial activities such as the number of operating factories may have decreased due to the limited consumer demand during COVID-19 crisis, which may have resulted in decreased domestic emissions [<xref rid=\"B30-ijerph-17-05279\" ref-type=\"bibr\">30</xref>].</p><p>Similar improvements in air quality were observed around the world since the COVID-19 crisis, with an estimated reduction of 9%, 29%, and 11% of ambient PM<sub>2.5</sub>, NO<sub>2</sub>, and ozone levels, respectively [<xref rid=\"B18-ijerph-17-05279\" ref-type=\"bibr\">18</xref>]. The reductions in carbon monoxide and NO<sub>2</sub> levels were observed after the partial lockdown (school closure, work from home, avoiding gatherings, shutting down commerce, and limiting public transportation) in the city of Rio de Janeiro and São Paulo State [<xref rid=\"B16-ijerph-17-05279\" ref-type=\"bibr\">16</xref>,<xref rid=\"B17-ijerph-17-05279\" ref-type=\"bibr\">17</xref>]. During the Movement Control Order (work from home and suspend industrial activities) to isolate the source of COVID-19 in Malaysia, up to a 58.4% decrease in PM<sub>2.5</sub> was observed [<xref rid=\"B15-ijerph-17-05279\" ref-type=\"bibr\">15</xref>]. By analyzing air quality monitoring data of 22 cities in India, 43% and 18% decreases in PM<sub>2.5</sub> and NO<sub>2</sub> were observed during the COVID-19 crisis [<xref rid=\"B31-ijerph-17-05279\" ref-type=\"bibr\">31</xref>].</p><p>The air pollution levels in Seoul cannot be evaluated without considering the effects of the neighboring countries, China and North Korea. By evaluating the source contribution of PM<sub>2.5</sub> on days with severe PM<sub>2.5</sub> concentrations (with 24-h average PM<sub>2.5</sub> concentration of over 100 μg/m<sup>3</sup>) in Seoul, China contributed to the PM<sub>2.5</sub> concentrations by up to 70% while the domestic contribution was 21% [<xref rid=\"B20-ijerph-17-05279\" ref-type=\"bibr\">20</xref>]. In addition, around 15% of PM<sub>2.5</sub> concentration in Seoul is affected by the emission from North Korea [<xref rid=\"B32-ijerph-17-05279\" ref-type=\"bibr\">32</xref>]. Among the 1638 mortalities attributable to the acute exposure to high levels of PM<sub>2.5</sub> in Korea in 2016, at least 258 and 26 deaths were estimated to have been due to the emissions from China and North Korea, respectively [<xref rid=\"B33-ijerph-17-05279\" ref-type=\"bibr\">33</xref>].</p><p>Because the air quality in China improved dramatically during the COVID-19 quarantine (10 February to 14 April 2020) [<xref rid=\"B12-ijerph-17-05279\" ref-type=\"bibr\">12</xref>,<xref rid=\"B13-ijerph-17-05279\" ref-type=\"bibr\">13</xref>,<xref rid=\"B14-ijerph-17-05279\" ref-type=\"bibr\">14</xref>,<xref rid=\"B34-ijerph-17-05279\" ref-type=\"bibr\">34</xref>], and considering the fact that China’s contribution to Seoul’s PM<sub>2.5</sub> concentration is generally greater in the spring and winter [<xref rid=\"B33-ijerph-17-05279\" ref-type=\"bibr\">33</xref>], the decrease in PM<sub>2.5</sub> observed in Seoul from January to April 2020 may partially be explained by the effects of China’s rigorous quarantine measures and decreased industrial activities during the COVID-19 crisis. If the observed decrease in PM<sub>2.5</sub> levels in Seoul in 2020 is indeed due to the changes related to domestic responses as well as China’s response against COVID-19, the estimated mortality benefit from the lowered PM<sub>2.5</sub> levels (37 persons) outweighs the number of the direct casualties from COVID-19 in Seoul (2 persons till 30 April 2020). Similar paradoxical phenomena, and a massive decrease in air-pollution-related mortalities and morbidities during the COVID-19 crisis are expected worldwide [<xref rid=\"B14-ijerph-17-05279\" ref-type=\"bibr\">14</xref>].</p><p>Another plausible explanation for the decrease in PM<sub>2.5</sub> concentration in Seoul in 2020 is the governmental effort to reduce the domestic sources of PM<sub>2.5</sub> [<xref rid=\"B19-ijerph-17-05279\" ref-type=\"bibr\">19</xref>]. With consultations with relevant ministries, a comprehensive set of measures to control the particulate matter in 2020 to 2024 was finalized on 1 November 2019 [<xref rid=\"B35-ijerph-17-05279\" ref-type=\"bibr\">35</xref>]. One of the measures is the seasonal management of the domestic sources of PM<sub>2.5</sub> from December to April, when PM<sub>2.5</sub> levels are usually high [<xref rid=\"B36-ijerph-17-05279\" ref-type=\"bibr\">36</xref>]. Efforts to reduce the domestic emission of PM<sub>2.5</sub> include the shutting down of the coal-fired power plants, voluntary reduction in emissions from business sites, nationwide surveillance of emission sources, designation of low-sailing zones, and transition to low sulfur fuels for ocean vessels. In addition to these comprehensive measures, an increase in rainfall and the number of days with high wind velocity may also help to explain the decrease in PM<sub>2.5</sub> observed in Seoul from January to March 2020 [<xref rid=\"B19-ijerph-17-05279\" ref-type=\"bibr\">19</xref>]. However, the relative humidity and wind speed in 2020 were similar to those of 2016 and 2017; limiting the effects of meteorological factors on PM<sub>2.5</sub> reduction.</p><p>We may not be able to distinguish the effects of diverse governmental measures from those of domestic and international changes related to COVID-19 on the reduced PM<sub>2.5</sub> levels in Seoul. However, it is reasonable to assume that the domestic measures (social distancing and avoidance of outside activities in Korea) and international effects (decreased PM<sub>2.5</sub> levels during the quarantine in China) related to COVID-19 may have played a significant role at the same time. We may be able to confirm the effects of COVID-19 by observing the air pollution levels after the COVID-19 measures are lifted and by evaluating whether the effects (decrease in air pollution) cease after the treatment (measures against COVID-19) ends [<xref rid=\"B37-ijerph-17-05279\" ref-type=\"bibr\">37</xref>].</p><p>Although not formally published as a journal article, several reports posted on medRxiv are showing the health benefits of reduced PM<sub>2.5</sub> levels after the COVID-19 crisis. One report estimated that the PM<sub>2.5</sub> and NO<sub>2</sub> levels dropped by 18.9 and 12.9 μg/m<sup>3</sup> in China during the COVID-19 quarantine period, which led to a decrease of 3214 PM<sub>2.5</sub>-related deaths and a decrease of 8911 NO<sub>2</sub>-related deaths [<xref rid=\"B13-ijerph-17-05279\" ref-type=\"bibr\">13</xref>]. By analyzing the air pollution data from over 10,000 monitoring stations around the globe, a 9%, 29%, and 11% reduction in ambient PM<sub>2.5</sub>, NO<sub>2</sub>, and ozone levels was estimated during February to April 2020, just after the global lockdown in response to COVID-19 [<xref rid=\"B18-ijerph-17-05279\" ref-type=\"bibr\">18</xref>]. The corresponding health benefits related to the decrease in air pollution levels were 7400 deaths and 6600 pediatric asthma cases.</p><p>After the first confirmed case of COVID-19 in Korea on 20 January 2020, the panic buying of particulate filtrating respirator led to instability in supply and demand. To tackle this issue, the Korean government adopted a “5-day rotation system for respirator” to provide equal opportunity for individuals to purchase two particulate-filtering respirators (particulate respirators capable of filtering at least 80% of the 0.6 μm nonoil particulates) per week. As the Korean government instructed its citizens to wear a respirator outside the house to control COVID-19, personal level of exposure to ambient PM<sub>2.5</sub> may have decreased from wearing a particulate respirator. With the results of the previous intervention study, we estimated that the effects of respirator use during COVID-19 crisis decreased the personal PM<sub>2.5</sub> exposure level by 27.4% in addition to the decrease in ambient PM<sub>2.5</sub> levels. We estimated that this decrease led to 102 averted deaths related to PM<sub>2.5</sub> during the 4-month period; this is higher compared to the conservative estimation of 37 lives saved with a 4.1 μg/m<sup>3</sup> decrease in ambient PM<sub>2.5</sub> concentration during the COVID-19 crisis estimated in our study. We believe that using a particulate filtrating respirator not only help to block the transmission of COVID-19, but also helped to limit the adverse health effects of PM<sub>2.5</sub>.</p><p>We have previously estimated that around 12,000 premature deaths were attributable to chronic PM<sub>2.5</sub> exposure in Korea in 2015, when the annual average of PM<sub>2.5</sub> concentration was 24.4 μg/m<sup>3</sup> [<xref rid=\"B38-ijerph-17-05279\" ref-type=\"bibr\">38</xref>]. We have also estimated that 1763 deaths solely occurred in Seoul. However, our study is different from the previous study in terms of the fact that it addressed the acute effects of PM<sub>2.5</sub> exposure by using the RRs from previous time-series studies. With the yearly mortality information and PM<sub>2.5</sub> measurement data for the entire year, we may be able to estimate the chronic PM<sub>2.5</sub> exposure burden for 2020 and compare the difference with previous years. If reduced PM<sub>2.5</sub> levels are maintained throughout 2020, we may be able to see a marked decrease in PM<sub>2.5</sub>-related burden.</p><p>Several limitations should be noted for this study. First, we adopted the RRs from previous time-series studies and ignored the possibility that the association between PM<sub>2.5</sub> and health outcomes may have changed during the COVID-19 crisis. The Korean government instructed its citizens to avoid outdoor activities or crowded areas, and to use a respirator outside the house. Due to social distancing and the use of a respirator, the beta coefficient of exposure–response relationship may have changed. If the daily mortality data become available in the future, we may be able to confirm the changes in beta coefficient by comparing the estimates from the time-series analyses before and during the COVID-19 crisis. In addition, although we tried to adjust for the effects of respirator by using the results from previous intervention study, we also assumed that the entire public were using the particulate respirator during the COVID-19 crisis, which is unlikely. Second, we ignored the daily lag effect of PM<sub>2.5</sub>. Because mortality data are not disclosed simultaneously, we were unable to conduct a day-to-day analysis accounting for the lag effects of PM<sub>2.5</sub>. With the full mortality data of 2020, a more precise estimation can be conducted by using the daily data rather than using the 4-month (January to April) data as a whole.</p><p>Due to insufficient data and assumptions used in our study, our study estimates may be biased. Assumptions regarding mortality rates and concentration-response functions have to be validated with a daily number of mortality data and PM<sub>2.5</sub> monitoring data in the future. However, based on the currently available data, our study may offer a glimpse into the acute health benefits of PM<sub>2.5</sub> reduction and changes in public behaviors, which are the tangible changes experienced by the citizens in their daily lives during the COVID-19 crisis.</p></sec><sec sec-type=\"conclusions\" id=\"sec5-ijerph-17-05279\"><title>5. Conclusions</title><p>We observed at least 4.1 μg/m<sup>3</sup> decrease in ambient PM<sub>2.5</sub> in Seoul from January to April 2020, and this decrease is believed to be the results of the changes related to COVID-19 crisis. With our conservative estimation, a total of 37 lives were saved due to the PM<sub>2.5</sub> reduction in Seoul from January to April 2020 compared to the same period in previous years. However, the health benefits related to the decrease in PM<sub>2.5</sub> may be greater because of the popular use of the particulate respirator by the public during the COVID-19 crisis in Korea. We may need to verify our study findings by observing the PM<sub>2.5</sub> levels after the COVID-19 crisis and conducting studies with a full set of daily mortality data.</p></sec></body><back><app-group><app id=\"app1-ijerph-17-05279\"><title>Supplementary Materials</title><p>The following are available online at <uri xlink:href=\"https://www.mdpi.com/1660-4601/17/15/5279/s1\">https://www.mdpi.com/1660-4601/17/15/5279/s1</uri>, Table S1: Concentration-response functions used in the study; Table S2: Estimated number of mortalities attributable to PM<sub>2.5</sub> exposure from January to April in each year from 2016 to 2020; Table S3: The daily average amount of vehicles entering the Seoul Metropolitan Area highways and number of citizens who used subways of Seoul Metropolitan area from January to April each year from 2016 to 2020; Figure S1: Locations of 25 PM<sub>2.5</sub> monitoring stations (red circle) and meteorological station (blue circle) covering Seoul; Figure S2: Average PM<sub>2.5</sub> concentration of Seoul from January to April and estimated number of mortalities attributable to PM<sub>2.5</sub> exposure (RRs from the Seoul study were used for the estimation); Figure S3: Mobility trends of Seoul City (Mobility on 13 January 2020 was regarded as 100%; Data was gathered from the Apple Maps Mobility Trends Reports: <uri xlink:href=\"https://www.apple.com/covid19/mobility/\">https://www.apple.com/covid19/mobility/</uri>).</p><supplementary-material content-type=\"local-data\" id=\"ijerph-17-05279-s001\"><media xlink:href=\"ijerph-17-05279-s001.pdf\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material></app></app-group><notes><title>Author Contributions</title><p>Conceptualization, C.H.; Data curation, C.H.; Formal analysis, C.H.; Funding acquisition, C.H.; Investigation, C.H.; Resources, C.H.; Software, C.H.; Supervision, Y.-C.H.; Writing—original draft, C.H.; Writing—review and editing, C.H. and Y.-C.H. 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Korean Med. Sci.</source><year>2018</year><volume>33</volume><fpage>e193</fpage><pub-id pub-id-type=\"doi\">10.3346/jkms.2018.33.e193</pub-id><pub-id pub-id-type=\"pmid\">30034305</pub-id></element-citation></ref></ref-list></back><floats-group><fig id=\"ijerph-17-05279-f001\" orientation=\"portrait\" position=\"float\"><label>Figure 1</label><caption><p>PM<sub>2.5</sub> concentration in Seoul from year 2016 to 2020 (January to April).</p></caption><graphic xlink:href=\"ijerph-17-05279-g001\"/></fig><fig id=\"ijerph-17-05279-f002\" orientation=\"portrait\" position=\"float\"><label>Figure 2</label><caption><p>Average PM<sub>2.5</sub> concentration of Seoul from January to April of 2016 to 2020 and estimated number of mortalities attributable to PM<sub>2.5</sub> exposure (RRs from the MCC study were used for the estimation).</p></caption><graphic xlink:href=\"ijerph-17-05279-g002\"/></fig><table-wrap id=\"ijerph-17-05279-t001\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05279-t001_Table 1</object-id><label>Table 1</label><caption><p>Average daily particulate matter (PM<sub>2.5</sub>) concentrations, temperature, relative humidity, wind speed, and number of days over the WHO (25 μg/m<sup>3</sup>) and Republic of Korea (ROK, 35 μg/m<sup>3</sup>) PM<sub>2.5</sub> 24-h average standards in Seoul (January to April each year from 2016 to 2020).</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Year</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Days (N)</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">PM<sub>2.5</sub> (μg/m<sup>3</sup>) <sup>(a)</sup></th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Temperature (°C) <sup>(a)</sup></th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Relative Humidity (%) <sup>(a)</sup></th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Wind Speed (m/s) <sup>(a)</sup></th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Days over WHO Standard <sup>(b)</sup></th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Days over ROK Standard <sup>(b)</sup></th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2016</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">121</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">28.1 (11.1)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.5 (7.8)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">52.7 (14.4)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.5 (0.7)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">64 (52.9)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">33 (27.3)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2017</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">120</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">31.7 (15.2)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.6 (7.1)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">52.1 (12.7)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.4 (0.7)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">71 (59.2)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">40 (33.3)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2018</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">120</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">30.6 (18.3)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.9 (8.4)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">51.8 (14.4)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.0 (0.7)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">64 (53.3)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">40 (33.3)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2019</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">120</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">34.6 (23.6)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.9 (6.0)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">48.8 (14.6)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.9 (0.6)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">63 (52.5)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">39 (32.5)</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2020</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">121</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">25.6 (12.2)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">5.8 (5.3)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">52.4 (13.5)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2.5 (0.8)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">55 (45.5)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">25 (20.7)</td></tr></tbody></table><table-wrap-foot><fn><p><sup>(a)</sup> Mean and standard deviation are presented, <sup>(b)</sup> Number of days and percentage are presented.</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"ijerph-17-05279-t002\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05279-t002_Table 2</object-id><label>Table 2</label><caption><p>Number of registered population, number of deaths, and mortality rates used in this study.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Year </th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2016</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2017</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2018</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2019</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2020</th></tr></thead><tbody><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Registered population at January, Seoul </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">10,018,537</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">9,930,478</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">9,851,767</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">9,766,288</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">9,733,509</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Non-accidental mortality (A00-R00, January to April)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> Number of deaths</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13,776</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13,243</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">14,445</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13,595 <sup>(a)</sup></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13,549 <sup>(a)</sup></td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"> Mortality rate (per 100,000)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">137.5</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">133.4</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">146.6</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">139.2 <sup>(b)</sup></td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">139.2 <sup>(b)</sup></td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Cardiovascular disease mortality (I00-I99, January to April)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> Number of deaths</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3080</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3129</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3316</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3125 <sup>(a)</sup></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3115 <sup>(a)</sup></td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"> Mortality rate (per 100,000)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">30.7</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">31.5</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">33.7</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">32.0 <sup>(b)</sup></td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">32.0 <sup>(b)</sup></td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Respiratory disease mortality (J00-J99, January to April)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> Number of deaths </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1533</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1462</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1789</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1572 <sup>(a)</sup></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1567 <sup>(a)</sup></td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"> Mortality rate (per 100,000)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">15.3</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">14.7</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">18.2</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">16.1 <sup>(b)</sup></td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">16.1 <sup>(b)</sup></td></tr></tbody></table><table-wrap-foot><fn><p><sup>(a)</sup> Calculated based on the estimated mortality rate, <sup>(b)</sup> Estimated by averaging year 2016–2018 mortality rate.</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"ijerph-17-05279-t003\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05279-t003_Table 3</object-id><label>Table 3</label><caption><p>Estimated PM<sub>2.5</sub> reduction levels and avoided mortality due to PM<sub>2.5</sub> exposure in January to April of 2020 compared to same month each year from 2016 to 2019.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Model 1 <sup>(a)</sup></th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Model 2 <sup>(b)</sup></th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Model 3 <sup>(c)</sup></th><th align=\"center\" valign=\"top\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Model 4 <sup>(d)</sup></th></tr></thead><tbody><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Reduction of PM<sub>2.5</sub> by comparing 2016–2019 and 2020 (μg/m<sup>3</sup>)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−5.6 (−9.0, −2.3)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−4.1 (−7.2, −0.9)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−15.1 (−27.1, −3.2)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−11.2 (−14.3, −8.2)</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Avoided cause-specific deaths</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Estimation using RRs from MCC study</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> Non-accidental mortality</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">51.3 (44.6, 58.1)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">37.6 (32.6, 42.5)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">137.9 (119.8, 156)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">102.5 (89.0, 115.9)</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> Cardiovascular disease mortality</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">9.6 (7.8, 11.5)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7 (5.7, 8.4)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">25.7 (21.0, 30.8)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">19.1 (15.6, 22.9)</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"> Respiratory disease mortality</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">6.5 (4.6, 8.3)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">4.7 (3.4, 6.1)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">17.3 (12.5, 22.2)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">12.9 (9.2, 16.5)</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Estimation using RRs from Seoul City study</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> Non-accidental mortality</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">25 (0.8, 49.8)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">18.3 (0.6, 36.5)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">67.2 (2.0, 133.9)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">49.9 (1.5, 99.5)</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> Cardiovascular disease mortality</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13.2 (2.1, 24.3)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">9.7 (1.5, 17.8)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">35.4 (5.6, 65.2)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">26.3 (4.2, 48.5)</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"> Respiratory disease mortality</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">15.3 (4.8, 25.8)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">11.2 (3.5, 18.9)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">41 (12.9, 68.6)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">30.5 (9.6, 51.2)</td></tr></tbody></table><table-wrap-foot><fn><p><sup>(a)</sup> Model 1 estimated by simple comparison of mean value, <sup>(b)</sup> Model 2 adjusting for daily average temperature, relative humidity, and wind speed, <sup>(c)</sup> Model 3 adjusting for daily average temperature, relative humidity, wind speed, years (in continuous variable), and months (as categorical variable), <sup>(d)</sup> Model 4 adjusting for daily average temperature, relative humidity, and wind speed and considered the effect of particulate filtering respirator.</p></fn></table-wrap-foot></table-wrap></floats-group></article>\n"
]
[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Int J Environ Res Public Health</journal-id><journal-id journal-id-type=\"iso-abbrev\">Int J Environ Res Public Health</journal-id><journal-id journal-id-type=\"publisher-id\">ijerph</journal-id><journal-title-group><journal-title>International Journal of Environmental Research and Public Health</journal-title></journal-title-group><issn pub-type=\"ppub\">1661-7827</issn><issn pub-type=\"epub\">1660-4601</issn><publisher><publisher-name>MDPI</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32751853</article-id><article-id pub-id-type=\"pmc\">PMC7432096</article-id><article-id pub-id-type=\"doi\">10.3390/ijerph17155541</article-id><article-id pub-id-type=\"publisher-id\">ijerph-17-05541</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Article</subject></subj-group></article-categories><title-group><article-title>Wellbeing at Work before and during the SARS-COV-2 Pandemic: A Brazilian Nationwide Study among Dietitians</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0003-1251-7966</contrib-id><name><surname>Matos</surname><given-names>Raquel Adjafre da Costa</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05541\">1</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0003-0699-7617</contrib-id><name><surname>Akutsu</surname><given-names>Rita de Cássia Coelho de Almeida</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05541\">1</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0003-0370-3089</contrib-id><name><surname>Zandonadi</surname><given-names>Renata Puppin</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05541\">1</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0003-3505-4538</contrib-id><name><surname>Rocha</surname><given-names>Ada</given-names></name><xref ref-type=\"aff\" rid=\"af2-ijerph-17-05541\">2</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0002-0369-287X</contrib-id><name><surname>Botelho</surname><given-names>Raquel Braz Assunção</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05541\">1</xref><xref rid=\"c1-ijerph-17-05541\" ref-type=\"corresp\">*</xref></contrib></contrib-group><aff id=\"af1-ijerph-17-05541\"><label>1</label>Department of Nutrition, Faculty of Health Sciences, University of Brasilia, Brasilia 70910-900, Brazil; <email>[email protected]</email> (R.A.d.C.M.); <email>[email protected]</email> (R.d.C.C.d.A.A.); <email>[email protected]</email> (R.P.Z.)</aff><aff id=\"af2-ijerph-17-05541\"><label>2</label>Faculdade de Ciencias da Nutrição e Alimentação, University of Porto, 4200-464 Porto, Portugal; <email>[email protected]</email></aff><author-notes><corresp id=\"c1-ijerph-17-05541\"><label>*</label>Correspondence: <email>[email protected]</email></corresp></author-notes><pub-date pub-type=\"epub\"><day>31</day><month>7</month><year>2020</year></pub-date><pub-date pub-type=\"ppub\"><month>8</month><year>2020</year></pub-date><volume>17</volume><issue>15</issue><elocation-id>5541</elocation-id><history><date date-type=\"received\"><day>06</day><month>7</month><year>2020</year></date><date date-type=\"accepted\"><day>28</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>© 2020 by the authors.</copyright-statement><copyright-year>2020</copyright-year><license license-type=\"open-access\"><license-p>Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>).</license-p></license></permissions><abstract><p>This study aimed to evaluate the perceptions of dietitians’ wellbeing at work before and during the SARS-COV-2 pandemic in Brazil. This cross-sectional study was performed using a previously validated instrument to investigate the wellbeing of dietitians at work in Brazil. The questionnaire on the wellbeing of dietitians was composed of 25 items (with a 5-point scale), characteristics, and questions about the SARS-COV-2 period. The application was carried out with GoogleForms<sup>®</sup> tool from 26 May to 7 June 2020. The weblink to access the research was sent via email, messaging apps, and social networks. Volunteers were recruited nationwide with the help of the Brazilian Dietitians Councils, support groups, as well as media outreach to reach as many dietitians as possible. Volunteers received, along with the research link, the invitation to participate, as well as the consent form. A representative sample of 1359 dietitians from all the Brazilian regions answered the questionnaire—mostly female (92.5%), Catholic (52.9%), from 25 to 39 years old (58.4%), with a partner (63.8%), and with no children (58%). Most of the participants continue working during the pandemic period (83.8%), but they did not have SARS-COV-2 (96%), nor did their family members (80.7%). The wellbeing at work before SARS-COV-2 was 3.88 ± 0.71, statistically different (<italic>p</italic> < 0.05) from during the pandemic, with the wellbeing of 3.71 ± 0.78. Wellbeing at work was higher before the pandemic for all the analyzed variables. Analyzing variables separately before and during the pandemic, dietitians with partners, children and a Ph.D. presented higher scores for wellbeing at work. Professionals receiving more than five times the minimum wage have higher scores. During the pandemic, better wellbeing was observed for dietitians working remotely.</p></abstract><kwd-group><kwd>SARS-COV-2</kwd><kwd>dietitians</kwd><kwd>pandemic</kwd><kwd>wellbeing at work</kwd></kwd-group></article-meta></front><body><sec sec-type=\"intro\" id=\"sec1-ijerph-17-05541\"><title>1. Introduction</title><p>The world is facing the unexpected SARS-COV-2 pandemic with several consequences for economic, social, mental, environmental, and health aspects. The SARS-COV-2 brought not only the risk of death from the viral infection but also unbearable psychological pressure on people in the world [<xref rid=\"B1-ijerph-17-05541\" ref-type=\"bibr\">1</xref>]. According to the World of Health Organization (WHO), until June 7, 2020, 6,799,713 cases of SARS-COV-2 were registered and there have been 397,388 deaths in the world [<xref rid=\"B2-ijerph-17-05541\" ref-type=\"bibr\">2</xref>]. Americas represent almost half of the registered cases (<italic>n</italic> = 3,234,875 cases; 47.6%) and deaths (<italic>n</italic> = 179,394 deaths; 45%) in the period. These numbers could be underestimated due to the lack of medical diagnosis at the beginning of the pandemic. Some countries face a few diagnostic tests. In South America, Brazil is the country with the most registered cases of SARS-COV-2 (60.3%, <italic>n</italic> = 645,771, until 7 June 2020) and deaths (72.7%, <italic>n</italic> = 35,026 in the same period) [<xref rid=\"B3-ijerph-17-05541\" ref-type=\"bibr\">3</xref>].</p><p>In Brazil, on the frontline of coping with SARS-COV-2, health professionals are acting on different fronts. To date, the future has been uncertain, and healthcare professionals have stepped out of their comfort zones [<xref rid=\"B4-ijerph-17-05541\" ref-type=\"bibr\">4</xref>]. There are several scenarios faced by health care professionals, especially ones working in clinics or hospitals, with the risk of catching SARS-COV-2, and the ones that are experiencing unemployment and family income reduction without knowing how they will face the economic crisis during and after the pandemic. In this sense, during the pandemic period, they may experience maladaptive psychological consequences of their jobs [<xref rid=\"B5-ijerph-17-05541\" ref-type=\"bibr\">5</xref>]. Work plays a central role in the individuals’ lives, bringing paradoxical consequences to the social, physical, and psychological integrity of workers [<xref rid=\"B6-ijerph-17-05541\" ref-type=\"bibr\">6</xref>,<xref rid=\"B7-ijerph-17-05541\" ref-type=\"bibr\">7</xref>], and low job satisfaction is the leading cause of turnover among health care professionals [<xref rid=\"B8-ijerph-17-05541\" ref-type=\"bibr\">8</xref>,<xref rid=\"B9-ijerph-17-05541\" ref-type=\"bibr\">9</xref>]. During the pandemic period, this can get worse.</p><p>As well as some other healthcare professionals, during the pandemic, dietitians may be facing some difficulties in their jobs, beyond the traditional ones such as low payment, lack of professional recognition, difficulty in getting their first jobs, and difficulty in geographical mobility [<xref rid=\"B10-ijerph-17-05541\" ref-type=\"bibr\">10</xref>,<xref rid=\"B11-ijerph-17-05541\" ref-type=\"bibr\">11</xref>,<xref rid=\"B12-ijerph-17-05541\" ref-type=\"bibr\">12</xref>,<xref rid=\"B13-ijerph-17-05541\" ref-type=\"bibr\">13</xref>]. In Brazil, dietitians present a wide range of work options, from directly working in hospitals visiting patients or conducting hospital foodservices, to working in clinics, schools, and commercial restaurants. All of these environments can bring direct contact with possibly infected people, presenting challenges in this new work scenario. The number of patients in the hospitals increased, and the way the food is served needed to change. Restaurants outside the hospitals had to close their doors, leading to unemployment, and facing many risks in reopening when permitted. It is essential to highlight those dietitians, despite being a specific niche of the health system, that are working directly with people infected with Sars-CoV-2. Often, their risks are ignored or underestimated by other health professionals and even by the Federal Government, which can negatively impact wellbeing at work. Recent legislation published on July 8, 2020 (Law No. 14.023) [<xref rid=\"B14-ijerph-17-05541\" ref-type=\"bibr\">14</xref>] dealing with the protection of professionals exposed to the risks of Sars-Cov-2, did not include dietitians on its lists.</p><p>Despite the enormous gathering of scientific data, to date, there is no treatment or vaccine for SARS-COV-2. This scenario of uncertainties about the disease, security itself, and their jobs can affect the perceptions of dietitians about their wellbeing at work. Studies conceive wellbeing at work as a process, defining it as the satisfaction of needs and the fulfillment of individuals’ desires as they fulfill their role in the profession [<xref rid=\"B15-ijerph-17-05541\" ref-type=\"bibr\">15</xref>,<xref rid=\"B16-ijerph-17-05541\" ref-type=\"bibr\">16</xref>]. This conception considers the role of work organizations in the health of individuals, and the development of healthy environments enables positive relationships and attitudes. The wellbeing of a professional category can be impacted, among other things, by the general and work values that are expressed in professional practice and social coexistence [<xref rid=\"B16-ijerph-17-05541\" ref-type=\"bibr\">16</xref>]. Both of them can be facing negative impacts during the SARS-COV-2 pandemic. Wellbeing at work has a significant impact on work performance and quality of life, and it brings paradoxical consequences for the social, physical, and psychological integrity of workers [<xref rid=\"B6-ijerph-17-05541\" ref-type=\"bibr\">6</xref>,<xref rid=\"B7-ijerph-17-05541\" ref-type=\"bibr\">7</xref>]. Low wellbeing at work and low job satisfaction are considered the leading causes of turnover among health care professionals [<xref rid=\"B8-ijerph-17-05541\" ref-type=\"bibr\">8</xref>,<xref rid=\"B9-ijerph-17-05541\" ref-type=\"bibr\">9</xref>]. Therefore, the knowledge about wellbeing at work is vital to improve the working environment and the quality of the service [<xref rid=\"B8-ijerph-17-05541\" ref-type=\"bibr\">8</xref>]. Wellbeing at work is related to lower levels of stress, work-related diseases, burnout, depression, and unhealthy personal practices (smoking, drinking, overeating, lack of exercise), and consequently, lower levels of non-communicable chronic diseases (NCD) [<xref rid=\"B17-ijerph-17-05541\" ref-type=\"bibr\">17</xref>]. There have been studies on the psychological impact of the epidemic on the general public, patients, medical staff, children, and older adults [<xref rid=\"B1-ijerph-17-05541\" ref-type=\"bibr\">1</xref>,<xref rid=\"B18-ijerph-17-05541\" ref-type=\"bibr\">18</xref>,<xref rid=\"B19-ijerph-17-05541\" ref-type=\"bibr\">19</xref>]. However, there is no study on the perception of dietitians’ wellbeing at work during the difficult times of pandemic. The hypothesis is that wellbeing at work will decrease during the pandemic period. In this sense, this study aimed to evaluate dietitians’ perceptions of wellbeing at work before and during the SARS-COV-2 pandemic in Brazil. The professional wellbeing knowledge among dietitians may lead to effective avenues to prevent or manage stress, unhealthy personal practices, and NCD. By evaluating the period before and during the SARS-COV-2 pandemic period, we expect that a clear understanding of the factors that influence dietitians’ wellbeing in these two moments may contribute to helping these professionals to recover after the pandemic period, exploring the areas that were most affected, and working on better professional valorization, improving the public’s trust in dietitians and the dynamics of the interprofessional healthcare team.</p></sec><sec id=\"sec2-ijerph-17-05541\"><title>2. Materials and Methods</title><sec id=\"sec2dot1-ijerph-17-05541\"><title>2.1. Study Design and Instrument</title><p>This exploratory and cross-sectional study was performed using a previously validated instrument [<xref rid=\"B16-ijerph-17-05541\" ref-type=\"bibr\">16</xref>] to investigate the wellbeing of dietitians before and during the SARS-COV-2 pandemic in Brazil. The questionnaire was composed of 25 items on the wellbeing of dietitians (with a 5-point scale that varies from 1 to 5). It also included characteristics from the original study [<xref rid=\"B16-ijerph-17-05541\" ref-type=\"bibr\">16</xref>] such as gender, age, marital status, the Brazilian state of current residency, religion, number of individuals living in the house, family income, children, educational level, occupational area as a dietitian, number of workplaces as a dietitian, how long ago graduation ended, and type of university. Researchers included three questions about the SARS-COV-2 period: Do you continue working during the SARS-COV-2 pandemic? Did you test positive for SARS-COV-2? and Did anybody in your family test positive for SARS-COV-2? The complete questionnaire is available in <xref ref-type=\"app\" rid=\"app2-ijerph-17-05541\">Appendix A</xref>.</p><p>The instrument application was carried out with GoogleForms<sup>®</sup> tool (Google LLC, Mountain View, CA, USA) from May 26 to June 7, 2020. The weblink to access the research was sent via email, messaging apps, and social networks. Volunteers were recruited nationwide with the help of the Brazilian Dietitians Councils, support groups, and media outreach to reach as many dietitians as possible. Volunteers received, along with the research link, the invitation to participate, as well as the consent form.</p></sec><sec id=\"sec2dot2-ijerph-17-05541\" sec-type=\"subjects\"><title>2.2. Participants and Ethics</title><p>Dietitians from the entire country were recruited to participate in the study. Researchers wanted to trace the wellbeing at work before and during the SARS-COV-2 of this population group in Brazil. Ethical approval was obtained for this study by the Ethics Committee University of Brasília (protocol No. 54822316.1.00000030). The study was conducted according to the guidelines laid down in the Declaration of Helsinki and followed the Recommendations for the Conduct, Reporting, Editing, and Publication of Scholarly work in Medical Journals.</p><p>The sampling size was calculated based on data from the Brazilian Federal Dietitians Council that presents 129,134 registered dietitians [<xref rid=\"B20-ijerph-17-05541\" ref-type=\"bibr\">20</xref>], considering an error (e) of 3% and a level of significance (α) of 5% [<xref rid=\"B21-ijerph-17-05541\" ref-type=\"bibr\">21</xref>]. The minimum estimated representative sample size would be of 1059 participants. The inclusion criteria were to be a dietitian and living and working in Brazil.</p></sec><sec id=\"sec2dot3-ijerph-17-05541\"><title>2.3. Statistical Analysis</title><p>Researchers extracted data from the GoogleForms<sup>®</sup> tool and analyzed using Statistical Package for the Social Sciences—SPSS 24.0 (version 24, SPSS Inc., Chicago, IL, USA). Exploratory and confirmatory analyses were conducted to determine the psychometric quality of the wellbeing at work instrument. We used the Kaiser–Meyer–Olkin (KMO), and Barlett’s sphericity test. For consistency, Cronbach’s alpha was used. Descriptive analyses were used to determine the measures of central tendency and dispersion of the sample. We compared means of the sample through paired <italic>t</italic>-tests (wellbeing before and during SARS-COV-2) and Analysis of Variance (ANOVA) with Tukey post-hoc.</p></sec></sec><sec sec-type=\"results\" id=\"sec3-ijerph-17-05541\"><title>3. Results</title><p>A representative sample of 1359 dietitians from all the 26 Brazilian states and the Federal District and regions answered the questionnaire. <xref ref-type=\"fig\" rid=\"ijerph-17-05541-f001\">Figure 1</xref> shows the distribution of the dietitians and participants by Brazilian regions.</p><p>The participants were mostly female (92.5%), Catholic (52.9%), aged from 25 to 39 years old (58.4%), with a partner (63.8%), and with no children (58%) (<xref rid=\"ijerph-17-05541-t001\" ref-type=\"table\">Table 1</xref>). Most of the participants continued working during the pandemic period (83.8%), but had not been diagnosed with SARS-COV-2 (96%) before answering the questionnaire, nor did their family members (80.7%).</p><p>Dietitians with less than 1 MW family income are mostly graduates (35.9%) or present specialization/residency (56.4%). However, dietitians with more than 20 MW as a family income have a master’s and Ph.D. (52.2%). Of 144 dietitians with Ph.D., 95.1% work in the teaching area (universities).</p><p><xref rid=\"ijerph-17-05541-t002\" ref-type=\"table\">Table 2</xref> shows data from the wellbeing at work before and during the pandemic period compared by participants’ characteristics. The instrument presented a KMO of 0.957 and a significant (0.000) Barlett’s sphericity test. For internal consistency, the wellbeing at work instrument presented a Cronbach’s alpha of 0.952. In the communalities, the extraction item was below 0.500 for questions 12 (social relations with my colleagues positively influence my work) and 25 (I consider my workload adequate), and they were not considered for the final score of wellbeing at work. The maintained questions from the instrument were divided into four factors. Factor 1 is related to exterior perception with questions 2, 4, 6, 7, 11 and 15 (<xref ref-type=\"app\" rid=\"app2-ijerph-17-05541\">Appendix A</xref>), factor 2 is concerned about the perception in itself (questions 1, 3, 5, 8, 13, 14, 18 and 21), factor 3 is the task perception (questions 19, 20, 22, 23 and 24), and factor 4 is the perception from the dietitians’ category (questions 9, 10, 16 and 17). Cronbach´s alpha was also calculated for each factor: factor 1, 0.871; factor 2, 0.881; factor 3, 0.881; factor 4, 0.884 (<xref ref-type=\"app\" rid=\"app1-ijerph-17-05541\">Supplementary File, Table S1</xref>).</p><p>In general, wellbeing at work before SARS-COV-2 was 3.88 ± 0.71, statistically different (<italic>p</italic> < 0.05) from during the pandemic, with the wellbeing of 3.71 ± 0.78. A comparison between before the pandemic and during the pandemic showed statistical differences for all variables (worse during the pandemic period) (<italic>p</italic> < 0.05).</p><p>Gender and number of workplaces did not influence wellbeing at work before and during the pandemic period. Individuals with a partner and with children had a better perception of wellbeing at work than the ones with no partner or children. Before and during the pandemic, master’s and Ph.D. individuals presented better wellbeing at work than graduates, and Ph.D. dietitians presented better wellbeing than dietitians with a master’s degree (<xref rid=\"ijerph-17-05541-t002\" ref-type=\"table\">Table 2</xref>).</p><p>For both periods, individuals that work in teaching present better wellbeing at work compared to the other areas of dietitians’ practice (clinic, foodservice administration, public health, and others). Before the pandemic period, individuals with family monthly income >5 MW present higher wellbeing at work than the ones up to 5 MW (<xref rid=\"ijerph-17-05541-t002\" ref-type=\"table\">Table 2</xref>). During the pandemic period, the results were a little different, showing differences among individuals up to 3 MW, from 3 to 5 MW and >5 MW, with increasing wellbeing perception with higher family income. Before the pandemic, the time from the undergraduate completion differed from up to 10 years to >15 years. During the pandemic, the higher time from undergraduate completion (>15 years) presented a higher mean of wellbeing at work than the lowest time of completion (≤2 years).</p><p>Before the pandemic period, individuals adept in Catholicism and Spiritism had a better perception of wellbeing at work than Protestants. During the pandemic, Catholics did not differ from Protestants, agnostics, or Spiritism followers. However, individuals following the Spiritism religion presented a better perception of wellbeing at work than Protestants (<xref rid=\"ijerph-17-05541-t002\" ref-type=\"table\">Table 2</xref>).</p><p>Dietitians that tested positive for SARS-COV-2 (<italic>n</italic> = 55) were predominantly working in-person (78.2%, <italic>n</italic> = 43), without or with adaptations (58.2%, <italic>n</italic> = 32; 20%, <italic>n</italic> = 11, respectively). Participants who reported not being working during the pandemic period (<italic>n</italic> = 220) have a job, but they are unable to work in-person or remotely. The wellbeing values were considered for this group of workers because they answered the questions and effectively have a job. There is no difference among perceptions of wellbeing at work between dietitians who had SARS-COV-2 and the ones that did not have, similarly to the results from the ones that had any family member test positive for SARS-COV-2.</p><p>When separating the wellbeing at work by factors (<xref ref-type=\"app\" rid=\"app1-ijerph-17-05541\">Table S1</xref>) and comparing by participant´s characteristics, factors 1 and 4 presented the lowest means before and after the pandemic, being lower and statistically different (<italic>p</italic> < 0.05) during the pandemic. Factor 1 relates to exterior perception and factor 4 to the category perception. Higher scores occurred for the perception of itself and the perception of the task, before and during the pandemic.</p></sec><sec sec-type=\"discussion\" id=\"sec4-ijerph-17-05541\"><title>4. Discussion</title><p>As the world grapples with the impact of the SARS-CoV-2 pandemic, health care workers face extraordinary challenges daily, in different contexts and conditions [<xref rid=\"B22-ijerph-17-05541\" ref-type=\"bibr\">22</xref>]. The media reports that health care professionals are hugely concerned for the health and wellbeing of their patients, their families, and themselves, facing pandemic issues [<xref rid=\"B22-ijerph-17-05541\" ref-type=\"bibr\">22</xref>,<xref rid=\"B23-ijerph-17-05541\" ref-type=\"bibr\">23</xref>]. However, the Brazilian government did not recognize dietitians as part of the health professionals facing the SARS-COV-2 pandemic [<xref rid=\"B14-ijerph-17-05541\" ref-type=\"bibr\">14</xref>]. These changes in life and work and the lack of recognition and support have a significant impact on their wellbeing, as shown by our results. Among Brazilian dietitians, there was a worse perception of wellbeing at work during the pandemic compared to the period before the pandemic for all variables (<italic>p</italic> < 0.05) (<xref rid=\"ijerph-17-05541-t002\" ref-type=\"table\">Table 2</xref>). In general, dietitians’ wellbeing at work was positive (above 2.5), which is the midpoint of the scale. The items that obtained the best scores were those that investigate the perception of the importance of the profession for themselves and society. The average scores were above 4.40 before the SARS-COV-2 pandemic and 4.20 during it. The items with the lowest scores and which need to be improved are related to compensation and technological support to perform the tasks assigned to the dietitian. Probably, wage improvement could come through professional qualification, as higher wages were linked to more years of study in our research. Besides, there is a need for an increase in the number of class entities (unions and councils) to fight for better wages of this professional category. In Brazil, even before the SARS-COV-2 pandemic, the country had high unemployment rates [<xref rid=\"B24-ijerph-17-05541\" ref-type=\"bibr\">24</xref>], a situation worsened by the pandemic, which made it difficult for workers to maintain their family income. Part of the dietitians’ work is in the foodservice area (schools, commercial and institutional restaurants), and most of these are closed or changed the policies of production due to the food safety and workers’ safety conditions.</p><p>Wellbeing at work before SARS-COV-2 was 3.88 ± 0.71, statistically different (<italic>p</italic> < 0.05) from during the pandemic (3.71 ± 0.78). Usually, these conditions would place health care professionals in a situation defined by threat and fear, both of which have been shown to have a detrimental effect on their ability to offer compassionate and person-centered care to their patients [<xref rid=\"B22-ijerph-17-05541\" ref-type=\"bibr\">22</xref>,<xref rid=\"B25-ijerph-17-05541\" ref-type=\"bibr\">25</xref>]. In these circumstances, there are high levels of conflict within teams and workplace adversity, leading to a working environment which is perceived as hostile, abusive, and unrewarding [<xref rid=\"B26-ijerph-17-05541\" ref-type=\"bibr\">26</xref>,<xref rid=\"B27-ijerph-17-05541\" ref-type=\"bibr\">27</xref>]. As a consequence, workplace adversity can be correlated with a decreased quality of care [<xref rid=\"B28-ijerph-17-05541\" ref-type=\"bibr\">28</xref>]. In April 2020, the Brazilian Health Ministry requested the registration of all health professionals, including dietitians, to reinforce the fight against SARS-CoV-2, in addition to the usual work of individuals [<xref rid=\"B29-ijerph-17-05541\" ref-type=\"bibr\">29</xref>]. This reinforcement is to assist managers of the Unified Health System (<italic>SUS</italic>) in coping with SARS-CoV-2, based on the work capacity of these professionals. It focuses on those who were available to go to the Brazilian states with the greatest need to strengthen health teams [<xref rid=\"B29-ijerph-17-05541\" ref-type=\"bibr\">29</xref>]. This fact also caused concern in several dietitians who were afraid to work facing SARS-COV-2 patients for various reasons. According to the Brazilian Health Ministry, from the 90,245 Brazilian registered dietitians to reinforce the fight against SARS-CoV-2, 33,624 were willing to work facing SARS-COV-2 [<xref rid=\"B30-ijerph-17-05541\" ref-type=\"bibr\">30</xref>]. These professionals were registered in the category to receive payment for their work to confront SARS-COV-2. However, the Brazilian Ministry registered other health care professions in a voluntary (unpaid) category of workers, and no dietitian volunteered until 28 April 2020 [<xref rid=\"B30-ijerph-17-05541\" ref-type=\"bibr\">30</xref>]. It was not stated, but it seems that the dietitians that were willing to work facing SARS-COV-2 were doing it to help the family income, which can potentially worsen their perception of wellbeing at work. Given the unknown and uncontrollable nature of the SARS-COV-2, some health care professionals need to stay away from their home and loved ones, possibly affecting emotional aspects and the relationship with their work [<xref rid=\"B23-ijerph-17-05541\" ref-type=\"bibr\">23</xref>].</p><p>In the pandemic situation, the protection measures for these professionals get worse due to the limitations of social distance. In some cases, this risk is higher, such as in hospitals, emergency services, outpatient clinics, vaccination clinics, screening lines, and other health care settings. In this environment, these professionals are even more exposed when their duty includes providing some assistance to infected people with SARS-COV-2. In foodservices, where these professionals do not work directly with individuals adequately tested for the new coronavirus, they face the same environment with asymptomatic individuals or those in the incubation phase of the disease [<xref rid=\"B31-ijerph-17-05541\" ref-type=\"bibr\">31</xref>].</p><p>Our sample was mostly composed of females (<xref rid=\"ijerph-17-05541-t001\" ref-type=\"table\">Table 1</xref>). The female hegemony among dietitians is common, as shown by other studies in different countries [<xref rid=\"B8-ijerph-17-05541\" ref-type=\"bibr\">8</xref>,<xref rid=\"B9-ijerph-17-05541\" ref-type=\"bibr\">9</xref>,<xref rid=\"B13-ijerph-17-05541\" ref-type=\"bibr\">13</xref>,<xref rid=\"B32-ijerph-17-05541\" ref-type=\"bibr\">32</xref>,<xref rid=\"B33-ijerph-17-05541\" ref-type=\"bibr\">33</xref>,<xref rid=\"B34-ijerph-17-05541\" ref-type=\"bibr\">34</xref>]. Female hegemony in the profession can represent repercussions on career, social prestige, and income [<xref rid=\"B12-ijerph-17-05541\" ref-type=\"bibr\">12</xref>]. A study showed that dietitians were mostly women and that, although the labor market has grown, the new jobs were mostly (86%) part-time, not only because women need to conciliate career and family care, but also because these positions get lower payment [<xref rid=\"B35-ijerph-17-05541\" ref-type=\"bibr\">35</xref>]. However, according to our data, gender did not influence wellbeing at work before nor during the pandemic period.</p><p>According to a research conducted by the Dietitians Federal Council in 2016/2017 with 1104 dietitians in Brazil, most of them are young, between 25 and 44 years old (81%), a higher percentage than our study (68.3%). Previous studies showed that 73% of the Brazilian dietitians are postgraduates [<xref rid=\"B12-ijerph-17-05541\" ref-type=\"bibr\">12</xref>,<xref rid=\"B16-ijerph-17-05541\" ref-type=\"bibr\">16</xref>,<xref rid=\"B36-ijerph-17-05541\" ref-type=\"bibr\">36</xref>], similar to our data (77.8%, <italic>n</italic> = 1058). Before and during the pandemic, master’s and Ph.D. individuals presented better wellbeing at work than graduates (<xref rid=\"ijerph-17-05541-t002\" ref-type=\"table\">Table 2</xref>). Most Ph.D. dietitians work in the teaching area, and for both periods, individuals that work in teaching present better wellbeing at work compared to the other areas of dietitians’ practice (clinic, foodservice administration, public health, and others). All schools, including universities, faced changes in how they conducted work. Professors had to search for technological tools and strategies in order to work during the pandemic period, impacting their perception of wellbeing at work, as shown by the lower mean during the pandemic. Undergraduate courses in the health area need practical classes and time inside the hospitals, and it is difficult to return to these activities in-person. The other work areas for dietitians did not present statistically different wellbeing, even for professionals that work in the clinical area and can be inside hospitals.</p><p>Before the pandemic period, individuals with family monthly income > 5 MW presented higher wellbeing at work than the ones up to 5 MW (<xref rid=\"ijerph-17-05541-t002\" ref-type=\"table\">Table 2</xref>). During the pandemic period, wellbeing perception increased with higher family income. These data are confirmed by other studies, indicating that lower wages decrease satisfaction at work [<xref rid=\"B13-ijerph-17-05541\" ref-type=\"bibr\">13</xref>,<xref rid=\"B16-ijerph-17-05541\" ref-type=\"bibr\">16</xref>,<xref rid=\"B37-ijerph-17-05541\" ref-type=\"bibr\">37</xref>,<xref rid=\"B38-ijerph-17-05541\" ref-type=\"bibr\">38</xref>,<xref rid=\"B39-ijerph-17-05541\" ref-type=\"bibr\">39</xref>]. During the pandemic period, most family members are isolated at home, increasing expenses with bills (water, energy, food, and others). This can influence the difference between the categories of family income compared to the period before the SARS-COV-2. According to the official data [<xref rid=\"B24-ijerph-17-05541\" ref-type=\"bibr\">24</xref>], unemployment increased in all Brazilian regions with SARS-COV-2, but mainly in the northeast region (from 13.6% in 2019 to 15.6% in 2020), followed by the North (from 10.6% to 11.4%) and the southeast region (from 11.4% to 12.4%), also impacting family income. It is noteworthy that the north and northeast regions were the ones presenting the largest proportional increase in official cases of SARS-COV-2 in Brazil until June 7, 2020 [<xref rid=\"B40-ijerph-17-05541\" ref-type=\"bibr\">40</xref>].</p><p>The Brazilian government published two provisional measures [<xref rid=\"B41-ijerph-17-05541\" ref-type=\"bibr\">41</xref>,<xref rid=\"B42-ijerph-17-05541\" ref-type=\"bibr\">42</xref>] during the pandemic changing the work relationship between employers and employees. Employers can reduce employees’ salaries during the SARS-COV-2 crisis, and rules were established for remote work and the suspension of some administrative measures related to safety at work. These legislative measures enable up to a 70% salary reduction and precarious work relationships, reflected by a lower perception of wellbeing at work during the pandemic.</p><p>Two characteristics influence wellbeing at work before and during the pandemic: marital status, and children. Dietitians with partners (63.8%) and with children (42%) had higher wellbeing before and during the pandemic. VanderWeele [<xref rid=\"B43-ijerph-17-05541\" ref-type=\"bibr\">43</xref>] discusses in his article that studies associate marriage with higher life satisfaction and happiness. This association can be related to better mental and physical health and longevity. With time, marriage is associated with a better relationship with others, including work partners, which can influence work wellbeing. VanderWeele [<xref rid=\"B43-ijerph-17-05541\" ref-type=\"bibr\">43</xref>] also highlights that marriage and family are vital to wellbeing. Despite this study evaluating wellbeing in life, not in the workplace, it could potentially explain higher scores of wellbeing in our study for dietitians with children and partners. Wilcox [<xref rid=\"B44-ijerph-17-05541\" ref-type=\"bibr\">44</xref>] discusses in his book that marriage is also associated with financial status and education. A meta-analytic study [<xref rid=\"B45-ijerph-17-05541\" ref-type=\"bibr\">45</xref>] suggested that people who are employed present better life, family, and marital satisfaction. Our study was only conducted with dietitians that have a job, because the instrument is related to wellbeing at work. Therefore, it is not possible to compare this with studies of unemployment. However, they show better wellbeing for people with partners, family, and education, such as our findings for wellbeing at work [<xref rid=\"B46-ijerph-17-05541\" ref-type=\"bibr\">46</xref>]. Ryff and Heidrich [<xref rid=\"B46-ijerph-17-05541\" ref-type=\"bibr\">46</xref>] stated that work and education experiences explain differences in the purpose of life. Higher levels of education are associated with happiness and satisfaction and strongly affect income [<xref rid=\"B47-ijerph-17-05541\" ref-type=\"bibr\">47</xref>]. As already discussed, dietitians with more education and income presented better scores for wellbeing before and during the pandemic.</p><p>Regarding religion, worldwide, 15% of people are agnostic [<xref rid=\"B48-ijerph-17-05541\" ref-type=\"bibr\">48</xref>], higher than in our study (5.2%), but closer to the Brazilian statistics of 8% (IBGE, 2010). Even though research has shown that religion brings higher subjective wellbeing because of social support and meaning in life, wellbeing at work was not lower among agnostic dietitians [<xref rid=\"B49-ijerph-17-05541\" ref-type=\"bibr\">49</xref>]. Sedikides [<xref rid=\"B50-ijerph-17-05541\" ref-type=\"bibr\">50</xref>] stated that religion is important for most people´s psychological conditions and subjective wellbeing. There are pieces of evidence that suggest that spirituality is a protective factor for health and psychological problems [<xref rid=\"B51-ijerph-17-05541\" ref-type=\"bibr\">51</xref>,<xref rid=\"B52-ijerph-17-05541\" ref-type=\"bibr\">52</xref>]. A study conducted by Ferreira, Pinto e Neto [<xref rid=\"B52-ijerph-17-05541\" ref-type=\"bibr\">52</xref>] with university students from Portugal, Mozambique, Angola, and Brazil showed that churchgoers present better spiritual wellbeing and better life satisfaction. During the pandemic, churches and temples were closed in many cities and states, and this can explain the lower wellbeing in our study for all the religions during the pandemic.</p><p>Dietitians that tested positive for SARS-COV-2 (<italic>n</italic> = 55) were predominantly working in-person (78.2%, <italic>n</italic> = 43), without or with adaptations (58.2%, <italic>n</italic> = 32; 20%, <italic>n</italic> = 11, respectively). Despite the need for adaptations, people working remotely during the pandemic had better wellbeing at work than the ones that are working in person. Probably, this is due to the possibility of a greater sense of security at home, and being near the family.</p><p>The new coronavirus pandemic arrived in Brazil at a time of economic stagnation, problems with the health and social protection systems, difficulties among the food security programs, accelerated increase in poverty, and, especially, extreme poverty, and a significant increase in the homeless population. Since March 2020, Brazil has accumulated a fall in gross domestic product (GDP) [<xref rid=\"B53-ijerph-17-05541\" ref-type=\"bibr\">53</xref>], and this retreat, partially caused by social isolation, has significantly increased formal and informal unemployment, in addition to precarious labor relations. This new scenario will directly impact dietitian’s work and wellbeing, not only their work conditions, incomes, and uncertainties, but also the feeling of helplessness when facing hunger in the country.</p><p>At the same time, the pandemic can bring a search for new strategies for better conditions for health professionals in hospitals and clinics. New routines and behaviors for food production can be developed, not thinking only of food safety inside the production area, but also the attitudes of consumers. Dietitians have the potential to show the importance of their work, to avoid contaminations in foodservices, and bring more discussion about eating habits and immunity.</p><p>Dietitians are health professionals at the front line for the population’s nutritional assistance. They work at all levels of complexity in the health care system, and may potentially reduce the risks of disease worsening, and contribute to the recovery of patients affected by Sars-Cov-2. The different spheres (population, governments, and other health professionals) must recognize the relevance of these professionals for public health in the country.</p></sec><sec sec-type=\"conclusions\" id=\"sec5-ijerph-17-05541\"><title>5. Conclusions</title><p>These data are essential to evaluating dietitians’ perceptions of wellbeing at work and potentially helping to understand the main challenges supporting them to emerge from the pandemic as a different type of health care practitioner. The instrument presented an excellent KMO and internal consistency as a whole or by its factors. For all the participants’ characteristics, wellbeing decreased during the pandemic, not showing specific influence among the analyzed variables. The hypothesis that the SARS-COV-2 pandemic period influences the wellbeing of Brazilian dietitians was confirmed. However, when evaluating wellbeing separately, before and during the pandemic, dietitians with partners, children, Ph.D., and receiving more than five MW presented higher wellbeing scores at work. During the pandemic period, dietitians working remotely also showed higher wellbeing. Regardless of the period, it is notable that, for dietitians’ wellbeing at work to improve, better compensation for and recognition of the profession is necessary, as well as the conditions for their activities to be carried out. Health policymakers should discuss the role of health professionals as a multidisciplinary team, highlighting the importance of each category for the health system. Much improvement has happened in the health system in Brazil to integrate professionals but, as discussed, dietitians still feel unrecognized for their work. This study can open doors for more research and discussion in the field of wellbeing at work for health professionals, as well as a clear understanding of the factors that influence dietitians’ wellbeing before and during the SARS-COV-2 pandemic, helping these professionals to recover after this period. Therefore, the data could help to explore the areas that most affected (before and during the pandemic period) wellbeing at work, favoring professional valorization. Further studies should be conducted after the pandemic period to evaluate the perceptions of dietitians’ wellbeing at work due to the potential changes in the work environment and conditions in Brazil, and also in other countries, allowing comparisons netweem them.</p></sec></body><back><ack><title>Acknowledgments</title><p>The authors acknowledge the participants, PPGNH/UnB, and CAPES.</p></ack><app-group><app id=\"app1-ijerph-17-05541\"><title>Supplementary Materials</title><p>The following are available online at <uri xlink:href=\"https://www.mdpi.com/1660-4601/17/15/5541/s1\">https://www.mdpi.com/1660-4601/17/15/5541/s1</uri>, Table S1. Wellbeing at work by factors and by socioeconomic and demographic variables of Brazilian dietitians before and during the SARS-COV-2 pandemic period (<italic>n</italic> = 1359).</p><supplementary-material content-type=\"local-data\" id=\"ijerph-17-05541-s001\"><media xlink:href=\"ijerph-17-05541-s001.pdf\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material></app></app-group><notes><title>Author Contributions</title><p>Conceptualization, R.d.C.C.d.A.A., R.B.A.B., and R.P.Z..; methodology, R.d.C.C.d.A.A., R.B.A.B., A.R., R.A.d.C.M., and R.P.Z.; validation, R.d.C.C.d.A.A.,; formal analysis, R.d.C.C.d.A.A., R.A.d.C.M., R.B.A.B., and R.P.Z..; investigation, R.d.C.C.d.A.A., R.A.d.C.M., R.B.A.B., and R.P.Z.; resources, R.A.d.C.M.; data curation, R.d.C.C.d.A.A., R.A.d.C.M., R.B.A.B., A.R.; writing—original draft preparation, R.d.C.C.d.A.A., R.A.d.C.M., R.B.A.B., and R.P.Z.; writing—review and editing, R.d.C.C.d.A.A., R.A.d.C.M., A.R., R.B.A.B., and R.P.Z.; visualization, A.R.; supervision, R.B.A.B.; project administration, R.d.C.C.d.A.A., R.B.A.B., and R.P.Z.; funding acquisition, R.A.d.C.M. All authors have read and agreed to the published version of the manuscript.</p></notes><notes><title>Funding</title><p>This research received no external funding.</p></notes><notes notes-type=\"COI-statement\"><title>Conflicts of Interest</title><p>The authors declare no conflict of interest.</p></notes><app-group><app id=\"app2-ijerph-17-05541\"><title>Appendix A. Brazilian-Portuguese Questionnaire to Evaluate the Dietitians’ Wellbeing at Work before and during the SARS-COV-2 Pandemic</title><sec id=\"secAdot1-ijerph-17-05541\"><title>Appendix A.1. Questionário de Avaliação Do Bem-Estar Do Nutricionista No Trabalho Antes E Durante A Pandemia da Sars-Cov-2/Nutritionist’s Wellbeing Assessment Questionnaire at Work before and during the Sars-Cov-2 Pandemic</title><p>A seção seguinte destina-se a recolher dados dos participantes da investigação, com a finalidade de permitir a análise de tendências de respostas em função de características pessoais e do trabalho. Por favor complete o questionário com estes dados, com a garantia de que nenhuma destas informações será utilizada para identificar qualquer participante da investigação./<italic>The next section is intended to collect data from the research participants, in order to allow the analysis of trends in responses according to personal and work characteristics. Please complete the questionnaire with this data, ensuring that none of this information will be used to identify any research participants.</italic></p><list list-type=\"order\"><list-item><p>Sexo Biológico/<italic>Gender</italic></p></list-item><list-item><p>Idade/<italic>Age</italic></p></list-item><list-item><p>Estado civil/<italic>Marital status</italic></p></list-item><list-item><p>Qual o estado brasileiro de residência atual?/<italic>What is your resindency state in Brazil?</italic></p></list-item><list-item><p>Qual a sua religião?/<italic>Religion</italic></p></list-item><list-item><p>Quantas pessoas vivem na sua casa incluindo você? <italic>Counting with you, how many persons live in the house</italic>?</p></list-item><list-item><p>Qual a sua renda familiar?/<italic>Family income</italic></p></list-item><list-item><p>Você tem filhos?/<italic>Do you have children</italic>?</p></list-item><list-item><p>Qual o seu nível educacional?/<italic>Educational level</italic></p></list-item><list-item><p>Qual a sua área de atuação? (você pode marcar mais de uma opção)/<italic>What is your area of working as a dietitian?</italic></p></list-item><list-item><p>Em quantos locais você exerce atividade como nutricionista?/<italic>In how many places do you work as a dietitian?</italic></p></list-item><list-item><p>Há quanto tempo terminou sua graduação em nutrição?/<italic>How long ago did you graduate in nutrition?</italic></p></list-item><list-item><p>Em que tipo de instituição cursou sua graduação em nutrição?/<italic>In what type of university did you study nutrition?</italic></p></list-item><list-item><p>Você continua trabalhando no período da pandemia da SARS-CoV-2?/<italic>Do you comtinue working during SARS-CoV-2 pandemic?</italic></p></list-item><list-item><p>Você testou positivo para a SARS-CoV-2?/<italic>Did you test positive for SARS-CoV-2?</italic></p></list-item><list-item><p>Alguma pessoa da sua família testou positivo para a SARS-CoV-2?/<italic>Has anyone in your family tested positive for SARS-CoV-2</italic></p></list-item></list></sec><sec id=\"secAdot2-ijerph-17-05541\"><title>Appendix A.2. Escala de Bemestar Individual No Trabalho/Wellbeing at Work</title><p>Este instrumento pretende avaliar o seu nível de bem-estar no exercício da profissão de nutricionista. Para tanto, você deve avaliar cada uma das afirmativas abaixo, preenchendo os espaços em branco conforme os códigos seguintes:</p><p>Nunca/<italic>never</italic> (0); Raramente/<italic>Rarely</italic> (1); Às Vezes/<italic>Sometimes</italic> (2); Frequentemente/<italic>Frequently</italic> (3); Sempre/<italic>Always</italic> (4)</p><list list-type=\"order\"><list-item><p>O trabalho como nutricionista é importante para mim/<italic>My work as a dietitian is important for me</italic></p></list-item><list-item><p>Percebo que minha profissão é valorizada onde trabalho/<italic>I realize that my profession is valued where I work</italic></p></list-item><list-item><p>Considero que exerço um trabalho importante para a sociedade<italic>/I comsider my work important for society</italic></p></list-item><list-item><p>Sou recompensado(a) por minha competência como nutricionista/<italic>I am rewarded for my competence as a dietitian</italic></p></list-item><list-item><p>Sou admirado(a) por meus colegas pelo trabalho que faço/<italic>I am admired by my colleagues for my work</italic></p></list-item><list-item><p>Tenho liberdade para executar minhas atividades com meu estilo pessoal/<italic>I am free to carry out my activities in my personal style</italic></p></list-item><list-item><p>Tenho a infraestrutura material necessária para a execução do meu trabalho/<italic>I have the necessary material infrastructure to carry out my work</italic></p></list-item><list-item><p>Tenho a possibilidade de me desenvolver profissionalmente<italic>/I have the possibility to improve professionally</italic></p></list-item><list-item><p>Sinto-me realizado(a) profissionalmente/<italic>I feel professionally fulfilled</italic></p></list-item><list-item><p>Sinto-me seguro(a) com a possibilidade de continuar trabalhando como nutricionista/<italic>I feel safe with the possibility of continuing to work as a dietitian</italic></p></list-item><list-item><p>Tenho um bom suporte tecnológico para executar o meu trabalho/<italic>I have good technological support to do my job</italic></p></list-item><list-item><p>As relações sociais com meus colegas influenciam positivamente o meu trabalho/<italic>Social relations with my colleagues positively influence my work</italic></p></list-item><list-item><p>Sinto-me bem com o relacionamento com meus chefes/<italic>I feel good about my relationship with my bosses</italic></p></list-item><list-item><p>Sinto-me bem com o relacionamento com meus subordinados/<italic>I feel good about my relationship with my employees</italic></p></list-item><list-item><p>Considero justo o salário que recebo/<italic>My salary is fare</italic></p></list-item><list-item><p>Tenho orgulho de pertencer à categoria profissional de nutricionista/<italic>I am proud to be part of the dietitans professional category.</italic></p></list-item><list-item><p>Sinto-me bem trabalhando como nutricionista./<italic>I feel good to be working as a dietitian</italic></p></list-item><list-item><p>Sou admirado pela sociedade/clientes pelo trabalho que faço<italic>/I am admired by society/clients for the work I peform</italic></p></list-item><list-item><p>Considero meu trabalho criativo e estimulante/<italic>I consider my work criative and challenging</italic></p></list-item><list-item><p>Quero permanecer sempre trabalhando como nutricionista/<italic>I want to continue working as a dietitian</italic></p></list-item><list-item><p>Considero que as tarefas que executo para atender meus clientes são importantes para a qualidade de vida deles/<italic>I believe that the tasks I perform to my clients are important to their quality of life</italic></p></list-item><list-item><p>Sinto-me estimulado(a) a estar sempre atualizado(a)/<italic>I feel encouraged to always be up to date</italic></p></list-item><list-item><p>Considero que os avanços da ciência da nutrição melhoram o meu desempenho profissional<italic>/I believe that advances in nutrition science improve my professional performance</italic></p></list-item><list-item><p>Considero que as novas tecnologias criadas melhoram meu desempenho profissional<italic>/I believe that the new technologies improve my professional performance</italic></p></list-item><list-item><p>Considero minha carga horária de trabalho adequada/<italic>I consider my workload adequate</italic></p></list-item></list></sec></app></app-group><ref-list><title>References</title><ref id=\"B1-ijerph-17-05541\"><label>1.</label><element-citation publication-type=\"journal\"><person-group 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Geografia e Estatística Produto Interno Bruto—PIB</article-title><comment>Available online: <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ibge.gov.br/explica/pib.php\">https://www.ibge.gov.br/explica/pib.php</ext-link></comment><date-in-citation content-type=\"access-date\" iso-8601-date=\"2020-06-17\">(accessed on 17 June 2020)</date-in-citation></element-citation></ref></ref-list></back><floats-group><fig id=\"ijerph-17-05541-f001\" orientation=\"portrait\" position=\"float\"><label>Figure 1</label><caption><p>Distribution of dietitians and participants among Brazilian regions.</p></caption><graphic xlink:href=\"ijerph-17-05541-g001\"/></fig><table-wrap id=\"ijerph-17-05541-t001\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05541-t001_Table 1</object-id><label>Table 1</label><caption><p>Characteristics of Brazilian dietitians and SARS-COV-2 questions (<italic>n</italic> = 1359).</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Variable</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>n</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">%</th></tr></thead><tbody><tr><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Gender</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Female</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1258</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">92.5</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Male</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">102</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">7.5</td></tr><tr><td rowspan=\"7\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Age group</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">21 to 24 y/o</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">149</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">11.0</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">25 to 29 y/o</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">275</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">20.2</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">30 to 34 y/o</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">272</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">20.0</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">35 to 39 y/o</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">248</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">18.2</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">40 to 44 y/o</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">134</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">9.9</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">45 to 49 y/o</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">188</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6.5</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">50 to older</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">194</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">14.3</td></tr><tr><td rowspan=\"5\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Religion</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Catholic</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">720</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">52.9</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Protestant</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">283</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">20.8</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Spiritism</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">213</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">15.7</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Agnostic</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">71</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.2</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Others</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">73</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">5.4</td></tr><tr><td rowspan=\"4\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Level of education (highest degree)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Graduate</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">302</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">22.2</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Especialization/Residency</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">677</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">49.8</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Master</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">237</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">17.4</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">PhD</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">144</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10.6</td></tr><tr><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Marital status</td><td align=\"center\" valign=\"middle\" style=\"border-top:solid thin\" rowspan=\"1\" colspan=\"1\">Without partner</td><td align=\"center\" valign=\"middle\" style=\"border-top:solid thin\" rowspan=\"1\" colspan=\"1\">493</td><td align=\"center\" valign=\"middle\" style=\"border-top:solid thin\" rowspan=\"1\" colspan=\"1\">36.3</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">With partner</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">867</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">63.8</td></tr><tr><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Children</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Yes</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">571</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">42.0</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">No</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">789</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">58.0</td></tr><tr><td rowspan=\"7\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Family monthly income</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">≤1 MW</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">39</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.9</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">>1 to 2 MW</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">111</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8.2</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">>2 to 3 MW</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">199</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">14.6</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">>3 tp 5 MW</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">297</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">21.8</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">>5 to 10 MW</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">414</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">30.4</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">>10 to 20 MW</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">229</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">16.8</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">>20 MW</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">71</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">5.2</td></tr><tr><td rowspan=\"5\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Number of household members</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">140</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10.6</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">366</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">27.7</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">370</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">28.1</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">320</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">24.3</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">>5</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">123</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">9.3</td></tr><tr><td rowspan=\"6\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Area of Practice</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Clinic</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">327</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">24.0</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Foodservice administration</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">173</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">12.7</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Public health</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">117</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8.6</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Teaching</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">99</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7.3</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Others</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">72</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.3</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">More than one area of practice</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>572</italic>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>42.1</italic>\n</td></tr><tr><td rowspan=\"4\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Number of workplaces</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">858</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">63.1</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">353</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">26.0</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">85</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6.3</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">>3</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">64</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">4.7</td></tr><tr><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Type of institution where you finished your undergraduate degree</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Public</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">656</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">48.2</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Private</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">704</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">51.8</td></tr><tr><td rowspan=\"5\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Time from the undergraduate completion</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">≤2 years</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">288</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">21.2</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">>2 to 5 years</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">232</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">17.1</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">>5 to 10 years</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">255</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">18.8</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">>10 to 15 years</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">253</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">18.6</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">>15 years</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">332</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">24.4</td></tr><tr><td rowspan=\"4\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Do you continue working during SARS-COV-2?</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">no</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">220</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">16.2</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">yes remotely</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">486</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">35.7</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">yes in person with some adaptations</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">360</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">26.5</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">yes in person</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">294</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">21.6</td></tr><tr><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Did you test positive for SARS-COV-2?</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">No</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1304</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">96.0</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Yes</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">55</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">4.0</td></tr><tr><td rowspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Did any family members test positive for SARS-COV-2?</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">No</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1097</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">80.7</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Yes (does not live with me)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">69</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.1</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Yes (living with me)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">194</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">14.3</td></tr></tbody></table><table-wrap-foot><fn><p>MW—Minimum Wage in Brazil (June 7th 2020)—U$ 213.0.; y/o—years old.</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"ijerph-17-05541-t002\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05541-t002_Table 2</object-id><label>Table 2</label><caption><p>Wellbeing at work by socioeconomic and demographic variables of Brazilian dietitians before and during the pandemic period (<italic>n</italic> = 1359).</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th rowspan=\"2\" colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\">Variable</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin\" rowspan=\"1\" colspan=\"1\">Before * Pandemic</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin\" rowspan=\"1\" colspan=\"1\">During * Pandemic</th></tr><tr><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Mean ± SD</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Mean ± SD</th></tr></thead><tbody><tr><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Gender</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Female</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.88 <sup>a</sup> ± 0.71</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.70 <sup>a</sup> ± 0.78</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Male</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3.92 <sup>a</sup> ± 0.71</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3.79 <sup>a</sup> ± 0.79</td></tr><tr><td rowspan=\"7\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Age group</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">21 to 24 y/o</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.77 <sup>abc</sup> ± 0.77</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.63 <sup>a</sup> ± 0.82</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">25 to 29 y/o</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.79 <sup>abc</sup> ± 0.76</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.65 <sup>a</sup> ± 0.80</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">30 to 34 y/o</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.81 <sup>b</sup> ± 0.71</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.64 <sup>a</sup> ± 0.75</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">35 to 39 y/o</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.94 <sup>abc</sup> ± 0.65</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.77 <sup>ab</sup> ± 0.78</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">40 to 44 y/o</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.90 <sup>abc</sup> ± 0.70</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.71 <sup>ab</sup> ± 0.81</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">45 to 49 y/o</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.98 <sup>abc</sup> ± 0.63</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.75 <sup>ab</sup> ± 0.81</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">50 to older</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">4.05 <sup>ac</sup> ± 0.65</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3.86 <sup>b</sup> ± 0.73</td></tr><tr><td rowspan=\"5\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Brazilian region</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">North</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.76 <sup>a</sup> ± 0.79</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.61 <sup>a</sup> ± 0.83</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Northeast</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.86 <sup>ab</sup> ± 0.70</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.67 <sup>ab</sup> ± 0.77</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Midwest</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.89 <sup>ab</sup> ± 0.66</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.69 <sup>ab</sup> ± 0.76</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Southeast</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.95 <sup>b</sup> ± 0.73</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.81 <sup>b</sup> ± 0.80</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">South</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3.98 <sup>b</sup> ± 0.64</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3.80 <sup>ab</sup> ± 0.76</td></tr><tr><td rowspan=\"5\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Religion</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Catholic</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.91 <sup>a</sup> ± 0.69</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.73 <sup>abc</sup> ± 0.77</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Protestant</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.72 <sup>b</sup> ± 0.77</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.59 <sup>b</sup> ± 0.82</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Spiritism</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.99 <sup>a</sup> ± 0.64</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.83 <sup>c</sup> ± 0.74</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Agnostic</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.89 <sup>ab</sup> ± 0.70</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.71 <sup>abc</sup> ± 0.80</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Others</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3.88 <sup>ab</sup> ± 0.78</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3.68 <sup>abc</sup> ± 0.88</td></tr><tr><td rowspan=\"4\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Level of education (highest degree)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Graduate</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.75 <sup>a</sup> ± 0.75</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.57 <sup>a</sup> ± 0.80</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Specialization/Residency</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.85 <sup>ab</sup> ± 0.71</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.68 <sup>ab</sup> ± 0.79</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Master’s</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.97 <sup>b</sup> ± 0.68</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.78 <sup>b</sup> ± 0.77</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">PhD</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.17 <sup>c</sup> ± 0.55</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.01 <sup>c</sup> ± 0.65</td></tr><tr><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Marital status</td><td align=\"center\" valign=\"middle\" style=\"border-top:solid thin\" rowspan=\"1\" colspan=\"1\">Without partner</td><td align=\"center\" valign=\"middle\" style=\"border-top:solid thin\" rowspan=\"1\" colspan=\"1\">3.79 <sup>a</sup> ± 0.74</td><td align=\"center\" valign=\"middle\" style=\"border-top:solid thin\" rowspan=\"1\" colspan=\"1\">3.60 <sup>a</sup> ± 0.81</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">With partner</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.93 <sup>b</sup> ± 0.69</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.77 <sup>b</sup> ± 0.76</td></tr><tr><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Children</td><td align=\"center\" valign=\"middle\" style=\"border-top:solid thin\" rowspan=\"1\" colspan=\"1\">Yes</td><td align=\"center\" valign=\"middle\" style=\"border-top:solid thin\" rowspan=\"1\" colspan=\"1\">3.94 <sup>a</sup> ± 0.68</td><td align=\"center\" valign=\"middle\" style=\"border-top:solid thin\" rowspan=\"1\" colspan=\"1\">3.77 <sup>a</sup> ± 0.75</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">No</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3.84 <sup>b</sup> ± 0.72</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3.66 <sup>b</sup> ± 0.80</td></tr><tr><td rowspan=\"7\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Family monthly income</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">≤1 MW</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.49 <sup>a</sup> ± 0.85</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.29 <sup>a</sup> ± 0.90</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">>1 to 2 MW</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.53 <sup>a</sup> ± 0.80</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.33 <sup>a</sup> ± 0.83</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">>2 to 3 MW</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.74 <sup>a</sup> ± 0.79</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.55 <sup>a</sup> ± 0.84</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">>3 to 5 MW</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.81 <sup>a</sup> ± 0.67</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.61 <sup>b</sup> ± 0.78</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">>5 to 10 MW</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.01 <sup>b</sup> ± 0.63</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.86 <sup>c</sup> ± 0.71</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">>10 to 20 MW</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.06 <sup>b</sup> ± 0.61</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.91 <sup>c</sup> ± 0.70</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">>20 MW</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">4.01 <sup>b</sup> ± 0.69</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3.85 <sup>c</sup> ± 0.78</td></tr><tr><td rowspan=\"6\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Area of Practice</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Clinic</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.87 <sup>a</sup> ± 0.73</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.72 <sup>ac</sup> ± 0.78</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Teaching</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.20 <sup>b</sup> ± 0.55</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.99 <sup>b</sup> ± 0.69</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Foodservice administration</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.73 <sup>a</sup> ± 0.76</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.58 <sup>ac</sup> ± 0.83</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Public health</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.75 <sup>a</sup> ± 0.67</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.49 <sup>ac</sup> ± 0.77</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">More than one area of practice</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.90 <sup>a</sup> ± 0.71</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.74 <sup>ab</sup> ± 0.78</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Others</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3.90 <sup>a</sup> ± 0.62</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3.69 <sup>ac</sup> ± 0.75</td></tr><tr><td rowspan=\"4\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Number of workplaces</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3.85 <sup>a</sup> ± 0.72</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3.67 <sup>a</sup> ± 0.79</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.90 <sup>a</sup> ± 0.68</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.74 <sup>a</sup> ± 0.75</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.99 <sup>a</sup> ± 0.74</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.82 <sup>a</sup> ± 0.78</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">>3</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">4.02 <sup>a</sup> ± 0.67</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3.82 <sup>a</sup> ± 0.82</td></tr><tr><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Type of institution where you finished your undergraduate degree</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Public</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.92 <sup>a</sup> ± 0.67</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.74 <sup>a</sup> ± 0.73</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Private</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3.84 <sup>b</sup> ± 0.74</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3.68 <sup>a</sup> ± 0.83</td></tr><tr><td rowspan=\"5\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Time from the undergraduate completion</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">≤2 years</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.76 <sup>a</sup> ± 0.74</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.59 <sup>a</sup> ± 0.83</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">>2 to 5 years</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.80 <sup>a</sup> ± 0.77</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.66 <sup>ab</sup> ± 0.79</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">>5 to 10 years</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.85 <sup>a</sup> ± 0.72</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.68 <sup>ab</sup> ± 0.79</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">>10 to 15 years</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.93 <sup>ab</sup> ± 0.70</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.76 <sup>ab</sup> ± 0.80</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">>15 years</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">4.03 <sup>b</sup> ± 0.60</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3.83 <sup>b</sup> ± 0.71</td></tr><tr><td rowspan=\"4\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Do you continue working during SARS-COV-2?</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">No</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.65 *8 ± 0.76</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.33 <sup>a</sup> ± 0.85</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Yes, in person</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.80 *8 ± 0.68</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.65 <sup>b</sup> ± 0.72</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Yes, in person with some adaptations</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.90 *8 ± 0.72</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.74 <sup>b</sup> ± 0.79</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">yes remotely</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">4.02 *8 ± 0.66</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3.89 <sup>c</sup> ± 0.72</td></tr><tr><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Did you test positive for SARS-COV-2?</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">No</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.89 * ± 0.71</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.71 <sup>a</sup> ± 0.79</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Yes</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3.76 * ± 0.71</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3.65 <sup>a</sup> ± 0.70</td></tr><tr><td rowspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Did any family members test positive for SARS-COV-2?</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">No</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.89 * ± 0.70</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.71 <sup>a</sup> ± 0.78</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Yes (does not live with me)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.88 * ± 0.64</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.74 <sup>a</sup> ± 0.67</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Yes (living with me)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3.84 * ± 0.79</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3.68 <sup>a</sup> ± 0.83</td></tr></tbody></table><table-wrap-foot><fn><p>* Comparison between before the pandemic and during the pandemic showed statistical differences for all variables (worse during the pandemic period); Different lowercase letters inside each column and for each variable show statistically different results (<italic>p</italic> < 0.05); y/o—years old.; MW—Minimum Wage in Brazil (June 7th 2020)—U$ 213.0.</p></fn></table-wrap-foot></table-wrap></floats-group></article>\n"
]
[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Int J Environ Res Public Health</journal-id><journal-id journal-id-type=\"iso-abbrev\">Int J Environ Res Public Health</journal-id><journal-id journal-id-type=\"publisher-id\">ijerph</journal-id><journal-title-group><journal-title>International Journal of Environmental Research and Public Health</journal-title></journal-title-group><issn pub-type=\"ppub\">1661-7827</issn><issn pub-type=\"epub\">1660-4601</issn><publisher><publisher-name>MDPI</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32717868</article-id><article-id pub-id-type=\"pmc\">PMC7432097</article-id><article-id pub-id-type=\"doi\">10.3390/ijerph17155300</article-id><article-id pub-id-type=\"publisher-id\">ijerph-17-05300</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Article</subject></subj-group></article-categories><title-group><article-title>Assessment of Attitudes Toward Physical Education by the Implementation of an Extracurricular Program for Obese Children</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Romero-Pérez</surname><given-names>Ena Monserrat</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05300\">1</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0001-6573-6762</contrib-id><name><surname>Núñez Enríquez</surname><given-names>Oscar</given-names></name><xref ref-type=\"aff\" rid=\"af2-ijerph-17-05300\">2</xref><xref rid=\"c1-ijerph-17-05300\" ref-type=\"corresp\">*</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0002-8931-1125</contrib-id><name><surname>Gastélum-Cuadras</surname><given-names>Gabriel</given-names></name><xref ref-type=\"aff\" rid=\"af2-ijerph-17-05300\">2</xref></contrib><contrib contrib-type=\"author\"><name><surname>Horta-Gim</surname><given-names>Mario Alberto</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05300\">1</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0002-7298-9060</contrib-id><name><surname>González-Bernal</surname><given-names>Jerónimo J</given-names></name><xref ref-type=\"aff\" rid=\"af3-ijerph-17-05300\">3</xref><xref rid=\"c1-ijerph-17-05300\" ref-type=\"corresp\">*</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0002-4389-1777</contrib-id><name><surname>de Paz</surname><given-names>José Antonio</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05300\">1</xref><xref ref-type=\"aff\" rid=\"af4-ijerph-17-05300\">4</xref></contrib></contrib-group><aff id=\"af1-ijerph-17-05300\"><label>1</label>Division of Biological Sciences and Health, University of Sonora, 83000 Hermosillo, Sonora, Mexico; <email>[email protected]</email> (E.M.R.-P.); <email>[email protected]</email> (M.A.H.-G.); <email>[email protected]</email> (J.A.d.P.)</aff><aff id=\"af2-ijerph-17-05300\"><label>2</label>Faculty of Physical Culture Sciences, Autonomous University of Chihuahua, 31000 Chihuahua, Chih, Mexico; <email>[email protected]</email></aff><aff id=\"af3-ijerph-17-05300\"><label>3</label>Department of Health Sciences, University of Burgos, 09001 Burgos, Spain</aff><aff id=\"af4-ijerph-17-05300\"><label>4</label>Institute of Biomedicine, University of León, 24071 León, Spain</aff><author-notes><corresp id=\"c1-ijerph-17-05300\"><label>*</label>Correspondence: <email>[email protected]</email> (O.N.E.); <email>[email protected]</email> (J.J.G.-B.); Tel.: +34-947-499-108 (J.J.G.-B.)</corresp></author-notes><pub-date pub-type=\"epub\"><day>23</day><month>7</month><year>2020</year></pub-date><pub-date pub-type=\"ppub\"><month>8</month><year>2020</year></pub-date><volume>17</volume><issue>15</issue><elocation-id>5300</elocation-id><history><date date-type=\"received\"><day>25</day><month>6</month><year>2020</year></date><date date-type=\"accepted\"><day>18</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>© 2020 by the authors.</copyright-statement><copyright-year>2020</copyright-year><license license-type=\"open-access\"><license-p>Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>).</license-p></license></permissions><abstract><p>The World Health Organization (WHO) identifies the importance of implementing physical activity programs such as physical education (PE) classes in schools. This study identifies the attitudes of obese children toward PE, before and after participation in a vigorous-intensity physical exercise program without the participation of normal-weight peers using a questionnaire on Attitudes toward Physical Education (CAEF). 98 children between 8–11 years of age were randomized in an Experimental Group (GE) (<italic>n</italic> = 48) and a Control Group (CG) (<italic>n</italic> = 47). They were assessed using a questionnaire on Attitudes toward Physical Education (CAEF). All the study participants exhibited a BMI Z-score ≥ 2. Before the intervention, the only difference between boys and girls was “empathy to teacher and physical education subject” (<italic>p</italic> = 0.001, d de Cohen = 0.72, r = 0.34). The interaction between gender and training was only present in empathy for the teacher, with a medium effect size (<italic>η</italic><sup>2</sup> = 0.055). The implementation of PE with two hours per week elicits only a few effects over the attitude of obese children, even though with a certain engagement of gender through training in the adjustment of empathy for teachers and the PE class.</p></abstract><kwd-group><kwd>childhood obesity</kwd><kwd>attitudes</kwd><kwd>moderate and vigorous activity</kwd><kwd>physical education</kwd></kwd-group></article-meta></front><body><sec sec-type=\"intro\" id=\"sec1-ijerph-17-05300\"><title>1. Introduction</title><p>Obesity has been defined as an increase in body weight resulting from excess fat, which in turn, jeopardizes health significantly [<xref rid=\"B1-ijerph-17-05300\" ref-type=\"bibr\">1</xref>]. It is a multifactorial disease caused by social, physiological, metabolic, genetic, and psychologic factors; excess food intake and reduced caloric output combine to increase the incidence in children, which rises every day [<xref rid=\"B2-ijerph-17-05300\" ref-type=\"bibr\">2</xref>]. Among all the health-related and psychological factors affected by obesity, it has been discussed that children and youth present a lack of energy and low self-esteem [<xref rid=\"B3-ijerph-17-05300\" ref-type=\"bibr\">3</xref>]. The National Health Survey in 2016 confirmed that Mexico has been documenting a growing tendency toward overweight and obesity in school children under the age of 11 years [<xref rid=\"B4-ijerph-17-05300\" ref-type=\"bibr\">4</xref>].</p><p>According to Weigley [<xref rid=\"B5-ijerph-17-05300\" ref-type=\"bibr\">5</xref>], the Body Mass Index (BMI) is a reasonable estimate of the accumulated fat in the pediatric population. It is, however, difficult to establish cut-off points for diagnosis in children since they undergo a constant height and weight evolution; therefore, for diagnosis, the comparative reference are children of the same age and gender, using percentile tables [<xref rid=\"B6-ijerph-17-05300\" ref-type=\"bibr\">6</xref>]. This confirms the diagnostic criteria for overweight as any percentile above 85 and obesity as any percentile above 95 [<xref rid=\"B7-ijerph-17-05300\" ref-type=\"bibr\">7</xref>]. The BMI z-score is also employed in the diagnosis of obesity in children [<xref rid=\"B8-ijerph-17-05300\" ref-type=\"bibr\">8</xref>] (number of standard deviations above or below the average value of BMI in regards to the group median of the same age and gender). The BMI z-score relates to the percentile, depending on the reference population; the 95 percentile usually corresponds to a BMI z-score of 1.68 [<xref rid=\"B9-ijerph-17-05300\" ref-type=\"bibr\">9</xref>]. However, the cut-off points used by the WHO for obesity in children are a BMI z-score of 2 [<xref rid=\"B10-ijerph-17-05300\" ref-type=\"bibr\">10</xref>], which has been used to define obesity in children in the study sample.</p><p>Several approaches have been used in Mexico as preventive measures according to the Mexican Official Norm [NOM] [<xref rid=\"B11-ijerph-17-05300\" ref-type=\"bibr\">11</xref>], which in its suggestions includes the design of low-calorie diet programs along with high-intensity physical activity (PA) as per the needs identified in a community. As such, it appears that the natural scenario, ideal for the application of designed protocols regarding obesity turns out to be the school setting, (by nature the place where a child spends a considerable number of hours a day interacting with a large number of peers) specifically in PE class [<xref rid=\"B12-ijerph-17-05300\" ref-type=\"bibr\">12</xref>,<xref rid=\"B13-ijerph-17-05300\" ref-type=\"bibr\">13</xref>,<xref rid=\"B14-ijerph-17-05300\" ref-type=\"bibr\">14</xref>,<xref rid=\"B15-ijerph-17-05300\" ref-type=\"bibr\">15</xref>]. This is because there is clear evidence presenting that enforced programs in the schools exert a positive effect on the conduct and healthy lifestyles [<xref rid=\"B16-ijerph-17-05300\" ref-type=\"bibr\">16</xref>,<xref rid=\"B17-ijerph-17-05300\" ref-type=\"bibr\">17</xref>,<xref rid=\"B18-ijerph-17-05300\" ref-type=\"bibr\">18</xref>].</p><p>Since its inclusion in the school curriculums, PE has been strongly linked to health [<xref rid=\"B13-ijerph-17-05300\" ref-type=\"bibr\">13</xref>]. Moreover, this association allowed an understanding of the health benefits and also helped recognize the barriers faced by children and youth engaging in PA settings like physical education. In Mexico, elementary education is provided from first grade (6 years old) to sixth grade (11 years old). Studies demonstrate this stage to be of utmost importance as the acquisition of most healthy habits occurs at this stage [<xref rid=\"B19-ijerph-17-05300\" ref-type=\"bibr\">19</xref>,<xref rid=\"B20-ijerph-17-05300\" ref-type=\"bibr\">20</xref>,<xref rid=\"B21-ijerph-17-05300\" ref-type=\"bibr\">21</xref>]. PE class turns out to be a favorable environment for the enforcement of these habits within the school schedule [<xref rid=\"B22-ijerph-17-05300\" ref-type=\"bibr\">22</xref>]. It is important to mention that the PE teacher should be one of the main agents of change to facilitate the adoption of healthy habits. The pedagogic approach in these settings must encourage and demonstrate a promising attitude as a way to acquire healthy habits. However, in most cases, a good number of PE classes do not have such emphasis [<xref rid=\"B23-ijerph-17-05300\" ref-type=\"bibr\">23</xref>].</p><p>According to Escarti [<xref rid=\"B24-ijerph-17-05300\" ref-type=\"bibr\">24</xref>], PE class must be a tool for facilitating instead of suppressing behavior. This is consistent with Kirk’s [<xref rid=\"B25-ijerph-17-05300\" ref-type=\"bibr\">25</xref>] work, who mentioned that a boring and unexciting environment forms a barrier for children and youth in PE. This means a PE setting should be a space where personal and collective agreements must be made for the achievement of healthy habits within school premises. Thus, having an impact on attitudes that create benefits in the development of school children at a psychological level, in their lifestyles, and their emotional and social aspects is a pre-requisite [<xref rid=\"B17-ijerph-17-05300\" ref-type=\"bibr\">17</xref>].</p><p>In a PE class, obese students may face specific issues that do not represent a definite personality disorder, although, such children more often show a higher rate of psychological barriers in comparison to children with normal weight [<xref rid=\"B26-ijerph-17-05300\" ref-type=\"bibr\">26</xref>]. Children with obesity tend to show feelings of low self-worth and self-limitations facing social isolation, and stigmatization; sometimes they also develop a feeling that the PE class is a hostile environment where they are victims of bullying and disrespect, thus creating negativity in respect to their self-esteem and body image [<xref rid=\"B3-ijerph-17-05300\" ref-type=\"bibr\">3</xref>,<xref rid=\"B27-ijerph-17-05300\" ref-type=\"bibr\">27</xref>,<xref rid=\"B28-ijerph-17-05300\" ref-type=\"bibr\">28</xref>]. This situation can either be cared for or worsened by the perception of physical education classes these students have, as in case of an inadequate balance between the cooperative learning between teachers and students.</p><p>The objectives of the present study include identification of the attitudes of obese boys and girls toward PE class; an analysis of modifications in attitudes of obese boys and girls toward the PE class after participating in a vigorous-intensity physical exercise program without normal-weight peers; and in case of changes, to determine if these changes are the same in boys and girls.</p></sec><sec id=\"sec2-ijerph-17-05300\"><title>2. Material and Methods</title><p>A total of 104 school children were grouped in an experimental and a control group separately, both with children with obesity. The <italic>Questionnaire for attitudes toward physical education</italic> (CAEF) was used to screen the study participants for a 20-week pre- and post-intervention period [<xref rid=\"B29-ijerph-17-05300\" ref-type=\"bibr\">29</xref>]. This questionnaire was applied in both groups before and after of intervention.</p><sec id=\"sec2dot1-ijerph-17-05300\"><title>2.1. Sample</title><p>The study participants comprised a total of 104 school children from 3 different elementary schools in the city of Hermosillo, Sonora, and Mexico, aged 8 to 11 years. However, only 95 participants completed the study. The participants had a BMI z-score > 2 and were randomly assigned to either an experimental group (<italic>n</italic> = 48) and a control group (<italic>n</italic> = 47) (boys and girls separately), using a computer-generated block randomization sequence (block sizes of 4) (as shown in <xref rid=\"ijerph-17-05300-t001\" ref-type=\"table\">Table 1</xref>). An open invitation was given from the research group to the principals of the schools. As a way to raise awareness on the topic, the open invitation included one presentation per school (three in total) highlighting the benefits of exercise and the risks involved in being overweight and obese during school age. The experimental group was engaged in an exercise program. It was suggested to the control group to engage during the intervention in their regular daily activities; however, the group was offered to enroll in the same exercise program once the 2-week evaluation was complete. The inclusion criteria necessitated the diagnosis of clinical obesity according to the criteria of the WHO at the time of the study, based on a standard deviation of BMI z-score > 2. The exclusion criteria were based on the presence of every single type of disability in the participants that deterred them from performing any physical activities and the presence of chronic conditions like hypothyroidism and uncontrolled juvenile diabetes Type 1. Informed consent was obtained from both the parents and the children. The Bioethics Committee of the University of Sonora supported the study (Reg. DMCS/CBIDMCS/D21).</p></sec><sec id=\"sec2dot2-ijerph-17-05300\"><title>2.2. Exercise Program</title><p>The exercise program was implemented 2 times per week for 20 weeks. A total of 40 sessions for 60 min each were completed and involved varied aerobic and strength activities that worked on conditional and coordination skills, physical displacements with different length and intensity, throws of balls with hands and legs, jumps, and physical activities with the opposition without complete pause between the different activities, at an average intensity during the session with a 79.8% of their maximum heart rate, (estimated by the formula, HRmax = 220-age), which was monitored with a POLAR Model FT7 heart rate monitor (Seppo Säynäjäkangas, Kempele, Filand). These sessions were held in addition to the regular PE classes already established in their school program and only children with obesity participated in the activities of this program.</p><p>The exercise program was conducted in the participant’s school. The exercises were taught by the first author and researcher, who is not their regular PE teacher. The program was implemented on separate days such that it did not interfere with their weekly mandatory PE class.</p></sec><sec id=\"sec2dot3-ijerph-17-05300\"><title>2.3. Instruments</title><p>The participants were weighed on a Model 803 digital SECA scale (seca gmbh & co. kg, Hamburg, Germany), with a maximum capacity of 150 kg and a sensitivity of d = 0.1 kg. For measurement of height, a Seca<sup>®</sup> 213 (seca gmbh & co. kg, Hamburg, Germany) measuring rod with a maximum height of 2 m (6.5 feet) was used. The BMI was calculated using the data thus obtained according to the following formula: Weight/size<sup>2</sup> (kg/m<sup>2</sup>). BMI z-score based on age and gender was calculated using the WHO software version 3.2., 2011 (WHO and UNICEF, Genève, Switzerland) [<xref rid=\"B30-ijerph-17-05300\" ref-type=\"bibr\">30</xref>].</p></sec><sec id=\"sec2dot4-ijerph-17-05300\"><title>2.4. Physical Capacity</title><p>The physical capacity was assessed by evaluating Curl Up, Shoulder stretch, Push up, Trunk lift, performed using the Fitnessgram methodology [<xref rid=\"B31-ijerph-17-05300\" ref-type=\"bibr\">31</xref>], along with horizontal jump feet together with a run time of 400 m(s).</p></sec><sec id=\"sec2dot5-ijerph-17-05300\"><title>2.5. Attitudes toward Physical Education</title><p>Questionnaire for attitudes toward physical education [CAEF] [<xref rid=\"B29-ijerph-17-05300\" ref-type=\"bibr\">29</xref>]: It consists of 56 items wherein the students are enquired about their degree of satisfaction; they can answer on a scale of 1 to 4 where 1 denotes total disagreement and 4 signifies total agreement. This questionnaire has been applied in different studies [<xref rid=\"B32-ijerph-17-05300\" ref-type=\"bibr\">32</xref>,<xref rid=\"B33-ijerph-17-05300\" ref-type=\"bibr\">33</xref>,<xref rid=\"B34-ijerph-17-05300\" ref-type=\"bibr\">34</xref>,<xref rid=\"B35-ijerph-17-05300\" ref-type=\"bibr\">35</xref>]. Puhl and Heuer [<xref rid=\"B35-ijerph-17-05300\" ref-type=\"bibr\">35</xref>] mention that children with obesity tend to be bullied in PE class, which in turn affects their future engagement in a healthy lifestyle. Contreras et al. [<xref rid=\"B32-ijerph-17-05300\" ref-type=\"bibr\">32</xref>] mention similarities stating that peer interaction directly affects their personality development. However, there is no literature about the use of this instrument in a Mexican PE context as yet.</p><p>The instrument considers the range of attitudes toward PE in 7 hypothetical factors that are related to the corresponding items for each point.</p><list list-type=\"simple\"><list-item><p>Assessment of the most essential aspect of the PE, i.e., faculty and subject.</p></list-item><list-item><p>Subject acceptance in terms of its comparison with other subjects.</p></list-item><list-item><p>The validity of the subject and its contents for the integral formation of the student body.</p></list-item><list-item><p>Concerns of PE faculty toward the students.</p></list-item><list-item><p>Performance of PE subject.</p></list-item><list-item><p>Attitude toward PE and sports.</p></list-item><list-item><p>Comparison between PE and sports.</p></list-item></list></sec><sec id=\"sec2dot6-ijerph-17-05300\"><title>2.6. Statistical Analysis</title><p>Statistical analysis was performed with SPSS v.23.0 (SPSS Inc., IBM, Armonk, NY, USA). Data are expressed as mean ± standard deviation. The Kolmogorov–Smirnov test was performed to test the normality assumption. Student’s independent t-tests were used to examine differences between groups and Student’s paired t-tests for pre-post means within each group, and Cohen’s d was used to measure the effect size. The main outcome measure, the modification of attitude of obese boys and girls toward PE classes, was analyzed with the two-way ANOVA (sex and intervention group), interaction analysis partial eta squared (<italic>η</italic><sup>2</sup>) as a measure of effect size. A level of significance was established at <italic>p</italic> < 0.05.</p></sec></sec><sec sec-type=\"results\" id=\"sec3-ijerph-17-05300\"><title>3. Results</title><p><xref rid=\"ijerph-17-05300-t002\" ref-type=\"table\">Table 2</xref> depicts the values of participants related to age, weight, size, BMI, BMI z-score, and abdominal perimeter. However, no differences were observed in the baseline values between boys and girls.</p><p><xref rid=\"ijerph-17-05300-t003\" ref-type=\"table\">Table 3</xref>, shows the results of the physical condition tests, before and after the intervention. In EG changes are observed in Curl Up, Shoulder strech and 400 m. In the remaining tests, there were significant changes in both groups.</p><p><xref ref-type=\"fig\" rid=\"ijerph-17-05300-f001\">Figure 1</xref> shows the characteristic elements of the PA program concerning the participating children’s average heart rate throughout the 40 sessions for exercise during 20 weeks.</p><p><xref rid=\"ijerph-17-05300-t004\" ref-type=\"table\">Table 4</xref> presents the data collected from the different items on the CAEF questionnaire for boys and girls in the baseline; the only difference between them was in the item relating to “the empathy for the teacher and the PE subject”. This factor contains items that describe the children’s ability to establish a good relationship with their PE teacher by identifying a number of his/her traits such as the degree of concern shown toward his/her students, the PE teacher is more “fun” than other teachers, or the student has a better relationship with the PE teacher than with the rest of the teachers.</p><p><xref rid=\"ijerph-17-05300-t005\" ref-type=\"table\">Table 5</xref> illustrates the values for different items of the questionnaire in all the groups, both before and after the period of intervention. Girls from the GC show a significant difference in the pre and post results on their assessment of the PE subject and teacher, whereas boys from the GC show empathy for the PE teacher and subject.</p><p><xref rid=\"ijerph-17-05300-t005\" ref-type=\"table\">Table 5</xref> shows the values for the different factors of the questionnaire in all groups, both before and after the intervention period, and the interaction effect of intervention and gender. The organization and concordance results are unchanged and very similar for the control groups; for the experimental groups, both girls and boys showed a decrease in the initial score.</p><p>In the difficulty factor, the scores were interpreted in reverse, and it was observed that girls from both the groups (GE and GC) showed higher levels than boys; so for them, the PE class was not difficult; however, after the exercise, the values decreased for girls in the GE, which can be interpreted as an increased perception for the difficulty of the class.</p><p>Regarding the empathy factor, the girls in GC did not reflect variations in their scores over time, while the girls in the GE, on the contrary, showed a decrease in results after participating in the PE program, i.e., they reported lowered empathy. Children in both groups acquired quite similar values and modified the teacher’s perception.</p><p>In the factor that evaluated physical education as a sport, the participants in the GC showed no difference in these concepts. For girls in the GE, conceptualization about EF class and sport was interchangeable after exercise, whereas boys in this group had similar views but with higher scores, not establishing a clear differentiation between the two activities.</p><p>In the preference for PE and Sport in GC vs. GE, participants in GE indicated a decrease in their preference for this subject after the exercise. Boys in the GC had lower values than girls, and the values did not change in post-major. On the other hand, despite having higher values at the beginning of the intervention, participants from the GE showed a tendency of the degree of preference for the PE and sport to worsen after the intervention, significantly in girls.</p><p>The usefulness of PE on evaluation reflected the little worth perceived by children from both the groups. However, in children from the GE, a change was reflected after the exercise that was not quite significant, where possible for that group of children; PE is an important subject the usefulness of which would be reflected in the future.</p><p>After the intervention period, girls and boys from the GE showed significant changes in their assessment of the PE class. This aspect grouped those test items in which students assigned either more or less importance, variations in degrees of satisfaction with their PE class, whether they consider that the knowledge they are receiving is necessary and important, how fair and unbiased they feel the teacher’s evaluations are, the motivation provided by the teacher, the use of educational materials, and the equal treatment shown by the teacher for boys and girls.</p><p>Concerning the subject’s degree of difficulty and the preference for PE and sports, the girls from the GE showed significant differences after the training session. This difficulty was in comparison with other subjects that the children studied every day, and it also involved items related to how easy the children thought it was to pass the course in comparison with other subjects. Their values were interpreted in reverse; therefore, after the moderate to vigorous training, the girls from the GE expressed that the PE class had a higher degree of difficulty after the added training sessions.</p><p>Regarding the preference shown by boys and girls toward PE class, significant changes were observed in the girls’ GE after the training since they now preferred to be physically active than to be with friends or watch TV. The remaining factors assessed did not show any differences between the boys and girls as far as gender is concerned.</p><p>On analyzing the interaction between gender and training, there was no interaction, except on the empathy for the PE teacher and subject. However, since the effect size (partial eta squared) is considered small when the partial eta squared value is ≤0.02, medium when the value is ≤0.06, and large when the value is ≤0.26, it should be emphasized that even though this interaction is significant, it is also medium and by itself does not explain nine percent of the differences.</p><p><xref rid=\"ijerph-17-05300-t005\" ref-type=\"table\">Table 5</xref> demonstrates the evaluations of physical tests carried out. Improvements were observed in both the groups in which the lumbar force, the articular amplitude of the shoulder, and the upper and lower extremities were evaluated along with the abdominal force. Although the greatest improvements (delta: post-pre value) were presented in the EG, in the run time of 400 m, the abdominal and lumbar force, as well as a greater decrease in the sum of the cutaneous folds, was noted.</p></sec><sec sec-type=\"discussion\" id=\"sec4-ijerph-17-05300\"><title>4. Discussion</title><p>The improvement in physical fitness was not one of the main objectives of the study, although the PE has been observed to have brought changes in some aspects of physical fitness. Some of these changes could be attributed to the growth and maturation of these children throughout the 20 weeks of the intervention as the GC also showed improvements in the manifestations of upper and lower extremity strength, lumbar strength, shoulder joint width, and decrease in the summation of skin folds. These changes occur widely in the manifestations of physical condition throughout growth [<xref rid=\"B36-ijerph-17-05300\" ref-type=\"bibr\">36</xref>].</p><p>However, the group of children in whom exercise sessions were implemented twice a week showed a higher gain in abdominal strength, 400-m performance, and a greater decrease in subcutaneous fat. Nevertheless, the main results of this research differ from those of Moreno, Rodriguez, and Gutierrez [<xref rid=\"B29-ijerph-17-05300\" ref-type=\"bibr\">29</xref>] and Cook-Cottone, Case, and Feeley [<xref rid=\"B37-ijerph-17-05300\" ref-type=\"bibr\">37</xref>] where boys showed a clear preference for attending PE classes in comparison to girls. This is similar to Breslin et al. [<xref rid=\"B38-ijerph-17-05300\" ref-type=\"bibr\">38</xref>] where they mention that PA is pleasing to both genders; however, vigorous activities requiring a considerable effort are not appealing to girls.</p><sec><title>Pre-Post Intragroup Questionnaire</title><p>To know the degree to which the differences found between the pre-and-post values of the group that participated in the intervention are attributable to the exercise program, intragroup comparisons were made. On analyzing variations in attitudes toward PE as assessed by the CAEF Questionnaire, it was observed that the consistency with which the teacher organized the class, perception of the teacher’s attire, the time of taking classes, and the fact that the program lacked greater practicality for the group of children show a significant change (<italic>p</italic> < 0.05), with a significant size of the effect on boys (Cohen’s d-0.72, r-0.52) and girls (Cohen’s d-0.50, r-0.25) essentially, 52% and 25% of pre-post differences in boys and girls, respectively, were explained by exercise intervention.</p><p>The perception of the degree of difficulty in the PE class was evaluated on the level of effort that represented to accredit the contents of the subject in comparison to other classes. It was inversely graded and the results showed a significant change (<italic>p</italic> < 0.05) before and after the intervention in the girls of the GE and the size of the effect (Cohen’s d-0.61, r-0.38). This points out that 38% of these changes could have been developed by the physical exercise program.</p><p>The factor related to the degree of preference for PE and sport (relating to the conceptualization that the student has about these two ideas, which sometimes are understood as synonyms) showed how the girls of GE had significant changes (<italic>p</italic> < 0.05) with an effect size (Cohen’s d = 73, r = 54) that determines how 54% of these changes are attributable to the intervention.</p><p>The usefulness of physical education was evaluated through items that questioned the validity of contents during the integral training of the student and were obtained from answers such as “Physical education is boring”, “what I learn in physical education is useless”, etc. It is evaluated in reverse so that high scores on the scale such as those shown by this study, reflect the little usefulness perceived by children of both the groups in this subject. Only the girls in the GC exhibited a significant change (<italic>p</italic> < 0.05) and showed a size of the effect (Cohen’s d-0.12, r-0.02), and hence, only 2% of these changes should be attributed to the passage of time since they did not participate in the exercise program.</p><p>PE elements such as sport and PE utility are factors that showed no significant changes before and after the program, regardless of gender.</p><p>The factor empathy reflects the teacher’s ability to engage with the students, considering the teacher “more fun” or the “teacher with whom they relate better than the rest”.</p><p>Regarding the attitude before the PE class, only differences in empathy toward the teacher were observed (<italic>p =</italic> 0.001) between the boys and girls, with a small effect size (Cohen’s d = 0.72, r <italic>=</italic> 0.34) before the intervention. This means that only 34% of the differences are explained by gender. Regarding the perceived difficulty in PE class, even if not significant, a change in the GE after participating in the PE program was noted; a similar result was obtained with the factor relating to the preference for PE and sports in the trained GE. It is important to mention that difficulty in the perception of the PE class is a factor that children consider to engage and increase their performance [<xref rid=\"B39-ijerph-17-05300\" ref-type=\"bibr\">39</xref>]. The only factor showing significant changes in the boys and girls from the GE before and after a vigorous exercise program is the one related to empathy for the PE teacher and subject; these results are in accordance with those obtained by Mowatt, DePaw, and Hulac [<xref rid=\"B40-ijerph-17-05300\" ref-type=\"bibr\">40</xref>].</p><p>However, the parameters of evaluation on the part of the students have changed, showing the importance of the teacher’s performance for this purpose in the process of coordinated learning as well as the perception of support in certain psychological needs such as autonomy and social relations and also the role that the teacher plays as a facilitator for these [<xref rid=\"B41-ijerph-17-05300\" ref-type=\"bibr\">41</xref>].</p><p>The recent years have seen a renewed appreciation for the importance of PE class in school settings; the PE teachers have sought specialization and professionalization of their practice, and therefore, the students’ appreciation for the subject holds immense importance. The same was established by O’Brien, Hunter, and Banks [<xref rid=\"B42-ijerph-17-05300\" ref-type=\"bibr\">42</xref>] clearly showing that the main objective of PE classes is to generate positive attitudes and interest toward it.</p><p>The literature now reports strong scientific evidence on the importance of promoting PA programs [<xref rid=\"B17-ijerph-17-05300\" ref-type=\"bibr\">17</xref>,<xref rid=\"B43-ijerph-17-05300\" ref-type=\"bibr\">43</xref>,<xref rid=\"B44-ijerph-17-05300\" ref-type=\"bibr\">44</xref>]. The present research also includes designing programs promoting PE among obese children, which has helped focus on school settings where PE classes are carried out [<xref rid=\"B43-ijerph-17-05300\" ref-type=\"bibr\">43</xref>]. This suggests, that PE is an ideal place to utilize an appropriate curriculum that is attractive to children, in this case with obesity, promoting engagement in PA for a lifetime [<xref rid=\"B25-ijerph-17-05300\" ref-type=\"bibr\">25</xref>,<xref rid=\"B44-ijerph-17-05300\" ref-type=\"bibr\">44</xref>]. It also encourages the analysis of different ways of delivering the planned content, implementing an approach that is not only based on the teacher–student relationship [<xref rid=\"B45-ijerph-17-05300\" ref-type=\"bibr\">45</xref>,<xref rid=\"B46-ijerph-17-05300\" ref-type=\"bibr\">46</xref>] but one that promotes a surrounding where all students feel comfortable sharing their abilities and possibilities to create an appropriate pedagogical environment that would help them become active for a lifetime.</p><p>Undoubtedly, it would be of great interest to analyze the level of motivation that students possess during the class, in subsequent studies, considering this aspect as a factor that generates a positive attitude and favors participation, especially when it comes to overweight and obese populations.</p><p>It is pertinent to highlight the importance of the role played by the PE teacher in promoting positive experiences within the class, which guarantees adherence to the development and maintenance of physical-sports habits [<xref rid=\"B47-ijerph-17-05300\" ref-type=\"bibr\">47</xref>,<xref rid=\"B48-ijerph-17-05300\" ref-type=\"bibr\">48</xref>]. This work possesses some limitations, the first one being reduced intervention time, while lack of sensitiveness in the measuring instrument is the second one as insufficient sensitivity of the instrument in detecting change could prove to be a major drawback. Perhaps, adding an interview process would help understand the attitudes toward physical education class in a deeper sense. Besides, the use of heart rate monitoring may not be the best way to quantify the intensity of exercise in children.</p></sec></sec><sec sec-type=\"conclusions\" id=\"sec5-ijerph-17-05300\"><title>5. Conclusions</title><p>This paper presents answers to two questions raised by researchers. In regards to whether the training generates change, it can be proved that implementing PE classes through an additional two-hour weekly exercise program of vigorous intensity for obese children only produces little effects on the students’ attitude toward PE. This effect is regardless of gender, even though there is a clear gender interaction in the training, as the only change observed was in the empathy for the teacher and the PE class. However, it is essential to point out that certain limitations such as the location of the school and school-time form barriers for further exploration of different possibilities among obese children in terms of implementation of extra hours every week.</p><p>However, this also helped understand that the PE teacher and/or facilitator must use empathy to his/her favor and their good relationship with the students to increase their sensitivity and knowledge. Utilization of an attractive curriculum where the teacher/facilitator designs activities that motivate and encourage students’ participation in intense physical exercises to establish a program with motivating strategies that would promote more participation of obese children can best be implemented by PE classes.</p></sec></body><back><notes><title>Author Contributions</title><p>E.M.R.-P., O.N.E., and J.A.d.P. were responsible for the conception and design of the study. G.G.-C. and M.A.H.-G. were responsible for the literature review. G.G.-C. and M.A.H.-G. designed the exercise program. J.A.d.P. and J.J.G.-B. were responsible for the analysis and interpretation of the data. E.M.R.-P., O.N.E., and G.G.-C. were responsible for drafting the manuscript. E.M.R.-P., O.N.E., and J.J.G.-B. were responsible for critical revision of the article and final approval of the manuscript. All authors have read and agreed to the published version of the manuscript.</p></notes><notes><title>Funding</title><p>This research received no external funding.</p></notes><notes notes-type=\"COI-statement\"><title>Conflicts of Interest</title><p>The authors declare no conflict of interest.</p></notes><ref-list><title>References</title><ref id=\"B1-ijerph-17-05300\"><label>1.</label><element-citation publication-type=\"web\"><person-group person-group-type=\"author\"><collab>World Health Organization</collab></person-group><article-title>Obesity: Preventing and Managing the Global Epidemic</article-title><year>2000</year><comment>Available online: <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.who.int/nutrition/publications/obesity/WHO_TRS_894/en/\">https://www.who.int/nutrition/publications/obesity/WHO_TRS_894/en/</ext-link></comment><date-in-citation content-type=\"access-date\" iso-8601-date=\"2020-01-15\">(accessed on 15 January 2020)</date-in-citation></element-citation></ref><ref id=\"B2-ijerph-17-05300\"><label>2.</label><element-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Onis</surname><given-names>M.</given-names></name><name><surname>Blossner</surname><given-names>M.</given-names></name><name><surname>Borghi</surname><given-names>E.</given-names></name></person-group><article-title>Global prevalence and trends of overweight and obesity among preschool children</article-title><source>Am. 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Educ. Física Deporte Recreación</source><year>2017</year><volume>31</volume><fpage>98</fpage><lpage>102</lpage></element-citation></ref></ref-list></back><floats-group><fig id=\"ijerph-17-05300-f001\" orientation=\"portrait\" position=\"float\"><label>Figure 1</label><caption><p>Mean heart rate per session.</p></caption><graphic xlink:href=\"ijerph-17-05300-g001\"/></fig><table-wrap id=\"ijerph-17-05300-t001\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05300-t001_Table 1</object-id><label>Table 1</label><caption><p>Depiction of the sample according to age and group.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</th><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Control Group (CG)</th><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Experimental Group (EG)</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Age (year)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">8–9</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">10–11</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">8–9</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">10–11</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">(<italic>n</italic>)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">25</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">22</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">25</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">23</td></tr></tbody></table></table-wrap><table-wrap id=\"ijerph-17-05300-t002\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05300-t002_Table 2</object-id><label>Table 2</label><caption><p>Age and anthropometric characteristics of girls and boys in the sample (Mean and SD).</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Variable</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Girls</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Boys</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>p</italic>\n</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Age (year)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">10.3(0.8)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">10.4(0.9)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.684</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Weight (kg)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">53.4(11.6)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">54.0(9.6)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.284</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Height (m)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">139.5(9.1)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">142.1(7.6)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.138</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">BMI (kg/m<sup>2</sup>)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">27.2(3.3)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">26.6(3.1)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.408</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Zscore BMI</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2.9(0.5)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3.1(0.8)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold><italic>0.031</italic></bold>\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Waist–hip Index</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">89.4(8.0)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">88.8(8.6)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.712</td></tr></tbody></table><table-wrap-foot><fn><p>(Bold and italic = statistically significant).</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"ijerph-17-05300-t003\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05300-t003_Table 3</object-id><label>Table 3</label><caption><p>Evaluation of the physical condition before and after the intervention and the pre-post differences.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">Test</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">CG</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">EG</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</th></tr><tr><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Baseline</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Post</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>p</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Delta</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Baseline</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Post</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>p</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Delta</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><italic>p</italic> Delta</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Curl Up (<italic>n</italic>)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">8.1(3.9)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">8.6(4.2)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.206</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.5(2.3)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">7.0(5.7)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">8.5(5.9) *</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold><italic>0.034</italic></bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2.0(0.3) #</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold><italic>0.000</italic></bold>\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Shoulder strech (cm)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">4.9(4.0)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">5.6(3.9)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.282</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.7(1.5)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">5.1(7.9)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">6.1(6.9) *</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold><italic>0.040</italic></bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.2(2.7)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.078</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Push_up (s)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3.9(2.0)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">5.2(1.7) *</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold><italic>0.000</italic></bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.4(1.5)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2.8(2.9)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3.5(2.3) *</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold><italic>0.035</italic></bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.6(2.1)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.083</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">400m (s)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">127(34.1)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">129.8(3.9)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.255</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2.6(17.3)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">111.3(26.5)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">106.0(26.2) *</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold><italic>0.034</italic></bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−5.4(12.8) #</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold><italic>0.002</italic></bold>\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Trunk_lift(cm)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">32.7(5.2)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">34.8(5.0) *</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold><italic>0.003</italic></bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2.1(1.9)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">32.8(6.3)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">34.2(6.2) *</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold><italic>0.044</italic></bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.5(2.5)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.063</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">H.jump feet together(m)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.97(0.16)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.99(0.17) *</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold><italic>0.048</italic></bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.02 (0.05)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.97(0.17)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.00(0.16) *</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold><italic>0.047</italic></bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.03(0.08)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.137</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">BMI Zscore</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2.9(0.4)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2.7(0.4)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold><italic>0.053</italic></bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.2(0.1)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3.1(0.8)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3.0(0.8)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold><italic>0.063</italic></bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.1(0.5)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.074</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Pli(mm)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">141.2(29.8)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">134.9(27.4) *</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold><italic>0.033</italic></bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−5.5(12.0)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">138.1(27.0)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">124.2(30.3) *</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold><italic>0.002</italic></bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−14.6(26.3) #</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold><italic>0.002</italic></bold>\n</td></tr></tbody></table><table-wrap-foot><fn><p><italic>p</italic> = pre-post intra group; * = <italic>p</italic> < 0.05; delta = pre-post intra group diference; <italic>p</italic> delta = inter grup delta; # = <italic>p</italic> < 0.05 delta from the control group. (Bold and italic = statistically significant).</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"ijerph-17-05300-t004\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05300-t004_Table 4</object-id><label>Table 4</label><caption><p>Data from the questionnaire on attitude toward physical education in girls and boys (Mean and SD)—pre-intervention.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Item</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Girls</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Boys</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>p</italic>\n</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Subjet organization concordancy</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">15.2(3.8)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">14.5(3.6)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.373</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">PE Diifficulty</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">17.5(3.7)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">16.7(3.3)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.266</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Emphaty towards teachers and subject</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">16.3(3.7)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">13.9(2.9)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold><italic>0.001</italic></bold>\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">PE as sport</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">10.5(3.0)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">9.9(2.6)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.284</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Preference for PE and Sport</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">10.5(2.6)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">10.9(3.0)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.499</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Utility of PE</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">22.5(4.7)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">22.3(4.5)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.847</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Value subject and PE Teacher</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">32.2(6.7)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">29.9(6.7)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.091</td></tr></tbody></table><table-wrap-foot><fn><p>(Bold and italic = statistically significant).</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"ijerph-17-05300-t005\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05300-t005_Table 5</object-id><label>Table 5</label><caption><p>Pre and post values of the study subjects on each dimension.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">Item</th><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">\n</th><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Girls</th><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Boys</th><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">\n</th></tr><tr><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Control</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Experimental</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Control</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Experimental</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>p</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>η</italic>\n<sup>2</sup>\n</th></tr></thead><tbody><tr><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Subjet organization concordancy</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Pretest</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">14.2(4.1)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">16.2(3.4)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">12.8(3.3)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">16.0(3.2)</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">0.536</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">0.04</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Postest</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">14.3(3.9)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">14.4(3.8) <sup>*</sup></td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">12.9(3.6)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">13.5(3.7) <sup>*</sup></td></tr><tr><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">PE Difficulty</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Pretest</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">17.2(4.4)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">17.8(3.0)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">16.7(3.5)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">16.8(3.2)</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">0.2</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">0.056</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Postest</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">17.1(3.9)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">15.7(3.8) <sup>*</sup></td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">17.0(3.7)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">17.5(3.2)</td></tr><tr><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Emphaty towards teachers and subject</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Pretest</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">16.7(3.9)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">15.9(3.4)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">14.3(2.2)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">13.5(3.3)</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">\n<bold><italic>0.021</italic></bold>\n</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">0.055</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Postest</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">16.8(3.5)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">14.3(3.9) <sup>*</sup></td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">15.1(2.5) <sup>*</sup></td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">15.0(342) <sup>*</sup></td></tr><tr><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">PE as sport</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Pretest</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">11.4(3.1)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">9.7(2.7)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">9.9(3.3)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">10.0(1.8)</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">0.101</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">0.028</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Postest</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">11.3(2.7)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">8.6(3.3) <sup>*</sup></td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">9.7(2.6)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">10.8(2.4)</td></tr><tr><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Preference for PE and Sport</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Pretest</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">10.2(2.5)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">10.8(2.8)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">9.7(2.4)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">12.0(3.0)</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">0.261</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">0.013</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Postest</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">10.0(2.2)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">8.6(3.2) <sup>*</sup></td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">9.6(2.4)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">10.9(2.7)</td></tr><tr><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Utility of PE</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Pretest</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">24.3(4.9)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">20.7(3.)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">21.1(4.4)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">23.3(4.5)</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">0.315</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">0.011</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Postest</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">23.9(5.1)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">20.7(4.0)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">21.5(4.2)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">24.3(5.3)</td></tr><tr><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Value subject and PE Teacher</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Pretest</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">30.5(6.6)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">33.9(6.5)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">28.6(8.0)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">31.0(5.2)</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">0.077</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">0.033</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Postest</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">31.3(6.4) <sup>*</sup></td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">30.7(8.1)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">29.3(3.2)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">32.8(7.7)</td></tr></tbody></table><table-wrap-foot><fn><p>* <italic>p</italic> < 0.05 pre-post intra group; <italic>p = p</italic> value interaction gender × exercise; <italic>η</italic><sup>2</sup> = effect size of the interaction (Mean and SD). (Bold and italic = statistically significant).</p></fn></table-wrap-foot></table-wrap></floats-group></article>\n"
]
[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Int J Mol Sci</journal-id><journal-id journal-id-type=\"iso-abbrev\">Int J Mol Sci</journal-id><journal-id journal-id-type=\"publisher-id\">ijms</journal-id><journal-title-group><journal-title>International Journal of Molecular Sciences</journal-title></journal-title-group><issn pub-type=\"epub\">1422-0067</issn><publisher><publisher-name>MDPI</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32752303</article-id><article-id pub-id-type=\"pmc\">PMC7432098</article-id><article-id pub-id-type=\"doi\">10.3390/ijms21155533</article-id><article-id pub-id-type=\"publisher-id\">ijms-21-05533</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Article</subject></subj-group></article-categories><title-group><article-title>Cytotoxic Effects of Cannabinoids on Human HT-29 Colorectal Adenocarcinoma Cells: Different Mechanisms of THC, CBD, and CB83</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Cerretani</surname><given-names>Daniela</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05533\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Collodel</surname><given-names>Giulia</given-names></name><xref ref-type=\"aff\" rid=\"af2-ijms-21-05533\">2</xref></contrib><contrib contrib-type=\"author\"><name><surname>Brizzi</surname><given-names>Antonella</given-names></name><xref ref-type=\"aff\" rid=\"af3-ijms-21-05533\">3</xref></contrib><contrib contrib-type=\"author\"><name><surname>Fiaschi</surname><given-names>Anna Ida</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05533\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Menchiari</surname><given-names>Andrea</given-names></name><xref ref-type=\"aff\" rid=\"af4-ijms-21-05533\">4</xref></contrib><contrib contrib-type=\"author\"><name><surname>Moretti</surname><given-names>Elena</given-names></name><xref ref-type=\"aff\" rid=\"af2-ijms-21-05533\">2</xref></contrib><contrib contrib-type=\"author\"><name><surname>Moltoni</surname><given-names>Laura</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05533\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Micheli</surname><given-names>Lucia</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05533\">1</xref><xref rid=\"c1-ijms-21-05533\" ref-type=\"corresp\">*</xref></contrib></contrib-group><aff id=\"af1-ijms-21-05533\"><label>1</label>Department of Medical and Surgical Sciences and Neurosciences, University of Siena, 53100 Siena, Italy; <email>[email protected]</email> (D.C.); <email>[email protected]</email> (A.I.F.); <email>[email protected]</email> (L.M.)</aff><aff id=\"af2-ijms-21-05533\"><label>2</label>Department of Molecular and Developmental Medicine, University of Siena, 53100 Siena, Italy; <email>[email protected]</email> (G.C.); <email>[email protected]</email> (E.M.)</aff><aff id=\"af3-ijms-21-05533\"><label>3</label>Department of Biotechnology, Chemistry and Pharmacy, University of Siena, 53100 Siena, Italy; <email>[email protected]</email></aff><aff id=\"af4-ijms-21-05533\"><label>4</label>Department of Business and Law, University of Siena, 53100 Siena, Italy; <email>[email protected]</email></aff><author-notes><corresp id=\"c1-ijms-21-05533\"><label>*</label>Correspondence: <email>[email protected]</email>; Tel.: +39-057-723-3285; Fax: +39-057-723-3102</corresp></author-notes><pub-date pub-type=\"epub\"><day>01</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><month>8</month><year>2020</year></pub-date><volume>21</volume><issue>15</issue><elocation-id>5533</elocation-id><history><date date-type=\"received\"><day>15</day><month>7</month><year>2020</year></date><date date-type=\"accepted\"><day>30</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>© 2020 by the authors.</copyright-statement><copyright-year>2020</copyright-year><license license-type=\"open-access\"><license-p>Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>).</license-p></license></permissions><abstract><p>In this study, we investigated the effects of exposition to IC<sub>50</sub> dose for 24 h of a new synthetic cannabinoid (CB83) and of phytocannabinoids Δ9-tetrahydrocannabinol (THC) and cannabidiol (CBD) on HT-29 colorectal carcinoma cells. Cell viability and proliferative activity evaluated using the MTT, lactate dehydrogenase (LDH), and CyQUANT assays showed that cell viability was significantly affected when CB83, THC, and CBD were administered to cells. The results obtained showed that the reduced glutathione/oxidized glutathione ratio was significantly reduced in the cells exposed to CBD and significantly increased in the cells treated with the CB83 when compared to the controls. CBD treatment causes a significant increase in malondialdehyde content. The catalase activity was significantly reduced in HT-29 cells after incubation with CB83, THC, and CBD. The activities of glutathione reductase and glutathione peroxidase were significantly increased in cells exposed to THC and significantly decreased in those treated with CBD. The ascorbic acid content was significantly reduced in cells exposed to CB83, THC, and CBD. The ultrastructural investigation by TEM highlighted a significantly increased percentage of cells apoptotic and necrotic after CB83 exposition. The Annexin V-Propidium Iodide assay showed a significantly increased percentage of cells apoptotic after CB83 exposition and necrotic cells after CBD and THC exposition. Our results proved that only CBD induced oxidative stress in HT-29 colorectal carcinoma cells via CB receptor-independent mechanisms and that CB83 caused a mainly CB2 receptor-mediated antiproliferative effect comparable to 5-Fuorouracil, which is still the mainstay drug in protocols for colorectal cancer.</p></abstract><kwd-group><kwd>HT-29 cells</kwd><kwd>synthetic cannabinoid</kwd><kwd>Δ<sup>9</sup>-tetrahydrocannabinol</kwd><kwd>cannabidiol</kwd><kwd>oxidative stress</kwd><kwd>apoptosis</kwd></kwd-group></article-meta></front><body><sec sec-type=\"intro\" id=\"sec1-ijms-21-05533\"><title>1. Introduction</title><p>Cannabinoids obtained from <italic>Cannabis sativa</italic> and their derivatives produce many biological effects, mainly through interactions with specific receptors such as CB1 and CB2, which have been cloned and characterized [<xref rid=\"B1-ijms-21-05533\" ref-type=\"bibr\">1</xref>,<xref rid=\"B2-ijms-21-05533\" ref-type=\"bibr\">2</xref>]. Moreover, the orphan G protein coupled receptor 55 (GPR55), the transient receptor potential cation channel subfamily V member 1 (TRPV1), and peroxisome proliferator-activated receptors (PPARs) have been reported as possible receptors for endogenous cannabinoids [<xref rid=\"B3-ijms-21-05533\" ref-type=\"bibr\">3</xref>,<xref rid=\"B4-ijms-21-05533\" ref-type=\"bibr\">4</xref>]. Given the many effects of cannabinoids and the evidence demonstrated by preclinical studies, it is possible to assume a potential use of these substances in the medical field. To date, cannabinoids have been used in the treatment of nausea and vomiting in cancer patients undergoing chemotherapy, but the use of cannabinoids in oncology is likely to be limited, although there is evidence showing that cannabinoids are able to inhibit cell growth in different cancer cell lines [<xref rid=\"B5-ijms-21-05533\" ref-type=\"bibr\">5</xref>] and to exert antitumor effects in experimental animal models [<xref rid=\"B6-ijms-21-05533\" ref-type=\"bibr\">6</xref>].</p><p>Through cannabinoid receptor and nonreceptor signaling pathways, cannabinoids show specific cytotoxicity against tumor cells while protecting healthy tissue from apoptosis. Bogdanović et al. [<xref rid=\"B7-ijms-21-05533\" ref-type=\"bibr\">7</xref>] investigated the proapoptotic and antiproliferative effects of cannabinoids and associated signaling pathways in different cancer cell lines, and it has been demonstrated that natural and synthetic cannabinoids cause a CB1 and/or CB2 receptor-dependent decrease in the proliferation of breast and intestinal cancer cells [<xref rid=\"B5-ijms-21-05533\" ref-type=\"bibr\">5</xref>,<xref rid=\"B8-ijms-21-05533\" ref-type=\"bibr\">8</xref>]. Cannabinoids impair tumor progressions at various levels. Their main effect is the induction of cancer cell death by apoptosis and the inhibition of cancer cell proliferation. At least one of those actions has been demonstrated in almost all cancer cell types tested [<xref rid=\"B9-ijms-21-05533\" ref-type=\"bibr\">9</xref>]. Cannabinoid treatments affect directly the viability of a great variety of cancer cells via the induction of apoptosis or cell cycle arrest [<xref rid=\"B10-ijms-21-05533\" ref-type=\"bibr\">10</xref>,<xref rid=\"B11-ijms-21-05533\" ref-type=\"bibr\">11</xref>]. The psychotropic cannabinoid, the Δ<sup>9</sup>-tetrahydrocannabinol (THC, <xref ref-type=\"fig\" rid=\"ijms-21-05533-f001\">Figure 1</xref>), induces apoptosis in a variety of transformed and nontransformed cells, including those of immune origin [<xref rid=\"B10-ijms-21-05533\" ref-type=\"bibr\">10</xref>,<xref rid=\"B12-ijms-21-05533\" ref-type=\"bibr\">12</xref>]. It was observed that THC treatment induces significant levels of apoptosis in leukemias and lymphocytes in culture, as well as in the murine thymus and spleen [<xref rid=\"B12-ijms-21-05533\" ref-type=\"bibr\">12</xref>,<xref rid=\"B13-ijms-21-05533\" ref-type=\"bibr\">13</xref>,<xref rid=\"B14-ijms-21-05533\" ref-type=\"bibr\">14</xref>], showing that THC may impair T-cell functions through the induction of apoptosis. Moreover, cannabidiol (CBD, <xref ref-type=\"fig\" rid=\"ijms-21-05533-f001\">Figure 1</xref>), a nonpsychotropic cannabinoid, has also been reported to induce apoptosis in several transformed or immortalized cells, including K-ras-thyroid epithelial, C6 glioma, malondhyaldhehyde (MDA)-MB-231 breast carcinoma, HL-60, and Jurkat leukemia cells [<xref rid=\"B5-ijms-21-05533\" ref-type=\"bibr\">5</xref>,<xref rid=\"B15-ijms-21-05533\" ref-type=\"bibr\">15</xref>,<xref rid=\"B16-ijms-21-05533\" ref-type=\"bibr\">16</xref>]. In addition, many evidences suggest that cannabinoids damage tumor angiogenesis and block invasion and metastasis [<xref rid=\"B6-ijms-21-05533\" ref-type=\"bibr\">6</xref>,<xref rid=\"B17-ijms-21-05533\" ref-type=\"bibr\">17</xref>]. The role of reactive oxygen species (ROS) in regulating apoptosis is supported by many evidences [<xref rid=\"B18-ijms-21-05533\" ref-type=\"bibr\">18</xref>], and the production of ROS during apoptosis has been described in various models of apoptotic cell death [<xref rid=\"B19-ijms-21-05533\" ref-type=\"bibr\">19</xref>]. Cancer cells seem to possess higher levels of endogenous ROS compared to normal cells, but events that increase ROS levels above a certain threshold seem to be incompatible with the cellular survival. Thus, compounds that increase the ROS level or that impair the cellular antioxidant system will shift the redox balance, inducing cancer cell cytotoxicity [<xref rid=\"B20-ijms-21-05533\" ref-type=\"bibr\">20</xref>].</p><p>Our previous study in the cannabinoid field led to the development of a new class of synthetic cannabinoid ligands [<xref rid=\"B21-ijms-21-05533\" ref-type=\"bibr\">21</xref>,<xref rid=\"B22-ijms-21-05533\" ref-type=\"bibr\">22</xref>,<xref rid=\"B23-ijms-21-05533\" ref-type=\"bibr\">23</xref>,<xref rid=\"B24-ijms-21-05533\" ref-type=\"bibr\">24</xref>,<xref rid=\"B25-ijms-21-05533\" ref-type=\"bibr\">25</xref>] chemically characterized by a substituted resorcinol nucleus linked to fatty acid amides. In fact, their structure merges the crucial pharmacophoric requirements for the cannabinoid receptor binding of both THC and anandamide (AEA, <xref ref-type=\"fig\" rid=\"ijms-21-05533-f001\">Figure 1</xref>), the main endogenous cannabinoid, such as a rigid aromatic backbone bearing an alkyl tail and a flexible saturated chain with an amidic head. Among these derivatives, compound CB83 [<xref rid=\"B24-ijms-21-05533\" ref-type=\"bibr\">24</xref>], <xref ref-type=\"fig\" rid=\"ijms-21-05533-f001\">Figure 1</xref>, belonging to the 5-(1′,1′-dimethylheptyl)resorcinol class, was selected for its balanced potency (<italic>K</italic>i CB1 = 310 nM and <italic>K</italic>i CB2 = 30 nM) and selectivity.</p><p>The aim of this in vitro study was to investigate the effects of the synthetic cannabinoid CB83 and the traditional phytocannabinoids, THC and CBD, on the viability, proliferation, ultrastructure, and apoptosis in human colorectal carcinoma HT-29 cells. We also evaluated the effect of the treatment with cannabinoids on the HT-29 cellular redox state. For this purpose, we determined the parameters of oxidative stress, such as the ratio of reduced glutathione (GSH) to oxidized glutathione (GSSG); the levels of malondhyaldhehyde (MDA), a marker of lipid peroxidation; the antioxidant ascorbic acid (AA); and the cellular antioxidant enzyme activities, such as catalase (CAT), glutathione peroxidase (GPx), and glutathione reductase (GR). Moreover, we compared the CB effects with those observed with the pyrimidine antagonist 5-Fluorouracil (5FU), which is still the mainstay drug in protocols for colorectal cancer.</p></sec><sec sec-type=\"results\" id=\"sec2-ijms-21-05533\"><title>2. Results</title><sec id=\"sec2dot1-ijms-21-05533\"><title>2.1. CB83, THC, CBD, and 5FU Induce Cytotoxicity and Inhibit the Viability of HT-29 Cells</title><p>The results from the MTT assay showed that CB83, THC, CBD, and 5FU were cytotoxic and suppressed the viability of HT-29 cells after 24h, with IC<sub>50</sub> values of 1.0 ± 0.10 µM, 30.0 ± 1.01 µM, 30.0 ± 3.02 µM, and 34.0 ± 13.89 µM, respectively (<xref rid=\"ijms-21-05533-t001\" ref-type=\"table\">Table 1</xref>). The HT-29 cells were relatively more sensitive to synthetic cannabinoid CB83, followed by THC, CBD, and 5FU.</p><p>In order to verify the cytotoxic activity of CB83, THC, CBD, and 5FU, the viability of HT-29 cells was determined in cellular lysate using the lactate dehydrogenase (LDH) assay. According to the cytotoxic effect found in HT-29 cells, all the substances mentioned above significantly suppressed the viability of the HT-29 cell line after 24 h of treatment, with the maximum effect observed with THC, followed by CB83, 5FU, and CBD, with LDH ratio values of 10.9 ± 0.31, <italic>p</italic> < 0.001; 5.9 ± 1.01, <italic>p</italic> < 0.05; 5.9 ± 0.62, <italic>p</italic> < 0.001; and 5.1 ± 0.72, <italic>p</italic> < 0.05, respectively vs. the control LDH ratio value (3.0 ± 0.07) (<xref rid=\"ijms-21-05533-t001\" ref-type=\"table\">Table 1</xref>).</p><p>The results of the CyQUANT cell proliferation assay, a highly sensitive, fluorescence-based microplate assay, are shown in <xref rid=\"ijms-21-05533-t001\" ref-type=\"table\">Table 1</xref>. CB83, THC, CBD, and 5FU cause a significant inhibition of proliferation at 24 h (53.1 ± 5.47, <italic>p</italic> < 0.001; 70.9 ± 5.59, <italic>p</italic> < 0.01; 66.8 ± 7.90, <italic>p</italic> < 0.01; and 57.5 ± 6.15, <italic>p</italic> < 0.001, respectively) respective to the control (100.0 ± 10.57).</p><p>The results achieved using the incubation with CB1 antagonist AM251 and CB2 antagonist AM630 in HT-29 cells exposed to CBD show that the blockade of CB1 and CB2 receptors did not influence the cytotoxic effect of CBD. The treatment of HT-29 cells with THC in the presence of AM251 did not produce significant effects on the cellular viability compared to the control cells, while using AM630 THC showed a significant reduction of cellular viability (<italic>p</italic> < 0.05 vs. control cells). The treatment of HT-29 cells with CB83 in the presence of CB2 antagonist AM630 did not show significant effects on the cellular viability compared to the control cells, while the cytotoxic effect was maintained in the presence of AM251 (<italic>p</italic> < 0.05 vs. control cells) (<xref ref-type=\"fig\" rid=\"ijms-21-05533-f002\">Figure 2</xref>).</p></sec><sec id=\"sec2dot2-ijms-21-05533\"><title>2.2. Effects of CB83, THC, CBD, and 5FU on the HT-29 Cellular Redox State</title><p>In this study we explored the effects of CB83, THC, CBD, and 5FU at the respective IC<sub>50</sub> doses on the antioxidant cellular defense system in HT-29 cells. For this purpose, the GSH/GSSG ratio; the AA content; the MDA levels as the index of lipid peroxidation; and the activity of antioxidant enzymes CAT, GR, and GPx were determined in cellular lysates after 24 h of incubation. The GSH/GSSG ratio was essentially unchanged in THC and 5FU-treated HT-29 cells, while in the cells exposed to CBD, the GSH/GSSG ratio was significantly reduced (13.7 ± 0.58; <italic>p</italic> < 0.05) respective to the control (16.1 ± 1.14). Conversely, the HT-29 cells treated with the CB83 synthetic cannabinoid showed a significant increase in the GSH/GSSG ratio (26.4 ± 0.83; <italic>p</italic> < 0.001) compared to untreated cells (<xref ref-type=\"fig\" rid=\"ijms-21-05533-f003\">Figure 3</xref>a). The nonenzymatic AA antioxidant content of cells kept for 24 h with CB83, THC, and CBD was significantly reduced (10.5 ± 1.13, 4.8 ± 0.85, and 6.8 ± 0.94 nmol/mL; <italic>p</italic> < 0.01, <italic>p</italic> < 0.001, and <italic>p</italic> < 0.001, respectively) while remaining unchanged in those exposed to 5FU (15.5 ± 1.27 nmol/mL) compared to untreated HT-29 cells (13.7 ± 1.06 nmol/mL) (<xref ref-type=\"fig\" rid=\"ijms-21-05533-f003\">Figure 3</xref>b).</p><p>The effects of the treatment of HT-29 with CB83, THC, CBD, and 5FU on MDA levels showed that only the cells exposed to CBD exhibited a significant increase in the MDA content (2.6 ± 0.18 nmol/mL; <italic>p</italic> < 0.01) respective to the untreated cells (1.6 ± 0.27 nmol/mL) to indicate the presence of oxidative damage (<xref ref-type=\"fig\" rid=\"ijms-21-05533-f003\">Figure 3</xref>c).</p><p>The activity of antioxidant enzyme CAT was significantly reduced in HT-29 cells after 24-h incubation with CB83, THC, CBD, and 5FU (121.5 ± 14.03, <italic>p</italic> < 0.001; 76.5 ± 21.92, <italic>p</italic> < 0.001; 146.5 ± 17.26, <italic>p</italic> < 0.05; and 32.9 ± 3.78, <italic>p</italic> < 0.001 U/mg protein, respectively) respective to the control cells (173.1 ± 3.27 U/mg protein) (<xref ref-type=\"fig\" rid=\"ijms-21-05533-f004\">Figure 4</xref>a).</p><p>The GR activity appeared to be unaltered in CB83-treated cells (0.5 ± 0.06 U/mg protein), while it showed a significant increase in HT-29 cells exposed to THC (0.7 ± 0.07 U/mg protein, <italic>p</italic> < 0.001). Conversely, the cells treated with CBD and 5FU showed a significant decrease in the enzymatic activity (0.1 ± 0.01 U/mg protein, <italic>p</italic> < 0.01 and 0.2 ± 0.03 U/mg protein, <italic>p</italic> < 0.01, respectively) compared to the control cells (0.3 ± 0.01 U/mg protein) (<xref ref-type=\"fig\" rid=\"ijms-21-05533-f004\">Figure 4</xref>b).</p><p>The GPx activity measured in the cells treated with the CB83 proved to be significantly increased (18.7 ± 1.87 U/mg protein, <italic>p</italic> < 0.001), as well as in cells exposed to THC (23.5 ± 3.53 U/mg protein, <italic>p</italic> < 0.001); instead, the HT-29 cells exposed to CBD and 5FU showed a significant decrease in GPx enzymatic activity (9.2 ± 0.79 and 5.2 ± 1.69 U/mg protein, <italic>p</italic> < 0.05 and <italic>p</italic> < 0.01, respectively) respective to the control cells (10.8 ± 0.91 U/mg protein) (<xref ref-type=\"fig\" rid=\"ijms-21-05533-f004\">Figure 4</xref>c).</p></sec><sec id=\"sec2dot3-ijms-21-05533\"><title>2.3. CB83, THC, CBD, and 5FU Cause Morphological Alterations in HT-29 Cells</title><sec id=\"sec2dot3dot1-ijms-21-05533\"><title>2.3.1. AnV/PI Assay</title><p>The percentage of intact viable cells was significantly decreased in cells treated with 5FU (<italic>p</italic> < 0.001) and with CB83 (<italic>p</italic> < 0.01) compared to the control cells, while the viability of cells treated with CBD and with THC did not show significant differences, despite an evident increase with that detected in the controls (<xref rid=\"ijms-21-05533-t002\" ref-type=\"table\">Table 2</xref>). In particular, a significant increase of AnV-positive cells (<italic>p</italic> < 0.001) was shown in CB83 cells with respect to the controls, and a significant increase of necrotic cells (PI-positive) was detected in 5FU (<italic>p</italic> < 0.001), CBD (<italic>p</italic> < 0.01), and THC (<italic>p</italic> < 0.05) cells compared to the controls.</p></sec><sec id=\"sec2dot3dot2-ijms-21-05533\"><title>2.3.2. Transmission Electron Microscopy (TEM)</title><p>TEM analysis highlighted an increased percentage of apoptotic and necrotic cells in all treated samples respective to the controls (<xref rid=\"ijms-21-05533-t003\" ref-type=\"table\">Table 3</xref>)—in particular, significant values were detected when the cells were treated with 5FU (<italic>p</italic> < 0.01 and <italic>p</italic> < 0.001, respectively) and with CB83 (<italic>p</italic> < 0.001 and <italic>p</italic> < 0.01, respectively) compared to the control cells.</p><p>Normal cells showed normal chromatin textures and cytoplasm containing rough endoplasmic reticulum, Golgi bodies, and mitochondria (<xref ref-type=\"fig\" rid=\"ijms-21-05533-f005\">Figure 5</xref>a). Necrotic cells (<xref ref-type=\"fig\" rid=\"ijms-21-05533-f005\">Figure 5</xref>b) displayed altered chromatin texture and cytoplasm, deeply impaired and devoid of organelles; the plasma membrane was frequently broken. The apoptotic cell characteristics regarded marginated chromatin and swollen mitochondria; moreover, another ultrastructural peculiar alteration was the presence of a very vacuolated cytoplasm (<xref ref-type=\"fig\" rid=\"ijms-21-05533-f005\">Figure 5</xref>c). In all treated samples, an interesting feature was the presence of a percentage (8–12%) of cells showing the cytoplasm rich in enlargements (<xref ref-type=\"fig\" rid=\"ijms-21-05533-f005\">Figure 5</xref>d). This feature was absent in the control cells.</p></sec></sec></sec><sec sec-type=\"discussion\" id=\"sec3-ijms-21-05533\"><title>3. Discussion</title><p>Cannabinoids act by a modulation of the signaling pathways crucial in the control of cell proliferation and survival, and many in vitro and in vivo experiments have shown that cannabinoids inhibit the proliferation of cancer cells and stimulate autophagy and apoptosis. Here, we studied the effects of cannabinoids THC, CBD, and CB83 on the viability, proliferation, ultrastructure, apoptosis, and cellular redox state of human colorectal carcinoma HT-29 cells. </p><p>MTT results, LDH analyses, and the nucleic acid content determination showed that all the cannabinoids tested and 5FU inhibit cellular proliferation. The HT-29 cells were relatively more sensitive to synthetic cannabinoid CB83, followed by THC, CBD, and 5FU. At the same time, only CBD caused oxidative stress in HT-29 cells. Particularly, in CBD-treated HT-29 cells, the decrease in the GSH/GSSG ratio suggests a redox imbalance towards the appearance of oxidative stress, a condition that did not occur in cells treated with THC, CB83, and 5FU. The evaluation of the activity of cellular antioxidant defense enzymes showed that all cannabinoids tested and 5FU reduced the activity of CAT in HT-29 cells, while the activity of GR was significantly increased in cells treated with THC, reduced in HT-29 cells exposed to CBD and 5FU, and unchanged in the CB83 treatment. The GPx activity was significantly increased in HT-29 cells treated with CB83 and THC, whereas it was reduced in cells exposed to CBD and 5FU. Thus, CBD appears to induce oxidative stress in HT-29 cells, possibly through ROS production, which causes GSH consumption and inhibits CAT, GR, and GPx activities. These data were confirmed by the presence, in CBD-treated cells, of significantly higher MDA levels than in control cells, while they remained substantially unchanged in HT-29 cells exposed to THC, CB83, and 5FU.</p><p>The decrease of AA, a nonenzymatic antioxidant, in the cells exposed to cannabinoids can be explained by the increased demand for reducing equivalents necessary to maintain GSH levels through the “sparing” effect of AA on GSSG [<xref rid=\"B26-ijms-21-05533\" ref-type=\"bibr\">26</xref>]. In HT-29 cells treated with CBD, the request for reducing equivalents was not satisfied, possibly due to the excess of ROS, and the GSH/GSSG ratio remained below the value of the control, contributing to causing oxidative stress. Massi et al. [<xref rid=\"B15-ijms-21-05533\" ref-type=\"bibr\">15</xref>] showed that the CBD-dependent production of ROS was accompanied by a reduction in GSH and GSH-related enzymes. The origin of stress induced by CBD came in part from the mitochondria and led to the activation of multiple caspases involved in the intrinsic and extrinsic pathways of apoptosis. The production of ROS is the most supported hypothesis for the CBD-dependent inhibition of cancer cell aggressiveness in experimental models in cultures, [<xref rid=\"B27-ijms-21-05533\" ref-type=\"bibr\">27</xref>,<xref rid=\"B28-ijms-21-05533\" ref-type=\"bibr\">28</xref>,<xref rid=\"B29-ijms-21-05533\" ref-type=\"bibr\">29</xref>] and Singer et al. [<xref rid=\"B30-ijms-21-05533\" ref-type=\"bibr\">30</xref>] observed, for the first time in vivo, that the CBD-dependent generation of ROS is, in part, responsible for the antitumor activity of the cannabinoid.</p><p>It is reported that CBD has low affinity for cannabinoid receptors and acts independently of them. In fact, CBD seems to interact with other receptors such as TRPV1, GPR55, or PPARs [<xref rid=\"B3-ijms-21-05533\" ref-type=\"bibr\">3</xref>,<xref rid=\"B4-ijms-21-05533\" ref-type=\"bibr\">4</xref>]. However, other authors suggested that CBD induces apoptosis in cancer cells partially through the direct or indirect activation of CB2 receptors [<xref rid=\"B5-ijms-21-05533\" ref-type=\"bibr\">5</xref>,<xref rid=\"B31-ijms-21-05533\" ref-type=\"bibr\">31</xref>]. The results obtained using the incubation with CB1 antagonist AM251 and CB2 antagonist AM630 seem to support the hypothesis that CBD-induced cytotoxicity in HT-29 cells occurs through a CB1 and CB2 receptor-independent mechanism and, possibly, via ROS production, which leads to apoptotic cell death [<xref rid=\"B31-ijms-21-05533\" ref-type=\"bibr\">31</xref>,<xref rid=\"B32-ijms-21-05533\" ref-type=\"bibr\">32</xref>,<xref rid=\"B33-ijms-21-05533\" ref-type=\"bibr\">33</xref>]. Regarding the cytotoxic effects induced in HT-29 by THC and CB83, no signs of oxidative stress were evident in the cells treated with these cannabinoids. Furthermore, THC in the presence of AM251 did not produce significant effects on the cellular viability compared to the control cells; while using AM630, THC showed a significant reduction of the cellular viability to demonstrate that it is an agonist of the CB1 receptor, with less efficacy at the CB2 ones [<xref rid=\"B34-ijms-21-05533\" ref-type=\"bibr\">34</xref>].</p><p>This result is in-line with what was observed by other authors [<xref rid=\"B35-ijms-21-05533\" ref-type=\"bibr\">35</xref>], even if it was reported that antitumor activities of THC were associated with both cannabinoid CB1 and CB2 receptors [<xref rid=\"B36-ijms-21-05533\" ref-type=\"bibr\">36</xref>,<xref rid=\"B37-ijms-21-05533\" ref-type=\"bibr\">37</xref>,<xref rid=\"B38-ijms-21-05533\" ref-type=\"bibr\">38</xref>]. Otherwise, the treatment of HT-29 cells with CB83 in the presence of CB2 antagonist AM630 does not show significant effects on the cellular viability compared to the control cells, while the cytotoxic effect is maintained in the presence of AM251, the selective CB1 receptor antagonist. The synthetic cannabinoid CB83 has been shown to have a higher CB2 affinity (<italic>K</italic>i = 30 nM), rather than for CB1 receptors (<italic>K</italic>i = 310 nM). These data seem to confirm that the cytotoxic effect of CB83 is mainly mediated by CB2 receptors. The inhibition of the cancer cell proliferation and induction of apoptosis in cancer cells by CB1 and CB2 activation is described, and the most upstream key molecule that can initiate death signals appears to be ceramide [<xref rid=\"B39-ijms-21-05533\" ref-type=\"bibr\">39</xref>]. The increases of ceramide production via a mechanism that involves tumor necrosis factor-alpha (TNF-α) [<xref rid=\"B34-ijms-21-05533\" ref-type=\"bibr\">34</xref>] induces an activation of the endoplasmic reticulum stress-related signaling pathway, which, finally, leads to the activation of the intrinsic apoptosis pathway [<xref rid=\"B31-ijms-21-05533\" ref-type=\"bibr\">31</xref>].</p><p>The analysis by the Annexin V-Propidium Iodide assay and TEM confirmed that all the tested cannabinoids increased the percentages of the apoptotic and necrotic cells, even if only the synthetic cannabinoid CB83 determined a significant increase of these pathologies. Specifically, this compound resulted as effective as 5FU. These results agree with the data obtained by the MTT test.</p><p>Probably through cannabinoid receptors and nonreceptor signaling pathways, cannabinoids showed specific cytotoxicity against tumor cells [<xref rid=\"B7-ijms-21-05533\" ref-type=\"bibr\">7</xref>]. By TEM analysis, this cytotoxic effect, particularly evident in samples incubated with CB83, was highlighted by the significant increase of cells with marginated chromatin, swollen mitochondria, and vacuolated cytoplasm or disrupted chromatin and broken plasma membrane, typical features, respectively, of apoptosis and necrosis.</p><p>The cannabinoid ligands have been shown to sensitize cancer cells and, synergistically, may interact with members of the TNF receptor, thus suggesting that the combination of cannabinoids with death receptor ligands induces additive or synergistic tumor cell death [<xref rid=\"B40-ijms-21-05533\" ref-type=\"bibr\">40</xref>]. Recently, it has been reported that different compounds induced cell death by necroptotic and apoptotic mechanisms in cancer cells, with a concomitant mitochondrial metabolism failure that triggers lower production of ATP and ROS overproduction [<xref rid=\"B41-ijms-21-05533\" ref-type=\"bibr\">41</xref>].</p></sec><sec id=\"sec4-ijms-21-05533\"><title>4. Materials and Methods</title><sec id=\"sec4dot1-ijms-21-05533\"><title>4.1. Chemicals</title><p>All reagents were purchased from Sigma-Aldrich (St. Louis, MO, USA). All solutions were prepared with deionized water of resistivity no less than 18.2 MΩ·cm<sup>–1</sup> (Milli-Q Ultrapure Water System, Merck KGaA, Darmstadt, Germany).</p></sec><sec id=\"sec4dot2-ijms-21-05533\"><title>4.2. Cell Cultures</title><p>Human colorectal carcinoma HT-29 cells were purchased from ATCC (American Type Culture Collection, Manassas, VA, USA), and they were cultured in Dulbecco’s modified Eagle’s medium (MEM) + GlutaMAX™-1 supplemented with 10% fetal bovine serum (FBS) and 1% penicillin-streptomycin (Gibco/Invitrogen, Carlsbad, CA, USA). The cells were grown in T-75 flasks at 37 °C in a humidified atmosphere with 5% CO<sub>2</sub> in the air, and the culture medium was replaced every 2 to 3 days, and passages were performed 1 to 2 days per week.</p></sec><sec id=\"sec4dot3-ijms-21-05533\"><title>4.3. Cell Viability Assays</title><sec id=\"sec4dot3dot1-ijms-21-05533\"><title>4.3.1. Assay for Cytotoxicity (MTT Assay)</title><p>To explore the antiproliferative effect on the HT-29 cell line, the cells were exposed to CB83, THC, CBD, and 5FU for 24 h, and the effect on the cell viability was determined using the MTT assay.</p><p>The HT-29 cells were preincubated in a 96-well plate at a density of 1.0 × 10<sup>4</sup> cells/well for 24 h; cells were treated with CB83, THC, CBD, and 5FU at different concentrations (range from 0.1 mM to 0.1 nM) to determine the IC<sub>50</sub> values. After incubation for 24 h, the MTT reagent (5 mg/mL) was added to each well, and the plate was incubated for an additional 4 h at 37 °C. At the end of incubation, the media were removed, and the intracellular formazan product was dissolved in 100 μL of isopropyl alcohol. The absorbance of each well was measured at 540 nm using an ELISA reader (iMarkTM; Bio-Rad Laboratories, Inc., Hercules CA, USA), and the MTT reduction rate was calculated by setting each of the control survivals equal to 100%.</p><p>In order to investigate the role of the CB receptors, the MTT assay was again performed with specific antagonists of the CB1 and CB2 receptors. The cells were pretreated for 30 min with CB1 and CB2 antagonists AM251 (1 μM) and AM630 (1 μM), respectively, before the addition of CB83, THC, and CBD.</p></sec><sec id=\"sec4dot3dot2-ijms-21-05533\"><title>4.3.2. Lactate Dehydrogenase (LDH) Determination</title><p>To evaluate the cellular viability in HT-29 cells, the cells were plated at an initial density of 2 × 10<sup>5</sup> cells/well in 6-well plates, and they were allowed to attach overnight. Afterwards, the medium was removed by aspiration, and the cells were exposed to the IC<sub>50</sub> values of CB83, THC, CBD, and 5FU for 24 h. Cells lysate and culture media samples were recovered to the assay LDH activity. LDH catalyzed the conversion of pyruvate to L-lactate, while the reduced nicotinamide adenine dinucleotide (NADH) was oxidized. The rate of oxidation, which is directly proportional to the LDH activity [<xref rid=\"B42-ijms-21-05533\" ref-type=\"bibr\">42</xref>], was monitored by measuring the decrease in absorbance at 340 nm using an UV-Vis Spectrophotometer (Lambda 35, Perkin Elmer, Norwalk, CT, USA). Total LDH activity was evaluated by calculation ΔA/min in lysate and media samples. Viability was expressed as the LDH ratio (LDH media/LDH lysate). </p></sec><sec id=\"sec4dot3dot3-ijms-21-05533\"><title>4.3.3. Cell Proliferation Assay</title><p>The cell viability was determined by using the CyQUANT cell proliferation assay kit, which measures the total nucleic acid content. The cells were plated in 6-well plates at a seeding density of 2 × 10<sup>5</sup> in 3 mL of culture medium containing 1% FBS. Then, the cells were treated with CB83, THC, CBD, and 5FU for 24 h at the respective IC<sub>50</sub> doses. The cells were harvested and were seeded (5000 cells/well) into a 96-well microplate in 200 µL of culture medium containing 1% FBS for 4 h. The medium was aspirated, and the plates frozen at −80 °C until used. The plates were thawed at room temperature and processed according to the manufacturer’s instructions. The analysis was carried out using a VICTOR Multilabel plate reader (excitation 485 nm/emission 520 nm; Perkin Elmer Victor 3V, Waltham, MA, USA).</p></sec></sec><sec id=\"sec4dot4-ijms-21-05533\"><title>4.4. Cellular Redox Systems Evaluation</title><p>HT-29 cells were exposed to CB83, THC, CBD, and 5FU to evaluate a panel of factors involved in the oxidative stress response.</p><p>The cells were plated at an initial density of 2 × 10<sup>5</sup> cells/well in 6-well plates, allowing them to attach overnight. Afterwards, the medium was removed by aspiration, and the cells were treated to respective doses IC<sub>50</sub> of CB83, THC, CBD, and 5FU for 24 h. Then, cells were detached and centrifuged at 1500× <italic>g</italic> for 2 min. The pellets were resuspended in 1 mL of phosphate-buffered saline (PBS) and exposed to three cycles of freeze-thaw, freezing at −80 °C in the freezer.</p><p>The supernatants of cells destroyed by freezing and thawing were aliquoted and processed as described below in the respective methods.</p><sec id=\"sec4dot4dot1-ijms-21-05533\"><title>4.4.1. Glutathione Oxidized and Reduced</title><p>An aliquot of cell lysates was added to an equal volume of 10% metaphosphoric acid and centrifuged at low speed (2000× <italic>g</italic>) for 10 min at 0 °C. The supernatant was removed and stored at −80 °C until use. Total GSH and GSSG levels were quantified in the supernatant using a a micro-assay procedure [<xref rid=\"B43-ijms-21-05533\" ref-type=\"bibr\">43</xref>] based on an enzymatic method with the reading at 415 nm. Results were expressed in nmol/mg protein.</p></sec><sec id=\"sec4dot4dot2-ijms-21-05533\"><title>4.4.2. Proteins Assay</title><p>Protein concentrations were determined by the method of Lowry et al. [<xref rid=\"B44-ijms-21-05533\" ref-type=\"bibr\">44</xref>], and the calibration curves were prepared with dry bovine serum albumin.</p></sec><sec id=\"sec4dot4dot3-ijms-21-05533\"><title>4.4.3. AA Assay</title><p>AA levels were measured in the aliquot of cell lysates acidified with metaphosphoric acid using an HPLC method, as described by Ross [<xref rid=\"B45-ijms-21-05533\" ref-type=\"bibr\">45</xref>], with minor modifications. The supernatants were filtered (Anotop 0.2 μm, Merck), and 20 μL were injected into a high-performance liquid chromatography (HPLC) column. The AA was quantified by UV reverse-phase HPLC using a Waters 600 E System Controller HPLC (Milford, MA, USA) equipped with a Waters Dual λ 2487 UV detector (Milford, MA, USA) set at 262 nm. A 5-µm ultrasphere ODS column (Beckman, San Ramon, CA, USA) was used with the acetonitrile-water (49/51, <italic>v</italic>/<italic>v</italic>) as the mobile phase at the flow rate of 0.8 mL/min. The AA concentrations (nmol/mL) were calculated by peak areas, determined using an Agilent 3395 integrator (Agilent Technologies, Santa Clara, CA, USA).</p></sec><sec id=\"sec4dot4dot4-ijms-21-05533\"><title>4.4.4. Malondialdehyde Assessment</title><p>The extent of lipid peroxidation in cell lysates was estimated by calculating the MDA levels according the method of Shara et al. [<xref rid=\"B46-ijms-21-05533\" ref-type=\"bibr\">46</xref>], with minor modifications. To prevent artifact oxidations of polyunsaturated free fatty acids, immediately after the freezing and thawing, an aliquot of 0.2 mL was added to 0.2 mL of tris-HCl 0.04 M and acetonitrile containing 0.1% butylated hydroxytoluene. After centrifugation at 2000× <italic>g</italic> for 10 min at 0 °C, the supernatant was frozen at −80 °C until use.</p><p>At the time of analysis, the supernatant was derivatized with 2,4-dinitrophenylhydrazine and immediately stirred and extracted with 5 mL of pentane; finally, the samples were dried using nitrogen. A calibration curve with concentrations of MDA in the range from 0.5 nmol/mL to 10 nmol/mL was used.</p><p>The MDA hydrazone was quantified by isocratic HPLC using a Waters 600 E System Controller HPLC (Milford, MA, USA) equipped with a Waters Dual λ 2487 UV detector (Milford, MA, USA) set at 307 nm. A 5-µm ultrasphere ODS column C18 (Beckman, San Ramon, CA, USA) was used to separate the hydrazone derivative at the flow rate of 0.8 mL/min with the acetonitrile (45%)-HCl 0.01 N (55%) as the mobile phase. The MDA concentrations (nmol/mL) were calculated by peak areas determined using an Agilent 3395 integrator (Agilent Technologies, Santa Clara, CA, USA).</p></sec><sec id=\"sec4dot4dot5-ijms-21-05533\"><title>4.4.5. Catalase Activity</title><p>An aliquot of cell lysates was centrifuged at 4000× <italic>g</italic> for 15 min at 4 °C, and the supernatants were frozen at −80 °C until use. To determine the CAT, a micro-assay procedure was used [<xref rid=\"B47-ijms-21-05533\" ref-type=\"bibr\">47</xref>].</p><p>This method is based on the reaction of the CAT with methanol in the presence of an optimal concentration of hydrogen peroxide. The formaldehyde production was measured spectrophotometrically at 540 nm with 4-amino-3-hydrazino-5-mercapto-1,2,4-triazole (Purpald) as a chromogen. One unit of CAT activity is defined as the amount of enzyme that will cause the formation of 1 nmol of formaldehyde per minute at 25 °C. The results were expressed as U/mg protein.</p></sec><sec id=\"sec4dot4dot6-ijms-21-05533\"><title>4.4.6. Glutathione Reductase Activity</title><p>For the evaluation of the GR activity, an aliquot of cell lysates was diluted (1:1) in cold 0.25-M sucrose in 0.1-M phosphate buffer, pH 7.4, and centrifuged at 40,000× <italic>g</italic> for 20 min at 4 °C. The supernatants were stored at −80 °C until analyzed. The method is based on the increase in absorbance at 415 nm when 5,5′-dithiobis(2-nitrobenzoic acid) is reduced by GSH generated from an excess of GSSG [<xref rid=\"B48-ijms-21-05533\" ref-type=\"bibr\">48</xref>].</p><p>Samples were prepared in 96-well plates, and absorbance was measured every 30 s for 3 min with a programmable microplate reader. The rate of increase in absorbance was directly proportional to the amount of GR in the sample. The results were expressed as U/mg protein.</p></sec><sec id=\"sec4dot4dot7-ijms-21-05533\"><title>4.4.7. Glutathione Peroxidase Assay</title><p>The cell lysates were treated using the same procedure as described for GR. The GPx activity is quantitated by measuring the change in absorbance at 340 nm caused by the oxidation of NADPH [<xref rid=\"B49-ijms-21-05533\" ref-type=\"bibr\">49</xref>]. One unit of GPx activity is defined as the amount of enzyme that oxidizes 1 µmol of NADPH at 37 °C per minute. Enzyme activity was expressed as U/mg protein.</p></sec></sec><sec id=\"sec4dot5-ijms-21-05533\"><title>4.5. Morphological Studies</title><p>Apoptosis was initially induced by the incubation of cells 2 × 10<sup>4</sup> cells/well in complete growing medium for 24 h with the respective IC<sub>50</sub> doses of CB83, THC, CBD, and 5FU.</p><sec id=\"sec4dot5dot1-ijms-21-05533\"><title>4.5.1. Transmission Electron Microscopy (TEM)</title><p>Samples were fixed in cold Karnovsky fixative and maintained at 4 °C for 2 h. Fixed cells were washed in 0.1-M cacodylate buffer (pH 7.2) for 12 h, post-fixed in 1% buffered osmium tetroxide for 1 h at 4 °C, then dehydrated and embedded in Epon Araldite. Ultra-thin sections were cut with a Supernova ultramicrotome (Reickert Jung, Vienna, Austria), mounted on copper grids, stained with uranyl acetate and lead citrate, and then observed and photographed with a Philips CM12 transmission electron microscope (TEM; Philips Scientifics, Eindhoven, The Netherlands and Centro di Microscopie Elettroniche “Laura Bonzi”, ICCOM, Consiglio Nazionale delle Ricerche—CNR, Via Madonna del Piano, 10 Firenze, Italy).</p><p>One-hundred ultra-thin cell sections were analyzed from each sample. Major submicroscopic characteristics were recorded by two highly trained examiners who were blind to the experiment applying the same evaluation criteria. Cell conditions (normal, apoptosis, and necrosis) were defined by typical ultrastructural characteristics. Marginated chromatin, cytoplasmatic translucent vacuoles, and swollen and badly assembled mitochondria were the typical ultrastructural markers of apoptosis, whereas cells with a broken plasma membrane and nuclei with disrupted chromatin were affected by necrosis. TEM evaluation was carried out in three different experiments.</p></sec><sec id=\"sec4dot5dot2-ijms-21-05533\"><title>4.5.2. Annexin V/Propidium Iodide Assay</title><p>The detection of phosphatidylserine (PS) externalization was performed with the Vybrant Apoptosis Assay kit (Invitrogen Ltd., Paisley, UK) made up of Annexin (An) V-fluorescein isothiocyanate (FITC) and propidium iodide (PI), which are able to differentiate viable from apoptotic/necrotic cells. The samples were washed with PBS, centrifuged, and suspended in Annexin-binding buffer (ABB) to obtain a cell density of ~1 × 10<sup>6</sup>. Following the manufacturer’s instructions, 10 µL of conjugated-FITC AnV and 1 µL of PI (100 µg/mL) working solution to each cell suspension were added. Cells were incubated in the dark for 15 min at 37 °C. After a careful wash with ABB, a drop of cell suspension was smeared on each glass slide. Slides were mounted in glycerol containing 5% n-propylgallate. Observations were made with a Leitz Aristoplan (Leica, Wetzlar, Germany) light microscope equipped with a fluorescence apparatus. A total of 300 cells from each sample were counted. By staining cells with FITC-AnV (AnV, green fluorescence) and, simultaneously, with the nonvital dye (PI, red fluorescence), it is possible to recognize intact cells (AnV-negative and PI-negative), early-apoptotic cells with PS externalization (AnV-positive and PI-negative), cells with PS externalization and damaged membranes (AnV-positive and PI-positive), and necrotic cells (AnV-negative and PI-positive). The results were expressed as the percentages of apoptotic cells (green).</p></sec></sec><sec id=\"sec4dot6-ijms-21-05533\"><title>4.6. Statistical Analysis</title><p>All experiments were independently repeated three times, with multiple replicates within each run. The data represented the mean of three independent experiments in triplicate and were expressed as means ± SD. Statistical analysis was performed using SPSS v.19 Chicago: SPSS Inc. (Chicago, IL, USA).</p><p>The IC<sub>50</sub> value was defined as the concentration causing proliferation inhibition by 50% compared to the control. The IC<sub>50</sub> values are given as mean values ± SD and were calculated according to the method of Litchfield and Wilcoxon [<xref rid=\"B50-ijms-21-05533\" ref-type=\"bibr\">50</xref>].</p><p>Data was tested for normality using a Kolomogorov-Smirnov Test.</p><p>Statistical comparisons were made by one-way ANOVA or Kruskal–Wallis test.</p><p>Tukey’s post-hoc test was used under homoscedasticity conditions. Games-Howell post-hoc test was used on violations of the assumption of homoscedasticity. Dunn’s multiple comparisons test for Kruskal–Wallis tests. The values were considered significantly different when <italic>p</italic> ≤ 0.05.</p></sec></sec><sec sec-type=\"conclusions\" id=\"sec5-ijms-21-05533\"><title>5. Conclusions</title><p>In summary, we found that CB83, a new synthetic cannabinoid characterized by an alkylresorcinol nucleus, has an effect comparable to 5FU, which is still the mainstay drug in protocols for colorectal cancer. CB83 appears to produce its effects mainly through CB2 receptors and is more effective than CBD and THC in the induction of apoptosis and necrosis in HT-29 cells. Furthermore, in this work we observed, like other authors, the cytotoxic effect of CBD on HT-29 cells appears to be independent of the activation of CB1 and CB2 receptors and, instead, to involve the production of ROS and, consequently, oxidative stress, which leads cells to apoptosis and necrosis. Therefore, a combined treatment that exploits the synergistic cytotoxic effects of CB83 and CBD on colon cancer cells could represent an interesting new strategy in colon cancer therapy. Further studies will be needed to evaluate the efficacy and toxicity of CB83 and the CB83 + CBD association in animal models.</p></sec></body><back><ack><title>Acknowledgments</title><p>The authors thank Stefano Menchiari for the excellent technical assistance in making the figures.</p></ack><notes><title>Author Contributions</title><p>Conceptualization, G.C. and D.C.; methodology, A.I.F. and E.M.; validation, A.I.F.; formal analysis, A.M.; investigation, L.M. (Laura Moltoni); resources, A.B.; data curation, G.C. writing—original draft preparation, D.C.; writing—review and editing, L.M. (Lucia Micheli); supervision, L.M. (Lucia Micheli); project administration, D.C. 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Data were statistically evaluated. * <italic>p</italic> < 0.05 and ** <italic>p</italic> < 0.01 vs. control.</p></caption><graphic xlink:href=\"ijms-21-05533-g002\"/></fig><fig id=\"ijms-21-05533-f003\" orientation=\"portrait\" position=\"float\"><label>Figure 3</label><caption><p>Nonenzymatic antioxidant contents: (<bold>a</bold>) glutathione/oxidized glutathione (GSH/GSSG) ratio, (<bold>b</bold>) ascorbic acid (AA) levels, and (<bold>c</bold>) malondhyaldhehyde (MDA) levels in HT-29 cells exposed to IC50 of CB83, THC, CBD, and 5-Fluorouracil (5FU). Data are representative of three independent experiments. For each treatment, the experimental measurements (left side with circle marker) and mean values ± S.D. (right side) are reported. Mean value markers represent the significance vs. control (C): triangle for <italic>p</italic> < 0.05, diamond for <italic>p</italic> < 0.01, and square for <italic>p</italic> < 0.001; hyphen if dataset is not significant.</p></caption><graphic xlink:href=\"ijms-21-05533-g003\"/></fig><fig id=\"ijms-21-05533-f004\" orientation=\"portrait\" position=\"float\"><label>Figure 4</label><caption><p>Antioxidant enzymes activity: (<bold>a</bold>) catalase (CAT), (<bold>b</bold>) glutathione reductase (GR), and (<bold>c</bold>) glutathione peroxidase (GPx) in HT-29 cells exposed to IC<sub>50</sub> of CB83, THC, CBD, and 5FU. Data are representative of three independent experiments. For each treatment, the experimental measurements (left side with circle marker) and mean values ± S.D. (right side) are reported. Mean value markers represent the significance vs. control (C): triangle for <italic>p</italic> < 0.05, diamond for <italic>p</italic> < 0.01, and square for <italic>p</italic> < 0.001; hyphen if dataset is not significant.</p></caption><graphic xlink:href=\"ijms-21-05533-g004\"/></fig><fig id=\"ijms-21-05533-f005\" orientation=\"portrait\" position=\"float\"><label>Figure 5</label><caption><p>Transmission electron microscopy (TEM) sections of cultured HT-29 cells. (<bold>a</bold>) Baseline conditions. The cells showed normal nuclei (N), regular chromatin texture, and cytoplasm (Cy) containing the typical organelles’ mitochondria (M). Bar 3.5 μm. (<bold>b</bold>–<bold>d</bold>) Incubation with CB83, THC, and CBD. In all samples, a high percentage of cells with necrotic and apoptotic features was described. In (<bold>b</bold>), a necrotic cell is evidenced in an altered chromatin texture (aN) and a cytoplasm devoid of organelles (aCy) and, in (<bold>c</bold>), is represented in an apoptotic cell with a cytoplasm rich in vacuoles (V). The cell showed in (<bold>d</bold>) highlights enlargements in the cytoplasm (arrows). This feature was detected in a percentage of 8–12% in all treated samples. (<bold>b</bold>,<bold>d</bold>) Bar 3.5 μm. (<bold>c</bold>) Bar 3 μm.</p></caption><graphic xlink:href=\"ijms-21-05533-g005\"/></fig><table-wrap id=\"ijms-21-05533-t001\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijms-21-05533-t001_Table 1</object-id><label>Table 1</label><caption><p>Results from the MTT, lactate dehydrogenase (LDH), and CyQUANT assays.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Treatment </th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">MTT IC<sub>50</sub> (μM)</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">LDH ratio</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">CyQUANT %</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">C</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.0 ± 0.07</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0 ± 10.57</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CB83</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.0 ± 0.10</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.9 ± 1.01 *</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">53.1 ± 5.47 ***</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">THC</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">30.0 ±1.01 <sup>##</sup></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10.9 ± 0.31 ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">70.9 ± 5.59 **</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CBD</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">30.0 ± 3.02 <sup>##</sup></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.1 ± 0.72 *</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">66.8 ± 7.90 **</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">5FU</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">34.0 ± 13.89 <sup>#</sup></td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">5.9 ± 0.62 ***</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">57.5± 6.15 ***</td></tr></tbody></table><table-wrap-foot><fn><p>Data are representative of three independent experiments and are presented as mean ± S.D. Data were statistically evaluated. <sup>#</sup>\n<italic>p</italic> < 0.05 and <sup>##</sup>\n<italic>p</italic> < 0.01 vs. CB83 and * <italic>p</italic> < 0.05, ** <italic>p</italic> < 0.01, and *** <italic>p</italic> < 0.001 vs. control (C). THC: tetrahydrocannabinol, CBD: cannabidiol, and 5FU: 5-Fluorouracil.</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"ijms-21-05533-t002\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijms-21-05533-t002_Table 2</object-id><label>Table 2</label><caption><p>Results from screening with the Annexin V(AnV)-fluorescein isothiocyanate (FITC) and propidium iodide (PI) assay. Intact cells appeared unstained (AnV- or PI-), apoptotic cells with phosphatidylserine (PS) externalization were green stained with FITC-Annexin (AnV+ PI-), and necrotic cells were red stained (AnV- PI+ or AnV+ PI+) with the broken plasma membrane.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Intact %<break/>AnV-PI-</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Apoptosis %<break/>AnV+PI-</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Necrosis %<break/>AnV+PI+</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">C</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">84.0 ± 1.01</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">11.0 ± 1.15</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.7 ± 1.15</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5FU</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">38.7 ± 1.53 ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">31.3 ± 1.53</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">32.3 ± 2.09 ***</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CB83</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">43.3 ± 0.58 **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">37.3 ± 1.15 ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">19.3 ± 1.53</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CBD</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">53.0 ± 0.10</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">20.6 ± 1,16</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">27.0 ± 1.05 **</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">THC</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">56.3 ± 2.52</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">19.6 ± 2.08</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">25.0 ± 1.73 *</td></tr></tbody></table><table-wrap-foot><fn><p>Data are representative of three independent experiments and are presented as mean ± S.D. Data were statistically evaluated. * <italic>p</italic> < 0.05, ** <italic>p</italic> < 0.01, and *** <italic>p</italic> < 0.001 vs. control (C).</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"ijms-21-05533-t003\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijms-21-05533-t003_Table 3</object-id><label>Table 3</label><caption><p>Results from screening with a transmission electron microscopy (TEM analysis. One hundred cell sections were analyzed for each sample. A cell was considered apoptotic when marginated chromatin, translucent vacuoles, and swollen and badly assembled mitochondria were detected. A cell was considered necrotic when broken plasma membrane and disrupted chromatin were observed.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Apoptosis %</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Necrosis %</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">C</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">12.7 ± 2.08</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.3 ± 1.53</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5FU</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">27.3 ± 1.53 **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">28.3 ± 1.54 ***</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CB83</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">35.0 ± 2.01 ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">26.0 ± 2.05 **</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CBD</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">22.0 ± 1.10</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">22.7 ± 2.52</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">THC</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">20.0 ± 0.01</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">20.0 ± 1.73</td></tr></tbody></table><table-wrap-foot><fn><p>Data are representative of three independent experiments and are presented as mean ±S.D. Data were statistically evaluated ** <italic>p</italic> < 0.01, and *** <italic>p</italic> < 0.001.</p></fn></table-wrap-foot></table-wrap></floats-group></article>\n"
]
[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Int J Environ Res Public Health</journal-id><journal-id journal-id-type=\"iso-abbrev\">Int J Environ Res Public Health</journal-id><journal-id journal-id-type=\"publisher-id\">ijerph</journal-id><journal-title-group><journal-title>International Journal of Environmental Research and Public Health</journal-title></journal-title-group><issn pub-type=\"ppub\">1661-7827</issn><issn pub-type=\"epub\">1660-4601</issn><publisher><publisher-name>MDPI</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32752122</article-id><article-id pub-id-type=\"pmc\">PMC7432099</article-id><article-id pub-id-type=\"doi\">10.3390/ijerph17155559</article-id><article-id pub-id-type=\"publisher-id\">ijerph-17-05559</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Article</subject></subj-group></article-categories><title-group><article-title>Higher Academic Stress Was Associated with Increased Risk of Overweight and Obesity among College Students in China</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Chen</surname><given-names>Yonghua</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05559\">1</xref><xref ref-type=\"aff\" rid=\"af2-ijerph-17-05559\">2</xref><xref ref-type=\"author-notes\" rid=\"fn1-ijerph-17-05559\">†</xref></contrib><contrib contrib-type=\"author\"><name><surname>Liu</surname><given-names>Xi</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05559\">1</xref><xref ref-type=\"author-notes\" rid=\"fn1-ijerph-17-05559\">†</xref></contrib><contrib contrib-type=\"author\"><name><surname>Yan</surname><given-names>Ni</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05559\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Jia</surname><given-names>Wanru</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05559\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Fan</surname><given-names>Yahui</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05559\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Yan</surname><given-names>Hong</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05559\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Ma</surname><given-names>Lu</given-names></name><xref ref-type=\"aff\" rid=\"af3-ijerph-17-05559\">3</xref><xref rid=\"c1-ijerph-17-05559\" ref-type=\"corresp\">*</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0001-7592-9779</contrib-id><name><surname>Ma</surname><given-names>Le</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05559\">1</xref><xref rid=\"c1-ijerph-17-05559\" ref-type=\"corresp\">*</xref></contrib></contrib-group><aff id=\"af1-ijerph-17-05559\"><label>1</label>School of Public Health, Xi’an Jiaotong University Health Science Center, Xi’an 710061, China; <email>[email protected]</email> (Y.C.); <email>[email protected]</email> (X.L.); <email>[email protected]</email> (N.Y.); <email>[email protected]</email> (W.J.); <email>[email protected]</email> (Y.F.); <email>[email protected]</email> (H.Y.)</aff><aff id=\"af2-ijerph-17-05559\"><label>2</label>Research Centre on College Students Ideological Education and Practice, Xi’an Jiaotong University, Xi’an 710061, China</aff><aff id=\"af3-ijerph-17-05559\"><label>3</label>Global Health Institute, School of Public Health, Xi’an Jiaotong University Health Science Center, Xi’an 710061, China</aff><author-notes><corresp id=\"c1-ijerph-17-05559\"><label>*</label>Correspondence: <email>[email protected]</email> (L.M.); <email>[email protected]</email> (L.M.)</corresp><fn id=\"fn1-ijerph-17-05559\"><label>†</label><p>The authors contributed equally to this manuscript.</p></fn></author-notes><pub-date pub-type=\"epub\"><day>31</day><month>7</month><year>2020</year></pub-date><pub-date pub-type=\"ppub\"><month>8</month><year>2020</year></pub-date><volume>17</volume><issue>15</issue><elocation-id>5559</elocation-id><history><date date-type=\"received\"><day>10</day><month>5</month><year>2020</year></date><date date-type=\"accepted\"><day>27</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>© 2020 by the authors.</copyright-statement><copyright-year>2020</copyright-year><license license-type=\"open-access\"><license-p>Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>).</license-p></license></permissions><abstract><p>This study examined associations between academic stress and overweight and obesity, and moderation effects of gender, grade, and types of college on such associations. Data on academic stress, negative learning events, weight, and height were self-reported by 27,343 college students in China in 2018. About 23% and 91% of students perceived high academic stress and suffered from at least one negative learning event during the past six months, respectively, especially for females, undergraduates, and students major in humanities and social science subject groups. Perceived academic stress was associated with increased risk of overweight and obesity among all students (OR = 1.05, 95%CI: 1.00–1.10), male (OR = 1.09, 95%CI: 1.03–1.15), undergraduate (OR = 1.06, 95%CI: 1.00–1.11), and students from subordinate universities (OR = 1.13, 95%CI: 1.01–1.26). Negative learning events were associated with increased risk of overweight and obesity among all students (OR = 1.05, 95%CI: 1.01–1.09), undergraduates (OR = 1.05, 95%CI: 1.01–1.09), and students from local universities (OR = 1.07, 95%CI: 1.00–1.14). Interventions are needed to reduce the high academic stress of college students, considering the modifying effects of gender, grade, and college type. Such interventions may further contribute to the prevention of overweight and obesity among college students.</p></abstract><kwd-group><kwd>overweight</kwd><kwd>obesity</kwd><kwd>college students</kwd><kwd>academic stress</kwd><kwd>negative learning events</kwd></kwd-group></article-meta></front><body><sec sec-type=\"intro\" id=\"sec1-ijerph-17-05559\"><title>1. Introduction</title><p>Youth overweight and obesity has become a serious public health concern, and the prevalence has been increasing at an alarming rate especially in developing countries [<xref rid=\"B1-ijerph-17-05559\" ref-type=\"bibr\">1</xref>]. Among Chinese college students, 22.7% of males and 8.4% of females aged 19–22 years were overweight or obese [<xref rid=\"B2-ijerph-17-05559\" ref-type=\"bibr\">2</xref>]. Excess weight in college students could increase the risk of developing physical (e.g., type 2 diabetes, hypertension) [<xref rid=\"B3-ijerph-17-05559\" ref-type=\"bibr\">3</xref>] and psychological health problems (e.g., depression and weight stigma) [<xref rid=\"B4-ijerph-17-05559\" ref-type=\"bibr\">4</xref>].</p><p>High levels of psychological stress have numerous deleterious effects on academic (e.g., impeding learning abilities), physical, and mental health outcomes among university students [<xref rid=\"B5-ijerph-17-05559\" ref-type=\"bibr\">5</xref>,<xref rid=\"B6-ijerph-17-05559\" ref-type=\"bibr\">6</xref>]. Psychological stress has been found to be a risk factor of overweight and obesity through multiple interacting biological and behavioral pathways [<xref rid=\"B7-ijerph-17-05559\" ref-type=\"bibr\">7</xref>]. A meta-analysis of 14 longitudinal studies showed that stresses including general life stress (caregiver stress, major life events) and job strain were positively associated with risk of obesity, albeit with a modest effect size [<xref rid=\"B8-ijerph-17-05559\" ref-type=\"bibr\">8</xref>]. Furthermore, previous studies suggested that the associations varied by socio-demographic factors. For example, higher perceived life stress was associated with overweight or obesity only among male students [<xref rid=\"B9-ijerph-17-05559\" ref-type=\"bibr\">9</xref>]. Previous study has indicated that leaving home to attend a post-secondary school, itself, is an important reason for stress, and compared with graduates, the transition mainly affected the freshmen and caused the series of adverse outcomes [<xref rid=\"B10-ijerph-17-05559\" ref-type=\"bibr\">10</xref>]. Furthermore, for the students in different majors, especially medical, a large number of studies have shown their high level of stress and the effect on both physical health and mental health [<xref rid=\"B11-ijerph-17-05559\" ref-type=\"bibr\">11</xref>,<xref rid=\"B12-ijerph-17-05559\" ref-type=\"bibr\">12</xref>].</p><p>As one of the most common chronic stressors for college students, prior research has indicated that academic stress may impair self-control and deteriorate health behaviors such as changing the dietary pattern [<xref rid=\"B13-ijerph-17-05559\" ref-type=\"bibr\">13</xref>], in turn, increasing the risk of overweight and obesity. For example, a laboratory study reported that college students took many more calories, carbohydrates, and sugars when academically stressed [<xref rid=\"B14-ijerph-17-05559\" ref-type=\"bibr\">14</xref>]. However, no study has focused on the effects of academic stress and negative learning events on overweight and obesity among college students. More and more college students were reported to be plagued with crippling bouts of academic stress, which refers to stress that occurs in the educational field, such as the worrying of the difficult tasks and being assessed by various tests [<xref rid=\"B15-ijerph-17-05559\" ref-type=\"bibr\">15</xref>], including perceived academic stress and the experience of negative learning events. In China, students’ academic excellence has been a social criterion and even become the only standard for Chinese parents to judge their children [<xref rid=\"B16-ijerph-17-05559\" ref-type=\"bibr\">16</xref>]. Rapid social changes and the implementation of the one-child policy have contributed to elevate academic and job competition and directly increase college student’s academic pressure [<xref rid=\"B17-ijerph-17-05559\" ref-type=\"bibr\">17</xref>]. It was indicated that the Chinese college students were confronting the highest level of academic stress compared with their counterparts in Japan and Korea, and tended to report more school-related negative events than those in United States [<xref rid=\"B18-ijerph-17-05559\" ref-type=\"bibr\">18</xref>,<xref rid=\"B19-ijerph-17-05559\" ref-type=\"bibr\">19</xref>]. However, no study has investigated the academic stress or negative learning events in Chinese college students until now.</p><p>With a large-scale sample of Chinese college students, this study aimed to investigate the associations between academic stress (perceived academic stress and negative learning events) and overweight and obesity. Furthermore, we also examined the potential modifying effects of gender, grade, and college type on such associations. We hypothesized that higher perceived academic stress and negative learning events experiences were associated with increased overweight and obesity among college students in China. Findings would provide insights to design interventions targeting academic stress to fight the obesity epidemic among college students.</p></sec><sec id=\"sec2-ijerph-17-05559\"><title>2. Materials and Methods</title><sec id=\"sec2dot1-ijerph-17-05559\" sec-type=\"subjects\"><title>2.1. Study Design and Participants</title><p>A total of 30,000 students participated in this cross-sectional study in 2018, of which 28,000 were undergraduates and 2000 were graduates. Stratified multilevel cluster random sampling was used to recruit participants at 30 colleges in Shan’xi Province, stratified by college types (subordinates, local, private, and vocational universities). Specifically, 800 undergraduates (200 for each grade from 1 to 4) and 200 graduates were invited in 4 subordinate and 6 local universities, respectively. In the other 6 local colleges and 4 private universities, 1000 undergraduates were recruited, where 300 students were randomly selected from grade 1 and grade 2, while 200 students were selected from grade 3 and grade 4, respectively. The number of the students in the range from grade 1 to 3 who participated in the project were 400, 300, and 300 in vocational universities. All students in the sampled grades were invited to complete the self-administered paper questionnaire in classroom settings in the absence of teachers being supported by a research assistant. It took about 20 min to complete the questionnaire. Trained and experienced research assistants briefed the participants about the study and questions in the questionnaire. Socio-demographic information, perceived academic stress, negative learning events, height, and body weight were used for data analyses. A sample of 27,343 participants (43.5% were boys) with complete key variables data were included in the final data analyses (missing rate = 8.86%).</p><p>Consent was sought from the school principals before the survey. It was announced that return of the completed questionnaire implied informed consent by students. No incentive was provided to the participants. The study was approved by the ethical committee of Xi’an Jiao Tong University, bearing registration number 2017-788 on 11 November 2017.</p></sec><sec id=\"sec2dot2-ijerph-17-05559\"><title>2.2. Variables and Measurements</title><sec id=\"sec2dot2dot1-ijerph-17-05559\"><title>2.2.1. Outcomes</title><p>Overweight and obesity: BMI was calculated based on self-reported body weight (kg) divided by self-reported height (m<sup>2</sup>) squared. Overweight and obesity were defined based on the standard recommended by the Working Group for Obesity in China for Chinese adults (Normal weight: 18.5 kg/m<sup>2</sup> < BMI ≤ 23.9 kg/m<sup>2</sup>, overweight: 24.0 kg/m<sup>2</sup> ≤ BMI ≤ 27.9 kg/m<sup>2</sup>, Obese: BMI ≥ 28.0 kg/m<sup>2</sup>) [<xref rid=\"B20-ijerph-17-05559\" ref-type=\"bibr\">20</xref>].</p></sec><sec id=\"sec2dot2dot2-ijerph-17-05559\"><title>2.2.2. Exposure Variables</title><p>The exposure variables were perceived academic stress and negative learning events. Perceived academic stress was measured by one self-reported item “How much academic stress do you feel in the study?” using a 5-point Likert scale where 1 = “No”, 2 = “relatively low”, 3 = “average/general”, 4 = “relatively high” and 5 = “extremely heavy”, with a higher score indicating more perceived academic stress. The response categories were categorized into three categories in the descriptive analyses: low stress including “no = 1” or “relatively light = 2”, medium stress including “average/general = 3”, and high stress including “relatively high = 4” and “extremely heavy = 5”.</p><p>Negative learning events were measured by asking the participants whether the four negative learning events happened in the past six months (Response: yes or no). The four negative learning events included: “Failure in the exam or unsatisfactory performance”, “Heavy study load”, “family financial difficulties”, and “Pressure for further study”. Students who answered that these events happened in the past 6 months were asked to rate the degree to which these events affected them using a 5-point Likert scale from 1 = “never” to 5 = “extremely heavy”. The average scores of four events were calculated to indicate the negative impacts of the negative learning events, with higher score indicating more negative impacts.</p></sec><sec id=\"sec2dot2dot3-ijerph-17-05559\"><title>2.2.3. Covariates</title><p>Covariates included gender; major (Science and technology, Medical science, Agronomy, Humanities and social science, Economics and management, and Sports and art); college type (Subordinate universities, Local universities, Private universities, and Vocational universities); grades (undergraduate and graduate); academic attainment (below average, average, and above average); and the characteristics of their parents: paternal and maternal education level (≤junior middle school, senior middle school/vocational schools, and ≥college), and household income (<40,000/year, 50,000–100,000/year, and >100,000/year).</p></sec></sec><sec id=\"sec2dot3-ijerph-17-05559\"><title>2.3. Statistical Analysis</title><p>Descriptive statistics were calculated. A Chi-square test (for categorical variables) and <italic>t</italic>-tests (for continuous variables) were conducted to test for the differences of perceived academic stress and the impacts of negative learning events across gender, grade, major, and college types.</p><p>Linear/logistic mixed-effects models were used to examine the associations of academic stress and negative learning events with overweight and obesity after adjusting for covariates using enter procedure. Effect sizes were presented as a beta coefficient with a standard error or odds ratio and a 95% confidence interval (95% CI). Stratified analyses were conducted by gender, grade, and college types to examine the potential modifying effects. Mixed-effects modeling was used to account for clustering effects. Stata 14 (StataCorp, College Station, TX, USA) was used in data analysis Statistical significance was set at <italic>p</italic> < 0.05.</p></sec></sec><sec sec-type=\"results\" id=\"sec3-ijerph-17-05559\"><title>3. Results</title><sec id=\"sec3dot1-ijerph-17-05559\" sec-type=\"subjects\"><title>3.1. Characteristics of Participants</title><p>The basic characteristics of the 27,343 included college students are shown in <xref rid=\"ijerph-17-05559-t001\" ref-type=\"table\">Table 1</xref> (<italic>n</italic> = 11,888 boys (43.4%) and <italic>n</italic> = 15,295 girls). About 13% of them were overweight or obese. Boys were more likely to be overweight and obese than girls (boys vs. girls: 20.8% vs. 7.3%). Most of them (93.4%) were undergraduate students. Most of the students majored in science and technology, and boys were more likely to study science and technology, while girls were more likely to study humanities and social science. About 90% of the students reported their academic attainment as average or above average. The majority of students’ fathers (83.6%) and mothers (86.5%) only attained junior middle school or lower education, and had a household income <40,000 per year (80.8%).</p></sec><sec id=\"sec3dot2-ijerph-17-05559\"><title>3.2. Characteristics of Perceived Academic Stress and Negative Learning Events among College Students</title><p>The prevalence of perceived high academic stress was 22.9%. Girls (23.3%) or students who study humanities and social science (26.5%) were more likely to report high perceived academic stress compared with boys (22.5%) and other major students (proportions ranged from 20.2% to 23.6%), respectively (<xref rid=\"ijerph-17-05559-t002\" ref-type=\"table\">Table 2</xref>).</p><p>Regarding experiencing negative learning events, about 91% of the college students experienced negative learning events in the past 6 months. Females (91.9%), undergraduate students (91.1%), and students studying humanities and social science (92.1%) were more likely to experience negative learning events compared with males (89.4%), graduate students (87.8%), and other major students (proportions ranged from 89.1% to 92.0%), respectively (<italic>p</italic> < 0.001) (<xref rid=\"ijerph-17-05559-t002\" ref-type=\"table\">Table 2</xref>).</p><p>The negative impacts of negative learning events on girls (mean = 8.66), undergraduate students (mean = 8.25), medical science students (mean = 9.22), and vocational university students (mean = 9.11) were significantly (<italic>p</italic> < 0.001) higher than that of males (mean = 8.27), graduate students (mean = 7.05), other major students (mean score ranged from 8.10 to 8.55), and students of other college types (mean score ranged from 7.93 to 8.48), respectively (<xref rid=\"ijerph-17-05559-t002\" ref-type=\"table\">Table 2</xref>).</p></sec><sec id=\"sec3dot3-ijerph-17-05559\"><title>3.3. Associations between Perceived Academic Stress and Overweight and Obesity among College Students, Stratified by Gender, Grade and College Types</title><p>As shown in <xref rid=\"ijerph-17-05559-t003\" ref-type=\"table\">Table 3</xref>, perceived academic stress was significantly associated with increased risk for overweight or obesity for all students (OR = 1.05, 95%CI: 1.00, 1.39), males (OR = 1.09, 95%CI: 1.03, 1.15), undergraduate students (OR = 1.06, 95%CI: 1.00, 1.11), and students from Subordinate universities (OR = 1.13, 95%CI: 1.01, 1.26) after adjusting for covariates. However, these associations were not significant for females, graduates, and students from other types of college.</p></sec><sec id=\"sec3dot4-ijerph-17-05559\"><title>3.4. Associations between Negative Learning Events and Overweight and Obesity among College Students, Stratified by Gender, Grade, and College Type</title><p>As shown in <xref rid=\"ijerph-17-05559-t004\" ref-type=\"table\">Table 4</xref>, all students who reported a higher negative learning events score were more likely to be overweight or obese (OR = 1.05, 95% CI: 1.01, 1.09). No significant gender difference was found. The undergraduate students but not graduate students who reported higher negative learning events score were more likely to be overweight or obese (OR = 1.05, 95% CI: 1.01, 1.09). Moreover, the association between negative learning events and overweight or obesity was also significant among students in local universities (OR = 1.07, 95% CI: 1.00, 1.14).</p></sec></sec><sec sec-type=\"discussion\" id=\"sec4-ijerph-17-05559\"><title>4. Discussion</title><p>This large-scale study found that both academic stress and negative learning events were positively associated with overweight and obesity among Chinese college students, although the association was modest. Additionally, the study indicated that the association is, to some extent, influenced by some demographic factors: only male students, undergraduates, and students in subordinate universities with higher perceived life stress were more likely to be overweight or obese. The effects of negative learning events on overweight and obesity were only found in undergraduates and students in local universities.</p><p>As expected, most of the Chinese college students perceived academic stress, experienced negative learning events, and were impacted by them, which may lead to a series of negative consequences [<xref rid=\"B21-ijerph-17-05559\" ref-type=\"bibr\">21</xref>]. Consistent with the findings of previous studies [<xref rid=\"B22-ijerph-17-05559\" ref-type=\"bibr\">22</xref>], this study indicated that female students reported higher levels of academic stress compared to male students. Females may be more sensitive to the stressors; when being in a stressful situation or suffering negative events they may be more easily affected by the problems and more willing to report their emotions [<xref rid=\"B23-ijerph-17-05559\" ref-type=\"bibr\">23</xref>]. Results of the major differences replicated the previous study which showed that students of medical majors were more academically stressed than students from other majors. Medical students are under much more pressure due to extensive curricula, frequent exams, and strain from working with patients [<xref rid=\"B24-ijerph-17-05559\" ref-type=\"bibr\">24</xref>]. The study also indicates that undergraduates were more easily likely to encounter negative learning events than graduate students. Graduates may master more professional and time management skills and social support than undergraduates which may help them deal with the negative events and be influenced less.</p><p>Psychological stress has been found to be one of the determinants of weight gain through multiple pathways [<xref rid=\"B25-ijerph-17-05559\" ref-type=\"bibr\">25</xref>]. Biological responses to stress include the impairment of gastrointestinal function that slow gastric emptying and promote energy storage, HPA axis dysregulation, and secretion changes of many biochemical substances that are relevant to weight (e.g., cortisol, leptin, ghrelin and neuropeptide Y) [<xref rid=\"B7-ijerph-17-05559\" ref-type=\"bibr\">7</xref>]. Moreover, stress can affect behavior such as an increased food assumption and sedentary lifestyle. A study conducted in seven cities of China reported that college students prefer to choose ready-to-eat food and energy-dense foods and increase their food consumption when perceived as having high stress which in turn promotes positive-energy imbalance and increases the risk of obesity [<xref rid=\"B21-ijerph-17-05559\" ref-type=\"bibr\">21</xref>].</p><p>Epidemiological evidence that linked stress as one of the determinants to the development and maintenance of overweight and obesity was accumulating. A cohort study of Hispanic-Latino adults showed that chronic stress is positively related to obesity, with the obese individuals reporting over three chronic stressors [<xref rid=\"B26-ijerph-17-05559\" ref-type=\"bibr\">26</xref>]. Using data from the Jackson Heart Study, Samson Y et al. showed that African-American adults who perceived higher stress levels were more likely to be obese [<xref rid=\"B27-ijerph-17-05559\" ref-type=\"bibr\">27</xref>]. The epidemiological data of children and adolescents has also showed an association of higher psychological stress with higher odds of obesity [<xref rid=\"B28-ijerph-17-05559\" ref-type=\"bibr\">28</xref>]. This study differs in that we focus on the specific academic stressor conducted in Chinese college students, and across these young adults, perceived stress may result in diverse changes in both physiology and behavior compared to children or older adults. In previous studies, multiple stressors often coexist and have greater influence on individuals than one stressor which may explain why the effect of the academic stress on obesity is smaller than prior similar studies [<xref rid=\"B8-ijerph-17-05559\" ref-type=\"bibr\">8</xref>]. Different measurements of stress and adiposity across the studies may also contribute to the discrepancy. The current study also suggested that a higher incidence and impact of negative learning events may contribute to the likelihood of being overweight and obese. It is possible that people may perceive higher stress after negative events and trigger a stronger stress response which may explain the association [<xref rid=\"B29-ijerph-17-05559\" ref-type=\"bibr\">29</xref>]. Indeed, negative learning events such as exam failure and financial hardship were often dealt with by avoidance through unhealthy behaviors including overeating, drinking, and smoking more [<xref rid=\"B30-ijerph-17-05559\" ref-type=\"bibr\">30</xref>]. Although no known researchers have specifically paid attention to negative learning events, the finding concurred with the Dutch study that showed recent stressful life events were positively related to BMI among young adults [<xref rid=\"B31-ijerph-17-05559\" ref-type=\"bibr\">31</xref>].</p><p>Gender-stratified analyses showed that the negative impacts of academic stress on overweight and obesity were more remarkable in male students. The finding is partly consistent with the study in 50 Chinese universities which showed a positive association between perceived life stress and overweight and obesity [<xref rid=\"B9-ijerph-17-05559\" ref-type=\"bibr\">9</xref>]. Males were found to be more reactive to the stress exposure such as a greater neuroendocrine activation and a high release of cortisol [<xref rid=\"B32-ijerph-17-05559\" ref-type=\"bibr\">32</xref>]. Moreover, male students are more likely to lose behavior control, eating more and exercising less, while female students seem better able to maintain a healthy lifestyle and a healthy weight when in a stressful situation [<xref rid=\"B33-ijerph-17-05559\" ref-type=\"bibr\">33</xref>].</p><p>Perceived academic stress and negative learning events only affected the risk of obesity for undergraduates, not graduates. The different coping strategies response to stressful events of graduates and undergraduates may explain the difference. It was indicated that undergraduates chose to escape from the plight by internet addiction and overeating, which may lead to overweight or obesity, while graduates master more living skills and prefer to solve the problem in a planned way when being in stressful academic situations [<xref rid=\"B34-ijerph-17-05559\" ref-type=\"bibr\">34</xref>].</p><p>Academic life may account for a significant part of college life for students from subordinate universities and local universities. They may have more excellent academic performance which, in turn, leads to higher expectations, and the failure of exams or worry about further study may have greater influence on them compared to students from other colleges. Hence, the association may be more remarkable than others.</p><p>This study has several important strengths. First, the large sample size, about 30,000 college students from different majors, grades, and types of college were recruited in western China which enable us to comprehensively explore the characteristics of academic stress and negative learning events in college students. Another strength was that we analyzed the effects in detail of academic stress and negative learning events on overweight and obesity among college students in different genders, grades, and college types.</p><p>The study also has some limitations. First, the cross-sectional design could not address the causal effects of academic stress variables on overweight and obesity among college students. Other factors associated with academic stress (e.g., the intellectual ability and head circumference) [<xref rid=\"B35-ijerph-17-05559\" ref-type=\"bibr\">35</xref>] and the nutritional status (e.g., dietary intake, physical activity) [<xref rid=\"B36-ijerph-17-05559\" ref-type=\"bibr\">36</xref>] of college students need to be investigated and considered in future work. It is important to continually monitor changes in academic stress over time alongside changes in nutritional status to observe possible effects of each other in cross-lagged analyses. Second, body weight and height were self-reported, which might have weakened the observed associations. Self-reported height and weight are widely used in population-based studies and are closely correlated to those measured in most cases [<xref rid=\"B37-ijerph-17-05559\" ref-type=\"bibr\">37</xref>]. This is especially thought to be the case among college students, who pay much attention to their weight status and may measure their weight frequently, and thus may be more likely to report accurately their weight and height. Third, while we observed the association between academic stress and obesity, we did not further explore the mediators of such associations (e.g., food choice, food intake, and physical activities). Future studies may explore the mediators on the associations between academic stress and obesity. Studies that include participants with different educational stages (e.g., primary and secondary school students) are needed to obtain a better understanding of the associations between academic stress and obesity. Longitudinal studies are also warranted to examine the causal relationship of academic stress and negative learning events with overweight and obesity in college students.</p></sec><sec sec-type=\"conclusions\" id=\"sec5-ijerph-17-05559\"><title>5. Conclusions</title><p>In conclusion, we found that a majority of college students were exposed to high academic stress and negative learning events. Perceived academic stress and negative learning events could increase the risk of being overweight and obese. Interventions are needed to reduce academic stress among college students and may also reduce the prevalence of overweight and obesity.</p></sec></body><back><notes><title>Author Contributions</title><p>The authors’ responsibilities were as follows: L.M. (Lu Ma) and L.M. (Le Ma) designed the research; Y.C. provided essential materials; X.L., N.Y., W.J., H.Y. performed statistical analyses; Y.F., X.L. drafted the manuscript; X.L. and L.M. (Lu Ma) had primary responsibility for the final content and are the guarantors; all authors critically helped in the interpretation of results, revised the manuscript, and provided relevant intellectual input; and all authors have read and agreed to the published version of the manuscript.</p></notes><notes><title>Funding</title><p>This research was partly funded by grants from the National Natural Science Foundation of China (NSFC-81473059), the Natural Science Foundation of Shaanxi Province of China (2017JM8041), New-star Plan of Science and Technology of Shaanxi Province (2015LJXX-07), the China Postdoctoral Science Special Foundation (2015T81036), the Fundamental Research Funds for the Central Universities (qngz2016004), and the Nutrition Research Foundation Fund of the Chinese Nutrition Society–DSM Special Research Foundation (CNSDSM 2016–041).</p></notes><notes notes-type=\"COI-statement\"><title>Conflicts of Interest</title><p>The authors declare no conflict of interest.</p></notes><ref-list><title>References</title><ref id=\"B1-ijerph-17-05559\"><label>1.</label><element-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Flegal</surname><given-names>K.M.</given-names></name><name><surname>Carroll</surname><given-names>M.D.</given-names></name><name><surname>Kit</surname><given-names>B.K.</given-names></name><name><surname>Ogden</surname><given-names>C.L.</given-names></name></person-group><article-title>Prevalence of obesity and trends in the distribution of body mass index among US adults, 1999–2010</article-title><source>JAMA</source><year>2012</year><volume>307</volume><fpage>491</fpage><lpage>497</lpage><pub-id pub-id-type=\"doi\">10.1001/jama.2012.39</pub-id><pub-id pub-id-type=\"pmid\">22253363</pub-id></element-citation></ref><ref id=\"B2-ijerph-17-05559\"><label>2.</label><element-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Zhang</surname><given-names>Y.X.</given-names></name><name><surname>Wang</surname><given-names>S.R.</given-names></name><name><surname>Zhao</surname><given-names>J.S.</given-names></name><name><surname>Chu</surname><given-names>Z.H.</given-names></name></person-group><article-title>Prevalence of overweight and central obesity and their relationship with blood pressure among college students in Shandong, China</article-title><source>Blood Press Monit.</source><year>2016</year><volume>21</volume><fpage>251</fpage><lpage>254</lpage><pub-id pub-id-type=\"doi\">10.1097/MBP.0000000000000189</pub-id><pub-id pub-id-type=\"pmid\">27089507</pub-id></element-citation></ref><ref id=\"B3-ijerph-17-05559\"><label>3.</label><element-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Campbell</surname><given-names>P.T.</given-names></name></person-group><article-title>Obesity: A certain and avoidable cause of cancer</article-title><source>Lancet</source><year>2014</year><volume>384</volume><fpage>727</fpage><lpage>728</lpage><pub-id pub-id-type=\"doi\">10.1016/S0140-6736(14)61172-7</pub-id><pub-id pub-id-type=\"pmid\">25129326</pub-id></element-citation></ref><ref id=\"B4-ijerph-17-05559\"><label>4.</label><element-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Hunger</surname><given-names>J.M.</given-names></name><name><surname>Tomiyama</surname><given-names>A.J.</given-names></name></person-group><article-title>Weight labeling and obesity: A longitudinal study of girls aged 10 to 19 years</article-title><source>JAMA Pediatr.</source><year>2014</year><volume>168</volume><fpage>579</fpage><lpage>580</lpage><pub-id pub-id-type=\"doi\">10.1001/jamapediatrics.2014.122</pub-id><pub-id pub-id-type=\"pmid\">24781349</pub-id></element-citation></ref><ref id=\"B5-ijerph-17-05559\"><label>5.</label><element-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Turner</surname><given-names>K.</given-names></name><name><surname>McCarthy</surname><given-names>V.L.</given-names></name></person-group><article-title>Stress and anxiety among nursing students: A review of intervention strategies in literature between 2009 and 2015</article-title><source>Nurse Educ. 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Behav.</source><year>2007</year><volume>92</volume><fpage>548</fpage><lpage>553</lpage><pub-id pub-id-type=\"doi\">10.1016/j.physbeh.2007.04.032</pub-id><pub-id pub-id-type=\"pmid\">17537466</pub-id></element-citation></ref></ref-list></back><floats-group><table-wrap id=\"ijerph-17-05559-t001\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05559-t001_Table 1</object-id><label>Table 1</label><caption><p>Demographic characteristics of the Chinese college students (Mean (SD)/%) in 2018.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Characteristics</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Overall<break/>(<italic>n</italic> = 27,343)</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Boys<break/>(<italic>n</italic> = 11,888)</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Girls<break/>(<italic>n</italic> = 15,295)</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><italic>p</italic>-Value<break/>across Gender <sup>a</sup></th></tr></thead><tbody><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Grade</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.693</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> Undergraduate</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">93.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">93.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">93.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"> Graduate</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">6.6</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">6.5</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">6.6</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Major</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.001</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> Science and technology</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">37.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">57.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">21.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> Medical science</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">18.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> Agronomy</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> Humanities and social science</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">19.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">28.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> Economics and management</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">18.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">14.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">22.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"> Sports and art</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">5.7</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">5.5</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">6.3</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">BMI (kg/m<sup>2</sup>)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">20.9 ± 3.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">21.8 ± 4.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">20.1 ± 3.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.001</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Overweight and obesity <sup>c</sup></td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">13.2</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">20.8</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">7.3</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><0.001</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Academic attainment</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.001</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> Below average</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">28.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3180 (27.0%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4348 (28.7%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> Average</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">58.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6529 (55.4%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">9318 (61.6%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"> Above average</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">13.1</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2081 (17.7%)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1464 (9.7%)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Paternal education level</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.001</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> ≤Junior middle school</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">83.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">82.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">84.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> Senior middle school/vocational schools</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">14.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">15.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"> ≥College</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.7</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.5</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Maternal education level</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.001</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> ≤Junior middle school</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">86.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">85.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">87.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> Senior middle school/vocational schools</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">12.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">11.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"> ≥College</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.4</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.7</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.1</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Household income</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> <40,000/year</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">80.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">78.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">82.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.001</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> 50,000–100,000/year</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">14.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"> >100,000/year</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">5.3</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">6.6</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">4.3</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"/></tr></tbody></table><table-wrap-foot><fn><p>Abbreviation: BMI: body mass index; Undergraduate include undergraduate students in Grade 1, 2, 3, 4, and 5; Graduate include graduate students in Grade 1, 2, and 3. <sup>a</sup>: <italic>p</italic>-value was based on Chi-square test for categorical variables and <italic>t</italic>-tests for continuous variables across genders; <sup>c</sup>: Overweight and obesity were defined by Working Group for Obesity in China for Chinese adults (overweight: Body mass index: 24.0–27.9 kg/m<sup>2</sup>; obesity: Body mass index: ≥28 kg/m<sup>2</sup>).</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"ijerph-17-05559-t002\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05559-t002_Table 2</object-id><label>Table 2</label><caption><p>Characteristics of academic stress and negative learning events among college students in China stratified by gender, grade, major, and college type.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">All and Subgroups</th><th colspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Perceived Academic Stress</th><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">\n<italic>p</italic>\n<sup>a</sup>\n</th><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">Negative Learning Events (Yes/%)</th><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">\n<italic>p</italic>\n<sup>a</sup>\n</th><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">Impact of Negative Learning Events <sup>c</sup></th><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">\n<italic>p</italic>\n<sup>a</sup>\n</th></tr><tr><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Low</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Medium</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">High</th></tr></thead><tbody><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">All</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">18.9%</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">58.2%</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">22.9%</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">90.7</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">8.48 ± 4.39</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Gender</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.001</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.001</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> Male</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">21.5%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">56.1%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">22.5%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">89.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.001</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8.27 ± 4.55</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"> Female</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">16.9%</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">59.8%</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">23.3%</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">91.9</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">8.66 ± 4.24</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Grade <sup>b</sup></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.074</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.001</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.001</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> Undergraduate</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">20.3%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">55.9%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">23.7%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">91.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8.25 ± 4.28</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"> Graduate</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">18.5%</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">56.3%</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">25.0%</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">87.8</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">7.05 ± 4.36</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Major</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.001</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.001</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.001</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> Science and technology</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">19.5%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">57.5%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">23.0%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">90.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8.35 ± 4.40</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> Medical science</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">14.5%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">61.9%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">23.6%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">92.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">9.22 ± 4.27</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> Agronomy</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">21.6%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">56.4%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">22.0%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">89.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8.10 ± 4.32</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> Humanities and social science</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">16.4%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">57.0%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">26.5%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">92.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8.51 ± 4.30</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> Economics and management</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">21.2%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">58.5%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">20.3%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">90.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8.55 ± 4.40</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"> Sports and art</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">22.0%</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">57.9%</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">20.2%</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">89.1</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">7.88 ± 4.37</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">College type</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.308</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.199</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.001</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> Subordinates universities</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">22.4%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">55.9%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">24.7%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">91.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7.93 ± 4.15</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> Local universities</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">19.5%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">57.0%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">23.4%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">91.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8.36 ± 4.31</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> Private universities</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">18,4%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">60.3%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">21.3%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">90.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8.48 ± 4.39</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"> Vocational universities</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">17.1%</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">61.1%</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">21.8%</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">91.4</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">9.11 ± 4.39</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"/></tr></tbody></table><table-wrap-foot><fn><p>Variable definition: Score of “impact of negative learning events” was calculated by summing the scores of the four items in the scale, and higher score more negative impact of negative learning events; <sup>a</sup>: <italic>p</italic>-value was based on Chi-square test for perceived academic stress and negative learning events across genders, grade, major, and college type. <sup>b</sup>: The difference of academic stress between graduate and undergraduate was only compared among students from Subordinate and local universities (sample size: 14,494), as only these universities had graduate students. <sup>c</sup>: The participants were asked to rate the degree to which the four negative learning events affected them using a 5-point Likert scale where “never = 1” to “extremely heavy = 5”. The average scores of four events were calculated to indicate the negative impacts of the negative learning events, with higher score indicating more negative events.</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"ijerph-17-05559-t003\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05559-t003_Table 3</object-id><label>Table 3</label><caption><p>Mixed-effects model for the associations between perceived academic stress and overweight and obesity among college students in China, stratified by gender, grade, and college type (<italic>n</italic> = 27,343).</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">All and Subgroups</th><th colspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Overweight and Obesity</th></tr><tr><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">OR</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">95%CI</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>p</italic>\n</th></tr></thead><tbody><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">All <sup>a</sup></td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>1.05</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>(1.00, 1.10)</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>0.039</bold>\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Gender <sup>b</sup></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> Male</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>1.09</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>(1.03, 1.15)</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>0.002</bold>\n</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"> Female</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.949</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">(0.87, 1.03)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.235</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Grade <sup>c</sup></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> Undergraduate</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>1.06</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>(1.00, 1.11)</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>0.032</bold>\n</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"> Graduate</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.98</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">(0.84, 1.14)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.753</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">College type <sup>d</sup></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> Subordinates universities</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>1.13</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>(1.01, 1.26)</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>0.038</bold>\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> Local universities</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.02</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">(0.94, 1.10)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.659</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> Private universities</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.07</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">(0.95, 1.22)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.266</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"> Vocational universities</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.04</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">(0.96, 1.14)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.331</td></tr></tbody></table><table-wrap-foot><fn><p>Overweight and obesity were defined by Working Group for Obesity in China for Chinese adults (overweight: Body mass index: 24.0–27.9 kg/m<sup>2</sup>; obesity: Body mass index: ≥28 kg/m<sup>2</sup>); Non-overweight = 0, overweight or obesity = 1. Linear/logistic mixed-effects model was used to analyze the associations between perceived academic stress and weight status. <sup>a</sup>: The mixed-effects model adjusted gender, grade, major, college type, father and mother education level, household income, and academic attainment. <sup>b</sup>: The mixed-effects model adjusted grade, major, college type, father and mother education level, household income, and academic attainment. <sup>c</sup>: The mixed-effects model adjusted gender, major, college type, father and mother education level, household income, and academic attainment. <sup>d</sup>: The mixed-effects model adjusted gender, major, grade, father and mother education level, household income, and academic attainment. Numbers in bold indicate statistical significance.</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"ijerph-17-05559-t004\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05559-t004_Table 4</object-id><label>Table 4</label><caption><p>Mixed-effects model for the associations between negative learning events and overweight and obesity among college students in China, stratified by gender, grade, and college type (<italic>n</italic> = 27,343).</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">All and Subgroups</th><th colspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Overweight and Obesity</th></tr><tr><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">OR</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">95% CI</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>p</italic>\n</th></tr></thead><tbody><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">All <sup>a</sup></td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>1.05</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>(1.01, 1.09)</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>0.009</bold>\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Gender <sup>b</sup></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> Male</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"> Female</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.02</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">(0.95, 1.09)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.608</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Grade <sup>c</sup></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> Undergraduate</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>1.05</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>(1.01, 1.09)</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>0.011</bold>\n</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"> Graduate</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.05</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">(0.94, 1.19)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.352</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">College type <sup>d</sup></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> Subordinates universities</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.05</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">(0.96, 1.16)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.228</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> Local universities</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>1.07</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>(1.00, 1.14)</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>0.022</bold>\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> Private universities</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.04</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">(0.95, 1.14)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.411</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"> Vocational universities</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.05</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">(0.98, 1.11)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.155</td></tr></tbody></table><table-wrap-foot><fn><p>Overweight and obesity were defined by Working Group for Obesity in China for Chinese adults (overweight: Body mass index: 24.0–27.9 kg/m<sup>2</sup>; obesity: Body mass index: 28 kg/m<sup>2</sup>); Non-overweight = 0, overweight or obesity = 1. Linear/logistic mixed-effects model was used to analyze the associations between negative learning events and weight status. <sup>a</sup>: The mixed-effects model adjusted gender, grade, major, college type, father and mother education level, household income, and academic attainment. <sup>b</sup>: The mixed-effects model adjusted grade, major, college type, father and mother education level, household income, and academic attainment. <sup>c</sup>: The mixed-effects model adjusted gender, major, college type, father and mother education level, household income, and academic attainment. <sup>d</sup>: The mixed-effects model adjusted gender, major, grade, father and mother education level, household income, and academic attainment. Numbers in bold indicate statistical significance.</p></fn></table-wrap-foot></table-wrap></floats-group></article>\n"
]
[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Int J Environ Res Public Health</journal-id><journal-id journal-id-type=\"iso-abbrev\">Int J Environ Res Public Health</journal-id><journal-id journal-id-type=\"publisher-id\">ijerph</journal-id><journal-title-group><journal-title>International Journal of Environmental Research and Public Health</journal-title></journal-title-group><issn pub-type=\"ppub\">1661-7827</issn><issn pub-type=\"epub\">1660-4601</issn><publisher><publisher-name>MDPI</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32752119</article-id><article-id pub-id-type=\"pmc\">PMC7432100</article-id><article-id pub-id-type=\"doi\">10.3390/ijerph17155558</article-id><article-id pub-id-type=\"publisher-id\">ijerph-17-05558</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Protocol</subject></subj-group></article-categories><title-group><article-title>Health Promoting School Interventions in Latin America: A Systematic Review Protocol on the Dimensions of the RE-AIM Framework</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Bastos</surname><given-names>Patrícia de Oliveira</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05558\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Cavalcante</surname><given-names>Ana Suelen Pedroza</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05558\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Pereira</surname><given-names>Wallingson Michael Gonçalves</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05558\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>de Castro</surname><given-names>Victor Hugo Santos</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05558\">1</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0002-9483-8060</contrib-id><name><surname>Ferreira Júnior</surname><given-names>Antonio Rodrigues</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05558\">1</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0003-4239-0716</contrib-id><name><surname>Guerra</surname><given-names>Paulo Henrique</given-names></name><xref ref-type=\"aff\" rid=\"af2-ijerph-17-05558\">2</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0002-7356-1680</contrib-id><name><surname>da Silva</surname><given-names>Kelly Samara</given-names></name><xref ref-type=\"aff\" rid=\"af3-ijerph-17-05558\">3</xref></contrib><contrib contrib-type=\"author\"><name><surname>da Silva</surname><given-names>Maria Rocineide Ferreira</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05558\">1</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0002-4769-4068</contrib-id><name><surname>Barbosa Filho</surname><given-names>Valter Cordeiro</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05558\">1</xref><xref ref-type=\"aff\" rid=\"af4-ijerph-17-05558\">4</xref><xref rid=\"c1-ijerph-17-05558\" ref-type=\"corresp\">*</xref></contrib></contrib-group><aff id=\"af1-ijerph-17-05558\"><label>1</label>Post-graduate Program in Collective Health, Ceara State University, Fortaleza 60714-903, Brazil; <email>[email protected]</email> (P.d.O.B.); <email>[email protected]</email> (A.S.P.C.); <email>[email protected]</email> (W.M.G.P.); <email>[email protected]</email> (V.H.S.d.C.); <email>[email protected]</email> (A.R.F.J.); <email>[email protected]</email> (M.R.F.d.S.)</aff><aff id=\"af2-ijerph-17-05558\"><label>2</label>Federal University of Fronteira Sul, Chapeco 89815-899, Brazil; <email>[email protected]</email></aff><aff id=\"af3-ijerph-17-05558\"><label>3</label>Research Center for Physical Activity and Health, Federal University of Santa Catarina, Florianopolis 88040-900, Brazil; <email>[email protected]</email></aff><aff id=\"af4-ijerph-17-05558\"><label>4</label>Federal Institute of Education, Science and Technology of Ceara, Aracati 62800-000, Brazil</aff><author-notes><corresp id=\"c1-ijerph-17-05558\"><label>*</label>Correspondence: <email>[email protected]</email></corresp></author-notes><pub-date pub-type=\"epub\"><day>31</day><month>7</month><year>2020</year></pub-date><pub-date pub-type=\"ppub\"><month>8</month><year>2020</year></pub-date><volume>17</volume><issue>15</issue><elocation-id>5558</elocation-id><history><date date-type=\"received\"><day>12</day><month>6</month><year>2020</year></date><date date-type=\"accepted\"><day>29</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>© 2020 by the authors.</copyright-statement><copyright-year>2020</copyright-year><license license-type=\"open-access\"><license-p>Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>).</license-p></license></permissions><abstract><p>Understanding the dimensions of internal and external validities (e.g., using the RE-AIM model: Reach, Effectiveness/Efficacy, Adoption, Implementation, and Maintenance) of school interventions is important to guide research and practice in this context. The aim of this systematic review protocol is to synthesize evidence on the RE-AIM dimensions in interventions based on the Health Promoting School (HPS) approach from the World Health Organization (WHO) in Latin America. Studies of interventions based on HPS-WHO that were carried out in Latin America involving the population of 5 to 18-year-olds will be eligible. Searches in nine electronic databases, a study repository, the gray literature, and the retrieved articles’ reference lists will be performed, without year or publication language limits. Study selection and data extraction will be conducted by independent researchers. Data on intervention implementation will be summarized in categories of HPS-WHO actions: (1) school curriculum, (2) changes in the social and/or physical environment of schools, and (3) actions with families and the community. A previously validated tool will be used to summarize the information on the dimensions of the RE-AIM model. The strengths and limitations of the included studies will be evaluated using the Critical Appraisal Skills Program (CASP) tool, and the confidence level of evidence will be assessed according to the GRADE CERQual tool.</p></abstract><kwd-group><kwd>health promotion at school</kwd><kwd>school health</kwd><kwd>systematic review</kwd><kwd>Latin America</kwd><kwd>policy making</kwd><kwd>implementation science</kwd><kwd>school age populations</kwd><kwd>program evaluation</kwd></kwd-group></article-meta></front><body><sec sec-type=\"intro\" id=\"sec1-ijerph-17-05558\"><title>1. Introduction</title><p>Health promotion in childhood and adolescence has a significant impact on health, society, and the economy [<xref rid=\"B1-ijerph-17-05558\" ref-type=\"bibr\">1</xref>,<xref rid=\"B2-ijerph-17-05558\" ref-type=\"bibr\">2</xref>]. Globally, more than 1.1 million deaths due to preventable or treatable causes [<xref rid=\"B3-ijerph-17-05558\" ref-type=\"bibr\">3</xref>] are estimated among adolescents aged between 10 and 19 years old (more than 3000 per day). The countries of Latin America and the Caribbean are characterized by being the most unequal region in the world in terms of income distribution [<xref rid=\"B4-ijerph-17-05558\" ref-type=\"bibr\">4</xref>]. This matrix of social inequality results in health inequities, as it directly influences the choice of quality food, housing conditions, risk behaviors and access to health services [<xref rid=\"B5-ijerph-17-05558\" ref-type=\"bibr\">5</xref>]. Latin American and Caribbean countries stand out on the global scenario with the highest child homicide rates and on worrying estimates of risk factors for the development of chronic non-communicable diseases [<xref rid=\"B2-ijerph-17-05558\" ref-type=\"bibr\">2</xref>,<xref rid=\"B5-ijerph-17-05558\" ref-type=\"bibr\">5</xref>,<xref rid=\"B6-ijerph-17-05558\" ref-type=\"bibr\">6</xref>]. Therefore, confrontation of health harms and health promotion for the young population of this region is imperative in order to decrease these health inequities.</p><p>The school has been recognized as an appropriate context for consolidating actions in health promotion and confronting social vulnerabilities suffered in childhood and adolescence [<xref rid=\"B7-ijerph-17-05558\" ref-type=\"bibr\">7</xref>,<xref rid=\"B8-ijerph-17-05558\" ref-type=\"bibr\">8</xref>,<xref rid=\"B9-ijerph-17-05558\" ref-type=\"bibr\">9</xref>]. In 1997, the World Health Organization (WHO) issued the technical report “Promoting Health Through Schools”, which recommends measures and political actions that allow the school to use its full potential to improve the health of children, adolescents, their families, school staff, and the community [<xref rid=\"B10-ijerph-17-05558\" ref-type=\"bibr\">10</xref>].</p><p>Thus, the WHO’s Health Promoting Schools (HPS-WHO) proposal includes a wide school health care model that goes beyond the biomedical perspectives and traditional education [<xref rid=\"B10-ijerph-17-05558\" ref-type=\"bibr\">10</xref>]. In this perspective, school health intervention strategies should seek planning with a view to education and to health, promoting flexibility in curricula in a participative manner that focuses on meeting students’ well-being and health needs in their totality [<xref rid=\"B7-ijerph-17-05558\" ref-type=\"bibr\">7</xref>,<xref rid=\"B11-ijerph-17-05558\" ref-type=\"bibr\">11</xref>,<xref rid=\"B12-ijerph-17-05558\" ref-type=\"bibr\">12</xref>].</p><p>Dimensions related to internal validity (which refers to how true the results of the study are for the studied population) and external validity (aimed at extending the results to other representatives of the population of interest) are important to elucidate the potential impact on public health of populational-scale intervention [<xref rid=\"B13-ijerph-17-05558\" ref-type=\"bibr\">13</xref>,<xref rid=\"B14-ijerph-17-05558\" ref-type=\"bibr\">14</xref>]. In this regard, the RE-AIM model (Reach, Effectiveness/Efficacy, Adoption, Implementation, and Maintenance) aims to determine if the dimensions can assist in the evaluation of the potential impact on public health interventions and in the creation and implementation of policies and programs on a large-scale [<xref rid=\"B15-ijerph-17-05558\" ref-type=\"bibr\">15</xref>,<xref rid=\"B16-ijerph-17-05558\" ref-type=\"bibr\">16</xref>,<xref rid=\"B17-ijerph-17-05558\" ref-type=\"bibr\">17</xref>]. One of the main dimensions of the RE-AIM model for stakeholders, police-makers, and practitioners is the implementation [<xref rid=\"B18-ijerph-17-05558\" ref-type=\"bibr\">18</xref>,<xref rid=\"B19-ijerph-17-05558\" ref-type=\"bibr\">19</xref>]. Elements such as acceptability, appropriateness, feasibility, fidelity, cost, and penetration are necessary for decision-making on whether or not to carry out new treatments, practices, and health services [<xref rid=\"B20-ijerph-17-05558\" ref-type=\"bibr\">20</xref>,<xref rid=\"B21-ijerph-17-05558\" ref-type=\"bibr\">21</xref>]. Knowing these elements can assist administrators in carrying out these actions on a large scale and in analyzing whether these elements have increased the chances of success in the school population’s health [<xref rid=\"B21-ijerph-17-05558\" ref-type=\"bibr\">21</xref>]. Thus, the RE-AIM model is favorable because it allows a balance between the elements of internal and external validity of interventions at both the organizational and the individual levels [<xref rid=\"B17-ijerph-17-05558\" ref-type=\"bibr\">17</xref>,<xref rid=\"B22-ijerph-17-05558\" ref-type=\"bibr\">22</xref>,<xref rid=\"B23-ijerph-17-05558\" ref-type=\"bibr\">23</xref>], thus its widespread use worldwide [<xref rid=\"B17-ijerph-17-05558\" ref-type=\"bibr\">17</xref>].</p><p>Previous reports have summarized the relevant information about interventions based on HPS-WHO [<xref rid=\"B7-ijerph-17-05558\" ref-type=\"bibr\">7</xref>,<xref rid=\"B8-ijerph-17-05558\" ref-type=\"bibr\">8</xref>,<xref rid=\"B11-ijerph-17-05558\" ref-type=\"bibr\">11</xref>,<xref rid=\"B24-ijerph-17-05558\" ref-type=\"bibr\">24</xref>,<xref rid=\"B25-ijerph-17-05558\" ref-type=\"bibr\">25</xref>]. Two studies have synthesized data on the HPS-WHO and showed that interventions promote positive changes in different scholars’ health indicators, such as nutritional status, physical fitness, physical activity, fruit and vegetable intake, tobacco use, and bullying [<xref rid=\"B7-ijerph-17-05558\" ref-type=\"bibr\">7</xref>,<xref rid=\"B11-ijerph-17-05558\" ref-type=\"bibr\">11</xref>]. However, only two of the 67 interventions analyzed were carried out in a Latin America context, and these were conducted in Mexico. Another systematic review summarized data of studies focused on the dimensions that may facilitate or hinder the process of implementing interventions based on HPS-WHO worldwide; however, none have been conducted in the region in question [<xref rid=\"B11-ijerph-17-05558\" ref-type=\"bibr\">11</xref>]. Finally, a systematic review that synthesized studies on HPS-WHO in Latin America from 1996 to 2009 identified only eight studies, but only two of them had experimental designs [<xref rid=\"B9-ijerph-17-05558\" ref-type=\"bibr\">9</xref>,<xref rid=\"B25-ijerph-17-05558\" ref-type=\"bibr\">25</xref>].</p><p>Considering these gaps, studies in countries and contexts of social, economic, and political vulnerabilities that impact young population’s health need to be encouraged and summarized [<xref rid=\"B1-ijerph-17-05558\" ref-type=\"bibr\">1</xref>,<xref rid=\"B2-ijerph-17-05558\" ref-type=\"bibr\">2</xref>,<xref rid=\"B5-ijerph-17-05558\" ref-type=\"bibr\">5</xref>,<xref rid=\"B6-ijerph-17-05558\" ref-type=\"bibr\">6</xref>,<xref rid=\"B10-ijerph-17-05558\" ref-type=\"bibr\">10</xref>,<xref rid=\"B25-ijerph-17-05558\" ref-type=\"bibr\">25</xref>,<xref rid=\"B26-ijerph-17-05558\" ref-type=\"bibr\">26</xref>]. In particular, Latin American countries have peculiar political, economic, and social contexts that are different from countries in other regions in which interventions based on HPS-WHO are carried out (mainly North America and Europe), which fact may influence both children and adolescents’ health and how policies and interventions regarding school health promotion are implemented. A synthesis of evidence with methodological rigor and specific data from these countries may guide the different actors involved in their schools (politicians, managers, teachers, family members, and scholars) in the development and implementation of viable strategies for health promotion in Latin American schools.</p><p>Therefore, this is a systematic review protocol that will evaluate whether the dimensions of internal and external validities (based on the RE-AIM model) are addressed in interventions based on the HPS-WHO in Latin America. A secondary aim of this protocol is to synthesize information (qualitative and quantitative) on the implementation process of the selected interventions.</p></sec><sec id=\"sec2-ijerph-17-05558\"><title>2. Materials and Methods</title><sec id=\"sec2dot1-ijerph-17-05558\"><title>2.1. Protocol and Registration</title><p>This systematic review was based on the methodological guidelines for evaluation of implementation studies proposed by Cochrane [<xref rid=\"B27-ijerph-17-05558\" ref-type=\"bibr\">27</xref>,<xref rid=\"B28-ijerph-17-05558\" ref-type=\"bibr\">28</xref>]. This protocol was registered on the PROSPERO platform (International Prospective Register of Systematic Reviews), under registration number CRD42020168069, with its writing following the guidelines of the Systematic Review and Meta-analysis Protocols (PRISMA-P) (<xref ref-type=\"app\" rid=\"app1-ijerph-17-05558\">Table S1</xref>) [<xref rid=\"B29-ijerph-17-05558\" ref-type=\"bibr\">29</xref>].</p></sec><sec id=\"sec2dot2-ijerph-17-05558\"><title>2.2. Elegibility Criteria</title><p>Eligibility criteria were organized based on the items suggested by the SPIDER strategy (a version of the PICO strategy adapted for qualitative/mixed-method studies, based on Sample, Phenomenon of Interest, Design, Evaluation, and Research type) [<xref rid=\"B30-ijerph-17-05558\" ref-type=\"bibr\">30</xref>].</p><list list-type=\"order\"><list-item><p>Sample: School-aged children and adolescents (aged 5 to 18 years old or the mean age in this age group) who are students of schools located in Latin American countries. Studies regarding exclusively school staff (for example, teachers) and other members of the school community will not be included.</p></list-item><list-item><p>Phenomenon of Interest: Potential studies must have conducted one or more actions in each of the three dimensions of the HPS-WHO model: (1) school curriculum, (2) changes in the social and/or physical environment of schools, and (3) actions with families and the community [<xref rid=\"B11-ijerph-17-05558\" ref-type=\"bibr\">11</xref>]. Due to the plurality of possible results, the presence of the term or direct reference to the WHO report will not be required [<xref rid=\"B7-ijerph-17-05558\" ref-type=\"bibr\">7</xref>]. In addition, strategies should focus on a component of health or well-being (for example, mental health, healthy lifestyle, sexual health, oral health, hygiene, vaccination, substance use, and multi-component interventions) [<xref rid=\"B7-ijerph-17-05558\" ref-type=\"bibr\">7</xref>].</p></list-item><list-item><p>Evaluation: Studies will be included if they present information on one of the aims of this protocol: (1) internal and external validity (RE-AIM model) and (2) evaluation of the implementation process:<list list-type=\"bullet\"><list-item><p>Studies will be considered whether they reported information will include at least one of the five dimensions that represent the RE-AIM model [<xref rid=\"B23-ijerph-17-05558\" ref-type=\"bibr\">23</xref>,<xref rid=\"B31-ijerph-17-05558\" ref-type=\"bibr\">31</xref>]. The reach (R) will represent the absolute number, proportion, and representativeness of individuals who are willing to participate in a given intervention or initiative compared to those who have given up or who have not joined. The effectiveness/efficacy (E) will represent the impact or repercussion of an intervention on important outcomes, whether negative, positive, or financial. Adoption (A) will represent the absolute number, proportion, and representativeness of the agents or organizations that are willing to join the program. Implementation (I) will be at the individual level when the agents use the intervention strategies themselves. At the organizational level, it will refer to the loyalty of the action agents to the various stages of an intervention protocol. Finally, maintenance (M) will represent the long-term beneficial effects at the individual level. At the organizational level, it will represent these effects as a program becomes institutionalized over time, thus being part of the routine, practice, or local policy [<xref rid=\"B15-ijerph-17-05558\" ref-type=\"bibr\">15</xref>,<xref rid=\"B23-ijerph-17-05558\" ref-type=\"bibr\">23</xref>,<xref rid=\"B31-ijerph-17-05558\" ref-type=\"bibr\">31</xref>].</p></list-item><list-item><p>The implementation process will consider the taxonomy of Proctor et al. [<xref rid=\"B21-ijerph-17-05558\" ref-type=\"bibr\">21</xref>]: acceptability, adoption, appropriateness, feasibility, fidelity, implementation cost, penetration, and sustainability. These elements can be measured by evaluating subjects in the intervention plan (children, teachers, school staff, parents, and others), through qualitative, mixed or quantitative methods to measure the results related to the process (if planning or training was carried out, what resources were used, amount, and reach), people/agents (if there was fidelity to the intervention proposal, what adaptations were necessary for each type of context) and products (if objectives were achieved) [<xref rid=\"B20-ijerph-17-05558\" ref-type=\"bibr\">20</xref>,<xref rid=\"B21-ijerph-17-05558\" ref-type=\"bibr\">21</xref>].</p></list-item></list></p></list-item><list-item><p>Research type: Finally, amidst the wide variety of study designs for evaluating the implementation of policies, programs, or practices [<xref rid=\"B19-ijerph-17-05558\" ref-type=\"bibr\">19</xref>], controlled intervention studies will be included, without necessarily requiring randomization for allocation into groups. They will be included regardless of whether data collection and analysis processes were based on quantitative, qualitative, or mixed methods.</p></list-item></list></sec><sec id=\"sec2dot3-ijerph-17-05558\"><title>2.3. Information Sources</title><p>No limits regarding year, language, or publication status will be applied, provided that they will attend the eligibility criteria.</p><p>Searches for potential articles started in May 2020 and will be updated if more than 12 months pass before the publication. The following platforms will be searched:<list list-type=\"bullet\"><list-item><p>Electronic databases (<italic>n</italic> = 9): Medline, PubMed, LILACS, Web of Science, Scopus, PsycINFO, Eric, SciELO, and Cochrane Library;</p></list-item><list-item><p>Gray literature (<italic>n</italic> = 5): Brazilian Digital Library of Theses and Dissertations (BTDT) (<uri xlink:href=\"http://bdtd.ibict.br/vufind/\">http://bdtd.ibict.br/vufind/</uri>), Networked Digital Library of Theses and Dissertations (NDLTD) (<uri xlink:href=\"http://www.ndltd.org/\">http://www.ndltd.org/</uri>), International Clinical Trials Registry Platform (ICTRP) (<uri xlink:href=\"http://apps.who.int/trialsearch/\">http://apps.who.int/trialsearch/</uri>), Brazilian Clinical Trials Registry (REBEC) (<uri xlink:href=\"http://www.ensaiosclinicos.gov.br/\">http://www.ensaiosclinicos.gov.br/</uri>) and Google Scholar (screening of the first 200 results).</p></list-item><list-item><p>Complementary strategies: Search in specialized websites on the theme (<uri xlink:href=\"www.iuhpe.org\">www.iuhpe.org</uri>; www.ashaweb.org; <uri xlink:href=\"www.who.int\">www.who.int</uri>; www.cdc.gov; <uri xlink:href=\"www.unesco.org\">www.unesco.org</uri>; www.freshschools.org; <uri xlink:href=\"www.hbsc.org\">www.hbsc.org</uri>; and <uri xlink:href=\"www.paho.org\">www.paho.org</uri>). Complementary searches will include the screening of the titles in the references of the chosen articles and literature reviews on the theme [<xref rid=\"B7-ijerph-17-05558\" ref-type=\"bibr\">7</xref>,<xref rid=\"B8-ijerph-17-05558\" ref-type=\"bibr\">8</xref>,<xref rid=\"B11-ijerph-17-05558\" ref-type=\"bibr\">11</xref>,<xref rid=\"B22-ijerph-17-05558\" ref-type=\"bibr\">22</xref>,<xref rid=\"B25-ijerph-17-05558\" ref-type=\"bibr\">25</xref>,<xref rid=\"B26-ijerph-17-05558\" ref-type=\"bibr\">26</xref>,<xref rid=\"B32-ijerph-17-05558\" ref-type=\"bibr\">32</xref>].</p></list-item></list></p></sec><sec id=\"sec2dot4-ijerph-17-05558\"><title>2.4. Search Strategy</title><p>In order to produce the best results from the search strategy, the terms were tested and defined based on the DeCS (Health Sciences Descriptors) and MeSH (Medical Subject Headings) of the National Library of Medicine, which were aligned with text words from the scientific literature pertinent to the area, as explained in <xref ref-type=\"app\" rid=\"app1-ijerph-17-05558\">Tables S2 and S3</xref> The search strategy was developed with terms related to the population of interest (children and adolescents from Latin American countries), schools, school health interventions, and experimental studies. Terms were defined in consensus meetings between researchers, including researchers with experience in searching electronic databases, and with the support of reviews on child and adolescent health [<xref rid=\"B1-ijerph-17-05558\" ref-type=\"bibr\">1</xref>,<xref rid=\"B2-ijerph-17-05558\" ref-type=\"bibr\">2</xref>], Health Promoting School [<xref rid=\"B10-ijerph-17-05558\" ref-type=\"bibr\">10</xref>,<xref rid=\"B11-ijerph-17-05558\" ref-type=\"bibr\">11</xref>,<xref rid=\"B24-ijerph-17-05558\" ref-type=\"bibr\">24</xref>], and school-based interventions [<xref rid=\"B7-ijerph-17-05558\" ref-type=\"bibr\">7</xref>]. The Boolean connectors (“OR”, “AND”, and “NOT”) were arranged according to the characteristics and search guidance of each database. Searching in all databases will be performed using terms in English, and databases originated from Brazil (LILACS, SciELO, BTDT, and REBEC) will be searched in Portuguese complementarily. Search strategies were developed acording to the recommendations of the Peer Review of Electronic Search Strategies (PRESS) Statement [<xref rid=\"B33-ijerph-17-05558\" ref-type=\"bibr\">33</xref>].</p></sec><sec id=\"sec2dot5-ijerph-17-05558\"><title>2.5. Study Selection</title><p>All results will be exported to EndNote Web, and one of the authors (P.B.) will remove any duplicates. Two reviewers (P.B. and A.S) will independently analyze the titles and abstracts of each of the results, which will be selected based on the inclusion criteria. Articles that do not meet these criteria will be excluded. Any discrepancies between the two reviewers regarding the included studies will be discussed; then, consensus will be reached by a third author (V.C.).</p><p>After this first search, the full texts will be read independently by the two authors (P.B. and A.S). Disagreements will again be discussed with a third author (V.C.) to reach a consensus. All studies excluded at this stage will be listed and included in the “Table of Excluded Studies”, with statements about the rationale for exclusion. For selected texts that are not available in full, that are unpublished, or that are ongoing studies, the authors will contact the authors to request any necessary information. The PRISMA flow chart will be used to report the study selection process.</p></sec><sec id=\"sec2dot6-ijerph-17-05558\"><title>2.6. Data Extraction</title><p>Data will be extracted by one reviewer and verified by the other (P.B. and A.S.) using a standardized data extraction spreadsheet. Disagreements during this process will be resolved during a consensus meeting involving a third author (V.C.). Authors of unpublished and ongoing studies will be contacted to request any necessary information. Each study included will receive an identifier consisting of the authors’ names and the primary reference year. Publications representing the same study will be combined into a single identifier. For studies with the same authors and the same years, letters in alphabetical order will be added as a third identifier.</p><p>Data related to the implementation of HPS-WHO interventions in Latin American schools will be extracted according to the form contained in <xref ref-type=\"app\" rid=\"app1-ijerph-17-05558\">Table S4</xref>. This form was produced according to guidance provided by Moore et al. [<xref rid=\"B20-ijerph-17-05558\" ref-type=\"bibr\">20</xref>] and Proctor et al. [<xref rid=\"B21-ijerph-17-05558\" ref-type=\"bibr\">21</xref>] on the evaluation of the implementation of data extraction.</p><p>Data regarding intervention studies’ internal and external validity will be extracted. Then, a checklist (<xref ref-type=\"app\" rid=\"app1-ijerph-17-05558\">Table S5</xref>) will be produced according to the RE-AIM model and adapted by Brito et al. [<xref rid=\"B31-ijerph-17-05558\" ref-type=\"bibr\">31</xref>] for use in systematic reviews, with data about the characterization of the studies added. In total, the checklist will consist of 21 items, which will be related to each segment of the RE-AIM model: Reach (five items), Effectiveness/efficacy (four items), Adoption (six items), Implementation (three items), and Maintenance (three items) [<xref rid=\"B31-ijerph-17-05558\" ref-type=\"bibr\">31</xref>].</p></sec><sec id=\"sec2dot7-ijerph-17-05558\"><title>2.7. Assessment of Risk of Bias</title><p>The Critical Assessment Skills Program (CASP) quality checklist will be used to assess the risk of bias in the included studies, considering the possible distinctions between the study designs (e.g., randomized controlled trial, cluster randomized controlled trial, and nonrandomized controlled trial) [<xref rid=\"B34-ijerph-17-05558\" ref-type=\"bibr\">34</xref>]. This tool has been recommended for evidence synthesis that address complex interventions and the evaluation of implementation process [<xref rid=\"B28-ijerph-17-05558\" ref-type=\"bibr\">28</xref>].</p><p>CASP assesses studies’ risk of bias based on 10 questions related to the following domains: objectives, methodology, research design, recruitment strategy, data collection, rigor of data analysis, reflexivity-related issues, ethical issues, discoveries, and contribution to the research. The possible answers to these ten questions are yes, no, unclear, or not applicable.</p><p>Two independent reviewers (P.B. and A.S.) will critically assess the risk of bias in studies referred to in the synthesis. A third reviewer (V.C.) will be consulted for resolution of doubts, agreement or consensus. No study will be excluded based on assessment of the risk of bias; on the contrary, the methodological rigor of each study will be considered for the confidence assessments of each finding in the review. Nevertheless, the strengths and methodological limitations of the studies will be discussed among the authors until they reach a consensus. This will be done based on each item and its impact on the main inferences from the studies and the review. In other words, the use of the total scores or the classification of the methodological quality of the included studies will not be applied [<xref rid=\"B28-ijerph-17-05558\" ref-type=\"bibr\">28</xref>,<xref rid=\"B34-ijerph-17-05558\" ref-type=\"bibr\">34</xref>].</p></sec><sec id=\"sec2dot8-ijerph-17-05558\"><title>2.8. Data Synthesis</title><p>A descriptive synthesis will summarize information about the methodological and general characteristics of the included studies (for example, year and country of implementation, population group, and intervention strategies).</p><p>For the synthesis of evidence on the dimensions of internal and external validity, the scores of the 21-item checklist for each study will be calculated, considering the general and specific score of the domain. The overall quality of the studies will also be assessed and determined based on the reports’ frequency of meeting the 21 items related to the RE-AIM framework. To that end, criteria proposed by Brito et al. [<xref rid=\"B31-ijerph-17-05558\" ref-type=\"bibr\">31</xref>] will be applied in order to classify the overall score as low (0 to 7 points), moderate (8 to 14 points), or high (15 or higher) quality.</p><p>The results of the implementation process will be presented according to the following dimensions: acceptability, adoption, appropriateness, feasibility, fidelity, implementation cost, penetration, and sustainability [<xref rid=\"B20-ijerph-17-05558\" ref-type=\"bibr\">20</xref>,<xref rid=\"B21-ijerph-17-05558\" ref-type=\"bibr\">21</xref>]. Quantitative information will be organized in tables and presented in a narrative synthesis (considering a preliminary heterogeneity of data). Qualitative data will be organized using the IRAMUTEQ (R INTERFACE for multidimensional analysis of texts and questionnaires) software in different stages: similarity analysis, word cloud, classic textual statistics, research on group specificities, and descending hierarchical classification. This categorization will be discussed and defined by two researchers (P.B. and A.S.) during the meetings.</p><p>Finally, the level of confidence of each finding in the qualitative summaries of the implementation process’s dimensions will be analyzed using GRADE CERQual, © Lewin et al., Oslo, Norway [<xref rid=\"B35-ijerph-17-05558\" ref-type=\"bibr\">35</xref>]. This tool provides a systematic and transparent framework for assessing the level of confidence in the conclusions of a review based on the following components: (1) methodological limitations, (2) coherence of the review finding, (3) adequacy of data supporting a review finding, and (4) relevance of the included studies for the review question. Four levels are used to describe the overall assessment of confidence: high, moderate, low, and very low, considering the recommendations for this tool. Two researchers will perform this process (P.B. and A.S.) in a consensus meeting to validate the coding.</p><p>Potential publication biases will be analyzed narratively. For example, the authors will assess whether information about implementation or scores in the RE-AIM dimensions will be different according to the publication status (journal articles vs. unpublished documents), period, and language of publication. This protocol is exempt from ethical approval since it will be a review of previously published articles. Ethical aspects of the included studies will be reported and discussed based on the CASP domain on ethical issues [<xref rid=\"B34-ijerph-17-05558\" ref-type=\"bibr\">34</xref>].</p></sec></sec><sec id=\"sec3-ijerph-17-05558\"><title>3. Final Considerations</title><p>The HPS-WHO approach has been recognized as a suitable model in interventions aimed at combining health and education in an integral way in schools [<xref rid=\"B1-ijerph-17-05558\" ref-type=\"bibr\">1</xref>,<xref rid=\"B7-ijerph-17-05558\" ref-type=\"bibr\">7</xref>,<xref rid=\"B9-ijerph-17-05558\" ref-type=\"bibr\">9</xref>,<xref rid=\"B10-ijerph-17-05558\" ref-type=\"bibr\">10</xref>,<xref rid=\"B25-ijerph-17-05558\" ref-type=\"bibr\">25</xref>]. Considering the potentiality and particularities of this model in Latin America, we propose this systematic review in order to synthesize the level and quality of evidence on intervention of the HPS model in these countries. By emphasizing the implementation process, we can expose which elements are essential for the assertiveness of future interventions or even political decisions about the student’s health. We expect that the clarity of the evidence on the process of implementing these interventions will provide relevant information for the formulation of interventions and public policies to be widely implemented in schools in Latin America. The main components that favor or hinder its implementation in this context will also be addressed.</p><p>Moreover, the evidence-based decision making on health policies and programs in schools in in Latin American countries may consider the results of this protocol because it will summarize dimensions of internal (e.g., whether HPS-WHO intervention may impact students’ health behaviors) and external (e.g., whether HPS-WHO are feasible and suitable for the schools from different areas) validities. Notwithstanding, knowing the quality of the study reports on the elements of internal and external validities in interventions (based on the RE-AIM approach) will indicate which elements need improvement in terms of description and scientific evaluation. This will guide future research and interventions aimed at students’ health in Latin America.</p></sec></body><back><app-group><app id=\"app1-ijerph-17-05558\"><title>Supplementary Materials</title><p>The following are available online at <uri xlink:href=\"https://www.mdpi.com/1660-4601/17/15/5558/s1\">https://www.mdpi.com/1660-4601/17/15/5558/s1</uri>. Table S1: Prisma-P Check List. Table S2: Example of search terms, which will be used at Medline/Pubmed. Table S3: Example of search terms, which will be used at Lilacs, Scielo, BTDT and ReBEC. Table S4: Characteristics to be extracted related to implementation. Table S5: Characteristics to be extracted related to RE-AIM.</p><supplementary-material content-type=\"local-data\" id=\"ijerph-17-05558-s001\"><media xlink:href=\"ijerph-17-05558-s001.pdf\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material></app></app-group><notes><title>Author Contributions</title><p>Conceptualization: P.d.O.B., A.S.P.C., W.M.G.P., V.H.S.d.C., and V.C.B.F.; methodology: P.d.O.B., A.S.P.C., W.M.G.P., V.H.S.d.C., and V.C.B.F.; software: P.d.O.B. and A.S.P.C.; validation: P.d.O.B., A.S.P.C., W.M.G.P., V.H.S.d.C., V.C.B.F., A.R.F.J., M.R.F.d.S., P.H.G., and K.S.d.S.; formal analysis: P.d.O.B., A.S.P.C., W.M.G.P., V.H.S.d.C., V.C.B.F., A.R.F.J., M.R.F.d.S., P.H.G., and K.S.d.S.; investigation: P.d.O.B., A.S.P.C., W.M.G.P., V.H.S.d.C., and V.C.B.F.; data curation, P.d.O.B., A.S.P.C., W.M.G.P., V.H.S.d.C., V.C.B.F., A.R.F.J., M.R.F.d.S., P.H.G., and K.S.d.S.; writing—original draft preparation: P.d.O.B., W.M.G.P., and V.C.B.F.; writing—review and editing: P.d.O.B., A.S.P.C., W.M.G.P., V.H.S.d.C., V.C.B.F., A.R.F.J., M.R.F.d.S., P.H.G., and K.S.d.S.; visualization: P.d.O.B., A.S.P.C., W.M.G.P., V.H.S.d.C., V.C.B.F., A.R.F.J., M.R.F.d.S., P.H.G., and K.S.d.S.; supervision: V.C.B.F., A.R.F.J., M.R.F.d.S., P.H.G., and K.S.d.S.; project administration: P.d.O.B., A.S.P.C., V.H.S.d.C., and V.C.B.F.; All authors have read and agreed to the published version of the manuscript.</p></notes><notes><title>Funding</title><p>This study has no funding for its implementation. Some authors received individual grants from the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior—Brasil Finance Coode 001 (A.S., N. 88882.447752/2019-01; W.M., N. 88882.447540/2019-01; V.H., N. 88887.473351/2020-00). The funding agencies had no participation in the interpretation, analysis, writing, and approval of this manuscript.</p></notes><notes notes-type=\"COI-statement\"><title>Conflicts of Interest</title><p>The authors declare no conflicts of interest.</p></notes><ref-list><title>References</title><ref id=\"B1-ijerph-17-05558\"><label>1.</label><element-citation publication-type=\"book\"><person-group person-group-type=\"author\"><collab>WHO</collab></person-group><source>Global School Health Initiatives: Achieving Health and Education Outcomes. 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[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"review-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Int J Mol Sci</journal-id><journal-id journal-id-type=\"iso-abbrev\">Int J Mol Sci</journal-id><journal-id journal-id-type=\"publisher-id\">ijms</journal-id><journal-title-group><journal-title>International Journal of Molecular Sciences</journal-title></journal-title-group><issn pub-type=\"epub\">1422-0067</issn><publisher><publisher-name>MDPI</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32722576</article-id><article-id pub-id-type=\"pmc\">PMC7432101</article-id><article-id pub-id-type=\"doi\">10.3390/ijms21155297</article-id><article-id pub-id-type=\"publisher-id\">ijms-21-05297</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Review</subject></subj-group></article-categories><title-group><article-title>Subcellular Localization Relevance and Cancer-Associated Mechanisms of Diacylglycerol Kinases</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Fazio</surname><given-names>Antonietta</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05297\">1</xref><xref ref-type=\"author-notes\" rid=\"fn1-ijms-21-05297\">†</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0003-0403-9466</contrib-id><name><surname>Owusu Obeng</surname><given-names>Eric</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05297\">1</xref><xref ref-type=\"author-notes\" rid=\"fn1-ijms-21-05297\">†</xref></contrib><contrib contrib-type=\"author\"><name><surname>Rusciano</surname><given-names>Isabella</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05297\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Marvi</surname><given-names>Maria Vittoria</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05297\">1</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0001-8838-5408</contrib-id><name><surname>Zoli</surname><given-names>Matteo</given-names></name><xref ref-type=\"aff\" rid=\"af2-ijms-21-05297\">2</xref><xref ref-type=\"aff\" rid=\"af3-ijms-21-05297\">3</xref></contrib><contrib contrib-type=\"author\"><name><surname>Mongiorgi</surname><given-names>Sara</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05297\">1</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0001-9684-714X</contrib-id><name><surname>Ramazzotti</surname><given-names>Giulia</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05297\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Follo</surname><given-names>Matilde Yung</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05297\">1</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0001-6027-3156</contrib-id><name><surname>McCubrey</surname><given-names>James A.</given-names></name><xref ref-type=\"aff\" rid=\"af4-ijms-21-05297\">4</xref></contrib><contrib contrib-type=\"author\"><name><surname>Cocco</surname><given-names>Lucio</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05297\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Manzoli</surname><given-names>Lucia</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05297\">1</xref><xref rid=\"c1-ijms-21-05297\" ref-type=\"corresp\">*</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0002-6258-5345</contrib-id><name><surname>Ratti</surname><given-names>Stefano</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05297\">1</xref></contrib></contrib-group><aff id=\"af1-ijms-21-05297\"><label>1</label>Cellular Signalling Laboratory, Department of Biomedical and Neuromotor Sciences (DIBINEM), University of Bologna, Via Irnerio 48, 40126 Bologna, Italy; <email>[email protected]</email> (A.F.); <email>[email protected]</email> (E.O.O.); <email>[email protected]</email> (I.R.); <email>[email protected]</email> (M.V.M.); <email>[email protected]</email> (S.M.); <email>[email protected]</email> (G.R.); <email>[email protected]</email> (M.Y.F.); <email>[email protected]</email> (L.C.); <email>[email protected]</email> (S.R.)</aff><aff id=\"af2-ijms-21-05297\"><label>2</label>Center for the Diagnosis and Treatment of Hypothalamic-Pituitary Diseases-Pituitary Unit, IRCCS Istituto delle Scienze Neurologiche di Bologna (Institute of Neurological Sciences of Bologna), 40126 Bologna, Italy; <email>[email protected]</email></aff><aff id=\"af3-ijms-21-05297\"><label>3</label>Department of Biomedical and Neuromotor Sciences (DIBINEM), University of Bologna, 40126 Bologna, Italy</aff><aff id=\"af4-ijms-21-05297\"><label>4</label>Department of Microbiology and Immunology, Brody School of Medicine at East Carolina University, Greenville, NC 27858, USA; <email>[email protected]</email></aff><author-notes><corresp id=\"c1-ijms-21-05297\"><label>*</label>Correspondence: <email>[email protected]</email></corresp><fn id=\"fn1-ijms-21-05297\"><label>†</label><p>These authors contributed equally to this work.</p></fn></author-notes><pub-date pub-type=\"epub\"><day>26</day><month>7</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><month>8</month><year>2020</year></pub-date><volume>21</volume><issue>15</issue><elocation-id>5297</elocation-id><history><date date-type=\"received\"><day>24</day><month>6</month><year>2020</year></date><date date-type=\"accepted\"><day>24</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>© 2020 by the authors.</copyright-statement><copyright-year>2020</copyright-year><license license-type=\"open-access\"><license-p>Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>).</license-p></license></permissions><abstract><p>An increasing number of reports suggests a significant involvement of the phosphoinositide (PI) cycle in cancer development and progression. Diacylglycerol kinases (DGKs) are very active in the PI cycle. They are a family of ten members that convert diacylglycerol (DAG) into phosphatidic acid (PA), two-second messengers with versatile cellular functions. Notably, some DGK isoforms, such as DGKα, have been reported to possess promising therapeutic potential in cancer therapy. However, further studies are needed in order to better comprehend their involvement in cancer. In this review, we highlight that DGKs are an essential component of the PI cycle that localize within several subcellular compartments, including the nucleus and plasma membrane, together with their PI substrates and that they are involved in mediating major cancer cell mechanisms such as growth and metastasis. DGKs control cancer cell survival, proliferation, and angiogenesis by regulating Akt/mTOR and MAPK/ERK pathways. In addition, some DGKs control cancer cell migration by regulating the activities of the Rho GTPases Rac1 and RhoA.</p></abstract><kwd-group><kwd>diacylglycerol</kwd><kwd>phosphoinositide</kwd><kwd>PI3K/Akt/mTOR</kwd><kwd>DGKs</kwd><kwd>lipids</kwd><kwd>cancer</kwd></kwd-group></article-meta></front><body><sec sec-type=\"intro\" id=\"sec1-ijms-21-05297\"><title>1. Introduction</title><p>Phosphoinositides (PIs) represent a tiny component of the total phospholipid content in eukaryotic cell membranes, but they regulate numerous cellular activities such as cell adhesion [<xref rid=\"B1-ijms-21-05297\" ref-type=\"bibr\">1</xref>], migration [<xref rid=\"B2-ijms-21-05297\" ref-type=\"bibr\">2</xref>], apoptosis [<xref rid=\"B3-ijms-21-05297\" ref-type=\"bibr\">3</xref>], vesicular trafficking [<xref rid=\"B4-ijms-21-05297\" ref-type=\"bibr\">4</xref>], and post-translational modifications [<xref rid=\"B5-ijms-21-05297\" ref-type=\"bibr\">5</xref>]. These processes are consistent with cancer-associated cellular mechanisms. PI metabolism is controlled by several kinases, phosphatases, and phospholipases following their stimulation by different external stimuli. An increasing number of studies report that alterations in the PI cycle, resulting from dysfunctional PI metabolic enzymes, are involved in cancer [<xref rid=\"B6-ijms-21-05297\" ref-type=\"bibr\">6</xref>,<xref rid=\"B7-ijms-21-05297\" ref-type=\"bibr\">7</xref>,<xref rid=\"B8-ijms-21-05297\" ref-type=\"bibr\">8</xref>].</p><p>Diacylglycerol kinases (DGKs) are a family of ten PI metabolic kinases (α, β, γ, δ, ε, ζ, η, θ, ι, and κ) that participate in the PI cycle by catalyzing the phosphorylation of diacylglycerol (DAG) to generate phosphatidic acid (PA), hence, regulating these two-second messengers [<xref rid=\"B9-ijms-21-05297\" ref-type=\"bibr\">9</xref>]. DAG and PA are involved in the regulation of several critical enzymes, including mammalian target of rapamycin (mTOR) [<xref rid=\"B10-ijms-21-05297\" ref-type=\"bibr\">10</xref>], phosphatidylinositol-4-phosphate 5-kinase (PIP5K), the Ras GTPase-activating protein (GAP), rapidly accelerated fibrosarcoma-1 (Raf-1) kinase, protein kinases C (PKCs), mammalian uncoordinated 13 (Unc-13), Chimaerins (which activate Rac GTPase), and rat sarcoma virus guanyl nucleotide-releasing protein (RasGRP), which are involved in cell proliferation and migration [<xref rid=\"B11-ijms-21-05297\" ref-type=\"bibr\">11</xref>,<xref rid=\"B12-ijms-21-05297\" ref-type=\"bibr\">12</xref>]. Although DGKs can utilize different molecular DAG species independent of PI turnover pathways [<xref rid=\"B13-ijms-21-05297\" ref-type=\"bibr\">13</xref>], the conversion of DAG to PA by DGKs represents one of the first steps in PI resynthesis, where phosphatidylinositol 4,5 biphosphate (PtdIns(4,5)P<sub>2</sub>) levels are replenished [<xref rid=\"B14-ijms-21-05297\" ref-type=\"bibr\">14</xref>]. As such, DGK signaling is pivotal in modulating the balance between these two essential bioactive lipids, DAG, and PA. </p><p>Accumulating evidence demonstrates that DGKs, as well as phospholipases C (PLCs) and PKCs, are distributed across several subcellular compartments together with their substrates [<xref rid=\"B15-ijms-21-05297\" ref-type=\"bibr\">15</xref>,<xref rid=\"B16-ijms-21-05297\" ref-type=\"bibr\">16</xref>,<xref rid=\"B17-ijms-21-05297\" ref-type=\"bibr\">17</xref>] and, as PLCs and PKCs, they are involved in cell regulation [<xref rid=\"B18-ijms-21-05297\" ref-type=\"bibr\">18</xref>,<xref rid=\"B19-ijms-21-05297\" ref-type=\"bibr\">19</xref>]. Nuclear localization allows DGKs to participate in a PI cycle which is independent of that of the plasma membrane [<xref rid=\"B20-ijms-21-05297\" ref-type=\"bibr\">20</xref>,<xref rid=\"B21-ijms-21-05297\" ref-type=\"bibr\">21</xref>]. Therefore, DGK activity may regulate distinct cellular functions and explain the complexities that surround DGK signaling [<xref rid=\"B17-ijms-21-05297\" ref-type=\"bibr\">17</xref>]. In fact, DGKs regulate cytokine/growth factor-mediated cell proliferation and migration, cell growth, seizure activity, and insulin receptor-mediated glucose metabolism, suggesting that DGKs may also be involved in several diseases including epilepsy and diabetes [<xref rid=\"B22-ijms-21-05297\" ref-type=\"bibr\">22</xref>,<xref rid=\"B23-ijms-21-05297\" ref-type=\"bibr\">23</xref>]. </p><p>Of particular note is the involvement of DGKs in cancer development and progression [<xref rid=\"B24-ijms-21-05297\" ref-type=\"bibr\">24</xref>,<xref rid=\"B25-ijms-21-05297\" ref-type=\"bibr\">25</xref>,<xref rid=\"B26-ijms-21-05297\" ref-type=\"bibr\">26</xref>]. For instance, mutations in the DGKα gene can drive pancreatic cancer [<xref rid=\"B24-ijms-21-05297\" ref-type=\"bibr\">24</xref>] as well as promote hepatocellular carcinoma (HCC) progression by activating the mitogen-activated protein kinase (MAPK) pathway [<xref rid=\"B27-ijms-21-05297\" ref-type=\"bibr\">27</xref>]. In 3D colon and breast cancer models, DGKα was reported to promote cell survival by regulating Src [<xref rid=\"B28-ijms-21-05297\" ref-type=\"bibr\">28</xref>]. For these reasons, there are several reports suggesting that DGKα may be a promising therapeutic target in cancer therapy [<xref rid=\"B7-ijms-21-05297\" ref-type=\"bibr\">7</xref>,<xref rid=\"B29-ijms-21-05297\" ref-type=\"bibr\">29</xref>]. Meanwhile, in colorectal cancer (CRC), DGKγ plays tumor-suppressive roles, while DGKζ activity promotes tumor progression [<xref rid=\"B26-ijms-21-05297\" ref-type=\"bibr\">26</xref>,<xref rid=\"B30-ijms-21-05297\" ref-type=\"bibr\">30</xref>,<xref rid=\"B31-ijms-21-05297\" ref-type=\"bibr\">31</xref>]. Moreover, DGKη and DGKδ regulate cell growth and proliferation in cervical cancer cell lines [<xref rid=\"B32-ijms-21-05297\" ref-type=\"bibr\">32</xref>,<xref rid=\"B33-ijms-21-05297\" ref-type=\"bibr\">33</xref>], whereas epigenetic changes in DGKι are reported in glioblastoma and HCC cells [<xref rid=\"B34-ijms-21-05297\" ref-type=\"bibr\">34</xref>,<xref rid=\"B35-ijms-21-05297\" ref-type=\"bibr\">35</xref>]. Despite the numerous reports of the involvement of DGKs in cancer as well as their clinical potential, the comprehension of the specific cellular functions regulated by DGKs in cancer is not yet complete. This review aims to provide up-to-date knowledge of the regulatory roles played by DGKs in cancer cell survival and metastasis, while also highlighting the downstream regulation of DGKs, the role of their cellular localization and summing up current knowledge on targeting DGKs in cancer therapies.</p></sec><sec id=\"sec2-ijms-21-05297\"><title>2. Activation and Regulation of DGK Isozymes</title><p>The 10 members of the mammalian DGK family are classified into 5 different subtypes depending on their structural motifs [<xref rid=\"B36-ijms-21-05297\" ref-type=\"bibr\">36</xref>]: type I (DGKs α, β, and γ), type II (DGKs δ, η, and κ), type III (DGKε), type IV (DGKs ζ, and ι) and type V (DGKθ). Moreover, some DGK isoforms undergo further alternative splicing which often changes either the distribution or activity of the enzyme [<xref rid=\"B37-ijms-21-05297\" ref-type=\"bibr\">37</xref>,<xref rid=\"B38-ijms-21-05297\" ref-type=\"bibr\">38</xref>]. Their differences may be attributed to their evolution, in order to regulate specific cellular processes evident in higher vertebrates [<xref rid=\"B39-ijms-21-05297\" ref-type=\"bibr\">39</xref>]. DGKs are ubiquitous kinases that are mainly expressed in the brain and hemopoietic tissue [<xref rid=\"B40-ijms-21-05297\" ref-type=\"bibr\">40</xref>]. They are prominently distributed across several different regions of the brain, including the cerebellum, hippocampus, and the olfactory bulb, therefore suggesting involvement of DGKs in central nervous system functions [<xref rid=\"B41-ijms-21-05297\" ref-type=\"bibr\">41</xref>]. Some DGK isoforms are also expressed in the retina (ε, γ, ι) [<xref rid=\"B41-ijms-21-05297\" ref-type=\"bibr\">41</xref>], in striated (δ and ζ) and cardiac muscle (β and ε) [<xref rid=\"B41-ijms-21-05297\" ref-type=\"bibr\">41</xref>,<xref rid=\"B42-ijms-21-05297\" ref-type=\"bibr\">42</xref>] and in the lungs (α, ε, ζ and η) [<xref rid=\"B43-ijms-21-05297\" ref-type=\"bibr\">43</xref>]. So far, it has been shown that different DGK isoforms can be co-expressed in the same tissue and even in the same cell, suggesting that each subtype may carry out tissue or cell-specific functions [<xref rid=\"B41-ijms-21-05297\" ref-type=\"bibr\">41</xref>].</p><p>Until now, all recognized mammalian DGKs possess two common kinase domains comprising a conserved catalytic domain, which is characterized by a highly conserved ATP binding site and an accessory domain [<xref rid=\"B44-ijms-21-05297\" ref-type=\"bibr\">44</xref>]. The possession of additional distinct domains, that seems to have regulatory roles, as shown in DGK family types, contributes to isoform-specific functions and diversity among mammalian DGK isoforms [<xref rid=\"B17-ijms-21-05297\" ref-type=\"bibr\">17</xref>]. For instance, type I isoforms (α, β, and γ) participate in calcium (Ca<sup>2+</sup>) signaling because of their Ca<sup>2+</sup> binding motif, while the carboxyl terminus-based sterile alpha motif (SAM) of DGKδ, which is a type II DGK, promotes protein-protein interactions [<xref rid=\"B45-ijms-21-05297\" ref-type=\"bibr\">45</xref>]. In addition, DGKδ possesses a PH domain that weakly binds to phosphatidylinositols [<xref rid=\"B46-ijms-21-05297\" ref-type=\"bibr\">46</xref>]. The nuclear localization of type IV DGKs (ζ and ι) is enhanced via their nuclear localization sequence (NLS). This domain also serves as substrate for conventional PKCs and is homologous to the phosphorylation domain of the myristoylated alanine-rich kinase substrate (MARCKS) protein. DGKθ, which is the only type V DGK isoform, can be differentiated by its PH domain, three C1 domains, and a Ras-associating domain that mediates Ras signaling [<xref rid=\"B47-ijms-21-05297\" ref-type=\"bibr\">47</xref>].</p><p>All DGKs possess at least two cysteine-rich regions similar to the DAG-binding C1A and C1B domains of PKC [<xref rid=\"B48-ijms-21-05297\" ref-type=\"bibr\">48</xref>]. The C1 regions of DGKs allow membrane binding either through protein interactions, as demonstrated by several DGKs and β-arrestin [<xref rid=\"B49-ijms-21-05297\" ref-type=\"bibr\">49</xref>] or through lipid interactions, as shown by DGKα and the lipid product of phosphoinositide 3-kinase (PI3K) [<xref rid=\"B50-ijms-21-05297\" ref-type=\"bibr\">50</xref>,<xref rid=\"B51-ijms-21-05297\" ref-type=\"bibr\">51</xref>]. C1 domains are recognized phorbol ester or DAG binding regions, but several studies have tried to evaluate the binding potential of the C1 domains of some DGKs to a DAG analog or phorbol ester reported that only the C1A domain of DGK β and γ displays successful binding [<xref rid=\"B52-ijms-21-05297\" ref-type=\"bibr\">52</xref>]. Therefore, it is not clear whether the C1 domains of all DGKs can actually bind DAG. </p><p>The activation and regulation of DGKs is a very complex process that needs further studies to be fully comprehended [<xref rid=\"B37-ijms-21-05297\" ref-type=\"bibr\">37</xref>]. Given the structural and subcellular localization differences, it may be possible that different activation mechanisms exist for each individual DGK isoform. Considering also the ability of DGKs to translocate to different cellular sites, the presence of post-translational modifications and their binding to different cofactors, such as membrane lipids and Ca<sup>2+</sup>, may produce some degree of diversity in their functions. DAG is accessed by DGKs upon their translocation to DAG-producing cellular membranes, where DGKs are proposed to be activated during agonist or kinase promoted phosphorylation or following their binding to some cofactors or to other proteins [<xref rid=\"B39-ijms-21-05297\" ref-type=\"bibr\">39</xref>]. Indeed, some studies reported that the distinct activities observed in the various DGK isoforms may depend on the type of agonist and the cofactors they bind to during their activation [<xref rid=\"B53-ijms-21-05297\" ref-type=\"bibr\">53</xref>]. For example, DGKα, which is one of the most studied DGK isoforms, demonstrates this complexity in T lymphocytes. Based on the type of agonist used to activate DGKα in T cells, it translocates to two different membrane compartments: stimulation with interleukin 2 (IL-2) induces the translocation of DGKα from the cytosol to the perinuclear region [<xref rid=\"B54-ijms-21-05297\" ref-type=\"bibr\">54</xref>], whereas the translocation from the cytosol to the plasma membrane occurs when DGKα is stimulated by T-cell antigen receptor [<xref rid=\"B55-ijms-21-05297\" ref-type=\"bibr\">55</xref>]. Several different cofactors, such as Ca<sup>2+</sup>, which binds to the EF-hand motif and membrane lipids, including phosphatidylserine (PS), sphingosine, the PI3K lipid products, PtdIns(3,4)P<sub>2</sub>, and PtdIns(3,4,5)P<sub>3</sub> have been reported to modify DGKα activity both in vitro and in vivo [<xref rid=\"B56-ijms-21-05297\" ref-type=\"bibr\">56</xref>]. As for other DGK isoforms, activation of DGKδ may be enhanced by the binding of its PH domain to phosphatidylinositols [<xref rid=\"B41-ijms-21-05297\" ref-type=\"bibr\">41</xref>], DGKε is inhibited by both PtdIns(4,5)P<sub>2</sub> and PS, while DGKζ is activated by both PtdIns(4,5)P<sub>2</sub> and PS [<xref rid=\"B57-ijms-21-05297\" ref-type=\"bibr\">57</xref>]. Moreover, the protein-protein interaction between DGKθ and RhoA is involved in the regulation of the activity of DGKθ, where its kinase activity is completely reduced by RhoA [<xref rid=\"B17-ijms-21-05297\" ref-type=\"bibr\">17</xref>]. </p><p>Several studies showed that the specificity of DGK activities could also be attributed to its association with or inhibition of DAG-activated proteins, such as the RasGRP proteins [<xref rid=\"B58-ijms-21-05297\" ref-type=\"bibr\">58</xref>]. Generally, when DAG is abundant, RasGRPs activate either Rap or Ras, or both, and this mechanism is RasGRP-isoform specific. Consequently, the downstream effects of DGKs diverge because DGK isozymes bind to different isoforms of RasGRP [<xref rid=\"B59-ijms-21-05297\" ref-type=\"bibr\">59</xref>]. Hence, the functional specificity of DGKs depends on their interactions. For example, type IV DGKs, ζ, and ι, are all structurally similar, but they induce opposing effects on Ras signaling. DGKζ attenuates Ras signaling both in vitro [<xref rid=\"B58-ijms-21-05297\" ref-type=\"bibr\">58</xref>,<xref rid=\"B60-ijms-21-05297\" ref-type=\"bibr\">60</xref>] and in vivo [<xref rid=\"B60-ijms-21-05297\" ref-type=\"bibr\">60</xref>], whereas DGKι enhances it [<xref rid=\"B59-ijms-21-05297\" ref-type=\"bibr\">59</xref>]. These opposing effects were mainly dependent on the ability of DGKs ζ and ι to bind and inhibit specific RasGRP enzymes, respectively RasGRP1 and RasGRP3 [<xref rid=\"B58-ijms-21-05297\" ref-type=\"bibr\">58</xref>,<xref rid=\"B59-ijms-21-05297\" ref-type=\"bibr\">59</xref>]. Since the activities of DGKs maintain a balance between DAG and PA levels, DGKs can also be associated with proteins whose activities are regulated by PA. In fact, DGKs regulate either directly or indirectly Rac1 [<xref rid=\"B30-ijms-21-05297\" ref-type=\"bibr\">30</xref>], mTOR [<xref rid=\"B10-ijms-21-05297\" ref-type=\"bibr\">10</xref>], PIP5K type 1α [<xref rid=\"B17-ijms-21-05297\" ref-type=\"bibr\">17</xref>], and atypical PKCs [<xref rid=\"B50-ijms-21-05297\" ref-type=\"bibr\">50</xref>], all regulated by PA while mediating several essential cellular effects such as cell survival, migration, and vesicle trafficking [<xref rid=\"B11-ijms-21-05297\" ref-type=\"bibr\">11</xref>,<xref rid=\"B12-ijms-21-05297\" ref-type=\"bibr\">12</xref>].</p><p>DGKs were initially described as modulators of the classical and novel PKC family members. However, some DGKs form complexes with certain DAG-sensitive PKC isoforms, thus being regulated by PKC-dependent phosphorylation [<xref rid=\"B36-ijms-21-05297\" ref-type=\"bibr\">36</xref>,<xref rid=\"B50-ijms-21-05297\" ref-type=\"bibr\">50</xref>]. Indeed, these DGKs and their respective PKC counterparts mutually regulate each other’s enzyme activities, as seen in the case of DGKζ and PKCα. At immune and nervous synapses, the activation of DGKζ and PKCα is mutually regulated by both kinases [<xref rid=\"B50-ijms-21-05297\" ref-type=\"bibr\">50</xref>]. At basal conditions, DGKζ phosphorylates DAG and prevents PKCα activation [<xref rid=\"B61-ijms-21-05297\" ref-type=\"bibr\">61</xref>] but upon stimulation, there is an overproduction of local DAG levels that is too abundant for DGKζ to phosphorylate, leading to the availability of excess DAG. Consequently, high levels of DAG activate PKCα, which phosphorylates DGKζ, causing their physical dissociation [<xref rid=\"B61-ijms-21-05297\" ref-type=\"bibr\">61</xref>] and promoting transient or even prolonged activation of PKCα. In the context of cancer, the DGKζ-PKCα axis could be important in regulating signaling in tumor cells. For instance, DGKζ undergoes a PKCα-dependent phosphorylation to enhance its shuttle from the nucleus to the cytosol [<xref rid=\"B62-ijms-21-05297\" ref-type=\"bibr\">62</xref>], a biological process which is implicated in cancer cells responding to stress conditions [<xref rid=\"B63-ijms-21-05297\" ref-type=\"bibr\">63</xref>]. Moreover, DGKα is involved in inflammation in tumor cells by positively regulating tumor necrosis factor α (TNFα)-induced nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB) activation via a PKCζ-mediated Ser311 phosphorylation of the NF-κB subunit p65/RelA [<xref rid=\"B64-ijms-21-05297\" ref-type=\"bibr\">64</xref>].</p><p>Overall, DGKs can be activated by several mechanisms which might be isoform-specific, and they produce different cellular outcomes based on the type of co-factors and proteins they associate with. The concept of DGK-isoform specific functions is supported by mouse knockout studies showing that targeted deletion of specific DGK isoforms leads to a distinct phenotype [<xref rid=\"B59-ijms-21-05297\" ref-type=\"bibr\">59</xref>,<xref rid=\"B65-ijms-21-05297\" ref-type=\"bibr\">65</xref>,<xref rid=\"B66-ijms-21-05297\" ref-type=\"bibr\">66</xref>,<xref rid=\"B67-ijms-21-05297\" ref-type=\"bibr\">67</xref>,<xref rid=\"B68-ijms-21-05297\" ref-type=\"bibr\">68</xref>]. </p></sec><sec id=\"sec3-ijms-21-05297\"><title>3. Cellular Localization and Distribution of DGKs</title><p>As previously reported, DGKs can translocate to various distinct cellular compartments depending on the type of agonist. This supports the fact that the activities of DGKs may be confined to specific DAG pools produced after receptor activation [<xref rid=\"B17-ijms-21-05297\" ref-type=\"bibr\">17</xref>]. This is the case of DGKε, which only phosphorylates DAG species that possess an arachidonoyl group at the <italic>sn</italic>-2 position [<xref rid=\"B50-ijms-21-05297\" ref-type=\"bibr\">50</xref>]. Moreover, recent studies have also shown the presence of an alternative DAG metabolic pathway where individual DGK isoforms phosphorylate different molecular DAG pools or species independently from PI turnover [<xref rid=\"B13-ijms-21-05297\" ref-type=\"bibr\">13</xref>]. For instance, DGKδ2 interacts via its SAM domain with the ER enzyme sphingomyelin synthase related protein (SMSr) generating DAG from phosphatidylethanolamine and ceramide [<xref rid=\"B69-ijms-21-05297\" ref-type=\"bibr\">69</xref>]. Through a flip-flop mechanism, DAG produced by SMSr in the ER crosses the ER membrane from the lumen region to the cytosolic region to give access to DGKδ2 [<xref rid=\"B69-ijms-21-05297\" ref-type=\"bibr\">69</xref>]. A previous work by van der Bend and colleagues showed that receptor activation of DGK in cells induces a physiological DAG generation while treating cells with exogenous PLC induces a global, non-specific DAG production [<xref rid=\"B17-ijms-21-05297\" ref-type=\"bibr\">17</xref>,<xref rid=\"B70-ijms-21-05297\" ref-type=\"bibr\">70</xref>]. Moreover, receptor activation of DGKs exhibited a significant increase in kinase activity, compared to the kinase activity produced from treating cells with exogenous PLC. Following this observation, the authors suggested that DGKs are activated only in spatially restricted subcellular sites characterized by DAG production because DGKs cannot use DAG pools that are randomly generated in the plasma membrane [<xref rid=\"B17-ijms-21-05297\" ref-type=\"bibr\">17</xref>,<xref rid=\"B70-ijms-21-05297\" ref-type=\"bibr\">70</xref>]. </p><p>DGKs usually localize within several cell compartments, with majority localizing at least partially within the plasma membrane. This is either constitutively, as seen in the case of DGKκ [<xref rid=\"B71-ijms-21-05297\" ref-type=\"bibr\">71</xref>], or following stimulation with specific agonists, such as DGKδ1, which is translocated to the plasma membrane upon exposure to phorbol esters [<xref rid=\"B72-ijms-21-05297\" ref-type=\"bibr\">72</xref>], or DGKα, following engagement of the T cell receptor [<xref rid=\"B55-ijms-21-05297\" ref-type=\"bibr\">55</xref>]. Moreover, DGKs θ and ζ are found at the plasma membrane upon activation of some G protein-coupled receptors (<xref rid=\"ijms-21-05297-t001\" ref-type=\"table\">Table 1</xref>) [<xref rid=\"B73-ijms-21-05297\" ref-type=\"bibr\">73</xref>,<xref rid=\"B74-ijms-21-05297\" ref-type=\"bibr\">74</xref>]. Several studies showed that the nuclear inositide signaling is involved in regulating essential cellular processes, such as cell cycle and differentiation [<xref rid=\"B75-ijms-21-05297\" ref-type=\"bibr\">75</xref>,<xref rid=\"B76-ijms-21-05297\" ref-type=\"bibr\">76</xref>,<xref rid=\"B77-ijms-21-05297\" ref-type=\"bibr\">77</xref>,<xref rid=\"B78-ijms-21-05297\" ref-type=\"bibr\">78</xref>], while it is also implicated in several pathologies, including myelodysplastic syndromes, brain diseases, and cancer [<xref rid=\"B79-ijms-21-05297\" ref-type=\"bibr\">79</xref>,<xref rid=\"B80-ijms-21-05297\" ref-type=\"bibr\">80</xref>,<xref rid=\"B81-ijms-21-05297\" ref-type=\"bibr\">81</xref>,<xref rid=\"B82-ijms-21-05297\" ref-type=\"bibr\">82</xref>]. Interestingly, DGKs, which have been discovered in nearly all cell compartments, were also found in the nucleus. Notably, DGKs play a critical role in the PI cycle, and the conversion of DAG to PA by DGKs represents the first step in resynthesizing PIs [<xref rid=\"B14-ijms-21-05297\" ref-type=\"bibr\">14</xref>]. In addition, different agonists, such as insulin-like growth factor 1 (IGF-1) or thrombin, can generate DAG in the nucleus but not in the plasma membrane. Moreover, nuclear DAG levels fluctuate independently of extranuclear DAG during cell cycle [<xref rid=\"B20-ijms-21-05297\" ref-type=\"bibr\">20</xref>]. Among the nuclear DGKs, DGK α, ζ, and ι translocate in and out of the nucleus [<xref rid=\"B17-ijms-21-05297\" ref-type=\"bibr\">17</xref>], while DGKγ is shuttled from the cytoplasm to the nucleus [<xref rid=\"B83-ijms-21-05297\" ref-type=\"bibr\">83</xref>] and a significant fraction of DGKθ localize mainly within nuclear speckles [<xref rid=\"B84-ijms-21-05297\" ref-type=\"bibr\">84</xref>,<xref rid=\"B85-ijms-21-05297\" ref-type=\"bibr\">85</xref>]. DGKθ can also translocate from the cytosol to the plasma membrane upon stimulation by PKCs and activated GPCRs [<xref rid=\"B86-ijms-21-05297\" ref-type=\"bibr\">86</xref>]. In addition, nuclear DGKs ζ and ι localize within distinct nuclear compartments [<xref rid=\"B17-ijms-21-05297\" ref-type=\"bibr\">17</xref>,<xref rid=\"B84-ijms-21-05297\" ref-type=\"bibr\">84</xref>] whereas DGKα localizes mainly at the nuclear periphery [<xref rid=\"B17-ijms-21-05297\" ref-type=\"bibr\">17</xref>,<xref rid=\"B71-ijms-21-05297\" ref-type=\"bibr\">71</xref>].</p><p>DGKs also localize within other organelles and this may be cell type-specific [<xref rid=\"B87-ijms-21-05297\" ref-type=\"bibr\">87</xref>,<xref rid=\"B88-ijms-21-05297\" ref-type=\"bibr\">88</xref>]. Localization and expression of DGKs in the brain remain elusive. However, DGKε localizes to the subsurface cisterns of cerebellar Purkinje cells and colocalizes with inositol trisphosphate receptor-1 (InsP<sub>3</sub>R-1) in dendrites and axons of the brain, thus confirming the involvement of DGKε in neuronal and brain functions [<xref rid=\"B89-ijms-21-05297\" ref-type=\"bibr\">89</xref>]. Moreover, DGKε can also localize within the endoplasmic reticulum and the plasma membrane [<xref rid=\"B87-ijms-21-05297\" ref-type=\"bibr\">87</xref>]. In adrenal cells, a group of PI signaling molecules are expressed significantly in zona glomerulosa cells and medullary chromaffin cells in the adrenal gland. The same study showed that DGKγ localizes to the Golgi complex, DGKε to the plasma membrane, and DGKζ to the nucleus of adrenal cells [<xref rid=\"B88-ijms-21-05297\" ref-type=\"bibr\">88</xref>]. Furthermore, DGKs δ and η localize to endosomes [<xref rid=\"B37-ijms-21-05297\" ref-type=\"bibr\">37</xref>,<xref rid=\"B45-ijms-21-05297\" ref-type=\"bibr\">45</xref>].</p><p>Even though the specific functions of DGKs are not clearly known yet, several reports demonstrated the presence of DGK activity in cellular contents containing cytoskeletal components and the possible involvement of DGKs in cytoskeletal remodeling and cellular morphology [<xref rid=\"B90-ijms-21-05297\" ref-type=\"bibr\">90</xref>,<xref rid=\"B91-ijms-21-05297\" ref-type=\"bibr\">91</xref>]. DGKs can interact with proteins associated with cytoskeletal reorganization, such as Rac and Rho GTPases, PIP5K, Cdc42, and Rho-GDI [<xref rid=\"B17-ijms-21-05297\" ref-type=\"bibr\">17</xref>,<xref rid=\"B91-ijms-21-05297\" ref-type=\"bibr\">91</xref>,<xref rid=\"B92-ijms-21-05297\" ref-type=\"bibr\">92</xref>]. For example, DGKζ binds directly to Rac1 to form a complex with Rho-GDI and PAK1 [<xref rid=\"B93-ijms-21-05297\" ref-type=\"bibr\">93</xref>], DGKβ co-localized with actin filaments [<xref rid=\"B94-ijms-21-05297\" ref-type=\"bibr\">94</xref>], whereas endogenous DGKζ co-purified with cytoskeletal proteins and localized to the leading edge of C2 myoblasts [<xref rid=\"B42-ijms-21-05297\" ref-type=\"bibr\">42</xref>] and glioblastoma cells [<xref rid=\"B58-ijms-21-05297\" ref-type=\"bibr\">58</xref>].</p></sec><sec id=\"sec4-ijms-21-05297\"><title>4. The Impact of DGKs in the Regulation of Cancer Cell Mechanisms</title><p>Despite all the progress made in medicine, cancer remains one of the frequently occurring causes of death in humans. Therefore, it is important to better understand the various mechanisms associated with cancer development and progression, in order to pave the way for new personalized medicine approaches. Interestingly, abnormal levels of the DGK substrate, DAG, are involved in malignant cell transformation, as increased DAG levels induce tumor-promoting effects. Consequently, a decreased expression or activity of DGKs could lead to higher DAG levels that could promote malignant cell transformation (<xref ref-type=\"fig\" rid=\"ijms-21-05297-f001\">Figure 1</xref>) [<xref rid=\"B17-ijms-21-05297\" ref-type=\"bibr\">17</xref>,<xref rid=\"B35-ijms-21-05297\" ref-type=\"bibr\">35</xref>]. </p><p>DGKζ activity may explain clearly how DGKs negatively regulate DAG in order to limit the transforming potential of DAG in cancer [<xref rid=\"B37-ijms-21-05297\" ref-type=\"bibr\">37</xref>]. Following T cell receptor stimulation, DGKζ negatively regulates RasGRP1, an enzyme involved in cell proliferation. As such, excessive activation of the oncogenic protein Ras is observed in DGKζ-deficient lymphocytes upon T cell receptor stimulation and this correlates with high DAG levels. Even DGKα seems to modulate RasGRP1 and its deletion induces hyperproliferation in T cells [<xref rid=\"B58-ijms-21-05297\" ref-type=\"bibr\">58</xref>,<xref rid=\"B65-ijms-21-05297\" ref-type=\"bibr\">65</xref>]. Besides, high levels of DGKα expression correlate positively with lung cancer patient survival [<xref rid=\"B95-ijms-21-05297\" ref-type=\"bibr\">95</xref>], whereas in HCC, the downregulation of DGKα inhibits cell proliferation and metastasis [<xref rid=\"B27-ijms-21-05297\" ref-type=\"bibr\">27</xref>]. Hence, DGKs act as both tumor suppressors and tumor promoters in cancer but this is cancer cell type-dependent.</p><p>All these reports demonstrate that DGK signaling may be essential in cancer development and progression. As such, this part of the review highlights the impact of DGKs in the regulation of cell growth, proliferation, and metastasis in cancer.</p><sec id=\"sec4dot1-ijms-21-05297\"><title>4.1. Cell Growth and Proliferation in Cancer</title><p>Cell growth and proliferation are essential factors in cancer development and progression. These processes have been shown to be regulated by the altered expression and/or activity of cell cycle-related proteins in cancer cells [<xref rid=\"B17-ijms-21-05297\" ref-type=\"bibr\">17</xref>,<xref rid=\"B96-ijms-21-05297\" ref-type=\"bibr\">96</xref>]. Several reports demonstrated the involvement of the DGK family in cell cycle regulation. For example, DGKs α and ζ play contrasting roles in regulating the cell cycle. DGKζ inhibited the progression of cells from G1 to S phase of the cell cycle [<xref rid=\"B62-ijms-21-05297\" ref-type=\"bibr\">62</xref>], while DGKα-induced PA was required for IL-2 mediated progression of cells to the S phase [<xref rid=\"B54-ijms-21-05297\" ref-type=\"bibr\">54</xref>]. DGKζ is a negative regulator of cell cycle progression in C2C12 mouse myoblasts: DGKζ overexpression blocked cells at the G1 phase of the cell cycle via its interaction with the Retinoblastoma protein (pRb) which is a tumor suppressor and a cell cycle regulator, and DGKζ downregulation increased the number of cells at both S and G2/M phases of the cell cycle [<xref rid=\"B97-ijms-21-05297\" ref-type=\"bibr\">97</xref>]. Interestingly, DGKζ is highly expressed in patient-derived acute myeloid leukemia cells and the knockdown of DGKζ in HL-60 promyelocytic cells induces a cell cycle arrest at the G2M checkpoint, inhibiting cell proliferation while increasing apoptosis (<xref rid=\"ijms-21-05297-t002\" ref-type=\"table\">Table 2</xref>) [<xref rid=\"B98-ijms-21-05297\" ref-type=\"bibr\">98</xref>]. In addition, DGKζ inhibition in U251 and U87 glioblastoma cells caused a marked decrease in cyclin D1 (CCND1) protein expression, which led to an arrest of cells at the G0/G1 phase [<xref rid=\"B99-ijms-21-05297\" ref-type=\"bibr\">99</xref>]. Furthermore, major regulators of cancer cell growth and proliferation, such as the phosphorylated forms of Akt and mTOR, were also decreased, resulting in a significant reduction of cell proliferation in DGKζ knockdown cells compared to control cells. The authors also showed in an in vivo model that the tumorigenic capability of glioblastoma cells was reduced when DGKζ expression was decreased. Hence, DGKζ inhibition may confer advantages to glioblastoma patients [<xref rid=\"B99-ijms-21-05297\" ref-type=\"bibr\">99</xref>]. </p><p>In other cancer cells, such as K562 human erythroleukemia cells, DGKα can modulate cell cycle progression by influencing the phosphorylated status of pRb, which subsequently induces cell cycle arrest by impairing the G1/S transition [<xref rid=\"B100-ijms-21-05297\" ref-type=\"bibr\">100</xref>]. In HCC cells, DGKα knockdown significantly suppresses cell proliferation, whereas overexpressing wildtype DGKα but not the kinase-dead mutant in the same cells significantly enhances proliferation. Similar results were obtained in HCC xenograft model experiments, where DGKα regulates cell proliferation via activation of the MAPK pathway. Specifically, DGKα downregulation impaired mitogen-activated protein kinase (MEK) and extracellular signal-regulated kinase (ERK) phosphorylation, both of which are crucial in the regulation of cell growth and migration [<xref rid=\"B27-ijms-21-05297\" ref-type=\"bibr\">27</xref>]. Moreover, a novel DGKα specific inhibitor CU-3, which was successfully obtained after a high-throughput screening of about 9600 chemical compounds, induced apoptosis in HepG2 HCC cells and HeLa cervical cancer cells, while simultaneously enhancing immune response by promoting IL-2 production [<xref rid=\"B101-ijms-21-05297\" ref-type=\"bibr\">101</xref>]. Consistent with these data, it was also reported that silencing or inhibiting DGKα activity with short interfering RNA (siRNA) or small-molecule inhibitor R59022 caused increased death of glioblastoma and melanoma cells by interrupting essential oncogenic pathways [<xref rid=\"B29-ijms-21-05297\" ref-type=\"bibr\">29</xref>]. DGKα knockdown decreased both total and phosphorylated forms of mTOR, hypoxia-inducible factor 1-alpha (HIF1α), c-Myc levels, and phosphorylation of Akt in glioblastoma cells. Xenograft experiments also demonstrated that DGKα knockdown and inhibition affects tumor growth, angiogenesis, and survival of mice with intracranial and subcutaneous tumors. Intriguingly, knockdown of DGKα in non-cancerous cells, such as astrocytes and fibroblasts, showed no form of cytotoxicity as revealed in both glioblastoma and melanoma cells. Hence, small-molecule inhibition of DGKα is selectively toxic to human cancer cells but not normal human cells, thus making DGKα inhibition a promising therapeutic target [<xref rid=\"B29-ijms-21-05297\" ref-type=\"bibr\">29</xref>].</p><p>Several studies demonstrated an active role of DGKα also in Src oncogenic functions [<xref rid=\"B8-ijms-21-05297\" ref-type=\"bibr\">8</xref>,<xref rid=\"B28-ijms-21-05297\" ref-type=\"bibr\">28</xref>]. Src is a regulator of mitogenic and survival signaling pathways that are downstream of receptor and non-receptor tyrosine kinases, such as the vascular endothelial growth factor receptor (VEGFR), human epidermal growth factor receptor-2 (HER2) and focal adhesion kinase (FAK), which are often aberrantly expressed in colon, breast, and pancreatic cancer [<xref rid=\"B8-ijms-21-05297\" ref-type=\"bibr\">8</xref>]. Using 3D colon and breast cancer cell cultures, it was demonstrated that DGKα is essential in cell growth and survival by promoting the stabilization of Src activation. Importantly, DGKα enzymatic activity is necessary for Src activation. Pharmacological or genetic DGKα silencing restricted tumor growth in vivo, thus confirming the function of DGKα in malignant transformation [<xref rid=\"B28-ijms-21-05297\" ref-type=\"bibr\">28</xref>].</p><p>Furthermore, DGK is involved in the major biological features of the transformed phenotype of Kaposi’s sarcoma (KS) cells, where DGK is essential for cell proliferation and DGK inhibitors could be promising for therapy [<xref rid=\"B104-ijms-21-05297\" ref-type=\"bibr\">104</xref>]. Indeed, the DGK pharmacological inhibitor R59949 strongly reduces hepatocyte growth factor (HGF)-induced KS proliferation and anchorage-independent growth without affecting cell survival or the classical Akt and MAPK pathways, which are usually implicated in KS.</p><p>On the other hand, further studies showed that CHO-K1 ovary cells expressing the kinase negative mutant of DGKγ exhibited a larger size, slower growth rate, and an extended S phase, suggesting that the increase of cell size was induced by protein synthesis during the extended S phase and that DGKγ regulates cell cycle [<xref rid=\"B61-ijms-21-05297\" ref-type=\"bibr\">61</xref>]. Even though the activities of DGKs in cancer seem to support tumor-promoting roles, there is evidence that DGKs can also support tumor suppressor activities [<xref rid=\"B102-ijms-21-05297\" ref-type=\"bibr\">102</xref>]. For instance, DGKγ expression is downregulated in HCC tumor tissues and colorectal cancer (CRC) cell lines when compared to non-tumor control tissues, and this correlates with poor clinical outcomes [<xref rid=\"B102-ijms-21-05297\" ref-type=\"bibr\">102</xref>]. Interestingly, DGKγ downregulation in HCC is due to epigenetic mutations induced by histone H3 and H4 deacetylation. In addition, an analysis of methylation of the CpG islands of DGK promoter genes in CRC cells revealed that DGKγ is hypermethylated in CRC cells but not in normal colonic tissue, and this corresponds with reduced DGKγ expression in CRC cell lines compared to control cells [<xref rid=\"B26-ijms-21-05297\" ref-type=\"bibr\">26</xref>]. However, both constitutively active and kinase-dead DGKγ mutants induced inhibitory effects on CRC cell proliferation [<xref rid=\"B26-ijms-21-05297\" ref-type=\"bibr\">26</xref>]. Notably, the ectopic expression of DGKγ in HCC cells decreased cell growth by downregulating glucose transporter 1 (GLUT1) expression and inhibiting cell glycolysis. In fact, GLUT1 expression is high in HCC and promotes tumorigenicity, therefore DGKγ plays tumor suppressor roles in HCC by lowering GLUT1 levels [<xref rid=\"B102-ijms-21-05297\" ref-type=\"bibr\">102</xref>]. </p><p>DGKε activity can also regulate the Ras/RAF/MEK/ERK signaling in cervical cancer cell line models [<xref rid=\"B33-ijms-21-05297\" ref-type=\"bibr\">33</xref>]. This pathway plays pivotal roles in the regulation of cell proliferation, survival, and differentiation. The study showed that siRNA downregulation of DGKε impairs the epidermal growth factor (EGF)-activated Ras/RAF/MEK/ERK signaling cascade in HeLa cells. However, the mechanism through which DGKε regulates this pathway is still unknown [<xref rid=\"B33-ijms-21-05297\" ref-type=\"bibr\">33</xref>]. Additionally, the potential of DGKη to regulate MAPK signaling, which is a downstream target of epidermal growth factor receptor (EGFR), led a group to study the oncogenic effects of DGKη in lung cancer, which is often characterized by mutations in EGFR and KRAS. The authors reported that silencing DGKη in lung cancer models, characterized by EGFR and KRAS mutations, reduced cancer cell growth while enhancing the cells’ sensitivity to EGFR inhibitor Afatinib [<xref rid=\"B103-ijms-21-05297\" ref-type=\"bibr\">103</xref>].</p></sec><sec id=\"sec4dot2-ijms-21-05297\"><title>4.2. Cell Migration, Invasiveness, and Metastasis</title><p>The motility and invasion of cancer cells from the primary tumor to a distant organ is an essential step in tumor metastasis. This event requires chemotactic migration of cancer cells and crossing of extracellular matrix barriers that surround the tumor [<xref rid=\"B105-ijms-21-05297\" ref-type=\"bibr\">105</xref>]. As previously stated, DGKγ plays tumor-suppressive roles in CRC. The ectopic expression of wildtype, as well as kinase active and inactive mutant forms of DGKγ, restricts cell migration and invasion in CRC cells by inhibiting Rac1 activity [<xref rid=\"B26-ijms-21-05297\" ref-type=\"bibr\">26</xref>]. Rac1 is a member of the Rho GTPases, which are small GTP-binding proteins that regulate cytoskeletal dynamics and activate essential protein kinases involved in Epithelial to Mesenchymal Transition (EMT) [<xref rid=\"B106-ijms-21-05297\" ref-type=\"bibr\">106</xref>]. EMT involves the reprogramming of epithelial cells into mesenchymal cells, leading to morphological changes, specifically more elongated and spindle-like forms with increased migratory and invasive properties [<xref rid=\"B107-ijms-21-05297\" ref-type=\"bibr\">107</xref>]. Notably, Rac1 is highly expressed in different stages of colorectal tumors. Its activity in CRC tissues positively correlates with poor prognosis of CRC patients by promoting EMT-mediated invasion of CRC cells via the activation of the signal transducers and activators of transcription 3 (STAT3) pathway [<xref rid=\"B106-ijms-21-05297\" ref-type=\"bibr\">106</xref>]. Therefore, it would be interesting to elucidate the mechanisms associated with DGKγ-mediated inhibition of Rac1 activity for potential CRC therapy. DGKγ also plays tumor suppressor roles in HCC cells by reducing cell migration when DGKγ is overexpressed [<xref rid=\"B102-ijms-21-05297\" ref-type=\"bibr\">102</xref>]. Conversely, DGKα is highly expressed in HCC and promotes tumorigenicity [<xref rid=\"B27-ijms-21-05297\" ref-type=\"bibr\">27</xref>]. In fact, knockdown of DGKα suppresses cell migration by impairing the Ras/RAF/MEK/ERK pathway in HCC cells. The Ras/RAF/MEK/ERK pathway is indeed frequently deregulated in HCC and the activation of this pathway is significantly involved in cancer cell invasion [<xref rid=\"B27-ijms-21-05297\" ref-type=\"bibr\">27</xref>]. In fact, ERK signaling is a critical mediator of cell migration, although it is also a classic mediator of cell growth, proliferation, and differentiation. ERK activates several proteins that regulate cell-matrix adhesion, cell protrusion, and retraction, all of which are essential processes recognized during cell motility [<xref rid=\"B108-ijms-21-05297\" ref-type=\"bibr\">108</xref>]. Moreover, ERK controls EMT-regulated cell migration through Rac1/Fox01 activation [<xref rid=\"B107-ijms-21-05297\" ref-type=\"bibr\">107</xref>]. DGKα activity has also been reported to be a key factor in the migratory and invasive responses induced by several growth factors, including HGF and vascular endothelial growth factor (VEGF) in endothelial, epithelial, and leukemic cells [<xref rid=\"B109-ijms-21-05297\" ref-type=\"bibr\">109</xref>,<xref rid=\"B110-ijms-21-05297\" ref-type=\"bibr\">110</xref>,<xref rid=\"B111-ijms-21-05297\" ref-type=\"bibr\">111</xref>,<xref rid=\"B112-ijms-21-05297\" ref-type=\"bibr\">112</xref>]. In line with the potential to regulate migration in endothelial cells by DGKα, a study employing both DGKα specific siRNA and/or DGK pharmacological inhibitor R59949 demonstrated that DGKα activity is a key regulator of migration in Hec-1A endometrial cancer cell line [<xref rid=\"B112-ijms-21-05297\" ref-type=\"bibr\">112</xref>]. Inhibition of DGKα indeed reduced cell migration towards estrogen chemoattractant as well as abolished ruffle formation in Hec-1A cells [<xref rid=\"B112-ijms-21-05297\" ref-type=\"bibr\">112</xref>]. In addition, DGKα promotes invasive migration in H1299 lung cancer cells and A2780 ovarian carcinoma cells by controlling Rab coupling protein (RCP)-driven integrin trafficking [<xref rid=\"B113-ijms-21-05297\" ref-type=\"bibr\">113</xref>]. Furthermore, R59949 significantly reduced HGF-induced motility in KS cell lines with limited effects on cell adhesion and spreading [<xref rid=\"B104-ijms-21-05297\" ref-type=\"bibr\">104</xref>], but it did not affect MAPK and Akt signaling pathways.</p><p>The downstream product of DGKs, PA has been associated with the receptor tyrosine kinase (RTK) signaling, which is an upstream regulator of the Ras/RAF/MEK/ERK cascade [<xref rid=\"B114-ijms-21-05297\" ref-type=\"bibr\">114</xref>]. Another study attributed a potential role of PA in regulating tumor metastasis due to its ability to induce the secretion of Type 1 matrix metalloproteases (MMP1), enzymes able to promote metastasis [<xref rid=\"B115-ijms-21-05297\" ref-type=\"bibr\">115</xref>]. However, these studies refer to PA generated by phospholipase D (PLD). Hence, it would be important to understand whether PA generated by DGKs performs the same functions. Interestingly, the application of nanomolar concentrations of PA increased cell migration in invasive MDA-MB-231 human breast cancer cells but had no effect on non-neoplastic control cells. Moreover, applying <italic>Clostridium difficile</italic> Toxin B to the PA-treated MDA-MB-231 breast cancer cells inhibited Rho activity and was followed by a marked decrease in cell migration [<xref rid=\"B116-ijms-21-05297\" ref-type=\"bibr\">116</xref>]. These data strengthen the link between DGK/PA and Rho GTPases in cytoskeletal organization and subsequent cell migration. In addition, PA may be central in the regulation of cell motility by controlling the activity of type I PIP5K isozymes and PtdIns(4,5)P<sub>2</sub> [<xref rid=\"B117-ijms-21-05297\" ref-type=\"bibr\">117</xref>]. In fact, PA stimulates PIP5K, that participates in actin reorganization by generating PtdIns(4,5)P<sub>2</sub>, which is a primary regulator of cytoskeletal organization, so that PA signaling is also critical in PtdIns(4,5)P<sub>2</sub> resynthesis [<xref rid=\"B117-ijms-21-05297\" ref-type=\"bibr\">117</xref>]. </p><p>A study reported that DGKζ deficiency in fibroblast cells induces a reduction in Rac1 and RhoA activation, as well as a significant reduction in cell migration [<xref rid=\"B118-ijms-21-05297\" ref-type=\"bibr\">118</xref>]. Considering this finding, the authors extended their study by elucidating the impact of DGKζ signaling in CRC metastasis [<xref rid=\"B30-ijms-21-05297\" ref-type=\"bibr\">30</xref>]. In tumor-derived CRC cell lines, knocking down DGKζ expression produced similar results as those seen in fibroblasts. A significant decrease in Rac1 and RhoA activity in DGKζ knockdown CRC cells was also observed and was followed by a decrease in cell invasion. Concomitantly, DGKζ depletion decreased the invasiveness of prostate cancer and metastatic breast cancer cells [<xref rid=\"B30-ijms-21-05297\" ref-type=\"bibr\">30</xref>]. Thus, opposite to the tumor-suppressive roles of DGKγ as described above, DGKζ may also promote tumorigenesis by potentiating cell invasion and migration in several cancer types by regulating Rac1 and RhoA activity. This may be due to the fact that coordinated events between Rac1 and RhoA are necessary for effective migration in cancer metastasis. Indeed, RhoA is involved in the maintenance of actin stress fibers and focal adhesions, while Rac1 regulates the generation of lamellipodia, membrane ruffles formation, and Cdc42 signaling in filopodia production [<xref rid=\"B119-ijms-21-05297\" ref-type=\"bibr\">119</xref>]. </p><p>As for other DGKs, such as DGKδ and DGKι, there are a few reports demonstrating their participation in the development and progression of cancer. Downregulation of DGKδ in cervical and lung adenocarcinoma cell line models induced a downregulation of Akt activity, leading to a decrease in cell migration and proliferation. Moreover, DGKδ can control Akt activity through pleckstrin homology domain leucine-rich repeat protein phosphatase 2 (PHLPP2) [<xref rid=\"B32-ijms-21-05297\" ref-type=\"bibr\">32</xref>]. Epigenetic studies have also revealed that DGKι may be methylated in cancer, including glioblastoma and HCC [<xref rid=\"B34-ijms-21-05297\" ref-type=\"bibr\">34</xref>,<xref rid=\"B35-ijms-21-05297\" ref-type=\"bibr\">35</xref>], while it is still unknown whether DGKι mutations may produce effects directly involved in metastasis of these cancer types.</p></sec></sec><sec id=\"sec5-ijms-21-05297\"><title>5. Targeting DGKs in Cancer Therapies</title><p>The development of effective therapies to fight cancer continues to be one of the major challenges in modern medicine. Chemotherapy and radiotherapy are somehow successful, but these approaches are non-specific and often lead to short- or long-term adverse effects which usually affect quality of life [<xref rid=\"B7-ijms-21-05297\" ref-type=\"bibr\">7</xref>]. Recently, exploring immune-based therapeutic systems, such as blockage of immune checkpoints or adoptive cell transfer (ACT) that stimulate antitumor immunity by targeting and attacking tumor cells, seems promising because of its specificity. The starting point of this immune response is represented by chimeric antigen receptor (CAR)-T cells [<xref rid=\"B120-ijms-21-05297\" ref-type=\"bibr\">120</xref>]. Interestingly, incoming reports suggest that targeting DGK activity could be a strong approach to reinforce the anti-tumor functions of T cells [<xref rid=\"B7-ijms-21-05297\" ref-type=\"bibr\">7</xref>].</p><p>Currently, DGKα holds much promise in cancer therapy [<xref rid=\"B7-ijms-21-05297\" ref-type=\"bibr\">7</xref>,<xref rid=\"B29-ijms-21-05297\" ref-type=\"bibr\">29</xref>], as its inhibition presents toxicity in various cancer, but not normal human cells [<xref rid=\"B29-ijms-21-05297\" ref-type=\"bibr\">29</xref>]. For instance, in T cells, the inhibition of DGKα activity may generate a simultaneous response of reinforcing T cell attack on tumor cells, while directly inhibiting tumor growth [<xref rid=\"B7-ijms-21-05297\" ref-type=\"bibr\">7</xref>]. </p><p>The inhibition of DGKα by R59949 in KS and endometrial cancer cells leads to decreased cell proliferation, growth, and migration [<xref rid=\"B104-ijms-21-05297\" ref-type=\"bibr\">104</xref>,<xref rid=\"B112-ijms-21-05297\" ref-type=\"bibr\">112</xref>]. On the other hand, using the small molecule inhibitor of DGKs R59022, DGKα was inhibited in glioblastoma, cervical cancer, melanoma, and breast cancer cell lines. In these cells, the percentage of cell death was increased compared to control normal fibroblasts and astrocytes [<xref rid=\"B29-ijms-21-05297\" ref-type=\"bibr\">29</xref>]. Indeed, in glioblastoma in vivo tumor models, the same authors showed that DGKα inhibition decreases angiogenesis, tumor growth, and survival of mice with tumors [<xref rid=\"B29-ijms-21-05297\" ref-type=\"bibr\">29</xref>]. Targeting DGKs may be important in cancer immunotherapy, as intratumoral injection of DGK knockout T-cells into U87MGvIII glioblastoma tumor models, obtained by CRISPR/Cas9, caused significant suppression of the tumors [<xref rid=\"B25-ijms-21-05297\" ref-type=\"bibr\">25</xref>]. More importantly, this result was due to an enhancement in the effector functions of T-cells in the xenograft model. The authors also showed that the CRISPR/Cas9 generated DGK-knockout in CAR-T cells potentiates T-cell functions by increasing cluster of differentiation 3 (CD3) signaling. Consequently, the cells became resistant to immunosuppressive factors such as transforming growth factor-β (TGFβ) and prostaglandin E2, which are known mediators of cancer cell survival [<xref rid=\"B25-ijms-21-05297\" ref-type=\"bibr\">25</xref>]. </p><p>Other studies tested CU-3, a DGK pharmacological inhibitor with a higher specificity for DGKα than R59949 and R59022, mainly due to its specific targeting of the ATP binding site in the catalytic domain of DGKα. This molecule induced apoptosis in several cancer cells, while enhancing immune response [<xref rid=\"B101-ijms-21-05297\" ref-type=\"bibr\">101</xref>]. Similarly, compound A, which specifically inhibits type I DGKs and especially DGKα, induced apoptosis and reduced viability of melanoma and several other cancer cell lines [<xref rid=\"B121-ijms-21-05297\" ref-type=\"bibr\">121</xref>]. In addition, two novel DGKα inhibitor compounds, namely 11 and 20 (with an IC<sub>50</sub> of 1.6 and 1.8 µM respectively, thus representing the most potent DGKα inhibitors until now), decreased cell migration in cancer cells (<xref ref-type=\"fig\" rid=\"ijms-21-05297-f002\">Figure 2</xref>) [<xref rid=\"B122-ijms-21-05297\" ref-type=\"bibr\">122</xref>].</p><p>On the other hand, Ritanserin, an established serotonin receptor inhibitor, has recently been identified as a DGKα inhibitor. Interestingly, it is more potent than R59022, although these two compounds differ structurally by just a single fluorine [<xref rid=\"B123-ijms-21-05297\" ref-type=\"bibr\">123</xref>]. Ritanserin has already been shown to be well-tolerated and safe for human use in clinical trials, potentially paving the way to use it clinically as a DGKα inhibitor [<xref rid=\"B123-ijms-21-05297\" ref-type=\"bibr\">123</xref>]. In fact, treatment of several cancer cells with Ritanserin has yielded similar results as other DGK inhibitors [<xref rid=\"B7-ijms-21-05297\" ref-type=\"bibr\">7</xref>,<xref rid=\"B124-ijms-21-05297\" ref-type=\"bibr\">124</xref>]. For example, the mesenchymal subtypes of lung and pancreatic carcinoma, as well as the mesenchymal subtype of glioblastoma, are sensitive to Ritanserin. Indeed, DGKα inhibition by Ritanserin induced cell death in glioblastoma stem cells and this was partially mediated by apoptosis [<xref rid=\"B124-ijms-21-05297\" ref-type=\"bibr\">124</xref>]. Additionally, Ritanserin, as with other small molecule inhibitors of DGKα, also enhanced T cell signaling but failed to promote long-term T-cell activation [<xref rid=\"B125-ijms-21-05297\" ref-type=\"bibr\">125</xref>]. </p></sec><sec sec-type=\"conclusions\" id=\"sec6-ijms-21-05297\"><title>6. Conclusions</title><p>All the reported studies highlight DGK signaling as a promising target for cancer therapy. However, more studies are needed to fully comprehend DGK specific roles in cancer development and progression. Due to the isoform-specific functions observed in different types of cancer cells and even subcellular sites, it would be crucial to fully understand how the specific DGK isoforms control downstream oncogenic signaling, as these pathways can regulate proliferation, growth, angiogenesis, immunity, and migration. To better understand DGK signaling it would also be essential to explore the potential crosstalk among the various DGK isoforms, the possible redundancy or compensatory functions of other isoforms during the inhibition of one or more DGK isoforms, as well as consider the DGK specific subcellular localization relevance. Moreover, the discovery of more potent DGK-specific isoform inhibitors may be useful to study the isoform-specific functions and develop new cancer targeted therapies. Future studies may also benefit from the combinatorial effect of DGK inhibitors and other standard cancer therapies, such as radiation and chemotherapy. Furthermore, since PA is involved in several cellular processes, the combination of both DGK inhibitors and inhibitors of PA-synthesizing enzymes may prove to be more beneficial in cancer therapy than when used individually. Finally, since recent reports demonstrated that DGKs may have a preference for specific DAG species, which are independent of PI turnover pathways, it would be strategic to further investigate the cellular signaling pathways associated with PA, produced by both PI independent and PI dependent pathways. </p></sec></body><back><notes><title>Author Contributions</title><p>Conceptualization, investigation L.M., L.C. and S.R., formal analysis, A.F., E.O.O., I.R., M.V.M., J.A.M. and M.Z. writing-original draft preparation, A.F. and E.O.O. writing-review and editing, S.M., M.Y.F., G.R., M.Z., J.A.M., S.R., L.C.; and L.M. visualization, I.R. and M.V.M., funding acquisition, L.M., G.R., M.Y.F., S.R. and L.C. All authors have read and agreed to the published version of the manuscript.</p></notes><notes><title>Funding</title><p>This research was funded by Italian PRIN-MIUR grants (to L.M., G.R., M.Y.F.), Fondazione Cassa di Risparmio di Bologna grant (to S.R.), Fondazione del Monte di Bologna e Ravenna grant and Intesa S. Paolo Foundation grant (to L.C.).</p></notes><notes notes-type=\"COI-statement\"><title>Conflicts of Interest</title><p>The authors declare no conflict of interest.</p></notes><glossary><title>Abbreviations</title><array orientation=\"portrait\"><tbody><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3D</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Three-dimensional</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Ca<sup>2+</sup></td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Calcium</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ACT</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Adoptive cell transfer</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">AML</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Acute myeloid leukemia</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CAR</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Chimeric antigen receptor</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CCND1</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Cyclin D1</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CD3</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Cluster of differentiation 3</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CRC</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Colorectal cancer</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">DAG</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Diacylglycerol</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">DGKs</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Diacylglycerol kinases</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">EGF</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Epidermal growth factor</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">EGFR</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Epidermal growth factor receptor</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">EMT</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Epithelial to mesenchymal transition</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ERK</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Extracellular signal-regulated kinase</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">FAK</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Focal adhesion kinase</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">GAP</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">GTPase-activating protein</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">GLUT1</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Glucose transporter 1</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">HCC</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Hepatocellular carcinoma</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">HER2</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Human epidermal growth factor receptor-2</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">HGF</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Hepatocyte growth factor</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">HIF1α</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Hypoxia-inducible factor 1-alpha</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">IL-2</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Interleukin 2</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">InsP3R</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Inositol trisphosphate receptor</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">KS</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Kaposi’s sarcoma</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">MAPK</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Mitogen activated protein kinase</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">MARCKS</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Myristoylated alanine rich kinase substrate</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">MEK</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Mitogen-activated protein kinase</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">MMP1</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Type 1 matrix metalloproteases</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">mTOR</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Mammalian target of rapamycin</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">NLS</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Nuclear localization sequence</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">PA</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Phosphatidic acid</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">PHLPP2</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Pleckstrin homology domain leucin-rich repeat protein phosphatase 2</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">PI</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Phosphoinositide</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">PI3K</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Phosphoinositide 3-kinase</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">PIP5K</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Phosphatidylinositol-4-phosphate 5-kinase</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">PKC</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Protein kinase C</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">PLC</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Phospholipases C</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">PLD</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Phospholipase D</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">pRb</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Retinoblastoma protein</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">PS</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Phosphatidylserine</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">PtdIns(4,5)P2</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Phosphatidylinositol 4,5 biphosphate</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">RCP<break/>Raf-1</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Rab-coupling protein<break/>Rapidly accelerated fibrosarcoma-1</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">RasGRP</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Rat sarcoma virus guanyl nucleotide-releasing protein</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">RTK</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Receptor tyrosine kinase</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">SAM</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Sterile α motif</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">siRNA</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Short interfering RNA</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">SMSr</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Sphingomyelin synthase related protein</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">STAT3</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Signal transducers and activators of transcription 3</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">TGFβ</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Transforming growth factor β</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Unc-13</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Mammalian uncoordinated 13</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">VEGF</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Vascular endothelial growth factor</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">VEGFR</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Vascular endothelial growth factor receptor</td></tr></tbody></array></glossary><ref-list><title>References</title><ref id=\"B1-ijms-21-05297\"><label>1.</label><element-citation publication-type=\"journal\"><person-group 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On the other hand, DAG activates several critical proteins including both conventional and novel PKCs, mammalian Unc-13, chimaerins which activate Rac GTPase, and RasGRPs, which are involved in cell proliferation and migration.</p></caption><graphic xlink:href=\"ijms-21-05297-g001\"/></fig><fig id=\"ijms-21-05297-f002\" orientation=\"portrait\" position=\"float\"><label>Figure 2</label><caption><p>Functional effects produced by DGKα inhibition in cancer. The use of DGKα specific or pan inhibitors to inhibit DGKα activity in cancer cells seems to block cancer progression through the inhibition of cancer-promoting mechanisms, such as growth and survival. Intriguingly, inhibition of DGKα activity increases apoptosis in several cancer cell lines and in vivo tumor models, while enhancing T-cell activity or immune response.</p></caption><graphic xlink:href=\"ijms-21-05297-g002\"/></fig><table-wrap id=\"ijms-21-05297-t001\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijms-21-05297-t001_Table 1</object-id><label>Table 1</label><caption><p>Distribution of DGKs across distinct subcellular compartments.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">DGK Isoforms</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Subcellular Localization</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>DGKα</italic>\n</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Plasma membrane, Cytosol, Nucleus</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>DGKβ</italic>\n</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Cytoskeleton</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>DGKγ</italic>\n</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Nucleus, Golgi, Cytosol</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>DGKδ</italic>\n</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Plasma membrane, Endoplasmic reticulum, Endosomes</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>DGKε</italic>\n</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Plasma membrane, Endoplasmic reticulum</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>DGKζ</italic>\n</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Nuclear speckles, Plasma membrane, Cytosol, Cytoskeleton</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>DGKθ</italic>\n</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Nuclear speckles, Plasma membrane, Cytosol</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>DGKι</italic>\n</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Nucleus, Cytosol</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>DGKκ</italic>\n</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Plasma membrane</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>DGKη</italic>\n</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Endosomes</td></tr></tbody></table></table-wrap><table-wrap id=\"ijms-21-05297-t002\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijms-21-05297-t002_Table 2</object-id><label>Table 2</label><caption><p>Functional effects on cancer upon the downregulation of DGKs.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">DGK Isoform</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Cancer Type</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Effects Caused by Downregulating DGKs</th></tr></thead><tbody><tr><td rowspan=\"5\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">\n<bold>DGKα</bold>\n</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Glioblastoma</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Decreases Akt/mTOR, HIF1α, c-Myc activity [<xref rid=\"B29-ijms-21-05297\" ref-type=\"bibr\">29</xref>]</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">HCC</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Inhibits the Ras/RAF/MEK/ERK pathway [<xref rid=\"B27-ijms-21-05297\" ref-type=\"bibr\">27</xref>]</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Acute Myeloid Leukemia</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Impairs pRb signaling [<xref rid=\"B100-ijms-21-05297\" ref-type=\"bibr\">100</xref>]</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Cervical cancer, HCC</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Promotes IL-2 signaling [<xref rid=\"B101-ijms-21-05297\" ref-type=\"bibr\">101</xref>]</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Colon, Breast cancer</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Impairs Src activity [<xref rid=\"B28-ijms-21-05297\" ref-type=\"bibr\">28</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>DGKδ</bold>\n</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Cervical cancer, Lung adenocarcinoma</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Suppresses Akt activity [<xref rid=\"B32-ijms-21-05297\" ref-type=\"bibr\">32</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>DGKε</bold>\n</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Cervical cancer</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Impairs Ras/RAF/MEK/ERK signaling [<xref rid=\"B33-ijms-21-05297\" ref-type=\"bibr\">33</xref>]</td></tr><tr><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">\n<bold>DGKγ</bold>\n</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">HCC</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Inhibition of GLUT1 expression and glycolysis [<xref rid=\"B102-ijms-21-05297\" ref-type=\"bibr\">102</xref>]</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Colorectal cancer</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Inhibits Rac1 activity [<xref rid=\"B26-ijms-21-05297\" ref-type=\"bibr\">26</xref>]</td></tr><tr><td rowspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">\n<bold>DGKζ</bold>\n</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Glioblastoma</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Suppresses Cyclin D1 expression [<xref rid=\"B99-ijms-21-05297\" ref-type=\"bibr\">99</xref>]<break/>Decreases Akt/mTOR activity [<xref rid=\"B99-ijms-21-05297\" ref-type=\"bibr\">99</xref>]<break/>Increases CD3 expression to enhance T-cell functions [<xref rid=\"B25-ijms-21-05297\" ref-type=\"bibr\">25</xref>] </td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Colorectal cancer</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Decreases Rac1 and RhoA activity [<xref rid=\"B30-ijms-21-05297\" ref-type=\"bibr\">30</xref>]</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Acute Myeloid Leukemia</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Induces cell cycle arrest at G2M, Inhibits cell proliferation and increases apoptosis [<xref rid=\"B98-ijms-21-05297\" ref-type=\"bibr\">98</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>DGKη</bold>\n</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Lung cancer</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Impairs MAPK signaling [<xref rid=\"B103-ijms-21-05297\" ref-type=\"bibr\">103</xref>]</td></tr></tbody></table></table-wrap></floats-group></article>\n"
]
[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Int J Mol Sci</journal-id><journal-id journal-id-type=\"iso-abbrev\">Int J Mol Sci</journal-id><journal-id journal-id-type=\"publisher-id\">ijms</journal-id><journal-title-group><journal-title>International Journal of Molecular Sciences</journal-title></journal-title-group><issn pub-type=\"epub\">1422-0067</issn><publisher><publisher-name>MDPI</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32731552</article-id><article-id pub-id-type=\"pmc\">PMC7432102</article-id><article-id pub-id-type=\"doi\">10.3390/ijms21155363</article-id><article-id pub-id-type=\"publisher-id\">ijms-21-05363</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Article</subject></subj-group></article-categories><title-group><article-title>Changes in Proteome of Fibroblasts Isolated from Psoriatic Skin Lesions</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Gęgotek</surname><given-names>Agnieszka</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05363\">1</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0002-8060-7675</contrib-id><name><surname>Domingues</surname><given-names>Pedro</given-names></name><xref ref-type=\"aff\" rid=\"af2-ijms-21-05363\">2</xref></contrib><contrib contrib-type=\"author\"><name><surname>Wroński</surname><given-names>Adam</given-names></name><xref ref-type=\"aff\" rid=\"af3-ijms-21-05363\">3</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0001-5397-7139</contrib-id><name><surname>Skrzydlewska</surname><given-names>Elżbieta</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05363\">1</xref><xref rid=\"c1-ijms-21-05363\" ref-type=\"corresp\">*</xref></contrib></contrib-group><aff id=\"af1-ijms-21-05363\"><label>1</label>Department of Analytical Chemistry, Medical University of Bialystok, Mickiewicza 2D, 15-222 Bialystok, Poland; <email>[email protected]</email></aff><aff id=\"af2-ijms-21-05363\"><label>2</label>Mass Spectrometry Centre, LAQV REQUIMTE, Department of Chemistry, University of Aveiro, 3810-193 Aveiro, Portugal; <email>[email protected]</email></aff><aff id=\"af3-ijms-21-05363\"><label>3</label>Dermatological Specialized Center “DERMAL” NZOZ in Bialystok, 15-453 Bialystok, Poland; <email>[email protected]</email></aff><author-notes><corresp id=\"c1-ijms-21-05363\"><label>*</label>Correspondence: <email>[email protected]</email>; Tel.: +48-85-748-5708</corresp></author-notes><pub-date pub-type=\"epub\"><day>28</day><month>7</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><month>8</month><year>2020</year></pub-date><volume>21</volume><issue>15</issue><elocation-id>5363</elocation-id><history><date date-type=\"received\"><day>06</day><month>7</month><year>2020</year></date><date date-type=\"accepted\"><day>27</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>© 2020 by the authors.</copyright-statement><copyright-year>2020</copyright-year><license license-type=\"open-access\"><license-p>Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>).</license-p></license></permissions><abstract><p>The dermal fibroblasts are in constant contact with the cells of the immune system and skin epidermis. Therefore, they are essential for the development of lesions in psoriasis. The aim of this study was to assess the changes in the proteomic profile of fibroblasts in the dermis of psoriasis patients, and to discuss the most significant changes and their potential consequences. The proteomic results indicate that fibroblast dysfunction arises from the upregulation of proinflammatory factors and antioxidant proteins, as well as those involved in signal transduction and participating in proteolytic processes. Moreover, downregulated proteins in psoriatic fibroblasts are mainly responsible for the transcription/translation processes, glycolysis/ adenosine triphosphate synthesis and structural molecules. These changes can directly affect intercellular signaling and promote the hyperproliferation of epidermal cells. A better understanding of the metabolic effects of the proteomic changes observed could guide the development of new pharmacotherapies for psoriasis.</p></abstract><kwd-group><kwd>psoriasis</kwd><kwd>skin fibroblasts</kwd><kwd>proteomic profile</kwd><kwd>inflammation</kwd><kwd>oxidative conditions</kwd><kwd>intracellular signal transduction</kwd></kwd-group></article-meta></front><body><sec sec-type=\"intro\" id=\"sec1-ijms-21-05363\"><title>1. Introduction</title><p>Psoriasis is a chronic disease that occurs with increasing frequency in developed countries. In European countries and the United States, the prevalence of psoriasis can reach 3% [<xref rid=\"B1-ijms-21-05363\" ref-type=\"bibr\">1</xref>,<xref rid=\"B2-ijms-21-05363\" ref-type=\"bibr\">2</xref>]. Psoriasis occurs mainly due to a dysfunction of the immune system, and its development might be associated with other diseases, including arthritis, metabolic syndrome, heart disease, polycystic ovarian syndrome, chronic obstructive pulmonary disease and even cancer [<xref rid=\"B3-ijms-21-05363\" ref-type=\"bibr\">3</xref>]. However, the main symptom of the disease is excessive skin exfoliation [<xref rid=\"B4-ijms-21-05363\" ref-type=\"bibr\">4</xref>]. As a result, psoriasis also affects the psychophysical health of patients by lowering their self-esteem and disrupting their social behavior. Depressive symptoms and sleep disturbances are also common in psoriatic patients [<xref rid=\"B5-ijms-21-05363\" ref-type=\"bibr\">5</xref>,<xref rid=\"B6-ijms-21-05363\" ref-type=\"bibr\">6</xref>].</p><p>The pathogenesis of psoriasis is the result of impaired immunity, and has a genetic component linked to the immune genes and their encoded pathways, as well as to environmental factors such as drugs, smoking, diet, alcohol and mental stress [<xref rid=\"B7-ijms-21-05363\" ref-type=\"bibr\">7</xref>]. Regardless of the particular mechanisms involved, psoriasis develops due to the chronic activation of the cells of the peripheral immune system, resulting in the increased proliferation and differentiation of skin cells [<xref rid=\"B8-ijms-21-05363\" ref-type=\"bibr\">8</xref>,<xref rid=\"B9-ijms-21-05363\" ref-type=\"bibr\">9</xref>]. Significant changes occur in the epidermis, where the accelerated cell cycle of keratinocytes results in intensified keratinization and the formation of cutaneous psoriatic lesions. Epidermal keratinocytes are stimulated to proliferate by signaling molecules, primarily released by lymphocytes. This process has been well examined and described previously [<xref rid=\"B8-ijms-21-05363\" ref-type=\"bibr\">8</xref>].</p><p>Undoubtedly, the release of signaling molecules that can reach and interact with the epidermis will also have an impact on cells that build the other layers of the skin, including dermal fibroblasts. Under physiological conditions, skin fibroblasts are primarily responsible for the production of collagen and other intercellular matrix substances present in the dermis, which are intimately linked to the condition and function of the skin [<xref rid=\"B10-ijms-21-05363\" ref-type=\"bibr\">10</xref>]. However, the metabolic activity of fibroblasts in psoriatic skin has not been extensively studied in recent years, compared to keratinocytes, which have been the subject of extensive research [<xref rid=\"B11-ijms-21-05363\" ref-type=\"bibr\">11</xref>,<xref rid=\"B12-ijms-21-05363\" ref-type=\"bibr\">12</xref>,<xref rid=\"B13-ijms-21-05363\" ref-type=\"bibr\">13</xref>,<xref rid=\"B14-ijms-21-05363\" ref-type=\"bibr\">14</xref>,<xref rid=\"B15-ijms-21-05363\" ref-type=\"bibr\">15</xref>,<xref rid=\"B16-ijms-21-05363\" ref-type=\"bibr\">16</xref>,<xref rid=\"B17-ijms-21-05363\" ref-type=\"bibr\">17</xref>,<xref rid=\"B18-ijms-21-05363\" ref-type=\"bibr\">18</xref>].</p><p>Oxidative stress is a characteristic of the tissue of patients with psoriasis [<xref rid=\"B19-ijms-21-05363\" ref-type=\"bibr\">19</xref>]. Currently, it is known that oxidative stress in dermal fibroblasts is higher in scaly skin than in unchanged tissue [<xref rid=\"B20-ijms-21-05363\" ref-type=\"bibr\">20</xref>]. It is important to note that the increase in oxidative stress and the decrease in the total antioxidant capacity of dermal fibroblasts are even greater than in the keratinocytes isolated from the same skin biopsy [<xref rid=\"B20-ijms-21-05363\" ref-type=\"bibr\">20</xref>]. Under such conditions, the molecules present in intracellular fibroblasts may undergo oxidative modifications, which can trigger an increase in oxidative lipid metabolism [<xref rid=\"B21-ijms-21-05363\" ref-type=\"bibr\">21</xref>]. As a result, there is an increase in lipid peroxidation products, including reactive α, β-unsaturated aldehydes and isoprostanes [<xref rid=\"B22-ijms-21-05363\" ref-type=\"bibr\">22</xref>]. Additionally, the increase in the enzymatic lipid metabolism of psoriatic fibroblasts promotes the production of bioactive mediators, including eicosanoids, sphingolipids and ceramides. These mediators are involved in skin biology, inflammation and immunity, and even cell apoptosis [<xref rid=\"B23-ijms-21-05363\" ref-type=\"bibr\">23</xref>,<xref rid=\"B24-ijms-21-05363\" ref-type=\"bibr\">24</xref>].</p><p>Increased levels of electrophilic molecules, mainly reactive oxygen species (ROS), as well as reactive aldehydes, especially 4-hydroxynenenal (4-HNE) and malondialdehyde (MDA), can also lead to modifications of proteins in patients with psoriasis. These modifications have been observed in lymphocytes and keratinocytes, and included the formation of protein adducts with lipid peroxidation products [<xref rid=\"B17-ijms-21-05363\" ref-type=\"bibr\">17</xref>,<xref rid=\"B25-ijms-21-05363\" ref-type=\"bibr\">25</xref>] and a significant increase in protein carbonylation in skin fibroblasts [<xref rid=\"B20-ijms-21-05363\" ref-type=\"bibr\">20</xref>]. The presence of these protein modifications in psoriatic fibroblasts also leads to the activation of redox-sensitive signaling pathways, including those that depend on the mitogen-activated protein kinases (mitogen-activated protein kinase (MAPK), p38, extracellular signal-regulated kinase (ERK) and c-Jun N-terminal kinase (JNK)) [<xref rid=\"B21-ijms-21-05363\" ref-type=\"bibr\">21</xref>], as well as protein kinase C (PKC) [<xref rid=\"B26-ijms-21-05363\" ref-type=\"bibr\">26</xref>]. Consistently, PKC in the cell membranes of psoriatic fibroblasts is significantly activated, which could make these cells very sensitive in response to hormones or growth factors [<xref rid=\"B26-ijms-21-05363\" ref-type=\"bibr\">26</xref>]. Moreover, psoriatic fibroblasts, unlike unmodified dermal cells, have been shown to stimulate the proliferation of keratinocytes after receiving activation signals [<xref rid=\"B27-ijms-21-05363\" ref-type=\"bibr\">27</xref>]. An example of such action in psoriatic fibroblasts stimulated by inflammatory cytokines is the observation that increased expression of the insulin-like growth factor-I (IGF-I) significantly promotes the proliferation of keratinocytes [<xref rid=\"B28-ijms-21-05363\" ref-type=\"bibr\">28</xref>]. Metabolic disturbances in psoriatic fibroblasts also cause increased expression of interleukin 8 (IL-8), resulting in the stimulation of neutrophils, monocytes and T lymphocytes, which migrate into the skin layers [<xref rid=\"B29-ijms-21-05363\" ref-type=\"bibr\">29</xref>]. In addition, the changes observed following psoriatic epidermal exfoliation are linked to changes in the metabolism of fibroblasts, not only locally but also in regions distant from the exfoliation site. The expression of factors such as α5 integrin, fibronectin or keratinocyte growth factor (KGF) is high, in particular in non-lesional psoriatic skin fibroblasts [<xref rid=\"B30-ijms-21-05363\" ref-type=\"bibr\">30</xref>]. In agreement with this, it is suggested that these factors play a crucial role in the pathogenesis of psoriasis by influencing the inflammation and hyperproliferation of keratinocytes.</p><p>The abundance of evidence highlighting the critical role of fibroblasts in the development of psoriasis lesions has led us to investigate in more detail the molecular mechanisms leading to the pathogenesis of the disease. To achieve this, we sought to determine the differences in the proteomic profiles of fibroblasts isolated from the dermis of psoriatic patients, compared to unmodified skin cells.</p></sec><sec sec-type=\"results\" id=\"sec2-ijms-21-05363\"><title>2. Results</title><p>The results presented in this study show that the proteome of fibroblasts isolated from the dermis of psoriatic patients has a different profile than that of control cells. The data obtained from our proteomic analysis allowed us to identify and semi-quantitatively determine the expressions of 718 proteins, 684 of which were found in control fibroblasts, and 690 in cells isolated from the skin of psoriatic patients ( <xref ref-type=\"app\" rid=\"app1-ijms-21-05363\">Supplementary Table S1</xref>). The distribution of these proteins between the samples is shown on a Venn diagram (<xref ref-type=\"fig\" rid=\"ijms-21-05363-f001\">Figure 1</xref>).</p><p>Using principal component analysis (PCA), we found that changes in the proteomic profiles of skin fibroblast cells led to the clustering of the experimental groups (PC1—41.5%, PC2—17.4%). In the case of control fibroblasts, the samples clustered in the left quadrant, while the psoriatic fibroblasts clustered mainly in the lower right quadrant (<xref ref-type=\"fig\" rid=\"ijms-21-05363-f002\">Figure 2</xref>). Statistical analysis indicated that the expressions of 242 of the proteins identified were significantly different between the control fibroblasts and the psoriatic fibroblasts ( <xref ref-type=\"app\" rid=\"app1-ijms-21-05363\">Supplementary Table S2</xref>). A volcano plot displaying differentially enriched proteins highlighted that the most significantly changed proteins in psoriatic fibroblasts were downregulated; β-catenin (P35222), importin-8 (O15397), protein kinase C (Q05655) and galectin-3 (P17931). The following, on the other hand, were upregulated: keratin (P35527), tubulin (Q9BVA1), 26S proteasome (Q5VWC4), protein transport protein Sec24C (P53992), glutathione S transferase 1 (P08263) and high mobility group protein B2 (P26583) (<xref ref-type=\"fig\" rid=\"ijms-21-05363-f003\">Figure 3</xref>).</p><p>The clustering and functions of the 50 most significant proteins were visualized in a two-dimensional hierarchical clustering heat map (<xref ref-type=\"fig\" rid=\"ijms-21-05363-f004\">Figure 4</xref>). The analyzed proteins were divided into two clusters. Cluster 1 contained proteins downregulated in psoriatic fibroblasts compared to controls. These proteins were primarily involved in the transcription/translation processes, protein folding and glycolysis/ATP synthesis, as well as having structural functions. Cluster 2 contained proteins upregulated in psoriasis and had various different biological functions. Some of them were proinflammatory proteins, including NFκB (Q00653), TNFα (P01375) and S100A8/9 (P05109/P06702) (<xref ref-type=\"fig\" rid=\"ijms-21-05363-f005\">Figure 5</xref>A). Another group of proteins upregulated in psoriatic fibroblasts was those with antioxidant properties. These included thioredoxin (Q99757), peroxiredoxin (P32119), glutaredoxin (O76003), Nrf2 (Q16236), glutathione S transferase 1 (P08263) and thioredoxin-dependent peroxide reductase 2 (P30048) (<xref ref-type=\"fig\" rid=\"ijms-21-05363-f005\">Figure 5</xref>B). Finally, psoriatic fibroblasts were characterized by higher expressions of proteins involved in signal transduction (such as 14-3-3 proteins (P31947, P63104), kinases (P55263, P67775, Q5U5J2) and intracellular channel protein 4 (Q6FIC5)) (<xref ref-type=\"fig\" rid=\"ijms-21-05363-f006\">Figure 6</xref>A), intracellular transport (such as the Ran-specific GTPase-activating protein (F6WQW2), GTP-binding nuclear protein Ran (J3KQE5) and transport protein Sec24C (P53992)) (<xref ref-type=\"fig\" rid=\"ijms-21-05363-f006\">Figure 6</xref>B) and the proteins involved in the proteolytic processes (calpain (P07384) and 26S proteasome (Q5VWC4)) (<xref ref-type=\"fig\" rid=\"ijms-21-05363-f006\">Figure 6</xref>C).</p></sec><sec sec-type=\"discussion\" id=\"sec3-ijms-21-05363\"><title>3. Discussion</title><p>Psoriasis is an immune-mediated disease characterized by increased activation of lymphocytes. Once the lymphocytes have entered the skin, they cause the proliferation, maturation and desquamation of keratinocytes [<xref rid=\"B25-ijms-21-05363\" ref-type=\"bibr\">25</xref>]. The resulting skin lesions disrupt the functioning of the epidermis and can lead to a reduction in the quality of life of those affected [<xref rid=\"B5-ijms-21-05363\" ref-type=\"bibr\">5</xref>,<xref rid=\"B31-ijms-21-05363\" ref-type=\"bibr\">31</xref>]. However, the development of psoriasis is not only linked to changes in the interaction between lymphocytes and keratinocytes; it also involves the activity of other cells of the immune system. For example, the actions of granulocytes [<xref rid=\"B32-ijms-21-05363\" ref-type=\"bibr\">32</xref>,<xref rid=\"B33-ijms-21-05363\" ref-type=\"bibr\">33</xref>] can significantly alter the profile of the signaling molecules in the plasma of psoriatic patients [<xref rid=\"B34-ijms-21-05363\" ref-type=\"bibr\">34</xref>]. Such changes do not exclusively affect keratinocytes, and also have consequences for other skin-building cells, including dermal fibroblasts [<xref rid=\"B24-ijms-21-05363\" ref-type=\"bibr\">24</xref>,<xref rid=\"B30-ijms-21-05363\" ref-type=\"bibr\">30</xref>,<xref rid=\"B35-ijms-21-05363\" ref-type=\"bibr\">35</xref>].</p><p>Fibroblasts, as the primary cells of the dermis, are responsible for the synthesis of proteoglycans, glycosaminoglycans and collagen, which are the main components of the extracellular matrix. These cells are essential for maintaining the appropriate thickness of the dermis when the exfoliated epidermis becomes too thin to fulfill a protective function. Therefore, increased collagen biosynthesis in these cells is observed in psoriatic skin [<xref rid=\"B36-ijms-21-05363\" ref-type=\"bibr\">36</xref>,<xref rid=\"B37-ijms-21-05363\" ref-type=\"bibr\">37</xref>]. Moreover, fibroblasts directly promote the proliferation of keratinocytes by generating and transporting growth factors [<xref rid=\"B27-ijms-21-05363\" ref-type=\"bibr\">27</xref>].</p><p>These observations highlight the need for further research into how changes in the proteome of these cells could contribute to the development of psoriasis. To date, complex changes in the proteome of psoriatic patients have been identified in keratinocytes and biopsies of the whole skin, as well as in blood cells and plasma [<xref rid=\"B35-ijms-21-05363\" ref-type=\"bibr\">35</xref>,<xref rid=\"B38-ijms-21-05363\" ref-type=\"bibr\">38</xref>,<xref rid=\"B39-ijms-21-05363\" ref-type=\"bibr\">39</xref>,<xref rid=\"B40-ijms-21-05363\" ref-type=\"bibr\">40</xref>,<xref rid=\"B41-ijms-21-05363\" ref-type=\"bibr\">41</xref>,<xref rid=\"B42-ijms-21-05363\" ref-type=\"bibr\">42</xref>,<xref rid=\"B43-ijms-21-05363\" ref-type=\"bibr\">43</xref>]. However, in fibroblasts from psoriatic patients, only changes in candidate proteins were analyzed [<xref rid=\"B20-ijms-21-05363\" ref-type=\"bibr\">20</xref>,<xref rid=\"B21-ijms-21-05363\" ref-type=\"bibr\">21</xref>,<xref rid=\"B26-ijms-21-05363\" ref-type=\"bibr\">26</xref>,<xref rid=\"B28-ijms-21-05363\" ref-type=\"bibr\">28</xref>,<xref rid=\"B30-ijms-21-05363\" ref-type=\"bibr\">30</xref>,<xref rid=\"B44-ijms-21-05363\" ref-type=\"bibr\">44</xref>]. Our study investigated changes in the proteome of psoriatic fibroblasts. In this report, we present the most significant modifications and discuss their potential consequences. The changes described include the upregulated proteins that are involved in inflammation, the antioxidant response, signal transduction and proteolytic processes, as well as the downregulated proteins mainly responsible for the transcription/translation processes, glycolysis/ATP synthesis and structural components.</p><p>Psoriatic skin exfoliation is intrinsically linked to the response of skin cells to proinflammatory factors generated by immune cells [<xref rid=\"B45-ijms-21-05363\" ref-type=\"bibr\">45</xref>]. These agents activate cell membrane receptors leading to stimulation of the intracellular pro-inflammatory pathways. Consequently, the expression of NFκB is increased in dermal fibroblasts, but also in epidermal keratinocytes [<xref rid=\"B46-ijms-21-05363\" ref-type=\"bibr\">46</xref>]. The action of NFκB not only leads to the expression of the genes responsible for the inflammatory reaction, but also stimulates other pro-inflammatory factors, including TNFα, which intensify pro-inflammatory signaling within the cell and between adjacent cells. In psoriatic plaques, the skin levels of TNFα are increased, which promotes infiltration by macrophages that also express TNFα [<xref rid=\"B47-ijms-21-05363\" ref-type=\"bibr\">47</xref>,<xref rid=\"B48-ijms-21-05363\" ref-type=\"bibr\">48</xref>,<xref rid=\"B49-ijms-21-05363\" ref-type=\"bibr\">49</xref>]. As a result, keratinocytes are continuously stimulated to proliferate [<xref rid=\"B50-ijms-21-05363\" ref-type=\"bibr\">50</xref>]. Until now, there has been no unambiguous data indicating the levels of these factors in psoriatic fibroblasts. However, the presented results allow us to suggest that the increased expression of NFκB and TNFα in fibroblasts can enhance intercellular proinflammatory signaling, thus contributing to the stimulation of keratinocytes proliferation.</p><p>Moreover, the increase in the level of pro-inflammatory factors in psoriatic fibroblasts is accompanied by an increased expression of the proteins participating in the proteolytic processes, such as 26S proteasome components and S100A8/A9. The ubiquitin–proteasome pathway activated during the development of psoriasis also plays a central role in the selective degradation of intracellular proteins, including those involved in the control of inflammatory processes [<xref rid=\"B51-ijms-21-05363\" ref-type=\"bibr\">51</xref>]. The increase in the level of 26S proteasome subunits observed in this study could contribute to an increased degradation of IκB, which is a cytosolic inhibitor of NFκB [<xref rid=\"B52-ijms-21-05363\" ref-type=\"bibr\">52</xref>,<xref rid=\"B53-ijms-21-05363\" ref-type=\"bibr\">53</xref>]. As a result, NFκB, by improving the inflammatory response, promotes T cell responses in psoriasis [<xref rid=\"B54-ijms-21-05363\" ref-type=\"bibr\">54</xref>]. In vitro and in vivo experiments have shown that 26S proteasome inhibitors inhibit cell proliferation and migration under inflammatory conditions [<xref rid=\"B55-ijms-21-05363\" ref-type=\"bibr\">55</xref>], highlighting the potential for the inhibition of the proteasome as a treatment option for inflammatory disorders such as psoriasis [<xref rid=\"B51-ijms-21-05363\" ref-type=\"bibr\">51</xref>]. Another protein with proteolytic activity associated with inflammation is a calcium-dependent neutral protease called calpain. The main role of calpain is the regulation of various fundamental cellular functions, such as the cell cycle and apoptosis, but it is also involved in the initiation of inflammation by the degradation of IκB and the activation of NFκB [<xref rid=\"B56-ijms-21-05363\" ref-type=\"bibr\">56</xref>]. An increase in the level of calpain in psoriatic patients has already been identified in the skin tissue [<xref rid=\"B57-ijms-21-05363\" ref-type=\"bibr\">57</xref>,<xref rid=\"B58-ijms-21-05363\" ref-type=\"bibr\">58</xref>]. However, calpain also stimulates the migration of fibroblasts and myoblasts, which is necessary for the treatment of damaged skin [<xref rid=\"B59-ijms-21-05363\" ref-type=\"bibr\">59</xref>].</p><p>Our data also show an increase in the level of S100A8/A9 proteins in psoriatic fibroblasts. Previous studies have determined that the source of S100A proteins in skin tissue are the activated phagocytes in inflammatory conditions associated with psoriasis lesions [<xref rid=\"B60-ijms-21-05363\" ref-type=\"bibr\">60</xref>]. The main role of S100A8/A9 in the psoriatic epidermis is to activate the complement component 3 protein (C3) in keratinocytes. After activation, C3 is translocated to the dermis, where it stimulates immune cells to produce cytokines, interleukins and growth factors, thereby contributing to the development of psoriasis [<xref rid=\"B61-ijms-21-05363\" ref-type=\"bibr\">61</xref>,<xref rid=\"B62-ijms-21-05363\" ref-type=\"bibr\">62</xref>,<xref rid=\"B63-ijms-21-05363\" ref-type=\"bibr\">63</xref>]. It is unknown whether psoriatic fibroblasts can synthesize S100A8/A9 or can only accumulate proteins when produced by other cells. However, our data, which show a significant increase in these proteins in dermal cells, suggest an additional role for fibroblasts in pro-inflammatory signaling, which leads to the hyperproliferation of keratinocytes in psoriasis.</p><p>Inflammatory diseases, such as psoriasis, are associated with pro-oxidative conditions, leading to oxidative stress [<xref rid=\"B64-ijms-21-05363\" ref-type=\"bibr\">64</xref>,<xref rid=\"B65-ijms-21-05363\" ref-type=\"bibr\">65</xref>]. In response, the level and activity of components of the antioxidant system increase in patients with psoriasis [<xref rid=\"B66-ijms-21-05363\" ref-type=\"bibr\">66</xref>,<xref rid=\"B67-ijms-21-05363\" ref-type=\"bibr\">67</xref>]. Our results confirm that in the fibroblasts of psoriasis patients, one of the main groups of significantly modified proteins is the proteins involved in the antioxidant response. These include the transcription factor Nrf2—a redox-sensitive protein responsible for the expression of cytoprotective proteins. Various investigations into psoriatic keratinocytes have observed changes in Nrf2 levels. One study found that a decrease in the levels of Nrf2 was associated with the development of psoriasis [<xref rid=\"B68-ijms-21-05363\" ref-type=\"bibr\">68</xref>], while others observed an increased expression of Nrf2, which led to the elevated expression of keratins and promoted the proliferation of keratinocytes, leading to the pathogenesis of psoriasis [<xref rid=\"B69-ijms-21-05363\" ref-type=\"bibr\">69</xref>,<xref rid=\"B70-ijms-21-05363\" ref-type=\"bibr\">70</xref>]. The transcriptional activity of Nrf2 leads to the expression of genes coding for antioxidant enzymes, in particular thioredoxin-dependent peroxide reductase and glutathione S transferase 1 [<xref rid=\"B71-ijms-21-05363\" ref-type=\"bibr\">71</xref>], the levels of which are increased in psoriatic fibroblasts. A previous study also indicated that the level of these enzymes is increased in fibroblasts under oxidative stress induced by UV, which is probably a defense mechanism against adverse conditions in the cell [<xref rid=\"B72-ijms-21-05363\" ref-type=\"bibr\">72</xref>]. Moreover, the increased level of thioredoxin-dependent peroxide reductase is accompanied by a high level of thioredoxin, which is associated with the increased activity of this enzyme. Simultaneously, the levels of peroxiredoxin and glutaredoxin are increased. These proteins can reduce thiol groups in oxidized proteins and also control the peroxide levels induced by cytokines [<xref rid=\"B73-ijms-21-05363\" ref-type=\"bibr\">73</xref>]. Previous reports confirm the increase in the mentioned parameters of the antioxidant system in skin biopsies of psoriatic patients [<xref rid=\"B74-ijms-21-05363\" ref-type=\"bibr\">74</xref>]. Along with the previously published data, our findings indicate that fibroblasts from psoriasis patients are subject to high levels of oxidative stress, and these cells activate pathways to limit these oxidative conditions.</p><p>Signal transduction between cells involved in psoriatic lesion development is one of the fundamental elements to consider in designing effective treatments for psoriasis [<xref rid=\"B75-ijms-21-05363\" ref-type=\"bibr\">75</xref>,<xref rid=\"B76-ijms-21-05363\" ref-type=\"bibr\">76</xref>,<xref rid=\"B77-ijms-21-05363\" ref-type=\"bibr\">77</xref>]. So far, the role of fibroblasts in this intercellular communication has not been described. In this study, we found that fibroblasts in psoriatic skin display the upregulation of 14-3-3 sigma (σ) and zeta/delta (ζ/δ) protein isoforms. Other studies show that 14-3-3 protein levels in psoriatic skin biopsies are changed in various ways, depending on the isoform; 14-3-3τ and σ are upregulated [<xref rid=\"B78-ijms-21-05363\" ref-type=\"bibr\">78</xref>,<xref rid=\"B79-ijms-21-05363\" ref-type=\"bibr\">79</xref>,<xref rid=\"B80-ijms-21-05363\" ref-type=\"bibr\">80</xref>], while 14-3-3β and 14-3-3ζ are downregulated [<xref rid=\"B81-ijms-21-05363\" ref-type=\"bibr\">81</xref>]. 14-3-3 is involved in the regulation of transcription and translation through its interaction with DNA/mRNA-binding proteins, such as tristetraprolin (TTP), which induces the destabilization and degradation of cytokine mRNA (including TNFα mRNA). After phosphorylation, TTP can bind to 14-3-3, which inhibits the mRNA-degrading capabilities of TTP. Therefore, in several skin diseases characterized by hyperproliferative keratinocytes, increased levels of 14-3-3 result in the overexpression of cytokines [<xref rid=\"B78-ijms-21-05363\" ref-type=\"bibr\">78</xref>]. These changes are accompanied by the upregulation of kinases, as shown in this study and in previous work on a psoriatic skin model [<xref rid=\"B82-ijms-21-05363\" ref-type=\"bibr\">82</xref>]. Conversely, in the case of DNA-damage or the overexpression of dysregulated genes, 14–3–3σ is upregulated by a p53-dependent pathway, and partially prevents cells from entering mitosis [<xref rid=\"B83-ijms-21-05363\" ref-type=\"bibr\">83</xref>].</p><p>In psoriatic skin fibroblasts, the expression of the chloride intracellular channel protein 4 (ClIC4) is increased. The activity of this transmembrane protein is linked to angiogenesis and to the differentiation of keratinocytes [<xref rid=\"B84-ijms-21-05363\" ref-type=\"bibr\">84</xref>]. In the case of fibroblasts, the overexpression of ClIC4 leads to the activation of the transforming growth factor-β1 (TGF-β1) and to conversion to myofibroblasts, which is a known feature of diseases characterized by the hyperproliferation of cells, including cancer [<xref rid=\"B44-ijms-21-05363\" ref-type=\"bibr\">44</xref>,<xref rid=\"B85-ijms-21-05363\" ref-type=\"bibr\">85</xref>]. Consistent with this finding, increased levels of TGF-β1 have been observed in the keratinocytes, plasma and lymphocytes of psoriatic patients [<xref rid=\"B86-ijms-21-05363\" ref-type=\"bibr\">86</xref>,<xref rid=\"B87-ijms-21-05363\" ref-type=\"bibr\">87</xref>,<xref rid=\"B88-ijms-21-05363\" ref-type=\"bibr\">88</xref>]. Therefore, the increased ClIC4 level in fibroblasts from psoriatic patients suggests the role of these cells in enhancing the expression of TGF-β1 in the tissues of people affected by psoriasis.</p><p>Psoriatic fibroblasts are also characterized by the increased expression of proteins involved in intracellular transport. Intracellular transport is essential for cells with accelerated proliferation, as well as those that are constantly exposed to the signaling molecules released by cells of the immune system. At the top of the list of the most-changing expressed proteins are the Ran-specific GTPase-activating protein (RANBP1) and the GTP-binding nuclear protein Ran. The actions of these proteins lead to the selective activation of Ran [<xref rid=\"B89-ijms-21-05363\" ref-type=\"bibr\">89</xref>], a protein involved in the transport of proteins across the nuclear membrane. Ran carries out nuclear transport by binding to importins or exportins, and thus participates in the activity of regulation of the transcription factor [<xref rid=\"B89-ijms-21-05363\" ref-type=\"bibr\">89</xref>]. So far, the increase in the level of Ran and its activators has been identified in activated human lymphocytes T [<xref rid=\"B90-ijms-21-05363\" ref-type=\"bibr\">90</xref>]. However, despite the importance of the Ran-related pathway in regulating the cell cycle, it has not been widely analyzed in samples from psoriatic patients. Moreover, the Sec24C protein is also upregulated in psoriatic fibroblasts. The activity of Sec24C facilitates the selection of proteins for transport to the cell nucleus, but is also involved in the transport of proteins to the endoplasmic reticulum [<xref rid=\"B91-ijms-21-05363\" ref-type=\"bibr\">91</xref>]. Therefore, Sec24C is responsible for the proper biosynthesis, maturation and secretion of collagen [<xref rid=\"B92-ijms-21-05363\" ref-type=\"bibr\">92</xref>], suggesting that its upregulation plays a specific role in the fibroblasts of regularly exfoliated psoriatic skin.</p><p>The fibroblasts of psoriatic skin lesions also contain a large group of proteins whose expression is reduced compared to healthy cells. The main proteins are β-catenin, importin-8 and galectin-3. All of these proteins are involved in cell–cell adhesion, which is an essential process in the formation of the skin layers [<xref rid=\"B93-ijms-21-05363\" ref-type=\"bibr\">93</xref>,<xref rid=\"B94-ijms-21-05363\" ref-type=\"bibr\">94</xref>,<xref rid=\"B95-ijms-21-05363\" ref-type=\"bibr\">95</xref>]. Decreased levels of β-catenin have been found in the cytoplasm of keratinocytes in psoriatic skin [<xref rid=\"B93-ijms-21-05363\" ref-type=\"bibr\">93</xref>]. β-catenin is responsible for the transmission of the contact inhibition signal, which causes the division of cells to stop. Therefore, its deficiency in psoriasis does not stop cell division. However, in psoriatic cells, β-catenin accumulates in the nucleus, where it can upregulate gene expression and influence cell growth [<xref rid=\"B93-ijms-21-05363\" ref-type=\"bibr\">93</xref>]. Conversely, gene expression might also be dysregulated by a decrease in the level of importin-8. This protein is responsible for the transport of mature miRNAs from the cytoplasm to the nucleus, which causes gene silencing [<xref rid=\"B94-ijms-21-05363\" ref-type=\"bibr\">94</xref>]. Cells deficient in importin-8 display uncontrolled gene expression [<xref rid=\"B96-ijms-21-05363\" ref-type=\"bibr\">96</xref>] which, in the skin, can lead to the hyperproliferation of keratinocytes and the formation of psoriatic lesions.</p><p>Another downregulated protein in psoriatic fibroblasts associated with the regulation of gene expression is galectin-3. This protein is strongly expressed in epithelial cells, such as keratinocytes and skin fibroblasts, and is involved in the pathogenesis of inflammatory skin diseases by regulating the functions of immune cells, including lymphocytes [<xref rid=\"B95-ijms-21-05363\" ref-type=\"bibr\">95</xref>,<xref rid=\"B97-ijms-21-05363\" ref-type=\"bibr\">97</xref>]. Its decreased level in psoriatic keratinocytes has been shown to be a primary mechanism of cellular hyperproliferation, through the activation of the JNK pathway or the accumulation of neutrophils associated with S100A7-9 overexpression [<xref rid=\"B98-ijms-21-05363\" ref-type=\"bibr\">98</xref>]. On the other hand, an increase in the levels of galectin-3 in the blood of psoriatic patients leads to inflammation and increased profibrotic activity [<xref rid=\"B99-ijms-21-05363\" ref-type=\"bibr\">99</xref>]. However, the exact role of the downregulation of fibroblast galectin-3 in the development of psoriasis remains undefined.</p><p>Current evidence indicates that the development of psoriasis is likely associated with changes in the function of the immune system, as well as in skin cells. Psoriatic skin exfoliation is the result of changes in the metabolism of keratinocytes; however, metabolic changes in fibroblasts also contribute significantly to these symptoms.</p></sec><sec id=\"sec4-ijms-21-05363\"><title>4. Materials and Methods</title><sec id=\"sec4dot1-ijms-21-05363\"><title>4.1. Sample Collection and Preparation</title><p>Skin biopsy fragments were collected from five untreated patients with a diagnosis of psoriasis vulgaris. The patients were two men and three women; age range 27–48 years, mean 38. They were selected from a cohort of 70 patients because their skin lesions were the most characteristic of typical psoriasis. Biopsies were also taken from five healthy people who had moles, and the adjacent skin removed. These individuals forming a control group were sex-matched to the patient group. They had an age range of 28–50 years, mean 38.</p><p>The individuals selected for the patient group had had a diagnosis of plaque psoriasis for at least six months, with at least 10% of the total body surface area affected. The severity of psoriasis was assessed using the PASI score (Psoriasis Area and Severity Index) (median 18; range 12–25). None of the patients or healthy subjects had received topical, injectable or oral medications during the four weeks before the study. Individuals whose history indicated any other disorders were excluded from the study. None of the participants were smokers. All subjects gave their informed consent for inclusion before they participated in the study. The study was conducted in accordance with the Declaration of Helsinki, and the protocol was approved by the Local Bioethics Committee Medical University of Bialystok (Poland), No. R-I-002/502/2015 (17 December 2015). After the biopsy, skin fragments were taken for histopathological examination, and the remaining material was used for molecular analysis.</p><p>The samples were washed in PBS with 50 U/mL penicillin and 50 μg/mL streptomycin and incubated overnight at 4 °C in 1 mg/mL dispase to separate the epidermis from the dermis. The obtained dermis was sliced and placed in culture plates in fibroblast culture medium consisting of DMEM (Dulbecco’s Modified Eagle Medium), fetal bovine serum (10%) and penicillin (50 U/mL)/streptomycin (50 μg/mL). Samples were incubated in a humidified atmosphere of 5% CO<sub>2</sub> at 37 °C until the fibroblasts emigrating from the slices reached full confluence.</p><p>Fibroblasts were collected from the plates by scraping on ice, and were suspending in buffer Tris-HCl (50 mM, pH 7.5, 4 °C) containing 0.1% SDS and protease inhibitor cocktail. All samples were lysed by sonification on ice. The total protein content in the cell lysates was measured using the Bradford assay [<xref rid=\"B100-ijms-21-05363\" ref-type=\"bibr\">100</xref>].</p></sec><sec id=\"sec4dot2-ijms-21-05363\"><title>4.2. SDS-PAGE and In-Gel Digestion</title><p>Each sample (containing 25 μg of protein) was mixed 1:1 with Laemmle buffer, containing 5% 2-mercaptoethanol, and heated at 100 °C for 7 min. After cooling to room temperature, the samples were separated on 12% Tris-Glycine SDS-PAGE gels. Gels were fixed for 1 h in methanol:acetic acid:water (4:1:5) and stained for 4 h with Coomassie Brilliant Blue R-250. All detected bands were cut out of the gel, sliced, and washed out of the dye by acetonitrile (ACN) and 25 mM AMBIC (ammonium bicarbonate). Proteins in each gel fragment were reduced for 1 h with 10 mM DTT, alkylated for 1 h with 50 mM iodoacetamide, and overnight in-gel digested with sequencing grade trypsin (Promega, Madison, WI, USA). The digestion was stopped by the addition of 10% FA (formic acid) and evaporated.</p></sec><sec id=\"sec4dot3-ijms-21-05363\"><title>4.3. Liquid Chromatography-Mass Spectrometry (LC-MS/MS) Analysis</title><p>The dried peptides were dissolved in 5% ACN with 0.1% FA immediately before analysis and loaded onto a 150 mm x 75 µm PepMap RSLC capillary analytical C18 column with 2 μm particle size (LC Packings) using Ultimate 3000 HPLC system (Dionex, Idstein, Germany). Peptide separation was done at a flow rate of 0.300 µl/min, and the solvents gradient started at 3 min and was ramped to 60% Buffer B (90% ACN + 0.03% FA) over 60 min. Eluted peptides were analyzed using a Q Exactive HF mass spectrometer with a nanoelectrospray ionization source (ESI) (Thermo Fisher Scientific, Bremen, Germany). The mass spectrometer was externally calibrated and operated in positive and data-dependent modes. Survey MS scans were conducted in the 200–2000 m/z range, with a resolution of 120,000. The top ten most intense ions were fragmented with 30 eV collision energy on an HCD collision cell and analyzed with a resolution of 30,000. A 10 s dynamic exclusion window was applied, and an isolation window of 4 m/z was used to collect suitable tandem mass spectra. The obtained data were acquired with the Xcalibur software version 4.1 (Thermo Fisher Scientific, Bremen, Germany).</p></sec><sec id=\"sec4dot4-ijms-21-05363\"><title>4.4. Protein Identification and Label-Free Quantification</title><p>For protein identification, Proteome Discoverer 2.0 (Thermo Fisher Scientific, Bremen, Germany) was used with the following search parameters: peptide mass tolerance set to 10 ppm, MS/MS mass tolerance set to 0.02 Da, up to two missed cleavages allowed, cysteine carbamidomethylation/carboxymethylation and methionine oxidation set as a dynamic modification, a minimum peptide length set to 6 amino acids, and the minimum number of identified unique peptides for each protein set to two peptides. Input data were searched against the UniProtKB-SwissProt database (taxonomy: Homo sapiens, release 2019-04). In the case of proteins that were identified in at least 60% of the examined samples from the control or psoriatic group, the missing values were estimated as half of the lowest recorded intensity (half minimum imputation). Other proteins were removed from the analysis as artefacts.</p></sec><sec id=\"sec4dot5-ijms-21-05363\"><title>4.5. Statistical Analysis</title><p>The analysis of each sample was performed in three independent replicates. Data from individual protein label-free quantifications were log and Z-score transformed. Statistical analysis of data was performed using free available MetaboAnalyst 4.0 software (<uri xlink:href=\"http://www.metaboanalyst.ca\">http://www.metaboanalyst.ca</uri>) (Xia Lab, Montreal, Quebec, Canada) [<xref rid=\"B101-ijms-21-05363\" ref-type=\"bibr\">101</xref>], RStudio software (R version 3.6.2 (2019-12-12)) [<xref rid=\"B102-ijms-21-05363\" ref-type=\"bibr\">102</xref>] and Perseus 1.6.10.43 [<xref rid=\"B103-ijms-21-05363\" ref-type=\"bibr\">103</xref>]. Data were analyzed using the standard statistical analysis methods, including univariate analysis (one-way ANOVA), and only proteins with an FDR-corrected significant q-value were taken into account in the discussion. Protein molecular function and protein class were assigned according to the Gene Ontology database in the free available STRING version 11 (<uri xlink:href=\"https://string-db.org/\">https://string-db.org/</uri>) (ELIXIR, Hinxton, Cambridgeshire, UK) [<xref rid=\"B104-ijms-21-05363\" ref-type=\"bibr\">104</xref>].</p></sec></sec><sec sec-type=\"conclusions\" id=\"sec5-ijms-21-05363\"><title>5. Conclusions</title><p>As this study shows, fibroblast dysfunction results from the upregulation of pro-inflammatory factors and proteins with antioxidant properties, as well as factors involved in signal transduction and participating in proteolytic processes. The changes described may directly affect intercellular signaling and promote the hyperproliferation of epidermal cells. Therefore, a better understanding of their exact molecular mechanisms can contribute to the development of more effective pharmacotherapy.</p></sec></body><back><ack><title>Acknowledgments</title><p>Cooperation between co-authors was financed by the Polish National Agency for Academic Exchange (NAWA) as part of the International Academic Partnerships (PPI/APM/2018/00015/U/001). Thanks are due to the University of Aveiro and FCT/MCT for the financial support to QOPNA ((FCT UID/QUI/00062/2019) and LAQV/REQUIMTE (UIDB/50006/2020), and to RNEM (Rede Nacional de Espectrometria de Massa), Portuguese Mass Spectrometry Network, (LISBOA-01-0145-FEDER-402-022125) through national funds and, where applicable, co-financed by the FEDER (Fundo Europeu de Desenvolvimento Regional), within the PT2020 Partnership Agreement.</p></ack><app-group><app id=\"app1-ijms-21-05363\"><title>Supplementary Materials</title><p>The following are available online at <uri xlink:href=\"https://www.mdpi.com/1422-0067/21/15/5363/s1\">https://www.mdpi.com/1422-0067/21/15/5363/s1</uri>, Table S1: Names and ID of proteins indicated in fibroblasts isolated from skin of psoriatic patients (<italic>n</italic> = 5) and healthy people (<italic>n</italic> = 5). Table S2: The p-values and fold change (FC) for individual statistically significant proteins indicated in fibroblasts isolated from skin of psoriatic patients (<italic>n</italic> = 5) and healthy people (<italic>n</italic> = 5).</p><supplementary-material content-type=\"local-data\" id=\"ijms-21-05363-s001\"><media xlink:href=\"ijms-21-05363-s001.pdf\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material></app></app-group><notes><title>Author Contributions</title><p>Conceptualization, E.S.; Data curation, A.G. and P.D.; Formal analysis, A.W.; Funding acquisition, E.S.; Investigation, A.G. and A.W.; Methodology, P.D. and A.W.; Project administration, E.S.; Supervision, E.S.; Validation, A.G.; Visualization, A.G.; Writing: original draft, A.G.; Writing: review and editing, P.D. and E.S. All authors have read and agreed to the published version of the manuscript.</p></notes><notes><title>Funding</title><p>This study was financed by the National Science Centre Poland (NCN) grant no. 2016/23/B/NZ7/02350.</p></notes><notes notes-type=\"COI-statement\"><title>Conflicts of Interest</title><p>The authors declare no conflict of interest.</p></notes><glossary><title>Abbreviations</title><array orientation=\"portrait\"><tbody><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4-<bold>HNE</bold></td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4-hydroxynenenal</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ACN</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">acetonitrile</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">AMBIC</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ammonium bicarbonate</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ANOVA</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">analysis of variance</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">C3</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">component 3</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">DMEM</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Dulbecco’s Modified Eagle Medium</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ERK</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">extracellular signal-regulated kinase</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ESI</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">nanoelectrospray ionization</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">FA</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">formic acid</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">FDR</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">false discovery rate</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">HPLC</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">high performance liquid chromatography</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">IGF-I</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">insulin-like growth factor-I</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">IL-8</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">interleukin 8</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">JNK</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">c-Jun N-terminal kinase</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">KGF</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">keratinocyte growth factor</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">MAPK</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">mitogen-activated protein kinase</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">MDA</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">malondialdehyde</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">MS</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">mass spectrometry</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">NFκB</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">nuclear factor kappa-light-chain-enhancer of activated B cells</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Nrf2</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">nuclear factor erythroid 2-related factor 2</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">PASI</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Psoriasis Area and Severity Index</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">PCA</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">principal component analysis</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">PKC</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">protein kinase C</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">RANBP1</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Ran-specific GTPase-activating protein 1</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ROS</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">reactive oxygen species</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">SDS-PAGE</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">sodium dodecyl sulfate polyacrylamide gel electrophoresis</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">TGF-β1</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">transforming growth factor-β1</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">TNFα</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">tumor necrosis factor α.</td></tr></tbody></array></glossary><ref-list><title>References</title><ref id=\"B1-ijms-21-05363\"><label>1.</label><element-citation publication-type=\"journal\"><person-group 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The names and ID of all the proteins identified are contained in the <xref ref-type=\"app\" rid=\"app1-ijms-21-05363\">Supplementary Table S1</xref>.</p></caption><graphic xlink:href=\"ijms-21-05363-g001\"/></fig><fig id=\"ijms-21-05363-f002\" orientation=\"portrait\" position=\"float\"><label>Figure 2</label><caption><p>Principal component analysis (PCA) of the proteins in fibroblasts isolated from skin of psoriatic patients (<italic>n</italic> = 5) and healthy controls (<italic>n</italic> = 5).</p></caption><graphic xlink:href=\"ijms-21-05363-g002\"/></fig><fig id=\"ijms-21-05363-f003\" orientation=\"portrait\" position=\"float\"><label>Figure 3</label><caption><p>Volcano plot of fibroblasts proteins isolated from the skin of psoriatic patients (<italic>n</italic> = 5) and healthy controls (<italic>n</italic> = 5). Red dots indicate proteins of statistical significance among the groups tested. The <italic>p-</italic>values and the fold change (FC) for each protein are included in <xref ref-type=\"app\" rid=\"app1-ijms-21-05363\">Supplementary Table S2</xref>.</p></caption><graphic xlink:href=\"ijms-21-05363-g003\"/></fig><fig id=\"ijms-21-05363-f004\" orientation=\"portrait\" position=\"float\"><label>Figure 4</label><caption><p>Heat map and clustering of the 50 most significantly changing proteins of fibroblasts isolated from the skin of psoriatic patients (<italic>n</italic> = 5) and healthy controls (<italic>n</italic> = 5).</p></caption><graphic xlink:href=\"ijms-21-05363-g004\"/></fig><fig id=\"ijms-21-05363-f005\" orientation=\"portrait\" position=\"float\"><label>Figure 5</label><caption><p>The level of significantly changed proteins involved in the antioxidant response (<bold>A</bold>) and inflammation (<bold>B</bold>) of fibroblasts isolated from skin of psoriatic patients (<italic>n</italic> = 5) and healthy controls (<italic>n</italic> = 5).</p></caption><graphic xlink:href=\"ijms-21-05363-g005\"/></fig><fig id=\"ijms-21-05363-f006\" orientation=\"portrait\" position=\"float\"><label>Figure 6</label><caption><p>The level of significantly changed proteins involved in signal transduction/phosphorylation (<bold>A</bold>), intracellular transport (<bold>B</bold>) and proteolysis (<bold>C</bold>) of fibroblasts isolated from the skin of psoriatic patients (<italic>n</italic> = 5) and healthy controls (<italic>n</italic> = 5).</p></caption><graphic xlink:href=\"ijms-21-05363-g006\"/></fig></floats-group></article>\n"
]
[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Int J Environ Res Public Health</journal-id><journal-id journal-id-type=\"iso-abbrev\">Int J Environ Res Public Health</journal-id><journal-id journal-id-type=\"publisher-id\">ijerph</journal-id><journal-title-group><journal-title>International Journal of Environmental Research and Public Health</journal-title></journal-title-group><issn pub-type=\"ppub\">1661-7827</issn><issn pub-type=\"epub\">1660-4601</issn><publisher><publisher-name>MDPI</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32752247</article-id><article-id pub-id-type=\"pmc\">PMC7432103</article-id><article-id pub-id-type=\"doi\">10.3390/ijerph17155569</article-id><article-id pub-id-type=\"publisher-id\">ijerph-17-05569</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Article</subject></subj-group></article-categories><title-group><article-title>Resilient Resources in Youth Athletes and Their Relationship with Anxiety in Different Team Sports</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0002-6640-0352</contrib-id><name><surname>González-Hernández</surname><given-names>Juan</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05569\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Gomariz-Gea</surname><given-names>Marcial</given-names></name><xref ref-type=\"aff\" rid=\"af2-ijerph-17-05569\">2</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0002-4317-1665</contrib-id><name><surname>Valero-Valenzuela</surname><given-names>Alfonso</given-names></name><xref ref-type=\"aff\" rid=\"af2-ijerph-17-05569\">2</xref><xref ref-type=\"aff\" rid=\"af3-ijerph-17-05569\">3</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0002-4595-3994</contrib-id><name><surname>Gómez-López</surname><given-names>Manuel</given-names></name><xref ref-type=\"aff\" rid=\"af2-ijerph-17-05569\">2</xref><xref ref-type=\"aff\" rid=\"af3-ijerph-17-05569\">3</xref><xref rid=\"c1-ijerph-17-05569\" ref-type=\"corresp\">*</xref></contrib></contrib-group><aff id=\"af1-ijerph-17-05569\"><label>1</label>Department of Personality, Evaluation and Psychological Treatment, University of Granada, 18071 Granada, Spain; <email>[email protected]</email></aff><aff id=\"af2-ijerph-17-05569\"><label>2</label>Department of Physical Activity and Sport, Faculty of Sport Sciences, University of Murcia, Santiago de la Ribera, 30720 Murcia, Spain; <email>[email protected]</email> (M.G.-G.); <email>[email protected]</email> (A.V.-V.)</aff><aff id=\"af3-ijerph-17-05569\"><label>3</label>Campus of International Excellence “Mare Nostrum”, University of Murcia, 30720 Murcia, Spain</aff><author-notes><corresp id=\"c1-ijerph-17-05569\"><label>*</label>Correspondence: <email>[email protected]</email>; Tel.: +34-868-888-674</corresp></author-notes><pub-date pub-type=\"epub\"><day>01</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"ppub\"><month>8</month><year>2020</year></pub-date><volume>17</volume><issue>15</issue><elocation-id>5569</elocation-id><history><date date-type=\"received\"><day>29</day><month>6</month><year>2020</year></date><date date-type=\"accepted\"><day>29</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>© 2020 by the authors.</copyright-statement><copyright-year>2020</copyright-year><license license-type=\"open-access\"><license-p>Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>).</license-p></license></permissions><abstract><p>The objective of this study is to show the links and differences in the expressions of competitive anxiety in the face of the existence of resilient resources in young athletes, according to sporting (years of experience) and personal (gender) characteristics. To meet these aims, the participants answered the Resilience Scale (RS-14) and the Competitive State Anxiety Inventory-2R (CSAI-2R). The sample consisted of 241 adolescent handball and basketball players between 14 and 17 years old. Different analyses were performed, including a differential and multivariate descriptive, a correlation, and a multiple regression. The results showed that anxiety was negatively related to resilience in its acceptance dimension. It was shown that girls showed higher levels of somatic anxiety, while boys showed higher levels of acceptance. Statistically significant differences were found in the resources for acceptance in favor of boys, while there were significantly different indicators in somatic anxiety and self-confidence in favor of girls. The sports experience was positively related to resilience and negatively to anxiety. Although the existence of indicators of cognitive anxiety (e.g., recurrent thoughts or rhyming), coaches and athletes need to understand that they are also indicators of a necessary activation for psychological functioning. Channeling such a process through psychological training of different skills will enhance the capacities for self-confidence.</p></abstract><kwd-group><kwd>handball</kwd><kwd>basketball</kwd><kwd>resilience</kwd><kwd>self-confidence</kwd><kwd>acceptance</kwd><kwd>competence</kwd></kwd-group></article-meta></front><body><sec sec-type=\"intro\" id=\"sec1-ijerph-17-05569\"><title>1. Introduction</title><p>The ability to obtain an optimal and stable psychological state during competition is of utmost importance to coaches and athletes [<xref rid=\"B1-ijerph-17-05569\" ref-type=\"bibr\">1</xref>,<xref rid=\"B2-ijerph-17-05569\" ref-type=\"bibr\">2</xref>,<xref rid=\"B3-ijerph-17-05569\" ref-type=\"bibr\">3</xref>]. Physical, social, cognitive, and emotional factors factor into such functioning, as these resources restore balance when a young athlete is in the face of adversity [<xref rid=\"B4-ijerph-17-05569\" ref-type=\"bibr\">4</xref>,<xref rid=\"B5-ijerph-17-05569\" ref-type=\"bibr\">5</xref>,<xref rid=\"B6-ijerph-17-05569\" ref-type=\"bibr\">6</xref>]. In this sense, the study of anxiety has been one of the most prolific lines of research, due to the emotional responses it provokes and that can affect athletes’ performance [<xref rid=\"B7-ijerph-17-05569\" ref-type=\"bibr\">7</xref>].</p><p>Anxiety, which is considered a psychological response produced as a consequence of the differences between an athlete’s response capacity and the demands of the environment, accompanied by a high degree of psycho-physiological activation [<xref rid=\"B8-ijerph-17-05569\" ref-type=\"bibr\">8</xref>], shows functional conditions that facilitate adaptation (e.g., helps to prepare for action) but also other dysfunctions that weaken or hinder behavior (e.g., nervousness, impulsiveness or rumination) [<xref rid=\"B9-ijerph-17-05569\" ref-type=\"bibr\">9</xref>].</p><p>Among the different classifications that exist around this psychological response and the instruments used in research to measure it, we must highlight somatic anxiety (physiological activation that a person perceives when faced with a stressful situation) and cognitive anxiety (negative expectations and concerns about oneself and the current situation and its possible consequences) [<xref rid=\"B10-ijerph-17-05569\" ref-type=\"bibr\">10</xref>].</p><p>In sports situations, somatic anxiety often occurs in competitions perceived as significant where there are uncontrollable elements and under conditions of social evaluation that lead to stress and a hormonal response, such as increased levels of cortisol in saliva, or physiological aspects, such as increased heart rate or muscle tension [<xref rid=\"B11-ijerph-17-05569\" ref-type=\"bibr\">11</xref>,<xref rid=\"B12-ijerph-17-05569\" ref-type=\"bibr\">12</xref>]. To understand the relationship between anxiety and sports performance, Martens, Vealey and Burton [<xref rid=\"B13-ijerph-17-05569\" ref-type=\"bibr\">13</xref>] established the multidimensional theory, in which they proposed that there is a negative relationship between cognitive anxiety and performance on the one hand, and a curvilinear relationship between somatic anxiety and sports performance on the other.</p><p>Anxiety is also related to sociodemographic variables such as age and gender in relation with sport contexts, due to it being multifactorial. Different studies carried out in the sports context have shown that the older the athlete, the greater the experience and competitive level in practicing a sport, and the lower their levels of anxiety, because more experienced athletes have greater and better control over their emotions [<xref rid=\"B14-ijerph-17-05569\" ref-type=\"bibr\">14</xref>,<xref rid=\"B15-ijerph-17-05569\" ref-type=\"bibr\">15</xref>], greater technical-tactical mastery, and greater experience in stressful and adverse competitive situations that favor self-confidence [<xref rid=\"B16-ijerph-17-05569\" ref-type=\"bibr\">16</xref>] and cognitive abilities [<xref rid=\"B17-ijerph-17-05569\" ref-type=\"bibr\">17</xref>]. With regard to gender, after an extensive review of the literature, no studies were found that show statistically significant differences, although it is true that some studies have shown that women have higher levels of anxiety and less self-confidence than men, and that the production of anxiety in men and women can come from different routes. In men it is be produced by the poor management of a situation, and in women by their level of motivation [<xref rid=\"B12-ijerph-17-05569\" ref-type=\"bibr\">12</xref>,<xref rid=\"B18-ijerph-17-05569\" ref-type=\"bibr\">18</xref>,<xref rid=\"B19-ijerph-17-05569\" ref-type=\"bibr\">19</xref>]. Likewise, with regard to the type of sport practiced, the results of the literature review indicate that athletes who practice team sports in which there is physical contact show higher levels of anxiety than athletes who practice individual sports [<xref rid=\"B17-ijerph-17-05569\" ref-type=\"bibr\">17</xref>,<xref rid=\"B20-ijerph-17-05569\" ref-type=\"bibr\">20</xref>].</p><p>In a confluent direction with anxiety, resilience protects young athletes from stressful situations or threatening scenarios, giving athletes the means to avoid them [<xref rid=\"B21-ijerph-17-05569\" ref-type=\"bibr\">21</xref>]. Among the many definitions of resilience, Deen, Truner and Wong [<xref rid=\"B22-ijerph-17-05569\" ref-type=\"bibr\">22</xref>] explained it as a set of cognitive aspects and behavioral responses to acute or chronic adversity. Luthar, Cichetti and Becker [<xref rid=\"B23-ijerph-17-05569\" ref-type=\"bibr\">23</xref>] defined it as a dynamic process of positive adaptations within a context of adversity. Fletcher and Sarkar [<xref rid=\"B24-ijerph-17-05569\" ref-type=\"bibr\">24</xref>] argued that resilience is based on overcoming adversity and on positive adaptation. On the other hand, Wu et al. [<xref rid=\"B25-ijerph-17-05569\" ref-type=\"bibr\">25</xref>] highlighted effective efforts to environmental challenges and ultimate resistance to the unhealthy effects of stress.</p><p>Studied in sports contexts, resilience, like anxiety, is a variable that is dependent on different factors, such as emotions, supports, experiences, strategies, motivation, and self-concept [<xref rid=\"B5-ijerph-17-05569\" ref-type=\"bibr\">5</xref>,<xref rid=\"B26-ijerph-17-05569\" ref-type=\"bibr\">26</xref>,<xref rid=\"B27-ijerph-17-05569\" ref-type=\"bibr\">27</xref>], all of which facilitate optimal sports performance. This concept is really important in youth, with important implications in cognitive, physical, and social development that occurs in the adolescence [<xref rid=\"B28-ijerph-17-05569\" ref-type=\"bibr\">28</xref>], and its influence on growth on the personal level and in sporting abilities [<xref rid=\"B5-ijerph-17-05569\" ref-type=\"bibr\">5</xref>,<xref rid=\"B29-ijerph-17-05569\" ref-type=\"bibr\">29</xref>].</p><p>As for the relationship between resilience and the sociodemographic variables of age and gender, it has not been possible to establish firm conclusions since resilience has been little studied in the context of sports [<xref rid=\"B30-ijerph-17-05569\" ref-type=\"bibr\">30</xref>]. However, some studies have shown that athletes are more resilient, which is probably due to their greater experience in the face of adversity both in professionals [<xref rid=\"B15-ijerph-17-05569\" ref-type=\"bibr\">15</xref>] and youth athletes [<xref rid=\"B28-ijerph-17-05569\" ref-type=\"bibr\">28</xref>,<xref rid=\"B31-ijerph-17-05569\" ref-type=\"bibr\">31</xref>,<xref rid=\"B32-ijerph-17-05569\" ref-type=\"bibr\">32</xref>]. With respect to their relationship with gender, there are contradictions, as there are studies that have not shown significant differences in the levels of resilience between men and women, and others that point to higher levels of resilience in men caused by a better perception of personal competence [<xref rid=\"B33-ijerph-17-05569\" ref-type=\"bibr\">33</xref>].</p><p>With regard to the relationship between resilience and sports variables such as sporting experience or the sports modality practiced, the results of the review carried out on the first of the variables show contradictory results, since there are studies that, although they have not obtained statistically significant outcomes, have found that professional athletes with higher levels of competence are more resilient than amateur athletes [<xref rid=\"B34-ijerph-17-05569\" ref-type=\"bibr\">34</xref>,<xref rid=\"B35-ijerph-17-05569\" ref-type=\"bibr\">35</xref>]; on the other hand, there are works that have reflected that athletes with less sporting experience, like youth athletes, had more resilience capacity [<xref rid=\"B32-ijerph-17-05569\" ref-type=\"bibr\">32</xref>]. As for the sport practiced, different studies have shown that athletes who practice individual sports have higher levels of resilience [<xref rid=\"B28-ijerph-17-05569\" ref-type=\"bibr\">28</xref>,<xref rid=\"B36-ijerph-17-05569\" ref-type=\"bibr\">36</xref>].</p><p>Self-confidence is understood as conviction (based on what has been learned and lived before) about the ability to perform a certain task successfully. It is evident that young people have less sporting experience and a greater probability of not being confident in their own skills and resources (e.g., several consecutive errors or failures would affect the perception of self-confidence), which would lead to living with greater perceptions of anxiety. However, different aspects could generate their improvement by looking for self-reflection before the tasks [<xref rid=\"B37-ijerph-17-05569\" ref-type=\"bibr\">37</xref>], reducing the dramatism for the mistake [<xref rid=\"B38-ijerph-17-05569\" ref-type=\"bibr\">38</xref>] or generating support bonds with colleagues [<xref rid=\"B39-ijerph-17-05569\" ref-type=\"bibr\">39</xref>] and rational expression of emotions [<xref rid=\"B40-ijerph-17-05569\" ref-type=\"bibr\">40</xref>].</p><p>Moreover, regarding the relationship between anxiety and resilience in sports contexts, it should be noted that based on the literature review, there is little scientific evidence on this issue in adolescent samples and outside the clinical setting. As an example, Martin-Krumm, Sarrazin, Peterson and Famose [<xref rid=\"B41-ijerph-17-05569\" ref-type=\"bibr\">41</xref>], in a study conducted in France with adolescent children, demonstrated the relationship between resilience and anxiety, since the results reflected that children with a higher level of resilience controlled their anxiety levels better when performing an ability. Likewise, Ortín-Montero, De la Vega and Gonsálvez-Botella [<xref rid=\"B42-ijerph-17-05569\" ref-type=\"bibr\">42</xref>], in their study about optimism, anxiety, and self-confidence in handball players, showed that the most resilient players faced the competition with greater confidence and with lower levels of anxiety.</p><p>Therefore, and on the basis of what has been reviewed so far, the objectives of the study are as follows: (a) to describe the levels of resilience and competitive anxiety, (b) to analyze the differences in terms of gender and years of sports experience, (c) to study the relationship between resiliency resources and competitive anxiety, and (d) to examine the variables that predict both resiliency resources and self-confidence in players.</p></sec><sec id=\"sec2-ijerph-17-05569\"><title>2. Materials and Methods</title><sec id=\"sec2dot1-ijerph-17-05569\" sec-type=\"subjects\"><title>2.1. Participants</title><p>A total of 241 team sports players (handball and basketball) belonging to different clubs in the Murcia region and Valencia communities participated (160 boys, 66.4%; 81 girls, 33.6%), with an average age of 15.36 years (SD = 1.07). According to age, 60.6% (<italic>n</italic> = 146) of the participants were <15 years, while 39.4% (<italic>n</italic> = 95) were >15 years old. Most of them stated that they had an average of 1.56 years of experience as a federated player in their sport (SD = 0.50) and they spent 1.93 training sessions per week (SD = 0.36).</p></sec><sec id=\"sec2dot2-ijerph-17-05569\"><title>2.2. Measurement Instruments</title><p>Resilience scale. The Spanish version of the Resilience Scale (RS-14) [<xref rid=\"B43-ijerph-17-05569\" ref-type=\"bibr\">43</xref>] was used, along with the 14-Item Resilience Scale (ER-14) [<xref rid=\"B44-ijerph-17-05569\" ref-type=\"bibr\">44</xref>], which is an abbreviated version of the original 25-item version [<xref rid=\"B45-ijerph-17-05569\" ref-type=\"bibr\">45</xref>]. This scale measures a person’s degree of individual resilience when such is considered a positive personality characteristic, one that allows people to adapt to adverse situations. The scale is composed of 14 items grouped in two dimensions that measure personal competence (11 items: self-confidence, independence, decision, ingenuity and perseverance, etc.; e.g., “I am not afraid to suffer difficulties because I have already experienced them in the past”), and the acceptance of oneself and life (3 items: adaptability, balance, and flexibility and stable life perspective; e.g., “I am a person with adequate self-esteem”). Every item on the scale should correspond with the respondent’s feelings. The answers were on a 7-point Likert-type scale that ranged from strongly disagree (1) to strongly agree (7). The internal consistency for the sample collected was satisfactory (α = 0.80).</p><p>Competitive State Anxiety Inventory-2R. The Competitive State Anxiety Inventory-2R (CSAI-2R) [<xref rid=\"B46-ijerph-17-05569\" ref-type=\"bibr\">46</xref>] comes from the original CSAI-2 version consisting of 27 items [<xref rid=\"B20-ijerph-17-05569\" ref-type=\"bibr\">20</xref>]. The Spanish version of this second revision, which was validated for athletes, was used in this study [<xref rid=\"B47-ijerph-17-05569\" ref-type=\"bibr\">47</xref>]. The CSAI-2R is a specific inventory for the sports context, but with a one-dimensional conception of the state of anxiety. The scale is composed of 16 items grouped in three dimensions that measure somatic state anxiety (6 items; e.g., “My heart is racing”), self-confidence (5 items; e.g., “I am confident”), and cognitive state anxiety (5 items; e.g., “I worry that others will be disappointed with my performance”). Each item of the scale had to be answered in terms of how the player felt just before the competition. The answers were collected on a 4-point Likert-type scale ranging from none (1) to very much (4). The internal consistency for the collected sample was satisfactory (α = 0.78).</p></sec><sec id=\"sec2dot3-ijerph-17-05569\"><title>2.3. Procedure</title><p>The study was carried out in different clubs and sport federations of Murcia region and Valencia community (Spain). A letter explaining the objectives of the research and how it was to be carried out, accompanied by two models of informed consent and permission (one for the parents and another for the young athletes), together with a copy of the measures, was sent to the clubs and federations (first for presidents) before the data collection. The questionnaire was administered by the researchers in the sports facilities of the clubs during the training sessions and completed by the participants in 20–30 min. All the participants were informed of the objectives and of their rights as participants in the study, as well as of the voluntary nature and the absolute confidentiality of the answers and handling of the data. It was explained that there were no correct or incorrect answers, so that participants would answer with the most sincerity and honesty. This study was carried out in accordance with the ethical guidelines of the American Psychological Association (APA). The protocol was approved by the Ethics Committee of the Universidad de Murcia (ID: 1494/2017). All subjects gave written informed consent in accordance with the Declaration of Helsinki [<xref rid=\"B48-ijerph-17-05569\" ref-type=\"bibr\">48</xref>].</p></sec><sec id=\"sec2dot4-ijerph-17-05569\"><title>2.4. Statistical Analysis</title><p>Through the analysis carried out with the statistical software SPSS. 23.0, descriptive measures and homogeneity (X<sup>2</sup> Chi-squared) were obtained for the distribution of the observed sample, internal consistency of the instruments (Cronbach’s alpha), differential analysis (<italic>t</italic>-test, multivariate analysis and effect size with etha-square) to describe any differences in the sample (according to gender and sport experience), correlations, and multiple regressions (5000 bootstrap samples with bias-corrected confidence intervals 95.00) for the determination of the strength of the relationship between the study variables.</p></sec></sec><sec sec-type=\"results\" id=\"sec3-ijerph-17-05569\"><title>3. Results</title><sec id=\"sec3dot1-ijerph-17-05569\"><title>3.1. Descriptive and Differential Analysis</title><p>The sample distribution reflected the existence of a statistically significant association between both variables (<xref rid=\"ijerph-17-05569-t001\" ref-type=\"table\">Table 1</xref>) for the achievement of normality, which was assumed to be a link of dependence between the distributions by gender and sport experience (years). Similarly, this circumstance was again obtained when considering the distribution by gender and the hours of weekly training.</p><p>As can be seen in <xref rid=\"ijerph-17-05569-t002\" ref-type=\"table\">Table 2</xref>, the sample reflected higher means of self and life acceptance in terms of resilience, while higher means of self-confidence were observed in the perception of anxiety. Participants’ response tendency (asymmetry) tended to be concentrated between 5 and 7 points (Likert response orientation was 1–7) for personal competence as well as for acceptance, with pronounced peaks at 5 and 8 (kurtosis). Likewise, the participants’ response tendency in anxiety perception tended to concentrate (with a Likert response orientation from 1 to 4), between 2 and 3 points for somatic anxiety, and between 2.5 and 3.5 for cognitive anxiety and self-confidence, with pronounced peaks at 2.5 for somatic anxiety and 3 for cognitive anxiety and self-confidence.</p><p>Differential analysis (<xref rid=\"ijerph-17-05569-t003\" ref-type=\"table\">Table 3</xref>) showed the existence of statistically significant differences with respect to gender and sports experience. By gender, resources for acceptance (<italic>p</italic> < 0.02) were shown in favor of girls and self-confidence (<italic>p</italic> = 0.00) in favor of boys. The effect size of the established comparisons indicated that all the variables are very high, except for competition and cognitive anxiety, aspects that were repeated in the analyses carried out both by gender and by years of sports experience. According to sports experience, the most experienced young athletes showed differences in acceptance (<italic>p</italic> < 0.03) and self-confidence (<italic>p</italic> < 0.01).</p><p>In order to check whether there were differences in resiliency resources and anxiety responses based on gender and years of sports experience, an analysis of multivariate variance (MANOVA) was carried out with the scores obtained in all the variables under study (<xref rid=\"ijerph-17-05569-t004\" ref-type=\"table\">Table 4</xref>). The results (Pillai’s Trace) indicated statistically significant differences in favor of girls (F<sub>(5.232)</sub> = 9.08; <italic>p</italic> < 0.01) with the highest magnitude of the effect (η<sup>2</sup> = 731; <italic>r</italic> = 0.46). Statistically significant differences were shown based on years of sports experience in favor of the younger, most experienced ones, but the interaction with the gender variables was significant (F<sub>(5.232)</sub> = 7.512; <italic>p</italic> > 0.03), with a moderate effect size (η<sup>2</sup> = 779; <italic>r</italic> = 0.51). More specifically, analysis of the variance of effects between subjects showed significant differences in competence, acceptance, cognitive anxiety, and self-confidence by gender (in competence and self-confidence in favor of boys, and in cognitive anxiety and acceptance in favor of girls). In the years of sports experience, the differences were in acceptance, cognitive anxiety, and self-confidence in favor of the younger, more experienced ones. No significant differences were shown in the gender*sport experience interaction.</p></sec><sec id=\"sec3dot2-ijerph-17-05569\"><title>3.2. Correlational Analysis</title><p>Statistically significant two-way relationships appeared between the variables under study (<xref rid=\"ijerph-17-05569-t005\" ref-type=\"table\">Table 5</xref>), so that older athletes had higher perceptions of competence as shown in their lower levels of managing resiliently. Acceptance was inversely and significantly related to both cognitive and somatic anxiety. Conversely it was directly related to self-confidence and logically to a high perception of competence. All this led us to think that higher perceptions of resilient acceptance had a direct influence on the athlete’s ability to interpret anxiety. The relationship between the two resilient variables (acceptance and competence) with the development of self-confidence in a young handball player is very significant, in a positive way.</p></sec><sec id=\"sec3dot3-ijerph-17-05569\"><title>3.3. Multiple Regression Analysis</title><p>The results of the regression analysis (<xref rid=\"ijerph-17-05569-t006\" ref-type=\"table\">Table 6</xref>) indicated a model in which as the athletes showed better levels of acceptance, competence, and cognitive anxiety, they also reduced their somatic anxiety indicators, and increased in self-confidence, where F<sub>2.239</sub> = 23.59; <italic>p</italic> < 0.01 explains 33.5% of the total variance.</p><p>The results of the regression analysis (<xref rid=\"ijerph-17-05569-t007\" ref-type=\"table\">Table 7</xref>) also indicate a model in which younger athletes had better levels of acceptance and self-confidence and more competence, where F<sub>2.239</sub> = 33.96; <italic>p</italic> < 0.01 explains 42.1% of the total variance. On the other hand, acceptance was achieved through the predictive model established by an increase in competence and self-confidence, and the existence of low levels of cognitive anxiety.</p></sec></sec><sec sec-type=\"discussion\" id=\"sec4-ijerph-17-05569\"><title>4. Discussion</title><p>The objectives of this study were as follows: (a) to describe the levels of resilience and competitive anxiety, (b) to analyze the differences in terms of gender and years of sports experience, (c) to study the relationship between resiliency resources and competitive anxiety, and (d) to examine the variables that predict young players’ resiliency resources and self-confidence.</p><sec id=\"sec4dot1-ijerph-17-05569\"><title>4.1. Resilience Levels and Competitive Anxiety</title><p>The results indicated that both the levels of self-acceptance and life and competence were high. These outcomes imply that athletes best face adversity with high levels of resilience [<xref rid=\"B21-ijerph-17-05569\" ref-type=\"bibr\">21</xref>]. This finding is also consistent with previous studies, most of which were conducted in team sports such as handball and football with young sport team athletes [<xref rid=\"B28-ijerph-17-05569\" ref-type=\"bibr\">28</xref>,<xref rid=\"B30-ijerph-17-05569\" ref-type=\"bibr\">30</xref>,<xref rid=\"B31-ijerph-17-05569\" ref-type=\"bibr\">31</xref>,<xref rid=\"B32-ijerph-17-05569\" ref-type=\"bibr\">32</xref>,<xref rid=\"B35-ijerph-17-05569\" ref-type=\"bibr\">35</xref>]. As for anxiety levels, the results reflected moderate and high levels of self-confidence. It should be noted that moderate levels of anxiety could optimize athletes’ performance [<xref rid=\"B49-ijerph-17-05569\" ref-type=\"bibr\">49</xref>]. These results are consistent with those found in other studies conducted with samples belonging mostly to individual sports [<xref rid=\"B12-ijerph-17-05569\" ref-type=\"bibr\">12</xref>,<xref rid=\"B16-ijerph-17-05569\" ref-type=\"bibr\">16</xref>,<xref rid=\"B18-ijerph-17-05569\" ref-type=\"bibr\">18</xref>,<xref rid=\"B50-ijerph-17-05569\" ref-type=\"bibr\">50</xref>] and contradict the high levels of somatic [<xref rid=\"B14-ijerph-17-05569\" ref-type=\"bibr\">14</xref>,<xref rid=\"B17-ijerph-17-05569\" ref-type=\"bibr\">17</xref>] and cognitive [<xref rid=\"B19-ijerph-17-05569\" ref-type=\"bibr\">19</xref>] anxiety reached by others.</p></sec><sec id=\"sec4dot2-ijerph-17-05569\"><title>4.2. Differences in Levels of Resilience and Competitive Anxiety Depending on Gender and Years of Sports Experience</title><p>In relation to resilience, the results showed that acceptance levels were higher in boys, while competence levels were higher in girls. The literature indicates that it is usually boys who show higher levels of resilience due to an increase in physical self-concept and that girls are much more affected by the sports context [<xref rid=\"B51-ijerph-17-05569\" ref-type=\"bibr\">51</xref>]. In terms of sports experience, it was shown that both acceptance and competence levels were higher in players with greater sports experience. These results are consistent with findings in previous studies in similar samples, most of which were conducted with team sports [<xref rid=\"B31-ijerph-17-05569\" ref-type=\"bibr\">31</xref>,<xref rid=\"B32-ijerph-17-05569\" ref-type=\"bibr\">32</xref>].</p><p>On the other hand, higher levels of somatic anxiety were found in girls and cognitive anxiety in boys. The literature provides different explanations for why girls tend to have higher levels of anxiety than boys. Abrahamsen et al. [<xref rid=\"B12-ijerph-17-05569\" ref-type=\"bibr\">12</xref>] attributed the difference to boys’ interpretation of performance; Brunet and Sabiston [<xref rid=\"B18-ijerph-17-05569\" ref-type=\"bibr\">18</xref>] attributed it to a greater perception of competence and motivation in boys; Peñaloza et al. [<xref rid=\"B50-ijerph-17-05569\" ref-type=\"bibr\">50</xref>], León-Prados, Fuentes and Calvo [<xref rid=\"B51-ijerph-17-05569\" ref-type=\"bibr\">51</xref>], and Arbinaga and Caracuel [<xref rid=\"B52-ijerph-17-05569\" ref-type=\"bibr\">52</xref>] attributed it to girls’ greater concern about the loss of social recognition caused by poor performance. In regard to self-confidence and gender, studies were found that differed from the results reported. Morillo et al. [<xref rid=\"B19-ijerph-17-05569\" ref-type=\"bibr\">19</xref>] and Peñaloza et al. [<xref rid=\"B50-ijerph-17-05569\" ref-type=\"bibr\">50</xref>] revealed higher levels of self-confidence in boys. The controversy between the results of the study and those found in the literature may be due to the age difference of the analyzed sample. Finally, in other studies such as the one conducted by Montero, Moreno-Murcia, González, Pulido, and Cervelló [<xref rid=\"B53-ijerph-17-05569\" ref-type=\"bibr\">53</xref>] there were no statistically significant differences in relation to gender, nor in the levels of anxiety or self-confidence.</p><p>As regards the relationship between anxiety levels and sports experience, although no statistically significant results were found, higher levels of somatic anxiety and cognitive anxiety and lower levels of self-confidence were reported in athletes with less sports experience, thus coinciding with findings from previous studies conducted with samples belonging to both team and individual sports. Hanton et al. [<xref rid=\"B14-ijerph-17-05569\" ref-type=\"bibr\">14</xref>] with adults and Hernández et al. [<xref rid=\"B16-ijerph-17-05569\" ref-type=\"bibr\">16</xref>] with youths showed that athletes with greater sports experience faced anxiety with higher levels of self-confidence and managed to interpret anxiety better than those with less experience.</p></sec><sec id=\"sec4dot3-ijerph-17-05569\"><title>4.3. Relationship between Resiliency Resources and Competitive Anxiety</title><p>The results of the correlational analysis reflected a negative association between the two dimensions of anxiety (cognitive and somatic anxiety) and of acceptance, showing that competence was negatively related to somatic anxiety and positively to cognitive anxiety. The papers reviewed showed that the two dimensions of resilience were negatively related to those of anxiety [<xref rid=\"B37-ijerph-17-05569\" ref-type=\"bibr\">37</xref>,<xref rid=\"B38-ijerph-17-05569\" ref-type=\"bibr\">38</xref>,<xref rid=\"B54-ijerph-17-05569\" ref-type=\"bibr\">54</xref>,<xref rid=\"B55-ijerph-17-05569\" ref-type=\"bibr\">55</xref>]. Similar studies were conducted in team sports such as handball, basketball, football, and rugby, and found that youth athletes with higher levels of resilience managed to moderate their anxiety levels significantly, recovering or adapting better to threatening situations [<xref rid=\"B38-ijerph-17-05569\" ref-type=\"bibr\">38</xref>,<xref rid=\"B41-ijerph-17-05569\" ref-type=\"bibr\">41</xref>,<xref rid=\"B42-ijerph-17-05569\" ref-type=\"bibr\">42</xref>,<xref rid=\"B54-ijerph-17-05569\" ref-type=\"bibr\">54</xref>]. In addition, resilient athletes may be better able to protect themselves in the face of adversity, while those with lower levels may have greater failures, thus causing low self-confidence and higher levels of anxiety [<xref rid=\"B41-ijerph-17-05569\" ref-type=\"bibr\">41</xref>].</p></sec><sec id=\"sec4dot4-ijerph-17-05569\"><title>4.4. Resiliency Resources and Self-Confidence Predicting Variables</title><p>The regression analysis showed that self-confidence is likely to increase as athletes have better levels of acceptance, competence, and cognitive anxiety and that it is likely to reduce their indicators of somatic anxiety. These results are in line with the findings of Ortin-Montero et al. [<xref rid=\"B42-ijerph-17-05569\" ref-type=\"bibr\">42</xref>] in their study of handball players, in which the players with higher levels of resilience showed higher levels of self-confidence and coped better with adverse situations.</p><p>Furthermore, the regression analysis also indicated that younger athletes have higher levels of competence with higher levels of acceptance and self-confidence. In addition, the acceptance was greater when combined with competence, self-confidence, and less cognitive anxiety. The literature reviewed shows that young athletes tend to be the most resilient [<xref rid=\"B32-ijerph-17-05569\" ref-type=\"bibr\">32</xref>,<xref rid=\"B55-ijerph-17-05569\" ref-type=\"bibr\">55</xref>].</p><p>The present study, in addition to providing new information on the relationship between resiliency resources and anxiety levels in athletes, has some limitations that should be highlighted. One of them is that the generalization of the results is limited, due to the sample (cadets and juniors), so these results should be considered as preliminary, needing future replication in other categories such as children and other sports, thus covering all ages of adolescence and sports, and both team and individual sports.</p><p>In terms of strengths, it is important to highlight the novel information provided in relation to the field of study and the instruments used, as these have been used in a multitude of works and with a high degree of consistency. Likewise, it would be necessary to complement future studies with the analysis of other variables related to anxiety and resilience such as stress and optimism. Finally, another interesting aspect would be to be able to contrast resiliency resources and anxiety levels in collective and individual sports and see the possible differences that are generated.</p></sec></sec><sec sec-type=\"conclusions\" id=\"sec5-ijerph-17-05569\"><title>5. Conclusions</title><p>The consequent negative relationship between resilience resources (especially acceptance and competition) and levels of competitive anxiety, is a reflection that to enhance the former makes it possible to regulate up to control the latter. Despite the existence of indicators of cognitive anxiety (e.g., recurrent thoughts or rhyming), coaches and athletes need to understand that they are also indicators of a necessary activation for psychological functioning. Channeling such a process through psychological training of different skills will enhance the capacities for self-confidence. Making young athletes grow up on self-acceptance (e.g., reducing the belief that they cannot lose), for the younger ones, and on the perception of competition (e.g., learning to observe their improvements) already in adolescence, will undoubtedly consolidate key aspects for an effective and adapted psychological response to sports situations.</p><p>Highlighting how to work on skills, such as understanding situations of uncertainty, cooperation, or the search for social support at early ages and with strategies that integrate such resources in the dynamics of training and sports growth, will enhance the resources for the much needed coping with stress and anxiety, as well as consolidate beliefs and attitudes that can reduce dysfunctional responses that alter (e.g., blaming others or oneself, creating fears of failure after several mistakes).</p></sec></body><back><ack><title>Acknowledgments</title><p>We are grateful for the support received from the Royal Spanish Handball Federation (RFEBM) which allowed this study to be carried out.</p></ack><notes><title>Author Contributions</title><p>Conceptualization, J.G.-H. and M.G.-L.; data curation, J.G.-H. and M.G.-G.; formal analysis, J.G.-H.; investigation, M.G.-G. and M.G.-L.; methodology, J.G.-H.; project administration, M.G.-L.; supervision, M.G.-L.; writing—original draft, J.G.-H., M.G.-G., and M.G.-L.; writing—review and editing, J.G.-H., A.V.-V., and M.G.-L. 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Psicol. Deporte</source><year>2018</year><volume>18</volume><fpage>43</fpage><lpage>54</lpage></element-citation></ref></ref-list></back><floats-group><table-wrap id=\"ijerph-17-05569-t001\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05569-t001_Table 1</object-id><label>Table 1</label><caption><p>Distribution and homogeneity of intervariable variances (gender, sport experience, and weekly training hours).</p></caption><table frame=\"hsides\" rules=\"groups\"><tbody><tr><td rowspan=\"4\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">Chi-squared (<italic>X<sup>2</sup></italic>)<sub>2</sub> = 19.26; <italic>p</italic> = 0.402 *</td><td colspan=\"4\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">\n<bold>Sport Experience</bold>\n</td></tr><tr><td colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">\n<bold>≤</bold>\n<bold>5 years (<italic>n</italic> = 106)</bold>\n</td><td colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">\n<bold>> 5 years (<italic>n</italic> = 135)</bold>\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>Mean (SD) age</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold><italic>n</italic> (%)</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>Mean (SD) age</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold><italic>n</italic> (%)</bold>\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">15.67 (0.51)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">15.14 (0.49)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Boys = 160 (66.39%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Boys <italic>n</italic> (%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">78 (73.58%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">82 (60.74%)</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Girls = 81 (33.61%)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Girls <italic>n</italic> (%)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">28 (26.42%)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">53 (39.26%)</td></tr><tr><td rowspan=\"6\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Chi-squared (<italic>X<sup>2</sup></italic>)<sub>2</sub> = 14.39; <italic>p</italic> = 0.236 *</td><td colspan=\"4\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">\n<bold>Weekly Training Hours</bold>\n</td></tr><tr><td colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">\n<bold>≤</bold>\n<bold>2 h/week (<italic>n</italic> = 17)</bold>\n</td><td colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">\n<bold>> 2 h/week (<italic>n</italic> = 224)</bold>\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>Mean (SD) age</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold><italic>n</italic> (%)</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>Mean (SD) age</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold><italic>n</italic> (%)</bold>\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">15.71 (1.05)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">15.33 (1.07)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Boys <italic>n</italic> (%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">17 (100%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">143 (63.84)</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Girls <italic>n</italic> (%)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">81 (36.16%)</td></tr></tbody></table><table-wrap-foot><fn><p>* <italic>p</italic> < 0.05.</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"ijerph-17-05569-t002\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05569-t002_Table 2</object-id><label>Table 2</label><caption><p>Means, standard deviations, and measures of dispersion of the variables studied for the sample.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><italic>n</italic> = 241</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Means</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Standard Deviation</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Asymmetry</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Kurtosis</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Competence</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.46</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.79</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.47</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.29</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Acceptance</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7.37</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.56</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.20</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.10</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Somatic anxiety</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.45</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.77</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.18</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.85</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Self-confidence</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.01</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.59</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.34</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.05</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Cognitive Anxiety</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2.75</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.71</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.29</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.37</td></tr></tbody></table></table-wrap><table-wrap id=\"ijerph-17-05569-t003\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05569-t003_Table 3</object-id><label>Table 3</label><caption><p>Differential analysis (<italic>t</italic>-test), according to the gender and sports experience of the participants.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\"><italic>n</italic> = 241; gl<sub>2.239</sub></th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">(<italic>n</italic> = 160)<break/>Girls</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">(<italic>n</italic> = 81)<break/>Boys</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>t(p)</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>d</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">(<italic>n</italic> = 106)<break/>≤5 years</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">(<italic>n</italic> = 135)<break/>>5 years</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>t(p)</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>d</italic>\n</th></tr><tr><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">M (SD)</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">M (SD)</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">M (SD)</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">M (SD)</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Competence</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.39 (0.79)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.58 (0.86)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−3.43</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.40</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.45 (0.81)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.46 (0.78)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.03</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.03</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Acceptance</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7.83 (0.78)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7.05 (0.86)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.25 (0.02 *)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.64</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7.33 (0.70)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7.90 (0.64)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−1.62 (0.03 *)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.07</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Somatic anxiety</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.21 (0.73)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.14 (0.76)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−3.21</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.98</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.48 (0.75)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.43 (0.78)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.08</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.11</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Cognitive anxiety</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.04 (0.58)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.94 (0.62)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−4.13</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.28</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.03 (0.66)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.00 (0.54)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.48</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.07</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Self-confidence</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2.61 (0.68)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3.02 (0.68)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−4.32 (0.00 **)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.01</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2.68 (0.73)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3.40 (0.69)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−1.83 (0.01 *)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.78</td></tr></tbody></table><table-wrap-foot><fn><p><italic>* p</italic> < 0.05; ** <italic>p</italic> < 0.01</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"ijerph-17-05569-t004\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05569-t004_Table 4</object-id><label>Table 4</label><caption><p>Analysis of multivariate variance of resiliency resources and anxiety, according to gender and years of federated sport experience.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th rowspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">\n</th><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">≤5 Years</th><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">>5 Years</th><th rowspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">F<sub>(5.232)</sub><break/>Gender</th><th rowspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">F<sub>(5.232)</sub><break/>Years</th><th rowspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">F<sub>(5.232)</sub><break/>Gender × Years</th></tr><tr><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Boys<break/>(<italic>n</italic> = 78)</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Girls<break/>(<italic>n</italic> = 28)</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Boys<break/>(<italic>n</italic> = 82)</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Girls<break/>(<italic>n</italic> = 53)</th></tr><tr><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">M (SD)</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">M (SD)</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">M (SD)</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">M (SD)</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Competence</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">5.85 (0.79)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">5.18 (0.83)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">5.72 (0.78)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">5.33 (0.77)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.05 *</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Acceptance</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">6.51 (0.59)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">7.26 (0.88)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">7.32 (0.38)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">7.68 (0.52)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.01 *</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.03 *</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Somatic anxiety</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2.41 (0.73)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2.64 (0.75)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2.23 (0.73)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2.75 (0.77)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Cognitive anxiety</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2.61 (0.73)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2.94 (0.65)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2.93 (0.64)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3.26 (0.70)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.02 **</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.02 *</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Self-confidence</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3.10 (0.62)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2.82 (0.72)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3.53 (0.53)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3.21 (0.56)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.00 **</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.00 **</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td></tr></tbody></table><table-wrap-foot><fn><p>* <italic>p</italic> < 0.05; ** <italic>p</italic> < 0.01</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"ijerph-17-05569-t005\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05569-t005_Table 5</object-id><label>Table 5</label><caption><p>Correlational analysis between resilience dimensions and anxiety responses.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">M</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">4</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">5</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">6</th></tr></thead><tbody><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1. Somatic anxiety</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.45 (0.77)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.533 **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.123</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.073</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.193 **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.110</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2. Cognitive anxiety</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.01 (0.59)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.049</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.013</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.144 *</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.125</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3. Self-confidence</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.75 (0.71)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.547 **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.387 **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.033</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4. Competence</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.46 (0.79)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.494 **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.164 *</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5. Acceptance</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7.37 (1.59)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.051</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">6. Age</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">15.36 (1.07)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1</td></tr></tbody></table><table-wrap-foot><fn><p>* <italic>p</italic> < 0.05; ** <italic>p</italic> < 0.01</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"ijerph-17-05569-t006\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05569-t006_Table 6</object-id><label>Table 6</label><caption><p>Variable predictors of young players’ self-confidence.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Dependent Variable</th><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">Step</th><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">Independent Variables</th><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">Beta</th><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">\n<italic>t</italic>\n</th><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">\n<italic>p</italic>\n</th></tr><tr><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Self-confidence</th></tr></thead><tbody><tr><td rowspan=\"5\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\"><italic>R</italic> = 0.579; <italic>R<sup>2</sup></italic> = 0.335; <italic>F</italic><sub>2.239</sub>(<italic>p</italic>) = 23.59(0.00)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Age</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.023</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.34</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.46</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Competence</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.354</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7.46</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.00 **</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Acceptance</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.057</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.35</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.01 *</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Somatic anxiety</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.100</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−2.02</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.04 *</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">5</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Cognitive anxiety</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.112</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2.09</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.03 *</td></tr></tbody></table><table-wrap-foot><fn><p>* <italic>p</italic> < 0.05; ** <italic>p</italic> < 0.01</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"ijerph-17-05569-t007\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05569-t007_Table 7</object-id><label>Table 7</label><caption><p>Predicting variables of resiliency resources in players.</p></caption><table frame=\"hsides\" rules=\"groups\"><tbody><tr><td align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>Dependent Variable</bold>\n</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">\n<bold>Step</bold>\n</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">\n<bold>Independent Variables</bold>\n</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">\n<bold>Beta</bold>\n</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">\n<bold><italic>t</italic></bold>\n</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">\n<bold><italic>p</italic></bold>\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>Competence</bold>\n</td></tr><tr><td rowspan=\"5\" align=\"center\" valign=\"middle\" colspan=\"1\"><italic>R</italic> = 0.648; <italic>R</italic><sup>2</sup> = 0.421; <italic>F</italic><sub>2.239</sub>(<italic>p</italic>) = 33.96 (0.00)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Age</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.123</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−3.30</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.63</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Acceptance</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.177</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6.31</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.00 **</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Somatic anxiety</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.021</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.34</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.73</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Cognitive anxiety</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.018</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.26</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.79</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Self-confidence</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.543</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7.46</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.00 **</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>Dependent Variable</bold>\n</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">\n<bold>Step</bold>\n</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">\n<bold>Independent Variables</bold>\n</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">\n<bold>Beta</bold>\n</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">\n<bold><italic>t</italic></bold>\n</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">\n<bold><italic>p</italic></bold>\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>Competence</bold>\n</td></tr><tr><td rowspan=\"5\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\"><italic>R</italic> = 0.648; <italic>R</italic><sup>2</sup> = 0.421; <italic>F</italic><sub>2.239</sub>(<italic>p</italic>) = 33.96 (0.00)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Age</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.150</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.836</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.68</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Acceptance</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.823</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6.308</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.00 **</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Somatic anxiety</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.154</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−1.159</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.24</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Cognitive anxiety</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.217</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−1.515</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.02 *</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">5</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Self-confidence</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.407</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2.358</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.06 *</td></tr></tbody></table><table-wrap-foot><fn><p>* <italic>p</italic> < 0.05; ** <italic>p</italic> < 0.01</p></fn></table-wrap-foot></table-wrap></floats-group></article>\n"
]
[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Int J Environ Res Public Health</journal-id><journal-id journal-id-type=\"iso-abbrev\">Int J Environ Res Public Health</journal-id><journal-id journal-id-type=\"publisher-id\">ijerph</journal-id><journal-title-group><journal-title>International Journal of Environmental Research and Public Health</journal-title></journal-title-group><issn pub-type=\"ppub\">1661-7827</issn><issn pub-type=\"epub\">1660-4601</issn><publisher><publisher-name>MDPI</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32751107</article-id><article-id pub-id-type=\"pmc\">PMC7432104</article-id><article-id pub-id-type=\"doi\">10.3390/ijerph17155454</article-id><article-id pub-id-type=\"publisher-id\">ijerph-17-05454</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Article</subject></subj-group></article-categories><title-group><article-title>Non-Cancer Chronic Pain Conditions and Risk for Incident Alzheimer’s Disease and Related Dementias in Community-Dwelling Older Adults: A Population-Based Retrospective Cohort Study of United States Medicare Beneficiaries, 2001–2013</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Khalid</surname><given-names>Sumaira</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05454\">1</xref><xref rid=\"c1-ijerph-17-05454\" ref-type=\"corresp\">*</xref></contrib><contrib contrib-type=\"author\"><name><surname>Sambamoorthi</surname><given-names>Usha</given-names></name><xref ref-type=\"aff\" rid=\"af2-ijerph-17-05454\">2</xref></contrib><contrib contrib-type=\"author\"><name><surname>Innes</surname><given-names>Kim E.</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05454\">1</xref></contrib></contrib-group><aff id=\"af1-ijerph-17-05454\"><label>1</label>Department of Epidemiology, West Virginia University School of Public Health, Morgantown, WV 26506, USA; <email>[email protected]</email></aff><aff id=\"af2-ijerph-17-05454\"><label>2</label>Department of Pharmaceutical Systems and Policy, West Virginia University School of Pharmacy, Morgantown, WV 26506, USA; <email>[email protected]</email></aff><author-notes><corresp id=\"c1-ijerph-17-05454\"><label>*</label>Correspondence: <email>[email protected]</email></corresp></author-notes><pub-date pub-type=\"epub\"><day>29</day><month>7</month><year>2020</year></pub-date><pub-date pub-type=\"ppub\"><month>8</month><year>2020</year></pub-date><volume>17</volume><issue>15</issue><elocation-id>5454</elocation-id><history><date date-type=\"received\"><day>24</day><month>6</month><year>2020</year></date><date date-type=\"accepted\"><day>23</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>© 2020 by the authors.</copyright-statement><copyright-year>2020</copyright-year><license license-type=\"open-access\"><license-p>Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>).</license-p></license></permissions><abstract><p>Accumulating evidence suggests that certain chronic pain conditions may increase risk for incident Alzheimer’s disease and related dementias (ADRD). Rigorous longitudinal research remains relatively sparse, and the relation of overall chronic pain condition burden to ADRD risk remains little studied, as has the potential mediating role of sleep and mood disorders. In this retrospective cohort study, we investigated the association of common non-cancer chronic pain conditions (NCPC) at baseline to subsequent risk for incident ADRD, and assessed the potential mediating effects of mood and sleep disorders, using baseline and 2-year follow-up data using 11 pooled cohorts (2001–2013) drawn from the U.S. Medicare Current Beneficiaries Survey (MCBS). The study sample comprised 16,934 community-dwelling adults aged ≥65 and ADRD-free at baseline. NCPC included: headache, osteoarthritis, joint pain, back or neck pain, and neuropathic pain, ascertained using claims data; incident ADRD (N = 1149) was identified using claims and survey data. NCPC at baseline remained associated with incident ADRD after adjustment for sociodemographics, lifestyle characteristics, medical history, medications, and other factors (adjusted odds ratio (AOR) for any vs. no NCPC = 1.21, 95% confidence interval (CI) = 1.04–1.40; <italic>p</italic> = 0.003); the strength and magnitude of this association rose significantly with increasing number of diagnosed NCPCs (AOR for 4+ vs. 0 conditions = 1.91, CI = 1.31–2.80, <italic>p</italic>-trend < 0.00001). Inclusion of sleep disorders and/or depression/anxiety modestly reduced these risk estimates. Sensitivity analyses yielded similar findings. NCPC was significantly and positively associated with incident ADRD; this association may be partially mediated by mood and sleep disorders. Additional prospective studies with longer-term follow-up are warranted to confirm and extend our findings.</p></abstract><kwd-group><kwd>Alzheimer’s disease</kwd><kwd>dementia</kwd><kwd>ADRD</kwd><kwd>modifiable risk factors</kwd><kwd>Chronic pain</kwd><kwd>elderly population</kwd><kwd>longitudinal study</kwd><kwd>arthritis</kwd><kwd>headache</kwd></kwd-group></article-meta></front><body><sec sec-type=\"intro\" id=\"sec1-ijerph-17-05454\"><title>1. Introduction</title><p>Alzheimer’s disease and related dementia (ADRD) is a group of serious neurodegenerative disorders characterized by progressive decline in cognitive and psychomotor function [<xref rid=\"B1-ijerph-17-05454\" ref-type=\"bibr\">1</xref>,<xref rid=\"B2-ijerph-17-05454\" ref-type=\"bibr\">2</xref>]. Prevalence of ADRD continues to increase worldwide, exacting enormous social, economic and healthcare costs. For example, in the United States, at least 5.8 million adults are living with Alzheimer’s disease (AD), the most common form of dementia, with attributable Medicare costs alone exceeding $146 billion annually; these figures are expected to rise steeply in coming years [<xref rid=\"B3-ijerph-17-05454\" ref-type=\"bibr\">3</xref>]. ADRD is also a significant contributor to mortality and number of life years lived with disability [<xref rid=\"B3-ijerph-17-05454\" ref-type=\"bibr\">3</xref>,<xref rid=\"B4-ijerph-17-05454\" ref-type=\"bibr\">4</xref>]. With no cure for ADRD, and no effective disease-modifying treatments yet available despite decades of clinical research [<xref rid=\"B5-ijerph-17-05454\" ref-type=\"bibr\">5</xref>], efforts are increasingly shifting to prevention and early intervention, including intensive approaches to identify and target modifiable risk factors for ADRD [<xref rid=\"B6-ijerph-17-05454\" ref-type=\"bibr\">6</xref>].</p><p>To date, several factors have been linked to elevated ADRD risk. These include non-modifiable factors such as advancing age, female sex, rurality, and specific genetic profiles, as well as modifiable risk factors [<xref rid=\"B7-ijerph-17-05454\" ref-type=\"bibr\">7</xref>]. The latter include: poor education and poverty [<xref rid=\"B7-ijerph-17-05454\" ref-type=\"bibr\">7</xref>,<xref rid=\"B8-ijerph-17-05454\" ref-type=\"bibr\">8</xref>]; physical inactivity, midlife obesity, and other lifestyle factors [<xref rid=\"B7-ijerph-17-05454\" ref-type=\"bibr\">7</xref>,<xref rid=\"B9-ijerph-17-05454\" ref-type=\"bibr\">9</xref>]; history of head injury or stroke [<xref rid=\"B10-ijerph-17-05454\" ref-type=\"bibr\">10</xref>,<xref rid=\"B11-ijerph-17-05454\" ref-type=\"bibr\">11</xref>]; and certain chronic physical health disorders (e.g., diabetes, cardiovascular disease, midlife hypertension, respiratory illness [<xref rid=\"B7-ijerph-17-05454\" ref-type=\"bibr\">7</xref>,<xref rid=\"B9-ijerph-17-05454\" ref-type=\"bibr\">9</xref>,<xref rid=\"B12-ijerph-17-05454\" ref-type=\"bibr\">12</xref>]). Specific mental health conditions (e.g., depression, anxiety) and chronic sleep impairment have also been shown to predict subsequent cognitive decline and conversion to ADRD [<xref rid=\"B13-ijerph-17-05454\" ref-type=\"bibr\">13</xref>,<xref rid=\"B14-ijerph-17-05454\" ref-type=\"bibr\">14</xref>,<xref rid=\"B15-ijerph-17-05454\" ref-type=\"bibr\">15</xref>,<xref rid=\"B16-ijerph-17-05454\" ref-type=\"bibr\">16</xref>]. In addition, there is growing evidence from observational and experimental research that chronic pain, an increasingly common and highly burdensome condition, may also contribute to elevated risk for neurocognitive impairment and development of ADRD [<xref rid=\"B17-ijerph-17-05454\" ref-type=\"bibr\">17</xref>,<xref rid=\"B18-ijerph-17-05454\" ref-type=\"bibr\">18</xref>,<xref rid=\"B19-ijerph-17-05454\" ref-type=\"bibr\">19</xref>,<xref rid=\"B20-ijerph-17-05454\" ref-type=\"bibr\">20</xref>,<xref rid=\"B21-ijerph-17-05454\" ref-type=\"bibr\">21</xref>,<xref rid=\"B22-ijerph-17-05454\" ref-type=\"bibr\">22</xref>,<xref rid=\"B23-ijerph-17-05454\" ref-type=\"bibr\">23</xref>]. </p><p>Although limited at present, there is evidence from longitudinal studies that chronic pain and common non-cancer chronic pain conditions (NCPC) may increase risk for incident cognitive impairment and ADRD [<xref rid=\"B24-ijerph-17-05454\" ref-type=\"bibr\">24</xref>,<xref rid=\"B25-ijerph-17-05454\" ref-type=\"bibr\">25</xref>,<xref rid=\"B26-ijerph-17-05454\" ref-type=\"bibr\">26</xref>,<xref rid=\"B27-ijerph-17-05454\" ref-type=\"bibr\">27</xref>,<xref rid=\"B28-ijerph-17-05454\" ref-type=\"bibr\">28</xref>,<xref rid=\"B29-ijerph-17-05454\" ref-type=\"bibr\">29</xref>,<xref rid=\"B30-ijerph-17-05454\" ref-type=\"bibr\">30</xref>,<xref rid=\"B31-ijerph-17-05454\" ref-type=\"bibr\">31</xref>,<xref rid=\"B32-ijerph-17-05454\" ref-type=\"bibr\">32</xref>,<xref rid=\"B33-ijerph-17-05454\" ref-type=\"bibr\">33</xref>,<xref rid=\"B34-ijerph-17-05454\" ref-type=\"bibr\">34</xref>]. For example, recent retrospective cohort investigations of Taiwanese nationals [<xref rid=\"B26-ijerph-17-05454\" ref-type=\"bibr\">26</xref>,<xref rid=\"B28-ijerph-17-05454\" ref-type=\"bibr\">28</xref>,<xref rid=\"B29-ijerph-17-05454\" ref-type=\"bibr\">29</xref>,<xref rid=\"B34-ijerph-17-05454\" ref-type=\"bibr\">34</xref>] and prospective cohort studies of Japanese and Norwegian elders [<xref rid=\"B25-ijerph-17-05454\" ref-type=\"bibr\">25</xref>,<xref rid=\"B27-ijerph-17-05454\" ref-type=\"bibr\">27</xref>,<xref rid=\"B33-ijerph-17-05454\" ref-type=\"bibr\">33</xref>] have reported significantly increased risk of incident dementia in adults previously diagnosed with fibromyalgia [<xref rid=\"B28-ijerph-17-05454\" ref-type=\"bibr\">28</xref>], osteoarthritis or knee pain [<xref rid=\"B26-ijerph-17-05454\" ref-type=\"bibr\">26</xref>,<xref rid=\"B33-ijerph-17-05454\" ref-type=\"bibr\">33</xref>], and headache [<xref rid=\"B25-ijerph-17-05454\" ref-type=\"bibr\">25</xref>,<xref rid=\"B27-ijerph-17-05454\" ref-type=\"bibr\">27</xref>,<xref rid=\"B29-ijerph-17-05454\" ref-type=\"bibr\">29</xref>,<xref rid=\"B34-ijerph-17-05454\" ref-type=\"bibr\">34</xref>]. Likewise, findings from a handful of longitudinal studies in US [<xref rid=\"B24-ijerph-17-05454\" ref-type=\"bibr\">24</xref>,<xref rid=\"B30-ijerph-17-05454\" ref-type=\"bibr\">30</xref>,<xref rid=\"B32-ijerph-17-05454\" ref-type=\"bibr\">32</xref>] and British adults [<xref rid=\"B31-ijerph-17-05454\" ref-type=\"bibr\">31</xref>] suggest that non-specific chronic pain may predict subsequent deterioration in memory [<xref rid=\"B30-ijerph-17-05454\" ref-type=\"bibr\">30</xref>,<xref rid=\"B32-ijerph-17-05454\" ref-type=\"bibr\">32</xref>], accelerated cognitive decline [<xref rid=\"B32-ijerph-17-05454\" ref-type=\"bibr\">32</xref>], new onset cognitive impairment [<xref rid=\"B32-ijerph-17-05454\" ref-type=\"bibr\">32</xref>], and incident dementia [<xref rid=\"B24-ijerph-17-05454\" ref-type=\"bibr\">24</xref>,<xref rid=\"B32-ijerph-17-05454\" ref-type=\"bibr\">32</xref>], although studies varied widely in design, study population, length of follow-up, and measures. However, to our knowledge, no study has investigated the collective and incremental association of common chronic pain conditions to ADRD risk, nor the potential mediating role of depression, anxiety and sleep disorders, conditions strongly and bidirectionally associated with chronic pain and linked to ADRD risk. </p><p>In this study, we investigate the association of common NCPC to ADRD risk using multiple retrospective cohorts from a linked database of nationally representative sample of older US Medicare beneficiaries. We hypothesized that the presence of NCPC at baseline will be associated with significantly increased risk of incident ADRD, and that the magnitude of this association will increase with rising number of NCPC at baseline. We further hypothesized that depression, anxiety and/or sleep impairment, will partially mediate the association between NCPC and incident ADRD.</p></sec><sec sec-type=\"methods\" id=\"sec2-ijerph-17-05454\"><title>2. Methods</title><sec id=\"sec2dot1-ijerph-17-05454\"><title>2.1. Study Design</title><p>Our study used a retrospective cohort design to assess the association of baseline common non-cancer chronic pain conditions (NCPC) to incident ADRD using data from the Medicare Current Beneficiary Survey (MCBS) [<xref rid=\"B35-ijerph-17-05454\" ref-type=\"bibr\">35</xref>] linked with Medicare fee-for-service claims. MCBS is a nationally representative survey of adults participating in Medicare health insurance program. As detailed below, multiple three-year MCBS cohorts (2001–2013) were pooled to construct the analytic sample for this study.</p></sec><sec id=\"sec2dot2-ijerph-17-05454\"><title>2.2. Data Source</title><p>First initiated in 1991, MCBS has been recruiting survey participants each year using a complex stratified, three-stage probability sampling design described in detail elsewhere [<xref rid=\"B36-ijerph-17-05454\" ref-type=\"bibr\">36</xref>,<xref rid=\"B37-ijerph-17-05454\" ref-type=\"bibr\">37</xref>]. MCBS participants undertake structured in-person interviews at baseline and follow-up rounds each year for up to three years. MCBS generates comprehensive cross-sectional and longitudinal data on participants’ health status, health services utilization, prescription medications, and payment sources using a combination of survey and administrative records. Our study used data from MCBS Cost and Use files linked with Medicare fee-for-service claims to ascertain demographics, access to care, lifestyle factors, medical history and medication. Based on the recommendations by MCBS investigators [<xref rid=\"B36-ijerph-17-05454\" ref-type=\"bibr\">36</xref>,<xref rid=\"B37-ijerph-17-05454\" ref-type=\"bibr\">37</xref>,<xref rid=\"B38-ijerph-17-05454\" ref-type=\"bibr\">38</xref>] and sampling strategies documented in prior published studies using MCBS [<xref rid=\"B36-ijerph-17-05454\" ref-type=\"bibr\">36</xref>,<xref rid=\"B38-ijerph-17-05454\" ref-type=\"bibr\">38</xref>,<xref rid=\"B39-ijerph-17-05454\" ref-type=\"bibr\">39</xref>], we combined 11 MCBS cohorts in order to maximize reliability and precision of our study estimates; these included the following cohorts: 2001–2003; 2002–2004; 2003–2005; 2004–2006; 2005–2007; 2006–2008; 2007–2009; 2008–2010; 2009–2011; 2010–2012 and 2011–2013.</p></sec><sec id=\"sec2dot3-ijerph-17-05454\"><title>2.3. Study Sample</title><p>The study sample comprised continuously fee-for-service enrolled community-dwelling Medicare beneficiaries, aged 65 years or over, who had complete information on NCPC status and were still alive at the end of follow-up. Institutionalized participants were excluded, as were participants with diagnosed ADRD at baseline. As depicted in the sample selection flow chart (see <xref ref-type=\"app\" rid=\"app1-ijerph-17-05454\">supplementary Figure S1</xref>), application of all a priori exclusion criteria yielded a final sample size of 16,934 adults (N’s by year = 1874 (2001–2003); 1768 (2002–2004); 1884 (2003–2005); 1766 (2004–2006); 1735 (2005–2007); 1650 (2006–2008); 1495 (2007–2009); 1278 (2008–2010); 1034 (2009–2011); 1159 (2010–2012); and 1291 (2011–2013)). </p><p>IRB/ethics approval: The present study was approved as exempt protocol by the WVU Institutional Review Board (IRB), due to the deidentified nature of the data used in the study.</p></sec><sec id=\"sec2dot4-ijerph-17-05454\"><title>2.4. Dependent Variable: Incident Alzheimer’s Disease and Related Dementia (ADRD) at Follow-Up– Yes/No</title><p>The Medicare fee-for-service (FFS) claims for inpatient (IP), skilled nursing facility (SNF), outpatient (OT), home health agency (HHA), and physician office (PO) visits for years 2001–2013 as well as MCBS self-reported Health Status and Functioning files were used to ascertain the presence of ADRD at baseline and follow-up. The presence of ADRD at both baseline (year 1) and follow-up (years 2 and 3) was ascertained using a validated CMS algorithm (Centers for Medicare and Medicaid Services (CMS) Chronic Condition Algorithms) [<xref rid=\"B40-ijerph-17-05454\" ref-type=\"bibr\">40</xref>] of at least one fee-for-service claim with any of these International Classification of Diseases, ninth Edition, clinical modification (ICD-9-CM) diagnostic codes: 331.0, 331.11, 331.19, 331.2, 331.7, 290.0, 290.10, 290.11, 290.12, 290.13, 290.20, 290.21, 290.3, 290.40, 290.41, 290.42, 290.43, 294.0, 294.10, 294.11, 294.20, 294.21, 294.8, and 797, or an affirmative response to the self-reported Health Status question “Has a doctor ever told you that you had Alzheimer’s?” [<xref rid=\"B41-ijerph-17-05454\" ref-type=\"bibr\">41</xref>]. Using a combination of claims and survey data to ascertain ADRD has been recommended by MCBS investigators to increase capture of ADRD and has been shown to yield results similar to those of expert in-person-assessment [<xref rid=\"B42-ijerph-17-05454\" ref-type=\"bibr\">42</xref>].</p></sec><sec id=\"sec2dot5-ijerph-17-05454\"><title>2.5. Key Independent Variable: Non-Cancer Chronic Pain Condition (NCPC) and Number of NCPCs</title><p>Baseline NCPC were identified using Medicare fee-for-service claims. NCPC included five common NCPC (back or neck pain, headache, joint pain, neuropathic pain, and osteoarthritis). The presence of any NCPC was identified using either two outpatient claims (90 days apart) or one inpatient claim using ICD9-CM codes as recommended by the Centers for Medicare and Medicaid Services [<xref rid=\"B40-ijerph-17-05454\" ref-type=\"bibr\">40</xref>] and consistent with prior studies of NCPC [<xref rid=\"B43-ijerph-17-05454\" ref-type=\"bibr\">43</xref>,<xref rid=\"B44-ijerph-17-05454\" ref-type=\"bibr\">44</xref>]. Any NCPC was assessed as a binary variable (yes/no) during baseline. Relative NCPC burden was ascertained with a count variable (0–5 NCPCs). </p></sec><sec id=\"sec2dot6-ijerph-17-05454\"><title>2.6. Covariates</title><p>To account for the influence of potential confounding, specific baseline characteristics known or suspected to be associated with ADRD risk and/or chronic pain based on prior research were selected a priori for inclusion as covariates in our multivariable models. These included age group (65–69, 70–74, 75–79, and ≥80 years), based on prior research indicating the risk in adults doubles approximately every 4 years after age 65 [<xref rid=\"B3-ijerph-17-05454\" ref-type=\"bibr\">3</xref>,<xref rid=\"B45-ijerph-17-05454\" ref-type=\"bibr\">45</xref>]. Other biological factors i.e., sex (female/male), and race/ethnicity (Non-Hispanic White, African American, Hispanic, and other); other demographic characteristics, i.e., marital status (married, widowed, divorce/separated, other), educational level (less than high school, high school, some college, college), family income, measured as percentage of federal poverty line (FPL) (poor/low-income (<200%), (middle to high-income (≥200%)); health insurance status (insured by Medicaid (yes/no) or private insurance (yes/no)); rurality (metropolitan areas, yes/no); and lifestyle factors, i.e., smoking status (current smoker, past smoker, never smoked) and body mass index (BMI, kg/m<sup>2</sup>) (underweight (<18.5), normal (18.5–24.9), overweight (25–29.9), and obese ≥30). </p><p>Also included as covariates were other chronic physical health conditions reported at baseline (yes/no). These included hypertension, diabetes, heart disease, respiratory illness, and history of stroke, traumatic brain injury (TBI), and cancer (with the exception of non-melanotic skin cancer), as well as specific auto-immune conditions associated with chronic pain, including rheumatoid arthritis (RA), and systemic lupus erythematosus (SLE). In addition, certain commonly used medications that have been linked to ADRD risk and/or used in chronic pain management were also evaluated as covariates. These included non-steroidal anti-inflammatory drugs (NSAIDS) [<xref rid=\"B46-ijerph-17-05454\" ref-type=\"bibr\">46</xref>], opioid analgesics [<xref rid=\"B47-ijerph-17-05454\" ref-type=\"bibr\">47</xref>], benzodiazepines [<xref rid=\"B48-ijerph-17-05454\" ref-type=\"bibr\">48</xref>] and certain other psychotropic drugs (e.g., selective serotonin reuptake inhibitors (SSRIs), serotonin norepinephrine reuptake inhibitors (SNRIs), monoamino oxidase inhibitors (MAOIs), tricyclic antidepressants (TCAs), and atypical antidepressants) [<xref rid=\"B49-ijerph-17-05454\" ref-type=\"bibr\">49</xref>]. Benzodiazepine use was evaluated as a separate medication category given that: (1) a significant number of study participants (10%) were prescribed these medications; and (2) prior research has suggested that benzodiazepines not only has multidimensional neurological effects [<xref rid=\"B50-ijerph-17-05454\" ref-type=\"bibr\">50</xref>,<xref rid=\"B51-ijerph-17-05454\" ref-type=\"bibr\">51</xref>], but is also associated with elevated risk for ADRD [<xref rid=\"B48-ijerph-17-05454\" ref-type=\"bibr\">48</xref>].</p><p><italic>Mediators:</italic> To examine the potential mediating influence of mood and sleep disorders on the relation of NCPC to incident ADRD, we evaluated the effects of diagnosed depression, anxiety and insomnia-related sleep disorders both separately and in combination (see <xref ref-type=\"app\" rid=\"app1-ijerph-17-05454\">supplementary Table S1</xref> for ICD-9-CM diagnostic codes). </p></sec><sec id=\"sec2dot7-ijerph-17-05454\"><title>2.7. Statistical Analysis</title><p>Potential differences in baseline characteristics by NCPC and ADRD status were determined using Rao-Scott chi-square tests (categorical variables). Logistic regressions were used to assess the unadjusted and independent association of NCPC and NCPC burden to incident ADRD. In these multivariable logistic regressions, we carried out block-wise adjustment for demographic and socioeconomic factors; lifestyle factors and chronic physical health conditions; and analgesics and psychotropic medications to assess the adjusted associations of NCPC to ADRD risk. Similar nested multivariable logistic regression models were used to evaluate potential mediating effects of baseline diagnoses of depression, anxiety, and sleep disorders both individually and in combination. To assess the linear relationship of NCPC to risk of ADRD, regression models with polynomial contrast were used. For our sensitivity analyses, we used multinomial logistic regression to build competing risk of death models with the following four outcomes: ADRD-free and alive at the end of follow-up (N = 15,785) (referent category); ADRD-free and not alive at the end of follow-up (N = 1220); ADRD positive and Alive at the end of follow-up (N = 1149); ADRD positive and not alive at the end of follow-up (N = 259). </p><p>All bivariate and multivariable analyses were carried out using MCBS complex survey design sampling weights. MCBS data-cycles are released with cross-sectional as well as longitudinal weights. We used 3-year backward longitudinal weights for analysis of our pooled cohorts to yield up to two years of continuous follow-up. All analyses were performed using SAS survey procedure (SAS version 9.4, SAS Institute, Inc.).</p></sec></sec><sec sec-type=\"results\" id=\"sec3-ijerph-17-05454\"><title>3. Results</title><p>Our analytical sample comprised 16,934 eligible participants with a mean age at baseline of 74 ± 0.07 years. The majority of the cohort were female (57%), white (83%), married (56.5%) and reported a family income at or above 200% of the FPL. Study participants were predominantly from metropolitan areas (71%) and most had private insurance (79%).</p><p>Overall baseline prevalence of any NCPC in this study was 36% (weighted), with significant differences by cohort (range 31% to 39%, <italic>p</italic> < 0.0001). Pain burden in this population was high, with 37.5% reporting at least two co-existing NCPCs and 15% indicating three or more NCPCs. A significantly higher percentage of participants with baseline NCPC reported a history of chronic physical health conditions associated with ADRD, including hypertension (82% vs. 68.7%), heart disease (31.5% vs. 19.9%), stroke (14% vs. 10%), diabetes (23.5% vs. 19%) (<italic>p</italic>’s < 0.0001). Participants with baseline NCPC were also significantly more likely to report mood disorders, including depression (11% vs. 4.5%) and anxiety (7.9% vs. 3.5%), as well as sleep disorders (25.5% vs. 10.2%) when compared to participants without baseline NCPC (<xref rid=\"ijerph-17-05454-t001\" ref-type=\"table\">Table 1</xref>).</p><p><xref rid=\"ijerph-17-05454-t002\" ref-type=\"table\">Table 2</xref> summarizes the baseline characteristics of the study sample by incident ADRD status. A total of 1149 participants were diagnosed with incident ADRD during the 2-year follow-up period (overall incidence rate = 6.8 per 100 participants). Incident ADRD rates did not differ by study cohorts (range 4.9 to 6.2 per 100, <italic>p</italic> = 0.9), and were significantly higher in women (6.2% vs. 5.0% in men), and in participants who were black (7.4% vs. 5.5% in Non-Hispanic whites), widowed (8.2% vs. 4.5% in married), and more poorly educated (<italic>p</italic>’s < 0.0001). The proportion of participants diagnosed with incident ADRD was also significantly higher in those who were <200% of the federal poverty level (7.4% vs. 4.3%), were on Medicaid (9.1% vs. 5.2%) and who lacked private insurance (7.2% vs. 5.3%) (<italic>p</italic>’s < 0.0001). In addition, the percentage diagnosed with ADRD was significantly greater in those who indicated a history of specific physical health conditions at baseline, including hypertension, heart disease, stroke, and TBI, as well as in those who reported use of opioid analgesics (6.9% vs. 5.4%), benzodiazepines (8.5% vs. 5.4%), or other psychotropics (9.8% vs. 4.8%) (<italic>p</italic>’s < 0.0001). The proportion of participants with incident ADRD increased significantly with rising number of comorbid physical health conditions (range 4.6% to 10.6%, <italic>p</italic> < 0.0001). Participants who reported sleep, depression or anxiety disorders at baseline also included a significantly higher proportion of ADRD cases (<italic>p</italic>’s < 0.0001). Notably, as detailed in <xref rid=\"ijerph-17-05454-t002\" ref-type=\"table\">Table 2</xref>, incident ADRD rates were significantly higher in those who reported any NCPC at baseline (6.6 vs. 4.3 per 100), and rose with increasing number of NCPC conditions, from 6.2 in those with 1 NCPC condition to 17.3 per 100 participants in those with 4 or more NCPC conditions (<italic>p</italic>’s < 0.0001).</p><sec id=\"sec3dot1-ijerph-17-05454\"><title>3.1. Association of NCPC to Incident ADRD</title><p>In our unadjusted logistic regression model, those with baseline NCPC were 54% more likely to have incident ADRD (odds ratio (OR) = 1.54, 95% confidence interval (CI) = 1.34–1.78, <italic>p</italic> < 0.0001) (<xref rid=\"ijerph-17-05454-t003\" ref-type=\"table\">Table 3</xref>). Adjustment for demographic and socioeconomic characteristics (sex, race, age, education, poverty, Medicaid insurance, private insurance, and marital status) attenuated but did not eliminate this association (adjusted OR (AOR) = 1.33, 95% confidence interval (CI) =1.14–1.53, <italic>p</italic> < 0.0001). </p><p>The magnitude of this association was modestly reduced after additional adjustment for lifestyle factors and comorbid physical health conditions (AOR = 1.28, CI 1.10–1.48, <italic>p</italic> = 0.001), and further attenuated by inclusion of medications in the model (AOR adjusted for NSAIDs and opioid analgesics (AOR = 1.25, CI 1.08–1.45, <italic>p</italic> = 0.003) (<xref rid=\"ijerph-17-05454-t003\" ref-type=\"table\">Table 3</xref>). Further adjustment for benzodiazepines and other psychotropics only slightly reduced the magnitude of this estimate (AOR = 1.21 (1.04–1.40, <italic>p</italic> = 0.01)). As detailed in <xref rid=\"ijerph-17-05454-t003\" ref-type=\"table\">Table 3</xref>, the association of NCPC to incident ADRD rose significantly in both strength and magnitude with increasing number of pain conditions. Relative to beneficiaries with no NCPC at baseline, those with ≥4 NCPCs were twice as likely to be subsequently diagnosed with ADRD after adjustment for demographics, socioeconomic status, lifestyle factors, and medical conditions (AOR = 1.98, CI = 1.36–2.89, <italic>p</italic>-trend <0.00001). Further adjustment for analgesic medications only slightly attenuated these associations (AOR for ≥4 vs. no NCPC = 1.91, CI 1.31–2.80, <italic>p</italic>-trend = 0.0008). When number of NCPCs at baseline were assessed as a continuous variable, risk for incident ADRD increased 12% for each additional NCPC in the fully adjusted model (AOR = 1.12, CI = 1.06–1.20, <italic>p</italic> = 0.0007).</p></sec><sec id=\"sec3dot2-ijerph-17-05454\"><title>3.2. Sensitivity Analyses</title><p>Analyses using competing risk of death models yielded findings consistent with those from our primary analyses (<xref rid=\"ijerph-17-05454-t004\" ref-type=\"table\">Table 4</xref>). Relative to participants without NCPC at baseline, those with any NCPC and still alive at follow-up were significantly more likely to be diagnosed with incident ADRD after adjustment for demographics, lifestyle characteristics, physical health conditions, analgesics, and other factors (AOR for any NCPC = 1.26, 95% CI = 1.08–1.46, <italic>p</italic> < 0.003). In contrast, baseline NCPC status was unrelated to death during the follow-up period, either with or without a diagnosis of incident ADRD (AORs, respectively = 0.99 and 0.97, <italic>p</italic>’s ≥ 0.6). Further adjustment for psychotropics did not appreciably change these estimates. These findings suggest survival bias is unlikely to explain the positive association observed between baseline NCPC and incident ADRD.</p></sec><sec id=\"sec3dot3-ijerph-17-05454\"><title>3.3. Potential Mediating Effects of Sleep and Mood Disorders</title><p>As illustrated in <xref rid=\"ijerph-17-05454-t005\" ref-type=\"table\">Table 5</xref>, inclusion of mood disorders in fully adjusted model modestly attenuated the relation of NCPC to incident ADRD (AORs for any NCPC and 4+ NCPCs vs. no NCPC, respectively = 1.17 (1.01–1.37) and 1.57 (1.06–2.34), <italic>p</italic>’s ≤ 0.04). Likewise, the addition of sleep disorders to the model attenuated the association of NCPC to ADRD risk (AORs for any NCPC and 4+ NCPCs vs. no NCPC, respectively = 1.20 (1.04–1.39) and 1.66 (1.12–2.45) <italic>p</italic>’s < 0.02) as did that of both sleep and mood disorders (AORs for any NCPC and 4+ NCPCs, respectively = 1.18 (1.01–1.38) and 1.41 (0.94–2.12)). Adding mood and sleep disorders to models adjusted only for sociodemographics yielded similar risk estimates (AORs for any NCPC = 1.19, CI 1.02–1.47, <italic>p</italic> < 0.05), again suggesting that mood and sleep disorders may in part mediate the relation of NCPC to incident ADRD. Additional adjustment for benzodiazepines and psychotropics did not appreciably alter these associations (AOR = 1.17, CI = 1.01–1.37, <italic>p</italic> = 0.04). A number of demographic, lifestyle, and health-related factors remained significantly associated with incident ADRD in our fully adjusted models. These include: black (vs. non-Hispanic white) race (AOR = 1.48, CI 1.09–2.00); age (80+ vs. 65–69, AOR 6.12, CI = 4.7–7.9); poverty (<200% vs. >200%, AOR 1.17, CI = 1.02–1.34); Medicaid insurance (AOR 1.27, 95% CI = 1.01–1.6); BMI (underweight vs. normal, AOR = 1.48, CI = 1.04–2.12); history of stroke (AOR 1.87, CI = 1.6–2.2) or TBI (AOR = 1.52, CI = 1.04–2.23); and psychotropic medications (AOR = 2.04, CI = 1.74–2.39) (<italic>p</italic>’s < 0.05). Use of neither NSAIDs nor opioids was significantly associated with ADRD risk in the adjusted analyses (AORs respectively = 0.96 (0.80–1.16) and 1.03 (0.87–1.22)), nor was use of benzodiazepines (AOR = 1.17, CI = 0.97–1.40) (<italic>p</italic>’s > 0.05).</p></sec></sec><sec sec-type=\"discussion\" id=\"sec4-ijerph-17-05454\"><title>4. Discussion</title><p>In this retrospective cohort study of older community-dwelling Medicare fee-for-service enrollees, diagnosed NCPC at baseline remained significantly and positively associated with risk for incident ADRD after adjustment for demographics, socioeconomic factors, medical history, medications, and other factors. The strength and magnitude of this association rose with increasing number of NCPCs, indicating increasing risk for incident ADRD with rising chronic pain condition burden. The relation between NCPC and risk of ADRD appeared to be mediated in part by the presence of sleep and mood disorders. To our knowledge, this investigation is the first large retrospective cohort study to assess the collective and incremental association of NCPC to incident ADRD, and to explore the potential mediating role of mood and sleep disorders in this association. </p><p>The significant positive associations NCPC to ADRD risk observed in this study are consistent with those of prior longitudinal studies regarding the relation of specific chronic pain conditions to risk for dementia. Recent large retrospective matched cohort studies in Taiwanese nationals [<xref rid=\"B26-ijerph-17-05454\" ref-type=\"bibr\">26</xref>,<xref rid=\"B28-ijerph-17-05454\" ref-type=\"bibr\">28</xref>,<xref rid=\"B29-ijerph-17-05454\" ref-type=\"bibr\">29</xref>,<xref rid=\"B34-ijerph-17-05454\" ref-type=\"bibr\">34</xref>,<xref rid=\"B52-ijerph-17-05454\" ref-type=\"bibr\">52</xref>], prospective cohort investigations in Norwegian adults [<xref rid=\"B25-ijerph-17-05454\" ref-type=\"bibr\">25</xref>,<xref rid=\"B27-ijerph-17-05454\" ref-type=\"bibr\">27</xref>] and a small retrospective cohort study of Canadian elders [<xref rid=\"B53-ijerph-17-05454\" ref-type=\"bibr\">53</xref>] all indicated significantly increased risk for incident all-cause dementia in those diagnosed with headache [<xref rid=\"B25-ijerph-17-05454\" ref-type=\"bibr\">25</xref>,<xref rid=\"B27-ijerph-17-05454\" ref-type=\"bibr\">27</xref>,<xref rid=\"B29-ijerph-17-05454\" ref-type=\"bibr\">29</xref>,<xref rid=\"B34-ijerph-17-05454\" ref-type=\"bibr\">34</xref>,<xref rid=\"B52-ijerph-17-05454\" ref-type=\"bibr\">52</xref>,<xref rid=\"B53-ijerph-17-05454\" ref-type=\"bibr\">53</xref>], OA/knee pain [<xref rid=\"B26-ijerph-17-05454\" ref-type=\"bibr\">26</xref>,<xref rid=\"B33-ijerph-17-05454\" ref-type=\"bibr\">33</xref>], and fibromyalgia [<xref rid=\"B28-ijerph-17-05454\" ref-type=\"bibr\">28</xref>] after adjusting for demographics, comorbid conditions, and other potential confounders. Although the few longitudinal studies investigating the association of non-specific chronic pain to subsequent cognitive deterioration or incident ADRD, all in British [<xref rid=\"B31-ijerph-17-05454\" ref-type=\"bibr\">31</xref>] and U.S. adults [<xref rid=\"B24-ijerph-17-05454\" ref-type=\"bibr\">24</xref>,<xref rid=\"B30-ijerph-17-05454\" ref-type=\"bibr\">30</xref>,<xref rid=\"B32-ijerph-17-05454\" ref-type=\"bibr\">32</xref>], have varied widely in study population, design, and methodology, results have likewise indicated that severe chronic pain [<xref rid=\"B30-ijerph-17-05454\" ref-type=\"bibr\">30</xref>,<xref rid=\"B31-ijerph-17-05454\" ref-type=\"bibr\">31</xref>], persistent pain [<xref rid=\"B32-ijerph-17-05454\" ref-type=\"bibr\">32</xref>], and/or reported pain interference [<xref rid=\"B24-ijerph-17-05454\" ref-type=\"bibr\">24</xref>] may predict subsequent worsening in memory [<xref rid=\"B30-ijerph-17-05454\" ref-type=\"bibr\">30</xref>,<xref rid=\"B31-ijerph-17-05454\" ref-type=\"bibr\">31</xref>], accelerated cognitive decline [<xref rid=\"B32-ijerph-17-05454\" ref-type=\"bibr\">32</xref>], and dementia [<xref rid=\"B24-ijerph-17-05454\" ref-type=\"bibr\">24</xref>,<xref rid=\"B32-ijerph-17-05454\" ref-type=\"bibr\">32</xref>].</p><p>While some longitudinal studies investigating the association between pain and dementia risk have included depression and/or anxiety as covariates in their adjusted models [<xref rid=\"B24-ijerph-17-05454\" ref-type=\"bibr\">24</xref>,<xref rid=\"B28-ijerph-17-05454\" ref-type=\"bibr\">28</xref>,<xref rid=\"B29-ijerph-17-05454\" ref-type=\"bibr\">29</xref>,<xref rid=\"B30-ijerph-17-05454\" ref-type=\"bibr\">30</xref>,<xref rid=\"B32-ijerph-17-05454\" ref-type=\"bibr\">32</xref>,<xref rid=\"B34-ijerph-17-05454\" ref-type=\"bibr\">34</xref>], no studies to our knowledge have explored the potential mediating role of mood disorders or evaluated the influence of sleep impairment on the association of chronic pain to incident ADRD. Sleep and mood disorders are common in older adults, have been strongly and reciprocally associated with chronic pain [<xref rid=\"B54-ijerph-17-05454\" ref-type=\"bibr\">54</xref>,<xref rid=\"B55-ijerph-17-05454\" ref-type=\"bibr\">55</xref>], and have been shown to be independent risk factors for ADRD [<xref rid=\"B13-ijerph-17-05454\" ref-type=\"bibr\">13</xref>,<xref rid=\"B14-ijerph-17-05454\" ref-type=\"bibr\">14</xref>,<xref rid=\"B15-ijerph-17-05454\" ref-type=\"bibr\">15</xref>,<xref rid=\"B56-ijerph-17-05454\" ref-type=\"bibr\">56</xref>,<xref rid=\"B57-ijerph-17-05454\" ref-type=\"bibr\">57</xref>], suggesting that sleep and mood impairment may mediate the observed associations between pain and ADRD risk. The role of sleep and mood neuromodulators, such as serotonin [<xref rid=\"B58-ijerph-17-05454\" ref-type=\"bibr\">58</xref>,<xref rid=\"B59-ijerph-17-05454\" ref-type=\"bibr\">59</xref>], dopamine and histamine [<xref rid=\"B60-ijerph-17-05454\" ref-type=\"bibr\">60</xref>,<xref rid=\"B61-ijerph-17-05454\" ref-type=\"bibr\">61</xref>] has been well documented in pain expression, likely contributing to the documented bidirectional relationships of sleep [<xref rid=\"B54-ijerph-17-05454\" ref-type=\"bibr\">54</xref>] and mood disorders [<xref rid=\"B55-ijerph-17-05454\" ref-type=\"bibr\">55</xref>,<xref rid=\"B62-ijerph-17-05454\" ref-type=\"bibr\">62</xref>] to pain conditions. In the current study, inclusion of depression, anxiety, and insomnia-related sleep disorders in the model weakened but did not eliminate the observed associations between NCPC and incident ADRD risk, suggesting a partial mediating role. In agreement with prior research, sleep [<xref rid=\"B15-ijerph-17-05454\" ref-type=\"bibr\">15</xref>,<xref rid=\"B54-ijerph-17-05454\" ref-type=\"bibr\">54</xref>] and mood disorders [<xref rid=\"B13-ijerph-17-05454\" ref-type=\"bibr\">13</xref>,<xref rid=\"B14-ijerph-17-05454\" ref-type=\"bibr\">14</xref>,<xref rid=\"B57-ijerph-17-05454\" ref-type=\"bibr\">57</xref>] remained strongly and positively associated with both baseline NCPC and with risk for incident ADRD in this cohort after adjustment for other factors.</p><p><italic>NCPC and increased ADRD risk; possible pathways:</italic> although the mechanisms underlying the observed association of NCPC to incident ADRD remain speculative, chronic pain may operate via several pathways to increase risk for dementia [<xref rid=\"B21-ijerph-17-05454\" ref-type=\"bibr\">21</xref>,<xref rid=\"B23-ijerph-17-05454\" ref-type=\"bibr\">23</xref>,<xref rid=\"B63-ijerph-17-05454\" ref-type=\"bibr\">63</xref>,<xref rid=\"B64-ijerph-17-05454\" ref-type=\"bibr\">64</xref>,<xref rid=\"B65-ijerph-17-05454\" ref-type=\"bibr\">65</xref>]. Adults experiencing chronic pain have demonstrated diminished attention, impaired learning and memory, altered processing speed, reduced psychomotor efficiency, and compromised executive function [<xref rid=\"B17-ijerph-17-05454\" ref-type=\"bibr\">17</xref>,<xref rid=\"B18-ijerph-17-05454\" ref-type=\"bibr\">18</xref>,<xref rid=\"B19-ijerph-17-05454\" ref-type=\"bibr\">19</xref>,<xref rid=\"B22-ijerph-17-05454\" ref-type=\"bibr\">22</xref>,<xref rid=\"B23-ijerph-17-05454\" ref-type=\"bibr\">23</xref>], hallmarks of cognitive decline that may ultimately presage the development of cognitive impairment and dementia. Contributing to the documented decline in cognitive function that accompanies chronic pain, NCPC may promote specific adverse neurostructural and neurofunctional changes. For example, experimental and neuroimaging studies have demonstrated neurodegenerative changes in subjects with chronic pain that parallel those observed in ADRD [<xref rid=\"B21-ijerph-17-05454\" ref-type=\"bibr\">21</xref>,<xref rid=\"B65-ijerph-17-05454\" ref-type=\"bibr\">65</xref>,<xref rid=\"B66-ijerph-17-05454\" ref-type=\"bibr\">66</xref>], including reduction in grey matter volume in the amygdala, hippocampus and frontal cortices, the brain regions integrally involved in cognitive and behavioral functioning [<xref rid=\"B66-ijerph-17-05454\" ref-type=\"bibr\">66</xref>,<xref rid=\"B67-ijerph-17-05454\" ref-type=\"bibr\">67</xref>]. The increases in both peripheral and systemic inflammation that have been linked to chronic pain [<xref rid=\"B68-ijerph-17-05454\" ref-type=\"bibr\">68</xref>,<xref rid=\"B69-ijerph-17-05454\" ref-type=\"bibr\">69</xref>] may contribute to these neurodegenerative changes by contributing to neuroinflammation [<xref rid=\"B63-ijerph-17-05454\" ref-type=\"bibr\">63</xref>]. For example, chronic pain-induced microglial neuroinflammation has been directly implicated in Alzheimer’s disease pathogenesis via production of amyloid beta plaques and neurofibrillary tangles [<xref rid=\"B21-ijerph-17-05454\" ref-type=\"bibr\">21</xref>]; persistent inflammation negatively affects neuroplasticity and synaptic performance via reduction in brain-derived neurotrophic factors [<xref rid=\"B66-ijerph-17-05454\" ref-type=\"bibr\">66</xref>,<xref rid=\"B68-ijerph-17-05454\" ref-type=\"bibr\">68</xref>,<xref rid=\"B69-ijerph-17-05454\" ref-type=\"bibr\">69</xref>]. Neuronal receptors in the brain are neither infinite nor mutually exclusive and serve a range of neurologic functions under limited resources. During persistent pain, nerve endings fire rapid pain impulses to the brain for remedial actions, which in turn, exhausts the neuronal resources that are also involved in cognitive functions [<xref rid=\"B69-ijerph-17-05454\" ref-type=\"bibr\">69</xref>,<xref rid=\"B70-ijerph-17-05454\" ref-type=\"bibr\">70</xref>,<xref rid=\"B71-ijerph-17-05454\" ref-type=\"bibr\">71</xref>]. In addition, the presence of chronic pain conditions has been correlated with dysregulation of noradrenergic-modulated endogenous pain autoinhibition [<xref rid=\"B69-ijerph-17-05454\" ref-type=\"bibr\">69</xref>], which has, in turn, been linked to negative cognitive outcomes, including decline in working and long-term memory [<xref rid=\"B70-ijerph-17-05454\" ref-type=\"bibr\">70</xref>,<xref rid=\"B71-ijerph-17-05454\" ref-type=\"bibr\">71</xref>]. </p><p>In agreement with previously published research [<xref rid=\"B3-ijerph-17-05454\" ref-type=\"bibr\">3</xref>,<xref rid=\"B7-ijerph-17-05454\" ref-type=\"bibr\">7</xref>,<xref rid=\"B45-ijerph-17-05454\" ref-type=\"bibr\">45</xref>], risk for ADRD increased strongly with age in this study, and was elevated among African Americans and in adults who had less education or lower family income, were on Medicaid, or had a history of diabetes, stroke, or TBI. Similarly, the significant positive relationships between baseline sleep and mood disorders and likelihood of incident ADRD observed in this sample of US Medicare beneficiaries are consistent with the findings of numerous prior investigations [<xref rid=\"B14-ijerph-17-05454\" ref-type=\"bibr\">14</xref>,<xref rid=\"B15-ijerph-17-05454\" ref-type=\"bibr\">15</xref>]. Both psychotropics and analgesics are frequently employed to manage chronic mood disorders, insomnia, and pain conditions in the older population. NSAIDs have been reported to reduce risk of ADRD in most but not all studies [<xref rid=\"B46-ijerph-17-05454\" ref-type=\"bibr\">46</xref>], whereas prior research has demonstrated modest or no association of opioid analgesics to ADRD risk [<xref rid=\"B47-ijerph-17-05454\" ref-type=\"bibr\">47</xref>]. In this study, use of neither NSAIDs nor opioid analgesics was significantly related to risk for ADRD. In agreement with some [<xref rid=\"B49-ijerph-17-05454\" ref-type=\"bibr\">49</xref>] but not all previous studies [<xref rid=\"B48-ijerph-17-05454\" ref-type=\"bibr\">48</xref>,<xref rid=\"B72-ijerph-17-05454\" ref-type=\"bibr\">72</xref>], use of psychotropics, but not benzodiazepines, remained significantly associated with incident ADRD after adjustment for multiple confounders. However, as noted above, adjustment for these medications only slightly attenuated the observed relationships of NCPCs to incident ADRD.</p><sec><title>Strengths and Limitations</title><p>This study has several strengths, including the population-based design, the use of longitudinal data from multiple cohorts, and the large, nationally representative sample of U.S. community-dwelling elders enrolled in FFS Medicare plans. Comprehensive information was available on a broad range of demographic and lifestyle characteristics, as well as on medical history, medication use, and other factors, allowing us to assess the potential confounding influence of these factors. Furthermore, NCPC’s, medication use, and history of other health conditions were ascertained using claims data and established algorithms. ADRD was identified using a combination of Medicare claims and survey data, likely leading to greater capture of this often under-reported and underdiagnosed outcome [<xref rid=\"B41-ijerph-17-05454\" ref-type=\"bibr\">41</xref>,<xref rid=\"B73-ijerph-17-05454\" ref-type=\"bibr\">73</xref>,<xref rid=\"B74-ijerph-17-05454\" ref-type=\"bibr\">74</xref>]. Notably, previous studies have indicated high specificity (89-95%) and acceptable sensitivity (64-85%) for the ascertainment of ADRD using multiple years of Medicare claims data [<xref rid=\"B41-ijerph-17-05454\" ref-type=\"bibr\">41</xref>,<xref rid=\"B73-ijerph-17-05454\" ref-type=\"bibr\">73</xref>,<xref rid=\"B74-ijerph-17-05454\" ref-type=\"bibr\">74</xref>]. We used a 3-year backward cohort design, with a two-year continuous follow-up and incident ADRD measured at two time points. </p><p>Our study also has a number of limitations, including the relatively short follow-up period and lack of information on NCPC duration or on chronic pain symptoms, precluding a more comprehensive assessment of the potential role of chronic pain and chronic pain conditions in the development of ADRD. Given that ADRD is often underdiagnosed and progression is generally slow and insidious, and that the study follow-up period was short, under-ascertainment of ADRD is likely in this study, potentially biasing our risk estimates toward the null. In addition, given that we used a conservative method [<xref rid=\"B75-ijerph-17-05454\" ref-type=\"bibr\">75</xref>] for ascertaining chronic pain (i.e., ≥1 inpatient visit or two outpatient visits for any chronic pain conditions 90 days apart), and that NCPC is typically under-reported in medical claims data [<xref rid=\"B76-ijerph-17-05454\" ref-type=\"bibr\">76</xref>,<xref rid=\"B77-ijerph-17-05454\" ref-type=\"bibr\">77</xref>,<xref rid=\"B78-ijerph-17-05454\" ref-type=\"bibr\">78</xref>], NCPC may have been under-ascertained in this study, again potentially biasing risk estimates towards the null. Moreover, as sleep and mood disorders often accompany the development of ADRD, these disorders may have reflected prodromal ADRD in some who were diagnosed with incident ADRD. While we were able to adjust for a wide range of potential confounders, including smoking and BMI, we lacked information on certain lifestyle-related and other factors strongly linked to ADRD risk, including alcohol consumption, physical activity, genetic and familial predisposition, and social isolation [<xref rid=\"B7-ijerph-17-05454\" ref-type=\"bibr\">7</xref>,<xref rid=\"B9-ijerph-17-05454\" ref-type=\"bibr\">9</xref>]. Due to small cell sizes, we were also unable to adjust for Parkinson’s disease and related movement disorders, conditions which have been linked to chronic pain, as well as to mood and sleep disorders and cognitive impairment [<xref rid=\"B79-ijerph-17-05454\" ref-type=\"bibr\">79</xref>,<xref rid=\"B80-ijerph-17-05454\" ref-type=\"bibr\">80</xref>]. However, small cell sizes would also suggest that any residual confounding from these movement disorders is unlikely to explain our findings. Both ADRD and chronic pain have been associated with increased mortality [<xref rid=\"B81-ijerph-17-05454\" ref-type=\"bibr\">81</xref>,<xref rid=\"B82-ijerph-17-05454\" ref-type=\"bibr\">82</xref>,<xref rid=\"B83-ijerph-17-05454\" ref-type=\"bibr\">83</xref>], introducing potential survival bias. However, competing risk of death analyses yielded findings similar to those of our primary analyses, arguing against a substantive effect of survival bias on our study results. Finally, definitive conclusions regarding causality are not possible due to the short follow-up period in this study and the insidious nature of ADRD development and progression. However, while reverse causality cannot be ruled out, a growing literature suggests chronic pain can disrupt neurocognitive function and may increase risk for cognitive decline and incident dementia [<xref rid=\"B18-ijerph-17-05454\" ref-type=\"bibr\">18</xref>,<xref rid=\"B65-ijerph-17-05454\" ref-type=\"bibr\">65</xref>,<xref rid=\"B84-ijerph-17-05454\" ref-type=\"bibr\">84</xref>], whereas evidence for an inverse relationship remains sparse [<xref rid=\"B22-ijerph-17-05454\" ref-type=\"bibr\">22</xref>]. </p></sec></sec><sec sec-type=\"conclusions\" id=\"sec5-ijerph-17-05454\"><title>5. Conclusions</title><p>In this large, population-based study in a nationally representative sample of US community-dwelling elders enrolled in FFS Medicare, NCPC at baseline remained significantly and positively associated with risk for incident ADRD after adjustment for demographics, lifestyle factors, medical history, medications, and other factors. This association increased in magnitude with increasing NCPC burden and appeared to be partially mediated by the presence of mood and sleep disorders. Additional large, prospective studies with longer term follow-up are warranted to confirm our findings; to further elucidate the potentially important contribution of chronic pain to accelerated cognitive decline, new onset cognitive impairment and the development of ADRD; to clarify the potential mediating role of sleep and mood disorders; and to explore possible underlying mechanisms.</p></sec></body><back><ack><title>Acknowledgments</title><p>Research reported in this publication was supported in part by the National Institute of General Medical Sciences of the National Institutes of Health (Award Number 2U54GM104942-02)<bold>,</bold> the WVCTSI and the Alzheimer’s Research and Prevention Foundation (ARPF). S.K. received a Fulbright fellowship for doctoral research. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health, the ARPF or the Fulbright fellowship program.</p></ack><app-group><app id=\"app1-ijerph-17-05454\"><title>Supplementary Materials</title><p>The following are available online at <uri xlink:href=\"https://www.mdpi.com/1660-4601/17/15/5454/s1\">https://www.mdpi.com/1660-4601/17/15/5454/s1</uri>, Figure S1: ADRD-NCPC Study Cohort: Medicare Current Beneficiary Survey (MCBS), 2001–2013, Table S1: ICD-9_Clinial Modification (ICD-9-CM) Diagnostic Codes.</p><supplementary-material content-type=\"local-data\" id=\"ijerph-17-05454-s001\"><media xlink:href=\"ijerph-17-05454-s001.zip\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material></app></app-group><notes><title>Author Contributions</title><p>Conceptualization, design, and methodology, and to the interpretation and presentation of results, S.K., U.S. and K.E.I.; statistical analyses, S.K. and U.S.; manuscript draft, S.K.; subsequent iterations, S.K., U.S. and K.E.I.; critical review of the final draft, U.S. and K.E.I. All authors have read and agreed to the published version of the manuscript.</p></notes><notes><title>Funding</title><p>This research was funded by the National Institute of General Medical Sciences of the National Institutes of Health (Award Number 2U54GM104942-02)<bold>,</bold> the WVCTSI and the Alzheimer’s Research and Prevention Foundation (ARPF).</p></notes><notes notes-type=\"COI-statement\"><title>Conflicts of Interest</title><p>The authors declare no conflict of interest.</p></notes><ref-list><title>References</title><ref id=\"B1-ijerph-17-05454\"><label>1.</label><element-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Wilson</surname><given-names>R.S.</given-names></name><name><surname>Segawa</surname><given-names>E.</given-names></name><name><surname>Boyle</surname><given-names>P.A.</given-names></name><name><surname>Anagnos</surname><given-names>S.E.</given-names></name><name><surname>Hizel</surname><given-names>L.P.</given-names></name><name><surname>Bennett</surname><given-names>D.A.</given-names></name></person-group><article-title>The natural history of cognitive decline in Alzheimer’s disease</article-title><source>Psychol. 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style=\"border-top:solid thin\" rowspan=\"1\">Any NCPC</th><th rowspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\"><italic>p</italic>-Value <sup>§</sup></th></tr><tr><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">\n</th><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">Yes</th><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">No</th></tr><tr><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">N <sup>¥</sup></th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Wt.%</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">N</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Wt.%</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">N</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Wt. %</th></tr></thead><tbody><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold><italic>ALL</italic><sup>¥</sup></bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">16,934</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6369</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10,565</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold><italic>Sociodemographics</italic></bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> <bold>Sex</bold></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.0001</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> Female</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">9669</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">56.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4215</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">66.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5454</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">51.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> Male</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7265</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">43.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2154</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">33.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5111</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">48.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Age in Years</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.0001</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> 65–69</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4188</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">31.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1335</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">26.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2853</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">34.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> 70–74</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3484</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">22.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1230</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">22.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2254</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">22.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> 75–79</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3674</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">21.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1411</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">22.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2263</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">20.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> 80+</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5588</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">24.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2393</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">28.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3195</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">21.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> <bold>Race/Ethnicity</bold></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.842</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> White</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">14,085</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">83.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5293</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">83.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8792</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">83.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> Black</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1159</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">438</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">721</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> Hispanic</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">956</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">359</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">597</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> Other</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">709</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">268</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">441</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> <bold>Education</bold></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.0001</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> <High School</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4605</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">25.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1744</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">25.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2861</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">24.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> High School</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6174</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">36.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2349</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">37.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3825</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">36.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> Some College</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2431</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">15.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">950</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">15.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1481</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">14.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> College</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3675</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">23.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1306</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">21.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2369</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">24.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> <bold>Marital Status</bold></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.0001</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> Married</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">9139</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">56.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3208</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">52.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5931</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">58.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> Widowed</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5803</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">30.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2491</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">35.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3312</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">27.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> Divorce/separated</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1455</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">9.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">490</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">965</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> Other</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">532</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">179</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">353</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> <bold>Household Income</bold></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.0001</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> <200% Federal Poverty Level</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8260</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">45.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3221</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">48.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5039</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">44.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> ≥200% Federal Poverty Level</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8674</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">54.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3148</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">51.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5526</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">55.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold><italic>Insurance</italic></bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> <bold>Medicaid</bold></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.0001</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> Yes</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2147</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">11.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1005</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">15.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1142</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">9.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> No</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">14,787</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">88.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5364</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">84.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">9423</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">90.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> <bold>Private Insurance</bold></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.001</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> Yes</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13,418</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">79.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5147</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">80.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8271</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">78.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> No</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3514</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">20.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1221</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">19.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2293</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">21.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold><italic>Residence</italic></bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> <bold>Metropolitan Status</bold></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.0001</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> Metro </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">11,375</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">70.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4415</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">72.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6960</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">69.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> Rural</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5558</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">29.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1954</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">27.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3604</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">30.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold><italic>Lifestyle Characteristics</italic></bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> <bold>Body Mass Index</bold></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.0001</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> Underweight</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">312</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">212</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> Normal</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5867</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">34.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1984</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">30.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3883</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">36.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> Overweight</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6709</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">40.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2491</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">39.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4218</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">40.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> Obese</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3881</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">24.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1734</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">29.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2147</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">21.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>Mean BMI 27.2 (±0.052)</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> <bold>Smoking Status</bold></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.0001</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> Current</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1594</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">457</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1137</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">11.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> Past</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7976</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">47.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2965</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">47.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5011</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">47.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> Never</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7326</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">42.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2936</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">45.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4390</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">40.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold><italic>Baseline Health History</italic></bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold><italic>Physical Health Conditions</italic></bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> <bold>Hypertension</bold></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.0001</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> Yes</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">12,713</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">73.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5263</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">82.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7450</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">68.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> No</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4221</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">26.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1106</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">18.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3115</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">31.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> <bold>Heart disease</bold></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.0001</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> Yes</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4357</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">24.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2097</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">31.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2260</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">19.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> No</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">12,577</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">75.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4272</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">68.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8305</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">80.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> <bold>Stroke</bold></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.0001</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> Yes</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2139</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">11.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">979</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">14.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1160</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">9.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> No</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">14,795</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">88.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5390</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">85.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">9405</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">90.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> <bold>Diabetes</bold></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.0001</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> Yes</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3495</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">20.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1468</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">23.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2027</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">19.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> No</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13,439</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">79.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4901</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">76.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8538</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">80.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> <bold>Respiratory disease</bold></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.0001</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> Yes</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2423</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">14.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1045</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">16.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1378</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">12.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> No</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">14,511</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">85.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5324</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">83.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">9187</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">87.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> <bold>Cancer</bold></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.0001</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> Yes</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7101</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">40.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2911</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">44.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4190</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">38.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> No</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">9833</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">59.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3458</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">55.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6375</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">62.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> <bold>Traumatic Brain Injury</bold></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.0001</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> Yes</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">221</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">146</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">75</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> No</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">16,713</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">98.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6223</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">97.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10,490</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">99.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> <bold>Rheumatoid Arthritis</bold></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.0001</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> Yes</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">508</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">349</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">159</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> No</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">16,426</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">97.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6020</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">94.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10,406</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">98.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> <bold>Lupus</bold></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.0001</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> Yes</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">136</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">94</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">42</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> No</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">16,798</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">99.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6275</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">98.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10,523</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">99.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Number of NCPCs</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> 0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6385</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">39.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> 1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4194</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">24.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> 2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3818</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">22.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> 3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1908</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> ≥4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">629</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold><italic>Sleep and Mood Disorders</italic></bold>\n</td><td colspan=\"7\" align=\"center\" valign=\"middle\" rowspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> <bold>Sleep Disorder</bold></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.0001</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> Yes</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2728</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">15.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1630</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">25.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1098</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> No</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">14,206</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">84.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4739</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">74.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">9467</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">89.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> <bold>Depression</bold></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.0001</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> Yes</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1158</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">686</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">11.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">472</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> No</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">15,776</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">93.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5683</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">88.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10,093</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">95.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> <bold>Anxiety</bold></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.0001</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> Yes</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">862</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">492</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">370</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> No</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">16,072</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">94.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5877</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">92.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10,195</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">96.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold><italic>Medication Use</italic></bold>\n</td><td colspan=\"7\" align=\"center\" valign=\"middle\" rowspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> <bold>NSAIDs</bold></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.0001</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> Yes</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3241</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">18.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1936</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">30.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1305</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">12.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> No</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13,693</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">81.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4433</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">69.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">9260</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">88.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> <bold>Opioid Analgesics</bold></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.0001</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> Yes</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3353</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">19.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2059</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">32.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1294</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">12.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> No</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13,581</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">80.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4310</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">67.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">9271</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">88.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> <bold>Benzodiazepines</bold></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.0001</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> Yes</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1734</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">9.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">935</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">14.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">799</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> No</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">15,200</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">90.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5434</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">85.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">9766</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">92.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> <bold>Psychotropic medications</bold></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.0001</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> Yes</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2871</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">17.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1448</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">23.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1423</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"> No</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">14,063</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">83.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">4921</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">76.7</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">9142</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">86.5</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td></tr></tbody></table><table-wrap-foot><fn><p>* Based on 11 pooled cohorts of 16,934 older community-dwelling adults (age > 65 years), enrolled in fee-for service Medicare, alive at the end of follow-up. <sup>¥</sup> Numbers for some variables may not add up to N = 16,934, due to missing values. <sup>§</sup> Statistically significant group differences in presence of NCPC were examined with Rao-Scott chi-square test. Abbreviation: NCPC, non-cancer chronic pain conditions; BMI, body mass index; NSAIDs, nonsteroidal anti-inflammatory drugs.</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"ijerph-17-05454-t002\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05454-t002_Table 2</object-id><label>Table 2</label><caption><p>Baseline characteristics by incident Alzheimer’s disease and related dementias (ADRD) in older Medicare beneficiaries *, analyzed using linked data from the Medicare Current Beneficiary Survey and Medicare claims, 2001-2013.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">Variables</th><th colspan=\"4\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Incident ADRD</th><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\"><italic>p</italic>-Value <sup>§</sup></th></tr><tr><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">Yes</th><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">No</th></tr><tr><th align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> </th><th align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">N</th><th align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Wt. %</th><th align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">N</th><th align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Wt. %</th><th align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</th></tr></thead><tbody><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold><italic>ALL</italic><sup>¥</sup></bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1149</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">15,785</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">93.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> <bold>Any NCPC</bold></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.0001</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> Yes</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">798</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">9677</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">93.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> No</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">351</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6108</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">95.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> <bold>Number of NCPC’s</bold></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.0001</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> 0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">351</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6108</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">95.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> 1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">304</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3948</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">93.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> 2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">273</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3605</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">94.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> 3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">155</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1742</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">92.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> ≥4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">66</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">17.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">382</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">82.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold><italic>Sociodemographic</italic></bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> <bold>Sex</bold></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.0001</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> Female</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">725</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8944</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">93.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> Male</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">424</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6841</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">95</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> <bold>Age in Years</bold></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.0001</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> 65–69</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">101</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4087</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">98.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> 70–74</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">139</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3345</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">96</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> 75–79</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">220</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3454</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">94.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> 80+</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">689</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">12</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4899</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">88</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> <bold>Race/Ethnicity</bold></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.066</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> White</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">922</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13,163</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">94.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> Black</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">106</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1053</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">92.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> Hispanic</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">70</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">886</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">94.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> Other</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">51</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">658</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">93.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> <bold>Education</bold></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.0001</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> <High School</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">417</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4188</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">92.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> High School</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">398</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5776</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">94.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> Some College</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">141</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2290</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">95.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> College</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">186</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3489</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">95.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> <bold>Marital Status</bold></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.0001</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> Married</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">494</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8645</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">95.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> Widowed</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">541</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5262</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">91.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> Divorce/separated</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">76</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1379</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">95.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> Other</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">38</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">494</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">94</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> <bold>Household Income</bold></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.0001</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> <200% Federal Poverty Level</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">699</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7561</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">92.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> ≥200% Federal Poverty Level</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">450</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8224</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">95.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold><italic>Insurance</italic></bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> <bold>Medicaid</bold></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.0001</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> Yes</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">223</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">9.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1924</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">90.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> No</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">926</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13,861</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">94.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> <bold>Private Insurance</bold></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.0001</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> Yes</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">847</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">12,571</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">94.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> No</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">300</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3214</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">92.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold><italic>Residence</italic></bold>\n</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">\n</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">\n</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">\n</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">\n</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">0.649</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> <bold>Metropolitan Status</bold></td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> Metro </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">787</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10,588</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">94.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> Rural</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">362</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5196</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">94.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold><italic>Lifestyle Characteristics</italic></bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> <bold>Body Mass Index</bold></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.0001</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> Underweight</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">43</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">12.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">269</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">87.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> Normal</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">484</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5383</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">93.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> Overweight</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">417</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6292</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">94.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> Obese</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">195</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3686</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">95.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> <bold>Smoking Status</bold></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.0001</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> Current</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">96</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1498</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">95.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> Past</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">472</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7504</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">95</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> Never</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">580</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6746</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">93.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold><italic>Baseline Health History</italic></bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold><italic>Physical Health Conditions</italic></bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> <bold>Hypertension</bold></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.0001</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> Yes</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">935</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">11,778</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">93.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> No</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">214</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4007</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">95.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> <bold>Heart disease</bold></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.0001</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> Yes</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">368</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3989</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">92.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> No</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">781</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">11,796</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">94.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> <bold>Stroke</bold></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.0001</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> Yes</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">281</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">11.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1858</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">88.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> No</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">868</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13,927</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">95.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> <bold>Diabetes</bold></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.032</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> Yes</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">264</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3231</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">93.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> No</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">885</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">12,554</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">94.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> <bold>Respiratory disease</bold></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.576</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> Yes</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">170</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2253</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">94.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> No</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">979</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13,532</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">94.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> <bold>Cancer</bold></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.043</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> Yes</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">505</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6596</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">93.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> No</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">644</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">9189</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">94.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> <bold>Traumatic Brain Injury</bold></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.0001</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> Yes</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">30</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">12.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">191</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">87.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> No</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1119</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">15,594</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">94.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> <bold>Rheumatoid Arthritis</bold></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.247</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> Yes</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">39</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">469</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">93.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> No</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1110</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">15,316</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">94.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> <bold>Lupus</bold></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.119</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> Yes</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">123</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">91.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> No</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1136</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">15,662</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">94.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold><italic>Sleep and Mood Disorders</italic></bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> <bold>Sleep Disorder</bold></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.0001</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> Yes</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">267</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2461</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">91.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> No</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">882</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13,324</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">94.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> <bold>Depression</bold></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.0001</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> Yes</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">170</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">12.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">988</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">87.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> No</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">979</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">14,797</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">94.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> <bold>Anxiety</bold></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.0001</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> Yes</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">109</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">753</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">89.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> No</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1040</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">15,032</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">94.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold><italic>Medication Use</italic></bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> <bold>NSAIDs</bold></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.496</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> Yes</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">226</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3015</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">94</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> No</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">923</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">12,770</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">94.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> <bold>Opioid Analgesics</bold></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.0001</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> Yes</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">266</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3087</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">93.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> No</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">883</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">12,698</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">94.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> <bold>Benzodiazepines</bold></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.0001</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> Yes</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">167</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1567</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">91.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"> No</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">982</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">5.4</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">14,218</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">94.6</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td></tr></tbody></table><table-wrap-foot><fn><p><italic>*</italic> Based on 11 pooled cohorts of 16,934 older community-dwelling adults (age ≥ 65 years), enrolled in fee-for service Medicare, alive at the end of follow-up. <sup>¥</sup> Numbers for some variables may not add up to N = 1149, due to missing values. <sup>§</sup> Statistically significant group differences in presence of NCPC were examined with Rao-Scott chi-square test. Abbreviations: NCPC, non-cancer chronic pain conditions; ADRD, Alzheimer’s disease and related dementias; NSAIDs, nonsteroidal anti-inflammatory drugs.</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"ijerph-17-05454-t003\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05454-t003_Table 3</object-id><label>Table 3</label><caption><p>Association of baseline non-cancer chronic pain conditions (NCPC) and burden to incident Alzheimer’s disease and related dementias (ADRD) in older Medicare beneficiaries *: analysis using linked Medicare Current Beneficiary Survey and Medicare claims data, 2001–2013 (odds ratios (OR) and adjusted odds ratios (AOR) with 95% confidence intervals (CI) calculated using separate logistic regression models).</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">NCPC Presence/Number</th><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Unadjusted Model</th><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Model 1 <sup>¥</sup></th><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Model 2 <sup>¥¥</sup></th><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Model 3 <sup>ⱡⱡ</sup></th></tr><tr><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">OR (95% CI)</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>p</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">AOR (95% CI)</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>p</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">AOR (95% CI)</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>p</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">AOR (95% CI)</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>p</italic>\n</th></tr></thead><tbody><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Any NCPC ** vs. None</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.54 (1.34,1.78)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.0001</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.33 (1.14,1.53)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.0001</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.28 (1.10,1.48)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.001</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.25 (1.08,1.45)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.003</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Number of NCPCs</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">  None (referent)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.00</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.00</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.00</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.00</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">  One</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.47 (1.26,1.71)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.0001</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.27 (1.08,1.50)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0039</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.24 (1.05,1.46)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0125</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.23 (1.04,1.45)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0179</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">  Two</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.42 (1.20,1.68)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.0001</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.25 (1.05,1.49)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0121</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.22 (1.02,1.45)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0278</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.20 (1.01,1.43)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0435</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">  Three</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.75 (1.42,2.16)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.0001</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.45 (1.17,1.80)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0007</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.38 (1.11,1.71)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0041</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.34 (1.07,1.68)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0122</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">  Four or more</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.03 (2.14,4.29)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.0001</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.32 (1.62,3.31)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.0001</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.98 (1.36,2.89)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0004</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.91 (1.31,2.80)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0008</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold><italic> P for linear trend <sup>++</sup></italic></bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.0001</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.0001</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.0001</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.0001</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Number of NCPCs</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">(per additional NCPC)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.23 (1.16,1.30)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><0.0001</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.16 (1.09,1.23)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><0.0001</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.13 (1.06,1.20)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.0002</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.12 (1.05,1.20)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.0007</td></tr></tbody></table><table-wrap-foot><fn><p>* Based on 11 pooled cohorts of 16,934 older community-dwelling adults (age > 65 years), enrolled in fee-for-service Medicare. ** Including back and neck pain, headache and migraine, joint pain, neuropathic pain and osteoarthritis. <sup>¥</sup> Socio-demographics: sex, age, race/ethnicity, education, income, Medicaid insurance, private insurance, marital status, region. <sup>¥¥</sup> Lifestyle and chronic physical health conditions: smoking, body mass index, hypertension, diabetes, heart disease, cancer, respiratory disorder, history of stroke, traumatic brain injury. <sup>ⱡⱡ</sup> Analgesics: nonsteroidal anti-inflammatory drugs and opioid analgesics. <sup>++</sup> Regression results from polynomial contrast for linear relation indicate a strong linear effect of NCPC on risk of ADRD.</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"ijerph-17-05454-t004\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05454-t004_Table 4</object-id><label>Table 4</label><caption><p>Association of baseline non-cancer chronic pain conditions to incident Alzheimer’s disease and related dementias in older Medicare beneficiaries *: competing risk analysis using linked Medicare Current Beneficiary Survey (MCBS) and Medicare claims data, 2001–2013 (odds ratios (OR) and adjusted odds ratios (AOR) with 95% confidence intervals (CI) calculated from multinomial logistic regression).</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">Any Non-Cancer Chronic Pain Condition (NCPC) **</th><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Alive at Follow-Up, No ADRD (Referent)</th><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Alive at Follow-Up, Incident ADRD</th><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Died before Follow-Up, Incident ADRD</th><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Died before Follow-Up, No ADRD</th></tr><tr><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">OR </th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>p</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">OR (95% CI)</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>p</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">OR (95% CI)</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>p</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">OR (95% CI)</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>p</italic>\n</th></tr></thead><tbody><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> <bold>Model 1</bold>. Unadjusted model</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.00</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.55 (1.34,1.78)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.0001</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.29 (1.03,1.62)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.029</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.25 (1.12,1.40)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.0001</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> <bold>Model 2</bold>. Adjusted for sociodemographics <sup>¥</sup></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.00</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.34 (1.16,1.55)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.0001</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.10 (0.87,1.40)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.42</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.21 (1.07,1.36)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0024</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> <bold>Model 3</bold>. Also adjusted for lifestyle and chronic physical health conditions <sup>¥¥</sup></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.00</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.28 (1.11,1.49)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0009</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.03 (0.79,1.34)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.8287</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.05 (0.93,1.18)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.4461</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"> <bold>Model 4</bold>. Also adjusted for NSAID and opioid analgesic use</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.00</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.26 (1.08,1.46)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.0026</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.99 (0.75,1.31)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.96</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.97 (0.85,1.10)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.60</td></tr></tbody></table><table-wrap-foot><fn><p>* Based on 11 pooled cohorts of older community-dwelling adults (age > 65 years), enrolled in fee-for-service Medicare. ** Referent for Any NCPC ‘No NCPC’. <sup>¥</sup> Sex, age, race/ethnicity, education, income, Medicaid insurance, private insurance, marital status, region. <sup>¥¥</sup> Smoking status, body mass index, hypertension, diabetes, heart disease, cancer, respiratory illnesses, history of stroke and traumatic brain injury.</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"ijerph-17-05454-t005\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05454-t005_Table 5</object-id><label>Table 5</label><caption><p>Association of baseline non-cancer chronic pain conditions (NCPC’s) and burden to incident Alzheimer’s disease and related dementias in older Medicare beneficiaries *: analysis using linked Medicare Current Beneficiary Survey and Medicare claims data, 2001–2013: Potential mediating influence of diagnosed mood and sleep disorders (odds ratios (OR) and adjusted odds ratios (AOR) with 95% confidence intervals (CI)).</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">NCPC Presence/Number</th><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Fully Adjusted Model <sup>¥</sup></th><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Model <sup>¥</sup> + Mood Disorders <sup>§</sup></th><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Model <sup>¥</sup> + Sleep Disorders <sup>§§</sup></th><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Model <sup>¥</sup> + Mood and Sleep Disorders</th></tr><tr><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">OR (95% CI)</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>p</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">OR (95% CI)</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>p</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">OR (95% CI)</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>p</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">OR (95% CI)</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>p</italic>\n</th></tr></thead><tbody><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Any NCPC vs. None **</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.25 (1.08,1.45)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0032</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.17 (1.01,1.37)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0388</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.20 (1.04,1.39)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0155</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.18 (1.01,1.38)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0336</td></tr><tr><td colspan=\"9\" align=\"left\" valign=\"middle\" rowspan=\"1\">\n<bold>Number of NCPCs</bold>\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> None (referent)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.00 (referent)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.00 (referent)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.00 (referent)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.00 (referent)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> One</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.23 (1.04,1.45)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0179</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.19 (1.01,1.41)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0408</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.20 (1.02,1.43)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0311</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.17 (0.99,1.39)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0594</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> Two</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.20 (1.01,1.43)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0435</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.13 (0.95,1.36)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.1722</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.16 (0.97,1.38)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.1008</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.11 (0.92,1.32)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.2713</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> Three</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.34 (1.07,1.68)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0122</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.22 (0.96,1.55)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0962</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.25 (0.99,1.58)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0589</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.17 (0.92,1.48)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.2112</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> Four or more</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.91 (1.31,2.80)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0008</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.57 (1.06,2.34)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0255</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.66 (1.12,2.45)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0113</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.41 (0.94,2.12)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0941</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold><italic>p for linear trend</italic><sup>++</sup></bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.0001</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.0001</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.0001</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.0001</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Number of NCPCs</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">(per additional NCPC)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.12 (1.05,1.20)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.0007</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.08 (1.01,1.16)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.027</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.09 (1.02,1.16)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.0101</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.06 (0.99,1.13)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.1001</td></tr></tbody></table><table-wrap-foot><fn><p>* Based on 11 pooled cohorts of older community-dwelling adults (age > 65 years), enrolled in fee-for-service Medicare. ** Including back or neck pain, headache and migraine, joint pain, neuropathic pain, and osteoarthritis. <sup>¥</sup> sex, age, race/ethnicity, education, income, Medicaid insurance, private insurance, marital status, region, smoking status, body mass index, chronic physical health conditions (hypertension, diabetes, heart disease, cancer, respiratory disorder, history of stroke, traumatic brain injury), NSAID and opioid analgesics. <sup>§</sup> Depression and anxiety; <sup>§§</sup> insomnia-related sleep disorders. <sup>++</sup> Regression results from polynomial contrast for linear relation indicate a strong linear effect of NCPC on risk of ADRD.</p></fn></table-wrap-foot></table-wrap></floats-group></article>\n"
]
[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"review-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Int J Mol Sci</journal-id><journal-id journal-id-type=\"iso-abbrev\">Int J Mol Sci</journal-id><journal-id journal-id-type=\"publisher-id\">ijms</journal-id><journal-title-group><journal-title>International Journal of Molecular Sciences</journal-title></journal-title-group><issn pub-type=\"epub\">1422-0067</issn><publisher><publisher-name>MDPI</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32751945</article-id><article-id pub-id-type=\"pmc\">PMC7432105</article-id><article-id pub-id-type=\"doi\">10.3390/ijms21155484</article-id><article-id pub-id-type=\"publisher-id\">ijms-21-05484</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Review</subject></subj-group></article-categories><title-group><article-title>Current Status and Future Perspectives of Checkpoint Inhibitor Immunotherapy for Prostate Cancer: A Comprehensive Review</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Kim</surname><given-names>Tae Jin</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05484\">1</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0001-7303-6256</contrib-id><name><surname>Koo</surname><given-names>Kyo Chul</given-names></name><xref ref-type=\"aff\" rid=\"af2-ijms-21-05484\">2</xref><xref rid=\"c1-ijms-21-05484\" ref-type=\"corresp\">*</xref></contrib></contrib-group><aff id=\"af1-ijms-21-05484\"><label>1</label>Department of Urology, C.H.A. Bundang Medical Center, University College of Medicine, Seongnam 13496, Korea; <email>[email protected]</email></aff><aff id=\"af2-ijms-21-05484\"><label>2</label>Department of Urology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul 06229, Korea</aff><author-notes><corresp id=\"c1-ijms-21-05484\"><label>*</label>Correspondence: <email>[email protected]</email>; Tel.: +82-2-2019-3470</corresp></author-notes><pub-date pub-type=\"epub\"><day>31</day><month>7</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><month>8</month><year>2020</year></pub-date><volume>21</volume><issue>15</issue><elocation-id>5484</elocation-id><history><date date-type=\"received\"><day>03</day><month>7</month><year>2020</year></date><date date-type=\"accepted\"><day>30</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>© 2020 by the authors.</copyright-statement><copyright-year>2020</copyright-year><license license-type=\"open-access\"><license-p>Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>).</license-p></license></permissions><abstract><p>The clinical spectrum of prostate cancer (PCa) varies from castration-naive to metastatic castration-resistant disease. Despite the administration of androgen synthesis inhibitors and chemotherapy regimens for castration-resistant prostate cancer, the treatment options for this entity are limited. The utilization of the immune system against cancer cells shows potential as a therapeutic modality for various solid tumors and hematologic malignancies. With technological advances over the last decade, immunotherapy has become an integral treatment modality for advanced solid tumors. The feasibility of immunotherapy has shown promise for patients with PCa, and with advances in molecular diagnostic platforms and our understanding of immune mechanisms, immunotherapy is reemerging as a potential treatment modality for PCa. Various combinations of individualized immunotherapy and immune checkpoint blockers with androgen receptor-targeted therapies and conventional cytotoxic agents show promise. This article will review the current status of immunotherapy, including new discoveries and precision approaches to PCa, and discuss future directions in the continuously evolving landscape of immunotherapy.</p></abstract><kwd-group><kwd>biomarkers</kwd><kwd>clinical trials</kwd><kwd>immune checkpoint inhibitor</kwd><kwd>immunotherapy</kwd><kwd>prostatic neoplasm</kwd></kwd-group></article-meta></front><body><sec sec-type=\"intro\" id=\"sec1-ijms-21-05484\"><title>1. Introduction</title><p>Prostate cancer (PCa) is the most commonly diagnosed malignancy among men and the second most common cause of cancer-associated death in industrialized nations [<xref rid=\"B1-ijms-21-05484\" ref-type=\"bibr\">1</xref>]. The standard primary treatments for localized PCa are radical prostatectomy or radiation therapy (RT) with or without androgen-deprivation therapy (ADT), while the primary treatment modality for PCa patients in the advanced setting is ADT. Despite initial therapeutic responses with ADT, the majority of patients are destined to progress to metastatic castration-resistant PCa (mCRPC) [<xref rid=\"B2-ijms-21-05484\" ref-type=\"bibr\">2</xref>]. Therefore, novel therapeutic approaches with durable response rates for advanced disease are warranted.</p><p>Current agents approved by the US Food and Drug Administration (FDA) that have shown efficacy in terms of overall survival (OS) in patients with mCRPC include docetaxel, cabazitaxel, radium-223, sipuleucel-T, and androgen receptor axis-targeted agents including abiraterone and enzalutamide [<xref rid=\"B3-ijms-21-05484\" ref-type=\"bibr\">3</xref>,<xref rid=\"B4-ijms-21-05484\" ref-type=\"bibr\">4</xref>,<xref rid=\"B5-ijms-21-05484\" ref-type=\"bibr\">5</xref>,<xref rid=\"B6-ijms-21-05484\" ref-type=\"bibr\">6</xref>,<xref rid=\"B7-ijms-21-05484\" ref-type=\"bibr\">7</xref>,<xref rid=\"B8-ijms-21-05484\" ref-type=\"bibr\">8</xref>]. Since the approval of sipuleucel-T in 2010 [<xref rid=\"B4-ijms-21-05484\" ref-type=\"bibr\">4</xref>], there have been significant advancements and innovations in the field of immunotherapy. However, with the exception of sipuleucel-T, no other immunotherapeutic treatment has been approved for the treatment of mCRPC, owing to limited response rates and modest clinical efficacy. Nevertheless, with the introduction of next-generation genomic diagnostic platforms and advancements in our understanding of the molecular pathophysiology, we are now observing a considerable shift in the paradigm of immunotherapy for the treatment of PCa.</p><p>This review will highlight the re-emergence of immunotherapeutic approaches in the treatment of PCa, mainly focusing on immune checkpoint inhibitors (ICIs), which have shown the potential to revolutionize the treatment of both localized and advanced PCa.</p></sec><sec id=\"sec2-ijms-21-05484\"><title>2. Pathophysiology of Prostate Cancer</title><p>The pathogenesis of PCa is a slow, ongoing progression that involves small developing cancers that gradually evolve into different clonal entities with various clinical outcomes [<xref rid=\"B9-ijms-21-05484\" ref-type=\"bibr\">9</xref>,<xref rid=\"B10-ijms-21-05484\" ref-type=\"bibr\">10</xref>,<xref rid=\"B11-ijms-21-05484\" ref-type=\"bibr\">11</xref>]. Previous studies have shown that chronic inflammation is frequent in the prostates of elderly men and that this change is associated with an increased risk of PCa development [<xref rid=\"B12-ijms-21-05484\" ref-type=\"bibr\">12</xref>,<xref rid=\"B13-ijms-21-05484\" ref-type=\"bibr\">13</xref>]. The underlying mechanisms of chronic prostate inflammation and its clinical relevance to the development of PCa are unclear. Nevertheless, clinical evidence shows that chronic inflammation is a potential risk factor for disease progression and poor clinical outcomes [<xref rid=\"B14-ijms-21-05484\" ref-type=\"bibr\">14</xref>,<xref rid=\"B15-ijms-21-05484\" ref-type=\"bibr\">15</xref>,<xref rid=\"B16-ijms-21-05484\" ref-type=\"bibr\">16</xref>,<xref rid=\"B17-ijms-21-05484\" ref-type=\"bibr\">17</xref>].</p><p>Cellular pathways that modulate the proliferation, migration, and survival of cancer cells are upregulated through cytokines and signaling molecules, including tumor necrosis factor-alpha (TNF-α), transforming growth factor-beta (TGF-β), C-C motif chemokine ligand 2 (CCL-2), and interleukins (IL-2, IL-6, IL-8, and IL-10) [<xref rid=\"B17-ijms-21-05484\" ref-type=\"bibr\">17</xref>]. T cell activity and the associated autoimmune reaction induce epithelial and stromal cell proliferation. Infiltrating B cells are associated with increased antibody production, and recent studies have reported that specific subsets, namely regulatory B cells, may also play a significant role in tumor progression [<xref rid=\"B18-ijms-21-05484\" ref-type=\"bibr\">18</xref>,<xref rid=\"B19-ijms-21-05484\" ref-type=\"bibr\">19</xref>,<xref rid=\"B20-ijms-21-05484\" ref-type=\"bibr\">20</xref>]. Moreover, increased B cell infiltrates have been observed in PCa [<xref rid=\"B21-ijms-21-05484\" ref-type=\"bibr\">21</xref>]. Although T and B cells are the immune cell types that are predominantly observed in PCa, various ILs and inflammatory cell cytokines that dwell in the tumor stroma, such as granulocytes, macrophages, natural killer (NK) cells, myeloid-derived suppressor cells (MDSCs), and monocytes, additionally play an essential role in promoting the autocrine or paracrine proliferation of PCa cells [<xref rid=\"B22-ijms-21-05484\" ref-type=\"bibr\">22</xref>,<xref rid=\"B23-ijms-21-05484\" ref-type=\"bibr\">23</xref>,<xref rid=\"B24-ijms-21-05484\" ref-type=\"bibr\">24</xref>,<xref rid=\"B25-ijms-21-05484\" ref-type=\"bibr\">25</xref>].</p><p>Along with the aforementioned immune cells, androgen receptors (ARs) are a crucial factor in the tumorigenesis of PCa. The AR is a ligand-dependent transcription factor that is densely populated in thymic epithelial cells (TECs) and is essential for the development and function of male accessory sex organs [<xref rid=\"B26-ijms-21-05484\" ref-type=\"bibr\">26</xref>,<xref rid=\"B27-ijms-21-05484\" ref-type=\"bibr\">27</xref>]. ARs have a crucial role in the proliferation of PCa cells. The downregulation of gene expression required for regular TEC activity results in decreased thymocyte proliferation and an increase in apoptosis, which ultimately leads to thymic involution [<xref rid=\"B27-ijms-21-05484\" ref-type=\"bibr\">27</xref>]. AR signaling directly affects the activity of circulating T cells by increasing the transcription of the protein tyrosine phosphatase non-receptor type 1 (PTPN 1). This downregulates the Janus kinase/signal transducers and activators of transcription (JAK/STAT) signaling pathway and subsequently suppress T helper type 1 (Th1) cell differentiation [<xref rid=\"B28-ijms-21-05484\" ref-type=\"bibr\">28</xref>]. Moreover, androgen cessation can inversely affect the involution of the thymus by upregulating thymocyte proliferation and differentiation and can subsequently stimulate T cell infiltration [<xref rid=\"B29-ijms-21-05484\" ref-type=\"bibr\">29</xref>,<xref rid=\"B30-ijms-21-05484\" ref-type=\"bibr\">30</xref>]. Evidence from numerous studies has shown that PCa cells consist of a highly reactive stroma, which is featured by upregulated T cell infiltrates densely populated with regulatory T cells that have immunosuppressive tendencies [<xref rid=\"B31-ijms-21-05484\" ref-type=\"bibr\">31</xref>,<xref rid=\"B32-ijms-21-05484\" ref-type=\"bibr\">32</xref>].</p><p>As integral members of the innate immune system, myeloid cells can be stratified into two individual groups. The first group consists of mononuclear cells such as dendritic cells, macrophages, and monocytes, and the second consists of polymorphonuclear cells, which include basophils, eosinophils, mast cells, and neutrophils. Many studies have shown a tumor-promoting function of terminally differentiated myeloid-derived cells, as they can promote cancer cell proliferation and migration, tumor angiogenesis, and immune suppression [<xref rid=\"B33-ijms-21-05484\" ref-type=\"bibr\">33</xref>,<xref rid=\"B34-ijms-21-05484\" ref-type=\"bibr\">34</xref>,<xref rid=\"B35-ijms-21-05484\" ref-type=\"bibr\">35</xref>,<xref rid=\"B36-ijms-21-05484\" ref-type=\"bibr\">36</xref>,<xref rid=\"B37-ijms-21-05484\" ref-type=\"bibr\">37</xref>]. In the PCa microenvironment, the cessation of androgen induces CCL2 upregulation and activation of the STAT 3 pathway, which initiates the recruitment of macrophages and induces polarization of M2 macrophages [<xref rid=\"B38-ijms-21-05484\" ref-type=\"bibr\">38</xref>,<xref rid=\"B39-ijms-21-05484\" ref-type=\"bibr\">39</xref>,<xref rid=\"B40-ijms-21-05484\" ref-type=\"bibr\">40</xref>]. Activated M1 macrophages have the ability to promote a Th1 response and can exert an efficient antigen-presenting function with the capability of terminating tumors. However, by initiating the proliferation of epithelial and stromal cells, neovascularization, and suppression of the immune system, the anti-inflammatory characteristics of M2 macrophages stimulate the Th2 response and tissue remodeling, all of which creates favorable changes in the microenvironment for disease progression [<xref rid=\"B41-ijms-21-05484\" ref-type=\"bibr\">41</xref>].</p></sec><sec id=\"sec3-ijms-21-05484\"><title>3. Immunotherapy Resistance of Prostate Cancer</title><p>Various clinical trials of PCa immunotherapy have been targeted at metastatic disease, with specific advances being made for novel therapies for CRPC. Patients with metastatic PCa have a dysfunctional and compromised immune system [<xref rid=\"B42-ijms-21-05484\" ref-type=\"bibr\">42</xref>]. The immunogenicity of the cancer cells is the main determining factor in the immunotherapeutic approach to tumor eradication. Cancer cells are integrated into the self-immune system and, therefore, do not express foreign antigens. However, cancer cells exhibit tumor self-antigens, which can be immunogenic. Tumor neoantigens are the result of somatic mutations that are amassed in dividing cancer cells and are clinically correlated with the degree of mutation [<xref rid=\"B43-ijms-21-05484\" ref-type=\"bibr\">43</xref>]. The load of tumor mutation has been associated with clinical outcomes of immunotherapy; however, tumor immunogenicity is influenced by numerous components that are regulated by the cancer cells [<xref rid=\"B44-ijms-21-05484\" ref-type=\"bibr\">44</xref>,<xref rid=\"B45-ijms-21-05484\" ref-type=\"bibr\">45</xref>,<xref rid=\"B46-ijms-21-05484\" ref-type=\"bibr\">46</xref>,<xref rid=\"B47-ijms-21-05484\" ref-type=\"bibr\">47</xref>,<xref rid=\"B48-ijms-21-05484\" ref-type=\"bibr\">48</xref>,<xref rid=\"B49-ijms-21-05484\" ref-type=\"bibr\">49</xref>].</p><p>Patients with metastatic PCa are known to have disrupted cellular immunity as well as a tumor microenvironment with increased immunosuppressive qualities. The compromised immune system of patients with PCa is characterized by a reduction in NK cell activity and renewal and inhibited expression of CD3 in NK and T cells, which may ultimately result in the reduction of T cell receptors and NK cell-activating receptors [<xref rid=\"B50-ijms-21-05484\" ref-type=\"bibr\">50</xref>,<xref rid=\"B51-ijms-21-05484\" ref-type=\"bibr\">51</xref>,<xref rid=\"B52-ijms-21-05484\" ref-type=\"bibr\">52</xref>]. Moreover, patients with mCRPC also have a reduced number of total T cells [<xref rid=\"B53-ijms-21-05484\" ref-type=\"bibr\">53</xref>] and an increased number of myeloid suppressor cells and regulatory T cells in the tumor microenvironment and circulation [<xref rid=\"B54-ijms-21-05484\" ref-type=\"bibr\">54</xref>,<xref rid=\"B55-ijms-21-05484\" ref-type=\"bibr\">55</xref>,<xref rid=\"B56-ijms-21-05484\" ref-type=\"bibr\">56</xref>]. Another potential explanation for the resistance and tolerance to immunotherapy in PCa is the slow disease progression [<xref rid=\"B57-ijms-21-05484\" ref-type=\"bibr\">57</xref>,<xref rid=\"B58-ijms-21-05484\" ref-type=\"bibr\">58</xref>]. The low mutational tumor burden in patients with PCa may contribute to de novo immunotherapy resistance (<xref ref-type=\"fig\" rid=\"ijms-21-05484-f001\">Figure 1</xref>) [<xref rid=\"B59-ijms-21-05484\" ref-type=\"bibr\">59</xref>]. This view, however, is still under debate, as a recent genomic analysis indicated that patients with PCa had a higher tumor mutation burden than patients with renal cell carcinoma [<xref rid=\"B60-ijms-21-05484\" ref-type=\"bibr\">60</xref>].</p><p>Further research is warranted to elucidate the exact pathological mechanism underlying the resistance of patients with PCa to immunotherapy to pave the way for an effective immunotherapeutic regimen for metastatic PCa.</p></sec><sec id=\"sec4-ijms-21-05484\"><title>4. Immune Checkpoint Inhibitors for the Treatment of Prostate Cancer</title><p>The development of ICIs has been one of the most dynamic advances during the last decade. To date, sipuleucel-T, a cancer vaccine based on autologous antigen-presenting cell (APC) immunotherapy, is the only immunotherapeutic agent that is FDA-approved for the treatment of PCa [<xref rid=\"B4-ijms-21-05484\" ref-type=\"bibr\">4</xref>]. ICIs, which are antibodies inhibiting the immune checkpoint receptors, cytotoxic T lymphocyte-associated protein 4 (CTLA-4), and programmed death 1/programed death ligand 1 (PD-1/PD-L1), have shown potential therapeutic benefits by producing antitumor effects and long-term survival benefits in a broad spectrum of malignancies (<xref ref-type=\"fig\" rid=\"ijms-21-05484-f002\">Figure 2</xref>) [<xref rid=\"B61-ijms-21-05484\" ref-type=\"bibr\">61</xref>,<xref rid=\"B62-ijms-21-05484\" ref-type=\"bibr\">62</xref>,<xref rid=\"B63-ijms-21-05484\" ref-type=\"bibr\">63</xref>]. However, numerous clinical trials in patients with mCRPC utilizing CTLA-4 or PD-1/PD-L1 inhibitors have been unsatisfactory, with limited survival benefit when administered as a monotherapy in unselected patients [<xref rid=\"B64-ijms-21-05484\" ref-type=\"bibr\">64</xref>,<xref rid=\"B65-ijms-21-05484\" ref-type=\"bibr\">65</xref>,<xref rid=\"B66-ijms-21-05484\" ref-type=\"bibr\">66</xref>,<xref rid=\"B67-ijms-21-05484\" ref-type=\"bibr\">67</xref>,<xref rid=\"B68-ijms-21-05484\" ref-type=\"bibr\">68</xref>]. The sole exception is the FDA-approved usage of pembrolizumab for all malignancies with mismatch repair (MMR) deficiency or a microsatellite instability (MSI)-high status [<xref rid=\"B69-ijms-21-05484\" ref-type=\"bibr\">69</xref>,<xref rid=\"B70-ijms-21-05484\" ref-type=\"bibr\">70</xref>]. To overcome the therapeutic limitation, combination regimens consisting of ICIs with other modalities, or in a specific subset of patients who would most likely benefit, have resulted in better treatment efficacies in patients with a specific subtype of mCRPC.</p><sec id=\"sec4dot1-ijms-21-05484\"><title>4.1. ICI Monotherapy</title><p>Ipilimumab, a monoclonal antibody blocking the immune properties of CTLA-4, was the first effective ICI to show potential efficacy for melanoma [<xref rid=\"B71-ijms-21-05484\" ref-type=\"bibr\">71</xref>]. The therapeutic success of ipilimumab and its FDA-approval in 2011 triggered an interest in its use in PCa. An open-label phase I/II multicenter clinical trial (NCT00323882) investigated ipilimumab with or without RT in patients with mCRPC [<xref rid=\"B72-ijms-21-05484\" ref-type=\"bibr\">72</xref>]. After stratifying the patients into two individual dose escalation arms, the study endpoints included adverse events (AE), prostate-specific antigen (PSA) decline, and tumor response. A select number of patients in both study groups exhibited antitumor activity as measured by PSA response. Eight (16%) patients in the combination arm (10 mg/kg) showed a PSA decline >50%, while one (2.0%) patient showed a complete response (CR) lasting 11 months. Six (12.0%) patients had stable disease which lasted for 2.8 to 6.1 months [<xref rid=\"B72-ijms-21-05484\" ref-type=\"bibr\">72</xref>]. Encouraging clinical evidence of antitumor activity in previous studies incited further examination of ipilimumab in several phase III clinical trials. In the CA184-043 phase III trial (NCT00861614), patients with mCRPC who had at least one bone metastasis following taxane-based chemotherapy were allocated to either ipilimumab or placebo following palliative RT every three weeks [<xref rid=\"B67-ijms-21-05484\" ref-type=\"bibr\">67</xref>]. Although the study barely missed the primary endpoint in terms of OS between the ipilimumab group and the placebo group (HR 0.85, CI 0.72–1.00; <italic>p</italic> = 0.053), progression-free survival (PFS) was significantly superior in the ipilimumab group. Moreover, subset analysis revealed that ipilimumab was the most therapeutically efficacious in the subgroup of patients who exhibited favorable prognostic characteristics, including normal serum alkaline phosphatase levels, normal hemoglobin concentration, and no visceral metastases. The OS outcomes in this subgroup of patients were superior with ipilimumab compared to placebo [<xref rid=\"B67-ijms-21-05484\" ref-type=\"bibr\">67</xref>]. In a recent randomized, double-blind phase III trial (NCT01057810), ipilimumab monotherapy was compared to a placebo in patients with asymptomatic or minimally symptomatic chemotherapy-naïve mCRPC [<xref rid=\"B65-ijms-21-05484\" ref-type=\"bibr\">65</xref>]. The median OS was 28.7 months in the ipilimumab group and 29.7 months in the placebo group (<italic>p</italic> = 0.367). Median PFS was 5.6 months in the ipilimumab group and 3.8 months in the placebo group (HR = 0.67; 95.87% CI 0.55–0.81). This trial concluded that ipilimumab failed to prolong OS in patients with mCRPC. However, after observing a longer median PFS in the ipilimumab group compared to the placebo group (5.6 months vs. 3.8 months) and a decline in serum PSA values, the investigators concluded that these findings suggest the presence of antitumor activity in a specific subgroup of patients [<xref rid=\"B65-ijms-21-05484\" ref-type=\"bibr\">65</xref>]. Indeed, future studies are required to determine how to measure such antitumor activity, preferably with biomarkers, to identify the subgroup of patients who would most benefit from ipilimumab.</p><p>Several studies focused on PD-1/PD-L1 inhibition in patients with metastatic PCa. In a phase Ib trial of nivolumab (NCT00730639), 296 patients with non-small cell lung cancer, advanced melanoma, renal cancer carcinoma, or CRPC received nivolumab every two weeks [<xref rid=\"B68-ijms-21-05484\" ref-type=\"bibr\">68</xref>]. Favorable objective response rates (ORRs) were shown in patients with melanoma, non-small cell lung cancer, and renal cell carcinoma. However, there were no responses observed in 17 patients with CRPC. PCa samples collected from the patients yielded negative results for PD-L1 expression [<xref rid=\"B68-ijms-21-05484\" ref-type=\"bibr\">68</xref>]. Martin et al. focused on the deficiency of PD-L1 expression in PCa and emphasized another challenge in the ongoing battle of PCa treatment [<xref rid=\"B73-ijms-21-05484\" ref-type=\"bibr\">73</xref>]. Contrary to the phase I nivolumab trial, the non-randomized phase Ib KEYNOTE-028 (NCT02054806) clinical trial only included patients with PD-L1 expression in ≥1% of tumor or stroma cells [<xref rid=\"B66-ijms-21-05484\" ref-type=\"bibr\">66</xref>]. Patients received pembrolizumab 10mg/kg every two weeks for up to two years, with the primary endpoint being ORR. Preliminary results showed an ORR of 17.4%, with 34.8% of patients exhibiting stable disease. The median period of response was 13.5 months [<xref rid=\"B66-ijms-21-05484\" ref-type=\"bibr\">66</xref>]. Similar to various solid tumor types, more studies would be needed in order to elucidate the prognostic impact of PD-L1 expression as a biomarker of response to ICIs in patients with PCa.</p><p>In general, PCa has a relatively low expression level of PD-L1 [<xref rid=\"B73-ijms-21-05484\" ref-type=\"bibr\">73</xref>]. Bishop et al. investigated whether the targets of immunotherapy, namely PD-1, PD-L1/2, and CTLA-4, are overexpressed in patients with mCRPC who were resistant to enzalutamide. The results revealed that tumor resistance to enzalutamide is associated with an upregulation in the expression of PD-L1 [<xref rid=\"B74-ijms-21-05484\" ref-type=\"bibr\">74</xref>]. Evidence of antitumor activity was documented in a phase II study in which the anti-PD-1 antibody pembrolizumab was added to patients receiving enzalutamide treatment who had progressive disease [<xref rid=\"B75-ijms-21-05484\" ref-type=\"bibr\">75</xref>]. Three (30%) patients experienced a rapid decline in serum PSA level to below 0.2 ng/mL. Two patients with measurable disease at enrolment both showed a partial response. One of the partial responders was revealed to be MSI-positive, which validates the observations of previous reports of MSI as a prognosticator of response to anti-PD-1 antibodies [<xref rid=\"B76-ijms-21-05484\" ref-type=\"bibr\">76</xref>]. Although only one patient in the aforementioned trial of pembrolizumab had PCa, this ICI is now approved for the treatment of patients with MSI-positive tumors. <xref rid=\"ijms-21-05484-t001\" ref-type=\"table\">Table 1</xref> summarizes clinical trials evaluating the clinical efficacies of ICI monotherapies.</p></sec><sec id=\"sec4dot2-ijms-21-05484\"><title>4.2. Combination Immunotherapy Regimens</title><p>Several studies have investigated the efficacy of anti-PD-1 inhibitors in combination with various pharmacotherapeutic regimens for the treatment of advanced PCa. The two most promising combination immunotherapeutics for the treatment of mCRPC are the combined usage of two individual ICIs or one ICI with the augmentation of enzalutamide, a novel androgen receptor-axis-targeted agent approved for mCRPC. The phase II CHECKMATE-650 (NCT02985957) clinical trial administered a combination of the PD-1 inhibitor nivolumab with the anti-CTLA4 antibody ipilimumab as a second- or third-line therapy for patients with asymptomatic or minimally symptomatic mCRPC [<xref rid=\"B77-ijms-21-05484\" ref-type=\"bibr\">77</xref>]. In this clinical trial, the study population was stratified into two arms based on the previous administration of taxane-based chemotherapy. A total of 78 patients were enrolled, with a minimum observational period of six months. A 10% ORR was observed in the combined ICI arm, which included patients who previously received chemotherapy. However, a superior ORR of 26% was observed in patients who were chemotherapy-naïve [<xref rid=\"B77-ijms-21-05484\" ref-type=\"bibr\">77</xref>].</p><p>In the setting of PCa in which ICI monotherapy has shown limited success, the combination of nivolumab and ipilimumab showed promising clinical results. Further investigations regarding this combination would likely explore alternative dosages and administration intervals of both ICIs. Ongoing studies are determining whether this combination would be effective for a different subgroup of patients. In 2012, an open-labeled phase I dose-escalation trial (NCT01510288) evaluated ipilimumab in combination with the granulocyte-macrophage colony-stimulating factor-transduced cell-based allogeneic prostate cancer vaccine (GVAX) [<xref rid=\"B78-ijms-21-05484\" ref-type=\"bibr\">78</xref>]. Patients with chemotherapy-naïve mCRPC enrolled in the study showed promising antitumor activity, with 25% of the patients exhibiting a PSA decline of greater than 50% and overall good tolerance to the agent [<xref rid=\"B78-ijms-21-05484\" ref-type=\"bibr\">78</xref>]. The prospective phase II STARVE-PC trial (NCT02601014) enrolled patients with metastatic PCa with androgen receptor splice variant 7 (AR-V7) positive phenotypes [<xref rid=\"B79-ijms-21-05484\" ref-type=\"bibr\">79</xref>]. Patients were administered nivolumab 3 mg/kg with ipilimumab 1 mg/kg every 3 weeks for four doses, and maintenance therapy was performed with nivolumab 3 mg/kg every 2 weeks. For the determination of DNA repair deficiency, targeted next-generation sequencing was utilized. The outcomes were generally more favorable in the patient population with AR-V7-positive PCa with DNA repair deficiency. The currently ongoing single-arm phase II NEPTUNES clinical trial (NCT03061539) is investigating the efficacy of ICI combination therapy in patients with higher tumor mutation burden (TMB) due to a defective DNA damage response (dDDR) or DNA mismatch repair gene mutations (dMMR) and in patients with high tumor-infiltrating lymphocytes [<xref rid=\"B80-ijms-21-05484\" ref-type=\"bibr\">80</xref>].</p><p>Improvements in our molecular biology knowledge have shown that the AR signaling pathway is a critical element in CRPC progression and is, therefore, a rational target for the treatment of advanced disease and CRPC. Novel combination regimens involving PD-1 inhibitors with enzalutamide have shown promising results in this clinical field [<xref rid=\"B81-ijms-21-05484\" ref-type=\"bibr\">81</xref>]. KEYNOTE-365 (NCT02861573) was a phase Ib/II umbrella study that tested various combination therapies in patients with CRPC [<xref rid=\"B82-ijms-21-05484\" ref-type=\"bibr\">82</xref>]. The study cohort consisted of 69 patients with mCRPC who have progressed on chemo-naïve abiraterone and were treated with pembrolizumab and enzalutamide. Primary endpoints were safety and PSA response rate. The study results revealed an ORR of 20% and a PSA response rate of 33% [<xref rid=\"B82-ijms-21-05484\" ref-type=\"bibr\">82</xref>]. A phase II trial (NCT02312557) enrolled 58 patients with chemotherapy-naïve mCRPC whose disease had progressed during enzalutamide and received four doses of 200mg IV pembrolizumab every three weeks while still on enzalutamide [<xref rid=\"B75-ijms-21-05484\" ref-type=\"bibr\">75</xref>]. The primary endpoint was a PSA response greater than 50% (PSA<sub>50</sub>). Five of 28 (18%) patients achieved PSA<sub>50</sub>, while three of 12 patients (25%) with measurable disease at baseline had objective responses documented by radiographic imaging [<xref rid=\"B75-ijms-21-05484\" ref-type=\"bibr\">75</xref>]. With promising results in the aforementioned clinical trials, KEYNOTE-641 (NCT03834493), a randomized double-blind phase III trial, has started accrual of patients with mCRPC as of July 2019 to evaluate the efficacy and therapeutic safety of the pembrolizumab and enzalutamide combination vs. enzalutamide plus placebo [<xref rid=\"B83-ijms-21-05484\" ref-type=\"bibr\">83</xref>]. <xref rid=\"ijms-21-05484-t002\" ref-type=\"table\">Table 2</xref> summarizes clinical trials evaluating the clinical efficacies of combination immunotherapies.</p></sec><sec id=\"sec4dot3-ijms-21-05484\"><title>4.3. The Utilization of Genomic Selection for Checkpoint Inhibitor Immunotherapy</title><p>Another strategy for the use of ICIs in the treatment of PCa focuses on specifically tailored patient selection. This approach of patient selection first started with colon cancer patients, as these patients also exhibit limited treatment response to ICI monotherapy [<xref rid=\"B76-ijms-21-05484\" ref-type=\"bibr\">76</xref>]. Investigators noted that patients with dMMR variants of colon cancer have more somatic mutations compared to those with MMR-proficient tumors and higher immune infiltration [<xref rid=\"B76-ijms-21-05484\" ref-type=\"bibr\">76</xref>]. A breakthrough study demonstrated the clinical importance of MMR gene mutations and analyzed clinical response to pembrolizumab monotherapy in cancers with and without dMMR variants. Patients with dMMR cancers exhibited an ORR of 40% and a 12-month PFS rate of 78%, while those with MMR-proficient colorectal cancer showed no objective responses and a 12-month PFS rate of 11% [<xref rid=\"B84-ijms-21-05484\" ref-type=\"bibr\">84</xref>]. Based on this finding, pembrolizumab was FDA-approved in 2017 for the treatment of unresectable or metastatic solid organ malignancies of any histologic origin exhibiting dMMR or MSI-high expression [<xref rid=\"B85-ijms-21-05484\" ref-type=\"bibr\">85</xref>]. This FDA-approval of pembrolizumab was the first cancer treatment to be approved based on a biomarker instead of the primary site of origin [<xref rid=\"B85-ijms-21-05484\" ref-type=\"bibr\">85</xref>].</p><p>According to a clinical genomic analysis reported by Robinson et al., 3% to 5% of advanced PCa expresses MMR gene mutations, which supports the notion that this disease spectrum could be susceptible to pembrolizumab [<xref rid=\"B11-ijms-21-05484\" ref-type=\"bibr\">11</xref>]. To date, retrospective studies have analyzed the therapeutic efficacy of PD-1 inhibitors in patients with mCRPC with dMMR mutations. Antonarakis et al. utilized the Johns Hopkins somatic genomic database for pathogenic loss of function MMR mutations in 13 patients with a deleterious mutation in the MMR gene. Results showed that six (46%) patients had <italic>MSH6</italic> mutations, while two of four (50%) patients who had received PD-1 inhibitors showed a clinically meaningful PSA<sub>50</sub> [<xref rid=\"B70-ijms-21-05484\" ref-type=\"bibr\">70</xref>]. Interestingly, this subset of patients was also observed to be sensitive to standard ADT, exhibiting a ≥90% PSA response rate of 85% and a median PSA PFS of 55 months [<xref rid=\"B70-ijms-21-05484\" ref-type=\"bibr\">70</xref>]. In another study, 1551 tumors from 1346 patients with PCa were reviewed. Among 1033 patients who were eligible for the study, 32 (3.1%) patients with mCRPC exhibited MSI-high or dMMR tumors. Among these patients, 11 (34.4%) had been treated with a single-agent anti-PD-1 or PD-L1 treatment. It was observed that six (54.5%) patients achieved PSA<sub>50</sub>, and four (36.4%) patients showed a radiographic response [<xref rid=\"B69-ijms-21-05484\" ref-type=\"bibr\">69</xref>]. Although the MSI-high/dMMR phenotype is uncommon and the subset of this PCa population is small, the biomarker-specific approval of pembrolizumab and the clinical evidence of therapeutic efficacy for this specific subgroup of patients with PCa supports the prospect of tumor sequencing for MMR deficiency in all patients with mCRPC, as recommended in the 2019 version of the National Comprehensive Cancer Network Guidelines [<xref rid=\"B86-ijms-21-05484\" ref-type=\"bibr\">86</xref>].</p><p>Along with the MSI-high/dMMR genetic expression, clinical trials are ongoing to discover other molecular subtypes that may respond to ICIs. In the open-label phase II KEYNOTE-199 (NCT02787005) trial, the clinical efficacy of pembrolizumab for patients with chemotherapy-resistant mCRPC was assessed in three parallel cohorts [<xref rid=\"B64-ijms-21-05484\" ref-type=\"bibr\">64</xref>]. Pembrolizumab monotherapy showed a 12% ORR in patients with somatic <italic>ATM</italic> or <italic>BRCA1/2</italic> mutations, which was higher than the ORR of 4% for patients without the aforementioned mutations [<xref rid=\"B64-ijms-21-05484\" ref-type=\"bibr\">64</xref>]. A comparable trend was observed in the CHECKMATE-650 trial, in which a superior response rate was observed in patients with homologous repair-deficient mCRPC [<xref rid=\"B77-ijms-21-05484\" ref-type=\"bibr\">77</xref>]. The ImmunoProst (NCT03040791) study is a multicenter, single-arm, open-label phase II trial of nivolumab in patients with mCRPC who have progressed to a previous taxane-based chemotherapy regimen. The study will analyze germline and somatic DNA repair defects in that patient cohort [<xref rid=\"B87-ijms-21-05484\" ref-type=\"bibr\">87</xref>]. Another multicenter phase II trial (NCT03248570) is investigating the effects of pembrolizumab in patients with mCRPC stratified according to the presence of defects in DNA damage repair [<xref rid=\"B88-ijms-21-05484\" ref-type=\"bibr\">88</xref>].</p><p>There is accumulating clinical evidence that a subgroup of patients with CDK12 inactivation may benefit from immunotherapy. Due to numerous focal tandem duplications and increased gene fusion activity, along with an upregulation of neoantigens and T cell infiltration, solid malignancies with CDK12 biallelic mutations have been suggested to harbor a distinctive immune signature [<xref rid=\"B89-ijms-21-05484\" ref-type=\"bibr\">89</xref>]. This observation infers the existence of another potential genomic subtype of PCa that could be susceptible to ICIs [<xref rid=\"B90-ijms-21-05484\" ref-type=\"bibr\">90</xref>]. Various studies estimate that 4.7% of patients with mCRPC harbor this mutation, making PCa the tumor population with the greatest distribution of biallelic inactivation of CDK12 among all solid tumors [<xref rid=\"B89-ijms-21-05484\" ref-type=\"bibr\">89</xref>,<xref rid=\"B91-ijms-21-05484\" ref-type=\"bibr\">91</xref>,<xref rid=\"B92-ijms-21-05484\" ref-type=\"bibr\">92</xref>]. Wu et al. reported the efficacy of PD-1 inhibitor treatment in four patients with mCRPC harboring CDK12 mutations and showed that two (50%) patients showed antitumor activity in terms of PSA decline [<xref rid=\"B89-ijms-21-05484\" ref-type=\"bibr\">89</xref>]. A recently conducted multicenter trial in patients with advanced PCa with loss of function CDK12 mutations evaluated the efficacies of the poly (ADP-ribose) polymerase and PD-1 inhibitors. Three of eight (38%) patients with CDK12-mutated mCRPC had a significant PSA decline, with a median PFS of 6.6 months [<xref rid=\"B93-ijms-21-05484\" ref-type=\"bibr\">93</xref>]. The ongoing IMPACT (NCT03570619) trial is investigating whether this subtype of mCRPC is more susceptible to a combination of ICIs consisting of ipilimumab and nivolumab followed by nivolumab monotherapy [<xref rid=\"B94-ijms-21-05484\" ref-type=\"bibr\">94</xref>].</p><p>With advances in next-generation tumor DNA sequencing, new subsets of patients with PCa are being identified through precision medicine. POLE mutations, or mutations in the exonuclease domain of the proofreading DNA polymerase epsilon enzyme, have been observed in patients with PCa. These mutation phenotypes were observed despite an MSI-stable signature [<xref rid=\"B95-ijms-21-05484\" ref-type=\"bibr\">95</xref>]. Although POLE mutations are reported to affect only 0.1% of patients with mCRPC, this gene mutation may have potential therapeutic efficacy with anti-PD1 antibodies due to the vast quantity of predicted mutation-associated neoantigens [<xref rid=\"B96-ijms-21-05484\" ref-type=\"bibr\">96</xref>].</p><p>Despite recent advances and achievements of checkpoint inhibition and other modalities of immunotherapy, inconsistencies continue to occur between treatment and response across the different tumor spectra. PCa develops complex genotype mutations that significantly affect the outcome of immunotherapy [<xref rid=\"B97-ijms-21-05484\" ref-type=\"bibr\">97</xref>]. The classification and identification of patients who will successfully respond to immunotherapy is of paramount importance and is dependent on the future development of biomarkers. It is crucial to establish reliable biomarkers that can aid the initial decision to determine which individual group of patients will be more responsive to a specific mode of immunotherapy. Clinical trials focusing on genomic and DNA transcription data are ongoing to unravel immune signatures to aid the prediction of therapeutic efficacy and response [<xref rid=\"B97-ijms-21-05484\" ref-type=\"bibr\">97</xref>,<xref rid=\"B98-ijms-21-05484\" ref-type=\"bibr\">98</xref>,<xref rid=\"B99-ijms-21-05484\" ref-type=\"bibr\">99</xref>].</p></sec><sec id=\"sec4dot4-ijms-21-05484\"><title>4.4. Adoptive Cellular Therapy in the Treatment of Prostate Cancer</title><p>Adoptive cellular therapy (ACT) is an emerging treatment strategy in immunotherapy, in which the autologous T cells of the patient are extracted and genetically manipulated prior to reinfusion. Various approaches for ACT, such as engineered T cell receptors, tumor-infiltrating lymphocytes, and chimeric antigen receptors (CAR) T cells, have been developed for the treatment of PCa patients with metastases or in advanced disease settings [<xref rid=\"B100-ijms-21-05484\" ref-type=\"bibr\">100</xref>]. Of the aforementioned engineered T cells, CAR-T cells have shown the most therapeutic potential. These cells are genetically constructed to express a specific CAR gene ex vivo to target antigens utilizing an antibody derived single-chain variable fragment (scFv) [<xref rid=\"B101-ijms-21-05484\" ref-type=\"bibr\">101</xref>]. After infusion, CAR-T cells activate an inflammatory reaction that results in the cytolysis of the antigen-expressing tumor cell [<xref rid=\"B102-ijms-21-05484\" ref-type=\"bibr\">102</xref>].</p><p>In patients with PCa, CAR-T cells are used to target prostate-specific membrane antigen (PSMA), which is a type II transmembrane glycoprotein that is specifically upregulated in PCa cells [<xref rid=\"B103-ijms-21-05484\" ref-type=\"bibr\">103</xref>]. In a phase I clinical trial conducted by Junghans et al., “designer” CAR-T cells targeting PSMA were continuously infused with a low dose of IL-2 [<xref rid=\"B104-ijms-21-05484\" ref-type=\"bibr\">104</xref>]. Three of the five enrolled patients met the 20% engraftment target of CAR-T cells, and there were no anti PSMA toxicities, while two of the three engraftment successful patients exhibited a PSA50 response [<xref rid=\"B104-ijms-21-05484\" ref-type=\"bibr\">104</xref>]. In another phase I trial, the safety and dosage of modified autologous PSMA targeted T cells was analyzed. The study concluded that a dosage up of 3 × 107 cells/kg could be safely administered, with persistence of T cell activity lasting for up to 2 weeks [<xref rid=\"B105-ijms-21-05484\" ref-type=\"bibr\">105</xref>]. Interestingly, one patient experienced a long-term response over 16 months, which suggests clinical activity [<xref rid=\"B105-ijms-21-05484\" ref-type=\"bibr\">105</xref>].</p><p>Similar to other cellular-based immunotherapies, CAR-T cell therapy elicits limited therapeutic responses in the treatment of solid tumors, which includes PCa [<xref rid=\"B101-ijms-21-05484\" ref-type=\"bibr\">101</xref>]. The major obstacle in CAR-T cell therapy is the tumor microenvironment, which has immunosuppressive properties. To overcome this hindrance, a PSMA directed/TGF-β insensitive CAR-T cell is undergoing a phase I clinical trial (NCT03089203) [<xref rid=\"B106-ijms-21-05484\" ref-type=\"bibr\">106</xref>]. The distinctive TGF-β insensitive features of this cell product have the potential to show therapeutic efficacy in the immunosuppressive PCa microenvironment caused by high levels of TGF-β [<xref rid=\"B106-ijms-21-05484\" ref-type=\"bibr\">106</xref>]. With the occurrence of neoantigens with resistant clones, further clinical studies and research focusing on next-generation CAR-T cells with multiple scFv targeting multiple tumor-specific antigens are warranted.</p></sec></sec><sec sec-type=\"conclusions\" id=\"sec5-ijms-21-05484\"><title>5. Conclusions</title><p>Considerable progress has been made in the immunotherapeutic approach to the treatment of PCa. However, further research is still warranted to fully understand the immunological mechanisms involved in the PCa microenvironment. For now, the treatment benefits of ICIs show promise and potential. Along with standard monotherapeutic strategies utilizing novel ICIs, the efficacy of the combined usage of two individual ICIs and the administration of one ICI with standard therapies still needs to be elucidated. In the present clinical setting, these regimens will likely be used in some capacity and show therapeutic benefit in a select group of patients with PCa. In the near future, there will be other classes of immunotherapeutic agents, which may become an appropriate treatment for patients with mCRPC. For now, the classification of patients and administration modalities of novel immunotherapeutic agents remain unsolved. Moreover, studies are warranted to investigate how the modification in the sequencing of prior agents could affect the efficacy of the novel agents.</p></sec></body><back><notes><title>Author Contributions</title><p>Conceptualization, T.J.K. and K.C.K.; writing—original draft preparation, T.J.K.; writing—review and editing, K.C.K.; supervision and project administration, K.C.K. All authors have read and agreed to the published version of the manuscript.</p></notes><notes><title>Funding</title><p>This study was supported by a research grant from the National Research Foundation of Korea (2020R1F1A1073833) and a research grant from Gangnam Severance Hospital Prostate Cancer Center Research Committee (7523110). 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Clin. Oncol.</source><year>2019</year><volume>37</volume><fpage>TPS347</fpage><pub-id pub-id-type=\"doi\">10.1200/JCO.2019.37.7_suppl.TPS347</pub-id></element-citation></ref></ref-list></back><floats-group><fig id=\"ijms-21-05484-f001\" orientation=\"portrait\" position=\"float\"><label>Figure 1</label><caption><p>Factors contributing to resistance to immunotherapy. Several potential tumor-related, host-related, and environmental factors may elicit resistance to immunotherapies.</p></caption><graphic xlink:href=\"ijms-21-05484-g001\"/></fig><fig id=\"ijms-21-05484-f002\" orientation=\"portrait\" position=\"float\"><label>Figure 2</label><caption><p>Molecular mechanisms of CTLA4 and PD-1 attenuation of T cell activation. Schematic diagram of the molecular interactions and downstream signaling induced by ligation of CTLA4 and PD-1 by their respective ligands. APC: antigen-presenting cell, CAR: chimeric antigen receptor, CTLA-4: cytotoxic T lymphocyte-associated protein 4, PD-1: programmed death 1, PL-L1: programed death ligand 1, TCR: T cell receptor.</p></caption><graphic xlink:href=\"ijms-21-05484-g002\"/></fig><table-wrap id=\"ijms-21-05484-t001\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijms-21-05484-t001_Table 1</object-id><label>Table 1</label><caption><p>Clinical trials evaluating immune checkpoint inhibitor monotherapy for prostate cancer.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Agent</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Mechanism</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Clinical Phase</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Identifier</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Indication</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Primary Endpoints</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Ipilimumab</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Immunotherapy + radiotherapy</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">I/II</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">NCT00323882 [<xref rid=\"B72-ijms-21-05484\" ref-type=\"bibr\">72</xref>]</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Metastatic CRPC</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">AE, PSA response, and tumor response</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Ipilimumab</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Immunotherapy</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">III</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">NCT00861614 (CA184-043) [<xref rid=\"B67-ijms-21-05484\" ref-type=\"bibr\">67</xref>]</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Metastatic CRPC, post-docetaxel</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">OS</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Ipilimumab</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Immunotherapy</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">III</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">NCT01057810 [<xref rid=\"B65-ijms-21-05484\" ref-type=\"bibr\">65</xref>]</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Metastatic CPRC, chemotherapy-naïve</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">OS</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Nivolumab</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Immunotherapy</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Ib</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">NCT00730639 (MDX-1106) [<xref rid=\"B68-ijms-21-05484\" ref-type=\"bibr\">68</xref>]</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">CRPC</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Safety, antitumor activity, and pharmacokinetic properties</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Pembrolizumab</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Immunotherapy</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Ib</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">NCT02054806 (KEYNOTE-028) [<xref rid=\"B66-ijms-21-05484\" ref-type=\"bibr\">66</xref>]</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Advanced prostate cancer with PD-L1 expression ≥ 1% of tumor or stromal cells</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">ORR</td></tr></tbody></table><table-wrap-foot><fn><p>AE: adverse event; CRPC: castration-resistant prostate cancer; ORR: objective response rate; OS: overall survival; PD-L1: programmed death-ligand 1; PSA: prostate-specific antigen.</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"ijms-21-05484-t002\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijms-21-05484-t002_Table 2</object-id><label>Table 2</label><caption><p>Clinical trials evaluating combination immunotherapies for prostate cancer.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Combination Agents</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Mechanism</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Clinical Phase</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Trial ID</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Indication</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Primary Endpoints</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Ipilimumab + nivolumab</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Dual checkpoint blockade</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">II</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">NCT02985957 (CHECKMATE-650) [<xref rid=\"B77-ijms-21-05484\" ref-type=\"bibr\">77</xref>]</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Metastatic CRPC</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">ORR and rPFS</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Ipilimumab + GVAX</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Vaccination + immunotherapy</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">I</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">NCT01510288 [<xref rid=\"B78-ijms-21-05484\" ref-type=\"bibr\">78</xref>]</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Metastatic CRPC</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">AE</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Ipilimumab + nivolumab</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Dual checkpoint blockade</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">II</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">NCT02601014 (STARVE-PC) [<xref rid=\"B79-ijms-21-05484\" ref-type=\"bibr\">79</xref>]</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Metastatic CRPC with detectable AR-V7 transcript</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">PSA response</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Ipilimumab + nivolumab</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Dual checkpoint blockade</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">II</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">NCT03061539 (NEPTUNES) [<xref rid=\"B80-ijms-21-05484\" ref-type=\"bibr\">80</xref>]</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Metastatic CRPC with TMB</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">CRR</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Pembrolizumab + enzalutamide</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Checkpoint blockade + ADT</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Ib/II</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">NCT02861573 (KEYNOTE-365) [<xref rid=\"B82-ijms-21-05484\" ref-type=\"bibr\">82</xref>]</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Metastatic CRPC</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">AE, PSA response, ORR</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Pembrolizumab + enzalutamide</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Checkpoint blockade + ADT</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">III</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">NCT03834493 (KEYNOTE-641) [<xref rid=\"B83-ijms-21-05484\" ref-type=\"bibr\">83</xref>]</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Metastatic CRPC</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">OS and rPFS</td></tr></tbody></table><table-wrap-foot><fn><p>ADT: androgen-deprivation therapy; AE: adverse event; CRPC: castration-resistant prostate cancer; CRR: composite response rate; ORR: objective response rate; OS: overall survival; PSA: prostate-specific antigen; rPFS: radiographic progression-free survival; TMB: tumor mutation burden.</p></fn></table-wrap-foot></table-wrap></floats-group></article>\n"
]
[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"case-report\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Int J Mol Sci</journal-id><journal-id journal-id-type=\"iso-abbrev\">Int J Mol Sci</journal-id><journal-id journal-id-type=\"publisher-id\">ijms</journal-id><journal-title-group><journal-title>International Journal of Molecular Sciences</journal-title></journal-title-group><issn pub-type=\"epub\">1422-0067</issn><publisher><publisher-name>MDPI</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32708014</article-id><article-id pub-id-type=\"pmc\">PMC7432106</article-id><article-id pub-id-type=\"doi\">10.3390/ijms21155200</article-id><article-id pub-id-type=\"publisher-id\">ijms-21-05200</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Case Report</subject></subj-group></article-categories><title-group><article-title>Diabetes Mellitus and Hypertension—A Case of Sugar and Salt?</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Sondermann</surname><given-names>Marcus</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05200\">1</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0002-3289-1050</contrib-id><name><surname>Holecki</surname><given-names>Michał</given-names></name><xref ref-type=\"aff\" rid=\"af2-ijms-21-05200\">2</xref></contrib><contrib contrib-type=\"author\"><name><surname>Kirsch</surname><given-names>Andrea Marita</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05200\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Bastian</surname><given-names>Manuela</given-names></name><xref ref-type=\"aff\" rid=\"af3-ijms-21-05200\">3</xref></contrib><contrib contrib-type=\"author\"><name><surname>Fischer</surname><given-names>Dagmar-Christiane</given-names></name><xref ref-type=\"aff\" rid=\"af4-ijms-21-05200\">4</xref></contrib><contrib contrib-type=\"author\"><name><surname>Willenberg</surname><given-names>Holger Sven</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05200\">1</xref><xref rid=\"c1-ijms-21-05200\" ref-type=\"corresp\">*</xref></contrib></contrib-group><aff id=\"af1-ijms-21-05200\"><label>1</label>Division of Endocrinology and Metabolism, Rostock University Medical Center, 18057 Rostock, Germany; <email>[email protected]</email> (M.S.); <email>[email protected]</email> (A.M.K.)</aff><aff id=\"af2-ijms-21-05200\"><label>2</label>Department of Internal, Autoimmune and Metabolic Diseases, Medical Faculty in Katowice, Medical University of Silesia, 40-055 Katowice, Poland; <email>[email protected]</email></aff><aff id=\"af3-ijms-21-05200\"><label>3</label>Institute for Clinical Chemistry and Laboratory Medicine, Rostock University Medical Center, 18057 Rostock, Germany; <email>[email protected]</email></aff><aff id=\"af4-ijms-21-05200\"><label>4</label>Department of Pediatrics, Rostock University Medical Centre, Rostock, 18057 Rostock, Germany; <email>[email protected]</email></aff><author-notes><corresp id=\"c1-ijms-21-05200\"><label>*</label>Correspondence: <email>[email protected]</email></corresp></author-notes><pub-date pub-type=\"epub\"><day>22</day><month>7</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><month>8</month><year>2020</year></pub-date><volume>21</volume><issue>15</issue><elocation-id>5200</elocation-id><history><date date-type=\"received\"><day>28</day><month>5</month><year>2020</year></date><date date-type=\"accepted\"><day>21</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>© 2020 by the authors.</copyright-statement><copyright-year>2020</copyright-year><license license-type=\"open-access\"><license-p>Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>).</license-p></license></permissions><abstract><p>The majority of patients with diabetes mellitus (DM) have hypertension (HTN). A specific mechanism for the development of HTN in DM has not been described. In the Zucker, Endothel, und Salz (sugar, endothelium, and salt) study (ZEuS), indices of glucose metabolism and of volume regulation are recorded. An analysis of these parameters shows that glucose concentrations interfere with plasma osmolality and that changes in glycemic control have a significant impact on fluid status and blood pressure. The results of this study are discussed against the background of the striking similarities between the regulation of sugar and salt blood concentrations, introducing the view that DM is probably a sodium-retention disorder that leads to a state of hypervolemia.</p></abstract><kwd-group><kwd>diabetes mellitus</kwd><kwd>hypertension</kwd><kwd>adrenal</kwd><kwd>glucose</kwd><kwd>sodium</kwd><kwd>osmolality</kwd><kwd>cortisol</kwd><kwd>aldosterone</kwd><kwd>SGLT2</kwd><kwd>water homeostasis</kwd></kwd-group></article-meta></front><body><sec sec-type=\"intro\" id=\"sec1-ijms-21-05200\"><title>1. Introduction</title><p>There are multiple conditions and risk factors leading to a chronic increase in blood glucose concentrations. As such, diabetes mellitus (DM) is a heterogeneous group of disorders [<xref rid=\"B1-ijms-21-05200\" ref-type=\"bibr\">1</xref>] and in more than 60% of patients with DM, hypertension (HTN) is present as well [<xref rid=\"B2-ijms-21-05200\" ref-type=\"bibr\">2</xref>]. DM is more prevalent in the elderly and comes with a typical pattern of clinical and laboratory features at the time of diagnosis [<xref rid=\"B3-ijms-21-05200\" ref-type=\"bibr\">3</xref>]. Notably, people above 55 years of age have a 90% chance of developing HTN, while patients with DM experience such a high prevalence of HTN approximately 10 years earlier [<xref rid=\"B4-ijms-21-05200\" ref-type=\"bibr\">4</xref>]. A specific mechanism for the development of HTN in DM has not been found and a number of factors have been addressed so far, including overactivity of the sympathetic nervous and the renin–angiotensin–aldosterone systems (RAAS), aberrant renal sodium handling, endothelial dysfunction, blood vessel damage, insulin resistance, chronic inflammation, and activation of adrenal steroid hormone secretion [<xref rid=\"B5-ijms-21-05200\" ref-type=\"bibr\">5</xref>,<xref rid=\"B6-ijms-21-05200\" ref-type=\"bibr\">6</xref>,<xref rid=\"B7-ijms-21-05200\" ref-type=\"bibr\">7</xref>,<xref rid=\"B8-ijms-21-05200\" ref-type=\"bibr\">8</xref>].</p><p>However, principal considerations on the regulation of glucose and salt plasma concentrations show striking similarities and suggest the view that DM is probably a sodium-retention disorder. As sketched in <xref ref-type=\"fig\" rid=\"ijms-21-05200-f001\">Figure 1</xref>, blood concentrations of glucose and sodium are also increased via hormonal mechanisms involving the adrenal gland and secretion of glucocorticoids and/or mineralocorticoids and bind to their respective receptors. Glucose and sodium bind water, raise the osmotic pressure, and stimulate release of vasopressin, which leads to thirst and reabsorption of water in the kidney. Hypervolemia develops, disguising the total body content of glucose and sodium, and promotes the development of hypertension for which vasopressin concentrations may become inadequately high.</p><p>In <xref ref-type=\"fig\" rid=\"ijms-21-05200-f001\">Figure 1</xref>, the thoughts for such an assumption are further developed in additional aspects and substantiated with results collected within the frame of the Zucker, Endothel, und Salz (sugar, endothelium, and salt) study (ZEuS).</p></sec><sec sec-type=\"results\" id=\"sec2-ijms-21-05200\"><title>2. Results</title><p>Within the frame of the ZEuS study, data on plasma osmolality in relation to the results of an oral 75 g glucose challenge (arm A, ZEuS/oral glucose tolerance test (oGTT)) or indices of glucose metabolism and blood pressure (arm B, ZEuS/DM) were obtained (<xref rid=\"ijms-21-05200-t001\" ref-type=\"table\">Table 1</xref>). All hypertensive patients were on antihypertensive treatment, including beta-blockade (43.3%), anti-angiotensin agents (38.3%), diuretics (30.5%), or calcium channel blockers (20.3%).</p><p>Out of 73 individuals undergoing an oral 75 g glucose challenge test, 19 (26%) were diagnosed with impaired glucose tolerance and four (5.5%) with DM. Interestingly, insulin and homeostasis model assessment-insulin resistance (HOMA-IR) values were normally distributed (15.9 ± 11.2 µU/mL and 3.7 ± 2.5, respectively), and correlated well with systolic blood pressure (Person-<italic>r</italic> = 0.753/<italic>p</italic> < 0.05 and 0.816/<italic>p</italic> < 0.01, respectively) and with plasma osmolality (Person-<italic>r</italic> = 0.719/<italic>p</italic> < 0.01).</p><p>Notably, 20 (27%) had plasma osmolality values higher than 300 mosmol/kg, whereby half of them were in the group of individuals with elevated glucose concentrations after the oral glucose challenge (<italic>p</italic> < 0.05 in the Chi-square test). There was also a relevant correlation between plasma osmolality and serum copeptin concentrations (Spearman-<italic>r</italic> = 0.321, <italic>p</italic> < 0.05) with a steep increase in copeptin concentrations for plasma osmolality values of more than 300 mosmol/kg (<xref ref-type=\"fig\" rid=\"ijms-21-05200-f002\">Figure 2</xref>, Panel a).</p><p>Of the 24 patients with DM—all on intensified insulin treatment, including three with an insulin pump—more than 78% had type 2 and 21% had type 1 diabetes, whereby patients with type 1 diabetes were obese and regarded as having double diabetes. As expected, comorbidities were present in a number of patients and distributed as follows: peripheral polyneuropathy in 47.8%, nicotine in 39.1%, chronic kidney injury in 13.0%, glaucoma in 4.3%, and cardiovascular disease in 65%, with parental hypertension in 69.9%. Please see <xref rid=\"ijms-21-05200-t001\" ref-type=\"table\">Table 1</xref> for further details.</p><p>In these individuals, plasma osmolality showed a significant correlation with systolic (Spearman-<italic>r</italic> = 0.402, <italic>p</italic> = 0.05), but not with diastolic blood pressure. Systolic blood pressure did not change significantly during the study (SPB 128.6 ± 11.7 vs. 131.2 ± 19.5 mmHg), and diastolic blood pressure dropped slightly and scarcely failed significance (77.9 ± 8.0 vs. 71.2 ± 7.4 mmHg). In this group of patients with DM, education and changes in insulin treatment led to the expected drop in hemoglobin A1c (HbA1c) (8.40 [7.65–9.05] % vs. 7.25 [6.40–8.05] %; <italic>p</italic> < 0.001). In 10 of these patients, data on serum N-terminal brain natriuretic peptide (NT-proBNP) concentrations obtained prior and after improved glucose metabolism by the modified insulin therapy revealed a nonsignificant increase of NT-proBNP, in parallel to the intensification of insulin therapy (102 [59.2–260.2] vs. 123 [57.8–278.5] ng/mL). Concentrations of the NT-proBNP, however, were lower the more HbA1c dropped after intervention (<xref ref-type=\"fig\" rid=\"ijms-21-05200-f002\">Figure 2</xref>, Panels b–d). Changes in renin (17.0 [4.2–37.2] vs. 15.8 [8.1–85.4] pg/mL) and aldosterone concentrations (104 [81–231] vs. 149 [75–282] pmol/L) were in favor of relief from volume stress but did not, however, reach statistical significance (the distribution was too wide for the number of patients).</p></sec><sec sec-type=\"discussion\" id=\"sec3-ijms-21-05200\"><title>3. Discussion</title><p>The majority of patients with DM develop HTN. On one hand, HTN in patients with DM is thought to arise from common risk factors related to the development of DM, e.g., genetic disposition, overactivity of the sympathetic nervous system, chronic inflammation, and excess adrenal steroid hormone secretion, along with RAAS activation as a consequence of surplus adipose tissue and a long list of factors generated by these fat cells [<xref rid=\"B4-ijms-21-05200\" ref-type=\"bibr\">4</xref>,<xref rid=\"B5-ijms-21-05200\" ref-type=\"bibr\">5</xref>,<xref rid=\"B6-ijms-21-05200\" ref-type=\"bibr\">6</xref>,<xref rid=\"B7-ijms-21-05200\" ref-type=\"bibr\">7</xref>,<xref rid=\"B8-ijms-21-05200\" ref-type=\"bibr\">8</xref>,<xref rid=\"B10-ijms-21-05200\" ref-type=\"bibr\">10</xref>]. On the other hand, HTN is also discussed to be already a consequence of diabetes with renal dysfunction, RAAS activation and abnormal renal sodium handling, endothelial dysfunction, blood vessel damage, and chronic inflammation [<xref rid=\"B4-ijms-21-05200\" ref-type=\"bibr\">4</xref>,<xref rid=\"B5-ijms-21-05200\" ref-type=\"bibr\">5</xref>,<xref rid=\"B6-ijms-21-05200\" ref-type=\"bibr\">6</xref>,<xref rid=\"B9-ijms-21-05200\" ref-type=\"bibr\">9</xref>]. A certain degree of vagueness may be the reason that until now, DM has not been considered a cause of secondary HTN [<xref rid=\"B11-ijms-21-05200\" ref-type=\"bibr\">11</xref>,<xref rid=\"B12-ijms-21-05200\" ref-type=\"bibr\">12</xref>].</p><p>However, multiple studies have shown that a salt-mediated increase in osmolality triggers the release of arginine-vasopressin [<xref rid=\"B13-ijms-21-05200\" ref-type=\"bibr\">13</xref>,<xref rid=\"B14-ijms-21-05200\" ref-type=\"bibr\">14</xref>]. Our data on osmolality and copeptin reflect this relationship and the dose-response curve is similar to the one reviewed recently [<xref rid=\"B15-ijms-21-05200\" ref-type=\"bibr\">15</xref>]. Interestingly, it is known that acute effects of salt administration on blood pressure are explained by an increase in osmolality [<xref rid=\"B16-ijms-21-05200\" ref-type=\"bibr\">16</xref>]. Likewise, vasopressin-deficient Brattleboro rats do not develop hypertension when fed salt and sodium-retaining agents before active vasopressin is administered [<xref rid=\"B10-ijms-21-05200\" ref-type=\"bibr\">10</xref>,<xref rid=\"B17-ijms-21-05200\" ref-type=\"bibr\">17</xref>,<xref rid=\"B18-ijms-21-05200\" ref-type=\"bibr\">18</xref>,<xref rid=\"B19-ijms-21-05200\" ref-type=\"bibr\">19</xref>,<xref rid=\"B20-ijms-21-05200\" ref-type=\"bibr\">20</xref>]. Similar to the results of our study, the relation of osmolality and blood pressure is also a chronic one. From a physiological and historical point of view, sugar is as much a valuable crystal as is sodium chloride. While salt was once also called “white gold”, 80% of slaves in the Caribbean worked for sugar cane farmers [<xref rid=\"B21-ijms-21-05200\" ref-type=\"bibr\">21</xref>]. Both molecules are most likely tasty for evolutionary reasons and extensively used today by the food industry [<xref rid=\"B22-ijms-21-05200\" ref-type=\"bibr\">22</xref>]. They bind water, are up-regulated by adrenal gland hormones (sodium by mineralocorticoids, sugar by glucocorticoids), which are synthesized by different enzymes that evolved by gene duplication (aldosterone synthase, <italic>CYP11B2,</italic> and 11β-hydroxylase, CYP11B1), and they bind to closely related receptors (mineralocorticoid or glucocorticoid receptors, respectively) after inactivation into cortisone or reactivation into cortisol by 11β-hydroxysteroiddehydrogenase type II or type I action, respectively (<xref ref-type=\"fig\" rid=\"ijms-21-05200-f001\">Figure 1</xref>).</p><p>However, an important aspect is that sodium and glucose are reclaimed from the kidney filtrate through sodium/glucose cotransporters (SGLT). Thus, as a result of the renal threshold and continuous SGLT action, overfeeding with sugar and hyperglycemia would induce a perpetual recycling process of glucose leading to an increase in total body glucose and to sodium retention. As pointed out in more detail elsewhere, the retention of sodium and glucose results in a state of hypervolemia that develops through induction of thirst and the action of vasopressin, and is reflected by blood pressure, copeptin, and NT-proBNP levels [<xref rid=\"B10-ijms-21-05200\" ref-type=\"bibr\">10</xref>,<xref rid=\"B23-ijms-21-05200\" ref-type=\"bibr\">23</xref>]. Within this context, it is noteworthy that NT-proBNP concentrations indeed dropped in patients with steep decreases in HbA1c values. Diastolic blood pressure values also declined after the intervention in glucose metabolism, however, they just missed statistical significance. A state of hypervolemia may increase cardiac preload and cause shear stress in vessels, thus promoting chronic heart failure and conditions with inflammation in patients with diabetes or hyperaldosteronism. All in all, this would also explain the excellent cardiovascular outcome of patients in studies with SGLT-2 inhibitors [<xref rid=\"B24-ijms-21-05200\" ref-type=\"bibr\">24</xref>] and is not contradicted by the decrease in urinary sodium excretion in patients on long-term treatment with such agents, as the latter resembles a physiological aldosterone escape phenomenon [<xref rid=\"B10-ijms-21-05200\" ref-type=\"bibr\">10</xref>].</p><p>Problems in volume regulation may also come with the progression of chronic kidney disease (CKD). Thus, our results may change at different stages of disturbed glucose metabolism or in the presence of glucosuria, as well as CKD stages in combination with electrolyte imbalances and sensitivity to aldosterone. Notably, an octet of changes were found to underlie the development of type 2 diabetes mellitus, whereby the increase in glucose reabsorption is one feature only, and therefore, explains the development of hypertension only partially [<xref rid=\"B25-ijms-21-05200\" ref-type=\"bibr\">25</xref>,<xref rid=\"B26-ijms-21-05200\" ref-type=\"bibr\">26</xref>]. Our findings may also be altered by interference with volume changes in the treatment of CKD or provoked by hyperfiltration in different stages of hypertension. Notably, any therapeutic intervention into the renin-angiotensin-aldosterone system may have an effect that outbalances the salt-retention provoked by reabsorption of glucose and could change our findings. However, because of this, therapeutic interventions to inhibit the activity of the renin-angiotensin-aldosterone system and to lower salt and volume load may be especially effective in the therapy of CKD in individuals with diabetes. Targeting diabetic kidney disease will significantly reduce morbidity, and eventually, mortality. Notably, promising agents have been shown to interfere with inflammatory and hemodynamic pathways [<xref rid=\"B27-ijms-21-05200\" ref-type=\"bibr\">27</xref>]. As such, atrasentan and SGLT2 inhibitors decrease albuminuria as well as systolic and diastolic blood pressure indices [<xref rid=\"B27-ijms-21-05200\" ref-type=\"bibr\">27</xref>].</p><p>However, we noticed a nonsignificant slight elevation of NT-proBNP concentrations with intensified insulin treatment in a portion of our patients. This might explain the hitherto noted weight gain with insulin treatment, and at least in part, the fact that insulin treatment has a U-shaped curve regarding survival [<xref rid=\"B28-ijms-21-05200\" ref-type=\"bibr\">28</xref>]. On the basis of these findings and our observations, it might be an option to combine insulin therapy with oral glucose-lowering agents, such as SGLT-2 inhibitors, to improve the outcome.</p></sec><sec id=\"sec4-ijms-21-05200\"><title>4. Patients and Methods</title><p>In the ZEuS study, the relation of parameters reflecting the glucose metabolism were studied along with indices of salt, water, and volume homeostasis. The study was approved by the institutional review boards and local ethics committees and informed consent was obtained from all individual participants included in the study.</p><p>In arm A (ZEuS/oGTT), adult individuals undergoing an oral 75 g glucose load were investigated for anthropometric measures, glucose, insulin, osmolality, and copeptin concentrations at baseline, after 60 as well as 120 min. The main reason was to look for diabetes or other abnormalities in glucose metabolism as well as for the presence of insulin resistance in patients with weight gain or obesity. This program was established simultaneously at the Rostock University Medical Center in Germany and the Medical University of Silesia in Poland. Plasma samples scheduled for determination of copeptin were stored at −80 °C until analysis. The Kryptor system (B.R.A.H.M.S./ThermoFisher Scientific) was used for copeptin measurements and the Osmomat 030 freezing point osmometer (Gonotec, Berlin) for osmolality determinations throughout the study—they were all performed at the Institute for Clinical Chemistry and Laboratory Medicine, Rostock University Medical Center.</p><p>In arm B (ZEuS/DM), patients with DM were investigated for anthropometric measures, multiple parameters of glucose metabolism, including HbA1c (photometry), insulin, or C-peptide (both ECLIA Cobas e411, Roche, Mannheim, Germany), and indices of salt-water homeostasis, including renin (Liaison platform, DiaSorin, Saluggia, Italy), aldosterone (Chromogene LC-MS/MS assay), NT-proBNP (Cobas ECLIA), blood pressure, and volume status. In this study arm, patients were included who were in need of specific education and changes in their insulin treatment regiments, e.g., a switch from a conservative to a more intensified form of insulin injection therapy, or from multiple insulin injections to therapy with an insulin pump. Patients with polyuria did not qualify for this study. All in all, 24 adult patients who were capable of full self-management of their diabetes were included and studied before and re-evaluated after 3–6 months of change in therapy. It is important to mention that the medication not changing during the ZEuS/DM study was an exclusion criterion for follow-up in arm B. Furthermore, medically-related forms of diabetes were not included. The aim was to ensure a lifestyle of rather high caloric intake, which was evaluated beforehand.</p><p>Methodological limitations are to be seen in the number of patients in the follow-up study, which was caused in part by the exclusion of patients who experienced a change in the blood pressure modifying medication—irrespective of the fact that negative assertions were made on larger case numbers. We also studied indices of glucose and sodium concentrations and not actually total body glucose, total body sodium, or intravascular volume. Apart from these issues, our conclusions therefore maintain a certain degree of interpretation of our results, rather than direct evidence. Furthermore, the osmotic properties of glucose cannot be discerned from the osmotic properties of salt along with the regulatory consequences, when both systems are interrelated as described above. Nevertheless, this close connection should be kept in mind when reading other study results. Since the number of diabetic probands was rather manageable and since we sought to include patients with type 2 DM and/or obesity, we could not differentiate between diabetes types. Therefore, it may well be that our findings cannot be translated to individuals with other forms of DM.</p></sec><sec sec-type=\"conclusions\" id=\"sec5-ijms-21-05200\"><title>5. Conclusions</title><p>With the abovementioned limitations in mind, we conclude that the regulation of glucose concentrations by cortisol has many aspects in common with the regulation of sodium concentrations by aldosterone. Both sugar and salt regulate blood osmolality, which triggers thirst as well as copeptin/vasopressin release and which is related to blood pressure. Through reabsorption of sugar and salt in the kidney, elevations in plasma glucose concentrations promote sodium retention and elevations in NT-proBNP, most likely by fluid load. Thus, DM is probably a sodium retention disorder with all its negative consequences.</p></sec></body><back><notes><title>Author Contributions</title><p>Conceptualization, D.-C.F. and H.S.W.; methodology, M.S., M.H., M.B., D.-C.F., and H.S.W.; software, M.S., A.M.K., and H.S.W.; validation, M.S., A.M.K., M.B., D.-C.F., and H.S.W.; formal analysis, M.S., M.H., A.M.K., M.B., and H.S.W.; investigation, M.S., M.H., and H.S.W.; resources, M.H., D.-C.F. and H.S.W; data curation, M.S., A.M.K., M.B., D.-C.F., and H.S.W.; writing—original draft preparation, H.S.W.; writing—review and editing, all authors; visualization, M.S., A.M.K. and H.S.W.; supervision, M.H., D.-C.F. and H.S.W.; project administration, M.H., D.-C.F. and H.S.W.; funding acquisition, M.H., M.B., D.-C.F. and H.S.W. All authors have read and agreed to the published version of the manuscript.</p></notes><notes><title>Funding</title><p>This research received no external funding.</p></notes><notes notes-type=\"COI-statement\"><title>Conflicts of Interest</title><p>The authors declare no conflict of interest.</p></notes><glossary><title>Abbreviations</title><array orientation=\"portrait\"><tbody><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">MDPI</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Multidisciplinary Digital Publishing Institute</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">DOAJ</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Directory of open access journals</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">TLA</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Three letter acronym</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">LD</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">linear dichroism</td></tr></tbody></array></glossary><ref-list><title>References</title><ref id=\"B1-ijms-21-05200\"><label>1.</label><element-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><collab>American Diabetes Association</collab></person-group><article-title>2. 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Pharmacother.</source><year>2017</year><volume>51</volume><fpage>401</fpage><lpage>409</lpage><pub-id pub-id-type=\"doi\">10.1177/1060028017689878</pub-id><pub-id pub-id-type=\"pmid\">28133970</pub-id></element-citation></ref><ref id=\"B28-ijms-21-05200\"><label>28.</label><element-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Currie</surname><given-names>C.J.</given-names></name><name><surname>Peters</surname><given-names>J.R.</given-names></name><name><surname>Tynan</surname><given-names>A.</given-names></name><name><surname>Evans</surname><given-names>M.</given-names></name><name><surname>Heine</surname><given-names>R.J.</given-names></name><name><surname>Bracco</surname><given-names>O.L.</given-names></name><name><surname>Zagar</surname><given-names>T.</given-names></name><name><surname>Poole</surname><given-names>C.D.</given-names></name></person-group><article-title>Survival as a function of HbA(1c) in people with type 2 diabetes: A retrospective cohort study</article-title><source>Lancet</source><year>2010</year><volume>375</volume><fpage>481</fpage><lpage>489</lpage><pub-id pub-id-type=\"doi\">10.1016/S0140-6736(09)61969-3</pub-id><pub-id pub-id-type=\"pmid\">20110121</pub-id></element-citation></ref></ref-list></back><floats-group><fig id=\"ijms-21-05200-f001\" orientation=\"portrait\" position=\"float\"><label>Figure 1</label><caption><p>Parallelisms in the regulation of glucose and salt blood concentrations. This figure is a further development of the scheme in [<xref rid=\"B9-ijms-21-05200\" ref-type=\"bibr\">9</xref>]. Notably, both glucose and sodium are reclaimed in the kidney through the action of sodium-glucose cotransporters.</p></caption><graphic xlink:href=\"ijms-21-05200-g001\"/></fig><fig id=\"ijms-21-05200-f002\" orientation=\"portrait\" position=\"float\"><label>Figure 2</label><caption><p>The ZEuS/oGTT study (arm A) showed that there is a significant correlation between baseline osmolality and copeptin concentrations (<bold>a</bold>). Patient education and changes in insulin treatment regiments reduced HbA1c values significantly in the ZEuS/DM study (arm B) of 24 patients with diabetes mellitus (<bold>b</bold>). In 10 of these patients, serum NT-proBNP concentrations were available and shown to rather increase with intensification of insulin therapy but remained lower the more HbA1c values dropped (<bold>c</bold>), negative figures indicate a smaller difference between pre- and postinterventional values. Plasma osmolality correlated with systolic blood pressure values (<bold>d</bold>).</p></caption><graphic xlink:href=\"ijms-21-05200-g002\"/></fig><table-wrap id=\"ijms-21-05200-t001\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijms-21-05200-t001_Table 1</object-id><label>Table 1</label><caption><p>Baseline characterization of individuals in the two arms of the ZEuS study. While the portion of female patients was higher in arm A intensivation of insulin therapy was more frequently done in male patients (arm B). The patients in arm A were younger and suffered less frequently from hypertension. Their BMI, however, was higher.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin\" rowspan=\"1\" colspan=\"1\">Parameter</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin\" rowspan=\"1\" colspan=\"1\">Age</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin\" rowspan=\"1\" colspan=\"1\">Gender</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin\" rowspan=\"1\" colspan=\"1\">BMI</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin\" rowspan=\"1\" colspan=\"1\">SBP</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin\" rowspan=\"1\" colspan=\"1\">DBP</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin\" rowspan=\"1\" colspan=\"1\">Glucose</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin\" rowspan=\"1\" colspan=\"1\">HbA1c</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin\" rowspan=\"1\" colspan=\"1\">HTN</th></tr><tr><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">(unit)</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">(years)</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">(% female)</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">(kg/m<sup>2</sup>)</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">(mmHg)</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">(mmHg)</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">(mmol/L)</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">(%)</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">(%)</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">ZEuS/oGTT<break/>(arm A, <italic>n</italic> = 73)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">49.1 ± 18.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">71.6</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">32.2 ± 7.7</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">131.4 ± 13.6</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">78.8 ± 10.3</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">5.2 ± 0.6</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">5.5 ± 0.4</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">58.3</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">ZEuS/DM<break/>(arm B, <italic>n</italic> = 24)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">57.8 ± 16.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">20.8</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">29.8 ± 8.2</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">128.6 ± 11.7</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">77.9 ± 8.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">12.1 ± 4.3</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">9.2 ± 2.7</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">65.7</td></tr></tbody></table><table-wrap-foot><fn><p>Abbreviations: body mass index (BMI), diastolic blood pressure (DBP), diabetes mellitus (DM), hypertension (HTN), oral glucose tolerance test (oGTT), systolic blood pressure (SBP), Zucker, Endothel, und Salz (sugar, endothelium, and salt) study (ZEuS).</p></fn></table-wrap-foot></table-wrap></floats-group></article>\n"
]
[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Int J Environ Res Public Health</journal-id><journal-id journal-id-type=\"iso-abbrev\">Int J Environ Res Public Health</journal-id><journal-id journal-id-type=\"publisher-id\">ijerph</journal-id><journal-title-group><journal-title>International Journal of Environmental Research and Public Health</journal-title></journal-title-group><issn pub-type=\"ppub\">1661-7827</issn><issn pub-type=\"epub\">1660-4601</issn><publisher><publisher-name>MDPI</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32707799</article-id><article-id pub-id-type=\"pmc\">PMC7432107</article-id><article-id pub-id-type=\"doi\">10.3390/ijerph17155269</article-id><article-id pub-id-type=\"publisher-id\">ijerph-17-05269</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Article</subject></subj-group></article-categories><title-group><article-title>Mercury Exposure through Fish Consumption in Traditional Communities in the Brazilian Northern Amazon</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Hacon</surname><given-names>Sandra de Souza</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05269\">1</xref><xref rid=\"c1-ijerph-17-05269\" ref-type=\"corresp\">*</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0002-8004-9079</contrib-id><name><surname>Oliveira-da-Costa</surname><given-names>Marcelo</given-names></name><xref ref-type=\"aff\" rid=\"af2-ijerph-17-05269\">2</xref></contrib><contrib contrib-type=\"author\"><name><surname>Gama</surname><given-names>Cecile de Souza</given-names></name><xref ref-type=\"aff\" rid=\"af3-ijerph-17-05269\">3</xref></contrib><contrib contrib-type=\"author\"><name><surname>Ferreira</surname><given-names>Renata</given-names></name><xref ref-type=\"aff\" rid=\"af4-ijerph-17-05269\">4</xref></contrib><contrib contrib-type=\"author\"><name><surname>Basta</surname><given-names>Paulo Cesar</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05269\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Schramm</surname><given-names>Ana</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05269\">1</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0002-1876-782X</contrib-id><name><surname>Yokota</surname><given-names>Decio</given-names></name><xref ref-type=\"aff\" rid=\"af4-ijerph-17-05269\">4</xref></contrib></contrib-group><aff id=\"af1-ijerph-17-05269\"><label>1</label>Fundação Oswaldo Cruz, Escola Nacional de Saúde Pública Sérgio Arouca, Rio de Janeiro 21041-210, Brazil; <email>[email protected]</email> (P.C.B.); <email>[email protected]</email> (A.S.)</aff><aff id=\"af2-ijerph-17-05269\"><label>2</label>WWF-Brasil, Brasília 70377-540, Brazil; <email>[email protected]</email></aff><aff id=\"af3-ijerph-17-05269\"><label>3</label>Instituto de Pesquisas Científicas e Tecnológicas do Amapá, Av. Feliciano Coelho, 1509. Trem, Amapá 68901-025, Brazil; <email>[email protected]</email></aff><aff id=\"af4-ijerph-17-05269\"><label>4</label>Iepé-Instituto de Pesquisa e Formação Indígena, Macapá, Amapá 68908-120, Brazil; <email>[email protected]</email> (R.F.); <email>[email protected]</email> (D.Y.)</aff><author-notes><corresp id=\"c1-ijerph-17-05269\"><label>*</label>Correspondence: <email>[email protected]</email></corresp></author-notes><pub-date pub-type=\"epub\"><day>22</day><month>7</month><year>2020</year></pub-date><pub-date pub-type=\"ppub\"><month>8</month><year>2020</year></pub-date><volume>17</volume><issue>15</issue><elocation-id>5269</elocation-id><history><date date-type=\"received\"><day>29</day><month>6</month><year>2020</year></date><date date-type=\"accepted\"><day>16</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>© 2020 by the authors.</copyright-statement><copyright-year>2020</copyright-year><license license-type=\"open-access\"><license-p>Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>).</license-p></license></permissions><abstract><p>Artisanal small-scale gold mining (ASGM) is the main source of anthropogenic mercury emissions and contamination in Latin America. In the Brazilian northern Amazon, ASGM has contaminated the environment and people over the past century. The main contamination route is through fish consumption, which endangers the food security and livelihoods of traditional communities. Our study aims to assess the potential toxicological health risks caused by the consumption of Hg-contaminated fish across five regions in Amapá State. We sampled 428 fish from 18 sites across inland and coastal aquatic systems. We measured the total mercury content in fish samples, and the results were applied to a mercury exposure risk assessment targeting three distinct groups (adults, women of childbearing age, and children). Mercury contamination was found to exceed the World Health Organization’s safe limit in 28.7% of all fish samples, with higher prevalence in inland zones. Moreover, the local preference for carnivorous fish species presents a serious health risk, particularly for communities near inland rivers in the region. This is the first study to provide clear recommendations for reducing the mercury exposure through fish consumption in Amapá State. It builds scientific evidence that helps decision-makers to implement effective policies for protecting the health of riverine communities.</p></abstract><kwd-group><kwd>protected areas</kwd><kwd>traditional communities</kwd><kwd>mercury contamination</kwd><kwd>health risk assessment</kwd><kwd>Amazon</kwd></kwd-group></article-meta></front><body><sec sec-type=\"intro\" id=\"sec1-ijerph-17-05269\"><title>1. Introduction</title><p>Artisanal small-scale gold mining (ASGM) activities have significantly expanded in the Amazon over the last two decades. ASGM is a major cause of deforestation and habitat degradation in the Brazilian northern Amazon [<xref rid=\"B1-ijerph-17-05269\" ref-type=\"bibr\">1</xref>,<xref rid=\"B2-ijerph-17-05269\" ref-type=\"bibr\">2</xref>,<xref rid=\"B3-ijerph-17-05269\" ref-type=\"bibr\">3</xref>], particularly on the borders between Guyana, French Guiana, Suriname, and Venezuela, and the Brazilian states of Roraima, Amapá, and Pará (also known as the Guiana Shield Ecoregion) [<xref rid=\"B4-ijerph-17-05269\" ref-type=\"bibr\">4</xref>]. The ecoregion covers 250 million hectares and contains one of the largest complexes of uninterrupted primary tropical forests on Earth [<xref rid=\"B5-ijerph-17-05269\" ref-type=\"bibr\">5</xref>]; the region therefore plays a critical role in mitigating global climate change and preserving biodiversity and freshwater ecosystems.</p><p>The Brazilian state of Amapá borders French Guiana and Suriname, and almost 72% of its territory is formally protected. It consists of five indigenous territories, four quilombolas territories, and nineteen conservation units (twelve federal, five state, and two municipal), covering a total area of 115.656 km<sup>2</sup>. These areas are crucial for human livelihoods, especially for communities that rely on their natural resources. However, the protection of these areas has been hampered by weak governance, lack of financial resources for protective management, and different anthropogenic pressures. Among those threats, the widespread use of mercury (Hg) in ASGM has led to the contamination of both the environment and the regional population.</p><p>The ASGM sector is the leading source of mercury emissions, contamination, and consumption in Latin America and the Caribbean [<xref rid=\"B6-ijerph-17-05269\" ref-type=\"bibr\">6</xref>]; thus, the traditional communities that depend on natural resources for their survival are highly vulnerable to ASGM activities. This activity is generally coordinated by illegal networks and conducted by wildcat gold miners, and dealers [<xref rid=\"B7-ijerph-17-05269\" ref-type=\"bibr\">7</xref>]. ASGM has adversely affected the health of riverside communities—particularly indigenous people [<xref rid=\"B8-ijerph-17-05269\" ref-type=\"bibr\">8</xref>,<xref rid=\"B9-ijerph-17-05269\" ref-type=\"bibr\">9</xref>]—that rely on fish as their main protein source [<xref rid=\"B10-ijerph-17-05269\" ref-type=\"bibr\">10</xref>,<xref rid=\"B11-ijerph-17-05269\" ref-type=\"bibr\">11</xref>]. Fisheries in the Amazon provide food security and reduce malnutrition rates, as fish are the dominant dietary source of essential amino acids, lipids with fatty acids, minerals, and vitamins [<xref rid=\"B12-ijerph-17-05269\" ref-type=\"bibr\">12</xref>,<xref rid=\"B13-ijerph-17-05269\" ref-type=\"bibr\">13</xref>].</p><p>In ASGM-dominated regions, human exposure to Hg is mainly through bioaccumulation and biomagnification of methylmercury from fish consumption. In the Brazilian Amazon, indigenous people, riverside communities, fishermen, quilombolas, peasants, and extractivists inhabit the areas near rivers, bays, and streams and are therefore highly exposed to Hg compounds. This reality is aggravated by the social vulnerability of exposed communities, including reduced access to healthcare, formal education, regular income, basic sanitation, and potable water. In addition, these communities suffer from high rates of malnutrition, particularly in children under 5 years of age. They are also vulnerable to a high number of infectious and respiratory diseases (such as malaria, hantavirus pulmonary syndrome, leishmaniasis, yellow fever, etc.) that are exacerbated by both changes in land use and climate [<xref rid=\"B14-ijerph-17-05269\" ref-type=\"bibr\">14</xref>].</p><p>Therefore, mercury contamination in the Amazon is both a national and international concern. In this study, we aimed to develop a better understanding of the risks associated with fish consumption in various regions of the northern Amazon. Although a few studies have shown mercury contamination in fish in the region [<xref rid=\"B5-ijerph-17-05269\" ref-type=\"bibr\">5</xref>], this is the first to conduct an investigation on human Hg exposure in the study region. We assessed the potential toxicological health risks from mercury exposure through fish consumption for different age groups across five regions in Amapá State.</p></sec><sec sec-type=\"methods\" id=\"sec2-ijerph-17-05269\"><title>2. Methods</title><sec id=\"sec2dot1-ijerph-17-05269\"><title>2.1. Study Area</title><p>We assessed five regions in the Amapá State territory, including some of the most biodiverse and economically significant river basins in the region (<xref ref-type=\"fig\" rid=\"ijerph-17-05269-f001\">Figure 1</xref>). Watersheds in the state play an important social, cultural, and economic role, and fish resources in the region are heavily influenced by the Amazon River and Atlantic Ocean. We chose rivers that bordered protected areas (<xref rid=\"ijerph-17-05269-t001\" ref-type=\"table\">Table 1</xref>) due to the higher occurrence of mineral deposits [<xref rid=\"B15-ijerph-17-05269\" ref-type=\"bibr\">15</xref>] and higher number of ASGM sites surrounding Amapá’s protected areas [<xref rid=\"B16-ijerph-17-05269\" ref-type=\"bibr\">16</xref>]. Sampling sites were set up at potential ASGM sites (active and inactive). These sites were identified based on gold mining deforestation maps [<xref rid=\"B3-ijerph-17-05269\" ref-type=\"bibr\">3</xref>], literature reviews [<xref rid=\"B5-ijerph-17-05269\" ref-type=\"bibr\">5</xref>,<xref rid=\"B17-ijerph-17-05269\" ref-type=\"bibr\">17</xref>], the presence of local communities, the influence of coastal tides or inland waters, sampling logistics, the distance to protected areas, and interviews with managers of protected areas and locals with knowledge of the regional gold mining history. Sampling sites 1, 2, and 3 are directly influenced by coastal tides and are therefore considered coastal zones, while sites 4 and 5 are located in inland waters and are therefore referred to as inland zones.</p></sec><sec id=\"sec2dot2-ijerph-17-05269\"><title>2.2. Fish Sampling and Preparation</title><p>Between August 2017 and May 2018, we collected tissue samples from 428 fish from 45 different species at 18 sites across the five regions (<xref rid=\"ijerph-17-05269-t001\" ref-type=\"table\">Table 1</xref>). The fish were caught by local fishermen hired to support the research project. According to the local fishermen, each fish species was captured using specific gear; therefore, gillnets, hooks and hand lines, fishing rods and long lines were used. Each specimen was identified to the species level, and a minimum of 70 g of muscle tissue (free of skin) was extracted from the dorsal region of the fish body. All dissection tools were sterilized before sampling to avoid contamination. Samples were stored in ice boxes and frozen for transportation to the laboratory. The study was conducted in accordance with the Brazilian regulation (IN 03/2014), under the license SISBIO 58296-1. </p></sec><sec id=\"sec2dot3-ijerph-17-05269\"><title>2.3. Mercury Analysis</title><p>Total mercury (THg) was measured by cold vapor atomic fluorescence spectrometry at the Analytical Chemistry Laboratory of the Pontific University of Rio de Janeiro [<xref rid=\"B23-ijerph-17-05269\" ref-type=\"bibr\">23</xref>]. The analytical quality was determined by control strict blanks with duplicate analysis and compared to the analytical results of certified reference materials (DORM-2, Dogfish Muscle Certified Reference Material for trace elements, National Research Council, Ottawa, ON, Canada). Average recovery values were >96% of the certified value analytical results; and the relative standard deviation (RSD) was <15%. Total mercury analysis from the wet weight was adopted.</p></sec><sec id=\"sec2dot4-ijerph-17-05269\"><title>2.4. Data Analysis</title><p>The data are presented as mean ± standard error and percentiles. We applied the Kruskal–Wallis test to determine the mean differences in THg concentrations between sampling locations and trophic levels.</p><p>The risk assessment approach in this study was based on the method used by the US Government Agency for Toxic Substances and Diseases Registry [<xref rid=\"B24-ijerph-17-05269\" ref-type=\"bibr\">24</xref>]. The method includes four steps: (i) hazard identification, (ii) dose–response assessment, (iii) exposure assessment, and (iv) risk characterization. Most risk assessment studies use secondary data; however, our Hg exposure assessment used information on the most consumed fish species in the region [<xref rid=\"B5-ijerph-17-05269\" ref-type=\"bibr\">5</xref>] as well as informal interviews with local fishermen and individuals from indigenous and riverine communities.</p></sec><sec id=\"sec2dot5-ijerph-17-05269\"><title>2.5. Estimated Daily Intake</title><p>To assess the health risks of total mercury exposure, we incorporated two scenarios for toxicological risk: (i) the critical scenario and (ii) the current scenario. The critical scenario assumes that some of the local communities—including the indigenous communities—consume larger quantities of fish due to the lack of other protein sources. The current scenario represents the eating habits of most of the communities in the study area. The estimated exposure dose of fish consumers from both coastal and inland zones were divided into three groups: (i) adults of both genders (age: 17 to 75), (ii) women of childbearing age (age: 17 to 49), and (iii) children/juveniles (age: 5 to 16). We estimated their daily intake (EDI) to determine each group’s daily Hg consumption [<xref rid=\"B25-ijerph-17-05269\" ref-type=\"bibr\">25</xref>]. The average daily fish consumption was based on 16 studies (<xref rid=\"ijerph-17-05269-t002\" ref-type=\"table\">Table 2</xref>) that assessed the dietary patterns and fish consumption of Amazonian communities.</p><p>The potential exposure dose (µg/kg/d) was calculated according to Equation (1) [<xref rid=\"B26-ijerph-17-05269\" ref-type=\"bibr\">26</xref>]:<disp-formula id=\"FD1-ijerph-17-05269\"><label>(1)</label><mml:math id=\"mm1\"><mml:mrow><mml:mrow><mml:msub><mml:mi>D</mml:mi><mml:mrow><mml:mi>p</mml:mi><mml:mi>o</mml:mi><mml:mi>t</mml:mi></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:mi>C</mml:mi><mml:mo>×</mml:mo><mml:mfrac><mml:mrow><mml:mi>I</mml:mi><mml:mi>R</mml:mi><mml:mo>×</mml:mo><mml:mi>E</mml:mi><mml:mi>F</mml:mi><mml:mo>×</mml:mo><mml:mi>E</mml:mi><mml:mi>D</mml:mi></mml:mrow><mml:mrow><mml:mi>B</mml:mi><mml:mi>W</mml:mi></mml:mrow></mml:mfrac><mml:mo>×</mml:mo><mml:mfrac><mml:mn>1</mml:mn><mml:mrow><mml:mi>A</mml:mi><mml:mi>T</mml:mi></mml:mrow></mml:mfrac></mml:mrow></mml:mrow></mml:math></disp-formula>\nwhere <italic>D<sub>pot</sub></italic> is the potential mercury intake dose (µg/kg/day); <italic>C</italic> is the total mercury concentration of fish species (µg/g); <italic>IR</italic> is the fish intake rate (kg/meal); <italic>EF</italic> is the frequency of exposure (meals /day); <italic>ED</italic> is the exposure duration (day, week); <italic>AT</italic> corresponds to the time weighted (day) (time weighted considers the duration of exposure); and <italic>BW</italic> is the body weight (kg).</p><p>The risk ratio was calculated as the ratio between the potential intake dose and the reference dose using the risk quotient equation (Equation (2)):<disp-formula>QR = <italic>D<sub>pot</sub></italic>/RfD<label>(2)</label></disp-formula>\nwhere QR is the risk quotient; <italic>D<sub>pot</sub></italic> is the potential mercury intake dose (µg/kg/day); and RfD is the reference dose (1.6 μg/kg/week or 0.23 μg/kg/day [<xref rid=\"B25-ijerph-17-05269\" ref-type=\"bibr\">25</xref>]) of the substance or chemical element (Hg) that can be consumed weekly over a lifetime without appreciable risk to health. A QR of <1 indicates a low likelihood of adverse health effects, while QR > 1 indicates the possibility of adverse non-carcinogenic effects.</p></sec><sec id=\"sec2dot6-ijerph-17-05269\"><title>2.6. Model Input Variables</title><p>The description of the input variables for the mercury exposure model for both the critical and current scenarios are shown in <xref rid=\"ijerph-17-05269-t003\" ref-type=\"table\">Table 3</xref>. We assumed a 50% consumption of fish species in age groups with larger average Hg concentrations (carnivorous and omnivorous) as well as the groups with the lowest Hg concentration (herbivorous and detritivores). The literature review showed a high frequency of fish consumption and a high variability in the quantity of meals, ranging from 1 to 20 meals per week [<xref rid=\"B38-ijerph-17-05269\" ref-type=\"bibr\">38</xref>,<xref rid=\"B40-ijerph-17-05269\" ref-type=\"bibr\">40</xref>]. We therefore set an average of 10 meals of fish consumed per week, varying from 160 to 430 g/day, based on the characteristics of each group. We estimated the fish daily intake values based on the literature review (<xref rid=\"ijerph-17-05269-t002\" ref-type=\"table\">Table 2</xref>). The average body weight of the residents in the communities was obtained from research on family budgets that presented the anthropometry and nutritional status of children, adolescents, and adults in the state of Amapá [<xref rid=\"B19-ijerph-17-05269\" ref-type=\"bibr\">19</xref>].</p><p>To quantify exposure, we calculated the mean Hg concentrations of carnivorous and omnivorous fish as well as the 95% confidence interval (CI) of each mean. We also conducted a literature review to determine the frequency of fish meal consumption and estimated potential doses by assigning the 95% upper limit of the mean fish consumption. It was assumed 100% of THg concentration was methylmercury, since methyl Hg constitutes up to 98% of the Hg concentration in fish samples in the Amazon basin. The body weight was the same for both scenarios, and the factor absorption of total Hg as methyl Hg by exposed residents was assumed to be 100% for both scenarios. We used the lognormal function to estimate the exposure dose.</p></sec></sec><sec sec-type=\"results\" id=\"sec3-ijerph-17-05269\"><title>3. Results</title><sec id=\"sec3dot1-ijerph-17-05269\"><title>3.1. Mercury Levels in Fish Species and Trophic Levels</title><p>All of the 428 fish sampled in this study had detectable levels of mercury, and 28.7% exceeded the World Health Organization’s (WHO) mercury threshold (0.5 μg/g) for human consumption. The Hg concentration in fish exceeded the safe limit in 77.6% of carnivores, 20% of omnivorous, and 2.4% of herbivores. Carnivorous fish accounted for 76% (<italic>n</italic> = 325) of the total samples, with average Hg concentrations of 0.4 μg/g (SD = 0.38) and a concentration range of 0.008–2.1 μg/g. Interestingly, the Hg concentration in omnivorous fish averaged 0.60 μg/g (SD = 0.42) and reached a maximum of 1.8 μg/g. This could be explained by changes in fish feeding habits. For instance, fish from the omnivorous species <italic>Pimelodus ornatus</italic> can change their diet depending on the resources available in the environment. Two herbivorous species showed unexpectedly high Hg concentrations of 1.0 μg/g for <italic>Mylesinus paraschombourgkii</italic> (flaviano) and 0.85 μg/g for <italic>Myloplus ternetzi</italic> (pacu). We also observed significant differences in the average Hg concentrations between trophic levels (<italic>p</italic> < 0.001) (<xref ref-type=\"fig\" rid=\"ijerph-17-05269-f002\">Figure 2</xref>).</p><p>Four of the seven species with the highest Hg concentrations (<xref ref-type=\"fig\" rid=\"ijerph-17-05269-f003\">Figure 3</xref>) are among the most consumed species in the region [<xref rid=\"B5-ijerph-17-05269\" ref-type=\"bibr\">5</xref>]. The highest level (2.1 μg/g) was detected in <italic>Boulengerella cuvieri</italic> (pirapucu), followed by <italic>Cichla monoculus</italic> (tucunaré), and <italic>Hoplias aimara</italic> (traírão), which are all carnivorous species.</p></sec><sec id=\"sec3dot2-ijerph-17-05269\"><title>3.2. Mercury Levels at Sampling Sites (Inland and Coastal Zone)</title><p>The median Hg contamination of inland fish samples was 0.580 µg/g for carnivores (<italic>n</italic> = 131), 0.045 µg/g for herbivores (<italic>n</italic> = 15), and 0.680 µg/g for omnivores (<italic>n</italic> = 33); samples of detritivorous species were not collected. In coastal zones, the median Hg concentrations were 0.165 µg/g for carnivores (<italic>n</italic> = 194), 0.019 µg/g for herbivores (<italic>n</italic> = 5), 0.290 µg/g for omnivores (<italic>n</italic> = 15), and 0.044 µg/g for detritivores (<italic>n</italic> = 35). The median Hg levels of carnivorous and omnivorous species showed no statistical differences in both aquatic systems. Hg contamination exceeded the WHO limit in 28.7% of all samples, in 5.2% of coastal samples, and 61.5% of inland samples (<italic>p</italic> < 0.001) (<xref ref-type=\"fig\" rid=\"ijerph-17-05269-f004\">Figure 4</xref>). It is therefore 29 times more likely to observe Hg levels above the safe limit in fish samples from inland areas (95% CI, 15.3–54.6) relative to those in coastal zones. The regional variability of Hg levels in the study region is likely related to the different hydrological, physicochemical, and environmental dynamics between inland and coastal areas.</p><p>The highest Hg concentrations were detected in <italic>Serrasalmus rhombeus</italic> (piranha preta), <italic>Pimelodus ornatus</italic> (mandi casaca), and <italic>Hoplias aimara</italic> (traírão) in the inland water zone, and <italic>Sciades herzbergii</italic> (bagre guribu), <italic>Megalops atlanticus</italic> (pirapema), and <italic>Hoplias malabaricus</italic> (traíra) in the coastal zone.</p></sec><sec id=\"sec3dot3-ijerph-17-05269\"><title>3.3. Health Risk Assessment</title><p>Fish from the inland zone had higher levels of Hg compared to fish from the coastal zone (<xref ref-type=\"fig\" rid=\"ijerph-17-05269-f004\">Figure 4</xref>b). This is reflected by the potential dose and toxicological QR of total mercury for both exposure scenarios in coastal and inland zones (<xref rid=\"ijerph-17-05269-t004\" ref-type=\"table\">Table 4</xref>). The potential THg dose of the three groups was mostly above the acceptable reference dose of 1.6 μg/kg/week or 0.23 μg/kg/day [<xref rid=\"B25-ijerph-17-05269\" ref-type=\"bibr\">25</xref>]. In the critical scenario in both zones, the potential THg dose showed high risks of adverse health effects in all groups, regardless of location. The exception was the current scenario for women of childbearing age in the coastal zone (0.08 μg/kg/day). Children/juveniles in the inland zone showed significantly greater risks (<italic>p</italic> ≤ 0.005) under the critical scenario (55) relative to the current scenario (37.5). Our results highlight the elevated health risks associated with fish consumption in the study area.</p></sec></sec><sec sec-type=\"discussion\" id=\"sec4-ijerph-17-05269\"><title>4. Discussion</title><p>High levels of mercury were detected in different fish species and trophic levels in remote and legally protected areas of the northern Amazon; this suggests a high risk of Hg contamination in local fish consumers. Based on the risk assessment of fish Hg concentrations from 18 sites in Amapá State we recommend a low-risk fish diet for the three different groups. We found that the local preference for carnivorous fish species presents a significant health risk for local communities, particularly in inland water zones. As top predators, carnivorous fish bioaccumulate larger quantities of Hg throughout their lifecycle.</p><p>The mercury dynamics in aquatic ecosystems are complex and influenced by many factors, including soil type, river flow, fish trophic level, age of fish, season, pH, the productivity of aquatic ecosystems, characteristics of methylation sites, and others [<xref rid=\"B41-ijerph-17-05269\" ref-type=\"bibr\">41</xref>,<xref rid=\"B42-ijerph-17-05269\" ref-type=\"bibr\">42</xref>]. As most of the fish samples (81%) were caught during the dry season (with only one field campaign during the rainy season), our study may be limited by the lack of seasonal data; however, we observed no significant difference in Hg concentration in the fish sampled in the dry and rainy seasons (<italic>p</italic> ≥ 0.05). Another potential source of bias is the lack of detailed primary data on local eating habits, including dietary patterns and meal composition, particularly with regard to the fish intake dose. To minimize this potential bias, we incorporated a conservative approach by assuming a low daily fish intake in relation to the current exposure scenario and in response to the high frequency and quantity of weekly meals [<xref rid=\"B27-ijerph-17-05269\" ref-type=\"bibr\">27</xref>,<xref rid=\"B40-ijerph-17-05269\" ref-type=\"bibr\">40</xref>]; this is due to the high regional heterogeneity in the Amazon region, where fish consumption is higher in some communities due to the lack of alternative protein sources.</p><p>Furthermore, erosion can significantly influence THg levels in Amazonian aquatic ecosystems by mobilizing inherited Hg in soils. Consequently, efforts to reduce Hg exposure—particularly in populations downstream of ASGM sites—must also address soil erosion, with a particular focus on vulnerable rivers and streams in riparian forests [<xref rid=\"B43-ijerph-17-05269\" ref-type=\"bibr\">43</xref>]. In addition, changes in land cover/use leads to frequent forest fires, which releases large quantities of Hg to the atmosphere [<xref rid=\"B44-ijerph-17-05269\" ref-type=\"bibr\">44</xref>] and aquatic systems [<xref rid=\"B42-ijerph-17-05269\" ref-type=\"bibr\">42</xref>]. These issues require the political will to implement effective policies to reduce the drivers of deforestation in the Amazon.</p><p>Comparing the current and critical scenarios in the coastal zone, we observed no significant difference in the potential doses of children/juveniles based on the acceptable methylmercury dose (1.6 μg/kg/week or 0.23 μg/kg/day) [<xref rid=\"B25-ijerph-17-05269\" ref-type=\"bibr\">25</xref>]. In contrast, a significant difference was observed in the potential doses of children in the inland zone (<italic>p</italic> ≤ 0.005). For this group, we assumed children consume the same fish species as adults but in smaller quantities. Furthermore, children are at significant risk of Hg exposure because of their higher Hg doses and lower body weights (approximately half that of adults). The potential dose for the other two groups (adults and pregnant women) in both zones was double the WHO’s daily recommended dose at 0.5 μg/kg/day. The potential dose for women of childbearing age in the coastal zone was within the safe limit at 0.08 μg/kg/day. Nevertheless, under the critical scenario in the coastal zone, the toxicological risks for adults (men and women) was 26 and 20 times higher than the daily reference dose for adults and women of childbearing age, respectively. Our results suggest that both groups in the coastal zone that consume carnivorous fish five times per week on average have significantly higher health risks from THg contamination, even in areas where lower fish Hg levels were observed.</p><p>Both scenarios showed a high risk of Hg contamination in the inland zone. The highest risks were observed in children at doses of 37.5 μg/kg/week and 55 μg/kg/week. The consumption of fish with high levels of Hg therefore poses significant health risks for this group. Adults under the current and critical scenarios in both zones had a potential dose of 1.9 μg/kg/day and 9.9 μg/kg/day, respectively, inferring a low risk for the current scenario and very high risk for the critical scenario. For pregnant women, the potential dose was 1.8 μg/kg/day under the current scenario, and the dose for the critical scenario was 4.3 times that of the current scenario. Many studies assessing riverbank populations in the Amazon show that a small proportion of the communities consume carnivorous fish at a frequency of 10 meals a week [<xref rid=\"B15-ijerph-17-05269\" ref-type=\"bibr\">15</xref>,<xref rid=\"B27-ijerph-17-05269\" ref-type=\"bibr\">27</xref>,<xref rid=\"B36-ijerph-17-05269\" ref-type=\"bibr\">36</xref>,<xref rid=\"B39-ijerph-17-05269\" ref-type=\"bibr\">39</xref>].</p><p>These communities have been exposed to Hg contamination for over a century through the ingestion of mercury contaminated fish. This long-term Hg exposure has led to potential adverse health effects, such as fetal impairment in women of childbearing age [<xref rid=\"B45-ijerph-17-05269\" ref-type=\"bibr\">45</xref>,<xref rid=\"B46-ijerph-17-05269\" ref-type=\"bibr\">46</xref>,<xref rid=\"B47-ijerph-17-05269\" ref-type=\"bibr\">47</xref>].</p><p>Our findings suggest that the consumption of the carnivorous fish species <italic>Cichla monoculus, Hoplias malabaricus, Aspistor quadriscutis, Ageneiosus inermis, Plagioscion auratus,</italic> and <italic>Sciades herzbergii</italic> should not exceed 200 g per week. This is equivalent to a dose of 1.4 μg/kg/week for a 70 kg adult. In contrast, we identified no health risks regarding the consumption of herbivorous (such as <italic>Mugil</italic> sp. (tainha)) and detritivorous species (such as the <italic>Hypostomus</italic> sp. (acari/cascudo) and <italic>Curimata inornata</italic> (branquinha)) in the coastal communities. Moreover, to minimize health risks, <italic>Ageneiosus inermis</italic> (mandubé), <italic>Boulengerella cuvieri</italic> (pirapucu), <italic>Cichla monoculus</italic> (tucunaré), and <italic>Hoplias aimara</italic> (traírão) should only be consumed once a month. Pregnant women should avoid consuming carnivorous fish and reduce the intake of omnivorous fish during the pregancy. </p></sec><sec sec-type=\"conclusions\" id=\"sec5-ijerph-17-05269\"><title>5. Conclusions</title><p>To our knowledge, this is the first study to provide clear recommendations for reducing the Hg exposure through fish consumption in local communities in the northern Amazon. High levels of mercury were detected in different fish species in the study area, suggesting a high risk of Hg contamination in local fish consumers. The highest risk of Hg contamination was observed in children in the inland zone. To minimize health risks, we proposed a maximum weekly fish intake for different trophic levels based on both the toxic characteristics of Hg and human susceptibility. We suggest that the consumption of the carnivorous fish species should not exceed 200 g per week, with special attention to the consumption of mandubé, pirapucu, tucunaré and trairão, that should be consumed once a month.</p><p>The study locations were selected in protected and conserved areas, including Brazil’s largest natural reserve (Tumucumaque National Park). These regions provide ecosystem services for traditional communities and maintain their social and cultural values, including the incorporation of fish resources for human livelihoods. </p><p>Due to the persistence of Hg in the environment, legal and illegal ASGM activities in Amapá continue to impact the health and livelihoods of traditional communities who are dependent on fish as a primary protein source. This situation has been exacerbated by the global increase in the price of gold, which has driven the expansion of illegal ASGM activities in the Amazon and further threatens the environment and the human rights of local communities. This also inhibits the ability of vulnerable populations to achieve their sustainable development goals.</p><p>In this context, it is critical to assess the impacts of Hg contamination on local communities and their environment. There is a strong need to build scientific evidence that helps to develop effective mitigation measures and policies to tackle these issues. It is crucial to assess the impact of mercury contamination on human health at regional scales. Policy makers must also devise and implement effective mitigation measures to guarantee the food security of indigenous communities; this includes the proposition of nutritional alternatives and the moderation of fish consumption without compromising cultural traditions.</p></sec></body><back><ack><title>Acknowledgments</title><p>This research was led by the WWF in close partnership with the Instituto de Pesquisa e Formação Indígena (Iepé), Fundação Oswaldo Cruz, ICMBio, and the Amapá Institute of Scientific and Technological Research (IEPA). We thank the researchers, civil society, fishermen, peasants, quilombolas, indigenous communities, and local and federal institutions who participated in this project. We acknowledge the support from ICMBio in the design and implementation of field work, particularly Iranildo Coutinho, Ricardo Pires, and Christoph Jaster. We thank the Amapá Institute of Scientific and Technological Research (IEPA) and the local inhabitants for their assistance with field work. We also thank Ana Claudia Vasconcellos and André Perisse for participating in the discussions, preparing the database and visiting the indigenous communities and health services in Amapá.</p></ack><notes><title>Author Contributions</title><p>General coordination: M.O.-d.-C.; conceptualization: M.O.-d.-C., C.d.S.G., R.F. and D.Y.; Field work: C.d.S.G., D.Y. and R.F.; data analysis and method: S.d.S.H., P.C.B. and A.S.; manuscript draft: S.d.S.H., M.O.-d.-C., P.C.B. and A.S.; review: S.d.S.H., M.O.-d.-C., C.d.S.G., R.F., P.C.B., A.S. and D.Y. 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Health Perspect.</source><year>2005</year><volume>113</volume><fpage>1376</fpage><lpage>1380</lpage><pub-id pub-id-type=\"doi\">10.1289/ehp.8041</pub-id><pub-id pub-id-type=\"pmid\">16203250</pub-id></element-citation></ref></ref-list></back><floats-group><fig id=\"ijerph-17-05269-f001\" orientation=\"portrait\" position=\"float\"><label>Figure 1</label><caption><p>Map of the study area indicating the sampling sites, protected areas, and indigenous territories.</p></caption><graphic xlink:href=\"ijerph-17-05269-g001\"/></fig><fig id=\"ijerph-17-05269-f002\" orientation=\"portrait\" position=\"float\"><label>Figure 2</label><caption><p>Distribution of mercury concentrations in fish (μg/g) by trophic level.</p></caption><graphic xlink:href=\"ijerph-17-05269-g002\"/></fig><fig id=\"ijerph-17-05269-f003\" orientation=\"portrait\" position=\"float\"><label>Figure 3</label><caption><p>Sankey diagram categorizing the studied fish species and trophic levels by total mercury concentrations (≥0.5 μg/g and ≤0.5 μg/g).</p></caption><graphic xlink:href=\"ijerph-17-05269-g003\"/></fig><fig id=\"ijerph-17-05269-f004\" orientation=\"portrait\" position=\"float\"><label>Figure 4</label><caption><p>Distribution of mercury concentrations in fish (μg/g) in (<bold>a</bold>) the sampling sites and (<bold>b</bold>) the aggregated zones (Kruskal–Wallis: 95% CI, <italic>p</italic> < 0.001).</p></caption><graphic xlink:href=\"ijerph-17-05269-g004\"/></fig><table-wrap id=\"ijerph-17-05269-t001\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05269-t001_Table 1</object-id><label>Table 1</label><caption><p>Description of the sampling sites and the surrounding regions.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Sites and Main River</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Tributaries-Sampling Sites</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Coordinates of Sampling Sites</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Characteristics of the Region</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Correlation to Gold Mining Activity</th></tr></thead><tbody><tr><td rowspan=\"4\" align=\"center\" valign=\"middle\" colspan=\"1\">1-Cassiporé River (CR)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Maruani River</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">03°32′27.71″ N<break/> 51°11′34.23″ W </td><td rowspan=\"4\" align=\"left\" valign=\"middle\" colspan=\"1\">Coastal zone. Muddy water. High fishing pressure. Influence of tides and pororoca. Protected Areas: Cabo Orange National Park and Amapá State Forest. Communities (pop): Oiapoque, 27.270 <sup>a</sup>; Calçoene, 11.117 <sup>a</sup>; Vila Velha, 2.723 <sup>b</sup>; Cunani (Quilombo) 940 <sup>b</sup>. Indigenous lands: Juminã, 61 <sup>c</sup>; Uaçá, 4.462 <sup>c</sup>; Galibi, 151 <sup>d</sup>.</td><td rowspan=\"4\" align=\"left\" valign=\"middle\" colspan=\"1\">Gold mining activity for over 250 years. Influence of Garimpo do Lourenço, located at the headwaters of the Cassiporé River. On-going mining processes in the indigenous land of Uaçá for gold research and exploration. </td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CR-Bridge</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">02°57′25.57″ N<break/> 51°25′18.10″ W</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CR </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">03°17′16.87″ N<break/> 51°10′36.68″ N</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CR-Vila Velha</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">03°08′34.16″ N<break/> 51°12′36.16″ W</td></tr><tr><td rowspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin\" colspan=\"1\">2-Amapá Grande River (AG)</td><td align=\"center\" valign=\"middle\" style=\"border-top:solid thin\" rowspan=\"1\" colspan=\"1\">AG</td><td align=\"center\" valign=\"middle\" style=\"border-top:solid thin\" rowspan=\"1\" colspan=\"1\">02°10′16.19″ N 50°47′39.41″ W </td><td rowspan=\"3\" align=\"left\" valign=\"middle\" style=\"border-top:solid thin\" colspan=\"1\">Coastal zone. Muddy river bottom. High fishing pressure. Influence of tide and pororoca. Protected Areas: Cariaú River reserve, Maracá-Jipioca Ecological Station. Communities (pop): Calçoene, 11.117 <sup>a</sup>; Pracuúba, 5.120 <sup>a</sup>; Amapá, 9.109 <sup>a</sup> Tartarugalzinho, 17.315 <sup>a</sup>.</td><td rowspan=\"3\" align=\"left\" valign=\"middle\" style=\"border-top:solid thin\" colspan=\"1\">This region is influenced by Garimpo do Tartarugalzinho. At least five other areas in the region constitute environmental liabilities: Bananal, Buracão, Fofoca, Mandiocal and Mineiro.</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">AG-Confluence</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">02°07′13.50″ N<break/> 50°45′17.52″ W</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Flechal</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">01°53′19.07″ N<break/> 50°47′25.27″ W</td></tr><tr><td rowspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin\" colspan=\"1\">3-Oiapoque River (OR)</td><td align=\"center\" valign=\"middle\" style=\"border-top:solid thin\" rowspan=\"1\" colspan=\"1\">Uaçá River</td><td align=\"center\" valign=\"middle\" style=\"border-top:solid thin\" rowspan=\"1\" colspan=\"1\">04°0′11.66″ N<break/> 51°28′16.28″ W</td><td rowspan=\"3\" align=\"left\" valign=\"middle\" style=\"border-top:solid thin\" colspan=\"1\">Coastal zone. Muddy water. High fishing pressure. Large population in the surroundings. Borders French Guiana. Communities (pop): Oiapoque, 27.270 <sup>a</sup> Calçoene, 11.117 <sup>a</sup>. Indigenous lands: Juminã, 121 <sup>c</sup>; Uaçá: 4 462 <sup>c</sup>; Galibi: 151 <sup>d</sup>.</td><td rowspan=\"3\" align=\"left\" valign=\"middle\" style=\"border-top:solid thin\" colspan=\"1\">Displacement of illegal ASGM toward French Guiana from 2002. Influence from ASGM sites in the whole basin from both countries.</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">OR</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">04°9′49.10″ N<break/> 51°37′59.97″ W</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">OR (Bay)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">04°20′9.00″ N<break/> 51°34′50.20″ W</td></tr><tr><td rowspan=\"4\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">4-Araguari River (AR)</td><td align=\"center\" valign=\"middle\" style=\"border-top:solid thin\" rowspan=\"1\" colspan=\"1\">Tajauí River</td><td align=\"center\" valign=\"middle\" style=\"border-top:solid thin\" rowspan=\"1\" colspan=\"1\">01°23′27.10″ N<break/> 52°01′28.02″ W</td><td rowspan=\"4\" align=\"left\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">Inland zone. Sandy River bottom. Illegal fishing.<break/>Protected Areas: Tumucumaque National Park, Amapá National Forest. Communities (pop): Pedra Branca do Amapari, 16.502 <sup>a</sup>; Porto Grande, 21.971 <sup>a</sup>; Ferreira Gomes, 7.780 <sup>a</sup>.</td><td rowspan=\"4\" align=\"left\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">Influence of the Amapari, Lourenço, Vila Nova, Capivara, Mururé ASGM sites. </td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Falsino River</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">00°58′09.70″ N<break/> 51°36′16.90″ W</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Mureré River</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">01°26′16.75″ N<break/> 52°03′14.30″ W</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Mutum River</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">01°19′02.84″ N<break/> 51°59′23.09″ W</td></tr><tr><td rowspan=\"4\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">5-Amapari River(AMR)</td><td align=\"center\" valign=\"middle\" style=\"border-top:solid thin\" rowspan=\"1\" colspan=\"1\">AMR</td><td align=\"center\" valign=\"middle\" style=\"border-top:solid thin\" rowspan=\"1\" colspan=\"1\">01°35′16.86″ N<break/> 52°32′38.18″ W</td><td rowspan=\"4\" align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Inland zone. Sandy River bottom. Protected Areas: Tumucumaque Mountains National Park, Amapá National Forest. Communities (pop): Pedra Branca do Amapari, 16.502 <sup>a</sup>; Serra do Navio, 5.397 <sup>a</sup>. Indigenous: Wajãpi, 1.200 <sup>d</sup>.</td><td rowspan=\"4\" align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Four legal active ASGM sites. Illegal gold mining sites within PAs and prospection projects. The Tucano’s Gold Mine (Pedra Branca do Amapari) is the second largest gold mine in Brazil.</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Anacuí River</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">01°36′04.52″ N<break/> 52°29′26.99″ W</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">AMR/Jurupá River</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">01°11′13.52″ N<break/> 52°22′14.66″ W</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">AMR (Porto Terezinha)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">00°52′53.52″ N<break/> 52°00′36.84″ W</td></tr></tbody></table><table-wrap-foot><fn><p><sup>a</sup> [<xref rid=\"B18-ijerph-17-05269\" ref-type=\"bibr\">18</xref>]. <sup>b</sup> [<xref rid=\"B19-ijerph-17-05269\" ref-type=\"bibr\">19</xref>]. <sup>c</sup> [<xref rid=\"B20-ijerph-17-05269\" ref-type=\"bibr\">20</xref>]. <sup>d</sup> [<xref rid=\"B21-ijerph-17-05269\" ref-type=\"bibr\">21</xref>]. <sup>e</sup> [<xref rid=\"B22-ijerph-17-05269\" ref-type=\"bibr\">22</xref>].</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"ijerph-17-05269-t002\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05269-t002_Table 2</object-id><label>Table 2</label><caption><p>Summary of articles that quantified fish consumption in communities in the Brazilian Amazon.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th colspan=\"7\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Reference</th></tr><tr><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Reference</th><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Local</th><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Year</th><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Population</th><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">\n<italic>N</italic>\n</th><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">Consumption (g/Day; Meals/Week; Daily Eat %)</th></tr><tr><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Average (SD)</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Min-Max</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B27-ijerph-17-05269\" ref-type=\"bibr\">27</xref>] </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Monte Alegre/PA</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1993–1995</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Families</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">35</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">369</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B28-ijerph-17-05269\" ref-type=\"bibr\">28</xref>] </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Rio Madeira/RO</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1991 and 1993</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">All ages</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">607</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">243 (135)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">100–300</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B29-ijerph-17-05269\" ref-type=\"bibr\">29</xref>] </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Rio Cuiabá/MT</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1995–1996</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">>12 </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">153</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">110.4 (152.5)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">20–372</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B30-ijerph-17-05269\" ref-type=\"bibr\">30</xref>] </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Maroni River, Community Wayana/French Guyana</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1997</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><1–14<break/>Adults (15–45)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">109<break/>118</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">195 ± 9<break/>372 ± 116</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B31-ijerph-17-05269\" ref-type=\"bibr\">31</xref>] </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Rio Negro/AM</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1998–1999</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Woman (1545–)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">31</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">170.5</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">23–293</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B32-ijerph-17-05269\" ref-type=\"bibr\">32</xref>] </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Alta Floresta/MT</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2000–2002</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">All ages</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">251</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3–180</td></tr><tr><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">[<xref rid=\"B33-ijerph-17-05269\" ref-type=\"bibr\">33</xref>]</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Indigenous/PA</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">−</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Munduruku</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">249</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">30.0 (16.6)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Kayabi</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">47</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">110.4 (60.6)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−</td></tr><tr><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">[<xref rid=\"B34-ijerph-17-05269\" ref-type=\"bibr\">34</xref>]</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Rio Tapajós/PA</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">2003</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Woman > 15</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">121</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">124 (65)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Men > 15</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">135</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">189.7 (105.5)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B35-ijerph-17-05269\" ref-type=\"bibr\">35</xref>] </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Lago Puruzinho/PA</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2005–2006</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">All ages</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">120</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">406 (204.1)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B36-ijerph-17-05269\" ref-type=\"bibr\">36</xref>] </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Madeira River, Curiã Lake, Porto Velho</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2009–2011</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Grouped Villages</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Daily eat (%)<break/>5.8%–57.7%</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td rowspan=\"4\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">[<xref rid=\"B37-ijerph-17-05269\" ref-type=\"bibr\">37</xref>]</td><td rowspan=\"4\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Madeira River/RO</td><td rowspan=\"4\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">2015</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">All</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">51</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">34.29</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">7.1–330</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Young</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">23</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">21.4</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">7.1–180</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Woman</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">12</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">34.3</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">10–328.6</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Adults (1549–)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">28</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">51.1</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">10–330</td></tr><tr><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">[<xref rid=\"B38-ijerph-17-05269\" ref-type=\"bibr\">38</xref>]</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Community of Brasília Legal, Tapajós/PA</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">2004</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Woman</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">46</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">4.9 Meals/week</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Men</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">22</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">6 Meals/week</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−</td></tr><tr><td rowspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">[<xref rid=\"B39-ijerph-17-05269\" ref-type=\"bibr\">39</xref>]</td><td rowspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Lower Amazon River<break/>Trombetas River<break/>Purus River</td><td rowspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">2006–2008</td><td rowspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Families</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">85</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">416.39 (209.12)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">183</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">490 (240.69)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">54</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">469 (207.72)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B40-ijerph-17-05269\" ref-type=\"bibr\">40</xref>] </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Tapajós River–Community of Barreiras/PA</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Men<break/>(015–)<break/>(1660–)<break/>(5060–)<break/>Woman<break/>(015–)<break/>(1660–)<break/>(5060–)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"> <break/>28<break/>21<break/>55<break/> <break/>26<break/>40<break/>17</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Meals/week<break/>15 ± 0<break/>20 ± 1<break/>20 ± 0<break/>Meals/week<break/>14 ± 0<break/>20 ± 0<break/>20 ± 0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B7-ijerph-17-05269\" ref-type=\"bibr\">7</xref>]</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Middle Madeira River/RO</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2009–2010</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Grouped Villages</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">132</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">440 </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">270–460</td></tr></tbody></table></table-wrap><table-wrap id=\"ijerph-17-05269-t003\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05269-t003_Table 3</object-id><label>Table 3</label><caption><p>Exposure parameters used for the health risk assessment of residents in areas influenced by coastal tides (sites 1, 2, and 3) and inland zones (sites 4 and 5).</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th rowspan=\"2\" colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\">Exposure Parameters by Scenario </th><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin\" rowspan=\"1\">Child & Juvenile<break/>(5–16)</th><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin\" rowspan=\"1\">Women and Men Adults <break/>(17–75)</th><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin\" rowspan=\"1\">Women of<break/>Childbearing Age <break/>(16–49)</th></tr><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Coastal Tidal</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Inland</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Coastal Tidal</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Inland</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Coastal Tidal</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Inland</th></tr></thead><tbody><tr><td rowspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Current</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">[Hg]-omnivorous and carnivorous–Average (SD) µg/g </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.50 (0.37)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.66 (0.61)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.50 (0.37)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.66 (0.61)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.50 (0.37)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.66 (0.61)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">[Hg]-detritivore and herbivore– Average (SD) µg/g </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.17 (0.18)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.17 (0.22)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.17 (0.18)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.17 (0.22)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.17 (0.18)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.17 (0.22)</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Average fish consumption (kg/d) adjusted for trophic level</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.160 (0.49)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.160 (0.49)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.300 (0.154)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.300 (0.154)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.255 (0.135)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.255 (0.135)</td></tr><tr><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Critical</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">[Hg] (omnivorous & carnivore) 95% µg/g</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.2 (0.37)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.48 (0.64)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.2 (0.37)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.48 (0.64)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.2 (0.37)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.48 (0.64)</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Average fish consumption (kg/d) 95% carnivorous</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.192</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.192</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.430</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.430</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.300</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.300</td></tr><tr><td colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">Average body weight<break/>kg (SD)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">25 (12.0)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">25 (12.0)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">65 (6.3)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">65 (6.3)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">58 (4.7)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">58 (4.7)</td></tr></tbody></table><table-wrap-foot><fn><p>SD—standard deviation.</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"ijerph-17-05269-t004\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05269-t004_Table 4</object-id><label>Table 4</label><caption><p>Potential dose (<italic>D<sub>pot</sub></italic>) and toxicological risk quotient (QR) of total mercury for both exposure scenarios in coastal and inland zones.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Exposure Scenario</th><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Pot. Dose<break/>Child & Juvenile<break/>(5–16)</th><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Pot. Dose<break/>Women and Men Adults (17–75)</th><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Pot. Dose<break/>Woman of Childbearing Age<break/>(16–49)</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">μg/kg/day</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">μg/kg/week</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">μg/kg/day</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">μg/kg/week</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">μg/kg/day</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">μg/kg/week</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><inline-formula><mml:math id=\"mm2\"><mml:mrow><mml:mrow><mml:msub><mml:mi>D</mml:mi><mml:mrow><mml:mi>p</mml:mi><mml:mi>o</mml:mi><mml:mi>t</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mrow></mml:math></inline-formula>/d</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><inline-formula><mml:math id=\"mm3\"><mml:mrow><mml:mrow><mml:msub><mml:mi>D</mml:mi><mml:mrow><mml:mi>p</mml:mi><mml:mi>o</mml:mi><mml:mi>t</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mrow></mml:math></inline-formula>/w</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><inline-formula><mml:math id=\"mm4\"><mml:mrow><mml:mrow><mml:msub><mml:mi>D</mml:mi><mml:mrow><mml:mi>p</mml:mi><mml:mi>o</mml:mi><mml:mi>t</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mrow></mml:math></inline-formula>/d </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><inline-formula><mml:math id=\"mm5\"><mml:mrow><mml:mrow><mml:msub><mml:mi>D</mml:mi><mml:mrow><mml:mi>p</mml:mi><mml:mi>o</mml:mi><mml:mi>t</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mrow></mml:math></inline-formula>/w</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><inline-formula><mml:math id=\"mm6\"><mml:mrow><mml:mrow><mml:msub><mml:mi>D</mml:mi><mml:mrow><mml:mi>p</mml:mi><mml:mi>o</mml:mi><mml:mi>t</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mrow></mml:math></inline-formula>/d</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><inline-formula><mml:math id=\"mm7\"><mml:mrow><mml:mrow><mml:msub><mml:mi>D</mml:mi><mml:mrow><mml:mi>p</mml:mi><mml:mi>o</mml:mi><mml:mi>t</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mrow></mml:math></inline-formula>/w</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Coastal Zone</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Current scenario </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">34</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.08</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.4</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Critical scenario </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">6.9</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">34.5</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">6.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">30</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">4.7</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">23.5</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Inland Zone</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Current scenario</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">37.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">9.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">9</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Critical scenario</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">11</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">55</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">9.9</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">49.5</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">7.7</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">38.5</td></tr></tbody></table></table-wrap></floats-group></article>\n"
]
[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"review-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Int J Mol Sci</journal-id><journal-id journal-id-type=\"iso-abbrev\">Int J Mol Sci</journal-id><journal-id journal-id-type=\"publisher-id\">ijms</journal-id><journal-title-group><journal-title>International Journal of Molecular Sciences</journal-title></journal-title-group><issn pub-type=\"epub\">1422-0067</issn><publisher><publisher-name>MDPI</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32751239</article-id><article-id pub-id-type=\"pmc\">PMC7432108</article-id><article-id pub-id-type=\"doi\">10.3390/ijms21155389</article-id><article-id pub-id-type=\"publisher-id\">ijms-21-05389</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Review</subject></subj-group></article-categories><title-group><article-title>Gut Microbiota Manipulation as a Tool for Colorectal Cancer Management: Recent Advances in Its Use for Therapeutic Purposes</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Perillo</surname><given-names>Federica</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05389\">1</xref><xref ref-type=\"author-notes\" rid=\"fn1-ijms-21-05389\">†</xref></contrib><contrib contrib-type=\"author\"><name><surname>Amoroso</surname><given-names>Chiara</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05389\">1</xref><xref ref-type=\"author-notes\" rid=\"fn1-ijms-21-05389\">†</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0001-7217-3355</contrib-id><name><surname>Strati</surname><given-names>Francesco</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05389\">1</xref><xref rid=\"c1-ijms-21-05389\" ref-type=\"corresp\">*</xref></contrib><contrib contrib-type=\"author\"><name><surname>Giuffrè</surname><given-names>Maria Rita</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05389\">1</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0003-2096-1223</contrib-id><name><surname>Díaz-Basabe</surname><given-names>Angélica</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05389\">1</xref><xref ref-type=\"aff\" rid=\"af2-ijms-21-05389\">2</xref></contrib><contrib contrib-type=\"author\"><name><surname>Lattanzi</surname><given-names>Georgia</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05389\">1</xref><xref ref-type=\"aff\" rid=\"af2-ijms-21-05389\">2</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0002-2541-9428</contrib-id><name><surname>Facciotti</surname><given-names>Federica</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05389\">1</xref><xref rid=\"c1-ijms-21-05389\" ref-type=\"corresp\">*</xref></contrib></contrib-group><aff id=\"af1-ijms-21-05389\"><label>1</label>Department of Experimental Oncology, IEO European Institute of Oncology IRCCS, 20139 Milan, Italy; <email>[email protected]</email> (F.P.); <email>[email protected]</email> (C.A.); <email>[email protected]</email> (M.R.G.); <email>[email protected]</email> (A.D.-B.); <email>[email protected]</email> (G.L.)</aff><aff id=\"af2-ijms-21-05389\"><label>2</label>Department of Oncology and Hemato-Oncology, Università degli Studi di Milano, 20135 Milan, Italy</aff><author-notes><corresp id=\"c1-ijms-21-05389\"><label>*</label>Correspondence: <email>[email protected]</email> (F.S.); <email>[email protected]</email> (F.F.)</corresp><fn id=\"fn1-ijms-21-05389\"><label>†</label><p>These authors equally contributed.</p></fn></author-notes><pub-date pub-type=\"epub\"><day>29</day><month>7</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><month>8</month><year>2020</year></pub-date><volume>21</volume><issue>15</issue><elocation-id>5389</elocation-id><history><date date-type=\"received\"><day>13</day><month>7</month><year>2020</year></date><date date-type=\"accepted\"><day>28</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>© 2020 by the authors.</copyright-statement><copyright-year>2020</copyright-year><license license-type=\"open-access\"><license-p>Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>).</license-p></license></permissions><abstract><p>Colorectal cancer (CRC) is a multifaceted disease influenced by both environmental and genetic factors. A large body of literature has demonstrated the role of gut microbes in promoting inflammatory responses, creating a suitable microenvironment for the development of skewed interactions between the host and the gut microbiota and cancer initiation. Even if surgery is the primary therapeutic strategy, patients with advanced disease or cancer recurrence after surgery remain difficult to cure. Therefore, the gut microbiota has been proposed as a novel therapeutic target in light of recent promising data in which it seems to modulate the response to cancer immunotherapy. The use of microbe-targeted therapies, including antibiotics, prebiotics, live biotherapeutics, and fecal microbiota transplantation, is therefore considered to support current therapies in CRC management. In this review, we will discuss the importance of host−microbe interactions in CRC and how promoting homeostatic immune responses through microbe-targeted therapies may be useful in preventing/treating CRC development.</p></abstract><kwd-group><kwd>colorectal cancer</kwd><kwd>gut microbiome</kwd><kwd>live biotherapeutic products</kwd></kwd-group></article-meta></front><body><sec id=\"sec1-ijms-21-05389\"><title>1. Background</title><p>Colorectal cancer (CRC) is the third most commonly diagnosed malignancy and the fourth leading cause of cancer death in the world [<xref rid=\"B1-ijms-21-05389\" ref-type=\"bibr\">1</xref>]. CRC is a heterogeneous disease with a wide range of long-term outcomes and responses to treatment. Genetic and environmental factors contribute to the etiology and progression of the disease. Several risk factors have been identified, including positive family history, smoking, alcohol intake, lifestyle, and cultural and social practices [<xref rid=\"B2-ijms-21-05389\" ref-type=\"bibr\">2</xref>]. Moreover, emerging evidence suggests that diet has an important impact on the risk of CRC development [<xref rid=\"B3-ijms-21-05389\" ref-type=\"bibr\">3</xref>]. Over the past three decades, molecular genetic studies have revealed some critical mutations underlying the pathogenesis of the sporadic and inherited forms of CRC [<xref rid=\"B4-ijms-21-05389\" ref-type=\"bibr\">4</xref>], including mutational inactivation of the adenomatous polyposis coli (APC) tumor suppressor [<xref rid=\"B5-ijms-21-05389\" ref-type=\"bibr\">5</xref>], resulting in overactivation of the Wnt/β-catenin signaling pathway, dysregulated cell proliferation and adenoma development [<xref rid=\"B6-ijms-21-05389\" ref-type=\"bibr\">6</xref>], or microsatellite instability, assessed with the detection of mono- and di-nucleotide tracts selected by the National Cancer Institute consensus conference [<xref rid=\"B4-ijms-21-05389\" ref-type=\"bibr\">4</xref>,<xref rid=\"B7-ijms-21-05389\" ref-type=\"bibr\">7</xref>].</p><p>Current treatments for CRC include endoscopic and surgical local excision, downstaging preoperative radiotherapy and systemic therapy, extensive surgery for locoregional and metastatic disease, local ablative therapies for metastases, palliative chemotherapy, targeted therapy, and immunotherapy [<xref rid=\"B8-ijms-21-05389\" ref-type=\"bibr\">8</xref>]. Although these treatments have doubled the overall survival of patients up to 3 years for advanced stages of the disease, CRC remains associated with poor prognosis and very low rates of long-term survival [<xref rid=\"B9-ijms-21-05389\" ref-type=\"bibr\">9</xref>]. The development and progression of CRC are multi-factorial processes which are associated also with the progressive failure of immunosurveillance, which is the natural and/or therapy-stimulated capacity of the immune system to control cancer progression [<xref rid=\"B10-ijms-21-05389\" ref-type=\"bibr\">10</xref>]. However, the role of altered mucosal immune surveillance in the development of CRC has not yet been fully understood [<xref rid=\"B11-ijms-21-05389\" ref-type=\"bibr\">11</xref>].</p><p>The role of the gut microbiota in cancer biology has been increasingly recognized as an environmental factor favoring CRC development. Indeed, the gut microenvironment harbors a complex microbial ecosystem comprising approximately 3 × 10<sup>13</sup> bacteria and other microorganisms such as fungi, phages, archaea, and protists [<xref rid=\"B12-ijms-21-05389\" ref-type=\"bibr\">12</xref>], which are confined into the intestinal lumen by the epithelial cell lining, the mucus layers, and the production of antibacterial peptides and bioactive molecules [<xref rid=\"B13-ijms-21-05389\" ref-type=\"bibr\">13</xref>]. The mutual interaction between the gut microbiota and the host is further highlighted by its role in inducing immune maturation [<xref rid=\"B14-ijms-21-05389\" ref-type=\"bibr\">14</xref>]. For this reason, a CRC-associated microbial dysbiosis can alter the delicate equilibrium between the gut microbiota and the host’s immune system, contributing to cancer initiation and/or progression [<xref rid=\"B15-ijms-21-05389\" ref-type=\"bibr\">15</xref>]. The spatial organization of multispecies bacterial communities in higher-order structures, termed biofilms, appears to be indispensable for CRC initiation. According to the adenoma–carcinoma sequence model proposed by Fearon and Vogelstein [<xref rid=\"B16-ijms-21-05389\" ref-type=\"bibr\">16</xref>], microbial biofilm may be regarded as an independent “driver” at an early stage of CRC carcinogenesis, before the malignant transformation from adenoma to carcinoma. Here, we will recapitulate how the interconnection between the intestinal microenvironment, CRC onset, and the gut microbiota composition may influence therapies’ outcomes. We will discuss the importance of gut microbiota modulation in CRC patients through dietary interventions as well as microbiome biomodulators, including anti-, pro-, pre-, and post-biotics and fecal microbiota transplantation, and how these therapeutic strategies can help in preventing/treating CRC.</p></sec><sec id=\"sec2-ijms-21-05389\"><title>2. Role of Gut Microbiota in CRC</title><sec id=\"sec2dot1-ijms-21-05389\"><title>2.1. Pro-Tumorigenic Roles of the Gut Microbiota in CRC</title><p>The gut microbiota has been linked to carcinogenesis and colorectal tumor progression [<xref rid=\"B15-ijms-21-05389\" ref-type=\"bibr\">15</xref>]. Pathobionts (microorganisms commonly living in the gut that become harmful under certain circumstances) colonizing the intestine may produce genotoxins, metabolites that induce DNA damage, which can lead to alterations in the tumor microenvironment (TME), inducing subsequent changes in the abundance of colonic intrinsic pathogenic members (<xref rid=\"ijms-21-05389-t001\" ref-type=\"table\">Table 1</xref>) [<xref rid=\"B17-ijms-21-05389\" ref-type=\"bibr\">17</xref>]. For instance, <italic>Escherichia coli</italic> is a bacterium commonly found in the human gut. However, some strains are pathogenic and can promote disease via several virulence factors [<xref rid=\"B18-ijms-21-05389\" ref-type=\"bibr\">18</xref>]. The <italic>E. coli</italic>-derived colibactin toxin, encoded by the pathogenicity island <italic>pks</italic>, is frequently associated with human colorectal carcinogenesis, as demonstrated in human and animal models in which <italic>pks</italic><sup>+</sup>\n<italic>E. coli</italic> strains induce double-strand DNA breaks, mutations, chromosomal rearrangements, and cell cycle arrest [<xref rid=\"B19-ijms-21-05389\" ref-type=\"bibr\">19</xref>]. Accordingly, <italic>pks</italic><sup>+</sup>\n<italic>E. coli</italic> strains induce single-base substitution/indels mutational patterns, which are predominantly detected in CRC patients [<xref rid=\"B20-ijms-21-05389\" ref-type=\"bibr\">20</xref>]. In addition, the enterotoxigenic <italic>B. fragilis</italic> (ETBF), an anaerobic bacterium found in the human intestinal microbiota, produces an enterotoxin (<italic>B. fragilis</italic> toxin, BFT), which is highly associated with CRC [<xref rid=\"B21-ijms-21-05389\" ref-type=\"bibr\">21</xref>], by activating β-catenin signaling as well as the secretion of interleukin (IL)-8 in colonic epithelial cells, leading to persistent cellular proliferation [<xref rid=\"B22-ijms-21-05389\" ref-type=\"bibr\">22</xref>]. The alteration of the gut microbiota composition, known as “dysbiosis”, has been observed in CRC patients [<xref rid=\"B23-ijms-21-05389\" ref-type=\"bibr\">23</xref>]. A large body of literature has reported that fecal and intestinal mucosa samples from CRC patients display a lower bacterial diversity compared to healthy individuals [<xref rid=\"B24-ijms-21-05389\" ref-type=\"bibr\">24</xref>,<xref rid=\"B25-ijms-21-05389\" ref-type=\"bibr\">25</xref>]. In addition, CRC patients show significant alterations in specific bacterial taxa, with a potentially detrimental impact on mucosal immune responses [<xref rid=\"B25-ijms-21-05389\" ref-type=\"bibr\">25</xref>]. In particular, the CRC-associated microbiota is characterized by the increased abundance of <italic>Enterobacteriaceae</italic>, <italic>Streptococcus</italic>, and typical genera belonging to the oral microbiota such as <italic>Fusobacterium</italic>, <italic>Gemella</italic>, <italic>Peptostreptococcus</italic>, <italic>Prevotella</italic>, <italic>Solobacterium</italic>, and <italic>Parvimonas</italic> [<xref rid=\"B26-ijms-21-05389\" ref-type=\"bibr\">26</xref>,<xref rid=\"B27-ijms-21-05389\" ref-type=\"bibr\">27</xref>,<xref rid=\"B28-ijms-21-05389\" ref-type=\"bibr\">28</xref>]. On the contrary, the <italic>Firmicutes</italic> phylum (especially the <italic>Ruminococcaceae</italic> and <italic>Lachnospiraceae</italic> families) as well as <italic>Bifidobacterium, Odoribacter</italic>, and <italic>Streptococcus</italic> are substantially underrepresented in CRC patients [<xref rid=\"B29-ijms-21-05389\" ref-type=\"bibr\">29</xref>,<xref rid=\"B30-ijms-21-05389\" ref-type=\"bibr\">30</xref>]. In fact, gut microbiota transplant from CRC patients into mice is sufficient to disrupt the intestinal barrier, leading to low-grade inflammation and dysbiosis. Indeed, conventional and germ-free (GF) mice transplanted with stool samples from CRC patients developed high-grade dysplasia and macroscopic polyps concomitantly with a higher proportion of colonic Ki-67 positive proliferating cells as well as increased expression of C-X-C motif chemokine receptor 1, C-X-C motif chemokine receptor 2, IL-17A, IL-22, and IL-23A cytokines. [<xref rid=\"B31-ijms-21-05389\" ref-type=\"bibr\">31</xref>]. Similarly, Apc<sup>Min/+</sup> mice (carrying a mutation predisposing them to intestinal adenoma and tumor formation) transplanted with feces from CRC patients showed an increase in the number of intestinal tumors, downregulated expression of mucin-2, regenerating islet-derived protein and intestinal secretory immunoglobulin A, upregulation of the NLRP3 inflammasome, and increased production of pro-inflammatory cytokines IL-1β and tumor necrosis factor (TNF)-α. The hyperproliferative and pro-inflammatory phenotype of the CRC associated-mucosa can be attributed to a dysbiotic microbiota, defined as an “imbalanced” gut microbial composition, associated with disease, that promotes the activation of the Wnt signaling pathway [<xref rid=\"B32-ijms-21-05389\" ref-type=\"bibr\">32</xref>]. Indeed, alterations of the gut microbiota can also influence tumor development and progression by impairing immunosurveillance [<xref rid=\"B33-ijms-21-05389\" ref-type=\"bibr\">33</xref>], leading to features of exhaustion once tumors are established [<xref rid=\"B34-ijms-21-05389\" ref-type=\"bibr\">34</xref>], together with a pro-inflammatory phenotype at early stages [<xref rid=\"B23-ijms-21-05389\" ref-type=\"bibr\">23</xref>,<xref rid=\"B35-ijms-21-05389\" ref-type=\"bibr\">35</xref>,<xref rid=\"B36-ijms-21-05389\" ref-type=\"bibr\">36</xref>,<xref rid=\"B37-ijms-21-05389\" ref-type=\"bibr\">37</xref>,<xref rid=\"B38-ijms-21-05389\" ref-type=\"bibr\">38</xref>]. <italic>Fusobacterium nucleatum</italic> (<italic>Fn</italic>) is commonly found in colorectal tissue with high-grade dysplasia as well as in adenomas. It has been shown that <italic>Fn</italic> modulates several immune responses towards CRC tumor progression, influencing the pre-tumoral environment [<xref rid=\"B39-ijms-21-05389\" ref-type=\"bibr\">39</xref>], inducing the production of inflammatory mediators (IL-6, IL-8, IL-1β) [<xref rid=\"B40-ijms-21-05389\" ref-type=\"bibr\">40</xref>], transforming growth factor (TGF)-β and TNF-α [<xref rid=\"B41-ijms-21-05389\" ref-type=\"bibr\">41</xref>], and suppressing antitumor immunity by the secretion of Fap2, a natural killer inhibitory ligand [<xref rid=\"B42-ijms-21-05389\" ref-type=\"bibr\">42</xref>]. Similarly to colibactin and BFT, <italic>Fn</italic> mediates DNA damage and promotes tumor cell proliferation through the Wnt/β-catenin pathway [<xref rid=\"B43-ijms-21-05389\" ref-type=\"bibr\">43</xref>]. Furthermore, bacterial metabolism might affect CRC progression by the production of oxidative stress molecules [<xref rid=\"B30-ijms-21-05389\" ref-type=\"bibr\">30</xref>,<xref rid=\"B44-ijms-21-05389\" ref-type=\"bibr\">44</xref>,<xref rid=\"B45-ijms-21-05389\" ref-type=\"bibr\">45</xref>], promoting chronic inflammation and disrupting intestinal barrier integrity [<xref rid=\"B36-ijms-21-05389\" ref-type=\"bibr\">36</xref>,<xref rid=\"B46-ijms-21-05389\" ref-type=\"bibr\">46</xref>]. Moreover, choline degradation by gut bacteria contributes to colon cancer via the synthesis of trimethylamine N-oxide, a potential carcinogen [<xref rid=\"B28-ijms-21-05389\" ref-type=\"bibr\">28</xref>].</p></sec><sec id=\"sec2dot2-ijms-21-05389\"><title>2.2. Anti-Oncogenic Effects of the Gut Microbiota in Colon Cancer</title><p>On the other hand, many microbes within the gut microbiota have shown anticancer activities (<xref rid=\"ijms-21-05389-t001\" ref-type=\"table\">Table 1</xref>). Some lines of evidence have shown that <italic>Lactobacillus, Bifidobacterium</italic>, and non-enterotoxigenic <italic>Bacteroides fragilis</italic> (NTBF) can control DNA damage by promoting epithelium renewal through lactic acid production, the modulation of the immune system by reducing the Th17 pool, enhancing major histocompatibility complex-II expression on dendritic cells, and improving natural killer cell and cytotoxic T cell recruitment and cytotoxicity [<xref rid=\"B10-ijms-21-05389\" ref-type=\"bibr\">10</xref>,<xref rid=\"B47-ijms-21-05389\" ref-type=\"bibr\">47</xref>,<xref rid=\"B48-ijms-21-05389\" ref-type=\"bibr\">48</xref>,<xref rid=\"B49-ijms-21-05389\" ref-type=\"bibr\">49</xref>,<xref rid=\"B50-ijms-21-05389\" ref-type=\"bibr\">50</xref>,<xref rid=\"B51-ijms-21-05389\" ref-type=\"bibr\">51</xref>]. Furthermore, the anticancer activities of the gut microbiota are also promoted by short chain fatty acid (SCFA) production. Indeed, one of the most prominent signatures of a healthy microbiota is the presence of SCFA-producing bacteria, such as members of the <italic>Lachnospiraceae</italic> and <italic>Ruminococcaceae</italic> families, among others [<xref rid=\"B10-ijms-21-05389\" ref-type=\"bibr\">10</xref>,<xref rid=\"B52-ijms-21-05389\" ref-type=\"bibr\">52</xref>]. SCFAs, by modulating histone deacetylase inhibitory activity, promote the accumulation and differentiation of Treg cells [<xref rid=\"B53-ijms-21-05389\" ref-type=\"bibr\">53</xref>,<xref rid=\"B54-ijms-21-05389\" ref-type=\"bibr\">54</xref>,<xref rid=\"B55-ijms-21-05389\" ref-type=\"bibr\">55</xref>] controlling tumor progression. Accordingly, two anti-tumorigenic strains of the microbiota recently identified, <italic>Faecalibaculum rodentium</italic> and its human homologue, <italic>Holdemanella biformis,</italic> were able to control protein acetylation and tumor cell proliferation through the production of SCFAs, inhibiting the calcineurin and cytoplasmic 3 (NFATc3) activation pathway in mouse and human settings [<xref rid=\"B56-ijms-21-05389\" ref-type=\"bibr\">56</xref>].\n<table-wrap id=\"ijms-21-05389-t001\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijms-21-05389-t001_Table 1</object-id><label>Table 1</label><caption><p>Summary of bacteria known to be involved in colorectal cancer progression and prevention.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Name</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">(Potential) Role in CRC Oncogenicity</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Mechanism of Action</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">References</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Proteobacteria, especially the <italic>Enterobacteriaceae</italic> family</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Pro-oncogenic</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Opportunistic pathogens, promotion of inflammation</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B22-ijms-21-05389\" ref-type=\"bibr\">22</xref>,<xref rid=\"B37-ijms-21-05389\" ref-type=\"bibr\">37</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><italic>Escherichia coli</italic></td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Pro-oncogenic</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">DNA damage by colibactin, induction of a pro-inflammatory environment</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B18-ijms-21-05389\" ref-type=\"bibr\">18</xref>,<xref rid=\"B19-ijms-21-05389\" ref-type=\"bibr\">19</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Enterotoxigenic <italic>Bacteroides fragilis</italic> (ETBF)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Pro-oncogenic</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Colon cell hyperproliferation by β-catenin pathway activation and IL-8 production</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B20-ijms-21-05389\" ref-type=\"bibr\">20</xref>,<xref rid=\"B21-ijms-21-05389\" ref-type=\"bibr\">21</xref>,<xref rid=\"B50-ijms-21-05389\" ref-type=\"bibr\">50</xref>,<xref rid=\"B57-ijms-21-05389\" ref-type=\"bibr\">57</xref>,<xref rid=\"B58-ijms-21-05389\" ref-type=\"bibr\">58</xref>,<xref rid=\"B59-ijms-21-05389\" ref-type=\"bibr\">59</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><italic>Fusobacterium nucleatum</italic></td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Pro-oncogenic</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Promotion of inflammation, impairment of antitumor immunity, activation of β-catenin pathway, DNA damage</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B39-ijms-21-05389\" ref-type=\"bibr\">39</xref>,<xref rid=\"B40-ijms-21-05389\" ref-type=\"bibr\">40</xref>,<xref rid=\"B41-ijms-21-05389\" ref-type=\"bibr\">41</xref>,<xref rid=\"B42-ijms-21-05389\" ref-type=\"bibr\">42</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><italic>Ruminococcaceae</italic> family</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Anti-oncogenic</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">SCFA production</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B9-ijms-21-05389\" ref-type=\"bibr\">9</xref>,<xref rid=\"B51-ijms-21-05389\" ref-type=\"bibr\">51</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><italic>Lachnospiraceae</italic> family</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Anti-oncogenic</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">SCFA production</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B9-ijms-21-05389\" ref-type=\"bibr\">9</xref>,<xref rid=\"B51-ijms-21-05389\" ref-type=\"bibr\">51</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><italic>Bifidobacteria</italic></td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Anti-oncogenic</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">SCFA production, reduction of pro-inflammatory cytokines, epithelial cell renewal</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B9-ijms-21-05389\" ref-type=\"bibr\">9</xref>,<xref rid=\"B45-ijms-21-05389\" ref-type=\"bibr\">45</xref>,<xref rid=\"B46-ijms-21-05389\" ref-type=\"bibr\">46</xref>,<xref rid=\"B47-ijms-21-05389\" ref-type=\"bibr\">47</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><italic>Lactobacilli,</italic> including <italic>L. casei, L. plantarum, L. rhamnosus, and L. acidophilus</italic></td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Anti-oncogenic</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">SCFA production, reduction of pro-inflammatory cytokines, enhancement of antitumor immunity, epithelial cell renewal</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B46-ijms-21-05389\" ref-type=\"bibr\">46</xref>,<xref rid=\"B47-ijms-21-05389\" ref-type=\"bibr\">47</xref>,<xref rid=\"B50-ijms-21-05389\" ref-type=\"bibr\">50</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Non-enterotoxigenic <italic>Bacteroides fragilis</italic> (NTBF)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Anti-oncogenic</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Boost of antitumor immunity, amelioration of inflammation by PSA production</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B48-ijms-21-05389\" ref-type=\"bibr\">48</xref>,<xref rid=\"B50-ijms-21-05389\" ref-type=\"bibr\">50</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><italic>Faecalibaculum rodentium</italic> and <italic>Holdemanella biformis</italic></td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Anti-oncogenic</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">SCFA production</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B55-ijms-21-05389\" ref-type=\"bibr\">55</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><italic>Akkermansia muciniphila</italic></td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Anti-oncogenic</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">SCFA production, regulation of intestinal barrier integrity</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B9-ijms-21-05389\" ref-type=\"bibr\">9</xref>,<xref rid=\"B60-ijms-21-05389\" ref-type=\"bibr\">60</xref>,<xref rid=\"B61-ijms-21-05389\" ref-type=\"bibr\">61</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><italic>Enterococcus faecalis</italic></td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Anti-oncogenic</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Improvement of intestinal inflammation</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B62-ijms-21-05389\" ref-type=\"bibr\">62</xref>]</td></tr></tbody></table></table-wrap></p></sec></sec><sec id=\"sec3-ijms-21-05389\"><title>3. Lifestyle, Diet, and Microbiota in the Early Onset of CRC</title><p>Early-onset colorectal cancer (EOCRC) is the second most common cancer and the third leading cause of cancer mortality in people <50 years of age in the USA. The incidence of EOCRC has been on the rise over the past four decades and it is expected to increase by >140% by 2030. At present, different established cancer drivers have been linked to EOCRC including diet, sedentary lifestyle, smoking, and alcohol [<xref rid=\"B63-ijms-21-05389\" ref-type=\"bibr\">63</xref>]. The gut microbiota is probably at the intersection of these risk factors and EOCRC. Indeed, the gut microbiome and inflammation are key players and master regulators of CRC onset and progression, as discussed above. The World Cancer Research Foundation (London, UK) and the American Institute for Cancer Research (Washington, DC, USA) consider diet to be one of the most important exogenous factors in CRC etiology [<xref rid=\"B64-ijms-21-05389\" ref-type=\"bibr\">64</xref>]. The use of dietary modifications to supplement conventional cancer therapy is, therefore, a practical approach that is receiving growing attention. Dietary composition also dictates nutrient availability in the TME. Manipulation of the metabolic environment of cancer cells markedly changes their metabolic activity, producing shifts in drug sensitivity, proliferation rate, and metabolic requirements. The diet also influences the composition of the gut microbiota and, thus, the effect that gut microbes exert on the above-mentioned mechanisms.</p><p>It is now widely recognized that the adoption of a westernized diet rich in red meat and saturated fat and low in fiber exerts a negative effect on intestinal homeostasis [<xref rid=\"B65-ijms-21-05389\" ref-type=\"bibr\">65</xref>], also promoting gut dysbiosis and inflammation [<xref rid=\"B50-ijms-21-05389\" ref-type=\"bibr\">50</xref>,<xref rid=\"B66-ijms-21-05389\" ref-type=\"bibr\">66</xref>,<xref rid=\"B67-ijms-21-05389\" ref-type=\"bibr\">67</xref>,<xref rid=\"B68-ijms-21-05389\" ref-type=\"bibr\">68</xref>,<xref rid=\"B69-ijms-21-05389\" ref-type=\"bibr\">69</xref>,<xref rid=\"B70-ijms-21-05389\" ref-type=\"bibr\">70</xref>]. The putative indication of colon cancer risk and a high-fat, low-fiber western diet was evaluated in a two-week diet exchange study among rural Africans, who usually have a low-fat, high-fiber diet, and African Americans, who usually consume the western diet. The diet exchange resulted in remarkable reciprocal changes in microbiota composition, as demonstrated by a shift in African Americans fed with the high fiber diet from <italic>Bacteroides</italic> and butyrate-producing bacteria (e.g., <italic>Roseburia intestinalis</italic> and <italic>Clostridium symbiosum</italic>) towards stronger co-occurrence patterns, including <italic>Firmicutes</italic>, which are typically associated with complex carbohydrate fermentation. On the other hand, the low-fiber/high-fat intervention was associated with an increase in <italic>F. nucleatum</italic>. Moreover, the latter were characterized by an increase in proliferative and inflammatory markers such as Ki67 and cluster of differentiation (CD)3<sup>+</sup> intraepithelial lymphocytes and CD68<sup>+</sup> lamina propria macrophages [<xref rid=\"B71-ijms-21-05389\" ref-type=\"bibr\">71</xref>]. It has been postulated that the higher risk of developing CRC through a high-fat diet (HFD) may be due to an increase in the intestinal secretion of primary bile acids, converted into secondary bile acids by the gut microbiota, such as deoxycholic acid and lithocholic acid, which have been associated with a great increase in intestinal tumor formation and inflammatory damage in mice [<xref rid=\"B72-ijms-21-05389\" ref-type=\"bibr\">72</xref>].</p><p>Changes not only in the diet but also in the use of food additives (used to extend the shelf-life of processed foods) have resulted in a considerable shift in food quality and increased risk of CRC onset. It is well known that nitrite and nitrate consumption, rich in processed meats, can lead to the formation of N-nitroso compounds by gut microbes, some of which are carcinogenic [<xref rid=\"B73-ijms-21-05389\" ref-type=\"bibr\">73</xref>]. The health and regulatory issues related to the addition of food ingredients are too vast to cover in this review and have been covered elsewhere [<xref rid=\"B74-ijms-21-05389\" ref-type=\"bibr\">74</xref>]; nevertheless, it is worthwhile to consider a few examples of food additives that modulate the gut microbiome and the host inflammatory status, factors associated with CRC development [<xref rid=\"B73-ijms-21-05389\" ref-type=\"bibr\">73</xref>]. Monosodium glutamate is an additive used to enhance the flavor of savory foods able to induce obesity and diabetes. Interestingly, monosodium glutamate increases the susceptibility to CRC in models of inflammation-induced colorectal carcinogenesis [<xref rid=\"B75-ijms-21-05389\" ref-type=\"bibr\">75</xref>]. Additionally, the food additive titanium dioxide, commonly used as a whitening and brightening agent, promotes colon inflammation and neoplastic lesions in chemically-induced carcinogenesis models [<xref rid=\"B76-ijms-21-05389\" ref-type=\"bibr\">76</xref>,<xref rid=\"B77-ijms-21-05389\" ref-type=\"bibr\">77</xref>]. Relatively low concentrations of two commonly used emulsifiers, carboxymethylcellulose and polysorbate-80, altered the gut microbiota composition and promoted low-grade intestinal inflammation in animal models [<xref rid=\"B78-ijms-21-05389\" ref-type=\"bibr\">78</xref>,<xref rid=\"B79-ijms-21-05389\" ref-type=\"bibr\">79</xref>]. These data suggest a role of food additives in the incidence of CRC development in humans. Altogether, these studies support a mechanistic link between the gut microbiota and CRC, since external factors that modulate the gut microbiome include not only stress and dietary factors but also elements previously thought to be disconnected from colon health, such as birth mode, breastfeeding behaviors, and maternal stress and nutrition [<xref rid=\"B80-ijms-21-05389\" ref-type=\"bibr\">80</xref>,<xref rid=\"B81-ijms-21-05389\" ref-type=\"bibr\">81</xref>,<xref rid=\"B82-ijms-21-05389\" ref-type=\"bibr\">82</xref>].</p></sec><sec id=\"sec4-ijms-21-05389\"><title>4. Relevance of the Gut Microbiota in the Efficiency of Cancer Therapies</title><p>Advancements in CRC pathophysiological understanding have increased the array of treatment options for local and advanced disease, leading to individual therapeutic plans. Surgery is the cornerstone of curative treatment for patients with non-metastasized CRC [<xref rid=\"B83-ijms-21-05389\" ref-type=\"bibr\">83</xref>]. In more advanced cases, neoadjuvant treatments, including preoperative chemotherapy, chemoradiotherapy, or radiotherapy, can reduce tumor load and stage and might be necessary to optimize the chances of a successful resection [<xref rid=\"B84-ijms-21-05389\" ref-type=\"bibr\">84</xref>]. Current chemotherapies include both single-agent therapy, which is mainly fluoropyrimidine (5-FU)-based, and multiple agent regimens containing oxaliplatin (OX), irinotecan (IRI) and capecitabine (CAP or XELODA or XEL) [<xref rid=\"B85-ijms-21-05389\" ref-type=\"bibr\">85</xref>]. Radical surgery and various chemotherapeutic agents can perceptibly create a state of dysbiosis, further exaggerating the influence of deleterious bacteria, reducing efficacy, and exacerbating the toxicity of chemotherapy [<xref rid=\"B86-ijms-21-05389\" ref-type=\"bibr\">86</xref>]. In particular, compared with preoperative samples, fecal samples collected postoperatively exhibit a significant decrease in obligate anaerobes, tumor-related bacteria, and butyric acid-producing bacteria. However, a relevant increase in some conditional pathogens, such as <italic>Bilophila</italic>, <italic>Eggerthella</italic>, and <italic>Anaerostipes</italic>, was observed [<xref rid=\"B87-ijms-21-05389\" ref-type=\"bibr\">87</xref>]. Moreover, chemotherapy also alters the intestinal microbiota through the so called “rebound effect”, which is characterized by a dramatic increase in pathogens and a shift in lactate-utilizing bacteria from <italic>Veillonella</italic> to <italic>Butyricimonas</italic> and <italic>Butyricicoccus</italic>, as well as a decrease in commensals [<xref rid=\"B87-ijms-21-05389\" ref-type=\"bibr\">87</xref>]. Accordingly, stool samples from resected stage III CRC patients characterized by CapeOX chemotherapy-induced diarrhea presented lower bacterial community richness and diversity, with <italic>Klebsiella pneumoniae</italic> being the most predominant microbial species [<xref rid=\"B88-ijms-21-05389\" ref-type=\"bibr\">88</xref>]. Specific members of the gut microbiota have been found to play a vital role in chemoresistance to 5-FU and OX therapy by mediating autophagy [<xref rid=\"B89-ijms-21-05389\" ref-type=\"bibr\">89</xref>]. Indeed, the potential relationship between <italic>Fn</italic> infection and the chemotherapeutic efficacy of 5-FU was investigated both in vitro and in vivo. <italic>F. nucleatum</italic> load reduced the chemosensitivity of CRC cells to 5-FU by targeting MYD88 innate immune signaling and specific microRNAs responsible for the activation of the autophagy pathway. All these results demonstrate how <italic>Fn</italic> abundance is well correlated with a lower response in advanced CRC patients to 5-FU-based adjuvant chemotherapy after radical surgery [<xref rid=\"B89-ijms-21-05389\" ref-type=\"bibr\">89</xref>,<xref rid=\"B90-ijms-21-05389\" ref-type=\"bibr\">90</xref>]. In addition, other gut microbes might aggravate chemotherapy-related adverse reactions via the microbial metabolism of chemotherapy agents [<xref rid=\"B91-ijms-21-05389\" ref-type=\"bibr\">91</xref>,<xref rid=\"B92-ijms-21-05389\" ref-type=\"bibr\">92</xref>]. For instance, IRI effectiveness is severely limited by gastrointestinal tract toxicity caused by gut bacterial β-glucuronidase enzymes [<xref rid=\"B91-ijms-21-05389\" ref-type=\"bibr\">91</xref>,<xref rid=\"B93-ijms-21-05389\" ref-type=\"bibr\">93</xref>]. </p><p>Besides these canonical treatments, other therapies have been explored for CRC management. Targeted therapies include four main groups of drugs: monoclonal antibodies against epidermal growth factor receptor (EGFR) (cetuximab and panitumumab), monoclonal antibodies against vascular endothelial growth factor (VEGF)-A (bevacizumab), fusion proteins that target multiple proangiogenic growth factors (e.g., aflibercept), and small-molecule-based multikinase inhibitors (e.g., regorafenib) [<xref rid=\"B84-ijms-21-05389\" ref-type=\"bibr\">84</xref>]. These treatments can work on cancerous cells by directly inhibiting cell proliferation, differentiation, and migration, but they can also alter the TME to slow down tumor growth and promote a stronger immunosurveillance response [<xref rid=\"B85-ijms-21-05389\" ref-type=\"bibr\">85</xref>]. Immune escape has been frequently identified in various cancers, including CRC [<xref rid=\"B94-ijms-21-05389\" ref-type=\"bibr\">94</xref>]. One major explanation is tumor-related T cell inactivation and exhaustion via activation of co-inhibitory receptors, the so-called immune checkpoint receptors, on the surface of T cells [<xref rid=\"B60-ijms-21-05389\" ref-type=\"bibr\">60</xref>], which include programmed cell death protein (PD)-1 and cytotoxic T lymphocyte antigen 4 (CTLA-4). Checkpoint inhibitors are now a standard of care in microsatellite-instable CRC patients [<xref rid=\"B61-ijms-21-05389\" ref-type=\"bibr\">61</xref>,<xref rid=\"B95-ijms-21-05389\" ref-type=\"bibr\">95</xref>,<xref rid=\"B96-ijms-21-05389\" ref-type=\"bibr\">96</xref>]. The gut microbiota is an important player affecting the efficacy of the immune checkpoint blockade [<xref rid=\"B97-ijms-21-05389\" ref-type=\"bibr\">97</xref>]. Initial findings by Vetizou et al. showed that the CTLA-4-targeting antibody ipilimumab could treat specific-pathogen-free mice but not GF mice. In addition, antibiotics including ampicillin, colistin, and streptomycin compromise the antitumor effects of this antibody, indicating the key role of the gut microbiota in immunotherapy outcomes [<xref rid=\"B98-ijms-21-05389\" ref-type=\"bibr\">98</xref>]. Ipilimumab induces significant changes in the microbiome—in particular, a decrease in the bacterial orders <italic>Bacteroidales</italic> and <italic>Burkholderiales</italic>. Similarly, the presence of <italic>Bifidobacterium</italic> was positively correlated with anti-tumor T-cell responses in melanoma and bladder cancer mouse models treated with an anti-PD-L1 agent [<xref rid=\"B99-ijms-21-05389\" ref-type=\"bibr\">99</xref>]. Gut microbes also affect the capacity of cytotoxic T cells to infiltrate the TME. Gut colonization with different <italic>Bacteroidales</italic> and non-<italic>Bacteroidales</italic> strains enhanced the efficacy of PD-1 and CTLA-4 monoclonal antibody therapies in GF mice due to the stronger immune-protective infiltration of CD8<sup>+</sup> T cells. Unfortunately, the adverse effects of immunotherapy are dominated by autoimmune complications, such as fatal forms of colitis, as seen in metastatic melanoma of Ipilimumab-treated patients, which developed intestinal inflammation within the first 16 weeks of treatment [<xref rid=\"B100-ijms-21-05389\" ref-type=\"bibr\">100</xref>].</p></sec><sec id=\"sec5-ijms-21-05389\"><title>5. Clinical Applications of the Gut Microbiota Modulation for CRC Prevention and Management</title><p>Several approaches, among which dietary interventions, antibiotic treatments, probiotics, prebiotics, and postbiotics, as well as fecal microbiota transplantation (FMT), have been explored to target and modulate gut microbiota composition, including both microbial physiology and/or their metabolites that cause or contribute to CRC directly or indirectly (<xref ref-type=\"fig\" rid=\"ijms-21-05389-f001\">Figure 1</xref>). Various experimental studies have deepened the understanding of the role of gut biomodulators and microbe-based treatments as antineoplastic agents, although a practical clinical application in CRC prevention and management is still largely lacking.</p><sec id=\"sec5dot1-ijms-21-05389\"><title>5.1. Dietary Interventions</title><p>As mentioned above, diet plays a significant role in shaping the microbiome and, therefore, in the management of CRC. While the impact of a westernized high-fat diet on CRC and on the gut microbiota has been well characterized, the protective effects of grain diets, known to be associated with low CRC risk, remain uncertain at the microbiota level. Yang et al. assessed the capacity of seven different grains to reduce CRC risk in mice fed with an HFD, showing that the consumption of non-glutinous rice, glutinous rice, and sorghum led to the highest reduction in CRC risk. In particular, non-glutinous rice stabilized key altered genera associated with CRC, including <italic>Bacteroides</italic>, <italic>Lactobacillus</italic>, <italic>Ruminococcus</italic>, and <italic>Acinetobacter</italic> [<xref rid=\"B101-ijms-21-05389\" ref-type=\"bibr\">101</xref>]. Moreover, through a prospective cohort study, a diet rich in whole grains and dietary fiber was associated with a lower risk of developing <italic>F. nucleatum</italic>-positive CRC but not <italic>F. nucleatum</italic>-negative CRC, supporting a potential role of intestinal microbiota in the development of CRC [<xref rid=\"B102-ijms-21-05389\" ref-type=\"bibr\">102</xref>]. Vitamin D supplementation reduces cancer incidence in mouse models of bacteria-driven colitis and CRC [<xref rid=\"B57-ijms-21-05389\" ref-type=\"bibr\">57</xref>]. Azoxymethane/dextran sodium sulfate (AOM/DSS)-induced CRC mice fed with high doses of vitamin D showed not only improved body weight gain and less colon shortening but also a lower expression of inflammatory cytokines such as IL-6 and TNF-α. Vitamin D has also a significant regulatory effect on the homeostasis of the microbiota, especially on the regulation of the intestinal barrier integrity mediated by <italic>Akkermansia muciniphila</italic>, a mucin-utilizing bacterium [<xref rid=\"B58-ijms-21-05389\" ref-type=\"bibr\">58</xref>,<xref rid=\"B59-ijms-21-05389\" ref-type=\"bibr\">59</xref>].</p><p>Since there are no clear guidelines on the type of nutrition that could have a major impact on cancer incidence, various forms of reduced caloric intake, such as fasting, demonstrate a wide range of beneficial effects in preventing malignancies and increasing the efficacy of cancer therapies [<xref rid=\"B103-ijms-21-05389\" ref-type=\"bibr\">103</xref>]. One of the main mechanisms through which fasting induces metabolic improvements is certainly mediated by the gut microbiota. For instance, every-other-day fasting led to an alteration of the gut microbiota composition, elevating fermentation products like acetate and lactate. Moreover, this dietary regimen enriched the levels of <italic>Firmicutes</italic> and also the production of SCFAs, decreasing <italic>Bacteroidetes</italic>, <italic>Actinobacteria</italic>, and <italic>Tenericutes</italic> [<xref rid=\"B104-ijms-21-05389\" ref-type=\"bibr\">104</xref>]. In particular, food withdrawal decreased the abundance of potentially pathogenic <italic>Proteobacteria</italic> while elevating <italic>A. muciniphila</italic> in mice fed with HFD [<xref rid=\"B105-ijms-21-05389\" ref-type=\"bibr\">105</xref>]. Due to various deficiencies in one or both key mitochondrial enzymes, tumors are not able to metabolize ketone bodies as an energetic source. Thus, the administration of a ketogenic diet (KD) may be a reasonable therapeutic strategy to inhibit tumor growth [<xref rid=\"B106-ijms-21-05389\" ref-type=\"bibr\">106</xref>]. KDs are low-carbohydrate, high-fat diets, mimicking the metabolic state of fasting by inducing a physiological rise in acetoacetate and beta-hydroxybutyrate [<xref rid=\"B107-ijms-21-05389\" ref-type=\"bibr\">107</xref>]. For cancer prevention, a high intake of mono-unsaturated fatty acids and n-3 polyunsaturated fatty acids could be hypothesized to be beneficial for promoting gut health [<xref rid=\"B108-ijms-21-05389\" ref-type=\"bibr\">108</xref>], as demonstrated by the delayed tumor growth induced by a KD rich in omega-3 fatty acids in a CRC mouse xenograft model [<xref rid=\"B106-ijms-21-05389\" ref-type=\"bibr\">106</xref>]. Lastly, supplementation with α-ketoglutarate, an important intermediary in the nuclear factor kappa light chain enhancer of activated B cells (NF-κB)-mediated inflammatory pathway, offered significant protection against CRC development in mice. Thus, α-ketoglutarate not only exhibited immunomodulatory effects mediated via the downregulation of IL-6, IL-22, TNF-α, and IL-1β cytokines but also minimized the frequency of opportunistic pathogens (<italic>Escherichia</italic> and <italic>Enterococcus</italic>), while it increased the populations of <italic>Akkermansia</italic>, <italic>Butyricicoccus</italic>, <italic>Clostridium</italic>, and <italic>Ruminococcus</italic>, suggesting that dietary α-ketoglutarate intervention may protect against inflammation-related CRC [<xref rid=\"B109-ijms-21-05389\" ref-type=\"bibr\">109</xref>]. </p></sec><sec id=\"sec5dot2-ijms-21-05389\"><title>5.2. Antibiotics</title><p>Modulation of the gut microbiota through the use of antibiotics was partially evaluated in CRC, with only few studies present in the literature [<xref rid=\"B110-ijms-21-05389\" ref-type=\"bibr\">110</xref>]. Cefoxitin, a semi-synthetic and broad-spectrum cephalosporin, induced a complete and durable clearance of enterotoxigenic <italic>B. fragilis</italic> colonization in previously ETBF-inoculated mice, with a concomitant decrease in median adenoma formation [<xref rid=\"B111-ijms-21-05389\" ref-type=\"bibr\">111</xref>]. Consistent with the pro-tumorigenic Th17 immune response of ETBF [<xref rid=\"B112-ijms-21-05389\" ref-type=\"bibr\">112</xref>,<xref rid=\"B113-ijms-21-05389\" ref-type=\"bibr\">113</xref>], its eradication was accompanied by an abrupt reduction in colonic IL-17A levels, suggesting that other microbes are implicated in the IL-17 response [<xref rid=\"B111-ijms-21-05389\" ref-type=\"bibr\">111</xref>]. Erythromycin has the ability to suppress the transcriptional activity of NF-κB and the activator protein-1 (AP-1), as well as its downstream targets, IL-6 and cyclooxygenase-2 (COX-2), in human CRC cells. Moreover, a reduction in <italic>Il</italic>-6 and <italic>cox-2</italic> mRNA expression was also observed in Apc<sup>Min/+</sup> mice, in which the number of intestinal polyps was reduced as well [<xref rid=\"B114-ijms-21-05389\" ref-type=\"bibr\">114</xref>]. Berberine (BBR), an isoquinoline molecule with antibacterial activity [<xref rid=\"B115-ijms-21-05389\" ref-type=\"bibr\">115</xref>], has been used to treat <italic>F. nucleatum</italic> colonization in Apc<sup>Min/+</sup> mouse models. BBR not only was able to reverse the microbiota imbalance induced by <italic>Fn</italic> but also blocked the secretion of mucosal immune factors, such as IL-21, IL-22, IL-31, and CD40L. In addition, this compound inhibited the <italic>Fn</italic>-induced activation of the Janus kinase/signal transducer and activator of transcription (JAK/STAT) and mitogen-activated protein kinase/extracellular signal-regulated kinases (MAPK/ERK) pathway [<xref rid=\"B116-ijms-21-05389\" ref-type=\"bibr\">116</xref>]. Moreover, the microbial structure alteration, characterized by the increase in <italic>Tenericutes</italic> and <italic>Verrucomicrobia</italic>, was dramatically reversed in <italic>Fn</italic>-infected mice after BBR intervention, suggesting an antimicrobial intervention as a potential treatment for patients with <italic>Fn</italic>-associated CRC [<xref rid=\"B117-ijms-21-05389\" ref-type=\"bibr\">117</xref>]. Metronidazole has also been explored as an alternative to treat <italic>F. nucleatum</italic> colonization. This antibiotic reduced the <italic>Fn</italic> load, cancer cell proliferation, and overall tumor growth in mice bearing colon cancer xenografts [<xref rid=\"B117-ijms-21-05389\" ref-type=\"bibr\">117</xref>]. However, antibiotic administration, being the most aggressive means of manipulating gut microbiota composition, has been controversial in its role in cancer management. Although gut microbiome depletion was shown to inhibit cancer progression, accumulating lines of evidence highlight how antibiotics can compromise immunotherapy efficacy or induce disease progression by creating further microbial dysbiosis [<xref rid=\"B118-ijms-21-05389\" ref-type=\"bibr\">118</xref>,<xref rid=\"B119-ijms-21-05389\" ref-type=\"bibr\">119</xref>].</p></sec><sec id=\"sec5dot3-ijms-21-05389\"><title>5.3. Probiotics</title><p>For CRC prevention and management, another potential strategy is represented by probiotics. Probiotics are living microorganisms which can confer positive effects on health by impacting on the resident microbiota, intestinal epithelium cells, and, globally, the immune system [<xref rid=\"B120-ijms-21-05389\" ref-type=\"bibr\">120</xref>]. Nowadays, several bacterial species are used as probiotics, which are commercially available (<xref rid=\"ijms-21-05389-t002\" ref-type=\"table\">Table 2</xref>).</p><p>Among them, lactic acid bacteria are the most frequently used, not only for their ability to contribute to colonization resistance [<xref rid=\"B121-ijms-21-05389\" ref-type=\"bibr\">121</xref>] but also for their immunomodulatory effects [<xref rid=\"B122-ijms-21-05389\" ref-type=\"bibr\">122</xref>]. Specific bacterial strains are able to prevent tumor development through the modulation of the immune system in CRC murine models. Oral treatment with <italic>Lactobacillus casei</italic> BL23, a probiotic strain well known for its anti-inflammatory [<xref rid=\"B123-ijms-21-05389\" ref-type=\"bibr\">123</xref>] and anticancer properties [<xref rid=\"B124-ijms-21-05389\" ref-type=\"bibr\">124</xref>], significantly protected mice against CRC development. In addition, this probiotic showed not only immunomodulatory effects by downregulating colonic IL-22 but also an antiproliferative effect mediated by the upregulation of caspase (casp)-7, casp-9, and Bik, as well as a decrease in Ki67 expression [<xref rid=\"B125-ijms-21-05389\" ref-type=\"bibr\">125</xref>]. Probiotics have also been recently exploited to counteract chemotherapy-dependent dysbiosis, mucositis (inflammatory lesions of the oral and/or gastrointestinal tract caused by high-dose cancer therapies), post-surgical microbiota intestinal alterations, and relapses in CRC patients. Preclinical studies revealed that <italic>L. rhamnosus</italic> (Lcr35) reduces the severity of diarrhea and intestinal mucositis caused by adjuvant 5-FU-based chemotherapy in mice injected with CT26 colorectal adenocarcinoma cells. Moreover, Lcr35 treatment normalized the increased number of BCL2-associated X protein apoptotic and NF-κB-activated cells as well as the upregulated expression of TNF-α and IL-6 [<xref rid=\"B126-ijms-21-05389\" ref-type=\"bibr\">126</xref>]. A recent randomized, double-blind, placebo-controlled trial (NCT03782428) revealed that treatment with six viable microorganisms of <italic>Lactobacillus</italic> and <italic>Bifidobacterium</italic> strains significantly reduced the levels of pro-inflammatory cytokines such as TNF-α, IL-6, IL-10, IL-12, IL-17A, IL-17C, and IL-22 and prevented post-surgical complications as well [<xref rid=\"B127-ijms-21-05389\" ref-type=\"bibr\">127</xref>]. Probiotics are generally considered safe and well-tolerated for healthy subjects, but in patients with underlying medical conditions, their safety profile is uncertain [<xref rid=\"B62-ijms-21-05389\" ref-type=\"bibr\">62</xref>]. Probiotic translocation, which refers to the entry of viable bacteria into extraintestinal sites, leads to the ensuing systemic or localized infections. Indeed, various case reports of probiotic-associated bacteremia, endocarditis, liver abscess, and pneumonia have been published [<xref rid=\"B128-ijms-21-05389\" ref-type=\"bibr\">128</xref>]. Nevertheless, another theoretical risk regarding long-term probiotic use is the possible transmission of antibiotic-resistant genes via horizontal gene transfer [<xref rid=\"B62-ijms-21-05389\" ref-type=\"bibr\">62</xref>].</p></sec><sec id=\"sec5dot4-ijms-21-05389\"><title>5.4. Prebiotics</title><p>Prebiotics are non-digestible dietary compounds that stimulate the growth and activity of probiotics, conferring a competitive advantage to commensal bacteria capable of metabolizing these substrates or by increasing the production of beneficial metabolic products, such SCFAs, that result from their fermentation [<xref rid=\"B129-ijms-21-05389\" ref-type=\"bibr\">129</xref>,<xref rid=\"B130-ijms-21-05389\" ref-type=\"bibr\">130</xref>]. The health benefits of prebiotics goes beyond nutrition and they are gaining popularity among people (<xref rid=\"ijms-21-05389-t003\" ref-type=\"table\">Table 3</xref>). However, great care must be taken to ensure their therapeutic efficacy, especially regarding intestinal tumors. The chemopreventive effect of galacto-oligosaccharides derived from lactulose revealed a remarkable reduction in colonic tumor numbers in a CRC animal model. Moreover, a significant decrease in pro-inflammatory bacteria was observed, as well as a substantial increase in beneficial populations such as <italic>Bifidobacterium</italic> [<xref rid=\"B131-ijms-21-05389\" ref-type=\"bibr\">131</xref>]. Similarly, ginsenoside-Rb3 and ginsenoside-Rd effectively reduced the size and the number of the polyps and reinstated the dysbiotic gut microbial composition and intestinal microenvironment in Apc<sup>Min/+</sup> mice by promoting the growth of SCFA-producing bacteria [<xref rid=\"B132-ijms-21-05389\" ref-type=\"bibr\">132</xref>]. Given the encouraging results obtained from studies conducted both in vitro and in vivo, prebiotic administration has also been evaluated in more structured, randomized clinical trials involving CRC patients. A randomized, double-blind, no-treatment parallel control clinical trial involving 140 perioperative CRC patients was performed to investigate the effects of prebiotics containing fructooligosaccharides, xylooligosaccharides, polydextrose, and resistant dextrin on the immune system and the intestinal microbiota. In the preoperative interval, prebiotics upregulated serum levels of IgG, IgM, and transferrin, while in the postoperative period, they enhanced levels of IgG, IgA, CD8+ T cells, and total B lymphocytes. Prebiotic administration increased the abundance of <italic>Bifidobacterium</italic> and <italic>Enterococcus</italic> but decreased the abundance of <italic>Bacteroides</italic> in the preoperative timeframe. On the other hand, in the postoperative period, the abundance of <italic>Bacteroides</italic> was decreased, while <italic>Escherichia-Shigella</italic> was increased, suggesting that prebiotic intake is recommended to improve serum immunologic indicators in patients with CRC 7 days before operation, since surgical trauma can alter the gut microbiome [<xref rid=\"B133-ijms-21-05389\" ref-type=\"bibr\">133</xref>]. However, recent studies have demonstrated that prebiotic interventions may exert variable effects in different individuals, probably due to differences in the host genetic background, which may plausibly explain the different tumor phenotype, oncogenic pathways, and, subsequently, the response to a specific intervention [<xref rid=\"B134-ijms-21-05389\" ref-type=\"bibr\">134</xref>].</p></sec><sec id=\"sec5dot5-ijms-21-05389\"><title>5.5. Postbiotics</title><p>Postbiotics are chemical compounds of microbial origin including short chain fatty acids, enzymes, peptides, teichoic acids, peptidoglycan-derived muropeptides, endo- and exo-polysaccharides, cell surface proteins, vitamins, plasmalogens, and organic acids [<xref rid=\"B140-ijms-21-05389\" ref-type=\"bibr\">140</xref>]. The chemopreventive effects of acetate, butyrate, and propionate mixture were evaluated in AOM/DSS-treated mice, determining a significant reduction in tumor incidence and size. Moreover, SCFAs suppressed pro-inflammatory cytokine expression, including that of IL-6, TNF-α, and IL-17, as well as COX-2 and NF-κB [<xref rid=\"B141-ijms-21-05389\" ref-type=\"bibr\">141</xref>]. The prophylactic effects of postbiotics were also shown by the oral intake of mitochonic acid 35, an indole compound, which ameliorated the disease activity index score and survival rate, reducing the macroscopic formation of colonic tumors in murine models of CRC. In addition, it was able to downregulate colonic TNF-α, IL-6, TGF-β1, and fibronectin 1 expression, suggesting its ability to inhibit CRC carcinogenesis [<xref rid=\"B142-ijms-21-05389\" ref-type=\"bibr\">142</xref>]. Among the non-viable microbial cells, researchers have exploited the possible therapeutic effects of <italic>Enterococcus faecalis.</italic> In this regard, it was observed that pre-treatment of THP-1-derived macrophages with heat-killed <italic>E. faecalis</italic> inhibited NLRP3 inflammasome activation in response to fecal content or commensal microbes, <italic>Proteus mirabilis</italic> or <italic>E. coli</italic>, according to the reduction in casp-1 activation and IL-1β maturation. Moreover, experiments in vivo showed that <italic>E. faecalis</italic> improved the severity of intestinal inflammation, protecting mice from the formation of colon tumors [<xref rid=\"B143-ijms-21-05389\" ref-type=\"bibr\">143</xref>]. Interestingly, postbiotics have also been evaluated as adjuvants of anti-cancer therapies. The combined efficacy of <italic>L. acidophilus</italic> cell lysates with an anti-CTLA-4 monoclonal antibody was tested in vivo. In contrast to anti-CTLA-4 monotherapy, <italic>L. acidophilus</italic> lysates attenuated body weight loss and the combined administration significantly protected mice against CRC development, suggesting an enhancement of anti-CTLA-4 antitumor activity. Moreover, the synergistic combination led to an increase in CD8+T cells, especially the effector memory T cells, a decrease in Tregs, and it alternatively activated macrophages (M2) in the TME. Additionally, pre-treatment with <italic>L. acidophilus</italic> lysate in vitro showed an immunomodulatory effect through the inhibition of M2 polarization and of IL-10 production by lipopolysaccharide-activated macrophages. Lastly, the combined administration significantly inhibited the abnormal increase in the relative abundance of <italic>Proteobacteria</italic> and partly counterbalanced CRC-induced dysbiosis in mice. Thus, CTLA-4 blocking antibodies in combination with the present lysates may be of importance for the development of new therapeutic strategies against CRC to be tested in clinical trials [<xref rid=\"B144-ijms-21-05389\" ref-type=\"bibr\">144</xref>]. The postbiotic field is as yet a highly unknown area, considering that the number and diversity of bacterial metabolites are vast. Thus, their safety profile is still under preclinical and clinical evaluation [<xref rid=\"B62-ijms-21-05389\" ref-type=\"bibr\">62</xref>].</p></sec><sec id=\"sec5dot6-ijms-21-05389\"><title>5.6. FMT</title><p>Fecal microbiota transplantation (FMT)—i.e., the transfer of a microbial ecology from a healthy donor into a patient—is currently being explored as a therapeutic strategy to restore normobiosis, the normal state of the human intestinal microbiota, in different pathological contexts [<xref rid=\"B145-ijms-21-05389\" ref-type=\"bibr\">145</xref>]. Since CRC is characterized by a status of dysbiosis, FMT is considered as a potential clinical application in patients. To date, FMT has only proved to be highly successful in treating recurrent and antibiotic refractory <italic>Clostridiodes difficile</italic> (<italic>C. difficile</italic>) infection, with cure rates of approximately 90% [<xref rid=\"B129-ijms-21-05389\" ref-type=\"bibr\">129</xref>]. In a CRC mouse model, FMT was able to normalize the gut microbiota through the reduction of tumor growth. FMT contributed to reducing the levels of inflammation by decreasing IL-1β, IL-6, and TNF-α levels and increasing anti-inflammatory cytokines such as IL-10 and TGF-β through the inhibition of canonical NF-κB activity and cellular proliferation. Moreover, FMT treatment triggered the accumulation of Tregs but not Th1, Th2, and Th17 cells [<xref rid=\"B146-ijms-21-05389\" ref-type=\"bibr\">146</xref>]. Additionally, the chemopreventive potential of FMT on FOLFOX-induced mucosal injury was evaluated. Microbiota transplantation reduced the severity of diarrhea and intestinal mucositis, suppressed IL-6 levels, and restored the number of goblet cells, zonula occludens-1, apoptotic, and NF-κB-positive cells, as well as the expression of toll-like receptors and MYD88. All these beneficial effects were accompanied by a restoration of the gut microbiota composition without causing bacteremia [<xref rid=\"B147-ijms-21-05389\" ref-type=\"bibr\">147</xref>]. However, it is worth noting that the impact of FMT on the recipient immune system is complicated and unpredictable, and the risk of dissemination of unknown pathogens cannot be prevented. In addition, numerous questions remain to be answered, including the features that a “good donor” should present, the routes of administration, and the long-term effects of this therapy [<xref rid=\"B148-ijms-21-05389\" ref-type=\"bibr\">148</xref>].</p></sec></sec><sec sec-type=\"conclusions\" id=\"sec6-ijms-21-05389\"><title>6. Conclusions and Perspective</title><p>The core of CRC carcinogenesis is also defined by gut microbiota metabolic activity and a dysbiotic composition. Hence, a consortium of inflammatory responses, virulence factors, and impaired epithelial signaling create a suitable microenvironment for the development of disrupted and irregular interactions between the host and the gut microbiota [<xref rid=\"B149-ijms-21-05389\" ref-type=\"bibr\">149</xref>]. Even if surgery is the primary therapeutic option, patients with advanced disease or cancer recurrence after surgery remain difficult to cure. Since the gut microbiota is gaining more attention, a deeper knowledge of its interaction with the host’s immune system will elucidate the outcomes of cancer therapeutic strategies. Lastly, research is currently assessing the impact of personalized diets and biomodulators to restore a eubiotic condition for the prevention and treatment of CRC [<xref rid=\"B150-ijms-21-05389\" ref-type=\"bibr\">150</xref>,<xref rid=\"B151-ijms-21-05389\" ref-type=\"bibr\">151</xref>].</p></sec></body><back><notes><title>Author Contributions</title><p>Writing—original draft preparation C.A., F.P.; writing—review and editing F.S., M.R.G., A.D.-B., G.L., F.F.; supervision F.S., F.F.; funding acquisition F.F. All authors have read and agreed to the published version of the manuscript.</p></notes><notes><title>Funding</title><p>This work was made possible through a grant from AIRC, Associazione Italiana per la Ricerca sul Cancro, to FF (IG-2019 22923).</p></notes><notes notes-type=\"COI-statement\"><title>Conflicts of Interest</title><p>The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.</p></notes><glossary><title>Abbreviations</title><array orientation=\"portrait\"><tbody><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Adenomatous polyposis coli</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">APC</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Azoxymethane/dextran sodium sulfate</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">AOM/ASS</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">B. fragilis toxin</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">BFT</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Berberine</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">BBR</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Calcineurin and cytoplasmic 3</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Nfatc3</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Capecitabine</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">CAP or XELODA or XEL</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Capecitabine oxaliplatin</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">CAPeOX</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Caspase</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">casp</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Cluster of differentiation</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">CD</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Colorectal cancer</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">CRC</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Cyclooxygenase-2</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">COX-2</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Cytotoxic T lymphocyte antigen 4</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">CTLA-4</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Early-onset colorectal cancer</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">EOCRC</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Enterotoxigenic <italic>B. fragilis</italic></td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">ETBF</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Epidermal growth factor receptor</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">EGFR</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Fecal microbiota transplantation</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">FMT</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Fluorouracile</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">5-FU</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Fusobacterium nucleatum</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Fn</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Germ-free</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">GF</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">High fat diet</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">HFD</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Interleukin</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">IL</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Irinotecan</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">IRI</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Janus kinase/signal transducer and activator of transcription</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">JAK/STAT</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Ketogenic diet</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">KD</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Mitogen-activated protein kinase/extracellular signal-regulated kinases</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">MAPK/ERK</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Non-enterotoxigenic <italic>Bacteroides fragilis</italic></td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">NTBF</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Nuclear factor kappa light chain enhancer of activated B cells</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">NF-κB</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Oxaliplatin</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">OX</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Programmed cell death protein</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">PD-1</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Short chain fatty acids</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">SCFAs</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Transforming growth factor</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">TGF</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Tumor microenvironment</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">TME</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Tumor necrosis factor</td><td align=\"left\" 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Probiotics act through different anticancerogenic mechanisms: (i) probiotics can inhibit the colonization of pathogenic bacteria, (ii) they can enhance barrier function increasing mucin production and tight junction protein expression, (iii) they promote homeostatic immune responses, contributing to the expansion of anti-inflammatory responses by Treg cells and the modulation of pro-inflammatory cytokine release, (iv) they promote apoptosis on cancer cells. Postbiotics induce selective cytotoxicity against tumor cells as well as the control of tumor cell proliferation by inhibiting NFATc3 activation. Finally, fecal microbiota transplantation (FMT) could be used in CRC management to restore microbiome normobiosis and therefore induce homeostatic immune responses; nevertheless, potential complications associated with FMT include the risk of introducing new pathobionts and the spreading of disease-associated genes.</p></caption><graphic xlink:href=\"ijms-21-05389-g001\"/></fig><table-wrap id=\"ijms-21-05389-t002\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijms-21-05389-t002_Table 2</object-id><label>Table 2</label><caption><p>Examples of some commercially available probiotics.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Brand Name</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Strain</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Producer</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Dicoflor</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>Lactobacillus rhamnosus GG</italic>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">AGPHARMA</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Enterogermina</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>Bacillus clausii</italic>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">SANOFI</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Enterolactis</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>Lactobacillus casei</italic>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">SOFAR</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Nutriflor</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>Lactobacillus acidophilus DDS-1</italic>\n<break/>\n<italic>Lactobacillus bulgaricus DDS-14</italic>\n<break/>\n<italic>Bifidobacterium bifidum</italic>\n<break/>\n<italic>Lactobacillus rhamnosus</italic>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">NUTRIGEA</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Probactiol Duo</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>Lactobacillus acidophilus NCFM</italic>\n<break/>\n<italic>Lactobacillus paracasei Lpc-37</italic>\n<break/>\n<italic>Bifidobacterium lactis Bi-07</italic>\n<break/>\n<italic>Bifidobacterium lactis Bi-04</italic>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">METAGENETICS</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">VSL#3</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>Streptococcus thermophilus</italic>\n<break/>\n<italic>Bifidobacterium breve</italic>\n<break/>\n<italic>Bifidobacterium longum</italic>\n<break/>\n<italic>Bifidobacterium infantis</italic>\n<break/>\n<italic>Lactobacillus acidophilus</italic>\n<break/>\n<italic>Lactobacillus plantarum</italic>\n<break/>\n<italic>Lactobacillus paracasei</italic>\n<break/>\n<italic>Lactobacillus delbrueckii subsp. bulgaricus</italic>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">FERRING FARMACEUTICI</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Yakult</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>Lactobacillus casei Shirota</italic>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">YAKULT (Tokio)</td></tr></tbody></table></table-wrap><table-wrap id=\"ijms-21-05389-t003\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijms-21-05389-t003_Table 3</object-id><label>Table 3</label><caption><p>Prebiotic-rich foods and their effects on human health.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Prebiotic</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Origin</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Clinical Benefit</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">References</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Fructo-Oligosaccharides (FOS)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Vegetables, cereals (onion, garlic, artichokes)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Crohn’s disease<break/>Colitis<break/>CRC<break/>Obesity</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B135-ijms-21-05389\" ref-type=\"bibr\">135</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Gluco-Oligosaccharides (GOS)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Legumes (lentils, chickpeas and broad beans)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Crohn’s disease<break/>Colitis<break/>Obesity</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B135-ijms-21-05389\" ref-type=\"bibr\">135</xref>,<xref rid=\"B136-ijms-21-05389\" ref-type=\"bibr\">136</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Ginsenoside-Rb3</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Panax Ginseng</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Myeloid leukemia<break/>CRC<break/>Heart failure </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B132-ijms-21-05389\" ref-type=\"bibr\">132</xref>,<xref rid=\"B137-ijms-21-05389\" ref-type=\"bibr\">137</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Inulin</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Asparagus and artichokes</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Crohn’s disease<break/>Colitis<break/>CRC<break/>Obesity<break/>Diabetes</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B138-ijms-21-05389\" ref-type=\"bibr\">138</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Lactulose</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Boiled milk</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Constipation</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B139-ijms-21-05389\" ref-type=\"bibr\">139</xref>]</td></tr></tbody></table></table-wrap></floats-group></article>\n"
]
[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Int J Environ Res Public Health</journal-id><journal-id journal-id-type=\"iso-abbrev\">Int J Environ Res Public Health</journal-id><journal-id journal-id-type=\"publisher-id\">ijerph</journal-id><journal-title-group><journal-title>International Journal of Environmental Research and Public Health</journal-title></journal-title-group><issn pub-type=\"ppub\">1661-7827</issn><issn pub-type=\"epub\">1660-4601</issn><publisher><publisher-name>MDPI</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32751437</article-id><article-id pub-id-type=\"pmc\">PMC7432109</article-id><article-id pub-id-type=\"doi\">10.3390/ijerph17155494</article-id><article-id pub-id-type=\"publisher-id\">ijerph-17-05494</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Article</subject></subj-group></article-categories><title-group><article-title>Qualitative Approach to Environmental Risk Assessment in Transport</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0002-8320-1419</contrib-id><name><surname>Dvorak</surname><given-names>Zdenek</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05494\">1</xref><xref rid=\"c1-ijerph-17-05494\" ref-type=\"corresp\">*</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0002-4617-0553</contrib-id><name><surname>Rehak</surname><given-names>David</given-names></name><xref ref-type=\"aff\" rid=\"af2-ijerph-17-05494\">2</xref></contrib><contrib contrib-type=\"author\"><name><surname>David</surname><given-names>Andrej</given-names></name><xref ref-type=\"aff\" rid=\"af3-ijerph-17-05494\">3</xref></contrib><contrib contrib-type=\"author\"><name><surname>Cekerevac</surname><given-names>Zoran</given-names></name><xref ref-type=\"aff\" rid=\"af4-ijerph-17-05494\">4</xref></contrib></contrib-group><aff id=\"af1-ijerph-17-05494\"><label>1</label>Faculty of Security Engineering, University of Zilina, 01026 Zilina, Slovakia</aff><aff id=\"af2-ijerph-17-05494\"><label>2</label>Faculty of Safety Engineering, VSB—Technical University of Ostrava, 70030 Ostrava, Czech Republic; <email>[email protected]</email></aff><aff id=\"af3-ijerph-17-05494\"><label>3</label>Faculty of Operation and Economics of Transport and Communications, University of Zilina, 01026 Zilina, Slovakia; <email>[email protected]</email></aff><aff id=\"af4-ijerph-17-05494\"><label>4</label>Faculty of Business and Law, Union—Nikola Tesla University, 11000 Belgrade, Serbia; <email>[email protected]</email></aff><author-notes><corresp id=\"c1-ijerph-17-05494\"><label>*</label>Correspondence: <email>[email protected]</email>; Tel.: +421-415-136-854</corresp></author-notes><pub-date pub-type=\"epub\"><day>30</day><month>7</month><year>2020</year></pub-date><pub-date pub-type=\"ppub\"><month>8</month><year>2020</year></pub-date><volume>17</volume><issue>15</issue><elocation-id>5494</elocation-id><history><date date-type=\"received\"><day>29</day><month>6</month><year>2020</year></date><date date-type=\"accepted\"><day>27</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>© 2020 by the authors.</copyright-statement><copyright-year>2020</copyright-year><license license-type=\"open-access\"><license-p>Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>).</license-p></license></permissions><abstract><p>The purpose of this paper is to present the development of a qualitative approach to environmental risk assessment (QAERA) in transport. The approach is described as a model developed for the future software tool which will be utilizable as a risk decision support system. The basic part is aimed on developing a quantitative environmental risk assessment. Thus, this paper describes a set of 6 pillars of safety and security. Accordingly, the paper contains both chosen safety and security indicators and selected criteria for assessing the risk of launching the environmental change of global model thinking in the transport sector. The environmental risk assessment as a global model of thinking was originally based on historical experience but, nowadays, it is changing. Based on new expert knowledge, more precisely, on input of new global data, paper displays an environmental risk assessment with actual interpretation. The discussion of the paper is oriented to support research results, a new knowledge-oriented approach to global climate changes, using suitable risk assessment methods and technics. The result of the paper is a new approach for the modeling of environmental risk assessment in the transport sector.</p></abstract><kwd-group><kwd>risk assessment</kwd><kwd>transport sector</kwd><kwd>environment</kwd><kwd>common safety method</kwd><kwd>decision support system</kwd><kwd>expert evaluation</kwd></kwd-group></article-meta></front><body><sec sec-type=\"intro\" id=\"sec1-ijerph-17-05494\"><title>1. Introduction</title><p>Transport, as one of the most important sectors of economics, has been developing gradually. Development of mankind is linked to the development of transport systems. The history of transport is mainly associated with the invention of the wheel and the development of inland waterway transport. In addition, maritime transport has played a key role in discovering new continents. With the development of the industrialization of society, railway transport began to develop significantly hand in hand with the discovery of a steam engine. The discovery of an internal combustion and diesel engine led to the development of road transport. The latest results of science and research have been fundamental elements at gradual development of each mode of transport. For many decades, the impact of transport on the environment has not been fundamentally addressed. To be specific, the first research did not take place until the middle of the twentieth century. Globally, the number of vehicles is increasing from year to year, and although the technical parameters of internal combustion engines are improving, global emissions are growing.</p><p>Emphasizing the environmental aspect of individual modes of transport should be an important aspect of the transport policy of an advanced society. The European Commission regularly issues recommendations and directives that address some areas of transport development. Individual states should define the priorities of transport development in their transport policy, mainly for ensuring transport services, but also for expected environmental impacts. Essentially, the authors of the article come from central and eastern Europe. In conjunction with this, the examples of the transport policy of the Czech Republic, Slovakia, and Serbia show that these countries have a remarkably similar transport policy, where the aspect of environmental protection plays a less important role.</p><p>The traditional scientific research plan has historically been focused on technical and technological processes. The challenge today is security with its multi-level and multi-union complexity. If safety research is understood in the above concept as a multi-level and multi-sectoral problem, then the challenge of addressing the issue of the environmental risk assessment will be met. The methodology will become a key element for setting appropriate indicators that will take into account not only the economic benefits for the company but will also address the societal impact in the social, health, and environmental fields. Drawing on this, simplifying efficiency and risk assessments have now led to absurd shipments of raw materials and semi-finished products in globalized companies, regardless of the full cost.</p><p>Current methods of environmental risk assessment are mainly of a quantitative or hybrid nature. In engineering, these methods are most commonly used in industry, where they account for more than 50% of existing risk assessment methods [<xref rid=\"B1-ijerph-17-05494\" ref-type=\"bibr\">1</xref>]. In the field of transport, these methods are used mainly in the context of the assessment of mobile risk sources [<xref rid=\"B2-ijerph-17-05494\" ref-type=\"bibr\">2</xref>,<xref rid=\"B3-ijerph-17-05494\" ref-type=\"bibr\">3</xref>]. However, these methods require a large amount of specific data, which is very time consuming to collect [<xref rid=\"B4-ijerph-17-05494\" ref-type=\"bibr\">4</xref>]. For this reason, it is convenient to use at first some of the appropriate qualitative methods for the preliminary risk analysis [<xref rid=\"B5-ijerph-17-05494\" ref-type=\"bibr\">5</xref>,<xref rid=\"B6-ijerph-17-05494\" ref-type=\"bibr\">6</xref>]. These methods are time-saving but, on the contrary, also quite general.</p><p>Referring to the above, it can be stated that even today there is no suitable available method dealing with the qualitative assessment of environmental risks arising from normal traffic. Based on this, the paper aims to present a newly proposed qualitative approach to environmental risk assessment in the transport sector. The essence of this approach is a preliminary environmental risk assessment, which is based on the application of selected safety and security indicators and selected risk assessment criteria in the implementation of environmental change in the global model of thinking in the transport sector.</p></sec><sec id=\"sec2-ijerph-17-05494\"><title>2. Literature Review</title><p>The preparation of a paper focusing on environmental risk assessment in transport requires a broader framework of solutions. Therefore, we primarily examined information sources that play a decisive role in the field of environmental protection. Subsequently, attention was paid to the areas of sustainable development, transport, safety and security.</p><sec id=\"sec2dot1-ijerph-17-05494\"><title>2.1. Environmental Area</title><p>The first paper to be discussed is the paper Biodiversity Loss and its Impact on Humanity, written by a broad team of authors led by Cardinale et al. [<xref rid=\"B7-ijerph-17-05494\" ref-type=\"bibr\">7</xref>]. The paper has focused on research since 1980, evaluating the last 40 years of life on Earth. Gradually, significant climate changes are taking place, which are accompanied by extreme weather events and often lead to the extinction of some animal and plant species. Entire ecosystems disappear irretrievably. As mentioned in the paper, in 2005, the Millennium Ecosystem Assessment was published for the first time. The condition and trends in the world’s ecosystems, and the services they provide, highlighted two distinct foci of biodiversity and ecosystem functioning (BEF) and biodiversity and ecosystem services (BES) research. Selecting from the published Consensus statements, there is currently clear evidence that biodiversity loss reduces the efficiency of ecological communities that capture biologically important resources, produce biomass, decompose, and recycle biologically important nutrients. Furthermore, there is evidence that greater biodiversity increases the stability of ecosystem functions over time. Moreover, four emerging trends were defined in the research. In our research on environmental risk assessment, we found that diversity effects grow stronger with time, and may increase at larger spatial scales. In BES research—for example, if we want to know how biodiversity affects the ability of ecosystems to remove carbon dioxide from the atmosphere and store carbon in the long term, we need to consider the net impact of biodiversity on photosynthesis (exchange of carbon dioxide for oxygen).</p><p>Changes in the environment have taken place continuously in the history of the planet Earth. The research is focused on different parts of the environment. An example to be used in our publication is the article 2500-Year Climate and Environmental Record Inferred from Subfossil Chironomids from Lugu Lake, by the author team led by Chang et al. [<xref rid=\"B8-ijerph-17-05494\" ref-type=\"bibr\">8</xref>] from 2018. The research focused on multi-proxy indicators for catchment erosion at Lugu Lake puts forward a methodological approach that the issue of environmental change must be considered in a long time series.</p><p>Another relevant source is Connor et al. [<xref rid=\"B9-ijerph-17-05494\" ref-type=\"bibr\">9</xref>] Long-Term Population Dynamics: Theory and Reality in a Peatland Ecosystem, which is focused on the research of environmental change in a selected region. Jimenez and Flores [<xref rid=\"B10-ijerph-17-05494\" ref-type=\"bibr\">10</xref>] focused on reducing carbon dioxide emissions in large agglomerations, where transport plays a big role. Kondratyev et al. [<xref rid=\"B11-ijerph-17-05494\" ref-type=\"bibr\">11</xref>] was involved in research of global carbon cycle and climate change. Similarly, Koblen et al. [<xref rid=\"B12-ijerph-17-05494\" ref-type=\"bibr\">12</xref>] focused on reducing emissions from aviation transport. This research was followed by Koscak et al. [<xref rid=\"B13-ijerph-17-05494\" ref-type=\"bibr\">13</xref>] which dealt with negative environmental impact of winter airport maintenance.</p><p>Within the European Universities Association, there was an established waterborne technology platform, which integrates research activities within maritime transport. The platform has defined seven key trends for maritime transport, which touch on the topic addressed and will be addressed in the paper [<xref rid=\"B14-ijerph-17-05494\" ref-type=\"bibr\">14</xref>].</p></sec><sec id=\"sec2dot2-ijerph-17-05494\"><title>2.2. Sustainable Development Area</title><p>As a part of global solutions, world conferences on environmental protection were organized in the past. Gradually, this agenda became one of the 17 goals of the transforming world called the 2030 Agenda for Sustainable Development. This agenda was approved by the United Nations and it contains 197 targets in addition to 17 basic goals. Some of them, directly and indirectly, affect the issues of the environment and sustainable transport [<xref rid=\"B15-ijerph-17-05494\" ref-type=\"bibr\">15</xref>]. Jakobsen [<xref rid=\"B16-ijerph-17-05494\" ref-type=\"bibr\">16</xref>] subsequently brought a new perspective on ecological economics.</p><p>As for these goals and targets, four goals are relevant to our research: Goal 4—Ensure lifelong learning opportunities; Goal 9—Built resilient infrastructure, promote inclusive and sustainable industrialization and foster innovation; Goal 11—Make city and human settlements inclusive safe, resilient, and sustainable; and Goal 13—Take urgent action to combat climate change and its impacts. In the next part, attention will be paid to them [<xref rid=\"B15-ijerph-17-05494\" ref-type=\"bibr\">15</xref>].</p></sec><sec id=\"sec2dot3-ijerph-17-05494\"><title>2.3. Transport Area</title><p>A lot of research teams around the world are involved in assessing environmental change and its impact on transport and transport infrastructure. Extreme weather events, seismic and geological changes occur in all regions of the world. However, the first limitation of our research is the focus on the central European environment, as examples will be events from the Czech Republic, Slovakia, and Serbia. All three countries have remarkably similar relief. On the whole, there are mountain ranges with transport infrastructure and lowlands with large rivers. The nature of the threats to transport and transport infrastructure is similar. For this reason, the writers focus their research on four basic key components—Modes, Infrastructures, Networks, and Flows (see <xref ref-type=\"fig\" rid=\"ijerph-17-05494-f001\">Figure 1</xref>).</p><p>As a significant contribution to the field of environmental risk assessment in transport, we present the RAIN project (i.e. Risk Analysis of Infrastructure Networks) solved in the years 2014–2016. The project pointed up the impact of weather extremes on transport and energy. The project resulted in building up a new framework for assessing the risks posed by weather extremes—floods, lightning, landslides, extreme droughts, frosts, and storms [<xref rid=\"B18-ijerph-17-05494\" ref-type=\"bibr\">18</xref>].</p><p>The theoretical framework of risk assessment and information support in railway and road transport was also addressed in the publications Assessment of Critical Infrastructure Elements in Transport [<xref rid=\"B19-ijerph-17-05494\" ref-type=\"bibr\">19</xref>], Crisis Management Decision Support System in Railway Infrastructure Company [<xref rid=\"B20-ijerph-17-05494\" ref-type=\"bibr\">20</xref>], and Multi-agent System for Decreasing of Risk in Road Transport [<xref rid=\"B21-ijerph-17-05494\" ref-type=\"bibr\">21</xref>].</p><p>Dzunda et al. [<xref rid=\"B22-ijerph-17-05494\" ref-type=\"bibr\">22</xref>] dealt with the synthesis of selected aspects of navigation systems to increase air safety. Havel et al. [<xref rid=\"B23-ijerph-17-05494\" ref-type=\"bibr\">23</xref>] focused their attention on a new route intersection-minimum distance model. Sousek and Dvorak [<xref rid=\"B24-ijerph-17-05494\" ref-type=\"bibr\">24</xref>] researched the transport of dangerous shipments. Fuchs et al. [<xref rid=\"B25-ijerph-17-05494\" ref-type=\"bibr\">25</xref>], within the framework of the BIOTRA project and a series of publications, concentrated on the transport of dangerous goods and the assessment of its possible impacts on the environment in the vicinity of roads (line objects). Hoterova and Dvorak [<xref rid=\"B26-ijerph-17-05494\" ref-type=\"bibr\">26</xref>] discussed comparing CO<sub>2</sub> emissions in rail and road transport.</p><p>The Water Transport Technology Platform welcomes the Council Conclusions entitled “EU Water Transport Sector—Looking to the future: Towards a carbon-neutral, zero-accident, automated and competitive EU water transport sector”, endorsed by the Council on 5 June 2020. These conclusions are politically very relevant as they clearly demonstrate the views of the Member States on European policy on the future of waterborne transport. The conclusions underline this strategy—the growing importance of the European waterway sector and the need for enhanced research, development, and innovation to achieve the objectives set out in the Council conclusions. Key trends influencing the maritime sector are, e.g., climate change, continuous population growth and urbanization, food and water demand, increasing expectations for health, safety and security and, last but not least, developing countries. On the whole, these constituent factors will increase their share in global economic growth, increase in energy consumption, and fast development of information and communication technologies [<xref rid=\"B27-ijerph-17-05494\" ref-type=\"bibr\">27</xref>].</p></sec><sec id=\"sec2dot4-ijerph-17-05494\"><title>2.4. Safety and Security Area</title><p>Transport, as a specific area of economic activity, and the provision of transport services for people requires the continuous improvement of all activities and subsystems. During the evaluating of traffic, people focus on attributes such as time, price, quality, safety, and environmental impact. As a part of the evaluation of safety and security aspects, researchers write of the point, line, and area objects. These attributes and aspects were defined within the projects RAIN [<xref rid=\"B18-ijerph-17-05494\" ref-type=\"bibr\">18</xref>], OKISD (i.e. Critical Infrastructure Protection in Sector Transportation in the Slovak language) [<xref rid=\"B28-ijerph-17-05494\" ref-type=\"bibr\">28</xref>], and RESILIENCE 2015 [<xref rid=\"B29-ijerph-17-05494\" ref-type=\"bibr\">29</xref>].</p><p>The risk assessment for the environmental area was addressed in the following publications: Dobes et al. [<xref rid=\"B30-ijerph-17-05494\" ref-type=\"bibr\">30</xref>], Approach of the Czech Republic to the Prevention of Environmentally Oriented Terrorism. Kelemen and Jevcak [<xref rid=\"B31-ijerph-17-05494\" ref-type=\"bibr\">31</xref>] addressed the specifics of security management in aviation. Kirschenstein et al. [<xref rid=\"B32-ijerph-17-05494\" ref-type=\"bibr\">32</xref>] discussed research into storms and the resulting threats to air traffic. Polishchuk et al. [<xref rid=\"B33-ijerph-17-05494\" ref-type=\"bibr\">33</xref>], in their research, dealt with a fuzzy model of risk assessment for environmental for start-up projects in the air transporter sector. Rehak et al. [<xref rid=\"B34-ijerph-17-05494\" ref-type=\"bibr\">34</xref>] spoke of Safety and Security Issues in Technical Infrastructures.</p><p>The decade since 2010 has been aimed at significantly reducing CO<sub>2</sub> production by car and aircraft engines. Because of that, many studies were carried out to compare the production of emissions in different modes of transport. The effects of extreme weather events pose a visible response of the Earth to increasing global greenhouse gas emissions. Gradual climate change is mainly reflected in the ever-increasing global temperature, which is leading to the gradual melting of glaciers around the North and South Pole. The significant reduction in the world’s glaciers is also the result of the global deforestation and forest systems. Currently, 40% of the land on Earth is covered by deserts, and their size is growing from year to year. First, according to historical meteorological measurements over the last hundred years, seven of the ten warmest years occurred between 2000 and 2019. Second, according to research by the European Severe Storms Laboratory (next ESSL), extreme weather events increase every year in Europe. Many of these extremes have a devastating impact on transport (relocation of goods and people in vehicles) as well as on transport infrastructure (roads, railways, waterways, and transport facilities). Third, according to the conclusions and recommendations from the RAIN project, it is necessary to take into account more significant extremes of weather in the reconstruction of transport facilities than was the case according to technical standards around 1970 (50 years ago). In our research, the worst-case scenarios for transport infrastructure are landslides, snow avalanches, flooding of transport infrastructure, extremely high and extremely low temperatures, storms, intense storms, and forest fires [<xref rid=\"B35-ijerph-17-05494\" ref-type=\"bibr\">35</xref>].</p></sec></sec><sec id=\"sec3-ijerph-17-05494\"><title>3. Materials and Methods </title><p>In our research, standard scientific methods were used, from analysis and comparison to synthesis, from top-down solutions to bottom-up solutions. The basis of all methodological procedures is the definition of the object/system where the risks will be assessed. Probabilities and possible implications for the identified typological scenarios are required as input for all scenarios. Thus, risk assessment can rely on the pillars of system security management of the organization (see <xref ref-type=\"fig\" rid=\"ijerph-17-05494-f002\">Figure 2</xref>).</p><p>A comprehensive assessment of the safety, security, and resilience of transport infrastructure requires new approaches. Our primary objective should be specifically to reduce the volume of transoceanic traffic in the future. All goods and commodities transported over more than 10,000 km should be subject to a special tax. Moreover, the new scientific department of ecological economics recommends re-evaluating long-distance transport. In the current conditions, there are cases where the raw material obtained on one continent is transported to another continent. The product made of the raw material travels thousands of kilometers to another continent, and if not sold there, it is, in turn, transported to another continent. These cases are possible thanks to a non-comprehensive global solution, wherein some countries do not give attention to the environmental impacts of transport [<xref rid=\"B10-ijerph-17-05494\" ref-type=\"bibr\">10</xref>]. On the contrary, the security solution requires a comprehensive approach, the security pillars shown in the <xref ref-type=\"fig\" rid=\"ijerph-17-05494-f002\">Figure 2</xref> according to the research results must be evaluated using a comprehensive set of measurable indicators.</p><p><xref ref-type=\"fig\" rid=\"ijerph-17-05494-f002\">Figure 2</xref> shows environmental security as one of the pillars of an organization’s security. Environmental risk assessment is part of a general approach to risk assessment. The current basic framework for risk assessment is ISO 31,000 Risk Management—Guidelines [<xref rid=\"B37-ijerph-17-05494\" ref-type=\"bibr\">37</xref>]. Various techniques and methodologies for risk assessment are used for different areas of society. As part of the methodology aimed at increasing the security of the organization, it is necessary to link these pillars with the PDCA method (i.e. Plan-Do-Check-Act), which sets out to continuously improve business processes.</p><p>In addressing environmental risk assessment, it is necessary to build on previously conducted research published in these papers and sources focused on air transport [<xref rid=\"B32-ijerph-17-05494\" ref-type=\"bibr\">32</xref>,<xref rid=\"B35-ijerph-17-05494\" ref-type=\"bibr\">35</xref>,<xref rid=\"B38-ijerph-17-05494\" ref-type=\"bibr\">38</xref>,<xref rid=\"B39-ijerph-17-05494\" ref-type=\"bibr\">39</xref>,<xref rid=\"B40-ijerph-17-05494\" ref-type=\"bibr\">40</xref>,<xref rid=\"B41-ijerph-17-05494\" ref-type=\"bibr\">41</xref>] and other modes of transport [<xref rid=\"B33-ijerph-17-05494\" ref-type=\"bibr\">33</xref>]. A multi-disciplinary and multi-level approach is the basic method for addressing risks. In the risk assessment, it is important to have relevant statistical files for the last period. Another important part of objective risk assessment is the study of best practices in international research. Some innovative approaches are not based on real research tasks but arise as a real need of society. An example of this is the comparison of CO<sub>2</sub> emission from transport published in Norway as a real answer for the discussion of experts. The real measurements that were taken, gave the results shown in <xref ref-type=\"fig\" rid=\"ijerph-17-05494-f003\">Figure 3</xref>. The conversion involved the transport of one ton of cargo over one km and, after the calculation, the real emissions in grams were converted. The lowest value was given by a freight train with a diesel drive. In second place, there is a container ship carrying more than two thousand containers. On the opposite side of the line, was the carriage of one ton of cargo by plane.</p><p>For the most part, the aim of future research will be the interconnection of individual subsystems, presented by individual pillars of safety/security and their mutual integration. Future security audits must be based on dozens of indicators for each safety/security pillar. The interconnection and thorough evaluation of the measured values will give a clear answer as to which indicators in which pillar appear the weakest and which we must pay primary attention to. The environmental dimension of the solution must include the entire biodiversity of the environment, starting with microorganisms across plants and animals [<xref rid=\"B7-ijerph-17-05494\" ref-type=\"bibr\">7</xref>].</p><p>The growing number of people on the Earth, the occupation of an ever-increasing area of previously untouched nature for agricultural activity, the growth of urban agglomerations, and the depopulation of rural settlements has long been indeterminate. If the principle of sustainable development of society is correct, it cannot be tested in laboratories, but in natural conditions, thus, it is dysfunctional. In relation to this, it is necessary to perceive every extraordinary event, every deviation from normal, as a challenge for the future in this principle. Research teams must continuously prepare scenarios for even the less probable events. Due to this fact, it is necessary to learn from the crown of the crisis and prepare for similar events [<xref rid=\"B15-ijerph-17-05494\" ref-type=\"bibr\">15</xref>].</p><p>The current crisis caused by COVID-19 shows that even the least likely events can cause gigantic material damage, financial and human loss. Based on these facts, it is necessary to look for new approaches to addressing environmental risk assessment. Routine, template approaches obscure the mind and lead us to a dead end.</p></sec><sec sec-type=\"results\" id=\"sec4-ijerph-17-05494\"><title>4. Results</title><p>Real research results have been realistically tested by various methods at different levels. One of the possibilities was brainstorming within scientific conferences in combination with a comparison of the results of foreign research projects. The ambition of the authors is to shift knowledge in the field of theory as a theoretical contribution and cooperation with practice as a practical contribution. Another proven method is the preparation of research projects in close cooperation between academia and companies. This trend was applied until 2018. Then, however, there was a turning point, in that currently, projects focused dealing with safety and risks are prepared in cooperation with the state, region, city, municipality with academia, technology companies. Moreover, the fourth group, we invite to cooperate are non-profit stakeholders, associations, and relevant organizations of active people.</p><p>This approach brings us significant added value, which lies in a comprehensive understanding of security and risks. All relevant players must be involved in solving these projects. A good example is the current cooperation of regional self-government with the university, companies, state authorities in the region, and interest volunteer associations in the preparation of projects focused on SMART/SAFE city/region.</p><p>In this context, the advantage of our faculties is that the authors have been working for a long time on partial methodologies, detailed procedures and solutions that lead to an increase in the level of security. Every year, our security faculties in Zilina and Ostrava discuss dozens of final theses dealing with selected scenarios to prepare the best measures for resolving crisis events.</p><p>If we use the division of security areas as presented in <xref ref-type=\"fig\" rid=\"ijerph-17-05494-f002\">Figure 2</xref>, one can choose from the reservoirs of partial solutions focused on physical security. These projects have been solved by students in three levels of university studies for more than 10 years. The basic ambition is to prepare security projects designed for all types of facilities, starting with elements of critical infrastructure, across the protection of soft targets to the protection of common facilities according to the requirements of practice.</p><p>The second important area where our faculties work closely together and have more than 10 years of results are fire safety projects. These projects are divided into two large groups. Whereas the first one is focused on preventative measures, the second group of projects comprises improving the intervention activities of the fire and rescue corps.</p><p>The third area we pay attention to, is the area of occupational safety and health. Primarily, the Faculty of Safety Engineering of the VSB—Technical University of Ostrava has a long tradition of such final theses. This tradition has lasted for more than 20 years. Cooperation in the field of occupational safety and health at the international level also leads to participation in jointly organized conferences and international projects.</p><p>The field of environmental safety has markedly a cross-sectional character. Every year, individual final theses are created, the aim of which is to look for methods, processes, and techniques on how to improve environmental protection. The young generation at our faculties is involved in a wide range of youth activities aimed at protecting the environment.</p><p>Another important area that has taken on significance within the COVID-19 pandemic is information security. Telework/home office, the use of new communication tools, and the company’s interest in the further development of informatization are the driving force for students to focus their upcoming challenges in the field of cybersecurity in their final theses and projects.</p><p>The last presented pillar is technological/technical safety, which is associated with the object/service or real activities. Measures in this area are specified depending on the sector. Within transport, each mode of transport has its own historical development in the field of risk assessment. In the first place, there are space flights, where the required level of safety/acceptable risk has safety indicators set very high. Beside the space flights, attention will also be drawn to the common modes of transport used daily by the population. Next in line is air transport, where previous incidents and air disasters have set safety/security level requirements and safety/security indicators from commonly used modes of transport to the highest level. Safety/risks in air transport are the most important parameter for passengers. As a result of this, in case of any problem before the take-off of the aircraft, each passenger accepts the decision of the captain of the aircraft to postpone the take-off. It is common for passengers who boarded a plane to wait patiently for the captain’s next decision. If they are asked to disembark the aircraft and return to the departure hall, they shall respect such a decision with understanding. In other modes of transport, we will not encounter such an attitude on the part of passengers [<xref rid=\"B12-ijerph-17-05494\" ref-type=\"bibr\">12</xref>,<xref rid=\"B32-ijerph-17-05494\" ref-type=\"bibr\">32</xref>,<xref rid=\"B38-ijerph-17-05494\" ref-type=\"bibr\">38</xref>].</p><p>An example of good practice in maritime transport is seaports, which also faces increased control to reduce externalities such as air pollution, noise, and other environmental impacts. Possible sources of emissions in ports include seagoing vessels, port vessels (pilot boats, police boats, pusher tugs), equipment for handling cargo in road transport, semi-trailers, or the trailer sets transporting goods respectively, diesel locomotives, small power plants, industrial, and production centers located in the port.</p><p>The emissions from seagoing ships in ports endanger not only the port workers themselves but also the people living near those ports. These emissions cause the population various respiratory diseases such as asthma, lung cancer, and others. For this reason, seaports take various measures to reduce emissions, e.g.,</p><list list-type=\"bullet\"><list-item><p>use of alternative fuels, or fuels that contain a lower percentage of sulfur;</p></list-item><list-item><p>the supply of electricity to vessels at berth in ports (in smaller seaports, where there is not enough electricity capacity, vessels generate electricity themselves by burning fuel oil, or diesel);</p></list-item><list-item><p>improving the exchange of information between vessels and port management centers, which monitor the movement of vessels in ports so that ships can sail at optimum speeds;</p></list-item><list-item><p>only vessels whose engines meet the technical criteria set out in MARPOL Annex VI may sail in the water area of the port;</p></list-item><list-item><p>strengthening the control of vessels by the authorities carrying out checks on their technical condition;</p></list-item><list-item><p>the identification of other areas in the world for the regulation of emissions because of the differences among the countries of the world.</p></list-item></list><p>Inland water transport has an exceptionally long history. Risk reduction measures within the technological/technical phase have gradually come with the development of engineering, electronics, energy, and information and communication technologies. At present, however, inland waterway transport lags far behind air transport. In the field of safety, national and international rules and regulations apply. These rules and regulations are accepted, checked, and amended from time to time. Hence, risk assessment in water transport is in its infancy, because some current legal norms and regulations do not accept the current level of knowledge (applies to the countries of central Europe) [<xref rid=\"B43-ijerph-17-05494\" ref-type=\"bibr\">43</xref>].</p><p>Speaking of railway transport, the railway risk assessment was launched within the European Railway Agency by the approval of the Common Safety Methods. A pivotal role of this approach is to find the best practices in other railway companies. The strict separation of the management of railway infrastructure from rail transport has initially led to problems, especially in dealing with the consequences of incidents and railway accidents. The system of renewal of railway infrastructure has for many decades used all the forces and resources of the state railway company for renewal. In addition to this, separating freight and passenger transport from rail infrastructure management has led to complications, prolonging recovery times, and reducing the efficiency of the use of forces and resources. The current situation can be assessed in our countries as follows. Already in the period 1994–1998, the Czech Republic reconstructed a large part of the main transit lines (part of TEN-T) to a speed of 160 km per hour. Currently in the years 2018–2020, it continues in the next phase of reconstruction and modernization of corridor lines. In contrast, Slovakia and Serbia have failed to modernize corridor lines in the last 30 years. The technological progress made by top railway companies in China, Japan, France, and Germany is also an inducement for the countries of central and eastern Europe. The European Railway Agency has defined a role for individual national railway infrastructure administrations and railway undertakings to implement rail risk assessment systems by 2012. Research institutions in the Czech Republic, Slovakia, and Serbia have been intensively involved in this topic. Unfortunately, the transfer of the results of research projects into practice has not taken place yet [<xref rid=\"B19-ijerph-17-05494\" ref-type=\"bibr\">19</xref>,<xref rid=\"B34-ijerph-17-05494\" ref-type=\"bibr\">34</xref>,<xref rid=\"B44-ijerph-17-05494\" ref-type=\"bibr\">44</xref>].</p><p>Risk assessment in road transport was addressed in several research projects and the results were published, e.g., in an article by Dvorak et al. [<xref rid=\"B21-ijerph-17-05494\" ref-type=\"bibr\">21</xref>] and the paper by Fuchs et al. [<xref rid=\"B25-ijerph-17-05494\" ref-type=\"bibr\">25</xref>]. The development of intelligent transport systems/telematics solutions was started in the Czech Republic, Slovakia, and Serbia around the year 2000. All three countries significantly built the highway network and at the same time implement the results of applied research into the safety of tunnels, which have currently become a good basis for building networks supporting autonomous transport systems [<xref rid=\"B34-ijerph-17-05494\" ref-type=\"bibr\">34</xref>].</p><p>In addition to the area of risk assessment, the writers of the article, along with research teams, attended to the assessment of the vulnerability and resilience of transport and transport infrastructure. OKISD projects [<xref rid=\"B28-ijerph-17-05494\" ref-type=\"bibr\">28</xref>] were gradually solved, where attention was paid to the foundation of a theoretical framework for the construction of critical infrastructure in transport. The researchers focused mainly on road and rail transport. All workplaces of the Faculty of Security Engineering in Zilina were involved in the solution of the project. Ultimately, the result was more than 100 publications, where the results of the research were presented from different perspectives. A multi-disciplinary and multi-level approach has become the basis of the solution proposed. In the previous phase of the solution, we defined the technical terms that were used in the entire project solution.</p><p>The second important contribution was the 7FP RAIN project [<xref rid=\"B18-ijerph-17-05494\" ref-type=\"bibr\">18</xref>]. Within this project, attention was paid to assessing the risks of extreme weather conditions with priority given to transport infrastructure. As a result of project, vulnerability assessment tools, emphasizing the social vulnerability of society in the event of failures in the energy and transport sectors have been identified.</p><p>The most recent project, which brought together the efforts of researchers from Ostrava and Zilina, was the RESILIENCE 2015 project [<xref rid=\"B29-ijerph-17-05494\" ref-type=\"bibr\">29</xref>]. The project focused on creating a resilience framework for important sectors—energy, transport, and information and communication technologies. Certified methodologies were developed within the project and the results were published in Safety and Security Issues in Technical Infrastructures [<xref rid=\"B34-ijerph-17-05494\" ref-type=\"bibr\">34</xref>], Dynamic Impact Modeling as a Road Transport Crisis Management Support Tool [<xref rid=\"B45-ijerph-17-05494\" ref-type=\"bibr\">45</xref>] and in Complex Approach to Assessing Resilience of Critical Infrastructure Elements [<xref rid=\"B46-ijerph-17-05494\" ref-type=\"bibr\">46</xref>]. The newly developed resilience methodology used a system where resilience consists of adaptability, robustness, and resilience.</p><p>Based on the above, it can be stated that the essence of the proposed qualitative approach to environmental risk assessment (QAERA) in transport is the information support of entities in risk management. This approach is determined by 6 pillars of safety and protection of infrastructure elements in the transport sector. More precisely, the environmental risk assessment itself is based on the application of selected safety and security indicators and selected risk assessment criteria in the implementation of environmental change in the global model of thinking in the transport sector. The process of evaluation and use of indicators and criteria is presented in <xref ref-type=\"fig\" rid=\"ijerph-17-05494-f004\">Figure 4</xref>.</p><p>Step 1: Select the Mode of Transport</p><p>The article is devoted to environmental risk assessment in transport and since each mode of transport has certain peculiarities, it is necessary to focus first on one selected mode of transport (air, water, road, or rail).</p><p>Step 2: Select the Object Type</p><p>After selecting the mode of transport, it is necessary to choose whether the evaluator’s attention will be focused on the transport infrastructure or the transport and transportation processes. When determining reference objects, the division into area (railway stations), line (line sections), or point objects (stop, person, train) is used [<xref rid=\"B46-ijerph-17-05494\" ref-type=\"bibr\">46</xref>].</p><p>The specifics of both types of objects are unambiguous. When assessing threats, the most significant threats to transport infrastructure are, for example, floods and landslides. When assessing threats to transport and transportation processes, the most significant threats include, for example, the intentional act of an employee, the economic crisis or neglecting of work duties.</p><p>Step 3: Select Type Object (Criteria)</p><p>This step sets out to define the database of type threats (i.e., risk type criteria) of specific reference objects. Events, activation mechanisms, event locations, and causes must be clarified within the database (see <xref rid=\"ijerph-17-05494-t001\" ref-type=\"table\">Table 1</xref>).</p><p>After combining the activation mechanism and the source of risk, the following disruptive events arise, for example: malfunction of the security device; immobility of the towing vehicle; illness of staff; panic of the traveling public; panic of railway employees; insolvency of a railway organization; insolvency of debtors of the railway organization; violation of the geometric position of the track; damage to the structure in the body of the track; and fire near the track.</p><p>Step 4: Implement Measurable Indicators of Safety/Security Pillars with a Focus on Environmental Risk Assessment</p><p>Subsequently, in the fourth step, all safety/security pillars (different viewing angles) must be evaluated. When implementing safety indicators, it is recommended to define 30–50 measurable indicators for each safety/security pillar. As stated above, the paper presents a qualitative approach to environmental risk assessment, which is based on the document of the United Nations [<xref rid=\"B15-ijerph-17-05494\" ref-type=\"bibr\">15</xref>]:<list list-type=\"bullet\"><list-item><p>Goal 4\n<list list-type=\"simple\"><list-item><label>Target 4.7</label><p>To ensure that all learners acquire the knowledge and skills needed to promote sustainable development.</p></list-item></list></p></list-item><list-item><p>Goal 9\n<list list-type=\"simple\"><list-item><label>Target 9.1</label><p>To develop quality, reliable, sustainable, and resilient infrastructure, including regional and transborder infrastructure, to support economic development and human well-being, with a focus on affordable and equitable access for all.</p></list-item></list></p></list-item><list-item><p>Goal 11\n<list list-type=\"simple\"><list-item><label>Target 11.4</label><p>To strengthen efforts to protect and safeguard the world’s cultural and natural heritage.</p></list-item><list-item><label>Target 11.6</label><p>By 2030, to reduce the adverse per capita environmental impact of cities, including by paying special attention to air quality and municipal and other waste management.</p></list-item><list-item><label>Target 11.7</label><p>By 2030, to provide universal access to safe, inclusive and accessible, green and public spaces, in particular for women and children, older persons, and persons with disabilities.</p></list-item><list-item><label>Target 11.a</label><p>To support positive economic, social, and environmental links between urban, peri-urban, and rural areas by strengthening national and regional development planning.</p></list-item><list-item><label>Target 11.b</label><p>By 2020, to substantially increase the number of cities and human settlements adopting and implementing integrated policies and plans towards inclusion, resource efficiency, mitigation and adaptation to climate change, resilience to disasters, and develop and implement, in line with the Sendai Framework for Disaster Risk Reduction 2015–2030, holistic disaster risk management at all levels.</p></list-item></list></p></list-item><list-item><p>Goal 13\n<list list-type=\"simple\"><list-item><label>Target 13.1</label><p>To strengthen resilience and adaptive capacity to climate-related hazards and natural disasters in all countries.</p></list-item><list-item><label>Target 13.2</label><p>To integrate climate change measures into national policies, strategies, and planning.</p></list-item><list-item><label>Target 13.3</label><p>To improve education, awareness-raising, and human and institutional capacity on climate change mitigation, adaptation, impact reduction, and early warning.</p></list-item></list></p></list-item></list></p><p>However, the writer’ research was aimed at defining the required number of environmental safety indicators. For this reason, 38 indicators were defined for the real information system. In the following, we introduce proposals concerning some measurable indicators. The evaluation of measurable indicators using the procedure described below will give a clear conclusion about the level of safety of the reference object against a specific source of risk with a proposal for specific measures to reduce it. Examples of some indicators are presented in <xref rid=\"ijerph-17-05494-t002\" ref-type=\"table\">Table 2</xref>, <xref rid=\"ijerph-17-05494-t003\" ref-type=\"table\">Table 3</xref> and <xref rid=\"ijerph-17-05494-t004\" ref-type=\"table\">Table 4</xref>.</p><p>The issue of environmental safety/security indicators is closely related to technical standards, laws, and regulations. Without deep experience in the field of environmental protection, it is not possible to set indicators that will objectively measure environmental security.</p><p>Step 5: Evaluation of Measurable Indicators</p><p>The same way as the color displays the resulting values in the traffic light (green, orange, and red), it is possible to determine the current level of safety. Briefly, if the values are, for example, on the orange scale, it is appropriate to monitor the indicator. If it is found out that an indicator was included in the green zone during testing, then it is not necessary to conduct safety measures. When entering the orange zone, it is necessary to increase vigilance, limit the speed of traffic, and, if necessary, choose appropriate safety measures to increase safety and security. If the indicator is included in the red zone, it is necessary to stop the transport process and take appropriate safety/security measures immediately.</p><p>Step 6: Selection and Implementation of Security Measures</p><p>The sixth step is a set of measures based on practical experience. Sets of proposed measures are created for the areas:<list list-type=\"bullet\"><list-item><p>technical and technological,</p></list-item><list-item><p>organizational—legal framework,</p></list-item><list-item><p>workforce,</p></list-item><list-item><p>information support.</p></list-item></list></p><p>The technical and technological area is very important, as there is a system of planned inspection and maintenance. Namely, rail transport has a proven system of real technical and technological measures for years to guarantee track safety. Organizational measures are based on four sources. The first source is legal acts—laws, and decrees at the national level, the second is regulations and recommendations at the international level, the third is technical standards, and the fourth is the internal regulations of the infrastructure manager and transport companies. All listed resources are updated at regular intervals. In the area of manpower, there exist measures for the selection, training, and continuous training of employees. That is why railway companies have a long-established training system. For the most part, there are significant changes in the area of information support. Historical security and notification devices have been gradually replaced by high-quality information and communication technology. A current problem in this area is the provision of information security, which is carried out on an ongoing basis in all railway undertakings. Measures in this area focus on people, hardware, software, and data protection (GDPR, sensitive information, and know-how).</p><p>Practical application of the decision-making process qualitative approach to environmental risk assessment (QAERA) provides transport. As part of the verification process proposed by QAERA, several scenarios had to be prepared where the whole approach would be tested. The real events from the recent past played decisive role in the selection of scenarios. The first scenario was set by prolonged heavy rainfall which caused large-scale floods. The second scenario for landslides was made up after intense flash floods. The third scenario focused on the intentional act of a railway company employee. Each of these scenarios has a significant environmental impact. Each is relatively likely with a possible high impact on rail transport.</p></sec><sec sec-type=\"discussion\" id=\"sec5-ijerph-17-05494\"><title>5. Discussion</title><p>The qualitative approach to environmental risk assessment in transport is based on long-term research, which relies on the historical basis of our research organizations. The second key part is the company’s interest in environmental safety as one of the priorities [<xref rid=\"B7-ijerph-17-05494\" ref-type=\"bibr\">7</xref>,<xref rid=\"B8-ijerph-17-05494\" ref-type=\"bibr\">8</xref>,<xref rid=\"B15-ijerph-17-05494\" ref-type=\"bibr\">15</xref>,<xref rid=\"B25-ijerph-17-05494\" ref-type=\"bibr\">25</xref>,<xref rid=\"B30-ijerph-17-05494\" ref-type=\"bibr\">30</xref>,<xref rid=\"B47-ijerph-17-05494\" ref-type=\"bibr\">47</xref>]. If we compare different sectors of the national economy, we will find out that the issue of environmental risk assessment is addressed at different levels. Good practice is mostly elaborated in the field of chemical industry and nuclear facilities. In transport, good practice is exemplified in Polishchuk et al. [<xref rid=\"B33-ijerph-17-05494\" ref-type=\"bibr\">33</xref>] in a fuzzy model of risk assessment for environmental issues. In rail transport, the attention is drawn to the implementation of Common Safety Methods [<xref rid=\"B48-ijerph-17-05494\" ref-type=\"bibr\">48</xref>], also with reference to examples of good practice.</p><p>The current approaches, in which the risk assessment is narrowed down to a defined system, the identification of threats, the analysis of risks, and the design of measures for the selected risk for a specific object/service, are, as mentioned, very narrowed down. The innovativeness of our solution rests on the interconnection of several approaches from vulnerability assessment, alternatively ranging from the assessment of resilience to security indicators as part of the safety/security pillars. The complexity of the solution should take into account all interests—global, national, regional, corporate, and individual. Creating an intelligent/SMART and comprehensive environmental risk assessment solution requires a real scientific basis, long-term experience, and a multidimensional perspective solution [<xref rid=\"B35-ijerph-17-05494\" ref-type=\"bibr\">35</xref>,<xref rid=\"B49-ijerph-17-05494\" ref-type=\"bibr\">49</xref>]. Previously implemented projects focused on static evaluation, however, the current trend is focused on the dynamic dimension of the solution [<xref rid=\"B45-ijerph-17-05494\" ref-type=\"bibr\">45</xref>]. The writers of the article are aware of the need to create a theoretical framework, followed by a methodological procedure and a detailed algorithm of selected scenarios, which will be the basis for the IT solution. In the past, it was not possible to create a comprehensive information system that would provide real-time technology using of the technologies, e.g., the internet of things, big data, and cloud, to gain all the necessary data for decision-making by top managers responsible for environmental risk assessment.</p><p>The interconnection of computer science, environmental, and security sciences is a challenging us at present. A multidisciplinary solution, using the knowledge of all relevant disciplines, is a key tool for a comprehensive qualitative approach to environmental risk assessment. Transport, as one of the economic sectors, requires further innovation and research. The real possibility is the continuous improvement of technical and technological processes for individual transport subsystems: transport technology, transport infrastructure, control and information systems in transport, and education and training of transport workers. This original method was intended for the regional and national levels. It does not only address global issues that require a different understanding of current needs and risk management. Moreover, when seeking a global solution, a multi-criteria assessment, which appropriately favors environmental aspects, must be used. Thousands of unused trucks cannot drive on the roads together with the thousands of overloaded trucks. Furthermore, environmentally friendly modes of transport such as water and rail cannot have smaller volumes of transported goods from year to year (situation in central Europe). Furthermore, it is not reasonable to increase the volume of goods transported in maritime transport every year, without full utilization of domestic resources in individual countries.</p><p>It is necessary to gradually fulfil the global model of sustainable development [<xref rid=\"B15-ijerph-17-05494\" ref-type=\"bibr\">15</xref>] step by step so that each activity carried out, reflects the protection of the environment. Promoting globally defined 17 goals and 197 targets are one of the right paths to global environmental protection.</p><p>Current quantitative approaches to risk assessment are based on long-term records and available risk data. However, individual countries in this area are creating a database only gradually. Therefore, at this stage of development, the attention of researchers was focused on qualitative evaluation, which also uses other data and thus does not need an extensive database. Thus, the result of this assessment is the identification of risk areas that can be evaluated in the next step using quantitative methods.</p></sec><sec sec-type=\"conclusions\" id=\"sec6-ijerph-17-05494\"><title>6. Conclusions</title><p>The paper aimed to present the ideas, reflections, projects, and publications of writers who have long been engaged in research in the fields of transport, safety, and the environment. We are aware of environmental impacts possibly affecting the functioning of transport and the operability of transport infrastructure. With regard to this, they certainly affect people’s health and lives. Every year, millions of people around the world are exposed to new extremes in the weather. Due to them, there is a movement of nations, whose inhabitants are forced to leave their homes and search for new places to live. Globally, the 50 percent limit of urban settlement has been exceeded, thus with more than half of mankind currently living in cities and conurbations with an absolute dependence on the functioning of critical infrastructures.</p><p>Research on dynamic resilience has shown us the complexity of current systems, their interdependence and considerable vulnerability. The more complicated and intelligent systems that control the sources of human needs is, the greater the possible impact on people’s lives and the environment.</p><p>Safety/security research leads us to ever new challenges how to increase the level of resilience, how to improve the protection of people, companies, institutions, municipalities, cities, regions, and, in general, of the globe. In our research, we have repeatedly encountered the limits of what is feasible, as we solved clearly defined problems and, interestingly, new and new questions have emerged as solutions.</p><p>The current limitations of the research play pivotal role, as they need to be based mainly on the multi-specialization of the solution. Creating broad teams of experts in practice and connecting researchers at the international level can bring about a shift in risk assessment in transport. Another limitation is the absence of legal acts that individual countries should issue in order to reduce environmental risks. Last but not least, there is the stereotype that people are used to pollute the environment and do not consider it a major problem. Awareness, education, and training must be coordinated and clear standards for environmental education and training must be defined in the future.</p><p>The field of applied research and innovation must be directed to the creation of new approaches, new methodologies, and new techniques in the field of environmental risk in transport. A comprehensive approach and timely identification of sources of risk is key. The writers demonstrated a new approach applicable in environmental risk assessment in transport by proposing safety pillars and a set of indicators.</p><p>The issue of environmental risk assessment in the countries of central and eastern Europe is addressed only in research organizations. It cannot be shifted into a real transport policy. It solves other seemingly more important problems—the moral obsolescence of transport infrastructure, non-coordination of individual modes of transport, corrupt behavior, and the absence of SMART solutions/technologies. Research results are rarely used as a basis for government negotiations at national and regional levels. In the area of transport policy, there is a lack of coordination of solutions at national and regional level. To sum up, the proposed approach provides six basic pillars of safety and dozens of indicators that could be part of the transport agenda at local, regional, and national level.</p><p>Based on the above, safety/security research requires teamwork, preferably with the representation of experts from different disciplines, because the perception of security is very individual. If we want to bring truly comprehensive solutions, it is necessary to start from all relevant research areas and look for synergistic solutions that can bring a new quality of security to society.</p></sec></body><back><notes><title>Author Contributions</title><p>Conceptualization, Z.D. and D.R.; methodology, Z.D. and D.R.; Validation, Z.D. and D.R.; formal analysis, Z.D., D.R., A.D. and Z.C; resources, Z.D. and D.R.; data curation, Z.D. and D.R.; writing—original draft preparation, Z.D., D.R., A.D. and Z.C.; writing—review and editing, Z.D. and D.R.; visualization, D.R.; supervision, Z.D.; project administration, D.R.; funding acquisition, Z.D. and D.R. All authors have read and agreed to the published version of the manuscript.</p></notes><notes><title>Funding</title><p>This research was funded by the European Regional Development Fund, grant number 313011T462, and by the Ministry of the Interior of the Czech Republic, grant number VI20192022151.</p></notes><notes notes-type=\"COI-statement\"><title>Conflicts of Interest</title><p>The authors declare no conflict of interest.</p></notes><ref-list><title>References</title><ref id=\"B1-ijerph-17-05494\"><label>1.</label><element-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Marhavilas</surname><given-names>P.K.</given-names></name><name><surname>Koulouriotis</surname><given-names>D.</given-names></name><name><surname>Gemeni</surname><given-names>V.</given-names></name></person-group><article-title>Risk Analysis and Assessment Methodologies in the Work Sites: On a Review, Classification and Comparative Study of the Scientific Literature of the Period 2000–2009</article-title><source>J. 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Public Health</source><year>2019</year><volume>16</volume><elocation-id>4008</elocation-id><pub-id pub-id-type=\"doi\">10.3390/ijerph16204008</pub-id></element-citation></ref><ref id=\"B42-ijerph-17-05494\"><label>42.</label><element-citation publication-type=\"web\"><article-title>Faktisk is it True that Emissions from a Container Ship Correspond to 50 Million Cars?</article-title><comment>Available online: <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.faktisk.no/artikler/MA/stemmer-det-at-utslippene-fra-et-containerskip-tilsvarer-50-millioner-biler\">https://www.faktisk.no/artikler/MA/stemmer-det-at-utslippene-fra-et-containerskip-tilsvarer-50-millioner-biler</ext-link></comment><date-in-citation content-type=\"access-date\" iso-8601-date=\"2020-06-06\">(accessed on 6 June 2020)</date-in-citation></element-citation></ref><ref id=\"B43-ijerph-17-05494\"><label>43.</label><element-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Pavolová</surname><given-names>H.</given-names></name><name><surname>Tobisová</surname><given-names>A.</given-names></name></person-group><article-title>The Model of Supplier Quality Management in Transport Company</article-title><source>Nase More</source><year>2013</year><volume>60</volume><fpage>123</fpage><lpage>126</lpage></element-citation></ref><ref id=\"B44-ijerph-17-05494\"><label>44.</label><element-citation publication-type=\"confproc\"><person-group person-group-type=\"author\"><name><surname>Luskova</surname><given-names>M.</given-names></name><name><surname>Dvorak</surname><given-names>Z.</given-names></name><name><surname>Leitner</surname><given-names>B.</given-names></name></person-group><article-title>Impact of Extreme Weather Events on Land Transport Infrastructure</article-title><source>Proceedings of the 19th International Scientific Conference Transport Means 2015</source><conf-loc>Kaunas, Lithuania</conf-loc><conf-date>22–23 October 2015</conf-date><fpage>306</fpage><lpage>309</lpage></element-citation></ref><ref id=\"B45-ijerph-17-05494\"><label>45.</label><element-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Rehak</surname><given-names>D.</given-names></name><name><surname>Radimsky</surname><given-names>M.</given-names></name><name><surname>Hromada</surname><given-names>M.</given-names></name><name><surname>Dvorak</surname><given-names>Z.</given-names></name></person-group><article-title>Dynamic Impact Modeling as a Road Transport Crisis Management Support Tool</article-title><source>Adm. 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Prot.</source><year>2019</year><volume>25</volume><fpage>125</fpage><lpage>138</lpage><pub-id pub-id-type=\"doi\">10.1016/j.ijcip.2019.03.003</pub-id></element-citation></ref><ref id=\"B47-ijerph-17-05494\"><label>47.</label><element-citation publication-type=\"book\"><person-group person-group-type=\"author\"><name><surname>Crncevic</surname><given-names>D.</given-names></name></person-group><article-title>Transport Risk Analysis</article-title><source>Transportation Systems and Engineering: Concepts, Methodologies, Tools, and Applications</source><publisher-name>IGI Global</publisher-name><publisher-loc>Hershey, PA, USA</publisher-loc><year>2015</year><volume>Volume 3</volume><fpage>1</fpage><lpage>31</lpage><pub-id pub-id-type=\"doi\">10.4018/978-1-4666-8473-7.ch001</pub-id><isbn>978-1-4666-8473-7</isbn></element-citation></ref><ref id=\"B48-ijerph-17-05494\"><label>48.</label><element-citation publication-type=\"web\"><article-title>Directive (EU) 2016/798 of the European Parliament and of the Council of 11 May 2016 on Railway Safety</article-title><comment>Available online: <ext-link ext-link-type=\"uri\" xlink:href=\"https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX%3A32016L0798\">https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX%3A32016L0798</ext-link></comment><date-in-citation content-type=\"access-date\" iso-8601-date=\"2020-06-27\">(accessed on 27 June 2020)</date-in-citation></element-citation></ref><ref id=\"B49-ijerph-17-05494\"><label>49.</label><element-citation publication-type=\"book\"><person-group person-group-type=\"author\"><name><surname>Vidrikova</surname><given-names>D.</given-names></name><name><surname>Boc</surname><given-names>K.</given-names></name><name><surname>Dvorak</surname><given-names>Z.</given-names></name><name><surname>Rehak</surname><given-names>D.</given-names></name></person-group><source>Critical Infrastructure and Integrated Protection</source><publisher-name>Association of Fire and Safety Engineering</publisher-name><publisher-loc>Ostrava, Czech Republic</publisher-loc><year>2017</year><isbn>978-80-7385-190-3</isbn></element-citation></ref></ref-list></back><floats-group><fig id=\"ijerph-17-05494-f001\" orientation=\"portrait\" position=\"float\"><label>Figure 1</label><caption><p>Components of transport modes [<xref rid=\"B17-ijerph-17-05494\" ref-type=\"bibr\">17</xref>].</p></caption><graphic xlink:href=\"ijerph-17-05494-g001\"/></fig><fig id=\"ijerph-17-05494-f002\" orientation=\"portrait\" position=\"float\"><label>Figure 2</label><caption><p>Security pillars of the organization [<xref rid=\"B36-ijerph-17-05494\" ref-type=\"bibr\">36</xref>].</p></caption><graphic xlink:href=\"ijerph-17-05494-g002\"/></fig><fig id=\"ijerph-17-05494-f003\" orientation=\"portrait\" position=\"float\"><label>Figure 3</label><caption><p>Comparison of CO<sub>2</sub> emission from the transport of one ton of goods per km [<xref rid=\"B42-ijerph-17-05494\" ref-type=\"bibr\">42</xref>].</p></caption><graphic xlink:href=\"ijerph-17-05494-g003\"/></fig><fig id=\"ijerph-17-05494-f004\" orientation=\"portrait\" position=\"float\"><label>Figure 4</label><caption><p>Environmental risk assessment procedure in transport.</p></caption><graphic xlink:href=\"ijerph-17-05494-g004\"/></fig><table-wrap id=\"ijerph-17-05494-t001\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05494-t001_Table 1</object-id><label>Table 1</label><caption><p>Criteria determining the type of risk.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Areas</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Criteria</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Event naming:</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1. Exclusion of traffic—the threat may manifest itself in that it is not possible to operate train transport.<break/>2. Change of the schedule—the threat may manifest itself in such a way that it is possible to operate traffic, but to a limited extent.<break/>3. Technology change—threats can manifest themselves as an impact on the work system.<break/>4. Change in the scope of services—the threat may manifest itself in such a way that the infrastructure is not disrupted, but there has been a change in services.</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Activation mechanisms:</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1. Mass infection.<break/>2. Assault on operating personnel.<break/>3. Device malfunction.<break/>4. Illness of staff.<break/>5. Panic.<break/>6. Fire.<break/>7. Landslide.<break/>8. Flooding of the runway.</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Event locations:</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1. Train—point object.<break/>2. Track section—line object.<break/>3. Station—area object.<break/>4. Surroundings of the track—area object.</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Causes of events:</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1. Unintentional errors of transport participants.<break/>2. Failure of supplies necessary for operation.<break/>3. Problems in state administration.<break/>4. Extreme natural phenomena and weather.<break/>5. Technical failure.<break/>6. Crime.</td></tr></tbody></table></table-wrap><table-wrap id=\"ijerph-17-05494-t002\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05494-t002_Table 2</object-id><label>Table 2</label><caption><p>Indicator—Amount of carbon dioxide (CO<sub>2</sub>) released into the air.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Value</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Verbal Expression</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Description</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Units/Percentages</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">excellent</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Amount of CO<sub>2</sub> emissions produced from biomass used as fuel, including CO<sub>2</sub> emissions that were not caused by the use of biomass in quantities ranging from 0 to 110 Mg per year.</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">from 0 to 110 Mg per year</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">good</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Amount of CO<sub>2</sub> emissions produced from biomass used as fuel, including CO<sub>2</sub> emissions that were not caused by the use of biomass in the range of 110 to 162 Mg per year.</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">from 110 to 162 Mg per year</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">unacceptable</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Amount of CO<sub>2</sub> emissions produced from biomass used as fuel, including CO<sub>2</sub> emissions that were not caused by the use of biomass in quantities exceeding 162 Mg per year.</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">more than 162 Mg per year</td></tr></tbody></table><table-wrap-foot><fn><p>Comment: The indicator presents the measurable production of CO<sub>2</sub> emissions from biomass.</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"ijerph-17-05494-t003\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05494-t003_Table 3</object-id><label>Table 3</label><caption><p>Indicator—Amount of investments aimed at environmental protection.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Value</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Verbal Expression</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Description</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Units/Percentages</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">excellent</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">The railway infrastructure manager spends € 100,000 or more per year on environmental protection.</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">100,000 euros and more</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">good</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">The railway infrastructure manager spends between €30,000 and €99,999 per year on environmental protection.</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">30,000 to 99,999 euros</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">unacceptable</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">The railway infrastructure manager spends less than €30,000 a year on environmental protection.</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">less than 30,000 euros</td></tr></tbody></table><table-wrap-foot><fn><p>Comment: The indicator presents the amount of investments of the railway infrastructure manager intended for environmental protection.</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"ijerph-17-05494-t004\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05494-t004_Table 4</object-id><label>Table 4</label><caption><p>Indicator—Status of equipment for measuring meteorological data.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Value</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Verbal Expression</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Description</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Units/Percentages</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">excellent</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Checks of the functionality of measuring equipment are performed at regular intervals.</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">90%</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">good</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Checks of the functionality of measuring equipment are performed at irregular intervals.</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">70%</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">unacceptable</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">The state of functionality of measuring equipment is outdated, the results of measured values are inadequate.</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">30%</td></tr></tbody></table><table-wrap-foot><fn><p>Comment: The indicator presents the status of the meteorological equipment of the infrastructure manager used as a database for telematics solutions in transport.</p></fn></table-wrap-foot></table-wrap></floats-group></article>\n"
]
[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Int J Environ Res Public Health</journal-id><journal-id journal-id-type=\"iso-abbrev\">Int J Environ Res Public Health</journal-id><journal-id journal-id-type=\"publisher-id\">ijerph</journal-id><journal-title-group><journal-title>International Journal of Environmental Research and Public Health</journal-title></journal-title-group><issn pub-type=\"ppub\">1661-7827</issn><issn pub-type=\"epub\">1660-4601</issn><publisher><publisher-name>MDPI</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32722097</article-id><article-id pub-id-type=\"pmc\">PMC7432110</article-id><article-id pub-id-type=\"doi\">10.3390/ijerph17155325</article-id><article-id pub-id-type=\"publisher-id\">ijerph-17-05325</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Article</subject></subj-group></article-categories><title-group><article-title>The Utility of Virtual Patient Simulations for Clinical Reasoning Education</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0002-9322-8455</contrib-id><name><surname>Watari</surname><given-names>Takashi</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05325\">1</xref><xref rid=\"c1-ijerph-17-05325\" ref-type=\"corresp\">*</xref></contrib><contrib contrib-type=\"author\"><name><surname>Tokuda</surname><given-names>Yasuharu</given-names></name><xref ref-type=\"aff\" rid=\"af2-ijerph-17-05325\">2</xref></contrib><contrib contrib-type=\"author\"><name><surname>Owada</surname><given-names>Meiko</given-names></name><xref ref-type=\"aff\" rid=\"af3-ijerph-17-05325\">3</xref></contrib><contrib contrib-type=\"author\"><name><surname>Onigata</surname><given-names>Kazumichi</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05325\">1</xref></contrib></contrib-group><aff id=\"af1-ijerph-17-05325\"><label>1</label>Postgraduate Clinical Training Center, Shimane University Hospital, 89–1, Enya-cho, Izumo shi Shimane 693-8501, Japan; <email>[email protected]</email></aff><aff id=\"af2-ijerph-17-05325\"><label>2</label>Okinawa Muribushi Project for Teaching Hospitals, Okinawa 901-2132, Japan; <email>[email protected]</email></aff><aff id=\"af3-ijerph-17-05325\"><label>3</label>Nursing Department, Toho University Hospital Omori Medical Center, Tokyo 143-8541, Japan; <email>[email protected]</email></aff><author-notes><corresp id=\"c1-ijerph-17-05325\"><label>*</label>Correspondence: <email>[email protected]</email>; Tel.: +81-853-20-2005</corresp></author-notes><pub-date pub-type=\"epub\"><day>24</day><month>7</month><year>2020</year></pub-date><pub-date pub-type=\"ppub\"><month>8</month><year>2020</year></pub-date><volume>17</volume><issue>15</issue><elocation-id>5325</elocation-id><history><date date-type=\"received\"><day>25</day><month>5</month><year>2020</year></date><date date-type=\"accepted\"><day>23</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>© 2020 by the authors.</copyright-statement><copyright-year>2020</copyright-year><license license-type=\"open-access\"><license-p>Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>).</license-p></license></permissions><abstract><p>Virtual Patient Simulations (VPSs) have been cited as a novel learning strategy, but there is little evidence that VPSs yield improvements in clinical reasoning skills and medical knowledge. This study aimed to clarify the effectiveness of VPSs for improving clinical reasoning skills among medical students, and to compare improvements in knowledge or clinical reasoning skills relevant to specific clinical scenarios. We enrolled 210 fourth-year medical students in March 2017 and March 2018 to participate in a real-time pre-post experimental design conducted in a large lecture hall by using a clicker. A VPS program (<sup>®</sup>Body Interact, Portugal) was implemented for one two-hour class session using the same methodology during both years. A pre–post 20-item multiple-choice questionnaire (10 knowledge and 10 clinical reasoning items) was used to evaluate learning outcomes. A total of 169 students completed the program. Participants showed significant increases in average total post-test scores, both on knowledge items (pre-test: median = 5, mean = 4.78, 95% CI (4.55–5.01); post-test: median = 5, mean = 5.12, 95% CI (4.90–5.43); <italic>p</italic>-value = 0.003) and clinical reasoning items (pre-test: median = 5, mean = 5.3 95%, CI (4.98–5.58); post-test: median = 8, mean = 7.81, 95% CI (7.57–8.05); <italic>p</italic>-value < 0.001). Thus, VPS programs could help medical students improve their clinical decision-making skills without lecturer supervision.</p></abstract><kwd-group><kwd>clinical reasoning</kwd><kwd>virtual reality simulation</kwd><kwd>symptomatology</kwd><kwd>education support</kwd></kwd-group></article-meta></front><body><sec sec-type=\"intro\" id=\"sec1-ijerph-17-05325\"><title>1. Introduction</title><p>Traditional lectures in Japanese medical schools have been primarily conducted as didactic lectures in large classrooms, with little interactivity. According to a previous study, for digital native students, such one-way, passive lectures are not effective and their learning outcomes may be relatively low [<xref rid=\"B1-ijerph-17-05325\" ref-type=\"bibr\">1</xref>,<xref rid=\"B2-ijerph-17-05325\" ref-type=\"bibr\">2</xref>,<xref rid=\"B3-ijerph-17-05325\" ref-type=\"bibr\">3</xref>,<xref rid=\"B4-ijerph-17-05325\" ref-type=\"bibr\">4</xref>,<xref rid=\"B5-ijerph-17-05325\" ref-type=\"bibr\">5</xref>]. Further, other reports have indicated that future medical education will make advancements through the implementation of digital tools such as video, audio, and simulators [<xref rid=\"B1-ijerph-17-05325\" ref-type=\"bibr\">1</xref>,<xref rid=\"B4-ijerph-17-05325\" ref-type=\"bibr\">4</xref>,<xref rid=\"B6-ijerph-17-05325\" ref-type=\"bibr\">6</xref>]. In fact, since the 1990s, research has especially focused on the application of virtual simulation technology to medical education [<xref rid=\"B7-ijerph-17-05325\" ref-type=\"bibr\">7</xref>,<xref rid=\"B8-ijerph-17-05325\" ref-type=\"bibr\">8</xref>,<xref rid=\"B9-ijerph-17-05325\" ref-type=\"bibr\">9</xref>,<xref rid=\"B10-ijerph-17-05325\" ref-type=\"bibr\">10</xref>,<xref rid=\"B11-ijerph-17-05325\" ref-type=\"bibr\">11</xref>,<xref rid=\"B12-ijerph-17-05325\" ref-type=\"bibr\">12</xref>]. Currently, virtual simulation including virtual patient is helpful in pre-clinical education, which can now utilize 3D images to teach subjects such as anatomy and pathology [<xref rid=\"B13-ijerph-17-05325\" ref-type=\"bibr\">13</xref>,<xref rid=\"B14-ijerph-17-05325\" ref-type=\"bibr\">14</xref>], training for pediatric surgery and laparoscopy [<xref rid=\"B15-ijerph-17-05325\" ref-type=\"bibr\">15</xref>,<xref rid=\"B16-ijerph-17-05325\" ref-type=\"bibr\">16</xref>], bioethics [<xref rid=\"B17-ijerph-17-05325\" ref-type=\"bibr\">17</xref>], and tracheal intubation techniques [<xref rid=\"B18-ijerph-17-05325\" ref-type=\"bibr\">18</xref>]. The technology has already been applied in the virtual simulation of hearing and vision loss to enhance medical students’ empathy for elderly patients [<xref rid=\"B19-ijerph-17-05325\" ref-type=\"bibr\">19</xref>]. A systematic review reported that the use of virtual patients can more effectively improve medical students’ skills and achieve at least the same degree of knowledge as traditional methods. The findings suggest that skills can be improved in a targeted way. The improved skills include procedural skills, a mixture of procedural and team skills, and some clinical reasoning skills. Moreover, virtual patient simulations (VPSs) in abnormal clinical scenarios may provide an accelerated breakthrough in improving clinical reasoning education [<xref rid=\"B20-ijerph-17-05325\" ref-type=\"bibr\">20</xref>]. However, there is inadequate evidence of the usefulness of VPSs for improving clinical reasoning skills for undergraduate medical students without lecturer supervision [<xref rid=\"B21-ijerph-17-05325\" ref-type=\"bibr\">21</xref>,<xref rid=\"B22-ijerph-17-05325\" ref-type=\"bibr\">22</xref>]. To the best of our knowledge, few studies have used VPSs to comparatively measure which areas of relevant case knowledge and clinical reasoning skills in symptomatology lead to better outcomes. Thus, this study had two objectives: (1) to clarify the effectiveness of VPSs for developing clinical reasoning skills among medical students without lecturer supervision, and (2) to elucidate whether VPSs improve clinical reasoning skills or knowledge of a particular case. To address these objectives, we used a pre–post experimental design to study a VPS.</p></sec><sec id=\"sec2-ijerph-17-05325\"><title>2. Materials and Methods</title><sec id=\"sec2dot1-ijerph-17-05325\" sec-type=\"subjects\"><title>2.1. Study Setting and Participants</title><p>This pre–post study was conducted at the Shimane University School of Medicine, a national medical school in Japan. It took place during one two-hour introductory class in clinical clerkship education in March 2017 and March 2018. Medical school programs in Japan are six years in length, and the students who participated in the study were at the end of their fourth year of courses, having recently passed the Objective Structured Clinical Examination. About 105 students are enrolled in each year of this medical school, so we arranged to perform the exact intervention in the same setting for a total of 210 people over two years. Before the intervention, we explained to each possible candidate: (1) the research intent, (2) the contents of the class, (3) the expected effects, (4) that data collection would be fully anonymous and collected with the distributed clicker, (5) that participation was free and voluntary, (6) that there was no conflict of interest, and (7) that we would not use these data for student evaluation.</p><p>A total of 191 participants attended the classes in this study; 19 of the original candidates did not participate due to absence. Of these, we analyzed a total of 169 participants (88.5%) after excluding 13 participants who did not provide consent and 9 participants who had trouble connecting their clickers and left the class early. The answers were not divided according to sex, as the data were collected anonymously using randomly distributed clickers. However, in 2017 and 2018, the proportion of fourth-year female students was 38% and 36%, respectively.</p></sec><sec id=\"sec2dot2-ijerph-17-05325\"><title>2.2. Study Design</title><p>This was a single center pre–post study of the effects of VPSs on clinical reasoning. A VPS software program (<sup>®</sup>Body Interact, Coimbra, Portugal) was used as an experimental intervention in both two-hour classes using an experimental design [<xref rid=\"B23-ijerph-17-05325\" ref-type=\"bibr\">23</xref>]. As the VPS in this study was based on a screen rather than a headset, it can be considered a partial VPS. In this software (<xref ref-type=\"fig\" rid=\"ijerph-17-05325-f001\">Figure 1</xref>), the virtual patient shows the dynamic pathophysiological response to user decisions. Participants can act as real clinicians, ordering almost any tests or treatments as needed [<xref rid=\"B23-ijerph-17-05325\" ref-type=\"bibr\">23</xref>]. We first provided guidance on study participation, confirming that the responses of the participants who consented would be collected using a real-time audience response system (<sup>®</sup>TurningPoint clicker, Tokyo, Japan), and that the study would exclude the data of the participants who subsequently withdrew consent [<xref rid=\"B24-ijerph-17-05325\" ref-type=\"bibr\">24</xref>]. An audience response system clicker was randomly distributed to each participant to answer the questions in real time (<xref ref-type=\"fig\" rid=\"ijerph-17-05325-f002\">Figure 2</xref>). The data were transmitted and saved to the computer at the front of the room, ensuring anonymity and making it impossible to identify individuals. To evaluate the VPS learning outcomes, we prepared a 20-item multiple-choice question (MCQ) quiz, which included 10 knowledge items and 10 clinical reasoning items for each of two scenarios (<xref ref-type=\"app\" rid=\"app1-ijerph-17-05325\">Supplementary File S1</xref> List of 20 MCQs). We categorized the questions into knowledge items and clinical reasoning items from the past questions of the Japanese National Medical Practitioners Qualifying Examination in Japan, which were relevant to each scenario. The items were randomly arranged and selected from the Japanese National Medical Practitioners Qualifying Examination. The same MCQ quiz was administered to participants pre-study and post-study to evaluate the VPS outcomes. We explained that the participants could not discuss the answers with others. The difference between the scores indicated what the participants had learned through the intervention.</p><p>Two scenarios were used: (1) a 55-year-old male with altered mental status, and (2) a 65-year-old male with acute chest pain. Since the simulation software was partially in English, a faculty member (the same for each class) performed some minimal translations into Japanese as necessary. Another faculty member guided the participants in how to operate the VPS; this faculty member was instructed to intervene as little as possible during the class, especially regarding knowledge and clinical reasoning skills that were directly relevant to the answers for the pre-post tests. For each scenario, participants were allotted 20 min to make a diagnosis and treat the patient. During this period, participants were required to conduct a medical interview, physical examination, interpretation of results, and treatment as if they were practicing physicians. Following each scenario, the participants were automatically presented with a summary of their decision-making. Participants’ pre-post test responses were automatically sent to the computer as a CSV file. To ensure uniform study conditions and reduce confounding factors as much as possible, we used the same large lecture hall and ensured that all participants had the same level of experience in both 2017 and 2018. This was meant to minimize differences in VPS usage time and maintain fairness. The same interventions and methodology were used for all participants.</p></sec><sec id=\"sec2dot3-ijerph-17-05325\"><title>2.3. Statistical Analyses</title><p>We used an interquartile range (IQR) along with the 25th and 75th percentiles to indicate skewed data. The Shapiro–Wilk test was implemented to examine the total score of pre-post test distributions. McNemar’s test was used for paired nominal data pre–post study with Bonferroni correction, while the Wilcoxon signed-rank test was employed for nonparametric repeated measurements with skewed continuous variables. All analyses were performed using Stata statistical software, version 14.0 (<italic>Stata 14 Base Reference Manual</italic>; Stata Corp., College Station, TX, USA). All tests were two-sided with a <italic>p</italic>-value < 0.05 (Bonferroni correction in <xref rid=\"ijerph-17-05325-t001\" ref-type=\"table\">Table 1</xref>; <italic>p</italic>-value < 0.0025), which was considered statistically significant.</p></sec><sec id=\"sec2dot4-ijerph-17-05325\"><title>2.4. Ethics</title><p>The study was conducted in accordance with the Declaration of Helsinki. The Ethics Committee of the Shimane University Hospital did not conduct a review for the following reasons: participants’ personal information was not made available, the research data were automatically converted electronically to nondiscriminating data, participants volunteered and provided informed consent, the safety of VPS research is well-established, VPS research is widely used, and there was no risk of harm to the participants.</p></sec></sec><sec sec-type=\"results\" id=\"sec3-ijerph-17-05325\"><title>3. Results</title><p>Compared with pre-test baseline scores, participating students showed significant increase in average total post-test scores (<italic>p</italic>-value < 0.001). The total pre-test scores showed a median of 10 IQR (8–12), a mean of 10.1, and 95% CI (9.6–10.5); the post-test scores showed a median of 13 IQR (11–15), a mean of 13.0, and 95% CI (12.6–13.4). <xref ref-type=\"fig\" rid=\"ijerph-17-05325-f003\">Figure 3</xref> presents histograms of the pre-test and post-test scores for the 169 participants. The pre-test scores showed a normal distribution, while the post-test scores after the VPS intervention skewed to the right (Shapiro–Wilk test, <italic>p</italic>-value < 0.022). Furthermore, no significant differences were observed for these fourth-year medical students between the years 2017 (pre-test scores: median = 10 IQR (8–11), mean = 9.7, 95% CI (9.1–10.3); post-test scores: median = 13, IQR (11–15), mean = 12.7, 95% CI (12.1–13.2)) and 2018 (pre-test scores: median = 10 IQR (9–12), mean = 10.4, 95% CI (9.9–10.3); post-test scores: median = 13 IQR (12–16), mean = 13.3, 95% CI (12.7–13.9)).</p><p><xref rid=\"ijerph-17-05325-t001\" ref-type=\"table\">Table 1</xref> shows that the 10 items related to knowledge (K) and the 10 items related to clinical reasoning (CR), comprising past questions of the Japanese National Examination for Physicians, were randomly arranged. We compared the correct answer rate of the pre-post tests and performed McNemar’s test for each quiz and Bonferroni correction for all 20 items. As a result, 7 of the 10 CR items showed a statistically significant increase in the correct answer rate, whereas only 4 of the 10 K items showed a significant increase. The rate of change between CR items (median = +22.8% IQR 11.8% to 39.1%, mean = 25.3, 95% CI (12.1–38.5)) was significantly higher than that between K items (median = +4.2% IQR −1.8% to 11.8%, mean = 3.84, 95% CI (−5.1 to 12.8)), with <italic>p</italic>-value < 0.008. Notably, for the CR items, although the pre-test correct answer rate was low, the correct answer rate for some post-test items increased by 50 points or more (i.e., items 1 and 14). The items with a large increase in the correct answer rate included a direct question regarding diagnosis of an altered mental status and management of acute chest pain. On the other hand, some items requiring simple knowledge showed a decrease in the percentage of correct answers.</p><p><xref ref-type=\"fig\" rid=\"ijerph-17-05325-f004\">Figure 4</xref> presents box plots of the scores for the 10 CR items and the 10 K items. CR scores increased significantly (pre-test: median = 5, mean = 5.3 95%, CI (4.98–5.58); post-test: median = 8, mean = 7.81, 95% CI (7.57–8.05); <italic>p</italic>-value < 0.001). K item scores overall also increased statistically, though the median was unchanged (pre-test: median = 5, mean = 4.78, 95% CI (4.55–5.01); post-test: median = 5, mean = 5.12, 95% CI (4.90–5.43); <italic>p</italic>-value = 0.003).</p></sec><sec sec-type=\"discussion\" id=\"sec4-ijerph-17-05325\"><title>4. Discussion</title><p>As expected, overall scores were higher after the intervention. This can be explained by the fact that the histogram of overall scores was significantly skewed towards the higher scores. However, as a general rule with certain educational interventions, total scores will always rise immediately afterwards. This study assessed CR outcomes by using a VPS to train participants on diagnosing two clinical scenarios. The results show that VPSs improve CR ability even when used to instruct a large group in a lecture hall. On the other hand, little improvement was observed in K item scores when teacher intervention and instruction were minimized. There are several possible causes for this result. First, even if the participants had already learned the information necessary to answer all K items, these items may be difficult for fourth-year students because they were taken from the Japanese National Medical Practitioners Qualifying Examination, and the students have not yet performed their clinical clerkships. Second, the information is necessary to answer K items encompassed medical knowledge only, and may have been difficult to learn based solely on VPS unless teachers explained the scenario. For example, it is challenging to learn to interpret either electrocardiograms of patients with chest pain or contrast CTs based on anatomical knowledge simply by engaging with a VR program and without reading a textbook or listening to a lecture. Third, improving knowledge item scores in a short period of time may be challenging without essential knowledge of pathology, pharmacology, physiology, and anatomy. Furthermore, the knowledge acquired in this pre-clinical stage may still be perceived as complex when based only on engagement with the VPS. For example, the scores for some of the K items decreased in the opposite direction (items 9 and 18). One possible reason may be that analyzing the chest pain and altered mental status cases through the VPS program caused learner bias due to confusion over each scenario, and the answers were pulled in an unexpected direction. On the other hand, the scores increased for problems related to CR regarding altered mental status and acute chest pain. This is because these scenarios are more closely related to the diagnostic process of listing differential diagnoses for chest pains and determining necessary tests, as well as initially ruling out hypoglycemia for the differential diagnosis of altered mental status. For these reasons, we believe that engaging with a VPS is more useful for learning CR compared to acquiring medical knowledge, and for students to attain clinical experience by repeatedly utilizing the VPS.</p><p>For today’s digital native students, we believe it is necessary to implement new learning methods that include video, music, YouTube, and social media, rather than traditional methods [<xref rid=\"B1-ijerph-17-05325\" ref-type=\"bibr\">1</xref>]. One previous study compared three educational methods for teaching CR: live discussion, watching a video of the discussion, and learning from a textbook [<xref rid=\"B25-ijerph-17-05325\" ref-type=\"bibr\">25</xref>]. Notably, immediately after the lesson, the students who participated in the live discussion had a statistically significant outcome. However, an evaluation two weeks later showed no significant difference in knowledge retention from watching the discussion video or from participating in the live discussion, and both methods were found to be more effective than textbook learning.</p><p>In addition, another randomized controlled trial showed that the authenticity of CR (that is, how similar it is to actual practical experience) in traditional pre-clerkship instruction might not be high, as measured against pre-clerkship and clerkship outcome measures [<xref rid=\"B26-ijerph-17-05325\" ref-type=\"bibr\">26</xref>].</p><p>In this study, since we implemented a nonimmersive simulation without a virtual reality (VR) head set, we cannot consider it a true VR simulation by narrow definition. However, we gathered the participants in a large lecture hall with a shared screen to ensure uniformity of participant experience and to minimize confounding factors as much as possible. A fully immersive VR simulation, using a VR headset, may have a higher educational effect than a large shared screen [<xref rid=\"B27-ijerph-17-05325\" ref-type=\"bibr\">27</xref>]. It is not easy, however, for all participants to simultaneously use a fully immersive VR simulation, and it currently requires a tremendous amount of money and high-tech equipment [<xref rid=\"B28-ijerph-17-05325\" ref-type=\"bibr\">28</xref>]. Moreover, another comparative study found no overall significant differences in efficiency of educational outcomes and participant satisfaction between a shared screen and a fully immersive virtual reality simulation [<xref rid=\"B29-ijerph-17-05325\" ref-type=\"bibr\">29</xref>].</p><p>Furthermore, the greatest value of VPS is that the patient is not required to be involved in student training, and thus there is no risk to the patient [<xref rid=\"B2-ijerph-17-05325\" ref-type=\"bibr\">2</xref>,<xref rid=\"B8-ijerph-17-05325\" ref-type=\"bibr\">8</xref>,<xref rid=\"B30-ijerph-17-05325\" ref-type=\"bibr\">30</xref>,<xref rid=\"B31-ijerph-17-05325\" ref-type=\"bibr\">31</xref>,<xref rid=\"B32-ijerph-17-05325\" ref-type=\"bibr\">32</xref>,<xref rid=\"B33-ijerph-17-05325\" ref-type=\"bibr\">33</xref>]. There are also other merits, such as the ability to repeat the learning experience until the educational goal is achieved. Of course, it is not possible to complete all medical instruction through simulation education. However, we believe that if the strengths of traditional educational methods are combined appropriately with the VPS, further educational benefits can be expected.</p><sec><title>Limitations</title><p>Although we attempted to ensure a uniform study setting, there are some limitations to our study. First, this is not an experiment comparing VPS to other teaching methods. For that reason, we cannot say whether it is better than traditional teaching methods, such as lectures and case discussions. This discussion point is important, and further comparative experiments need to be conducted. Second, the intervention should ideally be reassessed afterwards to ensure that clinical reasoning skills have indeed improved and have been retained. Further, we cannot say whether performing multiple VPS scenarios will actually develop sufficient clinical reasoning skills for a clinician. However, our study could not be followed up because the data were taken anonymously using a clicker. Third, this study was conducted in a Japanese university hospital. Since clinical medical education systems vary from country to country, there is a question of external validity. It is uncertain whether these results are applicable to other countries or institutions. Fourth, variations in the degree of participation may be possible due to the type of VPS used. Further, although we tried to ensure consistency in the study setting, there may have been some unavoidable differences, such as the individual students’ active participation, concentration, and screen visibility due to seating positions. Fifth, about 10% of students were absent since no participation incentives were provided, and this may have created a selection bias. Finally, no similar studies have been conducted, and the validity of the test questions is uncertain, because they were selected from the Japanese National Medical Practitioners Qualifying Examination.</p></sec></sec><sec sec-type=\"conclusions\" id=\"sec5-ijerph-17-05325\"><title>5. Conclusions</title><p>Our study suggests that VPS programs are more effective for increasing CR scores than K scores among medical students. VPS software programs could thus help medical students improve their clinical decision-making skills with minimal supervision from lecturers. In summary, the widespread use of VPS software programs in clinical education could help maximize the effectiveness of medical school curriculum.</p></sec></body><back><ack><title>Acknowledgments</title><p>The authors would like to thank Satoru Hattori at Uhida Yoko Co. for providing user information and careful guidance about Body Interact. We also thank all of the medical students who participated in this study.</p></ack><app-group><app id=\"app1-ijerph-17-05325\"><title>Supplementary Materials</title><p>The following are available online at <uri xlink:href=\"https://www.mdpi.com/1660-4601/17/15/5325/s1\">https://www.mdpi.com/1660-4601/17/15/5325/s1</uri>, File S1: List of 20 MCQs.</p><supplementary-material content-type=\"local-data\" id=\"ijerph-17-05325-s001\"><media xlink:href=\"ijerph-17-05325-s001.pdf\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material></app></app-group><notes><title>Author Contributions</title><p>Conceptualization, T.W.; methodology, T.W. and Y.T.; software, T.W. and M.O.; validation, T.W., M.O., and Y.T.; formal analysis, T.W. and Y.T.; investigation, T.W.; resources, T.W.; data curation, T.W. and M.O.; writing—original draft preparation, T.W.; writing—review and editing, T.W. and Y.T.; visualization, T.W.; supervision, Y.T. and K.O.; project administration, T.W. and K.O.; funding acquisition, T.W., and K.O. All authors have read and agreed to the published version of the manuscript.</p></notes><notes><title>Funding</title><p>This work was funded by JSPS KAKENHI, grant numbers 20H03913.</p></notes><notes notes-type=\"COI-statement\"><title>Conflicts of Interest</title><p>The authors declare no conflict of interest.</p></notes><ref-list><title>References</title><ref id=\"B1-ijerph-17-05325\"><label>1.</label><element-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Schwartzstein</surname><given-names>R.M.</given-names></name><name><surname>Roberts</surname><given-names>D.H.</given-names></name></person-group><article-title>Saying Goodbye to Lectures in Medical School-Paradigm Shift or Passing Fad?</article-title><source>N. Engl. J. 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Med. Internet Res.</source><year>2019</year><volume>21</volume><fpage>e14676</fpage><pub-id pub-id-type=\"doi\">10.2196/14676</pub-id><pub-id pub-id-type=\"pmid\">31267981</pub-id></element-citation></ref></ref-list></back><floats-group><fig id=\"ijerph-17-05325-f001\" orientation=\"portrait\" position=\"float\"><label>Figure 1</label><caption><p>The virtual reality simulation software (<sup>®</sup>Body Interact, Coimbra, Portugal).</p></caption><graphic xlink:href=\"ijerph-17-05325-g001\"/></fig><fig id=\"ijerph-17-05325-f002\" orientation=\"portrait\" position=\"float\"><label>Figure 2</label><caption><p>Clickers being distributed during the pre-test (March 2018).</p></caption><graphic xlink:href=\"ijerph-17-05325-g002\"/></fig><fig id=\"ijerph-17-05325-f003\" orientation=\"portrait\" position=\"float\"><label>Figure 3</label><caption><p>Histogram with a bell curve of participant scores (<bold>a</bold>) on the pre-test and (<bold>b</bold>) on the post-test.</p></caption><graphic xlink:href=\"ijerph-17-05325-g003\"/></fig><fig id=\"ijerph-17-05325-f004\" orientation=\"portrait\" position=\"float\"><label>Figure 4</label><caption><p>Total scores on knowledge (<bold>a</bold>) and clinical reasoning (<bold>b</bold>) items.</p></caption><graphic xlink:href=\"ijerph-17-05325-g004\"/></fig><table-wrap id=\"ijerph-17-05325-t001\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05325-t001_Table 1</object-id><label>Table 1</label><caption><p>Pre-post test values.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Item No.</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Category</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Main Topic of Quiz</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Pre-Test Score (<italic>n</italic> = 169)</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Post-Test Score (<italic>n</italic> = 169)</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Fluctuation (%)</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Adjusted <italic>p</italic>-Value</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CR</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Management of altered mental status</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">25.4%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">75.7%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">+50.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.0001 *</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">K</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Electrocardiogram and syncope</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">52.1%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">58.0%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">+5.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.1573</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">K</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Type of hormone secretion during hypoglycemia</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">64.5%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">87.0%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">+22.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.0001 *</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">K</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Referred pain of acute coronary syndrome</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">51.5%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">53.8%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">+2.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5862</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CR</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Time course of syncope (cardiogenic)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">59.2%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">82.8%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">+23.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.0001 *</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">K</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Pathophysiology of pulmonary failure</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">34.9%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">31.4%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−3.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.1573</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">K</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Electrocardiogram of ST elevation</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">34.3%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">36.7%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">+2.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5791</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CR</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Vital signs of sepsis</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">73.4%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">85.8%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">+12.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.0001 *</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">K</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Anatomy of aortic dissection</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">55%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">53.3%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−1.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.6015</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CR</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Management of each type of shock</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">66.9%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">78.7%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">+11.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0032</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">11</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">K</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Contrast CT of aortic dissection</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">67.5%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">79.3%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">+11.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0016 *</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">12</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CR</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Treatment strategy of shock</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">56.2%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">62.7%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">+6.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0630</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">K</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Jugular venous pressure</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">42.6%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">49.7%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">+7.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0455</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">14</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CR</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Differential diagnosis of hypoglycemia</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">24.3%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">82.2%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">+58.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.0001 *</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">15</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CR</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Management of altered mental status</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">75.1%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">97.0%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">+21.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.0001 *</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">16</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">K</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Chest radiograph of heart failure</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">24.3%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">39.6%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">+15.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.0001 *</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">17</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CR</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Management of syncope</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">53.8%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">79.3%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">+25.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.0001 *</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">18</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">K</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Symptoms of hypoglycemia</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">51.5%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">27.8%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−23.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.0001 *</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">19</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CR</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Treatment of sepsis shock</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">47.3%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">50.9%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">+3.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.3428</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">20</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">CR</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Management of chest pain</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">46.7%</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">85.8%</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">+39.1</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><0.0001 *</td></tr></tbody></table><table-wrap-foot><fn><p>Notes: K = knowledge, CR = clinical reasoning, * = statistically significant, <italic>p</italic>-value < 0.0025.</p></fn></table-wrap-foot></table-wrap></floats-group></article>\n"
]
[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Int J Environ Res Public Health</journal-id><journal-id journal-id-type=\"iso-abbrev\">Int J Environ Res Public Health</journal-id><journal-id journal-id-type=\"publisher-id\">ijerph</journal-id><journal-title-group><journal-title>International Journal of Environmental Research and Public Health</journal-title></journal-title-group><issn pub-type=\"ppub\">1661-7827</issn><issn pub-type=\"epub\">1660-4601</issn><publisher><publisher-name>MDPI</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32731593</article-id><article-id pub-id-type=\"pmc\">PMC7432111</article-id><article-id pub-id-type=\"doi\">10.3390/ijerph17155439</article-id><article-id pub-id-type=\"publisher-id\">ijerph-17-05439</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Article</subject></subj-group></article-categories><title-group><article-title>Assessment of COVID-19 Waste Flows During the Emergency State in Romania and Related Public Health and Environmental Concerns</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Mihai</surname><given-names>Florin-Constantin</given-names></name></contrib></contrib-group><aff id=\"af1-ijerph-17-05439\">Department of Research, Faculty of Geography and Geology, Alexandru Ioan Cuza University, Carol I Blvd, Nr.20 A, RO-700505 Iasi, Romania; <email>[email protected]</email></aff><pub-date pub-type=\"epub\"><day>28</day><month>7</month><year>2020</year></pub-date><pub-date pub-type=\"ppub\"><month>8</month><year>2020</year></pub-date><volume>17</volume><issue>15</issue><elocation-id>5439</elocation-id><history><date date-type=\"received\"><day>28</day><month>6</month><year>2020</year></date><date date-type=\"accepted\"><day>23</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>© 2020 by the author.</copyright-statement><copyright-year>2020</copyright-year><license license-type=\"open-access\"><license-p>Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>).</license-p></license></permissions><abstract><p>This paper provides a rapid assessment method of potentially infectious waste flow related to the coronavirus disease (COVID-19) pandemic in Romania focusing on the emergency state (from 16 March to 14 May 2020) where a national lockdown was in force with restrictive and social distancing measures concerning population mobility and economic activities. Medical and municipal waste management systems are critical services in combating the virus spread in the community. This assessment is useful due to poor available data of medical waste flow in environmental reports and it covers COVID-19 patients, quarantined, and self-isolated persons as the main potential infectious waste sources. The proposed model estimates that COVID-19 related waste flow is 4312 t at the national level from 25 February to 15 June of which 2633 t in the emergency state period. This assessment is correlated with deficiencies of medical and municipal waste management systems in Romania before the COVID-19 pandemic as stress factors of public health and environment. This study points out the main challenges of waste operators and reveals some best practices during this pandemic crisis. Based on the results and discussion section, several recommendations are proposed to COVID-19 waste-related issues and points out the crucial role of the reliable medical and municipal waste database in managing such biologic hazards at national and EU levels. Monitoring of COVID-19 waste flow through such models are important for decision-makers, particularly in low and middle-income countries which are facing waste management deficiencies and gaps in waste statistics, to reduce other contamination risks or related environmental threats.</p></abstract><kwd-group><kwd>COVID-19</kwd><kwd>waste management</kwd><kwd>medical waste</kwd><kwd>municipal waste</kwd><kwd>public health</kwd><kwd>pollution</kwd></kwd-group></article-meta></front><body><sec sec-type=\"intro\" id=\"sec1-ijerph-17-05439\"><title>1. Introduction</title><p>The geographic expansion of the COVID-19 outbreak around the world forced the World Health Organization (WHO) to declare the pandemic status on 11 March 2020. On 24 March, the United Nations Environment Programme (UNEP) pointed out that waste management is an essential public service in avoiding secondary impacts upon public health and environment and special attention should be paid to handle medical, household and other hazardous items in such a period [<xref rid=\"B1-ijerph-17-05439\" ref-type=\"bibr\">1</xref>]. Massive amounts of medical waste have already been generated and disposed into the natural environment [<xref rid=\"B2-ijerph-17-05439\" ref-type=\"bibr\">2</xref>] and waste mismanagement practices can increase the contamination risks in developing countries [<xref rid=\"B3-ijerph-17-05439\" ref-type=\"bibr\">3</xref>,<xref rid=\"B4-ijerph-17-05439\" ref-type=\"bibr\">4</xref>] or among urban poor which live in informal settlements [<xref rid=\"B5-ijerph-17-05439\" ref-type=\"bibr\">5</xref>,<xref rid=\"B6-ijerph-17-05439\" ref-type=\"bibr\">6</xref>] or among hundreds of millions of people without basic water, sanitation, and waste management facilities in rural areas [<xref rid=\"B7-ijerph-17-05439\" ref-type=\"bibr\">7</xref>].</p><p>Romania declared a state of emergency on 16 March due to the pandemic crisis in which a national lockdown was implemented since the first case of COVID-19 patient was registered on 26 February 2020 [<xref rid=\"B8-ijerph-17-05439\" ref-type=\"bibr\">8</xref>]. The National Public Health Institute of Romania declared household waste generated in quarantines as infectious waste on 18 March 2020. Therefore, there is a shift from usual municipal waste management practices towards the special collection, transport and disposal requirements of hazardous wastes [<xref rid=\"B9-ijerph-17-05439\" ref-type=\"bibr\">9</xref>]. On 30 March, the European Center for Disease Prevention and Control (ECDPC) released an infection prevention and control guideline at the household level for suspected or confirmed COVID-19 disease with mild symptoms in self-isolation status [<xref rid=\"B10-ijerph-17-05439\" ref-type=\"bibr\">10</xref>]. This document provides some recommendations regarding the safe management of household waste. One keynote is to have a double waste bag for used tissues, face masks and other waste, which should be disposed of in the residual bin. The European Commission (EC) released a guideline on 14 April regarding waste management practices in the context of coronavirus crisis [<xref rid=\"B11-ijerph-17-05439\" ref-type=\"bibr\">11</xref>]. This document requires to maintain quality standards of municipal waste collection services and medical waste treatment and any measures must comply with EU regulations. Zero Waste Europe (ZWE) argues that the COVID-19 crisis should not undermine the EU’s long-term circular economy objectives by returning to business as usual [<xref rid=\"B12-ijerph-17-05439\" ref-type=\"bibr\">12</xref>]. The transition towards a circular economy should be further supported while 2020 is the first year where the recycling rate of 50% should be achieved by the EU Member States. However, this target is impossible to be achieved in Romania where the recycling rate of municipal waste was around 14% in 2017 [<xref rid=\"B13-ijerph-17-05439\" ref-type=\"bibr\">13</xref>].</p><p>Romania is coping with several mismanagement practices such as poor efficiency of source-separated waste collection schemes in urban areas, the prevalence of landfills as a main waste management option, delays in implementation of regional integrated waste management systems, lack of a reliable waste statistics database, etc. Furthermore, rural Romania is facing illegal waste disposal practices (wild dumps, open burning practices), plastic pollution of freshwater bodies and limited waste collection coverage of the rural population in some counties [<xref rid=\"B14-ijerph-17-05439\" ref-type=\"bibr\">14</xref>,<xref rid=\"B15-ijerph-17-05439\" ref-type=\"bibr\">15</xref>]. The circular economy remains underdeveloped despite the promising potential in this area [<xref rid=\"B16-ijerph-17-05439\" ref-type=\"bibr\">16</xref>], but the new circular economy plan promoted EU could catalyze the effort in the right direction [<xref rid=\"B17-ijerph-17-05439\" ref-type=\"bibr\">17</xref>]. On the other side, medical waste management issues are less examined in Romania, poor details and waste statistics data provided in environmental reports and several environmental crimes investigated by environmental authorities and revealed to the general public through national and local mass-media with a particular focus to hazardous waste incinerators. Neither the new national waste management plans provide a solid and updated analysis of medical waste management status in Romania [<xref rid=\"B18-ijerph-17-05439\" ref-type=\"bibr\">18</xref>]. According to WHO, about 85% of medical waste generated by healthcare activities is nonhazardous and the remaining 15% is considered hazardous material that can be infectious, toxic or radioactive [<xref rid=\"B19-ijerph-17-05439\" ref-type=\"bibr\">19</xref>]. However, COVID-19 related waste flow is considered infectious waste which must be properly treated [<xref rid=\"B20-ijerph-17-05439\" ref-type=\"bibr\">20</xref>,<xref rid=\"B21-ijerph-17-05439\" ref-type=\"bibr\">21</xref>], but serious concerns are raised around consumption and management of plastic products associated with personal protective equipment (PPE) [<xref rid=\"B22-ijerph-17-05439\" ref-type=\"bibr\">22</xref>,<xref rid=\"B23-ijerph-17-05439\" ref-type=\"bibr\">23</xref>]. A weak infectious waste management system could speed up the growth of COVID-19 in developing countries [<xref rid=\"B24-ijerph-17-05439\" ref-type=\"bibr\">24</xref>].</p><p>On this background, this paper aims: (i) to provide a rapid assessment of potentially infectious waste broken down in three main sources such as COVID-19 patients (hospitalized), people in quarantined sites (abroad working/travel Romanian citizens from countries/regions heavily affected by coronavirus), self-isolated people (persons from abroad or contacts with confirmed COVID-19 cases); (ii) to reveal national and some regional flows of potentially infectious waste generated; (iii) to identify critical issues during the emergency state in Romania related to medical and municipal waste management backgrounds; (iv) to reveal some best practices related to waste management sector; (v) to provide some recommendations related to waste management policies and data support at national and EU levels.</p><p>This study intends to provide a rapid assessment method to estimate COVID-19 related waste flow in Central and Eastern European countries where medical and municipal waste management systems must be further improved to comply with EU standards. Furthermore, such analysis is useful in case of low and middle-income countries around the world which face poor waste management infrastructure and gaps in waste statistics data, as an alternative solution to estimate the potential COVID-19 related waste flow necessary for decision-makers to reduce other contamination risk routes and environmental pollution threats.</p></sec><sec id=\"sec2-ijerph-17-05439\"><title>2. Materials and Methods</title><sec id=\"sec2dot1-ijerph-17-05439\"><title>2.1. COVID-19 Status in Romania</title><p>The first case of COVID-19 patient waste detected on 26 February 2020 increasing to 168 cases on 16 March (0 deaths and 9 recovered) when the state of emergency was implemented. The first deaths were registered one week later (9 on 23 March). After the first month of emergency state (15 April) the COVID-19 statistics revealed 7216 (cumulative cases), 419 deaths, 1270 recovered and at the end of the emergency state (and beginning of alert state on 15 May) the situation was 16,247 cumulative cases of which 6020 active cases, 1090 deaths and 9370 recovered. By the end of the first stage of the alert state (15 June) statistics show 22,165 cumulative cases of which 4938 active cases, 1410 deaths and 15,817 recovered.</p><p>The peak of active cases was recorded in the second stage of emergency state with over 7000 cases from 23 April to 13 May, dropping during the alert state under 5000 cases from 28 May to 15 June. The number of patients in intensive care units (ICU) increased since 19 March (6 cases) to the peak of 288 cases (22 April), maintain the threshold of plus 200 cases until the end of the emergency state (15 May). However, the cases of intensive care patients decreased in the alert state below 200 cases until 15 June. The number of persons in quarantine has increased since 15 March (2855) to the peak on 9 April (25,586) dropping to 11,631 (28 April) with a slight increase by the end of emergency state 14,441 (15 May) followed by a continuous decreasing trend during the alert state (1331 on 15 June). The number of persons self-isolated increase from 14 March (14,680) to the peak on 29 March (132,641) with a continuous descending trend by the end of emergency state such as 14,789 (15 May). However, there is an ascending trend during the alert state from 19 May (24,415) peaking by the end of month 98,403 (30 May) and maintaining this trend in June surpassing 100,000 persons on 11 June than declining by 15 June (92,734 persons).</p><p>These statistics are important in the assessment process of Covid-19 related waste flow in Romania as further described in the following section.</p></sec><sec id=\"sec2dot2-ijerph-17-05439\"><title>2.2. Estimation of COVID-19 Waste Flow in Healthcare Facilities</title><p>High-income countries generate on average up to 0.5 kg of hazardous waste per hospital bed per day while low-income countries generate on average 0.2 kg [<xref rid=\"B19-ijerph-17-05439\" ref-type=\"bibr\">19</xref>]. On the other side, there are no separate collections (hazardous vs nonhazardous items) in developing countries that increase the ratio of hazardous items through contamination [<xref rid=\"B25-ijerph-17-05439\" ref-type=\"bibr\">25</xref>]. At the national level, medical waste as follows:<disp-formula>M<sub>w</sub> = No. of active COVID-19 cases per day × M<sub>wgr</sub> (kg∙bed∙day<sup>−1</sup>)<label>(1)</label></disp-formula>\nwhere M<sub>wgr</sub> = medical waste generation rate—1 kg∙bed∙day<sup>−1</sup> The number of active cases is available at <uri xlink:href=\"https://www.graphs.ro/\">https://www.graphs.ro/</uri> at the national level. Active cases represent confirmed COVID-19 patients that are treated in hospitals without recovered or dead patients. In general, all COVID-19 cases were treated in hospitals during the emergency state.\n<disp-formula>Subnational level (county): Mw = confirmed COVID-19 cases per day × Mwgr (kg∙bed∙day<sup>−1</sup>).<label>(2)</label></disp-formula></p><p>There is no available information about the active COVID-19 statistics at the county level and some inconsistencies could appear between confirmed cases, deaths, and recovered people at this scale. However, daily data provided by the strategic communication group of the Romanian government are considered for estimation to reduce this aspect. Therefore, COVID-19 medical waste flow is calculated from active cases at national level, whereas subnational is calculated from daily confirmed cases.</p><p>The assessment of healthcare waste at subnational levels is more difficult to achieve, the reporting systems are made by regional public health authorities (county departments) to the National Institute of Public Health. However, national authorities stopped reporting data at county levels from 19 March until 2 April. On 20 March, a manifesto initiated by Geo-spatial.org and supported by 17 non-governmental organizations (NGOs) was addressed to national authorities to release such key daily COVID-19 statistics data at county level (including confirmed cases, recovered, deaths, no. of people in quarantine or self-isolated and details about age and gender in each category for data consistency, no. of tests performed each day) for better monitoring of COVID-19 status in Romania and to avoid misinformation and spreading of fake news [<xref rid=\"B26-ijerph-17-05439\" ref-type=\"bibr\">26</xref>].</p><p>The average medical waste generation rate was around 1 kg∙bed∙day<sup>−1</sup> in Infectious Diseases Clinical Hospital of Craiova used as the baseline for this paper [<xref rid=\"B27-ijerph-17-05439\" ref-type=\"bibr\">27</xref>]. This value is consistent with the recommendation of El-Haggar [<xref rid=\"B28-ijerph-17-05439\" ref-type=\"bibr\">28</xref>] as the average amount of medical waste generated per bed per day.</p><p>The highest rate of medical waste 1.814 kg∙bed<sup>−1</sup>∙day<sup>−1</sup> was calculated in the case of the Emergency Clinical Hospital of Craiova [<xref rid=\"B29-ijerph-17-05439\" ref-type=\"bibr\">29</xref>]. The medical waste generation rates in main hospitals vary between 1–1.8 kg∙bed<sup>−1</sup>∙day<sup>−1</sup> compared to smaller healthcare facilities < 0.5 kg∙bed<sup>−1</sup>·day<sup>−1</sup> [<xref rid=\"B27-ijerph-17-05439\" ref-type=\"bibr\">27</xref>,<xref rid=\"B29-ijerph-17-05439\" ref-type=\"bibr\">29</xref>].</p><p>In Italy, Vaccari et al. [<xref rid=\"B30-ijerph-17-05439\" ref-type=\"bibr\">30</xref>] found that the highest medical waste generation in departments of anesthetics (5.96 kg∙bed<sup>−1</sup>∙day<sup>−1</sup>) pediatric and intensive care (3.37 kg∙bed<sup>−1</sup>∙day<sup>−1</sup>). In Wuhan (China) the amount of medical waste peaked to 240 t per day [<xref rid=\"B24-ijerph-17-05439\" ref-type=\"bibr\">24</xref>]. Therefore, in the COVID-19 pandemic context, medical waste flow associated with intensive care patients is expected to be higher than the general average of infectious disease and upper limited detected by previous studies in Romania (1.8 kg) is further considered in the analysis.\n<disp-formula>M<sub>w</sub> = No. of COVID-19 patients in ICU. day<sup>−1</sup> × 1.8 kg∙bed<sup>−1</sup>∙day<sup>−1</sup><label>(3)</label></disp-formula></p><p>The ICU waste is included in the general COVID-19 medical waste flow as calculated by Equation (1).</p></sec><sec id=\"sec2dot3-ijerph-17-05439\"><title>2.3. Estimation of Potentially Infectious Waste Generated in Quarantine Places or by Self-Isolated People in Households</title><p>Quarantine (14 days) is established for all people who have no symptoms but returning from areas with extended community transmission of new coronavirus (COVID-19 red areas) in special locations (e.g., hotels, touristic pensions) supervised by local authorities and county departments of public health. In the first phase, these locations were served by municipal waste operators, but national authorities considered these wastes as infectious. In this situation, the quarantine places must have contracts with special waste operators licensed to collect and transport such hazardous wastes to waste incinerators.</p><p>Self-isolation (14 days) is established for people who do not show symptoms, but: (i) have traveled in the last 14 days to regions/localities in areas affected by COVID-19 other than those with extended community transmission (yellow areas); (ii) direct contact with people with symptoms and who traveled to areas with extended community transmission; (iii) direct contact with people who were confirmed with coronavirus (COVID-19); (iv) family member in above-mentioned cases. These waste generated in such households should be disposed of in residual bins and collected by waste operators and transported to landfills.</p><p>The estimation of waste flow data takes into consideration the municipal waste generated in households or quarantine places.</p><p>In Romania, municipal waste generation rate is 0.64 kg∙inhab∙day<sup>−1</sup> in urban areas and 0.31 kg∙inhab∙day<sup>−1</sup> in rural areas according to the new national waste management plan [<xref rid=\"B18-ijerph-17-05439\" ref-type=\"bibr\">18</xref>].However, regional differences are expected between larger cities (e.g., county capitals) and the other urban areas.</p><p>The regional waste management plans of Dolj county points out that Craiova city has a per-capita waste generation rate of 0.7 kg∙inhab∙day<sup>−1</sup> compared to other cities 0.55 kg∙inhab∙day<sup>−1.</sup> [<xref rid=\"B31-ijerph-17-05439\" ref-type=\"bibr\">31</xref>]. In the latter case, this per-capita generation rate is used as a conservative option to estimate the municipal waste flow in quarantine places and self-isolated cases in households.\n<disp-formula>Quarantine/self-isolation waste = no. of people in quarantine/self-isolated (national & subnational levels) × W<sub>GR</sub>, W<sub>GR</sub> = waste generation rate (0.55 kg∙inhab∙day<sup>−1</sup>).<label>(4)</label></disp-formula></p><p>Regional assessments of such wastes depend on available data at county levels regarding the number of persons in quarantine or self-isolated. These data are less available to the public than confirmed cases at the county levels. At the beginning of April, all three main categories (confirmed, quarantine, self-isolated) were available for a short time (see <xref ref-type=\"fig\" rid=\"ijerph-17-05439-f001\">Figure 1</xref>). The number of quarantine and isolated are daily provided by the press release notes at the national level (<uri xlink:href=\"https://stirioficiale.ro/informatii\">https://stirioficiale.ro/informatii</uri>) by the strategic communication group of the Romanian government. Some data are available for certain counties through government representatives at county levels (Prefecture Institution), but the lack of cohesion across all counties reduce the possibility to perform a spatial analysis during all emergency period at subnational levels. In this context, a snapshot of regional disparities is presented for the beginning of April (all three sources of COVID-19 related waste flow) and completed with certain case studies (e.g., Neamt County).</p><p>Plastic packaging materials are significant waste streams generated in quarantine places due to the single-use of cups, tableware and dishes according to three daily meals. An increase in plastic and glass was observed in Turin (Italy), but that overall MSW production in March 2020 was 11.5% lower than the one registered in March 2019 [<xref rid=\"B32-ijerph-17-05439\" ref-type=\"bibr\">32</xref>]. In addition, plastic waste increased in Thailand, but in Indonesia the amounts of waste sent to landfills decreased up to 40% due to the closure of offices, restaurants and industries [<xref rid=\"B24-ijerph-17-05439\" ref-type=\"bibr\">24</xref>].</p></sec><sec id=\"sec2dot4-ijerph-17-05439\"><title>2.4. Limitations of the Study</title><p>The paper aims to provide a rapid estimation of potentially infectious waste related to coronavirus in Romania at national and subnational levels in the most critical period (emergency state 16 March to 15 May) and the first stage of the alert state (15 May to 15 June). This assessment depends on the reliability of COVID-19 statistics in Romania and the transparency of local and national authorities in this regard as mentioned in <xref ref-type=\"sec\" rid=\"sec2dot2-ijerph-17-05439\">Section 2.2</xref> and <xref ref-type=\"sec\" rid=\"sec2dot3-ijerph-17-05439\">Section 2.3</xref> (number of confirmed cases, active cases, number of population in quarantine/self-isolate in homes, number of tests, etc.) and on the other hand, the medical waste generation rate (kg∙bed∙day<sup>−1</sup>) and municipal waste generation rates are taken into consideration and supported by previous studies. Some inconsistences between the reported cases from one county to another could be expected, but daily official statistics are taking into account. In the medium term, experimental studies at various COVID-19 hospitals and quarantined places should be made to adjust waste generation rates and to determine the medical waste composition/municipal waste composition. Another possible objective is to determine the medical waste generation rate broken down per intensive care patients and non-intensive care patients. Such field measurements are necessary for Romania and beyond to update the baseline scenario in both healthcare facilities and households and ultimately to improve the current models. However, special attention should be given in the international comparison of COVID-19 pandemic data due to various reporting systems and the number of tests performed [<xref rid=\"B33-ijerph-17-05439\" ref-type=\"bibr\">33</xref>].</p></sec></sec><sec sec-type=\"results\" id=\"sec3-ijerph-17-05439\"><title>3. Results and Discussions</title><sec id=\"sec3dot1-ijerph-17-05439\"><title>3.1. COVID-19 Waste Flow at National Level</title><p><xref rid=\"ijerph-17-05439-t001\" ref-type=\"table\">Table 1</xref> shows that most of the COVID-19 related waste was generated during the emergency state where medical waste (COVID-19 patients) has a ratio of 10.86% and quarantine waste 17.22% of the total waste flow.</p><p>These two sources cumulated 739.48 t of waste which should be incinerated according to national authority guidelines adding other 262 t during the alert state. Per the total period 1007.7 t infectious wastes (healthcare facilities + quarantine wastes) should have been collected by special hazardous waste operators and disposed of in the waste incineration plants or using alternative options for medical waste flow as outlined in <xref ref-type=\"sec\" rid=\"sec3dot4-ijerph-17-05439\">Section 3.4</xref>.</p><p>For example, in Jakarta (Indonesia), the amount of medical waste reached 12,740 t 60 days after the virus attacked the region and in Wuhan (China) 5200 t of medical waste was collected from individual containers which served hospitals, isolation areas and shelters [<xref rid=\"B24-ijerph-17-05439\" ref-type=\"bibr\">24</xref>].</p><p>Self-isolation wastes prevailed in all periods as amounts generated, but this waste stream has a lower infectious potential being managed by ordinary municipal waste operators and disposed of in landfills. However, some local authorities treat the self-isolation wastes as quarantine wastes with special transport and disposal treatment.</p><p>The ICU patients generated around 6% of total medical waste during the emergency and the alert state states (16 March to 15 June) with no such waste stream before the emergency state. Prior the emergency state, the amounts of potentially infectious waste flow was over 10 times smaller than both emergency and alert states which point out the exponential growth of COVID-19 cases and on the other hand, the large number of people who returned in Romania from countries seriously affected by COVID-19 pandemics such as Italy or Spain.</p><p>National Institute of Public Health (NIPH) updated the list of red and yellow areas destinations in which quarantine and self-isolation are compulsory when citizens return in Romania which was regularly updated during the emergency state [<xref rid=\"B34-ijerph-17-05439\" ref-type=\"bibr\">34</xref>]. The list of extended community transmission comprised on 26 February (first COVID-19 case detected in Romania): Mainland China (Hubei Province), Wenzhou, Hangzhou, Ningbo, Taizhou Cities in Zhejiang Province or one of the 12 localities in Italy (Lombardia and Veneto regions). The list comprises on 15 March 2020 (beginning of emergency state) in (i) red areas (quarantine): Hubei region (China), Italy, Iran, South Korea (Daegu city and Cheongdo County); (ii) yellow areas (self-isolation)—countries with over 500 cases (Austria, Belgium, South Korea, Denmark, Switzerland, France, Germany, Japan, UK, Norway, Netherlands, USA, Spain, Sweden, rest of China. At the end of the emergency state (15 May), the self-isolation is compulsory for all international travels (including family members in the same household). To avoid exposure of family members the traveling person can require to be self-isolated in special quarantine places supervised by authorities.</p><p>During the emergency state, the model estimates that the self-isolation waste flow peaked 28 and 29 March over 70 t per day surpassing the threshold of 50 t per day from 25 March to 7 April followed by a descending trend where the daily amounts dropped around 10 t as shown in <xref ref-type=\"fig\" rid=\"ijerph-17-05439-f001\">Figure 1</xref>. However, this waste stream regained an ascendant trend during the alert state reaching the threshold of 50 t per day from 26 May to 15 June as shown in <xref ref-type=\"fig\" rid=\"ijerph-17-05439-f002\">Figure 2</xref>.</p><p>In both periods, the self-isolation waste is the largest COVID-19 related waste flow, but without severe regulations in their collection and treatment practices as the wastes generated by healthcare facilities or quarantine places. Medical waste stream comprised the ITU patients and those with mild symptoms treated in dedicated hospitals. It is estimated that ITU patients generated an average of 0.286 t per day during the emergency state peaking on 22 April (0.548 t) then it dropped to around 0.2–0.3.</p><p>The wastes generated by confirmed COVID-19 patients from healthcare facilities are around 4.8 t per day on average during the emergency state peaking on 29 April (7.91 t). However, this peak was not reached in the alert state (15 May to 15 June), but the average was slightly higher around 5.23 t per day. A key aspect is that medical waste flow is larger than quarantine flow from 22 May until the end of the alert state on 15 June. The daily average of quarantine waste is around 3 t per day during the alert state compared to 7.55 t per day during the emergency period. In the latter case, quarantine wastes reached the threshold of 10 t from 6 April to 18 April peaking to 14 t on both 8–9 April. Per total COVID-19 waste flow (medical + quarantine + self-isolation) the daily average is around 44 t per day during the emergency state and around 49 t during the alert state. The total maxim value of COVID-19 related waste flow reached 79.29 t on 29 March (emergency state) and 61.6 t on 12 June (alert state). In the next section, a regional analysis of COVID-19 flow is performed.</p></sec><sec id=\"sec3dot2-ijerph-17-05439\"><title>3.2. COVID-19 Waste Flow at Subnational Levels</title><p>As mentioned to the material and method section, the COVID-19 related data at subnational levels (county data) is difficult to obtain during the emergency state, particularly for quarantine or self-isolation persons. <xref ref-type=\"fig\" rid=\"ijerph-17-05439-f003\">Figure 3</xref> provides a regional snapshot of total potential infectious waste related to COVID-19 in Romania as of 4 April 2020. The map shows regional disparities of such waste flows with a prevalence of Bucharest capital city compared to other counties.</p><p>Total potential infectious waste is 73,870.7 kg of which 3386 kg medical waste (COVID-19 patients) and 8566.8 kg quarantine waste. These two waste streams (11,942.8 kg) should be collected by special waste operators and incinerated according to authorities’ recommendations. The rest of the 61,917.9 kg. is self-isolation waste. The medical waste (green section of the pie chart) is the most hazardous flow with larger amounts in Suceava County (967 kg), Bucharest (550 kg), Neamt County (148 kg), Timis County (136 kg), Brasov County (127 kg), Cluj County (121 kg). The lowest values are calculated in the case of Harghita (1 kg), Salaj (5 kg), Valcea (9 kg), etc.</p><p>Most quarantine wastes (red section of the pie chart) are generated in Constanta County (539 kg), 400–410 kg (Bucharest, Dolj, Ilfov) which are covered by waste incineration plants. At that time few quarantine wastes (<100 kg) were generated in Hunedoara, Sibiu, Covasna and Harghita counties. Self-isolation wastes are mostly generated in Bucharest city (4354 kg) followed by Arges (3166 kg), Brasov (2607 kg), Mures (2300 kg) which drop below 1000 kg (Dambovita, Valcea, Caras-Severin, Alba, Arad, etc.) lowest values in all three waste categories being in Covasna County.</p><p>Three are several counties where medical and quarantine wastes are at the lowest levels compared to self-isolation wastes (e.g., Tulcea, Vaslui, Gorj, etc.) which decrease the contamination risk of spreading COVID-19 disease by infectious waste.</p><p>In the case of the Neamt County, the model suggest that medical wastes generated by COVID-19 patients were increasing toward the end of the emergency state as the virus spreading in this area peaking in the last day to 805 kg. On the opposite side, the amounts of self-isolation wastes peaked in the first stage of the emergency state (27–29 March) surpassing 2000 kg of waste per day. The descend trend started by the end of March and the first week of April when the amounts of waste dropped below 500 kg per day until the end of the emergency state (14 May). The high number of self-isolation persons is explained by the large number of people who worked abroad and returned from the yellow areas of Italy or Spain. Fewer persons were quarantined (red zones) which generated fewer wastes (9160 kg) compared to self-isolation persons (37,000 kg). However, quarantine wastes are considered infectious waste which must be transported by special waste operators towards waste incineration plants as a medical waste of COVID-19 patients. In the latter case, it is estimated that the total amount generated during the emergency is almost twice (18,090 kg) than quarantine wastes as cumulative cases are still increasing during the alert state. Infectious wastes (medical + quarantine) reached 27,250.8 kg of total 64,256 kg COVID-19 related waste flow during the emergency state. There is a gap of data between 9 May to 12 May regarding the number of people in quarantine and self-isolation as shown in <xref ref-type=\"fig\" rid=\"ijerph-17-05439-f004\">Figure 4</xref> therefore, the total COVID-19 related flow is difficult to estimate at subnational levels. In the COVID-19 context, waste-related data are scarce and fragmented, and this situation is similar in other countries [<xref rid=\"B24-ijerph-17-05439\" ref-type=\"bibr\">24</xref>,<xref rid=\"B32-ijerph-17-05439\" ref-type=\"bibr\">32</xref>]. At the national level, the amounts of quarantine wastes are larger than healthcare wastes during the emergency state, but this situation is changing during the alert state as shown in <xref rid=\"ijerph-17-05439-t001\" ref-type=\"table\">Table 1</xref>.</p></sec><sec id=\"sec3dot3-ijerph-17-05439\"><title>3.3. Public Health and Environmental Concerns</title><p>In the first weeks of COVID-19 cases in Romania, the wastes collected from quarantine places were handled by municipal waste operators and disposed into landfills. Because such wastes were declared by national authorities as infectious wastes (first notification on 18 March 2020) the regional public health departments supervise the waste collection process between these sites and special waste operators licensed to transport hazardous waste to disposal facilities (e.g., hazardous waste incinerators). However, these wastes operators do not have wide geographic coverage and in times of emergency had difficulties to fulfill the requests from hospitals and quarantine sites. As an example, piles of potentially infectious waste bags formed in front of a quarantine site in Galati city because there is only one waste operator authorized to transport such wastes in the South-East Region. Mass media reveals cases where full bins and piles of medical wastes (outdoor) from hospitals remained uncollected in Deva with COVID-19 patients [<xref rid=\"B35-ijerph-17-05439\" ref-type=\"bibr\">35</xref>].</p><p>Such wastes should be temporarily stored in special locations maximum 24 h unless the wastes are stored in a location provided with a cooling system that constantly ensures a temperature of less than 4 °C, a situation in which the storage period can be a maximum of 7 days according to technical norms of Ministry of Health [<xref rid=\"B36-ijerph-17-05439\" ref-type=\"bibr\">36</xref>].</p><p>Ministry of Environment, Water and Forests emitted an order during the emergency state in Romania (2 April 2020) that all public land should be cleared by illegal dumping sites until 30 May 2020. These illegal dumping sites are considered by environmental authorities as an additional factor for the spreading of the coronavirus. Previous controls made by the National Environmental Guard in the first trimester of this year detected several wild dumpsites. Local authorities are responsible to collect and properly dispose of such wastes. Uncontrolled waste disposal practice is a serious environmental issue in Romania where peri-urban and rural regions are the most exposed to such practices [<xref rid=\"B15-ijerph-17-05439\" ref-type=\"bibr\">15</xref>]. Each spring season, the local authorities must perform sanitation activities around their localities. Seasonal floods feed freshwater pollution of dam lakes or downstream localities. Plastic pollution reached alarming levels in Romania due to waste mismanagement activities [<xref rid=\"B14-ijerph-17-05439\" ref-type=\"bibr\">14</xref>]. By the end of alert state 15 June 2020, local authorities of Bals city cope with massive plastic pollution and wood wastes carried out by Oltet river from upstream localities due to the summer flash floods.</p><p>Sorting stations, where dry recyclables are manually sorted, are an additional source of contamination for workers. Some household items that can be included in the category of infectious waste (napkins, masks, gloves, cutlery, containers, etc.) should not be separately collected, but to be disposed of in the residual bins. Some sorting lines were stopped to avoid the contamination risk of workers due to the presence of such items in recyclables bins. Environmental authorities detected (during March) massive illegal burning practices of hazardous and nonhazardous waste items (e.g., used tires, electric cables) in Ilfov County (Sintesti village of Vidra commune) which polluted the air of Bucharest city. The circular economy is limited in Romania where landfill is still the most prevalent option in case of municipal waste stream despite the most ambitious plan supported by the EU [<xref rid=\"B17-ijerph-17-05439\" ref-type=\"bibr\">17</xref>].</p><p>Medical waste management in Romania is less studied than the municipal waste stream and available data are scarce. However, some studies reveal that improper management of medical wastes can lead to water pollution [<xref rid=\"B37-ijerph-17-05439\" ref-type=\"bibr\">37</xref>] and other environmental threats [<xref rid=\"B38-ijerph-17-05439\" ref-type=\"bibr\">38</xref>]. Before the EU accession, the medical waste flow was disposed of in old and improper crematories of hospitals [<xref rid=\"B39-ijerph-17-05439\" ref-type=\"bibr\">39</xref>]. Despite some early guidelines on healthcare waste management practices [<xref rid=\"B40-ijerph-17-05439\" ref-type=\"bibr\">40</xref>] the problem of this waste stream is far to be solved across the EU. There is no Europe wide overview of amounts of unused pharmaceuticals and their return rate which poses further risks to the environment [<xref rid=\"B41-ijerph-17-05439\" ref-type=\"bibr\">41</xref>].</p><p>Environmental authorities made field controls on 14 May about the illegal waste disposal of hazardous medical wastes collected from Bucharest hospitals on open dumps in rural areas of Ilfov County as shown in <xref ref-type=\"fig\" rid=\"ijerph-17-05439-f005\">Figure 5</xref>. These wastes were collected and transported by a special waste operator under the supervision of environmental authorities.</p><p>The problem of pharmaceutical waste-related issues gains attention in recent studies which point out the necessity of cohesion between legislative framework, waste management infrastructure, public policies and awareness campaigns [<xref rid=\"B42-ijerph-17-05439\" ref-type=\"bibr\">42</xref>,<xref rid=\"B43-ijerph-17-05439\" ref-type=\"bibr\">43</xref>]. Medical waste disposal is provided also by hazardous waste incinerators. However, hazardous waste incinerators are a controversial topic in the environmental policies of Romania.</p><p>In Dolj County, the medical waste incinerator which operates in Sopot commune was fined by the National Environmental Guard (80,000 lei) because of several non-compliances with environmental permits and measures were imposed regarding the proper storage and disposal of waste as well as related to the incinerator furnaces parameters [<xref rid=\"B44-ijerph-17-05439\" ref-type=\"bibr\">44</xref>].</p><p>In Suceava County, the environmental authorities found 1019.8 kg of hazardous medical waste improperly stored in a hall. In 2018, 11 hazardous waste incinerators (which also dispose of the medical waste), 14 treatment facilities (thermal decontamination at low temperatures) and 23 on-site treatment facilities (hospitals) operated in Romania [<xref rid=\"B45-ijerph-17-05439\" ref-type=\"bibr\">45</xref>]. According to the Ministry of Environment, there are 9 hazardous waste incinerators in Romania which operate in 2020 which can burn 100–500 kg of medical waste per hour and 15 medical waste sterilization facilities which can treat 45 t of medical waste per day in the COVID-19 pandemic context [<xref rid=\"B46-ijerph-17-05439\" ref-type=\"bibr\">46</xref>].The national waste management plan point out the small number of thermal decontamination treatment facilities at low temperatures of hazardous medical waste (inside the sanitary units or in a centralized system). In Romania, there are 14 counties with no such treatment facilities increasing dependency on hazardous waste incinerators as alternatives for waste disposal options [<xref rid=\"B18-ijerph-17-05439\" ref-type=\"bibr\">18</xref>]. Environmental NGO’s request national authorities to stop medical and hazardous waste incineration in facilities located near human settlements during the emergency state in Romania because air pollution could increase the COVID-19 cases, particularly in large cities [<xref rid=\"B47-ijerph-17-05439\" ref-type=\"bibr\">47</xref>]. These organizations draw attention on several environmental concerns such as illegal activities related to recent waste burning practices must be investigated by environmental authorities; to stop the import of any type of waste or second–hand goods; to speed up the acquisition process of monitoring equipment in case of dioxins, furans and PCBs and made that data publicly available.</p></sec><sec id=\"sec3dot4-ijerph-17-05439\"><title>3.4. Best Practices During the Emergency State Conditions</title><p>The Ministry of Health, National Institute of Public Health and the Ministry of Environment, Water and Forests released a notification for the general public regarding the management of municipal waste stream which transposes the UNEP and ECDC guidelines on this matter: (i) municipal waste generated in households with confirmed/suspected COVID-19 cases must be disposed of in the residual bin (without separate collection), the bag must be carefully enclosed without any compression, closed access to animal companions near the waste bags/bins, these items will be collected by municipal waste operators and directed transported to landfills; (ii) municipal waste generated in households without confirmed/suspected cases will be managed as usual with proper separate collection schemes. Clear communication with the general public is an essential step towards sound waste management practices of potential infectious wastes during the COVID-19 pandemic.</p><p>The National Institute of Public Health declared household waste generated in quarantine places as infectious wastes (18 March 2020), therefore, a strict waste management procedure must be enforced in this regard. These wastes must be collected by special waste operators and transported at −4 °C to hazardous waste incinerators. Waste operators and/or inter-community development associations that supervise waste management activities at regional level release guidelines with specific requirements on how to handle municipal waste stream in case of confirmed, quarantine or self-isolated citizens [<xref rid=\"B48-ijerph-17-05439\" ref-type=\"bibr\">48</xref>].</p><p>In Bucharest city, local authorities of District 4 and 6 provide special bags for waste collection of quarantine places and from self-isolated persons. District 4 bought two special vehicles (capacity of 840 L each, seven bins of 120 L) dedicated to collect and transport potential infectious wastes at −4 °C to waste incinerators from Dolj and Prahova counties [<xref rid=\"B49-ijerph-17-05439\" ref-type=\"bibr\">49</xref>]. These vehicles will cover the quarantine site and self-isolated persons with a twice collection frequency per week in District 4 of Bucharest city. The workers have full protection equipment as shown in <xref ref-type=\"fig\" rid=\"ijerph-17-05439-f006\">Figure 6</xref>.</p><p>Waste workers equipped with PEE equipment are compulsory and they must have access to daily disinfectants. In Neamt County, waste operators changed the waste collection frequency of dry recyclables to once per month in rural areas. In addition, the dry recyclables bags must be enclosed and have indicated by the generator on the date of closing the bag. However, the reduction of waste collection frequency in rural areas can lead to illegal dumping or open burning practices [<xref rid=\"B50-ijerph-17-05439\" ref-type=\"bibr\">50</xref>].</p><p>Optimized routes for medical waste collection at regional levels are necessary to avoid additional risks in the context of COVID-19 pandemic when hospitals generate higher amounts of medical waste and there is increasing pressure on hazardous waste transporters and waste incinerators [<xref rid=\"B51-ijerph-17-05439\" ref-type=\"bibr\">51</xref>]. During the peak of COVID-19 pandemic, storage and waste disposal facilities are surpassed by the huge amounts of medical wastes in countries like in China and Italy [<xref rid=\"B32-ijerph-17-05439\" ref-type=\"bibr\">32</xref>,<xref rid=\"B52-ijerph-17-05439\" ref-type=\"bibr\">52</xref>].</p><p>The COVID-19 pandemic reveals that medical waste management treatment facilities must be further developed and to decrease the dependency on hazardous waste incinerators. Civil society and mass-media reveal several serious issues related to public health and environmental pollution of waste incineration plants in Romania in the last years. This waste disposal option is a critical environmental concern around the world [<xref rid=\"B53-ijerph-17-05439\" ref-type=\"bibr\">53</xref>].</p><p>On-site treatment facilities (hospitals) can be cost-efficient compared to the collection, transportation and incineration costs [<xref rid=\"B32-ijerph-17-05439\" ref-type=\"bibr\">32</xref>,<xref rid=\"B54-ijerph-17-05439\" ref-type=\"bibr\">54</xref>].</p><p>The National Police, National Environmental Guard and Health Inspection performed 283 controls (from 9 June to 1 July) at the sanitary units and at the economic agents that carried out operations in the field of hazardous waste management infested with SARS-CoV-2 virus. This operation led to safely disposing of 210 t hazardous waste, 43 fines (amounting to 454,800 lei) 40 warnings, 6 criminal cases and temporary closure of an incinerator [<xref rid=\"B55-ijerph-17-05439\" ref-type=\"bibr\">55</xref>].</p><p>Alternatives options to waste incineration is mandatory in the medium term in Romania such as [<xref rid=\"B56-ijerph-17-05439\" ref-type=\"bibr\">56</xref>]: (i) thermal processes at low temperature (105 °C–177 °C) using hot air inactivation equipment, steam equipment, microwave inactivation equipment or inactivation equipment by crushing, steaming, drying process; (ii) chemical processes—medical waste is first crushed and mixed to increase its exposure to the action of chemicals (sodium hypochlorite, peracetic acid or inorganic chemicals) and the disinfectant solution is recycled; (iii) Irradiation-based technologies refer to the exposure of medical waste to the action of electronic particles, cobalt-60 rays or UV rays; (iv) biologic processes use enzymes to break down organic matter. In Romania, the thermal process at low temperatures is the most widespread medical waste treatment as an alternative to waste incineration plants. However, only 6% of healthcare facilities investigated in 2018 (out of 832) treat medical waste in its facilities (thermal process at low temperature) and 15% had contracts with such specialized economic agents [<xref rid=\"B45-ijerph-17-05439\" ref-type=\"bibr\">45</xref>]. Most healthcare facilities (65%) are reliant on waste operators which transport medical waste flow to hazardous waste incinerators while 14% dispose of their nonhazardous waste through landfilling [<xref rid=\"B45-ijerph-17-05439\" ref-type=\"bibr\">45</xref>]. Improvements in staff awareness related to proper medical waste management practices and green purchasing suppliers are further steps in reducing pollution threats [<xref rid=\"B57-ijerph-17-05439\" ref-type=\"bibr\">57</xref>]. Contingency plans to target waste management sector under various scenarios should be continuously developed and adjusted [<xref rid=\"B58-ijerph-17-05439\" ref-type=\"bibr\">58</xref>].</p></sec></sec><sec id=\"sec4-ijerph-17-05439\"><title>4. Further Recommendations</title><p>Based on medical and municipal waste management issues raised by coronavirus spreading in Romania (<xref ref-type=\"sec\" rid=\"sec3-ijerph-17-05439\">Section 3</xref>), a set of recommendations is proposed at the EU and Romania levels to better monitor the COVID-19 waste flow, municipal, and medical waste stream in general, as a key support for decision-makers.</p><sec id=\"sec4dot1-ijerph-17-05439\"><title>4.1. EU Commission</title><p>Policy and monitoring issues must be addressed and improved among EU countries under the supervision of the EU Commission to provide reliable data about waste flows during the COVID-19 pandemic in short and medium-term such as:<list list-type=\"bullet\"><list-item><p>Special reports about waste management challenges in both medical and municipal waste management sectors associated with COVID-19 pandemic;</p></list-item><list-item><p>Eurostat must include medical waste management statistics as a compulsory special waste stream broken down by waste items (infectious waste included) beside the existing ones (e.g.WEEE, etc.);</p></list-item><list-item><p>Infectious-waste generation rates (kg∙bed∙day<sup>−1</sup>) and other similar medical waste flows must be determined by experimental studies across EU countries.</p></list-item></list></p><p>In the long term, waste management policies should have the following key objectives:<list list-type=\"bullet\"><list-item><p>The European Commission must include the issues of healthcare waste management activities in annual environmental reports and healthcare performance status with separate case reports for each country;</p></list-item><list-item><p>Comprehensive health and waste-related statistics and common policies to cope with future outbreaks;</p></list-item><list-item><p>Circular economy policies must include the medical waste management sector with clear guidelines and best practices;</p></list-item><list-item><p>EU funds to develop and support sound medical waste treatment facilities as an alternative to hazardous waste incineration plants.</p></list-item></list></p></sec><sec id=\"sec4dot2-ijerph-17-05439\"><title>4.2. Romania</title><p>Poor publicly available data related to medical and municipal waste management flows was the norm before the COVID-19 pandemic in Romania, particularly at the county level, which makes it more difficult to assess the COVID-19 related waste flow in such time of crisis.</p><p>This situation was worsening during the emergency state due to the lack of COVID-19 related data at subnational levels as pointed out in <xref ref-type=\"sec\" rid=\"sec2-ijerph-17-05439\">Section 2</xref> coupled with waste management deficiencies as highlighted in <xref ref-type=\"sec\" rid=\"sec3-ijerph-17-05439\">Section 3</xref>. Therefore, in the short and medium-term, national authorities should improve the transparency of key data:<list list-type=\"bullet\"><list-item><p>Centralized daily COVID-19 statistics available at county levels including a wider range of indicators (as requested by Geo-spatial.org [<xref rid=\"B26-ijerph-17-05439\" ref-type=\"bibr\">26</xref>]) with open access data;</p></list-item><list-item><p>Publicly available data about environmental crimes detected by National Environmental Guard regarding waste collection and disposal (incineration/landfilling) of hazardous waste including medical waste flow;</p></list-item><list-item><p>Intensive controls of incinerator’s operations, hazardous waste collection, open burning and illegal dumping activities during COVID-19 pandemic supervised by special environmental commissions with inter-institutional members;</p></list-item><list-item><p>Special reports on such environmental crimes with available data at national and county levels for general public and mass-media;</p></list-item><list-item><p>Regional public health and environmental authorities must cooperate to provide reliable monitoring of hospitals with COVID-19 patients in terms of possible environmental and public health threats associated with medical waste mismanagement practices;</p></list-item><list-item><p>Incinerators and waste operators dealing with hazardous waste (including medical waste fractions) should mandatorily publish on their websites: environmental authorization, annual environmental performance report with detailed statistics about the wastes collected, treated and disposed of. Monthly reports of medical waste collected and treated during the COVID-19 pandemic;</p></list-item><list-item><p>Medical-waste composition data and generation rates supported by experimental studies in each county with special attention to COVID-19 hospitals.</p></list-item></list></p><p>To enhance environmental policy decisions at national and regional levels in a post-COVID-19 pandemic context and to be better prepared for other possible outbreaks, the public access to basic waste related data must be improved:<list list-type=\"bullet\"><list-item><p>Regional and local waste management plans must have a special section of medical waste management systems with updated data;</p></list-item><list-item><p>Environmental reports (national and regional levels) must include an updated analysis of medical waste management status in collaboration with the National Public Health Institute;</p></list-item><list-item><p>Special regulations/amendments in reporting waste statistics data and to be available to the general public in electronic format (open data);</p></list-item><list-item><p>Waste statistics should be available on national and subnational levels including NUTS3 regions of Romania;</p></list-item><list-item><p>Waste statistics portal in web GIS format with open data available at the national, county and local administrative levels (LAU-2—cities and communes).</p></list-item></list></p><p>Investments to support alternative and environmentally friendly medical waste treatment facilities to waste incineration plants must be a continual effort in Romania.</p></sec></sec><sec sec-type=\"conclusions\" id=\"sec5-ijerph-17-05439\"><title>5. Conclusions</title><p>This paper provides a rapid assessment of potential COVID-19 medical waste flow in Romania during the emergency state (15 March to 14 May) and alert state (15 May to 15 June) at the national level and some regional statistics. This flow is feed by confirmed COVID-19 cases, people in quarantine places, and those suspected and self-isolated in households.</p><p>The model estimates a total COVID-19 related flow of 4312 t at the national level from 25 February to 15 June of which 2633 t in the emergency state period.</p><p>The self-isolation source dominates in this waste flow for each period analyzed, but this waste stream has the lowest potential infectious risks compared to those generated by healthcare facilities (confirmed COVID-19 patients) and quarantine places. This waste flow is often mixed collected by municipal waste operators and disposed of through landfills. Both healthcare and quarantine wastes are considered infectious wastes by national authorities, therefore, around 1008 t should have been collected by special hazardous waste operators and disposed of in the waste incineration plants or using alternative options for medical waste flow as outlined in <xref ref-type=\"sec\" rid=\"sec3dot4-ijerph-17-05439\">Section 3.4</xref>. The paper reveals a shift between quarantine and medical wastes in the alert state, where the estimated amounts of waste generated by confirmed COVID-19 patients in healthcare facilities (167.47 t) are larger than those in quarantine places (94.51 t).</p><p>There are regional disparities regarding the amounts of infectious waste (healthcare + quarantine sources) compared to self-isolation wastes among Romanian counties as shown by the map (<xref ref-type=\"fig\" rid=\"ijerph-17-05439-f003\">Figure 3</xref>), but the capital city of Bucharest is the largest contributor to the overall COVID-19 related waste flow and self-isolation waste on this regional snapshot (4 April). Furthermore, Suceava County is the largest contributor to medical waste and Constanta County for quarantine wastes.</p><p>The COVID-19 waste flow cannot be determined for all periods at subnational levels due to the limited data. However, the Neamt County (one of the top five counties affected by COVID-19) is examined as a case study of local COVID-19 related waste flow during the emergency state.</p><p>The paper points out several critical issues related to medical and municipal waste management sectors in Romania. Monitoring of COVID-19 waste flow through the proposed model is important for decision-makers, particularly in low and middle-income countries like Romania which are facing waste management deficiencies and gaps in waste statistics, to reduce other contamination risks or related environmental threats. The regional analysis (subnational levels) of COVID-19 related waste flow provided by spatial statistics and thematic cartography is crucial in this regard, but more transparency and public access to such COVID-19 related data are required from national authorities.</p><p>The mentioned recommendations would enhance better decisions across EU and Romania in mitigating the waste-related secondary impacts on public health status and environmental factors during such biologic hazards or other possible hazards (natural or anthropogenic) caused by climate change or environmental pollution.</p><p>Both medical and municipal waste management services are critical around the world and these systems must cope with serious challenges particularly in low and middle-income countries. Further investigations and research studies are necessary to adjust the COVID-19 waste-related flows in Romania and other countries affected at considerable levels by this pandemic.</p></sec></body><back><notes><title>Funding</title><p>This project is funded by the Ministry of Research and Innovation within Program 1—Development of the national RD system, Subprogram 1.2—Institutional Performance—RDI excellence funding projects, Contract no.34PFE/19.10.2018. 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Chem. Pharm.</source><year>2019</year><volume>13</volume><fpage>100163</fpage><pub-id pub-id-type=\"doi\">10.1016/j.scp.2019.100163</pub-id></element-citation></ref><ref id=\"B58-ijerph-17-05439\"><label>58.</label><element-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Klemeš</surname><given-names>J.J.</given-names></name><name><surname>Van Fan</surname><given-names>Y.</given-names></name><name><surname>Tan</surname><given-names>R.R.</given-names></name><name><surname>Jiang</surname><given-names>P.</given-names></name></person-group><article-title>Minimising the present and future plastic waste, energy and environmental footprints related to COVID-19</article-title><source>Renew. Sustain. Energy Rev.</source><year>2020</year><volume>127</volume><fpage>109883</fpage><pub-id pub-id-type=\"doi\">10.1016/j.rser.2020.109883</pub-id></element-citation></ref></ref-list></back><floats-group><fig id=\"ijerph-17-05439-f001\" orientation=\"portrait\" position=\"float\"><label>Figure 1</label><caption><p>COVID-19 related waste flow in Romania during the emergency state.</p></caption><graphic xlink:href=\"ijerph-17-05439-g001\"/></fig><fig id=\"ijerph-17-05439-f002\" orientation=\"portrait\" position=\"float\"><label>Figure 2</label><caption><p>COVID-19 related waste flow in Romania during the alert state.</p></caption><graphic xlink:href=\"ijerph-17-05439-g002\"/></fig><fig id=\"ijerph-17-05439-f003\" orientation=\"portrait\" position=\"float\"><label>Figure 3</label><caption><p>Regional disparities in COVID19-related waste flows as of 4 April 2020 (emergency state) Counties info: <uri xlink:href=\"https://en.wikipedia.org/wiki/Counties_of_Romania\">https://en.wikipedia.org/wiki/Counties_of_Romania</uri>.</p></caption><graphic xlink:href=\"ijerph-17-05439-g003\"/></fig><fig id=\"ijerph-17-05439-f004\" orientation=\"portrait\" position=\"float\"><label>Figure 4</label><caption><p>COVID-19 related waste flow in Neamt County during the emergency state.</p></caption><graphic xlink:href=\"ijerph-17-05439-g004\"/></fig><fig id=\"ijerph-17-05439-f005\" orientation=\"portrait\" position=\"float\"><label>Figure 5</label><caption><p>Illegal dumping of medical waste in Dobroiesti commune, Ilfov County. 14 May 2020 (source: National Environmental Guard).</p></caption><graphic xlink:href=\"ijerph-17-05439-g005\"/></fig><fig id=\"ijerph-17-05439-f006\" orientation=\"portrait\" position=\"float\"><label>Figure 6</label><caption><p>Collection of COVID-19 infectious waste in District 4 of Bucharest city Source: Bucharest District Hall 4.</p></caption><graphic xlink:href=\"ijerph-17-05439-g006\"/></fig><table-wrap id=\"ijerph-17-05439-t001\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05439-t001_Table 1</object-id><label>Table 1</label><caption><p>COVID-19 waste flow at the national level from 26 February to 15 June 2020.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Period / Tons Generated</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">ICU Patients Related<break/>Waste</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Medical Waste<break/>COVID-19</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Quarantine<break/>Waste</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Self-Isolation Waste</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Total COVID-19 Related Waste</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Prior emergency state (26 February–15 March)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.44</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.79</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">109.72</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">115.96</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Emergency state (16 March–14 May)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">17.17</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">286.04</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">453.44</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1893.56</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2633.05</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Alert state<break/>(15 May–15 June)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">167.47</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">94.51</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1301.81</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1563.79</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Total period<break/>(26 February–15 June)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">27.28</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">453.95</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">553.75</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3305.1</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">4312.81</td></tr></tbody></table></table-wrap></floats-group></article>\n"
]
[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Int J Environ Res Public Health</journal-id><journal-id journal-id-type=\"iso-abbrev\">Int J Environ Res Public Health</journal-id><journal-id journal-id-type=\"publisher-id\">ijerph</journal-id><journal-title-group><journal-title>International Journal of Environmental Research and Public Health</journal-title></journal-title-group><issn pub-type=\"ppub\">1661-7827</issn><issn pub-type=\"epub\">1660-4601</issn><publisher><publisher-name>MDPI</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32751191</article-id><article-id pub-id-type=\"pmc\">PMC7432112</article-id><article-id pub-id-type=\"doi\">10.3390/ijerph17155463</article-id><article-id pub-id-type=\"publisher-id\">ijerph-17-05463</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Article</subject></subj-group></article-categories><title-group><article-title>Synthetic Evaluation of China’s Regional Low-Carbon Economy Challenges by Driver-Pressure-State-Impact-Response Model</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Pan</surname><given-names>Wenyan</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05463\">1</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0002-5802-0245</contrib-id><name><surname>Gulzar</surname><given-names>Muhammad Awais</given-names></name><xref ref-type=\"aff\" rid=\"af2-ijerph-17-05463\">2</xref><xref ref-type=\"aff\" rid=\"af3-ijerph-17-05463\">3</xref><xref rid=\"c1-ijerph-17-05463\" ref-type=\"corresp\">*</xref></contrib><contrib contrib-type=\"author\"><name><surname>Hassan</surname><given-names>Waseem</given-names></name><xref ref-type=\"aff\" rid=\"af4-ijerph-17-05463\">4</xref></contrib></contrib-group><aff id=\"af1-ijerph-17-05463\"><label>1</label>School of Safety Science and Emergency Management, Wuhan University of Technology, Wuhan 430070, China; <email>[email protected]</email></aff><aff id=\"af2-ijerph-17-05463\"><label>2</label>University of Waikato Joint Institute, Zhejiang University City College, Hangzhou 310015, China</aff><aff id=\"af3-ijerph-17-05463\"><label>3</label>Waikato Management School, University of Waikato, Hamilton 3240, New Zealand</aff><aff id=\"af4-ijerph-17-05463\"><label>4</label>NUST Business School, National University of Sciences and Technology, Islamabad 44000, Pakistan; <email>[email protected]</email></aff><author-notes><corresp id=\"c1-ijerph-17-05463\"><label>*</label>Correspondence: <email>[email protected]</email></corresp></author-notes><pub-date pub-type=\"epub\"><day>29</day><month>7</month><year>2020</year></pub-date><pub-date pub-type=\"ppub\"><month>8</month><year>2020</year></pub-date><volume>17</volume><issue>15</issue><elocation-id>5463</elocation-id><history><date date-type=\"received\"><day>09</day><month>7</month><year>2020</year></date><date date-type=\"accepted\"><day>24</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>© 2020 by the authors.</copyright-statement><copyright-year>2020</copyright-year><license license-type=\"open-access\"><license-p>Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>).</license-p></license></permissions><abstract><p>The “driver–pressure–state–impact–response” (DPSIR) model has recently become a popular approach to deal with environmental problems. The combination of DPSIR with analytic hierarchy process (AHP) is a useful method to study low-carbon evaluation because the AHP model has a special advantage in multi-indexes evaluation. This paper constructs the low-carbon economy evaluation system and comprehensively evaluates the numerical value of low-carbon economic development of China’s 30 regions from 2000 to 2015 by using the AHP method. It shows that the numerical value of low-carbon economy of China’s 30 regions varies in terms of growth rate. The numerical value of east regional low-carbon economy shows a pattern that is gradually higher than that of the west region. The numerical value of low carbon economic development in the south region is higher than that of the north region by degrees. In addition, based on the model of coordination degree in 2015, the result indicated that the four subsystems have primary coordination in the east area and bare coordination in the central and west areas. It is indicated that the four sub-indexes should be developed at the same pace and promoting the development of a low-carbon economy in the mid-west areas is the key in China. Finally, we proposed that environmental regulations and policies should be formulated to improve coordination in various aspects and various departments. Calculating the degree of low-carbon economic coupling coordination may be helpful for policy makers to formulate effective policies and take actions in the future.</p></abstract><kwd-group><kwd>CO<sub>2</sub> emissions</kwd><kwd>low-carbon economy</kwd><kwd>DPSIR model</kwd><kwd>coupling coordination model</kwd></kwd-group></article-meta></front><body><sec sec-type=\"intro\" id=\"sec1-ijerph-17-05463\"><title>1. Introduction</title><p>The 26th meeting of the Conference of the Parties (COP26) of the United Nations Framework Convention on Climate Change (UNFCCC), originally scheduled for November in Glasgow, United Kingdom has been postponed until 2021 [<xref rid=\"B1-ijerph-17-05463\" ref-type=\"bibr\">1</xref>]. Patricia Espinosa (Executive Secretary of UNFCCC) said COVID-19 is the most urgent threat facing humanity today, but we cannot forget that climate change is the biggest threat over the long term. It clarifies the content of anthropogenic climate change and the political statements of reducing greenhouse gas emissions of each country. An important contribution made by the Paris Agreement (COP21) was the Nationally Determined Contributions (NDCs) [<xref rid=\"B2-ijerph-17-05463\" ref-type=\"bibr\">2</xref>]. This is the key to achieving long-term goals. The NDCs identified the efforts and commitments of each country to reduce greenhouse gas emissions, improving the impact of climate change. The aim is to work together to respond to the challenge of climate change and the greenhouse effect. The Paris Agreement stipulates that global warming needs to be limited between 1.5 and 2 °C above pre-industrial levels. The European Union (EU) and China as the largest economies and emitters have announced that they will strengthen their collaboration and step-up their efforts to deal with climate change [<xref rid=\"B3-ijerph-17-05463\" ref-type=\"bibr\">3</xref>]. Greenhouse gas emissions are a very serious environmental problem that the planet is facing today, and it has become a nightmare for countries throughout the world. This issue is mainly attributed to the typical high carbon economy. It is extremely important for the countries around the globe to deal with climate change and achieve a low-carbon economy. In order to explore the corresponding environmental protection measures, the calculation of carbon dioxide emissions and evaluation of low-carbon economic development at the regional level is of great theoretical and practical value [<xref rid=\"B4-ijerph-17-05463\" ref-type=\"bibr\">4</xref>,<xref rid=\"B5-ijerph-17-05463\" ref-type=\"bibr\">5</xref>,<xref rid=\"B6-ijerph-17-05463\" ref-type=\"bibr\">6</xref>,<xref rid=\"B7-ijerph-17-05463\" ref-type=\"bibr\">7</xref>].</p><p>The core objective/essence of low-carbon economy is to reduce CO<sub>2</sub> emissions. For the purpose of dealing with the impacts of climatic change on the global economy and human existence, the emission of CO<sub>2</sub> in production and consumption must be strictly controlled [<xref rid=\"B8-ijerph-17-05463\" ref-type=\"bibr\">8</xref>,<xref rid=\"B9-ijerph-17-05463\" ref-type=\"bibr\">9</xref>,<xref rid=\"B10-ijerph-17-05463\" ref-type=\"bibr\">10</xref>]. It is indicated that CO<sub>2</sub> emissions from burning fossil fuels had increased 49% from the year 1990 to the year 2008 [<xref rid=\"B2-ijerph-17-05463\" ref-type=\"bibr\">2</xref>]. According to the statistics given by the International Energy Agency [<xref rid=\"B11-ijerph-17-05463\" ref-type=\"bibr\">11</xref>], China became the world’s largest carbon dioxide emitter and the largest energy consumer [<xref rid=\"B12-ijerph-17-05463\" ref-type=\"bibr\">12</xref>,<xref rid=\"B13-ijerph-17-05463\" ref-type=\"bibr\">13</xref>]. All these statistics show that as the global climate problem is becoming more and more serious, the low-carbon economy has become a global hotspot all around the world [<xref rid=\"B1-ijerph-17-05463\" ref-type=\"bibr\">1</xref>]. In decoupling theory, we know that the amount of materials consumed grows with the economy at the beginning of industrialization. However, this situation will reverse at a certain stage in future, which will realize the growth of economy while decreasing consumption of materials. The meaning on low-carbon economy varies with the places. Different paths and choices should be adopted for CO<sub>2</sub> emission reduction to areas that are at different stages of development. For example, the developed countries that have achieved industrialization and high human development must be able to achieve absolute reduction of CO<sub>2</sub> emissions. However, in the developing countries that are still in the process of industrialization, due to their booming population and unachieved basic goals of human development, CO<sub>2</sub> emissions will continue to grow. Therefore, if they ensure social development along with constant economic growth, the relative reduction of CO<sub>2</sub> emissions should be regarded as low-carbon economy. Due to different developmental stages in different regions, the problems they are facing vary greatly. For all these reasons, constructing the low-carbon economy evaluation index system as well as finding specific problems and solutions are of great importance. </p><p>The low-carbon economy, which is the outcome of constricting emission requirements and increasing human development at a good level is kind of an economic foundation. The reason we develop a low-carbon economy is to control emissions of CO<sub>2</sub>, which is a global shared vision [<xref rid=\"B14-ijerph-17-05463\" ref-type=\"bibr\">14</xref>,<xref rid=\"B15-ijerph-17-05463\" ref-type=\"bibr\">15</xref>]. Therefore, the low-carbon economy should be at the base of the development of production, environmental protection and resource optimization. A thriving low-carbon economy can be developed only when these factors proceed simultaneously, along with the unification of economy, ecosystem and social benefit. Besides, while we are researching the low-carbon economy, we need to integrate resources science, environmental science and the social sciences effectively. For this purpose, an instructional method is required that can not only specify complex problems but also effectively combine each part. As a result, the DPSIR model serves as a good research tool for the low-carbon economic evaluation. </p><p>We aimed to build a comprehensive low-carbon economic system by combining two models, DPSIR and AHP, so as to give a numerical value of the low-carbon economic development of China’s 30 regions from 2000 to 2015. In addition, the spatial distribution was analyzed in the four aspects: “pressure”, “state”, “effect” and “response”. The study largely discussed the applicability of DPSIR model to evaluate China’s regional low-carbon economic challenges; a conceptual model was provided, establishing indicator system–data processing–analysis–making decisions.</p></sec><sec id=\"sec2-ijerph-17-05463\"><title>2. Theoretical Framework</title><p>The DPSIR model is considered as a fundamental and effective tool to thoroughly observe the causation between human activities and the natural environment [<xref rid=\"B16-ijerph-17-05463\" ref-type=\"bibr\">16</xref>,<xref rid=\"B17-ijerph-17-05463\" ref-type=\"bibr\">17</xref>]. In the late 1980s, the OECD proposed a Pressure–State–Response (PSR) frame model [<xref rid=\"B18-ijerph-17-05463\" ref-type=\"bibr\">18</xref>]. On that basis, the United Nations proposed the Driving Force–State–Response frame model. Finally, the European Environment Agency and others combined the benefits of the previous two index systems and proposed a new conceptual frame model: Driving Force-Pressure–State–Impact–Response (DPSIR) [<xref rid=\"B19-ijerph-17-05463\" ref-type=\"bibr\">19</xref>,<xref rid=\"B20-ijerph-17-05463\" ref-type=\"bibr\">20</xref>]. It can be seen that the DPSIR model is the expansion and revision of PSR model, which adds the “Driving Force” factor as the cause of the “Pressure” factor and the “Impact” force caused by the environment [<xref rid=\"B21-ijerph-17-05463\" ref-type=\"bibr\">21</xref>]. The scholars who use the model think that it is people’s activities such as production and consumption that put the natural environment under pressure, consequently leading to change in the natural environment. However, we should respond to the effects that the climate change brings. The DPSIR model is widely used for analyzing problems about resources, environment, economy, and society. For instance, Maxim et al. supplemented the DPSIR model by using the complex system theory, and then, the model was used to analyze the risk of biological diversity in the aspects of policy, society and economy [<xref rid=\"B22-ijerph-17-05463\" ref-type=\"bibr\">22</xref>]; Ness et al. think the DPSIR model is quite up to the requirements of sustainable Scientific Outlook on Development, and with the help of that, they can research the eutrophication in the Baltic Sea [<xref rid=\"B23-ijerph-17-05463\" ref-type=\"bibr\">23</xref>]. Atkins et al. create a model which supports marine environment decision by integrating the DPSIR model with features of ecosystem service and social welfare [<xref rid=\"B24-ijerph-17-05463\" ref-type=\"bibr\">24</xref>]. The DPSIR model was selected due to its simplicity and because it is the most powerful communication tool between environment, economy and society [<xref rid=\"B25-ijerph-17-05463\" ref-type=\"bibr\">25</xref>,<xref rid=\"B26-ijerph-17-05463\" ref-type=\"bibr\">26</xref>].</p><p>From these aforementioned examples, it is clear that the applicability of DSPIR model is very high and that’s why it is used by many scholars for the analysis of all kinds of resources and in environmental as well as social and economic problems. DPSIR model particularly focuses on economic operations, their effects on the environment and their interrelations. Thus, it is a very systematic, comprehensive, widespread and flexible model.</p><p>The “Driving force” indicates the potential cause of the changes in environmental conditions, which mainly refers to the development trend of economic activities, social activities and changes in the industrial structure. The “Pressure” is the influence of human activities on natural environment, energy and resources. The “State” refers to the situation of environment under the above-mentioned pressures and mainly considers regional resource consumption and environment degradation. The “Impact” is the influence of system’s status on environment and social economic structure. Lastly, the “Response” shows the methods and effective policies that people create when facing environmental impact.</p><p>Firstly, the analytical framework of a low-carbon economy system is constructed, in which the DPSIR model is used considering human needs, social progress, economic development, energy demand, carbon emissions, resource status, low-carbon consumption and economic development. This progress reflects the features of a low-carbon economy, as a complex system relevant to the mutual effect of human activities and natural resources. Showing the connotation of a scientific outlook on development, the low-carbon economic system should be human-oriented and development-coordinated. The analytical framework of the system based on DPSIR model is shown in <xref ref-type=\"fig\" rid=\"ijerph-17-05463-f001\">Figure 1</xref>.</p><p>In order to construct a multi-dimensional and scientific evaluation index system that not only can conduct the horizontal comparison in all regions but also the vertical reflection of steady shift, the principles mentioned below must be followed.</p><p>For the purpose of ensuring that the evaluation of the system completely and objectively reflects its comprehensiveness and shift result, plenty of work needs to be done. In addition to that, the overlap between indexes must be avoided. Therefore, the index system must be made clear by level diversification and index classification according to system structure.</p><p>The evaluation index should also scientifically and moderately reveal the properties and transitional characteristics of a low-carbon economy. Besides, every evaluation with its calculation should be standardized, normalized and clearly defined; even those statistics that cannot be obtained from the existing statistical source can be brought into the index system if they have the ability to reflect the practicality and the embodiment of the low-carbon economy.</p><p>As a whole, the system should include various indexes that can affect the level of the low-carbon economy. Although it is impossible to cover all the relevant indexes, the system must be capable of reflecting all aspects that can affect the level of the low-carbon economy in the current social economy. In addition, the selected indexes should reflect the main character and status of the evaluated system from different aspects. In order to make the index system easy to use, while choosing indexes, we must emphasize on their depiction and avoid redundancy.</p><p>The worldwide recognized common index must be followed after altering it according to required standards, and concurrently, the internationally accepted names, concepts and calculations must be adopted. In addition to that, whatever the evaluation index, system construct is a global perspective oriented job. Therefore, we must take full account of the dynamic change of the system when designing the index system and embrace innovation. </p><p>In this paper, the system is established based on DPSIR model, as shown in <xref rid=\"ijerph-17-05463-t001\" ref-type=\"table\">Table 1</xref>. The “impact” was removed from the DPSIR model because indicators in “impact” overlap with those in “Pressure” and “State”. At the same time, the relevant statistics for economic losses and natural hazard caused by air pollution are hard to get.</p><p>The connotation of “Driver” is reflected by setting the “Driver for social development” (D1) and “Driver for economic development” (D2), completely representing the essence of people’s needs and social development of Scientific Outlook on Development. The connotation of “Pressure” is reflected by setting the “resource pressure” (P1) and “environmental pressure” (P2), symbolizing the damages and losses that social progress and human development do to the environment and resources. The connotation of “State” is reflected by setting the “State of low-carbon consumption” (S1) and “State of low-carbon resources” (S2), describing the objective condition of resources and environment in the low-carbon economy system. The connotation of “Response” is reflected by setting the “Scientific Response” (R1), “Human Response” (R2) and “Policy Response” (R3), embodying the positive response of countries towards environmental pollution, resources consumption and ecological destruction. At the same time, the frequently used indexes from the relevant studies about low-carbon economy are selected.</p></sec><sec sec-type=\"methods\" id=\"sec3-ijerph-17-05463\"><title>3. Methodology</title><p>This paper focuses on the coordination degree among four sub-indexes. Degrees of interaction among the four sub-indexes are defined by their respective characteristics as the degree of coupling coordination. The equation is as follows:<disp-formula id=\"FD1-ijerph-17-05463\"><label>(1)</label><mml:math id=\"mm1\"><mml:mrow><mml:mrow><mml:mi>T</mml:mi><mml:mo>=</mml:mo><mml:mi>μ</mml:mi><mml:mi>d</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>x</mml:mi><mml:mo stretchy=\"false\">)</mml:mo><mml:mo>+</mml:mo><mml:mi>ϕ</mml:mi><mml:mi>p</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>y</mml:mi><mml:mo stretchy=\"false\">)</mml:mo><mml:mo>+</mml:mo><mml:mi>ξ</mml:mi><mml:mi>s</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>z</mml:mi><mml:mo stretchy=\"false\">)</mml:mo><mml:mo>+</mml:mo><mml:mi>θ</mml:mi><mml:mi>p</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>k</mml:mi><mml:mo stretchy=\"false\">)</mml:mo><mml:mo>=</mml:mo><mml:mi>μ</mml:mi><mml:mstyle displaystyle=\"true\"><mml:munderover><mml:mo>∑</mml:mo><mml:mrow><mml:mi>i</mml:mi><mml:mo>=</mml:mo><mml:mn>1</mml:mn></mml:mrow><mml:mi>m</mml:mi></mml:munderover><mml:mrow><mml:msub><mml:mi>α</mml:mi><mml:mi>i</mml:mi></mml:msub><mml:msubsup><mml:mi>x</mml:mi><mml:mi>i</mml:mi><mml:mo>,</mml:mo></mml:msubsup></mml:mrow></mml:mstyle><mml:mo>+</mml:mo><mml:mi>ϕ</mml:mi><mml:mstyle displaystyle=\"true\"><mml:munderover><mml:mo>∑</mml:mo><mml:mrow><mml:mi>i</mml:mi><mml:mo>=</mml:mo><mml:mn>1</mml:mn></mml:mrow><mml:mi>n</mml:mi></mml:munderover><mml:mrow><mml:msub><mml:mi>β</mml:mi><mml:mi>i</mml:mi></mml:msub><mml:msubsup><mml:mi>y</mml:mi><mml:mi>i</mml:mi><mml:mo>,</mml:mo></mml:msubsup></mml:mrow></mml:mstyle><mml:mo>+</mml:mo><mml:mi>ξ</mml:mi><mml:mstyle displaystyle=\"true\"><mml:munderover><mml:mo>∑</mml:mo><mml:mrow><mml:mi>i</mml:mi><mml:mo>=</mml:mo><mml:mn>1</mml:mn></mml:mrow><mml:mi>j</mml:mi></mml:munderover><mml:mrow><mml:msub><mml:mi>σ</mml:mi><mml:mi>i</mml:mi></mml:msub><mml:msubsup><mml:mi>z</mml:mi><mml:mi>i</mml:mi><mml:mo>,</mml:mo></mml:msubsup></mml:mrow></mml:mstyle><mml:mo>+</mml:mo><mml:mi>θ</mml:mi><mml:mstyle displaystyle=\"true\"><mml:munderover><mml:mo>∑</mml:mo><mml:mrow><mml:mi>i</mml:mi><mml:mo>=</mml:mo><mml:mn>1</mml:mn></mml:mrow><mml:mi>l</mml:mi></mml:munderover><mml:mrow><mml:msub><mml:mi>ω</mml:mi><mml:mi>i</mml:mi></mml:msub><mml:msubsup><mml:mi>k</mml:mi><mml:mi>i</mml:mi><mml:mo>,</mml:mo></mml:msubsup></mml:mrow></mml:mstyle></mml:mrow></mml:mrow></mml:math></disp-formula>\nwhere <italic>T</italic> represents the development degree of the comprehensive evaluation index, which is the reflection of the development level of low-carbon economy. <inline-formula><mml:math id=\"mm2\"><mml:mrow><mml:mrow><mml:msubsup><mml:mi>x</mml:mi><mml:mi>i</mml:mi><mml:mo>,</mml:mo></mml:msubsup></mml:mrow></mml:mrow></mml:math></inline-formula> represents the “Driver” indexes. <inline-formula><mml:math id=\"mm3\"><mml:mrow><mml:mrow><mml:msubsup><mml:mi>y</mml:mi><mml:mi>i</mml:mi><mml:mo>,</mml:mo></mml:msubsup></mml:mrow></mml:mrow></mml:math></inline-formula> represents “Pressure” indexes. <inline-formula><mml:math id=\"mm4\"><mml:mrow><mml:mrow><mml:msubsup><mml:mi>z</mml:mi><mml:mi>i</mml:mi><mml:mo>,</mml:mo></mml:msubsup></mml:mrow></mml:mrow></mml:math></inline-formula> represents “State” indexes. <inline-formula><mml:math id=\"mm5\"><mml:mrow><mml:mrow><mml:msubsup><mml:mi>k</mml:mi><mml:mi>i</mml:mi><mml:mo>,</mml:mo></mml:msubsup></mml:mrow></mml:mrow></mml:math></inline-formula> represents “Response” indexes. The weight values are <inline-formula><mml:math id=\"mm6\"><mml:mrow><mml:mi>μ</mml:mi></mml:mrow></mml:math></inline-formula>, <inline-formula><mml:math id=\"mm7\"><mml:mrow><mml:mi>ϕ</mml:mi></mml:mrow></mml:math></inline-formula>, <inline-formula><mml:math id=\"mm8\"><mml:mrow><mml:mi>ξ</mml:mi></mml:mrow></mml:math></inline-formula>, <inline-formula><mml:math id=\"mm9\"><mml:mrow><mml:mi>θ</mml:mi></mml:mrow></mml:math></inline-formula>, <inline-formula><mml:math id=\"mm10\"><mml:mrow><mml:mrow><mml:msub><mml:mi>α</mml:mi><mml:mi>i</mml:mi></mml:msub></mml:mrow></mml:mrow></mml:math></inline-formula>, <inline-formula><mml:math id=\"mm11\"><mml:mrow><mml:mrow><mml:msub><mml:mi>β</mml:mi><mml:mi>i</mml:mi></mml:msub></mml:mrow></mml:mrow></mml:math></inline-formula>, <inline-formula><mml:math id=\"mm12\"><mml:mrow><mml:mrow><mml:msub><mml:mi>σ</mml:mi><mml:mi>i</mml:mi></mml:msub></mml:mrow></mml:mrow></mml:math></inline-formula>.\n<disp-formula id=\"FD2-ijerph-17-05463\"><label>(2)</label><mml:math id=\"mm13\"><mml:mrow><mml:mrow><mml:mi>C</mml:mi><mml:mo>=</mml:mo><mml:msup><mml:mrow><mml:mrow><mml:mo>{</mml:mo><mml:mrow><mml:mfrac bevelled=\"true\"><mml:mrow><mml:mi>d</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>x</mml:mi><mml:mo stretchy=\"false\">)</mml:mo><mml:mo>×</mml:mo><mml:mi>p</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>y</mml:mi><mml:mo stretchy=\"false\">)</mml:mo><mml:mo>×</mml:mo><mml:mi>s</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>z</mml:mi><mml:mo stretchy=\"false\">)</mml:mo><mml:mo>×</mml:mo><mml:mi>p</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>k</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow><mml:mrow><mml:msup><mml:mrow><mml:mrow><mml:mo>[</mml:mo><mml:mrow><mml:mfrac><mml:mrow><mml:mi>d</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>x</mml:mi><mml:mo stretchy=\"false\">)</mml:mo><mml:mo>+</mml:mo><mml:mi>p</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>y</mml:mi><mml:mo stretchy=\"false\">)</mml:mo><mml:mo>+</mml:mo><mml:mi>s</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>z</mml:mi><mml:mo stretchy=\"false\">)</mml:mo><mml:mo>+</mml:mo><mml:mi>p</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>k</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow><mml:mn>4</mml:mn></mml:mfrac></mml:mrow><mml:mo>]</mml:mo></mml:mrow></mml:mrow><mml:mn>4</mml:mn></mml:msup></mml:mrow></mml:mfrac></mml:mrow><mml:mo>}</mml:mo></mml:mrow></mml:mrow><mml:mrow><mml:mfrac><mml:mn>1</mml:mn><mml:mn>4</mml:mn></mml:mfrac></mml:mrow></mml:msup></mml:mrow></mml:mrow></mml:math></disp-formula>\nwhere <italic>C</italic> represents the coupling degree, which is a measure of coordinated development. <italic>C</italic> ranges from 0 to 1. The greater the coupling degree among the systems the closer the <italic>C</italic> is to 1. Contrary to it, the smaller the coupling degree among the systems, the closer the <italic>C</italic> is to 0. Besides, there is no need to develop it while the sub-indexes are irrelevant.\n<disp-formula id=\"FD3-ijerph-17-05463\"><label>(3)</label><mml:math id=\"mm14\"><mml:mrow><mml:mrow><mml:mi>D</mml:mi><mml:mo>=</mml:mo><mml:msqrt><mml:mrow><mml:mi>C</mml:mi><mml:mo>×</mml:mo><mml:mi>T</mml:mi></mml:mrow></mml:msqrt></mml:mrow></mml:mrow></mml:math></disp-formula>\n<italic>D</italic> is coupling coordinated degree. <italic>T</italic> is development degree. It measures the situation as a whole of the development of coordination in the system [<xref rid=\"B27-ijerph-17-05463\" ref-type=\"bibr\">27</xref>].</p><p>The classification criterion of <italic>D</italic> is as in <xref rid=\"ijerph-17-05463-t002\" ref-type=\"table\">Table 2</xref>.</p></sec><sec id=\"sec4-ijerph-17-05463\"><title>4. Data and Variables</title><sec id=\"sec4dot1-ijerph-17-05463\"><title>4.1. Measurement of CO<sub>2</sub> Emissions from Energy Consumption</title><p>In the current literature and research, the calculation caliber of regional carbon emissions data is difficult to unify. We use the methodology in the IPCC guidelines:<disp-formula id=\"FD4-ijerph-17-05463\"><label>(4)</label><mml:math id=\"mm15\"><mml:mrow><mml:mrow><mml:mrow><mml:mi mathvariant=\"normal\">C</mml:mi><mml:mo>=</mml:mo></mml:mrow><mml:mstyle displaystyle=\"true\"><mml:munder><mml:mo>∑</mml:mo><mml:mi>i</mml:mi></mml:munder><mml:mrow><mml:msub><mml:mi>T</mml:mi><mml:mi>i</mml:mi></mml:msub><mml:mo>×</mml:mo><mml:msub><mml:mi>F</mml:mi><mml:mi>i</mml:mi></mml:msub></mml:mrow></mml:mstyle></mml:mrow></mml:mrow></mml:math></disp-formula>\nwhere <italic>C</italic> represents total CO<sub>2</sub> emissions from regional energy consumption; <italic>i</italic> is the type of energy; <inline-formula><mml:math id=\"mm16\"><mml:mrow><mml:mrow><mml:msub><mml:mi>T</mml:mi><mml:mi>i</mml:mi></mml:msub></mml:mrow></mml:mrow></mml:math></inline-formula> is the terminal consumption for <italic>i</italic>; <inline-formula><mml:math id=\"mm17\"><mml:mrow><mml:mrow><mml:msub><mml:mi>F</mml:mi><mml:mi>i</mml:mi></mml:msub></mml:mrow></mml:mrow></mml:math></inline-formula> is the coefficient of CO<sub>2</sub> emissions for <italic>i</italic>:<disp-formula id=\"FD5-ijerph-17-05463\"><label>(5)</label><mml:math id=\"mm18\"><mml:mrow><mml:mrow><mml:msub><mml:mi>F</mml:mi><mml:mi>i</mml:mi></mml:msub><mml:mo>=</mml:mo><mml:msub><mml:mi>A</mml:mi><mml:mi>i</mml:mi></mml:msub><mml:mo>×</mml:mo><mml:msub><mml:mi>B</mml:mi><mml:mi>i</mml:mi></mml:msub><mml:mo>×</mml:mo><mml:mo stretchy=\"false\">(</mml:mo><mml:mn>44</mml:mn><mml:mo>/</mml:mo><mml:mn>12</mml:mn><mml:mo stretchy=\"false\">)</mml:mo><mml:mo>×</mml:mo><mml:msup><mml:mrow><mml:mn>10</mml:mn></mml:mrow><mml:mn>3</mml:mn></mml:msup><mml:mo>×</mml:mo><mml:mn>4186.8</mml:mn><mml:mo>×</mml:mo><mml:msup><mml:mrow><mml:mn>10</mml:mn></mml:mrow><mml:mrow><mml:mo>−</mml:mo><mml:mn>9</mml:mn></mml:mrow></mml:msup><mml:mo>×</mml:mo><mml:msup><mml:mrow><mml:mn>10</mml:mn></mml:mrow><mml:mrow><mml:mo>−</mml:mo><mml:mn>3</mml:mn></mml:mrow></mml:msup><mml:mo>×</mml:mo><mml:msub><mml:mi>E</mml:mi><mml:mi>i</mml:mi></mml:msub></mml:mrow></mml:mrow></mml:math></disp-formula>\n<inline-formula><mml:math id=\"mm19\"><mml:mrow><mml:mrow><mml:msub><mml:mi>A</mml:mi><mml:mi>i</mml:mi></mml:msub></mml:mrow></mml:mrow></mml:math></inline-formula> is the coefficient of carbon emissions for <italic>i</italic> (kgC/GJ); <inline-formula><mml:math id=\"mm20\"><mml:mrow><mml:mrow><mml:msub><mml:mi>B</mml:mi><mml:mi>i</mml:mi></mml:msub></mml:mrow></mml:mrow></mml:math></inline-formula> is the oxidation rate in the combustion for <italic>i</italic> type energy; <inline-formula><mml:math id=\"mm21\"><mml:mrow><mml:mrow><mml:msub><mml:mi>A</mml:mi><mml:mi>i</mml:mi></mml:msub></mml:mrow></mml:mrow></mml:math></inline-formula> and <inline-formula><mml:math id=\"mm22\"><mml:mrow><mml:mrow><mml:msub><mml:mi>B</mml:mi><mml:mi>i</mml:mi></mml:msub></mml:mrow></mml:mrow></mml:math></inline-formula> are derived from IPCC (2006). In <xref rid=\"ijerph-17-05463-t003\" ref-type=\"table\">Table 3</xref>, <inline-formula><mml:math id=\"mm23\"><mml:mrow><mml:mrow><mml:msub><mml:mi>C</mml:mi><mml:mi>i</mml:mi></mml:msub></mml:mrow></mml:mrow></mml:math></inline-formula> is the coefficient of CO<sub>2</sub> emissions for <italic>i</italic> (kgCO<sup>2</sup>/TJ); <inline-formula><mml:math id=\"mm24\"><mml:mrow><mml:mrow><mml:msub><mml:mi>D</mml:mi><mml:mi>i</mml:mi></mml:msub></mml:mrow></mml:mrow></mml:math></inline-formula> is the original coefficient for i type of energy (Kg CO<sub>2</sub>/Kcal); <inline-formula><mml:math id=\"mm25\"><mml:mrow><mml:mrow><mml:msub><mml:mi>E</mml:mi><mml:mi>i</mml:mi></mml:msub></mml:mrow></mml:mrow></mml:math></inline-formula> is the calorific value of unit fuel in China.</p><p>This paper calculates CO<sub>2</sub> emissions in China’s 30 regions (1995–2016). The data for these provinces are derived from China’s Statistical yearbook. The reason we removed the refinery gas and liquefied petroleum gas is because they are difficult to gather, and they are used less. We calculated the total CO<sub>2</sub> emissions from 1995 to 2016, which show a tendency to increase year after year (<xref rid=\"ijerph-17-05463-t004\" ref-type=\"table\">Table 4</xref>).</p></sec><sec id=\"sec4dot2-ijerph-17-05463\"><title>4.2. Other Data and Variables</title><p>We evaluate the level of the low-carbon economy development level in China’s 30 regions from 2000 to 2015 as shown in <xref rid=\"ijerph-17-05463-t001\" ref-type=\"table\">Table 1</xref>. The demographic data are obtained from “China Population Statistic Yearbook”, the per capita GDP, the GDP growth and the average income of rural and urban families are obtained from the statistics in “China Statistical Yearbook”. The energy data are obtained through the calculation of statistics in “China Energy Statistical Yearbook” and China’s 30 regions’ statistical yearbooks. The scientific data are attained from “China Statistical Yearbook on Science and Technology”. We can set the 6‰ and 75% as moderate indexes, which is the ideal value of natural population growth rate and urbanization rate.</p></sec></sec><sec id=\"sec5-ijerph-17-05463\"><title>5. Regional Evaluation of the Low-Carbon Economy</title><sec id=\"sec5dot1-ijerph-17-05463\"><title>5.1. Index Calculation</title><p>Urbanization rate (D12). Urbanization rate refers to the percentage that the urban population accounts for the entire population, which witnesses city’s history and development. Since the current statistics do not cover all sub-provincial cities’ statistical data of urbanization rate, this paper adopts the method that “non-agricultural population accounts for total population proportion” to calculate the urbanization rate, which is authorized by police departments. The method can be summarized as “the urbanization rate = non-agricultural population/total population.</p><p>Per capita carbon emissions (S11). From 1990s to the year 2000, the global average annual CO<sub>2</sub> emission has increased from 22 billion tons to 264 billion tons. Therefore, the primary goal of developing the low-carbon economy is to limit CO<sub>2</sub> emissions. Due to unavailability of authorized statistics on carbon emissions, this paper estimates the carbon emissions produced by fossil resources (coal, oil and natural gas) based on other existing statistics. The total carbon emissions = Σ (each resource consumption × carbon emissions per unit of energy consumption), summarized as per capita carbon emissions = total carbon emissions/population.</p><p>Carbon emissions of residents’ consumption (S12) and government’s consumption (S13). They refer to the carbon emissions created by government’s and residents’ final consumption on goods and services over a period of time, which can reflect the influence of consumption level and structure on carbon emissions. The government’s consumption on carbon emissions refers to the carbon emissions created by government’s consumption on public services, which can reflect the impact of local government governance level on carbon emissions. We account these two indexes according to the proportion of residents’ consumption in GDP (known as final consumption rate) and carbon intensity per unit of economy. That is, carbon emissions of residents’ consumption = total carbon emissions/residents’ consumption, carbon emissions of government’s consumption = total carbon emissions/government’s consumption.</p><p>The efficiency of energy process and conversion (R12). The process of energy conversion is a production link of the energy process system, which is the important index for evaluating the advancement of the energy conversion device, the production technique and the quality of administration. The improvement of efficiency of energy process and conversion means that the low energy input can create high energy output. Its formula is as follows: efficiency of energy process and conversion = energy output/energy input.</p><p>Carbon productivity (R13). It is considered as the core index of measuring low carbonization, which is the GDP per unit of carbon emitted. This index links the GDP output with the carbon emissions of energy consumption directly. Therefore, it can visually reflect the carbon use efficiency of the whole social economy and measure the overall level of low-carbon technology. In addition, as a result of its relation with economic structure, the carbon productivity can have the potential of further reduction of carbon emissions intensity of unit consumption when the country’s technology has accumulated to a certain extent. Its formula is carbon productivity = GDP/carbon emissions.</p></sec><sec id=\"sec5dot2-ijerph-17-05463\"><title>5.2. Dimensionless Evaluation Factors</title><p>The collected original data must be dimensionless through range conversion as they cannot be compared directly.</p><p>We set <inline-formula><mml:math id=\"mm26\"><mml:mrow><mml:mrow><mml:msub><mml:mi>x</mml:mi><mml:mrow><mml:mi>k</mml:mi><mml:mi>i</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mrow></mml:math></inline-formula> as the value of the index <italic>k</italic> that has been normalized in the evaluated area <italic>i</italic>, and set the <inline-formula><mml:math id=\"mm27\"><mml:mrow><mml:mrow><mml:msub><mml:mi>v</mml:mi><mml:mrow><mml:mi>k</mml:mi><mml:mi>i</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mrow></mml:math></inline-formula> as the value of the index <italic>k</italic> in the evaluated area <italic>i</italic>, the <italic>n</italic> as the quantity of the evaluated area. Positive data was calculated:<disp-formula id=\"FD6-ijerph-17-05463\"><label>(6)</label><mml:math id=\"mm28\"><mml:mrow><mml:mrow><mml:msub><mml:mi>x</mml:mi><mml:mrow><mml:mi>k</mml:mi><mml:mi>i</mml:mi></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:mfrac><mml:mrow><mml:msub><mml:mi>v</mml:mi><mml:mrow><mml:mi>k</mml:mi><mml:mi>i</mml:mi></mml:mrow></mml:msub><mml:mo>−</mml:mo><mml:munder><mml:mrow><mml:mi>min</mml:mi></mml:mrow><mml:mrow><mml:mn>1</mml:mn><mml:mo>≤</mml:mo><mml:mi>i</mml:mi><mml:mo>≤</mml:mo><mml:mi>n</mml:mi></mml:mrow></mml:munder><mml:mo stretchy=\"false\">(</mml:mo><mml:msub><mml:mi>v</mml:mi><mml:mrow><mml:mi>k</mml:mi><mml:mi>i</mml:mi></mml:mrow></mml:msub><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow><mml:mrow><mml:munder><mml:mrow><mml:mi>max</mml:mi></mml:mrow><mml:mrow><mml:mn>1</mml:mn><mml:mo>≤</mml:mo><mml:mi>i</mml:mi><mml:mo>≤</mml:mo><mml:mi>n</mml:mi></mml:mrow></mml:munder><mml:mo stretchy=\"false\">(</mml:mo><mml:msub><mml:mi>v</mml:mi><mml:mrow><mml:mi>k</mml:mi><mml:mi>i</mml:mi></mml:mrow></mml:msub><mml:mo stretchy=\"false\">)</mml:mo><mml:mo>−</mml:mo><mml:munder><mml:mrow><mml:mi>min</mml:mi></mml:mrow><mml:mrow><mml:mn>1</mml:mn><mml:mo>≤</mml:mo><mml:mi>i</mml:mi><mml:mo>≤</mml:mo><mml:mi>n</mml:mi></mml:mrow></mml:munder><mml:mo stretchy=\"false\">(</mml:mo><mml:msub><mml:mi>v</mml:mi><mml:mrow><mml:mi>k</mml:mi><mml:mi>i</mml:mi></mml:mrow></mml:msub><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:mfrac></mml:mrow></mml:mrow></mml:math></disp-formula></p><p>Negative data was calculated:<disp-formula id=\"FD7-ijerph-17-05463\"><label>(7)</label><mml:math id=\"mm29\"><mml:mrow><mml:mrow><mml:msub><mml:mi>x</mml:mi><mml:mrow><mml:mi>k</mml:mi><mml:mi>i</mml:mi></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:mfrac><mml:mrow><mml:munder><mml:mrow><mml:mi>max</mml:mi></mml:mrow><mml:mrow><mml:mn>1</mml:mn><mml:mo>≤</mml:mo><mml:mi>i</mml:mi><mml:mo>≤</mml:mo><mml:mi>n</mml:mi></mml:mrow></mml:munder><mml:mo stretchy=\"false\">(</mml:mo><mml:msub><mml:mi>v</mml:mi><mml:mrow><mml:mi>k</mml:mi><mml:mi>i</mml:mi></mml:mrow></mml:msub><mml:mo stretchy=\"false\">)</mml:mo><mml:mo>−</mml:mo><mml:msub><mml:mi>v</mml:mi><mml:mrow><mml:mi>k</mml:mi><mml:mi>i</mml:mi></mml:mrow></mml:msub></mml:mrow><mml:mrow><mml:munder><mml:mrow><mml:mi>max</mml:mi></mml:mrow><mml:mrow><mml:mn>1</mml:mn><mml:mo>≤</mml:mo><mml:mi>i</mml:mi><mml:mo>≤</mml:mo><mml:mi>n</mml:mi></mml:mrow></mml:munder><mml:mo stretchy=\"false\">(</mml:mo><mml:msub><mml:mi>v</mml:mi><mml:mrow><mml:mi>k</mml:mi><mml:mi>i</mml:mi></mml:mrow></mml:msub><mml:mo stretchy=\"false\">)</mml:mo><mml:mo>−</mml:mo><mml:munder><mml:mrow><mml:mi>min</mml:mi></mml:mrow><mml:mrow><mml:mn>1</mml:mn><mml:mo>≤</mml:mo><mml:mi>i</mml:mi><mml:mo>≤</mml:mo><mml:mi>n</mml:mi></mml:mrow></mml:munder><mml:mo stretchy=\"false\">(</mml:mo><mml:msub><mml:mi>v</mml:mi><mml:mrow><mml:mi>k</mml:mi><mml:mi>i</mml:mi></mml:mrow></mml:msub><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:mfrac></mml:mrow></mml:mrow></mml:math></disp-formula></p><p>Regarding, moderate interrelation for those factors, the closer it approached to ideal value, the better. In reference to a large number of previous literature, this paper sets the planning objective of the country’s natural population growth rate in China (V1 = 6‰) as the ideal value of natural population growth rate (D11) and sets the average urbanization rate in developed countries (V2 = 75%) as the ideal value of the urbanization rate (D12). We set <inline-formula><mml:math id=\"mm30\"><mml:mrow><mml:mrow><mml:msub><mml:mi>x</mml:mi><mml:mrow><mml:mi>k</mml:mi><mml:mi>i</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mrow></mml:math></inline-formula> as the value of the index <italic>k</italic> that has been normalized in the evaluated area <italic>i</italic>, set the <inline-formula><mml:math id=\"mm31\"><mml:mrow><mml:mrow><mml:msub><mml:mi>v</mml:mi><mml:mrow><mml:mi>k</mml:mi><mml:mi>i</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mrow></mml:math></inline-formula> as the value of the index <italic>k</italic> in the evaluated area <italic>i</italic>, the <inline-formula><mml:math id=\"mm32\"><mml:mrow><mml:mrow><mml:msubsup><mml:mi>v</mml:mi><mml:mrow><mml:mi>k</mml:mi><mml:mi>i</mml:mi></mml:mrow><mml:mn>0</mml:mn></mml:msubsup></mml:mrow></mml:mrow></mml:math></inline-formula> as the ideal value and the <italic>n</italic> as the quantity of the evaluated area. Moderate data was calculated:<disp-formula id=\"FD8-ijerph-17-05463\"><label>(8)</label><mml:math id=\"mm33\"><mml:mrow><mml:mrow><mml:msub><mml:mi>x</mml:mi><mml:mrow><mml:mi>k</mml:mi><mml:mi>i</mml:mi></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:mrow><mml:mo>{</mml:mo><mml:mrow><mml:mtable><mml:mtr><mml:mtd><mml:mrow><mml:mn>1</mml:mn><mml:mo>−</mml:mo><mml:mfrac><mml:mrow><mml:msubsup><mml:mi>v</mml:mi><mml:mrow><mml:mi>k</mml:mi><mml:mi>i</mml:mi></mml:mrow><mml:mn>0</mml:mn></mml:msubsup><mml:mo>−</mml:mo><mml:msub><mml:mi>v</mml:mi><mml:mrow><mml:mi>k</mml:mi><mml:mi>i</mml:mi></mml:mrow></mml:msub></mml:mrow><mml:mrow><mml:mi>max</mml:mi><mml:mrow><mml:mo>[</mml:mo><mml:mrow><mml:msubsup><mml:mi>v</mml:mi><mml:mrow><mml:mi>k</mml:mi><mml:mi>i</mml:mi></mml:mrow><mml:mn>0</mml:mn></mml:msubsup><mml:mo>−</mml:mo><mml:munder><mml:mrow><mml:mi>min</mml:mi></mml:mrow><mml:mrow><mml:mn>1</mml:mn><mml:mo>≤</mml:mo><mml:mi>i</mml:mi><mml:mo>≤</mml:mo><mml:mi>n</mml:mi></mml:mrow></mml:munder><mml:mo stretchy=\"false\">(</mml:mo><mml:msub><mml:mi>v</mml:mi><mml:mrow><mml:mi>k</mml:mi><mml:mi>i</mml:mi></mml:mrow></mml:msub><mml:mo stretchy=\"false\">)</mml:mo><mml:mo>,</mml:mo><mml:munder><mml:mrow><mml:mi>max</mml:mi></mml:mrow><mml:mrow><mml:mn>1</mml:mn><mml:mo>≤</mml:mo><mml:mi>i</mml:mi><mml:mo>≤</mml:mo><mml:mi>n</mml:mi></mml:mrow></mml:munder><mml:mo stretchy=\"false\">(</mml:mo><mml:msub><mml:mi>v</mml:mi><mml:mrow><mml:mi>k</mml:mi><mml:mi>i</mml:mi></mml:mrow></mml:msub><mml:mo stretchy=\"false\">)</mml:mo><mml:mo>−</mml:mo><mml:msubsup><mml:mi>v</mml:mi><mml:mrow><mml:mi>k</mml:mi><mml:mi>i</mml:mi></mml:mrow><mml:mn>0</mml:mn></mml:msubsup></mml:mrow><mml:mo>]</mml:mo></mml:mrow></mml:mrow></mml:mfrac><mml:mo>,</mml:mo><mml:munder><mml:mrow><mml:mi>min</mml:mi></mml:mrow><mml:mrow><mml:mn>1</mml:mn><mml:mo>≤</mml:mo><mml:mi>i</mml:mi><mml:mo>≤</mml:mo><mml:mi>n</mml:mi></mml:mrow></mml:munder><mml:mo stretchy=\"false\">(</mml:mo><mml:msub><mml:mi>v</mml:mi><mml:mrow><mml:mi>k</mml:mi><mml:mi>i</mml:mi></mml:mrow></mml:msub><mml:mo stretchy=\"false\">)</mml:mo><mml:mo>≤</mml:mo><mml:msub><mml:mi>v</mml:mi><mml:mrow><mml:mi>k</mml:mi><mml:mi>i</mml:mi></mml:mrow></mml:msub><mml:mo>≤</mml:mo><mml:msubsup><mml:mi>v</mml:mi><mml:mrow><mml:mi>k</mml:mi><mml:mi>i</mml:mi></mml:mrow><mml:mi>o</mml:mi></mml:msubsup></mml:mrow></mml:mtd></mml:mtr><mml:mtr><mml:mtd><mml:mrow><mml:mn>1</mml:mn><mml:mo>−</mml:mo><mml:mfrac><mml:mrow><mml:msubsup><mml:mi>v</mml:mi><mml:mrow><mml:mi>k</mml:mi><mml:mi>i</mml:mi></mml:mrow><mml:mn>0</mml:mn></mml:msubsup><mml:mo>−</mml:mo><mml:msub><mml:mi>v</mml:mi><mml:mrow><mml:mi>k</mml:mi><mml:mi>i</mml:mi></mml:mrow></mml:msub></mml:mrow><mml:mrow><mml:mi>max</mml:mi><mml:mrow><mml:mo>[</mml:mo><mml:mrow><mml:msubsup><mml:mi>v</mml:mi><mml:mrow><mml:mi>k</mml:mi><mml:mi>i</mml:mi></mml:mrow><mml:mn>0</mml:mn></mml:msubsup><mml:mo>−</mml:mo><mml:munder><mml:mrow><mml:mi>min</mml:mi></mml:mrow><mml:mrow><mml:mn>1</mml:mn><mml:mo>≤</mml:mo><mml:mi>i</mml:mi><mml:mo>≤</mml:mo><mml:mi>n</mml:mi></mml:mrow></mml:munder><mml:mo stretchy=\"false\">(</mml:mo><mml:msub><mml:mi>v</mml:mi><mml:mrow><mml:mi>k</mml:mi><mml:mi>i</mml:mi></mml:mrow></mml:msub><mml:mo stretchy=\"false\">)</mml:mo><mml:mo>,</mml:mo><mml:munder><mml:mrow><mml:mi>max</mml:mi></mml:mrow><mml:mrow><mml:mn>1</mml:mn><mml:mo>≤</mml:mo><mml:mi>i</mml:mi><mml:mo>≤</mml:mo><mml:mi>n</mml:mi></mml:mrow></mml:munder><mml:mo stretchy=\"false\">(</mml:mo><mml:msub><mml:mi>v</mml:mi><mml:mrow><mml:mi>k</mml:mi><mml:mi>i</mml:mi></mml:mrow></mml:msub><mml:mo stretchy=\"false\">)</mml:mo><mml:mo>−</mml:mo><mml:msubsup><mml:mi>v</mml:mi><mml:mrow><mml:mi>k</mml:mi><mml:mi>i</mml:mi></mml:mrow><mml:mn>0</mml:mn></mml:msubsup></mml:mrow><mml:mo>]</mml:mo></mml:mrow></mml:mrow></mml:mfrac><mml:mo>,</mml:mo><mml:msubsup><mml:mi>v</mml:mi><mml:mrow><mml:mi>k</mml:mi><mml:mi>i</mml:mi></mml:mrow><mml:mn>0</mml:mn></mml:msubsup><mml:mo>≤</mml:mo><mml:msub><mml:mi>v</mml:mi><mml:mrow><mml:mi>k</mml:mi><mml:mi>i</mml:mi></mml:mrow></mml:msub><mml:mo>≤</mml:mo><mml:munder><mml:mrow><mml:mi>max</mml:mi></mml:mrow><mml:mrow><mml:mn>1</mml:mn><mml:mo>≤</mml:mo><mml:mi>i</mml:mi><mml:mo>≤</mml:mo><mml:mi>n</mml:mi></mml:mrow></mml:munder><mml:mo stretchy=\"false\">(</mml:mo><mml:msub><mml:mi>v</mml:mi><mml:mrow><mml:mi>k</mml:mi><mml:mi>i</mml:mi></mml:mrow></mml:msub><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:mtd></mml:mtr></mml:mtable></mml:mrow></mml:mrow></mml:mrow></mml:mrow></mml:math></disp-formula></p></sec><sec id=\"sec5dot3-ijerph-17-05463\"><title>5.3. Weight of Evaluation Factors</title><p>AHP model was developed by Satty [<xref rid=\"B28-ijerph-17-05463\" ref-type=\"bibr\">28</xref>]; it includes both qualitative and quantitative indicators, and ranks programs based on multiple criteria. It has been widely used in macro (complex and realistic) and human (management subjective) problems [<xref rid=\"B29-ijerph-17-05463\" ref-type=\"bibr\">29</xref>,<xref rid=\"B30-ijerph-17-05463\" ref-type=\"bibr\">30</xref>,<xref rid=\"B31-ijerph-17-05463\" ref-type=\"bibr\">31</xref>]. Nowadays, it is used in many research fields [<xref rid=\"B32-ijerph-17-05463\" ref-type=\"bibr\">32</xref>,<xref rid=\"B33-ijerph-17-05463\" ref-type=\"bibr\">33</xref>,<xref rid=\"B34-ijerph-17-05463\" ref-type=\"bibr\">34</xref>,<xref rid=\"B35-ijerph-17-05463\" ref-type=\"bibr\">35</xref>]. </p><p>In this study, the low-carbon economy system includes four levels (<xref rid=\"ijerph-17-05463-t001\" ref-type=\"table\">Table 1</xref>).</p><p>If <inline-formula><mml:math id=\"mm34\"><mml:mrow><mml:mrow><mml:msub><mml:mi>a</mml:mi><mml:mrow><mml:mi>i</mml:mi><mml:mi>j</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mrow></mml:math></inline-formula> is the result of comparing <italic>i</italic> with <italic>j.</italic> The matrix is\n<disp-formula id=\"FD9-ijerph-17-05463\"><label>(9)</label><mml:math id=\"mm35\"><mml:mrow><mml:mrow><mml:mi>A</mml:mi><mml:mo>=</mml:mo><mml:mrow><mml:mo>[</mml:mo><mml:mrow><mml:mtable><mml:mtr><mml:mtd><mml:mrow><mml:msub><mml:mi>a</mml:mi><mml:mrow><mml:mn>11</mml:mn></mml:mrow></mml:msub></mml:mrow></mml:mtd><mml:mtd><mml:mrow><mml:msub><mml:mi>a</mml:mi><mml:mrow><mml:mn>12</mml:mn></mml:mrow></mml:msub></mml:mrow></mml:mtd><mml:mtd><mml:mrow><mml:mo>…</mml:mo></mml:mrow></mml:mtd><mml:mtd><mml:mrow><mml:msub><mml:mi>a</mml:mi><mml:mrow><mml:mn>1</mml:mn><mml:mi>n</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mtd></mml:mtr><mml:mtr><mml:mtd><mml:mrow><mml:msub><mml:mi>a</mml:mi><mml:mrow><mml:mn>21</mml:mn></mml:mrow></mml:msub></mml:mrow></mml:mtd><mml:mtd><mml:mrow><mml:msub><mml:mi>a</mml:mi><mml:mrow><mml:mn>22</mml:mn></mml:mrow></mml:msub></mml:mrow></mml:mtd><mml:mtd><mml:mrow><mml:mo>…</mml:mo></mml:mrow></mml:mtd><mml:mtd><mml:mrow><mml:msub><mml:mi>a</mml:mi><mml:mrow><mml:mn>2</mml:mn><mml:mi>n</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mtd></mml:mtr><mml:mtr><mml:mtd><mml:mrow><mml:mo>…</mml:mo></mml:mrow></mml:mtd><mml:mtd><mml:mrow><mml:mo>…</mml:mo></mml:mrow></mml:mtd><mml:mtd><mml:mrow><mml:mo>…</mml:mo></mml:mrow></mml:mtd><mml:mtd><mml:mrow><mml:mo>…</mml:mo></mml:mrow></mml:mtd></mml:mtr><mml:mtr><mml:mtd><mml:mrow><mml:msub><mml:mi>a</mml:mi><mml:mrow><mml:mi>n</mml:mi><mml:mn>1</mml:mn></mml:mrow></mml:msub></mml:mrow></mml:mtd><mml:mtd><mml:mrow><mml:msub><mml:mi>a</mml:mi><mml:mrow><mml:mi>n</mml:mi><mml:mn>2</mml:mn></mml:mrow></mml:msub></mml:mrow></mml:mtd><mml:mtd><mml:mrow><mml:mo>…</mml:mo></mml:mrow></mml:mtd><mml:mtd><mml:mrow><mml:msub><mml:mi>a</mml:mi><mml:mrow><mml:mi>n</mml:mi><mml:mi>n</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mtd></mml:mtr></mml:mtable></mml:mrow><mml:mo>]</mml:mo></mml:mrow></mml:mrow></mml:mrow></mml:math></disp-formula></p><p>We employed the Delphi method to analyze the relative importance of D, P, S and R. In this study, we provided a scoring table for 30 experts from the universities, scientific institutions and government decision-making departments. Then we calculated the judgment matrix, obtained the indicator weighting and established pair-wise comparison matrix for A (<xref rid=\"ijerph-17-05463-t005\" ref-type=\"table\">Table 5</xref>).Finally, we found that “State” (S) was crucial to the low-carbon economy level, and it was the base for low-carbon economy, Therefore, weight of S was the highest. </p><p><inline-formula><mml:math id=\"mm36\"><mml:mrow><mml:mrow><mml:msub><mml:mi>m</mml:mi><mml:mi>i</mml:mi></mml:msub></mml:mrow></mml:mrow></mml:math></inline-formula> is the product of every row: <disp-formula id=\"FD10-ijerph-17-05463\"><label>(10)</label><mml:math id=\"mm37\"><mml:mrow><mml:mrow><mml:msub><mml:mi>m</mml:mi><mml:mi>i</mml:mi></mml:msub><mml:mo>=</mml:mo><mml:mstyle displaystyle=\"true\"><mml:munderover><mml:mo>∏</mml:mo><mml:mrow><mml:mi>i</mml:mi><mml:mo>=</mml:mo><mml:mn>1</mml:mn></mml:mrow><mml:mn>4</mml:mn></mml:munderover><mml:mrow><mml:msub><mml:mi>a</mml:mi><mml:mrow><mml:mi>i</mml:mi><mml:mi>j</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mstyle><mml:mo>,</mml:mo><mml:mo> </mml:mo><mml:mo stretchy=\"false\">(</mml:mo><mml:mi mathvariant=\"normal\">i</mml:mi><mml:mo>=</mml:mo><mml:mn>1</mml:mn><mml:mo>,</mml:mo><mml:mo> </mml:mo><mml:mn>2</mml:mn><mml:mo>,</mml:mo><mml:mo> </mml:mo><mml:mn>3</mml:mn><mml:mo>,</mml:mo><mml:mo> </mml:mo><mml:mn>4</mml:mn><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:mrow></mml:math></disp-formula>\n<disp-formula id=\"FD11-ijerph-17-05463\"><label>(11)</label><mml:math id=\"mm38\"><mml:mrow><mml:mrow><mml:mrow><mml:mover accent=\"true\"><mml:mrow><mml:msub><mml:mi>w</mml:mi><mml:mi>i</mml:mi></mml:msub></mml:mrow><mml:mo stretchy=\"true\">¯</mml:mo></mml:mover><mml:mo>=</mml:mo><mml:mroot><mml:mrow><mml:msub><mml:mi>m</mml:mi><mml:mi>i</mml:mi></mml:msub></mml:mrow><mml:mn>4</mml:mn></mml:mroot></mml:mrow><mml:mrow><mml:mo>;</mml:mo><mml:mo> </mml:mo><mml:mover accent=\"true\"><mml:mi>w</mml:mi><mml:mo stretchy=\"true\">¯</mml:mo></mml:mover><mml:mo>=</mml:mo><mml:msup><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mover accent=\"true\"><mml:mrow><mml:msub><mml:mi>w</mml:mi><mml:mn>1</mml:mn></mml:msub></mml:mrow><mml:mo stretchy=\"true\">¯</mml:mo></mml:mover><mml:mo>,</mml:mo><mml:mover accent=\"true\"><mml:mrow><mml:msub><mml:mi>w</mml:mi><mml:mn>2</mml:mn></mml:msub></mml:mrow><mml:mo stretchy=\"true\">¯</mml:mo></mml:mover><mml:mo>,</mml:mo><mml:mo>…</mml:mo><mml:mo>,</mml:mo><mml:mover accent=\"true\"><mml:mrow><mml:msub><mml:mi>w</mml:mi><mml:mi>n</mml:mi></mml:msub></mml:mrow><mml:mo stretchy=\"true\">¯</mml:mo></mml:mover><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow><mml:mi>T</mml:mi></mml:msup></mml:mrow></mml:mrow></mml:mrow></mml:math></disp-formula>\n<disp-formula id=\"FD13-ijerph-17-05463\"><label>(12)</label><mml:math id=\"mm39\"><mml:mrow><mml:mrow><mml:mrow><mml:msub><mml:mi>w</mml:mi><mml:mi>i</mml:mi></mml:msub><mml:mo>=</mml:mo><mml:mfrac><mml:mrow><mml:mover accent=\"true\"><mml:mrow><mml:msub><mml:mi>w</mml:mi><mml:mi>i</mml:mi></mml:msub></mml:mrow><mml:mo stretchy=\"true\">¯</mml:mo></mml:mover></mml:mrow><mml:mrow><mml:mstyle displaystyle=\"true\"><mml:munderover><mml:mo>∑</mml:mo><mml:mrow><mml:mi>i</mml:mi><mml:mo>=</mml:mo><mml:mn>1</mml:mn></mml:mrow><mml:mi>n</mml:mi></mml:munderover><mml:mrow><mml:mover accent=\"true\"><mml:mrow><mml:msub><mml:mi>w</mml:mi><mml:mi>i</mml:mi></mml:msub></mml:mrow><mml:mo stretchy=\"true\">¯</mml:mo></mml:mover></mml:mrow></mml:mstyle></mml:mrow></mml:mfrac></mml:mrow><mml:mrow><mml:mo>,</mml:mo><mml:mo> </mml:mo><mml:mo stretchy=\"false\">(</mml:mo><mml:mi mathvariant=\"normal\">i</mml:mi><mml:mo>=</mml:mo><mml:mn>1</mml:mn><mml:mo>,</mml:mo><mml:mo> </mml:mo><mml:mn>2</mml:mn><mml:mo>,</mml:mo><mml:mo> </mml:mo><mml:mn>3</mml:mn><mml:mo>,</mml:mo><mml:mo> </mml:mo><mml:mn>4</mml:mn><mml:mo stretchy=\"false\">)</mml:mo><mml:mo>;</mml:mo><mml:mo> </mml:mo><mml:msub><mml:mi>λ</mml:mi><mml:mrow><mml:mi>max</mml:mi></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:mfrac><mml:mn>1</mml:mn><mml:mi>n</mml:mi></mml:mfrac><mml:mstyle displaystyle=\"true\"><mml:munderover><mml:mo>∑</mml:mo><mml:mrow><mml:mi>i</mml:mi><mml:mo>=</mml:mo><mml:mn>1</mml:mn></mml:mrow><mml:mi>n</mml:mi></mml:munderover><mml:mrow><mml:mfrac><mml:mrow><mml:msub><mml:mrow><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mi>A</mml:mi><mml:mi>W</mml:mi></mml:mrow><mml:mo>)</mml:mo></mml:mrow></mml:mrow><mml:mi>i</mml:mi></mml:msub></mml:mrow><mml:mrow><mml:msub><mml:mi>w</mml:mi><mml:mi>i</mml:mi></mml:msub></mml:mrow></mml:mfrac></mml:mrow></mml:mstyle></mml:mrow></mml:mrow></mml:mrow></mml:math></disp-formula>\n<disp-formula id=\"FD15-ijerph-17-05463\"><label>(13)</label><mml:math id=\"mm40\"><mml:mrow><mml:mrow><mml:mi>A</mml:mi><mml:mi>W</mml:mi><mml:mo>=</mml:mo><mml:mrow><mml:mo>[</mml:mo><mml:mrow><mml:mtable><mml:mtr><mml:mtd><mml:mrow><mml:msub><mml:mi>a</mml:mi><mml:mrow><mml:mn>11</mml:mn></mml:mrow></mml:msub></mml:mrow></mml:mtd><mml:mtd><mml:mrow><mml:msub><mml:mi>a</mml:mi><mml:mrow><mml:mn>12</mml:mn></mml:mrow></mml:msub></mml:mrow></mml:mtd><mml:mtd><mml:mrow><mml:mo>…</mml:mo></mml:mrow></mml:mtd><mml:mtd><mml:mrow><mml:msub><mml:mi>a</mml:mi><mml:mrow><mml:mn>1</mml:mn><mml:mi>n</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mtd></mml:mtr><mml:mtr><mml:mtd><mml:mrow><mml:msub><mml:mi>a</mml:mi><mml:mrow><mml:mn>21</mml:mn></mml:mrow></mml:msub></mml:mrow></mml:mtd><mml:mtd><mml:mrow><mml:msub><mml:mi>a</mml:mi><mml:mrow><mml:mn>22</mml:mn></mml:mrow></mml:msub></mml:mrow></mml:mtd><mml:mtd><mml:mrow><mml:mo>…</mml:mo></mml:mrow></mml:mtd><mml:mtd><mml:mrow><mml:msub><mml:mi>a</mml:mi><mml:mrow><mml:mn>2</mml:mn><mml:mi>n</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mtd></mml:mtr><mml:mtr><mml:mtd><mml:mrow><mml:mo>…</mml:mo></mml:mrow></mml:mtd><mml:mtd><mml:mrow><mml:mo>…</mml:mo></mml:mrow></mml:mtd><mml:mtd><mml:mrow><mml:mo>…</mml:mo></mml:mrow></mml:mtd><mml:mtd><mml:mrow><mml:mo>…</mml:mo></mml:mrow></mml:mtd></mml:mtr><mml:mtr><mml:mtd><mml:mrow><mml:msub><mml:mi>a</mml:mi><mml:mrow><mml:mi>n</mml:mi><mml:mn>1</mml:mn></mml:mrow></mml:msub></mml:mrow></mml:mtd><mml:mtd><mml:mrow><mml:msub><mml:mi>a</mml:mi><mml:mrow><mml:mi>n</mml:mi><mml:mn>2</mml:mn></mml:mrow></mml:msub></mml:mrow></mml:mtd><mml:mtd><mml:mrow><mml:mo>…</mml:mo></mml:mrow></mml:mtd><mml:mtd><mml:mrow><mml:msub><mml:mi>a</mml:mi><mml:mrow><mml:mi>n</mml:mi><mml:mi>n</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mtd></mml:mtr></mml:mtable></mml:mrow><mml:mo>]</mml:mo></mml:mrow><mml:mo>×</mml:mo><mml:mrow><mml:mo>|</mml:mo><mml:mrow><mml:mtable><mml:mtr><mml:mtd><mml:mrow><mml:msub><mml:mi>w</mml:mi><mml:mn>1</mml:mn></mml:msub></mml:mrow></mml:mtd></mml:mtr><mml:mtr><mml:mtd><mml:mrow><mml:msub><mml:mi>w</mml:mi><mml:mn>2</mml:mn></mml:msub></mml:mrow></mml:mtd></mml:mtr><mml:mtr><mml:mtd><mml:mo>•</mml:mo></mml:mtd></mml:mtr><mml:mtr><mml:mtd><mml:mrow><mml:msub><mml:mi>w</mml:mi><mml:mi>j</mml:mi></mml:msub></mml:mrow></mml:mtd></mml:mtr></mml:mtable></mml:mrow><mml:mo>|</mml:mo></mml:mrow></mml:mrow></mml:mrow></mml:math></disp-formula></p><p>Finally, we calculated the weight of D, P, S and R were 0.1186, 0.2162, 0.4141 and 0.2511. The eigenvalue <inline-formula><mml:math id=\"mm41\"><mml:mrow><mml:mrow><mml:msub><mml:mi>λ</mml:mi><mml:mrow><mml:mi>max</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mrow></mml:math></inline-formula> = 4.1576.</p><p>Then, we tested CI (the consistency index) = 0.0525. CR (the consistency ratio) = 0.059 ≤ 0.10.\n<disp-formula id=\"FD16-ijerph-17-05463\"><label>(14)</label><mml:math id=\"mm42\"><mml:mrow><mml:mrow><mml:mrow><mml:mi>C</mml:mi><mml:mi>I</mml:mi><mml:mo>=</mml:mo><mml:mfrac><mml:mrow><mml:msub><mml:mi>λ</mml:mi><mml:mrow><mml:mi>max</mml:mi></mml:mrow></mml:msub><mml:mo>−</mml:mo><mml:mi>n</mml:mi></mml:mrow><mml:mrow><mml:mi>n</mml:mi><mml:mo>−</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:mfrac></mml:mrow><mml:mspace linebreak=\"newline\"/><mml:mrow><mml:mi>C</mml:mi><mml:mi>R</mml:mi><mml:mo>=</mml:mo><mml:mfrac><mml:mrow><mml:mi>C</mml:mi><mml:mi>I</mml:mi></mml:mrow><mml:mrow><mml:mi>R</mml:mi><mml:mi>I</mml:mi></mml:mrow></mml:mfrac></mml:mrow></mml:mrow></mml:mrow></mml:math></disp-formula>\n<italic>RI</italic> is used to modify the <italic>CI</italic>. In this paper, <italic>RI</italic> = 0.90.</p></sec><sec id=\"sec5dot4-ijerph-17-05463\"><title>5.4. The synthetic Index and Sub-Index of the Low-Carbon Economy</title><p>We calculated the indexes according to the aforesaid principles, then used a composite index that contains rule hierarchy composite index and comprehensive composite index (using the hundred percentage point system). In other words, the method of linear weighted sum is used to calculate index value in all rule hierarchies, and then from its result the low-carbon evaluation index (LCEI) value is obtained. </p><p>We use <inline-formula><mml:math id=\"mm43\"><mml:mrow><mml:mrow><mml:msub><mml:mi>z</mml:mi><mml:mi>j</mml:mi></mml:msub></mml:mrow></mml:mrow></mml:math></inline-formula> to express the numerical value of second grades in the comprehensive system; in order, there are D index, P index, S index and R index. Then we set <inline-formula><mml:math id=\"mm44\"><mml:mrow><mml:mrow><mml:msub><mml:mi>x</mml:mi><mml:mi>k</mml:mi></mml:msub></mml:mrow></mml:mrow></mml:math></inline-formula> as the dimensionless index after normalization and the <inline-formula><mml:math id=\"mm45\"><mml:mrow><mml:mrow><mml:msub><mml:mi>w</mml:mi><mml:mi>k</mml:mi></mml:msub></mml:mrow></mml:mrow></mml:math></inline-formula> as the weight of the indexes (<xref rid=\"ijerph-17-05463-t001\" ref-type=\"table\">Table 1</xref>).\n<disp-formula id=\"FD18-ijerph-17-05463\"><label>(15)</label><mml:math id=\"mm46\"><mml:mrow><mml:mrow><mml:mrow><mml:msub><mml:mi>z</mml:mi><mml:mi>j</mml:mi></mml:msub><mml:mo>=</mml:mo><mml:mo>∑</mml:mo><mml:mrow><mml:msub><mml:mi>x</mml:mi><mml:mi>k</mml:mi></mml:msub><mml:msub><mml:mi>w</mml:mi><mml:mi>k</mml:mi></mml:msub></mml:mrow><mml:mo> </mml:mo><mml:mo>(</mml:mo><mml:mi mathvariant=\"normal\">j</mml:mi><mml:mo>=</mml:mo><mml:mi mathvariant=\"normal\">D</mml:mi><mml:mo>,</mml:mo><mml:mi mathvariant=\"normal\">P</mml:mi><mml:mo>,</mml:mo><mml:mi mathvariant=\"normal\">R</mml:mi><mml:mo>,</mml:mo><mml:mi mathvariant=\"normal\">S</mml:mi><mml:mo>)</mml:mo><mml:mo>;</mml:mo><mml:mo> </mml:mo></mml:mrow><mml:mspace linebreak=\"newline\"/><mml:mrow><mml:mi>L</mml:mi><mml:mi>C</mml:mi><mml:mi>E</mml:mi><mml:mi>I</mml:mi><mml:mo>=</mml:mo><mml:msub><mml:mi>z</mml:mi><mml:mi>D</mml:mi></mml:msub><mml:mo>+</mml:mo><mml:msub><mml:mi>z</mml:mi><mml:mi>P</mml:mi></mml:msub><mml:mo>+</mml:mo><mml:msub><mml:mi>z</mml:mi><mml:mi>S</mml:mi></mml:msub><mml:mo>+</mml:mo><mml:msub><mml:mi>z</mml:mi><mml:mi>R</mml:mi></mml:msub></mml:mrow></mml:mrow></mml:mrow></mml:math></disp-formula></p><p>We present the low-carbon economy level of China’s 30 regions in <xref rid=\"ijerph-17-05463-t006\" ref-type=\"table\">Table 6</xref>. </p></sec></sec><sec id=\"sec6-ijerph-17-05463\"><title>6. Assessing Coupling Coordination between the Sub-Indexes</title><p>As mentioned above, the horizontal comparisons of the coordination degrees are shown in <xref rid=\"ijerph-17-05463-t007\" ref-type=\"table\">Table 7</xref>, the coordination degrees of the 30 regions in China were calculated and analyzed and were based on the model of coordination degree in 2015.</p><p>The coupling degree (C) results show a pattern for that central area lower than the west area and the west area lower than the east area. The average degree of the whole country is 0.6723, which is higher than west and central areas but lower than the east area. </p><p>The coordination degree (D) results show a pattern for the west area lower than the central area and the central area lower than the east area. The average degree of the whole country is 0.5840, which is higher than the west and central areas but lower than the east area.</p><p>Moreover, based on the model of coordination degree in 2015, the coordination degrees of the 30 regions in China were calculated and analyzed.</p><p>The results indicated the four sub systems were primary coordination in the east area and bare coordination in the central and west areas in 2015. It indicated that four sub-indexes should be developed at the same pace and be improved based on stable development. The construction of low-carbon economy in mid-west areas is the key in China.</p></sec><sec sec-type=\"results\" id=\"sec7-ijerph-17-05463\"><title>7. Results and Analysis</title><sec id=\"sec7dot1-ijerph-17-05463\"><title>7.1. Results</title><p>In <xref rid=\"ijerph-17-05463-t008\" ref-type=\"table\">Table 8</xref>, the numerical value increases gradually; all provinces, municipalities and autonomous regions show varying increasing rates; the level of low-carbon economy varies by province. The index level shows a gradually decreasing trend from southeastern coastal areas to the north on spatial variation.</p><p>The <xref ref-type=\"fig\" rid=\"ijerph-17-05463-f002\">Figure 2</xref> reflects the distribution of development capability of regional low-carbon economy in China in 2015. It can be seen from <xref ref-type=\"fig\" rid=\"ijerph-17-05463-f002\">Figure 2</xref> that in the spatial distribution, the numerical value of development capability shows a pattern that indicates the southeast is gradually higher than the north. While the clear winner would be the east, which overall stands on the top. This spatial distributive characteristic is closely related to China’s industrial structure, economic development and resource distribution features. For example, Shanxi and Inner Mongolia are important coal-producing bases where coal mines and resources are so crowded that they have weak low-carbon economy development. However, Guangdong and Fujian relatively have lack of natural resources so that their economy has reached a higher level, and the low-carbon economy develops well. Although Beijing is located in northern China and is very near the coal resource centers geographically, with coal making up such a large proportion in its energy structure, it has a well-developed economy; especially, the proportion of its tertiary industry is over 80%. Its industry is mainly based on modern manufacturing and tech industry; therefore, Beijing is under less pressure on low-carbon economy and It ranks 1st in low-carbon economy. However, industrial manufacturing still accounts for a definite proportion of the industrial structure in Shanghai; as a result, Shanghai is under more pressure on low-carbon economy than Beijing even though its economy is better.</p><p>In order to reveal the figures of low-carbon economy in different regions, we used Ward’s method to do cluster analysis on China’s 30 regions based on SPSS statistical software. For instance, the results of 2015 were shown in <xref rid=\"ijerph-17-05463-t008\" ref-type=\"table\">Table 8</xref>.</p><p>We found that the 30 regions can be divided into 4 categories. These 4 categories correspond respectively to the 4 statuses: leading status, good status, medium status and poor status. The evaluation results show that the numerical value varies greatly in different regions (<xref rid=\"ijerph-17-05463-t009\" ref-type=\"table\">Table 9</xref>). The leading status is almost all in the east area except Jiangxi, Guangxi and Hunan, which are included in the central area. Shanxi, Inner Mongolia and Ningxia fall into poor status.</p><p><xref ref-type=\"fig\" rid=\"ijerph-17-05463-f003\">Figure 3</xref> and <xref ref-type=\"fig\" rid=\"ijerph-17-05463-f004\">Figure 4</xref> compare the difference of average low-carbon economy level in different regions. They show that the numerical value of east regional low-carbon economy shows a pattern that is gradually higher than the west region. The numerical value of low carbon economic development in the south region is higher than the north region by degrees. Almost all parts of the west are low level. Ningxia is considered to have very low level. We find that the average level has shown a downward trend since 2010. This is due to the decline in economic growth and GDP. Growth rate was 10.3% in 2010 and 7.4% in 2015.</p></sec><sec id=\"sec7dot2-ijerph-17-05463\"><title>7.2. Analysis of DPSR Sub-Indexes</title><p>In view of the driving sub-index, state sub-index and pressure sub-index, there are some differences in all regions, and the leading status is significantly better than the poor status. As seen from the “Response” sub-index, the resource consumption and degree of pollution vary from province to province, as well as pollution abatement and efforts for environmental protection. All these factors reflect that the more efforts the government, system, society and individual make to improve the status of resources, the better the low-carbon economy develops.</p><p>The drive sub-index varies greatly from province to province. It shows a pattern with the indication of northeast gradually lower than southeast, which is determined by China’s terrains and regional development policies. From <xref ref-type=\"fig\" rid=\"ijerph-17-05463-f005\">Figure 5</xref>, we can see that the regions with high grades of drive sub-index value are all located in coastal areas. This is because these areas have significant regional advantages, which make the input cost of commerce activities and production activities much lower than in other regions. Therefore, their input and output efficiency in economic activities is relatively high. In addition to that, in the earlier stage of reform and opening, China has given special importance to the economic development in coastal areas by providing support in policies. Various regional preferential policies were introduced to form advantages for development; thus, their level of driving force on low-carbon economy is higher than the average level in China.</p><p>The situation of the pressure sub-index generally indicates the north being gradually lower than the south, the west gradually lower than the east, and hopping in some areas as shown in (<xref ref-type=\"fig\" rid=\"ijerph-17-05463-f006\">Figure 6</xref>), which is determined by various causes of development foundations, regional conditions and development policies in all regions. For quite a long time, the speed of economic development in China’s eastern coastal areas was much faster than in central and western areas. In addition, the coming years offer a critical period for western areas to accelerate industrialization; thus, the low-carbon pressure in east is relatively lower than in the west. We can conclude that the index value in the rule hierarchy of pressure has gradually decreasing effects around the main energy supplement region and the surrounding areas, so Shanxi, Inner Mongolia, Ningxia and Qinghai have the most pressure on low-carbon economy.</p><p>The situation of the state sub-index indicates the north being gradually lower than the south; therefore, the ranks of Hainan, Guangxi and Fujian are relatively higher, while Shaannxi, Hunan and Hubei are in the second tier (<xref ref-type=\"fig\" rid=\"ijerph-17-05463-f007\">Figure 7</xref>). This is because southeastern coastal areas have good economic development with external supply of energy, oil and natural gas. Hydroelectricity and nuclear electricity occupy a large proportion in the structure of energy consumption, so the low-carbon economy in these regions is in good status. However, although central and southern areas have certain coal resources, the consumption far outstrips the production. As a result, the external supply of energy is badly needed. At the same time, these areas that are rich in forest resources vigorously develop hydroelectricity and pursue rational use of energy, so the numerical value in these regions is better than it is in the north.</p><p>The situation of the response sub-index shows a pattern of hopping and overlapping in some areas. Beijing, Shanghai and Guangdong rank the first three in 2015, while in the north, northwest and southeast, the index values are gradually low (<xref ref-type=\"fig\" rid=\"ijerph-17-05463-f008\">Figure 8</xref>). This is due to the combined impact of the economic and scientific development level, energy consumption efficiency and industrial structure. According to the statistical yearbook, major consumption of energy in west regions is from coal, and there are more coal resources without desulfurization, which combines with low economic development and inadequate input in environment protection. As a result, the efficiency of energy process and conversion is low, and the productivity of carbon falls behind.</p></sec><sec id=\"sec7dot3-ijerph-17-05463\"><title>7.3. Analysis of the Degree of Coordination among the Four Sub-Indexes</title><p>As mentioned above, the horizontal comparisons of the coordination degrees are shown in <xref rid=\"ijerph-17-05463-t008\" ref-type=\"table\">Table 8</xref>; the coordination degrees of the 30 regions in China were calculated and analyzed and were based on the model of coordination degree in 2015.</p><p>The coupling degree (C) results show a pattern for the central area lower than the west area and the west area lower than the east area. The average degree of the whole country is 0.6723, which is higher than the west and central areas but lower than the east area. </p><p>The coordination degree (D) results show a pattern for the west area lower than the central area and the central area lower than the east area. The average degree of the whole country is 0.5840, which is higher than the west and central areas but lower than the east area.</p><p>Moreover, based on the model of coordination degree in 2015, the coordination degrees of the 30 regions in China were calculated and analyzed. </p><p>The results indicated the four sub-systems were primary coordination in the east area and bare coordination in the central and west areas in 2015.In addition, four sub-indexes should be developed at the same pace and improved on the basis of stable development. It shows that promoting the development of a low-carbon economy in mid-west areas is the key in China.</p></sec></sec><sec sec-type=\"conclusions\" id=\"sec8-ijerph-17-05463\"><title>8. Conclusions</title><p>Based on the above results, the following conclusions are drawn:</p><p>All 30 regions show varying increasing rates. The regional distributions of the sub-indexes are the combined impacts of regional economic development level, resource endowment and policy support. The numerical values in China’s 30 regions vary greatly; as a whole, they indicates the north is gradually lower than the southeastern coastal areas, and the east is higher than the west; the south is higher than the north, and there is overlapping in some areas. Beijing and Shanghai are far ahead while Shanxi, Inner Mongolia and Ningxia are backward.</p><p>As compared to the developed countries that have already achieved industrialization, China focuses particularly on achieving sustainable development by combining industrialization with ecological civilization. It is worth noting that China has large regional differences. Compared with the developed regions in the east, there are still a lot of heavy-duty industries in the central and western regions. Industrial structure transformation is more difficult. Therefore, promoting the development of low-carbon economy in mid-west areas is the key in China.</p><p>The improvement of people’s living quality and social welfare can best embody the development of low-carbon economy. The result of this study may be helpful for policy makers to formulate effective policies and take actions in the future.</p><p>We firmly believe that ecological civilization construction, industrialization and urbanization are not mutually exclusive at all. We must emphasize realizing sustainable development through the low-carbon system while people’s material life has benefited a lot. Therefore, we ought to change the bad social consumption mode, as well as wrong consuming concepts and patterns. Besides, we must make great efforts to turn the low-carbon way of life into a conscious behavior while improving our quality of life and stick to it in concrete activities.</p></sec></body><back><notes><title>Author Contributions</title><p>Conceptualization, W.P, M.A.G and W.H.; methodology, W.P and M.A.G.; formal analysis, W.P.; writing—original draft preparation, W.P.; writing—review and editing, M.A.G and W.H.; supervision, W.P and M.A.G. All authors have read and agreed to the published version of the manuscript.</p></notes><notes><title>Funding</title><p>This research was funded by the Fundamental Research Funds for the Central University (WUT: 2020VI011) and the National Natural Science Foundation of China (71772143).</p></notes><notes notes-type=\"COI-statement\"><title>Conflicts of Interest</title><p>Declare conflicts of interest or state “The authors declare no conflict of interest.”</p></notes><ref-list><title>References</title><ref id=\"B1-ijerph-17-05463\"><label>1.</label><element-citation publication-type=\"book\"><person-group person-group-type=\"author\"><collab>IPCC</collab></person-group><source>Climate Change 2014: Synthesis Report</source><publisher-name>IPCC</publisher-name><publisher-loc>Geneva, Switzerland</publisher-loc><year>2014</year><fpage>151</fpage></element-citation></ref><ref id=\"B2-ijerph-17-05463\"><label>2.</label><element-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Michalena</surname><given-names>E.</given-names></name><name><surname>Kouloumpis</surname><given-names>V.</given-names></name><name><surname>Hills</surname><given-names>J.M.</given-names></name></person-group><article-title>Challenges for Pacific Small Island Developing States in achieving their Nationally Determined Contributions (NDC)</article-title><source>Energy Policy</source><year>2018</year><volume>114</volume><fpage>508</fpage><lpage>518</lpage><pub-id pub-id-type=\"doi\">10.1016/j.enpol.2017.12.022</pub-id></element-citation></ref><ref id=\"B3-ijerph-17-05463\"><label>3.</label><element-citation publication-type=\"confproc\"><person-group person-group-type=\"author\"><collab>UNFCCC</collab></person-group><article-title>Adoption of the Paris Agreement</article-title><source>Proceedings of the Conference of the Parties, 21 session, Durban Platform for Enhanced Action</source><conf-loc>Paris, France</conf-loc><conf-date>20 November–11 December 2016</conf-date><comment>Available online: <ext-link ext-link-type=\"uri\" xlink:href=\"https://unfccc.int/resource/docs/2015/cop21/eng/l09r01.pdf\">https://unfccc.int/resource/docs/2015/cop21/eng/l09r01.pdf</ext-link></comment><date-in-citation content-type=\"access-date\" iso-8601-date=\"2020-05-20\">(accessed on 20 May 2020)</date-in-citation></element-citation></ref><ref id=\"B4-ijerph-17-05463\"><label>4.</label><element-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Boucher</surname><given-names>O.</given-names></name><name><surname>Bellassen</surname><given-names>V.</given-names></name><name><surname>Benveniste</surname><given-names>H.</given-names></name><name><surname>Ciais</surname><given-names>P.</given-names></name><name><surname>Criqui</surname><given-names>P.</given-names></name><name><surname>Guivarch</surname><given-names>C.</given-names></name><name><surname>Le Treut</surname><given-names>H.</given-names></name><name><surname>Mathy</surname><given-names>S.</given-names></name><name><surname>Séférian</surname><given-names>R.</given-names></name></person-group><article-title>In the wake of Paris agreement, scientists must embrace new directions for climate change research</article-title><source>Proc. 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id=\"ijerph-17-05463-f004\" orientation=\"portrait\" position=\"float\"><label>Figure 4</label><caption><p>Comparison of the average low-carbon economy level of the three areas.</p></caption><graphic xlink:href=\"ijerph-17-05463-g004\"/></fig><fig id=\"ijerph-17-05463-f005\" orientation=\"portrait\" position=\"float\"><label>Figure 5</label><caption><p>Map of drive sub-index in 2000 and 2015.</p></caption><graphic xlink:href=\"ijerph-17-05463-g005\"/></fig><fig id=\"ijerph-17-05463-f006\" orientation=\"portrait\" position=\"float\"><label>Figure 6</label><caption><p>Map of pressure sub-index in 2000 and 2015.</p></caption><graphic xlink:href=\"ijerph-17-05463-g006\"/></fig><fig id=\"ijerph-17-05463-f007\" orientation=\"portrait\" position=\"float\"><label>Figure 7</label><caption><p>Map of state sub-index in 2000 and 2015.</p></caption><graphic xlink:href=\"ijerph-17-05463-g007\"/></fig><fig id=\"ijerph-17-05463-f008\" orientation=\"portrait\" position=\"float\"><label>Figure 8</label><caption><p>Map of response sub-index in 2000 and 2015.</p></caption><graphic xlink:href=\"ijerph-17-05463-g008\"/></fig><table-wrap id=\"ijerph-17-05463-t001\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05463-t001_Table 1</object-id><label>Table 1</label><caption><p>Index system of low-carbon economy.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">First Grade</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Second Grade</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Weight</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Third Grade</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Weight</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Fourth Grade</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Weight</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Positive or Negative</th></tr></thead><tbody><tr><td rowspan=\"24\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Comprehensive level of low-carbon economy<break/>(A)</td><td rowspan=\"6\" align=\"center\" valign=\"middle\" colspan=\"1\">Driver (D)</td><td rowspan=\"6\" align=\"center\" valign=\"middle\" colspan=\"1\">0.1186</td><td rowspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Driver for social development (D1)</td><td rowspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">0.0593</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Natural Population Growth (D11, ‰)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.0272</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Moderate</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Urbanization Rate (D12, %)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.0247</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Moderate</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Engel’s coefficient on urban and rural households (D13, %)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.0075</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Negative</td></tr><tr><td rowspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Driver for economic development (D2)</td><td rowspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">0.0593</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">per capita GDP (D21, yuan per capita)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.0183</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Positive</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">GDP growth rate (D22, %)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.0065</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Positive</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">The average income of rural and urban family (D23, yuan per capita)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.0345</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Positive</td></tr><tr><td rowspan=\"5\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Pressure<break/>(P)</td><td rowspan=\"5\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">0.2162</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">resource pressure (P1)</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">0.0865</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">The energy consumption per unit of GDP (P11, tons of coal per 10,000 yuan)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.0605</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Negative</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Electricity consumption per capita (P12, kwh per capita)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.0259</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Negative</td></tr><tr><td rowspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">environmental pressure (P2)</td><td rowspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">0.1297</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">The industrial waste-gas discharge per unit of GDP (P21, cubic meter per 10,000 yuan)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.0741</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Negative</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">SO<sub>2</sub> emission per unit of GDP (P22, ton per 10,000 yuan)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.0371</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Negative</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Public transportations per 10,000 people (P23)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.0185</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Positive</td></tr><tr><td rowspan=\"6\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Status<break/>(S)</td><td rowspan=\"6\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">0.4141</td><td rowspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Status of low-carbon consumption (S1)</td><td rowspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">0.2071</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Carbon emissions per capita (S11, ton per capita)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.1305</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Negative</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">The carbon emissions of residents’ consumption (S12, ton per 10,000 yuan)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.0541</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Negative</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">The carbon emissions of government consumption (S13, ton per 10,000 yuan)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.0225</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Negative</td></tr><tr><td rowspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Status of low-carbon resources (S2)</td><td rowspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">0.2071</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Proportion of consumption of raw coal (S21, %)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.0941</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Negative</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Carbon emissions per unit of energy (S22, ton of CO<sub>2</sub> per ton of coal)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.0188</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Negative</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Forest coverage (S23, %)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.0941</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Positive</td></tr><tr><td rowspan=\"7\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Response<break/>(R)</td><td rowspan=\"7\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">0.2511</td><td rowspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Scientific Response (R1)</td><td rowspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">0.1758</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Expenditure on science and technology per capita (R11, yuan per capita)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.0189</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Positive</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">The efficiency of energy process and conversion (R12, %)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.0181</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Positive</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Carbon productivity (R13, 10,000 yuan per ton)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.1387</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Positive</td></tr><tr><td rowspan=\"4\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Policy Response (R2)</td><td rowspan=\"4\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">0.0753</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">The proportion the tertiary industry output value accounts for GDP (R21, %)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.0442</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Positive</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">The development plan of low-carbon economy (R22)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.0077</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Positive</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Policy of carbon tax (R23)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.0081</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Positive</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Supervision and statistics system of carbon emissions (R24)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.0154</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Positive</td></tr></tbody></table></table-wrap><table-wrap id=\"ijerph-17-05463-t002\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05463-t002_Table 2</object-id><label>Table 2</label><caption><p>Classification of the coupling coordinated degree.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Coordination Degree</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Coordination Level</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Coordination Degree</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Coordination Level</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.01 < CD2 ≤ 0.10</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Extreme disorder ≤</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.51 < CD2 ≤ 0.60</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Bare coordination</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.11 <CD2 ≤ 0.20</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Serious disorder</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.61 < CD2 ≤ 0.70</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Primary coordination</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.21 <CD2 ≤ 0.30</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Moderate disorder</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.71 < CD2 ≤0.80</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Intermediate coordination</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.31 <CD2 ≤ 0.40</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Mild disorder</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.81 < CD2 ≤0.90</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Favorable coordination</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.41 <CD2 ≤ 0.50</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">On the verge of disorder</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.91 < CD2 ≤ 0.10</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Quality coordination</td></tr></tbody></table></table-wrap><table-wrap id=\"ijerph-17-05463-t003\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05463-t003_Table 3</object-id><label>Table 3</label><caption><p>Coefficient of CO<sub>2</sub> emissions in China.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th rowspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">Energy Type</th><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">A</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">B</th><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">C = A × B × (44 / 12) × 1000</th><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">D = C × 4186.8 × 10−9 × 10−3</th><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">E</th><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">F = D × E</th></tr><tr><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">IPCC (2006)</th><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Oxidation Rate in the Combustion</th><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">IPCC (2006)</th><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">Original Coefficient</th><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">Calorific Value of Unit Fuel in China</th><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">Suggested Coefficient</th></tr><tr><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Carbon<break/>Emission<break/>Coefficient</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Measurement</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">CO<sub>2</sub> Emission Coefficient</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Measurement</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Original Coefficient</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Measurement</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Average Calorific Value of Unit Fuel in China</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Measurement</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Coefficient</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Measurement</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Crude coal</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">25.8</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">kgC/GJ</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">94,600</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">kgCO<sub>2</sub>/TJ</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.000396071</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">kgCO<sub>2</sub>/Kcal</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">5000</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Kcal/Kg</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.98</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">kgCO<sub>2</sub>/Kg</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Coke</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">29.2</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">kgC/GJ</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">107,066.67</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">kgCO<sub>2</sub>/TJ</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.000448267</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">kgCO<sub>2</sub>/Kcal</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">6800</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Kcal/Kg</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3.05</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">kgCO<sub>2</sub>/Kg</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Crude oil</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">20</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">kgC/GJ</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">73,333.33</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">kgCO<sub>2</sub>/TJ</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.000307032</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">kgCO<sub>2</sub>/Kcal</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">10,000</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Kcal/Kg</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3.07</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">kgCO<sub>2</sub>/Kg</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Gasoline</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">18.9</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">kgC/GJ</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">69,300</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">kgCO<sub>2</sub>/TJ</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.000290145</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">kgCO<sub>2</sub>/Kcal</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">10,300</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Kcal/Kg</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2.99</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">kgCO<sub>2</sub>/Kg</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Jet kerosene</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">19.6</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">kgC/GJ</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">71,866.67</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">kgCO<sub>2</sub>/TJ</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.000300891</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">kgCO<sub>2</sub>/Kcal</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">10,300</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Kcal/Kg</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3.10</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">kgCO<sub>2</sub>/Kg</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Diesel oil</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">20.2</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">kgC/GJ</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">74,066.67</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">kgCO<sub>2</sub>/TJ</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.000310102</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">kgCO<sub>2</sub>/Kcal</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">10,200</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Kcal/Kg</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3.16</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">kgCO<sub>2</sub>/Kg</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Fuel oil</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">21.1</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">kgC/GJ</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">77,366.67</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">kgCO<sub>2</sub>/TJ</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.000323919</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">kgCO<sub>2</sub>/Kcal</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">10,000</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Kcal/Kg</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3.24</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">kgCO<sub>2</sub>/Kg</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Refinery gas</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">16.8</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">kgC/GJ</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">61,600</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">kgCO<sub>2</sub>/TJ</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.000257907</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">kgCO<sub>2</sub>/Kcal</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">11,000</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Kcal/Kg</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2.84</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">kgCO<sub>2</sub>/Kg</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Natural gas</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">15.3</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">kgC/GJ</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">56,100</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">kgCO<sub>2</sub>/TJ</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.000234879</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">kgCO<sub>2</sub>/Kcal</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">9310</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">kgCO<sub>2</sub>/M3</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2.19</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">kgCO<sub>2</sub>/M3</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Liquefied petroleum gas</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">17.2</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">kgC/GJ</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">63,066.67</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">kgCO<sub>2</sub>/TJ</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.000264048</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">kgCO<sub>2</sub>/Kcal</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">12,000</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">kgCO<sub>2</sub>/M3</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3.17</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">kgCO<sub>2</sub>/M3</td></tr></tbody></table></table-wrap><table-wrap id=\"ijerph-17-05463-t004\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05463-t004_Table 4</object-id><label>Table 4</label><caption><p>Energy consumption and CO<sub>2</sub> emissions.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Year</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Crude Coal Consumption (104 Tons)</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Coke Consumption (104 Tons)</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Crude Oil Consumption (104 Tons)</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Gasoline Oil Consumption (104 Tons)</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Jet Kerosene Consumption (104 Tons)</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Diesel Oil Consumption (104 Tons)</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Fuel Oil Consumption (104 Tons)</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Natural Gas Consumption (104 Tons)</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">CO<sub>2</sub> Emissions</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1995</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">137,677</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10,725.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">14,886.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2910</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">512.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4321</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3693.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">177.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">49.46</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1996</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">144,734.46</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">11,865</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">15,865.01</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3182.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">555.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4825.14</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3632.31</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">185.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">51.56</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1997</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">139,248.26</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10,927</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">17,367.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3312</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">681.71</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5291.21</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3848.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">196.44</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">51.58</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1998</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">129,492.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">11,447.82</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">17,395.31</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3328.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">706.41</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5309.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3828.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">202.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">49.77</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1999</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">139,336.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10,460.52</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">18,949.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3389.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">824.21</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6231.63</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3934.11</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">214.94</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">52.58</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2000</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">135,690</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10,840.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">21,232</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3505</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">871.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6578.57</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3872.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">245</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">52.64</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2001</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">143,063</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">11,931.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">21,410.74</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3597.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">890.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6917.58</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3850.22</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">274.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">54.55</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2002</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">153,585</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">12,803.12</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">22,694.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3804.32</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">919.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7560.87</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3723.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">292</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">57.16</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2003</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">183,760</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">15,926.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">25,180.72</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4118.52</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">921.61</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8498.16</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4330.34</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">339.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">67.21</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2004</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">212,162</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">18,067.01</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">29,009.31</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4695.72</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1060.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10,072.94</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4844.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">397</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">77.04</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2005</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">243,375</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">25,105.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">30,088.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4855</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1076.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10,889.42</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4244.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">466.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">84.06</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2006</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">270,639</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">28,298</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">32,245.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5242.55</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1124.74</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">11,652.71</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4471.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">573.33</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">92.20</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2007</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">290,410</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">31,168.12</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">34,031.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5519.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1243.72</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">12,420.25</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4157.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">705.23</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">96.89</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2008</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">300,605</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">32,120.23</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">35,510.34</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6145.52</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1294.01</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13,475.46</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3236.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">813</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">97.20</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2009</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">325,003</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">36,350</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">38,128.59</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6172.69</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1450.49</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13,494.83</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2828.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">895.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">102.87</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2010</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">349,008</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">38,702.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">42,874.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6956</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1765.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">14,655.17</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3758</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1080.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">113.50</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2011</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">388,961</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">42,063.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">43,965.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7596</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1816.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">15,593.54</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3662.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1341.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">122.97</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2012</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">411,727</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">44,805.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">46,678.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8166</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1956.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">16,900.67</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3683.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1497</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">129.84</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2013</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">424,426</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">45,851.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">48,652.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">9366</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2164.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">17,106.75</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3954</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1705.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">134.65</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2014</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">411,613</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">46,884.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">51,547</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">9776</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2335.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">17,127.02</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4400.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1868.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">134.94</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2015</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">397,014</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">44,058.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">54,088.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">11,368</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2663.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">17,280.44</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4662</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1931.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">133.44</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2016</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">384,560</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">45,462.4</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">56,025.9</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">11,866</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2970.7</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">16,736.39</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">4631</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2078.1</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">131.97</td></tr></tbody></table></table-wrap><table-wrap id=\"ijerph-17-05463-t005\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05463-t005_Table 5</object-id><label>Table 5</label><caption><p>Pair-wise comparison matrix for A.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">A</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">D</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">P</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">S</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">R</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">D</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1/4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1/7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1/5</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">P</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1/4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1/2</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">S</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">R</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">5</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1/4</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1</td></tr></tbody></table></table-wrap><table-wrap id=\"ijerph-17-05463-t006\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05463-t006_Table 6</object-id><label>Table 6</label><caption><p>Low-carbon economy level in China.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Regions and Areas</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2000</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2003</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2007</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2010</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2015</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Average</th></tr></thead><tbody><tr><td rowspan=\"12\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">East area</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Beijing</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5882</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.6603</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.7980</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.7829</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.8634</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.7386</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Tianjin</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.3411</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.3505</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5329</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5340</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5367</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.4590</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Hebei</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.3783</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.3715</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.3581</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.3881</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.4660</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.3924</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Liaoning</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.4309</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.4731</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.4136</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.4341</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.4443</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.4392</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Shanghai</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5259</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5750</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.6332</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.6509</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5744</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5919</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Jiangsu</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.4942</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5364</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5451</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5581</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5613</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5390</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Zhejiang</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.6057</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.6564</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.6469</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.6721</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.6414</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.6445</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Fujian</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.6265</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.6241</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.6821</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.6832</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.6345</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.6501</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Shandong</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5299</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5250</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.4755</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.4623</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.4724</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.4930</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Guangdong</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.6256</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.6393</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.7191</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.7179</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.6618</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.6727</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Hainan</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.6299</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.6274</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.6862</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.6307</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5528</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.6254</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Average</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.5251</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.5490</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.5901</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.5922</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.5826</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.5678</td></tr><tr><td rowspan=\"11\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Central area</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Shanxi</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.2756</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.2484</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.2420</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.2315</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.2481</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.2491</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Inner Mongolia</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.3232</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.3058</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.2181</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.2437</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.2763</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.2734</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Jilin</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5001</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.4981</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.4843</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.4932</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5051</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.4962</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Heilongjiang</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5075</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5535</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5469</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5176</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5312</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5313</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Anhui</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.4491</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.4673</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.4833</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.4943</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5580</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.4904</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Jiangxi</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5635</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5855</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5913</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.6032</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5998</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5887</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Henan</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.4452</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.4812</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.4452</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.4623</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5291</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.4726</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Hubei</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.4957</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5213</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5254</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5597</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5741</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5352</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Hunan</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5963</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.6012</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5755</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.6115</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.6025</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5974</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Guangxi</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5446</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5641</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5897</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.6170</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5758</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5782</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Average</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.4701</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.4826</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.4702</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.4834</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.5000</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.4813</td></tr><tr><td rowspan=\"10\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">West area</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Chongqing</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5936</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.6194</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.4067</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5733</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5760</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5538</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Sichuan</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5045</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5237</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5419</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5956</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5657</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5463</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Guizhou</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.3203</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.3694</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.3351</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.4159</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5176</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.3917</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Yunnan</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.4982</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5059</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.4910</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5487</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5369</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5161</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Shaanxi</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5032</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5078</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5039</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.4795</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.4925</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.4974</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Gansu</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.3910</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.4013</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.4197</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.4389</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.4422</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.4186</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Qinghai</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.4259</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.4622</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.3845</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.4671</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.3813</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.4242</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Ningxia</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.2546</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.2366</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.1530</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.1505</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.1931</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.1976</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Xinjiang</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.4137</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.4530</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.3866</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.3323</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.2478</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.3667</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Average</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.4339</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.4533</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.4025</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.4447</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.4392</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.4347</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Whole country</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Average</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.4791</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.4979</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.4933</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.5112</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.5073</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.4978</td></tr></tbody></table></table-wrap><table-wrap id=\"ijerph-17-05463-t007\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05463-t007_Table 7</object-id><label>Table 7</label><caption><p>The cluster results.</p></caption><table frame=\"hsides\" rules=\"groups\"><tbody><tr><td align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Category one (Leading status)</td><td align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Beijing, Guangdong, Hainan, Fujian, Zhejiang, Shanghai, Jiangxi, Guangxi, Hunan</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Category two (Good status)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Heilongjiang, Sichuan, Jiangsu, Tianjin, Hubei, Shaanxi, Yunnan, Jilin, Anhui, Shandong</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Category three (Medium status)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Chongqing, Henan, Gansu, Liaoning, Xinjiang, Qinghai, Hebei, Guizhou</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Category four (Poor status)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Shanxi, Inner Mongolia, Ningxia</td></tr></tbody></table></table-wrap><table-wrap id=\"ijerph-17-05463-t008\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05463-t008_Table 8</object-id><label>Table 8</label><caption><p>The regional comparisons in 2015.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Regions and Areas</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">d(x)</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">p(y)</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">s(z)</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">p(k)</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">C</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">T</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">D</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Coordination Level</th></tr></thead><tbody><tr><td rowspan=\"12\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">East area</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Beijing</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0897</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.2108</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.3488</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.2142</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.8980</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.8634</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.8805</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Favorable coordination</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Tianjin</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0696</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.1959</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.2401</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0311</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.7486</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5367</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.6338</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Primary coordination</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Hebei</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0468</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.1429</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.2593</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0169</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.6314</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.4660</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5424</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Bare coordination</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Liaoning</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0411</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.1494</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.2279</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0259</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.6984</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.4443</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5570</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Bare coordination</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Shanghai</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0847</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.1795</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.2541</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0562</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.8451</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5744</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.6968</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Primary coordination</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Jiangsu</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0668</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.1920</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.2719</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0306</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.7241</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5613</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.6375</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Primary coordination</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Zhejiang</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0777</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.1838</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.3493</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0306</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.6933</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.6414</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.6668</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Primary coordination</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Fujian</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0696</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.1850</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.3619</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0181</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.6038</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.6345</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.6189</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Primary coordination</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Shandong</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0589</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.1756</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.2203</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0176</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.6740</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.4724</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5643</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Bare coordination</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Guangdong</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0820</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.1906</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.3698</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0194</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.6219</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.6618</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.6415</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Primary coordination</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Hainan</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0346</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.1789</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.3071</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0322</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.6402</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5528</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5949</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Bare coordination</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Average</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.0656</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.1804</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.2918</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.0448</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.7656</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.5826</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.6679</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Primary coordination</td></tr><tr><td rowspan=\"11\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Central area</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Shanxi</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0427</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0841</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0906</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0307</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.9064</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.2481</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.4742</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">On the verge of disorder</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Inner Mongolia</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0566</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0990</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.1145</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0061</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.6442</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.2763</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.4219</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">On the verge of disorder</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Jilin</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0313</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.1632</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.2972</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0134</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5316</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5051</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5182</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Bare coordination</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Heilongjiang</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0293</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.1830</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.2925</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0264</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.6043</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5312</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5666</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Bare coordination</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Anhui</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0450</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.1733</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.3249</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0148</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5606</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5580</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5593</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Bare coordination</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Jiangxi</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0389</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.1838</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.3677</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0095</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.4707</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5998</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5314</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Bare coordination</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Henan</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0381</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.1792</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.2958</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0160</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5700</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5291</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5491</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Bare coordination</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Hubei</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0485</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.1777</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.3334</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0145</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5601</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5741</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5670</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Bare coordination</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Hunan</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0491</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.1802</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.3542</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0190</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5835</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.6025</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5929</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Bare coordination</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Guangxi</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0350</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.1713</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.3633</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0062</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.4215</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5758</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.4926</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">On the verge of disorder</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Average</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.0414</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.1595</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.2834</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.0157</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.5888</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.5000</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.5426</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Bare coordination</td></tr><tr><td rowspan=\"10\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">West area</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Chongqing</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0479</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.1764</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.3335</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0181</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5873</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5760</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5816</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Bare coordination</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Sichuan</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0293</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.1807</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.3409</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0148</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5081</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5657</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5361</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Bare coordination</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Guizhou</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0357</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.1626</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.2938</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0255</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.6276</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5176</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5700</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Bare coordination</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Yunnan</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0353</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.1332</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.3596</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0088</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.4620</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5369</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.4980</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">On the verge of disorder</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Shaanxi</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0391</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.1683</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.2649</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0201</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.6254</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.4925</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5550</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Bare coordination</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Gansu</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0355</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.1268</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.2547</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0251</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.6628</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.4422</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5414</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Bare coordination</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Qinghai</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0344</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0809</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.2607</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0052</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.4634</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.3813</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.4203</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">On the verge of disorder</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Ningxia</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0371</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0453</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0951</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0157</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.8236</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.1931</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.3988</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Mild disorder</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Xinjiang</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0239</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0725</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.1354</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0160</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.7105</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.2478</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.4196</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">On the verge of disorder</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Average</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.0354</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.1274</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.2599</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.0166</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.6046</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.4392</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.5153</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Bare coordination</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Whole country</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Average</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.0475</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.1558</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.2784</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.0257</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.6723</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.5073</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.5840</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Bare coordination</td></tr></tbody></table></table-wrap><table-wrap id=\"ijerph-17-05463-t009\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05463-t009_Table 9</object-id><label>Table 9</label><caption><p>The regional comparisons in 2015.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Regions and Areas</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">d(x)</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">p(y)</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">s(z)</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">p(k)</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">C</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">T</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">D</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Coordination Level</th></tr></thead><tbody><tr><td rowspan=\"12\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">East area</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Beijing</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0897</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.2108</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.3488</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.2142</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.8980</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.8634</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.8805</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Favorable coordination</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Tianjin</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0696</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.1959</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.2401</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0311</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.7486</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5367</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.6338</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Primary coordination</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Hebei</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0468</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.1429</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.2593</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0169</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.6314</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.4660</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5424</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Bare coordination</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Liaoning</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0411</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.1494</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.2279</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0259</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.6984</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.4443</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5570</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Bare coordination</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Shanghai</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0847</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.1795</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.2541</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0562</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.8451</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5744</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.6968</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Primary coordination</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Jiangsu</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0668</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.1920</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.2719</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0306</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.7241</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5613</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.6375</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Primary coordination</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Zhejiang</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0777</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.1838</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.3493</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0306</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.6933</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.6414</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.6668</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Primary coordination</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Fujian</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0696</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.1850</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.3619</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0181</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.6038</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.6345</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.6189</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Primary coordination</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Shandong</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0589</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.1756</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.2203</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0176</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.6740</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.4724</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5643</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Bare coordination</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Guangdong</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0820</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.1906</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.3698</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0194</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.6219</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.6618</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.6415</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Primary coordination</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Hainan</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0346</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.1789</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.3071</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0322</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.6402</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5528</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5949</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Bare coordination</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Average</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.0656</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.1804</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.2918</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.0448</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.7656</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.5826</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.6679</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Primary coordination</td></tr><tr><td rowspan=\"11\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Central area</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Shanxi</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0427</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0841</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0906</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0307</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.9064</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.2481</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.4742</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">On the verge of disorder</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Inner Mongolia</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0566</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0990</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.1145</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0061</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.6442</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.2763</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.4219</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">On the verge of disorder</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Jilin</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0313</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.1632</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.2972</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0134</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5316</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5051</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5182</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Bare coordination</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Heilongjiang</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0293</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.1830</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.2925</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0264</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.6043</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5312</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5666</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Bare coordination</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Anhui</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0450</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.1733</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.3249</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0148</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5606</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5580</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5593</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Bare coordination</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Jiangxi</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0389</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.1838</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.3677</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0095</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.4707</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5998</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5314</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Bare coordination</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Henan</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0381</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.1792</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.2958</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0160</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5700</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5291</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5491</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Bare coordination</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Hubei</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0485</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.1777</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.3334</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0145</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5601</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5741</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5670</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Bare coordination</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Hunan</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0491</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.1802</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.3542</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0190</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5835</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.6025</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5929</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Bare coordination</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Guangxi</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0350</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.1713</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.3633</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0062</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.4215</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5758</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.4926</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">On the verge of disorder</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Average</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.0414</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.1595</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.2834</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.0157</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.5888</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.5000</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.5426</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Bare coordination</td></tr><tr><td rowspan=\"10\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">West area</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Chongqing</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0479</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.1764</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.3335</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0181</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5873</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5760</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5816</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Bare coordination</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Sichuan</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0293</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.1807</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.3409</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0148</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5081</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5657</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5361</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Bare coordination</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Guizhou</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0357</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.1626</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.2938</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0255</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.6276</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5176</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5700</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Bare coordination</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Yunnan</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0353</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.1332</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.3596</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0088</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.4620</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5369</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.4980</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">On the verge of disorder</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Shaanxi</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0391</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.1683</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.2649</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0201</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.6254</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.4925</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5550</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Bare coordination</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Gansu</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0355</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.1268</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.2547</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0251</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.6628</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.4422</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5414</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Bare coordination</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Qinghai</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0344</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0809</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.2607</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0052</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.4634</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.3813</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.4203</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">On the verge of disorder</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Ningxia</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0371</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0453</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0951</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0157</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.8236</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.1931</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.3988</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Mild disorder</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Xinjiang</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0239</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0725</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.1354</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0160</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.7105</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.2478</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.4196</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">On the verge of disorder</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Average</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.0354</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.1274</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.2599</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.0166</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.6046</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.4392</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.5153</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Bare coordination</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Whole country</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Average</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.0475</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.1558</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.2784</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.0257</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.6723</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.5073</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.5840</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Bare coordination</td></tr></tbody></table></table-wrap></floats-group></article>\n"
]
[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Int J Mol Sci</journal-id><journal-id journal-id-type=\"iso-abbrev\">Int J Mol Sci</journal-id><journal-id journal-id-type=\"publisher-id\">ijms</journal-id><journal-title-group><journal-title>International Journal of Molecular Sciences</journal-title></journal-title-group><issn pub-type=\"epub\">1422-0067</issn><publisher><publisher-name>MDPI</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32759830</article-id><article-id pub-id-type=\"pmc\">PMC7432113</article-id><article-id pub-id-type=\"doi\">10.3390/ijms21155571</article-id><article-id pub-id-type=\"publisher-id\">ijms-21-05571</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Article</subject></subj-group></article-categories><title-group><article-title>Anti-Angiogenic and Anti-Proliferative Graphene Oxide Nanosheets for Tumor Cell Therapy</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Verde</surname><given-names>Valeria</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05571\">1</xref><xref ref-type=\"author-notes\" rid=\"fn1-ijms-21-05571\">†</xref></contrib><contrib contrib-type=\"author\"><name><surname>Longo</surname><given-names>Anna</given-names></name><xref ref-type=\"aff\" rid=\"af2-ijms-21-05571\">2</xref><xref ref-type=\"author-notes\" rid=\"fn1-ijms-21-05571\">†</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0001-6127-5962</contrib-id><name><surname>Cucci</surname><given-names>Lorena Maria</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05571\">1</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0002-4269-187X</contrib-id><name><surname>Sanfilippo</surname><given-names>Vanessa</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05571\">1</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0003-1556-934X</contrib-id><name><surname>Magrì</surname><given-names>Antonio</given-names></name><xref ref-type=\"aff\" rid=\"af3-ijms-21-05571\">3</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0001-5348-5863</contrib-id><name><surname>Satriano</surname><given-names>Cristina</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05571\">1</xref><xref rid=\"c1-ijms-21-05571\" ref-type=\"corresp\">*</xref></contrib><contrib contrib-type=\"author\"><name><surname>Anfuso</surname><given-names>Carmelina Daniela</given-names></name><xref ref-type=\"aff\" rid=\"af2-ijms-21-05571\">2</xref></contrib><contrib contrib-type=\"author\"><name><surname>Lupo</surname><given-names>Gabriella</given-names></name><xref ref-type=\"aff\" rid=\"af2-ijms-21-05571\">2</xref><xref rid=\"c1-ijms-21-05571\" ref-type=\"corresp\">*</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0002-4193-7612</contrib-id><name><surname>La Mendola</surname><given-names>Diego</given-names></name><xref ref-type=\"aff\" rid=\"af4-ijms-21-05571\">4</xref></contrib></contrib-group><aff id=\"af1-ijms-21-05571\"><label>1</label>Nano Hybrid BioInterfaces Lab (NHBIL), Department of Chemical Sciences, University of Catania, Viale Andrea Doria 6, 95125 Catania Italy; <email>[email protected]</email> (V.V.); <email>[email protected]</email> (L.M.C.); <email>[email protected]</email> (V.S.)</aff><aff id=\"af2-ijms-21-05571\"><label>2</label>Department of Biomedical and Biotechnological Sciences, University of Catania, Via Santa Sofia 97, 95123 Catania, Italy; <email>[email protected]</email> (A.L.); <email>[email protected]</email> (C.D.A.)</aff><aff id=\"af3-ijms-21-05571\"><label>3</label>Institute of Crystallography—National Council of Research, via Paolo Gaifami 18, 95126 Catania, Italy; <email>[email protected]</email></aff><aff id=\"af4-ijms-21-05571\"><label>4</label>Department of Pharmacy, University of Pisa, via Bonanno Pisano 6, 56126 Pisa, Italy; <email>[email protected]</email></aff><author-notes><corresp id=\"c1-ijms-21-05571\"><label>*</label>Correspondence: <email>[email protected]</email> (C.S.); <email>[email protected]</email> (G.L.); Tel.: +39-095-7385136 (C.S.); +39-095-4781158 (G.L.)</corresp><fn id=\"fn1-ijms-21-05571\"><label>†</label><p>These authors contributed equally to this work.</p></fn></author-notes><pub-date pub-type=\"epub\"><day>04</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><month>8</month><year>2020</year></pub-date><volume>21</volume><issue>15</issue><elocation-id>5571</elocation-id><history><date date-type=\"received\"><day>21</day><month>7</month><year>2020</year></date><date date-type=\"accepted\"><day>01</day><month>8</month><year>2020</year></date></history><permissions><copyright-statement>© 2020 by the authors.</copyright-statement><copyright-year>2020</copyright-year><license license-type=\"open-access\"><license-p>Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>).</license-p></license></permissions><abstract><p>Graphene oxide (GO) is a bidimensional novel material that exhibits high biocompatibility and angiogenic properties, mostly related to the intracellular formation of reactive oxygen species (ROS). In this work, we set up an experimental methodology for the fabrication of GO@peptide hybrids by the immobilization, via irreversible physical adsorption, of the Ac-(GHHPH)<sub>4</sub>-NH<sub>2</sub> peptide sequence, known to mimic the anti-angiogenic domain of the histidine-proline-rich glycoprotein (HPRG). The anti-proliferative capability of the graphene-peptide hybrids were tested in vitro by viability assays on prostate cancer cells (PC-3 line), human neuroblastoma (SH-SY5Y), and human retinal endothelial cells (primary HREC). The anti-angiogenic response of the two cellular models of angiogenesis, namely endothelial and prostate cancer cells, was scrutinized by prostaglandin E2 (PGE<sub>2</sub>) release and wound scratch assays, to correlate the activation of inflammatory response upon the cell treatments with the GO@peptide nanocomposites to the cell migration processes. Results showed that the GO@peptide nanoassemblies not only effectively induced toxicity in the prostate cancer cells, but also strongly blocked the cell migration and inhibited the prostaglandin-mediated inflammatory process both in PC-3 and in HRECs. Moreover, the cytotoxic mechanism and the internalization efficiency of the theranostic nanoplatforms, investigated by mitochondrial ROS production analyses and confocal microscopy imaging, unraveled a dose-dependent manifold mechanism of action performed by the hybrid nanoassemblies against the PC-3 cells, with the detection of the GO-characteristic cell wrapping and mitochondrial perturbation. The obtained results pointed out to the very promising potential of the synthetized graphene-based hybrids for cancer therapy.</p></abstract><kwd-group><kwd>theranostics</kwd><kwd>nanomaterials</kwd><kwd>nanomedicine</kwd><kwd>HPRG</kwd><kwd>peptides</kwd><kwd>cell migration</kwd><kwd>prostaglandins</kwd><kwd>human prostate cancer cells</kwd><kwd>ROS</kwd><kwd>confocal microscopy</kwd></kwd-group></article-meta></front><body><sec sec-type=\"intro\" id=\"sec1-ijms-21-05571\"><title>1. Introduction</title><p>Graphene is a 2-dimensional (2D) carbon nanomaterial consisting of planar sheets of sp<sup>2</sup>-hybridized carbon atoms arranged in a honeycomb lattice [<xref rid=\"B1-ijms-21-05571\" ref-type=\"bibr\">1</xref>] and due to this monoatomic layer structure is the thinnest material produced so far [<xref rid=\"B2-ijms-21-05571\" ref-type=\"bibr\">2</xref>]. Over the past 15 years, graphene has attracted the attention of the whole scientific community thanks to its unique mechanical and electrochemical properties, which include high density, chemical inertness, optical transmittance, very high hydrophobicity and molecular barrier abilities [<xref rid=\"B3-ijms-21-05571\" ref-type=\"bibr\">3</xref>,<xref rid=\"B4-ijms-21-05571\" ref-type=\"bibr\">4</xref>,<xref rid=\"B5-ijms-21-05571\" ref-type=\"bibr\">5</xref>]. It is one of the most resistant materials ever tested with tensile strengths greater than 100 GPa and a traction module of 1 TPa [<xref rid=\"B6-ijms-21-05571\" ref-type=\"bibr\">6</xref>]. Other remarkable features include the high planar surface (calculated value, 2630 m<sup>2</sup>/g [<xref rid=\"B7-ijms-21-05571\" ref-type=\"bibr\">7</xref>], which allows for its higher drug loading capacity than other nanomaterials [<xref rid=\"B8-ijms-21-05571\" ref-type=\"bibr\">8</xref>] and the thermal conductivity (5000 W/mK) [<xref rid=\"B9-ijms-21-05571\" ref-type=\"bibr\">9</xref>]. Such properties make graphene particularly promising for applications in several fields, like the aerospace, electronics, energy, mechanical, environmental, and food industries as well as in biomedicine [<xref rid=\"B10-ijms-21-05571\" ref-type=\"bibr\">10</xref>]. However, the poor solubility and agglomeration of the nanosheets in solution, caused by van der Waals forces and π–π stacking interactions, greatly limits its uses, makes it difficult to produce and significantly impacts its toxicity [<xref rid=\"B11-ijms-21-05571\" ref-type=\"bibr\">11</xref>,<xref rid=\"B12-ijms-21-05571\" ref-type=\"bibr\">12</xref>]. The oxygen-functionalized graphene derivative, namely graphene oxide (GO), maintaining a similar hexagonal carbon structure to graphene, overcomes these limits [<xref rid=\"B13-ijms-21-05571\" ref-type=\"bibr\">13</xref>]. GO, indeed, shows a high density of functional oxygen groups namely carboxyl (–COOH) and hydroxyl (C–OH) groups, typically located at the edges of the sheets, and carbonyl (C=O) and epoxy groups (C–O–C) on the basal plane of the graphene sheets [<xref rid=\"B14-ijms-21-05571\" ref-type=\"bibr\">14</xref>].</p><p>The unique chemical and physical properties of GO and its reduced derivative (rGO) have aroused a strong interest especially for biological studies [<xref rid=\"B15-ijms-21-05571\" ref-type=\"bibr\">15</xref>], since the presence of defective oxygen-bound sp<sup>3</sup> carbon atoms induces a strong hydrophilicity and contributes to the formation of dispersions highly stable colloidal in aqueous solvents, preventing the uncontrolled aggregation of the nanosheets caused by van der Waals and the hydrophobic interactions [<xref rid=\"B16-ijms-21-05571\" ref-type=\"bibr\">16</xref>]. Moreover, the presence of hydrophilic functional groups on the GO surface offers high versatility for the derivatization of nanosheets and makes GO a suitable platform for the development of drug delivery systems [<xref rid=\"B17-ijms-21-05571\" ref-type=\"bibr\">17</xref>] with potential application in tissue engineering [<xref rid=\"B17-ijms-21-05571\" ref-type=\"bibr\">17</xref>], biosensing [<xref rid=\"B18-ijms-21-05571\" ref-type=\"bibr\">18</xref>], and bioimaging [<xref rid=\"B19-ijms-21-05571\" ref-type=\"bibr\">19</xref>], thus opening new horizons in the field of nanomedicine. In particular, graphene oxide based targeted drug carriers are becoming of great interest in the treatment of cancer diseases [<xref rid=\"B20-ijms-21-05571\" ref-type=\"bibr\">20</xref>]. To this respect biological therapies, based on the delivery of biomolecules and gene therapy as well as phototherapies against cancer, including photodynamic and photothermal treatments, which use GO as a carrier, have showed high biocompatibility and good results both in <italic>in vitro</italic> and <italic>in vivo</italic> studies [<xref rid=\"B21-ijms-21-05571\" ref-type=\"bibr\">21</xref>]. Thus, Liu et al. formulated transferrin modified graphene oxide for glioma-targeted drug delivery [<xref rid=\"B22-ijms-21-05571\" ref-type=\"bibr\">22</xref>], Li et al. used functionalized nano-graphene oxide particles for targeted fluorescence imaging and photothermic therapy of glioma U251 cells [<xref rid=\"B23-ijms-21-05571\" ref-type=\"bibr\">23</xref>], while Song and colleagues evaluated hyaluronic acid-decorated graphene oxide nanohybrids as carriers for targeted and pH-responsive anticancer therapy [<xref rid=\"B24-ijms-21-05571\" ref-type=\"bibr\">24</xref>]. Furthermore, GO shows intrinsic biological properties, including antimicrobial activity [<xref rid=\"B25-ijms-21-05571\" ref-type=\"bibr\">25</xref>] and the capability to control the function of immune cells [<xref rid=\"B26-ijms-21-05571\" ref-type=\"bibr\">26</xref>] and to modulate angiogenesis. This latter feature provides additional advantages in cancer therapy, since formation of new blood vessels is involved in both tumor growth and development of metastases [<xref rid=\"B21-ijms-21-05571\" ref-type=\"bibr\">21</xref>,<xref rid=\"B27-ijms-21-05571\" ref-type=\"bibr\">27</xref>]. Hence, the anti-angiogenic action of GO can be very effective to fight cancer. To note, there are plenty of examples in the literature on the development of GO and modified GO platforms for anti-cancer therapy [<xref rid=\"B28-ijms-21-05571\" ref-type=\"bibr\">28</xref>,<xref rid=\"B29-ijms-21-05571\" ref-type=\"bibr\">29</xref>,<xref rid=\"B30-ijms-21-05571\" ref-type=\"bibr\">30</xref>,<xref rid=\"B31-ijms-21-05571\" ref-type=\"bibr\">31</xref>].</p><p>It has been demonstrated that GO sheets present pro-angiogenic properties at low doses (1–50 ng/mL), due to the controlled production of intracellular reactive oxygen species (ROS) (H<sub>2</sub>O<sub>2</sub> and O<sub>2</sub><sup>•−</sup>) induced by this material, while show anti-angiogenic features at high doses (≥100 ng/mL), attributed to the excessive generation of intracellular ROS [<xref rid=\"B32-ijms-21-05571\" ref-type=\"bibr\">32</xref>]. In general, the mechanisms underlying GO toxicity in addition to oxidative stress and excessive ROS production also include DNA damage, apoptosis, autophagy, and immune responses, which widely varied in relation to the physical-chemical properties of GO, such as surface chemistry, layer number, lateral dimension, and purity [<xref rid=\"B33-ijms-21-05571\" ref-type=\"bibr\">33</xref>].</p><p>The histidine-proline-rich glycoprotein (HPRG) is a single polypeptide chain protein of 70–75 kDa, with a multidomain structure. In humans, the protein is synthesized in the liver and is present in plasma at relatively high concentrations of 100–150 µg/mL (1.5 µM) [<xref rid=\"B34-ijms-21-05571\" ref-type=\"bibr\">34</xref>,<xref rid=\"B35-ijms-21-05571\" ref-type=\"bibr\">35</xref>]. HPRG ability to simultaneously interact with several ligands suggests that it may act as an adapter molecule which regulates numerous biological processes, including blood coagulation and fibrinolysis, adhesion, and cell migration, as well as anti-/pro-angiogenic activity [<xref rid=\"B36-ijms-21-05571\" ref-type=\"bibr\">36</xref>].</p><p>Indeed, the HPRG protein promotes angiogenesis by inhibiting the activity of the antiangiogenic thrombospondin-1 (TPS-1) [<xref rid=\"B37-ijms-21-05571\" ref-type=\"bibr\">37</xref>], by binding to plasminogen/plasmin onto the surface of endothelial cells as well as by promoting cell migration and invasion [<xref rid=\"B38-ijms-21-05571\" ref-type=\"bibr\">38</xref>], which are critical phases of the new blood vessels formation. On the other hand, HPRG has also a demonstrated antiangiogenic activity, mainly localized in its histidine-proline-rich domain (H/P) and occurring by the blocking of the interaction between fibroblast growth factor (FGF-1 and FGF-2) and heparan sulphate in the extracellular matrix (ECM) and the surface of endothelial cells [<xref rid=\"B39-ijms-21-05571\" ref-type=\"bibr\">39</xref>].</p><p>The Ac–(GHHPH)<sub>4</sub>–NH<sub>2</sub> peptide has been shown to be an active HPRG mimic system, and it has been demonstrated effective as antitumor agent in two syngeneic cancer models, namely Lewis lung cancer (3LL) and melanoma (B16F1) growth in mice [<xref rid=\"B40-ijms-21-05571\" ref-type=\"bibr\">40</xref>,<xref rid=\"B41-ijms-21-05571\" ref-type=\"bibr\">41</xref>].</p><p>Based on these premises, in this work GO was functionalized with a with the Ac–(GHHPH)<sub>4</sub>GK–NH<sub>2</sub> peptide [<xref rid=\"B41-ijms-21-05571\" ref-type=\"bibr\">41</xref>] covalently bound to a 5,6-carboxyfluorescein (Fam) moiety, hereinafter named Tetra(HPRG)-Fam. The integration of the therapeutic potential from both GO and the Tetra(HPRG) peptide and the imaging capability through the fluorescence of the dye-labelled peptide makes the hybrid graphene oxide-Tetra(HPRG)Fam system (hereinafter named GO@T) a potential theranostic platform. The physicochemical characterization was carried out by mean of spectroscopic analyses of UV-visible, fluorescence and ATR-FTIR, to scrutinize the hybrid biointerface between the nanosheets and the peptide molecules in terms of electron transfer processes and average peptide molecular structural conformation. In vitro cellular experiments were carried out on human neuroblastoma (SH-SY5Y) and prostate cancer (PC-3) cell lines, as cellular tumor models to test the anti-angiogenic potential of our platforms in anti-cancer therapy. Both cell lines are aggressively-growing tumors, associated also to uncontrolled tumor vascularization [<xref rid=\"B42-ijms-21-05571\" ref-type=\"bibr\">42</xref>]. Moreover, PC-3 can form vessel-like structures through a process denoted as vascular mimicry [<xref rid=\"B43-ijms-21-05571\" ref-type=\"bibr\">43</xref>]. As endothelial cellular model of angiogenesis, we tested also our nanoformulation on primary human retinal endothelial cells (HREC) [<xref rid=\"B44-ijms-21-05571\" ref-type=\"bibr\">44</xref>,<xref rid=\"B45-ijms-21-05571\" ref-type=\"bibr\">45</xref>].</p><p>Specifically, the nanotoxicity and the pro-or anti-proliferative activity were investigated for GO@T hybrids compared to bare GO sheets and to the fragment peptide. Also, the nanocomposites effect on mitochondrial dysfunctions were scrutinized by measuring the mitochondrial production of O<sub>2</sub><sup>•−</sup> as well as their effects on cellular migration and prostaglandin E2 (PGE<sub>2</sub>) release. Intracellular imaging using confocal laser scanning microscopy (LSM) was performed to evaluate the mechanism of interaction and internalization of hybrid systems on the studied cancer cell line.</p></sec><sec sec-type=\"results\" id=\"sec2-ijms-21-05571\"><title>2. Results</title><p>UV-visible, circular dichroism (CD), fluorescence and attenuated total reflectance Fourier transform infrared (ATR/FTIR) spectroscopies were used to characterize the hybrid graphene oxide nanosheets functionalized with the Tetra(HPRG)-Fam peptide (samples thereafter named GO@T). Two weight ratios for the peptide/GO mixtures were used for the preparation of the samples, namely, two concentrations of graphene oxide, i.e., 1 mg/mL (GO_A) or 0.5 mg/mL (GO_B) incubated for 2 h under stirring with a fixed concentration of peptide (0.2 mM).</p><p><xref ref-type=\"fig\" rid=\"ijms-21-05571-f001\">Figure 1</xref> displays the absorption spectra of reference samples of the GO nanosheets and of the Tetra(HPRG)-Fam peptide, both before and after the mixing (<xref ref-type=\"fig\" rid=\"ijms-21-05571-f001\">Figure 1</xref>a) as well as for the corresponding pellets and supernatants recovered after the centrifugation and washing step (<xref ref-type=\"fig\" rid=\"ijms-21-05571-f001\">Figure 1</xref>b), to remove the loosely bound peptide molecules from the GO platforms.</p><p><xref ref-type=\"fig\" rid=\"ijms-21-05571-f001\">Figure 1</xref>a shows, for the Tetra(HPRG)-Fam sample, the characteristic <italic>π</italic> → <italic>π</italic>* transition absorption band of the peptide bond in the 180–230 nm range as well as the absorption of the aromatic side-chains of His residues, in the range of 230–300 nm [<xref rid=\"B46-ijms-21-05571\" ref-type=\"bibr\">46</xref>]. Moreover, related to the chromophore activity of the Fam moiety [<xref rid=\"B47-ijms-21-05571\" ref-type=\"bibr\">47</xref>,<xref rid=\"B48-ijms-21-05571\" ref-type=\"bibr\">48</xref>], two weak bands, respectively at 280 nm and in the 350–440 nm region, and a main absorption peak at 497 nm are evident. As to the GO nanosheets, the UV-visible spectra validate the approximation of using the Lambert-Beer law of solutions for these colloidal dispersions. Indeed, both GO_A and GO_B samples exhibit the same spectral features just scaled in a 1 to 0.5 ratio; namely, one absorption peak at 238 nm and a shoulder at around 300 nm, due to the π → π* and n → π* transitions, respectively [<xref rid=\"B49-ijms-21-05571\" ref-type=\"bibr\">49</xref>]. The spectra of the hybrid GO@T systems display both hypochromic and bathochromic/hypsochromic shifts compared to the reference GO and Tetra(HPRG)-Fam systems, respectively. Such findings point to the occurrence of electron transfer processes at the interface established between the GO nanosheets and the dye covalently linked to the peptide. To note, the spectra of the mixture systems are mostly given by the sum of the spectra of each component, the GO and the Tetra(HPRG)-Fam, with the GO absorption bands buried by the background signal of the Fam moiety in the UV region. Also, only for GO_B+T the Fam absorption peak being significantly decreased, due to the quenching effect of GO, but with no evidence of shift in the wavelength at the maximum of absorption. These findings point to a lower level of interaction between the GO sheets and the peptide molecules in the mixture than in the hybrid nanocomposite.</p><p>After the centrifugation and washing steps (<xref ref-type=\"fig\" rid=\"ijms-21-05571-f001\">Figure 1</xref>b), the spectra of bare GO demonstrate a different relative ratio of sp<sup>3</sup> (oxidized defective) to sp<sup>2</sup> (graphitic-like) carbon than the pristine GO, as disclosed both by the hypsochromic shift (~6 nm) of the π → π* peak and by the slight hyperchromic shift in the shoulder related to the n → π* transitions, respectively. In particular, the heavier (and larger) GO nanosheets, which accumulate preferentially in the pellet, exhibit a relative ratio of the sp<sup>2</sup> to sp<sup>3</sup> domains higher than the lighter (and smaller) nanosheets that mostly gathered into the supernatant, and hence have more defective oxygen-containing groups, especially at the edges [<xref rid=\"B50-ijms-21-05571\" ref-type=\"bibr\">50</xref>,<xref rid=\"B51-ijms-21-05571\" ref-type=\"bibr\">51</xref>]. To note, an additional contribution to these shifts can derive by the decrease of conjugative effect of chromophore aggregation by washing-induced loss of weakly bound oxygen-containing species, which influences the π–π* plasmon peak [<xref rid=\"B52-ijms-21-05571\" ref-type=\"bibr\">52</xref>]. Significantly, the pellets of GO_A@T and GO_B@T samples (<xref ref-type=\"fig\" rid=\"ijms-21-05571-f001\">Figure 1</xref>b), which display spectral features of both GO and Tetra(HPRG)-Fam components, do not show significant changes in the peak wavelength position with respect to those of the hybrid systems before the centrifugation and rinsing steps (<xref ref-type=\"fig\" rid=\"ijms-21-05571-f001\">Figure 1</xref>a). These findings point to the successful and irreversible immobilization of the peptide molecules at the GO nanosheets surface.</p><p>As to the mixtures, also in this case the spectra evidence an irreversible binding of the Fam-labeled peptide to the GO substrates. For both GO@T hybrids and GO+T mixture samples, the spectra of supernatants collected after first washing step do not display any significant indication of the presence of peptide neither of GO. By using the molar extinction coefficients calculated for Tetra(HPRG)-Fam in the hybrid nanocomposite, a peptide loading of about 95 µM for 1 mg/mL of GO in GO_A@T or GO_A+T and of 88 µM for 1 mg/mL of GO in GO_B@T or GO_B+T could be estimated, respectively.</p><p>The analysis of CD spectra for both hybrids and mixture samples, in the comparison with the spectrum of the peptide alone, evidenced: (i) no detectable peptide signals for GO_A@T and GO_A+T (data not shown), likely due to the low relative concentration of the peptide compared to the GO matrix; (ii) a progressive intensity decrease and a red-shift of the minimum band at 202 nm for both GO_B+T (at 206 nm) and GO_B@T (at 207 nm) (<xref ref-type=\"fig\" rid=\"ijms-21-05571-f002\">Figure 2</xref>). The redshift is explained as result of the increase in the polyproline II (PPII) conformers [<xref rid=\"B53-ijms-21-05571\" ref-type=\"bibr\">53</xref>]; the intensity change is due to the optical coupling between the peptide chromophore moieties and the GO substrates. Over all, these findings point to a strong interaction between the nanosheets and the Tetra(HPRG)-Fam, which affects at different levels (higher in the hybrids than in the mixtures) the peptide conformational structure and its freedom degrees.</p><p>The electron transfer processes at the interface between the GO nanosheets and the Fam-labelled peptide were confirmed by the fluorescence spectra (<xref ref-type=\"fig\" rid=\"ijms-21-05571-f003\">Figure 3</xref>), which display a strong quenching of the Fam peak at 521 nm. In particular, the fluorescein emission decreases of 99.9% in GO_A@T and to 99.1% in GO_B@T samples (see inset <xref ref-type=\"fig\" rid=\"ijms-21-05571-f003\">Figure 3</xref>), respectively.</p><p>The characterization by ATR-FTIR analyses (<xref ref-type=\"fig\" rid=\"ijms-21-05571-f004\">Figure 4</xref>) confirmed the effective immobilization of the peptide on the GO nanosheets in the hybrid nanocomposite. As to the uncoated GO, the spectrum displays a broad band in the wavenumber range of 3700–3000 cm<sup>−1</sup>, assigned to O-H stretching vibrations, as well as two peaks at ~1730 cm<sup>−1</sup> and 1620 cm<sup>−1</sup>, due to the C=O stretching of the carboxylic acid groups and the aromatic C–C stretching, respectively [<xref rid=\"B54-ijms-21-05571\" ref-type=\"bibr\">54</xref>]. Moreover, a weak signal at ~1400 cm<sup>−1</sup> corresponding to the O–H deformation and an absorption peak at ~1050 cm<sup>−1</sup> attributed to the alkoxy C–O stretching vibrations are also visible [<xref rid=\"B55-ijms-21-05571\" ref-type=\"bibr\">55</xref>,<xref rid=\"B56-ijms-21-05571\" ref-type=\"bibr\">56</xref>]. The spectrum of Tetra(HPRG)-Fam shows characteristic peaks from both the 5,6-carboxyfluorescein unit and the peptide backbone. Specifically, the O–H stretching vibrations in the broad band from 3600 to 3300 cm<sup>−1</sup> as well as the signals at 1667 cm<sup>−1</sup> and 2852 cm<sup>−1</sup>, which correspond respectively to the stretching of the C=O of carboxyl groups and the C–H stretching [<xref rid=\"B57-ijms-21-05571\" ref-type=\"bibr\">57</xref>] are visible, together with the typical IR bands for amino acids and peptide (the “bio-fingerprint region”) at 1630 cm<sup>−1</sup> (amide I) and 1543 cm<sup>−1</sup> (amide II) are visible [<xref rid=\"B58-ijms-21-05571\" ref-type=\"bibr\">58</xref>]. Moreover, the signals at 1202 cm<sup>−1</sup> can be assigned to the C=C stretching and the N–H bending vibrations of the histidine residue, while the weak band in range of 1400 and 1465 cm<sup>−1</sup> can be attributed to the C–N stretching of the proline and the sharp peak at 1133 cm<sup>−1</sup> corresponds to the C–O stretching vibrations of the peptide [<xref rid=\"B59-ijms-21-05571\" ref-type=\"bibr\">59</xref>].</p><p>As to the GO@T hybrids, the signals at 1156 and 1080 cm<sup>−1</sup>, related respectively to the C=C stretching and the N–H bending vibrations of the histidine residue confirm the presence of peptide-related bands in the nanocomposite sample. Such signals are shifted to higher wavenumbers and the peak are broader with respect to those observed for free peptide, thus suggesting the formation of hydrogen binding between the hydrophilic groups of graphene oxide and the side chain imidazole groups of histidine in the peptide [<xref rid=\"B60-ijms-21-05571\" ref-type=\"bibr\">60</xref>]. Moreover, the fingerprint signals from the amide I and amide II vibrations are strongly attenuated, likely due to a different conformational state of the peptide immobilized in the adlayer on the GO surface compared to the unbound peptide molecules.</p><p>The response of prostate cancer (PC-3), neuroblastoma (SH-SY5Y) and non-tumor endothelial (HREC) cells to the GO@T hybrid nanocomposites was investigated in terms of toxicity through MTT assay. Cell viability assays were performed on cells incubated for 24 h and 48 h with increasing concentrations of GO nanosheets (10, 12, 30 µg/mL), Tetra(HPRG)-Fam (2, 4, 6 µM) and three hybrid (GO@T) and mixture (GO+T) samples, spanning the same concentration range in the GO and the peptide (<xref ref-type=\"fig\" rid=\"ijms-21-05571-f005\">Figure 5</xref>).</p><p>In general, the cellular treatments with GO samples resulted not toxic after 24 h of incubation time for all the three cell lines tested. After 48 h of incubation, we detected a statistically significant decrease in cell viability in PC-3 (30 µg/mL GO: 88.5 ± 2.6% of CTRL; ** <italic>p</italic> < 0.01) and SH-SY5Y (10 µg/mL GO: 78.0 ± 5.1% of CTRL; * <italic>p</italic> < 0.05; 12 µg/mL GO: 68.1 ± 5.6% of CTRL; ** <italic>p</italic> < 0.01; 30 µg/mL GO: 50.1 ± 5.9% of CTRL; * <italic>p</italic> < 0.01;) but not in HRECs. Also, our findings are consistent with many other studies, reporting that GO is non-toxic for multiple cell types [<xref rid=\"B61-ijms-21-05571\" ref-type=\"bibr\">61</xref>,<xref rid=\"B62-ijms-21-05571\" ref-type=\"bibr\">62</xref>,<xref rid=\"B63-ijms-21-05571\" ref-type=\"bibr\">63</xref>].</p><p>Interestingly, the treatment with Tetra(HPRG)-Fam alone induced an increased viability both in prostate cancer (<xref ref-type=\"fig\" rid=\"ijms-21-05571-f005\">Figure 5</xref>a, 24 h: ~120% of CTRL: ** <italic>p</italic> < 0.01; <xref ref-type=\"fig\" rid=\"ijms-21-05571-f005\">Figure 5</xref>d, 48 h: up to ~130% of CTRL: *** <italic>p</italic> < 0.001) and endothelial cells (<xref ref-type=\"fig\" rid=\"ijms-21-05571-f005\">Figure 5</xref>c, 24 h: ~130% of CTRL: * <italic>p</italic> < 0.05; <xref ref-type=\"fig\" rid=\"ijms-21-05571-f005\">Figure 5</xref>f, 48 h: ~140% of CTRL: ** <italic>p</italic> < 0.01) cells, while no significant changes in cell viability were detected in neuroblastoma cells for both 24 h (<xref ref-type=\"fig\" rid=\"ijms-21-05571-f005\">Figure 5</xref>b) and 48 h (<xref ref-type=\"fig\" rid=\"ijms-21-05571-f005\">Figure 5</xref>d) of incubation.</p><p>A comparable anti-proliferative effect was induced in PC-3 by the treatment either with the GO@T hybrids or the GO+T mixtures, for both the incubation times of 24 and 48 h. In particular, a statistically significant dose-dependent decrease of viability was detected with respect to both the untreated cells as well as the GO-treated cells, with a maximum of cytotoxicity found for the mixture of 30 µg/mL GO with 6 µM Tetra(HPRG)-Fam (24 h: 54.3 ± 4.8% of CTRL: *** <italic>p</italic> < 0.001; 48 h: 39.8 ± 2.2% of CTRL: **** <italic>p</italic> < 0.0001).</p><p>As to SH-SY5Y cells, a significant and comparable decrease in cell viability was detected only in the cells incubated for 48 h with the highest concentrations of GO@T hybrid (~80% of CTRL: * <italic>p</italic> < 0.05) or GO+T mixture (~88–70% of CTRL: * <italic>p</italic> < 0.05), respectively. To note, the same treatment conditions resulted however proliferative in comparison to the incubations with corresponding positive controls of GO.</p><p>The HREC treatments with the highest concentrations of GO@T hybrids resulted anti-proliferative both after 24 h (~70% of CTRL: * <italic>p</italic> < 0.05) and 48 h (~40% of CTRL: * <italic>p</italic> < 0.05) of incubation. On the other hand, the endothelial cells incubation with the GO+T mixture at the highest concentrations showed only a slightly proliferative effect at 24 h with respect to the positive control of GO (~120% of CTRL: <sup>##</sup>\n<italic>p</italic> < 0.01).</p><p>According to the results of anti-proliferative effects induced by GO@T hybrids in PC-3 and HREC but not in SH-SY5Y, the following investigations on the inhibition of cell migration and PGE<sub>2</sub> release were carried out only on the prostate cancer and endothelial cells.</p><p>The modulation of PC-3 and HREC scratch crossing was monitored for 24 h and 48 h after monolayer wounding (<xref ref-type=\"fig\" rid=\"ijms-21-05571-f006\">Figure 6</xref>).</p><p>Representative photographs and the quantitative analysis of wound healing for PC-3 (<xref ref-type=\"fig\" rid=\"ijms-21-05571-f006\">Figure 6</xref>a,c) and HREC (<xref ref-type=\"fig\" rid=\"ijms-21-05571-f006\">Figure 6</xref>b,d) incubated in absence (negative control) or in presence of the positive controls of GO nanosheets (10, 12, 30 µg/mL),Tetra(HPRG)-Fam (2, 4, 6 µM) and the corresponding GO+T mixtures are shown in the comparison with those of cells incubated with the hybrid GO@T samples.</p><p>The control untreated PC-3 cells were able to cross the wound at 24 h, until the wound closed after 48 h. Conversely, the migration ability of PC-3 treated with GO was reduced significantly in comparison with control cells. A dose-dependent partial inhibition of cell invasion was observed in presence of Tetra(HPRG)-Fam, especially evident at 24 h of incubation (2 µM: ~70% of CTRL: ** <italic>p</italic> < 0.01; 4 and 6 µM: ~30% of CTRL: *** <italic>p</italic> < 0.001) than after 48 h of treatment (2, 4, and 6 µM: ~80% of CTRL: ** <italic>p</italic> < 0.01). The treatment of PC-3 either with the GO@T hybrids or the GO+T mixtures induced a significant and comparable reduction of the wound closure (at the two highest concentration tested: less than 20% of CTRL: **** <italic>p</italic> < 0.0001), indicating that the association of the peptide to the GO nanosheets efficiently reduces the PC-3 cells migration, inhibiting their metastatic potential.</p><p>HREC migration was modulated differently from cancer cells. Indeed, whereas the cells incubation with GO@T hybrids exhibited a significant inhibitory effect on wound closure for all three concentration tested (less than 20% of CTRL: **** <italic>p</italic> < 0.0001), and no differences in comparison with the negative control of untreated cells were observed for the cells incubated with the peptide alone, both the GO and the GO+T mixtures showed a similar trend, i.e., a partial inhibition for the lowest concentration (10 µg/mL: ~40% at 24 h and ~60% at 48 h of CTRL: *** <italic>p</italic> < 0.0001) and a very efficient inhibition of wound closure (less than 20% of CTRL: **** <italic>p</italic> < 0.0001) at the other two concentrations for both incubation times.</p><p>Inflammation is increasingly recognized as a critical mediator of angiogenesis [<xref rid=\"B64-ijms-21-05571\" ref-type=\"bibr\">64</xref>]. It has been demonstrated that PGE<sub>2</sub> promotes the in vitro tube formation of human microvascular endothelial cells via activation of EP4 receptors and by triggering PKA Cγ signaling pathway, <italic>ex vivo</italic> vessel outgrowth of aortic rings, and <italic>in vivo</italic> angiogenesis [<xref rid=\"B65-ijms-21-05571\" ref-type=\"bibr\">65</xref>,<xref rid=\"B66-ijms-21-05571\" ref-type=\"bibr\">66</xref>,<xref rid=\"B67-ijms-21-05571\" ref-type=\"bibr\">67</xref>]. Moreover, during the inflammatory process, immune cells secrete pro-angiogenic factors for the promotion of new vessel formation [<xref rid=\"B64-ijms-21-05571\" ref-type=\"bibr\">64</xref>].</p><p>The ability of GO@T hybrids to modulate the release of PGE<sub>2</sub> in PC-3 and HREC was investigated as marker for the activation of inflammatory processes, which can have a crucial role both in carcinogenesis and cancer progression as well as in the angiogenic switch-on mechanism.</p><p>Cell culture supernatants collected after incubation of PC-3 or HREC cells for 48 h in absence (control) or in presence of GO nanosheets, Tetra(HPRG)-Fam, the hybrid GO@T or the mixture GO+T, were assayed for their PGE<sub>2</sub> levels (<xref rid=\"ijms-21-05571-t001\" ref-type=\"table\">Table 1</xref> and <xref rid=\"ijms-21-05571-t002\" ref-type=\"table\">Table 2</xref>).</p><p>As to prostate cancer cells, upon the incubations with the different samples, the PGE<sub>2</sub> production by PC-3 cells decreased in comparison to untreated control cells. Specifically, <xref rid=\"ijms-21-05571-t001\" ref-type=\"table\">Table 1</xref> shows a dose-dependent effect (about 35–40%) for the treatments with GO, and a reduction in the PEG<sub>2</sub> release by about 26% for Tetra(HPRG)-Fam, and by about 39% for the GO+T mixture, respectively. Finally, the treatments with GO@T hybrids determined a significant decrease by about 60% for all used concentrations.</p><p>Analogous investigation on HREC (<xref rid=\"ijms-21-05571-t002\" ref-type=\"table\">Table 2</xref>) showed that PGE<sub>2</sub> levels were significantly reduced after the cellular treatments with GO or GO+T (by 20–29% or 39%, respectively) but not after incubation with the three different concentrations of Tetra(HPRG)-Fam. On the other hand, a significant and large decrease in comparison to untreated cells (by 64%), comparable to that found in the PC-3 case, was detected for HREC cells incubated with GO@T hybrids.</p><p>These results confirmed the role played in prostate cancer by PGE<sub>2</sub>, the most abundant pro-inflammatory mediator, promoting cancer cell invasion by induction of protein and mRNA expression of metalloproteinases, and, in turn, allow for the proliferation and migration of PC-3 cells [<xref rid=\"B68-ijms-21-05571\" ref-type=\"bibr\">68</xref>]. Moreover, the nanocomposites of GO and Tetra(HPRG)-Fam peptide, although to different extend, were able to modulate the PGE<sub>2</sub> release and thus the new vessel formation as well as the tumor progression. Interestingly, the same trend measured in HREC for both the cell migration and the PEG<sub>2</sub> release upon the treatment with the peptide alone, i.e., no significant changes with respect to the negative control of untreated cells, points to a certain role of the switch-on process of angiogenesis in the mechanism involved.</p><p>The ability of GO@T to modulate the release of PGE<sub>2</sub> in PC3 cell makes them potential anti-inflammatory agents for use in the conditions under which inflammation amplifies the pathological process. In particular, chronic inflammation is a risk factor for the development and progression of prostate cancer [<xref rid=\"B69-ijms-21-05571\" ref-type=\"bibr\">69</xref>]. It has been demonstrated in PC3 that PGE<sub>2</sub> binds to a prostaglandin transporter which thus determines an increase in their intracellular concentration and, with an intracrine mechanism, they induce the proliferation, migration and invasion of PC-3 tumor cells [<xref rid=\"B70-ijms-21-05571\" ref-type=\"bibr\">70</xref>].</p><p>To shed light on the molecular mechanisms involved in toxicity induced by the hybrid GO@T platforms, the oxidative stress induced by the production of reactive oxygen species (ROS) was measured through the fluorescent assays of MitoSOX Red, and dichlorohydrofluorescein (DCF), to measure mitochondrial superoxide, and cytosol hydrogen peroxide production, respectively. Hence, PC-3 cells were treated for 24 h with three different concentrations of GO, Tetra(HPRG)-Fam and GO@Tetra(HPRG)-Fam. <xref ref-type=\"fig\" rid=\"ijms-21-05571-f007\">Figure 7</xref> shows that the PC-3 cells treatment with GO nanosheets increases the ROS in a dose-depended manner, in agreement with the literature on the GO-induced cytotoxicity mechanism by intracellular ROS production [<xref rid=\"B27-ijms-21-05571\" ref-type=\"bibr\">27</xref>,<xref rid=\"B49-ijms-21-05571\" ref-type=\"bibr\">49</xref>]. On the other hand, no significant ROS production was detected upon the cells treatment by Tetra(HPRG)-Fam as well as by the hybrid GO@T samples in the concentration range tested.</p><p>To tackle with the correlation between the pro-tumor effect of PGE<sub>2</sub> and the particular ability of GO@T to reduce their synthesis we carried out intracellular imaging LSM to evaluate the cellular uptake of the GO nanosheets decorated by the fluorescent Tetra(HPRG)-Fam peptide as well as to image the perturbation on the mitochondria, as analyzed by the organelle staining by the MitoTracker Deep Red fluorescent probe. <xref ref-type=\"fig\" rid=\"ijms-21-05571-f008\">Figure 8</xref> shows the representative LSM micrographs of PC-3 cells after 24 h of treatment followed by cellular staining and fixation.</p><p>As to the GO-treated cells (<xref ref-type=\"fig\" rid=\"ijms-21-05571-f008\">Figure 8</xref>b), several dark areas in the optical light field micrographs (not observed in the control untreated cells, <xref ref-type=\"fig\" rid=\"ijms-21-05571-f008\">Figure 8</xref>a), show the cellular wrapping by the GO sheets aggregates, which is one of the mechanism invoked for the GO cytoxicity [<xref rid=\"B71-ijms-21-05571\" ref-type=\"bibr\">71</xref>]. Interestingly, such GO sheets are detected in close contact to the cell membrane but also intracellularly, as evidenced by the shadowing of the nuclear staining (see arrows in <xref ref-type=\"fig\" rid=\"ijms-21-05571-f008\">Figure 8</xref>a), thus pointing to point to the effective cellular uptake of the GO nanosheets. As to the cells incubated with the free peptide (<xref ref-type=\"fig\" rid=\"ijms-21-05571-f008\">Figure 8</xref>c), a green fluorescence that increases in a dose-dependent manner is clearly visible in the cell cytoplasm and, mostly, accumulating at the cell membrane for the highest peptide concentration tested. Finally, the cells incubated with the three GO@T samples at the different relative concentration of peptide and GO, show a more diffuse green fluorescence inside the cells, as well as peptide-decorated (i.e., green emitting) GO aggregates that wrap/trap the cells in a ‘sheet-form blanket’. To note, the isoelectric pH value of 7.9 calculated for the free peptide at the physiological pH suggests an interaction driven by electrostatic forces with the negatively charged cell membranes. On the other hand, for the GO-immobilized peptide a more effective transport into the cells is offered by the graphene-based platform [<xref rid=\"B20-ijms-21-05571\" ref-type=\"bibr\">20</xref>]. These findings validate the self-assembly-based approach used to fabricate the nanocomposite for their use in the theranostics concept.</p><p>The quantitative analysis (<xref ref-type=\"fig\" rid=\"ijms-21-05571-f008\">Figure 8</xref>e) confirms the effective internalization of the free peptide in a dose dependent manner, and suggest, by the increase of the detected emission of the MitoTracker Deep Red probe compared to the control untreated cells, a general mitochondrial insult, especially for the bare GO nanosheets and the GO@T hybrid with the lowest concentration of peptide among those tested.</p></sec><sec sec-type=\"discussion\" id=\"sec3-ijms-21-05571\"><title>3. Discussion</title><p>Small peptide molecules preferentially interact via electrostatic interaction with the edge or basal plane surface of the GO nanosheets. Indeed, the hydrophilic groups of graphene oxide, such as –COOH and –OH, allow for the versatile immobilization of biomolecules via either covalent grafting or non-covalent interactions that include hydrogen bonds, ion–dipole, or van der Waal forces, while the free surface π electrons are capable of forming π–π and CH–π and interactions [<xref rid=\"B49-ijms-21-05571\" ref-type=\"bibr\">49</xref>,<xref rid=\"B72-ijms-21-05571\" ref-type=\"bibr\">72</xref>]. Noteworthy, aromatic amino acids, such as the histidine included in the aminoacidic sequence of the Tetra(HPRG) peptide, preferably orient themselves in parallel with respect to the basal plane of the graphene sheets, through predominant π–π interactions [<xref rid=\"B73-ijms-21-05571\" ref-type=\"bibr\">73</xref>,<xref rid=\"B74-ijms-21-05571\" ref-type=\"bibr\">74</xref>].</p><p>A strong and irreversible interaction between the physisorbed Tetra(HPRG) peptide and GO was detected by means of UV-visible (<xref ref-type=\"fig\" rid=\"ijms-21-05571-f001\">Figure 1</xref>) and fluorescence (<xref ref-type=\"fig\" rid=\"ijms-21-05571-f003\">Figure 3</xref>) spectroscopies, as demonstrated in terms of electron transfer processes between the nanosheets and the 5,6-carboxyfluorescein fluorophore moiety covalently bound to the Tetra(HPRG) peptide sequence.</p><p>In particular, the bathochromic shift (~2 nm) and, most noticeably, the hypochromic shift of the Fam-related main absorption peak at ~500 nm, pointed to the significant decrease, in a GO-dose dependent manner, of the molar extinction coefficient from the value of ε<sub>497</sub> = 5.6 × 10<sup>5</sup> M<sup>−1</sup>cm<sup>−1</sup> of the free peptide to ε<sub>499</sub> = 1.3 × 10<sup>5</sup> M<sup>−1</sup>cm<sup>−1</sup> and ε<sub>499</sub> = 1.7 × 10<sup>5</sup> M<sup>−1</sup>cm<sup>−1</sup> in the GO_A@T and GO_B@T hybrid samples, respectively. Moreover, while the bare GO_A and GO_B nanosheets have similar molar extinction coefficients for the GO characteristic π → π* transition peak, as calculated by using the Lambert-Beer law (ε<sub>238</sub> ~3 × 10<sup>2</sup> mg<sup>−1</sup> mL cm<sup>−1</sup>), the hybrid GO@T samples exhibit both hypsochromic (~16 nm) and hypochromic shifts, the latter effect being depending on the GO concentration, with the values of ε<sub>232</sub> = 73 mg<sup>−1</sup> mL cm<sup>−1</sup> for GO_A@T and ε<sub>232</sub> = 93 mg<sup>−1</sup> mL cm<sup>−1</sup> for GO_B@T estimated from the spectra of the hybrid samples subtracted by that of the free peptide.</p><p>GO, which is itself fluorescent, is a quencher for the fluorescence signals of fluorophores, with a quenching efficiency superior to that of conventional organic molecules [<xref rid=\"B75-ijms-21-05571\" ref-type=\"bibr\">75</xref>]. Its quenching mechanism is related either to fluorescence resonance energy transfer (FRET) [<xref rid=\"B76-ijms-21-05571\" ref-type=\"bibr\">76</xref>] or photo-induced electron transfer (PET) processes [<xref rid=\"B77-ijms-21-05571\" ref-type=\"bibr\">77</xref>]. Hence, when the GO nanosheet and the fluorophore molecules are close to each other, the energy or excited electron transfers from the fluorophore (donor) to the GO (acceptor) and the fluorescence signal of the fluorophore is quenched. GO is largely used as energy acceptor for the fabrication of biosensors [<xref rid=\"B78-ijms-21-05571\" ref-type=\"bibr\">78</xref>]. Our findings on the strong quenching of the Fam fluorescence (<99% decrease in the emission intensity measured in the absence of GO) in the GO@T hybrids strongly support the successful and irreversible immobilization of the Tetra(HPRG)-Fam molecules on the GO nanosheets (<xref ref-type=\"fig\" rid=\"ijms-21-05571-f003\">Figure 3</xref>).</p><p>The CD (<xref ref-type=\"fig\" rid=\"ijms-21-05571-f002\">Figure 2</xref>) and ATR-FTIR (<xref ref-type=\"fig\" rid=\"ijms-21-05571-f004\">Figure 4</xref>) spectra also confirmed the successful immobilization of the peptide molecules at the GO surface, with conformational changes of the peptide molecules due to the strong interaction with the GO nanosheets. In particular, in the infrared spectra, the shifts to higher wavenumbers and the peak broadening of the signals at 1156 and 1080 cm<sup>−1</sup>, related respectively to the C=C stretching and the N–H bending vibrations of the histidine residue, point out to the formation of hydrogen bonds between the hydrophilic groups of graphene oxide and the side chain imidazole groups of histidine in the peptide [<xref rid=\"B60-ijms-21-05571\" ref-type=\"bibr\">60</xref>]. Also, the spectral changes observed in the protein-fingerprint region of amide I and amide II stretching vibrations pointed to significant conformational changes of the peptide molecules in the adlayer at the surface of the GO compared to the unbound form of the free peptide in solution. Specifically, the amide I vibration, absorbing near 1650 cm<sup>−1</sup>, arises mainly from the C=O stretching with minor contributions from the out-of-phase C–N stretching. The amide II, absorbing near 1550 cm<sup>−1</sup>, is the combination of N–H and C–N stretching vibration with smaller contributions from the C–O, C–C and N–C stretching vibrations. Both amide I and amide II vibration peaks of proteins may provide valuable structural information and secondary structure prediction [<xref rid=\"B59-ijms-21-05571\" ref-type=\"bibr\">59</xref>]. For instance, the amide I peak may encompass different conformational contributions that represent extended strands, β-turns, 3(10)-helix, polyproline I, and polyproline II [<xref rid=\"B79-ijms-21-05571\" ref-type=\"bibr\">79</xref>]. The HPRG domain has been reported to have a predominant polyproline II conformation rather than random coil one for the high content of proline residues [<xref rid=\"B80-ijms-21-05571\" ref-type=\"bibr\">80</xref>]. We found by CD analyses supporting evidences on the presence of PPII conformers of the free Tetra(HPRG) peptide in equilibrium with the random coil configuration. Indeed, the CD spectrum of the free peptide (<xref ref-type=\"fig\" rid=\"ijms-21-05571-f002\">Figure 2</xref>), shows a broad negative peak at 202 nm, a maximum at 222 nm, and another minimum at 234 nm. This combination has been shown to occur in polypeptides with extended chain polyproline II conformation, whereas the random conformation has the minimum is at 199 nm [<xref rid=\"B81-ijms-21-05571\" ref-type=\"bibr\">81</xref>].</p><p>To note, the HPRG domain is able to bind different ligands by exploiting the imidazole side chain, partially protonated at physiological pH, and the conformational features of its secondary structure. The HPRG protein has a multidomain structure composed of 2 N-terminal regions (called N1 and N2) homologous to cystatin but lacking the function of inhibitor of cysteine proteinase; a central element, histidine rich region (HRR) flanked by 2 regions rich in proline (PRR1 and PRR2) and a C-terminal domain or (C) [<xref rid=\"B82-ijms-21-05571\" ref-type=\"bibr\">82</xref>]. This domain also prevents heparanase-mediated release of angiogenic growth factors from ECM by blocking the heparanase cleavage sites in the EPM heparan sulphate [<xref rid=\"B83-ijms-21-05571\" ref-type=\"bibr\">83</xref>] and by binding to tropomyosin on the cell provides antiangiogenic signals to endothelial cells [<xref rid=\"B41-ijms-21-05571\" ref-type=\"bibr\">41</xref>]. Interestingly, these processes could be enhanced by high extracellular Zn<sup>2+</sup> (generally provided by platelets) and low pH levels, which cause conformational changes in the molecule [<xref rid=\"B34-ijms-21-05571\" ref-type=\"bibr\">34</xref>]. Noteworthy, the multidomain structure of HPRG allows for its interaction with a variety of molecules such as heme group, fibrinogen, heparinase, thrombospondin (TSP), divalent metal cations zinc, copper, mercury, cadmium and nickel, but also with associated molecules to cells, including heparan sulphate (HS), tropomyosin ATP synthase, DNA [<xref rid=\"B36-ijms-21-05571\" ref-type=\"bibr\">36</xref>]. Specifically, HPRG has been detected on the surface of the cells involved in the immune response such as: Leukocytes, macrophages and monocytes, as well as in the platelet and megakaryocyte granules [<xref rid=\"B35-ijms-21-05571\" ref-type=\"bibr\">35</xref>]. Numerous studies have demonstrated the effectiveness of HPRG-derived peptides in inhibiting tumor cell proliferation indirectly, by reducing angiogenesis and starving the tumor. To date, there are no studies conducted on tumor cells after incubation with the Tetra(HPRG) peptide or graphene-based nanocomposites either of the whole protein or of the peptide.</p><p>The different viability results (<xref ref-type=\"fig\" rid=\"ijms-21-05571-f005\">Figure 5</xref>) showed by the cells incubated with GO-immobilized peptide, both hybrids GO@T and mixtures GO+T, compared to the free Tetra(HPRG)-Fam peptide could be therefore related to the different binding capabilities of the HPRG backbone in the free and surface-immobilized conformational state.</p><p>Specifically, the opposite trends detected on both PC-3 and HREC, i.e., proliferative effect for the peptide alone and anti-proliferative effect for the GO-immobilized peptide, can be related to the equilibrium shift for the latter towards the PPII configuration. To note, according to the confocal imaging analyses (<xref ref-type=\"fig\" rid=\"ijms-21-05571-f008\">Figure 8</xref>), the cells exhibited a dose-dependent cellular uptake of the free peptide with preferential gathering at the cell membrane, while a more diffuse cytoplasm internalization was observed for the GO-immobilized peptides. Accordingly, different signaling pathways resulting in the proliferative or anti-proliferative effects could be activated.</p><p>The effects induced by the GO@T hybrids and GO+T mixtures on PC-3 and HREC cell viability confirm that, depending on the incubation time and the dose of treatment, it could be also used as an anti-cancer molecule and not only as a drug-delivery agent [<xref rid=\"B63-ijms-21-05571\" ref-type=\"bibr\">63</xref>]. What is more, the combination Tetra(HPRG)-Fam peptide to the GO platform in the GO@T hybrids blocked the cancer cells proliferation to a greater extent than each individual component. In the other hand, in the angiogenic cellular model, the GO@T hybrids inhibit the endothelial cell proliferation while the GO+T mixture do not, thus highlighting a promising potential to block unwanted vessel formation.</p><p>The different effects of GO and GO@T hybrids on PC-3, SH-SY5Y and HREC are in agreement with other studies showing that graphene oxide inhibits tumor-sphere formation in different cancer types (breast, ovarian, prostate, lung, and pancreatic cancers, as well as glioblastoma), by inhibiting several signal transduction pathways, but with no toxic for normal fibroblasts [<xref rid=\"B84-ijms-21-05571\" ref-type=\"bibr\">84</xref>]. Also, the time- and dose-dependent response on the treatment of SH-SY5Y cells with GO is in agreement with literature data [<xref rid=\"B85-ijms-21-05571\" ref-type=\"bibr\">85</xref>] and previous works from this group which demonstrated that the GO toxicity in neuroblastoma cells depends on the functional groups and the oxidation level of GO [<xref rid=\"B27-ijms-21-05571\" ref-type=\"bibr\">27</xref>,<xref rid=\"B50-ijms-21-05571\" ref-type=\"bibr\">50</xref>]. These findings are in general very promising since they elucidate on the potential of graphene oxide as an effective non-toxic therapeutic strategy for the eradication of cancer cells.</p><p>As to the strong inhibitory effect of the migration of PC-3 and HREC cells in the presence of GO or GO@T hybrids (<xref ref-type=\"fig\" rid=\"ijms-21-05571-f006\">Figure 6</xref>), we found a consistent trend with the PEG<sub>2</sub> release (see <xref rid=\"ijms-21-05571-t001\" ref-type=\"table\">Table 1</xref> and <xref rid=\"ijms-21-05571-t002\" ref-type=\"table\">Table 2</xref>), with a significant inhibitory effect in both the cell types.</p><p>For PC-3, the inhibition in cell migration can be due also to the inhibition of the electron transport chain, as highlighted by the high levels of ROS produced in the cells after incubation with GO (<xref ref-type=\"fig\" rid=\"ijms-21-05571-f007\">Figure 7</xref>). This hypothesis is confirmed by other studies, reporting a blockage of the electrons transfer to the iron-sulfur centers, since GO would subtract electrons, having a higher electron affinity than the iron-sulfur centers [<xref rid=\"B86-ijms-21-05571\" ref-type=\"bibr\">86</xref>]. Prolonged damage over time causes inactivation of the free radical scavenger enzymes (superoxide dismutase and glutathione peroxidase) and consequently damage to proteins, DNA and lipids, which greatly thereby influencing the cell metabolism and signaling pathways [<xref rid=\"B84-ijms-21-05571\" ref-type=\"bibr\">84</xref>]. Moreover, GO exposure can induce phosphorylation of ERK signaling molecules, which are related to cell cycle regulation, triggering apoptosis [<xref rid=\"B87-ijms-21-05571\" ref-type=\"bibr\">87</xref>]. The reduced production of ATP by oxidative phosphorylation would result in an impairment in the F-actin cytoskeleton assembly, which is an ATP-consuming process, thus inhibiting cancer cells migration. Interestingly, the increased ROS levels for cells incubated with the GO@T hybrids is less evident than the treatment with the GO alone. Such a result is most likely due to a protective effect of the peptide which covers the surface of GO sheets thereby reducing the interaction with the oxygen molecules and as consequences the ROS production [<xref rid=\"B88-ijms-21-05571\" ref-type=\"bibr\">88</xref>]. The generation of ROS by the GO, indeed, may be mediated by the adsorption of O<sub>2</sub> on its surface to form a surface-bound C(O<sub>2</sub>) intermediate which is capable to oxidize the glutathione enzyme GSH to GSSG, thus restoring the carbon surface to its original state and releasing H<sub>2</sub>O<sub>2</sub> [<xref rid=\"B88-ijms-21-05571\" ref-type=\"bibr\">88</xref>].</p><p>Since the presence of the peptide in GO@T hybrid led to a reduction in the release of free radicals than the treatment with the GO alone, the inhibition of the PC-3 cells migration in the presence of the hybrid could be due to a mechanism not strictly related to the reduction of the ATP generation. One of the mechanisms proposed in modulating tumor cell migration is the activation of signal pathways, involved in the inflammatory process. It has indeed been demonstrated that PGE<sub>2</sub> is the most abundant pro-inflammatory mediator in prostate tissues and its levels are increasing in prostate carcinoma [<xref rid=\"B89-ijms-21-05571\" ref-type=\"bibr\">89</xref>]. Moreover, it has been shown that cAMP-PKA/PI3K-Akt signaling pathway, activated in prostate cancer cells by the binding of PGE<sub>2</sub> to its EP3 receptor, is involved in PC-3 cells proliferation [<xref rid=\"B68-ijms-21-05571\" ref-type=\"bibr\">68</xref>]. The strong inhibition of the PGE<sub>2</sub> release by PC-3 (<xref rid=\"ijms-21-05571-t001\" ref-type=\"table\">Table 1</xref>) and HREC (<xref rid=\"ijms-21-05571-t002\" ref-type=\"table\">Table 2</xref>) treated with GO@T, may provide novel insight into the therapeutic potential of this hybrid nanocomposite. Indeed, GO triggers profound effects through the metabolic reprogramming of the cell, thereby exerting its anti-inflammatory effects through the downregulation of specific genes, leading to activation of the transcription factor nuclear factor erythroid 2-related factor 2, which inhibited expression of pro-inflammatory cytokines such as IL-1β and IL-6 [<xref rid=\"B90-ijms-21-05571\" ref-type=\"bibr\">90</xref>]. A down-regulation by GO, via epigenetic mechanisms, of cyclooxygenase 2 (cox2), releasing PGE<sub>2</sub>, in human embryonic kidney 293T cells has been demonstrated [<xref rid=\"B91-ijms-21-05571\" ref-type=\"bibr\">91</xref>]. The ability of GO to trigger physical interactions between the downstream factors and the cox2 promoter gives GO peculiar characteristics that make it an effective molecule in the treatment of tumor forms. In the same study, aminated GO and poly (acrylic acid)-functionalized GO were demonstrated to reduce the inflammatory response, with a weaker effect on chromatin architecture. Therefore, GO-mediated chromatin interactions may minimize toxicity in practical applications.</p><p>The association between Tetra(HPRG) peptide and the ROS-producing GO nanosheets, with the related enhanced cellular uptake and mitochondrial perturbation (<xref ref-type=\"fig\" rid=\"ijms-21-05571-f008\">Figure 8</xref>) are therefore able to strongly inhibit both the tumor cells proliferation and the cell migration process. Mitochondria, in fact, are intracellular organelles involved in energy metabolism, which show a central role in the ATP synthesis. According to the data from mitochondrial organelle staining shown in the <xref ref-type=\"fig\" rid=\"ijms-21-05571-f008\">Figure 8</xref>, the GO@T hybrids at the higher concentrations of peptide and GO interfere less with mitochondrial activity than the peptide and GO alone.</p></sec><sec id=\"sec4-ijms-21-05571\"><title>4. Materials and Methods</title><sec id=\"sec4dot1-ijms-21-05571\"><title>4.1. Chemicals</title><p>Graphene oxide water dispersion (0.4 wt% concentration) was purchased from Graphenea Inc., (Gipuzkoa, Spain). TETRA-HPRG (Ac-(GHHPH)<sub>4</sub>G-NH<sub>2</sub> with the addition of 6-Fam (6-carboxyfluorescein) were purchased from Caslo (Kongens Lyngby, Denmark). Phosphate buffered saline purchased by Sigma-Aldrich (St. Louis, MO, USA). Ultrapure Milli-Q water was used (18.2 mΩ∙cm at 25 °C, Millipore, Burlington, MA, USA). RPMI 1640, Dulbecco’s modified eagle medium (DMEM)-F12, fetal bovine serum (FBS), penicillin streptomycin solution and amphotericin solution for cell cultures. 2′,7′-dichlorofluorescein (DCF) were purchased from Sigma-Aldrich (St. Louis, MO, USA). MitoSOX™ red mitochondrial superoxide indicator and 2′-[4-ethoxyphenyl]-5-[4-methyl-1-piperazinyl]-2,5′-bi-1H-benzimidazole trihydrochloride trihydrate (Hoechst33342) were purchased from Thermo Fisher Scientific (Waltham, MA, USA).</p></sec><sec id=\"sec4dot2-ijms-21-05571\"><title>4.2. Synthesis of GO@T Hybrid</title><p>The aqueous dispersion GO was dried 70 °C, 400 rpm overnight in Termomixer (Thermomixer Comfort model, Eppendorf, Hamburg, Germany) and 3 mg of GO dried were resuspended in Phosphate Buffered Saline (PBS) 10 mM to reach a final concentration of 1 mg/mL. Therefore, to obtain homogeneous dispersion, GO dispersion was sonicated in Labsonic Ultrasound bath at 59 Hz for 120 min maintaining the temperature at 25 °C. The obtained dispersion has a characteristic dark brown coloration. For the synthesis of the GO@T hybrid nanosystem, 20 µL of peptide (10 mM) in Milli-Q ultrapure water was added to 1 mL of the GO dispersion (1 mg/mL or 0.5 mg/mL, for GO_A@T or GO_B@T, respectively). The sample were incubated for 120 min in Thermomixer (Thermomixer Comfort model, Eppendorf) at 37 °C and 400 rpm. To remove the excess of reactants, the synthetized hybrid system was washed with PBS (10 mM) twice by centrifugation (2 min at 2700 RCF). The same washing procedure was also applied to the GO alone.</p></sec><sec id=\"sec4dot3-ijms-21-05571\"><title>4.3. UV-Visible and Fluorescence Spectroscopy</title><p>UV-visible spectra of the samples in PBS were recorded using spectrometer Lambda S2 (Perkin Elmer, Waltham, MA, USA) and quartz cuvettes with an optical path length of 0.1 cm in the range of 200–700 nm. Fluorescence spectra were recorded on a LS55 (Perkin Elmer, Waltham, MA, USA) fluorimeter using quartz cuvettes with an optical path length of 0.1 cm.</p></sec><sec id=\"sec4dot4-ijms-21-05571\"><title>4.4. ATR/FTIR and CD Spectroscopies</title><p>The vibrational spectra were recorded using a Perkin Elmer FT-IR spectrophotometer (Spectrum Two, Waltham, MA, USA) in the range of 4000–400 cm<sup>−1</sup>. To carry out the measures the solid samples of GO and GO@T hybrid dried in Thermomixer (Thermomixer Comfort model, Eppendorf, Hamburg, Germany) at 70 °C and 400 rpm overnight, have been placed on the surface of the crystal and then locked with a “clutch-type” lever before starting the measure. Each spectrum was acquired at a resolution of 2 cm<sup>−1</sup> (10 scans).</p><p>The CD spectra of samples, both GO@T hybrids and GO+T mixtures, were obtained at 25 °C under a constant flow of nitrogen on a Jasco model 810 spectropolarimeter at a scan rate of 50 nm min<sup>−1</sup> and a resolution of 0.1 nm. The path length was 1 cm. The spectra were recorded as average of 10 scans in the 190–260 nm range.</p></sec><sec id=\"sec4dot5-ijms-21-05571\"><title>4.5. Cell Cultures and Maintenance</title><p>Prostate cancer cells (PC-3) and neuroblastoma (SH-SY5Y) cells were cultured in 25 cm<sup>2</sup> tissue-culture treated Corning<sup>®</sup> flasks (Sigma-Aldrich, St. Louis, MO, USA) in RPMI-1640 and Dulbecco’s modified eagle medium (DMEM)-F12, respectively. The medium was supplemented with 10% <italic>v/v</italic> fetal bovine serum (FBS), and contained 2 mM L-glutamine, 100 IU/mL penicillin and 0.1 mg/mL streptomycin. HREC cells were cultured in EGM-2 medium supplemented with 5% FBS. Cells were grown in an incubator (Heraeus Hera Cell 150C incubator), under a humidified atmosphere at 37 °C in 5% CO<sub>2</sub>.</p></sec><sec id=\"sec4dot6-ijms-21-05571\"><title>4.6. MTT Assay</title><p>For cell viability assays, the 3-[4,5–dimethylthiazol-2-yl]-2,5-diphenyl tetrasodium bromide (MTT) assay was used (Chemicon, Temecula, CA, US). Cell lines were seeded in 96-well plates at the cell density per well of 1.5 × 10<sup>4</sup> for PC-3 and HREC cells and 2 × 10<sup>4</sup> for SH-SY5Y cells, respectively. Cells were incubated overnight at 37 °C before experiment. Afterwards, cells were treated for 24 h and 48 h in the absence (negative control) or the presence of GO nanosheets (10, 12, 30 µg/mL), Tetra(HPRG)-Fam peptide (2, 4, 6 µM) and hybrid GO@T samples (10 µg/Ml-2 µM, 12 µg/mL-4 µM, 30 µg/mL-6 µM) and the corresponding GO+T mixtures. After incubation periods, 10 µL of MTT reagent (5 mg/mL) were added to each well and the cells were incubated for 3 h at 37 °C. The formazan crystals were solubilized with 100 µL DMSO and plates were shaken for 10 min. The absorbance was measured at 570 nm with plate reader (Synergy 2-bioTek).</p></sec><sec id=\"sec4dot7-ijms-21-05571\"><title>4.7. Cell Migration</title><p>PC-3 and HREC migration was measured using a standard scratch assay, performed as previously reported [<xref rid=\"B92-ijms-21-05571\" ref-type=\"bibr\">92</xref>]. Cells were incubated for 24 h and 48 h in the absence (negative control) or the presence of GO nanosheets (10, 12, 30 µg/mL), Tetra(HPRG)-Fam peptide (2, 4, 6 µM) and hybrid GO@T samples (10 µg/m-2 µM, 12 µg/mL-4 µM, 30 µg/mL-6 µM) and the corresponding GO+T mixtures. Migration was followed by an inverted Leica DM IRB microscope equipped with CCD camera. Time zero represents the time immediately after the scratch for all conditions.</p></sec><sec id=\"sec4dot8-ijms-21-05571\"><title>4.8. PGE<sub>2</sub> Assay</title><p>To determine PGE<sub>2</sub> release, PC-3 and HREC were incubated for 48 h in the absence (negative control) or the presence of GO nanosheets (10, 12, 30 µg/mL), Tetra(HPRG)-Fam peptide (2, 4, 6 µM) and hybrid GO@T samples (10 µg/mL-2 µM, 12 µg/mL-4 µM, 30 µg/mL-6 µM of GO-peptide concentration). Aliquots of culture medium were analyzed by using ELISA kits (Cayman Chemical, Ann Arbor, MI, USA), according to the manufacturer’s instructions. Three different experiments were analyzed in triplicate.</p></sec><sec id=\"sec4dot9-ijms-21-05571\"><title>4.9. Confocal Microscopy Analysis</title><p>LSM imaging was performed with an Olympus FV1000 confocal laser scanning microscope (LSM), equipped with diode UV (405 nm, 50 mW), multiline Argon (457 nm, 488 nm, 515 nm, total 30 mW), HeNe(G) (543 nm, 1 mW) and HeNe(R) (633 nm, 1 mW) lasers. An oil immersion objective (60xO PLAPO) and spectral filtering systems were used. The detector gain was fixed at a constant value and images were collected, in sequential mode, randomly all through the area of the well. To perform the experiment PC-3 cells were seeded in glass bottom dishes (WillCo-dish<sup>®</sup>, Willco Wells, B.V., Amsterdam, The Netherland) with 12 mm of glass diameter at a density of 3 × 10<sup>4</sup> cells per dish in RPMI-1640 medium supplemented with 1% FBS. Thereafter, cells were treated for 24 h in the absence (negative control) or the presence of GO, Tetra(HPRG)-Fam peptide, the hybrid GO@T and the GO+T mixtures. Before fixing, cells were stained with nuclear dye Hoechst33342 (0.251 µg/mL) and MitoTracker Deep Red (200 nM). Cells were rinsed with fresh PBS and cellular fixation was performed with high purity paraformaldehyde (2% <italic>w/v</italic>) in PBS (pH 7.3).</p></sec><sec id=\"sec4dot10-ijms-21-05571\"><title>4.10. Total and Mitochondrial ROS Production</title><p>The oxidative stress induced by the cell treatment with GO, Tetra(HPRG)-Fam and the hybrid GO@T was evaluated through DCF assay and MitoSOX assay on PC-3 cells measuring the level of reactive oxygen species (ROS) produced. To perform the assay, cells were plated into a 96-well plate in complete medium at a density of 10 × 10<sup>3</sup> cells per well. Cells were treated for 24 h in 3 replicate wells with: GO nanosheets (10, 12, 30 µg/mL), Tetra(HPRG)-Fam peptide (2, 4, 6 µM) and hybrid GO@T samples (10 µg/mL-2 µM, 12 µg/mL-4 µM, 30 µg/mL-6 µM of GO-peptide concentration). The incubation was carried out in complete medium supplemented with 1% FBS. Then, cells were stained with 0.12 µg/mL Hoechst33342 for 20 min, 3 µM 2′,7′-dichlorofluorescein (DCF) for 15 min (total ROS) or 5 µM MitoSOX for 5 min (mitochondrial O<sub>2</sub><sup>•−</sup>) at room temperature and analyzed by measuring the fluorescence emission (λex = 493 nm, λem = 523 nm for the nuclear staining; λex = 493 nm, λem = 523 nm for DCF; λex = 510, λem = 580 for MitoSOX, respectively) using a fluorescence spectrophotometers a LS55 (Perkin Elmer, Waltham, MA, US) and quartz cuvettes with an optical path length of 0.1 cm. Results are normalized to the Hoechst emission and represented as the increase in DCF or MitoSOX signals with respect to the untreated control. Data are presented as the means ± SEM of three replicas.</p></sec></sec><sec sec-type=\"conclusions\" id=\"sec5-ijms-21-05571\"><title>5. Conclusions</title><p>In summary, we assembled here a theranostic platform made of graphene oxide and a tetra repeat sequence of HPRG.</p><p>Results of cell viability in human prostate cancer (PC-3), neuroblastoma (SH-SY5Y) and retinal endothelial (HREC) cells incubated with GO-immobilized peptide, both hybrids GO@T and mixtures GO+T, compared to the treatments with the free Tetra(HPRG)-Fam peptide, pointed to different binding capabilities of the HPRG backbone in the free and surface-immobilized conformational state.</p><p>In particular, the proliferative effect for the peptide alone and the anti-proliferative effect for the GO-immobilized peptide, detected both in PC-3 and HREC, were related to the predominance of poly-Proline II conformation of the peptide immobilized on the GO nanosheets. Also, results of confocal imaging analyses suggested the activation of different signaling pathways resulting by different peptide internalization and sub-cellular localization. The strong inhibitory effect observed on the migration of PC-3 and HREC cells in the presence of GO or GO@T hybrids was correlated to the PEG<sub>2</sub> release.</p><p>Such findings may provide novel insights into the therapeutic potential of these nanocomposite. In fact, we demonstrated that a 21-mer peptide analogue mimics the anti-angiogenic activity of the whole HPRG protein and is also able to inhibit tumor growth when associated to the graphene oxide. Such a dual action could represent the basis for obtaining a new, more effective, anticancer therapeutic approach. To this aim, further studies are necessary to deepen the signaling pathway involved in the implementation of the overall function.</p></sec></body><back><ack><title>Acknowledgments</title><p>C.S. and D.L. acknowledge the Consorzio Interuniversitario di Ricerca in Chimica dei Metalli nei Sistemi Biologici (C.I.R.C.M.S.B.), Bari, Italy.</p></ack><notes><title>Author Contributions</title><p>Conceptualization, C.S., G.L. and D.L.M.; investigation, V.V., A.L., L.M.C., V.S., A.M.; data curation, L.M.C., V.S. and C.S.; writing—original draft preparation, V.V., A.L. and C.D.A.; writing—review and editing, C.S., G.L. and D.L.M. All authors have read and agreed to the published version of the manuscript.</p></notes><notes><title>Funding</title><p>This research was partially supported by MIUR PRIN grant, 2017WBZFHL), EraNet Cofund-M-ERA.NET 2 (project SmartHyCAR, n. 2471) and University of Catania (Piano della Ricerca di Ateneo, Linea di Intervento 2, 2018–2020).</p></notes><notes notes-type=\"COI-statement\"><title>Conflicts of Interest</title><p>The authors declare no conflict of interest.</p></notes><ref-list><title>References</title><ref id=\"B1-ijms-21-05571\"><label>1.</label><element-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Allen</surname><given-names>M.J.</given-names></name><name><surname>Tung</surname><given-names>V.C.</given-names></name><name><surname>Kaner</surname><given-names>R.B.</given-names></name></person-group><article-title>Honeycomb Carbon: A Review of Graphene</article-title><source>Chem. 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(<bold>b</bold>) UV-visible spectra in PBS of the pellets for hybrid GO_A@T and GO_B@T samples (solid lines), the GO_A+T and GO_B+T mixtures (dot lines) and, for comparison, of bare GO_A and GO_B pellets (dashed lines) after two centrifugation (2700 RCF, 2 min, RT) and washing steps. The spectra of supernatants (dashed dot lines) of GO@T samples collected after the first washing step are included.</p></caption><graphic xlink:href=\"ijms-21-05571-g001\"/></fig><fig id=\"ijms-21-05571-f002\" orientation=\"portrait\" position=\"float\"><label>Figure 2</label><caption><p>CD spectra in phosphate buffer saline solution (PBS, pH = 7.4) of Tetra(HPRG)-Fam (green, solid line, peptide concentration = 1 × 10<sup>−5</sup> M) and the pellets (10× dilution) for hybrid GO_B@T (dark green, solid line) and mixture GO_B+T (dark green, dotted line) samples.</p></caption><graphic xlink:href=\"ijms-21-05571-g002\"/></fig><fig id=\"ijms-21-05571-f003\" orientation=\"portrait\" position=\"float\"><label>Figure 3</label><caption><p>Fluorescence spectra (λ excitation = 488 nm) in PBS (pH = 7.4) of free Tetra(HPRG)-Fam peptide (dot green line) and GO@T hybrids (GO_A@T: dark green line; GO_A@T: light green line). In the inset the magnified region for the pellets (solid lines) and supernatants (dashed-dot lines).</p></caption><graphic xlink:href=\"ijms-21-05571-g003\"/></fig><fig id=\"ijms-21-05571-f004\" orientation=\"portrait\" position=\"float\"><label>Figure 4</label><caption><p>Attenuated total reflectance Fourier transform infrared (ATR/FTIR) spectra of graphene oxide (GO) (black curve), Tetra(histidine-proline-rich glycoprotein (HPRG))-Fam (green curve) and GO@T (dark cyan curve) hybrid samples.</p></caption><graphic xlink:href=\"ijms-21-05571-g004\"/></fig><fig id=\"ijms-21-05571-f005\" orientation=\"portrait\" position=\"float\"><label>Figure 5</label><caption><p>Cell viability from MTT assays of prostate cancer (<bold>a</bold>,<bold>d</bold>), neuroblastoma (<bold>b</bold>,<bold>e</bold>) and endothelial (<bold>c</bold>,<bold>f</bold>) cells incubated for 24 h (<bold>a</bold>–<bold>c</bold>) or 48 h (<bold>d</bold>–<bold>f</bold>) with the hybrid GO@T samples at increasing content of GO nanosheets (10–30 µg/mL concentration range) and Tetra(HPRG)-Fam peptide (3–6 µM concentration range). The negative control of untreated cells (CTRL) and the positive controls of cells treated with the peptide alone or the bare GO or the peptide mixtures are included as reference. Values (means ± SEM) are from three independent experiments. Results are expressed as percentage with respect to CTRL. Student’s <italic>t</italic>-test was used to compare cell viability measurements in all experimental conditions. (*) <italic>p</italic> < 0.05, (**) <italic>p</italic> < 0.01, (***), <italic>p</italic> < 0.001, (****) <italic>p</italic> < 0.0001 vs. CTRL, (<sup>#</sup>) <italic>p</italic> < 0.05, (<sup>##</sup>) <italic>p</italic> < 0.01, (<sup>###</sup>) <italic>p</italic> < 0.001, (<sup>####</sup>) <italic>p</italic> < 0.0001 vs. the corresponding GO reference sample.</p></caption><graphic xlink:href=\"ijms-21-05571-g005\"/></fig><fig id=\"ijms-21-05571-f006\" orientation=\"portrait\" position=\"float\"><label>Figure 6</label><caption><p>Representative micrographs (<bold>a</bold>,<bold>b</bold>) and quantitative analysis of cell migration (<bold>c</bold>,<bold>d</bold>) for prostate cancer (<bold>a</bold>,<bold>c</bold>) and endothelial (<bold>b</bold>,<bold>d</bold>) cells in absence (negative control, CTRL) or in presence of the different treatments at time 0, 24 h and 48 h after scratch: GO nanosheets (10, 12, 30 µg/mL), Tetra(HPRG)-Fam peptide (2, 4, 6 µM), GO@T hybrids (10 µg/mL 2 µM, 12 µg/mL 4 µM, 30 µg/mL 6 µM of GOpeptide concentration) and GO+T mixtures. PC-3 and HREC cells were wounded as described in the Materials and Methods section. The quantitative analysis of migration assay (wound edge advancement in percent vs. time) is expressed as means values (± SEM) from three independent experiments. Statistical analysis was performed by pairwise Student’s <italic>T</italic>-test results are expressed as percentage of wound closure with respect to time 0. (**) <italic>p</italic> < 0.01, (***) <italic>p</italic> < 0.001, (****) <italic>p</italic> < 0.0001 vs. CTRL, (<sup>#</sup>) <italic>p</italic> < 0.05, (<sup>##</sup>) <italic>p</italic> < 0.01, (<sup>###</sup>) <italic>p</italic> < 0.001 vs. the corresponding GO reference sample.</p></caption><graphic xlink:href=\"ijms-21-05571-g006\"/></fig><fig id=\"ijms-21-05571-f007\" orientation=\"portrait\" position=\"float\"><label>Figure 7</label><caption><p>Ratio of mitochondrial-reactive oxygen species (ROS)/total-ROS levels measured on prostate cancer cells (PC-3) measured by the MitoSOX and dichlorohydrofluorescein (DCF) fluorescence assays. Cells were incubated for 24 h in absence (negative control, CTRL) or in presence of the different treatments: GO nanosheets (10, 12, 30 µg/mL), Tetra(HPRG)-Fam peptide (2, 4, 6 µM) and hybrid GO@T samples (10 µg/mL-2 µM, 12 µg/mL-4 µM, 30 µg/mL-6 µM of GO-peptide concentration). Values (means ± SEM) are from three independent experiments. Statistical analysis was performed by One-way ANOVA test. Symbols indicate the significance versus CTRL: ** <italic>p</italic> < 0.01. Results are expressed as ratio of MitoSOX with respect to DCF emission intensities.</p></caption><graphic xlink:href=\"ijms-21-05571-g007\"/></fig><fig id=\"ijms-21-05571-f008\" orientation=\"portrait\" position=\"float\"><label>Figure 8</label><caption><p>Laser scanning microscopy (LSM) micrographs of PC-3 cells incubated for 24 h in absence (<bold>a</bold>: CTRL) or in presence of the different treatments: GO nanosheets (<bold>b</bold>: 10, 12, 30 µg/mL), Tetra(HPRG)-Fam peptide (<bold>c</bold>: 2, 4, 6 µM) and hybrid GO@T samples (<bold>d</bold>: 10 µg/mL-2 µM, 12 µg/mL-4 µM, 30 µg/mL-6 µM of GO-peptide concentration). For each treatment condition, the merged confocal images of nuclei (Hoechst, λex/em = 405/425–450 nm) and mitochondria (MitoTracker Deep Red, λex/em = 633/650–700) (upper panel), and the merged fluorescence image of the dye-labelled peptide (Fam, λex/em = 543/550–600 nm) and the optical bright field micrograph (lower panel) are shown. Scale bar 30 µm. The white arrows guide the eye to the GO sheets. In (<bold>e</bold>): quantitative analysis of Fam and MitoTracker Deep Red emission intensity. Results are presented as mean +/-SEM from experiments in triplicate and normalized with respect to the control untreated cells. Asterisks represent the correlation significant at the (*) <italic>p</italic> < 0.05, (**) <italic>p</italic> < 0.01, (***) <italic>p</italic> < 0.001 (****) <italic>p</italic> < 0.0001 vs. CTRL, (One-way ANOVA).</p></caption><graphic xlink:href=\"ijms-21-05571-g008\"/></fig><table-wrap id=\"ijms-21-05571-t001\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijms-21-05571-t001_Table 1</object-id><label>Table 1</label><caption><p>Prostaglandin (PGE<sub>2</sub>) production in PC-3 medium, after different treatments. Values (means ± SEM) are from three independent experiments (<italic>n</italic> = 3). ANOVA was used to compare PGE2 production in all experimental conditions. * <italic>p</italic> < 0.01 vs. control cells.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">PC-3 Treatment</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">PGE<sub>2</sub> (pg/mL)</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CTRL</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">103.1 ± 9.2</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">GO (10 µg/mL)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">64.5 ± 6.3 *</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">GO (12 µg/mL)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">66.3 ± 6.0 *</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">GO (30 µg/mL)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">60.2 ± 4.8 *</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Tetra(HPRG)-Fam (2 µM)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">78.1 ± 6.2 *</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Tetra(HPRG)-Fam (4 µM)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">71.7 ± 6.4 *</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Tetra(HPRG)-Fam (6 µM)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">76.3 ± 5.8 *</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">GO_A@T ([GO] = 10 µg/mL, [Tetra(HPRG)-Fam] = 2 µM</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">40.5 ± 3.7 *</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">GO_B@T ([GO] = 12 µg/mL, [Tetra(HPRG)-Fam] = 4 µM</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">44.3 ± 4.1 *</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">GO_A@T ([GO] = 30 µg/mL, [Tetra(HPRG)-Fam] = 6 µM</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">41.1 ± 4.8 *</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">GO_A+T ([GO] = 10 µg/mL, [Tetra(HPRG)-Fam] = 2 µM</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">62.2 ± 6.1 *</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">GO_B+T ([GO] = 12 µg/mL, [Tetra(HPRG)-Fam] = 4 µM</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">66.0 ± 5.5 *</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">GO_A+T ([GO] = 30 µg/mL, [Tetra(HPRG)-Fam] = 6 µM</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">57.8 ± 6.4 *</td></tr></tbody></table></table-wrap><table-wrap id=\"ijms-21-05571-t002\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijms-21-05571-t002_Table 2</object-id><label>Table 2</label><caption><p>PGE<sub>2</sub> production in human retinal endothelial cells (HREC), after different treatments. Values (means ± SEM) are from three independent experiments (<italic>n</italic> = 3). Student’s <italic>t</italic>-test used to compare PGE<sub>2</sub> production in all experimental conditions. * <italic>p</italic> < 0.01 vs. control cells.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">HREC Treatment</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">PGE<sub>2</sub> (pg/mL)</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CTRL</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">71.9 ± 6.1</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">GO (10 µg/mL)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">55.3 ± 4.2 *</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">GO (12 µg/mL)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">50.9 ± 5.4 *</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">GO (30 µg/mL)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">57.6 ± 5.2 *</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Tetra(HPRG)-Fam (2 µM)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">67.7 ± 5.8</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Tetra(HPRG)-Fam (4 µM)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">70.1 ± 5.5</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Tetra(HPRG)-Fam (6 µM)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">73.9 ± 6.3</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">GO_A@T ([GO] = 10 µg/mL, [Tetra(HPRG)-Fam] = 2 µM</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">25.4 ± 2.2 *</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">GO_B@T ([GO] = 12 µg/mL, [Tetra(HPRG)-Fam] = 4 µM</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">28.2 ± 3.8 *</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">GO_A@T ([GO] = 30 µg/mL, [Tetra(HPRG)-Fam] = 6 µM</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">23.5 ± 2.1 *</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">GO_A+T ([GO] = 10 µg/mL, [Tetra(HPRG)-Fam] = 2 µM</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">43.1 ± 5.2 *</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">GO_B+T ([GO] = 12 µg/mL, [Tetra(HPRG)-Fam] = 4 µM</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">46.4 ± 6.3 *</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">GO_A+T ([GO] = 30 µg/mL, [Tetra(HPRG)-Fam] = 6 µM</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">41.7 ± 4.8 *</td></tr></tbody></table></table-wrap></floats-group></article>\n"
]
[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Front Psychiatry</journal-id><journal-id journal-id-type=\"iso-abbrev\">Front Psychiatry</journal-id><journal-id journal-id-type=\"publisher-id\">Front. Psychiatry</journal-id><journal-title-group><journal-title>Frontiers in Psychiatry</journal-title></journal-title-group><issn pub-type=\"epub\">1664-0640</issn><publisher><publisher-name>Frontiers Media S.A.</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32848948</article-id><article-id pub-id-type=\"pmc\">PMC7432114</article-id><article-id pub-id-type=\"doi\">10.3389/fpsyt.2020.00784</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Psychiatry</subject><subj-group><subject>Original Research</subject></subj-group></subj-group></article-categories><title-group><article-title>Gray Matter Differences Between Premature Pubertal Girls With and Without the Reactivation of the Hypothalamic—Pituitary-Gonadal Axis</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Fu</surname><given-names>Yuchuan</given-names></name><xref ref-type=\"aff\" rid=\"aff1\">\n<sup>1</sup>\n</xref><uri xlink:type=\"simple\" xlink:href=\"https://loop.frontiersin.org/people/630338\"/></contrib><contrib contrib-type=\"author\"><name><surname>Zhang</surname><given-names>Wenjing</given-names></name><xref ref-type=\"aff\" rid=\"aff2\">\n<sup>2</sup>\n</xref><uri xlink:type=\"simple\" xlink:href=\"https://loop.frontiersin.org/people/122373\"/></contrib><contrib contrib-type=\"author\"><name><surname>Tao</surname><given-names>Bo</given-names></name><xref ref-type=\"aff\" rid=\"aff2\">\n<sup>2</sup>\n</xref></contrib><contrib contrib-type=\"author\"><name><surname>Yang</surname><given-names>Beisheng</given-names></name><xref ref-type=\"aff\" rid=\"aff2\">\n<sup>2</sup>\n</xref></contrib><contrib contrib-type=\"author\"><name><surname>Yang</surname><given-names>Di</given-names></name><xref ref-type=\"aff\" rid=\"aff3\">\n<sup>3</sup>\n</xref><uri xlink:type=\"simple\" xlink:href=\"https://loop.frontiersin.org/people/624681\"/></contrib><contrib contrib-type=\"author\"><name><surname>Xie</surname><given-names>Xiaoling</given-names></name><xref ref-type=\"aff\" rid=\"aff1\">\n<sup>1</sup>\n</xref></contrib><contrib contrib-type=\"author\"><name><surname>Liu</surname><given-names>Peining</given-names></name><xref ref-type=\"aff\" rid=\"aff4\">\n<sup>4</sup>\n</xref></contrib><contrib contrib-type=\"author\"><name><surname>Zhu</surname><given-names>Yaxin</given-names></name><xref ref-type=\"aff\" rid=\"aff1\">\n<sup>1</sup>\n</xref></contrib><contrib contrib-type=\"author\"><name><surname>Zhou</surname><given-names>Lu</given-names></name><xref ref-type=\"aff\" rid=\"aff1\">\n<sup>1</sup>\n</xref><uri xlink:type=\"simple\" xlink:href=\"https://loop.frontiersin.org/people/888361\"/></contrib><contrib contrib-type=\"author\"><name><surname>Chen</surname><given-names>Tao</given-names></name><xref ref-type=\"aff\" rid=\"aff1\">\n<sup>1</sup>\n</xref></contrib><contrib contrib-type=\"author\"><name><surname>Liu</surname><given-names>Xiaozheng</given-names></name><xref ref-type=\"aff\" rid=\"aff1\">\n<sup>1</sup>\n</xref><uri xlink:type=\"simple\" xlink:href=\"https://loop.frontiersin.org/people/408181\"/></contrib><contrib contrib-type=\"author\"><name><surname>Yan</surname><given-names>Zhihan</given-names></name><xref ref-type=\"aff\" rid=\"aff1\">\n<sup>1</sup>\n</xref><xref ref-type=\"author-notes\" rid=\"fn001\">\n<sup>*</sup>\n</xref></contrib></contrib-group><aff id=\"aff1\">\n<sup>1</sup>\n<institution>Department of Radiology, Second Affiliated Hospital and Yuying Children’s Hospital of Wenzhou Medical University</institution>, <addr-line>Wenzhou</addr-line>, <country>China</country>\n</aff><aff id=\"aff2\">\n<sup>2</sup>\n<institution>Department of Radiology, West China Hospital of Sichuan University</institution>, <addr-line>Chengdu</addr-line>, <country>China</country>\n</aff><aff id=\"aff3\">\n<sup>3</sup>\n<institution>Department of Radiology, Zhejiang Hospital</institution>, <addr-line>Hangzhou</addr-line>, <country>China</country>\n</aff><aff id=\"aff4\">\n<sup>4</sup>\n<institution>Department of Child Health Care, The Second Affiliated Hospital and Yuying Children’s Hospital of Wenzhou Medical University</institution>, <addr-line>Wenzhou</addr-line>, <country>China</country>\n</aff><author-notes><fn fn-type=\"edited-by\"><p>Edited by: Hesheng Liu, Harvard Medical School, United States</p></fn><fn fn-type=\"edited-by\"><p>Reviewed by: Ji-Won Chun, Catholic University of Korea, South Korea; Deniz Vatansever, Fudan University, China</p></fn><corresp id=\"fn001\">*Correspondence: Zhihan Yan, <email xlink:href=\"mailto:[email protected]\" xlink:type=\"simple\">[email protected]</email>\n</corresp><fn fn-type=\"other\" id=\"fn002\"><p>This article was submitted to Computational Psychiatry, a section of the journal Frontiers in Psychiatry</p></fn></author-notes><pub-date pub-type=\"epub\"><day>11</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><year>2020</year></pub-date><volume>11</volume><elocation-id>784</elocation-id><history><date date-type=\"received\"><day>09</day><month>1</month><year>2020</year></date><date date-type=\"accepted\"><day>22</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>Copyright © 2020 Fu, Zhang, Tao, Yang, Yang, Xie, Liu, Zhu, Zhou, Chen, Liu and Yan</copyright-statement><copyright-year>2020</copyright-year><copyright-holder>Fu, Zhang, Tao, Yang, Yang, Xie, Liu, Zhu, Zhou, Chen, Liu and Yan</copyright-holder><license xlink:href=\"http://creativecommons.org/licenses/by/4.0/\"><license-p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</license-p></license></permissions><abstract><p>The onset of puberty and related hormones exerts significant effects on brain morphometric and psychosocial development. The biological mechanisms underlying how the reactivation of the hypothalamic-pituitary-gonadal (HPG) axis and puberty-related hormonal maturation sculpts human brain architecture remain elusive. To address this question, 105 premature pubertal girls (age 8–11 years) without menstruation underwent brain structural scanning on a 3T MR system, and the luteinizing hormone releasing hormone (LHRH) stimulation test was used to identify the reactivation of the HPG axis. Among the 105 girls, 63 were positive for HPG axis reactivation (HPG+), while the others showed negative (HPG-). Cortical thickness was calculated and compared between the two groups after adjusting for age. The brain regions showing inter-group differences were then extracted and correlated with the peak value of serum hormone after the LHRH stimulation test in entire sample. Compared to HPG- girls, HPG+ girls showed reduced cortical thickness mainly in the the right precuneus, right inferior temporal gyrus, and right superior frontal gyrus, while increased cortical thickness primarily in the left superior parietal lobe and right inferior parietal lobe. Linear-regression analysis revealed negative correlations between the cortical thickness of the right inferior parietal lobe with the peak value of FSH and the right precuneus with LH and E. These findings provide evidence to support the notion that the reactivation of HPG axis and changes of hormones during the early phase of hormonal maturation exert influences on the development of gray matter.</p></abstract><kwd-group><kwd>cortical thickness</kwd><kwd>follicle stimulating hormone</kwd><kwd>luteinizing hormone</kwd><kwd>estradiol</kwd><kwd>hypothalamic-pituitary-gonadal axis</kwd></kwd-group><funding-group><award-group><funding-source id=\"cn001\">Natural Science Foundation of Zhejiang Province<named-content content-type=\"fundref-id\">10.13039/501100004731</named-content></funding-source></award-group></funding-group><counts><fig-count count=\"2\"/><table-count count=\"3\"/><equation-count count=\"0\"/><ref-count count=\"43\"/><page-count count=\"8\"/><word-count count=\"4917\"/></counts></article-meta></front><body><sec sec-type=\"intro\" id=\"s1\"><title>Introduction</title><p>Puberty is a critical period in which dramatic hormone-related changes parallel changes in emotion, behavior, cognition, physical, and brain development (<xref rid=\"B1\" ref-type=\"bibr\">1</xref>, <xref rid=\"B2\" ref-type=\"bibr\">2</xref>). Notably, the effect of puberty on psychosocial development in adolescence is believed to vary at different stages, with the early phase of puberty appearing to play a more critical role than those in middle and late adolescence (<xref rid=\"B3\" ref-type=\"bibr\">3</xref>). The onset of puberty is often deemed as a trigger of several neuroendocrine-related changes, such as a vast increase in sexual hormone, which occur prior to the physical changes. The released gonadotropin releasing hormone (GnRH) is distributed in the hypothalamus in a pulsatile fashion and initiates a cascade of events that have been recognized as the onset of puberty. Following the secretion of GnRH, the pituitary responds by secreting luteinizing hormone (LH) and follicular stimulating hormone (FSH). These major hormonal changes occur just after the reactivation of the hypothalamic-pituitary-gonadal (HPG) axis, causing gonadal maturation and the production of sex steroids, most notably estrogen (E) and testosterone (TES). The first activation of the HPG axis begins at the prenatal and the early postnatal months. After the first year of birth, the HPG axis lays dormant until the resurrection of the GnRH, which facilitate the onset of puberty (<xref rid=\"B4\" ref-type=\"bibr\">4</xref>). The increased secretion of LH from the pituitary gland is the first measurable endocrinological marker of puberty (<xref rid=\"B5\" ref-type=\"bibr\">5</xref>). Generally, a serum LH level of ≥5 IU/L using modern ultrasensitive automated chemiluminescence assays after the luteinizing hormone releasing hormone (LHRH) stimulation test from patient samples (either 30 or 60 min) can be recognized as being positive for HPG axis reactivation, otherwise being negative for HPG axis reactivation following previously established clinical criteria (<xref rid=\"B6\" ref-type=\"bibr\">6</xref>–<xref rid=\"B9\" ref-type=\"bibr\">9</xref>). In the occasional child whose LH is not quite as high, they showed the auxological and clinical signs of puberty, a LHRH stimulation test may be required to confirm the diagnosis. However, sexual maturation in the previous research was defined by using biological features or clinical features [i.e., Tanner stage (<xref rid=\"B10\" ref-type=\"bibr\">10</xref>, <xref rid=\"B11\" ref-type=\"bibr\">11</xref>) or the self-report of children (<xref rid=\"B12\" ref-type=\"bibr\">12</xref>)]. Therefore, it is necessary to specify the brain regions on which the HPG axis reactivation showed association in early adolescence.</p><p>Previous studies have found that brain morphology differs at different stages of puberty. A few studies found that the pubertal stage, as assessed by Tanner staging of sexual maturation, is related to brain morphology. For instance, girls in mid/late puberty had significantly lower right and left cortical gray matter volumes than girls in earlier stages of puberty (<xref rid=\"B13\" ref-type=\"bibr\">13</xref>). The gray matter volume of inferior frontal gyrus in adolescents was larger in early pubertal stages compared to that in late puberty (<xref rid=\"B12\" ref-type=\"bibr\">12</xref>). The volume in the right hippocampus decreased with increasing Tanner hair stage, while the volume of the right and left amygdala decreased with increasing Tanner breast stage (<xref rid=\"B14\" ref-type=\"bibr\">14</xref>). In addition, greater increases in Tanner stage predicted less cortical thickness in the right superior frontal gyrus and right superior temporal gyrus and decreases in the left precuneus surface area in the girls (<xref rid=\"B15\" ref-type=\"bibr\">15</xref>). Goddings and her colleagues also demonstrated that the volume of the amygdala and hippocampus increased and the volume of nucleus accumbens, caudate, putamen, and globus pallidus decreased across puberty as measured by Tanner stage (<xref rid=\"B16\" ref-type=\"bibr\">16</xref>). While in a large group of 9-year-old twins, including males, the children that showed signs of physical maturation (Tanner stage ≥ 2) was associated with decreased parietal gray matter densities compared to the children without any secondary pubertal signs (Tanner stage ≤ 1) (<xref rid=\"B17\" ref-type=\"bibr\">17</xref>). However, how brain development is regulated by pubertal changes, especially at the early stage premenarche, remains poorly understood (<xref rid=\"B14\" ref-type=\"bibr\">14</xref>). Previous studies either in animals or humans found that puberty-related hormones were associated with the development of the gray matter in brain. In animal models, puberty-related hormonal changes have been shown to induce changes in brain maturation, including structural reorganization and neuronal circuit pruning, with these effects starting at puberty onset and continuing through adolescence (<xref rid=\"B18\" ref-type=\"bibr\">18</xref>–<xref rid=\"B20\" ref-type=\"bibr\">20</xref>). Several studies in humans have been conducted to examine the associations between changes in serum hormones and brain structure during adolescence, and the findings were inconsistent. One study examined the influence of sex steroids on structural brain maturation and found that there were no effects of sex steroids on cortical thickness in adolescents aged 8–25 years (<xref rid=\"B12\" ref-type=\"bibr\">12</xref>). One study demonstrated that higher TES levels were associated with thinner left calcarine sulcus and right lingual gyrus cortex in girls (<xref rid=\"B21\" ref-type=\"bibr\">21</xref>). Another study demonstrated that higher E levels, but not TES, were associated with smaller gray matter density in prefrontal, parietal, and middle temporal areas and greater gray matter density in the middle frontal, inferior temporal, and middle occipital gyri in girls between 10 and 15 years of age (<xref rid=\"B22\" ref-type=\"bibr\">22</xref>). Another two studies also showed that changes in the TES and E levels were associated with white matter, gray matter, right amygdala, and bilateral caudate volumes, cortical thickness and surface area (<xref rid=\"B15\" ref-type=\"bibr\">15</xref>, <xref rid=\"B23\" ref-type=\"bibr\">23</xref>). Higher FSH levels have also been associated with greater gray matter density in left prefrontal areas, hippocampus, anterior cingulate, precuneus, and right cerebellum among girls aged 9 to 12 years (<xref rid=\"B24\" ref-type=\"bibr\">24</xref>). Previous studies did not find an association between LH and gray matter (<xref rid=\"B12\" ref-type=\"bibr\">12</xref>, <xref rid=\"B24\" ref-type=\"bibr\">24</xref>, <xref rid=\"B25\" ref-type=\"bibr\">25</xref>); however, a trend for negative association was been suggested between the gray matter density of temporal areas and the LH in the 9-year-old twins (<xref rid=\"B25\" ref-type=\"bibr\">25</xref>). Nonetheless, these studies indicated that puberty-related hormones exerted positive or/and negative influences on gray matter structural development and played different roles in brain areas. Due to variable findings in previous studies and the importance of studying gonadotropin concentration at an early stage of hormonal maturation, more research is needed to identify hormone-related influences on brain organization during the initiation of puberty.</p><p>To achieve this aim, we enrolled girls aged 8–11 years with thelarche as participants whose bone age and uterine volume were advanced than their chronologic age, seeking to identify the influences of the HPG axis reactivation on brain organization during the initiation of puberty. We conducted high-resolution structural MRI brain scanning in young girls who underwent testing for HPG axis reactivation determined by stimulated LH concentration. We used HPG+ to represent being positive for HPG axis reactivation and HPG- for being negative for HPG axis reactivation. We hypothesized that the girls whose HPG axis reactivation was positive (HPG+) would show cortical thickness differences compared to the girls whose HPG axis reactivation was negative (HPG-), and there were associations between the cortical thickness of several regions showing differences between the two groups and the peak value of puberty-related hormone after the LHRH stimulation test in the entire sample.</p></sec><sec id=\"s2\"><title>Methods</title><sec id=\"s2_1\"><title>Subjects</title><p>This study was approved by the ethics committee of the Second Affiliated Hospital and Yuying Children’s of Wenzhou Medical University. The LHRH stimulation test is an invasive procedure; it needs the intravenous injection of the medication and the collection of the blood sample three times in the test. Therefore, it is difficult to obtain the consent of the guardians to allow healthy girls to take it. We chose to look at these younger girls with thelarche whose bone age and uterine volume were advanced than their chronologic age as study participants. The positive for HPG axis reactivation was diagnosed premature puberty. The girls with premature puberty were recommended to non-clinical intervention such as exercise more and pay attention to their diets and so on and clinical following-up by doctor (<xref rid=\"B6\" ref-type=\"bibr\">6</xref>). Their parents also wanted to know their children’s height development thereafter. Because of the HPG-axis reactivation, the process is followed by the activation of the growth axis leading to growth spurts in puberty.</p><p>All the subjects provided written informed consent from the guardians before participation. One hundred five right-handed girls aged 8–11 years were recruited <italic>via</italic> the Child Healthcare Department of the Second Affiliated Hospital and Yuying Children’s of Wenzhou Medical University. The exclusion criteria were as follows: (1) intelligence quotient (IQ) < 70 estimated by the Chinese Wechsler Intelligence Scale for Children (C-WISC) (<xref rid=\"B17\" ref-type=\"bibr\">17</xref>), (2) history of premature birth or postterm birth, (3) history of neurological or psychiatric disorders in the participants or their first-degree relatives, (4) history of head trauma, (5) have any systemic physical illness, (6) any medications known to affect brain function or hormone levels, (7) menstruation, and (8) contraindications to MRI scanning. Finally, 105 girls were recruited: 63 were in the HPG+ group, and the others were in the HPG group. Details of the demographics of all subjects are shown in <xref rid=\"T1\" ref-type=\"table\">\n<bold>Table 1</bold>\n</xref>.</p><table-wrap id=\"T1\" position=\"float\"><label>Table 1</label><caption><p>Demographic and clinical characteristics of girls.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th valign=\"top\" rowspan=\"2\" colspan=\"2\" align=\"left\">Characteristic</th><th valign=\"top\" colspan=\"2\" align=\"center\" rowspan=\"1\">HPG+ (n = 63)</th><th valign=\"top\" colspan=\"2\" align=\"center\" rowspan=\"1\">HPG- (n = 42)</th><th valign=\"top\" rowspan=\"2\" align=\"center\" colspan=\"1\">\n<italic>p</italic>\n</th></tr><tr><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Mean</th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">SD</th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Mean</th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">SD</th></tr></thead><tbody><tr><td valign=\"top\" colspan=\"2\" align=\"left\" rowspan=\"1\">Age (years)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">9.13</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.57</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">8.92</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.36</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.064</td></tr><tr><td valign=\"top\" colspan=\"2\" align=\"left\" rowspan=\"1\">BMI (kg/m<sup>2</sup>)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">17.08</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.16</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">16.21</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.66</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.071</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">LH(IU/L)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">LH max</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">21.09</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">15.60</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.36</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.86</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><0.000</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">LH baseline</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.04</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.197</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.117</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.065</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><0.000</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">FSH(IU/L)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">FSH max</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">16.59</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">6.53</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">12.94</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5.13</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><0.001</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">FSH baseline</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4.67</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5.06</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.18</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.88</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.0022</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">E2(pg/ml)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">E2max</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">39.44</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">15.43</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">32.45</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">14.04</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><0.010</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">E2 baseline</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">37.84</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">16.17</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">31.25</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">14.25</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.043</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">CBCL</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Total score</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">10.52</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">10.64</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">12</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">10.51</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.156</td></tr><tr><td valign=\"top\" colspan=\"2\" align=\"left\" rowspan=\"1\">IQ</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">91.11</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">14.50</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">89.79</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">12.05</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.12</td></tr></tbody></table><table-wrap-foot><fn><p>SD, standard deviation; BMI, body mass index (BMI, calculated as Weight/Height<sup>2</sup>); HPG+/HPG-, with/without reactivated the HPG-axis; LH, luteinizing hormone; FSH, follicle stimulating hormone; E2, estradiol; TES, testosterone; CBCL, Child Behavior Checklist; IQ, intelligence quotient. p < 0.05 was considered statistically significant in all the tests.</p></fn></table-wrap-foot></table-wrap></sec><sec id=\"s2_2\"><title>Assessment Scales and Pubertal Assessment</title><p>Intelligence tests were performed by means of Wechsler Young Children Scales of Intelligence (C-WISC) in all the subjects to screen for mental retardation before the MRI scanning. Then, assisted by a child care physician, the primary caretaker of each child completed the child behavior checklist (CBCL) (<xref rid=\"B26\" ref-type=\"bibr\">26</xref>), which is a self-report questionnaire used to assess the behavioral and emotional problems of the children. The details are shown in <xref rid=\"T1\" ref-type=\"table\">\n<bold>Table 1</bold>\n</xref>. Tanner staging was used to examine pubertal status. Mean ages and the standard deviations, the frequencies for Tanner breast stage and hair stage are showed in <xref rid=\"T2\" ref-type=\"table\">\n<bold>Table 2</bold>\n</xref>.</p><table-wrap id=\"T2\" position=\"float\"><label>Table 2 </label><caption><p>Tanner stage.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th valign=\"top\" rowspan=\"2\" align=\"left\" colspan=\"1\">Tanner stage</th><th valign=\"top\" colspan=\"2\" align=\"center\" rowspan=\"1\">Tanner breast (mean age ± SD; frequency)</th><th valign=\"top\" colspan=\"2\" align=\"center\" rowspan=\"1\">Tanner hair (mean age ± SD; frequency)</th></tr><tr><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">HPG+</th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">HPG-</th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">HPG+</th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">HPG-</th></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">9.13 ± 0.57<break/>63</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">8.92 ± 0.36<break/>42</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">2</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">9.13 ± 0.57<break/>63</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">8.92 ± 0.36<break/>42</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0</td></tr></tbody></table><table-wrap-foot><fn><p>Mean age, standard deviations (in years), and frequencies for Tanner breast and hair stages.</p></fn><fn><p>Tanner breast 1: preadolescent, 2: appearance of the breast bud. Tanner hair 1: no public hair.</p></fn></table-wrap-foot></table-wrap></sec><sec id=\"s2_3\"><title>LHRH Stimulation Test</title><p>All subjects received the LHRH stimulation test after the imaging data acquisition. Both exams were completed within a week. Following overnight fasting, the participants were told to arrive at the hospital at approximately 8:00 am. Four to five milliliters of blood were collected (0-min sample), and then the LHRH was injected as an intravenous bolus of 2.5 μg/kg (maximum dose < 100 μg) (<xref rid=\"B27\" ref-type=\"bibr\">27</xref>) <italic>via</italic> an indwelling catheter. At 30 and 60 min after injection, 2 ml of blood were drawn again.</p><p>The bloods samples from 0 min were sent immediately to the clinical laboratory for analysis, and the 30- and 60-min samples after the LHRH injection were sent immediately to analyze respectively. The samples were centrifuged, separated, and assayed. We assayed LH, FSH, and estradiol (E) using modern ultrasensitive automated chemiluminescence assays, and the lower limit of detection was 0.07 IU/L for LH, 0.3 IU/L for FSH, 10.7 pg/ml for E, and 0.1 ng/ml for TES separately. Intra-assay coefficient of variation (CV) was 3.72 and 3% at 31.98 and 63.02 IU/L for LH, 5.08 and 3.97% at 36.1 and 83.65 IU/L for FSH, 5.55 and 4.61% at 265.22 and 552.67 pg/ml for E, respectively. Inter-assay CV was 5.13 and 4.63% at 30.44 and 59.96 IU/L for LH, 5.58 and 5.07% at 36.68 and 83.91 IU/L for FSH, and 6.08 and 3.81% at 260.37 and 547.73 pg/ml for E, respectively. A serum LH level of ≥5 IU/L after the LHRH stimulation test from patient samples (either 30 or 60 min) is considered evidence of the positive for HPG-axis reactivation otherwise negative for HPG axis reactivation following previously established criteria (<xref rid=\"B6\" ref-type=\"bibr\">6</xref>–<xref rid=\"B9\" ref-type=\"bibr\">9</xref>). We used HPG+ to represent being positive for HPG axis reactivation and HPG- for being negative for HPG axis reactivation. The peak value of serum puberty-related hormone after the LHRH stimulation test (either 30min or 60min) was used for linear regression analysis. Details of the hormone levels are shown in <xref rid=\"T1\" ref-type=\"table\">\n<bold>Table 1</bold>\n</xref>.</p></sec><sec id=\"s2_4\"><title>Structural Data Acquisition</title><p>The participants were scanned on a 3T GE HDxt scanner (General Electric, Milwaukee, Wisconsin) with an eight-channel phase array head coil. High-resolution T1-weighted images were acquired axially for the whole brain with a volumetric three-dimensional fast spoiled gradient recall (FSPGR) sequence. The scan parameters were as follows: TE/TR = 8.88 ms/4.02 ms, flip angle = 15°, FOV = 256 × 256, voxel size = 1 mm × 1 mm × 1 mm, and 160 slices with no gap. T2 FLAIR and T2-weighted images were acquired to screen the brain abnormalities by two experienced neuroradiologists. The quality of all the images was also controlled by them.</p></sec><sec id=\"s2_5\"><title>Image Processing</title><p>The FreeSurfer package (version 5.30, <uri xlink:type=\"simple\" xlink:href=\"http://surfer.nmr.mgh.harvard.edu/\">http://surfer.nmr.mgh.harvard.edu/</uri>) was used for preprocessing of cortical thickness in high-resolution T1-weighted images. This method has high test-retest reliability (<xref rid=\"B28\" ref-type=\"bibr\">28</xref>, <xref rid=\"B29\" ref-type=\"bibr\">29</xref>). The FreeSurfer pipeline performs motion correction on T1-images. The post-processing procedures have been explained in detail in previous studies (<xref rid=\"B30\" ref-type=\"bibr\">30</xref>, <xref rid=\"B31\" ref-type=\"bibr\">31</xref>) and contained the following main steps: bias-field correction, Talairach and Tournoux atlas conversion, signal strength standardization, skull-stripped, automatically delineated gray/white matter boundaries, and the pial matter topology correction. Cortical thickness was calculated as the shortest distance between the white matter interface and the pial matter at each vertex across the cortical boundaries (<xref rid=\"B31\" ref-type=\"bibr\">31</xref>). These post-processing procedures were performed separately on each cerebral hemisphere.</p></sec><sec id=\"s2_6\"><title>Statistical Analysis</title><p>Differences between HPG (+) and HPG (-) girls on demographic characteristics (e.g., age, BMI, CBCL total score, and IQ) and the hormone concentrations were examined using the independent sample Mann-Whitney U test.</p><p>The vertex-wise comparison of the cortical maps was performed on QDEC (Query, Design, Estimate, Contrast) with age as a covariate, and a whole-brain statistical threshold correction was performed using the Monte Carlo simulation method. Statistical significance was set at a cluster-wise–corrected P value < 0.05. A smoothing step using a Gaussian filter with 10-mm full-width at half maximum was initiated to average cortical thickness data across participants in a common spherical coordinate system.</p><p>Brain areas with statistical inter-group differences in cortical thickness were selected as brain region of interest (ROI). The average cortical thickness data of the ROI were extracted from the clusters. Correlation analysis between the regional cortical thickness values and stimulated hormone levels was assessed by using a general linear model and age as nuisance variables in the entire sample. Statistical significance was set at alpha < 0.05.</p></sec></sec><sec sec-type=\"results\" id=\"s3\"><title>Results</title><sec id=\"s3_1\"><title>Demographic Statistics</title><p>No statistically significant differences were found in the age (p = 0.064, >0.05), BMI (p = 0.071, >0.05), CBCL total score (p = 0.156, >0.05), IQ (p = 0.12, >0.05), and Tanner breast stage and hair stage between the HPG+ girls and HPG- girls. There were statistically significant differences in the levels of LH (p < 0.0001, <0.05), FSH (p = 0.0030, <0.05), and E (p = 0.0204, <0.05) between the two groups (<xref rid=\"T1\" ref-type=\"table\">\n<bold>Table 1</bold>\n</xref>).</p></sec><sec id=\"s3_2\"><title>Group Differences in Cortical Thickness</title><p>Compared to HPG(-) girls, it showed difference in cortical thickness of 10 brain regions in HPG(+) girls, less cortical thickness primarily in the right precuneus (p = 0.002, <0.05), right inferior temporal gyrus (p = 0.006, <0.05), right superior frontal gyrus (p = 0.044, <0.05) and in the left rostral middle frontal gyrus (p = 0.028, <0.05), and higher cortical thickness in the right temporal pole (p = 0.006, <0.05), right inferior parietal lobe (p = 0.003, <0.05), right rostral middle frontal gyrus (p = 0.04, <0.05) and in the left superior parietal lobe (p = 0.001, <0.05), lateral occipital gyrus (p = 0.009, <0.05), and superior temporal gyrus (p = 0.01, <0.05) (<xref ref-type=\"fig\" rid=\"f1\">\n<bold>Figure 1</bold>\n</xref> and <xref rid=\"T3\" ref-type=\"table\">\n<bold>Table 3</bold>\n</xref>)</p><fig id=\"f1\" position=\"float\"><label>Figure 1</label><caption><p>Differences in cortical thickness between the HPG+ girls and the HPG- girls. Decreased cortical thickness colored by blue and increased cortical thickness colored by red. HPG+ girls show decreased cortical thickness in the left rostral middle frontal gyrus, right precuneus, right inferior temporal gyrus, right superior frontal gyrus and increased cortical thickness in the right temporal pole, right inferior parietal lobe, right rostral middle frontal gyrusin, left superior parietal lobe, left lateral occipital gyrus, left superior temporal gyrus compared to the HPG- girls. Threshold at <italic>p</italic> < 0.05.</p></caption><graphic xlink:href=\"fpsyt-11-00784-g001\"/></fig><table-wrap id=\"T3\" position=\"float\"><label>Table 3</label><caption><p>Brain regions with significantly differences in cortical thickness between HPG+ and HPG-.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th valign=\"top\" rowspan=\"2\" align=\"left\" colspan=\"1\">\n<bold>Brain region HPG+ and HPG-</bold>\n</th><th valign=\"top\" rowspan=\"2\" align=\"center\" colspan=\"1\">\n<bold>Cluster size (mm<sup>2</sup>)</bold>\n</th><th valign=\"top\" colspan=\"3\" align=\"center\" rowspan=\"1\">\n<bold>MNI coordinates</bold>\n</th><th valign=\"top\" rowspan=\"2\" align=\"center\" colspan=\"1\">\n<bold><italic>P</italic></bold>\n</th></tr><tr><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">X</th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Y</th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Z</th></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold>Less</bold>\n</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold>Precuneus-R</bold>\n</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">116.79</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5.8</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-65.1</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">34.3</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.002</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold>ITG-R</bold>\n</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">70.18</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">48</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-43.9</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-16.3</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.006</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold>SFG-R</bold>\n</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">23.91</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">10.2</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.8</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">51.7</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.044</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold>rMFG-L</bold>\n</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">24.30</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-28.2</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">32.0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">30.7</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.028</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold>Higher</bold>\n</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold>Temporalpole-R</bold>\n</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">105.91</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">33.1</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">14.3</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-35.5</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.006</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold>IPL-R</bold>\n</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">53.85</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">47.1</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-54.4</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">44.6</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.003</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold>rMFG-R</bold>\n</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">30.43</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">19.0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">55.8</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-13.9</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.04</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold>SPL-L</bold>\n</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">61.69</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-21.4</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-51.6</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">62.1</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.001</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold>LOG-L</bold>\n</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">184.07</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-35.4</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-82.5</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-14.9</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.009</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold>STG-L</bold>\n</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">55.43</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-57.7</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-43.2</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">15.8</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.01</td></tr></tbody></table><table-wrap-foot><fn><p>Less, cortical thickness less in the HPG+ than in the HPG-; higher, cortical thickness higher in the HPG+ than in the HPG-; ITG-R, right inferior temporal gyrus; SFG-R, right superior frontal gyrus; rMFG-L, left rostral middle frontal gyrus; IPL-R, right inferior parietal lobe; rMFG-R, right rostral middle frontal gyrus; SPL-L, left superior parietal lobe; LOG-L, left lateral occipital gyrus; STG-L, left superior temporal gyrus.</p></fn></table-wrap-foot></table-wrap></sec><sec id=\"s3_3\"><title>Associations Between Hormone Levels and Cortical Thickness in the Entire Sample</title><p>We conducted a linear regression analysis to detect hormone-related variation in the cortical thickness of the differential brain region (10 ROIs) in the two groups. The results showed that the peak value of FSH after the LHRH stimulation test was negatively correlated to cortical thickness in the right inferior parietal lobe (r = -0.2630, p = 0.0067, <0.05), the peak value of LH and E were negatively correlation with cortical thickness in the right precuneus (r = -0.2043, p = 0.0365, <0.05; r = -0.2189, p = 0.0249, <0.05) (<xref ref-type=\"fig\" rid=\"f2\">\n<bold>Figure 2</bold>\n</xref>). After the FDR correction, the results showed that the peak values of FSH after the LHRH stimulation test were negatively correlated to cortical thickness in the right inferior parietal lobe (p = 0.0403, <0.05).</p><fig id=\"f2\" position=\"float\"><label>Figure 2</label><caption><p>Associations between hormone levels and cortical thickness. <bold>(A)</bold> Negative correlation between the LH level and the cortical thickness in the right precuneus. <bold>(B)</bold> Negative correlation between the E levels and the cortical thickness in the right precuneus. <bold>(C)</bold> Negative correlation between the FSH levels and the cortical thickness in the right inferior parietal lobe.</p></caption><graphic xlink:href=\"fpsyt-11-00784-g002\"/></fig></sec></sec><sec sec-type=\"discussion\" id=\"s4\"><title>Discussion</title><p>To our knowledge, this study is the first to investigate the impact of HPG axis reactivation defined by LHRH stimulation testing on cortical thickness. Our main findings were (1) HPG+ girls who showed reduced cortical thickness mainly in the right precuneus, right inferior temporal gyrus, right superior frontal gyrus, and the left rostral middle frontal gyrus while increased cortical thickness primarily in the left superior parietal lobe and right inferior parietal lobe, the right temporal pole, right rostral middle frontal gyrus, left lateral occipital gyrus, and left superior temporal gyrus. (2) The correlation analysis revealed negative correlations between the peak value of LH and E after LHRH stimulation test and cortical thickness in the right precuneus, the peak value of FSH, and the right inferior parietal lobe in the two groups. Our results suggest that the reactivation of the HPG axis and related hormonal maturation may be involved in sculpting human brain cortical architecture.</p><p>One of the main interesting findings is the bidirectional changes of cortical thickness. In fact, previous studies indicated changes in the gray matter during different stage of puberty. For example, Herting et al. reported greater increases in Tanner stage predicted decreased cortical thickness including the right superior frontal gyrus and right superior temporal gyrus in puberty girls (<xref rid=\"B15\" ref-type=\"bibr\">15</xref>). While in a large group of 9-year-old twins, including males, the children that showed any signs of physical maturation (Tanner stage ≥ 2) was associated with decreased parietal gray matter densities compared to the children without any secondary pubertal signs (Tanner stage ≤ 1) (<xref rid=\"B17\" ref-type=\"bibr\">17</xref>). No studies have examined the difference in cortical thickness between prepuberty girls and girls of the same age at the onset of puberty. Our findings suggested that the reactivation of the HPG axis played a different role in the cortical thickness. The reactivation of the HPG axis drives dramatic changes in physical appearance, such as facial structure and the appearance of secondary sexual characteristics, and adolescents enter a stage of profound psychological transition in the onset of puberty, which may negatively or positively affect brain maturation. To reach a stable adult role, there are changes in the structure and function of the brain. Furthermore, multiple studies showed the receptors for HPG axis hormones are found in various brain cortical especially for learning and memory (<xref rid=\"B32\" ref-type=\"bibr\">32</xref>, <xref rid=\"B33\" ref-type=\"bibr\">33</xref>). Most brain areas in our result were involved in learning and/or memory function. The reactivation of the HPG axis caused a cascade secretion of hormone, which attached to the receptors in the brain cortical. It might be one mechanism that the reactivation of the HPG axis sculpts the brain cortical thickness in our result.</p><p>Another important result demonstrates the correlations between the gray matter changes and the level of hormones. Sex steroids are known to affect the brain in two different ways: organizational effects and activational effects. The organizational effects of sex hormones were played by permanently changing the structure of the brain, such as neuronal number, myelination, and dendritic branching (<xref rid=\"B34\" ref-type=\"bibr\">34</xref>). Previous animal studies found that E levels could inhibit cortical myelination during puberty (<xref rid=\"B35\" ref-type=\"bibr\">35</xref>), and it is also involved in apoptotic processes (<xref rid=\"B36\" ref-type=\"bibr\">36</xref>). Another study found that estradiol altered dendritic spine density and dendritic spines in brain regions related to cognition and memory were sensitive to estrogen fluctuations (<xref rid=\"B37\" ref-type=\"bibr\">37</xref>). The precuneus was deemed to play a central role in a wide spectrum of highly integrated tasks, including self-centered mental imagery strategies, consciousness, and episodic memory retrieval (<xref rid=\"B38\" ref-type=\"bibr\">38</xref>). Yates and Juraska found that puberty ovarian hormone exposure reduced the number of myelinated axons in the splenium of corpus callosum in the rats (<xref rid=\"B18\" ref-type=\"bibr\">18</xref>). Koss and the colleagues also found that the number of neurons and glia were increased in the medial prefrontal cortex of female gonadectomy before puberty rats (<xref rid=\"B20\" ref-type=\"bibr\">20</xref>). Therefore, we hypothesize that higher estradiol levels might contribute to changes in the cortical thickness of the precuneus by decreasing the cortical number of neurons and/or myelinated axons.</p><p>Previous animal and human studies have shown that LH and FSH receptors have been found in various brain areas including the parietal cortex (<xref rid=\"B39\" ref-type=\"bibr\">39</xref>, <xref rid=\"B40\" ref-type=\"bibr\">40</xref>). However, how circulating LH and FSH levels exert their effects on cerebral gray matter remains unclear. Bukovsky and colleagues speculated that microglial cells (among cells of the mononuclear phagocyte system) and some nerve cells in the gray matter of the human brain cortex might be influenced by LH. High LH levels might also activate resident macrophages to remove some nerve cells in which the expression of LH receptors was weaker to change gray matter (<xref rid=\"B40\" ref-type=\"bibr\">40</xref>). More studies are needed to further investigate the mechanisms. With regard to the relationship between the FSH level and gray matter, previous study showed that the gray matter volume of the right inferior parietal gyrus was decreased in perimenopausal women than premenopausal women (<xref rid=\"B41\" ref-type=\"bibr\">41</xref>). As we all know, the serum FSH level in perimenopausal women was obviously increased than in premenopausal. Another study found that the genetic variant for an FSH receptor gene strongly affected the onset of puberty in girls (<xref rid=\"B42\" ref-type=\"bibr\">42</xref>).</p><p>However, in contrast to our study, previous study of adolescence with an age of 8–25 years reported no effects of sex steroids on cortical thickness (<xref rid=\"B12\" ref-type=\"bibr\">12</xref>) and no association between the LH concentrations and gray matter (<xref rid=\"B12\" ref-type=\"bibr\">12</xref>, <xref rid=\"B24\" ref-type=\"bibr\">24</xref>, <xref rid=\"B25\" ref-type=\"bibr\">25</xref>). The possible explanations might be that (1) the hormonal level was determined in morning saliva or urine samples in previous studies and (2) the age of the sample. In that study the subjects were between 8 and 25 years. (3) The period the participants enrolled in [before or after hormonal maturation; middle or late puberty (<xref rid=\"B23\" ref-type=\"bibr\">23</xref>)] and the handling of confounding factors [stratification by sex or time in menstrual cycle (<xref rid=\"B24\" ref-type=\"bibr\">24</xref>)]. However, the onset age of puberty was between 7 and 13 in the girls, and the mean age of menarche was approximately 12 years old (<xref rid=\"B43\" ref-type=\"bibr\">43</xref>). To eliminate the effect of the menstrual cycle, the age of the participants was limited to 8–11 years old. All the subjects in our study were girls, none of them had menstruation, and their LH and FSH concentrations were peak-stimulated serum LH and FSH levels after the LHRH stimulation test.</p><p>The limitation regarding our study should also be noted. First, the age range of the sample is narrow. The age of the HPG (+) girls was 8 to 11 years, and that of the HPG (-) girls was 8 to 10 years. However, the onset age of puberty was between 7 and 13 in the girls, and the mean age of menarche was approximately 12 years old (<xref rid=\"B43\" ref-type=\"bibr\">43</xref>). Hence, to eliminate the effect of the menstrual cycle, the age of the participants was limited to 8 to 11 years old. Second, the serum concentration of LH, FSH, and E were the total hormone levels, encompass both bound and unbound hormone levels. Its precision to analysis the correlation between the puberty-related hormone and the brain regions used the unbound hormone levels. Thirdly, the current study was unable to detect more robust effects of the reactivation of the HPG axis; as we acknowledged, our results didn’t pass the statistical threshold at recommended corrected significance levels, the findings need for replication given the possibility of false-positive results in larger cohorts.</p><p>Despite the limitations, it’s important to emphasize the originality of the results by investigate the association of HPG axis reactivation defined by LHRH stimulation testing with cortical thickness in girls at the stage of premenarche. The current results would be useful to study the cellular mechanisms about how the sexual hormone effect the brain gray matter development, especially in the default mode network and executive control network highly associated with psychoneurosis at puberty.</p></sec><sec id=\"s5\"><title>Conclusion</title><p>The present study provides evidence that reactivation of the HPG axis is associated with the development of cortical thickness, especially involving the default mode network and executive control network, in puberty along with hormone-related levels. These findings provide evidence for a better understanding of the relevance of the HPG-axis with brain maturation. Future studies should combine behavioral and functional measurements to illuminate the structure–function-hormone relationship.</p></sec><sec sec-type=\"data-availability\" id=\"s6\"><title>Data Availability Statement</title><p>The datasets generated for this study are available on request to the corresponding author.</p></sec><sec id=\"s7\"><title>Ethics Statement</title><p>The studies involving human participants were reviewed and approved by the ethics committee of the Second Affiliated Hospital and Yuying Children’s of Wenzhou Medical University. Written informed consent to participate in this study was provided by the participants’ legal guardian/next of kin.</p></sec><sec id=\"s8\"><title>Author Contributions</title><p>ZY conceived the study and designed the protocol. YF, DY, LX, PL, YZ, LZ, and TC did the experiments. WZ, BT, BY, and XL conducted the statistical analyses. YF, WZ, BT, BY, and ZY interpreted study findings and contributed to developing the manuscript. YF wrote the first draft of the manuscript that was revised by all authors.</p></sec><sec id=\"s9\"><title>Funding</title><p>This research was supported by Natural Science Foundation of Zhejiang Province (grant number LY19H180003), Zhejiang Medical Health Science and Technology Program (grant numbers 2017KY108 and 2017ZD024), and Wenzhou Municipal Sci-Tech Bureau (grant numbers Y20150291 and Y20190185).</p></sec><sec id=\"s10\"><title>Conflict of Interest</title><p>The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p></sec></body><back><ref-list><title>References</title><ref id=\"B1\"><label>1</label><mixed-citation publication-type=\"journal\">\n<person-group person-group-type=\"author\"><name><surname>Hemphill</surname><given-names>SA</given-names></name><name><surname>Kotevski</surname><given-names>A</given-names></name><name><surname>Herrenkohl</surname><given-names>TI</given-names></name><name><surname>Toumbourou</surname><given-names>JW</given-names></name><name><surname>Carlin</surname><given-names>JB</given-names></name><name><surname>Catalano</surname><given-names>RF</given-names></name><etal/></person-group>\n<article-title>Pubertal stage and the prevalence of violence and social/relational aggression</article-title>. <source>Pediatrics</source> (<year>2010</year>) <volume>126</volume>(<issue>2</issue>):<page-range>e298–305</page-range>.  <pub-id pub-id-type=\"doi\">10.1542/peds.2009-0574</pub-id>\n</mixed-citation></ref><ref id=\"B2\"><label>2</label><mixed-citation publication-type=\"journal\">\n<person-group person-group-type=\"author\"><name><surname>Crocetti</surname><given-names>E</given-names></name><name><surname>Klimstra</surname><given-names>TA</given-names></name><name><surname>Hale</surname><given-names>WW</given-names></name><name><surname>Koot</surname><given-names>HM</given-names></name><name><surname>Meeus</surname><given-names>W</given-names></name></person-group>\n<article-title>Impact of early adolescent externalizing problem behaviors on identity development in middle to late adolescence: a prospective 7-year longitudinal study</article-title>. <source>J Youth Adolescence</source> (<year>2013</year>) <volume>42</volume>(<issue>11</issue>):<page-range>1745–58</page-range>.  <pub-id pub-id-type=\"doi\">10.1007/s10964-013-9924-6</pub-id>\n</mixed-citation></ref><ref id=\"B3\"><label>3</label><mixed-citation publication-type=\"journal\">\n<person-group person-group-type=\"author\"><name><surname>Glaser</surname><given-names>B</given-names></name><name><surname>Gunnell</surname><given-names>D</given-names></name><name><surname>Timpson</surname><given-names>NJ</given-names></name><name><surname>Joinson</surname><given-names>C</given-names></name><name><surname>Zammit</surname><given-names>S</given-names></name><name><surname>Smith</surname><given-names>GD</given-names></name><etal/></person-group>\n<article-title>Age and puberty-dependent association between IQ score in early childhood and depressive symptoms in adolescence</article-title>. <source>Psychol Med</source> (<year>2011</year>) <volume>41</volume>(<issue>2</issue>):<page-range>333–43</page-range>.  <pub-id pub-id-type=\"doi\">10.1017/S0033291710000814</pub-id>\n</mixed-citation></ref><ref id=\"B4\"><label>4</label><mixed-citation publication-type=\"journal\">\n<person-group person-group-type=\"author\"><name><surname>Knobil</surname><given-names>E</given-names></name></person-group>\n<article-title>The neuroendocrine control of ovulation</article-title>. <source>Hum Reprod</source> (<year>1988</year>) <volume>3</volume>(<issue>4</issue>):<page-range>469–72</page-range>.  <pub-id pub-id-type=\"doi\">10.1016/0090-8258(88)90059-5</pub-id>\n</mixed-citation></ref><ref id=\"B5\"><label>5</label><mixed-citation publication-type=\"journal\">\n<person-group person-group-type=\"author\"><name><surname>Delemarre-van de Waal</surname><given-names>HA</given-names></name><name><surname>Wennink</surname><given-names>JM</given-names></name><name><surname>Odink</surname><given-names>RJ</given-names></name></person-group>\n<article-title>Gonadotrophin and growth hormone secretion throughout puberty</article-title>. <source>Acta Paediatr Scand</source> (<year>1991</year>) <volume>Suppl 372</volume>:<fpage>26–31</fpage>.  <pub-id pub-id-type=\"doi\">10.1111/j.1651-2227.1991.tb17964.x</pub-id>\n</mixed-citation></ref><ref id=\"B6\"><label>6</label><mixed-citation publication-type=\"journal\">\n<person-group person-group-type=\"author\"><name><surname>Carel</surname><given-names>JC</given-names></name><name><surname>Léger</surname><given-names>J</given-names></name></person-group>\n<article-title>Clinical practice. 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[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"review-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Int J Mol Sci</journal-id><journal-id journal-id-type=\"iso-abbrev\">Int J Mol Sci</journal-id><journal-id journal-id-type=\"publisher-id\">ijms</journal-id><journal-title-group><journal-title>International Journal of Molecular Sciences</journal-title></journal-title-group><issn pub-type=\"epub\">1422-0067</issn><publisher><publisher-name>MDPI</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32718086</article-id><article-id pub-id-type=\"pmc\">PMC7432115</article-id><article-id pub-id-type=\"doi\">10.3390/ijms21155238</article-id><article-id pub-id-type=\"publisher-id\">ijms-21-05238</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Review</subject></subj-group></article-categories><title-group><article-title>Interleukin-6 in Rheumatoid Arthritis</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Pandolfi</surname><given-names>Franco</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05238\">1</xref><xref rid=\"c1-ijms-21-05238\" ref-type=\"corresp\">*</xref><xref ref-type=\"author-notes\" rid=\"fn1-ijms-21-05238\">†</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0001-8638-7565</contrib-id><name><surname>Franza</surname><given-names>Laura</given-names></name><xref ref-type=\"aff\" rid=\"af2-ijms-21-05238\">2</xref><xref ref-type=\"author-notes\" rid=\"fn1-ijms-21-05238\">†</xref></contrib><contrib contrib-type=\"author\"><name><surname>Carusi</surname><given-names>Valentina</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05238\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Altamura</surname><given-names>Simona</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05238\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Andriollo</surname><given-names>Gloria</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05238\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Nucera</surname><given-names>Eleonora</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05238\">1</xref></contrib></contrib-group><aff id=\"af1-ijms-21-05238\"><label>1</label>Allergy and Clinical Immunology, Fondazione Policlinico Universitario A, Gemelli IRCCS, Catholic University, 00168 Rome, Italy; <email>[email protected]</email> (V.C.); <email>[email protected]</email> (S.A.); <email>[email protected]</email> (G.A.); <email>[email protected]</email> (E.N.)</aff><aff id=\"af2-ijms-21-05238\"><label>2</label>Emergency Medicine, Fondazione Policlinico Universitario A, Gemelli IRCCS, Università Cattolica del Sacro Cuore, 00168 Rome, Italy; <email>[email protected]</email></aff><author-notes><corresp id=\"c1-ijms-21-05238\"><label>*</label>Correspondence: <email>[email protected]</email></corresp><fn id=\"fn1-ijms-21-05238\"><label>†</label><p>These authors contributed equally to this work.</p></fn></author-notes><pub-date pub-type=\"epub\"><day>23</day><month>7</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><month>8</month><year>2020</year></pub-date><volume>21</volume><issue>15</issue><elocation-id>5238</elocation-id><history><date date-type=\"received\"><day>02</day><month>7</month><year>2020</year></date><date date-type=\"accepted\"><day>20</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>© 2020 by the authors.</copyright-statement><copyright-year>2020</copyright-year><license license-type=\"open-access\"><license-p>Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>).</license-p></license></permissions><abstract><p>The role of interleukin (IL)-6 in health and disease has been under a lot of scrutiny in recent years, particularly during the recent COVID-19 pandemic. The inflammatory pathways in which IL-6 is involved are also partly responsible of the development and progression of rheumatoid arthritis (RA), opening interesting perspectives in terms of therapy. Anti-IL-6 drugs are being used with variable degrees of success in other diseases and are being tested in RA. Results have been encouraging, particularly when anti-IL-6 has been used with other drugs, such as metothrexate (MTX). In this review we discuss the main immunologic aspects that make anti-IL-6 a good candidate in RA, but despite the main therapeutic options available to target IL-6, no gold standard treatment has been established so far.</p></abstract><kwd-group><kwd>IL-6</kwd><kwd>rheumatoid arthritis</kwd><kwd>inflammation</kwd></kwd-group></article-meta></front><body><sec id=\"sec1-ijms-21-05238\"><title>1. Interleukin-6 in Health and Disease</title><p>Interleukin (IL)-6 is a prototypical cytokine, featuring pleiotropic and redundant functional activity. IL-6 belongs to a family of cytokines, which includes IL-6, IL-11, IL-27, IL-31, oncostatin M (OSM), leukaemia inhibitory factor (LIF), ciliary neurotrophic factor (CNTF), cardiotrophin 1 (CT-1), and cardiotrophin-like cytokine factor 1 (CLCF1); all these cytokines use the common IL-6 signal transducer gp130 [<xref rid=\"B1-ijms-21-05238\" ref-type=\"bibr\">1</xref>]. IL-6 production can be induced by both infection and other types of inflammation. Indeed, IL-6 is promptly produced, mainly by macrophages, in response to pathogens or inflammation-related damage-associated molecular patterns [<xref rid=\"B2-ijms-21-05238\" ref-type=\"bibr\">2</xref>] and performs a protective function by removing infectious agents and healing damaged tissue via induction of acute phase and immune responses. IL-6 is crucial for both innate and adaptive immunity. Several cell types produce IL-6, including monocytes, T-lymphocytes, fibroblasts, and endothelial cells, and production is strongly enhanced at sites of inflammation [<xref rid=\"B3-ijms-21-05238\" ref-type=\"bibr\">3</xref>].</p><p>In infections, toll-like receptors (TLRs) directly recognize the bacteria, virus, or fungi and can directly or indirectly, depending on the type of stimuli, induce the production of IL-6 and other inflammatory cytokines, such as IL-1 or tumor necrosis factor α (TNFα), through the nuclear factor-kappa B (NF-kB) signaling pathway. Interestingly, the production of IL-1 and TNFα also stimulate the production of IL-6 [<xref rid=\"B4-ijms-21-05238\" ref-type=\"bibr\">4</xref>]. Overall, dysregulated IL-6 production leads to persistent inflammation [<xref rid=\"B5-ijms-21-05238\" ref-type=\"bibr\">5</xref>].</p><p>IL-6 demonstrates its biological activities only by binding to its specific receptor, IL-6R. Neither IL-6 nor IL-6R have affinity for gp130 (also known as CD130). However, as a complex, IL-6 and IL-6R can bind to and activate the IL-6Rβ-subunit, gp130, leading to its dimerization and intracellular signaling through Janus kinase (JAKs: JAK1, JAK2, and TYK2), in particular, signal transducer and activator of transcription (STAT)1 and 3. STAT1 and 3 are phosphorylated and translocated to the nucleus and induce the transcription of target genes in the nucleus, while SH2 domain-containing protein-tyrosine phosphatase 2 (SHP-2) activates the Ras-MAP kinase pathway [<xref rid=\"B6-ijms-21-05238\" ref-type=\"bibr\">6</xref>]. While these molecules activate the transcription process, they also start a negative feedback signaling pathway, which suppresses cytokine signaling 1 (SOCS1) and SOCS3 proteins [<xref rid=\"B4-ijms-21-05238\" ref-type=\"bibr\">4</xref>].</p><p>IL-6 was thought in the past to target only the liver, since hepatocytes express high IL-6R and gp130, but in recent years it has appeared more and more clear that IL-6 does not work on a single target organ [<xref rid=\"B7-ijms-21-05238\" ref-type=\"bibr\">7</xref>]. IL-6 acts as an acute phase protein, enhancing the inflammatory response of the body. Most of its effects are indeed obtained through the liver, where it is processed and starts an inflammatory cascade, inducing C-reactive protein (CRP), serum amyloid A (SAA), fibrinogen, haptoglobin, and α1-antichymotrypsin. IL-6 also triggers a reduction in the levels of albumin, zinc, and iron through various mechanisms [<xref rid=\"B5-ijms-21-05238\" ref-type=\"bibr\">5</xref>].</p><p>IL-6 also guides cell differentiation in the bone marrow, stimulating the production of neutrophils and megakariocytes, triggering liver production of thrombopoietin, which stimulates the differentiation of megakaryocytes in platelets, with IL-3 [<xref rid=\"B8-ijms-21-05238\" ref-type=\"bibr\">8</xref>]. Indeed, like many other inflammatory agents, IL-6 production is also associated to anemia; its effects on hepatocytes include in-vitro production of hepcidin, not yet confirmed in vivo [<xref rid=\"B9-ijms-21-05238\" ref-type=\"bibr\">9</xref>].</p><p>The effect of IL-6 on the immune system is particularly interesting; its effects are evident both on innate and acquired immunity. In terms of innate immunity, IL-6 is responsible for the maturation of inflammatory infiltrate, promoting neutrophil migration and mononuclear cell infiltration. It also acts as a chemoceptor for monocytes at the site of inflammation [<xref rid=\"B10-ijms-21-05238\" ref-type=\"bibr\">10</xref>,<xref rid=\"B11-ijms-21-05238\" ref-type=\"bibr\">11</xref>], while in terms of acquired immunity, IL-6 also expresses its action both on T-cells and on B-cells. On the latter, it has a differentiative effect towards active plasma-cells, increasing levels of serum gamma-globulins. Indeed, these two conditions are both present in Castelman’s disease, which is responsive to anti-IL-6 treatment [<xref rid=\"B12-ijms-21-05238\" ref-type=\"bibr\">12</xref>]. During the 1980s, IL-6 was indeed known as B cell stimulating factor 2, yet, the role of IL-6 in the production of gamma-globulins, even though relevant, is not essential, as it is for other inflammatory mediators. Gamma-globulin production is indeed a complex process, in which different inflammatory mediators have redundant functions: IL-2 in particular is associated to immunoglobulin production, while IL-4 and TGF-β work as inhibitory molecules [<xref rid=\"B13-ijms-21-05238\" ref-type=\"bibr\">13</xref>]. Interestingly, in other contexts, the effect of IL-6 and TGF-β are synergic.</p><p>The effects IL-6 has on T-cells are also quite relevant and associated to various pathologies. IL-6 acts mainly on the differentiation of CD4+ T-cells; in particular, it stimulates the Th17 pathway while inhibiting regulatory-T-cells (T-reg) differentiation. Th17 differentiation is important in fighting inflammation and promotes IL-6 production, further enhancing Th17 differentiation. Cytokines produced by Th17 lymphocytes also include TNFα, IL-1β, IL-17, IL-21, and IL-22, which stimulate inflammation and the fibrotic reaction of the tissue [<xref rid=\"B14-ijms-21-05238\" ref-type=\"bibr\">14</xref>]. IL-6 on the other hand inhibits the production of T-reg cells through transforming growth factor β (TGFβ). In the right conditions, IL-6 is indeed an autoinflammatory mediator; not only does it reduce the capacity of the body to distinguish self from not self, it also promotes fibrosis and the inflammatory reaction. The importance of its role in many autoimmune diseases has been demonstrated and targeted [<xref rid=\"B15-ijms-21-05238\" ref-type=\"bibr\">15</xref>,<xref rid=\"B16-ijms-21-05238\" ref-type=\"bibr\">16</xref>].</p></sec><sec id=\"sec2-ijms-21-05238\"><title>2. Rheumatoid Arthritis and Immunologic Pathways</title><p>Rheumatoid arthritis (RA) is an autoimmune disease characterized by chronic inflammation affecting the joints and cartilage, which can lead to various degrees of osteoarthritis, and it can cause various degrees of disability. Even though the development and progression of RA is still not completely understood, different therapeutic options are available, and they have completely changed the prognosis of the disease [<xref rid=\"B17-ijms-21-05238\" ref-type=\"bibr\">17</xref>]. Disease modifying antirheumatic drugs (DMARD), such as methotrexate (MTX), allow patients to keep the disease under control and lead normal lives. Unfortunately, not all patients respond to these drugs and their quality of life is severely impaired [<xref rid=\"B18-ijms-21-05238\" ref-type=\"bibr\">18</xref>]. Factors involved in the development of RA can be roughly distinguished into environmental and genetic, with the former influencing the second</p><p>Overall, it is widely accepted that RA occurs in genetically predisposed individuals following the interaction with different environmental factors [<xref rid=\"B19-ijms-21-05238\" ref-type=\"bibr\">19</xref>,<xref rid=\"B20-ijms-21-05238\" ref-type=\"bibr\">20</xref>]. The presence of T-cell receptor (TCR) restriction at the joints has also been described, suggesting T-cell attack of a joint antigen. It may also prove important in terms of predicting poor response to conventional therapy and severity of disease [<xref rid=\"B21-ijms-21-05238\" ref-type=\"bibr\">21</xref>,<xref rid=\"B22-ijms-21-05238\" ref-type=\"bibr\">22</xref>].</p><p>The common amino acid motif (QKRAA) in the HLA-DRB1 region, commonly known as the shared epitope, confers particular susceptibility [<xref rid=\"B23-ijms-21-05238\" ref-type=\"bibr\">23</xref>]. Given the importance in determining T-cell repertoire of this region, the central role of T-cells appears obvious.</p><p>Joint damage results from the degradation of connective tissue by tissue-destroying matrix metalloproteinases (MMP) and the stimulation of osteoclastogenesis through the receptor activator of the nuclear factor-kB ligand (RANKL).</p><p>Among the environmental factors for RA, smoking is an important one, causing alterations in the oral microbiota, which might trigger autoimmune cross-reactions [<xref rid=\"B24-ijms-21-05238\" ref-type=\"bibr\">24</xref>]. Something similar also happens with some infections, such as Epstein-Barr and cytomegalovirus infections: These infections over-stimulate the immune system, favoring suppression of T-regs and proliferation of Th17 lymphocytes [<xref rid=\"B25-ijms-21-05238\" ref-type=\"bibr\">25</xref>]. In susceptible individuals, the chronic exposure to citrullinated peptides in conditions of chronic inflammation, as in the above-mentioned infections, is a key step in the pathogenesis of RA [<xref rid=\"B26-ijms-21-05238\" ref-type=\"bibr\">26</xref>]. The role of the microbiota also needs to be acknowledged: The microbiota is a well-known regulation agent of inflammation and immunity, as it can act as a pro-inflammatory stimulus or reduce systemic inflammation, also modifying the pattern of cytokine production of the organism [<xref rid=\"B27-ijms-21-05238\" ref-type=\"bibr\">27</xref>]. In RA, oral microbiota appears to be particularly significant. Indeed, it stimulates both production of aberrant neutrophils, which appear to play, in general, a central role in RA, and also favors cross-reactivity with proteins expressed in the joints [<xref rid=\"B28-ijms-21-05238\" ref-type=\"bibr\">28</xref>]. <italic>Lactobacillus salivarius</italic> is the most represented bacteria in patients suffering from RA, and molecular mimicry of antigens involved in RA has been described. Interestingly, once patients are treated for RA, their oral microbiota changes and becomes similar to that of the general population [<xref rid=\"B29-ijms-21-05238\" ref-type=\"bibr\">29</xref>].</p><p>From an immunologic point of view, both the adaptive and the innate immune systems are involved. Neutrophils and monocytes are the most important actors as far as innate immunity is concerned in the pathogenesis of RA, as they directly influence the interaction between toll-like-receptors (TLR) and other signaling molecules. In particular, myeloid-related protein (MRP) 8/14 further activates monocytes and creates an inflammatory environment [<xref rid=\"B30-ijms-21-05238\" ref-type=\"bibr\">30</xref>]. The role of macrophages has also been acknowledged: They are capable of stimulating TNF and IL-1β production and they activate a wide number of immune effectors. They are also important in terms of clinical presentation, as some of the enzymes they produce are directly involved in cartilage deterioration [<xref rid=\"B31-ijms-21-05238\" ref-type=\"bibr\">31</xref>]. As for the adaptive immunity, both B-lymphocytes and T-lymphocytes are involved.</p><p>The importance of B-cells in RA has been questioned in the past, but has been confirmed by the efficacy of treating affected patients with rituximab, an anti-CD20 antibody [<xref rid=\"B32-ijms-21-05238\" ref-type=\"bibr\">32</xref>]. Plasma cells, which produce antibodies, do not express CD20, proving that the role of B-cells goes beyond autoimmune antibody production. Interestingly, activated CD4+ T cells also stimulate B cells to produce immunoglobulins.</p><sec><title>TH17 in RA</title><p>The Th1 pathway is traditionally considered the most important in RA, while Th17 CD4 lymphocytes have only recently been acknowledged as important mediators in the pathogenesis of RA [<xref rid=\"B14-ijms-21-05238\" ref-type=\"bibr\">14</xref>]. Precursor Th17 lineage cells expressing CD161 or lectin-like transcript 1 (LLT1) accumulate in patients suffering from RA, playing a central role in the pathogenesis [<xref rid=\"B33-ijms-21-05238\" ref-type=\"bibr\">33</xref>]. IL-17 production is central in stimulating synovial fibroblasts and it also stimulates osteoclastogenesis in a RANKL-dependent and RANKL-independent fashion: In the first pathway, it acts directly to produce osteolysis. IL-17 also induces osteoblasts to produce RANKL, which act in synergy with TNF-α, resulting in increased osteoclastogenesis. Th-17 also produces IL-21 and 22 and TNF-α, in addition to IL-17, potentially inducing a vicious circle of inflammation [<xref rid=\"B34-ijms-21-05238\" ref-type=\"bibr\">34</xref>]. The activation of all these mechanisms seems to be determined at least in part by IL-6, which also determines a reduction in the number of T-regs. Interestingly, IL-6 is further stimulated by IL-17, further maintaining inflammation. The imbalance between the inflammatory pathway and the suppressive one is, indeed, central in the pathogenesis of RA: T-regs in patients with RA appear not only reduced in quantity, but also in quality [<xref rid=\"B35-ijms-21-05238\" ref-type=\"bibr\">35</xref>].</p><p>In RA, the signaling pathway of the TLRs and the expression of inflammatory cytokines (e.g., IL-1, TNFα, and IL-17) work as IL-6 promoter-activators, as do other factors [<xref rid=\"B36-ijms-21-05238\" ref-type=\"bibr\">36</xref>]. Once at the joint, IL-6 has a crucial role in the inflammatory process, in osteoclast-mediated bone resorption and in pannus development; these processes help the development of IgM and IgG rheumatoid factors along with antibodies to citrullinated peptides, which are characteristically increased in RA [<xref rid=\"B37-ijms-21-05238\" ref-type=\"bibr\">37</xref>]. At a joint level, IL-6 works on many different paths; its importance has been evidenced in obese patients suffering from osteoarthritis. Obesity is linked to high levels of inflammatory proteins, such as IL-6, in the blood, but also, for instance in the synovial liquid. At this level, inflammatory proteins favor the production of MMPs, particularly, MMP-1, MMP-3, MMP-13, and aggrecanase 1 and 2 (ADAMTS-4, ADAMTS-5) [<xref rid=\"B38-ijms-21-05238\" ref-type=\"bibr\">38</xref>]. As previously discussed, MMP activation is also key in the progression of RA.</p><p>Other immunologic pathways are also involved in the development of RA and can eventually lead to IL-6 overexpression. For instance, the nuclear factor kappa-light-chain-enhancer of activated B cells (NF-kB) pathway is a key inflammatory mediator in RA, determining an increase in TNFα, which in turn increases IL-6 levels.</p><p>IL-6 might also be involved in a more subtle way in the development of RA: It has been observed that IL-6 is involved in cytokine release syndrome complicated by T-cell therapy. Blocking IL-6 in these cases gave good results, proving the central part it plays in inflammatory syndromes [<xref rid=\"B39-ijms-21-05238\" ref-type=\"bibr\">39</xref>,<xref rid=\"B40-ijms-21-05238\" ref-type=\"bibr\">40</xref>]. IL-6 seems to be responsible for other systemic symptoms associated with RA, in particular in the nervous and cardiovascular systems.</p><p>Overall, in patients with RA, elevated serum levels of both IL-6 and IL-6R are found in serum and synovial fluid of affected joints [<xref rid=\"B41-ijms-21-05238\" ref-type=\"bibr\">41</xref>].</p><p>Other important cytokines that are currently being studied as possible therapeutic targets are IL-17 and granulocyte-macrophage colony-stimulating factor (GM-CSF) [<xref rid=\"B42-ijms-21-05238\" ref-type=\"bibr\">42</xref>], further demonstrating the interaction between both innate and adaptive immunity.</p><p>Even though RA is usually thought of as a disease affecting mostly the joints, patients who suffer from this disease often face a number of comorbidities and are at a higher risk of developing certain types of disease: Cardiovascular disease is particularly common in this population, for instance, and this seems to be directly affected by IL-6 [<xref rid=\"B43-ijms-21-05238\" ref-type=\"bibr\">43</xref>]. A similar situation has been described for psychiatric disorders. Patients suffering from chronic conditions, autoimmune diseases in particular, are known to be at a higher risk of developing a broad range of psychiatric disorders, particularly depression. In this group of patients, those with higher levels of IL-6 and CRP seem to be at a higher risk of developing psychiatric comorbidities [<xref rid=\"B44-ijms-21-05238\" ref-type=\"bibr\">44</xref>]. Thus, targeting IL-6 might improve overall health in patients suffering from RA.</p><p>Overall, inflammatory cytokines, especially TNF-α, and two interleukins, IL-1β and IL-6, are key in driving inflammation and joint damage. Cytokines such as IL-23, IL-17A, as discussed above, and interferon gamma (IFN-γ) also play crucial roles in the pathogenesis of RA. IL-4 and IL-10, on the other hand, have been suggested to improve arthritis [<xref rid=\"B45-ijms-21-05238\" ref-type=\"bibr\">45</xref>]. While there is overall consensus on the inflammatory role of IL-6 in the pathogenesis of RA, there are some studies pointing out that its role is not clear-cut: It has emerged that IL-1 signaling reduces IL-6 signaling in RA, overall worsening patients’ conditions. The possibility of acting directly on IL-1 is thus interesting; at the moment, the most interesting results have been found for patients suffering from other chronic inflammatory diseases, such as type-2 diabetes, in which patients whose IL-1 pathway is blocked may have benefits both in RA and diabetes control [<xref rid=\"B46-ijms-21-05238\" ref-type=\"bibr\">46</xref>]. IL-6 acts differently on different cell types, and it can even inhibit fibrosis, but when in a crosstalk with IL-1, its inflammatory characteristics seem to overtake its anti-inflammatory role [<xref rid=\"B47-ijms-21-05238\" ref-type=\"bibr\">47</xref>]. Yet, this hypothesis is based primarily on in vitro studies and needs to be confirmed further.</p></sec></sec><sec id=\"sec3-ijms-21-05238\"><title>3. Interleukin-6 in Rheumatoid Arthritis and Other Diseases</title><p>The IL-6 pathway is involved in different inflammatory diseases and could be a potential target in a vast array of clinical conditions in different branches of medicine [<xref rid=\"B48-ijms-21-05238\" ref-type=\"bibr\">48</xref>] spanning RA, cytokine released syndrome in CART cells [<xref rid=\"B49-ijms-21-05238\" ref-type=\"bibr\">49</xref>], or COVID-19 infection [<xref rid=\"B50-ijms-21-05238\" ref-type=\"bibr\">50</xref>,<xref rid=\"B51-ijms-21-05238\" ref-type=\"bibr\">51</xref>].</p><p>IL-6 blockade has been successfully used in Castelman’s disease, a lymphoproliferative disorder with a wide spectrum of manifestations, and juvenile idiopathic arthritis (JIA). In some countries it has also been used with various degrees of success in other rheumatologic diseases, such as giant cell arteritis (GCA), Takayasu arteritis (TA), and cytokine releasing syndrome (CRS).</p><p>In the past few months, IL-6 has gained visibility because of its effects on COVID-19 infection. COVID-19 is a disease caused by a novel coronavirus and it can cause a vast array of symptoms, ranging from mild, flu-like symptoms to severe respiratory failure [<xref rid=\"B52-ijms-21-05238\" ref-type=\"bibr\">52</xref>]. Many different therapeutic approaches have been tried, mostly anti-inflammatory therapies, ranging from steroids to antimalaria drugs; even non-conventional therapies, such as ozone-therapy, have been tested [<xref rid=\"B53-ijms-21-05238\" ref-type=\"bibr\">53</xref>]. The most severe symptoms seem to be caused, at least in part, by an autoinflammatory reaction, with characteristics of a cytokine storm [<xref rid=\"B52-ijms-21-05238\" ref-type=\"bibr\">52</xref>]: Binding of the novel coronavirus to TLRs causes an increase in the levels of IL-1β, which mediate fibrosis and inflammation of the lung [<xref rid=\"B50-ijms-21-05238\" ref-type=\"bibr\">50</xref>]. The role of IL-6 in promoting fibrosis has been studied in other pathologies, particularly in the liver [<xref rid=\"B54-ijms-21-05238\" ref-type=\"bibr\">54</xref>]. Blocking IL-6 in this context has proven useful: Patients have been treated with tocilizumab (TCZ), a monoclonal antibody against IL-6R, and results have been encouraging [<xref rid=\"B51-ijms-21-05238\" ref-type=\"bibr\">51</xref>]. The role played by IL-6 in COVID-19-related inflammation is confirmed by the fact that patients with higher circulating levels of IL-6 and other inflammatory cytokines, such as persons suffering from Down syndrome, are at a higher risk of developing more severe forms of COVID-19 infection [<xref rid=\"B55-ijms-21-05238\" ref-type=\"bibr\">55</xref>].</p><p>In the treatment of RA, IL-6 blockade has proven to be very useful for those patients who do not respond to conventional therapy or even less standard ones. For instance, TNF-α had been a promising target in patients with severe RA, but up to two thirds of patients do not have a full response; in this group, targeting IL-6 is not only useful, but even improves the overall response to therapy of these patients [<xref rid=\"B56-ijms-21-05238\" ref-type=\"bibr\">56</xref>].</p><p>An increase in serum IL-6 and IL-21 levels is associated with markers of B cell activation, and IL-6 is associated with radiographic progression in patients with RA [<xref rid=\"B57-ijms-21-05238\" ref-type=\"bibr\">57</xref>]. Based on studies emphasizing the critical role of IL-6 in development and progression of RA, targeting IL-6 has been proposed as a tool to add to the armamentarium to treat RA. Targeting IL-6 can be achieved by either direct targeting of the cytokine or targeting of its receptor. Targeting the receptor (instead of the cytokine itself) may have the advantage of blocking other cytokines of the IL-6 family.</p></sec><sec id=\"sec4-ijms-21-05238\"><title>4. Targeting Interleukin-6: Available Options</title><p>As discussed above, targeting IL-6 has proven effective in many contexts. Drugs targeting IL-6 can be roughly divided into direct inhibitors and IL-6 receptor inhibitors. The latter offer an advantage, as they have the capacity to also block other interleukins of the same family, such as IL-1, which also play an important role in the pathogenesis of RA [<xref rid=\"B58-ijms-21-05238\" ref-type=\"bibr\">58</xref>].</p><p>Different types of IL-6 inhibitors will be discussed in the following paragraphs.</p><p>TCZ was introduced in 2008 and reduced disease activity in RA with inadequate response to DMARD [<xref rid=\"B59-ijms-21-05238\" ref-type=\"bibr\">59</xref>].</p><p>In the last few years, several trials in over 23,700 patients have confirmed the efficacy and safety of TCZ (alone or in combination with DMARD) in RA [<xref rid=\"B36-ijms-21-05238\" ref-type=\"bibr\">36</xref>], demonstrating the higher efficacy of a combination of DMARDs than MTX on its own.</p><p>The randomized OPTION study on 622 patients proved that the use of TCZ reduces signs and symptoms of RA, proving that the inhibition of IL-6 signaling is effective in moderate-to-severe RA [<xref rid=\"B60-ijms-21-05238\" ref-type=\"bibr\">60</xref>].</p><p>In the TAMARA trial, 286 patients with moderate to severe RA were treated with TCZ and it was concluded that TCZ is effective in improving patients’ condition, the most solid results being seen at about 24 weeks [<xref rid=\"B61-ijms-21-05238\" ref-type=\"bibr\">61</xref>].</p><p>Different routes of administration of TCZ have also been studied, demonstrating that the sub-cutaneous (SC) route is also safe [<xref rid=\"B59-ijms-21-05238\" ref-type=\"bibr\">59</xref>,<xref rid=\"B62-ijms-21-05238\" ref-type=\"bibr\">62</xref>,<xref rid=\"B63-ijms-21-05238\" ref-type=\"bibr\">63</xref>,<xref rid=\"B64-ijms-21-05238\" ref-type=\"bibr\">64</xref>,<xref rid=\"B65-ijms-21-05238\" ref-type=\"bibr\">65</xref>,<xref rid=\"B66-ijms-21-05238\" ref-type=\"bibr\">66</xref>,<xref rid=\"B67-ijms-21-05238\" ref-type=\"bibr\">67</xref>]. Extensive experience has firmly established the short- and long-term efficacy of both intravenous and SC TCZ in adults with moderate-to-severe RA who failed to respond to therapy with synthetic or biologic DMARDs (review in [<xref rid=\"B62-ijms-21-05238\" ref-type=\"bibr\">62</xref>]). In one of the first pivotal studies (SAMURAI), it was shown that in 306 patients with active RA, TCZ monotherapy was generally well tolerated and provided radiographic benefit [<xref rid=\"B68-ijms-21-05238\" ref-type=\"bibr\">68</xref>]. Another important study, AMBITION, randomized 673 patients, showing that TCZ was better than MTX [<xref rid=\"B69-ijms-21-05238\" ref-type=\"bibr\">69</xref>].</p><p>The large LITHE study further demonstrated the disease-modifying effects on IL-6R antagonism. The study enrolled 1196 patients, suffering from RA, ranging from moderate to severe forms of the disease. All participants did not show an adequate response to methotrexate (MTX) therapy. During the study, all patients were treated with MTX but they were also randomly divided in a placebo group and a group receiving TCZ (4 or 8 mg/kg, IV) every 4 weeks in combination with MTX. At week 52, patients who were administered TCZ 8 mg/kg showed a mean change in the Genant-modified Sharp, demonstrating a significantly lower radiographic progression of the disease when compared to those on the placebo group (0.29 vs. 1.13; <italic>p</italic> < 0.0001) [<xref rid=\"B70-ijms-21-05238\" ref-type=\"bibr\">70</xref>], further confirming the safety and effectiveness of TCZ. The incidence of adverse effects was 1.2% in patients with no risk factors and 11.2% in patients with three or more risk factors. The most frequent adverse effects were infections, particularly respiratory ones, both bacterial and viral [<xref rid=\"B71-ijms-21-05238\" ref-type=\"bibr\">71</xref>]. In this regard, a separate study, which enrolled 63 RA patients, showed that a short course of TCZ increased the risk of hepatitis B reactivation [<xref rid=\"B63-ijms-21-05238\" ref-type=\"bibr\">63</xref>].</p><p>Another recent paper by Rutherford, 2018 [<xref rid=\"B72-ijms-21-05238\" ref-type=\"bibr\">72</xref>] compared the incidence of serious infections in RA patients treated with different DMARDs, even though there was little directly comparative literature of infection risk between TCZ and other drugs. Kerschbaumer et al. studied 19,282 patients with 46,771 global years of follow-up and reported that the incidence of serious infections (SI) was 5.51 cases per 100 patient years for the entire cohort (95% confidence interval (CI) 5.29 to 5.71). Compared with etanercept, TCZ had a higher risk of serious infections (HR 1.22, 95% CI 1.02 to 1.47) especially sepsis and respiratory infections ([<xref rid=\"B73-ijms-21-05238\" ref-type=\"bibr\">73</xref>]).</p><p>Overall, TCZ has a well-characterized and tolerable safety profile [<xref rid=\"B70-ijms-21-05238\" ref-type=\"bibr\">70</xref>].</p><p>Another IL-6 R inhibitor, sarilumab (SAR), has also been approved in RA in combination with MTX [<xref rid=\"B74-ijms-21-05238\" ref-type=\"bibr\">74</xref>,<xref rid=\"B75-ijms-21-05238\" ref-type=\"bibr\">75</xref>,<xref rid=\"B76-ijms-21-05238\" ref-type=\"bibr\">76</xref>,<xref rid=\"B77-ijms-21-05238\" ref-type=\"bibr\">77</xref>,<xref rid=\"B78-ijms-21-05238\" ref-type=\"bibr\">78</xref>]. In the MONARCH study, it was demonstrated that SAR monotherapy was more efficient than adalimumab (an anti-TNFα) monotherapy, improving signs and symptoms and physical functions in patients with RA who could not continue taking MTX [<xref rid=\"B79-ijms-21-05238\" ref-type=\"bibr\">79</xref>]. In addition, atlizumab, an IL-6R inhibitor, was proven to be beneficial [<xref rid=\"B80-ijms-21-05238\" ref-type=\"bibr\">80</xref>].</p><p>As stated above, targeting IL-6 can been achieved by biological reagents, namely monoclonal antibodies directed against IL-6 itself such as siltuximab [<xref rid=\"B81-ijms-21-05238\" ref-type=\"bibr\">81</xref>], which is approved for multicentric Castleman’s disease [<xref rid=\"B82-ijms-21-05238\" ref-type=\"bibr\">82</xref>] and has shown efficacy in RA [<xref rid=\"B83-ijms-21-05238\" ref-type=\"bibr\">83</xref>,<xref rid=\"B84-ijms-21-05238\" ref-type=\"bibr\">84</xref>].</p><p>Sirukumab is another anti-IL-6 drug, and it has been approved for Castelman’s disease, with positive preliminary data in RA. The SIRROUND-D study showed improvements both in the Clinical Disease Activity Index and clinically. Meaningful improvements in patient-reported outcomes were sustained at week 104 among patients initially randomized to sirukumab [<xref rid=\"B85-ijms-21-05238\" ref-type=\"bibr\">85</xref>]. Another randomized study in 38 healthy patients showed similar results, confirming sirukumab’s well-tolerated safety profile [<xref rid=\"B86-ijms-21-05238\" ref-type=\"bibr\">86</xref>].</p><p>However, the license request for treatment in RA was denied due to excess mortality in the sirukumab arm in comparison to placebo: On 2 Auguest 2017 the Food and Drug Administration (FDA Advisory Committee Meeting denied approval. The majority of the committee (11 versus 2) agreed that it was not clear whether the imbalance in all-cause mortality is actually a side-effect of the drug or rather a consequence of the design of the study. Overall, the committee agreed that more studies should be conducted with sirukumab because, the “safety profile seemed to be consistent with a class effect showing similar adverse effects and laboratory abnormality profiles. However, death rates in sirukumab arms compared with placebo, especially in the controlled period, raised safety concerns, which led to the decision by the FDA to decline the approval of SRK in August 2017. The majority of the committee (11 to 2) did not agree that the safety profile of SRK 50 mg SC every 4 weeks is adequate to support the approval of sirukumab for the treatment of adult patients with moderately to severely active RA who have had an inadequate response or are intolerant to one or more DMARDs”.</p><p>Following this decision, the company, albeit convinced that more data may show its efficacy, stopped pursuing this line of research [<xref rid=\"B87-ijms-21-05238\" ref-type=\"bibr\">87</xref>].</p><p>Clazakizumab is also an anti-IL-6 drug with promising results. In a study with 418 participants, patients with RA received SC clazakizumab once a month at 25, 100, or 200 mg plus MTX, once-monthly only SC clazakizumab at 100 or 200 mg as monotherapy, or MTX plus a placebo (in other words, MTX alone). Patients who received clazakizumab had a significantly greater response at week 12 when compared to patients receiving MTX alone [<xref rid=\"B88-ijms-21-05238\" ref-type=\"bibr\">88</xref>].</p><p>Olokizumab, also an anti-IL-6 reagent, gave better results than placebo in 119 randomized patients [<xref rid=\"B89-ijms-21-05238\" ref-type=\"bibr\">89</xref>].</p><p>In addition to the above-mentioned agents, a number of reagents against IL-6R and IL-6 have been developed and used in RA (<xref rid=\"ijms-21-05238-t001\" ref-type=\"table\">Table 1</xref>).</p><p>An IL-6 blockade has proven to be effective not only in classic RA, but also in adult onset Still’s disease. Still’s disease is a rare generalized form of RA, which can present with a vast array of symptoms. Given its rarity, no systemic studies have been conducted on therapeutic strategies, and therapies are based mostly on clinicians’ experience. Targeting IL-1 and TNF-α has proven effective in the context of the articular symptoms of the disease, but, to date, only IL-6 has proven consistently effective in treating systemic symptoms [<xref rid=\"B90-ijms-21-05238\" ref-type=\"bibr\">90</xref>].</p><p>The safety and efficacy of an IL-6 blockade has also been demonstrated in patients suffering from JIA, a form of RA affecting children and young adults. There are many forms of JIA, some of which closely resemble adult RA, but therapeutic strategies are limited by the young age of patients and differences in the immune systems of children and adults. DMARDs normally used in RA do not always offer satisfactory results. Recently, though, TCZ has been tested in this population and results have been encouraging, demonstrating the central role of IL-6 in autoimmune diseases [<xref rid=\"B91-ijms-21-05238\" ref-type=\"bibr\">91</xref>].</p><p>Overall, both direct and indirect IL-6 inhibitors have demonstrated acceptable safety profiles, even though some studies have pointed out that mortality rates associated to direct IL-6 inhibition are higher than the ones associated to those of an IL-6 receptor blockade. Studies highlighting this discrepancy, though, compared the mortality rates between those taking an IL-6 inhibitor to those under placebo, so the interpretation of these data is not clear [<xref rid=\"B58-ijms-21-05238\" ref-type=\"bibr\">58</xref>].</p></sec><sec sec-type=\"conclusions\" id=\"sec5-ijms-21-05238\"><title>5. Conclusions</title><p>The development of a vast array of reagents against IL-6 and IL-6R is a demonstration per se that the perfect drug to treat RA has not been found yet. Interesting results have indeed been obtained by treating patients with different IL-6 antagonists in combination with different DMARDs: Patients showed improvement in terms of symptoms and quality of life, further proving the central role of IL-6 in the pathogenesis of RA.</p><p>Targeting IL-6 or its receptor is a safe and effective treatment, but further studies are required to identify the gold standard of treatment, because direct drug-to-drug comparison trials are still lacking and, even though scarce, there is information suggesting that the safety profile of these drugs may not be fitting for all patients and may even vary from drug to drug. Overall, in those patients who do not adequately respond to classic therapy, anti-IL-6 targeting is an interesting strategy that needs to be evaluated, given its potential capacity to completely modify patients’ prognosis and reduce the burden of disease.</p></sec></body><back><notes><title>Author Contributions</title><p>F.P. has suggested the topic, L.F. has developed the paper, V.C., E.N., S.A. and G.A. have helped review the paper. All authors have read and agree to the published version of the manuscript.</p></notes><notes><title>Funding</title><p>This research received no external funding</p></notes><notes notes-type=\"COI-statement\"><title>Conflicts of Interest</title><p>The authors declare no conflict of interest.</p></notes><ref-list><title>References</title><ref id=\"B1-ijms-21-05238\"><label>1.</label><element-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Kang</surname><given-names>S.</given-names></name><name><surname>Narazaki</surname><given-names>M.</given-names></name><name><surname>Metwally</surname><given-names>H.</given-names></name><name><surname>Kishimoto</surname><given-names>T.</given-names></name></person-group><article-title>Historical overview of the interleukin-6 family cytokine</article-title><source>J. Exp. 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Med.</source><year>2019</year><volume>137</volume><fpage>517</fpage><lpage>522</lpage><pub-id pub-id-type=\"doi\">10.1590/1516-3180.2018.0489220719</pub-id><pub-id pub-id-type=\"pmid\">32159638</pub-id></element-citation></ref></ref-list></back><floats-group><table-wrap id=\"ijms-21-05238-t001\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijms-21-05238-t001_Table 1</object-id><label>Table 1</label><caption><p>IL-6 and IL-6R mAbs.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Biological</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Other Names</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Commercial Name</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Producer</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Target</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Siltuximab</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Sylvant **</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">IL-6</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Clazakizumab</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">LD518 and BMS-945429</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Adler Biopharma</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">IL-6</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Olokizumab</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">UCB</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">IL-6</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Vobarilizumab</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Il-6R</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Olamkicept</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">FE-301; FE-999301; TJ-301</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Ferring</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">IL-6R</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Sarilumab</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Kevzara</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Sanofi</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">IL-6R</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Sirukumab **</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Janssen</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">IL-6</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Olamkicept</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Ferring</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Satralizumab</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Sapelizumab, SA237</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Roche</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">IL-6R</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Tocilizumab</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Tocilizumab, Actemra e RoActemra.</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Roche</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">IL-6R</td></tr></tbody></table><table-wrap-foot><fn><p>** this drug is approved for multicentric Castleman disease.</p></fn></table-wrap-foot></table-wrap></floats-group></article>\n"
]
[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Int J Mol Sci</journal-id><journal-id journal-id-type=\"iso-abbrev\">Int J Mol Sci</journal-id><journal-id journal-id-type=\"publisher-id\">ijms</journal-id><journal-title-group><journal-title>International Journal of Molecular Sciences</journal-title></journal-title-group><issn pub-type=\"epub\">1422-0067</issn><publisher><publisher-name>MDPI</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32731483</article-id><article-id pub-id-type=\"pmc\">PMC7432116</article-id><article-id pub-id-type=\"doi\">10.3390/ijms21155355</article-id><article-id pub-id-type=\"publisher-id\">ijms-21-05355</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Article</subject></subj-group></article-categories><title-group><article-title>Free Your Mind: Emotional Expressive Flexibility Moderates the Effect of Stress on Post-Traumatic Stress Disorder Symptoms</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Levy-Gigi</surname><given-names>Einat</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05355\">1</xref><xref ref-type=\"aff\" rid=\"af2-ijms-21-05355\">2</xref><xref rid=\"c1-ijms-21-05355\" ref-type=\"corresp\">*</xref></contrib><contrib contrib-type=\"author\"><name><surname>Donner</surname><given-names>Reut</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05355\">1</xref><xref ref-type=\"aff\" rid=\"af3-ijms-21-05355\">3</xref></contrib><contrib contrib-type=\"author\"><name><surname>Bonanno</surname><given-names>George A.</given-names></name><xref ref-type=\"aff\" rid=\"af4-ijms-21-05355\">4</xref></contrib></contrib-group><aff id=\"af1-ijms-21-05355\"><label>1</label>School of Education Bar-Ilan University, Ramat-Gan 52900, Israel; <email>[email protected]</email></aff><aff id=\"af2-ijms-21-05355\"><label>2</label>Gonda Multidisciplinary Brain Research Center, Bar-Ilan University, Ramat-Gan 52900, Israel</aff><aff id=\"af3-ijms-21-05355\"><label>3</label>Psychology Department, University of Haifa, Haifa 34988, Israel</aff><aff id=\"af4-ijms-21-05355\"><label>4</label>Teachers College, Columbia University, New York, NY 10027, USA; <email>[email protected]</email></aff><author-notes><corresp id=\"c1-ijms-21-05355\"><label>*</label>Correspondence: <email>[email protected]</email>; Tel.: +972-3-5317963</corresp></author-notes><pub-date pub-type=\"epub\"><day>28</day><month>7</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><month>8</month><year>2020</year></pub-date><volume>21</volume><issue>15</issue><elocation-id>5355</elocation-id><history><date date-type=\"received\"><day>28</day><month>6</month><year>2020</year></date><date date-type=\"accepted\"><day>21</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>© 2020 by the authors.</copyright-statement><copyright-year>2020</copyright-year><license license-type=\"open-access\"><license-p>Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>).</license-p></license></permissions><abstract><p>Servicemen are exposed to high levels of stress as part of their daily routine, however, studies which tested the relationship between stress and clinical symptoms reached inconsistent results. The present study examines the role of expressive flexibility, which was determined according to the ability to enhance or suppress either negative or positive emotional expression in conflictual situations, as a possible moderator between stress and Post-Traumatic Stress Disorder (PTSD) symptoms. A total of 82 active-duty firefighters (all men, age range = 25–66, M = 33.59, SD = 9.56, range of years in duty service = 2–41, M = 14.37, SD = 11.79), with different duty-related repeated traumatic exposure, participated in the study. We predicted and found that firefighters with low, but not high, expressive flexibility showed a significant positive correlation between duty-related traumatic exposure and PTSD symptomology (<italic>t</italic>(81) = 3.85, <italic>p</italic> < 0.001). Hence, the greater the exposure the higher level of symptoms they exhibited. In addition, we found a difference between the moderating roles of suppressing positive and negative emotional expression, as high but not low, ability to suppress the expression of negative emotions (<italic>t</italic>(81) = 1.76, <italic>p</italic> > 0.05), as low but not high, ability to suppress the expression of positive emotions (<italic>t</italic>(81) = 1.6, <italic>p</italic> > 0.05), served as a protective factor in buffering the deleterious effect of repeated traumatic exposure. The results provide a pivotal support for the growing body of evidence that a flexible emotional profile is an adaptive one, in dealing with negative life events. However, while there is a need to update behavior, the direction of the adaptive update may differ as a function of valance.</p></abstract><kwd-group><kwd>stress</kwd><kwd>expressive flexibility</kwd><kwd>PTSD</kwd><kwd>servicemen</kwd></kwd-group></article-meta></front><body><sec sec-type=\"intro\" id=\"sec1-ijms-21-05355\"><title>1. Introduction</title><p>The relationship between repeated exposure to trauma and Post-Traumatic Stress Disorder (PTSD) remains unclear, as studies with first responders—who are frequently exposed to traumatic events as part of their occupational routine—have shown inconsistent results. Some studies report significant positive correlation between duty-related traumatic events and PTSD, i.e., the more traumatic events one is exposed to, the higher level of PTSD symptomology, e.g., [<xref rid=\"B1-ijms-21-05355\" ref-type=\"bibr\">1</xref>,<xref rid=\"B2-ijms-21-05355\" ref-type=\"bibr\">2</xref>]; yet others show no direct effect [<xref rid=\"B3-ijms-21-05355\" ref-type=\"bibr\">3</xref>,<xref rid=\"B4-ijms-21-05355\" ref-type=\"bibr\">4</xref>,<xref rid=\"B5-ijms-21-05355\" ref-type=\"bibr\">5</xref>]. These mixed findings suggest that other variables likely moderate the relationship between trauma exposure and PTSD. Previous studies reveal that emotional and cognitive flexibility may play an important role in this relationship [<xref rid=\"B6-ijms-21-05355\" ref-type=\"bibr\">6</xref>,<xref rid=\"B7-ijms-21-05355\" ref-type=\"bibr\">7</xref>]. The current study aims to expand these finding by focusing on the moderating role of flexibility in the regulation of emotional expression.</p><p>Emotion regulation refers to one’s ability to manage his or her emotional experience, when faced with a given situation [<xref rid=\"B8-ijms-21-05355\" ref-type=\"bibr\">8</xref>]. Researchers within the field of emotion regulation differentiate between two main types of regulatory strategies: one, disengagement strategies such as distraction, in which blocking an emotional stimulus from capturing selective attention and thus preventing further cognitive processing; two, engaging strategies such as reappraisal which involves allowing elaborated cognitive processing and providing semantic meaning to the stimulus. Past studies have practiced a categorical approach, which characterized distraction as always maladaptive, and reappraisal as always adaptive, in dealing with all negative situations [<xref rid=\"B9-ijms-21-05355\" ref-type=\"bibr\">9</xref>,<xref rid=\"B10-ijms-21-05355\" ref-type=\"bibr\">10</xref>,<xref rid=\"B11-ijms-21-05355\" ref-type=\"bibr\">11</xref>,<xref rid=\"B12-ijms-21-05355\" ref-type=\"bibr\">12</xref>,<xref rid=\"B13-ijms-21-05355\" ref-type=\"bibr\">13</xref>]. A more recent approach offers a person–situation interactionist model, which claims there is no “right” or “wrong” regulation strategy; different strategies predict different emotional outcomes, depending on context [<xref rid=\"B8-ijms-21-05355\" ref-type=\"bibr\">8</xref>,<xref rid=\"B13-ijms-21-05355\" ref-type=\"bibr\">13</xref>]. Specifically, several studies have shown it is better to use reappraisal when faced with low-intensity negative contexts (e.g., when watching a picture of two sad people, use reappraisal by thinking that they support each other and soon would feel better), and distraction when faced with highly aversive situations (use distraction by thinking of yellow triangles when watching a picture of a dead body) [<xref rid=\"B6-ijms-21-05355\" ref-type=\"bibr\">6</xref>,<xref rid=\"B8-ijms-21-05355\" ref-type=\"bibr\">8</xref>]. Based on this work, investigators have argued that regulatory flexibility, i.e., the ability to choose and apply a proper emotion regulation strategy in a manner that corresponds with contextual demands, plays a crucial role in the successful adaptation to negative events [<xref rid=\"B14-ijms-21-05355\" ref-type=\"bibr\">14</xref>].</p><p>In the current study, we focused on a unique population of active-duty firefighters. Firefighters are exposed to an annual average of hundreds of duty-related traumatic events, including buildings and factory fires, car accidents and rescuing trapped people (see [<xref rid=\"B6-ijms-21-05355\" ref-type=\"bibr\">6</xref>] for estimated number of exposures to different traumatic events in this population). Hence, they represent individuals well with repeated traumatic exposure. A recent study with active-duty firefighters demonstrated that one aspect of regulatory flexibility, related to strategy choice, moderated the relationship between repeated traumatic exposure and PTSD [<xref rid=\"B6-ijms-21-05355\" ref-type=\"bibr\">6</xref>]. In this study, participants watched a series of negative computer images, with low-to-high emotional intensity. After watching each image, participants were asked to choose and apply one of two regulation strategies (distraction or reappraisal) in order to cope with the emotions triggered by the image. Participants with high regulatory choice flexibility, who successfully alternated between reappraisal in the low intensity images and distraction in the high intensity images, showed no correlation between the amount of duty-related traumatic events they experienced, and the amount of PTSD symptoms presented. However, participants with low regulatory choice flexibility, who favored only one regulation strategy or alternated between reappraisal and distraction with no regard to the contextual intensity, showed increased levels of PTSD symptomology across exposure.</p><p>In addition to the emotional experience, and the ability to regulate it, emotion theorists refer to another element of the “emotion phenomena”: Emotional expression, one’s ability to enhance or suppress an outward emotional reaction, i.e., display more or less emotion than one actually feels, respectively [<xref rid=\"B14-ijms-21-05355\" ref-type=\"bibr\">14</xref>]. Previous studies have identified situations in which being highly emotionally expressive was beneficiary—for example, when trying to develop and maintain social interactions [<xref rid=\"B15-ijms-21-05355\" ref-type=\"bibr\">15</xref>,<xref rid=\"B16-ijms-21-05355\" ref-type=\"bibr\">16</xref>]. Others have found situations in which it is best to be more discrete [<xref rid=\"B17-ijms-21-05355\" ref-type=\"bibr\">17</xref>]—for example, during argument mediation (for a full review, see [<xref rid=\"B14-ijms-21-05355\" ref-type=\"bibr\">14</xref>]). Expressive flexibility refers to the ability to alternate between enhancement and suppression, in a manner that corresponds with contextual demands. Similar to the person–situation interactionist model discussed earlier, which emphasizes the importance of being flexible and in accord with situational demands when regulating one’s emotional experience, growing evidence suggests the importance of expressive flexibility—as opposed to favoring either the enhancement or the suppression of emotional expression—in successful adjustment to negative events [<xref rid=\"B14-ijms-21-05355\" ref-type=\"bibr\">14</xref>,<xref rid=\"B18-ijms-21-05355\" ref-type=\"bibr\">18</xref>].</p><p>For example, Bonanno et al. [<xref rid=\"B14-ijms-21-05355\" ref-type=\"bibr\">14</xref>] tested expressive flexibility among New York students soon after the 9/11 attacks and found it could predict changes in their emotional health and distress levels throughout a two-year time period. In this study, participants were asked to view a set of pleasant and unpleasant emotion-evoking images on a computer monitor. For each image, participants were instructed to either enhance their emotional reactions, suppress their emotion reactions, or simply view the images as they would normally. Facial expressiveness was subsequently coded from videotapes. Expressive enhancement and expressive suppression abilities were then determined for each participant relative to that participant’s own level of expressiveness in the normal view condition. An overall expressive flexibility score was also calculated, representing the ability to both enhance and suppress emotional expression. Both the individual enhancement and suppression ability scores and the overall expressive flexibility score predicted better adjustment, i.e., reduced distress, two years after the 9/11 attacks. A limitation of this study, however, was that it did not examine differences in trauma exposure among participants.</p><p>There are also more general limitations of the expressive flexibility paradigm [<xref rid=\"B19-ijms-21-05355\" ref-type=\"bibr\">19</xref>]. First, being a laboratory task, it cannot easily be employed for prospective field research. Second, the requirement that participants express or suppress emotion while sitting alone at a computer lacks ecological validity. In order to accommodate these limitations, Burton and Bonanno [<xref rid=\"B19-ijms-21-05355\" ref-type=\"bibr\">19</xref>] developed the Flexible Regulation of Emotional Expression (FREE) scale as an easy to use questionnaire measure of expressive flexibility. Crucially, the FREE scale showed good validity, including correlation with expressive flexibility performance in the experimental paradigm.</p><p>The current study used the FREE scale to assess expressive flexibility among Israeli firefighters and examine the role of expressive flexibility in the elusive relationship between repeated traumatic exposure and PTSD symptomology. Based on previous findings, our main hypothesis was that expressive flexibility would moderate the relationship between trauma exposure and PTSD. Specifically, we predicted that among individuals with low, but not high, expressive flexibility, repeated traumatic exposure would be related to increased levels of PTSD symptomology. Our second and third hypotheses were that the abilities to flexibly enhance or suppress emotional expression would separately function as moderators, in the same way.</p></sec><sec id=\"sec2-ijms-21-05355\"><title>2. Method</title><sec id=\"sec2dot1-ijms-21-05355\" sec-type=\"subjects\"><title>2.1. Participants</title><p>A total of 82 active-duty firefighters (all men, age range = 25–66, M = 33.59, SD = 9.56, 34.1% single; 56.1% married; 9.8% divorced; M years of education = 12.33, SD = 1.01; range years in service 2–41, mean years = 14.37, SD = 11.79), who serve in fire stations in central Israel, volunteered to participate in this cross-sectional study. All the participants hold operational positions and hence they keep a regular fitness routine. Since the vast majority of Israeli firefighters are men, no woman participated in the study. Exclusion criteria for all participants were current or past diagnosis of Axis I and II psychopathology other than PTSD; risk of suicidal/homicidal ideation; any substance dependence or abuse within the past 6 months (excluding nicotine); a history of concussion or other clinically significant head injury including loss of consciousness for over 10 min; or a history of neurological disorders such as epilepsy, multiple sclerosis, stroke, or encephalitis; acute hearing loss or color blindness. The study was conducted in accordance with the Helsinki declaration and was approved by the ethics committee of the Fire and Rescue Service.</p></sec><sec id=\"sec2dot2-ijms-21-05355\"><title>2.2. Measures</title><p>Duty-related repeated traumatic exposure—repeated traumatic exposure was measured by a self-report exposure questionnaire. In this questionnaire, participants were asked to rate the frequency in which they were personally exposed to 16 different life-threatening, duty-related events during an average year of service (e.g., building fires, car accidents, gas leaks), on a scale of 1 (“was not exposed to at all”) to 6 (“was exposed to on a daily basis”). The number of traumatic duty-related events on an average year (range = 26–66, M = 43.62, SD = 9.57) was then multiplied by each participant’s number of years as an active firefighter (range = 2–41 years, M = 14.37, SD = 11.79), in order to obtain an adequate estimation of cumulative repeated trauma throughout the service (range = 52–2440, M = 671.34, SD = 632.27).</p><p>The Flexible Regulation of Emotional Expression (FREE) scale—in the FREE scale [<xref rid=\"B19-ijms-21-05355\" ref-type=\"bibr\">19</xref>], participants were instructed to indicate how well they would be able to be more or less expressive, when feeling negative or positive emotions and faced with given, hypothetical social scenarios, on a scale of 1 (“Unable”) to 6 (“Very able”). The scale contains 16 items, which are divided into two subscales: enhancement and suppression. Each subscale is divided into two groups: positive emotional expression, and negative emotional expression. Four items refer to the ability to enhance positive emotional expression (e.g., “You receive a gift from a family member but it’s a shirt you dislike”). Four items refer to the ability to enhance negative emotional expression (e.g., “Your friend is telling you about what a terrible day they had”). Four items refer to the ability to suppress positive emotional expression (e.g., “You are in a training session and you see an accidentally funny typo in the presenter’s slideshow”). Four items refer to the ability to suppress negative emotional expression (e.g., “You have just heard about the death of a close relative right before an important work meeting”). The items of each group were summed into four different scores: positive enhancement, negative enhancement, positive suppression and negative suppression. Both positive and negative enhancement scores were summed into an “enhancement score” (from here on enhancement flexibility); both positive and negative suppression scores were into a “suppression score” (from here on suppression flexibility). Expressive flexibility was calculated by subtracting the absolute value of the average difference between enhancement and suppression scores, from the average sum of the two scores (range = 4.75–10.74, M = 7.77, SD = 1.33). Higher scores indicate high expressive flexibility; lower scores indicate low expressive flexibility.</p><p>The PTSD checklist for DSM-5 (PCL-5) questionnaire [<xref rid=\"B20-ijms-21-05355\" ref-type=\"bibr\">20</xref>]—this was administered to assess levels of PTSD symptomology (Weathers et al., 2013). In this questionnaire, participants were asked to indicate how much they had been bothered by different problems in the last month, on a scale of 1 (“Not at all”) to 5 (“Extremely”). The PCL-5 questionnaire contains 21 items, which were summed in order to get a total symptom severity score (range = 21–68, M = 29.24, SD = 10.78). </p></sec><sec id=\"sec2dot3-ijms-21-05355\"><title>2.3. Procedure</title><p>All participants provided a written informed consent at the beginning of the experiment after the nature of the procedure had been fully explained. Firefighters were approached in their fire station, during their shift, and were asked to fill out our three questionnaires in the following order: duty-related traumatic exposure, FREE scale and PCL-5. Firefighter trainees were approached in the National Fire and Rescue Academy in Rishon LeZion during their final week of an advanced training course, and were asked to fill out two questionnaires—FREE scale and PCL-5—in the same order. Each participant performed the experiment individually.</p></sec><sec id=\"sec2dot4-ijms-21-05355\"><title>2.4. Data Analysis </title><p>Data were analyzed with SPSS (version 25) software. In order to test our hypotheses, regarding the role of expressive flexibility, enhancement flexibility and suppression flexibility in the relationship between repeated traumatic exposure and PTSD symptomology, Hayes’s [<xref rid=\"B21-ijms-21-05355\" ref-type=\"bibr\">21</xref>] PROCESS macro for moderator analysis was employed, using 5000 bootstrap resampling for calculation of confidence intervals (Model 1; for the advantages of using this macro see [<xref rid=\"B22-ijms-21-05355\" ref-type=\"bibr\">22</xref>]). Duty-related repeated traumatic exposure was treated as the independent variable and PTSD symptomology was treated as the outcome. Expressive flexibility, enhancement flexibility and suppression flexibility were treated, separately, as moderators, in accordance with our three hypotheses.</p></sec></sec><sec sec-type=\"results\" id=\"sec3-ijms-21-05355\"><title>3. Results</title><p>Zero-order correlations between all measures are reported in <xref rid=\"ijms-21-05355-t001\" ref-type=\"table\">Table 1</xref>. We found a significant positive correlation between duty-related repeated traumatic exposure and PTSD symptomology, a significant negative correlation between enhancement flexibility and PTSD symptomology and a significant negative correlation between expressive flexibility and PTSD symptomology. We found no correlation between enhancement flexibility and suppression flexibility, indicating their independence. Both flexibility variables were positively correlated with expressive flexibility, confirming that higher abilities to enhance or suppress an emotional expression are reflected in a higher expressive flexibility score.</p><p>First, we examined our main prediction with expressive flexibility as a possible moderator, in the relationship between repeated traumatic exposure and PTSD symptomology. The estimated coefficients of the main findings and their significance levels are described in <xref rid=\"ijms-21-05355-t002\" ref-type=\"table\">Table 2</xref>. The general model was significant, <italic>R</italic><sup>2</sup> = 0.25, <italic>F</italic>(3,78) = 8.56, <italic>p</italic> < 0.001. Consistent with our hypothesis, there was a significant interaction between duty-related repeated traumatic exposure and expressive flexibility, which accounted for an additional 3.7% of the variance above. To interpret the interactive effect on PTSD symptomology, we computed bootstrapping confidence intervals (95%) evaluating the magnitude of the relationship between repeated traumatic exposure and PTSD symptoms for individuals with low (−1 SD) and high expressive flexibility (+1 SD). As expected, the results revealed a significant positive relationship between duty-related repeated traumatic exposure and PTSD symptomology for individuals with low expressive flexibility, <italic>β =</italic> 0.012, 95% CI = 0.006, 0.018, <italic>t</italic>(81) = 3.85, <italic>p</italic> < 0.001. However, no such relationship was found among individuals with high expressive flexibility, <italic>β</italic> = 0.001, 95% CI = −0.01, 0.01, <italic>t</italic>(81) = 0.12, <italic>p ></italic> 0.05. These results indicate that among low (but not high) expressive flexibility individuals, an increase in duty-related traumatic exposure is associated with enhanced PTSD symptomology.</p><p>The findings regarding the moderating role of enhancement flexibility did not support our second hypothesis. While the general model was significant, <italic>R</italic><sup>2</sup> = 0.22, <italic>F</italic>(3,78) = 7.24, <italic>p</italic> < 0.001, the interaction between duty-related repeated traumatic exposure and enhancement flexibility was not significant, <italic>t</italic>(81) = −0.56, <italic>p ></italic> 0.05. Additionally, the results did not support our third hypothesis, regarding the role of suppression flexibility as a possible moderator. While the general model was significant, <italic>R</italic><sup>2</sup> = 0.19, <italic>F</italic>(3,78) = 6.22, <italic>p</italic> < 0.001, the interaction between duty-related repeated traumatic exposure and suppression flexibility was not significant, <italic>t</italic>(81) = −1, <italic>p ></italic> 0.05.</p><p>To further examine the role of the different components of expressive flexibility in the elusive relationship between duty-related repeated traumatic exposure and PTSD symptomology, we separated between the expression of positive and negative emotions and tested four different variables as possible moderators: negative enhancement, positive enhancement (i.e., the abilities to enhance the expression of positive and negative emotions, respectively), negative suppression and positive suppression (i.e., the ability to suppress the expression of positive and negative emotions, respectively). Both positive enhancement and negative enhancement yielded significant general models, however, the interactions between repeated traumatic exposure and the two variables were not significant.</p><p>The findings regarding <italic>negative suppression</italic> as a possible moderator are reported in <xref rid=\"ijms-21-05355-t003\" ref-type=\"table\">Table 3</xref>. The general model was significant, <italic>R</italic><sup>2</sup> = 0.26, <italic>F</italic>(3,78) = 9, <italic>p</italic> < 0.001, and there was a significant interaction between repeated exposure and negative suppression which accounted for an additional 4.3% of the variance above. Further examination of this interactive effect revealed a significant positive relationship between repeated duty-related traumatic exposure and PTSD symptomology for individuals with a low ability to suppress the expression of negative emotions, <italic>β</italic> = 0.012, 95% CI = 0.007, 0.018, <italic>t</italic>(81) = 4.35, <italic>p</italic> < 0.001. However, no such relationship was found among individuals with a high ability to suppress the expression of negative emotions, <italic>β</italic> = 0.004, 95% CI = −0.001, 0.01, <italic>t</italic>(81) = 1.76, <italic>p ></italic> 0.05. These results are consistent with our general assumption, as they indicate that among low (but not high) expressive flexibility individuals—i.e., individuals with a low ability to suppress the expression of negative emotions—an increase in duty-related traumatic exposure is associated with enhanced PTSD symptomatology (as described in <xref ref-type=\"fig\" rid=\"ijms-21-05355-f001\">Figure 1</xref>).</p><p>The findings regarding positive suppression as a possible moderator are also reported in <xref rid=\"ijms-21-05355-t003\" ref-type=\"table\">Table 3</xref>. The general model was significant, <italic>R</italic><sup>2</sup> = 0.23, <italic>F</italic>(3,78) = 7.79, <italic>p</italic> < 0.001, and there was a significant interaction between repeated exposure and positive suppression which accounted for an additional 6.4% of the variance above. Further examination of this interactive effect revealed a significant positive relationship between repeated duty-related traumatic exposure and PTSD symptomology for individuals with a high ability to suppress the expression of positive emotions, <italic>β</italic> = 0.02, 95% CI = 0.01, 0.02, <italic>t</italic>(81) = 4.3, <italic>p</italic> < 0.001. However, no such relationship was found among individuals with a low ability to suppress the expression of positive emotions, <italic>β</italic> = 0.004, 95% CI = −0.001, 0.01, <italic>t</italic>(81) = 1.6, <italic>p ></italic> 0.05. These results are inconsistent with our general assumption, as they indicate that among high (but not low) expressive flexibility individuals—i.e., individuals with a high ability to suppress the expression of positive emotions—an increase in duty-related traumatic exposure is associated with enhanced PTSD symptomatology (as described in <xref ref-type=\"fig\" rid=\"ijms-21-05355-f002\">Figure 2</xref>).</p><p>This pattern of results indicates that the abilities to suppress the expression of negative and positive emotions have opposing roles in the relationship between repeated traumatic exposure and PTSD symptomology.</p></sec><sec sec-type=\"discussion\" id=\"sec4-ijms-21-05355\"><title>4. Discussion</title><p>The aim of the current study was to examine the role of expressive flexibility—i.e., the ability to enhance and suppress an outward emotional expression—in the relationship between repeated exposure to traumatic events and PTSD symptomology. In accordance with our prediction, we found that expressive flexibility moderates the relationship between repeated traumatic exposure and PTSD symptoms. Specifically, while individuals with low expressive flexibility showed a positive association between level of repeated exposure and PTSD symptoms, no such relationship was found in individuals with high expressive flexibility. These results are in line with previous studies which showed that high expressive flexibility predicts emotional adaptation following both exposures to general trauma [<xref rid=\"B14-ijms-21-05355\" ref-type=\"bibr\">14</xref>,<xref rid=\"B23-ijms-21-05355\" ref-type=\"bibr\">23</xref>] or loss of partner [<xref rid=\"B24-ijms-21-05355\" ref-type=\"bibr\">24</xref>]. Moreover, it complements and expands previous results on active duty firefighters, which show a similar effect of regulatory choice flexibility [<xref rid=\"B6-ijms-21-05355\" ref-type=\"bibr\">6</xref>]. Finally, the results provide additional support for the importance of emotional flexibility in psychological well-being, and—for the first time, to our knowledge—provide specific evidence for the crucial role of expressive flexibility in the successful adaptation to repeated traumatic exposure.</p><p>While these results may tell a simple and clear story, focusing on the specific abilities to suppress and enhance emotions, revealed a more complex picture. First, we found that contrary to our prediction, the ability to display more emotion, neither positive nor negative, did not buffer the deleterious effects of repeated exposure to trauma. These results may question the traditional approach, which assumes that expressing emotion is therapeutic and beneficiary, and hence may represent an adaptive way to cope with trauma. Indeed, actively disclosing emotions regarding upsetting life events—whether to a close friend, a therapist or even writing about the experience—is often associated with an immediate emotional relief, and beneficial to mental and physical health [<xref rid=\"B25-ijms-21-05355\" ref-type=\"bibr\">25</xref>,<xref rid=\"B26-ijms-21-05355\" ref-type=\"bibr\">26</xref>,<xref rid=\"B27-ijms-21-05355\" ref-type=\"bibr\">27</xref>,<xref rid=\"B28-ijms-21-05355\" ref-type=\"bibr\">28</xref>,<xref rid=\"B29-ijms-21-05355\" ref-type=\"bibr\">29</xref>]. Abstaining from emotional expression of past or recent adverse experiences, on the other hand, is commonly linked to poorer health [<xref rid=\"B27-ijms-21-05355\" ref-type=\"bibr\">27</xref>,<xref rid=\"B30-ijms-21-05355\" ref-type=\"bibr\">30</xref>], increased emotional and physical arousal [<xref rid=\"B30-ijms-21-05355\" ref-type=\"bibr\">30</xref>,<xref rid=\"B31-ijms-21-05355\" ref-type=\"bibr\">31</xref>] and even to cognitive impairments [<xref rid=\"B32-ijms-21-05355\" ref-type=\"bibr\">32</xref>]. However, more recent studies present a different approach. Kennedy-Moore and Watson [<xref rid=\"B26-ijms-21-05355\" ref-type=\"bibr\">26</xref>] have pointed out a paradox in expressing negative emotions, suggesting that while disclosure of emotions is an important part of a successful therapeutic process, it may also indicate severe emotional distress, and even serves as a warning sign. Moreover, the relationship between emotional expression and psychological well-being seems more complicated with possible mediators such as different personality traits and coping styles, initial physical state and the level of social support [<xref rid=\"B33-ijms-21-05355\" ref-type=\"bibr\">33</xref>,<xref rid=\"B34-ijms-21-05355\" ref-type=\"bibr\">34</xref>]. Finally, it was suggested that the personal and social elements of emotional expression play a crucial role in this relationship. Specifically, it was found that sharing emotions is most helpful in reducing distress when it is done in a supportive social environment. However, if those in need or their potential supporters feel uncomfortable communicating emotions, or when the content of the expressed emotion is too intense or overwhelming, the recipients might react in a diminishing or minimizing manner and the person in need would end up feeling dismissed or rejected. Taken together, it is possible that in our study, the intense lifestyle of male, active-duty firefighters, who are repeatedly exposed to overwhelming sights, addresses certain personal coping styles and/or social communication habits in which expressing emotions is not common nor considered as worthwhile. Alternatively, it is possible that while the majority of previous research revealed similar results in firefighters and other servicemen who are repeatedly exposed to trauma [<xref rid=\"B35-ijms-21-05355\" ref-type=\"bibr\">35</xref>,<xref rid=\"B36-ijms-21-05355\" ref-type=\"bibr\">36</xref>,<xref rid=\"B37-ijms-21-05355\" ref-type=\"bibr\">37</xref>,<xref rid=\"B38-ijms-21-05355\" ref-type=\"bibr\">38</xref>], future studies may aim to test the moderating role of expressive flexibility in the relationship between trauma exposure and PTSD symptoms in other populations including not only soldiers and police officers, but also men and women civilians who live in conflict zones and experience repeated trauma.</p><p>When we tested the general ability to suppress emotional expression, we found no significant effect on the relationship between repeated exposure to trauma and PTSD symptoms. However, further examination revealed an interesting difference between the role of suppressing negative and positive emotions. Specifically, both the ability to suppress negative emotions and the tendency to allow and avoid suppression of positive emotions served as protective factors against the effects of repeated traumatic exposure. These findings may suggest that not only suppressing negative emotions but also allowing positive emotions plays an important role in coping with trauma. Indeed, there is growing evidence for the unique role of positive emotions in stress-related situations. For example, a recent study suggests that it is the lack of positive experiences, and not the excess of negative emotions, that differentiates between individuals with social anxiety disorder and healthy controls [<xref rid=\"B39-ijms-21-05355\" ref-type=\"bibr\">39</xref>]. Folkman [<xref rid=\"B40-ijms-21-05355\" ref-type=\"bibr\">40</xref>] refers in her review to the important function of positive emotions in restoring physical and psychological coping resources when facing a stressful event. Similarly, the Broaden-and-Build Theory [<xref rid=\"B41-ijms-21-05355\" ref-type=\"bibr\">41</xref>,<xref rid=\"B42-ijms-21-05355\" ref-type=\"bibr\">42</xref>] emphasizes the crucial role of positive emotions in the down-regulation of sympathetic arousal caused by stress, and the broadening of important coping functions such as attention, creativity and flexible thinking. With that being said, it should be noted that flexibility is the capacity to use the best mechanism, depending on the situation. Thus, enhancement might be best in one situation, or at one point in time in dealing with the situation, while suppression might be best later in the same situation or in another situation. Hence, while some aspects are more related to reduced PTSD symptoms, adaptive behavior is the ability to use enhancement and suppression when they are most appropriate.</p><p>The current study has several limitations. First, we did not address the possible relationship between emotional experience and emotional expression. Previous studies which tried to answer this question have yielded inconsistent results. For example, Gross and Levenson [<xref rid=\"B43-ijms-21-05355\" ref-type=\"bibr\">43</xref>] showed heightened sympathetic activity among participants who were asked to suppress all outward signs of emotional expression. Interestingly, despite this increase in physiological stress levels, participants did not report an increase in their negative emotional experience. Moreover, in some situations the expressing of negative emotions was linked to a relief from negative emotional experience [<xref rid=\"B33-ijms-21-05355\" ref-type=\"bibr\">33</xref>], although this was not the case for all the participants who expressed their aversive feelings. [<xref rid=\"B44-ijms-21-05355\" ref-type=\"bibr\">44</xref>,<xref rid=\"B45-ijms-21-05355\" ref-type=\"bibr\">45</xref>]. Future studies may aim to test the relationship between expressive flexibility and regulatory flexibility in conditions of repeated traumatic exposure. A positive correlation, for example, would indicate that an improvement in one aspect of emotional flexibility could benefit the other. Both expressive and regulatory flexibility seem to play a crucial role in buffering the effect of traumatic exposure. Hence, a better understanding of the relationship between the two could have important theoretical and therapeutic implications. Another limitation relates to the fact that we tested participants only at one time-point. While such a design serves as a proof of concept and allows to reach important conclusions regarding the associations between the variables, future studies may aim to use a longitudinal approach, in order to base a causal relationship.</p><p>The results of the present study may serve to develop interventions which aim to minimize the deleterious effect of repeated trauma both before and during active service. Specifically, it is possible that if first responders will acquire and practice adaptive expressive flexibility skills, it will help them to better cope with stressful situations and to invigorate through positive experiences.</p><p>To summarize, the current study reveals that a flexible emotional expressive profile is important when dealing with negative life events. Most importantly, among individuals who are repeatedly exposed to trauma, a resilient expressive profile is not necessarily having high abilities to suppress and enhance positive and negative emotional expressions, but adjusting these abilities in correspondence with the complex context of their lives.</p></sec></body><back><notes><title>Author Contributions</title><p>E.L.-G. and G.A.B. developed the concept of the paper; R.D. administrated the study and analyzed the results under the supervision of E.L.-G.; E.L.-G. and R.D. wrote a first draft of the report and G.A.B. read and commented on it. All authors have read and agreed to the published version of the manuscript.</p></notes><notes><title>Funding</title><p>The study was funded by the US–Israel Binational Science Foundation (BSF), Grant #2015_143 to E.L.-G. and G.A.B.</p></notes><notes notes-type=\"COI-statement\"><title>Conflicts of Interest</title><p>The authors declare no conflict of interest</p></notes><glossary><title>Abbreviations</title><array orientation=\"portrait\"><tbody><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">PTSD</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Post-Traumatic Stress Disorder</td></tr></tbody></array></glossary><ref-list><title>References</title><ref id=\"B1-ijms-21-05355\"><label>1.</label><element-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Benedek</surname><given-names>D.M.</given-names></name><name><surname>Fullerton</surname><given-names>C.</given-names></name><name><surname>Ursano</surname><given-names>R.J.</given-names></name></person-group><article-title>First Responders: Mental Health Consequences of Natural and Human-Made Disasters for Public Health and Public Safety Workers</article-title><source>Annu. 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position=\"float\"><label>Figure 1</label><caption><p>Post-Traumatic Stress Disorder (PTSD) symptomology as a function of duty-related repeated traumatic exposure and negative suppression.</p></caption><graphic xlink:href=\"ijms-21-05355-g001\"/></fig><fig id=\"ijms-21-05355-f002\" orientation=\"portrait\" position=\"float\"><label>Figure 2</label><caption><p>Post-Traumatic Stress Disorder (PTSD) symptomology as a function of duty-related repeated traumatic exposure and positive suppression.</p></caption><graphic xlink:href=\"ijms-21-05355-g002\"/></fig><table-wrap id=\"ijms-21-05355-t001\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijms-21-05355-t001_Table 1</object-id><label>Table 1</label><caption><p>Zero-order correlation between enhancement flexibility, suppression flexibility, expressive flexibility, PTSD symptomology and duty-related repeated traumatic exposure.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Variable</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">4</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">5</th></tr></thead><tbody><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.   Enhancement flexibility</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.203</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.69 **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.29 **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.17</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.   Suppression flexibility</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.203</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.67 **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.20</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.18</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.   Expressive flexibility</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.69 **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.67 **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.29 **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.20</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.   PTSD symptomology</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.29 **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.20</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.29 **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.41 **</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">5.   Duty-related repeated traumatic exposure</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.17</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.18</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.20</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.41 **</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1</td></tr></tbody></table><table-wrap-foot><fn><p>** <italic>p</italic> < 0.01. Post-Traumatic Stress Disorder (PTSD).</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"ijms-21-05355-t002\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijms-21-05355-t002_Table 2</object-id><label>Table 2</label><caption><p>Estimated coefficients, standard errors, and 95% confidence intervals for independent variable and moderator in the model predicting PTSD symptomology.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">Variable</th><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">\n<italic>β</italic>\n</th><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">SE</th><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\"><italic>t</italic> Value</th><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">95% CI</th></tr><tr><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Low</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">High</th></tr></thead><tbody><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Duty-related repeated traumatic exposure</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.006</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.002</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3 **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.002</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.01</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Expressive flexibility</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−2.26</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.85</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−2.65 **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−3.96</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.56</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Interaction</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.004</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.002</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−1.98 *</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.001</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0</td></tr></tbody></table><table-wrap-foot><fn><p><italic>β</italic> = Unstandardized Estimated Coefficients; SE = Standard Error; CI = Confidence Intervals. * <italic>p</italic> = 0.05. ** <italic>p</italic> < 0.01.</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"ijms-21-05355-t003\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijms-21-05355-t003_Table 3</object-id><label>Table 3</label><caption><p>Estimated coefficients, standard errors, and 95% confidence intervals for independent variable and moderators in the model predicting PTSD symptomology.</p></caption><table frame=\"hsides\" rules=\"groups\"><tbody><tr><td colspan=\"6\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">\n<bold>Negative Suppression as a Moderator</bold>\n</td></tr><tr><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">\n<bold>Variable</bold>\n</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">\n<bold><italic>β</italic></bold>\n</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">\n<bold>SE</bold>\n</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">\n<bold><italic>t</italic> Value</bold>\n</td><td colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">\n<bold>95% CI</bold>\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>Low</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>High</bold>\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Duty-related repeated traumatic exposure</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.001</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.002</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.21 **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.004</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.012</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Negative suppression</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.55</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.26</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−2.14 *</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−1.06</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.04</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Interaction</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.001</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.000</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−2.12 *</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.002</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.000</td></tr><tr><td colspan=\"6\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">\n<bold>Positive Suppression as a Moderator</bold>\n</td></tr><tr><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">\n<bold>Variable</bold>\n</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">\n<bold><italic>β</italic></bold>\n</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">\n<bold>SE</bold>\n</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">\n<bold><italic>t</italic> Value</bold>\n</td><td colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">\n<bold>95% CI</bold>\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>Low</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>High</bold>\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Duty-related repeated traumatic exposure</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.01</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.002.</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.77 **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.006</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.015</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Positive suppression</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.23</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.30</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.76</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.37</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.83</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Interaction</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.001</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.00</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2.54 *</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.000</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.003</td></tr></tbody></table><table-wrap-foot><fn><p><italic>β</italic> = Unstandardized Estimated Coefficients; SE = Standard Error; CI = Confidence Intervals. * <italic>p</italic> < 0.05. ** <italic>p</italic> < 0.01.</p></fn></table-wrap-foot></table-wrap></floats-group></article>\n"
]
[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Int J Environ Res Public Health</journal-id><journal-id journal-id-type=\"iso-abbrev\">Int J Environ Res Public Health</journal-id><journal-id journal-id-type=\"publisher-id\">ijerph</journal-id><journal-title-group><journal-title>International Journal of Environmental Research and Public Health</journal-title></journal-title-group><issn pub-type=\"ppub\">1661-7827</issn><issn pub-type=\"epub\">1660-4601</issn><publisher><publisher-name>MDPI</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32731381</article-id><article-id pub-id-type=\"pmc\">PMC7432117</article-id><article-id pub-id-type=\"doi\">10.3390/ijerph17155417</article-id><article-id pub-id-type=\"publisher-id\">ijerph-17-05417</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Article</subject></subj-group></article-categories><title-group><article-title>The Impact of Environmental Social Media Publications on User Satisfaction with and Trust in Tourism Businesses</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Martínez-Navalón</surname><given-names>Juan Gabriel</given-names></name></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0001-5951-6392</contrib-id><name><surname>Gelashvili</surname><given-names>Vera</given-names></name></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0002-9457-7745</contrib-id><name><surname>Saura</surname><given-names>José Ramón</given-names></name><xref rid=\"c1-ijerph-17-05417\" ref-type=\"corresp\">*</xref></contrib></contrib-group><aff id=\"af1-ijerph-17-05417\">Department of Business Economics, Rey Juan Carlos University, 28033 Madrid, Spain; <email>[email protected]</email> (J.G.M.-N.); <email>[email protected]</email> (V.G.)</aff><author-notes><corresp id=\"c1-ijerph-17-05417\"><label>*</label>Correspondence: <email>[email protected]</email></corresp></author-notes><pub-date pub-type=\"epub\"><day>28</day><month>7</month><year>2020</year></pub-date><pub-date pub-type=\"ppub\"><month>8</month><year>2020</year></pub-date><volume>17</volume><issue>15</issue><elocation-id>5417</elocation-id><history><date date-type=\"received\"><day>06</day><month>7</month><year>2020</year></date><date date-type=\"accepted\"><day>24</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>© 2020 by the authors.</copyright-statement><copyright-year>2020</copyright-year><license license-type=\"open-access\"><license-p>Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>).</license-p></license></permissions><abstract><p>The main aim of the present study was to analyze whether publications related to environmental sustainability in social media directly and positively influence user satisfaction with and trust in tourism businesses. Our second goal was to determine whether the influence of environmental sustainability and satisfaction is moderated by users’ gender. Data collection was performed using a questionnaire. The questionnaire responses were analyzed using the partial least squares-structural equation modeling (PLS-SEM) methodology. The results have shown that there is a positive relationship between environmental sustainability, satisfaction, and trust generated by tourism companies through their publications on social media, and that this relationship is not conditioned by users’ gender. The results of the present study contribute to the literature by bridging the gap in research on tourism enterprises and their strategies regarding social media publications. Our findings also provide important implications related to the content of environmental sustainability strategies and social media communication for tourism companies.</p></abstract><kwd-group><kwd>environmental sustainability</kwd><kwd>satisfaction</kwd><kwd>trust</kwd><kwd>social media</kwd><kwd>SEM</kwd></kwd-group></article-meta></front><body><sec sec-type=\"intro\" id=\"sec1-ijerph-17-05417\"><title>1. Introduction</title><p>In recent decades, the emergence of social media has provided companies with direct and fast contact with their customers [<xref rid=\"B1-ijerph-17-05417\" ref-type=\"bibr\">1</xref>]. Nowadays, social media are among the “best possibilities available” means for companies to get in touch with potential customers [<xref rid=\"B2-ijerph-17-05417\" ref-type=\"bibr\">2</xref>]. By means of social media, companies can announce offers, promote new products and services, get to know about their customers’ preferences, and directly relate their offers [<xref rid=\"B3-ijerph-17-05417\" ref-type=\"bibr\">3</xref>,<xref rid=\"B4-ijerph-17-05417\" ref-type=\"bibr\">4</xref>]. Furthermore, several studies have demonstrated that the costs of traditional marketing are considerably higher than social media marketing [<xref rid=\"B5-ijerph-17-05417\" ref-type=\"bibr\">5</xref>,<xref rid=\"B6-ijerph-17-05417\" ref-type=\"bibr\">6</xref>,<xref rid=\"B7-ijerph-17-05417\" ref-type=\"bibr\">7</xref>,<xref rid=\"B8-ijerph-17-05417\" ref-type=\"bibr\">8</xref>]. </p><p>Other advantages of using social media for companies include transparency, efficiency, and openness [<xref rid=\"B8-ijerph-17-05417\" ref-type=\"bibr\">8</xref>,<xref rid=\"B9-ijerph-17-05417\" ref-type=\"bibr\">9</xref>]. Several studies have highlighted the beneficial role of social media for business, showing that appropriate use of social media can help businesses to capture new customers and turn interested people into future customers [<xref rid=\"B6-ijerph-17-05417\" ref-type=\"bibr\">6</xref>,<xref rid=\"B10-ijerph-17-05417\" ref-type=\"bibr\">10</xref>,<xref rid=\"B11-ijerph-17-05417\" ref-type=\"bibr\">11</xref>,<xref rid=\"B12-ijerph-17-05417\" ref-type=\"bibr\">12</xref>]. </p><p>Furthermore, companies also use social media to promote sustainability [<xref rid=\"B8-ijerph-17-05417\" ref-type=\"bibr\">8</xref>,<xref rid=\"B13-ijerph-17-05417\" ref-type=\"bibr\">13</xref>]. In recent years, sustainability, a recently emerging concept, has become a popular word among the general public [<xref rid=\"B14-ijerph-17-05417\" ref-type=\"bibr\">14</xref>,<xref rid=\"B15-ijerph-17-05417\" ref-type=\"bibr\">15</xref>]. While the meaning of sustainability varies depending on its context of use, such as social, economic, or ecological [<xref rid=\"B16-ijerph-17-05417\" ref-type=\"bibr\">16</xref>,<xref rid=\"B17-ijerph-17-05417\" ref-type=\"bibr\">17</xref>], most interpretations of sustainable development converge in that a company’s policies and actions must be respectful of the environment and be socially equitable if they want to contribute to economic growth [<xref rid=\"B18-ijerph-17-05417\" ref-type=\"bibr\">18</xref>]. That means that sustainable development should be responsible and include conscientious management of the environment to create a positive impact [<xref rid=\"B19-ijerph-17-05417\" ref-type=\"bibr\">19</xref>]. Accordingly, since sustainability issues are considered to be strategic topics to ensure companies’ success and operability, companies have been trying to strengthen and grow their sustainability [<xref rid=\"B20-ijerph-17-05417\" ref-type=\"bibr\">20</xref>,<xref rid=\"B21-ijerph-17-05417\" ref-type=\"bibr\">21</xref>,<xref rid=\"B22-ijerph-17-05417\" ref-type=\"bibr\">22</xref>]. In the long run, sustainability is a determining factor for firms’ achieving superior competitive advantage and financial performance [<xref rid=\"B23-ijerph-17-05417\" ref-type=\"bibr\">23</xref>].</p><p>Numerous studies have analyzed the sustainability of Spanish companies operating in different sectors [<xref rid=\"B24-ijerph-17-05417\" ref-type=\"bibr\">24</xref>,<xref rid=\"B25-ijerph-17-05417\" ref-type=\"bibr\">25</xref>,<xref rid=\"B26-ijerph-17-05417\" ref-type=\"bibr\">26</xref>]. Many of these studies have highlighted several important benefits that companies can obtain when they apply sustainability in their management model. However, although several studies emphasized the importance of tourism businesses in Spain [<xref rid=\"B27-ijerph-17-05417\" ref-type=\"bibr\">27</xref>], relevant research on the sustainability of tourism enterprises in Spain remains scarce. </p><p>At present, due to the rapid increase in the awareness of the population about sustainability issues, environmental sustainability is a key objective of many tourism companies. Accordingly, in order to follow this trend, along with other modifications [<xref rid=\"B27-ijerph-17-05417\" ref-type=\"bibr\">27</xref>], companies should modify their sustainability policies.</p><p>As demonstrated by Reyes-Menendez et al. [<xref rid=\"B28-ijerph-17-05417\" ref-type=\"bibr\">28</xref>], most tourism companies in Spain are sustainable, and their stakeholders are aware of it. However, the question is whether investing in environmental sustainability and informing stakeholders about it can increase customer satisfaction with and trust in tourism companies. To date, none of the previous studies has addressed this issue. To bridge this gap in the literature, the present study evaluates the influence of social media publications about sustainability on customer satisfaction with and trust in tourism businesses.</p><p>The main aim of the present study is to analyze the impact of social media on promoting environmental sustainability. The two research questions addressed in this study are as follows: (1) Can social media publications by tourism businesses about sustainability influence user satisfaction? (2) If so, can user satisfaction affect user trust in tourism businesses that share publications about sustainability in social media? Accordingly, the present study analyzes whether social media publications related to environmental sustainability directly and positively influence satisfaction and, indirectly and positively, user trust in tourism companies. Specific attention is paid to user gender. The analyses are performed from the user perspective, i.e., using a survey. </p><p>The remainder of this paper is structured as follows. <xref ref-type=\"sec\" rid=\"sec2-ijerph-17-05417\">Section 2</xref> presents a review of relevant literature on marketing and sustainability in social media. In <xref ref-type=\"sec\" rid=\"sec3-ijerph-17-05417\">Section 3</xref>, hypotheses are formulated and the sample and methodology are presented. <xref ref-type=\"sec\" rid=\"sec4-ijerph-17-05417\">Section 4</xref> reports the results that are discussed further in <xref ref-type=\"sec\" rid=\"sec5-ijerph-17-05417\">Section 5</xref>. <xref ref-type=\"sec\" rid=\"sec6-ijerph-17-05417\">Section 6</xref> concludes.</p></sec><sec id=\"sec2-ijerph-17-05417\"><title>2. Literature Review</title><sec id=\"sec2dot1-ijerph-17-05417\"><title>2.1. Social Media Marketing</title><p>In the 21st century, social media have become a new way of communication that enables people to obtain information and express their beliefs and ideas in a new way [<xref rid=\"B29-ijerph-17-05417\" ref-type=\"bibr\">29</xref>]. Social platforms, such as Facebook, Twitter, YouTube, Instagram, or LinkedIn, have entered people’s daily lives; today, people increasingly use these media for social interaction and information exchange [<xref rid=\"B30-ijerph-17-05417\" ref-type=\"bibr\">30</xref>,<xref rid=\"B31-ijerph-17-05417\" ref-type=\"bibr\">31</xref>]. Nowadays, social media are used in different fields like culture, entertainment, education, politics, economy, or business. In the business area, social media offer business owners’ new ways to communicate, collaborate, create [<xref rid=\"B32-ijerph-17-05417\" ref-type=\"bibr\">32</xref>], or receive feedback from their users [<xref rid=\"B33-ijerph-17-05417\" ref-type=\"bibr\">33</xref>,<xref rid=\"B34-ijerph-17-05417\" ref-type=\"bibr\">34</xref>].</p><p>One of the areas where social media are actively employed is marketing [<xref rid=\"B2-ijerph-17-05417\" ref-type=\"bibr\">2</xref>,<xref rid=\"B3-ijerph-17-05417\" ref-type=\"bibr\">3</xref>,<xref rid=\"B35-ijerph-17-05417\" ref-type=\"bibr\">35</xref>,<xref rid=\"B36-ijerph-17-05417\" ref-type=\"bibr\">36</xref>]. Therefore, since the emergence of social media, companies have changed the way they market their products and services [<xref rid=\"B37-ijerph-17-05417\" ref-type=\"bibr\">37</xref>]. Marketing through social media has become a tool that helps companies keep in touch with their users, promote the products and services, and transform this relationship into sales in the best case [<xref rid=\"B38-ijerph-17-05417\" ref-type=\"bibr\">38</xref>]. </p><p>Many previous studies have demonstrated that using social media marketing can increase companies’ revenue and efficiency, reduce the expenses, help to stay in touch with potential and actual users, increase the impact on promotional activities, and so on [<xref rid=\"B3-ijerph-17-05417\" ref-type=\"bibr\">3</xref>,<xref rid=\"B4-ijerph-17-05417\" ref-type=\"bibr\">4</xref>,<xref rid=\"B39-ijerph-17-05417\" ref-type=\"bibr\">39</xref>]. Another advantage of social media marketing is its higher cost efficiency as compared to traditional marketing [<xref rid=\"B5-ijerph-17-05417\" ref-type=\"bibr\">5</xref>,<xref rid=\"B7-ijerph-17-05417\" ref-type=\"bibr\">7</xref>,<xref rid=\"B40-ijerph-17-05417\" ref-type=\"bibr\">40</xref>]. Owing to the advantages offered by social media marketing, companies are increasingly trying to use this type of marketing to promote their products and services and attract more customers.</p><p>However, marketing through social media also has several limitations. For instance, Maecker et al., [<xref rid=\"B11-ijerph-17-05417\" ref-type=\"bibr\">11</xref>] found that customers of the companies or specific products that use social media marketing have more service requests. Therefore, if enterprises have more service requests, they should invest more money in this area. Furthermore, Todor [<xref rid=\"B40-ijerph-17-05417\" ref-type=\"bibr\">40</xref>] reported that disadvantages of social media marketing include the lack of user trust due to the large number of frauds related to virtual promotions, cash-on-delivery systems, copyright issues, and so forth. Accordingly, if companies in areas as diverse as tourism, trade, or culture want to enjoy all the advantages offered by the use of marketing through social media, they should carefully consider potential hazards of this channel of marketing.</p></sec><sec id=\"sec2dot2-ijerph-17-05417\"><title>2.2. Environmental Sustainability in Social Media Marketing of Tourism Industry in Spain</title><p>The first official definition of sustainability comes from the United Nations General Assembly Report of the World Commission on Environment and Development. It was initially called “Our common future” and then renamed as the \"Brundtland report\" prepared by Brundtland et al. [<xref rid=\"B41-ijerph-17-05417\" ref-type=\"bibr\">41</xref>]. The report defines sustainability as “development that meets the needs of the present without compromising the ability of future generations to meet their own needs” (p.39). The report distinguished three different types of sustainability: environmental, economic, and social. According to Basiago [<xref rid=\"B42-ijerph-17-05417\" ref-type=\"bibr\">42</xref>], economic sustainability is understood as a production system that meets current consumption levels without compromising future needs, while social sustainability consists of a system of social organization that reduces poverty.</p><p>Finally, environmental sustainability requires the maintenance and protection of environmental resources for future generations. Therefore, environmental sustainability refers to different production and engineering processes that support and protect the environmental system [<xref rid=\"B43-ijerph-17-05417\" ref-type=\"bibr\">43</xref>]. Accordingly, environmental sustainability concerns the preservation and protection of natural resources and the development of alternative sources of energy, as well as maintenance of sustainable consumption levels, development of green products, and promotion of biodegradable products.</p><p>Since customers are the main marketing variable for the company, and all properly transferred information determines the success or failure of a business, marketing is an important factor related to sustainability [<xref rid=\"B44-ijerph-17-05417\" ref-type=\"bibr\">44</xref>]. This has given rise to the emergence of the new concept “sustainable marketing”, which is a combination of marketing and sustainability. </p><p>Fuller [<xref rid=\"B45-ijerph-17-05417\" ref-type=\"bibr\">45</xref>] defined sustainable marketing as “the process of planning, implementing, and controlling the development, pricing, promotion, and distribution of products in a manner that satisfies the following three criteria: customer needs are met, organizational goals are attained, and the process is compatible with ecosystem” (p. 4). However, despite the centrality of this concept today, only very few previous studies in the area of marketing have analyzed how enterprises communicate sustainability to the public [<xref rid=\"B46-ijerph-17-05417\" ref-type=\"bibr\">46</xref>].</p><p>Numerous studies demonstrated that social media belong to the most popular marketing tools [<xref rid=\"B2-ijerph-17-05417\" ref-type=\"bibr\">2</xref>,<xref rid=\"B3-ijerph-17-05417\" ref-type=\"bibr\">3</xref>,<xref rid=\"B36-ijerph-17-05417\" ref-type=\"bibr\">36</xref>,<xref rid=\"B39-ijerph-17-05417\" ref-type=\"bibr\">39</xref>]. Therefore, similarities between sustainable marketing and social media can be detected. For instance, Du et al. [<xref rid=\"B13-ijerph-17-05417\" ref-type=\"bibr\">13</xref>] pointed out that consumers increasingly seem to prefer eco-friendly offers. Accordingly, companies focus on their new sustainable product performance and pass that information on to their users. Using social media to promote the sustainable marketing of new products and services would be beneficial for companies. Several previous studies have investigated the effect of sustainable marketing through social media and its benefits for the companies [<xref rid=\"B13-ijerph-17-05417\" ref-type=\"bibr\">13</xref>,<xref rid=\"B47-ijerph-17-05417\" ref-type=\"bibr\">47</xref>]. However, relevant research on Spain, including its tourism sector that significantly contributes to the country’s economic growth, has been scarce [<xref rid=\"B48-ijerph-17-05417\" ref-type=\"bibr\">48</xref>].</p><p>The tourism industry plays an important role in the Spanish economy, generating a considerable dragging effect on other sectors [<xref rid=\"B49-ijerph-17-05417\" ref-type=\"bibr\">49</xref>]. According to a report prepared by the business association World Travel and Tourism [<xref rid=\"B50-ijerph-17-05417\" ref-type=\"bibr\">50</xref>], the tourism sector amounted to 14.6% of Spanish GDP.</p><p>In addition, travel and tourism created 1 in 5 new jobs over the last five years. Several authors have studied different aspects of the tourism industry, such as accessible tourism for everyone [<xref rid=\"B51-ijerph-17-05417\" ref-type=\"bibr\">51</xref>], climate change and its associated risks for the tourism industry [<xref rid=\"B52-ijerph-17-05417\" ref-type=\"bibr\">52</xref>], the importance of evaluating sustainable tourism and tourist destinations [<xref rid=\"B53-ijerph-17-05417\" ref-type=\"bibr\">53</xref>], cultural tourism and its evolution in the last years [<xref rid=\"B54-ijerph-17-05417\" ref-type=\"bibr\">54</xref>], or rural tourism facilities [<xref rid=\"B55-ijerph-17-05417\" ref-type=\"bibr\">55</xref>]. All these studies have emphasized the importance of the tourism industry for the Spanish economy and society. For instance, Rodríguez-Antón et al. [<xref rid=\"B56-ijerph-17-05417\" ref-type=\"bibr\">56</xref>] reported that, despite the recent emergence of the phenomenon of the so-called collaborative economy, there has been enormous growth and future potential of the collaborative tourism model in the tourism industry. This implies that the tourism industry will continue to grow positively and contribute to the economic development of the country.</p></sec></sec><sec id=\"sec3-ijerph-17-05417\"><title>3. Hypotheses Development</title><sec id=\"sec3dot1-ijerph-17-05417\"><title>3.1. Hypotheses</title><p>This section presents the hypotheses to be tested in the present study. Numerous previous studies have shown a strong link between marketing and sustainability and have come to the conclusion that environmental sustainability is a crucial factor for companies to create green products, be competitive in the market, increase profits, maintain employee loyalty, or maintain users trust and satisfaction towards them [<xref rid=\"B57-ijerph-17-05417\" ref-type=\"bibr\">57</xref>,<xref rid=\"B58-ijerph-17-05417\" ref-type=\"bibr\">58</xref>,<xref rid=\"B59-ijerph-17-05417\" ref-type=\"bibr\">59</xref>]. For instance, Walsh and Dodds [<xref rid=\"B59-ijerph-17-05417\" ref-type=\"bibr\">59</xref>] pointed out that the company’s activities have a significant impact on the environment and society in general. Accordingly, customers become increasingly sensible of the environmental, social, and economic implications of their actions towards reasonable consumption and, therefore, put some pressure on companies to implement environmentally friendly policies. </p><p>In this connection, a study by Pereira-Moliner et al. [<xref rid=\"B60-ijerph-17-05417\" ref-type=\"bibr\">60</xref>] showed that environmental management practices positively influence financial performance, market success, and the satisfaction of users and employees. However, few studies directly explored the relationship between environmental sustainability and user satisfaction [<xref rid=\"B31-ijerph-17-05417\" ref-type=\"bibr\">31</xref>]. Based on the considerations discussed above, the following hypothesis can be formulated: </p><statement><label><bold>H1.</bold> </label><p><italic>Social media publications related to environmental sustainability would directly and positively influence user satisfaction</italic>.</p></statement><p>According to McMurrian and Matulich [<xref rid=\"B61-ijerph-17-05417\" ref-type=\"bibr\">61</xref>], companies that generate the trust of their users are more likely to expand and improve their income. Numerous previous studies have analyzed user trust in marketing and management [<xref rid=\"B62-ijerph-17-05417\" ref-type=\"bibr\">62</xref>,<xref rid=\"B63-ijerph-17-05417\" ref-type=\"bibr\">63</xref>,<xref rid=\"B64-ijerph-17-05417\" ref-type=\"bibr\">64</xref>]. For instance, a study on travel agencies demonstrated that the more satisfied is a customer in his/her relationship with the travel agency, the more trust s/he attributed to that agency [<xref rid=\"B65-ijerph-17-05417\" ref-type=\"bibr\">65</xref>]. Likewise, Kassim and Asiah [<xref rid=\"B66-ijerph-17-05417\" ref-type=\"bibr\">66</xref>] found that customer satisfaction in e-commerce settings has a positive impact on customer trust. Based on the evidence reviewed above, the following hypothesis can be formulated: </p><statement><label><bold>H2.</bold> </label><p><italic>Satisfaction of social media users with tourism companies directly and positively influences their trust in those companies</italic>.</p></statement><p>Furthermore, while previous research on sustainability and satisfaction has demonstrated that more and more consumers support sustainable companies [<xref rid=\"B67-ijerph-17-05417\" ref-type=\"bibr\">67</xref>,<xref rid=\"B68-ijerph-17-05417\" ref-type=\"bibr\">68</xref>]. A study by Hou and Elliott [<xref rid=\"B69-ijerph-17-05417\" ref-type=\"bibr\">69</xref>] showed that compared to men, women are more involved in the search for information, dedicating more time and attention to what they need to know before making their purchase. Similarly, Trivedi and Teichert [<xref rid=\"B70-ijerph-17-05417\" ref-type=\"bibr\">70</xref>] found gender differences in consumer preferences and purchase intention. Based on this evidence, in the present study, the following hypothesis will be tested: </p><statement><label><bold>H3.</bold> </label><p><italic>The influence of environmental sustainability and satisfaction is mediated by user gender</italic>.</p></statement><p>Based on the hypotheses formulated above, the following model is proposed (see <xref ref-type=\"fig\" rid=\"ijerph-17-05417-f001\">Figure 1</xref>).</p></sec><sec id=\"sec3dot2-ijerph-17-05417\" sec-type=\"methods\"><title>3.2. Sampling and Methodology</title><p>This study aims to investigate whether social media publications about environmental sustainability influence user satisfaction and trust in tourism businesses. To this end, an on-line questionnaire was constructed. Data collection was performed in August 2019. The invitation to participate in the questionnaire was disseminated through e-mail and social media (Twitter, Inc., San Francisco, CA, USA) and Facebook (Facebook, Inc., Menlo Park, CA, USA)). Data collection was supported by active tourism companies and hotels in the province of Albacete (Spain); these companies granted the authors access to their customers through social media. The province of Albacete was chosen because this study is part of a series of investigations that aim to measure different variables in Spanish regions that suffer from the risk of depopulation, and where tourism can be one of the industries that can prevent such depopulation. The advantage of selecting this province is the interest shown by the companies to participate in the research since the results will allow them to improve in the future. However, since Spain is a very touristic country, the results of this study might not be generalizable to less touristic countries. </p><p>The questionnaire consisted of two blocks. The first block contained questions about demographic characteristics of the participants; in the second block, the participants were asked to rate a number of items on a 5-point Likert scale (1 = ‘strongly disagree’; 5 = ‘strongly agree’). </p><p>For the data analysis and the validation of the hypotheses, the model for the study of structural equations was used, which has its origin in variances (SEM). This model makes it possible to statistically analyze the predicted relationships by predicting the dependent variables, which makes it possible to calculate and quantify the direct and indirect effects of the variables on each other [<xref rid=\"B71-ijerph-17-05417\" ref-type=\"bibr\">71</xref>,<xref rid=\"B72-ijerph-17-05417\" ref-type=\"bibr\">72</xref>]. </p><p>While there are several techniques based on variances, in the present study, the partial least squares (PLS) was used. This technique enables the analysis of composed and factor models, which makes it possible to measure variables and estimate the proposed model [<xref rid=\"B71-ijerph-17-05417\" ref-type=\"bibr\">71</xref>,<xref rid=\"B73-ijerph-17-05417\" ref-type=\"bibr\">73</xref>,<xref rid=\"B74-ijerph-17-05417\" ref-type=\"bibr\">74</xref>].</p><p>As argued in many previous studies, the PLS-SEM technique is one of the most complete methods for the analysis of models where relationships between variables are identified and the influence of these among other options is measured [<xref rid=\"B73-ijerph-17-05417\" ref-type=\"bibr\">73</xref>,<xref rid=\"B75-ijerph-17-05417\" ref-type=\"bibr\">75</xref>,<xref rid=\"B76-ijerph-17-05417\" ref-type=\"bibr\">76</xref>,<xref rid=\"B77-ijerph-17-05417\" ref-type=\"bibr\">77</xref>]. In addition, this technique is used to perform a multi-group analysis [<xref rid=\"B31-ijerph-17-05417\" ref-type=\"bibr\">31</xref>].</p><p>In the present study, a total of 362 questionnaire responses were obtained; however, since some responses were incomplete, only 351 questionnaires were included in the final sample. Therefore, the sample was sufficient for the analysis using the PLS-SEM technique. The use of this technique is also justified in investigating a novel topic on which available literature is scarce and where many different relationships are to be explored. Accordingly, this technique is widely used as an effective tool for exploratory analysis [<xref rid=\"B78-ijerph-17-05417\" ref-type=\"bibr\">78</xref>]. Using the PLS-SEM technique is also recommended when some of the analyzed variables are composed of dimensions [<xref rid=\"B31-ijerph-17-05417\" ref-type=\"bibr\">31</xref>], as well as when the proposed model is complex [<xref rid=\"B79-ijerph-17-05417\" ref-type=\"bibr\">79</xref>]. The PLS-SEM analysis was performed using SmartPLS 3.0 (SmartPLS GmbH, Bönningstedt, Germany), a widely used and reliable software [<xref rid=\"B73-ijerph-17-05417\" ref-type=\"bibr\">73</xref>,<xref rid=\"B80-ijerph-17-05417\" ref-type=\"bibr\">80</xref>]. </p></sec></sec><sec id=\"sec4-ijerph-17-05417\"><title>4. Analysis of Results</title><sec id=\"sec4dot1-ijerph-17-05417\"><title>4.1. Descriptive Analysis</title><p>As can be seen in <xref rid=\"ijerph-17-05417-t001\" ref-type=\"table\">Table 1</xref>, gender distribution in the sample was balanced (52% women; 48% men). Most participants (52%) were 31–45 years old, followed by those aged 18–30 years old (19.9%) and 46–55 years old (17.6%). Almost two-thirds of the participants (62.4%) were employees, followed by self-employed (16.8%). In terms of time spent using social media, 37% used social media between 30 and 60 min a day, followed by 28.5% who used them for less than 30 min. </p><p>In terms of the preferred social platform, the respondents were given the possibility to check more than one option. Most participants (88.3%) used Facebook, followed by Instagram (59.8%) and YouTube (54.4%). Snapchat was regularly used by only 5.1% of the participants.</p></sec><sec id=\"sec4dot2-ijerph-17-05417\"><title>4.2. Measurement Model Analysis</title><p>The analysis using PLS-SEM unfolded in several steps [<xref rid=\"B73-ijerph-17-05417\" ref-type=\"bibr\">73</xref>]. First, the validation of the measurement scale was carried out. Second, the structural model analysis was performed. Third, the multi-group analysis investigating whether there was a moderation effect in the predicted relations according to user gender was run.</p><p>In the present study, the measurement scale was validated twice: first with the items of the multidimensional variable and, second, with the already grouped dimensions. This led to the establishment of the first- and second-order models [<xref rid=\"B81-ijerph-17-05417\" ref-type=\"bibr\">81</xref>].</p><p>In the first step, we validated the measurement scale twice. For the first-order model, all items of the variables were reflective; thus, the criteria to be tested were individual reliability, composite reliability, convergent validity, and discriminant validity.</p><p>In the first section, the items favorably outperformed the cutting indices of the first three criteria used, exceeding the value of 0.707 proposed by Carmines and Zeller [<xref rid=\"B82-ijerph-17-05417\" ref-type=\"bibr\">82</xref>] for individual reliability, Cronbach alpha of 0.70 in the criterion of Nunnally and Bernstein [<xref rid=\"B83-ijerph-17-05417\" ref-type=\"bibr\">83</xref>] for composite reliability, and 0.5 of the criterion of Fornell and Larcker [<xref rid=\"B84-ijerph-17-05417\" ref-type=\"bibr\">84</xref>] which sets the minimum level of average variance extracted (AVE) [<xref rid=\"B75-ijerph-17-05417\" ref-type=\"bibr\">75</xref>]. In the results, the Cronbach alpha values were very close to 0.9, which can incur possible errors [<xref rid=\"B74-ijerph-17-05417\" ref-type=\"bibr\">74</xref>]. Therefore, the Dijkstra-Henseler indicator (rho_A) was also analyzed, which gives the study results greater robustness, as it is considered the most reliable measure for the analysis of composite reliability [<xref rid=\"B31-ijerph-17-05417\" ref-type=\"bibr\">31</xref>,<xref rid=\"B85-ijerph-17-05417\" ref-type=\"bibr\">85</xref>]. The analysis was positive, as all constructs exceeded 0.7. All of the above indicated that all analyzed constructs were reliable and that they accounted for over 50% of the variance of the corresponding items [<xref rid=\"B73-ijerph-17-05417\" ref-type=\"bibr\">73</xref>]. Said differently, all constructs exceeded the minimum values of composite reliability and convergent validity (see <xref rid=\"ijerph-17-05417-t002\" ref-type=\"table\">Table 2</xref>).</p><p>In the last step of the validation of the first-order measurement scale, discriminant validity was analyzed. This was done by means of two analyses. The first analysis was based on Fornell and Larcker’s [<xref rid=\"B84-ijerph-17-05417\" ref-type=\"bibr\">84</xref>] method that analyzes the amount of variance captured by a variable from its indicators (AVE); this amount must be greater than the variance that this variable shares with other variables in the model [<xref rid=\"B73-ijerph-17-05417\" ref-type=\"bibr\">73</xref>].</p><p>The second analysis, called heterotrait-monotrait (HTMT), allows for a more rigorous analysis of the discriminatory validity criterion [<xref rid=\"B86-ijerph-17-05417\" ref-type=\"bibr\">86</xref>]. In this last criterion that can be seen in <xref rid=\"ijerph-17-05417-t003\" ref-type=\"table\">Table 3</xref>, not all items exceeded the threshold. Therefore, the items SAT2, HON2, COM1, and COM2 were eliminated (see <xref rid=\"ijerph-17-05417-t002\" ref-type=\"table\">Table 2</xref>).</p><p>Once the first-order measurement scale was validated, a grouping of the items of the multidimensional variable was performed, allowing for the second validation of the second-order model. In this model, the dimensions of the multidimensional variable “trust” had a formative character, which is why other analyses were carried out to validate the second-order scale.</p><p>First, all criteria previously made for reflective items were studied; however, in the second-order model, all items were kept (<xref rid=\"ijerph-17-05417-t004\" ref-type=\"table\">Table 4</xref>). Subsequently, the analysis was carried out for the formative variable to rule out collinearity problems. This was done by means of the evaluation of the variance inflation factor (VIF; Hair et al. [<xref rid=\"B73-ijerph-17-05417\" ref-type=\"bibr\">73</xref>,<xref rid=\"B78-ijerph-17-05417\" ref-type=\"bibr\">78</xref>]). Then, the magnitude of the variable’s weights [<xref rid=\"B87-ijerph-17-05417\" ref-type=\"bibr\">87</xref>] and significance [<xref rid=\"B81-ijerph-17-05417\" ref-type=\"bibr\">81</xref>] was evaluated. <xref rid=\"ijerph-17-05417-t004\" ref-type=\"table\">Table 4</xref> shows the three dimensions of confidence that fully validate the measurement scale.</p></sec><sec id=\"sec4dot3-ijerph-17-05417\"><title>4.3. Structural Model Analysis</title><p>Furthermore, the analysis of the structural model was performed. This analysis aimed to evaluate the predictive capacity of the model and the relations predicted by the hypotheses. Before proceeding to the model analysis, collinearity in the structural model should be ruled out [<xref rid=\"B88-ijerph-17-05417\" ref-type=\"bibr\">88</xref>]. This is done by means of VIF values of the model which must not exceed 5 [<xref rid=\"B73-ijerph-17-05417\" ref-type=\"bibr\">73</xref>], followed by the analysis of the algebraic sign, the significance and magnitude of the coefficients: Path coefficients (β), R2 values (variance explained), the size of the effect ƒ2 and the Q2 test (validated cross redundancy) in order to measure the predictive relevance of the model [<xref rid=\"B73-ijerph-17-05417\" ref-type=\"bibr\">73</xref>,<xref rid=\"B89-ijerph-17-05417\" ref-type=\"bibr\">89</xref>,<xref rid=\"B90-ijerph-17-05417\" ref-type=\"bibr\">90</xref>]. </p><p>With regard to the analysis of the predictive power, i.e., the amount of variance (0.75 relevant, 0.50 moderate, and 0.25 weak; see Hair et al. [<xref rid=\"B78-ijerph-17-05417\" ref-type=\"bibr\">78</xref>]; Henseler et al. [<xref rid=\"B75-ijerph-17-05417\" ref-type=\"bibr\">75</xref>]) of a variable explained by another predictive variable (determination coefficient R2), the values obtained for the variables were moderate (60%) for trust and weak (36%) for satisfaction.</p><p>Subsequently, the effect size of (f2) was analyzed; this value shows the level to which an exogenous variable contributes to explain an endogenous variable. <xref rid=\"ijerph-17-05417-t005\" ref-type=\"table\">Table 5</xref> reports the results, and the two studied relationships have a large effect size.</p><p>As for the cross-validated redundancy indices (Q2), which help to examine the predictive relevance of the theoretical/structural model [<xref rid=\"B87-ijerph-17-05417\" ref-type=\"bibr\">87</xref>], these indices were greater than zero. Therefore, the model had a satisfactory predictive relevance.</p><p>Therefore, the relationships predicted by Hypotheses 1–2 were significant and had high predictive power and a significant influence effect. Therefore, Hypotheses 1 and 2 were supported by the results. The results of Hypothesis 3 are reported in <xref ref-type=\"sec\" rid=\"sec4dot4-ijerph-17-05417\">Section 4.4</xref>.</p></sec><sec id=\"sec4dot4-ijerph-17-05417\"><title>4.4. Multi-Group Moderating Effect</title><p>To perform the analysis of the multi-group moderating effect, the measurement invariance test of the compounds must be carried out [<xref rid=\"B31-ijerph-17-05417\" ref-type=\"bibr\">31</xref>,<xref rid=\"B81-ijerph-17-05417\" ref-type=\"bibr\">81</xref>]. Such an analysis, called MICOM, is a prerequisite for carrying out a multi-group study [<xref rid=\"B73-ijerph-17-05417\" ref-type=\"bibr\">73</xref>,<xref rid=\"B91-ijerph-17-05417\" ref-type=\"bibr\">91</xref>,<xref rid=\"B92-ijerph-17-05417\" ref-type=\"bibr\">92</xref>]. In the present study, MICOM unfolded in the following three steps. First, we observed that the two subsamples (men and women) were tested for having the same configurational invariance, i.e., on whether they had exactly the same model. Second, compound invariance was searched for. It was stable, as the scores of a compound using the weights of group 1 did not differ from those created using the weights of group 2. Third, 3a and 3b are divided into two parts: 3a analyses equality of variances and 3b analyses equality of measures. <xref rid=\"ijerph-17-05417-t006\" ref-type=\"table\">Table 6</xref> shows the results of the MICOM analysis.</p><p>The results of the MICOM analysis showed complete invariance, suggesting that both subsamples could be grouped together. Once the satisfactory result of the invariance was obtained, the multi-group analysis was performed using two non-parametric techniques, PLS-MGA and PERMUTATIONS, and one parametric technique, the Parametric Test. <xref rid=\"ijerph-17-05417-t007\" ref-type=\"table\">Table 7</xref> shows that the results obtained using the different analysis techniques of multi-group moderation (Parametric Test, PLS-MGA, and Permutations).</p><p>The results of these analyses showed that user gender did not exert a moderating effect on the relationships between satisfaction-trust and environmental sustainability-satisfaction. Therefore, Hypothesis 3 was not supported by the results. <xref ref-type=\"fig\" rid=\"ijerph-17-05417-f002\">Figure 2</xref> shows the model validated for this study.</p></sec></sec><sec sec-type=\"discussion\" id=\"sec5-ijerph-17-05417\"><title>5. Discussion</title><p>This study investigated the relationship between social network publications by tourism companies where these companies promote their sustainable strategies and respect for the environment, on the one hand, and user satisfaction and trust, on the other hand. Supporting H1, our results showed that social media publications related to the environment and sustainability influence user satisfaction. This evidence is consistent with the results reported by Reilly and Hynan [<xref rid=\"B17-ijerph-17-05417\" ref-type=\"bibr\">17</xref>] and suggests that companies should focus on disseminating and preparing communication plans that support sustainable practices and are respectful towards the environment.</p><p>According to Azhar and Fauzan [<xref rid=\"B93-ijerph-17-05417\" ref-type=\"bibr\">93</xref>], user satisfaction is a key factor that plays a fundamental role in the marketing and development strategies of hotel companies, various tourism businesses, or local businesses that travelers explore while traveling. Similarly, to Saura et al. [<xref rid=\"B34-ijerph-17-05417\" ref-type=\"bibr\">34</xref>], the results of the present study showed that user satisfaction when browsing through social networks must be positive, as it has a real impact on user engagement with the organization. </p><p>Furthermore, supporting H2, our results showed that the satisfaction of social media users with tourism companies directly and positively influences their trust in those companies. This result is consistent with the findings reported by Cori et al. [<xref rid=\"B94-ijerph-17-05417\" ref-type=\"bibr\">94</xref>], in the political field. Therefore, companies should increase travelers confidence in their sustainable policies, rather than in their messages related to promotions, discounts, or elements that do not add value to the marketing strategy.</p><p>In addition, in line with Apperson et al. [<xref rid=\"B95-ijerph-17-05417\" ref-type=\"bibr\">95</xref>], and Bopp et al. [<xref rid=\"B96-ijerph-17-05417\" ref-type=\"bibr\">96</xref>], our results showed that, while the strategy of social media publications can improve user trust in tourism businesses, such elements as pollution, society, unsustainable strategies or environmental pollutants in the tourism environment can make tourists feel uncomfortable during their travel.</p><p>Based on these findings, it can be concluded that the appropriate content strategy for managers of tourism companies would be to create messages that focus on maintaining values related to the environment [<xref rid=\"B72-ijerph-17-05417\" ref-type=\"bibr\">72</xref>]. As suggested by Heinonen [<xref rid=\"B97-ijerph-17-05417\" ref-type=\"bibr\">97</xref>], the messages revealed by companies on social networks can influence user behavior. Another important aspect to consider is how users organize themselves in communities on social networks and interact with the messages that companies launch [<xref rid=\"B98-ijerph-17-05417\" ref-type=\"bibr\">98</xref>]. These strategies based on social media posts demonstrate user interest in maintaining two-way communication with companies in digital environments [<xref rid=\"B99-ijerph-17-05417\" ref-type=\"bibr\">99</xref>].</p><p>The results of the present study confirm that social media communications should follow a sustainable policy and disseminate relevant environment-related achievements and policies adopted by tourism companies. In this way, users will be able to share the publications with their peers, use those publications for their own purposes, or demonstrate their pride in the fact that they are using the services of an environmentally conscious tourism company to their peers in social network communities. </p><p>Finally, our results showed that gender does not moderate user appreciation of social media publications related to sustainability and the environment. Therefore, our findings do not support [<xref rid=\"B100-ijerph-17-05417\" ref-type=\"bibr\">100</xref>] as the conclusion about gender influence on user behavior in social networks, particularly in the sustainable tourism sector. In contrast, other authors, such as Palos-Sanchez et al. [<xref rid=\"B101-ijerph-17-05417\" ref-type=\"bibr\">101</xref>], consider the study of gender as an important factor in drawing robust conclusions.</p><p>However, considering that our study is the first attempt to investigate the impact of gender on user appreciation of environment- and sustainability-related publications in social media, this topic deserves further in-depth investigation in future research.</p><p>As indicated above, social network publications have been studied from different perspectives [<xref rid=\"B102-ijerph-17-05417\" ref-type=\"bibr\">102</xref>]. However, there is room for improvement to study the feelings of users when their followers support their publications, adding likes, and sharing content on social networks [<xref rid=\"B103-ijerph-17-05417\" ref-type=\"bibr\">103</xref>]. </p><p>From this analytical perspective, the study of how users obtain immediate rewards on an emotional level on social media is interesting to set future research objectives in this area. By analyzing these findings, it will be possible to understand how sustainable publications can contribute to creating a positive impact on consumers through social media [<xref rid=\"B104-ijerph-17-05417\" ref-type=\"bibr\">104</xref>].</p><p>In addition, we agree with the results of Tomomi [<xref rid=\"B105-ijerph-17-05417\" ref-type=\"bibr\">105</xref>], since companies should add environmental supportive content to their communication and marketing plans. A relevant example would be companies’ communicating the content related to supporting sustainable projects and actions, which will both boost their environmental reputation online and make their followers support their content marketing plans by sharing this kind of content.</p></sec><sec sec-type=\"conclusions\" id=\"sec6-ijerph-17-05417\"><title>6. Conclusions</title><p>In the present study, the analysis unfolded in the following two blocks. In the first block, the proposed model of how environmental sustainability, directly and indirectly, influences social media users’ satisfaction and trust was analyzed. In a second block, we investigated whether the model can be moderated by user gender. Of note, previous studies that analyzed the relationship between sustainability and social media user satisfaction and trust are scarce, which complicated formulating the hypotheses based on available literature. </p><p>The results showed that environmentally sustainable tourism companies generate greater satisfaction among their users. In addition, environmentally sustainable tourism companies also indirectly induced user trust. Therefore, the first two hypotheses tested in this study (H1 and H2) were supported by the results. </p><p>However, our prediction that user gender may moderate the aforementioned relationships was not supported by the results. Therefore, H3 had to be rejected.</p><sec id=\"sec6dot1-ijerph-17-05417\"><title>6.1. Theoretical Implications</title><p>As discussed in <xref ref-type=\"sec\" rid=\"sec1-ijerph-17-05417\">Section 1</xref>, this study aimed to bridge a gap in previous literature by investigating the impact of social media publications by tourism companies on user satisfaction and trust. In doing so, the present study contributes to available knowledge about the role of social media publications in the tourism sector. Furthermore, to the best of our knowledge, the present study is the first to investigate whether user gender plays a role in user evaluation of social media publications related to sustainability and the environment. The results of the present study suggest meaningful directions of further research in this area, including elements such as the organization of users in communities in social networks, the type of interaction according to the message published in the tourism ecosystem, and the concerns and behaviors of users when interacting with this type of content. Our findings also highlight that the content and communication strategies developed by companies in the tourism sector should focus on actions related to environmental sustainability.</p></sec><sec id=\"sec6dot2-ijerph-17-05417\"><title>6.2. Practical Implications</title><p>As for the practical implications for the improvement of the management of tourism companies, it should be pointed out that environmental sustainability is a growing asset within the tourism sector. This implies that an organization committed to environmental sustainability and the one that explicitly reveals this message to its customers will substantially improve user satisfaction and the trust of its stakeholders. The implementation of waste separation policies, the use of products with ecological packaging, reduction of energy costs, and other relevant eco-friendly policies will help the company to pollute less and increase its customers’ purchasing possibilities.</p><p>Our findings highlight that the communication strategies of tourism companies should explicitly inform their customers about various sustainability policies implemented. This will improve companies’ public profiles, lead to an increase in the number of customers, and thus expand companies’ influence on society.</p><p>Finally, since we did not find any gender differences, managers of tourism companies should take into account that in communicating policies, the strategies should not differentiate between men and women, since both groups have the same perceptions of sustainability and environmental issues. Therefore, campaigns should be carried out in the same way for both genders.</p></sec><sec id=\"sec6dot3-ijerph-17-05417\"><title>6.3. Limitations and Future Research</title><p>The present study has several limitations. First, the size of the sample was rather limited, particularly given that there are possibilities to get larger samples in the tourism industry. Second, due to the paucity of relevant prior research, it was difficult to ground the formulated hypotheses in past work. Thirdly, due to the novelty of the topic, the theoretical framework that the present study is based on is exploratory. In order to expand the present investigation, further research that would replicate the present study design in other sectors or countries, as well as expand the model by adding other marketing variables, such as commitment, loyalty, economic sustainability, or social sustainability. </p></sec></sec></body><back><notes><title>Author Contributions</title><p>Conceptualization, J.G.M.-N., V.G. and J.R.S.; methodology, J.G.M.-N.; software, J.G.M.-N.; validation, V.G. and J.R.S; formal analysis, J.G.M.-N., V.G. and J.R.S.; investigation, J.G.M.-N., V.G. and J.R.S.; data curation, J.G.M.-N.; writing—original draft preparation, J.G.M.-N., V.G. and J.R.S.; writing—review and editing, J.G.M.-N., V.G. and J.R.S; supervision, J.R.S. 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Manag.</source><year>2010</year><volume>9</volume><fpage>265</fpage><lpage>280</lpage><pub-id pub-id-type=\"doi\">10.1057/abm.2010.6</pub-id></element-citation></ref></ref-list></back><floats-group><fig id=\"ijerph-17-05417-f001\" orientation=\"portrait\" position=\"float\"><label>Figure 1</label><caption><p>Research model used in the present study.</p></caption><graphic xlink:href=\"ijerph-17-05417-g001\"/></fig><fig id=\"ijerph-17-05417-f002\" orientation=\"portrait\" position=\"float\"><label>Figure 2</label><caption><p>Proposed research model results.</p></caption><graphic xlink:href=\"ijerph-17-05417-g002\"/></fig><table-wrap id=\"ijerph-17-05417-t001\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05417-t001_Table 1</object-id><label>Table 1</label><caption><p>Characteristics of the study participants (<italic>n</italic> = 351).</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Classification Variable</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Variable</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Frequency</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Percentage</th></tr></thead><tbody><tr><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Gender</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Female</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">182</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">52%</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Male</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">169</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">48%</td></tr><tr><td rowspan=\"5\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Age</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">18–30</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">70</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">19.9%</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">31–45</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">184</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">52.4%</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">46–55</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">62</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">17.6%</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">56–65</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">19</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.4%</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">>65</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">16</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">4.6%</td></tr><tr><td rowspan=\"5\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Employment</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Student</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">32</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">9.1%</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Housewife/man</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">14</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.9%</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Unemployed</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">27</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7.6%</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Employed</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">219</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">62.4%</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Self-Employed</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">59</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">16.8%</td></tr><tr><td rowspan=\"5\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Minutes devoted to social media per day</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><30</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">28.5%</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">30–60</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">130</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">37%</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">60–90</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">40</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">11.4%</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">90–120</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">36</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10.3%</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">>120</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">45</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">12.8%</td></tr><tr><td rowspan=\"7\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Social media used<break/>(multiple responses)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Twitter</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">126</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">35.9%</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Facebook</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">310</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">88.3%</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Instagram</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">210</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">59.8%</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">LinkedIn</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">160</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">45.6%</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">YouTube</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">191</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">54.4%</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Snapchat</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">18</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.1%</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Pinterest</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">65</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">18.5%</td></tr></tbody></table></table-wrap><table-wrap id=\"ijerph-17-05417-t002\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05417-t002_Table 2</object-id><label>Table 2</label><caption><p>Measurement items first order.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Constructs </th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Items</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Correlation Loading</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">CA</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">CR</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">rho_A</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">AVE</th></tr></thead><tbody><tr><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Satisfaction</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">(SAT1) I am satisfied with the knowledge I get from the social media of the tourism companies I follow.</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.885 ***</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">0.790</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">0.904</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">0.820</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">0.824</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">(SAT3) The social media of the tourism companies I follow meet my expectations.</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.930 *** </td></tr><tr><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Honesty</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">(HON1) The tourism companies that I follow on social media keep their promises.</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.933 ***</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">0.805</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">0.832</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">0.832</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">0.835</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">(HON3) The social media of the tourism companies I follow are managed in an ethical and transparent way.</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.894 ***</td></tr><tr><td rowspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Benevolence</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">(BEN1) The social media of the tourism companies I follow offer beneficial advice and recommendations.</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.884 ***</td><td rowspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">0.883</td><td rowspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">0.928</td><td rowspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">0.885</td><td rowspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">0.811</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">(BEN2) The tourism companies that I follow in social media develop actions taking into account how they will affect their interest groups.</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.871 ***</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">(BEN3) The tourism companies I follow on social media are concerned about the interests and benefits, both present and future, of their stakeholders.</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.956 ***</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Competence</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">(COM3) The tourism companies that I follow have a knowledge of their users that allows them to adapt to users’ needs.</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.904 ***</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.00</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.00</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.00</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.00</td></tr><tr><td rowspan=\"5\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Environmental Sustainability </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">(SUST1) The tourism companies that I follow on social media have recycling policies.</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.740 ***</td><td rowspan=\"5\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">0.882</td><td rowspan=\"5\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">0.914</td><td rowspan=\"5\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">0.894</td><td rowspan=\"5\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">0.680</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">(SOST2) The social media accounts of the tourism companies that I follow promote positive environmental ethics.</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.869 ***</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">(SUST3) The tourism companies that I follow in social media value and protect the environment.</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.859 ***</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">(SUST4) The social media of the tourism companies I follow publish pollution awareness messages.</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.834 ***</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">(SUST5) The social media of the tourism companies that I follow defend the diversity of nature and promote its value and protection. </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.816 ***</td></tr></tbody></table><table-wrap-foot><fn><p>Note: CA = Cronbach’s alpha; CR = Composite Reliability; rho_A = Dijkstra-Henseler indicator; AVE = Average Variance Extracted; *** <italic>p</italic>-value < 0.001.</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"ijerph-17-05417-t003\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05417-t003_Table 3</object-id><label>Table 3</label><caption><p>Measurement of the first-order model (discriminant validity).</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</th><th colspan=\"5\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Fornell-Larcker Criterion</th><th colspan=\"5\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Heterotrait-Monotrait Ratio (HTMT)</th></tr><tr><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Constructs</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">BEN</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">COM</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">HON</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">SAT</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">SUST</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">BEN</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">COM</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">HON</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">SAT</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">SUST</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">BEN</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">9.00</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">COM</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.693</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.00</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.737</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">HON</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.739</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.715</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.914</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.874</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.807</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">SAT</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.714</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.644</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.727</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.908</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.850</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.716</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.894</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">SUST</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.656</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.538</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.580</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.598</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.825</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.728</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.561</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.669</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.683</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td></tr></tbody></table><table-wrap-foot><fn><p>Note: BEN = Benevolence; COM = Competence; HON = Honesty; SAT = Satisfaction; SUST = Environmental.</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"ijerph-17-05417-t004\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05417-t004_Table 4</object-id><label>Table 4</label><caption><p>Second-order measurement model of the formative construct.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Construct</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Dimensions</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Correlation<break/>(Weights)</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">CA</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">CR</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">AVE</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">VIF</th></tr></thead><tbody><tr><td rowspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">TRUST</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Honesty (HON)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.483 ***</td><td rowspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">n/a</td><td rowspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">n/a</td><td rowspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">n/a</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.669</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Benevolence (BEN)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.432 ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.511</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Competence (COM)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.184 **</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2.328</td></tr></tbody></table><table-wrap-foot><fn><p>Note: CA = Cronbach’s alpha; CR = Composite Reliability; AVE = Average Variance Extracted; VIF = Variance inflation factor; ** <italic>p</italic>-value < 0.01, *** <italic>p</italic>-value < 0.001; n/a = Not applicable.</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"ijerph-17-05417-t005\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05417-t005_Table 5</object-id><label>Table 5</label><caption><p>Results for Hypotheses 1–2.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">\n</th><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">Path Coeff (β)</th><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">Statistics t (β/STDEV)</th><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">f2</th><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Confidence Interval</th></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">5.0%</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">95.0%</td></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">H1. Environmental sustainability → Satisfaction</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.600 ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">12.227</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.563</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.723</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.823</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">H2. Satisfaction → Trust</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.779 ***</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">25.992</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.547</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.512</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.675</td></tr></tbody></table><table-wrap-foot><fn><p>R2: Trust = 0.607; Satisfaction = 0.360; Adjusted R2: Trust = 0.606; Satisfaction = 0.358; Q2: Trust = 0.460; Satisfaction = 0.275. Students in single queue *** <italic>p</italic> < 0.001.</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"ijerph-17-05417-t006\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05417-t006_Table 6</object-id><label>Table 6</label><caption><p>Results of invariance measurement testing using permutation.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">Constructs</th><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">Invariance</th><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Composition Invariance</th><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">Partial Invariance</th><th colspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Equal Mean Assessment</th><th colspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Equal Variance Assessment</th><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">Full Measurement Invariance Established</th></tr><tr><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">C = 1</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Confidence Interval</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Differences</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Confidence Interval</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Equal</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Differences</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Confidence Interval</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Equal</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">TRUST</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Yes</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.999</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">(0.975/1.000)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Yes</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.043</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">(−0.219/0.221)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Yes</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.046</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">(−0.482/0.469)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Yes</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Yes</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">SAT</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Yes</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.000</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">(0.999/1.000)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Yes</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.144</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">(−0.225/0.219)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Yes</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.074</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">(−0.393/0.390)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Yes</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Yes</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">SUST</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Yes</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.000</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">(0.997/1.000)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Yes</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.010</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">(−0.217/0.221)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Yes</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.197</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">(−0.403/0.400)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Yes</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Yes</td></tr></tbody></table><table-wrap-foot><fn><p>Note: SAT = Satisfaction; SUST = Environmental Sustainability.</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"ijerph-17-05417-t007\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05417-t007_Table 7</object-id><label>Table 7</label><caption><p>Result for Hypothesis 3.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</th><th colspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Path Coefficient</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Confidence Interval<break/>(2.5%; 97.5%)</th><th colspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\"><italic>p</italic>-Value Difference</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</th></tr><tr><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Relationship</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Men</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Women</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Difference</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">PLS-MGA</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Permutation Test</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Parametric Test</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Supported</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">SAT→TRUST</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.752</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.806</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.054</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">(−0.117; 0.118)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.821</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.360</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.185</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">NO/NO/NO</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">SUST→SAT</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.593</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.609</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.016</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">(−0.194; 0.192)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.569</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.872</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.435</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">NO/NO/NO</td></tr></tbody></table><table-wrap-foot><fn><p>Note: SAT = Satisfaction; SUST = Environmental Sustainability.</p></fn></table-wrap-foot></table-wrap></floats-group></article>\n"
]
[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Front Psychol</journal-id><journal-id journal-id-type=\"iso-abbrev\">Front Psychol</journal-id><journal-id journal-id-type=\"publisher-id\">Front. Psychol.</journal-id><journal-title-group><journal-title>Frontiers in Psychology</journal-title></journal-title-group><issn pub-type=\"epub\">1664-1078</issn><publisher><publisher-name>Frontiers Media S.A.</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32849073</article-id><article-id pub-id-type=\"pmc\">PMC7432118</article-id><article-id pub-id-type=\"doi\">10.3389/fpsyg.2020.01821</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Psychology</subject><subj-group><subject>Brief Research Report</subject></subj-group></subj-group></article-categories><title-group><article-title>Social Distancing and Stigma: Association Between Compliance With Behavioral Recommendations, Risk Perception, and Stigmatizing Attitudes During the COVID-19 Outbreak</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Tomczyk</surname><given-names>Samuel</given-names></name><xref ref-type=\"corresp\" rid=\"c001\"><sup>*</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/942530/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Rahn</surname><given-names>Maxi</given-names></name><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/942532/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Schmidt</surname><given-names>Silke</given-names></name></contrib></contrib-group><aff><institution>Department Health and Prevention, Institute of Psychology, University of Greifswald</institution>, <addr-line>Greifswald</addr-line>, <country>Germany</country></aff><author-notes><fn fn-type=\"edited-by\"><p>Edited by: Nuno Guerreiro Piçarra, University Institute of Lisbon (ISCTE), Portugal</p></fn><fn fn-type=\"edited-by\"><p>Reviewed by: Mark Conner, University of Leeds, United Kingdom; Galina Alexandrovna Arina, Lomonosov Moscow State University, Russia; Joseph DeLuca, Icahn School of Medicine at Mount Sinai, United States</p></fn><corresp id=\"c001\">*Correspondence: Samuel Tomczyk, <email>[email protected]</email></corresp><fn fn-type=\"other\" id=\"fn004\"><p>This article was submitted to Health Psychology, a section of the journal Frontiers in Psychology</p></fn></author-notes><pub-date pub-type=\"epub\"><day>11</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><year>2020</year></pub-date><pub-date pub-type=\"pmc-release\"><day>11</day><month>8</month><year>2020</year></pub-date><!-- PMC Release delay is 0 months and 0 days and was based on the <pub-date pub-type=\"epub\"/>. --><volume>11</volume><elocation-id>1821</elocation-id><history><date date-type=\"received\"><day>17</day><month>4</month><year>2020</year></date><date date-type=\"accepted\"><day>01</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>Copyright © 2020 Tomczyk, Rahn and Schmidt.</copyright-statement><copyright-year>2020</copyright-year><copyright-holder>Tomczyk, Rahn and Schmidt</copyright-holder><license xlink:href=\"http://creativecommons.org/licenses/by/4.0/\"><license-p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</license-p></license></permissions><abstract><p><bold>Introduction:</bold> Following behavioral recommendations is key to successful containment of the COVID-19 pandemic. Therefore, it is important to identify causes and patterns of non-compliance in the population to further optimize risk and health communication.</p><p><bold>Methods:</bold> A total of 157 participants [80% female; mean age = 27.82 years (<italic>SD</italic> = 11.01)] were surveyed regarding their intention to comply with behavioral recommendations issued by the German government. Latent class analysis examined patterns of compliance, and subsequent multinomial logistic regression models tested sociodemographic (age, gender, country of origin, level of education, region, and number of persons per household) and psychosocial (knowledge about preventive behaviors, risk perception, stigmatizing attitudes) predictors.</p><p><bold>Results:</bold> Three latent classes were identified: <italic>high compliance</italic> (25%) with all recommendations; <italic>public compliance</italic> (51%), with high compliance regarding public but not personal behaviors; and <italic>low compliance</italic> (24%) with most recommendations. Compared to high compliance, low compliance was associated with male gender [relative risk ratio (<italic>RRR</italic>) = 0.08 (0.01; 0.85)], younger age [<italic>RRR</italic> = 0.72 (0.57; 0.93)], and lower public stigma [<italic>RRR</italic> = 0.21 (0.05; 0.88)]. Low compliers were also younger than public compliers [<italic>RRR</italic> = 0.76 (0.59; 0.98)].</p><p><bold>Discussion:</bold> With 25% of the sample reporting full compliance, and 51% differing in terms of public and personal compliance, these findings challenge the sustainability of strict regulatory measures. Moreover, young males were most likely to express low compliance, stressing the need for selective health promotion efforts. Finally, the positive association between public stigma and compliance points to potential othering effects of stigma during a pandemic, but further longitudinal research is required to examine its impact on health and social processes throughout the pandemic.</p></abstract><kwd-group><kwd>COVID-19</kwd><kwd>stigma</kwd><kwd>public health</kwd><kwd>risk communication</kwd><kwd>latent class analysis</kwd><kwd>infection prevention</kwd><kwd>cross-sectional</kwd></kwd-group><counts><fig-count count=\"1\"/><table-count count=\"3\"/><equation-count count=\"0\"/><ref-count count=\"64\"/><page-count count=\"9\"/><word-count count=\"0\"/></counts></article-meta></front><body><sec id=\"S1\"><title>Introduction</title><p>The current outbreak of the coronavirus SARS-CoV-2 and the associated disease, COVID-19, is transfixing the world with over 2 million confirmed infections by April 16, 2020<sup><xref ref-type=\"fn\" rid=\"footnote1\">1</xref></sup>. In addition to its physical threat, this outbreak also causes psychological distress, anxiety, and depression (<xref rid=\"B60\" ref-type=\"bibr\">Wang et al., 2020</xref>). Moreover, research on the coronavirus-associated SARS pandemic in 2002/2003 points to potentially long-lasting adverse consequences, such as depression, stigmatization, diminished quality of life, and post-traumatic stress (<xref rid=\"B27\" ref-type=\"bibr\">Ko et al., 2006</xref>; <xref rid=\"B31\" ref-type=\"bibr\">Lee et al., 2006</xref>; <xref rid=\"B51\" ref-type=\"bibr\">Siu, 2008</xref>; <xref rid=\"B21\" ref-type=\"bibr\">Gardner and Moallef, 2015</xref>).</p><p>To contain infectious diseases like COVID-19, experts and government officials alike recommend a series of preventive behaviors, such as hand hygiene, and avoidance behaviors, such as social distancing or (voluntary) quarantine (e.g., <xref rid=\"B22\" ref-type=\"bibr\">Glass et al., 2006</xref>; <xref rid=\"B19\" ref-type=\"bibr\">Durham and Casman, 2012</xref>; <xref rid=\"B17\" ref-type=\"bibr\">Ding, 2014</xref>; <xref rid=\"B25\" ref-type=\"bibr\">Karimi et al., 2015</xref>; <xref rid=\"B61\" ref-type=\"bibr\">Weston et al., 2018</xref>; <xref rid=\"B35\" ref-type=\"bibr\">Lewnard and Lo, 2020</xref>). Previous simulations and current reports affirm that a combination of all strategies has the greatest success rates in containing the disease (<xref rid=\"B26\" ref-type=\"bibr\">Kelso et al., 2009</xref>; <xref rid=\"B28\" ref-type=\"bibr\">Kupferschmidt and Cohen, 2020</xref>). And yet, successful containment depends on adequate public compliance. While predictors of compliance can be explicated via a behavior theory (e.g., the theory of planned behavior; <xref rid=\"B1\" ref-type=\"bibr\">Ajzen, 1991</xref>), and they are well-documented for certain health behaviors (e.g., adherence in chronical illness; <xref rid=\"B44\" ref-type=\"bibr\">Rich et al., 2015</xref>), far less is known about compliance in pandemics.</p><p>To date, several studies have identified perceived personal risk (i.e., susceptibility, anticipated severity, and anticipatory worry) and knowledge of adaptive behaviors as facilitators of compliance (c. <xref rid=\"B55\" ref-type=\"bibr\">Tang and Wong, 2003</xref>, <xref rid=\"B56\" ref-type=\"bibr\">2005</xref>; <xref rid=\"B10\" ref-type=\"bibr\">Cheng and Ng, 2006</xref>; <xref rid=\"B32\" ref-type=\"bibr\">Leppin and Aro, 2009</xref>; <xref rid=\"B29\" ref-type=\"bibr\">Kwok et al., 2020</xref>), although an explicit theoretical framework is often missing (<xref rid=\"B6\" ref-type=\"bibr\">Bish and Michie, 2010</xref>). Moreover, barriers to adherence (i.e., non-compliance) have received less attention presumably due to preventive and avoidance behaviors being very easy to carry out.</p><p>In a review of 26 studies on preventive behaviors in pandemics (<xref rid=\"B6\" ref-type=\"bibr\">Bish and Michie, 2010</xref>), however, compliance rates varied greatly, for example, between 4% for wearing a mask, 41.3% for “one or more specific actions” (<xref rid=\"B8\" ref-type=\"bibr\">Brug et al., 2004</xref>), and up to 95% for quarantine (<xref rid=\"B7\" ref-type=\"bibr\">Blendon et al., 2004</xref>). Despite the variety of illnesses, time frames, populations, and research methods in these studies, a general implication seems to be that a substantial proportion of the population does not adhere to the recommended behaviors. Composite measures of preventive behaviors revealed even lower compliance: 30.7% of a representative sample in Singapore practiced six or more out of eight (<xref rid=\"B43\" ref-type=\"bibr\">Quah and Hin-Peng, 2004</xref>), 48.7% in Hong Kong practiced five or more out of seven (<xref rid=\"B34\" ref-type=\"bibr\">Leung et al., 2003</xref>), and 37.8% in England practiced one or more out of three measures (<xref rid=\"B45\" ref-type=\"bibr\">Rubin et al., 2009</xref>).</p><p>In this respect, a qualitative study on (non)compliance with SARS quarantine identified ethical (e.g., civic duty), legal (e.g., monetary sanctions), and social (e.g., peer pressure) reasons to publicly comply with quarantine, while acceptance of quarantine differed markedly within households and private environments (<xref rid=\"B9\" ref-type=\"bibr\">Cava et al., 2005</xref>). Another study also identified practical issues (e.g., disposal of used tissues), selfishness, and responsibility shift (<xref rid=\"B39\" ref-type=\"bibr\">Morrison and Yardley, 2009</xref>) as core barriers to compliance. Responsibility shift refers to the belief that infected persons are particularly responsible for (not) spreading the illness, thus protecting others, whereas healthy persons are responsible for protecting themselves from becoming infected, leading to a shift in personal priorities in protective behaviors depending on one’s infection status.</p><p>Moreover, sociodemographic variables gender and age (i.e., male, younger age) consistently predicted non-compliance (<xref rid=\"B34\" ref-type=\"bibr\">Leung et al., 2003</xref>; <xref rid=\"B55\" ref-type=\"bibr\">Tang and Wong, 2003</xref>). This might be connected to a generally lower risk perception, particularly a lower perceived susceptibility, in young males (<xref rid=\"B15\" ref-type=\"bibr\">De Zwart et al., 2009</xref>). Regarding educational attainment, higher levels of education have been discussed as barriers to as well as facilitators of behavioral compliance in different populations (<xref rid=\"B34\" ref-type=\"bibr\">Leung et al., 2003</xref>; <xref rid=\"B56\" ref-type=\"bibr\">Tang and Wong, 2005</xref>; <xref rid=\"B15\" ref-type=\"bibr\">De Zwart et al., 2009</xref>; <xref rid=\"B6\" ref-type=\"bibr\">Bish and Michie, 2010</xref>).</p><p>To capture the existing heterogeneity in (non)compliance, this study utilizes a latent class approach (<xref rid=\"B11\" ref-type=\"bibr\">Collins and Lanza, 2010</xref>). Latent classes are often used to analyze behavioral patterns in non-communicable diseases, such as substance use (e.g., <xref rid=\"B57\" ref-type=\"bibr\">Tomczyk et al., 2015</xref>, <xref rid=\"B58\" ref-type=\"bibr\">2016</xref>). However, to our knowledge, only one study applied latent class analysis to population behaviors following a novel virus outbreak [i.e., influenza A (H7N9)] in Hong Kong (<xref rid=\"B36\" ref-type=\"bibr\">Liao et al., 2015</xref>), despite the method’s statistical advantages in modeling behavioral patterns (e.g., flexibility, integration of measurement error). <xref rid=\"B36\" ref-type=\"bibr\">Liao et al. (2015)</xref> identified three latent classes of behavioral compliance, namely, moderate hygiene compliance (moderate personal hygiene, low avoidance behaviors), good hygiene compliance (high personal hygiene, low avoidance), and vigilance (high hygiene and avoidance). Moderate hygiene compliance was the largest class (about 50% of the sample) and was significantly associated with male gender, lower age, poor education, and lower risk perception, thus stressing the need for selective prevention and health promotion.</p><p>Finally, the current study also focuses on stigmatizing attitudes in the context of compliance due to the impact of stigma on fear, psychosocial stress, and social rejection during infectious diseases, such as SARS (<xref rid=\"B50\" ref-type=\"bibr\">Sim and Chua, 2004</xref>; <xref rid=\"B30\" ref-type=\"bibr\">Lee et al., 2005</xref>; <xref rid=\"B27\" ref-type=\"bibr\">Ko et al., 2006</xref>; <xref rid=\"B51\" ref-type=\"bibr\">Siu, 2008</xref>). Stigmatization can occur at different levels (e.g., individual, social, structural) and is connected to social identity processes (<xref rid=\"B54\" ref-type=\"bibr\">Tajfel and Turner, 1986</xref>; <xref rid=\"B3\" ref-type=\"bibr\">Bandura, 1998</xref>, <xref rid=\"B4\" ref-type=\"bibr\">2004</xref>; <xref rid=\"B37\" ref-type=\"bibr\">Link and Phelan, 2001</xref>), where in-groups (i.e., individuals or groups that a person identifies with) and out-groups (i.e., individuals or groups a person does not identify with) are constructed based on certain characteristics (e.g., profession, illness symptoms). Out-groups are subsequently devaluated, for instance, by being labeled irresponsible or dangerous. This devaluation can further lead to verbal discrimination or interpersonal violence (<xref rid=\"B42\" ref-type=\"bibr\">Parker and Aggleton, 2003</xref>; <xref rid=\"B12\" ref-type=\"bibr\">Corrigan et al., 2004</xref>). Moreover, public stigma comprises support for a restriction of public opportunities (e.g., vote, utilize health care) for the devaluated out-group, in this instance, symptomatic and/or infected persons. In fact, survivors of the SARS epidemic experienced blame and social rejection (<xref rid=\"B30\" ref-type=\"bibr\">Lee et al., 2005</xref>; <xref rid=\"B38\" ref-type=\"bibr\">Mak et al., 2006</xref>), while persons of Asian descent reported victimization, regardless of their personal infection status (<xref rid=\"B64\" ref-type=\"bibr\">Zheng et al., 2005</xref>). These experiences of being blamed and ostracized oftentimes outlasted the epidemic and were associated with continued psychosocial stress (<xref rid=\"B8\" ref-type=\"bibr\">Brug et al., 2004</xref>; <xref rid=\"B51\" ref-type=\"bibr\">Siu, 2008</xref>; <xref rid=\"B24\" ref-type=\"bibr\">Jiang et al., 2009</xref>). In addition, an increase in influenza infections also corresponded to an increase in stigmatizing attitudes (e.g., a lack of trust, increased hostility) in previous research (<xref rid=\"B62\" ref-type=\"bibr\">Williams and Gonzalez-Medina, 2011</xref>).</p><p>Furthermore, qualitative studies argue that anticipated stigma might even prohibit personal preventive behaviors during infectious diseases, such as wearing masks, to avoid future stigmatization (<xref rid=\"B51\" ref-type=\"bibr\">Siu, 2008</xref>; <xref rid=\"B24\" ref-type=\"bibr\">Jiang et al., 2009</xref>); this hypothesis is supported by cross-sectional, quantitative research (<xref rid=\"B32\" ref-type=\"bibr\">Leppin and Aro, 2009</xref>). Similarly, perceived differences in responsibility for personal (healthy persons) and public protection (infected persons) during a pandemic (<xref rid=\"B39\" ref-type=\"bibr\">Morrison and Yardley, 2009</xref>) might reinforce stigma-associated social identity processes and increase the salience of group differences.</p><p>In sum, stigmatization might differentially affect behavioral compliance. On the one hand, it might be beneficial from a prevention perspective by fostering social distancing toward and isolation of infected people, primarily by stigmatizing persons and defining them as a relevant out-group (so-called <italic>othering</italic>; see <xref rid=\"B16\" ref-type=\"bibr\">Deacon, 2006</xref>). On the other hand, it might reduce compliance with official recommendations among stigmatized and/or infected persons due to fear of social isolation, stress, or discrimination (<xref rid=\"B62\" ref-type=\"bibr\">Williams and Gonzalez-Medina, 2011</xref>; <xref rid=\"B52\" ref-type=\"bibr\">Smith and Hughes, 2014</xref>). Therefore, to investigate compliance and the role of stigmatization during pandemics, this exploratory study aims to:</p><list list-type=\"simple\"><list-item><label>1.</label><p>Examine patterns of intentions to comply with behavioral recommendations to contain the COVID-19 pandemic in the German population via latent class analysis.</p></list-item><list-item><label>2.</label><p>Inspect the role of stigma in non-compliance while considering sociodemographic differences, risk perception, and knowledge of adaptive behaviors.</p></list-item><list-item><label>3.</label><p>Explore intercultural similarities and differences of compliance by focusing on the German population, whereas previous research mostly focused on Asian populations.</p></list-item></list></sec><sec id=\"S2\"><title>Methods</title><sec id=\"S2.SS1\"><title>Sample</title><p><italic>Via</italic> an online survey, a community sample of 157 German adults [80% female; M (SD)<sub><italic>age</italic></sub> = 27.82 (11.01)] provided information about their knowledge of preventive measures, risk perception, intentions to comply with official behavioral recommendations and guidelines as well as their stigmatizing attitudes toward people suffering from COVID-19. Participants received gift vouchers (€5) as incentives. The survey was conducted via convenience sampling between March 13 and March 27 by placing online advertisements on social media, for instance, on Facebook. During this time, far-reaching social isolation measures were implemented in Germany, for instance, restricting public meetings to two people (except for households) and establishing guidelines for a safety distance of 1.5–2.0 m in public spaces. In addition, behavioral recommendations on personal hygiene and avoidance behaviors were repeatedly and consistently issued by the government. The study procedure included informed consent in alignment with the Declaration of Helsinki and received ethical approval by a local ethics committee (BB 169/18).</p></sec><sec id=\"S2.SS2\"><title>Measures</title><p>Sociodemographic data comprised age, gender [1 (<italic>female</italic>), 2 (<italic>male</italic>)], country of origin [0 (<italic>Germany</italic>), 1 (<italic>other</italic>)], level of education [0 (<italic>lower secondary education</italic>), 1 (<italic>higher secondary education</italic>, i.e., university entry level), 2 (<italic>tertiary education</italic>, e.g., bachelor’s degree)], region [0 (<italic>rural</italic>, i.e., up to 100,000 inhabitants), 1 (<italic>urban</italic>, i.e., more than 100,000 inhabitants)], and number of persons in one’s household [continuous; recoded as 1 (<italic>1</italic>), 2 (<italic>2</italic>), 3 (<italic>3 or more</italic>)]. For analysis purposes, categorical variables were dummy-coded.</p><p>Measures of stigmatizing attitudes were adapted from previous research on mental health stigma, assessing support for discrimination (<xref rid=\"B47\" ref-type=\"bibr\">Schomerus et al., 2007</xref>, <xref rid=\"B48\" ref-type=\"bibr\">2019</xref>) with three items (“Persons with COVID-19 should not be allowed to hold public office,” “Persons with COVID-19 should not be allowed to have a driver’s license,” “If persons with COVID-19 do not consent to medical treatment, they should receive compulsory treatment”), and blame (<xref rid=\"B13\" ref-type=\"bibr\">Corrigan et al., 2006</xref>; <xref rid=\"B48\" ref-type=\"bibr\">Schomerus et al., 2019</xref>) with four items (e.g., “Persons with COVID-19 are to blame for their problems”) rated on a five-point scale each, from 1 (<italic>don’t agree at all</italic>) to 5 (<italic>agree completely</italic>). Support for discrimination (Cronbach’s α = 0.71) and blame (α = 0.73) showed satisfactory internal consistency.</p><p>Risk perception comprised two items representing cognitive and affective aspects of perceived risk, namely, perceived susceptibility (“How likely will you become infected?”; 0 to 100%) and anticipated fear [“How afraid would you feel if you became infected?”; 1 (<italic>not at all</italic>) to 5 (<italic>very</italic>)].</p><p>Intentions to comply with official recommendations were assessed by asking participants how likely [1 (<italic>not at all</italic>) to 5 (<italic>very</italic>)] they would follow the following nine recommendations: (1) covering mouth and nose with flexed elbow or tissue when coughing or sneezing; (2) avoid handshakes; (3) avoid touching one’s face (i.e., eyes, nose, and mouth) as much as possible; (4) dispose of used tissue immediately and securely; (5) frequent ventilation; (6) increased hand hygiene; (7) stay at home when sick/symptomatic; (8) avoid personal contact to symptomatic persons; (9) avoid mass events. Since strictly following these recommendations is the safest way to contain further spreading of the infection, we recoded items to reflect likelihood of compliance [1 (<italic>very high likelihood</italic>), 0 (<italic>other</italic>)]. These nine indicators were then subjected to latent class analysis. In addition, a single item measuring subjective knowledge of adaptive behaviors was rated from 1 (<italic>very low</italic>) to 5 (<italic>very high</italic>). All measures are listed in <xref ref-type=\"supplementary-material\" rid=\"TS1\">Supplementary Table S1</xref>.</p></sec><sec id=\"S2.SS3\"><title>Statistical Analysis</title><p>Following an inspection of missing data and descriptive data analysis, latent class models were computed to examine patterns of (non)compliance in the population. Subsequent multinomial logistic regression models inspected sociodemographic and psychosocial predictors of compliance patterns. Descriptive data analysis was performed with Stata 15.1 (<xref rid=\"B53\" ref-type=\"bibr\">StataCorp, 2017</xref>), and latent class models and multinomial logistic regression models were computed with Mplus 7.4 (<xref rid=\"B40\" ref-type=\"bibr\">Muthén and Muthén, 1998–2015</xref>). All analyses were based on α = 0.05.</p><p>We estimated latent class models of compliance via robust maximum likelihood estimation with 2,000 sets of random start values. The estimation process started with two latent classes (indicating full compliance and non-compliance), the number of latent classes was subsequently increased up to five, while comparing model fit between models. Model selection considered overall model fit, parameter sparseness, classification quality, and theoretical tenability (<xref rid=\"B41\" ref-type=\"bibr\">Nylund et al., 2007</xref>; <xref rid=\"B58\" ref-type=\"bibr\">Tomczyk et al., 2016</xref>, <xref rid=\"B59\" ref-type=\"bibr\">2018</xref>). As an overall fit measure, the bootstrapped likelihood ratio test (BLRT) compared the estimated model to a model with one less class: a significant value indicated better fit of the current model. To achieve reliable estimates, we chose 50 random starts with 50 bootstrap draws for each comparison. The Akaike Information Criterion (AIC) and the sample size-adjusted Bayes Information Criterion (BIC) indicated sparseness of the model; a lower value meant a sparser model. Average latent class probabilities (AL) and entropy demonstrated classification quality that is the differentiation between latent classes. Values range between 0 and 1; the closer to 1, the better the fit; an entropy of at least 0.6 pointed to reliable estimates (<xref rid=\"B2\" ref-type=\"bibr\">Asparouhov and Muthén, 2014</xref>). Finally, latent classes needed to be interpreted based on the literature and theoretical background. Therefore, the best latent class solution was selected on statistical criteria as well as content validity.</p><p>Using the three-step approach (<xref rid=\"B2\" ref-type=\"bibr\">Asparouhov and Muthén, 2014</xref>), we calculated multinomial logistic regressions to predict compliance patterns by sociodemographic data and psychological variables (stigmatizing attitudes, risk perception, and subjective knowledge). For each regression model, relative risk ratios (RRRs) including 95% confidence intervals were reported as effect sizes.</p></sec></sec><sec id=\"S3\"><title>Results</title><sec id=\"S3.SS1\"><title>Descriptive Statistics</title><p>Missing data were low (37 missing values; 0.01% overall) and equally distributed among variables, suggesting missing at random. Therefore, complete cases were analyzed for descriptive statistics (<xref rid=\"B46\" ref-type=\"bibr\">Schafer, 1999</xref>; <xref rid=\"B18\" ref-type=\"bibr\">Dong and Peng, 2013</xref>), while full information maximum likelihood was used for latent class estimation. The sample was predominantly female, most persons did not have a migration background, and about a fifth lived in single households. Due to the very high level of education, the variable “education” was dichotomized for further analysis [1 (<italic>tertiary</italic>), 0 (<italic>secondary</italic>)]. Intentions to comply were mixed but particularly low for immediate disposal of used tissues, frequent ventilation, and reduced hand-to-face contact (<xref rid=\"T1\" ref-type=\"table\">Table 1</xref>).</p><table-wrap id=\"T1\" position=\"float\"><label>TABLE 1</label><caption><p>Overview of mean values and relative frequencies of sociodemographic data, risk perception, knowledge, intentions to comply with recommendations, and stigmatizing attitudes in a German community sample (complete cases with listwise deletion; <italic>N</italic> = 154–157).</p></caption><table frame=\"hsides\" rules=\"groups\" cellspacing=\"5\" cellpadding=\"5\"><thead><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Variable</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold><italic>M</italic> (<italic>SD</italic>) or <italic>N</italic> (%)</bold></td></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Age (range: 18–77)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">27.82 (11.01)</td></tr><tr><td valign=\"top\" align=\"left\" colspan=\"2\" rowspan=\"1\"><bold>Gender</bold></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Female</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">124 (80.0)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Male</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">31 (20.0)</td></tr><tr><td valign=\"top\" align=\"left\" colspan=\"2\" rowspan=\"1\"><bold>Level of education</bold></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Lower secondary</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4 (2.6)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Higher secondary</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">91 (59.0)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Tertiary</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">59 (38.3)</td></tr><tr><td valign=\"top\" align=\"left\" colspan=\"2\" rowspan=\"1\"><bold>Region</bold></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Rural</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">105 (73.2)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Urban</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">42 (26.8)</td></tr><tr><td valign=\"top\" align=\"left\" colspan=\"2\" rowspan=\"1\"><bold>Country of origin</bold></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Germany</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">150 (95.5)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Other</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">7 (4.5)</td></tr><tr><td valign=\"top\" align=\"left\" colspan=\"2\" rowspan=\"1\"><bold>Persons in one’s household</bold></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">One</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">30 (19.5)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Two</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">63 (38.9)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Three or more</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">61 (39.6)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Support for discrimination (range: 1–5)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.50 (0.82)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Blame (range: 1–5)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.42 (0.54)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Risk perception</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Susceptibility (range: 1–100%)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">62.17 (20.27)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Fear (range: 1–5)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.11 (1.05)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Subjective knowledge about adaptive behaviors (range: 1–5)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.80 (0.76)</td></tr><tr><td valign=\"top\" align=\"left\" colspan=\"2\" rowspan=\"1\"><bold>Intentions to comply with behavioral recommendations (very high)</bold></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">(1) Covering mouth and nose when coughing or sneezing</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">144 (91.7)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">(2) Avoid handshakes</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">121 (77.6)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">(3) Avoid touching one’s face as much as possible</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">28 (17.8)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">(4) Dispose of used tissue immediately and securely</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">81 (52.3)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">(5) Frequent ventilation</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">55 (35.3)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">(6) Increased hand hygiene</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">113 (72.9)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">(7) Stay at home when sick</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">128 (81.5)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">(8) Avoid personal contact to symptomatic persons</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">124 (79.0)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">(9) Avoid mass events</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">128 (81.5)</td></tr></tbody></table></table-wrap></sec><sec id=\"S3.SS2\"><title>Latent Class Models</title><p>Model fit criteria for latent class models are printed in <xref rid=\"T2\" ref-type=\"table\">Table 2</xref>. While entropy and information criteria were in favor of a model with four classes, the difference to a three-class model was only marginal (ΔAIC = 0.04; ΔSSABIC = 1.14), and according to the BLRT, the latter was preferable. Moreover, a fourth class would have been very small (<italic>n</italic> = 6; 4.8%) with similar conditional response probabilities to class 1 of the three-class model. Since it also showed good entropy and latent class separation (ALCP > 0.8) compared to the remaining models, the three-class model was chosen. The following descriptions of latent class counts and proportions are based on most likely latent class membership.</p><table-wrap id=\"T2\" position=\"float\"><label>TABLE 2</label><caption><p>Model fit criteria for latent class models of intentions to comply with behavioral recommendations regarding infection prevention in a German community sample (<italic>N</italic> = 157).</p></caption><table frame=\"hsides\" rules=\"groups\" cellspacing=\"5\" cellpadding=\"5\"><thead><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>2 classes</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>3 classes</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>4 classes</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>5 classes</bold></td></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Free parameters</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">19  </td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">29  </td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">39</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">149 </td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">BLRT</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">77.28***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>29.01***</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">20.41</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">15.46 </td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">AIC</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1423.81      </td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1414.80      </td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>1414.76</bold>   </td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1419.42    </td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">SSABIC</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1421.74      </td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1411.64      </td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>1410.50</bold>   </td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1414.07    </td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Entropy</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.60</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.70</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>0.74</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.74</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">ALCP</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.89</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.86</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.00</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.85</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.88</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.81</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.82</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.77</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.91</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.90</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.85</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.80</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.00</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.84</td></tr></tbody></table><table-wrap-foot><attrib><italic><italic>BLRT, bootstrapped likelihood ratio test; AIC, Akaike Information Criterion; SSABIC, sample size-adjusted Bayes Information Criterion; ALCP, average latent class probabilities. ***p < 0.001; fit criteria indicating the best model are printed in bold.</italic></italic></attrib></table-wrap-foot></table-wrap><p>The first class was labeled “low compliance” (<italic>n</italic> = 37; 24%), with low to moderate intentions to comply with most recommendations except for covering one’s mouth and nose when sneezing or coughing. The second class was labeled “high compliance” (<italic>n</italic> = 40; 25%), with high probabilities of following most recommendations and moderate compliance with reducing hand-to-face contact. Finally, the third class, “public compliance” (<italic>n</italic> = 80; 51%), had high intentions regarding compliance with public and avoidance behaviors (e.g., social distancing) but low intentions regarding personal behaviors (i.e., avoidance of face contact, tissue disposal, frequent ventilation). Conditional response probabilities for each class can be seen in <xref ref-type=\"fig\" rid=\"F1\">Figure 1</xref>.</p><fig id=\"F1\" position=\"float\"><label>FIGURE 1</label><caption><p>Conditional response probabilities and latent class proportions of three latent classes of (non)compliance with behavioral recommendations regarding infection prevention in a German community sample (<italic>N</italic> = 157). The probabilities correspond to the dichotomized likelihood of complying with recommendations [0 (<italic>not at all likely</italic> to <italic>quite likely</italic>); 1 (<italic>very likely</italic>)], thus a higher probability indicates higher compliance.</p></caption><graphic xlink:href=\"fpsyg-11-01821-g001\"/></fig><p>Multinomial logistic regression compared sociodemographic data, stigmatizing attitudes, knowledge, and risk perception between latent classes (<xref rid=\"T3\" ref-type=\"table\">Table 3</xref>). To complement multinomial models, detailed descriptive comparisons of latent classes are provided in <xref ref-type=\"supplementary-material\" rid=\"TS2\">Supplementary Table S2</xref>. Compared to high compliance (class 2), low compliance (class 1) was associated with being male [<italic>RRR</italic> = 0.08 (0.01; 0.85)], younger [<italic>RRR</italic> = 0.72 (0.57; 0.93)], and expressing lower support for discrimination [<italic>RRR</italic> = 0.21 (0.05; 0.88)], whereas public compliance (class 3) and high compliance did not differ on sociodemographic data, stigmatizing attitudes or risk perception, although support for discrimination was considerably lower in public compliers than in high compliers [<italic>RRR</italic> = 0.27 (0.06; 1.21); <italic>p</italic> = 0.09]. Furthermore, low compliers were significantly younger [RRR = 0.76 (0.59; 0.98)] than public compliers and, by trend, were less fearful of a possible infection [RRR = 0.46 (0.20; 1.06); <italic>p</italic> = 0.07].</p><table-wrap id=\"T3\" position=\"float\"><label>TABLE 3</label><caption><p>Multinomial logistic regression of latent classes of intentions to comply with behavioral recommendations regarding infection prevention in a German community sample (<italic>N</italic> = 157).</p></caption><table frame=\"hsides\" rules=\"groups\" cellspacing=\"5\" cellpadding=\"5\"><thead><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Predictor</bold></td><td valign=\"top\" align=\"center\" colspan=\"3\" rowspan=\"1\"><bold>Public compliance (class 3) vs. high compliance (class 2)</bold><hr/></td><td valign=\"top\" align=\"center\" colspan=\"3\" rowspan=\"1\"><bold>Low compliance (class 1) vs. high compliance (class 2)</bold><hr/></td><td valign=\"top\" align=\"center\" colspan=\"3\" rowspan=\"1\"><bold>Low compliance (class 1) vs. public compliance (class 3)</bold><hr/></td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>RRR</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>95% CI</bold></td><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>RRR</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>95% CI</bold></td><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>RRR</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>95% CI</bold></td><td rowspan=\"1\" colspan=\"1\"/></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Age</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.95</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.87</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"> 1.04</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>0.72*</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.57</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"> 0.93</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>0.76*</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.59</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"> 0.98</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Gender (ref. male)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.38</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.05</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"> 3.16</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>0.08*</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.01</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"> 0.85</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.22</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.02</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"> 1.90</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Level of education (ref. secondary)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.20</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.12</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">11.68</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.82</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.41</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">19.58</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.44</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.03</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"> 6.60</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Region (ref. rural)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.00</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.36</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">24.95</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.39</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.37</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">30.75</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.37</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.02</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"> 5.67</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Country of origin (ref. Germany)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.54</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.08</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"> 3.67</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.25</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.03</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"> 1.76</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5.19</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.53</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">50.83 </td></tr><tr><td valign=\"top\" align=\"left\" colspan=\"10\" rowspan=\"1\"><bold>Persons per household (ref. One)</bold></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Two</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.40</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.68</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">17.13</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.52</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.07</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"> 4.16</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.00</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.18</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"> 5.49</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Three or more</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.15</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.01</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"> 4.12</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.11</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.03</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">41.84</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.60</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.02</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">119.22 </td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Support for discrimination</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.27</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.06</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"> 1.21</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>0.21*</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.05</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"> 0.88</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.77</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.12</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"> 5.06</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Blame</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.94</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.24</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"> 3.67</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.46</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.33</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"> 6.39</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.55</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.28</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"> 8.66</td></tr><tr><td valign=\"top\" align=\"left\" colspan=\"10\" rowspan=\"1\"><bold>Risk perception</bold></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Susceptibility</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.01</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.97</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"> 1.04</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.03</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.99</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"> 1.06</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.02</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.97</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"> 1.06</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Fear</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.74</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.61</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"> 4.96</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.80</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.34</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"> 1.89</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.46</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.20</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"> 1.06</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Subjective knowledge</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.46</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.13</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"> 1.67</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.25</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.05</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"> 1.26</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.55</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.08</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"> 3.84</td></tr></tbody></table><table-wrap-foot><attrib><italic><italic>RRR, relative risk ratio; 95% CI, 95% confidence interval. Significant coefficients are printed in bold; *p < 0.05.</italic></italic></attrib></table-wrap-foot></table-wrap></sec></sec><sec id=\"S4\"><title>Discussion</title><p>As one of the first studies examining patterns of (non)compliance with behavioral recommendations in the general population during the COVID-19 pandemic, this study revealed that only a quarter of the surveyed German population expressed intentions to fully comply with recommendations, while a majority (about 51%) intended to follow some public actions but was less willing to enact personal hygiene behaviors (i.e., swift disposal of tissues, reduction of hand-to-face contact, ventilation). Young males were significantly less likely to comply with recommendations, and aspects of public stigma were also linked to compliance intentions.</p><p>In a virus outbreak, such as the COVID-19 pandemic, personal hygiene and social distancing in the general population are paramount to containment of the illness (<xref rid=\"B63\" ref-type=\"bibr\">Wu et al., 2006</xref>; <xref rid=\"B25\" ref-type=\"bibr\">Karimi et al., 2015</xref>; <xref rid=\"B61\" ref-type=\"bibr\">Weston et al., 2018</xref>). And yet, only a minority was ready to comply with the main recommendations, with 25% reaching high compliance in this sample and similar, albeit slightly higher, proportions of 30.7% (<xref rid=\"B43\" ref-type=\"bibr\">Quah and Hin-Peng, 2004</xref>), 37.8% (<xref rid=\"B62\" ref-type=\"bibr\">Williams and Gonzalez-Medina, 2011</xref>), and 48.7% (<xref rid=\"B30\" ref-type=\"bibr\">Lee et al., 2005</xref>) in previous studies. Since Germany was not affected by previous pandemics (e.g., H1N1, SARS) as strongly as Hong Kong, for instance, and measures like wearing face masks are not as common in Europe (e.g., <xref rid=\"B45\" ref-type=\"bibr\">Rubin et al., 2009</xref>), we assume the lack of familiarity with such strict preventive measures to be responsible for this lower level of compliance.</p><sec id=\"S4.SS1\"><title>Patterns and Predictors of Non-compliance</title><p>To further explore cultural differences of compliance during a pandemic and connect our findings to previous research, we compare our findings (Germany) to <xref rid=\"B36\" ref-type=\"bibr\">Liao et al. (2015)</xref>, who analyzed latent classes of behavior patterns in Hong Kong during a virus outbreak. They also identified three latent classes, with the class <italic>moderate hygiene</italic> being the largest group, followed by <italic>good hygiene</italic> and <italic>vigilance</italic>. Moreover, younger males, persons with lower educational attainment, and lower risk perception were also more likely to belong to the moderate hygiene class (i.e., exhibit low compliance), similar to our findings. This trend of older persons and females reporting higher risk perception and willingness to perform preventive behaviors was consistently found in a variety of health risks (<xref rid=\"B20\" ref-type=\"bibr\">Flynn et al., 1994</xref>), among them also pandemics (<xref rid=\"B6\" ref-type=\"bibr\">Bish and Michie, 2010</xref>; <xref rid=\"B29\" ref-type=\"bibr\">Kwok et al., 2020</xref>), presumably due to a higher perceived susceptibility in these groups. Since older people have a higher risk of manifesting COVID-19 symptoms (<xref rid=\"B14\" ref-type=\"bibr\">Davies et al., 2020</xref>), which was promulgated via mass media reports, this might have led to lower susceptibility perceptions among younger people. Across cultures and scenarios, young males tend to report lower risk perception and compliance intentions. By corroborating these associations in the context of COVID-19, our findings stress the need for selective prevention targeting young males to improve their compliance and thereby public health.</p><p>Despite these similarities, we observed differing intentions regarding personal hygiene behaviors but overall high intentions to comply with avoidance behaviors, in contrast to <xref rid=\"B36\" ref-type=\"bibr\">Liao et al. (2015)</xref>. While studies in other Western countries, that is, Canada (Toronto) and the United States (<xref rid=\"B7\" ref-type=\"bibr\">Blendon et al., 2004</xref>), also indicated high compliance with quarantine and social distancing strategies, it should be noted that avoidance measures are generally easier to implement than specific preventive behaviors that require personal action (<xref rid=\"B6\" ref-type=\"bibr\">Bish and Michie, 2010</xref>). Therefore, it is possible that in this early phase of the COVID-19 outbreak in Germany, personal responsibility was not as salient in the general population. This might be connected to the lack of familiarity with pandemics and appropriate preventive action in the German population. Nevertheless, personal preventive actions may yet increase over time, coinciding with an increase in vigilance, knowledge, and positive attitudes, if supported by concerted action, as suggested by previous SARS outbreak trajectories (<xref rid=\"B34\" ref-type=\"bibr\">Leung et al., 2003</xref>, <xref rid=\"B33\" ref-type=\"bibr\">2005</xref>).</p><p>To concur, in their analysis of repeated cross-sectional surveys, <xref rid=\"B36\" ref-type=\"bibr\">Liao et al. (2015)</xref> observed fairly stable behavioral patterns (i.e., robust latent classes) across time but an increase in public vigilance and perceived threat throughout the epidemic (i.e., an increase in latent class proportions in favor of vigilance). To foster vigilance, the media and governmental institutions are therefore urged to provide clear guidance, openly communicate and justify new measures to increase trust, and strengthen self-efficacy at early stages of a pandemic, as shown in previous health crises (e.g., <xref rid=\"B49\" ref-type=\"bibr\">Seeger, 2006</xref>; <xref rid=\"B5\" ref-type=\"bibr\">Bean et al., 2015</xref>; <xref rid=\"B23\" ref-type=\"bibr\">Jha et al., 2018</xref>).</p></sec><sec id=\"S4.SS2\"><title>Non-compliance and Stigmatizing Attitudes</title><p>In addition to compliance patterns, this study also examined the impact of stigmatizing attitudes on intentions to comply with behavioral recommendations. While <xref rid=\"B62\" ref-type=\"bibr\">Williams and Gonzalez-Medina (2011)</xref> connected an increase in influenza infections to an increase in stigmatizing attitudes, in this study, blame was low (mean = 1.42 on scale of 1–5) and did not predict compliance. Instead, support for discrimination was significantly associated with higher compliance intentions. Drawing on social psychiatric research, this type of discrimination might be described as <italic>intentional structural discrimination</italic>, where a worldview is actively supported that restricts patients’ rights (by law), for example, regarding their opportunities to vote or to hold public office (<xref rid=\"B12\" ref-type=\"bibr\">Corrigan et al., 2004</xref>, <xref rid=\"B13\" ref-type=\"bibr\">2006</xref>; <xref rid=\"B47\" ref-type=\"bibr\">Schomerus et al., 2007</xref>). In the context of COVID-19, a support for discrimination implies a desired restriction of access to sociopolitical resources for infected persons.</p><p>As a result, while high compliance represents law-abiding and theoretically desirable behavior, its connection to discrimination, particularly in this highly educated sample, is noteworthy. In line with the reasoning behind selfishness and responsibility shift in confronting the SARS pandemic (<xref rid=\"B39\" ref-type=\"bibr\">Morrison and Yardley, 2009</xref>), a support for discrimination might indicate a way to maximize differences between relevant in-groups (i.e., responsible, healthy) and out-groups (i.e., irresponsible, reckless) to affirm social identity status (<xref rid=\"B54\" ref-type=\"bibr\">Tajfel and Turner, 1986</xref>; <xref rid=\"B37\" ref-type=\"bibr\">Link and Phelan, 2001</xref>) and – at least symbolically – reduce the risk of infection. Since blame did not differ between latent classes and was generally low, we assume that in this sample, stigma facilitated othering but not discriminatory action (<xref rid=\"B16\" ref-type=\"bibr\">Deacon, 2006</xref>). Although this hypothesis requires further research in larger, longitudinal samples using more elaborate measures of stigmatizing attitudes, it is clearly in line with evidence-based demands of a more nuanced debate of the functional properties of stigmatization and its connection to discrimination in infectious diseases (<xref rid=\"B16\" ref-type=\"bibr\">Deacon, 2006</xref>).</p></sec><sec id=\"S4.SS3\"><title>Strengths and Limitations</title><p>Finally, this study is not without limitations, as the sample is a small convenience sample that is not representative of the German population. In fact, the sample was highly educated, predominantly female, and mostly without migration background. However, we still observed substantial heterogeneity in intentions, despite females and highly educated persons being generally more likely to report high compliance in previous studies. In addition, this study was cross-sectional and exploratory and used short but validated measures of core constructs, hence, effects of risk perception, for example, were not fully explored. Components like anticipatory worry could also affect compliance intentions and should be studied in more detail (<xref rid=\"B32\" ref-type=\"bibr\">Leppin and Aro, 2009</xref>). Furthermore, items measuring stigmatizing attitudes were adapted to COVID-19 for this study, therefore, a thorough psychometric validation is necessary. Moreover, we did not assess other important factors that might be connected to (non)compliance, such as ethnicity, interpersonal contact with infected persons, or trust in the government. Finally, we captured behavioral intentions, but we did not assess actual behaviors, as the pandemic had just reached the German population, and official recommendations were first issued at the beginning of data collection. Therefore, future studies should also focus on behavioral performance. When investigating the connection between compliance intentions and behavioral performance, health behaviors models like the theory of planned behavior should be applied to incorporate relevant intermediary variables, such as self-efficacy (<xref rid=\"B1\" ref-type=\"bibr\">Ajzen, 1991</xref>; <xref rid=\"B6\" ref-type=\"bibr\">Bish and Michie, 2010</xref>). Overall, more comprehensive, longitudinal, and experimental studies are necessary to validate our findings in the context of COVID-19 in diverse populations. Nevertheless, we think this study provides an important look at patterns of compliance at early stages of the COVID-19 outbreak and impactful sociodemographic and attitudinal factors, such as support for discrimination, that underline the need for selective preventive action.</p></sec></sec><sec sec-type=\"data-availability\" id=\"S5\"><title>Data Availability Statement</title><p>The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation, to any qualified researcher.</p></sec><sec id=\"S6\"><title>Ethics Statement</title><p>The studies involving human participants were reviewed and approved by the Ethics Committee of the University Medicine Greifswald, University Medicine Greifswald. The patients/participants provided their written informed consent to participate in this study.</p></sec><sec id=\"S7\"><title>Author Contributions</title><p>ST, MR, and SS contributed to the conception and design of the study. ST and MR were responsible for the data collection and statistical analysis. ST wrote the first draft of the manuscript. All authors contributed to the article and approved the submitted version.</p></sec><sec id=\"conf1\"><title>Conflict of Interest</title><p>The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p></sec></body><back><ack><p>We would like to thank Simon Barth, Isabel Buck, and Hanna Groth for their assistance in the collection and preparation of data.</p></ack><fn-group><fn id=\"footnote1\"><label>1</label><p><ext-link ext-link-type=\"uri\" xlink:href=\"https://coronavirus.jhu.edu/\">https://coronavirus.jhu.edu/</ext-link></p></fn></fn-group><sec id=\"S9\" sec-type=\"supplementary material\"><title>Supplementary Material</title><p>The Supplementary Material for this article can be found online at: <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.frontiersin.org/articles/10.3389/fpsyg.2020.01821/full#supplementary-material\">https://www.frontiersin.org/articles/10.3389/fpsyg.2020.01821/full#supplementary-material</ext-link></p><supplementary-material content-type=\"local-data\" id=\"TS1\"><media xlink:href=\"Table_1.docx\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"TS2\"><media xlink:href=\"Table_2.docx\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material></sec><ref-list><title>References</title><ref id=\"B1\"><mixed-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Ajzen</surname><given-names>I.</given-names></name></person-group> (<year>1991</year>). <article-title>The theory of planned behavior.</article-title>\n<source><italic>Organ. 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[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"review-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Front Psychiatry</journal-id><journal-id journal-id-type=\"iso-abbrev\">Front Psychiatry</journal-id><journal-id journal-id-type=\"publisher-id\">Front. Psychiatry</journal-id><journal-title-group><journal-title>Frontiers in Psychiatry</journal-title></journal-title-group><issn pub-type=\"epub\">1664-0640</issn><publisher><publisher-name>Frontiers Media S.A.</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32848957</article-id><article-id pub-id-type=\"pmc\">PMC7432119</article-id><article-id pub-id-type=\"doi\">10.3389/fpsyt.2020.00802</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Psychiatry</subject><subj-group><subject>Review</subject></subj-group></subj-group></article-categories><title-group><article-title>Local and Interregional Neurochemical Associations Measured by Magnetic Resonance Spectroscopy for Studying Brain Functions and Psychiatric Disorders</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Shen</surname><given-names>Jun</given-names></name><xref ref-type=\"author-notes\" rid=\"fn001\"><sup>*</sup></xref><uri xlink:type=\"simple\" xlink:href=\"https://loop.frontiersin.org/people/65877\"/></contrib><contrib contrib-type=\"author\"><name><surname>Shenkar</surname><given-names>Dina</given-names></name></contrib><contrib contrib-type=\"author\"><name><surname>An</surname><given-names>Li</given-names></name></contrib><contrib contrib-type=\"author\"><name><surname>Tomar</surname><given-names>Jyoti Singh</given-names></name></contrib></contrib-group><aff id=\"aff1\"><institution>Molecular Imaging Branch, National Institute of Mental Health</institution>, <addr-line>Bethesda, MD</addr-line>, <country>United States</country></aff><author-notes><fn fn-type=\"edited-by\"><p>Edited by: Maria Concepcion Garcia Otaduy, University of São Paulo, Brazil</p></fn><fn fn-type=\"edited-by\"><p>Reviewed by: Mark Mikkelsen, Johns Hopkins University, United States; Stefania Schiavone, University of Foggia, Italy</p></fn><corresp id=\"fn001\">*Correspondence: Jun Shen, <email xlink:href=\"mailto:[email protected]\" xlink:type=\"simple\">[email protected]</email></corresp><fn fn-type=\"other\" id=\"fn002\"><p>This article was submitted to Molecular Psychiatry, a section of the journal Frontiers in Psychiatry</p></fn></author-notes><pub-date pub-type=\"epub\"><day>11</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><year>2020</year></pub-date><volume>11</volume><elocation-id>802</elocation-id><history><date date-type=\"received\"><day>08</day><month>6</month><year>2020</year></date><date date-type=\"accepted\"><day>27</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>Copyright © 2020 Shen, Shenkar, An and Tomar</copyright-statement><copyright-year>2020</copyright-year><copyright-holder>Shen, Shenkar, An and Tomar</copyright-holder><license xlink:href=\"http://creativecommons.org/licenses/by/4.0/\"><license-p>This work is authored by Jun Shen, Dina Shenkar, Li An, and Jyoti Singh Tomar on behalf of the U.S. Government and, as regards Dr. Shen, Dr. Shenkar, Dr. An, Dr. Tomar, and the U.S. Government, is not subject to copyright protection in the United States. Foreign and other copyrights may apply. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</license-p></license></permissions><abstract><p>Magnetic resonance spectroscopy (MRS) studies have found significant correlations among neurometabolites (<italic>e.g.</italic>, between glutamate and GABA) across individual subjects and altered correlations in neuropsychiatric disorders. In this article, we discuss neurochemical associations among several major neurometabolites which underpin these observations by MRS. We also illustrate the role of spectral editing in eliminating unwanted correlations caused by spectral overlapping. Finally, we describe the prospects of mapping macroscopic neurochemical associations across the brain and characterizing excitation–inhibition balance of neural networks using glutamate- and GABA-editing MRS imaging.</p></abstract><kwd-group><kwd>glutamate</kwd><kwd>GABA</kwd><kwd>neurochemical correlations</kwd><kwd>magnetic resonance spectroscopy</kwd><kwd>spectral editing</kwd><kwd>psychiatric disorders</kwd></kwd-group><counts><fig-count count=\"5\"/><table-count count=\"1\"/><equation-count count=\"6\"/><ref-count count=\"130\"/><page-count count=\"11\"/><word-count count=\"4897\"/></counts></article-meta></front><body><sec sec-type=\"intro\" id=\"s1\"><title>Introduction</title><p><italic>In vivo</italic> MRS is the only noninvasive technique that can directly measure brain chemicals <italic>in vivo</italic>. Using techniques similar to MRI, MRS can measure concentrations of many neurometabolites as well as metabolic fluxes from localized brain regions (<xref rid=\"B1\" ref-type=\"bibr\">1</xref>). Over the past decades, MRS studies have found biochemical abnormalities in essentially all neuropsychiatric disorders, providing important insights into our understanding of etiologies and treatments of various brain diseases. These studies have, in most cases, focused on alterations in the concentrations of individual brain neurometabolites by comparing them among different cohorts and/or effects of treatments.</p><p>Many significant correlations among neurometabolites and between individual neurometabolites and non-MRS measures of brain function and disorders have been reported more recently. Altered correlations have been found in neuropsychiatric disorders, revealing abnormal neurochemical associations under pathophysiological conditions [e.g., (<xref rid=\"B2\" ref-type=\"bibr\">2</xref>–<xref rid=\"B12\" ref-type=\"bibr\">12</xref>)]. The strong correlations among N-acetylaspartate (NAA)/choline in precentral gyrus, midcingulate cortex, and thalamus found in healthy subjects were absent in patients with amyotrophic lateral sclerosis (<xref rid=\"B4\" ref-type=\"bibr\">4</xref>). Correlation between hippocampal Glx (glutamate + glutamine) and NAA has been demonstrated to be a more sensitive biomarker differentiating between healthy controls and schizophrenia patients than either neurometabolite alone (<xref rid=\"B6\" ref-type=\"bibr\">6</xref>, <xref rid=\"B7\" ref-type=\"bibr\">7</xref>). In patients with subclinical hepatic encephalopathy the occipital lobe phosphodiester measured by <sup>31</sup>P MRS and Glx levels were found to be negatively correlated (<xref rid=\"B13\" ref-type=\"bibr\">13</xref>). Interregional correlations of glutamate and GABA levels have also been reported in many studies [e.g., (<xref rid=\"B8\" ref-type=\"bibr\">8</xref>, <xref rid=\"B14\" ref-type=\"bibr\">14</xref>–<xref rid=\"B17\" ref-type=\"bibr\">17</xref>)]. These studies have clearly demonstrated that neurochemical associations are abnormally altered in many brain disorders, but the absolute strengths of these correlations measured by different MRS methodologies have been inconsistent or controversial [e.g., (<xref rid=\"B5\" ref-type=\"bibr\">5</xref>, <xref rid=\"B14\" ref-type=\"bibr\">14</xref>, <xref rid=\"B15\" ref-type=\"bibr\">15</xref>, <xref rid=\"B18\" ref-type=\"bibr\">18</xref>–<xref rid=\"B23\" ref-type=\"bibr\">23</xref>)]. In particular, the effects of spectral overlap on the observed neurometabolite correlations have yet to be illustrated although they can significantly confound the intrinsic neurochemical correlations of interest.</p><p>Many studies of neurochemical associations rely on spectral fitting [e.g., (<xref rid=\"B24\" ref-type=\"bibr\">24</xref>)] to extract neurometabolite concentrations from overlapping signals. When there is significant spectral overlap between two signals overestimate of one signal is statistically correlated with underestimate of the other signal and <italic>vice versa</italic>, even when there exists no neurochemical correlation between the two signals. In addition, this type of statistical correlations can propagate due to the intensity constraints imposed by LCModel (<xref rid=\"B24\" ref-type=\"bibr\">24</xref>) or overlapping with neurochemically correlated signals. When neurometabolite concentrations are correlated with other measurements (e.g., behavior, resting state fMRI functional connectivity, or gene expression), statistical correlations due to spectral overlap among MRS measurements can also affect correlations between MRS measurements and non-MRS measurements.</p><p>In this article we review dominant metabolic pathways connecting major neurometabolites (<xref rid=\"B25\" ref-type=\"bibr\">25</xref>–<xref rid=\"B27\" ref-type=\"bibr\">27</xref>) which underpin the neurochemical associations detected by MRS correlation studies. Non-MRS neurochemical studies of animal models that found correlated changes in neurometabolite concentrations under various pathophysiological conditions are also discussed. Monte Carlo simulations are performed to demonstrate the existence of statistical correlations that originated from spectral overlap. Finally, we discuss MRS techniques that eliminate spectral overlap and associated statistical correlations. We hope that these discussions will spur interest in developing MRS techniques for mapping neurochemical associations across the brain to facilitate a variety of clinical investigations. In particular, since glutamate and GABA play dominant roles in the excitation–inhibition balance (<xref rid=\"B28\" ref-type=\"bibr\">28</xref>, <xref rid=\"B29\" ref-type=\"bibr\">29</xref>) and in many neuropsychiatric disorders (<xref rid=\"B30\" ref-type=\"bibr\">30</xref>–<xref rid=\"B33\" ref-type=\"bibr\">33</xref>), MRS characterization of glutamate–GABA associations among the nodes of neural networks may provide considerable insight into the interactions between glutamatergic and GABAergic systems and their abnormalities.</p></sec><sec id=\"s2\"><title>Metabolic Pathways Underlying Neurochemical Associations</title><p>Predominant metabolic pathways connecting NAA, glutamate, glutamine, and GABA are reviewed in detail. For clarity, a table summarizing these pathways is provided (<xref rid=\"T1\" ref-type=\"table\"><bold>Table 1</bold></xref>).</p><table-wrap id=\"T1\" position=\"float\"><label>Table 1</label><caption><p>Predominant metabolic pathways of NAA, glutamate, glutamine and GABA in brain.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Neurometabolites</th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Anabolic enzymes</th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Catabolic enzymes</th></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">NAA</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">NAA synthase</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">aspartoacylase</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">glutamate</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">aspartate aminotransferase, glutaminase, glutamate dehydrogenase</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">aspartate aminotransferase, glutamine synthetase, glutamate dehydrogenase, glutamic acid decarboxylase</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">glutamine</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">glutamine synthetase</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">glutaminase</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">GABA</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">glutamic acid decarboxylase</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">GABA transaminase</td></tr></tbody></table></table-wrap><sec id=\"s2_1\"><title>NAA-Glutamate Association</title><p>NAA is the most abundant free amino acid derivative in the CNS. NAA is found almost exclusively within the nervous system. In the adult brain, it is mostly confined to neurons. As such, it is of great clinical interest as it has been considered as a neuronal marker for assessment of neuronal viability in a variety of neuropsychiatric disorders using proton MRS. For example, NAA level is markedly reduced within the infarct of stroke patients (<xref rid=\"B34\" ref-type=\"bibr\">34</xref>), while a higher NAA level is associated with a better clinical outcome (<xref rid=\"B35\" ref-type=\"bibr\">35</xref>). Despite the intense interest in NAA, both its physiological and metabolic roles in normal brain functions as well as in neuropsychiatric disorders remain poorly understood [for a review of the putative role of NAA, see (<xref rid=\"B36\" ref-type=\"bibr\">36</xref>)].</p><p>Important metabolic associations exist among major neurometabolites observable by MRS through precursor–product relationships and sharing common substrates (<xref rid=\"B26\" ref-type=\"bibr\">26</xref>, <xref rid=\"B37\" ref-type=\"bibr\">37</xref>, <xref rid=\"B38\" ref-type=\"bibr\">38</xref>). Glutamate, the most abundant intracellular amino acids in mammals, is a key component of intermediary metabolism and a precursor of numerous cellular components including proteins as well as neurometabolites such as GABA, N-acetylaspartylglutamate (NAAG), and glutathione (<xref rid=\"B26\" ref-type=\"bibr\">26</xref>). As glutamate is also the primary excitatory neurotransmitter in the CNS, it is not surprising that the proton MRS has found abnormal glutamate levels in many neuropsychiatric disorders including multiple sclerosis (<xref rid=\"B39\" ref-type=\"bibr\">39</xref>), major depression (<xref rid=\"B40\" ref-type=\"bibr\">40</xref>), and bipolar disorder (<xref rid=\"B41\" ref-type=\"bibr\">41</xref>) where glutamatergic dysfunction is broadly implicated.</p><p>Glutamate is primarily synthesized by transamination from <italic>α</italic>-ketoglutarate catalyzed by aspartate aminotransferase (<xref rid=\"B25\" ref-type=\"bibr\">25</xref>):</p><disp-formula><mml:math id=\"M1\"><mml:mrow><mml:mtext>glutamate</mml:mtext><mml:mo>+</mml:mo><mml:mtext>oxaloacetate</mml:mtext><mml:mo>↔</mml:mo><mml:mi>α</mml:mi><mml:mo>-</mml:mo><mml:mtext>ketoglutarate </mml:mtext><mml:mo>+</mml:mo><mml:mtext> aspartate</mml:mtext></mml:mrow></mml:math></disp-formula><p>Glutamate also is produced, to a much lesser extent, from <italic>α</italic>-ketoglutarate and ammonium <italic>via</italic> glutamate dehydrogenase, from glutamine <italic>via</italic> hydrolysis catalyzed by phosphate-activated glutaminase, by other transamination reactions that use <italic>α</italic>-ketoglutarate as receptor of the amino group, and during protein turnover (<xref rid=\"B42\" ref-type=\"bibr\">42</xref>).</p><p>The transaminases of importance for maintenance of glutamate homeostasis in the brain are mainly aspartate aminotransferase, branched-chain aminotransferase, and alanine aminotransferase with aspartate aminotransferase dominating overwhelmingly, representing >97% of the glutamate-related aminotransferase activities (<xref rid=\"B26\" ref-type=\"bibr\">26</xref>). <sup>13</sup>C magnetization transfer MRS experiments have shown that the aspartate aminotransferase reaction is extremely fast in the brain <italic>in vivo</italic> (<xref rid=\"B43\" ref-type=\"bibr\">43</xref>). This rapid transamination by aspartate aminotransferase predominates in the formation of glutamate in the CNS, forming strong metabolic coupling between glutamate and aspartate (<xref rid=\"B25\" ref-type=\"bibr\">25</xref>). The tight connection between glutamate and aspartate becomes conspicuous under many pathophysiological conditions where the brain is challenged or perturbed metabolically. For example, during hypoglycemia a decrease in glutamate concentration was accompanied by an increase in aspartate concentration (<xref rid=\"B44\" ref-type=\"bibr\">44</xref>–<xref rid=\"B46\" ref-type=\"bibr\">46</xref>). Similarly, barbiturate anesthesia and hypothermia were also found to lower the concentration of <italic>α</italic>-ketoglutarate accompanied by reduced glutamate concentration and increased aspartate concentration (<xref rid=\"B47\" ref-type=\"bibr\">47</xref>–<xref rid=\"B49\" ref-type=\"bibr\">49</xref>). These correlated changes in glutamate and aspartate were explained by a sizable shift in the aspartate aminotransferase reaction towards aspartate formation at the cost of a reduction in glutamate concentration (<xref rid=\"B25\" ref-type=\"bibr\">25</xref>, <xref rid=\"B26\" ref-type=\"bibr\">26</xref>). In contrast, both hypocapnia and hypoxic hypoxia are associated with an increase in glutamate concentration and a reduction in aspartate concentration with the aspartate aminotransferase reaction shifting in the opposite direction (<xref rid=\"B25\" ref-type=\"bibr\">25</xref>, <xref rid=\"B26\" ref-type=\"bibr\">26</xref>, <xref rid=\"B50\" ref-type=\"bibr\">50</xref>).</p><p>A strong metabolic coupling between glutamate and aspartate mediated by the rapid and ubiquitous aspartate aminotransferase reaction also affects NAA, the dominant signal in proton MRS, as NAA is primarily synthesized from acetyl coenzyme A (CoA) and aspartate by NAA synthase (<xref rid=\"B51\" ref-type=\"bibr\">51</xref>):</p><disp-formula><mml:math id=\"M3\"><mml:mrow><mml:mtext>acetyl</mml:mtext><mml:mo>-</mml:mo><mml:mtext>CoA</mml:mtext><mml:mo>+</mml:mo><mml:mtext>aspartate</mml:mtext><mml:mo>→</mml:mo><mml:mtext>CoA</mml:mtext><mml:mo>+</mml:mo><mml:msubsup><mml:mtext>H</mml:mtext><mml:mo>\n</mml:mo><mml:mo>+</mml:mo></mml:msubsup><mml:mo>+</mml:mo><mml:mtext>NAA</mml:mtext></mml:mrow></mml:math></disp-formula><p>In addition to this indirect connection from glutamate to NAA synthesis <italic>via</italic> aspartate, both glutamate and NAA are products of NAAG catabolism catalyzed by N-acetylated-α-linked-amino dipeptidase (<xref rid=\"B52\" ref-type=\"bibr\">52</xref>–<xref rid=\"B54\" ref-type=\"bibr\">54</xref>). The deacetylation of NAA catalyzed by aspartoacylase has also been proposed as a significant metabolic pathway for NAA to act as a reservoir for glutamate in brain (<xref rid=\"B55\" ref-type=\"bibr\">55</xref>).</p></sec><sec id=\"s2_2\"><title>Glutamate–Glutamine Association</title><p>In contrast to glutamate, which is predominantly located in glutamatergic neurons, glutamine is primarily an astrocytic chemical. In the MRS literature glutamate + glutamine is often collectively referred to as Glx as at lower magnetic fields it has been difficult to separate the two spectroscopically. Abnormal glutamine concentrations have been found in several brain disorders including cancer, hepatic encephalopathy, and other neuropsychiatric disorders (<xref rid=\"B56\" ref-type=\"bibr\">56</xref>, <xref rid=\"B57\" ref-type=\"bibr\">57</xref>).</p><p>Although the overall glutamate pool in neural tissues rapidly turns over fueled by primarily glucose under normal physiological conditions, glutamate released from nerve terminals is replenished by astroglial glutamine <italic>via</italic> the glutamate–glutamine neurotransmitter cycle [<xref ref-type=\"fig\" rid=\"f1\"><bold>Figure 1</bold></xref>; (<xref rid=\"B26\" ref-type=\"bibr\">26</xref>, <xref rid=\"B59\" ref-type=\"bibr\">59</xref>)]. The negatively charged highly hydrophilic glutamate cannot diffuse across cell membranes. The concentration of glutamate in the extracellular space is extremely low due to its rapid uptake into the astroglia facilitated by high-affinity Na<sup>+</sup>-dependent transport systems against a large concentration gradient (<xref rid=\"B60\" ref-type=\"bibr\">60</xref>–<xref rid=\"B62\" ref-type=\"bibr\">62</xref>). Once taken up into the astroglial cells, glutamate is converted into glutamine by glutamine synthetase:</p><disp-formula><mml:math id=\"M5\"><mml:mrow><mml:mtext>glutamate</mml:mtext><mml:mo>+</mml:mo><mml:msub><mml:mrow><mml:mtext>NH</mml:mtext></mml:mrow><mml:mn>3</mml:mn></mml:msub><mml:mo>+</mml:mo><mml:mtext>adenosin triphosphate </mml:mtext><mml:mo>→</mml:mo><mml:mtext>glutamine</mml:mtext><mml:mo>+</mml:mo><mml:mtext>adenosine diphosphate</mml:mtext><mml:mo>+</mml:mo><mml:msub><mml:mtext>P</mml:mtext><mml:mtext>i</mml:mtext></mml:msub></mml:mrow></mml:math></disp-formula><fig id=\"f1\" position=\"float\"><label>Figure 1</label><caption><p>Schematic diagram of the glutamate–glutamine neurotransmitter cycle between neurons and astroglia (<xref rid=\"B58\" ref-type=\"bibr\">58</xref>). Glutamate (Glu) is taken up from the synaptic cleft into astroglia. There glutamate is converted to glutamine (Gln) by glutamine synthetase. The inactive glutamine is released by the astroglia, enters the neurons, and then is converted into glutamate by phosphate-activated glutaminase. Glc, glucose; <italic>α</italic>-KG, <italic>α</italic>-ketoglutarate; NH<sub>3</sub>, ammonia.</p></caption><graphic xlink:href=\"fpsyt-11-00802-g001\"/></fig><p>or oxidized by assimilation into the tricarboxylic acid cycle of astroglial cells (<xref rid=\"B26\" ref-type=\"bibr\">26</xref>, <xref rid=\"B63\" ref-type=\"bibr\">63</xref>). Once formed, glutamine readily enters nerve terminals by its own low affinity transport system or by simple diffusion. There the phosphate-activated glutaminase converts it into glutamate (<xref rid=\"B26\" ref-type=\"bibr\">26</xref>):</p><disp-formula><mml:math id=\"M7\"><mml:mrow><mml:mtext>glutamine</mml:mtext><mml:mo>+</mml:mo><mml:msub><mml:mtext>H</mml:mtext><mml:mn>2</mml:mn></mml:msub><mml:mtext>O</mml:mtext><mml:mo>→</mml:mo><mml:mtext>glutamate </mml:mtext><mml:mo>+</mml:mo><mml:msubsup><mml:mrow><mml:mtext> NH</mml:mtext></mml:mrow><mml:mn>4</mml:mn><mml:mo>+</mml:mo></mml:msubsup></mml:mrow></mml:math></disp-formula><p>A large number of neurochemical as well as autoradiographic studies have confirmed that glutamate is selectively taken up by astroglial cells and then converted into glutamine, while glutamine preferentially enters the neurons and is converted into glutamate there (<xref rid=\"B64\" ref-type=\"bibr\">64</xref>). <italic>In vivo</italic>\n<sup>13</sup>C and <sup>15</sup>N MRS studies have quantitatively measured the glutamate–glutamine cycling flux in rodent and human brains [e.g., (<xref rid=\"B58\" ref-type=\"bibr\">58</xref>, <xref rid=\"B65\" ref-type=\"bibr\">65</xref>–<xref rid=\"B69\" ref-type=\"bibr\">69</xref>)]. Results from these studies have demonstrated that the glutamate–glutamine cycle between glutamatergic neurons and astroglia is metabolically significant, providing a major connection between glutamate and glutamine in the brain (<xref rid=\"B70\" ref-type=\"bibr\">70</xref>). In addition, over the range of glutamate concentrations found in the nerve terminals, product inhibition appears to be the main mechanism of control of glutaminase activity with glutamate significantly attenuating the activity of glutaminase (<xref rid=\"B71\" ref-type=\"bibr\">71</xref>).</p><p>The metabolic connection between glutamate and glutamine is manifested in many brain disorders and animal models. For example, elevated ammonia level in the brain is associated with increased glutamine synthesis for ammonia detoxification (<xref rid=\"B67\" ref-type=\"bibr\">67</xref>). It has been reported that in hyperammonemia and hepatic encephalopathy the elevation of glutamine level in the brain is accompanied by a reduced glutamate level as glutamate acts as a receptor for the excessive ammonia (<xref rid=\"B72\" ref-type=\"bibr\">72</xref>). The changes in glutamate and glutamine levels, however, do not necessarily go in opposite directions. Both glutamate and glutamine are abundant in the brain. Despite efforts to understand the roles of glutamate and glutamine, the reason for maintaining relatively high concentrations of glutamate and glutamine in the brain is still poorly understood. It is possible that a high concentration of glutamate and glutamine facilitates the generally high metabolic activities in the brain because glutamate and glutamine are key components of intermediary metabolism (<xref rid=\"B25\" ref-type=\"bibr\">25</xref>). They are also precursors of many other cellular components (<xref rid=\"B26\" ref-type=\"bibr\">26</xref>). Their role as “energy reservoirs” is particularly clear when the brain is under metabolic stress. For example, when glucose is scarce, such as in hypoglycemia, both glutamate and glutamine act as energy fuels. As a result, the concentrations of both glutamate and glutamine are reduced in synchrony during hypoglycemia and in many other pathophysiological conditions when normal oxidative metabolism is impaired (<xref rid=\"B45\" ref-type=\"bibr\">45</xref>, <xref rid=\"B73\" ref-type=\"bibr\">73</xref>).</p></sec><sec id=\"s2_3\"><title>Glutamate–GABA Association</title><p>While glutamate is the major excitatory neurotransmitter in the mammalian brain, GABA is the major inhibitory neurotransmitter. Since the initial detection of reduced GABA levels in epilepsy patients by MRS (<xref rid=\"B74\" ref-type=\"bibr\">74</xref>) and a strong correlation between GABA levels and seizure control in epilepsy patients treated by vigabatrin (<xref rid=\"B75\" ref-type=\"bibr\">75</xref>), MRS of GABA has greatly advanced both in terms of MRS methodologies and their clinical applications in studying GABAergic abnormalities in neuropsychiatric disorders.</p><p>Like glutamate, GABA metabolism proceeds through important intermediates of the tricarboxylic acid cycle. When GABA was first discovered (<xref rid=\"B76\" ref-type=\"bibr\">76</xref>) it was realized that GABA was formed from glutamate. Later studies identified that the principal pathway of GABA goes through <italic>α</italic>-decarboxylation of glutamate <italic>via</italic> glutamic acid decarboxylase which converts glutamate directly into GABA (<xref rid=\"B77\" ref-type=\"bibr\">77</xref>):</p><disp-formula><mml:math id=\"M9\"><mml:mrow><mml:mtext>glutamate</mml:mtext><mml:mo>→</mml:mo><mml:mtext>GABA </mml:mtext><mml:mo>+</mml:mo><mml:msub><mml:mrow><mml:mtext> CO</mml:mtext></mml:mrow><mml:mn>2</mml:mn></mml:msub></mml:mrow></mml:math></disp-formula><p>The source of the GABA precursor is believed to be dominated by neuronal glucose with astroglial glutamine playing a smaller role (<xref rid=\"B78\" ref-type=\"bibr\">78</xref>–<xref rid=\"B80\" ref-type=\"bibr\">80</xref>). GABA synthesis from putrescine and other polyamines is metabolically insignificant in the brain although polyamines play an important role in the developing brain (<xref rid=\"B81\" ref-type=\"bibr\">81</xref>).</p><p>A fundamental aspect of glutamate–GABA association is the excitation–inhibition balance in the brain (<xref rid=\"B32\" ref-type=\"bibr\">32</xref>, <xref rid=\"B82\" ref-type=\"bibr\">82</xref>) because of their roles as the dominant excitatory and inhibitory neurotransmitters, respectively, in the CNS. Glutamatergic neurons (e.g., cortical pyramidal neurons) receive a significant degree of GABA<sub>A</sub>-mediated inhibition through interneurons (<xref rid=\"B83\" ref-type=\"bibr\">83</xref>). Balanced excitation and inhibition facilitate normal brain functions, and failure to maintain excitation–inhibition balance underlies dysfunction in many brain disorders (<xref rid=\"B32\" ref-type=\"bibr\">32</xref>, <xref rid=\"B33\" ref-type=\"bibr\">33</xref>, <xref rid=\"B84\" ref-type=\"bibr\">84</xref>). Many studies have also revealed altered glutamate and GABA. For example, GABA levels were found to decrease, whereas Glx levels increased with increasing visual input in the occipital cortex of healthy subjects (<xref rid=\"B85\" ref-type=\"bibr\">85</xref>). Both glutamate and GABA increased following vigorous exercise (<xref rid=\"B86\" ref-type=\"bibr\">86</xref>). In autistic patients the frontal lobe [GABA]/[Glu] ratio was found to be significantly lower, suggesting abnormality in the regulation between GABA and glutamate (<xref rid=\"B87\" ref-type=\"bibr\">87</xref>). Abnormalities in MRS measures of glutamate and/or GABA in many other neuropsychiatric disorders have also been reported and reviewed [e.g., (<xref rid=\"B40\" ref-type=\"bibr\">40</xref>, <xref rid=\"B88\" ref-type=\"bibr\">88</xref>–<xref rid=\"B90\" ref-type=\"bibr\">90</xref>)].</p><p>Long range excitatory and inhibitory interactions between functionally connected brain regions are well established (<xref rid=\"B91\" ref-type=\"bibr\">91</xref>–<xref rid=\"B93\" ref-type=\"bibr\">93</xref>). The strong coupling between glutamatergic neurotransmission and total GABA level is supported by a large body of <italic>in vitro</italic> and <italic>in vivo</italic> evidence [e.g., (<xref rid=\"B16\" ref-type=\"bibr\">16</xref>, <xref rid=\"B75\" ref-type=\"bibr\">75</xref>, <xref rid=\"B94\" ref-type=\"bibr\">94</xref>–<xref rid=\"B100\" ref-type=\"bibr\">100</xref>)]. A large number of neuroimaging studies have also shown that total glutamate or Glx concentration is significantly correlated with neural activity or glutamatergic neurotransmission [e.g., (<xref rid=\"B28\" ref-type=\"bibr\">28</xref>, <xref rid=\"B101\" ref-type=\"bibr\">101</xref>–<xref rid=\"B103\" ref-type=\"bibr\">103</xref>)]. Correlations of total glutamate and total GABA levels locally and among regions functionally connected in a specific neural network have also been reported [e.g., (<xref rid=\"B15\" ref-type=\"bibr\">15</xref>–<xref rid=\"B17\" ref-type=\"bibr\">17</xref>, <xref rid=\"B19\" ref-type=\"bibr\">19</xref>)].</p></sec></sec><sec id=\"s3\"><title>Statistical Correlations Among MRS Signals Due to Spectral Overlap</title><p>Many neurometabolites have similar resonant frequencies, leading to spectral overlap among MRS signals. Effects of spectral overlap on the Cremer–Rao lower bounds of extracted neurometabolites have been analyzed previously (<xref rid=\"B104\" ref-type=\"bibr\">104</xref>). Extracting the concentrations of neurometabolites by spectral fitting is essentially mapping the acquired MRS spectrum (<bold>spec</bold>) into a vector (<bold>conc</bold>) consisting of concentrations by inverting the matrix equation <bold>spec</bold> = <bold>basis</bold> • <bold>conc</bold> (<xref rid=\"B24\" ref-type=\"bibr\">24</xref>). Here, <bold>basis</bold> is a matrix consisting of basis spectra of the component neurometabolites, which transforms the concentration vector <bold>conc</bold> into the fitted spectrum that approximates <bold>spec</bold> in the sense of least squares. Overlapping neurometabolites become statistically correlated through the covariance matrix (COV) of <bold>conc</bold> (<xref rid=\"B105\" ref-type=\"bibr\">105</xref>):</p><disp-formula><mml:math id=\"M11\"><mml:mrow><mml:mtext>COV</mml:mtext><mml:mo stretchy=\"false\">(</mml:mo><mml:mi mathvariant=\"bold\">conc</mml:mi><mml:mo stretchy=\"false\">)</mml:mo><mml:mo>=</mml:mo><mml:msup><mml:mi>σ</mml:mi><mml:mn>2</mml:mn></mml:msup><mml:msup><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mi mathvariant=\"bold\">basi</mml:mi><mml:msup><mml:mi mathvariant=\"bold\">s</mml:mi><mml:mo>†</mml:mo></mml:msup><mml:mo>•</mml:mo><mml:mtext> </mml:mtext><mml:mi mathvariant=\"bold\">basis</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow><mml:mrow><mml:mo>−</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:msup></mml:mrow></mml:math></disp-formula><p>where <sup>†</sup> denotes Hermitian transposition and <italic>σ</italic><sup>2</sup> is the noise variance of the measured spectrum <bold>spec</bold>. The off-diagonal elements (proportional to the square of cross-correlation coefficients) of COV(<bold>conc</bold>) depend on the frequency separation among the resonances of the neurometabolites in <bold>basis</bold>. <xref ref-type=\"fig\" rid=\"f2\"><bold>Figure 2</bold></xref> shows Monte Carlo simulations of the correlation between two singlet peaks as a function of their separation in the frequency domain. <xref ref-type=\"fig\" rid=\"f2\"><bold>Figure 2A</bold></xref> shows the correlation between conc<sub>1</sub> and conc<sub>2</sub> across individual fits when spectral overlap between the two signals is minimal. The correlation between conc<sub>1</sub> and conc<sub>2</sub> when their frequency separation equals their half-height linewidth is plotted in <xref ref-type=\"fig\" rid=\"f2\"><bold>Figure 2B</bold></xref>. Finally, the correlation between conc<sub>1</sub> and conc<sub>2</sub> when their frequency separation equals 0.5* half-height linewidth is plotted in <xref ref-type=\"fig\" rid=\"f2\"><bold>Figure 2C</bold></xref>. As shown by <xref ref-type=\"fig\" rid=\"f2\"><bold>Figure 2</bold></xref> statistical correlation between these two neurochemically unrelated signals increases as their spectral overlap increases. Here the large correlation values occur when the two signals happen to exhibit similar chemical shifts, not because one signal influences the other neurochemically. This simple example of two overlapping singlets illustrates a point of caution in the interpretation of neurometabolite correlation results. For multiplets and neurometabolites with multiple resonances, spectral overlap occurs when two resonance lines overlap each other even when the chemical shifts are not very close.</p><fig id=\"f2\" position=\"float\"><label>Figure 2</label><caption><p>Monte Carlo simulation of statistical correlation between two unrelated Lorentzian singlets with signal-to-noise ratio = 20. The half-height linewidth of the two peaks is 8 Hz. The same spectral fitting process was repeated 1,000 times with the same noise level but different noise realizations. For 40 Hz <bold>(A)</bold>, 8 Hz <bold>(B)</bold>, and 4 Hz <bold>(C)</bold> frequency separations between the two singlets, the correlation coefficient was found to be −0.04, −0.63, and −0.98, respectively.</p></caption><graphic xlink:href=\"fpsyt-11-00802-g002\"/></fig><p>The off-diagonal elements of the covariance matrix become highly significant in the presence of severe spectral overlap such as in short echo time MRS spectra. In addition to overlapping neurometabolites, a strong baseline can also cause statistical correlations (<xref rid=\"B106\" ref-type=\"bibr\">106</xref>, <xref rid=\"B107\" ref-type=\"bibr\">107</xref>) because the baseline, which arises from macromolecules and/or lipids and residual water, overlaps with essentially all neurometabolite signals. <xref ref-type=\"fig\" rid=\"f3\"><bold>Figure 3</bold></xref> compares spectral fitting of two 3 T short echo time single voxel <italic>in vivo</italic> spectra by the commercial LCModel software. The spectrum on the left was acquired using short echo time Point RESolved Spectroscopy (PRESS) technique from a cubic voxel in the anterior cingulate cortex of a healthy subject at 3 T (echo time = 35 ms, voxel size = 8 ml). The spectrum on the right was generated by broadening the linewidths of the spectrum on the left by 2.0 Hz and adding random noise to maintain the same signal-to-noise ratio (<xref rid=\"B107\" ref-type=\"bibr\">107</xref>). Both spectra in <xref ref-type=\"fig\" rid=\"f3\"><bold>Figure 3</bold></xref> were fitted using LCModel with the same default settings (<xref rid=\"B24\" ref-type=\"bibr\">24</xref>). With 2.0 Hz line-broadening the LCModel baseline was conspicuously stronger around the spectral region near 2.35 ppm where glutamate and the aspartyl moiety of NAA resonate. Both NAA and glutamate levels reported by LCModel were lowered by approximately the same amount (~11%) after the line-broadening [n = 10; (<xref rid=\"B107\" ref-type=\"bibr\">107</xref>)]. This reduction in the extracted metabolite concentrations was found to be generally more pronounced with greater line-broadening. Although the concentrations of neurometabolites in the two spectra of <xref ref-type=\"fig\" rid=\"f3\"><bold>Figure 3</bold></xref> are identical, the LCModel produced lower neurometabolite levels and a more intense baseline after 2.0 Hz line-broadening. Because of the spectral overlap between baseline and neurometabolites, overestimating (underestimating) the baseline causes underestimating (overestimating) neurometabolites and <italic>vice versa</italic>. Neurometabolites overlapping with the same broad baseline peak are similarly underestimated (overestimated) due to the broad baseline signals. This in turn, contributes to positive statistical correlations among those neurometabolites regardless of the underlying neurochemical associations.</p><fig id=\"f3\" position=\"float\"><label>Figure 3</label><caption><p><bold>(A)</bold> Single voxel short echo time spectrum acquired from the anterior cingulate cortex of a healthy subject at 3 T. Data was fitted using LCModel. <bold>(B)</bold> LCModel generated a different baseline from the same data after 2.0 Hz line-broadening and noise injection to maintain the same signal-to-noise ratio. The large change in baseline around 2.35 ppm (marked by an arrow) simultaneously reduced fitted NAA and glutamate concentrations, therefore, causing positive correlation between NAA and glutamate even though the only difference between the two spectra is their linewidth (<italic>i.e.</italic>, no correlations). Reprinted from reference (<xref rid=\"B107\" ref-type=\"bibr\">107</xref>) with permission from Elsevier.</p></caption><graphic xlink:href=\"fpsyt-11-00802-g003\"/></fig><p>Spectral fitting techniques such as the LCModel heavily rely on the linewidth difference between neurometabolites and background signals to separate them. Broad neurometabolite peaks in the presence of a strong baseline, as often seen in clinical short echo time MRS data, can lead to significant quantification errors and unwanted statistical correlations because of the large baseline-metabolite covariances. The results in <xref ref-type=\"fig\" rid=\"f3\"><bold>Figure 3</bold></xref> are also corroborated by an earlier study which quantitatively analyzed the estimation uncertainties caused by the baseline using Cramer–Rao lower bound (CRLB) of the baseline (<xref rid=\"B106\" ref-type=\"bibr\">106</xref>), confirming that the estimation uncertainty significantly increases with decreased baseline smoothness and increased spectral linewidths.</p></sec><sec id=\"s4\"><title>Prospects for Mapping Macroscopic Neurochemical Associations By MRS Imaging</title><p>Although group comparison of neurometabolite correlations between healthy controls and patients reveals altered neurochemical associations in brain disorders, determining the absolute strength of these correlations is important for interpreting clinical findings (<xref rid=\"B20\" ref-type=\"bibr\">20</xref>, <xref rid=\"B21\" ref-type=\"bibr\">21</xref>) and for potentially relaying interregional associations across the brain. As linewidth variations in clinical MRS studies are very common, our analysis of statistical correlations that originated from the spectral overlap in the section <italic>Statistical Correlations Among MRS Signals Due to Spectral Overlap</italic> has demonstrated that spectral overlap should be eliminated or minimized when the absolute strength of neurometabolite correlations is to be determined. To facilitate measurement of the absolute strength of neurochemical associations, MRS techniques that result in flat baselines and isolated signals of interest would be ideal. Many existing single voxel spectral editing techniques generate flat or weak baselines while eliminating or minimizing overlapping resonances (<xref rid=\"B74\" ref-type=\"bibr\">74</xref>, <xref rid=\"B108\" ref-type=\"bibr\">108</xref>–<xref rid=\"B115\" ref-type=\"bibr\">115</xref>), which are likely suited for measuring local or intraregional neurochemical associations.</p><p>Mapping macroscopic neurochemical associations across the human brain has the exciting potential to broadly impact studies of normal brain functions as well as neuropsychiatric disorders (<xref rid=\"B32\" ref-type=\"bibr\">32</xref>, <xref rid=\"B116\" ref-type=\"bibr\">116</xref>, <xref rid=\"B117\" ref-type=\"bibr\">117</xref>). Here we discuss the prospects for measuring interregional excitation–inhibition balance (<xref rid=\"B32\" ref-type=\"bibr\">32</xref>, <xref rid=\"B118\" ref-type=\"bibr\">118</xref>–<xref rid=\"B120\" ref-type=\"bibr\">120</xref>) by spectroscopic imaging of spectrally resolved glutamate and GABA. Participant motion is a major issue in scanning many patients of neuropsychiatric disorders. Studying these patients using chemical shift imaging is technically challenging because of the relatively long scan time required for phase encoding. As artifacts in chemical shift images caused by motion are hard to detect, they can lead to erroneous diagnosis and data interpretation (<xref rid=\"B121\" ref-type=\"bibr\">121</xref>). It is well-known that participant motion inside a magnetic field causes changes in resonant frequencies. Incorporating spectral editing techniques based on highly selective radiofrequency pulses into chemical shift imaging therefore can lead to even larger errors due to the additional effects of carrier frequency mismatch on spectral editing yield.</p><p>To minimize error due to unavoidable patient movement during extended scan time necessary for phase encoding, we focus on techniques that can resolve glutamate or GABA in a single shot with relatively weak baselines at 7 T. Spectral isolation of glutamate or GABA accompanied with a weak baseline will minimize the unwanted correlations that originated from spectral overlap (<xref rid=\"B107\" ref-type=\"bibr\">107</xref>). The emphasis on minimizing spectral overlap for measuring interregional neurometabolite correlations may seem counterintuitive. However, it is necessary because, for example, overlapping with interregionally correlated signals can relay the correlation to overlapped signals. To spectrally resolve glutamate or GABA over an extended brain region, highly frequency-selective pulses popular for single voxel spectral editing cannot be used because of the unavoidable and significant residual B<sub>0</sub> inhomogeneity across a large volume in the brain, especially at high magnetic field strength. Highly frequency-selective pulses are sensitive to patient movement, system instability, and B<sub>0</sub> inhomogeneity as they will miss or partially miss the editing target in part of the slice(s) where resonance frequencies are shifted away (<xref rid=\"B122\" ref-type=\"bibr\">122</xref>, <xref rid=\"B123\" ref-type=\"bibr\">123</xref>).</p><p>Weak or nearly flat baselines are automatically produced at long echo times because of the shorter T<sub>2</sub> values of macromolecules (<xref rid=\"B1\" ref-type=\"bibr\">1</xref>). Serendipitously, the strongly coupled glutamate H4 (2.35 ppm) forms an intense pseudo singlet at a relatively long echo time (~100 ms) at 7 T (<xref rid=\"B111\" ref-type=\"bibr\">111</xref>, <xref rid=\"B112\" ref-type=\"bibr\">112</xref>). The resonances of glutamine H4 at 2.45 ppm and glutathione glutamyl H4 at 2.49 ppm also form pseudo singlets at ~100 ms echo time (see <xref ref-type=\"fig\" rid=\"f4\"><bold>Figure 4</bold></xref>). Therefore, glutamate can be spectrally resolved in a single shot without using any spectrally selective pulses at ~100 ms echo time at 7 T (<xref rid=\"B111\" ref-type=\"bibr\">111</xref>). At 7 T the multiplet signal of the aspartyl moiety of NAA at 2.49 ppm still overlaps with glutamine H4 and glutathione glutamyl H4 at ~100 ms echo time (<xref rid=\"B111\" ref-type=\"bibr\">111</xref>). The overlapping NAA aspartyl moiety signals can be eliminated using a J-suppression pulse acting on the <italic>α</italic>-H of the aspartyl moiety of NAA at 4.38 ppm (<xref rid=\"B112\" ref-type=\"bibr\">112</xref>). The J suppression pulse can be made band-selective with a flat top frequency profile (<xref rid=\"B124\" ref-type=\"bibr\">124</xref>) to accommodate variations in B<sub>0</sub>. Either because no frequency selective pulses are needed (<xref rid=\"B111\" ref-type=\"bibr\">111</xref>) or with band-selective J suppression (<xref rid=\"B112\" ref-type=\"bibr\">112</xref>), chemical shift imaging of glutamate is feasible at 7 T in the presence of significant patient motion and residual static magnetic field inhomogeneity across the slice(s).</p><fig id=\"f4\" position=\"float\"><label>Figure 4</label><caption><p>Single voxel 7 T spectrum acquired using a single-shot echo time optimized PRESS sequence (<xref rid=\"B112\" ref-type=\"bibr\">112</xref>) without signal averaging. Voxel size = 2 × 2 × 2 cm<sup>3</sup>. Echo time = 106 ms. Line broadening = 8 Hz. Number of averages = 1. NAA, N-acetylaspartate; Glu, glutamate; Gln, glutamine; GSH, glutathione; tCr, total creatine; tCho, total choline; mI, myo-inositol. Glutamate H4 at 2.35 ppm was spectrally resolved with an approximately flat baseline.</p></caption><graphic xlink:href=\"fpsyt-11-00802-g004\"/></fig><p>Multiple quantum filtering can be used to edit GABA in a single shot (<xref rid=\"B125\" ref-type=\"bibr\">125</xref>–<xref rid=\"B128\" ref-type=\"bibr\">128</xref>). It is also possible to generate a flat baseline using multiple quantum filtering (<xref rid=\"B127\" ref-type=\"bibr\">127</xref>). Chemical shift imaging of GABA over a large volume in the brain is challenging even at the high magnetic field strength of 7 T as all available GABA editing techniques rely on frequency selective pulses that differentially act on GABA H3 at 1.91 ppm and GABA H4 at 3.02 ppm. At 7 T, the chemical shift dispersion between GABA H3 and GABA H4 is 328 Hz. This relatively large chemical shift difference makes it possible to use band-selective pulses with a flat top (<xref rid=\"B124\" ref-type=\"bibr\">124</xref>) to accommodate changes in B<sub>0</sub> due to patient movement, system instability, and residual B<sub>0</sub> inhomogeneity while still affording a close to uniform editing yield. <xref ref-type=\"fig\" rid=\"f5\"><bold>Figure 5</bold></xref> proposes a band-selective multiple quantum filtering scheme that combines spectral selectivity while allowing signal to shift within the flat passband. This multiple quantum GABA editing scheme is expected to tolerate a large frequency shift with GABA H3 lying within the flat bandwidth and GABA H4 staying outside of the downfield transition band of the band-selective pulse acting on GABA H3.</p><fig id=\"f5\" position=\"float\"><label>Figure 5</label><caption><p>A proposed scheme for band-selective multiple quantum filtering of GABA. A band-selective refocusing pulse prepares GABA into multiple quantum state while leaving glutathione in single quantum state by avoiding refocusing its cysteinyl <italic>α</italic>-H at 4.56 ppm. A band-selective 90<sup>°</sup> pulse converts the double quantum coherence into observable single quantum coherence. The frequency separation between GABA H4 and GABA H3 at 7 T is 164 × 2 = 328 Hz. Asp, aspartate; GSH, glutathione; Cr, creatine. The chemical shifts (in ppm) of the resonance signals are placed below their labels.</p></caption><graphic xlink:href=\"fpsyt-11-00802-g005\"/></fig><p>Although we have focused on discussing chemical shift imaging of spectrally resolved glutamate and GABA without using highly selective editing pulses for the magnetic field strength of 7 T, similar ideas may also be developed for lower magnetic field strengths such as 3 T. For example, multiecho time averaging at 3 T can isolate glutamate with a nearly flat baseline without using any spectral editing pulses (<xref rid=\"B109\" ref-type=\"bibr\">109</xref>). Directly combining this approach with conventional phase-encoding for chemical shift imaging would not be feasible due to the large number [e.g., (<xref rid=\"B32\" ref-type=\"bibr\">32</xref>)] of echoes required for each phase encoding step. Instead of using evenly spaced echo times, the averaging effect may be obtained using fewer echo times with numerical optimization. Many fast imaging strategies such as echo-planar readout can also greatly accelerate data acquisition of chemical shift imaging experiments.</p><p>The coordinated variations of glutamate and GABA across the brain can be assessed using chemical shift images of spectrally resolved glutamate and GABA. The absolute strengths of interregional correlations of spectrally resolved glutamate and GABA have the exciting potential for characterizing excitatory–inhibitory connections among the nodes of neural networks, therefore providing novel parameters for gauging interregional excitation–inhibition balance, the disruption of which is implicated in many neuropsychiatric disorders [e.g., (<xref rid=\"B33\" ref-type=\"bibr\">33</xref>, <xref rid=\"B103\" ref-type=\"bibr\">103</xref>, <xref rid=\"B117\" ref-type=\"bibr\">117</xref>, <xref rid=\"B129\" ref-type=\"bibr\">129</xref>, <xref rid=\"B130\" ref-type=\"bibr\">130</xref>)].</p></sec><sec sec-type=\"conclusions\" id=\"s5\"><title>Conclusions</title><p>Previous neurochemical studies of animal models have revealed coordinated changes in major neurometabolites under various pathophysiological conditions which were attributed to the well-studied metabolic pathways connecting them. Recent MRS findings of neurometabolite correlations in healthy subjects and altered correlations in patients have further corroborated these associations, and their disruption is a hallmark of many neuropsychiatric disorders. To measure the absolute strength of these correlations, it is necessary to use spectral editing techniques to minimize or eliminate statistical correlations among MRS signals that originated from spectral overlap. Finally, chemical shift imaging of spectrally resolved glutamate and GABA is technically feasible at 7 T. It is hoped that the prospects for eliminating the confounding statistical correlations due to spectral overlap will reduce controversies in the field and generate further interest in characterizing local and interregional neurochemical associations especially glutamate–GABA interactions in the brain for studying neuropsychiatric disorders.</p></sec><sec id=\"s6\"><title>Author Contributions</title><p>JS conceived the paper and performed literature search. DS and LA conducted laboratory research. JST performed literature search. JS, LA, and JST wrote the paper. All authors contributed to the article and approved the submitted version.</p></sec><sec sec-type=\"funding-information\" id=\"s7\"><title>Funding</title><p>This work was supported by the Intramural Research Program of National Institute of Mental Health, NIH.</p></sec><sec id=\"s8\"><title>Conflict of Interest</title><p>The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p></sec></body><back><ref-list><title>References</title><ref id=\"B1\"><label>1</label><mixed-citation publication-type=\"book\">\n<person-group person-group-type=\"author\"><name><surname>de Graaf</surname><given-names>RA</given-names></name></person-group>\n<source>In vivo NMR spectroscopy.</source>\n<publisher-name>John Wiley & Sons</publisher-name>: <publisher-loc>Chichester, UK</publisher-loc> (<year>1998</year>).\n</mixed-citation></ref><ref id=\"B2\"><label>2</label><mixed-citation 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[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"correction\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Front Genet</journal-id><journal-id journal-id-type=\"iso-abbrev\">Front Genet</journal-id><journal-id journal-id-type=\"publisher-id\">Front. Genet.</journal-id><journal-title-group><journal-title>Frontiers in Genetics</journal-title></journal-title-group><issn pub-type=\"epub\">1664-8021</issn><publisher><publisher-name>Frontiers Media S.A.</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32849776</article-id><article-id pub-id-type=\"pmc\">PMC7432120</article-id><article-id pub-id-type=\"doi\">10.3389/fgene.2020.00732</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Genetics</subject><subj-group><subject>Correction</subject></subj-group></subj-group></article-categories><title-group><article-title>Corrigendum: Exact Distribution of Linkage Disequilibrium in the Presence of Mutation, Selection, or Minor Allele Frequency Filtering</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Qu</surname><given-names>Jiayi</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Kachman</surname><given-names>Stephen D.</given-names></name><xref ref-type=\"aff\" rid=\"aff2\"><sup>2</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Garrick</surname><given-names>Dorian</given-names></name><xref ref-type=\"aff\" rid=\"aff3\"><sup>3</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/37713/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Fernando</surname><given-names>Rohan L.</given-names></name><xref ref-type=\"aff\" rid=\"aff4\"><sup>4</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/22048/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Cheng</surname><given-names>Hao</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><xref ref-type=\"corresp\" rid=\"c001\"><sup>*</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/774102/overview\"/></contrib></contrib-group><aff id=\"aff1\"><sup>1</sup><institution>Department of Animal Science, University of California, Davis</institution>, <addr-line>Davis, CA</addr-line>, <country>United States</country></aff><aff id=\"aff2\"><sup>2</sup><institution>Department of Statistics, University of Nebraska Lincoln</institution>, <addr-line>Lincoln, NE</addr-line>, <country>United States</country></aff><aff id=\"aff3\"><sup>3</sup><institution>School of Agriculture, Massey University</institution>, <addr-line>Wellington</addr-line>, <country>New Zealand</country></aff><aff id=\"aff4\"><sup>4</sup><institution>Department of Animal Science, Iowa State University</institution>, <addr-line>Ames, IA</addr-line>, <country>United States</country></aff><author-notes><fn fn-type=\"edited-by\"><p>Edited and reviewed by: Jacob A. Tennessen, Harvard University, United States</p></fn><corresp id=\"c001\">*Correspondence: Hao Cheng <email>[email protected]</email></corresp><fn fn-type=\"other\" id=\"fn001\"><p>This article was submitted to Evolutionary and Population Genetics, a section of the journal Frontiers in Genetics</p></fn></author-notes><pub-date pub-type=\"epub\"><day>11</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><year>2020</year></pub-date><pub-date pub-type=\"pmc-release\"><day>11</day><month>8</month><year>2020</year></pub-date><!-- PMC Release delay is 0 months and 0 days and was based on the <pub-date pub-type=\"epub\"/>. --><volume>11</volume><elocation-id>732</elocation-id><history><date date-type=\"received\"><day>18</day><month>5</month><year>2020</year></date><date date-type=\"accepted\"><day>16</day><month>6</month><year>2020</year></date></history><permissions><copyright-statement>Copyright © 2020 Qu, Kachman, Garrick, Fernando and Cheng.</copyright-statement><copyright-year>2020</copyright-year><copyright-holder>Qu, Kachman, Garrick, Fernando and Cheng</copyright-holder><license xlink:href=\"http://creativecommons.org/licenses/by/4.0/\"><license-p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</license-p></license></permissions><related-article related-article-type=\"corrected-article\" id=\"d38e175\" ext-link-type=\"doi\" xlink:href=\"10.3389/fgene.2020.00362\" journal-id=\"Front Genet\" journal-id-type=\"nlm-ta\" vol=\"11\" page=\"362\">A Corrigendum on <article-title>Exact Distribution of Linkage Disequilibrium in the Presence of Mutation, Selection, or Minor Allele Frequency Filtering</article-title> by Qu, J., Kachman, S. D., Garrick, D., Fernando, R. L., and Cheng, H. (2020). Front. Genet. 11:362. doi: <object-id>10.3389/fgene.2020.00362</object-id></related-article><kwd-group><kwd>linkage disequilibrium</kwd><kwd>effective population size</kwd><kwd>mutation rate</kwd><kwd>selection</kwd><kwd>minor allele frequency filtering</kwd></kwd-group><counts><fig-count count=\"1\"/><table-count count=\"0\"/><equation-count count=\"0\"/><ref-count count=\"0\"/><page-count count=\"2\"/><word-count count=\"431\"/></counts></article-meta></front><body><p>In the original article, there was a mistake in <xref ref-type=\"fig\" rid=\"F1\">Figure 1</xref> as published. A wrong graph was used in <xref ref-type=\"fig\" rid=\"F1\">Figure 1A</xref> when c = 0.01. The corrected <xref ref-type=\"fig\" rid=\"F1\">Figure 1</xref> appears below.</p><fig id=\"F1\" position=\"float\"><label>Figure 1</label><caption><p>Comparison of Sved's and Hill's approximation to exact distribution of <inline-formula><mml:math id=\"M1\"><mml:msubsup><mml:mrow><mml:mi>r</mml:mi></mml:mrow><mml:mrow><mml:mi>E</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msubsup></mml:math></inline-formula> (scatter points) derived from transition-matrix approach. Mean square errors are shown in the parentheses. “Calibrated” denotes the non-linear regression formula derived from the transition-matrix approach. <bold>(A)</bold> In the absence of mutation and selection. <bold>(B)</bold> In the presence of mutation but no selection.</p></caption><graphic xlink:href=\"fgene-11-00732-g0001\"/></fig><p>The authors apologize for this error and state that this does not change the scientific conclusions of the article in any way. The original article has been updated.</p></body></article>\n"
]
[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Int J Mol Sci</journal-id><journal-id journal-id-type=\"iso-abbrev\">Int J Mol Sci</journal-id><journal-id journal-id-type=\"publisher-id\">ijms</journal-id><journal-title-group><journal-title>International Journal of Molecular Sciences</journal-title></journal-title-group><issn pub-type=\"epub\">1422-0067</issn><publisher><publisher-name>MDPI</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32751838</article-id><article-id pub-id-type=\"pmc\">PMC7432121</article-id><article-id pub-id-type=\"doi\">10.3390/ijms21155474</article-id><article-id pub-id-type=\"publisher-id\">ijms-21-05474</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Article</subject></subj-group></article-categories><title-group><article-title>Mechanosensitivity Is a Characteristic Feature of Cultured Suburothelial Interstitial Cells of the Human Bladder</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0003-2484-6994</contrib-id><name><surname>Neuhaus</surname><given-names>Jochen</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05474\">1</xref><xref rid=\"c1-ijms-21-05474\" ref-type=\"corresp\">*</xref></contrib><contrib contrib-type=\"author\"><name><surname>Gonsior</surname><given-names>Andreas</given-names></name><xref ref-type=\"aff\" rid=\"af2-ijms-21-05474\">2</xref></contrib><contrib contrib-type=\"author\"><name><surname>Cheng</surname><given-names>Sheng</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05474\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Stolzenburg</surname><given-names>Jens-Uwe</given-names></name><xref ref-type=\"aff\" rid=\"af2-ijms-21-05474\">2</xref></contrib><contrib contrib-type=\"author\"><name><surname>Berger</surname><given-names>Frank Peter</given-names></name><xref ref-type=\"aff\" rid=\"af3-ijms-21-05474\">3</xref></contrib></contrib-group><aff id=\"af1-ijms-21-05474\"><label>1</label>Department of Urology, Research Laboratory, University of Leipzig, Liebigstr. 19, 04103 Leipzig, Germany; <email>[email protected]</email></aff><aff id=\"af2-ijms-21-05474\"><label>2</label>Department of Urology, University Hospital Leipzig, Liebigstr. 20, 04103 Leipzig, Germany; <email>[email protected]</email> (A.G.); <email>[email protected]</email> (J.-U.S.)</aff><aff id=\"af3-ijms-21-05474\"><label>3</label>Department of Urology, University Hospital Jena, Am Klinikum 1, 07747 Jena, Germany; <email>[email protected]</email></aff><author-notes><corresp id=\"c1-ijms-21-05474\"><label>*</label>Correspondence: <email>[email protected]</email>; Tel.: +49-341-9717688</corresp></author-notes><pub-date pub-type=\"epub\"><day>31</day><month>7</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><month>8</month><year>2020</year></pub-date><volume>21</volume><issue>15</issue><elocation-id>5474</elocation-id><history><date date-type=\"received\"><day>16</day><month>6</month><year>2020</year></date><date date-type=\"accepted\"><day>29</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>© 2020 by the authors.</copyright-statement><copyright-year>2020</copyright-year><license license-type=\"open-access\"><license-p>Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>).</license-p></license></permissions><abstract><p>Bladder dysfunction is characterized by urgency, frequency (pollakisuria, nocturia), and dysuria and may lead to urinary incontinence. Most of these symptoms can be attributed to disturbed bladder sensitivity. There is growing evidence that, besides the urothelium, suburothelial interstitial cells (suICs) are involved in bladder afferent signal processing. The massive expansion of the bladder during the filling phase implicates mechanical stress delivered to the whole bladder wall. Little is known about the reaction of suICs upon mechanical stress. Therefore, we investigated the effects of mechanical stimulation in cultured human suICs. We used fura-2 calcium imaging as a major physiological readout. We found spontaneous intracellular calcium activity in 75 % of the cultured suICs. Defined local pressure application via a glass micropipette led to local increased calcium activity in all stimulated suICs, spreading over the whole cell. A total of 51% of the neighboring cells in a radius of up to 100 µm from the stimulated cell showed an increased activity. Hypotonic ringer and shear stress also induced calcium transients. We found an 18-times increase in syncytial activity compared to unstimulated controls, resulting in an amplification of the primary calcium signal elicited in single cells by 50%. Our results speak in favor of a high sensitivity of suICs for mechanical stress and support the view of a functional syncytium between suICs, which can amplify and distribute local stimuli. Previous studies of connexin expression in the human bladder suggest that this mechanism could also be relevant in normal and pathological function of the bladder in vivo.</p></abstract><kwd-group><kwd>human urinary bladder</kwd><kwd>afferent signaling</kwd><kwd>mechanical stimulation</kwd><kwd>shear stress</kwd><kwd>fura-2 calcium imaging</kwd><kwd>intercellular coupling</kwd><kwd>functional syncytium</kwd><kwd>platelet derived growth factor receptor-alpha</kwd><kwd>calreticulin</kwd><kwd>vimentin</kwd><kwd>alpha-smooth muscle cell actin</kwd><kwd>connexin 43</kwd></kwd-group></article-meta></front><body><sec sec-type=\"intro\" id=\"sec1-ijms-21-05474\"><title>1. Introduction</title><p>The urinary bladder has a dual function, periodical stretching during the filling phase and exhibiting mass contraction for micturition. The realization of this requires a complex coordination with the sphincter systems of the bladder itself, called the vesical or internal sphincter (musculus sphincter vesicae) and the split external or urethral sphincter, comprising an internal smooth muscle part (musculus sphincter urethrae glaber) and an external striated part (musculus sphincter urethrae transversostriatus) [<xref rid=\"B1-ijms-21-05474\" ref-type=\"bibr\">1</xref>]. While the neuronal control via spinal cord and higher central nervous system centers are well known [<xref rid=\"B2-ijms-21-05474\" ref-type=\"bibr\">2</xref>], the local signal processing in the bladder wall is still enigmatic. Many details of the lower urinary tract function have been learned from diseased states in humans and in animal models, such as bladder outlet obstruction (BOO) and overactive bladder (OAB), and interstitial cystitis/bladder pain processing in the bladder wall is still enigmatic. Many details of the lower urinary tract function have been learned from diseased states in humans and in animal models, such as bladder outlet obstruction (BOO), overactive bladder (OAB), and interstitial cystitis/bladder pain syndrome (IC/BPS) [<xref rid=\"B3-ijms-21-05474\" ref-type=\"bibr\">3</xref>,<xref rid=\"B4-ijms-21-05474\" ref-type=\"bibr\">4</xref>]. While the urothelium acts as the major sensor of bladder filling, the suburothelial interstitial cells are emerging as important modulators of the afferent signal generation [<xref rid=\"B5-ijms-21-05474\" ref-type=\"bibr\">5</xref>]. Twenty-eight transient receptor potential (TRP) channels have been described so far in mammals, of which TRPV1, 2, 4 (vanilloid), TRPM8 (melastatin), and TRPA1 (ankyrin) seem to play an important role in sensory signaling in the bladder [<xref rid=\"B6-ijms-21-05474\" ref-type=\"bibr\">6</xref>]. Recently, our own group described TRPA1 expression in interstitial cells (ICs) of the human, guinea pig, and rat, supporting the hypothesis of ICs being mechanosensitive [<xref rid=\"B7-ijms-21-05474\" ref-type=\"bibr\">7</xref>]. The role of the ICs in the bladder is still not fully understood and recent studies indicate that there are different subtypes of ICs. The major differences in their distribution define sublayers of the lamina propria, referred to as the upper lamina propria and deeper lamina propria [<xref rid=\"B8-ijms-21-05474\" ref-type=\"bibr\">8</xref>]. Recent comparative studies on ICs in human and the major laboratory animals suggest that the understanding of the roles of ICs partly needs revision [<xref rid=\"B7-ijms-21-05474\" ref-type=\"bibr\">7</xref>,<xref rid=\"B9-ijms-21-05474\" ref-type=\"bibr\">9</xref>,<xref rid=\"B10-ijms-21-05474\" ref-type=\"bibr\">10</xref>].</p><p>Coupling between ICs, the urothelium, and detrusor smooth muscle cells is another important issue which determines the normal function of the bladder and is significantly altered in disease, as demonstrated in BOO [<xref rid=\"B11-ijms-21-05474\" ref-type=\"bibr\">11</xref>,<xref rid=\"B12-ijms-21-05474\" ref-type=\"bibr\">12</xref>,<xref rid=\"B13-ijms-21-05474\" ref-type=\"bibr\">13</xref>,<xref rid=\"B14-ijms-21-05474\" ref-type=\"bibr\">14</xref>,<xref rid=\"B15-ijms-21-05474\" ref-type=\"bibr\">15</xref>,<xref rid=\"B16-ijms-21-05474\" ref-type=\"bibr\">16</xref>], OAB [<xref rid=\"B17-ijms-21-05474\" ref-type=\"bibr\">17</xref>,<xref rid=\"B18-ijms-21-05474\" ref-type=\"bibr\">18</xref>], and IC/BPS [<xref rid=\"B19-ijms-21-05474\" ref-type=\"bibr\">19</xref>]. Furthermore, cell culture studies showed that cytokines can control connexin (Cx)43 and Cx45 expression in bladder smooth muscle cells and interstitial cells [<xref rid=\"B20-ijms-21-05474\" ref-type=\"bibr\">20</xref>,<xref rid=\"B21-ijms-21-05474\" ref-type=\"bibr\">21</xref>,<xref rid=\"B22-ijms-21-05474\" ref-type=\"bibr\">22</xref>]. Besides the formation of functional syncytia via gap junction coupling, connexins and the closely related family of pannexins can form hemichannels in the plasma membranes, mediating, for example, cellular ATP release [<xref rid=\"B23-ijms-21-05474\" ref-type=\"bibr\">23</xref>,<xref rid=\"B24-ijms-21-05474\" ref-type=\"bibr\">24</xref>].</p><p>In the present study, we used fura-2 calcium imaging as a direct readout for (i) spontaneous Ca<sup>2+</sup> activity, (ii) mechanical pressure stimulation, (iii) shear stress, and (iv) hypotonic stretch in cultured human suburothelial interstitial cells of myoid differentiation (suICs). We describe the Ca<sup>2+</sup> peak morphology, time resolved intracellular traveling of the Ca<sup>2+</sup> wave, and the signal propagation to neighboring cells.</p></sec><sec sec-type=\"results\" id=\"sec2-ijms-21-05474\"><title>2. Results</title><sec id=\"sec2dot1-ijms-21-05474\"><title>2.1. Spontaneous Ca<sup>2+</sup> Activity in Cultured Human suICs</title><p>After bulk loading of the cells with 2.5 mM of fura-2-acetoxymethylester (fura-2AM), spontaneous Ca<sup>2+</sup> activity was recorded for 250 s (<italic>n</italic> = 11 experiments). In total, 1173 cells were analyzed. At least one transient elevation of the intracellular calcium concentration (calcium transients) was recorded in 879 (75%) of the cells, while 25% of the cells showed no spontaneous calcium activity (<xref ref-type=\"fig\" rid=\"ijms-21-05474-f001\">Figure 1</xref>A). Detailed analysis of the peak characteristics revealed a mean of 0.4 peaks /min (<italic>n</italic> = 879 cells, SD 0.21, 95% CI (0.42–0.45)). The mean peak amplitude was ΔFI = 200 (<italic>n</italic> = 1592 peaks, SD 262, 95% CI (188–214)) and the mean peak duration was 38.5 s (<italic>n</italic> = 1592 peaks, SD 23.8, 95% CI (37–40)). Notably, the area under the curves (AUCs) and the peak amplitudes varied considerably (<xref ref-type=\"fig\" rid=\"ijms-21-05474-f001\">Figure 1</xref>B–E). We found a strong positive correlation between the peak amplitude and the AUC (Spearman’s rho <italic>r</italic><sub>s</sub> = 0.959, <italic>p</italic> < 0.001) and a significant but very weak correlation between amplitude and peak duration (<italic>r</italic><sub>s</sub> = 0.045, <italic>p</italic> = 0.045). AUC weakly correlated with the peak duration (<italic>r</italic><sub>s</sub> = 0.267, <italic>p</italic> = 0.001). While the peaks varied in amplitude and frequency, their morphology was remarkably consistent; a fast rise of [Ca<sup>2+</sup>]<sub>i</sub> followed by a slow signal decay (<xref ref-type=\"fig\" rid=\"ijms-21-05474-f002\">Figure 2</xref>) was observed. We observed no plateau phase, but the decay often undulated (<xref ref-type=\"fig\" rid=\"ijms-21-05474-f001\">Figure 1</xref>A, arrows).</p><p>To answer the question of whether the amplitude influences the overall shape of the peaks, we investigated the peak morphology by grouping the peaks according to their maximum amplitude into six groups (<xref rid=\"ijms-21-05474-t001\" ref-type=\"table\">Table 1</xref>) and centering the transients to their maximum. We found that the peak shape was very similar at different amplitudes (<xref ref-type=\"fig\" rid=\"ijms-21-05474-f002\">Figure 2</xref>).</p></sec><sec id=\"sec2dot2-ijms-21-05474\"><title>2.2. Single Cell Mechanical Stimulation by Glass Micropipette</title><p>We analyzed 14 independent experiments (two cell cultures). Cells were bulk-loaded with fura-2AM, as described in <xref ref-type=\"sec\" rid=\"sec4dot4-ijms-21-05474\">Section 4.4</xref>. Ratio images (340 nm/380 nm) were acquired at a rate of 10 frames per second (fps) in seven experiments and at a rate of 4 fps in seven experiments. The fluorescence intensity (FI) was calculated after background correction as FI = F340 nm/F380 nm ×1000. The mean time of observation after initiation of the calcium signal was 128 s (range: 75 s–199 s). The FI was false color-coded (blue = min FI to white = max FI). An example of a typical experiment is depicted in <xref ref-type=\"fig\" rid=\"ijms-21-05474-f003\">Figure 3</xref>. A calcium transient was initiated in each of the 14 experiments. Compared to the spontaneous calcium transients, the transients had higher mean (±SD) amplitudes (2242 ± 967 FI vs. 599 ± 319 FI, <italic>p</italic> < 0.0001, unpaired <italic>t</italic>-test), larger AUCs (476737 ± 430178 vs. 41535 ± 37684, <italic>p</italic> < 0.0001, Mann–Whitney test) and lasted longer (40.99 ± 20.02 vs. 28.29 ± 14.13 s, <italic>p</italic> = 0.0363, Mann–Whitney test).</p><p>The calcium increase spread radially over the whole cell (<xref ref-type=\"fig\" rid=\"ijms-21-05474-f004\">Figure 4</xref>). The peak amplitude showed a linear decay by 50% at a distance of over 80 µm from the site of initiation (<xref ref-type=\"fig\" rid=\"ijms-21-05474-f004\">Figure 4</xref>C). The mean propagation velocity was 32.8 µm/s (<italic>n</italic> = 32, 95% CI = 12.9–52.6) for region of interest (ROI) distances up to 50 µm (<xref ref-type=\"fig\" rid=\"ijms-21-05474-f004\">Figure 4</xref>D, circles). At higher ROI distances, the velocity was slightly lower (mean = 23.2 µm/s, <italic>n</italic> = 120, 95% CI = 14.4–32.1); however, the difference was not significant (single-sided Mann–Whitney-Test, <italic>p</italic> = 0.25).</p><p>We evaluated the morphology of the peaks at different distances from the initial calcium signal by averaging peaks measured in seven experiments at 4 fps time resolution. The peak morphology was comparable in respect to signal rise and decay within the 10 s before and after the peak, respectively. However, with increasing distance from the initial peak, the curve flattened out. None of the cells reached the basic pre-stimulation calcium concentration within the observation time of 65 sec (<xref ref-type=\"fig\" rid=\"ijms-21-05474-f005\">Figure 5</xref>).</p></sec><sec id=\"sec2dot3-ijms-21-05474\"><title>2.3. Intercellular Propagation of the Ca<sup>2+</sup> Signal</title><p>In our experiments, we observed a rise in calcium activity following single cell stimulation in neighboring suICs (Video S1). Therefore, we analyzed this phenomenon in 14 independent experiments (<italic>n</italic> = 2 cell cultures). The stimulated cell was placed in the center of the field of view after mechanical stimulation of the cell by lowering a glass micropipette onto the cell surface. The micropipette was retracted as soon as a calcium rise was observed in the cell. We recorded at least for 60 s at 4 fps (<italic>n</italic> = 7) or 10 fps (<italic>n</italic> = 7). Automatic analysis was done using a self-written Python 3 program [<xref rid=\"B25-ijms-21-05474\" ref-type=\"bibr\">25</xref>]. The first ROI (stimulation site) was placed in the stimulated cell and further ROIs were placed in each neighboring cell center close to the nucleus (<xref ref-type=\"fig\" rid=\"ijms-21-05474-f006\">Figure 6</xref>). We calculated the radial distance of the neighboring cells (ROIs) to the stimulation site and grouped the ROIs (group 50: distance ≤ 50 µm etc.).</p><p>To reduce background noise, we filtered the peaks for a duration ≥ 10 s and an amplitude ΔFI ≥ 200. All the stimulated cells (14/14) showed a calcium transient. In each case, the first peak occurring in a cell was analyzed. Of the 604 neighboring cells examined, 40% showed a calcium peak within 60 s after stimulation. A representative experiment is depicted in <xref ref-type=\"fig\" rid=\"ijms-21-05474-f006\">Figure 6</xref>C–K.</p><p>With growing distance from the stimulation site, we found (i) a reduction in the fraction of active cells, (ii) a smaller peak amplitude, (iii) reduced duration of the calcium transient, (iv) diminished AUC and (v) an increase in the delay of the peak start (<xref ref-type=\"fig\" rid=\"ijms-21-05474-f007\">Figure 7</xref>, <xref rid=\"ijms-21-05474-t002\" ref-type=\"table\">Table 2</xref>). Within a radius of 100 µm, 51.27% of the neighboring cells showed increased calcium activity, and within 250 µm, 39.24% of the neighboring cells showed increased calcium activity.</p><p>For peak analysis, we included only peaks which showed a higher amplitude compared to the spontaneous peaks measured before the stimulation experiment (control in <xref rid=\"ijms-21-05474-t002\" ref-type=\"table\">Table 2</xref>). The threshold was defined as peak amplitude > 2 * standard deviation of the control. According to this criterion, only approximately 2.5% of the observed peaks should have been spontaneous.</p><p>The intercellular propagation velocity of the calcium wave was between 6.5 and 18.5 µm/s in the central 50% of the neighboring cells (median = 9.6, range from 2.7 to 465.9 µm/s). The mean velocity was 25.2 ± 50.4 µm/s, mean ± SD. There was no significant difference between the propagation velocity to close cells within the radius of 50 µm (32.77 ± 55.11 µm/s) or far cells within a distance of > 50 -250 µm (23.23 ± 49.11 µm/s) of the stimulated cell (<xref ref-type=\"fig\" rid=\"ijms-21-05474-f007\">Figure 7</xref>D).</p></sec><sec id=\"sec2dot4-ijms-21-05474\"><title>2.4. Shear Stress Evoked Calcium Transients</title><p>In addition to the single cell mechanical stimulation, we applied physical deformation of the plasma membrane by changing the flow in the laminar perfusion chamber, generating shear stress. Important for the experimental setup was the habituation of the cells to a constant ringer flow of 1 mL/min for 10 min. In total, we analyzed nine experiments with <italic>n</italic> = 596 cells (<italic>n</italic> = 3 cell cultures).</p><p>The cells showed a significant increase in their calcium activity in section B, i.e., with increased laminar flow (<xref ref-type=\"fig\" rid=\"ijms-21-05474-f008\">Figure 8</xref>).</p><p>Interestingly, the reactions of the cells were not fully synchronized but there was a significant increase in the overall activity (peak frequency, amplitude, AUC and duration; <xref rid=\"ijms-21-05474-t003\" ref-type=\"table\">Table 3</xref>).</p></sec><sec id=\"sec2dot5-ijms-21-05474\"><title>2.5. Hypotonic Stimulation</title><p>We used the hypotonic Ringer solution as a different method of mechanical stimulation as it leads to defined reversible swelling of the cells and stress on the cytoskeleton. The experimental setup corresponded to the stimulation by shear stress (see <xref ref-type=\"sec\" rid=\"sec2dot4-ijms-21-05474\">Section 2.4</xref>). In brief, the cells were adapted to standard Ringer (309 mOsm/L) at a flow of 1 mL/s for 15 min (<xref ref-type=\"fig\" rid=\"ijms-21-05474-f009\">Figure 9</xref>A). We recorded during the last 3 min of the preincubation. Then, the cells were challenged with either Hypo25 (232 mOsm/L) or Hypo50 (154 mOsm/L) for 3 min <xref ref-type=\"fig\" rid=\"ijms-21-05474-f009\">Figure 9</xref>B) followed by another 3 min recording in 309 mOsm/L Ringer (<xref ref-type=\"fig\" rid=\"ijms-21-05474-f009\">Figure 9</xref>C). We included control experiments with corresponding Ringer changes but using 309 mOsm/L in B and C. We analyzed a total of 111 cells in three independent experiments.</p><p><xref ref-type=\"fig\" rid=\"ijms-21-05474-f009\">Figure 9</xref> shows a representative experiment (14 cells of each condition included). We found a dramatic concentration dependent increase in the calcium activity elicited by hypotonic Ringer. The number of active cells increased from 5.9% in control Ringer to 100% under hypotonic stimulation; the peaks had a 22- (65-) fold higher amplitude and a 4.4- (5.5-) fold longer duration. This resulted in a 39-fold increase in 232 mOsm/L and a 93-fold increase in 154 mOsm/L in AUC (<xref rid=\"ijms-21-05474-t004\" ref-type=\"table\">Table 4</xref>).</p></sec><sec id=\"sec2dot6-ijms-21-05474\"><title>2.6. Immunocytochemical Characterization of Cultured suICs</title><p>We used cell cultures of passage 4-6 to examine the expression of characteristic marker proteins. We performed multiplex immunocytochemical detection of CALR/VIM/aSMA, PDGFRa/aSMA/CALR and PDGFRa/aSMA/Cx43 to define the suIC subtypes. All cells showed VIM-IR, classifying them as mesenchymal ICs (<xref ref-type=\"fig\" rid=\"ijms-21-05474-f010\">Figure 10</xref>B). Most cells were PDGFRa+/aSMA−/CALR+ with medium-sized ovoid nuclei and fusiform cell shapes (<xref ref-type=\"fig\" rid=\"ijms-21-05474-f010\">Figure 10</xref> and <xref ref-type=\"fig\" rid=\"ijms-21-05474-f011\">Figure 11</xref>, <xref ref-type=\"app\" rid=\"app1-ijms-21-05474\">Figure S2</xref>). A subpopulation was characterized by small elongated nuclei and at least one long process, which often extended to over 200 µm (<xref ref-type=\"fig\" rid=\"ijms-21-05474-f010\">Figure 10</xref>; <xref ref-type=\"fig\" rid=\"ijms-21-05474-f011\">Figure 11</xref>, arrow-heads). Only a small cell population (about 6%) showed classical myofibroblast differentiation, with strong aSMA staining of actin stress fibers, lacking PDGFRa expression (<xref ref-type=\"fig\" rid=\"ijms-21-05474-f010\">Figure 10</xref>, asterisks). In conclusion, most of the cultured suICs resembled the typical (type 1, PDGFRa+/CALR+/aSMA−) telocyte and a smaller fraction showed the characteristic staining pattern of hybrid (type 2, PDGFRa+/CALR+/aSMA+) telocyte, as defined by Vannucchi and coworkers [<xref rid=\"B26-ijms-21-05474\" ref-type=\"bibr\">26</xref>].</p><p>In triple immunostaining experiments, we found that Cx43-IR was associated with PDGFRa+/aSMA+ or aSMA− cells. Cx43-IR was significantly lower in myofibroblasts (<italic>p</italic> = 0.01842, two-sided <italic>t</italic>-test, data not shown) in typical myofibroblasts showing extensive stress fibers (<xref ref-type=\"fig\" rid=\"ijms-21-05474-f011\">Figure 11</xref>).</p></sec></sec><sec sec-type=\"discussion\" id=\"sec3-ijms-21-05474\"><title>3. Discussion</title><p>We here demonstrated for the first time that mechanical stimulation of cultured human suburothelial interstitial cells (suICs) evoked intracellular calcium transients. This observation is relevant for the assumed function of the suICs within the afferent signaling pathway in the bladder [<xref rid=\"B27-ijms-21-05474\" ref-type=\"bibr\">27</xref>,<xref rid=\"B28-ijms-21-05474\" ref-type=\"bibr\">28</xref>,<xref rid=\"B29-ijms-21-05474\" ref-type=\"bibr\">29</xref>]. The current view is that the primary sensor of bladder filling—i.e., stretching of the bladder wall—is the urothelium, and it releases adenosine triphosphate (ATP), acetylcholine (ACh), nitric oxide (NO), prostaglandins (PGs), cytokines and many others involved in signal transduction to the ICs and nerve fibers in the lamina propria [<xref rid=\"B30-ijms-21-05474\" ref-type=\"bibr\">30</xref>,<xref rid=\"B31-ijms-21-05474\" ref-type=\"bibr\">31</xref>].</p><p>We observed calcium transients in three different conditions:<list list-type=\"bullet\"><list-item><p>(i) Spontaneous calcium transients occurred regularly in the cultured human suICs and were confirmed in previous in vitro and in vivo studies in rats, guinea pigs and humans [<xref rid=\"B29-ijms-21-05474\" ref-type=\"bibr\">29</xref>,<xref rid=\"B32-ijms-21-05474\" ref-type=\"bibr\">32</xref>,<xref rid=\"B33-ijms-21-05474\" ref-type=\"bibr\">33</xref>,<xref rid=\"B34-ijms-21-05474\" ref-type=\"bibr\">34</xref>,<xref rid=\"B35-ijms-21-05474\" ref-type=\"bibr\">35</xref>].</p></list-item><list-item><p>(ii) Mechanical stimulation of human suICs either by direct indentation of the plasma membrane, swelling of the cells by hypotonic Ringer solution or membrane deformation by sheer stress induced calcium transients. Interestingly, calcium transients were also elicited when releasing the sheer stress by reduction of the flow rate to normal flow. This indicates that the cells are also capable of sensing the changing of the pressure in addition to the force of the pressure.</p></list-item><list-item><p>(iii) Calcium signals propagated not only intracellularly but were transmitted to neighboring cells. This indicates that the suICs formed a functional syncytium, as previously shown [<xref rid=\"B21-ijms-21-05474\" ref-type=\"bibr\">21</xref>,<xref rid=\"B22-ijms-21-05474\" ref-type=\"bibr\">22</xref>]. Interestingly, we observed two populations of neighboring cells, with approximately 87% of the cells showing a delay of calcium activation after single cell stimulation, resulting in an intercellular signal propagation velocity of about 20 µm/s, while in 20 of 152 cells (13%), the delay was much shorter, with a calculated propagation speed of ≥ 50 µm/s. These cells were similarly found in close proximity to the stimulated cell (<50 µm radius), as well as within a distance of 50 to 250 µm (<xref ref-type=\"fig\" rid=\"ijms-21-05474-f007\">Figure 7</xref>D). We hypothesized that the latter cells were directly connected to the stimulated cell via long thin cell processes, thus resembling telocytes [<xref rid=\"B36-ijms-21-05474\" ref-type=\"bibr\">36</xref>], which also have been proposed to be present in the upper lamina propria (ULP) of the human bladder [<xref rid=\"B26-ijms-21-05474\" ref-type=\"bibr\">26</xref>,<xref rid=\"B37-ijms-21-05474\" ref-type=\"bibr\">37</xref>]. This idea was directly supported by dye coupling experiments, regularly demonstrating cells with long cytoplasmic processes in cultured suICs (<xref ref-type=\"fig\" rid=\"ijms-21-05474-f012\">Figure 12</xref>), and our immunocytochemical experiments indicating the presence of typical type 1 telocytes (PDGFRa+/CALR+/aSMA−) and of PDGFRa+/CALR+/aSMA+ hybrid (type 2) telocytes (<xref ref-type=\"fig\" rid=\"ijms-21-05474-f010\">Figure 10</xref>, <xref ref-type=\"fig\" rid=\"ijms-21-05474-f011\">Figure 11</xref>, <xref ref-type=\"app\" rid=\"app1-ijms-21-05474\">Figure S2</xref>). We found type 1 telocytes, which were characterized by very small nuclei and small cell bodies showing long thin bipolar cell protrusions spanning up to 200 µm. These cells were observed at a rate of 12.5% in early cell cultures and decreased to around 7% with increasing confluency. Since we used the cell cultures at ≥ 80% confluency (e.g., <xref ref-type=\"fig\" rid=\"ijms-21-05474-f004\">Figure 4</xref>), it is most probable that we did not directly stimulate these cells. However, the small fraction of typical telocytes would well correspond to the observed 13% of cells with short Ca2+ signal delay in up to 250 µm distance (<xref ref-type=\"fig\" rid=\"ijms-21-05474-f007\">Figure 7</xref>D), thus promoting a fast calcium wave distribution over the cellular syncytium. Interestingly, we found no Cx43-IR in typical myofibroblasts accounting for less than 1% of the cells. These very large cells, therefore, would not be considered to contribute to the propagation of the calcium signal.</p></list-item></list></p><p>The intercellular propagation velocities of 32.8 µm/s and data from the literature promote the idea that IP3 rather than Ca<sup>2+</sup> proper is responsible for the triggering of calcium transients in neighboring cells. IP3 has an up to 22-times higher diffusion rate and a 10-fold higher lifetime and is thus able to trigger Ca<sup>2+</sup> release from neighboring cells in a range of 24 µm from the point of initiation [<xref rid=\"B39-ijms-21-05474\" ref-type=\"bibr\">39</xref>,<xref rid=\"B40-ijms-21-05474\" ref-type=\"bibr\">40</xref>]. In addition, there is evidence that Ca<sup>2+</sup> rapidly closes GJ, possibly via binding to calmodulin (CaM), which in turn closes the GJ channel by binding to Cx43 [<xref rid=\"B41-ijms-21-05474\" ref-type=\"bibr\">41</xref>]; review [<xref rid=\"B42-ijms-21-05474\" ref-type=\"bibr\">42</xref>].</p><p>Interestingly, the propagation velocities of the calcium waves in the cell cultures were well below the velocities reported by Kanai et al. in bladder wholemounts of normal rats (4700 ± 700 µm/s) [<xref rid=\"B29-ijms-21-05474\" ref-type=\"bibr\">29</xref>]. However, in contrast to the single cell stimulation in our cell culture experiments, the Ca<sup>2+</sup> waves in the bladder wholemounts were initiated by stretching of the whole bladder preparation. As we have demonstrated in our hypotonic experiments, stretch can significantly enhance the spontaneous activity of suIC. Therefore, the cells in the wholemount experiments were pre-stimulated when calcium activity rose at a single site of the active bladder dome, facilitating the rise of [Ca<sup>2+</sup>]<sub>i</sub> in neighboring cells and the Ca<sup>2+</sup> wave propagation. In addition, synergisms of several signaling mechanisms in vivo—e.g., gap junction coupling, mechanical coupling and fast short-range sensitization of Ca<sup>2+</sup> entry channels by the release of soluble activators—could further enhance the Ca<sup>2+</sup> wave propagation, as indicated by the higher conduction velocities measured after carbachol stimulation [<xref rid=\"B29-ijms-21-05474\" ref-type=\"bibr\">29</xref>].</p><p>In a previous study, we found that the same cell cultures used in the present study expressed Cx43 and Cx45, which were differentially regulated by cytokines and growth factors, modulating intercellular coupling [<xref rid=\"B21-ijms-21-05474\" ref-type=\"bibr\">21</xref>]. To study the morphology and the Cx43 expression of suICs in situ and in vitro, we used immunohistochemical multiplexing. The interstitial cells directly adjacent to the urothelium presented small cell bodies with two or more processes. Most of them showed a strong expression of Cx43, the molecular correlates of intercellular coupling. The majority of the cells in the ULP stained positive for the cytoskeletal markers vimentin (VIM) and alpha-smooth muscle actin (aSMA), as depicted in the confocal 3D projection (<xref ref-type=\"fig\" rid=\"ijms-21-05474-f013\">Figure 13</xref>, <xref ref-type=\"app\" rid=\"app1-ijms-21-05474\">Supplementary Video S2</xref>).</p><p>Our study provides further evidence for a mechanism of non-neuronal signaling in the human bladder by a network of mesenchymal interstitial cells [<xref rid=\"B5-ijms-21-05474\" ref-type=\"bibr\">5</xref>]. The relevance of these cells for the physiologic and pathologic function of the bladder has been implied by evidence at several levels. Their strategical positioning at the border between the urothelial layer and the deep lamina propria is accompanied by characteristic receptor expression [<xref rid=\"B7-ijms-21-05474\" ref-type=\"bibr\">7</xref>,<xref rid=\"B9-ijms-21-05474\" ref-type=\"bibr\">9</xref>] and by the expression of Cx43, leading to the formation of a functional syncytium in vivo [<xref rid=\"B43-ijms-21-05474\" ref-type=\"bibr\">43</xref>].</p><p>In the present study, we focused on calcium transients in suICs activated by various mechanical stimuli. Though we did not directly show the involvement of TRPA1 channels in the mechanical activation of calcium transients, it is well documented in the literature that extracellular calcium is essential for eliciting intracellular calcium transients and intracellular calcium oscillations [<xref rid=\"B44-ijms-21-05474\" ref-type=\"bibr\">44</xref>,<xref rid=\"B45-ijms-21-05474\" ref-type=\"bibr\">45</xref>,<xref rid=\"B46-ijms-21-05474\" ref-type=\"bibr\">46</xref>]. Arora and colleagues described how the graded stretch activated slow calcium oscillations in human gingival fibroblasts. These oscillations depended on extracellular [Ca<sup>2+</sup>]<sub>e</sub>, actin cytoskeleton and pertussis toxin-sensitive subunits of G-proteins but not on intracellular calcium pools, indicating the involvement of stretch-activated ion channels [<xref rid=\"B46-ijms-21-05474\" ref-type=\"bibr\">46</xref>].</p><p>Calcium waves arise from massive calcium release from intracellular calcium stores upon activation of inositol triphosphate receptors (IP<sub>3</sub>Rs) and ryanodine receptors (RyRs) interacting with various members of the TRP family [<xref rid=\"B45-ijms-21-05474\" ref-type=\"bibr\">45</xref>,<xref rid=\"B47-ijms-21-05474\" ref-type=\"bibr\">47</xref>,<xref rid=\"B48-ijms-21-05474\" ref-type=\"bibr\">48</xref>]. In the presence of extracellular Ca<sup>2+</sup> and the selective TRPA1 agonists allyl isothiocyanate (AITC) and 15-deoxy-delta-12, 14-prostaglandin J2 (PGJ2) induced intracellular calcium transients in human cardiac fibroblasts, indicating the triggering of intracellular Ca<sup>2+</sup> release via extracellular Ca2+ influx [<xref rid=\"B44-ijms-21-05474\" ref-type=\"bibr\">44</xref>].</p><p>Additional support for the hypothesis of suICs being active elements in afferent signaling processing in the bladder comes from experiments demonstrating increased ATP release from cultured pig bladder ICs by hypotonic stimulation [<xref rid=\"B49-ijms-21-05474\" ref-type=\"bibr\">49</xref>]. Our own group has previously shown that human suICs (denoted myofibroblasts in older literature) are highly sensitive to ATP, inducing calcium transients at very low concentrations [<xref rid=\"B33-ijms-21-05474\" ref-type=\"bibr\">33</xref>].</p><p>In our single cell stimulation experiments, we did not observe any correlation between the direction of the Ringer flow with the spreading of the calcium wave in neighboring cells. Therefore, we conclude that a possible release of ATP from the locally stimulated suIC does not add to the calcium signal measured in our experiments and that the distribution of the signal is truly mediated via the cytoplasmic coupling of the cells.</p><p>Cultured suICs express aSMA, allowing cell contraction, especially in the fraction of myofibroblast-like cells forming stress fibers (<xref ref-type=\"app\" rid=\"app1-ijms-21-05474\">Figure S1</xref>). However, our immunocytochemical experiments indicated that these myofibroblast-like cells are not included in the functional syncytium of the suIC under cell culture conditions. Besides strong aSMA expression, the typical fibroblast-like appearance of this cell—i.e., its large flat cell body with large nuclei and multiple cell processes—has provoked the name “myofibroblast”. Under normal conditions, such cells should not account for the majority of the aSMA+ ICs in the bladder. Therefore, these cells might indeed represent pathological altered suICs and it will be interesting to further investigate their contribution to pathological states of the bladder.</p><p>Since most non-myofibroblast suICs (type 2 telocytes) strongly express aSMA in situ and a rise in intracellular calcium can trigger cell contraction, it is conjecturable that local contractions evoked by ATP signaling from the urothelium could also lead to local signaling during the bladder filling phase. Therefore, the role of suICs in afferent signaling could be the enhancement and distribution of local signals over larger areas of the bladder wall. This view is supported by the excellent experiments of Kanai and colleagues in wholemount rat bladders, directly demonstrating the coordinated spreading of local (spontaneous) calcium signals from the dome to the bladder neck. Furthermore, they demonstrated the propagation of the signals from the mucosal initiation site to the serosa of the bladder [<xref rid=\"B43-ijms-21-05474\" ref-type=\"bibr\">43</xref>].</p><p>If the suICs take part in afferent signal processing in the bladder, one should assume that local stimulation of a single cell, as was conducted in <xref ref-type=\"sec\" rid=\"sec2dot2-ijms-21-05474\">Section 2.2</xref>, should influence the calcium activity in the functional syncytium. We therefore compared the activity after stimulation in the neighboring cells with the approximated background activity measured in control experiments. We found that, after mechanical stimulation of a single cell, the syncytial activity (activity in the neighboring cells) increased by 18 times based on the AUCs in the first peaks. The primary calcium signal evoked by a single short-term membrane indentation of a single cell was radially distributed in the cell layer for at least 250 µm, activating an area of approximately 0.2 mm<sup>2</sup>. The strength of activation decreased with distance from 500% to 200% in a radius of 50 µm and 250 µm, respectively. In total, the primary calcium signal was amplified by 50%. These findings strongly support the notion of an amplifier function of suICs.</p><p>The major limitation of this study is the use of a 2D adherent cell culture system. In addition, while we provide some evidence of several subtypes of cells in our cell culture, we did not further characterize these subtypes to allow a more detailed view of the roles of these cells in the bladder. Since the urinary bladder wall is composed of multiple cell types embedded in a complex structured extracellular matrix, the results cannot be transferred directly to the in vivo situation. Physiological stretching of the bladder wall during bladder filling results in multiple cellular interactions, including the release of transmitter substances and mechanical stress. Therefore, the 2D model system can only highlight a small part of the complex sequence of events.</p><p>In the future, it will be interesting to investigate the role of suIC functional syncytia in 3D model systems of overactive bladder and especially of interstitial cystitis/bladder pain syndrome (IC/BPS).</p></sec><sec id=\"sec4-ijms-21-05474\"><title>4. Materials and Methods</title><sec id=\"sec4dot1-ijms-21-05474\"><title>4.1. Ethical Statement</title><p>This study was approved by the Ethics Committee of the University of Leipzig (ethical approval code: 036-2007, Date of issue: 26.05.2015) and was conducted according to the principles expressed in the World Medical Association Declaration of Helsinki [<xref rid=\"B50-ijms-21-05474\" ref-type=\"bibr\">50</xref>]. Written informed consent was obtained from all patients.</p></sec><sec id=\"sec4dot2-ijms-21-05474\"><title>4.2. Cell Cultures</title><p>Human bladder tissue was collected from patients undergoing cystectomy for bladder carcinoma treatment or due to gynecologic tumors. We were able to culture suICs from five female and three male patients, aged (mean ± SD) 60.2 ± 11.7 yrs and 64.3 ± 6.6 yrs, respectively. Cell cultures of suburothelial interstitial cells (suICs) were established from macroscopic tumor free bladder tissue by sharp microsurgical dissection of the urothelium from the smooth muscle layer of the detrusor, ensuring no contamination with detrusor smooth muscle cells. The tissue was then cut into small fragments of approximately 0.5 mm edge length and cultured in smooth muscle cell growth medium 2 (PromoCell GmbH, Heidelberg, Germany). This medium did not support the growth of urothelial cells and the resulting cell cultures were free of contaminating urothelial cells, as proven by polymerase chain reaction (PCR), Western blot and immunocytochemistry. We described the procedure in detail in a previous study [<xref rid=\"B21-ijms-21-05474\" ref-type=\"bibr\">21</xref>]. In brief, the fragments were plated into tissue culture flasks (TPP AG, Trasadingen, Switzerland) and incubated at 37 °C in 5% CO<sub>2</sub> humidified atmosphere until reaching 80% confluency. Cell passages 2 to 5 were used in the experiments. For the calcium imaging experiments, the cells were plated onto collagen A (Biochrome AG, Berlin, Germany) coated 13 mm glass cover slips and grown to 80% confluency.</p></sec><sec id=\"sec4dot3-ijms-21-05474\"><title>4.3. Chemicals and Solutions</title><p>We used a modified Krebs–Ringer solution for the calcium imaging experiment (in mmol/L): CaCl<sub>2</sub> 1.9; NaCl 120.9; NaHCO<sub>3</sub> 14.4; KCl 5.9; MgCl<sub>2</sub> 1.2; NaH<sub>2</sub>PO<sub>4</sub> 1.55; Hepes 4.2; Glucose 11.49; pH = 7.2; 309 mOsm/L (all chemicals from Sigma-Aldrich, Steinheim, Germany). All solutions were freshly prepared with cell culture grade distilled water on the day of usage. For hypotonic solutions, we diluted the Krebs–Ringer solution at the ratio of 3:1 and 1:1 to yield 232 mOsm/L and 154 mOsm/L solution, respectively.</p></sec><sec id=\"sec4dot4-ijms-21-05474\"><title>4.4. Calcium Imaging</title><p>Cells cultured onto glass cover slips were washed 3 times with Krebs–Ringer solution at 37 °C and thereafter incubated for 35 min at 37 °C with 2.5 mMol/L fura-2 AM (fura-2 acetoxymethylester) dissolved in DMSO (dimethylsulfoxid) and 2% pluronic (Invitrogen by Thermo Fisher Scientific, Schwerte, Germany). The cells were washed again in Krebs–Ringer solution and placed into a 37 °C heated lamina flow Diamond Bath Oocyte Recording Chamber (RC-26Z, Warner Instruments, Hamden, CT, USA). To exclude calcium activity caused by the washing and transfer procedure, the cells were adapted to the experimental conditions (37 °C, oxygenation of the Krebs–Ringer solution with 95% O<sub>2</sub> and 5% CO<sub>2</sub>, constant flow of 1 mL/min) for ten minutes before the start of the experiments. We used an IX-71 inverted microscope equipped with a LCPLN20xIR objective (Olympus Microscopy, Hamburg, Germany) and a TILL IMAGO-QE digital camera (TillPhotonics, Gräfelfing, Germany). Experimental control and image acquisition were done using TILLvisION Software V. 4.5 (TillPhotonics). Image series were recorded at 1, 4 or 10 frames per second (fps). Fura-2 fluorescence was excited by 30 ms pulse application of 340nm and 380nm monochromatic light (Polychrome V monochromator, TillPhotonics) and the fluorescence signal was detected at a wavelength of 520 nm. The specific fluorescence intensity (FI) was calculated after subtraction of the background fluorescence: FI = F340 nm/F380 nm × 1000. The FI was color-coded and exported as TIFF video. We used a self-written Python [<xref rid=\"B25-ijms-21-05474\" ref-type=\"bibr\">25</xref>] program for analysis of the calcium changes over time (calcium transients). The core of the Python program was the automatic calcium peak detection, which was fitted to cope with the heteromorphic calcium traces. The analysis can be restricted to pre-defined parts of the curves, allowing precise analysis of the peak morphology. For the analysis of intercellular calcium waves, the software allows the definition of a reference (stimulated) cell and calculates the distances of all regions of interest (ROI) and the time course of the calcium signal in relation to this cell. Details are described in the <xref ref-type=\"app\" rid=\"app1-ijms-21-05474\">Supplementary Materials</xref>.</p></sec><sec id=\"sec4dot5-ijms-21-05474\"><title>4.5. Data Analysis and Statistics</title><p>We used Microsoft Excel (Microsoft Corporation, Redmond, WA, USA) and Graph Pad Prism 5/8 (GraphPad Software, San Diego, CA, USA) for data processing and statistical analysis (Wilcoxon matched-pairs signed rank test; two-tailed Mann–Whitney test).</p></sec><sec id=\"sec4dot6-ijms-21-05474\"><title>4.6. Stimulation Experiments</title><p>As a starting point, spontaneous calcium activity was recorded after 10 min of adaptation of the cells before starting the real experiment. We investigated the cells’ reactions to three different mechanical stimuli.</p><sec id=\"sec4dot6dot1-ijms-21-05474\"><title>4.6.1. Single Cell Mechanical Stimulation</title><p>Single cell mechanical stimulation requires ultraprecise micromanipulators. We used a motorized MP-285 Nano-Micromanipulator (Sutter Instruments, Novato, CA, USA), allowing step sizes down to 40 nm. We mechanically stimulated single cells by defined deflection of the plasma membrane using a glass micropipette with a fine tip, which was closed and rounded to avoid (i) capillary flow-in of ringer and (ii) mechanical injury of the membrane. The membrane deflection was applied by stepwise lowering the micropipette in steps of 40 nm (<xref ref-type=\"fig\" rid=\"ijms-21-05474-f014\">Figure 14</xref>).</p></sec><sec id=\"sec4dot6dot2-ijms-21-05474\"><title>4.6.2. Mechanical Stimulation by Shear Stress</title><p>Cells were adapted to the experimental conditions by 15 min superfusion with 1 mL/min. We then recorded the calcium activity for 5 min in each section: (A) 1 mL/min, (B) 2 mL/min, (C) 1 mL/min.</p></sec><sec id=\"sec4dot6dot3-ijms-21-05474\"><title>4.6.3. Hypotonic Stimulation</title><p>After adaptation for 15 min to the experimental conditions (309 mOsm/L, 1 mL/min flow), cells were challenged either with 232 mOsm/L or 154 mOsm/L for 3 min. Recordings of the calcium activity were done for three minutes in each section (A,B,C). For control, the flow was switched to 309 mOsm/L in section B.</p></sec></sec><sec id=\"sec4dot7-ijms-21-05474\"><title>4.7. Dye-Coupling Experiments</title><p>Single cells were dialyzed for 10 min with 3% lucifer yellow (LY) dissolved in intracellular pipette solution (130 mM/L KCl, 5 mM/L K-pyruvate, 5 mM/L K-oxalacetate, 5 mM/L K-succinate, 10 mM/l HEPES, 0.02 mM EGTA, pH 7.4) [<xref rid=\"B51-ijms-21-05474\" ref-type=\"bibr\">51</xref>] by using a 50 MΩ micropipettes for sharp penetration of the cell membrane. LY was injected into the cell by short reproducible pressure pulses by using a PV800 Pneumatic PicoPump (World Precision Instruments, Berlin, Germany), as described previously [<xref rid=\"B52-ijms-21-05474\" ref-type=\"bibr\">52</xref>]. LY-fluorescence was visualized using a fluorescein isothiocyanate (FITC) filter set.</p></sec><sec id=\"sec4dot8-ijms-21-05474\"><title>4.8. Confocal Imaging</title><p>Confocal images of bladder tissue were acquired at a LSM800 (Carl Zeiss Microscopy, Jena, Germany) equipped with a Plan-Apochromat 63x/1.40 Oil DIC M27; in cell cultures, a 40x/0.95 objective was used for acquisition of 5 × 5 large tiles, which were stitched to yield an image of 556 µm × 556 µm. In brief: human bladder tissue was fixed in 4% buffered formalin for 24 h and embedded in paraffin. Ten µm thick paraffin sections were immunofluorescence stained for VIM, aSMA, Cx43 (<xref rid=\"ijms-21-05474-t005\" ref-type=\"table\">Table 5</xref>). Cell cultures were cultured onto collagen A covered glass coverslips. Cells were fixed for 2 min in –20 °C methanol before processing for primary antibody incubation overnight at 4 °C. Nuclei were stained with DAPI (4′,6-diamidin-2-phenylindol, 1:5000, Carl Roth, Karlsruhe, Germany). Image processing and analysis was done in ImageJ 1.51s [<xref rid=\"B53-ijms-21-05474\" ref-type=\"bibr\">53</xref>] for mac and Adobe Photoshop CC V. 20.0.10 (Adobe, San José, CA, USA).</p></sec></sec></body><back><ack><title>Acknowledgments</title><p>The authors want to thank A. Weimann for the excellent technical assistance.</p></ack><app-group><app id=\"app1-ijms-21-05474\"><title>Supplementary Materials</title><p>Supplementary materials can be found at <uri xlink:href=\"https://www.mdpi.com/1422-0067/21/15/5474/s1\">https://www.mdpi.com/1422-0067/21/15/5474/s1</uri>.</p><supplementary-material content-type=\"local-data\" id=\"ijms-21-05474-s001\"><media xlink:href=\"ijms-21-05474-s001.zip\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material></app></app-group><notes><title>Author Contributions</title><p>Conceptualization, J.N. and F.P.B.; Data curation, A.G. and F.P.B.; Formal analysis, J.N. and F.P.B.; Funding acquisition, J.N.; Investigation, S.C. and F.P.B.; Methodology, J.N., S.C. and F.P.B.; Project administration, J.N.; Resources, J.N., A.G. and J.-U.S.; Software, F.P.B.; Supervision, J.N. and F.P.B.; Validation, J.N., A.G. and F.P.B.; Visualization, J.N. and F.P.B.; Writing—original draft, F.P.B.; Writing—review and editing, J.N., A.G. and J.-U.S. All authors have read and agreed to the published version of the manuscript.</p></notes><notes><title>Funding</title><p>This research was funded by Deutsche Forschungsgemeinschaft, grant number NE425/4-4. This research received funding from the Leipzig University for Open Access Publishing.</p></notes><notes notes-type=\"COI-statement\"><title>Conflicts of Interest</title><p>The authors declare no conflict of interest.</p></notes><glossary><title>Abbreviations</title><array orientation=\"portrait\"><tbody><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">AUC</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">area under the curve</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">aSMA</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">alpha-smooth muscle actin</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">BOO</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">bladder outlet obstruction</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CALR</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">calreticulin</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Cx43</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">connexin 43</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">DAPI</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4′,6-diamidin-2-phenylindol</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">FITC</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">fluorescein isothiocyanate</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">fps</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">frames per second</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">fura-2AM</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">fura-2-acetoxymethylester</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">IC/BPS</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">interstitial cystitis/bladder pain syndrome</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">OAB</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">overactive bladder</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">PDGFRa</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">platelet derived growth factor receptor-alpha</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">SD</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">standard deviation</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">SEM</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">standard error of the mean</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">suIC</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">suburothelial interstitial cells</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">TRP</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">transient receptor potential</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">VIM</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">vimentin</td></tr></tbody></array></glossary><ref-list><title>References</title><ref id=\"B1-ijms-21-05474\"><label>1.</label><element-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Dorschner</surname><given-names>W.</given-names></name><name><surname>Stolzenburg</surname><given-names>J.U.</given-names></name><name><surname>Neuhaus</surname><given-names>J.</given-names></name></person-group><article-title>Structure and function of the bladder neck</article-title><source>Adv. 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(<bold>A</bold>) Representative traces of calcium transients measured with the ratiometric fura-2 fluorescent Ca<sup>2+</sup> indicator; arrows indicate undulated decay of the amplitued in the red colored trace; (<bold>B</bold>–<bold>E</bold>) quantitative analysis of peak frequency (<bold>B</bold>), peak amplitude (<bold>C</bold>), peak duration (<bold>D</bold>) and integrated peak area (area under the curve = AUC (<bold>E</bold>); inset in (<bold>E</bold>) explains the peak measures (for detailed information, see Methods section).</p></caption><graphic xlink:href=\"ijms-21-05474-g001\"/></fig><fig id=\"ijms-21-05474-f002\" orientation=\"portrait\" position=\"float\"><label>Figure 2</label><caption><p>Peak morphology of spontaneous Ca<sup>2+</sup> transients is homogeneous. Amplitude sorted transients were overlaid at their maximum. Note that the morphology does not alter with the amplitude. Mean (solid lines), standard error of the mean (SEM, dashed lines); only peaks with at least 20 time points were included.</p></caption><graphic xlink:href=\"ijms-21-05474-g002\"/></fig><fig id=\"ijms-21-05474-f003\" orientation=\"portrait\" position=\"float\"><label>Figure 3</label><caption><p>Positioning of the glass micropipette (<bold>A</bold>, phase contrast); lowering of the micropipette (<bold>B</bold>, arrow) provoked a local intracellular calcium rise (<bold>C</bold>) in the foremost quiet cell (arrow); the calcium signal spread across the cell within several seconds (<bold>C</bold>–<bold>F</bold>); arrow indicates micropipette tip position; inset in (<bold>C</bold>) = fluorescence intensity (FI) color-coding.</p></caption><graphic xlink:href=\"ijms-21-05474-g003\"/></fig><fig id=\"ijms-21-05474-f004\" orientation=\"portrait\" position=\"float\"><label>Figure 4</label><caption><p>Intracellular propagation of the calcium signal. (<bold>A</bold>) Position of the glass micropipette (phase contrast): circles indicate the regions of interest (ROIs) of fluorescence measurements, color corresponds to the color-coding of the time resolved fluorescence intensity traces in (<bold>B</bold>); scale bar = 200 µm; (<bold>B</bold>) the fluorescence peak intensity decreased with increasing distance from the site of signal initiation; traces smoothed; (<bold>C</bold>) peak amplitude (%) versus distance to the site of stimulation (binned data, <italic>x</italic>-axis = center of bin; bin 5 = > 0 to < 10; bin 20 = ≥ 10 to < 30)—note the linear decay; (<bold>D</bold>) plotting of the peak delay versus the distance to the site of stimulation showed mainly linear correlation with increasing scattering at higher distances.</p></caption><graphic xlink:href=\"ijms-21-05474-g004\"/></fig><fig id=\"ijms-21-05474-f005\" orientation=\"portrait\" position=\"float\"><label>Figure 5</label><caption><p>Peak morphology of calcium transients depending on the distance to the initial peak. Note that the peaks show similar rising and decay characteristics within 10 s before and after the peak maximum.</p></caption><graphic xlink:href=\"ijms-21-05474-g005\"/></fig><fig id=\"ijms-21-05474-f006\" orientation=\"portrait\" position=\"float\"><label>Figure 6</label><caption><p>Analysis of the intercellular propagation of the calcium signal. (<bold>A</bold>) Position of the micropipette (stimulation site); ROIs (red circles); white circles indicate sections: 50 µm, 100 µm, 150 µm); nucleus of stimulated cell (white arrow); phase contrast image; scale bar = 50 µm. (<bold>B</bold>) Typical recording of calcium transients in the stimulated cell (red trace) and the neighboring cells (black traces); recording speed: 10 fps. (<bold>C</bold>–<bold>K</bold>) Surface plot of the propagation of the calcium wave over the functional syncytium: (<bold>C</bold>) pre stimulation; (<bold>D</bold>) 0.1 s after stimulation; white arrow indicates stimulation site. Note that first calcium peaks in neighboring cells already occurred within 1 s after stimulation (<bold>E</bold>–<bold>G</bold>); maximum excitation is seen about 3-5 s after stimulation (<bold>I</bold>,<bold>J</bold>); excitation decay is seen after 7 s (<bold>K</bold>); instant of time since stimulation is indicated bottom right; color-coding ranged from FI = 0–3200 (inset in <bold>C</bold>).</p></caption><graphic xlink:href=\"ijms-21-05474-g006\"/></fig><fig id=\"ijms-21-05474-f007\" orientation=\"portrait\" position=\"float\"><label>Figure 7</label><caption><p>Analysis of the calcium peaks and transients in neighboring cells compared to the first peaks in the stimulated cells; (<bold>A</bold>–<bold>C</bold>) the neighboring cells were grouped into distance groups (upper bin limit indicated); (<bold>D</bold>) the propagation speed of the calcium signal was compared between near (≤50 µm) and far (>50–250 µm) neighboring cells. Note the split of the <italic>y</italic>-axis and the bimodal distribution of speeds; controls = cells with spontaneous activity; green dotted line indicates upper 95% CI of the controls.</p></caption><graphic xlink:href=\"ijms-21-05474-g007\"/></fig><fig id=\"ijms-21-05474-f008\" orientation=\"portrait\" position=\"float\"><label>Figure 8</label><caption><p>Effect of shear stress on the calcium activity of cultures suIC. In 5 min recordings, acceleration of the laminar flow from 1ml/s (<bold>A</bold>) to 2 mL/s (<bold>B</bold>) led to increased calcium activity. This activity decreased only slowly, as seen in the increased activity recorded 5 min after switching back to 1 mL/min flow (<bold>C</bold>).</p></caption><graphic xlink:href=\"ijms-21-05474-g008\"/></fig><fig id=\"ijms-21-05474-f009\" orientation=\"portrait\" position=\"float\"><label>Figure 9</label><caption><p>Effect of stimulation with hypotonic Ringer solutions. (<bold>A</bold>) conditioning of cells by 3 min preincubation in 309 mOsm/L Ringer; (<bold>B</bold>) 3 min period of challenging the cells with different Ringer solutions; Hypotonic Ringer led to a concentration dependent increase in calcium activity. This increase was almost synchronous and disappeared immediately after switching back to 309 mOsm/L Ringer solution (<bold>C</bold>). Representative traces of 14 cells per condition are depicted.</p></caption><graphic xlink:href=\"ijms-21-05474-g009\"/></fig><fig id=\"ijms-21-05474-f010\" orientation=\"portrait\" position=\"float\"><label>Figure 10</label><caption><p>Immunocytochemical characterization of the suIC in cell culture. Triple staining of (<bold>A</bold>) calreticulin (CALR, orange), (<bold>B</bold>) vimentin (VIM, green) and (<bold>C</bold>) α-smooth muscle cell actin (aSMA, red); all cells show immunoreactivity (IR) for CALR and VIM, while only very few cells show strong aSMA staining of stress fibers (asterisks); nuclei are stained with 4′,6-diamidin-2-phenylindol (DAPI, blue).</p></caption><graphic xlink:href=\"ijms-21-05474-g010\"/></fig><fig id=\"ijms-21-05474-f011\" orientation=\"portrait\" position=\"float\"><label>Figure 11</label><caption><p>Triple staining of (<bold>A</bold>) platelet derived growth factor receptor alpha (PDGFRa, orange), (<bold>B</bold>) aSMA (green) and (<bold>C</bold>) connexin 43 (Cx43, red). Note the cell membrane associated punctuate or even patchy Cx43-IR present on cells with very small elongated nuclei and small cell bodies (arrows); no Cx43-IR can be attributed to the strongly stained myofibroblast characterized by the presence of stress fibers; nuclei are stained with DAPI (blue); (<bold>D</bold>) merged image channels.</p></caption><graphic xlink:href=\"ijms-21-05474-g011\"/></fig><fig id=\"ijms-21-05474-f012\" orientation=\"portrait\" position=\"float\"><label>Figure 12</label><caption><p>Lucifer Yellow (LY) injection revealed functional syncytia in cultured suICs. (<bold>A</bold>) Phase contrast, MP (micro pipette); (<bold>B</bold>) injected cell filled with LY after 90 s; (<bold>C</bold>) cell culture with two injected cells (arrows) showing distribution of the LY, defining variably sized functional syncytia; (<bold>D</bold>) the LY injected cell (arrow) and a direct neighboring cell show bright LY-fluorescence signal; arrow-heads indicate a fine process contacting another cell, spanning a distance of approximately 100 µm; nuclei (DAPI, blue); modified reproduction with permission from Neuhaus et al. 2007 [<xref rid=\"B38-ijms-21-05474\" ref-type=\"bibr\">38</xref>].</p></caption><graphic xlink:href=\"ijms-21-05474-g012\"/></fig><fig id=\"ijms-21-05474-f013\" orientation=\"portrait\" position=\"float\"><label>Figure 13</label><caption><p>The 3D projection of a confocal image stack (voxel size (<italic>yxz</italic>): 0.06 µm × 0.06 µm × 0.39 µm); (<bold>A</bold>) VIM (green, ex 488 nm); (<bold>B</bold>) aSMA (red, ex 640 nm); (<bold>C</bold>) nuclei stained with DAPI (blue, ex 405nm); (<bold>D</bold>) merged image including Cx43 staining (magenta, excitation at 561 nm). Note that only a few cells are negative for aSMA (arrow); U = urothelium.</p></caption><graphic xlink:href=\"ijms-21-05474-g013\"/></fig><fig id=\"ijms-21-05474-f014\" orientation=\"portrait\" position=\"float\"><label>Figure 14</label><caption><p>Mechanical stimulation using a glass micropipette. The micropipette is placed close to the plasma membrane (<bold>A</bold>); by stepwise (40 nm steps) lowering of the micropipette, the plasma membrane is dented until a calcium signal is initiated (<bold>B</bold>,<bold>C</bold>); red arrows indicate movement of the micropipette (<bold>B</bold>) and the deflection of the cell membrane (<bold>C</bold>).</p></caption><graphic xlink:href=\"ijms-21-05474-g014\"/></fig><table-wrap id=\"ijms-21-05474-t001\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijms-21-05474-t001_Table 1</object-id><label>Table 1</label><caption><p>Maximum amplitudes of the peak groups.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Group</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Mean</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">SD</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>N</italic>\n</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">56.5466</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">27.46916</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1913</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">221.3514</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">70.75953</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">582</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">488.9613</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">72.17626</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">245</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">743.6639</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">69.71185</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">134</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">978.3084</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">66.73132</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">70</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">6</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1239.981</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">66.15836</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">29</td></tr></tbody></table><table-wrap-foot><fn><p>SD = standard deviation; <italic>N</italic> = number of peaks analyzed.</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"ijms-21-05474-t002\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijms-21-05474-t002_Table 2</object-id><label>Table 2</label><caption><p>Number of cells observed and number of peaks analyzed.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Cell Group</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">50</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">100</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">150</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">200</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">250</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Control</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Distance (µm)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Stimulated</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><= 50</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">>50 ≤ 100</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">>100 ≤150</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">>150 ≤ 200</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">>200≤ 250</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">NA</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Number of cells</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">14</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">81</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">155</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">213</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">139</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">16</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">619</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Active cells (<italic>n</italic>)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">14</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">44</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">77</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">67</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">41</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">34</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Mean (%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">54.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">49.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">31.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">29.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">50</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.5</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">95% CI (%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">43.2–65.4 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">40.3–50.2 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">25.2–37.7 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">21.8–37.2 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">12.9–51.6 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.7–7.3 </td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Peak ampl. mean</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2242</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1025</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">954</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">887</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">848</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1090</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">599</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">95% CI</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1683–2800</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">831–1219</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">840–1068</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">766–1008</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">639–1058</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">363–1817</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">489–711</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Peak AUC (au)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">476737</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">189641</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">175652</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">157005</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">115,647</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">97981</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">41535</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">95% CI</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">228,359–725,115</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">119,516–259,766</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">139,339–211,966</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">122,889–191,121</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">80,430–150,865</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">9981–185,982</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">28,387–54,684</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Peak duration (s)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">40.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">36.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">36.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">33.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">28.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">39.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">28.3</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">95% CI</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">29.4–52.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">32.8–40.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">34.1–39.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">30.5–36.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">24.6–31.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">19.4–39.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">23.4–33.2</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Peak delay (s)</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">NA</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6.534</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">11.65</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">18.34</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">18.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">15.68</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">NA</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">95% CI</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">4.1–8.9</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">9.4–13.9</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">15.4–21.3</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">12.9–23.3</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">10.1–21.3</td></tr></tbody></table><table-wrap-foot><fn><p>NA = data not available; control = spontaneous peaks before stimulation.</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"ijms-21-05474-t003\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijms-21-05474-t003_Table 3</object-id><label>Table 3</label><caption><p>Analysis of the shear stress experiments.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Peak</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Section A</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Section B</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Section C</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><italic>p</italic>–Value</th></tr></thead><tbody><tr><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">Frequency (min<sup>−1</sup>)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.62 (522)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.70 (554)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.75 (559)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">A/B: <0.0001 §</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">[0.59–0.64]</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">[0.68–0.73]</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">[0.72–0.78]</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">B/C: 0.0039 §</td></tr><tr><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">Amplitude (FI)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">269.4 (1345)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">344.7 (1628)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">207.6 (1757)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">A/B: <0.0001 $</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">[245.6–293.19]</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">[319.4–370.0]</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">[192.1–223.2]</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">B/C: <0.0001 $</td></tr><tr><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">Duration (s)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">34.26 (1345)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">39.43 (1628)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">35.74 (1757)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">A/B: <0.0001 $</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">[32.99–35.54]</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">[38.03–40.83]</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">[34.59–36.90]</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">B/C: <0.0001 $</td></tr><tr><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">AUC</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5789 (1345)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8860 (1628)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4602 (1757)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">A/B: <0.0001 §</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">[5149–6430]</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">[8060–9660]</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">[4172–5032]</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">B/C: <0.0001 §</td></tr></tbody></table><table-wrap-foot><fn><p>Mean (<italic>n</italic>), [95% CI]; § Wilcoxon matched pairs signed rank test; $ two-tailed Mann–Whitney test.</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"ijms-21-05474-t004\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijms-21-05474-t004_Table 4</object-id><label>Table 4</label><caption><p>Analysis of the hypotonic stimulation experiments.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin\" rowspan=\"1\" colspan=\"1\">Osmotic Conc.</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin\" rowspan=\"1\" colspan=\"1\">309</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin\" rowspan=\"1\" colspan=\"1\">232</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin\" rowspan=\"1\" colspan=\"1\">154</th><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\"><italic>p</italic>–Value</th></tr><tr><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">(mOsm/L)</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Control</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Hypo25</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Hypo50</th></tr></thead><tbody><tr><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">frequency (min<sup>–1</sup>)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.02 (44) </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.45 (36)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.35(58) </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Hypo25/Hypo50</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">[0.0–0.06]</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">[0.39–0.49]</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">[0.34–0.36]</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.0001 $</td></tr><tr><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">amplitude (FI)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">32.63 (1)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">733.2 (47)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2147 (59)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Hypo25/Hypo50</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">[NA]</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">[579.8–886.7]</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">[1891–2403]</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.0001 $</td></tr><tr><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">duration (s)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">21 (1)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">93 (47)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">114.6 (59)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Hypo25/Hypo50</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">[NA]</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">[80.19–105.8]</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">[109.4–119.8]</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0021 $</td></tr><tr><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">AUC</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">419.6 (1)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">37,707 (47)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">119,702 (59)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Hypo25/Hypo50</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">[NA]</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">[27,896–47,517]</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">[102,882–136,421]</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><0.0001 $</td></tr></tbody></table><table-wrap-foot><fn><p>In control, only one cell showed spontaneous activity; mean (<italic>n</italic>), [95% CI]; $ two-tailed Mann–Whitney test.</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"ijms-21-05474-t005\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijms-21-05474-t005_Table 5</object-id><label>Table 5</label><caption><p>Primary and secondary antibodies used in confocal imaging experiments.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Target</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Host Isotype</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Class</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Conjugate</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Dilution</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Product Number</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Source</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">vimentin (VIM)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">mouse gG1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Monoclonal</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">na</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1:200</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">V6389</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">a</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">alpha-smooth muscle actin <break/>(aSMA)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">mouse IgG2a</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Monoclonal</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">na</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1:1000</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">A2547</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">a</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">platelet-derived growth factor receptor-alpha (PDGFRa)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">goat</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Polyclonal</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">na</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1:100</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">AF-307-NA</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">b</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">calreticulin (CALR)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">rabbit</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Polyclonal</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">na</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1:600</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ab2907</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">c</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">connexin 43 (Cx43)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">rabbit</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Polyclonal</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">na</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1:500</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">C6219</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">a</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">mouse IgG1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">goatIgG</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Polyclonal</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Alexa Fluor® 488</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1:500</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">A21121</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">d</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">mouse IgG2a</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">goat IgG</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Polyclonal</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Alexa Fluor® 633</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1:500</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">A21136</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">d</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">rabbit IgG</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">goat IgG (H+L)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">polyclonal</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Alexa Fluor® 555</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1:500</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">A21121</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">d</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">mouse IgG</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">donkey IgG (H+L)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">monoclonal</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Alexa Fluor® 488</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1:500</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">A21202</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">d</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">goat IgG</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">donkey IgG</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">polyclonal</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Alexa Fluor® 555</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1:500</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">A41232</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">d</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">rabbit IgG</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">donkey IgG</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">polyclonal</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Dylight® 649</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1:500</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">711-495-152</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">e</td></tr></tbody></table><table-wrap-foot><fn><p>(a) Sigma-Aldrich (Taufkirchen, Germany); (b) R&D Systems, Mineapolis, MN, USA; (c) Abcam, Cambridge, MA, USA; (d) Invitrogen by Thermo Fisher Scientific, Schwerte, Germany; (e) Dianova, Hamburg, Germany; na = not applicable.</p></fn></table-wrap-foot></table-wrap></floats-group></article>\n"
]
[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Int J Environ Res Public Health</journal-id><journal-id journal-id-type=\"iso-abbrev\">Int J Environ Res Public Health</journal-id><journal-id journal-id-type=\"publisher-id\">ijerph</journal-id><journal-title-group><journal-title>International Journal of Environmental Research and Public Health</journal-title></journal-title-group><issn pub-type=\"ppub\">1661-7827</issn><issn pub-type=\"epub\">1660-4601</issn><publisher><publisher-name>MDPI</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32751311</article-id><article-id pub-id-type=\"pmc\">PMC7432122</article-id><article-id pub-id-type=\"doi\">10.3390/ijerph17155477</article-id><article-id pub-id-type=\"publisher-id\">ijerph-17-05477</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Article</subject></subj-group></article-categories><title-group><article-title>Correlation between COVID-19 Morbidity and Mortality Rates in Japan and Local Population Density, Temperature, and Absolute Humidity</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0001-6595-0742</contrib-id><name><surname>Kodera</surname><given-names>Sachiko</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05477\">1</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0001-6571-9807</contrib-id><name><surname>Rashed</surname><given-names>Essam A.</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05477\">1</xref><xref ref-type=\"aff\" rid=\"af2-ijerph-17-05477\">2</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0001-8336-1140</contrib-id><name><surname>Hirata</surname><given-names>Akimasa</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05477\">1</xref><xref ref-type=\"aff\" rid=\"af3-ijerph-17-05477\">3</xref><xref rid=\"c1-ijerph-17-05477\" ref-type=\"corresp\">*</xref></contrib></contrib-group><aff id=\"af1-ijerph-17-05477\"><label>1</label>Department of Electrical and Mechanical Engineering, Nagoya Institute of Technology, Nagoya 466-8555, Japan; <email>[email protected]</email> (S.K.); <email>[email protected]</email> (E.A.R.)</aff><aff id=\"af2-ijerph-17-05477\"><label>2</label>Department of Mathematics, Faculty of Science, Suez Canal University, Ismailia 41522, Egypt</aff><aff id=\"af3-ijerph-17-05477\"><label>3</label>Center of Biomedical Physics and Information Technology, Nagoya Institute of Technology, Nagoya 466-8555, Japan</aff><author-notes><corresp id=\"c1-ijerph-17-05477\"><label>*</label>Correspondence: <email>[email protected]</email>; Tel.: +81-52-735-7916</corresp></author-notes><pub-date pub-type=\"epub\"><day>29</day><month>7</month><year>2020</year></pub-date><pub-date pub-type=\"ppub\"><month>8</month><year>2020</year></pub-date><volume>17</volume><issue>15</issue><elocation-id>5477</elocation-id><history><date date-type=\"received\"><day>22</day><month>6</month><year>2020</year></date><date date-type=\"accepted\"><day>27</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>© 2020 by the authors.</copyright-statement><copyright-year>2020</copyright-year><license license-type=\"open-access\"><license-p>Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>).</license-p></license></permissions><abstract><p>This study analyzed the morbidity and mortality rates of the coronavirus disease (COVID-19) pandemic in different prefectures of Japan. Under the constraint that daily maximum confirmed deaths and daily maximum cases should exceed 4 and 10, respectively, 14 prefectures were included, and cofactors affecting the morbidity and mortality rates were evaluated. In particular, the number of confirmed deaths was assessed, excluding cases of nosocomial infections and nursing home patients. The correlations between the morbidity and mortality rates and population density were statistically significant (<italic>p</italic>-value < 0.05). In addition, the percentage of elderly population was also found to be non-negligible. Among weather parameters, the maximum temperature and absolute humidity averaged over the duration were found to be in modest correlation with the morbidity and mortality rates. Lower morbidity and mortality rates were observed for higher temperature and absolute humidity. Multivariate linear regression considering these factors showed that the adjusted determination coefficient for the confirmed cases was 0.693 in terms of population density, elderly percentage, and maximum absolute humidity (<italic>p</italic>-value < 0.01). These findings could be useful for intervention planning during future pandemics, including a potential second COVID-19 outbreak.</p></abstract><kwd-group><kwd>COVID-19</kwd><kwd>temperature</kwd><kwd>absolute humidity</kwd><kwd>morbidity rate</kwd><kwd>mortality rate</kwd><kwd>Japan</kwd></kwd-group></article-meta></front><body><sec sec-type=\"intro\" id=\"sec1-ijerph-17-05477\"><title>1. Introduction</title><p>The COVID-19 outbreak was first reported in China in 2019 [<xref rid=\"B1-ijerph-17-05477\" ref-type=\"bibr\">1</xref>,<xref rid=\"B2-ijerph-17-05477\" ref-type=\"bibr\">2</xref>] and spread worldwide in early 2020. Japan declared a state of emergency in seven (of 47) prefectures on 7 April 2020 and extended it to all prefectures on 13 April 2020. The state of emergency was withdrawn on 25 May 2020. During this state of emergency, unlike many other countries where city lockdowns were enforced, in Japan, citizens self-isolated. The mortality rate (per population) in Japan is relatively low compared to the global rate; the total number of confirmed deaths in Japan is 846 (25 May 2020), corresponding to 6.72 per million people [<xref rid=\"B3-ijerph-17-05477\" ref-type=\"bibr\">3</xref>]. Although a straightforward comparison is infeasible, this number is smaller than that of many other countries with the same order of magnitude of population: 541, 504, 435, and 295 in Italy, the United Kingdom, France, and the United States, respectively, but larger than 5.18 and 4.0 in Indonesia and Australia, respectively (25 July 2020).</p><p>An additional difficulty in understanding the morbidity rate is the unreliability of the diagnosis of COVID-19. The number of polymerase chain reaction (PCR) tests, a simple and cost-effective method, is limited in Japan, partly because of its reliability. Therefore, chest CT is used for a fast-track, highly accurate diagnosis [<xref rid=\"B4-ijerph-17-05477\" ref-type=\"bibr\">4</xref>]. Some patients do not exhibit any common pandemic symptoms [<xref rid=\"B5-ijerph-17-05477\" ref-type=\"bibr\">5</xref>], thereby complicating morbidity rate assessment in different areas (e.g., population composition).</p><p>Morbidity and mortality statistics have been updated every day in each prefecture in Japan, which provides a good opportunity for local studies. A notable feature of Japan is that no medical collapse has been reported. In addition, due to the health insurance system, free medical care for COVID-19 has been offered. Thus, the reliability of the mortality rate is more accurate than that of the morbidity rate, because patients with weak or mild symptoms may not be tested. However, to avoid nosocomial infections and medical resource shortages, it was suggested that people with specific symptoms (e.g., fever with temperature >37.5 °C for no more than four consecutive days) stay home and avoid seeking medical attention, unless they had been in close contact with an infected person(s) or had recently visited foreign countries. Such a policy may result in longer latency in the reported cases.</p><p>In general, coronaviruses are considered to spread mainly by respiratory droplets and contact via droplets [<xref rid=\"B6-ijerph-17-05477\" ref-type=\"bibr\">6</xref>]. Droplets tend to fall to the ground close to the infected host. Droplet transmission is typically limited to short distances, generally less than 2 m. There exist some hypotheses about transmission due to airborne transmission that remain in flight for one hour or longer [<xref rid=\"B7-ijerph-17-05477\" ref-type=\"bibr\">7</xref>]. For both mechanisms, the ambient condition potentially influences the duration of droplet and airborne spread. Several co-factors potentially influence COVID-19 morbidity/mortality rates [<xref rid=\"B8-ijerph-17-05477\" ref-type=\"bibr\">8</xref>,<xref rid=\"B9-ijerph-17-05477\" ref-type=\"bibr\">9</xref>,<xref rid=\"B10-ijerph-17-05477\" ref-type=\"bibr\">10</xref>,<xref rid=\"B11-ijerph-17-05477\" ref-type=\"bibr\">11</xref>,<xref rid=\"B12-ijerph-17-05477\" ref-type=\"bibr\">12</xref>,<xref rid=\"B13-ijerph-17-05477\" ref-type=\"bibr\">13</xref>,<xref rid=\"B14-ijerph-17-05477\" ref-type=\"bibr\">14</xref>]); among them, ambient conditions have been considered here.</p><p>The effect of ambient temperature on the mortality was discussed in Wuhan [<xref rid=\"B8-ijerph-17-05477\" ref-type=\"bibr\">8</xref>]. Positive and negative associations were found between daily COVID-19 death counts and daily temperature difference and absolute humidity, respectively. The effect of high temperature and humidity on the transmission of COVID-19 was discussed using relative humidity as a measure [<xref rid=\"B9-ijerph-17-05477\" ref-type=\"bibr\">9</xref>]. Their finding suggested that high temperature and humidity may suppress COVID transmission. Furthermore, the effect of weather on COVID-19 cases employing a case in Jakarta was presented in [<xref rid=\"B10-ijerph-17-05477\" ref-type=\"bibr\">10</xref>]. They reported that only average temperature is correlated with the pandemic spread. The effect of ambient temperature on the confirmed cases was discussed in more than 100 Chinese cities, and it was concluded that there is no evidence supporting that COVID-19 case counts would decline when the weather becomes warmer [<xref rid=\"B12-ijerph-17-05477\" ref-type=\"bibr\">12</xref>]. The effect of ambient temperature and absolute humidity on the confirmed cases was investigated in cities in China, and the researchers commented that the epidemic might gradually ease partially due to rising temperatures [<xref rid=\"B13-ijerph-17-05477\" ref-type=\"bibr\">13</xref>]. Instead, no correlation with UV and temperature on the transmission of COVID-19 was reported in [<xref rid=\"B15-ijerph-17-05477\" ref-type=\"bibr\">15</xref>].</p><p>Following Chinese studies, case studies in different countries have been reported. Briz-Redón and Serrano-Aroca [<xref rid=\"B16-ijerph-17-05477\" ref-type=\"bibr\">16</xref>] evaluated the spatiotemporal analysis of temperature in the cases of early COVID-19 evolution in Spain. Pirouz et al. [<xref rid=\"B17-ijerph-17-05477\" ref-type=\"bibr\">17</xref>] discussed the correlation between daily confirmed cases and temperature, humidity, and velocity with multivariate analysis in Italy. Application of neural networks for its estimation is also discussed in [<xref rid=\"B18-ijerph-17-05477\" ref-type=\"bibr\">18</xref>]. A similar attempt has been made in Oslo, Norway [<xref rid=\"B19-ijerph-17-05477\" ref-type=\"bibr\">19</xref>]. Recent studies have confirmed the effect of temperature and relative humidity on morbidity rates in Brazil [<xref rid=\"B20-ijerph-17-05477\" ref-type=\"bibr\">20</xref>,<xref rid=\"B21-ijerph-17-05477\" ref-type=\"bibr\">21</xref>]. From these studies, it is difficult to derive a consistent conclusion on the effect of the weather on the spread of COVID-19. Studies of influenza suggested the importance of ambient conditions for its spread: lower spread for higher humidity (e.g., [<xref rid=\"B22-ijerph-17-05477\" ref-type=\"bibr\">22</xref>,<xref rid=\"B23-ijerph-17-05477\" ref-type=\"bibr\">23</xref>]).</p><p>Studies with wider scopes included global data analysis, discussing how temperature and humidity are correlated with the infection and fatality rates of the COVID-19 pandemic [<xref rid=\"B24-ijerph-17-05477\" ref-type=\"bibr\">24</xref>,<xref rid=\"B25-ijerph-17-05477\" ref-type=\"bibr\">25</xref>]. The region of interest is wide (country level) in these studies, and thus it is not directly applicable to the ruling or regulation. In addition, some modeling studies have been proposed. However, parameter setting is not easy for this type of novel virus spread [<xref rid=\"B14-ijerph-17-05477\" ref-type=\"bibr\">14</xref>,<xref rid=\"B26-ijerph-17-05477\" ref-type=\"bibr\">26</xref>]; in most modeling studies, the parameters relating to the weather or population cannot be given explicitly. Instead, the effect of population density on the spreading effect of the epidemic has been discussed under some assumptions [<xref rid=\"B27-ijerph-17-05477\" ref-type=\"bibr\">27</xref>].</p><p>Nevertheless, none of the aforementioned research and modeling studies simultaneously considered the impact of population density and ambient conditions. A question that arises here is To what extent do ambient conditions and population density influence morbidity and mortality rates in different cities? Unlike the aforementioned studies, a major feature of Japan is the relative homogeneity of the health insurance and care system without medical collapse during this pandemic. In addition, the difference in household wealth is relatively small in Japan [<xref rid=\"B28-ijerph-17-05477\" ref-type=\"bibr\">28</xref>]. The average annual salary per population is USD 34,400 to 39,900 (USD 1 = JPY 107). The standard deviation of household consumption in each prefecture is 10% or less [<xref rid=\"B28-ijerph-17-05477\" ref-type=\"bibr\">28</xref>]. With all these demographic factors, the data sample discussed here provides a convenient case study with less bias. In a recent study [<xref rid=\"B29-ijerph-17-05477\" ref-type=\"bibr\">29</xref>], we examined the time course of the morbidity rate of different prefectures in Japan and found that the durations of the spread and decay stages can be characterized by population density, temperature, and absolute humidity. An additional factor would be the ratio of the elderly to the entire population; in Japan, this ratio reached 28.4% [<xref rid=\"B30-ijerph-17-05477\" ref-type=\"bibr\">30</xref>], which is ranked the highest globally.</p><p>This novel study aimed to evaluate the effect of ambient temperature and humidity on mortality and morbidity rates in different prefectures in Japan. Additionally, it considered the influence of population density and composition. To the best of the authors’ knowledge, this is the first study to highlight the environmental factors’ effect during COVID-19 in Japan. The model of Japan provides an interesting case study for different factors, as the medical service and social reaction is almost uniform nationwide and high-quality data were recorded properly. If the correlation of the pandemic with population density and ambient conditions is significant, the findings will be useful to set the level and duration for a strict lockdown period for each city considering the environmental factors and in planning future pandemic measures.</p><p>The organization of this study is as follows. In <xref ref-type=\"sec\" rid=\"sec2-ijerph-17-05477\">Section 2</xref>, the data sources of COVID-19 in Japan and weather data are mentioned. Then, the statistical method for data processing is explained briefly. In <xref ref-type=\"sec\" rid=\"sec3-ijerph-17-05477\">Section 3</xref>, effect of population density, elderly population, and ambient conditions on the morbidity/mortality rates are evaluated statistically. Based on the evaluation, multivariate linear regression has been conducted to estimate the morbidity/mortality rate from these parameters. In <xref ref-type=\"sec\" rid=\"sec4-ijerph-17-05477\">Section 4</xref>, provides discussion of the results including the limitation. The conclusion is given in <xref ref-type=\"sec\" rid=\"sec5-ijerph-17-05477\">Section 5</xref>.</p></sec><sec id=\"sec2-ijerph-17-05477\"><title>2. Material and Methods</title><sec id=\"sec2dot1-ijerph-17-05477\"><title>2.1. Data Source</title><p>In this study, we used three datasets. The first involved the confirmed daily positive cases and deaths in each prefecture [<xref rid=\"B31-ijerph-17-05477\" ref-type=\"bibr\">31</xref>]. This dataset is based on the report by the Ministry of Health Labor and Welfare [<xref rid=\"B32-ijerph-17-05477\" ref-type=\"bibr\">32</xref>]. We used time-integrated data until 25 May 2020—when the emergency state was terminated. According to the dataset, 16 prefectures had confirmed total deaths and daily positive counts higher than 4 and 10, respectively. These prefectures were defined as infected. The remaining prefectures were excluded due to a lower number of infected cases, which is simply because of the self-isolation, including the discouragement from moving to other prefectures after the declaration. In Japan, to avoid nosocomial infections and medical resource shortages, it was suggested that people with symptoms (e.g., fever >37.5 °C for no more than four consecutive days) stay home and not seek immediate medical attention unless they had been in close contact with infected people or had recently visited a foreign country. Some patients have been reported to be asymptomatic [<xref rid=\"B5-ijerph-17-05477\" ref-type=\"bibr\">5</xref>], making the statistical study of COVID-19 more complex. Then, the positive rate of the test varied from 2.2% to 34.8% for different prefectures. Unlike other diseases, the number of confirmed cases/deaths are counted even when patients are found to be infected after their death. For this data collection, we use the mortality rate in this study as a metric rather than the case fatality rate.</p><p>For comparison, two sets of data were prepared: (1) the number of confirmed deaths excluding and including nosocomial/nursing facility infection, and (2) the total confirmed positive cases. This is to avoid the data of cluster infection for high-risk groups, resulting in a higher possibility of death.</p><p>Note that among some of the 16 selected prefectures, as shown in <xref ref-type=\"fig\" rid=\"ijerph-17-05477-f001\">Figure 1</xref>, the number of victims due to nosocomial/nursing facility infection was not always reported. Thus, such prefectures were excluded from the comparisons. Among others, the area of Hokkaido is one to two magnitudes larger than the other prefectures. Thus, several peaks in the number of cases are observed in different cities with larger distances between them than those in other adjacent prefectures. In the Gunma prefecture, three confirmed deaths occurred, except for in Isezaki City, where substantial nosocomial infections were reported (15 victims). Thus, we used data from the Gunma prefecture, excluding Isezaki City, for accurate comparison of mortality rates. In addition, Saitama was also excluded since it did not report humidity data (see below for the third dataset). Two prefectures with unclear nosocomial/nursing facility infections were also excluded.</p><p>The second dataset comprises the population and the area of the prefectures. Based on the evidence that more than 90% of the victims are older than 60 years and because the retirement age in Japan is 65, which may potentially influence morbidity rates, we set the threshold as 65. For a total of 14 prefectures and one city, the first and second datasets are listed in <xref rid=\"ijerph-17-05477-t001\" ref-type=\"table\">Table 1</xref>. Note that the rationale for choosing 25 May as a reference date is the end of state of emergency, and then, the daily confirmed death over Japan was 20 (128 million population); the daily confirmed death was smaller than 100 for one month after that.</p><p>The third dataset comprises weather data for each prefecture. They are extracted from the weather reports generated by the Japan Meteorological Agency [<xref rid=\"B33-ijerph-17-05477\" ref-type=\"bibr\">33</xref>]. In our previous study, we had studied the correlation between environmental conditions and the duration of the pandemic from its spread to decay periods [<xref rid=\"B29-ijerph-17-05477\" ref-type=\"bibr\">29</xref>]. We extended this investigation to the prefectures defined in <xref rid=\"ijerph-17-05477-t001\" ref-type=\"table\">Table 1</xref>. We then estimated the start and end dates of the spread and decay stages, as defined in <xref rid=\"ijerph-17-05477-t002\" ref-type=\"table\">Table 2</xref>. To validate the effect of ambient factors in different phases of the pandemic, we computed ambient features for three time frames: during the spreading stage <italic>D<sub>S</sub></italic> (from <italic>T<sub>SS</sub></italic> to <italic>T<sub>SE</sub></italic>), during the decaying stage <italic>D<sub>D</sub></italic> (from <italic>T<sub>DS</sub></italic> to <italic>T<sub>DE</sub></italic>), and during both stages (from <italic>T<sub>SS</sub></italic> to <italic>T<sub>DE</sub></italic>).</p><p>To consider the mortality rate, which is affected by many factors, it should be noted that the metrics are averaged over the duration of the spread stage, decay stage, and the entire period. The duration-averaged values of temperature, absolute humidity, wind velocity, and daylight hours were calculated from the data available from the internet site mentioned above, as listed in <xref rid=\"ijerph-17-05477-t003\" ref-type=\"table\">Table 3</xref>. The latitude of Japan considered here is N 33°36′ (Fukuoka) to N 36°35′ (Ishikawa), except for Okinawa of N 26°12′), and thus, total solar radiation may be marginally influenced with this measure.</p></sec><sec id=\"sec2dot2-ijerph-17-05477\"><title>2.2. Statistical Analysis</title><p>A statistical study was conducted to analyze the correlation of different factors on both mortality and morbidity rates. The software JMP (SAS Institute, Cary, NC, USA) was used in this study. In order to specify dominant factors influencing the rates, <italic>p</italic>-value was used. We determined the pairwise correlations by calculating the Spearman’s rank correlation between the number of confirmed positive cases, confirmed death cases, and different environmental and demographic parameters. Correlation matrix with partial correlation probability and CI of correlation were calculated. After that, with the same software, multivariate analysis [<xref rid=\"B34-ijerph-17-05477\" ref-type=\"bibr\">34</xref>] was conducted in terms of the factors. We considered linear regression for data least-squares fitting after considering multicollinearity. Statistical significance was accepted at <italic>p</italic> < 0.05.</p></sec></sec><sec sec-type=\"results\" id=\"sec3-ijerph-17-05477\"><title>3. Results</title><sec id=\"sec3dot1-ijerph-17-05477\"><title>3.1. Effect of Population Density and Elderly Population</title><p><xref ref-type=\"fig\" rid=\"ijerph-17-05477-f002\">Figure 2</xref> shows the relationship between confirmed positive cases and confirmed deaths, including and excluding nosocomial infections and nursing home patients. A modest correlation was observed between positive cases per million and population density (<italic>R</italic><sup>2</sup> = 0.394), whereas a slight and mild correlation was observed for confirmed deaths (<italic>R</italic><sup>2</sup> = 0.097) and excluding nosocomial infection (<italic>R</italic><sup>2</sup> = 0.259). This result suggests that population density should be considered as a factor that implicitly represents social distancing, as is similar to our previous study that discussed the pandemic’s duration [<xref rid=\"B29-ijerph-17-05477\" ref-type=\"bibr\">29</xref>].</p><p>When the cases and deaths for the elderly population were considered, the same tendency was observed; <italic>R</italic><sup>2</sup> = 0.363 for cases, and <italic>R</italic><sup>2</sup> = 0.078 and <italic>R</italic><sup>2</sup> = 0.210 for deaths with and without nosocomial infections (not shown to avoid repetition), respectively. Instead, as shown in <xref ref-type=\"fig\" rid=\"ijerph-17-05477-f003\">Figure 3</xref>, the morbidity and mortality rates normalized by population density are modestly correlated with the percentage of the elderly, especially for confirmed deaths excluding nosocomial infections (<italic>R</italic><sup>2</sup> = 0.482). This factor is thus considered in the multivariate analysis study presented later.</p></sec><sec id=\"sec3dot2-ijerph-17-05477\"><title>3.2. Effect of Ambient Conditions</title><p>Several ambient factors potentially influence morbidity and mortality rates. Our study considered temperature and absolute humidity. Most previous studies reported the maximum, average, or difference (diurnal variation range) of ambient temperature (e.g., see [<xref rid=\"B8-ijerph-17-05477\" ref-type=\"bibr\">8</xref>] and [<xref rid=\"B35-ijerph-17-05477\" ref-type=\"bibr\">35</xref>]). Our study also considered the minimum temperature. Recent reports on influenza suggest the importance of absolute humidity rather than its relative value [<xref rid=\"B22-ijerph-17-05477\" ref-type=\"bibr\">22</xref>,<xref rid=\"B23-ijerph-17-05477\" ref-type=\"bibr\">23</xref>]; however, we considered the maximum, average, minimum, and difference values of absolute humidity as parameters. The daily average wind velocity and daylight hours were also considered. Regression analysis was conducted for all metrics averaged over the duration of the spread and decay stages and the total duration.</p><p><xref rid=\"ijerph-17-05477-t004\" ref-type=\"table\">Table 4</xref> lists the coefficients of determination for different metrics. For most parameters, the averaged values over the total stage provided the highest correlation rather than those over the other two durations. As an example, <xref ref-type=\"fig\" rid=\"ijerph-17-05477-f004\">Figure 4</xref> shows the correlation between the number of confirmed positive cases and fatality normalized by the population density and the daily maximum temperature and diurnal absolute humidity. A moderate correlation was observed among the daily maximum temperature, diurnal absolute humidity, and cases per population density. <xref rid=\"ijerph-17-05477-t005\" ref-type=\"table\">Table 5</xref> lists the Spearman’s rank correlation for different parameters. The ambient factor was normalized by population density as aforementioned. A moderate correlation was also observed with the daily maximum temperature, daily maximum, and diurnal absolute humidity and percentage of elderly population. Correlation was weak with wind velocity and daylight hours.</p></sec><sec id=\"sec3dot3-ijerph-17-05477\"><title>3.3. Multivariate Linear Regression</title><p>In this subsection, the morbidity/mortality rates are estimated in terms of different factors. In <xref ref-type=\"sec\" rid=\"sec3dot1-ijerph-17-05477\">Section 3.1</xref>, population density and percentage of the elderly were found to be modest, at least non-negligible factors for multivariate analysis [<xref rid=\"B34-ijerph-17-05477\" ref-type=\"bibr\">34</xref>]. In <xref ref-type=\"sec\" rid=\"sec3dot2-ijerph-17-05477\">Section 3.2</xref>, maximum temperature and absolute humidity difference were found to be relatively important. No consistency was observed between mortality and morbidity rates. The data in Ishikawa and Toyama prefectures were considered as outliers from hierarchical clustering (see also [<xref rid=\"B29-ijerph-17-05477\" ref-type=\"bibr\">29</xref>]).</p><p>The difference in absolute humidity is derived from the maximum and minimum absolute humidity; at least two parameters are needed. In addition, the maximum temperature is also related to the maximum absolute humidity. In terms of variance inflation factors (VIFs), the multicollinearity was evaluated. The threshold value to differentiate small from large is generally taken as 10 [<xref rid=\"B36-ijerph-17-05477\" ref-type=\"bibr\">36</xref>]. From this analysis, a set of population density, elderly percentage, and absolute humidity provided estimation without multicollinearity: VIF < 3.78 for spread duration, VIF < 3.23 for decay duration, and VIF < 3.68 for total duration. Note that the maximum ambient temperature was excluded due to strong correlation with absolute humidity.</p><p><xref ref-type=\"fig\" rid=\"ijerph-17-05477-f005\">Figure 5</xref> shows the multivariate linear regression of cases and deaths per million. <xref rid=\"ijerph-17-05477-t006\" ref-type=\"table\">Table 6</xref> shows the determination coefficients for the three durations. As shown in <xref ref-type=\"fig\" rid=\"ijerph-17-05477-f005\">Figure 5</xref>, the predicted and actual data are of good correlation with the averaged value over three stages. The highest contribution rates were the population density in the multivariate analysis (74.4%, 80.0%, and 84.5% in the cases per million, deaths per million including, and excluding nosocomial infection, respectively).</p></sec></sec><sec sec-type=\"discussion\" id=\"sec4-ijerph-17-05477\"><title>4. Discussion</title><p>In this study, we analyzed the morbidity and mortality rates in different prefectures in Japan, where the number of confirmed deaths and daily confirmed positive counts were higher than 4 and 10, respectively. A major feature of Japan was the relative homogeneity of the health insurance and care system without medical collapse during this pandemic, in addition to household wealth. The Japanese strategy included identifying infection clusters at an early stage, to the best possible extent. However, the criteria for conducting tests (diagnosis) on potential patients may not be uniform in different prefectures; some patients may exhibit weak symptoms. Thus, after retracting the state of emergency on 25 May 2020, we processed the data for morbidity and mortality rates in 14 prefectures.</p><p>The morbidity/mortality rates were then shown to be proportional to the population density. In previous studies, this factor was not considered [<xref rid=\"B12-ijerph-17-05477\" ref-type=\"bibr\">12</xref>] nor was correlation between different cities considered [<xref rid=\"B17-ijerph-17-05477\" ref-type=\"bibr\">17</xref>]. After excluding the number of confirmed deaths in cluster infections related to hospital and care services, we observed modest correlation among different cities in terms of population density. It is worth noting that no strict closure was applied in Japan. Next, we found a good correlation between population density and the spread of COVID-19. This finding implicitly represents social distancing. In Tokyo and Osaka, which are considered among cities with the highest population densities worldwide, infection is potentially more likely to occur compared to other less dense regions. However, this may not be the case reported in other countries where strict lockdown was implemented. In Wuhan (China), the duration of the decaying stage was only 10 days, with almost no contact during the period. However, such strict lockdown may not be allowed in most countries to avoid severe social and economic damage. Therefore, this study demonstrates that population density should be considered for avoiding potential spread in future pandemics. Moreover, this finding may be useful to improve the simulation model of epidemic transmission [<xref rid=\"B37-ijerph-17-05477\" ref-type=\"bibr\">37</xref>,<xref rid=\"B38-ijerph-17-05477\" ref-type=\"bibr\">38</xref>]. The maximum temperature and absolute humidity differences were the dominant ambient factors characterizing morbidity and mortality rates. As shown in <xref ref-type=\"fig\" rid=\"ijerph-17-05477-f004\">Figure 4</xref>, cases and deaths in Ishikawa and Toyama prefectures have a different tendency than that in other prefectures—as COVID-19 occurred in a very limited area in these prefectures. In general, for higher temperature and absolute humidity, the morbidity and mortality rates were decreased. For example, the population density of Hyogo (650.4 capita/km<sup>2</sup>) is nearly equal to that of Okinawa (637.5 capita/km<sup>2</sup>). However, the total cases in Hyogo were 8.6 times that of Okinawa. The daily maximum temperature in Hyogo was 7 °C lower than in Okinawa. This relationship can be observed in other prefectures but not all due to mild correlation with weather condition. The reason for higher correlation with absolute humidity difference is unclear. However, one potential reason would be the relatively small variation in a limited period (from mid-March to mid-May). Further study of key factors would be needed. The ambient conditions in Okinawa prefecture differ the most from those of other prefectures in Japan. If the data of Okinawa are excluded, the correlation of confirmed cases and deaths improved. In particular, the total cases and deaths normalized by population have a mild correlation with the maximum temperature and absolute humidity averaged over spread duration (from 0.13 < <italic>R</italic><sup>2</sup> < 0.18 to 0.37 < <italic>R</italic><sup>2</sup> < 0.55).</p><p>The effect of ambient conditions on the morbidity and mortality rates was shown to be modest over multiple prefecture studies. As mentioned in the introduction, this was a controversial COVID-19 issue. Our study hypothesized that this may be caused by population density, which was not considered in previous studies, as well as the uniformity of the policy, health insurance system, household wealth, etc.</p><p>The morbidity and mortality rates were roughly derived via multivariate analysis. Note that the ambient parameters are cross-correlated with each other, and thus further research and investigation are needed. Their adjusted-R<sup>2</sup> was almost the same; 0.69 (<italic>p <</italic> 0.01) for positive cases, and those for confirmed deaths including and excluding nosocomial infection were 0.53 (<italic>p</italic> < 0.05) and 0.15 (<italic>p</italic> = 0.25), respectively. This statistical finding may be improved for modeling studies. The correlation with the mortality rate excluding nosocomial infection was relatively low, suggesting that nosocomial infection would be a part of COVID-19 transmission at least in Japan.</p><p>Unlike previous studies that discussed the correlation with ambient condition in each city (e.g., [<xref rid=\"B17-ijerph-17-05477\" ref-type=\"bibr\">17</xref>]), our study explores common factors over 14 prefectures, resulting in lower <italic>p</italic>-value as compared to such studies. In such cases, the uncertainty of measured ambient condition would also be another factor to influence the correlation. For example, no correlation with ambient condition was observed in the analysis of 122 cities in China [<xref rid=\"B12-ijerph-17-05477\" ref-type=\"bibr\">12</xref>].</p><p>Note that according to the record of the Ministry of Health in Japan, no pandemic has been reported in the last 50 years [<xref rid=\"B39-ijerph-17-05477\" ref-type=\"bibr\">39</xref>]. Thus, a comparison with other epidemics is infeasible. Common influenza has been recorded, but only at fixed points (hospitals), making proper comparison difficult [<xref rid=\"B39-ijerph-17-05477\" ref-type=\"bibr\">39</xref>]. However, the finding of this study that presents the effect of population density and ambient conditions may be useful when considering measures for potential future pandemics.</p></sec><sec sec-type=\"conclusions\" id=\"sec5-ijerph-17-05477\"><title>5. Conclusions</title><p>A mild correlation was found of mortality and morbidity rates with the population density and the percentage of the elderly population, in addition to maximum absolute humidity averaged over the spread stage under Japanese policy. The multivariate linear regression provided adjusted coefficients of determination, which were 0.69 and 0.53 (<italic>p</italic> < 0.05) for positive cases and confirmed deaths, respectively. Our results suggested that with population and weather data, we can estimate the number of cases and deaths, at least in Japanese cities. Although the date and duration of the pandemic were different even in Japan, our estimation presented mild correlation, providing useful information for the planning of policy and medical resources. With our findings, more customized guidelines can be developed, specific to where and when different measures can be applied to restrict the adverse effects caused by a potential pandemic in the future, including a second wave of COVID-19. The limitation of this study is that the weather data in different prefectures are similar to each other due to the limited period (March to May 2020), and thus further data are needed for a general conclusion. The controversy in previous studies may be potentially caused by the population density and elderly population percentage, as those were not considered in most studies. Thus, these factors should be included for proper comparisons with the tendencies of international cities.</p></sec></body><back><notes><title>Author Contributions</title><p>Conceptualization, A.H.; data curation, S.K. and E.A.R.; formal analysis, S.K., E.A.R. and A.H.; investigation, S.K. and E.A.R.; methodology, A.H.; project administration, A.H.; resources, E.A.R.; software, S.K. and E.A.R.; validation, E.A.R.; visualization, S.K.; writing—original draft, A.H.; writing—review and editing, S.K., E.A.R. and A.H. 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The number of (<bold>a</bold>) positive cases, confirmed deaths (<bold>b</bold>) including and (<bold>c</bold>) excluding those caused by nosocomial infection.</p></caption><graphic xlink:href=\"ijerph-17-05477-g002\"/></fig><fig id=\"ijerph-17-05477-f003\" orientation=\"portrait\" position=\"float\"><label>Figure 3</label><caption><p>Correlation between the number of confirmed positive cases and fatality normalized by the population density and the percentage of the elderly population. The number of (<bold>a</bold>) positive cases, confirmed deaths (<bold>b</bold>) including and (<bold>c</bold>) excluding those caused by nosocomial infection.</p></caption><graphic xlink:href=\"ijerph-17-05477-g003\"/></fig><fig id=\"ijerph-17-05477-f004\" orientation=\"portrait\" position=\"float\"><label>Figure 4</label><caption><p>Correlation between the number of confirmed positive cases and fatality normalized by the population density and (<bold>a</bold>–<bold>c</bold>) the daily maximum temperature and (<bold>d</bold>–<bold>f</bold>) diurnal absolute humidity averaged over total duration. The number of (<bold>a</bold>,<bold>d</bold>) positive cases, confirmed deaths (<bold>b</bold>,<bold>e</bold>) including and (<bold>c</bold>,<bold>f</bold>) excluding those caused by nosocomial infection.</p></caption><graphic xlink:href=\"ijerph-17-05477-g004\"/></fig><fig id=\"ijerph-17-05477-f005\" orientation=\"portrait\" position=\"float\"><label>Figure 5</label><caption><p>Multivariate linear regression with population density, elderly percentage, daily maximum absolute humidity averaged over spread duration. The number of (<bold>a</bold>) positive cases, and confirmed deaths (<bold>b</bold>) including and (<bold>c</bold>) excluding those caused by nosocomial infection.</p></caption><graphic xlink:href=\"ijerph-17-05477-g005\"/></fig><table-wrap id=\"ijerph-17-05477-t001\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05477-t001_Table 1</object-id><label>Table 1</label><caption><p>Population and population density of 14 prefectures, in addition to the percentage of the elderly population, where confirmed deaths and daily confirmed positives are larger than 4 and 10, respectively. The data of confirmed cases and deaths were counted until 25 May 2020.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Prefectures</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Population (×1000)</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Density (capita/km<sup>2</sup>)</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Total Cases </th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Confirmed Deaths</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Confirmed Deaths (Ex.) <sup>†</sup></th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Cases/1M</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Elderly (>65 years) (%)</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Aichi</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7552</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1460.0 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">507</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">34</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">16</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">67.1 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">25.1 </td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Chiba</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6259</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1217.4 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">904</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">44</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">27</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">144.4 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">27.8 </td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Fukuoka</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5104</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1024.8 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">672</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">25</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">20</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">131.7 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">27.9 </td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Gifu</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1987</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">187.3 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">150</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">75.5 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">30.1 </td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Gunma</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1942</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">304.6 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">149</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">19</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">19</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">76.7 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">29.9 </td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Hyogo</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5466</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">650.4 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">699</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">40</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">33</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">127.9 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">29.1 </td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Ibaraki</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2860</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">470.4 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">168</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">58.7 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">29.5 </td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Ishikawa</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1138</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">271.7 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">296</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">24</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">260.1 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">29.6 </td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Kanagawa</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">9198</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3807.5 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1336</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">76</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">59</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">145.2 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">25.3 </td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Kyoto</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2583</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">560.1 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">358</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">15</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">15</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">138.6 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">29.2 </td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Okinawa</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1453</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">637.5 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">81</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">55.7 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">22.2 </td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Osaka</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8809</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4631.0 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1781</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">80</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">45</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">202.2 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">27.6 </td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Tokyo</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13,921</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6354.8 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5170</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">292</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">210</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">371.4 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">23.1 </td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Toyama</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1044</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">245.6 </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">227</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">21</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">10</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">217.4 </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">32.3 </td></tr></tbody></table><table-wrap-foot><fn><p><sup>†</sup> Excluding nosocomial infection in confirmed deaths.</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"ijerph-17-05477-t002\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05477-t002_Table 2</object-id><label>Table 2</label><caption><p>Starting and terminating dates of the spread and decay stages of COVID-19 in different prefectures in Japan. <italic>T<sub>SS</sub></italic> (<italic>T<sub>DS</sub></italic>) and <italic>T<sub>SE</sub></italic> (<italic>T<sub>DE</sub></italic>) denote the start and end dates for the spread (decay) stages of the pandemic.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">Prefectures</th><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Spread Stage</th><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Decay Stage</th></tr><tr><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>T<sub>SS</sub></italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>T<sub>SE</sub></italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>T<sub>DS</sub></italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>T<sub>DE</sub></italic>\n</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Aichi</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">22-February</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">30-March</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1-April</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">27-April</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Chiba</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">19-March</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2-April</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13-April</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5-May</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Fukuoka</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">22-March</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1-April</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">9-April</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">27-April</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Gifu</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">25-March</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4-April</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6-April</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">17-April</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Gunma</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">25-March</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5-April</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">9-April</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">22-April</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Hyogo</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">19-March</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4-April</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7-April</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4-May</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Ibaraki</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">16-March</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">28-March</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8-April</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">23-April</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Ishikawa</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">24-March</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3-April</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8-April</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8-May</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Kanagawa</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">19-March</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3-April</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">11-April</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">19-May</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Kyoto</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">16-March</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2-April</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5-April</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">9-May</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Okinawa</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">28-March</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3-April</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10-April</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">25-April</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Osaka</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">18-March</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6-April</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13-April</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6-May</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Tokyo</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">17-March</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3-April</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10-April</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7-May</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Toyama</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1-April</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">13-April</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">18-April</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">30-April</td></tr></tbody></table></table-wrap><table-wrap id=\"ijerph-17-05477-t003\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05477-t003_Table 3</object-id><label>Table 3</label><caption><p>Duration-averaged temperature (<italic>T</italic>), absolute humidity (<italic>H</italic>), wind velocity (<italic>V<sub>air</sub></italic>), and daylight hours (<italic>DL</italic>) in each prefecture. <italic>D<sub>S</sub></italic> and <italic>D<sub>D</sub></italic> represent time frames during the spread and decay stages of the pandemic, respectively, as listed in <xref rid=\"ijerph-17-05477-t002\" ref-type=\"table\">Table 2</xref>. <italic>T<sub>ave</sub></italic>, <italic>T<sub>max</sub></italic>, and <italic>T<sub>min</sub></italic> represent the daily average, maximum, and minimum temperatures, respectively. <italic>H<sub>ave</sub></italic>, <italic>H<sub>max</sub></italic>, and <italic>H<sub>min</sub></italic> represent the daily average, maximum, and minimum absolute humidity values, respectively. <italic>V<sub>air</sub></italic> represents the daily averaged wind velocity.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th colspan=\"11\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Spread Duration (<italic>D<sub>S</sub>)</italic></th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>Prefectures</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold><italic>T<sub>ave</sub></italic></bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold><italic>T<sub>max</sub></italic></bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold><italic>T<sub>min</sub></italic></bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold><italic>T<sub>diff</sub></italic></bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold><italic>H<sub>ave</sub></italic></bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold><italic>H<sub>max</sub></italic></bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold><italic>H<sub>min</sub></italic></bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold><italic>H<sub>diff</sub></italic></bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold><italic>V<sub>air</sub></italic></bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold><italic>DL</italic></bold>\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Aichi</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">14.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.3</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Chiba</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">12.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">16.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">9.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.5</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Fukuoka</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">14.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">17.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">11.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">11.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.5</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Gifu</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">12.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">16.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.2</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Gunma</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">15.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">9.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.4</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Hyogo</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">12.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">16.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">9.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">9.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.1</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Ibaraki</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">17.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7.0</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Ishikawa</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">9.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">14.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">9.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.4</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Kanagawa</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">12.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">16.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">9.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.6</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Kyoto</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">11.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">16.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">9.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.6</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Okinawa</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">21.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">24.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">18.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">14.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">17.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">12.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.9</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Osaka</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">12.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">17.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.1</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Tokyo</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">11.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">16.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">9.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.4</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Toyama</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">9.7</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">14.6</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">5.2</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">9.4</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">6.3</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">7.7</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">4.7</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3.2</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">4.4</td></tr><tr><td colspan=\"11\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">\n<bold>Decay Duration (<italic>D<sub>D</sub>)</italic></bold>\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>T<sub>ave</sub></italic>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>T<sub>max</sub></italic>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>T<sub>min</sub></italic>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>T<sub>diff</sub></italic>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>H<sub>ave</sub></italic>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>H<sub>max</sub></italic>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>H<sub>min</sub></italic>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>H<sub>diff</sub></italic>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>V<sub>air</sub></italic>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>DL</italic>\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Aichi</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">18.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">9.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6.1</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Chiba</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">15.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">19.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">11.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.6</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Fukuoka</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">14.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">17.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">9.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.7</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Gifu</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">12.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">18.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6.5</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Gunma</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">11.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">16.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">9.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.2</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Hyogo</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">15.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">19.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">12.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">9.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.3</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Ibaraki</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">15.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">9.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.7</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Ishikawa</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">17.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">9.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.5</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Kanagawa</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">16.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">20.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">9.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">11.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.9</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Kyoto</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">14.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">20.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">9.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.0</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Okinawa</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">19.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">22.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">17.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">11.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">14.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.2</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Osaka</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">16.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">20.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">12.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.3</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Tokyo</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">14.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">19.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">9.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">9.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.0</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Toyama</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">12.1</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">17.6</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">7.7</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">9.9</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">7.5</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">9.1</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">5.8</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3.3</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3.7</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3.9</td></tr><tr><td colspan=\"11\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">\n<bold>All Duration, from <italic>T<sub>SS</sub></italic> to <italic>T<sub>DE</sub></italic></bold>\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>T<sub>ave</sub></italic>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>T<sub>max</sub></italic>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>T<sub>min</sub></italic>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>T<sub>diff</sub></italic>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>H<sub>ave</sub></italic>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>H<sub>max</sub></italic>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>H<sub>min</sub></italic>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>H<sub>diff</sub></italic>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>V<sub>air</sub></italic>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>DL</italic>\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Aichi</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">11.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">16.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">11.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.5</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Chiba</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">17.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">9.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">9.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.9</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Fukuoka</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">14.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">19.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">11.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">14.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.5</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Gifu</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">12.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">17.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">12.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.5</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Gunma</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">11.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">16.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">11.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.2</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Hyogo</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">14.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">17.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">9.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">14.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.6</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Ibaraki</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">15.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.5</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Ishikawa</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">12.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">16.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">12.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.7</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Kanagawa</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">15.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">19.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">11.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">15.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.0</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Kyoto</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">18.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.9</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Okinawa</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">20.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">22.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">17.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">12.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">14.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">20.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.5</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Osaka</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">14.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">18.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">9.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">14.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.4</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Tokyo</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">18.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">9.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.3</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Toyama</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">10.9</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">16.1</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">6.2</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">6.8</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">8.4</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">5.2</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3.2</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">10.9</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3.4</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">4.3</td></tr></tbody></table></table-wrap><table-wrap id=\"ijerph-17-05477-t004\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05477-t004_Table 4</object-id><label>Table 4</label><caption><p>Coefficient of determination for different metrics: (i) cases, (ii) death, (iii) death excluding nosocomial infection, (iv) cases normalized by density, (v) death normalized density, and (vi) death excluding nosocomial infection normalized by population density.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">\n</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">(i)</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">(ii)</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">(iii)</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">(iv)</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">(v)</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">(vi)</th></tr></thead><tbody><tr><td colspan=\"2\" align=\"center\" valign=\"middle\" rowspan=\"1\">Population density</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.393</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.097</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.259</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">—</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">—</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">—</td></tr><tr><td colspan=\"2\" align=\"center\" valign=\"middle\" rowspan=\"1\">Elderly density</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.363</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.078</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.210</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.225</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.185 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.295</td></tr><tr><td colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">Elderly percentage</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.009</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.014</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.007</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.405</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.360 </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.482</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>T<sub>ave</sub></italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>D<sub>S</sub></italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.073</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.143</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.041</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.151</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.157 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.122</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>D<sub>D</sub></italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.000</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.035</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.011</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.164</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.173 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.274</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Total</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.009</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.075</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.020</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.158</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.173 </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.216</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>T<sub>max</sub></italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>D<sub>S</sub></italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.089</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.161</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.035</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.175</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.181 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.130</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>D<sub>D</sub></italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.008</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.019</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.001</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.143</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.166 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.229</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Total</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.003</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.081</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.006</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.202</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.229 </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.242</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>T<sub>min</sub></italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>D<sub>S</sub></italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.053</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.114</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.054</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.105</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.116 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.112</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>D<sub>D</sub></italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.001</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.041</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.019</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.147</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.147 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.246</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Total</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.013</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.069</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.034</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.122</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.134 </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.192</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>T<sub>diff</sub></italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>D<sub>S</sub></italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.007</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.027</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.047</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.015</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.021 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.043</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>D<sub>D</sub></italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.026</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.042</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.048</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.071</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.055 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.128</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Total</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.026</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.042</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.078</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.036</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.036 </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.101</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>H<sub>ave</sub></italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>D<sub>S</sub></italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.076</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.091</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.043</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.055</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.055 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.048</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>D<sub>D</sub></italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.017</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.002</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.019</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.099</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.061 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.142</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Total</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.006</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.026</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.004</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.095</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.080 </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.127</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>H<sub>max</sub></italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>D<sub>S</sub></italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.069</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.123</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.032</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.152</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.149 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.131</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>D<sub>D</sub></italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.016</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.001</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.019</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.127</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.081 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.160</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Total</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.005</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.038</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.003</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.160</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.138 </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.191</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>H<sub>min</sub></italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>D<sub>S</sub></italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.086</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.084</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.036</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.044</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.039 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.025</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>D<sub>D</sub></italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.011</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.002</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.016</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.089</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.051 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.117</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Total</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.011</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.024</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.004</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.079</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.060 </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.089</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>H<sub>diff</sub></italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>D<sub>S</sub></italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.001</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.107</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.002</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.463</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.488 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.546</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>D<sub>D</sub></italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.052</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.001</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.031</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.347</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.277 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.384</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Total</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.006</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.074</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.000</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.485</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.509 </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.635</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>V<sub>air</sub></italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>D<sub>S</sub></italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.034 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.058 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.007 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.020 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.022 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.044 </td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>D<sub>D</sub></italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.035 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.000 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.091 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.023 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.027 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.003 </td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Total</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.001 </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.008 </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.032 </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.015 </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.017 </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.015 </td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>DL</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>D<sub>S</sub></italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.023 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.007 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.012 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.012 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.010 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.014 </td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>D<sub>D</sub></italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.021 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.077 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.025 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.045 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.086 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.018 </td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Total</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.008 </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.007 </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.000 </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.035 </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.053 </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.029 </td></tr></tbody></table></table-wrap><table-wrap id=\"ijerph-17-05477-t005\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05477-t005_Table 5</object-id><label>Table 5</label><caption><p>Spearman’s rank correlation for cases normalized by density, death normalized density, and death, excluding nosocomial infection normalized by population density.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Parameters</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</th><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Cases/Density</th><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Deaths/Density</th><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Deaths/Density (Ex.)</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>p</italic>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><italic>p</italic>-value</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>p</italic>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><italic>p</italic>-value</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>p</italic>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><italic>p</italic>-value</td></tr><tr><td colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">Elderly percentage</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.864 </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><0.0001</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.824 </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><0.001</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.842 </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><0.001</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>T<sub>ave</sub></italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.456 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.101 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.489 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.076 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.456 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.101 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.101 </td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.565 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.05</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.539 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.05</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.543 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.05</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.005</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.503 </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.067 </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.543 </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><0.05</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.508 </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.064 </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.064 </td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>T<sub>max</sub></italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.526 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.050 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.551 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.05</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.471 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.089 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.089 </td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.631 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.05</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.574 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.05</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.560 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.05</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.005</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Total</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.475 </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.086 </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.535 </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><0.05</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.473 </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.088 </td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>T<sub>min</sub></italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>D<sub>S</sub></italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.385 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.175 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.446 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.110 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.442 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.114 </td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>D<sub>D</sub></italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.524 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.055 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.506 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.065 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.511 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.062 </td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Total</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.429 </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.126 </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.477 </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.084 </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.453 </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.104 </td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>T<sub>diff</sub></italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>D<sub>S</sub></italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.234 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.422 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.280 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.333 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.311 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.280 </td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>D<sub>D</sub></italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.317 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.269 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.273 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.345 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.289 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.317 </td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Total</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.315 </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.273 </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.326 </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.255 </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.375 </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.187 </td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>H<sub>ave</sub></italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>D<sub>S</sub></italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.314 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.275 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.353 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.215 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.331 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.248 </td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>D<sub>D</sub></italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.560 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.05</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.465 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.094 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.469 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.091 </td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Total</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.496 </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.071 </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.476 </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.085 </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.450 </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.107 </td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>H<sub>max</sub></italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>D<sub>S</sub></italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.578 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.05</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.569 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.05</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.534 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.05</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>D<sub>D</sub></italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.570 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.05</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.497 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.070 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.488 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.076 </td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Total</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.601 </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><0.05</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.579 </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><0.05</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.542 </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><0.05</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>H<sub>min</sub></italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>D<sub>S</sub></italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.080 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.787 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.113 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.701 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.060 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.839 </td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>D<sub>D</sub></italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.532 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.050 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.439 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.116 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.444 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.112 </td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Total</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.495 </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.072 </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.493 </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.073 </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.453 </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.104 </td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>H<sub>diff</sub></italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>D<sub>S</sub></italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.665 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.01</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.583 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.05</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.579 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.05</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>D<sub>D</sub></italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.777 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.005</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.736 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.005</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.699 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.01</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Total</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.669 </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><0.01</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.636 </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><0.05</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.623 </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><0.05</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>V<sub>air</sub></italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>D<sub>S</sub></italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.160 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.584 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.081 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.782 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.187 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.523 </td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>D<sub>D</sub></italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.024 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.935 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.077 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.794 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.029 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.923 </td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Total</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.108 </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.714 </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.007 </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.982 </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.103 </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.725 </td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>DL</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>D<sub>S</sub></italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.464 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.095 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.411 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.144 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.446 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.110 </td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>D<sub>D</sub></italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.169 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.563 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.222 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.446 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.231 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.427 </td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Total</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.191 </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.513 </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.301 </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.296 </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.319 </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.267 </td></tr></tbody></table></table-wrap><table-wrap id=\"ijerph-17-05477-t006\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05477-t006_Table 6</object-id><label>Table 6</label><caption><p>Coefficients of determination and adjusted R<sup>2</sup> values for multivariate linear regression.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">\n</th><th colspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Cases</th><th colspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Deaths</th><th colspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Deaths (Ex.) <sup>†</sup></th></tr><tr><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">R<sup>2</sup></th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">adj. R<sup>2</sup></th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><italic>p</italic>-Value</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">R<sup>2</sup></th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">adj. R<sup>2</sup></th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><italic>p</italic>-Value</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">R<sup>2</sup></th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">adj. R<sup>2</sup></th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><italic>p</italic>-Value</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>D<sub>S</sub></italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.777 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.693 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.01</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.659 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.532 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.05 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.384</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.153 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.251 </td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>D<sub>D</sub></italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.773</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.688 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.01</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.653 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.523 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.05</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.383</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.151</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.253 </td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Total</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.776 </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.692</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><0.01</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.662 </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.536 </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><0.05</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.386</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.155</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.249 </td></tr></tbody></table><table-wrap-foot><fn><p><sup>†</sup> Excluding nosocomial infection in confirmed deaths.</p></fn></table-wrap-foot></table-wrap></floats-group></article>\n"
]
[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Int J Environ Res Public Health</journal-id><journal-id journal-id-type=\"iso-abbrev\">Int J Environ Res Public Health</journal-id><journal-id journal-id-type=\"publisher-id\">ijerph</journal-id><journal-title-group><journal-title>International Journal of Environmental Research and Public Health</journal-title></journal-title-group><issn pub-type=\"ppub\">1661-7827</issn><issn pub-type=\"epub\">1660-4601</issn><publisher><publisher-name>MDPI</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32751203</article-id><article-id pub-id-type=\"pmc\">PMC7432123</article-id><article-id pub-id-type=\"doi\">10.3390/ijerph17155464</article-id><article-id pub-id-type=\"publisher-id\">ijerph-17-05464</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Article</subject></subj-group></article-categories><title-group><article-title>Prevalence of Misophonia and Correlates of Its Symptoms among Inpatients with Depression</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0001-8161-3367</contrib-id><name><surname>Siepsiak</surname><given-names>Marta</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05464\">1</xref><xref rid=\"c1-ijerph-17-05464\" ref-type=\"corresp\">*</xref></contrib><contrib contrib-type=\"author\"><name><surname>Sobczak</surname><given-names>Anna Maria</given-names></name><xref ref-type=\"aff\" rid=\"af2-ijerph-17-05464\">2</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0003-2216-5888</contrib-id><name><surname>Bohaterewicz</surname><given-names>Bartosz</given-names></name><xref ref-type=\"aff\" rid=\"af2-ijerph-17-05464\">2</xref></contrib><contrib contrib-type=\"author\"><name><surname>Cichocki</surname><given-names>Łukasz</given-names></name><xref ref-type=\"aff\" rid=\"af3-ijerph-17-05464\">3</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0002-3349-6562</contrib-id><name><surname>Dragan</surname><given-names>Wojciech Łukasz</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05464\">1</xref></contrib></contrib-group><aff id=\"af1-ijerph-17-05464\"><label>1</label>Faculty of Psychology, University of Warsaw, 00-183 Warsaw, Poland; <email>[email protected]</email></aff><aff id=\"af2-ijerph-17-05464\"><label>2</label>Department of Cognitive Neuroscience and Neuroergonomics, Institute of Applied Psychology, Jagiellonian University, 30-348 Cracow, Poland; <email>[email protected]</email> (A.M.S.); <email>[email protected]</email> (B.B.)</aff><aff id=\"af3-ijerph-17-05464\"><label>3</label>Department of Psychiatry, Andrzej Frycz Modrzewski Cracow Academy, 30-705 Cracow, Poland; <email>[email protected]</email></aff><author-notes><corresp id=\"c1-ijerph-17-05464\"><label>*</label>Correspondence: <email>[email protected]</email></corresp></author-notes><pub-date pub-type=\"epub\"><day>29</day><month>7</month><year>2020</year></pub-date><pub-date pub-type=\"ppub\"><month>8</month><year>2020</year></pub-date><volume>17</volume><issue>15</issue><elocation-id>5464</elocation-id><history><date date-type=\"received\"><day>16</day><month>6</month><year>2020</year></date><date date-type=\"accepted\"><day>16</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>© 2020 by the authors.</copyright-statement><copyright-year>2020</copyright-year><license license-type=\"open-access\"><license-p>Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>).</license-p></license></permissions><abstract><p>Misophonia is an underexplored condition that significantly decreases the quality of life of those who suffer from it. It has neurological and physiological correlates and is associated with a variety of psychiatric symptoms; however, a growing body of data suggests that it is a discrete disorder. While comorbid diagnoses among people with misophonia have been a matter of research interest for many years there is no data on the frequency of misophonia among people with psychiatric disorders. This could be the next step to reveal additional mechanisms underlying misophonia. Until recently, the use of a variety of non-validated questionnaires and the dominance of internet-based studies have been also a major obstacles to a proper definition of misophonia. A total of 94 inpatients diagnosed with depression were assessed for misophonia with face-to-face interviews as well as with MisoQuest—a validated misophonia questionnaire. The prevalence of misophonia among these patients and the congruence of MisoQuest with face-to-face interviews were evaluated. Additionally, the patients filled in a series of questionnaires that measured a variety of psychiatric symptoms and psychological traits. Anxiety, depression, impulsivity, somatic pain, vegetative symptoms, post-traumatic stress disorder (PTSD) symptoms, gender, and age were analyzed in relation to the severity of symptoms of misophonia. Between 8.5 to 12.76% of inpatients with depression were diagnosed with misophonia (depending on measurement and inclusion criteria). MisoQuest accuracy was equal to 92.55%, sensitivity-66.67% and specificity-96.34%. Severity of misophonia symptoms was positively correlated to the greatest extent with anxiety. Moderate positive correlation was also found between severity of misophonia symptoms and depressive symptoms, intrusions, and somatic pain; a weak positive correlation was found between severity of misophonia and non-planning impulsivity, motor impulsivity, avoidance, and vegetative symptoms. There was no relationship between the severity of misophonia symptoms and attentional impulsivity or the age of participants.</p></abstract><kwd-group><kwd>misophonia</kwd><kwd>depression</kwd><kwd>anxiety</kwd><kwd>impulsivity</kwd><kwd>PTSD</kwd><kwd>MisoQuest</kwd><kwd>questionnaire for assessing misophonia</kwd><kwd>misophonia prevalence</kwd></kwd-group></article-meta></front><body><sec sec-type=\"intro\" id=\"sec1-ijerph-17-05464\"><title>1. Introduction</title><p>Misophonia is a kind of decreased sound tolerance [<xref rid=\"B1-ijerph-17-05464\" ref-type=\"bibr\">1</xref>,<xref rid=\"B2-ijerph-17-05464\" ref-type=\"bibr\">2</xref>] which significantly influences the quality of life of those who suffer from it. It is still underexplored and does not have either an International Classification of Diseases (ICD-11) or Diagnostic and Statistical Manual of Mental Disorders (DSM-5) classification. When people affected by misophonia are exposed to certain sound stimuli particular to each individual (e.g., sniffing, breathing, or tapping), they experience unwanted emotions (such as anger, disgust, irritation, or anxiety) which they usually consider excessive [<xref rid=\"B3-ijerph-17-05464\" ref-type=\"bibr\">3</xref>,<xref rid=\"B4-ijerph-17-05464\" ref-type=\"bibr\">4</xref>,<xref rid=\"B5-ijerph-17-05464\" ref-type=\"bibr\">5</xref>,<xref rid=\"B6-ijerph-17-05464\" ref-type=\"bibr\">6</xref>]. Moreover, they report physical sensations such as pressure in the chest, shoulders, head, or whole body as well as an increase in body temperature, pain, or breathing difficulties [<xref rid=\"B3-ijerph-17-05464\" ref-type=\"bibr\">3</xref>,<xref rid=\"B7-ijerph-17-05464\" ref-type=\"bibr\">7</xref>]. Although recent studies indicate that misophonia is a rather unique disorder, it is strongly associated with a variety of psychiatric symptoms [<xref rid=\"B8-ijerph-17-05464\" ref-type=\"bibr\">8</xref>,<xref rid=\"B9-ijerph-17-05464\" ref-type=\"bibr\">9</xref>,<xref rid=\"B10-ijerph-17-05464\" ref-type=\"bibr\">10</xref>]. Some results have indicated that anxiety symptoms act as a mediator of anger outbursts among individuals with misophonia [<xref rid=\"B11-ijerph-17-05464\" ref-type=\"bibr\">11</xref>]. These results have been replicated on a group of Chinese students [<xref rid=\"B12-ijerph-17-05464\" ref-type=\"bibr\">12</xref>]. Furthermore, anxiety symptoms turned out to be strongly related to the severity of misophonia in a study conducted on a population of Singaporean patients with various psychiatric diagnoses [<xref rid=\"B13-ijerph-17-05464\" ref-type=\"bibr\">13</xref>]. Moreover, patients with misophonia had significantly more symptoms of anxiety than the control group in the study of Schröder et al. [<xref rid=\"B14-ijerph-17-05464\" ref-type=\"bibr\">14</xref>]. We hypothesized that in our sample of patients with a diagnosis of depression, anxiety symptoms would be positively associated with severity of misophonia symptoms.</p><p>Rouw and Erfanian [<xref rid=\"B5-ijerph-17-05464\" ref-type=\"bibr\">5</xref>] found that post-traumatic stress disorder (PTSD) was one of the most common diagnoses in people with misophonia (occurring in 12% of cases). Remarkably, they noted that PTSD was the only comorbid disorder related to the severity of misophonia symptoms. Other studies have also found PTSD to be one of the most common comorbid disorders, being present in from 15.38% [<xref rid=\"B9-ijerph-17-05464\" ref-type=\"bibr\">9</xref>] to 30% [<xref rid=\"B15-ijerph-17-05464\" ref-type=\"bibr\">15</xref>] of cases. Its presence was associated with the severity of misophonia symptoms. Therefore, we assumed in this study that there would be a positive correlation between the severity of misophonia symptoms and PTSD symptoms, such as hyperarousal, avoidance, and intrusions.</p><p>Other research and observations on misophonia have led researchers to conclude that misophonia is strongly associated with obsessive-compulsive personality disorder (OCPD) and obsessive-compulsive disorder (OCD) [<xref rid=\"B6-ijerph-17-05464\" ref-type=\"bibr\">6</xref>,<xref rid=\"B11-ijerph-17-05464\" ref-type=\"bibr\">11</xref>,<xref rid=\"B16-ijerph-17-05464\" ref-type=\"bibr\">16</xref>]. The available data gives frequencies of the occurrence of OCD in people with misophonia at 2.4% [<xref rid=\"B6-ijerph-17-05464\" ref-type=\"bibr\">6</xref>], 11.53% [<xref rid=\"B9-ijerph-17-05464\" ref-type=\"bibr\">9</xref>], and 28% [<xref rid=\"B15-ijerph-17-05464\" ref-type=\"bibr\">15</xref>] while as many as 52.4% of people characterized with misophonia meet DSM diagnostic criteria for OCPD [<xref rid=\"B6-ijerph-17-05464\" ref-type=\"bibr\">6</xref>]. However, the occurrence of OCD characteristics such as obsession, compulsivity, and impulsivity [<xref rid=\"B6-ijerph-17-05464\" ref-type=\"bibr\">6</xref>] in misophonia is yet to be explored, as the data remain inconsistent. In a study by Cusack et al. [<xref rid=\"B8-ijerph-17-05464\" ref-type=\"bibr\">8</xref>], obsessive symptoms were more related to misophonia than compulsive symptoms. In contrast, the results of McKay et al. 2018 showed that, while some symptoms of OCD, e.g., ordering and harm avoidance, were higher among people with misophonia, other symptoms such as obsessions, neutralizing, and washing were lower [<xref rid=\"B10-ijerph-17-05464\" ref-type=\"bibr\">10</xref>]. Likewise, Schröderet al. 2013 state that impulsivity is an actual link between obsessive compulsive spectrum disorder and misophonia [<xref rid=\"B6-ijerph-17-05464\" ref-type=\"bibr\">6</xref>]. Taking into consideration the aforementioned reports, this study focused on impulsivity as a trait.</p><p>There is a large body of data indicating that people with OCD have increased impulsivity [<xref rid=\"B17-ijerph-17-05464\" ref-type=\"bibr\">17</xref>,<xref rid=\"B18-ijerph-17-05464\" ref-type=\"bibr\">18</xref>]; however, this relation appears to be quite complex [<xref rid=\"B19-ijerph-17-05464\" ref-type=\"bibr\">19</xref>]. While some aspects of impulsivity are positively correlated with OCD, others are associated negatively or not associated at all [<xref rid=\"B20-ijerph-17-05464\" ref-type=\"bibr\">20</xref>,<xref rid=\"B21-ijerph-17-05464\" ref-type=\"bibr\">21</xref>,<xref rid=\"B22-ijerph-17-05464\" ref-type=\"bibr\">22</xref>]. Moreover, self-report studies on people with OCD can be biased; therefore, they may not exactly reflect neurocognitive impulsivity [<xref rid=\"B23-ijerph-17-05464\" ref-type=\"bibr\">23</xref>]. Due to these disputes and discrepancies in the data on the role of impulsivity in OCD and because our goal was to explore the psychological construct of impulsivity rather than its comorbidity with other disorders, we excluded people with a diagnosis of OCD from this study. We suspected that, in spite of the exclusion of people with OCD, there would be a positive correlation between impulsivity and severity of misophonia symptoms.</p><p>Another disorder frequently reported among people with misophonia is depression, which was diagnosed in 22% of a sample of 50 people suffering from misophonia [<xref rid=\"B15-ijerph-17-05464\" ref-type=\"bibr\">15</xref>] and in 9.61% of a sample of 52 [<xref rid=\"B9-ijerph-17-05464\" ref-type=\"bibr\">9</xref>]. Both studies revealed a positive correlation between depression and the severity of misophonia symptoms. An internet-based study conducted on students of psychology also confirmed relationship between misophonia and symptoms of depression [<xref rid=\"B11-ijerph-17-05464\" ref-type=\"bibr\">11</xref>,<xref rid=\"B12-ijerph-17-05464\" ref-type=\"bibr\">12</xref>]. Another self-report study found that depression was reported by 13% of participants with misophonia [<xref rid=\"B5-ijerph-17-05464\" ref-type=\"bibr\">5</xref>]. In this study, we wanted to investigate whether in a clinical group of people with diagnoses of depression there is a positive correlation between severity of depressive symptoms and misophonia symptoms.</p><p>A growing body of data shows that being annoyed by noise is related to symptoms of anxiety and depression [<xref rid=\"B24-ijerph-17-05464\" ref-type=\"bibr\">24</xref>,<xref rid=\"B25-ijerph-17-05464\" ref-type=\"bibr\">25</xref>], while symptoms of depression has been found in a third of patients with tinnitus or hyperacusis [<xref rid=\"B26-ijerph-17-05464\" ref-type=\"bibr\">26</xref>]. Additionally, sound sensitivity has been shown to be associated with multiple vegetative symptoms of depression and pain [<xref rid=\"B27-ijerph-17-05464\" ref-type=\"bibr\">27</xref>,<xref rid=\"B28-ijerph-17-05464\" ref-type=\"bibr\">28</xref>,<xref rid=\"B29-ijerph-17-05464\" ref-type=\"bibr\">29</xref>]. In this study, we wanted to explore whether vegetative symptoms and pain are also associated with severity of misophonia symptoms.</p><p>In 2018, Queck et al. investigated factors related to the severity of misophonia symptoms in psychiatric patients, half of whom were diagnosed with depression [<xref rid=\"B13-ijerph-17-05464\" ref-type=\"bibr\">13</xref>]. Likewise, we decided to narrow down the research group to inpatients diagnosed with depression in order to avoid potential confounding effects due to other disorders. While there is no evidence that misophonia coexists with any particular disorder, at least 50% of people with misophonia suffer from some other psychiatric condition. Finding a trait or set of characteristics related to the severity of misophonia could shed light on its underlying mechanisms. Therefore, we explored how misophonia symptoms are related to psychological constructs rather than nosological entities in a homogenous group of psychiatric patients. We focused on attentional, motor, and non-planning impulsivity, symptoms of anxiety, alongside intrusions, hyperarousal and avoidance as components of PTSD. We also investigated the intensity of depressive symptoms, as the research group consisted of people who had been diagnosed with depression. Additionally, we controlled for somatic pain and vegetative symptoms that could impact auditory sensitivity.</p><p>Apart from the above, the main goal of this study was to assess the prevalence of misophonia among the patients with the diagnosis of depression. While there is already some data on the percentage of people with misophonia who suffer from depression, there is a gap in knowledge on how many people with depression suffer from misophonia. Revealing this relationship could be an additional cue on possible mechanisms underlying misophonia. Additionally, we aimed to assess the external validity of MisoQuest [<xref rid=\"B30-ijerph-17-05464\" ref-type=\"bibr\">30</xref>], a new questionnaire for assessing misophonia.</p></sec><sec id=\"sec2-ijerph-17-05464\"><title>2. Materials and Methods</title><p>This study was approved by The Ethics Committee at the Faculty of Psychology at the University of Warsaw (Psychologiczne korelaty mizofonii u osób z depresją, 21/05/2019).</p><sec id=\"sec2dot1-ijerph-17-05464\" sec-type=\"subjects\"><title>2.1. Participants</title><p>A total of 100 patients with a current diagnosis of depression were enrolled in the study. Data was missing for six participants, so 94 participants were analyzed. The psychiatric diagnoses were made by a team of hospital psychiatrists based on ICD-10 classification, prior to and independent of the study. Exclusion criteria were as follows: (a) cognitive impairment, (b) psychosis, (c) obsessive-compulsive disorder, (d) drug and/or alcohol addiction, and (e) experiencing a bipolar depressive episode. The reason the participants were in a patient unit was depression, although we excluded from the study participants with psychotic depression, depression in a progress of bipolar disorder, and when the state of depression was mainly a consequence of an alcoholic addiction. Patients were mainly treated for depression, but some of them had also diagnoses of personality or anxiety disorders. The time of their hospitalization was between two weeks and two months, and we recruited them with the help of their psychiatric doctors, who recommended patients who fulfilled the criteria of participation for this study.</p><p>Both male and female participants were recruited from psychiatric care wards in Kraków (Dr Jozef Babinski Clinical Hospital) and Warsaw (Institute of Psychiatry and Neurology). Participants’ ages ranged from 18 to 79 years old (<italic>M</italic> = 39.95, <italic>SD</italic> = 14.9). There was no difference in the average age (<italic>t</italic>(55) = 0.08; <italic>p</italic> = 0.935) of the men (<italic>M</italic> = 39.58, <italic>SD</italic> = 13.38) and women (<italic>M</italic> = 39.90, <italic>SD</italic> = 16.17). There were slightly more women than men in this study (45.7%; missing data for 19.1% of participants).</p></sec><sec id=\"sec2dot2-ijerph-17-05464\"><title>2.2. Assessments</title><p>The participants were individually evaluated by researchers trained in the diagnosis of misophonia using the measures described below.</p><p>Misophonia was identified using both the MisoQuest questionnaire [<xref rid=\"B30-ijerph-17-05464\" ref-type=\"bibr\">30</xref>] and a semi-structured interview based on Schröder et al.’s diagnostic criteria for misophonia [<xref rid=\"B6-ijerph-17-05464\" ref-type=\"bibr\">6</xref>]. During the interview, the diagnostician asked whether the patients agree or not with statements (see <xref ref-type=\"app\" rid=\"app1-ijerph-17-05464\">Supplementary File S1</xref>) derived directly from the diagnostic criteria. After each statement, according to the situation, the researcher asked additional questions in order to eliminate possible misunderstandings and to verify whether the given symptoms have clinical meaning. MisoQuest, a 14-item self-report questionnaire for assessing misophonia, was developed and validated on a Polish population. It has excellent psychometric properties and, therefore, was suitable for the purposes of this study. Previously [<xref rid=\"B30-ijerph-17-05464\" ref-type=\"bibr\">30</xref>], the cutoff for clinically significant misophonia symptoms was not specified. In this study, we proposed a cutoff of 61 out of 70 points. This derives from the difference between the mean score (<italic>M</italic> = 65.72) and the standard deviation (<italic>SD</italic> = 4.3) for people classified as having misophonia in the first MisoQuest validation study conducted in 2018. The same cutoff has been chosen to identify people with misophonia in an ongoing study further assessing the external validity of MisoQuest as well as in all other studies using MisoQuest associated with the “Psychological and psychophysiological correlates of misophonia” project being conducted at the Faculty of Psychology at the University of Warsaw.</p><p>Impulsivity was evaluated with the Barratt Impulsivity Scale (BIS-11)—a 30-item self-report questionnaire which assesses three aspects of impulsivity: attentional, motor, and non-planning. The Polish adaptation was used in this study [<xref rid=\"B31-ijerph-17-05464\" ref-type=\"bibr\">31</xref>].</p><p>Severity of depressive and anxiety symptoms was assessed with The Hospital Anxiety Depression Scale (HADS) [<xref rid=\"B32-ijerph-17-05464\" ref-type=\"bibr\">32</xref>,<xref rid=\"B33-ijerph-17-05464\" ref-type=\"bibr\">33</xref>] and The Symptom Checklist-27-plus (SCL-27-plus). The HADS consists of 14 items, 7 of which relate to anxiety and 7 of which relate to depression. The SCL-27-plus [<xref rid=\"B34-ijerph-17-05464\" ref-type=\"bibr\">34</xref>,<xref rid=\"B35-ijerph-17-05464\" ref-type=\"bibr\">35</xref>] measures the intensity of depressive symptoms, both those currently experienced and those which have occurred throughout one’s life as well as vegetative symptoms, agoraphobia, social phobia, and somatic pain.</p><p>PTSD components were identified using the Impact Event Scale-Revised (IES-R)—a 22-item self-reported questionnaire proposed by Weiss & Marmar [<xref rid=\"B36-ijerph-17-05464\" ref-type=\"bibr\">36</xref>] and adapted to Polish by Juczyński and Ogińska-Bulik [<xref rid=\"B37-ijerph-17-05464\" ref-type=\"bibr\">37</xref>]. The IES-R is used as a screening tool for an initial diagnosis of PTSD as it contains three subscales that include the symptoms of PTSD: intrusion, hyperarousal, and avoidance. Intrusion refers to recurring images, dreams, perceptual thoughts, or impressions connected with trauma. Hyperarousal is characterized by heightened alertness, anxiety, impatience, and difficulties concentrating. Avoidance manifests in attempts to eschew thoughts, emotions, or conversations connected to the trauma.</p></sec><sec id=\"sec2dot3-ijerph-17-05464\"><title>2.3. Data Analysis</title><p>Patients with misophonia were identified based on MisoQuest results and face-to-face interviews in order to evaluate the prevalence of misophonia as well as the external validity of MisoQuest. Next, the sensitivity, specificity, and accuracy of MisoQuest were calculated with exact Clopper–Pearson confidence intervals.</p><p>In order to check the difference in severity of misophonia symptoms between men and women, a <italic>t</italic>-test was conducted on the entire sample (94 participants). Next, Pearson correlations between the severity of misophonia symptoms (measured by MisoQuest) and all the factors described above, as well as the age of the participants, were calculated on the entire sample.</p><p>Row data is provided in <xref ref-type=\"app\" rid=\"app1-ijerph-17-05464\">Supplementary File S2</xref>.</p></sec></sec><sec sec-type=\"results\" id=\"sec3-ijerph-17-05464\"><title>3. Results</title><sec id=\"sec3dot1-ijerph-17-05464\"><title>3.1. Prevalence of Misophonia among Inpatients with Depression</title><p>Eight out of 94 subjects were diagnosed with misophonia (8.5%), meeting criteria for misophonia in both face-to-face interview and MisoQuest.</p><p>According to the assumed cutoff in MisoQuest (61 out of 70 points), 11 patients should be classified as having misophonia (11.7%). However, two of them (61 and 62 points) did not meet the criteria in the face-to-face interview, and in one case, the symptoms were explained by PTSD (62 points). There were also 4 patients (52, 59, 59, and 60 points) who met criteria for mild misophonia [<xref rid=\"B6-ijerph-17-05464\" ref-type=\"bibr\">6</xref>] in face-to-face interviews but did not report a significant severity of misophonia symptoms on MisoQuest.</p></sec><sec id=\"sec3dot2-ijerph-17-05464\"><title>3.2. External Validity of MisoQuest</title><p>The data from the questionnaire—MisoQuest were compared with face-to-face misophonia diagnosis. For sensitivity, specificity and accuracy of MisoQuest, see <xref rid=\"ijerph-17-05464-t001\" ref-type=\"table\">Table 1</xref>.</p></sec><sec id=\"sec3dot3-ijerph-17-05464\"><title>3.3. Gender Differences in Misophonia Symptoms Severity and Correlates of Severity of Misophonia Symptoms</title><p>Women (<italic>M</italic> = 44.72; SD = 14.66) manifested a significantly higher severity of misophonia symptoms (<italic>t</italic>(74) = 2.5; <italic>p</italic> = 0.015; Cohen’s d = 0.57) than men (<italic>M</italic> = 35.52; SD = 17.48).</p><p>There was no correlation between age and severity of symptoms of misophonia. The severity of misophonia symptoms was moderately correlated with symptoms of anxiety and severity of depression. A moderate correlation was also found with intrusions and arousal, while avoidance correlated weakly. There was a weak correlation with non-planning impulsivity and motor impulsivity and no correlation with attentional impulsivity. Moderate and weak correlation with somatic pain and vegetative symptoms was found (<xref rid=\"ijerph-17-05464-t002\" ref-type=\"table\">Table 2</xref>).</p></sec></sec><sec sec-type=\"discussion\" id=\"sec4-ijerph-17-05464\"><title>4. Discussion</title><p>This study showed that the prevalence of people with misophonia among patients with depression ranges from 8.5 to 12.76%. Additionally, comparing MisoQuest to face-to-face interviews allowed us to verify its external validity in a population of patients, proving its high accuracy. Severity of misophonia symptoms was positively correlated to the greatest extent with anxiety. Moreover, severity of misophonia symptoms was weakly to moderately associated with a variety of symptoms of pathologies and traits (impulsivity).</p><p>A total of 8.5% of patients met the criteria for misophonia. If we included the 4 cases with mild misophonia, with very limited impact on one’s life, this would be 12.76%. This is the first study investigating the prevalence of misophonia among inpatients with psychiatric diagnoses and also the first study, on any population, to evaluate the prevalence of misophonia using face-to-face interviews. These percentages of individuals found to have misophonia are half those found in other studies [<xref rid=\"B11-ijerph-17-05464\" ref-type=\"bibr\">11</xref>,<xref rid=\"B12-ijerph-17-05464\" ref-type=\"bibr\">12</xref>]. However, it is not possible to draw a conclusion from this comparison. The difference in the percentage of people with misophonia is probably due to differences in the measurement tools used. We believe that previously used misophonia questionnaires might have also captured more general sound sensitivities and considered lower severities of symptoms to constitute misophonia.</p><p>Analysis of Pearson’s correlation revealed a moderate association between depressive symptoms and severity of misophonia. The relation between misophonia and depression and its symptoms is already supported by data from several studies [<xref rid=\"B5-ijerph-17-05464\" ref-type=\"bibr\">5</xref>,<xref rid=\"B11-ijerph-17-05464\" ref-type=\"bibr\">11</xref>,<xref rid=\"B15-ijerph-17-05464\" ref-type=\"bibr\">15</xref>]. Chronic stress might link these phenomena, as it is strongly associated with misophonia and, at the same time, is one of the leading causes of depression [<xref rid=\"B38-ijerph-17-05464\" ref-type=\"bibr\">38</xref>]. Reactions characteristic of misophonia—tension related to trigger sounds and, consequently, avoidance of many social situations—can lead to overactivity of the hypothalamic–pituitary–adrenal tract [<xref rid=\"B39-ijerph-17-05464\" ref-type=\"bibr\">39</xref>,<xref rid=\"B40-ijerph-17-05464\" ref-type=\"bibr\">40</xref>]. Although it is still hypothetical that depression and depressive symptoms can be caused by misophonia, this notion could be supported by comparing our results with those of other studies: depression appears to be more prevalent among people with misophonia than misophonia is among people with depression. Nonetheless, it is highly possible that this difference is due to the use of different diagnostic criteria (MisoQuest vs. the Misophonia Questionnaire) as well as the way the data was gathered (internet-based study vs. face-to-face interviews). Therefore, the exploration of the incidence of misophonia, measured with comparable criteria and questionnaires, among homogenous groups of patients as well as in the general population should be done in the future. This could help to reveal the direction of the relation between misophonia and depression as well as other disorders.</p><p>This is yet another study indicating the meaningful role of anxiety in misophonia. Anxiety was correlated to a greater degree with misophonia severity than were any of the other measured variables. These results are similar to those of Queck et al. [<xref rid=\"B13-ijerph-17-05464\" ref-type=\"bibr\">13</xref>], who found that anxiety was the only significant factor in multivariate regression, predicting misophonia severity in a group of psychiatric patients. While the relation between misophonia and symptoms of anxiety as a pathology is already well documented, anxiety disorders (such as generalized anxiety disorder, panic disorder, or social phobia) are not the disorders most frequently found alongside misophonia and are not reported as those most significantly related with increased misophonia symptoms. The exceptions to this are OCD, in which the role in misophonia is still disputed, and PTSD, which is an event-related disorder, in contrast to other anxiety disorders where the more diffuse nature of the anxiety facilitates interaction with misophonia. At the same time, there is also significant data on the role of increased neuroticism in misophonia [<xref rid=\"B14-ijerph-17-05464\" ref-type=\"bibr\">14</xref>,<xref rid=\"B41-ijerph-17-05464\" ref-type=\"bibr\">41</xref>]. As the role of anxiety in predicting the severity of misophonia symptoms seems to be already well described, a deeper investigation into anxiety as a trait and personality characteristics such as neuroticism versus anxiety disorders could be the next step in unveiling the mechanisms of misophonia related to anxiety.</p><p>Excluding OCD patients allowed us to assess self-described impulsivity without the risk of overlap. As a result, unlike attentional impulsivity, non-planning impulsivity and motor impulsivity were weakly but significantly correlated with misophonia symptoms. Notably, attentional impulsivity was one of only two variables (the other being age) in this study that were not correlated with the severity of misophonia; the other aspects of impulsivity were much less associated with severity of misophonia than were all the other variables. Hence, the role of impulsivity was undermined in this study, but nonetheless, strong conclusions about impulsivity should not be drawn—this relationship needs further exploration.</p><p>Regarding symptoms of PTSD, intrusions and arousal were moderately associated with misophonia symptoms while avoidance was only weakly correlated. There were 6 participants (7.4%) who reported that they had been diagnosed by their psychiatrist with PTSD; their average score on MisoQuest was 10 points higher (<italic>M</italic> = 48.83, <italic>SD</italic> = 12.37) than patients without such a diagnosis (<italic>M</italic> = 38.52, <italic>SD</italic> = 16.85). The increased severity of misophonia symptoms in people with PTSD has already been reported by Rouw and Erfanian [<xref rid=\"B5-ijerph-17-05464\" ref-type=\"bibr\">5</xref>]. Unfortunately, our study did not include enough participants diagnosed with PTSD to conduct a statistical analysis and to draw reliable conclusions. Nonetheless, no patients with PTSD were diagnosed with misophonia in the face-to-face interviews. Therefore, our results undermine the potential relationship between misophonia and PTSD. These results might rather indicate relations between more general sound sensitivity and psychological distress and hyperarousal [<xref rid=\"B42-ijerph-17-05464\" ref-type=\"bibr\">42</xref>,<xref rid=\"B43-ijerph-17-05464\" ref-type=\"bibr\">43</xref>].</p><p>Vegetative symptoms were found to be weakly yet significantly correlated with the severity of misophonia symptoms, while somatic pain was found to be moderately significantly correlated. Although many studies discuss the vegetative symptoms experienced by people with misophonia in the presence of their misophonic triggers, to the best of our knowledge, this is the first study to measure the association between the above symptoms and misophonia severity in general. We are not surprised with these results. We expected that vegetative symptoms and somatic pain would be related to misophonia symptom severity to a certain but limited extent based on the associations between sound sensitivity and somatic disturbances and pain discussed in the Introduction section. Future research should check whether these variables are more related to general sound sensitivity than to misophonia. It is possible that, because misophonia is a more specific condition than general sound sensitivity/hyperacusis, the presence of pain and vegetative symptoms could be lower in misophonia.</p><p>Women manifested a significantly higher severity of misophonia symptoms than did men. Our results are in line with recent research conducted by Queck et al. [<xref rid=\"B13-ijerph-17-05464\" ref-type=\"bibr\">13</xref>] or Erfanian et al. [<xref rid=\"B9-ijerph-17-05464\" ref-type=\"bibr\">9</xref>]. However, we draw different conclusions to previous studies [<xref rid=\"B11-ijerph-17-05464\" ref-type=\"bibr\">11</xref>,<xref rid=\"B12-ijerph-17-05464\" ref-type=\"bibr\">12</xref>] which found no gender differences in severity of misophonia. Gender differences in accepted standards for certain scales that are used in clinical practice are usually not taken into account in research studies. As with other constructs, the standards for genders might differ also for misophonia.</p><p>The severity of misophonia symptoms was not associated with the age of participants. However, much age data was missing, which could have affected the results. In a study by Queck et al. [<xref rid=\"B13-ijerph-17-05464\" ref-type=\"bibr\">13</xref>] on patients with psychiatric diagnoses, a negative correlation between the severity of misophonia symptoms and age was observed, but only before adjusting for confounding factors. Similarly, there was no relation with age in the study by Wu et al. [<xref rid=\"B11-ijerph-17-05464\" ref-type=\"bibr\">11</xref>]. Nonetheless, the data are inconclusive: in the study conducted by Rouw & Erfanian [<xref rid=\"B5-ijerph-17-05464\" ref-type=\"bibr\">5</xref>], while severity of misophonia symptoms decreased with age and there was negative correlation with a small effect between age and severity of misophonia, they noted that it increased in the two oldest age categories (over 65). Thus, the association between age and severity of misophonia needs further exploration.</p><p>Assessment of MisoQuest’s additional external validity showed that, with its high accuracy, it is a good tool for assessing misophonia. It has lower sensitivity than specificity, which means that there is a risk of false negative cases. In fact, 4 patients diagnosed in face-to-face interviews with misophonia with limited impact on one’s life did not reach the cutoff for misophonia in MisoQuest. Results slightly below or above the cutoff for misophonia should be evaluated carefully, especially in populations of patients with psychiatric diagnoses. MisoQuest was created with an aim to identify people whose quality of life is significantly lower because of misophonia [<xref rid=\"B30-ijerph-17-05464\" ref-type=\"bibr\">30</xref>], and apparently, it captures more severe cases. Further assessment of validity of MisoQuest is needed on the general population.</p><sec><title>Limitations</title><p>This study has two meaningful weaknesses. The major drawback of the study is the significant amount of missing data (age and gender) that was a result of mistakes during the data collection. Another limitation is that caution must be used when comparing these data with those of other studies (especially studies in which only questionnaires were used, with no face-to-face interviews). This is because MisoQuest is based on a slightly different approach to misophonia than the Misophonia Questionnaire (MQ), which has, to date, been used in the majority of studies. At the time of conducting this study, there was no Polish version of the MQ available. In the future, it would be worthwhile to compare characteristics of people diagnosed with misophonia by those two different questionnaires.</p></sec></sec><sec sec-type=\"conclusions\" id=\"sec5-ijerph-17-05464\"><title>5. Conclusions</title><p>The prevalence of misophonia among patients with depression was evaluated in this study and stands at 8.5–12.76%. Additionally, the results show that MisoQuest can be helpful for identifying misophonia among patients with psychiatric diagnoses. The severity of misophonia symptoms was associated weakly to moderately with various symptoms and psychological characteristics and was the most associated with anxiety. Anxiety, however, is a more general psychological construct that not only is related to multiple psychiatric conditions but also for which intensity differs based on the temperamental characteristics of individuals. The aforementioned results are in line with other studies, suggesting that misophonia might be an independent disorder.</p></sec></body><back><app-group><app id=\"app1-ijerph-17-05464\"><title>Supplementary Materials</title><p>The following are available online at <uri xlink:href=\"https://www.mdpi.com/1660-4601/17/15/5464/s1\">https://www.mdpi.com/1660-4601/17/15/5464/s1</uri>, File S1: Semi-structured interview; File S2: Row data.</p><supplementary-material content-type=\"local-data\" id=\"ijerph-17-05464-s001\"><media xlink:href=\"ijerph-17-05464-s001.zip\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material></app></app-group><notes><title>Author Contributions</title><p>Conceptualization, M.S., A.M.S., and B.B.; methodology, M.S., A.M.S., B.B., and W.Ł.D.; formal analysis, M.S.; investigation, M.S., A.M.S., B.B., and Ł.C.; writing—original draft preparation, M.S. and A.M.S.; writing—review and editing, B.B., Ł.C., and W.Ł.D.; supervision, W.Ł.D.; project administration, M.S., A.M.S., B.B., and Ł.C.; funding acquisition, M.S. All authors have read and agreed to the published version of the manuscript.</p></notes><notes><title>Funding</title><p>This work was supported by the Faculty of Psychology, University of Warsaw, from the funds awarded by the Ministry of Science and Higher Education in the form of a subsidy for the maintenance and development of research potential in 2020 (501-D125-01-1250000-5011000251), the Polish National Science Center grant number 2018/29/N/HS6/01108 and the Foundation for Polish Science (FNP) project (Bio-inspired Artificial Neural Networks ID: POIR.04.04.00-00-14DE/18-00.</p></notes><notes notes-type=\"COI-statement\"><title>Conflicts of Interest</title><p>The authors declare no conflict of interest.</p></notes><ref-list><title>References</title><ref id=\"B1-ijerph-17-05464\"><label>1.</label><element-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Brout</surname><given-names>J.J.</given-names></name><name><surname>Edelstein</surname><given-names>M.</given-names></name><name><surname>Erfanian</surname><given-names>M.</given-names></name><name><surname>Mannino</surname><given-names>M.</given-names></name><name><surname>Miller</surname><given-names>L.J.</given-names></name><name><surname>Rouw</surname><given-names>R.</given-names></name><name><surname>Kumar</surname><given-names>S.</given-names></name><name><surname>Rosenthal</surname><given-names>M.Z.</given-names></name></person-group><article-title>Investigating misophonia: A review of the empirical literature, clinical implications, and a research agenda</article-title><source>Front. 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Neuropsychol.</source><year>2019</year><volume>26</volume><fpage>365</fpage><lpage>373</lpage><pub-id pub-id-type=\"doi\">10.1080/23279095.2018.1433179</pub-id><pub-id pub-id-type=\"pmid\">29465307</pub-id></element-citation></ref></ref-list></back><floats-group><table-wrap id=\"ijerph-17-05464-t001\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05464-t001_Table 1</object-id><label>Table 1</label><caption><p>Sensitivity, specificity, and accuracy of MisoQuest.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">MisoQuest</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Value %</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">(95% CI)</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Sensitivity</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">66.67</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">(34.89–90.08)</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Specificity</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">96.34</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">(89.68–99.24)</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Accuracy</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">92.55</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">(85.26–96.95)</td></tr></tbody></table></table-wrap><table-wrap id=\"ijerph-17-05464-t002\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05464-t002_Table 2</object-id><label>Table 2</label><caption><p>Correlations with severity of misophonia symptoms assessed with MisoQuest.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Age</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">HADS Anxiety</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">BIS Attentional</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">BIS Motor</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">BIS Non-Planning</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">SCL Vegetative</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">SCL Pain</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">HADS Depression</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">IES Intrusions</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">IES Arousal</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">IES Avoidance</th></tr></thead><tbody><tr><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">MisoQuest Pearson’s correlation <italic>p</italic>-value</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.024</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.444</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.178</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.205</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.204</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.276</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.307</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.325</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.324</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.325</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.263</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.856</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.000</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.086</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.047</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.049</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.007</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.003</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.001</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.001</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.001</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.010</td></tr></tbody></table><table-wrap-foot><fn><p>HADS—Hospital Anxiety and Depression scale; BIS—Barrat Impulsivity Scale; SCL—Symptom Checklist Scale; IES—Impact of Event Scale.</p></fn></table-wrap-foot></table-wrap></floats-group></article>\n"
]
[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Int J Mol Sci</journal-id><journal-id journal-id-type=\"iso-abbrev\">Int J Mol Sci</journal-id><journal-id journal-id-type=\"publisher-id\">ijms</journal-id><journal-title-group><journal-title>International Journal of Molecular Sciences</journal-title></journal-title-group><issn pub-type=\"epub\">1422-0067</issn><publisher><publisher-name>MDPI</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32751332</article-id><article-id pub-id-type=\"pmc\">PMC7432124</article-id><article-id pub-id-type=\"doi\">10.3390/ijms21155395</article-id><article-id pub-id-type=\"publisher-id\">ijms-21-05395</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Article</subject></subj-group></article-categories><title-group><article-title>Polymorphisms in the Angiogenesis-Related Genes <italic>EFNB2</italic>, <italic>MMP2</italic> and <italic>JAG1</italic> Are Associated with Survival of Colorectal Cancer Patients</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0003-4430-296X</contrib-id><name><surname>Scherer</surname><given-names>Dominique</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05395\">1</xref><xref ref-type=\"aff\" rid=\"af2-ijms-21-05395\">2</xref></contrib><contrib contrib-type=\"author\"><name><surname>Deutelmoser</surname><given-names>Heike</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05395\">1</xref><xref ref-type=\"aff\" rid=\"af2-ijms-21-05395\">2</xref></contrib><contrib contrib-type=\"author\"><name><surname>Balavarca</surname><given-names>Yesilda</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05395\">1</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0002-6096-1052</contrib-id><name><surname>Toth</surname><given-names>Reka</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05395\">1</xref><xref ref-type=\"aff\" rid=\"af3-ijms-21-05395\">3</xref></contrib><contrib contrib-type=\"author\"><name><surname>Habermann</surname><given-names>Nina</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05395\">1</xref><xref ref-type=\"aff\" rid=\"af4-ijms-21-05395\">4</xref></contrib><contrib contrib-type=\"author\"><name><surname>Buck</surname><given-names>Katharina</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05395\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Kap</surname><given-names>Elisabeth Johanna</given-names></name><xref ref-type=\"aff\" rid=\"af5-ijms-21-05395\">5</xref></contrib><contrib contrib-type=\"author\"><name><surname>Botma</surname><given-names>Akke</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05395\">1</xref><xref ref-type=\"aff\" rid=\"af5-ijms-21-05395\">5</xref></contrib><contrib contrib-type=\"author\"><name><surname>Seibold</surname><given-names>Petra</given-names></name><xref ref-type=\"aff\" rid=\"af5-ijms-21-05395\">5</xref></contrib><contrib contrib-type=\"author\"><name><surname>Jansen</surname><given-names>Lina</given-names></name><xref ref-type=\"aff\" rid=\"af6-ijms-21-05395\">6</xref></contrib><contrib contrib-type=\"author\"><name><surname>Lorenzo Bermejo</surname><given-names>Justo</given-names></name><xref ref-type=\"aff\" rid=\"af2-ijms-21-05395\">2</xref></contrib><contrib contrib-type=\"author\"><name><surname>Weigl</surname><given-names>Korbinian</given-names></name><xref ref-type=\"aff\" rid=\"af6-ijms-21-05395\">6</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0002-7238-6956</contrib-id><name><surname>Benner</surname><given-names>Axel</given-names></name><xref ref-type=\"aff\" rid=\"af7-ijms-21-05395\">7</xref></contrib><contrib contrib-type=\"author\"><name><surname>Hoffmeister</surname><given-names>Michael</given-names></name><xref ref-type=\"aff\" rid=\"af6-ijms-21-05395\">6</xref></contrib><contrib contrib-type=\"author\"><name><surname>Ulrich</surname><given-names>Alexis</given-names></name><xref ref-type=\"aff\" rid=\"af8-ijms-21-05395\">8</xref><xref ref-type=\"aff\" rid=\"af9-ijms-21-05395\">9</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0002-6129-1572</contrib-id><name><surname>Brenner</surname><given-names>Hermann</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05395\">1</xref><xref ref-type=\"aff\" rid=\"af6-ijms-21-05395\">6</xref><xref ref-type=\"aff\" rid=\"af10-ijms-21-05395\">10</xref></contrib><contrib contrib-type=\"author\"><name><surname>Burwinkel</surname><given-names>Barbara</given-names></name><xref ref-type=\"aff\" rid=\"af11-ijms-21-05395\">11</xref><xref ref-type=\"aff\" rid=\"af12-ijms-21-05395\">12</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0001-8919-1971</contrib-id><name><surname>Chang-Claude</surname><given-names>Jenny</given-names></name><xref ref-type=\"aff\" rid=\"af5-ijms-21-05395\">5</xref><xref ref-type=\"aff\" rid=\"af13-ijms-21-05395\">13</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0001-7641-059X</contrib-id><name><surname>Ulrich</surname><given-names>Cornelia M.</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05395\">1</xref><xref ref-type=\"aff\" rid=\"af14-ijms-21-05395\">14</xref><xref ref-type=\"aff\" rid=\"af15-ijms-21-05395\">15</xref><xref rid=\"c1-ijms-21-05395\" ref-type=\"corresp\">*</xref></contrib></contrib-group><aff id=\"af1-ijms-21-05395\"><label>1</label>Division of Preventive Oncology, National Center for Tumor Diseases (NCT) and German Cancer Research Center (DKFZ), 69117 Heidelberg, Germany; <email>[email protected]</email> (D.S.); <email>[email protected]</email> (H.D.); <email>[email protected]</email> (Y.B.); <email>[email protected]</email> (R.T.); <email>[email protected]</email> (N.H.); <email>[email protected]</email> (K.B.); <email>[email protected]</email> (A.B.); <email>[email protected]</email> (H.B.)</aff><aff id=\"af2-ijms-21-05395\"><label>2</label>Institute of Medical Biometry and Informatics, University of Heidelberg, 69117 Heidelberg, Germany; <email>[email protected]</email></aff><aff id=\"af3-ijms-21-05395\"><label>3</label>Division of Cancer Epigenomics and Cancer Risk Factors, German Cancer Research Center (DKFZ), 69117 Heidelberg, Germany</aff><aff id=\"af4-ijms-21-05395\"><label>4</label>European Molecular Biology Laboratory (EMBL), Genome Biology, 69117 Heidelberg, Germany</aff><aff id=\"af5-ijms-21-05395\"><label>5</label>Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), 69117 Heidelberg, Germany; <email>[email protected]</email> (E.J.K.); <email>[email protected]</email> (P.S.); <email>[email protected]</email> (J.C.-C.)</aff><aff id=\"af6-ijms-21-05395\"><label>6</label>Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), 69117 Heidelberg, Germany; <email>[email protected]</email> (L.J.); <email>[email protected]</email> (K.W.); <email>[email protected]</email> (M.H.)</aff><aff id=\"af7-ijms-21-05395\"><label>7</label>Division of Biostatistics, German Cancer Research Center (DKFZ), 69117 Heidelberg, Germany; <email>[email protected]</email></aff><aff id=\"af8-ijms-21-05395\"><label>8</label>Department of General, Visceral and Transplantation Surgery, University Hospital Heidelberg, 69117 Heidelberg, Germany; <email>[email protected]</email></aff><aff id=\"af9-ijms-21-05395\"><label>9</label>Chirurgische Klinik I, Lukaskrankenhaus Neuss, 41464 Neuss, Germany</aff><aff id=\"af10-ijms-21-05395\"><label>10</label>German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), 69117 Heidelberg, Germany</aff><aff id=\"af11-ijms-21-05395\"><label>11</label>Division of Molecular Epidemiology, German Cancer Research Center (DKFZ), 69117 Heidelberg, Germany; <email>[email protected]</email></aff><aff id=\"af12-ijms-21-05395\"><label>12</label>Division Molecular Biology of Breast Cancer, Department of Gynecology and Obstetrics, University of Heidelberg, 69117 Heidelberg, Germany</aff><aff id=\"af13-ijms-21-05395\"><label>13</label>Cancer Epidemiology Group, University Cancer Center Hamburg, University Medical Center Hamburg-Eppendorf, 20246 Hamburg, Germany</aff><aff id=\"af14-ijms-21-05395\"><label>14</label>Huntsman Cancer Institute, Salt Lake City, UT 84112, USA</aff><aff id=\"af15-ijms-21-05395\"><label>15</label>Department of Population Health Sciences, University of Utah, Salt Lake City, UT 84112, USA</aff><author-notes><corresp id=\"c1-ijms-21-05395\"><label>*</label>Correspondence: <email>[email protected]</email>; Tel.: +1-801-213-5716</corresp></author-notes><pub-date pub-type=\"epub\"><day>29</day><month>7</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><month>8</month><year>2020</year></pub-date><volume>21</volume><issue>15</issue><elocation-id>5395</elocation-id><history><date date-type=\"received\"><day>03</day><month>7</month><year>2020</year></date><date date-type=\"accepted\"><day>22</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>© 2020 by the authors.</copyright-statement><copyright-year>2020</copyright-year><license license-type=\"open-access\"><license-p>Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>).</license-p></license></permissions><abstract><p>An individual’s inherited genetic variation may contribute to the ‘angiogenic switch’, which is essential for blood supply and tumor growth of microscopic and macroscopic tumors. Polymorphisms in angiogenesis-related genes potentially predispose to colorectal cancer (CRC) or affect the survival of CRC patients. We investigated the association of 392 single nucleotide polymorphisms (SNPs) in 33 angiogenesis-related genes with CRC risk and survival of CRC patients in 1754 CRC cases and 1781 healthy controls within DACHS (Darmkrebs: Chancen der Verhütung durch Screening), a German population-based case-control study. Odds ratios and 95% confidence intervals (CI) were estimated from unconditional logistic regression to test for genetic associations with CRC risk. The Cox proportional hazard model was used to estimate hazard ratios (HR) and 95% CIs for survival. Multiple testing was adjusted for by a false discovery rate. No variant was associated with CRC risk. Variants in <italic>EFNB2</italic>, <italic>MMP2</italic> and <italic>JAG1</italic> were significantly associated with overall survival. The association of the <italic>EFNB2</italic> tagging SNP rs9520090 (<italic>p</italic> < 0.0001) was confirmed in two validation datasets (<italic>p</italic>-values: 0.01 and 0.05). The associations of the tagging SNPs rs6040062 in <italic>JAG1</italic> (<italic>p</italic>-value 0.0003) and rs2241145 in <italic>MMP2</italic> (<italic>p</italic>-value 0.0005) showed the same direction of association with overall survival in the first and second validation sets, respectively, although they did not reach significance (<italic>p</italic>-values: 0.09 and 0.25, respectively). <italic>EFNB2</italic>, <italic>MMP2</italic> and <italic>JAG1</italic> are known for their functional role in angiogenesis and the present study points to novel evidence for the impact of angiogenesis-related genetic variants on the CRC outcome.</p></abstract><kwd-group><kwd>angiogenesis</kwd><kwd>colorectal cancer</kwd><kwd>survival</kwd><kwd>single nucleotide polymorphism</kwd></kwd-group></article-meta></front><body><sec sec-type=\"intro\" id=\"sec1-ijms-21-05395\"><title>1. Introduction</title><p>Colorectal cancer (CRC) is the second most common cancer and the second leading cause of cancer death among men and women throughout the world asserting major public health problems [<xref rid=\"B1-ijms-21-05395\" ref-type=\"bibr\">1</xref>]. Disease risk and prognosis are to a large proportion modifiable with obesity, red meat consumption and sedentary lifestyle increasing the risk and physical activity, NSAID (non-steroidal anti-inflammatory drug) use and fiber intake being protective [<xref rid=\"B2-ijms-21-05395\" ref-type=\"bibr\">2</xref>,<xref rid=\"B3-ijms-21-05395\" ref-type=\"bibr\">3</xref>,<xref rid=\"B4-ijms-21-05395\" ref-type=\"bibr\">4</xref>]. However, there is also a genetic component to the disease, which is estimated to explain up to 35% of the heritability in colorectal cancer risk [<xref rid=\"B5-ijms-21-05395\" ref-type=\"bibr\">5</xref>,<xref rid=\"B6-ijms-21-05395\" ref-type=\"bibr\">6</xref>]. Some studies point to genetic associations with the CRC outcome [<xref rid=\"B7-ijms-21-05395\" ref-type=\"bibr\">7</xref>,<xref rid=\"B8-ijms-21-05395\" ref-type=\"bibr\">8</xref>,<xref rid=\"B9-ijms-21-05395\" ref-type=\"bibr\">9</xref>]. However, evidence is still limited.</p><p>Angiogenesis, the generation of new blood vessels from pre-existing vessels, is crucial for tumor growth, progression and metastasis as it provides nutrients and oxygen to the growing tumor [<xref rid=\"B10-ijms-21-05395\" ref-type=\"bibr\">10</xref>]. While in normal tissue, angiogenesis is tightly controlled and only transiently turned on, in carcinogenic progression an ‘angiogenic switch’ perpetuates the formation of new blood vessels [<xref rid=\"B11-ijms-21-05395\" ref-type=\"bibr\">11</xref>].</p><p>VEGF (vascular endothelial growth factor) as one of the most potent endothelial cell mitogens is a major driver of angiogenesis. It can act in an autocrine or paracrine manner through interaction with its receptors (VEGFR1-3, also named FLT1 (fms related tyrosine kinase 1), KDR (kinase insert domain receptor) and FLT4 (fms related tyrosine kinase 4), respectively) and co-receptors (neuropilins, NRPs) [<xref rid=\"B12-ijms-21-05395\" ref-type=\"bibr\">12</xref>]. Such interactions may further be regulated through ephrinB2 (EFNB2) and finally stimulate proliferation, migration of endothelial cells, cell degradation, remodeling of the extracellular matrix (ECM) and angiogenesis [<xref rid=\"B13-ijms-21-05395\" ref-type=\"bibr\">13</xref>]. Ephrins and their receptors (Eph receptors) are crucial in numerous biological processes [<xref rid=\"B14-ijms-21-05395\" ref-type=\"bibr\">14</xref>]. Their bidirectional signaling triggers cascades in both, the receptor- and the ligand-bearing cells and deregulated Eph/ephrin activation as well as altered expression of the receptors or their ligands have been frequently reported in a variety of tumors including colorectal neoplasms [<xref rid=\"B14-ijms-21-05395\" ref-type=\"bibr\">14</xref>,<xref rid=\"B15-ijms-21-05395\" ref-type=\"bibr\">15</xref>]. Based on these observations, targeted interference with Eph/ephrin signaling has been proposed for cancer therapy [<xref rid=\"B16-ijms-21-05395\" ref-type=\"bibr\">16</xref>].</p><p>Matrix metalloproteinases (MMPs) are critical in ECM remodeling [<xref rid=\"B13-ijms-21-05395\" ref-type=\"bibr\">13</xref>,<xref rid=\"B17-ijms-21-05395\" ref-type=\"bibr\">17</xref>]. In addition to their capability to promote bioavailability of VEGF, the proteolytic activities of MMPs facilitate protein degradation and support vessel formation [<xref rid=\"B18-ijms-21-05395\" ref-type=\"bibr\">18</xref>]. VEGF signaling also regulates DLL4 expression, which is one of two key ligands of NOTCH4 (notch receptor 4), the second ligand being JAG1 (jagged canonical Notch ligand 1) [<xref rid=\"B19-ijms-21-05395\" ref-type=\"bibr\">19</xref>]. Concurrently, NOTCH4 promotes VEGFR expression [<xref rid=\"B20-ijms-21-05395\" ref-type=\"bibr\">20</xref>].</p><p>An individual’s inherited genetic background may modify angiogenesis-related signaling cascades that affect blood supply of premalignant lesions and/or macroscopic tumors [<xref rid=\"B21-ijms-21-05395\" ref-type=\"bibr\">21</xref>]. We hypothesize that this may result in altered cancer risk or disease outcome after colorectal cancer diagnosis.</p><p>In the present study, we investigated the association between angiogenesis-related genetic variants and risk of CRC, overall survival (OS) and recurrence-free survival (RFS) of colorectal cancer patients in 1754 CRC cases and 1781 healthy controls using a comprehensive targeted genotyping approach and validated some of the associations.</p></sec><sec sec-type=\"results\" id=\"sec2-ijms-21-05395\"><title>2. Results</title><p>In this study 392 SNPs in 33 angiogenesis-related genes (<xref ref-type=\"app\" rid=\"app1-ijms-21-05395\">Table S1</xref>) were investigated for their association with risk of colorectal cancer and survival of colorectal cancer patients in a population of 1754 colorectal cancer cases and 1781 healthy individuals (<xref ref-type=\"app\" rid=\"app1-ijms-21-05395\">Tables S2, S3 and S4</xref>, respectively). Characteristics of the study population are presented in <xref rid=\"ijms-21-05395-t001\" ref-type=\"table\">Table 1</xref>.</p><sec id=\"sec2dot1-ijms-21-05395\"><title>2.1. Risk</title><p>Higher education and the use of NSAIDs were associated with reduced risk of colorectal cancer. In addition, cases were more likely to smoke regularly, consume higher amounts of red meat and alcohol and were more often diabetic and obese (<xref rid=\"ijms-21-05395-t001\" ref-type=\"table\">Table 1</xref>a).</p><p>No variant was associated with the risk of colorectal cancer after adjustment for multiple testing (<xref ref-type=\"app\" rid=\"app1-ijms-21-05395\">Table S2</xref>). Furthermore, we did not observe statistically significant interactions of any of the investigated polymorphisms with NSAID use or smoking in relation to the risk of colorectal cancer.</p></sec><sec id=\"sec2dot2-ijms-21-05395\"><title>2.2. Overall and Recurrence-Free Survival</title><p>During a median follow up of five years, 538 patients died and 468 had a recurrent event. Deceased patients were older, diagnosed with higher disease stages and tumor grade. They were less likely to consume alcohol and to be overweight or obese (<xref rid=\"ijms-21-05395-t001\" ref-type=\"table\">Table 1</xref>b).</p><p>Eleven tagging SNPs in four genes (<italic>EFNB2</italic>, <italic>MMP2</italic>, <italic>JAG1</italic> and <italic>KDR</italic>) were significantly associated with overall survival (<xref rid=\"ijms-21-05395-t002\" ref-type=\"table\">Table 2</xref>, <xref ref-type=\"app\" rid=\"app1-ijms-21-05395\">Figure S1</xref> and <xref ref-type=\"app\" rid=\"app1-ijms-21-05395\">Table S3</xref>) of colorectal cancer patients. Five variants in <italic>EFNB2</italic>, none of which were in high LD (r<sup>2</sup> < 0.8), were associated with overall survival. The strongest associations with overall survival were observed for the protective variant rs2391333 (HR<sub>C > T</sub>: 0.77, 95% CI: 0.68–0.87, <italic>p</italic>-value: <0.0001) and for the risk variant rs9520090 (HR<sub>G > C</sub>: 1.29, 95% CI: 1.15–1.46, <italic>p</italic>-value: <0.0001). Furthermore, rs9520087 (HR<sub>G > A</sub>: 1.24, 95% CI: 1.10–1.40, <italic>p</italic>-value: 0.0006), rs9520088 (HR<sub>TT</sub> vs. <sub>TC/CC</sub>: 1.37, 95% CI: 1.15–1.62, <italic>p</italic>-value: 0.0003) and rs2057408 (HR<sub>AA</sub> vs. <sub>AG/GG</sub>: 1.35, 95% CI: 1.14–1.62, <italic>p</italic>-value: 0.0008) were significantly associated with worse overall survival (<xref rid=\"ijms-21-05395-t002\" ref-type=\"table\">Table 2</xref>a). All associations with overall survival remained statistically significant after correction for multiple testing (<italic>q</italic> < 0.05).</p><p>Four tagging SNPs (none in high LD with r<sup>2</sup> < 0.8) in the region of <italic>MMP2</italic> were significantly associated with overall survival of colorectal cancer patients (<xref rid=\"ijms-21-05395-t002\" ref-type=\"table\">Table 2</xref>a). Rs17301608 was significantly associated with poorer OS (HR<sub>A > G</sub>: 1.25, 95% CI: 1.11–1.41, <italic>p</italic>-value: 0.0003, <xref rid=\"ijms-21-05395-t002\" ref-type=\"table\">Table 2</xref>a). The variants rs2241145 (HR<sub>G > C</sub>: 1.23, 95% CI: 1.09–1.38, <italic>p</italic>-value: 0.0005) and rs1561219 (HR<sub>C > A</sub>: 1.32, 95% CI: 1.11–1.56, <italic>p</italic>-value: 0.0009) were also associated with decreased overall survival. In contrast, the variant rs243847 significantly improved overall survival (HR<sub>T > C</sub>: 0.81, 95% CI: 0.71–0.92, <italic>p</italic>-value: 0.0010). All associations remained statistically significant after multiple testing correction (<italic>q</italic> < 0.05).</p><p>One intronic variant in <italic>JAG1</italic> (rs6040062) was significantly associated with shorter overall survival (HR<sub>G > C</sub>: 1.36, 95% CI: 1.15–1.61, <italic>p</italic>-value: 0.0003, <xref rid=\"ijms-21-05395-t002\" ref-type=\"table\">Table 2</xref>a) and recurrence-free survival (HR<sub>G > C</sub>: 1.31, 95% CI: 1.09–1.57, <italic>p</italic>-value: 0.004, <xref ref-type=\"app\" rid=\"app1-ijms-21-05395\">Table S4</xref>) of colorectal cancer patients. Only the association with overall survival remained significant after multiple testing correction (<italic>q</italic> < 0.05).</p><p>Finally, the V297I variant (rs2305948) in <italic>KDR</italic> was associated with decreased overall survival (HR<sub>CC</sub> vs. <sub>CT/TT</sub>: 1.28, 95% CI: 1.04–1.57, <italic>p</italic>-value: 0.02, <xref rid=\"ijms-21-05395-t002\" ref-type=\"table\">Table 2</xref>b). For its deleterious and probably damaging SIFT and PolyPhen scores, respectively, this polymorphism was a selected candidate.</p><p>SNPs in <italic>MMP2</italic>, <italic>EFNB2</italic> and <italic>JAG1,</italic> which were significantly associated with patients’ survival, were subsequently analyzed in a first validation set of 2165 (for <italic>MMP2</italic> and <italic>JAG1</italic>), and 1638 (for <italic>EFNB2</italic>) DACHS colorectal cancer patients and a second validations set of 372 colorectal cancer patients from TCGA where genome-wide data was available.</p><p>In the first validation set of imputed genotypes from the DACHS study with a median follow up of five years, the association with rs9520090 in <italic>EFNB2</italic> was validated in a dominant model (HR<sub>GG</sub> vs. <sub>GC/CC</sub>: 1.22, 95% CI: 1.00–1.47, <italic>p</italic>-value: 0.05). The variant rs6040062 in <italic>JAG1</italic> showed a similar direction (HR: 1.10) in the validation set, however the association was not significant (<italic>p</italic>-value: 0.25). No other association was validated in this dataset (<xref ref-type=\"app\" rid=\"app1-ijms-21-05395\">Table S5</xref>).</p><p>For the second validation set, we retrieved data of 372 colorectal cancer patients with a median follow up of 0.6 years from The Cancer Genome Atlas (TCGA) Colon Adenocarcinoma (COAD) and Rectal Adenocarcinoma (READ) to validate significant associations in an external dataset. The variant rs7983579, which is in LD with rs9520090 (r<sup>2</sup> = 0.8) in <italic>EFNB2,</italic> was significantly associated with the overall survival of colorectal cancer patients (HR<sub>T > G</sub>: 1.76, 95% CI: 1.14–2.73, <italic>p</italic>-value: 0.01). The variant rs9520090 itself showed the same direction of association with overall survival, although it did not reach significance (HR<sub>G > C</sub>: 1.28, 95% CI: 0.84–1.94, <italic>p</italic>-value: 0.25). Furthermore, the variants rs9923304 (HR<sub>T > C</sub>: 1.49, 95% CI: 0.94–2.38, <italic>p</italic>-value: 0.09) and rs1477017 (HR<sub>A > G</sub>: 1.42, 95% CI: 0.91–2.22, <italic>p</italic>-value: 0.12) in <italic>MMP2</italic> linked to rs2241145 (r<sup>2</sup> = 0.83) and rs17301608 (r<sup>2</sup> = 0.93), respectively, were marginally associated with overall survival. No TCGA data were available for variants in <italic>JAG1</italic>.</p><p>The gene-environment interaction between overall survival and smoking, NSAID use, adjuvant 5-FU-based chemotherapy and microsatellite status was investigated for all variants (<xref ref-type=\"app\" rid=\"app1-ijms-21-05395\">Table S7</xref>). Some indication for interaction was observed for two tagging SNPs in <italic>ETS1</italic> (rs11221322 and rs7121854: <italic>p</italic><sub>interaction</sub>: 0.0004 and 0.001, respectively) and the candidate missense variant in <italic>TEK</italic> V486I (<italic>p</italic><sub>interaction</sub>: 0.003) with MSI (microsatellite instability), for the candidate missense variant <italic>KDR</italic> Q472H (rs1870377; <italic>p</italic><sub>interaction</sub>: 0.03) with smoking, for the candidate missense variant <italic>TEK</italic> V486I (rs1334811, <italic>p</italic><sub>interaction</sub>: 0.03) with NSAID use and for the <italic>IL10</italic> candidate rs1800890 (<italic>p</italic><sub>interaction</sub>: 0.01) with adjuvant 5-FU-based chemotherapy for overall survival of colorectal cancer patients. These associations did not remain significant after adjustment for multiple testing.</p></sec></sec><sec sec-type=\"discussion\" id=\"sec3-ijms-21-05395\"><title>3. Discussion</title><p>Genetic variation may contribute to an ‘angiogenic switch’, which is essential for blood supply and tumor growth in microscopic and macroscopic tumors. In the present study, we investigated the association of polymorphisms in angiogenesis-related genes with the risk of colorectal cancer as well as with overall survival and recurrence-free survival of colorectal cancer patients in the DACHS population-based case-control study using 1754 CRC cases and 1781 controls. We chose a targeted candidate gene approach to obtain a more complete coverage of the hypothesized genes and avoided potentially high numbers of false-negative findings, which are often the result of large-scale genotyping studies. We observed significant associations between variants in <italic>EFNB2</italic>, <italic>MMP2</italic> and <italic>JAG1</italic> and overall survival. Some of the associations could be validated.</p><p>Ephrins are membrane-associated ligands that function through interaction with their receptors, erythropoietin-producing hepatocellular (Eph) receptor tyrosine kinase. The unique ligand-receptor interaction, which requires cell-cell contact, can trigger bidirectional signaling, with effects in both the ligand- and the receptor-bearing cell [<xref rid=\"B22-ijms-21-05395\" ref-type=\"bibr\">22</xref>]. Downstream or upstream signaling cascades regulate cell proliferation, migration, adhesion, differentiation, survival and angiogenesis and are crucial for homeostasis in epithelial tissues. In this study, we observed significant associations between five variants in ephrin B2 (<italic>EFNB2</italic>) and OS of colorectal cancer patients. Rs9520087 is located in the 3′UTR of <italic>EFNB2</italic>, which may suggest regulatory effects on gene expression of <italic>EFNB2</italic>, however, the in silico functional follow up did not reveal any associations with gene expression. It is not linked to any known variant and it is covered only by a small number of genome-wide genotyping arrays. Imputation of the variant in a validation set of 2165 CRC cases resulted in 10% of inaccurately imputed alleles, which may be the reason why this variant has not been identified earlier [<xref rid=\"B23-ijms-21-05395\" ref-type=\"bibr\">23</xref>]. Similarly, other <italic>EFNB2</italic> variants are only present on a limited number of genome-wide genotyping arrays. Rs3742159, linked to rs9520088 (r<sup>2</sup> = 1) displayed a RegulomeDB rank of 3a. It is located within a region, which likely affects binding of transcription factors, one of which was identified to be NFKB [<xref rid=\"B24-ijms-21-05395\" ref-type=\"bibr\">24</xref>]. None of the other linked SNPs seems to be functionally important. The SNPs rs2391333 and rs9520090 modify the methylation status of the site chr13:107163855 (methylation probe cg05493945) within <italic>EFNB2</italic>. Whether this results in changed gene expression of <italic>EFNB2</italic> needs to be further investigated. A number of studies have shown that <italic>EFNB2</italic> and its receptor <italic>EPHB4</italic> are transcriptionally upregulated in colon cancer carcinoma cell lines and in colon cancer tissue as compared to adjacent mucosa, supporting a role of EFNB2 in carcinogenesis potentially through modulation of angiogenic signals, even though the exact mechanisms remain to be fully elucidated [<xref rid=\"B25-ijms-21-05395\" ref-type=\"bibr\">25</xref>]. No other gene, which may be relevant for cancer progression is located in the vicinity of this region, thus it seems plausible, that the identified associations with survival of colorectal cancer patients are linked to <italic>EFNB2</italic>.</p><p>We observed a statistically significant association with OS of colorectal cancer patients for four <italic>MMP2</italic> tagging SNPs (rs17301608, rs2241145, rs1561217 and rs243847), all located in introns or in non-coding exon regions, while a previous association of the <italic>MMP2</italic> rs243865 (−1306 C > T-in our study the linked SNP rs243866 was genotyped, r<sup>2</sup>: 0.99) in smaller studies of OS or CRC risk was not confirmed [<xref rid=\"B26-ijms-21-05395\" ref-type=\"bibr\">26</xref>,<xref rid=\"B27-ijms-21-05395\" ref-type=\"bibr\">27</xref>]. The major task of MMPs is the degradation of proteins, which is an essential step in many hallmarks of cancer in regulating cell growth, differentiation, apoptosis, migration, invasion, immune surveillance and cancer angiogenesis [<xref rid=\"B18-ijms-21-05395\" ref-type=\"bibr\">18</xref>]. Consequently, MMPs play a crucial role in malignant transformation and cancer progression. To our knowledge, neither of the polymorphisms or any of the linked variants has previously been reported for their association with OS of colorectal cancer patients. In silico functional follow up of rs17301608 revealed that three of the linked variants (rs1477017, rs11646643 and rs9302671; r<sup>2</sup> > 0.8) ranked low (1f) at RegulomeDB, supporting a regulatory role. They likely affect the binding of transcription factors (TF), potentially of NR2F2 (nuclear receptor subfamily 2 group F member 2), which was previously shown to regulate cell growth, invasiveness, metastasis and angiogenesis [<xref rid=\"B28-ijms-21-05395\" ref-type=\"bibr\">28</xref>,<xref rid=\"B29-ijms-21-05395\" ref-type=\"bibr\">29</xref>]. Furthermore, all three variants linked to rs17301608 are reported expression quantitative trait loci (eQTLs) for <italic>LPCAT2</italic> (lysophosphatidylcholine acyltransferase 2), which was previously associated with the progression of breast cancer, ovarian cancer and CRC in human tissue and in cell lines [<xref rid=\"B30-ijms-21-05395\" ref-type=\"bibr\">30</xref>]. Finally, data from TCGA supports the function of rs2241145 and rs17301608 as eQTLs on <italic>LPCAT2</italic> expression in prostate adenocarcinoma [<xref rid=\"B31-ijms-21-05395\" ref-type=\"bibr\">31</xref>].</p><p>Aberrant NOTCH signaling promotes the development and progression of colorectal cancer and is partly driven by the overexpression of JAG1, a ligand of NOTCH4 [<xref rid=\"B32-ijms-21-05395\" ref-type=\"bibr\">32</xref>]. We identified one tagging SNP (rs6040062) in <italic>JAG1</italic>, which was associated with shorter OS of colorectal cancer patients. The variant and its linked SNPs rs3748480 (r<sup>2</sup>: 1.0) and rs112946915 (r<sup>2</sup>: 0.97) are intronic. The lowest functional RegulomeDB rank was reached by rs112946915 (3a), which modifies binding motifs for transcription factors STAT1 and IRF1 among others. JAG1 has been considered as an attractive target for colorectal cancer therapy, as it seems to be a more specific target than other members of the NOTCH pathway [<xref rid=\"B33-ijms-21-05395\" ref-type=\"bibr\">33</xref>].</p><p>Our results are based on more than 1700 CRC cases and 1700 healthy controls, with comprehensive clinical, epidemiological and demographic data, which is a major strength of the study. The detailed information on lifestyle factors as well as the availability of clinical patients’ characteristics enabled us to adequately account for relevant environmental factors and test for effect modification. Although our sample size was fairly large, some of the interaction analyses were based on small strata. Unfortunately, the MSI status was missing for 22% of the population and 9% of investigated tumors were MSI-high. Thus, associations should be considered with caution. Furthermore, we had access to two validation sets and validated the association of the <italic>EFNB2</italic> tagging SNP rs9520090 (or its linked variant rs7983579) with the overall survival of colorectal cancer. In addition, tagging SNPs in <italic>JAG1</italic> and <italic>MMP2</italic> showed the same direction of association with overall survival in the first and second validation set, respectively.</p><p>The signals of the first validation set were weaker than we expected considering that the discovery and first validation sets were derived from the same study population. We thus compared both sample sets with respect to genetic, epidemiological and clinical data. For up to 67 individuals, imputed genotypes from genome-wide data and at the same time directly genotyped genotypes were available. We observed a discrepancy of 5%–25% between the directly genotyped and imputed genotypes, suggesting an insufficient imputation accuracy for these regions and supporting the choice of a targeted candidate gene approach. The accuracy of imputed genotypes strongly relies on linkage disequilibrium (LD), consequently they depend on the genotypes measured within the same LD-block. <italic>EFNB2</italic> and <italic>JAG</italic> are located in regions of rather low LD, which reduces the probability to correctly impute missing genotypes [<xref rid=\"B34-ijms-21-05395\" ref-type=\"bibr\">34</xref>]. We further observed differences between the two study sample sets with respect to overall survival, alcohol consumption and tumor grade of patients. We adjusted for these variables, however, other confounders not captured within this study may explain the discrepant association strengths between the two study samples. While 98% of participants reported to be of German nationality, the ethnic background of study participants was not assessed as part of this study. We cannot rule out an effect of ethnicity on the identified associations, even though this is likely to be very small, because they are expected to be of Caucasian origin.</p><p>In conclusion, we identified novel genetic associations with the overall survival of colorectal cancer patients. To our knowledge, no previous study has reported associations between SNPs in <italic>EFNB2</italic> or <italic>JAG1</italic> with the CRC outcome, both genes known for their functional role in angiogenesis and their impact on disease progression. The present study cannot point to the causal variant or elucidate the functional role of the identified polymorphisms and additional investigations in other populations are required to further validate these associations and to unravel the causal variants and their functionalities.</p></sec><sec id=\"sec4-ijms-21-05395\"><title>4. Materials and Methods </title><sec id=\"sec4dot1-ijms-21-05395\"><title>4.1. Study Population</title><p>The study population includes patients with colorectal cancer who participated in a long-term follow-up study of patients of the German population-based case-control study DACHS (“Darmkrebs: Chancen der Verhuetung durch Screening”; described in detail elsewhere) [<xref rid=\"B35-ijms-21-05395\" ref-type=\"bibr\">35</xref>,<xref rid=\"B36-ijms-21-05395\" ref-type=\"bibr\">36</xref>,<xref rid=\"B37-ijms-21-05395\" ref-type=\"bibr\">37</xref>,<xref rid=\"B38-ijms-21-05395\" ref-type=\"bibr\">38</xref>]. In brief, colorectal cancer patients with a primary, confirmed diagnosis of colorectal cancer were recruited from hospitals of the Rhein-Neckar-Odenwald region 2003–2007 if they were aged 30 years or older, resident in the study region and able to complete an in-person interview. Baseline interviews were performed using standardized questionnaires to collect information on demographic and established or suggested colorectal cancer risk factors as well as possible prognostic factors. Follow-up information on vital status was collected at three, five and ten years after diagnosis. Causes of death were verified by death certificates and coded based on ICD−10 classifications. Information on recurrences and secondary tumors were collected from general practitioners and specialists as applicable. In addition, clinical and histological data were extracted from patient and pathological records. Patients without baseline blood samples (post-diagnostic) or without mouthwash (<1%) were excluded from the study.</p><p>Population controls were randomly selected from lists of residents of the population registries of the cities and counties. Controls were matched to cases by sex, county of residence and 5-year age group. Controls were eligible if they were aged 30 years or older with no history of colorectal cancer.</p><p>The study was approved by the ethics committee (approved on 6 December 2001, project identification code 310/2001) of the University of Heidelberg and State Medical Boards of Baden-Wuerttemberg and Rhineland-Palatinate and was conducted in agreement with the Helsinki Declaration. Written informed consent was provided by all participants at the baseline and during follow-up.</p></sec><sec id=\"sec4dot2-ijms-21-05395\"><title>4.2. Single Nucleotide Polymorphism (SNP) Selection and Genotyping</title><p>We selected 33 genes related to angiogenesis based on a comprehensive literature search, gene ontology data bases and external expertise. The genes comprised: <italic>AKT1, ANGPT1, ANGPT2, DKK4, DLL1, DLL4, EFNB2, EPHB4, ETS1, FGF2, FLT1, FLT4, HIF1A, ID1, JAG1, KDR, MAPK1, MMP2, MMP9, NOTCH4, NRP1, NRP2, PDGFA, PDGFRB, PGF, PIK3CA, PIK3CG, TEK, TIE1, VEGFA, VEGFB, VEGFC</italic> and <italic>VHL</italic> (<xref ref-type=\"app\" rid=\"app1-ijms-21-05395\">Table S1</xref>).</p><p>We used Haploview 4.2 (Broad Institute, Cambridge, MA, USA) to identify tagging SNPs located in the assigned genes as well as in the 5′ and 3′ flanking regions (up to 10 kb) for coverage of the genetic variation across the gene. The tagging SNP selection was based on the HapMap project (CEU [Utah Residents (CEPH) with Northern and Western European Ancestry] population, Phase II/Release 24), using a pairwise tagging approach applying r<sup>2</sup> ≥ 0.8 as the cutoff.</p><p>Genomic DNA was extracted from EDTA (Ethylenediaminetetraacetic acid) anticoagulated blood or mouthwash samples using the FlexiGene DNA kit (Qiagen GmbH, Hilden, Germany) and quantified using Quant-iT PicoGreen dsDNA reagent and kit (Invitrogen/Life Technologies, Darmstadt, Germany). Genotyping was performed using the Illumina GoldenGate assay or iPLEX assay (Sequenom, Hamburg, Germany) for the MassArray system. For quality control, all assays were validated using the 72 CEPH controls, distributed randomly among the study samples. Each assay batch contained negative and positive controls and 5% of the total number of samples were re-genotyped to confirm reproducibility. Call rates for all genotypes exceeded 96%. The concordance for blinded duplicates was >99.7%. Deviation from the Hardy-Weinberg equilibrium was evaluated using a χ2 statistic in all control subjects. A total of 392 single nucleotide polymorphisms in 33 genes were investigated in this study, which passed quality control, were not in linkage disequilibrium (LD) and had a cell count of at least 10.</p></sec><sec id=\"sec4dot3-ijms-21-05395\"><title>4.3. Imputation</title><p>Imputation of environmental factors was performed using multiple imputation as most variables, except for tumor grade (11%), had only few missing values (<1%). For grading, we compared no imputation and multiple imputation of grading with best-case (all missing values set to grade 1) and worst-case (all missing values set to grade 4) resulting in similar effect estimates (<10% difference). Genotype imputation in the DACHS study was performed by using IMPUTE2 and the 1000 Genomes reference panel [<xref rid=\"B39-ijms-21-05395\" ref-type=\"bibr\">39</xref>,<xref rid=\"B40-ijms-21-05395\" ref-type=\"bibr\">40</xref>].</p></sec><sec id=\"sec4dot4-ijms-21-05395\"><title>4.4. Statistical Analysis</title><p>The associations between polymorphisms and the risk of colorectal cancer were assessed using unconditional logistic regression models estimating odds ratios (OR) and 95% confidence intervals (CI). Associations were also adjusted by relevant environmental factors, which were obtained using the backward elimination procedure. These factors were: age (<60, 60–69, 70–79 and 80+ years), sex, alcohol consumption (no alcohol consumption, 0.1–5.7, 5.7–13.3, 13.3–28.6 and ≥28.6 g alcohol/day during lifetime), BMI category 5–14 years ago (≤18.5 kg/m<sup>2</sup>, 18.5 to < 25kg/m<sup>2</sup> (reference category), 25 to <30 kg/m<sup>2</sup> and ≥30 kg/m<sup>2</sup>), first degree family history of CRC (yes, no/unknown), education (low, medium and high), prior colonoscopy (ever colorectal endoscopy after consideration of endoscopy report, yes, no/unknown), diabetes, regular smoking (never, yes), NSAID use (never, ever used NSAIDs more than once/month ≥1 year) and red meat consumption (low, medium and high). </p><p>For each polymorphism, the risk of colorectal cancer was estimated using log-additive, and dominant models. Differences in baseline characteristics between deceased and non-deceased patients were tested using the χ2 statistic and <italic>t</italic>-tests for categorical and continuous variables, respectively. As an outcome, time to survival and recurrence-free survival times were calculated as the time from the diagnosis to the event of interest (death and recurrence or death, respectively) or censoring. Median follow-up time was calculated using the reverse Kaplan-Meier estimator and the cumulative survival curves for overall survival were generated using the Kaplan-Meier curves [<xref rid=\"B41-ijms-21-05395\" ref-type=\"bibr\">41</xref>].</p><p>Cox proportional hazard models were used to estimate hazard ratios (HR) for overall survival and recurrence-free survival, and their 95% CIs associated with genetic variants. Dominant and log-additive modes of inheritance were assumed for the genetic variants. The models also included adjustment for relevant environmental and clinical factors obtained from a backward elimination procedure. The analyses were adjusted for age (<60, 60–70, 70–80 and 80+ years), sex, stage (I-IV), tumor grade (1 and 2 versus 3 and 4), current BMI (<18.5 kg/m<sup>2</sup>, 18.5 to <25 kg/m<sup>2</sup> (reference category), 25 to <30 kg/m<sup>2</sup> and ≥30 kg/m<sup>2</sup>), and alcohol intake (no alcohol intake and quartiles: 0.1–6.1, 6.1–15.6, 15.6–32.6 and ≥32.6 g alcohol/day in the last year).</p><p>The effect modification was tested by using multiplicative interaction terms and stratified analyses for adjuvant 5-FU-based chemotherapy (yes/no) and microsatellite status (high/low; only for overall survival), NSAID use (never, ever used NSAIDs more than once/month ≥1 year), and smoking (never/yes).</p><p>All statistical analyses were two-sided with a significance level of 0.05 and were performed using SAS (version 9.3, SAS Institute, Cary, NC, USA) and R (version 3.0.2, R Foundation for Statistical Computing, Vienna, Austria).</p><p>The false discovery rate (FDR, indicated as the <italic>q</italic>-value) correction method was used to adjust for multiple testing of non-candidate polymorphisms [<xref rid=\"B42-ijms-21-05395\" ref-type=\"bibr\">42</xref>].</p><p>In silico functional follow up of the SNPs (<xref ref-type=\"app\" rid=\"app1-ijms-21-05395\">Table S6</xref>) that were statistically significantly associated with risk of CRC or overall survival of CRC patients was performed using Haploreg, LDlink, RegulomeDB, PancanQTL, Pancan-meQTL, GTExPortal and (variant effect predictor) VEP [<xref rid=\"B24-ijms-21-05395\" ref-type=\"bibr\">24</xref>,<xref rid=\"B31-ijms-21-05395\" ref-type=\"bibr\">31</xref>,<xref rid=\"B43-ijms-21-05395\" ref-type=\"bibr\">43</xref>,<xref rid=\"B44-ijms-21-05395\" ref-type=\"bibr\">44</xref>,<xref rid=\"B45-ijms-21-05395\" ref-type=\"bibr\">45</xref>,<xref rid=\"B46-ijms-21-05395\" ref-type=\"bibr\">46</xref>,<xref rid=\"B47-ijms-21-05395\" ref-type=\"bibr\">47</xref>].</p></sec><sec id=\"sec4dot5-ijms-21-05395\"><title>4.5. Validation Sets</title><p>Validation of significant associations was performed using two datasets. The first set included DNA samples from an independent sample of 2165 DACHS colorectal cancer patients recruited 2007–2010, which were genotyped using Illumina’s Global Screening Array, OmniExpress BeadChip and OncoArray as described previously [<xref rid=\"B48-ijms-21-05395\" ref-type=\"bibr\">48</xref>]. Genotypes were imputed separately for each gene (±1.5 MB) and for each array using the Michigan Imputation Server [<xref rid=\"B49-ijms-21-05395\" ref-type=\"bibr\">49</xref>]. The Haplotype Reference Consortium (HRC) reference panel (HRC r1.1 2016) has been used for imputation and Eagle for phasing. Subsequently, the imputed genotypes of each array were combined. The second set included genotype data from The Cancer Genome Atlas (TCGA) Colon Adenocarcinoma (COAD) cohort and Rectal Adenocarcinoma (TCGA-READ) cohort, which were downloaded from the NCI Genomic Data Commons.</p><p>A statistical analysis from our respective findings was performed for both validation datasets as described above. TCGA analyses were adjusted for the available variables age, sex and stage.</p><p>The data that support the findings of this study are available from the corresponding author upon reasonable request.</p></sec></sec></body><back><ack><title>Acknowledgments</title><p>We thank all participants of the DACHS study, the interviewers, as well as the physicians and staff of the following hospitals and cooperating institutions: Chirurgische Universitätsklinik Heidelberg, Klinik am Gesundbrunnen Heilbronn, St. Vincentiuskrankenhaus Speyer, St. Josefskrankenhaus Heidelberg, Chirurgische Universitätsklinik Mannheim, Diakonissenkrankenhaus Speyer, Krankenhaus Salem Heidelberg, Kreiskrankenhaus Schwetzingen, St. Marienund St. Annastiftkrankenhaus Ludwigshafen, Klinikum Ludwigshafen, Stadtklinik Frankenthal, Diakoniekrankenhaus Mannheim, Kreiskrankenhaus Sinsheim, Klinikum am Plattenwald Bad Friedrichshall, Kreiskrankenhaus Weinheim, Kreiskrankenhaus Eberbach, Kreiskrankenhaus Buchen, Kreiskrankenhaus Mosbach, Enddarmzentrum Mannheim, Kreiskrankenhaus Brackenheim, and the Cancer Registry of Rhineland-Palatinate. We thank the microarray unit of the Genomics and Proteomics Core Facility of the German Cancer Research Center for genotyping, particularly Matthias Schick, and also thank Muhabbet Celik, Ursula Eilber, Sabine Behrens and Ute Handte-Daub for excellent technical assistance. The results shown here are in part based upon data generated by the TCGA Research Network: <uri xlink:href=\"https://www.cancer.gov/tcga\">https://www.cancer.gov/tcga</uri>.</p></ack><app-group><app id=\"app1-ijms-21-05395\"><title>Supplementary Materials</title><p>Supplementary Materials can be found at <uri xlink:href=\"https://www.mdpi.com/1422-0067/21/15/5395/s1\">https://www.mdpi.com/1422-0067/21/15/5395/s1</uri>.</p><supplementary-material content-type=\"local-data\" id=\"ijms-21-05395-s001\"><media xlink:href=\"ijms-21-05395-s001.zip\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material></app></app-group><notes><title>Author Contributions</title><p>D.S. prepared the statistical analysis plan, interpreted the data and drafted the manuscript. Y.B., H.D. and K.B. analyzed the data, P.S., B.B., R.T., N.H., A.B. (Akke Botma) and E.J.K. contributed to SNP selection, A.B. ( Axel Benner) and J.L.B. provided statistical support. A.U., K.W., L.J., J.C.-C., M.H., H.B., designed and implemented the different studies included in this analysis. C.M.U. conceived the study, provided input to interpreting the data and writing the manuscript. All authors discussed the results, provided critical feedback and approved the final manuscript. All authors have read and agreed to the published version of the manuscript.</p></notes><notes><title>Funding</title><p>D. Scherer was supported by an Olympia Morata fellowship of the Medical Faculty, University of Heidelberg and the German Federal Ministry of Education and Research (01KT1510). The DACHS study was supported by grants from the German Research Council (Deutsche Forschungsgemeinschaft, grant numbers BR 1704/6–1, BR 1704/6–3, BR 1704/6–4, BR 1704/6–6, CH 117/1–1, HO 5117/2–1, HE 5998/2–1, KL 2354/3–1, RO 2270/8–1, and BR 1704/17–1), and the German Federal Ministry of Education and Research (grant numbers 01KH0404, 01ER0814, 01ER0815, 01ER1505A and 01ER1505B). CM Ulrich was supported by R01 CA189184, U01 CA206110 and R01 CA 207371.</p></notes><notes notes-type=\"COI-statement\"><title>Conflicts of Interest</title><p>The authors declare no conflict of interest.</p></notes><glossary><title>Abbreviations</title><array orientation=\"portrait\"><tbody><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CRC</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Colorectal cancer</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">OS</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Overall survival</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">RFS</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Recurrence-free survival</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">SNP</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Single nucleotide polymorphism</td></tr></tbody></array></glossary><ref-list><title>References</title><ref id=\"B1-ijms-21-05395\"><label>1.</label><element-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Bray</surname><given-names>F.</given-names></name><name><surname>Ferlay</surname><given-names>J.</given-names></name><name><surname>Soerjomataram</surname><given-names>I.</given-names></name><name><surname>Siegel</surname><given-names>R.L.</given-names></name><name><surname>Torre</surname><given-names>L.A.</given-names></name><name><surname>Jemal</surname><given-names>A.</given-names></name></person-group><article-title>Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries</article-title><source>Cancer J. 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Genet.</source><year>2016</year><volume>48</volume><fpage>1284</fpage><lpage>1287</lpage><pub-id pub-id-type=\"doi\">10.1038/ng.3656</pub-id><pub-id pub-id-type=\"pmid\">27571263</pub-id></element-citation></ref></ref-list></back><floats-group><table-wrap-group id=\"ijms-21-05395-t001\" orientation=\"portrait\" position=\"float\"><label>Table 1</label><caption><p>Characteristics of the study population. (<bold>a</bold>) Total population; (<bold>b</bold>) Case population.</p></caption><table-wrap id=\"ijms-21-05395-t001a\" orientation=\"portrait\" position=\"anchor\"><object-id pub-id-type=\"pii\">ijms-21-05395-t001a_Table 1</object-id><caption><p>(<bold>a</bold>)</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Controls</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Cases</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><italic>p</italic>-Value</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>Gender (Male/Female)</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1066/715</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1027/727</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.43</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>Age (Mean ± SD)</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">68.72 ± 10.18</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">68.25 ± 10.42</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.32</td></tr><tr><td rowspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">\n<bold>First Degree Relatives With CRC</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><italic>n</italic> (available)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1781</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1754</td><td rowspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">0.009</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Yes (%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">11.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">14.3</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">No (%)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">88.7</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">85.8</td></tr><tr><td rowspan=\"4\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">\n<bold>Education</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><italic>n</italic> (available)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1781</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1754</td><td rowspan=\"4\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\"><0.0001</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Low (%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">53.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">62.9</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Medium (%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">25.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">20.5</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">High (%)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">21.4</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">16.6</td></tr><tr><td rowspan=\"4\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">\n<bold>Red meat Consumption</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><italic>n</italic> (available)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1781</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1754</td><td rowspan=\"4\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\"><0.0001</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Low (%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8.2</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Medium (%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">85.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">84.7</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">High (%)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">4.2</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">7.1</td></tr><tr><td rowspan=\"6\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">\n<bold>Alcohol Consumption (g/day)</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><italic>n</italic> (available)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1781</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1754</td><td rowspan=\"6\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">0.012</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">no intake</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">14.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">16.3</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.1–5.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">23.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">21.0</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.7–13.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">21.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">20.8</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13.3–28.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">23.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">21.1</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">≥28.6</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">17.2</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">20.9</td></tr><tr><td rowspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">\n<bold>Regular Smoking</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><italic>n</italic> (available)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1781</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1754</td><td rowspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">0.011</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Never (%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">52.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">47.9</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Current (%)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">47.8</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">52.1</td></tr><tr><td rowspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">\n<bold>Diabetes</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><italic>n</italic> (available)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1781</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1754</td><td rowspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">0.0003</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Yes (%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">18.2</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">No (%)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">86.4</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">81.8</td></tr><tr><td rowspan=\"5\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">\n<bold>BMI (kg/m<sup>2</sup>)</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><italic>n</italic> (available)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1781</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1754</td><td rowspan=\"5\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\"><0.0001</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><18.5 (%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.97</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">18.5–25 (%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">37.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">31.07</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">25–30 (%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">47.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">47.32</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">>30 (%)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">15.2</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">20.64</td></tr><tr><td rowspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">\n<bold>NSAID Use</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><italic>n</italic> (available)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1781</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1754</td><td rowspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\"><0.0001</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">No (%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">63.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">74.0</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Yes (%)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">37.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">26.0</td></tr><tr><td rowspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">\n<bold>Ever Colonoscopy</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><italic>n</italic> (available)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1781</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1754</td><td rowspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\"><0.0001</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">No (%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">46.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">79.2</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Yes (%)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">53.1</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">20.8</td></tr></tbody></table></table-wrap><table-wrap id=\"ijms-21-05395-t001b\" orientation=\"portrait\" position=\"anchor\"><object-id pub-id-type=\"pii\">ijms-21-05395-t001b_Table 1</object-id><caption><p>(<bold>b</bold>)</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</th><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Overall Survival (Discovery)</th><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Overall Survival (Validation) </th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>Overall <italic>n</italic>/Events</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">1675/538</td><td colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">2165/689</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">HR (95% CI)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>p</italic>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">HR (95% CI)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>p</italic>\n</td></tr><tr><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">\n<bold>Gender</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Male</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B1-ijms-21-05395\" ref-type=\"bibr\">1</xref>]</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B1-ijms-21-05395\" ref-type=\"bibr\">1</xref>]</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Female</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.89 (0.75–1.05)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.16</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.27 (1.09–1.49)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.003</td></tr><tr><td rowspan=\"4\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">\n<bold>Age Category</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><60</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B1-ijms-21-05395\" ref-type=\"bibr\">1</xref>]</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B1-ijms-21-05395\" ref-type=\"bibr\">1</xref>]</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">60–69</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.24 (0.94–1.62)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.13</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.31 (1.00–1.72)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.047</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">70–79</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.45 (1.10–1.90)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.007</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.84 (1.43–2.36)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.0001</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">>80</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2.84 (2.14–3.77)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><0.0001</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3.40 (2.63–4.39)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><0.0001</td></tr><tr><td rowspan=\"4\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">\n<bold>Stage at Diagnosis</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">I</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B1-ijms-21-05395\" ref-type=\"bibr\">1</xref>]</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B1-ijms-21-05395\" ref-type=\"bibr\">1</xref>]</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">II</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.53 (1.10–2.12)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.01</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.81 (1.38–2.37)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.0001</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">III</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.73 (2.01–3.69)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.0001</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.70 (2.08–3.52)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.0001</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">IV</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">13.28 (9.81–17.98)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><0.0001</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">13.33 (10.26–17.31)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><0.0001</td></tr><tr><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">\n<bold>Grade</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">G1-G2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B1-ijms-21-05395\" ref-type=\"bibr\">1</xref>]</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B1-ijms-21-05395\" ref-type=\"bibr\">1</xref>]</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">G3-G4</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.27 (1.06–1.52)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.01</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.85 (1.57–2.18)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><0.0001</td></tr><tr><td rowspan=\"5\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">\n<bold>Alcohol Consumption</bold>\n<break/>\n<bold>(g/day)</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">no intake</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B1-ijms-21-05395\" ref-type=\"bibr\">1</xref>]</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B1-ijms-21-05395\" ref-type=\"bibr\">1</xref>]</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.1–6.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.69 (0.53–0.88)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.003</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.79 (0.64–0.98)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.03</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6.1–15.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.70 (0.54–0.91)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.007</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.76 (0.61–0.95)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.02</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">15.6–32.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.67 (0.52–0.86)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.002</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.88 (0.70–1.09)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.24</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">≥32.6</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.74 (0.58–0.96)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.02</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.93 (0.73–1.18)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.54</td></tr><tr><td rowspan=\"4\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">\n<bold>BMI (kg/m<sup>2</sup>)</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">18.5–25></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B1-ijms-21-05395\" ref-type=\"bibr\">1</xref>]</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B1-ijms-21-05395\" ref-type=\"bibr\">1</xref>]</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><18.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.72 (1.075–2.75)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.02</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.18 (0.71–1.95)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.53</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">25–30></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.77 (0.64–0.93)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.007</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.76 (0.65–0.90)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.002</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">≥30</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.71 (0.56–0.91)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.007</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.78 (0.63–0.96)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.02</td></tr></tbody></table><table-wrap-foot><fn><p>Abbreviations: HR: hazard ratio; CI: confidence interval; BMI: body mass index.</p></fn></table-wrap-foot></table-wrap></table-wrap-group><table-wrap-group id=\"ijms-21-05395-t002\" orientation=\"portrait\" position=\"float\"><label>Table 2</label><caption><p>Significant associations of genetic variants in <italic>MMP2</italic>, <italic>EFNB2</italic> and <italic>JAG1</italic> with overall and recurrence-free survival of colorectal cancer patients in the discovery cohort. (<bold>a</bold>) Associations of tagging SNPs; (<bold>b</bold>) Association of candidate SNP.</p></caption><table-wrap id=\"ijms-21-05395-t002a\" orientation=\"portrait\" position=\"anchor\"><object-id pub-id-type=\"pii\">ijms-21-05395-t002a_Table 2</object-id><caption><p>(<bold>a</bold>)</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</th><th colspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Overall Survival</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</th><th colspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Recurrence-Free Survival</th></tr><tr><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Gene</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">SNP</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Location</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Alleles</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Alive</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Deceased</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">HR <sup>1</sup> (95%-CI)</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>p</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>q</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Alive</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Deceased</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">HR <sup>1</sup> (95%-CI)</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>p</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>q</italic>\n</th></tr></thead><tbody><tr><td rowspan=\"20\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">\n<italic>EFNB2</italic>\n</td><td rowspan=\"4\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">rs2391333</td><td rowspan=\"4\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">intronic</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CC</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">455</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">223</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B1-ijms-21-05395\" ref-type=\"bibr\">1</xref>]</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">474</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">204</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B1-ijms-21-05395\" ref-type=\"bibr\">1</xref>]</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CT</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">526</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">259</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">0.77 (0.68–0.87)</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\"><0.0001</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">0.008</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">538</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">247</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">0.85 (0.74–0.97)</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">0.02</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">0.73</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">TT</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">174</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">64</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">168</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">70</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">CT/TT</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">700</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">323</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.76 (0.64–0.90)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.002</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.060</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">706</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">317</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.83 (0.69–0.99)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.05</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.91</td></tr><tr><td rowspan=\"4\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">rs9520090</td><td rowspan=\"4\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">intronic</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">GG</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">338</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">134</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B1-ijms-21-05395\" ref-type=\"bibr\">1</xref>]</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">339</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">133</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B1-ijms-21-05395\" ref-type=\"bibr\">1</xref>]</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">GC</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">580</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">270</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">1.29 (1.15–1.46)</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\"><0.0001</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">0.008</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">586</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">264</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">1.20 (1.06–1.37)</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">0.005</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">0.68</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CC</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">237</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">142</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">255</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">124</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">GC/GC</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">817</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">412</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.42 (1.16–1.73)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.0006</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.04</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">841</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">388</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.2 (1.03–1.56)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.03</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.68</td></tr><tr><td rowspan=\"4\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">rs9520087</td><td rowspan=\"4\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">3′UTR</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">GG</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">369</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">148</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B1-ijms-21-05395\" ref-type=\"bibr\">1</xref>]</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">371</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">146</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B1-ijms-21-05395\" ref-type=\"bibr\">1</xref>]</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">GA</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">562</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">297</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">1.24 (1.10–1.40)</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">0.0006</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">0.04</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">575</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">284</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">1.15 (1.00–1.31)</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">0.05</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">0.86</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">AA</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">224</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">101</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">234</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">91</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">GA/AA</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">786</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">398</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.42 (1.17–1.71)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.0003</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.03</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">809</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">375</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.28 (1.05–1.57)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.14</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.68</td></tr><tr><td rowspan=\"4\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">rs2057408</td><td rowspan=\"4\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">downstream</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">AA</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">509</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">204</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B1-ijms-21-05395\" ref-type=\"bibr\">1</xref>]</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">513</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">200</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B1-ijms-21-05395\" ref-type=\"bibr\">1</xref>]</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">AG</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">488</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">271</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">1.19 (1.05–1.34)</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">0.006</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">0.13</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">507</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">252</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">1.13 (0.99–1.29)</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">0.07</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">0.87</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">GG</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">158</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">71</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">160</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">69</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">AG/GG</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">646</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">342</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.35 (1.14–1.62)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.0008</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.04</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">667</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">321</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.26 (1.04–1.52)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.02</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.68</td></tr><tr><td rowspan=\"4\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">rs9520088</td><td rowspan=\"4\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">intronic</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">TT</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">638</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">269</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B1-ijms-21-05395\" ref-type=\"bibr\">1</xref>]</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">641</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">266</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B1-ijms-21-05395\" ref-type=\"bibr\">1</xref>]</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">TC</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">429</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">239</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">1.24 (1.08–1.41)</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">0.002</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">0.06</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">446</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">222</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">1.15 (0.99–1.33)</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">0.07</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">0.87</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CC</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">88</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">38</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">93</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">33</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">TC/CC</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">517</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">277</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.37 (1.15–1.62)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.0003</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.03</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">539</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">255</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.24 (1.03–1.49)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.02</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.68</td></tr><tr><td rowspan=\"14\" align=\"center\" valign=\"middle\" colspan=\"1\">\n<italic>MMP2</italic>\n</td><td rowspan=\"4\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">rs17301608</td><td rowspan=\"4\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">intronic</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">AA</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">432</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">180</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B1-ijms-21-05395\" ref-type=\"bibr\">1</xref>]</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">431</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">181</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B1-ijms-21-05395\" ref-type=\"bibr\">1</xref>]</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">AG</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">540</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">267</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">1.25 (1.11–1.41)</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">0.0003</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">0.03</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">551</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">256</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">1.11 (0.98–1.27)</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">0.11</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">0.87</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">GG</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">183</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">99</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">198</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">84</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">AG/GG</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">723</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">366</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.42 (1.18–1.71)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.0002</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.03</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">749</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">340</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.23 (1.01–1.49)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.04</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.91</td></tr><tr><td rowspan=\"4\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">rs1561219 </td><td rowspan=\"4\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">intronic</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CC</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">877</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">378</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B1-ijms-21-05395\" ref-type=\"bibr\">1</xref>]</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">877</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">378</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B1-ijms-21-05395\" ref-type=\"bibr\">1</xref>]</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CA</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">255</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">160</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">1.32 (1.11–1.56)</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">0.0009</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">0.04</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">277</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">138</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">1.11 (0.92–1.34)</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">0.26</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">0.89</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">AA</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">23</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">26</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">CA/AA</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">278</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">168</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.37 (1.14–1.65)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.0009</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.04</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">303</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">143</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.17 (0.95–1.40)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.14</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.91</td></tr><tr><td rowspan=\"4\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">rs2241145</td><td rowspan=\"4\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">intronic</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">GG</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">341</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">133</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B1-ijms-21-05395\" ref-type=\"bibr\">1</xref>]</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">334</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">140</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B1-ijms-21-05395\" ref-type=\"bibr\">1</xref>]</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">GC</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">547</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">281</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">1.23 (1.09–1.38)</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">0.0005</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">0.04</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">566</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">262</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.11 (0.98–1.26)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.11</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.87</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CC</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">267</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">132</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">280</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">119</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">GC/CC</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">814</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">413</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.50 (1.23–1.83)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><0.0001</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.03</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">846</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">381</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.24 (1.01–1.52)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.05</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.91</td></tr><tr><td rowspan=\"4\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">rs243847</td><td rowspan=\"4\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">intronic</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">TT</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">432</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">213</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B1-ijms-21-05395\" ref-type=\"bibr\">1</xref>]</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">448</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">197</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B1-ijms-21-05395\" ref-type=\"bibr\">1</xref>]</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">TC</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">542</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">267</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">0.81 (0.71–0.92)</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">0.001</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">0.04</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">555</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">254</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">0.88 (0.76–1.00)</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">0.05</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">0.87</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CC</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">181</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">66</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">177</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">70</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">TC/CC</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">723</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">333</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.82 (0.68–0.97)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.024</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.32</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">732</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">324</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.86 (0.71–1.04)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.11</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.91</td></tr><tr><td rowspan=\"4\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">\n<italic>JAG1</italic>\n</td><td rowspan=\"4\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">rs6040062</td><td rowspan=\"4\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">intronic</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">TT</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">876</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">391</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B1-ijms-21-05395\" ref-type=\"bibr\">1</xref>]</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">886</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">381</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B1-ijms-21-05395\" ref-type=\"bibr\">1</xref>]</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">TG</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">259</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">142</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">1.36 (1.15–1.61)</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">0.0003</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">0.02</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">274</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">127</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">1.31 (1.09–1.57)</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">0.004</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">0.68</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">GG</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">20</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">20</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">TG/GG</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">279</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">155</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.38 (1.14–1.67)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.0007</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.04</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">294</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">140</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.29 (1.05–1.59)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.01</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.68</td></tr></tbody></table></table-wrap><table-wrap id=\"ijms-21-05395-t002b\" orientation=\"portrait\" position=\"anchor\"><object-id pub-id-type=\"pii\">ijms-21-05395-t002b_Table 2</object-id><caption><p>(<bold>b</bold>)</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</th><th colspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Overall Survival</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</th><th colspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Recurrence-Free Survival</th></tr><tr><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Gene</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">SNP</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Location</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Alleles</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Alive</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Deceased</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">HR <sup>1</sup> (95%-CI)</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>p</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>q</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Alive</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Deceased</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">HR <sup>1</sup> (95%-CI)</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>p</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>q</italic>\n</th></tr></thead><tbody><tr><td rowspan=\"4\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">\n<italic>KDR</italic>\n</td><td rowspan=\"4\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">rs2305948</td><td rowspan=\"4\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">V297I</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CC</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">934</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">424</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B1-ijms-21-05395\" ref-type=\"bibr\">1</xref>]</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">944</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">414</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B1-ijms-21-05395\" ref-type=\"bibr\">1</xref>] </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CT</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">209</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">113</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">1.27 (1.06–1.53)</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">0.01</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">NA</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">225</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">97</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">1.14 (0.92–1.41)</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">0.23</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">NA</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">TT</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">12</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">11</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">CT/TT</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">221</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">122</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.28 (1.04–1.57)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.02</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">NA</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">236</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">107</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.09 (0.86–1.37)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.47</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">NA</td></tr></tbody></table><table-wrap-foot><fn><p><sup>1</sup> Hazard ratios for log-additive and dominant models, adjusted for age, sex, stage, grade, BMI, alcohol consumption. Abbreviations: SNP: single nucleotide polymorphism; HR: hazard ration; CI: confidence interval; NA: not applicable.</p></fn></table-wrap-foot></table-wrap></table-wrap-group></floats-group></article>\n"
]
[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"review-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Int J Mol Sci</journal-id><journal-id journal-id-type=\"iso-abbrev\">Int J Mol Sci</journal-id><journal-id journal-id-type=\"publisher-id\">ijms</journal-id><journal-title-group><journal-title>International Journal of Molecular Sciences</journal-title></journal-title-group><issn pub-type=\"epub\">1422-0067</issn><publisher><publisher-name>MDPI</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32752176</article-id><article-id pub-id-type=\"pmc\">PMC7432125</article-id><article-id pub-id-type=\"doi\">10.3390/ijms21155514</article-id><article-id pub-id-type=\"publisher-id\">ijms-21-05514</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Review</subject></subj-group></article-categories><title-group><article-title>Update on Cuticular Wax Biosynthesis and Its Roles in Plant Disease Resistance</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Wang</surname><given-names>Xiaoyu</given-names></name><xref ref-type=\"author-notes\" rid=\"fn1-ijms-21-05514\">†</xref></contrib><contrib contrib-type=\"author\"><name><surname>Kong</surname><given-names>Lingyao</given-names></name><xref ref-type=\"author-notes\" rid=\"fn1-ijms-21-05514\">†</xref></contrib><contrib contrib-type=\"author\"><name><surname>Zhi</surname><given-names>Pengfei</given-names></name></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0001-6672-4704</contrib-id><name><surname>Chang</surname><given-names>Cheng</given-names></name><xref rid=\"c1-ijms-21-05514\" ref-type=\"corresp\">*</xref></contrib></contrib-group><aff id=\"af1-ijms-21-05514\">College of Life Sciences, Qingdao University, Qingdao 266071, Shandong, China; <email>[email protected]</email> (X.W.); <email>[email protected]</email> (L.K.); <email>[email protected]</email> (P.Z.)</aff><author-notes><corresp id=\"c1-ijms-21-05514\"><label>*</label>Correspondence: <email>[email protected]</email>; Tel.: +86-532-85953227</corresp><fn id=\"fn1-ijms-21-05514\"><label>†</label><p>These authors contributed equally to this work.</p></fn></author-notes><pub-date pub-type=\"epub\"><day>01</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><month>8</month><year>2020</year></pub-date><volume>21</volume><issue>15</issue><elocation-id>5514</elocation-id><history><date date-type=\"received\"><day>29</day><month>6</month><year>2020</year></date><date date-type=\"accepted\"><day>30</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>© 2020 by the authors.</copyright-statement><copyright-year>2020</copyright-year><license license-type=\"open-access\"><license-p>Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>).</license-p></license></permissions><abstract><p>The aerial surface of higher plants is covered by a hydrophobic layer of cuticular waxes to protect plant tissues against enormous environmental challenges including the infection of various pathogens. As the first contact site between plants and pathogens, the layer of cuticular waxes could function as a plant physical barrier that limits the entry of pathogens, acts as a reservoir of signals to trigger plant defense responses, and even gives cues exploited by pathogens to initiate their infection processes. Past decades have seen unprecedented proceedings in understanding the molecular mechanisms underlying the biosynthesis of plant cuticular waxes and their functions regulating plant–pathogen interactions. In this review, we summarized the recent progress in the molecular biology of cuticular wax biosynthesis and highlighted its multiple roles in plant disease resistance against bacterial, fungal, and insect pathogens.</p></abstract><kwd-group><kwd>cuticular wax</kwd><kwd>wax biosynthesis</kwd><kwd>plant–pathogen interaction</kwd><kwd>plant disease resistance</kwd></kwd-group></article-meta></front><body><sec sec-type=\"intro\" id=\"sec1-ijms-21-05514\"><title>1. Introduction</title><p>In the natural environment, plants encounter various pathogens such as viruses, bacteria, fungi, and even herbivorous insects, which seriously threaten plant growth and crop production. It was estimated that these pathogens have contributed to at least 20% of yield loss in important crops including wheat, rice, maize, potatoes, and soybeans [<xref rid=\"B1-ijms-21-05514\" ref-type=\"bibr\">1</xref>,<xref rid=\"B2-ijms-21-05514\" ref-type=\"bibr\">2</xref>]. Therefore, diseases caused by pathogenic microorganisms and herbivores are major factors affecting agriculture [<xref rid=\"B3-ijms-21-05514\" ref-type=\"bibr\">3</xref>,<xref rid=\"B4-ijms-21-05514\" ref-type=\"bibr\">4</xref>,<xref rid=\"B5-ijms-21-05514\" ref-type=\"bibr\">5</xref>,<xref rid=\"B6-ijms-21-05514\" ref-type=\"bibr\">6</xref>]. Unlike animals that could escape from predators and pathogens, plants must withstand pathogen attacks at the sites of growth [<xref rid=\"B1-ijms-21-05514\" ref-type=\"bibr\">1</xref>,<xref rid=\"B2-ijms-21-05514\" ref-type=\"bibr\">2</xref>]. Increasing evidence from studies in the model plant <italic>Arabidopsis thaliana</italic> revealed that plants have acquired a battery of sophisticated defense mechanisms to defend themselves against pathogen attacks during their co-evolution with various pathogens [<xref rid=\"B7-ijms-21-05514\" ref-type=\"bibr\">7</xref>].</p><p>As the preformed defense, physical barriers such as spines, hairs, trichomes, thorns, and cuticles cover the aerial parts of plants [<xref rid=\"B8-ijms-21-05514\" ref-type=\"bibr\">8</xref>,<xref rid=\"B9-ijms-21-05514\" ref-type=\"bibr\">9</xref>]. In contrast, plant innate immunity acts as an example of induced defense. For instance, pattern-triggered immunity (PTI) was induced by the recognition of chemical molecules in the pathogen, microbial, and/or damage-associated molecular patterns (PAMPs, MAMPs, and/or DAMPs, respectively), and these chemical molecules are released during pathogen infection and recognized by pattern recognition receptors (PRRs) [<xref rid=\"B10-ijms-21-05514\" ref-type=\"bibr\">10</xref>,<xref rid=\"B11-ijms-21-05514\" ref-type=\"bibr\">11</xref>]. Generally, PTI is involved in a wide range of defenses, including the production of reactive oxygen species (ROS) and the cascade of mitogen-activated protein kinases (MAPKs) [<xref rid=\"B12-ijms-21-05514\" ref-type=\"bibr\">12</xref>,<xref rid=\"B13-ijms-21-05514\" ref-type=\"bibr\">13</xref>,<xref rid=\"B14-ijms-21-05514\" ref-type=\"bibr\">14</xref>,<xref rid=\"B15-ijms-21-05514\" ref-type=\"bibr\">15</xref>]. To interfere with pattern-triggered immunity, pathogens usually secrete effectors, which could be recognized by specific resistance (R) proteins, inducing effector-triggered immunity (ETI) [<xref rid=\"B16-ijms-21-05514\" ref-type=\"bibr\">16</xref>,<xref rid=\"B17-ijms-21-05514\" ref-type=\"bibr\">17</xref>,<xref rid=\"B18-ijms-21-05514\" ref-type=\"bibr\">18</xref>,<xref rid=\"B19-ijms-21-05514\" ref-type=\"bibr\">19</xref>,<xref rid=\"B20-ijms-21-05514\" ref-type=\"bibr\">20</xref>]. Compared with PTI, ETI usually triggers the hypersensitive response (HR) and systemic acquired resistance (SAR) in host plants [<xref rid=\"B21-ijms-21-05514\" ref-type=\"bibr\">21</xref>,<xref rid=\"B22-ijms-21-05514\" ref-type=\"bibr\">22</xref>,<xref rid=\"B23-ijms-21-05514\" ref-type=\"bibr\">23</xref>,<xref rid=\"B24-ijms-21-05514\" ref-type=\"bibr\">24</xref>,<xref rid=\"B25-ijms-21-05514\" ref-type=\"bibr\">25</xref>]. Traditionally, breeding for disease resistance in crops such as wheat, rice, potato, barley, and even soybean mainly relies on host resistance regulated by Resistance (R) genes [<xref rid=\"B26-ijms-21-05514\" ref-type=\"bibr\">26</xref>]. However, in many cases, due to the variation of new pathogen strains, the resistance mediated by the R gene is less effective in the field [<xref rid=\"B26-ijms-21-05514\" ref-type=\"bibr\">26</xref>]. These new pathogen strains can escape the recognition of the R gene and the R gene-mediated downstream defense [<xref rid=\"B26-ijms-21-05514\" ref-type=\"bibr\">26</xref>]. Increasing evidence from studies in model plants and crops such as <italic>Arabidopsis</italic>, tomato (<italic>Solanum lycopersicum</italic>), and rice have revealed that phytohormones and their signaling pathways usually function at the downstream of pattern-triggered immunity and effector-triggered immunity to regulate plant defense, which were also considered to be effective plant defense mechanisms [<xref rid=\"B27-ijms-21-05514\" ref-type=\"bibr\">27</xref>,<xref rid=\"B28-ijms-21-05514\" ref-type=\"bibr\">28</xref>]. For instance, phytohormones salicylic acid (SA), jasmonic acid (JA), and ethylene (ET) are well known as the main regulators in plant defense responses [<xref rid=\"B29-ijms-21-05514\" ref-type=\"bibr\">29</xref>]. In addition, phytohormones abscisic acid (ABA), auxin, gibberellin (GA), brassinosteroid (BR), and cytokinin (CK) also act as indirect factors involved in plant-pathogen interactions [<xref rid=\"B27-ijms-21-05514\" ref-type=\"bibr\">27</xref>,<xref rid=\"B28-ijms-21-05514\" ref-type=\"bibr\">28</xref>].</p><p>As the outmost layer exposed to the environment, cuticle covers plant aerial organs and protects plant tissues against enormous environmental challenges such as dehydration, excessive UV radiation, mechanical damage, and even pathogen infections, which have been summarized in prior reviews [<xref rid=\"B30-ijms-21-05514\" ref-type=\"bibr\">30</xref>,<xref rid=\"B31-ijms-21-05514\" ref-type=\"bibr\">31</xref>,<xref rid=\"B32-ijms-21-05514\" ref-type=\"bibr\">32</xref>,<xref rid=\"B33-ijms-21-05514\" ref-type=\"bibr\">33</xref>,<xref rid=\"B34-ijms-21-05514\" ref-type=\"bibr\">34</xref>,<xref rid=\"B35-ijms-21-05514\" ref-type=\"bibr\">35</xref>,<xref rid=\"B36-ijms-21-05514\" ref-type=\"bibr\">36</xref>,<xref rid=\"B37-ijms-21-05514\" ref-type=\"bibr\">37</xref>,<xref rid=\"B38-ijms-21-05514\" ref-type=\"bibr\">38</xref>,<xref rid=\"B39-ijms-21-05514\" ref-type=\"bibr\">39</xref>,<xref rid=\"B40-ijms-21-05514\" ref-type=\"bibr\">40</xref>,<xref rid=\"B41-ijms-21-05514\" ref-type=\"bibr\">41</xref>,<xref rid=\"B42-ijms-21-05514\" ref-type=\"bibr\">42</xref>,<xref rid=\"B43-ijms-21-05514\" ref-type=\"bibr\">43</xref>,<xref rid=\"B44-ijms-21-05514\" ref-type=\"bibr\">44</xref>,<xref rid=\"B45-ijms-21-05514\" ref-type=\"bibr\">45</xref>]. In addition to its protective roles, cuticle also gets involved in regulating plant development [<xref rid=\"B33-ijms-21-05514\" ref-type=\"bibr\">33</xref>]. Although the composition of the cuticle varies among plant species, tissues, developmental stages, and even environmental conditions, plant cuticle is mainly composed of a cutin scaffold impregnated by and covered with cuticular waxes [<xref rid=\"B46-ijms-21-05514\" ref-type=\"bibr\">46</xref>,<xref rid=\"B47-ijms-21-05514\" ref-type=\"bibr\">47</xref>]. As a mixture of very-long-chain (VLC, >C20) fatty acids and their derivatives, cuticular waxes play multiple roles, from acting as a plant physical barrier, limiting the entry of pathogens, to functioning as a cue exploited by pathogens to initiate their prepenetration and infection processes in regulating the plant-pathogen interactions. Therefore, cuticular waxes have gained increasing attention in the study of plant disease resistance [<xref rid=\"B9-ijms-21-05514\" ref-type=\"bibr\">9</xref>,<xref rid=\"B48-ijms-21-05514\" ref-type=\"bibr\">48</xref>]. In this review, we summarized the recent advances in the molecular biology of cuticular wax biosynthesis and discussed their multiple roles in plant disease resistance against bacterial, fungal, and insect pathogens.</p></sec><sec id=\"sec2-ijms-21-05514\"><title>2. Molecular Mechanism of Cuticular Wax Biosynthesis</title><p>Plant cuticular waxes are organic solvent-extractable complex mixtures comprising very-long-chain fatty acids and their derivatives, such as aldehydes, alkanes, primary and secondary alcohols, esters, and ketones [<xref rid=\"B49-ijms-21-05514\" ref-type=\"bibr\">49</xref>,<xref rid=\"B50-ijms-21-05514\" ref-type=\"bibr\">50</xref>,<xref rid=\"B51-ijms-21-05514\" ref-type=\"bibr\">51</xref>]. In some plant species, secondary metabolites such as flavonoids, pentacyclic triterpenoids, and tocopherols have also been identified as wax components [<xref rid=\"B49-ijms-21-05514\" ref-type=\"bibr\">49</xref>,<xref rid=\"B50-ijms-21-05514\" ref-type=\"bibr\">50</xref>,<xref rid=\"B51-ijms-21-05514\" ref-type=\"bibr\">51</xref>]. In the model plant <italic>Arabidopsis</italic>, cuticular wax biosynthetic mechanisms have been characterized with the contribution of wax biosynthetic mutants and transcriptomic/proteomic analysis [<xref rid=\"B49-ijms-21-05514\" ref-type=\"bibr\">49</xref>,<xref rid=\"B50-ijms-21-05514\" ref-type=\"bibr\">50</xref>,<xref rid=\"B51-ijms-21-05514\" ref-type=\"bibr\">51</xref>].</p><p>As summarized in <xref ref-type=\"fig\" rid=\"ijms-21-05514-f001\">Figure 1</xref>, the biosynthesis of cuticular waxes in <italic>Arabidopsis</italic> can be divided into three steps: (1) the de novo synthesis of C16 or C18 fatty acids; (2) the extension of C16 and C18 fatty acids to form very-long-chain fatty acids (VLCFAs), which are used as direct precursors for wax synthesis in the endoplasmic reticulum (ER); and (3) the synthesis of derivatives of VLCFAs, such as aldehydes, alcohols, alkanes, ketones, and esters [<xref rid=\"B49-ijms-21-05514\" ref-type=\"bibr\">49</xref>,<xref rid=\"B51-ijms-21-05514\" ref-type=\"bibr\">51</xref>]. In the plastids of epidermal cells, acetyl coenzyme A (CoA) was converted into CoA-C2 by the catalysis of fatty acid synthetase complex (FAS), and after many reaction cycles, it can generate C16 or C18 acyl-acyl carrier protein (ACP), which was hydrolyzed by a fatty acyl-ACP thioesterase (FAT) to produce C16 or C18 fatty acids (<xref ref-type=\"fig\" rid=\"ijms-21-05514-f001\">Figure 1</xref>). These C16 or C18 fatty acids were activated to acyl-CoAs by the long-chain acyl-coenzyme A synthases (LACSs) and then transported to the endoplasmic reticulum (ER) [<xref rid=\"B51-ijms-21-05514\" ref-type=\"bibr\">51</xref>,<xref rid=\"B52-ijms-21-05514\" ref-type=\"bibr\">52</xref>]. C16 and C18 acyl-CoAs served as precursors for the formation of very-long-chain acyl-CoAs (up to C34), which was catalyzed by the enzymes of the fatty acid elongase (FAE) complex and the ECERIFERUM2 (CER2) proteins (<xref ref-type=\"fig\" rid=\"ijms-21-05514-f001\">Figure 1</xref>) [<xref rid=\"B53-ijms-21-05514\" ref-type=\"bibr\">53</xref>,<xref rid=\"B54-ijms-21-05514\" ref-type=\"bibr\">54</xref>]. In the FAE complex, β-ketoacyl-CoA synthase (KCS), β-ketoacyl-CoA reductase (KCR), 3-hydroxyacyl-CoA dehydratase (HCD), and enoyl-CoA reductase (ECR) catalyzed the sequential condensation/reduction/dehydration/reduction reactions in the formation of very-long-chain acyl-CoAs (<xref ref-type=\"fig\" rid=\"ijms-21-05514-f001\">Figure 1</xref>) [<xref rid=\"B55-ijms-21-05514\" ref-type=\"bibr\">55</xref>,<xref rid=\"B56-ijms-21-05514\" ref-type=\"bibr\">56</xref>,<xref rid=\"B57-ijms-21-05514\" ref-type=\"bibr\">57</xref>,<xref rid=\"B58-ijms-21-05514\" ref-type=\"bibr\">58</xref>]. These elongated very-long-chain acyl-CoAs were then modified into alkanes by the ECERIFERUM1 (CER1)/ECERIFERUM3 (CER3)/CYTOCHROME B5 (CYTB5) complex in an alkane-forming pathway, and these very-long-chain alkanes could be further oxidized to secondary alcohols and ketones by the CYP95A family cytochrome P450 enzymes MIDCHAIN ALKANE HYDROXYLASE1 (MAH1) (<xref ref-type=\"fig\" rid=\"ijms-21-05514-f001\">Figure 1</xref>) [<xref rid=\"B59-ijms-21-05514\" ref-type=\"bibr\">59</xref>,<xref rid=\"B60-ijms-21-05514\" ref-type=\"bibr\">60</xref>,<xref rid=\"B61-ijms-21-05514\" ref-type=\"bibr\">61</xref>]. In the alcohol-forming pathway, very-long-chain acyl-CoAs were converted into the n-6 monounsaturated fatty acids by the acyl desaturase ECERIFERUM17 (CER17), which was followed by the formation of primary alcohols catalyzed by the fatty acyl-CoA reductase ECERIFERUM4 (CER4) (<xref ref-type=\"fig\" rid=\"ijms-21-05514-f001\">Figure 1</xref>) [<xref rid=\"B62-ijms-21-05514\" ref-type=\"bibr\">62</xref>,<xref rid=\"B63-ijms-21-05514\" ref-type=\"bibr\">63</xref>]. In addition, the <italic>Arabidopsis</italic> WAX SYNTHASE/ACYL-COA: DIACYLGLYCEROL ACYLTRANSFERASE 1 (WSD1) catalyzed the formation of wax esters through using acyl-CoAs and primary alcohols as precursors in the alcohol-forming pathway (<xref ref-type=\"fig\" rid=\"ijms-21-05514-f001\">Figure 1</xref>) [<xref rid=\"B64-ijms-21-05514\" ref-type=\"bibr\">64</xref>]. These generated waxes components were transported from the ER to the plasma membrane (PM)via the Golgi and trans-Golgi network (TGN)-trafficking pathways, and finally exported out of the plant cell to the cuticle via the PM-localized ATP binding cassette G (ABCG) subfamily half transporters and the lipid transfer proteins (LTPs) (<xref ref-type=\"fig\" rid=\"ijms-21-05514-f001\">Figure 1</xref>) [<xref rid=\"B49-ijms-21-05514\" ref-type=\"bibr\">49</xref>,<xref rid=\"B50-ijms-21-05514\" ref-type=\"bibr\">50</xref>,<xref rid=\"B51-ijms-21-05514\" ref-type=\"bibr\">51</xref>].</p><p>Increasing evidence from studies in <italic>Arabidopsis</italic> revealed that the biosynthesis of cuticular waxes is regulated by multiple transcriptional regulators [<xref rid=\"B49-ijms-21-05514\" ref-type=\"bibr\">49</xref>,<xref rid=\"B50-ijms-21-05514\" ref-type=\"bibr\">50</xref>,<xref rid=\"B51-ijms-21-05514\" ref-type=\"bibr\">51</xref>]. As the first reported transcriptional regulator, the <italic>Arabidopsis</italic> APETALA2-Ethylene responsive factor (AP2-EREBP)-type transcription factor WAX INDUCER1/SHINE1 (WIN1/SHN1) and its close homologs SHN2 and SHN3 activated cuticular wax biosynthesis by upregulation of biosynthesis genes β-Ketoacyl-CoA synthase 1 (<italic>KCS1</italic>), <italic>CER1</italic>, and <italic>CER2</italic> [<xref rid=\"B65-ijms-21-05514\" ref-type=\"bibr\">65</xref>,<xref rid=\"B66-ijms-21-05514\" ref-type=\"bibr\">66</xref>]. Similarly, Myeloblastosis (MYB) family transcription factors MYB94 and MYB96 potentiated the cuticular wax biosynthesis under drought by directly activating expression of <italic>KCS2</italic>, <italic>ECR</italic>, <italic>CER2</italic>, and <italic>WSD1</italic> genes in <italic>Arabidopsis</italic> [<xref rid=\"B67-ijms-21-05514\" ref-type=\"bibr\">67</xref>,<xref rid=\"B68-ijms-21-05514\" ref-type=\"bibr\">68</xref>]. In contrast, the <italic>Arabidopsis</italic> AP2/ERF-type transcription factor DECREASE WAX BIOSYNTHESIS (DEWAX) was reported to negatively regulate cuticular wax biosynthesis in the light/dark cycle by directly suppressing long chain acyl-CoA synthase 2 (<italic>LACS2</italic>), <italic>CER1</italic>, and <italic>ECR</italic> genes [<xref rid=\"B69-ijms-21-05514\" ref-type=\"bibr\">69</xref>,<xref rid=\"B70-ijms-21-05514\" ref-type=\"bibr\">70</xref>]. Moreover, the biosynthesis of cuticular wax in <italic>Arabidopsis</italic> is regulated at the post-transcriptional and post-translational levels. For instance, ECERIFERUM7 (CER7), a core subunit of the exosome, regulated the accumulation of trans-acting small interfering RNA class of small RNAs involved in direct silencing of <italic>CER3</italic> in <italic>Arabidopsis</italic> [<xref rid=\"B71-ijms-21-05514\" ref-type=\"bibr\">71</xref>]. Another recent study in <italic>Arabidopsis</italic> revealed that CER16, a protein with no known domains or motifs, also inhibited post-transcriptional gene silencing of CER3 to regulate alkane biosynthesis [<xref rid=\"B72-ijms-21-05514\" ref-type=\"bibr\">72</xref>]. In addition, the Kelch repeat F-box protein SMALL AND GLOSSY LEAVES1 (SAGL1) mediated proteasome-dependent degradation of CER3, thereby negatively regulating cuticular wax biosynthesis in <italic>Arabidopsis</italic> [<xref rid=\"B73-ijms-21-05514\" ref-type=\"bibr\">73</xref>].</p></sec><sec id=\"sec3-ijms-21-05514\"><title>3. Regulation of Plant–Bacterial Pathogen Interaction by Cuticular Waxes</title><p>On the plant cuticle, bacterial pathogens usually produce extracellular polymeric substances and form large aggregates to help them to withstand the harsh surrounding conditions. It is well known that the bacterial pathogen <italic>Pseudomonas syringae</italic> can produce syringafactin, a compound with surfactant properties, to facilitate its motility and increase the permeability of <italic>Arabidopsis</italic> cuticle [<xref rid=\"B74-ijms-21-05514\" ref-type=\"bibr\">74</xref>]. Increasing evidence has revealed that the survival and infection of bacterial pathogens on plant surfaces are affected by the integrity and permeability of the plant cuticular wax layer. A more permeable plant cuticular wax layer could lead to either enhanced resistance or susceptibility to pathogen infections [<xref rid=\"B48-ijms-21-05514\" ref-type=\"bibr\">48</xref>]. For instance, <italic>Arabidopsis sma4</italic> (symptoms to multiple-regulated avr4) is a loss-of-function mutant of the <italic>SMA4</italic> gene, which encodes the cuticular wax biosynthetic component LACS2 [<xref rid=\"B75-ijms-21-05514\" ref-type=\"bibr\">75</xref>]. Tang et al. found that the <italic>sma4</italic> mutant exhibited enhanced susceptibility to the hemibiotrophic bacterial pathogen <italic>Pseudomonas syringae</italic> pv <italic>tomato</italic> strain DC3000 (<italic>Pst</italic> DC3000) but displayed enhanced resistance against the necrotrophic fungal pathogen <italic>Botrytis cinerea</italic> (<italic>B. cinerea</italic>) [<xref rid=\"B76-ijms-21-05514\" ref-type=\"bibr\">76</xref>].</p><p>MYB family transcription factors have been reported to be involved in plant cuticular wax biosynthesis and disease resistance against bacterial pathogens [<xref rid=\"B48-ijms-21-05514\" ref-type=\"bibr\">48</xref>,<xref rid=\"B51-ijms-21-05514\" ref-type=\"bibr\">51</xref>]. For instance, <italic>Arabidopsis</italic> transcription factor MYB30 functions as a positive regulator of a cell death pathway, conditioning the hypersensitive response [<xref rid=\"B77-ijms-21-05514\" ref-type=\"bibr\">77</xref>]. Raffaele et al. demonstrated that the exacerbated hypersensitive response phenotype of MYB30-overexpressing <italic>Arabidopsis</italic> lines was altered by the loss of function of the acyl-ACP thioesterase gene acyl-ACP thioesterase B <italic>(FATB)</italic>, which causes severe defects in the supply of fatty acids for the biosynthesis of very-long-chain fatty acids, suggesting that MYB30 modulates hypersensitive response via controlling the biosynthesis of very-long-chain fatty acids in <italic>Arabidopsis</italic> [<xref rid=\"B77-ijms-21-05514\" ref-type=\"bibr\">77</xref>]. Similarly, Zhang et al. reported that ectopic expression of apple (<italic>Medicago truncatula</italic>) <italic>MdMYB30</italic>, which encodes an R2R3 MYB transcription factor, in <italic>Arabidopsis</italic> increased the transcription levels of wax biosynthesis-related genes, including wax synthesis regulatory gene 1 (<italic>AtWRI1</italic>), <italic>AtWIN1</italic>, <italic>AtKCS1</italic>, acyl-CoA binding protein 1 (<italic>AtACBP1</italic>), <italic>AtLACS2</italic>, <italic>AtSHINE2</italic>, and <italic>AtSHINE3</italic> [<xref rid=\"B78-ijms-21-05514\" ref-type=\"bibr\">78</xref>]. The accumulation of wax compositions, such as C29 alkanes, C31 alcohols, C29 aldehydes, C16 fatty acids, C29 ketones, and C29 and C30 esters were significantly enhanced in <italic>MdMYB30</italic>-ectopic-expression <italic>Arabidopsis</italic> lines [<xref rid=\"B78-ijms-21-05514\" ref-type=\"bibr\">78</xref>]. Interestingly, MdMYB30 also contributed to the increased resistance against <italic>Pst</italic> DC3000 in <italic>Arabidopsis</italic>, suggesting that the changed epicuticular wax content and composition might cause disease resistance against the bacterial pathogen <italic>Pst</italic> DC3000 [<xref rid=\"B78-ijms-21-05514\" ref-type=\"bibr\">78</xref>]. In addition, MYB96, another MYB transcription factor (TF) in <italic>Arabidopsis</italic>, directly binds to the promoters of wax biosynthetic genes including <italic>KCS1</italic>, <italic>KCS2/DAISY</italic>, <italic>KCS6</italic>, <italic>KCR1</italic>, and <italic>CER3</italic>, and actives the expression of these genes under drought- and ABA-inducible conditions [<xref rid=\"B79-ijms-21-05514\" ref-type=\"bibr\">79</xref>]. Notably, MYB96 activation tagging <italic>Arabidopsis</italic> lines showed increased wax accumulation and enhanced resistance to <italic>Pst</italic> DC3000 by potentiating the SA biosynthesis [<xref rid=\"B80-ijms-21-05514\" ref-type=\"bibr\">80</xref>]. However, the accumulation of cuticular wax components does not necessarily contribute to the plant disease resistance against bacterial pathogens. For instance, VLC alkanes were accumulated but the susceptibility to <italic>Pst</italic> DC3000 was enhanced in the <italic>Arabidopsis</italic> lines over-expressing <italic>CER1</italic> [<xref rid=\"B59-ijms-21-05514\" ref-type=\"bibr\">59</xref>]. Interestingly, increasing evidence from studies in fungal pathogens <italic>Blumeria</italic> and <italic>Colletotrichum</italic> revealed that certain wax components such as very-long-chain aldehyde and terpenoids could be exploited by certain fungal pathogens to trigger their infection processes, but other components, including free fatty acid RR (resistance-related) metabolites, contribute to plant resistance against fungal pathogens. Therefore, characterizing the function of specific wax components in regulating bacterial growth and infections, as well as plant defense responses, might contribute to understanding the roles of cuticular waxes in the regulation of plant–bacterial pathogen interactions in the future research.</p></sec><sec id=\"sec4-ijms-21-05514\"><title>4. Regulation of Plant–Fungal Pathogen Interaction by Cuticular Waxes</title><p>During the infection process, fungal pathogens could synthesize and secrete hydrolytic enzymes such as cutinases and lipases to degrade the cuticular wax layer [<xref rid=\"B81-ijms-21-05514\" ref-type=\"bibr\">81</xref>]. For instance, cutinase2 (<italic>CUT2</italic>) gene in rice blast (<italic>Magnaporthe grisea</italic>) is activated during appressorial development and fungal penetration [<xref rid=\"B82-ijms-21-05514\" ref-type=\"bibr\">82</xref>]. During these processes, fungal pathogens would be recognized by the plant immune systems and trigger the immune responses. Several <italic>Arabidopsis</italic> plants over-accumulating fungal cutinase exhibited increased cuticle permeability and enhanced resistance to fungal pathogens. For example, the <italic>Arabidopsis</italic> AP2/ERF-type transcription factor DECREASE WAX BIOSYNTHESIS (DEWAX) negatively regulates cuticular wax biosynthesis by suppressing cuticular wax biosynthesis genes (<italic>CER1</italic>, <italic>LACS2</italic>, ATP-citrate lyase A2 and <italic>ECR</italic>) [<xref rid=\"B83-ijms-21-05514\" ref-type=\"bibr\">83</xref>]. Interestingly, over-expression <italic>DEWAX</italic> in <italic>Arabidopsis</italic> led to enhanced disease resistance against grey mildew (<italic>Botrytis cinerea</italic>) [<xref rid=\"B69-ijms-21-05514\" ref-type=\"bibr\">69</xref>]. Further analyses revealed that DEWAX acts as a transcriptional activator, binding to the promoters of defense-related genes including plant defensin 1.2a (<italic>PDF1.2a</italic>), indole glucosinolate O-methyltransferase 1 (<italic>IGMT1</italic>), and peroxidase 37 (<italic>PRX37</italic>), and upregulating the expression of these genes in <italic>Arabidopsis</italic> [<xref rid=\"B69-ijms-21-05514\" ref-type=\"bibr\">69</xref>]. These results suggest that cuticle wax biosynthesis genes could regulate plant disease resistance through direct targeting defense-related genes. Indeed, the <italic>Arabidopsis sma4</italic> mutant plants display increased resistance to grey mildew (<italic>B. cinerea</italic>), and these processes were independent of jasmonic acid (JA)- and ethylene (ET)-signaling pathways [<xref rid=\"B76-ijms-21-05514\" ref-type=\"bibr\">76</xref>].</p><p>Interestingly, <italic>Arabidopsis</italic> mutants such as long-chain acyl-CoA synthetase-2 (<italic>lacs-2</italic>) and <italic>-3</italic>, and myeloblast transcription factor 96 (<italic>myb96</italic>) with increased cuticle permeability exhibited enhanced disease resistance against grey mildew (<italic>B. cinerea</italic>) [<xref rid=\"B76-ijms-21-05514\" ref-type=\"bibr\">76</xref>,<xref rid=\"B80-ijms-21-05514\" ref-type=\"bibr\">80</xref>]. L’Haridon et al. reported that a permeable cuticle in <italic>Arabidopsis</italic> is associated with the release of reactive oxygen species (ROS) and induction of innate immunity, demonstrating the importance of fungal suppression by reactive oxygen species formation [<xref rid=\"B84-ijms-21-05514\" ref-type=\"bibr\">84</xref>]. In contrast, Cui et al. demonstrated that <italic>Botrytis</italic> immunity conferred by cuticle permeability can be genetically uncoupled from phosphatase2c-regulated abscisic acid (ABA) sensitivity but requires negative regulation of a parallel ABA-dependent cell death pathway [<xref rid=\"B85-ijms-21-05514\" ref-type=\"bibr\">85</xref>]. In addition, several studies revealed that cuticular wax accumulation also contributes to disease resistance against fungal pathogens. For instance, Zhang et al. showed that the apple (<italic>M. truncatula</italic>) transcription factor MdMYB30 could bind to the promoter region of <italic>MdKCS1</italic> to activate its expression and induce wax biosynthesis [<xref rid=\"B78-ijms-21-05514\" ref-type=\"bibr\">78</xref>]. Notably, the infection of apple canker pathogen <italic>Botryosphaeria dothidea</italic> could induce the accumulation of wax crystals and transcription of pathogenesis-related genes, such as <italic>MdNPR1</italic>, <italic>MdPR1</italic>, <italic>MdPR5</italic>, <italic>MdEDS1</italic>, and <italic>MdPAL</italic> at <italic>B. dothidea</italic> injection sites in <italic>MdMYB30</italic>-overexpression apple lines [<xref rid=\"B78-ijms-21-05514\" ref-type=\"bibr\">78</xref>]. Consistently, <italic>MdMYB30</italic>-overexpression transgenic apple calli exhibited strengthened resistance against apple canker (<italic>B. dothidea</italic>). These results indicated that MdMYB30 positively modulates waxes biosynthesis of apple fruit and enhances apple resistance to certain fungal pathogens [<xref rid=\"B78-ijms-21-05514\" ref-type=\"bibr\">78</xref>].</p><p>Powdery mildew caused by the fungal pathogen <italic>Blumeria graminis</italic> is a devastating disease in barley and wheat. Increasing evidence has revealed that <italic>B. graminis</italic> could utilize plant cuticular wax components to initiate their prepenetration processes such as conidial germination and appressorial development [<xref rid=\"B86-ijms-21-05514\" ref-type=\"bibr\">86</xref>,<xref rid=\"B87-ijms-21-05514\" ref-type=\"bibr\">87</xref>,<xref rid=\"B88-ijms-21-05514\" ref-type=\"bibr\">88</xref>,<xref rid=\"B89-ijms-21-05514\" ref-type=\"bibr\">89</xref>,<xref rid=\"B90-ijms-21-05514\" ref-type=\"bibr\">90</xref>,<xref rid=\"B91-ijms-21-05514\" ref-type=\"bibr\">91</xref>,<xref rid=\"B92-ijms-21-05514\" ref-type=\"bibr\">92</xref>]. Hansjakob et al. reported that very-long-chain aldehydes could stimulate the in vitro conidial germination of <italic>B. graminis</italic> in a dose-dependent manner [<xref rid=\"B86-ijms-21-05514\" ref-type=\"bibr\">86</xref>,<xref rid=\"B87-ijms-21-05514\" ref-type=\"bibr\">87</xref>]. Recently, Wang et al. and Kong et al. reported that the silencing of the wheat 3-ketoacyl-CoA synthase <italic>(TaKCS6)</italic> and enoyl-CoA reductase (<italic>TaECR</italic>) led to the reduction of cuticular wax load and attenuated conidial germination of <italic>Blumeria graminis</italic> f.sp. <italic>tritici</italic> (<italic>Bgt</italic>). Interestingly, the <italic>Bgt</italic> germination penalty on the <italic>TaKCS6-</italic> or <italic>TaECR</italic>-silenced wheat plants could be fully restored by the application of wild-type cuticular waxes or very-long-chain aldehydes, suggesting that the very-long-chain aldehydes were the wax signals provided by <italic>TaKCS6</italic> and <italic>TaECR</italic> for stimulating <italic>Bgt</italic> conidia germination in bread wheat [<xref rid=\"B91-ijms-21-05514\" ref-type=\"bibr\">91</xref>,<xref rid=\"B92-ijms-21-05514\" ref-type=\"bibr\">92</xref>]. In <italic>Arabidopsis</italic>, Inada and Savory reported that the powdery mildew pathogen <italic>Golovinomyces orontii</italic> (<italic>G. orontii</italic>) could infect the mature rosette leaves of <italic>Arabidopsis</italic>, but its prepenetration processes such as conidial germination and appressorial formation were strongly inhibited on stems, fruits, and roots of <italic>Arabidopsis</italic> [<xref rid=\"B93-ijms-21-05514\" ref-type=\"bibr\">93</xref>]. In addition, they found that inhibition of prepenetration processes of powdery mildew pathogen <italic>G. orontii</italic> on <italic>Arabidopsis</italic> stems was more severe in the mutant <italic>cer3</italic> but not in <italic>cer1</italic>, which is consistent with the fact that <italic>CER1</italic> gets involved in the biosynthesis of very-long-chain alkanes, but <italic>CER3</italic> mediates the formation of very-long-chain aldehydes [<xref rid=\"B93-ijms-21-05514\" ref-type=\"bibr\">93</xref>]. Therefore, stimulating germination of powdery mildew conidia by very-long-chain aldehydes might be conserved during the interactions of powdery mildew pathogens with monocots and dicots.</p><p>Fusarium head blight (FHB) caused by the fungal pathogen <italic>Fusarium graminearum</italic> is another devastating disease in wheat and barley. Silencing of barley WAX INDUCER1 (<italic>HvWIN1</italic>), a gene essential for the regulation of cuticular wax biosynthesis, resulted in enhanced susceptibility to FHB [<xref rid=\"B94-ijms-21-05514\" ref-type=\"bibr\">94</xref>]. Further study showed the contents of free fatty acid RR (resistance-related) metabolites such as linoleic and palmitic acids, and the transcript abundance of genes involved in cuticular wax biosynthesis, including <italic>CYP86A2</italic>, <italic>CYP89A2</italic>, and <italic>LACS2</italic>, were significantly reduced in the <italic>HvWIN1</italic>-silenced barley leaves upon pathogen inoculation, suggesting that HvWIN1 regulates the expression of free fatty acid biosynthesis genes to reinforce cuticle to resist head blight in barley [<xref rid=\"B94-ijms-21-05514\" ref-type=\"bibr\">94</xref>].</p><p>As a typical fungal pathogen, rust has evolved special mechanisms for invading plants. Upon rust infection, fungal urediniospores need to attach to the surface of leaves and subsequently form germ tubes. In general, rust pathogen requires specific plant surface topography and chemical signals to trigger the formation of prepenetration structures [<xref rid=\"B95-ijms-21-05514\" ref-type=\"bibr\">95</xref>,<xref rid=\"B96-ijms-21-05514\" ref-type=\"bibr\">96</xref>]. Barrel medic (<italic>Medicago truncatula</italic>) gene <italic>IRG1</italic>, encoding a Cys(2) His(2) zinc finger transcription factor, contributes to the plant nonhost resistance to fungal pathogens. <italic>Phakopsora pachyrhizi</italic> and <italic>Puccinia emaculata</italic> are the main pathogens causing soybean (<italic>Glycine max</italic>) and switchgrass (<italic>Panicum virgatum</italic>) rust, respectively [<xref rid=\"B97-ijms-21-05514\" ref-type=\"bibr\">97</xref>,<xref rid=\"B98-ijms-21-05514\" ref-type=\"bibr\">98</xref>]. The barrel medic inhibitor of rust germ tube differentation1 (<italic>irg1</italic>) mutant showed retarded prepenetration structures of two rust pathogens, <italic>P. pachyrhizi</italic> and <italic>P. emaculata</italic>, and one anthracnose pathogen, <italic>Colletotrichum trifoli</italic> [<xref rid=\"B99-ijms-21-05514\" ref-type=\"bibr\">99</xref>]. Further analyses revealed that abaxial epicuticular wax crystals were completely lost and surface hydrophobicity was reduced in barrel medic (<italic>M. truncatula</italic>) <italic>irg1</italic> mutant [<xref rid=\"B99-ijms-21-05514\" ref-type=\"bibr\">99</xref>]. Meanwhile, the compositions of epicuticular waxes were changed in <italic>irg1</italic> mutant, with fewer C30 primary alcohols as well as more C29 and C30 alkanes [<xref rid=\"B99-ijms-21-05514\" ref-type=\"bibr\">99</xref>]. Transcriptome analysis found that ECERIFERUM 4, an enzyme involved in primary alcohol biosynthesis, and MYB96, a major transcription factor regulating wax biosynthesis, were downregulated in <italic>irg1</italic> mutant, suggesting that IRG1 executes a regulating role in the biosynthesis of epidermal wax, which might affect the germination and appressorial formation of nonhost fungal pathogens in barrel medic (<italic>M. truncatula</italic>) [<xref rid=\"B99-ijms-21-05514\" ref-type=\"bibr\">99</xref>]. Similarly, a barley gene, <italic>CYP96B22</italic>, encoding a putative cytochrome P450 monooxygenase, was also known to be involved in cuticular wax biosynthesis [<xref rid=\"B100-ijms-21-05514\" ref-type=\"bibr\">100</xref>]. The expression of <italic>CYP96B22</italic> was induced by the inoculation of rice blast <italic>Magnaporthe orzae</italic> at the nonhost barley leaves [<xref rid=\"B100-ijms-21-05514\" ref-type=\"bibr\">100</xref>]. Meanwhile, the silencing of <italic>CYP96B22</italic> using barley stripe mosaic virus-mediated gene silencing (BSMV-VIGS) led to a decrease in penetration resistance of barley plants to host and nonhost isolates of blast <italic>Magnaporthe</italic>, suggesting that CYP96B22 plays a role in the disease resistance against rice blast <italic>M. orzae</italic> infection [<xref rid=\"B100-ijms-21-05514\" ref-type=\"bibr\">100</xref>]. Further studying the roles of IRG1 and CYP96B22 might help us to improve understanding of the significance of cuticular wax deposition on plant disease resistance against fungal infection in the future.</p></sec><sec id=\"sec5-ijms-21-05514\"><title>5. Regulation of Plant–Insect Interaction by Cuticular Waxes</title><p>Growing in their natural environment, plants are usually attacked by a variety of herbivorous insects. It was estimated that insect infestation leads to yield losses of more than 20% in wheat, soybean, and cotton [<xref rid=\"B101-ijms-21-05514\" ref-type=\"bibr\">101</xref>]. As summarized in a prior review, plant–insect interactions are regulated by plant cuticular waxes at multiple levels [<xref rid=\"B102-ijms-21-05514\" ref-type=\"bibr\">102</xref>]. First, cuticular waxes contribute to the slippery nature of plant surfaces, thus affecting plant-insect interactions [<xref rid=\"B96-ijms-21-05514\" ref-type=\"bibr\">96</xref>]. For instance, cuticular waxes coating stems of many <italic>Macaranga</italic> ant plants (<italic>Euphorbiaceae</italic>) contain a large amount of triterpenoids, rendering the surface very slippery for most insects and allowing its symbiotic ants to survive in a competitor-free environment [<xref rid=\"B103-ijms-21-05514\" ref-type=\"bibr\">103</xref>,<xref rid=\"B104-ijms-21-05514\" ref-type=\"bibr\">104</xref>]. Second, certain cuticular wax components such as long-chain alkanes could be exploited by insects for host selection [<xref rid=\"B105-ijms-21-05514\" ref-type=\"bibr\">105</xref>,<xref rid=\"B106-ijms-21-05514\" ref-type=\"bibr\">106</xref>,<xref rid=\"B107-ijms-21-05514\" ref-type=\"bibr\">107</xref>,<xref rid=\"B108-ijms-21-05514\" ref-type=\"bibr\">108</xref>,<xref rid=\"B109-ijms-21-05514\" ref-type=\"bibr\">109</xref>]. Spencer J.L. reported that the addition of long-chain alkanes in sinigrin and cabbage homogenates could stimulate oviposition by the diamondback moth <italic>Plutella xylostella</italic> [<xref rid=\"B110-ijms-21-05514\" ref-type=\"bibr\">110</xref>]. Third, egg deposition of insects could significantly affect the composition of plant cuticular waxes, which could be sensed by egg parasitoids [<xref rid=\"B111-ijms-21-05514\" ref-type=\"bibr\">111</xref>]. Blenn et al. reported that oviposition by the large cabbage white butterfly <italic>Pieris brassicae</italic> led to changes in the amounts of the wax composition such as the fatty acids tetracosanoic acid and tetratriacontanoic acid in <italic>Arabidopsis</italic>, and that the tetracosanoic acid could attract the egg parasitoid <italic>Trichogramma brassicae</italic> [<xref rid=\"B111-ijms-21-05514\" ref-type=\"bibr\">111</xref>].</p><p>In addition to studies on the correlation of insect behaviors and plant wax compositions, the plant transcriptomic analysis also contributes to our understanding of the regulation of plant-insect interaction by cuticular waxes. For instance, tea green leafhopper, <italic>Empoasca</italic> (<italic>Matsumurasca</italic>) <italic>onukii</italic> Matsuda, is one of the most harmful pests to tea plants (<italic>Camellia sinensis</italic>), seriously threatening tea yield and quality [<xref rid=\"B112-ijms-21-05514\" ref-type=\"bibr\">112</xref>]. A recent transcriptomic analysis revealed that genes involved in cuticle wax biosynthesis were significantly upregulated by tea green leafhopper infestation in tea plants [<xref rid=\"B112-ijms-21-05514\" ref-type=\"bibr\">112</xref>]. In particular, the transcription level of a <italic>CER1</italic> homolog involved in the formation of cuticular wax alkane was most significantly elevated, and C29 alkanes in tea leaf waxes were increased [<xref rid=\"B112-ijms-21-05514\" ref-type=\"bibr\">112</xref>]. These results suggested that the <italic>CER1</italic> homolog plays a pivotal role in tea wax alkane formation and is probably involved in responding to tea green leafhopper and other environmental stresses. Therefore, it is intriguing to characterize the function of other cuticular wax biosynthesis genes in regulating plant-insect interaction in future research.</p></sec><sec sec-type=\"conclusions\" id=\"sec6-ijms-21-05514\"><title>6. Conclusions and Perspectives</title><p>In this review, we discussed the recent progress in the understanding of plant cuticular wax biosynthesis and its important roles in regulating plant-pathogen interactions (as summarized in <xref ref-type=\"fig\" rid=\"ijms-21-05514-f001\">Figure 1</xref>). Although past decades have seen a great advance in understanding the function of cuticular wax biosynthesis genes in model plants, we still have a long way to go towards fully understanding the biosynthesis of plant cuticular wax. For instance, exact enzymes mediating biosynthesis of certain cuticular wax components such as very-long-chain aldehydes need to be identified. Furthermore, biosynthesis of cuticular wax shares many precursors and energies with metabolisms of other substances such as saccharides, lipids, and even amino acids, but how plants orchestrate these biosynthetic processes is still unknown. In addition, increasing evidence has revealed that the biosynthesis of cuticular wax is regulated by developmental signals and environmental conditions, and their underlying mechanisms also remain to be disclosed.</p><p>As summarized in <xref rid=\"ijms-21-05514-t001\" ref-type=\"table\">Table 1</xref>, many cuticular wax biosynthesis genes get involved in plant–pathogen interaction. As we know, the mixture of cuticular waxes is mainly composed of very-long-chain fatty acids and their derivatives, such as aldehydes, alkanes, primary and secondary alcohols, esters, and ketones, but the exact cuticular wax components responsible for limiting pathogen infection or inducing plant defense responses remains to be identified. Although it was demonstrated that very-long-chain aldehydes function as wax signals to trigger the conidial germination and appresorial development of <italic>B. graminis</italic> in barley and wheat, wherein the chemical regulation seems to be very specific between plant-pathogen interactions. Therefore, identifying these cuticular wax components that induce plant defense or pathogen infection and characterizing their underlying mechanisms would certainly improve our understanding of the function of cuticular wax biosynthesis in plant disease resistance. In addition, most of our knowledge about plant cuticular wax biosynthesis and their roles in plant disease resistance come from the study of the model plant-pathogen interaction, such as <italic>Arabidopsis</italic> and the fungal pathogen <italic>Botrytis cinerea</italic>, or bacterial pathogen <italic>Pst</italic> DC3000. However, the roles and mechanisms of cuticular waxes in regulating crop-pathogen interactions remain to be explored in future research.</p><p>Our knowledge of cuticular wax biosynthesis and their roles in plant–pathogen interactions could bring us valuable information to develop new strategies for crop protection. For instance, based on an understanding about the cuticular wax biosynthetic pathway and the function of exact cuticular wax components in plant-pathogen interaction, we could employ genome-editing systems such as the clustered regularly interspaced short palindromic repeats and CRISPR associated protein 9 (CRISPR-Cas9) system to create genome-edited crops producing the “ideal” layer of cuticular waxes without wax cues exploited by pathogens [<xref rid=\"B113-ijms-21-05514\" ref-type=\"bibr\">113</xref>]. In addition, the knowledge of cuticular wax biosynthesis and their functions in plant-pathogen interaction would help us to synthesize the “elicitor” cuticular wax components to prime plant defense responses and limit pathogen infection.</p></sec></body><back><notes><title>Author Contributions</title><p>C.C., L.K., X.W., and P.Z. wrote this manuscript. All authors have read and agreed to the published version of the manuscript.</p></notes><notes><title>Funding</title><p>This research was funded by the National Natural Science Foundation of China (31701412, 31701986), the Natural Science Foundation of Shandong Province (ZR2017BC109), and the Qingdao Science and Technology Bureau Fund (17-1-1-50-jch, 18-2-2-51-jch).</p></notes><notes notes-type=\"COI-statement\"><title>Conflicts of Interest</title><p>The authors declare no conflict of interest.</p></notes><ref-list><title>References</title><ref id=\"B1-ijms-21-05514\"><label>1.</label><element-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Zhou</surname><given-names>J.M.</given-names></name><name><surname>Zhang</surname><given-names>Y.</given-names></name></person-group><article-title>Plant immunity: Danger perception and signaling</article-title><source>Cell</source><year>2020</year><volume>181</volume><fpage>978</fpage><lpage>989</lpage><pub-id pub-id-type=\"doi\">10.1016/j.cell.2020.04.028</pub-id><pub-id pub-id-type=\"pmid\">32442407</pub-id></element-citation></ref><ref id=\"B2-ijms-21-05514\"><label>2.</label><element-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Gust</surname><given-names>A.A.</given-names></name><name><surname>Pruitt</surname><given-names>R.</given-names></name><name><surname>Nürnberger</surname><given-names>T.</given-names></name></person-group><article-title>Sensing danger: Key to activating plant immunity</article-title><source>Trends Plant. 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Sci.</source><year>2020</year><volume>10</volume><elocation-id>1705</elocation-id><pub-id pub-id-type=\"doi\">10.3389/fpls.2019.01705</pub-id><pub-id pub-id-type=\"pmid\">32010173</pub-id></element-citation></ref><ref id=\"B113-ijms-21-05514\"><label>113.</label><element-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Gupta</surname><given-names>D.</given-names></name><name><surname>Bhattacharjee</surname><given-names>O.</given-names></name><name><surname>Mandal</surname><given-names>D.</given-names></name><name><surname>Sen</surname><given-names>M.K.</given-names></name><name><surname>Dey</surname><given-names>D.</given-names></name><name><surname>Dasgupta</surname><given-names>A.</given-names></name><name><surname>Kazi</surname><given-names>T.A.</given-names></name><name><surname>Gupta</surname><given-names>R.</given-names></name><name><surname>Sinharoy</surname><given-names>S.</given-names></name><name><surname>Acharya</surname><given-names>K.</given-names></name><etal/></person-group><article-title>CRISPR-Cas9 system: A new-fangled dawn in gene editing</article-title><source>Life Sci.</source><year>2019</year><volume>232</volume><elocation-id>116636</elocation-id><pub-id pub-id-type=\"doi\">10.1016/j.lfs.2019.116636</pub-id><pub-id pub-id-type=\"pmid\">31295471</pub-id></element-citation></ref></ref-list></back><floats-group><fig id=\"ijms-21-05514-f001\" orientation=\"portrait\" position=\"float\"><label>Figure 1</label><caption><p>A simplified model for the cuticular wax biosynthesis and its roles in regulating plant–pathogen interactions. The biosynthesis of the cuticular wax mixture starts from the elongation of C16 or C18 fatty acid-coenzyme A (CoA) by fatty acid elongase (FAE) complex and ECERIFERUM2 (CER2) proteins. The elongated very-long-chain (VLC) acyl-CoAs are then modified into aldehydes, alkanes, secondary alcohols, and ketones by an alkane-forming pathway (shown in blue) or into primary alcohols and wax esters by an alcohol-forming pathway (shown in pink). Names shown in red denote proteins involved in the regulation of plant-pathogen interactions. For steps involving multiple paralogs, only the gene subfamily name is given in black. Circle, square, and triangle denote plant-bacterial pathogen interaction, plant-fungal pathogen interaction, and plant–insect interaction, respectively. Positive and negative regulations of plant–pathogen interaction are individually shown in green and red colors, respectively. The model for the cuticular wax biosynthesis was built on Yeast and Rose. 2013, and Lewandowska et al., 2020 [<xref rid=\"B8-ijms-21-05514\" ref-type=\"bibr\">8</xref>,<xref rid=\"B49-ijms-21-05514\" ref-type=\"bibr\">49</xref>].</p></caption><graphic xlink:href=\"ijms-21-05514-g001\"/></fig><table-wrap id=\"ijms-21-05514-t001\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijms-21-05514-t001_Table 1</object-id><label>Table 1</label><caption><p>Cuticular wax biosynthesis genes involved in plant-pathogen interactions.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"> Gene Name</th><th align=\"left\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Gene Product</th><th align=\"left\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Gene Product Family </th><th align=\"left\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Plant Species</th><th align=\"left\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Function of Gene Product </th><th align=\"left\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Involvement of Gene Product in Plant-Pathogen Interaction and Evidence</th><th align=\"left\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Reference</th></tr></thead><tbody><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>DEWAX</italic>\n</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">DEWAX</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">AP2/ERF-type transcription factor</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>Arabidopsis thaliana</italic>\n</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Transcriptional suppression of cuticular waxes biosynthesis genes</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">DEWAX acts as transcriptional activator of defense-related genes and positively regulates disease resistance against <italic>Botrytis cinerea.</italic></td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B69-ijms-21-05514\" ref-type=\"bibr\">69</xref>]</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>SMA4</italic>\n</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">LACS2</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Long chain acyl-CoA synthetase</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>Arabidopsis thaliana</italic>\n</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Biosynthesis of C16 or C18 acyl-CoAs</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><italic>sma4</italic> mutant exhibited enhanced susceptibility to bacterial pathogen <italic>Pst</italic> DC3000 but enhanced resistance against fungal pathogen <italic>B. cinerea.</italic></td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B76-ijms-21-05514\" ref-type=\"bibr\">76</xref>]</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>MYB30</italic>\n</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">MYB30</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">R2R3-type MYB family transcription factor</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>Arabidopsis thaliana</italic>\n</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Transcriptional activation of cuticular waxes biosynthesis genes </td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Hypersensitive response was exacerbated in <italic>MYB30</italic>-overexpressing lines.</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B77-ijms-21-05514\" ref-type=\"bibr\">77</xref>]</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>MdMYB30</italic>\n</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">MdMYB30</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">R2R3-type MYB family transcription factor</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>Malus domestica</italic>\n</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Transcriptional activation of cuticular waxes biosynthesis genes</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Ectopic expression of <italic>MdMYB30</italic> in <italic>Arabidopsis</italic> increases resistance to <italic>Pst</italic> DC3000.</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B78-ijms-21-05514\" ref-type=\"bibr\">78</xref>]</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>MYB96</italic>\n</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">MYB96 </td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">R2R3-type MYB family transcription factor</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>Arabidopsis thaliana</italic>\n</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Transcriptional activation of cuticular waxes biosynthesis genes</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">MYB96 activation-tagging <italic>Arabidopsis</italic> lines exhibited enhanced resistance to <italic>Pst</italic> DC3000 by potentiating SA biosynthesis.</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B80-ijms-21-05514\" ref-type=\"bibr\">80</xref>]</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>CER1</italic>\n</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">CER1</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">VLC-aldehyde decarbonylase putative</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>Arabidopsis thaliana</italic>\n</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Formation of VLC alkanes </td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">The susceptibility to <italic>Pst</italic> DC3000 were enhanced in the <italic>Arabidopsis</italic> plants over-expressing <italic>CER1.</italic></td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B59-ijms-21-05514\" ref-type=\"bibr\">59</xref>]</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>TaKCS6</italic>\n</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">TaKCS6</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3-Ketoacyl-CoA synthase</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>Triticum aestivum</italic>\n</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Biosynthesis of VLC acyl-CoAs</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Silencing of <italic>TaKCS6</italic> attenuated <italic>Bgt</italic> conidia germination in bread wheat.</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B91-ijms-21-05514\" ref-type=\"bibr\">91</xref>]</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>TaECR</italic>\n</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">TaECR</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Enoyl-CoA reductase</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>Triticum aestivum</italic>\n</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Biosynthesis of VLC acyl-CoAs</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Silencing of <italic>TaECR</italic> attenuated <italic>Bgt</italic> conidia germination in bread wheat.</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B92-ijms-21-05514\" ref-type=\"bibr\">92</xref>]</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>CER3</italic>\n</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">CER3</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">VLC-acyl-CoA reductase putative</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>Arabidopsis thaliana</italic>\n</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Formation of VLC alkanes</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">The inhibition of prepenetration processes of <italic>Golovinomyces orontii</italic> on <italic>Arabidopsis</italic> stems is more severe in the mutant <italic>cer3.</italic></td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B93-ijms-21-05514\" ref-type=\"bibr\">93</xref>]</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>HvWIN1</italic>\n</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">HvWIN1</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">AP2-EREBP-type transcription factor</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>Hordeum vulgare</italic>\n</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Transcriptional activation of cuticular waxes biosynthesis genes</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Silencing of <italic>HvWIN1</italic> resulted in enhanced susceptibility to FHB.</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B94-ijms-21-05514\" ref-type=\"bibr\">94</xref>]</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>IRG1</italic>\n</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">IRG1</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Cys<sub>2</sub>His<sub>2</sub> zinc finger transcription factor</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>Medicago truncatula</italic>\n</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Formation of epicuticular wax crystals</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><italic>irg1</italic> mutant showed retarded prepenetration of two rust pathogens and one anthracnose pathogen.</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B99-ijms-21-05514\" ref-type=\"bibr\">99</xref>]</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>CYP96B2</italic>\n</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">CYP96B2</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Cytochrome P450 monooxygenase putative</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>Hordeum vulgare</italic>\n</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Cuticular waxes biosynthesis</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Silencing of <italic>CYP96B22</italic> led to a decrease in penetration resistance of barley plants to blast pathogen <italic>Magnaporthe</italic>.</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B100-ijms-21-05514\" ref-type=\"bibr\">100</xref>]</td></tr></tbody></table></table-wrap></floats-group></article>\n"
]
[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Int J Environ Res Public Health</journal-id><journal-id journal-id-type=\"iso-abbrev\">Int J Environ Res Public Health</journal-id><journal-id journal-id-type=\"publisher-id\">ijerph</journal-id><journal-title-group><journal-title>International Journal of Environmental Research and Public Health</journal-title></journal-title-group><issn pub-type=\"ppub\">1661-7827</issn><issn pub-type=\"epub\">1660-4601</issn><publisher><publisher-name>MDPI</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32717867</article-id><article-id pub-id-type=\"pmc\">PMC7432126</article-id><article-id pub-id-type=\"doi\">10.3390/ijerph17155299</article-id><article-id pub-id-type=\"publisher-id\">ijerph-17-05299</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Article</subject></subj-group></article-categories><title-group><article-title>Enhanced Biodegradation of Phthalic Acid Esters’ Derivatives by Plasticizer-Degrading Bacteria (<italic>Burkholderia cepacia</italic>, <italic>Archaeoglobus fulgidus</italic>, <italic>Pseudomonas aeruginosa</italic>) Using a Correction 3D-QSAR Model</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0002-1075-8603</contrib-id><name><surname>Zhang</surname><given-names>Haigang</given-names></name><xref rid=\"c1-ijerph-17-05299\" ref-type=\"corresp\">*</xref></contrib><contrib contrib-type=\"author\"><name><surname>Zhao</surname><given-names>Chengji</given-names></name></contrib><contrib contrib-type=\"author\"><name><surname>Na</surname><given-names>Hui</given-names></name></contrib></contrib-group><aff id=\"af1-ijerph-17-05299\">Alan G. MacDiarmid Institute, College of Chemistry, Jilin University, Changchun 130012, China; <email>[email protected]</email> (C.Z.); <email>[email protected]</email> (H.N.)</aff><author-notes><corresp id=\"c1-ijerph-17-05299\"><label>*</label>Correspondence: <email>[email protected]</email>; Tel.: +86-0431-85168870; Fax: +86-0431-85168870</corresp></author-notes><pub-date pub-type=\"epub\"><day>23</day><month>7</month><year>2020</year></pub-date><pub-date pub-type=\"ppub\"><month>8</month><year>2020</year></pub-date><volume>17</volume><issue>15</issue><elocation-id>5299</elocation-id><history><date date-type=\"received\"><day>27</day><month>6</month><year>2020</year></date><date date-type=\"accepted\"><day>21</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>© 2020 by the authors.</copyright-statement><copyright-year>2020</copyright-year><license license-type=\"open-access\"><license-p>Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>).</license-p></license></permissions><abstract><p>A phthalic acid ester’s (PAEs) comprehensive biodegradability three-dimensional structure-activity relationship (3D-QSAR) model was established, to design environmentally friendly PAE derivatives, which could be simultaneously degraded by plasticizer-degrading bacteria, such as <italic>Burkholderia cepacia</italic>, <italic>Archaeoglobus fulgidus</italic>, and <italic>Pseudomonas aeruginosa</italic>. Only three derivatives of diethyl phthalate (DEP (DEP-27, DEP-28 and DEP-29)) were suited for their functionality and environmental friendliness, which had an improved stability in the environment and improved the characteristics (bio-toxicity, bioaccumulation, persistence, and long-range migration) of the persistent organic pollutants (POPs). The simulation inference of the microbial degradation path before and after DEP modification and the calculation of the reaction energy barrier exhibited the energy barrier for degradation being reduced after DEP modification and was consistent with the increased ratio of comprehensive biodegradability. This confirmed the effectiveness of the comparative molecular similarity index analysis (CoMSIA) model of the PAE’s comprehensive biodegradability. In addition, a molecular dynamics simulation revealed that the binding of the DEP-29 derivative with the three plasticizer-degradation enzymes increased significantly. DEP-29 could be used as a methyl phthalate derivative that synergistically degrades with microplastics, providing directional selection and theoretical designing for plasticizer replacement.</p></abstract><kwd-group><kwd>diethyl phthalate</kwd><kwd>plasticizer-degrading bacteria</kwd><kwd>biodegradation</kwd><kwd>molecular modification</kwd><kwd>molecular dynamics</kwd></kwd-group></article-meta></front><body><sec sec-type=\"intro\" id=\"sec1-ijerph-17-05299\"><title>1. Introduction</title><p>In industrial production, a plasticizer is an indispensable part of microplastics and increases the flexibility and durability of plastic products. The phthalic acid esters (PAEs) are the most widely used plasticizers [<xref rid=\"B1-ijerph-17-05299\" ref-type=\"bibr\">1</xref>]. About 8.4 million tons of PAE plasticizers are used annually, accounting for 70% of its total use worldwide. Among these, diethyl phthalate (DEP) accounts for a high proportion [<xref rid=\"B2-ijerph-17-05299\" ref-type=\"bibr\">2</xref>]. The extensive use of phthalate plasticizers creates great commercial value, though it also poses environmental health risks that we cannot ignore. PAE plasticizers are not directly connected to plastic polymers, and they easily release into the environment during use [<xref rid=\"B3-ijerph-17-05299\" ref-type=\"bibr\">3</xref>]. Agricultural plastic film is one of the primary sources of microplastics in the soil. The PAE content in a vegetable greenhouse base could reach up to 9.68 mg/kg [<xref rid=\"B4-ijerph-17-05299\" ref-type=\"bibr\">4</xref>]. The PAEs in microplastics belong to refractory organic compounds, which are detected in soil [<xref rid=\"B5-ijerph-17-05299\" ref-type=\"bibr\">5</xref>], rivers [<xref rid=\"B6-ijerph-17-05299\" ref-type=\"bibr\">6</xref>], drinking water [<xref rid=\"B7-ijerph-17-05299\" ref-type=\"bibr\">7</xref>], food [<xref rid=\"B8-ijerph-17-05299\" ref-type=\"bibr\">8</xref>], household garbage [<xref rid=\"B9-ijerph-17-05299\" ref-type=\"bibr\">9</xref>], sewage sludge [<xref rid=\"B10-ijerph-17-05299\" ref-type=\"bibr\">10</xref>], industrial wastewater [<xref rid=\"B11-ijerph-17-05299\" ref-type=\"bibr\">11</xref>], marine sediment [<xref rid=\"B12-ijerph-17-05299\" ref-type=\"bibr\">12</xref>] and landfill leach [<xref rid=\"B13-ijerph-17-05299\" ref-type=\"bibr\">13</xref>]. Toxicological studies proved that PAE plasticizers remaining in the environment could enter human and animal bodies through inhalation, diet and skin contact, causing great harm to human health and environmental safety [<xref rid=\"B14-ijerph-17-05299\" ref-type=\"bibr\">14</xref>]. Although PAEs are not acutely toxic to organisms, exposure to large doses could lead to teratogenic, carcinogenic and mutagenic effects in animals [<xref rid=\"B15-ijerph-17-05299\" ref-type=\"bibr\">15</xref>]. In addition, animal experiments confirmed that some phthalates cause liver and kidney damage in animals [<xref rid=\"B16-ijerph-17-05299\" ref-type=\"bibr\">16</xref>]. The US Environmental Protection Agency, the European Union, and the China National Environmental Monitoring Center have listed PAEs as the priority pollutants [<xref rid=\"B17-ijerph-17-05299\" ref-type=\"bibr\">17</xref>]. The degradation and transformation of phthalate plasticizers in environmental residual has become a topic of interest in recent years.</p><p>Microbial degradation is a key process that mineralizes the organic pollutants in the environment. Hoellein et al. [<xref rid=\"B18-ijerph-17-05299\" ref-type=\"bibr\">18</xref>] studied the biological effects of microplastics in the water surrounding sewage treatment plants. They observed that only specific microorganisms could affect migration behavior and the biodegradability of plasticizer. During microbial degradation, the PAEs could simultaneously be degraded by a variety of plasticizer-degrading bacteria. A study reported that 50% of the PAEs degraded after an inoculation of aerobic microorganisms in sewage sludge for 28 days [<xref rid=\"B19-ijerph-17-05299\" ref-type=\"bibr\">19</xref>]. Sugatt et al. [<xref rid=\"B20-ijerph-17-05299\" ref-type=\"bibr\">20</xref>] studied the biodegradability of 14 PAE plasticizers commercially used in microplastics using a shake flask test. They detected the PAE plasticizers were sensitive to the mixed microbial community in the natural environment, and they gradually degraded and mineralized. To improve the microbiological degradation efficiency of plasticizers, several scientists attempted to isolate a single species of bacteria with a high-efficiency of degradation from nature. Wu et al. [<xref rid=\"B21-ijerph-17-05299\" ref-type=\"bibr\">21</xref>] screened and isolated <italic>Ochrobactrum lupini</italic> and <italic>Agrobacterium tumefaciens</italic> that used dibutyl phthalate (DBP) as their only carbon source and energy source from polluted river sludge and completely mineralized it. The degrading enzymes (esterase, carboxylesterase and hydratase) play a crucial role in the synergistic degradation of PAE plasticizers in microplastics [<xref rid=\"B22-ijerph-17-05299\" ref-type=\"bibr\">22</xref>]. Under different environmental conditions, microorganisms degrade the PAE plasticizers, but the degradation cycle is relatively long [<xref rid=\"B23-ijerph-17-05299\" ref-type=\"bibr\">23</xref>]. Therefore, it is of immense significance to improve the biodegradability of the PAE plasticizers to inhibit their adverse effects on the environment and human health.</p><p>In this paper, we employed the range normalization method combined with the entropy weight method to characterize the comprehensive biodegradability of PAEs by enzymes from three plasticizer-degrading bacteria [<xref rid=\"B24-ijerph-17-05299\" ref-type=\"bibr\">24</xref>]. We constructed a three-dimensional structure-activity relationship (3D-QSAR) model of the comprehensive biodegradability of the PAEs. DEP was taken as the target molecule for molecular modification, and we screened for DEP derivatives suitable for degradation by plasticizer-degrading bacteria. Further, we evaluated the DEP derivatives for their functionality and environmental friendliness, providing directional selection for plasticizer replacement.</p></sec><sec id=\"sec2-ijerph-17-05299\"><title>2. Materials and Methods</title><sec id=\"sec2dot1-ijerph-17-05299\"><title>2.1. Data Source</title><p>The <italic>Burkholderia</italic>, <italic>Archaeoglobus</italic> [<xref rid=\"B22-ijerph-17-05299\" ref-type=\"bibr\">22</xref>] and <italic>Pseudomonas</italic> [<xref rid=\"B25-ijerph-17-05299\" ref-type=\"bibr\">25</xref>] species are typical plasticizer-degrading bacteria that effectively degrade PAE plasticizers. We obtained the structures of phthalate dioxygenase reductase from <italic>Burkholderia cepacia</italic> (PDB ID: 2PIA), esterase from <italic>Archaeoglobus fulgidus</italic> (PDB ID: 2ZYI) and carboxylesterase from <italic>Pseudomonas aeruginosa</italic> (PDB ID: 3CN7) from the Protein Data Bank (PDB (<uri xlink:href=\"http://www.rcsb.org/pdb\">http://www.rcsb.org/pdb</uri>)) [<xref rid=\"B26-ijerph-17-05299\" ref-type=\"bibr\">26</xref>]. Using the molecular docking module in Sybyl-x2.0 software, we docked 17 different PAEs with the three degrading enzymes to obtain the corresponding scoring function value [<xref rid=\"B27-ijerph-17-05299\" ref-type=\"bibr\">27</xref>], which formed the source of the degradability data of the PAEs.</p></sec><sec id=\"sec2dot2-ijerph-17-05299\"><title>2.2. Construction of the Comparative Molecular Similarity Index Analysis (CoMSIA) Model of the Comprehensive Biodegradability of PAEs</title><p>We employed the range normalization method to standardize the degradation data of the three plasticizer-degrading bacteria and the entropy weight method to objectively weigh the standardized data [<xref rid=\"B28-ijerph-17-05299\" ref-type=\"bibr\">28</xref>]. Finally, we obtained the value of the comprehensive biodegradability of the PAE molecules, degraded by the three bacteria. The following are the range normalization formulas of the scoring function values of 17 PAE molecules after docking with the three plasticizer-degrading enzymes:\n<disp-formula id=\"FD1-ijerph-17-05299\"><label>(1)</label><mml:math id=\"mm1\"><mml:mrow><mml:mrow><mml:msub><mml:mrow><mml:mrow><mml:mi mathvariant=\"normal\">Y</mml:mi></mml:mrow></mml:mrow><mml:mrow><mml:mi>i</mml:mi><mml:mi>j</mml:mi></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:mfenced><mml:mrow><mml:mn>1</mml:mn><mml:mo>−</mml:mo><mml:mi>a</mml:mi></mml:mrow></mml:mfenced><mml:mo>+</mml:mo><mml:mi>a</mml:mi><mml:mfrac><mml:mrow><mml:msub><mml:mi>X</mml:mi><mml:mrow><mml:mi>i</mml:mi><mml:mi>j</mml:mi></mml:mrow></mml:msub><mml:mo>−</mml:mo><mml:msub><mml:mi>X</mml:mi><mml:mrow><mml:mi>m</mml:mi><mml:mi>i</mml:mi><mml:mi>n</mml:mi><mml:mi>j</mml:mi></mml:mrow></mml:msub></mml:mrow><mml:mrow><mml:msub><mml:mi>X</mml:mi><mml:mrow><mml:mi>m</mml:mi><mml:mi>a</mml:mi><mml:mi>x</mml:mi><mml:mi>j</mml:mi></mml:mrow></mml:msub><mml:mo>−</mml:mo><mml:msub><mml:mi>X</mml:mi><mml:mrow><mml:mi>m</mml:mi><mml:mi>i</mml:mi><mml:mi>n</mml:mi><mml:mi>j</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mfrac></mml:mrow></mml:mrow></mml:math></disp-formula>\n<disp-formula id=\"FD2-ijerph-17-05299\"><label>(2)</label><mml:math id=\"mm2\"><mml:mrow><mml:mrow><mml:msub><mml:mrow><mml:mrow><mml:mi mathvariant=\"normal\">Y</mml:mi></mml:mrow></mml:mrow><mml:mrow><mml:mi>i</mml:mi><mml:mi>j</mml:mi></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:mfenced><mml:mrow><mml:mn>1</mml:mn><mml:mo>−</mml:mo><mml:mi>a</mml:mi></mml:mrow></mml:mfenced><mml:mo>+</mml:mo><mml:mi>a</mml:mi><mml:mfrac><mml:mrow><mml:msub><mml:mi>X</mml:mi><mml:mrow><mml:mi>m</mml:mi><mml:mi>a</mml:mi><mml:mi>x</mml:mi><mml:mi>j</mml:mi></mml:mrow></mml:msub><mml:mo>−</mml:mo><mml:msub><mml:mi>X</mml:mi><mml:mrow><mml:mi>i</mml:mi><mml:mi>j</mml:mi></mml:mrow></mml:msub></mml:mrow><mml:mrow><mml:msub><mml:mi>X</mml:mi><mml:mrow><mml:mi>m</mml:mi><mml:mi>a</mml:mi><mml:mi>x</mml:mi><mml:mi>j</mml:mi></mml:mrow></mml:msub><mml:mo>−</mml:mo><mml:msub><mml:mi>X</mml:mi><mml:mrow><mml:mi>m</mml:mi><mml:mi>i</mml:mi><mml:mi>n</mml:mi><mml:mi>j</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mfrac></mml:mrow></mml:mrow></mml:math></disp-formula></p><p>Equation (1) is the processing formula for the positive index, where a higher index is desirable. Equation (2) is the processing formula for the negative index, where a lower index is desirable. Among them, <italic>i</italic> represents the different PAEs (<italic>i</italic> = 1, 2, 3, …, 17); <italic>j</italic> represents the degradability index of the different degradative enzymes (<italic>j</italic> = 1, 2, 3; corresponding to the enzymes from <italic>B. cepacia</italic>, <italic>P. aeruginosa</italic> and <italic>A. fulgidus</italic>, respectively); <italic>Y<sub>ij</sub></italic> is the standardized value of each scoring function; <italic>X<sub>ij</sub></italic> is the scoring function value of the <italic>j</italic>-th biodegradability index of the <italic>i</italic>-th PAE; <italic>X<sub>maxj</sub></italic> and <italic>X<sub>minj</sub></italic> are the maximum and minimum values of the corresponding indexes, a ∈ (0,1), which are generally 0.9. The scoring function value characterizes the index of microbial degradability with a higher score being desirable. Therefore, we used the processing formula for the positive index to standardize the microbial degradability data. </p><p>The entropy weight method is an objective method for weight processing [<xref rid=\"B29-ijerph-17-05299\" ref-type=\"bibr\">29</xref>], which uses information entropy for processing the weight. The calculation steps are as follows:</p><p>Step 1: calculate the information entropy (<italic>E<sub>j</sub></italic>) of the <italic>j</italic>-th biodegradability index of the <italic>i</italic>-th PAE using the following formula:<disp-formula id=\"FD3-ijerph-17-05299\"><label>(3)</label><mml:math id=\"mm3\"><mml:mrow><mml:mrow><mml:msub><mml:mi>E</mml:mi><mml:mi>j</mml:mi></mml:msub><mml:mo>=</mml:mo><mml:mo>−</mml:mo><mml:mi>k</mml:mi><mml:mo>⋅</mml:mo><mml:munderover><mml:mstyle mathsize=\"140%\" displaystyle=\"true\"><mml:mo>∑</mml:mo></mml:mstyle><mml:mrow><mml:mi>i</mml:mi><mml:mo>=</mml:mo><mml:mn>1</mml:mn></mml:mrow><mml:mi>m</mml:mi></mml:munderover><mml:msub><mml:mi>Y</mml:mi><mml:mrow><mml:mi>i</mml:mi><mml:mi>j</mml:mi></mml:mrow></mml:msub><mml:mo>⋅</mml:mo><mml:mi>ln</mml:mi><mml:msub><mml:mi>Y</mml:mi><mml:mrow><mml:mi>i</mml:mi><mml:mi>j</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mrow></mml:math></disp-formula>\nwhere <italic>k</italic> = 1/lnm, m is the total number of PAE molecules (i.e., 17), and <italic>E<sub>j</sub></italic> is the information entropy of the <italic>j</italic>-th biodegradability index.</p><p>Step 2: calculate the difference coefficient (<italic>H<sub>j</sub></italic>) of the <italic>j</italic>-th biodegradability index, reflecting the difference in the degree of each index using the following formula:<disp-formula id=\"FD4-ijerph-17-05299\"><label>(4)</label><mml:math id=\"mm4\"><mml:mrow><mml:mrow><mml:msub><mml:mi>H</mml:mi><mml:mi>j</mml:mi></mml:msub><mml:mo>=</mml:mo><mml:mn>1</mml:mn><mml:mo>−</mml:mo><mml:msub><mml:mi>E</mml:mi><mml:mi>j</mml:mi></mml:msub></mml:mrow></mml:mrow></mml:math></disp-formula>\nwhere <italic>H<sub>j</sub></italic> is the difference coefficient of the <italic>j</italic>-th biodegradability index.</p><p>Step 3: normalize the index difference coefficient (<italic>H<sub>j</sub></italic>) to estimate the weight (<italic>W<sub>j</sub></italic>) of each biodegradability index using the following formula:<disp-formula id=\"FD5-ijerph-17-05299\"><label>(5)</label><mml:math id=\"mm5\"><mml:mrow><mml:mrow><mml:msub><mml:mi>W</mml:mi><mml:mi>j</mml:mi></mml:msub><mml:mo>=</mml:mo><mml:mfrac bevelled=\"true\"><mml:mrow><mml:msub><mml:mi>H</mml:mi><mml:mi>j</mml:mi></mml:msub></mml:mrow><mml:mrow><mml:msubsup><mml:mstyle mathsize=\"70%\" displaystyle=\"true\"><mml:mo>∑</mml:mo></mml:mstyle><mml:mrow><mml:mi>j</mml:mi><mml:mo>=</mml:mo><mml:mn>1</mml:mn></mml:mrow><mml:mi>n</mml:mi></mml:msubsup><mml:msub><mml:mi>H</mml:mi><mml:mi>j</mml:mi></mml:msub></mml:mrow></mml:mfrac></mml:mrow></mml:mrow></mml:math></disp-formula>\nwhere <italic>W<sub>j</sub></italic> is the weight of the <italic>j</italic>-th biodegradability index, and <italic>n</italic> is the total number of biodegradability indicators (i.e., 3).</p><p>Step 4: calculate the comprehensive value of the biodegradability of the three plasticizer-degrading bacteria for each PAE using the following formula:<disp-formula id=\"FD6-ijerph-17-05299\"><label>(6)</label><mml:math id=\"mm6\"><mml:mrow><mml:mrow><mml:msub><mml:mi>Z</mml:mi><mml:mrow><mml:mi>i</mml:mi><mml:mi>n</mml:mi></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:munderover><mml:mstyle mathsize=\"140%\" displaystyle=\"true\"><mml:mo>∑</mml:mo></mml:mstyle><mml:mrow><mml:mi>j</mml:mi><mml:mo>=</mml:mo><mml:mn>1</mml:mn></mml:mrow><mml:mi>n</mml:mi></mml:munderover><mml:msub><mml:mi>W</mml:mi><mml:mi>j</mml:mi></mml:msub><mml:mo>⋅</mml:mo><mml:msub><mml:mi>Y</mml:mi><mml:mrow><mml:mi>i</mml:mi><mml:mi>j</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mrow></mml:math></disp-formula>\nwhere <italic>Z<sub>in</sub></italic> is the comprehensive value of the biodegradability of the three plasticizer-degrading bacteria of the <italic>i</italic>-th PAE, and n is the total number of biodegradability indicators (i.e., 3).</p><p>The molecular structure of the PAEs and the comprehensive biodegradability CoMSIA model of the three plasticizer-degrading bacteria were mainly constructed by using the 3D-QSAR module in Sybyl-x 2.0 software (Tripos, Princeton, NJ, USA). The minimize module in the software optimized the molecular structure of the PAEs [<xref rid=\"B30-ijerph-17-05299\" ref-type=\"bibr\">30</xref>].To obtain a stable conformation of the lowest energy state of the molecule, the Powell’s conjugate gradient method was employed and combined with the Tripos molecular force field. The latter sets the charge of the molecule to the Gasterger–Hückle charge [<xref rid=\"B31-ijerph-17-05299\" ref-type=\"bibr\">31</xref>]. The energy convergence standard was adjusted to 0.005 kcal mol<sup>−1</sup>, and the number of iterations was set to 10,000 times [<xref rid=\"B32-ijerph-17-05299\" ref-type=\"bibr\">32</xref>]. In this paper, the diundecyl phthalate (DUP) with the highest comprehensive value of the biodegradability index of the three plasticizer-degrading bacteria was used as the template molecule (<xref ref-type=\"fig\" rid=\"ijerph-17-05299-f001\">Figure 1</xref>). The Align Database module of the software was selected to select the common part of the optimized PAE molecular structure as the common skeleton for superposition [<xref rid=\"B33-ijerph-17-05299\" ref-type=\"bibr\">33</xref>].</p><p>Sixteen molecules were randomly selected from the comprehensive biodegradation values of 17 PAEs molecules and three plasticizer degrading bacteria. According to the ratio of 4:1, 13 PAE molecules were used as the training set, and the remaining three PAE molecules were used as the test set. The template molecule DUP was used in both the test set and the training set. The least squares method (PLS) was used to analyze the training set compounds. The training set was cross-validated under the extraction method module to obtain the cross-validation coefficient (q<sup>2</sup>) and the optimal number of principal components (<italic>n</italic>). The training set compounds were analyzed using the no validation regression to obtain the cross-validation coefficient (R<sup>2</sup>), standard deviation (SEE), test value (F) and the contribution rate of the force field (stereo field, electrostatic field). The scrambling stability test assessed the robustness of the model by evaluating the scrambling stability test parameters (Q<sup>2</sup>), cross-validated standard error of prediction (cSDEP), and dQ<sup>2</sup>/dr<sup>2</sup>yy. The cross-validation method assessed the external prediction ability of the built model to the test set compounds with the external verification parameter (r<sup>2</sup><sub>pred</sub>). Based on the information prompted by the contour maps of the constructed CoMSIA model, the substitution sites and substitution groups that had a greater impact on the comprehensive score value were screened, forming the theoretical basis for the subsequent molecular modification. In this paper, DEP was used as the target molecule for modification, which is the most widely used and frequently detected in the environment. <xref ref-type=\"fig\" rid=\"ijerph-17-05299-f002\">Figure 2</xref> displays the molecular structure of the target molecule DEP.</p></sec><sec id=\"sec2dot3-ijerph-17-05299\"><title>2.3. Evaluation of Functionality and Environmental Friendliness of DEP Derivatives Based on Density Functional Theory (DFT)</title><p>Stability was used as an evaluation index for the molecular functionality of the DEP derivatives [<xref rid=\"B34-ijerph-17-05299\" ref-type=\"bibr\">34</xref>] with the frequency, total energy and energy gap as the parameters. The above parameters were calculated by Gaussian 2009 software (Gaussian Inc., Wallingford, CT, USA) based on density functional theory (DFT) at the unit level of b3pw91/6-31G* [<xref rid=\"B35-ijerph-17-05299\" ref-type=\"bibr\">35</xref>]; environmental friendliness was evaluated by the four persistent organic pollutants’ (POPs) characteristics of toxicity (LC<sub>50</sub>), persistence (logt<sub>1/2</sub>), bioconcentration (logBCF) and migration (−logPL) [<xref rid=\"B36-ijerph-17-05299\" ref-type=\"bibr\">36</xref>].</p></sec></sec><sec sec-type=\"results\" id=\"sec3-ijerph-17-05299\"><title>3. Results and Discussion</title><sec id=\"sec3dot1-ijerph-17-05299\"><title>3.1. Construction and Evaluation of the CoMSIA Model for the Comprehensive Biodegradability of the PAEs by Plasticizer-Degrading Bacteria</title><sec id=\"sec3dot1dot1-ijerph-17-05299\"><title>3.1.1. Calculation of the Comprehensive Biodegradation Values of the PAEs by Three Plasticizer-Degrading Bacteria of PAE Molecules</title><p>The docking score values, range normalized conversion values, weight and comprehensive biodegradation values of the combination of PAE molecules with phthalate dioxygenase reductase from <italic>B. cepacia</italic> (PDB ID: 2PIA), <italic>A. fulgidus</italic> (PDB ID: 2ZYI) and <italic>P. aeruginosa</italic> (PDB ID: 3CN7) are listed in <xref rid=\"ijerph-17-05299-t001\" ref-type=\"table\">Table 1</xref>.</p></sec><sec id=\"sec3dot1dot2-ijerph-17-05299\"><title>3.1.2. Construction of the CoMSIA Model for the Comprehensive Biodegradability of the PAE Molecules by Three Plasticizer-Degrading Bacteria</title><p>DUP exhibited the highest comprehensive biodegradability among all the PAEs. It was the template molecule for the common skeleton superpositioning of the training set compounds to construct a CoMSIA model of the comprehensive biodegradability of the PAEs by the three plasticizer-degrading bacteria. In <xref rid=\"ijerph-17-05299-t001\" ref-type=\"table\">Table 1</xref>, a and b represent the training set and the test set of the model, respectively. <xref rid=\"ijerph-17-05299-t002\" ref-type=\"table\">Table 2</xref> lists the evaluation parameters of the CoMSIA model. The cross-validation coefficient (q<sup>2</sup>), calculated by the cross-validation analysis, was 0.731 (>0.5), and the principal component number (<italic>n</italic>) of the model was 8. Both of these indicate that the CoMSIA model had good internal prediction capabilities [<xref rid=\"B37-ijerph-17-05299\" ref-type=\"bibr\">37</xref>]. The standard deviation (SEE) calculated by the non-cross-validation analysis was 0.013 (<0.95), the test value (F) was 578.738, and the non-cross-validation coefficient (R<sup>2</sup>) was 0.999 (>0.9). These indicate that the model had a high fitting ability [<xref rid=\"B38-ijerph-17-05299\" ref-type=\"bibr\">38</xref>]. The perturbation stability test parameter (Q<sup>2</sup>) was 0.85, cSDEP was 0.251, and dq<sup>2</sup>/dr<sup>2</sup>yy was 1.488, indicating that the model had a high stability [<xref rid=\"B39-ijerph-17-05299\" ref-type=\"bibr\">39</xref>]. The external validation coefficient (r<sup>2</sup><sub>pred</sub>), estimated by the external validation analysis of the test set compound, was 0.761 (>0.6), indicating that the model exhibited a good external prediction ability [<xref rid=\"B40-ijerph-17-05299\" ref-type=\"bibr\">40</xref>]. In addition, the contribution rate of each force field was 34.7% in the steric field (S), 11.1% in the electrostatic field (E), 46.8% in the hydrophobic field (H), 7.4% in the hydrogen bond acceptor field (D) and 0.0% in the hydrogen bond donor field (A).</p></sec><sec><title>3.1.3. Contour Map Analysis of the CoMSIA Model </title><p>The contribution rates of the steric, electrostatic field and hydrophobic field were the highest. Therefore, the force field information of the electrostatic and hydrophobic fields in the contour maps of the comprehensive biodegradability of the DEP molecule was analyzed (<xref ref-type=\"fig\" rid=\"ijerph-17-05299-f003\">Figure 3</xref>). The steric field information map (<xref ref-type=\"fig\" rid=\"ijerph-17-05299-f003\">Figure 3</xref>a) displays that introducing small-volume groups into the yellow region could improve the comprehensive biodegradation value of the DEP molecules by the three plasticizer-degrading bacteria. In other words, the groups with volumes less than that of -CH<sub>2</sub>CH<sub>3</sub> were introduced at the C<sub>1</sub> position. In the electrostatic field (<xref ref-type=\"fig\" rid=\"ijerph-17-05299-f003\">Figure 3</xref>b), introducing positively charged groups into the blue region or negatively charged groups into the red region could effectively improve the comprehensive biodegradability of the DEP molecules [<xref rid=\"B41-ijerph-17-05299\" ref-type=\"bibr\">41</xref>]. However, the red region was located in the common skeleton and was difficult to replace and modify. Therefore, a group with a positive charge greater than -H introduced into the C<sub>1</sub> position could achieve the purpose of increasing the comprehensive biodegradability. In the hydrophobic field (<xref ref-type=\"fig\" rid=\"ijerph-17-05299-f003\">Figure 3</xref>c), introducing strong hydrophilic substituents in the white area at the C<sub>2</sub> position was conducive to increase the comprehensive biodegradability of the DEP molecules. In summary, to improve the comprehensive biodegradability of DEP, it was necessary to modify its structure by introducing single and double substitutions: (i) at the C1 position, a group with a volume smaller than -CH<sub>2</sub>CH<sub>3</sub> and a group with an electropositivity greater than -H, and (ii) at the C<sub>2</sub> position, a hydrophilic group.</p></sec></sec><sec id=\"sec3dot2-ijerph-17-05299\"><title>3.2. Molecular Modification of DEP for Enhanced Biodegradability Based on the CoMSIA Model</title><sec id=\"sec3dot2dot1-ijerph-17-05299\"><title>3.2.1. Molecular Modification and Prediction of Comprehensive Biodegradability of DEP</title><p>According to the force field information from the contour maps of the CoMSIA model, the groups was substituted at the C<sub>1</sub> and C<sub>2</sub> positions to conduct single and double substitutions on the DEP molecule. The groups with volumes smaller than -CH<sub>2</sub>CH<sub>3</sub> (-CH<sub>3</sub>, -OH, -H, -CN, -NH<sub>2</sub>, -CHO) and groups with an electropositivity greater than -H (-CH<sub>3</sub>, -CH<sub>2</sub>CH<sub>3</sub>, -CH(CH<sub>3</sub>)<sub>2</sub>, -C(CH<sub>3</sub>)<sub>3</sub>) were introduced at the C<sub>1</sub> position. At the C<sub>2</sub> position, the groups with higher hydrophilicities (-OH, -CHO, -COOH, -NH<sub>2</sub>, -COCH<sub>3</sub>, -CONH<sub>2</sub>) were introduced. These substitutions would improve the comprehensive biodegradability of derivative molecules. Thus, a total of 30 kinds of DEP derivatives were designed accordingly. Using the constructed CoMSIA model to predict the comprehensive biological properties of the modified derivatives, it was found that the comprehensive biodegradability of most of the DEP derivatives enhanced significantly (<xref rid=\"ijerph-17-05299-t003\" ref-type=\"table\">Table 3</xref>). Among them, the comprehensive biodegradability of 12 DEP derivatives increased by more than 15%, and the comprehensive biodegradation value of DEP-26 molecules had the highest growth rate, reaching 50.37%. DEP-23 molecules had the same substitution groups at site C<sub>2</sub> as in the DEP-26 molecules. However, the H atom at site C<sub>1</sub> (H<sub>1</sub> atom) in DEP-26 resulted in a higher comprehensive biodegradation value than the corresponding substituent in DEP-23. DEP-21 was introduced to more hydrophilic groups, increasing its comprehensive biodegradability value to more than that of DEP-15. In summary, the prediction of the comprehensive biodegradability of the DEP derivatives by the three plasticizer-degrading bacteria was consistent with the information presented in the contour maps of the CoMSIA model.</p></sec><sec id=\"sec3dot2dot2-ijerph-17-05299\"><title>3.2.2. Verification of the CoMSIA Model for the Comprehensive Biodegradability of PAEs Molecules</title><p>In this paper, Sybyl-x2.0 software was also used to construct the 3D-QSAR model of the single biodegradability of the PAEs by the three plasticizer-degrading bacteria (<xref rid=\"ijerph-17-05299-t004\" ref-type=\"table\">Table 4</xref>). The DEP derivative molecules were screened through the biodegradable 3D-QSAR model of the PAEs’ molecular plasticizer-degrading bacteria, whose degradation efficiency ratio was close to the weight ratios of the entropy weight method in the comprehensive biodegradability model (<xref rid=\"ijerph-17-05299-t003\" ref-type=\"table\">Table 3</xref>). The docking score of the DEP derivative with phthalate dioxygenase reductase was 4.939–6.725, with a growth rate of up to 20.74%. Compared with the growth rate before modification, the docking score values of the DEP derivative binding with esterase ranged from −2.46% to 27.71%. The scoring functions for carboxylesterase and DEP derivatives exhibited a significant increase, with the growth rate ranging from 15.86% to 24.96%. The ratio of the increase in the biodegradation effect value of DEP-23–DEP-30 by three plasticizer-degrading bacteria was the closest to that calculated and weighted by the entropy weight method. These results verified that the CoMSIA model effectively provides the biodegradability information of DEP derivatives when degraded by three plasticizer-degrading bacteria. Additionally, this result demonstrated that the CoMSIA model modified by the entropy weight method is reliable with a good predictive ability, and can be applied to the design modifications in PAE molecules.</p></sec><sec id=\"sec3dot2dot3-ijerph-17-05299\"><title>3.2.3. Evaluation of the Functionality and Environmental Friendliness of DEP Derivatives</title><p>Molecular stability was the primary index for evaluating the functionality of DEP derivatives, characterized by the energy value, energy gap value, and positive frequency value. The persistent organic pollutant (POP) characteristics determined the environmental friendliness of the DEP derivatives, before and after modification, by evaluating their bio-toxicity (logLC<sub>50</sub>), bioaccumulation (logBCF), persistence (logt<sub>1/2</sub>) and long-range migration (log<italic>K</italic><sub>OA</sub>) [<xref rid=\"B36-ijerph-17-05299\" ref-type=\"bibr\">36</xref>]. The energy value was inversely proportional to the stability of the molecules in the environment [<xref rid=\"B42-ijerph-17-05299\" ref-type=\"bibr\">42</xref>]. A higher positive frequency value indicated that the DEP derivative molecules could exist in the environment [<xref rid=\"B43-ijerph-17-05299\" ref-type=\"bibr\">43</xref>]. The bio-toxicity and long-range migration of the DEP derivatives were inversely proportional to the predicted logLC<sub>50</sub> and log<italic>K</italic><sub>OA</sub> values, respectively. On the contrary, the bioaccumulation and persistence of the DEP derivatives were directly proportional to the predicted logBCF and logt<sub>1/2</sub> values, respectively. <xref rid=\"ijerph-17-05299-t005\" ref-type=\"table\">Table 5</xref> summarizes the predicted values of the parameters evaluating the functionality and environmental friendliness of the DEP derivatives, before and after modification.</p><p><xref rid=\"ijerph-17-05299-t005\" ref-type=\"table\">Table 5</xref> displays that the energy values of the DEP derivatives were significantly lower than that of the DEP molecule, with a reduction of 19.91–37.39%. The energy gap values did not change significantly before and after modification, indicating that the modified DEP derivatives were highly stable. In addition, the positive frequency values of the DEP derivatives were greater than zero, indicating that the modified molecules could exist in the environment. Among the eight DEP derivatives, only DEP-27, DEP-28, and DEP-29 exhibited no significant changes in their bio-toxicity compared with the unmodified molecule. The bio-toxicity of the remaining five derivatives significantly increased, with the highest increase-rate going up to 38.91%. An organic compound with a logBCF value less than 100 does not easily accumulate in organisms and has a small impact on the environment [<xref rid=\"B44-ijerph-17-05299\" ref-type=\"bibr\">44</xref>]. Except for the DEP-25 and DEP-26 molecules, the logBCF values of the DEP derivatives were much lower than 100. Among them, the logBCF values of DEP-27, DEP-28, and DEP-29 after modification did not change significantly. The predicted half-lives of the DEP derivatives were slightly higher than that of DEP, indicating that the degradability of these derivatives improved marginally. The predicted log<italic>K</italic><sub>OA</sub> values of the eight DEP derivatives were significantly higher than that of DEP, suggesting that their mobilities were decreasing. In conclusion, among the eight DEP derivatives, only DEP-27, DEP-28 and DEP-29 exhibited significantly higher biodegradation values, and their functionalities and environmental friendliness were also better than the unmodified DEP molecule.</p></sec></sec><sec id=\"sec3dot3-ijerph-17-05299\"><title>3.3. Analysis of the Microbial Degradation Mechanism of DEP and Its Derivatives Based on a Microbial Degradation Path Simulation</title><sec id=\"sec3dot3dot1-ijerph-17-05299\"><title>3.3.1. Simulation of Microbial Degradation of DEP and Its Derivative Molecules </title><p>Plasticizer residue poses a serious threat to the environment. Among the possible ways of degradation, microbial degradation is the primary elimination process of PAE plasticizers. It achieves the purpose of recycling elements and balancing the ecosystem [<xref rid=\"B45-ijerph-17-05299\" ref-type=\"bibr\">45</xref>]. Gram-negative and Gram-positive bacteria degrade PAEs via different pathways, but both eventually form protocatechate [<xref rid=\"B46-ijerph-17-05299\" ref-type=\"bibr\">46</xref>]. Protocatechate can convert into pyruvate, succinate, and oxaloacetate, entering the tricarboxylic acid cycle and finally mineralizing into CO<sub>2</sub> and H<sub>2</sub>O [<xref rid=\"B47-ijerph-17-05299\" ref-type=\"bibr\">47</xref>]. Ren et al. [<xref rid=\"B48-ijerph-17-05299\" ref-type=\"bibr\">48</xref>] found that esterase plays a crucial role in the microbial degradation of PAEs while studying their microbial degradation mechanism. Ester bond hydrolysis forms the key initial step in the microbial degradation of PAEs [<xref rid=\"B49-ijerph-17-05299\" ref-type=\"bibr\">49</xref>]. Based on the microbial degradation path of PAEs, the screened DEP derivative molecules (DEP-27, DEP-28 and DEP-29) were taken as examples to simulate and derive the microbial degradation path before and after DEP molecular modification (<xref ref-type=\"fig\" rid=\"ijerph-17-05299-f004\">Figure 4</xref>). A Gaussian calculation was carried out for the energy barrier of the reaction to compare the difficulty of the biodegradation process before and after the DEP molecular modification (<xref rid=\"ijerph-17-05299-t006\" ref-type=\"table\">Table 6</xref>).</p><p>As shown in <xref ref-type=\"fig\" rid=\"ijerph-17-05299-f004\">Figure 4</xref>, the microbial degradation transformation paths of DEP and its derivatives are divided into the transformation paths of Gram-negative bacteria and Gram-positive bacteria. Under the action of the plasticizer-degrading enzymes, DEP first hydrolyzed to phthalate monoesters (M0-1) and then hydrolyzed to form phthalate (M0-2). Phthalate 4,5-dioxygenase in Gram-negative bacteria oxidized the hydroxyl phthalate to produce 4,5-dihydroxyphthalic acid (M0-3), which decarboxylated to protocatechate (M0-5) [<xref rid=\"B50-ijerph-17-05299\" ref-type=\"bibr\">50</xref>]. Gram-positive bacteria hydrolyzed phthalic acid at C<sub>3</sub> and C<sub>4</sub> to produce 3, 4-dihydroxyphthalic acid (M0-4), which decarboxylated to protocatechate (M0-5) [<xref rid=\"B51-ijerph-17-05299\" ref-type=\"bibr\">51</xref>]. Finally, protocatechate mineralized into CO<sub>2</sub> and H<sub>2</sub>O through the tricarboxylic acid cycle, culminating in the complete degradation of PAEs [<xref rid=\"B47-ijerph-17-05299\" ref-type=\"bibr\">47</xref>]. When the C<sub>2</sub> site of DEP was replaced with -CONH<sub>2</sub>, the resulting derivative was difficult to hydrolyze into phthalic acid [<xref rid=\"B52-ijerph-17-05299\" ref-type=\"bibr\">52</xref>]. So the degradation process was slightly different from that of DEP.</p></sec><sec id=\"sec3dot3dot2-ijerph-17-05299\"><title>3.3.2. Calculation of the Reaction Energy Barrier for Microbial Degradation Transformation Paths of DEP and Its Derivatives</title><p>The reaction energy barrier required for the microbial degradation of the three DEP derivatives was lower than that required for the degradation of DEP (<xref rid=\"ijerph-17-05299-t006\" ref-type=\"table\">Table 6</xref>). A smaller reaction barrier is desirable as it improves the likelihood of the reaction path to occur. This indicates that the microorganisms could easily degrade the three DEP derivatives compared with DEP. The change rates of the reaction energy barrier for DEP-27, DEP-28, and DEP-29 were −20.15%, −23.42%, and −30.26%, respectively. These were consistent with the enhanced comprehensive biodegradability predicted by the comprehensive biodegradability model (DEP-27: 23.33%, DEP-28: 27.04% and DEP-29: 31.85%). This validates the reliability of the comprehensive biodegradation model, and indicates that the biodegradability of DEP can improve by molecular modification.</p></sec><sec id=\"sec3dot3dot3-ijerph-17-05299\"><title>3.3.3. Simulation and Verification of the Molecular Dynamics of the Microbial Degradation of DEP and Its Derivatives</title><p>In this paper, GROMACS 4.6.5 software was used to simulate the molecular dynamics (MD) of the ligand complex structures of DEP and its derivatives in Sybyl-x2.0, docked with phthalate dioxygenase reductase, esterase and acid esterase. The Poisson–Boltzmann surface area (MM-PBSA) method was used to calculate the binding free energy in the molecular dynamics [<xref rid=\"B53-ijerph-17-05299\" ref-type=\"bibr\">53</xref>]. Compared with the scoring function of molecular docking, the degree of binding energy characterized the biodegradability of DEP molecules before and after modification (<xref rid=\"ijerph-17-05299-t007\" ref-type=\"table\">Table 7</xref>).</p><p>Higher docking scores, indicate a stronger binding of the molecules to the degradative enzyme [<xref rid=\"B54-ijerph-17-05299\" ref-type=\"bibr\">54</xref>]. Additionally, the binding energy is directly proportional to the affinity between the molecule and the degrading enzyme [<xref rid=\"B55-ijerph-17-05299\" ref-type=\"bibr\">55</xref>], indicating a higher biodegradability of the molecule. Compared with the DEP molecules, DEP-27 and DEP-28 had lower scoring functions and higher free energy of binding to the degrading enzymes (<xref rid=\"ijerph-17-05299-t007\" ref-type=\"table\">Table 7</xref>). The scoring functions of only DEP-29 binding to the three degrading enzymes increased significantly, and its binding free energy also decreased significantly. The results demonstrated that DEP-29 was the only derivative that could easily bind to the three degrading enzymes at the same time and that the degree of binding was significant. This not only proves the rationality of the comprehensive biodegradation model of PAEs to modify the derivatives, but also forms the means to screen the PAE derivatives that synergistically degrade with microplastics.</p></sec></sec></sec><sec sec-type=\"conclusions\" id=\"sec4-ijerph-17-05299\"><title>4. Conclusions</title><p>In this paper, a 3D-QSAR model of the comprehensive biodegradability of the phthalic acid esters (PAEs) was constructed by combining the range normalization and entropy weight methods. In combination with molecular modifications, this model was successfully applied to design environmentally friendly PAEs that can co-degrade with microplastics. These PAE derivatives significantly improve the biodegradability of PAE plasticizers in microplastics, and can reduce the residual PAEs in the natural environment, relieve the adverse effects of plasticizer residues in the human body and environment, and provide the directional selection and theoretical support for the replacement of plasticizers. Further research work will be focused on whether the designed PAE derivatives could be degraded synergistically with the plasticizer-degrading bacteria or not, and the synthesis and biodegradation of these derivatives would provide an effective verification for the molecular modification method available in this study.</p></sec></body><back><notes><title>Author Contributions</title><p>Conceptualization, H.Z. and C.Z.; methodology, H.Z.; software, H.Z.; validation, C.Z. and H.N.; formal analysis, H.Z.; investigation, H.Z.; resources, H.Z.; data curation, H.Z.; writing—original draft preparation, H.Z.; writing—review and editing, H.Z., C.Z.; visualization, H.Z.; supervision, C.Z.; project administration, C.Z. 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A: The transformation path of gram-negative bacteria; B: the transformation path of Gram-positive bacteria; M0: the microbial degradation products of DEP; Mr: the microbial degradation products of DEP derivative molecules; R: -CH<sub>3</sub>, -CH<sub>2</sub>CH<sub>3</sub>, -CH (CH<sub>3</sub>)<sub>2</sub>; r = 1, 2, 3.</p></caption><graphic xlink:href=\"ijerph-17-05299-g004\"/></fig><table-wrap id=\"ijerph-17-05299-t001\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05299-t001_Table 1</object-id><label>Table 1</label><caption><p>Biodegradability score values and comprehensive biodegradability values of the phthalic acid ester (PAE) molecules by three plasticizer-degrading bacteria.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Compounds</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Docking Score Value of 2PIA</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Converted Values of 2PIA</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Docking Score Value of 2ZYI</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Converted Values of 2ZYI</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Docking Score Value of 3CN7</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Converted Values of 3CN7</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Comprehensive Biodegradation Values</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">BBP <sup>a</sup></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7.070</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.509</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.908</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.100</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.116</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.380</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.323</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">DAP <sup>a</sup></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.918</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.385</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.461</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.357</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.275</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.409</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.380</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">DBP <sup>b</sup></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.754</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.367</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.551</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.364</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6.047</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.547</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.408</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">DEP <sup>a</sup></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.574</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.347</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.711</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.303</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.548</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.100</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.272</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">DHP <sup>a</sup></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7.455</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.551</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7.221</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.485</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6.964</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.711</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.564</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">DIBP <sup>a</sup></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.824</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.266</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.081</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.330</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.006</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.361</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.313</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">DIHP <sup>a</sup></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8.634</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.679</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10.570</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.727</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7.870</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.872</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.743</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">DIHXP</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7.462</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.552</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10.548</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.726</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6.660</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.656</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.643</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">DIPP <sup>a</sup></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6.313</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.427</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.916</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.390</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6.061</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.549</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.442</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">DIPRP <sup>a</sup></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.339</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.322</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.609</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.296</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.885</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.518</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.358</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">DMEP <sup>b</sup></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7.085</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.511</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.088</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.258</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6.876</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.695</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.459</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">DMP <sup>a</sup></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.293</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.100</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.624</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.297</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.940</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.170</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.191</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">DNOP <sup>a</sup></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10.420</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.873</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.797</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.309</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8.333</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.955</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.679</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">DPP <sup>a</sup></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.484</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.338</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.970</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.322</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6.313</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.594</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.393</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">DPRP <sup>b</sup></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6.273</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.423</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6.681</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.446</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.746</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.314</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.406</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">DTDP <sup>a</sup></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">9.112</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.731</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13.722</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.956</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8.584</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.000</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.880</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">DUP *</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">11.595</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.000</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">14.334</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.000</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">8.561</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.996</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.999</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>Ej</italic>\n</td><td colspan=\"2\" align=\"center\" valign=\"middle\" rowspan=\"1\">1.799</td><td colspan=\"2\" align=\"center\" valign=\"middle\" rowspan=\"1\">1.792</td><td colspan=\"2\" align=\"center\" valign=\"middle\" rowspan=\"1\">1.497</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>H<sub>j</sub></italic>\n</td><td colspan=\"2\" align=\"center\" valign=\"middle\" rowspan=\"1\">0.799</td><td colspan=\"2\" align=\"center\" valign=\"middle\" rowspan=\"1\">0.792</td><td colspan=\"2\" align=\"center\" valign=\"middle\" rowspan=\"1\">0.497</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>W<sub>j</sub></italic>\n</td><td colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">38.28%</td><td colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">37.93%</td><td colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">23.79%</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−</td></tr></tbody></table><table-wrap-foot><fn><p><sup>a</sup>: Training set; <sup>b</sup>: test set; *: template molecule.</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"ijerph-17-05299-t002\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05299-t002_Table 2</object-id><label>Table 2</label><caption><p>Evaluation parameters of the comparative molecular similarity index analysis (CoMSIA) model for the biodegradability of the PAEs by three plasticizer-degrading bacteria.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Model</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">q<sup>2</sup></th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>n</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">SEE</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">R<sup>2</sup></th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">F</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">r<sup>2</sup><sub>pred</sub></th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Q<sup>2</sup></th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">cSDEP</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">dq<sup>2</sup>/dr<sup>2</sup>yy</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">S</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">E</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">H</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">D</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">A</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">CoMSIA</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.731</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">8</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.013</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.999</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">578.738</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.761</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.385</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.251</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.488</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">34.7%</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">11.1%</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">46.8%</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">7.4%</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.0%</td></tr></tbody></table></table-wrap><table-wrap id=\"ijerph-17-05299-t003\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05299-t003_Table 3</object-id><label>Table 3</label><caption><p>Prediction of the CoMSIA model of comprehensive and the single biodegradability of DEP derivatives and their change ratios.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">No.</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Substituent Group</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Comprehensive Biodegradation Values</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Change Rate<break/>(%)</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Docking Score Value of 2PIA</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Change Rate<break/>(%)</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Docking Score Value of 2ZYI</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Change Rate<break/>(%)</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Docking Score Value of 3CN7</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Change Rate<break/>(%)</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Ratio</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">DEP</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.27</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.57</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.71</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.55</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">38.28:37.93:23.79</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">DEP-1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">H<sub>1</sub>-CH<sub>3</sub></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.293</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8.52%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.637</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.20%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.232</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">11.08%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.319</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">21.66%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">DEP-2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">H<sub>1</sub>-CH<sub>2</sub>CH<sub>3</sub></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.311</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">15.19%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.762</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.45%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.353</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13.65%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.329</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">21.94%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">DEP-3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">H<sub>1</sub>-CH(CH<sub>3</sub>)<sub>2</sub></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.328</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">21.48%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.876</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.49%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.397</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">14.59%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.399</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">23.92%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">DEP-4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">H<sub>1</sub>-C(CH<sub>3</sub>)<sub>3</sub></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.349</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">29.26%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.976</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7.29%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.673</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">20.45%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.36</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">22.82%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">DEP-5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">C<sub>1</sub>-CH<sub>3</sub></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.269</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.37%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.500</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−1.26%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.827</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.48%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.262</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">20.06%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">DEP-6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">C<sub>1</sub>-OH</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.228</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−15.56%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.828</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−13.32%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.838</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.72%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.224</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">18.99%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">DEP-7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">C<sub>1</sub>-H</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.272</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.74%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.059</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−9.17%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.784</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.57%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.372</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">23.15%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">DEP-8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">C<sub>1</sub>-CN</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.255</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−5.56%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.266</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−5.46%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.874</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.48%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.172</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">17.52%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">DEP-9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">C<sub>1</sub>-NH<sub>2</sub></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.225</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−16.67%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.967</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−10.83%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.677</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.70%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.327</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">21.89%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">DEP-10</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">C<sub>1</sub>-CHO</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.255</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−5.56%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.938</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−11.35%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.077</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7.79%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.113</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">15.86%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">DEP-11</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">C<sub>2</sub>-OH</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.249</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−7.78%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.658</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.58%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.594</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−2.46%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.29</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">20.85%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">DEP-12</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">C<sub>2</sub>-CHO</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.262</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−2.96%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.737</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.00%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.871</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.42%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.282</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">20.62%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">DEP-13</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">C<sub>2</sub>-COOH</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.269</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.37%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.892</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.78%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.921</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.48%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.312</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">21.46%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">DEP-14</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">C<sub>2</sub>-NH<sub>2</sub></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.256</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−5.19%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.504</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−1.18%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.911</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.27%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.199</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">18.28%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">DEP-15</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">C<sub>2</sub>-COCH<sub>3</sub></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.289</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7.04%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6.109</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">9.68%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.969</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.50%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.378</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">23.32%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">DEP-16</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">C<sub>2</sub>-CONH<sub>2</sub></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.235</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−12.96%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.755</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.32%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.776</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.40%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.436</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">24.96%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">DEP-17</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">C<sub>2</sub>-(OH)<sub>2</sub></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.267</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−1.11%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.484</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−1.54%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.495</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">16.67%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.170</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">17.46%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">DEP-18</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">C<sub>2</sub>-(CHO)<sub>2</sub></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.267</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−1.11%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.409</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−2.89%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.73</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">21.66%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.131</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">16.37%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">DEP-19</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">C<sub>2</sub>-(COOH)<sub>2</sub></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.284</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.19%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.621</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.92%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.826</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">23.69%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.119</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">16.03%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">DEP-20</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">C<sub>2</sub>-(NH<sub>2</sub>)<sub>2</sub></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.259</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−4.07%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.444</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−2.26%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.434</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">15.37%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.295</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">20.99%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">DEP-21</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">C<sub>2</sub>-(COCH<sub>3</sub>)<sub>2</sub></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.310</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">14.81%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.799</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.11%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6.015</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">27.71%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.188</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">17.97%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">DEP-22</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">C<sub>2</sub>-(CONH<sub>2</sub>)<sub>2</sub></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.261</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−3.33%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.362</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−3.73%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.946</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">26.24%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.175</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">17.61%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">DEP-23</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">H<sub>1</sub>-CH<sub>3</sub>-C<sub>2</sub>-COCH<sub>3</sub></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.357</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">32.22%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6.317</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13.41%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.547</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">17.77%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.198</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">18.25%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">27.13:35.95:36.92</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">DEP-24</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">H<sub>1</sub>-CH<sub>2</sub>CH<sub>3</sub>-C<sub>2</sub>-COCH<sub>3</sub></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.377</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">39.63%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6.453</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">15.85%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.725</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">21.55%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.169</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">17.44%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">28.91:39.30:31.80</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">DEP-25</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">H<sub>1</sub>-CH(CH<sub>3</sub>)<sub>2</sub>-C<sub>2</sub>-COCH<sub>3</sub></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.389</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">44.07%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6.565</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">17.86%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.708</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">21.19%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.200</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">18.31%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">31.14:36.94:31.92</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">DEP-26</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">H<sub>1</sub>-C(CH<sub>3</sub>)<sub>3</sub>-C<sub>2</sub>-COCH<sub>3</sub></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.406</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">50.37%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6.725</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">20.74%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.787</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">22.87%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.282</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">20.62%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">32.29:35.60:32.11</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">DEP-27</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">H<sub>1</sub>-CH<sub>3</sub>-C<sub>2</sub>-CONH<sub>2</sub></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.333</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">23.33%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6.077</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">9.10%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.778</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">22.68%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.129</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">16.31%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">18.93:47.15:33.92</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">DEP-28</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">H<sub>1</sub>-CH<sub>2</sub>CH<sub>3</sub>-C<sub>2</sub>-CONH<sub>2</sub></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.343</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">27.04%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6.151</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10.43%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.898</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">25.22%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.130</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">16.34%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">20.06:48.51:31.42</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">DEP-29</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">H<sub>1</sub>-CH(CH<sub>3</sub>)<sub>2</sub>-C<sub>2</sub>-CONH<sub>2</sub></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.356</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">31.85%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6.263</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">12.44%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.879</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">24.82%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.144</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">16.73%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">23.04:45.97:30.99</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">DEP-30</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">H<sub>1</sub>-C(CH<sub>3</sub>)<sub>3</sub>-C<sub>2</sub>-CONH<sub>2</sub></td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.370</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">37.04%</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">6.382</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">14.58%</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">5.953</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">26.39%</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">4.230</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">19.15%</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">24.25:43.89:31.86</td></tr></tbody></table><table-wrap-foot><fn><p>Note: H<sub>1</sub> stands for the H atom on the C<sub>1</sub> site.</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"ijerph-17-05299-t004\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05299-t004_Table 4</object-id><label>Table 4</label><caption><p>Evaluation parameters of <italic>B. cepacia</italic> (a), <italic>A. fulgidus</italic> (b), <italic>P. aeruginosa</italic> (c). Biodegradation 3D-QSAR model of PAEs.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Model</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3D-QSAR</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">q<sup>2</sup></th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">n</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">SEE</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">R<sup>2</sup></th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">F</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">r<sup>2</sup><sub>pred</sub></th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Q<sup>2</sup></th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">cSDEP</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">dq<sup>2</sup>/dr<sup>2</sup>yy</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">a</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CoMSIA</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.627</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.172</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.998</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">284.548</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.657</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.559</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.78</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.958</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">b</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CoMFA</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.697</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.518</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.986</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">207.862</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.918</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.422</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.306</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.372</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">c</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">CoMFA</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.68</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">10</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.001</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">465107.312</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.618</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.491</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3.021</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.690</td></tr></tbody></table></table-wrap><table-wrap id=\"ijerph-17-05299-t005\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05299-t005_Table 5</object-id><label>Table 5</label><caption><p>Prediction parameters for DEP’s molecular function and environmental friendliness before and after modification.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">No.</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Total Energy (a.u.)</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Change Rate<break/>(%)</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Energy Gap (eV)</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Frequency (cm<sup>−1</sup>)</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Bio-Toxicity<break/>(logLC<sub>50</sub>)</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Change Rate<break/>(%)</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Bioaccumulation (logBCF)</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">BCF</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Persistence<break/>(logt<sub>1/2</sub>)</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Change Rate<break/>(%)</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Long-Range Migration (log<italic>K<sub>OA</sub></italic>)</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Change Rate<break/>(%)</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">DEP</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−766.62</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.32</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">24.02</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.100</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.264</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">18.37</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.156</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7.505</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">DEP-23</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−919.26</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−19.91%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.15</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">15.09</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.781</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">29.00%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.879</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">75.68</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.271</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−3.64%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8.540</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13.79%</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">DEP-24</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−958.57</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−25.04%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.16</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">18.09</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.744</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">32.36%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.992</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">98.17</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.250</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−2.98%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8.546</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13.87%</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">DEP-25</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−997.89</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−30.17%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.14</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">17.65</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.737</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">33.00%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.041</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">109.90</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.230</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−2.34%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8.563</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">14.10%</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">DEP-26</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−1037.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−35.30%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.06</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">15.38</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.672</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">38.91%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.116</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">130.62</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.215</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−1.87%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8.766</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">16.80%</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">DEP-27</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−935.32</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−22.01%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.00</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">19.32</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.070</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.73%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.569</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">37.07</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.386</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−7.29%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8.329</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10.98%</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">DEP-28</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−974.64</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−27.13%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.97</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">16.33</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.047</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.82%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.600</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">39.81</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.378</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−7.03%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8.352</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">11.29%</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">DEP-29</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−1013.95</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−32.26%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.96</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">16.24</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.038</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.64%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.653</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">44.98</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.357</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−6.37%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8.374</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">11.58%</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">DEP-30</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−1053.26</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−37.39%</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">4.94</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">15.99</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.923</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">16.09%</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.716</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">52.00</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3.349</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−6.12%</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">8.489</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">13.11%</td></tr></tbody></table></table-wrap><table-wrap id=\"ijerph-17-05299-t006\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05299-t006_Table 6</object-id><label>Table 6</label><caption><p>Calculation of the reaction energy barrier of microbial degradation transformation paths of DEP and its derivatives.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th colspan=\"5\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">DEP</th><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">Change Rate<break/>(%)</th><th colspan=\"4\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">DEP-27</th><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">Change Rate<break/>(%)</th></tr><tr><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Path</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Reactants</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Reaction<break/>Products</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Energy Barrier (kJ/mol)</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Total Energy Barrier (kJ/mol)</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Reactants</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Reaction<break/>Products</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Energy Barrier (kJ/mol)</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Total Energy Barrier (kJ/mol)</th></tr></thead><tbody><tr><td rowspan=\"4\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Path1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">DEP</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">M0-1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">27.57</td><td rowspan=\"4\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">127.33</td><td rowspan=\"4\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">−</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">DEP-27</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">M1-1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">28.88</td><td rowspan=\"4\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">113.16</td><td rowspan=\"4\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">−11.13</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">M0-1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">M0-2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">15.75</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">M1-1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">M1-2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13.92</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">M0-2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">M0-3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">53.82</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">M1-2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">M1-3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">56.97</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">M0-3</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">M0-5</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">30.19</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">M1-3</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">M1-5</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">13.39</td></tr><tr><td rowspan=\"4\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Path2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">DEP</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">M0-1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">27.57</td><td rowspan=\"4\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">139.68</td><td rowspan=\"4\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">−</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">DEP-27</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">M1-1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">28.88</td><td rowspan=\"4\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">127.07</td><td rowspan=\"4\" align=\"center\" valign=\"middle\" colspan=\"1\">−9.02</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">M0-1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">M0-2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">15.75</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">M1-1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">M1-2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13.92</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">M0-2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">M0-4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">68.79</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">M1-2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">M1-4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">72.99</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">M0-4</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">M0-5</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">27.57</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">M1-4</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">M1-5</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">11.29</td></tr><tr><td colspan=\"5\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td colspan=\"4\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\"><bold>Total Change rate</bold> (%)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−20.15</td></tr><tr><td colspan=\"5\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">\n<bold>DEP-28</bold>\n</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">\n<bold>Change Rate</bold>\n<break/>\n<bold>(%)</bold>\n</td><td colspan=\"4\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">\n<bold>DEP-29</bold>\n</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">\n<bold>Change Rate</bold>\n<break/>\n<bold>(%)</bold>\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>Path</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>Reactants</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>Reaction</bold>\n<break/>\n<bold>Products</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>Energy Barrier (kJ/mol)</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>Total Energy Barrier (kJ/mol)</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>Path</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>Reactants</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>Reaction</bold>\n<break/>\n<bold>Products</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>Energy Barrier (kJ/mol)</bold>\n</td></tr><tr><td rowspan=\"4\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Path1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">DEP-28</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">M2-1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">24.94</td><td rowspan=\"4\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">110.98</td><td rowspan=\"4\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">−12.84</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">DEP-29</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">M3-1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">17.07</td><td rowspan=\"4\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">99.77</td><td rowspan=\"4\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">−21.65</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">M2-1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">M2-2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">15.67</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">M3-1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">M3-2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">12.34</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">M2-2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">M2-3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">56.97</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">M3-2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">M3-3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">56.97</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">M2-3</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">M2-5</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">13.39</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">M3-3</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">M3-5</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">13.39</td></tr><tr><td rowspan=\"4\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Path2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">DEP-28</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">M2-1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">24.94</td><td rowspan=\"4\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">124.90</td><td rowspan=\"4\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">−10.58</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">DEP-29</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">M3-1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">17.07</td><td rowspan=\"4\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">113.68</td><td rowspan=\"4\" align=\"center\" valign=\"middle\" colspan=\"1\">−18.61</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">M2-1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">M2-2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">15.67</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">M3-1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">M3-2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">12.34</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">M2-2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">M2-4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">72.99</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">M3-2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">M3-4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">72.99</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">M2-4</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">M2-5</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">11.29</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">M3-4</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">M3-5</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">11.29</td></tr><tr><td colspan=\"5\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\"><bold>Total Change rate</bold> (%)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−23.42</td><td colspan=\"4\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\"><bold>Total Change rate</bold> (%)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−30.26</td></tr></tbody></table></table-wrap><table-wrap id=\"ijerph-17-05299-t007\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05299-t007_Table 7</object-id><label>Table 7</label><caption><p>Molecular docking scores and molecular dynamics simulation of the binding energy calculation for DEP and its derivative molecules.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">No.</th><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">2PIA</th><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">2ZYI</th><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">3CN7</th></tr><tr><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Docking Score Value</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">△G<sub>bind</sub> (kJ/mol)</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Docking Score Value</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">△G<sub>bind</sub> (kJ/mol)</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Docking Score Value</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">△G<sub>bind</sub> (kJ/mol)</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">DEP</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.574</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−62.400</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.711</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−138.694</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.548</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−108.742</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">DEP-27</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.323↓</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−126.613↓</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.491↓</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−158.330↓</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.661↑</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−102.247↑</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">DEP-28</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.793↑</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−74.505↓</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.993↓</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−138.588↑</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6.139↑</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−136.861↓</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">DEP-29</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">5.717↑</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−108.149↓</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">7.535↑</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−177.961↓</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">7.042↑</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−160.312↓</td></tr></tbody></table></table-wrap></floats-group></article>\n"
]
[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Front Plant Sci</journal-id><journal-id journal-id-type=\"iso-abbrev\">Front Plant Sci</journal-id><journal-id journal-id-type=\"publisher-id\">Front. Plant Sci.</journal-id><journal-title-group><journal-title>Frontiers in Plant Science</journal-title></journal-title-group><issn pub-type=\"epub\">1664-462X</issn><publisher><publisher-name>Frontiers Media S.A.</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32849733</article-id><article-id pub-id-type=\"pmc\">PMC7432127</article-id><article-id pub-id-type=\"doi\">10.3389/fpls.2020.01184</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Plant Science</subject><subj-group><subject>Original Research</subject></subj-group></subj-group></article-categories><title-group><article-title>Genome-Wide Association Mapping Identifies Novel Loci for Quantitative Resistance to Blackleg Disease in Canola</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Raman</surname><given-names>Harsh</given-names></name><xref ref-type=\"aff\" rid=\"aff1\">\n<sup>1</sup>\n</xref><xref ref-type=\"author-notes\" rid=\"fn001\">\n<sup>*</sup>\n</xref><uri xlink:type=\"simple\" xlink:href=\"https://loop.frontiersin.org/people/55456\"/></contrib><contrib contrib-type=\"author\"><name><surname>McVittie</surname><given-names>Brett</given-names></name><xref ref-type=\"aff\" rid=\"aff1\">\n<sup>1</sup>\n</xref><uri xlink:type=\"simple\" xlink:href=\"https://loop.frontiersin.org/people/485514\"/></contrib><contrib contrib-type=\"author\"><name><surname>Pirathiban</surname><given-names>Ramethaa</given-names></name><xref ref-type=\"aff\" rid=\"aff2\">\n<sup>2</sup>\n</xref><uri xlink:type=\"simple\" xlink:href=\"https://loop.frontiersin.org/people/993013\"/></contrib><contrib contrib-type=\"author\"><name><surname>Raman</surname><given-names>Rosy</given-names></name><xref ref-type=\"aff\" rid=\"aff1\">\n<sup>1</sup>\n</xref><uri xlink:type=\"simple\" xlink:href=\"https://loop.frontiersin.org/people/421690\"/></contrib><contrib contrib-type=\"author\"><name><surname>Zhang</surname><given-names>Yuanyuan</given-names></name><xref ref-type=\"aff\" rid=\"aff3\">\n<sup>3</sup>\n</xref><uri xlink:type=\"simple\" xlink:href=\"https://loop.frontiersin.org/people/219342\"/></contrib><contrib contrib-type=\"author\"><name><surname>Barbulescu</surname><given-names>Denise M.</given-names></name><xref ref-type=\"aff\" rid=\"aff4\">\n<sup>4</sup>\n</xref></contrib><contrib contrib-type=\"author\"><name><surname>Qiu</surname><given-names>Yu</given-names></name><xref ref-type=\"aff\" rid=\"aff1\">\n<sup>1</sup>\n</xref></contrib><contrib contrib-type=\"author\"><name><surname>Liu</surname><given-names>Shengyi</given-names></name><xref ref-type=\"aff\" rid=\"aff3\">\n<sup>3</sup>\n</xref><uri xlink:type=\"simple\" xlink:href=\"https://loop.frontiersin.org/people/363568\"/></contrib><contrib contrib-type=\"author\"><name><surname>Cullis</surname><given-names>Brian</given-names></name><xref ref-type=\"aff\" rid=\"aff2\">\n<sup>2</sup>\n</xref></contrib></contrib-group><aff id=\"aff1\">\n<sup>1</sup>\n<institution>NSW Department of Primary Industries, Wagga Wagga Agricultural Institute</institution>, <addr-line>Wagga Wagga, NSW</addr-line>, <country>Australia</country>\n</aff><aff id=\"aff2\">\n<sup>2</sup>\n<institution>Centre for Bioinformatics and Biometrics, National Institute for Applied Statistics Research Australia, University of Wollongong</institution>, <addr-line>Wollongong, NSW</addr-line>, <country>Australia</country>\n</aff><aff id=\"aff3\">\n<sup>3</sup>\n<institution>Oil Crops Research Institute, Chinese Academy of Agricultural Sciences</institution>, <addr-line>Wuhan</addr-line>, <country>China</country>\n</aff><aff id=\"aff4\">\n<sup>4</sup>\n<institution>Department of Jobs, Precincts and Regions, Agriculture Victoria</institution>, <addr-line>Horsham, VIC</addr-line>, <country>Australia</country>\n</aff><author-notes><fn fn-type=\"edited-by\"><p>Edited by: Ryo Fujimoto, Kobe University, Japan</p></fn><fn fn-type=\"edited-by\"><p>Reviewed by: Habibur Rahman, University of Alberta, Canada; Takahiro Kawanabe, Tokai University, Japan</p></fn><corresp id=\"fn001\">*Correspondence: Harsh Raman, <email xlink:href=\"mailto:[email protected]\" xlink:type=\"simple\">[email protected]</email>; <uri xlink:href=\"https://orcid.org/0000-0001-9761-1518\" xlink:type=\"simple\">orcid.org/0000-0001-9761-1518</uri>\n</corresp><fn fn-type=\"other\" id=\"fn002\"><p>This article was submitted to Plant Breeding, a section of the journal Frontiers in Plant Science</p></fn></author-notes><pub-date pub-type=\"epub\"><day>11</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><year>2020</year></pub-date><volume>11</volume><elocation-id>1184</elocation-id><history><date date-type=\"received\"><day>07</day><month>5</month><year>2020</year></date><date date-type=\"accepted\"><day>21</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>Copyright © 2020 Raman, McVittie, Pirathiban, Raman, Zhang, Barbulescu, Qiu, Liu and Cullis</copyright-statement><copyright-year>2020</copyright-year><copyright-holder>Raman, McVittie, Pirathiban, Raman, Zhang, Barbulescu, Qiu, Liu and Cullis</copyright-holder><license xlink:href=\"http://creativecommons.org/licenses/by/4.0/\"><license-p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</license-p></license></permissions><abstract><p>Blackleg disease, caused by the fungal pathogen <italic>Leptosphaeria maculans</italic>, continues to be a major concern for sustainable production of canola (<italic>Brassica napus</italic> L.) in many parts of the world. The deployment of effective quantitative resistance (QR) is recognized as a durable strategy in providing natural defense to pathogens. Herein, we uncover loci for resistance to blackleg in a genetically diverse panel of canola accessions by exploiting historic recombination events which occurred during domestication and selective breeding by genome-wide association analysis (GWAS). We found extensive variation in resistance to blackleg at the adult plant stage, including for upper canopy infection. Using the linkage disequilibrium and genetic relationship estimates from 12,414 high quality SNPs, GWAS identified 59 statistically significant and “suggestive” SNPs on 17 chromosomes of <italic>B. napus</italic> genome that underlie variation in resistance to blackleg, evaluated under field and shade-house conditions. Each of the SNP association accounted for up to 25.1% of additive genetic variance in resistance among diverse panel of accessions. To understand the homology of QR genomic regions with <italic>Arabidopsis thaliana</italic> genome, we searched the synteny between QR regions with 22 ancestral blocks of Brassicaceae. Comparative analyses revealed that 25 SNP associations for QR were localized in nine ancestral blocks, as a result of genomic rearrangements. We further showed that phenological traits such as flowering time, plant height, and maturity confound the genetic variation in resistance. Altogether, these findings provided new insights on the complex genetic control of the blackleg resistance and further expanded our understanding of its genetic architecture.</p></abstract><kwd-group><kwd>natural variation</kwd><kwd>resistance to blackleg</kwd><kwd><italic>Leptosphaeria maculans</italic></kwd><kwd>canola</kwd><kwd>genome-wide association analysis</kwd><kwd>linkage disequilibrium</kwd></kwd-group><funding-group><award-group><funding-source id=\"cn001\">Grains Research and Development Corporation<named-content content-type=\"fundref-id\">10.13039/501100000980</named-content></funding-source><award-id rid=\"cn001\">DAN00117, DAN00208, DAN1701</award-id></award-group></funding-group><counts><fig-count count=\"6\"/><table-count count=\"4\"/><equation-count count=\"2\"/><ref-count count=\"84\"/><page-count count=\"20\"/><word-count count=\"12379\"/></counts></article-meta></front><body><sec sec-type=\"intro\" id=\"s1\"><title>Introduction</title><p>Canola, a polyploid crop (<italic>Brassica napus</italic> L, 2n = 4× = 36, genome A<sub>n</sub>A<sub>n</sub>C<sub>n</sub>C<sub>n</sub>) that was only domesticated about 500 years ago (<xref rid=\"B32\" ref-type=\"bibr\">Gómez-Campo and Prakash, 1999</xref>), has now taken central stage for meeting global demands of healthy vegetable oil for human consumption. Currently, it is grown on over 37 M ha worldwide with production of 75 m tonnes (<uri xlink:type=\"simple\" xlink:href=\"http://www.fao.org/faostat/\">http://www.fao.org/faostat/</uri>). Blackleg, caused by the fungal pathogen <italic>Leptosphaeria maculans</italic>, is one of the most serious diseases of canola in many parts of the world and accounts for significant yield losses (<xref rid=\"B74\" ref-type=\"bibr\">Sosnowski et al., 2004</xref>; <xref rid=\"B24\" ref-type=\"bibr\">Fitt et al., 2006</xref>). The infection occurs at various stages of plant development and causes lesions on underground and the above ground parts; cotyledon, leaf, stem, branches, flowers, and siliques (<xref rid=\"B76\" ref-type=\"bibr\">Sprague et al., 2018</xref>). Current disease management strategies include deployment of host resistance, mediated by qualitative (race-specific <italic>R</italic> genes) and uncharacterized quantitative resistance (race nonspecific QTL, QR) genes, crop rotations, and application of fungicides.</p><p>Several sources of qualitative and quantitative resistance to blackleg has been identified in <italic>Brassica</italic> species (<xref rid=\"B52\" ref-type=\"bibr\">Mithen et al., 1987</xref>; <xref rid=\"B11\" ref-type=\"bibr\">Chevre et al., 1996</xref>; <xref rid=\"B12\" ref-type=\"bibr\">Chèvre et al., 1997</xref>; <xref rid=\"B13\" ref-type=\"bibr\">Christianson et al., 2006</xref>; <xref rid=\"B70\" ref-type=\"bibr\">Rimmer, 2006</xref>; <xref rid=\"B47\" ref-type=\"bibr\">Leflon et al., 2007</xref>; <xref rid=\"B63\" ref-type=\"bibr\">Raman et al., 2013</xref>; <xref rid=\"B27\" ref-type=\"bibr\">Fredua-Agyeman et al., 2014</xref>; <xref rid=\"B29\" ref-type=\"bibr\">Gaebelein et al., 2019</xref>); and several of them were deployed by canola breeders to develop resistant cultivars. Genetics underlying both types of resistance has been investigated in different structured mapping populations and unstructured diverse panels of canola (<xref rid=\"B70\" ref-type=\"bibr\">Rimmer, 2006</xref>; <xref rid=\"B18\" ref-type=\"bibr\">Delourme et al., 2011</xref>; <xref rid=\"B63\" ref-type=\"bibr\">Raman et al., 2013</xref>). However, loci associated with <italic>R</italic> gene mediated resistance are understood in greater detail compared to QR (<xref rid=\"B42\" ref-type=\"bibr\">Larkan et al., 2013</xref>; <xref rid=\"B43\" ref-type=\"bibr\">Larkan et al., 2014</xref>; <xref rid=\"B46\" ref-type=\"bibr\">Larkan et al., 2019</xref>). Though, incorporation of the <italic>R</italic> genes has been effective in mitigating risks for yield loss due to blackleg; many of them have become ineffective over-time, when deployed singly or in “stack” (<xref rid=\"B71\" ref-type=\"bibr\">Rouxel et al., 2003</xref>; <xref rid=\"B48\" ref-type=\"bibr\">Li et al., 2006</xref>; <xref rid=\"B75\" ref-type=\"bibr\">Sprague et al., 2006</xref>; <xref rid=\"B80\" ref-type=\"bibr\">Van De Wouw et al., 2014</xref>). In contrast, QR is recognized for its durability in providing natural defense to pathogens, including <italic>L. maculans</italic>, as it conveys incomplete resistance, probably due to decreased selective pressure to overcome host resistance (<xref rid=\"B38\" ref-type=\"bibr\">Johnson, 1984</xref>; <xref rid=\"B4\" ref-type=\"bibr\">Boyd, 2006</xref>; <xref rid=\"B28\" ref-type=\"bibr\">Fukuoka et al., 2009</xref>; <xref rid=\"B20\" ref-type=\"bibr\">Delourme et al., 2014</xref>).</p><p>Several hundred quantitative trait loci (QTL) for QR were identified in mapping populations following classical QTL (<xref rid=\"B17\" ref-type=\"bibr\">Delourme et al., 2006</xref>; <xref rid=\"B40\" ref-type=\"bibr\">Kaur et al., 2009</xref>; <xref rid=\"B36\" ref-type=\"bibr\">Jestin et al., 2011</xref>; <xref rid=\"B61\" ref-type=\"bibr\">Raman et al., 2012a</xref>; <xref rid=\"B62\" ref-type=\"bibr\">Raman et al., 2012b</xref>; <xref rid=\"B25\" ref-type=\"bibr\">Fopa Fomeju et al., 2014</xref>; <xref rid=\"B37\" ref-type=\"bibr\">Jestin et al., 2015</xref>; <xref rid=\"B44\" ref-type=\"bibr\">Larkan et al., 2016a</xref>; <xref rid=\"B64\" ref-type=\"bibr\">Raman et al., 2016</xref>; <xref rid=\"B41\" ref-type=\"bibr\">Kumar et al., 2018</xref>; <xref rid=\"B65\" ref-type=\"bibr\">Raman et al., 2018</xref>; <xref rid=\"B67\" ref-type=\"bibr\">Raman et al., 2020</xref>) and genome-wide association (GWAS) approaches in canola (<xref rid=\"B36\" ref-type=\"bibr\">Jestin et al., 2011</xref>; <xref rid=\"B25\" ref-type=\"bibr\">Fopa Fomeju et al., 2014</xref>; <xref rid=\"B26\" ref-type=\"bibr\">Fopa Fomeju et al., 2015</xref>; <xref rid=\"B60\" ref-type=\"bibr\">Rahman et al., 2016</xref>; <xref rid=\"B64\" ref-type=\"bibr\">Raman et al., 2016</xref>; <xref rid=\"B41\" ref-type=\"bibr\">Kumar et al., 2018</xref>). However, only a limited number of QTL provided stable QR to blackleg across environments (<xref rid=\"B57\" ref-type=\"bibr\">Pilet et al., 2001</xref>; <xref rid=\"B35\" ref-type=\"bibr\">Huang et al., 2016</xref>; <xref rid=\"B44\" ref-type=\"bibr\">Larkan et al., 2016a</xref>; <xref rid=\"B65\" ref-type=\"bibr\">Raman et al., 2018</xref>). Combining QR loci with complementary mode of action and combining QR with <italic>R</italic> genes could provide effective and durable resistance (<xref rid=\"B6\" ref-type=\"bibr\">Brun et al., 2010</xref>; <xref rid=\"B51\" ref-type=\"bibr\">Michelmore et al., 2017</xref>; <xref rid=\"B58\" ref-type=\"bibr\">Pilet-Nayel et al., 2017</xref>; <xref rid=\"B69\" ref-type=\"bibr\">Rimbaud et al., 2018</xref>). The latter approach has not been extremely successful in some parts of Australia, particularly when pathogen pressure is too high under field conditions (<xref rid=\"B62\" ref-type=\"bibr\">Raman et al., 2012b</xref>; <xref rid=\"B67\" ref-type=\"bibr\">Raman et al., 2020</xref>). Sources of effective QR resistance and underlying mode of action for QR genes is largely unknown in canola. Despite of immense interest in exploiting QR in canola breeding programs, comprehensive GWAS studies using larger sets of diverse panel and markers, and field-based assessments of resistance to blackleg across multiple locations and environments were lacking. In addition, upper canopy infection (UCI); characterized by lesions on siliques, pedicel, branches, and stem, at terminal stages of plant development, has become another concern for Australian canola industry in recent years (<xref rid=\"B76\" ref-type=\"bibr\">Sprague et al., 2018</xref>). Currently, there is a very limited knowledge on the extent of genetic variation for resistance to UCI in canola germplasm as well as underlying loci involved in resistance. Delineation of loci involved in multifaceted components of resistance and their uniqueness/redundancy in canola germplasm would provide potential genetic solution to reduce yield losses, caused by blackleg disease.</p><p>Herein, we assess a diverse panel of canola accessions across multiple environments and reveal significant marker-trait associations for resistance, evaluated as plant survival/mortality, internal infection (stem/crown canker) and severity of infection in upper canopy. To understand whether duplicated homoeologous regions are involved in QR, we searched the synteny between QR regions with 22 ancestral blocks (AK) of Brassicaceae (<xref rid=\"B72\" ref-type=\"bibr\">Schranz et al., 2006</xref>; <xref rid=\"B19\" ref-type=\"bibr\">Delourme et al., 2013</xref>; <xref rid=\"B25\" ref-type=\"bibr\">Fopa Fomeju et al., 2014</xref>). This analysis revealed that majority of QR regions have originated as a result of homoeologous and paralogous exchanges. We further showed that phenological traits such as flowering time, plant height and maturity confound the genetic variation for resistance. Our results provide new insights into the genetic and developmental basis for variation in QR among diverse panel of accessions. Identification of genetic variation for resistance to stem canker and UCI, in combination with molecular tools would enable the incorporation of QR into the elite germplasm, to develop new cultivars with overall reduced susceptibility to blackleg infection in canola.</p></sec><sec id=\"s2\"><title>Material and Methods</title><sec id=\"s2_1\"><title>Diversity Panel</title><p>A diverse panel of 421 accessions, comprising 395 of <italic>Brassica napus</italic>, 21 of <italic>B. napus</italic>/<italic>Brassica juncea</italic> derivatives, one of <italic>B. juncea</italic> and four <italic>of Brassica carinata</italic> was used in this study (<xref ref-type=\"supplementary-material\" rid=\"SM1\">\n<bold>Supplementary Table 1</bold>\n</xref>). The <italic>B. napus</italic> panel includes 368 homozygous doubled haploid (DH) diverse accessions, representing different geographical locations, that were utilized earlier for mapping loci associated with photoperiodic response and flowering time (<xref rid=\"B66\" ref-type=\"bibr\">Raman et al., 2019</xref>). The DH accessions were generated <italic>via</italic> the microspore culture technique at the Haplotech Inc. (Manitoba, Canada).</p></sec><sec id=\"s2_2\"><title>Phenotypic Evaluation of Diverse DH Accessions for Resistance</title><p>The diverse panel of accessions was evaluated for resistance to <italic>L. maculans</italic> in five experiments. Two experiments were conducted under semi-controlled shade-house conditions (SH16, SH17) across two years (2016 and 2017) at Grains Innovation Park, Horsham, Victoria, Australia (36°43’14.6”S 142°10’24.5”E) and three experiments (FT17, FT18 and FT19) were conducted under field conditions across three years (2017, 2018 and 2019) at Wagga Wagga Agricultural Institute (WWAI), Wagga Wagga, NSW, Australia (35°02′27.0″S 147°19′12.6″E).</p><sec id=\"s2_2_1\"><title>Phenotyping for Resistance With Ascospore Shower Test (Shade-House Experiments)</title><sec id=\"s2_2_1_1\"><title>Experimental Design</title><p>The shade-house experiments included a total of 327 DH accessions in two runs: in SH16, 244 accessions together with 10 current commercial cultivars (as “check”) were evaluated whereas in SH17, the remaining 77 accessions along with the same 10 “check” cultivars were evaluated (<xref ref-type=\"supplementary-material\" rid=\"SM1\">\n<bold>Supplementary Table 1</bold>\n</xref>). Connectivity between both experiments with 10 check cultivars and four DH accessions (46C04, ATR-Beacon, ATR-Signal and ATR-Hyden) that were used in SH16 experiment ensured that the assessment of disease expression is consistent across both experiments (<xref ref-type=\"supplementary-material\" rid=\"SM1\">\n<bold>Supplementary Table 2</bold>\n</xref>). In the SH16 experiment, the accessions were arranged in a Completely Randomized Design with three pot replicates for each accession (<xref ref-type=\"supplementary-material\" rid=\"SM1\">\n<bold>Supplementary Figure 1A</bold>\n</xref>). A total of 762 pots were located in three adjacent bays. Each bay consisted of a rectangular array of pots; each bay had either four or two columns (herein referred to as ranges) with a varying number of rows (80, 79, 32, and 31, respectively). Individual pots were the experimental units where, each pot contained 4 plants of the same accession. In the SH17 experiment, accessions were allocated to pots within three (complete) blocks. These blocks were located within one rectangular array of pots (Bay) with four ranges and 66 rows. The whole experiment had a total of 264 pots (<xref ref-type=\"supplementary-material\" rid=\"SM1\">\n<bold>Supplementary Figure 1B</bold>\n</xref>).</p><p>Ascospores, released from a mixed stubble were used as inoculum for the “ascospore shower” test (<xref rid=\"B34\" ref-type=\"bibr\">Huang et al., 2006b</xref>). Stubble was collected from 2015 commercial crops of Australian canola cultivars: ATR-Gem (group A: <italic>Rlm1</italic>, Cudal, NSW), CB-Telfer (group B: <italic>Rlm4</italic>, Arthurton, South Australia), ATR-Stingray (group C: <italic>Rlm3</italic>, Minyip, Victoria), Hyola450TT (group ABD: <italic>Rlm1</italic>, <italic>Rlm4</italic>, <italic>LepR3</italic>, Streatham, Victoria), Hyola650TT (group ABD, Grenfell, NSW), T28156 (group F: <italic>Rlm6</italic>, unreleased breeding line, Parkes, NSW) and ATR-Marlin (group S: <italic>Rlm1</italic>, <italic>LepR</italic>3, Parkes, NSW). Details of the “ascospore shower” test are given in <xref rid=\"B50\" ref-type=\"bibr\">Marcroft et al. (2012)</xref>. Essentially, seedlings of test accessions were grown in punnets for approximately 10 to 15 days and then four seedlings/accession were subjected to the “ascospore shower.” The seedlings were placed onto trays and were then transferred to two growth (humidity) chambers, where the relative humidity was set at 100% at 20°C, for 48 h to encourage disease progression. After 21 days of inoculation, seedlings were transplanted into plastic pots (20 cm diameter) in a shade-house, each pot containing 4 plants per accession and subsequently raised according to the described experimental designs until the physiological maturity stage (GS 83-85). Plants were cut at the crowns and their cross-sections were assessed visually for disease severity as described previously (<xref rid=\"B50\" ref-type=\"bibr\">Marcroft et al., 2012</xref>).</p></sec></sec><sec id=\"s2_2_2\"><title>Phenotypic Measurements of Diversity Panel Under Natural Field Conditions</title><p>A subset of the diverse panel, comprising 300 homozygous accessions (<xref ref-type=\"supplementary-material\" rid=\"SM1\">\n<bold>Supplementary Table 1</bold>\n</xref>) was evaluated for resistance in blackleg nursery containing mixed stubble of triazine tolerant (TT) cultivars (<xref rid=\"B65\" ref-type=\"bibr\">Raman et al., 2018</xref>) and Westar stubble at the experimental farm of WWAI (35°02’27.0”S 147°19’12.6”E).</p><sec id=\"s2_2_2_1\"><title>Experimental Design</title><p>In each of the three field experiments, accessions were allocated to plots within two (complete) blocks. Plots consisted of a single row of at most 100 plants. These plants were grown in canola stubble of mixed TT cultivars and Westar as described in <xref rid=\"B65\" ref-type=\"bibr\">Raman et al. (2018)</xref>. Each complete block comprised a rectangular array of plots with 30 rows by 10 columns separated by a buffer row of SturtTT (see <xref ref-type=\"supplementary-material\" rid=\"SM1\">\n<bold>Supplementary Figure 1C</bold>\n</xref>). The experiments were carried-out in canola growing seasons (April-November). The blackleg nursery was irrigated frequently (5-6 times, approximately 150 mm water applied) using lateral move, in addition to natural rainfall. The weather data and conditions for both experiments is presented in <xref ref-type=\"supplementary-material\" rid=\"SM1\">\n<bold>Supplementary Figure 2</bold>\n</xref>. Plant emergence was recorded by counting number of plants present in each plot after 45 days of sowing. Number of surviving plants were recorded thrice at growth stage (GS) 15-16 (after 5 weeks of emergence), GS 60-65 (at flowering stage) and at the GS 83-85 (physiological maturity). Plant survival was calculated as the proportion between number of plants at emergence and at maturity. At the physiological maturity, ten plants per accession were cut at the crown and visually assessed for internal stem infection on a 0 to 100 percentage scale based on area showing necrosis (<xref rid=\"B62\" ref-type=\"bibr\">Raman et al., 2012b</xref>).</p><p>Resistance for UCI severity was assessed in the two experiments conducted in 2018 and 2019. Number of plants infected and the extent of UCI on ten randomly selected plants from each plot were scored. An ordinal disease severity rating scale was used based on visual assessment; 0: immune reaction (no visible symptoms of UCI); (1) disease present on up to 25% of plant tissue (small lesions, resistant to UCI); (2) disease present on 26%–50% of plant tissue; (3) disease present on 51%–75% of plant tissue, (4) disease present on >75% but less than 90% of plant tissue and (5) 100% susceptibility, affecting all tissues, characterized with extensive discoloration, snapping-off vegetative (main and secondary branches) and reproductive structures (siliques).</p></sec></sec></sec><sec id=\"s2_3\"><title>Measurement of Phenological Traits</title><p>Each field experiment was also assessed for three key developmental traits: flowering time, plant height and maturity. Flowering time (days to first flower, GS 65) was recorded, as the difference between the date when 50% of plants in a plot had the first flower opened and the day of sowing. The date of first flower was recorded for each plot three times in a week. Plant height (cm) was measured from top to base with a scale at the silique-filling stage (GS 80) from five plants selected randomly in the middle of each plot while, plant maturity (0–9 scale) was recorded, just 1–2 days before the assessment of blackleg severity.</p></sec><sec id=\"s2_4\"><title>SNP Genotyping and Selection of Marker Panel</title><p>DNA was genotyped with Illumina Infinium SNP markers (<xref rid=\"B14\" ref-type=\"bibr\">Clarke et al., 2016</xref>), as detailed in our earlier study (<xref rid=\"B66\" ref-type=\"bibr\">Raman et al., 2019</xref>). Across all accessions and SNPs, there was a total of 2.7% missing values. Missing values were imputed using the <italic>k</italic> nearest neighbor method (<xref rid=\"B79\" ref-type=\"bibr\">Troyanskaya et al., 2001</xref>) using the <italic>Pedicure</italic> package (<xref rid=\"B9\" ref-type=\"bibr\">Butler, 2019</xref>) in <italic>R</italic> statistical computing environment (<xref rid=\"B59\" ref-type=\"bibr\">R Core Team, 2019</xref>). In this method, a missing value for a marker is replaced by the mean of its <italic>k</italic> nearest neighbors with nonmissing data for that individual. SNPs that had <80% call rate, and <2% MAF (minor allele frequency) were discarded prior to GWAS analysis. We examined redundant SNPs based on the Hamming distance between marker pairs. Sets of markers, whose Hamming distance was less than a threshold of 0.001 were considered as identical and were removed prior to analysis. We also removed SNPs which could not be anchored to the 19 linkage groups of A<sub>n</sub>, and C<sub>n</sub> subgenomes (plus Ann_random and Cnn_random linkage groups) of reference sequenced genome of <italic>B. napus</italic> cv. <italic>“</italic>Darmor-<italic>bzh”</italic> version 4.1. A final set of 12,414 high quality SNPs for 344 accessions were selected for GWAS analysis.</p></sec><sec id=\"s2_5\"><title>Statistical Analysis</title><p>The approach used herein for the determination of genomic regions that influence the expression of the traits associated with blackleg resistance is an extension of the whole genome, single step QTL analysis developed by <xref rid=\"B81\" ref-type=\"bibr\">Verbyla et al. (2007)</xref>. Our approach uses an alternative working model which assumes the variance of each set of markers on different linkage groups (LGs) have different variances. That is if <italic>α</italic> is the <italic>r</italic>-vector of marker effects presented in map order, then our working model assumes that <italic>α</italic> is Gaussian, mean zero, variance</p><disp-formula><mml:math id=\"M1\"><mml:mrow><mml:mi>v</mml:mi><mml:mi>a</mml:mi><mml:mi>r</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:mi>α</mml:mi><mml:mo>)</mml:mo></mml:mrow><mml:mo>=</mml:mo><mml:mo> </mml:mo><mml:msubsup><mml:mo>⊕</mml:mo><mml:mrow><mml:mi>i</mml:mi><mml:mo>=</mml:mo><mml:mn>1</mml:mn></mml:mrow><mml:mi>c</mml:mi></mml:msubsup><mml:msubsup><mml:mi>σ</mml:mi><mml:mrow><mml:msub><mml:mi>α</mml:mi><mml:mi>i</mml:mi></mml:msub></mml:mrow><mml:mn>2</mml:mn></mml:msubsup><mml:msub><mml:mi>I</mml:mi><mml:mrow><mml:msub><mml:mi>r</mml:mi><mml:mi>i</mml:mi></mml:msub></mml:mrow></mml:msub></mml:mrow></mml:math></disp-formula><p>where <italic>c</italic> is the number of LGs, <italic>r<sub>i</sub></italic> is the number of markers in the <italic>i</italic>th LG, <inline-formula><mml:math id=\"im1\"><mml:mrow><mml:msub><mml:mi>I</mml:mi><mml:mrow><mml:msub><mml:mi>r</mml:mi><mml:mi>i</mml:mi></mml:msub></mml:mrow></mml:msub></mml:mrow></mml:math></inline-formula> is the identity matrix of order <italic>r<sub>i</sub></italic>, <inline-formula><mml:math id=\"im2\"><mml:mo>⊕</mml:mo></mml:math></inline-formula> is the direct sum operator and <inline-formula><mml:math id=\"im3\"><mml:mrow><mml:mi>r</mml:mi><mml:mo>=</mml:mo><mml:mo> </mml:mo><mml:mstyle displaystyle=\"true\"><mml:msubsup><mml:mo>∑</mml:mo><mml:mrow><mml:mi>i</mml:mi><mml:mo>=</mml:mo><mml:mn>1</mml:mn></mml:mrow><mml:mi>c</mml:mi></mml:msubsup><mml:mrow><mml:msub><mml:mi>r</mml:mi><mml:mi>i</mml:mi></mml:msub></mml:mrow></mml:mstyle></mml:mrow></mml:math></inline-formula>. The working model of <xref rid=\"B81\" ref-type=\"bibr\">Verbyla et al. (2007)</xref> assumes that <inline-formula><mml:math id=\"im4\"><mml:mrow><mml:msubsup><mml:mi>σ</mml:mi><mml:mrow><mml:msub><mml:mi>α</mml:mi><mml:mi>i</mml:mi></mml:msub></mml:mrow><mml:mn>2</mml:mn></mml:msubsup><mml:mo>=</mml:mo><mml:mo> </mml:mo><mml:msubsup><mml:mi>σ</mml:mi><mml:mi>α</mml:mi><mml:mn>2</mml:mn></mml:msubsup></mml:mrow></mml:math></inline-formula>, for <italic>i</italic> = 1,…, <italic>c</italic>. Hence their model is equivalent to the standard GBLUP model used in models for genomic selection (<xref rid=\"B78\" ref-type=\"bibr\">Tolhurst et al., 2019</xref>).</p><p>Construction of the appropriate genetic and nongenetic working model requires the inclusion of terms which represent the plot structure of the experiment(s), as well as accounting for other significant sources of nongenetic variation which occur in either shade-house studies or field trials (<xref rid=\"B31\" ref-type=\"bibr\">Gilmour et al., 1997</xref>; <xref rid=\"B73\" ref-type=\"bibr\">Smith et al., 2006</xref>). The working model (referred to as model M1) must also include terms which partition residual variance for those traits where the experimental unit is not the observational unit (<xref rid=\"B2\" ref-type=\"bibr\">Bailey, 2008</xref>). These traits include internal infection, for FT17, FT18, and FT19; internal infection and mortality for SH16, and SH17; and UCI and plant height for FT18 and FT19. All models also include a term representing polygenic effects, and the integrity of the experimental designs are preserved by including an additional term as a fixed effect, representing the effects of those accessions which were not genotyped but had phenotypic data (<xref rid=\"B78\" ref-type=\"bibr\">Tolhurst et al., 2019</xref>). All trials and traits were analyzed individually except the two runs of the shade-house experiments SH16 and SH17 which were analyzed jointly considering years as “environments”.</p><p>The models were fitted either as a linear mixed model (LMM) using residual maximum likelihood (REML) (<xref rid=\"B55\" ref-type=\"bibr\">Patterson and Thompson, 1971</xref>) for the traits internal infection, UCI, days to flower, plant height and maturity which were considered to be approximately Gaussian on a suitable transformed scale, or as a generalized linear mixed model (GLMM) using penalized quasi likelihood (<xref rid=\"B5\" ref-type=\"bibr\">Breslow and Clayton, 1993</xref>) for the traits: plant survival and mortality. All analyses were conducted using <italic>ASReml-R</italic> (<xref rid=\"B8\" ref-type=\"bibr\">Butler et al., 2018</xref>), which provides REML estimates of variance parameters, empirical best linear unbiased predictions (EBLUPs) of random effects and empirical best linear unbiased estimates (EBLUEs) of fixed effects.</p><p>Heritability (reliability) for each experiment and trait was computed using a generalized definition for heritability developed using the methods of <xref rid=\"B15\" ref-type=\"bibr\">Cullis et al. (2006)</xref>:</p><disp-formula><mml:math id=\"M2\"><mml:mrow><mml:msup><mml:mi>h</mml:mi><mml:mn>2</mml:mn></mml:msup><mml:mo>=</mml:mo><mml:mn>1</mml:mn><mml:mo>−</mml:mo><mml:mo> </mml:mo><mml:mi>P</mml:mi><mml:mi>E</mml:mi><mml:mi>V</mml:mi><mml:mo stretchy=\"false\">/</mml:mo><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mn>2</mml:mn><mml:msubsup><mml:mi>θ</mml:mi><mml:mrow><mml:mi>g</mml:mi><mml:mi>t</mml:mi></mml:mrow><mml:mn>2</mml:mn></mml:msubsup></mml:mrow><mml:mo>)</mml:mo></mml:mrow></mml:mrow></mml:math></disp-formula><p>where <italic>PEV</italic> is the average pair-wise prediction error variance of genotype effects and <inline-formula><mml:math id=\"im5\"><mml:mrow><mml:msubsup><mml:mi>θ</mml:mi><mml:mrow><mml:mi>g</mml:mi><mml:mi>t</mml:mi></mml:mrow><mml:mn>2</mml:mn></mml:msubsup></mml:mrow></mml:math></inline-formula> is the genetic variance of trial <italic>t</italic>. Pair-wise marker additive genetic correlations (<italic>r<sub>a</sub></italic>) between traits were computed using the EBLUPs of the marker additive genetic effects of those accessions evaluated for each trait.</p></sec><sec id=\"s2_6\"><title>GWAS Approach</title><p>Using the working model, M1, genome scans for each linkage group with a positive REML estimate of the marker variance was performed. During this scan, each marker was fitted as a fixed effect, the other markers on the same linkage group were dropped from the model, the marker variances for the other linkage groups were held fixed at the values from the base working model while all other variance parameters were re-estimated. Potential QTLs were chosen from this scan, along with their positions and LOD scores, i.e., –<italic>log</italic>\n<sub>10</sub>(<italic>P</italic> – <italic>value</italic>). The potential set of markers was then thinned using a modification of the approach of <xref rid=\"B3\" ref-type=\"bibr\">Benjamini and Hochberg (1995)</xref>. Given the strong dependence of the tests within a linkage group, the squared LD (r<sup>2</sup>) was computed for each pair of markers and any markers with an estimated LD of greater than 0.7 and within 100 kb, were deemed to be within the same linkage block. Markers which were in the same linkage block and also chosen as potential QTLs were thinned again, to leave only one potential QTL within any given linkage block. The linkage block marker was chosen as the marker with the lowest <italic>P</italic>-value and the least number of missing values, for that linkage block. The remaining set of markers obtained from this were then included as the baseline multi-QTL model which included all of the same terms as the working baseline model as well as the set of QTLs fitted as fixed effects. Markers were dropped from this baseline multi-QTL model using a Bonferroni based backward elimination approach until all remaining markers were significant at a significance level of 0.05. All remaining markers in the fixed component of the final multi-QTL model are reported here as putative QTLs. Of which the markers with <italic>P</italic>-value ≤ 0.001, i.e., LOD score ≥ 3 are considered as “significant,” and the markers with 0.001 < <italic>P</italic> value < 0.05 are considered as “suggestive” (<xref rid=\"B68\" ref-type=\"bibr\">Rexroad et al., 2012</xref>).</p></sec><sec id=\"s2_7\"><title>Putative Candidate Genes for Resistance</title><p>Marker sequences that revealed significant associations with quantitative resistance to blackleg in this GWAS study and previous studies (<xref rid=\"B36\" ref-type=\"bibr\">Jestin et al., 2011</xref>; <xref rid=\"B25\" ref-type=\"bibr\">Fopa Fomeju et al., 2014</xref>; <xref rid=\"B26\" ref-type=\"bibr\">Fopa Fomeju et al., 2015</xref>; <xref rid=\"B44\" ref-type=\"bibr\">Larkan et al., 2016a</xref>; <xref rid=\"B64\" ref-type=\"bibr\">Raman et al., 2016</xref>; <xref rid=\"B41\" ref-type=\"bibr\">Kumar et al., 2018</xref>) were aligned by searching their physical positions with the reference Darmor-<italic>bzh</italic> gene assembly version 4.1 (<xref rid=\"B10\" ref-type=\"bibr\">Chalhoub et al., 2014</xref>). Biological functions of those genes in relation to <italic>R</italic> and QR loci were described.</p></sec><sec id=\"s2_8\"><title>Comparative Mapping of SNP Associations in Relation to AK of Brassicaceae and Disease Resistance <italic>R</italic> Genes</title><p>Marker sequences of significant SNP associations for resistance were aligned with the sequence of <italic>A. thaliana</italic> to identify the location of QR loci and its relationship with AK of Brassicaceae, as described previously (<xref rid=\"B84\" ref-type=\"bibr\">Zou et al., 2016</xref>). <italic>Brassica napus</italic>\n<italic>R</italic> genes (<xref rid=\"B10\" ref-type=\"bibr\">Chalhoub et al., 2014</xref>; <xref rid=\"B1\" ref-type=\"bibr\">Alamery et al., 2018</xref>) that map in the vicinity of genomic regions associated with resistance to blackleg were also searched for their synteny with AK blocks.</p></sec></sec><sec sec-type=\"results\" id=\"s3\"><title>Results</title><sec id=\"s3_1\"><title>Genetic Variation for Resistance to Blackleg</title><p>Despite of the extreme high day-temperatures and drought conditions across field environments (<xref ref-type=\"supplementary-material\" rid=\"SM1\">\n<bold>Supplementary Figure 2</bold>\n</xref>), we were able to raise blackleg nurseries, suitable for evaluation of germplasm for resistance. Frequent overhead watering, with the lateral move irrigator ensured that condition for blackleg infection were congenial throughout the growing season. Across environments, blackleg infection occurred, right from the cotyledon stage (after 4–6 weeks of seedling emergence) to silique maturation (<xref ref-type=\"supplementary-material\" rid=\"SM1\">\n<bold>Supplementary Figure 3A</bold>\n</xref>). The maximum disease expression for UCI was observed at the silique maturation stage, when the disease severity for UCI was assessed. Frequency distributions exhibited that severity of blackleg scores, measured as internal infection and plant mortality, were variable across environments; blackleg severity was higher under shade-house (ascospore shower test) compared to disease nurseries under field conditions (<xref ref-type=\"fig\" rid=\"f1\">\n<bold>Figures 1A, B</bold>\n</xref>).</p><fig id=\"f1\" position=\"float\"><label>Figure 1</label><caption><p>Variation for resistance to blackleg disease, caused by the fungus, <italic>Leptosphaeria maculans</italic> in canola. Box plots showing variation for resistance to blackleg disease, measured as per cent internal infection <bold>(A)</bold> and as plant survival/mortality <bold>(B)</bold> assessed across five environments. In total, disease severity scores from 421 diverse accessions are evaluated across five environments (for details of accessions evaluated, see <xref ref-type=\"supplementary-material\" rid=\"SM1\">\n<bold>Supplementary Table 1</bold>\n</xref>). <bold>(C)</bold> The frequency distribution of upper canopy infection scores among diverse accessions. <bold>(D)</bold> Accessions showing extremes in resistance and susceptibility to upper canopy blackleg infection, evaluated across two field environments (2018, 2019) at Wagga Wagga, NSW. Variation for blackleg severity was assessed under field conditions (in blackleg nurseries) and under shade-house conditions (with ascospore shower method). <bold>(E)</bold> Scatter plots showing relationship between plant mortality and internal infection among diverse accessions, assessed for resistance using ascospore shower test. Stubble collected from commercial crops of Australian canola cultivars: ATR-Gem (group A; Cudal, NSW), CB-Telfer (group B, Arthurton, South Australia), ATR-Stingray (group C, Minyip, Victoria), Hyola450TT (group ABD, Streatham, Victoria), Hyola650TT (group ABD, Grenfell, NSW), T28156 (group F, unreleased breeding line, Parkes, NSW) and ATR-Marlin (group S, Parkes, NSW) grown in 2015, was used for “ascospore shower.” Empirical best linear unbiased predictions (EBLUPs) of genetic effects are plotted.</p></caption><graphic xlink:href=\"fpls-11-01184-g001\"/></fig><p>Joint analysis of the two shade-house experiments, where accessions were evaluated with the “ascospore shower” method, revealed that the majority of them were susceptible to pathotypes, present on the infested stubble derived from the Australian canola cultivars: ATR-Gem, CB-Telfer, ATR-Stingray, Hyola450TT, Hyola650TT, T28156 and ATR-Marlin (<xref ref-type=\"supplementary-material\" rid=\"SM1\">\n<bold>Supplementary Table 3</bold>\n</xref>). We identified 21 accessions of canola, and one accession of Ethiopian mustard (<italic>B. carinata</italic>, <italic>2n =4x =34</italic>, subgenome B<sub>c</sub>B<sub>c</sub>C<sub>c</sub>C<sub>c,</sub> ATC93184) that had ≤10% internal infection in the shade-house experiments (<xref ref-type=\"supplementary-material\" rid=\"SM1\">\n<bold>Supplementary Table 3</bold>\n</xref>). Resistant <italic>B. napus</italic> accessions were of Australian (43C80CL, 46C04, AGA99, ATR-Beacon, ATR-Mako, ATR-Signal, AV-Ruby, BononzaTT, HurricaneTT, ScaddanTT, Surpass501TT, ThunderTT, WarriorCL, RQ01-02), European (Global, Columbus, Beluga, Lindore) and Asian (Gan-You and Tosharsu) origins. The level of resistance in 16 accessions was similar to contemporary and modern commercial cultivars: GT42 (<italic>Rlm1,4,6)</italic>, Hyola575 (<italic>LepR1, Rlm1</italic>) and Hyola970 (<italic>Rlm7</italic>) (<xref ref-type=\"supplementary-material\" rid=\"SM1\">\n<bold>Supplementary Table 3</bold>\n</xref>). Several cultivars, carrying the race-specific resistance genes, such as Stingray (<italic>Rlm3</italic>,9), Ripper (<italic>Rlm2,4,9</italic>) and CB-Telfer (<italic>Rlm4</italic>) were susceptible under shade-house and field conditions, suggesting that major genes were ineffective in conferring resistance, while some of the resistant accessions such as Scaddan (<italic>Rlm1,4</italic>), Bonanza (<italic>Rlm2,9</italic>) and ThunderTT (<italic>Rlm4,9</italic>) had a very low scores for internal infection when challenged with ascospores released from cultivars carrying <italic>Rlm1, Rlm2, Rlm4 and Rlm9</italic> genes (either single or in stack), hinting that those accessions may have loci for quantitative disease resistance or a combination of both novel <italic>R</italic> and QR loci, this requires further verification. Diverse panel of canola accession showed a continuous distribution of disease severity scores for UCI, suggesting that multiple genes control resistance to UCI (<xref ref-type=\"fig\" rid=\"f1\">\n<bold>Figure 1C</bold>\n</xref>). Among different accessions, Beluga, Gundula, Jet Neuf - a donor source of quantitative resistance in Darmor-<italic>bzh</italic> and other winter cultivars (<xref rid=\"B57\" ref-type=\"bibr\">Pilet et al., 2001</xref>), Lindore, Rafal, and two accessions of <italic>B. carinata</italic> (ATC94047, ATC94458) were highly resistant to UCI, while P3083, Ding474 and Wesbell were rated as highly susceptible (<xref ref-type=\"fig\" rid=\"f1\">\n<bold>Figure 1D</bold>\n</xref>).</p><p>Our analysis showed that the significant source of genetic variation in blackleg resistance was from the additive component, i.e., genetic markers; VAF<sub>a</sub>, which ranged from 33.8% for plant survival (FT17) to 100% for internal infection (SH17, FT17) and mortality (SH17) (<xref rid=\"T1\" ref-type=\"table\">\n<bold>Table 1</bold>\n</xref>). The values for the reliabilities (broad sense genomic heritability) for each trait measured were generally high (<xref rid=\"T2\" ref-type=\"table\">\n<bold>Table 2</bold>\n</xref>) and found to be trait and environment dependent. For example, plant survival had low reliability (42%) in 2018, but higher values were obtained in 2017 (87%) and in 2019 (99%) environments. Flowering time had higher values for reliability across all environments (97% to 98%) compared to other traits. High reliability values suggest that the variation in different traits measured is heritable and therefore is suitable for genetic analysis as well as for exploiting in the canola breeding programs for enhancing genetic gains.</p><table-wrap id=\"T1\" position=\"float\"><label>Table 1</label><caption><p>Summary of the residual maximum likelihood (REML) estimates for additive and nonadditive (residual) genetic variance from the models before (Base marker model: M1) and after identifying putative quantitative trait loci (QTL) (Final multi QTL model: M2) for each of the trait and trial.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Trait</th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Experiment</th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Additive (M1)</th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Additive (M2)</th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Nonadditive (M1)</th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Nonadditive (M2)</th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">VAF<sub>a</sub> (%)</th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">VAF<sub>m</sub> (%)</th></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Internal infection</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">SH16</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.46</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.91</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.27</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.30</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">84.4</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">28.3</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Internal infection</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">SH17</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5.21</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.81</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.00</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.00</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">100.0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">43.6</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Internal infection</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">SH16/17</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.68</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.66</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.17</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.10</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">94.0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">37.0</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Internal infection</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">FT17</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.86</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.64</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.00</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.00</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">100.0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">22.6</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Internal infection</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">FT18</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.35</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.67</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.04</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.00</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">97.4</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">51.8</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Internal infection</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">FT19</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.58</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.48</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.750</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.75</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">82.7</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">28.5</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">UCI</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">FT18</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.27</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.06</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.17</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.19</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">61.2</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">47.7</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">UCI</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">FT19</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.24</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.06</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.05</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.07</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">81.5</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">59.0</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Height</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">FT18</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">129.75</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">46.42</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">51.53</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">55.69</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">71.6</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">43.2</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Height</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">FT19</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">117.81</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">63.68</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">56.17</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">53.41</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">67.7</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">31.2</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">DTF</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">FT17</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">36.50</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">20.90</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5.09</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5.12</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">87.8</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">34.4</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">DTF</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">FT18</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">158.48</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">68.18</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">13.41</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">13.96</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">91.1</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">48.9</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">DTF</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">FT19</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">48.56</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">23.67</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">7.12</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">7.06</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">87.5</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">39.8</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Plant survival</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">FT17</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.31</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.27</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.61</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.58</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">33.8</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">7.8</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Plant survival</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">FT18</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.67</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.42</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.23</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.12</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">74.1</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">38.7</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Plant survival</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">FT19</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.57</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.42</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.57</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.44</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">49.7</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">27.2</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Mortality</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">SH16</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.68</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.32</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.21</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.16</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">76.0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">46.9</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Mortality</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">SH17</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.79</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.92</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.00</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.00</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">100.0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">27.8</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Mortality</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">SH16/17</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.04</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.55</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.19</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.21</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">84.6</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">35.6</td></tr></tbody></table><table-wrap-foot><fn><p>VAF<sub>a</sub> is the percentage of total genetic variance accounted by the markers. VAF<sub>m</sub> shows the percentage of genetic variance accounted by the identified putative QTLs. Resistance to blackleg was assessed as internal infection (stem canker, %) and plant survival/mortality (0–1) across five environments in 2016, 2017, 2018, and 2019. SH, shade-house; FT, Field; DTF, Days to flower; UCI, Upper canopy infection (1-5 scale); SH16/17, joint analysis of SH16 and SH17.</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"T2\" position=\"float\"><label>Table 2</label><caption><p>Summary of heritability, mean, minimum and maximum values of the predicted means for each trait and trial in the diverse panel of canola accessions.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Phenotypic Environment</th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Year</th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Trait</th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Trial</th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Heritability (%)</th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Mean</th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Minimum</th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Maximum</th></tr></thead><tbody><tr><td valign=\"top\" rowspan=\"6\" align=\"left\" colspan=\"1\">Shade-house</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2016</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Internal infection</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">SH16</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">80</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">83.79</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">11.67</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">98.88</td></tr><tr><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2017</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Internal infection</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">SH17</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">84</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">74.26</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.81</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">98.84</td></tr><tr><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2016</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Mortality</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">SH16</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">72</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.46</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.13</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.85</td></tr><tr><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2017</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Mortality</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">SH17</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">77</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.34</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.06</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.90</td></tr><tr><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2016/2017</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Internal infection</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">SH16/17</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">81</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">51.89</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.82</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">86.35</td></tr><tr><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2016/2017</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Mortality</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">SH16/17</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">75</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.50</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.10</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.92</td></tr><tr><td valign=\"top\" rowspan=\"13\" align=\"left\" colspan=\"1\">Field</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2017</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Internal infection</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">FT17</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">66</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4.75</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.55</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">24.47</td></tr><tr><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2018</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Internal infection</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">FT18</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">67</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">22.45</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.60</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">91.84</td></tr><tr><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2019</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Internal infection</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">FT19</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">81</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">22.45</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.60</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">91.84</td></tr><tr><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2018</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">UCI</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">FT18</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">74</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.99</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.18</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4.41</td></tr><tr><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2019</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">UCI</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">FT19</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">76</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.76</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.34</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4.45</td></tr><tr><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2018</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Height</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">FT18</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">89</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">128.98</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">91.86</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">160.44</td></tr><tr><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2019</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Height</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">FT19</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">90</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">90.35</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">28.46</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">123.36</td></tr><tr><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2017</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">DTF</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">FT17</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">98</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">120.39</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">109.20</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">139.57</td></tr><tr><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2018</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">DTF</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">FT18</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">98</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">122.17</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">102.95</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">155.60</td></tr><tr><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2019</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">DTF</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">FT19</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">97</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">115.42</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">100.81</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">139.35</td></tr><tr><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2017</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Plant survival</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">FT17</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">87</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.81</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.23</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.98</td></tr><tr><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2018</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Plant survival</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">FT18</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">42</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.98</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.86</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.99</td></tr><tr><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2019</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Plant survival</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">FT19</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">99</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.66</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.08</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.98</td></tr></tbody></table><table-wrap-foot><fn><p>Resistance to blackleg was assessed as internal infection (%) and plant survival/mortality (proportion) across five environments in 2016, 2017, 2018, and 2019. SH, shade-house; FT, Field; DTF, Days to flower; UCI, Upper canopy infection (1–5 scale). Data on SH16/17 is based on the joint analysis of SH16 and SH17 trials.</p></fn></table-wrap-foot></table-wrap></sec><sec id=\"s3_2\"><title>Genetic Correlation Between Blackleg Traits</title><p>We investigated the pair-wise additive genetic correlation between different measures of blackleg scores among accessions. There was a high positive correlation (<italic>r<sub>a</sub></italic> = 0.91) between internal infection and mortality in ascospore shower test (<xref ref-type=\"fig\" rid=\"f1\">\n<bold>Figure 1E</bold>\n</xref>). Likewise, internal infection scores showed negative correlation with plant survival in 2017 (<italic>r<sub>a</sub></italic> = −0.56), 2018 (<italic>r<sub>a</sub></italic> = −0.54) and in 2019 field environments (<italic>r<sub>a</sub></italic> = −0.66) (<xref ref-type=\"fig\" rid=\"f2\">\n<bold>Figures 2A–C</bold>\n</xref>). Resistance to UCI, evaluated across two years also showed a negative correlation with plant survival across both environments (2018<italic>; r<sub>a</sub></italic> = −0.50, 2019; <italic>r<sub>a</sub></italic> = −0.42). To determine the effectiveness of quantitative resistance across environments (field and shade-house), we further investigated the relationship between internal infection (stem canker) scores among accessions. We observed a high additive genetic correlation (<italic>r<sub>a</sub></italic> = 0.60 to 0.77), suggesting that diverse accessions have effective resistance to blackleg (<xref ref-type=\"fig\" rid=\"f2\">\n<bold>Figure 2D</bold>\n</xref>).</p><fig id=\"f2\" position=\"float\"><label>Figure 2</label><caption><p>Genetic (additive) correlations for resistance to blackleg evaluated in disease nurseries across three environments in 2017 <bold>(A)</bold>, 2018 <bold>(B)</bold>, and 2019 <bold>(C)</bold> at Wagga Wagga, NSW, Australia. Durability of resistance (assessed as internal infection) across three field experiments and shade-house (joint analysis of 2016 and 2017 experiments) also shown <bold>(D)</bold> Blackleg severity was evaluated on the basis of plant survival (proportion), internal infection (crown canker, %) and upper canopy infection (UCI, 1–5 scale) among diverse accessions (for details, see <xref ref-type=\"supplementary-material\" rid=\"SM1\">\n<bold>Supplementary Table 1</bold>\n</xref>). Plant development traits: plant height (cm) was measured for five plants per plot; maturity was scored for each plot; flowering time (days to first flower, GS 65) was determined, when 50% of plants have first open flower from the day of sowing as days to flower. Pair-wise correlations (r<sub>a</sub>) between traits were computed using the EBLUPs of the marker additive genetic effects.</p></caption><graphic xlink:href=\"fpls-11-01184-g002\"/></fig></sec><sec id=\"s3_3\"><title>Genetic Control of Blackleg Resistance</title><p>To gain an understanding of genetic loci controlling variation in QR against <italic>L.</italic>\n<italic>maculans</italic>, we performed GWAS analysis. This was accomplished by using 12,414 genome-wide distributed SNPs across all 19 linkage groups (and Ann_random and Cnn_random linkage groups) of <italic>B. napus</italic> (<xref ref-type=\"fig\" rid=\"f3\">\n<bold>Figure 3A</bold>\n</xref>). Marker density on the A<sub>n</sub> subgenome was higher (n = 6,480) compared to the C<sub>n</sub> subgenome (n = 5,939) and covered 0.77 Gb of the reference Darmor-<italic>bzh</italic> genome (<xref ref-type=\"supplementary-material\" rid=\"SM1\">\n<bold>Supplementary Table 4</bold>\n</xref>). The LD was estimated at the whole genome level, using the full set of SNP markers, for each chromosome and both A<sub>n</sub> and C<sub>n</sub> subgenomes (<xref ref-type=\"fig\" rid=\"f3\">\n<bold>Figures 3B, C</bold>\n</xref>, <xref ref-type=\"supplementary-material\" rid=\"SM1\">\n<bold>Supplementary Figure 4</bold>\n</xref>). The LD decay (<italic>r</italic>\n<sup>2</sup> = 0.2) of the A<sub>n</sub> and C<sub>n</sub> subgenomes varied from 0.6 Mb to 2 Mb, respectively. After thinning markers controlling for false discovery rate, and accounting LD among diverse accessions, GWAS revealed 26 highly significant (LOD score of ≥3, <italic>P</italic> ≤ 0.001) and 33 “suggestive” (LOD score of <3, 0.001 < <italic>P</italic> < 0.05) associations for resistance to <italic>L. maculans</italic>, scored as per cent internal canker infection, plant survival and UCI on 17 chromosomes of canola, with the exception of C07 and C08 (<xref ref-type=\"supplementary-material\" rid=\"SM1\">\n<bold>Supplementary Table 5</bold>\n</xref>). SNP markers explained 7.8 to 59.0% of genetic variance for resistance to blackleg (<xref rid=\"T1\" ref-type=\"table\">\n<bold>Table 1</bold>\n</xref>).</p><fig id=\"f3\" position=\"float\"><label>Figure 3</label><caption><p>Distribution of SNP markers <bold>(A)</bold> and LD (<italic>r</italic>\n<sup>2</sup>) among diverse accessions of canola across 19 linkage groups of <italic>B. napus</italic> (A01-C09) and unanchored An_random (Ann) and Cn_random (Cnn) linkage groups. LD decay across the A<sub>n</sub> subgenome <bold>(B)</bold> and C<sub>n</sub> subgenome <bold>(C)</bold>. LD decay occurs (<italic>r</italic>\n<sup>2 </sup>= 0.2) approximately 0.61 Mb and 1 Mb in A<sub>n</sub> and C<sub>n</sub> subgenomes, respectively. The <italic>r</italic>\n<sup>2</sup> is estimated for each pair of SNPs used for GWAS.</p></caption><graphic xlink:href=\"fpls-11-01184-g003\"/></fig><sec id=\"s3_3_1\"><title>SNP Associations Detected for Resistance Using “Ascospore Shower” Screens</title><p>Joint analysis of the two shade-house experiments identified 18 (four significant and 14 suggestive) SNP associations for resistance against pathotypes present on stubble of groups A, B, C, D, E, F, and S commercial canola cultivars (<xref rid=\"T3\" ref-type=\"table\">\n<bold>Table 3</bold>\n</xref>; <xref ref-type=\"supplementary-material\" rid=\"SM1\">\n<bold>Supplementary Table 5</bold>\n</xref>). For plant mortality, eight SNP associations, comprising two significant and six “suggestive,” were identified on chromosomes A01, C04, and C06 (<xref ref-type=\"fig\" rid=\"f4\">\n<bold>Figure 4A</bold>\n</xref>, <xref ref-type=\"supplementary-material\" rid=\"SM1\">\n<bold>Supplementary Table 5</bold>\n</xref>). The maximum genetic variance (9.1%) was explained by each of the two “suggestive” SNPs, Bn-A01-p6747706 (on A01) and Bn-scaff_16576_1-p256367 on chromosome C04. For internal infection, 10 SNP associations; two significant and eight “suggestive,” were identified on chromosomes A01, A03, A04, C01, C05, and C06 (<xref ref-type=\"fig\" rid=\"f4\">\n<bold>Figure 4B</bold>\n</xref>). The maximum genetic variance (8.3%) was explained by the Bn-scaff_15838_1-p1403775 SNP on C01, followed by Bn-scaff_17088_3-p242403 on C06 (6.4%). As both mortality/plant survival and internal infection (stem canker) phenotypes were highly correlated (<italic>r<sub>a</sub></italic> = 0.91, <xref ref-type=\"fig\" rid=\"f1\">\n<bold>Figure 1E</bold>\n</xref>), we identified two genomic regions that were collocated on chromosome A01 (<xref ref-type=\"supplementary-material\" rid=\"SM1\">\n<bold>Supplementary Table 5</bold>\n</xref>). To determine the effect of population size on marker-trait association, we undertook GWAS analysis for each environment independently of ascospore shower screens, conducted across the two experiments (SH16 and SH17). In the SH16 experiment, four significant SNP associations were identified: two were identified for resistance to internal infection on chromosome A01 within genomic interval of 6.17 Mb to 10.87 Mb, while, four SNP associations were identified for plant survival on A01, A03, and C06 (<xref rid=\"T3\" ref-type=\"table\">\n<bold>Table 3</bold>\n</xref>, <xref ref-type=\"supplementary-material\" rid=\"SM1\">\n<bold>Supplementary Table 5</bold>\n</xref>). In SH17 experiment, two significant SNP associations were identified: the first for internal infection on A03 (0.94 Mb), and second for plant survival on A06 (1.58 Mb) and none of them were detected in SH16 (<xref ref-type=\"supplementary-material\" rid=\"SM1\">\n<bold>Supplementary Table 5</bold>\n</xref>). This discrepancy was likely due to the effective population size affecting both population structure as well as local LD among accessions. Overlaying results from single experiments (SH16 and SH17) with from joint analysis (SH16/SH17), we found that two regions for resistance on A01 (5.3 Mb; 6.17 Mb); one region on A03 (14.8 Mb); and one region on C06 (33.2 to 33.5 Mb, within LD) were detected across individual and joint analyses (<xref ref-type=\"supplementary-material\" rid=\"SM1\">\n<bold>Supplementary Table 5</bold>\n</xref>).</p><table-wrap id=\"T3\" position=\"float\"><label>Table 3</label><caption><p>Genome-wide association analysis showing significant statistical association between Illumina SNP markers and resistance to <italic>L. maculans</italic>.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Trait</th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">SNP marker</th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Experiment</th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Linkage group</th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Position (bp)</th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">\n<italic>P</italic>-value</th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">LOD</th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">\n<italic>R</italic>\n<sup>2</sup> (%)</th></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold>Mortality</bold>\n</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold>Bn-A01-p6747706</bold>\n</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold>SH16</bold>\n</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold>A01</bold>\n</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">\n<bold>6173561</bold>\n</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">\n<bold>0.001</bold>\n</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">\n<bold>3.11</bold>\n</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">\n<bold>9.6</bold>\n</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold>Internal infection</bold>\n</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold>Bn-A01-p6747706</bold>\n</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold>SH16</bold>\n</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold>A01</bold>\n</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">\n<bold>6173561</bold>\n</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">\n<bold>0</bold>\n</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">\n<bold>3.33</bold>\n</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">\n<bold>6.5</bold>\n</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Internal infection</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Bn-A01-p12622123</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">SH16</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">A01</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">10872512</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.001</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.27</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5.1</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Plant survival</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Bn-A02-p27743910</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">FT18</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">A02</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">24724645</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.001</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.02</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">9.7</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Internal infection</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Bn-A03-p1308820</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">SH17</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">A03</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">945040</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.001</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.01</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">16.5</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Internal infection</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Bn-A03-p4352837</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">FT19</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">A03</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3885180</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.59</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">6.9</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Upper canopy infection</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Bn-A03-p8475614</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">FT19</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">A03</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">7780069</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5.05</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">19.2</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Mortality</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Bn-A03-p15815965</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">SH16</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">A03</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">14892225</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4.26</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">10.1</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold>Upper canopy infection</bold>\n</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold>Bn-A04-p2448580</bold>\n</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold>FT19</bold>\n</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold>A04</bold>\n</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">\n<bold>2133626</bold>\n</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">\n<bold>0</bold>\n</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">\n<bold>5.97</bold>\n</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">\n<bold>23.4</bold>\n</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold>Upper canopy infection</bold>\n</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold>Bn-A04-p2713564</bold>\n</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold>FT18</bold>\n</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold>A04</bold>\n</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">\n<bold>2419904</bold>\n</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">\n<bold>0</bold>\n</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">\n<bold>3.51</bold>\n</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">\n<bold>10.2</bold>\n</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Upper canopy infection</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Bn-A04-p4099958</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">FT19</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">A04</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4169810</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">11.7</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Upper canopy infection</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Bn-A05-p22141046</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">FT19</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">A05</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">20252157</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">6.73</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">25.1</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Mortality</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Bn-A06-p18000461</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">SH17</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">A06</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1585510</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.8</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">8</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Plant survival</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Bn-scaff_15705_1-p84236</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">FT19</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">A06</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">19564715</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4.53</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.9</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Internal infection</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Bn-A07-p11185122</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">FT18</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">A07</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">12477068</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.46</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">13.5</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Upper canopy infection</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Bn-A02-p771951</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">FT18</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">A07</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">13400889</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.001</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.01</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">7</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Internal infection</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Bn-A07-p16036626</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">FT19</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">A07</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">17951152</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4.57</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">8</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Upper canopy infection</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Bn-A07-p18226326</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">FT18</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">A07</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">20118101</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.001</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.96</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">11.6</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Internal infection</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Bn-A09-p10092753</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">FT17</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">A09</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">9086363</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4.17</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">13.9</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Internal infection</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Bn-A09-p20157367</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">FT18</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">A09</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">17156001</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.73</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">10.8</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Plant survival</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Bn-A10-p2048923</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">FT19</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">A10</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1671039</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.4</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">6.3</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Internal infection</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Bn-A10-p15021776</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">FT18</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">A10</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">14967128</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4.7</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">13.3</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Plant survival</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Bn-scaff_15838_1-p790905</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">FT18</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">C01</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1224850</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.001</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">11.9</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Internal infection</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Bn-scaff_22790_1-p1264986</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">SH16/17</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">C01</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">16610817</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4.99</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.1</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Plant survival</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Bn-scaff_15712_6-p292471</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">FT17</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">C02</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">37086215</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5.01</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">7.7</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Internal infection</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Bn-scaff_18917_1-p533499</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">FT17</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">C03</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">29919378</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.51</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">10.9</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Internal infection</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Bn-A05-p17894045</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">FT18</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">C05</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">33067591</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4.11</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">13.5</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Internal infection</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Bn-scaff_20294_1-p250512</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">SH16/17</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">C06</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3244625</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.39</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.4</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Mortality</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Bn-scaff_15746_1-p145442</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">SH16/17</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">C06</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">21016294</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.53</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">7.5</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Mortality</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Bn-scaff_23957_1-p151052</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">SH16</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">C06</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">30618173</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.001</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.26</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.5</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold>Mortality</bold>\n</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold>Bn-scaff_25466_1-p33838</bold>\n</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold>SH16/17</bold>\n</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold>C06</bold>\n</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">\n<bold>33209209</bold>\n</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">\n<bold>0</bold>\n</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">\n<bold>3.51</bold>\n</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">\n<bold>6.3</bold>\n</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold>Mortality</bold>\n</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold>Bn-scaff_20294_1-p395143</bold>\n</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold>SH16</bold>\n</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold>C06</bold>\n</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">\n<bold>33566786</bold>\n</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">\n<bold>0</bold>\n</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">\n<bold>3.86</bold>\n</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">\n<bold>22</bold>\n</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Internal infection</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Bn-scaff_17554_1-p19888</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">FT19</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">C09</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">39720977</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4.3</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">8.1</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Upper canopy infection</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Bn-scaff_15576_1-p115266</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">FT19</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">C09</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">41180043</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.001</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.27</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">14</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Upper canopy infection</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Bn-scaff_16361_1-p1072715</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">FT18</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Cnn_random</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">62936531</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4.33</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">12.8</td></tr></tbody></table><table-wrap-foot><fn><p>Resistance was assessed as plant mortality (proportion) and internal infection (%) using “ascospore shower” test under shade-house conditions across 2016 (SH16) and 2017 (SH17) whereas it was assessed as plant survival (proportion), internal stem infection (%) and upper canopy infection (1–5 scale) across three field environments (2017: FT17, 2018: FT18, and 2019: FT19). Only markers that revealed the LOD score ≥3 are shown herein. Marker associations with LOD scores of <3 are given in a <xref ref-type=\"supplementary-material\" rid=\"SM1\">\n<bold>Supplementary Table 5</bold>\n</xref>. The physical positions of SNPs are based on the map position on the Darmor-bzh gene assembly (<xref rid=\"B14\" ref-type=\"bibr\">Clarke et al., 2016</xref>). Associations in bold represent to those which appeared across experiments and in LD. The LD was estimated in the diverse panel of canola accessions. R<sup>2</sup> and SH16/17 refer to the percentage of genetic variance explained by the SNP marker and joint analysis of SH16 and SH17 experiments, respectively.</p></fn></table-wrap-foot></table-wrap><fig id=\"f4\" position=\"float\"><label>Figure 4</label><caption><p>Genetic loci associated with resistance to blackleg, assessed using “ascospore shower test” in diverse accessions of canola. Manhattan plots showing LOD scores (-log<sub>10</sub>\n<italic>P</italic>) for association between Illumina SNP and disease severity: plant mortality <bold>(A)</bold> and internal infection <bold>(B)</bold> in 285 (genotyped) of 327 canola accessions (for details, see <xref ref-type=\"supplementary-material\" rid=\"SM1\">\n<bold>Supplementary Table 1</bold>\n</xref>). LOD scores presented in the Manhattan plots are from Base marker model M1. The red dash line indicates the threshold value for highly significant SNPs at LOD ≥ 3. Positions (kb) of the highly significant (LOD ≥ 3, P ≤ 0.001) and “suggestive” (LOD < 3, 0.001 < P < 0.05) markers from the final multi-QTL model (M2) are labeled. The physical positions of SNPs are based on the map position on the Darmor-<italic>bzh</italic> gene assembly (<xref rid=\"B14\" ref-type=\"bibr\">Clarke et al., 2016</xref>, <xref ref-type=\"supplementary-material\" rid=\"SM1\">\n<bold>Supplementary Table 5</bold>\n</xref>). <bold>(C)</bold> Box plot showing the distribution of the EBLUPs for internal infection partitioned into allele combinations for the SNP markers that are significantly associated with resistance (LOD ≥ 3) assessed under shade-house conditions with “ascospore shower” test in a diverse panel of GWAS accessions. Major (present in high proportion) and minor (present in low proportion) allele for each marker are shown in different colors. Number of alleles (n) for each significant SNPs associated with resistance are also shown. Distributions of SNP marker Bn-A03-p13088220 allele represent to SH17 experiment, where 68 canola accessions were evaluated for resistance to blackleg using the “ascospore shower test.”.</p></caption><graphic xlink:href=\"fpls-11-01184-g004\"/></fig><p>To identify SNP markers suitable for tracking QR genes in <italic>B. napus</italic> germplasm, we compared relationship between marker alleles that showed significant associations to <italic>L. maculans</italic> with “ascospore shower” and variation in resistance to blackleg, assessed by the severity of internal infection. Our analysis showed that single SNP association could not sufficiently account for resistance. However, using multiple alleles for different QTL regions, resistance could be tracked in a diverse panel of canola accessions (<xref ref-type=\"fig\" rid=\"f4\">\n<bold>Figure 4C</bold>\n</xref>). For example, SNP alleles present in a greater proportion (TT, Bn-scaff-20294-1-p250512) and in smaller proportion on chromosomes A01 (GG, Bn-A01-p6747706), A03 (GG, Bn-A03-p1308820), and C01 (GG, Bn-scaff-22790-1-p1264986), contributed in increasing resistance to internal infection under shade-house conditions.</p></sec><sec id=\"s3_3_2\"><title>SNP Associations for Plant Survival in Field</title><p>GWAS identified 13 SNPs (five significant and eight “suggestive”) that were associated with resistance to <italic>L. maculans,</italic> based on the plant survival scores under field conditions across three environments (<xref ref-type=\"fig\" rid=\"f5\">\n<bold>Figure 5A</bold>\n</xref>, <xref ref-type=\"supplementary-material\" rid=\"SM1\">\n<bold>Supplementary Table 5</bold>\n</xref>). Of the five significant associations (LOD ≥3) identified on A02, A06, A10, C01, and C02 chromosomes, the SNP Bn-scaff_15838_1-p790905 on C01 explained the maximum genetic variance (11.9%) for plant survival, followed by Bn-A02-p27743910 (9.7%) on A02 (<xref rid=\"T3\" ref-type=\"table\">\n<bold>Table 3</bold>\n</xref>). In total, GWAS associations explained 7.8% (2017) to 38.7% (2018) of the genetic variance (<xref rid=\"T1\" ref-type=\"table\">\n<bold>Table 1</bold>\n</xref>). Two genomic regions, the first delimited with two SNPs, Bn-A06-p17332795 and Bn-A06-p17042021, and the second region by the Bn-scaff_15705_1-p84236 and Bn-A06-p18432812 markers on A06, were detected within 0.26 Mb (in LD) across 2018 and 2019 environments (<xref ref-type=\"supplementary-material\" rid=\"SM1\">\n<bold>Supplementary Table 5</bold>\n</xref>).</p><fig id=\"f5\" position=\"float\"><label>Figure 5</label><caption><p>Manhattan plots showing genomic regions associated with resistance to blackleg in diverse accessions. Resistance was assessed by plant survival <bold>(A)</bold>, internal infection <bold>(B)</bold> in blackleg nursery across three field environments in three consecutive years (2017 to 2019) at Wagga Wagga, while upper canopy infection <bold>(C)</bold> was assessed across two years (2018 and 2019). LOD scores (-log<sub>10</sub>\n<italic>P</italic>) for association between Illumina SNP and disease severity in diverse canola accessions are shown on y-axis. LOD scores presented in the Manhattan plots are from Base marker model M1. The red dash line indicates the threshold value for significant SNPs at LOD ≥ 3. Positions (kb) of the highly significant (LOD ≥ 3, P ≤ 0.001) and “suggestive” (LOD < 3, 0.001 < P < 0.05) markers from the final multi-QTL model (M2) are labeled. The physical positions of SNPs (x-axis) are based on the map position on the Darmor-<italic>bzh</italic> genome assembly (<xref rid=\"B14\" ref-type=\"bibr\">Clarke et al., 2016</xref>, for detail, see <xref ref-type=\"supplementary-material\" rid=\"SM1\">\n<bold>Supplementary Table 5</bold>\n</xref>). <bold>(D)</bold> Circos plot with 19 chromosomes showing localization of quantitative trait loci (QTL) associated with resistance to blackleg and phenological components in a diverse panel of canola accessions on the 19 chromosomes of canola. Physical positions of SNP markers were inferred on the positions of the Darmor-<italic>bzh</italic> reference genome assembly. Scatter plots and lines represent LOD scores and R<sup>2</sup> values of SNP associations for different traits evaluated in a GWAS panel. Lines represent to R<sup>2</sup> values (inner most shell). Outer most shell represents to different traits (I: internal infection; S: plant survival, M: plant mortality, Mat: plant maturity; H: plant height; U: upper canopy infection; F: flowering time), evaluated in a GWAS panel. The second outer most shell illustrates the ancestral blocks. Localization of SNP markers associated with resistance to blackleg is only shown on the ancestral blocks of Brassicaceae, as defined by <xref rid=\"B72\" ref-type=\"bibr\">Schranz et al., 2006</xref> by aligning <italic>A. thaliana</italic> genes to the Darmor reference genome.</p></caption><graphic xlink:href=\"fpls-11-01184-g005\"/></fig></sec><sec id=\"s3_3_3\"><title>SNP Associations for Resistance to Internal Infection in Field</title><p>Seventeen SNP associations (nine significant and eight “suggestive”) were identified for resistance to internal infection across three field environments (2017-2019) on chromosomes A03, A07, A08, A09, A10, C03, C05, and C09 (<xref ref-type=\"fig\" rid=\"f5\">\n<bold>Figure 5B</bold>\n</xref>, <xref ref-type=\"supplementary-material\" rid=\"SM1\">\n<bold>Supplementary Table 5</bold>\n</xref>). SNP marker, Bn-A09-p10092753 on chromosome A09 accounted for 13.9% of genetic variation for internal infection (<xref rid=\"T3\" ref-type=\"table\">\n<bold>Table 3</bold>\n</xref>). Of the SNP associations, 82% of them (14/17) were mapped on the A<sub>n</sub> subgenome, suggesting that the A genome is the richer reservoir of blackleg resistance genes compared to the C<sub>n</sub> subgenome. This is consistent with earlier findings that majority of <italic>R</italic> genes (<italic>Rlm1</italic>, <italic>Rlm2</italic>, <italic>Rlm3</italic>, <italic>Rlm4</italic>, <italic>Rlm7</italic>, <italic>Rlm9</italic>, <italic>Rlm12</italic>, <italic>LepR</italic>1-4) identified so far, are mapped on the A<sub>n</sub> subgenome, while only a few, <italic>Rlm13</italic> is mapped on the C<sub>n</sub> subgenome (Raman et al, unpublished). Among SNP associations, only one genomic region, delimited with Bn-A07-p15812852 to Bn-A07-p16036626 (17.7 to 17.9 Mb sequence of Darmor-<italic>bzh</italic>) on chromosome A07 was detected repeatedly in LD across two field environments (FT18 and FT19; <xref ref-type=\"supplementary-material\" rid=\"SM1\">\n<bold>Supplementary Table 5</bold>\n</xref>).</p></sec><sec id=\"s3_3_4\"><title>SNP Associations for Resistance to UCI</title><p>Herein, we identified 14 associations (eight significant and six “suggestive”) for resistance against UCI on A03, A04, A05, A07, and C09 and Cnn_random contig of Darmor-<italic>bzh</italic> assembly (<xref rid=\"T3\" ref-type=\"table\">\n<bold>Table 3</bold>\n</xref>, <xref ref-type=\"fig\" rid=\"f5\">\n<bold>Figure 5C</bold>\n</xref>). The number of SNP associations varied from 6 to 8, depending upon the phenotypic environment. Of the SNP associations, Bn-A05-p22141046 on A05 accounted for the maximum genetic variance (<italic>R</italic>\n<sup>2</sup> = 25.1%), followed by Bn-A04-p2448580 (<italic>R</italic>\n<sup>2</sup> = 23.4%) on A04. More than 42% of the genetic associations (6/14) were detected on A04, suggesting that multiple loci for resistance are localized on this chromosome. Across environments, one genomic region of the Darmor-<italic>bzh</italic> sequence (2.13 to 2.42 Mb); demarked with markers: Bn-A04-p2448580/Bn-A04-p2713564 was repeatedly detected in LD on A04 and accounted for up to 23.4% of the genetic variance (<xref rid=\"T3\" ref-type=\"table\">\n<bold>Table 3</bold>\n</xref>, <xref ref-type=\"supplementary-material\" rid=\"SM1\">\n<bold>Supplementary Table 5</bold>\n</xref>). Identification of multiple genomic regions associated with resistance, having small additive allelic effects, suggests that UCI is a quantitative trait and is controlled by multiple loci in canola.</p></sec></sec><sec id=\"s3_4\"><title>Hot-Spot Genetic Regions for Resistance to Blackleg</title><p>To identify hot-spot genomic regions for resistance to blackleg, we compared the physical positions of sequences of SNPs that reveal significant associations for plant survival, stem canker and UCI in diverse panel of canola accessions. The A<sub>n</sub> subgenome had 39 SNP associations, while 20 associations were detected on the C<sub>n</sub> subgenome (<xref ref-type=\"fig\" rid=\"f5\">\n<bold>Figure 5D</bold>\n</xref>). Among the 19 linkage groups, A04, and A07 had 2.3 to 2.7 times more associations (QTL) for resistance to blackleg, compared to average genome-wide SNP (2.95/linkage group) associations (<xref ref-type=\"supplementary-material\" rid=\"SM1\">\n<bold>Supplementary Figure 5</bold>\n</xref>).</p></sec><sec id=\"s3_5\"><title>Significantly Associated SNPs Overlap With Previously Identified QR and Known <italic>R</italic> Genes</title><p>To determine the uniqueness/redundancy of loci involved in QR resistance, we compared the physical positions of SNP associations identified in this study with the previous studies (<xref rid=\"B36\" ref-type=\"bibr\">Jestin et al., 2011</xref>; <xref rid=\"B25\" ref-type=\"bibr\">Fopa Fomeju et al., 2014</xref>; <xref rid=\"B44\" ref-type=\"bibr\">Larkan et al., 2016a</xref>; <xref rid=\"B60\" ref-type=\"bibr\">Rahman et al., 2016</xref>; <xref rid=\"B64\" ref-type=\"bibr\">Raman et al., 2016</xref>; <xref rid=\"B41\" ref-type=\"bibr\">Kumar et al., 2018</xref>; <xref rid=\"B65\" ref-type=\"bibr\">Raman et al., 2018</xref>; <xref rid=\"B67\" ref-type=\"bibr\">Raman et al., 2020</xref>). We identified several marker loci for QR to blackleg on chromosomes A01, A02, A03, A04, A05, A06, A07, A08, A09, A10, C01, C02, C05, and C06 that were mapped near the SNP associations identified in the present study (<xref ref-type=\"fig\" rid=\"f6\">\n<bold>Figure 6</bold>\n</xref>). This suggests that QR loci identified here are relevant to international germplasm, comprising spring, semispring/winter and winter types. We also identified new QR loci on chromosomes A01 (10.8 Mb), A04 (1.1 Mb to 2.41 Mb), A06 (5.88 Mb), A08 (1.30 Mb, 16.94 Mb), A10 (1.67 Mb), C01 (1.80 Mb, 161.61 Mb), C03 (29.91 Mb), and C05 (7.03 Mb). These loci provide new targets for genetic improvement in canola breeding programs.</p><fig id=\"f6\" position=\"float\"><label>Figure 6</label><caption><p>Comparison between genomic regions identified in a diverse panel of canola (this study) and previous studies. Marker loci highlighted in different colors represent to different studies. For clarity, we showed only the highly significant SNP associations for resistance (<xref rid=\"T3\" ref-type=\"table\">\n<bold>Tables 3 </bold>\n</xref>and <xref rid=\"T4\" ref-type=\"table\">\n<bold>4</bold>\n</xref>) here. Candidate genes associated with <italic>R</italic> genes which map within 0.25 Mb are shown here. For details, see <xref ref-type=\"supplementary-material\" rid=\"SM1\">\n<bold>Supplementary Table 6</bold>\n</xref>.</p></caption><graphic xlink:href=\"fpls-11-01184-g006\"/></fig><p>To identify plausible candidate genes in the QR genomic regions, we compared the physical locations of annotated <italic>R</italic> genes in the Darmor-<italic>bzh</italic> assembly (<xref rid=\"B10\" ref-type=\"bibr\">Chalhoub et al., 2014</xref>; <xref rid=\"B1\" ref-type=\"bibr\">Alamery et al., 2018</xref>). Forty-two <italic>R</italic> genes were located within 1 Mb of QR loci (<xref ref-type=\"supplementary-material\" rid=\"SM1\">\n<bold>Supplementary Table 6</bold>\n</xref>) of which 12 disease resistance <italic>R</italic> genes were located within 200 kb from the SNP associations (QTLs) on A01, A06, A07, A10, C01, C05; C06, and C09 (<xref rid=\"T4\" ref-type=\"table\">\n<bold>Table 4</bold>\n</xref>). These annotated <italic>R</italic> genes encode disease resistance proteins with the TIR-NSB-LRR and non-TIT-NBS-LRR (CNL), RLP and NB-ARC-LRR motifs, which are implicated in ETI (<xref rid=\"B39\" ref-type=\"bibr\">Jones and Dangl, 2006</xref>).</p><table-wrap id=\"T4\" position=\"float\"><label>Table 4</label><caption><p>Annotated disease resistance genes located in the vicinity of quantitative resistance to blackleg disease in canola grown across different environments.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th valign=\"top\" rowspan=\"2\" align=\"left\" colspan=\"1\">Trait</th><th valign=\"top\" rowspan=\"2\" align=\"center\" colspan=\"1\">SNP marker</th><th valign=\"top\" rowspan=\"2\" align=\"center\" colspan=\"1\">Linkage group</th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Position</th><th valign=\"top\" rowspan=\"2\" align=\"center\" colspan=\"1\">\n<italic>P</italic>-value</th><th valign=\"top\" rowspan=\"2\" align=\"center\" colspan=\"1\">LOD</th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">\n<italic>R</italic>\n<sup>2</sup> (%)</th><th valign=\"top\" colspan=\"3\" align=\"center\" rowspan=\"1\">Candidate <italic>R</italic> gene</th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Distance from candidate gene</th><th valign=\"top\" rowspan=\"2\" align=\"center\" colspan=\"1\">\n<italic>R</italic> gene motif</th></tr><tr><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">(bp)</th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">ID</th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Start</th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">End</th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">(bp)</th></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Mortality (SH16/17)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Bn-A01-p23905191</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">A01</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">20068468</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.007</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.19</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4.40</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">BnaA01g28560D</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">19873137</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">19874480</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">195331</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">CC – NBS</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Plant survival (FT18)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Bn-A02-p27743910</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">A02</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">24724645</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.001</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.02</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">9.66</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">BnaA02g36900D (A2_random)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">24581776</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">24580933</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">142869</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">TIR-NBS-LRR</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Plant survival (FT19)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Bn-A06-p18000403</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">A06</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1585452</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.000</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4.26</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.71</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">BnaA06g39650D (A6_ran) BnaA06g02940D</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1655327<break/>1789462</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1658600<break/>1792348</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">69817<break/>204,010</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">CC-NBS-LRR</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">UCIS (FT18)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Bn-A02-p771951</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">A07</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">13400889</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.000</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.54</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">7.02</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">BnaA07g15400D</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">13339537</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">13343565</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">57,324</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">TIR-NBS-LRR</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Internal infection (FT18)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Bn-A07-p16036626</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">A07</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">17951152</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.001</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.10</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.56</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">BnaA07g24250D<break/>BnaA07g24260D</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">18138909<break/>18149353</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">18137486<break/>18145885</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">193,122<break/>187757</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">NBS - LRR<break/>TIR-NBS-LRR</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Internal infection (FT18)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Bn-A10-p9802574</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">A10</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">11216126</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.001</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.22</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4.78</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">BnaA10g13740D</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">11035464</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">11038549</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">177,577</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"> NB-ARC domain-containing protein</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Plant survival (FT18)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Bn-scaff_15838_1-p790905</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">C01</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1224850</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.001</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.21</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">12.84</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">chrC01_random-snap.407 BnaC01g02460D</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1246230<break/>1302496</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1244036<break/>1303729</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">21380<break/>77,646</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">TIR-NBS-LRR</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Internal infection (SH16/17)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Bn-scaff_18181_1-p622258</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">C05</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">7039786</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.001</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.97</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4.08</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">BnaC05g12130D</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">7046069</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">7053047</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">6,283</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">CC-NBS-LRR</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Internal infection (SH16/17)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Bn-scaff_20294_1-p250512</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">C06</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3244625</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.000</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.39</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.39</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">BnaC06g43800D (c06_random)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3240778</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3,241,715</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3847</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">TIR-NBS-LRR</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Mortality (SH16)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Bn-scaff_20294_1-p395143</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">C06</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">33566786</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.002</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.69</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">16.03</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">BnaC06g34000D</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">33631584</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">33634387</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">67,601</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">TIR-NBS-LRR</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Internal infection (FT19)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Bn-scaff_17554_1-p19888</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">C09</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">39720977</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.000</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4.44</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">8.03</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">BnaC09g36260D</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">39561973</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">39561973</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">159004</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">NB-ARC-LRR, RLK</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">UCI (FT19)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Bn-scaff_15576_1-p115266</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">C09</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">41180043</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.001</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.28</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">14.03</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">BnaC09g38280D</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">41200106</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">41205204</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">20,063</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Zinc finger</td></tr></tbody></table><table-wrap-foot><fn><p>Only SNP associations that were detected within 200 kb are shown herein. Detailed information is presented in a <xref ref-type=\"supplementary-material\" rid=\"SM1\">\n<bold>Supplementary Table 5</bold>\n</xref>. Data on SH16 and SH17 experiments relates to ascospore shower test, while FT18, FT18, and FT19 experiments relate to field screening of GWAS panel. Data on SH16/17 is based on the joint analysis of SH16 and SH17 trials. R<sup>2</sup> refers to the percentage of genetic variance explained by the SNP marker.</p></fn></table-wrap-foot></table-wrap></sec><sec id=\"s3_6\"><title>Several QR Loci of Canola May Represent to Ancestral Genes of Brassicaceae</title><p>To determine and verify whether QR genomic regions are located in the homoeologous regions of <italic>B. napus</italic> genome, we searched the synteny between genomic regions that showed significant associations with QR, using SNP markers as proxies, and 22 AK blocks of Brassicaceae (<xref rid=\"B72\" ref-type=\"bibr\">Schranz et al., 2006</xref>). We delimited 37 SNP associations (identified on chromosomes A01, A02, A03, A04, A05, A06, A07, A009, A10, C001, C02, C3, C04, C05, C06, and C09) in the 17 AK blocks: A, B, C, D, E, F, H, I, J, K, N, O, Q, R, S, T, U, and X (<xref ref-type=\"fig\" rid=\"f5\">\n<bold>Figure 5D</bold>\n</xref>). Six SNP associations, which were mapped on chromosomes A03, A04, A09, A10, C04 and C06 could not be located to the AK blocks (<xref ref-type=\"supplementary-material\" rid=\"SM1\">\n<bold>Supplementary Table 7</bold>\n</xref>). Of the 44 significant SNP associations for resistance, 25 (56.7%) were localized in the duplicated regions of the 9 AK blocks; the maximum SNP associations (n = 6) were located in each of the blocks: E, F, and U, while the minimum associations (n = 1) were identified in each of the B, D, H, I, K, and T blocks (<xref ref-type=\"supplementary-material\" rid=\"SM1\">\n<bold>Supplementary Table 7</bold>\n</xref>). In addition to the E, J, R, and U blocks identified for resistance to blackleg in European canola (<xref rid=\"B26\" ref-type=\"bibr\">Fopa Fomeju et al., 2015</xref>), we also mapped SNP association in the duplicated regions on A, B, C, D, F, H, I, K, N, Q, S, T and X AK (<xref ref-type=\"supplementary-material\" rid=\"SM1\">\n<bold>Supplementary Table 7</bold>\n</xref>). In this study, no SNP association for resistance could be assigned to the W block (<xref rid=\"B25\" ref-type=\"bibr\">Fopa Fomeju et al., 2014</xref>). In addition, one SNP association for resistance, assessed based on plant mortality on chromosome C04 could not be mapped onto the same ancestral karyotype J block, identified in a previous study (<xref rid=\"B26\" ref-type=\"bibr\">Fopa Fomeju et al., 2015</xref>).</p><p>We further investigated the locations of genomic regions for QR with subgenomic structure of AK blocks, determined on the level of fractionation (<xref rid=\"B54\" ref-type=\"bibr\">Parkin, 2011</xref>; <xref rid=\"B53\" ref-type=\"bibr\">Parkin et al., 2014</xref>). The maximum SNP associations were present in the least fractionated subgenome (LF = 18), followed by seven associations in the moderately fractionated subgenome (MF1) whereas, the minimum SNP associations (6) were in the most fractionated subgenome (MF2). Consistent with a previous study (<xref rid=\"B26\" ref-type=\"bibr\">Fopa Fomeju et al., 2015</xref>), one to six homoeologous regions for QR were identified in the LF, MF1, and MF2 subgenomes of duplicated regions of E, J, R, U, and W (<xref ref-type=\"supplementary-material\" rid=\"SM1\">\n<bold>Supplementary Table 7</bold>\n</xref>). For example, we identified SNP association for UCI to 39.93 Mb region on chromosome C09 (R block, LF subgenome). The same regions for QR was also identified at 39.93 Mb region on chromosome C09 in a <italic>B. napus</italic> GWAS panel of European origin (<xref rid=\"B26\" ref-type=\"bibr\">Fopa Fomeju et al., 2015</xref>). At least 18 disease resistance genes of <italic>A. thaliana</italic> (localized in AK blocks C, D, E, F, H, I, J, N, Q, R, S, U, and X) were located within 100 kb regions, suggesting that these <italic>R</italic> genes were retained in <italic>B. napus</italic> genome, despite of long term genome fractionation, evolution and domestication (<xref rid=\"B10\" ref-type=\"bibr\">Chalhoub et al., 2014</xref>; <xref rid=\"B53\" ref-type=\"bibr\">Parkin et al., 2014</xref>). This study and the previously reported ones revealed that majority of QR regions have originated as a result of homoeologous and paralogous exchanges occurred between <italic>A. thaliana</italic> and <italic>B. napus</italic> genomes.</p></sec><sec id=\"s3_7\"><title>Relationship Between Resistance and Phenological Traits</title><p>Since resistance to blackleg seems to be conditioned with other plant developmental traits such as plant height and maturity (<xref rid=\"B57\" ref-type=\"bibr\">Pilet et al., 2001</xref>; <xref rid=\"B16\" ref-type=\"bibr\">Delourme et al., 2004</xref>; <xref rid=\"B35\" ref-type=\"bibr\">Huang et al., 2016</xref>; <xref rid=\"B65\" ref-type=\"bibr\">Raman et al., 2018</xref>), we scored these traits in the replicated trials conducted under field conditions (2017–2019). Pair-wise correlations of the additive genetic effects between different traits: blackleg resistance, flowering time, plant height and maturity are presented in <xref ref-type=\"fig\" rid=\"f2\">\n<bold>Figure 2</bold>\n</xref>. Utilizing the data on the genetic variation in the developmental traits, we further (i) located loci associated with flowering time, plant height and maturity in the diversity panel and (ii) investigated their physical localization in relation to significant loci associated with resistance to blackleg. GWAS analysis identified 30 SNP associations for flowering time on chromosomes A01, A02, A03, A10, C03, and C07 (<xref ref-type=\"supplementary-material\" rid=\"SM1\">\n<bold>Supplementary Table 5</bold>\n</xref>, <xref ref-type=\"supplementary-material\" rid=\"SM1\">\n<bold>Supplementary Figure 6</bold>\n</xref>); at least one of them is in the vicinity of associations for QR to blackleg on A02 (24.6 to 24.7 Mb). This genomic region maps within 27.5 kb from the AGAMOUS-LIKE 69/MADS AFFECTING FLOWERING 4; MAF4, AT5G65070, 24.6 Mb on the Darmor assembly). Likewise, GWAS identified 13 SNP associations for plant height on chromosomes A02, A08, A10, C03, C06, and C07 (<xref ref-type=\"fig\" rid=\"f5\">\n<bold>Figure 5D</bold>\n</xref>, <xref ref-type=\"supplementary-material\" rid=\"SM1\">\n<bold>Supplementary Figure 6</bold>\n</xref>). Of which, one marker association on A02 that was identified with flowering time (24.6 to 24.7 Mb), was also located in the same genomic region for plant survival in 2018 field environment (<xref ref-type=\"supplementary-material\" rid=\"SM1\">\n<bold>Supplementary Table 5</bold>\n</xref>). No QTL for plant height was detected on chromosome A06, where QR locus for blackleg was identified earlier in the Darmor-<italic>bzh</italic>/Yudal population (<xref rid=\"B35\" ref-type=\"bibr\">Huang et al., 2016</xref>; <xref rid=\"B41\" ref-type=\"bibr\">Kumar et al., 2018</xref>; <xref rid=\"B65\" ref-type=\"bibr\">Raman et al., 2018</xref>). Of the 14 SNP associations identified on A05, A08, A10, Ann_random, C02, C05 and C09 for plant maturity, possibly four genomic regions on chromosomes A08 (1.03 Mb to 1.30 Mb), A10 (1.61 to 1.67 Mb, and 14.6 to 14.9 Mb), and C09 (39.7 Mb) were detected near the SNP maker loci for resistance to blackleg (<xref ref-type=\"supplementary-material\" rid=\"SM1\">\n<bold>Supplementary Table 5</bold>\n</xref>). Moderate correlation between phenotypes and co-location of QTLs for resistance to blackleg and phenological traits (<xref ref-type=\"fig\" rid=\"f2\">\n<bold>Figures 2A–C</bold>\n</xref> and <xref ref-type=\"fig\" rid=\"f5\">\n<bold>Figure 5D</bold>\n</xref>) suggest that genes, underlying QTLs are either linked or may have pleiotropic allelic effects. It was difficult to measure causal relationship between plant development traits and resistance, as blackleg disease may have impacted their expression.</p><p>We looked if variation in flowering modulate blackleg infection. To understand this, we compared genetic variation in flowering time revealed in FT17 and FT18 experiments (this study) and previous experiments conducted at WWAI in the same environments (2017, 2018) on the same set of 300 diverse accessions (<xref rid=\"B66\" ref-type=\"bibr\">Raman et al., 2019</xref>). We observed a high correlation across environments (<italic>r</italic> = 0.98). The possibility of involvement of flowering time loci was also excluded by comparing the physical locations of SNP associations for flowering time, which were identified in the same set of GWAS panel evaluated under the similar field conditions, in the absence of blackleg disease; similar SNP-trait associations were detected, as found in experiments conducted in disease nurseries. This observation suggests variation in flowering time did not significantly affect blackleg disease in experimental conditions used in this study.</p></sec></sec><sec sec-type=\"discussion\" id=\"s4\"><title>Discussion</title><sec id=\"s4_1\"><title>Ascospore Shower and Field Screens Identify Additional Sources for Quantitative Resistance</title><p>In this study, we included a larger set of germplasm as compared to previous studies (<xref rid=\"B17\" ref-type=\"bibr\">Delourme et al., 2006</xref>; <xref rid=\"B40\" ref-type=\"bibr\">Kaur et al., 2009</xref>; <xref rid=\"B49\" ref-type=\"bibr\">Light et al., 2011</xref>; <xref rid=\"B62\" ref-type=\"bibr\">Raman et al., 2012b</xref>; <xref rid=\"B44\" ref-type=\"bibr\">Larkan et al., 2016a</xref>; <xref rid=\"B64\" ref-type=\"bibr\">Raman et al., 2016</xref>; <xref rid=\"B65\" ref-type=\"bibr\">Raman et al., 2018</xref>) and identified additional sources for resistance (<xref ref-type=\"fig\" rid=\"f1\">\n<bold>Figure 1C</bold>\n</xref>, <xref ref-type=\"supplementary-material\" rid=\"SM1\">\n<bold>Supplementary Table 3</bold>\n</xref>). Optimal conditions for blackleg development are required for assessing variation in resistance. For example, under high disease pressure, it may be difficult to reveal variation in resistance to blackleg, as <italic>L. maculans</italic> is a hemibiotroph and can colonize on resistant accessions of canola and related species. We observed moderately high values for reliability, which are comparable to previous studies (<xref rid=\"B56\" ref-type=\"bibr\">Pilet et al., 1998</xref>; <xref rid=\"B62\" ref-type=\"bibr\">Raman et al., 2012b</xref>; <xref rid=\"B37\" ref-type=\"bibr\">Jestin et al., 2015</xref>; <xref rid=\"B44\" ref-type=\"bibr\">Larkan et al., 2016a</xref>; <xref rid=\"B64\" ref-type=\"bibr\">Raman et al., 2016</xref>; <xref rid=\"B65\" ref-type=\"bibr\">Raman et al., 2018</xref>; <xref rid=\"B66\" ref-type=\"bibr\">Raman et al., 2019b</xref>) (<xref rid=\"T2\" ref-type=\"table\">\n<bold>Table 2</bold>\n</xref>). Low reliability values (12%) observed in some environments could be due to empirical visual assessment of resistance and genotype x environment interactions, as observed in canola DH populations from Skipton/Ag-Spectrum and RP04/Ag-Outback and Darmor-<italic>bzh</italic>/Yudal (<xref rid=\"B62\" ref-type=\"bibr\">Raman et al., 2012b</xref>; <xref rid=\"B35\" ref-type=\"bibr\">Huang et al., 2016</xref>; <xref rid=\"B67\" ref-type=\"bibr\">Raman et al., 2020</xref>). Under moderate disease pressure, we could assess genetic variation in resistance to UCI to blackleg consistently across environments (reliability scores: 74 to 76%, <xref rid=\"T2\" ref-type=\"table\">\n<bold>Table 2</bold>\n</xref>). Hereby, we emphasize the genetic variation in resistance to UCI among the accessions of the diverse panel and report several sources of resistance and susceptibility (<xref ref-type=\"supplementary-material\" rid=\"SM1\">\n<bold>Supplementary Table 3</bold>\n</xref>). This information can be useful for (i) understanding the genetic basis of resistance to UCI in mapping populations as well as (ii) developing protocols for high-throughput phenotyping for comprehensive assessment of UCI. The resistant accessions can further be exploited in breeding programs for developing improved cultivars with overall resistance to blackleg in canola and its related species.</p></sec><sec id=\"s4_2\"><title>Genome-Wide Distributed Loci Are Associated With Quantitative Resistance</title><p>We have delineated numerous loci for resistance to blackleg in a diverse panel of canola using a GWAS approach, although causative SNPs are not identified in this study. Majority of the Australian canola germplasm has either qualitative and/or quantitative “background” resistance. In fact, it is not common to find Australian cultivars that do not have either qualitative and/or quantitative resistance, as it has been the “backbone” trait of canola industry. Therefore, it is difficult to access Australian germplasm (with effective population size for GWAS analysis) that does not have <italic>R</italic> genes and utilize for the identification of QR genes comprehensively.</p><p>Most of the significant SNP associations identified in our screens do not correspond to the “known” <italic>R</italic> genes reported on chromosomes A01, A02, A06, A07, and A10 (<xref rid=\"B63\" ref-type=\"bibr\">Raman et al., 2013</xref>; <xref rid=\"B64\" ref-type=\"bibr\">Raman et al., 2016</xref>) and were not consistently detected across environments (<xref rid=\"T3\" ref-type=\"table\">\n<bold>Table 3</bold>\n</xref>). The results indicate that environment played a significant role in quantitative resistance expression. Similar observations were made in previous QTL and GWAS studies (<xref rid=\"B57\" ref-type=\"bibr\">Pilet et al., 2001</xref>; <xref rid=\"B36\" ref-type=\"bibr\">Jestin et al., 2011</xref>; <xref rid=\"B37\" ref-type=\"bibr\">Jestin et al., 2015</xref>; <xref rid=\"B35\" ref-type=\"bibr\">Huang et al., 2016</xref>; <xref rid=\"B41\" ref-type=\"bibr\">Kumar et al., 2018</xref>; <xref rid=\"B65\" ref-type=\"bibr\">Raman et al., 2018</xref>). Presence of SNP associations accounting low genetic variance (despite of moderate to high reliability, <xref rid=\"T3\" ref-type=\"table\">\n<bold>Table 3</bold>\n</xref>) on multiple genomic regions also indicate that QR is governed by quantitative genes (polygenic) rather than monogenic <italic>R</italic> genes, as the latter provide the complete resistance and account for larger proportion of genetic variance (<xref rid=\"B61\" ref-type=\"bibr\">Raman et al., 2012a</xref>; <xref rid=\"B62\" ref-type=\"bibr\">Raman et al., 2012b</xref>; <xref rid=\"B45\" ref-type=\"bibr\">Larkan et al., 2016b</xref>; <xref rid=\"B65\" ref-type=\"bibr\">Raman et al., 2018</xref>; <xref rid=\"B67\" ref-type=\"bibr\">Raman et al., 2020</xref>). We also observed that some SNPs accounted for variable LOD scores and genotypic variance (<xref ref-type=\"supplementary-material\" rid=\"SM1\">\n<bold>Supplementary Table 5</bold>\n</xref>). For example, Bn-A07-p16036626 on A07 (17.9 Mb) had “suggestive” LOD score of 1.82 in 2018, while the same SNP association had significant LOD score of 4.52 in 2019, suggesting that significance of trait-marker association relies on environment (phenotypic conditions including pathogen population) and “suggestive” associations should not be discounted in genetic improvement programs. More than 50% (32/59) of the SNP associations, detected on A01, A02, A03, A04, A05, A06, A07, A09, C02, C05, C06, and C09 chromosomes, were identified within 100 kb distance from the previously identified QTLs (<xref ref-type=\"supplementary-material\" rid=\"SM1\">\n<bold>Supplementary Table 6</bold>\n</xref>, <xref ref-type=\"fig\" rid=\"f6\">\n<bold>Figure 6</bold>\n</xref>), at least in one of the mapping populations (<xref rid=\"B44\" ref-type=\"bibr\">Larkan et al., 2016a</xref>; <xref rid=\"B64\" ref-type=\"bibr\">Raman et al., 2016</xref>; <xref rid=\"B41\" ref-type=\"bibr\">Kumar et al., 2018</xref>; <xref rid=\"B65\" ref-type=\"bibr\">Raman et al., 2018</xref>; <xref rid=\"B67\" ref-type=\"bibr\">Raman et al., 2020</xref>). Other SNPs associations on A04, A10 and C01 were mapped 0.5 to 1 Mb from QTL identified in Aviso/Bristol, Canberra/Bristol, Darmor/Bristol, and Grizzly/Bristol populations (<xref ref-type=\"supplementary-material\" rid=\"SM1\">\n<bold>Supplementary Table 6</bold>\n</xref>). These results suggest the common QR loci for resistance to blackleg in the winter (<xref rid=\"B36\" ref-type=\"bibr\">Jestin et al., 2011</xref>) and diverse panel of <italic>B. napus</italic> germplasm utilized in this study. Unlike previous studies (<xref rid=\"B36\" ref-type=\"bibr\">Jestin et al., 2011</xref>; <xref rid=\"B44\" ref-type=\"bibr\">Larkan et al., 2016a</xref>; <xref rid=\"B41\" ref-type=\"bibr\">Kumar et al., 2018</xref>), significant QTL (LOD ≥3) on chromosome A08 could not be detected in our GWAS panel. However, there were “suggestive” associations (with LOD <3) on A08. This could be attributed to <italic>L. maculans</italic> population structure, environmental conditions and/or the origin of germplasm. Previous studies (<xref rid=\"B36\" ref-type=\"bibr\">Jestin et al., 2011</xref>; <xref rid=\"B26\" ref-type=\"bibr\">Fopa Fomeju et al., 2015</xref>; <xref rid=\"B41\" ref-type=\"bibr\">Kumar et al., 2018</xref>) utilized a smaller set of accessions of winter rapeseed for GWAS which were evaluated under European phenotypic environments, whereas herein we used a larger diverse set of germplasm comprising spring, semispring/winter and winter types under Australian growing environments.</p><p>Some of the SNP associations for QR to blackleg were present in the vicinity of known <italic>R</italic> genes (<italic>Rlm3, Rlm4, Rlm7, and Rlm9)</italic> on chromosomes A07 (17.7 to 17.9 Mb), <italic>LepR3/Rlm2</italic> on A10 (14.4 Mb), <italic>LepR1</italic> on A02, and <italic>LepR4</italic> on chromosome A06 (<xref rid=\"B82\" ref-type=\"bibr\">Yu et al., 2008</xref>; <xref rid=\"B83\" ref-type=\"bibr\">Yu et al., 2013</xref>; <xref rid=\"B45\" ref-type=\"bibr\">Larkan et al., 2016b</xref>; <xref rid=\"B64\" ref-type=\"bibr\">Raman et al., 2016</xref>; <xref rid=\"B65\" ref-type=\"bibr\">Raman et al., 2018</xref>). The 17.7 to 17.9 Mb genomic region of A07 represents to a cluster of <italic>R</italic> genes and maps approximately 2Mb apart the <italic>Rlm9</italic>, encoding Wall-Associated Kinase like protein (BnaA07g20220D, 15.91 Mb) that was recently cloned in canola. (<xref rid=\"B46\" ref-type=\"bibr\">Larkan et al., 2019</xref>). Previously, <italic>Rlm9</italic> was mapped to a 15.95 Mb region in a DH population from Darmor-<italic>bzh</italic>/Yudal population (<xref rid=\"B65\" ref-type=\"bibr\">Raman et al., 2018</xref>). The genetic region, delineated by the Bn-A07-p18226326 marker at the 20.1 Mb on the Darmor assembly for UCI could represent the <italic>Rlm1</italic> gene in the AK block, E (subgenome LF). It was interesting that the SNP associations on A07 (Bn-A07-p15812852/Bn-A07-p16036626); 17.7 to 17.9 Mb for resistance to internal infection (<xref ref-type=\"supplementary-material\" rid=\"SM1\">\n<bold>Supplementary Table 5</bold>\n</xref>) were not detected with ascospore shower test. This was due to the utilization of stubbles derived from cultivars having <italic>Rlm1/Rlm3/Rlm4/Rlm7/Rlm9</italic> and unknown QR genes, which likely have rendered <italic>Rlm</italic> genes on A07 ineffective, leading to no variation for genetic analyses to detect. Effectiveness of qualitative <italic>R</italic> genes under field conditions identified hereby is in contrast to earlier studies, where the effectiveness of <italic>Rlm3</italic>, <italic>Rlm4</italic>, and <italic>Rlm9</italic> was questioned under the Australian field conditions (<xref rid=\"B49\" ref-type=\"bibr\">Light et al., 2011</xref>; <xref rid=\"B61\" ref-type=\"bibr\">Raman et al., 2012a</xref>; <xref rid=\"B44\" ref-type=\"bibr\">Larkan et al., 2016a</xref>; <xref rid=\"B64\" ref-type=\"bibr\">Raman et al., 2016</xref>; <xref rid=\"B65\" ref-type=\"bibr\">Raman et al., 2018</xref>; <xref rid=\"B67\" ref-type=\"bibr\">Raman et al., 2020</xref>). This may have occurred due to fungal population structure and environmental conditions, which may have impacted the frequency and distribution of Avirulence genes in the <italic>L. maculans</italic> population in the disease nurseries. It is reiterated that field environments across 2017, 2018 and 2019 were less conducive for the development of high disease pressure (<xref ref-type=\"supplementary-material\" rid=\"SM1\">\n<bold>Supplementary Figures 2A, B</bold>\n</xref>), due to record excessive drought conditions and high temperatures (plus 40°C during day time).The strong effect of weather conditions including the temperature on the severity of blackleg (stem canker) is reported previously (<xref rid=\"B22\" ref-type=\"bibr\">Evans et al., 2008</xref>). Further work is required to established whether there is any interaction between QR and <italic>R</italic> genes (described above) and or QR identified under field conditions is due to the genetic effect of <italic>R</italic> genes (e.g. <italic>Rlm12</italic> and QTL region on A01; 5.35Mb to 6.33Mb on A02, <italic>Rlm2/LepR3</italic> and QTL region 14.6 Mb to 15.3Mb on A10; <xref ref-type=\"supplementary-material\" rid=\"SM1\">\n<bold>Supplementary Table 6</bold>\n</xref>).</p><p>Our findings suggest that the “ascospore shower” test using ascospores released from mixed stubble sources (from cultivars carrying <italic>R</italic> genes such as <italic>Rlm1, Rlm4, Rlm6, LepR1</italic>, and <italic>LepR3</italic>, used in this study) was found to be suitable for the identification of QR genes. In the present study, we identified several genomic regions for QR which were detected variably across environments. For example, we identified genomic regions for resistance on chromosome A01, including <italic>Rlm12</italic> under shade-house conditions, which were not detected under field conditions. These results suggest that complementary approaches should be explored for genetic analysis of QR to blackleg. Our findings and previous studies revealed that variation in quantitative resistance is due to quantitative genes, distributed genome-wide with smaller allelic effects, suggesting that variation in resistance has evolved in several independent genes in canola germplasm.</p></sec><sec id=\"s4_3\"><title>GWAS Analysis Reveals Genomic Regions for Resistance to UCI</title><p>GWAS analysis has been extensively used to detect marker associations with trait of interest, especially which are in LD with QTL. Relying on this approach with genetic relationship estimates, we detected 16 associations between SNPs and UCI. Majority (56%) of the SNP associations were detected on chromosome A04. Detection of multiple SNPs in small genomic interval on A04 suggest that this region harbors gene(s) for resistance to UCI and is one of the candidates for fine mapping. In addition, SNP marker Bn-A03-p8475614, accounting for 19.2% of genetic variance was mapped in the proximity of QTL involved in resistance to blackleg (~180 kb) on chromosome A03 (7.59 Mb, Raman et al, 2016). In earlier studies, significant marker associations for resistance to both single spore isolates of <italic>L. maculans</italic> and stem canker were also detected on the homoeologous group 3 (A03/C03) chromosome (<xref rid=\"B37\" ref-type=\"bibr\">Jestin et al., 2015</xref>; <xref rid=\"B64\" ref-type=\"bibr\">Raman et al., 2016</xref>). Two genomic regions (13.4 Mb and 20.1 Mb) for resistance to UCI were identified on chromosome A07. The 13.4 Mb locus was mapped ~129.7 kb apart from the GWAS association for resistance at the cotyledon stage (<xref rid=\"B64\" ref-type=\"bibr\">Raman et al., 2016</xref>). One of the disease resistance genes, BnaA07g15400D (GSBRNA2T00098616001) was also located 57.3 kb proximal to this QR locus (<xref ref-type=\"supplementary-material\" rid=\"SM1\">\n<bold>Supplementary Table 5</bold>\n</xref>), implying that this genomic region is likely to be involved in resistance to blackleg. The 20.1 Mb genomic region for UCI may represent to the <italic>Rlm1</italic> gene. The physical position of <italic>Rlm1</italic> in the GWAS panel is consistent with our results on the mapping of <italic>Rlm1</italic> in a high-resolution mapping population of canola (Raman et al, unpublished). This current study also hints that the <italic>R</italic> gene mediated resistances to internal infection (mapped to the 17.7 Mb to 17.9 Mb of the Darmor-<italic>bzh</italic> sequence, <italic>Rlm3,4/7/9</italic>) and to UCI (mapped to the 20.11 Mb, <italic>Rlm1</italic>), are stable under high temperatures in field and is not prone to high temperatures. These results are in contrast with a previous study which reported that <italic>Rlm6</italic> gene is not stable under high temperatures (<xref rid=\"B33\" ref-type=\"bibr\">Huang et al., 2006a</xref>).</p><p>Consistent with our earlier and other GWAS studies (<xref rid=\"B60\" ref-type=\"bibr\">Rahman et al., 2016</xref>; <xref rid=\"B64\" ref-type=\"bibr\">Raman et al., 2016</xref>), we identified several significant associations for field resistance (disease nurseries in field and ascospore shower in shade-house) near genomic regions identified with single spore isolates. In addition, we detected genomic regions for QR near <italic>R</italic> genes, suggesting that mechanisms underlying host Pathogen-Associated Molecular Pattern triggered Immunity (PTI) and Effector-Triggered Immunity (ETI) are shared for qualitative and quantitative resistance (<xref rid=\"B77\" ref-type=\"bibr\">Thomma et al., 2011</xref>). Pathogens like <italic>L. maculans</italic> must overcome PTI to colonize on host and then to interact with ETI for defense response. It is not established here which <italic>R</italic>/<italic>QR</italic> loci are responsible for PTI or weaker ETI (<xref rid=\"B21\" ref-type=\"bibr\">Doehlemann and Hemetsberger, 2013</xref>). It is also possible that some of the genomic regions identified here could be associated with qualitative resistance due to (i) the genetic effects of <italic>R</italic> genes, (ii) ubiquitous presence of races of <italic>L. maculans</italic> in a canola crop and (iii) <italic>Avr</italic> genes that are in low frequency and not subjected to mutations, leading to virulence toward <italic>R</italic> genes.</p></sec><sec id=\"s4_4\"><title>Possible Role of Plant Developmental Traits on Blackleg Resistance</title><p>There was a positive relationship between UCI, flowering time, maturity, and plant height in 2018, however no such consistent relationship was found in 2019. This could have been attributed to the water stress and high temperatures in 2019 (<xref ref-type=\"supplementary-material\" rid=\"SM1\">\n<bold>Supplementary Figure 2</bold>\n</xref>). Moderate temperatures and high humidity are known to favor blackleg disease (<xref rid=\"B30\" ref-type=\"bibr\">Ghanbarnia et al., 2009</xref>). One genomic region for plant survival on A02, demarked with markers: Bn-A02-p27327804 (24.6 Mb), Bn-A02-p27336483 (24.6 Mb) and Bn-A02-p27743910 (24.7 Mb) was mapped within 100 kb from the SNP associations for flowering time and plant height (<xref ref-type=\"supplementary-material\" rid=\"SM1\">\n<bold>Supplementary Table 6</bold>\n</xref>). These results suggest that phenology genes may have pleiotropic effects on the level of blackleg expression.</p></sec></sec><sec sec-type=\"conclusions\" id=\"s5\"><title>Conclusions</title><p>In summary, we have delineated natural variation for resistance to blackleg in a diverse panel of canola. The specific accessions identified herein provide a useful resource for improving resistance to blackleg, and increasing genetic diversity in resistance genes, which can be exploited in the breeding programs. GWAS analysis identified 59 significant and “suggestive” loci for resistance to blackleg under shade-house and field conditions. These loci can only explain a small proportion of the genetic variance in resistance to blackleg; the sizable proportion of variance is accounted by G× E interaction, indicating that additional loci remain to be identified. Further research is required to assess the stability of QTL associations across canola growing regions in Australia and elsewhere. Given the adaptive nature of <italic>L. maculans</italic>, it is also important to demonstrate the uniqueness of genes conferring effective quantitative resistance to blackleg in <italic>Brassica</italic> species to expand the genetic base of canola - one of the most recent domesticated oil seed crops in recent history. As this and previous studies showed that QR is controlled by several genes with smaller allelic effects, introgression of a large number of loci is difficult <italic>via</italic> traditional marker-assisted selection (<xref rid=\"B23\" ref-type=\"bibr\">Fikere et al., 2018</xref>). However, favorable alleles for stable QR including for plant height and flowering time, which don’t confound the expression of QR can be enriched in the elite breeding lines using genomic selection approaches. Altogether with the results of previous studies, our results suggest that the gene network involved in QR is rather complex and evolved in a multifaceted manner to unleash genomic variation that contributed to natural variation in resistance to blackleg disease in canola.</p></sec><sec sec-type=\"data-availability\" id=\"s6\"><title>Data Availability Statement</title><p>The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.</p></sec><sec id=\"s7\"><title>Author Contributions</title><p>HR conceived the research, planned experiments and wrote the paper. HR, BM, and RR conducted research and contributed to phenotyping. BC developed the statistical methods for GWAS analysis. RP performed statistical analysis and GWAS analysis in consultation with HR. SM and DB evaluated accessions for resistance using ascospore shower, in consultation with HR. YZ, SL, and HR performed comparative mapping of significantly associated SNP markers. All authors contributed to the article and approved the submitted version.</p></sec><sec sec-type=\"funding-information\" id=\"s8\"><title>Funding</title><p>This study was supported by the NSW Department of Primary Industries, the Grains Research and Development Corporation, and Agriculture Victoria Research (AVR under the investments in National Brassica Germplasm Improvement Program project to HR (DAN00117, DAN00208, and DAN1707).</p></sec><sec id=\"s9\"><title>Conflict of Interest</title><p>The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p></sec></body><back><ack><title>Acknowledgments</title><p>We thank Dean MacCullum, Hannah Roe, Shane Hildebrand, and Bashar Thejer for technical assistance in the field. Authors are thankful to Dr Steve Marcroft, Marcroft Grains Pathology, Horsham for evaluating canola accessions using ascospore shower test.</p></ack><sec id=\"s10\" sec-type=\"supplementary-material\"><title>Supplementary Material</title><p>The Supplementary Material for this article can be found online at: <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.frontiersin.org/articles/10.3389/fpls.2020.01184/full#supplementary-material\">https://www.frontiersin.org/articles/10.3389/fpls.2020.01184/full#supplementary-material</ext-link>\n</p><supplementary-material content-type=\"local-data\" id=\"SM1\"><media xlink:href=\"Presentation_3.pptx\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"SF1\"><media xlink:href=\"Image_1.tif\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"ST1\"><media xlink:href=\"DataSheet_1.xlsx\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"ST2\"><media xlink:href=\"DataSheet_2.docx\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"ST3\"><media xlink:href=\"DataSheet_3.xlsx\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"ST4\"><media xlink:href=\"DataSheet_4.docx\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"ST5\"><media xlink:href=\"DataSheet_5.xlsx\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"ST6\"><media xlink:href=\"DataSheet_6.docx\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"ST7\"><media xlink:href=\"DataSheet_7.xlsx\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"ST8\"><media xlink:href=\"DataSheet_8.docx\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"ST9\"><media xlink:href=\"DataSheet_9.docx\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"ST10\"><media xlink:href=\"DataSheet_10.docx\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"ST11\"><media xlink:href=\"DataSheet_11.docx\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"ST12\"><media xlink:href=\"DataSheet_12.docx\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material></sec><ref-list><title>References</title><ref id=\"B1\"><mixed-citation publication-type=\"journal\">\n<person-group person-group-type=\"author\"><name><surname>Alamery</surname><given-names>S.</given-names></name><name><surname>Tirnaz</surname><given-names>S.</given-names></name><name><surname>Bayer</surname><given-names>P.</given-names></name><name><surname>Tollenaere</surname><given-names>R.</given-names></name><name><surname>Chaloub</surname><given-names>B.</given-names></name><name><surname>Edwards</surname><given-names>D.</given-names></name><etal/></person-group> (<year>2018</year>). <article-title>Genome-wide identification and 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[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"review-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Int J Mol Sci</journal-id><journal-id journal-id-type=\"iso-abbrev\">Int J Mol Sci</journal-id><journal-id journal-id-type=\"publisher-id\">ijms</journal-id><journal-title-group><journal-title>International Journal of Molecular Sciences</journal-title></journal-title-group><issn pub-type=\"epub\">1422-0067</issn><publisher><publisher-name>MDPI</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32722327</article-id><article-id pub-id-type=\"pmc\">PMC7432128</article-id><article-id pub-id-type=\"doi\">10.3390/ijms21155270</article-id><article-id pub-id-type=\"publisher-id\">ijms-21-05270</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Review</subject></subj-group></article-categories><title-group><article-title>Gingival Crevicular Fluid Peptidome Profiling in Healthy and in Periodontal Diseases</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Preianò</surname><given-names>Mariaimmacolata</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05270\">1</xref><xref ref-type=\"author-notes\" rid=\"fn1-ijms-21-05270\">†</xref></contrib><contrib contrib-type=\"author\"><name><surname>Savino</surname><given-names>Rocco</given-names></name><xref ref-type=\"aff\" rid=\"af2-ijms-21-05270\">2</xref><xref ref-type=\"author-notes\" rid=\"fn1-ijms-21-05270\">†</xref></contrib><contrib contrib-type=\"author\"><name><surname>Villella</surname><given-names>Chiara</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05270\">1</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0002-4236-7367</contrib-id><name><surname>Pelaia</surname><given-names>Corrado</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05270\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Terracciano</surname><given-names>Rosa</given-names></name><xref ref-type=\"aff\" rid=\"af3-ijms-21-05270\">3</xref><xref rid=\"c1-ijms-21-05270\" ref-type=\"corresp\">*</xref></contrib></contrib-group><aff id=\"af1-ijms-21-05270\"><label>1</label>Department of Health Sciences, University “Magna Græcia”, 88100 Catanzaro, Italy; <email>[email protected]</email> (M.P.); <email>[email protected]</email> (C.V.); <email>[email protected]</email> (C.P.)</aff><aff id=\"af2-ijms-21-05270\"><label>2</label>Department of Medical and Surgical Sciences, University “Magna Græcia”, 88100 Catanzaro, Italy; <email>[email protected]</email></aff><aff id=\"af3-ijms-21-05270\"><label>3</label>Department of Experimental and Clinical Medicine, University “Magna Græcia”, 88100 Catanzaro, Italy</aff><author-notes><corresp id=\"c1-ijms-21-05270\"><label>*</label>Correspondence: <email>[email protected]</email>; Tel.: +39-0961-3694085</corresp><fn id=\"fn1-ijms-21-05270\"><label>†</label><p>Co-first author, these authors contributed equally to this work.</p></fn></author-notes><pub-date pub-type=\"epub\"><day>24</day><month>7</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><month>8</month><year>2020</year></pub-date><volume>21</volume><issue>15</issue><elocation-id>5270</elocation-id><history><date date-type=\"received\"><day>16</day><month>6</month><year>2020</year></date><date date-type=\"accepted\"><day>22</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>© 2020 by the authors.</copyright-statement><copyright-year>2020</copyright-year><license license-type=\"open-access\"><license-p>Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>).</license-p></license></permissions><abstract><p>Given its intrinsic nature, gingival crevicular fluid (GCF) is an attractive source for the discovery of novel biomarkers of periodontal diseases. GCF contains antimicrobial peptides and small proteins which could play a role in specific immune-inflammatory responses to guarantee healthy gingival status and to prevent periodontal diseases. Presently, several proteomics studies have been performed leading to increased coverage of the GCF proteome, however fewer efforts have been done to explore its natural peptides. To fill such gap, this review provides an overview of the mass spectrometric platforms and experimental designs aimed at GCF peptidome profiling, including our own data and experiences gathered from over several years of matrix-assisted laser desorption ionization/time of flight mass spectrometry (MALDI-TOF MS) based approach in this field. These tools might be useful for capturing snapshots containing diagnostic clinical information on an individual and population scale, which may be used as a specific code not only for the diagnosis of the nature or the stage of the inflammatory process in periodontal disease, but more importantly, for its prognosis, which is still an unmet medical need. As a matter of fact, current peptidomics investigations suffer from a lack of standardized procedures, posing a serious problem for data interpretation. Descriptions of the efforts to address such concerns will be highlighted.</p></abstract><kwd-group><kwd>gingival crevicular fluid</kwd><kwd>MALDI-TOF</kwd><kwd>peptides</kwd><kwd>antimicrobial peptides</kwd><kwd>peptidomics</kwd><kwd>periodontal diseases</kwd><kwd>mass spectrometry</kwd></kwd-group></article-meta></front><body><sec sec-type=\"intro\" id=\"sec1-ijms-21-05270\"><title>1. Introduction</title><sec id=\"sec1dot1-ijms-21-05270\"><title>1.1. Gingivitis and Periodontitis</title><p>Periodontal diseases are complex, multifactorial, highly prevalent chronic inflammatory disorders affecting the tooth supporting tissues. Among them, periodontitis is a complex immune–inflammatory disease representing a significant health issue due to its high prevalence worldwide with approximately 10–15% of individuals being affected by severe forms [<xref rid=\"B1-ijms-21-05270\" ref-type=\"bibr\">1</xref>]. It is characterized by microbially-associated, host-mediated inflammation that, in the absence of advanced treatment, could lead to a loss of periodontal attachment and also to alveolar bone resorption, resulting in deep periodontal lesions that may cause significant tooth loss in the advanced stage of pathology [<xref rid=\"B2-ijms-21-05270\" ref-type=\"bibr\">2</xref>]. The accumulation of activated inflammatory cells like polynuclear leukocytes, macrophages, and lymphocytes in response to dental plaque amplifies the inflammatory process by the release of pro-inflammatory mediators [<xref rid=\"B3-ijms-21-05270\" ref-type=\"bibr\">3</xref>]. Instead, gingivitis is recognized as a reversible condition, associated with microbial plaque accumulation and gingival inflammation that often resolves after restoration of oral hygiene procedures but, if untreated, may ultimately progress to periodontitis, thus representing the transitional phase between health and periodontal disease [<xref rid=\"B1-ijms-21-05270\" ref-type=\"bibr\">1</xref>]. Therefore, a crucial interest has been growing in delineating the mechanism underlying this inflammatory stage, before it evolves to periodontitis. Periodontitis diagnosis is based on clinical examination, medical and dental history, tooth mobility, and radiographic assessment. Clinical evaluation is based on the assessment parameters, such oral hygiene, gingival status, probing depth, bleeding on probing, clinical attachment loss, alveolar bone status, but also on procedures such as periodontal microbiology testing, systemic health profiling by blood analysis, and finally, histological studies [<xref rid=\"B4-ijms-21-05270\" ref-type=\"bibr\">4</xref>]. The main limit of most of these clinical methods is a certain degree of subjectivity, since they are examiner dependent. In addition, the biological events which trigger the progression from health to disease conditions are still unclear, therefore there is an urgent need to discover and validate reliable biomarkers able to diagnose early stage of pathology, possibly before clinical manifestations of periodontitis, in order to allow a more effective prompt therapeutic intervention.</p><p>Diagnostic tests are generally not necessary to discriminate between an inflamed site and a healthy site because clinical indices of inflammation, determined as described above, correspond well enough with periodontium histological inflammation. However, from a diagnostic point of view, the ability to discriminate between an inflamed-progressive (active) site and a less critical inflamed but stable site is of paramount importance [<xref rid=\"B5-ijms-21-05270\" ref-type=\"bibr\">5</xref>,<xref rid=\"B6-ijms-21-05270\" ref-type=\"bibr\">6</xref>]. Now, it is well accepted that both the outcomes of the immune/inflammatory response present in saliva/gingival crevicular fluid (GCF) and the impact of behavioral and environmental factors deeply affect the clinical picture of periodontopathy [<xref rid=\"B7-ijms-21-05270\" ref-type=\"bibr\">7</xref>]. Following the “omics” burst, many researchers have tried to unravel molecular markers of periodontitis, identifying several genes, transcripts, proteins, and metabolites related to the pathology. Therefore, in the attempt to screen molecular profiles of periodontal disease, GCF has gained much attention, as will be discussed in the following sections.</p></sec><sec id=\"sec1dot2-ijms-21-05270\"><title>1.2. Nature and Function of GCF</title><p>GCF is released in the dental crevice or gingival sulcus, defined as the space between the tooth and the overlying unattached gingiva. It mirrors serum composition because it not only derives from the gingival plexus of blood capillaries in the gingival corium, but also contains molecules derived from both host tissues and subgingival plaque, as well as a number of different cells, including epithelial cells, leukocytes and bacteria from the adjacent plaque mass [<xref rid=\"B8-ijms-21-05270\" ref-type=\"bibr\">8</xref>]. In contrast, specialized glands, in particular the major salivary glands (submandibular, sublingual and parotid glands), and also roughly 600 minor salivary glands that are dispersed throughout the oral mucosa, have evolved to secrete saliva in the mouth. Obviously, GCF flows from the dental crevice into the buccal cavity, mixing with saliva and becoming part of it. Therefore, saliva is an aqueous solution (water content of 99%) containing electrolytes, small organic molecules, and nucleic acids, but most of all, proteins. In fact, over 3000 proteins have been found in saliva, the most represented being mucins, amylases, proline-rich proteins, cystatins, statherin, and histatins [<xref rid=\"B4-ijms-21-05270\" ref-type=\"bibr\">4</xref>].</p><p>GCF exists as a serum transudate, changing into an inflammatory exudate as the inflammatory events progress [<xref rid=\"B4-ijms-21-05270\" ref-type=\"bibr\">4</xref>]. In fact, this fluid is described as a mixture of molecules derived from the blood, host tissues, and subgingival plaque, and it contains electrolytes, small organic molecules, and various kind of proteins, among which antibodies, cytokines, bacterial antigens, and finally, enzymes of both host and bacterial origin [<xref rid=\"B9-ijms-21-05270\" ref-type=\"bibr\">9</xref>]. Cytological examination of the GCF also demonstrates that it contains a variety of different cell types, including bacteria from the neighboring plaque mass, desquamated epithelial cells (which are passively washed out of the dental crevice into the oral cavity by the outward GCF flow), and transmigrating leukocytes (among which, lymphocytes monocytes/macrophages, and polymorphonuclear cells). Erythrocyte detection in the GCF should be considered an auxiliary finding, indicating injury to the microvessels that is typical of progressive inflammatory process [<xref rid=\"B10-ijms-21-05270\" ref-type=\"bibr\">10</xref>]. With the onset of inflammation, the GCF flow rate increases and its composition begins to look like that of an inflammatory exudate. The augmented GCF flow contributes to host immune defense by flushing both bacteria and their metabolites away from the dental crevice. The main route for the diffusion of GCF is through the basement membrane, and subsequently, through the junctional epithelium into the dental crevice [<xref rid=\"B11-ijms-21-05270\" ref-type=\"bibr\">11</xref>]. A particularly attractive feature of GCF is that this fluid is “periodontal specific” as it stems from a local site deeply involved in the manifestation of periodontal disease. For this reason it is considered to represent a faithful mirror of the periodontal health state of an individual [<xref rid=\"B4-ijms-21-05270\" ref-type=\"bibr\">4</xref>]. Due to its composition, GCF might be a source of new biomarkers of periodontitis/gingivitis. The oral cavity is indeed a reserve of the microbiome and, when adverse changes occur within the oral cavity, it results in pathological changes, such as gingivitis, periodontitis, and dental caries [<xref rid=\"B8-ijms-21-05270\" ref-type=\"bibr\">8</xref>]. As a biomonitoring fluid, GCF may play a key role both in the diagnosis and in the prognosis of oral diseases, in particular for periodontal diseases. It can be included among the most non-traumatic proximal fluid to obtain information about periodontal tissue status, including the conditions of the connective tissue and the level of hard destruction [<xref rid=\"B12-ijms-21-05270\" ref-type=\"bibr\">12</xref>]. A non-invasive GCF collection (paper strip, paper points) technique helps obtaining samples of all age groups of human subjects, allowing also multiple sites sampling within the oral cavity [<xref rid=\"B13-ijms-21-05270\" ref-type=\"bibr\">13</xref>]. So far, diverse inflammatory mediators have been isolated from GCF, including cytokines, phosphatase, proteinase and local tissue degradation products [<xref rid=\"B14-ijms-21-05270\" ref-type=\"bibr\">14</xref>]. Several proteomics studies in the last decade shed light on GCF as potential source of biomarkers of both gingivitis and periodontitis and provided evidence for its diagnostic usefulness. The differential expression level of several proteins, such as defensins, annexins, plastin-2, lipocalin-2, and S100-P, was reported in the GCF between health and disease [<xref rid=\"B15-ijms-21-05270\" ref-type=\"bibr\">15</xref>].</p></sec><sec id=\"sec1dot3-ijms-21-05270\"><title>1.3. Low Molecular Weight Profiling of GCF</title><p>Despite the fact that naturally occurring peptides present in bodily fluids are known to play both local and/or systemic functions, only a few proteomics/peptidomics investigations targeted the GCF peptidome [<xref rid=\"B6-ijms-21-05270\" ref-type=\"bibr\">6</xref>,<xref rid=\"B16-ijms-21-05270\" ref-type=\"bibr\">16</xref>,<xref rid=\"B17-ijms-21-05270\" ref-type=\"bibr\">17</xref>,<xref rid=\"B18-ijms-21-05270\" ref-type=\"bibr\">18</xref>,<xref rid=\"B19-ijms-21-05270\" ref-type=\"bibr\">19</xref>,<xref rid=\"B20-ijms-21-05270\" ref-type=\"bibr\">20</xref>,<xref rid=\"B21-ijms-21-05270\" ref-type=\"bibr\">21</xref>,<xref rid=\"B22-ijms-21-05270\" ref-type=\"bibr\">22</xref>,<xref rid=\"B23-ijms-21-05270\" ref-type=\"bibr\">23</xref>,<xref rid=\"B24-ijms-21-05270\" ref-type=\"bibr\">24</xref>,<xref rid=\"B25-ijms-21-05270\" ref-type=\"bibr\">25</xref>,<xref rid=\"B26-ijms-21-05270\" ref-type=\"bibr\">26</xref>,<xref rid=\"B27-ijms-21-05270\" ref-type=\"bibr\">27</xref>,<xref rid=\"B28-ijms-21-05270\" ref-type=\"bibr\">28</xref>,<xref rid=\"B29-ijms-21-05270\" ref-type=\"bibr\">29</xref>]. Even with such limited studies, the knowledge of the biological mechanisms that can be gained from peptidomics applied both to the identification of markers of disease and to the development of therapies which has encouraged the use of in the context of progress made in this field [<xref rid=\"B6-ijms-21-05270\" ref-type=\"bibr\">6</xref>,<xref rid=\"B16-ijms-21-05270\" ref-type=\"bibr\">16</xref>,<xref rid=\"B17-ijms-21-05270\" ref-type=\"bibr\">17</xref>,<xref rid=\"B18-ijms-21-05270\" ref-type=\"bibr\">18</xref>,<xref rid=\"B19-ijms-21-05270\" ref-type=\"bibr\">19</xref>,<xref rid=\"B20-ijms-21-05270\" ref-type=\"bibr\">20</xref>,<xref rid=\"B21-ijms-21-05270\" ref-type=\"bibr\">21</xref>,<xref rid=\"B22-ijms-21-05270\" ref-type=\"bibr\">22</xref>,<xref rid=\"B23-ijms-21-05270\" ref-type=\"bibr\">23</xref>,<xref rid=\"B24-ijms-21-05270\" ref-type=\"bibr\">24</xref>,<xref rid=\"B25-ijms-21-05270\" ref-type=\"bibr\">25</xref>,<xref rid=\"B26-ijms-21-05270\" ref-type=\"bibr\">26</xref>,<xref rid=\"B27-ijms-21-05270\" ref-type=\"bibr\">27</xref>,<xref rid=\"B28-ijms-21-05270\" ref-type=\"bibr\">28</xref>,<xref rid=\"B29-ijms-21-05270\" ref-type=\"bibr\">29</xref>]. In the last decade, the impact of proteomics to the understanding of human GCF composition has progressively increased, underlining the potential diagnostic role of this fluid as invaluable source of periodontal disease-specific markers [<xref rid=\"B6-ijms-21-05270\" ref-type=\"bibr\">6</xref>,<xref rid=\"B14-ijms-21-05270\" ref-type=\"bibr\">14</xref>,<xref rid=\"B16-ijms-21-05270\" ref-type=\"bibr\">16</xref>,<xref rid=\"B17-ijms-21-05270\" ref-type=\"bibr\">17</xref>,<xref rid=\"B18-ijms-21-05270\" ref-type=\"bibr\">18</xref>,<xref rid=\"B19-ijms-21-05270\" ref-type=\"bibr\">19</xref>,<xref rid=\"B20-ijms-21-05270\" ref-type=\"bibr\">20</xref>,<xref rid=\"B21-ijms-21-05270\" ref-type=\"bibr\">21</xref>,<xref rid=\"B22-ijms-21-05270\" ref-type=\"bibr\">22</xref>,<xref rid=\"B23-ijms-21-05270\" ref-type=\"bibr\">23</xref>,<xref rid=\"B24-ijms-21-05270\" ref-type=\"bibr\">24</xref>,<xref rid=\"B25-ijms-21-05270\" ref-type=\"bibr\">25</xref>,<xref rid=\"B26-ijms-21-05270\" ref-type=\"bibr\">26</xref>,<xref rid=\"B27-ijms-21-05270\" ref-type=\"bibr\">27</xref>,<xref rid=\"B28-ijms-21-05270\" ref-type=\"bibr\">28</xref>,<xref rid=\"B29-ijms-21-05270\" ref-type=\"bibr\">29</xref>,<xref rid=\"B30-ijms-21-05270\" ref-type=\"bibr\">30</xref>,<xref rid=\"B31-ijms-21-05270\" ref-type=\"bibr\">31</xref>,<xref rid=\"B32-ijms-21-05270\" ref-type=\"bibr\">32</xref>,<xref rid=\"B33-ijms-21-05270\" ref-type=\"bibr\">33</xref>,<xref rid=\"B34-ijms-21-05270\" ref-type=\"bibr\">34</xref>,<xref rid=\"B35-ijms-21-05270\" ref-type=\"bibr\">35</xref>,<xref rid=\"B36-ijms-21-05270\" ref-type=\"bibr\">36</xref>,<xref rid=\"B37-ijms-21-05270\" ref-type=\"bibr\">37</xref>,<xref rid=\"B38-ijms-21-05270\" ref-type=\"bibr\">38</xref>,<xref rid=\"B39-ijms-21-05270\" ref-type=\"bibr\">39</xref>,<xref rid=\"B40-ijms-21-05270\" ref-type=\"bibr\">40</xref>,<xref rid=\"B41-ijms-21-05270\" ref-type=\"bibr\">41</xref>,<xref rid=\"B42-ijms-21-05270\" ref-type=\"bibr\">42</xref>,<xref rid=\"B43-ijms-21-05270\" ref-type=\"bibr\">43</xref>,<xref rid=\"B44-ijms-21-05270\" ref-type=\"bibr\">44</xref>,<xref rid=\"B45-ijms-21-05270\" ref-type=\"bibr\">45</xref>,<xref rid=\"B46-ijms-21-05270\" ref-type=\"bibr\">46</xref>,<xref rid=\"B47-ijms-21-05270\" ref-type=\"bibr\">47</xref>].</p><p>With the progress of bottom-up MS-based approaches, enormous amounts of data have been generated, especially concerning the protein content of GCF, and extraordinary attention has been paid to comparative analysis in order to identify differentially expressed proteins between healthy and periodontal disease [<xref rid=\"B48-ijms-21-05270\" ref-type=\"bibr\">48</xref>,<xref rid=\"B49-ijms-21-05270\" ref-type=\"bibr\">49</xref>]. While from one point of view, it appears remarkable to categorize these studies as based on discovery or on quantitative proteomics approaches, as recently outlined by both Bostanci and colleagues [<xref rid=\"B15-ijms-21-05270\" ref-type=\"bibr\">15</xref>,<xref rid=\"B48-ijms-21-05270\" ref-type=\"bibr\">48</xref>] and Tsuchida and collaborators [<xref rid=\"B49-ijms-21-05270\" ref-type=\"bibr\">49</xref>], it could also be of interest to appraise the efforts done by the proteomics community to assess the invaluable role played by naturally occurring peptides in GCF. Therefore, in the present review we highlight peptidomics studies which offer significant opportunities to detect and to identify peptides and small proteins in GCF of interest for periodontal diseases (<xref ref-type=\"scheme\" rid=\"ijms-21-05270-sch001\">Scheme 1</xref>, <xref rid=\"ijms-21-05270-t001\" ref-type=\"table\">Table 1</xref> and <xref rid=\"ijms-21-05270-t002\" ref-type=\"table\">Table 2</xref>). A special focus will be given to the study design of several investigations, which are described from the collection of the samples to their processing and to the MS platform and strategy adopted, all with the aim to better address current limitations which hurdle GCF peptidomics research. Concerning the methodological strategy, all the studies reviewed here were carried out with MS profiling approaches in which protein content of GCF did not undergo protease digestion before MS analysis. The absence of a digestion step preceding the MS analysis overcome the intrinsic limitation of not discriminating between the naturally occurring peptides found in GCF and those artificially generated by proteolysis in the preceding step required for a bottom up protocol. The relevance of the top down profiling approaches reviewed here resides therefore in the fact that such investigations might reveal in a more direct and clear manner specific proteins/peptides functions and mechanisms of actions, as top down approaches preserve valuable information about all of proteoforms including proteolytic cleavage from larger proteins by the action of proteases of human or bacterial origin.</p><p>Generally, the peptide profiling investigations that we summarize in this review make use of surface enhanced laser desorption ionization (SELDI) [<xref rid=\"B17-ijms-21-05270\" ref-type=\"bibr\">17</xref>,<xref rid=\"B18-ijms-21-05270\" ref-type=\"bibr\">18</xref>] and matrix-assisted laser desorption ionization (MALDI)-MS [<xref rid=\"B6-ijms-21-05270\" ref-type=\"bibr\">6</xref>,<xref rid=\"B19-ijms-21-05270\" ref-type=\"bibr\">19</xref>,<xref rid=\"B20-ijms-21-05270\" ref-type=\"bibr\">20</xref>,<xref rid=\"B21-ijms-21-05270\" ref-type=\"bibr\">21</xref>,<xref rid=\"B22-ijms-21-05270\" ref-type=\"bibr\">22</xref>,<xref rid=\"B23-ijms-21-05270\" ref-type=\"bibr\">23</xref>,<xref rid=\"B24-ijms-21-05270\" ref-type=\"bibr\">24</xref>,<xref rid=\"B25-ijms-21-05270\" ref-type=\"bibr\">25</xref>,<xref rid=\"B26-ijms-21-05270\" ref-type=\"bibr\">26</xref>] approaches which result more suitable and easily applicable for the analysis of natural peptides of GCF (<xref rid=\"ijms-21-05270-t001\" ref-type=\"table\">Table 1</xref>). However, few investigations based on electrospray ionization (ESI)-MS have also been carried out to explore peptidic components and small proteins found in GCF in a top-down fashion [<xref rid=\"B27-ijms-21-05270\" ref-type=\"bibr\">27</xref>,<xref rid=\"B28-ijms-21-05270\" ref-type=\"bibr\">28</xref>,<xref rid=\"B29-ijms-21-05270\" ref-type=\"bibr\">29</xref>] (<xref rid=\"ijms-21-05270-t001\" ref-type=\"table\">Table 1</xref>).</p><p>.</p></sec></sec><sec id=\"sec2-ijms-21-05270\"><title>2. An Overview of MS Profiling Tools and Investigations to Detect GCF Peptides</title><p>Some, but not many investigations based on MS have been performed to explore the GCF peptidome both under physiological [<xref rid=\"B20-ijms-21-05270\" ref-type=\"bibr\">20</xref>,<xref rid=\"B22-ijms-21-05270\" ref-type=\"bibr\">22</xref>,<xref rid=\"B23-ijms-21-05270\" ref-type=\"bibr\">23</xref>,<xref rid=\"B27-ijms-21-05270\" ref-type=\"bibr\">27</xref>,<xref rid=\"B28-ijms-21-05270\" ref-type=\"bibr\">28</xref>] and pathological conditions [<xref rid=\"B6-ijms-21-05270\" ref-type=\"bibr\">6</xref>,<xref rid=\"B16-ijms-21-05270\" ref-type=\"bibr\">16</xref>,<xref rid=\"B17-ijms-21-05270\" ref-type=\"bibr\">17</xref>,<xref rid=\"B18-ijms-21-05270\" ref-type=\"bibr\">18</xref>,<xref rid=\"B19-ijms-21-05270\" ref-type=\"bibr\">19</xref>,<xref rid=\"B21-ijms-21-05270\" ref-type=\"bibr\">21</xref>,<xref rid=\"B24-ijms-21-05270\" ref-type=\"bibr\">24</xref>,<xref rid=\"B25-ijms-21-05270\" ref-type=\"bibr\">25</xref>,<xref rid=\"B26-ijms-21-05270\" ref-type=\"bibr\">26</xref>,<xref rid=\"B29-ijms-21-05270\" ref-type=\"bibr\">29</xref>]. Targeted identifications of specific peptidic component in GCF have been reported by Lundy et al. [<xref rid=\"B16-ijms-21-05270\" ref-type=\"bibr\">16</xref>]. In this study, Calgranulin A (S100-A8) was purified by high-performance liquid chromatography (HPLC) and then identified using N-terminal amino-acid sequencing. S100-A8 is expressed in cells of myeloid origin and secreted in the case of cell damage or activation of phagocytes and monocytes. It exerts many roles in periodontal inflammation including chemotaxis, phagocytosis, or inhibition of microbial growth [<xref rid=\"B16-ijms-21-05270\" ref-type=\"bibr\">16</xref>]. The amount of Calgranulin A was determined in GCF from healthy, gingivitis and periodontitis sites both in periodontitis patients (<italic>n</italic> = 15) and also from controls (<italic>n</italic> = 5) with healthy gingival status. The results showed significant differences in the amounts of Calgranulin A in relation to disease status, with higher expression levels in periodontitis and gingivitis sites than in the healthy sites in both periodontitis subjects and controls (<xref rid=\"ijms-21-05270-t002\" ref-type=\"table\">Table 2</xref>) [<xref rid=\"B16-ijms-21-05270\" ref-type=\"bibr\">16</xref>].</p><sec id=\"sec2dot1-ijms-21-05270\"><title>2.1. SELDI MS GCF Peptidome Profiling</title><p>In SELDI MS, samples to be analyzed are applied directly to a biochip coated with specific chemical “bait” matrices, which may be comprised of supports routinely used in chromatographic techniques (anionic, cationic, hydrophobic surfaces, etc.) or biochemical bait molecules, for instance purified ligands, proteins (like antibodies, receptors) or also DNA oligonucleotides [<xref rid=\"B50-ijms-21-05270\" ref-type=\"bibr\">50</xref>]. The protein population bound to the biochip can then be washed with different kinds of washing buffers, in such a way that only the proteins sharing common chemical features remain on the surface and, subsequently, the mass map of all proteins retained is obtained at the same time. This analysis gives rise to a protein “fingerprint” formed by the exact molecular weight of each and every single protein first bound and then ionized off the specific bait surface used. SELDI-time of flight (TOF) MS, is one of the best analytical techniques for profiling both peptides and also low MW proteins (<20 kDa). Indeed, it has been extensively used in proteomics studies and, due to its high throughput, it became a promising tool for the identification of diagnostic patterns of disease [<xref rid=\"B51-ijms-21-05270\" ref-type=\"bibr\">51</xref>,<xref rid=\"B52-ijms-21-05270\" ref-type=\"bibr\">52</xref>,<xref rid=\"B53-ijms-21-05270\" ref-type=\"bibr\">53</xref>]. However, this technology suffers a number of drawbacks preventing its widespread application as a routine platform in clinical diagnostics [<xref rid=\"B54-ijms-21-05270\" ref-type=\"bibr\">54</xref>]. One of the main disadvantages of SELDI-TOF MS in peptidomics analysis is that its resolution is generally not very high (i.e., several different peptides can be present within each single ion peak, thus preventing direct assignment of the ion peaks of interest) [<xref rid=\"B55-ijms-21-05270\" ref-type=\"bibr\">55</xref>]. Another point of weakness of the platform is the poor reproducibility, both within and also between laboratories. In this respect, a number of efforts have already been made to set up standardized protocols among the various research groups [<xref rid=\"B56-ijms-21-05270\" ref-type=\"bibr\">56</xref>]. Another limitation of SELDI-TOF instruments is that they are not able to directly identify putative biomarkers as they lack MS/MS capabilities [<xref rid=\"B57-ijms-21-05270\" ref-type=\"bibr\">57</xref>]. Finally, concerns can also be risen related to both the sensitivity of the technique and also the specificity of the biomarkers discovered by SELDI analysis [<xref rid=\"B54-ijms-21-05270\" ref-type=\"bibr\">54</xref>].</p><p>The first peptidomics study on GCF was pioneered by Diamond and colleagues [<xref rid=\"B18-ijms-21-05270\" ref-type=\"bibr\">18</xref>]. With the aim to understand the physiologic role of beta-defensins in the oral environment, Diamond and colleagues examined the media of cultured human oral keratinocytes and GCF by ProteinChip Array SELDI-TOF MS technology. The authors found that both the 47-amino acid form of human β-Defensin 1 (hBD-1, <italic>m</italic>/<italic>z</italic> = 5069) and the 41-amino acid form of human β-Defensin 2 (hBD-2, <italic>m</italic>/<italic>z</italic> = 4330) were best detected in GCF by SELDI-TOF measurements when a chip (among the others) coated with a hydrophilic phase was used. Peptides with the predicted masses of the alpha-defensins HNP-1 (<italic>m</italic>/<italic>z</italic> = 3442), HNP-2 (<italic>m</italic>/<italic>z</italic> = 3371) and HNP-3 (<italic>m</italic>/<italic>z</italic> = 3486) were also detected. However, only the identity of hBD-1 and hBD-2 was indirectly confirmed by immunoaffinity capture with anti-hBD-1 and anti-hBD-2 polyclonal antibodies on the ProteinChip surface, although hBD-1, as the authors hypothesized, was found in several smaller forms suggesting extracellular proteolysis. Immunoaffinity capture experiments unmasked inter-individual variation between the two subjects (<italic>n</italic> = 2) in the relative amount of hBD-2 and the hBD-1 peptides. Specifically, more hBD-2 and alpha-defensins, with little hBD-1, was seen in the case of the subject with greater inflammation. β-Defensins are secreted by gingival epithelial cells and exert combined antimicrobial, chemotactic, and anti-inflammatory properties [<xref rid=\"B18-ijms-21-05270\" ref-type=\"bibr\">18</xref>]</p><p>With the same technology, some years later, Dommisch and colleagues optimized the experimental conditions to obtain the simultaneous detection of human neutrophil antimicrobial peptides (AMPs) in GCF as well as human cathelicidin LL-37 in SELDI-TOF profiles [<xref rid=\"B17-ijms-21-05270\" ref-type=\"bibr\">17</xref>]. LL-37 is secreted by neutrophils and plays a key role in innate immune responses [<xref rid=\"B17-ijms-21-05270\" ref-type=\"bibr\">17</xref>]. In this study, they compared GCF specimen from healthy periodontal sites to those from sites with early gingival inflammation. As each single donor also provided GCF samples from healthy periodontal sites as an internal control, GCF samples from sites diagnosed as gingivitis were analyzed individually in a donor-specific manner, thus allowing relative quantification. The authors observed that donors (four out of eight donors) that showed significantly higher intensity peaks corresponding to the mass of LL-37 (<italic>p</italic> = 0.01) also displayed greater (although not statistically significant) intensity peaks corresponding to the molecular masses of HNP-1 and HNP-2 in samples obtained from inflamed as compared to healthy periodontal sites (see <xref rid=\"ijms-21-05270-t002\" ref-type=\"table\">Table 2</xref>). However, also in this SELDI study, the main limitations are due to the loss of unambiguous sequence-based identifications of the putative <italic>m</italic>/<italic>z</italic> peaks detected in the mass spectra and to the inherent semi quantitative assay.</p></sec><sec id=\"sec2dot2-ijms-21-05270\"><title>2.2. MALDI MS GCF Peptidome Profiling</title><p>In the MALDI-TOF MS, a large excess of matrix (which is a small organic aromatic UV absorbing molecule) is used in comparison to the amount of the analytes (in this case peptides that need to be analyzed). Peptides are co-crystallized with the matrix into a solid-phase onto a stainless-steel target plate. When a laser beam hits the surface of the solid mixture, the matrix absorbs the laser energy and transfers it to the protonated peptides. Simultaneously, the rapid heating determines desorption of both the matrix and the newly formed [M + H]<sup>+</sup> protonated peptides directly into the gas phase. MALDI ion source is commonly coupled to TOF mass analyzer [<xref rid=\"B58-ijms-21-05270\" ref-type=\"bibr\">58</xref>]. In the recent mass analyzers, the ions generated in the source are then accelerated to a fixed amount of kinetic energy and travel along a flight tube. Having all the same kinetic energy, the small ions have a higher velocity and are therefore recorded by a detector before the larger ones. As a consequence, each <italic>m</italic>/<italic>z</italic> value displayed in a TOF spectrum is proportional to the time required to reach the detector for each given analyte.</p><p>Specifically, MALDI-TOF MS is a very versatile and fast analytical platform for analyzing both small and large polypeptides contained in various kind of biological samples. Some intrinsic properties of this analytical technique include low sample consumption, simple sample preparation, remarkable tolerance toward salts and buffers, high degree of automation and high throughput analysis and, finally, high sensitivity and mass accuracy. All these features make MALDI-TOF MS highly suitable for diagnostic purposes [<xref rid=\"B59-ijms-21-05270\" ref-type=\"bibr\">59</xref>,<xref rid=\"B60-ijms-21-05270\" ref-type=\"bibr\">60</xref>,<xref rid=\"B61-ijms-21-05270\" ref-type=\"bibr\">61</xref>,<xref rid=\"B62-ijms-21-05270\" ref-type=\"bibr\">62</xref>,<xref rid=\"B63-ijms-21-05270\" ref-type=\"bibr\">63</xref>]. The versatility of the MALDI-TOF MS platform was exploited in different approaches to analyze GCF peptidome [<xref rid=\"B6-ijms-21-05270\" ref-type=\"bibr\">6</xref>,<xref rid=\"B19-ijms-21-05270\" ref-type=\"bibr\">19</xref>,<xref rid=\"B20-ijms-21-05270\" ref-type=\"bibr\">20</xref>,<xref rid=\"B21-ijms-21-05270\" ref-type=\"bibr\">21</xref>,<xref rid=\"B22-ijms-21-05270\" ref-type=\"bibr\">22</xref>,<xref rid=\"B23-ijms-21-05270\" ref-type=\"bibr\">23</xref>,<xref rid=\"B24-ijms-21-05270\" ref-type=\"bibr\">24</xref>,<xref rid=\"B25-ijms-21-05270\" ref-type=\"bibr\">25</xref>,<xref rid=\"B26-ijms-21-05270\" ref-type=\"bibr\">26</xref>].</p><p>Lundy and colleagues used MALDI-TOF MS to qualitatively analyze neutrophil alpha-defensins in unfractionated GCF samples [<xref rid=\"B19-ijms-21-05270\" ref-type=\"bibr\">19</xref>]. Starting from the rationale that the triad of alpha-defensins peptides (HNP-1, HNP-2, and HNP-3), due to their similar primary sequence, might show an equal ionization efficiency during the MALDI-TOF MS measurements, these investigators assumed that the ion intensities of the peaks representing the alpha-defensins paralleled the relative amount of these peptides. Therefore, they assessed the relative intensity of the peaks corresponding to HNP-1, HNP-2 and HNP-3 detected in the MALDI-TOF mass spectra of the unfractionated GCF collected from 12 periodontally healthy and 11 periodontally diseased subjects. From a comparison of the relative abundance of defensins, they found that the peak corresponding to the mass of HNP-1 was always the highest of the triplet while the peak matching with HNP-3 was always the lowest. These findings are consistent with the speculation that HNP-2 could be produced selectively by proteolysis of HNP-3, but not of HNP-1 [<xref rid=\"B64-ijms-21-05270\" ref-type=\"bibr\">64</xref>]. Additionally, on the basis of ion intensity scores, the authors observed that the defensins were more abundant in a higher proportion of the healthy sites in comparison to the diseased (although the differences were not statistically significant). Therefore, they hypothesized that high expression of HNPs in GCF of periodontally healthy gingivae serves as barrier against bacterial colonization. These findings on defensins expression based on MALDI-MS data from Lundy [<xref rid=\"B19-ijms-21-05270\" ref-type=\"bibr\">19</xref>] were however in contrast with those observed later by Dommisch [<xref rid=\"B17-ijms-21-05270\" ref-type=\"bibr\">17</xref>] in the SELDI-based investigations (<xref rid=\"ijms-21-05270-t002\" ref-type=\"table\">Table 2</xref>).</p><p>Wen et al. performed a profiling proteomic approach using GCF from 20 pubertal and 20 post-pubertal subjects in order to identify novel candidate biomarkers in GCF useful for the diagnosis of pubertal growth peak [<xref rid=\"B20-ijms-21-05270\" ref-type=\"bibr\">20</xref>]. The GCF samples were collected by paper points and stored at −80 °C until MALDI-TOF analysis. Sample processing was performed using TFA, without the use of a protease inhibitor cocktail (PIC) (see <xref rid=\"ijms-21-05270-t001\" ref-type=\"table\">Table 1</xref>). The unfractionated samples were analyzed directly by MALDI-TOF MS. The statistical analysis was performed after normalization for peak intensity and the results showed that six peptides were significantly different between the two groups. In particular, the peaks at <italic>m</italic>/<italic>z</italic> 1660.2, 1783.0 and 6108.9 were upregulated in the pubertal group compared to the post-pubertal group, while the peaks at <italic>m</italic>/<italic>z</italic> 5064.9, 2912.5 and 4178.6 were downregulated in the pubertal group compared to the post-pubertal group. Although primary sequence of these potential candidate biomarkers has not been identified, still the study provided a statistical model based on MALDI-TOF MS data able to detect the pubertal growth peak, demonstrating the potential use of this approach as a periodontal diagnostic tool. It should be taken into account that normalization for protein concentration was not performed, nor was the time of storage at −80 °C indicated.</p><p>Ngo and colleagues were the first to apply the sequencing capability of MALDI-TOF/TOF MS to identify the endogenous peptides of GCF collected from inflamed sites [<xref rid=\"B6-ijms-21-05270\" ref-type=\"bibr\">6</xref>]. By means of this technology, 33 endogenous peptides were identified with a molecular weight ranging from 1067 Da to 3700 Da. In particular, of these 33 peptides, 10 peptides were identified in unfractionated GCF from single sites from direct TOF/TOF sequencing experiments while the other 23 peptides were identified in GCF samples pooled from multiple inflamed sites and fractionated by nano-HPLC. Several peptides resulted to be fragments derived from salivary precursor proteins, possibly reflecting the protease activity in the periodontal pocket (see <xref rid=\"ijms-21-05270-t001\" ref-type=\"table\">Table 1</xref>). In this study, however, the identification of the three putative alpha-defensins peaks (HNP<sub>1–3</sub>) (<italic>m</italic>/<italic>z</italic> = 3370.9, <italic>m</italic>/<italic>z</italic> = 3442.2, and <italic>m</italic>/<italic>z</italic> = 3486.5), detected in the MALDI-TOF spectra and also revealed by previous investigations [<xref rid=\"B17-ijms-21-05270\" ref-type=\"bibr\">17</xref>,<xref rid=\"B19-ijms-21-05270\" ref-type=\"bibr\">19</xref>] was complicated because precursors ions did not fragment by collision-induced dissociation in MS/MS mode, due to the presence of the three characteristic internal disulfide bonds inside defensins. Therefore, the authors validated a putative assignment by using a reducing agent and showing that the resulting MALDI-TOF mass spectra displayed a +6 Da shift only in the mass of three peaks, finding consistent with the reduction of three internal disulfide bonds. Other peptides and small proteins were also identified alternatively by SDS-PAGE followed by in-gel digestion and MS analyses using both nano liquid chromatography (LC)-ESI-MS/MS and MALDI-TOF/TOF MS. All the peptides and small proteins of this study are summarized in <xref rid=\"ijms-21-05270-t001\" ref-type=\"table\">Table 1</xref>, while medium-size and larger proteins, also identified by the authors, are not listed in <xref rid=\"ijms-21-05270-t001\" ref-type=\"table\">Table 1</xref> because they’re outside of the scope of the present review.</p><p>Later on, the same group demonstrated that the combined use of MALDI-TOF MS with bioinformatics may allow the site-specific prediction of periodontal disease progression in a cohort of forty-one periodontal maintenance subjects [<xref rid=\"B21-ijms-21-05270\" ref-type=\"bibr\">21</xref>]. Each individual and specific site were followed over 12 months, with clinical measurements taken both at baseline and then every three months, collecting GCF to be analyzed at each visit [<xref rid=\"B21-ijms-21-05270\" ref-type=\"bibr\">21</xref>]. The authors compared the MALDI-TOF MS spectra of GCF of both healthy and diseased sites from patients with moderate to severe chronic periodontitis. The purpose of the study was to use these spectra to create models for disease diagnosis. In particular, to pursue this, they used a genetic algorithm to create a pattern analysis-based model to predict specifically sites which were undergoing attachment loss. Interestingly, the statistical algorithm showed that there was more similarity in the GCF profile between patients, than between sites with different severity of inflammation. However, even with these differences, MALDI profiles may be taken into account for the prediction of periodontal attachment loss at a site level with a very significant positive predictive value and, most importantly, before it can be measured by clinical examination. This valuable result can be achieved with one single test on each individual GCF sample. Remarkably, this was the first study which explored and preliminarily demonstrated the potential use of MALDI-TOF MS technology in the field of periodontal disease diagnostics.</p><p>Our group set up a standardized protocol for both GCF collection from healthy gingival tissue belonging to subjects diagnosed healthy by clinical examination and further GCF processing [<xref rid=\"B22-ijms-21-05270\" ref-type=\"bibr\">22</xref>]. Pre-analytical and analytical variables expected to influence the GCF peptidome profiling were assessed by MALDI-TOF MS analysis. More specifically, in a comparative study, we explored how the robustness and reproducibility of MALDI-TOF MS profiles are influenced by the use of paper points vs. paper strips, by the conditions of centrifugal elution and by the utilization of PIC (<xref rid=\"ijms-21-05270-t001\" ref-type=\"table\">Table 1</xref>). Moreover, the effects of both sinapinic acid composition and sample-to-matrix ratio in MALDI-TOF MS analysis were systematically estimated, with the aim of optimizing both the quality and robustness of signals in GCF MALDI-TOF spectra. Definitively, a simple and high throughput procedure was optimized to rapidly obtain reproducible peptidome profiles of GCF from healthy sites of clinically healthy subjects. Sample composition and dilution severely affected the peptidome profiles obtained in MALDI-TOF analysis, therefore in this study, the optimal elution volume was evaluated in the assessment of a standardized procedure. Among other findings, elution by TFA generated richer gingival fingerprints compared to those obtained by the use of acetic acid. PIC supplementation and centrifugation speed did not change GCF profiles. Matrix composition and matrix/sample volume ratio were also assessed to obtain robustness of the MS analysis ensuring CV less than 10% for peak area and signal-to-noise ratio. These results are of interest for standardizing both sample collection and handling methods for GCF peptidomic-based biomarker identification, although we are aware that the procedure we set up may have to be re-visited for clinical conditions differing from a healthy status (for instance, gingival inflammation or periodontal disease), in which it is known that the nature of the GCF changes dramatically.</p><p>As diagnostic peptide fingerprints may vary as a function of GCF collection, handling, and storage, our research group analyzed if storage conditions have an influence on the quality and the reproducibility of MALDI-TOF profiles for this biological fluid [<xref rid=\"B23-ijms-21-05270\" ref-type=\"bibr\">23</xref>]. Indeed, our data strongly suggest that sample storage conditions indeed affect the GCF peptide pattern over time in a quite significant manner. Specifically, a procedure of immediate extraction of GCF from paper strips, followed by storage of the extracted material, generates a lower amount of variation in molecular profiles as compared to the extraction performed after the storage on paper strips. Indeed, significant spectral changes were observed for GCF samples stored at −20 °C directly on the paper strips for three months and then extracted, in comparison to samples extracted immediately and then stored for three months in the same conditions. Moreover, a significant decrease in the peak area of HNP-3, of S100-A8 and of both full-length S100-A9 and its truncated form was detected after 3 months even at −80 °C when storage was on paper strips. The artifacts found in the “paper-stored GCF” profile may not only influence the pattern-based biomarker discovery, but also make its use not suitable for in vitro diagnostic test targeting. So S100-A8 and S100-A9 are proposed as periodontal disease potential diagnostic biomarkers [<xref rid=\"B49-ijms-21-05270\" ref-type=\"bibr\">49</xref>]. This study definitively shows that the signatures closest to those obtained with immediate elution and analysis were attained for the GCF samples eluted in TFA and then immediately stored at temperatures not higher than −80 °C for at most one month. The information gained from our findings on protein/patterns stability after storage may be useful in defining standardized protocols enabling optimal preservation of GCF samples.</p><p>Very recently, our group proposed a fast and easy to perform method for processing GCF before MS analysis, thus minimizing the risk of false positive detection due to degradation of particularly sensitive molecular species within GCF samples [<xref rid=\"B24-ijms-21-05270\" ref-type=\"bibr\">24</xref>]. In this investigation, twenty subjects (ten healthy and ten suffering from gingivitis) were recruited in order to perform comparative proteomics profiling studies between healthy and gingivitis subjects (<xref rid=\"ijms-21-05270-t001\" ref-type=\"table\">Table 1</xref> and <xref rid=\"ijms-21-05270-t002\" ref-type=\"table\">Table 2</xref>). So, we proposed a standardized methodology for GCF analysis by MALDI-TOF/TOF-MS in order to identify a characteristic peptide signature of gingivitis. Storage times were established of one and three months concerning GCF specimen to analyze, in order to better evaluate any degradation and changes in GCF peptidome patterns. Comparative analysis of normalized peak area from peptidome profiles showed a pattern of five <italic>m</italic>/<italic>z</italic> peaks (3371, 4136, 4964, 13,153, and 13,458) which discriminate the two groups in a statistically significant manner. Specifically, the normalized peak area for <italic>m</italic>/<italic>z</italic> 3371, 4136, and 13,458 was significantly higher in GCF from gingivitis subjects compared to GCF from the healthy group, while an inverse trend was observed for <italic>m</italic>/<italic>z</italic> = 4964 and for <italic>m</italic>/<italic>z</italic> = 13,153 (see <xref rid=\"ijms-21-05270-t002\" ref-type=\"table\">Table 2</xref>). The peak with <italic>m</italic>/<italic>z</italic> = 3371 was identified as the HNP-2 belonging to the triplet of alpha-defensins. Curiously, no significant change was observed for HNP-1 and HNP-3 between healthy and gingivitis groups. The other four peptides were identified as the C-terminal fragment of alpha-1-antitrypsin (AAT), namely C-36 peptide (<italic>m</italic>/<italic>z</italic> = 4136), as the β-thymosin (<italic>m</italic>/<italic>z</italic> = 4964) and, finally, two different post-translational modifications (PTMs) of the full-length S100-A9 protein were found (<italic>m</italic>/<italic>z</italic> = 13153 and <italic>m</italic>/<italic>z</italic> = 13458) (see <xref rid=\"ijms-21-05270-t001\" ref-type=\"table\">Table 1</xref>). The study also demonstrated the emerging role of MALDI-TOF MS in the high-throughput characterization of naturally occurring peptides from complex bodily fluid and its potential to directly identify constituents of the GCF peptidome and their proteoforms.</p><p>In a very recent study, Tang et al. analyzed GCF, saliva, and serum samples from 50 gender- and age-matched subjects suffering from chronic periodontitis (<italic>n</italic> = 17) and gingivitis (<italic>n</italic> = 17), and from healthy subjects (<italic>n</italic> = 16), in order to identify potential biomarkers of periodontal diseases by using MALDI-TOF MS [<xref rid=\"B25-ijms-21-05270\" ref-type=\"bibr\">25</xref>]. GCF samples were collected by paper strips, immediately deposited into a tube containing PBS; supernatants were recovered after centrifugation and stored at −80 °C (<xref rid=\"ijms-21-05270-t001\" ref-type=\"table\">Table 1</xref>). GCF, saliva and serum samples were fractionated using a weak cation exchange magnetic beads and analyzed immediately on a MALDI-TOF mass spectrometer or stored at −20 °C and analyzed within 24 h. In the comparison between chronic periodontitis and healthy groups, four GCF peptides were significantly higher in chronic periodontitis group compared with healthy (3434.4 Da, 4126.6 Da, 5407.7 Da, and 5416.0 Da) (see <xref rid=\"ijms-21-05270-t002\" ref-type=\"table\">Table 2</xref>). Among these, only the peak at <italic>m</italic>/<italic>z</italic> 3434.4 was successfully identified to be derived from haptoglobin by nano-LC/ESI-MS/MS analysis and its upregulation in chronic periodontitis patients was confirmed also for saliva samples. The authors identify other two peptides derived from haptoglobin both in serum and saliva (3874.9 Da and 1147.1 Da respectively) with an increased expression level in chronic periodontitis and gingivitis patients, thus confirming the results obtained with GCF. One strength of this study relies on the possibility to obtain specific molecular signatures for a given bodily fluid and then to compare the potential biomarkers identified across different bodily fluids. However, an important limitation, as pointed out by the authors themselves, is the absence of any kind of normalization for protein concentration. Furthermore, samples stored for the same amount of time need to be compared in order to ensure the robustness of the results; indeed, as discussed above, the protein profile of a sample analyzed immediately by MS may not be comparable to that of the same sample stored at −20 °C and analyzed within 24 h [<xref rid=\"B23-ijms-21-05270\" ref-type=\"bibr\">23</xref>,<xref rid=\"B24-ijms-21-05270\" ref-type=\"bibr\">24</xref>].</p><p>On the basis of blind MALDI-TOF analysis presently used in clinical microbiology for conventional bacterial strain identification, Antezack and colleagues adapted this analytical approach to MALDI peptide profiles acquired from GCF, saliva and dental plaque with the aim to develop a rapid diagnostic test for periodontitis [<xref rid=\"B26-ijms-21-05270\" ref-type=\"bibr\">26</xref>]. They compared the MALDI spectra to assess if a classification may be performed among individuals according to their periodontal status. In particular, the authors used the binary discriminant analysis method [<xref rid=\"B65-ijms-21-05270\" ref-type=\"bibr\">65</xref>] to evaluate differences between the groups of periodontitis (<italic>n</italic> = 67) and healthy periodontal (<italic>n</italic> = 74) subjects on the basis of a discriminant peak list (<xref rid=\"ijms-21-05270-t001\" ref-type=\"table\">Table 1</xref>). Based on the differentially expressed peaks from MALDI peptide fingerprints, a diagnostic decision tree for periodontitis was built for each type of sample. In the case of GCF, a pattern of nine peaks was chosen to set up a decision tree with a specificity of 98% and a sensitivity of 96%. In a blind experiment GCF data demonstrated the ability to segregate periodontitis patients from control individuals showing a specificity of 75.7% (±0.195) and a sensitivity of 79.6% (±0.188). Although the authors claim their study as the first “to demonstrate that MALDI-TOF MS differentiates periodontitis from healthy periodontium by blind identification of specific patterns in mass signals from protein profiles in saliva, GCF and dental plaque” [<xref rid=\"B26-ijms-21-05270\" ref-type=\"bibr\">26</xref>], the investigation suffers several limitations, in particular the absence of preliminary experiments designed to assess the impact of short-term storage at 4 °C of the analyzed samples and the total absence of quantification of total protein concentration in the same samples. In fact, concerning the first limitation (impact of short-term storage at 4 °C) although data in the literature show the small molecules like uric acid or cortisol are stable when saliva was stored for at 4 °C for four weeks [<xref rid=\"B66-ijms-21-05270\" ref-type=\"bibr\">66</xref>,<xref rid=\"B67-ijms-21-05270\" ref-type=\"bibr\">67</xref>] this may absolutely not hold true for protein or peptide components in the same fluid [<xref rid=\"B23-ijms-21-05270\" ref-type=\"bibr\">23</xref>,<xref rid=\"B24-ijms-21-05270\" ref-type=\"bibr\">24</xref>]. Further studies are absolutely necessary in order to increase the robustness of the diagnostic approach.</p></sec><sec id=\"sec2dot3-ijms-21-05270\"><title>2.3. ESI MS GCF Peptidome Profiling</title><p>Unlike the MALDI source which ionizes and sublimates the samples out of a crystalline, dry matrix by laser, the ESI source ionizes the analytes out of a solution and is therefore usually coupled to LC instruments [<xref rid=\"B68-ijms-21-05270\" ref-type=\"bibr\">68</xref>]. In the ESI source, the solution containing peptides analytes is passed through a needle to which a high voltage (~2–5 kV) is applied [<xref rid=\"B69-ijms-21-05270\" ref-type=\"bibr\">69</xref>]. This leads to the generation of a spray of small charged droplets. As the solvent evaporates, desolvation of peptide/protein-solvent droplets resulted in a multicharged ions formations. The ions are then accelerated into the mass analyzer for subsequent measurement of their mass-to-charge ratios and of ion intensity. A mathematical deconvolution performed by the software on the ESI spectrum allows the mass of the analyte to be determined with an accuracy of roughly 1/10,000 amu. The combined information of both the time of elution (obtained, when possible, by using protein standards) and precise mass value of the protein quite often allows non ambiguous identifications to be obtained. This platform may be used successfully for the analysis of small complex samples because it allows high sensitivity detection of different analytes at the same time.</p><p>The mass analyzers routinely used with ESI are quadrupole, linear ion trap, and Orbitrap, however we concisely describe only the last two analyzers, those used in the peptidomic investigations here reviewed. Briefly, in a linear ion trap, ions are confined in radial direction by a two-dimensional radio frequency field, and in axial direction by stopping potentials applied to end electrodes [<xref rid=\"B70-ijms-21-05270\" ref-type=\"bibr\">70</xref>]. The trapped ions are manipulated employing direct current and radio frequency electric fields in a series of carefully timed events. They can work as stand-alone mass spectrometers or into hybrid configurations combined with high-resolution mass analyzers. A correlation between the concentration of a given peptide and the ionic current, which is measured by the ion trap mass spectrometer, is however possible only under very reproducible conditions. Indeed, as the ionic current is directly proportional to the protonation level of the peptide, it is necessary to standardize the treatment of the samples. The linear ion trap mass spectrometer has become popular because of its robustness, fast scan speed, sensitivity, user friendliness and relatively low cost. However, resolution, mass accuracy and dynamic range are still not comparable to other mass spectrometers such as Orbitrap instruments [<xref rid=\"B71-ijms-21-05270\" ref-type=\"bibr\">71</xref>]. Orbitrap is a benchtop instrument that is cost effective, accessible, and applicable in hybrid architectures. The Orbitrap MS is composed of a barrel-like outer electrode and a spindle-like central electrode [<xref rid=\"B72-ijms-21-05270\" ref-type=\"bibr\">72</xref>]. In this electric field, ions rotate around the central electrode while oscillating down the length of the electrode; the frequency of these harmonic ion oscillations, undergone by the orbitally trapped ions, is used for <italic>m</italic>/<italic>z</italic> measurement [<xref rid=\"B72-ijms-21-05270\" ref-type=\"bibr\">72</xref>]. The main advantage of the Orbitrap analyzers are enhanced resolution (the highest available for a benchtop mass spectrometer mostly in hybrid architectures), high mass accuracy, high sensitivity, wide dynamic range, fast acquisition rates and multi-stage MS<sup>(<italic>n</italic>)</sup> capabilities for tandem MS experiments [<xref rid=\"B71-ijms-21-05270\" ref-type=\"bibr\">71</xref>,<xref rid=\"B73-ijms-21-05270\" ref-type=\"bibr\">73</xref>]</p><p>Pisano and colleagues were the first group to analyze GCF by reverse-phase (RP)-HPLC coupled to ESI MS [<xref rid=\"B27-ijms-21-05270\" ref-type=\"bibr\">27</xref>]. The authors analyzed the acidic-soluble protein content of GCF collected under physiological conditions. After the chromatographic separation, the eluate was directly introduced into an ion-trap mass spectrometer through ESI. Concerning peptides and small proteins, the HNPs 1–3 (alpha-defensins 1, 2, and 3) were the principal components detected, while only minor amounts of HNP-4, statherin, PB peptide, cystatin A were observed. Additionally, other unidentified components were detected with low mass values of 1067.4, 1109.4, and 1151.4 amu (probably corresponding to the same molecule showing a different degree of acetylation) and other peaks with mass values of 4135.0, 4936.4, 4964.0 and 4980.0 amu. This was also the first study to analytically demonstrate that GCF represents a physiological entity distinct from saliva. In fact, the authors did not detect the presence in chromatogram of the major proteins/peptides characteristic of human saliva such as acidic and basic proline-rich proteins. Although this approach does not allow for the detection of proteins that are not soluble in acidic solution, it contributed to the initial effort to discover the still under-represented GCF peptidome by an innovative MS-top down approach.</p><p>The same group (Inzitari et al.) demonstrated later, not only by HPLC-ESI-MS but also by immunohistochemical analysis, that gingival sulcus is the main source of Tβ4 in the oral cavity and that GFC contains high levels of Tβ4 and also of a second member of the same peptide family, namely Tβ10 [<xref rid=\"B28-ijms-21-05270\" ref-type=\"bibr\">28</xref>]. Of note, these peptides play a key role in diverse cellular functions such as tissue development, migration, angiogenesis, and wound healing [<xref rid=\"B28-ijms-21-05270\" ref-type=\"bibr\">28</xref>].</p><p>Very recently, Dassatti and colleagues also performed a pilot study analyzing the GCF proteomic profile by RP-HPLC coupled to ESI MS. GCF samples were collected during the first 15 post-partum days from 10 female patients with a clinical scenario of gingivitis and periodontitis (assessed by clinical periodontal parameters), and from the same patients after three months in order to evaluate the effects of a professional oral hygiene session and to analyze the associated variations of proteomic profile of GCF. A control group was created in order to compare the results with GCF samples from 10 not pregnant fertile women [<xref rid=\"B29-ijms-21-05270\" ref-type=\"bibr\">29</xref>]. The analysis of clinical periodontal parameters at three months post-partum revealed the therapeutic support of the professional oral hygiene session performed during the first visit. Proteomic profiling of GCF, performed by RP-HPLC-ESI coupled to a hybrid ion trap-Orbitrap mass spectrometer, highlighted the presence of peptides playing a key role during the inflammatory process and exerting defense mechanisms. These peptides, were then identified by MS/MS experiments as α-defensins, thymosin-β4 with one of its fragments (21–44), thymosin-β10, fibrinopeptide A and its fragments (fragments 21–35 and 22–35) and fibrinopeptide B (<xref rid=\"ijms-21-05270-t001\" ref-type=\"table\">Table 1</xref>). The levels of all the defensins (α-defensins 1–4) and of all the fibrinopeptides, were always higher during post-partum gingivitis and periodontitis, compared to the three months recall group and the control group (<xref rid=\"ijms-21-05270-t002\" ref-type=\"table\">Table 2</xref>). The value of thymosin-β4 decreased in the group of patients with periodontitis, while it was significantly increased in the three months recall group. A similar trend was also observed for thymosin-β10 (<xref rid=\"ijms-21-05270-t002\" ref-type=\"table\">Table 2</xref>). Otherwise, the expression level of thymosin β4 fragment (21–44) was higher during post-partum periodontitis compared to the three months recall group and the control group (<xref rid=\"ijms-21-05270-t002\" ref-type=\"table\">Table 2</xref>). These findings demonstrate that pregnant women may be at an increased risk of pathological dental conditions and underscore the importance of a good oral hygiene procedures in helping to prevent problems with dental health. It is also interesting to associate the study of the expression level of peptides involved in the inflammatory process and in defense mechanisms with the variation of the clinical parameters normally used for the evaluation of the gingival health status. However, the paper lacks some important information about sample collection and processing.</p></sec></sec><sec id=\"sec3-ijms-21-05270\"><title>3. The GCF Antimicrobial Peptidome</title><p>This section will focus in particular on the main findings concerning naturally occurring peptides, identified in GCF by profiling strategies performed without a previous proteolysis step propaedeutic to mass analysis. Considerations about their putative role and how their analysis might lead to the identification of novel biomarkers of periodontal health or disease will be discussed. As emerged from the literature reviewed in the previous sections, the top-down analysis of GCF peptides and their proteoforms (as they occurred in their intact form) poses several challenges for MS-based proteomics platforms. One of the main limitation arises in such cases from the short sequences of detected peptides, therefore the probability to find in their sequence basic aminoacidic residues is very low and as consequence those peptides may not be favorably charged for MS detection, as they are often singly charged and more difficult to efficiently fragment. Additionally, unlike bottom up approaches, the absence of Lys and Arg as non-tryptic peptides, hinders their detection. Furthermore, the conventional database search approach, which is normal routine in proteomics, is not suitable in this case. However these issues are extensively addressed also elsewhere [<xref rid=\"B74-ijms-21-05270\" ref-type=\"bibr\">74</xref>,<xref rid=\"B75-ijms-21-05270\" ref-type=\"bibr\">75</xref>]. Although definitive identification of naturally occurring peptides is particularly challenging, some MS instruments for example MALDI-TOF/TOF and ESI-ion trap or ESI-ion trap/Orbitrap provide not only a rapid screening of proteoforms [<xref rid=\"B18-ijms-21-05270\" ref-type=\"bibr\">18</xref>,<xref rid=\"B22-ijms-21-05270\" ref-type=\"bibr\">22</xref>,<xref rid=\"B23-ijms-21-05270\" ref-type=\"bibr\">23</xref>,<xref rid=\"B27-ijms-21-05270\" ref-type=\"bibr\">27</xref>], but also their sequencing and identifications [<xref rid=\"B6-ijms-21-05270\" ref-type=\"bibr\">6</xref>,<xref rid=\"B24-ijms-21-05270\" ref-type=\"bibr\">24</xref>,<xref rid=\"B25-ijms-21-05270\" ref-type=\"bibr\">25</xref>,<xref rid=\"B28-ijms-21-05270\" ref-type=\"bibr\">28</xref>,<xref rid=\"B29-ijms-21-05270\" ref-type=\"bibr\">29</xref>]. In the light of the above considerations, it is worth highlighting important results from several groups; for example Ngo and colleagues detected and identified for the first time histone protein or peptide in GCF by MALDI-TOF/TOF (<xref rid=\"ijms-21-05270-t001\" ref-type=\"table\">Table 1</xref>) [<xref rid=\"B6-ijms-21-05270\" ref-type=\"bibr\">6</xref>]. Interestingly, histone fragments, have been shown to have antibacterial activity [<xref rid=\"B76-ijms-21-05270\" ref-type=\"bibr\">76</xref>]. Other examples are the discovery of haptoglobin fragment in GCF by Tang et al. [<xref rid=\"B25-ijms-21-05270\" ref-type=\"bibr\">25</xref>] (<xref rid=\"ijms-21-05270-t001\" ref-type=\"table\">Table 1</xref>) as well as fibrinopeptide fragments by Dassatti et al. [<xref rid=\"B29-ijms-21-05270\" ref-type=\"bibr\">29</xref>] (<xref rid=\"ijms-21-05270-t001\" ref-type=\"table\">Table 1</xref>). It would possible to speculate that these fragments might play a more specific role in GCF compared to those played by their precursor protein. It is well known that haptoglobin is an acute-phase protein, which promotes anti-inflammation activities and could also act indirectly as an antioxidant and bacteriostatic agent [<xref rid=\"B77-ijms-21-05270\" ref-type=\"bibr\">77</xref>]. Its expression increases in response to injury or infection as it contributes to the recovery of homeostasis after systemic or local infection [<xref rid=\"B77-ijms-21-05270\" ref-type=\"bibr\">77</xref>]. Therefore, the authors hypothesized that the up-regulation of haptoglobin derived fragment levels in chronic periodontitis patients compared to healthy subjects (<xref rid=\"ijms-21-05270-t002\" ref-type=\"table\">Table 2</xref>) may reflect the anti-inflammatory and reestablishment process of the periodontium. Fibrinogen and fibrinopeptides are involved in hemostasis, wound healing, inflammation, angiogenesis, vascularization after injury, and other biological functions [<xref rid=\"B78-ijms-21-05270\" ref-type=\"bibr\">78</xref>]. So, the authors speculated that the increased levels in periodontal disease conditions of fibrinopeptide A and its fragments along with fibrinopeptide B (see <xref rid=\"ijms-21-05270-t002\" ref-type=\"table\">Table 2</xref>) may be due to their involvement in the healing process and revascularization of the area affected by periodontal disease.</p><p>Another point worthy of note was the detection of Thymosin β4, together with its sulfoxide form, and of Thymosin β10 by Inzitari and colleagues [<xref rid=\"B28-ijms-21-05270\" ref-type=\"bibr\">28</xref>] which demonstrated that gingival sulcus is the main source of Tβ4 in the oral cavity and that GFC is the main source of Tβ4 and Tβ10 [<xref rid=\"B28-ijms-21-05270\" ref-type=\"bibr\">28</xref>]. Although several bottom-up approaches documented the presence of Thymosin β4, they failed to identify Thymosin β10 or the sulfoxide form of Thymosin β4 [<xref rid=\"B14-ijms-21-05270\" ref-type=\"bibr\">14</xref>,<xref rid=\"B32-ijms-21-05270\" ref-type=\"bibr\">32</xref>,<xref rid=\"B39-ijms-21-05270\" ref-type=\"bibr\">39</xref>,<xref rid=\"B42-ijms-21-05270\" ref-type=\"bibr\">42</xref>,<xref rid=\"B45-ijms-21-05270\" ref-type=\"bibr\">45</xref>]. These peptides have multiple diverse cellular functions, including tissue development, migration, angiogenesis, and wound healing [<xref rid=\"B79-ijms-21-05270\" ref-type=\"bibr\">79</xref>]. In particular, multifunctional roles are attributed to Tβ4 in protecting cells against damage by acting as antimicrobial, anti-inflammatory, and anti-apoptotic agent [<xref rid=\"B80-ijms-21-05270\" ref-type=\"bibr\">80</xref>].</p><p>Our group detected two proteoforms of S100-A9 with <italic>m</italic>/<italic>z</italic> = 13,153 and 13,458 and the oxidized forms of S100-A8 [<xref rid=\"B23-ijms-21-05270\" ref-type=\"bibr\">23</xref>,<xref rid=\"B24-ijms-21-05270\" ref-type=\"bibr\">24</xref>]. S100-A9 together with S100 A8 belong to the damage-associated molecular pattern family members of danger signals that initiate and amplify local inflammation and innate immune responses [<xref rid=\"B81-ijms-21-05270\" ref-type=\"bibr\">81</xref>]. S100-A9 is frequently associated with S100-A8 to form the S100-A8/S100-A9 heterodimer, known as calprotectin, with anti-microbial, chemotactic, pro-apoptotic and anti-proliferative activities [<xref rid=\"B81-ijms-21-05270\" ref-type=\"bibr\">81</xref>]. Several studies shed light on the key role of calprotectin and its subunits in the pathobiology of a number of inflammatory conditions including periodontal diseases [<xref rid=\"B49-ijms-21-05270\" ref-type=\"bibr\">49</xref>,<xref rid=\"B81-ijms-21-05270\" ref-type=\"bibr\">81</xref>,<xref rid=\"B82-ijms-21-05270\" ref-type=\"bibr\">82</xref>]. In addition the GCF calprotectin levels were positively correlated with clinical parameters of periodontitis [<xref rid=\"B81-ijms-21-05270\" ref-type=\"bibr\">81</xref>]. Specifically, in our comparative study, we detected the acetylated form (<italic>m</italic>/<italic>z</italic> = 13153) and the acetylated and glutathionylated form of the S100-A9 (<italic>m</italic>/<italic>z</italic> = 13458) [<xref rid=\"B24-ijms-21-05270\" ref-type=\"bibr\">24</xref>]. The first was found decreased in gingivitis patients compared to the healthy controls, while the latter was found increased [<xref rid=\"B24-ijms-21-05270\" ref-type=\"bibr\">24</xref>]. It is common knowledge that GCF contains significant levels of reduced glutathione which is responsible for the local antioxidant capacity, typical of periodontally healthy subjects [<xref rid=\"B83-ijms-21-05270\" ref-type=\"bibr\">83</xref>] and that glutathionylation of S100-A9 alters its ability to form complexes with S100-A8, to bind endothelial cells, and limits neutrophil migration in inflammatory lesions [<xref rid=\"B84-ijms-21-05270\" ref-type=\"bibr\">84</xref>]. Based on these results, our group speculated that the glutathionylation in S100-A9 may result in a protective effect against oxidative process at the site of inflammation thus providing a coherent explanation to the observed reversed ratio between the <italic>m</italic>/<italic>z</italic> = 13,458 (both glutathionylated and acetylated) and the <italic>m</italic>/<italic>z</italic> = 13,153 (acetylated only) forms of S100-A9 peptide in gingivitis patients compared to healthy subjects [<xref rid=\"B24-ijms-21-05270\" ref-type=\"bibr\">24</xref>].</p><p>Lundy and colleagues, in line with other transcriptomics and proteomics studies [<xref rid=\"B4-ijms-21-05270\" ref-type=\"bibr\">4</xref>], found increased expression levels of S100-A8 in periodontitis and gingivitis sites compared to the healthy ones (<xref rid=\"ijms-21-05270-t002\" ref-type=\"table\">Table 2</xref>) [<xref rid=\"B16-ijms-21-05270\" ref-type=\"bibr\">16</xref>]. However, it is worth noting that, in some comparative proteomics studies, S100-A8 and S100-A9 are not differentially expressed, even if detected [<xref rid=\"B32-ijms-21-05270\" ref-type=\"bibr\">32</xref>,<xref rid=\"B33-ijms-21-05270\" ref-type=\"bibr\">33</xref>,<xref rid=\"B34-ijms-21-05270\" ref-type=\"bibr\">34</xref>,<xref rid=\"B41-ijms-21-05270\" ref-type=\"bibr\">41</xref>], suggesting various hypotheses as already reported elsewhere [<xref rid=\"B23-ijms-21-05270\" ref-type=\"bibr\">23</xref>] and discussed in the next sections.</p><p>Among several AMPs detected by the MS profiling platforms, here described and summarized in <xref rid=\"ijms-21-05270-t001\" ref-type=\"table\">Table 1</xref> and <xref rid=\"ijms-21-05270-t002\" ref-type=\"table\">Table 2</xref>, important families such as human alpha-defensins (HNPs 1–4) and human beta-defensins (hBD-1 and hBD-2) and also the cathelicidin antimicrobial LL-37 peptide were studied.</p><p>Concerning the alpha defensins (HNPs 1–4) the results of comparative studies between healthy and gingivitis groups or healthy and periodontitis groups (<xref rid=\"ijms-21-05270-t002\" ref-type=\"table\">Table 2</xref>) showed that defensins, with the exclusion of one study [<xref rid=\"B19-ijms-21-05270\" ref-type=\"bibr\">19</xref>], are generally increased in periodontal conditions (gingivitis and periodontitis, <xref rid=\"ijms-21-05270-t002\" ref-type=\"table\">Table 2</xref>). Similarly to other AMPs, defensins exert their protective role against microbes entering the oral cavity resulting in effective control of infections. However, even with a broad bactericidal effect, HNPs have been demonstrated to be rather ineffective in vitro against various periodontal pathogens, at least at concentrations in which they are found in healthy gingival crevice [<xref rid=\"B85-ijms-21-05270\" ref-type=\"bibr\">85</xref>]. Increased expression of α-defensins may induce tissue damage both directly, by their observed cytotoxic effects on cells [<xref rid=\"B86-ijms-21-05270\" ref-type=\"bibr\">86</xref>], and indirectly, by competing with neutrophil elastase for the binding of α-1-antitrypsin (AAT), an inhibitor of neutrophil elastase [<xref rid=\"B87-ijms-21-05270\" ref-type=\"bibr\">87</xref>]. Consequently, defensins may influence the balance between proteases and their inhibitors and increased defensins levels observed both in peptidomics studies (<xref rid=\"ijms-21-05270-t002\" ref-type=\"table\">Table 2</xref>) [<xref rid=\"B17-ijms-21-05270\" ref-type=\"bibr\">17</xref>,<xref rid=\"B18-ijms-21-05270\" ref-type=\"bibr\">18</xref>,<xref rid=\"B24-ijms-21-05270\" ref-type=\"bibr\">24</xref>,<xref rid=\"B29-ijms-21-05270\" ref-type=\"bibr\">29</xref>] and also in proteomics investigations [<xref rid=\"B34-ijms-21-05270\" ref-type=\"bibr\">34</xref>,<xref rid=\"B38-ijms-21-05270\" ref-type=\"bibr\">38</xref>,<xref rid=\"B40-ijms-21-05270\" ref-type=\"bibr\">40</xref>] may cause augmented proteolytic activity, thus contributing to the inflammation and the tissue damage observed in gingivitis. As shown in <xref rid=\"ijms-21-05270-t002\" ref-type=\"table\">Table 2</xref>, in our studies we detected significantly increased levels of only HNP-2 in the gingivitis group while no significant change was observed for HNP-1 and HNP-3 between healthy and gingivitis groups [<xref rid=\"B24-ijms-21-05270\" ref-type=\"bibr\">24</xref>]. Our results were partially in agreement with the findings of Dommisch et al. who observed increased levels of HNP-2 and, to a lesser extent, of HNP-1 (although both not statistically significant) in gingivitis sites in comparison to the healthy sites while no variation of HNP-3 was reported [<xref rid=\"B17-ijms-21-05270\" ref-type=\"bibr\">17</xref>]. However, the general observed trend of increase of defensins in periodontal diseases was inverted in the study of Lundy and colleagues as they found that the defensins expressions were more abundant in a higher proportion of the healthy sites in comparison to the diseased (although the differences were not statistically significant) [<xref rid=\"B19-ijms-21-05270\" ref-type=\"bibr\">19</xref>].</p><p>Interestingly, Diamond and colleagues detected in GCF not only the alfa defensins, but also the β -defensins hBD-1 and hBD-2, although hBD-1 was also found “in several smaller forms” suggesting extracellular proteolysis [<xref rid=\"B18-ijms-21-05270\" ref-type=\"bibr\">18</xref>]. It is interesting to note that a higher amount of both hBD-2 and α-defensins, with little hBD-1, was observed in the subject with higher levels of inflammation (individual 1, <xref rid=\"ijms-21-05270-t002\" ref-type=\"table\">Table 2</xref>). This observation is in line with increased levels of both hBD-2 and neutrophils in inflammation. β-defensins are small cationic peptides produced by gingival epithelium that are involved in the innate host defense against the bacterial challenge, which is continuously present in the oral cavity [<xref rid=\"B88-ijms-21-05270\" ref-type=\"bibr\">88</xref>]. β-defensins play a key role in the awakening of the innate immune response to increased bacterial exposure in the gingival epithelium and exert combined antimicrobial, chemotactic and anti-inflammatory properties. These peptides contribute to the healing process of gingival wounds, regenerating the damaged epithelium by promoting the attachment and proliferation of fibroblasts on the diseased root surfaces [<xref rid=\"B88-ijms-21-05270\" ref-type=\"bibr\">88</xref>].</p><p>Another relevant antimicrobial peptide detected by Dommisch and colleagues is the human cathelicidin LL-37, a peptide of 37 amino acid starting with two leucine residues which has a broad spectrum of antimicrobial activity [<xref rid=\"B89-ijms-21-05270\" ref-type=\"bibr\">89</xref>]. It is stored as biologically inactive precursor in the secondary granules of neutrophils and it acquires its antimicrobial potency after proteolysis exerted by proteinase 3. LL-37 regulates inflammatory and immune responses, promotes angiogenesis and wound healing and, finally, also neutralizes lipopolysaccharides. The increased expression of this peptide in samples obtained from sites characterized by gingivitis compared to healthy periodontal sites (<xref rid=\"ijms-21-05270-t002\" ref-type=\"table\">Table 2</xref>), together with the increased expression in the same diseased sites of HNP-1 and HNP-2, induced Dommisch and colleagues to speculate that LL-37 might be a good indicator of the antimicrobial capabilities of the gingival apparatus provided by neutrophils and that there might be an association between a parallel secretion of these AMPs due to the increased numbers of neutrophils in inflammation.</p><p>Our group identified for the first time the sequence of the peptide <italic>m</italic>/<italic>z</italic> = 4136 already detected in GCF in previous top down proteomics studies, but not structurally elucidated [<xref rid=\"B27-ijms-21-05270\" ref-type=\"bibr\">27</xref>]. Despite technical issues, due not only to the complexity of GCF mixture but also to the high molecular weight of the precursor ion (<italic>m</italic>/<italic>z</italic> = 4136), a direct high-energy CID fragmentation of the selected peak allowed us to identify it as C-36 peptide corresponding to the residue fragment 383–418 of AAT protein [<xref rid=\"B24-ijms-21-05270\" ref-type=\"bibr\">24</xref>] (<xref rid=\"ijms-21-05270-t001\" ref-type=\"table\">Table 1</xref>). This peptide showed a statistically significant increase in gingivitis group compared to healthy group as previously described in the <xref ref-type=\"sec\" rid=\"sec2dot2-ijms-21-05270\">Section 2.2</xref> [<xref rid=\"B24-ijms-21-05270\" ref-type=\"bibr\">24</xref>] (<xref rid=\"ijms-21-05270-t002\" ref-type=\"table\">Table 2</xref>). Interestingly, C-36 peptide deriving from AAT, exerts significant pro-inflammatory activity [<xref rid=\"B90-ijms-21-05270\" ref-type=\"bibr\">90</xref>]. In fact, it was found expressed in human lung tissue as pro-inflammatory activator of human monocytes [<xref rid=\"B91-ijms-21-05270\" ref-type=\"bibr\">91</xref>]. Moreover, the proteolytic cleavage of AAT at sites of inflammation may reverse the anti-inflammatory effect of AAT thus contributing to neutrophil recruitment and activation [<xref rid=\"B92-ijms-21-05270\" ref-type=\"bibr\">92</xref>]. Considering that inflamed sites are populated by several bacterial species secreting proteolytic enzymes able to generate protein-breakdown products [<xref rid=\"B93-ijms-21-05270\" ref-type=\"bibr\">93</xref>], it is tempting to speculate that the C-36 peptide, which was found increased in gingivitis patients as compared to healthy subjects [<xref rid=\"B24-ijms-21-05270\" ref-type=\"bibr\">24</xref>], might be derived by AAT bacterial proteolytic breakdown. It should be stressed that a bottom-up LC MS/MS approach would have failed to address the levels of AAT endogenous fragment, due to the requirement for the proteomic mixture to be digested before it can be mass analyzed, whereas our top down MALDI-TOF platform allowed for the detection of increased levels of C-36 peptide in the GCF of gingivitis patients [<xref rid=\"B24-ijms-21-05270\" ref-type=\"bibr\">24</xref>].</p><p>All these findings further demonstrate the utility of peptidomics profiling platforms for screening and detecting proteoforms, including products of specific proteolytic cleavages, which can have different effects and important biological roles on periodontal diseases processes. The ability to characterize these species as they occur in GCF is essential for understanding the mechanism of the pathobiological response to oral diseases.</p></sec><sec id=\"sec4-ijms-21-05270\"><title>4. Relevance and Challenge of Pre-Analytical and Analytical Variables</title><p>To date, the effects of different sampling protocols and the influence of both pre-analytical and analytical conditions on GCF proteomic profiling have not been extensively addressed in many of the proteomics and peptidomics investigations which analyzed this specific biological fluid. The inherent limitations of GCF, due to its tiny amount in healthy gums as well as its heterogeneity in periodontal diseases, pose many concerns especially in comparative studies, which require rigorous protocols for protein recovery and normalization of MS data. Other concerns arise from artefacts due to storage conditions. Furthermore, in many cases, investigations which are biased by inter-individual variability of GCF samples, were performed on too small size groups to guarantee sufficiently robust statistical analysis. Last but not least, the comparison of the results obtained among several research groups is not so straightforward and the strategy adopted (bottom-up or top-down) should also be taken into account.</p><sec id=\"sec4dot1-ijms-21-05270\"><title>4.1. Quantitation Issues</title><p>More than five decades of scientific literature have definitively demonstrated that different sampling methodologies, different sampling times and different processing protocols significantly affected both the quality and the quantity of GCF samples [<xref rid=\"B94-ijms-21-05270\" ref-type=\"bibr\">94</xref>]. Up to date, only a few quantitative proteomics investigations have been performed with the aim to differentiate GCF protein profiles in periodontal health and disease [<xref rid=\"B14-ijms-21-05270\" ref-type=\"bibr\">14</xref>,<xref rid=\"B30-ijms-21-05270\" ref-type=\"bibr\">30</xref>,<xref rid=\"B33-ijms-21-05270\" ref-type=\"bibr\">33</xref>,<xref rid=\"B38-ijms-21-05270\" ref-type=\"bibr\">38</xref>,<xref rid=\"B39-ijms-21-05270\" ref-type=\"bibr\">39</xref>,<xref rid=\"B40-ijms-21-05270\" ref-type=\"bibr\">40</xref>,<xref rid=\"B44-ijms-21-05270\" ref-type=\"bibr\">44</xref>]. In MALDI-TOF MS, but also in other MS platforms, the ionization is influenced not only by the analyte concentration, but also by its primary structure (in fact, the presence of one or more basic residues in a peptide/protein promotes the ionization). Therefore, in the MS investigations which analyze peptides without an hydrolysis preceding step, MS spectra and MS tandem spectra could result poor and in such cases the spectra interpretation may result challenging in comparison to those obtained by bottom-up procedures in which the presence of tryptic peptides facilitates the ionization and the fragmentation step in MS/MS analysis. The lack in some peptides of a positive charge on the N-terminal residue due to PTMs (acetylation/pyroglutamylation) and the simultaneous absence of basic residues such as Lys, Arg, or His hinder the detection of these peptides [<xref rid=\"B74-ijms-21-05270\" ref-type=\"bibr\">74</xref>,<xref rid=\"B75-ijms-21-05270\" ref-type=\"bibr\">75</xref>].</p><p>Concerning peptidomics studies, the quest for quantitative approach is stringently desirable in order to make published data comparable between each other. The lack of data uniformity is due to several factors, including the difficulty in accurately measuring the small volumes of GCF obtained (especially for healthy sites) and the differences both in collection methodology and processing protocols, as pointed out in <xref rid=\"ijms-21-05270-t001\" ref-type=\"table\">Table 1</xref>. An additional problem, which complicates the analysis and standardization issues in GCF analysis, is the amount of volume used to elute the sample collected on paper strips or on paper points. In particular, this is an important issue in the case of SELDI and MALDI based investigations (<xref rid=\"ijms-21-05270-t001\" ref-type=\"table\">Table 1</xref>). In fact, it is well accepted that MALDI-TOF MS spectra of complex bodily fluids are heavily influenced by the extent of sample dilution [<xref rid=\"B63-ijms-21-05270\" ref-type=\"bibr\">63</xref>,<xref rid=\"B95-ijms-21-05270\" ref-type=\"bibr\">95</xref>,<xref rid=\"B96-ijms-21-05270\" ref-type=\"bibr\">96</xref>]. To address this issue in our investigations, experiments at different elution volumes have been performed and both the number of peaks and total peak area in MALDI-TOF MS spectra have been assessed [<xref rid=\"B22-ijms-21-05270\" ref-type=\"bibr\">22</xref>]. Indeed, the observation that more concentrated solutions showed both a lower number of peaks and a lower total peak area in comparison to more diluted samples was the most evident and expected behavior; not surprisingly, this was consistent with the MALDI ionization process. Furthermore, since GCF is a complex biological sample for the presence of endogenous peptides and proteins of different structures and different concentrations, the occurrence of ion suppression effects may also take place [<xref rid=\"B97-ijms-21-05270\" ref-type=\"bibr\">97</xref>,<xref rid=\"B98-ijms-21-05270\" ref-type=\"bibr\">98</xref>].</p><p>The effect of heterogeneity of both sample/matrix and ion signals determines poor MALDI data reproducibility, making this kind of analysis unsuitable for quantitative purposes [<xref rid=\"B62-ijms-21-05270\" ref-type=\"bibr\">62</xref>]. In order to make MS profiling analysis more robust the use of internal standard and/or a better assessment of peak area normalization are required. Several studies have shown that with the appropriate internal standard, linear calibration curves can be generated providing a correct quantification especially for small molecules and peptides, even in complex biological matrices [<xref rid=\"B62-ijms-21-05270\" ref-type=\"bibr\">62</xref>,<xref rid=\"B99-ijms-21-05270\" ref-type=\"bibr\">99</xref>,<xref rid=\"B100-ijms-21-05270\" ref-type=\"bibr\">100</xref>,<xref rid=\"B101-ijms-21-05270\" ref-type=\"bibr\">101</xref>]. The use of internal standard can be avoided when a linear correlation between analyte concentration and ion counts can be demonstrated [<xref rid=\"B102-ijms-21-05270\" ref-type=\"bibr\">102</xref>]. A possible alternative to the utilization of an internal standard is the use of Ionic-Liquid Matrices [<xref rid=\"B103-ijms-21-05270\" ref-type=\"bibr\">103</xref>].</p></sec><sec id=\"sec4dot2-ijms-21-05270\"><title>4.2. PIC</title><p>The presence in the GCF of active proteases, of both human and bacterial origin might constitute serious drawbacks making GCF peptidomics analysis very challenging. As in saliva and in other bodily fluids, the degradation and/or protease activity in GCF should be taken in great account. In light of possible protease activity, in such peptidomics studies after sample collection the use of protease inhibitors or of other precautions to prevent protease activity might be highly recommended. Concerning the solutions or buffer used to elute GCF collected on paper strips, in our experience, the use of acidic aqueous solutions is preferable to neutral or basic aqueous/organic ones, because acidic elution quenches protease activity, preserving proteins from potential protease degradations [<xref rid=\"B27-ijms-21-05270\" ref-type=\"bibr\">27</xref>,<xref rid=\"B104-ijms-21-05270\" ref-type=\"bibr\">104</xref>]. It is interesting to underline that the use of acidic buffer in peptidomics investigations might turn useful also for other reasons. In fact, high molecular weight proteins such as mucins, lactoferrin, α-amylases, and myeloperoxidases, found to be present in GCF [<xref rid=\"B14-ijms-21-05270\" ref-type=\"bibr\">14</xref>,<xref rid=\"B30-ijms-21-05270\" ref-type=\"bibr\">30</xref>,<xref rid=\"B31-ijms-21-05270\" ref-type=\"bibr\">31</xref>,<xref rid=\"B33-ijms-21-05270\" ref-type=\"bibr\">33</xref>,<xref rid=\"B34-ijms-21-05270\" ref-type=\"bibr\">34</xref>,<xref rid=\"B36-ijms-21-05270\" ref-type=\"bibr\">36</xref>,<xref rid=\"B37-ijms-21-05270\" ref-type=\"bibr\">37</xref>,<xref rid=\"B38-ijms-21-05270\" ref-type=\"bibr\">38</xref>,<xref rid=\"B40-ijms-21-05270\" ref-type=\"bibr\">40</xref>,<xref rid=\"B41-ijms-21-05270\" ref-type=\"bibr\">41</xref>,<xref rid=\"B42-ijms-21-05270\" ref-type=\"bibr\">42</xref>,<xref rid=\"B43-ijms-21-05270\" ref-type=\"bibr\">43</xref>,<xref rid=\"B44-ijms-21-05270\" ref-type=\"bibr\">44</xref>,<xref rid=\"B45-ijms-21-05270\" ref-type=\"bibr\">45</xref>,<xref rid=\"B46-ijms-21-05270\" ref-type=\"bibr\">46</xref>,<xref rid=\"B47-ijms-21-05270\" ref-type=\"bibr\">47</xref>] and which could hinder the detection of peptidic components [<xref rid=\"B105-ijms-21-05270\" ref-type=\"bibr\">105</xref>], are insoluble in acidic conditions [<xref rid=\"B27-ijms-21-05270\" ref-type=\"bibr\">27</xref>]. Studies on peptidome stability of GCF samples with PIC supplementation at neutral or basic pH are not yet reported in literature. Many research groups make use of acidic buffer to elute or dilute GCF immediately after the collection [<xref rid=\"B6-ijms-21-05270\" ref-type=\"bibr\">6</xref>,<xref rid=\"B17-ijms-21-05270\" ref-type=\"bibr\">17</xref>,<xref rid=\"B18-ijms-21-05270\" ref-type=\"bibr\">18</xref>,<xref rid=\"B20-ijms-21-05270\" ref-type=\"bibr\">20</xref>,<xref rid=\"B21-ijms-21-05270\" ref-type=\"bibr\">21</xref>,<xref rid=\"B22-ijms-21-05270\" ref-type=\"bibr\">22</xref>,<xref rid=\"B23-ijms-21-05270\" ref-type=\"bibr\">23</xref>,<xref rid=\"B24-ijms-21-05270\" ref-type=\"bibr\">24</xref>,<xref rid=\"B27-ijms-21-05270\" ref-type=\"bibr\">27</xref>,<xref rid=\"B28-ijms-21-05270\" ref-type=\"bibr\">28</xref>] and, as indicated in <xref rid=\"ijms-21-05270-t001\" ref-type=\"table\">Table 1</xref>, for all of the investigations reviewed, the use [<xref rid=\"B22-ijms-21-05270\" ref-type=\"bibr\">22</xref>,<xref rid=\"B23-ijms-21-05270\" ref-type=\"bibr\">23</xref>,<xref rid=\"B24-ijms-21-05270\" ref-type=\"bibr\">24</xref>] or not [<xref rid=\"B6-ijms-21-05270\" ref-type=\"bibr\">6</xref>,<xref rid=\"B16-ijms-21-05270\" ref-type=\"bibr\">16</xref>,<xref rid=\"B17-ijms-21-05270\" ref-type=\"bibr\">17</xref>,<xref rid=\"B18-ijms-21-05270\" ref-type=\"bibr\">18</xref>,<xref rid=\"B19-ijms-21-05270\" ref-type=\"bibr\">19</xref>,<xref rid=\"B20-ijms-21-05270\" ref-type=\"bibr\">20</xref>,<xref rid=\"B21-ijms-21-05270\" ref-type=\"bibr\">21</xref>,<xref rid=\"B25-ijms-21-05270\" ref-type=\"bibr\">25</xref>,<xref rid=\"B26-ijms-21-05270\" ref-type=\"bibr\">26</xref>,<xref rid=\"B27-ijms-21-05270\" ref-type=\"bibr\">27</xref>,<xref rid=\"B28-ijms-21-05270\" ref-type=\"bibr\">28</xref>,<xref rid=\"B29-ijms-21-05270\" ref-type=\"bibr\">29</xref>] of PIC is shown.</p><p>The levels of the peptides present in bodily fluids might reflect the activity of protease(s) generating them, which could in turn be influenced by various biological events. Thus, the amount of certain peptides can be used as indirect sensors of the biological state of an individual and, in the end, could provide invaluable information for clinical diagnoses [<xref rid=\"B57-ijms-21-05270\" ref-type=\"bibr\">57</xref>]. Among the peptidomics studies reviewed here, many investigations have shown in GCF the presence of peptide fragments derived by proteolytic cleavage from larger proteins such as Fibrinopeptide A fragments (21–35) and (22–35) and Fibrinopeptide B [<xref rid=\"B29-ijms-21-05270\" ref-type=\"bibr\">29</xref>], haptoglobin derived peptide [<xref rid=\"B25-ijms-21-05270\" ref-type=\"bibr\">25</xref>], Thymosin β-4 fragment (21–44) [<xref rid=\"B29-ijms-21-05270\" ref-type=\"bibr\">29</xref>], hBD-1 derived peptides [<xref rid=\"B18-ijms-21-05270\" ref-type=\"bibr\">18</xref>] and peptides fragments derived from salivary and serum precursor proteins [<xref rid=\"B6-ijms-21-05270\" ref-type=\"bibr\">6</xref>], C36 peptide AAT [<xref rid=\"B24-ijms-21-05270\" ref-type=\"bibr\">24</xref>]. Many of these peptides, arising from precursor proteins after exquisitely specific proteolytic cleavages, could exert significant biological activity against various kinds of microorganisms as already outlined in previous section. However, these findings currently remain only mere hypotheses and targeted experiments are required in order to support these possible speculations.</p><p>Careful attention should be used in the interpretation of the results of all the studies in which neither protease inhibitors nor other precautions to prevent protease activity were used during sample collection, as the presence of fragments identified as putative biomarkers of pathology may simply be due to the action of uninhibited proteases.</p><p>Ultimately, the presence of glutathione in stored GCF determines glutathionylation patterns of such protein target [<xref rid=\"B23-ijms-21-05270\" ref-type=\"bibr\">23</xref>] lowering the concentration of the protein as demonstrated in our previous work [<xref rid=\"B23-ijms-21-05270\" ref-type=\"bibr\">23</xref>]. The above considerations, together with the degradation patterns observed during storage in various conditions [<xref rid=\"B23-ijms-21-05270\" ref-type=\"bibr\">23</xref>,<xref rid=\"B24-ijms-21-05270\" ref-type=\"bibr\">24</xref>], underline the requirement to better asses the concentration and storage issues (see below) not only in top down but also in bottom up approaches in order to make the results comparable among each other for a consistency in data interpretation.</p></sec><sec id=\"sec4dot3-ijms-21-05270\"><title>4.3. Storage</title><p>Among the various pre-analytical issues that deeply affect the peptide profiles of GCF, the storage conditions may strongly compromise the correct interpretation of data analysis if not properly assessed. To address this issue, our group studied the stability of GCF MALDI-TOF profiles after sample collection under different storage conditions [<xref rid=\"B23-ijms-21-05270\" ref-type=\"bibr\">23</xref>,<xref rid=\"B24-ijms-21-05270\" ref-type=\"bibr\">24</xref>]. Specifically, the storage of GCF immediately extracted from paper strips was found to generate less variations in molecular profiles compared to those obtained when the extraction is performed after the storage. In fact, significant spectral changes were detected for those samples stored at −20 °C directly on the paper strips and extracted after three months, in comparison to the freshly extracted control [<xref rid=\"B23-ijms-21-05270\" ref-type=\"bibr\">23</xref>]. Still, even in the case of GCF samples immediately extracted from paper strips, a significant decrease in the peak area of HNP-3, S100-A8, full-length S100-A9, and its truncated form were detected after three months of storage, even at a temperature as low as −80 °C [<xref rid=\"B23-ijms-21-05270\" ref-type=\"bibr\">23</xref>].</p><p>Based on the above findings, we conclude that the storage conditions may strongly compromise the correct data interpretation, since alterations of “stored GCF” profile may influence the pattern-based biomarker discovery. As a consequence, the samples to be compared (healthy versus periodontal diseases) should be stored for the same amount of time in order to minimize the chance that the results of the study may be biased by potential artifacts.</p></sec><sec id=\"sec4dot4-ijms-21-05270\"><title>4.4. The Inter-Individual Variability</title><p>Considering the intrinsic inter-individual variability of GCF samples, large cohorts are necessary in order to compare the proteomes in the presence or absence of periodontitis. As reported in <xref rid=\"ijms-21-05270-t002\" ref-type=\"table\">Table 2</xref>, only few case-control reports describing differences between healthy and gingivitis [<xref rid=\"B24-ijms-21-05270\" ref-type=\"bibr\">24</xref>] or healthy and periodontitis subjects [<xref rid=\"B19-ijms-21-05270\" ref-type=\"bibr\">19</xref>,<xref rid=\"B25-ijms-21-05270\" ref-type=\"bibr\">25</xref>] or sites [<xref rid=\"B16-ijms-21-05270\" ref-type=\"bibr\">16</xref>,<xref rid=\"B17-ijms-21-05270\" ref-type=\"bibr\">17</xref>,<xref rid=\"B29-ijms-21-05270\" ref-type=\"bibr\">29</xref>] were reported; moreover only one experimental model of feasibility blinded test [<xref rid=\"B26-ijms-21-05270\" ref-type=\"bibr\">26</xref>] emerged (<xref rid=\"ijms-21-05270-t002\" ref-type=\"table\">Table 2</xref>).</p><p>With the exception of the studies by Tang and colleagues (<italic>n</italic> = 50) [<xref rid=\"B25-ijms-21-05270\" ref-type=\"bibr\">25</xref>] and by Antezack et al. (<italic>n</italic> = 141) [<xref rid=\"B26-ijms-21-05270\" ref-type=\"bibr\">26</xref>], a limitation of the reports summarized in <xref rid=\"ijms-21-05270-t002\" ref-type=\"table\">Table 2</xref> is the small sample size.</p><p>It is quite conceivable that the subset of potential biomarkers identified in a small sample size might not be confirmed on a larger cohort of patients. Conversely, the use of larger sample sizes should provide increased statistical power allowing the identification of additional biomarkers. Investigations on larger cohorts could provide a more accurate qualitative interpretation of peptide signatures, for well-defined clinical applications.</p></sec></sec><sec id=\"sec5-ijms-21-05270\"><title>5. Bioinformatics Approaches</title><p>It should be considered that the majority of data in spectral libraries are built from in-silico enzymatic digestion of proteins, limiting the analysis only to those peptides derived from expected enzyme cleavage sites and limiting the number of PTMs considered; for this reason the datasets are difficult to leverage for peptidomics [<xref rid=\"B74-ijms-21-05270\" ref-type=\"bibr\">74</xref>]. Moreover, the limited length of the aminoacidic sequences makes peptidomics spectral data analysis and interpretation more challenging in comparison to conventional bottom-up proteomics. As already underlined in <xref ref-type=\"sec\" rid=\"sec3-ijms-21-05270\">Section 3</xref>, the studies here reported relate to ‘non-tryptic’ peptides which generate MS/MS patterns less informative in comparison to the ‘tryptic digested peptides’. In fact, due to their intrinsic nature, the absence of basic Lysine or Arginine at the C-terminal sequence might hurdle the fragmentation process and, as a consequence, generation of poor MS/MS spectra could be observed. In line with these observations, the same bioinformatics strategies used for bottom-up approaches do not perform efficiently when less predictive and informative MS/MS patterns are obtained from endogenous peptides [<xref rid=\"B75-ijms-21-05270\" ref-type=\"bibr\">75</xref>]. It is important to highlight that, unlike bottom-up proteomics, software solutions for peptidome characterization are not fully developed and data analysis often requires laborious manual interpretation and rigorous validation especially when the identification is based on a single peptide [<xref rid=\"B74-ijms-21-05270\" ref-type=\"bibr\">74</xref>,<xref rid=\"B75-ijms-21-05270\" ref-type=\"bibr\">75</xref>]. <italic>De novo</italic> sequencing algorithms assisted by classical database search programs for sensitive and accurate peptide identification are also currently available [<xref rid=\"B106-ijms-21-05270\" ref-type=\"bibr\">106</xref>,<xref rid=\"B107-ijms-21-05270\" ref-type=\"bibr\">107</xref>]. <italic>De novo</italic> sequence assignments (when manually performed) often require skilled and experienced personnel and, besides fragmentation patterns, further acquired confirmations for identification can possibly be the assessment of ion intensities and in the case of LC-ESI experiments, evaluation of accurate retention times [<xref rid=\"B27-ijms-21-05270\" ref-type=\"bibr\">27</xref>]. The use of software for predicted fragmentations is one of most cost-effective way to validate the identification [<xref rid=\"B108-ijms-21-05270\" ref-type=\"bibr\">108</xref>]. For example, concerning both MALDI-TOF/TOF and ESI experiments, when the automated software identify with low confidence endogenous peptide and their fragments, in order to overcame these difficulties and to reach high confidence identification/validation, the experimental mass value of the peptide derived from unspecific cleavage of precursor protein/endogenous peptide can be compared with average theoretical mass values using the FindPept or PeptideMass software (available at <uri xlink:href=\"http://www.expasy.org/tools\">http://www.expasy.org/tools</uri>) and the experimental MS/MS spectrum can be compared to the MS/MS spectrum generated from the Protein Prospector (<uri xlink:href=\"http://prospector.ucsf.edu/\">http://prospector.ucsf.edu/</uri>). Furthermore, several tools have been developed for peptide mapping such as iPiG, EnzymePredictor, PatternLab, Peptigram, UStags, PepNovo, and MS-Tag [<xref rid=\"B74-ijms-21-05270\" ref-type=\"bibr\">74</xref>].</p></sec><sec sec-type=\"conclusions\" id=\"sec6-ijms-21-05270\"><title>6. Conclusions</title><p>Peptidomics research represents one of the most interesting and challenging area of proteomics. GCF peptidome could be a goldmine for the discovery of novel biomarkers of periodontal diseases. While many proteomics-based bottom-up approaches have been pursued, resulting in a burgeoning of proteins occurring in GCF, still these approaches are not adequate enough to completely disclose all the proteoforms present in the sample. On the other hand, peptidomics investigations, mainly based on profiling (top-down) strategies, which are well suited for proteoforms detection, are very few and therefore, up to date, the picture of naturally occurring peptides and their role in GCF is still incomplete. In this scenario, the main limitations are due to the lack of standardization of pre-analytical and analytical variables affecting the different processing steps from the sample collection, to the potential artefacts coming from storage of GCF and from the protein concentration normalization and, finally, to the robustness of MS analysis. So, differences derived from heterogeneity in collection, handling, and storage make the results difficult to compare and to establish their correct interpretation due to their inconsistency. Controversies arising from defensins and calgranulins are clear examples of these data misinterpretations.</p><p>With the appropriate standardization, MS profiling strategies will allow to harvest intact peptide signatures from GCF which could help to delineate the subtle boundaries existing among the different stages of gingival inflammation. Obviously, each single biomarker forming the signature can be validated by more traditional biochemical approaches like, for instance, Western blot analysis or ELISA assays, which both present an advantage in terms of exquisite specificity of purposely developed monoclonal antibodies raised against the peptide identified by MS. This will open the possibility to recognize a specific phenotypic pattern for the early diagnosis and the progression to periodontal diseases. Moreover, an increased coverage of both GCF peptidome and proteome might assist the comprehension of molecular mechanisms underpinning the pathogenesis of periodontitis. Although the focus of this review is on periodontal diseases, in recent years it was proposed that GCF and saliva should be kept in consideration also in “the wider contexts of oral and systemic health” [<xref rid=\"B109-ijms-21-05270\" ref-type=\"bibr\">109</xref>]. Indeed, it has been reported that saliva has shown potential for the development of diagnostic assays for systemic pathologies such as acquired immunodeficiencies, diabetes mellitus, Sjögren syndrome, cerebrovascular/cardiovascular diseases, systemic sclerosis, and even for cancers like breast cancer and, not surprisingly, more localized tumors such as tumors of oral cavity, salivary gland tumor larynx carcinoma, and head and neck carcinoma ([<xref rid=\"B4-ijms-21-05270\" ref-type=\"bibr\">4</xref>] and references therein). Considering that GCF has a different origin compared to saliva (it is not secreted from the salivary glands) it is not surprising that these two biofluids do not have a perfect overlap as diagnostic predictors in systemic health, however it has been reported that periodontal diseases (focus of this review) and the consequent periodontal inflammation can have an effect on the progression of important systemic disorders, such as cardiovascular and cerebrovascular diseases [<xref rid=\"B94-ijms-21-05270\" ref-type=\"bibr\">94</xref>]. In light of these considerations, MS profiling strategies will allow to harvest intact peptide signatures from GCF which can be used for diagnostics and prognostics assays for important systemic disorders, such as cardiovascular and cerebrovascular diseases.</p></sec></body><back><ack><title>Acknowledgments</title><p>In this section you can acknowledge any support given which is not covered by the author contribution or funding sections. This may include administrative and technical support, or donations in kind (e.g., materials used for experiments).</p></ack><notes><title>Author Contributions</title><p>Conceptualization, R.T.; writing-original draft preparation, M.P., R.S., C.V., and R.T.; writing-reviewing and editing, M.P., R.S., C.V., C.P., and R.T.; table preparation, M.P. and R.S.; supervision, R.S. and R.T. All authors have read and agreed to the published version of the manuscript.</p></notes><notes><title>Funding</title><p>This work was supported in part by funding from the Department of Health Sciences of the University of Catanzaro. MP has been supported by funding from research grant PON-MIUR 03PE000_78—Nutramed. 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Peptidomics workflow, without a digestion step, allows to preserve the endogenous information of peptides from GCF, including post-translational modifications and proteolytic products.</p></caption><graphic xlink:href=\"ijms-21-05270-sch001\"/></fig><table-wrap id=\"ijms-21-05270-t001\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijms-21-05270-t001_Table 1</object-id><label>Table 1</label><caption><p>Gingival crevicular fluid (GCF) peptidomics profiling studies.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Ref.</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Collection<break/>Devices/Elution Buffer/<break/>Pre-Processing</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Storage Conditions</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Protein Quantitation</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">MS <break/>Normalization </th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">MS-Approach</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Naturally Occurring Peptides &Small Proteins<break/>Detected: Main Results</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Lundy et al. [<xref rid=\"B16-ijms-21-05270\" ref-type=\"bibr\">16</xref>]</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Paper strips/sodium phosphate containing NaCl; NO PIC; RP-HPLC.</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−70 °C, time not specified.</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Yes</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Not required <sup>(a)</sup></td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">N-terminal aminoacid sequencing and LC-MS</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">S100-A8</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Diamond et al. [<xref rid=\"B18-ijms-21-05270\" ref-type=\"bibr\">18</xref>]</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Periopaper strips/5% acetic acid; NO PIC; ProteinChip.</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Not specified.</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">No </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">No</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">SELDI-TOF</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">α-defensins 1–3, hBD-1 derived peptides, hBD-2</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Dommisch et al. [<xref rid=\"B17-ijms-21-05270\" ref-type=\"bibr\">17</xref>]</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Periopaper strips/5% acetic acid; NO PIC; ProteinChip.</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Not specified.</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Yes <sup>(b)</sup></td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">No</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">SELDI-TOF</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">α-defensins 1–3, Cathelicidin antimicrobial peptide LL-37</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Lundy et al. [<xref rid=\"B19-ijms-21-05270\" ref-type=\"bibr\">19</xref>] </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Periopaper strips/sodium phosphate containing NaCl; NO PIC; Unfractionated.</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−80 °C, time not specified.</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Yes</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">No</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">MALDI-TOF</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">α-defensins 1–3</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Wen et al. [<xref rid=\"B20-ijms-21-05270\" ref-type=\"bibr\">20</xref>]</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Paper points/2.5% TFA; NO PIC; Unfractionated.</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−80 °C, time not specified.</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">No</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Yes</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">MALDI-TOF</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><italic>m</italic>/<italic>z</italic> = 1660.2, 1783.0, 2912.5, 4178.6, 5064.9, 6108.9</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Ngo et al. [<xref rid=\"B6-ijms-21-05270\" ref-type=\"bibr\">6</xref>]</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Glass microcapillary tubes/water or 0.1% TFA for HPLC fractionation; NO PIC; ZipTips or RP-HPLC.</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−70 °C, time not specified.</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">No</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">No</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">MALDI-TOF/TOF</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">α-defensins 1–3,<break/>Peptides fragments from:<break/>Peptide salivary low MW, Proline-rich phosphoprotein, Beta globin, Peptide PA saliva, (Peptide PB saliva, Peptide PC saliva, H2A histone family, Ig light chain variable region, Glyceraldehyde-3-phosphate, Fibroblast growth factor, Albumin, Collagen, Ig heavy chain, Thioredoxin, Statherin, Hemoglobin alpha 1, Albumin Dbox binding protein, Collagen R-1 type III, Metallopeptidase</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Ngo et al. [<xref rid=\"B21-ijms-21-05270\" ref-type=\"bibr\">21</xref>]</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Glass microcapillary tubes/GCF (0.2–1.5 µL) was dispensed with 2.5% TFA; NO PIC; ZipTips.</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−70 °C, time not specified.</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">No</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Yes</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">MALDI-TOF</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Attachment loss sites vs. stable sites (<italic>m</italic>/<italic>z</italic>):<break/>2023.50, 4042.80, 4490.30, 4525.30, 5232.30, 5502.20, 6890.10, 10939.00, 12833.00, 14008.00</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Preianò et al. [<xref rid=\"B22-ijms-21-05270\" ref-type=\"bibr\">22</xref>]</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Paper points, Paper strips/2.5% TFA or 5% acetic acid; PIC and NO PIC; Unfractionated.</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">GCF was immediately analyzed by MS.</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Yes</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Yes</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">MALDI-TOF/TOF</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">α-defensins 1–4, Thymosin β4</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Preianò et al. [<xref rid=\"B23-ijms-21-05270\" ref-type=\"bibr\">23</xref>]</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Paper strips/2.5%TFA; PIC and NO PIC; Unfractionated.</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−20 °C/1 or 3 months, −80 °C/1 or 3 months and immediately analyzed </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Yes</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Yes</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">MALDI-TOF/TOF</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">α-defensins 1–4, hBD-2, Thymosin β4, S100-A8 and its oxidized forms, S100-A9 and its isoforms, Lysozyme C</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Preianò et al. [<xref rid=\"B24-ijms-21-05270\" ref-type=\"bibr\">24</xref>]</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Paper strips/2.5%TFA; PIC and NO PIC, Unfractionated.</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−20 °C/1 month, −80 °C/1 month and immediately analyzed. </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Yes</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Yes</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">MALDI-TOF/TOF</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">α-defensins 1–3, hBD-2, C-36 peptide of AAT, Thymosin β4, Thymosin β10, S100-A8 and its oxidized forms, S100-A9 and its isoforms, Lysozyme C</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Tang et al. [<xref rid=\"B25-ijms-21-05270\" ref-type=\"bibr\">25</xref>]</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Paper strips/phosphate-buffered saline; NO PIC; Weak cation exchange magnetic beads.</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−80 °C, time not specified <sup>(c)</sup>.</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"> No</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Yes</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">MALDI-TOF</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Haptoglobin derived peptide and other unidentified peptides: <italic>m</italic>/<italic>z</italic> = 4126.6, 5407.7, 5416.0</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Antezack et al. [<xref rid=\"B26-ijms-21-05270\" ref-type=\"bibr\">26</xref>]</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Paper points/HPLC-grade water; NO PIC; Unfractionated.</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">+4 °C and analyzed within 24 to 48 h.</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">No</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Yes</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">MALDI-TOF</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><italic>m</italic>/<italic>z</italic> = 3775, 4235, 4944, 5296, 5728, 5893, 10586, 11324, 11,359 and 11447</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Pisano et al. [<xref rid=\"B27-ijms-21-05270\" ref-type=\"bibr\">27</xref>]</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Paper cones/0.2% TFA aqueous solution; NO PIC; RP-HPLC.</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−80 °C, time not specified.</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">No</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Yes</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">ESI-Ion Trap</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">α-defensins 1–4, Statherin, Peptide PB, Cystatin A</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Inzitari et al. [<xref rid=\"B28-ijms-21-05270\" ref-type=\"bibr\">28</xref>]</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Paper cones/0.2% TFA aqueous solution; NO PIC; RP-HPLC.</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Storage conditions not specified.</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Yes</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Yes</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">ESI-Ion Trap</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Thymosin β4, Thymosin β4 sulfoxide, Thymosin β10</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Dassatti et al. [<xref rid=\"B29-ijms-21-05270\" ref-type=\"bibr\">29</xref>]</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Paper cones/Buffer not specified; Use of PIC not specified; RP-HPLC.</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−80 °C, time not specified.</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">No </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Yes</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">ESI-Ion Trap/<break/>Orbitrap</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">α-defensins 1–4, Thymosin β4, Thymosin β4 fragment (21–44), Thymosin β10, Fibrinopeptide A, Fibrinopeptide A fragments (21–35) and (22–35), Fibrinopeptide B</td></tr></tbody></table><table-wrap-foot><fn><p><sup>(a)</sup> Peak normalization is not required as it is a targeted study. <sup>(b)</sup> Protein quantitation: samples from healthy periodontal sites served as internal control (baseline) for quantitative analysis. <sup>(c)</sup> After storage at −80 °C, GCF samples were fractioned using a weak cation exchange magnetic bead kit and then immediately analyzed on MALDI-TOF or stored at −20 °C and analyzed within 24 h.</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"ijms-21-05270-t002\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijms-21-05270-t002_Table 2</object-id><label>Table 2</label><caption><p>Naturally occurring peptides found in gingival crevicular fluid (GCF) from different study groups. Expression levels are shown as increased (↑) or decreased (↓), as determined by the referred investigation in relation to the appropriate control group. H = Healthy, G = Gingivitis, P = periodontitis, CP = Chronic periodontitis.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Naturally Occurring Peptides</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Methods</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Study Groups<break/>and Number of Subjects (<italic>n</italic>)</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Study Groups and Protein Expression Level</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Ref.</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">C-36 AAT</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">MALDI-TOF</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">H (10) vs. G (10)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">↓H/↑G *</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Preianò et al. [<xref rid=\"B24-ijms-21-05270\" ref-type=\"bibr\">24</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Cathelicidin antimicrobial peptide LL-37</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">SELDI-TOF</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">H sites (<italic>n</italic> = 8) and G sites (<italic>n</italic> = 8) in subjects with good general health (4). </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">↓H sites/↑G sites in H *</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Dommisch et al. [<xref rid=\"B17-ijms-21-05270\" ref-type=\"bibr\">17</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Fibrinopeptide A</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">ESI-Ion Trap/Orbitrap</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\nG and P sites in women after pregnancy (10) and not pregnant women as H controls (10).\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">↓H/↑P sites *<break/>↓H/↑G sites *</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Dassatti et al. [<xref rid=\"B29-ijms-21-05270\" ref-type=\"bibr\">29</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Fibrinopeptide A fragment (21–35)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">ESI-Ion Trap/Orbitrap</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\nG and P sites in women after pregnancy (10) and not pregnant women as H controls (10).\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">↓H/↑P sites * <break/>↓H/↑G sites *</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Dassatti et al. [<xref rid=\"B29-ijms-21-05270\" ref-type=\"bibr\">29</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Fibrinopeptide A fragment (22–35)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">ESI-Ion Trap/Orbitrap</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\nG and P sites in women after pregnancy (10) and not pregnant women as H controls (10).\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">↓H/↑P sites *<break/>↓H/↑G sites *</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Dassatti et al. [<xref rid=\"B29-ijms-21-05270\" ref-type=\"bibr\">29</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Fibrinopeptide B</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">ESI-Ion Trap/Orbitrap</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">G and P sites in women after pregnancy (10) and not pregnant women as H controls (10).</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">↓H/↑P sites *<break/>↓H/↑G sites *</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Dassatti et al. [<xref rid=\"B29-ijms-21-05270\" ref-type=\"bibr\">29</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Haptoglobin derived peptide</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">MALDI-TOF</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">CP (17) and H (16).</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">↓H/↑CP *</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Tang et al. [<xref rid=\"B25-ijms-21-05270\" ref-type=\"bibr\">25</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">hBD-1 </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">SELDI-TOF</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Patients with mild to moderate gingival inflammation (2).</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">↑mild gingival inflammation/↓moderate gingival inflammation</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Diamond et al. [<xref rid=\"B18-ijms-21-05270\" ref-type=\"bibr\">18</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">hBD-2</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">SELDI-TOF</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Patients with mild to moderate gingival inflammation (2).</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">↓mild gingival inflammation/↑moderate gingival inflammation</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Diamond et al. [<xref rid=\"B18-ijms-21-05270\" ref-type=\"bibr\">18</xref>]</td></tr><tr><td rowspan=\"4\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">HNP-1</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">SELDI-TOF</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Patients with mild to moderate gingival inflammation (2).</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">↓mild gingival inflammation/↑moderate gingival inflammation</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Diamond et al. [<xref rid=\"B18-ijms-21-05270\" ref-type=\"bibr\">18</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">SELDI-TOF</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">H sites (<italic>n</italic> = 8) and G sites (<italic>n</italic> = 8) in subjects with good general health (4).</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">↓H sites/↑G sites in H</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Dommisch et al. [<xref rid=\"B17-ijms-21-05270\" ref-type=\"bibr\">17</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">MALDI-TOF</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">P(11) and H (12)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">↑H/↓P</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Lundy et al. [<xref rid=\"B19-ijms-21-05270\" ref-type=\"bibr\">19</xref>] </td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">ESI-Ion Trap/Orbitrap</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">G and P sites in women after pregnancy (10) and not pregnant women as H controls (10).</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">↓H/↑P sites *<break/>↓H/↑G sites *</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Dassatti et al. [<xref rid=\"B29-ijms-21-05270\" ref-type=\"bibr\">29</xref>]</td></tr><tr><td rowspan=\"5\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">HNP-2</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">SELDI-TOF</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Patients with mild to moderate gingival inflammation (2)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">↓mild gingival inflammation/↑moderate gingival inflammation</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Diamond et al. [<xref rid=\"B18-ijms-21-05270\" ref-type=\"bibr\">18</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">SELDI-TOF</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">H sites (<italic>n</italic> = 8) and G sites (<italic>n</italic> = 8) in subjects with good general health (4)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">↓H sites/↑G sites in H</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Dommisch et al. [<xref rid=\"B17-ijms-21-05270\" ref-type=\"bibr\">17</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">MALDI-TOF</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">P (11) and H (12)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">↑H/↓P</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Lundy et al. [<xref rid=\"B19-ijms-21-05270\" ref-type=\"bibr\">19</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">MALDI-TOF</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">H (10) vs. G (10)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">↓H/↑G *</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Preianò et al. [<xref rid=\"B24-ijms-21-05270\" ref-type=\"bibr\">24</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">ESI-Ion Trap/Orbitrap</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">G and P sites in women after pregnancy (10) and not pregnant women as H controls (10).</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">↓H/↑P sites *<break/>↓H/↑G sites *</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Dassatti et al. [<xref rid=\"B29-ijms-21-05270\" ref-type=\"bibr\">29</xref>]</td></tr><tr><td rowspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">HNP-3</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">SELDI-TOF</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Patients with mild to moderate gingival inflammation (2).</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">↓mild gingival inflammation/↑moderate gingival inflammation</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Diamond et al. [<xref rid=\"B18-ijms-21-05270\" ref-type=\"bibr\">18</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">MALDI-TOF</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">P (11) and H (12)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">↑H/↓P</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Lundy et al. [<xref rid=\"B19-ijms-21-05270\" ref-type=\"bibr\">19</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">ESI-Ion Trap/Orbitrap</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">G and P sites in women after pregnancy (10) and not pregnant women as H controls (10).</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">↓H/↑P sites *<break/>↓H/↑G sites *</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Dassatti et al. [<xref rid=\"B29-ijms-21-05270\" ref-type=\"bibr\">29</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">HNP-4</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">ESI-Ion Trap/Orbitrap</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">G and P sites in women after pregnancy (10) and not pregnant women as H controls (10).</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">↓H/↑P sites <break/>↓H/↑G sites </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Dassatti et al. [<xref rid=\"B29-ijms-21-05270\" ref-type=\"bibr\">29</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">S100-A8</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">N-terminal amino acid sequencing and LC-MS</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">H, G and P sites in patients with P (15) and H sites in H subjects (5).</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">↓H/↑G sites in P *<break/>↓H/↑P sites in P *<break/>↓H sites in P/↑G sites in P *<break/>↓H sites in P/↑P sites in P *</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Lundy et al. [<xref rid=\"B16-ijms-21-05270\" ref-type=\"bibr\">16</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">S100-A9</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">MALDI-TOF</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">H (10) vs. G (10)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">↑H/↓G *</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Preianò et al. [<xref rid=\"B24-ijms-21-05270\" ref-type=\"bibr\">24</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">S100-A9 Glutathionylated</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">MALDI-TOF</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">H (10) vs. G (10)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">↓H/↑G *</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Preianò et al. [<xref rid=\"B24-ijms-21-05270\" ref-type=\"bibr\">24</xref>]</td></tr><tr><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Thymosin β-4</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">MALDI-TOF</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">H (10) vs. G (10)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">↑H/↓G *</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Preianò et al. [<xref rid=\"B24-ijms-21-05270\" ref-type=\"bibr\">24</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">ESI-Ion Trap/Orbitrap</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">G and P sites in women after pregnancy (10) and not pregnant women as H controls (10).</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">↑H/↓P sites *<break/>↑H/↓G sites *</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Dassatti et al. [<xref rid=\"B29-ijms-21-05270\" ref-type=\"bibr\">29</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Thymosin β-4 fragment (21–44)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">ESI-Ion Trap/Orbitrap</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">G and P sites in women after pregnancy (10) and not pregnant women as H controls (10).</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">↓H/↑P sites *<break/>↓H/↑G sites *</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Dassatti et al. [<xref rid=\"B29-ijms-21-05270\" ref-type=\"bibr\">29</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Thymosin β-10</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">ESI-Ion Trap/Orbitrap</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">G and P sites in women after pregnancy (10) and not pregnant women as H controls (10).</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">↑H/↓P sites *<break/>↑H/↓G sites *</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Dassatti et al. [<xref rid=\"B29-ijms-21-05270\" ref-type=\"bibr\">29</xref>]</td></tr></tbody></table><table-wrap-foot><fn><p>* Statistically significant difference.</p></fn></table-wrap-foot></table-wrap></sec></back></article>\n"
]
[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Front Psychol</journal-id><journal-id journal-id-type=\"iso-abbrev\">Front Psychol</journal-id><journal-id journal-id-type=\"publisher-id\">Front. Psychol.</journal-id><journal-title-group><journal-title>Frontiers in Psychology</journal-title></journal-title-group><issn pub-type=\"epub\">1664-1078</issn><publisher><publisher-name>Frontiers Media S.A.</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32849080</article-id><article-id pub-id-type=\"pmc\">PMC7432129</article-id><article-id pub-id-type=\"doi\">10.3389/fpsyg.2020.01838</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Psychology</subject><subj-group><subject>Original Research</subject></subj-group></subj-group></article-categories><title-group><article-title>Predicting Late Adolescent Anxiety From Early Adolescent Environmental Stress Exposure: Cognitive Control as Mediator</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Tsai</surname><given-names>Nancy</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><xref ref-type=\"corresp\" rid=\"c001\"><sup>*</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/866975/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Jaeggi</surname><given-names>Susanne M.</given-names></name><xref ref-type=\"aff\" rid=\"aff2\"><sup>2</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/117160/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Eccles</surname><given-names>Jacquelynne S.</given-names></name><xref ref-type=\"aff\" rid=\"aff2\"><sup>2</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Atherton</surname><given-names>Olivia E.</given-names></name><xref ref-type=\"aff\" rid=\"aff3\"><sup>3</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Robins</surname><given-names>Richard W.</given-names></name><xref ref-type=\"aff\" rid=\"aff3\"><sup>3</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/1014746/overview\"/></contrib></contrib-group><aff id=\"aff1\"><sup>1</sup><institution>McGovern Institute for Brain Research, Massachusetts Institute of Technology</institution>, <addr-line>Cambridge, MA</addr-line>, <country>United States</country></aff><aff id=\"aff2\"><sup>2</sup><institution>School of Education, University of California</institution>, <addr-line>Irvine, Irvine, CA</addr-line>, <country>United States</country></aff><aff id=\"aff3\"><sup>3</sup><institution>Department of Psychology, University of California</institution>, <addr-line>Davis, Davis, CA</addr-line>, <country>United States</country></aff><author-notes><fn fn-type=\"edited-by\"><p>Edited by: Mathias Weymar, University of Potsdam, Germany</p></fn><fn fn-type=\"edited-by\"><p>Reviewed by: Andrea T. Shafer, National Institute on Aging, National Institutes of Health (NIH), United States; Swann Pichon, Université de Genève, Switzerland</p></fn><corresp id=\"c001\">*Correspondence: Nancy Tsai, <email>[email protected]</email></corresp><fn fn-type=\"other\" id=\"fn004\"><p>This article was submitted to Emotion Science, a section of the journal Frontiers in Psychology</p></fn></author-notes><pub-date pub-type=\"epub\"><day>11</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><year>2020</year></pub-date><volume>11</volume><elocation-id>1838</elocation-id><history><date date-type=\"received\"><day>03</day><month>3</month><year>2020</year></date><date date-type=\"accepted\"><day>03</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>Copyright © 2020 Tsai, Jaeggi, Eccles, Atherton and Robins.</copyright-statement><copyright-year>2020</copyright-year><copyright-holder>Tsai, Jaeggi, Eccles, Atherton and Robins</copyright-holder><license xlink:href=\"http://creativecommons.org/licenses/by/4.0/\"><license-p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</license-p></license></permissions><abstract><p>Early exposure to stressful life events is associated with greater risk of chronic diseases and mental health problems, including anxiety. However, there is significant variation in how individuals respond to environmental adversity, perhaps due to individual differences in processing and regulating emotional information. Differences in cognitive control – processes necessary for implementing goal directed behavior – have been linked to both stress exposure and anxiety, but the directionality of these links is unclear. The present study investigated the longitudinal pathway of environmental stress exposure during early adolescence on later adolescent anxiety, and the possible mediating mechanism of cognitive control. Participants were 674 Mexican-origin adolescents (mean<sub>age</sub> = 10.8 years, 50% male) enrolled in the California Families Project, an ongoing longitudinal study of Mexican-origin families. In the current analysis, we examined self-reports of environmental stressors at age 14 (Time 1), cognitive control at age 16 (Time 2), and anxiety at age 18 (Time 3). Structural equation modeling revealed that environmental stressors (Time 1) had both direct and indirect effects on later anxiety (Time 3) through their effects on cognitive control (Time 2), even when accounting for prior levels of anxiety (Time 2). Cognitive control accounted for 18% of the association between environmental stressors and adolescent anxiety: an increase in stressors decreased cognitive control (β = −0.20, <italic>p</italic> < 0.001), however, cognitive control buffers against anxiety (β = −0.10, <italic>p</italic> = 0.004). These findings deepen our understanding of the mechanisms underlying the development of anxiety and highlight the importance of cognitive control as a potential protective factor.</p></abstract><kwd-group><kwd>cognitive control</kwd><kwd>executive function</kwd><kwd>self-regulation</kwd><kwd>mental health</kwd><kwd>stress</kwd></kwd-group><counts><fig-count count=\"1\"/><table-count count=\"1\"/><equation-count count=\"0\"/><ref-count count=\"71\"/><page-count count=\"9\"/><word-count count=\"0\"/></counts></article-meta></front><body><sec id=\"S1\"><title>Introduction</title><p>Exposure to stressful life events is associated with greater risk of developing chronic diseases and mental health disorders, including anxiety – the most prevalent psychiatric disorder experienced by youth (<xref rid=\"B58\" ref-type=\"bibr\">Pérez-Edgar and Fox, 2005</xref>; <xref rid=\"B59\" ref-type=\"bibr\">Pine, 2007</xref>; <xref rid=\"B60\" ref-type=\"bibr\">Rapee et al., 2009</xref>). However, there is significant variation in how individuals respond to environmental adversity, and those individual differences might be related to the processing and regulation of information. These cognitive control processes necessary for implementing goal-directed behavior – an umbrella term for a collection of related yet distinct processes that are also labeled effortful control, executive function, and self-regulation, depending on the field of study (<xref rid=\"B71\" ref-type=\"bibr\">Zhou et al., 2012</xref>) – have been significantly associated with anxiety, as well as a range of other psychiatric disorders such as depression and substance abuse (e.g., <xref rid=\"B7\" ref-type=\"bibr\">Baler and Volkow, 2006</xref>; <xref rid=\"B36\" ref-type=\"bibr\">Hirsch and Mathews, 2012</xref>; <xref rid=\"B70\" ref-type=\"bibr\">Zainal and Newman, 2018</xref>). Cognitive models of generalized anxiety disorder have converged on cognitive control as a potential mechanism of psychopathology (<xref rid=\"B41\" ref-type=\"bibr\">Joormann, 2006</xref>; <xref rid=\"B40\" ref-type=\"bibr\">Joorman et al., 2009</xref>; <xref rid=\"B36\" ref-type=\"bibr\">Hirsch and Mathews, 2012</xref>). For example, the Attentional Control Theory (<xref rid=\"B28\" ref-type=\"bibr\">Eysenck and Derakshan, 2011</xref>) and the Processing Efficiency Theory (<xref rid=\"B27\" ref-type=\"bibr\">Eysenck and Calvo, 1992</xref>) were put forth to explain the reallocation of cognitive resources when processes such as inhibition, stress, and negative thoughts co-occur. These theories postulate that compromised cognitive control is linked to excessive and uncontrollable worry, a core symptom of anxiety. Indeed, cross-sectional studies have found an association between a range of cognitive control functions and anxiety disorders (e.g., <xref rid=\"B68\" ref-type=\"bibr\">Toren et al., 2000</xref>; <xref rid=\"B54\" ref-type=\"bibr\">Muris and Ollendick, 2005</xref>; <xref rid=\"B29\" ref-type=\"bibr\">Fujii et al., 2013</xref>), as well as the degree of cognitive control impairment being commensurate with the severity of anxiety among patients with generalized anxiety disorder (<xref rid=\"B32\" ref-type=\"bibr\">Hallion et al., 2017</xref>). The few studies examining longitudinal associations have found that executive function is related to anxiety problems in an adolescent population two years later (<xref rid=\"B33\" ref-type=\"bibr\">Han et al., 2016</xref>) and in adults nine years later (<xref rid=\"B70\" ref-type=\"bibr\">Zainal and Newman, 2018</xref>). The scarcity of studies examining longitudinal associations between of cognitive control and anxiety begs the question of directionality and whether cognitive control is an underlying mechanism that might mediate the effect of stress exposure on the development of anxiety.</p><p>Stress and cognitive control are processed by closely related neural systems (e.g., <xref rid=\"B35\" ref-type=\"bibr\">Herman et al., 2005</xref>), leading some researchers to speculate that stress exposure during childhood and adolescence, sensitive periods of neurocognitive development, can compromise the development of the neural regions that underlie the development of cognitive control (<xref rid=\"B66\" ref-type=\"bibr\">Shonkoff and Phillips, 2000</xref>; <xref rid=\"B49\" ref-type=\"bibr\">Lupien et al., 2009</xref>). For example, a longitudinal study of infants raised in a predominantly low-income environment found that the chronic physical and psychosocial stress exposure associated with poverty predicted later executive function in pre-kindergarten (<xref rid=\"B8\" ref-type=\"bibr\">Berry et al., 2012</xref>). In their longitudinal study examining childhood poverty (age 9) and later adult emotion regulation (age 24), <xref rid=\"B43\" ref-type=\"bibr\">Kim et al. (2013)</xref> found that cumulative chronic stress mediated the relationship, mimicking findings highlighting the mediating role of stress between childhood poverty and later cognitive control (e.g., <xref rid=\"B25\" ref-type=\"bibr\">Evans and Schamberg, 2009</xref>; <xref rid=\"B10\" ref-type=\"bibr\">Blair et al., 2011</xref>; <xref rid=\"B21\" ref-type=\"bibr\">Evans and Fuller-Rowell, 2013</xref>; <xref rid=\"B44\" ref-type=\"bibr\">Kim et al., 2018</xref>). These findings underscore the link between early stress exposure to later diminished executive function abilities (<xref rid=\"B31\" ref-type=\"bibr\">Gunnar et al., 2009</xref>; <xref rid=\"B9\" ref-type=\"bibr\">Blair, 2010</xref>; <xref rid=\"B69\" ref-type=\"bibr\">Ursache et al., 2013</xref>), but whether these relationships together explain anxiety outcomes remains unclear.</p><p>Although the aforementioned links all strongly suggest a mechanism by which early experiences of stress contribute to anxiety outcomes, few published studies to date have explicitly examined the relationship between stress exposure, cognitive control, and anxiety together. In a recent study, <xref rid=\"B38\" ref-type=\"bibr\">Huh et al. (2017)</xref> found mediating effects of emotion regulation (i.e., cognitive control in emotionally salient contexts) on the relationship between acute childhood stressors and adult anxiety symptoms in a clinical population using a cross-sectional design. Similarly, <xref rid=\"B2\" ref-type=\"bibr\">Affrunti and Woodruff-Borden (2015)</xref> found that executive functions mediated the relationship between fear perception and anxiety in 7- to 10-year-old children. With a short-term longitudinal design, <xref rid=\"B30\" ref-type=\"bibr\">Gulley et al. (2016)</xref> examined the interaction of effortful control and stressors on the development of anxiety over a 3-month period finding that, at low levels of stress, high level of effortful control protected against the development of anxious symptoms. With little to almost no prospective studies to draw from, some have speculated that adolescents with more effective cognitive control abilities are better able to process negative emotional information, which in turn lowers their risk for psychopathology (<xref rid=\"B50\" ref-type=\"bibr\">Martel et al., 2007</xref>; <xref rid=\"B53\" ref-type=\"bibr\">Micco et al., 2009</xref>). Similarly, <xref rid=\"B55\" ref-type=\"bibr\">Nigg (2006)</xref> theorizes that anxiety arises from experiences of both negative affect and impaired cognitive control. That is, greater exposure to stressors paired with lower levels of cognitive control may contribute to increased anxiety and depression (<xref rid=\"B4\" ref-type=\"bibr\">Anthony et al., 2002</xref>; <xref rid=\"B17\" ref-type=\"bibr\">Eisenberg et al., 2005</xref>). However, no studies to date have tested these theories by examining the longitudinal relations between early environmental stress exposure, cognitive control, and later anxiety, and thus, the directionality of these relationships remains unclear and beckons the need for further research.</p><p>In the present study, we conducted a prospective mediation analysis to evaluate the effect of environmental stress exposure during early adolescence on late adolescent anxiety and examine the possible mediating mechanism of cognitive control. Given previous findings, we hypothesize that:</p><list list-type=\"simple\"><list-item><label>(1)</label><p>Increased stress exposure is associated with higher levels of anxiety.</p></list-item><list-item><label>(2)</label><p>This relation between stress exposure and anxiety is partially mediated by cognitive control, with increased stress exposure leading to impaired cognitive control, whereas cognitive control in turn buffers against the development of anxiety.</p></list-item></list><p>Previous research examining the association between environmental stress exposure and anxiety has often operationalized environmental stress as poverty, leaving a vast range of other possible environmental stressors overlooked and/or under examined. Thus, the present study uses data from a sample of Mexican-origin youth in the United States who face unique challenges and may experience greater exposure to adversity ranging from fewer material and emotional resources to increased exposure to discrimination and neighborhood violence, and more chaotic and less stable home environments (<xref rid=\"B22\" ref-type=\"bibr\">Evans and Kantrowitz, 2002</xref>; <xref rid=\"B23\" ref-type=\"bibr\">Evans and Kim, 2010</xref>), experiences that can cause chronic stress beyond those of financial origins. Moreover, data from this sample of youth provide an opportunity to examine changes in cognition and anxiety in the context of adolescence – a unique developmental time period marked by notable neurocognitive changes and heightened prevalence of stress-related psychological disorders (<xref rid=\"B52\" ref-type=\"bibr\">Merikangas et al., 2010</xref>; <xref rid=\"B61\" ref-type=\"bibr\">Romeo, 2017</xref>). Thus, it is critical for research to elucidate the potential risk factors that lead to the development of these disorders during this period of enhanced vulnerability.</p></sec><sec sec-type=\"materials|methods\" id=\"S2\"><title>Materials and Methods</title><sec id=\"S2.SS1\"><title>Participants</title><p>Participants were 674 Mexican-origin adolescents (mean<sub>age</sub> = 10.8 years, 50% male) enrolled in the California Families Project, an ongoing 12-year longitudinal study of Mexican-origin families (for additional details about the study, see <xref rid=\"B51\" ref-type=\"bibr\">Martin et al., 2019</xref>; <xref rid=\"B5\" ref-type=\"bibr\">Atherton et al., 2020</xref>). Of the 674 participants, 551 participants had longitudinal data for all our variables of interest at ages 14 (Time 1), 16 years (Time 2), and 18 years (Time 3) and were included in the analysis. Potential participants were drawn at random from rosters of students from the Sacramento and Woodland, CA, school districts. To be eligible for participation in this study, the focal child had to be in the fifth grade, of Mexican origin, and living with his/her biological mother; 72.6% of the eligible families consented to participate in the study, which was granted approval by the Institutional Review Board of University of California, Davis.</p></sec><sec id=\"S2.SS2\"><title>Measures</title><sec id=\"S2.SS2.SSS1\"><title>Environmental Stressors</title><p>We measured environmental stress exposure using a composite of three separate scales that were all administered at age 14: neighborhood criminal events, neighborhood quality dissatisfaction, and adolescent reports of discrimination experiences. Similar to other composite measures such as socioeconomic status, which is often defined as a composite of occupation, education, and income, our measure of environmental stress exposure is a formative construct: the events are largely independent of each other but collectively contribute to the construct (see <xref rid=\"B16\" ref-type=\"bibr\">Edwards and Bagozzi, 2000</xref>). Therefore, environmental stress exposure was calculated by summing the average scores of all three risk factors. Additive indices of cumulative stress exposure are robust and consistently predict mental health outcomes better than indices of singular stress exposure or alternative multiple stress exposure metrics (<xref rid=\"B24\" ref-type=\"bibr\">Evans et al., 2013</xref>; <xref rid=\"B43\" ref-type=\"bibr\">Kim et al., 2013</xref>; <xref rid=\"B19\" ref-type=\"bibr\">Evans and Cassells, 2014</xref>) and have been established as a reasonable method in capturing the confluence of physical and psychosocial challenges associated with adolescent adversity (<xref rid=\"B20\" ref-type=\"bibr\">Evans and English, 2002</xref>).</p></sec><sec id=\"S2.SS2.SSS2\"><title>Neighborhood Criminal Events Scale</title><p>The adolescent reported on neighborhood-level violence using the Neighborhood Criminal Events Scale, which consists of 10 items. These items assess the extent to which there is violence and disorder in the neighborhood (<xref rid=\"B3\" ref-type=\"bibr\">Aneshensel and Sucoff, 1996</xref>; <xref rid=\"B11\" ref-type=\"bibr\">Bowen and Chapman, 1996</xref>; <xref rid=\"B65\" ref-type=\"bibr\">Sampson et al., 1997</xref>; <xref rid=\"B13\" ref-type=\"bibr\">Cutrona et al., 2000</xref>; <xref rid=\"B63\" ref-type=\"bibr\">Ross and Jang, 2000</xref>). The scale includes items, such as “How often did [violent crimes including stabbings, shootings, and violent assaults] happen in your neighborhood in the past year?” and “How often did [kids sell illegal drugs] in your neighborhood in the past year?” Ratings were made on a four-point scale ranging from 1 (almost never or never) to 4 (almost always to always). Higher scores indicated greater exposure to crime. The scale demonstrated good internal reliability (α = 0.88).</p></sec><sec id=\"S2.SS2.SSS3\"><title>Neighborhood Quality Dissatisfaction</title><p>The adolescent reported on his/her personal evaluation of attractiveness of the neighborhood using an abbreviated version of Neighborhood Quality Evaluation Scale (<xref rid=\"B62\" ref-type=\"bibr\">Roosa et al., 2005</xref>), which consists of six items. A typical item is “Your neighborhood is clean and attractive” and “Overall, you are satisfied with your neighborhood.” Ratings were made on a four-point scale ranging from 1 (not at all true) to 4 (very true). Higher scores indicated higher perceptions of neighborhood quality. The average score was then reversed to reflect negative neighborhood quality, with higher scores indicating poorer perceptions of neighborhood quality, which was then used as part of the cumulative stressor score. This scale demonstrated excellent internal reliability (α = 0.93).</p></sec><sec id=\"S2.SS2.SSS4\"><title>Perceived Ethnic Discrimination</title><p>The adolescent reported his/her perceived personal experiences with ethnic discrimination using four items, which were adapted for use in the La Familia Project (<xref rid=\"B39\" ref-type=\"bibr\">Johnston and Delgado, 2004</xref>) from questions on the Racism in the Workplace Scale (<xref rid=\"B37\" ref-type=\"bibr\">Hughes and Dodge, 1997</xref>) and Schedule of Sexist Events (<xref rid=\"B45\" ref-type=\"bibr\">Klonoff and Landrine, 1995</xref>). Sample items include “You have heard your teachers at school making jokes or saying bad things about [Mexicans/Mexican–Americans]” and “Teachers think kids who speak Spanish don’t do as well at school.” Ratings were made on a four-point Likert scale, ranging from 1 (almost never or never) to 4 (almost always or always). Higher scores indicated greater experiences of discrimination. The scale demonstrated adequate internal reliability (α = 0.68).</p></sec><sec id=\"S2.SS2.SSS5\"><title>Cognitive Control</title><p>Adolescents completed the effortful control scale (16 items) from the short form of the Early Adolescent Temperament Questionnaire – Revised when the adolescent was 16 years old (EATQ-R; <xref rid=\"B18\" ref-type=\"bibr\">Ellis and Rothbart, 2001</xref>). The 16-item EATQ-R scale assesses various aspects of cognitive control including the capacity to perform an action when there is a strong tendency to avoid it, the capacity to focus and shift attention when desired, and the capacity to suppress and regulate dominant impulses. This scale includes items such as “When someone tells you to stop doing something, it is easy for you to stop.” and “You pay close attention when someone tells you how to do something.” Ratings were made on a four-point scale ranging from 1 (not at all true of you) to 4 (very true of you). Higher scores indicated greater cognitive control. The full scale demonstrated adequate reliability (α = 0.65). Cognitive control assessed at age 16 (Time 2) was included as our mediator of interest.</p></sec><sec id=\"S2.SS2.SSS6\"><title>Anxiety</title><p>Anxiety was assessed using the Mini-Mood and Anxiety Symptom Questionnaire (<xref rid=\"B12\" ref-type=\"bibr\">Casillas and Clark, 2000</xref>). For our measure of anxiety, we composited the anxiety (three items; “How much have you felt keyed up or on edge”) and anxious arousal (10 items; “Have you had trouble swallowing”) items into an overall anxiety scale. Participants rated how much they “felt or experienced” each symptom “during the past week” using a four-point scale ranging from 1 (not at all) to 4 (very much). Higher scores indicated more anxiety. The scale demonstrated good reliability (α = 0.87). Anxiety assessed at age 16 (Time 2) was included as a covariate given that our outcome of interest was anxiety at age 18 (Time 3).</p></sec></sec><sec id=\"S2.SS3\"><title>Analytical Approach</title><p>Our prospective mediation analysis was framed around three time points (Time 1, 2, 3) in order to capture a full prospective mediation model. Several considerations informed the development of our analytical model: (1) the temporal sequence of variables required in a mediation model; (2) the need to account for the stability of anxiety over time, to ensure that the effects of environmental stress and cognitive control are, in fact, prospectively predicting anxiety (and not just due to the fact that anxiety symptoms are stable across adolescence); and (3) the equivalent distance of time between measurements. Our final model was determined by these constraints and captures the development of these constructs during the peak of adolescence. Thus, we examined self-reports of environmental stressors at age 14 (Time 1), cognitive control at age 16 (Time 2), and anxiety at age 18 (Time 3), while including anxiety at age 16 as a covariate<sup><xref ref-type=\"fn\" rid=\"footnote1\">1</xref></sup>.</p><p>To address our research questions, we conducted a prospective mediation analysis using SEM in Stata Version 13 (<xref rid=\"B67\" ref-type=\"bibr\">StataCorp, 2013</xref>). Bootstrapping procedures in SEM were used to test the significance of the mediation effects of cognitive control. In this study, 200 bootstrapping samples were generated from the original data set by random sampling to determine indirect effects of mediating variables and analyze the corresponding confidence intervals. This statistical approach is considered to be a robust method of analyzing indirect effects (<xref rid=\"B34\" ref-type=\"bibr\">Hayes, 2009</xref>).</p></sec></sec><sec id=\"S3\"><title>Results</title><p>Descriptive statistics, correlations, and α reliability estimates for all study variables were calculated prior to addressing our research questions and are displayed in <xref rid=\"T1\" ref-type=\"table\">Table 1</xref>. The hypothesized structural model comprised three observed variables: environmental stressors at age 14 (Time 1), cognitive control at age 16 (Time 2), and anxiety at age 18 (Time 3). In addition, we included anxiety at age 16 (Time 2) as a predictor of anxiety at age 18 (Time 3) in order to account for the fact that anxiety symptoms are likely stable over time and allow us to draw stronger inferences about prospective effects.</p><table-wrap id=\"T1\" position=\"float\"><label>TABLE 1</label><caption><p>Mean, Standard Deviations (SD), and pairwise correlations among study measures.</p></caption><table frame=\"hsides\" rules=\"groups\" cellspacing=\"5\" cellpadding=\"5\"><thead><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Measure</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Time at measurement</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Age atmeasurement</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><italic>Mean</italic></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><italic>SD</italic></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3</td></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1. Environmental stressors</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">T1</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">14</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.67</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.44</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">2. Cognitive control</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">T2</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">16</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.93</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.37</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−0.19**</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">3. Anxiety at Age 16</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">T2</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">16</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.3</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.3</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.18**</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−0.33**</td><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">4. Anxiety at age 18</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">T3</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">18</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.22</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.26</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.17**</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−0.21**</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.35**</td></tr></tbody></table><table-wrap-foot><attrib><italic>The presentation of the pairwise correlations focus on the measures at timepoints relevant to the analysis. Correlation is significant at **p < 0.001.</italic></attrib></table-wrap-foot></table-wrap><p>We hypothesized that individuals with higher levels of environmental stress exposure would later report higher levels of anxiety, as compared with peers with lower levels of environmental stress exposure (Hypothesis 1). Furthermore, we predicted that this effect would be mediated by cognitive control (Hypothesis 2). Indeed, structural equation modeling revealed that cumulative environmental stressors at age 14 had both direct (path c′, <xref ref-type=\"fig\" rid=\"F1\">Figure 1B</xref>) and indirect (paths a and b, <xref ref-type=\"fig\" rid=\"F1\">Figure 1B</xref>) effects on later anxiety at age 18 through their effects on cognitive control at age 16 even when previously reported anxiety at age 16 was included as a covariate. <xref ref-type=\"fig\" rid=\"F1\">Figure 1</xref> shows the results of a test of the full model, including the total (<xref ref-type=\"fig\" rid=\"F1\">Figure 1A</xref>) and indirect effects (<xref ref-type=\"fig\" rid=\"F1\">Figure 1B</xref>) among cumulative environmental stressors, cognitive control, and anxiety.</p><fig id=\"F1\" position=\"float\"><label>FIGURE 1</label><caption><p>Path model showing the effect of cumulative environmental stressors on anxiety mediated by cognitive control. Total effects model <bold>(A)</bold> and indirect effects model <bold>(B)</bold> demonstrate the cognitive control at age 16 partially mediated the relation between environmental stressors at age 14 and anxiety at age 18. Anxiety at age 16 is included as a covariale. Residual variances of cognitive control and anxiety at age 16 are correlated to account for association due to unmeasured common causes (<italic>r</italic> = −0.03). Both standardized and unstandardized coefficients are shown. <italic>p</italic> < 0.05, <sup>∗∗</sup><italic>p <</italic> 0.01, <sup>∗∗∗</sup><italic>p</italic> < 0.001.</p></caption><graphic xlink:href=\"fpsyg-11-01838-g001\"/></fig><p>Consistent with Hypothesis 1, our results show that adolescents who report higher levels of cumulative environmental stressors at age 14 later reported greater anxiety at age 18 (c; β = 0.11, <italic>B</italic> = 0.06, <italic>z</italic> = 2.68, <italic>p</italic> = 0.02). Consistent with Hypothesis 2, which predicts that cognitive control would mediate the relation between cumulative environmental stressors and early adulthood anxiety, our results demonstrate a significant effect of environmental stressors on cognitive control (a; β = −0.20, <italic>B</italic> = −0.17, <italic>z</italic> = −4.52, <italic>p</italic> < 0.001), of cognitive control on anxiety (b; β = −0.10, <italic>B</italic> = −0.07, <italic>z</italic> = −2.91, <italic>p</italic> = 0.004), and of environmental stressors on anxiety (c′; β = 0.09, <italic>B</italic> = 0.05, <italic>z</italic> = 2.00, <italic>p</italic> = 0.05). These effects remained statistically significant even while controlling for anxiety at age 16, which suggests that cognitive control as a mediator is prospectively predicting anxiety at age 18 over and above prior levels of anxiety. That is, those reporting higher levels of environmental stressors tended to have lower cognitive control. Higher cognitive control, in turn, was associated with lower levels of late adolescent anxiety. The bootstrapped unstandardized indirect effect was <italic>B</italic> = 0.012, confidence interval [0.002, 0.02], and thus, the indirect effect was statistically significant. As a partial mediator, cognitive control accounted for 18% of the association between environmental cumulative stressors and adolescent anxiety (indirect effect/total effect): individuals reporting higher levels of cumulative environmental stress exposure tended to have decreased cognitive control (β = −0.20, <italic>B</italic> = −0.17, <italic>p</italic> < 0.001), cognitive control in turn was associated with decreased anxiety (β = −0.10, <italic>B</italic> = −0.07, <italic>p</italic> = 0.004). Taken together, these findings are consistent with the hypothesis that cumulative environmental stress exposure is associated with later anxiety at least in part because stress exposure impairs cognitive control, a critical factor in buffering against the development of anxiety.</p></sec><sec id=\"S4\"><title>Discussion</title><p>The purpose of the current study was to investigate the potential meditational role of cognitive control in the longitudinal relation between cumulative environmental stress exposure and the development of late adolescent anxiety. Given previous research, we tested the hypotheses that (1) increased stress exposure would be associated with higher levels of anxiety, and (2) this association would be partially mediated by cognitive control, with increased stress exposure being associated with impaired cognitive control, which in turn is linked to increased anxiety.</p><p>In line with our first hypothesis, our findings revealed a statistically significant positive association between early adolescent cumulative environmental stress exposure and later adolescent anxiety, albeit with β = 0.11, the effect is considered small (small = 2%, medium = 15%, and large = 25%; <xref rid=\"B48\" ref-type=\"bibr\">Lachowicz et al., 2018</xref>). This is consistent with a large body of research demonstrating a link between early exposure to adverse experiences and a range of later physical and mental health outcomes (see <xref rid=\"B56\" ref-type=\"bibr\">Nusslock and Miller, 2016</xref>, for review). However, our findings point to the importance of examining stress exposure from different sources. Previous studies have examined child maltreatment, poverty, family instability, socioeconomic status, and trauma to operationalize stress and adversity. In our unique sample, we touched upon a small fraction of the breadth of stressors one may be exposed to during development. We included reports of discrimination, exposure to criminal activity, and neighborhood quality in our measure of cumulative environmental stress. Sources of stress are wide ranging – from health inequalities to experiences of racism and discrimination – therefore, we urge these diverse experiences of stress to be reflected in future research and to be considered for their potential cumulative effects.</p><p>In line with our second hypothesis, we found the aforementioned relationship was partially explained by adolescent cognitive control. Specifically, our findings demonstrated that cognitive control mediated the relation between cumulative environmental stress exposure at age 14 and anxiety at age 18: those with greater exposure to environmental stressors tended to then have lower cognitive control (medium effect; β = −0.20), but higher cognitive control, in turn, was associated with lower levels of late adolescent anxiety (small effect; β = −0.10). In fact, even after controlling for anxiety at age 16, our tested model demonstrates that cognitive control accounts for 18% of the total effect between environmental stress exposure at age 14 and anxiety at age 18, which indicates a medium proportion of explained variance (<xref rid=\"B48\" ref-type=\"bibr\">Lachowicz et al., 2018</xref>). As the first study to examine the three constructs in a prospective, longitudinal manner, our results converge with evidence from developmental psychology, public health, and neuroscience to chronicle the role of social systems in shaping the development of our mental and emotional health. More importantly, our findings uniquely identify cognitive control as an underlying mechanism, a protective factor that is both vulnerable to the influences of environmental stress yet potentially buffers against these deleterious effects on anxiety outcomes. Thus, efforts to mitigate mental health outcomes for youth ought to consider the role and malleability of cognitive control. Although results from cognitive control interventions are mixed (see <xref rid=\"B6\" ref-type=\"bibr\">Au et al., 2015</xref> for meta-analysis), a growing number of interventions studies have shown some promise in improving mental health outcomes (<xref rid=\"B57\" ref-type=\"bibr\">Owens et al., 2013</xref>; <xref rid=\"B46\" ref-type=\"bibr\">Koster et al., 2017</xref>; <xref rid=\"B42\" ref-type=\"bibr\">Jopling et al., 2020</xref>). The prospect of optimizing this function is critical in promoting resilience, particularly during adolescence, a unique period of neurocognitive development and enhanced vulnerability.</p><p>It is important to note that despite the fact that the above relations were statistically significant, their β values ranged from small to medium. Specifically, the relation between cumulative environmental stress at age 14 and later anxiety at age 18 may be meaningful but smaller than expected given the findings from previous literature. It is important to note, however, that previous findings were either cross-sectional in nature and/or examined only two constructs, which may magnify the strength of the relationships. A longitudinal study examining poverty, chronic stress, and later cognitive control – as indexed by neural activity – reported similar β values for poverty and cognitive control ranging between 0.03 and 0.05, and for chronic stress and cognitive control ranging between −0.13 and −0.14 (<xref rid=\"B43\" ref-type=\"bibr\">Kim et al., 2013</xref>), which closely mirrors our findings for the direct effects of path a and c′ (<xref ref-type=\"fig\" rid=\"F1\">Figure 1</xref>). As such, modest values may reflect the challenges in isolating causal mechanisms that are inherent to longitudinal work, where the dynamic relationship of variables gets diluted over time as other factors come into play. For example, our findings demonstrate that concurrent measurement of cognitive control and anxiety at age 16 leads to a stronger relationship (<italic>r</italic> = −0.33, <italic>p</italic> < 0.001) than cognitive control at age 16 and anxiety at age 18 (<italic>r</italic> = −0.21, <italic>p</italic> < 0.001). Note, however, that the indirect effect of cognitive control accounted for 18% of the total relationship, despite the weakened association over the 2 years and controlling for anxiety at age 16.</p><p>The present results should be considered in light of a few limitations. First, our measure of environmental stressors aimed to encapsulate the cumulative effects of stress through measures of environmental adversity. The range of measures included – from perceived discrimination to neighborhood quality – resulted in modest correlation between measures. However, this modest correlation may reflect the methodological issues of assessing environmental stress. Measurement has taken on a variety of forms in attempt to capture the broad range of physical to psychosocial sources (see <xref rid=\"B20\" ref-type=\"bibr\">Evans and English, 2002</xref> for review). One promising approach indexes environmental stress exposure as a cumulative construct in attempt to capture the confluence of multiple external demands that may lead to suboptimal outcomes for youth. Literature on chronic stress shows that the <italic>quantity</italic> of risk factors encountered, as captured by a cumulative index, and not the particular <italic>type</italic> that seems to better predict outcomes (<xref rid=\"B47\" ref-type=\"bibr\">Kraemer et al., 2005</xref>; <xref rid=\"B64\" ref-type=\"bibr\">Sameroff, 2006</xref>; <xref rid=\"B19\" ref-type=\"bibr\">Evans and Cassells, 2014</xref>). With our cumulative score from three questionnaires, the self-reported levels of chronic stress were low in our sample (mean = 1.67, range = 0–4), which could reflect measurement issues and/or the possibility that our sample was not exposed to high levels of environmental stress. Future work would benefit from including a greater breadth of measures for a more robust index.</p><p>Second, our measure of cognitive control relied on self-report. In a meta-analysis of 282 studies of self-control, correlations within and across types of self-control measures were weak (<xref rid=\"B14\" ref-type=\"bibr\">Duckworth and Kern, 2011</xref>). Future work including some combination of behavioral, observational, and self-report may improve measurement validity. Lastly, the direction of the relationship between cognitive control and anxiety is arguable. That is, there is literature indicating impaired cognitive control <italic>causes</italic> anxiety (<xref rid=\"B1\" ref-type=\"bibr\">Abravanel and Sinha, 2015</xref>), anxiety <italic>causes</italic> impaired cognitive control (<xref rid=\"B15\" ref-type=\"bibr\">Edwards et al., 2016</xref>), or that cognitive control moderates the relationship between stress and adversity on poor mental health outcomes (<xref rid=\"B26\" ref-type=\"bibr\">Extremera and Rey, 2015</xref>). Although correlational in nature, our novel findings hint at the first causal effect – that is, greater cognitive control is associated with later decreased anxiety – but all three effects have not been adequately examined together (as competing or complementary processes) in a longitudinal context.</p><p>The current study set out to synthesize findings from stress, cognition, and mental health literature and test previously untested theories on the directionality of these relationships during adolescence. As the first prospective longitudinal study in this area, our results deepen our understanding of the mechanism underlying early stress exposure and the development of anxiety during a developmentally sensitive period. More importantly, our findings underscore the importance of preserving cognitive control as a means of combating mental health disorders and as a possible protective factor in promoting resilience.</p></sec><sec sec-type=\"data-availability\" id=\"S5\"><title>Data Availability Statement</title><p>The datasets generated for this study will not be made publicly available due to confidentiality reasons. Data is available upon application through administrators of the California Families Project.</p></sec><sec id=\"S6\"><title>Ethics Statement</title><p>The studies involving human participants were reviewed and approved by the Institutional Review Board of University of California, Davis. Written informed consent to participate in this study was provided by the participants’ legal guardian/next of kin.</p></sec><sec id=\"S7\"><title>Author Contributions</title><p>NT, SJ, JE, and RR contributed to the theoretical development of the study. OA supplied resources needed for study analysis. NT performed the data analysis and interpretation under the supervision of SJ, JE, and RR. NT drafted the manuscript. SJ, JE, RR, and OA provided revisions. All authors approved the final version of the manuscript for submission.</p></sec><sec id=\"conf1\"><title>Conflict of Interest</title><p>The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p></sec></body><back><fn-group><fn fn-type=\"financial-disclosure\"><p><bold>Funding.</bold> This study was supported by award no. MRP-17-454825 from the University of California Consortium Adolescence Seed Grant. 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[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Int J Mol Sci</journal-id><journal-id journal-id-type=\"iso-abbrev\">Int J Mol Sci</journal-id><journal-id journal-id-type=\"publisher-id\">ijms</journal-id><journal-title-group><journal-title>International Journal of Molecular Sciences</journal-title></journal-title-group><issn pub-type=\"epub\">1422-0067</issn><publisher><publisher-name>MDPI</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32756462</article-id><article-id pub-id-type=\"pmc\">PMC7432130</article-id><article-id pub-id-type=\"doi\">10.3390/ijms21155552</article-id><article-id pub-id-type=\"publisher-id\">ijms-21-05552</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Article</subject></subj-group></article-categories><title-group><article-title>The N-Terminal Region of Soybean PM1 Protein Protects Liposomes during Freeze-Thaw</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Chen</surname><given-names>Liyi</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05552\">1</xref><xref ref-type=\"author-notes\" rid=\"fn1-ijms-21-05552\">†</xref></contrib><contrib contrib-type=\"author\"><name><surname>Sun</surname><given-names>Yajun</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05552\">1</xref><xref ref-type=\"author-notes\" rid=\"fn1-ijms-21-05552\">†</xref></contrib><contrib contrib-type=\"author\"><name><surname>Liu</surname><given-names>Yun</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05552\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Zou</surname><given-names>Yongdong</given-names></name><xref ref-type=\"aff\" rid=\"af2-ijms-21-05552\">2</xref></contrib><contrib contrib-type=\"author\"><name><surname>Huang</surname><given-names>Jianzi</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05552\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Zheng</surname><given-names>Yizhi</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05552\">1</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0003-1335-4775</contrib-id><name><surname>Liu</surname><given-names>Guobao</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05552\">1</xref><xref rid=\"c1-ijms-21-05552\" ref-type=\"corresp\">*</xref></contrib></contrib-group><aff id=\"af1-ijms-21-05552\"><label>1</label>Guangdong Provincial Key Laboratory for Plant Epigenetics, College of Life Sciences and Oceanography, Shenzhen University, Shenzhen 518060, China; <email>[email protected]</email> (L.C.); <email>[email protected]</email> (Y.S.); <email>[email protected]</email> (Y.L.); <email>[email protected]</email> (J.H.); <email>[email protected]</email> (Y.Z.)</aff><aff id=\"af2-ijms-21-05552\"><label>2</label>The Instrumental Analysis Center of Shenzhen University (Lihu Campus), Shenzhen University, Shenzhen 518060, China; <email>[email protected]</email></aff><author-notes><corresp id=\"c1-ijms-21-05552\"><label>*</label>Correspondence: <email>[email protected]</email>; Tel.: +86-755-26558081</corresp><fn id=\"fn1-ijms-21-05552\"><label>†</label><p>These authors contributed equally to this work.</p></fn></author-notes><pub-date pub-type=\"epub\"><day>03</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><month>8</month><year>2020</year></pub-date><volume>21</volume><issue>15</issue><elocation-id>5552</elocation-id><history><date date-type=\"received\"><day>22</day><month>7</month><year>2020</year></date><date date-type=\"accepted\"><day>01</day><month>8</month><year>2020</year></date></history><permissions><copyright-statement>© 2020 by the authors.</copyright-statement><copyright-year>2020</copyright-year><license license-type=\"open-access\"><license-p>Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>).</license-p></license></permissions><abstract><p>Late embryogenesis abundant (LEA) group 1 (LEA_1) proteins are intrinsically disordered proteins (IDPs) that play important roles in protecting plants from abiotic stress. Their protective function, at a molecular level, has not yet been fully elucidated, but several studies suggest their involvement in membrane stabilization under stress conditions. In this paper, the soybean LEA_1 protein PM1 and its truncated forms (PM1-N: N-terminal half; PM1-C: C-terminal half) were tested for the ability to protect liposomes against damage induced by freeze-thaw stress. Turbidity measurement and light microscopy showed that full-length PM1 and PM1-N, but not PM1-C, can prevent freeze-thaw-induced aggregation of POPC (1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine) liposomes and native thylakoid membranes, isolated from spinach leaves (<italic>Spinacia oleracea</italic>). Particle size distribution analysis by dynamic light scattering (DLS) further confirmed that PM1 and PM1-N can prevent liposome aggregation during freeze-thaw. Furthermore, PM1 or PM1-N could significantly inhibit membrane fusion of liposomes, but not reduce the leakage of their contents following freezing stress. The results of proteolytic digestion and circular dichroism experiments suggest that PM1 and PM1-N proteins bind mainly on the surface of the POPC liposome. We propose that, through its N-terminal region, PM1 functions as a membrane-stabilizing protein during abiotic stress, and might inhibit membrane fusion and aggregation of vesicles or other endomembrane structures within the plant cell.</p></abstract><kwd-group><kwd>LEA protein</kwd><kwd>liposome</kwd><kwd>freeze-thaw</kwd><kwd>intrinsically disordered</kwd><kwd>membrane stabilizing protein</kwd></kwd-group></article-meta></front><body><sec sec-type=\"intro\" id=\"sec1-ijms-21-05552\"><title>1. Introduction</title><p>Plants have developed various adaptations to maintain normal physiological processes under adverse abiotic stress, such as soil salinity, drought and extreme temperatures. For instance, it has been demonstrated experimentally that late embryogenesis abundant (LEA) proteins are involved in plant tolerance to abiotic stress [<xref rid=\"B1-ijms-21-05552\" ref-type=\"bibr\">1</xref>,<xref rid=\"B2-ijms-21-05552\" ref-type=\"bibr\">2</xref>,<xref rid=\"B3-ijms-21-05552\" ref-type=\"bibr\">3</xref>]. Based on Pfam domains within their sequences, LEA proteins can be classified into eight subgroups [<xref rid=\"B3-ijms-21-05552\" ref-type=\"bibr\">3</xref>]. Most research has focused on the protective functions of the LEA_4, LEA_5 and dehydrin subgroups, generating data on expression profile, cellular localization, and the role of the respective genes and proteins. There is relatively little research on the protective roles of other groups, including the LEA_1 proteins, which are highly expressed in mature seeds and also accumulate in vegetative tissues under stress conditions [<xref rid=\"B4-ijms-21-05552\" ref-type=\"bibr\">4</xref>,<xref rid=\"B5-ijms-21-05552\" ref-type=\"bibr\">5</xref>,<xref rid=\"B6-ijms-21-05552\" ref-type=\"bibr\">6</xref>]. Some genetic studies have been performed showing that the over-expression of LEA_1 genes, such as <italic>LEA4-1</italic> from <italic>Brassica napus</italic>, <italic>BhLEA1</italic> and <italic>BhLEA2</italic> from <italic>Boea hygrometrica</italic>, <italic>AtLEA4-5</italic> from <italic>Arabidopsis thaliana</italic>, and <italic>XsLEA1-8</italic> from <italic>Xerophyta schlechteri</italic>, confer tolerance to salt, drought and osmotic stress in transgenic plants [<xref rid=\"B5-ijms-21-05552\" ref-type=\"bibr\">5</xref>,<xref rid=\"B6-ijms-21-05552\" ref-type=\"bibr\">6</xref>,<xref rid=\"B7-ijms-21-05552\" ref-type=\"bibr\">7</xref>]. The over-expression of recombinant <italic>Gastrodia elata GeLEA1-1</italic> or <italic>Xerophyta schlechteri XsLEA1-8</italic> enhances <italic>Escherichia coli</italic> viability under low-temperature or heat stress [<xref rid=\"B7-ijms-21-05552\" ref-type=\"bibr\">7</xref>,<xref rid=\"B8-ijms-21-05552\" ref-type=\"bibr\">8</xref>]. In addition, in vitro, the recombinant LEA_1 proteins AtLEA4-2 and AtLEA4-5 (both <italic>A. thaliana</italic>) and XsLEA1-8 can preserve lactate dehydrogenase activity and prevent this enzyme aggregating during freeze–thaw, heat, desiccation and oxidative stress [<xref rid=\"B7-ijms-21-05552\" ref-type=\"bibr\">7</xref>,<xref rid=\"B9-ijms-21-05552\" ref-type=\"bibr\">9</xref>,<xref rid=\"B10-ijms-21-05552\" ref-type=\"bibr\">10</xref>]. Liu et al. suggested that BhLEA1 and BhLEA2 play a general protective role in the plant cell during dehydration stress and increase membrane and protein stability, as indicated by the relatively low electrolyte leakage and higher SOD and POD activity in transgenic plant leaves [<xref rid=\"B5-ijms-21-05552\" ref-type=\"bibr\">5</xref>]. In contrast, <italic>A. thaliana</italic> basic protein LEA18 (LEA_1) specifically aggregates and destabilizes negatively charged liposomes, which suggests that LEA18 does not function as a membrane-stabilizing protein, but could modulate membrane stability depending on membrane composition [<xref rid=\"B11-ijms-21-05552\" ref-type=\"bibr\">11</xref>].</p><p>The LEA proteins are a broad family of proteins, many of which are thought to be intrinsically disordered proteins (IDPs) with “moonlighting” activity [<xref rid=\"B3-ijms-21-05552\" ref-type=\"bibr\">3</xref>,<xref rid=\"B10-ijms-21-05552\" ref-type=\"bibr\">10</xref>]. However, to our knowledge, relatively few individual LEA proteins have been shown to have multifunctional protective activities. One example of a multifunctional protein, PM1, a LEA_1 protein from soybean, can interact with a range of other molecules, such as non-reducing sugars, poly-<sc>l</sc>-lysine and phospholipids, during dehydration. PM1 is likely to be an important component of the cellular, organic glass that may stabilize desiccation-sensitive proteins and membranes in stressed plants [<xref rid=\"B12-ijms-21-05552\" ref-type=\"bibr\">12</xref>]. PM1 contains a high proportion of basic amino acids, including 13 lysine, 5 arginine and 10 histidine residues, but these are distributed unequally along the length of the protein. There are 12 lysine residues and 3 arginine residues located in the N-terminal half (residues 1–84), while 9 histidine residues and 2 arginine residues are located in the C-terminal region (residues 85–173). In previous papers, we have demonstrated that PM1 protein can bind some metal ions (Fe<sup>3+</sup>, Ni<sup>2+</sup>, Cu<sup>2+</sup> and Zn<sup>2+</sup>) and so play an important protective role in reducing oxidative damage and ion toxicity in plants exposed to abiotic stress [<xref rid=\"B13-ijms-21-05552\" ref-type=\"bibr\">13</xref>]. In addition, PM1 or its truncated version, PM1-C (C-terminal half only), but not PM1-N, can form oligomers and high molecular weight (HMW) complexes via its C-terminal histidine residues both in vitro and <italic>in planta</italic>. Crucially, binding of Cu<sup>2+</sup> at high concentrations, which takes place through the same histidine residues, seems to promote oligomerization and the formation of HMW complexes by PM1 [<xref rid=\"B14-ijms-21-05552\" ref-type=\"bibr\">14</xref>].</p><p>In the present paper, we investigate whether soybean LEA_1 protein PM1 and its two truncated forms, PM1-N and PM1-C, possess the ability to suppress freeze-thaw-induced damage of liposomes. We show that only PM1 and PM1-N can effectively prevent aggregation of POPC liposomes caused by freeze-thaw treatment. Furthermore, we show that, although PM1 and PM1-N inhibit membrane fusion events, they do not reduce leakage of POPC liposomes. On the basis of these observations, we propose that the N-terminal region of PM1 protein has a membrane stabilizing function during abiotic stress.</p></sec><sec sec-type=\"results\" id=\"sec2-ijms-21-05552\"><title>2. Results</title><sec id=\"sec2dot1-ijms-21-05552\"><title>2.1. PM1 and PM1-N Proteins Inhibit the Freeze-Thaw-Induced Increase in Turbidity of a Liposome Suspension</title><p>The cell membrane is composed of lipids <styled-content style=\"color:#222222\">(phospholipids and cholesterol)</styled-content>, proteins and carbohydrate groups. Among the phospholipids, p<styled-content style=\"color:#222222\">hosphatidylcholine (PC)</styled-content><styled-content style=\"color:#222222\"> represents a</styled-content> major component of biological membranes and the PC, POPC (1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine), can be used to produce liposomes that model cell membranes. Light scattering due to aggregation of POPC liposomes can be measured by apparent absorbance at 400 nm [<xref rid=\"B15-ijms-21-05552\" ref-type=\"bibr\">15</xref>]. The OD<sub>400</sub> of a freshly prepared POPC liposome suspension was ~0.3 and the addition of one of the proteins thaumatin, PM1, PM1-N or PM1-C to POPC liposomes did not obviously affect their turbidity before exposure to stress (<xref ref-type=\"fig\" rid=\"ijms-21-05552-f001\">Figure 1</xref>).</p><p>The turbidity, as shown by OD<sub>400</sub>, of the liposome suspension increased markedly to 0.8 after three freeze-thaw cycles, indicating that the freeze-thaw treatment causes liposome aggregation. When 0.8 mg/mL thaumatin (molar ratio of protein:lipid~1:3300) or 0.8 mg/mL PM1-C protein (molar ratio of protein:lipid~1:1650) was added to the POPC liposome suspension, its OD<sub>400</sub> increased to 0.6 after the freeze-thaw treatment, which is significantly different (<italic>p</italic> < 0.01) to that of liposomes only. Thaumatin was chosen as a negative control in the following study because of having no known function in membrane protection [<xref rid=\"B11-ijms-21-05552\" ref-type=\"bibr\">11</xref>] and its similar molecular size to PM1 protein. There was no statistical difference in turbidity between liposome/PM1-C and liposome/thaumatin. However, when PM1 or PM1-N protein (0.04–0.4 mg/mL) was included in the liposome suspension, the turbidity increase after freeze-thaw was appreciably suppressed and the extent of this suppression was dependent on the concentration of the added protein, being significantly (<italic>p</italic> < 0.05) or extremely significantly different (<italic>p</italic> < 0.01) from that of thaumatin. When the concentration of PM1 (molar ratio of protein:lipid ~1:3300) or PM1-N protein (molar ratio of protein:lipid ~1:1650) reached 0.8 mg/mL, the post-treatment turbidity was 0.4 at OD<sub>400</sub>, i.e., similar to that before freeze-thaw.</p></sec><sec id=\"sec2dot2-ijms-21-05552\"><title>2.2. PM1 and PM1-N Proteins Can Inhibit or Prevent Freeze-Thaw-Induced Aggregation of Liposomes and Isolated Thylakoid membranes</title><p>The inhibitory effect of PM1, PM1-N or PM1-C protein on liposome aggregation can be followed by light microscopy. Before freeze-thaw treatment, POPC liposomes were small in size and evenly distributed in the suspension (<xref ref-type=\"fig\" rid=\"ijms-21-05552-f002\">Figure 2</xref>A). The addition of one of thaumatin, PM1, PM1-N or PM1-C proteins to POPC liposomes did not cause aggregation before freeze-thaw treatment, but in the absence of added proteins, liposome aggregation could be seen to occur after freeze-thaw treatment. With either 0.8 mg/mL thaumatin (negative control) or 0.8 mg/mL PM1-C protein in the suspension, the liposomes still aggregated after freeze-thaw. In contrast, when 0.8 mg/mL PM1 or 0.8 mg/mL PM1-N protein was added to liposomes, the suspension remained homogeneous even after freeze-thaw.</p><p>Next, the anti-aggregation effect of the PM1, PM1-N or PM1-C proteins on natural biological material was tested using thylakoid membranes isolated from spinach leaves (<italic>Spinacia oleracea</italic>). Under the light microscope, the thylakoid membranes appeared as evenly distributed, small particles in suspension (<xref ref-type=\"fig\" rid=\"ijms-21-05552-f002\">Figure 2</xref>B). The addition of thaumatin, PM1, PM1-N or PM1-C proteins individually to thylakoid membranes did not cause aggregation. However, after freeze-thaw treatment, the thylakoid membranes aggregated markedly, as was apparent by light microscopy, and even being directly visible to the naked eye. The addition of 0.8 mg/mL thaumatin (negative control) or 0.8 mg/mL PM1-C protein did not prevent the formation of thylakoid aggregates when the suspension was subjected to freeze-thaw. In contrast, when 0.8 mg/mL PM1 or 0.8 mg/mL PM1-N protein was added to the thylakoid suspension, there was almost no aggregation after freeze-thaw.</p></sec><sec id=\"sec2dot3-ijms-21-05552\"><title>2.3. The Addition of Either PM1 or PM1-N Protein Can Stabilize the Liposome Particle Size</title><p>The particle size distribution of POPC liposomes was measured and a single peak was obtained by dynamic light scattering (DLS) centering around 130 nm before freeze-thaw treatment (<xref ref-type=\"fig\" rid=\"ijms-21-05552-f003\">Figure 3</xref>). The addition of thaumatin, PM1, PM1-N or PM1-C individually to liposomes did not affect the particle size distribution (<xref ref-type=\"fig\" rid=\"ijms-21-05552-f003\">Figure 3</xref>A). After freeze-thaw treatment, another peak of 800 nm was observed besides the main peak of 130 nm, indicating that freeze-thaw can cause liposome adhesion, involving liposome fusion and/or aggregation. When either 0.8 mg/mL thaumatin or 0.8 mg/mL PM1-C protein was added to the liposome suspension, a peak at 250–270 nm was present besides the main peak of 130 nm. In contrast, when either 0.8 mg/mL PM1 or 0.8 mg/mL PM1-N protein was added to the liposome suspension, a small peak of particle size ~50 nm emerged besides the main peak of 140 nm (<xref ref-type=\"fig\" rid=\"ijms-21-05552-f003\">Figure 3</xref>B).</p><p>Together, the above results suggest that PM1 and PM1-N, but not PM1-C, can inhibit aggregation of liposomes and thylakoids caused by freeze-thaw treatment; the effect was concentration-dependent within a certain range and also specific. Furthermore, PM1 and PM1-N protein can prevent membrane fusion in POPC liposomes due to freeze-thaw.</p></sec><sec id=\"sec2dot4-ijms-21-05552\"><title>2.4. PM1 and PM1-N Proteins Cannot Prevent Freeze-Thaw-Induced Leakage of Liposomes</title><p>Freeze-thaw treatment can cause the collapse of liposome membranes, causing leakage of liposome contents. To obtain a deeper insight into the protective effects of PM1 protein and its N-terminal region, a leakage experiment, using the fluorescent probe, carboxyfluorescein (CF), trapped within liposomes, was carried out according to [<xref rid=\"B16-ijms-21-05552\" ref-type=\"bibr\">16</xref>]. As shown in <xref ref-type=\"fig\" rid=\"ijms-21-05552-f004\">Figure 4</xref>, the leakage rate of pure POPC liposomes after freeze-thaw treatment was 91.9%, suggesting that freeze-thaw almost entirely destroys their integrity. In the presence of the proteins (all at 0.8 mg/mL) used in this study, the post-treatment leakage rates of POPC liposomes were as follows: thaumatin, 86.8%; PM1, 80.1%; PM1-N, 80.5%; PM1-C, 79.6%. There was no statistical difference in CF leakage rates between any of the liposome/protein combinations and the liposome-only samples, suggesting that PM1 and its truncated versions prevent leakage no better than thaumatin or, indeed, no protein (<xref ref-type=\"fig\" rid=\"ijms-21-05552-f004\">Figure 4</xref>).</p></sec><sec id=\"sec2dot5-ijms-21-05552\"><title>2.5. PM1 and PM1-N Proteins Can Effectively Inhibit or Prevent Membrane Fusion</title><p>The leakage of soluble contents from liposomes is often accompanied by membrane fusion [<xref rid=\"B17-ijms-21-05552\" ref-type=\"bibr\">17</xref>]. The degree of fusion of the membrane can be assessed quantitatively by fluorescence resonance energy transfer (FRET) [<xref rid=\"B18-ijms-21-05552\" ref-type=\"bibr\">18</xref>]. In the present paper, POPC liposomes whose membranes contained a pair of fluorescently labeled phospholipids (Rh-PE and NBD-PE) that can undergo FRET were mixed with unlabeled liposomes. When membrane fusion between labeled and unlabeled liposomes occurs, the degree of FRET between Rh-PE and NBD-PE will be reduced, due to dilution of the fluorescent probes. As shown as <xref ref-type=\"fig\" rid=\"ijms-21-05552-f005\">Figure 5</xref>, about 41.6% of liposomes underwent membrane fusion after freeze-thaw. When 0.8 mg/mL thaumatin was added to the liposome suspension, the degree of membrane fusion was 37.1% after freeze-thaw is not significantly different from that of liposomes only. When 0.8 mg/mL PM1-C protein was added to the liposome suspension, the degree of membrane fusion was 31.7% after freeze-thaw, showing a significant difference (<italic>p</italic> < 0.05) with liposomes only or thaumatin as negative control. However, in the presence of 0.8 mg/mL PM1 or 0.8 mg/mL PM1-N, the degree of membrane fusion reduced markedly to 10.6% or 8.6%, respectively, showing significant differences (<italic>p</italic> < 0.01) to liposomes only or thaumatin as negative control. Thus, the ability of PM1 to reduce membrane fusion in POPC liposomes caused by freeze-thaw largely resides in its N-terminal sequence.</p><p>The above results show that PM1 or PM1-N protein do not reduce the leakage of CF from POPC liposomes subjected to freeze-thaw, although they can significantly inhibit membrane fusion. Compared to thaumatin protein as negative control, on the other hand, PM1-C protein did not reduce leakage, and only reduced membrane fusion of POPC liposomes to some extent. </p></sec><sec id=\"sec2dot6-ijms-21-05552\"><title>2.6. The Presence of POPC Liposomes Does Not Affect Digestion of PM1 by Trypsin</title><p>PM1 is an IDP, a group of proteins that are on the whole degraded ~100 times faster than folded proteins [<xref rid=\"B19-ijms-21-05552\" ref-type=\"bibr\">19</xref>]. Liposomes can increase the folding of IDPs, and this can be measured by a limited proteolysis test [<xref rid=\"B20-ijms-21-05552\" ref-type=\"bibr\">20</xref>,<xref rid=\"B21-ijms-21-05552\" ref-type=\"bibr\">21</xref>]. Trypsin is a Lys-specific protease that can recognize the Lys residues located exclusively within the N-terminal half of the soybean PM1 sequence. Besides the main band at ~20 kDa, the purified PM1 protein contains several smaller bands, which also can be detected by PM1 specific antibody [<xref rid=\"B14-ijms-21-05552\" ref-type=\"bibr\">14</xref>], probably indicating a small amount of degradation in protein preparation. <xref ref-type=\"fig\" rid=\"ijms-21-05552-f006\">Figure 6</xref>A shows that PM1 was readily and gradually degraded by trypsin over 30 min, as indicated by the main band at ~20 kDa becoming weaker and by the appearance of smaller bands below 17 kDa. This pattern of degradation did not change in the presence of POPC liposomes, suggesting that the structure of PM1 protein remains disordered state and is not influenced by the presence of liposomes.</p></sec><sec id=\"sec2dot7-ijms-21-05552\"><title>2.7. The Presence of POPC Liposomes Does Not Change the Secondary Structure of PM1 Protein</title><p>It has been reported that <italic>Zea mays</italic> dehydrin DHN1 and <italic>A. thaliana</italic> dehydrin Lti30 bind the negatively charged head groups of phospholipids and that this is accompanied by an increase in α-helicity of both LEA proteins [<xref rid=\"B15-ijms-21-05552\" ref-type=\"bibr\">15</xref>,<xref rid=\"B22-ijms-21-05552\" ref-type=\"bibr\">22</xref>,<xref rid=\"B23-ijms-21-05552\" ref-type=\"bibr\">23</xref>,<xref rid=\"B24-ijms-21-05552\" ref-type=\"bibr\">24</xref>]. We tested the effect of POPC liposomes on PM1 secondary structure using circular dichroism (CD) (<xref ref-type=\"fig\" rid=\"ijms-21-05552-f006\">Figure 6</xref>B). We observed that PM1 protein remained unstructured both before, and after, freeze-thaw in the absence or presence of liposomes, as indicated by an ellipticity minimum at 198 nm. </p><p>Together, the results of trypsin digestion and CD analysis suggest that PM1 interacts mainly with the surface of the uncharged POPC liposomes.</p></sec></sec><sec sec-type=\"discussion\" id=\"sec3-ijms-21-05552\"><title>3. Discussion</title><p>LEA proteins, a large and highly diverse protein family, accumulate during seed desiccation in the later stages of embryogenesis. Given the diversity and compartmentalization of LEA proteins in plant tissues, a detailed understanding of their function involves characterization of each protein individually. The over-expression of some LEA protein genes in transgenic plants confers tolerance to low-temperature stress. For example, over-expression of <italic>A. thaliana COR15A</italic> and <italic>COR15B</italic> (LEA_4) in Arabidopsis or of <italic>Citrus unshiu CuCOR19</italic> (dehydrin) in tobacco (<italic>Nicotiana tabacum</italic>) increases the freezing tolerance of the resulting transgenic plants, as indicated by reduced electrolyte leakage compared to controls [<xref rid=\"B25-ijms-21-05552\" ref-type=\"bibr\">25</xref>,<xref rid=\"B26-ijms-21-05552\" ref-type=\"bibr\">26</xref>,<xref rid=\"B27-ijms-21-05552\" ref-type=\"bibr\">27</xref>]. In vitro, COR15A and COR15B stabilize liposomes, either in terms of freeze-induced solute leakage or membrane fusion [<xref rid=\"B26-ijms-21-05552\" ref-type=\"bibr\">26</xref>]. Similarly, <italic>Pisum sativum</italic> LEAM (LEA_4) or the <italic>Artemia franciscana</italic> AfrLEA2 and AfrLEA3m (LEA_4) increase liposome stability after freeze-thawing, as illustrated by a reduced leakage of entrapped CF molecules [<xref rid=\"B28-ijms-21-05552\" ref-type=\"bibr\">28</xref>,<xref rid=\"B29-ijms-21-05552\" ref-type=\"bibr\">29</xref>]. In contrast, three <italic>A. thaliana</italic> LEA proteins, LEA1, LEA26 and LEA27 (LEA_2), increase CF leakage from liposomes after freeze-thaw treatment [<xref rid=\"B16-ijms-21-05552\" ref-type=\"bibr\">16</xref>]. Arabidopsis Lti30 (dehydrin) assembles lipid vesicles and native thylakoid membranes into aggregates by a possible role in membrane cross-linking [<xref rid=\"B15-ijms-21-05552\" ref-type=\"bibr\">15</xref>], while Arabidopsis LEA18 protein (LEA_1) increases CF leakage, liposome membrane fusion and liposome aggregation under non-freezing conditions [<xref rid=\"B11-ijms-21-05552\" ref-type=\"bibr\">11</xref>]. </p><p>Shih et al. reported that soybean PM1 protein can interact with POPC lipids to maintain the liquid-crystal phase over a wide temperature range in the dry state [<xref rid=\"B12-ijms-21-05552\" ref-type=\"bibr\">12</xref>]. In the present paper, we found that at high concentrations PM1/PM1-N protein specifically binds to or interacts with the surface of liposome membranes in solution and in the frozen state. For this reason, we speculate that, in a solution of PM1 or PM1-N, the liposomes are kept separate from each other by the protein molecules. During freeze-thaw, liposomes break down or fragment such that their contents (CF in our experiments) leak out [<xref rid=\"B26-ijms-21-05552\" ref-type=\"bibr\">26</xref>]. In the absence of PM1 and PM1-N proteins, the resulting membrane fragments can fuse and POPC liposomes aggregate to such an extent that they become visible under the light microscope and even to the naked eye. In the presence of PM1 and PM1-N, the adherence of liposome membranes was reduced and membrane fusion and liposome aggregation was reduced, even though leakage of CF contents was not prevented. Therefore, our observations support a protection mechanism for PM1 on cells and cell membranes that is very similar to the “molecular shield” model. This model was proposed by Wise and Tunnacliffe to explain the protective function of LEA proteins towards other proteins under water stress conditions [<xref rid=\"B30-ijms-21-05552\" ref-type=\"bibr\">30</xref>].</p><p>Some dehydrins, such as <italic>Zea mays</italic> DHN1, <italic>Thellungiella salsuginea</italic> TsDHN1 and TsDHN2, and <italic>A. thaliana</italic> ERD14 and Lti30, bind phospholipids in vitro [<xref rid=\"B15-ijms-21-05552\" ref-type=\"bibr\">15</xref>,<xref rid=\"B19-ijms-21-05552\" ref-type=\"bibr\">19</xref>,<xref rid=\"B22-ijms-21-05552\" ref-type=\"bibr\">22</xref>,<xref rid=\"B23-ijms-21-05552\" ref-type=\"bibr\">23</xref>,<xref rid=\"B31-ijms-21-05552\" ref-type=\"bibr\">31</xref>]. These interactions between dehydrins and phospholipid molecules are driven by electrostatics and depend on the positively charged residues in the characteristic lysine-rich K-segment of the dehydrins, which pair with the negatively charged head groups of the lipids [<xref rid=\"B24-ijms-21-05552\" ref-type=\"bibr\">24</xref>]. Ananlysis of the amino acid composition of soybean basic PM1 protein shows that the N-terminal is also rich in lysine residues and has +6 positively charged amino acids in solution at pH 7.4. It is reasonable to speculate that the N-terminal region of PM1 might interact with the surface of liposomes, especially with the negatively charged head groups of phospholipid molecules. </p><p>On the other hand, the C-terminal region of PM1 contains 9 histidine residues and has only +1 positively charged amino acids at pH 7.4. In contrast to the PM1 and PM1-N proteins, PM1-C cannot effectively prevent liposome aggregation and membrane fusion during freeze-thaw. This could be because PM1-C, with many fewer charged residues, can only attach to the liposome surface with low affinity. Therefore, it is not as effective at keeping liposomes separate and consequently preventing them from aggregating. Therefore, the anti-aggregation function of PM1 protein on cell membranes is likely mainly conferred by its N-terminal region.</p><p><italic>A. thaliana</italic> basic LEA18 protein and soybean PM1 protein both belong to the LEA_1 subgroup of LEA proteins. However, there is only 30% similarity between the two protein sequences. Intriguingly, LEA18 does not function as a membrane-stabilizing protein, in contrast to PM1. Instead, the LEA18 protein causes aggregation and destabilization of negatively charged liposomes [<xref rid=\"B11-ijms-21-05552\" ref-type=\"bibr\">11</xref>]. </p><p>We have carried out a series of studies on the protective functions of soybean PM1 protein. The amino acid sequence shows that its histidine residues are mainly located in the C-terminal half, while its lysine residues are mostly in the N-terminal region. The C-terminal region has the potential to bind metal ions, scavenge hydroxyl radicals, and form oligomers and HMW complexes. These functions are directly related to the number of histidine residues in the C-terminal region [<xref rid=\"B13-ijms-21-05552\" ref-type=\"bibr\">13</xref>,<xref rid=\"B14-ijms-21-05552\" ref-type=\"bibr\">14</xref>]. In contrast to the disordered nature of the C-terminus, the highly conserved N-terminal half of PM1 can be induced to form significant levels of α-helical structure by SDS or trifluoroethanol [<xref rid=\"B14-ijms-21-05552\" ref-type=\"bibr\">14</xref>]. At the same time, this N-terminal region of PM1 has a protective effect on LDH activity (<xref ref-type=\"fig\" rid=\"ijms-21-05552-f0A1\">Figure A1</xref>) and can also effectively prevent liposome aggregation. Both these functions may relate to the number of lysine residues in the N-terminal region. It has been speculated that the two halves of PM1 could perform different functions by binding different targets. The conformational flexibility and structural plasticity of PM1, as an IDP, may be the molecular basis for its multiple protective functions.</p></sec><sec id=\"sec4-ijms-21-05552\"><title>4. Materials and Methods </title><sec id=\"sec4dot1-ijms-21-05552\"><title>4.1. Protein Over-Expression and Purification</title><p>Immature soybean <italic>(G. max</italic> L. Merr. cv Bainong 6#) seeds were collected from pods 35–45 d after flowering and then total RNA was extracted from plant material with Trizol reagent (TAKARA, Otsu, Japan). The full-length ORF of the <italic>PM1</italic> gene was amplified by RT-PCR using the PrimeScript<sup>TM</sup> one-step RT-PCR kit (TAKARA, Otsu, Japan). Constructs containing the full-length soybean <italic>PM1</italic>, <italic>PM1-N</italic> and <italic>PM1-C</italic> sequences were described previously [<xref rid=\"B13-ijms-21-05552\" ref-type=\"bibr\">13</xref>,<xref rid=\"B14-ijms-21-05552\" ref-type=\"bibr\">14</xref>]. Recombinant PM1, PM1-N and PM1-C proteins were purified using affinity chromatography, and His-tags were removed by incubation with thrombin as previously described [<xref rid=\"B14-ijms-21-05552\" ref-type=\"bibr\">14</xref>]. The proteins were lyophilized and then stored at −80°C for later use. The proteins were resuspended in a suitable buffer before use. </p></sec><sec id=\"sec4dot2-ijms-21-05552\"><title>4.2. Preparation of Liposomes</title><p>POPC (1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine) was obtained from Avanti Polar Lipids (Alabaster, AL, USA). Large unilaminar vesicles (LUVs, 100 nm) of POPC were prepared by the extrusion method described previously [<xref rid=\"B15-ijms-21-05552\" ref-type=\"bibr\">15</xref>]. Briefly, the lipids were dissolved in chloroform, and lipid mixtures were dried under a gentle liquid nitrogen flow and subsequently rehydrated in 10 mM Na<sub>2</sub>HPO<sub>4</sub>-KH<sub>2</sub>PO<sub>4</sub> buffer, pH7.4. The lipid solution was extruded in an Avanti Mini Extruder (100 nm polycarbonate filter), repeated 15 times and then liposomes were collected. </p><p>For the leakage experiment, liposomes were made according to [<xref rid=\"B16-ijms-21-05552\" ref-type=\"bibr\">16</xref>]. Briefly, an appropriate amount of POPC lipid was hydrated in buffer (250 μL 100 mM CF (carboxyfluorescein) in 10 mM Na<sub>2</sub>HPO<sub>4</sub>-KH<sub>2</sub>PO<sub>4</sub> buffer, pH 7.4). The lipid solution was extruded as above and liposomes were collected. To remove external CF, the lipid samples were passed through a Sephadex G-25 column (NAP-5, GE Healthcare, Milwaukee, WI, USA) in buffer (10 mM Na<sub>2</sub>HPO<sub>4</sub>-KH<sub>2</sub>PO<sub>4</sub>, 0.1 mM EDTA and 50 mM NaCl). </p><p>To measure membrane fusion, two liposome samples were prepared: One containing 1 mol % of both NBD-PE and Rh-PE, while the other contained only unlabeled lipids. After extrusion, the two samples were mixed at a 1:9 (labeled:unlabeled) ratio. All liposomes were diluted to 20 mg/mL before use.</p></sec><sec id=\"sec4dot3-ijms-21-05552\"><title>4.3. Thylakoid Membrane Preparation</title><p>Thylakoids were isolated from spinach (<italic>Spinacia oleracea</italic>) as previously described [<xref rid=\"B32-ijms-21-05552\" ref-type=\"bibr\">32</xref>]. Briefly, 40 g spinach leaves and 100 mL cold A buffer (0.3 M Suc, 50 mM Na-phosphate, pH 7.4, and 5 mM MgCl<sub>2</sub>) was mixed for 5 × 10 s with a coldmixer. The solution was filtered with 50 μM nylon mesh and centrifuged at 3000 rpm for 3 min. The pellet was suspended in 30 mL A buffer and centrifuged at 4500 rpm for 5 min. The solution was homogenized in 30 mL B buffer (10 mM phosphate, pH 7.4, 5 mM MgCl<sub>2</sub>, and 5 mM NaCl) and centrifuged at 4500 rpm for 5 min. The pellet (about 200 mg) was homogenized in 10 mL buffer (0.1 M Suc, 10 mM phosphate, pH 7.4, 5 mM MgCl<sub>2</sub> and 5 mM NaCl).</p></sec><sec id=\"sec4dot4-ijms-21-05552\"><title>4.4. Freeze-Thaw Treatment of Liposomes</title><p>Either liposomes (20 mg/mL) or thylakoid membranes (about 20 mg/mL) were mixed with the same volume of the PM1, PM1-N and PM1-C proteins at concentrations of 0.08, 0.2, 0.8 or 1.6 mg/mL or the negative control protein thaumatin (from <italic>Thaumatococcus daniellii</italic>; Sigma, St. Louis, MO, USA) at a concentration of 1.6 mg/mL in 10 mM Na<sub>2</sub>HPO<sub>4</sub>-KH<sub>2</sub>PO<sub>4</sub> buffer, pH 7.4, or with buffer alone. The freeze-thaw treatment was performed as previously described [<xref rid=\"B26-ijms-21-05552\" ref-type=\"bibr\">26</xref>]; samples were then frozen in an ethylene glycol bath at −20 °C for 2 h and allowed to thaw for 30 min at 28°C. This freeze-thaw cycle was repeated up to three times. </p></sec><sec id=\"sec4dot5-ijms-21-05552\"><title>4.5. Turbidity Measurement and Dynamic Light Scattering (DLS) </title><p>A simple aggregation assay was performed by measuring apparent absorbance [<xref rid=\"B33-ijms-21-05552\" ref-type=\"bibr\">33</xref>], namely turbidity, due to light scattering at 400 nm (denoted as OD<sub>400</sub>), with an Ultro Spec 2000 (GE Healthcare, Milwaukee, WI, USA). In addition, particle size distribution in suspension was measured using <styled-content style=\"color:#333333\">a</styled-content> DLS analyzer (Zetaplus Zeta Potential Analyzer, Brookhaven Instruments, Holtsville, NY, USA) [<xref rid=\"B11-ijms-21-05552\" ref-type=\"bibr\">11</xref>]. For both of these measurements, the samples were diluted to avoid saturation. </p></sec><sec id=\"sec4dot6-ijms-21-05552\"><title>4.6. Light Microscopy </title><p>For light microscopy, 6 μL samples were placed on a glass slide, and images were examined using the optical microscope Olympus BX51 (Olympus Corporation, Tokyo, Japan).</p></sec><sec id=\"sec4dot7-ijms-21-05552\"><title>4.7. CF Leakage Experiments</title><p>The leakage experiments were performed as previously reported [<xref rid=\"B16-ijms-21-05552\" ref-type=\"bibr\">16</xref>]. Same of 12 μL were diluted with 300 μL Na<sub>2</sub>HPO<sub>4</sub>-KH<sub>2</sub>PO<sub>4</sub> buffer in 96-well plates. CF fluorescence was measured with a Varioskan Flash multimode reader<styled-content style=\"color:#545454\"> (</styled-content>Thermo Scientific, Waltham, MA, USA) with the following settings: Excitation 444 nm; emission 555 nm. While fluorescence is strongly quenched at the high concentration inside intact liposomes, fluorescence will increase when CF is released into the surrounding buffer. The 100% fluorescence level for leakage<styled-content style=\"color:#545454\"> (</styled-content>i.e., complete release of entrapped CF) was obtained by detergent lysis of the liposomes with 5 μL 0.1% Triton X-100 solution.</p></sec><sec id=\"sec4dot8-ijms-21-05552\"><title>4.8. Membrane Fusion Measurements</title><p>Membrane fusion was measured as a reduction of fluorescence resonance energy transfer (FRET) between Rh-PE and NBD-PE by measuring the increase in NBD fluorescence, due to the dilution of the probes after fusion of labeled, and unlabeled, liposome samples [<xref rid=\"B18-ijms-21-05552\" ref-type=\"bibr\">18</xref>]. This is using a Hitachi F-4500 fluorescence instrument (Hitachi, Tokyo, Japan) at an excitation wavelength of 450 nm and an emission wavelength of 530 nm. The 100% fusion level (i.e., maximal NBD fluorescence in each sample) was determined after lysis of the liposomes with Triton X-100 solution.</p></sec><sec id=\"sec4dot9-ijms-21-05552\"><title>4.9. Limited Proteolysis </title><p>Protease sensitivity was tested with trypsin [<xref rid=\"B15-ijms-21-05552\" ref-type=\"bibr\">15</xref>]. Typically, PM1 (1 mg/mL) was mixed with the same volume of liposomes (1 mg/mL). To start the digestion, trypsin was added at a mass ratio of 1:200, trypsin to PM1. After the samples were incubated at room temperature for 0–30 min, the reactions were terminated by adding 1 mM phenylmethanesulfonyl fluorid, a trypsin inhibitor. All samples were mixed with 5×loading buffer, heated at 95 °C for 5 min and then run on a 15% ready-made SDS-PAGE gel. </p></sec><sec id=\"sec4dot10-ijms-21-05552\"><title>4.10. Far UV-Circular Dichroism Spectroscopy</title><p>PM1 protein (20 μM) was mixed with the same volume of liposomes (2 mg/mL) or with pure buffer with or without freeze-thaw treatment. Far UV-circular dichroism (CD) spectra were recorded using a Jasco J-815 CD spectropolarimeter (JASCO Analytical Instruments, Tokyo, Japan). The acquisition parameters were 0.5 nm resolution, 1.0 nm bandwidth, 0.5 s response and 250–190 nm wavelength range. Three spectra were averaged and smoothed to reduce noise.</p></sec><sec id=\"sec4dot11-ijms-21-05552\"><title>4.11. In Vitro Lactate Dehydrogenase Assays</title><p>Lactate dehydrogenase (LDH) assays were adapted from [<xref rid=\"B34-ijms-21-05552\" ref-type=\"bibr\">34</xref>]. In short, LDH from rabbit muscle (Roche, Mannheim, Germany) was diluted in 100 mM sodium phosphate buffer (pH 7.0) to a final concentration of 0.357 μM. PM1, PM1-N and PM1-C proteins or the negative control protein lysozyme were added to equal volumes of LDH at molar ratios of 1:1 (test protein: LDH). The enzyme with and without added proteins was frozen in an ethylene glycol bath at −20 °C for 2 h and allowed to thaw for 30 min at 28 °C. This freeze-thaw cycle was repeated up to five times. The activity of LDH was assayed according to [<xref rid=\"B34-ijms-21-05552\" ref-type=\"bibr\">34</xref>].</p></sec><sec id=\"sec4dot12-ijms-21-05552\"><title>4.12. Statistics</title><p><italic>p</italic>-Value was determined using SPSS 9 software (SPSS, Inc., Chicago, IL, USA). One-way ANOVA and Tukey post hoc test using InStat3 (GraphPad Software, San Diego, CA, USA) were performed for all the experiments. All experiments were performed at least in triplicate.</p></sec></sec></body><back><ack><title>Acknowledgments</title><p>We thank the Instrumental Analysis Center of Shenzhen University (Lihu Campus) for providing research instruments.</p></ack><notes><title>Author Contributions</title><p>G.L. and Y.Z. (Yizhi Zheng) designed the project. L.C. performed most of the experiments, and Y.S. participated in the experiments. Y.Z. (Yongdong Zou) and J.H. analyzed the leakage experiments and the FRET data. L.C. and Y.S. wrote the draft of the manuscript, J.H., Y.Z. (Yizhi Zheng) and G.L. revised the draft. Funding acquisition and project administration were done by Y.L., Y.Z. (Yizhi Zheng) and G.L. All authors have read and approved the submitted vision of the manuscript.</p></notes><notes><title>Funding</title><p>This research was funded by National Nature Science Foundation of China (Grant No. 31370289 and 31600203) and Shenzhen Fundamental Research fund (Grant No.JCYJ20170818142241972).</p></notes><notes notes-type=\"COI-statement\"><title>Conflicts of Interest</title><p>The authors declare no conflict of interest.</p></notes><glossary><title>Abbreviations</title><array orientation=\"portrait\"><tbody><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">LEA</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">late embryogenesis abundant</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">IDP</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">intrinsically disordered protein</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">DLS</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">dynamic light scattering</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">PC</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">phosphatidylcholine</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">POPC</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CF</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">carboxyfluorescein</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">HMW</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">high molecular weight</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">LUV</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">large unilaminar vesicle</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">FRET</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">fluorescence resonance energy transfer</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CD</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">circular dichroism</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">LDH</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">lactate dehydrogenase</td></tr></tbody></array></glossary><app-group><app id=\"app1-ijms-21-05552\"><title>Appendix A</title><p>To validate whether PM1 could protect cellular proteins under stress conditions, we analyzed the protective effects of PM1, PM1-N or PM1-C protein on LDH during freeze−thaw cycles. <xref ref-type=\"fig\" rid=\"ijms-21-05552-f0A1\">Figure A1</xref>. shows that, PM1 and PM1-N, but not PM1-C gave a significantly better protection of LDH against freeze−thaw-induced inactivation (<italic>p</italic> < 0.01).</p><fig id=\"ijms-21-05552-f0A1\" orientation=\"portrait\" position=\"anchor\"><label>Figure A1</label><caption><p>Protective effect of PM1, PM1-N and PM1-C on LDH. LDH was subjected to freeze-thaw in the absence or presence of PM1, PM1-N or PM1-C, or lysozyme as a negative control protein. The samples were frozen in an ethylene glycol bath at −20 °Cfor 2 h and allowed to thaw for 30 min at 28 °C. 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This freeze-thaw cycle was repeated up to three times. ** denotes significance at <italic>p</italic> < 0.01, * denotes <italic>p</italic> < 0.05 and “ns” denotes not significant using one-way ANOVA, plus a Tukey post-hoc test.</p></caption><graphic xlink:href=\"ijms-21-05552-g001\"/></fig><fig id=\"ijms-21-05552-f002\" orientation=\"portrait\" position=\"float\"><label>Figure 2</label><caption><p>PM1 and its N-terminal region prevent POPC liposome or thylakoid aggregation resulting from freeze-thaw treatment as assessed by light microscopy. (<bold>A</bold>) POPC liposomes; (<bold>B</bold>) spinach leaf thylakoids. Bars in (<bold>A</bold>) and (<bold>B</bold>) represent 100 μm. FT: freeze-thaw treatment.</p></caption><graphic xlink:href=\"ijms-21-05552-g002\"/></fig><fig id=\"ijms-21-05552-f003\" orientation=\"portrait\" position=\"float\"><label>Figure 3</label><caption><p>Size distribution of POPC liposomes before and after freeze-thaw treatment in the presence and absence of PM1, PM1-N or PM1-C. (<bold>A</bold>) Size distribution of POPC liposomes before treatment; (<bold>B</bold>) size distribution of POPC liposomes after freeze-thaw treatment.</p></caption><graphic xlink:href=\"ijms-21-05552-g003\"/></fig><fig id=\"ijms-21-05552-f004\" orientation=\"portrait\" position=\"float\"><label>Figure 4</label><caption><p>Influence of PM1, PM1-N and PM1-C on CF leakage from liposomes after freeze-thaw treatment. POPC liposomes were subjected to freeze-thaw in the absence or presence of PM1, PM1-N or PM1-C, or thaumatin as a negative control protein. “ns” denotes not significant using one-way ANOVA, plus a Tukey post-hoc test.</p></caption><graphic xlink:href=\"ijms-21-05552-g004\"/></fig><fig id=\"ijms-21-05552-f005\" orientation=\"portrait\" position=\"float\"><label>Figure 5</label><caption><p>Effect of PM1, PM1-N and PM1-C on membrane fusion caused by freeze-thaw treatment. POPC liposomes were subjected to freeze-thaw in the absence or presence of PM1, PM1-N or PM1-C, or thaumatin as a negative control protein. Membrane fusion was determined by a FRET assay. ** denotes significance at <italic>p</italic> < 0.01and * denotes <italic>p</italic> < 0.05 using one-way ANOVA, plus a Tukey post-hoc test.</p></caption><graphic xlink:href=\"ijms-21-05552-g005\"/></fig><fig id=\"ijms-21-05552-f006\" orientation=\"portrait\" position=\"float\"><label>Figure 6</label><caption><p>PM1 does not gain structure in the presence of liposomes. (<bold>A</bold>) Digestion of PM1 by trypsin in the presence or absence of POPC liposomes. (<bold>B</bold>) CD spectra of PM1 alone and PM1 in the presence of liposomes before and after freeze-thaw treatment.</p></caption><graphic xlink:href=\"ijms-21-05552-g006\"/></fig></floats-group></article>\n"
]
[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Int J Mol Sci</journal-id><journal-id journal-id-type=\"iso-abbrev\">Int J Mol Sci</journal-id><journal-id journal-id-type=\"publisher-id\">ijms</journal-id><journal-title-group><journal-title>International Journal of Molecular Sciences</journal-title></journal-title-group><issn pub-type=\"epub\">1422-0067</issn><publisher><publisher-name>MDPI</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32717877</article-id><article-id pub-id-type=\"pmc\">PMC7432131</article-id><article-id pub-id-type=\"doi\">10.3390/ijms21155213</article-id><article-id pub-id-type=\"publisher-id\">ijms-21-05213</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Article</subject></subj-group></article-categories><title-group><article-title>The In Vitro Effect of Prostaglandin E<sub>2</sub> and F<sub>2α</sub> on the Chemerin System in the Porcine Endometrium during Gestation</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Dobrzyn</surname><given-names>Kamil</given-names></name><xref rid=\"c1-ijms-21-05213\" ref-type=\"corresp\">*</xref><xref ref-type=\"author-notes\" rid=\"fn1-ijms-21-05213\">†</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0002-8677-5282</contrib-id><name><surname>Kiezun</surname><given-names>Marta</given-names></name><xref ref-type=\"author-notes\" rid=\"fn1-ijms-21-05213\">†</xref></contrib><contrib contrib-type=\"author\"><name><surname>Zaobidna</surname><given-names>Ewa</given-names></name></contrib><contrib contrib-type=\"author\"><name><surname>Kisielewska</surname><given-names>Katarzyna</given-names></name></contrib><contrib contrib-type=\"author\"><name><surname>Rytelewska</surname><given-names>Edyta</given-names></name></contrib><contrib contrib-type=\"author\"><name><surname>Gudelska</surname><given-names>Marlena</given-names></name></contrib><contrib contrib-type=\"author\"><name><surname>Kopij</surname><given-names>Grzegorz</given-names></name></contrib><contrib contrib-type=\"author\"><name><surname>Bors</surname><given-names>Kinga</given-names></name></contrib><contrib contrib-type=\"author\"><name><surname>Szymanska</surname><given-names>Karolina</given-names></name></contrib><contrib contrib-type=\"author\"><name><surname>Kaminska</surname><given-names>Barbara</given-names></name></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0003-1643-026X</contrib-id><name><surname>Kaminski</surname><given-names>Tadeusz</given-names></name></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0001-5364-1387</contrib-id><name><surname>Smolinska</surname><given-names>Nina</given-names></name><xref rid=\"c1-ijms-21-05213\" ref-type=\"corresp\">*</xref></contrib></contrib-group><aff id=\"af1-ijms-21-05213\">Department of Animal Anatomy and Physiology, Faculty of Biology and Biotechnology, University of Warmia and Mazury in Olsztyn, Oczapowskiego 1A, 10-719 Olsztyn-Kortowo, Poland; <email>[email protected]</email> (M.K.); <email>[email protected]</email> (E.Z.); <email>[email protected]</email> (K.K.); <email>[email protected]</email> (E.R.); <email>[email protected]</email> (M.G.); <email>[email protected]</email> (G.K.); <email>[email protected]</email> (K.B.); <email>[email protected]</email> (K.S.); <email>[email protected]</email> (B.K.); <email>[email protected]</email> (T.K.)</aff><author-notes><corresp id=\"c1-ijms-21-05213\"><label>*</label>Correspondence: <email>[email protected]</email> (K.D.); <email>[email protected]</email> (N.S.)</corresp><fn id=\"fn1-ijms-21-05213\"><label>†</label><p>These authors contributed equally to this work.</p></fn></author-notes><pub-date pub-type=\"epub\"><day>23</day><month>7</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><month>8</month><year>2020</year></pub-date><volume>21</volume><issue>15</issue><elocation-id>5213</elocation-id><history><date date-type=\"received\"><day>21</day><month>5</month><year>2020</year></date><date date-type=\"accepted\"><day>21</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>© 2020 by the authors.</copyright-statement><copyright-year>2020</copyright-year><license license-type=\"open-access\"><license-p>Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>).</license-p></license></permissions><abstract><p>Chemerin belongs to the group of adipocyte-derived hormones known as adipokines, which are responsible mainly for the control of energy homeostasis. Adipokine exerts its influence through three receptors: Chemokine-like receptor 1 (CMKLR1), G protein-coupled receptor 1 (GPR1), and C-C motif chemokine receptor-like 2 (CCRL2). A growing body of evidence indicates that chemerin participates in the regulation of the female reproductive system. According to the literature, the expression of chemerin and its receptors in reproductive structures depends on the local hormonal milieu. The aim of this study was to investigate the in vitro effect of prostaglandins E<sub>2</sub> (PGE<sub>2</sub>) and F<sub>2α</sub> (PGF<sub>2α</sub>) on chemerin and chemerin receptor (chemerin system) mRNAs (qPCR) and proteins (ELISA, Western blotting) in endometrial tissue explants collected from early-pregnant gilts. Both PGE<sub>2</sub> and PGF<sub>2α</sub> significantly influenced the expression of the chemerin gene, hormone secretion, and the expression of chemerin receptor genes and proteins. The influence of both prostaglandins on the expression of the chemerin system varied between different stages of gestation. This is the first study to describe the modulatory effect of PGE<sub>2</sub> and PGF<sub>2α</sub> on the expression of the chemerin system in the porcine uterus during early gestation. </p></abstract><kwd-group><kwd>early pregnancy</kwd><kwd>chemerin</kwd><kwd>chemerin receptors</kwd><kwd>porcine uterus</kwd><kwd>PGE<sub>2</sub></kwd><kwd>PGF<sub>2α</sub></kwd></kwd-group></article-meta></front><body><sec sec-type=\"intro\" id=\"sec1-ijms-21-05213\"><title>1. Introduction</title><p>The relationship between nutritional status and reproductive success in animals has been studied for many years. Adipokines are a group of adipocyte-derived agents that are responsible mainly for the regulation of energy metabolism, and they are perceived as the key link between metabolic status and reproductive functions. The adipokine family includes chemerin, the product of the retinoic acid receptor responder 2 gene (<italic>RARRES2</italic>), also known as tazarotene-induced gene 2 (<italic>TIG2</italic>) [<xref rid=\"B1-ijms-21-05213\" ref-type=\"bibr\">1</xref>]. Chemerin circulates in the blood as prochemerin, which consists of 143 amino acids and originates from a 163 amino acid precursor known as pre-prochemerin [<xref rid=\"B2-ijms-21-05213\" ref-type=\"bibr\">2</xref>]. Interestingly, chemerin has been found to exert pleiotropic effects, including modulation of insulin sensitivity that affects food intake, energy homeostasis, and adipose tissue function [<xref rid=\"B3-ijms-21-05213\" ref-type=\"bibr\">3</xref>,<xref rid=\"B4-ijms-21-05213\" ref-type=\"bibr\">4</xref>]. Chemerin has been found to have a chemotactic properties for immune cells, through promoting the immune cells migration to the inflammation sites. It has also been reported that chemerin may inhibit pro-inflammatory cytokines actions, such as interleukin-6 (IL-6) and tumor necrosis factor-α (TNF-α) [<xref rid=\"B3-ijms-21-05213\" ref-type=\"bibr\">3</xref>,<xref rid=\"B5-ijms-21-05213\" ref-type=\"bibr\">5</xref>]. In the body, chemerin binds to three G-protein coupled receptors: Chemokine-like receptor 1 (CMKLR1), G protein-coupled receptor 1 (GPR1), and C-C motif chemokine receptor-like 2 (CCRL2). Chemokine-like receptor 1, also known as ChemR23 in humans, has been most extensively studied. G protein-coupled receptor 1 is structurally similar to CMKLR1, but its role has not been elucidated to date. It is believed that GPR1 has different functions than those of CMKLR1 because both receptors are expressed in different tissues. Chemokine-like receptor 1 has been detected mainly in macrophages, natural killer cells, plasmacytoid dendritic cells, and myeloid dendritic cells, whereas GPR1 is expressed in cells related to the central nervous system [<xref rid=\"B6-ijms-21-05213\" ref-type=\"bibr\">6</xref>,<xref rid=\"B7-ijms-21-05213\" ref-type=\"bibr\">7</xref>,<xref rid=\"B8-ijms-21-05213\" ref-type=\"bibr\">8</xref>]. CMKLR1 has been found to use MAPK/ERK1/2, AMPK, and PI3K/Akt kinases to transduce signals, whereas GPR1 acts mainly through the AMPK signaling pathway [<xref rid=\"B9-ijms-21-05213\" ref-type=\"bibr\">9</xref>]. The third receptor, CCRL2, differs from CMKLR1 and GPR1. C-C motif chemokine receptor-like 2 is unable to transduce the signal, but it binds the N-terminal region of chemerin and exposes the C-terminal region of the hormone molecule to CMKLR1 that is expressed on the membranes of the neighboring cells [<xref rid=\"B10-ijms-21-05213\" ref-type=\"bibr\">10</xref>]. The expression of the CCLR2 gene and protein was confirmed in neutrophils, T cells, and macrophages [<xref rid=\"B11-ijms-21-05213\" ref-type=\"bibr\">11</xref>].</p><p>A growing body of evidence suggests that chemerin may be responsible for the control of reproductive functions. It has been indicated that the inhibition of the chemerin signaling via the CMKLR1 receptor in the mice decidua results in embryo abortion [<xref rid=\"B12-ijms-21-05213\" ref-type=\"bibr\">12</xref>]. Furthermore, it has been observed that in women who had early spontaneous abortions, the concentrations of chemerin in the blood plasma were decreased, whereas the expression of CMKLR1 in the decidua was found to be elevated [<xref rid=\"B12-ijms-21-05213\" ref-type=\"bibr\">12</xref>]. The expression of the chemerin system (a collective term for chemerin and its receptors) was confirmed in brain structures responsible for reproductive functions, as well as in the lower branches of the hypothalamic-pituitary-ovarian axis and the female reproductive tract. Chemerin expression was confirmed in the hypothalami of mice and pigs [<xref rid=\"B13-ijms-21-05213\" ref-type=\"bibr\">13</xref>,<xref rid=\"B14-ijms-21-05213\" ref-type=\"bibr\">14</xref>]. Chemerin receptor expression was reported in porcine and bovine pituitaries and hypothalami [<xref rid=\"B14-ijms-21-05213\" ref-type=\"bibr\">14</xref>,<xref rid=\"B15-ijms-21-05213\" ref-type=\"bibr\">15</xref>,<xref rid=\"B16-ijms-21-05213\" ref-type=\"bibr\">16</xref>]. It has been suggested that chemerin plays an important role in the structural remodeling of the hypothalamus, and may be responsible for the regulation of the secretion of hypothalamic hormones connected with feeding behavior [<xref rid=\"B13-ijms-21-05213\" ref-type=\"bibr\">13</xref>,<xref rid=\"B17-ijms-21-05213\" ref-type=\"bibr\">17</xref>]. The expression of chemerin and its receptors in the porcine hypothalamic structures responsible for gonadoliberin (GnRH) production, as well as in the pituitary gonadotrophs suggests the potential involvement of the adipokine in the regulation of the reproductive functions [<xref rid=\"B14-ijms-21-05213\" ref-type=\"bibr\">14</xref>,<xref rid=\"B15-ijms-21-05213\" ref-type=\"bibr\">15</xref>]. Chemerin system expression was observed in the human uterus [<xref rid=\"B18-ijms-21-05213\" ref-type=\"bibr\">18</xref>,<xref rid=\"B19-ijms-21-05213\" ref-type=\"bibr\">19</xref>], in human and rat placenta [<xref rid=\"B1-ijms-21-05213\" ref-type=\"bibr\">1</xref>,<xref rid=\"B20-ijms-21-05213\" ref-type=\"bibr\">20</xref>], in the ovaries of women and rodents [<xref rid=\"B1-ijms-21-05213\" ref-type=\"bibr\">1</xref>,<xref rid=\"B21-ijms-21-05213\" ref-type=\"bibr\">21</xref>,<xref rid=\"B22-ijms-21-05213\" ref-type=\"bibr\">22</xref>], and in porcine ovaries, uteri, trophoblasts, and conceptuses [<xref rid=\"B23-ijms-21-05213\" ref-type=\"bibr\">23</xref>,<xref rid=\"B24-ijms-21-05213\" ref-type=\"bibr\">24</xref>]. The expression of the chemerin system components in the endometrium, trophoblasts, placentas, and conceptuses suggests that the adipokine may play an important role in the foeto–maternal crosstalk during early pregnancy, as well as, through the placenta, across the whole gestation period. Chemerin decreases the in vitro steroidogenesis of granulosa cells, blocks oocyte meiotic progression in cattle, and suppresses FSH-induced progesterone (P<sub>4</sub>) and estradiol (E<sub>2</sub>) secretion in rat preantral follicles and granulosa cells [<xref rid=\"B21-ijms-21-05213\" ref-type=\"bibr\">21</xref>,<xref rid=\"B22-ijms-21-05213\" ref-type=\"bibr\">22</xref>,<xref rid=\"B25-ijms-21-05213\" ref-type=\"bibr\">25</xref>]. Chemerin also reduced the hCG-induced production of P<sub>4</sub> in cultured follicles and corpora lutea of mice [<xref rid=\"B26-ijms-21-05213\" ref-type=\"bibr\">26</xref>]. Our previous research revealed changes in the expression of the chemerin system in porcine uterine and ovarian tissues and demonstrated changes in serum chemerin concentrations during the estrous cycle and early gestation. These findings suggest that chemerin system expression may be dependent on the hormonal milieu [<xref rid=\"B14-ijms-21-05213\" ref-type=\"bibr\">14</xref>,<xref rid=\"B23-ijms-21-05213\" ref-type=\"bibr\">23</xref>,<xref rid=\"B24-ijms-21-05213\" ref-type=\"bibr\">24</xref>]. Similar observations were made by Yang et al., [<xref rid=\"B26-ijms-21-05213\" ref-type=\"bibr\">26</xref>] who reported that chemerin and GPR1 expression in the ovaries of mice varied during the estrous cycle. Treatment involving PGF<sub>2α</sub> decreased GPR1 receptor expression at both gene and protein levels. The above findings supported the formulation of the research hypothesis postulating that prostaglandins, the key factors that regulate uterine functions in early pregnancy, affect the endometrial expression of the chemerin system. Therefore, the aim of the present study was to investigate the influence of prostaglandin E<sub>2</sub> (PGE<sub>2</sub>) and F<sub>2α</sub> (PGF<sub>2α</sub>) on the endometrial expression of chemerin, <italic>CMKLR1</italic>, <italic>GPR1,</italic> and <italic>CCRL2</italic> genes, and on the concentrations of chemerin receptor proteins and chemerin secretion by endometrial tissue explants during early gestation.</p></sec><sec sec-type=\"results\" id=\"sec2-ijms-21-05213\"><title>2. Results</title><sec id=\"sec2dot1-ijms-21-05213\"><title>2.1. The Effect of PGE<sub>2</sub> on Chemerin Gene Expression and Protein Secretion by the Endometrial Tissue Explants</title><p>In the endometrium, on days 10 to 11 of gestation, PGE<sub>2</sub> at the dose of 250 ng/mL enhanced, but at the dose of 100 ng/mL decreased chemerin gene expression (<xref ref-type=\"fig\" rid=\"ijms-21-05213-f001\">Figure 1</xref>A). On days 12 to 13 of gestation, PGE<sub>2</sub> at all the tested doses caused an increase in chemerin mRNA content (<xref ref-type=\"fig\" rid=\"ijms-21-05213-f001\">Figure 1</xref>C). On days 15 to 16 of pregnancy, PGE<sub>2</sub> enhanced chemerin gene expression (100 ng/mL), but decreased the hormone secretion (100 and 500 ng/mL; <xref ref-type=\"fig\" rid=\"ijms-21-05213-f001\">Figure 1</xref>E,F). Prostaglandin E<sub>2</sub> suppressed the endometrial chemerin gene expression on days 27 to 28 of pregnancy (100, 250, 500 ng/mL), as well as on days 10 to 11 of the estrous cycle (250 ng/mL; <xref ref-type=\"fig\" rid=\"ijms-21-05213-f001\">Figure 1</xref>G,I; <italic>p</italic> < 0.05).</p><p>In general, PGE<sub>2</sub> treatment resulted in an increase of chemerin mRNA expression on days 12 to 13 and 15 to 16 of pregnancy and a decrease on days 27 to 28 of gestation and days 10 to 11 of the cycle. PGE<sub>2</sub> decreased chemerin protein secretion on days 15 to 16 of gestation.</p></sec><sec id=\"sec2dot2-ijms-21-05213\"><title>2.2. The Effect of PGE<sub>2</sub> on CMKLR1 Gene and Protein Expression in the Endometrial Tissue Explants</title><p>In the endometrium, on days 10 to 11 of gestation, PGE<sub>2</sub> (100, 250, 500 ng/mL) caused a decrease in <italic>CMKLR1</italic> gene expression (<xref ref-type=\"fig\" rid=\"ijms-21-05213-f002\">Figure 2</xref>A). During the same days, PGE<sub>2</sub> at the dose of 250 ng/mL increased, whereas at doses of 100 and 500 ng/mL decreased the receptor protein concentration (<xref ref-type=\"fig\" rid=\"ijms-21-05213-f002\">Figure 2</xref>B). On days 12 to 13 of pregnancy, PGE<sub>2</sub> increased (250, 500 ng/mL) <italic>CMKLR1</italic> mRNA content and decreased (100, 250, 500 ng/mL) the receptor protein concentration (<xref ref-type=\"fig\" rid=\"ijms-21-05213-f002\">Figure 2</xref>C,D). On days 15 to 16 of gestation, PGE<sub>2</sub> at the dose of 250 ng/mL enhanced, but at the dose of 100 ng/mL suppressed <italic>CMKLR1</italic> mRNA content (<xref ref-type=\"fig\" rid=\"ijms-21-05213-f002\">Figure 2</xref>E). During the same days, PGE<sub>2</sub> caused a decrease in the receptor protein concentration (100, 250, 500 ng/mL; <xref ref-type=\"fig\" rid=\"ijms-21-05213-f002\">Figure 2</xref>F). On days 27 to 28 of pregnancy, PGE<sub>2</sub> inhibited <italic>CMKLR1</italic> gene expression (250 ng/mL) and enhanced the receptor protein concentration (100, 250, 500 ng/mL; <xref ref-type=\"fig\" rid=\"ijms-21-05213-f002\">Figure 2</xref>G,H). On days 10 to 11 of the estrous cycle, PGE<sub>2</sub> at all tested doses caused an increase in <italic>CMKLR1</italic> gene expression but decreased the receptor protein concentration (<xref ref-type=\"fig\" rid=\"ijms-21-05213-f002\">Figure 2</xref>J; <italic>p</italic> < 0.05).</p><p>In general, PGE<sub>2</sub> decreased <italic>CMKLR1</italic> gene expression on days 10 to 11 and 27 to 28 of gestation and protein expression on days 12 to 13 and 15 to 16 of gestation, and 10 to 11 of the cycle. PGE<sub>2</sub> enhanced the receptor gene expression on days 12 to 13 of gestation and 10 to 11 of the cycle, and increased CMKLR1 protein content on days 27 to 28 of pregnancy.</p></sec><sec id=\"sec2dot3-ijms-21-05213\"><title>2.3. The Effect of PGE<sub>2</sub> on GPR1 Gene and Protein Expression in the Endometrial Tissue Explants</title><p>On days 10 to 11 of gestation, PGE<sub>2</sub> at all tested doses caused a decrease in GPR1 expression at both the gene and protein levels (<xref ref-type=\"fig\" rid=\"ijms-21-05213-f003\">Figure 3</xref>A,B). On days 12 to 13 of gestation, PGE<sub>2</sub> at the dose of 500 ng/mL increased, but at the doses of 100 and 250 ng/mL inhibited <italic>GPR1</italic> gene expression. During the same days, PGE<sub>2</sub> (250 ng/mL) caused an increase in the endometrial GPR1 protein concentration (<xref ref-type=\"fig\" rid=\"ijms-21-05213-f003\">Figure 3</xref>C,D). On days 15 to 16 of pregnancy, PGE<sub>2</sub> caused a decrease in <italic>GPR1</italic> mRNA content (100, 250, 500 ng/mL) and increased the receptor protein concentration (100, 500 ng/mL; <xref ref-type=\"fig\" rid=\"ijms-21-05213-f003\">Figure 3</xref>E,F). On days 27 to 28 of pregnancy, PGE<sub>2</sub> at all the tested doses caused a decrease in the receptor gene expression and provoked an increase in GPR1 protein concentration (<xref ref-type=\"fig\" rid=\"ijms-21-05213-f003\">Figure 3</xref>G,H). On days 10 to 11 of the estrous cycle, PGE<sub>2</sub> at the doses of 250 and 500 ng/mL enhanced, but at the dose of 100 ng/mL inhibited the endometrial <italic>GPR1</italic> expression. During the same period, PGE<sub>2</sub> at the dose of 100 ng/mL was found to increase the receptor protein concentration (<xref ref-type=\"fig\" rid=\"ijms-21-05213-f003\">Figure 3</xref>I,J; <italic>p</italic> < 0.05).</p><p>Generally, PGE<sub>2</sub> decreased the receptor mRNA content on days 10 to 11, 15 to 16, and 27 to 28 of pregnancy and GPR1 protein concentration on days 10 to 11 of pregnancy. The prostaglandin increased GPR1 protein concentration on days 12 to 13, 15 to 16, and 27 to 28 of pregnancy, and 10 to 11 of the cycle.</p></sec><sec id=\"sec2dot4-ijms-21-05213\"><title>2.4. The Effect of PGE<sub>2</sub> on CCRL2 Gene and Protein Expression in the Endometrial Tissue Explants</title><p>On days 10 to 11 of gestation, PGE<sub>2</sub> was found to exert an inhibitory effect on the endometrial expression of CCRL2 at both the gene (100, 250, 500 ng/mL) and protein (100, 250 ng/mL) levels (<xref ref-type=\"fig\" rid=\"ijms-21-05213-f004\">Figure 4</xref>A,B). On days 12 to 13 of gestation, PGE<sub>2</sub> caused a decrease (100, 250, 500 ng/mL) in <italic>CCRL2</italic> expression, but increased (100, 250 ng/mL) the receptor protein concentration (<xref ref-type=\"fig\" rid=\"ijms-21-05213-f004\">Figure 4</xref>C,D). On days 15 to 16 of pregnancy, PGE<sub>2</sub> at the dose of 500 ng/mL caused an increase in CCRL2 protein concentration (<xref ref-type=\"fig\" rid=\"ijms-21-05213-f004\">Figure 4</xref>F). On days 27 to 28 of pregnancy, PGE<sub>2</sub> at the dose of 100 ng/mL increased, whereas at the dose of 500 ng/mL decreased <italic>CCRL2</italic> mRNA content (<xref ref-type=\"fig\" rid=\"ijms-21-05213-f004\">Figure 4</xref>G). On these days, PGE<sub>2</sub> at all the tested doses caused an increase in the endometrial CCRL2 protein concentration (<xref ref-type=\"fig\" rid=\"ijms-21-05213-f004\">Figure 4</xref>H). On days 10 to 11 of the estrous cycle, PGE<sub>2</sub> at the dose of 250 ng/mL enhanced, but at the dose of 100 ng/mL inhibited <italic>CCRL2</italic> expression (<xref ref-type=\"fig\" rid=\"ijms-21-05213-f004\">Figure 4</xref>I). On these days, PGE<sub>2</sub> was observed to decrease (100, 250, 500 ng/mL) the endometrial CCRL2 protein concentration (<xref ref-type=\"fig\" rid=\"ijms-21-05213-f004\">Figure 4</xref>J; <italic>p</italic> < 0.05).</p><p>In summary, PGE<sub>2</sub> inhibited CCRL2 gene expression on days 10 to 11 and 12 to 13 of gestation, and the receptor protein content on days 10 to 11 of pregnancy and 10 to 11 of the cycle. On days 12 to 13, 15 to 16, and 27 to 28, PGE<sub>2</sub> stimulated CCRL2 protein expression.</p></sec><sec id=\"sec2dot5-ijms-21-05213\"><title>2.5. The Effect of PGF<sub>2α</sub> on Chemerin Gene Expression and Protein Secretion by the Endometrial Tissue Explants</title><p>In the endometrium, on days 10 to 11 of pregnancy, PGF<sub>2α</sub> caused an increase (250, 500 ng/mL) in chemerin gene expression (<xref ref-type=\"fig\" rid=\"ijms-21-05213-f005\">Figure 5</xref>A). On days 12 to 13 of pregnancy, PGF<sub>2α</sub> caused an increase in chemerin mRNA content (100, 250, 500 ng/mL), but decreased (500 ng/mL) its protein secretion (<xref ref-type=\"fig\" rid=\"ijms-21-05213-f005\">Figure 5</xref>C,D). On days 15 to 16 of gestation, PGF<sub>2α</sub> caused a decrease in chemerin gene expression (100, 500 ng/mL), as well as its protein secretion (100, 250, 500 ng/mL; <xref ref-type=\"fig\" rid=\"ijms-21-05213-f005\">Figure 5</xref>E,F). On days 27 to 28 of pregnancy, PGF<sub>2α</sub> also decreased both chemerin gene expression (100, 250, 500 ng/mL) and protein secretion (250 ng/mL) (<xref ref-type=\"fig\" rid=\"ijms-21-05213-f005\">Figure 5</xref>G,H; <italic>p</italic> < 0.05).</p><p>Briefly, PGF<sub>2α</sub> decreased chemerin mRNA content on days 15 to 16 and 27 to 28 of pregnancy, and protein secretion on days 12 to 13, 15 to 16, and 27 to 28 of pregnancy. On days 10 to 11 and 12 to 13 of gestation, the prostaglandin enhanced chemerin gene expression.</p></sec><sec id=\"sec2dot6-ijms-21-05213\"><title>2.6. The Effect of PGF<sub>2α</sub> on CMKLR1 Gene and Protein Expression in the Endometrial Tissue Explants</title><p>On days 10 to 11 of gestation, PGF<sub>2α</sub> caused a decrease (100, 250, 500 ng/mL) in <italic>CMKLR1</italic> expression and increased (250, 500 ng/mL) the receptor protein concentration (<xref ref-type=\"fig\" rid=\"ijms-21-05213-f006\">Figure 6</xref>A,B). On days 12 to 13 of pregnancy, PGF<sub>2α</sub> at the dose of 250 ng/mL enhanced <italic>CMKLR1</italic> expression (<xref ref-type=\"fig\" rid=\"ijms-21-05213-f006\">Figure 6</xref>C). On those days, PGF<sub>2α</sub> at the dose of 500 ng/mL increased, but at the doses of 100 and 250 ng/mL, decreased the receptor protein concentration (<xref ref-type=\"fig\" rid=\"ijms-21-05213-f006\">Figure 6</xref>D). On days 15 to 16 of pregnancy, PGF<sub>2α</sub> caused a decrease in CMKLR1 expression at both the gene (250, 500 ng/mL) and protein (100, 250 ng/mL) levels (<xref ref-type=\"fig\" rid=\"ijms-21-05213-f006\">Figure 6</xref>E,F). On days 27 to 28 of pregnancy, PGF<sub>2α</sub> at the dose of 100 ng/mL enhanced, but at the dose of 500 ng/mL inhibited <italic>CMKLR1</italic> expression (<xref ref-type=\"fig\" rid=\"ijms-21-05213-f006\">Figure 6</xref>G). On those days, PGF<sub>2α</sub> at the doses of 250 and 500 ng/mL was found to enhance the receptor protein expression (<xref ref-type=\"fig\" rid=\"ijms-21-05213-f006\">Figure 6</xref>H). On days 10 to 11 of the estrous cycle, PGF<sub>2α</sub> provoked an increase in both CMKLR1 gene (100, 250, 500 ng/mL) and protein (500 ng/mL) expression (<xref ref-type=\"fig\" rid=\"ijms-21-05213-f006\">Figure 6</xref>I,J; <italic>p</italic> < 0.05).</p><p>Summarizing, PGF<sub>2α</sub> suppressed <italic>CMKLR</italic>1 gene expression on days 10 to 11 and 15 to 16 of pregnancy, and the receptor protein expression on days 15 to 16 of pregnancy. On days 12 to 13 of gestation and 10 to 11 of the cycle, PGF<sub>2α</sub> enhanced the receptor gene expression, whereas on days 10 to 11, 27 to 28 of pregnancy and 10 to 11 of the cycle, it increased CMKlR1 protein expression. </p></sec><sec id=\"sec2dot7-ijms-21-05213\"><title>2.7. The Effect of PGF<sub>2α</sub> on GPR1 Gene and Protein Expression in the Endometrial Tissue Explants</title><p>In the endometrium, on days 10 to 11 of gestation, PGF<sub>2α</sub> at all the tested doses caused a decrease in GPR1 expression at both gene and protein levels (<xref ref-type=\"fig\" rid=\"ijms-21-05213-f007\">Figure 7</xref>A,B). On days 12 to 13 of gestation, PGF<sub>2α</sub> at the dose of 250 ng/mL increased, whereas at the doses of 100 and 500 ng/mL decreased <italic>GPR1</italic> expression (<xref ref-type=\"fig\" rid=\"ijms-21-05213-f007\">Figure 7</xref>C). On those days, PGF<sub>2α</sub> at the dose of 500 ng/mL enhanced the receptor protein expression (<xref ref-type=\"fig\" rid=\"ijms-21-05213-f007\">Figure 7</xref>D). On days 15 to 16 of pregnancy, PGF<sub>2α</sub> caused a decrease (100, 250, 500 ng/mL) in <italic>GPR1</italic> expression but increased (250 ng/mL) the endometrial receptor protein concentration (<xref ref-type=\"fig\" rid=\"ijms-21-05213-f007\">Figure 7</xref>E,F). On days 27 to 28 of pregnancy, PGF<sub>2α</sub> at the dose of 250 ng/mL increased, whereas at the doses of 100 and 500 ng/mL decreased the <italic>GPR1</italic> mRNA content (<xref ref-type=\"fig\" rid=\"ijms-21-05213-f007\">Figure 7</xref>G). On those days, PGF<sub>2α</sub> at all the tested doses decreased the receptor protein concentration (<xref ref-type=\"fig\" rid=\"ijms-21-05213-f007\">Figure 7</xref>H). On days 10 to 11 of the estrous cycle, PGF<sub>2α</sub> enhanced (250, 500 ng/mL) GPR1 expression and decreased (500 ng/mL) the receptor protein concentration (<xref ref-type=\"fig\" rid=\"ijms-21-05213-f007\">Figure 7</xref>I,J; <italic>p</italic> < 0.05).</p><p>Generally, PGF<sub>2α</sub> decreased <italic>GPR1</italic> gene expression on days 10 to 11 and 15 to 16 of pregnancy and the receptor protein content on days 10 to 11 and 27 to 28 of gestation and 10 to 11 of the cycle. On days 10 to 11 of the cycle PGF<sub>2α</sub> increased <italic>GPR1</italic> gene expression, and on days 12 to 13 and 15 to 16 of pregnancy, it increased the receptor protein content. </p></sec><sec id=\"sec2dot8-ijms-21-05213\"><title>2.8. The Effect of PGF<sub>2α</sub> on CCRL2 Gene and Protein Expression in the Endometrial Tissue Explants</title><p>In the endometrium, on days 10 to 11 of gestation, PGF<sub>2α</sub> enhanced (100 ng/mL) <italic>CCRL2</italic> expression and decreased (100, 250, 500 ng/mL) the receptor protein concentration (<xref ref-type=\"fig\" rid=\"ijms-21-05213-f008\">Figure 8</xref>A,B). On days 12 to 13 of pregnancy, PGF<sub>2α</sub> decreased (100, 500 ng/mL) <italic>CCRL2</italic> expression and increased (100, 250, 500 ng/mL) the receptor protein concentration (<xref ref-type=\"fig\" rid=\"ijms-21-05213-f008\">Figure 8</xref>C,D). On days 15 to 16 of pregnancy, PGF<sub>2α</sub> caused a decrease in CCRL2 expression at both the gene (100 ng/mL) and protein (100, 250, 500 ng/mL) levels (<xref ref-type=\"fig\" rid=\"ijms-21-05213-f008\">Figure 8</xref>E,F). On days 27 to 28 of gestation, PGF<sub>2α</sub> provoked an increase in <italic>CCRL2</italic> expression (100, 500 ng/mL), as well as in the receptor protein concentration (100, 250 ng/mL; <xref ref-type=\"fig\" rid=\"ijms-21-05213-f008\">Figure 8</xref>G,H). On days 10 to 11 of the estrous cycle, PGF<sub>2α</sub> at all tested doses stimulated <italic>CCRL2</italic> expression, but decreased the receptor protein concentration (<xref ref-type=\"fig\" rid=\"ijms-21-05213-f008\">Figure 8</xref>I,J; <italic>p</italic> < 0.05).</p><p>In general, PGF<sub>2α</sub> caused a decrease in <italic>CCRL2</italic> gene expression on days 12 to 13 and 15 to 16 of gestation, and in the receptor protein content on days 10 to 11 and 15 to 16 of gestation and 10 to 11 of the cycle. On days 10 to 11 and 27 to 28 of pregnancy and 10 to 11 of the cycle, PGF<sub>2α</sub> enhanced <italic>CCRL2</italic> gene expression, whereas on days 12 to 13 and 27 to 28 of pregnancy, it increased the receptor protein content.</p></sec></sec><sec sec-type=\"discussion\" id=\"sec3-ijms-21-05213\"><title>3. Discussion</title><p>The expression of the chemerin system in the porcine reproductive tract during the estrous cycle and early gestation has been reported previously [<xref rid=\"B23-ijms-21-05213\" ref-type=\"bibr\">23</xref>]. Chemerin, CMKLR1, GPR1, and CCRL2 have been detected in both the endometrium and the myometrium. However, the relationship between the chemerin system and uterine-derived factors responsible for the regulation of reproductive functions, such as steroid hormones and prostaglandins, has not been previously investigated. This is the first study to demonstrate the modulatory influence of PGE<sub>2</sub> and PGF<sub>2α</sub> on chemerin system expression in the endometrium of early-pregnant gilts. The obtained results indicate the dose-dependent influence of prostaglandins (PGs) on the endometrial expression of chemerin system at both the gene and protein levels. Our unpublished results (Smolinska et al., unpublished) indicate that chemerin may also enhance the expression of the key enzymes responsible for the synthesis of prostaglandins such as microsomal prostaglandin E synthase-1 (mPGES-1), cyclooxygenase-2 (COX-2), prostaglandin F synthase (PGFS), and prostaglandin E 9-keto-reductase (CBR1), as well as modulate the secretion of PGE<sub>2</sub> and PGF<sub>2α</sub>, depending on the adipokine dose and the pregnancy period. The above points to the presence of a complex regulatory loop between chemerin and prostaglandins in the porcine endometrium. This study revealed differences in the expression patterns of chemerin system genes and the corresponding proteins. The observed differences in the expression patterns of mRNA and the corresponding proteins are not surprising. Schwanhäusser et al. [<xref rid=\"B27-ijms-21-05213\" ref-type=\"bibr\">27</xref>] reported that the differences in mRNA expression in mammals explained approximately 40% of the variation in protein levels, and the coefficient of determination (R<sup>2</sup>) between the content of mRNA and protein was only 0.41. The R<sup>2</sup> value increased to 0.95 when translation rate constants were taken into account. These suggest that the protein abundance may be regulated mainly by the translation efficiency. The presented mechanism may be beneficial for the metabolism of cell because of the savings in the substrates for protein synthesis and the minimization in the energy expenditure associated with the translation process. The discrepancies between gene and protein expression may also arise from the differences in the stability of proteins and gene transcripts, transcriptional and post-transcriptional regulation, inhibition of post-transcriptional processes through high mRNA content, or attenuation of gene expression due to high protein concentrations, as well as due to miRNA-induced mRNA degradation [<xref rid=\"B28-ijms-21-05213\" ref-type=\"bibr\">28</xref>,<xref rid=\"B29-ijms-21-05213\" ref-type=\"bibr\">29</xref>,<xref rid=\"B30-ijms-21-05213\" ref-type=\"bibr\">30</xref>]. The observed changes in the expression of the endometrial chemerin system under the influence of prostaglandins could be explained by variations in the expression of prostaglandin receptors during the analyzed pregnancy periods. The concentrations of PGE<sub>2</sub> and PGF<sub>2α</sub> receptors fluctuate during pregnancy in mice and during the estrous cycle and early gestation in pigs [<xref rid=\"B31-ijms-21-05213\" ref-type=\"bibr\">31</xref>,<xref rid=\"B32-ijms-21-05213\" ref-type=\"bibr\">32</xref>,<xref rid=\"B33-ijms-21-05213\" ref-type=\"bibr\">33</xref>]. In pigs, differences were observed in the endometrial content of PGF<sub>2α</sub> receptor (PTGFR) protein during the estrous cycle and gestation [<xref rid=\"B32-ijms-21-05213\" ref-type=\"bibr\">32</xref>]. Estradiol and PGE<sub>2</sub>, the main factors responsible for the maternal recognition of pregnancy, stimulate the endometrial expression of PGE<sub>2</sub> receptors PTGER2 and PTGER4 [<xref rid=\"B33-ijms-21-05213\" ref-type=\"bibr\">33</xref>]. Varied responses to prostaglandins may also be explained by differences in the activity of prostaglandin 9-ketoreductase (CBR1) in explants collected during different gestation periods. The prostaglandin 9-ketoreductase enzyme converts PGE<sub>2</sub> into PGF<sub>2α</sub>. The expression of the CBR1 gene and protein was confirmed in the porcine uterus during early pregnancy and the oestrous cycle [<xref rid=\"B34-ijms-21-05213\" ref-type=\"bibr\">34</xref>], which could point to the conversion of prostaglandins in tissue explants during the studied periods. Therefore, the differences in the concentration of chemerin system elements may be explained by the characteristic response for each of the studied dose of PGs. Beside the concentration and ratio of both prostaglandins, their receptors expression patterns, which are strongly connected with the individual periods of early gestation, should also be considered as an important factor that may affect chemerin and its receptors’ mRNA and protein content. The differences observed in the present study could also be attributed to changes in the hormonal microenvironment induced by different concentration patterns of steroid hormones, endogenous prostaglandins, as well as the occurrence of accompanying processes characteristic for the studied periods.</p><p>Early pregnancy in pigs is one of the most dynamic and critical periods during gestation. Around day 11 of porcine gestation, migrating conceptuses begin to secrete E<sub>2</sub>, which initiates a number of processes related to the maternal recognition of pregnancy [<xref rid=\"B35-ijms-21-05213\" ref-type=\"bibr\">35</xref>]. During this time, the direction of PGF<sub>2α</sub> secretion is altered from endocrine to exocrine, and PGE<sub>2</sub> production is stimulated, which promotes the secretory functions of the corpus luteum and ensures the proper course of gestation [<xref rid=\"B36-ijms-21-05213\" ref-type=\"bibr\">36</xref>,<xref rid=\"B37-ijms-21-05213\" ref-type=\"bibr\">37</xref>]. During our previous study we indicated the changes of chemerin system protein expression across early gestation which may be caused by a number of factors present in the uterine microenvironment [<xref rid=\"B23-ijms-21-05213\" ref-type=\"bibr\">23</xref>]. The analysis of the results presented herein indicates many similarities between the basal and PG-stimulated chemerin system expression patterns on the corresponding days of gestation. The comparison of the response patterns indicates that changes in the basal chemerin system expression during early pregnancy seem to be connected with the local presence of PGE<sub>2</sub> and PGF<sub>2α</sub>. Furthermore, due to a fact that PGs are secreted in a pulsatile manner, and their secretion patterns during early pregnancy are similar, the PGE<sub>2</sub>:PGF<sub>2α</sub> ratio is another agent, which should be taken into consideration [<xref rid=\"B38-ijms-21-05213\" ref-type=\"bibr\">38</xref>]. Beside its luteoprotective effects, the redirection of prostaglandin secretion also promotes endometrial reconstruction and modulation of the angiogenesis process during the peri-implantation period [<xref rid=\"B32-ijms-21-05213\" ref-type=\"bibr\">32</xref>,<xref rid=\"B39-ijms-21-05213\" ref-type=\"bibr\">39</xref>,<xref rid=\"B40-ijms-21-05213\" ref-type=\"bibr\">40</xref>]. The chemerin system seems to be involved in the stimulation of angiogenesis. Chemerin has been found to promote angiogenesis in vivo in mice and in vitro in the human umbilical vein endothelial cell line. Similar effects were observed in the human microvascular endothelial cell line [<xref rid=\"B41-ijms-21-05213\" ref-type=\"bibr\">41</xref>,<xref rid=\"B42-ijms-21-05213\" ref-type=\"bibr\">42</xref>]. Furthermore, it has also been found that chemerin, besides its influence on the vascularization process, may also, mainly via CMKLR1 receptor, influence the vascular tone and blood pressure [<xref rid=\"B43-ijms-21-05213\" ref-type=\"bibr\">43</xref>]. Our unpublished data also indicate that chemerin enhances the endometrial secretion of several angiogenic factors, including vascular endothelial growth factors (VEGFA and VEGFB), placental growth factor, and basic fibroblast growth factor, as well as modulates the expression of their receptor proteins, depending on chemerin dose and the stage of gestation. In the present study, PGE<sub>2</sub> decreased CMKLR1 protein concentrations on days 12 to 13 and 15 to 16 of gestation, whereas increased CCRL2 protein expression. PGF<sub>2α</sub> generally exerts a similar effect on the CMKLR1 receptor on days 12 to 16 of gestation and enhances CCRL2 protein content on days 12 to 13. Both prostaglandins exerted the opposite effects on GPR1 protein expression on days 12 to 16 of gestation. Chemerin promotes angiogenesis mainly via the CMKLR1 receptor [<xref rid=\"B41-ijms-21-05213\" ref-type=\"bibr\">41</xref>], whereas the GPR1 receptor is responsible mainly for glucose homeostasis [<xref rid=\"B44-ijms-21-05213\" ref-type=\"bibr\">44</xref>], which could suggest that by suppressing CMKLR1 and promoting GPR1 expression, prostaglandins could regulate tissue remodeling, prevent excessive vascular development, and ensure the availability of nutrients for implanting embryos. These findings could be confirmed by the observation that the lack of GPR1 expression in pregnant mice resulted in glucose intolerance, disrupted lipid metabolism, and decreased insulin levels [<xref rid=\"B45-ijms-21-05213\" ref-type=\"bibr\">45</xref>]. We propose that the regulation of these processes may take place not only through the inhibition of CMKLR1 receptor expression, but also via the stimulation of CCRL2 protein expression. An increase of CCRL2 protein content may result in an increased chemerin uptake from the circulating blood and, in consequence, enhanced GPR1 receptor expression in the tissue. Interestingly, both prostaglandins stimulated the expression of CMKLR1 and CCRL2 proteins on days 27 to 28 of gestation, which implies that the chemerin system could be involved in intensive angiogenesis processes during this period. The present results suggest that the chemerin system and prostaglandins could be a part of a mechanism that regulates endometrial tissue vascularization and, consequently, nutrient availability.</p><p>In mammals, gestation is a unique period during which the maternal organism accepts conceptuses carrying different genetic material. Developing conceptuses harbor only one half of genetic material which is identical to the maternal DNA, and they are perceived as natural semi-allografts. The presence of foreign genetic material in the uterine environment raises the risk of immune system mobilization and, consequently, embryo rejection. The suppression of immune activity in the pregnant uterus is a natural mechanism that prevents embryo rejection. Implanting conceptuses secrete a number of factors that modulate the maternal immune system, including interleukin 1β (IL1β), interferons δ and γ, interleukin 6 (IL6), epidermal growth factor, transforming growth factor β, and leukemia inhibitory factor (LIF), which are crucial for maternal–fetal crosstalk [<xref rid=\"B46-ijms-21-05213\" ref-type=\"bibr\">46</xref>,<xref rid=\"B47-ijms-21-05213\" ref-type=\"bibr\">47</xref>,<xref rid=\"B48-ijms-21-05213\" ref-type=\"bibr\">48</xref>,<xref rid=\"B49-ijms-21-05213\" ref-type=\"bibr\">49</xref>,<xref rid=\"B50-ijms-21-05213\" ref-type=\"bibr\">50</xref>]. However, these processes must be strictly controlled to prevent conceptus rejection [<xref rid=\"B51-ijms-21-05213\" ref-type=\"bibr\">51</xref>,<xref rid=\"B52-ijms-21-05213\" ref-type=\"bibr\">52</xref>]. Progesterone and PGE<sub>2</sub> are the key factors that suppress the immune system during gestation. The presence of PGE<sub>2</sub> during the first trimester of human gestation restrains the activation of maternal leukocytes in the decidua, which inhibits their anti-trophoblast killer function [<xref rid=\"B53-ijms-21-05213\" ref-type=\"bibr\">53</xref>]. In contrast, PGF<sub>2α</sub> is an important inflammatory mediator in the reproductive system [<xref rid=\"B54-ijms-21-05213\" ref-type=\"bibr\">54</xref>]. Chemerin is also a potential modulator of the immune response. Chemerin acts through CMKLR1 to promote chemotaxis of immature dendritic cells (DCs) and macrophages [<xref rid=\"B6-ijms-21-05213\" ref-type=\"bibr\">6</xref>]. Chemerin levels were elevated in tissues and fluids during inflammation, and CMKLR1-expressing immune cells were involved in several chronic inflammatory diseases [<xref rid=\"B5-ijms-21-05213\" ref-type=\"bibr\">5</xref>]. However, chemerin can also exert anti-inflammatory effects. Cash et al. (2008) reported that chemerin significantly downgraded neutrophil and monocyte recruitment and decreased proinflammatory cytokine expression in mice [<xref rid=\"B55-ijms-21-05213\" ref-type=\"bibr\">55</xref>]. Our unpublished data indicate that chemerin may modulate the endometrial secretion of various cytokines, including interleukins 1β, IL6 and 8, tumor necrosis factor α (TNFα), transforming growth factor α (TGFα), and LIF. We revealed that chemerin may inhibit and/or enhance the secretion of IL8, IL1β, IL6, TNFα, TGFα, and LIF in a dose- and time-dependent manner. Chemerin not only influences cytokine secretion, but also controls cytokine activity by regulating the expression of cytokine receptors in endometrial tissue. We found that the effect of chemerin on the cytokine receptor expression in the endometrium varied depending on the pregnancy period and the dose of the adipokine (Smolinska et al., unpublished). Due to the fact that chemerin acts as the chemoattractant for the immune cells, one of the most important sources of cytokines, further studies concerning the role of this adipokine in the endometrial immune cells recruitment are necessary to receive the full picture of its effect on the uterine immunological milieu during the early gestation period. The current study revealed that PGE<sub>2</sub> inhibits chemerin secretion on days 15 to 16 of pregnancy and stimulates CCRL2 protein expression, whereas PGF<sub>2α</sub> exerts the same effect on days 12 to 28 of gestation in the case of chemerin and promotes the expression of CCRL2 on days 12 to 13 and 27 to 28. During implantation, on days 15 to 16 of pregnancy, both prostaglandins could inhibit chemerin production to prevent the recruitment of immune cells and pregnancy failure. Furthermore, the enhanced expression of CCRL2 protein in the tissue may result in further chemerin level reduction. In pigs, the conceptuses initiate an acute-phase inflammatory response during implantation and placental attachment; therefore, PGF<sub>2α</sub> could inhibit chemerin secretion during the maternal recognition of pregnancy and implantation to counteract its anti-inflammatory effects and establish a supportive environment for gestation [<xref rid=\"B56-ijms-21-05213\" ref-type=\"bibr\">56</xref>].</p></sec><sec id=\"sec4-ijms-21-05213\"><title>4. Materials and Methods</title><sec id=\"sec4dot1-ijms-21-05213\"><title>4.1. Animals and Tissue Collection</title><p>Twenty-five mature crossbred gilts (Large White × Polish Landrace, 130–140 kg, of weight and age of 7–8 months) were randomly assigned to one of the following experimental groups (n = 5 per group): Gilts on days 10 to 11 (transuterine migration of embryos), 12 to 13 (maternal recognition of pregnancy), 15 to 16 (beginning of implantation), and 27 to 28 (end of implantation) of pregnancy, and days 10 to 11 of the estrous cycle (mid-luteal phase; the activity of corpora lutea (CL) is comparable to the CL activity during pregnancy). The quantity of CLs and embryos are specified in the <xref ref-type=\"app\" rid=\"app1-ijms-21-05213\">Supplementary Table S1</xref>. Animals were maintained and fed in accordance with current Polish standards. The daily dose of compound feed was approximately 2.7 kg/gilt at 32.4 MJ of metabolizable energy intake (12 MJ per kg of feed). The feed contained 135 g/kg of total digestible protein, 5.4 g/kg methionine and cystine, 7.3 g/kg lysine, 2.1 g/kg tryptophan, 4.8 g/kg threonine, 5.7 g/kg total phosphorus, 8.9 g/kg calcium, 1.7 g/kg sodium, 10% fiber, and the addition of other macro- and microelements. All individuals were given access to fresh water and forage <italic>ad libitum</italic>. Cyclic gilts were monitored daily for estrus behavior in the presence of boar. The day of the onset of the second estrus was recognized as day 0 of the cycle. The phase of the estrus cycle was confirmed on the basis of the ovary morphology [<xref rid=\"B57-ijms-21-05213\" ref-type=\"bibr\">57</xref>]. The inseminations were conducted by natural mating with the use of the same, crossbreed boar. The insemination was carried out on the first or second day of the estrous cycle, and the first day after coitus was counted as a first day of gestation. The phase of gestation was further confirmed based on the conceptuses morphology [<xref rid=\"B58-ijms-21-05213\" ref-type=\"bibr\">58</xref>]. Additionally, the phase of the estrous cycle and pregnancy was confirmed by determining the level P<sub>4</sub>, as described in Nitkiewicz et al. [<xref rid=\"B59-ijms-21-05213\" ref-type=\"bibr\">59</xref>] and Dobrzyn et al. [<xref rid=\"B60-ijms-21-05213\" ref-type=\"bibr\">60</xref>]. The concentrations of P<sub>4</sub> in the porcine plasma and uterine luminal flushing are detailed in the <xref ref-type=\"app\" rid=\"app1-ijms-21-05213\">Supplementary Table S1</xref>. The post-mortem-obtained uteri were transported immediately in ice-cold PBS supplemented with antibiotic-antimycotic solution (Sigma-Aldrich, Saint Louis, MO, USA) for an in vitro tissue culture procedure. <xref ref-type=\"fig\" rid=\"ijms-21-05213-f009\">Figure 9</xref> presents the experimental design scheme.</p></sec><sec id=\"sec4dot2-ijms-21-05213\"><title>4.2. Endometrial Explant Cultures</title><p>The in vitro cultures of endometrial explants were conducted as described by Smolinska et al. [<xref rid=\"B61-ijms-21-05213\" ref-type=\"bibr\">61</xref>]. In order to investigate the impact of PGE<sub>2</sub> and PGF<sub>2α</sub> on the chemerin system expression, after preincubation (2 h) in phenol-red free medium M199 (Sigma-Aldrich, USA), explants were incubated in the presence of PGE<sub>2</sub> (100, 250, 500 ng/mL; Sigma-Aldrich, USA), PGF<sub>2α</sub> (100, 250, 500 ng/mL; Sigma-Aldrich, USA), or without any treatment (control group) for 24 h (37 °C, 95% O<sub>2</sub>, 5% CO<sub>2</sub>). The doses of PGE<sub>2</sub> and PGF<sub>2α</sub> were chosen based on the work of Morgan et al. [<xref rid=\"B62-ijms-21-05213\" ref-type=\"bibr\">62</xref>] and Gregoraszczuk and Michas [<xref rid=\"B63-ijms-21-05213\" ref-type=\"bibr\">63</xref>]. Morgan et al. measured PGF<sub>2α</sub> levels in porcine uterine flushing on day 11 of gestation under exposure to estradiol valerate. The concentration of PGF<sub>2α</sub> in the control group was 198 ng/mL on the above-mentioned day. Since both PGs are secreted in a pulsatile manner and their concentrations are not constant, their physiological levels could be determined in the range of 100–500 ng/mL. The cultures were run in five separate experiments per group (n = 5, one gilt per each experiment) and each treatment combination was prepared in duplicates. The lactate dehydrogenase (LDH) activity measurement after preincubation and incubation periods was used to define the viability of tissue explants. The analysis was performed using Liquick Cor-LDH kit (Cormay, Poland) in accordance with the manufacturer’s instructions. The obtained LDH activity in the culture media was compared to the enzyme activity in the fully disintegrated tissue (maximal LDH release, positive control). The mean activity of LDH in the culture media after 24 h of incubation was 184 ± 14 U/L (0.93% of maximal LDH release).</p></sec><sec id=\"sec4dot3-ijms-21-05213\"><title>4.3. Total RNA Isolation and Reverse Transcription</title><p>Total RNA from endometrial tissue explants was isolated using TRI Reagent<sup>®</sup> RNA Isolation Reagent (Sigma-Aldrich) following the producer’s instructions. The quantity and purity of the obtained RNA samples were inspected spectrophotometrically using Infinite M200 Pro (Tecan, Mannedorf, Switzerland). One microgram of each RNA sample was reverse transcribed (RT) with the use of Omniscript RT Kit (Qiagen, Germantown, MD, USA) in the presence of 0.5 μg oligo(dT)<sub>15</sub> (Roche, Basel, Switzerland) in a final volume of 20 μL. The RT reaction was conducted at 37 °C for 1 h and was terminated by the incubation at 93 °C for 5 min.</p></sec><sec id=\"sec4dot4-ijms-21-05213\"><title>4.4. Quantitative Real-Time PCR Analysis</title><p>Quantitative real-time PCR (qPCR) analysis was carried out using Power SYBR Green Master Mix (Applied Biosystems, Carlsbad, CA, USA) as described by Smolinska et al. [<xref rid=\"B14-ijms-21-05213\" ref-type=\"bibr\">14</xref>]. Specific primer pairs used to amplify parts of chemerin, <italic>CMKLR1, GPR1, CCRL2,</italic> cyclophilin (<italic>PPIA</italic>), and β-actin (<italic>ACTB</italic>) genes are included in <xref rid=\"ijms-21-05213-t001\" ref-type=\"table\">Table 1</xref>. The constitutively expressed genes <italic>PPIA</italic> and <italic>ACTB</italic> were used as the internal controls to verify the method. Our preliminary studies revealed that the endometrial <italic>PPIA</italic> and <italic>ACTB</italic> expression was stable during the estrous cycle and pregnancy, as well as with and without treatments. Quantitative real-time PCR reaction mixtures contained: cDNA, primers, Power SYBR Green PCR Master Mix (12.5 μL; Applied Biosystems), and RNase-free water (to the final volume of 25 μL). In negative controls, the cDNA was substituted by water, or RT was not performed before qPCR. All reactions were run in duplicates. The specificity of the reaction was confirmed at the end of the run by the analysis of the melting-curve. The purity of the amplification product was confirmed by agarose gel electrophoresis. The calculation of chemerin and chemerin receptors genes’ relative expression was conducted with the use of the comparative cycle threshold method (ΔΔCT) and normalized using the geometrical means of the reference genes Ct values.</p></sec><sec id=\"sec4dot5-ijms-21-05213\"><title>4.5. Enzyme-Linked Immunosorbent Assay (ELISA) of Chemerin</title><p>Chemerin concentrations in the culture media were determined using a commercial ELISA kit (FineTest, Wuhan, China) following the manufacturer’s protocol. The range of standard curve was 0.156–10 ng/mL. The sensitivity of the assay was approximately 0.1 ng/mL. The sensitivity of the assays was defined as the lowest protein concentration that could be differentiated from zero samples. Absorbance was measured at 450 nm with the use of Infinite M200 PRO reader with Tecan i-control software (Tecan, Switzerland). The data were linearized by plotting the log of chemerin concentration versus the log of the optical density, and the best fit line was determined by regression analysis. Intra- and inter-assay coefficients of variation of the assay were 3.22% ± 0.39 and 8.1%, respectively.</p><p>The basal concentrations of chemerin in the uterine luminal flushing were determined by [<xref rid=\"B23-ijms-21-05213\" ref-type=\"bibr\">23</xref>] and are detailed in the <xref ref-type=\"app\" rid=\"app1-ijms-21-05213\">Supplementary Table S1</xref>.</p></sec><sec id=\"sec4dot6-ijms-21-05213\"><title>4.6. Protein Isolation and Western Blotting</title><p>The endometrial tissue explants were homogenized in T-PER Tissue Protein Extraction Reagent (Thermo Fischer Scientific, Waltham, MA, USA) in the presence of peptidase and phosphatase inhibitors (Sigma-Aldrich). The lysates were cleared by double centrifugation at 10,000× <italic>g</italic> for 5 min. The protein concentrations were measured with the use of Bradford dye-binding procedure with the dilutions of bovine serum albumin (BSA) as standards.</p><p>Western blotting analysis was performed as described by Smolinska et al. [<xref rid=\"B14-ijms-21-05213\" ref-type=\"bibr\">14</xref>]. Endometrial tissue lysates (40 μg) from control, PGE<sub>2</sub>-, and PGF<sub>2α</sub>-treated samples were resolved by SDS-PAGE electrophoresis in the 12.5% polyacrylamide gels and transferred onto PVDF membrane (Whatman, USA). Subsequently, membranes were blocked for 1 h in Tris-buffered saline Tween-20 containing 5% skimmed milk powder. After blocking, membranes were incubated for 12 h at 4 °C with rabbit polyclonal antibodies to CMKLR1 (1:1000; ab230442; Abcam, UK), mouse polyclonal antibodies to GPR1 (1:500; ab169331; Abcam, UK), rabbit polyclonal antibodies to CCRL2 (1:600; ab85224; Abcam, UK), and rabbit polyclonal antibodies to actin (1:200; A2066; Sigma-Aldrich, USA). Actin was used as a control to normalize the results of chemerin receptors protein concentration. Subsequently, to identify immunoreactive products, membranes were incubated for 1.5 h at RT with goat anti-rabbit IgG for CMKLR1, CCRL2, and actin (1:5000; sc-2054; Santa Cruz, USA), and goat anti-mouse IgG for GPR1 (1:2500; 115-035-003; Jackson ImmunoResearch Laboratories, Baltimore Pike, PA, USA) conjugated with horseradish peroxidase (HRP). For negative control blots, primary antibodies were substituted by nonspecific fetal calf serum (MP Biomedicals, Santa Ana, CA, USA). Immunocomplexes were visualized with Immobilon Western Chemiluminescent HRP Substrate (Merck Millipore, Kenilworth, NJ, USA) on the G: Box EF Gel Documentation System (Syngene, Cambridge, UK). The same protocol was performed in relation to the adipose tissue used as the positive controls. The results were quantified by densitometric analysis of immunoblots with the use of Image Studio Lite version 5.2 software (LI-COR, Lincoln, NE, USA). Data were expressed as the ratio of chemerin receptors proteins relative to actin protein in arbitrary optical density units.</p></sec><sec id=\"sec4dot7-ijms-21-05213\"><title>4.7. Statistical Analysis</title><p>Statistica software (StatSoft Inc., Tulsa, OK, USA) was used to perform the statistical analysis of results. All variables were analyzed using descriptive statistics (mean, standard deviation, sample minimum, and sample maximum). To determine the differences in genes expression and proteins concentration between control groups and PGE<sub>2</sub> or PGF<sub>2α</sub> treated groups, a one-way ANOVA followed by Duncan’s post-hoc test were used. Results were presented as the means ± S.E.M. from five independent observations. Values for <italic>p</italic> < 0.05 were considered statistically significant.</p></sec></sec><sec sec-type=\"conclusions\" id=\"sec5-ijms-21-05213\"><title>5. Conclusions</title><p>This is the first study to demonstrate that prostaglandins affect the expression of the chemerin system in the porcine uterus during early gestation. The presented results indicate that the analyzed prostaglandins exert different effects on the endometrial chemerin system expression, which depend on the dose of the used PGs. Furthermore, we revealed that the response pattern for PGs is specific for each of the studied pregnancy periods. The above findings confirm our hypothesis assuming the regulatory influence of PGs on chemerin system expression in the porcine early-pregnant uterus. In the light of the existing knowledge, the presented findings also suggest that the chemerin system could be an important element of the regulatory mechanism responsible for the proper course of gestation, and that its expression depends on the local hormonal microenvironment.</p></sec></body><back><app-group><app id=\"app1-ijms-21-05213\"><title>Supplementary Materials</title><p>Supplementary materials can be found at <uri xlink:href=\"https://www.mdpi.com/1422-0067/21/15/5213/s1\">https://www.mdpi.com/1422-0067/21/15/5213/s1</uri>. Supplementary Table S1. Progesterone and chemerin concentrations, as well as and number of corpora lutea (CL) and embryos in the research groups. Supplementary Figure S1. The standard curve of Chemerin ELISA kit used in the study.</p><supplementary-material content-type=\"local-data\" id=\"ijms-21-05213-s001\"><media xlink:href=\"ijms-21-05213-s001.pdf\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material></app></app-group><notes><title>Author Contributions</title><p>Conceptualization, N.S., T.K., K.D., M.K.; methodology, N.S., M.K., and K.D.; validation, M.K., K.D., E.R.; formal analysis, N.S., M.G., K.K., E.Z.; investigation, M.K., K.D., N.S., E.R., K.K., E.Z.; resources, K.B., G.K., K.S.; data curation, M.K., and B.K.; writing—original draft preparation, M.K. and K.D.; writing—review and editing, M.K., N.S., T.K., B.K., K.D.; supervision, N.S. and T.K.; project administration, N.S.; funding acquisition, N.S. All authors have read and agreed to the published version of the manuscript.</p></notes><notes><title>Funding</title><p>This research was supported by the National Science Centre (projects no: 2017/25/B/NZ9/00040).</p></notes><notes notes-type=\"COI-statement\"><title>Conflicts of Interest</title><p>The authors declare no competing financial interests.</p></notes><notes><title>Animal Welfare Statement</title><p>The authors confirm that the ethical policies of the journal, as noted on the journal’s author guidelines page, have been adhered to. 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(<italic>n</italic> = 5). Bars with different superscripts differ (<italic>p</italic> < 0.05).</p></caption><graphic xlink:href=\"ijms-21-05213-g001\"/></fig><fig id=\"ijms-21-05213-f002\" orientation=\"portrait\" position=\"float\"><label>Figure 2</label><caption><p>The influence of prostaglandin E<sub>2</sub> (PGE<sub>2</sub>; 100, 250, 500 ng/mL) on chemokine-like receptor 1 (CMKLR1) mRNA (<bold>A</bold>,<bold>C</bold>,<bold>E</bold>,<bold>G</bold>,<bold>I</bold>) and protein (<bold>B</bold>,<bold>D</bold>,<bold>F</bold>,<bold>H</bold>,<bold>J</bold>) expression in the porcine endometrium on days 10 to 11, 12 to 13, 15 to 16, and 27 to 28 of the pregnancy, and on days 10 to 11 of the estrous cycle. The gene expression was determined by quantitative real-time PCR. The protein concentration was determined by the western blotting analysis; upper panels: Representative immunoblots; lower panels: Densitometric analysis of CMKLR1 protein relative to actin protein. Results are reported as the means ± S.E.M. (<italic>n</italic> = 5). Bars with different superscripts differ (<italic>p</italic> < 0.05).</p></caption><graphic xlink:href=\"ijms-21-05213-g002\"/></fig><fig id=\"ijms-21-05213-f003\" orientation=\"portrait\" position=\"float\"><label>Figure 3</label><caption><p>The influence of prostaglandin E<sub>2</sub> (PGE<sub>2</sub>; 100, 250, 500 ng/mL) on G protein-coupled receptor 1 (GPR1) mRNA (<bold>A</bold>,<bold>C</bold>,<bold>E</bold>,<bold>G</bold>,<bold>I</bold>) and protein (<bold>B</bold>,<bold>D</bold>,<bold>F</bold>,<bold>H</bold>,<bold>J</bold>) expression in the porcine endometrium on days 10 to 11, 12 to 13, 15 to 16, and 27 to 28 of the pregnancy, and on days 10 to 11 of the estrous cycle. The gene expression was determined by quantitative real-time PCR. The protein concentration was determined by the western blotting analysis; upper panels: Representative immunoblots; lower panels: Densitometric analysis of GPR1 protein relative to actin protein. Results are reported as the means ± S.E.M. (<italic>n</italic> = 5). Bars with different superscripts differ (<italic>p</italic> < 0.05).</p></caption><graphic xlink:href=\"ijms-21-05213-g003\"/></fig><fig id=\"ijms-21-05213-f004\" orientation=\"portrait\" position=\"float\"><label>Figure 4</label><caption><p>The influence of prostaglandin E<sub>2</sub> (PGE<sub>2</sub>; 100, 250, 500 ng/mL) on C-C motif chemokine receptor like 2 (CCRL2) mRNA (<bold>A</bold>,<bold>C</bold>,<bold>E</bold>,<bold>G</bold>,<bold>I</bold>) and protein (<bold>B</bold>,<bold>D</bold>,<bold>F</bold>,<bold>H</bold>,<bold>J</bold>) expression in the porcine endometrium on days 10 to 11, 12 to 13, 15 to 16, and 27 to 28 of the pregnancy, and on days 10 to 11 of the estrous cycle. The gene expression was determined by quantitative real-time PCR. The protein concentration was determined by the western blotting analysis; upper panels: Representative immunoblots; lower panels: Densitometric analysis of CCRL2 protein relative to actin protein. Results are reported as the means ± S.E.M. (<italic>n</italic> = 5). Bars with different superscripts differ (<italic>p</italic> < 0.05).</p></caption><graphic xlink:href=\"ijms-21-05213-g004\"/></fig><fig id=\"ijms-21-05213-f005\" orientation=\"portrait\" position=\"float\"><label>Figure 5</label><caption><p>The influence of prostaglandin F<sub>2α</sub> (PGF<sub>2α</sub>; 100, 250, 500 ng/mL) on chemerin mRNA expression (<bold>A</bold>,<bold>C</bold>,<bold>E</bold>,<bold>G</bold>,<bold>I</bold>) and chemerin protein secretion (<bold>B</bold>,<bold>D</bold>,<bold>F</bold>,<bold>H</bold>,<bold>J</bold>) in the porcine endometrial tissue explants on days 10 to 11, 12 to 13, 15 to 16, and 27 to 28 of the pregnancy, and on days 10 to 11 of the estrous cycle. The gene expression was determined by quantitative real-time PCR. The proteins secretion was determined by an ELISA test. Results are reported as the means ± S.E.M. (<italic>n</italic> = 5). Bars with different superscripts differ (<italic>p</italic> < 0.05).</p></caption><graphic xlink:href=\"ijms-21-05213-g005\"/></fig><fig id=\"ijms-21-05213-f006\" orientation=\"portrait\" position=\"float\"><label>Figure 6</label><caption><p>The influence of prostaglandin F<sub>2α</sub> (PGF<sub>2α</sub>; 100, 250, 500 ng/mL) on chemokine-like receptor 1 (CMKLR1) mRNA (<bold>A</bold>,<bold>C</bold>,<bold>E</bold>,<bold>G</bold>,<bold>I</bold>) and protein (<bold>B</bold>,<bold>D</bold>,<bold>F</bold>,<bold>H</bold>,<bold>J</bold>) expression in the porcine endometrium on days 10 to 11, 12 to 13, 15 to 16, and 27 to 28 of the pregnancy, and on days 10 to 11 of the estrous cycle. The gene expression was determined by quantitative real-time PCR. The protein concentration was determined by the western blotting analysis; upper panels: Representative immunoblots; lower panels: Densitometric analysis of CMKLR1 protein relative to actin protein. Results are reported as the means ± S.E.M. (<italic>n</italic> = 5). Bars with different superscripts differ (<italic>p</italic> < 0.05).</p></caption><graphic xlink:href=\"ijms-21-05213-g006\"/></fig><fig id=\"ijms-21-05213-f007\" orientation=\"portrait\" position=\"float\"><label>Figure 7</label><caption><p>The influence of prostaglandin F<sub>2α</sub> (PGF<sub>2α</sub>; 100, 250, 500 ng/mL) on G protein-coupled receptor 1 (GPR1) mRNA (<bold>A</bold>,<bold>C</bold>,<bold>E</bold>,<bold>G</bold>,<bold>I</bold>) and protein (<bold>B</bold>,<bold>D</bold>,<bold>F</bold>,<bold>H</bold>,<bold>J</bold>) expression in the porcine endometrium on days 10 to 11, 12 to 13, 15 to 16, and 27 to 28 of the pregnancy, and on days 10 to 11 of the estrous cycle. The gene expression was determined by quantitative real-time PCR. The protein concentration was determined by the western blotting analysis; upper panels: Representative immunoblots; lower panels: Densitometric analysis of GPR1 protein relative to actin protein. Results are reported as the means ± S.E.M. (<italic>n</italic> = 5). Bars with different superscripts differ (<italic>p</italic> < 0.05).</p></caption><graphic xlink:href=\"ijms-21-05213-g007\"/></fig><fig id=\"ijms-21-05213-f008\" orientation=\"portrait\" position=\"float\"><label>Figure 8</label><caption><p>The influence of prostaglandin F<sub>2α</sub> (PGF<sub>2α</sub>; 100, 250, 500 ng/mL) on C-C motif chemokine receptor like 2 (CCRL2) mRNA (<bold>A</bold>,<bold>C</bold>,<bold>E</bold>,<bold>G</bold>,<bold>I</bold>) and protein (<bold>B</bold>,<bold>D</bold>,<bold>F</bold>,<bold>H</bold>,<bold>J</bold>) expression in the porcine endometrium on days 10 to 11, 12 to 13, 15 to 16, and 27 to 28 of the pregnancy, and on days 10 to 11 of the estrous cycle. The gene expression was determined by quantitative real-time PCR. The protein concentration was determined by the western blotting analysis; upper panels: Representative immunoblots; lower panels: Densitometric analysis of CCRL2 protein relative to actin protein. Results are reported as the means ± S.E.M. (<italic>n</italic> = 5). Bars with different superscripts differ (<italic>p</italic> < 0.05).</p></caption><graphic xlink:href=\"ijms-21-05213-g008\"/></fig><fig id=\"ijms-21-05213-f009\" orientation=\"portrait\" position=\"float\"><label>Figure 9</label><caption><p>Experimental design graph.</p></caption><graphic xlink:href=\"ijms-21-05213-g009\"/></fig><table-wrap id=\"ijms-21-05213-t001\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijms-21-05213-t001_Table 1</object-id><label>Table 1</label><caption><p>Characteristics of the primers used in the study of gene expression in porcine endometrial explants. <italic>RARRES2</italic>: Chemerin; <italic>CCRL2</italic>: C-C motif chemokine receptor like 2; <italic>CMLKR1</italic>: chemokine-like receptor 1; <italic>GPR1</italic>: G protein-coupled receptor 1; <italic>PPIA</italic>: Cyclophilin; <italic>ACTB</italic>: β-actin; F: Forward; R: Reverse.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Gene</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Primers Sequences</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Accession Number</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Primer, nM</th><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Reaction Conditions</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Reference</th></tr></thead><tbody><tr><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">\n<italic>RARRES2</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">F: 5′-TGGAGGAGTTCCACAAGCAC-3′</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">EU660865</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">500</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">\n<inline-formula><mml:math id=\"mm1\"><mml:mrow><mml:mrow><mml:mtable><mml:mtr><mml:mtd><mml:mi>Activation</mml:mi><mml:mo>:</mml:mo><mml:mo> </mml:mo><mml:mn>95</mml:mn><mml:mo> </mml:mo><mml:mo>°</mml:mo><mml:mi mathvariant=\"normal\">C</mml:mi><mml:mo>—</mml:mo><mml:mn>10</mml:mn><mml:mo> </mml:mo><mml:mi>min</mml:mi></mml:mtd></mml:mtr><mml:mtr><mml:mtd><mml:mrow><mml:mrow><mml:mtable><mml:mtr><mml:mtd><mml:mrow><mml:mi>Denaturation</mml:mi><mml:mo>:</mml:mo><mml:mo> </mml:mo><mml:mn>95</mml:mn><mml:mo> </mml:mo><mml:mo>°</mml:mo><mml:mi mathvariant=\"normal\">C</mml:mi><mml:mo>—</mml:mo><mml:mn>15</mml:mn><mml:mo> </mml:mo><mml:mi mathvariant=\"normal\">s</mml:mi></mml:mrow></mml:mtd></mml:mtr><mml:mtr><mml:mtd><mml:mrow><mml:mi>Annealing</mml:mi><mml:mo>:</mml:mo><mml:mo> </mml:mo><mml:mn>60</mml:mn><mml:mo> </mml:mo><mml:mo>°</mml:mo><mml:mi mathvariant=\"normal\">C</mml:mi><mml:mo>—</mml:mo><mml:mn>1</mml:mn><mml:mo> </mml:mo><mml:mi>min</mml:mi></mml:mrow></mml:mtd></mml:mtr><mml:mtr><mml:mtd><mml:mrow><mml:mi>Elongation</mml:mi><mml:mo>:</mml:mo><mml:mo> </mml:mo><mml:mn>72</mml:mn><mml:mo> </mml:mo><mml:mo>°</mml:mo><mml:mi mathvariant=\"normal\">C</mml:mi><mml:mo>—</mml:mo><mml:mn>1</mml:mn><mml:mo> </mml:mo><mml:mi>min</mml:mi></mml:mrow></mml:mtd></mml:mtr></mml:mtable></mml:mrow><mml:mo>}</mml:mo></mml:mrow></mml:mtd></mml:mtr></mml:mtable></mml:mrow></mml:mrow></mml:math></inline-formula>\n</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">40</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">[<xref rid=\"B14-ijms-21-05213\" ref-type=\"bibr\">14</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">R: 5′-GCTTTCTTCCAGTCCCTCTTC-3′</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">500</td></tr><tr><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">\n<italic>CCRL2</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">F: 5′-GAGCAGCAGCTACTTACTTCC-3′</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">NM_001001617.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">200</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">\n<inline-formula><mml:math id=\"mm2\"><mml:mrow><mml:mrow><mml:mtable><mml:mtr><mml:mtd><mml:mi>Activation</mml:mi><mml:mo>:</mml:mo><mml:mo> </mml:mo><mml:mn>95</mml:mn><mml:mo> </mml:mo><mml:mo>°</mml:mo><mml:mi mathvariant=\"normal\">C</mml:mi><mml:mo>—</mml:mo><mml:mn>10</mml:mn><mml:mo> </mml:mo><mml:mi>min</mml:mi></mml:mtd></mml:mtr><mml:mtr><mml:mtd><mml:mrow><mml:mrow><mml:mtable><mml:mtr><mml:mtd><mml:mi>Denaturation</mml:mi><mml:mo>:</mml:mo><mml:mo> </mml:mo><mml:mn>95</mml:mn><mml:mo> </mml:mo><mml:mo>°</mml:mo><mml:mi mathvariant=\"normal\">C</mml:mi><mml:mo>—</mml:mo><mml:mn>15</mml:mn><mml:mo> </mml:mo><mml:mi mathvariant=\"normal\">s</mml:mi></mml:mtd></mml:mtr><mml:mtr><mml:mtd><mml:mi>Annealing</mml:mi><mml:mo>:</mml:mo><mml:mo> </mml:mo><mml:mn>60</mml:mn><mml:mo> </mml:mo><mml:mo>°</mml:mo><mml:mi mathvariant=\"normal\">C</mml:mi><mml:mo>—</mml:mo><mml:mn>1</mml:mn><mml:mo> </mml:mo><mml:mi>min</mml:mi></mml:mtd></mml:mtr><mml:mtr><mml:mtd><mml:mi>Elongation</mml:mi><mml:mo>:</mml:mo><mml:mo> </mml:mo><mml:mn>72</mml:mn><mml:mo> </mml:mo><mml:mo>°</mml:mo><mml:mi mathvariant=\"normal\">C</mml:mi><mml:mo>—</mml:mo><mml:mn>1</mml:mn><mml:mo> </mml:mo><mml:mi>min</mml:mi></mml:mtd></mml:mtr></mml:mtable></mml:mrow><mml:mo>}</mml:mo></mml:mrow></mml:mtd></mml:mtr></mml:mtable></mml:mrow></mml:mrow></mml:math></inline-formula>\n</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">40</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">[<xref rid=\"B14-ijms-21-05213\" ref-type=\"bibr\">14</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">R: 5′-CTGCCCACTGACCGAGTTC-3′</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">200</td></tr><tr><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">\n<italic>CMKLR1</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">F: 5′-GGACTACCACTGGGTGTTCG-3′</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">EU660866</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">200</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">\n<inline-formula><mml:math id=\"mm3\"><mml:mrow><mml:mrow><mml:mtable><mml:mtr><mml:mtd><mml:mi>Activation</mml:mi><mml:mo>:</mml:mo><mml:mo> </mml:mo><mml:mn>95</mml:mn><mml:mo> </mml:mo><mml:mo>°</mml:mo><mml:mi mathvariant=\"normal\">C</mml:mi><mml:mo>—</mml:mo><mml:mn>10</mml:mn><mml:mo> </mml:mo><mml:mi>min</mml:mi></mml:mtd></mml:mtr><mml:mtr><mml:mtd><mml:mrow><mml:mrow><mml:mtable><mml:mtr><mml:mtd><mml:mi>Denaturation</mml:mi><mml:mo>:</mml:mo><mml:mo> </mml:mo><mml:mn>95</mml:mn><mml:mo> </mml:mo><mml:mo>°</mml:mo><mml:mi mathvariant=\"normal\">C</mml:mi><mml:mo>—</mml:mo><mml:mn>15</mml:mn><mml:mo> </mml:mo><mml:mi mathvariant=\"normal\">s</mml:mi></mml:mtd></mml:mtr><mml:mtr><mml:mtd><mml:mi>Annealing</mml:mi><mml:mo>:</mml:mo><mml:mo> </mml:mo><mml:mn>60</mml:mn><mml:mo> </mml:mo><mml:mo>°</mml:mo><mml:mi mathvariant=\"normal\">C</mml:mi><mml:mo>—</mml:mo><mml:mn>1</mml:mn><mml:mo> </mml:mo><mml:mi>min</mml:mi></mml:mtd></mml:mtr><mml:mtr><mml:mtd><mml:mi>Elongation</mml:mi><mml:mo>:</mml:mo><mml:mo> </mml:mo><mml:mn>72</mml:mn><mml:mo> </mml:mo><mml:mo>°</mml:mo><mml:mi mathvariant=\"normal\">C</mml:mi><mml:mo>—</mml:mo><mml:mn>1</mml:mn><mml:mo> </mml:mo><mml:mi>min</mml:mi></mml:mtd></mml:mtr></mml:mtable></mml:mrow><mml:mo>}</mml:mo></mml:mrow></mml:mtd></mml:mtr></mml:mtable></mml:mrow></mml:mrow></mml:math></inline-formula>\n</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">40</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">[<xref rid=\"B14-ijms-21-05213\" ref-type=\"bibr\">14</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">R: 5′-GCCATGTAAGCCAGTCGGA-3′</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">200</td></tr><tr><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">\n<italic>GPR1</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">F: 5′-ACCGACTTGGAGGAGAAAGC -3’</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">FJ234899.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">200</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">\n<inline-formula><mml:math id=\"mm4\"><mml:mrow><mml:mrow><mml:mtable><mml:mtr><mml:mtd><mml:mi>Activation</mml:mi><mml:mo>:</mml:mo><mml:mo> </mml:mo><mml:mn>95</mml:mn><mml:mo> </mml:mo><mml:mo>°</mml:mo><mml:mi mathvariant=\"normal\">C</mml:mi><mml:mo>—</mml:mo><mml:mn>10</mml:mn><mml:mo> </mml:mo><mml:mi>min</mml:mi></mml:mtd></mml:mtr><mml:mtr><mml:mtd><mml:mrow><mml:mrow><mml:mtable><mml:mtr><mml:mtd><mml:mi>Denaturation</mml:mi><mml:mo>:</mml:mo><mml:mo> </mml:mo><mml:mn>95</mml:mn><mml:mo> </mml:mo><mml:mo>°</mml:mo><mml:mi mathvariant=\"normal\">C</mml:mi><mml:mo>—</mml:mo><mml:mn>15</mml:mn><mml:mo> </mml:mo><mml:mi mathvariant=\"normal\">s</mml:mi></mml:mtd></mml:mtr><mml:mtr><mml:mtd><mml:mi>Annealing</mml:mi><mml:mo>:</mml:mo><mml:mo> </mml:mo><mml:mn>60</mml:mn><mml:mo> </mml:mo><mml:mo>°</mml:mo><mml:mi mathvariant=\"normal\">C</mml:mi><mml:mo>—</mml:mo><mml:mn>1</mml:mn><mml:mo> </mml:mo><mml:mi>min</mml:mi></mml:mtd></mml:mtr><mml:mtr><mml:mtd><mml:mi>Elongation</mml:mi><mml:mo>:</mml:mo><mml:mo> </mml:mo><mml:mn>72</mml:mn><mml:mo> </mml:mo><mml:mo>°</mml:mo><mml:mi mathvariant=\"normal\">C</mml:mi><mml:mo>—</mml:mo><mml:mn>1</mml:mn><mml:mo> </mml:mo><mml:mi>min</mml:mi></mml:mtd></mml:mtr></mml:mtable></mml:mrow><mml:mo>}</mml:mo></mml:mrow></mml:mtd></mml:mtr></mml:mtable></mml:mrow></mml:mrow></mml:math></inline-formula>\n</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">40</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">[<xref rid=\"B14-ijms-21-05213\" ref-type=\"bibr\">14</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">R: 5′-ATTGAGGAACCAGAGCGTGG -3’</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">200</td></tr><tr><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">\n<italic>PPIA</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">F: 5′-GCACTGGTGGCAAGTCCAT-3’</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">U48832</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">300</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">\n<inline-formula><mml:math id=\"mm5\"><mml:mrow><mml:mrow><mml:mtable><mml:mtr><mml:mtd><mml:mn>50</mml:mn><mml:mo> </mml:mo><mml:mo>°</mml:mo><mml:mi mathvariant=\"normal\">C</mml:mi><mml:mo>—</mml:mo><mml:mn>2</mml:mn><mml:mo> </mml:mo><mml:mi>min</mml:mi><mml:mo> </mml:mo><mml:mo>°</mml:mo><mml:mi mathvariant=\"normal\">C</mml:mi></mml:mtd></mml:mtr><mml:mtr><mml:mtd><mml:mrow><mml:mrow><mml:mtable><mml:mtr><mml:mtd><mml:mi>Activation</mml:mi><mml:mo>:</mml:mo><mml:mo> </mml:mo><mml:mn>95</mml:mn><mml:mo> </mml:mo><mml:mo>°</mml:mo><mml:mi mathvariant=\"normal\">C</mml:mi><mml:mo>—</mml:mo><mml:mn>10</mml:mn><mml:mi>min</mml:mi><mml:mo> </mml:mo><mml:mo>°</mml:mo><mml:mi mathvariant=\"normal\">C</mml:mi></mml:mtd></mml:mtr><mml:mtr><mml:mtd><mml:mi>Denaturation</mml:mi><mml:mo>:</mml:mo><mml:mo> </mml:mo><mml:mn>95</mml:mn><mml:mo> </mml:mo><mml:mo>°</mml:mo><mml:mi mathvariant=\"normal\">C</mml:mi><mml:mo>—</mml:mo><mml:mn>15</mml:mn><mml:mo> </mml:mo><mml:mi mathvariant=\"normal\">s</mml:mi></mml:mtd></mml:mtr><mml:mtr><mml:mtd><mml:mi>Annealing</mml:mi><mml:mo>:</mml:mo><mml:mo> </mml:mo><mml:mn>60</mml:mn><mml:mo> </mml:mo><mml:mo>°</mml:mo><mml:mi mathvariant=\"normal\">C</mml:mi><mml:mo>—</mml:mo><mml:mn>1</mml:mn><mml:mo> </mml:mo><mml:mi>min</mml:mi></mml:mtd></mml:mtr></mml:mtable></mml:mrow><mml:mo>}</mml:mo></mml:mrow></mml:mtd></mml:mtr></mml:mtable></mml:mrow></mml:mrow></mml:math></inline-formula>\n</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">40</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">[<xref rid=\"B64-ijms-21-05213\" ref-type=\"bibr\">64</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">R: 5′-AGGACCCGTATGCTTCAGGA-3’</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">300</td></tr><tr><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">\n<italic>ACTB</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">F: 5′-ACATCAAGGAGAAGCTCTGCTACG-3’</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">U07786</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">500</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">\n<inline-formula><mml:math id=\"mm6\"><mml:mrow><mml:mrow><mml:mtable><mml:mtr><mml:mtd><mml:mi>Activation</mml:mi><mml:mo>:</mml:mo><mml:mo> </mml:mo><mml:mn>95</mml:mn><mml:mo> </mml:mo><mml:mo>°</mml:mo><mml:mi mathvariant=\"normal\">C</mml:mi><mml:mo>—</mml:mo><mml:mn>10</mml:mn><mml:mo> </mml:mo><mml:mi>min</mml:mi></mml:mtd></mml:mtr><mml:mtr><mml:mtd><mml:mrow><mml:mrow><mml:mtable><mml:mtr><mml:mtd><mml:mi>Denaturation</mml:mi><mml:mo>:</mml:mo><mml:mo> </mml:mo><mml:mn>95</mml:mn><mml:mo> </mml:mo><mml:mo>°</mml:mo><mml:mi mathvariant=\"normal\">C</mml:mi><mml:mo>—</mml:mo><mml:mn>15</mml:mn><mml:mo> </mml:mo><mml:mi mathvariant=\"normal\">s</mml:mi></mml:mtd></mml:mtr><mml:mtr><mml:mtd><mml:mi>Annealing</mml:mi><mml:mo>:</mml:mo><mml:mo> </mml:mo><mml:mn>61</mml:mn><mml:mo> </mml:mo><mml:mo>°</mml:mo><mml:mi mathvariant=\"normal\">C</mml:mi><mml:mo>—</mml:mo><mml:mn>15</mml:mn><mml:mo> </mml:mo><mml:mi>min</mml:mi></mml:mtd></mml:mtr><mml:mtr><mml:mtd><mml:mi>Elongation</mml:mi><mml:mo>:</mml:mo><mml:mo> </mml:mo><mml:mn>72</mml:mn><mml:mo> </mml:mo><mml:mo>°</mml:mo><mml:mi mathvariant=\"normal\">C</mml:mi><mml:mo>—</mml:mo><mml:mn>1</mml:mn><mml:mo> </mml:mo><mml:mi>min</mml:mi></mml:mtd></mml:mtr></mml:mtable></mml:mrow><mml:mo>}</mml:mo></mml:mrow></mml:mtd></mml:mtr></mml:mtable></mml:mrow></mml:mrow></mml:math></inline-formula>\n</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">40</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">[<xref rid=\"B65-ijms-21-05213\" ref-type=\"bibr\">65</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">R: 5′-GAGGGGCGATGATCTTGATCTTCA-3’</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">500</td></tr></tbody></table></table-wrap></floats-group></article>\n"
]
[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Int J Environ Res Public Health</journal-id><journal-id journal-id-type=\"iso-abbrev\">Int J Environ Res Public Health</journal-id><journal-id journal-id-type=\"publisher-id\">ijerph</journal-id><journal-title-group><journal-title>International Journal of Environmental Research and Public Health</journal-title></journal-title-group><issn pub-type=\"ppub\">1661-7827</issn><issn pub-type=\"epub\">1660-4601</issn><publisher><publisher-name>MDPI</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32722623</article-id><article-id pub-id-type=\"pmc\">PMC7432132</article-id><article-id pub-id-type=\"doi\">10.3390/ijerph17155374</article-id><article-id pub-id-type=\"publisher-id\">ijerph-17-05374</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Article</subject></subj-group></article-categories><title-group><article-title>Reduced Gray Matter Volume and Risk of Falls in Parkinson’s Disease with Dementia Patients: A Voxel-Based Morphometry Study</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0003-3883-2104</contrib-id><name><surname>Cheng</surname><given-names>Kai-Lun</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05374\">1</xref><xref ref-type=\"aff\" rid=\"af2-ijerph-17-05374\">2</xref><xref ref-type=\"aff\" rid=\"af3-ijerph-17-05374\">3</xref><xref ref-type=\"author-notes\" rid=\"fn1-ijerph-17-05374\">†</xref></contrib><contrib contrib-type=\"author\"><name><surname>Lin</surname><given-names>Li-Han</given-names></name><xref ref-type=\"aff\" rid=\"af4-ijerph-17-05374\">4</xref><xref ref-type=\"author-notes\" rid=\"fn1-ijerph-17-05374\">†</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0002-6386-2850</contrib-id><name><surname>Chen</surname><given-names>Po-Cheng</given-names></name><xref ref-type=\"aff\" rid=\"af5-ijerph-17-05374\">5</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0002-3875-6677</contrib-id><name><surname>Chiang</surname><given-names>Pi-Ling</given-names></name><xref ref-type=\"aff\" rid=\"af4-ijerph-17-05374\">4</xref></contrib><contrib contrib-type=\"author\"><name><surname>Chen</surname><given-names>Yueh-Sheng</given-names></name><xref ref-type=\"aff\" rid=\"af4-ijerph-17-05374\">4</xref></contrib><contrib contrib-type=\"author\"><name><surname>Chen</surname><given-names>Hsiu-Ling</given-names></name><xref ref-type=\"aff\" rid=\"af4-ijerph-17-05374\">4</xref></contrib><contrib contrib-type=\"author\"><name><surname>Chen</surname><given-names>Meng-Hsiang</given-names></name><xref ref-type=\"aff\" rid=\"af4-ijerph-17-05374\">4</xref></contrib><contrib contrib-type=\"author\"><name><surname>Chou</surname><given-names>Kun-Hsien</given-names></name><xref ref-type=\"aff\" rid=\"af6-ijerph-17-05374\">6</xref><xref ref-type=\"aff\" rid=\"af7-ijerph-17-05374\">7</xref></contrib><contrib contrib-type=\"author\"><name><surname>Li</surname><given-names>Shau-Hsuan</given-names></name><xref ref-type=\"aff\" rid=\"af8-ijerph-17-05374\">8</xref></contrib><contrib contrib-type=\"author\"><name><surname>Lu</surname><given-names>Cheng-Hsien</given-names></name><xref ref-type=\"aff\" rid=\"af9-ijerph-17-05374\">9</xref></contrib><contrib contrib-type=\"author\"><name><surname>Lin</surname><given-names>Wei-Che</given-names></name><xref ref-type=\"aff\" rid=\"af4-ijerph-17-05374\">4</xref><xref rid=\"c1-ijerph-17-05374\" ref-type=\"corresp\">*</xref></contrib></contrib-group><aff id=\"af1-ijerph-17-05374\"><label>1</label>Department of Medical Imaging, Chung Shan Medical University Hospital, Taichung 402, Taiwan; <email>[email protected]</email></aff><aff id=\"af2-ijerph-17-05374\"><label>2</label>School of Medical Imaging and Radiological Sciences, Chung Shan Medical University, Taichung 402, Taiwan</aff><aff id=\"af3-ijerph-17-05374\"><label>3</label>Department of Veterinary Medicine, National Chung Hsing University, Taichung 402, Taiwan</aff><aff id=\"af4-ijerph-17-05374\"><label>4</label>Department of Diagnostic Radiology, Kaohsiung Chang Gung Memorial Hospital, and Chang Gung University College of Medicine, Kaohsiung 833, Taiwan; <email>[email protected]</email> (L.-H.L.); <email>[email protected]</email> (P.-L.C.); <email>[email protected]</email> (Y.-S.C.); <email>[email protected]</email> (H.-L.C.); <email>[email protected]</email> (M.-H.C.)</aff><aff id=\"af5-ijerph-17-05374\"><label>5</label>Department of Physical Medicine and Rehabilitation, Kaohsiung Chang Gung Memorial Hospital, and Chang Gung University College of Medicine, Kaohsiung 833, Taiwan; <email>[email protected]</email></aff><aff id=\"af6-ijerph-17-05374\"><label>6</label>Brain Research Center, National Yang-Ming University, Taipei 112, Taiwan; <email>[email protected]</email></aff><aff id=\"af7-ijerph-17-05374\"><label>7</label>Institute of Neuroscience, National Yang-Ming University, Taipei 112, Taiwan</aff><aff id=\"af8-ijerph-17-05374\"><label>8</label>Department of Oncology and Hematology, Kaohsiung Chang Gung Memorial Hospital, and Chang Gung University College of Medicine, Kaohsiung 833, Taiwan; <email>[email protected]</email></aff><aff id=\"af9-ijerph-17-05374\"><label>9</label>Department of Neurology, Kaohsiung Chang Gung Memorial Hospital, and Chang Gung University College of Medicine, Kaohsiung 833, Taiwan; <email>[email protected]</email></aff><author-notes><corresp id=\"c1-ijerph-17-05374\"><label>*</label>Correspondence: <email>[email protected]</email>; Tel.: +886-7-731-7123</corresp><fn id=\"fn1-ijerph-17-05374\"><label>†</label><p>These authors contributed equally to this work.</p></fn></author-notes><pub-date pub-type=\"epub\"><day>26</day><month>7</month><year>2020</year></pub-date><pub-date pub-type=\"ppub\"><month>8</month><year>2020</year></pub-date><volume>17</volume><issue>15</issue><elocation-id>5374</elocation-id><history><date date-type=\"received\"><day>15</day><month>6</month><year>2020</year></date><date date-type=\"accepted\"><day>23</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>© 2020 by the authors.</copyright-statement><copyright-year>2020</copyright-year><license license-type=\"open-access\"><license-p>Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>).</license-p></license></permissions><abstract><p><italic>Purpose</italic>: Risk of falls is a common sequela affecting patients with Parkinson’s disease (PD). Although motor impairment and dementia are correlated with falls, associations of brain structure and cognition deficits with falls remain unclear. <italic>Material and Methods</italic>: Thirty-five PD patients with dementia (PDD), and 37 age- and sex-matched healthy subjects were recruited for this study. All participants received structural magnetic resonance imaging (MRI) scans, and disease severity and cognitive evaluations. Additionally, patient fall history was recorded. Regional structural differences between PDD with and without fall groups were performed using voxel-based morphometry processing. Stepwise logistic regression analysis was used to predict the fall risk in PDD patients. <italic>Results:</italic> The results revealed that 48% of PDD patients experienced falls. Significantly lower gray matter volume (GMV) in the left calcarine and right inferior frontal gyrus in PDD patients with fall compared to PDD patients without fall were noted. The PDD patients with fall exhibited worse UPDRS-II scores compared to PDD patients without fall and were negatively correlated with lower GMV in the left calcarine (<italic>p</italic>/<italic>r</italic> = 0.004/−0.492). Furthermore, lower GMV in the left calcarine and right inferior frontal gyrus correlated with poor attention and executive functional test scores. Multiple logistic regression analysis showed that the left calcarine was the only variable (<italic>p</italic> = 0.004, 95% CI = 0.00–0.00) negatively associated with the fall event. <italic>Conclusions:</italic> PDD patients exhibiting impaired motor function, lower GMV in the left calcarine and right inferior frontal gyrus, and notable cognitive deficits may have increased risk of falls.</p></abstract><kwd-group><kwd>Parkinson’s disease</kwd><kwd>dementia</kwd><kwd>fall</kwd><kwd>brain structure</kwd><kwd>executive function</kwd></kwd-group></article-meta></front><body><sec id=\"sec1-ijerph-17-05374\"><title>1. Background</title><p>Common symptoms of Parkinson’s disease with dementia (PDD), including tremors, bradykinesia, rigidity, postural instability, memory impairment, and visual hallucinations can result in an increased risk of falls [<xref rid=\"B1-ijerph-17-05374\" ref-type=\"bibr\">1</xref>]. Postural instability, especially during gait initiation, is considered one of the primary factors leading to falls in patients with PDD. The instability worsens with visual impairment, as visual input contributes significantly to the maintenance of upright posture when walking. Other motor network disruptions, and cognitive decline may also affect patient balance, further increasing the risk of falls. Meanwhile, repeated falls can result in fractures requiring hospitalization, and in more serious cases, may be fatal, particularly in the elderly and female [<xref rid=\"B2-ijerph-17-05374\" ref-type=\"bibr\">2</xref>]. Furthermore, long-term complications include atrophy caused by disuse, lifestyle disruptions caused by fear of falls, or possible need for institutionalization [<xref rid=\"B3-ijerph-17-05374\" ref-type=\"bibr\">3</xref>]. To date, however, the relationship between disease severity or cognitive function and risks of falls in PDD patients has not yet to be further investigated.</p><p>In recent years, the application of neuroimaging in exploring the etiology of neurodegenerative diseases has gained in popularity, with Parkinson’s disease (PD) being one of the most commonly studied disorders. These imaging methods have contributed significantly to a broadening of our understanding of the disease, beyond simply impaired dopaminergic transmissions [<xref rid=\"B4-ijerph-17-05374\" ref-type=\"bibr\">4</xref>]. In fact, some neuroimaging studies have reported alterations in networks related to motor and cognitive symptoms [<xref rid=\"B5-ijerph-17-05374\" ref-type=\"bibr\">5</xref>,<xref rid=\"B6-ijerph-17-05374\" ref-type=\"bibr\">6</xref>,<xref rid=\"B7-ijerph-17-05374\" ref-type=\"bibr\">7</xref>,<xref rid=\"B8-ijerph-17-05374\" ref-type=\"bibr\">8</xref>,<xref rid=\"B9-ijerph-17-05374\" ref-type=\"bibr\">9</xref>,<xref rid=\"B10-ijerph-17-05374\" ref-type=\"bibr\">10</xref>]. Radiotracer imaging, such as positron emission tomography (PET) or single-photon emission computed tomography (SPECT), can be used to study the dopaminergic and other neurochemical systems [<xref rid=\"B11-ijerph-17-05374\" ref-type=\"bibr\">11</xref>]; while widely available magnetic resonance imaging (MRI) has demonstrated effectiveness at measuring anatomical changes of the brain, with several MRI studies reporting cortical atrophy in Parkinson’s patients with and without dementia [<xref rid=\"B12-ijerph-17-05374\" ref-type=\"bibr\">12</xref>,<xref rid=\"B13-ijerph-17-05374\" ref-type=\"bibr\">13</xref>,<xref rid=\"B14-ijerph-17-05374\" ref-type=\"bibr\">14</xref>,<xref rid=\"B15-ijerph-17-05374\" ref-type=\"bibr\">15</xref>], decreasing gray matter volume (GMV) of left calcarine in patients with impaired cognition in Huntington’s disease [<xref rid=\"B16-ijerph-17-05374\" ref-type=\"bibr\">16</xref>], or that of right inferior frontal gyrus in unaffected siblings of schizophrenia patients [<xref rid=\"B17-ijerph-17-05374\" ref-type=\"bibr\">17</xref>]. However, clarification of the pathophysiology within the brain and the association with risks of fall in PDD patients require further investigation.</p><p>In this study, we aim to relate the clinical disease severity and cognitive function of patients with PDD to the MRI findings. We hypothesized that PDD patients with histories of falls demonstrated relatively diminished physical and cognitive functions, which would be associated with distinct brain regions, different from those regions affected in PDD patients without histories of falls.</p></sec><sec id=\"sec2-ijerph-17-05374\"><title>2. Materials and Methods</title><sec id=\"sec2dot1-ijerph-17-05374\"><title>2.1. Subjects</title><p>Thirty-five patients with PDD (8 men and 27 women; mean age, 64.00 ± 5.85 years) and 37 age- and sex-matched healthy subjects (10 men and 27 women; mean age, 63.14 ± 5.34 years) were recruited for the study. Patients received MRI scans and neuropsychological tests (NPT) at the time of Parkinson disease diagnosis, while the control group received scans and tests at the initial enrollment in the study. Exclusion criteria for this study included a history of neurologic or psychiatric illness, the presence of developmental disorders, use of medication for unrelated conditions, and head injuries. The 35 PDD patients were further divided into two subgroups, based on the presence or absence of a history of falls (18 in the non-fall group, 3 men and 15 women; mean age, 62.78 ± 5.61 years; and 17 in the fall group, 5 men and 12 women; mean age, 65.29 ± 5.99 years). The Chang Gung Memorial Hospital Institutional Review Committees approved the study (IRB is 103-6906A3), and written informed consents were obtained from all subjects. </p></sec><sec id=\"sec2dot2-ijerph-17-05374\"><title>2.2. Assessment of Clinical Disease Severity</title><p>The clinical features recorded were age at enrollment (or age at the time of the first reported symptom attributable to the disease), education level, and history of falls. Data on falls were prospectively collected from either the clinical records starting at enrollment or information provided by patients during the study period. The Morse fall scale assessed the risk of falling for hospital in-patients or those in long-term care and included considerations such as presence/absence of intravenous therapy [<xref rid=\"B18-ijerph-17-05374\" ref-type=\"bibr\">18</xref>]. Since all patients were enrolled in the study from the Neurology Out-patient Clinic, the scale was slightly modified by deletion of the item, “intravenous therapy or not”. The severity of PDD was graded according to the scores of the Unified Parkinson’s Disease Rating Scale (UPDRS), the Hoehn and Yahr staging, and activity of daily living assessment [<xref rid=\"B19-ijerph-17-05374\" ref-type=\"bibr\">19</xref>,<xref rid=\"B20-ijerph-17-05374\" ref-type=\"bibr\">20</xref>]. An experienced neurology nursing specialist who was blinded to the patients’ clinical and biochemical data was trained to measure these functional scores at the time of enrollment.</p></sec><sec id=\"sec2dot3-ijerph-17-05374\"><title>2.3. Neuropsychological Tests (NPT)</title><p>The NPT battery focusing on attention, executive function, speech and language, memory, and visuospatial functions were performed by a clinical psychologist blinded to each patient’s status. Attention was evaluated by letter number sequencing and digit span score from the Wechsler Adult Intelligence Scale-III (WAIS-III) [<xref rid=\"B21-ijerph-17-05374\" ref-type=\"bibr\">21</xref>], and by orientation score and attention score from the Cognitive Ability Screening Instrument (CASI) [<xref rid=\"B22-ijerph-17-05374\" ref-type=\"bibr\">22</xref>]. Executive functions were assessed using arithmetic, picture arrangement, digit symbol coding, and matrix reasoning scores from the WAIS-III, as well as abstract thinking scores from the CASI. Memory functions were assessed using short- and long-term memory scores from the CASI, and information scores from the WAIS-III. Speech and language ability were assessed using vocabulary, comprehension, and similarity scores from the WAIS-III, and language and semantic fluency scores from the CASI. Visuospatial functions were assessed using picture completion and block design scores from the WAIS-III and drawing score from the CASI. Raw scores for each NPT were transformed to <italic>z</italic> scores based on normative data. Cognitive domain scores were calculated by averaging <italic>z</italic> scores for NPT within specific domains, thereby accounting for any unequal distribution of tests per domain [<xref rid=\"B23-ijerph-17-05374\" ref-type=\"bibr\">23</xref>,<xref rid=\"B24-ijerph-17-05374\" ref-type=\"bibr\">24</xref>]. Impairment was defined as a <italic>z</italic> score ≤ 2 standard deviation (SD) compared to normal subjects for a given domain. PDD was defined as: (1) a diagnosis of idiopathic PD; (2) PD developed prior to the onset of dementia; (3) Mini-Mental State Examination (MMSE) < 26; (4) impairment in at least two of the neuro-psychological battery domains [<xref rid=\"B25-ijerph-17-05374\" ref-type=\"bibr\">25</xref>].</p></sec><sec id=\"sec2dot4-ijerph-17-05374\"><title>2.4. MRI Image Acquisition</title><p>All MRI scans were performed on the same 3-Tesla GE Signa whole-body MRI system (General Electric Healthcare, Milwaukee, WI, USA), equipped with an eight-channel head coil. A T1-weighted three-dimensional fluid-attenuated inversion-recovery fast spoiled gradient-recalled echo pulse sequence was used with the following imaging parameters for each participant and time-point: TR/TE/TI = 9.5/3.9/450 ms; flip angle, 15; NEX = 1; matrix size = 512 × 512; voxel size = 0.47 × 0.47 × 1.3 mm<sup>3</sup>; and 110 axial slices. An experienced neuroradiologist, blinded to the participants’ status, visually examined all the MRI scans to verify they were free from gross anatomical abnormalities. Subsequently, none of the participants in the study were excluded.</p></sec><sec id=\"sec2dot5-ijerph-17-05374\"><title>2.5. Voxel-Based Morphometry Processing</title><p>All structural MRI were post-processed by the same neuroradiologist, unaware of patients’ information. Voxel-based morphometry (VBM) was performed using Statistical Parametric Mapping software (SPM12) [<xref rid=\"B1-ijerph-17-05374\" ref-type=\"bibr\">1</xref>] running on Matlab 2015b (Matworks, Natick, MA, USA). The details of image processing and analysis of regional GMV differences were described in the <xref ref-type=\"app\" rid=\"app1-ijerph-17-05374\">Supplementary Materials</xref> (Method: Voxel-based morphometry processing and analysis).</p></sec><sec id=\"sec2dot6-ijerph-17-05374\"><title>2.6. Statistical Analysis</title><sec id=\"sec2dot6dot1-ijerph-17-05374\"><title>2.6.1. Analysis of Demographic Data between Groups</title><p>Statistical analysis was performed using the statistics computer software SPSS 18 (SPSS Inc., Chicago, IL, USA). Descriptive statistics were expressed as mean ± SD for continuous variables, and as numbers for categorical variables. The clinical data of PDD patients and normal subjects were analyzed by chi-square test or independent <italic>t</italic> test where appropriate. One-way analysis of covariance (ANCOVA) was used to compare NPT with age, sex, and education as covariates. The threshold for statistical significance was <italic>p</italic> < 0.05. </p></sec><sec id=\"sec2dot6dot2-ijerph-17-05374\"><title>2.6.2. Relationships among Disease Severity, Cognition Function, and Gray Matter Volume</title><p>For further relationship analysis, the regional GMV was extracted from the clusters with significant statistical difference from the PDD group comparisons. </p><p>Partial correlation analysis, adjusted for age and sex, was performed to correlate GMVs showing significant differences in the PDD with fall and without fall groups with disease severity and NPT. Significance was set at a Bonferroni corrected <italic>p</italic> < 0.05, accounting for multiple region of interest (ROI) comparisons [<xref rid=\"B26-ijerph-17-05374\" ref-type=\"bibr\">26</xref>]. </p><p>Stepwise logistic regression analysis with forward method was used to evaluate the relationships among disease severity, NPT, and areas with significant GMV differences in the PDD with fall and without fall groups, with adjustments made for other potential confounding factors.</p></sec></sec></sec><sec sec-type=\"results\" id=\"sec3-ijerph-17-05374\"><title>3. Results</title><sec id=\"sec3dot1-ijerph-17-05374\"><title>3.1. Clinical Characteristics, NPT, and Disease Severity among Groups</title><p>The demographic characteristics and NPT of PDD patients and normal controls are shown in <xref rid=\"ijerph-17-05374-t001\" ref-type=\"table\">Table 1</xref>. Except for age and sex, there were significant differences in education duration, MMSE, and NPT between the PDD patients and normal controls. All patients, regardless of fall status, performed significantly worse than those in the control group in all NPT (<italic>p</italic> < 0.05) (<xref rid=\"ijerph-17-05374-t001\" ref-type=\"table\">Table 1</xref>). There were no significant differences in MMSE and NPT between the PDD with fall group and the without fall group.</p><p>The disease severity of PDD patients with and without fall groups are shown in <xref rid=\"ijerph-17-05374-t002\" ref-type=\"table\">Table 2</xref>. Except for UPDRS-II (F = 2.540; <italic>p</italic> = 0.028), there were no significant differences in UPDRS, UPDRS I, UPDRS III, Hoehn and Yahr staging, activity of daily living assessment, and Morse fall scale between the PDD with fall and without fall groups.</p></sec><sec id=\"sec3dot2-ijerph-17-05374\"><title>3.2. Regional Gray Matter Volume (GMV) Aberrations among Groups</title><p>The regions with significant GMV differences are presented in <xref rid=\"ijerph-17-05374-t003\" ref-type=\"table\">Table 3</xref>.</p><sec id=\"sec3dot2dot1-ijerph-17-05374\"><title>3.2.1. Comparison between all PDD Patients and Normal Controls</title><p>All PDD patients exhibited lower NPT results, and significantly lower GMV in the right superior temporal gyrus, left putamen, bilateral cerebellum, bilateral middle frontal gyri, right fusiform, bilateral precentral gyri, left superior frontal gyrus, left middle occipital gyrus, and left superior medial frontal gyrus (uncorrected <italic>p</italic> < 0.001) compared to the control group. </p></sec><sec id=\"sec3dot2dot2-ijerph-17-05374\"><title>3.2.2. Comparison between PDD without Fall and Control Groups, and between PDD with Fall and Control Groups</title><p>By using a <italic>p</italic>-value of 0.001 (uncorrected), the PDD with fall group displayed lower GMV in the left middle temporal gyrus, left cuneus, left medial frontal gyrus, left middle occipital gyrus, bilateral inferior parietal gyri, right middle occipital gyrus, left middle temporal gyrus, right cingulate gyrus, and right anterior cingulate. Meanwhile, the PDD without fall group had lower GMV in the right cerebellum, and right superior temporal gyrus compared to the control group (uncorrected <italic>p</italic> < 0.001).</p></sec><sec id=\"sec3dot2dot3-ijerph-17-05374\"><title>3.2.3. Comparison between the PDD with Fall and without Fall Groups</title><p>Based on a nonstationary cluster extent threshold of <italic>p</italic> < 0.05 corrected for multiple comparisons with FWE, there was a significantly lower GMV in the left calcarine and right inferior frontal gyrus in PDD patient with fall group compared to PDD patients without fall group. </p><p>By using an uncorrected <italic>p</italic>-value of 0.001, PDD with fall group showed significantly lower GMV in the left calcarine, right inferior frontal gyrus, left inferior temporal gyrus, right precentral gyrus, left inferior frontal gyrus, right middle temporal gyrus, right inferior frontal gyrus, left Rolandic operculum, left caudate head, and right middle occipital gyrus compared to PDD without fall group (<xref ref-type=\"fig\" rid=\"ijerph-17-05374-f001\">Figure 1</xref>). </p></sec></sec><sec id=\"sec3dot3-ijerph-17-05374\" sec-type=\"subjects\"><title>3.3. Relationship among Disease Severity Profiles, NPT, and Extracted Regional GMV from PDD Patients </title><p>UPDRS-II was the only disease severity score showing significant difference between the PDD with fall and without fall groups; moreover, it exhibited a significantly negative correlation with a low GMV in the left calcarine (<italic>p</italic>/<italic>r</italic> = 0.004/−0.492) (<xref ref-type=\"fig\" rid=\"ijerph-17-05374-f002\">Figure 2</xref>). </p><p>There are positive correlations between NPT (attention and executive function) and the significantly lower GMV in PDD with fall group compared to PDD without fall group. The poor attention domain correlated with low GMV in the left calcarine (<italic>p</italic>/<italic>r</italic> = 0.002/0.520) and right inferior frontal gyrus (<italic>p</italic>/<italic>r</italic> = 0.003/0.503); while the low executive function correlated with low GMV in the left calcarine (<italic>p</italic>/<italic>r</italic> = 0.007/0.460) (<xref ref-type=\"fig\" rid=\"ijerph-17-05374-f002\">Figure 2</xref>).</p><p>Multiple logistic regression analysis with forward method showed that the left calcarine was the only variable (<italic>p</italic> = 0.004, B: −37.6, 95% CI = 0.00−0.00) negatively associated with the fall event, meaning that lower GMV of the left calcarine could predict the occurrence of a fall event.</p></sec></sec><sec sec-type=\"discussion\" id=\"sec4-ijerph-17-05374\"><title>4. Discussion</title><sec id=\"sec4dot1-ijerph-17-05374\"><title>4.1. Brief Summary and Potential Implications</title><p>There were significant differences in education level and the NPT between the PDD patients and controls, but no significant differences in the NPT between the PDD patients with and without fall groups. In the assessment of disease severity, the PDD with fall group only had significantly higher scores in UPDRS-II compared to the PDD without fall group. In the imaging study, all PDD patients showed significantly lower GMV in the right superior temporal gyrus, left putamen, bilateral cerebellum, bilateral middle frontal gyri, right fusiform, bilateral precentral gyri, left superior frontal gyrus, left middle occipital gyrus, and left superior medial frontal gyrus compared to the control group. There was a significantly lower GMV in left calcarine, and right inferior frontal gyrus in the PDD with fall group compared to the PDD without fall group. In terms of relationships, UPDRS-II showed a negative correlation with GMV in the left calcarine. Attention function was positively correlated with the GMV in the left calcarine, and right inferior frontal gyrus, while executive function was positively correlated with the GMV only in the left calcarine. Of note, the regression analysis showed that the GMV of the left calcarine was negatively associated with fall events.</p><p>Studies have reported the use of NPT to assess clinical characteristics of PDD patients [<xref rid=\"B27-ijerph-17-05374\" ref-type=\"bibr\">27</xref>,<xref rid=\"B28-ijerph-17-05374\" ref-type=\"bibr\">28</xref>,<xref rid=\"B29-ijerph-17-05374\" ref-type=\"bibr\">29</xref>]. Although differences existed in the evaluation tools, declining functions in many domains of NPT were noted in PDD patients, similar to the results reported herein. However, the specific domain accounting for falls has yet to be clarified. Although Papapetropoulos et al. [<xref rid=\"B30-ijerph-17-05374\" ref-type=\"bibr\">30</xref>] reported that PDD was one of the independent risk factors attributing to falls, no NPT were performed in the study. Another study found that mild cognitive impairment was related to increasing risk of fall, but the cognitive functions of the participants were rated by Clinical Dementia Rating Scale, which focused on the ability for self-care [<xref rid=\"B31-ijerph-17-05374\" ref-type=\"bibr\">31</xref>]. Meanwhile, Allcock et al. [<xref rid=\"B32-ijerph-17-05374\" ref-type=\"bibr\">32</xref>] reported on a relationship between impaired attention and fall frequency due to increasing difficulty in performing concurrent tasks and compensatory movements to prevent falls in PD patients. One systematic review also supported the concept that cognitive impairment, especially in the domains of attention and executive function, can disturb multitasking and thereby result in falls [<xref rid=\"B33-ijerph-17-05374\" ref-type=\"bibr\">33</xref>]. In this study, complete NP tests were assessed for all participants, and the PDD patients had significantly lower z scores compared to the controls. Although the z scores of most domains of the NPT in the PDD with fall group appeared to be lower than that of the PDD without fall group, no statistical difference was noted, possibly attributable to the small sample size.</p><p>In this study, multiple disease severity scales, including UPDRS, UPDRS I, II, III, Hoehn and Yahr stage, activity of daily living, and Morse fall scale, were assessed for all PDD participants. Although the disease severity in the PDD with fall group appeared to be severer than that of the PDD without fall group, no statistical difference was noted except for UPDRS-II, possibly attributable to the small sample size or similarity of disease severity of PDD participants. One systematic review reported that disease severity as measured by Hoehn and Yahr stage or by the UPDRS was found to be significantly associated with recurrent falls in PD [<xref rid=\"B34-ijerph-17-05374\" ref-type=\"bibr\">34</xref>]. Matinolli et al. [<xref rid=\"B35-ijerph-17-05374\" ref-type=\"bibr\">35</xref>] reported that UPDRS-II was a significant risk factor for predicting fall in PD, similar to the results reported herein. The UPDRS-II, specific for motor aspects of experiences of daily living, has 13 self-assessed items, including freezing when walking, a phenomenon closely related to fall. These results demonstrate UPDRS-II might be a tool to assess the fall risk in PD.</p><p>Many structural MRI studies have reported on the existence of cortical atrophy in PD patients, particularly atrophy of the hippocampus and the amygdala in patients with PD with or without dementia [<xref rid=\"B12-ijerph-17-05374\" ref-type=\"bibr\">12</xref>,<xref rid=\"B13-ijerph-17-05374\" ref-type=\"bibr\">13</xref>,<xref rid=\"B14-ijerph-17-05374\" ref-type=\"bibr\">14</xref>,<xref rid=\"B15-ijerph-17-05374\" ref-type=\"bibr\">15</xref>]. Recently, VBM brain MRI has been used in the study of patients with PD. These studies have demonstrated that patients with PDD present with limbic and widespread neocortical gray matter loss, while Parkinson’s patients without dementia mainly present with atrophy in the frontal and temporal regions [<xref rid=\"B36-ijerph-17-05374\" ref-type=\"bibr\">36</xref>,<xref rid=\"B37-ijerph-17-05374\" ref-type=\"bibr\">37</xref>,<xref rid=\"B38-ijerph-17-05374\" ref-type=\"bibr\">38</xref>,<xref rid=\"B39-ijerph-17-05374\" ref-type=\"bibr\">39</xref>]. Cristina et al. had found that PDD with visual hallucination presented gray matter volumetric reductions in the left orbitofrontal lobe when compared to normal subjects [<xref rid=\"B2-ijerph-17-05374\" ref-type=\"bibr\">2</xref>]. In the present study, the GMV of most neocortex regions of the PDD patients were less than those of the control group. The PDD with fall group mainly showed significantly decreased GMV in the left frontal, temporal, and occipital lobes, compared to the control group, similar to the results of other studies. Prior to this study, no imaging study had focused specifically on regions of the brain associated with fall risk. However, this study finds that the PDD with fall group had significantly less GMV in the left calcarine and right inferior frontal gyrus than that of the PDD without fall group. This finding is similar to a study comparing the brain structures of PD patients with and without rapid eye movement sleep behavior disorder [<xref rid=\"B40-ijerph-17-05374\" ref-type=\"bibr\">40</xref>]. Furthermore, the association between sleep disorders and falls in PD patients has been reported in a separate case-control study [<xref rid=\"B41-ijerph-17-05374\" ref-type=\"bibr\">41</xref>]. It is believed that sleep disorders can increase the risk of falls via several mechanisms, such as poor attention, slower reaction time, and impaired balance.</p><p>The calcarine region, part of the occipital lobe, is where the primary visual cortex is concentrated. The relationship between visuospatial ability and motor function has been demonstrated in previous studies [<xref rid=\"B42-ijerph-17-05374\" ref-type=\"bibr\">42</xref>,<xref rid=\"B43-ijerph-17-05374\" ref-type=\"bibr\">43</xref>]. Consistent with a previous study [<xref rid=\"B44-ijerph-17-05374\" ref-type=\"bibr\">44</xref>], this study demonstrates a negative correlation in PD patients between the GMV of the left calcarine and the UPDRS-II, which tests and rates the motor aspects associated with daily activities. Moreover, we identify associations between the GMV of the left calcarine and attention, implying a relationship between visual function and attention in PDD patients. Stuart et al. [<xref rid=\"B45-ijerph-17-05374\" ref-type=\"bibr\">45</xref>] conducted a cohort study and built a structural equation model showing associations between attention, visual function, saccade frequency, and gait impairment in PD patients. The abovementioned factors may result in poor executive function. Of note, the association between the GMV of the right inferior frontal gyrus and executive function is exhibited in this study, consistent with a previous imaging study which reported on the important role of the right inferior frontal gyrus in executive control [<xref rid=\"B46-ijerph-17-05374\" ref-type=\"bibr\">46</xref>]. Taken together, the results of the present study and separate studies suggest that visual function and specific domains of cognitive impairment play critical roles in fall events.</p></sec><sec id=\"sec4dot2-ijerph-17-05374\"><title>4.2. Limitations</title><p>Although this study demonstrates that specific domains of cognitive impairment and brain regions are reported to be related to falls in PDD patients, some limitations need to be addressed. First, this study applies a cross-sectional design, and further causal relationships should be investigated by future cohort study. Second, the small sample size limits the statistical power of demonstrating differences among groups. Third, the male to female odds ratio of developing dementia in PD patients between 65 and 69 years old has been reported to be 2:1 by a large cross-sectional study [<xref rid=\"B47-ijerph-17-05374\" ref-type=\"bibr\">47</xref>]; however, in this study, the number of female participants is higher than that of the male participants. This kind of selection bias may deviate our results from the real-world evidence, and future investigators should consider this when designing any future clinical trial.</p></sec></sec><sec sec-type=\"conclusions\" id=\"sec5-ijerph-17-05374\"><title>5. Conclusions</title><p>Those PDD patients with falls may suffer from higher degrees of structural and functional degeneration of the brain, especially in the calcarine and inferior frontal regions. These brain regions play important roles in attention and visual function, and aberrations in these regions may be used to predict future risk of falls in PDD patients. The findings of this study suggest that closer attention to these comorbidities be paid in clinical practice for early intervention and prevention of fall events.</p></sec></body><back><app-group><app id=\"app1-ijerph-17-05374\"><title>Supplementary Materials</title><p>The following are available online at <uri xlink:href=\"https://www.mdpi.com/1660-4601/17/15/5374/s1\">https://www.mdpi.com/1660-4601/17/15/5374/s1</uri>, Method: Voxel-based morphometry processing and analysis.</p><supplementary-material content-type=\"local-data\" id=\"ijerph-17-05374-s001\"><media xlink:href=\"ijerph-17-05374-s001.pdf\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material></app></app-group><notes><title>Author Contributions</title><p>Conceptualization, K.-L.C., L.-H.L. and W.-C.L.; Data curation, K.-L.C. and C.-H.L.; Formal analysis, K.-L.C. and P.-L.C.; Funding acquisition, C.-H.L. and W.-C.L.; Methodology, P.-L.C. and K.-H.C.; Resources, Y.-S.C., M.-H.C., C.-H.L. and W.-C.L.; Software, H.-L.C. and S.-H.L.; Supervision, L.-H.L., P.-C.C., Y.-S.C., H.-L.C., M.-H.C., S.-H.L. and W.-C.L.; Writing—original draft, K.-L.C.; Writing—review & editing, P.-L.C. and W.-C.L. All authors have read and agreed to the published version of the manuscript.</p></notes><notes><title>Funding</title><p>The authors declare no competing financial interests. This study received funding from the National Science Council of Taiwan (MOST 106-2314-B-182A-031-MY2, to W.-C.L.) and from Chang Gang Memorial Hospital (CMRPG8H0951 and CMRPG8I0371 to W.-C.L.)</p></notes><notes notes-type=\"COI-statement\"><title>Conflicts of Interest</title><p>The authors declare no conflict of interest.</p></notes><ref-list><title>References</title><ref id=\"B1-ijerph-17-05374\"><label>1.</label><element-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Lees</surname><given-names>A.J.</given-names></name><name><surname>Hardy</surname><given-names>J.</given-names></name><name><surname>Revesz</surname><given-names>T.</given-names></name></person-group><article-title>Parkinson’s disease</article-title><source>Lancet</source><year>2009</year><volume>373</volume><fpage>2055</fpage><lpage>2066</lpage><pub-id pub-id-type=\"doi\">10.1016/S0140-6736(09)60492-X</pub-id><pub-id pub-id-type=\"pmid\">19524782</pub-id></element-citation></ref><ref id=\"B2-ijerph-17-05374\"><label>2.</label><element-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Wielinski</surname><given-names>C.L.</given-names></name><name><surname>Erickson-Davis</surname><given-names>C.</given-names></name><name><surname>Wichmann</surname><given-names>R.</given-names></name><name><surname>Walde-Douglas</surname><given-names>M.</given-names></name><name><surname>Parashos</surname><given-names>S.A.</given-names></name></person-group><article-title>Falls and injuries resulting from falls among patients with Parkinson’s disease and other parkinsonian syndromes</article-title><source>Mov. 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Disord.</source><year>2016</year><volume>26</volume><fpage>67</fpage><lpage>72</lpage><pub-id pub-id-type=\"doi\">10.1016/j.parkreldis.2016.02.024</pub-id><pub-id pub-id-type=\"pmid\">26952697</pub-id></element-citation></ref></ref-list></back><floats-group><fig id=\"ijerph-17-05374-f001\" orientation=\"portrait\" position=\"float\"><label>Figure 1</label><caption><p>Lower gray matter volumes (GMVs) in PDD patients with fall vs without fall, with highlighted significant areas. Compared to PDD patients without fall, PDD patients with fall showed significantly lower GMV in the left inferior temporal gyrus, right inferior frontal gyrus, left caudate head, right middle occipital gyrus, left inferior frontal gyrus, left Rolandic operculum, right inferior frontal gyrus, right middle temporal gyrus, left calcarine, and right precentral gyrus. Among these areas, the clusters of left calcarine and R inferior frontal gyrus survived from statistical threshold of FWE-corrected <italic>p</italic> < 0.05.</p></caption><graphic xlink:href=\"ijerph-17-05374-g001\"/></fig><fig id=\"ijerph-17-05374-f002\" orientation=\"portrait\" position=\"float\"><label>Figure 2</label><caption><p>Correlations between NP tests (attention and executive) and UPDRS-II and regional GMV. (<bold>a</bold>) Left calcarine and attention. (<bold>b</bold>) Left calcarine and executive. (<bold>c</bold>) left calcarine and UPDRS-II. (<bold>d</bold>) Right inferior frontal gyrus and attention.</p></caption><graphic xlink:href=\"ijerph-17-05374-g002\"/></fig><table-wrap id=\"ijerph-17-05374-t001\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05374-t001_Table 1</object-id><label>Table 1</label><caption><p>Demographic characteristics of PDD patients and normal controls.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">Demographics</th><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">Normal Group</th><th colspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">PDD</th><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">F +</th><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\"><italic>p</italic>-Value +</th></tr><tr><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">With Fall</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Without Fall</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">All Patients</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Number of cases</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">37</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">17</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">18</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">35</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Sex (<italic>n</italic> = men/women)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10/27</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5 / 12</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3 / 15</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8/27</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.630</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Ages (years)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">63.1 ± 5.34</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">65.3 ± 5.99</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">62.8 ± 5.61</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">64.0 ± 5.85</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.112</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.335</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Education</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">9.95 ± 4.73 <sup>#</sup></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.53 ± 4.14 <sup>#</sup></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6.72 ± 4.78</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6.14 ± 4.45</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6.416</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.003</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">MMSE</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">26.7 ± 2.22<sup>§#</sup></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">19.2 ± 4.10 <sup>#</sup></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">21.1 ± 4.30 <sup>§</sup></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">20.1 ± 4.25</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">36.481</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.001</td></tr><tr><td colspan=\"7\" align=\"center\" valign=\"middle\" rowspan=\"1\">\n<bold>Neuropsychological domains</bold>\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Attention</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.46 ± 0.48 <sup>§ #</sup></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.70 ±-0.66 <sup>#</sup></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.30 ± 0.72 <sup>§</sup></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.49 ± 0.71</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.871</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.001</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Executive function</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.43 ± 0.76 <sup>§ #</sup></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.54 ± 0.62 <sup>#</sup></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.41 ± 0.65 <sup>§</sup></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.48 ± 0.63</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.461</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.001</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Memory</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.41 ± 0.53 <sup>§ #</sup></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.54 ± 0.85 <sup>#</sup></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.34 ± 0.65 <sup>§</sup></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.43 ± 0.75</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.140</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.001</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Speech and language</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.46 ± 0.71 <sup>§ #</sup></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.52 ± 0.58 <sup>#</sup></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.50 ± 0.69 <sup>§</sup></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.51 ± 0.63</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.317</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.001</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Visuospatial function</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.46 ± 0.62 <sup>§ #</sup></td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.45 ± 0.77 <sup>#</sup></td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.53 ± 0.74 <sup>§</sup></td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.49 ± 0.74</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.054</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><0.001</td></tr></tbody></table><table-wrap-foot><fn><p>F<sup>+</sup> and <italic>p</italic>\n<sup>+</sup> represent the comparison between all PDD patients vs. the control group. Sex was compared by chi-square test. Age, education, and MMSE data were compared by independent <italic>t</italic> test. NP data were compared by ANCOVA after controlling for age, sex, and education. Data are presented as mean ± SD. <sup>#</sup> Significant differences between the control group and the PDD with fall group. <sup>§</sup> Significant differences between the control group and the PDD without fall group.</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"ijerph-17-05374-t002\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05374-t002_Table 2</object-id><label>Table 2</label><caption><p>Disease severity of PDD patients.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">Disease Severity</th><th colspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">PD with Dementia</th><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">F <sup>+</sup></th><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\"><italic>p</italic>-Value <sup>+</sup></th></tr><tr><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">With Fall</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Without Fall</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">All Patients</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Number of Cases</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">17</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">18</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">35</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Unified Parkinson’s Disease Rating Scale (UPDRS) <sup>a</sup></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">57.0 ± 28.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">42.6 ± 22.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">49.6 ± 26.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.559</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.106</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">UPDRS I <sup>b</sup></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.88 ± 2.91</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.72 ± 2.95</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.28 ± 2.95</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.040</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.250</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">UPDRS-II <sup>c</sup></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">16.7 ± 9.98</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10.5 ± 5.54</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13.5 ± 8.49</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.540</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.028 *</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">UPDRS III <sup>d</sup></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">35.4 ± 16.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">28.3 ± 16.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">31.8 ± 16.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.010</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.213</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Hoehn and Yahr staging</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.53 ± 0.89</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.14 ± 0.98</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.33 ± 0.95</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.294</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.228</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Activity of daily living</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">76.5 ± 15.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">80.6 ± 16.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">78.6 ± 15.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.022</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.446</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Morse fall scale</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">62.9 ± 27.7</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">49.7 ± 26.8</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">56.1 ± 27.7</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.102</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.161</td></tr></tbody></table><table-wrap-foot><fn><p>F <sup>+</sup> and <italic>p</italic>\n<sup>+</sup> represent the comparison between PDD with fall group vs. PDD without fall group. <sup>a</sup> Total UPDRS score is the combined sum of parts I, II, and III. Theoretical minimum and maximum values are 0 and 176, respectively (176 represents the worst disability and 0 no disability).<sup>b</sup> I. Mentation, behavior, and mood. Theoretical minimum and maximum values are 0 and 16, respectively. (16 represents the worst disability and 0 no disability). <sup>c</sup> II. Activities of daily living (ADL). Theoretical minimum and maximum values are 0 and 52, respectively. (52 represents the worst disability and 0 no disability). <sup>d</sup> III. Motor examination. Theoretical minimum and maximum values are 0 and 108, respectively. (108 represents the worst disability and 0 no disability). UPDRS, UPDRS I/II/III, Hoehn and Yahr staging, Activity of daily living, and Morse fall scale were compared by independent <italic>t</italic> test. Data are presented as mean ± SD.* Significant differences between the PDD with fall group and PDD without fall group.</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"ijerph-17-05374-t003\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05374-t003_Table 3</object-id><label>Table 3</label><caption><p>Regions of statistically significant lower GMV in PD with dementia patients compared to those of control.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Gray Matter Volume</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Anatomical Regions</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">x</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">y</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">z</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Cluster Size</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">T-Value</th></tr></thead><tbody><tr><td colspan=\"7\" align=\"center\" valign=\"middle\" rowspan=\"1\">\n<bold>Normal > all PDD</bold>\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">R superior temporal gyrus *</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">59</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−11</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">14770</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.90</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">L putamen *</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−27</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">15</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10899</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.68</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">R cerebellum *</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">20</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−74</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−47</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">14753</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.54</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">R middle frontal gyrus *</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">29</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">50</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">591</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.40</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">L middle frontal gyrus *</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−29</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">42</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">30</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">304</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.94</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">R fusiform</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">41</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−57</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−18</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">289</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.55</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">R precentral gyrus</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">53</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">47</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">711</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.51</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">L precentral gyrus</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−44</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">47</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">646</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.42</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">L superior frontal gyrus</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−18</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">60</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">445</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.26</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">L middle occipital gyrus</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−33</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−81</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">14</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">195</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.20</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">L superior medial frontal gyrus</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−11</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−20</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">238</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.11</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">L cerebellum</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−30</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−68</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-36</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">648</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.52</td></tr><tr><td colspan=\"7\" align=\"center\" valign=\"middle\" rowspan=\"1\">\n<bold>Normal > PDD with fall</bold>\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">L middle temporal gyrus *</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−47</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−35</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">53327</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7.82</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">L cuneus *</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−86</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">28263</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7.14</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">L medial frontal gyrus *</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−12</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">39</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">18</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2027</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.34</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">L middle occipital gyrus</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−33</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−81</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">14</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">641</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6.10</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">R inferior parietal gyrus</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">57</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−56</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">44</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">270</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.23</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">L inferior parietal gyrus</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−59</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−47</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">45</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">281</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.79</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">R middle occipital gyrus</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">42</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−80</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">532</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.67</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">L middle temporal gyrus</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−41</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−65</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">12</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">441</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.40</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">R cingulate gyrus</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">14</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">11</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">44</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">165</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.92</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">R anterior cingulate</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">14</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">44</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">234</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.87</td></tr><tr><td colspan=\"7\" align=\"center\" valign=\"middle\" rowspan=\"1\">\n<bold>Normal > PDD without fall</bold>\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">R cerebellum</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">17</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−74</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−45</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1860</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.00</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">R superior temporal gyrus</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">62</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">236</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.63</td></tr><tr><td colspan=\"7\" align=\"center\" valign=\"middle\" rowspan=\"1\">\n<bold>PDD with fall < PDD without fall</bold>\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">L calcarine *</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−68</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">20</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3735</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.97</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">R inferior frontal gyrus *(pars triangularis)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">48</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">20</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">17</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">333</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6.01</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">L inferior temporal gyrus</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−47</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−35</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">717</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.97</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">R precentral gyrus</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">41</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−15</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">38</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">363</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.77</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">L inferior frontal gyrus (pars opercularis)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−47</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">15</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">15</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">444</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.73</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">R middle temporal gyrus</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">50</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-53</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">20</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">390</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.52</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">R inferior frontal gyrus (pars orbitalis)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">30</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">35</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−14</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">385</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.32</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">L Rolandic operculum</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−42</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">15</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">312</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.06</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">L caudate head</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−14</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">15</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">248</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.82</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">R middle occipital gyrus</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">41</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−78</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">9</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">205</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3.72</td></tr></tbody></table><table-wrap-foot><fn><p>Voxel-based morphometry of PDD patients compared to those of controls at an uncorrected <italic>p</italic>-Value (<0.001). * Indicated for statistical threshold: uncorrected <italic>p</italic> < 0.001 with a cluster extent correction family-wise error (FWE)-corrected <italic>p</italic>-Value < 0.05. (x, y, and z) refer to the Montreal Neurological Institute coordinates. The T-value is determined by dividing the estimated regression coefficient by its standard error. Abbreviations: R Right; L Left.</p></fn></table-wrap-foot></table-wrap></floats-group></article>\n"
]
[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Front Oncol</journal-id><journal-id journal-id-type=\"iso-abbrev\">Front Oncol</journal-id><journal-id journal-id-type=\"publisher-id\">Front. Oncol.</journal-id><journal-title-group><journal-title>Frontiers in Oncology</journal-title></journal-title-group><issn pub-type=\"epub\">2234-943X</issn><publisher><publisher-name>Frontiers Media S.A.</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32850301</article-id><article-id pub-id-type=\"pmc\">PMC7432133</article-id><article-id pub-id-type=\"doi\">10.3389/fonc.2020.00872</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Oncology</subject><subj-group><subject>Original Research</subject></subj-group></subj-group></article-categories><title-group><article-title>Radiomic-Based Quantitative CT Analysis of Pure Ground-Glass Nodules to Predict the Invasiveness of Lung Adenocarcinoma</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Xu</surname><given-names>Fangyi</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/861729/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Zhu</surname><given-names>Wenchao</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/971738/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Shen</surname><given-names>Yao</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><xref ref-type=\"aff\" rid=\"aff2\"><sup>2</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Wang</surname><given-names>Jian</given-names></name><xref ref-type=\"aff\" rid=\"aff3\"><sup>3</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Xu</surname><given-names>Rui</given-names></name><xref ref-type=\"aff\" rid=\"aff4\"><sup>4</sup></xref><xref ref-type=\"aff\" rid=\"aff5\"><sup>5</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/921059/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Outesh</surname><given-names>Chooah</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Song</surname><given-names>Lijiang</given-names></name><xref ref-type=\"aff\" rid=\"aff6\"><sup>6</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Gan</surname><given-names>Yi</given-names></name><xref ref-type=\"aff\" rid=\"aff7\"><sup>7</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Pu</surname><given-names>Cailing</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Hu</surname><given-names>Hongjie</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><xref ref-type=\"corresp\" rid=\"c001\"><sup>*</sup></xref></contrib></contrib-group><aff id=\"aff1\"><sup>1</sup><institution>Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine</institution>, <addr-line>Hangzhou</addr-line>, <country>China</country></aff><aff id=\"aff2\"><sup>2</sup><institution>Department of Radiology, Yinzhou Hospital Affiliated With the School of Medicine of Ningbo University</institution>, <addr-line>Ningbo</addr-line>, <country>China</country></aff><aff id=\"aff3\"><sup>3</sup><institution>Department of Radiology, Tongde Hospital of Zhejiang Province</institution>, <addr-line>Hangzhou</addr-line>, <country>China</country></aff><aff id=\"aff4\"><sup>4</sup><institution>DUT-RU International School of Information Science & Engineering, Dalian University of Technology</institution>, <addr-line>Dalian</addr-line>, <country>China</country></aff><aff id=\"aff5\"><sup>5</sup><institution>DUT-RU Co-Research Center of Advanced ICT for Active Life</institution>, <addr-line>Dalian</addr-line>, <country>China</country></aff><aff id=\"aff6\"><sup>6</sup><institution>Department of Cardiothoracic Surgery, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine</institution>, <addr-line>Hangzhou</addr-line>, <country>China</country></aff><aff id=\"aff7\"><sup>7</sup><institution>Department of Pathology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine</institution>, <addr-line>Hangzhou</addr-line>, <country>China</country></aff><author-notes><fn fn-type=\"edited-by\"><p>Edited by: Marco Lucchi, University of Pisa, Italy</p></fn><fn fn-type=\"edited-by\"><p>Reviewed by: Ahmad Chaddad, Guilin University of Electronic Technology, China; Niha Beig, Case Western Reserve University, United States; Shuling Chen, First Affiliated Hospital of Sun Yat-sen University, China</p></fn><corresp id=\"c001\">*Correspondence: Hongjie Hu <email>[email protected]</email></corresp><fn fn-type=\"other\" id=\"fn001\"><p>This article was submitted to Thoracic Oncology, a section of the journal Frontiers in Oncology</p></fn></author-notes><pub-date pub-type=\"epub\"><day>11</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><year>2020</year></pub-date><volume>10</volume><elocation-id>872</elocation-id><history><date date-type=\"received\"><day>19</day><month>12</month><year>2019</year></date><date date-type=\"accepted\"><day>04</day><month>5</month><year>2020</year></date></history><permissions><copyright-statement>Copyright © 2020 Xu, Zhu, Shen, Wang, Xu, Outesh, Song, Gan, Pu and Hu.</copyright-statement><copyright-year>2020</copyright-year><copyright-holder>Xu, Zhu, Shen, Wang, Xu, Outesh, Song, Gan, Pu and Hu</copyright-holder><license xlink:href=\"http://creativecommons.org/licenses/by/4.0/\"><license-p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</license-p></license></permissions><abstract><p><bold>Objectives:</bold> To investigate the performance of radiomic-based quantitative analysis on CT images in predicting invasiveness of lung adenocarcinoma manifesting as pure ground-glass nodules (pGGNs).</p><p><bold>Methods:</bold> A total of 275 lung adenocarcinoma cases, with 322 pGGNs resected surgically and confirmed pathologically, from January 2015 to October 2017 were enrolled in this retrospective study. All nodules were split into training and test cohorts randomly with a ratio of 4:1 to establish models to predict between pGGN-like adenocarcinoma <italic>in situ</italic> (AIS)/minimally invasive adenocarcinoma (MIA) and invasive adenocarcinoma (IVA). Radiomic feature extraction was performed using Pyradiomics with semi-automatically segmented tumor regions on CT scans that were contoured with an in-house plugin for 3D-Slicer. Random forest (RF) and support vector machine (SVM) were used for feature selection and predictive model building in the training cohort. Three different predictive models containing conventional, radiomic, and combined models were built on the basis of the selected clinical, radiological, and radiomic features. The predictive performance of each model was evaluated through the receiver operating characteristic curve (ROC) and the area under the curve (AUC). The predictive performance of two radiologists (A and B) and our radiomic predictive model were further investigated in the test cohort to see if radiomic predictive model could improve radiologists' performance in prediction between pGGN-like AIS/MIA and IVA.</p><p><bold>Results:</bold> Among 322 nodules, 48 (14.9%) were AIS and 102 (31.7%) were MIA with 172 (53.4%) for IVA. Age, diameter, density, and nine meaningful radiomic features were selected for model building in the training cohort. Three predictive models showed good performance in prediction between pGGN-like AIS/MIA and IVA (AUC > 0.8, <italic>P</italic> < 0.05) in both training and test cohorts. The AUC values in the test cohort were 0.824 (95% CI, 0.723–0.924), 0.833 (95% CI, 0.733–0.934), and 0.848 (95% CI, 0.750–0.946) for conventional, radiomic, and combined models, respectively. The predictive accuracy was 73.44 and 59.38% for radiologist A and radiologist B in the test cohort and was improved dramatically to 79.69 and 75.00% with the aid of our radiomic predictive model.</p><p><bold>Conclusion:</bold> The predictive models built in our study showed good predictive power with good accuracy and sensitivity, which provided a non-invasive, convenient, economic, and repeatable way for the prediction between IVA and AIS/MIA representing as pGGNs. The radiomic predictive model outperformed two radiologists in predicting pGGN-like AIS/MIA and IVA, and could significantly improve the predictive performance of the two radiologists, especially radiologist B with less experience in medical imaging diagnosis. The selected radiomic features in our research did not provide more useful information to improve the combined predictive model's performance.</p></abstract><kwd-group><kwd>radiomics</kwd><kwd>lung cancer</kwd><kwd>adenocarcinoma</kwd><kwd>computed tomography</kwd><kwd>machine learning</kwd></kwd-group><counts><fig-count count=\"6\"/><table-count count=\"3\"/><equation-count count=\"0\"/><ref-count count=\"50\"/><page-count count=\"12\"/><word-count count=\"8282\"/></counts></article-meta></front><body><sec id=\"s1\"><title>Background</title><p>A new classification for lung adenocarcinoma was proposed in 2011 by the International Association for the Study of Lung Cancer/American Thoracic Society/European Respiratory Society (IASLC/ATS/ERS) (<xref rid=\"B1\" ref-type=\"bibr\">1</xref>), which was also issued as the 4th edition WHO lung cancer classification in 2015 (<xref rid=\"B2\" ref-type=\"bibr\">2</xref>). According to the new classification, lung adenocarcinoma can be divided into preinvasive lesion, minimally invasive adenocarcinoma (MIA), and invasive adenocarcinoma (IVA), and preinvasive lesion includes atypical adenomatous hyperplasia (AAH) and adenocarcinoma <italic>in situ</italic> (AIS) (<xref rid=\"B1\" ref-type=\"bibr\">1</xref>). The improvement of medical technology and the generalization of lung cancer screening project have led more attention to pure ground-glass nodules (pGGNs) detected on computed tomography (CT) images (<xref rid=\"B3\" ref-type=\"bibr\">3</xref>, <xref rid=\"B4\" ref-type=\"bibr\">4</xref>).</p><p>Approximately 20% of lung adenocarcinoma including AIS, MIA, and even some early-stage IVA could present as pGGNs on CT images (<xref rid=\"B4\" ref-type=\"bibr\">4</xref>), which makes it quite difficult for radiologists and clinicians to make a precise diagnosis with conventional radiological parameters like size, density, etc. Kakinuma et al. reported that growth was observed in approximately 10% of pGGNs ≤5 mm, of which 1% would develop into IVA or MIA in their study (<xref rid=\"B5\" ref-type=\"bibr\">5</xref>). In another study, 57.8% of pGGNs showed growth during follow-up and 26.3% of them were adenocarcinoma (<xref rid=\"B6\" ref-type=\"bibr\">6</xref>). Eguchi et al. examined 124 cases with pGGNs, and 64 pGGNs (51.6%) showed growth during their 2-year follow-up (<xref rid=\"B7\" ref-type=\"bibr\">7</xref>). Several previous research revealed that nearly 50% of pGGNs were invasive lesions (<xref rid=\"B3\" ref-type=\"bibr\">3</xref>, <xref rid=\"B8\" ref-type=\"bibr\">8</xref>–<xref rid=\"B10\" ref-type=\"bibr\">10</xref>). In clinical practice, pGGNs are usually prescribed to be followed up but data above demonstrated that more detailed diagnosis and more individualized management should be made for pGGNs.</p><p>Compared with IVA, AIS, and MIA are considered as indolent lung adenocarcinoma because of the excellent prognosis (<xref rid=\"B11\" ref-type=\"bibr\">11</xref>, <xref rid=\"B12\" ref-type=\"bibr\">12</xref>). AIS/MIA could be followed up or treated with sublobar resection while more aggressive surgical interventions should be taken for IVA (<xref rid=\"B13\" ref-type=\"bibr\">13</xref>, <xref rid=\"B14\" ref-type=\"bibr\">14</xref>). Several previous studies revealed that the 5-year survival rates of AIS and MIA could be 100% and near 100% with a complete resection while that of IVA in stage Ia is no more than 75% (<xref rid=\"B11\" ref-type=\"bibr\">11</xref>, <xref rid=\"B13\" ref-type=\"bibr\">13</xref>, <xref rid=\"B14\" ref-type=\"bibr\">14</xref>). Thus, it might provide some guidance for clinical therapeutic decision-making if pGGN-like IVA could be figured out on preoperative CT images.</p><p>There were many studies investigating the difference in radiological features among lung adenocarcinoma subtypes. Wang et al. found that the mean CT attenuation and lesion size differed significantly between MIA and non-invasive lesions and internal air bronchograms were more often seen in adenocarcinoma (<xref rid=\"B15\" ref-type=\"bibr\">15</xref>). Several investigations reported that the notched signs, spiculations, bubbly lucencies, and rapid volume expansion were more common in IVA (<xref rid=\"B15\" ref-type=\"bibr\">15</xref>–<xref rid=\"B17\" ref-type=\"bibr\">17</xref>). However, those radiological features could be subtly different because of the small size of pGGN-like adenocarcinoma. Furthermore, the assessment of those parameters tends to be subjective, which could be influenced by radiologists' experience and diagnostic ability. Percutaneous biopsy is one method used in clinical practice to determine the nature of pulmonary nodules, which could provide relatively accurate pathological information. However, percutaneous biopsy is an invasive operation, and patients may have some operation-related complications (<xref rid=\"B18\" ref-type=\"bibr\">18</xref>, <xref rid=\"B19\" ref-type=\"bibr\">19</xref>). Considering the heterogeneity in adenocarcinoma, small pieces of tissue obtained by biopsy cannot represent the characteristics of the whole lesion (<xref rid=\"B20\" ref-type=\"bibr\">20</xref>). What is more, in some cases, it is difficult to complete biopsy due to patient's physical condition and bad cooperation as well as the location and size of nodules (<xref rid=\"B20\" ref-type=\"bibr\">20</xref>, <xref rid=\"B21\" ref-type=\"bibr\">21</xref>). Thus, the accurate diagnosis of pGGNs remains a key point and a challenge in the field of medical imaging diagnosis.</p><p>Radiomics is an emerging subject that could extract a large amount of invisible features from medical images for clinical decision-making (<xref rid=\"B20\" ref-type=\"bibr\">20</xref>, <xref rid=\"B22\" ref-type=\"bibr\">22</xref>). Radiomics has had remarkable progress in central nervous system malignancies, thoracic imaging diagnosis, discrimination of hepatic mass, and some other diseases (<xref rid=\"B23\" ref-type=\"bibr\">23</xref>–<xref rid=\"B26\" ref-type=\"bibr\">26</xref>). Chaddad et al. performed retrospective analysis involving 315 patients diagnosed as non-small cell lung cancer (NSCLC) and significant correlation was observed between radiomic features and survival (<xref rid=\"B27\" ref-type=\"bibr\">27</xref>). Also, radiomics' promising performance in the distinction of benign and malignant pulmonary nodules and the discrimination of adenocarcinoma subtypes had been validated in several researches (<xref rid=\"B11\" ref-type=\"bibr\">11</xref>, <xref rid=\"B28\" ref-type=\"bibr\">28</xref>, <xref rid=\"B29\" ref-type=\"bibr\">29</xref>). However, in most previous radiomic studies, all types of pulmonary nodules including solid and subsolid nodules were recruited as the study population. Few studies focused on the use of machine learning in early-stage lung adenocarcinoma representing as pGGNs, which are usually very difficult to manage. Since the diagnosis of solid components in pulmonary nodules on CT images is relatively uncomplicated while pGGNs remain a big challenge for medical imaging diagnosis, we aimed to explore the potential value of radiomic-based quantitative analysis to predict the invasiveness of pGGN-like adenocarcinoma to establish a comprehensive predictive model for clinical decision-making.</p></sec><sec sec-type=\"materials and methods\" id=\"s2\"><title>Materials and Methods</title><p>This retrospective study was approved by the institutional review committee of the Sir Run Run Shaw Hospital (No. 20190520-162) with an abstention of informed consents from all the patients involved according to the guidelines of the Council for International Organizations of Medical Science (CIOMS).</p><sec><title>Patients</title><p>We reviewed all the materials of 1,610 patients undergoing surgical resection for primary lung adenocarcinoma with complete clinical data and preoperative CT images from January 2015 to October 2017 in Sir Run Run Shaw Hospital, and reinterpreted the preoperative CT images from the Picture Archiving and Communication Systems (PACS) one by one. Clinical data like age, gender, and smoking status of all cases were collected from digital records. Patients who met any one of the following criteria were excluded: (1) nodules with solid components (<italic>n</italic> = 776), (2) nodule diameter >3 cm (<italic>n</italic> = 292), (3) patients with a history of other malignant diseases (<italic>n</italic> = 87), (4) CT images with bad quality (<italic>n</italic> = 70), and (5) patients who accepted thoracic surgical intervention, radiation, or any chemotherapeutics (<italic>n</italic> = 110).</p><p>Finally, 275 patients (72 men and 203 women, age range, 25~78 years) with 322 pGGNs (82 men and 240 women, age range, 25~78 years) were enrolled into this retrospective study (detailed in <xref ref-type=\"fig\" rid=\"F1\">Figure 1</xref> and <xref rid=\"T1\" ref-type=\"table\">Table 1</xref>). The median time from the last preoperative CT scan to surgery was 6 (0–92) days.</p><fig id=\"F1\" position=\"float\"><label>Figure 1</label><caption><p>The flowchart of patient selection.</p></caption><graphic xlink:href=\"fonc-10-00872-g0001\"/></fig><table-wrap id=\"T1\" position=\"float\"><label>Table 1</label><caption><p>Analysis of clinical and radiological features of pGGNs in the training and test cohort.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th rowspan=\"1\" colspan=\"1\"/><th valign=\"top\" align=\"center\" colspan=\"4\" style=\"border-bottom: thin solid #000000;\" rowspan=\"1\"><bold>Training cohort</bold></th><th valign=\"top\" align=\"center\" colspan=\"4\" style=\"border-bottom: thin solid #000000;\" rowspan=\"1\"><bold>Test cohort</bold></th></tr><tr><th rowspan=\"1\" colspan=\"1\"/><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Total</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>AIS/MIA</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>IVA</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold><italic>P</italic></bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Total</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>AIS/MIA</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>IVA</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold><italic>P</italic></bold></th></tr></thead><tbody><tr><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">258</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">120 (46.5%)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">138 (53.5%)</td><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">64</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">30 (46.9%)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">34 (53.1%)</td><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" colspan=\"9\" rowspan=\"1\"><bold>Gender</bold></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">    Male</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">70 (27.1%)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">31 (25.8%)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">39 (28.3%)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.662</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">12 (18.8%)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3 (10.0%)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">9 (26.5%)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.092</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">    Female</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">188 (72.9%)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">89 (74.2%)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">99 (71.7%)</td><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">52 (81.3%)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">27 (90.0%)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">25 (73.5%)</td><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Age (years)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">53 (25, 78)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">51 (25, 69)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">55 (26, 78)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.001</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">54 (30, 72)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">51 (31, 72)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">55 (30, 72)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.106<xref ref-type=\"table-fn\" rid=\"TN1\"><sup>#</sup></xref></td></tr><tr><td valign=\"top\" align=\"left\" colspan=\"9\" rowspan=\"1\"><bold>Smoking history</bold></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">    Never</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">230 (89.1%)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">109 (90.8%)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">121 (87.7%)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.417</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">59 (92.2%)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">29 (96.7%)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">30 (88.2%)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.360<xref ref-type=\"table-fn\" rid=\"TN2\"><sup>*</sup></xref></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">    Current or ever</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">28 (10.9%)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">11 (9.2%)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">17 (12.3%)</td><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5 (7.8%)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1 (3.3%)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4 (11.8%)</td><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">    Diameter (cm)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.94 (0.24, 2.94)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.76 (0.24, 1.92)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.13 (0.51, 2.94)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><0.001</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.93 (0.34, 2.04)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.70 (0.34, 1.41)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.09 (0.61, 2.04)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><0.001<xref ref-type=\"table-fn\" rid=\"TN1\"><sup>#</sup></xref></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Density (HU)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−583.0 (−829.2, −122.5)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−615.0 (−801.7, −122.5)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−529.2 (−829.2, −176.3)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><0.001</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−525.8 (−763.2, −179.8)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−544.7 (−763.2, −298.9)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−509.0 (−739.1, −179.8)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.088<xref ref-type=\"table-fn\" rid=\"TN1\"><sup>#</sup></xref></td></tr></tbody></table><table-wrap-foot><fn id=\"TN1\"><label>#</label><p><italic>Two-sample t-test</italic>.</p></fn><fn id=\"TN2\"><label>*</label><p><italic>Fisher exact probability test</italic>.</p></fn><p><italic>Continuous variables were represented in median (minimum, maximum) without any special statement</italic>.</p><p><italic>pGGNs, pure ground-glass nodules; AIS, adenocarcinoma in situ; MIA, minimally invasive adenocarcinoma; IVA, invasive adenocarcinoma</italic>.</p></table-wrap-foot></table-wrap></sec><sec><title>Histological Evaluation</title><p>All surgical specimens were fixed with formalin and stained with hematoxylin–eosin (HE). Two pathologists evaluated all slides using a multi-headed microscope and discussed about the diagnosis until a consensus was reached. According to the classification of lung adenocarcinoma issued in the 4th edition WHO lung cancer classification in 2015, each nodule was classified as AIS, MIA, and IVA (<xref rid=\"B26\" ref-type=\"bibr\">26</xref>, <xref rid=\"B30\" ref-type=\"bibr\">30</xref>). Each histological pattern presented in targeted lesion including lepidic, acinar, solid, papillary, and micropapillary patterns was recorded in 5% increments (<xref rid=\"B30\" ref-type=\"bibr\">30</xref>, <xref rid=\"B31\" ref-type=\"bibr\">31</xref>).</p></sec><sec><title>Image Acquisition</title><p>All the plain CT images were obtained using multidetector computed tomography scanners (Siemens SOMATOM Definition Flash, Siemens FORCE CT, Siemens Sensation 16, Siemens Definition AS 40, and GE LightSpeed VCT). The protocol parameters for each scanning were detailed in <xref ref-type=\"supplementary-material\" rid=\"SM1\">Supplemental Table 1</xref>. Plain spiral acquisitions were obtained from thoracic inlet to lung bases on patients accepting breath-hold training. Images were reconstructed using a standard reconstruction kernels in lung window settings (mean, −500 HU; width, 1,500 HU). All images underwent multi-planar reconstruction (MPR) including coronal and sagittal reconstruction utilizing the post-processing station.</p></sec><sec><title>Nodule Analysis and Segmentation</title><p>All transverse CT images were interpreted jointly by two radiologists who were both blind to the clinical and pathological information of all cases (radiologist A, with 10 years' experience in thoracic imaging diagnosis; radiologist B, with 2 years' experience in medical imaging diagnosis) on our professional reading screen. Conventional quantitative radiological features that were widely used in clinical diagnosis involving diameter (cm) and density (Hounsfield Unit, HU) were determined for each nodule. Nodule diameter was measured on the average of long- and short-axis diameters, both of which should be obtained on the same transverse revealing the greatest dimensions. The nodule density was measured at three different parts of each nodule avoiding vessels and bronchus and the mean value of the three results was calculated. The mean value of diameter and density measured by the two radiologists was calculated for our study.</p><p>Segmentation data consisted of all the 322 pGGNs. Plain CT images were loaded into 3D-slicer (<ext-link ext-link-type=\"uri\" xlink:href=\"http://www.slicer.org\">http://www.slicer.org</ext-link>) (<xref rid=\"B32\" ref-type=\"bibr\">32</xref>), an open source image processing software, implemented with in-house algorithm for automatic nodule detection and segmentation. Radiologist B would verify the regions of interest (ROIs) of automatic segmentation and made some modifications when the ROIs were not satisfactory. Radiologist A would have a second review for the results of radiologist B's semi-automatic segmentation. A consensus would be achieved via negotiation between two radiologists for each case when meeting a collision on reviewing. While modifying ROIs, two radiologists would delineate manually around the nodule boundary on each section avoiding the bronchus and vessels as much as they could.</p></sec><sec><title>Radiomic Feature Extraction and Predictive Models Building</title><p>Segmentation data were analyzed with Pyradiomics to extract radiomic features describing tumor phenotypes (<xref rid=\"B33\" ref-type=\"bibr\">33</xref>). All the segmentation data had a voxel resampling of 0.7 × 0.7 × 0.7 mm<sup>3</sup> for standardization to reduce the impact from the heterogeneity of image acquisition. In the end, we obtained nine types totaling 960 radiomic features for each nodule, which have been listed in <xref ref-type=\"supplementary-material\" rid=\"SM1\">Supplemental Table 2</xref>. Features are commonly grouped as follows: (1) first-order statistical features: these describe the voxel intensity distribution in the delineated ROI. They are usually calculated on the basis of the intensity histogram, including energy, entropy, standard deviation, skewness, kurtosis, uniformity, mean, minimum, and maximum intensity values and so on (<xref rid=\"B20\" ref-type=\"bibr\">20</xref>, <xref rid=\"B26\" ref-type=\"bibr\">26</xref>). (2) Shape-based features: descriptors of the two- and three-dimensional size and shape of the ROI. (3) Textural features: these contain gray level co-occurrence matrix (GLCM), gray level run length matrix (GLRLM), gray level size zone matrix (GLSZM), neighboring gray size zone matrix (NGZDM), and gray-level dependence matrix (GLDM). They are computed on the analysis of the three-dimensional directions within the tumor and the consideration of the spatial location of each voxel in the ROI (<xref rid=\"B26\" ref-type=\"bibr\">26</xref>). (4) Transformed features: features in the first and third groups extracted from images applied with a series of wavelet or Laplacian-of-Gaussian filtration.</p><p>Random forest (RF) and support vector machine (SVM) with cross-validation (CV) was used for radiomic feature selection and predictive model building to distinct pGGN-like IVA from indolent adenocarcinoma (AIS/MIA). Multivariate models were made in training cohort and were tested in a separate test cohort. All pGGNs were split into training and test cohorts randomly by a ratio of 4:1. Three predictive models were created in our research: (1) Conventional (selected clinical and radiological quantitative features), (2) Radiomic (selected radiomic features), and (3) Combined (selected conventional and radiomic features) predictive models. Subsequently, a binary analysis in which pGGN-like IVA was set as positive while AIS/MIA was thought as negative was applied to compare the predictive performance between radiomic predictive model and two radiologists (A and B) in the test cohort. Two weeks later, two radiologists, knowing the performance of our radiomic predictive model and its diagnosis for each pGGN in the test cohort, reevaluated all pGGNs in the test cohort.</p></sec><sec><title>Statistical Analysis</title><p>All the statistical analysis was applied using SPSS 25.0 (IBM, Armonk, NY, USA) and MedCalc 15.8 (MedCalc Software, Acacialaan 22, Ostend, Belgium). Tables and figures in our study were made with GraphPad Prism 5 (GraphPad Software Inc., San Diego, CA, USA) and Microsoft Office 2019 (Microsoft, Redmond, WDC, USA).</p><p>Thirty pGGNs were selected randomly to test the repeatability of nodule diameter and density measurement. Radiologist A and B did the measurement work of those 30 pGGNs, respectively. Two weeks later, radiologist B measured the diameter and density of these 30 pGGNs, again according to the same measurement criteria. Inter-/intra-observer correlation coefficient (ICC) was calculated for repeatability assessment.</p><p>For the assessment of clinical, quantitative radiological, and selected radiomic features, chi-square test or Fisher exact probability test was utilized for categorical variables. Two-sample <italic>t</italic>-test was adopted, if the continuous variables met the normal test and variance homogeneity test; otherwise, Wilcoxon signed-rank test was used. Predictive power of each predictive model was evaluated using receiver operating characteristic (ROC) curve and area under the curve (AUC). Models with an AUC > 0.50 and a <italic>P</italic> < 0.05 were thought to be predictive. McNemar's test and Kappa analysis were used to compare the binary diagnosis of two radiologists and radiomic predictive model.</p></sec></sec><sec sec-type=\"results\" id=\"s3\"><title>Results</title><p>The measurement of nodule diameter and density between senior radiologist A and junior radiologist B was highly consistent (ICC > 0.9, <italic>P</italic> < 0.05). Two weeks later, radiologist B did the measurement for the 30 selected pGGNs, and the ICC values were up to 0.955 (<italic>P</italic> < 0.05) and 0.984 (<italic>P</italic> < 0.05) for diameter and density measurement.</p><sec><title>Clinical Data and Conventional Image Features</title><p>A total of 322 pGGNs were recruited into this study with 80% in the training cohort and 20% in the test cohort. The analysis of clinical and quantitative radiological features in the training and test cohort were listed in <xref rid=\"T1\" ref-type=\"table\">Table 1</xref>. In the training cohort, the median age was 53 years (age range, 25–78 years) and the majority of cases were female (72.9%) with 28 (10.9%) having a smoking history. In the test cohort, 52 (81.3%) were female with a median age of 54 years (age range, 30–72 years) and 59 (92.2%) never smoke. Diameter showed statistical discrepancy between AIS/MIA and IVA in both training and test cohort (<italic>P</italic> < 0.001) while nothing significantly different existed in gender and smoking status between the two groups. Age and density exhibited evident difference in training cohort between AIS/MIA and IVA (<italic>P</italic> = 0.01 and <italic>P</italic> < 0.001), but no significant difference (<italic>P</italic> > 0.05) appeared between the two groups in test cohort.</p></sec><sec><title>Radiomic Feature Selection and Predictive Model Building</title><p>A RF algorithm with 4-fold cross-validation was taken to calculate the contribution value of each radiomic feature in the training cohort for the prediction of pGGN-like IVA from AIS/MIA. Predictive model building was performed using SVM also combined with 4-fold cross-validation. All the extracted radiomic features were listed in descending order by the contribution value for the classifier and were added one by one as the input for the SVM model training in each iteration of cross-validation calculation (detailed in <xref ref-type=\"fig\" rid=\"F2\">Figure 2</xref>). In the process of gradual accumulation of model training, the overall accuracy of the training cohort was recorded. When sequencing extracted radiomic features by their contribution value for the classifier in each iteration, we found that the first 20 radiomic features contributed much more than other features whose contribution values were <0.01 and even close to 0. Meanwhile, the first 20 radiomic features could increase the classifying performance dramatically while inclusion of extra features did not make a big difference to the performance of the SVM classifier in each iteration. To improve the generalization of our predictive model, only radiomic features appearing more than three times in the first 20 of contribution value rank in four iterations of the 4-fold cross validation were selected for final model building. Those radiomic features that were used for final predictive model building incorporated four from GLCM, four from GLSZM, and one from GLRLM. All the nine features are detailed in <xref rid=\"T2\" ref-type=\"table\">Table 2</xref>.</p><fig id=\"F2\" position=\"float\"><label>Figure 2</label><caption><p>The flowchart of the whole study.</p></caption><graphic xlink:href=\"fonc-10-00872-g0002\"/></fig><table-wrap id=\"T2\" position=\"float\"><label>Table 2</label><caption><p>Nine selected radiomic features.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th rowspan=\"1\" colspan=\"1\"/><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Selected radiomic feature</bold></th><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Radiomic group</bold></th><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Filter associated</bold></th></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">R1</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Maximum probability</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">GLCM</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">wavelet-LLL</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">R2</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Joint entropy</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">GLCM</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">None</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">R3</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Sum entropy</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">GLCM</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">wavelet-LLL</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">R4</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Joint energy</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">GLCM</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">None</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">R5</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Gray level Non-uniformity</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">GLSZM</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">log-sigma-1-0-mm-3D</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">R6</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Gray level Non-uniformity</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">GLSZM</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">wavelet-LLH</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">R7</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Gray level Non-uniformity</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">GLSZM</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">log-sigma-3-0-mm-3D</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">R8</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Size zone Non-uniformity</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">GLSZM</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">None</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">R9</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Low gray level run emphasis</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">GLRLM</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">log-sigma-3-0-mm-3D</td></tr></tbody></table><table-wrap-foot><p><italic>GLCM, gray level co-occurrence matrix; GLSZM, gray level size zone matrix; GLRLM, gray level run length matrix</italic>.</p></table-wrap-foot></table-wrap><p>We first investigated whether the nine advanced radiomic features could discriminate IVA from indolent adenocarcinoma (AIS/MIA) representing as pGGNs. <xref ref-type=\"fig\" rid=\"F3\">Figure 3</xref> shows the comparison of those nine radiomic features for the distinction of pGGN-like AIS/MIA and IVA in training and test cohorts. All the selected radiomic features revealed significant difference between AIS/MIA and IVA in both training and test cohorts (<italic>P</italic> < 0.001). Among the nine radiomic features, three (Maximum Probability, Joint Energy, and Low Gray Level Run Emphasis) had larger median values for AIS/MIA while the median values of six other features (Joint Entropy, Sum Entropy, Gray Level Non-Uniformity, and Three Different Filtered Size Zone Non-Uniformity) were higher for IVA than that for AIS/MIA in both training and test cohorts (detailed in <xref ref-type=\"supplementary-material\" rid=\"SM1\">Supplemental Table 3</xref>).</p><fig id=\"F3\" position=\"float\"><label>Figure 3</label><caption><p><bold>(A–I)</bold> Selected radiomic features in training and test cohort. Nine selected radiomic features showed significant difference between AIS/MIA and IVA in both training and test cohorts. Maximum Probability, Joint Energy, and Low Gray Level Run Emphasis had larger median values for AIS/MIA, while the median values of Joint Entropy, Sum Entropy, Gray Level Non-Uniformity, and Size Zone Non-uniformity were higher for IVA in both training and test cohorts.</p></caption><graphic xlink:href=\"fonc-10-00872-g0003\"/></fig><p>Multivariate predictive models were created for each set of features, involving conventional (age, diameter, and density), radiomic (nine predictive features), and the combined (conventional and selected radiomic features) sets. The three different models presented good predictive power (AUC > 0.8, <italic>P</italic> < 0.05) in both training and test cohorts as shown in <xref ref-type=\"fig\" rid=\"F4\">Figure 4</xref>, <xref ref-type=\"supplementary-material\" rid=\"SM1\">Supplemental Table 4</xref>. Then, DeLong's test (<xref rid=\"B34\" ref-type=\"bibr\">34</xref>, <xref rid=\"B35\" ref-type=\"bibr\">35</xref>) was applied to complete the pairwise predictive performance comparison among the three models, in which no significant difference was observed (<italic>P</italic> > 0.05). <xref ref-type=\"fig\" rid=\"F5\">Figure 5</xref> showed comprehensive parameters including accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), misdiagnosis rate (MR), and missed diagnosis rate (MDR) of the three predictive models' and two radiologists' binary diagnosis in the test cohort (detailed in <xref ref-type=\"supplementary-material\" rid=\"SM1\">Supplemental Table 5</xref>). The accuracy was 76.56, 71.88, and 78.13% for radiomic, conventional, and combined predictive models but no big difference was noted in comprehensive assessment among the three models (<xref ref-type=\"fig\" rid=\"F5\">Figure 5</xref>), which was consistent with the results of DeLong's test above. In a word, no matter what features were used for model training (conventional or radiomic features), predictive models built with machine learning algorithm could predict pGGN-like IVA from AIS/MIA well. The combination of conventional and radiomic features could further improve the diagnosis accuracy of predictive model, but the improvement was not statistically significant in our study.</p><fig id=\"F4\" position=\"float\"><label>Figure 4</label><caption><p>The ROC analysis of the three different predictive model. Three predictive models presented good performance in discrimination between pGGN-like IVA and AIS/MIA. <bold>(A)</bold>, the predictive performance of three models in the 693 training cohort; <bold>(B)</bold>, the predictive performance of three models in the testing cohort.</p></caption><graphic xlink:href=\"fonc-10-00872-g0004\"/></fig><fig id=\"F5\" position=\"float\"><label>Figure 5</label><caption><p>The binary diagnosis of predictive models and two radiologists. (A), the first binary diagnosis of the senior radiologist A with 10-year experience in thoracic imaging diagnosis in test cohort; (B), the first binary diagnosis of the junior radiologist B with 2-year experience in medical imaging diagnosis in test cohort; R, the binary diagnosis of radiomic predictive model in test cohort; A+R, the second binary diagnosis of radiologist A in test cohort with the aid of radiomic predictive model; B+R, the second binary diagnosis of radiologist B in test cohort with the aid of radiomic predictive model. PPV, positive predictive value; NPV, negative predictive value; MR, misdiagnosis rate; MDR, missed diagnosis rate.</p></caption><graphic xlink:href=\"fonc-10-00872-g0005\"/></fig><p>To investigate whether the radiomic predictive model could help radiologists improve their predictive performance, we then compared the diagnosis of radiomic predictive model and two radiologists (<xref ref-type=\"fig\" rid=\"F5\">Figure 5</xref>, <xref rid=\"T3\" ref-type=\"table\">Table 3</xref>). Radiologist A performed better than radiologist B with higher diagnostic accuracy, sensitivity, specificity, PPV, and NPV. Either accuracy or sensitivity, radiomic predictive model outperformed radiologist A with the cost of decreased specificity. Significant difference was observed between the binary diagnosis of radiomic predictive model and that of two radiologists (A vs. R, χ<sup>2</sup> = 7.563, <italic>P</italic> = 0.004, B vs. R, χ<sup>2</sup> = 4, <italic>P</italic> = 0.043). Generally speaking, Radiomic predictive model showed better performance than two radiologists in the prediction between pGGN-like IVA and AIS/MIA. Two radiologists dramatically improved their diagnostic accuracy to 79.69 and 75.00% with the aid of radiomic predictive model (A vs. A+R, χ<sup>2</sup> = 4.9, <italic>P</italic> = 0.021, B vs. B+R, χ<sup>2</sup> = 5.042, <italic>P</italic> = 0.023). The comparison of the second diagnosis of two radiologists revealed that when having the guidance from radiomic predictive model, no statistical difference existed between two radiologists in prediction of pGGN-like IVA and AIS/MIA (A+R vs. B+R, χ<sup>2</sup> = 1.455, <italic>P</italic> = 0.227).</p><table-wrap id=\"T3\" position=\"float\"><label>Table 3</label><caption><p>Performance comparison between radiomic predictive model and two radiologists in test cohort.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th rowspan=\"1\" colspan=\"1\"/><th valign=\"top\" align=\"center\" colspan=\"2\" style=\"border-bottom: thin solid #000000;\" rowspan=\"1\"><bold>McNemar's test</bold></th><th valign=\"top\" align=\"center\" colspan=\"2\" style=\"border-bottom: thin solid #000000;\" rowspan=\"1\"><bold>Kappa analysis</bold></th></tr><tr><th rowspan=\"1\" colspan=\"1\"/><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>χ<sup><bold>2</bold></sup></bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold><italic>P</italic></bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>κ</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold><italic>P</italic></bold></th></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">A vs. B</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.000</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.316</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.018</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">A vs. R</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">7.563</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.004</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.517</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><0.001</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">B vs. R</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.043</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.241</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.070</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">A vs. A+R</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4.9</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.021</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.690</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><0.001</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">B vs. B+R</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5.042</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.023</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.275</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.022</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">A+R vs. B+R</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.455</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.227</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.654</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><0.001</td></tr></tbody></table><table-wrap-foot><p><italic>A, the first binary diagnosis of the senior radiologist A with 10-year experience in thoracic imaging diagnosis in test cohort; B, the first binary diagnosis of the junior radiologist B with 2-year experience in medical imaging diagnosis in test cohort; R, the binary diagnosis of radiomic predictive model in test cohort; A+R, the second binary diagnosis of radiologist A in the test cohort with the aid of radiomic predictive model; B+R, the second binary diagnosis of radiologist B in the test cohort with the aid of radiomic predictive model</italic>.</p></table-wrap-foot></table-wrap></sec></sec><sec sec-type=\"discussion\" id=\"s4\"><title>Discussion</title><p>When it comes to pure ground-glass pulmonary nodules, clinicians tend to choose follow-up as their first choice for management. However, according to previous studies (<xref rid=\"B4\" ref-type=\"bibr\">4</xref>, <xref rid=\"B5\" ref-type=\"bibr\">5</xref>, <xref rid=\"B31\" ref-type=\"bibr\">31</xref>), a certain proportion of IVA that needs surgical treatment could be pGGNs on CT scans. If a pGGN-like IVA was misdiagnosed as a benign lesion or indolent adenocarcinoma and was given a prescription of follow-up, it might progress during the interval or even metastasize and miss the optimal time for surgical intervention. Conventional radiological features like lobulated signs, spiculations, bubble lucencies, and pleura traction have been demonstrated to be helpful to differentiate the malignancy of pulmonary nodules and the invasiveness of lung adenocarcinoma (<xref rid=\"B36\" ref-type=\"bibr\">36</xref>, <xref rid=\"B37\" ref-type=\"bibr\">37</xref>). However, pGGN-like lung adenocarcinoma tends to be in small volume with a large similarity in morphological characteristics and the assessment of the conventional radiological features is easy to be affected by the subjectivity of doctors, it remains a challenge to make a precise judgement for pGGNs without surgical intervention. Percutaneous biopsies may be helpful in determining the nature of pulmonary nodules. Nevertheless, it is an invasive tissue extraction method with the possibility of postoperative complications, and due to the tumor heterogeneity, it is not persuasive to represent the characteristics of the entire lesion with only a tiny tissue (<xref rid=\"B20\" ref-type=\"bibr\">20</xref>, <xref rid=\"B38\" ref-type=\"bibr\">38</xref>). In some cases, because of nodules' location and size, percutaneous biopsies may not be a good choice for diagnosis. Thus, the diagnosis of pGGNs remains a thorny point for clinical research.</p><p>Heidinger et al. reported that two-dimensional diameter could provide enough information for pulmonary nodule risk classification in their quantitative analysis based on CT images (<xref rid=\"B4\" ref-type=\"bibr\">4</xref>, <xref rid=\"B39\" ref-type=\"bibr\">39</xref>). In our study, the diameter of pGGNs showed a significantly different distribution between IVA and indolent adenocarcinoma (AIS/MIA), which was consistent with previous reports. Kitami et al. acclaimed that almost all the pGGNs with a diameter <10 mm and a density of no more than −600 HU were demonstrated to be preinvasive lesions in their study (<xref rid=\"B40\" ref-type=\"bibr\">40</xref>). The medians of density in our training and test cohort were −583.0 and −525.8 HU, both slightly higher than −600 HU, which might have something to do with the difference in lung adenocarcinoma classification. We classified MIA into indolent adenocarcinoma, which might lead to the increase in density medians.</p><p>Radiomics is a new quantitative image analysis approach that allows thorough exploration in medical images and has attracted more and more attention in the field of medicine in recent years. In this study, we obtained plenty of quantitative radiomic features (960 for each nodule) from routine thoracic CT images using machine learning techniques and completed the quantitative analysis of pGGN-like adenocarcinoma classification on the basis of conventional clinical, quantitative radiological, and selected radiomic features. Nine selected radiomic features demonstrated good performance in distinction of the IVA and indolent adenocarcinoma representing pGGNs on unenhanced CT images. Nine selected radiomic features in our study consisted of four GLCM-based features, four GLSZM-based features, and one GLRLM-based feature, which were analyzed as follows:</p><p>(1) Four radiomic features from GLCM: Maximum Probability, Joint Entropy, Sum Entropy, and Joint Energy. GLCM was used to compare the gray level correlation between two points in a certain distance in a spatial position, which reflects the comprehensive information about pixel distribution including direction, distance, gray value, and the pattern of gray level arrangement (<xref rid=\"B41\" ref-type=\"bibr\">41</xref>, <xref rid=\"B42\" ref-type=\"bibr\">42</xref>). Maximum Probability refers to a pair of pixels with the highest frequency in a GLCM (<xref rid=\"B43\" ref-type=\"bibr\">43</xref>, <xref rid=\"B44\" ref-type=\"bibr\">44</xref>). Entropy is a parameter describing the complexity of an image, which means the larger the entropy value of an image is, the more complex the image is (<xref rid=\"B43\" ref-type=\"bibr\">43</xref>). Energy is related to the uniformity of gray level distribution and the roughness of image texture with a larger energy indicating a more regular and more stable texture (<xref rid=\"B42\" ref-type=\"bibr\">42</xref>, <xref rid=\"B43\" ref-type=\"bibr\">43</xref>). In our study, Maximum Probability and Joint Energy got higher median values for AIS/MIA in the training as well as test cohort while Joint Entropy and Sum Entropy in IVA were higher than that in AIS/MIA in both training and test cohort. This might have something to do with that AIS/MIA tend to be homogeneous, which results in a higher probability of finding pixels with same distribution pattern in AIS/MIA and the different entropy and energy values between AIS/MIA and IVA.</p><p>(2) Four radiomic features from GLSZM and one from GLRLM: Gray Level Non-Uniformity filtered with LOG or wavelet algorithm, Size Zone Non-Uniformity, and Low Gray Level Run Emphasis. GLSZM refers to the number of pixels that share the same gray level intensity and the same arrangement pattern in an image while GLRLM calculates the number of pixels with the same gray level value and distribution pattern in a certain direction (<xref rid=\"B45\" ref-type=\"bibr\">45</xref>–<xref rid=\"B47\" ref-type=\"bibr\">47</xref>). Gray Level Non-Uniformity and Size Zone Non-Uniformity indicate the variability of gray level and size zone volumes in an image, with a higher value referring to more heterogeneity in ROIs (<xref rid=\"B46\" ref-type=\"bibr\">46</xref>, <xref rid=\"B47\" ref-type=\"bibr\">47</xref>). In our study, the median values of Gray Level Non-Uniformity and Size Zone Non-Uniformity were higher for IVA, which might be related to the fact that IVA tends to be more heterogeneous. Low Gray Level Run Emphasis analyzes the distribution of low gray level values in an image (<xref rid=\"B47\" ref-type=\"bibr\">47</xref>). The homogeneity and relatively lower average density of AIS/MIA might lead to the higher median value of Low Gray Level Run Emphasis for AIS/MIA in our study.</p><p>Chen et al. picked 76 features meaningful for the distinction of malignancy of pulmonary nodules from 750 extracted radiomic features and built a predictive model whose accuracy was up to 84% using four selected advanced features (<xref rid=\"B20\" ref-type=\"bibr\">20</xref>). Yagi et al. carried out the texture analysis of high-resolution computed tomography (HRCT) and found that 90th percentile and entropy performed well in discrimination between AIS/MIA and IVA with an AUC value of 0.90 (95% CI: 0.84–0.95) (<xref rid=\"B13\" ref-type=\"bibr\">13</xref>). Three different predictive models set with clinical, radiological, and nine selected radiomic features from 960 features extracted from unenhanced CT images in our study all presented good predictive power in the discrimination between AIS/MIA (indolent adenocarcinoma) and IVA (AUC > 0.8, <italic>P</italic> < 0.05). She et al. extracted radiomic features from radiological data of 402 cases (207 for training and 195 for test) diagnosed with lung adenocarcinoma and selected five meaningful radiomic features to build a predictive model that outperformed significantly the predictive model only built with conventional radiological features including nodule diameter for the discrimination between IVA and AIS/MIA (<xref rid=\"B11\" ref-type=\"bibr\">11</xref>). However, no apparent difference existed between conventional and radiomic predictive models in our study (<italic>P</italic> > 0.05). Combining conventional and radiomic features could improve the AUC value of combined predictive model, but it was not statistically significant. This might be caused by the difference of our study population. All types of pulmonary nodules including solid and subsolid nodules, which have heterogeneous internal density in lesions, were used for She's study. Therefore, compared with conventional radiological features such as diameter and density, radiomic features could more thoroughly analyze the variability and distribution of gray level intensity in ROI, which would provide more valuable information for improvement in diagnostic performance of predictive models. However, the relatively obscure variability of gray level intensity in pGGNs might result in the limited reference value for radiomic features in prediction between pGGN-like IVA and AIS/MIA, which potentially led to the similar diagnostic performance among our three predictive models. Nevertheless, predictive models built using either radiomic or conventional radiological features presented good performance in distinction between pGGN-like IVA and AIS/MIA, which further confirmed the possibility of machine learning methods for the differentiation of the invasion of pGGN-like lung adenocarcinoma. In conclusion, the predictive models established in our study could still provide certain guidance for clinicians to make accurate diagnosis.</p><p>To assess whether the radiomic predictive model could improve radiologists' performance in diagnosis of pGGN-like lung adenocarcinoma, we further compared the dichotomous diagnosis of the radiomic predictive model and two radiologists. There was a dramatic difference between radiologist A and radiologist B in the values of diagnostic accuracy, sensitivity, and specificity. The Kappa analysis revealed bad consistency between the results of the two radiologists (κ = 0.316, <italic>P</italic> = 0.018) while McNemar's test between that of two radiologists showed no significant difference (<italic>P</italic> < 0.05). This was related to the mechanisms of two statistical methods. McNemar's test only compares results with collision in two diagnostic methods instead of using comprehensive data acquired in a study while Kappa analysis calculates the consistency in all data (<xref rid=\"B48\" ref-type=\"bibr\">48</xref>–<xref rid=\"B50\" ref-type=\"bibr\">50</xref>). <xref ref-type=\"fig\" rid=\"F6\">Figure 6</xref> shows the mechanism of McNemar's test and the formula for calculating the value of χ<sup>2</sup>. A small (<italic>b</italic> – <italic>c</italic>) leads to a small χ<sup>2</sup>-value, which results in a <italic>P-</italic>value of more than 0.05 no matter whether the data have actual clinical significance or not. In our test cohort, 32.8% (21/64) of the pGGNs had diverse diagnoses from two radiologists, while the value of (<italic>b</italic> – <italic>c</italic>) is only 1, which resulted in a value of 0 for χ<sup>2</sup>. In this special situation, McNemar's test and Kappa analysis should be combined with actual data distribution to complete the comparison of two radiologists' diagnosis. The comprehensive analysis showed that the diagnostic ability of senior radiologist A was higher than that of junior radiologist B and the radiomic predictive model outperformed two radiologists. When having the diagnosis of the radiomic predictive model for reference, two radiologists could significantly improve their performance in prediction pGGN-like IVA from AIS/MIA. What is more, no significant difference existed between the second diagnosis from two radiologists with the aid of the radiomic predictive model. The predictive model built using selected radiomic features in our research could obviously improve the ability of radiologists in prediction between pGGN-like IVA and AIS/MIA, especially for the junior radiologist; it could help radiologist B reach the level of the senior radiologist A's diagnostic ability, which had certain potential clinical meaning.</p><fig id=\"F6\" position=\"float\"><label>Figure 6</label><caption><p><bold>(A)</bold> The mechanism of McNemar's test. <bold>(B)</bold> The calculation formula for χ<sup>2</sup>-value. Formula (1) would be used if (<italic>b</italic> + <italic>c</italic>) ≥40; otherwise, formula (2) should be chosen.</p></caption><graphic xlink:href=\"fonc-10-00872-g0006\"/></fig><p>There were also some limitations in our study. First, this was a retrospective study in which all the cases were sorted according to rigorous exclusion criteria. There was certain inherent selection bias in it. Meanwhile, 322 pGGNs were not large enough for this quantitative study, compared with 960 features for each nodule. Further data collection including data from other clinic centers would be done to evaluate these models' predictive reproducibility. Second, the feature selection driven by restrictive algorithm (<1% features remaining after) might lead to a certain loss of potential predictive features for distinction. Despite the significant feature reduction, we were still able to find predictive features with high robustness. Third, limitations of this trial included the lack of standardization in image acquisition completed on various CT scanners. A voxel-resampling was applied to reduce the influence from the variability in image acquisition protocols in our study, but the above problem may still have a certain impact on the feature selection and model building. Thus, the standardization of image acquisition and establishment of database with high quality are urgently required for radiomic research. Finally, we chose a semi-automatic method to complete the pGGN segmentation. Though consistent segmentation for each pGGN had been reached through negotiation by two radiologists, there was still some interobserver difference existing in this procedure. A reliable automatic segmentation algorithm that can be applied in clinical practice is still to be developed.</p></sec><sec sec-type=\"conclusions\" id=\"s5\"><title>Conclusion</title><p>Nine selected radiomic features in our research showed different distribution between IVA and AIS/MIA, which could provide some guidance for clinical practice. The predictive models established using conventional clinical, radiological, or radiomic features could help to distinguish the invasiveness of pGGN-like lung adenocarcinoma, but radiomic features could not offer more meaningful information to improve the performance of the combined model created in our study. The diagnostic performance of the radiomic predictive model established in our study was better than that of the two radiologists, and the predictive model could provide auxiliary information for radiologists (especially for junior radiologists) to improve their diagnostic ability in discrimination between pGGN-like IVA and AIS/MIA.</p></sec><sec sec-type=\"data-availability\" id=\"s6\"><title>Data Availability Statement</title><p>The datasets generated for this study are available on request to the corresponding author.</p></sec><sec id=\"s7\"><title>Ethics Statement</title><p>This study was approved by the Institutional Review Committee of the Sir Run Run Shaw Hospital.</p></sec><sec id=\"s8\"><title>Author Contributions</title><p>FX and WZ conceived and designed this study. FX, YS, and CP applied the data collection for this study. FX and YS did the data reproof work during the manscript review and now they are collecting new data for further exploration. FX and JW did the segmentation work for each pGGN recruited for this study. WZ and RX contributed to the algorithm design for this study. YG completed the pathological interpretation of all pGGNs in this study. FX drafted the manuscript. LS and CQ and HH contributed equally to the manuscript review.</p></sec><sec id=\"s9\"><title>Conflict of Interest</title><p>The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p></sec></body><back><fn-group><fn fn-type=\"financial-disclosure\"><p><bold>Funding.</bold> This research was supported by Zhejiang Provincial Natural Science Foundation of China under Grant No. LQ20F030018, Key Research and Development Program of Zhejiang Province under Grant No. 2019C03064, Program Co-sponsored by Province and Ministry under Grant No. WKJ-ZJ-1926 and National Natural Science Foundation of China (NSFC) under Grant No. 61772106.</p></fn></fn-group><sec sec-type=\"supplementary-material\" id=\"s10\"><title>Supplementary Material</title><p>The Supplementary Material for this article can be found online at: <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.frontiersin.org/articles/10.3389/fonc.2020.00872/full#supplementary-material\">https://www.frontiersin.org/articles/10.3389/fonc.2020.00872/full#supplementary-material</ext-link></p><supplementary-material content-type=\"local-data\" id=\"SM1\"><media xlink:href=\"Data_Sheet_1.PDF\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material></sec><ref-list><title>References</title><ref id=\"B1\"><label>1.</label><mixed-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Travis</surname><given-names>WD</given-names></name><name><surname>Brambilla</surname><given-names>E</given-names></name><name><surname>Noguchi</surname><given-names>M</given-names></name><name><surname>Nicholson</surname><given-names>AG</given-names></name><name><surname>Geisinger</surname><given-names>KR</given-names></name><name><surname>Yatabe</surname><given-names>Y</given-names></name><etal/></person-group>. <article-title>International association for the study of lung cancer/american thoracic society/european respiratory society international multidisciplinary classification of lung adenocarcinoma</article-title>. <source>J Thorac Oncol.</source> (<year>2011</year>) <volume>6</volume>:<fpage>244</fpage>–<lpage>85</lpage>. <pub-id pub-id-type=\"doi\">10.1097/JTO.0b013e318206a221</pub-id><pub-id pub-id-type=\"pmid\">21252716</pub-id></mixed-citation></ref><ref id=\"B2\"><label>2.</label><mixed-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Travis</surname><given-names>WD</given-names></name><name><surname>Brambilla</surname><given-names>E</given-names></name><name><surname>Nicholson</surname><given-names>AG</given-names></name><name><surname>Yatabe</surname><given-names>Y</given-names></name><name><surname>Austin</surname><given-names>J</given-names></name><name><surname>Beasley</surname><given-names>MB</given-names></name><etal/></person-group>. <article-title>The (2015). 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[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Front Plant Sci</journal-id><journal-id journal-id-type=\"iso-abbrev\">Front Plant Sci</journal-id><journal-id journal-id-type=\"publisher-id\">Front. Plant Sci.</journal-id><journal-title-group><journal-title>Frontiers in Plant Science</journal-title></journal-title-group><issn pub-type=\"epub\">1664-462X</issn><publisher><publisher-name>Frontiers Media S.A.</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32849756</article-id><article-id pub-id-type=\"pmc\">PMC7432134</article-id><article-id pub-id-type=\"doi\">10.3389/fpls.2020.01222</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Plant Science</subject><subj-group><subject>Original Research</subject></subj-group></subj-group></article-categories><title-group><article-title>Spatial Patterns and Drivers of Angiosperm Sexual Systems in China Differ Between Woody and Herbaceous Species</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Wang</surname><given-names>Yunyun</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><xref ref-type=\"aff\" rid=\"aff2\"><sup>2</sup></xref><uri xlink:type=\"simple\" xlink:href=\"https://loop.frontiersin.org/people/925752\"/></contrib><contrib contrib-type=\"author\"><name><surname>Lyu</surname><given-names>Tong</given-names></name><xref ref-type=\"aff\" rid=\"aff2\"><sup>2</sup></xref><xref ref-type=\"aff\" rid=\"aff3\"><sup>3</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Luo</surname><given-names>Ao</given-names></name><xref ref-type=\"aff\" rid=\"aff2\"><sup>2</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Li</surname><given-names>Yaoqi</given-names></name><xref ref-type=\"aff\" rid=\"aff2\"><sup>2</sup></xref><uri xlink:type=\"simple\" xlink:href=\"https://loop.frontiersin.org/people/948227\"/></contrib><contrib contrib-type=\"author\"><name><surname>Liu</surname><given-names>Yunpeng</given-names></name><xref ref-type=\"aff\" rid=\"aff2\"><sup>2</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Freckleton</surname><given-names>Robert P.</given-names></name><xref ref-type=\"aff\" rid=\"aff4\"><sup>4</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Liu</surname><given-names>Shuguang</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Wang</surname><given-names>Zhiheng</given-names></name><xref ref-type=\"aff\" rid=\"aff2\"><sup>2</sup></xref><xref ref-type=\"author-notes\" rid=\"fn001\"><sup>*</sup></xref><uri xlink:type=\"simple\" xlink:href=\"https://loop.frontiersin.org/people/426382\"/></contrib></contrib-group><aff id=\"aff1\"><sup>1</sup><institution>National Engineering Laboratory for Applied Technology of Forestry & Ecology in Southern China, and College of Life Science and Technology, Central South University of Forest and Technology</institution>, <addr-line>Changsha</addr-line>, <country>China</country></aff><aff id=\"aff2\"><sup>2</sup><institution>Institute of Ecology and Key Laboratory for Earth Surface Processes of the Ministry of Education, College of Urban and Environmental Sciences, Peking University</institution>, <addr-line>Beijing</addr-line>, <country>China</country></aff><aff id=\"aff3\"><sup>3</sup><institution>School of Urban Planning and Design, Shenzhen Graduate School, Peking University</institution>, <addr-line>Shenzhen</addr-line>, <country>China</country></aff><aff id=\"aff4\"><sup>4</sup><institution>Department of Animal and Plant Sciences, University of Sheffield</institution>, <addr-line>Sheffield</addr-line>, <country>United Kingdom</country></aff><author-notes><fn fn-type=\"edited-by\"><p>Edited by: Hang Sun, Chinese Academy of Sciences, China</p></fn><fn fn-type=\"edited-by\"><p>Reviewed by: Hong Qian, Illinois State Museum, United States; Liangsheng Zhang, Zhejiang University, China</p></fn><corresp id=\"fn001\">*Correspondence: Zhiheng Wang, <email xlink:href=\"mailto:[email protected]\" xlink:type=\"simple\">[email protected]</email></corresp><fn fn-type=\"other\" id=\"fn002\"><p>This article was submitted to Functional Plant Ecology, a section of the journal Frontiers in Plant Science</p></fn></author-notes><pub-date pub-type=\"epub\"><day>11</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><year>2020</year></pub-date><volume>11</volume><elocation-id>1222</elocation-id><history><date date-type=\"received\"><day>10</day><month>3</month><year>2020</year></date><date date-type=\"accepted\"><day>27</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>Copyright © 2020 Wang, Lyu, Luo, Li, Liu, Freckleton, Liu and Wang</copyright-statement><copyright-year>2020</copyright-year><copyright-holder>Wang, Lyu, Luo, Li, Liu, Freckleton, Liu and Wang</copyright-holder><license xlink:href=\"http://creativecommons.org/licenses/by/4.0/\"><license-p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</license-p></license></permissions><abstract><p>Plant sexual systems play an important role in the evolution of angiosperm diversity. However, large-scale patterns in the frequencies of sexual systems (i.e. dioecy, monoecy, and hermaphroditism) and their drivers for species with different growth forms remain poorly known. Here, using a newly compiled database on the sexual systems and distributions of 19780 angiosperm species in China, we map the large-scale geographical patterns in frequencies of the sexual systems of woody and herbaceous species separately. We use these data to test the following two hypotheses: (1) the prevalence of sexual systems differs between woody and herbaceous assemblies because woody plants have taller canopies and are found in warm and humid climates; (2) the relative contributions of different drivers (specifically climate, evolutionary age, and mature plant height) to these patterns differ between woody and herbaceous species. We show that geographical patterns in proportions of different sexual systems (especially dioecy) differ between woody and herbaceous species. Geographical variations in sexual systems of woody species were influenced by climate, evolutionary age and plant height. In contrast, these have only weakly significant effects on the patterns of sexual systems of herbaceous species. We suggest that differences between species with woody and herbaceous growth forms in terms of biogeographic patterns of sexual systems, and their drivers, may reflect their differences in physiological and ecological adaptions, as well as the coevolution of sexual system with vegetative traits in response to environmental changes.</p></abstract><kwd-group><kwd>angiosperms</kwd><kwd>sexual systems</kwd><kwd>growth form</kwd><kwd>climate change</kwd><kwd>geographical pattern</kwd><kwd>macro evo-devo</kwd><kwd>plant height</kwd><kwd>China</kwd></kwd-group><funding-group><award-group><funding-source id=\"cn001\">National Natural Science Foundation of China<named-content content-type=\"fundref-id\">10.13039/501100001809</named-content></funding-source><award-id rid=\"cn001\">31901216, 31988102, 31911530102</award-id></award-group></funding-group><counts><fig-count count=\"2\"/><table-count count=\"3\"/><equation-count count=\"0\"/><ref-count count=\"82\"/><page-count count=\"10\"/><word-count count=\"5077\"/></counts></article-meta></front><body><sec sec-type=\"intro\" id=\"s1\"><title>Introduction</title><p>The sexual systems of plant species play a significant role in the evolution of angiosperm diversity (<xref rid=\"B12\" ref-type=\"bibr\">Charlesworth and Wright, 2001</xref>; <xref rid=\"B52\" ref-type=\"bibr\">Nazareno et al., 2013</xref>; <xref rid=\"B36\" ref-type=\"bibr\">Käfer et al., 2014</xref>; <xref rid=\"B61\" ref-type=\"bibr\">Sabath et al., 2016</xref>; <xref rid=\"B60\" ref-type=\"bibr\">Rosche et al., 2018</xref>), variations in reproductive strategies (<xref rid=\"B8\" ref-type=\"bibr\">Bawa, 1980</xref>), and population and community dynamics in response to climate changes (<xref rid=\"B57\" ref-type=\"bibr\">Queenborough et al., 2009</xref>; <xref rid=\"B76\" ref-type=\"bibr\">Wang et al., 2018</xref>). Understanding the spatial distribution of plant sexual systems at large scales is vital for understanding the functional biogeography of reproductive traits. Previous studies have shown that the geographical distributions of angiosperm sexual systems may ultimately reflect coevolution with vegetative traits that are tied closely to sexual systems (i.e. growth form, <xref rid=\"B72\" ref-type=\"bibr\">Vamosi et al., 2003</xref>; <xref rid=\"B48\" ref-type=\"bibr\">Moeller et al., 2017</xref>; <xref rid=\"B77\" ref-type=\"bibr\">Wang et al., 2020</xref>). Nonetheless, the determinants of geographical patterns of sexual systems remain controversial.</p><p>The evolution of plant sexual systems has been widely hypothesized to be closely linked to the evolution of plant growth forms (e.g. woody vs. herbaceous growth forms, <xref rid=\"B7\" ref-type=\"bibr\">Bawa et al., 1985</xref>; <xref rid=\"B58\" ref-type=\"bibr\">Renner and Ricklefs, 1995</xref>; <xref rid=\"B72\" ref-type=\"bibr\">Vamosi et al., 2003</xref>; <xref rid=\"B71\" ref-type=\"bibr\">Vamosi and Vamosi, 2004</xref>; <xref rid=\"B47\" ref-type=\"bibr\">Mitchell and Diggle, 2005</xref>; <xref rid=\"B68\" ref-type=\"bibr\">Soza et al., 2012</xref>). Generally, dioecy is significantly more frequent in woody flowering plant species with long lifespan than in other growth forms (<xref rid=\"B58\" ref-type=\"bibr\">Renner and Ricklefs, 1995</xref>; <xref rid=\"B63\" ref-type=\"bibr\">Sakai et al., 1995</xref>; <xref rid=\"B72\" ref-type=\"bibr\">Vamosi et al., 2003</xref>; <xref rid=\"B78\" ref-type=\"bibr\">Ward et al., 2005</xref>). In contrast, hermaphroditism is more common in herbaceous species with short lifespan (<xref rid=\"B64\" ref-type=\"bibr\">Senarath, 2008</xref>; <xref rid=\"B48\" ref-type=\"bibr\">Moeller et al., 2017</xref>). This is likely because obligately outcrossing dioecious species need a long lifespan to find mates, reproduce and complete their life cycle (<xref rid=\"B51\" ref-type=\"bibr\">Morgan et al., 1997</xref>; <xref rid=\"B1\" ref-type=\"bibr\">Aarssen, 2000</xref>). In contrast, hermaphrodites, being able to self-pollinate, are likely to accumulate more genetic load and suffer from an increased turnover rate (<xref rid=\"B40\" ref-type=\"bibr\">Klekowski and Godfrey, 1989</xref>; <xref rid=\"B14\" ref-type=\"bibr\">Chen and Li, 2008</xref>). Moreover, growth form may affect relationships between traits and the environment (<xref rid=\"B27\" ref-type=\"bibr\">Fitzjohn et al., 2014</xref>). For example, based on more than 88,400 species and six plant traits, <xref rid=\"B65\" ref-type=\"bibr\">Šímová et al. (2018)</xref> have found significant differences in trait-climate correlations between woody and herbaceous species. However, the biogeographical distributions of sexual systems of different growth forms across large-scale environmental gradients remain poorly understood.</p><p>Plant sexual systems and sexual reproduction are sensitive to climate variations (<xref rid=\"B33\" ref-type=\"bibr\">Hedhly et al., 2009</xref>; <xref rid=\"B22\" ref-type=\"bibr\">Eckert et al., 2010</xref>). Recent studies indicate that reductions in precipitation can change the allocation of resources to male and female functions within individual plants (<xref rid=\"B35\" ref-type=\"bibr\">Hultine et al., 2016</xref>), which further influences the expression and composition of plant sexual system in local floras. For example, dioecy is more common in humid areas, especially in rainforest trees (<xref rid=\"B44\" ref-type=\"bibr\">Lloyd, 1980</xref>; <xref rid=\"B62\" ref-type=\"bibr\">Sakai et al., 1995</xref>; <xref rid=\"B29\" ref-type=\"bibr\">Friedman and Barrett, 2009</xref>; <xref rid=\"B73\" ref-type=\"bibr\">Vary et al., 2011</xref>), although aridity has been shown to have contributed to the evolutionary transition from hermaphroditism to dioecy (<xref rid=\"B4\" ref-type=\"bibr\">Ashman, 2006</xref>). Climate warming can also affect the composition of sexual systems in local floras by causing a mismatch between the timing of flowering and pollinator abundance/presence, which may lead to declines of plants populations (<xref rid=\"B24\" ref-type=\"bibr\">Etterson and Mazer, 2016</xref>). Moreover, different growth forms have contrasting climate preferences. Warm and humid climates are more suitable for woody plants (<xref rid=\"B75\" ref-type=\"bibr\">Wang et al., 2011</xref>), while herbaceous plants often have broader climate adaptations and hence have widespread distributions (<xref rid=\"B19\" ref-type=\"bibr\">Curtis and Bradley, 2016</xref>). Therefore, the influences of climate on sexual system compositions may differ between woody and herbaceous species. However, the role of climate in driving spatial variations in sexual system compositions of woody and herbaceous species remains poorly understood (<xref rid=\"B30\" ref-type=\"bibr\">Gamble et al., 2018</xref>).</p><p>Evolutionary history might be expected to influence the distribution of sexual systems (<xref rid=\"B53\" ref-type=\"bibr\">Obbard et al., 2006</xref>; <xref rid=\"B5\" ref-type=\"bibr\">Barrett, 2013</xref>; <xref rid=\"B36\" ref-type=\"bibr\">Käfer et al., 2014</xref>). Self-pollinated populations likely accumulate harmful mutants, which may increase their extinction rate (<xref rid=\"B24\" ref-type=\"bibr\">Etterson and Mazer, 2016</xref>). Therefore, self-pollinated species may be more common in temperate environments with more short-lived species and younger flora than in tropical environments. In contrast, dioecy (obligate outcrossing) can reduce the expression of recessive deleterious mutations, which tends to reduce their extinction rate (<xref rid=\"B53\" ref-type=\"bibr\">Obbard et al., 2006</xref>). Therefore, dioecy may be more frequent in regions with older floras. These findings suggest that the composition of sexual systems within communities is likely associated with the evolutionary ages of species in a region. Additionally, woody and herbaceous species differ significantly in their evolutionary rates (<xref rid=\"B43\" ref-type=\"bibr\">Laroche et al., 1997</xref>; <xref rid=\"B38\" ref-type=\"bibr\">Kay et al., 2006</xref>). Woody species have longer reproductive cycles and lifespans, and tend to accumulate genetic changes more slowly than herbaceous species. Differences in evolutionary rates may lead to differences in the speed of climate-niche evolution between these two growth forms. For example, using more than 5,000 plant species, <xref rid=\"B66\" ref-type=\"bibr\">Smith and Beaulieu (2009)</xref> found that woody plants adapted to new climates at a rate two to ten times slower than herbs over the course of their evolution. Therefore, exploring the differences between the effects of evolutionary time on frequencies of sexual systems of woody and herbaceous species would improve our understanding of the evolutionary mechanisms underlying large-scale patterns in sexual system composition.</p><p>Here, using data on sexual systems and spatial distributions of 19,780 angiosperm species across China, we compare the geographical patterns in plant sexual system compositions between woody and herbaceous species, and explore the ecological and evolutionary determinants of these patterns. Specifically, we test the following hypotheses: (1) the geographical patterns in sexual system composition differ between woody and herbaceous species; (2) the relative contributions of different drivers (specifically climate, evolutionary age, and mature plant height) to these patterns differ between woody and herbaceous species. Dioecious species are more common in humid areas and in floras with older and more woody species, whereas hermaphroditic species are more common in temperate arid areas of northwestern China and in floras with younger and more herbaceous species.</p></sec><sec sec-type=\"materials|methods\" id=\"s2\"><title>Materials and Methods</title><sec id=\"s2_1\"><title>Sexual System</title><p>We compiled a database on the sexual systems of angiosperms in China using published sources: <italic>Flora of China</italic> (<xref rid=\"B79\" ref-type=\"bibr\">Wu et al., 1994-2013</xref>), <italic>Flora Republicae Popularis Sinicae</italic> (126 issues of 80 volumes), <italic>Seeds of Woody Plants in China</italic> (<xref rid=\"B13\" ref-type=\"bibr\">Chen and Huang, 2001</xref>), and efloras (<uri xlink:type=\"simple\" xlink:href=\"http://efloras.org/\">http://efloras.org/</uri>). The Tree of Sex (<xref rid=\"B3\" ref-type=\"bibr\">Ashman et al., 2014</xref>), TYR Plant Trait Database (<uri xlink:type=\"simple\" xlink:href=\"http://www.try-db.org\">www.try-db.org</uri>; <xref rid=\"B37\" ref-type=\"bibr\">Kattge et al., 2011</xref>), Botanical Information and Ecology Network (BIEN, <xref rid=\"B23\" ref-type=\"bibr\">Enquist et al., 2016</xref>, <uri xlink:type=\"simple\" xlink:href=\"http://bien.nceas.ucsb.edu/bien/biendata/bien-3/\">http://bien.nceas.ucsb.edu/bien/biendata/bien-3/</uri>) and journal publications (<xref rid=\"B61\" ref-type=\"bibr\">Sabath et al., 2016</xref>; <xref rid=\"B31\" ref-type=\"bibr\">Goldberg et al., 2017</xref>) were used to check and supplement the sexual system information in the database. For species with information of sexual systems from multiple sources, conflicting records were checked and removed. In total, we compiled data of sexual systems for 19,780 species from 2,506 genera and 262 families in China (<xref rid=\"T1\" ref-type=\"table\"><bold>Table 1A</bold></xref>).</p><table-wrap id=\"T1\" position=\"float\"><label>Table 1</label><caption><p>The correlations between the geographical patterns in proportions of sexual systems among different growth forms.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Sexual System</th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">All-Wood</th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">All-Herb</th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Wood-Herb</th></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Hermaphroditism</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>0.535***</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>0.702**</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>0.304*</bold></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Dioecy</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.172ns</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>0.636*</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−0.068ns</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Monoecy</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>0.593*</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>0.888***</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>0.382**</bold></td></tr></tbody></table><table-wrap-foot><p>All, all species; Wood, woody species; Herb, herbaceous species. The significance was estimated using the Dutilleul’s modified t test, which accounts for the inflation of significance due to spatial autocorrelation (Package “SpatialPack”; <xref rid=\"B21\" ref-type=\"bibr\">Dutilleul et al., 1993</xref>).</p><p>Significance codes: ***P < 0.001, **P < 0.01, *P < 0.05, ‘.’ P < 0.1. The text is bolded when the correlation is significant.</p></table-wrap-foot></table-wrap><p>We classified all species into three categories according to their sexual systems following <xref rid=\"B10\" ref-type=\"bibr\">Cardoso et al. (2018)</xref>: dioecy (separate male and female individuals), monoecy (having pistils and stamens on separate flowers of the same plant), and hermaphrodites (having both male and female sex organs on the same flower). The category of dioecy includes the <italic>androdioecious</italic>, <italic>gynodioecious</italic>, and <italic>polygamodioecious</italic> species, while monoecy includes the <italic>monoecious</italic>, <italic>andromonoecious</italic>, and <italic>gynomonoecious</italic> species. Considering that a few species vary intraspecifically in their sexual systems (e.g., <xref rid=\"B20\" ref-type=\"bibr\">Dorken et al., 2017</xref>) in response to local abiotic or biotic conditions (e.g., climate variables or pollinator densities; <xref rid=\"B6\" ref-type=\"bibr\">Barrett and Harder, 2017</xref>), we excluded such species from the final dataset used in this study.</p></sec><sec id=\"s2_2\"><title>Species Distribution Data</title><p>Data on the distributions of plant species were compiled from all national, provincial and local floras, including <italic>Flora of China</italic> (both Chinese and English versions), <italic>Higher Plants of China</italic>, <italic>The Atlas of Woody Plants in China</italic> (<xref rid=\"B26\" ref-type=\"bibr\">Fang et al., 2011</xref>; see <xref rid=\"B74\" ref-type=\"bibr\">Wang et al., 2009</xref> for more information), etc. From these resources, we compiled only species distribution records at county-level or finer scales. Species distributions were further supplemented with data from National Specimen Information Infrastructure (NSII, <uri xlink:type=\"simple\" xlink:href=\"http://www.nsii.org.cn/\">http://www.nsii.org.cn/</uri>), and screened. NSII included over fifteen million specimen records that are published recently. In total, our dataset consists the distributions of 19,780 Chinese plant species with data for sexual systems.</p><p>Species distributions were rasterized to a grid with a spatial resolution of 100 × 100 km to eliminate the potential bias of area on subsequent analyses. We removed the grid cells with less than 50% of their area along the country borders and coastal areas. In all, 949 grid cells were included in the analyses reported below.</p></sec><sec id=\"s2_3\"><title>Growth Form</title><p>Data on the growth forms of all angiosperm species in China were compiled from the <italic>Flora of China</italic> (<xref rid=\"B79\" ref-type=\"bibr\">Wu et al., 1994-2013</xref>) and <italic>Flora Reipublicae Popularis Bulgaricae</italic> (<xref rid=\"B41\" ref-type=\"bibr\">Kuzmanov and Kožuharov, 1968</xref>). All species were categorized into two growth forms: “woody” and “herbaceous”. Woody species included trees, shrubs and woody lianas, whereas “herbaceous” species included those recorded as herbs, herbaceous lianas, and subshrubs. Species with information on both sexual systems and geographical distributions included 10,200 woody species, 9,479 herbaceous species, and 101 undefined.</p></sec><sec id=\"s2_4\"><title>Climate Data</title><p>Climate can influence flower morphology through its influences on reproductive performance (e.g., pollen production) during the flowering period (<xref rid=\"B81\" ref-type=\"bibr\">Zhang, 2006</xref>), and hence could influence sexual systems of plants. Moreover, the reproductive (gametophytic) phase in flowering plants is often highly sensitive to temperature or water stresses. Thus we selected four variables to evaluate the influences of climate, namely, mean temperature of coldest quarter (MTCQ), mean precipitation of warmest quarter (MPWQ), actual evapotranspiration (AET), and aridity index (AI). AET reflects the influences of both water availability and environmental energy, and is significantly associated with ecosystem primary productivity. AET was calculated using the Thornthwaite equation (<xref rid=\"B70\" ref-type=\"bibr\">Thornthwaite and Hare, 1955</xref>) from the average monthly temperature and precipitation that were obtained from the WorldClim database (<xref rid=\"B34\" ref-type=\"bibr\">Hijmans et al., 2005</xref>). AI reflects the level of aridity and was calculated as the ratio of mean annual precipitation to annual potential evapotranspiration. Higher AI values represent lower aridity. The average and the full range of values for each climatic variable within each grid cell were estimated with the zonal statistics tool in ArcGIS 10.0.</p></sec><sec id=\"s2_5\"><title>Genus Age</title><p>To evaluate the effect of evolutionary time (i.e., genus age) on biogeographical patterns in sexual system compositions, we used the average genus age derived from three phylogenies including <xref rid=\"B80\" ref-type=\"bibr\">Zanne et al. (2014)</xref>, <xref rid=\"B45\" ref-type=\"bibr\">Lu et al. (2018)</xref>, and <xref rid=\"B67\" ref-type=\"bibr\">Smith and Brown (2018)</xref>. This is because (1) different dating methods may lead to different estimations of genus ages; and (2) incomplete phylogenies tend to overestimate the ages of some genera when their close relatives are missed from the phylogenies, and none of these phylogenies is complete at the genus level. For comparison, we also repeated all analyses using the genus ages of each phylogeny separately. It is noteworthy that the geographical patterns in mean genus age per grid cell estimated using the genus ages derived from each phylogeny were consistent with each other (<xref ref-type=\"fig\" rid=\"f1\"><bold>Figure 1A</bold></xref>). Moreover, all major findings based on these four age estimations were also highly consistent. Therefore, we included the results based on the average genus ages across the three phylogenies in the main text, and those based on the genus ages derived from each phylogeny in the supplementary materials for comparison.</p><fig id=\"f1\" position=\"float\"><label>Figure 1</label><caption><p>Spatial patterns in proportions of angiosperm species with different sexual systems at 100 × 100 km scale. From top down, the three rows represent the proportions of species with dioecy, monoecy, and hermaphroditism respectively. From left to right, the three columns represent all species, woody species and herbaceous species respectively. Grid cells without data are shown in white. The patterns in the proportions of sexual systems for woody species were updated from <xref rid=\"B77\" ref-type=\"bibr\">Wang et al., 2020</xref>.</p></caption><graphic xlink:href=\"fpls-11-01222-g001\"/></fig></sec><sec id=\"s2_6\"><title>Mature Plant Height</title><p>Plant mature height and longevity of plant species are expected to be positively associated (<xref rid=\"B46\" ref-type=\"bibr\">Marbà et al., 2007</xref>; <xref rid=\"B49\" ref-type=\"bibr\">Moles and Leishman, 2008</xref>; <xref rid=\"B50\" ref-type=\"bibr\">Moles et al., 2009</xref>). Here we used mature height as a proxy for longevity to test the association between sexual system and longevity. Mature height of plant species was obtained from <italic>Flora of China</italic> (<uri xlink:type=\"simple\" xlink:href=\"http://frps.eflora.cn/\">http://frps.eflora.cn/</uri>, accessed in November 2013; <uri xlink:type=\"simple\" xlink:href=\"http://www.efloras.org/flora_page.aspx?flora_id=2\">http://www.efloras.org/flora_page.aspx?flora_id=2</uri>, accessed in February 2014). As lower and upper limits of mature height are normally reported for most species in floras, we used the average of lower and upper limits to represent the mature height of species. Species without erect stems (e.g., woody lianas, scandent shrubs, climbers or epiphytes) were excluded from our analyses following <xref rid=\"B50\" ref-type=\"bibr\">Moles et al. (2009)</xref>. For each growth-form, we averaged the mature height across species within each grid cell to evaluate the effect of plant height on the biogeographical patterns of sexual system compositions (proportions of the three sexual systems per grid cell).</p></sec></sec><sec id=\"s3\"><title>Statistical Analyses</title><p>First, based on the data of sexual systems, growth forms, and distributions of all species, we calculated the proportions of species with different sexual systems within each grid cell for all species together and for each growth form separately. We used Pearson correlation coefficients to evaluate the similarity between the geographical patterns in the proportions of different sexual systems of woody and herbaceous species (e.g. woody dioecy vs. herbaceous dioecy, woody monoecy vs. herbaceous monoecy, woody hermaphroditism vs. herbaceous hermaphroditism) and used Dutilleul’s t test to evaluate the significance of these correlations due the influences of spatial autocorrelation on significance tests (using R package of “SpatialPack”; <xref rid=\"B21\" ref-type=\"bibr\">Dutilleul et al., 1993</xref>).</p><p>We used generalized linear models (GLMs) with quasi-Poisson residuals to evaluate the explanatory power of each predictor on the proportions of sexual systems per grid cell and this was conducted separately for each of the three species groups (i.e. overall, woody, and herbaceous). The climate variable (i.e., MTCQ, MPWQ, AET, and AI), genus age, and plant height were used as predictors and the proportions of each sexual systems per grid cell were used as response variables. The explanatory power of each variable was estimated as the adjusted R<sup>2</sup><sub>adj</sub> (%) of the GLM model. Modified t tests were used to correct for the effect of spatial autocorrelation on <italic>p</italic> values (<xref rid=\"B16\" ref-type=\"bibr\">Clifford et al., 1989</xref>).</p><p>To evaluate the influences of growth forms on the relationships between the proportion of sexual systems and different predictors, we first built spatial linear models (SLM) for the combined dataset of sexual system for both growth forms together using spatial simultaneous autoregressive error (SAR) models. SLMs could account for the effects of residual spatial autocorrelation on the significance tests of regression slopes (<xref rid=\"B39\" ref-type=\"bibr\">Kissling and Carl, 2008</xref>). The climate variables (i.e., AET, AI, MTCQ, and MPWQ), genus age, plant height, growth form (i.e., woody vs. herbaceous), and interaction terms between growth form and other predictors were included as the explanatory variables. All the predictors were standardized before fitting the models. Similarly, we also conducted SLMs for each growth form separately. Moran’s I was estimated for the residuals of SLMs, and indicated no spatial autocorrelations.</p><p>All analyses were performed in R 3.4.3 (<xref rid=\"B17\" ref-type=\"bibr\">Core Team R, 2017</xref>).</p></sec><sec sec-type=\"results\" id=\"s4\"><title>Results</title><sec id=\"s4_1\"><title>Patterns in the Proportions of Sexual Systems for Different Growth Forms</title><p>The spatial patterns in the proportions of hermaphroditic and monoecious species per grid cell were consistent between woody and herbaceous growth forms (r = 0.304 and p < 0.05 for proportion of hermaphroditic species; 0.382 and p < 0.05 for proportion of monoecious species; <xref rid=\"T1\" ref-type=\"table\"><bold>Table 1</bold></xref>). For all species and both growth forms, the proportion of hermaphroditic species per grid cell was higher in northwest China than in other regions, while the proportion of monoecious species was higher in eastern China (<xref ref-type=\"fig\" rid=\"f1\"><bold>Figure 1</bold></xref>). In contrast, spatial patterns in the proportion of dioecious species were not significantly correlated between woody and herbaceous growth forms (<italic>P</italic> > 0.05; <xref rid=\"T1\" ref-type=\"table\"><bold>Table 1</bold></xref>). The woody dioecious species were the most frequent in northeast China and medium in southern China, while herbaceous dioecious species were the most frequent in southwest China (<xref ref-type=\"fig\" rid=\"f1\"><bold>Figure 1</bold></xref>). Growth form was the strongest predictor for the proportions of all three sexual systems, and the effects of growth form on the proportions of sexual systems were higher than the other predictors by one order of magnitude (<xref rid=\"T2\" ref-type=\"table\"><bold>Tables 2</bold></xref>, <xref rid=\"T3\" ref-type=\"table\"><bold>3</bold></xref>, and <xref ref-type=\"supplementary-material\" rid=\"SM1\"><bold>A2</bold></xref>; <xref ref-type=\"fig\" rid=\"f2\"><bold>Figure 2</bold></xref>).</p><table-wrap id=\"T2\" position=\"float\"><label>Table 2</label><caption><p>The slopes of growth forms (woody vs. herbaceous), climate, genus age, plant height, and their interaction on proportions of sexual systems (i.e. dioecy, monoecy, and hermaphroditism) evaluated using spatial linear models (SLMs) with simultaneous autoregressive errors (SAR).</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Variables</th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Dioecy</th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Monoecy</th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Hermaphroditism</th></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Growth Form</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>0.169</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>−0.180</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−0.0192</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Height</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−0.0403</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>0.112</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−0.0473</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">AET</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>0.0101</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>0.00622</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−0.00657</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">AI</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>0.00673</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.00100</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−0.00174</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">MTCQ</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>0.00937</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>−0.00933</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−0.000256</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">MPWQ</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>−0.0135</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−0.0000633</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.000311</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Genus Age</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.00334</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−0.00214</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.000283</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Growth Form: AET</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−0.0000394</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−0.00167</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.000299</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Growth Form: AI</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−<bold>0.0142</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−<bold>0.0103</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>0.0244</bold></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Growth Form: MTCQ</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−<bold>0.0333</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>0.0145</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>0.0205</bold></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Growth Form: MPWQ</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>0.0282</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>0.0195</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−<bold>0.0475</bold></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Growth Form: age</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>0.0585</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>0.0266</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−<bold>0.0831</bold></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Growth Form: height</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>0.0507</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−<bold>0.113</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.0423</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">R<sup>2</sup></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.922</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.723</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.829</td></tr></tbody></table><table-wrap-foot><p>All continuous predictors were standardized prior analysis. The regression coefficients (slopes) with p < 0.05 is shown in bold. AET, actual evapotranspiration; AI, aridity index; MPWQ, mean precipitation of warmest quarter; MTCQ, mean temperature of coldest quarter; Age, evolutionary age. The genus age was calculated using the average genus age extracted from all three phylogenies including <xref rid=\"B80\" ref-type=\"bibr\">Zanne et al. (2014)</xref>, <xref rid=\"B45\" ref-type=\"bibr\">Lu et al. (2018)</xref>, <xref rid=\"B67\" ref-type=\"bibr\">Smith and Brown (2018)</xref>.</p></table-wrap-foot></table-wrap><table-wrap id=\"T3\" position=\"float\"><label>Table 3</label><caption><p>The slopes and significance of different predictors on proportions of sexual systems evaluated using spatial linear models (SLMs) with simultaneous autoregressive errors (SAR).</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th valign=\"top\" rowspan=\"2\" align=\"left\" colspan=\"1\">Variable</th><th valign=\"top\" colspan=\"3\" align=\"center\" rowspan=\"1\">All</th><th valign=\"top\" colspan=\"3\" align=\"center\" rowspan=\"1\">Herb</th><th valign=\"top\" colspan=\"3\" align=\"center\" rowspan=\"1\">Wood</th></tr><tr><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Herma.</th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Dioecy</th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Monoecy</th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Herma.</th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Dioecy</th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Monoecy</th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Herma.</th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Dioecy</th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Monoecy</th></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">AET</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>−0.0173</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>0.0120</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>0.00866</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>−0.00707</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>0.00258</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>0.00446</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>−0.0183</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>0.0137</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.00442</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">AI</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>−0.00219</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>0.00740</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>−0.00410</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>−0.00250</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>0.00206</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.000435</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>0.0126</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>−</bold>0.00434</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>−0.00840</bold></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">MTCQ</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>0.00738</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.00455</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>−0.0112</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>−</bold>0.000611</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>0.00260</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>−</bold>0.00193</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>0.0187</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>−0.0249</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>0.00619</bold></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">MPWQ</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.00130</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>−0.0154</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.00807</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.00138</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>−</bold>0.000430</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>−</bold>0.000962</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>−0.0279</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.00875</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>0.0195</bold></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Age</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>−0.00518</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>0.00994</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>−0.00585</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.000157</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>0.00428</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>−0.00444</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>−0.0378</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>0.0270</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>0.0108</bold></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Height</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>−0.0228</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>0.0221</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>0.00500</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>−0.00429</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>−0.00174</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>0.00598</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>−−0.00588</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>0.00885</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>-0.00301</bold></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">R<sup>2</sup></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.831</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.906</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.206</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.320</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.474</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.217</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.770</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.582</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.499</td></tr></tbody></table><table-wrap-foot><p>The regression coefficients (slopes) with p < 0.05 is shown in bold. Here genus age was average genus of the three phylogenies including <xref rid=\"B80\" ref-type=\"bibr\">Zanne et al. (2014)</xref>, <xref rid=\"B45\" ref-type=\"bibr\">Lu et al. (2018)</xref>, and <xref rid=\"B67\" ref-type=\"bibr\">Smith and Brown (2018)</xref>.</p></table-wrap-foot></table-wrap><fig id=\"f2\" position=\"float\"><label>Figure 2</label><caption><p>Proportions of species with different sexual systems among different growth forms in China. D, Dioecy; M, monoecy; H, hermaphroditism.</p></caption><graphic xlink:href=\"fpls-11-01222-g002\"/></fig></sec><sec id=\"s4_2\"><title>Effects of Climate on Distributions of Proportions of Sexual Systems</title><p>The proportions of all three sexual systems were relatively weakly related to climate for herbaceous species than for both all species and woody species (<xref rid=\"T3\" ref-type=\"table\"><bold>Table 3</bold></xref>). The growth form–climate interaction terms were significant in almost all cases (<xref rid=\"T2\" ref-type=\"table\"><bold>Table 2</bold></xref>). This indicates that the relationship between proportions of sexual systems and particular climate variables differed significantly between woody and herbaceous assemblages.</p><p>The effects of climate on proportions of sexual systems were significantly greater for woody species than for herbaceous species. For woody species, the proportion of hermaphroditism decreased with AET and MPWQ but increased with AI and MTCQ, while the proportion of dioecious showed almost opposite trends (<xref rid=\"T3\" ref-type=\"table\"><bold>Table 3</bold></xref>). For herbaceous species, all three sexual systems were weakly correlated with climate (R<sup>2</sup> ranged from 0.217 to 0.474 in <xref rid=\"T3\" ref-type=\"table\"><bold>Table 3</bold></xref>).</p></sec><sec id=\"s4_3\"><title>Effects of Genus Age on Geographical Distributions of Sexual Systems</title><p>Genus age was consistently a significant predictor of sexual system frequencies for woody species, but had only weak effect on herbaceous species. For woody species, the proportion of hermaphroditic was negatively correlated with genus age, while the proportion of dioecious species and monoecious species were positively correlated with genus age (all P < 0.05, <xref rid=\"T3\" ref-type=\"table\"><bold>Table 3</bold></xref>). The explanatory power of genus age was much stronger on the proportions of dioecious and hermaphroditic species than on monoecious species (<xref rid=\"T3\" ref-type=\"table\"><bold>Table 3</bold></xref>).</p></sec><sec id=\"s4_4\"><title>Effects of Mature Plant Height on the Geographical Frequency of Sexual Systems</title><p>Mature plant height per grid cell was a significant predictor for geographical patterns in the proportions of almost all sexual systems examined here (<xref rid=\"T2\" ref-type=\"table\"><bold>Tables 2</bold></xref> and <xref rid=\"T3\" ref-type=\"table\"><bold>3</bold></xref>), but its effect was much stronger for woody species than for herbaceous species (<xref rid=\"T3\" ref-type=\"table\"><bold>Tables 3</bold></xref> and <xref ref-type=\"supplementary-material\" rid=\"SM1\"><bold>A2</bold></xref>). For both growth forms, the proportion of hermaphroditic species decreased. For woody species, the proportion of dioecious species increased but the monoecious decreased with plant height per grid cell, whereas herbaceous species showed opposite trend (<xref rid=\"T3\" ref-type=\"table\"><bold>Table 3</bold></xref>).</p></sec></sec><sec sec-type=\"discussion\" id=\"s5\"><title>Discussion</title><p>Because different sexual systems are associated with variation of plant growth form, there are large-differences in the distributions of plant sexual systems between contrasting woody and herbaceous species. Compared with <xref rid=\"B77\" ref-type=\"bibr\">Wang et al. (2020)</xref>, we advanced our understanding of the spatial distribution of plant sexual systems and the determinants underlying these patterns, especially between growth forms. Growth form significantly differed the relationships between geographic patterns in the proportions of all three sexual systems with different biotic and abiotic factors. Climate, evolutionary age, and mature plant height were all significantly correlates of the distributions of sexual systems of woody species than herbaceous ones. These results are partially consistent with the existing evidence that species diversity of woody plants is more closely related with family age and climate than that of herbaceous plants (<xref rid=\"B54\" ref-type=\"bibr\">Oberle et al., 2009</xref>; <xref rid=\"B32\" ref-type=\"bibr\">Hawkins et al., 2011</xref>).</p><sec id=\"s5_1\"><title>Effect of Plant Height on Proportions of Sexual Systems</title><p>Plant height was a significant predictor of the proportions of the three sexual systems in almost all cases, which partly confirmed previous hypotheses about the coevolution between sexual systems and growth forms (<xref rid=\"B58\" ref-type=\"bibr\">Renner and Ricklefs, 1995</xref>; <xref rid=\"B72\" ref-type=\"bibr\">Vamosi et al., 2003</xref>). Both plant height and sexual systems could reflect the adaptation and evolutionary trend of plant life-history strategies to a certain extent, and they also tend to coevolve (<xref rid=\"B59\" ref-type=\"bibr\">Renner, 2014</xref>; <xref rid=\"B77\" ref-type=\"bibr\">Wang et al., 2020</xref>). Owing to limited mating opportunities, selection has tended to favor dioecy in long-lived woody species which are tall (<xref rid=\"B58\" ref-type=\"bibr\">Renner and Ricklefs, 1995</xref>; <xref rid=\"B53\" ref-type=\"bibr\">Obbard et al., 2006</xref>). Taller species with height dimorphism and greater dispersal investment may also benefit dioecious species (e.g. <italic>Salicaceae</italic>) by improving the efficiency in pollen and seed dispersal, which further promote the maintenance and continuation of species populations (<xref rid=\"B55\" ref-type=\"bibr\">Pickup and Barrett, 2012</xref>; <xref rid=\"B69\" ref-type=\"bibr\">Thomson et al., 2018</xref>). In contrast, hermaphroditism tends to be associated with short-lifespan species with low height because hermaphroditic species can self-pollinate and thus tend to accumulate much more deleterious mutations (<xref rid=\"B24\" ref-type=\"bibr\">Etterson and Mazer, 2016</xref>), and which may have higher mortality and extinction risks (<xref rid=\"B53\" ref-type=\"bibr\">Obbard et al., 2006</xref>; <xref rid=\"B15\" ref-type=\"bibr\">Cheptou, 2019</xref>).</p><p>The explanatory power of plant height on the frequency of sexual systems was stronger for woody than for herbaceous species. This may be likely because woody and herbaceous plant species have different adaptive strategies in resource allocation between reproductive and vegetative tissues (<xref rid=\"B2\" ref-type=\"bibr\">Abrahamson and Gadgil, 1973</xref>; <xref rid=\"B9\" ref-type=\"bibr\">Bonser and Aarssen, 2003</xref>). Woody species may have more resources for the investment to sexual systems and reproduction (<xref rid=\"B56\" ref-type=\"bibr\">Porth and El-Kassaby, 2014</xref>), while herbaceous species (e.g. the annual <italic>Mercurialis annua</italic>) tend to exhibit short life-cycles, fast rates of population but limited resource accumulation (<xref rid=\"B42\" ref-type=\"bibr\">Lanfear et al., 2013</xref>).</p></sec><sec id=\"s5_2\"><title>The Effects of Climate on the Proportions of Sexual Systems</title><p>The proportion of hermaphroditic species for all species combined was high in areas with high AI or MTCQ, consistent with <xref rid=\"B77\" ref-type=\"bibr\">Wang et al. (2020)</xref>. The proportion of dioecious species among all species combined (e.g. <italic>Anaphalis</italic>) was higher in southern China than in other regions. This finding supports the hypothesis that dioecy is more common in tropical and subtropical floras where climate is warm and humid (<xref rid=\"B28\" ref-type=\"bibr\">Freeman et al., 1976</xref>; <xref rid=\"B8\" ref-type=\"bibr\">Bawa, 1980</xref>; <xref rid=\"B7\" ref-type=\"bibr\">Bawa et al., 1985</xref>; <xref rid=\"B63\" ref-type=\"bibr\">Sakai et al., 1995</xref>; <xref rid=\"B73\" ref-type=\"bibr\">Vary et al., 2011</xref>). In contrast, the proportion of woody dioecious species (e.g. <italic>Rhamnus</italic> and <italic>Acer</italic>) is higher in Northeast China with relatively higher AET and MPWQ compared to other regions in China. These results reveal that the differences in requirements for hydrothermal conditions between woody and herbaceous growth forms may influence their geographical patterns in proportions of sexual systems, consistent with our hypothesis. Generally, woody species often have deep roots, and herbaceous species have relatively shallow roots. Therefore, herbaceous species have lower efficiencies in soil water usage and are more sensitive to reduced water supply compared with woody species (<xref rid=\"B25\" ref-type=\"bibr\">Franco, 2002</xref>). Correspondingly, the interaction terms between climate and growth form in the spatial linear models explaining the frequencies of different sexual systems were significant in most cases. The differences in the effects of climate on proportions of sexual systems between woody and herbaceous growth forms may be partly due to the coevolution of sexual systems with growth forms (<xref rid=\"B58\" ref-type=\"bibr\">Renner and Ricklefs, 1995</xref>; <xref rid=\"B72\" ref-type=\"bibr\">Vamosi et al., 2003</xref>; <xref rid=\"B11\" ref-type=\"bibr\">Case and Barrett, 2004</xref>). Moreover, our results further corroborate previous findings that climate may have influenced the spatial pattern of sexual systems <italic>via</italic> its effect on the composition of plant growth forms (<xref rid=\"B77\" ref-type=\"bibr\">Wang et al., 2020</xref>).</p><p>We also found that the sexual system composition of woody species was more closely associated with contemporary climate than that of herbaceous species, which is consistent with previous studies at broad scales (<xref rid=\"B54\" ref-type=\"bibr\">Oberle et al., 2009</xref>; <xref rid=\"B74\" ref-type=\"bibr\">Wang et al., 2009</xref>). Compared with herbaceous species that can be protected from frost by being annual/biennial or by the production of underground buds, woody plant species typically with longer life cycles and large sizes expose their stems and buds (with reproductive organs) in climate. Thus climate has been found to have significant influences on the spatial pattern of woody species (<xref rid=\"B18\" ref-type=\"bibr\">Currie and Paquin, 1987</xref>) compared with herbaceous species. These results suggest that the differences in the effects of climate on geographical patterns in proportions of sexual systems for woody and herbaceous species may also be associated with the differences in ecophysiological strategies related to resource acquisition and utilization between growth forms.</p></sec><sec id=\"s5_3\"><title>Effect of Evolutionary Time on Proportions of Sexual Systems</title><p>For both woody and all species, the proportion of dioecious species was positively correlated with mean genus age per grid cell, which support our hypothesis that dioecy tends to be maintained in regions with older floras. Similarly, the proportion of woody monoecious species was also positively correlated with mean genus age. These results partly support and expand the hypothesis that dioecy and monoecy are often found to be significantly associated with each other across phylogenies of woody angiosperms (<xref rid=\"B58\" ref-type=\"bibr\">Renner and Ricklefs, 1995</xref>). In contrast, the proportions of sexual systems for herbaceous species were weakly correlated with mean genus age (e.g. <italic>Apiaceae</italic>, <italic>Asteraceae</italic>, <italic>Cyperaceae</italic>), which might be due to the higher population turnover rate and faster adaptation to climate of herbaceous species than those of woody species (e.g. <italic>Acer</italic> and <italic>Salicaceae</italic>, <xref rid=\"B53\" ref-type=\"bibr\">Obbard et al., 2006</xref>; <xref rid=\"B66\" ref-type=\"bibr\">Smith and Beaulieu, 2009</xref>). Moreover, the effect of genus age on the proportions of woody sexual systems was much stronger than on those of herbaceous species. The differences in the effect of evolutionary age on proportions of sexual systems between woody and herbaceous growth forms might reflect their differences in evolutionary rates (i.e. speciation and extinction rates). Molecular studies have revealed that compared with herbaceous species, woody species such as trees tend to have longer generation times and lower evolutionary rate (<xref rid=\"B66\" ref-type=\"bibr\">Smith and Beaulieu, 2009</xref>), and thus may have lower chances to transit between sexual systems in response to environmental changes.</p></sec></sec><sec sec-type=\"conclusions\" id=\"s6\"><title>Conclusions</title><p>Here we mapped the geographical distributions of the frequencies of sexual systems and compared the effects of abiotic and biotic factors on sexual system frequencies for both woody and herbaceous flowering plant species in China. The results suggest that woody and herbaceous species differed significantly in their patterns in the frequencies of sexual systems, and also in the determinants underlying these patterns. The proportions of sexual systems of woody species were more strongly influenced by climate, evolutionary age, and mature height, whereas those of herbaceous species were less influenced by these factors. These findings shed light on previous hypotheses about the association between sexual systems and growth forms, and further demonstrate the necessity to differentiate woody and herbaceous growth forms when investigating the evolution and ecology of sexual systems. It is noteworthy that sexual systems may also vary intraspecifically across populations of a single species (e.g. <italic>Mercurialis annua</italic>, <xref rid=\"B20\" ref-type=\"bibr\">Dorken et al., 2017</xref>). Exploring the intraspecific sexual system variations for species with different growth forms along environmental gradients would further improve our understanding on the drivers of sexual system variations. Our study also suggests that compared with both dioecious and hermaphroditic species, more attention should be paid to monoecious species from both evolutionary and ecological perspectives. This study improves our understanding of the mechanisms underlying geographical patterns of reproductive trait diversity across different growth forms and their responses to climate.</p></sec><sec sec-type=\"data-availability\" id=\"s7\"><title>Data Availability Statement</title><p>The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation, to any qualified researcher.</p></sec><sec id=\"s8\"><title>Author Contributions</title><p>ZW and YW conceived the idea and design of the study. YW, TL, AL, YLi, and YLiu organized the database. YW and TL performed the statistical analysis. YW wrote the first draft of the manuscript. ZW has comprehensively revised the article. All authors contributed to the article and approved the submitted version.</p></sec><sec sec-type=\"funding-information\" id=\"s9\"><title>Funding</title><p>This work was supported by the National Key Research Development Program of China (#2017YFA0605101; #2018YFA0606104), the Strategic Priority Research Program of Chinese Academy of Sciences (#XDB31000000), and National Natural Science Foundation of China (#31901216, #31988102, #31911530102).</p></sec><sec id=\"s10\"><title>Conflict of Interest</title><p>The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p></sec></body><back><sec sec-type=\"supplementary-material\" id=\"s11\"><title>Supplementary Material</title><p>The Supplementary Material for this article can be found online at: <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.frontiersin.org/articles/10.3389/fpls.2020.01222/full#supplementary-material\">https://www.frontiersin.org/articles/10.3389/fpls.2020.01222/full#supplementary-material</ext-link></p><supplementary-material content-type=\"local-data\" id=\"SM1\"><media xlink:href=\"Table_1.doc\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material></sec><ref-list><title>References</title><ref id=\"B1\"><mixed-citation publication-type=\"journal\">\n<person-group person-group-type=\"author\"><name><surname>Aarssen</surname><given-names>L. 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[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Front Psychol</journal-id><journal-id journal-id-type=\"iso-abbrev\">Front Psychol</journal-id><journal-id journal-id-type=\"publisher-id\">Front. Psychol.</journal-id><journal-title-group><journal-title>Frontiers in Psychology</journal-title></journal-title-group><issn pub-type=\"epub\">1664-1078</issn><publisher><publisher-name>Frontiers Media S.A.</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32849113</article-id><article-id pub-id-type=\"pmc\">PMC7432135</article-id><article-id pub-id-type=\"doi\">10.3389/fpsyg.2020.01903</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Psychology</subject><subj-group><subject>Original Research</subject></subj-group></subj-group></article-categories><title-group><article-title>The Impact of Organizational Psychology Factors for the Cross-Border Legal Service Entrepreneurs</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Xu</surname><given-names>Chengjin</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><xref ref-type=\"corresp\" rid=\"c001\"><sup>*</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/961241/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Zhang</surname><given-names>Zhe</given-names></name><xref ref-type=\"aff\" rid=\"aff2\"><sup>2</sup></xref></contrib></contrib-group><aff id=\"aff1\"><sup>1</sup><institution>School of Law, Shandong Normal University</institution>, <addr-line>Jinan</addr-line>, <country>China</country></aff><aff id=\"aff2\"><sup>2</sup><institution>School of Economics and Management, Beijing Jiaotong University</institution>, <addr-line>Beijing</addr-line>, <country>China</country></aff><author-notes><fn fn-type=\"edited-by\"><p>Edited by: Chih-Hung Yuan, University of Electronic Science and Technology of China, China</p></fn><fn fn-type=\"edited-by\"><p>Reviewed by: Apple Liu, Sultan Idris Education University, Malaysia; Qian Song, Pukyong National University, South Korea</p></fn><corresp id=\"c001\">*Correspondence: Chengjin Xu, <email>[email protected]</email></corresp><fn fn-type=\"other\" id=\"fn004\"><p>This article was submitted to Organizational Psychology, a section of the journal Frontiers in Psychology</p></fn></author-notes><pub-date pub-type=\"epub\"><day>11</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><year>2020</year></pub-date><volume>11</volume><elocation-id>1903</elocation-id><history><date date-type=\"received\"><day>23</day><month>4</month><year>2020</year></date><date date-type=\"accepted\"><day>10</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>Copyright © 2020 Xu and Zhang.</copyright-statement><copyright-year>2020</copyright-year><copyright-holder>Xu and Zhang</copyright-holder><license xlink:href=\"http://creativecommons.org/licenses/by/4.0/\"><license-p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</license-p></license></permissions><abstract><p>Trade friction has always been a prominent feature in the current economic development of the world. Its impacts on multinational enterprises are self-evident, but its psychological effects on the multinational entrepreneurs are still unclear. In order to understand the impacts of trade friction on psychological effects of multinational legal service entrepreneurs, 305 multinational entrepreneurs were selected in this study for questionnaire survey, and Spearman’s correlation and regression models were used to analyze the correlation among economic pressure, the thought of recession, self-efficacy, and social support. The structural equation model was used to analyze the influence path of economic pressure and social support on the thought of entrepreneurial recession, as well as the influence path of multinational entrepreneurship orientation and value-chain potential on the international performance. The results show that economic pressure is significantly and positively correlated with the thought of recession and self-efficacy extremely and significantly and negatively correlated with objective support and support utilization extremely; social support will reverse the thought of entrepreneurial recession caused by the economic pressure; the indirect impact path coefficient of social support utilization in economic pressure and entrepreneurial recession is – 0.281; the indirect impact path coefficient of value-chain potential in multinational entrepreneurial motivation and international performance is – 0.424. It shows that trade friction will indirectly trigger the thought of entrepreneurial recession of entrepreneurs by reducing their economic incomes. Besides, the social support utilization can significantly regulate the relationship between the economic pressure and the thought of entrepreneurial recession. Therefore, the value-chain potential plays an intermediary role in multinational entrepreneurial motivation and international performance.</p></abstract><kwd-group><kwd>trade friction</kwd><kwd>economic pressure</kwd><kwd>structural equation model</kwd><kwd>social support</kwd><kwd>the thought of entrepreneurial recession of entrepreneurs</kwd><kwd>value-chain potential</kwd></kwd-group><counts><fig-count count=\"8\"/><table-count count=\"8\"/><equation-count count=\"0\"/><ref-count count=\"47\"/><page-count count=\"14\"/><word-count count=\"0\"/></counts></article-meta></front><body><sec id=\"S1\"><title>Introduction</title><p>In recent years, there is an upsurge of entrepreneurship in China. Nevertheless, there is a serious problem of oversupply in all fields; especially in the traditional fields, due to the increasing competitiveness in domestic market of China, it is difficult for more and more entrepreneurs to get success in the domestic market (<xref rid=\"B43\" ref-type=\"bibr\">Verbeke and Yuan, 2013</xref>). There are often many business opportunities in overseas markets, especially in developing countries. Therefore, Chinese entrepreneurs have shifted their entrepreneurial perspective to overseas markets in order to achieve success through overseas entrepreneurship. Multinational enterprise becomes one of the important forms of economic globalization, so it has been widely concerned by experts and scholars (<xref rid=\"B15\" ref-type=\"bibr\">Dimitratos et al., 2014</xref>; <xref rid=\"B13\" ref-type=\"bibr\">Da Silva Lopes et al., 2019</xref>). In order to enter the overseas market, multinational enterprises have made a lot of foreign investment, which can not only promote local economic development and employment but also benefit from technology spillovers (<xref rid=\"B42\" ref-type=\"bibr\">Tippmann et al., 2018</xref>). However, the work performance of multinational enterprises will also be affected by factors such as competition effects and government subsidy policies (<xref rid=\"B16\" ref-type=\"bibr\">Emelifeonwu and Valk, 2019</xref>). In addition, the entrepreneurship of multinational enterprises has to consider the profit transfer, fair trading, and other related points between the two countries. When there is a problem, there will be a “trade friction” (<xref rid=\"B24\" ref-type=\"bibr\">Jiang et al., 2019</xref>). China has now become the third largest trading country in the world, causing a shock on the original interest pattern, so the trade friction is inevitable. However, it is still unknown whether the trade frictions will affect the psychological effects of multinational entrepreneurs and in turn affect the work performance of these entrepreneurs.</p><p>With the deepening of economic globalization, the trend of entrepreneurial strategy and international diversification strategy are considered as key factors for multinational enterprises to succeed in the global market, and related studies have shown that the trend entrepreneurial strategy has a directly positive correlation with the entrepreneurial performance of multinational enterprises (<xref rid=\"B32\" ref-type=\"bibr\">McGee and Peterson, 2019</xref>; <xref rid=\"B36\" ref-type=\"bibr\">Palmer et al., 2019</xref>). The entrepreneur is the core of entrepreneurship, so the physical and psychological factors of entrepreneurs are critical, and the individual characteristics of entrepreneurs are the direct constraints of entrepreneurial activities in a specific entrepreneurial environment (<xref rid=\"B46\" ref-type=\"bibr\">Yueh et al., 2020</xref>). Studies have shown that the individual characteristics of entrepreneurs can also affect the entrepreneurial performance. Therefore, it is of great significance to explore the impacts of the trend of entrepreneurial strategy and the individual characteristics of entrepreneur on the work performance of multinational enterprises for enterprises to continue the multinational entrepreneurial activities and promote the development of enterprises (<xref rid=\"B14\" ref-type=\"bibr\">Dijkhuizen et al., 2018</xref>; <xref rid=\"B22\" ref-type=\"bibr\">Howell, 2018</xref>). Most of the entrepreneurial researches in the past had been dedicated to the outputs of small businesses and startups, instead of focusing on international acquisition scenarios.</p><p>In order to explore the impacts of the trend of internationalization on the psychological effects and work performance of multinational entrepreneurs, the legal service entrepreneurs of knowledge-based service companies were thus selected as the research objects. The impacts of trade friction on the psychological effects of multinational entrepreneurs and the impact mechanism of multinational entrepreneurial orientation on the international performance were explored in this study firstly. Finally, the validity and authenticity of hypotheses proposed in this study were verified through actual investigations. The results of this study aimed to lay a foundation for understanding the psychological effects of multinational entrepreneurs and promoting the international work performance of multinational enterprises.</p></sec><sec id=\"S2\"><title>Literature Overview</title><sec id=\"S2.SS1\"><title>Psychological Effect of Entrepreneurs</title><p>Entrepreneurship can help entrepreneurs achieve their personal ideals and promote economic development and technological innovation. Researches in entrepreneurship-related fields had been conducted from the perspectives of management, psychology, and sociology. <xref rid=\"B29\" ref-type=\"bibr\">Lee et al. (2019)</xref> explored the differences between family and non-family senior management teams in organizational and psychological ownership of work, and the results showed that there was no significant difference between them and no significant impact on the entrepreneurship of the enterprise. <xref rid=\"B21\" ref-type=\"bibr\">Hooshangi and Loewenstein (2018)</xref> found that reduction in the investment opportunities could threaten the entrepreneur’s willingness to invest, and reduced investment costs would inhibit the investment decisions of pioneer entrepreneurs, while loose proprietary systems could have a positive impact on psychology of entrepreneurship. <xref rid=\"B47\" ref-type=\"bibr\">Zou et al. (2016)</xref> explored the different interests and goals of the inherent conflict between entrepreneurs and venture capitalists based on the psychological capital and then put forward a series of theoretical propositions about solving venture capital and strategic countermeasures. These studies revealed that the individual characteristics of entrepreneurs had a significant impact on entrepreneurial decision-making and entrepreneurship. <xref rid=\"B26\" ref-type=\"bibr\">Kautto (2019)</xref> found that the psychosocial impact of exogenous policy intervention could promote the transfer of entrepreneurship. <xref rid=\"B35\" ref-type=\"bibr\">Muhammad et al. (2020)</xref> indicated that external pressure had a positive and direct impact on behaviors of entrepreneurs and that the sustainable orientation of strategy can provide differentiation for enterprisers. In summary, psychological differences of individuals and external pressures affect the behaviors of entrepreneurs, yet the impacts of changes in the external environment and policies on the psychological effects of multinational entrepreneurs still need to be further explored.</p></sec><sec id=\"S2.SS2\"><title>Impacts of International Performance of Multinational Entrepreneurs</title><p>Entrepreneurship is a significant form to achieve the social and economic development. In exploring the international entrepreneurship, it is very important to improve the performance of multinational enterprise. In order to explore the impact of strategic responsibilities of the multinational enterprises on the performance of their subsidiaries, <xref rid=\"B40\" ref-type=\"bibr\">Sarabi et al. (2020)</xref> found through building a model that the entrepreneurial leadership of the chief executive officer (CEO) of a subsidiary can improve the enterprise’s performance. <xref rid=\"B8\" ref-type=\"bibr\">Boone et al. (2019)</xref> revealed that the entrepreneurial spirit and the innovation enthusiasm of the top management team could affect the performance of the enterprise. In order to explore the impact of external crises on the performance of subsidiaries of the multinational enterprises, <xref rid=\"B31\" ref-type=\"bibr\">Martins et al. (2019)</xref> found that the sense of crisis affected the business performance of multinational enterprises through verification of the least-square structural equation model. <xref rid=\"B11\" ref-type=\"bibr\">Collings et al. (2019)</xref> revealed that the multinational strategy determined the goals of the global talent management system and also affected the performances of enterprises. From the perspective of individual human resources, it indicated that the personal performance could be improved by enlarging the human resources. <xref rid=\"B17\" ref-type=\"bibr\">Fernandes et al. (2018)</xref> constructed the framework of the multinational entrepreneur to affect the working performance, and the results showed that the entrepreneurial spirit can promote the working performance through management. Based on the above researches, it can be found that individual characteristics and strategic decisions of entrepreneurs can affect the work performance of multinational enterprises. However, the existing research lacks a definition of entrepreneurship-oriented dimension and its impact on the international performance of multinational companies.</p><p>To sum up, the multinational legal service entrepreneurs were taken as the research objects in this paper. In the form of questionnaire, the impact path of the economic pressure and social support of the trade friction on the entrepreneurial recession of entrepreneurs and the impact path of multinational entrepreneurial orientation and value-chain potential on the international performance of enterprises were analyzed. In this way, this study will lay the theoretical foundation for finding out the way to relieve from the psychological pressure of multinational entrepreneur and improving their working performance.</p></sec></sec><sec sec-type=\"materials|methods\" id=\"S3\"><title>Materials and Methods</title><sec id=\"S3.SS1\"><title>Research Objects</title><p>A total of 310 Chinese entrepreneurs of legal services who started businesses in the United States, Canada, Indonesia, Brazil, and the United Kingdom were selected as the research objects, the questionnaires were distributed online from June 2019 to December 2019, and 305 questionnaires were returned, so the response rate was 98.39%. The 305 subjects were 21–38 years old, with an average age of 26.88 ± 5.52. Studies show that the risk aversion of female entrepreneurs is higher than that of male and that self-efficiency has a strong predictive effect on the entrepreneurial behavior. Therefore, the gender, age, and self-efficiency were taken as the control variables for subsequent analysis.</p></sec><sec id=\"S3.SS2\"><title>Model Building of Effect on Entrepreneur’s Recession From Economic Pressure and Social Support From the Trade Friction</title><p>It is believed in this study that the economic pressure brought about by the trade friction could cause the entrepreneur to experience some negative emotions (anxiety, despair, depression, etc.). Based on the theory of learned helplessness, the theoretical model was built for effect of the economic pressure and social support caused by the trade friction on the entrepreneur’s thought of recession. The theoretical model is shown in <xref ref-type=\"fig\" rid=\"F1\">Figure 1</xref>.</p><fig id=\"F1\" position=\"float\"><label>FIGURE 1</label><caption><p>Model of effect of the entrepreneur’s thought of recession.</p></caption><graphic xlink:href=\"fpsyg-11-01903-g001\"/></fig><p>According to <xref ref-type=\"fig\" rid=\"F1\">Figure 1</xref>, the economic pressure is an independent variable, entrepreneurial recession psychology is the dependent variable, and the sub-dimension under the social support is the intermediary variable. Therefore, the following hypotheses were proposed in this study:</p><list list-type=\"simple\"><list-item><label>a.</label><p>The economic pressure could be promoted significantly by the trade friction;</p></list-item><list-item><label>b.</label><p>The thought of entrepreneurial recession could be promoted significantly by the economic pressure;</p></list-item><list-item><label>c.</label><p>The promotion of economic pressure on the thought of entrepreneurial recession could be regulated reversely by the social support;</p></list-item><list-item><label>c1.</label><p>The promotion of economic pressure on the thought of entrepreneurial recession could be regulated reversely by the subjective support;</p></list-item><list-item><label>c2.</label><p>The promotion of economic pressure on the thought of entrepreneurial recession could be regulated reversely by the objective support;</p></list-item><list-item><label>c3.</label><p>The promotion of economic pressure on the thought of entrepreneurial recession could be regulated reversely by the support utilization.</p></list-item></list></sec><sec id=\"S3.SS3\"><title>Model Building of Correlation on the Multinational Entrepreneurial Orientation and the International Performance</title><p>In order to explore the correlation on the multinational entrepreneurial orientation and international performance, the theoretical model shown in <xref ref-type=\"fig\" rid=\"F2\">Figure 2</xref> below was proposed in this study by taking the global value chain as the intermediary variable.</p><fig id=\"F2\" position=\"float\"><label>FIGURE 2</label><caption><p>Model of effect of the entrepreneurship international performance.</p></caption><graphic xlink:href=\"fpsyg-11-01903-g002\"/></fig><p>Based on the above model, the below hypotheses are proposed in this study:</p><list list-type=\"simple\"><list-item><label>d.</label><p>The international performance could be promoted by the multinational entrepreneurial orientation;</p></list-item><list-item><label>d1.</label><p>The international performance could be promoted by innovation in the multinational entrepreneurial orientation;</p></list-item><list-item><label>d2.</label><p>The international performance could be promoted by antecedence in the multinational entrepreneurial orientation;</p></list-item><list-item><label>d3.</label><p>The international performance could be promoted by adventure in the multinational entrepreneurial orientation;</p></list-item><list-item><label>e.</label><p>The international performance could be performed by the value-chain potential;</p></list-item><list-item><label>e1.</label><p>The international performance could be performed by complexity of the trade;</p></list-item><list-item><label>e2.</label><p>The international performance could be performed by codability of the product;</p></list-item><list-item><label>e3.</label><p>The international performance could be performed by the supply capacity;</p></list-item><list-item><label>f.</label><p>The value-chain potential is the intermediary variable for multinational entrepreneurial orientation to affect the international performance.</p></list-item></list></sec><sec id=\"S3.SS4\"><title>Design of Evaluation Tools</title><p>The evaluation scales used in this study were all widely used, reliable, and valid evaluation tools in line with the actual conditions.</p><sec id=\"S3.SS4.SSS1\"><title>Evaluation Tools of the Social Support</title><p>The Social Support Rating Scale (SSRS) was used by <xref rid=\"B5\" ref-type=\"bibr\">Basinska and Rozkwitalska (2020)</xref>. The scale included 10 items and 3 dimensions, which were the degree of subjective support, degree of objective support, and degree of support utilization. In addition, the Likert-4 rating method (1–4 scores indicate from “no” to “yes”) was used for evaluation in this scale. The subjective support included the support at the emotional level or resources from the relatives, friends, social networks, etc. The objective support refers to the actual support resources and also includes the material support provided by individuals, organizations, or the society. The support utilization refers to the utilization degree of the actual or perceived resource by individuals.</p></sec><sec id=\"S3.SS4.SSS2\"><title>Evaluation Tools of the Economic Pressure</title><p>The Economic Pressure Rating Scale (EPRS) (<xref rid=\"B37\" ref-type=\"bibr\">Pollack et al., 2012</xref>) was compiled to evaluate the economic pressure of entrepreneur under the trade friction. The scale was supplemented by that “the recent economic environment has negatively affected my company,” “the business has been in trouble in the past year,” and “I have to bear a lot of pressures and responsibilities, and play a lot of roles.” The Likert-7 rating method (1–7 points indicate from “strongly disagree” to “strongly agree”) was used for evaluation in this scale.</p></sec><sec id=\"S3.SS4.SSS3\"><title>Evaluation Tools of Entrepreneurial Recession</title><p>The exiting entrepreneurship assessment scale was used for recompilation and was applied to evaluate the entrepreneur’s thought of recession (<xref rid=\"B41\" ref-type=\"bibr\">Schuster et al., 2013</xref>). Three items were used for the evaluation, namely, “in the next 1 year, I will try my best to quit my current entrepreneurial activities,” “if I continue my business, I will be very worried,” and “continuing to start a business is not as passionate as when I started my business.” The Likert-7 rating method (1–7 points indicate from “strongly disagree” to “strongly agree”) was used for evaluation in this scale.</p></sec><sec id=\"S3.SS4.SSS4\"><title>Evaluation Tools of Self-Efficiency</title><p>The entrepreneurship self-efficacy assessment scale (<xref rid=\"B10\" ref-type=\"bibr\">Chen et al., 2019</xref>) was used for recompilation and was applied to evaluate the entrepreneur’s self-efficacy. In this study, 17 items were used for the evaluation. The Likert-5 rating method (1–5 points indicate from “strongly disagree” to “strongly agree”) was used for evaluation in this scale.</p></sec><sec id=\"S3.SS4.SSS5\"><title>Evaluation Tools of Multinational Entrepreneurial Orientation</title><p>Based on the three dimensions of innovativeness, proactiveness, and risk taking and the “9-item semantic difference scale” (<xref rid=\"B45\" ref-type=\"bibr\">Williams and Lee, 2009</xref>), the scale was improved by combining with the internationalization perspective of the multinational enterprises. The improved scale could be seen as below <xref rid=\"T1\" ref-type=\"table\">Table 1</xref>. When this scale was used for evaluation, it was indicated by “yes” for 1 score or “no” for 0 score of the evaluation.</p><table-wrap id=\"T1\" position=\"float\"><label>TABLE 1</label><caption><p>Evaluation scale of the multinational entrepreneurial orientation.</p></caption><table frame=\"hsides\" rules=\"groups\" cellspacing=\"5\" cellpadding=\"5\"><thead><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Dimensionality</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>No.</bold></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Item</bold></td></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Innovation</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Focusing on marketing, products, or services? Or focusing on investment and leadership and innovation of the R & D and technology</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Having new products/services?</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Having great changes or the product/service path?</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Proactiveness</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Response to the competitor, or initiated by itself?</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">The first to introduce the new product/service and the technology on management and operation?</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">6</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Avoiding competitive conflicts in the form of “peaceful coexistence” as often?</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Risk-taking</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">7</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Being inclined to the international project with low risk?</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">8</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Achieving the goals step by step in the international environment?</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">9</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Reducing the error rate of decision with an attitude of await-and-see?</td></tr></tbody></table></table-wrap></sec><sec id=\"S3.SS4.SSS6\"><title>Evaluation Tools of Value-Chain Potential</title><p>The value-chain potential was measured based on the trade complexity, product codability, and supply capacity (<xref rid=\"B38\" ref-type=\"bibr\">Rashid et al., 2019</xref>). The finally designed scale is shown in <xref rid=\"T2\" ref-type=\"table\">Table 2</xref>. When this scale was used for evaluation, it was indicated by “yes” for 1 score or “no” for 0 score of the evaluation.</p><table-wrap id=\"T2\" position=\"float\"><label>TABLE 2</label><caption><p>Evaluation scale of value-chain potential.</p></caption><table frame=\"hsides\" rules=\"groups\" cellspacing=\"5\" cellpadding=\"5\"><thead><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Dimensionality</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>No.</bold></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Item</bold></td></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Trade complexity</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Having the need for information exchange by exchange, communication, and share</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">High complexity for having the need for information exchange by exchange, communication, and share</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Product codability</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Quantity of information on related design or quality besides the formal documents</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">High complexity for information on related design or quality besides the formal documents</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Supply capacity</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Strong acceptance to demand changes of the market compared with other competitors</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">6</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Strong acceptance to higher requirements from the service-provided side compared with other competitors</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">7</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">High assets proprietary of the company</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">8</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">High ability to bargain in the product trade</td></tr></tbody></table></table-wrap></sec><sec id=\"S3.SS4.SSS7\"><title>Evaluation Tools of International Performance</title><p>Based on the research results of other scholars (<xref rid=\"B1\" ref-type=\"bibr\">Abosede et al., 2018</xref>; <xref rid=\"B25\" ref-type=\"bibr\">Karami and Tang, 2019</xref>), the international performance was evaluated based on the four dimensions of foreign sales growth, changes in international market share, international investment return rate, and foreign operation satisfaction. The finally designed evaluation scale is shown in <xref rid=\"T3\" ref-type=\"table\">Table 3</xref> below. When this scale was used for evaluation, it was indicated by “yes” for 1 score or “no” for 0 score of the evaluation.</p><table-wrap id=\"T3\" position=\"float\"><label>TABLE 3</label><caption><p>Evaluation scale of the international performance.</p></caption><table frame=\"hsides\" rules=\"groups\" cellspacing=\"5\" cellpadding=\"5\"><thead><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>No.</bold></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Item</bold></td></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Rapid increase of the company’s overseas sales revenue in recent years</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">2</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Rapider increase of the company’s sales revenue in the oversea markets than other major competitors</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">3</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Rapid increase of overseas sales profits in recent years</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">4</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Rapider increase of the company’s sales profits in the oversea markets than other major competitors</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">5</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Rapid increase of the company’s overseas market shares in recent years</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">6</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Rapider increase of the company’s overseas market shares than other major competitors</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">7</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Higher return rate of the company’s oversea investment in recent years than other major competitors</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">8</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Significant increase of the company’s sales rate in oversea market</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">9</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">High operational satisfaction of the company in oversea market in recent years</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">10</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">High satisfaction feedback of the company from the clients</td></tr></tbody></table></table-wrap></sec></sec><sec id=\"S3.SS5\"><title>Statistical Analysis</title><p>The statistical processing was performed by SPSS 19.0. The validity tests of the evaluation tools were performed by using the Bartlett’s spherical test and KMO value (it was deemed as appropriation when KMO value was higher than 0.7, and it was deemed as appropriation when Bartlett’s spherical test <italic>p</italic> < 0.05). The reliability tests of the evaluation tools were performed by using the Cronbach’s α (it was deemed as high reliability when Cronbach’s α was higher than 0.7). The principal component analysis (PCA) was used to extract the factors, whose eigenvalue was higher than 1, in the variable tables. The descriptive statistics was used for statistical analysis of the sample distribution of each variable. The correlation of each variable was analyzed with the Pearson correlation. Hypotheses proposed in this study were verified by the process of hierarchical regression. When <italic>P</italic> < 0.05, it is considered that it has a statistical significance.</p></sec></sec><sec id=\"S4\"><title>Results</title><sec id=\"S4.SS1\"><title>Analysis on the Current Status of the Global and China-Related Trade Friction</title><p>Comparing the changes of the total number of the global and China-related trade frictions from 2009 to 2019, <xref ref-type=\"fig\" rid=\"F3\">Figure 3</xref> indicates that in the past 10 years, the trade frictions in the world have been more than 150 cases and those in China have been more than 70 cases. In 2019, the China-related trade frictions accounted for 30.46% of the cases in the whole world. Overall, the number of China-related trade friction showed a high trend all the time. The annual growth rate of the China-related trade friction showed the same trend as the annual growth rate of trade friction all over the world.</p><fig id=\"F3\" position=\"float\"><label>FIGURE 3</label><caption><p>Change trends of total number of the global and China-related trade frictions from 2009 to 2019.</p></caption><graphic xlink:href=\"fpsyg-11-01903-g003\"/></fig><p>Comparing the proportions of the number of the global and China-related trade frictions from 2009 to 2019, <xref ref-type=\"fig\" rid=\"F4\">Figure 4</xref> shows that among all the global and China-related trade frictions, the anti-dumping cases accounted for the largest proportion, which was 80% (2363 cases) and 67% (715 cases), respectively, while the proportions of anti-subsidy were 12% (344 cases) and 14% (145 cases), respectively, and the proportions of safeguard precaution (8% (222 cases) and 9% (201 cases) had no great difference. In addition, there were also some trade frictions related to the special safeguards, accounting for 0% (10 cases) and 1% (10 cases), respectively. It shows that most countries in the world took the boycott measures such as increased taxation currently when dumping products from outside the country on the market. At the same time, the importing country offset the subsidies for imported products by imposing countervailing measures, price commitments, or import restrictions in order to protect the domestic industries and restore fair competition.</p><fig id=\"F4\" position=\"float\"><label>FIGURE 4</label><caption><p>Proportions of the number of the global and China-related trade frictions from 2009 to 2019 [Figure <bold>(A)</bold> is the proportion of the global trade frictions; Figure <bold>(B)</bold> is the proportion of China-related trade frictions].</p></caption><graphic xlink:href=\"fpsyg-11-01903-g004\"/></fig><p>The top 10 complaining countries and the top 10 complained industries in the China-related trade frictions in 2009–2019 were compared. It could be seen from <xref ref-type=\"fig\" rid=\"F5\">Figure 5A</xref> that, among the top 10 complaining countries, the cases both in the United States and in India were more than 150, and the number of complaints in the past 10 years was much higher than that in the European Union (EU) and other countries or organizations. It could be seen from <xref ref-type=\"fig\" rid=\"F5\">Figure 5B</xref> that, among the top 10 complained industries, the numbers of metal product industry (197 cases), steel industry (196 cases), and chemical raw materials and product industries (164 cases) are much higher than those in other industries, and the most complained industries belong to the manufacturing industry.</p><fig id=\"F5\" position=\"float\"><label>FIGURE 5</label><caption><p>The top 10 complaining countries and the complained industries in the China-related trade frictions in 2009–2019. Figure <bold>(A)</bold> is the top 10 complaining countries; Figure <bold>(B)</bold> is the top 10 complained countries; A is for the fabricated metal industry; B is for the steel industry; C is for the chemical raw material and product industry; D is for the non-metallic product industry; E is for the textile industry; F is for the electrical industry; G is for the paper manufacturing industry; H is for the non-ferrous metal industry; I is for the food industry; and J is for the rubber product industry.</p></caption><graphic xlink:href=\"fpsyg-11-01903-g005\"/></fig></sec><sec id=\"S4.SS2\"><title>Descriptive Statistical Analysis on Basic Personal Information of the Entrepreneur</title><p>Then, a descriptive statistical analysis on the basic personal information of the entrepreneur was performed based on 305 entrepreneurs of legal services surveyed in this study. The results are shown in <xref ref-type=\"fig\" rid=\"F6\">Figure 6</xref> below. It could be seen that, among all the 305 entrepreneurs of legal services, the proportion in male is higher than that in female, which is 61% (187 cases) and 39% (118 cases), respectively, mainly aging range of 26–30 years with 126 cases (41%), and most are masters and above (212 cases, 70%).</p><fig id=\"F6\" position=\"float\"><label>FIGURE 6</label><caption><p>Statistical results of basic data of the subjects [Figure <bold>(A)</bold> is the sex ratio; Figure <bold>(B)</bold> is the age range; and Figure <bold>(C)</bold> is the academic qualification].</p></caption><graphic xlink:href=\"fpsyg-11-01903-g006\"/></fig></sec><sec id=\"S4.SS3\"><title>Results on Reliability and Validity Test of the Evaluation Tools</title><p>First, the reliability and validity test of the scales used in this study was performed. It could be seen from <xref rid=\"T4\" ref-type=\"table\">Table 4</xref> that the Cronbach’s α is higher than 0.7, KMO is higher than 0.7, and <italic>p</italic> < 0.05 in the Bartlett spherical test. It shows that the evaluation tools used in this study have high reliability and validity, which lays the reliability of the subsequent evaluation results.</p><table-wrap id=\"T4\" position=\"float\"><label>TABLE 4</label><caption><p>Results on reliability and validity test of the evaluation tools.</p></caption><table frame=\"hsides\" rules=\"groups\" cellspacing=\"5\" cellpadding=\"5\"><thead><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Scale</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Cronbach’sα</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>KMO</bold></td><td valign=\"top\" align=\"center\" colspan=\"3\" rowspan=\"1\"><bold>Bartlett’s spherical test</bold><hr/></td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>χ <sup>2</sup></bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold><italic>df</italic></bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold><italic>p</italic></bold></td></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Social support</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.815</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.713</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">720.955</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">44</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.000</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Economic pressure</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.809</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.713</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">241.955</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.000</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Entrepreneurial recession</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.821</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.711</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">225.782</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.000</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Self-efficacy</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.952</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.712</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">267.893</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.000</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Multinational entrepreneurial orientation</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.833</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.713</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">268.392</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.000</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Value-chain potential</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.862</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.712</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">305.171</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.000</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">International performance</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.934</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.713</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">556.272</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.000</td></tr></tbody></table></table-wrap></sec><sec id=\"S4.SS4\"><title>Results of Correlation Analysis of Various Variables</title><p>Then, the correlation between the thought of entrepreneurial recession, the impact of entrepreneurial work performance, and other variables in this study were compared. The results are shown in <xref rid=\"T5\" ref-type=\"table\">Tables 5</xref>, <xref rid=\"T6\" ref-type=\"table\">6</xref> below. It could be seen from <xref rid=\"T4\" ref-type=\"table\">Table 4</xref> that the economic pressure has an extremely significant positive correlation with the thought of recession and self-efficacy (<italic>p</italic> < 0.01) and has an extremely significant negative correlation with the objective support and support utilization (<italic>p</italic> < 0.01). The thought of recession has an extremely significant positive correlation with the self-efficacy (<italic>p</italic> < 0.01) and has a significant negative correlation with the objective support and support utilization (<italic>p</italic> < 0.05). The self-efficacy has a significant negative correlation with the subjective support and objective support (<italic>p</italic> < 0.05) and has an extremely significant negative correlation with the support utilization (<italic>p</italic> < 0.01). The subjective support has an extremely significant negative correlation with the objective support and support utilization (<italic>p</italic> < 0.01). There is an extremely significant positive correlation between the objective support and support utilization (<italic>p</italic> < 0.01).</p><table-wrap id=\"T5\" position=\"float\"><label>TABLE 5</label><caption><p>Correlation analysis on related variables of the thought of entrepreneurial recession.</p></caption><table frame=\"hsides\" rules=\"groups\" cellspacing=\"5\" cellpadding=\"5\"><thead><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Variable</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Economic pressure</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Thought of recession</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Self-efficacy</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Subjective support</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Objective support</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Support utilization</bold></td></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Economic pressure</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Thought of recession</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.432**</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Self-efficacy</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.321**</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.452**</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Subjective support</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.032</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.101</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−0.173*</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Objective support</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−0.544**</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−0.193*</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−0.207*</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−0.282**</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1</td><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Support utilization</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−0.501**</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−0.704**</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−0.686**</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−0.457**</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.458**</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1</td></tr></tbody></table><table-wrap-foot><attrib><italic><italic>*Correlation was significant at the 0.05 level (2-tailed); **Correlation was significant at the 0.01 level (2-tailed).</italic></italic></attrib></table-wrap-foot></table-wrap><table-wrap id=\"T6\" position=\"float\"><label>TABLE 6</label><caption><p>Correlation analysis of related variables of the entrepreneurial working performance.</p></caption><table frame=\"hsides\" rules=\"groups\" cellspacing=\"5\" cellpadding=\"5\"><thead><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Variable</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Multinational entrepreneurial orientation</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Innovation</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Proactiveness</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Risk-bearing</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Value-chain potential</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Transaction complexity</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Product codability</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Supply capacity</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>International performance</bold></td></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Multinational entrepreneurial orientation</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Innovation</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.820**</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Proactiveness</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.810**</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.545**</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1</td><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Risk-bearing</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.808**</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.467**</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.455**</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1</td><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Value-chain potential</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.520**</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.460**</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.502**</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.348**</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1</td><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Transaction complexity</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.398**</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.387**</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.362**</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.298**</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.832**</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1</td><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Product codability</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.326**</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.178*</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.332**</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.274**</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.577**</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.202**</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1</td><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Supply capacity</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.487**</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.434**</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.454**</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.303**</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.862**</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.622**</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.274**</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1</td><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">International performance</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.566**</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.551**</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.508**</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.347**</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.678**</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.567**</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.263**</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.656**</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1</td></tr></tbody></table><table-wrap-foot><attrib><italic><italic>*Correlation was significant at the 0.05 level (2-tailed); **Correlation was significant at the 0.01 level (2-tailed).</italic></italic></attrib></table-wrap-foot></table-wrap><p>It could be seen from <xref rid=\"T6\" ref-type=\"table\">Table 6</xref> that the multinational entrepreneurial guide and its sub-dimensions (innovation, proactiveness, and risk-taking), value-chain potential, and its sub-dimensions (transaction complexity, product codability, and supply capacity) have a significant correlation with the international performance (<italic>p</italic> < 0.05).</p></sec><sec id=\"S4.SS5\"><title>Verification Results of the Proposed Hypotheses</title><p>The hypotheses proposed earlier in this study were analyzed by the regression analysis. It could be seen from <xref rid=\"T7\" ref-type=\"table\">Table 7</xref> that: a. The trade friction has an extremely significant positive impact on the economic pressure (β = 0.206; <italic>p</italic> < 0.01). b. The economic pressure has an extremely significant positive effect on the thought of entrepreneurial recession (β = 0.244; <italic>p</italic> < 0.01). c. The social support × economic pressure can negatively affect the thought of entrepreneurial recession, that is, the social support can regulate the thought of entrepreneurial recession caused by the economic pressure reversely (β = −0.192; <italic>p</italic> < 0.01). c1. and c2. The subjective support and objective support cannot regulate the thought of entrepreneurial recession caused by the economic pressure significantly (β = −0.078, −0.032; <italic>p</italic> > 0.05). c3. The support utilization can regulate the thought of entrepreneurial recession caused by the economic pressure (β = −0.281; <italic>p</italic> < 0.01). d. The multinational entrepreneurial orientation has a significant positive impact on the international performance (β = 0.532; <italic>p</italic> < 0.01). d1. The innovation dimension in multinational entrepreneurship has an extremely significant positive impact on the international performance (β = 0.288; <italic>p</italic> < 0.01). d2. The proactiveness dimension in multinational entrepreneurship has a significant positive impact on the international performance (β = 0.227; <italic>p</italic> < 0.01). d3. The risk-taking in the multinational entrepreneurship cannot affect the international performance (β = 0.036; <italic>p</italic> > 0.05). e. The value-chain potential has an extremely significant positive impact on the international performance (β = 0.798; <italic>p</italic> < 0.01). e1. The transaction complexity has an extremely significant positive impact on the international performance (β = 0.182; <italic>p</italic> < 0.01). e2. The product codability cannot affect the international performance (β = −0.092; <italic>p</italic> > 0.05). e3. The supply capacity has an extremely significant positive effect on the international performance (β = 0.424; <italic>p</italic> < 0.01). In addition, it is also found in this study that the multinational entrepreneurial orientation can affect the value-chain potential significantly (β = 0.415; <italic>p</italic> < 0.01).</p><table-wrap id=\"T7\" position=\"float\"><label>TABLE 7</label><caption><p>Analysis results of regression analysis models.</p></caption><table frame=\"hsides\" rules=\"groups\" cellspacing=\"5\" cellpadding=\"5\"><thead><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Model</bold></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Independent variable</bold></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Dependent variable</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>β</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>△<italic>R</italic><sup>2</sup></bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold><italic>p</italic></bold></td></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Trade friction</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Economic pressure</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.206</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.344</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.000</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">2</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Economic pressure</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Thought of entrepreneurial recession</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.244</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.022</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.000</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">3</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Economic pressure × social support</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Thought of entrepreneurial recession</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.192</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.021</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.000</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">4</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Economic pressure × subjective support</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Thought of entrepreneurial recession</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.078</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.003</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.173</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">5</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Economic pressure × objective support</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Thought of entrepreneurial recession</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.032</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.003</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.532</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">6</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Economic pressure × support utilization</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Thought of entrepreneurial recession</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.281</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.032</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.000</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">7</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Multinational entrepreneurial orientation</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">International performance</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.523</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.307</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.000</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">8</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Innovation</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">International performance</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.288</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.322</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.000</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">9</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">proactiveness</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">International performance</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.227</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.314</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.000</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">10</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Risk-bearing</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">International performance</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.036</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.004</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.102</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">11</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Value-chain potential</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">International performance</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.798</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.443</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.000</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">12</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Trade complexity</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">International performance</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.182</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.318</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.000</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">13</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Product codability</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">International performance</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.092</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.003</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.098</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">14</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Supply capacity</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">International performance</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.424</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.261</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.000</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">15</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Multinational entrepreneurial orientation</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Value-chain potential</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.415</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.257</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.000</td></tr></tbody></table><table-wrap-foot><attrib><italic><italic>× indicates that the social support and its sub-dimension exerts the intermediary role for impact of economic pressure on the thought of entrepreneurial recession.</italic></italic></attrib></table-wrap-foot></table-wrap><p>It could be seen from <xref rid=\"T8\" ref-type=\"table\">Table 8</xref> that all the hypotheses are true except c1., C2., d3., and e2. Therefore, the result graph of the impact path proposed in this study has to be redrawn based on the verification results of the hypotheses.</p><table-wrap id=\"T8\" position=\"float\"><label>TABLE 8</label><caption><p>Verification results of the hypotheses.</p></caption><table frame=\"hsides\" rules=\"groups\" cellspacing=\"5\" cellpadding=\"5\"><thead><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>No.</bold></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Hypotheses</bold></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Verification result</bold></td></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">a.</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">The economic pressure could be promoted significantly by the trade friction.</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Support</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">b.</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">The thought of entrepreneurial recession could be promoted significantly by the economic pressure.</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Support</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">c.</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">The promotion of economic pressure on the thought of entrepreneurial recession could be regulated reversely by the social support.</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Support</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">c1.</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">The promotion of economic pressure on the thought of entrepreneurial recession could be regulated reversely by the subjective support.</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Not support</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">c2.</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">The promotion of economic pressure on the thought of entrepreneurial recession could be regulated reversely by the objective support.</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Note support</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">c3.</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">The promotion of economic pressure on the thought entrepreneurial recession could be regulated reversely by the support utilization.</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Support</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">d.</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">The international performance could be promoted by the multinational entrepreneurial orientation.</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Support</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">d1.</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">The international performance could be promoted by innovation in the multinational entrepreneurial orientation.</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Support</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">d2.</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">The international performance could be promoted by antecedence in the multinational entrepreneurial orientation.</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Support</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">d3.</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">The international performance could be promoted by adventure in the multinational entrepreneurial orientation.</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Not support</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">e.</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">The international performance could be performed by the value-chain potential.</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Support</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">e1.</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">The international performance could be performed by complexity of the trade.</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Support</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">e2.</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">The international performance could be performed by codability of the product.</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Not support</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">e3.</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">The international performance could be performed by the supply capacity.</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Support</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">f</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">The value-chain potential is the intermediary variable for multinational entrepreneurial orientation to affect the international performance.</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Support</td></tr></tbody></table></table-wrap><p>The impact path of the regulated economic pressure and social support based on the trade friction on the thought of entrepreneurial recession is shown in <xref ref-type=\"fig\" rid=\"F7\">Figure 7</xref> below. It can be seen that the economic pressure could be impacted positively by the trade friction, while the occurrence of the thought of entrepreneurial recession could be impacted positively by the economic pressure. However, the thought of entrepreneurial recession caused by the economic pressure could be regulated by the social support and its support utilization.</p><fig id=\"F7\" position=\"float\"><label>FIGURE 7</label><caption><p>Impact paths of the economic pressure and social support based on the trade friction on the thought of entrepreneurial recession. ** represents <italic>P</italic> < 0.01.</p></caption><graphic xlink:href=\"fpsyg-11-01903-g007\"/></fig><p>The impact paths of the regulated multinational entrepreneurial motivation, value-chain potential, and international performance are shown in <xref ref-type=\"fig\" rid=\"F8\">Figure 8</xref> below. It can be seen that the international performance could be promoted by the multinational entrepreneurial motivation and its innovation and proactiveness, and the international performance could be promoted by the transaction complexity and supply capacity of the value-chain potential. The value-chain potential can play a role of intermediation in the relationship between the multinational entrepreneurial motivation and international performance.</p><fig id=\"F8\" position=\"float\"><label>FIGURE 8</label><caption><p>Impact path of the multinational entrepreneurial orientation, value-chain potential, and international performance. ** represents <italic>P</italic> < 0.01.</p></caption><graphic xlink:href=\"fpsyg-11-01903-g008\"/></fig></sec></sec><sec id=\"S5\"><title>Discussion</title><p>In this study, it is found that the United States, European Union, Association of Southeast Asian Nations, and Canada are main exporters of China, so the current export markets of China will continue to be the “main battlefield” of the trade friction in the future (<xref rid=\"B28\" ref-type=\"bibr\">Lawrence, 2018</xref>). On the other hand, exports from China to the emerging markets such as Russia, India, and Mexico also grow at 25% year by year, so the trade relations with the emerging markets will also be in the form of “no major problems and constant minor issues.” It is found that greater economic pressure to persons of multinational legal service can be promoted by the trade friction. Some early statistics in WTO show that 1 of 7 trade frictions is China-related (<xref rid=\"B20\" ref-type=\"bibr\">Holladay et al., 2017</xref>). In addition, after the trade frictions were analyzed in the whole world in the past 10 years, it is found that most trade frictions were China-related, and the China-related trade friction in 2019 only was more than 30%. With gradual development of the foreign trade, the trade friction has extended from a single product to the entire industry. Therefore, in the future, it should not only consider quelling trade disputes but also consider policy formulation and policy level of the trade friction (<xref rid=\"B23\" ref-type=\"bibr\">Ji et al., 2020</xref>).</p><p>The protection policies for the knowledge-based enterprises in various countries are not less than those for the manufacturing industry, and the incidents caused by trade friction for the knowledge-service enterprises entering their own countries are possible. Therefore, the legal service entrepreneurs have to bear greater economic pressure, so the trade friction may cause certain economic pressure on multinational entrepreneurs (<xref rid=\"B12\" ref-type=\"bibr\">Cui et al., 2019</xref>; <xref rid=\"B30\" ref-type=\"bibr\">Liu et al., 2019</xref>). When there are events such as trade friction that are beyond the scope of the entrepreneur’s own tolerance/control, they will have a helpless feeling. This negative emotion will greatly affect the entrepreneur’s pursuit of a sense of accomplishment, so excessive economic pressure will result in the psychological effect of entrepreneurial recession (<xref rid=\"B39\" ref-type=\"bibr\">Ryff, 2019</xref>). In the process of multinational entrepreneurship, even if there are a lot of subjective or objective social supports in the society, the entrepreneur fails to apply these supports in practice, then it will have no great significance for the economic pressure and psychological pressure of the entrepreneur. Thus, the support utilization in social support can regulate the thought of entrepreneurial recession caused by the economic pressure (<xref rid=\"B27\" ref-type=\"bibr\">Klyver et al., 2017</xref>; <xref rid=\"B33\" ref-type=\"bibr\">Monica et al., 2018</xref>). It indicated that entrepreneurs can improve the economic pressure caused by trade frictions in enterprises through reasonable utilization of the social support during the multinational entrepreneurship and then adjust their psychological effects. In the global market, the innovation of products, services, and management is the source of enterprise value. The advanced behavior of enterprise will bring first-mover advantages, which is conducive to seize the market share and thus obtain higher profits. Therefore, the innovation and advancement in the multinational entrepreneurial orientation can promote the international performance of enterprises (<xref rid=\"B3\" ref-type=\"bibr\">Álvarez Torres et al., 2019</xref>; <xref rid=\"B19\" ref-type=\"bibr\">Guzmán et al., 2019</xref>). In the process of product transactions, the multinational companies are committed to the development and improvement of services and other technologies, increase the complexity of market information in the transaction process, and improve the knowledge and other aspects. In this way, it can increase the initiative of the enterprise and satisfy the needs of customers to a greater extent. Therefore, the complexity of product transactions and supply capacity of product in the value-chain potential of entrepreneurship of the multinational enterprises can promote the international performance of enterprises (<xref rid=\"B9\" ref-type=\"bibr\">Bos et al., 2017</xref>; <xref rid=\"B18\" ref-type=\"bibr\">Ferreira et al., 2018</xref>; <xref rid=\"B6\" ref-type=\"bibr\">Biberhofer et al., 2019</xref>).</p><p>It is found that the entrepreneur’s thought of entrepreneurial recession can be affected positively by the size of economic pressure, which is similar to the results of <xref rid=\"B34\" ref-type=\"bibr\">Morris et al. (2020)</xref>. Then, the regulation role of the social support and its sub-dimensions of subjective support, objective support, and support utilization to the thought of recession caused by the economic pressure was explored. The results of this study show that the stronger the social support, the weaker the relationship between thought of entrepreneurial recession caused by the economic pressure. The reasons for the above results may be that the research objects of this study are the multinational entrepreneurs. No matter how much subjective or objective support it has in the society, such support has to be applied in practice to improve the economic pressure of the enterprise and then reduce the thought of entrepreneurial recession (<xref rid=\"B2\" ref-type=\"bibr\">Alby et al., 2019</xref>), which is consistent with the results in this study that the subjective support and objective support cannot regulate the impact of economic pressure on the thought of entrepreneurial recession reversely. It is found in this study that the international performance could be promoted significantly by the multinational entrepreneurial orientation, and the effects of innovation and proactiveness in the multinational entrepreneurial orientation are significant. It is consistent with the results of <xref rid=\"B4\" ref-type=\"bibr\">Augustie and Saad (2019)</xref>. The international performance cannot be impacted significantly by the risk-taking in the multinational entrepreneurial orientation. This may be because this experience in overseas operations of entrepreneurs is relatively weak in the international market, and the company’s management level is relatively insufficient, so that many problems behind the value-chain potential cannot be treated and solved skillfully and quickly. The value-chain potential is a key factor to drive the international performance (<xref rid=\"B44\" ref-type=\"bibr\">Vonsée et al., 2019</xref>). It is found in this study that the international performance can be promoted by the value-chain potential and its transaction complexity and supply capacity. This is because, in the transaction process, the enterprise is committed to the development and improvement of the services and other technologies, enhancement on complexity of the market information, and improvement of the knowledge. In this way, the initiative of the enterprise can be increased, and thus the clients can be satisfied to a larger extend. Entrepreneurship is a very complex and dynamic activity, and the difficulties experienced by multinational entrepreneurs are more than those experienced by domestic entrepreneurs (<xref rid=\"B8\" ref-type=\"bibr\">Boone et al., 2019</xref>). Regardless of any entrepreneurial behavior or activity, it is germinated in a specific entrepreneurial environment and developed step by step. Therefore, the entrepreneurial activities are inevitably affected by the entrepreneurial environment (<xref rid=\"B7\" ref-type=\"bibr\">Biryukov et al., 2020</xref>). In addition, it is found based on the codability of the products in this study that the codability of products can suppress the falseness of international performance. It is because codability of the products can facilitate the product or service management provided by the company in multinational enterprises, so that it is convenient to estimate and choose. Therefore, this variable has no inhibitory effect on the international performance of multinational enterprises.</p></sec><sec id=\"S6\"><title>Conclusion</title><p>With assistance of statistical methods combined with questionnaire surveys, regression model, and structural equation models, this study analyzed the impact mechanism of trade friction on the mentality of multinational entrepreneurs. It was found that trade friction can trigger the thought of entrepreneurial recession of entrepreneurs indirectly by reducing their income. The social support utilization can significantly regulate the relationship between economic pressure and the thought of entrepreneurial recession. The multinational entrepreneurship motivation can promote the international performance of multinational enterprises through the value-chain potential. It fails to consider other factors of the external environment (such as policies, etc.) and the interactions from the personal internal factors to the thought of entrepreneurial recession and the entrepreneurial working performance. Accordingly, the quantity of samples has to be increased in the future and to take specific research in a certain area to explore the effects on external environment and the internal factors of individuals on the thought of entrepreneurial recession and entrepreneurial working performance. In conclusion, the thought of entrepreneurial recession of the entrepreneur can be reduced and the subjective initiative of the entrepreneur can be played by using the social support to the maximal extent. International performance of the enterprise can be improved by complying with the international market and improving the service ability. Results in this study can provide a basis for understanding the impact mechanism of the thought of entrepreneurial recession of entrepreneur of multinational legal service and its work performance.</p></sec><sec sec-type=\"data-availability\" id=\"S7\"><title>Data Availability Statement</title><p>The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation, to any qualified researcher.</p></sec><sec id=\"S8\"><title>Ethics Statement</title><p>The studies involving human participants were reviewed and approved by the Shandong Normal University Ethics Committee. The patients/participants provided their written informed consent to participate in this study. Written informed consent was obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article.</p></sec><sec id=\"S9\"><title>Author Contributions</title><p>CX wrote the manuscript. ZZ reviewed the manuscript. Both authors contributed to the article and approved the submitted version.</p></sec><sec id=\"conf1\"><title>Conflict of Interest</title><p>The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p></sec></body><back><fn-group><fn fn-type=\"financial-disclosure\"><p><bold>Funding.</bold> This research was funded by the Shandong Normal University Young Teachers Research Project (Humanities and Social Sciences).</p></fn></fn-group><ref-list><title>References</title><ref id=\"B1\"><mixed-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Abosede</surname><given-names>J. 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[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"review-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Front Plant Sci</journal-id><journal-id journal-id-type=\"iso-abbrev\">Front Plant Sci</journal-id><journal-id journal-id-type=\"publisher-id\">Front. Plant Sci.</journal-id><journal-title-group><journal-title>Frontiers in Plant Science</journal-title></journal-title-group><issn pub-type=\"epub\">1664-462X</issn><publisher><publisher-name>Frontiers Media S.A.</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32849760</article-id><article-id pub-id-type=\"pmc\">PMC7432136</article-id><article-id pub-id-type=\"doi\">10.3389/fpls.2020.01231</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Plant Science</subject><subj-group><subject>Mini Review</subject></subj-group></subj-group></article-categories><title-group><article-title>Fertile Tetraploids: New Resources for Future Rice Breeding?</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Koide</surname><given-names>Yohei</given-names></name><xref ref-type=\"author-notes\" rid=\"fn001\"><sup>*</sup></xref><uri xlink:type=\"simple\" xlink:href=\"https://loop.frontiersin.org/people/812351\"/></contrib><contrib contrib-type=\"author\"><name><surname>Kuniyoshi</surname><given-names>Daichi</given-names></name><uri xlink:type=\"simple\" xlink:href=\"https://loop.frontiersin.org/people/959796\"/></contrib><contrib contrib-type=\"author\"><name><surname>Kishima</surname><given-names>Yuji</given-names></name><uri xlink:type=\"simple\" xlink:href=\"https://loop.frontiersin.org/people/862447\"/></contrib></contrib-group><aff id=\"aff1\"><institution>Laboratory of Plant Breeding, Research Faculty of Agriculture, Hokkaido University</institution>, <addr-line>Sapporo</addr-line>, <country>Japan</country></aff><author-notes><fn fn-type=\"edited-by\"><p>Edited by: Ryo Fujimoto, Kobe University, Japan</p></fn><fn fn-type=\"edited-by\"><p>Reviewed by: Kaoru Tonosaki, Yokohama City University, Japan; Muhammad Qasim Shahid, South China Agricultural University, China</p></fn><corresp id=\"fn001\">*Correspondence: Yohei Koide, <email xlink:href=\"mailto:[email protected]\" xlink:type=\"simple\">[email protected]</email></corresp><fn fn-type=\"other\" id=\"fn002\"><p>This article was submitted to Plant Breeding, a section of the journal Frontiers in Plant Science</p></fn></author-notes><pub-date pub-type=\"epub\"><day>11</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><year>2020</year></pub-date><volume>11</volume><elocation-id>1231</elocation-id><history><date date-type=\"received\"><day>22</day><month>4</month><year>2020</year></date><date date-type=\"accepted\"><day>27</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>Copyright © 2020 Koide, Kuniyoshi and Kishima</copyright-statement><copyright-year>2020</copyright-year><copyright-holder>Koide, Kuniyoshi and Kishima</copyright-holder><license xlink:href=\"http://creativecommons.org/licenses/by/4.0/\"><license-p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</license-p></license></permissions><abstract><p>Ploidy manipulation is an efficient technique for the development of novel phenotypes in plant breeding. However, in rice (<italic>Oryza sativa</italic> L.), severe seed sterility has been considered a barrier preventing cultivation of autotetraploids since the 1930s. Recently, a series of studies identified two fertile autotetraploids, identified herein as the PMeS (Polyploid Meiosis Stability) and Neo-Tetraploid lines. Here, we summarize their characteristics, focusing on the recovery of seed fertility, and discuss potential future directions of study in this area, providing a comprehensive understanding of current progress in the study of fertile tetraploid rice, a classical, but promising, concept for rice breeding.</p></abstract><kwd-group><kwd>rice</kwd><kwd>tetraploid</kwd><kwd>fertility</kwd><kwd>pollen</kwd><kwd>seed</kwd></kwd-group><funding-group><award-group><funding-source id=\"cn001\">Japan Society for the Promotion of Science<named-content content-type=\"fundref-id\">10.13039/501100001691</named-content></funding-source><award-id rid=\"cn001\">18K05565</award-id></award-group></funding-group><counts><fig-count count=\"0\"/><table-count count=\"2\"/><equation-count count=\"0\"/><ref-count count=\"48\"/><page-count count=\"6\"/><word-count count=\"2880\"/></counts></article-meta></front><body><sec sec-type=\"intro\" id=\"s1\"><title>Introduction</title><p>The phenomenon of polyploidy, which refers to the multiplication of chromosome sets within cells, often doubling a normal (diploid) set into a quadruple (tetraploid) set, is a widespread and distinctive feature of the higher plants (<xref rid=\"B38\" ref-type=\"bibr\">Stebbins, 1950</xref>). Polyploids are traditionally classified into autopolyploids, which originate from a single parent species (xx to xxxx), and allopolyploids, which originate from two hybridizing species (xx + yy to xxyy) (<xref rid=\"B10\" ref-type=\"bibr\">Clark and Donghe, 2018</xref>). In plants, polyploidization occurs frequently, and 25%–30% of flowering plants are suggested to be current polyploids, which have not yet diploidized (<xref rid=\"B41\" ref-type=\"bibr\">Van de Peer et al., 2017</xref>). The adaptive potential of polyploidy has long been discussed. <xref rid=\"B41\" ref-type=\"bibr\">Van de Peer et al. (2017)</xref> summarized the adaptive potential of polyploids with respect to the effects of interaction between species (i.e., changes in modes of reproduction and changes in interactions with pollinators and herbivores), environmental robustness (i.e., increasing tolerance to abiotic stresses), and species diversification. These changes in adaptive potential are reported to be caused by changes in the genomes/transcriptomes of the polyploid, including structural changes in genomes/genes, and transcriptional and functional alterations in duplicated genes (for a detailed review of these changes, see <xref rid=\"B37\" ref-type=\"bibr\">Renny-Byfield and Wendel (2014)</xref>]. Because the function of a duplicated gene should be redundant at the time of polyploidization, it can accumulate mutations, which may affect its function without reducing the fitness of the individual (<xref rid=\"B4\" ref-type=\"bibr\">Blanc and Wolfe, 2004</xref>). It is therefore widely accepted that polyploids have greater mutational robustness than diploids (<xref rid=\"B40\" ref-type=\"bibr\">Van de Peer et al., 2009</xref>). <xref rid=\"B13\" ref-type=\"bibr\">Fawcett et al. (2009)</xref> showed that the majority of the most recent duplication events in flowering plants are clustered in time and seem to coincide with the period of the most recent mass extinction. This suggests that polyploid plants coped better than diploid plants with the markedly changed environments of that period (<xref rid=\"B40\" ref-type=\"bibr\">Van de Peer et al., 2009</xref>).</p><p>In crop species, it has been frequently observed that polyploidization causes plants to grow larger, more quickly, and with higher yields, compared to their diploid relatives (<xref rid=\"B37\" ref-type=\"bibr\">Renny-Bayfield and Wendel, 2014</xref>), although some studies suggest that polyploidization did not have a selective advantage over diploid genomes during the domestication process (<xref rid=\"B21\" ref-type=\"bibr\">Hilu, 1993</xref>). In addition to improved crop yield, recent studies have shown that polyploidy generates novel or diversified characteristics such as changes to grain threshing properties (<xref rid=\"B47\" ref-type=\"bibr\">Zhang et al., 2011</xref>) and grain hardness (<xref rid=\"B7\" ref-type=\"bibr\">Chantret et al., 2004</xref>) in wheat, flowering time in sunflower (<xref rid=\"B3\" ref-type=\"bibr\">Blackman et al., 2010</xref>), growing condition in coffee (<xref rid=\"B11\" ref-type=\"bibr\">Combes et al., 2012</xref>), and quality of fiber in cotton (<xref rid=\"B24\" ref-type=\"bibr\">Jiang et al., 1998</xref>). These studies prompted us to survey the potential capacity for polyploidization in rice, a staple food for four billion people globally (CGIAR, <uri xlink:type=\"simple\" xlink:href=\"http://ricecrp.org/\">http://ricecrp.org/</uri>), as a means of providing new genetic materials for future crop improvement. In this review, we focus on autotetraploid rice, especially its fertility, because low seed fertility has been regarded as a barrier obstructing its use. In the course of summarizing two pioneering studies on autotetraploid rice with high seed fertility, we will discuss potential future directions for ploidy manipulation in rice, a classical but promising concept.</p></sec><sec id=\"s2\"><title>History of the Study of Tetraploid Rice</title><p>Asian cultivated rice (<italic>Oryza sativa</italic> L.) is a diploid species with two sets of 12 chromosomes (2n = 24). [Although Asian cultivated rice may have experienced whole genome duplication more than 96 million years ago (<xref rid=\"B16\" ref-type=\"bibr\">Guo et al., 2019</xref>), this species is regarded as diploid, as it did not experience a “recent” polyploidization. In this review, we therefore regard Asian cultivated rice as a diploid species and tetraploid rice as containing two sets of 24 chromosomes]. As far as we know, no autotetraploid or allotetraploid rice varieties are used in agriculture. The first report of spontaneously autotetraploid rice was by <xref rid=\"B33\" ref-type=\"bibr\">Nakamori (1933)</xref>, 2 decades after the determination of its chromosome number (<xref rid=\"B27\" ref-type=\"bibr\">Kuwada, 1910</xref>). Soon after the 1933 report, <xref rid=\"B22\" ref-type=\"bibr\">Ichijima (1934)</xref> reported artificially induced tetraploids. Some subsequent studies concerning the artificial induction of tetraploidy in rice have been reported (<xref rid=\"B12\" ref-type=\"bibr\">Cua, 1950</xref>; <xref rid=\"B34\" ref-type=\"bibr\">Oka, 1953</xref>). According to <xref rid=\"B17\" ref-type=\"bibr\">He et al. (2010)</xref>, research on polyploid rice breeding was initiated in 1953 in China.</p></sec><sec id=\"s3\"><title>Low Seed Fertility: A Barrier Preventing Tetraploid Rice Use</title><p>In general, polyploidization confers greater stress tolerance by fostering slower development, delayed reproduction, longer life span, improved defense against pathogens and herbivores, larger seeds, and lower reproductive effort with greater emphasis on vegetative reproduction [<xref rid=\"B21\" ref-type=\"bibr\">Hilu, 1993</xref>, see also review by <xref rid=\"B28\" ref-type=\"bibr\">Levin (1983)</xref>]. In rice, large grain and leaf size are often observed in autotetraploid plants. In addition, <xref rid=\"B39\" ref-type=\"bibr\">Tu et al. (2014)</xref> reported higher salt tolerance in autotetraploid rice than in diploid rice. These studies suggest the potential usefulness of autotetraploid rice for breeding. However, low numbers of spikelets per panicle and low fertility have frequently observed in tetraploid rice throughout its known existence. <xref rid=\"B35\" ref-type=\"bibr\">Oka (1954)</xref> surveyed the phenotypes of a diverse set of autotetraploid rice varieties. Although greater vegetative than reproductive growth was not specifically indicated (<xref rid=\"T1\" ref-type=\"table\"><bold>Table 1</bold></xref>), he showed that seed fertility in tetraploid varieties ranged from 0% to 55% (<xref rid=\"B35\" ref-type=\"bibr\">Oka, 1954</xref>; <xref rid=\"B36\" ref-type=\"bibr\">Oka, 1955</xref>). This low seed set rate has been the main barrier preventing use of autotetraploid for rice breeding (<xref rid=\"B6\" ref-type=\"bibr\">Cai et al., 2007</xref>).</p><table-wrap id=\"T1\" position=\"float\"><label>Table 1</label><caption><p>General characteristics of tetraploid rice lines (summary of <xref rid=\"B35\" ref-type=\"bibr\">Oka, 1954</xref>).</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Traits</th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Characteristics in tetraploids</th></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Panicle length</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Increased (0.95 to 1.30)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Grain length</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Increased (1.15 to 1.30)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Awn</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Developed</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Plant height</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Reduced (0.65 to 1.00)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Spikelet number per panicle</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Reduced (0.50 to 0.90)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Panicle number per plant</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Reduced (0.40 to 0.70)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Seed fertility</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">0% to 55%</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Pollen fertility</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">55% to 95%</td></tr></tbody></table><table-wrap-foot><p>Numbers in parenthesis indicate ratio of phenotype value of tetraploids compared to diploids.</p></table-wrap-foot></table-wrap><p>Lower polyploid fertilities have been considered to be due to abnormal behavior of the chromosomes during meiosis (<xref rid=\"B5\" ref-type=\"bibr\">Bomblies et al., 2016</xref>). In the first division of meiosis, a pair of homologous chromosomes forms a bivalent, which later segregates. However, in polyploids, especially autopolyploids, the proper formation of bivalents is often inhibited, because homology among three or more chromosomes means that they fail to provide the special intrinsic cues necessary for normal, diploid-like segregation (<xref rid=\"B5\" ref-type=\"bibr\">Bomblies et al., 2016</xref>). In autotetraploid rice, chromosomal behavior at meiosis has been surveyed by <xref rid=\"B6\" ref-type=\"bibr\">Cai et al. (2007)</xref> and <xref rid=\"B42\" ref-type=\"bibr\">Wu et al. (2014)</xref>. <xref rid=\"B6\" ref-type=\"bibr\">Cai et al. (2007)</xref> found that a tetraploid line, Dure-4X, displays abnormal meiotic behaviors including a higher rate of multivalents, univalents, and trivalents during prophase, lagging chromosomes during metaphase, and micronuclei during anaphase and telophase. In addition to such abnormalities, asynchrony of chromosomes and abnormal cell shape are also observed in meiosis in pollen mother cells in tetraploids with low fertility (<xref rid=\"B42\" ref-type=\"bibr\">Wu et al., 2014</xref>). These studies suggested that abnormal chromosomal behavior is one of the reasons for low seed fertility in autotetraploid rice. Recent studies have revealed the differences of epigenetic state and transcriptomes between the diploid and tetraploid rice (<xref rid=\"B46\" ref-type=\"bibr\">Xu et al., 2014</xref>; <xref rid=\"B48\" ref-type=\"bibr\">Zhang et al., 2015</xref>; <xref rid=\"B31\" ref-type=\"bibr\">Li et al., 2018</xref>). <xref rid=\"B48\" ref-type=\"bibr\">Zhang et al. (2015)</xref> suggested that the increased methylation level of transposable elements (TE) alters the expression of genes near by the TE in autotetraploid rice. Transcriptomic differences, including the expression of small RNAs, long non-coding RNAs, meiosis-related genes, and carbohydrate metabolism-related genes have been suggested to be related to meiotic abnormalities and the subsequent reduction of pollen fertility in autotetraploid rice (<xref rid=\"B29\" ref-type=\"bibr\">Li et al., 2016</xref>; <xref rid=\"B30\" ref-type=\"bibr\">Li et al., 2017</xref>; <xref rid=\"B8\" ref-type=\"bibr\">Chen et al., 2018</xref>; <xref rid=\"B32\" ref-type=\"bibr\">Li et al., 2020</xref>).</p></sec><sec id=\"s4\"><title>PMeS (Polyploid Meiosis Stability) and Neo-Tetraploidy: Emerging Fertile Autotetraploid Rice</title><p>Although its low seed fertility has long been a barrier preventing the use of autotetraploid rice in breeding, two new autotetraploid rice series with high seed and pollen fertilities, identified herein as the PMeS and Neo-Tetraploid lines, were developed recently (<xref rid=\"T2\" ref-type=\"table\"><bold>Table 2</bold></xref>). The PMeS lines have been developed and analyzed by <xref rid=\"B6\" ref-type=\"bibr\">Cai et al. (2007)</xref> and subsequent studies (<xref rid=\"B17\" ref-type=\"bibr\">He et al., 2010</xref>; <xref rid=\"B18\" ref-type=\"bibr\">He Y. C. et al., 2011</xref>; <xref rid=\"B39\" ref-type=\"bibr\">Tu et al., 2014</xref>). They derive from the progenies of crosses between <italic>indica</italic> and <italic>japonica</italic> rice subspecies. Unlike other autotetraploid rice lines, PMeS lines do not show abnormal chromosomal behavior at meiosis. In addition, their pollen development pattern appears normal at all stages, lacking the many abnormalities seen in other autotetraploid rice lines (<xref rid=\"B17\" ref-type=\"bibr\">He et al., 2010</xref>). These results suggest that stable meiosis, timely tapetum degradation, and normal mitochondrial development were the critical factors ensuring the high pollen fertility of PMeS lines (<xref rid=\"B17\" ref-type=\"bibr\">He et al., 2010</xref>). Recently, <xref rid=\"B45\" ref-type=\"bibr\">Xiong et al. (2019)</xref> revealed that the rice homolog of the gene <italic>MND1</italic>, which plays a role in meiosis in yeast and <italic>Arabidopsis</italic>, also plays a crucial role in improving the seed set rate in PMeS lines. They showed that the <italic>OsMND1</italic> expression level in panicles of a PMeS line was higher than in the diploid lines or in the other autotetraploid line with low seed set rate and <italic>OsMND1</italic> overexpression improved pollen fertility and seed set. The meiotic behavioral observations suggested that the effects of <italic>OsMND1</italic> might be maintaining the balance of synapsis and recombination (<xref rid=\"B45\" ref-type=\"bibr\">Xiong et al., 2019</xref>). However, it is still unknown why the expression level of <italic>OsMND1</italic> was lower in autotetraploid line with low seed set rate than the PMeS line. More research is necessary for a full understanding of the molecular mechanism of normal meiosis in PMeS lines (<xref rid=\"B45\" ref-type=\"bibr\">Xiong et al., 2019</xref>).</p><table-wrap id=\"T2\" position=\"float\"><label>Table 2</label><caption><p>Comparison of two fertile tetraploid rice series.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"> </th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Neo-Tetraploid lines</th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">PMeS lines</th></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Line name</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Huaduo1, 2, 3, 4, 5, 8<sup>a,b,c,d</sup></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Sg99012, HN-2026-4X<sup>e,f</sup></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Cross combination</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Inter-subspecific (<italic>japonica</italic> and <italic>indica</italic>)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Inter-subspecific (<italic>japonica</italic> and <italic>indica</italic>)<sup>f</sup></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Seed set</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">more than 68%<sup>a,b</sup></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Over 80%<sup>e,f</sup></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Pollen fertility</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">more than 92%<sup>a</sup></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">84.15% (stained), 78.52% (germinated)<sup>g</sup></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Meiotic behavior in male gametes</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Few abnormality were detected<sup>a</sup></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Normal<sup>e</sup></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Seed set of F1</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">83.07% (when crossed with <italic>japonica</italic> varieties)<sup>a</sup></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">67.18% (when crossed with Ballila-4X)<sup>e</sup></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"> </td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">76.45% (when crossed with <italic>indica</italic> varieties)<sup>a</sup></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"> </td></tr></tbody></table><table-wrap-foot><p><sup>a</sup><xref rid=\"B2\" ref-type=\"bibr\">Bei et al., 2019</xref>, <sup>b</sup><xref rid=\"B15\" ref-type=\"bibr\">Guo et al., 2017</xref>, <sup>c</sup><xref rid=\"B9\" ref-type=\"bibr\">Chen et al., 2019</xref>, <sup>d</sup><xref rid=\"B14\" ref-type=\"bibr\">Ghaleb et al., 2020</xref>, <sup>e</sup><xref rid=\"B18\" ref-type=\"bibr\">He Y. C. et al., 2011</xref>, <sup>f</sup><xref rid=\"B6\" ref-type=\"bibr\">Cai et al., 2007</xref>, <sup>g</sup><xref rid=\"B17\" ref-type=\"bibr\">He et al., 2010</xref>.</p></table-wrap-foot></table-wrap><p>The other autotetraploid rice varieties with high seed and pollen fertilities were named the Neo-Tetraploid lines by <xref rid=\"B15\" ref-type=\"bibr\">Guo et al. (2017)</xref>. These lines derived from the progenies of crosses between T44 (96025) and T45 (Jackson-4X) and showed a seed set rate of more than 80%, while their parents (T44 and T45) showed less than 32% (<xref rid=\"B15\" ref-type=\"bibr\">Guo et al., 2017</xref>; <xref rid=\"B2\" ref-type=\"bibr\">Bei et al., 2019</xref>). Cytological observation of the meiotic stages in Neo-Tetraploid rice lines showed that fewer abnormalities in these lines than their autotetraploid parents, which displayed different chromosomal configurations at diakinesis (<xref rid=\"B2\" ref-type=\"bibr\">Bei et al., 2019</xref>). Surprisingly, a total of 324 genes in the Neo-Tetraploid rice genome showed new mutations, which do not exist in its parents’ genomes (<xref rid=\"B2\" ref-type=\"bibr\">Bei et al., 2019</xref>). By means of a transcriptome analysis, <xref rid=\"B2\" ref-type=\"bibr\">Bei et al. (2019)</xref> suggested that genomic structural reprogramming, DNA variations, and differential expression of some important meiosis- and epigenetics-related genes might be associated with the high fertility of Neo-Tetraploid lines.</p></sec><sec id=\"s5\"><title>Autotetraploid Rice and Hybrid Sterility</title><p>Autotetraploid rice varieties with high seed fertilities are also considered to be important resources for hybrid rice breeding (<xref rid=\"B15\" ref-type=\"bibr\">Guo et al., 2017</xref>). Interestingly, both PMeS and Neo-Tetraploid lines produce F1 progenies with high seed setting rates when crossed with other autotetraploid rice lines (<xref rid=\"T2\" ref-type=\"table\"><bold>Table 2</bold></xref>, <xref rid=\"B18\" ref-type=\"bibr\">He Y. C. et al., 2011</xref>; <xref rid=\"B15\" ref-type=\"bibr\">Guo et al., 2017</xref>). In PMeS lines, <xref rid=\"B18\" ref-type=\"bibr\">He Y. C. et al. (2011)</xref> showed that the seed set rate of an F1 hybrid of Balilla-4X × HN2026-4X (a PMeS line) was 67.18%, while it was 37.26% in the F1 of Balilla-4X × NJ11-4X (a non-PMeS line). They suggested that the normal meiotic behavior in lines with a PMeS background led to the high frequency of successful fertilization, normal embryo development, and high seed set rate observed in its F1 progeny.</p><p>In Neo-Tetraploid lines, the agronomic traits of various autotetraploid hybrids were examined by <xref rid=\"B15\" ref-type=\"bibr\">Guo et al. (2017)</xref>. They found that the seed setting rate in the hybrids of Neo-Tetraploid rice was significantly higher than in the hybrids generated from other autotetraploid lines. Their transcriptome analysis revealed that genes expressed differently by the hybrid and its parents included meiosis stage-specific- and meiosis-related genes, such as <italic>RAD51</italic> and <italic>SMC2</italic>. Because <italic>RAD51</italic> and <italic>SMC2</italic> are involved in recombination between homologous chromosome and control of chromosomal structure, respectively, during meiosis (<xref rid=\"B1\" ref-type=\"bibr\">Aya et al., 2011</xref>), their transcriptional changes should affect to the fertility observed in autotetraploid hybrids. However, the understanding of the cause of transcriptional changes in hybrids still remains a challenging.</p><p>The seed fertility of autotetraploid hybrids is also controlled by several hybrid sterility loci. At such loci in rice, several alleles exist; some of them interact in the heterozygous state to cause abortion of gametes (see the review of <xref rid=\"B26\" ref-type=\"bibr\">Koide et al., 2008b</xref>). At this locus, the allele that does not cause abortion of gametes is called the neutral allele (<xref rid=\"B23\" ref-type=\"bibr\">Ikehashi and Araki, 1986</xref>; <xref rid=\"B25\" ref-type=\"bibr\">Koide et al., 2008a</xref>). <xref rid=\"B18\" ref-type=\"bibr\">He et al. (2011b)</xref> and <xref rid=\"B43\" ref-type=\"bibr\">Wu et al. (2015)</xref> showed that interaction between three hybrid sterility loci (<italic>Sa</italic>, <italic>Sb</italic>, and <italic>Sc</italic>) has significant effects on the pollen fertility of autotetraploid hybrids, and that pollen fertility further decreased with increasing allelic interaction. They also found abnormal chromosomal behavior in the hybrids with low pollen fertility and suggested that the gene interactions of hybrid sterility loci tend to increase the chromosomal abnormalities that cause the partial abortion of male gametes, leading to the decline in seed set of the autotetraploid rice hybrids (<xref rid=\"B18\" ref-type=\"bibr\">He et al., 2011b</xref>). Such abnormalities in chromosomal behavior were reduced in hybrids with neutral alleles (<italic>Sa</italic><sup>n</sup> and <italic>Sb</italic><sup>n</sup>) at loci <italic>Sa</italic> and <italic>Sb</italic>, respectively (<xref rid=\"B44\" ref-type=\"bibr\">Wu et al., 2017</xref>). <xref rid=\"B9\" ref-type=\"bibr\">Chen et al. (2019)</xref> developed hybrids between the Neo-Tetraploid line and autotetraploid varieties with neutral alleles. They found an improvement of seed set rate and suggested that meiosis-related and meiosis-specific genes, and those related to saccharide metabolism and starch synthase, were involved in these heterozygote-specific phenotypes (<xref rid=\"B9\" ref-type=\"bibr\">Chen et al., 2019</xref>).</p></sec><sec sec-type=\"discussion\" id=\"s6\"><title>Discussions and Conclusions</title><p>These two series of pioneer studies on the PMeS and Neo-Tetraploid lines have opened the door to the use of autotetraploid varieties in rice breeding and cultivation. To facilitate the development of fertile autotetraploid lines, further research aimed at understanding the origin of the genes/quantitative trait loci (QTLs) responsible for high fertility in these tetraploid rice varieties will be necessary. Both the PMeS and Neo-Tetraploid rice lines have been developed from the progenies of crosses between the <italic>indica</italic> and <italic>japonica</italic> rice subspecies. Since the parental autotetraploid varieties show seed sterility, one possibility for the origin of high-fertility genes/QTLs in the new varieties’ progenies is the nature of interactions between genes (or genomic regions) derived from different autotetraploid parents. If so, a QTL analysis of high seed fertility could be performed by developing recombinant inbred lines derived from crosses between autotetraploids. The Neo-Tetraploid lines show different expression of some important meiosis stage specific- and meiosis-related genes (<xref rid=\"B15\" ref-type=\"bibr\">Guo et al., 2017</xref>; <xref rid=\"B2\" ref-type=\"bibr\">Bei et al., 2019</xref>). Genes/QTLs responsible for high seed fertility might control the expression of these. Interestingly, the PMeS and Neo-Tetraploid lines show high seed fertility not only when selfing, but also in hybrids with other autotetraploid rice strains (<xref rid=\"B18\" ref-type=\"bibr\">He Y. C. et al., 2011</xref>; <xref rid=\"B15\" ref-type=\"bibr\">Guo et al., 2017</xref>). These results suggest that the genes/QTLs responsible for high seed fertility in PMeS and Neo-Tetraploid lines act dominantly, suppressing unstable chromosomal behavior during meiosis in hybrids. If this is so, the question arises as to why the initial hybrids of <italic>indica</italic> and <italic>japonica</italic> used for developing the PMeS and Neo-Tetraploid lines did not show high seed fertility. Another possibility for the origin of genes/QTLs responsible for high seed fertility is the new appearance of these genes/QTLs during the line selection process. The finding of new mutations, absent from the parental genome, in a Neo-Tetraploid line (<xref rid=\"B2\" ref-type=\"bibr\">Bei et al., 2019</xref>) may support this scenario. The activation of TEs followed by alteration of the epigenetic state (<xref rid=\"B48\" ref-type=\"bibr\">Zhang et al., 2015</xref>; <xref rid=\"B15\" ref-type=\"bibr\">Guo et al., 2017</xref>; <xref rid=\"B2\" ref-type=\"bibr\">Bei et al., 2019</xref>) may cause new appearance of genes/QTLs in tetraploid rice. Further study is necessary to unclear why many such mutations would occur in the autotetraploid lines.</p><p>Although their origin is unclear, identifying the genes responsible for high seed fertility in the PMeS and Neo-Tetraploid lines will enable the transfer of this technique to other autotetraploid rice varieties through marker-assisted selection or genome editing. A diverse set of fertile autotetraploid rice varieties will greatly increase their potential usefulness in future agriculture. Ploidy manipulation, a classical concept, will revive in the field of rice breeding in the near future.</p></sec><sec id=\"s7\"><title>Author Contributions</title><p>YKo, DK, and YKi contributed to conceptualization of this review. YKo wrote original draft. DK and YKi reviewed and edited the original manuscript.</p></sec><sec sec-type=\"funding-information\" id=\"s8\"><title>Funding</title><p>YKo and YKi are funded by JSPS KAKENHI Grant Numbers 18K05565 and 19H00937, respectively.</p></sec><sec id=\"s9\"><title>Conflict of Interest</title><p>The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p></sec></body><back><ack><title>Acknowledgments</title><p>We thank to I. Takamure for his support for the project. 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"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"brief-report\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Front Neurol</journal-id><journal-id journal-id-type=\"iso-abbrev\">Front Neurol</journal-id><journal-id journal-id-type=\"publisher-id\">Front. Neurol.</journal-id><journal-title-group><journal-title>Frontiers in Neurology</journal-title></journal-title-group><issn pub-type=\"epub\">1664-2295</issn><publisher><publisher-name>Frontiers Media S.A.</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32849224</article-id><article-id pub-id-type=\"pmc\">PMC7432137</article-id><article-id pub-id-type=\"doi\">10.3389/fneur.2020.00770</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Neurology</subject><subj-group><subject>Brief Research Report</subject></subj-group></subj-group></article-categories><title-group><article-title>Empiric Recurrence Risk Estimates for Chronic Tic Disorders: Implications for Genetic Counseling</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Heiman</surname><given-names>Gary A.</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><xref ref-type=\"corresp\" rid=\"c001\"><sup>*</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/312573/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Rispoli</surname><given-names>Jessica</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Seymour</surname><given-names>Christine</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Leckman</surname><given-names>James F.</given-names></name><xref ref-type=\"aff\" rid=\"aff2\"><sup>2</sup></xref><xref ref-type=\"aff\" rid=\"aff3\"><sup>3</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/291934/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>King</surname><given-names>Robert A.</given-names></name><xref ref-type=\"aff\" rid=\"aff2\"><sup>2</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/312862/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Fernandez</surname><given-names>Thomas V.</given-names></name><xref ref-type=\"aff\" rid=\"aff2\"><sup>2</sup></xref><xref ref-type=\"aff\" rid=\"aff3\"><sup>3</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/46802/overview\"/></contrib></contrib-group><aff id=\"aff1\"><sup>1</sup><institution>Department of Genetics and the Human Genetics Institute of New Jersey, Rutgers, The State University of New Jersey</institution>, <addr-line>Piscataway, NJ</addr-line>, <country>United States</country></aff><aff id=\"aff2\"><sup>2</sup><institution>Child Study Center, Yale University School of Medicine</institution>, <addr-line>New Haven, CT</addr-line>, <country>United States</country></aff><aff id=\"aff3\"><sup>3</sup><institution>Department of Psychiatry, Yale University School of Medicine</institution>, <addr-line>New Haven, CT</addr-line>, <country>United States</country></aff><author-notes><fn fn-type=\"edited-by\"><p>Edited by: Giuseppe De Michele, University of Naples Federico II, Italy</p></fn><fn fn-type=\"edited-by\"><p>Reviewed by: Oksana Suchowersky, University of Alberta, Canada; Jong-Min Kim, Seoul National University Bundang Hospital, South Korea</p></fn><corresp id=\"c001\">*Correspondence: Gary A. Heiman <email>[email protected]</email></corresp><fn fn-type=\"other\" id=\"fn001\"><p>This article was submitted to Movement Disorders, a section of the journal Frontiers in Neurology</p></fn></author-notes><pub-date pub-type=\"epub\"><day>11</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><year>2020</year></pub-date><volume>11</volume><elocation-id>770</elocation-id><history><date date-type=\"received\"><day>11</day><month>2</month><year>2020</year></date><date date-type=\"accepted\"><day>22</day><month>6</month><year>2020</year></date></history><permissions><copyright-statement>Copyright © 2020 Heiman, Rispoli, Seymour, Leckman, King and Fernandez.</copyright-statement><copyright-year>2020</copyright-year><copyright-holder>Heiman, Rispoli, Seymour, Leckman, King and Fernandez</copyright-holder><license xlink:href=\"http://creativecommons.org/licenses/by/4.0/\"><license-p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</license-p></license></permissions><abstract><p><bold>Background:</bold> Tourette disorder (TD) and other chronic tic disorders are neurodevelopmental/neuropsychiatric disorders characterized by motor and/or vocal tics. Family studies indicate that TD strongly aggregates within families and that other chronic tic disorders are biologically related such that studies typically combine them into any chronic tic disorder (CTD). Because of stigma, bullying, and comorbidity with other neuropsychiatric disorders, CTDs can severely impact the quality of life of individuals with these disorders.</p><p><bold>Objectives:</bold> The genetic architecture of CTDs is complex and heterogeneous, involving a myriad of genetic variants. Thus, providing familial recurrence risks is based on empirical recurrence risk estimates rather than genetic testing. Because empiric recurrence risks for CTDs have not been published, the purpose of this study is to calculate and report these recurrence risks estimates.</p><p><bold>Methods:</bold> Based on population prevalence and increased risk to different relatives from a large population-based family study, we calculated the empiric recurrent risk estimate for each relative type (full sibling, parents, offspring, all first-degree, and all second-degree).</p><p><bold>Results:</bold> The recurrence risk estimate for CTDs in first-degree relatives is 29.9% [95% confidence interval (CI) = 23.2–38.5%]. The risk is higher in males, 33.7% (95% CI = 26.2–43.3%), than females, 24.3% (95% CI = 18.9–31.3%).</p><p><bold>Conclusions:</bold> Given the complex, heterogeneous genetic architecture of CTDs, individuals concerned about recurrence risk should be referred to genetic counseling. Such counseling should include discussion of the derivation and limitations of these empiric recurrence risk estimates, including the upper and lower limits of the range of risk.</p></abstract><kwd-group><kwd>Tourette disorder</kwd><kwd>chronic tic disorders</kwd><kwd>genetic counseling</kwd><kwd>recurrence risk estimate</kwd><kwd>genetic</kwd></kwd-group><counts><fig-count count=\"0\"/><table-count count=\"2\"/><equation-count count=\"0\"/><ref-count count=\"41\"/><page-count count=\"6\"/><word-count count=\"4664\"/></counts></article-meta></front><body><sec sec-type=\"intro\" id=\"s1\"><title>Introduction</title><p>Tourette and other chronic tic disorders are neurodevelopmental/neuropsychiatric disorders characterized by motor and/or vocal tics that make their appearance before the age of 18 years. While the diagnostic criteria for Tourette disorder (TD) require the presence of multiple motor and at least one vocal tic over at least 1 year, the criteria for chronic motor tic (CMTD) or chronic vocal tic (CVTD) disorders require at least 1 year of having either motor or vocal tics, respectively, but not both, during the individual's lifetime (<xref rid=\"B1\" ref-type=\"bibr\">1</xref>, <xref rid=\"B2\" ref-type=\"bibr\">2</xref>). Tic severity symptoms characteristically wax and wane throughout the day, weeks, months, and years (<xref rid=\"B3\" ref-type=\"bibr\">3</xref>). The 95% lifetime prevalence confidence interval (CI) of TD ranges from 0.32 to 0.85% (<xref rid=\"B4\" ref-type=\"bibr\">4</xref>), and combined, the prevalence CI of all chronic tic disorders (CTDs) ranges from 0.92 to 2.83% (<xref rid=\"B5\" ref-type=\"bibr\">5</xref>). Chronic tic disorders are found across most ethnic groups worldwide and are more prevalent in males than females (3–4:1) (<xref rid=\"B6\" ref-type=\"bibr\">6</xref>, <xref rid=\"B7\" ref-type=\"bibr\">7</xref>). While follow-up studies suggest a third of individuals report tic resolution into adulthood (<xref rid=\"B8\" ref-type=\"bibr\">8</xref>), objective videotape evaluations show that nonetheless some of these individuals still manifest tics (<xref rid=\"B9\" ref-type=\"bibr\">9</xref>). Over and above their psychosocial sequelae such as stigma or bullying, CTDs can severely impact quality of life because of a high comorbidity rate with other neuropsychiatric disorders including obsessive–compulsive disorder (OCD, 50.0%) and attention-deficit/hyperactivity disorder (ADHD, 54.3%) (<xref rid=\"B6\" ref-type=\"bibr\">6</xref>, <xref rid=\"B10\" ref-type=\"bibr\">10</xref>, <xref rid=\"B11\" ref-type=\"bibr\">11</xref>).</p><p>Family studies consistently show that TD strongly aggregates within families and that TD, CMTD, and CVTD are biologically sufficiently related that genetic studies typically combine them into a category of any CTD rather than analyzing them separately (<xref rid=\"B6\" ref-type=\"bibr\">6</xref>, <xref rid=\"B7\" ref-type=\"bibr\">7</xref>, <xref rid=\"B11\" ref-type=\"bibr\">11</xref>–<xref rid=\"B16\" ref-type=\"bibr\">16</xref>). Heritability studies clearly indicate a genetic contribution to the etiology of CTDs, with estimates ranging from 25 to 50% in twin studies (<xref rid=\"B13\" ref-type=\"bibr\">13</xref>, <xref rid=\"B15\" ref-type=\"bibr\">15</xref>, <xref rid=\"B17\" ref-type=\"bibr\">17</xref>) to 77% in a population-based familial-clustering study (<xref rid=\"B18\" ref-type=\"bibr\">18</xref>). Thus, CTDs are among the most heritable neuropsychiatric conditions (<xref rid=\"B18\" ref-type=\"bibr\">18</xref>). The relationship between tic severity and genetic predisposition in these studies is mixed. While proband severity is not associated with the number of affected relatives (<xref rid=\"B19\" ref-type=\"bibr\">19</xref>), proband severity is associated with having at least one affected relative (i.e., positive family history) (<xref rid=\"B20\" ref-type=\"bibr\">20</xref>). However, tic severity has low heritability (<xref rid=\"B21\" ref-type=\"bibr\">21</xref>) and varies considerably within families (<xref rid=\"B22\" ref-type=\"bibr\">22</xref>, <xref rid=\"B23\" ref-type=\"bibr\">23</xref>). There is little evidence of assortative mating among individuals with tic disorders (<xref rid=\"B24\" ref-type=\"bibr\">24</xref>). However, when present, bilineal transmission has been associated with a higher level of tic severity in the offspring (<xref rid=\"B24\" ref-type=\"bibr\">24</xref>). In addition, multiple studies also indicate that OCD is genetically related to CTDs (<xref rid=\"B11\" ref-type=\"bibr\">11</xref>, <xref rid=\"B13\" ref-type=\"bibr\">13</xref>, <xref rid=\"B25\" ref-type=\"bibr\">25</xref>, <xref rid=\"B26\" ref-type=\"bibr\">26</xref>) with both shared and distinct genetic risk factors (<xref rid=\"B27\" ref-type=\"bibr\">27</xref>).</p><p>Because family and twin studies show that CTDs are familial and have, at least in part, a genetic causation, molecular genetic studies have been conducted using different study designs (<xref rid=\"B28\" ref-type=\"bibr\">28</xref>). Initially, when it was thought that highly penetrant mutations were necessary and sufficient to cause CTD, linkage studies were conducted using large multiplex families. While these studies often had intriguing initial findings, they could not be replicated or localized to a distinct genetic locus (<xref rid=\"B7\" ref-type=\"bibr\">7</xref>, <xref rid=\"B12\" ref-type=\"bibr\">12</xref>). Later, the “common variant–common disease” hypothesis and the “rare variant–common disease” hypothesis paradigms led, respectively, to genome-wide association studies (GWASs) (<xref rid=\"B16\" ref-type=\"bibr\">16</xref>, <xref rid=\"B29\" ref-type=\"bibr\">29</xref>) and whole-exome sequencing (WES) studies (<xref rid=\"B14\" ref-type=\"bibr\">14</xref>, <xref rid=\"B30\" ref-type=\"bibr\">30</xref>). In the GWASs of CTDs, only one single-nucleotide polymorphism surpassed the genome-wide significance threshold, but this finding was not replicated in an independent cohort (<xref rid=\"B16\" ref-type=\"bibr\">16</xref>). However, polygenic risk scores, based on GWAS results, are able to predict tic disorders in independent samples (<xref rid=\"B16\" ref-type=\"bibr\">16</xref>, <xref rid=\"B31\" ref-type=\"bibr\">31</xref>). Two recent WES studies, searching for <italic>de novo</italic> damaging variants (variants predicted to disrupt protein function or probably-damaging missense variants), have yielded important insights into the genetic architecture of CTDs (<xref rid=\"B14\" ref-type=\"bibr\">14</xref>, <xref rid=\"B30\" ref-type=\"bibr\">30</xref>). First, they estimate 400–500 genes that increase risk for CTDs when mutated in the form of a damaging high-penetrance variant. Second, 10–12% of CTDs are due to <italic>de novo</italic> damaging variants, including <italic>de novo</italic> copy number variants (CNVs). While <italic>de novo</italic> in one individual, these individuals can subsequently pass the variants on to their offspring. Finally, some of the risk genes found so far in WES studies are involved in cell polarity, suggesting that CTDs may be caused by a disruption in neurons arriving at the correct location and making correct connections during neurodevelopment. Taken together, these molecular genetic studies suggest that CTDs are caused by variants in many different genes, some with rare damaging high-penetrance variants, including CNVs, which may interact with a background genetic risk from common low-penetrance variants. Additionally, CTDs may be caused by an additive effect of common low-penetrance variants in many genes (i.e., polygenic), which may or may not overlap with the same genes that harbor rare high-penetrance variants. Future studies may also document the “co-action” of specific gene variants (<xref rid=\"B32\" ref-type=\"bibr\">32</xref>). Specific gene variants may also interact with environmental factors such as prenatal maternal smoking (<xref rid=\"B33\" ref-type=\"bibr\">33</xref>).</p><p>Given the complex and heterogeneous genetic architecture of CTDs, providing familial recurrence risks is challenging. Currently, there are no genetic tests available to determine individualized familial recurrence risk to relatives. Instead, available recurrence risks are empirical estimations based on results from population prevalence and family studies rather than a single risk based on Mendelian inheritance. Because these studies provide risk ranges (i.e., CIs), this makes providing empiric recurrence risk to at-risk individuals even more challenging (<xref rid=\"B34\" ref-type=\"bibr\">34</xref>, <xref rid=\"B35\" ref-type=\"bibr\">35</xref>). The purpose of this article is to provide the empirical recurrence risk estimates for CTDs for different familial relationships, as these recurrence risks have not been previously reported. The process of how to optimally present these empiric risk estimates to individuals seeking genetic counseling is beyond the scope of this article and can be found elsewhere (<xref rid=\"B35\" ref-type=\"bibr\">35</xref>). However, in the discussion, we present the implications for genetic counseling for CTDs, based on current knowledge of genetic architecture and from empiric recurrence estimates.</p></sec><sec sec-type=\"materials and methods\" id=\"s2\"><title>Materials and Methods</title><sec><title>General Population Prevalence of CTDs</title><p>There are two published meta-analyses of the general population lifetime prevalence of TD (<xref rid=\"B4\" ref-type=\"bibr\">4</xref>, <xref rid=\"B5\" ref-type=\"bibr\">5</xref>). One of these (<xref rid=\"B5\" ref-type=\"bibr\">5</xref>) provides estimates for TD and all CTDs combined. We used the CTDs general population prevalence data from this study (<xref rid=\"B5\" ref-type=\"bibr\">5</xref>) to calculate the empiric recurrence risk estimates. This meta-analysis found an overall general population prevalence for CTDs of 1.6% (95% CI = 0.9–2.8) with a higher prevalence in males (1.8%; 95% CI = 0.7–4.4%) than females (1.3%; 95% CI = 0.8–2.0%) (<xref rid=\"B5\" ref-type=\"bibr\">5</xref>).</p></sec><sec><title>Family Study Data: Increased Risk of CTDs in Relatives</title><p>Despite many family studies [for a review, see (<xref rid=\"B12\" ref-type=\"bibr\">12</xref>)], there is no published meta-analysis of increased risk of CTDs in relatives. We based our calculation of empiric recurrence risk on a recent family study because it is population-based, it has the largest sample size (<italic>n</italic> = 4,826), and it includes all CTDs rather than only TD (<xref rid=\"B18\" ref-type=\"bibr\">18</xref>).</p></sec><sec><title>Calculation of Empiric Recurrence Risk Estimates for CTDs</title><p>For each relative type (full sibling, parents, offspring, all first-degree, and all second-degree), we multiplied the CTD general population prevalence estimate (<xref rid=\"B5\" ref-type=\"bibr\">5</xref>) by the reported increased CTD risk to each relative type (<xref rid=\"B18\" ref-type=\"bibr\">18</xref>). We also provided the range of risk by multiplying the population prevalence by the 95% CI for CTD increased risk to each relative type. This was done overall as well as separately for each sex.</p></sec></sec><sec sec-type=\"results\" id=\"s3\"><title>Results</title><p>Overall, based on general population prevalence and increased risk to relatives, the recurrence risk estimate for CTDs in any first-degree relative is 29.9% (95% CI = 23.2–38.5%) (<xref rid=\"T1\" ref-type=\"table\">Table 1</xref>). The risk is higher in males, 33.7% (95% CI = 26.2–43.3%), than females, 24.3% (95% CI = 18.9–31.3%). For second-degree relatives, the recurrence risk estimates are considerably lower (7.4%; 95% CI = 5.1–10.4%), also higher in males (8.3%; 95% CI = 5.8–11.7%) than in females (6.0%; 95% CI = 4.2–8.5%).</p><table-wrap id=\"T1\" position=\"float\"><label>Table 1</label><caption><p>Empiric recurrence risk estimate for chronic tic disorders based on general population prevalence and increased risk to relatives.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Relative type</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>General population prevalence (<xref rid=\"B5\" ref-type=\"bibr\">5</xref>)</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Increased risk to relative OR (95% CI) (<xref rid=\"B18\" ref-type=\"bibr\">18</xref>)</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Recurrence risk % (95% CI)</bold></th></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" colspan=\"4\" style=\"background-color:#bbbdc0\" rowspan=\"1\"><bold>A. OVERALL</bold></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">All 1st degree</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.6%</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">18.7 (14.5–24.1)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">29.9 (23.2–38.5)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">  Full sibling</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.6%</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">17.7 (12.9–24.2)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">28.3 (20.6–38.7)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">  Parents</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.6%</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">21.1 (11.2–39.7)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">33.8 (17.9–63.5)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">  Offspring</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.6%</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">24.7 (12.4–49.3)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">39.5 (19.9–78.9)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">All 2nd degree</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.6%</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4.6 (3.2–6.5)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">7.4 (5.1–10.4)</td></tr><tr><td valign=\"top\" align=\"left\" colspan=\"4\" style=\"background-color:#bbbdc0\" rowspan=\"1\"><bold>B. MALES ONLY</bold></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">All 1st degree</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.8%</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">18.7 (14.5–24.1)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">33.7 (26.2–43.3)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">  Full sibling</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.8%</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">17.7 (12.9–24.2)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">31.9 (23.2–43.6)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">  Parents</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.8%</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">21.1 (11.2–39.7)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">38.0 (20.1–71.4)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">  Offspring</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.8%</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">24.7 (12.4–49.3)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">44.5 (22.4–88.7)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">All 2nd degree</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.8%</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4.6 (3.2–6.5)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">8.3 (5.8–11.7)</td></tr><tr><td valign=\"top\" align=\"left\" colspan=\"4\" style=\"background-color:#bbbdc0\" rowspan=\"1\"><bold>C. FEMALES ONLY</bold></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">All 1st degree</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.3%</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">18.7 (14.5–24.1)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">24.3 (18.9–31.3)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">  Full sibling</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.3%</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">17.7 (12.9–24.2)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">23.0 (16.8–31.5)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">  Parents</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.3%</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">21.1 (11.2–39.7)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">27.4 (14.5–51.6)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">  Offspring</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.3%</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">24.7 (12.4–49.3)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">32.1 (16.1–64.1)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">All 2nd degree</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.3%</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4.6 (3.2–6.5)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">6.0 (4.2–8.5)</td></tr></tbody></table></table-wrap></sec><sec sec-type=\"discussion\" id=\"s4\"><title>Discussion</title><p>For CTDs, the empiric 95% CI recurrence risk estimates for different first-degree relatives (sibling, parents, and offspring) range from 28.3 (sibling) to 39.5% (offspring). The higher population prevalence in males than in females (3–4:1) led to a higher recurrence risk for any first-degree relative in males (33.7% in males vs. 24.3% in females). These recurrence risks estimates are in line with the recurrence risk to offspring of parents with other severe psychiatric diagnoses (schizophrenia, bipolar disorder, major depressive disorder), based on a meta-analysis of high-risk family studies (<xref rid=\"B36\" ref-type=\"bibr\">36</xref>). In that study, children of a parent with one of these diagnoses had a 32% chance of having a psychotic or major mood disorder by early adulthood. On the other hand, our CTD empiric recurrence risks are higher than those for schizophrenia and bipolar disorder historically quoted for genetic counselors (<xref rid=\"B37\" ref-type=\"bibr\">37</xref>). However, the meta-analysis was published more recently than the genetic counseling quoted risks, and this could explain the difference. In support of the higher risks from the meta-analysis, a recent systematic review of multiple familial high-risk longitudinal studies found that adult offspring of one or more parents with schizophrenia had 15–40% risk of developing a psychotic disorder (<xref rid=\"B38\" ref-type=\"bibr\">38</xref>). Thus, the recurrence risks for CTDs are in line with the risks from these other neuropsychiatric disorders.</p><p>Our findings should be considered in the context of certain limitations. The prevalence estimate used in this study was from the 2012 meta-analysis by Knight et al. (<xref rid=\"B5\" ref-type=\"bibr\">5</xref>). A subsequent meta-analysis, published in 2014, by Scharf et al. (<xref rid=\"B4\" ref-type=\"bibr\">4</xref>), included additional studies and had a slightly lower prevalence of TD (Scharf and colleagues' TD prevalence = 0.5%; 95% CI = 0.3–0.9%, compared with Knight and colleagues' TD prevalence 0.8%; 95% CI = 0.4–1.5%). While Scharf and colleagues' article did not report other CTDs, the lower TD prevalence may indicate that the true CTD prevalence and thus the empiric recurrence estimates reported here are slightly lower. On the other hand, a recent meta-analysis of prevalence studies conducted in China, which was not included in either of the prior meta-analyses, found a prevalence of TD plus other CTDs that was similar to the prevalence in the study of Knight et al. (1.5% combined for Chinese study vs. 1.6% in Knight and colleagues' study). Thus, far, there has not been a meta-analysis of family studies. While we used the results from the largest general population family study to date, this is only an estimate of the true underlying increased risk to relatives. Combining multiple family studies would reduce the size of the 95% CI (e.g., offspring 95% CI = 19.9–78.9%). In addition, our reliance on the data from the Swedish national registries to estimate empiric recurrence risk may also be somewhat problematic as the registries overrepresent the more severe or complex cases (<xref rid=\"B18\" ref-type=\"bibr\">18</xref>) but may miss less severe cases (that never seek clinical attention) and hence may not generalize to milder forms of the disorder.</p><p>In summary, CTDs have a complex heterogeneous etiology that involves hundreds of genes, some with rare high-penetrance mutations and others with common low-penetrance variants that act in an additive fashion. In this article, the empiric recurrence risk estimates range between 30 and 40% for specific first-degree relatives (or all first-degree relatives combined), but the CIs are wide. The recurrence risk estimates for second-degree relatives are considerably lower (7.4%; 95% CI = 5.1–10.4%). The recurrence risks are higher in males than females. These recurrence risks are in line with published risks for other neuropsychiatric disorders, including schizophrenia (<xref rid=\"B36\" ref-type=\"bibr\">36</xref>, <xref rid=\"B38\" ref-type=\"bibr\">38</xref>).</p><sec><title>Genetic Counseling Implications</title><p>Genetic counseling is a process that helps individuals understand the medical implications, including recurrence risk, for a variety of inherited disorders. The goal of this article was to provide the empiric recurrence risk estimates for CTDs, not the process of how to provide these risk estimates during a genetic counseling session. For an excellent article discussing the process of psychiatric genetic counseling, see Inglis et al. (<xref rid=\"B35\" ref-type=\"bibr\">35</xref>).</p><p>Previous work has suggested that patients with a family history of psychiatric disorders and other multifactorial disorders are interested in, and benefit from, genetic counseling, despite the challenges and limitations of precise risk assessment (<xref rid=\"B35\" ref-type=\"bibr\">35</xref>, <xref rid=\"B39\" ref-type=\"bibr\">39</xref>). It is yet to be explored which families with CTDs might be interested in genetic counseling. However, recent insights into the genetic architecture of CTDs have implications for recurrence risk estimation for families seeking information and thus make genetic counseling for CTDs possible. Presently, genetic counseling for CTDs using the empiric recurrence risks found in this article would follow a comparable model, outlined by Inglis et al. (<xref rid=\"B35\" ref-type=\"bibr\">35</xref>), which details an approach for providing psychiatric genetic counseling. Similar to genetic counseling for CTDs, psychiatric genetic counseling often involves providing empiric recurrence risk estimates. That article (<xref rid=\"B35\" ref-type=\"bibr\">35</xref>) provides detailed descriptions of (a) exploring personal and family histories, (b) establishing a shared understanding and expectations for counseling, (c) discussing the known and unknown etiology of psychiatric illness, (d) discussing protective factors, (e) communicating risk to patients, and (f) deriving estimates of probabilities for children to develop the illness. Additionally, this article also provides text for clinicians to use for different issues that might arise in a genetic counseling setting [Tables 1–3 Inglis et al. (<xref rid=\"B35\" ref-type=\"bibr\">35</xref>)].</p><p><xref rid=\"T2\" ref-type=\"table\">Table 2</xref> outlines the key points for couples or patients seeking genetic counseling for CTDs. The key points of a consultation with a genetic counselor include receiving education regarding the hereditary nature of CTDs, understanding its complex and heterogeneous genetic architecture, and comprehending the lack of available genetic testing. Although empiric recurrence risk estimates are available, there is currently no genetic testing that would provide individualized recurrence risk estimates for CTDs or that would rule out any predisposing factor. Genetic counselors are trained to provide a balanced summary of the current understanding of the underlying genetic etiology while highlighting the limitations of such information. Physicians and other medical professionals are encouraged to refer interested parents or patients to genetic counseling. Information surrounding the recurrence risk of CTDs is expected to evolve over time, and genetic counselors are able to provide the most up-to-date information to families at risk.</p><table-wrap id=\"T2\" position=\"float\"><label>Table 2</label><caption><p>Key counseling points for a chronic tic disorder (CTD) genetic counseling appointment.</p></caption><table frame=\"hsides\" rules=\"groups\"><tbody><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Prevalence is higher in males than females (3–4:1).</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">CTDs are highly heritable.</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Complex and heterogeneous etiology with hundreds of genes involved, both rare high-penetrance and common low-penetrance variants, and these may interact with environmental factors <italic>in utero</italic>.</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Ten percent to 12% of CTDs are due to <italic>de novo</italic> mutations, and these <italic>de novo</italic> mutations in one generation can be passed on to children in the next generation.</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Lifetime worst-ever tic severity may be higher when there is at least one other family member is affected, but the number of relatives with tics does not predict severity. Worst-ever tic severity varies widely within family members (i.e., severity does not breed true). Also, there is limited evidence that if both parents are affected, their children will have more severe tics.</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">At present, genetic testing for Tourette disorder and other CTDs has limited utility.</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">The empiric recurrence risk estimates for CTD found in this study are an average risk across all family types, including families in which only one individual has a CTD and families in which multiple individuals have had CTD. The 95% confidence intervals show the range of uncertainty around an average value and not meant to imply a range that is clinically applicable to a certain family type. That is, we currently do not have a system to categorize families with higher or lower risk within these intervals.</td></tr></tbody></table></table-wrap><p>One aspect that we did not address is the risk to relatives for the associated disorders that are known to be often clinically comorbid with CTDs, such as ADHD or OCD, but that may be at increased risk even in offspring without tics (<xref rid=\"B10\" ref-type=\"bibr\">10</xref>, <xref rid=\"B25\" ref-type=\"bibr\">25</xref>). Furthermore, because genetic counseling is often sought in the context of couples contemplating conceiving in the context of a family history of CTD, preconceived notions about the quality of life with CTD may affect such decisions and be heavily colored by the specific experiences of affected family members. The quality of life over the life span of individuals with CTDs varies greatly with their clinical specifics, especially the presence or absence of comorbidities such as ADHD or OCD, age, family support, and treatment (<xref rid=\"B40\" ref-type=\"bibr\">40</xref>, <xref rid=\"B41\" ref-type=\"bibr\">41</xref>). Although these are not the purview of the genetic counselor <italic>per se</italic>, it is important that individuals and families with CTD be connected with clinicians with expertise in these disorders who can help to mitigate their potentially deleterious impact.</p></sec></sec><sec sec-type=\"data-availability\" id=\"s5\"><title>Data Availability Statement</title><p>All datasets generated for this study are included in the article.</p></sec><sec id=\"s6\"><title>Ethics Statement</title><p>Ethical approval and written informed consent were not required as per local legislation and national guidelines.</p></sec><sec id=\"s7\"><title>Author Contributions</title><p>GH, JR, CS, JL, RK, and TF contributed to the design, organization, and conception of the work. GH and TF contributed to the statistical analysis. GH, JR, and TF contributed to the interpretation of the data. GH, JR, and RK contributed to drafting the manuscript. All authors reviewed, critiqued, and gave final approval for the version to be published and agreed to be accountable for all aspects of the work.</p></sec><sec id=\"s8\"><title>Conflict of Interest</title><p>The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p></sec></body><back><fn-group><fn fn-type=\"financial-disclosure\"><p><bold>Funding.</bold> This study was supported by grants from the National Institute of Mental Health (R01MH115958 to GH) and the New Jersey Center for Tourette Syndrome and Associated Disorders (NJCTS; to GH).</p></fn></fn-group><ref-list><title>References</title><ref id=\"B1\"><label>1.</label><mixed-citation publication-type=\"book\"><person-group person-group-type=\"author\"><name><surname>Vahia</surname><given-names>VN</given-names></name></person-group>\n<article-title>Diagnostic and Statistical Manual of Mental 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"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Front Immunol</journal-id><journal-id journal-id-type=\"iso-abbrev\">Front Immunol</journal-id><journal-id journal-id-type=\"publisher-id\">Front. Immunol.</journal-id><journal-title-group><journal-title>Frontiers in Immunology</journal-title></journal-title-group><issn pub-type=\"epub\">1664-3224</issn><publisher><publisher-name>Frontiers Media S.A.</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32849666</article-id><article-id pub-id-type=\"pmc\">PMC7432138</article-id><article-id pub-id-type=\"doi\">10.3389/fimmu.2020.02014</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Immunology</subject><subj-group><subject>Perspective</subject></subj-group></subj-group></article-categories><title-group><article-title>Understanding the Pathophysiology of COVID-19: Could the Contact System Be the Key?</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Meini</surname><given-names>Simone</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><xref ref-type=\"corresp\" rid=\"c001\"><sup>*</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/954427/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Zanichelli</surname><given-names>Andrea</given-names></name><xref ref-type=\"aff\" rid=\"aff2\"><sup>2</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/589093/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Sbrojavacca</surname><given-names>Rodolfo</given-names></name><xref ref-type=\"aff\" rid=\"aff3\"><sup>3</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Iuri</surname><given-names>Federico</given-names></name><xref ref-type=\"aff\" rid=\"aff4\"><sup>4</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Roberts</surname><given-names>Anna Teresa</given-names></name><xref ref-type=\"aff\" rid=\"aff5\"><sup>5</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Suffritti</surname><given-names>Chiara</given-names></name><xref ref-type=\"aff\" rid=\"aff2\"><sup>2</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/588967/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Tascini</surname><given-names>Carlo</given-names></name><xref ref-type=\"aff\" rid=\"aff3\"><sup>3</sup></xref></contrib></contrib-group><aff id=\"aff1\"><sup>1</sup><institution>Internal Medicine Unit, Azienda USL Toscana Centro, Santa Maria Annunziata Hospital</institution>, <addr-line>Florence</addr-line>, <country>Italy</country></aff><aff id=\"aff2\"><sup>2</sup><institution>General Medicine Unit, ASST Fatebenefratelli Sacco, Ospedale Luigi Sacco-Università degli Studi di Milano</institution>, <addr-line>Milan</addr-line>, <country>Italy</country></aff><aff id=\"aff3\"><sup>3</sup><institution>Infectious Diseases Clinic, Santa Maria Misericordia Hospital, Università degli Studi di Udine</institution>, <addr-line>Udine</addr-line>, <country>Italy</country></aff><aff id=\"aff4\"><sup>4</sup><institution>Department of Emergency, Santa Maria Misericordia Hospital, Università degli Studi di Udine</institution>, <addr-line>Udine</addr-line>, <country>Italy</country></aff><aff id=\"aff5\"><sup>5</sup><institution>Medical Department, Azienda USL Toscana Nord-Ovest</institution>, <addr-line>Pisa</addr-line>, <country>Italy</country></aff><author-notes><fn fn-type=\"edited-by\"><p>Edited by: Gennady Bocharov, Institute of Numerical Mathematics (RAS), Russia</p></fn><fn fn-type=\"edited-by\"><p>Reviewed by: Klaus T. Preissner, University of Giessen, Germany; Zhenlong Liu, McGill University, Canada</p></fn><corresp id=\"c001\">*Correspondence: Simone Meini, <email>[email protected]</email></corresp><fn fn-type=\"other\" id=\"fn004\"><p>This article was submitted to Viral Immunology, a section of the journal Frontiers in Immunology</p></fn></author-notes><pub-date pub-type=\"epub\"><day>11</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><year>2020</year></pub-date><pub-date pub-type=\"pmc-release\"><day>11</day><month>8</month><year>2020</year></pub-date><!-- PMC Release delay is 0 months and 0 days and was based on the <pub-date pub-type=\"epub\"/>. --><volume>11</volume><elocation-id>2014</elocation-id><history><date date-type=\"received\"><day>16</day><month>4</month><year>2020</year></date><date date-type=\"accepted\"><day>24</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>Copyright © 2020 Meini, Zanichelli, Sbrojavacca, Iuri, Roberts, Suffritti and Tascini.</copyright-statement><copyright-year>2020</copyright-year><copyright-holder>Meini, Zanichelli, Sbrojavacca, Iuri, Roberts, Suffritti and Tascini</copyright-holder><license xlink:href=\"http://creativecommons.org/licenses/by/4.0/\"><license-p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</license-p></license></permissions><abstract><p>To date the pathophysiology of COVID-19 remains unclear: this represents a factor determining the current lack of effective treatments. In this paper, we hypothesized a complex host response to SARS-CoV-2, with the Contact System (CS) playing a pivotal role in innate immune response. CS is linked with different proteolytic defense systems operating in human vasculature: the Kallikrein–Kinin (KKS), the Coagulation/Fibrinolysis and the Renin–Angiotensin (RAS) Systems. We investigated the role of the mediators involved. CS consists of Factor XII (FXII) and plasma prekallikrein (complexed to high-molecular-weight kininogen-HK). Autoactivation of FXII by contact with SARS-CoV-2 could lead to activation of intrinsic coagulation, with fibrin formation (microthrombosis), and fibrinolysis, resulting in increased D-dimer levels. Activation of kallikrein by activated FXII leads to production of bradykinin (BK) from HK. BK binds to B2-receptors, mediating vascular permeability, vasodilation and edema. B1-receptors, binding the metabolite [des-Arg<sup>9</sup>]-BK (DABK), are up-regulated during infections and mediate lung inflammatory responses. BK could play a relevant role in COVID-19 as already described for other viral models. Angiotensin-Converting-Enzyme (ACE) 2 displays lung protective effects: it inactivates DABK and converts Angiotensin II (Ang II) into Angiotensin-(1-7) and Angiotensin I into Angiotensin-(1-9). SARS-CoV-2 binds to ACE2 for cell entry, downregulating it: an impaired DABK inactivation could lead to an enhanced activity of B1-receptors, and the accumulation of Ang II, through a negative feedback loop, may result in decreased ACE activity, with consequent increase of BK. Therapies targeting the CS, the KKS and action of BK could be effective for the treatment of COVID-19.</p></abstract><kwd-group><kwd>SARS-CoV-2</kwd><kwd>COVID-19</kwd><kwd>pathophysiology</kwd><kwd>Contact System</kwd><kwd>bradykinin</kwd><kwd>ACE</kwd><kwd>coagulation</kwd></kwd-group><funding-group><award-group><funding-source id=\"cn001\">CSL Behring<named-content content-type=\"fundref-id\">10.13039/100008322</named-content></funding-source></award-group></funding-group><counts><fig-count count=\"1\"/><table-count count=\"1\"/><equation-count count=\"0\"/><ref-count count=\"80\"/><page-count count=\"9\"/><word-count count=\"0\"/></counts></article-meta></front><body><sec id=\"S1\"><title>Introduction</title><p>Starting in December 2019 in Wuhan (Hubei Province, China), a novel coronavirus, designated SARS-CoV-2, has caused an international outbreak of a respiratory illness (COVID-19), rapidly evolving into a pandemic. The clinical spectrum of SARS-CoV-2 infection varies from asymptomatic or self-limiting mild forms, occurring in most cases, to severe progressive pneumonia with acute respiratory distress syndrome (ARDS), and death. In a yet to be defined percentage of cases, after about one week, there is a sudden and unpredictable worsening of clinical conditions (<xref rid=\"B1\" ref-type=\"bibr\">1</xref>).</p><p>At present, there is no vaccine or pharmacological treatments of proven efficacy for COVID-19 (<xref rid=\"B2\" ref-type=\"bibr\">2</xref>) and further investigation on effective drugs is required to face the current pandemic. One factor determining the lack of effective treatments is that the pathophysiology of COVID-19 remains largely unclear.</p><p>In this review, we try to address the complex link between the pathophysiology of COVID-19 and the different proteolytic defense systems operating in human vasculature, investigating the role of the mediators involved and speculating on the possibility of pharmacological modulation.</p></sec><sec id=\"S2\"><title>Clinical and Laboratory Findings in Patients With COVID-19</title><p>COVID-19 is mainly a respiratory illness, but a wide variety of clinical manifestations have been described, including the central nervous (<xref rid=\"B3\" ref-type=\"bibr\">3</xref>) and the digestive (<xref rid=\"B4\" ref-type=\"bibr\">4</xref>) systems. Most symptomatic COVID-19 patients display manifestations such as fever (98.6%), dry cough (59.4%), dyspnea (31.2%), myalgias (34.8%), sore throat (17.4%), diarrhea (10.1%), and other (<xref rid=\"B5\" ref-type=\"bibr\">5</xref>). Olfactory and gustatory dysfunctions are common symptoms, occurring in about 50% of patients and often presenting early in the clinical course (<xref rid=\"B6\" ref-type=\"bibr\">6</xref>, <xref rid=\"B7\" ref-type=\"bibr\">7</xref>). Low blood pressure values are frequently observed in hospitalized patients: Wang et al. (<xref rid=\"B5\" ref-type=\"bibr\">5</xref>) in their cohort reported a median of mean arterial pressure values of 90 mmHg despite 31% of patients having a history of hypertension.</p><p>The predominant findings of lung Computed Tomography are images of bilateral, peripheral and basal ground-glass opacities, crazy-paving pattern, consolidations, often in association (<xref rid=\"B8\" ref-type=\"bibr\">8</xref>), and ultrasonography precociously demonstrates a lung interstitial syndrome (<xref rid=\"B9\" ref-type=\"bibr\">9</xref>). These findings are consistent with a lung injury characterized by increased permeability, leaky blood vessels and edema, and have been confirmed by the histopathological data obtained from the lungs of patients who died from COVID-19, showing diffuse alveolar damage with necrosis of alveolar lining cells, pneumocyte type 2 hyperplasia, linear intra-alveolar fibrin deposition and increased lung weight due to edema; in addition, thrombi in pulmonary arteries with a diameter of 1–2 mm, without complete luminal obstruction, and massive alveolar capillary microthrombi were observed (<xref rid=\"B10\" ref-type=\"bibr\">10</xref>).</p><p>Concerning laboratory findings, an increase of lactate dehydrogenase levels and lymphocytopenia are common. Elevated levels of serum ferritin, as commonly found in viral infections, are detected in most patients (<xref rid=\"B11\" ref-type=\"bibr\">11</xref>). Levels of interleukin-6 (IL-6) are typically in the upper limit of the reference range and appear to correlate with disease severity (<xref rid=\"B12\" ref-type=\"bibr\">12</xref>, <xref rid=\"B13\" ref-type=\"bibr\">13</xref>). IL-6-induced high levels of C-reactive protein (CRP) are typically more related to bacterial rather than to viral infections (<xref rid=\"B11\" ref-type=\"bibr\">11</xref>): in COVID-19 patients CRP values are very variable. Even in non-critical patients, high D-dimer levels are found in most patients. Prothrombin time is often slightly increased.</p><p>Levels of inflammatory and coagulation biomarkers vary considerably among patients with COVID-19, suggesting the existence of different biochemical/clinical phenotypes, in which the predominant systems involved and the inflammatory and coagulopathy response patterns differ.</p><p>From a pathogenetic point of view, it is clear that a link (to date not yet fully clarified) exists between the clinical manifestations and alterations of the inflammatory and coagulation systems, and that these different systems are only apparently unrelated.</p></sec><sec id=\"S3\"><title>The Role of the Contact System in the Pathophysiology of COVID-19</title><p>The Contact System (CS) is part of the innate immune system and of inflammatory response mechanism against artificial material, misfolded and foreign proteins and microorganisms (including viruses), found in the intravascular compartment. It remains to be clarified whether contact factors bind and activate directly on the viral surface or on infected cells (<xref rid=\"B14\" ref-type=\"bibr\">14</xref>).</p><p>The main proteins of the CS are the Factor XII (FXII), the prekallikrein (PK) and the high-molecular-weight kininogen (HK). These proteins are produced by the liver and circulate as zymogens into the bloodstream. Virtually all plasma PK circulates in complex with HK.</p><p>Auto-activation of FXII to FXIIa by contact with a variety of artificial and biological negatively charged surfaces, including microorganisms, gives rise to CS cascade. Biological substances with the potential to support its activation include: DNA, RNA, polyphosphates retained on activated platelet surface, aggregated proteins, neutrophil extracellular traps (NETs) and ferritin (<xref rid=\"B15\" ref-type=\"bibr\">15</xref>–<xref rid=\"B19\" ref-type=\"bibr\">19</xref>). Kannemeier et al. (<xref rid=\"B20\" ref-type=\"bibr\">20</xref>) presented evidence that different forms of eukaryotic and prokaryotic RNA serve as promoters of blood coagulation, enhancing auto-activation of proteases of the CS, such as FXII and FXI. As the extracellular RNA derived from damaged or necrotic cells represented a “foreign surface” able to activate the CS, it could be speculated that the same process may be initiated by viral RNA. In addition, at times of cellular stress (i.e., hypoxia, hyperthermia, oxygen radical production) such as that observed during COVID-19, endogenous “alarmins” named “Danger-Associated Molecular Patterns” (DAMPs) are released from necrotic cells. These molecules are able to initiate appropriate defense reactions associated with “sterile” inflammation and tissue repair, engaging the “Pattern-Recognition Receptors” (PRRs), such as the cell membrane and endosomal Toll-like receptors (TLRs) (<xref rid=\"B21\" ref-type=\"bibr\">21</xref>). Moreover, during viral infections, TLRs represent a host primary line of defense for pathogen sensing, due to their properties to bind diverse exogenous ligands (the “Pathogen-Associated Molecular Patterns,” PAMPs), including viral RNA (<xref rid=\"B21\" ref-type=\"bibr\">21</xref>); DAMPs and PAMPs are able to activate the FXII and the CS.</p><p>HK, complexed with PK, binds to these “surfaces”: the domain 5 is the artificial surface-binding region of HK, while the domain 6 binds PK and FXI in order to initiate the intrinsic coagulation (<xref rid=\"B16\" ref-type=\"bibr\">16</xref>). After HK binds to a surface, PK is exposed to conversion to plasma kallikrein (KAL) by FXIIa: the binding induces a conformational change in PK so that it acquires enzymatic activity and can stoichiometrically cleave HK (<xref rid=\"B22\" ref-type=\"bibr\">22</xref>). In turn, KAL cleaves and activates more FXII, in a powerful positive feedback loop (<xref rid=\"B14\" ref-type=\"bibr\">14</xref>).</p><p>In addition, a vessel wall-associated serine protease, prolyl-carboxypeptidase (PRCP), is able to activate PK to KAL independent of FXIIa (<xref rid=\"B16\" ref-type=\"bibr\">16</xref>).</p><p>The CS is involved in inflammation and in coagulation: when sufficient amounts of FXII are activated, FXIIa also activates FXI (to FXIa), and the intrinsic (or contact) coagulation pathway can start, leading to subsequent thrombin activation and fibrin formation. KAL can influence the fibrinolytic pathway by activating plasminogen into plasmin, thus leading also to fibrin degradation (<xref rid=\"B23\" ref-type=\"bibr\">23</xref>). D-dimer is a soluble fibrin degradation product deriving from the plasmin-mediated degradation of cross-linked fibrin: it can therefore be considered a biomarker of concomitant activation of both coagulation and fibrinolysis (<xref rid=\"B24\" ref-type=\"bibr\">24</xref>).</p><p>It should be remembered that plasmin can also activate FXII (<xref rid=\"B25\" ref-type=\"bibr\">25</xref>), and that FXIIa can act as a plasminogen activator too (<xref rid=\"B26\" ref-type=\"bibr\">26</xref>): it has been speculated that in the very early stages of <italic>in vivo</italic> contact activation, when PK has yet to become activated, plasmin could have an initiating role (<xref rid=\"B26\" ref-type=\"bibr\">26</xref>), however it should be noted that plasmin is hardly present in plasma as an active protease due to the very effective action of its specific inhibitor, the α2-anti-plasmin: plasmin is protected from inactivation by this inhibitor only when bound to fibrin.</p><p>The coagulation cascade can be modernly considered as a component and one of the intravascular effectors of innate immunity (immunothrombosis) (<xref rid=\"B27\" ref-type=\"bibr\">27</xref>). It is debatable whether the main physiological function of FXII is the activation of the intrinsic coagulation pathway, or if to be a component of the CS should be considered its main physiological function. For physiological hemostasis to occur, FXII auto-activation is dispensable (<xref rid=\"B21\" ref-type=\"bibr\">21</xref>). The FXII-induced intrinsic coagulation pathway is involved in pathological thrombus formation but is not associated with abnormal hemostasis: FXII-deficient subjects present in fact a normal hemostatic capacity (<xref rid=\"B28\" ref-type=\"bibr\">28</xref>, <xref rid=\"B29\" ref-type=\"bibr\">29</xref>). Challenging the concept of the coagulation balance, targeting FXII or its activator polyphosphate can provide protection from thromboembolic diseases (and modulate immunothrombosis) without interfering with hemostasis and increasing the risk of bleeding (<xref rid=\"B14\" ref-type=\"bibr\">14</xref>, <xref rid=\"B16\" ref-type=\"bibr\">16</xref>, <xref rid=\"B18\" ref-type=\"bibr\">18</xref>, <xref rid=\"B30\" ref-type=\"bibr\">30</xref>, <xref rid=\"B31\" ref-type=\"bibr\">31</xref>).</p><p>COVID-19 is a condition clearly characterized by coagulopathy, as testified by the extensive microthrombosis reported in lung autopsies (<xref rid=\"B10\" ref-type=\"bibr\">10</xref>), and the high levels of D-dimer displayed by most patients indirectly testify the hyperactivation of both coagulation and fibrinolysis, and overwhelming immunothrombosis. It should be remembered that low-molecular weight heparins (LMWHs) have been extensively used in hospitalized COVID-19 patients for preventing venous thromboembolism and thrombotic complications, and are currently investigated in randomized controlled trials (i.e., <ext-link ext-link-type=\"uri\" xlink:href=\"https://clinicaltrials.gov\">ClinicalTrials.gov</ext-link> Identifier: NCT04401293).</p><p>It is interesting to note that in the physiological state FXII acts as a growth factor promoting angiogenesis and wound repair (<xref rid=\"B32\" ref-type=\"bibr\">32</xref>), but pathologically it can promote lung fibroblast proliferation leading to pulmonary fibrosis (<xref rid=\"B33\" ref-type=\"bibr\">33</xref>): COVID-19 may also evolve into pulmonary fibrosis.</p><p>The archetypal contact activation disease state is sepsis from any etiology. There is no specific data on the model of SARS-CoV-2, but data may be gathered from other viral models. It is known that herpes simplex virus type-1 (HSV1) can trigger and amplify coagulation through the contact phase and intrinsic coagulation pathway: both an inhibitor of FXIIa (corn trypsin inhibitor), and anti-FXII, anti-KAL and anti-FXI antibodies were able to inhibit HSV1-initiated clotting (<xref rid=\"B34\" ref-type=\"bibr\">34</xref>). Moreover, PK and FXII levels are significantly lower in patients with dengue hemorrhagic fever (DHF), probably due to activation and consumption (<xref rid=\"B35\" ref-type=\"bibr\">35</xref>).</p><p>It has been mentioned that CS is part of the innate immune system: it is known that non-structural protein 3 (nsp3) of coronaviruses results able to block the host innate immune response (<xref rid=\"B36\" ref-type=\"bibr\">36</xref>), and other nsp play a role in evading host recognition (<xref rid=\"B37\" ref-type=\"bibr\">37</xref>).</p></sec><sec id=\"S4\"><title>The Kallikrein–Kinin System</title><p>The Kallikrein–Kinin System (KKS) is mainly a host inflammatory response mechanism, and although KKS and CS overlap and interact in the intravascular compartment (plasma KAL is part of both systems), the use of the two terms has different implications. Activation of KKS finally leads to the liberation of bradykinin (BK), and plays an essential role in inflammation, but not in blood coagulation (<xref rid=\"B16\" ref-type=\"bibr\">16</xref>).</p><p>Upon activation by FXIIa, KAL cleaves HK, releasing from its domain 4 the nonapeptide bradykinin (BK-1-9 or BK) (<xref rid=\"B38\" ref-type=\"bibr\">38</xref>); BK is converted by a carboxypeptidase to [des-Arg<sup>9</sup>]-BK (BK-1-8 or DABK), an active metabolite (<xref rid=\"B39\" ref-type=\"bibr\">39</xref>). During inflammation, plasmin potentiates the cleavage of HK by KAL, thus enhancing BK production (<xref rid=\"B40\" ref-type=\"bibr\">40</xref>).</p><p>BK and DABK bind to two pharmacologically distinct G protein-coupled receptors: the bradykinin B2 receptor (B2R), whose ligand is BK, and the B1 receptor (B1R), whose main agonist is DABK (<xref rid=\"B39\" ref-type=\"bibr\">39</xref>). The B2R is widely and constitutively expressed in mammalian cells (e.g., endothelial and smooth muscle cells), whereas the B1R is mostly inducible under the effect of cytokines during infections and immunopathology (<xref rid=\"B41\" ref-type=\"bibr\">41</xref>).</p><p>After binding through its B2R, BK activates signaling pathways resulting in increased vascular permeability, vasodilation, edema formation, hypotension, pain, fever (<xref rid=\"B14\" ref-type=\"bibr\">14</xref>): all typical clinical features of COVID-19. BK is one of the most potent vasodilatory substances in humans: it is known that the BK-mediated angioedema is responsible for a very high percentage of serious morbidity and mortality (<xref rid=\"B42\" ref-type=\"bibr\">42</xref>). BK is also one of the most potent inflammatory mediators, able to stimulate the production of superoxide radicals and nitric oxide and to modulate the mobilization and release of histamine, arachidonic acid, prostaglandin E2, prostacyclin, pro-inflammatory interleukin-1, and tumor necrosis factor (TNF)-alpha (<xref rid=\"B41\" ref-type=\"bibr\">41</xref>). Thereafter, BK has shown to increase IL-6 production via B2R in colorectal cancer cell (<xref rid=\"B43\" ref-type=\"bibr\">43</xref>), and the B2R-antagonist icatibant was able to inhibit the BK-induced IL-6 release (<xref rid=\"B44\" ref-type=\"bibr\">44</xref>). This effect is interesting: also chloroquine, that has been extensively used and investigated for COVID-19 treatment, was able to reduce IL-6 production by monocytes/macrophages (<xref rid=\"B45\" ref-type=\"bibr\">45</xref>). BK also stimulates tissue plasminogen activator (t-PA) release from human endothelium through a B2R-dependent mechanism: this effect was significantly reduced in smokers (<xref rid=\"B46\" ref-type=\"bibr\">46</xref>). A strong link between KKS and the renin–angiotensin system (RAS) is testified by the fact that B2R forms homo- and heterodimers with several receptors of the RAS, that are important for some physiologic functions, including thrombosis risk regulation. The B2R also complexes with endothelial cell nitric oxide synthase, while the B1R couples with inducible nitric oxide synthase (<xref rid=\"B16\" ref-type=\"bibr\">16</xref>).</p><p>B1R mediates several responses including vasodilation, hypotension, and increased vascular permeability (<xref rid=\"B41\" ref-type=\"bibr\">41</xref>): all typical features of COVID-19.</p><p>Human kallikreins have been detected in many tissues (<xref rid=\"B47\" ref-type=\"bibr\">47</xref>), including the epithelia of the upper and lower respiratory tract: there are in fact two classical pathways for the generation of kinins, the plasma and the tissue KKS. As the substrate of plasma KAL is HK (leading to BK), the substrate of tissue kallikreins is the low-molecular-weight kininogen, leading to formation of the decapeptide Lys-bradykinin or kallidin (KD). A carboxypeptidase leads to the formation of the active metabolite [des-Arg<sup>10</sup>]-KD (DAKD) from KD. KD mainly binds to B2R, while B1R has a high affinity for DAKD (<xref rid=\"B48\" ref-type=\"bibr\">48</xref>).</p><p>It is not known if SARS-CoV-2 infection is specifically associated with kinins dysregulation, but this happens in several viral models. Low levels of HK have been observed in DHF patients, perhaps due to proteolysis and generation of BK (<xref rid=\"B49\" ref-type=\"bibr\">49</xref>). Taylor et al. (<xref rid=\"B50\" ref-type=\"bibr\">50</xref>) previously described a novel mechanism of hantavirus-induced vascular leakage involving activation of the KKS, showing that incubation of FXII, PK, and HK with hantavirus-infected endothelial cells results in increased cleavage of HK, higher enzymatic activities of FXII/KAL and increased liberation of BK, that dramatically increased cell permeability. Furthermore, the alterations in permeability could be prevented using inhibitors directly blocking BK binding, the activity of FXII, or the activity of KAL (<xref rid=\"B50\" ref-type=\"bibr\">50</xref>). Infection of guinea pigs by nasal instillation of parainfluenza-3 virus induced airway hyperreactivity and influx of inflammatory cells into lung tissues, and these responses were attenuated by B2R-antagonists (<xref rid=\"B51\" ref-type=\"bibr\">51</xref>). Tissue kallikrein 1 was shown to intervene early during influenza infection, enhancing the antiviral defense, and the decreased expression observed in patients with chronic obstructive pulmonary disease could contribute to the less favorable evolution of influenza in this group (<xref rid=\"B52\" ref-type=\"bibr\">52</xref>).</p><p>Therefore, the KKS appears to be involved in vascular leakage and inflammatory response observed during different viral infections (<xref rid=\"B14\" ref-type=\"bibr\">14</xref>). We can speculate that modulation of the CS and the KKS may limit the evolution towards a frankly dysregulated host response also in SARS-CoV-2 infection.</p><p>Moreover, a role of BK in COVID-19 pathogenesis is suggested by several clinical features and symptoms observed in patients: given the close interconnection with the RAS, these aspects will be further discussed in the next chapter.</p></sec><sec id=\"S5\"><title>The Renin–Angiotensin System (RAS) and the Interplay With KKS</title><p>The renin–angiotensin system (RAS) is classically known for its effects on the cardiovascular system and fluid homeostasis, but it has become clear that the RAS is present in many tissues, where evidently has a role to play (<xref rid=\"B53\" ref-type=\"bibr\">53</xref>).</p><p>Starting from angiotensinogen, whose primary source is the liver, the RAS leads to the production of the multi-functional peptide hormone Angiotensin II (Ang II). Renin first catalyzes the cleavage of the peptide Angiotensin I (Ang I) from the N-terminus of the angiotensinogen molecule, then, sequentially, the dicarboxyl-peptidase angiotensin converting enzyme (ACE) removes two amino-acids from the C-terminus of Ang I to form Ang II (<xref rid=\"B54\" ref-type=\"bibr\">54</xref>). Ang II exerts its main functions binding to two specific G-protein coupled receptors: the ATII type 1 receptor (AT1R) and ATII type 2 receptor (AT2R) (<xref rid=\"B54\" ref-type=\"bibr\">54</xref>).</p><p>ACE is present in many tissues and is particularly abundant on the endothelium of the lungs: it is mainly anchored to the plasma membrane through a single trans-membrane domain, but a soluble form has also been described (<xref rid=\"B53\" ref-type=\"bibr\">53</xref>).</p><p>Apart from its well-known role as a peptidyl-dipeptidase forming Ang II, ACE is also described as a kininase II, able to inactivate BK, as well as KD (<xref rid=\"B53\" ref-type=\"bibr\">53</xref>). The affinity of ACE appears to be higher for BK than for Ang I, suggesting that ACE-inhibition may really involve the BK degradation more than the Ang II production (<xref rid=\"B55\" ref-type=\"bibr\">55</xref>). BK-evoked sensitization of airway sensory nerves is believed to be the main mechanism for ACE-inhibitor-induced dry cough (<xref rid=\"B56\" ref-type=\"bibr\">56</xref>): considering that dry cough is very frequently observed in COVID-19 patients, this pathway could in part explain the pathogenesis of this symptom. Additionally, a role of the BK has been hypothesized also for gustatory and olfactory dysfunctions (<xref rid=\"B7\" ref-type=\"bibr\">7</xref>); again, ACE-inhibitors can cause olfactory dysfunction (<xref rid=\"B57\" ref-type=\"bibr\">57</xref>).</p><p>In addition, over the last 20 years, knowledge of the biology and physiology of another enzyme besides ACE, the angiotensin converting enzyme 2 (ACE2), has accumulated (<xref rid=\"B58\" ref-type=\"bibr\">58</xref>): ACE2 is widely expressed, including type 2 alveolar epithelial cells, endothelial cells and enterocytes (<xref rid=\"B10\" ref-type=\"bibr\">10</xref>, <xref rid=\"B58\" ref-type=\"bibr\">58</xref>).</p><p>Both ACE and ACE2 act as zinc metallopeptidases (ACE2 only acts as a carboxypeptidase), but differ for substrate specificities, displaying counterbalancing roles in the RAS.</p><p>ACE2 converts Ang I into Angiotensin (1-9), and Ang II into Angiotensin (1-7); unlike ACE, ACE2 does not cleave BK, and is insensitive to conventional ACE-inhibitors (<xref rid=\"B58\" ref-type=\"bibr\">58</xref>). Ang II can be converted to angiotensin (1-7) also by PRCP in the low-pH areas of the kidney (<xref rid=\"B59\" ref-type=\"bibr\">59</xref>).</p><p>Angiotensin 1-7, acting on Mas receptor, exerts vasodilatory effects, thus diminishing and opposing the vasoconstrictor effect, mainly AT1R-mediated, of Ang II; moreover, it displays anti-fibrotic, anti-oxidant and anti-hypertrophic protective properties (<xref rid=\"B58\" ref-type=\"bibr\">58</xref>).</p><p>Therefore, ACE2 expression seems to protect from lung injury. Sodhi et al. (<xref rid=\"B39\" ref-type=\"bibr\">39</xref>) observed that a reduction in pulmonary ACE2 activity contributes to the pathogenesis of lung inflammation, resulting in prompt onset of neutrophil infiltration and more severe inflammation. Imai et al. (<xref rid=\"B60\" ref-type=\"bibr\">60</xref>) showed that the loss of ACE2 expression in acute lung injury leads to leaky pulmonary blood vessels through AT1R stimulation, while the AT2R protects against lung injury during sepsis. Angiotensin 1-9 has shown beneficial biological effects via the AT2R, resulting in protective effects on cardiac and vascular remodeling (<xref rid=\"B58\" ref-type=\"bibr\">58</xref>) and against pulmonary arterial hypertension, inflammation and fibrosis (<xref rid=\"B61\" ref-type=\"bibr\">61</xref>).</p><p>SARS-CoV-2 binds ACE2 for host cell entry, through the binding of its major spike glycoprotein (S1) to the N-terminal region of the receptor (<xref rid=\"B62\" ref-type=\"bibr\">62</xref>); chloroquine seems to interfere with ACE2 glycosylation, thus possibly preventing SARS-CoV-2 binding to target cells (<xref rid=\"B63\" ref-type=\"bibr\">63</xref>). Following binding with SARS-CoV-2, a loss of ACE2 function occurs, driven by endocytosis and activation of proteolytic cleavage and processing (<xref rid=\"B58\" ref-type=\"bibr\">58</xref>, <xref rid=\"B62\" ref-type=\"bibr\">62</xref>). It can be assumed that this downregulation may be involved in the pathophysiology of COVID-19 and its manifestations. DABK is a substrate of ACE2, and the attenuation of ACE2 activity leads to impaired DABK inactivation and thus to enhanced B1R signaling.</p><p>In a mouse model, the lack of ACE2 function with consequent accumulation of Ang II, through a negative feedback loop, resulted in a secondary reduction of ACE activity (at the molecular level, Ang II downregulates renal ACE gene and enzymatic activity levels, as well as renin gene expression): these crosstalk effects between ACE2 and ACE appeared to be sex-dependent and more evident in males (<xref rid=\"B64\" ref-type=\"bibr\">64</xref>). It is known that COVID-19 affects male patients in a larger percentage (<xref rid=\"B65\" ref-type=\"bibr\">65</xref>) and with worse outcomes (<xref rid=\"B66\" ref-type=\"bibr\">66</xref>). The reduced activity of ACE is also expected to result in further BK accumulation. Moreover, it has been recognized <italic>in vitro</italic> that Ang II, through the stimulation of AT2R, is associated with increased expression of PRCP, leading to a KAL-mediated increased formation of BK (<xref rid=\"B67\" ref-type=\"bibr\">67</xref>).</p><p>Since SARS-CoV-2 binds to ACE2 receptors to enter host cells, and intravenous infusion of ACE-inhibitors and angiotensin receptor blockers (ARBs) in experimental animal models increased the amount of ACE2 receptors in the cardiopulmonary circulation, it has been speculated that patients chronically taking these drugs may be at increased risk of worse outcomes from COVID-19 (<xref rid=\"B68\" ref-type=\"bibr\">68</xref>). However, to date, there are no conclusive data demonstrating beneficial or adverse outcomes with background use of ACE-inhibitors, ARBs or other RAS antagonists among COVID-19 patients with a history of cardiovascular disease treated with these drugs (<xref rid=\"B69\" ref-type=\"bibr\">69</xref>–<xref rid=\"B71\" ref-type=\"bibr\">71</xref>). For the pathophysiological considerations previously made, however, in our opinion, it remains debatable if ACE-inhibitors, for their action on BK, should be temporarily suspended during the acute phase of illness, especially in the case of low blood pressure values.</p><p>Finally, it is interesting to observe that in an experimental malaria model (<italic>Plasmodium</italic> parasites during blood stages release kinins), exposure to captopril (an ACE-inhibitor that leads to the reduction of BK degradation) resulted in death in mice, while the concomitant administration of chloroquine protected them. B1R-knockout mice presented a significant reduction of survival when compared with wild-type mice, unlike the B2R-knockout ones (<xref rid=\"B72\" ref-type=\"bibr\">72</xref>). In this inflammation/infection model, chloroquine-induced upregulation of B1R expression proved protective: the full meaning of this result is unclear but might indicate that the selective inhibition of B2R could represent a rational modulation of dysregulated BK pathway during infection. Could the same considerations apply to COVID-19?</p></sec><sec id=\"S6\"><title>C1-Inhibitor and Its Potential Role in Viral Infections</title><p>Hereditary angioedema (HAE) represents the archetypal KKS disorder and can be due to a deficiency of C1-INH (Type 1), an abnormal C1-INH molecule (Type 2), or a gain-in-function of FXII with consequent plasma C1-INH consumption (Type 3) (<xref rid=\"B73\" ref-type=\"bibr\">73</xref>). Thrombin formation is not considered a feature of this disorder: even if patients with acute attacks have elevated D-dimer levels, they do not display an increased thrombotic risk (<xref rid=\"B74\" ref-type=\"bibr\">74</xref>). Clinical pictures of activation of CS and KKS without (such as HAE) and with thrombin formation (such as sepsis) can be in fact distinguished (<xref rid=\"B16\" ref-type=\"bibr\">16</xref>): COVID-19 evidently falls into the latter group.</p><p>C1-INH is a protein able to inhibit multiple serine proteases involved in the CS, KKS, Complement, Fibrinolysis, and Coagulation Systems: through the inhibition of C1r and C1s subcomponents of C1 complex, FXIIa, and KAL, C1-INH prevents the activation of CS and KKS. The N-terminal end (non-serpin domain) confers to C1-INH the capacity to bind lipopolysaccharides and E-selectin: owing to this moiety, C1-INH can also intervene in the regulation of inflammatory reactions (<xref rid=\"B75\" ref-type=\"bibr\">75</xref>). Moreover, C1-INH inhibits selectin-mediated leukocyte adhesion, regardless of its protease inhibitory activity (<xref rid=\"B76\" ref-type=\"bibr\">76</xref>).</p><p>Wygrecka et al. (<xref rid=\"B77\" ref-type=\"bibr\">77</xref>) showed that C1-INH is able to inhibit the cytotoxic activity of extracellular histones (that play a determining role in pulmonary injury leading to ARDS) and the release of several cytokines, such as TNF-alpha, IL-1b, and IL-6. It is interesting to note that accumulation of extracellular histones has been detected during infection due to influenza virus, and anti-histone antibodies have led to a marked decrease in the lung damage consisting of widespread pulmonary microvascular thrombosis, endothelial necrosis, hemorrhagic effusions and edema (<xref rid=\"B78\" ref-type=\"bibr\">78</xref>). These histopathological findings are observed also in COVID-19, although there are several differences compared to the influenza model (<xref rid=\"B10\" ref-type=\"bibr\">10</xref>) whose discussion goes beyond the scope of this review. Although there is actually no specific evidence regarding SARS-CoV-2 infection, it can be assumed that C1-INH might have beneficial effects also in this case, both through the inhibition of the CS and KKS, especially regarding the BK-induced vascular leakage and edema formation, and its anti-inflammatory activity mediated by inhibition of complement activation and histone toxicity.</p><p><xref ref-type=\"fig\" rid=\"F1\">Figure 1</xref> shows the interconnection between the different human proteolytic systems operating in the vasculature, proposing a picture of an integrated host response to SARS-CoV-2 infection.</p><fig id=\"F1\" position=\"float\"><label>FIGURE 1</label><caption><p>The Contact System (CS) as a plausible link between Coagulation/Fibrinolysis, Kallikrein–Kinin and Renin–Angiotensin Systems in the pathobiology of SARS-CoV-2 infection (based on the hypothesis of the activation of FXII and HK by SARS-CoV-2, directly or following cell invasion and/or damage). Black arrows indicate enzymatic activation, while red lines represent inhibition or degradation/downregulation. The sequences of black arrows imply the involvement of other molecules not shown to activate the molecule indicated by the final arrow. Dashed red lines imply the involvement of other molecules not shown to inhibit the molecule indicated at the end of the line. Gray arrows indicate the transformation of one molecule into another. <italic>ACE</italic>, angiotensin converting enzyme; <italic>ACE2</italic>, angiotensin converting enzyme 2; <italic>Ang I</italic>, Angiotensin I; <italic>Ang II</italic>, Angiotensin II; <italic>Ang</italic> (1-7), Angiotensin 1-7; <italic>Ang</italic> (1-9), Angiotensin 1-9; <italic>AT1R</italic>, ATII type 1 receptor; <italic>AT2R</italic>, ATII type 2 receptor; <italic>B1R</italic>, B1 receptor; <italic>B2R</italic>, B2 receptor; <italic>BK</italic>, bradykinin; <italic>C1-INH</italic>, C1-inhibitor; <italic>DABK</italic>, [des-Arg<sup>9</sup>]-BK; <italic>FXI</italic>, coagulation factor XI; <italic>FXII</italic>, coagulation factor XII; <italic>HK</italic>, high-molecular-weight kininogen; <italic>IL-6</italic>, interleukin-6; <italic>KAL</italic>, plasma kallikrein; <italic>MasR</italic>, Mas receptor; <italic>PK</italic>, plasma prekallikrein; <italic>t-PA</italic>, tissue plasminogen activator.</p></caption><graphic xlink:href=\"fimmu-11-02014-g001\"/></fig><p><xref rid=\"T1\" ref-type=\"table\">Table 1</xref> lists some available drugs potentially representing effective therapeutic approaches in COVID-19, by modulation of the pathways and systems whose involvement has been hypothesized in its pathogenesis.</p><table-wrap id=\"T1\" position=\"float\"><label>TABLE 1</label><caption><p>Potential therapeutic approaches able to modulate the systems involved in the pathogenesis of COVID-19.</p></caption><table frame=\"hsides\" rules=\"groups\" cellspacing=\"5\" cellpadding=\"5\"><thead><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Drug</bold></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Mechanism of action</bold></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Labeled indication</bold></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Potential role in COVID-19</bold></td></tr></thead><tbody><tr><td valign=\"top\" align=\"justify\" colspan=\"4\" rowspan=\"1\"><bold>Contact system</bold></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">C1-inhibitor</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Inhibition of CS, Coagulation/Fibrinolytic systems, complement and KKS</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Treatment and prevention of angioedema attacks in hereditary angioedema</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Inhibition of all systems involved</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Anti-factor XII (FXII) antibody</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Monoclonal antibody inhibiting FXII</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Phase II study ongoing; phase III study planned. Studied indication: prevention of angioedema attacks</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Inhibition of FXII and consequently of CS and KKS</td></tr><tr><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Prevention and treatment of thrombosis, without increasing bleeding risk (Action on Coagulation system)</td></tr><tr><td valign=\"top\" align=\"justify\" colspan=\"4\" rowspan=\"1\"><bold>Kallikrein–kinin system</bold></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Icatibant</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Bradykinin type 2 receptor (B2R)-antagonist</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Treatment of acute attacks in hereditary angioedema</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Inhibition of pro-inflammatory and vasoactive actions of BK</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Lanadelumab</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Monoclonal antibody inhibiting plasma KAL</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Prevention of attacks of hereditary angioedema</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Inhibition of KKS and BK generation</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Ecallantide</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Inhibition of plasma KAL</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Treatment of acute attacks in hereditary angioedema</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Inhibition of KKS and BK generation</td></tr><tr><td valign=\"top\" align=\"justify\" colspan=\"4\" rowspan=\"1\"><bold>Coagulation system</bold></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Low-Molecular-Weight Heparin/Fondaparinux</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Catalyzed inhibition of activated coagulation factor X by antithrombin</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Prevention and treatment of venous thromboembolism</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Prevention and treatment of thrombosis and pulmonary embolism (consider bleeding risk)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Anti-factor XII (FXII) antibody</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">See above</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">See above</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">See above</td></tr></tbody></table></table-wrap></sec><sec id=\"S7\"><title>Discussion and Conclusion</title><p>The hypothesis of the involvement of different human proteolytic defense systems operating in the vasculature in the pathogenesis of COVID-19 has recently been proposed also by other authors. van de Veerdonk et al. (<xref rid=\"B79\" ref-type=\"bibr\">79</xref>) hypothesized that a kinin-dependent local lung angioedema via B1R and eventually B2R is an important feature of COVID-19 and proposed that blocking the B2R and inhibiting plasma KAL activity might be beneficial in early disease, preventing ARDS. Roche and Roche (<xref rid=\"B80\" ref-type=\"bibr\">80</xref>) emphasized the pivotal role of BK and DABK, suggesting that the B2R-antagonist icatibant might be able to interrupt the dysregulated pathway, thereby improving clinical outcomes. Colarusso et al. (<xref rid=\"B23\" ref-type=\"bibr\">23</xref>) proposed instead to block pharmacologically the KKS upstream of the BK, by means of lanadelumab. Regarding B1R-antagonists, several companies have in past developed orally available molecules, and some of these entered phase II clinical trials, but none have been developed further; possible reasons for this failure may be inefficacy in humans due to species differences, or human specific adverse effects (<xref rid=\"B48\" ref-type=\"bibr\">48</xref>).</p><p>In our opinion, the rational for modulating these pathways is strong but to date few data for COVID-19 are available. However, the exceptional nature of this pandemic and the lack of effective interventions of proven efficacy makes it necessary to explore further therapeutic possibilities.</p><p>Understanding the pathogenetic mechanisms underlying COVID-19 is crucial for the development of new effective therapeutic approaches modulating the CS, the KKS, the RAS and the Coagulation/Fibrinolysis System. The KKS inhibitors lanadelumab and ecallantide, licensed for the treatment of HAE, and several oral KKS inhibitors in clinical development, should be assessed for their efficacy in the treatment of patients with COVID-19. The same holds for icatibant, a selective B2R antagonist used for on demand treatment in HAE. Other promising CS-linked targets or mediators that should be explored in COVID-19 include anti-FXIIa antibodies and C1-INH. This pathophysiological therapeutic approach could be of great value also for other viral infections.</p></sec><sec sec-type=\"data-availability\" id=\"S8\"><title>Data Availability Statement</title><p>Publicly available datasets were analyzed in this study. This data can be found at the appropriate doi link of every cited article.</p></sec><sec id=\"S9\"><title>Author Contributions</title><p>SM, AZ, RS, FI, and CT: conceptualization. SM, AZ, RS, FI, CS, AR, and CT: formal analysis. AZ: funding acquisition. SM, AZ, and CT: investigation and project administration. SM, AZ, RS, FI, AR, CS, and CT: methodology and resources. SM, AZ, AR, and CT: writing – original draft preparation and writing – review and editing. All authors contributed to the article and approved the submitted version.</p></sec><sec id=\"conf1\"><title>Conflict of Interest</title><p>AZ received speaker/consultancy fees and/or was a member of medical/advisory boards for CSL Behring, Shire/Takeda, and SOBI. All the authors declare that they have no financial competing interests about the topic of this article, except for the publishing support and journal styling services, that were provided by SEEd Medical Publishers and funded by CSL Behring, Italy.</p></sec></body><back><fn-group><fn fn-type=\"financial-disclosure\"><p><bold>Funding.</bold> The CSL Behring funded the publishing support and journal styling services, but had no role in the conduct of the research, preparation of the article, in study design, in the collection, analysis and interpretation of data, in the writing of the report, and in the decision to submit the article for publication.</p></fn></fn-group><ack><p>We acknowledge SE<italic>Ed</italic> Medical Publishers, that provided publishing support and journal styling services.</p></ack><ref-list><title>References</title><ref id=\"B1\"><label>1.</label><mixed-citation publication-type=\"book\"><person-group 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person-group-type=\"author\"><name><surname>Roche</surname><given-names>JA</given-names></name><name><surname>Roche</surname><given-names>R.</given-names></name></person-group>\n<article-title>A hypothesized role for dysregulated bradykinin signaling in COVID-19 respiratory complications.</article-title>\n<source><italic>FASEB J.</italic></source> (<year>2020</year>) <volume>00</volume>:<fpage>1</fpage>–<lpage>5</lpage>. <pub-id pub-id-type=\"doi\">10.1096/fj.202000967</pub-id>\n<pub-id pub-id-type=\"pmid\">32359101</pub-id></mixed-citation></ref></ref-list><glossary><title>Abbreviations</title><def-list id=\"DL1\"><def-item><term>ACE</term><def><p>Angiotensin-Converting Enzyme</p></def></def-item><def-item><term>ACE2</term><def><p>Angiotensin-Converting Enzyme 2</p></def></def-item><def-item><term>Ang I</term><def><p>Angiotensin I</p></def></def-item><def-item><term>Ang II</term><def><p>Angiotensin II</p></def></def-item><def-item><term>Ang (1-7)</term><def><p>Angiotensin 1-7</p></def></def-item><def-item><term>Ang (1-9)</term><def><p>Angiotensin 1-9</p></def></def-item><def-item><term>ARB</term><def><p>angiotensin receptor blocker</p></def></def-item><def-item><term>ARDS</term><def><p>Acute respiratory distress syndrome</p></def></def-item><def-item><term>AT1R</term><def><p>Angiotensin II type 1 receptor</p></def></def-item><def-item><term>AT2R</term><def><p>Angiotensin II type 2 receptor</p></def></def-item><def-item><term>BK</term><def><p>Bradykinin</p></def></def-item><def-item><term>B1R</term><def><p>Bradykinin B1 receptor</p></def></def-item><def-item><term>B2R</term><def><p>Bradykinin B2 receptor</p></def></def-item><def-item><term>COVID-19</term><def><p>COronaVIrus Disease 2019</p></def></def-item><def-item><term>CS</term><def><p>Contact System</p></def></def-item><def-item><term>C1-INH</term><def><p>C1-inhibitor</p></def></def-item><def-item><term>DABK</term><def><p>[des-Arg<sup>9</sup>]-Bradykinin</p></def></def-item><def-item><term>DAKD</term><def><p>[des-Arg<sup>10</sup>]-Kallidin</p></def></def-item><def-item><term>DAMPs</term><def><p>Danger-Associated Molecular Patterns</p></def></def-item><def-item><term>DHF</term><def><p>dengue hemorrhagic fever</p></def></def-item><def-item><term>FXI</term><def><p>Factor XI</p></def></def-item><def-item><term>FXII</term><def><p>Factor XII</p></def></def-item><def-item><term>HK</term><def><p>High-molecular-weight Kininogen</p></def></def-item><def-item><term>HSV1</term><def><p>Herpes simplex virus type-1</p></def></def-item><def-item><term>IL-6</term><def><p>Interleukin-6</p></def></def-item><def-item><term>KAL</term><def><p>Kallikrein</p></def></def-item><def-item><term>KD</term><def><p>Kallidin</p></def></def-item><def-item><term>KKS</term><def><p>Kallikrein–Kinin System</p></def></def-item><def-item><term>LMWH</term><def><p>Low-Molecular Weight Heparin</p></def></def-item><def-item><term>PAMPs</term><def><p>Pathogen-Associated Molecular Patterns</p></def></def-item><def-item><term>PK</term><def><p>Prekallikrein</p></def></def-item><def-item><term>PRCP</term><def><p>Prolyl-carboxypeptidase</p></def></def-item><def-item><term>PRR</term><def><p>Pattern-Recognition Receptor</p></def></def-item><def-item><term>RAS</term><def><p>Renin–Angiotensin System</p></def></def-item><def-item><term>SARS-CoV-2</term><def><p>Severe Acute Respiratory Syndrome – Coronavirus – 2</p></def></def-item><def-item><term>TLR</term><def><p>Toll-like receptor</p></def></def-item><def-item><term>TNF-alpha</term><def><p>tumor necrosis factor-alpha</p></def></def-item><def-item><term>t-PA</term><def><p>tissue-Plasminogen Activator.</p></def></def-item></def-list></glossary></back></article>\n"
]
[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">J Med Internet Res</journal-id><journal-id journal-id-type=\"iso-abbrev\">J. Med. Internet Res</journal-id><journal-id journal-id-type=\"publisher-id\">JMIR</journal-id><journal-title-group><journal-title>Journal of Medical Internet Research</journal-title></journal-title-group><issn pub-type=\"ppub\">1439-4456</issn><issn pub-type=\"epub\">1438-8871</issn><publisher><publisher-name>JMIR Publications</publisher-name><publisher-loc>Toronto, Canada</publisher-loc></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32687479</article-id><article-id pub-id-type=\"pmc\">PMC7432139</article-id><article-id pub-id-type=\"publisher-id\">v22i8e18374</article-id><article-id pub-id-type=\"doi\">10.2196/18374</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Original Paper</subject></subj-group><subj-group subj-group-type=\"article-type\"><subject>Original Paper</subject></subj-group></article-categories><title-group><article-title>Rejected Online Feedback From a Swiss Physician Rating Website Between 2008 and 2017: Analysis of 2352 Ratings</article-title></title-group><contrib-group><contrib contrib-type=\"editor\"><name><surname>Eysenbach</surname><given-names>Gunther</given-names></name></contrib></contrib-group><contrib-group><contrib contrib-type=\"reviewer\"><name><surname>Brunson</surname><given-names>Emily</given-names></name></contrib><contrib contrib-type=\"reviewer\"><name><surname>Schulz</surname><given-names>Peter</given-names></name></contrib><contrib contrib-type=\"reviewer\"><name><surname>Kaliyadan</surname><given-names>Feroze</given-names></name></contrib></contrib-group><contrib-group><contrib id=\"contrib1\" contrib-type=\"author\" corresp=\"yes\"><name><surname>McLennan</surname><given-names>Stuart</given-names></name><degrees>MBHL, PhD</degrees><contrib-id contrib-id-type=\"orcid\">https://orcid.org/0000-0002-2019-6253</contrib-id><xref ref-type=\"aff\" rid=\"aff1\">1</xref><xref ref-type=\"aff\" rid=\"aff2\">2</xref><address><institution>Institute of History and Ethics in Medicine</institution><institution>Technical University of Munich</institution><addr-line>Ismaninger Straße 22</addr-line><addr-line>Munich, 81675</addr-line><country>Germany</country><phone>49 89 4140 4041</phone><email>[email protected]</email></address></contrib></contrib-group><aff id=\"aff1\">\n<label>1</label>\n<institution>Institute of History and Ethics in Medicine</institution>\n<institution>Technical University of Munich</institution>\n<addr-line>Munich</addr-line>\n<country>Germany</country>\n</aff><aff id=\"aff2\">\n<label>2</label>\n<institution>Institute for Biomedical Ethics</institution>\n<institution>University of Basel</institution>\n<addr-line>Basel</addr-line>\n<country>Switzerland</country>\n</aff><author-notes><corresp>Corresponding Author: Stuart McLennan <email>[email protected]</email></corresp></author-notes><pub-date pub-type=\"collection\"><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"epub\"><day>3</day><month>8</month><year>2020</year></pub-date><volume>22</volume><issue>8</issue><elocation-id>e18374</elocation-id><history><date date-type=\"received\"><day>22</day><month>2</month><year>2020</year></date><date date-type=\"rev-request\"><day>15</day><month>4</month><year>2020</year></date><date date-type=\"rev-recd\"><day>22</day><month>5</month><year>2020</year></date><date date-type=\"accepted\"><day>11</day><month>6</month><year>2020</year></date></history><permissions><copyright-statement>©Stuart McLennan. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 03.08.2020.</copyright-statement><copyright-year>2020</copyright-year><license license-type=\"open-access\" xlink:href=\"https://creativecommons.org/licenses/by/4.0/\"><license-p><!--CREATIVE COMMONS-->This is an open-access article distributed under the terms of the Creative Commons Attribution License (<ext-link ext-link-type=\"uri\" xlink:href=\"https://creativecommons.org/licenses/by/4.0/\">https://creativecommons.org/licenses/by/4.0/</ext-link>), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.jmir.org/\">http://www.jmir.org/</ext-link>, as well as this copyright and license information must be included.</license-p></license></permissions><self-uri xlink:type=\"simple\" xlink:href=\"https://www.jmir.org/2020/8/e18374\"/><abstract><sec sec-type=\"background\"><title>Background</title><p>Previous research internationally has only analyzed publicly available feedback on physician rating websites (PRWs). However, it appears that many PRWs are not publishing all the feedback they receive. Analysis of this rejected feedback could provide a better understanding of the types of feedback that are currently not published and whether this is appropriate.</p></sec><sec sec-type=\"objective\"><title>Objective</title><p>The aim of this study was to examine (1) the number of patient feedback rejected from the Swiss PRW Medicosearch, (2) the evaluation tendencies of the rejected patient feedback, and (3) the types of issues raised in the rejected narrative comments.</p></sec><sec sec-type=\"methods\"><title>Methods</title><p>The Swiss PRW Medicosearch provided all the feedback that had been rejected between September 16, 2008, and September 22, 2017. The feedback were analyzed and classified according to a theoretical categorization framework of physician-, staff-, and practice-related issues.</p></sec><sec sec-type=\"results\"><title>Results</title><p>Between September 16, 2008, and September 22, 2017, Medicosearch rejected a total of 2352 patient feedback. The majority of feedback rejected (1754/2352, 74.6%) had narrative comments in the German language. However, 11.9% (279/2352) of the rejected feedback only provided a quantitative rating with no narrative comment. Overall, 25% (588/2352) of the rejected feedback were positive, 18.7% (440/2352) were neutral, and 56% (1316/2352) were negative. The average rating of the rejected feedback was 2.8 (SD 1.4). In total, 44 subcategories addressing the physician (n=20), staff (n=9), and practice (n=15) were identified. In total, 3804 distinct issues were identified within the 44 subcategories of the categorization framework; 75% (2854/3804) of the issues were related to the physician, 6.4% (242/3804) were related to the staff, and 18.6% (708/3804) were related to the practice. Frequently mentioned issues identified from the rejected feedback included (1) satisfaction with treatment (533/1903, 28%); (2) the overall assessment of the physician (392/1903, 20.6%); (3) recommending the physician (345/1903, 18.1%); (4) the physician’s communication (261/1903, 13.7%); (5) the physician’s caring attitude (220/1903, 11.6%); and (6) the physician’s friendliness (203/1903, 10.6%).</p></sec><sec sec-type=\"conclusions\"><title>Conclusions</title><p>It is unclear why the majority of the feedback were rejected. This is problematic and raises concerns that online patient feedback are being inappropriately manipulated. If online patient feedback is going to be collected, there needs to be clear policies and practices about how this is handled. It cannot be left to the whims of PRWs, who may have financial incentives to suppress negative feedback, to decide which feedback is or is not published online. Further research is needed to examine how many PRWs are using criteria for determining which feedback is published or not, what those criteria are, and what measures PRWs are using to address the manipulation of online patient feedback.</p></sec></abstract><kwd-group><kwd>physician rating websites</kwd><kwd>patient satisfaction</kwd><kwd>participatory medicine</kwd><kwd>patient feedback</kwd></kwd-group></article-meta></front><body><sec sec-type=\"introduction\"><title>Introduction</title><p>There remains relevant unwarranted variation in health care systems and deficiencies regarding all key aspects of health care [<xref rid=\"ref1\" ref-type=\"bibr\">1</xref>]. Members of the public, however, have traditionally had few ways of knowing who the “good” health care organizations and professionals are [<xref rid=\"ref2\" ref-type=\"bibr\">2</xref>]. As part of a wider move toward transparency, public reporting activities have been developed in a number of countries with the aim of providing information about health care organizations or professionals to the public to correct this asymmetry of information in order to inform patient decision-making and drive quality improvement [<xref rid=\"ref3\" ref-type=\"bibr\">3</xref>-<xref rid=\"ref6\" ref-type=\"bibr\">6</xref>].</p><p>One type of public reporting activity that has been developed in recent decades is physician rating websites (PRWs) [<xref rid=\"ref7\" ref-type=\"bibr\">7</xref>-<xref rid=\"ref10\" ref-type=\"bibr\">10</xref>]. PRWs represent a “bottom-up” approach to public reporting, allowing users to post ratings and comments regarding their physician as a source of information for others [<xref rid=\"ref11\" ref-type=\"bibr\">11</xref>-<xref rid=\"ref14\" ref-type=\"bibr\">14</xref>]. Although patients have always been able to share their opinions about their physicians with others, the ability to share these opinions via the internet and social media now means that these opinions have the potential to reach a far wider audience. With a growing number of patients utilizing the internet in relation to health care [<xref rid=\"ref15\" ref-type=\"bibr\">15</xref>], it is expected that PRWs will play an increasingly important role.</p><p>A recent systematic search of PRWs internationally analyzed 143 different websites from 12 countries [<xref rid=\"ref16\" ref-type=\"bibr\">16</xref>] and found that the majority (76.9%) of websites provided options to give feedback both on a predefined quantitative rating scale and as narrative comments. Previous research internationally has often focused on analyzing the ratings and comments publicly available on PRWs. This research has reported that many PRWs have incomplete lists of physicians, a low number of physicians rated, and a low number of ratings per physician that are overwhelmingly positive, which has raised concerns about the representativeness, validity, and usefulness of information on PRWs [<xref rid=\"ref14\" ref-type=\"bibr\">14</xref>,<xref rid=\"ref17\" ref-type=\"bibr\">17</xref>]. Furthermore, the medical profession has often expressed concerns that feedback on PRWs will be manipulated for “doctor bashing” or defamation [<xref rid=\"ref10\" ref-type=\"bibr\">10</xref>].</p><p>The first PRWs in Switzerland were established in 2008, at the same time as many international PRWs. However, in comparison with other countries that have established PRWs, there has been limited research conducted on PRWs in Switzerland. This author recently conducted a study involving a random stratified sample of 966 physicians generated from the regions of Zürich and Geneva [<xref rid=\"ref18\" ref-type=\"bibr\">18</xref>,<xref rid=\"ref19\" ref-type=\"bibr\">19</xref>]. Selected physicians were searched on a total of four websites (OkDoc, Medicosearch, DocApp, and Google) between November 2017 and July 2018, and it was recorded whether the physician could be found. Moreover, the physician’s rating, the number of ratings and narrative comments, and the text of narrative comments were recorded. As far as the author is aware, this was the first inclusion of Google in a study examining physician ratings internationally.</p><p>With regard to the frequency of quantitative ratings and narrative comments on Swiss PRWs, similar issues as those identified in the international literature were found. Many of the selected physicians could not be identified (the proportion of physicians who could be identified ranged from 42.4% on OkDoc to 87.3% on DocApp), few of the identifiable physicians had been rated quantitatively (4.5% on DocApp to 49.8% on Google) or received a narrative comment (4.5% on DocApp to 31.2% on Google) at least once, rated physicians had, on average, a low number of quantitative ratings (1.47 ratings on OkDoc to 3.74 rating on Google) and narrative comments (1.23 comments on OkDoc to 3.03 comments on Google), and all three websites that allowed quantitative ratings had very positive average ratings on a 5-star rating scale (DocApp, 4.71; Medicosearch, 4.69; and Google, 4.41) [<xref rid=\"ref18\" ref-type=\"bibr\">18</xref>].</p><p>With regard to the contents of narrative comments, it was found that the selected physicians had a total of 849 comments [<xref rid=\"ref19\" ref-type=\"bibr\">19</xref>]. Narrative comments were analyzed and classified according to a theoretical categorization framework previously developed by Emmert et al [<xref rid=\"ref10\" ref-type=\"bibr\">10</xref>]. In total, 43 subcategories addressing the physician, staff, and practice were identified. None of the PRWs’ comments covered all 43 subcategories of the categorization framework; comments on Google covered 86% of the subcategories, those on Medicosearch covered 72%, those on DocApp covered 60%, and those on OkDoc covered 56%. In total, 2441 distinct issues were identified within the 43 subcategories of the categorization framework; 83.65% of the issues were related to the physician, 6.63% were related to the staff, and 9.70% were related to the practice. Overall, 95% of the subcategories of the categorization framework and 81.60% of the distinct issues identified were concerning aspects of performance (interpersonal skills of the physician and staff, infrastructure, and organization and management of the practice) considered assessable by patients [<xref rid=\"ref19\" ref-type=\"bibr\">19</xref>]. Furthermore, this research raised concerns that user feedback is being suppressed by Swiss PRWs [<xref rid=\"ref18\" ref-type=\"bibr\">18</xref>,<xref rid=\"ref19\" ref-type=\"bibr\">19</xref>], which risks undermining the overall aim of PRWs of providing a reliable source of unbiased information regarding patients’ experiences and satisfaction with physicians.</p><p>As far as this author is aware, previous research internationally has only analyzed publicly available feedback on PRWs. However, as it appears that many PRWs are not publishing all the feedback they receive. Analysis of this rejected feedback could provide a better understanding of the types of feedback that are currently not published and whether this is appropriate. This study therefore aimed to examine (1) the number of patient feedback rejected from the Swiss PRW Medicosearch, (2) the evaluation tendencies of the rejected patient feedback, and (3) the types of issues raised in the rejected narrative comments. Gaining a better understanding of feedback rejected by PRWs may help to identify issues in the way PRWs are currently determining which feedback are and are not to be publicly published.</p></sec><sec sec-type=\"methods\"><title>Methods</title><sec><title>Sample</title><p>Switzerland is a Central European country with a population of about 8.4 million people and 4 official languages (German, French, Italian, and Romansh). The Swiss health care system is highly complex and decentralized, and all Swiss residents are required to purchase basic mandatory health insurance that is offered by competing nonprofit insurers. Mandatory health insurance covers most general practitioner and specialist services, and people not enrolled in managed care plans generally have free choice of professionals [<xref rid=\"ref18\" ref-type=\"bibr\">18</xref>]. The first PRWs in Switzerland, OkDoc and Medicosearch, were established in 2008. A systematic web-based search conducted in June 2016 identified that the websites DocApp and Google also allow users to view quantitative ratings and/or narrative comments about Swiss physicians in a structured manner without having to open an account or log onto the website [<xref rid=\"ref18\" ref-type=\"bibr\">18</xref>,<xref rid=\"ref19\" ref-type=\"bibr\">19</xref>]. It appears that other websites have also subsequently started to allow users to view quantitative ratings and/or narrative comments about Swiss physicians (eg, DeinDoktor and Doctena) [<xref rid=\"ref19\" ref-type=\"bibr\">19</xref>]. Nevertheless, out of the dedicated Swiss PRWs, Medicosearch appears to be one of the best established and used [<xref rid=\"ref18\" ref-type=\"bibr\">18</xref>,<xref rid=\"ref19\" ref-type=\"bibr\">19</xref>]. Medicosearch allows users to search for physicians by location and specialty. Physician profiles provide general information about the physician (specialties, languages spoken, and contact details). In recent years, Medicosearch has shifted its business strategy toward online appointments, where physicians pay a fee and their booking systems are integrated with Medicosearch, allowing patients to book an appointment with a physician directly on Medicosearch. Users can also leave reviews of physicians, but Medicosearch requires both a quantitative rating (5-star rating scale) and a narrative comment in every patient feedback. Although Medicosearch allows negative comments, it informs the concerned physician before publishing it on the website, so that the physician can decide whether to activate the negative feedback function. If the physician refuses, the feedback function is deactivated, removing positive comments as well [<xref rid=\"ref19\" ref-type=\"bibr\">19</xref>].</p><p>As part of a larger project examining Swiss PRWs, the author approached the CEO of Medicosearch, Beat Burger. Discussions confirmed that Medicosearch does not publish all the feedback they receive. The author enquired about the possibility of receiving these rejected feedback for analysis. Medicosearch agreed to provide the rejected feedback to the author in an anonymous form. On October 24, 2017, Medicosearch sent the author an excel file that included all the feedback that Medicosearch had rejected between September 16, 2008, and September 22, 2017. The details of the rated physicians were not included. Medicosearch did not provide any reasons for why the feedback were rejected. The following data were imported into a Statistical Package for the Social Sciences (SPSS version 26 for Windows, IBM Corporation) file: the date the feedback was created, the quantitative rating out of 5, and the narrative comments provided under “title” and “description.” In early 2019, the author inquired with Medicosearch whether it would be possible to receive the rejected feedback from September 23, 2017, to December 31, 2018. Medicosearch told the author on April 11, 2019, that owing to new data-protection rules, Medicosearch had deleted all rejected feedback, and therefore, it was not possible to provide an updated file.</p></sec><sec><title>Data Analysis</title><p>Medicosearch uses a 5-star rating scale; a rating of 4 to 5 stars was considered a positive rating, 3 stars was considered a neutral rating, and 1 to 2 stars was considered a negative rating. The content of each narrative comment was analyzed and classified by the author according to a theoretical categorization framework of physician-, staff-, and practice-related issues. The categorization framework from Emmert et al was initially used [<xref rid=\"ref10\" ref-type=\"bibr\">10</xref>], with modifications where necessary. This included removing categories that were not identified in the comments, adding categories that were identified but were not adequately covered by the previous framework, and separating categories (eg, friendliness and caring attitude) that were discussed in comments as distinct issues. Narrative comments were analyzed in their original language. Descriptive statistics included means and standard deviations for continuous variables and percentages for categorical variables. To analyze whether differences existed between German and French comments, chi-squared tests were used. All analyses were performed with the significance level α set to .05 and two-tailed tests, using SPSS version 26.</p></sec></sec><sec sec-type=\"results\"><title>Results</title><sec><title>Characteristics of Ratings</title><p>Between September 16, 2008, and September 22, 2017, Medicosearch rejected a total of 2352 patient feedback (<xref rid=\"table1\" ref-type=\"table\">Table 1</xref>).</p><p>The majority of feedback rejected (1754/2352, 74.6%) had narrative comments in German (<xref rid=\"table2\" ref-type=\"table\">Table 2</xref>). Other rejected feedback had narrative comments in French (275/2352, 11.7%), Italian (31/2352, 1.3%), English (12/2352, 0.5%), and Spanish (1/2352, 0.04%). However, 11.9% (279/2352) of the rejected feedback only provided a quantitative rating with no narrative comment.</p><p>Overall, 25% (588/2352) of the quantitative ratings were positive, 18.7% (440/2352) were neutral, and 56% (1316/2352) were negative (<xref rid=\"table3\" ref-type=\"table\">Table 3</xref>). Additionally, the average rating of the rejected feedback was 2.8 (SD 1.4).</p><table-wrap id=\"table1\" position=\"float\"><label>Table 1</label><caption><p>Distribution of rejected feedback according to year.</p></caption><table frame=\"hsides\" rules=\"groups\" width=\"1000\" cellpadding=\"5\" cellspacing=\"0\" border=\"1\"><col width=\"100\" span=\"1\"/><col width=\"100\" span=\"1\"/><col width=\"100\" span=\"1\"/><col width=\"100\" span=\"1\"/><col width=\"100\" span=\"1\"/><col width=\"100\" span=\"1\"/><col width=\"100\" span=\"1\"/><col width=\"100\" span=\"1\"/><col width=\"100\" span=\"1\"/><col width=\"100\" span=\"1\"/><thead><tr valign=\"top\"><td colspan=\"10\" rowspan=\"1\">Distribution of comments (year)<sup>a</sup> (N=2352), n (%)</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">2008</td><td rowspan=\"1\" colspan=\"1\">2009</td><td rowspan=\"1\" colspan=\"1\">2010</td><td rowspan=\"1\" colspan=\"1\">2011</td><td rowspan=\"1\" colspan=\"1\">2012</td><td rowspan=\"1\" colspan=\"1\">2013</td><td rowspan=\"1\" colspan=\"1\">2014</td><td rowspan=\"1\" colspan=\"1\">2015</td><td rowspan=\"1\" colspan=\"1\">2016</td><td rowspan=\"1\" colspan=\"1\">2017</td></tr></thead><tbody><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">26<break/>(1.1)</td><td rowspan=\"1\" colspan=\"1\">259<break/>(11.0)</td><td rowspan=\"1\" colspan=\"1\">232<break/>(9.9)</td><td rowspan=\"1\" colspan=\"1\">344<break/>(14.6)</td><td rowspan=\"1\" colspan=\"1\">392<break/>(16.7)</td><td rowspan=\"1\" colspan=\"1\">321<break/>(13.6)</td><td rowspan=\"1\" colspan=\"1\">268<break/>(11.4)</td><td rowspan=\"1\" colspan=\"1\">142<break/>(6.0)</td><td rowspan=\"1\" colspan=\"1\">236<break/>(10.0)</td><td rowspan=\"1\" colspan=\"1\">132<break/>(5.6)</td></tr></tbody></table><table-wrap-foot><fn id=\"table1fn1\"><p><sup>a</sup>From September 16, 2008, to September 22, 2017.</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"table2\" position=\"float\"><label>Table 2</label><caption><p>Distribution of rejected feedback according to language.</p></caption><table frame=\"hsides\" rules=\"groups\" width=\"1000\" cellpadding=\"5\" cellspacing=\"0\" border=\"1\"><col width=\"180\" span=\"1\"/><col width=\"160\" span=\"1\"/><col width=\"150\" span=\"1\"/><col width=\"170\" span=\"1\"/><col width=\"170\" span=\"1\"/><col width=\"170\" span=\"1\"/><thead><tr valign=\"top\"><td colspan=\"6\" rowspan=\"1\">Language (N=2352), n (%)</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">German</td><td rowspan=\"1\" colspan=\"1\">French</td><td rowspan=\"1\" colspan=\"1\">Italian</td><td rowspan=\"1\" colspan=\"1\">English</td><td rowspan=\"1\" colspan=\"1\">Spanish</td><td rowspan=\"1\" colspan=\"1\">Missing</td></tr></thead><tbody><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">1754 (74.6)</td><td rowspan=\"1\" colspan=\"1\">275 (11.7)</td><td rowspan=\"1\" colspan=\"1\">31 (1.3)</td><td rowspan=\"1\" colspan=\"1\">12 (0.5)</td><td rowspan=\"1\" colspan=\"1\">1 (0.04)</td><td rowspan=\"1\" colspan=\"1\">279 (11.9)</td></tr></tbody></table></table-wrap><table-wrap id=\"table3\" position=\"float\"><label>Table 3</label><caption><p>Quantitative rating evaluation results.</p></caption><table frame=\"hsides\" rules=\"groups\" width=\"1000\" cellpadding=\"5\" cellspacing=\"0\" border=\"1\"><col width=\"30\" span=\"1\"/><col width=\"110\" span=\"1\"/><col width=\"130\" span=\"1\"/><col width=\"120\" span=\"1\"/><col width=\"120\" span=\"1\"/><col width=\"120\" span=\"1\"/><col width=\"110\" span=\"1\"/><col width=\"130\" span=\"1\"/><col width=\"0\" span=\"1\"/><col width=\"130\" span=\"1\"/><thead><tr valign=\"top\"><td rowspan=\"2\" colspan=\"2\">Measure</td><td colspan=\"7\" rowspan=\"1\">Language<sup>a</sup></td><td rowspan=\"1\" colspan=\"1\">Total (N=2352), n (%)</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">German (N=1754), n (%)</td><td rowspan=\"1\" colspan=\"1\">French (N=275), n (%)</td><td rowspan=\"1\" colspan=\"1\">Italian (N=31), n (%)</td><td rowspan=\"1\" colspan=\"1\">English (N=12), n (%)</td><td rowspan=\"1\" colspan=\"1\">Spanish (N=1), n (%)</td><td rowspan=\"1\" colspan=\"1\">Missing (N=279), n (%)</td><td colspan=\"2\" rowspan=\"1\">\n<break/>\n</td></tr></thead><tbody><tr valign=\"top\"><td colspan=\"2\" rowspan=\"1\">\n<bold>Evaluation</bold>\n</td><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td colspan=\"2\" rowspan=\"1\">\n<break/>\n</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">Positive</td><td rowspan=\"1\" colspan=\"1\">399 (22.7)</td><td rowspan=\"1\" colspan=\"1\">81 (29.5)</td><td rowspan=\"1\" colspan=\"1\">4 (12.9)</td><td rowspan=\"1\" colspan=\"1\">4 (33.3)</td><td rowspan=\"1\" colspan=\"1\">1/1 (100)</td><td rowspan=\"1\" colspan=\"1\">99 (35.5)</td><td colspan=\"2\" rowspan=\"1\">588 (25.0)</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">Neutral</td><td rowspan=\"1\" colspan=\"1\">296 (16.9)</td><td rowspan=\"1\" colspan=\"1\">59 (21.5)</td><td rowspan=\"1\" colspan=\"1\">6 (19.4)</td><td rowspan=\"1\" colspan=\"1\">1 (8.3)</td><td rowspan=\"1\" colspan=\"1\">0 (0)</td><td rowspan=\"1\" colspan=\"1\">77 (26.3)</td><td colspan=\"2\" rowspan=\"1\">440 (18.7%)</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">Negative</td><td rowspan=\"1\" colspan=\"1\">1065 (60.4)</td><td rowspan=\"1\" colspan=\"1\">135 (49.1)</td><td rowspan=\"1\" colspan=\"1\">21 (67.7)</td><td rowspan=\"1\" colspan=\"1\">7 (58.3)</td><td rowspan=\"1\" colspan=\"1\">0 (0)</td><td rowspan=\"1\" colspan=\"1\">88 (30)</td><td colspan=\"2\" rowspan=\"1\">1316 (56%)</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">Missing</td><td rowspan=\"1\" colspan=\"1\">0 (0)</td><td rowspan=\"1\" colspan=\"1\">0 (0)</td><td rowspan=\"1\" colspan=\"1\">0 (0)</td><td rowspan=\"1\" colspan=\"1\">0 (0)</td><td rowspan=\"1\" colspan=\"1\">0 (0)</td><td rowspan=\"1\" colspan=\"1\">8 (2.9)</td><td colspan=\"2\" rowspan=\"1\">8 (0.3)</td></tr><tr valign=\"top\"><td colspan=\"2\" rowspan=\"1\">Average rating (SD)</td><td rowspan=\"1\" colspan=\"1\">2.7 (1.3)</td><td rowspan=\"1\" colspan=\"1\">3.0 (1.4)</td><td rowspan=\"1\" colspan=\"1\">2.4 (1.3)</td><td rowspan=\"1\" colspan=\"1\">2.9 (1.5)</td><td rowspan=\"1\" colspan=\"1\">5.0</td><td rowspan=\"1\" colspan=\"1\">3.2 (1.4)</td><td colspan=\"2\" rowspan=\"1\">2.8 (1.4)</td></tr></tbody></table></table-wrap><p>Of the 2073 ratings that provided narrative comments, analysis found that a total of 170 comments were not feedback from a patient concerning the physician, staff, or practice (92 comments were not comprehensible, 29 comments were explicitly labelled as test ratings, 15 comments were about the PRW, 10 comments reported that the person had not visited the physician yet, 10 comments simply reported that the physician’s details were not up to date, eight comments were abusive, four comments were second-hand reports, two comments were asking for advice regarding their or their family member’s condition, and two comments were not concerning a visit to a physician). Consequently, this feedback was excluded from the categorization framework.</p><p>The 1903 included narrative comments had a mean length of 158 characters (SD 214), ranging from 1 to 2788 characters. There was a significant difference in the mean character length between positive comments (mean 88, SD 130) and negative comments (mean 193, SD 241) (t<sub>1314</sub>=−11, <italic>P</italic><.001). There was no significant difference in the mean character length between German comments (mean 158, SD 205) and French comments (mean 154, SD 206) (t<sub>1862</sub>=0.2, <italic>P</italic>=.82).</p></sec><sec><title>Categorization of Issues</title><p>Content analysis of the included 1903 narrative comments identified 44 subcategories addressing the physician (n=20), staff (n=9), and practice (n=15) (<xref ref-type=\"boxed-text\" rid=\"box1\">Textbox 1</xref>).</p><boxed-text id=\"box1\" position=\"float\"><caption><title>Categorization framework.</title></caption><p>\n<bold>Physician (n=20)</bold>\n</p><p>Satisfaction with treatment; overall assessment; recommendation; communication; caring attitude; friendliness; competence; treatment cost/billing; being taken seriously; time spent with patient; trust; professionalism; cooperation with medical specialists; alternative medicine; telephone availability; privacy; health insurance differentiation; patient involvement; individualized service; child friendliness</p><p>\n<bold>Staff (n=9)</bold>\n</p><p>Friendliness; overall assessment; service/assistance; communication; professionalism; availability by telephone; time spent with patient; health insurance differentiation; trust</p><p>\n<bold>Practice (n=15)</bold>\n</p><p>Overall assessment; waiting time within the practice; atmosphere; organization; ability to get appointment; equipment; recommendation; consultation hours; location; waiting room entertainment; parking space; availability by telephone; privacy; barrier-free access; online appointment</p></boxed-text><p>In total, 3804 distinct issues were identified within the 44 subcategories of the categorization framework; 75% (2854/3804) of the issues were related to the physician, 6.4% (242/3804) were related to the staff, and 18.6% (708/3804) were related to the practice (<xref rid=\"table4\" ref-type=\"table\">Table 4</xref>). The most frequent issue mentioned in the rejected comments was satisfaction with treatment (533/1903, 28%); 73.2% (390/533) of these ratings were negative. Other frequently mentioned issues regarding the physician were as follows: 20.6% (392/1903) of comments provided an overall assessment of the physician (53.8% negative); 18.1% (345/1903) provided a recommendation regarding the physician (76.2% negative); 13.7% (261/1903) referred to the physician’s communication (77.8% negative); 11.6% (220/1903) referred to the physician’s caring attitude (75.9% negative); and 10.6% (203/1903) referred to the physician’s friendliness (73.9% negative). In relation to staff issues, the most frequently mentioned issue was regarding the staffs’ friendliness (109/1903; 5.7%); 43.1% of these ratings were negative. Concerning practice issues, the frequently mentioned issues were as follows: 15.5% (295/1903) of the comments provided an overall assessment (38.6% positive), 8.1% (155/1903) referred to the waiting time within the practice (58.7% negative), and 5% (96/1903) referred to the atmosphere of the practice (40.6% positive).</p><p>However, there were some significant differences between German and French comments in a number of subcategories (<xref ref-type=\"supplementary-material\" rid=\"app1\">Multimedia Appendix 1</xref>). For instance, German comments referred significantly more often to the physician’s friendliness (χ<sup>2</sup><sub>1</sub>=5.9, <italic>P</italic>=.01), being taken seriously by the physician (χ<sup>2</sup><sub>1</sub>=8.5, <italic>P</italic>=.002), staff friendliness (χ<sup>2</sup><sub>1</sub>=7.0, <italic>P</italic>=.005), waiting time in the practice (χ<sup>2</sup><sub>1</sub>=6.3, <italic>P</italic>=.01), and practice atmosphere (χ<sup>2</sup><sub>1</sub>=6.6, <italic>P</italic>=.007).</p><table-wrap id=\"table4\" position=\"float\"><label>Table 4</label><caption><p>Categorization of issues.</p></caption><table frame=\"hsides\" rules=\"groups\" width=\"1000\" cellpadding=\"5\" cellspacing=\"0\" border=\"1\"><col width=\"30\" span=\"1\"/><col width=\"360\" span=\"1\"/><col width=\"200\" span=\"1\"/><col width=\"140\" span=\"1\"/><col width=\"130\" span=\"1\"/><col width=\"140\" span=\"1\"/><thead><tr valign=\"top\"><td rowspan=\"2\" colspan=\"2\">Issue</td><td rowspan=\"2\" colspan=\"1\">Total (N=1903), n (%)</td><td colspan=\"3\" rowspan=\"1\">Quantitative rating evaluation</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">Positive, n (%)</td><td rowspan=\"1\" colspan=\"1\">Neutral, n (%)</td><td rowspan=\"1\" colspan=\"1\">Negative, n (%)</td></tr></thead><tbody><tr valign=\"top\"><td colspan=\"2\" rowspan=\"1\">\n<bold>Physician</bold>\n</td><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">Satisfaction with treatment</td><td rowspan=\"1\" colspan=\"1\">533 (28.0)</td><td rowspan=\"1\" colspan=\"1\">82 (15.4)</td><td rowspan=\"1\" colspan=\"1\">61 (11.4)</td><td rowspan=\"1\" colspan=\"1\">390 (73.2)</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">Overall assessment</td><td rowspan=\"1\" colspan=\"1\">392 (20.6)</td><td rowspan=\"1\" colspan=\"1\">122 (31.0)</td><td rowspan=\"1\" colspan=\"1\">59 (15.1)</td><td rowspan=\"1\" colspan=\"1\">211 (53.8)</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">Recommendation</td><td rowspan=\"1\" colspan=\"1\">345 (18.1)</td><td rowspan=\"1\" colspan=\"1\">35 (10.1)</td><td rowspan=\"1\" colspan=\"1\">47 (13.6)</td><td rowspan=\"1\" colspan=\"1\">263 (76.2)</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">Communication</td><td rowspan=\"1\" colspan=\"1\">261 (13.7)</td><td rowspan=\"1\" colspan=\"1\">39 (14.9)</td><td rowspan=\"1\" colspan=\"1\">19 (7.3)</td><td rowspan=\"1\" colspan=\"1\">203 (77.8)</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">Caring attitude</td><td rowspan=\"1\" colspan=\"1\">220 (11.6)</td><td rowspan=\"1\" colspan=\"1\">40 (18.2)</td><td rowspan=\"1\" colspan=\"1\">13 (5.9)</td><td rowspan=\"1\" colspan=\"1\">167 (75.9)</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">Friendliness</td><td rowspan=\"1\" colspan=\"1\">203 (10.6)</td><td rowspan=\"1\" colspan=\"1\">35 (17.2)</td><td rowspan=\"1\" colspan=\"1\">18 (8.9)</td><td rowspan=\"1\" colspan=\"1\">150 (73.9)</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">Treatment cost/billing</td><td rowspan=\"1\" colspan=\"1\">173 (9.1)</td><td rowspan=\"1\" colspan=\"1\">8 (4.6)</td><td rowspan=\"1\" colspan=\"1\">30 (17.3)</td><td rowspan=\"1\" colspan=\"1\">135 (78.0)</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">Competence</td><td rowspan=\"1\" colspan=\"1\">170 (8.9)</td><td rowspan=\"1\" colspan=\"1\">43 (25.3)</td><td rowspan=\"1\" colspan=\"1\">13 (7.6)</td><td rowspan=\"1\" colspan=\"1\">114 (67.1)</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">Being taken seriously</td><td rowspan=\"1\" colspan=\"1\">141 (7.4)</td><td rowspan=\"1\" colspan=\"1\">10 (7.1)</td><td rowspan=\"1\" colspan=\"1\">10 (7.1)</td><td rowspan=\"1\" colspan=\"1\">121 (85.8)</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">Time spent with patient</td><td rowspan=\"1\" colspan=\"1\">136 (7.1)</td><td rowspan=\"1\" colspan=\"1\">17 (12.5)</td><td rowspan=\"1\" colspan=\"1\">18 (13.2)</td><td rowspan=\"1\" colspan=\"1\">101 (74.3)</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">Trust</td><td rowspan=\"1\" colspan=\"1\">133 (6.9)</td><td rowspan=\"1\" colspan=\"1\">44 (33.1)</td><td rowspan=\"1\" colspan=\"1\">11 (8.3)</td><td rowspan=\"1\" colspan=\"1\">78 (58.6)</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">Professionalism</td><td rowspan=\"1\" colspan=\"1\">97 (5.1)</td><td rowspan=\"1\" colspan=\"1\">11 (11.3)</td><td rowspan=\"1\" colspan=\"1\">6 (6.2)</td><td rowspan=\"1\" colspan=\"1\">80 (82.5)</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">Cooperation with medical specialists</td><td rowspan=\"1\" colspan=\"1\">13 (0.7)</td><td rowspan=\"1\" colspan=\"1\">4 (30.8)</td><td rowspan=\"1\" colspan=\"1\">0</td><td rowspan=\"1\" colspan=\"1\">9 (69.2)</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">Alternative medicine</td><td rowspan=\"1\" colspan=\"1\">8 (0.4)</td><td rowspan=\"1\" colspan=\"1\">6 (75.0)</td><td rowspan=\"1\" colspan=\"1\">0</td><td rowspan=\"1\" colspan=\"1\">2 (25.0)</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">Telephone availability</td><td rowspan=\"1\" colspan=\"1\">8 (0.4)</td><td rowspan=\"1\" colspan=\"1\">1 (12.5)</td><td rowspan=\"1\" colspan=\"1\">3 (37.5)</td><td rowspan=\"1\" colspan=\"1\">4 (50.0)</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">Privacy</td><td rowspan=\"1\" colspan=\"1\">7 (0.4)</td><td rowspan=\"1\" colspan=\"1\">0</td><td rowspan=\"1\" colspan=\"1\">2 (28.6)</td><td rowspan=\"1\" colspan=\"1\">5 (71.4)</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">Health insurance differentiation</td><td rowspan=\"1\" colspan=\"1\">6 (0.3)</td><td rowspan=\"1\" colspan=\"1\">0</td><td rowspan=\"1\" colspan=\"1\">0</td><td rowspan=\"1\" colspan=\"1\">6 (100)</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">Patient involvement</td><td rowspan=\"1\" colspan=\"1\">5 (0.3)</td><td rowspan=\"1\" colspan=\"1\">0</td><td rowspan=\"1\" colspan=\"1\">1 (20.0)</td><td rowspan=\"1\" colspan=\"1\">4 (80.0)</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">Individualized service</td><td rowspan=\"1\" colspan=\"1\">2 (0.1)</td><td rowspan=\"1\" colspan=\"1\">0</td><td rowspan=\"1\" colspan=\"1\">0</td><td rowspan=\"1\" colspan=\"1\">2 (100)</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">Child friendliness</td><td rowspan=\"1\" colspan=\"1\">1 (0.04)</td><td rowspan=\"1\" colspan=\"1\">0</td><td rowspan=\"1\" colspan=\"1\">0</td><td rowspan=\"1\" colspan=\"1\">1 (100)</td></tr><tr valign=\"top\"><td colspan=\"2\" rowspan=\"1\">\n<bold>Staff</bold>\n</td><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">Friendliness</td><td rowspan=\"1\" colspan=\"1\">109 (5.7)</td><td rowspan=\"1\" colspan=\"1\">39 (35.8)</td><td rowspan=\"1\" colspan=\"1\">23 (21.1)</td><td rowspan=\"1\" colspan=\"1\">47 (43.1)</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">Overall assessment</td><td rowspan=\"1\" colspan=\"1\">60 (3.2)</td><td rowspan=\"1\" colspan=\"1\">19 (31.7)</td><td rowspan=\"1\" colspan=\"1\">18 (30.0)</td><td rowspan=\"1\" colspan=\"1\">23 (38.3)</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">Service/assistance</td><td rowspan=\"1\" colspan=\"1\">31 (1.6)</td><td rowspan=\"1\" colspan=\"1\">11 (35.5)</td><td rowspan=\"1\" colspan=\"1\">3 (9.7)</td><td rowspan=\"1\" colspan=\"1\">17 (54.8)</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">Communication</td><td rowspan=\"1\" colspan=\"1\">22 (1.2)</td><td rowspan=\"1\" colspan=\"1\">4 (18.2)</td><td rowspan=\"1\" colspan=\"1\">7 (31.8)</td><td rowspan=\"1\" colspan=\"1\">11 (50.0)</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">Professionalism</td><td rowspan=\"1\" colspan=\"1\">10 (0.5)</td><td rowspan=\"1\" colspan=\"1\">2 (20.0)</td><td rowspan=\"1\" colspan=\"1\">2 (20.0)</td><td rowspan=\"1\" colspan=\"1\">6 (60.0)</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">Availability by telephone</td><td rowspan=\"1\" colspan=\"1\">6 (0.3)</td><td rowspan=\"1\" colspan=\"1\">1 (16.7)</td><td rowspan=\"1\" colspan=\"1\">4 (66.7)</td><td rowspan=\"1\" colspan=\"1\">1 (16.7)</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">Time spent with patient</td><td rowspan=\"1\" colspan=\"1\">2 (0.1)</td><td rowspan=\"1\" colspan=\"1\">1 (50)</td><td rowspan=\"1\" colspan=\"1\">0</td><td rowspan=\"1\" colspan=\"1\">1 (50.0)</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">Health insurance differentiation</td><td rowspan=\"1\" colspan=\"1\">1 (0.04)</td><td rowspan=\"1\" colspan=\"1\">0</td><td rowspan=\"1\" colspan=\"1\">1 (100)</td><td rowspan=\"1\" colspan=\"1\">0</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">Trust</td><td rowspan=\"1\" colspan=\"1\">1 (0.04)</td><td rowspan=\"1\" colspan=\"1\">0</td><td rowspan=\"1\" colspan=\"1\">0</td><td rowspan=\"1\" colspan=\"1\">1 (100)</td></tr><tr valign=\"top\"><td colspan=\"2\" rowspan=\"1\">\n<bold>Practice</bold>\n</td><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">Overall assessment</td><td rowspan=\"1\" colspan=\"1\">295 (15.5)</td><td rowspan=\"1\" colspan=\"1\">114 (38.6)</td><td rowspan=\"1\" colspan=\"1\">90 (30.5)</td><td rowspan=\"1\" colspan=\"1\">91 (30.8)</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">Waiting time within practice</td><td rowspan=\"1\" colspan=\"1\">155 (8.1)</td><td rowspan=\"1\" colspan=\"1\">22 (14.2)</td><td rowspan=\"1\" colspan=\"1\">42 (27.1)</td><td rowspan=\"1\" colspan=\"1\">91 (58.7)</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">Atmosphere</td><td rowspan=\"1\" colspan=\"1\">96 (5.0)</td><td rowspan=\"1\" colspan=\"1\">39 (40.6)</td><td rowspan=\"1\" colspan=\"1\">29 (30.2)</td><td rowspan=\"1\" colspan=\"1\">28 (29.2)</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">Organization</td><td rowspan=\"1\" colspan=\"1\">37 (1.9)</td><td rowspan=\"1\" colspan=\"1\">7 (18.9)</td><td rowspan=\"1\" colspan=\"1\">11 (29.7)</td><td rowspan=\"1\" colspan=\"1\">19 (51.4)</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">Ability to get appointment</td><td rowspan=\"1\" colspan=\"1\">36 (1.9)</td><td rowspan=\"1\" colspan=\"1\">4 (11.1)</td><td rowspan=\"1\" colspan=\"1\">11 (30.6)</td><td rowspan=\"1\" colspan=\"1\">21 (58.3)</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">Equipment</td><td rowspan=\"1\" colspan=\"1\">31 (1.6)</td><td rowspan=\"1\" colspan=\"1\">8 (25.8)</td><td rowspan=\"1\" colspan=\"1\">10 (32.3)</td><td rowspan=\"1\" colspan=\"1\">13 (41.9)</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">Recommendation</td><td rowspan=\"1\" colspan=\"1\">25 (1.3)</td><td rowspan=\"1\" colspan=\"1\">4 (16.0)</td><td rowspan=\"1\" colspan=\"1\">5 (20.0)</td><td rowspan=\"1\" colspan=\"1\">16 (64.0)</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">Consultation hours</td><td rowspan=\"1\" colspan=\"1\">8 (0.4)</td><td rowspan=\"1\" colspan=\"1\">3 (37.5)</td><td rowspan=\"1\" colspan=\"1\">3 (37.5)</td><td rowspan=\"1\" colspan=\"1\">2 (25.0)</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">Location</td><td rowspan=\"1\" colspan=\"1\">7 (0.4)</td><td rowspan=\"1\" colspan=\"1\">2 (28.6)</td><td rowspan=\"1\" colspan=\"1\">2 (28.6)</td><td rowspan=\"1\" colspan=\"1\">3 (42.9)</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">Waiting room entertainment</td><td rowspan=\"1\" colspan=\"1\">5 (0.3)</td><td rowspan=\"1\" colspan=\"1\">4 (80.0)</td><td rowspan=\"1\" colspan=\"1\">1 (20.0)</td><td rowspan=\"1\" colspan=\"1\">0</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">Parking space</td><td rowspan=\"1\" colspan=\"1\">4 (0.2)</td><td rowspan=\"1\" colspan=\"1\">4 (100)</td><td rowspan=\"1\" colspan=\"1\">0</td><td rowspan=\"1\" colspan=\"1\">0</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">Availability by telephone</td><td rowspan=\"1\" colspan=\"1\">3 (0.2)</td><td rowspan=\"1\" colspan=\"1\">1 (33.3)</td><td rowspan=\"1\" colspan=\"1\">1 (33.3)</td><td rowspan=\"1\" colspan=\"1\">1 (33.3)</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">Privacy</td><td rowspan=\"1\" colspan=\"1\">3 (0.2)</td><td rowspan=\"1\" colspan=\"1\">0</td><td rowspan=\"1\" colspan=\"1\">3 (100)</td><td rowspan=\"1\" colspan=\"1\">0</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">Barrier-free access</td><td rowspan=\"1\" colspan=\"1\">2 (0.1)</td><td rowspan=\"1\" colspan=\"1\">0</td><td rowspan=\"1\" colspan=\"1\">1 (50.0)</td><td rowspan=\"1\" colspan=\"1\">1 (50.0)</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">Online appointment</td><td rowspan=\"1\" colspan=\"1\">1 (0.04)</td><td rowspan=\"1\" colspan=\"1\">0</td><td rowspan=\"1\" colspan=\"1\">1 (100)</td><td rowspan=\"1\" colspan=\"1\">0</td></tr></tbody></table></table-wrap></sec></sec><sec sec-type=\"discussion\"><title>Discussion</title><sec><title>Principal Findings</title><p>As far as this author is aware, this is the first study internationally to examine feedback that has been rejected from a PRW. The key findings of this study are as follows: (1) the Swiss PRW Medicosearch rejected a total of 2352 patient feedback between September 16, 2008, and September 22, 2017, (2) just over half of all the rejected feedback were negative, and (3) the most frequently mentioned issue in the rejected feedback was satisfaction with treatment. Medicosearch has shown a lot of transparency in providing this rejected feedback for analysis. It is, however, unclear why the majority of the feedback were rejected. This is problematic and raises concerns that online patient feedback are being inappropriately manipulated.</p><p>Medicosearch did not provide the reasons why it rejected the feedback, and as far as this author is aware, Medicosearch does not use formal criteria for determining which feedback should be published or rejected. Of the 2352 ratings rejected by Medicosearch, 170 comments were excluded from the categorization framework for various reasons. For example, the feedback were not comprehensible, were explicitly labelled as test ratings, were about the PRW, etc. These would also appear to be legitimate reasons for Medicosearch to reject the feedback. However, the appropriateness of rejecting the remaining 92.7% of feedback is less clear, particularly as they appear to be qualitatively the same as the published feedback for a sample of Swiss physicians recently analyzed [<xref rid=\"ref19\" ref-type=\"bibr\">19</xref>].</p><p>Twelve percent of the rejected feedback only provided a quantitative rating with no narrative comment. Medicosearch requires that both a quantitative rating and a narrative comment be provided in every patient feedback, and this is likely the reason for rejecting this feedback. Narrative comments often provide a richer source of information than quantitative ratings [<xref rid=\"ref10\" ref-type=\"bibr\">10</xref>]; however, making narrative comments mandatory seems inappropriate. Some patients may not be willing or able to describe what happened in a narrative comment, but may still want to share their satisfaction with their physician with others. It is unclear why these ratings should simply be excluded because the patient did not want to also write a narrative comment.</p><p>In terms of the evaluation tendencies of online patient feedback, previous Swiss and international research has found that the published online patient feedback on PRWs is overwhelmingly positive [<xref rid=\"ref8\" ref-type=\"bibr\">8</xref>,<xref rid=\"ref10\" ref-type=\"bibr\">10</xref>,<xref rid=\"ref14\" ref-type=\"bibr\">14</xref>,<xref rid=\"ref17\" ref-type=\"bibr\">17</xref>-<xref rid=\"ref32\" ref-type=\"bibr\">32</xref>]. However, recent research also raised concerns that negative feedback is being suppressed by Swiss PRWs [<xref rid=\"ref19\" ref-type=\"bibr\">19</xref>]. For instance, the PRW OkDoc explicitly states on its website that any negative comments will be deleted, and while Medicosearch allows negative comments, it informs the concerned physician before publishing it online, so the physician can decide whether to activate the negative feedback function [<xref rid=\"ref19\" ref-type=\"bibr\">19</xref>]. There was therefore an expectation that the majority of the rejected feedback would be negative. However, this analysis of 2352 rejected feedback from Medicosearch found that just over half of all rejected feedback were negative, and the average rejected rating was 2.8 out of 5.</p><p>The proportion of rejected negative feedback, however, is substantially higher than the proportion of negative feedback that has been published in the international literature [<xref rid=\"ref8\" ref-type=\"bibr\">8</xref>,<xref rid=\"ref10\" ref-type=\"bibr\">10</xref>,<xref rid=\"ref14\" ref-type=\"bibr\">14</xref>,<xref rid=\"ref17\" ref-type=\"bibr\">17</xref>-<xref rid=\"ref32\" ref-type=\"bibr\">32</xref>]. Analysis of the published feedback for a sample of Swiss physicians also reported that only 4.3% (10/234) of the feedback published on Medicosearch was negative and that the average rating was 4.68 out of 5. It is unclear why there is such a large discrepancy between published and rejected negative feedback. It has previously been suggested that Switzerland’s restrictive legal framework regarding data protection may be having a big impact on the types of online patient feedback that are published [<xref rid=\"ref18\" ref-type=\"bibr\">18</xref>]. However, it may also be that PRWs like Medicosearch are also deciding themselves not to publish a lot of the negative feedback they receive owing to conflicts of interest. Medicosearch has shifted its business strategy toward online appointments, where physicians pay a fee and their booking systems are integrated with Medicosearch, which allows patients to book an appointment with a physician directly on Medicosearch. Consequently, Medicosearch will likely be reluctant to upset paying physicians by publishing too much negative feedback, as their business is now reliant on physicians using their online appointment system.</p><p>Users of PRWs can also manipulate online patient feedback, and there is some indication that physicians or practice staff sometimes pose as patients on PRWs to post either positive comments about themselves or negative comments about competitors [<xref rid=\"ref33\" ref-type=\"bibr\">33</xref>]. Indeed, 25% (588/2352) of the rejected feedback were positive, and it is possible some of these were rejected because Medicosearch suspected that these were fake reviews. However, without a clear and consistent way to determine which feedback is rejected, there is a danger that feedback will be inappropriately rejected.</p><p>With regard to the contents of narrative comments, the most frequently mentioned issues identified from the rejected feedback included (1) satisfaction with treatment; (2) the overall assessment of the physician; (3) recommending the physician; (4) the physician’s communication; (5) the physician’s caring attitude; and (6) the physician’s friendliness. In comparison, the top five mentioned issues identified in the analysis of the published feedback for a sample of Swiss physicians were (1) the overall assessment of the physician and the physician’s competence; (2) the physician’s communication; (3) recommending the physician; (4) the physician’s friendliness; and (5) the physician’s caring attitude [<xref rid=\"ref19\" ref-type=\"bibr\">19</xref>]. This suggests that online patient feedback is raising similar issues, regardless of whether it is published or rejected. Indeed, just like in the analysis of the published feedback for a sample of Swiss physicians [<xref rid=\"ref19\" ref-type=\"bibr\">19</xref>], it is important to recognize that 95% (42/44) of the subcategories of the categorization framework and 81.5% (3101/3804) of the distinct issues identified were concerning aspects of performance (interpersonal skills of the physician and staff, infrastructure, and organization and management of the practice) that are considered to be assessable by patients.</p><p>If online patient feedback is going to be collected, there needs to be clear policies and practices about how this is handled. It cannot be left to the whims of PRWs, who may have financial incentives to suppress negative feedback, to decide which feedback is or is not published online. It has previously been recommended that “there is a need for consensus-based criteria that applies to all Swiss PRWs for determining which comments are and are not to be publicly published and which are clearly publicized so users of PRWs are aware of it” [<xref rid=\"ref19\" ref-type=\"bibr\">19</xref>]. This analysis of 2352 rejected feedback from Medicosearch further highlights the need for such a consensus-based criteria. To support this, further research is needed to examine how many Swiss PRWs are using criteria for determining which feedback is published or not, what those criteria are, and what measures PRWs are using to address the manipulation of online patient feedback. It appears that research examining these issues would be helpful in most countries that have PRWs.</p></sec><sec><title>Limitations</title><p>This study has some limitations that should be taken into account when interpreting the results. First, it is unknown how many patient feedback Medicosearch received in total during the time period covered. The author contacted Medicosearch asking for this information but never received a response, and the information is not freely available on the website. It would be helpful to know the proportion of patient feedback that is being rejected. Second, the sample of rejected feedback was only taken from one Swiss PRW. Although Medicosearch is one of the oldest and most used Swiss PRWs, it is unclear how generalizable the results are to other PRWs and other countries. Future research examining whether PRWs are using criteria for determining which feedback is published or not should include all Swiss PRWs. Third, the specialty and sociodemographic information of the rated physicians are unknown, and there may be important differences between the different specialties and physicians. Finally, the sociodemographic information of the rating patients is unknown and may not be representative of Swiss patients in general.</p></sec></sec></body><back><ack><p>This work was funded by the Swiss Academy of Medical Sciences’ Käthe-Zingg-Schwichtenberg-Fonds, which had no role in the project design; in the collection, analysis, or interpretation of data; in the writing of the article; or in the decision to submit the paper for publication.</p></ack><fn-group><fn fn-type=\"COI-statement\"><p>Conflicts of Interest: None declared.</p></fn></fn-group><app-group><app><title>Appendix</title><supplementary-material content-type=\"local-data\" id=\"app1\"><label>Multimedia Appendix 1</label><p>Categorization of issues by language.</p><media xlink:href=\"jmir_v22i8e18374_app1.docx\" xlink:title=\"DOCX File , 25 KB\" xlink:type=\"simple\" id=\"d38e1634\" position=\"anchor\"/></supplementary-material></app></app-group><glossary><title>Abbreviations</title><def-list><def-item><term id=\"abb1\">PRW</term><def><p>physician rating website</p></def></def-item></def-list></glossary><ref-list><ref id=\"ref1\"><label>1</label><element-citation publication-type=\"book\"><person-group person-group-type=\"author\"><collab>Institute of Medicine</collab></person-group><source>Best care at lower cost: The path to continuously learning health care in America</source><year>2013</year><publisher-loc>Washington, DC</publisher-loc><publisher-name>The National Academies Press</publisher-name></element-citation></ref><ref id=\"ref2\"><label>2</label><element-citation publication-type=\"book\"><person-group person-group-type=\"author\"><name><surname>Paterson</surname><given-names>R</given-names></name></person-group><source>The Good Doctor: What Patients Want</source><year>2010</year><publisher-loc>Auckland, New Zealand</publisher-loc><publisher-name>Auckland University Press</publisher-name></element-citation></ref><ref id=\"ref3\"><label>3</label><element-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Faber</surname><given-names>M</given-names></name><name><surname>Bosch</surname><given-names>M</given-names></name><name><surname>Wollersheim</surname><given-names>H</given-names></name><name><surname>Leatherman</surname><given-names>S</given-names></name><name><surname>Grol</surname><given-names>R</given-names></name></person-group><article-title>Public reporting in health care: how do consumers use quality-of-care information? 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[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Front Immunol</journal-id><journal-id journal-id-type=\"iso-abbrev\">Front Immunol</journal-id><journal-id journal-id-type=\"publisher-id\">Front. Immunol.</journal-id><journal-title-group><journal-title>Frontiers in Immunology</journal-title></journal-title-group><issn pub-type=\"epub\">1664-3224</issn><publisher><publisher-name>Frontiers Media S.A.</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32849667</article-id><article-id pub-id-type=\"pmc\">PMC7432140</article-id><article-id pub-id-type=\"doi\">10.3389/fimmu.2020.02020</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Immunology</subject><subj-group><subject>Original Research</subject></subj-group></subj-group></article-categories><title-group><article-title>The Safety and Efficacy of Live Viral Vaccines in Patients With Cartilage-Hair Hypoplasia</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Vakkilainen</surname><given-names>Svetlana</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><xref ref-type=\"aff\" rid=\"aff2\"><sup>2</sup></xref><xref ref-type=\"aff\" rid=\"aff3\"><sup>3</sup></xref><xref ref-type=\"corresp\" rid=\"c001\"><sup>*</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/601095/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Kleino</surname><given-names>Iivari</given-names></name><xref ref-type=\"aff\" rid=\"aff4\"><sup>4</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/1034808/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Honkanen</surname><given-names>Jarno</given-names></name><xref ref-type=\"aff\" rid=\"aff3\"><sup>3</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/725670/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Salo</surname><given-names>Harri</given-names></name><xref ref-type=\"aff\" rid=\"aff5\"><sup>5</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Kainulainen</surname><given-names>Leena</given-names></name><xref ref-type=\"aff\" rid=\"aff6\"><sup>6</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Gräsbeck</surname><given-names>Michaela</given-names></name><xref ref-type=\"aff\" rid=\"aff7\"><sup>7</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Kekäläinen</surname><given-names>Eliisa</given-names></name><xref ref-type=\"aff\" rid=\"aff4\"><sup>4</sup></xref><xref ref-type=\"aff\" rid=\"aff8\"><sup>8</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/103082/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Mäkitie</surname><given-names>Outi</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><xref ref-type=\"aff\" rid=\"aff2\"><sup>2</sup></xref><xref ref-type=\"aff\" rid=\"aff3\"><sup>3</sup></xref><xref ref-type=\"aff\" rid=\"aff9\"><sup>9</sup></xref><xref ref-type=\"aff\" rid=\"aff10\"><sup>10</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/535471/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Klemetti</surname><given-names>Paula</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/765814/overview\"/></contrib></contrib-group><aff id=\"aff1\"><sup>1</sup><institution>Children’s Hospital, Pediatric Research Center, University of Helsinki and Helsinki University Hospital</institution>, <addr-line>Helsinki</addr-line>, <country>Finland</country></aff><aff id=\"aff2\"><sup>2</sup><institution>Folkhälsan Research Center, Institute of Genetics</institution>, <addr-line>Helsinki</addr-line>, <country>Finland</country></aff><aff id=\"aff3\"><sup>3</sup><institution>Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki</institution>, <addr-line>Helsinki</addr-line>, <country>Finland</country></aff><aff id=\"aff4\"><sup>4</sup><institution>Translational Immunology Research Program, University of Helsinki</institution>, <addr-line>Helsinki</addr-line>, <country>Finland</country></aff><aff id=\"aff5\"><sup>5</sup><institution>Children’s Hospital, Clinicum, University of Helsinki</institution>, <addr-line>Helsinki</addr-line>, <country>Finland</country></aff><aff id=\"aff6\"><sup>6</sup><institution>Department of Pediatrics and Adolescents, Turku University Hospital, University of Turku</institution>, <addr-line>Turku</addr-line>, <country>Finland</country></aff><aff id=\"aff7\"><sup>7</sup><institution>Department of Pediatrics, Kymenlaakso Central Hospital</institution>, <addr-line>Kotka</addr-line>, <country>Finland</country></aff><aff id=\"aff8\"><sup>8</sup><institution>Helsinki University Hospital, HUSLAB, Division of Clinical Microbiology</institution>, <addr-line>Helsinki</addr-line>, <country>Finland</country></aff><aff id=\"aff9\"><sup>9</sup><institution>Department of Molecular Medicine and Surgery and Center for Molecular Medicine, Karolinska Institutet</institution>, <addr-line>Stockholm</addr-line>, <country>Sweden</country></aff><aff id=\"aff10\"><sup>10</sup><institution>Department of Clinical Genetics, Karolinska University Hospital</institution>, <addr-line>Stockholm</addr-line>, <country>Sweden</country></aff><author-notes><fn fn-type=\"edited-by\"><p>Edited by: Yu Lung Lau, The University of Hong Kong, Hong Kong</p></fn><fn fn-type=\"edited-by\"><p>Reviewed by: Taizo Wada, Kanazawa University, Japan; Surjit Singh, Post Graduate Institute of Medical Education and Research (PGIMER), India</p></fn><corresp id=\"c001\">*Correspondence: Svetlana Vakkilainen, <email>[email protected]</email>; <email>[email protected]</email></corresp><fn fn-type=\"other\" id=\"fn004\"><p>This article was submitted to Primary Immunodeficiencies, a section of the journal Frontiers in Immunology</p></fn></author-notes><pub-date pub-type=\"epub\"><day>11</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><year>2020</year></pub-date><volume>11</volume><elocation-id>2020</elocation-id><history><date date-type=\"received\"><day>10</day><month>6</month><year>2020</year></date><date date-type=\"accepted\"><day>27</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>Copyright © 2020 Vakkilainen, Kleino, Honkanen, Salo, Kainulainen, Gräsbeck, Kekäläinen, Mäkitie and Klemetti.</copyright-statement><copyright-year>2020</copyright-year><copyright-holder>Vakkilainen, Kleino, Honkanen, Salo, Kainulainen, Gräsbeck, Kekäläinen, Mäkitie and Klemetti</copyright-holder><license xlink:href=\"http://creativecommons.org/licenses/by/4.0/\"><license-p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</license-p></license></permissions><abstract><sec><title>Background</title><p>Live viral vaccines are generally contraindicated in patients with combined immunodeficiency including cartilage-hair hypoplasia (CHH); however, they may be tolerated in milder syndromes. We evaluated the safety and efficacy of live viral vaccines in patients with CHH.</p></sec><sec><title>Methods</title><p>We analyzed hospital and immunization records of 104 patients with CHH and measured serum antibodies to measles, mumps, rubella, and varicella zoster virus (VZV) in all patients who agreed to blood sampling (<italic>n</italic> = 50). We conducted a clinical trial (<ext-link ext-link-type=\"uri\" xlink:href=\"https://clinicaltrials.gov\">ClinicalTrials.gov</ext-link> identifier: NCT02383797) of live VZV vaccine on five subjects with CHH who lacked varicella history, had no clinical symptoms of immunodeficiency, and were seronegative for VZV; humoral and cellular immunologic responses were assessed post-immunization.</p></sec><sec><title>Results</title><p>A large proportion of patients have been immunized with live viral vaccines, including measles-mumps-rubella (MMR) (<italic>n</italic> = 40, 38%) and VZV (<italic>n</italic> = 10, 10%) vaccines, with no serious adverse events. Of the 50 patients tested for antibodies, previous immunization has been documented with MMR (<italic>n</italic> = 22), rubella (<italic>n</italic> = 2) and measles (<italic>n</italic> = 1) vaccines. Patients with CHH demonstrated seropositivity rates of 96%/75%/91% to measles, mumps and rubella, respectively, measured at a medium of 24 years post-immunization. Clinical trial participants developed humoral and cellular responses to VZV vaccine. One trial participant developed post-immunization rash and knee swelling, both resolved without treatment.</p></sec><sec><title>Conclusion</title><p>No serious adverse events have been recorded after immunization with live viral vaccines in Finnish patients with CHH. Patients generate humoral and cellular immune response to live viral vaccines. Immunization with live vaccines may be considered in selected CHH patients with no or clinically mild immunodeficiency.</p></sec></abstract><kwd-group><kwd>clinical trial</kwd><kwd>combined immunodeficiency</kwd><kwd>immunization</kwd><kwd>MMR</kwd><kwd>RMRP</kwd><kwd>vaccination</kwd><kwd>varicella zoster virus</kwd></kwd-group><counts><fig-count count=\"2\"/><table-count count=\"3\"/><equation-count count=\"0\"/><ref-count count=\"28\"/><page-count count=\"8\"/><word-count count=\"0\"/></counts></article-meta></front><body><sec id=\"S1\"><title>Introduction</title><p>Cartilage-hair hypoplasia (CHH, MIM #250250) is a rare skeletal dysplasia with combined immunodeficiency. CHH is caused by mutations in the <italic>RMRP</italic> gene, encoding the RNA component of the mitochondrial RNA-processing endoribonuclease RNase MPR (<xref rid=\"B1\" ref-type=\"bibr\">1</xref>). Impaired <italic>RMRP</italic> functioning results in cell cycle disturbances, altered telomere biology and changes in gene regulation (<xref rid=\"B2\" ref-type=\"bibr\">2</xref>–<xref rid=\"B4\" ref-type=\"bibr\">4</xref>). Patients with CHH demonstrate highly variable degree of immune defect, ranging from asymptomatic lymphopenia to severe combined immunodeficiency necessitating hematopoietic stem cell transplantation (<xref rid=\"B5\" ref-type=\"bibr\">5</xref>, <xref rid=\"B6\" ref-type=\"bibr\">6</xref>).</p><p>The first report on CHH among the Amish described fatal outcomes after varicella zoster virus (VZV) primary infection (<xref rid=\"B7\" ref-type=\"bibr\">7</xref>). Since then, several CHH patients with severe varicella, but not fatalities, have been reported (<xref rid=\"B8\" ref-type=\"bibr\">8</xref>–<xref rid=\"B12\" ref-type=\"bibr\">12</xref>). In the large Finnish case series published in 1990s, only two out of 56 patients with a history of varicella had required hospitalization (<xref rid=\"B13\" ref-type=\"bibr\">13</xref>). In a more recent Amish series of 25 patients, neither antiviral medications nor hospitalizations were needed for varicella (<xref rid=\"B14\" ref-type=\"bibr\">14</xref>).</p><p>Patients with CHH can develop fatal complications after administration of live viral vaccines against smallpox or polio (<xref rid=\"B15\" ref-type=\"bibr\">15</xref>, <xref rid=\"B16\" ref-type=\"bibr\">16</xref>). The development of humoral or cellular response after live vaccine administration has not been previously assessed in CHH. CD4+ cells producing interferon gamma (IFN-γ) contribute significantly to the immunity from VZV, and measurement of IFN-γ response represents a simple method for evaluation of VZV-specific cellular immunity (<xref rid=\"B17\" ref-type=\"bibr\">17</xref>, <xref rid=\"B18\" ref-type=\"bibr\">18</xref>).</p><p>Live vaccines are generally contraindicated in patients with combined immunodeficiency, although they can be tolerated in milder syndromes (<xref rid=\"B19\" ref-type=\"bibr\">19</xref>). Measles-mumps-rubella (MMR) and VZV vaccines are safe in children with partial DiGeorge syndrome who have CD4 + T cell counts of ≥500 cells/mm<sup>3</sup> (<xref rid=\"B19\" ref-type=\"bibr\">19</xref>). In children infected with human immunodeficiency virus, live vaccines are considered safe if CD4 + T cell count is >200 cells/mm<sup>3</sup> or >15% (<xref rid=\"B19\" ref-type=\"bibr\">19</xref>).</p><p>In order to enable evidence-based decisions on the immunization of patients with CHH, we collected clinical data on the course of live-vaccine preventable diseases and analyzed vaccination and serologic data for the administered live viral vaccines in a large cohort of Finnish CHH patients, and then conducted a clinical trial with live VZV vaccine in this patient population.</p></sec><sec sec-type=\"materials|methods\" id=\"S2\"><title>Materials and Methods</title><p>The study protocol and the clinical trial (<ext-link ext-link-type=\"uri\" xlink:href=\"https://clinicaltrials.gov\">ClinicalTrials.gov</ext-link> identifier: NCT02383797, registered on March 9, 2015) were approved by the Institutional Ethics Committee at Helsinki University Hospital and University of Helsinki, Finland. Additional approval for the clinical trial was obtained from the Finnish Medicines Agency (FIMEA). The study was conducted in accordance with the Declaration of Helsinki and national and institutional standards. All participants and/or caregivers signed informed consents.</p><sec id=\"S2.SS1\"><title>Clinical and Laboratory Data</title><p>Clinical data, including data on live-vaccine preventable diseases, were obtained from the patients directly, as well as retrospectively from health records, as part of our previous studies exploring characteristics and natural course of CHH in the Finnish cohort (<xref rid=\"B5\" ref-type=\"bibr\">5</xref>, <xref rid=\"B13\" ref-type=\"bibr\">13</xref>, <xref rid=\"B20\" ref-type=\"bibr\">20</xref>, <xref rid=\"B21\" ref-type=\"bibr\">21</xref>). Only provider-recorded data in the patients’ immunization certificates were considered reliable and were included in the analysis of immunizations. The researchers had access to data on all hospitalizations of the study patients, beginning from 1969, via the Health Registers described in detail previously (<xref rid=\"B21\" ref-type=\"bibr\">21</xref>).</p><p>Serum samples were obtained from patients during clinical visits as part of our previous study (<xref rid=\"B5\" ref-type=\"bibr\">5</xref>). All patients who agreed to blood sampling were included, except those with ongoing immunoglobulin replacement therapy. The patients were not selected on the basis of the history of live-vaccine preventable diseases or immunization with live vaccines. Samples were analyzed by enzyme immunoassays for the presence of antibodies to measles, mumps, rubella, and VZVs. Reference values of the local laboratory (HUSLAB) were applied, and the titers were considered protective if over 15 EIU for rubella and VZVs, while for antibodies against measles and mumps viruses, qualitative assays were used.</p></sec><sec id=\"S2.SS2\"><title>Clinical Trial of Live Varicella Vaccine</title><p>We conducted an open-label clinical trial on live VZV vaccine (<xref ref-type=\"fig\" rid=\"F1\">Figure 1</xref>). Of the 120 patients with CHH in the Finnish Skeletal Dysplasia Register, we recruited five patients. Inclusion criteria were: (1) genetically confirmed CHH, (2) age >12 months, and (3) no history of varicella. Exclusion criteria were (1) history of immunization against VZV, (2) positive serum antibodies for VZV, (3) low CD4 + T cell counts (<15% or <200 cells/mm<sup>3</sup>), (4) clinical signs of severe immunodeficiency, or (5) ongoing immunoglobulin replacement therapy.</p><fig id=\"F1\" position=\"float\"><label>FIGURE 1</label><caption><p>Recruitment to the clinical trial of safety and efficacy of live varicella zoster virus vaccine in patients with cartilage-hair hypoplasia.</p></caption><graphic xlink:href=\"fimmu-11-02020-g001\"/></fig><p>In 2015–2017, all recruited patients were given one or two doses of Varilrix<sup>®</sup>, a live-attenuated VZV Oka strain vaccine, subcutaneously, by the nurses in the health care facilities. One dose contained at least 10<sup>3</sup>.<sup>3</sup> plaque forming units of VZV. A second dose was administered if the serologic response after primary immunization was negative.</p><p>Blood samples were drawn pre-immunization and 4-6 weeks post-immunization for evaluation of humoral and cellular responses. Humoral responses were assessed by measurement of serum VZV-specific antibodies by enzyme immunoassay in HUSLAB. Cellular responses were assessed by elispot using cryopreserved peripheral blood mononuclear cells (PBMC). Control PBMC samples were obtained from (1) healthy adult volunteers with a history of varicella disease, (2) CHH families, healthy siblings who had been immunized against varicella, and 3) one patient with CHH and a history of varicella.</p><p>Peripheral blood mononuclear cells were isolated from heparinized blood samples by Ficoll centrifugation. The isolated cells were counted and frozen in RPMI1640 cell culture medium containing 10% heat-inactivated human male serum (Innovative Research, Peary Court Novi, MI, United States) and 10% Dimethyl sulfoxide. Freezing of the isolated PBMCs to −80°C was done gradually in a temperature-controlled way in 1.8 ml cryotubes using isopropanol-filled cryofreezing containers. The next day the frozen cells were transferred to automated liquid nitrogen freezer (AGA, Finland) for long term storage at −180°C until use.</p><p>Peripheral blood mononuclear cells from patients with CHH and controls were subjected to IFN-γ elispot antigen response analysis (3420-4HST-2, MabTech, Nacka Strand, Sweden). The elispot was performed according to manufacturer’s protocol with 24 h stimulation with VZV antigen preparation (PR-BA104, Jena Bioscience, Löbstedter, Germany). Shortly, thawed PBMC were cultured on IFN-γ elispot strips in CTL-test medium (CTLT-010, Cellular Technology Limited, OH, United States) in 5% CO<sub>2</sub> humidified incubator at 37°C. For antigen response, cells in triplicate wells were stimulated with 2 ug of VZV antigen and compared to CTL-test only negative controls. MabTech anti-CD3 (1:1000) was used as positive control to TCR-stimulant for T cells. The IFN-γ spots were made visible with streptavidin-HRP enzymatic assay (3420-4HST-2, MabTech) and the number of IFN-γ spots were counted with elispot reader system (Elispot Reader System ELRIFL04, AID, Strassberg, Germany) with cutoff settings for a size and staining intensity of the positive spots deriving from the corresponding positive and negative control spots. The identity of patient and control samples was blinded during the elispot analysis. The statistical analysis of the elispot data (Paired-Wilcoxon) was done in RStudio (1.25) running R (3.6.2) with packages (rstatix, ggpubr, and tidyverse) available in r-project (<ext-link ext-link-type=\"uri\" xlink:href=\"https://cran.r-project.org/\">cran.r-project.org</ext-link>). If patient had received two doses of vaccine, the data from the sample collected after the second dose was used in the analysis. One sample (vaccinated healthy control) was dropped from the data due to very low level of reactive T cells in anti-CD3 positive control.</p><p>All patients/caregivers were instructed to report any adverse reactions immediately to the researchers and were additionally contacted by phone 4–6 weeks post-immunization to record potential adverse events. In addition, patient records of all clinical trial participants were assessed at the time of writing (3–5 years post-immunization) to check for possible long-term adverse events or breakthrough VZV infection.</p></sec></sec><sec id=\"S3\"><title>Results</title><sec id=\"S3.SS1\"><title>Cohort Description</title><p>Of the identified 120 patients with CHH in Finland, informed consent for the use of clinical and laboratory data was obtained from 104 patients during our previous studies (<xref ref-type=\"fig\" rid=\"F1\">Figure 1</xref>). All patients were either homozygous or compound heterozygous for the g.71A > G <italic>RMRP</italic> mutation.</p><p>The clinical characteristics of this cohort have been reported in our recent publication (<xref rid=\"B20\" ref-type=\"bibr\">20</xref>), According to our clinical immunodeficiency categorization (<xref rid=\"B21\" ref-type=\"bibr\">21</xref>) and based on data on childhood symptoms available in 103/104 patients, 62 patients were classified as clinically asymptomatic, another 28 individuals demonstrated symptoms of humoral immunodeficiency with recurrent respiratory tract infections and/or sepsis, and the remaining 13 patients were regarded as having combined immunodeficiency in childhood, based on the additional features of autoimmunity or opportunistic infections. During lifetime, twelve patients had received immunoglobulin replacement therapy and one patient had undergone a hematopoietic stem cell transplant. Retrospective laboratory data demonstrated lymphopenia in 36/83 (43%) patients in childhood and in 37/74 (50%) patients in adulthood.</p></sec><sec id=\"S3.SS2\"><title>Live-Vaccine Preventable Diseases in Patients With CHH</title><p>A significant number of patients reported history of measles (<italic>n</italic> = 32), mumps (<italic>n</italic> = 24) and/or rubella (<italic>n</italic> = 18) (<xref rid=\"T1\" ref-type=\"table\">Table 1</xref>), that, in the majority of patients, was also confirmed from the hospital records. All cases occurred in unvaccinated patients, prior to the 1980s, and had been diagnosed clinically. No hospitalizations were linked to measles, mumps, or rubella. Also, no long-term complications have been documented, including no cases of measles-associated subacute sclerosing panencephalitis or rubella-associated granulomatous skin lesions.</p><table-wrap id=\"T1\" position=\"float\"><label>TABLE 1</label><caption><p>Live vaccine preventable disease history, live vaccine immunization history and serological testing in 104 patients with CHH.</p></caption><table frame=\"hsides\" rules=\"groups\" cellspacing=\"5\" cellpadding=\"5\"><thead><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Disease</bold></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Vaccine</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Clinical history of disease (<italic>n</italic>)</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Written evidence of immunization (<italic>n</italic>)</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Complications post-immunization (<italic>n</italic>)</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Positive serology<sup>a</sup> [<italic>n</italic>/tested (%)]</bold></td></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Measles</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Measles</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">32</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">12</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">44/50 (88)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Mumps</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Mumps</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">24</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">NA</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">NA</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">33/48 (69)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Rubella</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Rubella</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">18</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">8</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">40/48 (83)</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">MMR</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">NA</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">40</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">NA</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Polio</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Sabin</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">37</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">n/a</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Rota virus</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Rota virus</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">n/a</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">n/a</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Smallpox</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Smallpox</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">22</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">n/a</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Tuberculosis</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">BCG</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">78</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">n/a</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Varicella</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">VZV<sup>b</sup></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">73</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">10</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1<sup>c</sup></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">49/50 (98)<sup>d</sup></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Yellow fever</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Yellow fever</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">n/a</td></tr></tbody></table><table-wrap-foot><attrib><italic>BCG bacillus Calmette–Guérin vaccine, MMR measles, mumps and rubella combined vaccine, n number of patients, n/a not accessed, NA not applicable, VZV varicella zoster virus. <sup><italic>a</italic></sup>Post-vaccination serology results only were reported in those immunized against varicella. <sup><italic>b</italic></sup>Patients enrolled in the clinical trial were also included. <sup><italic>c</italic></sup>Complications are described in detail under the section <italic>Clinical trial of live varicella zoster virus vaccine in patients with CHH</italic>. <sup><italic>d</italic></sup>Seronegative patient had no history of varicella or VZV vaccine administration, but deceased before enrollment into VZV vaccine clinical trial.</italic></attrib></table-wrap-foot></table-wrap><p>A history of varicella was reported in 73 patients with CHH. In addition, five more patients were seropositive for VZV despite negative clinical history. Of these 78 patients, seven were hospitalized for varicella due to severe disease, at least two of whom had concomitant pneumonia, all seven recovered. In addition, nine more patients were treated with acyclovir immediately after the onset of rash, which successfully prevented the severe course of varicella, and eight more individuals were administered VZV-specific immunoglobulin after a contact with varicella and did not develop disease.</p></sec><sec id=\"S3.SS3\"><title>Immunization With Live Viral Vaccines in Patients With CHH</title><p>Historically, measles vaccine was used in Finland in 1975–1982, rubella vaccine in 1975–1988, and MMR vaccine has been used since 1982. Written evidence of immunization was available in 90/104 patients, and the majority of them (96%, 86/90) have been vaccinated with one or more live vaccines (<xref rid=\"T1\" ref-type=\"table\">Table 1</xref>). No hospitalizations or serious complications were reported after any live vaccine administration, not even after immunization against smallpox, polio and yellow fever.</p><p>For measles-containing vaccines, one patient received the first dose in adulthood, otherwise primary immunization was performed in childhood, at median age of 1.7 years (range 1.1–12.6 years).</p><p>Prior to the initiation of our clinical trial, we were aware of five patients with CHH successfully immunized with live VZV vaccine during the second year of life, with no adverse events.</p></sec><sec id=\"S3.SS4\"><title>Serology for Measles, Mumps, Rubella, and Varicella in Patients With CHH</title><p>Of the 50 patients tested for antibodies against measles (<italic>n</italic> = 50), mumps (<italic>n</italic> = 48), rubella (<italic>n</italic> = 48), and varicella (<italic>n</italic> = 50) viruses, previous immunization has been documented with MMR (<italic>n</italic> = 22), VZV (<italic>n</italic> = 9), rubella (<italic>n</italic> = 2) and measles (<italic>n</italic> = 1) vaccines.</p><p>For varicella, all patients with a history of disease or immunization showed durable humoral response with 100% (44/44) of seropositivity when tested (<xref rid=\"T2\" ref-type=\"table\">Table 2</xref>). For measles, 100% (15/15) of patients with a history of disease had measurable antibodies, as had 96% (22/23) immunized patients (<xref rid=\"T2\" ref-type=\"table\">Table 2</xref>). For mumps, seropositivity was observed in 86% (12/14) of patients with a history of disease and in 75% (15/20) of immunized patients (<xref rid=\"T2\" ref-type=\"table\">Table 2</xref>). For rubella, these proportions were 100% (13/13) and 91% (20/22), respectively (<xref rid=\"T2\" ref-type=\"table\">Table 2</xref>). In terms of the clinical staging of immunodeficiency, all seronegative patients were asymptomatic, except for two patients with combined immunodeficiency, of whom one did not develop antibodies against measles and the other against mumps after MMR vaccination.</p><table-wrap id=\"T2\" position=\"float\"><label>TABLE 2</label><caption><p>Results of antibody measurements for measles, mumps, rubella (MMR) and varicella zoster viruses from serum samples of patients with cartilage-hair hypoplasia.</p></caption><table frame=\"hsides\" rules=\"groups\" cellspacing=\"5\" cellpadding=\"5\"><thead><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Disease</bold></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Clinical history</bold></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Immunized</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Serology positive [<italic>n</italic>/tested (%)]</bold></td></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Measles</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Yes: 32</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Yes: 4</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3/3 (100)</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">No/no data: 28</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">12/12 (100)</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">No/no data: 72</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Yes: 39</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">19/20 (95)</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">No/no data: 34</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">10/15 (67)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Mumps</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Yes: 24</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Yes: 2</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1/1 (100)</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">No/no data: 22</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">11/13 (85)</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">No/no data: 80</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Yes: 37</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">15/20 (75)</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">No/no data: 43</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">6/14 (43)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Rubella</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Yes: 18</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Yes: 4</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2/2 (100)</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">No/no data: 14</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">11/11 (100)</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">No/no data: 86</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Yes: 43</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">18/20 (90)</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">No/no data: 43</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">9/15 (60)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Varicella</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Yes: 73</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Yes: 0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">No/no data 73</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">35/35 (100)</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">No/no data: 31</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Yes: 10</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">9/9 (100)</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">No/no data: 21</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5/6 (83)</td></tr></tbody></table></table-wrap><p>Median time between the last dose of measle-containing vaccines and serum sampling for serology was 24.6 years (range 0.3–36.9 years). This median time interval was similar for MMR and for rubella-containing vaccines (24.7 and 24.9 years, respectively). All five patients immunized with live VZV vaccine, had detectable antibodies against VZV in serum 5–19 years (median 17 years) post-immunization.</p></sec><sec id=\"S3.SS5\"><title>Clinical Trial of Live Varicella Zoster Virus Vaccine in Patients With CHH</title><p><xref rid=\"T3\" ref-type=\"table\">Table 3</xref> summarizes the results of the clinical trial on live VZV vaccine in five patients with CHH. We immunized four children and one adult, all with clinically silent immunodeficiency. Three patients were lymphopenic, with various abnormalities in lymphocyte subpopulations. Lymphocyte responses to stimulation with mitogens were normal in all patients. All five patients had normal levels of immunoglobulin G.</p><table-wrap id=\"T3\" position=\"float\"><label>TABLE 3</label><caption><p>Summary of the clinical trial of live varicella zoster virus vaccine in patients with cartilage-hair hypoplasia.</p></caption><table frame=\"hsides\" rules=\"groups\" cellspacing=\"5\" cellpadding=\"5\"><thead><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Patient</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>1</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>2</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>3</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>4</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>5</bold></td></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Age at primary immunization, years</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1⋅5</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2⋅7</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">6</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">16</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">45</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Symptoms of combined immunodeficiency prior to immunization<sup>a</sup></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">None</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">None</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">None</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">None</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">None</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Laboratory parameters prior to immunization</td><td valign=\"top\" colspan=\"5\" rowspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Total lymphocyte count, cells/mm<sup>3</sup></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>2112</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3020</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>960</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>1210</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1590</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">CD3 + T cell count, cells/mm<sup>3</sup></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>1150</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2240</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>580</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>810</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1200</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">CD4 + T cell count, cells/mm<sup>3</sup></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>827</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1482</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>294</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">543</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">625</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">CD8 + T cell count, cells/mm<sup>3</sup></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>140</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">530</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>110</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>140</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">590</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">CD19 + B cell count, cells/mm<sup>3</sup></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>290</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">580</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>120</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">130</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">160</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">CD16/56 + NK cell count, cells/mm<sup>3</sup></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">310</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>110</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">170</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">90</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">160</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Lymphocyte response to stimulation with PHA</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Normal</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Normal</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Normal</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Normal</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Normal</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Number of vaccine doses</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Time interval between vaccine doses, months</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">6</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">6</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">n/a</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4.5</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">n/a</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">VZV-specific immunoglobulin G after the first dose</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">neg</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">neg</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">pos</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">neg</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">pos</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">VZV-specific immunoglobulin G after the second dose</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">pos</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">pos</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">NA</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">pos</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">NA</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Cellular response by elispot after the first dose</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">neg</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">pos</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">pos</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">pos</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">pos</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Cellular response by elispot after the second dose</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">pos</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">n/a</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">NA</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">pos</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">NA</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Vaccine adverse events</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">None</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">None</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">None</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">None</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Yes<sup>b</sup></td></tr></tbody></table><table-wrap-foot><attrib><italic>Numbers in bold represent cell counts below the normal range for age. n/a not available, NA – not applicable, neg – negative, PHA – phytohemagglutinin, pos – positive, VZV – varicella zoster virus. <sup><italic>a</italic></sup>Including severe or opportunistic infections, autoimmunity, or malignancy. <sup><italic>b</italic></sup>Post-immunization adverse events (rash and knee swelling) are described in detail in text.</italic></attrib></table-wrap-foot></table-wrap><p>None of the four immunized children developed adverse events. All five patients are well 2.8–4.5 years post-immunization, and no breakthrough varicella has been reported. Patient 5 reported a self-limited upper respiratory tract infection two weeks post-immunization. He then developed a rash during the third week post-immunization when few painless yellow-reddish round papules and ulcers (but not vesicles) 0.5 cm in size appeared on his abdomen, neck, and head within several days. The rash persisted unchanged for a week and then gradually disappeared. In addition, the patient developed swelling and morning stiffness in his right knee three weeks post-immunization, which did not disturb his walking and cycling. The patient declined antiviral and anti-inflammatory therapy and reported no complaints on the follow-up seven weeks post-immunization. There was no viremia at the time of rash, confirmed by negative blood VZV nucleic acid testing.</p><p>Three patients who did not develop measurable humoral response after the first vaccine dose demonstrated seroconversion after the second immunization. All patients also demonstrated the development of cellular response post-immunization when measured by elispot, 4/5 after the first vaccine dose (<xref ref-type=\"fig\" rid=\"F2\">Figure 2</xref>).</p><fig id=\"F2\" position=\"float\"><label>FIGURE 2</label><caption><p>Varicella zoster virus antigen response in CHH patient samples. Peripheral blood mononuclear cells of naïve and post-vaccine paired patient samples showed statistically significant induction of cellular immunity in interferon-γ elispot antigen assay. The level of induction (mean, black lines) was similar to a single healthy control.</p></caption><graphic xlink:href=\"fimmu-11-02020-g002\"/></fig></sec></sec><sec id=\"S4\"><title>Discussion</title><p>This is the first study describing the administration of live viral vaccines in a large cohort of patients with CHH. We demonstrate the safety and efficacy of MMR and VZV vaccines in this patient population, based on the retrospective data on provider-recorded immunizations, absence of breakthrough infections or hospitalizations linked to immunizations, as well as the durable serologic response. We also present the results of a clinical trial of live VZV vaccine in patients with CHH, which showed the generation of both humoral and cellular immune responses. Among the participants of our clinical trial, the incidence of varicella-like post-vaccination rash was 20% (1/5), compared with the reported rates of 0–7% in healthy individuals (<xref rid=\"B22\" ref-type=\"bibr\">22</xref>).</p><p>Prior to the 1980s, when MMR immunization was introduced in Finland, many patients with CHH had contracted MMR-diseases with uneventful course. This is in line with the generally milder immunodeficiency phenotype in Finnish individuals with CHH who rarely develop severe opportunistic infections (<xref rid=\"B21\" ref-type=\"bibr\">21</xref>). However, varicella clearly is an exception, as many patients with CHH suffer from prolonged rash and fever and some develop pneumonia and require hospitalization (<xref rid=\"B13\" ref-type=\"bibr\">13</xref>). Many patients with CHH are pre-emptively treated with acyclovir and given VZV immunoglobulin for the post-exposure prophylaxis. Immunization against VZV may obviate the need for prophylaxis, antiviral treatment and hospitalizations. The same considerations can be applied to the MMR vaccine.</p><p>Patients with CHH mount robust humoral immune response to measles and varicella (both natural infection and immunization), which remains measurable for decades. Antibodies to mumps either do not develop or wane over time in a significant proportion (14–25%) of patients with CHH, raising the concern about insufficient immunity against mumps. Also, rarely, patients with CHH may not develop seropositivity for rubella after MMR administration. Therefore, antibody measurement should be routinely used to assess vaccination responses in patients with CHH and booster doses should be given in case of insufficient immunity.</p><p>The pattern of seropositivity to MMR and VZV in patients with CHH are similar to those in healthy individuals, with good responses to measles, varicella and rubella, but lower rates of seropositivity to mumps (<xref rid=\"B23\" ref-type=\"bibr\">23</xref>, <xref rid=\"B24\" ref-type=\"bibr\">24</xref>). However, in our clinical trial of VZV vaccine, 60% (3/5) of participants failed to develop antibodies against VZV after the single vaccine dose, while in healthy children this proportion is 24% (<xref rid=\"B25\" ref-type=\"bibr\">25</xref>).</p><p>This is the first study describing the production of IFN-γ in stimulated PBMC from patients with CHH. Our assessment of cellular response to VZV vaccine demonstrate that patients with CHH develop cellular immunity probably comparable to healthy immunized controls. The comparison was problematic due to the small number of healthy immunized controls. Antibodies are essential to neutralize pathogens in extracellular space. However, cellular immunity is critical for controlling and containing intracellular pathogens, especially viruses with the ability to form latency, such as VZV. Thus, the protection against VZV is provided by multitude of simultaneously acting immunological mechanisms (<xref rid=\"B26\" ref-type=\"bibr\">26</xref>). Here, we observed clear cellular responses in two CHH patients after the first vaccine dose despite no development of antibodies to VZV. This may be explained by delayed antibody production consistent with combined immunodeficiency in CHH. The activation of cellular response against VZV in CHH patients suggests that these individuals may have sufficient immune protection against VZV despite the decreased humoral response. Our data support the recommendation of two doses of VZV vaccine, not single dose, to ensure optimal immune response of both, humoral and cellular, arms of the immune system.</p><p>Although there were no reported long-term consequences of live vaccines in our cohort, clinicians and patients should be aware of the risk of delayed development of vaccine-strain rubella virus-associated skin granulomas in patients with CHH (<xref rid=\"B27\" ref-type=\"bibr\">27</xref>). In addition, there were no reactivation of vaccine-strain VZV in our patients, possibly reflecting the effective cellular immunity against VZV. However, such a reactivation, occurring even years after primary immunization, and accompanied by central nervous system infection, has been described, both in immunocompetent and immunocompromised adolescents (<xref rid=\"B28\" ref-type=\"bibr\">28</xref>).</p><p>Laboratory parameters do not correlate with clinical severity or prognosis in CHH (<xref rid=\"B21\" ref-type=\"bibr\">21</xref>), and our study reports successful immunization of lymphopenic patients, therefore mild lymphopenia itself should not be an absolute contraindication for live vaccine administration in CHH. Clinicians can utilize laboratory criteria used in the management of patients with human immunodeficiency virus infection or patients with partial DiGeorge syndrome to make decisions on immunization in patients with CHH. Importantly, all vaccinated patients in our clinical trial demonstrated normal lymphocyte proliferation responses to mitogens prior to immunization. Immunization may be considered in clinically asymptomatic patients with CHH or those with mild recurrent respiratory tract infections and no other clinical features of immunodeficiency. However, individuals considered for the immunization should be selected cautiously and the qualitative assessment of lymphocyte subsets and function should be performed prior to immunization. Even though several patients with clinical features of combined immunodeficiency have been immunized with MMR successfully, we advise against live vaccine administration to these patients.</p><p>The strengths of our study are (1) the large number of patients for whom clinical and laboratory data were available, (2) written evidence of immunization in the majority of the study participants, (3) long-term follow-up data and assessment of serologic response to immunization in the long-term, as well as (4) assessment of both, humoral and cellular immune responses in a clinical trial. The drawbacks of our clinical trial include small sample size and relatively short follow-up time, calling for further studies with longer follow-up to evaluate the efficacy and long-term safety of VZV vaccine.</p><p>In conclusion, we provide retrospective and prospective data on the safety and efficacy of live viral vaccines in Finnish patients with CHH. Immunization with live vaccines should be considered in selected CHH patients with clinically mild immunodeficiency.</p></sec><sec sec-type=\"data-availability\" id=\"S5\"><title>Data Availability Statement</title><p>All datasets presented in this study are included in the article/supplementary material.</p></sec><sec id=\"S6\"><title>Ethics Statement</title><p>The studies involving human participants were reviewed and approved by Institutional Ethics Committee at the Helsinki University Hospital and University of Helsinki, Finland. Written informed consent to participate in this study was provided by the participants’ legal guardian/next of kin.</p></sec><sec id=\"S7\"><title>Author Contributions</title><p>SV, PK, and OM planned the study. SV, PK, LK, and MG carried out a clinical trial. JH and HS prepared PBMC for further analysis. EK and IK planned and performed the elispot analysis. SV drafted the manuscript. All authors contributed to the writing and critical review of the manuscript and approved the final version.</p></sec><sec id=\"conf1\"><title>Conflict of Interest</title><p>The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p></sec></body><back><fn-group><fn fn-type=\"financial-disclosure\"><p><bold>Funding.</bold> This work was supported by the Sigrid Jusélius Foundation to OM, the Academy of Finland to OM, the Folkhälsan Research Foundation to OM, the Novo Nordisk Foundation to OM, the Helsinki University Hospital Research Funds to OM, the Swedish Childhood Cancer Foundation to OM, the Foundation for Pediatric Research to OM, and the Doctoral School in Health Sciences at the University of Helsinki to SV.</p></fn></fn-group><ack><p>We thank laboratory assistants Maria Kiikeri and Sinikka Tsupari for their help with PBMC separation. 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gamma</p></def></def-item><def-item><term>MMR</term><def><p>measles, mumps, rubella</p></def></def-item><def-item><term>PBMC</term><def><p>peripheral blood mononuclear cells</p></def></def-item><def-item><term>VZV</term><def><p>varicella oster virus.</p></def></def-item></def-list></glossary></back></article>\n"
]
[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">JMIR Ment Health</journal-id><journal-id journal-id-type=\"publisher-id\">JMH</journal-id><journal-title-group><journal-title>JMIR Mental Health</journal-title></journal-title-group><issn pub-type=\"epub\">2368-7959</issn><publisher><publisher-name>JMIR Publications</publisher-name><publisher-loc>Toronto, Canada</publisher-loc></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32559173</article-id><article-id pub-id-type=\"pmc\">PMC7432141</article-id><article-id pub-id-type=\"publisher-id\">v7i8e19778</article-id><article-id pub-id-type=\"doi\">10.2196/19778</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Personal Perspective</subject></subj-group><subj-group subj-group-type=\"article-type\"><subject>Personal Perspective</subject></subj-group></article-categories><title-group><article-title>Patient Innovation in Investigating the Effects of Environmental Pollution in Schizophrenia: Case Report of Digital Phenotyping Beyond Apps</article-title></title-group><contrib-group><contrib contrib-type=\"editor\"><name><surname>Fulford</surname><given-names>Daniel</given-names></name></contrib></contrib-group><contrib-group><contrib contrib-type=\"reviewer\"><name><surname>Campellone</surname><given-names>Tim</given-names></name></contrib><contrib contrib-type=\"reviewer\"><name><surname>Mote</surname><given-names>Jasmine</given-names></name></contrib></contrib-group><contrib-group><contrib id=\"contrib1\" contrib-type=\"author\"><name><surname>Vaidyam</surname><given-names>Aditya</given-names></name><degrees>MS</degrees><xref ref-type=\"aff\" rid=\"aff1\">1</xref><contrib-id contrib-id-type=\"orcid\">https://orcid.org/0000-0002-2900-4561</contrib-id></contrib><contrib id=\"contrib2\" contrib-type=\"author\"><name><surname>Roux</surname><given-names>Spencer</given-names></name><degrees>MS</degrees><xref ref-type=\"aff\" rid=\"aff1\">1</xref><contrib-id contrib-id-type=\"orcid\">https://orcid.org/0000-0001-5374-6859</contrib-id></contrib><contrib id=\"contrib3\" contrib-type=\"author\" corresp=\"yes\"><name><surname>Torous</surname><given-names>John</given-names></name><degrees>MD</degrees><contrib-id contrib-id-type=\"orcid\">https://orcid.org/0000-0002-5362-7937</contrib-id><xref ref-type=\"aff\" rid=\"aff1\">1</xref><address><institution>Division of Digital Psychiatry</institution><institution>Beth Isreal Deaconess Medical Center</institution><institution>Harvard Medical School</institution><addr-line>330 Brookline Avenue</addr-line><addr-line>Boston, MA, 02215</addr-line><country>United States</country><phone>1 7143359858</phone><email>[email protected]</email></address></contrib></contrib-group><aff id=\"aff1\">\n<label>1</label>\n<institution>Division of Digital Psychiatry</institution>\n<institution>Beth Isreal Deaconess Medical Center</institution>\n<institution>Harvard Medical School</institution>\n<addr-line>Boston, MA</addr-line>\n<country>United States</country>\n</aff><author-notes><corresp>Corresponding Author: John Torous <email>[email protected]</email></corresp></author-notes><pub-date pub-type=\"collection\"><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"epub\"><day>3</day><month>8</month><year>2020</year></pub-date><volume>7</volume><issue>8</issue><elocation-id>e19778</elocation-id><history><date date-type=\"received\"><day>30</day><month>4</month><year>2020</year></date><date date-type=\"rev-request\"><day>21</day><month>5</month><year>2020</year></date><date date-type=\"rev-recd\"><day>17</day><month>6</month><year>2020</year></date><date date-type=\"accepted\"><day>18</day><month>6</month><year>2020</year></date></history><permissions><copyright-statement>©Aditya Vaidyam, Spencer Roux, John Torous. Originally published in JMIR Mental Health (http://mental.jmir.org), 03.08.2020.</copyright-statement><copyright-year>2020</copyright-year><license license-type=\"open-access\" xlink:href=\"https://creativecommons.org/licenses/by/4.0/\"><license-p><!--CREATIVE COMMONS-->This is an open-access article distributed under the terms of the Creative Commons Attribution License (<ext-link ext-link-type=\"uri\" xlink:href=\"https://creativecommons.org/licenses/by/4.0/\">https://creativecommons.org/licenses/by/4.0/</ext-link>), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Mental Health, is properly cited. The complete bibliographic information, a link to the original publication on <ext-link ext-link-type=\"uri\" xlink:href=\"http://mental.jmir.org/\">http://mental.jmir.org/</ext-link>, as well as this copyright and license information must be included.</license-p></license></permissions><self-uri xlink:type=\"simple\" xlink:href=\"https://mental.jmir.org/2020/8/e19778\"/><abstract><p>This patient perspective highlights the role of patients in the innovation and codesign of digital mental health technology. Though digital mental health apps have evolved and become highly functional, many still act as data collection silos without adequate support for patients to understand and investigate potentially meaningful inferences in their own data. Few digital health platforms respect the patient’s agency and curiosity, allowing the individual to wear the hat of researcher and data scientist and share their experiences and insight with their clinicians. This case is cowritten with an individual with lived experiences of schizophrenia who has decided to openly share their name and experiences to share with others the methods and results of their curiosity and encourage and inspire others to follow their curiosity as well.</p></abstract><kwd-group><kwd>digital mental health</kwd><kwd>mHealth</kwd><kwd>apps</kwd><kwd>serious mental illness</kwd><kwd>schizophrenia</kwd><kwd>psychiatry</kwd><kwd>digital phenotyping</kwd></kwd-group></article-meta></front><body><sec sec-type=\"introduction\"><title>Introduction</title><p>Digital health software, including apps, are designed to serve people and help improve clinical outcomes. Although apps have become more functional and able to capture both surveys and sensor information (ie, digital phenotyping [<xref rid=\"ref1\" ref-type=\"bibr\">1</xref>]), technical limitations in smartphone software and hardware limit potential data capture. Highlighting the potential of health software beyond smartphones (apps), this case report demonstrates the potential for such software to transition between different devices and hardware toward better serving people. Building off a prior case report where an individual started with an app to track symptoms but found a tally counter more useful [<xref rid=\"ref2\" ref-type=\"bibr\">2</xref>], here we discuss a case where an app was used at first, followed by a transition to a novel device that proved to be a more comprehensive solution.</p></sec><sec><title>Brief Case</title><p>Spencer Roux (henceforth referred to as SR) is a male in his thirties who was diagnosed with schizophrenia in 2013. His symptoms are currently well managed with exercise, lifestyle interventions, and medications. Seeking to constantly improve his condition and curious about the impact of various environmental aspects on his mental health, SR sought to quantify such environmental aspects. SR also considered tracking noise level, temperature, and ambient lighting, the latter serving as a proxy for sunlight.</p><p>Mental health concerns related to environmental variations, including global warming, climate change, and pollution are rising while pollution itself is a known trigger for brain inflammation [<xref rid=\"ref3\" ref-type=\"bibr\">3</xref>-<xref rid=\"ref5\" ref-type=\"bibr\">5</xref>], which may be related to the onset and exacerbation of numerous illnesses. The strongest available evidence is for the relationship between pollution and depression [<xref rid=\"ref6\" ref-type=\"bibr\">6</xref>] but emerging evidence indicates that ambient pollution could be associated with hospitalization in illnesses such as schizophrenia [<xref rid=\"ref7\" ref-type=\"bibr\">7</xref>,<xref rid=\"ref8\" ref-type=\"bibr\">8</xref>]. However, tracking environmental conditions such as pollution is not possible today through the use of common smartphone apps, as no major smartphone devices support indoor precision air quality sensors. Although it is possible to purchase such sensors individually, there is no off-the-shelf indoor precision air quality sensor that also tracks mental health available on the market today.</p><p>Though he had no prior knowledge of this literature, SR wanted to assess the relationship between air quality and the number of auditory hallucinations he experienced day-to-day. As his symptoms are well controlled, he believed this was a practical time to learn more about environmental factors that may be related to his symptoms. Previously, SR used a tally counter device to identify a relationship between his auditory hallucination count and the number of times he responded to his hallucinations [<xref rid=\"ref2\" ref-type=\"bibr\">2</xref>], and here sought to capture new data. As with the tally counter, he wanted to track his symptoms in a manner that was inconspicuous and easy to engage with.</p><p>The Nordic Thingy prototyping platform (<xref rid=\"figure1\" ref-type=\"fig\">Figure 1</xref>; Nordic Semiconductor) was temporarily provided to SR at no cost as it would be suitable for his needs and is compatible with the LAMP platform, which enables visualization and statistical inference. The LAMP platform has been used in prior clinical cases for similar purposes [<xref rid=\"ref9\" ref-type=\"bibr\">9</xref>], using only sensors available on commercial smartphone devices. The Nordic Thingy prototyping platform has numerous sensors not available in a smartphone that are able to achieve a high fidelity of measurement recording. The device was preloaded with a long-term evolution (LTE) data plan. The sensors include the following: LTE, Bluetooth, Wi-Fi, near-field communication (NFC), button, temperature, humidity, air quality, air pressure, ambient color, ambient light, high-g accelerometer, low-power accelerometer, global position system (GPS), digital microphone, radio frequency (RF) antenna, barometer, altitude, 9-axis motion tracker. It also has a multicolor light emitting diode (LED) and digital buzzer (a low-quality speaker) for responding to the user if necessary.</p><fig id=\"figure1\" position=\"float\"><label>Figure 1</label><caption><p>The size of a Nordic Thingy shown in comparison to a smartphone device. The primary input method is the surface containing the Nordic Semiconductors logo, which acts as a pressure-insensitive button pad.</p></caption><graphic xlink:href=\"mental_v7i8e19778_fig1\"/></fig><p>Due to its literal “black box” nature, it was not distracting to use, and was convenient and easy to manage. Noting its primary purpose as a prototyping system, it was purchased from a Nordic Semiconductors distributor for US $120. As the Nordic Thingy can capture data but does not offer data management, integration, or visualization, the LAMP platform was then installed to enable digital mental health–related functionality [<xref rid=\"ref10\" ref-type=\"bibr\">10</xref>,<xref rid=\"ref11\" ref-type=\"bibr\">11</xref>]. The Nordic Thingy was charged roughly alongside SR's smartphone and captured or measured the environment of SR's home throughout the day, for most of which SR was present. The button press was used to record auditory hallucinations, similar to the tally counter SR had used previously.</p><p>SR independently collected over 200,000 data points via the Nordic Thingy in the span of seven days, producing about 65 MB of uncompressed data. The humidity, temperature, air pressure, and air quality data appeared as sensor data in the LAMP platform, and the button presses appeared as active data in the form of responses to a survey with a single yes or no question. With the assistance of the LAMP programming interfaces, the data were plotted and are shown in <xref rid=\"figure2\" ref-type=\"fig\">Figure 2</xref>.</p><p>Though his findings showed no significant relationship between indoor air quality and auditory hallucinations (McNemar chi-squared test with continuity correction = 3179.4, <italic>df</italic>=1, <italic>P</italic><.001), SR was able to devise a hypothesis, perform an experiment, analyze the data, and produce a tangible outcome within the span of 1 week. The installation of the LAMP platform on a non–smartphone device proved useful in yielding high-quality data suitable for actionable digital phenotyping as well as data visualization.</p><fig id=\"figure2\" position=\"float\"><label>Figure 2</label><caption><p>SR's experimental data. The red lines indicate a button press, the blue line indicates indoor air quality, and the purple line indicates the threshold for indoor air quality >100; higher values indicate poorer air quality.</p></caption><graphic xlink:href=\"mental_v7i8e19778_fig2\"/></fig></sec><sec><title>Brief Discussion</title><p>As a single case report, SR's example highlights the potential for innovation in and codesign of digital mental health technology by those with lived experiences of serious mental illness. As technology and sensors evolve, it is possible to collect novel data streams like pollution on a highly personalized scale. Understanding the role of digital mental health technology beyond just apps and ensuring it is flexible enough to move between devices and contexts ensures that both patients and clinicians can take full advantage of these advances in technology. The design and evolution of digital mental health tools and platforms will likely rapidly evolve toward devices similar to the one used in this case report, and must involve individuals such as SR, whose lived experiences offer insight into both the problems and solutions encountered when building and using such systems.</p><p>As novel sensors like those used in this case report continue to add new accessible data, it is also critical for the field to understand their clinical uses, personal meaning, and therapeutic targets. Although it is easy to use such data in machine learning algorithms or artificial intelligence systems, starting with patient-centric clinical use cases can minimize bias, increase utility, and accelerate clinical translation. From an experimental methods standpoint, it is also important to consider privacy, safety, and ethics when developing individual study protocols, ensuring that all parties involved obtain recorded consent and respect the wishes of other parties. Furthermore, as seen in this case example, there was actually no need for advanced analytical methods beyond simple visualization and a statistical test, highlighting how the right data used in the right clinical case can itself be definitive.</p><p>As SR's case yet again shows, a smartphone app may not always be the solution. Although smartphones and wearables are considered highly accessible and usable devices that are practically always within reach, the potential of other digital hardware should not be ignored. The software can be the same, as demonstrated by the use of the LAMP platform on a non–smartphone device. Focusing less on the device and more on how the software serves unique clinical purposes offers a useful reminder toward focusing on fluid and interoperable technology that is not tied to any one platform or device. SR's case shows the merits of using non–smartphone devices for their specialized sensors and unobtrusive behavior.</p><p>Through a unique experimental journey, SR's case presents a clear message to other curious and innovative individuals with lived experiences, as well as creators and implementors of digital mental health technologies. Clinicians may also choose to actively take part or passively encourage innovation, lending clinical context and insight in either case. Through creativity, innovation, and in small part the accessibility and actionability of data, individuals given the right tools can understand and impact their own mental health.</p></sec></body><back><fn-group><fn fn-type=\"COI-statement\"><p>Conflicts of Interest: JT reports unrelated research support for Otuska. 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[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">JMIR Med Inform</journal-id><journal-id journal-id-type=\"publisher-id\">JMI</journal-id><journal-title-group><journal-title>JMIR Medical Informatics</journal-title></journal-title-group><issn pub-type=\"epub\">2291-9694</issn><publisher><publisher-name>JMIR Publications</publisher-name><publisher-loc>Toronto, Canada</publisher-loc></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32744510</article-id><article-id pub-id-type=\"pmc\">PMC7432142</article-id><article-id pub-id-type=\"publisher-id\">v8i8e20071</article-id><article-id pub-id-type=\"doi\">10.2196/20071</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Original Paper</subject></subj-group><subj-group subj-group-type=\"article-type\"><subject>Original Paper</subject></subj-group></article-categories><title-group><article-title>Improving Diagnostic Classification of Stillbirths and Neonatal Deaths Using ICD-PM (International Classification of Diseases for Perinatal Mortality) Codes: Validation Study</article-title></title-group><contrib-group><contrib contrib-type=\"editor\"><name><surname>Eysenbach</surname><given-names>Gunther</given-names></name></contrib></contrib-group><contrib-group><contrib contrib-type=\"reviewer\"><name><surname>Della Mea</surname><given-names>Vincenzo</given-names></name></contrib></contrib-group><contrib-group><contrib id=\"contrib1\" contrib-type=\"author\"><name><surname>Luk</surname><given-names>Hiu Mei</given-names></name><degrees>MD, MMed</degrees><xref ref-type=\"aff\" rid=\"aff1\">1</xref><contrib-id contrib-id-type=\"orcid\">https://orcid.org/0000-0001-8912-5552</contrib-id></contrib><contrib id=\"contrib2\" contrib-type=\"author\"><name><surname>Allanson</surname><given-names>Emma</given-names></name><degrees>PhD, FRANZCOG</degrees><xref ref-type=\"aff\" rid=\"aff2\">2</xref><contrib-id contrib-id-type=\"orcid\">https://orcid.org/0000-0002-4786-4354</contrib-id></contrib><contrib id=\"contrib3\" contrib-type=\"author\"><name><surname>Ming</surname><given-names>Wai-Kit</given-names></name><degrees>MPH, MD, PhD</degrees><xref ref-type=\"aff\" rid=\"aff3\">3</xref><contrib-id contrib-id-type=\"orcid\">https://orcid.org/0000-0002-8846-7515</contrib-id></contrib><contrib id=\"contrib4\" contrib-type=\"author\" corresp=\"yes\"><name><surname>Leung</surname><given-names>Wing Cheong</given-names></name><degrees>MD, FRCOG</degrees><contrib-id contrib-id-type=\"orcid\">https://orcid.org/0000-0003-1851-2138</contrib-id><xref ref-type=\"aff\" rid=\"aff1\">1</xref><address><institution>Department of Obstetrics and Gynaecology</institution><institution>Kwong Wah Hospital</institution><addr-line>Hong Kong SAR</addr-line><country>China (Hong Kong)</country><phone>852 35175091</phone><email>[email protected]</email></address></contrib></contrib-group><aff id=\"aff1\">\n<label>1</label>\n<institution>Department of Obstetrics and Gynaecology</institution>\n<institution>Kwong Wah Hospital</institution>\n<addr-line>Hong Kong SAR</addr-line>\n<country>China (Hong Kong)</country>\n</aff><aff id=\"aff2\">\n<label>2</label>\n<institution>Institute of Health Research</institution>\n<institution>University of Notre Dame, Fremantle</institution>\n<addr-line>Western Australia</addr-line>\n<country>Australia</country>\n</aff><aff id=\"aff3\">\n<label>3</label>\n<institution>Department of Public Health and Preventive Medicine</institution>\n<institution>School of Medicine</institution>\n<institution>Jinan University</institution>\n<addr-line>Guangzhou</addr-line>\n<country>China</country>\n</aff><author-notes><corresp>Corresponding Author: Wing Cheong Leung <email>[email protected]</email></corresp></author-notes><pub-date pub-type=\"collection\"><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"epub\"><day>3</day><month>8</month><year>2020</year></pub-date><volume>8</volume><issue>8</issue><elocation-id>e20071</elocation-id><history><date date-type=\"received\"><day>11</day><month>5</month><year>2020</year></date><date date-type=\"rev-request\"><day>4</day><month>6</month><year>2020</year></date><date date-type=\"rev-recd\"><day>8</day><month>6</month><year>2020</year></date><date date-type=\"accepted\"><day>25</day><month>6</month><year>2020</year></date></history><permissions><copyright-statement>©Hiu Mei Luk, Emma Allanson, Wai-Kit Ming, Wing Cheong Leung. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 03.08.2020.</copyright-statement><copyright-year>2020</copyright-year><license license-type=\"open-access\" xlink:href=\"https://creativecommons.org/licenses/by/4.0/\"><license-p><!--CREATIVE COMMONS-->This is an open-access article distributed under the terms of the Creative Commons Attribution License (<ext-link ext-link-type=\"uri\" xlink:href=\"https://creativecommons.org/licenses/by/4.0/\">https://creativecommons.org/licenses/by/4.0/</ext-link>), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Medical Informatics, is properly cited. The complete bibliographic information, a link to the original publication on <ext-link ext-link-type=\"uri\" xlink:href=\"http://medinform.jmir.org/\">http://medinform.jmir.org/</ext-link>, as well as this copyright and license information must be included.</license-p></license></permissions><self-uri xlink:type=\"simple\" xlink:href=\"https://medinform.jmir.org/2020/8/e20071\"/><abstract><sec sec-type=\"background\"><title>Background</title><p>Stillbirths and neonatal deaths have long been imperfectly classified and recorded worldwide. In Hong Kong, the current code system is deficient (>90% cases with unknown causes) in providing the diagnoses of perinatal mortality cases.</p></sec><sec sec-type=\"objective\"><title>Objective</title><p>The objective of this study was to apply the International Classification of Diseases for Perinatal Mortality (ICD-PM) system to existing perinatal death data. Further, the aim was to assess whether there was any change in the classifications of perinatal deaths compared with the existing classification system and identify any areas in which future interventions can be made.</p></sec><sec sec-type=\"methods\"><title>Methods</title><p>We applied the ICD-PM (with International Statistical Classification of Diseases and Related Health Problems, 10th Revision) code system to existing perinatal death data in Kwong Wah Hospital, Hong Kong, to improve diagnostic classification. The study included stillbirths (after 24 weeks gestation) and neonatal deaths (from birth to 28 days). The retrospective data (5 years) from May 1, 2012, to April 30, 2017, were recoded by the principal investigator (HML) applying the ICD-PM, then validated by an overseas expert (EA) after she reviewed the detailed case summaries. The prospective application of ICD-PM from May 1, 2017, to April 30, 2019, was performed during the monthly multidisciplinary perinatal meetings and then also validated by EA for agreement.</p></sec><sec sec-type=\"results\"><title>Results</title><p>We analyzed the data of 34,920 deliveries, and 119 cases were included for analysis (92 stillbirths and 27 neonatal deaths). The overall agreement with EA of our codes using the ICD-PM was 93.2% (111/119); 92% (78/85) for the 5 years of retrospective codes and 97% (33/34) for the 2 years of prospective codes (<italic>P</italic>=.44). After the application of the ICD-PM, the overall proportion of unknown causes of perinatal mortality dropped from 34.5% (41/119) to 10.1% (12/119) of cases (<italic>P</italic><.001).</p></sec><sec sec-type=\"conclusions\"><title>Conclusions</title><p>Using the ICD-PM would lead to a better classification of perinatal deaths, reduce the proportion of unknown diagnoses, and clearly link the maternal conditions with these perinatal deaths.</p></sec></abstract><kwd-group><kwd>stillbirths</kwd><kwd>perinatal deaths</kwd><kwd>neonatal deaths</kwd><kwd>ICD-PM</kwd><kwd>ICD-10</kwd></kwd-group></article-meta></front><body><sec sec-type=\"introduction\"><title>Introduction</title><sec><title>Background</title><p>More than 5 million perinatal deaths occur globally each year, and this largely silent epidemic significantly impacts and burdens families [<xref rid=\"ref1\" ref-type=\"bibr\">1</xref>,<xref rid=\"ref2\" ref-type=\"bibr\">2</xref>]. Despite this, perinatal deaths have long been frequently invisible and poorly recorded worldwide.</p><p>A large proportion of stillbirth and neonatal death cases take place in less developed countries [<xref rid=\"ref3\" ref-type=\"bibr\">3</xref>]. Classification methods used in these countries can be obscure. The numbers of skilled medical personnel are often inadequate, which contributes to less than comprehensive recording of clinical data at the time of stillbirth and neonatal deaths [<xref rid=\"ref4\" ref-type=\"bibr\">4</xref>,<xref rid=\"ref5\" ref-type=\"bibr\">5</xref>]. In contrast, stillbirth affects proportionally fewer births in high-income countries, and data related to these deaths tend to be comprehensively recorded [<xref rid=\"ref6\" ref-type=\"bibr\">6</xref>]. Many high-income countries have developed their own classification systems for perinatal deaths; for example, the United Kingdom uses the Codac system [<xref rid=\"ref7\" ref-type=\"bibr\">7</xref>], Sweden has the Stockholm system [<xref rid=\"ref8\" ref-type=\"bibr\">8</xref>], Australia and New Zealand use the Perinatal Society of Australia and New Zealand perinatal death classification system [<xref rid=\"ref9\" ref-type=\"bibr\">9</xref>], and the Netherlands uses the Tulip classification system [<xref rid=\"ref10\" ref-type=\"bibr\">10</xref>]. In the United States, the Stillbirth Collaborative Research Network developed the initial causes of fetal death system [<xref rid=\"ref11\" ref-type=\"bibr\">11</xref>]. While we see an annual reduction in the rate in stillbirth globally [<xref rid=\"ref12\" ref-type=\"bibr\">12</xref>], this trend differs widely among high- and low- income countries. An internationally recognized classification system of perinatal mortality would be invaluable so that the data could easily be compared between different countries to facilitate classification and research in driving programs to reduce the overall perinatal mortality.</p><p>Based on the <italic>International Statistical Classification of Diseases and Related Health Problems, 10th Revision</italic> (ICD-10), the International Classification of Diseases for Perinatal Mortality (ICD-PM) was released by the World Health Organization in August 2016 [<xref rid=\"ref13\" ref-type=\"bibr\">13</xref>]. It is the first perinatal death classification system developed to be used worldwide. This system links the cause of perinatal death, using ICD-10 codes [<xref rid=\"ref14\" ref-type=\"bibr\">14</xref>], separated by timing of death, with the maternal conditions that contribute to perinatal death.</p><p>Currently, in Hong Kong under the Hospital Authority, the coding system (for stillbirths only) used [<xref rid=\"ref15\" ref-type=\"bibr\">15</xref>] is a direct one (ie, if the cause of the stillbirth could be identified, it would be stated directly such as congenital abnormalities, pregnancy-induced hypertension, cord accident, antepartum hemorrhage, maternal disease). The remaining cases would be placed in the categories unclassified, unexplained, and miscellaneous/uninvestigated, which essentially refer to unknown causes. The problem is that up to 92.8% (800/862) of stillbirths from 2012 to 2018 were classified under these 3 categories [<xref rid=\"ref15\" ref-type=\"bibr\">15</xref>]. This phenomenon has significantly impaired the potential of using this perinatal database to further study and design programs to reduce perinatal mortality.</p><p>We hypothesized that by using a globally applicable classification system such as ICD-PM that recognizes stillbirths and neonatal deaths together with the contributing maternal conditions, coding of stillbirth and neonatal death could be significantly improved. Therefore, a validation study was performed to apply the ICD-PM coding system to our stillbirths and neonatal death cases.</p></sec><sec><title>Outcome</title><p>The primary outcome was the reduction in unknown causes for stillbirth and neonatal death after applying the ICD-PM coding system. The secondary outcome was the percentage agreement with expert (EA) in applying ICD-PM codes.</p></sec></sec><sec sec-type=\"methods\"><title>Methods</title><sec><title>Recruitment</title><p>The ICD-PM system, the World Health Organization application of the ICD-10 to deaths during the perinatal period, was applied to existing perinatal death data from May 1, 2012, to April 30, 2019, in Kwong Wah Hospital, a regional public hospital with 34,920 deliveries during the 7-year study period.</p></sec><sec><title>Selection Criteria</title><p>Inclusion criteria:</p><list list-type=\"bullet\"><list-item><p>Stillbirth cases diagnosed after 24 completed weeks gestation</p></list-item><list-item><p>Neonatal death cases within 28 days of birth</p></list-item></list><p>Exclusion criteria:</p><list list-type=\"bullet\"><list-item><p>Miscarriage at less than 24 completed weeks of gestation</p></list-item><list-item><p>Termination of pregnancy due to fetal anomalies or maternal abnormal medical conditions</p></list-item></list></sec><sec><title>Ethics</title><p>Ethics approval for the study was granted by the Kowloon Central/Kowloon East Research Ethics Committee (KC/KE-19-0193/ER-1). As this clinical study did not involve active patient participation, no patient consent was required.</p></sec><sec><title>Diagnostic Classification</title><p>In our department, every case of stillbirth and neonatal death is discussed in our monthly perinatal meetings attended by consultants, specialists, trainees, and senior labor ward midwives and senior nurses from the departments of obstetrics and gynecology and pediatrics. A detailed case summary is presented by the resident trainee directly involved in the clinical management of that particular case. Diagnosis of the cause of stillbirth and neonatal death is based on the clinical findings and investigation results.</p><p>We performed routine investigations for stillbirths, including:</p><list list-type=\"bullet\"><list-item><p>Maternal: complete blood counts; liver and renal function tests; urate; clotting profile; thyroid function test; lupus anticoagulant; anticardiolipin antibodies; antinuclear antibodies +/– anti-ds DNA; rheumatoid factor; Kleihauer test; toxoplasmosis, rubella, cytomegalovirus, and herpes simplex virus tests; oral glucose tolerance test; hemoglobin A1c; high vaginal swab; midstream urine for culture; and others.</p></list-item><list-item><p>Placenta: swabs for culture, histopathology, karyotyping (if the stillbirth showed dysmorphic features)</p></list-item><list-item><p>Stillbirth: body surface swabs for culture, autopsy (with consent from parents)</p></list-item></list></sec><sec><title>Data Validation</title><p>Our validation study started in 2017 and was divided into two parts.</p><sec><title>Retrospective Validation Study (Five Years)</title><p>The recoding of the retrospective stillbirth and neonatal death data from May 1, 2012, to April 30, 2017, was performed by the principal investigator (HML) using the ICD-10 [<xref rid=\"ref14\" ref-type=\"bibr\">14</xref>] to get a specific ICD-10 code that was then converted to the ICD-PM categories [<xref rid=\"ref13\" ref-type=\"bibr\">13</xref>].</p><p>Our ICD-10 and ICD-PM codes together with the detailed case summaries for each stillbirth and neonatal death case were then forwarded to an overseas expert (EA; via emails without patient identity) for validation of our codes based on the detailed case summaries, further discussion, and verification. EA has extensive experience using the ICD-PM [<xref rid=\"ref1\" ref-type=\"bibr\">1</xref>-<xref rid=\"ref4\" ref-type=\"bibr\">4</xref>]. The proportion of ICD-PM codes EA disagreed with was noted.</p></sec><sec><title>Prospective Validation Study (Two Years)</title><p>The prospective application of ICD-10 and then ICD-PM was performed from May 1, 2017, to April 30, 2019. The coding for each stillbirth and neonatal death case was validated during our monthly perinatal meetings.</p><p>Our ICD-PM codes together with the detailed case summaries in this prospective case series were also forwarded to the overseas expert (EA) to be checked for agreement.</p></sec></sec><sec><title>Statistical Analysis</title><p>All frequency data were analyzed by summary statistics. SPSS Statistics version 25.0 (IBM Corporation) was used for the analysis. The Pearson chi-square test was used where appropriate, such as to determine whether there was a significant difference between the frequency of unknown causes of death classified in our original Hospital Authority coding system and the newly applied ICD-PM coding system. <italic>P</italic><.05 was considered statistically significant.</p></sec></sec><sec sec-type=\"results\"><title>Results</title><p>We analyzed data for 34,920 deliveries, and 119 cases were included for analysis (92 stillbirths and 27 neonatal deaths):</p><list list-type=\"bullet\"><list-item><p>Retrospective ICD-PM codes (5 years; May 1, 2012, to April 30, 2017)—total 85 cases</p></list-item><list-item><p>Prospective ICD-PM codes (2 years; May 1, 2017, to April 30, 2019)—total 34 cases</p></list-item></list><p>All stillbirth cases had the full set of routine investigations described under Methods. However, only 25.2% (30/119) of cases had a postmortem examination.</p><p>EA verified every single one of the 119 stillbirth and neonatal death cases during the study period. The overall agreement rate of our codes using ICD-10 and then ICD-PM was 93.2% (111/119 cases) with EA: 92% (78/85 cases) for the 5 years of retrospective cases and 97% (33/34 cases) for the 2 years of prospective cases (<italic>P</italic>=.44). It was interesting and educational to look at how EA disagreed with our codes (<xref rid=\"table1\" ref-type=\"table\">Table 1</xref>).</p><p><xref rid=\"table2\" ref-type=\"table\">Table 2</xref> illustrates the application of the ICD-PM for perinatal death and maternal condition in stillbirth and neonatal death cases, respectively. In the ICD-PM, there are 6 groups of antepartum causes for stillbirth (A1 to A6), 7 groups of intrapartum causes for stillbirth (I1 to I7), 11 groups of causes for neonatal death (N1 to N11), and 5 groups of maternal conditions (M1 to M5) to be associated with stillbirth or neonatal death.</p><p>The most common causes for antepartum stillbirths were A3 (antepartum hypoxia, 24/91, 26%), followed by A5 (disorders related to fetal growth, 17/91, 19%), and A1 (congenital malformations, deformations, and chromosomal abnormalities, 10/91, 11%). In this case series, there was only one intrapartum stillbirth (I1). The most common corresponding maternal conditions were M1 (complications of placenta, cord, and membranes, 39/91, 43%), followed by M4 (maternal medical and surgical conditions, 21/91, 23%), and M2 (maternal complications of pregnancy, 12/91, 13%). The most common associations were A3;M1 (20/91, 22%), A6;M5 (12/91, 13%), and A6;M4 (9/91, 10%).</p><p>On the other hand, the most common causes for neonatal deaths were N9 (low birth weight and prematurity, 9/27, 33%), followed by N8 (neonatal conditions, 5/27, 19%), N6 (infection, 4/27, 15%), and N1 (congenital malformations, deformations and chromosomal abnormalities, also 4/27, 15%). The most common corresponding maternal conditions were M1 (complications of placenta, cord, and membranes, 10/27, 37%), followed by M2 (maternal complications of pregnancy, 6/27, 22%) and M4 (maternal medical and surgical conditions, 5/27, 19%). The most common associations were N9;M1 (3/27, 11%), N9;M4 (3/27, 11%), and N6; M1 (3/27, 11%).</p><table-wrap id=\"table1\" position=\"float\"><label>Table 1</label><caption><p>The 8 cases in which the overseas expert (EA) disagreed with our codes.</p></caption><table frame=\"hsides\" rules=\"groups\" width=\"1000\" cellpadding=\"5\" cellspacing=\"0\" border=\"1\"><col width=\"70\" span=\"1\"/><col width=\"220\" span=\"1\"/><col width=\"100\" span=\"1\"/><col width=\"90\" span=\"1\"/><col width=\"520\" span=\"1\"/><thead><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">Case number</td><td rowspan=\"1\" colspan=\"1\">Our original codes</td><td rowspan=\"1\" colspan=\"1\">Our ICD-PM<sup>a</sup> codes</td><td rowspan=\"1\" colspan=\"1\">EA’s codes</td><td rowspan=\"1\" colspan=\"1\">Comment</td></tr></thead><tbody><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">1</td><td rowspan=\"1\" colspan=\"1\">Unknown</td><td rowspan=\"1\" colspan=\"1\">A6; M1</td><td rowspan=\"1\" colspan=\"1\">A6; M5</td><td rowspan=\"1\" colspan=\"1\">EA: wonder if the placental pathology showing chorioamnionitis (M1) is related to the stillbirth in the absence of other evidence</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">9</td><td rowspan=\"1\" colspan=\"1\">Preeclampsia with placenta abruptio</td><td rowspan=\"1\" colspan=\"1\">A3; M2</td><td rowspan=\"1\" colspan=\"1\">A3; M4</td><td rowspan=\"1\" colspan=\"1\">EA: would classify this as M4 (PET<sup>b</sup>) as the fetus died before the mother, likely as a result of the PET, rather than the maternal death (M2) being the cause of the fetal death</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">11</td><td rowspan=\"1\" colspan=\"1\">Twin-twin transfusion syndrome (TTTS<sup>c</sup>), postmortem–hypoplastic adrenals</td><td rowspan=\"1\" colspan=\"1\">A1, M1</td><td rowspan=\"1\" colspan=\"1\">A1, M5</td><td rowspan=\"1\" colspan=\"1\">EA: Do you think the hypoplastic adrenals (A1) was the cause of death? Or hypoxia as a result of the TTTS (M1) with the hypoplastic adrenals being a secondary issue?</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">19</td><td rowspan=\"1\" colspan=\"1\">Unknown</td><td rowspan=\"1\" colspan=\"1\">A6; M1</td><td rowspan=\"1\" colspan=\"1\">A6; M5</td><td rowspan=\"1\" colspan=\"1\">EA: As long as you are certain of the chorioamnionitis (M1) again, or is this a postmortem change (M5) between fetal death and delivery?</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">23</td><td rowspan=\"1\" colspan=\"1\">Unknown</td><td rowspan=\"1\" colspan=\"1\">A5; M4</td><td rowspan=\"1\" colspan=\"1\">A6; M4</td><td rowspan=\"1\" colspan=\"1\">EA: The birthweight is surely to be expected with the delay between fetal death and delivery, and there is no evidence of IUGR<sup>d</sup> (A5); keep M4 (GDM<sup>e</sup>).</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">31</td><td rowspan=\"1\" colspan=\"1\">Unknown</td><td rowspan=\"1\" colspan=\"1\">A6; M1</td><td rowspan=\"1\" colspan=\"1\">A6; M5</td><td rowspan=\"1\" colspan=\"1\">EA: Are you confident of the chorioamnionitis (M1) as the cause of death?</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">71</td><td rowspan=\"1\" colspan=\"1\">Fetal syndromal abnormality</td><td rowspan=\"1\" colspan=\"1\">A1; M3</td><td rowspan=\"1\" colspan=\"1\">A1; M5</td><td rowspan=\"1\" colspan=\"1\">EA: The cesarean delivery was done after the fetal death. Cesarean delivery as a cause is more when there are complications (M3) from the cesarean delivery that lead to the fetal death</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">89</td><td rowspan=\"1\" colspan=\"1\">Cord accident, drug addict</td><td rowspan=\"1\" colspan=\"1\">A3; M4</td><td rowspan=\"1\" colspan=\"1\">A3; M1</td><td rowspan=\"1\" colspan=\"1\">EA: Cord accident (A3; M1) as the cause of fetal death rather than mother is drug addict (M4)</td></tr></tbody></table><table-wrap-foot><fn id=\"table1fn1\"><p><sup>a</sup>ICD-PM: International Classification of Disease for Perinatal Mortality.</p></fn><fn id=\"table1fn2\"><p><sup>b</sup>PET: pre-eclampsia.</p></fn><fn id=\"table1fn3\"><p><sup>c</sup>TTTS: twin-twin transfusion syndrome.</p></fn><fn id=\"table1fn4\"><p><sup>d</sup>IUGR: intrauterine growth restriction.</p></fn><fn id=\"table1fn5\"><p><sup>e</sup>GDM: gestational diabetes mellitus.</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"table2\" position=\"float\"><label>Table 2</label><caption><p>ICD-PM codes for stillbirths (antepartum [A] and intrapartum [I]) and neonatal [N] deaths with maternal conditions (n=119).</p></caption><table frame=\"hsides\" rules=\"groups\" width=\"1000\" cellpadding=\"5\" cellspacing=\"0\" border=\"1\"><col width=\"0\" span=\"1\"/><col width=\"330\" span=\"1\"/><col width=\"90\" span=\"1\"/><col width=\"140\" span=\"1\"/><col width=\"130\" span=\"1\"/><col width=\"120\" span=\"1\"/><col width=\"90\" span=\"1\"/><col width=\"100\" span=\"1\"/><thead><tr valign=\"top\"><td colspan=\"2\" rowspan=\"1\">Perinatal cause of death</td><td colspan=\"6\" rowspan=\"1\">Maternal condition</td></tr><tr valign=\"top\"><td colspan=\"2\" rowspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">M1: complications of placenta, cord, and membrane</td><td rowspan=\"1\" colspan=\"1\">M2: maternal complications of pregnancy</td><td rowspan=\"1\" colspan=\"1\">M3: other complications of labor and delivery</td><td rowspan=\"1\" colspan=\"1\">M4: maternal medical and surgical conditions</td><td rowspan=\"1\" colspan=\"1\">M5: no maternal conditions</td><td rowspan=\"1\" colspan=\"1\">Total (%)</td></tr></thead><tbody><tr valign=\"top\"><td colspan=\"2\" rowspan=\"1\">Antenatal death (A)</td><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">A1: congenital malformations, deformations, and chromosomal abnormalities</td><td rowspan=\"1\" colspan=\"1\">2</td><td rowspan=\"1\" colspan=\"1\">2</td><td rowspan=\"1\" colspan=\"1\">0</td><td rowspan=\"1\" colspan=\"1\">2</td><td rowspan=\"1\" colspan=\"1\">4</td><td rowspan=\"1\" colspan=\"1\">10 (11.0)</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">A2: infection</td><td rowspan=\"1\" colspan=\"1\">6</td><td rowspan=\"1\" colspan=\"1\">0</td><td rowspan=\"1\" colspan=\"1\">0</td><td rowspan=\"1\" colspan=\"1\">0</td><td rowspan=\"1\" colspan=\"1\">1</td><td rowspan=\"1\" colspan=\"1\">7 (7.7)</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">A3: antepartum hypoxia</td><td rowspan=\"1\" colspan=\"1\">20</td><td rowspan=\"1\" colspan=\"1\">1</td><td rowspan=\"1\" colspan=\"1\">0</td><td rowspan=\"1\" colspan=\"1\">3</td><td rowspan=\"1\" colspan=\"1\">0</td><td rowspan=\"1\" colspan=\"1\">24 (26.4)</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">A4: other specified antepartum disorder</td><td rowspan=\"1\" colspan=\"1\">1</td><td rowspan=\"1\" colspan=\"1\">0</td><td rowspan=\"1\" colspan=\"1\">0</td><td rowspan=\"1\" colspan=\"1\">1</td><td rowspan=\"1\" colspan=\"1\">0</td><td rowspan=\"1\" colspan=\"1\">2 (2.2)</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">A5: disorders related to fetal growth</td><td rowspan=\"1\" colspan=\"1\">6</td><td rowspan=\"1\" colspan=\"1\">5</td><td rowspan=\"1\" colspan=\"1\">0</td><td rowspan=\"1\" colspan=\"1\">6</td><td rowspan=\"1\" colspan=\"1\">0</td><td rowspan=\"1\" colspan=\"1\">17 (18.7)</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">A6: fetal death of unspecified cause</td><td rowspan=\"1\" colspan=\"1\">4</td><td rowspan=\"1\" colspan=\"1\">4</td><td rowspan=\"1\" colspan=\"1\">2</td><td rowspan=\"1\" colspan=\"1\">9</td><td rowspan=\"1\" colspan=\"1\">12</td><td rowspan=\"1\" colspan=\"1\">31 (34.0)</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">Total (%)</td><td rowspan=\"1\" colspan=\"1\">39 (42.9)</td><td rowspan=\"1\" colspan=\"1\">12 (13.2)</td><td rowspan=\"1\" colspan=\"1\">2 (2.2)</td><td rowspan=\"1\" colspan=\"1\">21 (23.0)</td><td rowspan=\"1\" colspan=\"1\">17 (18.7)</td><td rowspan=\"1\" colspan=\"1\">91 (100.0)</td></tr><tr valign=\"top\"><td colspan=\"2\" rowspan=\"1\">Intrapartum death (I)</td><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">I1: congenital malformations, deformations, and chromosomal abnormalities</td><td rowspan=\"1\" colspan=\"1\">0</td><td rowspan=\"1\" colspan=\"1\">0</td><td rowspan=\"1\" colspan=\"1\">1</td><td rowspan=\"1\" colspan=\"1\">0</td><td rowspan=\"1\" colspan=\"1\">0</td><td rowspan=\"1\" colspan=\"1\">1 (100.0)</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">I2: birth trauma</td><td rowspan=\"1\" colspan=\"1\">0</td><td rowspan=\"1\" colspan=\"1\">0</td><td rowspan=\"1\" colspan=\"1\">0</td><td rowspan=\"1\" colspan=\"1\">0</td><td rowspan=\"1\" colspan=\"1\">0</td><td rowspan=\"1\" colspan=\"1\">0 (0.0)</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">I3: acute intrapartum event</td><td rowspan=\"1\" colspan=\"1\">0</td><td rowspan=\"1\" colspan=\"1\">0</td><td rowspan=\"1\" colspan=\"1\">0</td><td rowspan=\"1\" colspan=\"1\">0</td><td rowspan=\"1\" colspan=\"1\">0</td><td rowspan=\"1\" colspan=\"1\">0 (0.0)</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">I4: infection</td><td rowspan=\"1\" colspan=\"1\">0</td><td rowspan=\"1\" colspan=\"1\">0</td><td rowspan=\"1\" colspan=\"1\">0</td><td rowspan=\"1\" colspan=\"1\">0</td><td rowspan=\"1\" colspan=\"1\">0</td><td rowspan=\"1\" colspan=\"1\">0 (0.0)</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">I5: other specified intrapartum disorder</td><td rowspan=\"1\" colspan=\"1\">0</td><td rowspan=\"1\" colspan=\"1\">0</td><td rowspan=\"1\" colspan=\"1\">0</td><td rowspan=\"1\" colspan=\"1\">0</td><td rowspan=\"1\" colspan=\"1\">0</td><td rowspan=\"1\" colspan=\"1\">0 (0.0)</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">I6: disorders related to fetal growth</td><td rowspan=\"1\" colspan=\"1\">0</td><td rowspan=\"1\" colspan=\"1\">0</td><td rowspan=\"1\" colspan=\"1\">0</td><td rowspan=\"1\" colspan=\"1\">0</td><td rowspan=\"1\" colspan=\"1\">0</td><td rowspan=\"1\" colspan=\"1\">0 (0.0)</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">I7: intrapartum death of unspecified cause</td><td rowspan=\"1\" colspan=\"1\">0</td><td rowspan=\"1\" colspan=\"1\">0</td><td rowspan=\"1\" colspan=\"1\">0</td><td rowspan=\"1\" colspan=\"1\">0</td><td rowspan=\"1\" colspan=\"1\">0</td><td rowspan=\"1\" colspan=\"1\">0 (0.0)</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">Total (%)</td><td rowspan=\"1\" colspan=\"1\">0</td><td rowspan=\"1\" colspan=\"1\">0</td><td rowspan=\"1\" colspan=\"1\">1 (100.0)</td><td rowspan=\"1\" colspan=\"1\">0</td><td rowspan=\"1\" colspan=\"1\">0</td><td rowspan=\"1\" colspan=\"1\">1 (100.0)</td></tr><tr valign=\"top\"><td colspan=\"2\" rowspan=\"1\">Neonatal death (N)</td><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">N1: congenital malformations, deformations, and chromosomal abnormalities</td><td rowspan=\"1\" colspan=\"1\">1</td><td rowspan=\"1\" colspan=\"1\">1</td><td rowspan=\"1\" colspan=\"1\">2</td><td rowspan=\"1\" colspan=\"1\">0</td><td rowspan=\"1\" colspan=\"1\">0</td><td rowspan=\"1\" colspan=\"1\">4 (14.8)</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">N2: disorders related to fetal growth</td><td rowspan=\"1\" colspan=\"1\">0</td><td rowspan=\"1\" colspan=\"1\">0</td><td rowspan=\"1\" colspan=\"1\">0</td><td rowspan=\"1\" colspan=\"1\">1</td><td rowspan=\"1\" colspan=\"1\">0</td><td rowspan=\"1\" colspan=\"1\">1 (3.7)</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">N3: birth trauma</td><td rowspan=\"1\" colspan=\"1\">0</td><td rowspan=\"1\" colspan=\"1\">0</td><td rowspan=\"1\" colspan=\"1\">0</td><td rowspan=\"1\" colspan=\"1\">0</td><td rowspan=\"1\" colspan=\"1\">0</td><td rowspan=\"1\" colspan=\"1\">0 (0.0)</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">N4: complications of intrapartum events</td><td rowspan=\"1\" colspan=\"1\">1</td><td rowspan=\"1\" colspan=\"1\">0</td><td rowspan=\"1\" colspan=\"1\">0</td><td rowspan=\"1\" colspan=\"1\">0</td><td rowspan=\"1\" colspan=\"1\">0</td><td rowspan=\"1\" colspan=\"1\">1 (3.7)</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">N5: convulsions and disorders of cerebral status</td><td rowspan=\"1\" colspan=\"1\">0</td><td rowspan=\"1\" colspan=\"1\">0</td><td rowspan=\"1\" colspan=\"1\">0</td><td rowspan=\"1\" colspan=\"1\">0</td><td rowspan=\"1\" colspan=\"1\">0</td><td rowspan=\"1\" colspan=\"1\">0 (0.0)</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">N6: infection</td><td rowspan=\"1\" colspan=\"1\">3</td><td rowspan=\"1\" colspan=\"1\">1</td><td rowspan=\"1\" colspan=\"1\">0</td><td rowspan=\"1\" colspan=\"1\">0</td><td rowspan=\"1\" colspan=\"1\">0</td><td rowspan=\"1\" colspan=\"1\">4 (14.8)</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">N7: respiratory and cardiovascular disorders</td><td rowspan=\"1\" colspan=\"1\">0</td><td rowspan=\"1\" colspan=\"1\">2</td><td rowspan=\"1\" colspan=\"1\">1</td><td rowspan=\"1\" colspan=\"1\">0</td><td rowspan=\"1\" colspan=\"1\">0</td><td rowspan=\"1\" colspan=\"1\">3 (11.1)</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">N8: neonatal conditions</td><td rowspan=\"1\" colspan=\"1\">2</td><td rowspan=\"1\" colspan=\"1\">1</td><td rowspan=\"1\" colspan=\"1\">0</td><td rowspan=\"1\" colspan=\"1\">1</td><td rowspan=\"1\" colspan=\"1\">1</td><td rowspan=\"1\" colspan=\"1\">5 (18.5)</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">N9: low birth weight and prematurity</td><td rowspan=\"1\" colspan=\"1\">3</td><td rowspan=\"1\" colspan=\"1\">1</td><td rowspan=\"1\" colspan=\"1\">1</td><td rowspan=\"1\" colspan=\"1\">3</td><td rowspan=\"1\" colspan=\"1\">1</td><td rowspan=\"1\" colspan=\"1\">9 (33.4)</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">N10: miscellaneous</td><td rowspan=\"1\" colspan=\"1\">0</td><td rowspan=\"1\" colspan=\"1\">0</td><td rowspan=\"1\" colspan=\"1\">0</td><td rowspan=\"1\" colspan=\"1\">0</td><td rowspan=\"1\" colspan=\"1\">0</td><td rowspan=\"1\" colspan=\"1\">0 (0.0)</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">N11: neonatal death of unspecified cause</td><td rowspan=\"1\" colspan=\"1\">0</td><td rowspan=\"1\" colspan=\"1\">0</td><td rowspan=\"1\" colspan=\"1\">0</td><td rowspan=\"1\" colspan=\"1\">0</td><td rowspan=\"1\" colspan=\"1\">0</td><td rowspan=\"1\" colspan=\"1\">0 (0.0)</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">Total (%)</td><td rowspan=\"1\" colspan=\"1\">10 (37.1)</td><td rowspan=\"1\" colspan=\"1\">6 (22.2)</td><td rowspan=\"1\" colspan=\"1\">4 (14.8)</td><td rowspan=\"1\" colspan=\"1\">5 (18.5)</td><td rowspan=\"1\" colspan=\"1\">2 (7.4)</td><td rowspan=\"1\" colspan=\"1\">27 (100.0)</td></tr></tbody></table></table-wrap><p>Before the application of the ICD-PM coding system, 34.5% (41/119) of stillbirths and neonatal deaths were classified as having unknown causes. After the application of the ICD-PM system, the cases with unknown causes of perinatal death dropped to 10.1% (12/119) cases (<italic>P</italic><.001). In our study, all neonatal deaths had specific causes identified.</p><p>After retrospectively applying ICD-10 and ICD-PM codes in the cases with unknown causes (30/85, 35%) of stillbirth from our original classification on the 85 cases from 2012 to 2017 (retrospective validation study), we can further reduce the unknown causes (ie, A6;M5) to 8% (7/85) of cases (<italic>P</italic><.001). <xref rid=\"table3\" ref-type=\"table\">Table 3</xref> showed how we could change the 23 unknown (30 minus 7) to known causes using ICD-PM.</p><table-wrap id=\"table3\" position=\"float\"><label>Table 3</label><caption><p>In the retrospective 5-year validation study (May 1, 2012, to April 30, 2017), 23/30 unknown causes of stillbirth can be assigned a diagnosis category after applying ICD-PM code.</p></caption><table frame=\"hsides\" rules=\"groups\" width=\"1000\" cellpadding=\"5\" cellspacing=\"0\" border=\"1\"><col width=\"110\" span=\"1\"/><col width=\"130\" span=\"1\"/><col width=\"630\" span=\"1\"/><col width=\"130\" span=\"1\"/><thead><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">Case number</td><td rowspan=\"1\" colspan=\"1\">Original codes</td><td rowspan=\"1\" colspan=\"1\">Remarks</td><td rowspan=\"1\" colspan=\"1\">ICD-PM<sup>a</sup> codes</td></tr></thead><tbody><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">3</td><td rowspan=\"1\" colspan=\"1\">Unknown</td><td rowspan=\"1\" colspan=\"1\">Funisitis, GDM<sup>b</sup></td><td rowspan=\"1\" colspan=\"1\">A2; M1</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">8</td><td rowspan=\"1\" colspan=\"1\">Unknown</td><td rowspan=\"1\" colspan=\"1\">Maternal hypothyroidism</td><td rowspan=\"1\" colspan=\"1\">A6; M4</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">14</td><td rowspan=\"1\" colspan=\"1\">Unknown</td><td rowspan=\"1\" colspan=\"1\">History of deep vein thrombosis, placental histopathology: fetal thrombotic vasculopathy and placenta infarction</td><td rowspan=\"1\" colspan=\"1\">A3; M1</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">23</td><td rowspan=\"1\" colspan=\"1\">Unknown</td><td rowspan=\"1\" colspan=\"1\">480 g at 28+ weeks, GDM</td><td rowspan=\"1\" colspan=\"1\">A6; M4</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">28</td><td rowspan=\"1\" colspan=\"1\">Unknown</td><td rowspan=\"1\" colspan=\"1\">375 g at 24+ weeks, placenta: thrombotic vasculopathy</td><td rowspan=\"1\" colspan=\"1\">A5; M1</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">32</td><td rowspan=\"1\" colspan=\"1\">Unknown</td><td rowspan=\"1\" colspan=\"1\">Placenta: focal fetal thrombotic vasculopathy</td><td rowspan=\"1\" colspan=\"1\">A6; M1</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">39</td><td rowspan=\"1\" colspan=\"1\">Unknown</td><td rowspan=\"1\" colspan=\"1\">GDM</td><td rowspan=\"1\" colspan=\"1\">A6; M4</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">48</td><td rowspan=\"1\" colspan=\"1\">Unknown</td><td rowspan=\"1\" colspan=\"1\">GDM</td><td rowspan=\"1\" colspan=\"1\">A6; M4</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">50</td><td rowspan=\"1\" colspan=\"1\">Unknown</td><td rowspan=\"1\" colspan=\"1\">2520 g at 39 weeks; placenta: thrombotic vasculopathy</td><td rowspan=\"1\" colspan=\"1\">A5; M1</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">55</td><td rowspan=\"1\" colspan=\"1\">Unknown</td><td rowspan=\"1\" colspan=\"1\">400 g at 25 weeks; severe oligohydramnios</td><td rowspan=\"1\" colspan=\"1\">A5; M2</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">57</td><td rowspan=\"1\" colspan=\"1\">Unknown</td><td rowspan=\"1\" colspan=\"1\">Preeclampsia, DM<sup>c</sup> in pregnancy</td><td rowspan=\"1\" colspan=\"1\">A6; M4</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">58</td><td rowspan=\"1\" colspan=\"1\">Unknown</td><td rowspan=\"1\" colspan=\"1\">Polyhydramnios</td><td rowspan=\"1\" colspan=\"1\">A6; M2</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">59</td><td rowspan=\"1\" colspan=\"1\">Unknown</td><td rowspan=\"1\" colspan=\"1\">250 g at 25 weeks; twisted cord</td><td rowspan=\"1\" colspan=\"1\">A5; M1</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">60</td><td rowspan=\"1\" colspan=\"1\">Unknown</td><td rowspan=\"1\" colspan=\"1\">255 g at 24 weeks; GDM</td><td rowspan=\"1\" colspan=\"1\">A5; M4</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">61</td><td rowspan=\"1\" colspan=\"1\">Unknown</td><td rowspan=\"1\" colspan=\"1\">330 g at 24 weeks; GDM</td><td rowspan=\"1\" colspan=\"1\">A5; M4</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">62</td><td rowspan=\"1\" colspan=\"1\">Unknown</td><td rowspan=\"1\" colspan=\"1\">Breech presentation</td><td rowspan=\"1\" colspan=\"1\">A6; M3</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">65</td><td rowspan=\"1\" colspan=\"1\">Unknown</td><td rowspan=\"1\" colspan=\"1\">Twin pregnancy</td><td rowspan=\"1\" colspan=\"1\">A6; M2</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">66</td><td rowspan=\"1\" colspan=\"1\">Unknown</td><td rowspan=\"1\" colspan=\"1\">Twin pregnancy</td><td rowspan=\"1\" colspan=\"1\">A6; M2</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">72</td><td rowspan=\"1\" colspan=\"1\">Unknown</td><td rowspan=\"1\" colspan=\"1\">Placenta: chorioamnionitis (Escherichia coli); fetal thrombotic vasculopathy</td><td rowspan=\"1\" colspan=\"1\">A2; M1</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">73</td><td rowspan=\"1\" colspan=\"1\">Unknown</td><td rowspan=\"1\" colspan=\"1\">Placenta: fetal thrombotic vasculopathy</td><td rowspan=\"1\" colspan=\"1\">A6; M1</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">74</td><td rowspan=\"1\" colspan=\"1\">Unknown</td><td rowspan=\"1\" colspan=\"1\">Oligohydramnios</td><td rowspan=\"1\" colspan=\"1\">A6; M2</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">80</td><td rowspan=\"1\" colspan=\"1\">Unknown</td><td rowspan=\"1\" colspan=\"1\">Maternal infection with fever; placenta: focal intervillous thrombus</td><td rowspan=\"1\" colspan=\"1\">A2; M1</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">85</td><td rowspan=\"1\" colspan=\"1\">Unknown</td><td rowspan=\"1\" colspan=\"1\">Placenta: minor infarcted villi</td><td rowspan=\"1\" colspan=\"1\">A6; M1</td></tr></tbody></table><table-wrap-foot><fn id=\"table3fn1\"><p><sup>a</sup>ICD-PM: International Classification of Diseases for Perinatal Mortality.</p></fn><fn id=\"table3fn2\"><p><sup>b</sup>GDM: gestational diabetes mellitus.</p></fn><fn id=\"table3fn3\"><p><sup>c</sup>DM: diabetes mellitus.</p></fn></table-wrap-foot></table-wrap></sec><sec sec-type=\"discussion\"><title>Discussion</title><sec><title>Principal Findings</title><p>Perinatal deaths remain problematic worldwide. Before the advent of the ICD-PM, there were several independent systems in high-income countries and few systems in low- and middle- income countries, causing significant disparity in perinatal death data recorded among countries [<xref rid=\"ref16\" ref-type=\"bibr\">16</xref>]. The classification system used in Hong Kong Hospital Authority obstetrics units is no better, with 92.8% of cases having unknown causes [<xref rid=\"ref15\" ref-type=\"bibr\">15</xref>]. It seems that we were somehow reluctant to give a specific diagnosis under the original coding system unless the cause was crystal clear. Hence detailed and accurate information that can be retrieved from our original classification of perinatal mortality cases was scarce. The ICD-PM is the first system that addresses the issue internationally. ICD-10 and ICD-PM are user-friendly as shown by the high level of agreement (93.2% overall) between our codes with that by the international expert (EA). The agreement with EA of our codes using ICD-PM was even higher, reaching 97.1% for the 2 years of prospective cases compared with 91.8% for the 5 years of retrospective cases, although not statistically significant (<italic>P</italic>=.44). This further supports our observation that the final consensus code made during perinatal meetings with multidisciplinary discussion on the perinatal death cases gives the most accurate answer, although our principal investigator (HML) had already done a good job with the 5-year retrospective codes.</p><p>The ICD-PM is designed to be used for classifying stillbirths and neonatal deaths at three levels. First, it identifies the time of death as antepartum (before the onset of labor), intrapartum (during labor but before delivery), or neonatal (up to day 7 of postnatal life, can be extended to late neonatal deaths, so as within 28 days of life, like in our classification). Second, it is multilayered such that the depth of classification can reflect the locally available intensity of investigation. In our Hospital Authority obstetrics units, the investigations for stillbirths and neonatal deaths are quite in-depth, as described under Methods, but this was not be reflected by the original classification system. Third, the ICD-PM links the contributing maternal condition, if any, with perinatal death. This is very important because most of these contributing maternal conditions would not have shown up in our original coding system. Ultimately, the ICD-PM classification at all three levels allows easy identification of where a program intervention should be targeted in order to improve the perinatal outcomes.</p><p>After the application of the ICD-PM system, the ratio of unknown causes of perinatal mortality dropped from 34.5% to 10.1%, which was statistically significant (<italic>P</italic><.001). A total of 77% of stillbirth cases initially classified as unknown were now assigned a diagnostic category after applying the ICD-PM (<xref rid=\"table3\" ref-type=\"table\">Table 3</xref>). In our study, all neonatal deaths had specific causes identified. In other words, the ICD-PM is more useful for coding stillbirths than neonatal deaths in our locality.</p><p>The main benefit of using ICD-10 and ICD-PM codes was to have a better understanding of the perinatal deaths in terms of the timing of death, the depth of investigations, and any contributing maternal conditions. We also consider the ICD-PM code to be user-friendly for changing an existing local perinatal death classification to one that is global and can be compared with international data. Using the ICD-PM code can lead to better classification of perinatal deaths, reduce the proportion of unknown diagnoses, and clearly link the maternal conditions with these perinatal deaths. There are some countries such as the United Kingdom [<xref rid=\"ref3\" ref-type=\"bibr\">3</xref>], South Africa [<xref rid=\"ref3\" ref-type=\"bibr\">3</xref>,<xref rid=\"ref16\" ref-type=\"bibr\">16</xref>,<xref rid=\"ref17\" ref-type=\"bibr\">17</xref>], India [<xref rid=\"ref18\" ref-type=\"bibr\">18</xref>], and Colombia [<xref rid=\"ref19\" ref-type=\"bibr\">19</xref>] using the ICD-PM to interpret the perinatal mortality data which showed that the ICD-PM classification was feasible and enabling the characterization of perinatal mortality. This is the first validation study demonstrating the application of the ICD-PM coding system in Hong Kong.</p></sec><sec><title>Limitations</title><p>However, there are limitations to our study. The study was focused on one hospital only, so the sample size was small. The improvement in coding might be related to the enthusiastic principal investigator (HML) as the coder in the retrospective validation study; the code in the prospective validation study was done during our monthly perinatal meetings with multidisciplinary input from various stakeholders, which is our usual practice before and after the study. Whether there is an overall Hawthorne effect (improving performance of coding during the study period) could be seen by continuous monitoring of the diagnostic classification of stillbirths and neonatal deaths after the ICD-PM is formally used for coding from 2020 onward.</p><p>Further study can be performed in all Hong Kong Hospital Authority obstetrics units using the ICD-10 and ICD-PM so as to draw an overall picture of perinatal mortality across the territory.</p></sec><sec><title>Conclusions</title><p>The ICD-PM is a user-friendly system that can enhance the understanding of data [<xref rid=\"ref5\" ref-type=\"bibr\">5</xref>]. Using the ICD-PM coding system could lead to a more comprehensive classification of perinatal deaths, reduce the proportion of unknown causes as well as providing better linkage to maternal conditions in these perinatal deaths. The ICD-PM classifications are more extensive in covering diagnostic categories, with more specific details. Implementing this new coding system in Hong Kong Hospital Authority obstetrics units will be of great help in improving clinical practice and reducing perinatal mortality in the long run.</p></sec></sec></body><back><fn-group><fn fn-type=\"con\"><p>Authors' Contributions: All authors had full access to the data, contributed to the study, approved the final version for publication, and take responsibility for its accuracy and integrity. HML and WCL were responsible for concept or design of the study. HML acquired the data and drafted the article. All authors contributed to the analysis or interpretation of data. 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[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Front Psychiatry</journal-id><journal-id journal-id-type=\"iso-abbrev\">Front Psychiatry</journal-id><journal-id journal-id-type=\"publisher-id\">Front. Psychiatry</journal-id><journal-title-group><journal-title>Frontiers in Psychiatry</journal-title></journal-title-group><issn pub-type=\"epub\">1664-0640</issn><publisher><publisher-name>Frontiers Media S.A.</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">33192625</article-id><article-id pub-id-type=\"pmc\">PMC7432143</article-id><article-id pub-id-type=\"doi\">10.3389/fpsyt.2020.00757</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Psychiatry</subject><subj-group><subject>Original Research</subject></subj-group></subj-group></article-categories><title-group><article-title>Plasma microRNA Array Analysis Identifies Overexpressed miR-19b-3p as a Biomarker of Bipolar Depression Distinguishing From Unipolar Depression</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Chen</surname><given-names>Yu</given-names></name><xref ref-type=\"aff\" rid=\"aff1\">\n<sup>1</sup>\n</xref><uri xlink:type=\"simple\" xlink:href=\"https://loop.frontiersin.org/people/813629\"/></contrib><contrib contrib-type=\"author\"><name><surname>Shi</surname><given-names>Jiabo</given-names></name><xref ref-type=\"aff\" rid=\"aff1\">\n<sup>1</sup>\n</xref><uri xlink:type=\"simple\" xlink:href=\"https://loop.frontiersin.org/people/612384\"/></contrib><contrib contrib-type=\"author\"><name><surname>Liu</surname><given-names>Haiyan</given-names></name><xref ref-type=\"aff\" rid=\"aff1\">\n<sup>1</sup>\n</xref><uri xlink:type=\"simple\" xlink:href=\"https://loop.frontiersin.org/people/552388\"/></contrib><contrib contrib-type=\"author\"><name><surname>Wang</surname><given-names>Qiang</given-names></name><xref ref-type=\"aff\" rid=\"aff2\">\n<sup>2</sup>\n</xref><uri xlink:type=\"simple\" xlink:href=\"https://loop.frontiersin.org/people/787446\"/></contrib><contrib contrib-type=\"author\"><name><surname>Chen</surname><given-names>Xiangxiang</given-names></name><xref ref-type=\"aff\" rid=\"aff1\">\n<sup>1</sup>\n</xref></contrib><contrib contrib-type=\"author\"><name><surname>Tang</surname><given-names>Hao</given-names></name><xref ref-type=\"aff\" rid=\"aff1\">\n<sup>1</sup>\n</xref><uri xlink:type=\"simple\" xlink:href=\"https://loop.frontiersin.org/people/1046606\"/></contrib><contrib contrib-type=\"author\"><name><surname>Yan</surname><given-names>Rui</given-names></name><xref ref-type=\"aff\" rid=\"aff1\">\n<sup>1</sup>\n</xref><uri xlink:type=\"simple\" xlink:href=\"https://loop.frontiersin.org/people/637805\"/></contrib><contrib contrib-type=\"author\"><name><surname>Yao</surname><given-names>Zhijian</given-names></name><xref ref-type=\"aff\" rid=\"aff1\">\n<sup>1</sup>\n</xref><xref ref-type=\"aff\" rid=\"aff3\">\n<sup>3</sup>\n</xref><xref ref-type=\"aff\" rid=\"aff4\">\n<sup>4</sup>\n</xref><xref ref-type=\"author-notes\" rid=\"fn001\">\n<sup>*</sup>\n</xref><uri xlink:type=\"simple\" xlink:href=\"https://loop.frontiersin.org/people/610601\"/></contrib><contrib contrib-type=\"author\"><name><surname>Lu</surname><given-names>Qing</given-names></name><xref ref-type=\"aff\" rid=\"aff4\">\n<sup>4</sup>\n</xref><xref ref-type=\"aff\" rid=\"aff5\">\n<sup>5</sup>\n</xref><xref ref-type=\"author-notes\" rid=\"fn001\">\n<sup>*</sup>\n</xref><uri xlink:type=\"simple\" xlink:href=\"https://loop.frontiersin.org/people/518978\"/></contrib></contrib-group><aff id=\"aff1\">\n<sup>1</sup>\n<institution>Department of Psychiatry, The Affifiliated Brain Hospital of Nanjing Medical University, Nanjing Medical University</institution>, <addr-line>Nanjing</addr-line>, <country>China</country>\n</aff><aff id=\"aff2\">\n<sup>2</sup>\n<institution>Department of Medical Psychology; Nanjing Drum Tower Hospital, Medical School of Nanjing University</institution>, <addr-line>Nanjing</addr-line>, <country>China</country>\n</aff><aff id=\"aff3\">\n<sup>3</sup>\n<institution>Department of Psychiatry, Nanjing Brain Hospital, Medical School of Nanjing University</institution>, <addr-line>Nanjing</addr-line>, <country>China</country>\n</aff><aff id=\"aff4\">\n<sup>4</sup>\n<institution>School of Biological Sciences & Medical Engineering, Southeast University</institution>, <addr-line>Nanjing</addr-line>, <country>China</country>\n</aff><aff id=\"aff5\">\n<sup>5</sup>\n<institution>Child Development and Learning Science, Key Laboratory of Ministry of Education</institution>, <addr-line>Nanjing</addr-line>, <country>China</country>\n</aff><author-notes><fn fn-type=\"edited-by\"><p>Edited by: Xenia Gonda, Semmelweis University, Hungary</p></fn><fn fn-type=\"edited-by\"><p>Reviewed by: Célia Fourrier, University of Adelaide, Australia; Xi Chen, Nanjing University, China</p></fn><corresp id=\"fn001\">*Correspondence: Zhijian Yao, <email xlink:href=\"mailto:[email protected]\" xlink:type=\"simple\">[email protected]</email>; Qing Lu, <email xlink:href=\"mailto:[email protected]\" xlink:type=\"simple\">[email protected]</email>\n</corresp><fn fn-type=\"other\" id=\"fn002\"><p>This article was submitted to Mood and Anxiety Disorders, a section of the journal Frontiers in Psychiatry</p></fn></author-notes><pub-date pub-type=\"epub\"><day>11</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><year>2020</year></pub-date><volume>11</volume><elocation-id>757</elocation-id><history><date date-type=\"received\"><day>27</day><month>9</month><year>2019</year></date><date date-type=\"accepted\"><day>16</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>Copyright © 2020 Chen, Shi, Liu, Wang, Chen, Tang, Yan, Yao and Lu</copyright-statement><copyright-year>2020</copyright-year><copyright-holder>Chen, Shi, Liu, Wang, Chen, Tang, Yan, Yao and Lu</copyright-holder><license xlink:href=\"http://creativecommons.org/licenses/by/4.0/\"><license-p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</license-p></license></permissions><abstract><sec><title>Objectives</title><p>The clinical characteristics of bipolar disorder (current major depressive episode) (BD) overlap with unipolar depressive disorder (UD), which makes it difficult to perform an accurate diagnosis. We identified plasma microRNAs (miRNAs) that distinguished BD from UD and explored the relationship between miRNA expression levels and clinical characteristics.</p></sec><sec><title>Methods</title><p>Total miRNAs from blood plasma from seven UD patients, seven BD patients, and six controls were analyzed. The identified miRNAs were validated in a separate population group. Depression severity and early life adversities were assessed. Bioinformatic analysis was conducted to investigate the target genes that were identified and the pathways associated with the altered miRNAs.</p></sec><sec><title>Results</title><p>Compared to controls, 42 miRNAs were differentially expressed in patients. miR-19b-3p, miR-3921, and miR-1180-3p were selected to validate the microarray results. Only miR-19b-3p was validated as down-regulated in patients. The primary predicted genes associated with miR-19b-3p were MAPK1, PTEN, and PRKAA1. The most relevant KEGG pathways included mTOR, FoxO, and the PI3-K/Akt signaling pathway. BD patients were more likely to have higher expression levels of miR-19b-3p and more severe childhood trauma experience compared to UD patients.</p></sec><sec><title>Conclusions</title><p>Plasma miR-19b-3p is a potential non-invasive biomarker that might be useful in distinguishing UD from BD. miR-19b3p was predicted to be involved in the pathway of inflammatory dysregulation associated with experiencing early childhood trauma.</p></sec></abstract><kwd-group><kwd>bipolar disorder</kwd><kwd>unipolar depression</kwd><kwd>peripheral miRNAs</kwd><kwd>biomarker</kwd><kwd>gene expression</kwd></kwd-group><counts><fig-count count=\"3\"/><table-count count=\"2\"/><equation-count count=\"0\"/><ref-count count=\"70\"/><page-count count=\"12\"/><word-count count=\"5915\"/></counts></article-meta></front><body><sec sec-type=\"intro\" id=\"s1\"><title>Introduction</title><p>The clinical manifestations of bipolar disorder (current depressive episode) (BD) and unipolar depressive disorder (UD) have a number of similar characteristics. Both disorders present a substantial public health burden due to their high prevalence, recurrence, and degree of disability. Although the theoretical basis of clinical psychiatry has been developed for more than half a century from Kraepelin’s conceptual foundations of mental illness to descriptive symptomatological conception, it still is not easy to clearly distinguish BD from UD. An episode of major depression may occur in the early stage of BD such that the patient is misdiagnosed with UD due to the lack of any history of hypomanic or manic episodes. It is possible for clinicians to change a diagnosis over time when evidence of hypomanic or manic episodes appears. The time-delay gap between the onset and the accurate diagnosis of BD, on average, is approximately five to ten years (<xref rid=\"B1\" ref-type=\"bibr\">1</xref>). Delayed diagnosis of BD may lead to a deleterious outcome through the use of antidepressant monotherapy, which would increase the risk of ‘switching’ into hypomanic or manic episodes (<xref rid=\"B2\" ref-type=\"bibr\">2</xref>, <xref rid=\"B3\" ref-type=\"bibr\">3</xref>). Approximately 40% to 70% of BD patients have to confront the problem of receiving an appropriate diagnosis as early as possible (<xref rid=\"B4\" ref-type=\"bibr\">4</xref>, <xref rid=\"B5\" ref-type=\"bibr\">5</xref>). It is imperative for clinicians to make a precise diagnosis to discern BD from UD. A series of clinical assessments have been administered, including sub-threshold hypomania, atypical depressive symptoms, and other clinical characteristics (<xref rid=\"B6\" ref-type=\"bibr\">6</xref>–<xref rid=\"B8\" ref-type=\"bibr\">8</xref>). Nonetheless, it is not enough to effectively distinguish the disorders using only these clinical symptoms.</p><p>Given the significant heritability of UD and BD, candidate genes for both diseases have been identified (<xref rid=\"B9\" ref-type=\"bibr\">9</xref>–<xref rid=\"B11\" ref-type=\"bibr\">11</xref>). One genome-wide association study (GWAS) that focused on major depression and included over 135,000 individuals identified 44 loci with genome-wide significance (<xref rid=\"B12\" ref-type=\"bibr\">12</xref>). Another GWAS that included over 20,000 BD patients found 30 significant genomic loci (<xref rid=\"B13\" ref-type=\"bibr\">13</xref>). However, these GWAS presented poor replication and failed to find shared or distinct genetic markers of affective disorder. One reason for this failure is that psychiatric diseases result from environmental causes as well as predisposing genetic factors. Epigenetic mechanisms indicate the interaction of genetic and environmental factors (G*E interaction). Given the role of non-coding RNAs as post-transcriptional regulators of gene expression, microRNAs are considered to be one of the G*E pathological features of affective disorders. Thus, the use of microRNA expression is of considerable interest to detect and distinguish UD from BD (<xref rid=\"B14\" ref-type=\"bibr\">14</xref>–<xref rid=\"B17\" ref-type=\"bibr\">17</xref>).</p><p>MicroRNAs (miRNAs) are small, endogenously-expressed, non-coding RNAs (~22 nucleotides) that can repress translation to inhibit protein synthesis or promote degradation of target mRNAs with complementary sequences (<xref rid=\"B18\" ref-type=\"bibr\">18</xref>). One miRNA can target hundreds of different mRNAs, and multiple miRNAs may regulate a single mRNA. Nearly 70% of mammalian miRNAs are expressed in the brain that generally negatively regulate target gene expression. Emerging studies have demonstrated that miRNAs, which are regarded as “neural communication sculptors,” play an essential role in the proliferation, differentiation, and migration of neurons and participate in the regulation of neuropsychiatric disorders (<xref rid=\"B19\" ref-type=\"bibr\">19</xref>, <xref rid=\"B20\" ref-type=\"bibr\">20</xref>). Zhao et al. reported that sevoflurane-induced upregulation of miR-19-3p in neonatal rats post-transcriptionally inhibited protein translation of CCNA2, which contributed to the impairment of learning and memory (<xref rid=\"B21\" ref-type=\"bibr\">21</xref>). Clinical studies have reported altered miRNA expression in different brain regions of patients with schizophrenia, bipolar disorders, and major depressive disorder (<xref rid=\"B22\" ref-type=\"bibr\">22</xref>, <xref rid=\"B23\" ref-type=\"bibr\">23</xref>).</p><p>MiRNAs are also released and circulating in the serum, plasma, and other body fluids. The signatures of circulating miRNAs may be potentially useful non-invasive biomarkers for disease, such as psychiatric disorders. For example, circulating miRNAs were shown to be changed by electroconvulsive shock therapy in psychotic depression (<xref rid=\"B24\" ref-type=\"bibr\">24</xref>). Walker et al. identified differential miRNAs (miR-15b, miR-132, and miR-652) in whole-blood samples of bipolar disorder comparing to healthy controls (<xref rid=\"B25\" ref-type=\"bibr\">25</xref>). Another study found a set of circulating miRNAs (let-7a-5p, let-7d-5p, let-7f-5p, miR-24-3p, and miR-425-3p) that were specifically altered in major depressive patients, five miRNA transcripts (miR-140-3p, miR-30d-5p, miR-330-5p, miR-378-5p, and miR-21-3p) were specifically altered in bipolar disorder patients, and two miRNAs (miR-330-3p and miR-345-5p) were altered in both diseases (<xref rid=\"B26\" ref-type=\"bibr\">26</xref>). Though the potential use of circulating miRNAs for psychiatric disorders screening has emerged, whether circulating miRNAs can be used as biomarkers for distinguishing bipolar depression from unipolar depression is still unclear.</p><p>Among the numerous adverse environmental conditions that exist, early childhood trauma, such as emotional abuse or neglect, physical abuse or neglect, and sexual abuse, is the greatest risk factor for the onset and development of depression (<xref rid=\"B27\" ref-type=\"bibr\">27</xref>). Patients diagnosed with UD or BD experienced more childhood trauma than healthy subjects (<xref rid=\"B28\" ref-type=\"bibr\">28</xref>, <xref rid=\"B29\" ref-type=\"bibr\">29</xref>). Neuroimaging studies have shown that early life adversities were associated with structural and functional abnormalities in specific brain regions that are involved in cognitive processing and emotional regulation (<xref rid=\"B30\" ref-type=\"bibr\">30</xref>). Growing evidence supports a direct association between exposure to childhood trauma and elevated levels of inflammatory biomarkers, such as C-reactive protein, interleukin-6, and white blood cell counts (<xref rid=\"B31\" ref-type=\"bibr\">31</xref>). The impact of early life adversity and a dysregulated inflammatory profile would persist into adulthood, leading to greater vulnerability to depression (<xref rid=\"B32\" ref-type=\"bibr\">32</xref>, <xref rid=\"B33\" ref-type=\"bibr\">33</xref>). It is hypothesized that the activation of immunological processes might be the mediator between childhood trauma and psychopathological outcomes (<xref rid=\"B34\" ref-type=\"bibr\">34</xref>).</p><p>During the process of adaptation to environmental perturbations by the individual, gene expression is modulated dynamically to optimize responses to stress. Emerging evidence indicates that miRNAs are ideally positioned to coordinate the genomic response to stress (<xref rid=\"B35\" ref-type=\"bibr\">35</xref>). Stress-induced alterations in miRNA expression affect multiple biological processes, including neurotransmission, cytokine production, and inflammation. Understanding the role of miRNAs in regulating stress-induced gene expression could have diagnostic benefits in mood disorders. To date, no studies have been conducted that compare the interactions between alterations in miRNA expression and early life adversities in UD and BD patients. The present study used an array to assess genome-wide plasma miRNA expression in UD and BD patients in combination with the assessment of the environmental risk factor of childhood trauma. The results of this study present a novel and comprehensive molecular signature that can contribute to the differential pathogenesis of UD and BD.</p></sec><sec sec-type=\"materials|methods\" id=\"s2\"><title>Materials and Methods</title><sec id=\"s2_1\"><title>Participants</title><p>Patients who were experiencing a major depressive episode were recruited from the Department of Psychiatry of the Affiliated Nanjing Brain Hospital of Nanjing Medical University from 2015 to 2016. The inclusion criteria for patients were as follows. (i) The patients received a diagnosis from a senior psychiatrist of major depressive disorder (unipolar depression, UD) or bipolar disorder (type I or type II) (BD) according to the Structured Clinical Interview for DSM-IV (SCID-IV). (ii) The patients received a 24-item Hamilton Depression Rating Scale (HAMD-24) score equal to or greater than 20. (iii) The age of the patients was between 18 and 55 years. (iv) The patients did not receive any psychotropic medications (including antipsychotics, antidepressants, mood stabilizers, and benzodiazepines) for at least four weeks. The exclusion criteria included the following. (i) The patients were diagnosed with other DSM-IV psychiatric disorders. (ii) The patients had a history of severe head injury. (iii) The patients were diagnosed with any neurological diseases or severe physical diseases, as evaluated by laboratory tests or personal history. (iv) The patients had a history of alcohol or substance dependence or abuse. (v) The patients were pregnant or lactating. All patients who were included in the study were followed up every six months until December 2018. The different types of early life stress were assessed among all patients when they entered the study using the Childhood Trauma Questionnaire (CTQ) (<xref rid=\"B36\" ref-type=\"bibr\">36</xref>). At every follow-up time point, the UD patients were administered the 32-item hypomania checklist (HCL-32) to screen for hypomania symptoms (<xref rid=\"B37\" ref-type=\"bibr\">37</xref>). If the HCL-32 score was greater than 14 points, the patient’s diagnosis was switched to BD, and the patient was excluded from the study.</p><p>The healthy control group included individuals who were matched to the patients with respect to age, gender, and education. The control subjects were recruited from communities in Nanjing from 2015 to 2016 and were screened using the Mini International Neuropsychiatric Interview (MINI) (<xref rid=\"B38\" ref-type=\"bibr\">38</xref>). Healthy controls were excluded if they had any history of psychiatric disorders or had any family history of mental disorders in their first-degree relatives.</p><p>All subjects were genetically unrelated, ethnic Han Chinese, with at least six years of education. Each subject donated 5 ml of venous blood at the time of their recruitment. A two-phase study was designed. First, in the screening phase, we performed peripheral miRNA profiling using Affymetrix chips for UD patients, BD patients, and healthy controls who were randomly selected from the sample set. In the second, independent validation phase, we examined the expression levels of identified miRNAs in all participants and analyzed the results of the miRNA arrays using the Gene Ontology and KEGG Pathway assays. Finally, we assessed the shared and distinct correlations between the expression of selected miRNAs and childhood traumatic experiences in UD and BD patients.</p><p>This study was approved by the institutional review board of the Affiliated Nanjing Brain Hospital of Nanjing Medical University, and written informed consent was obtained from each participant.</p></sec><sec id=\"s2_2\"><title>Plasma Preparation and RNA Isolation</title><p>The plasma was separated from venous blood within 24h after collection by centrifugation at 12,000 r.p.m. for 15 min. The supernatant from the plasma samples was stored in 300 µl aliquots at −80°C in RNase-free microtubes until it was used for miRNA extraction. A modified method was utilized to isolate total RNA. Briefly, Trizol reagent (Invitrogen, Carlsbad, CA, USA) was used to break down cells and cellular components in the plasma, and total RNA was extracted and purified using a miRNeasy Serum/Plasma Kit (Qiagen, Valencia, CA, USA) according to the manufacturer’s instructions. RNA quality and quantity were evaluated using a NanoDrop ND-1000 spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA).</p></sec><sec id=\"s2_3\"><title>miRNA Array</title><p>During the screening phase, a volume corresponding to 500 ng of total RNA from each blood sample was processed using a FlashTag Biotin HSR RNA Labeling kit (Affymetrix, Santa Clara, CA, USA), following the manufacturer’s protocol. The RNA was subsequently hybridized onto Affymetrix GeneChip miRNA 4.0 Arrays that each contained 2,578 human miRNA sequences (Affymetrix, Santa Clara, CA, USA). The GeneChip miRNA 4.0 Arrays were washed and stained using aFluidics station 450 and a GeneChip Scanner 3000 7G (Affymetrix, Santa Clara, CA, USA), respectively.</p></sec><sec id=\"s2_4\"><title>qRT-PCR Validation</title><p>At the validation phase, quantitative real-time PCR (qRT-PCR) was performed using Taqman microRNA probes (Applied Biosystems Inc, CA, USA) to confirm the candidate miRNAs identified on the microarrays. Total RNA was reverse transcribed to complementary DNA using a miRNA 1st Strand cDNA Synthesis Kit, stem-loop RT primers (Vazyme, Nanjing, China), and the GeneAmp 9700 PCR System (Thermo Scientific, MA, USA). The reactions began in a 384-well optical plate at 95°C for 5 minutes, followed by 40 cycles of 95°C for 10 seconds and 60°C for 30 seconds. The quantitative detection of the miRNAs was performed using the miRNA Universal SYBR qPCR Master Mix (Vazyme, Nanjing, China) and implemented on the Agilent AriaMx platform (Agilent Technologies, Palo Alto, CA, USA). All reactions, including no-template controls, were performed in triplicate. To calculate the relative expression levels of the target miRNAs, U6 was used as the control miRNA for plasma samples (the sequences of the primers listed in <xref ref-type=\"supplementary-material\" rid=\"SM1\">\n<bold>Supplemental data 1</bold>\n</xref>).</p></sec><sec id=\"s2_5\"><title>Data Analysis</title><p>All statistical analyses were performed using R (version 3.5.0) and SPSS (version 24.0). Demographic and clinical characteristics among the three groups were compared using χ2 tests, the Student’s t-test, or one-way ANOVA.</p><p>CEL-files of raw data were produced using Affymetrix GeneChip Command Console Software, Version 4.0 (Affymetrix, Santa Clara, CA, USA). Arrays were normalized using quantile normalization, and the probe values were log2 transformed. All data have been submitted to the GEO repository (code number: GSE152267).</p><p>We utilized one-way ANOVA to detect differently expressed miRNAs among all three groups, and miRNAs were chosen as candidates for further confirmation by individual qRT-PCR according to the following three criteria: (i) <italic>p-</italic>value <0.05 for the ANOVA; (ii) assessed using Fisher’s Least Significant Difference test as the Post Hoc test for multiple comparisons; (iii) fold changes (FCs) ≥3/2 or ≤2/3 between every two groups (BD vs. controls, UD vs. controls, and BD vs. UD).</p><p>Concerning qRT-PCR validation, the Ct values were normalized according to the delta Ct (ΔCt) method based on the internal reference, U6. The relative expression levels of target miRNAs were calculated using 2<sup>-ΔΔCT</sup>, which were analyzed using the Student’s t-test for independent samples (<xref rid=\"B39\" ref-type=\"bibr\">39</xref>). Binary logistic regression was applied to determine correlations between miRNA expression levels and childhood traumatic variables. A <italic>p</italic>-value<0.05 was statistically significant for all analyses.</p></sec><sec id=\"s2_6\"><title>Target Gene Prediction and Pathway Analysis</title><p>Bioinformatic analysis (Genminix Informatics Ltd., Shanghai, China) was performed for the miRNAs expressed in significant amounts. TargetScan (<uri xlink:type=\"simple\" xlink:href=\"http://www.targetscan.org/\">http://www.targetscan.org/</uri>) and miRanda (<uri xlink:type=\"simple\" xlink:href=\"http://www.microrna.org/microrna/hom.do\">http://www.microrna.org/microrna/hom.do</uri>) were used to predict target genes for the validated candidate miRNAs, and only genes predicted by both databases were retained. To identify the potential biological mechanism of differentially expressed miRNAs among the BD, UD, and healthy controls, we performed gene ontology (GO, <uri xlink:type=\"simple\" xlink:href=\"http://www.geneontology.org/\">http://www.geneontology.org/</uri>) and Kyoto Encyclopedia of Genes and Genomes (KEGG, <uri xlink:type=\"simple\" xlink:href=\"http://www.genome.jp/kegg/\">http://www.genome.jp/kegg/</uri>) pathway enrichment analysis with the David web application (<uri xlink:type=\"simple\" xlink:href=\"https://david.ncifcrf.gov\">https://david.ncifcrf.gov</uri>). Fisher’s exact test was employed to determine whether a gene set was enriched for a specific gene using GO terms or KEGG pathways compared to the background information. The p-value was corrected for false discovery rates (FDR). GO terms with a p-value<0.01 and a KEGG pathway with a <italic>p-</italic>value<0.05 were considered significant. The miRNA-mRNA-gene network of miR-19b-3p was obtained by using Cytoscape (Version 2.8.2).</p></sec></sec><sec sec-type=\"results\" id=\"s3\"><title>Results</title><sec id=\"s3_1\"><title>Demographic and Clinical Characteristics of the Subjects</title><p>All subjects were followed up every six months to confirm whether they experienced any hypomania or mania episodes until December 2018. During three years of follow up, six UD patients (two males and four females, with a mean age of 26.2 ± 10.0 years) experienced a manic episode that lasted more than seven days. These six patients were excluded from the study since their diagnosis was switched from UD to BD. Thus, 32 UD patients and 27 BD patients were included in the final analysis (see <xref rid=\"T1\" ref-type=\"table\">\n<bold>Table 1</bold>\n</xref>). There was no statistical difference between these two groups regarding age, gender, education, or family history (<italic>p</italic>>0.05). The UD group recruited more first-episode patients compared to the BD group, while patients in the BD group had a longer duration of illness than the UD group. Clinical assessments showed that there were no significant differences in the total scores for HAMD between the two diagnostic groups. At the same time, the UD patients had higher anxiety or somatization scores (<italic>p</italic><0.05) and sleep disturbance scores (<italic>p</italic><0.01) compared to BD patients.</p><table-wrap id=\"T1\" position=\"float\"><label>Table 1</label><caption><p>Demographic and clinical characteristics of unipolar depression, bipolar depression, and healthy controls.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Variables</th><th valign=\"top\" colspan=\"3\" align=\"center\" rowspan=\"1\">Total participants</th><th valign=\"top\" colspan=\"2\" align=\"center\" rowspan=\"1\">Significance</th></tr><tr><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">UD (n = 32) Mean (SD)</th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">BD (n = 27) Mean (SD)</th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">HC (n = 18) Mean (SD)</th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">\n<italic>p</italic> (UD vs. BD)</th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">\n<italic>p</italic> (three groups)</th></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold>Age (years)</bold>\n</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">35.06 ± 8.20</td><td valign=\"top\" align=\"char\" char=\"±\" rowspan=\"1\" colspan=\"1\">31.06 ± 9.97</td><td valign=\"top\" align=\"char\" char=\"±\" rowspan=\"1\" colspan=\"1\">32.56 ± 6.64</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.114</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.192</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold>Age range (years)</bold>\n</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">19~51</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">18~54</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">21~46</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold>Female (n(%))</bold>\n</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">21 (66.7%)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">19 (70.4%)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">8 (44.4%)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.698</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.188</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold>Education (years)</bold>\n</td><td valign=\"top\" align=\"char\" char=\"±\" rowspan=\"1\" colspan=\"1\">13.53 ± 3.39</td><td valign=\"top\" align=\"char\" char=\"±\" rowspan=\"1\" colspan=\"1\">13.74 ± 2.64</td><td valign=\"top\" align=\"char\" char=\"±\" rowspan=\"1\" colspan=\"1\">13.67 ± 3.07</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.957</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.965</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold>Duration of illness (months)</bold>\n</td><td valign=\"top\" align=\"char\" char=\"±\" rowspan=\"1\" colspan=\"1\">17.44 ± 17.37</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">36.15 ± 42.37</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.026*</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold>Family history (n(%))</bold>\n</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">8 (25.0%)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">8 (29.6%)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.690</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold>Depression for first episode (n(%))</bold>\n</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">29 (90.6%)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">15(80.0%)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.002**</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" colspan=\"6\" align=\"left\" rowspan=\"1\">\n<bold>Clinical assessment</bold>\n</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold>Total score of HAM-D<sub>24</sub></bold>\n</td><td valign=\"top\" align=\"char\" char=\"±\" rowspan=\"1\" colspan=\"1\">31.38 ± 6.04</td><td valign=\"top\" align=\"char\" char=\"±\" rowspan=\"1\" colspan=\"1\">28.63 ± 5.20</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.069</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" colspan=\"6\" align=\"left\" rowspan=\"1\">\n<bold>Subscore of HAM-D<sub>24</sub></bold>\n</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold>  Anxiety/somatization</bold>\n</td><td valign=\"top\" align=\"char\" char=\"±\" rowspan=\"1\" colspan=\"1\">8.22 ± 3.20</td><td valign=\"top\" align=\"char\" char=\"±\" rowspan=\"1\" colspan=\"1\">6.33 ± 2.48</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.016*</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold>  Cognitive disturbance</bold>\n</td><td valign=\"top\" align=\"char\" char=\"±\" rowspan=\"1\" colspan=\"1\">3.97 ± 1.98</td><td valign=\"top\" align=\"char\" char=\"±\" rowspan=\"1\" colspan=\"1\">4.37 ± 1.76</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.417</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold>  Retardation</bold>\n</td><td valign=\"top\" align=\"char\" char=\"±\" rowspan=\"1\" colspan=\"1\">7.84 ± 1.55</td><td valign=\"top\" align=\"char\" char=\"±\" rowspan=\"1\" colspan=\"1\">8.26 ± 1.89</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.357</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold>  Hopelessness</bold>\n</td><td valign=\"top\" align=\"char\" char=\"±\" rowspan=\"1\" colspan=\"1\">5.22 ± 1.95</td><td valign=\"top\" align=\"char\" char=\"±\" rowspan=\"1\" colspan=\"1\">5.00 ± 2.11</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.681</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold>  Sleep disturbance</bold>\n</td><td valign=\"top\" align=\"char\" char=\"±\" rowspan=\"1\" colspan=\"1\">4.63 ± 1.29</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.67 ± 1.71</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.007**</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold>  Weight loss</bold>\n</td><td valign=\"top\" align=\"char\" char=\"±\" rowspan=\"1\" colspan=\"1\">1.19 ± 0.93</td><td valign=\"top\" align=\"char\" char=\"±\" rowspan=\"1\" colspan=\"1\">0.52 ± 0.89</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.719</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold>  Circadian fluctuation</bold>\n</td><td valign=\"top\" align=\"char\" char=\"±\" rowspan=\"1\" colspan=\"1\">0.31 ± 0.59</td><td valign=\"top\" align=\"char\" char=\"±\" rowspan=\"1\" colspan=\"1\">0.26 ± 0.53</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.603</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold>Total score of CTQ</bold>\n</td><td valign=\"top\" align=\"char\" char=\"±\" rowspan=\"1\" colspan=\"1\">50.75 ± 11.7</td><td valign=\"top\" align=\"char\" char=\"±\" rowspan=\"1\" colspan=\"1\">54.85 ± 14.11</td><td valign=\"top\" align=\"char\" char=\"±\" rowspan=\"1\" colspan=\"1\">44.67 ± 3.76</td><td valign=\"top\" align=\"char\" char=\"±\" rowspan=\"1\" colspan=\"1\">0.018*</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold>Subtypes of CTQ</bold>\n</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold>  Emotional abuse</bold>\n</td><td valign=\"top\" align=\"char\" char=\"±\" rowspan=\"1\" colspan=\"1\">8.94 ± 3.88</td><td valign=\"top\" align=\"char\" char=\"±\" rowspan=\"1\" colspan=\"1\">10.30 ± 5.53</td><td valign=\"top\" align=\"char\" char=\"±\" rowspan=\"1\" colspan=\"1\">6.78 ± 1.11</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.025*</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold>  Physical abuse</bold>\n</td><td valign=\"top\" align=\"char\" char=\"±\" rowspan=\"1\" colspan=\"1\">6.47 ± 2.26</td><td valign=\"top\" align=\"char\" char=\"±\" rowspan=\"1\" colspan=\"1\">6.89 ± 2.93</td><td valign=\"top\" align=\"char\" char=\"±\" rowspan=\"1\" colspan=\"1\">4.94 ± 0.24</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.019*</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold>  Sexual abuse</bold>\n</td><td valign=\"top\" align=\"char\" char=\"±\" rowspan=\"1\" colspan=\"1\">5.72 ± 1.87</td><td valign=\"top\" align=\"char\" char=\"±\" rowspan=\"1\" colspan=\"1\">5.81 ± 1.67</td><td valign=\"top\" align=\"char\" char=\"±\" rowspan=\"1\" colspan=\"1\">5.00 ± 0.00</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.195</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold>  Emotional neglect</bold>\n</td><td valign=\"top\" align=\"char\" char=\"±\" rowspan=\"1\" colspan=\"1\">13.38 ± 4.69</td><td valign=\"top\" align=\"char\" char=\"±\" rowspan=\"1\" colspan=\"1\">15.48 ± 5.32</td><td valign=\"top\" align=\"char\" char=\"±\" rowspan=\"1\" colspan=\"1\">10.89 ± 2.42</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.005**</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold>  Physical neglect</bold>\n</td><td valign=\"top\" align=\"char\" char=\"±\" rowspan=\"1\" colspan=\"1\">9.19 ± 3.73</td><td valign=\"top\" align=\"char\" char=\"±\" rowspan=\"1\" colspan=\"1\">9.41 ± 4.12</td><td valign=\"top\" align=\"char\" char=\"±\" rowspan=\"1\" colspan=\"1\">7.22 ± 2.39</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.110</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/></tr></tbody></table><table-wrap-foot><fn><p>UD, unipolar depression; BD, bipolar depression; HC, healthy control; HAM-D24, 24-item Hamilton Depression Scale; CTQ, Childhood Trauma Questionnaire. *p < 0.05, **p < 0.01.</p></fn></table-wrap-foot></table-wrap></sec><sec id=\"s3_2\"><title>miRNA Expression Profiles</title><p>Seven UD patients, seven BD patients, and six healthy controls were randomly selected from the total sample to undergo microarray chip inspection. There were no differences in the subjects’ demographic characteristics, the overall severity of depression, or childhood traumatic experiences (see <xref ref-type=\"supplementary-material\" rid=\"SM1\">\n<bold>Supplementary data 2</bold>\n</xref>). Total RNA extracted from peripheral venous blood was analyzed using Affymetrix miRNA 4.0 Arrays. The altered miRNA expression in UD and BD patients compared to healthy controls are listed in <xref ref-type=\"supplementary-material\" rid=\"SM1\">\n<bold>Supplementary data 3</bold>\n</xref>. Seventeen miRNAs were down-regulated, and 25 miRNAs were up-regulated (<italic>p</italic><0.05).</p><p>Hierarchical clustering was carried out for all covered human mature miRNAs and pre-miRNAs, and significant differences in expression values were observed between the patients diagnosed with UD and BD and healthy controls. (<xref ref-type=\"fig\" rid=\"f1\">\n<bold>Figure 1</bold>\n</xref>). Following hierarchical clustering, the array results were narrowed down to dysregulated miRNAs with a <italic>p</italic>-value < 0.05 from one-way ANOVA analysis of the three groups and a fold change ≥3/2 or ≤2/3 for all the two-group comparisons. Based on these criteria, three representative miRNAs (miR-19b-3p, miR-3921, and miR-1180-3p) were selected for PCR to validate the microarray results.</p><fig id=\"f1\" position=\"float\"><label>Figure 1</label><caption><p>\n<bold>(A)</bold> Volcano map of differentially expressed miRNAs. The horizontal axis is the logarithm of the fold change value based on 2, and the vertical axis is the negative logarithm of the p value based on 10. Blue dots represent down-regulated miRNAs with statistically significant differences in patients comparing healthy subjects. Red dots represent miRNAs that are up-regulated in patients, and grey dots represent miRNAs that have no significant differences in expression values between the patients and healthy controls. <bold>(B)</bold> Heat map of differentially expressed miRNAs. The abscissa represents the subject names between groups, and the ordinate represents the differentially expressed miRNAs. Differentiated miRNAs are expressed in red with high expression values and those with low expression values are in green.</p></caption><graphic xlink:href=\"fpsyt-11-00757-g001\"/></fig></sec><sec id=\"s3_3\"><title>Verification Using Quantitative Real-Time PCR</title><p>Quantitative real-time PCR (qRT-PCR) was performed to verify the differentially expressed miRNAs in an expanded sample comprised of 32 UD patients and 27 BD patients. Only miRNAs with a fold change ≥3/2 or ≤2/3 and a <italic>p</italic>-value < 0.05 from Student’s t-tests for each two-group comparison were validated. Of these, miR-19b-3p was verified to be down-regulated (see <xref ref-type=\"fig\" rid=\"f2\">\n<bold>Figure 2</bold>\n</xref>).</p><fig id=\"f2\" position=\"float\"><label>Figure 2</label><caption><p>Scatter plot figures show qRT-PCR validation of the differential expression of three miRNAs among three groups (unipolar patients (UD), bipolar patients (BD), and healthy controls (HC) <bold>(A)</bold> miR-19b-3p, <bold>(B)</bold> miR-3921, <bold>(C)</bold> miR-1180-3p). The values of 2-ΔΔCT represent the expressed level of filtered miRNAs. A <italic>p</italic>-value of < 0.05 means statistically significant.</p></caption><graphic xlink:href=\"fpsyt-11-00757-g002\"/></fig></sec><sec id=\"s3_4\"><title>Microarray-Based Gene Ontology and Signal Pathway Analysis</title><p>Based on the GO and KEGG signal pathway analyses, we first predicted mRNAs that could be regulated by miR-19b-3p. Then we performed an enrichment analysis to infer the putative biological pathways that might be involved in the miRNA regulation. We detected 288 mRNAs regulated by miR-19b-3p (see <xref ref-type=\"supplementary-material\" rid=\"SM1\">\n<bold>Supplementary Data 4</bold>\n</xref>). Significant gene functions and pathways putatively altered in UD and BD patients were selected based on the standards of <italic>p</italic><0.01 (GO) and <italic>p</italic><0.05 (KEGG). GO results revealed that most functions associated with miR-19b-3p regulation were related to signal transduction, neuron growth, cell differentiation, and apoptosis (<xref ref-type=\"fig\" rid=\"f3\">\n<bold>Figure 3A</bold>\n</xref>). The most relevant KEGG signal pathways were involved in enrichment of biological processes, which included mTOR, autophagy, FoxO, prolactin, p53, and the PI3-K/Akt signaling pathway (<xref ref-type=\"fig\" rid=\"f3\">\n<bold>Figure 3B</bold>\n</xref>). A more intuitive miRNA-mRNA-gene network diagram was produced to investigate the potential role of miR-19b-3p in the pathogenesis of depression (<xref ref-type=\"fig\" rid=\"f3\">\n<bold>Figure 3C</bold>\n</xref>). The most relevant target genes were MAPK1, PTEN, TGFBR2, PRKAA1, PIK3R3, and RAF1, which were involved in multiple pathways.</p><fig id=\"f3\" position=\"float\"><label>Figure 3</label><caption><p>\n<bold>(A)</bold> GO analysis of target genes predicted by miR-19b-3p. The ordinate is the name of the target gene function, and the abscissa is the negative logarithm of <italic>p</italic> value (-Lg<italic>p</italic>). The larger the -Lg<italic>p</italic> value, the smaller the <italic>p</italic> value and the higher the significance level of the target gene function. <bold>(B)</bold> KEGG signal pathway of target genes predicted by miR-19b-3p. The ordinate is the name of the target gene signal pathway, and the abscissa is the negative logarithm of <italic>p</italic> value (-Lg<italic>p</italic>). The larger the -Lg<italic>p</italic> value, the smaller the <italic>p</italic> value and the higher the significance level of the target gene signal pathway. <bold>(C)</bold> MicroRNA-Gene network diagram of miR-19b-3p. The blue square in the figure refers to miR-19b-3p, the red circle refers to target genes, and the green arrow refers to the signal pathways involved in the target gene. Lines represent the regulatory relationship between miR-19b-3p and target genes.</p></caption><graphic xlink:href=\"fpsyt-11-00757-g003\"/></fig></sec><sec id=\"s3_5\"><title>Correlation Between miRNA Expression and Stress-Related Psychological Variables</title><p>A binary logistic regression model was produced to examine the correlation between miR-19b-3p expression and exposure to childhood trauma in patients with UD and BD, with adjustments for demographic variables. The results showed that the expression level of miR-19b-3p (OR=5.717, 95%CI:1.497–21.835, <italic>p</italic>=0.011) and the overall severity of childhood trauma (OR=1.099, 95%CI:1.012-1.192, <italic>p</italic>=0.025) were significantly associated with greater risk of BD. Among the sub-types of childhood trauma (emotional abuse, physical abuse, sexual abuse, emotional neglect, and physical neglect), there was a weak association between physical neglect and UD (OR=0.773, 95%CI:0.598-0.999, <italic>p</italic>=0.049) (<xref rid=\"T2\" ref-type=\"table\">\n<bold>Table 2</bold>\n</xref>). The other specific types of childhood trauma were not significantly correlated with any morbid status.</p><table-wrap id=\"T2\" position=\"float\"><label>Table 2</label><caption><p>The logistic regression of miR-19b-3p and childhood trauma characteristics between the patients with UD and BD.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"> B</th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">\n<italic>p</italic> value</th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">OR</th><th valign=\"top\" colspan=\"2\" align=\"center\" rowspan=\"1\"> 95%CI</th></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">miR-19b-3p</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.743</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.011*</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5.717</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.497</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">21.835</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Total score of CTQ</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.094</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.025*</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.099</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.012</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.192</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Physical neglect</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−0.258</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.049*</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.773</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.598</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.999</td></tr></tbody></table><table-wrap-foot><p>UD, unipolar depression; BD, bipolar depression; CTQ, childhood trauma questionnaire. Physical neglect: a subtype of childhood trauma. *p < 0.05</p></table-wrap-foot></table-wrap></sec></sec><sec sec-type=\"discussion\" id=\"s4\"><title>Discussion</title><p>In the present study, we systematically investigated the genome-wide miRNA expression profile in plasma from patients with UD and BD as compared to healthy subjects. Among 42 differentially expressed miRNAs, three miRNAs (miR-19b-3p, miR-3921, and miR-1180-3p) were selected to validate the microarray chip results. The novel miR-3921 was significantly up-regulated in the UD group but was not verified. miR-1180-3p was up-regulated in UD and BD patients but also was not verified. To date, no published reports have associated these two miRNAs with psychiatric disorders. However, miR-19b-3p was significantly down-regulated in the patient group, which indicates that peripheral miRNAs are possible non-invasive biomarkers with the necessary diagnostic accuracy. Furthermore, a specific miRNA related to psychological factors might help differentiate BD from UD. The BD patients were more likely to exhibit the combination of over-expressed miR-19b-3p and more severe childhood trauma when UD and BD patients were compared.</p><sec id=\"s4_1\"><title>miR-19b-3p and Its Pathology in Psychiatric Diseases</title><p>MiR-19b-3p belongs to the miR-17/92 cluster, which exerts powerful effects on lymphocyte development, proliferation, activation, differentiation, and cytokine production (<xref rid=\"B40\" ref-type=\"bibr\">40</xref>, <xref rid=\"B41\" ref-type=\"bibr\">41</xref>). Gantier et al. first reported that miR-19b regulated the activity of nuclear factor-κB (NF-κB) signaling in inflammation (<xref rid=\"B42\" ref-type=\"bibr\">42</xref>). Over-expression of miR-19b-3p inhibited the production of IL-6 and IL-8, and the interaction of miR-19b-3p with its direct target gene, G protein-coupled receptor kinase 6 (GRK6), was discovered to affect inflammation (<xref rid=\"B43\" ref-type=\"bibr\">43</xref>).</p><p>TNFAIP3, which is negatively regulated by miR-19b-3p, is widely recognized as an important regulator of inflammation (<xref rid=\"B44\" ref-type=\"bibr\">44</xref>). An intriguing role for miR-19b-3p is to regulate the neuroinflammatory response induced by the Japanese encephalitis virus (JEV) <italic>via</italic> enhancement of NF-κB signaling (<xref rid=\"B45\" ref-type=\"bibr\">45</xref>). Dwivedi et al. reported that miR-19b was over-expressed in the prefrontal cortex of rats with stress-induced depression with chronic administration of exogenous corticosterone (<xref rid=\"B46\" ref-type=\"bibr\">46</xref>). Although the epigenetic mechanisms of miR-19b-3p in psychiatric disorders remain unclear, various studies point to the effects of this miRNA in the communication between the immune system and the brain, which is known as a new area in psychiatry, specifically, immunopsychiatry.</p></sec><sec id=\"s4_2\"><title>The Target Genes of miR-19b-3p in Unipolar Depression and Bipolar Disorder</title><p>Our in silico results predicted a series of target genes and biological pathways for miR-19b-3p. Since patients with major depression who experienced childhood trauma were susceptible to immune dysregulation, we first check the biological processes associated with miR-19b-3p in immunomodulation between UD patients and BD patients. GO analysis predicted that the target gene function of miR-19b-3p, was enriched in the Wnt signaling pathway. It is consistent with a previous study that dysregulated expression of Wnt-related genes was shown in BD (<xref rid=\"B47\" ref-type=\"bibr\">47</xref>). Numerous studies have shown that suppression of Wnt signaling could induce both manic and depressive behaviors and exacerbate a proinflammatory state leading to increased neuronal apoptosis (<xref rid=\"B48\" ref-type=\"bibr\">48</xref>). In our study, the Wnt pathway was the most significantly enriched biological function, indicating its potential role in identifying UD and BD patients.</p><p>KEGG enrichment analyses predicted that the most significant target gene pathway was the mammalian rapamycin (mTOR) signaling pathway. Accumulating evidences suggested that mTOR signaling was dysregulated in depression (<xref rid=\"B49\" ref-type=\"bibr\">49</xref>). Activated mTOR signaling might be related to antidepressant-like effects in the hippocampus by modulating inflammatory cytokines such as interleukin-1β (IL-1β), interleukin-6 (IL-6), and tumor necrosis factor-α (TNF-α) (<xref rid=\"B50\" ref-type=\"bibr\">50</xref>). Autophagy signaling was the second, riched pathway regulated by miR-19b-3p. Dysregulation of autophagy leads to various disease manifestations, such as inflammation, metabolic alterations, and neurodegeneration (<xref rid=\"B51\" ref-type=\"bibr\">51</xref>). Some antidepressants induce inflammatory suppression through decreased serum levels of IL-1β and IL-18 and decreased NLRP3 protein expression <italic>via</italic> the autophagy pathway (<xref rid=\"B52\" ref-type=\"bibr\">52</xref>). Recent findings indicated that the forkhead box O (FoxO) signaling pathway is involved in the development of major depression and constitutes a potential therapeutic target in the treatment of depression (<xref rid=\"B53\" ref-type=\"bibr\">53</xref>). Although there is no evidence for FoxO involvement in BD, our results suggested that this pathway played an important role <italic>via</italic> miR-19b-3p in both UD and BD.</p><p>The prolactin pathway was identified in the differentiation between UD and BD. Hyperprolactinemia is reported commonly in subjects with a psychotic disorder which could due to stress, while information regarding mood disorder patients was particularly lacking (<xref rid=\"B54\" ref-type=\"bibr\">54</xref>). The p53 signaling, identified differently between UD and BD patients in our study, has not been reported to be associated with psychiatric disorders previously. Although the phosphatidylinositol 3-kinase (PI3K)-Akt signaling pathway was less strongly associated with miR-19b-3p expression, it contains the largest number of target genes. PI3K-Akt signaling is involved in the inhibition of the inflammatory response of lipoteichoic acid-stimulated macrophages (<xref rid=\"B55\" ref-type=\"bibr\">55</xref>). Reports indicate that some antidepressants used in clinical practice exert therapeutic efficacy <italic>via</italic> the promotion of the hippocampal PI3k-Akt-mTOR signaling pathway (<xref rid=\"B56\" ref-type=\"bibr\">56</xref>).</p><p>Mitogen-activated protein kinase 1 (MAPK1) had the highest predictive value among all the target genes of miR-19b-3p. Emerging evidence has revealed that alteration of MAPK1 is associated with psychiatric disorders, such as major depressive disorders, bipolar disorder and schizophrenia (<xref rid=\"B57\" ref-type=\"bibr\">57</xref>, <xref rid=\"B58\" ref-type=\"bibr\">58</xref>). MAPK1 is highly expressed in the prefrontal cortex and hippocampus, and can modulate neuronal growth and differentiation, synaptic plasticity, and inflammatory processes <italic>via</italic> mTOR, FoxO, and other signaling pathways (<xref rid=\"B59\" ref-type=\"bibr\">59</xref>, <xref rid=\"B60\" ref-type=\"bibr\">60</xref>).</p><p>Phosphatase and tensin homolog (PTEN) is a significant target of miR-19b-3p, and it directly regulates the PI3-K-Akt-mTOR signaling pathway, which is regarded as the most critical pathway for many neurobiological functions in the brain. PTEN regulates neuron cell size and affects dendritic growth, and it also acts as a significant tumor suppressor gene through the modulation of the inflammatory process (<xref rid=\"B61\" ref-type=\"bibr\">61</xref>, <xref rid=\"B62\" ref-type=\"bibr\">62</xref>). Altered expression of PTEN in the blood is considered a biomarker for suicidal tendencies (<xref rid=\"B63\" ref-type=\"bibr\">63</xref>), as well as in the prefrontal cortex and hippocampus of suicide victims (<xref rid=\"B64\" ref-type=\"bibr\">64</xref>). The possibility of using miR-19b-3p and its target genes, such as PTEN to identify stress-related neuropathology in mood disorders, is encouraging. An upstream molecule in the mTOR pathway (PRKAA1) was down-regulated by miR-181a and facilitated hippocampal fear memory consolidation (<xref rid=\"B65\" ref-type=\"bibr\">65</xref>). Fear inhibition is related to trauma events (<xref rid=\"B66\" ref-type=\"bibr\">66</xref>). Therefore, PRKAA1 is one of significant target genes of miR-19b-3p that could be associated with stress-induced bipolar patients <italic>via</italic> activation of the mTOR pathway.</p></sec><sec id=\"s4_3\"><title>Childhood Traumatic Exposure and miRNA Alterations in Depressive Patients</title><p>Early life adversities, such as emotional, physical, and sexual abuse or neglect in childhood, are associated with poor psychological health outcomes in adults. Childhood traumatic experiences play an important role in developing BD, induction of more severe clinical symptoms, impairing emotion regulation and cognitive function, and lead to a much higher risk for suicide (<xref rid=\"B28\" ref-type=\"bibr\">28</xref>). Recent studies have explored the epigenetic mechanisms between childhood trauma and major depression, as well as schizophrenia (<xref rid=\"B14\" ref-type=\"bibr\">14</xref>). Our findings provide a significant association between childhood traumatic experiences and altered expression of miR-19b-3p in BD. Based on our bioanalysis results, plasma miR-19b-3p might be a biomarker associated with the impact of childhood trauma on UD and BD through the involvement of inflammatory processes.</p><p>Increased inflammation has been described in healthy individuals exposed to childhood trauma, suggesting a potentially causal role in the future onset of depression (<xref rid=\"B67\" ref-type=\"bibr\">67</xref>, <xref rid=\"B68\" ref-type=\"bibr\">68</xref>). The Dwivedi group focused on epigenetic mechanisms of depression and suicide and recently reported that proinflammatory cytokines (e.g., TNF-α) and miR-19a-3b were up-regulated in the dorsolateral prefrontal cortex (DLPFC) in suicide victims, and both molecules were increased in the peripheral blood mononuclear cells of depressed patients with severe suicidal ideation (<xref rid=\"B69\" ref-type=\"bibr\">69</xref>).</p><p>Another important finding showed that unipolar patients with the lowest expression of peripheral miR-19p-3b among the three groups experienced more physical neglect, which has not been reported previously. The effects of neglect can be as traumatic as or even more traumatic than the effects of abuse, and the traumatic effects persist into adulthood (<xref rid=\"B70\" ref-type=\"bibr\">70</xref>). Neglect has several forms. Physical neglect could prompt the individual to develop psychological unavailability, which impacts the development of depressive symptoms. Further research is needed on possible epigenetic mechanisms of neglect associated with miRNAs.</p></sec><sec id=\"s4_4\"><title>Strengths and Limitations</title><p>This study has some strengths. First, we stringently recruited subjects in the screening phase. There were nearly identical clinical manifestations of the unipolar and bipolar patients, which could exclude possible confounding caused by distinct profiling between the two groups. Second, the differentiated miRNAs were systematically screened through the whole miRNome profiling on unipolar and bipolar depressive patients and healthy controls and was validated in a larger independent sample to increase the reliability of the results. Third, we provided an in-depth discussion of the possible miRNA targets and the integration of potential molecular mechanisms with environmental factors. We explored the possible use of peripheral miRNAs as non-invasive diagnostic biomarkers of mood disorders from an immune-psychiatry perspective and prompted further research on the role of miR-19p-3b in depression.</p><p>Nevertheless, the relationship of miRNA expression in plasma and the brain is not clear, since we only observed alterations in peripheral miRNAs. It is a concern that there were two selected miRNAs eliminated in the validation phase, which might be due to the limited sample size. A miRNA panel would be a more robust method of diagnosis of UD or BD. We did not examine any circulating inflammatory factors that were potentially related to the disorders. Therefore, the association between miRNA expression and immunological regulation remains unclear. A well-designed study would need to be conducted to develop a psychoneuroimmunology network of childhood trauma, immunological indices, altered miRNAs, and the disorder.</p></sec></sec><sec sec-type=\"conclusions\" id=\"s5\"><title>Conclusions</title><p>This study showed that the expression profiling of plasma miRNAs was altered both in UD and BD. miR-19b-3p was down-regulated in patients with depression and was validated to be over-expressed in BD but not UD. The target genes for miR-19b-3p were primarily enriched in the Wnt signaling and mTOR pathways. These miRNA gene targets elucidate the pathways and mechanisms involved in neuroimmunology pathways and depression pathogenesis.</p></sec><sec sec-type=\"data-availability\" id=\"s6\"><title>Data Availability Statement</title><p>All data have been submitted to the GEO repository (code number: GSE152267).</p></sec><sec id=\"s7\"><title>Ethics Statement</title><p>The studies involving human participants were reviewed and approved by Affiliated Brain Hospital of Nanjing Medical University ethics committee. The patients/participants provided their written informed consent to participate in this study.</p></sec><sec id=\"s8\"><title>Author Contributions</title><p>YC: collected data, conducted the statistical analysis, drafted the manuscript, edited, and submitted the manuscript. JS, HL, QW, XC, HT, RY: collected data, reviewed, and revised the manuscript. QL: statistical analysis, critically reviewed, edited, and revised the manuscript. ZY: conceptualized and designed the study, critically reviewed, and revised the manuscript.</p></sec><sec sec-type=\"funding-information\" id=\"s9\"><title>Funding</title><p>This work was supported by National Key R&D Program of China under grant number 2018YFC1314600; The National Natural Science Foundation of China under grant number 81871066,81571639; Jiangsu Provincial Medical Innovation Team of the Project of Invigorating Health Care through Science, Technology and Education under grant number CXTDC2016004; Jiangsu Provincial key research and development program under grant number BE2018609; Jiangsu Provincial Medical Youth Talent-The Project of Invigorating Health Care through Science, Technology and Education, QNRC2016049; The Science and Technology Program of Nanjing, 201803030.</p></sec><sec id=\"s10\"><title>Conflict of Interest</title><p>The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p></sec></body><back><ack><title>Acknowledgments</title><p>We thank all subjects for participating in this study.</p></ack><sec id=\"s11\" sec-type=\"supplementary-material\"><title>Supplementary Material</title><p>The Supplementary Material for this article can be found online at: <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.frontiersin.org/articles/10.3389/fpsyt.2020.00757/full#supplementary-material\">https://www.frontiersin.org/articles/10.3389/fpsyt.2020.00757/full#supplementary-material</ext-link>\n</p><supplementary-material content-type=\"local-data\" id=\"SM1\"><media xlink:href=\"DataSheet_1.docx\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material></sec><ref-list><title>References</title><ref id=\"B1\"><label>1</label><mixed-citation publication-type=\"journal\">\n<person-group 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"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"methods-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Front Plant Sci</journal-id><journal-id journal-id-type=\"iso-abbrev\">Front Plant Sci</journal-id><journal-id journal-id-type=\"publisher-id\">Front. Plant Sci.</journal-id><journal-title-group><journal-title>Frontiers in Plant Science</journal-title></journal-title-group><issn pub-type=\"epub\">1664-462X</issn><publisher><publisher-name>Frontiers Media S.A.</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32849706</article-id><article-id pub-id-type=\"pmc\">PMC7432144</article-id><article-id pub-id-type=\"doi\">10.3389/fpls.2020.01148</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Plant Science</subject><subj-group><subject>Methods</subject></subj-group></subj-group></article-categories><title-group><article-title>Mathematical Modeling of Growth and Paclitaxel Biosynthesis in <italic>Corylus avellana</italic> Cell Culture Responding to Fungal Elicitors Using Multilayer Perceptron-Genetic Algorithm</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Salehi</surname><given-names>Mina</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><xref ref-type=\"author-notes\" rid=\"fn001\"><sup>*</sup></xref><uri xlink:type=\"simple\" xlink:href=\"https://loop.frontiersin.org/people/627971\"/></contrib><contrib contrib-type=\"author\"><name><surname>Farhadi</surname><given-names>Siamak</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><uri xlink:type=\"simple\" xlink:href=\"https://loop.frontiersin.org/people/364094\"/></contrib><contrib contrib-type=\"author\"><name><surname>Moieni</surname><given-names>Ahmad</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><xref ref-type=\"author-notes\" rid=\"fn001\"><sup>*</sup></xref><uri xlink:type=\"simple\" xlink:href=\"https://loop.frontiersin.org/people/440889\"/></contrib><contrib contrib-type=\"author\"><name><surname>Safaie</surname><given-names>Naser</given-names></name><xref ref-type=\"aff\" rid=\"aff2\"><sup>2</sup></xref><xref ref-type=\"author-notes\" rid=\"fn001\"><sup>*</sup></xref><uri xlink:type=\"simple\" xlink:href=\"https://loop.frontiersin.org/people/441184\"/></contrib><contrib contrib-type=\"author\"><name><surname>Ahmadi</surname><given-names>Hamed</given-names></name><xref ref-type=\"aff\" rid=\"aff3\"><sup>3</sup></xref><uri xlink:type=\"simple\" xlink:href=\"https://loop.frontiersin.org/people/265871\"/></contrib></contrib-group><aff id=\"aff1\"><sup>1</sup><institution>Department of Plant Genetics and Breeding, Faculty of Agriculture, Tarbiat Modares University</institution>, <addr-line>Tehran</addr-line>, <country>Iran</country></aff><aff id=\"aff2\"><sup>2</sup><institution>Department of Plant Pathology, Faculty of Agriculture, Tarbiat Modares University</institution>, <addr-line>Tehran</addr-line>, <country>Iran</country></aff><aff id=\"aff3\"><sup>3</sup><institution>Bioscience and Agriculture Modeling Research Unit, Department of Poultry Science, Tarbiat Modares University</institution>, <addr-line>Tehran</addr-line>, <country>Iran</country></aff><author-notes><fn fn-type=\"edited-by\"><p>Edited by: Xianwen Ren, Peking University, China</p></fn><fn fn-type=\"edited-by\"><p>Reviewed by: Bingqiang Liu, Shandong University, China; Junjie Yue, Institute of Biotechnology (CAAS), China</p></fn><corresp id=\"fn001\">*Correspondence: Mina Salehi, <email xlink:href=\"mailto:[email protected]\" xlink:type=\"simple\">[email protected]</email>; <email xlink:href=\"mailto:[email protected]\" xlink:type=\"simple\">[email protected]</email>; Ahmad Moieni, <email xlink:href=\"mailto:[email protected]\" xlink:type=\"simple\">[email protected]</email>; Naser Safaie, <email xlink:href=\"mailto:[email protected]\" xlink:type=\"simple\">[email protected]</email></corresp><fn fn-type=\"other\" id=\"fn002\"><p>This article was submitted to Computational Genomics, a section of the journal Frontiers in Plant Science</p></fn></author-notes><pub-date pub-type=\"epub\"><day>11</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><year>2020</year></pub-date><volume>11</volume><elocation-id>1148</elocation-id><history><date date-type=\"received\"><day>22</day><month>4</month><year>2020</year></date><date date-type=\"accepted\"><day>14</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>Copyright © 2020 Salehi, Farhadi, Moieni, Safaie and Ahmadi</copyright-statement><copyright-year>2020</copyright-year><copyright-holder>Salehi, Farhadi, Moieni, Safaie and Ahmadi</copyright-holder><license xlink:href=\"http://creativecommons.org/licenses/by/4.0/\"><license-p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</license-p></license></permissions><abstract><p>Paclitaxel is the top-selling anticancer medicine in the world. <italic>In vitro</italic> culture of <italic>Corylus avellana</italic> has been made known as a promising and inexpensive strategy for producing paclitaxel. Fungal elicitors have been named as the most efficient strategy for enhancing the biosynthesis of secondary metabolites in plant cell culture. In this study, endophytic fungal strain HEF<sub>17</sub> was isolated from <italic>C. avellana</italic> and identified as <italic>Camarosporomyces flavigenus</italic>. <italic>C. avellana</italic> cell suspension culture (CSC) elicited with cell extract (CE) and culture filtrate (CF) derived from strain HEF<sub>17</sub>, either individually or combined treatment, in mid and late log phase was processed for modeling and optimizing growth and paclitaxel biosynthesis regarding CE and CF concentration levels, elicitor adding day, and CSC harvesting time using multilayer perceptron-genetic algorithm (MLP-GA). The results displayed higher accuracy of MLP-GA models (0.89–0.95) than regression models (0.56–0.85). The great accordance between the predicted and observed values of output variables (dry weight, intracellular, extracellular and total yield of paclitaxel, and also extracellular paclitaxel portion) for both training and testing subsets supported the excellent performance of developed MLP-GA models. MLP-GA method presented a promising tool for selecting the optimal conditions for maximum paclitaxel biosynthesis. An Excel<sup>®</sup> estimator, HCC-paclitaxel, was designed based on MLP-GA model as an easy-to-use tool for predicting paclitaxel biosynthesis in <italic>C. avellana</italic> CSC responding to fungal elicitors.</p></abstract><kwd-group><kwd>secondary metabolite</kwd><kwd>endophytic fungus</kwd><kwd>cell extract</kwd><kwd>culture filtrate</kwd><kwd>artificial neural network</kwd></kwd-group><counts><fig-count count=\"6\"/><table-count count=\"3\"/><equation-count count=\"1\"/><ref-count count=\"54\"/><page-count count=\"12\"/><word-count count=\"5592\"/></counts></article-meta></front><body><sec sec-type=\"intro\" id=\"s1\"><title>Introduction</title><p>Paclitaxel is a potent mitotic inhibitor that is utilized for treating breast, lung and ovarian cancers, and Kaposi’s sarcoma (<xref rid=\"B51\" ref-type=\"bibr\">Weaver, 2014</xref>), so that it has been entitled the top-selling anticancer medicine in the world (<xref rid=\"B17\" ref-type=\"bibr\">Goodman and Walsh, 2001</xref>). Also, this impactful chemotherapeutic agent is used for off-label treatment of endometrial, gastroesophageal, prostate, cervical, and head and neck cancers (<xref rid=\"B51\" ref-type=\"bibr\">Weaver, 2014</xref>). Invaluable secondary metabolite “paclitaxel” was initially extracted from <italic>Taxus</italic> bark (<xref rid=\"B52\" ref-type=\"bibr\">Wheeler et al., 1992</xref>). But harvesting the bark of these valuable species in the natural areas speedily exceeded levels deemed as a sustainable one, and critical over-harvesting has caused <italic>Taxus</italic> wild populations to be on the brink of extinction worldwide (<xref rid=\"B46\" ref-type=\"bibr\">Shinwari and Qaiser, 2011</xref>). Plant cell factories are a promising environmentally sustainable alternative to paclitaxel mass production (<xref rid=\"B34\" ref-type=\"bibr\">Salehi et al., 2017</xref>; <xref rid=\"B12\" ref-type=\"bibr\">Espinosa-Leal et al., 2018</xref>; <xref rid=\"B39\" ref-type=\"bibr\">Salehi et al., 2019b</xref>; <xref rid=\"B40\" ref-type=\"bibr\">Salehi et al., 2019c</xref>). The rising demand for paclitaxel and <italic>Taxus</italic> recalcitrant behavior under <italic>in vitro</italic> conditions have caused extensive effort toward finding alternatives for producing this invaluable secondary metabolite.</p><p><italic>In vitro</italic> culture of hazel (<italic>Corylus avellana</italic>, European filbert) has been made known as a promising and inexpensive strategy for producing paclitaxel (<xref rid=\"B16\" ref-type=\"bibr\">Gallego et al., 2017</xref>; <xref rid=\"B34\" ref-type=\"bibr\">Salehi et al., 2017</xref>; <xref rid=\"B37\" ref-type=\"bibr\">Salehi et al., 2018c</xref>; <xref rid=\"B39\" ref-type=\"bibr\">Salehi et al., 2019b</xref>; <xref rid=\"B40\" ref-type=\"bibr\">Salehi et al., 2019c</xref>; <xref rid=\"B13\" ref-type=\"bibr\">Farhadi et al., 2020</xref>; <xref rid=\"B42\" ref-type=\"bibr\">Salehi et al., 2020</xref>). Biosynthesizing bioactive compounds in plants is influenced by various factors (<xref rid=\"B50\" ref-type=\"bibr\">Torkamani et al., 2014</xref>; <xref rid=\"B34\" ref-type=\"bibr\">Salehi et al., 2017</xref>; <xref rid=\"B35\" ref-type=\"bibr\">Salehi et al., 2018a</xref>; <xref rid=\"B36\" ref-type=\"bibr\">Salehi et al., 2018b</xref>; <xref rid=\"B37\" ref-type=\"bibr\">Salehi et al., 2018c</xref>; <xref rid=\"B38\" ref-type=\"bibr\">Salehi et al., 2019a</xref>; <xref rid=\"B39\" ref-type=\"bibr\">Salehi et al., 2019b</xref>; <xref rid=\"B40\" ref-type=\"bibr\">Salehi et al., 2019c</xref>; <xref rid=\"B41\" ref-type=\"bibr\">Salehi et al., 2019d</xref>). Previous studies (<xref rid=\"B39\" ref-type=\"bibr\">Salehi et al., 2019b</xref>; <xref rid=\"B40\" ref-type=\"bibr\">Salehi et al., 2019c</xref>; <xref rid=\"B13\" ref-type=\"bibr\">Farhadi et al., 2020</xref>; <xref rid=\"B42\" ref-type=\"bibr\">Salehi et al., 2020</xref>) demonstrated the positive influences of cell extract (CE) and culture filtrate (CF) of endophytic fungi on paclitaxel biosynthesis in cell suspension culture (CSC) of <italic>C. avellana</italic>. Fungal elicitor type, concentration and adding time, and also exposure time of cell culture (CSC harvesting time) should be optimized to achieve the maximum biosynthesis of paclitaxel in <italic>C. avellana</italic> CSC (<xref rid=\"B39\" ref-type=\"bibr\">Salehi et al., 2019b</xref>; <xref rid=\"B40\" ref-type=\"bibr\">Salehi et al., 2019c</xref>; <xref rid=\"B13\" ref-type=\"bibr\">Farhadi et al., 2020</xref>; <xref rid=\"B42\" ref-type=\"bibr\">Salehi et al., 2020</xref>). Precise analysis of the effects of these factors and their optimal selection would pave the way for the commercialization of bioprocessing <italic>C. avellana</italic> cells toward paclitaxel mass production. Paclitaxel biosynthesis and its elicitation are complex biological processes since they are affected by several factors and their nonlinear interactions. Optimizing these mentioned factors by experimenting is laborious, costly, and time-consuming. The mathematical models can effectively predict the optimized conditions for a multifactorial process (<xref rid=\"B48\" ref-type=\"bibr\">Struik et al., 2005</xref>; <xref rid=\"B15\" ref-type=\"bibr\">Gallego et al., 2011</xref>) such as paclitaxel biosynthesis.</p><p>Artificial intelligence (AI) technology is the algorithm capable of complex and intelligent computing similar to the routine performance of the human brain (<xref rid=\"B2\" ref-type=\"bibr\">Agatonovic-Kustrin and Beresford, 2000</xref>). Artificial neural network (ANN) is an AI method discovering complex nonlinear relationships among input (factors) and output (parameters) data (<xref rid=\"B32\" ref-type=\"bibr\">Patnaik, 1999</xref>; <xref rid=\"B33\" ref-type=\"bibr\">Plumb et al., 2005</xref>). Indeed, ANN is a brain-inspired method that imitates the way that the human brain works (<xref rid=\"B2\" ref-type=\"bibr\">Agatonovic-Kustrin and Beresford, 2000</xref>). It processes information and makes decision in systems involving vagueness and uncertainty (<xref rid=\"B32\" ref-type=\"bibr\">Patnaik, 1999</xref>; <xref rid=\"B14\" ref-type=\"bibr\">Gago et al., 2010</xref>). This technology has been widely used as a predictive instrument in a broad range of fields including ecology, food science, agriculture, environmental sciences, plant biology, pharmaceutical research, and biotechnology (<xref rid=\"B23\" ref-type=\"bibr\">Hilbert and Ostendorf, 2001</xref>; <xref rid=\"B9\" ref-type=\"bibr\">Daniel et al., 2008</xref>; <xref rid=\"B25\" ref-type=\"bibr\">Huang, 2009</xref>; <xref rid=\"B6\" ref-type=\"bibr\">Arab et al., 2018</xref>; <xref rid=\"B20\" ref-type=\"bibr\">Hesami et al., 2019a</xref>; <xref rid=\"B21\" ref-type=\"bibr\">Hesami et al., 2019b</xref>; <xref rid=\"B22\" ref-type=\"bibr\">Hesami et al., 2019c</xref>; <xref rid=\"B45\" ref-type=\"bibr\">Sheikhi et al., 2020</xref>). Multilayer perceptron (MLP), one of the most popular types of ANN, exhibits superior predictive ability as compared to traditional statistical methods to approximate the mathematical functions for analyzing and interpreting different unforeseeable data sets (<xref rid=\"B4\" ref-type=\"bibr\">Ahmadi and Golian, 2011</xref>; <xref rid=\"B26\" ref-type=\"bibr\">Jamshidi et al., 2016</xref>). However, training and designing of ANN face several problems. One of the biggest problems is assigning the weights in ANN structure which displays the direct influence on model performance. Basically, the network architecture and learning algorithm parameters control the weights. Also, other network parameters including the number of memory taps, the number of hidden layers and nodes and learning rates could influence ANN performance (<xref rid=\"B49\" ref-type=\"bibr\">Tahmasebi and Hezarkhani, 2009</xref>). To overcome these mentioned problems, ANN is hybridized with other optimization methods including genetic algorithm (GA) (<xref rid=\"B33\" ref-type=\"bibr\">Plumb et al., 2005</xref>; <xref rid=\"B44\" ref-type=\"bibr\">Shao et al., 2007</xref>; <xref rid=\"B4\" ref-type=\"bibr\">Ahmadi and Golian, 2011</xref>; <xref rid=\"B11\" ref-type=\"bibr\">Eftekhari et al., 2018</xref>; <xref rid=\"B45\" ref-type=\"bibr\">Sheikhi et al., 2020</xref>).</p><p>GA is the evolutionary algorithm making superb solutions to problems and has been applied for bioprocess optimization in plant biology (<xref rid=\"B31\" ref-type=\"bibr\">Osama et al., 2015</xref>; <xref rid=\"B26\" ref-type=\"bibr\">Jamshidi et al., 2016</xref>; <xref rid=\"B6\" ref-type=\"bibr\">Arab et al., 2018</xref>). Indeed, GA is a search algorithm inspired by natural selection and genetics concepts (<xref rid=\"B24\" ref-type=\"bibr\">Holland, 1992</xref>). The fundamental principles of GA are the creation of an initial population of search solutions (chromosomes), and then elite search solutions were selected for crossover using a roulette wheel selection method, which will ultimately be the best solution (fittest chromosome) (optimal value) among them (<xref ref-type=\"fig\" rid=\"f1\"><bold>Figure 1</bold></xref>).</p><fig id=\"f1\" position=\"float\"><label>Figure 1</label><caption><p>Steps of operation of multilayer perceptron-genetics algorithm (MLP-GA) intelligence.</p></caption><graphic xlink:href=\"fpls-11-01148-g001\"/></fig><p>Multilayer perceptron-genetic algorithm (MLP-GA), integrating MLP with GA (<xref ref-type=\"fig\" rid=\"f1\"><bold>Figure 1</bold></xref>), causes achieving an accurate model for prediction and optimization of biological process (<xref rid=\"B26\" ref-type=\"bibr\">Jamshidi et al., 2016</xref>; <xref rid=\"B6\" ref-type=\"bibr\">Arab et al., 2018</xref>; <xref rid=\"B11\" ref-type=\"bibr\">Eftekhari et al., 2018</xref>).</p><p>The objectives of this research were (a) to isolate endophytic fungi from <italic>C. avellana</italic> grown in Iran, (b) to develop regression and MLP-GA models to predict output variables “dry weight (DW), intracellular paclitaxel, extracellular paclitaxel, total yield of paclitaxel and extracellular paclitaxel portion” based on input variables “CE and CF concentration levels, elicitor adding day, and CSC harvesting time”, (c) to compare regression and MLP-GA performance in term of prediction accuracy of output variables, (d) to optimize the mentioned factors for maximum biosynthesis of paclitaxel, (e) to detect the most important factors for maximum biosynthesis of paclitaxel, and (f) to design an Excel<sup>®</sup> estimator which can easily be applied to predict the total yield of paclitaxel in <italic>C. avellana</italic> CSC based on input variables.</p></sec><sec sec-type=\"materials|methods\" id=\"s2\"><title>Materials and Methods</title><sec id=\"s2_1\"><title>Isolation of Endophytic Fungi</title><p>Healthy samples of the bud, stem, bark, and leaves were obtained from <italic>C. avellana</italic> grown in Iran during June to September 2018. The surfaces of the samples were sterilized as described by <xref rid=\"B37\" ref-type=\"bibr\">Salehi et al. (2018c</xref>; <xref rid=\"B39\" ref-type=\"bibr\">2019b)</xref>. The surface-sterilized plant samples were cut and transferred on PDAC [potato dextrose agar (PDA); supplemented with 250 mg l<sup>−1</sup> Chloramphenicol] in unique Petri dishes (100 × 15 mm), incubated at 25 °C. After the growth of endophytic fungi, the pure cultures of the isolates were established by hyphal tip culture (<xref rid=\"B47\" ref-type=\"bibr\">Strobel et al., 1996</xref>). All fungal endophytes were numbered as HEF# series and stored at 4 °C.</p></sec><sec id=\"s2_2\"><title>Molecular Identification of Endophytic Fungus</title><p>Fungal endophyte was cultured in potato dextrose broth (PDB) and incubated in a shaker incubator at 25 °C and 110 rpm for 10 days. The extraction of fungal genomic DNA was done as described by <xref rid=\"B37\" ref-type=\"bibr\">Salehi et al. (2018c</xref>; <xref rid=\"B39\" ref-type=\"bibr\">2019b)</xref>. The partial sequences of internal transcribed spacer (ITS) fragments (ITS1-5.8S-ITS2) and actin gene (<italic>ACT</italic>) were used to obtain DNA sequence information. ITS fragments were amplified using universal primers ITS1 and ITS4 (<xref rid=\"B53\" ref-type=\"bibr\">White et al., 1990</xref>) and <italic>ACT</italic> using primer pair ACT-512F and ACT-783R (<xref rid=\"B8\" ref-type=\"bibr\">Carbone and Kohn, 1999</xref>). PCR reaction mixtures (25 µl) consisted of 1 µl genomic DNA (~100 ng), 1 µl forward and reverse primers (10 pM), and 12.5 µl Premix Taq (TaKaRa Biotechnology Ltd., Japan), and 10.5 µl PCR ultrapure water. PCR reaction programs were an initial denaturation at 94 °C for 3 min, followed by 30 cycles of denaturation (94 °C for 30 s), annealing [56 °C (ITS) and 59 °C (ACT) for 30 s], extension (72 °C for 1 min), and a final extension at 72 °C for 5 min. PCR product analysis and purification, sequencing and the phylogenetic analysis were made as described previously (<xref rid=\"B37\" ref-type=\"bibr\">Salehi et al., 2018c</xref>; <xref rid=\"B39\" ref-type=\"bibr\">Salehi et al., 2019b</xref>).</p></sec><sec id=\"s2_3\"><title>Elicitation of <italic>C. avellana</italic> Cell Culture</title><p><italic>C. avellana</italic> CSC was established as described by <xref rid=\"B34\" ref-type=\"bibr\">Salehi et al. (2017</xref>; <xref rid=\"B37\" ref-type=\"bibr\">2018c</xref>; <xref rid=\"B39\" ref-type=\"bibr\">2019b</xref>; <xref rid=\"B40\" ref-type=\"bibr\">2019c)</xref>. The elicitors (CE and CF) were prepared as described previously (<xref rid=\"B39\" ref-type=\"bibr\">Salehi et al., 2019b</xref>). For elicitation, 1.5 ± 0.1 g of <italic>C. avellana</italic> cells (fresh mass) was cultured in 100 ml flasks containing 30 ml MS medium supplemented with 2 mg l<sup>−1</sup> 2,4-D and 0.2 mg l<sup>−1</sup> BAP.</p><p>Based on our previous studies (<xref rid=\"B39\" ref-type=\"bibr\">Salehi et al., 2019b</xref>; <xref rid=\"B40\" ref-type=\"bibr\">Salehi et al., 2019c</xref>; <xref rid=\"B13\" ref-type=\"bibr\">Farhadi et al., 2020</xref>), three concentrations [2.5, 5, and 10% (v/v)] of fungal elicitors “CE:CF (100:0, 75:25, 50:50, 25:75, 0:100 v/v)” and also mid (day 13) and late (day 17) log phase of <italic>C. avellana</italic> cell cultures were selected for adding fungal elicitors. Control received an equal volume of water (for CE)/PDB (for CF).</p></sec><sec id=\"s2_4\"><title>Cell Growth Measurement</title><p>Cell growth was determined by the measurement of cell dry weight (DW). Cell biomass was separated from the culture medium by filtration (Whatman No. 1) and washed with distilled water to remove the residual medium, afterward freeze-dried to a constant weight by a vacuum-freeze drier.</p></sec><sec id=\"s2_5\"><title>Quantification of Paclitaxel</title><p><italic>C. avellana</italic> cells were separated from the culture medium by a filter paper (Whatman No. 1). Intracellular and extracellular paclitaxel were extracted from the cells and culture broth using a procedure described by <xref rid=\"B34\" ref-type=\"bibr\">Salehi et al. (2017</xref>; <xref rid=\"B37\" ref-type=\"bibr\">2018c</xref>; <xref rid=\"B39\" ref-type=\"bibr\">2019b)</xref>. Filtering all samples was performed by 0.22 µm cellulose acetate syringe filters before HPLC analysis. Paclitaxel in the samples was analyzed by HPLC (Waters, USA) with a C18 analysis column (Machereye-Nagel EC 250/4.6 Nucleodur). Each sample (20 µl) was injected and detected at 230 nm using a UV detector. The mobile phase was methanol:water (80:20 v/v) at a flow rate of 1.0 ml min<sup>−1</sup>. The quantification of paclitaxel was based on an external standard of genuine paclitaxel (Sigma).</p></sec><sec id=\"s2_6\"><title>Experimental Design</title><p>The experiment was conducted based on randomized complete block design (RCBD) with factorial arrangement, three factors containing elicitor type with 10 levels [CE:CF (100:0, 75:25, 50:50, 25:75, 0:100 v/v) and water:PDB (100:0, 75:25, 50:50, 25:75, 0:100 v/v)], concentration with three levels [2.5, 5, and 10% (v/v)], elicitor adding day with two levels (days 13 and 17), and three replicates. The cultures were harvested at 2-day intervals after elicitation until the 23<sup>rd</sup> day.</p></sec><sec id=\"s2_7\"><title>Model Development</title><p>The data were randomly divided into a training subset (70%) and a testing subset (30%). The training subset was applied to develop multiple linear regression (MLR) and backward regression and also MLP-GA models, and testing subset was applied to test the predictability of developed models (<xref rid=\"B43\" ref-type=\"bibr\">Shao et al., 2006</xref>).</p></sec><sec id=\"s2_8\"><title>Regression Analysis</title><p>Regression analysis is one of well-known predictive modeling methods. The popularity of these models may be assigned to model parameter interpretability and its ease of use. Here, MLR and backward regression models were used to predict DW, intracellular, extracellular and total yield of paclitaxel, and also extracellular paclitaxel portion. Significance level for the independent variables to include in the model was set at 0.05.</p><p>To determine which model component is more important during the modeling process, sensitivity analysis was performed on developed regression models using analysis of variance (ANOVA) and absolute t value (|t value|) corresponding to model coefficients. It is noteworthy that a more important model component displays a higher |t value| (<xref rid=\"B4\" ref-type=\"bibr\">Ahmadi and Golian, 2011</xref>; <xref rid=\"B5\" ref-type=\"bibr\">Ahmadi and Rodehutscord, 2017</xref>).</p></sec><sec id=\"s2_9\"><title>Multilayer Perceptron (MLP) Model</title><p>Three-layered feed forward back-propagation neural network was used to define the influences of CE and CF concentration levels, elicitor adding day, and CSC harvesting time on DW, paclitaxel biosynthesis (intracellular, extracellular and total), and extracellular paclitaxel portion. Transfer functions for hidden and output layers were hyperbolic tangent sigmoid (tansig) and linear (purelin), respectively.</p><p>ANN capability to process the information is determined by its architecture. Evolutionary algorithms are used for searching the optimal architecture design (<xref rid=\"B54\" ref-type=\"bibr\">Yao, 1999</xref>).</p></sec><sec id=\"s2_10\"><title>Genetic Algorithm (GA)</title><p>The high number of hidden neurons leads to prolong the training time and also overfits the data. Too few hidden neurons lead to a low accuracy rate (<xref rid=\"B29\" ref-type=\"bibr\">Matignon, 2005</xref>). GA was used (i) to determine optimal MLP architecture design including the optimal numbers of neurons, and (ii) to optimize the values of input variables (CE and CF concentration, elicitor adding day, and CSC harvesting time) in developed MLP-GA models for maximum paclitaxel biosynthesis and its secretion. An initial population of 50, crossover rate of 0.85, generation number of 500 and mutation rate of 0.01 (<xref rid=\"B19\" ref-type=\"bibr\">Haupt and Haupt, 2004</xref>; <xref rid=\"B1\" ref-type=\"bibr\">Abramson, 2007</xref>) were set to establish fittest MLP structure and optimize input variables for maximum output variables.</p><p>The performance of MLP-GA models is determined by root mean square error (RMSE) and coefficient of determination (R<sup>2</sup>) as reported by <xref rid=\"B3\" ref-type=\"bibr\">Ahmadi (2017)</xref>, as well as mean absolute percentage error (MAPE) [Eq. (1)].</p><disp-formula><label>(1)</label><mml:math id=\"M1\"><mml:mrow><mml:mi>M</mml:mi><mml:mi>A</mml:mi><mml:mi>P</mml:mi><mml:mi>E</mml:mi><mml:mo>=</mml:mo><mml:mo> </mml:mo><mml:mn>1</mml:mn><mml:mo stretchy=\"false\">/</mml:mo><mml:mi>n</mml:mi><mml:mo> </mml:mo><mml:munderover><mml:mo>∑</mml:mo><mml:mrow><mml:mi>i</mml:mi><mml:mo>=</mml:mo><mml:mn>1</mml:mn></mml:mrow><mml:mi>n</mml:mi></mml:munderover><mml:mrow><mml:mo>|</mml:mo><mml:mrow><mml:mfrac><mml:mrow><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:msub><mml:mi>y</mml:mi><mml:mrow><mml:mi>a</mml:mi><mml:mi>c</mml:mi><mml:mi>t</mml:mi></mml:mrow></mml:msub><mml:mo>−</mml:mo><mml:msub><mml:mi>y</mml:mi><mml:mrow><mml:mi>e</mml:mi><mml:mi>s</mml:mi><mml:mi>t</mml:mi></mml:mrow></mml:msub></mml:mrow><mml:mo>)</mml:mo></mml:mrow></mml:mrow><mml:mrow><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:msub><mml:mi>y</mml:mi><mml:mrow><mml:mi>a</mml:mi><mml:mi>c</mml:mi><mml:mi>t</mml:mi></mml:mrow></mml:msub></mml:mrow><mml:mo>)</mml:mo></mml:mrow></mml:mrow></mml:mfrac></mml:mrow><mml:mo>|</mml:mo></mml:mrow><mml:mo>×</mml:mo><mml:mn>100</mml:mn></mml:mrow></mml:math></disp-formula><p>Where “<italic>y<sub>act</sub></italic>” are the actual values, “<italic>y<sub>est</sub></italic>” are the predicted values, and “<italic>n</italic>” is the number of data.</p></sec><sec id=\"s2_11\"><title>Sensitivity Analysis of the Models</title><p>Sensitivity analysis was done on MLP-GA models to determine the importance degree of the factors (CE and CF concentration levels, elicitor adding day, and CSC harvesting time) on the model parameters (DW, paclitaxel biosynthesis, and its secretion). The sensitivity of DW, paclitaxel biosynthesis (intracellular, extracellular, and total yield), and extracellular paclitaxel portion was determined by the criteria including variable sensitivity error (VSE) value displaying the performance (RMSE) of MLP-GA model when that particular input variable is unavailable in the model. Variable sensitivity ratio (VSR) value was calculated as the ratio of VSE and MLP-GA model error (RMSE value) when all input variables are available. Finally, calculated VSR values were rescaled within the range [0, 1]. The input variable with higher VSR was considered as the higher important variable in the model (<xref rid=\"B4\" ref-type=\"bibr\">Ahmadi and Golian, 2011</xref>).</p><p>The mathematical codes for the development and evaluation of MLR, backward regression, and MLP-GA models were written using MATLAB (<xref rid=\"B30\" ref-type=\"bibr\">Matlab, 2010</xref>) software, and the graphs were made by GraphPad Prism 5 (<xref rid=\"B18\" ref-type=\"bibr\">GraphPad Prism 5, 2005</xref>) software. “ANNGA_opt” program coded by MATLAB can be downloaded from <uri xlink:type=\"simple\" xlink:href=\"https://github.com/hahmadima/ANNGA_opt\">https://github.com/hahmadima/ANNGA_opt</uri>.</p></sec></sec><sec sec-type=\"results\" id=\"s3\"><title>Results</title><sec id=\"s3_1\"><title>Identification of Endophytic Fungus</title><p>Strain HEF<sub>17</sub> was isolated from the leaf of <italic>C. avellana</italic> and identified as <italic>Camarosporomyces flavigenus</italic> by analysis of the sequences of actin gene (<xref ref-type=\"fig\" rid=\"f2\"><bold>Figure 2</bold></xref>). Accession numbers used for phylogenetic study were reported by <xref rid=\"B10\" ref-type=\"bibr\">De Gruyter et al. (2013)</xref>. This is the first report of this endophytic fungus on <italic>C. avellana</italic> tree (matrix nova). The partial sequences of ITS rDNA and <italic>ACT</italic> obtained from <italic>C. flavigenus</italic> strain HEF<sub>17</sub> were deposited in GenBank (NCBI) under accession numbers MT176168 and MT224136, respectively.</p><fig id=\"f2\" position=\"float\"><label>Figure 2</label><caption><p>Molecular identification of strain HEF<sub>17</sub> based on the analysis of the sequences of actin gene. The tree was rooted to <italic>Plenodomus lingam</italic> (CBS 147.24).</p></caption><graphic xlink:href=\"fpls-11-01148-g002\"/></fig></sec><sec id=\"s3_2\"><title>Regression Analysis</title><p>Goodness of fit displayed no difference regarding the accuracy of MLR and backward regression for all output variables, 0.66, 0.56, 0.61, 0.58, and 0.85 for DW, intracellular paclitaxel, extracellular paclitaxel, total yield of paclitaxel, and extracellular paclitaxel portion, respectively, for the training subset (<xref rid=\"T1\" ref-type=\"table\"><bold>Table 1</bold></xref>). Accordingly, the results of backward regression showed that elicitor adding day and CSC harvesting time are only parameters among the four above-mentioned input variables which influenced DW (<xref rid=\"T1\" ref-type=\"table\"><bold>Table 1</bold></xref>). All input variables including CE and CF concentration levels, elicitor adding day and CSC harvesting time are important factors influencing intracellular, extracellular, and total yield of paclitaxel, and also paclitaxel secretion from cells to the culture medium (<xref rid=\"T1\" ref-type=\"table\"><bold>Table 1</bold></xref>). R<sup>2</sup> values for DW, intracellular paclitaxel, extracellular paclitaxel, total yield of paclitaxel, and extracellular paclitaxel portion were estimated 0.64, 0.58, 0.61, 0.61, and 0.85, respectively, for the testing subset (<xref ref-type=\"fig\" rid=\"f3\"><bold>Figure 3</bold></xref>).</p><table-wrap id=\"T1\" position=\"float\"><label>Table 1</label><caption><p>Backward regression models for estimating growth, paclitaxel biosynthesis, and secretion in <italic>Corylus avellana</italic> cell suspension culture (CSC) treated with fungal elicitors using cell extract (CE) and culture filtrate (CF) concentration levels [% (v/v)], elicitor adding day, and CSC harvesting time (day).</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Measured factors</th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Variable<xref ref-type=\"table-fn\" rid=\"fnT1_1\"><sup>a</sup></xref>\n</th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Coeffcient</th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Standard error</th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">t value</th></tr></thead><tbody><tr><td valign=\"top\" rowspan=\"3\" align=\"left\" colspan=\"1\"><bold>Dry weight (g l<sup>-1</sup>)</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Intercept</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-0.8950</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.5350</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-1.67</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Elicitor adding day</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.0995</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.0325</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.06</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">CSC harvesting time</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.4862</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.0239</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">20.34</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">R<sup>2</sup></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.6635</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">RMSE</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.9642</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">MAPE</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4.6650</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" rowspan=\"5\" align=\"left\" colspan=\"1\"><bold>Intracellular paclitaxel (µg g<sup>-1</sup> DW)</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Intercept</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-4.7600</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.3200</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-3.60</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">CE concentration level</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.4393</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.0531</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">8.28</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">CF concentration level</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.7936</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.0528</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">15.03</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Elicitor adding day</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.8277</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.0787</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">10.52</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">CSC harvesting time</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-0.2170</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.0578</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-3.75</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">R<sup>2</sup></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.5560</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">RMSE</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.3318</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">MAPE</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">59.1400</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" rowspan=\"5\" align=\"left\" colspan=\"1\"><bold>Extracellular paclitaxel (µg l<sup>-1</sup>)</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Intercept</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-123.5000</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">8.9600</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-13.78</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">CE concentration level</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.6740</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.3600</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">7.44</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">CF concentration level</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4.7370</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.3580</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">13.24</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Elicitor adding day</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5.8400</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.5330</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">10.96</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">CSC harvesting time</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.5470</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.3920</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">6.50</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">R<sup>2</sup></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.6060</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">RMSE</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">15.7977</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">MAPE</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">65.60000</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" rowspan=\"5\" align=\"left\" colspan=\"1\"><bold>Total yield of paclitaxel (µg l<sup>-1</sup>)</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Intercept</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-242.0000</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">23.0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-10.51</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">CE concentration level</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">7.1130</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.924</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">7.70</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">CF concentration level</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">12.7280</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.920</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">13.84</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Elicitor adding day</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">15.5000</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.37</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">11.31</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">CSC harvesting time</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.0100</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.01</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.99</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">R<sup>2</sup></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.5803</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">RMSE</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">40.5987</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">MAPE</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">67.7200</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" rowspan=\"5\" align=\"left\" colspan=\"1\"><bold>Extracellular</bold><break/><bold>paclitaxel portion</bold><break/><bold>(%)</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Intercept</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-18.6000</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.4200</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-13.07</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">CE concentration level</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.1976</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.0571</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.46</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">CF concentration level</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.3167</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.0569</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5.57</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Elicitor adding day</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.3119</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.0847</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.68</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">CSC harvesting time</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.2211</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.0623</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">35.68</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">R<sup>2</sup></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.8549</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">RMSE</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.51070</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">MAPE</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">21.18</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/></tr></tbody></table><table-wrap-foot><fn id=\"fnT1_1\"><label>a</label><p>Significant (p ≤ 0.05) variables included in the model. R<sup>2</sup>, coefficient of determination; RMSE, root mean square error; MAPE, mean absolute percentage error.</p></fn></table-wrap-foot></table-wrap><fig id=\"f3\" position=\"float\"><label>Figure 3</label><caption><p>Scatter plot of actual data against predicted values of dry weight, intracellular, extracellular and total yield of paclitaxel, and extracellular paclitaxel portion in <italic>Corylus avellana</italic> cell cultures using backward regression models in the testing subset. The solid line shows fitted simple regression line on scatter points.</p></caption><graphic xlink:href=\"fpls-11-01148-g003\"/></fig><p>Goodness of fit of DW model and absolute t values (<xref rid=\"T1\" ref-type=\"table\"><bold>Table 1</bold></xref>) showed that out of the investigated input variables, CSC harvesting time (|t value| = 20.3) was the most important parameter affecting DW, followed by elicitor adding day (|t value| = 3.1). Accordingly, CF concentration level (|t value| = 15.0) displayed the highest effect on intracellular paclitaxel, followed by elicitor adding day (|t value| = 10.5), CE concentration level (|t value| = 8.3) and CSC harvesting time (|t value| = 3.8). Also, CF concentration level (|t value| = 13.2) was the most effective component on extracellular paclitaxel, followed by elicitor adding day (|t value| = 11.0), CE concentration level (|t value| = 7.4) and CSC harvesting time (|t value| = 6.5). Furthermore, CF concentration level (|t value| = 13.8) was the most important factor influencing total yield of paclitaxel, followed by elicitor adding day (|t value| = 11.3), CE concentration level (|t value| = 7.7) and CSC harvesting time (|t value| = 3.0). Additionally, CSC harvesting time exhibited the highest effect on extracellular paclitaxel portion (|t value| = 35.7), followed by CF concentration level (|t value| = 5.6), elicitor adding day (|t value| = 3.7) and CE concentration level (|t value| = 3.5) (<xref rid=\"T1\" ref-type=\"table\"><bold>Table 1</bold></xref>).</p></sec><sec id=\"s3_3\"><title>Multilayer Perceptron-Genetics Algorithm Analysis</title><p>Initially, CE and CF concentration levels, elicitor adding day and CSC harvesting time were used as input variables and DW, intracellular, extracellular and total yield of paclitaxel, and also extracellular paclitaxel portion as output variables. Then, output variables were predicted according to developed MLP-GA models. To evaluate the performance of developed MLP-GA models, the predicted values were plotted against the observed values of training (<xref ref-type=\"fig\" rid=\"f4\"><bold>Figure 4A</bold></xref>) and testing (<xref ref-type=\"fig\" rid=\"f4\"><bold>Figure 4B</bold></xref>) subsets. The great accordance between the predicted and observed values of DW, intracellular, extracellular and total yield of paclitaxel, and also extracellular paclitaxel portion was observed for both training and testing subsets (<xref ref-type=\"fig\" rid=\"f4\"><bold>Figure 4</bold></xref>). Goodness of fit of developed MLP-GA models showed that the developed models could accurately (R<sup>2</sup> = 0.90, 0.89, 0.92, 0.95, and 0.91) (<xref rid=\"T2\" ref-type=\"table\"><bold>Table 2</bold></xref>) predict DW, intracellular, extracellular and total yield of paclitaxel, and also extracellular paclitaxel portion of the testing subset, not used during the training processes (<xref ref-type=\"fig\" rid=\"f4\"><bold>Figure 4</bold></xref>). Also, developed MLP-GA models displayed the balanced statistical values for both training and testing subsets (<xref rid=\"T2\" ref-type=\"table\"><bold>Table 2</bold></xref>).</p><fig id=\"f4\" position=\"float\"><label>Figure 4</label><caption><p>Scatter plot of actual data against predicted values of dry weight, intracellular, extracellular and total yield of paclitaxel and extracellular paclitaxel portion in <italic>Corylus avellana</italic> cell cultures using multilayer perceptron-genetics algorithm (MLP-GA) models in training <bold>(A)</bold> and testing <bold>(B)</bold> subsets. The solid line shows fitted simple regression line on scatter points.</p></caption><graphic xlink:href=\"fpls-11-01148-g004\"/></fig><table-wrap id=\"T2\" position=\"float\"><label>Table 2</label><caption><p>Statistics and information on multilayer perceptron-genetics algorithm (MLP-GA) models for growth, paclitaxel biosynthesis and secretion in <italic>Corylus avellana</italic> cell culture.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Measured factors</th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Neuron number</th><th valign=\"top\" colspan=\"3\" align=\"center\" rowspan=\"1\">Training subsets</th><th valign=\"top\" colspan=\"3\" align=\"center\" rowspan=\"1\">Testing subsets</th></tr><tr><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">R<sup>2</sup></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">RMSE</th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">MAPE</th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">R<sup>2</sup></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">RMSE</th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">MAPE</th></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Dry weight</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.90</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.53</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.45028</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.90</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.54</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.48036</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Intracellular paclitaxel</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">6</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.90</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.14</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.86184</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.89</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.98</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.78311</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Extracellular paclitaxel</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">7</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.95</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">6.78</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4.45092</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.92</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5.77</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4.12229</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Total yield of paclitaxel</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">7</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.95</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">14.27</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">10.9185</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.95</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">12.87</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">9.85688</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Extracellular paclitaxel portion</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.92</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.87</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.54511</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.91</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.89</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.59910</td></tr></tbody></table><table-wrap-foot><p>R<sup>2</sup>, coefficient of determination; RMSE, root mean square error; MAPE, mean absolute percentage error.</p></table-wrap-foot></table-wrap></sec><sec id=\"s3_4\"><title>Sensitivity Analysis of the Models</title><p>To rank the input variables based on their relative importance in the model, VSRs were estimated using all data lines (training and testing subsets). VSRs were obtained for each of output variables (DW, intracellular, extracellular and total yield of paclitaxel, and also extracellular paclitaxel portion) regarding CE and CF concentration levels, elicitor adding day and CSC harvesting time (<xref rid=\"T3\" ref-type=\"table\"><bold>Table 3</bold></xref>). Analysis of DW model indicated that DW of <italic>C. avellana</italic> cells was more sensitive to CSC harvesting time (VSR = 0.990), followed by elicitor adding day (VSR = 0.010), CE and CF concentration levels (VSR = 0.004). Intracellular paclitaxel displayed more sensitivity to CE concentration level (VSR = 0.530), followed by CF concentration level (VSR = 0.460), elicitor adding day (VSR = 0.180), and CSC harvesting time (VSR = 0.100). Extracellular paclitaxel showed more sensitivity to CSC harvesting time (VSR = 0.660), followed by CF concentration level (VSR = 0.250), CE concentration level (VSR = 0.110), and elicitor adding day (VSR = 0.100). Accordingly, total yield of paclitaxel exhibited more sensitivity to CE concentration level (VSR = 0.720), followed by CF concentration level (VSR = 0.500), CSC harvesting time (VSR = 0.190), and elicitor adding day (VSR = 0.070). Also, extracellular paclitaxel portion displayed more sensitivity to CSC harvesting time (VSR = 0.810), followed by elicitor adding day (VSR = 0.120), CE concentration level (VSR = 0.080), and CF concentration level (VSR = 0.050) (<xref rid=\"T3\" ref-type=\"table\"><bold>Table 3</bold></xref>).</p><table-wrap id=\"T3\" position=\"float\"><label>Table 3</label><caption><p>Importance (according to sensitivity analysis) and optimal levels of the different input variables including cell extract (CE), culture filtrate (CF) concentration levels (% (v/v)), elicitor adding day and cell suspension culture (CSC) harvesting time (day) for achieving maximum growth, paclitaxel biosynthesis and secretion in <italic>Corylus avellana</italic> CSC using multilayer perceptron-genetics algorithm (MLP-GA) models.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Criteria</th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Variable</th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Importance value<break/>(according to VSR<xref ref-type=\"table-fn\" rid=\"fnT3_1\"><sup>a</sup></xref>)</th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Optimal level</th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Output Optimal</th></tr></thead><tbody><tr><td valign=\"top\" rowspan=\"4\" align=\"left\" colspan=\"1\"><bold>Dry weight (g l<sup>-1</sup>)</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">CE concentration level</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.004</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5.67</td><td valign=\"top\" rowspan=\"4\" align=\"center\" colspan=\"1\">12.04</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">CF concentration level</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.004</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.60</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Elicitor adding day</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.010</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">15.17</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">CSC harvesting time</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.990</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">20.78</td></tr><tr><td valign=\"top\" rowspan=\"4\" align=\"left\" colspan=\"1\"><bold>Intracellular paclitaxel (µg g<sup>-1</sup> DW)</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">CE concentration level</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.530</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.37</td><td valign=\"top\" rowspan=\"4\" align=\"center\" colspan=\"1\">17.74</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">CF concentration level</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.460</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5.33</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Elicitor adding day</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.180</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">17.00</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">CSC harvesting time</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.100</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">20.27</td></tr><tr><td valign=\"top\" rowspan=\"4\" align=\"left\" colspan=\"1\"><bold>Extracellular paclitaxel (µg l<sup>-1</sup>)</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">CE concentration level</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.110</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5.29</td><td valign=\"top\" rowspan=\"4\" align=\"center\" colspan=\"1\">124.52</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">CF concentration level</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.250</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5.75</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Elicitor adding day</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.100</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">17.00</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">CSC harvesting time</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.660</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">20.90</td></tr><tr><td valign=\"top\" rowspan=\"4\" align=\"left\" colspan=\"1\"><bold>Total yield of paclitaxel (µg l<sup>-1</sup>)</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">CE concentration level</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.720</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.33</td><td valign=\"top\" rowspan=\"4\" align=\"center\" colspan=\"1\">369.67</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">CF concentration level</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.500</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5.25</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Elicitor adding day</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.070</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">17.00</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">CSC harvesting time</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.190</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">20.95</td></tr><tr><td valign=\"top\" rowspan=\"4\" align=\"left\" colspan=\"1\"><bold>Extracellular paclitaxel portion (%)</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">CE concentration level</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.080</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4.51</td><td valign=\"top\" rowspan=\"4\" align=\"center\" colspan=\"1\">48.07</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">CF concentration level</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.050</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5.10</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Elicitor adding day</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.120</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">17.00</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">CSC harvesting time</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.810</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">23.00</td></tr></tbody></table><table-wrap-foot><fn id=\"fnT3_1\"><label>a</label><p>Relative indication of the ratio between the variable sensitivity error and the error of the model when all variables are available. Calculated VSR values were rescaled within range [0, 1].</p></fn></table-wrap-foot></table-wrap></sec><sec id=\"s3_5\"><title>Model Optimization</title><p>The optimization analysis on developed MLP-GA models was performed using GA to determine the optimal levels of input variables for achieving maximum growth, paclitaxel biosynthesis, and its secretion in <italic>C. avellana</italic> CSC (<xref rid=\"T3\" ref-type=\"table\"><bold>Table 3</bold></xref>). The optimization results showed that adding 6.27% (v/v) of 90CE:10CF containing 5.67% (v/v) CE and 0.6% (v/v) CF on 15<sup>th</sup> day and harvesting CSC 134 h and 38 min after elicitation could result in maximum DW (12.04 g l<sup>−1</sup>) (<xref rid=\"T3\" ref-type=\"table\"><bold>Table 3</bold></xref>). The highest content of intracellular paclitaxel (17.74 µg g<sup>−1</sup> DW) may be produced by adding 8.70% (v/v) of 39CE:61CF containing 3.37% (v/v) CE and 5.33% (v/v) CF on 17<sup>th</sup> day and harvesting CSC 78 h and 29 min after elicitation (<xref rid=\"T3\" ref-type=\"table\"><bold>Table 3</bold></xref>). Also, the results showed that highest extracellular paclitaxel (124.52 µg l<sup>−1</sup>) can be produced by adding 11.13% (v/v) of 48CE:52CF containing 5.29% (v/v) CE and 5.75% (v/v) CF on 17<sup>th</sup> day and harvesting CSC 93 h and 36 min after elicitation (<xref rid=\"T3\" ref-type=\"table\"><bold>Table 3</bold></xref>). Additionally, CSC exposed with 8.58% (v/v) of 39CE:61CF containing 3.33% (v/v) CE and 5.25% (v/v) CF on 17<sup>th</sup> day and harvesting it 94 h and 48 min after elicitation may obtain the highest total yield of paclitaxel (369.67 µg l<sup>−1</sup>) (<xref rid=\"T3\" ref-type=\"table\"><bold>Table 3</bold></xref>). The results of MLP-GA model optimization displayed that adding 9.61% (v/v) of 47CE:53CF containing 4.51% (v/v) CE and 5.10% (v/v) CF on 17<sup>th</sup> day and harvesting CSC 144 h after elicitation may lead to highest extracellular paclitaxel portion (48.07) (<xref rid=\"T3\" ref-type=\"table\"><bold>Table 3</bold></xref>).</p></sec><sec id=\"s3_6\"><title>Comparison of MLP-GA and Backward Regression Models</title><p>The statistical values for MLP-GA models displayed higher prediction accuracy as compared to regression models as estimated R<sup>2</sup> for MLP-GA <italic>vs.</italic> regression models were: DW = 0.90 <italic>vs.</italic> 0.66, intracellular paclitaxel = 0.90 <italic>vs.</italic> 0.56, extracellular paclitaxel = 0.93 <italic>vs.</italic> 0.61, total yield of paclitaxel = 0.95 <italic>vs.</italic> 0.58, and extracellular paclitaxel portion = 0.92 <italic>vs.</italic> 0.85 (<xref rid=\"T1\" ref-type=\"table\"><bold>Tables 1</bold></xref> and <xref rid=\"T2\" ref-type=\"table\"><bold>2</bold></xref>). In the end, an Excel<sup>®</sup> total paclitaxel estimator, namely, HCC-paclitaxel, was created using developed MLP-GA model (<xref ref-type=\"fig\" rid=\"f5\"><bold>Figure 5</bold></xref>). The mentioned estimator was presented as supplementary material.</p><fig id=\"f5\" position=\"float\"><label>Figure 5</label><caption><p>HCC-paclitaxel: an Excel<sup>®</sup> estimator for predicting total paclitaxel value in <italic>Corylus avellana</italic> cell culture responding fungal elicitors using multilayer perceptron-genetic algorithm (MLP-GA) model. CE, cell extract; CF, culture filtrate. This estimator was presented as Supplementary Material.</p></caption><graphic xlink:href=\"fpls-11-01148-g005\"/></fig></sec></sec><sec sec-type=\"discussion\" id=\"s4\"><title>Discussion</title><p>Predicting the optimal amount of the effective factors on paclitaxel biosynthesis is highly promising and essential for its production increment and cost decrement. This is the first study on predicting the optimal conditions for maximum paclitaxel biosynthesis in <italic>C. avellana</italic> CSC exposed to fungal elicitors using the mathematical model. To accurately predict the optimal amounts of effective factors (CE and CF concentration levels, elicitor adding day, and CSC harvesting time) on paclitaxel biosynthesis in <italic>C. avellana</italic> CSC, using a trustworthy modeling system is essential.</p><p>In this study, regression and MLP-GA modeling were applied to evaluate the relationships among four studied factors “CE and CF concentration levels, elicitor adding day, and CSC harvesting time” and the parameters “DW, intracellular, extracellular, and total yield of paclitaxel and extracellular paclitaxel portion”, and also the possibility of predicting the growth and paclitaxel biosynthesis by the determined factors. Such mathematical predictions have not been described in this area. Higher accuracy of MLP-GA models as compared to regression models (<xref rid=\"T1\" ref-type=\"table\"><bold>Tables 1</bold></xref> and <xref rid=\"T2\" ref-type=\"table\"><bold>2</bold></xref>) was also reported in previous studies (<xref rid=\"B26\" ref-type=\"bibr\">Jamshidi et al., 2016</xref>; <xref rid=\"B11\" ref-type=\"bibr\">Eftekhari et al., 2018</xref>).</p><p>The fit of regression models was presented by R<sup>2</sup> (<xref ref-type=\"fig\" rid=\"f3\"><bold>Figure 3</bold></xref>) for testing subset, suggesting these models can explain 64, 58, 61, 61 and 85% of the variability in DW, intracellular paclitaxel, extracellular paclitaxel, total yield of paclitaxel and paclitaxel extracellular portion, respectively, when they face unseen data.</p><p>Our results suggested that MLP-GA models could accurately predict DW, intracellular paclitaxel, extracellular paclitaxel, total yield of paclitaxel and extracellular paclitaxel portion (R<sup>2</sup> = 0.90, 0.89, 0.92, 0.94, and 0.91, respectively) in the testing subset (<xref ref-type=\"fig\" rid=\"f4\"><bold>Figure 4</bold></xref>), not used in the training process. Also, the small number of hidden neuron and also closing the errors of training and testing subsets to each other (<xref rid=\"T2\" ref-type=\"table\"><bold>Table 2</bold></xref>) suggested that overlearning had not arisen in the training process, and developed MLP-GA models displayed good generalizability when they faced unseen data (<xref rid=\"B27\" ref-type=\"bibr\">Lou and Nakai, 2001</xref>; <xref rid=\"B4\" ref-type=\"bibr\">Ahmadi and Golian, 2011</xref>). Based on RMSE, R<sup>2</sup> and MAPE of the training and testing subsets (<xref rid=\"T2\" ref-type=\"table\"><bold>Table 2</bold></xref>), it can be concluded that tansig activation function effectively worked for modeling over all experiments. Small RMSE and MAPE (<xref rid=\"T2\" ref-type=\"table\"><bold>Table 2</bold></xref>) showed the high potential of MLP-GA models in predicting output variables.</p><p>Regardless of previous studies on the effects of CE and CF concentration levels, elicitor adding day and CSC harvesting time on paclitaxel biosynthesis and secretion, there remains the question to be answered: which input variables are the most important in paclitaxel biosynthesis? As previously mentioned, sensitivity analysis displayed that CE and CF concentration levels are the most important variables affecting total yield of paclitaxel (<xref rid=\"T3\" ref-type=\"table\"><bold>Table 3</bold></xref>). Endophytic fungi synthesize microbe-associated molecular patterns (MAMPs). The receptors localized on plant cell surface recognize MAMPs and thus induce plant defense system (<xref rid=\"B7\" ref-type=\"bibr\">Ausubel, 2005</xref>). Some of these MAMPs are found only in CE, a number of these exist only in CF, and others are found in both CE and CF with different concentrations (<xref ref-type=\"fig\" rid=\"f6\"><bold>Figure 6</bold></xref>). Therefore, paclitaxel biosynthesis elicitation potential of these fungal elicitors (CE and CF) is different. Extracellular paclitaxel content is important for paclitaxel production in a continuous system. Sensitivity analysis displayed that CSC harvesting time is the most important factor affecting extracellular paclitaxel (<xref rid=\"T3\" ref-type=\"table\"><bold>Table 3</bold></xref>). Paclitaxel biosynthesis is the complex biological process that requires the accurate techniques for modeling and optimization. MLP-GA has been efficiently used to solve problems with extremely difficult and unknown solution in various fields (<xref rid=\"B26\" ref-type=\"bibr\">Jamshidi et al., 2016</xref>; <xref rid=\"B6\" ref-type=\"bibr\">Arab et al., 2018</xref>; <xref rid=\"B11\" ref-type=\"bibr\">Eftekhari et al., 2018</xref>; <xref rid=\"B45\" ref-type=\"bibr\">Sheikhi et al., 2020</xref>). A growing interest in ANN has mostly been because of its power in solving the problems in a broad range of fields, their ability for modeling nonlinear and complex relationships, prediction ability of the unseen relationships on the unseen data, and having no need of a specification of data statistical distribution (<xref rid=\"B28\" ref-type=\"bibr\">Mahanta, 2017</xref>).</p><fig id=\"f6\" position=\"float\"><label>Figure 6</label><caption><p>Schematic design of microbe-associated molecular patterns (MAMPs) found in cell extract and culture filtrate, and total yield of paclitaxel in <italic>Corylus avellana</italic> cell cultures exposed with different concentrations of these fungal elicitors derived from <italic>Camarosporomyces flavigenus</italic>.</p></caption><graphic xlink:href=\"fpls-11-01148-g006\"/></fig><p>According to the high prediction accuracy of the training and testing subsets, it can be concluded that developed MLP-GA could accurately predict DW, paclitaxel biosynthesis, and secretion in <italic>C. avellana</italic> CSC.</p><p>Publishing developed MLP-GA models needs to share the connection weight matrices, which running ANN models requires the especial software. Therefore, we share developed MLP-GA model predicting total paclitaxel with the readers as HCC-paclitaxel Excel<sup>®</sup> estimator (<xref ref-type=\"fig\" rid=\"f5\"><bold>Figure 5</bold></xref>).</p></sec><sec id=\"s5\"><title>Conclusion</title><p>This research applied mathematical approaches for modeling and optimizing paclitaxel biosynthesis in <italic>C. avellana</italic> cell culture treated with fungal elicitors for the first time. The great accordance between the predicted and observed values of the output variables (DW, intracellular, extracellular and total yield of paclitaxel, and also extracellular paclitaxel portion) supported the excellent performance of developed MLP-GA models. HCC-paclitaxel Excel<sup>®</sup> estimator presents an easy-to-use tool to predict total yield of paclitaxel in <italic>C. avellana</italic> cell culture treated with fungal elicitors using MLP-GA model.</p></sec><sec id=\"s6\"><title>Author Contributions</title><p>MS directed the research, carried out all experiments and analyses. AM directed in vitro cell culture and elicitation experiment. NS directed the sections related to fungal elicitation. HA performed data modeling. MS and SF interpreted the results and wrote the manuscript. All authors read and approved the final manuscript.</p></sec><sec sec-type=\"funding-information\" id=\"s7\"><title>Funding</title><p>The authors acknowledge Iran National Science Foundation (INSF, No. 97010721), and Research Deputy of Tarbiat Modares University, Tehran for financial support of this research project.</p></sec><sec id=\"s8\"><title>Conflict of Interest</title><p>The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p></sec></body><back><ref-list><title>References</title><ref id=\"B1\"><mixed-citation publication-type=\"book\">\n<person-group person-group-type=\"author\"><name><surname>Abramson</surname><given-names>M.</given-names></name></person-group> (<year>2007</year>). <source>Genetic algorithm and direct search toolbox user’s guide.</source>\n<publisher-loc>Natick</publisher-loc>: <publisher-name>MathWorks Inc.</publisher-name>\n</mixed-citation></ref><ref id=\"B2\"><mixed-citation publication-type=\"journal\">\n<person-group person-group-type=\"author\"><name><surname>Agatonovic-Kustrin</surname><given-names>S.</given-names></name><name><surname>Beresford</surname><given-names>R.</given-names></name></person-group> (<year>2000</year>). <article-title>Basic concepts of artificial neural network (ANN) modeling and its application in pharmaceutical research</article-title>. <source>J. 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[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Front Public Health</journal-id><journal-id journal-id-type=\"iso-abbrev\">Front Public Health</journal-id><journal-id journal-id-type=\"publisher-id\">Front. Public Health</journal-id><journal-title-group><journal-title>Frontiers in Public Health</journal-title></journal-title-group><issn pub-type=\"epub\">2296-2565</issn><publisher><publisher-name>Frontiers Media S.A.</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32850612</article-id><article-id pub-id-type=\"pmc\">PMC7432145</article-id><article-id pub-id-type=\"doi\">10.3389/fpubh.2020.00461</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Public Health</subject><subj-group><subject>Original Research</subject></subj-group></subj-group></article-categories><title-group><article-title>A Predicting Nomogram for Mortality in Patients With COVID-19</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Pan</surname><given-names>Deng</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/948982/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Cheng</surname><given-names>Dandan</given-names></name><xref ref-type=\"aff\" rid=\"aff2\"><sup>2</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Cao</surname><given-names>Yiwei</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Hu</surname><given-names>Chuan</given-names></name><xref ref-type=\"aff\" rid=\"aff3\"><sup>3</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/974823/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Zou</surname><given-names>Fenglin</given-names></name><xref ref-type=\"aff\" rid=\"aff4\"><sup>4</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Yu</surname><given-names>Wencheng</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><xref ref-type=\"corresp\" rid=\"c001\"><sup>*</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/948733/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Xu</surname><given-names>Tao</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><xref ref-type=\"corresp\" rid=\"c002\"><sup>*</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/968531/overview\"/></contrib></contrib-group><aff id=\"aff1\"><sup>1</sup><institution>Department of Pulmonary and Critical Care Medicine, Affiliated Hospital of Qingdao University</institution>, <addr-line>Qingdao</addr-line>, <country>China</country></aff><aff id=\"aff2\"><sup>2</sup><institution>Department of Hematology, Tongji Medical College, Tongji Hospital, Huazhong University of Science and Technology</institution>, <addr-line>Wuhan</addr-line>, <country>China</country></aff><aff id=\"aff3\"><sup>3</sup><institution>Department of Joint Surgery, Affiliated Hospital of Qingdao University</institution>, <addr-line>Qingdao</addr-line>, <country>China</country></aff><aff id=\"aff4\"><sup>4</sup><institution>Department of Biliary-Pancreatic Surgery, Tongji Medical College, Tongji Hospital, Huazhong University of Science and Technology</institution>, <addr-line>Wuhan</addr-line>, <country>China</country></aff><author-notes><fn fn-type=\"edited-by\"><p>Edited by: Banu Cakir, Hacettepe University, Turkey</p></fn><fn fn-type=\"edited-by\"><p>Reviewed by: Eugenia M. Bastos, Independent Researcher, Sommerville, MA, United States; Lira Pi, The University of Iowa, United States</p></fn><corresp id=\"c001\">*Correspondence: Wencheng Yu <email>[email protected]</email></corresp><corresp id=\"c002\">Tao Xu <email>[email protected]</email></corresp><fn fn-type=\"other\" id=\"fn001\"><p>This article was submitted to Life-Course Epidemiology and Social Inequalities, a section of the journal Frontiers in Public Health</p></fn></author-notes><pub-date pub-type=\"epub\"><day>11</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><year>2020</year></pub-date><pub-date pub-type=\"pmc-release\"><day>11</day><month>8</month><year>2020</year></pub-date><!-- PMC Release delay is 0 months and 0 days and was based on the <pub-date pub-type=\"epub\"/>. --><volume>8</volume><elocation-id>461</elocation-id><history><date date-type=\"received\"><day>12</day><month>5</month><year>2020</year></date><date date-type=\"accepted\"><day>22</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>Copyright © 2020 Pan, Cheng, Cao, Hu, Zou, Yu and Xu.</copyright-statement><copyright-year>2020</copyright-year><copyright-holder>Pan, Cheng, Cao, Hu, Zou, Yu and Xu</copyright-holder><license xlink:href=\"http://creativecommons.org/licenses/by/4.0/\"><license-p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</license-p></license></permissions><abstract><p><bold>Background:</bold> The global COVID-19 epidemic remains severe, with the cumulative global death toll reaching more than 207,170 as of May 2, 2020 (<xref rid=\"B1\" ref-type=\"bibr\">1</xref>).</p><p><bold>Purpose:</bold> Our research objective is to establish a reliable nomogram to predict mortality in COVID-19 patients. The nomogram can help us distinguish between patients who are at high risk of death and need close attention.</p><p><bold>Patients and Methods:</bold> For the single-center retrospective study, we collected 21 cases of patients who died in the critical illness area of the Optical Valley Branch of Tongji Hospital, Huazhong University of Science and Technology, from February 9 to March 10. Additionally, we selected 99 patients discharged during this period for analysis. The nomogram was constructed to predict the mortality for COVID-19 patients using the primary group of 120 patients and was validated using an independent cohort of 84 patients. We used multivariable logistic regression analysis to construct the prediction model. The nomogram was evaluated for calibration, differentiation, and clinical usefulness.</p><p><bold>Results:</bold> The predictors included in the nomogram were c-reactive protein, PaO<sub>2</sub>/FiO<sub>2</sub>, and cTnI. The areas under the curves of the nomogram were 0.988 (95% CI: 0.972–1.000) and 0.956 (95% CI, 0.874–1.000) in the primary and validation groups, respectively. Decision curve analysis suggests that the nomogram may have clinical usefulness.</p><p><bold>Conclusion:</bold> This study provides a nomogram containing c-reactive protein, PaO<sub>2</sub>/FiO<sub>2</sub>, and cTnI that can be conveniently used to predict individual mortality in COVID-19 patients. Next, we will collect as many cases as possible from multiple centers to build a more reliable nomogram to predict mortality for COVID-19 patients.</p></abstract><kwd-group><kwd>nomogram</kwd><kwd>predict</kwd><kwd>mortality</kwd><kwd>COVID-19</kwd><kwd>patients</kwd></kwd-group><funding-group><award-group><funding-source id=\"cn001\">Qingdao City Science and Technology Special</funding-source><award-id rid=\"cn001\">20-4-1-5-nsh</award-id></award-group></funding-group><counts><fig-count count=\"3\"/><table-count count=\"2\"/><equation-count count=\"0\"/><ref-count count=\"46\"/><page-count count=\"6\"/><word-count count=\"4382\"/></counts></article-meta></front><body><sec sec-type=\"intro\" id=\"s1\"><title>Introduction</title><p>Since the outbreak of COVID-19 in Wuhan, Hubei province, in December 2019, it has had a major impact on people's lives, health, and property safety. It has rapidly expanded to 34 provincial divisions of China (<xref rid=\"B2\" ref-type=\"bibr\">2</xref>–<xref rid=\"B4\" ref-type=\"bibr\">4</xref>). As of April 28, 2020, the total number of confirmed cases in Wuhan has reached 68,128, accounting for 80.75% of the total number of confirmed cases in China, and 4,512 people have died in Wuhan, accounting for 97.18% of the total number of deaths in China (<xref rid=\"B5\" ref-type=\"bibr\">5</xref>).</p><p>SARS-CoV-2 is a binuclear virus that has a wide clinical spectrum of infection (<xref rid=\"B6\" ref-type=\"bibr\">6</xref>–<xref rid=\"B8\" ref-type=\"bibr\">8</xref>). In the past 2 months, only a few studies have analyzed prognostic factors for death, with repeated emphasis on factors such as age, lymphocytosis, leukocytosis, and elevated ALT (<xref rid=\"B7\" ref-type=\"bibr\">7</xref>). Thus, some of these potential prognostic factors need further validation, and a scoring system that accurately predicts the possible mortality risk is needed. Nomograms are used for multiple indicators to diagnose or predict disease onset or progression, making prognostic model results easier to read. Therefore, nomograms have been gradually applied in medical research and clinical practice (<xref rid=\"B9\" ref-type=\"bibr\">9</xref>, <xref rid=\"B10\" ref-type=\"bibr\">10</xref>).</p><p>Here, we randomly selected some patients with definite clinical outcome (death or discharge) who had been admitted to the critical care unit of the Optical Valley Branch of Tongji Hospital, Huazhong University of Science and Technology, as of March 10, 2020 (<xref rid=\"B11\" ref-type=\"bibr\">11</xref>). The aim of our study was to establish a nomogram that incorporated demographics, clinical characteristics, and laboratory results to predict individual mortality in COVID-19 patients.</p></sec><sec sec-type=\"materials and methods\" id=\"s2\"><title>Materials and Methods</title><sec><title>Patient Selection</title><p>We selected COVID-19 patients admitted to the critical illness area of the Optical Valley Branch of Tongji Hospital, Huazhong University of Science and Technology, from February 9, 2020, to March 10, 2020.</p><p>Patients were enrolled in this cohort if they: (1) were more than 18 years old; (2) were diagnosed with COVID-19 by multiplex real-time RT-PCR; (3) had complete clinical records of medical history and laboratory results. Patients were excluded if they: (1) were suspected with COVID-19; (2) had incomplete clinical records of medical history and laboratory results.</p></sec><sec><title>Study Variables</title><p>We collected demographics, comorbidities, routine laboratory tests, immunological indicators, radiological images, and inpatient treatments (<xref rid=\"B12\" ref-type=\"bibr\">12</xref>). For each patient, the baseline characters were screened: gender, age, leukocytes, platelet–lymphocyte ratio (PLR), neutrophil–lymphocyte ratio (NLR), Charlson Comorbidity Index (CCI), CURB-65 score, PaO<sub>2</sub>/FiO<sub>2</sub>, interleukin-1β (IL-1β), interleukin-2R (IL-2R), interleukin-6 (IL-6), interleukin-8 (IL-8), interleukin-10 (IL-10), tumor necrosis factor-α (TNF-α), cardiac troponin I (cTnI), brain natriuretic peptide (BNP), calcitonin, c-reactive protein (CRP), lactate dehydrogenase (LDH), d-dimer, fibrinogen, history of hypertension, heart failure, coronary heart disease (CHD), diabetes, cough, expectoration, diarrhea, shortness of breath, body temperature above 38°C, and X-ray or CT findings.</p></sec><sec><title>Construction of the Nomogram</title><p>We used the chi-square test for univariate analysis. Multivariable regression models were developed by incorporating meaningful factors in univariate analyses. Univariate and multivariate regression analysis was used to analyze the risk factors in the primary cohort (<xref rid=\"B13\" ref-type=\"bibr\">13</xref>). Then we construct a nomogram on the basis of the multivariate logistic regression model.</p></sec><sec><title>Statistical Analysis</title><p>Patients' demographic and clinical characteristics were analyzed using descriptive methods, with standard summary statistics including the median, interquartile range (IQR), and proportions. Categorical variables were processed by Chi-square or Fisher's exact tests, as appropriate. We tested the accuracy of the nomograms by discrimination and calibration both in the primary and the validation cohort. We used the receiver operating characteristic curve (ROC) to assess the discriminative ability of the nomogram and then assessed the area under the curve (AUC) (<xref rid=\"B14\" ref-type=\"bibr\">14</xref>, <xref rid=\"B15\" ref-type=\"bibr\">15</xref>). Calibration curves were used to compare the association between actual outcomes and predicted probabilities (<xref rid=\"B16\" ref-type=\"bibr\">16</xref>). Decision curve analysis (DCA) was performed by calculating the net benefits for a range of threshold probabilities to evaluate the clinical utility of the nomogram (<xref rid=\"B17\" ref-type=\"bibr\">17</xref>, <xref rid=\"B18\" ref-type=\"bibr\">18</xref>). Statistical analysis was performed using SPSS 23 (IBM, Chicago, IL, USA) and R version 3.4.4. All statistical tests were two-tailed, and a <italic>P</italic> < 0.05 was considered to be statistically significant.</p></sec></sec><sec sec-type=\"results\" id=\"s3\"><title>Results</title><sec><title>Patient Characteristics</title><p>Between February 9, 2020, and March 10, 2020, from more than 600 patients in the critical illness area of the Optical Valley Branch of Tongji Hospital, 120 cases were selected for inclusion in the study by means of a random number table. The average age of the 120 patients was 62.16 years old, and there were 50 females and 70 males (<xref rid=\"B19\" ref-type=\"bibr\">19</xref>). The majority of the patients were over 60 years old (69.20%). Among the 120 cases, 90 (75.0%) developed cough symptoms with or without expectoration, 62 (51.7%) had high fever (≥38°C), 68 (56.7%) had mild shortness of breath, and 15 (12.5%) developed diarrhea. The vast majority of patients exhibited bilateral invasion in chest X-ray and CT, mostly with a patchy and ground-glass appearance (<xref rid=\"T1\" ref-type=\"table\">Table 1</xref>).</p><table-wrap id=\"T1\" position=\"float\"><label>Table 1</label><caption><p>Results of the univariate association analyses.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Characteristics</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Death set</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Survival set</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold><italic>Z</italic>-value</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold><italic>P</italic>-value</bold></th></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Age, year</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">70 (66, 78.5)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">64 (51, 70)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−3.273</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.001</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Sex (male)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">17</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">53</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5.358</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.021</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Sex (female)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">46</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">WBC</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">9.01 (5.42, 14.62)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5.47 (4.49, 6.81)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−3.398</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.001</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">NLR</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">10.44 (5.70, 25.10)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.50 (1.80, 3.84)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−5.688</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.000</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">PLR</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">305.88 (143.23, 352.80)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">204.40 (149.43, 312.73)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−1.433</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.152</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">PCT</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.29 (0.15, 0.57)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.06 (0.05, 0.07)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−6.560</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.000</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">CRP</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">113.3 (63.55, 145.4)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">8.9 (2.4, 32.4)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−5.888</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.000</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">LDH</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">566 (327.5, 667.5)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">245 (190, 285)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−5.046</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.000</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Fibrinogen</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4.9 (3.22, 6.19)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5.18 (4.1, 5.85)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−0.597</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.550</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">PaO<sub>2</sub>/FiO<sub>2</sub></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">105.43 (76.05, 161.98)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">331.76 (233.89, 390.54)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−6.351</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.000</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Serumcalcium(L)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">19</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">77</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.043</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.307</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">TNI(H)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">11</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">29.615</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.000</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">BNP(H)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">18</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">23</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">30.070</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.000</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">CURB-65(0–1)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">10</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">93</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">26.879</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.000</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">CURB-65(2–5)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">11</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">6</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">CCI index(0–2)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">18</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">96</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.555</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.110</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">CCI index(3-)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">IL-1β(H)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">16</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.000</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.000</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">IL-2R(H)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">17</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">34</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">15.554</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.000</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">IL-6(H)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">19</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">36</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">20.434</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.000</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">IL-8(H)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4.091</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.043</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">IL-10(H)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">8</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">8</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">11.034</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.001</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">TNF-α(H)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">16</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">45</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">6.549</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.010</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">D-dimer(H)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">21</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">68</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">8.866</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.003</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Hypertension</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">13</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">31</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">6.982</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.008</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Heart failure</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0</td><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.175</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">CHD</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.083</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.079</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Diabetes</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">14</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.055</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.814</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Cough</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">14</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">76</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.943</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.332</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Expectoration</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">7</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">44</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.875</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.350</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Diarrhea</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">13</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.008</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.928</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">≥38°C</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">11</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">51</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.005</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.943</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">GGO</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">8</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">30</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.486</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.486</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Consolidation</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">13</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.883</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.170</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Pathy</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">15</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">77</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.116</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.733</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Fibrosis</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3</td><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.000</td></tr></tbody></table></table-wrap></sec><sec><title>Independent Prognostic Factors</title><p>We used univariate and multivariate analyses to identify the prognostic factors (<xref rid=\"B20\" ref-type=\"bibr\">20</xref>, <xref rid=\"B21\" ref-type=\"bibr\">21</xref>). The univariate analysis revealed that age (<italic>p</italic> = 0.001), Male/Female (<italic>p</italic> = 0.021), WBC (<italic>p</italic> = 0.001), NLR (<italic>p</italic> = 0.000), PCT (<italic>p</italic> = 0.000), CRP (<italic>p</italic> = 0.000), LDH (<italic>p</italic> = 0.000), PaO<sub>2</sub>/FiO<sub>2</sub> (<italic>p</italic> = 0.000), Serum calcium (<italic>p</italic> = 0.307), cTnI (<italic>p</italic> = 0.000), BNP (<italic>p</italic> = 0.000), CURB-65 (<italic>p</italic> = 0.000), IL-2R (<italic>p</italic> = 0.000), IL-6 (<italic>p</italic> = 0.000), IL-8 (<italic>p</italic> = 0.043), IL-10 (<italic>p</italic> = 0.001), TNF-α (<italic>p</italic> = 0.01), D-dimer (<italic>p</italic> = 0.003), and Hypertension (<italic>p</italic> = 0.008) were significant prognostic factors. The multivariate analysis revealed that CRP (<italic>p</italic> = 0.004), PaO<sub>2</sub>/FiO<sub>2</sub> (<italic>p</italic> = 0.002), and cTnI (<italic>p</italic> = 0.016) were independent prognostic factors for predicting the mortality of COVID-19 patients (<xref rid=\"B22\" ref-type=\"bibr\">22</xref>).</p></sec><sec><title>Development and Validation of a Nomogram</title><p>Multivariate logistic regression analysis determined that CRP, PaO<sub>2</sub>/FiO<sub>2</sub>, and cTnI were independent predictors (<xref rid=\"T2\" ref-type=\"table\">Table 2</xref>). The model with three independent predictors is established and represented by a nomogram (<xref ref-type=\"fig\" rid=\"F1\">Figure 1</xref>).</p><table-wrap id=\"T2\" position=\"float\"><label>Table 2</label><caption><p>Multivariate logistic regression of death in COVID-19 patients.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th rowspan=\"1\" colspan=\"1\"/><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>B</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>SE</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Wald</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>OR(95%CI)</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold><italic>P</italic>-value</bold></th></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">CRP</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.037</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.013</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">8.271</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.037 (1.012, 1.064)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.004</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">PaO2/FiO2</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−0.045</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.015</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">9.144</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.956 (0.929, 0.984)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.002</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">TNI</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5.417</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.244</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5.830</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">225.296 (2.773, 18303.1)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.016</td></tr></tbody></table></table-wrap><fig id=\"F1\" position=\"float\"><label>Figure 1</label><caption><p>Nomogram predicting mortality for COVID-19 patients with three available factors: CRP, PaO<sub>2</sub>/FiO<sub>2</sub>, and cTnI.</p></caption><graphic xlink:href=\"fpubh-08-00461-g0001\"/></fig><p>We predicted the probability of death in patients with COVID-19 by calculating the sum of points in the nomogram corresponding to patient characteristics. Regarding the clinical application of this nomogram, we assumed that, if a patient's CRP was 20 mg/L, the PaO<sub>2</sub>/FiO<sub>2</sub> was 350, and the cTnI was increased, then the corresponding score of this patient was 3+33+26, a total of 62 points, so the corresponding risk of death of this patient was between 0.001 and 0.01, and this patient was relatively safe.</p><p>The AUC for the prediction nomogram was 0.988, 95% CI, 0.972–1.000 (<xref rid=\"B23\" ref-type=\"bibr\">23</xref>). The AUC for CRP was 0.910. The AUC for PaO<sub>2</sub>/FiO<sub>2</sub> was 0.942. The AUC for cTnI was 0.737. The nomogram decision curve analysis is shown in <xref ref-type=\"fig\" rid=\"F2\">Figure 2</xref>. The calibration curve of the nomogram for the probability of mortality demonstrated good agreement between prediction and observation in the primary cohort (<xref ref-type=\"fig\" rid=\"F2\">Figure 2</xref>) (<xref rid=\"B24\" ref-type=\"bibr\">24</xref>). The decision curve showed that if the threshold probability of a patient or doctor is >0%, using the nomogram to predict the mortality for COVID-19 patients adds more benefit. Within this range, net benefit was comparable, with several overlaps, on the basis of the nomogram (<xref rid=\"B25\" ref-type=\"bibr\">25</xref>).</p><fig id=\"F2\" position=\"float\"><label>Figure 2</label><caption><p>From left to right: ROC curves of the nomogram and the three factors; calibration curve of the nomogram in the cohort; decision curve analysis for the nomogram. The y-axis measures the net benefit. The red line represents the nomogram. The gray line represents the assumption that all patients have died. The thin black link represents the assumption that no patients have died. The net benefit was calculated by subtracting the proportion of all patients who are false positive from the proportion who are true positive, weighting by the relative harm of forgoing treatment compared with the negative consequences of unnecessary treatment.</p></caption><graphic xlink:href=\"fpubh-08-00461-g0002\"/></fig><p>Validation was performed by using another 84 COVID-19 patients. In the validation cohort, the independent risk factors included in the nomogram were examined. The AUC of the nomogram was 0.956 (95% CI: 0.874–1.000). <xref ref-type=\"fig\" rid=\"F3\">Figure 3</xref> shows that the calibration plot for the probability of mortality demonstrated good agreement between the prediction by nomogram and actual observation.</p><fig id=\"F3\" position=\"float\"><label>Figure 3</label><caption><p>From left to right: ROC curves of the Nomogram and the three factors; calibration curve of the nomogram in the cohort; decision curve analysis for the nomogram.</p></caption><graphic xlink:href=\"fpubh-08-00461-g0003\"/></fig></sec></sec><sec sec-type=\"discussion\" id=\"s4\"><title>Discussion</title><p>Since 2003, coronavirus has caused a number of major public health events (<xref rid=\"B26\" ref-type=\"bibr\">26</xref>, <xref rid=\"B27\" ref-type=\"bibr\">27</xref>). Generally, it was not until the outbreak of severe acute respiratory syndrome (SARS) in Guangdong, China, in 2002 and 2003 that the coronavirus was found to be highly pathogenic to humans. Another highly pathogenic coronavirus is the Middle East respiratory syndrome (MERS) coronavirus, which emerged in 2012 in Middle Eastern countries (<xref rid=\"B28\" ref-type=\"bibr\">28</xref>–<xref rid=\"B31\" ref-type=\"bibr\">31</xref>). COVID-19 is another highly pathogenic coronavirus that has been added to human history. Although COVID-19 has a mortality rate of <3% compared to SARS (9.6%) and MERS (34%), its effects remain extremely sudden and devastating (<xref rid=\"B32\" ref-type=\"bibr\">32</xref>, <xref rid=\"B33\" ref-type=\"bibr\">33</xref>).</p><p>Previous studies have shown that coronaviruses can cause respiratory and intestinal infections (<xref rid=\"B34\" ref-type=\"bibr\">34</xref>). According to recent reports, the most common symptoms of COVID-19 patients are fever, cough, myalgia, and fatigue, while the less common are expectoration, hemoptysis, and diarrhea (<xref rid=\"B35\" ref-type=\"bibr\">35</xref>–<xref rid=\"B39\" ref-type=\"bibr\">39</xref>). Most patients have mild symptoms and good prognosis. Mild patients may have only a low fever and mild weakness, with no obvious signs of pneumonia (<xref rid=\"B40\" ref-type=\"bibr\">40</xref>). In the late stage of infection, a variety of complications occur in critically ill patients, including ARDS, septic shock, refractory metabolic acidosis, acute myocardial injury, disseminated intravascular coagulation, and even death (<xref rid=\"B5\" ref-type=\"bibr\">5</xref>, <xref rid=\"B6\" ref-type=\"bibr\">6</xref>). Therefore, early diagnosis and early assessment of the patient's prognosis are crucial for controlling the outbreak.</p><p>Corticosteroids were used extensively during the outbreaks of severe SARS and MERS and are being applied in COVID-19 patients. Corticosteroids suppress inflammation in the lungs but also inhibit immune responses and pathogen clearance at the same time (<xref rid=\"B41\" ref-type=\"bibr\">41</xref>). The clinical benefit of corticosteroids in COVID-19 needs further clinical trials to confirm (<xref rid=\"B42\" ref-type=\"bibr\">42</xref>, <xref rid=\"B43\" ref-type=\"bibr\">43</xref>).</p><p>In previous studies, several prognostic models have been developed. For example, the NLR was the most effective prognostic factor affecting prognosis for severe COVID-19 patients (<xref rid=\"B44\" ref-type=\"bibr\">44</xref>).</p><p>In our study, this nomogram based on three variables, CRP, PaO<sub>2</sub>/FiO<sub>2</sub>, and cTnI, can provide a more accurate assessment and prediction of mortality at admission for COVID-19 patients. If patients with high mortality rates are properly and promptly identified, they should be more likely to benefit from nutritional support in nursing care and close attention in clinical care, which will ultimately have a positive impact on their recovery (<xref rid=\"B45\" ref-type=\"bibr\">45</xref>, <xref rid=\"B46\" ref-type=\"bibr\">46</xref>).</p><p>The variables in the nomogram we constructed were easily accessible from routine clinical work. As a result, clinicians can use this simple, intuitive predictive tool to draw a few lines in a few seconds to make a quick assessment of a patient's prognosis with no computational difficulty. Additionally, our model can help clinical workers rationally allocate medical resources to reduce the fatality rate of COVID-19 when medical resources are scarce.</p><p>There are still some limitations to our study. First, the study was at a single center with a small sample, and the next step is to include as many cases as possible from multiple centers to build a more reliable predicting nomogram for mortality in COVID-19 patients. Second, we excluded patients whose case data were incomplete, which could have resulted in selection bias.</p></sec><sec sec-type=\"conclusions\" id=\"s5\"><title>Conclusions</title><p>To sum up, to better identify the prognosis of COVID-19 patients, identify severe patients as early as possible, improve the cure rate, and reduce the fatality rate, we constructed a nomogram based on 120 patients to predict mortality. The proposed nomogram considered three independent risk factors, namely, CRP, PaO<sub>2</sub>/FiO<sub>2</sub>, and cTnI. We have confirmed that this nomogram has excellent discrimination and clinical availability.</p></sec><sec sec-type=\"data-availability\" id=\"s6\"><title>Data Availability Statement</title><p>The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.</p></sec><sec id=\"s7\"><title>Ethics Statement</title><p>The studies involving human participants were reviewed and approved by Medical ethics committee of affiliated hospital of Qingdao university. The ethics committee waived the requirement of written informed consent for participation. Written informed consent was obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article.</p></sec><sec id=\"s8\"><title>Author Contributions</title><p>DP: data analysis and visualization. DP, DC, YC, and CH: writing original draft. FZ, TX, and WY: review and editing. All authors contributed to the article and approved the submitted version.</p></sec><sec id=\"s9\"><title>Conflict of Interest</title><p>The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p></sec></body><back><ack><p>Thanks to the medical workers who have contributed to the epidemic and those in all industries involved in the prevention and control of the epidemic. 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"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Front Psychol</journal-id><journal-id journal-id-type=\"iso-abbrev\">Front Psychol</journal-id><journal-id journal-id-type=\"publisher-id\">Front. Psychol.</journal-id><journal-title-group><journal-title>Frontiers in Psychology</journal-title></journal-title-group><issn pub-type=\"epub\">1664-1078</issn><publisher><publisher-name>Frontiers Media S.A.</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32849153</article-id><article-id pub-id-type=\"pmc\">PMC7432146</article-id><article-id pub-id-type=\"doi\">10.3389/fpsyg.2020.01969</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Psychology</subject><subj-group><subject>Brief Research Report</subject></subj-group></subj-group></article-categories><title-group><article-title>Beyond the Lab: Empirically Supported Treatments in the Real World</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Schneider</surname><given-names>Renee A.</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><xref ref-type=\"corresp\" rid=\"c001\"><sup>*</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/940656/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Grasso</surname><given-names>Joseph R.</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/1034153/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Chen</surname><given-names>Shih Yin</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Chen</surname><given-names>Connie</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><xref ref-type=\"aff\" rid=\"aff2\"><sup>2</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Reilly</surname><given-names>Erin D.</given-names></name><xref ref-type=\"aff\" rid=\"aff3\"><sup>3</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Kocher</surname><given-names>Bob</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><xref ref-type=\"aff\" rid=\"aff4\"><sup>4</sup></xref></contrib></contrib-group><aff id=\"aff1\"><sup>1</sup><institution>Lyra Health</institution>, <addr-line>Burlingame, CA</addr-line>, <country>United States</country></aff><aff id=\"aff2\"><sup>2</sup><institution>Department of General Internal Medicine, University of California, San Francisco</institution>, <addr-line>San Francisco, CA</addr-line>, <country>United States</country></aff><aff id=\"aff3\"><sup>3</sup><institution>Department of Psychiatry, University of Massachusetts System</institution>, <addr-line>Boston, MA</addr-line>, <country>United States</country></aff><aff id=\"aff4\"><sup>4</sup><institution>School of Medicine, Stanford University</institution>, <addr-line>Stanford, CA</addr-line>, <country>United States</country></aff><author-notes><fn fn-type=\"edited-by\"><p>Edited by: Emily K. Sandoz, University of Louisiana at Lafayette, United States</p></fn><fn fn-type=\"edited-by\"><p>Reviewed by: John Young, University of Mississippi, United States; Brandon Gaudiano, Brown University, United States</p></fn><corresp id=\"c001\">*Correspondence: Renee A. Schneider, <email>[email protected]</email></corresp><fn fn-type=\"other\" id=\"fn004\"><p>This article was submitted to Psychology for Clinical Settings, a section of the journal Frontiers in Psychology</p></fn></author-notes><pub-date pub-type=\"epub\"><day>11</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><year>2020</year></pub-date><volume>11</volume><elocation-id>1969</elocation-id><history><date date-type=\"received\"><day>17</day><month>4</month><year>2020</year></date><date date-type=\"accepted\"><day>16</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>Copyright © 2020 Schneider, Grasso, Chen, Chen, Reilly and Kocher.</copyright-statement><copyright-year>2020</copyright-year><copyright-holder>Schneider, Grasso, Chen, Chen, Reilly and Kocher</copyright-holder><license xlink:href=\"http://creativecommons.org/licenses/by/4.0/\"><license-p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</license-p></license></permissions><abstract><p>Laboratory studies of empirically supported treatments (ESTs) for mental health problems achieve much higher rates of clinical improvement than has been observed following treatment in the community. This discrepancy is likely to due to limited reliance on ESTs by therapists outside of academia. Concerns about the generalizability of ESTs to patients in the community, who may have comorbid problems, likely limit rates of adoption. The present study examined the impact of ESTs delivered in the real-world for 1,256 adults who received services through an employee assistance program specializing in the delivery of ESTs. Rates of anxiety and depression decreased significantly, following treatment with an EST, and 898 (71.5%) patients demonstrated reliable improvement. Even among patients comorbid for depression and anxiety at baseline, over half reported reliable improvement in both disorders. Findings suggest ESTs can be effectively delivered outside of academic RCTs. However, additional research is needed to understand and overcome barriers to disseminating ESTs to the broader community.</p></abstract><kwd-group><kwd>empirically supported treatment</kwd><kwd>evidence-based treatment</kwd><kwd>depression</kwd><kwd>anxiety</kwd><kwd>mental health problems</kwd></kwd-group><counts><fig-count count=\"1\"/><table-count count=\"2\"/><equation-count count=\"0\"/><ref-count count=\"34\"/><page-count count=\"6\"/><word-count count=\"0\"/></counts></article-meta></front><body><p>Empirically supported therapies (ESTs) are behavioral health interventions that have been rigorously tested in randomized controlled trials (RCTs) or a series of well-designed single-subject experiments and have demonstrated efficacy when compared to a control or active treatment condition (<xref rid=\"B5\" ref-type=\"bibr\">Chambless and Hollon, 1998</xref>; <xref rid=\"B31\" ref-type=\"bibr\">Tolin et al., 2015</xref>). ESTs have been developed for a range of behavioral health problems, including the most common disorders, depression and anxiety. In particular, cognitive behavioral therapy (CBT), cognitive therapy (CT), and interpersonal psychotherapy have all demonstrated efficacy in the treatment of moderate or severe depression, with evidence suggesting that CBT and CT may be as efficacious as antidepressant medication (<xref rid=\"B27\" ref-type=\"bibr\">Shapiro et al., 1994</xref>; <xref rid=\"B10\" ref-type=\"bibr\">Gloaguen et al., 1998</xref>; <xref rid=\"B9\" ref-type=\"bibr\">DeRubeis et al., 2005</xref>). Multiple meta-analyses also support the efficacy of CBT or behavioral therapies for the treatment of anxiety disorders, including panic, obsessive-compulsive disorder, social anxiety, post-traumatic stress disorder, and generalized anxiety disorder (<xref rid=\"B8\" ref-type=\"bibr\">Deacon and Abramowitz, 2004</xref>).</p><p>Reliable improvement, defined as symptom improvement not accounted for by measurement error alone (<xref rid=\"B15\" ref-type=\"bibr\">Jacobson and Truax, 1991</xref>), is the standard by which ESTs are often measured. Laboratory studies of ESTs, in which efficacy is tested in an RCT, demonstrate rates of reliable improvement at or greater than 50% (<xref rid=\"B14\" ref-type=\"bibr\">Hofmann et al., 2012</xref>; <xref rid=\"B22\" ref-type=\"bibr\">Loerinc et al., 2015</xref>). However, findings indicate that among adults treated with psychotherapy under naturalistic conditions in the community, fewer than 30% achieve reliable improvement (<xref rid=\"B13\" ref-type=\"bibr\">Hansen et al., 2002</xref>).</p><p>One explanation for the discrepancy in observed outcomes may be due to limited adoption of ESTs by providers in the community. Despite efforts to disseminate ESTs more broadly, they are underutilized outside of academia (<xref rid=\"B29\" ref-type=\"bibr\">Stewart and Chambless, 2007</xref>). Concerns about the external validity of RCTs are frequently cited by clinicians in the community (<xref rid=\"B23\" ref-type=\"bibr\">Nelson and Steele, 2007</xref>; <xref rid=\"B17\" ref-type=\"bibr\">Kazdin, 2008</xref>), who worry that ESTs are infeasible to implement in real-world settings (<xref rid=\"B1\" ref-type=\"bibr\">Addis et al., 1999</xref>) and that subjects in RCTs differ in important ways from patients in community settings (<xref rid=\"B34\" ref-type=\"bibr\">Westen and Morrison, 2001</xref>). In particular, it has been hypothesized that including patients with psychological comorbidities, which tends to be the norm in real-world clinical settings, could reduce the impact of ESTs (<xref rid=\"B25\" ref-type=\"bibr\">Shadish et al., 2000</xref>).</p><p>Unfortunately, treatment studies often focus only on a single diagnosis, with comorbid psychiatric diagnoses considered exclusion criteria, thus diminishing their application to real-world settings (<xref rid=\"B11\" ref-type=\"bibr\">Goldstein-Piekarski et al., 2016</xref>). There are only a small number of RCTs examining EST effectiveness with more real-world–like samples. For example, <xref rid=\"B7\" ref-type=\"bibr\">Craigie and Nathan (2009)</xref> found that both individual and group CBT could be effectively used to treat depression in a sample of adult community outpatients with high rates of comorbidity. <xref rid=\"B2\" ref-type=\"bibr\">Barlow et al. (2017)</xref> similarly demonstrated that ESTs could be effective in the treatment of adult anxiety disorders when applied to patients with multiple psychiatric diagnoses.</p><p>However, other studies on the generalizability of ESTs have produced mixed results, with some research suggesting that ESTs are less potent when delivered outside of more controlled settings (e.g., <xref rid=\"B33\" ref-type=\"bibr\">Weisz et al., 2006</xref>; <xref rid=\"B16\" ref-type=\"bibr\">Jonsson et al., 2015</xref>). Consistent with these findings, it has been suggested that as study conditions more closely resemble the real world, the efficacy of ESTs may diminish (<xref rid=\"B34\" ref-type=\"bibr\">Westen and Morrison, 2001</xref>; <xref rid=\"B30\" ref-type=\"bibr\">Stewart and Chambless, 2009</xref>). For example, when providers are not required to use a treatment manual or when treatment fidelity is not measured, CBT may be less potent, as therapists may “drift” from standardized practices (<xref rid=\"B32\" ref-type=\"bibr\">Waller, 2009</xref>). Further, therapists in well-controlled RCTs often receive more intensive training in ESTs and clinical consultation than therapists practicing in the community (<xref rid=\"B33\" ref-type=\"bibr\">Weisz et al., 2006</xref>; <xref rid=\"B4\" ref-type=\"bibr\">Becker and Stirman, 2011</xref>), which may affect treatment fidelity and outcomes.</p><p>The EST literature has been criticized for focusing on efficacy in tightly controlled clinical trials at the expense of real-world generalizability (<xref rid=\"B31\" ref-type=\"bibr\">Tolin et al., 2015</xref>), and there are few studies that examine the efficacy of ESTs under more naturalistic conditions. In particular, only a handful of studies have looked at how adult patients with psychological comorbidities fare when treated with an EST. In addition, most studies of ESTs rely on a randomized controlled design, which may limit the generalizability of findings as not all patients are willing to be randomized to conditions (<xref rid=\"B31\" ref-type=\"bibr\">Tolin et al., 2015</xref>). Studies of the portability of ESTs outside of academia are often conducted in the context of extensive clinical training and oversight, something that is generally lacking in the community. In revising the criteria for ESTs, <xref rid=\"B31\" ref-type=\"bibr\">Tolin et al. (2015)</xref> encouraged research that (1) did not involve randomizing subjects to conditions, (2) was conducted by clinicians outside of academia, and (3) involved patients with behavioral health comorbidities.</p><p>This present study applies a retrospective design to build on the criteria outlined by <xref rid=\"B31\" ref-type=\"bibr\">Tolin et al. (2015)</xref> to better understand whether ESTs for behavioral health problems are effective under real-world conditions. We examined rates of reliable improvement among patients with depression or anxiety who received an EST from a community therapist. Because research on the efficacy of ESTs for patients with psychological comorbidities is limited, we also report separately rates of reliable improvement among patients who started treatment with both depression and anxiety.</p><sec id=\"S1\"><title>Methods</title><sec id=\"S1.SS1\"><title>Participants</title><p>Adult patients, 18 years or older, who started individual therapy between July 1, 2018 and May 31, 2019, were included in the present study. All patients in the study were referred to a community therapist by an employee assistance program (EAP) that partners with therapists who utilize ESTs. Patients were employees or dependents of customers who had purchased the EAP. Patients were included in the study if they scored in the clinical range on a measure of depression or anxiety and completed a baseline assessment within 2 weeks of their first appointment (<xref ref-type=\"fig\" rid=\"F1\">Figure 1</xref>). Patients were sent electronically secure assessment questionnaires every 4 weeks following their first appointment. Baseline assessments were compared to the most recent assessment to estimate the impact of treatment. This study was deemed exempt from human patients review by the Western Institutional Review Board.</p><fig id=\"F1\" position=\"float\"><label>FIGURE 1</label><caption><p>Participant flow through the study.</p></caption><graphic xlink:href=\"fpsyg-11-01969-g001\"/></fig></sec><sec id=\"S1.SS2\"><title>Therapists</title><p>All therapists in the present study were in community private or group practices and had agreed to join the network of an EAP that specializes in referring patients to providers who practice ESTs. Prior to joining the EAP network, a vetting team reviewed each provider’s public presence (e.g., website) and application, if available, to determine whether they likely utilized ESTs. Those providers who passed this initial step were invited to participate in a clinical vetting interview designed to test their knowledge of and ability to apply ESTs. Sample components of the clinical interview include asking prospective therapists about their theoretical orientation, the therapies and interventions they use, how they measure treatment progress, the average length of treatment, and how they adapt treatment plans based on a patient’s response to treatment. In particular, we were looking for providers who used ESTs as defined by <xref rid=\"B5\" ref-type=\"bibr\">Chambless and Hollon (1998)</xref> and <xref rid=\"B31\" ref-type=\"bibr\">Tolin et al. (2015)</xref>, used validated measures to assess treatment progress and outcomes, and practiced short-term therapy in contrast to treatments of indeterminate length. Only those therapists who passed the rigorous clinical vetting interview were invited to join the network. Historically, approximately 5% of providers who applied to join the network have passed the review and vetting interview. Providers included in the study were compensated monetarily, as per standard community practice.</p></sec><sec id=\"S1.SS3\"><title>Measures</title><p>Assessments consisted of the Patient Health Questionnaire 9 (PHQ-9) and Generalized Anxiety Disorder scale (GAD-7), well-validated measures of depression and anxiety (<xref rid=\"B18\" ref-type=\"bibr\">Kroenke et al., 2001</xref>; <xref rid=\"B28\" ref-type=\"bibr\">Spitzer et al., 2006</xref>; <xref rid=\"B24\" ref-type=\"bibr\">Plummer et al., 2016</xref>). The PHQ-9 includes nine items rated on a 4-point scale, with scores ranging from 0 to a maximum of 27. The GAD-7 includes seven items rated on a 4-point scale, with scores ranging from 0 to 21. Both measures have been shown to be sensitive to treatment changes over time in psychiatric populations (<xref rid=\"B3\" ref-type=\"bibr\">Beard and Björgvinsson, 2014</xref>). For inclusion in the study, a clinical cutoff of 10 was used for the PHQ-9, as research suggests that patients who score at or greater than 10 are very likely to meet the criteria for major depression (<xref rid=\"B18\" ref-type=\"bibr\">Kroenke et al., 2001</xref>). A clinical cutoff of 8 was used for the GAD-7, as research suggests that scores at or greater than 8 are highly likely to correspond to an anxiety disorder diagnosis (<xref rid=\"B19\" ref-type=\"bibr\">Kroenke et al., 2007</xref>; <xref rid=\"B24\" ref-type=\"bibr\">Plummer et al., 2016</xref>).</p></sec><sec id=\"S1.SS4\"><title>Analyses</title><p>Multiple regression analyses were conducted to examine a possible association between number of sessions and reduction in PHQ-9 and GAD-7 scores, respectively, after controlling for baseline scores. Paired <italic>t-</italic>tests were used to test differences in PHQ-9 and GAD-7 scores between baseline and follow-up. For each measure, we compared the baseline assessment score to the last available assessment and calculated Cohen’s <italic>d</italic><sub><italic>rm</italic></sub>, a conservative measure of effect size for within-subjects designs that controls for correlation between measurements (<xref rid=\"B20\" ref-type=\"bibr\">Lakens, 2013</xref>). We calculated the number of patients who demonstrated reliable improvement on either measure, using the RC index proposed by <xref rid=\"B15\" ref-type=\"bibr\">Jacobson and Truax (1991)</xref>. The RC index allowed us to determine whether a patient made substantial improvement in symptomatology, beyond what could be attributed to measurement error. Consistent with previous research (<xref rid=\"B12\" ref-type=\"bibr\">Gyani et al., 2013</xref>), we used an RC index of 6 for the PHQ-9 and 4 for the GAD-7. We also calculated the percentage of patients who recovered, meaning they moved from the clinical range to below the clinical cutoff on either measure. In addition, we calculated the number of patients who demonstrated reliable recovery (<xref rid=\"B12\" ref-type=\"bibr\">Gyani et al., 2013</xref>) in that they both demonstrated reliable improvement and recovered on either the PHQ-9 or GAD-7. Finally, to better assess the impact of treatment on patients with psychological comorbidity, rates of reliable improvement and recovery are reported separately for patients who started in the clinical range on both the PHQ-9 and GAD-7.</p></sec></sec><sec id=\"S2\"><title>Results</title><p>Of the 1,256 patients included in the analyses, 54.3% (<italic>n</italic> = 682) identified as female, 33.3% (<italic>n</italic> = 418) identified as male, 12.3% (<italic>n</italic> = 155) did not specify gender, and gender was missing for 1 patient. Sixty-eight percent of patients were between the ages of 18 and 34 years. The mean age of patients was 32.8 (<italic>SD</italic> = 8.7) years (<xref rid=\"T1\" ref-type=\"table\">Table 1</xref>).</p><table-wrap id=\"T1\" position=\"float\"><label>TABLE 1</label><caption><p>Characteristics of subjects and therapists.</p></caption><table frame=\"hsides\" rules=\"groups\" cellspacing=\"5\" cellpadding=\"5\"><thead><tr><td valign=\"top\" align=\"left\" colspan=\"3\" rowspan=\"1\">Subjects <italic>n</italic> (%)</td></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Gender</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Female</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">682 (54.3)</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Male</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">418 (33.3)</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Unspecified</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">155 (12.3)</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Missing</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1 (0.1)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Age group, years</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">18–24</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">153 (12.2)</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">24–34</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">699 (55.7)</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">35–44</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">275 (21.9)</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">45–54</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">86 (6.8)</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">55–64</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">41 (3.3)</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">=65</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2 (0.2)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Clinical presentation</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">PHQ-9 =10</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">845 (67.3)</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">GAD-7 = 8</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1,097(87.3)</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">PHQ-9 and GAD-7 in clinical range</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">686 (54.6)</td></tr><tr><td valign=\"top\" align=\"left\" colspan=\"3\" rowspan=\"1\"><bold>Therapists <italic>n</italic> (%)</bold></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Therapist credential</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Doctoral level</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">242 (43.3)</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Master’s level</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">317 (56.7)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Therapist years of experience</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><5</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">137 (24.5)</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">5–10</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">195 (34.9)</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">10–15</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">108 (19.3)</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">15–20</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">64 (11.4)</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">>20</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">52 (9.3)</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Missing</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3 (0.5)</td></tr></tbody></table></table-wrap><p>The 1,256 patients saw 559 separate therapists (<xref rid=\"T1\" ref-type=\"table\">Table 1</xref>). On average, each therapist saw 2.1 (<italic>SD</italic> = 1.6) patients. Approximately 43.3% of therapists had a doctoral degree; 56.7% of therapists had a master’s degree (e.g., LMFT, LCSW, LPCC). Therapists delivered ESTs, as per their normal practice, with the exception that patients could receive up to a pre-specified number of session visits per calendar year, with the maximum number of sessions being 25. The average number of sessions delivered across the course of treatment was 9.4 (<italic>SD</italic> = 7.13). The average number of weeks patients spent in treatment was 13.1 (<italic>SD</italic> = 10.4). The number of sessions was inversely correlated with depression and anxiety at follow-up, in that patients with more sessions showed greater improvement on the PHQ-9 [β = −0.05, <italic>t</italic>(1253) = 2.60, <italic>p</italic> = 0.01] and the GAD-7 [β = −0.05, <italic>t</italic>(1094) = 2.65, <italic>p</italic> < 0.01], after controlling for baseline scores.</p><p>Independent-samples <italic>t</italic>-tests were conducted to compare baseline severities on the GAD-7 and PHQ-9 for patients who did and did not complete a second outcome assessment. In terms of baseline PHQ-9 scores, there was no significant difference in severity for patients who completed a second outcome (mean = 14.30, <italic>SD</italic> = 3.79) and patients who did not complete a second outcome (mean = 14.65, <italic>SD</italic> = 3.83); <italic>t</italic>(1418) = −1.69, <italic>p</italic> = 0.09. Similarly, there was not a significant difference in the severity of baseline GAD-7 scores for patients who completed a second outcome (mean = 12.69, <italic>SD</italic> = 3.61) and patients who did not complete a second outcome (mean = 12.70, <italic>SD</italic> = 3.62); <italic>t</italic>(1860) = −0.05, <italic>p</italic> = 0.96.</p><sec id=\"S2.SS1\"><title>Depression Symptoms</title><p>Of the 1,256 patients, 845 (67.3%) scored in the clinical range on the PHQ-9 at baseline. The baseline average score on the PHQ-9 was 14.30 (<italic>SD</italic> = 3.79), corresponding to the moderate range of depression severity. Results of paired <italic>t</italic>-tests revealed that among patients who started in the clinical range, depression scores decreased significantly between baseline and follow-up (mean = 7.58, <italic>SD</italic> = 4.45), with patients improving an average of 6.7 points on the PHQ-9 [95% confidence interval (CI), 6.4–7.1], <italic>t</italic>(844) = 39.00, <italic>p</italic> < 0.001. Cohen’s <italic>d</italic> suggested a large treatment effect size on depression (<italic>d</italic><sub><italic>rm</italic></sub> = 1.62). Reliable improvement in depression scores was observed in 509 patients (60.2%), and 602 patients (71.2%) recovered on the PHQ-9 (<xref rid=\"T2\" ref-type=\"table\">Table 2</xref>). A total of 448 patients (53.0%) demonstrated reliable recovery on the PHQ-9, defined as meeting criteria for both reliable improvement and recovery.</p><table-wrap id=\"T2\" position=\"float\"><label>TABLE 2</label><caption><p>Clinical change and recovery.</p></caption><table frame=\"hsides\" rules=\"groups\" cellspacing=\"5\" cellpadding=\"5\"><thead><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><italic>n</italic> (%)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Baseline positive screening for depression (<italic>n</italic> = 845)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Baseline positive screening for anxiety (<italic>n</italic> = 1,097)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Baseline positive screening for depression or anxiety (<italic>n</italic> = 1,256)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Baseline positive screening for depression and anxiety (<italic>n</italic> = 686)</td></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Reliable improvement</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">509 (60.2)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">750 (68.4)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">898 (71.5)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">361 (52.6)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Recovery</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">602 (71.2)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">682 (62.2)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">934 (74.4)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">350 (51.0)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Reliable recovery*</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">448 (53.0)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">595 (54.2)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">774 (61.6)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">269 (39.2)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Reliable improvement or recovery</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">663 (78.5)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">837 (76.3)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1,039(82.7)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">442 (64.4)</td></tr></tbody></table><table-wrap-foot><attrib><italic>* <italic>Reliable recovery is defined as reliable improvement and recovery.</italic></italic></attrib></table-wrap-foot></table-wrap></sec><sec id=\"S2.SS2\"><title>Anxiety Symptoms</title><p>At baseline, 1,097 patients (87.3%) scored in the clinical range on the GAD-7. The average baseline score on the GAD-7 was 12.69 (<italic>SD</italic> = 3.61), corresponding to the moderate range of anxiety severity. Anxiety scores decreased an average of 5.6 points (95% CI, 5.3–5.9) between baseline and follow-up (mean = 7.07, <italic>SD</italic> = 4.35), and results of paired <italic>t</italic>-tests revealed that this difference was statistically significant, <italic>t</italic>(1096) = 38.66, <italic>p</italic> < 0.001. Cohen’s <italic>d</italic> suggested a large treatment effect size on anxiety (<italic>d</italic><sub><italic>rm</italic></sub> = 1.40). Of the 1,097 patients with clinical scores on the GAD-7 at baseline, 750 (68.4%) met the criteria for reliable improvement, and 682 patients (62.2%) recovered (<xref rid=\"T2\" ref-type=\"table\">Table 2</xref>). Reliable recovery from anxiety was observed in 595 patients (54.2%).</p></sec><sec id=\"S2.SS3\"><title>Depression and Anxiety Symptoms</title><p>A total of 686 patients (54.6%) scored in the clinical range on both measures at baseline, suggesting they were comorbid for depression and anxiety. Of these, 361 patients (52.6%) showed reliable improvement on both measures, and 350 (51.0%) recovered on both measures (<xref rid=\"T2\" ref-type=\"table\">Table 2</xref>). Approximately 269 (39.2%) of the 686 patients demonstrated reliable recovery from both depression and anxiety.</p></sec></sec><sec id=\"S3\"><title>Discussion</title><p>Findings presented here demonstrate that ESTs can be efficacious under real-world conditions and deliver results that are comparable to those observed in RCTs (e.g., <xref rid=\"B14\" ref-type=\"bibr\">Hofmann et al., 2012</xref>). Among patients receiving an EST from a community provider, levels of depression and anxiety significantly decreased over the course of treatment. Of note, more than half of patients who were comorbid for depression and anxiety at baseline made meaningful improvement in both areas. In utilizing the criteria outlined by <xref rid=\"B31\" ref-type=\"bibr\">Tolin et al. (2015)</xref>, this study further supports the efficacy of ESTs and extends their usefulness to settings outside of academia.</p><p>It is widely known that a gap exists between academia and real-world clinical practice, with the majority of providers in the community relying on prior experience and professional preferences, rather than research data, to inform their clinical decisions (<xref rid=\"B29\" ref-type=\"bibr\">Stewart and Chambless, 2007</xref>; <xref rid=\"B21\" ref-type=\"bibr\">Lilienfeld et al., 2013</xref>). One reason that community providers cite for rejecting ESTs is a concern that subjects in RCTs are not representative of patients in the real world who may have higher rates of comorbidity (<xref rid=\"B26\" ref-type=\"bibr\">Shafran et al., 2009</xref>). Contrary to this hypothesis, findings presented here are consistent with previous research (<xref rid=\"B7\" ref-type=\"bibr\">Craigie and Nathan, 2009</xref>; <xref rid=\"B2\" ref-type=\"bibr\">Barlow et al., 2017</xref>) and suggest that, even for patients with psychiatric comorbidities, ESTs can produce significant symptom reduction. Further, treatment effectiveness was comparable to efficacy rates seen in RCTs (e.g., <xref rid=\"B14\" ref-type=\"bibr\">Hofmann et al., 2012</xref>) despite the lack of clinical oversight and standardized training in ESTs that are characteristic of most studies examining EST effectiveness in the community.</p><p>Some limitations to the present study should be considered. In particular, there was no measure of treatment fidelity, so we cannot be certain whether ESTs were delivered with strict adherence to treatment manuals. However, this limitation allowed us to examine how ESTs perform when delivered under more naturalistic conditions by therapists with heterogeneous training in ESTs and use of varied ESTs. It is also possible that therapists combined elements of different ESTs, and in fact, the research suggests that this approach may be associated with better outcomes for patients with psychological comorbidities (<xref rid=\"B6\" ref-type=\"bibr\">Chorpita et al., 2013</xref>).</p><p>It would have been helpful to capture what other comorbid behavioral health conditions patients may have been experiencing and more specific details on the types of anxiety or depressive disorders patients had. In addition, we did not account for any other treatments patients may have been receiving that could also have produced a change in depression or anxiety. Further, because this was a naturalistic study, the timing of when baseline and follow-up measures were completed varied across patients. It is possible that the last outcome measure we have for some patients was collected midtreatment, and we would have seen even higher rates of reliable improvement and recovery if all patients completed the final outcome survey immediately after the end of treatment. It should also be noted that this was not an intent-to-treat analysis, and there may have been important differences between those patients who completed outcomes assessments and those who did not or those who dropped out of treatment prematurely, possibly skewing findings in a more positive direction.</p><p>Finally, because of a possible therapist selection bias, it is unknown whether these results are generalizable to ESTs delivered by community providers who have not undergone extensive vetting. In the majority of studies testing the generalizability of ESTs beyond academia or with patients who have psychological comorbidities, providers receive considerable clinical training, and treatment fidelity is measured in an ongoing way (e.g., <xref rid=\"B33\" ref-type=\"bibr\">Weisz et al., 2006</xref>; <xref rid=\"B2\" ref-type=\"bibr\">Barlow et al., 2017</xref>). Additional research is therefore needed to determine whether most providers can deliver ESTs in the community without substantial clinical vetting or oversight.</p><p>Further research is also needed to understand which ESTs are most easily transported to community settings and what modifications, if any, therapists in the community must make to improve patient acceptability. In traditional research studies, there may be a self-selection bias favoring patients who are interested in more structured or novel interventions and who are better-educated around what ESTs typically entail. Patients in the community may be less familiar with therapy, in general, and likely have very different expectations for therapy relative to those participating in a research study at an academic center. Therapists in the community may be adapting ESTs to make them more appealing to patients, and an understanding of these adaptations could improve efforts to disseminate ESTs more broadly.</p><p>Despite the limitations, this study addresses an important gap in the empirical research on the external validity of ESTs. Given that research demonstrates that ESTs are effective outside of academia (e.g., <xref rid=\"B33\" ref-type=\"bibr\">Weisz et al., 2006</xref>; <xref rid=\"B7\" ref-type=\"bibr\">Craigie and Nathan, 2009</xref>), translational studies aimed at understanding and overcoming barriers to the adoption of ESTs in the real world are an important next step. Understanding what changes therapists in the community may make to ESTs to improve acceptability and how those changes affect treatment efficacy may enhance dissemination of ESTs outside of academic settings and increase the sustainability of EST implementation in clinical practice.</p></sec><sec sec-type=\"data-availability\" id=\"S4\"><title>Data Availability Statement</title><p>All datasets generated for this study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.</p></sec><sec id=\"S5\"><title>Ethics Statement</title><p>The study was granted exempt status by the Western IRB. Written informed consent from the participants was not required to participate in this study in accordance with the national legislation and the institutional requirements.</p></sec><sec id=\"S6\"><title>Author Contributions</title><p>RS wrote the initial draft of the manuscript. JG, CC, ER, and BK contributed to the development and editing of the manuscript. SC did the statistical analyses. All authors contributed to the article and approved the submitted version.</p></sec><sec id=\"conf1\"><title>Conflict of Interest</title><p>RS, JG, SC, BK, and CC were employed by or have equity in Lyra Health, Inc. The authors declare that this study received funding from Lyra Health, Inc. The funder had the following involvement in the study: funding of data collection process; employment of co-authors who designed the study, conducted analyses, and prepared the manuscript.</p></sec></body><back><fn-group><fn fn-type=\"financial-disclosure\"><p><bold>Funding.</bold> This research was funded by the Lyra Health.</p></fn></fn-group><ack><p>We would like to express their appreciation for the therapists in the Lyra network who work every day to support patients in leading more fulfilling, productive lives.</p></ack><ref-list><title>References</title><ref id=\"B1\"><mixed-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Addis</surname><given-names>M. E.</given-names></name><name><surname>Wade</surname><given-names>W. 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[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"review-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Front Cell Dev Biol</journal-id><journal-id journal-id-type=\"iso-abbrev\">Front Cell Dev Biol</journal-id><journal-id journal-id-type=\"publisher-id\">Front. Cell Dev. Biol.</journal-id><journal-title-group><journal-title>Frontiers in Cell and Developmental Biology</journal-title></journal-title-group><issn pub-type=\"epub\">2296-634X</issn><publisher><publisher-name>Frontiers Media S.A.</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32850817</article-id><article-id pub-id-type=\"pmc\">PMC7432147</article-id><article-id pub-id-type=\"doi\">10.3389/fcell.2020.00696</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Cell and Developmental Biology</subject><subj-group><subject>Review</subject></subj-group></subj-group></article-categories><title-group><article-title>Relevance Function of Linc-ROR in the Pathogenesis of Cancer</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Chen</surname><given-names>Wenjian</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><xref ref-type=\"author-notes\" rid=\"fn002\"><sup>†</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Yang</surname><given-names>Junfa</given-names></name><xref ref-type=\"aff\" rid=\"aff2\"><sup>2</sup></xref><xref ref-type=\"aff\" rid=\"aff3\"><sup>3</sup></xref><xref ref-type=\"author-notes\" rid=\"fn002\"><sup>†</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Fang</surname><given-names>Hui</given-names></name><xref ref-type=\"aff\" rid=\"aff4\"><sup>4</sup></xref><xref ref-type=\"author-notes\" rid=\"fn002\"><sup>†</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Li</surname><given-names>Lei</given-names></name><xref ref-type=\"aff\" rid=\"aff5\"><sup>5</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Sun</surname><given-names>Jun</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><xref ref-type=\"corresp\" rid=\"c001\"><sup>*</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/946140/overview\"/></contrib></contrib-group><aff id=\"aff1\"><sup>1</sup><institution>Anhui Provincial Children’s Hospital, Affiliated to Anhui Medical University</institution>, <addr-line>Hefei</addr-line>, <country>China</country></aff><aff id=\"aff2\"><sup>2</sup><institution>Key Laboratory of Anti-inflammatory and Immune Medicine, Ministry of Education, Institute of Clinical Pharmacology, Anhui Medical University</institution>, <addr-line>Hefei</addr-line>, <country>China</country></aff><aff id=\"aff3\"><sup>3</sup><institution>School of Pharmacy, Anhui Medical University</institution>, <addr-line>Hefei</addr-line>, <country>China</country></aff><aff id=\"aff4\"><sup>4</sup><institution>Department of Pharmacology, The Affiliated Hospital of Hangzhou Normal University</institution>, <addr-line>Hangzhou</addr-line>, <country>China</country></aff><aff id=\"aff5\"><sup>5</sup><institution>The Affiliated Hospital of Anhui Medical University</institution>, <addr-line>Hefei</addr-line>, <country>China</country></aff><author-notes><fn fn-type=\"edited-by\"><p>Edited by: Sridhar Muthusami, Karpagam Academy of Higher Education, India</p></fn><fn fn-type=\"edited-by\"><p>Reviewed by: Anca Maria Cimpean, Victor Babes University of Medicine and Pharmacy, Romania; Omar Torres-Quesada, University of Innsbruck, Austria</p></fn><corresp id=\"c001\">*Correspondence: Jun Sun, <email>[email protected]</email>; <email>[email protected]</email></corresp><fn fn-type=\"other\" id=\"fn002\"><p><sup>†</sup>These authors have contributed equally to this work</p></fn><fn fn-type=\"other\" id=\"fn004\"><p>This article was submitted to Molecular and Cellular Oncology, a section of the journal Frontiers in Cell and Developmental Biology</p></fn></author-notes><pub-date pub-type=\"epub\"><day>11</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><year>2020</year></pub-date><volume>8</volume><elocation-id>696</elocation-id><history><date date-type=\"received\"><day>04</day><month>4</month><year>2020</year></date><date date-type=\"accepted\"><day>09</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>Copyright © 2020 Chen, Yang, Fang, Li and Sun.</copyright-statement><copyright-year>2020</copyright-year><copyright-holder>Chen, Yang, Fang, Li and Sun</copyright-holder><license xlink:href=\"http://creativecommons.org/licenses/by/4.0/\"><license-p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</license-p></license></permissions><abstract><p>Long non-coding RNAs (lncRNAs) are the key components of non-coding RNAs (ncRNAs) with a length of 200 nucleotides. They are transcribed from the so-called “dark matter” of the genome. Increasing evidence have shown that lncRNAs play an important role in the pathophysiology of human diseases, particularly in the development and progression of tumors. Linc-ROR, as a new intergenic non-protein coding RNA, has been considered to be a pivotal regulatory factor that affects the occurrence and development of human tumors, including breast cancer (BC), colorectal cancer (CRC), pancreatic cancer (PC), hepatocellular carcinoma (HCC), and so on. Dysregulation of Linc-ROR has been closely related to advanced clinicopathological factors predicting a poor prognosis. Because linc-ROR can regulate cell proliferation, apoptosis, migration, and invasion, it can thus be used as a potential biomarker for patients with tumors and has potential clinical significance as a therapeutic target. This article reviewed the role of linc-ROR in the development of tumors, its related molecular mechanisms, and clinical values.</p></abstract><kwd-group><kwd>lncRNAs</kwd><kwd>ncRNAs</kwd><kwd>linc-ROR</kwd><kwd>cancers</kwd><kwd>biomarker</kwd></kwd-group><funding-group><award-group><funding-source id=\"cn001\">Natural Science Foundation of Anhui Province<named-content content-type=\"fundref-id\">10.13039/501100003995</named-content></funding-source></award-group></funding-group><counts><fig-count count=\"4\"/><table-count count=\"2\"/><equation-count count=\"0\"/><ref-count count=\"145\"/><page-count count=\"16\"/><word-count count=\"0\"/></counts></article-meta></front><body><sec id=\"S1\"><title>Introduction</title><p>Cancer is a serious disease that affects human health, being one of the main causes of death all over the worldwide. According to research in 2018, 59% of new tumor cases and 70% of the cancer-associated deaths occurred in low-income and developing countries (<xref rid=\"B54\" ref-type=\"bibr\">Kumar and Sharawat, 2018</xref>; <xref rid=\"B84\" ref-type=\"bibr\">Noorolyai et al., 2019</xref>). Owing to a shortage in effective screening methods and lack of identification of early symptoms, most patients were already in advanced stages when they were diagnosed with cancer (<xref rid=\"B10\" ref-type=\"bibr\">Bray et al., 2018</xref>; <xref rid=\"B53\" ref-type=\"bibr\">Koo et al., 2018</xref>). Additionally, some clinical studies have shown that polarity and adhesion of cancer cells was decreased, leading to heir increased mobility and invasion, which is a key step in the development of cancer (<xref rid=\"B126\" ref-type=\"bibr\">Yan et al., 2018</xref>). Therefore, studies have shown that the high mobility of cancer cells is the main factor leading to high mortality rates in patients with cancer. Currently, there are many ways employed in the treatment of cancer, including surgery, radiotherapy, chemotherapy, biotherapy and targeted therapy (<xref rid=\"B82\" ref-type=\"bibr\">Nie et al., 2016</xref>). However, in the past 5 years, the survival rate of patients with cancers remains dismal (<xref rid=\"B80\" ref-type=\"bibr\">Nakashima, 2018</xref>). Therefore, in the process of developing human antitumor strategies, it is particularly important to find new early biomarkers and thus identify potential regulatory mechanisms to improve the survival rate of patients with cancers.</p><p>Over the past 2 decades, ncRNAs constitute more than 90% of the RNAs made from the human genome, but most of the > 50,000 known non-coding RNAs (ncRNAs) have been discovered and remain largely unstudied (<xref rid=\"B6\" ref-type=\"bibr\">Bhan et al., 2017</xref>; <xref rid=\"B108\" ref-type=\"bibr\">Slack and Chinnaiyan, 2019</xref>). Transfer RNA (tRNA) (89%) and ribosomal RNA (rRNA) (8.9%) constitute the majority of ncRNAs, followed in abundance by messenger RNAs (mRNAs) (0.9%). Thus, the remaining ncRNAs, including circular RNA (circRNA), small nuclear RNA (snRNA), small nucleolar RNA (snoRNA), microRNA (miRNA), and long non-coding RNA (lncRNA) together account for ∼1% of total ncRNA. Despite their low abundance, these ncRNAs have been reported to play critical roles in transcription, post-transcriptional processing, and translation such as epigenetics, post-transcriptional regulation, chromatin modification, and regulation of the cell cycle (<xref rid=\"B45\" ref-type=\"bibr\">Huarte, 2015</xref>; <xref rid=\"B52\" ref-type=\"bibr\">Kondo et al., 2017</xref>; <xref rid=\"B95\" ref-type=\"bibr\">Peng et al., 2017b</xref>). Additionally, because ncRNAs can be packaged into extracellular vesicles (EV), including exosomes (<xref rid=\"B75\" ref-type=\"bibr\">Meldolesi, 2018</xref>), they have been shown to provide a mechanism for intercellular communication through the transfer of miRNA and lncRNA to recipient cells both locally and systemically (<xref rid=\"B111\" ref-type=\"bibr\">Sun et al., 2018</xref>). It is important to note that the expression of ncRNAs, their post-transcriptional modification (particularly lncRNAs), and their subcellular distribution have been shown to be important to when assigning their potential function (<xref rid=\"B88\" ref-type=\"bibr\">Palazzo and Lee, 2015</xref>). Recently, next-generation sequencing and bioinformatics technology have revealed that circRNAs play crucial role in diagnosis and prognosis of various diseases (<xref rid=\"B89\" ref-type=\"bibr\">Pamudurti et al., 2017</xref>). Briefly, circRNAs are single-stranded transcripts generated by back-splicing (<xref rid=\"B47\" ref-type=\"bibr\">Jeck and Sharpless, 2014</xref>), with covalently linked head-to-tail closed loop structures with neither 5′–3′ polarity nor a polyadenylated tail (<xref rid=\"B76\" ref-type=\"bibr\">Memczak et al., 2013</xref>), that range in length from a few hundred to thousands of nucleotides, and are widely expressed in mammals, thereby showing higher stability compared to that in linear RNAs (<xref rid=\"B11\" ref-type=\"bibr\">Chen J. et al., 2017</xref>), and exhibiting a cell-type- or developmental-stage-specific expression pattern (<xref rid=\"B3\" ref-type=\"bibr\">Barrett and Salzman, 2016</xref>; <xref rid=\"B119\" ref-type=\"bibr\">Wang J. J. et al., 2019</xref>). Many functions of circRNAs have also been identified, including their role as miRNA sponges, binding to RNA-binding proteins and protein decoys, and functioning as regulators of transcription (<xref rid=\"B33\" ref-type=\"bibr\">Hansen et al., 2013</xref>; <xref rid=\"B20\" ref-type=\"bibr\">Du et al., 2016</xref>; <xref rid=\"B133\" ref-type=\"bibr\">Yang Y. et al., 2018</xref>). Interestingly, many circRNAs have been shown to be dysregulated in pathophysiological processes, and circRNAs are known to regulate the expression of gene by acting as miRNA sponges, in a mechanism that is termed as competitive endogenous RNA (ceRNA) mechanism (<xref rid=\"B142\" ref-type=\"bibr\">Zheng et al., 2016</xref>; <xref rid=\"B119\" ref-type=\"bibr\">Wang J. J. et al., 2019</xref>). For example, circMTO1 have been demonstrated to harbor conventional miRNA binding sites and has been identified as an inhibitor of miRNA-9 in hepatocellular carcinoma (HCC) (<xref rid=\"B30\" ref-type=\"bibr\">Han et al., 2017</xref>). Additionally, our previous study has demonstrated that miRNA plays a role in limiting the development of liver fibrosis by markedly blocking the activation and proliferation of hepatic stellate cells (HSCs), suggesting that miRNAs might be involved in the development and progression of several forms of cancers (<xref rid=\"B129\" ref-type=\"bibr\">Yang J. et al., 2017</xref>; <xref rid=\"B130\" ref-type=\"bibr\">Yang et al., 2019</xref>). Of note, lncRNAs, which are mainly transcribed by RNA polymerase II, are a new kind of ncRNA that are longer than 200 nucleotides (<xref rid=\"B72\" ref-type=\"bibr\">Ma et al., 2016</xref>). Owing to the lack of open reading frames, lncRNAs have extremely limited or no protein coding capacity (<xref rid=\"B98\" ref-type=\"bibr\">Ruan et al., 2016</xref>; <xref rid=\"B59\" ref-type=\"bibr\">Li J. et al., 2017</xref>). These new regulators were initially regarded as transcriptional noise with no specific biological functions (<xref rid=\"B51\" ref-type=\"bibr\">Kim and Sung, 2012</xref>). Recently, our laboratory found that epigenetic silencing of lncRNA ANRIL promoted the progression of liver fibrosis, thereby indicating that lncRNAs were associated with the progression of cancers (<xref rid=\"B131\" ref-type=\"bibr\">Yang et al., 2020</xref>). Interestingly, increasing evidence have shown that cellular events, including differentiation, proliferation, invasion, apoptosis, and migration, have all been associated with lncRNAs (<xref rid=\"B29\" ref-type=\"bibr\">Guttman et al., 2011</xref>). Additionally, there has been new evidence suggesting that lncRNAs may regulate a variety of biological and disease processes, from gene transcription and translation to post-translational modifications (<xref rid=\"B18\" ref-type=\"bibr\">Davalos and Esteller, 2019</xref>; <xref rid=\"B92\" ref-type=\"bibr\">Pang et al., 2019</xref>). More importantly, lncRNAs have been reported to be used as tumor suppressor genes or oncogenes, thus affecting the proliferation and metastasis of various types of tumors during tumorigenesis (<xref rid=\"B12\" ref-type=\"bibr\">Chen Q. N. et al., 2017</xref>; <xref rid=\"B70\" ref-type=\"bibr\">Lu et al., 2017</xref>). Subsequent studies have demonstrated that lncRNAs may serve as ceRNAs for miRNAs and in chromatin remodeling during the development of cancers (<xref rid=\"B44\" ref-type=\"bibr\">Huang et al., 2019</xref>; <xref rid=\"B118\" ref-type=\"bibr\">Wang C. J. et al., 2019</xref>). <xref ref-type=\"fig\" rid=\"F1\">Figure 1</xref> illustrates the functions of lncRNAs at the molecular level. Regarding certain cancer-associated human lncRNAs, it was demonstrated that linc-ROR was demonstrated to be predominantly upregulated in tumors (<xref rid=\"B94\" ref-type=\"bibr\">Peng et al., 2017a</xref>). The abnormal expression of linc-ROR in tumors has been suggested to be one of the main leading factors driving the development. The main ways to revert this effect would be to affect cell growth, migration, and invasion, thus leading to the inhibition of epithelial-mesenchymal transition (EMT), enhancement of the sensitivity to chemotherapy, etc. (<xref rid=\"B16\" ref-type=\"bibr\">Chen et al., 2016a</xref>; <xref rid=\"B140\" ref-type=\"bibr\">Zhao et al., 2017</xref>). For example, the expression level of linc-ROR in HCC tissues was inhibited compared with the adjacent tissues. At the same time, the downregulation of linc-ROR was linked to the aggressive process of the disease in patients with HCC. Furthermore, the ability of migration and invasion of HCC cells may be delayed by the low expression level of linc-ROR. In this review, we attempted to introduce the latest research on the biological effects, potential clinical applications, and molecular mechanisms of linc-ROR in human tumors and discuss its prognostic and therapeutic values.</p><fig id=\"F1\" position=\"float\"><label>FIGURE 1</label><caption><p>Paradigms for the function of long ncRNAs. Recent studies have identified a variety of regulatory paradigms for the mechanism by which long ncRNAs function, many of which are highlighted here. Transcription from an upstream non-coding promoter (pink) can negatively <bold>(1)</bold> or positively <bold>(2)</bold> affect the expression of the downstream gene (purple) by inhibiting the recruitment of RNA polymerase II or inducing chromatin remodeling, respectively. <bold>(3)</bold> An antisense transcript (orange) is able to hybridize to the overlapping sense transcript (purple) and block the recognition of the splice sites by the spliceosome, thus resulting in an alternatively spliced transcript. <bold>(4)</bold> Alternatively, hybridization of the sense and antisense transcripts can allow Dicer to generate endogenous siRNAs. By binding to specific protein partners, a non-coding transcript (blue) can modulate the activity of the protein <bold>(5)</bold>, serve as a structural component that allows the formation of a larger RNA-protein complex <bold>(6)</bold>, or alter where the protein localizes in the cell <bold>(7)</bold>. <bold>(8)</bold> Long ncRNAs (green) can be processed to yield small RNAs, such as miRNAs, piRNAs, and other less well-characterized classes of small transcripts.</p></caption><graphic xlink:href=\"fcell-08-00696-g001\"/></fig></sec><sec id=\"S2\"><title>Overview of Linc-Ror</title><p>Among lncRNAs, linc-ROR is a novel and important carcinogenic 2.6 kb lncRNA located in chromosome 18, which was initially identified as a highly expressed transcript of pluripotent and embryonic stem cells (<xref rid=\"B17\" ref-type=\"bibr\">Chen et al., 2016b</xref>). Studies found that the octamer-binding transcription factor 4 (OCT4), SRY-box transcription factor 2 (SOX2), and Nanog homeobox (Nanog) key pluripotency factors could regulate linc-ROR (<xref rid=\"B122\" ref-type=\"bibr\">Wang et al., 2013</xref>). However, linc-ROR was also found to be expressed in several organs including lung, liver, breast, and colon. Since its discovery, research in this field has been extensively expanded during the past 6 years, revealing the important role of linc-ROR in tumorigenesis. Additionally, upregulation of linc-ROR has been suggested to mediate the re-expression of fetal and cardiomyocyte hypertrophy-related genes (<xref rid=\"B122\" ref-type=\"bibr\">Wang et al., 2013</xref>; <xref rid=\"B58\" ref-type=\"bibr\">Li et al., 2016</xref>). Many reports regarding linc-ROR and tumorigenesis in recent years, have revealed that the upregulation of linc-ROR is positively correlated with the clinicopathological characteristics and poor prognosis of tumors, including the stages of advanced tumor node metastasis (TNM), positive lymph node metastasis (LNM), and lower survival rate but higher recurrence rate.</p><p>Current evidence have strongly indicated that linc-ROR may exert an impact on a variety of cancers (<xref rid=\"B91\" ref-type=\"bibr\">Pan et al., 2016</xref>). Furthermore, both tumorigenesis and metastasis have been shown to be induced by linc-ROR <italic>via</italic> activation of the EMT in various cancers (<xref rid=\"B41\" ref-type=\"bibr\">Hou et al., 2014</xref>; <xref rid=\"B43\" ref-type=\"bibr\">Huang et al., 2016</xref>; <xref rid=\"B135\" ref-type=\"bibr\">Zhan et al., 2016</xref>). For example, linc-ROR was demonstrated to be upregulated, thereby promoting EMT in HCC (<xref rid=\"B59\" ref-type=\"bibr\">Li J. et al., 2017</xref>). Besides, it was also reported that self-renewal and differentiation of glioma stem cells was significantly affected by linc-ROR (<xref rid=\"B138\" ref-type=\"bibr\">Zhang et al., 2012</xref>; <xref rid=\"B24\" ref-type=\"bibr\">Feng et al., 2015</xref>). More importantly, the chemoresistance of pancreatic cancer (PC) and breast cancer (BC) (<xref rid=\"B58\" ref-type=\"bibr\">Li et al., 2016</xref>) as well as radio-resistance of colorectal cancer (CRC) cells were observed to be elevated by linc-ROR (<xref rid=\"B132\" ref-type=\"bibr\">Yang P. et al., 2017</xref>). Moreover, linc-ROR has also been shown to exert a significantly effect on the stem cell-like characteristics and tumorigenic potential of PC. Recently, it was also reported that linc-ROR could be used as a biomarker in the field of diagnosis and prognosis of BC and oral cancer (<xref rid=\"B1\" ref-type=\"bibr\">Arunkumar et al., 2017</xref>; <xref rid=\"B140\" ref-type=\"bibr\">Zhao et al., 2017</xref>). Notably, increasing studies showed that linc-ROR could be used as a ceRNA, thus exerting its impact in the post-transcriptional network of tumor pathogenesis. For example, in triple-negative BC, linc-ROR has been shown to serve as a ceRNA, therefore promoting the migration and invasion of BC cells (<xref rid=\"B106\" ref-type=\"bibr\">Signal et al., 2016</xref>). Overall, linc-ROR is a typical lncRNA that plays important regulatory roles in interaction with miRNAs and maintenance of stem cell pluripotency, triggering the EMT as well. Furthermore, linc-ROR has also been involved in various key roles under hypoxia and in the promotion of tumorigenesis (<xref ref-type=\"fig\" rid=\"F2\">Figure 2</xref>). Therefore, linc-ROR may be considered as an oncogene affecting the progression of tumor and a promising predictor for the poor prognosis in patients with cancer. The transition of linc-ROR from basic research to clinical application requires further investigations as early as possible.</p><fig id=\"F2\" position=\"float\"><label>FIGURE 2</label><caption><p>Linc-ROR is a typical lncRNA that plays important regulatory roles in interacting with miRNAs and maintaining stem cell pluripotency, as well as triggering the EMT as well. Linc-ROR is also involved in various key roles under various stresses and in epigenetic regulation.</p></caption><graphic xlink:href=\"fcell-08-00696-g002\"/></fig></sec><sec id=\"S3\"><title>Regulatory Role of Linc-Ror in Various Types of Cancer</title><p>Increasing evidence has shown that the linc-ROR was abnormal expression in many cancers, and its dysregulation was associated with cellular functions (<xref rid=\"B25\" ref-type=\"bibr\">Fu et al., 2017</xref>; <xref rid=\"B109\" ref-type=\"bibr\">Spinelli et al., 2018</xref>). Additionally, studies found that the expression level of linc-ROR was substantially upregulated in samples of papillary thyroid carcinomas (PTCs) and PTCs cell lines as well as in metastatic PTCs samples and PTCs cell lines (<xref rid=\"B136\" ref-type=\"bibr\">Zhang et al., 2018</xref>). Simultaneously, cell migration and invasion could be regulated by linc-ROR <italic>via</italic> affecting EMT (<xref rid=\"B93\" ref-type=\"bibr\">Pastushenko and Blanpain, 2019</xref>). More importantly, studies demonstrated that linc-ROR was abnormally expressed in several cancers and led to elevated the invasion and metastasis of cancer cells to promoting the progression of tumors (<xref rid=\"B35\" ref-type=\"bibr\">Hashemian et al., 2019</xref>; <xref rid=\"B62\" ref-type=\"bibr\">Li et al., 2020b</xref>, <xref rid=\"B63\" ref-type=\"bibr\">c</xref>). This review summarizes the status of linc-ROR research in various human cancers and discusses its mechanism and clinical significance in the development and progression of tumor. The expression pattern, functional role, and regulatory mechanism of linc-ROR are summarized in <xref rid=\"T1\" ref-type=\"table\">Table 1</xref> and depicted in <xref ref-type=\"fig\" rid=\"F3\">Figure 3</xref>.</p><table-wrap id=\"T1\" position=\"float\"><label>TABLE 1</label><caption><p>Linc-ROR in human cancers.</p></caption><table frame=\"hsides\" rules=\"groups\" cellspacing=\"5\" cellpadding=\"5\"><thead><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Cancer types</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Expression</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Role</bold></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Functional role</bold></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Clinical correlation</bold></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Regulatory molecules</bold></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>References</bold></td></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Breast cancer</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Upregulated</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Oncogenic</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Proliferation, apoptosis, migration, invasion, EMT</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">TNM stage, LNM, ROC curve</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">linc-ROR/miR-205</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><xref rid=\"B40\" ref-type=\"bibr\">Hou et al., 2018</xref></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Pancreatic cancer</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Upregulated</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Oncogenic</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Growth, proliferation, migration, invasion, EMT</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">TNM stage, LNM, poor survival</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">linc-ROR/ZEB1/p53 linc-ROR/miR-145/Nanog linc-ROR/miR-124/PTBP1/PKM2</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><xref rid=\"B27\" ref-type=\"bibr\">Gao et al., 2016</xref>; <xref rid=\"B58\" ref-type=\"bibr\">Li et al., 2016</xref>; <xref rid=\"B135\" ref-type=\"bibr\">Zhan et al., 2016</xref></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Hepatocellular carcinoma</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Upregulated</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Oncogenic</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Poliferation, migration, invasion, EMT</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">TNM stage, LNM, DFS, OS</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">linc-ROR/EZH2/E-cadherin linc-ROR/miR-145/ZEB2, linc-ROR/miR-876-5p/FOXM1</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><xref rid=\"B57\" ref-type=\"bibr\">Li C. et al., 2017</xref>; <xref rid=\"B15\" ref-type=\"bibr\">Chen et al., 2018</xref>; <xref rid=\"B143\" ref-type=\"bibr\">Zhi et al., 2019</xref></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Colorectal cancer</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Upregulated</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Oncogenic</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Proliferation, apoptosis, migration, invasion, EMT, radiotherapy resistance.</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">TNM stage, LNM, DFS, OS</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">linc-ROR/miR-6833-3p</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><xref rid=\"B127\" ref-type=\"bibr\">Yan and Sun, 2018</xref>; <xref rid=\"B61\" ref-type=\"bibr\">Li et al., 2020a</xref></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Lung cancer</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Upregulated</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Oncogenic</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Apoptosis, cell cycle migration, invasion, EMT, drug resistance</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">TNM stage, LNM, DFS, OS</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">linc-ROR/miR-145/FSCN1</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><xref rid=\"B90\" ref-type=\"bibr\">Pan et al., 2017</xref></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Thyroid cancer</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Upregulated</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Oncogenic</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Proliferation, apoptosis, migration, invasion, cell cycle, EMT</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">TNM stage, LNM, poor survival</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">linc-ROR/miR-145</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><xref rid=\"B136\" ref-type=\"bibr\">Zhang et al., 2018</xref></td></tr></tbody></table></table-wrap><fig id=\"F3\" position=\"float\"><label>FIGURE 3</label><caption><p>Underlying molecular mechanisms of linc-ROR in multiple cancers. <bold>(A)</bold> Linc-ROR binds to miR-205 to upregulate ZEB2, while it also regulated the expression of EMT markers. <bold>(B)</bold> Linc-ROR could interact with miR-145 to inhibit FSCN1 and upregulated EMT-associated proteins while it decreases G0/G1 arrest and facilitated drug resistance. <bold>(C)</bold> Linc-ROR facilitated EMT through upregulate EZHZ and regulated ZEB2 by competitively binding to miR-145, and ZEB2 overexpression leads to increased EMT. In addition, linc-ROR binds to miR-876-5p to upregulate FOXM1. <bold>(D)</bold> Linc-ROR downregulated through EMT production and repress the expression of miR-145. <bold>(E)</bold> Linc-ROR binds to miR-6833-3p, while also regulating the process of EMT. <bold>(F)</bold> Linc-ROR upregulated ZEB1 to attenuate the expression of p53, while also decreasing the expression of miR-145 to increase the level of Nanog and reduce that of miR-124 to suppress PKM2. Detailed mechanisms of linc-ROR in other cancers are provided in the review.</p></caption><graphic xlink:href=\"fcell-08-00696-g003\"/></fig><sec id=\"S3.SS1\"><title>Breast Cancer</title><p>BC, which accounts for a quarter of all female cancer cases, is the most commonly diagnosed cancer and the leading cause of cancer-associated deaths among women worldwide (<xref rid=\"B64\" ref-type=\"bibr\">Li Z. et al., 2019</xref>). In 2018, it was estimated that there would be 2.1 million or so newly diagnosed cases of female BC (<xref rid=\"B10\" ref-type=\"bibr\">Bray et al., 2018</xref>; <xref rid=\"B32\" ref-type=\"bibr\">Hannafon et al., 2019</xref>). The main risk factors for BC, which is the difficult to change due to prolonged exposure to endogenous hormones, is difficult to control (<xref rid=\"B99\" ref-type=\"bibr\">Rudel et al., 2014</xref>; <xref rid=\"B10\" ref-type=\"bibr\">Bray et al., 2018</xref>). However, comprehensive treatment approaches have resulted in relatively good clinical outcomes for some patients with BC (<xref rid=\"B99\" ref-type=\"bibr\">Rudel et al., 2014</xref>; <xref rid=\"B28\" ref-type=\"bibr\">Goel et al., 2016</xref>; <xref rid=\"B55\" ref-type=\"bibr\">Kumler et al., 2016</xref>). Nevertheless, it has been reported that about one-third of the patients with BC have the potential for cell metastasis, chemotherapy resistance, and even recurrence (<xref rid=\"B28\" ref-type=\"bibr\">Goel et al., 2016</xref>; <xref rid=\"B55\" ref-type=\"bibr\">Kumler et al., 2016</xref>). Hence, there is an urgent need to develop new therapies targeting various molecular mechanisms of tumorigenesis for the treatment of BC.</p><p>Hou et al. investigated the expression level of linc-ROR in 94 patients (<xref rid=\"B40\" ref-type=\"bibr\">Hou et al., 2018</xref>). Their results revealed that the level of linc-ROR was increased in BC tissues. Moreover, the level of the expression of linc-ROR in the peripheral blood of the patients with BC was shown to be closely related to TNM phase and LNM. In addition, the wound-healing assay showed that overexpression of linc-ROR increased BC cells (MCF10A) mobility. Transwell assay revealed that linc-ROR overexpression remarkably increased the migration ability (<xref rid=\"B40\" ref-type=\"bibr\">Hou et al., 2018</xref>). More importantly, they found that ectopic expression of linc-ROR induced an EMT program in MCF10A cells. Fluorescence activated cell sorter analysis demonstrated that the subpopulation of the stem cell phenotype CD44<sup>high</sup>/CD24<sup>low</sup> was elevated in MCF10A cells transfected with linc-ROR plasmid. Mechanistically, the results of bioinformatic analysis and RNA immunoprecipitation analysis from Hou et al. demonstrated that linc-ROR functions as a ceRNA to regulate miR-205 activity toward prevention of the degradation of transcripts of miR-205 target genes, such as ZEB1 and ZEB2, from degradation. Additionally, it was shown that the expression levels of miR-205 members were decreased upon linc-ROR overexpression in MCF10A cells (<xref rid=\"B40\" ref-type=\"bibr\">Hou et al., 2018</xref>). More importantly, Zhao et al. recently demonstrated that CRISPR/Cas9-generated BRCA1-knockdown adipose-derived stem cells stimulated a more aggressive behavior in BC cells than wild-type adipose-derived stem cells (<xref rid=\"B139\" ref-type=\"bibr\">Zhao et al., 2019</xref>). Therefore, we believe that CRISPR/Cas9 may be used to in the treatment of BC by inhibiting the expression of linc-ROR during the progression of BC. Conclusively, the linc-ROR/miRNAs axis has been reported to closely affect the occurrence and development of BC.</p></sec><sec id=\"S3.SS2\"><title>Pancreatic Cancer</title><p>PC is the fourth most common cause of cancer-related mortality worldwide, leading to approximately 227,000 deaths annually (<xref rid=\"B105\" ref-type=\"bibr\">Siegel et al., 2016</xref>; <xref rid=\"B101\" ref-type=\"bibr\">Sabater et al., 2018</xref>). The 5−year relative survival of patients with PC remained at approximately 8% for 2005–2011 (<xref rid=\"B105\" ref-type=\"bibr\">Siegel et al., 2016</xref>). Hence, PC has been proposed to be one of the top two cancers in terms of fatalities in the next decade (<xref rid=\"B97\" ref-type=\"bibr\">Rahib et al., 2014</xref>). Surgical resection remains the exclusive potential curative treatment (<xref rid=\"B124\" ref-type=\"bibr\">Xiong et al., 2017</xref>). However, approximately half of the patients present with metastasis at the time of diagnosis, missing the opportunity for an effective treatment (<xref rid=\"B117\" ref-type=\"bibr\">Vincent et al., 2011</xref>; <xref rid=\"B124\" ref-type=\"bibr\">Xiong et al., 2017</xref>). A growing body of literature has demonstrated that both metastasis and limited effective biomarker for the diagnosis and treatment are the main obstacles for the efficient medical therapy of PC (<xref rid=\"B117\" ref-type=\"bibr\">Vincent et al., 2011</xref>; <xref rid=\"B8\" ref-type=\"bibr\">Boj et al., 2015</xref>; <xref rid=\"B5\" ref-type=\"bibr\">Basuroy et al., 2018</xref>). Thus, it is an absolute necessity to identify potential biomarkers and therapeutic targets in PC.</p><p><xref rid=\"B135\" ref-type=\"bibr\">Zhan et al. (2016)</xref> have highlighted the oncogenic effects of linc-ROR in the initiation and progression of PC. Their study demonstrated that the level of linc-ROR was significantly elevated in PC tissues (<xref rid=\"B135\" ref-type=\"bibr\">Zhan et al., 2016</xref>). Moreover, the wound-healing assay and Boyden’s chamber assay results showed that linc-ROR silencing reduced the migratory capability and metastasis of PC cells (<xref rid=\"B135\" ref-type=\"bibr\">Zhan et al., 2016</xref>). Another study by Chen et al. showed that the proliferation rates of sh-ROR-cells in which the level of linc-ROR was suppressed were evidently lower than those of sh-NC-cells. This result was confirmed by colony formation assay, suggesting that linc-ROR accelerated the growth of PC cells (<xref rid=\"B16\" ref-type=\"bibr\">Chen et al., 2016a</xref>). Interestingly, silencing of linc-ROR was shown to result in increased levels of the epithelial markers E-cadherin and α-catenin, and decreased levels of mesenchymal markers N-cadherin and vimentin, indicating that linc-ROR plays an important role in the regulation of EMT in PC cells (<xref rid=\"B135\" ref-type=\"bibr\">Zhan et al., 2016</xref>; <xref rid=\"B13\" ref-type=\"bibr\">Chen et al., 2020</xref>). More importantly, microarray analysis identified ZEB1 as potential target gene of linc-ROR. Further, the expression of linc-ROR and ZEB1 were observed to be negatively correlated with that of p53, suggesting that linc-ROR might mediate migration, and metastasis in PC cells may partly <italic>via</italic> activation of ZEB1 through the inhibition of the expression of p53 (<xref rid=\"B135\" ref-type=\"bibr\">Zhan et al., 2016</xref>). Interestingly, the fluorescence <italic>in situ</italic> hybridization and luciferase reporter assay results showed that the expression of linc-ROR was demonstrated to be negatively correlated to that of miR-145. MiR-145 can induce posttranscriptional silencing of its targeted genes by binding to the mRNA 3’-UTR or linc-ROR specific sites, indicating that linc-ROR can act as a ceRNA to decrease miR-145 in PC cells, thereby activating expression of Nanog, thus leading to the proliferation of pancreatic cancer stem cells (PCSCs) (<xref rid=\"B27\" ref-type=\"bibr\">Gao et al., 2016</xref>). Additionally, <xref rid=\"B58\" ref-type=\"bibr\">Li et al. (2016)</xref> further proved that the impact of linc-ROR can be partly reversed by overexpression of miR-124. Consistently, linc-ROR was observed to exhibited a negative correlation with the expression of miR-124 (<xref rid=\"B58\" ref-type=\"bibr\">Li et al., 2016</xref>). Hence, a linc-ROR/miR-124/PTBP1/PKM2 axis was identified in PC, shedding new light on the lncRNA-based diagnosis and therapeutic approaches in PC (<xref rid=\"B58\" ref-type=\"bibr\">Li et al., 2016</xref>, <xref rid=\"B62\" ref-type=\"bibr\">2020b</xref>). Notably, recent studies showed that PC cell-derived EVs could be used as effective carriers of paclitaxel to their parental cells, thereby bringing the drug into cells through an endocytic pathway and increasing its cytotoxicity (<xref rid=\"B100\" ref-type=\"bibr\">Saari et al., 2015</xref>). Additionally, it was demonstrated that vesicle-containing ncRNAs could serve as EV-associated PC detection markers (<xref rid=\"B123\" ref-type=\"bibr\">Worst et al., 2019</xref>). Thus, the presence of linc-RORs in EVs from patients with PC could serve as a potential diagnostic biomarker and a novel target for the therapy of patients with PC; this is worthy of further and wider research attention.</p></sec><sec id=\"S3.SS3\"><title>Hepatocellular Carcinoma</title><p>As the sixth most international commonly occurring cancer in 2018, HCC has become the fourth cause of cancer-associated deaths worldwide. It has been estimated that 841,000 new cases and 782,000 deaths will occur each year (<xref rid=\"B10\" ref-type=\"bibr\">Bray et al., 2018</xref>). Briefly, HCC has been reported to account for 75–80% of all the liver cancer cases, half of which have been detected in China (<xref rid=\"B86\" ref-type=\"bibr\">Omata et al., 2017</xref>; <xref rid=\"B10\" ref-type=\"bibr\">Bray et al., 2018</xref>). As such, HCC poses a huge threat to the worldwide health, especially that of the Chinese people (<xref rid=\"B86\" ref-type=\"bibr\">Omata et al., 2017</xref>). About 70% of the patients is expected to recrudescent within 5 years after hepatectomy, and 30% of the patients will die from this tumor (<xref rid=\"B115\" ref-type=\"bibr\">Vigano et al., 2015</xref>). Therefore, on the basis of studying the pathogenesis of HCC, it is apparent to look for more effective molecular markers and therapeutic targets for the management of HCC.</p><p><xref rid=\"B57\" ref-type=\"bibr\">Li C. et al. (2017)</xref> and <xref rid=\"B15\" ref-type=\"bibr\">Chen et al. (2018)</xref> reported that the expression level of linc-ROR was obviously elevated in 20 HCC tissues and four cell lines compared to the corresponding non-tumor tissues and normal liver cell lines, respectively, suggesting that linc-ROR might be critical regulator in the progression of HCC. Furthermore, biological function assay demonstrated that linc-ROR could play promoting role in regulating migration and invasion of HCC (<xref rid=\"B15\" ref-type=\"bibr\">Chen et al., 2018</xref>). Moreover, downregulation of linc-ROR could result in a significant increase in G1/G0 phase and an obvious decrease in S phase (<xref rid=\"B57\" ref-type=\"bibr\">Li C. et al., 2017</xref>). More importantly, silencing of linc-ROR could lead to the increased expression of E−cadherin and decreased expression level of N−cadherin in HCC cell lines. <xref rid=\"B57\" ref-type=\"bibr\">Li C. et al. (2017)</xref> further confirmed that linc-ROR could bind to the zeste homolog 2 (EZH2), thereby affecting the expression of E-cadherin, further indicating that linc-ROR could regulate the progression of EMT. Moreover, linc-ROR was further determined to be associated with DNA repair. Currently, mounting studies have identified reliable indicators of DNA damage, such as phosphorylated histone H2AX (γ-H2AX). <xref rid=\"B15\" ref-type=\"bibr\">Chen et al. (2018)</xref> uncover that overexpression of linc-ROR could obviously decrease the expression level of γ-H2AX, illuminating the suppressive effects of the overexpression of linc-ROR on DNA repair in HCC.</p><p>Further research demonstrated that linc-ROR could interact with miR−145 and dramatically downregulate the expression of miR−145 in HCC cells (<xref rid=\"B57\" ref-type=\"bibr\">Li C. et al., 2017</xref>). The above-mentioned results revealed that linc-ROR might play a promoting role in the proliferation, migration, invasion, and EMT of the HCC cell, which was contrary to the influence of miR−145 enrichment (<xref rid=\"B57\" ref-type=\"bibr\">Li C. et al., 2017</xref>). It suggested that overexpressed miR−145 could effectively reverse the promotion of HCC tumorigenesis induced by the overexpression of linc-ROR (<xref rid=\"B57\" ref-type=\"bibr\">Li C. et al., 2017</xref>). Li and his colleagues proposed a mechanistic model that linc-ROR promotes HCC tumorigenesis and autophagy partly through negatively regulating the expression of miR−145. Aside from that, it has been reported that miR-145 represses EMT, tumor migration, and invasion by directly targeting the 3’-UTRs of ZEB2 in the tumor. The decrease in miR−145 and increase in ZEB2 can obviously reversed the inhibition of cell migration and invasion mediated by the linc-ROR knockdown. Therefore, it was suggested that targeting the linc-ROR/miR−145/ZEB2 axis might represent a novel therapeutic application in HCC (<xref rid=\"B57\" ref-type=\"bibr\">Li C. et al., 2017</xref>). Similarly, <xref rid=\"B143\" ref-type=\"bibr\">Zhi et al. (2019)</xref> similarly showed that the migration and invasion of cells was reduced by the knockdown of linc-ROR. Moreover, they further confirmed that FOXM1-mediated activation of linc-ROR contributes to the poor sensitivity of HCC cells to sorafenib <italic>via</italic> partially regulating the miR-876-5p/FOXM1 axis, which forms a positive-feedback loop. Further evaluation of the regulatory mechanism involving this axis may provide new insights for exploring a potential therapeutic strategy for the management of HCC (<xref rid=\"B143\" ref-type=\"bibr\">Zhi et al., 2019</xref>). Consequently, these studies may offer new insights regarding the pathology of HCC and provide potential strategies for lncRNA-directed treatment. However, both the <italic>in vivo</italic> influence and other underlying mechanisms of linc-ROR still remain to be determined and clarified in the future research.</p></sec><sec id=\"S3.SS4\"><title>Colorectal Cancer</title><p>There are approximately 1.3 million new CRC cases and 690,000 CRC-related deaths worldwide each year, thus making CRC the third most common cancer in the world (<xref rid=\"B114\" ref-type=\"bibr\">Torre et al., 2015</xref>). Although the treatment of CRC has significantly improved in recent decades, the prognosis remains unsatisfactory, especially in case of advanced tumors with distant metastases (<xref rid=\"B7\" ref-type=\"bibr\">Bogousslavsky, 1984</xref>; <xref rid=\"B114\" ref-type=\"bibr\">Torre et al., 2015</xref>). Current studies results showed that approximately 25% of cases with CRC have synchronous liver metastases during the time of diagnosis (<xref rid=\"B49\" ref-type=\"bibr\">Kawaguchi et al., 2019</xref>). These patients have inherently low survival rates of less than 10% within 5 years, with an even worse prognosis (<xref rid=\"B42\" ref-type=\"bibr\">Hu et al., 2019</xref>; <xref rid=\"B49\" ref-type=\"bibr\">Kawaguchi et al., 2019</xref>). Thus, there is an urgent need to better understand the progression of CRC and to identify novel and sensitive biomarkers for the diagnosis and treatment of patients with CRC.</p><p>Yang et al. detected the expression of linc-ROR in 30 CRC tissues compared to normal tissues by using qRT-PCR. They found that the expression of linc-ROR was remarkably increased in CRC tissues compared with normal tissues. Similarly, linc-ROR was shown to be overexpressed in five CRC cell lines (<xref rid=\"B127\" ref-type=\"bibr\">Yan and Sun, 2018</xref>; <xref rid=\"B61\" ref-type=\"bibr\">Li et al., 2020a</xref>). Then, they also performed a series of functional assays to clarify the biological effects of the aberrant expression of linc-ROR on proliferation, viability, apoptosis, migration, and invasion of CRC cells. Knockdown of linc-ROR was shown to effectively inhibit the proliferation of CRC cells, whereas its overexpression obviously increased the proliferative capacity of CRC cells. Accordingly, silencing of linc-ROR strongly inhibited the migratory and invasive abilities of CRC cells, compared with that in the control cells (<xref rid=\"B61\" ref-type=\"bibr\">Li et al., 2020a</xref>). In contrast, the migratory and invasive ability of cells was activated following the overexpression of linc-ROR. The MTS (3-(4,5-dimethylthiazol-2-yl)-5-(3-carboxymethoxyphenyl)-2-(4- sulfophenyl)-2H-tetrazolium) assay results showed that the overexpression of linc-ROR could enhance the viability of CRC cells. Furthermore, the flow cytometric analysis results revealed that the percentage of apoptotic cells in linc-ROR overexpression group was reduced by 9.74% ± 2.13%, indicating that the overexpression of linc-ROR could inhibit apoptosis in the CRC cell lines (<xref rid=\"B61\" ref-type=\"bibr\">Li et al., 2020a</xref>). More importantly, a recent study revealed the role of linc-ROR in the EMT. It was revealed that the upregulation of linc-ROR could increase the expression of N-cadherin and Vimentin as well as decrease the level of E-cadherin, leading to the promotion of the progression of EMT (<xref rid=\"B145\" ref-type=\"bibr\">Zhou et al., 2016</xref>; <xref rid=\"B127\" ref-type=\"bibr\">Yan and Sun, 2018</xref>). Meanwhile, the high expression of linc-ROR in CRC was also confirmed by <xref rid=\"B42\" ref-type=\"bibr\">Hu et al. (2019)</xref>. Mechanistically, <xref rid=\"B61\" ref-type=\"bibr\">Li et al. (2020a)</xref> proved that that linc-ROR could bind to miR-6833-3p, which was determined to be significantly downregulated in CRC tissues. Additionally, a negative correlation was exhibited between the expression of linc-ROR and miR-6833-3p in BC tissues. Anti-AGO2 RNA immunoprecipitation assay further confirmed these results (<xref rid=\"B61\" ref-type=\"bibr\">Li et al., 2020a</xref>). Besides, rescue assays demonstrated that downregulation of miR-6833-3p could partly reversed the inhibition of tumorigenesis induced by linc-ROR knockdown in BC cells. Li and his colleagues uncovered that linc-ROR exerted its oncogenic role through negatively regulating the expression of miR-6833-3p during the progression of CRC, which might give new insights into molecular diagnosis and treatment (<xref rid=\"B61\" ref-type=\"bibr\">Li et al., 2020a</xref>). In addition, <xref rid=\"B61\" ref-type=\"bibr\">Li et al. (2020a)</xref> further uncovered that linc-ROR could mediate the expression level of SMC by sponging miR-6833-3p in CRC cells, thus promoting CRC progression. As for the effects of linc-ROR on radiotherapy resistance. <xref rid=\"B127\" ref-type=\"bibr\">Yan and Sun (2018)</xref> showed that overexpression of linc-ROR increased the ability of CRC cells for radiotherapy resistance. Collectively, these findings indicated that linc-ROR might be engaged in the metastatic process of CRC cells and could promote the development of CRC through a variety of molecular mechanisms.</p></sec><sec id=\"S3.SS5\"><title>Lung Cancer</title><p>Lung cancer is the leading cause of cancer-related deaths worldwide (<xref rid=\"B10\" ref-type=\"bibr\">Bray et al., 2018</xref>). Non-small cell lung cancer (NSCLC) accounts for about 85% of the lung cancer types, including squamous cell carcinoma, large cell lung cancer, and lung adenocarcinoma (<xref rid=\"B38\" ref-type=\"bibr\">Herbertz et al., 2015</xref>; <xref rid=\"B10\" ref-type=\"bibr\">Bray et al., 2018</xref>; <xref rid=\"B9\" ref-type=\"bibr\">Brainard and Farver, 2019</xref>). Although there are various approaches for its diagnosis and treatments, the 5-year overall survival (OS) rate for patients with advanced lung cancer is less than 15% (<xref rid=\"B144\" ref-type=\"bibr\">Zhou et al., 2011</xref>). Therefore, in order to carry out an early diagnosis and treatment of lung cancer, the search for valuable and efficient tumor markers is very urgent.</p><p>In recent years, linc-ROR has appeared as an important regulator of lung cancer. Research from <xref rid=\"B96\" ref-type=\"bibr\">Qu et al. (2017)</xref> demonstrated that the expression of linc-ROR was increased in 299 NSCLC tissues compared to that in the normal tissues. The overexpression of linc-ROR was shown to be closely related to the poor prognosis of LNM, histological grade, and stage of TNM (<xref rid=\"B90\" ref-type=\"bibr\">Pan et al., 2017</xref>; <xref rid=\"B96\" ref-type=\"bibr\">Qu et al., 2017</xref>). Another study by <xref rid=\"B90\" ref-type=\"bibr\">Pan et al. (2017)</xref> demonstrated that the decreased expression of linc-ROR could obviously impair the proliferative capacity of lung adenocarcinoma (LAD) cells, cause a G0/G1 phase arrest, and increase the ratio of apoptotic LAD cells. Moreover, downregulation of linc-ROR substantially inhibited the invasive and metastatic ability of LAD cells. Meanwhile, forced expression of linc-ROR was observed to reduce the expression of E-cadherin and β-catenin, which are the characteristic biomarkers of epithelial cells, whereas it increased the expression of N-cadherin and Vimentin, thus displaying a mesenchymal phenotype. Conversely, downregulation of linc-ROR was demonstrated to result in increased the expression of epithelial markers and decreased the expression of mesenchymal markers. This result uncovered the fact that the pro-metastatic effects of linc-ROR were induced by the regulation of the expression of a number of genes involved in cell metastasis and EMT progress. Meanwhile, overexpression of linc-ROR was shown to enhance the resistance of LAD to docetaxel (DTX) (<xref rid=\"B90\" ref-type=\"bibr\">Pan et al., 2017</xref>), suggesting that linc-ROR-induced resistance of LAD cells to DTX and EMT through regulation the expression of miR-145, which was predicted to interact with linc-ROR. More importantly, further research confirmed that miR-145 could bind to linc-ROR, and its downregulation could partly inhibit the resistance of LAD cells to DTX and EMT. Aside from that, Pan and his colleagues discovered that a decrease in the expression of miR-145 and increase in the expression FSCN1 could obviously reverse the inhibition of cell proliferation, chemoresistance, and EMT mediated by linc-ROR knockdown. They identified that dysregulation of the linc-ROR/miR-145/FSCN1 axis was associated with the therapeutic resistance and EMT transition in LAD cells, thereby providing potential therapeutic strategies for managing drug resistance in patients with LAD (<xref rid=\"B90\" ref-type=\"bibr\">Pan et al., 2017</xref>). Taken together, these studies collectively suggested that linc-ROR could activate the malignant phenotype of NSCLC cells with the guidance of a mechanism involving miRNAs. Hence, more efforts should be made to elucidate other regulatory mechanisms and the clinical significance of linc-ROR in lung cancer.</p></sec><sec id=\"S3.SS6\"><title>Thyroid Cancer (TC)</title><p>TC continues to be the most common endocrine malignant tumor and has emerged as a major health issue (<xref rid=\"B46\" ref-type=\"bibr\">Ito et al., 2013</xref>). It is estimated that more than 60,000 people in the United States present with TC every year (<xref rid=\"B46\" ref-type=\"bibr\">Ito et al., 2013</xref>; <xref rid=\"B116\" ref-type=\"bibr\">Vigneri et al., 2015</xref>). Additionally, TC is the sixth most common malignancy among Chinese women, with an incidence rate of about 6.6 per a population of 100,000 (<xref rid=\"B14\" ref-type=\"bibr\">Chen et al., 2015</xref>). The major subtypes of TC include PTC, follicular thyroid cancer (FTC), poorly differentiated thyroid cancer (PDTC), and anaplastic thyroid cancer (ATC) originating from follicular cell-derived thyroid cells. PTC accounts for more than 85% of all the TC cases, and approximately 10–15% of the patients with PTC have been reported to exhibit relapse and metastasis after therapy, leading to a poor outcome (<xref rid=\"B23\" ref-type=\"bibr\">Fagin and Wells, 2016</xref>). On the other hand, ATC is the most aggressive and fatal subtype, with a total survival of merely 3–5 months after initial diagnosis (<xref rid=\"B23\" ref-type=\"bibr\">Fagin and Wells, 2016</xref>). The studies of molecular mechanism correlated with the development and progression of TC may considerably facilitate the understanding of the pathogenesis of TC (<xref rid=\"B120\" ref-type=\"bibr\">Wang et al., 2016</xref>, <xref rid=\"B121\" ref-type=\"bibr\">2017</xref>; <xref rid=\"B79\" ref-type=\"bibr\">Murugan et al., 2018</xref>). Thus, it is importantly to find potential biomarkers and therapeutic targets involved in TC tumorigenesis.</p><p><xref rid=\"B136\" ref-type=\"bibr\">Zhang et al. (2018)</xref> examined the expression levels of linc-ROR in TC cells. Accordingly, their results from <italic>in situ</italic> hybridization showed that the levels of linc-ROR was increased in TC tissues and TC cell lines. Then, the expression of linc-ROR was further validated by using qRT-PCR analysis in TC tissue samples and cell lines, as they found it to be higher compared to that in normal tissue samples and normal thyroid cell. Hence, linc-ROR was observed to be evidently upregulated during the progression of TC (<xref rid=\"B136\" ref-type=\"bibr\">Zhang et al., 2018</xref>). Then, a series of functional assays were performed to clarify the biological effects of linc-ROR on proliferation and invasion of TC cells. They showed that that decreased the expression of linc-ROR could suppress the proliferative and invasion capacity of TC cells, as well as the elevated apoptotic rate in TC cells (<xref rid=\"B136\" ref-type=\"bibr\">Zhang et al., 2018</xref>). Interestingly, this led to the reversed progress of EMT, which was presented as a reduction in the levels of E-cadherin and enrichment in those of N-cadherin, highlighting the involvement of linc-ROR in the regulation of EMT (<xref rid=\"B136\" ref-type=\"bibr\">Zhang et al., 2018</xref>). Specifically, <xref rid=\"B136\" ref-type=\"bibr\">Zhang et al. (2018)</xref> proved that linc-ROR could act as a molecular sponge to modulate miR-145, which was determined to be significantly downregulated in TC tissues, indicating that linc-ROR had a negative regulatory role on miR-145. In summary, Zhang et al. discovered that linc-ROR could act as an oncogene and suggested its utilization as a prognostic indicator of patients with TC. However, a larger cohort of tumor samples and deeper mechanistic research are urgently needed.</p></sec></sec><sec id=\"S4\"><title>Potential Clinical Application of Linc-Ror in Human Cancers</title><p>The detection of cancer is hard in early stages and thus losing the best chance for curative surgery results in the poor survival rates (<xref rid=\"B104\" ref-type=\"bibr\">Siegel et al., 2020</xref>). Specific cancer biomarkers that monitor the molecular differences associated with cancer, which may be used for early diagnosis, are essential and may help select the best treatment options and gain valuable treatment time for patients with cancer. In clinical settings, prognostic markers can be used to predict the clinical outcome of untreated patients with cancer. On the other hand, current studies have further indicated that the progression of cancer or differentiation of cancer subtypes can be predicted by the expression profiles of lncRNAs. However, because of the uncertain molecular function of lncRNAs, it is difficult to accurately predict the progress of tumors. The ideal and convenient biomarkers should possess several typical and important characteristics. Interestingly, studies have shown that linc-ROR is not only closely associated with multiple biological functions of cancer cells but may also be an ideal and convenient biomarker (<xref rid=\"B91\" ref-type=\"bibr\">Pan et al., 2016</xref>). For example, the expression of linc-ROR has been reported to be increased in HCC tissues with LNM or vascular infiltration compared with normal tissues. In addition, the advanced stage of TNM was associated with the overexpression of linc-ROR. Most importantly, the expression of linc-ROR was shown to be elevated in patients with HCC with recurrence compared with those without recurrence (<xref rid=\"B57\" ref-type=\"bibr\">Li C. et al., 2017</xref>). Furthermore, results obtained from the Kaplan-Meier analysis showed that patients with HCC with high expression of linc-ROR had worse prognosis compared to those with low linc-ROR expression, along with a shorter disease-free survival (DFS) and OS (<xref rid=\"B57\" ref-type=\"bibr\">Li C. et al., 2017</xref>).</p><p><xref rid=\"B140\" ref-type=\"bibr\">Zhao et al. (2017)</xref> found that the expression level of linc-ROR was increased in BC tissues, BC cell lines, and BC plasma samples. Concomitantly, they demonstrated that the level of linc-ROR in patients with LNM was elevated compared to that in patients without LNM. Besides, the expression of linc-ROR in ER-positive or PR-positive BC plasma also demonstrated to be significantly increased (<xref rid=\"B140\" ref-type=\"bibr\">Zhao et al., 2017</xref>). Moreover, the study showed that the area under the Receiver operating characteristic (ROC) curve (AUC) of linc-ROR was 0.758 (sensitivity 80.0%; specificity 73.3%), suggesting that the diagnostic ability of linc-ROR was elevated relative to that of Carcinoembryonic antigen (CEA) (AUC = 0.516; sensitivity 66.7%; specificity 50.0%) and CA153 (AUC = 0.663; sensitivity 73.3%; specificity 60.0%). More importantly, they found that linc-ROR had the strongest ability among the three indices in distinguishing healthy from BC-affected individuals in an evaluation of 45 early stage patients from the 96 patients with BC and 45 age and sex-matched healthy controls from the 90 healthy volunteers. Moreover, the combined detection of the three plasma indexes might enhance the overall diagnostic power (AUC = 0.846; sensitivity 83.3%; specificity 70.0%) (<xref rid=\"B140\" ref-type=\"bibr\">Zhao et al., 2017</xref>). In addition, <xref rid=\"B40\" ref-type=\"bibr\">Hou et al. (2018)</xref> examined the level of linc-ROR in the clinical prognosis of patients with BC. The group found that there was a close relationship between LNM and the expression of linc-ROR. More importantly, overexpression of linc-ROR was observed to exhibit a faster decline with increased survival (<xref rid=\"B16\" ref-type=\"bibr\">Chen et al., 2016a</xref>; <xref rid=\"B40\" ref-type=\"bibr\">Hou et al., 2018</xref>). Another study also revealed that the regulatory polymorphisms in linc-ROR had an impact on the risk for BC (<xref rid=\"B71\" ref-type=\"bibr\">Luo et al., 2018</xref>). Conclusively, all the above-mentioned results suggested that linc-ROR might be used as a diagnostic and prognostic biomarker for BC.</p><p><xref rid=\"B96\" ref-type=\"bibr\">Qu et al. (2017)</xref> identified that the level of linc-ROR was significantly elevated in NSCLC tissues compared to that in the adjacent tissues. Meanwhile, upregulation of linc-ROR was shown to be significantly linked to advanced TNM stage, LNM, and positive distant metastasis. The Kaplan-Meier curve analysis showed that patients with NSCLC with high expression of linc-ROR have shorter OS and DFS times compared to patients with low expression of linc-ROR (<xref rid=\"B21\" ref-type=\"bibr\">Duan et al., 2016</xref>; <xref rid=\"B48\" ref-type=\"bibr\">Jiang et al., 2016</xref>; <xref rid=\"B96\" ref-type=\"bibr\">Qu et al., 2017</xref>). Subsequently, the results of Cox proportional hazards regression analyses showed that the expression of linc-ROR was an independent prognostic indicator for OS (HR (heart rate) = 2.983) and DFS (HR (heart rate) = 3.421) in patients with NSCLC (<xref rid=\"B96\" ref-type=\"bibr\">Qu et al., 2017</xref>). In addition, <xref rid=\"B25\" ref-type=\"bibr\">Fu et al. (2017)</xref> also demonstrated that the expression level of linc-ROR was increased in PC tissues compared to that in adjacent tissues. The expression level of linc-ROR was further demonstrated to be closely linked to tumor size and the clinical stage of PC. Meanwhile, log-rank analysis indicated that the OS was significantly decreased in patients with higher expression of linc-ROR (<xref rid=\"B25\" ref-type=\"bibr\">Fu et al., 2017</xref>). Glioblastoma (GB) has been considered to be the most aggressive subtype of glioma and the most common adult malignant brain tumor in the world (<xref rid=\"B110\" ref-type=\"bibr\">Stupp et al., 2009</xref>; <xref rid=\"B65\" ref-type=\"bibr\">Linz, 2010</xref>; <xref rid=\"B19\" ref-type=\"bibr\">Davis, 2016</xref>). <xref rid=\"B113\" ref-type=\"bibr\">Toraih et al. (2019)</xref> reported that the level of linc-ROR was increased in 89.5% of the patients (<xref rid=\"B31\" ref-type=\"bibr\">Han et al., 2012</xref>; <xref rid=\"B24\" ref-type=\"bibr\">Feng et al., 2015</xref>). The overexpression of linc-ROR was observed to be closely linked to poor disease progression-free and OS in younger age of patients. In addition, Kaplan–Meier survival plot illustrated that patients with GB with high linc-ROR expression had low survival rates. Multivariate analysis demonstrated that patients with GB were divided into two different groups according to the expression profile of linc-ROR and OS of patients (<xref rid=\"B113\" ref-type=\"bibr\">Toraih et al., 2019</xref>). Thus, these results suggested that the expression of linc-ROR was complementary in playing a crucial role in the progress of cancers and thus could serve as a new potential biomarker for the evaluation of clinical prognosis.</p><p>Currently, the biggest obstacles to cancer treatment are delayed diagnosis, recurrence, and metastasis. Therefore, the search for the ideal cancer biomarkers is crucial for the improvement of the early diagnosis rate. The above-mentioned findings suggested that linc-ROR might be used as a potential marker for the diagnosis of several types of cancer. However, the exact molecular mechanism by which linc-ROR plays a role in various cancers remains unclear. As such, the functional role of linc-ROR in cancer needs to be further explored and verified, especially in clinical applications.</p></sec><sec id=\"S5\"><title>Regulatory Mechanism of Linc-Ror in Various Types of Cancer</title><p>It is well understood that improvement of the survival of patients requires a deeper understanding, as well as effective biomarkers for determination of cancer occurrence and metastasis. Additionally, detection of these invasive tumors in the early stages of disease development and development of efficient treatments are more effective ways to reduce cancer mortality. A large number of researches have shown that many signaling pathways were significantly associated with the occurrence and development of tumors (<xref rid=\"B16\" ref-type=\"bibr\">Chen et al., 2016a</xref>; <xref rid=\"B102\" ref-type=\"bibr\">Sahebi et al., 2016</xref>; <xref rid=\"B40\" ref-type=\"bibr\">Hou et al., 2018</xref>). Hence, we summarize that the related some regulatory mechanisms are associated with the tumor development and progression.</p><p>First, linc-ROR has been reported to directly affect the progression of various cancers through several signaling pathways (<xref ref-type=\"fig\" rid=\"F4\">Figure 4</xref> and <xref rid=\"T2\" ref-type=\"table\">Table 2</xref>). Of note, a number of studies have hinted that the aberrant activated MAPK/ERK cascade plays a critical role in many aspects of tumorigenesis. Nearly 50% of human malignancies are known to exhibit unregulated ERK signaling (<xref rid=\"B39\" ref-type=\"bibr\">Herrero et al., 2015</xref>). In BC, the upregulated MAPK/ERK (mitogen-activated protein kinase/extracellular regulated protein kinases) signaling has been correlated with poor survival in patients with triple-negative BC (<xref rid=\"B4\" ref-type=\"bibr\">Bartholomeusz et al., 2012</xref>). In addition, the MAPK/ERK pathway has also been shown to influence the chemotherapeutic drug resistance to doxorubicin and paclitaxel in BC cells (<xref rid=\"B74\" ref-type=\"bibr\">McCubrey et al., 2006</xref>). Moreover, It is well known that the MAPK/ERK signaling is negatively regulated by mitogen-activated protein kinase phosphatases, which form six distinct groups based on their physiological functions; among them, groups 1–4 are known to be involved in dephosphorylation of ERK, including DUSP7 (<xref rid=\"B87\" ref-type=\"bibr\">Owens and Keyse, 2007</xref>). Interestingly, Peng and his colleagues demonstrated that DUSP7 is downregulated in response to estrogen deprivation and overexpression of linc-ROR significantly decreased the half-life of DUSP7, possibly inhibiting the activation of ERK, resulting in estrogen-independent growth of BC cells (<xref rid=\"B94\" ref-type=\"bibr\">Peng et al., 2017a</xref>). The Wnt signaling cascade was highly conserved among species and controls a multitude of biological processes during animal development and life cycles. Because of its central role in the maintenance of tissue homeostasis, the Wnt pathway was tightly regulated at multiple levels, from the ligand-receptor interaction down to transcriptional and post-transcriptional levels; its aberrant activity has been implicated in a number of developmental disorders and diseases and, most prominently, in cancer (<xref rid=\"B85\" ref-type=\"bibr\">Nusse and Clevers, 2017</xref>; <xref rid=\"B81\" ref-type=\"bibr\">Nguyen et al., 2019</xref>; <xref rid=\"B137\" ref-type=\"bibr\">Zhang et al., 2019</xref>; <xref rid=\"B66\" ref-type=\"bibr\">Liu et al., 2020</xref>). The recent discovery of lncRNAs that are regulated by Wnt and/or participate in Wnt pathway modulation and outcome is particularly intriguing and has highlighted some of these gaps in our knowledge (<xref rid=\"B134\" ref-type=\"bibr\">Zarkou et al., 2018</xref>). For example, <xref rid=\"B69\" ref-type=\"bibr\">Lou et al. (2017)</xref> have discovered that the Wnt/β-catenin signaling pathway led to the elevated proliferation of ovarian cancer (OC) cells. Whereas, E-cadherin was shown to be decreased, vimentin, β-catenin, and c-myc were all increased in OC cells treated with LiC1 (a Wnt/β-catenin pathway activator) compared with untreated controls (<xref rid=\"B69\" ref-type=\"bibr\">Lou et al., 2017</xref>). More importantly, the study further revealed that the activation of Wnt/β-catenin signaling pathway and the progression of EMT, along with the abnormal expression of EMT markers and Wnt/β-catenin signaling pathway-related proteins (c-Myc, cyclin-D1, and β-catenin), was inhibited by knockdown of linc-ROR during the development of OC, indicating that linc-ROR promotes OC EMT at least in part by activating the Wnt/β-catenin pathway (<xref rid=\"B69\" ref-type=\"bibr\">Lou et al., 2017</xref>). However, the relationship between linc-ROR and the Wnt/β-catenin signaling pathway required deeper study in tumor initiation and progression. The PI3K/Akt/mTOR pathway has various cell functions, including cellular proliferation, survival, differentiation, and intrusion, and previous literature has proven that lncRNA silencing could reduce the phosphorylation of ERK, Akt, and mTOR (<xref rid=\"B26\" ref-type=\"bibr\">Fumarola et al., 2014</xref>). More importantly, aberrant activation of the PI3K/Akt/mTOR pathway was shown to be one of the mechanisms of targeted therapeutic resistance in patients with NSCLC (<xref rid=\"B26\" ref-type=\"bibr\">Fumarola et al., 2014</xref>). <xref rid=\"B125\" ref-type=\"bibr\">Xu et al. (2018)</xref> found that silencing of linc-ROR significantly inhibited the expression of p-PI3K, p-AKT3, and m-TOR. In contrast, the overexpression of linc-ROR was shown to increase the expression of p-PI3K, p-AKT3, and m-TOR, suggesting that the activation of PI3K/Akt/mTOR signaling pathway was mediated by linc-ROR in NSCLC (<xref rid=\"B125\" ref-type=\"bibr\">Xu et al., 2018</xref>). Similarly, <xref rid=\"B103\" ref-type=\"bibr\">Shi et al. (2017)</xref> further discovered that linc-ROR could inhibit the PI3K/Akt/mTOR signaling pathway, so that inactivation of linc-ROR or inhibition of PI3K/Akt/mTOR signaling pathway could increase the sensitivity of NSCLC to cisplatin (DDP), thereby promoting cell apoptosis, and inhibiting cell proliferation, migration, invasion, and tumor growth. Additionally, the Hippo/YAP pathway (also known as the Salvador–Warts–Hippo pathway) is an evolutionarily conserved regulator of tissue growth and cell fate (<xref rid=\"B34\" ref-type=\"bibr\">Harvey et al., 2013</xref>). The Hippo/YAP pathway was first postulated to be important for human cancer on the basis of the egregious overgrowth of <italic>Drosophila melanogaster</italic> tissues that harbor mutations in different Hippo/YAP pathway genes. In recent years, increasing numbers of mammalian studies have validated this hypothesis: Hippo/YAP pathway perturbation can trigger tumorigenesis in mice, and mutation and altered expression of a subset of Hippo pathway genes have been observed in human cancers (<xref rid=\"B34\" ref-type=\"bibr\">Harvey et al., 2013</xref>). Interestingly, the Hippo/YAP signaling pathway has also been reported to activated by linc-ROR (<xref rid=\"B77\" ref-type=\"bibr\">Mo et al., 2014</xref>). Inactivation of the Hippo signaling pathway could lead to the downregulation of MST1/LATS1 (the core factors of Hippo pathway) and upregulation of YAP1 (<xref rid=\"B36\" ref-type=\"bibr\">He et al., 2015</xref>). Furthermore, dysregulation of the Hippo signaling has been confirmed in many types of tumors and closely linked to the acquisition of malignant features. A recent study provided by Chen et al. demonstrated that the regulatory axis of linc-ROR/EMT/YAP1 regulatory axis exerted oncogenic effects in the progression of PC (<xref rid=\"B13\" ref-type=\"bibr\">Chen et al., 2020</xref>). They found that a knockdown of linc-ROR resulted in the downregulation of YAP, and MOB kinase activator 1 (MOB1), whereas there was upregulation of MST1, MST2, p-MOB1, p-LATS1, LATS1, and p-YAP in PC cells. In contrast, overexpression of linc-ROR led to the exact reverse condition (<xref rid=\"B13\" ref-type=\"bibr\">Chen et al., 2020</xref>). The current evidence for the existence of the linc-ROR/EMT/Hippo/YAP axis has indicated that combined targeting of the YAP1/Hippo signaling pathway may provide a potential direction for the treatment of PC. Many studies have recognized that the function of TGFβ in the progression of BC can be different depending on the stage of cancer (<xref rid=\"B2\" ref-type=\"bibr\">Bachman and Park, 2005</xref>; <xref rid=\"B78\" ref-type=\"bibr\">Moses and Barcellos-Hoff, 2011</xref>; <xref rid=\"B73\" ref-type=\"bibr\">Massague, 2012</xref>). Under normal conditions, TGF-β has been shown to inhibit cell cycle and promote apoptosis, which together significantly contribute to its suppressive role in the initiation and progression of tumorigenesis (<xref rid=\"B37\" ref-type=\"bibr\">Heldin et al., 2009</xref>). <xref rid=\"B40\" ref-type=\"bibr\">Hou et al. (2018)</xref> reported that knockdown of linc-ROR in BC cells led to diminished expression levels of TGF-β. As a result, the downstream factors, such as Smad2 and α-SMA, were also downregulated. This finding suggested that overexpression of linc-ROR might be required to constitutively upregulate critical factors in the TGF-β signaling pathway (<xref rid=\"B40\" ref-type=\"bibr\">Hou et al., 2018</xref>). Overall, these findings revealed that linc-ROR might provide a novel therapeutic target and biomarker for cancers in the future.</p><fig id=\"F4\" position=\"float\"><label>FIGURE 4</label><caption><p>Direct regulatory function of linc-ROR in various cancers. First, linc-ROR promotes the estrogen-independent growth and activation of the MAPK/ERK signaling pathway of tumor cells by regulating the DUSP7 ERK-specific phosphatase. Second, linc-ROR promotes the process of EMT through the Wnt/β-catenin and Hip/YAP signaling pathways. Additionally, linc-ROR strongly represses the sensitivity to cisplatin and significantly increases the proliferation, invasion, migration, and growth of tumor cells <italic>via</italic> the PI3K/AKT/m-TOR signaling pathway (associated proteins: p-PI3K, p-AKT, and p-mTOR). Simultaneously, linc-ROR significantly facilitates the progression of tumor through the TGF-β signaling pathway (associated proteins: p-PI3K, p-AKT, and p-mTOR).</p></caption><graphic xlink:href=\"fcell-08-00696-g004\"/></fig><table-wrap id=\"T2\" position=\"float\"><label>TABLE 2</label><caption><p>The involvement of linc-ROR in multiple signaling pathways.</p></caption><table frame=\"hsides\" rules=\"groups\" cellspacing=\"5\" cellpadding=\"5\"><thead><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Cancer types</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Role</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Expression</bold></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Related gene</bold></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Signaling pathway</bold></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>References</bold></td></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Breast cancer</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Oncogenic</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Upregulated</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">ERK1, ERK2 TGF-β, Smad2, and α-SMA</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">MAPK/ERK signaling pathway TGF-β signaling pathway</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><xref rid=\"B94\" ref-type=\"bibr\">Peng et al., 2017a</xref>; <xref rid=\"B40\" ref-type=\"bibr\">Hou et al., 2018</xref></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Pancreatic cancer</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Oncogenic</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Upregulated</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">MST1, MST2, p−MOB1, p−Lats1, Lats1, p−YAP</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Hippo/YAP signaling pathway</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><xref rid=\"B13\" ref-type=\"bibr\">Chen et al., 2020</xref></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Lung cancer</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Oncogenic</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Upregulated</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">p-PI3K, p-AKT3, m-TOR</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">PI3K/Akt/mTOR signaling pathway</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><xref rid=\"B103\" ref-type=\"bibr\">Shi et al., 2017</xref>; <xref rid=\"B125\" ref-type=\"bibr\">Xu et al., 2018</xref></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Ovarian cancer</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Oncogenic</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Upregulated</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">c-Myc, cyclin-D1, and β-catenin</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Wnt/β-catenin signaling pathway</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><xref rid=\"B69\" ref-type=\"bibr\">Lou et al., 2017</xref></td></tr></tbody></table></table-wrap></sec><sec id=\"S6\"><title>Future Prospects</title><p>A growing understanding of the role of linc-ROR in all types of cancers may opened up the possibility for many new treatment strategies. Therefore, increasingly, cancer researchers will focus on this lncRNA. Recent studies have displayed that the level of linc-ROR was increased in lung cancer, bladder cancer, and CRC cell lines. Moreover, both cell proliferation and migration have been reported to be promoted by linc-ROR in several types of cancers. In addition, increasing evidence have shown that the abnormal expression of linc-ROR in various cancers was closely linked to tumorigenesis, diagnosis, metastasis, and prognosis through some signaling pathways. Of note, a number of studies have confirmed that linc-ROR can be considered as a possible new target for cancer therapy and a biomarker for cancer diagnosis. However, the current knowledge on the biological functions of linc-ROR remains insufficient, and its efficacy is also not evident. Thus, additional studies on lncRNAs are required.</p><p>The abbreviation “EV” is actually a collective term that refers to a series of lipid bilayer membrane-bound organelles that are released by cells into their environment. Briefly, EVs are heterogeneous in size and released from nearly all cells under appropriate physiological and pathological conditions (<xref rid=\"B56\" ref-type=\"bibr\">Lee et al., 2011</xref>). Of note, a variety of cargos have been transferred from a cell to another cell <italic>via</italic> EVs (<xref rid=\"B22\" ref-type=\"bibr\">Ekstrom et al., 2012</xref>; <xref rid=\"B128\" ref-type=\"bibr\">Yang J. et al., 2018</xref>). More importantly, the EV cargo can reflect the cells of origin. A number of studies have demonstrated that exosomes carry different types of RNA compared to their parental cells (<xref rid=\"B107\" ref-type=\"bibr\">Skog et al., 2008</xref>; <xref rid=\"B22\" ref-type=\"bibr\">Ekstrom et al., 2012</xref>; <xref rid=\"B83\" ref-type=\"bibr\">Nolte-’t Hoen et al., 2012</xref>). It is well-known that unprotected ncRNAs are easily degraded by the RNases in the blood. However, it has been demonstrated that under the protection of EVs, ncRNAs avoid degradation, and thus their integrity and activity are maintained in the circulation. Recently, studies suggested that the expression of lncRNAs in the plasma of patients with HCC was considerably increased compared to that in healthy people. More importantly, Takahashi et al. assessed the role of EV signaling in tumor cell responses to TGF-β and also recognized specific EV-lncRNA mediators, such as linc-ROR, involved in the regulation of the chemotherapy response to chemotherapy (<xref rid=\"B112\" ref-type=\"bibr\">Takahashi et al., 2014</xref>; <xref rid=\"B109\" ref-type=\"bibr\">Spinelli et al., 2018</xref>). Targeting these intercellular signaling mechanisms and mediators may be useful in enhancing sensitivity and improving responses to conventional therapeutic agents that are used for the treatment of HCC. As such, the expression of linc-ROR, which plays an important role in cancer prevention, diagnosis, and treatment may be increased through the administration of exosomal linc-ROR.</p><p>On the other hand, CRISPR/Cas9, as a very powerful gene editing tool, has been successfully applied to the interruption of the protein-coding 24 sequence in various organisms (<xref rid=\"B60\" ref-type=\"bibr\">Li M. et al., 2019</xref>). Previous researches have demonstrated that CRISPR/Cas9 could regulate the progression and development of cancers. For example, CRISPR/Cas9-mediated synthesis and gate genetic circuits were shown to be efficiently employed in the identification of bladder cancer cells (<xref rid=\"B67\" ref-type=\"bibr\">Liu et al., 2014</xref>). Of note, CRISPR/Cas9 could successfully target lncRNAs and replace the overexpression of the sponge bait, thus negating the requirement for the introduction of transgenes (<xref rid=\"B50\" ref-type=\"bibr\">Kellner et al., 2018</xref>). In addition, CRISPR/Cas9 has achieved many successes as a powerful genome engineering tool for the treatment of many diseases due to its specificity, efficiency, simplicity, and versatility (<xref rid=\"B141\" ref-type=\"bibr\">Zhen et al., 2017</xref>). For example, both the transcription and infection by HIV-1 were shown to be increased by lnc-MALAT1. However, long terminal repeat-driven gene transcription of HIV-1 and viral replication were reported to be inhibited by CRISPR/Cas9-mediated lnc-MALAT1 knockdown. Similarly, knockdown of lnc-HTOR or IGF2BP1 by CRISPR/Cas9 gene-editing methods mimicked the effects and abolished the requirement for Triptonide in nasopharyngeal carcinoma cells (<xref rid=\"B68\" ref-type=\"bibr\">Liu et al., 2016</xref>). Therefore, there’s a good chance that the technology of CRISPR/Cas9 could be used to treat cancers by editing the expression level of linc-ROR. Thus, CRISPR/Cas9 might be used to treat cancers by regulating the expression of linc-ROR <italic>via</italic> the relevant molecular mechanism.</p></sec><sec id=\"S7\"><title>Conclusion</title><p>Increasing studies have demonstrated that lncRNAs could regulate the expression of genes and be involved in the tumorigenesis and development of tumors through complex tumor networks. The linc-ROR was reported to be abnormally expression in a variety of malignant tumors, such as BC, PC, HCC, CRC, NSCLC, and TC. The expression trend of this lncRNA was demonstrated to be almost similar in most of these cancers. In addition, studies confirmed that the linc-ROR served as an oncogene <italic>in vivo</italic> and <italic>in vitro</italic> and was closely associated with the enlarged tumor volume, advanced TNM stage, shortened OS, and metastasis. Meanwhile, cell proliferation, invasion, migration, and anti-apoptosis were observed to be regulated by linc-ROR in all the above-mentioned cancers. Retrospective studies of linc-ROR in human cancers may provide new targets for the diagnosis and treatment of different types of cancer. Furthermore, increasing studies have shown that the molecular mechanism, such as Wnt/β-catenin signaling pathway involved in the progress of cancer, was regulated by linc-ROR. There seems to be some competitions between linc-ROR and miRNAs, resulting in the inhibition of downstream target genes of these miRNAs. However, the exact mechanism of action of linc-ROR remains to be explored. Further experiments are needed to delineate the role of linc-ROR and its molecular mechanisms in other human malignancies.</p></sec><sec id=\"S8\"><title>Author Contributions</title><p>WC and JY drafted the manuscript. JS revised the manuscript. LL and HF reviewed and modified the manuscript. All authors agreed on the final version.</p></sec><sec id=\"conf1\"><title>Conflict of Interest</title><p>The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p></sec></body><back><fn-group><fn fn-type=\"financial-disclosure\"><p><bold>Funding.</bold> This project was supported by the Natural Science Foundation of Anhui Province (1808085MH288).</p></fn></fn-group><ref-list><title>References</title><ref id=\"B1\"><mixed-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Arunkumar</surname><given-names>G.</given-names></name><name><surname>Deva Magendhra Rao</surname><given-names>A. 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Commun.</italic></source>\n<volume>7</volume>:<issue>11215</issue>. <pub-id pub-id-type=\"doi\">10.1038/ncomms11215</pub-id>\n<pub-id pub-id-type=\"pmid\">27050392</pub-id></mixed-citation></ref><ref id=\"B143\"><mixed-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Zhi</surname><given-names>Y.</given-names></name><name><surname>Abudoureyimu</surname><given-names>M.</given-names></name><name><surname>Zhou</surname><given-names>H.</given-names></name><name><surname>Wang</surname><given-names>T.</given-names></name><name><surname>Feng</surname><given-names>B.</given-names></name><name><surname>Wang</surname><given-names>R.</given-names></name><etal/></person-group> (<year>2019</year>). <article-title>FOXM1-Mediated LINC-ROR regulates the proliferation and sensitivity to Sorafenib in hepatocellular carcinoma.</article-title>\n<source><italic>Mol. Ther. Nucleic Acids</italic></source>\n<volume>16</volume>\n<fpage>576</fpage>–<lpage>588</lpage>. <pub-id pub-id-type=\"doi\">10.1016/j.omtn.2019.04.008</pub-id>\n<pub-id pub-id-type=\"pmid\">31082791</pub-id></mixed-citation></ref><ref id=\"B144\"><mixed-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Zhou</surname><given-names>C.</given-names></name><name><surname>Wu</surname><given-names>Y. L.</given-names></name><name><surname>Chen</surname><given-names>G.</given-names></name><name><surname>Feng</surname><given-names>J.</given-names></name><name><surname>Liu</surname><given-names>X. 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Oncol. Res.</italic></source>\n<volume>22</volume>\n<fpage>733</fpage>–<lpage>740</lpage>. <pub-id pub-id-type=\"doi\">10.1007/s12253-016-0061-x</pub-id>\n<pub-id pub-id-type=\"pmid\">27071407</pub-id></mixed-citation></ref></ref-list><glossary><title>Abbreviations</title><def-list id=\"DL1\"><def-item><term>BC</term><def><p>breast cancer</p></def></def-item><def-item><term>ceNAs</term><def><p>competing endogenous RNA</p></def></def-item><def-item><term>EMT</term><def><p>epithelial–mesenchymal transition</p></def></def-item><def-item><term>HCC</term><def><p>hepatocellular cancer</p></def></def-item><def-item><term>iPSCs</term><def><p>induced pluripotent stem cells</p></def></def-item><def-item><term>linc-ROR</term><def><p>Long intergenic non-protein coding RNA, regulator of reprogramming</p></def></def-item><def-item><term>lncRNAs</term><def><p>Long non-coding RNAs</p></def></def-item><def-item><term>PC</term><def><p>pancreatic cancer.</p></def></def-item></def-list></glossary></back></article>\n"
]
[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Int J Environ Res Public Health</journal-id><journal-id journal-id-type=\"iso-abbrev\">Int J Environ Res Public Health</journal-id><journal-id journal-id-type=\"publisher-id\">ijerph</journal-id><journal-title-group><journal-title>International Journal of Environmental Research and Public Health</journal-title></journal-title-group><issn pub-type=\"ppub\">1661-7827</issn><issn pub-type=\"epub\">1660-4601</issn><publisher><publisher-name>MDPI</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32751907</article-id><article-id pub-id-type=\"pmc\">PMC7432148</article-id><article-id pub-id-type=\"doi\">10.3390/ijerph17155547</article-id><article-id pub-id-type=\"publisher-id\">ijerph-17-05547</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Article</subject></subj-group></article-categories><title-group><article-title>Distinct Associations of Hedonic and Eudaimonic Motives with Well-Being: Mediating Role of Self-Control</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Zeng</surname><given-names>Zhijia</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05547\">1</xref><xref rid=\"c1-ijerph-17-05547\" ref-type=\"corresp\">*</xref></contrib><contrib contrib-type=\"author\"><name><surname>Chen</surname><given-names>Hezhi</given-names></name><xref ref-type=\"aff\" rid=\"af2-ijerph-17-05547\">2</xref><xref rid=\"c1-ijerph-17-05547\" ref-type=\"corresp\">*</xref></contrib></contrib-group><aff id=\"af1-ijerph-17-05547\"><label>1</label>Student Affairs Department, Zhejiang University of Finance and Economics, Hangzhou 310018, China</aff><aff id=\"af2-ijerph-17-05547\"><label>2</label>Department of Psychology and Behavioral Sciences, Zhejiang University, Hangzhou 310058, China</aff><author-notes><corresp id=\"c1-ijerph-17-05547\"><label>*</label>Correspondence: <email>[email protected]</email> (Z.Z.); <email>[email protected]</email> (H.C.)</corresp></author-notes><pub-date pub-type=\"epub\"><day>31</day><month>7</month><year>2020</year></pub-date><pub-date pub-type=\"ppub\"><month>8</month><year>2020</year></pub-date><volume>17</volume><issue>15</issue><elocation-id>5547</elocation-id><history><date date-type=\"received\"><day>20</day><month>7</month><year>2020</year></date><date date-type=\"accepted\"><day>28</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>© 2020 by the authors.</copyright-statement><copyright-year>2020</copyright-year><license license-type=\"open-access\"><license-p>Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>).</license-p></license></permissions><abstract><p>The pursuit of hedonia and eudaimonia are two ways to fulfill the goal of a “good life”. While some studies report that both hedonic and eudaimonic motives improve well-being, others suggest that hedonic motives are counterproductive, raising the question of whether and why eudaimonic motives are more positively associated with well-being. We aimed to identify the distinct associations of hedonic and eudaimonic motives with well-being and investigate whether they are partly mediated by self-control. A total of 2882 college freshmen (1835 females, 1047 males, mean age 18.16 years) completed measures assessing hedonic and eudaimonic motives, self-control, life satisfaction, positive and negative affect, and eudaimonic well-being. Eudaimonic motives were associated with higher life satisfaction, more positive affect, less negative affect, and better eudaimonic well-being. In contrast, hedonic motives were positively associated with life satisfaction, while also being correlated with a greater degree of negative affect and impaired eudaimonic well-being. Self-control mediated the relationships between hedonic and eudaimonic motives and well-being. Eudaimonic and hedonic motives were positively and negatively related to self-control, respectively. Further, high self-control was associated with greater life satisfaction, positive affect, and eudaimonic well-being and lower negative affect. Thus, eudaimonic motives can lead to a better life than hedonic motives because the former enhance self-control, while the latter lower it.</p></abstract><kwd-group><kwd>hedonic motives</kwd><kwd>eudaimonic motives</kwd><kwd>self-control</kwd><kwd>well-being</kwd><kwd>happiness</kwd></kwd-group></article-meta></front><body><sec sec-type=\"intro\" id=\"sec1-ijerph-17-05547\"><title>1. Introduction</title><p>The pursuit of a good life is a fundamental motive for human beings. However, individuals can have very different ideas about what constitutes a “good life”. Specifically, the two most prominent views of a good life are the hedonic view and the eudaimonic view [<xref rid=\"B1-ijerph-17-05547\" ref-type=\"bibr\">1</xref>]. Pursuing hedonia, or hedonic motives, involves seeking personal enjoyment, pleasure, and comfort; pursuing eudaimonia, or eudaimonic motives, relates to seeking personal growth, excellence, meaning, and authenticity [<xref rid=\"B2-ijerph-17-05547\" ref-type=\"bibr\">2</xref>].</p><sec id=\"sec1dot1-ijerph-17-05547\"><title>1.1. Hedonic and Eudaimonic Motives and Well-Being</title><p>One issue that has received much scholarly attention is how hedonic and eudaimonic motives affect well-being. Aristotle claimed that people should strive for a virtuous life rather than seeking pleasure, because the latter does not bring happiness. Consistent with this view, numerous studies have suggested that while eudaimonic motives generally improve well-being, pursuing hedonia does not result in the achievement of the goal of a “good life” and might even backfire [<xref rid=\"B3-ijerph-17-05547\" ref-type=\"bibr\">3</xref>,<xref rid=\"B4-ijerph-17-05547\" ref-type=\"bibr\">4</xref>,<xref rid=\"B5-ijerph-17-05547\" ref-type=\"bibr\">5</xref>]. For example, in one study, eudaimonic motives were correlated with various well-being outcomes, including life satisfaction, vitality, positive affect, negative affect, carefreeness, self-connectedness, and meaning, whereas hedonic motives only predicted vitality, positive affect, and carefreeness [<xref rid=\"B4-ijerph-17-05547\" ref-type=\"bibr\">4</xref>]. In another study, eudaimonic motives were negatively correlated with depression and stress; in contrast, there was no significant correlation between hedonic motives and depression or stress [<xref rid=\"B5-ijerph-17-05547\" ref-type=\"bibr\">5</xref>]. Sheldon et al. [<xref rid=\"B3-ijerph-17-05547\" ref-type=\"bibr\">3</xref>] further showed that participants’ motivations for improving their subjective well-being were negatively correlated with concurrent subjective well-being and did not affect longitudinal subjective well-being.</p><p>However, some studies have reported the well-being-related benefits of both hedonic and eudaimonic motives [<xref rid=\"B6-ijerph-17-05547\" ref-type=\"bibr\">6</xref>,<xref rid=\"B7-ijerph-17-05547\" ref-type=\"bibr\">7</xref>,<xref rid=\"B8-ijerph-17-05547\" ref-type=\"bibr\">8</xref>]. For example, Peterson et al. [<xref rid=\"B8-ijerph-17-05547\" ref-type=\"bibr\">8</xref>] showed that endorsement of pleasure, engagement, and meaning as paths to happiness all predicted life satisfaction. Similarly, Huta and Ryan [<xref rid=\"B6-ijerph-17-05547\" ref-type=\"bibr\">6</xref>] showed that hedonic and eudaimonic motives had both overlapping and distinct effects on well-being; hedonic motives related more to positive affect and life satisfaction, while eudaimonic motives related more to meaning. Overall, a combination of hedonic and eudaimonic motives was associated with the greatest well-being.</p><p>In sum, whether hedonic and eudaimonic motives lead to different well-being outcomes remains inadequately explored. Further, the factors that account for the potential distinct associations of hedonic and eudaimonic motives with well-being remain unclear.</p></sec><sec id=\"sec1dot2-ijerph-17-05547\"><title>1.2. Hedonic and Eudaimonic Motives and Self-Control</title><p>We argue that one factor that could account for the distinct associations of hedonic and eudaimonic motives with well-being is self-control. Self-control is defined as the ability to override one’s own inner responses or interrupt a course of action, especially when conflicting with ideals, values, and social expectations, and to support the fulfillment of long-term goals [<xref rid=\"B9-ijerph-17-05547\" ref-type=\"bibr\">9</xref>,<xref rid=\"B10-ijerph-17-05547\" ref-type=\"bibr\">10</xref>]. Huta [<xref rid=\"B11-ijerph-17-05547\" ref-type=\"bibr\">11</xref>] pointed out two crucial distinctions between hedonic and eudaimonic motives. First, hedonic motives are concerned with satiating one’s own needs and desires in the present or near future; in contrast, eudaimonic motives are concerned with fulfilling long-term objectives. The second difference is that hedonic motives are concerned with what feels good, whereas eudaimonic motives relate to what one believes to be right. According to the self-concordance model, individuals will strive for goals more persistently when they fit their core values [<xref rid=\"B12-ijerph-17-05547\" ref-type=\"bibr\">12</xref>,<xref rid=\"B13-ijerph-17-05547\" ref-type=\"bibr\">13</xref>]. Similarly, the goal theory of happiness suggests that individuals actively engage in behaviors that are in line with their identified goals, which, in turn, further influence their well-being [<xref rid=\"B14-ijerph-17-05547\" ref-type=\"bibr\">14</xref>,<xref rid=\"B15-ijerph-17-05547\" ref-type=\"bibr\">15</xref>]. Consequently, when faced with conflicts such as those between short-term desires and long-term objectives, individuals pursuing eudaimonia are more likely to implement a higher level of self-control to achieve their goals than are those pursuing hedonia.</p><p>Although empirical research on the relationships of hedonic and eudaimonic motives with self-control is sparse, two studies have provided some supporting evidence [<xref rid=\"B7-ijerph-17-05547\" ref-type=\"bibr\">7</xref>,<xref rid=\"B16-ijerph-17-05547\" ref-type=\"bibr\">16</xref>]. Peterson et al. [<xref rid=\"B16-ijerph-17-05547\" ref-type=\"bibr\">16</xref>] showed that endorsement of engagement and meaning as routes to happiness was positively related to self-regulation. More recently, Anic and Tončić [<xref rid=\"B7-ijerph-17-05547\" ref-type=\"bibr\">7</xref>] showed that individuals pursuing eudaimonia but not hedonia had a high level of self-control, whereas those pursuing hedonia but not eudaimonia had a relatively low level of self-control.</p></sec><sec id=\"sec1dot3-ijerph-17-05547\"><title>1.3. Self-Control and Well-Being</title><p>Studies have indicated that self-control positively predicts hedonic well-being [<xref rid=\"B17-ijerph-17-05547\" ref-type=\"bibr\">17</xref>,<xref rid=\"B18-ijerph-17-05547\" ref-type=\"bibr\">18</xref>,<xref rid=\"B19-ijerph-17-05547\" ref-type=\"bibr\">19</xref>,<xref rid=\"B20-ijerph-17-05547\" ref-type=\"bibr\">20</xref>,<xref rid=\"B21-ijerph-17-05547\" ref-type=\"bibr\">21</xref>]. For example, Briki [<xref rid=\"B18-ijerph-17-05547\" ref-type=\"bibr\">18</xref>] showed that self-control was positively related to happiness and life satisfaction. Hofmann et al. [<xref rid=\"B17-ijerph-17-05547\" ref-type=\"bibr\">17</xref>] found that self-control promoted affective experience and life satisfaction by managing goal conflict and facilitating the achievement of multiple goals. Similarly, Cheung et al. [<xref rid=\"B19-ijerph-17-05547\" ref-type=\"bibr\">19</xref>] demonstrated that people with higher self-control were happier because they were more promotion-focused and less prevention-focused. In addition, a lack of self-control has been linked to various problematic behaviors, such as procrastination, impulsive purchasing, addictive behavior, and even criminal offenses [<xref rid=\"B22-ijerph-17-05547\" ref-type=\"bibr\">22</xref>,<xref rid=\"B23-ijerph-17-05547\" ref-type=\"bibr\">23</xref>,<xref rid=\"B24-ijerph-17-05547\" ref-type=\"bibr\">24</xref>,<xref rid=\"B25-ijerph-17-05547\" ref-type=\"bibr\">25</xref>], which, in turn, might increase depression, stress, guilt, and other negative feelings [<xref rid=\"B15-ijerph-17-05547\" ref-type=\"bibr\">15</xref>,<xref rid=\"B26-ijerph-17-05547\" ref-type=\"bibr\">26</xref>,<xref rid=\"B27-ijerph-17-05547\" ref-type=\"bibr\">27</xref>].</p><p>Furthermore, it has been suggested that one approach to enhance eudaimonic well-being is satisfying basic psychological needs, including autonomy, competence, and relatedness [<xref rid=\"B28-ijerph-17-05547\" ref-type=\"bibr\">28</xref>]. Self-control itself is essential to the attainment of autonomy [<xref rid=\"B29-ijerph-17-05547\" ref-type=\"bibr\">29</xref>]. In addition, self-control has been found to be associated with higher achievement and better interpersonal relationships [<xref rid=\"B20-ijerph-17-05547\" ref-type=\"bibr\">20</xref>,<xref rid=\"B30-ijerph-17-05547\" ref-type=\"bibr\">30</xref>,<xref rid=\"B31-ijerph-17-05547\" ref-type=\"bibr\">31</xref>], which might satisfy the need for competence and relatedness. Consequently, self-control might also result in greater eudaimonic well-being.</p></sec><sec id=\"sec1dot4-ijerph-17-05547\"><title>1.4. The Current Study</title><p>The aim of this study is to identify the distinct associations between hedonic and eudaimonic motives and well-being outcomes, and further investigate the possible mediating role of self-control. For this purpose, we collected cross-sectional data on hedonic and eudaimonic motives, self-control, hedonic well-being (life satisfaction, positive and negative affect), and eudaimonic well-being. We propose the following hypotheses:</p><statement><label><bold>Hypothesis</bold> <bold>1.</bold></label><p>\n<italic>Self-control mediates the relationships between hedonic motives and well-being outcomes. Hedonic motives are detrimental to well-being outcomes by lowering the self-control of Chinese college students.</italic>\n</p></statement><statement><label><bold>Hypothesis</bold> <bold>2.</bold></label><p>\n<italic>Self-control mediates the relationships between eudaimonic motives and well-being outcomes. Eudaimonic motives promote well-being outcomes by increasing the self-control of Chinese college students.</italic>\n</p></statement></sec></sec><sec id=\"sec2-ijerph-17-05547\"><title>2. Materials and Methods</title><sec id=\"sec2dot1-ijerph-17-05547\" sec-type=\"methods\"><title>2.1. Sample and Procedures</title><p>In September 2019, we carried out a cross-sectional survey with a sample of 2882 students (mean age = 18.16, standard deviation = 0.44; 1047 males, 1835 females) from Zhejiang University of Finance and Economics. The participants were freshmen who had to complete a general psychological survey for course credits. The students completed the online survey while seated in individual cubicles. The teachers gave instructions and maintained discipline. All responses were included in the analyses.</p><p>The study design and data collection procedures were both approved by the Ethics Committee of Zhejiang University of Finance and Economics. All students provided written informed consent.</p></sec><sec id=\"sec2dot2-ijerph-17-05547\"><title>2.2. Measures</title><p>All the scales used in the current study were originally in English. Therefore, all the survey measures were translated from English to Chinese and back to English. When there were discrepancies between the original English version and the back-translated English version, those items were further modified after discussion by the two authors.</p><sec id=\"sec2dot2dot1-ijerph-17-05547\"><title>2.2.1. Hedonic and Eudaimonic Motives</title><p>To assess motives, we used the Hedonic and Eudaimonic Motives for Activities—Revised scale developed by Huta [<xref rid=\"B32-ijerph-17-05547\" ref-type=\"bibr\">32</xref>]. This instrument utilizes five items to assess hedonic motives (e.g., “seeking pleasure”) and five for eudaimonic motives (e.g., “seeking to use the best in yourself”). Respondents were asked to report the degrees of each motive with which they typically approached their activities on a seven-point Likert scale ranging from 1 (not at all) to 7 (very much). The Cronbach’s α values were 0.83 for hedonic motives and 0.79 for eudaimonic motives.</p></sec><sec id=\"sec2dot2dot2-ijerph-17-05547\"><title>2.2.2. Self-Control</title><p>To assess personal self-control, we used the 13-item Brief Self-Control Scale [<xref rid=\"B20-ijerph-17-05547\" ref-type=\"bibr\">20</xref>]. A sample item is “I am good at resisting temptation.” Respondents indicated the degree to which each of the statements reflected their typical behavior on a five-point Likert scale ranging from 1 (not at all) to 5 (very much). The Cronbach’s α value was 0.82.</p></sec><sec id=\"sec2dot2dot3-ijerph-17-05547\"><title>2.2.3. Well-Being Outcomes</title><p>To assess life satisfaction, we used the Satisfaction with Life Scale, which has five items (e.g., “In most ways my life is close to my ideal”) [<xref rid=\"B33-ijerph-17-05547\" ref-type=\"bibr\">33</xref>] rated on a seven-point Likert scale ranging from 1 (strongly disagree) to 7 (strongly agree). The Cronbach’s α value was 0.84.</p><p>Participants also reported their positive and negative affect in the past two weeks on the Positive and Negative Affect Schedule on a five-point Likert scale ranging from 1 (not at all) to 5 (very much) [<xref rid=\"B34-ijerph-17-05547\" ref-type=\"bibr\">34</xref>]. The Cronbach’s α values were 0.90 for both positive and negative affect.</p><p>Thereafter, they completed the Questionnaire for Eudaimonic Well-Being, which comprises 21 items assessing various aspects of eudaimonic well-being, including self-discovery, perceived development of one’s potential, meaning in life, and involvement and enjoyment in personally expressive activities (e.g., “I find I get intensely involved in many of the things I do each day”) [<xref rid=\"B35-ijerph-17-05547\" ref-type=\"bibr\">35</xref>]. Participants indicated their agreement with each item on a five-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree). The Cronbach’s α value was 0.81.</p></sec><sec id=\"sec2dot2dot4-ijerph-17-05547\"><title>2.2.4. Demographic Information</title><p>Demographic information included gender and age.</p></sec></sec><sec id=\"sec2dot3-ijerph-17-05547\"><title>2.3. Analyses</title><p>All tests were performed using SPSS 23.0 software (IBM Corp., Armonk, NY, USA). First, descriptive statistics were used to describe hedonic and eudaimonic motives, self-control, and well-being outcomes. Next, we conducted correlational analyses to investigate the general relationships between hedonic and eudaimonic motives, self-control, and well-being outcomes. Furthermore, we tested the hypothesized mediation effects of hedonic and eudaimonic motives on well-being outcomes via self-control using the PROCESS macro for SPSS (model 4) [<xref rid=\"B36-ijerph-17-05547\" ref-type=\"bibr\">36</xref>,<xref rid=\"B37-ijerph-17-05547\" ref-type=\"bibr\">37</xref>]. Hedonic and eudaimonic motives were entered into the models as independent variables, self-control was entered as the mediating variable, and gender was entered as the control variable. The four well-being outcome variables were separately entered as dependent variables; thus, the mediation model was run four times. We reported hierarchical regression results, indirect effects, and the 95% bias-corrected confidence intervals (CIs) estimated by 5000 bootstrap samples. The indirect effect was considered significant if the 95% CI did not contain zero.</p></sec></sec><sec sec-type=\"results\" id=\"sec3-ijerph-17-05547\"><title>3. Results</title><sec id=\"sec3dot1-ijerph-17-05547\"><title>3.1. Descriptive Analyses</title><p>Descriptive statistics related to hedonic and eudaimonic motives, self-control, and well-being outcomes are presented in <xref rid=\"ijerph-17-05547-t001\" ref-type=\"table\">Table 1</xref>. Bivariate correlations between the study variables are presented in <xref rid=\"ijerph-17-05547-t002\" ref-type=\"table\">Table 2</xref>. Higher hedonic motives were related to higher life satisfaction (<italic>r</italic> = 0.05, <italic>p</italic> = 0.004), but more negative affect (<italic>r</italic> = 0.15, <italic>p</italic> < 0.001) and worse eudaimonic well-being (<italic>r</italic> = −0.08, <italic>p</italic> < 0.001); there was no significant correlation between hedonic motives and positive affect (<italic>r</italic> = −0.01, <italic>p</italic> = 0.503). Higher eudaimonic motives were related to higher life satisfaction (<italic>r</italic> = 0.16, <italic>p</italic> < 0.001), more positive affect (<italic>r</italic> = 0.38, <italic>p</italic> < 0.001), less negative affect (<italic>r</italic> = −0.08, <italic>p</italic> = 0.001), and better eudaimonic well-being (<italic>r</italic> = 0.51, <italic>p</italic> < 0.001). Self-control was negatively related to hedonic motives (<italic>r</italic> = −0.25, <italic>p</italic> < 0.001) and positively related to eudaimonic motives (<italic>r</italic> = 0.26, <italic>p</italic> < 0.001). In addition, self-control was associated with all well-being outcomes (life satisfaction: <italic>r</italic> = 0.35, <italic>p</italic> < 0.001; positive affect: <italic>r</italic> = 0.38, <italic>p</italic> < 0.001; negative affect: <italic>r</italic> = −0.38, <italic>p</italic> < 0.001; and eudaimonic well-being: <italic>r</italic> = 0.50, <italic>p</italic> < 0.001).</p></sec><sec id=\"sec3dot2-ijerph-17-05547\"><title>3.2. Mediation Analyses</title><p>The regression analyses are shown in <xref rid=\"ijerph-17-05547-t003\" ref-type=\"table\">Table 3</xref>. As expected, after adjusting for gender, hedonic motives were negatively correlated with self-control (<italic>ß</italic> = −0.29, <italic>p</italic> < 0.001), while eudaimonic motives were positively correlated with self-control (<italic>ß</italic> = 0.30, <italic>p</italic> < 0.001). In addition, after controlling for hedonic and eudaimonic motives, self-control demonstrated significant positive associations with life satisfaction (<italic>ß</italic> = 0.37, <italic>p</italic> < 0.001), positive affect (<italic>ß</italic> = 0.30, <italic>p</italic> < 0.001), and eudaimonic well-being (<italic>ß</italic> = 0.37, <italic>p</italic> < 0.001) and a significant negative association with negative affect (<italic>ß</italic> = −0.37, <italic>p</italic> < 0.001). Whether gender was included as a control variable or not, the main results did not change.</p><p>The estimated direct and indirect effects between hedonic and eudaimonic motives and the four well-being outcomes are presented in <xref rid=\"ijerph-17-05547-t004\" ref-type=\"table\">Table 4</xref>. The indirect effects of hedonic motives on life satisfaction (indirect effect = −0.106, CI: (−0.123, −0.089)), positive affect (indirect effect = −0.054, CI: (−0.064, −0.045)), and eudaimonic well-being (indirect effect = −0.039, CI: (−0.045, −0.033)) via self-control were all negative, and the indirect effect of hedonic motives on negative affect (indirect effect = 0.067, CI: (0.056, 0.078)) via self-control was positive. In contrast, the indirect effects of eudaimonic motives on life satisfaction (indirect effect = 0.134, CI: (0.113, 0.156)), positive affect (indirect effect = 0.068, CI: (0.057, 0.081)), and eudaimonic well-being (indirect effect = 0.049, CI: (0.043, 0.056)) via self-control were all positive, and the indirect effect of eudaimonic motives on negative affect (indirect effect = −0.084, CI: (−0.098, −0.072)) via self-control was negative. This is consistent with the argument that hedonic motives could be detrimental to well-being outcomes by decreasing self-control, whereas eudaimonic motives promote well-being outcomes by increasing self-control.</p></sec></sec><sec sec-type=\"discussion\" id=\"sec4-ijerph-17-05547\"><title>4. Discussion</title><sec id=\"sec4dot1-ijerph-17-05547\"><title>4.1. Findings and Implications</title><p>In this study, we examined the associations of hedonic and eudaimonic motives with well-being among Chinese college students. The current data demonstrated that, overall, eudaimonic motives were more positively associated with well-being than hedonic motives, which is consistent with previous studies [<xref rid=\"B3-ijerph-17-05547\" ref-type=\"bibr\">3</xref>]. Specifically, eudaimonic motives were found to be associated with improvements in all well-being outcomes, including higher life satisfaction, more positive affect, less negative affect, and better eudaimonic well-being. In contrast, the correlations of hedonic motives were more complex; while they were positively related to life satisfaction, they were not associated with positive affect and were even associated with an increase in negative affect. In addition, the pursuit of hedonia seemed to be detrimental to eudaimonic well-being.</p><p>More importantly, we investigated the mediating role of self-control, which shed light on the distinct associations of hedonic and eudaimonic motives with well-being. The results revealed that hedonic motives were detrimental to self-control, whereas eudaimonic motives could potentially enhance self-control, which is in accordance with previous results [<xref rid=\"B7-ijerph-17-05547\" ref-type=\"bibr\">7</xref>]. In addition, higher self-control was related to higher life satisfaction, more positive affect, less negative affect, and better eudaimonic well-being. In other words, eudaimonic motives generally promote well-being by enhancing self-control, whereas hedonic motives might be harmful by lowering self-control.</p><p>These findings highlight the potential detrimental effects of hedonic motives on well-being, which have been largely neglected in previous studies. Recent studies have shown that hedonic motives include both enjoyment and comfort [<xref rid=\"B38-ijerph-17-05547\" ref-type=\"bibr\">38</xref>,<xref rid=\"B39-ijerph-17-05547\" ref-type=\"bibr\">39</xref>,<xref rid=\"B40-ijerph-17-05547\" ref-type=\"bibr\">40</xref>], but only enjoyment motives might promote well-being [<xref rid=\"B38-ijerph-17-05547\" ref-type=\"bibr\">38</xref>]. Similarly, Huta [<xref rid=\"B11-ijerph-17-05547\" ref-type=\"bibr\">11</xref>] has suggested that while healthful approaches to hedonia would bring pleasure and enjoyment, excessive or unbalanced hedonic motives can have undesirable consequences. Combining previous findings with the current results, it can be suggested that the effects of hedonic motives on well-being are twofold. On the one hand, hedonic motives might encourage people to engage in entertaining activities, which can lead to improved well-being. On the other hand, hedonic motives could make people yield to temptation more easily, which might lead to dysfunctional behaviors such as addiction and procrastination, and further worsen well-being. Consequently, it is worthwhile to further explore functional and dysfunctional hedonic motives and hedonic behaviors.</p></sec><sec id=\"sec4dot2-ijerph-17-05547\"><title>4.2. Limitations and Future Research Directions</title><p>The present study has a number of limitations. First, we failed to implement an attention check, due to some accidental technical issues. Nevertheless, all scales achieved sufficient reliabilities. The distinct associations of hedonic and eudaimonic motives with well-being were unlikely to appear by chance. Consequently, we believe the results of the current study were reliable.</p><p>Second, the current data are correlational rather than causal. Future research should consider using longitudinal data to further determine the causal relationships between hedonic and eudaimonic motives, self-control, and well-being.</p><p>Third, the Questionnaire for Eudaimonic Well-Being is primarily a measure of functioning, whereas the other outcomes are all measures of experience. Further studies should use more suitable measures that represent eudaimonic experiences (e.g., a feeling of meaning) to replicate our findings.</p><p>Fourth, this paper only examines the mediating role of self-control in the relationships between hedonic and eudaimonic motives and well-being outcomes. However, as mentioned above, hedonic motives could encourage both healthful hedonic activities and dysfunctional behaviors. Investigating these factors simultaneously might facilitate a more comprehensive understanding of the effects of hedonic motives on well-being.</p><p>Last, our sample comprised solely Chinese college students. Prior research has suggested that cultural factors might moderate the relationships between hedonic and eudaimonic motives and well-being [<xref rid=\"B41-ijerph-17-05547\" ref-type=\"bibr\">41</xref>]. A future study direction would be exploring how and why the pursuit of happiness, especially hedonia, might affect well-being differently across cultures.</p></sec></sec><sec sec-type=\"conclusions\" id=\"sec5-ijerph-17-05547\"><title>5. Conclusions</title><p>Despite the limitations mentioned above, the present study contributes to an improved understanding of the associations of hedonic and eudaimonic motives with well-being outcomes. In sum, hedonic motives could be detrimental to well-being by harming self-control; in contrast, eudaimonic motives can contribute to better well-being by improving self-control. These results suggest that the pursuit of eudaimonia is more likely to further the goal of leading a good life than the pursuit of hedonia.</p></sec></body><back><ack><title>Acknowledgments</title><p>We would like to thank all the participants for attending the current research.</p></ack><notes><title>Author Contributions</title><p>Conceptualization, Z.Z. and H.C.; methodology, Z.Z.; formal analysis, Z.Z.; writing—original draft preparation, Z.Z.; writing—review and editing, H.C.; supervision, H.C. 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Gen.</source><year>2015</year><volume>144</volume><fpage>1053</fpage><pub-id pub-id-type=\"doi\">10.1037/xge0000108</pub-id><pub-id pub-id-type=\"pmid\">26347945</pub-id></element-citation></ref></ref-list></back><floats-group><table-wrap id=\"ijerph-17-05547-t001\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05547-t001_Table 1</object-id><label>Table 1</label><caption><p>Descriptive statistics for study variables.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Variables</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Minimum</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Maximum</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Mean</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">SD</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Hedonic motives</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.00</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7.00</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.63</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.11</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Eudaimonic motives</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.40</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7.00</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.62</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.91</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Self-control</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.15</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.92</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.98</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.59</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Life satisfaction</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.00</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7.00</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.17</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.09</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Positive affect</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.00</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.00</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.35</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.69</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Negative affect</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.00</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.00</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.10</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.69</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Eudaimonic well-being</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2.24</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">5.00</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3.65</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.40</td></tr></tbody></table></table-wrap><table-wrap id=\"ijerph-17-05547-t002\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05547-t002_Table 2</object-id><label>Table 2</label><caption><p>Bivariate correlations between study variables.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">TitleStudy Variables</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">4</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">5</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">6</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">7</th></tr></thead><tbody><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1. Hedonic motives</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2. Eudaimonic motives</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.15 ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3. Self-control</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.25 ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.26 ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4. Life satisfaction</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.05 **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.16 ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.35 ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5. Positive affect</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.01</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.38 ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.38 ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.46 ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6. Negative affect</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.15 ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.08 **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.38 ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.19 ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.05 **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">7. Eudaimonic well-being</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.08 ***</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.51 ***</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.50 ***</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.40 ***</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.53 ***</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.32 ***</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">-</td></tr></tbody></table><table-wrap-foot><fn><p>Note: ** <italic>p</italic> < 0.01, *** <italic>p</italic> < 0.001.</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"ijerph-17-05547-t003\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05547-t003_Table 3</object-id><label>Table 3</label><caption><p>Regression results.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Models</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>B</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>ß</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>p</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>R</italic>\n<sup>2</sup>\n</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Dependent variable: Self-control</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.15</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Gender (female = 0, male = 1)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.04</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.03</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.090</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Hedonic motives</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.16</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.29</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.001</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Eudaimonic motives</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.20</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.30</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><0.001</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Depedent variable: Life satisfaction</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.15</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Gender (female = 0, male = 1)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.13</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.06</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.001</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Hedonic motives</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.13</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.13</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.001</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Eudaimonic motives</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.06</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.05</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.009</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Self-control</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.68</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.37</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.001</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-top:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>Dependent variable: Positive affect</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-top:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-top:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-top:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-top:solid thin\" rowspan=\"1\" colspan=\"1\">0.24</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Gender (female = 0, male = 1)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.20</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.14</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.001</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Hedonic motives</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.00</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.00</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.824</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Eudaimonic motives</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.23</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.30</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.001</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Self-control</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.35</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.30</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><0.001</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Dependent variable: Negative affect</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.15</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Gender (female = 0, male = 1)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.02</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.02</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.379</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Hedonic motives</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.03</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.06</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.003</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Eudaimonic motives</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.00</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.00</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.931</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Self-control</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.43</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.37</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><0.001</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Dependent variable: Eudaimonic well-being</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.41</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Gender (female = 0, male = 1)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.04</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.05</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.002</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Hedonic motives</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.02</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.06</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.001</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Eudaimonic motives</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.19</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.43</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.001</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Self-control</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.25</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.37</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><0.001</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td></tr></tbody></table></table-wrap><table-wrap id=\"ijerph-17-05547-t004\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05547-t004_Table 4</object-id><label>Table 4</label><caption><p>Direct and indirect effects and 95% confidence intervals.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">Outcome Variables</th><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Hedonic Motives</th><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Eudaimonic Motives</th></tr><tr><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Direct Effects</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Indirect Effects</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Direct Effects</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Indirect Effects</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Life satisfaction</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.130 (0.095, 0.165)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.106 (−0.123, −0.089)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.057 (0.014, 0.101)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.134 (0.113, 0.156)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Positive affect</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.002 (−0.019, 0.023)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.054 (−0.064, −0.045)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.226 [0.200, 0.252)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.068 [0.057, 0.081)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Negative affect</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.034 (0.012, 0.057)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.067 (0.056, 0.078)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.001 [−0.026, 0.029)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.084 [−0.098, −0.072)</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Eudaimonic well-being</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.022 (−0.032, −0.011)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.039 (−0.045, −0.033)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.187 [0.174, 0.200)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.049 [0.043, 0.056)</td></tr></tbody></table></table-wrap></floats-group></article>\n"
]
[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">JMIR Form Res</journal-id><journal-id journal-id-type=\"publisher-id\">JFR</journal-id><journal-title-group><journal-title>JMIR Formative Research</journal-title></journal-title-group><issn pub-type=\"epub\">2561-326X</issn><publisher><publisher-name>JMIR Publications</publisher-name><publisher-loc>Toronto, Canada</publisher-loc></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32744509</article-id><article-id pub-id-type=\"pmc\">PMC7432149</article-id><article-id pub-id-type=\"publisher-id\">v4i8e18223</article-id><article-id pub-id-type=\"doi\">10.2196/18223</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Original Paper</subject></subj-group><subj-group subj-group-type=\"article-type\"><subject>Original Paper</subject></subj-group></article-categories><title-group><article-title>Shared Decision Making and Patient-Centered Care in Israel, Jordan, and the United States: Exploratory and Comparative Survey Study of Physician Perceptions</article-title></title-group><contrib-group><contrib contrib-type=\"editor\"><name><surname>Eysenbach</surname><given-names>Gunther</given-names></name></contrib></contrib-group><contrib-group><contrib contrib-type=\"reviewer\"><name><surname>Diouf</surname><given-names>Ndeye</given-names></name></contrib><contrib contrib-type=\"reviewer\"><name><surname>Wieringa</surname><given-names>Thomas</given-names></name></contrib><contrib contrib-type=\"reviewer\"><name><surname>Galasinski</surname><given-names>Dariusz</given-names></name></contrib></contrib-group><contrib-group><contrib id=\"contrib1\" contrib-type=\"author\" corresp=\"yes\"><name><surname>Zisman-Ilani</surname><given-names>Yaara</given-names></name><degrees>MA, PhD</degrees><contrib-id contrib-id-type=\"orcid\">https://orcid.org/0000-0001-6852-2583</contrib-id><xref ref-type=\"aff\" rid=\"aff1\">1</xref><address><institution>Department of Social and Behavioral Sciences</institution><institution>College of Public Health</institution><institution>Temple University</institution><addr-line>1700 North Broad St</addr-line><addr-line>Philadelphia, PA, 19122</addr-line><country>United States</country><phone>1 215 204 5618</phone><email>[email protected]</email></address></contrib><contrib id=\"contrib2\" contrib-type=\"author\"><name><surname>Obeidat</surname><given-names>Rana</given-names></name><degrees>PhD</degrees><xref ref-type=\"aff\" rid=\"aff2\">2</xref><contrib-id contrib-id-type=\"orcid\">https://orcid.org/0000-0002-6783-0813</contrib-id></contrib><contrib id=\"contrib3\" contrib-type=\"author\"><name><surname>Fang</surname><given-names>Lauren</given-names></name><degrees>BSc</degrees><xref ref-type=\"aff\" rid=\"aff3\">3</xref><contrib-id contrib-id-type=\"orcid\">https://orcid.org/0000-0001-5270-2416</contrib-id></contrib><contrib id=\"contrib4\" contrib-type=\"author\"><name><surname>Hsieh</surname><given-names>Sarah</given-names></name><degrees>BSc</degrees><xref ref-type=\"aff\" rid=\"aff3\">3</xref><contrib-id contrib-id-type=\"orcid\">https://orcid.org/0000-0001-8236-5078</contrib-id></contrib><contrib id=\"contrib5\" contrib-type=\"author\"><name><surname>Berger</surname><given-names>Zackary</given-names></name><degrees>MD, PhD</degrees><xref ref-type=\"aff\" rid=\"aff3\">3</xref><contrib-id contrib-id-type=\"orcid\">https://orcid.org/0000-0002-5871-0342</contrib-id></contrib></contrib-group><aff id=\"aff1\">\n<label>1</label>\n<institution>Department of Social and Behavioral Sciences</institution>\n<institution>College of Public Health</institution>\n<institution>Temple University</institution>\n<addr-line>Philadelphia, PA</addr-line>\n<country>United States</country>\n</aff><aff id=\"aff2\">\n<label>2</label>\n<institution>Faculty of Nursing</institution>\n<institution>Zarqa University</institution>\n<addr-line>Zarqa</addr-line>\n<country>Jordan</country>\n</aff><aff id=\"aff3\">\n<label>3</label>\n<institution>Johns Hopkins School of Medicine</institution>\n<addr-line>Baltimore, MD</addr-line>\n<country>United States</country>\n</aff><author-notes><corresp>Corresponding Author: Yaara Zisman-Ilani <email>[email protected]</email></corresp></author-notes><pub-date pub-type=\"collection\"><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"epub\"><day>3</day><month>8</month><year>2020</year></pub-date><volume>4</volume><issue>8</issue><elocation-id>e18223</elocation-id><history><date date-type=\"received\"><day>12</day><month>2</month><year>2020</year></date><date date-type=\"rev-request\"><day>23</day><month>3</month><year>2020</year></date><date date-type=\"rev-recd\"><day>21</day><month>4</month><year>2020</year></date><date date-type=\"accepted\"><day>13</day><month>5</month><year>2020</year></date></history><permissions><copyright-statement>©Yaara Zisman-Ilani, Rana Obeidat, Lauren Fang, Sarah Hsieh, Zackary Berger. Originally published in JMIR Formative Research (http://formative.jmir.org), 03.08.2020.</copyright-statement><copyright-year>2020</copyright-year><license license-type=\"open-access\" xlink:href=\"https://creativecommons.org/licenses/by/4.0/\"><license-p><!--CREATIVE COMMONS-->This is an open-access article distributed under the terms of the Creative Commons Attribution License (<ext-link ext-link-type=\"uri\" xlink:href=\"https://creativecommons.org/licenses/by/4.0/\">https://creativecommons.org/licenses/by/4.0/</ext-link>), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Formative Research, is properly cited. The complete bibliographic information, a link to the original publication on <ext-link ext-link-type=\"uri\" xlink:href=\"http://formative.jmir.org\">http://formative.jmir.org</ext-link>, as well as this copyright and license information must be included.</license-p></license></permissions><self-uri xlink:type=\"simple\" xlink:href=\"https://formative.jmir.org/2020/8/e18223\"/><abstract><sec sec-type=\"background\"><title>Background</title><p>Shared decision making (SDM) is a health communication model that evolved in Europe and North America and largely reflects the values and medical practices dominant in these areas.</p></sec><sec sec-type=\"objective\"><title>Objective</title><p>This study aims to understand the beliefs, perceptions, and practices related to SDM and patient-centered care (PCC) of physicians in Israel, Jordan, and the United States.</p></sec><sec sec-type=\"methods\"><title>Methods</title><p>A hypothesis-generating comparative survey study was administered to physicians from Israel, Jordan, and the United States.</p></sec><sec sec-type=\"results\"><title>Results</title><p>A total of 36 surveys were collected via snowball sampling (Jordan: n=15; United States: n=12; Israel: n=9). SDM was perceived as a way to inform patients and allow them to participate in their care. Barriers to implementing SDM varied based on place of origin; physicians in the United States mentioned limited time, physicians in Jordan reported that a lack of patient education limits SDM practices, and physicians in Israel reported lack of communication training. Most US physicians defined PCC as a practice for prioritizing patient preferences, whereas both Jordanian and Israeli physicians defined PCC as a holistic approach to care and to prioritizing patient needs. Barriers to implementing PCC, as seen by US physicians, were mostly centered on limited appointment time and insurance coverage. In Jordan and Israel, staff shortage and a lack of resources in the system were seen as major barriers to PCC implementation.</p></sec><sec sec-type=\"conclusions\"><title>Conclusions</title><p>The study adds to the limited, yet important, literature on SDM and PCC in areas of the world outside the United States, Canada, Australia, and Western Europe. The study suggests that perceptions of PCC might widely differ among these regions, whereas concepts of SDM might be shared. Future work should clarify these differences.</p></sec></abstract><kwd-group><kwd>shared decision making</kwd><kwd>patient-centered care</kwd><kwd>Middle East</kwd><kwd>physicians</kwd><kwd>perceptions</kwd></kwd-group></article-meta></front><body><sec sec-type=\"introduction\"><title>Introduction</title><p>Shared decision making (SDM) is a central health communication model for supporting patient engagement in health care [<xref rid=\"ref1\" ref-type=\"bibr\">1</xref>-<xref rid=\"ref3\" ref-type=\"bibr\">3</xref>] and a recommended approach to increasing patient engagement and patient-centered care (PCC) in clinical decision making [<xref rid=\"ref4\" ref-type=\"bibr\">4</xref>-<xref rid=\"ref6\" ref-type=\"bibr\">6</xref>]. SDM evolved in Europe and North America [<xref rid=\"ref7\" ref-type=\"bibr\">7</xref>] and largely reflects the values and medical practices dominant in these areas [<xref rid=\"ref8\" ref-type=\"bibr\">8</xref>,<xref rid=\"ref9\" ref-type=\"bibr\">9</xref>]. Although SDM has become more widely discussed in recent years in non-Western countries (eg, China, Peru, Malaysia, Taiwan, Iran) [<xref rid=\"ref10\" ref-type=\"bibr\">10</xref>], it has yet to be implemented on a wider scale, and less is known about how or whether attitudes, beliefs, and practices regarding PCC exist or differ in various other regions of the world.</p><p>The overall aim of the present exploratory study was to explore the factors that enable or impede SDM implementation in different geographical and political contexts. Specifically, we sought to conduct a hypothesis-generating study and to collect preliminary data to better understand SDM- and PCC-related beliefs, attitudes, and practices of physicians in four regions in the Middle East characterized by different health care systems, cultures, and political environments: Israel, Jordan, and the West Bank. As a point of reference, we conducted a similar survey among US physicians to serve as a benchmark for SDM- and PCC-related beliefs, attitudes, and practices. In addition, such a comparison may provide insights about the importance of a health care system that facilitates the practice of SDM and PCC. The study focused on the following specific questions: (1) What are physicians’ common understandings and perceptions of the concepts of SDM and PCC? and (2) Do physicians find SDM and PCC to be feasible in their practice and in health care?</p></sec><sec sec-type=\"methods\"><title>Methods</title><sec><title>Settings: Context of Participating Countries</title><p>The survey was intended to be administered to physicians from different geographical and political contexts in the Middle East characterized by different health care systems: Israel, Jordan, and the West Bank. Israel is a democratic state with an efficient health care system that has been ranked among the top 10 health care systems for several years [<xref rid=\"ref11\" ref-type=\"bibr\">11</xref>,<xref rid=\"ref12\" ref-type=\"bibr\">12</xref>]. Israel’s national health insurance system provides universal health coverage throughout the country, with a significant spread of hospital and clinics [<xref rid=\"ref13\" ref-type=\"bibr\">13</xref>-<xref rid=\"ref15\" ref-type=\"bibr\">15</xref>]. Residents can supplement the universal coverage with additional forms of private health insurance. Israel’s health policy legislation is supportive of SDM principles, including the right to be informed of treatment options and risks [<xref rid=\"ref16\" ref-type=\"bibr\">16</xref>]. Jordan is a constitutional monarchy state with a health care system characterized by diverse types of payers (public, private, and donors) [<xref rid=\"ref17\" ref-type=\"bibr\">17</xref>]. The public health care sector is the largest in Jordan; however, only about 70% of residents have some form of public health insurance. Jordan has a ratio of 2.3 physicians to 1000 residents, and hospitals are mostly centralized in the larger urban areas. The massive influx of Syrian refugees due to the start of the Arab Spring in 2011 has further increased burdens on the Jordanian health care system, especially on public health facilities. The West Bank is an independent Palestinian territory governed by the Palestinian National Authority. The West Bank has low-functioning, inefficient health care systems that rely heavily on medical services to and referrals of patients to Israel (14% in 2011) or Jordan (13% in 2011) [<xref rid=\"ref17\" ref-type=\"bibr\">17</xref>,<xref rid=\"ref18\" ref-type=\"bibr\">18</xref>].</p><p>A parallel survey was planned among US physicians to serve as a benchmark. The United States is a representative democracy with a hybrid health care system but without universal health care coverage. In 2016, 48% of US health care spending came from private funds, with 28% coming from households and 20% coming from private businesses. The federal government accounted for 28% of spending, while state and local governments accounted for 17% of spending [<xref rid=\"ref19\" ref-type=\"bibr\">19</xref>]. SDM in the United States is increasingly recognized as part of value-based care, and several federal initiatives have linked SDM to reimbursement [<xref rid=\"ref20\" ref-type=\"bibr\">20</xref>].</p></sec><sec><title>Survey Development and Structure</title><p>Because the purpose of the present study was to explore aspects of SDM and PCC and to provide data for testing hypotheses, we chose to develop a survey as a research strategy [<xref rid=\"ref20\" ref-type=\"bibr\">20</xref>]. As recommended by Enhancing the Quality and Transparency of Health Research (EQUATOR), we used “Good Practice in the Conduct and Reporting of Survey Research” as a reporting guideline [<xref rid=\"ref20\" ref-type=\"bibr\">20</xref>]. We developed a short standardized survey form with 24 questions divided into 3 parts (see <xref ref-type=\"supplementary-material\" rid=\"app1\">Multimedia Appendix 1</xref>): (1) demographic data, (2) qualitative evaluation, and (3) quantitative evaluation.</p><p>First, demographic data (eg, age and years in practice) was assessed with 9 questions.</p><p>Second, a qualitative evaluation was included. One part of this evaluation assessed the beliefs and attitudes of physicians around PCC and SDM in their health care setting (eg, “What do you see as barriers to implementing shared decision making in your practice?”). This comprised 5 open-ended questions and 1 multiple choice question. The second part of the qualitative evaluation assessed the understanding of physicians of their immediate environment of practice in the context of their larger health care system (eg, “What are the most important day-to-day problems in the practice of medicine or health care in your country or region?”). This included 3 open-ended questions.</p><p>Third, the quantitative evaluation of level of SDM practice was based on the Shared Decision Making Questionnaire, physician version (SDM-Q-DOC) scale [<xref rid=\"ref21\" ref-type=\"bibr\">21</xref>]. This included 9 questions on a 6-point Likert scale ranging from “completely disagree” (0) to “completely agree” (5). The SDM-Q-DOC was developed to measure patients’ and clinicians’ agreement with steps and actions defined by a medically driven SDM model at the end of a medical consultation. It has been used in numerous studies to measure physicians’ perspectives of SDM and was recommended for use in health policy survey responses targeting the implementation of SDM [<xref rid=\"ref22\" ref-type=\"bibr\">22</xref>-<xref rid=\"ref24\" ref-type=\"bibr\">24</xref>]</p><p>The qualitative part of the survey was developed through an iterative process based on SDM and PCC literature related to the delivery and perception of care and on the lead investigators’ (YZI and ZB) knowledge [<xref rid=\"ref25\" ref-type=\"bibr\">25</xref>-<xref rid=\"ref30\" ref-type=\"bibr\">30</xref>]. The development process included discussions among the coinvestigators and piloting among colleagues. We conducted forward and backward translations based on accepted guidelines [<xref rid=\"ref31\" ref-type=\"bibr\">31</xref>] to each new question in sections 1 and 2. We used the English version of the SDM-Q-DOC questionnaire [<xref rid=\"ref21\" ref-type=\"bibr\">21</xref>] and the Arabic [<xref rid=\"ref32\" ref-type=\"bibr\">32</xref>] and Hebrew [<xref rid=\"ref33\" ref-type=\"bibr\">33</xref>] translations of the 9-item Shared Decision Making Questionnaire (SDM-Q-9), a parallel patient version [<xref rid=\"ref34\" ref-type=\"bibr\">34</xref>], with the needed minor adaptions.</p></sec><sec><title>Procedure</title><p>A web-based survey developed for the study was emailed to physicians in Israel, the West Bank, and the United States using the Qualtrics platform (Qualtrics International Inc). In Jordan it was advised by one of the coauthors (RO) to administer the survey via face-to-face interviews, based on her previous experience conducting similar types of research in Jordan. A snowball sampling methodology was used to recruit physicians. Accordingly, participants were asked to identify and email the questionnaire to other colleagues. Surveys were administered in Hebrew, English, and Arabic, and all responses were anonymized. Data collection began in February 2017 and ended in June 2017. It was designed to stop after a sample of 15 in each country or after the maximum sample size closest to this threshold. As this was an exploratory study, this sample size target was based not on statistical considerations but on real-world experience with the number of respondents likely to provide a hypothesis-generating set of responses. At the end of the survey, participants were reimbursed via gift cards in the amount of US $10 or an equal value in the local currency.</p></sec><sec><title>Data Analysis</title><p>To summarize the qualitative results from the open-ended survey questions, we used an integrated approach that enabled both inductive (ie, data-driven) coding of participants’ responses and deductive (ie, theory-driven) framework organization of codes [<xref rid=\"ref35\" ref-type=\"bibr\">35</xref>]. Specifically, 2 coauthors (SH and ZB) and another research assistant read open-ended responses from participants and developed a draft of coding categories based on the responses’ contents. These categories were reviewed by the lead author (YZI) and revised accordingly. Responses to open-ended questions were coded independently by all coauthors; differences and disagreements between the coders were resolved through discussions until consensus was achieved. The final coding of open-ended responses was double-checked for accuracy by the first and last authors (YZI and ZB) after finalization of the coding guide. Then, guided by SDM and PCC theories, all coauthors discussed the interrelationships between codes to finalize the grouping of the codes into themes and subthemes.</p><p>Chi-square and one-way ANOVA tests were conducted to describe demographic characteristics of the survey respondents. As recommended by the developers [<xref rid=\"ref21\" ref-type=\"bibr\">21</xref>], multiplication of the raw score by 20/9 provided a transformed total score range from 0 to 100, where 0 indicates the lowest possible level and 100 indicates the highest possible level of SDM. A nonparametric Kruskal-Wallis test was conducted to compare the mean total score of SDM-Q-9 between the 3 countries. Results were considered significant below a <italic>P</italic> value of .05.</p></sec><sec><title>Ethics, Consent, and Permissions</title><p>Because the responses were anonymized and not identifiable and participation in the study was associated with minimal risk, the Institutional Review Board of the Johns Hopkins School of Medicine deemed this study exempt from requirements for approval (IRB00111847). All participants provided consent to participate through their responses to the survey.</p></sec></sec><sec sec-type=\"results\"><title>Results</title><sec><title>Participants</title><p>Eligible survey respondents were practicing physicians in the United States, Israel, Jordan, and the West Bank. A total of 36 survey responses were received from Israel (n=9), Jordan (n=15), and the United States (n=12). Throughout the study period, we received no responses from West Bank physicians to our emails or to our in-person attempts to contact them; therefore, we were unable to collect any data from that population. Most survey respondents were men (24/36, 67%), the mean age of survey respondents was 43.6 years (SD 11.2), and the mean years of clinical experience was 15.8 (SD 10.5). Comparison of demographic characteristics and clinical experience of clinicians in each country indicate similarity between US and Israeli respondents for gender distribution, age, and clinical experience (<xref rid=\"table1\" ref-type=\"table\">Table 1</xref>). The Jordanian respondents were significantly younger and less experienced, and almost all were men.</p><table-wrap id=\"table1\" position=\"float\"><label>Table 1</label><caption><p>Demographic and clinical experience characteristics of survey respondents.</p></caption><table frame=\"hsides\" rules=\"groups\" width=\"1000\" cellpadding=\"5\" cellspacing=\"0\" border=\"1\"><col width=\"30\" span=\"1\"/><col width=\"180\" span=\"1\"/><col width=\"0\" span=\"1\"/><col width=\"180\" span=\"1\"/><col width=\"0\" span=\"1\"/><col width=\"170\" span=\"1\"/><col width=\"0\" span=\"1\"/><col width=\"170\" span=\"1\"/><col width=\"0\" span=\"1\"/><col width=\"170\" span=\"1\"/><col width=\"0\" span=\"1\"/><col width=\"100\" span=\"1\"/><thead><tr valign=\"top\"><td colspan=\"3\" rowspan=\"1\">Characteristics</td><td colspan=\"2\" rowspan=\"1\">Total sample <break/>\n(N=36)</td><td colspan=\"2\" rowspan=\"1\">United States <break/>\n(n=12)</td><td colspan=\"2\" rowspan=\"1\">Israel <break/>\n(n=9)</td><td colspan=\"2\" rowspan=\"1\">Jordan <break/>\n(n=15)</td><td rowspan=\"1\" colspan=\"1\"><italic>P</italic> value</td></tr></thead><tbody><tr valign=\"top\"><td colspan=\"3\" rowspan=\"1\">\n<bold>Gender</bold>\n</td><td colspan=\"2\" rowspan=\"1\"/><td colspan=\"2\" rowspan=\"1\">\n<break/>\n</td><td colspan=\"2\" rowspan=\"1\">\n<break/>\n</td><td colspan=\"2\" rowspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">.01<sup>a</sup></td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">Men, n (%)</td><td colspan=\"2\" rowspan=\"1\">5 (42)</td><td colspan=\"2\" rowspan=\"1\">24 (67)</td><td colspan=\"2\" rowspan=\"1\">5 (56)</td><td colspan=\"2\" rowspan=\"1\">14 (93)</td><td colspan=\"2\" rowspan=\"1\">—<sup>b</sup>\n<break/>\n</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">Women, n (%)</td><td colspan=\"2\" rowspan=\"1\">7 (58)</td><td colspan=\"2\" rowspan=\"1\">12 (33)</td><td colspan=\"2\" rowspan=\"1\">4 (44)</td><td colspan=\"2\" rowspan=\"1\">1 (7)</td><td colspan=\"2\" rowspan=\"1\">—\n<break/>\n</td></tr><tr valign=\"top\"><td colspan=\"3\" rowspan=\"1\">Age (years), mean (SD)</td><td colspan=\"2\" rowspan=\"1\">43.6 (11.2)</td><td colspan=\"2\" rowspan=\"1\">47.0 (8.6)</td><td colspan=\"2\" rowspan=\"1\">48.7 (13.4)</td><td colspan=\"2\" rowspan=\"1\">37.4 (9.2)</td><td rowspan=\"1\" colspan=\"1\">.02<sup>c</sup></td></tr><tr valign=\"top\"><td colspan=\"3\" rowspan=\"1\">Years of clinical experience, mean (SD)</td><td colspan=\"2\" rowspan=\"1\">15.8 (10.5)</td><td colspan=\"2\" rowspan=\"1\">19.0 (9.8)</td><td colspan=\"2\" rowspan=\"1\">20.0 (14.2)</td><td colspan=\"2\" rowspan=\"1\">10.8 (6.0)</td><td rowspan=\"1\" colspan=\"1\">.05<sup>d</sup></td></tr></tbody></table><table-wrap-foot><fn id=\"table1fn1\"><p><sup>a</sup>Pearson <italic>χ<sup>2</sup></italic><sub>2</sub>=8.7.</p></fn><fn id=\"table1fn2\"><p><sup>b</sup>Not applicable.</p></fn><fn id=\"table1fn3\"><p><sup>c</sup>Analysis of variance <italic>F</italic> test (<italic>F</italic><sub>2,32</sub>=4.40).</p></fn><fn id=\"table1fn4\"><p><sup>d</sup>Analysis of variance <italic>F</italic> test (<italic>F</italic><sub>2,33</sub>=3.37).</p></fn></table-wrap-foot></table-wrap></sec><sec><title>Open-Ended Responses: Perception of SDM and PCC</title><p>We included in the analysis 34 survey responses with greater than 50% total completion (Jordan: n=15; United States: n=12; Israel: n=7). There were 12 responses to the open-ended questions from the US physicians, 7 from the Israeli physicians, and 14 from the Jordanian physicians.</p><p>Most respondents defined SDM as a process aimed at informing patients (United States: 8/12, 67%; Israel: 4/7, 57%; Jordan: 12/15, 80%). Whereas most US respondents also defined SDM as the participation of patients in their care (8/12, 67%), only a third of the Jordanian respondents defined it as patient participation (5/15, 33%), and 4 of the 7 (57%) Israeli respondents defined SDM also as collaboration between patient and physician.</p><disp-quote><p>[SDM is when] a patient makes decisions about medical tests and treatment that incorporate information about benefits and harms from the physician as well as the patient’s own understanding of his or her values and priorities.</p><attrib>US respondent</attrib></disp-quote><disp-quote><p>[SDM is] an open conversation with the patient, in which I [the doctor] suggest/advise a variety of treatment options that fit the patient’s medical condition, and together with the patient, choose the appropriate treatment method.</p><attrib>Israeli respondent</attrib></disp-quote><disp-quote><p>[SDM is] giving the patient information about his treatment options and his illness and giving him a chance to have a say in his treatment options.</p><attrib>Jordanian respondent</attrib></disp-quote><p>Most US and Israeli respondents indicated familiarity with the concept of PCC (Israel: 5/7, 71%; United States: 10/12, 83%), whereas only 6 of the 15 (40%) Jordanian respondents indicated their or their patients’ familiarity with the concept. Prioritizing or meeting patient needs was commonly described as a feature of PCC by most respondents regardless of country of origin. In addition, US respondents commonly described PCC as accounting for patients’ preferences, most Israeli respondents described PCC also as individualized care, and most Jordanian respondents also described PCC as a provision of holistic care.</p><disp-quote><p>[PCC refers to] care that balances the needs and desires of the person receiving care.</p><attrib>US respondent</attrib></disp-quote><disp-quote><p>[PCC aims] to provide the patient with all of the patient’s needs and not just to solve a problem in the field, while maintaining proper communication and respect for the patient’s values.</p><attrib>Israeli respondent</attrib></disp-quote><disp-quote><p>[PCC refers] to doing whatever is needed for the patient or referring him/her to someone who can.</p><attrib>Jordanian respondent</attrib></disp-quote><p>Respondents indicated several barriers affecting the provision of SDM and PCC systematically; however, the common barrier was related to the system itself. All US respondents (12/12, 100%) mentioned lack of time as a major barrier to SDM implementation, whereas only 5 of 12 (42%) mentioned it as a barrier to PCC implementation. The role of insurance companies and fragmentation of care were mentioned as additional possible barriers to PCC implementation. In Jordan, most respondents (9/15, 60%) mentioned patient-related barriers, low health literacy, and a lack of knowledge as barriers to SDM implementation, whereas system-related barriers, such as staff shortages and high patient loads, were identified as barriers to PCC.</p><disp-quote><p>Time, and often hard to do for many decisions: few are really straightforward. Would be nice to have tools readily available to do this & ways to facilitate it.</p><attrib>US respondent, regarding SDM barriers</attrib></disp-quote><disp-quote><p>Lack of knowledge among patients and patient unwillingness to be fully informed about his/her condition.</p><attrib>Jordanian respondent, regarding SDM barriers</attrib></disp-quote><disp-quote><p>The healthcare delivery system is still organized traditionally regarding appointment scheduling and how patients interact with doctors; short visits limit person-centered care.</p><attrib>US respondent, regarding PCC barriers</attrib></disp-quote><disp-quote><p>Bureaucracy of the Jordanian health care system and lack of medical specialties in the peripheral areas of the country.</p><attrib>Jordanian respondent, regarding PCC barriers</attrib></disp-quote><p>With respect to problems related to the patient-physician relationship, the responses of Israeli physicians emphasized a lack of time and training (eg, lack of time, lack of support for physicians during their work). Jordanian respondents emphasized disorganization of the health care system, and US physicians highlighted problems of cost, social determinants of health, and the role of insurance companies (eg, the payment system and its incentive structure, lack of universal health care, costs of pharmaceuticals).</p></sec><sec><title>SDM-Q-DOC Responses: Comparison of SDM Practice and PCC Behaviors</title><p>We included in the analysis 32 survey responses with greater than 50% total completion (Jordan: n=15; United States: n=10; Israel: n=7). Overall, physicians in our sample reported practicing SDM at a moderately high level (mean 76.6, SD 11.5; median 75.6), with a range of 53 to 100. This result is similar to findings in other studies [<xref rid=\"ref22\" ref-type=\"bibr\">22</xref>]. The Kruskal-Wallis test results showed no significant difference in SDM-Q-DOC scores between the 3 countries (H<sub>2</sub>=0.631, <italic>P</italic>=.73; United States: mean 74.9, SD 11.4; Israel: mean 78.7, SD 7.9; Jordan: mean 76.7, SD 13.4). Box plot diagrams of SDM-Q-DOC means and standard deviations imply that SDM practice and PCC behaviors vary more among the US and Jordanian respondents in our sample than among their Israeli counterparts, as seen in <xref rid=\"figure1\" ref-type=\"fig\">Figure 1</xref>.</p><p>In addition, we compared the individual item scores between respondents from the 3 countries. Only for the first item (“I make clear to my patient that a decision needs to be made”) was a significant difference noticed, with higher scores for Jordanian physicians (<xref rid=\"table2\" ref-type=\"table\">Table 2</xref>). A nonsignificant but notable difference was noticed in the second item (“I want to know exactly from my patient how he/she wants to be involved in making the decision”), with a higher mean score for Jordanian physicians.</p><fig id=\"figure1\" position=\"float\"><label>Figure 1</label><caption><p>Box plot diagrams of SDM-Q-DOC score per country. SDM-Q-DOC: Shared Decision Making Questionnaire, physician version.</p></caption><graphic xlink:href=\"formative_v4i8e18223_fig1\"/></fig><table-wrap id=\"table2\" position=\"float\"><label>Table 2</label><caption><p>Comparison of means and standard deviations of Shared Decision Making Questionnaire, physician version responses among respondents from the United States, Israel, and Jordan (n=32).</p></caption><table frame=\"hsides\" rules=\"groups\" width=\"1000\" cellpadding=\"5\" cellspacing=\"0\" border=\"1\"><col width=\"280\" span=\"1\"/><col width=\"100\" span=\"1\"/><col width=\"100\" span=\"1\"/><col width=\"200\" span=\"1\"/><col width=\"160\" span=\"1\"/><col width=\"160\" span=\"1\"/><thead><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">SDM-Q-DOC<sup>a</sup> item question</td><td rowspan=\"1\" colspan=\"1\">H statistic</td><td rowspan=\"1\" colspan=\"1\"><italic>P</italic> value</td><td rowspan=\"1\" colspan=\"1\">United States, mean (SD)<sup>b</sup></td><td rowspan=\"1\" colspan=\"1\">Israel, mean (SD)<sup>b</sup></td><td rowspan=\"1\" colspan=\"1\">Jordan, mean (SD)<sup>b</sup></td></tr></thead><tbody><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">1. I make clear to my patient that a decision needs to be made.</td><td rowspan=\"1\" colspan=\"1\">9.55</td><td rowspan=\"1\" colspan=\"1\">.008</td><td rowspan=\"1\" colspan=\"1\">3.60 (0.84)</td><td rowspan=\"1\" colspan=\"1\">3.57 (0.98)</td><td rowspan=\"1\" colspan=\"1\">4.47 (0.52)</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">2. I want to know exactly from my patient how he/she wants to be involved in making the decision.</td><td rowspan=\"1\" colspan=\"1\">4.66</td><td rowspan=\"1\" colspan=\"1\">.10</td><td rowspan=\"1\" colspan=\"1\">3.00 (0.67)</td><td rowspan=\"1\" colspan=\"1\">3.29 (0.76)</td><td rowspan=\"1\" colspan=\"1\">3.73 (0.80)</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">3. I tell my patient that there are different options for treating his/her medical condition.</td><td rowspan=\"1\" colspan=\"1\">1.83</td><td rowspan=\"1\" colspan=\"1\">.40</td><td rowspan=\"1\" colspan=\"1\">4.50 (0.71)</td><td rowspan=\"1\" colspan=\"1\">4.43 (0.98)</td><td rowspan=\"1\" colspan=\"1\">4.21 (0.58)</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">4. I precisely explain the advantages and disadvantages of the treatment options to my patient.</td><td rowspan=\"1\" colspan=\"1\">2.59</td><td rowspan=\"1\" colspan=\"1\">.27</td><td rowspan=\"1\" colspan=\"1\">3.80 (0.92)</td><td rowspan=\"1\" colspan=\"1\">4.43 (0.53)</td><td rowspan=\"1\" colspan=\"1\">4.13 (0.64)</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">5. I help my patient understand all the information.</td><td rowspan=\"1\" colspan=\"1\">2.06</td><td rowspan=\"1\" colspan=\"1\">.36</td><td rowspan=\"1\" colspan=\"1\">3.80 (0.92)</td><td rowspan=\"1\" colspan=\"1\">4.29 (0.76)</td><td rowspan=\"1\" colspan=\"1\">4.27 (0.96)</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">6. I ask my patient which treatment option he/she prefers.</td><td rowspan=\"1\" colspan=\"1\">1.00</td><td rowspan=\"1\" colspan=\"1\">.61</td><td rowspan=\"1\" colspan=\"1\">4.10 (0.74)</td><td rowspan=\"1\" colspan=\"1\">4.00 (1.00)</td><td rowspan=\"1\" colspan=\"1\">3.73 (0.96)</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">7. My patient and I thoroughly weigh the different treatment options.</td><td rowspan=\"1\" colspan=\"1\">2.53</td><td rowspan=\"1\" colspan=\"1\">.28</td><td rowspan=\"1\" colspan=\"1\">3.40 (0.52)</td><td rowspan=\"1\" colspan=\"1\">4.00 (0.82)</td><td rowspan=\"1\" colspan=\"1\">3.47 (0.92)</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">8. My patient and I select a treatment option together.</td><td rowspan=\"1\" colspan=\"1\">0.23</td><td rowspan=\"1\" colspan=\"1\">.89</td><td rowspan=\"1\" colspan=\"1\">3.50 (0.53)</td><td rowspan=\"1\" colspan=\"1\">3.43 (0.79)</td><td rowspan=\"1\" colspan=\"1\">3.13 (1.46)</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">9. My patient and I reach an agreement on how to proceed.</td><td rowspan=\"1\" colspan=\"1\">1.47</td><td rowspan=\"1\" colspan=\"1\">.48</td><td rowspan=\"1\" colspan=\"1\">8.84 (0.76)</td><td rowspan=\"1\" colspan=\"1\">4.00 (5.80)</td><td rowspan=\"1\" colspan=\"1\">3.67 (0.90)</td></tr></tbody></table><table-wrap-foot><fn id=\"table2fn1\"><p><sup>a</sup>SDM-Q-DOC: Shared Decision Making Questionnaire, physician version.</p></fn><fn id=\"table2fn2\"><p><sup>b</sup>Means represent level of agreement with each item on a 6-point Likert scale, where 0=completely disagree and 5=completely agree.</p></fn></table-wrap-foot></table-wrap></sec></sec><sec sec-type=\"discussion\"><title>Discussion</title><sec><title>Principal Results and Comparison With Prior Work</title><p>The present study describes findings of a small, exploratory hypothesis-generating survey [<xref rid=\"ref20\" ref-type=\"bibr\">20</xref>] of Israeli, Jordanian, and US physicians’ perceptions of SDM and PCC. Open-ended qualitative results suggest that respondents, regardless of country of origin, identify SDM as a process focused on providing information or as informed decision making, but PCC as a physician’s effort to meet patients’ individualized needs. These findings are aligned with the perception that SDM is “the pinnacle of PCC” [<xref rid=\"ref4\" ref-type=\"bibr\">4</xref>] but also emphasize that SDM remains commonly perceived by physicians as a mean for delivering information rather than a collaborative discussion [<xref rid=\"ref36\" ref-type=\"bibr\">36</xref>,<xref rid=\"ref37\" ref-type=\"bibr\">37</xref>]. Barriers to implementing SDM and PCC were also identified and attributed to system- and patient-related factors [<xref rid=\"ref25\" ref-type=\"bibr\">25</xref>]. The quantitative results of the total mean score of the SDM-Q-DOC show medium to high levels of SDM-related behaviors among all respondents. Item-focused analysis showed that Jordanian respondents scored significantly higher on item 1 (“I make clear to my patient that a decision needs to be made”), with a nonsignificant but notable difference for item 2 (“I want to know exactly from my patient how he/she wants to be involved in making the decision”). These are interesting results for the psychometric quality of the SDM-Q-DOC. Although there is less literature on the psychometric qualities of the SDM-Q-DOC, ample literature exists on the psychometric characteristics of the SDM-Q-9, showing mixed results for item 1 and suggesting eliminating the item to improve the factorial structure [<xref rid=\"ref22\" ref-type=\"bibr\">22</xref>]. In our small sample, item 1 served as a discriminate item.</p></sec><sec><title>Strengths and Limitations</title><p>The present study has several strengths. To the best of our knowledge, this is the first transnational comparison of the perceptions and practices of physicians in the United States, Israel, and Jordan. This survey study provided a conceptual overview of physicians’ understanding of SDM and PCC as well as an evaluation of SDM- and PCC-related behaviors. Although SDM is a communication model and practice and PCC is considered the conceptual framework [<xref rid=\"ref6\" ref-type=\"bibr\">6</xref>], the participating physicians’ interpretation and understanding of the two were different. SDM was generally perceived as a means for delivering information to patients, whereas PCC was commonly perceived as a method for meeting a patient’s individual needs. While US and Jordanian physicians in our sample interpreted SDM also as a patient-level process (ie, patient participation), Israeli physicians in our sample interpreted SDM as a dialogue- and dyadic-based process occurring between patient and physician. These insights can inform future research and education initiatives pertaining to SDM and PCC among physicians. Finally, a methodological strength is our ability to deliver surveys in Arabic, Hebrew, and English due to the multilingual expertise of our team.</p><p>There are also some limitations to note. Our sample is small and not random; thus, we are unable to make meaningful statistical inferences or to generalize our findings, decreasing the study’s validity. However, because the purpose of this exploratory study was to generate hypotheses, our snapshot of the SDM landscape and PCC perceptions among physicians in Israel and Jordan is new and will inform future scaled-up surveys. We surveyed physicians, not other members of the health care team or patients. Clearly, a future comprehensive comparison of these health systems and practitioners’ approaches to care requires surveying all stakeholders involved. The SDM-Q-DOC questionnaire was administered to explore physicians’ perceptions of what is important in an SDM encounter, but it was not used to evaluate an actual encounter. Although the SDM-Q-DOC was developed to rate physicians’ experiences of SDM in patient-physician encounters, recent literature indicates the feasibility of the Shared Decision Making Questionnaire family for use in surveys [<xref rid=\"ref23\" ref-type=\"bibr\">23</xref>,<xref rid=\"ref28\" ref-type=\"bibr\">28</xref>,<xref rid=\"ref38\" ref-type=\"bibr\">38</xref>]. The final limitations are that we were unable to recruit and collect data from physicians in the West Bank and that the survey in Jordan was conducted using face-to-face interviews rather than web-based surveys. Using email to initiate communication with potential Jordanian and West Bank respondents was challenging, as was administering a web-based survey. In Jordan, we learned that recruitment by phone call or an offline survey methodology that does not require an internet connection could be better for collecting responses. Therefore, we employed face-to-face interviews successfully, but this might have caused a social desirability bias; that is, physicians might have overestimated their support for and use of the SDM approach. However, because the web-based platform was the tool rather than the purpose, we believe it was not critical and that the benefits of collecting data face to face instead of via a web-based survey were more important for this project.</p></sec><sec><title>Conclusions</title><p>The results of the present study add to the limited, yet important, literature on SDM and PCC in areas of the world outside the United States, Canada, Australia, and Western Europe [<xref rid=\"ref9\" ref-type=\"bibr\">9</xref>,<xref rid=\"ref12\" ref-type=\"bibr\">12</xref>,<xref rid=\"ref39\" ref-type=\"bibr\">39</xref>-<xref rid=\"ref43\" ref-type=\"bibr\">43</xref>]. They also add to the psychometric evaluation of SDM and PCC measures [<xref rid=\"ref44\" ref-type=\"bibr\">44</xref>] and identify barriers to implementation. We hope this survey will motivate researchers and clinicians in Israel, Jordan, and other countries that are less represented in the SDM and PCC research and practice to encourage related discussions and practice and to facilitate implementation, measurements, and interventions.</p></sec></sec></body><back><ack><p>The authors wish to thank Young Shin Kim for her help in this research.</p></ack><fn-group><fn fn-type=\"COI-statement\"><p>Conflicts of Interest: None declared.</p></fn></fn-group><app-group><app><title>Appendix</title><supplementary-material content-type=\"local-data\" id=\"app1\"><label>Multimedia Appendix 1</label><p>Physician Perceptions of Shared Decision Making and Person-Centered Care Survey.</p><media xlink:href=\"formative_v4i8e18223_app1.docx\" xlink:title=\"DOCX File , 22 KB\" xlink:type=\"simple\" id=\"d38e969\" position=\"anchor\"/></supplementary-material></app></app-group><glossary><title>Abbreviations</title><def-list><def-item><term id=\"abb1\">EQUATOR</term><def><p>Enhancing the Quality and Transparency of Health Research</p></def></def-item><def-item><term id=\"abb2\">PCC</term><def><p>patient-centered care</p></def></def-item><def-item><term id=\"abb3\">SDM</term><def><p>shared decision making</p></def></def-item><def-item><term id=\"abb4\">SDM-Q-9</term><def><p>9-item Shared Decision Making Questionnaire</p></def></def-item><def-item><term id=\"abb5\">SDM-Q-DOC</term><def><p>Shared Decision Making Questionnaire, physician version</p></def></def-item></def-list></glossary><ref-list><ref id=\"ref1\"><label>1</label><element-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Gordon</surname><given-names>JE</given-names></name><name><surname>Leiman</surname><given-names>JM</given-names></name><name><surname>Deland</surname><given-names>EL</given-names></name><name><surname>Pardes</surname><given-names>H</given-names></name></person-group><article-title>Delivering value: provider efforts to improve the quality and reduce the cost of health care</article-title><source>Annu Rev Med</source><year>2014</year><volume>65</volume><fpage>447</fpage><lpage>58</lpage><pub-id pub-id-type=\"doi\">10.1146/annurev-med-100312-135931</pub-id><pub-id pub-id-type=\"medline\">24111890</pub-id><pub-id pub-id-type=\"pmid\">24111890</pub-id></element-citation></ref><ref id=\"ref2\"><label>2</label><element-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Charles</surname><given-names>C</given-names></name><name><surname>Gafni</surname><given-names>A</given-names></name><name><surname>Whelan</surname><given-names>T</given-names></name></person-group><article-title>Shared decision-making in the medical encounter: what does it mean? 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[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Front Psychiatry</journal-id><journal-id journal-id-type=\"iso-abbrev\">Front Psychiatry</journal-id><journal-id journal-id-type=\"publisher-id\">Front. Psychiatry</journal-id><journal-title-group><journal-title>Frontiers in Psychiatry</journal-title></journal-title-group><issn pub-type=\"epub\">1664-0640</issn><publisher><publisher-name>Frontiers Media S.A.</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32848898</article-id><article-id pub-id-type=\"pmc\">PMC7432150</article-id><article-id pub-id-type=\"doi\">10.3389/fpsyt.2020.00622</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Psychiatry</subject><subj-group><subject>Original Research</subject></subj-group></subj-group></article-categories><title-group><article-title>Victim Sensitivity and Its Neural Correlates Among Patients With Major Depressive Disorder</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Wang</surname><given-names>Xiaoming</given-names></name><xref ref-type=\"aff\" rid=\"aff1\">\n<sup>1</sup>\n</xref><xref ref-type=\"aff\" rid=\"aff2\">\n<sup>2</sup>\n</xref><xref ref-type=\"author-notes\" rid=\"fn002\">\n<sup>†</sup>\n</xref><uri xlink:type=\"simple\" xlink:href=\"https://loop.frontiersin.org/people/1052939\"/></contrib><contrib contrib-type=\"author\"><name><surname>Cui</surname><given-names>Shaojuan</given-names></name><xref ref-type=\"aff\" rid=\"aff3\">\n<sup>3</sup>\n</xref><xref ref-type=\"author-notes\" rid=\"fn002\">\n<sup>†</sup>\n</xref><uri xlink:type=\"simple\" xlink:href=\"https://loop.frontiersin.org/people/1052940\"/></contrib><contrib contrib-type=\"author\"><name><surname>Wu</surname><given-names>Michael Shengtao</given-names></name><xref ref-type=\"aff\" rid=\"aff4\">\n<sup>4</sup>\n</xref><uri xlink:type=\"simple\" xlink:href=\"https://loop.frontiersin.org/people/425373\"/></contrib><contrib contrib-type=\"author\"><name><surname>Wang</surname><given-names>Yun</given-names></name><xref ref-type=\"aff\" rid=\"aff5\">\n<sup>5</sup>\n</xref><uri xlink:type=\"simple\" xlink:href=\"https://loop.frontiersin.org/people/799240\"/></contrib><contrib contrib-type=\"author\"><name><surname>Gao</surname><given-names>Qinglin</given-names></name><xref ref-type=\"aff\" rid=\"aff1\">\n<sup>1</sup>\n</xref><xref ref-type=\"aff\" rid=\"aff2\">\n<sup>2</sup>\n</xref><uri xlink:type=\"simple\" xlink:href=\"https://loop.frontiersin.org/people/900972\"/></contrib><contrib contrib-type=\"author\"><name><surname>Zhou</surname><given-names>Yuan</given-names></name><xref ref-type=\"aff\" rid=\"aff1\">\n<sup>1</sup>\n</xref><xref ref-type=\"aff\" rid=\"aff2\">\n<sup>2</sup>\n</xref><xref ref-type=\"aff\" rid=\"aff5\">\n<sup>5</sup>\n</xref><xref ref-type=\"author-notes\" rid=\"fn001\">\n<sup>*</sup>\n</xref><uri xlink:type=\"simple\" xlink:href=\"https://loop.frontiersin.org/people/73449\"/></contrib></contrib-group><aff id=\"aff1\">\n<sup>1</sup>\n<institution>Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences</institution>, <addr-line>Beijing</addr-line>, <country>China</country>\n</aff><aff id=\"aff2\">\n<sup>2</sup>\n<institution>Department of Psychology, University of Chinese Academy of Sciences</institution>, <addr-line>Beijing</addr-line>, <country>China</country>\n</aff><aff id=\"aff3\">\n<sup>3</sup>\n<institution>Department of Psychology, Beijing Tongren Hospital, Capital Medical University</institution>, <addr-line>Beijing</addr-line>, <country>China</country>\n</aff><aff id=\"aff4\">\n<sup>4</sup>\n<institution>School of Sociology and Anthropology, Xiamen University</institution>, <addr-line>Xiamen</addr-line>, <country>China</country>\n</aff><aff id=\"aff5\">\n<sup>5</sup>\n<institution>The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University</institution>, <addr-line>Beijing</addr-line>, <country>China</country>\n</aff><author-notes><fn fn-type=\"edited-by\"><p>Edited by: Paul Stokes, King’s College London, United Kingdom</p></fn><fn fn-type=\"edited-by\"><p>Reviewed by: Guido van Wingen, University of Amsterdam, Netherlands; Chien-Han Lai, National Yang-Ming University, Taiwan</p></fn><corresp id=\"fn001\">*Correspondence: Yuan Zhou, <email xlink:href=\"mailto:[email protected]\" xlink:type=\"simple\">[email protected]</email>\n</corresp><fn fn-type=\"equal\" id=\"fn002\"><p>†These authors have contributed equally to this work</p></fn><fn fn-type=\"other\" id=\"fn003\"><p>This article was submitted to Mood and Anxiety Disorders, a section of the journal Frontiers in Psychiatry</p></fn></author-notes><pub-date pub-type=\"epub\"><day>11</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><year>2020</year></pub-date><volume>11</volume><elocation-id>622</elocation-id><history><date date-type=\"received\"><day>03</day><month>2</month><year>2020</year></date><date date-type=\"accepted\"><day>15</day><month>6</month><year>2020</year></date></history><permissions><copyright-statement>Copyright © 2020 Wang, Cui, Wu, Wang, Gao and Zhou</copyright-statement><copyright-year>2020</copyright-year><copyright-holder>Wang, Cui, Wu, Wang, Gao and Zhou</copyright-holder><license xlink:href=\"http://creativecommons.org/licenses/by/4.0/\"><license-p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</license-p></license></permissions><abstract><sec><title>Background</title><p>Dysfunctional beliefs about the self are common in the development of depressive symptoms, but it remains unclear how depressed patients respond to unfair treatment, both dispositionally and neurally. The present research is an attempt to explore the differences in sensitivity to injustice as a victim and its neural correlates in patients with major depressive disorder (MDD) versus healthy controls.</p></sec><sec><title>Methods</title><p>First episodic, drug-naïve patients with MDD (<italic>n</italic> = 30) and a control group (<italic>n</italic> = 30) were recruited to compare their differences in victim sensitivity. A second group of patients with MDD (<italic>n</italic> = 23) and their controls (<italic>n</italic> = 28) were recruited to replicate the findings and completed resting-state functional magnetic resonance imaging (fMRI) scanning. Spontaneous brain activity measured by fractional amplitude of low-frequency fluctuation (fALFF) was used to characterize the neural correlates of victim sensitivity both in patients and in healthy controls.</p></sec><sec><title>Results</title><p>Higher victim sensitivity was consistently found in patients with MDD than healthy controls in both datasets. Multiple regression analysis on the fALFF showed a significant interaction effect between diagnosis and victim sensitivity in the right dorsolateral prefrontal cortex (DLPFC).</p></sec><sec><title>Conclusions</title><p>The patients with MDD show higher sensitivity to injustice as a victim, which may be independent of their disease course. The MDD patients differ from healthy controls in the neural correlates of victim sensitivity. These findings shed light on the linkage between cognitive control subserved by the DLPFC and negative bias towards the self implicated by higher victim sensitivity among the depressed patients.</p></sec></abstract><kwd-group><kwd>victim sensitivity</kwd><kwd>major depressive disorder</kwd><kwd>dorsolateral prefrontal cortex</kwd><kwd>fractional amplitude of low-frequency fluctuation</kwd><kwd>resting-state fMRI</kwd></kwd-group><counts><fig-count count=\"2\"/><table-count count=\"1\"/><equation-count count=\"0\"/><ref-count count=\"61\"/><page-count count=\"9\"/><word-count count=\"5158\"/></counts></article-meta></front><body><sec sec-type=\"intro\" id=\"s1\"><title>Introduction</title><p>Major depressive disorder (MDD) is one of the most common mental disorders, with a high prevalence in current times (<xref rid=\"B1\" ref-type=\"bibr\">1</xref>–<xref rid=\"B3\" ref-type=\"bibr\">3</xref>). The cognitive model of depression speculates that MDD patients generally hold negative bias and dysfunctional beliefs about themselves. However, it is still unclear how and to what extent self-related personalities and its neural circuits play a role in depressive symptoms (<xref rid=\"B4\" ref-type=\"bibr\">4</xref>). Recent research suggests that hypervigilance towards one’s negative experience is linked with the development of MDD (<xref rid=\"B5\" ref-type=\"bibr\">5</xref>), and sensitivity to injustice as a victim is related to the stabilization of depressive symptoms (<xref rid=\"B6\" ref-type=\"bibr\">6</xref>).</p><p>Victim sensitivity captures the individual differences in response to unfair treatment towards oneself. Victim-sensitive individuals tend to detect injustice within a low threshold. Thus, they are likely to experience stronger anger, moral outrage, and exhibit uncooperative behavioral tendencies (<xref rid=\"B7\" ref-type=\"bibr\">7</xref>, <xref rid=\"B8\" ref-type=\"bibr\">8</xref>). Victim sensitivity is associated with a higher risk of having negative concerns and social emotions (<xref rid=\"B9\" ref-type=\"bibr\">9</xref>) and biased social judgment (for example, people who are higher in victim sensitivity would rate neutral and hostile faces as more untrustworthy and underestimate targets’ cooperativeness in order to avoid being exploited in the future (<xref rid=\"B10\" ref-type=\"bibr\">10</xref>)). In the personality space, victim sensitivity has a unique correlation with hostility (<xref rid=\"B11\" ref-type=\"bibr\">11</xref>), a facet of neuroticism, which is a predisposition toward depression (<xref rid=\"B12\" ref-type=\"bibr\">12</xref>, <xref rid=\"B13\" ref-type=\"bibr\">13</xref>). Furthermore, victim sensitivity was found to be positively related to emotional problems (<xref rid=\"B14\" ref-type=\"bibr\">14</xref>) and to mediate the link between attention deficit hyperactivity disorder and depressive symptoms in children and adolescents (<xref rid=\"B14\" ref-type=\"bibr\">14</xref>). In a longitudinal study, depressive symptoms at the first measurement predicted higher victim sensitivity one or two years later, while higher victim sensitivity promoted the stabilization of depressive symptoms in those with depressive symptoms at the baseline (<xref rid=\"B6\" ref-type=\"bibr\">6</xref>). All of the above findings demonstrate a connection between victim sensitivity and depressive symptoms and also suggest that victim sensitivity should be considered as a potential personality risk factor for the emergence and maintenance of depression. However, there is lack of direct evidence as to whether victim sensitivity was significantly endorsed in a clinical population with MDD, as compared to healthy controls.</p><p>We then ask the question: what is the neural basis of higher victim sensitivity in MDD patients? In healthy populations, task-based functional magnetic resonance (fMRI) studies found that the brain activity of anterior insula, anterior cingulate cortex, and dorsolateral prefrontal cortex were increased when participants were faced with unfair treatments (e.g., unequal division on a certain amount of money) (<xref rid=\"B15\" ref-type=\"bibr\">15</xref>–<xref rid=\"B17\" ref-type=\"bibr\">17</xref>), suggesting that these brain regions may be vital for processing injustice for a victim. Different from the healthy controls, MDD patients had weaker activity in the medial occipital lobe or striatum with increasing or decreasing inequality (<xref rid=\"B18\" ref-type=\"bibr\">18</xref>). A relevant study on the neural correlates of justice sensitivity found that victim sensitivity predicted subjective ratings of praise, but it was not correlated with neural activity during a moral judgment task (<xref rid=\"B19\" ref-type=\"bibr\">19</xref>). It should be noted that these findings are obtained by using task-based fMRI, in which task-induced brain activity is context-dependent. Personality differences in victim sensitivity may also be reflected in spontaneous brain activity measured by resting-state fMRI, which is a promising tool to uncover the neural basis of inter-individual differences in personality or propensity (<xref rid=\"B20\" ref-type=\"bibr\">20</xref>–<xref rid=\"B23\" ref-type=\"bibr\">23</xref>). Using an index of spontaneous brain activity that measures temporal synchronization of brain activity within a local brain region, one study found victim sensitivity was positively associated with regional spontaneous brain activity of the paracentral lobule (<xref rid=\"B24\" ref-type=\"bibr\">24</xref>). In spite of these descriptions, to our knowledge, no empirical data on the neural basis of victim sensitivity in MDD has been reported. The present study is a first step to fill this gap.</p><p>In brief, this current study first aims to test whether the patients with MDD are more sensitive to injustice as a victim. To this aim, we recruited a group of MDD patients who were first episodic and drug-naïve to reduce the potential confounders from long illness duration and medications. Then, we recruited another group of MDD patients with less severe depressive symptoms to test whether the abnormality in victim sensitivity is generalized in patients with MDD. Second, we further explored the neural basis underlying victim sensitivity in MDD using the resting-state fMRI. We selected an index called fractional amplitude of low-frequency fluctuation (fALFF) (<xref rid=\"B25\" ref-type=\"bibr\">25</xref>). The fALFF, similar to ALFF, measures low frequency oscillations of the blood oxygen level-dependent (BOLD) signal at rest, but the fALFF represents the ratio of the power spectrum of low-frequency to that of the entire frequency range and thus is less prone to noise compared to ALFF (<xref rid=\"B25\" ref-type=\"bibr\">25</xref>). The low frequency BOLD oscillation are closely related to spontaneous neuronal activity (<xref rid=\"B26\" ref-type=\"bibr\">26</xref>). As a method with high test-retest reliability to detect the intensity of regional spontaneous neuronal activity during rest (<xref rid=\"B27\" ref-type=\"bibr\">27</xref>), the fALFF has been widely used to investigate spontaneous brain activities in different psychiatric disorders, including MDD (<xref rid=\"B28\" ref-type=\"bibr\">28</xref>–<xref rid=\"B30\" ref-type=\"bibr\">30</xref>), and be linked with personality traits in healthy populations (<xref rid=\"B31\" ref-type=\"bibr\">31</xref>–<xref rid=\"B33\" ref-type=\"bibr\">33</xref>). In addition, fALFF is a data-driven approach and requires no <italic>a priori</italic> hypothesis and thus is appropriate for exploratory analyses, such as the present study. Therefore, here we attempted to employ fALFF to explore whether there are different neural correlates of victim sensitivity between patients with MDD and healthy controls.</p></sec><sec id=\"s2\"><title>Methods</title><sec id=\"s2_1\"><title>Participants</title><p>Two groups of patients with MDD and their matched healthy controls were recruited from two independent sites. Participants in the Tongren dataset were recruited for the aim of investigating whether MDD patients who are first episodic and drug-naïve are more sensitive to injustice as a victim on a behavioral level. We then used another group of participants (i.e. Anding dataset) to test whether the findings obtained in the Tongren dataset can be replicated in MDD patients with less severe depressive symptoms. The participants in the Anding dataset are from a project that initially aimed to study the neural basis of abnormal social decision-making behavior and related traits in patients with MDD, and thus made it possible to further investigate the differences in neural correlates of victim sensitivity among MDD patients versus healthy participants in the current study.</p><p>In the Tongren dataset, 30 patients with MDD were recruited from the Department of Psychology, Beijing Tongren Hospital, Capital Medical University. Thirty demographically-matched healthy controls were recruited <italic>via</italic> advertisements. All of these patients met the following criteria: (1) the Diagnostic and Statistical Manual of Mental Disorders, fourth edition (DSM-IV) criteria for a major depressive episode, diagnosed independently by two qualified psychiatrists who interviewed the patients personally; (2) seeking medical advice for the first time in this hospital and have never taken any psychotropic medication; (3) Hamilton Rating Scale for Depression (HAMD-17) scores >=17; and (4) age range is between 18 and 45 years old. The patients were excluded if they had any preexisting or concurrent co-morbid primary diagnosis that met the DSM-IV criteria for any Axis I disorder other than MDD. Participants in the control group had no current or past history of depression or any other psychiatric disorders and no family history of major psychiatric or neurological illness in first-degree relatives. Additional exclusion criteria for both of the groups were acutely suicidal or homicidal behaviors, history of trauma resulting in loss of consciousness, history of major neurological or physical disorders that could lead to an altered mental state, or current pregnancy or breastfeeding.</p><p>In the Anding dataset, 23 patients were recruited from Beijing Anding Hospital, Capital Medical University. Potential applicants for the patient group were from the outpatient departments. They were recommended by their doctors who had diagnosed them before, and then again diagnosed by a psychiatrist when they were recruited in this study. The inclusion and exclusion criteria of the patients were the same as the Tongren dataset, with the only exception that these patients were not experiencing an acute depressive episode and had less severity in depressive symptoms (for details, see <xref rid=\"T1\" ref-type=\"table\">\n<bold>Table 1</bold>\n</xref>). Among these MDD patients, 15 patients had at least two onsets of depressive episodes; eight patients had a recent depressive episode and their symptoms had been controlled after antidepressant treatments when they were recruitment. Only one patient did not take any psychotropic medication; the other patients received antidepressant medications (SSRIs and/or SNRIs), with one patient additionally taking low-dose benzodiazepines. Twenty-eight healthy controls were recruited by advertisements. Due to the relatively stable mental condition of the MDD patients in the Anding dataset, only participants in the Anding dataset participated in MRI scanning.</p><table-wrap id=\"T1\" position=\"float\"><label>Table 1</label><caption><p>Demographics and clinical information of patients with MDD and healthy controls within the two datasets.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th valign=\"top\" rowspan=\"2\" align=\"left\" colspan=\"1\">Characteristics</th><th valign=\"top\" colspan=\"3\" align=\"center\" rowspan=\"1\">Tongren dataset</th><th valign=\"top\" colspan=\"3\" align=\"center\" rowspan=\"1\">Anding dataset</th></tr><tr><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">MDD</th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">NC</th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">p<xref ref-type=\"table-fn\" rid=\"fnT1_1\">\n<sup>a</sup>\n</xref>\n</th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">MDD</th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">NC</th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">p<xref ref-type=\"table-fn\" rid=\"fnT1_1\">\n<sup>a</sup>\n</xref>\n</th></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Sample size</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">N=30</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">N=30</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">N=23</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">N=28</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Age (years)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">25.47 ± 6.40</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">27.67 ± 4.14</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.12</td><td valign=\"top\" align=\"char\" char=\"±\" rowspan=\"1\" colspan=\"1\">31.22 ± 5.70</td><td valign=\"top\" align=\"char\" char=\"±\" rowspan=\"1\" colspan=\"1\">29.57 ± 5.80</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.32</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Gender (M: F)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">6:24</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">6:24</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.00</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">13:10</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">16:12</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.96</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Education</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">14.33 ± 2.44</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">14.80 ± 2.19</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.44</td><td valign=\"top\" align=\"char\" char=\"±\" rowspan=\"1\" colspan=\"1\">14.91 ± 3.22</td><td valign=\"top\" align=\"char\" char=\"±\" rowspan=\"1\" colspan=\"1\">15.25 ± 2.55</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.68</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">HAMA</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">22.03 ± 4.92</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–</td><td valign=\"top\" align=\"char\" char=\"±\" rowspan=\"1\" colspan=\"1\">9.83 ± 6.86</td><td valign=\"top\" align=\"char\" char=\"±\" rowspan=\"1\" colspan=\"1\">0.64 ± 1.16</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><0.001</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">HAMD-17</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">24.90 ± 4.14</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–</td><td valign=\"top\" align=\"char\" char=\"±\" rowspan=\"1\" colspan=\"1\">13.13 ± 6.96</td><td valign=\"top\" align=\"char\" char=\"±\" rowspan=\"1\" colspan=\"1\">0.89 ± 1.72</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><0.001</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Victim Sensitivity</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">34.67 ± 9.06</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">25.10 ± 7.48</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><0.001</td><td valign=\"top\" align=\"char\" char=\"±\" rowspan=\"1\" colspan=\"1\">30.96 ± 9.50</td><td valign=\"top\" align=\"char\" char=\"±\" rowspan=\"1\" colspan=\"1\">25.41 ± 8.07</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.03</td></tr></tbody></table><table-wrap-foot><fn id=\"fnT1_1\"><label>a</label><p>Two-sample t-tests or chi-square test. HAMD-17, the Hamilton Rating Scale for Depression; HAMA, the Hamilton Rating Scale for Anxiety.</p></fn></table-wrap-foot></table-wrap><p>This study was approved by the Institutional Review Boards of Beijing Tongren Hospital, Beijing Anding Hospital, Capital Medical University, as well as the Institute of Psychology, Chinese Academy of Sciences. Written informed consent was obtained from all participants.</p></sec><sec id=\"s2_2\"><title>Assessment of Victim Sensitivity</title><p>Ten items were used to measure justice sensitivity as a victim (<xref rid=\"B11\" ref-type=\"bibr\">11</xref>, <xref rid=\"B34\" ref-type=\"bibr\">34</xref>). Examples of items for victim sensitivity are “It makes me angry when I am treated worse than others” or “ I ruminate for a long time when other people are treated better than me”. Each item was scored on a 6-point rating scale ranging from 0 (not at all) to 5 (exactly). The reliabilities were acceptable with the estimated reliability coefficients (Cronbach’s alpha) of the MDD patients and healthy controls amounting to 0.92 and 0.84 for the Tongren sample, and to 0.84 and 0.89 for the Anding sample.</p></sec><sec id=\"s2_3\"><title>MRI Data Acquisition</title><p>The fMRI data were acquired using a GE 3.0 T MRI scanner at the Magnetic Resonance Imaging Research Center, Institute of Psychology, Chinese Academy of Sciences. Structural and functional MRI scans were obtained for each participant. Structural MRI scans were acquired using a magnetization-prepared rapid acquisition gradient echo sequence with the following parameters: TI = 450 ms, receiver bandwidth = 31.25, matrix = 256 × 256, field of view (FOV) = 240 mm * 240 mm, slice thickness = 1.5 mm, flip angle = 12°. Functional images were acquired for each participant using an echo planar imaging (EPI) sequence with the following parameters: TR = 2000 ms, TE = 30 ms, flip angle = 70°, acquisition matrix = 64 * 64, and FOV = 220 mm * 220 mm; 33 axial slices, with a thickness of 4 mm and no gap. During the scanning, the participants were instructed to keep their eyes closed and not to focus their thoughts on anything. The duration of the resting-state fMRI was 10 minutes. After resting-state fMRI scanning, these participants performed a fMRI task, but the data were not used in the current study.</p></sec><sec id=\"s2_4\"><title>Image Preprocessing</title><p>Conventional functional imaging preprocessing was performed using the Data Processing Assistant for Resting-State fMRI (DPARSF 4.3, <uri xlink:type=\"simple\" xlink:href=\"http://rfmri.org/dpabi\">http://rfmri.org/dpabi</uri>) (<xref rid=\"B35\" ref-type=\"bibr\">35</xref>), which is based on Statistical Parametric Mapping (SPM12) (<uri xlink:type=\"simple\" xlink:href=\"http://www.fil.ion.ucl.ac.uk/spm\">http://www.fil.ion.ucl.ac.uk/spm</uri>) and the Resting-State fMRI Data Analysis Toolkit (REST 1.8, <uri xlink:type=\"simple\" xlink:href=\"http://www.restfmri.net\">http://www.restfmri.net</uri>) (<xref rid=\"B36\" ref-type=\"bibr\">36</xref>). Conventional preprocessing steps were conducted, including the removal of the first 5 volumes, slice timing realignment, co-registration, segmentation of the T1 map to generate the gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) (<xref rid=\"B37\" ref-type=\"bibr\">37</xref>), nuisance variable regression, spatial normalization with 2-mm cubic voxels, and spatial smoothing of 4 mm FWHM. The nuisance variables included 24 motion parameters (6 head motion parameters, 6 head motion parameters one time point before, and the 12 corresponding squared items), 5 principal components from the individual segmented CSF and WM regions (<xref rid=\"B38\" ref-type=\"bibr\">38</xref>), and linear and quadratic trends (<xref rid=\"B39\" ref-type=\"bibr\">39</xref>). To quantify head motion, the volume-based framewise displacement (FD) was computed (<xref rid=\"B40\" ref-type=\"bibr\">40</xref>).</p></sec><sec id=\"s2_5\"><title>fALFF Analysis</title><p>For each participant, we calculated the fALFF to characterize the regional spontaneous activity in a voxel-wise way using the DPARSF software. The fALFF is defined as the total power within a frequency band divided by the total power of the entire detectable frequency range (<xref rid=\"B25\" ref-type=\"bibr\">25</xref>). Thus, the square root was firstly calculated at each frequency in the power spectrum, and the averaged square root was computed across a low frequency range (0.01–0.08 Hz) at each voxel. Then, the fALFF was obtained as a fraction, which was the sum of the amplitude across 0.01–0.08 Hz divided by that across the whole frequency range. Finally, the normalized score of fALFF was obtained and an individual voxel-wise z-fALFF map was generated for each participant.</p></sec><sec id=\"s2_6\"><title>Statistical Analysis</title><p>Chi-square tests and independent sample t tests were conducted to compare the mean level of demographic, clinical variables, and victim sensitivity between the MDD and healthy controls for each site. Correlation analyses and partial correlation analyses were conducted to investigate the clinical correlates of victim sensitivity for each dataset. All of these analyses were conducted by SPSS v21.</p><p>To identify the brain regions where activities related to victim sensitivity may be different between patients with MDD and healthy controls, we firstly conducted a multiple regression analysis on the z-fALFF images of the total sample using SPM12. The z-fALFF of each voxel in the brain were regressed on the variables of diagnosis, victim sensitivity score, and the diagnosis by victim sensitivity score interaction, with age, gender, education level, and mean FD as covariates. To control for Type 1 error, a cluster-level family-wise error (FWE) corrected <italic>p</italic>-value of 0.05 was used for multiple comparisons correction (individual voxel height threshold of <italic>p</italic> < 0.001). Then, multiple regression analyses were conducted on the z-fALFF images for patients and controls, separately. The victim sensitivity score was entered as a regressor into the model, in addition to age, gender, education level, and mean FD which were included as variables of no interest. Due to the relatively small sample size, we conducted a Small Volume Correction (SVC) by using the result of the regression analysis of the total sample as a mask (individual voxel height threshold of <italic>p</italic> < 0.005, cluster-level FWE corrected <italic>p</italic> < 0.05 following SVC).</p></sec></sec><sec sec-type=\"results\" id=\"s3\"><title>Results</title><sec id=\"s3_1\"><title>Demographic and Clinical Data</title><p>There were no significant differences in gender composition, age, and educational level between patients and healthy controls in both of the two datasets (all <italic>p</italic> > 0.05) (<xref rid=\"T1\" ref-type=\"table\">\n<bold>Table 1</bold>\n</xref>). The patients in the Tongren dataset showed more severe depressive symptoms as assessed with the HAMD (p<0.001) and more severe anxiety symptoms as assessed with the Hamilton Rating Scale for Anxiety (HAMA) (p<0.001) compared with those in the Anding dataset.</p></sec><sec id=\"s3_2\"><title>Victim Sensitivity</title><p>We found higher victim sensitivity among MDD patients both in the Tongren dataset (MDD: 34.67 ± 9.06, NC: 25.10 ± 7.48; p<0.001, Cohen’s d = 1.15) and in the Anding dataset (MDD: 30.96 ± 9.50, NC: 25.41 ± 8.07; p=0.03, Cohen’s d = 0.63) (<xref rid=\"T1\" ref-type=\"table\">\n<bold>Table 1</bold>\n</xref>).</p><p>We also explored the clinical correlates of victim sensitivity. We found that there was no correlation between victim sensitivity and the HAMD score in the Tongren dataset (<italic>r</italic> = 0.13, <italic>p</italic> = 0.49) but positive correlation in the Anding dataset (<italic>r</italic> = 0.54, <italic>p</italic> = 0.007). However, the correlation in the Anding dataset was marginally significant after controlling for the HAMA score (<italic>r</italic> = 0.42, <italic>p</italic> = 0.053) or disappeared after excluding the remitted patients (HAMD score < =7; N=6) (<italic>r</italic> = 0.05, <italic>p</italic> = 0.86). All of these findings suggested that the correlation between victim sensitivity and the severity of depressive symptoms may not be reliable.</p></sec><sec id=\"s3_3\"><title>fALFF</title><p>A significant interaction effect between diagnosis and victim sensitivity was found in the right middle frontal gyrus (peak MNI coordinate: [34, 58, 28]; cluster size: 95 voxels; cluster-level FWE <italic>p</italic> < 0.05) (<xref ref-type=\"fig\" rid=\"f1\">\n<bold>Figure 1A</bold>\n</xref>). By searching this region in the Brainnetome Atlas Viewer (<xref rid=\"B41\" ref-type=\"bibr\">41</xref>), we found that this region was located in the area 46, which is often termed as the dorsolateral prefrontal cortex (DLPFC) (<xref ref-type=\"fig\" rid=\"f1\">\n<bold>Figure 1B</bold>\n</xref>). The interaction effect was shown in <xref ref-type=\"fig\" rid=\"f1\">\n<bold>Figure 1C</bold>\n</xref>, which illustrated the relationship between victim sensitivity and the z-fALFF value in the right the DLPFC for each group.</p><fig id=\"f1\" position=\"float\"><label>Figure 1</label><caption><p>Interaction effect between diagnosis and victim sensitivity on fALFF and the results of post hoc analysis. <bold>(A)</bold> The region showing significant interaction effect between diagnosis and victim sensitivity; <bold>(B)</bold> The region showing significant interaction effect was overlaid with the area 46 in the Brainnetome Atlas Reviewer (<uri xlink:type=\"simple\" xlink:href=\"http://atlas.brainnetome.org/\">http://atlas.brainnetome.org/</uri>). <bold>(C)</bold> Scatter plots for the relationship between the fALFF value and victim sensitivity within each group with the shadow parts representing 95% confidence interval. Abbreviation: rMFG, right middle frontal gyrus.</p></caption><graphic xlink:href=\"fpsyt-11-00622-g001\"/></fig><p>By separately analyzing the relationship between victim sensitivity and fALFF within each group, we found that there were no regions showing significant correlations with victim sensitivity score both in the MDD group and in the healthy controls group in a whole brain search (cluster-level FWE <italic>p</italic> < 0.05). But by using SVC with the results of the regression analysis of the total sample as a mask, we found a significantly negative correlation in the right DLPFC in the healthy controls group (SVC cluster-level FWE corrected <italic>p</italic> = 0.01, 26 voxels, T = -4.45, peak voxel coordinates: [32, 56, 26]) and significantly positive correlation in the right DLPFC in the MDD group (SVC cluster-level FWE corrected <italic>p</italic> = 0.04, 9 voxels, T = 3.35, peak voxel coordinates: [32, 58, 22]).</p><p>Significant diagnosis effect was found in the right superior occipital gyrus (SOG; peak MNI coordinate: [32, -84, 42]; cluster size: 104 voxels) (<xref ref-type=\"fig\" rid=\"f2\">\n<bold>Figure 2</bold>\n</xref>), suggesting that the patients showed decreased fALFF in this region compared to the healthy controls. We didn’t find the regions showing significant correlations with victim sensitivity across the two groups in the pre-specified threshold (cluster-level FWE <italic>p</italic> < 0.05).</p><fig id=\"f2\" position=\"float\"><label>Figure 2</label><caption><p>Diagnosis effect on fALFF. <bold>(A)</bold> Region showing significant group difference in fALFF between the patients with major depressive disorder (MDD) and the healthy controls (HC). <bold>(B)</bold> The fALFF value in this region within each group. Abbreviation: rSOG, right superior occipital gyrus.</p></caption><graphic xlink:href=\"fpsyt-11-00622-g002\"/></fig></sec></sec><sec sec-type=\"discussion\" id=\"s4\"><title>Discussion</title><p>The current study provides robust evidence that MDD patients have higher sensitivity to injustice as a victim, which was consistently shown in two clinical samples. The results also showed that the neural correlates of victim sensitivity differ between the MDD patients and the healthy controls. Specifically, the fALFF in the right DLPFC, the region for cognitive control, positively correlated with victim sensitivity in MDD patients, but was negatively correlated with victim sensitivity in healthy controls.</p><sec id=\"s4_1\"><title>Hypersensitivity to Injustice as a Victim</title><p>In line with previous studies on the association between victim sensitivity and depressive symptoms in non-clinical samples or personality traits related to depression (<xref rid=\"B6\" ref-type=\"bibr\">6</xref>, <xref rid=\"B42\" ref-type=\"bibr\">42</xref>, <xref rid=\"B43\" ref-type=\"bibr\">43</xref>), the present study found that patients with MDD had higher sensitivity to injustice as a victim. Particularly, this hypersensitivity can be found in a group of first episodic, drug-naïve patients with MDD, suggesting that the increased victim sensitivity was free from the confounding effect of chronic illness or antipsychotic drugs and could be observed even in the early stage of depression. In order to test whether this hypersensitivity can be generalized to a common clinical sample, we recruited another group of MDD patients, most of whom were medicated and with mild to moderate depressive symptoms (the Anding dataset). We again found increased victim sensitivity in these patients. On the contrary, we found the correlation between victim sensitivity and severity of depressive symptoms was not reliable because we only found the evidence that supported a positive correlation in the Anding dataset not in the Tongren dataset. More importantly, the correlation only showed the trend towards significance after controlling for the anxiety symptoms or disappeared after excluding the remitted patients who had extremely low HAMD scores. The non-repeatable correlation between victim sensitivity and severity of depressive symptoms suggests that the increased victim sensitivity may be independent of depressive severity; however, this conclusion should be verified with a larger sample size and in the remitted patients. All of these findings - the repeatable findings on the higher victim sensitivity in the MDD patients in both of the two sites and the non-repeatable clinical correlates of the victim sensitivity - suggest that higher victim sensitivity may be independent of illness course, severity, or medication. Combining the previous findings obtained in non-depressed samples with ours, it seems higher victim sensitivity may be an important psychopathological feature for depression. In a longitudinal study of German adolescents, victim sensitivity increased the stabilization of depressive symptoms if they were present at baseline, suggesting that victim sensitivity may be a maintaining factor for depression (<xref rid=\"B6\" ref-type=\"bibr\">6</xref>). In future work, the role of victim sensitivity in the occurrence and progression of depression should be further examined in a longitudinal study.</p><p>The present study indicated that patients with MDD are more likely to perceive injustice and respond to injustice to one’s own disadvantage. Negative bias and dysfunctional beliefs about the self may account for this hypersensitivity in MDD patients. Much evidence supports the notion that MDD patients may suffer from dysfunctional cognition, which leads them to generate negative perceptions of social interactions in line with the cognitive model of depression (<xref rid=\"B4\" ref-type=\"bibr\">4</xref>). A negative bias has repeatedly been observed among MDD patients when processing facial emotions and moral and social emotions (for a review, please see <xref rid=\"B44\" ref-type=\"bibr\">44</xref>). Dysfunctional beliefs about the self are also observed in MDD. For example, the patients with MDD showed increased affective responses to the same events as the healthy controls experience, as observed in the patients with chronic depression when they were facing mood induction materials individualized with autobiographical content (<xref rid=\"B45\" ref-type=\"bibr\">45</xref>). The MDD patients tended to judge a proposal as less fair when they are in disadvantaged situation (<xref rid=\"B46\" ref-type=\"bibr\">46</xref>), suggesting they were more sensitive to injustice. These patients were more likely to reject an unfair proposal as retaliation (<xref rid=\"B46\" ref-type=\"bibr\">46</xref>, <xref rid=\"B47\" ref-type=\"bibr\">47</xref>). Therefore, it is possible that this negative bias and dysfunctional cognition about the self makes MDD patients more sensitive to injustice. Finally, we should note that depressive symptoms often lead to deterioration of social relationships and vice versa (<xref rid=\"B48\" ref-type=\"bibr\">48</xref>, <xref rid=\"B49\" ref-type=\"bibr\">49</xref>), which will increase the likelihood of exposure to a disadvantaged situation and thus increase the chance of experiencing injustice as a victim.</p></sec><sec id=\"s4_2\"><title>Neural Basis of Increased Victim Sensitivity</title><p>Using fALFF as a search tool, we found a contrary pattern regarding the association between the spontaneous activity in the right DLPFC and victim sensitivity among the MDD patients versus healthy controls. Specifically, for healthy populations, the level of spontaneous activity in the right DLPFC was negatively correlated with the level of victim sensitivity. However, for MDD patients, the level of spontaneous activity in the right DLPFC was positively correlated with the level of victim sensitivity.</p><p>The right DLPFC is strongly associated with cognitive control (<xref rid=\"B50\" ref-type=\"bibr\">50</xref>, <xref rid=\"B51\" ref-type=\"bibr\">51</xref>). It is well-established that superior cognitive control is related to lower levels of unwanted emotional responses, such as anger and aggression (<xref rid=\"B52\" ref-type=\"bibr\">52</xref>). A temporal decrease in the activity of the right DLPFC using neuromodulation techniques increased revenge-seeking, emotion-driven behavior (<xref rid=\"B53\" ref-type=\"bibr\">53</xref>), supporting the role of cognitive control subserved by the right DLPFC in overcoming unwanted emotional responses. Further evidence for the role of cognitive control in victim sensitivity is seen in the relationship between decreased spontaneous activity in the DLPFC indicated by regional homogeneity and higher rumination in healthy subjects, which suggests less efficient inhibitory control on the increased negative self-focused conflicts (<xref rid=\"B54\" ref-type=\"bibr\">54</xref>). Taking all of this evidence together, it is possible that individuals with low sensitivity to injustice as a victim have already had efficient control resources to self-regulate the influence of the disadvantaged situations (i.e., less likely to respond to injustice to one’s own disadvantage <italic>via</italic> anger, sadness, and rumination), and this effortful self-control is reflected by the higher spontaneous activity in the right DLPFC. We noted that this finding is in contrast to a previous study, which found that victim sensitivity was positively correlated with regional activity of the paracentral lobule (<xref rid=\"B24\" ref-type=\"bibr\">24</xref>). This inconsistency might be due to the differences in the index measuring spontaneous brain activity. Different from the fALFF, which reflects the spontaneous brain activity in an area (<xref rid=\"B25\" ref-type=\"bibr\">25</xref>), used in the present study, regional homogeneity was used in Wu’s paper, which measures the temporal synchronization of the time series of an area’s nearest neighbors (<xref rid=\"B55\" ref-type=\"bibr\">55</xref>).</p><p>It’s interesting that, unlike the healthy controls, the MDD patients revealed a positive correlation between victim sensitivity and the fALFF in the right DLPFC. This reverse pattern between depressed patients and healthy controls is also seen in previous studies, such as the relationship between the DLPFC volume and rumination (<xref rid=\"B54\" ref-type=\"bibr\">54</xref>). MDD is characterized by a failure in cognitive control of emotion (<xref rid=\"B56\" ref-type=\"bibr\">56</xref>). Although many studies found hypoactivity in the DLPFC in the MDD patients, increased or unchanged brain activity in this region compared to the healthy controls are also reported. For example, increased activity in the DLPFC to negative stimuli has been consistently found in young MDD patients based on a meta-analysis, suggesting that MDD patients have inefficient emotional regulation during affective processing (<xref rid=\"B57\" ref-type=\"bibr\">57</xref>). MDD patients also showed increased brain activity in the DLPFC to maintain a similar level of behavioral performance as controls during cognitive control (<xref rid=\"B58\" ref-type=\"bibr\">58</xref>). The hyperactivity in the DLPFC may be a reflection of inefficient cognitive control or compensation in the MDD patients. It needs to be noted that in this study we found that the spontaneous activity in the right DLPFC in the MDD patients was comparable with that in the healthy controls, but it was positively correlated with victim sensitivity in the patients. This finding may suggest that, even though the MDD patients recruit cognitive control resources to regulate their emotional responses (e.g., anger or rumination) when they are in an unjust situation, they fail in inhibiting the unwanted emotions or ruminating on one’s own disadvantage due to inefficient cognitive control or failure in recruiting more resources, and are thus more likely to respond to unjust situations <italic>via</italic> emotional responses (i.e., high sensitivity to injustice as a victim).</p><p>The cognitive control explanation subserved by the DLPFC is compatible with negative bias, which may be the candidate psychological process behind the hypersensitivity in the MDD patients as we discussed. Previous studies on the neural basis of negative bias have already indicated that impaired top-down cognitive control reflected by deficits in the DLPFC function play a vital role in negative bias (<xref rid=\"B59\" ref-type=\"bibr\">59</xref>). However, the exact psychological mechanisms still need to be determined in the future.</p></sec><sec id=\"s4_3\"><title>Limitations</title><p>There are several possible limitations in this study. First, even though higher victim sensitivity was found in both of the two samples, the neural basis of higher victim sensitivity in the MDD patients was only examined in one sample, some of whom were in the remission period and most of whom were medicated. Previous studies suggest differences in spontaneous brain activities and connectivity between MDD patients experiencing their first episode and remitted MDD patients or between drug-naïve MDD patients and medicated MDD patients (<xref rid=\"B29\" ref-type=\"bibr\">29</xref>, <xref rid=\"B60\" ref-type=\"bibr\">60</xref>, <xref rid=\"B61\" ref-type=\"bibr\">61</xref>). Thus, further validation is needed in a group of patients with higher homogeneity in clinical profiles, such as a group of patients who are experiencing their first episode and are medication free, or a group of remitted patients. It is more interesting to investigate whether the correlations between victim sensitivity and spontaneous brain activities is trait- or state-related. Secondly, the sample size is relatively small in the current study, especially in the part of neuroimaging analysis; however it is comparable to previous studies that involve the recruitment of MDD patients for fALFF analyses (for a review, please see also <xref rid=\"B29\" ref-type=\"bibr\">29</xref>). The current findings need to be validated using a larger sample size. Thirdly, our findings suggest that the right DLPFC plays a significant role in victim sensitivity, both in the healthy controls and in depressed patients; however, the exact function of the DLPFC in victim sensitivity still needs to be determined by the adoption of task-based fMRI. For example, in an ultimatum game, increased activity in the right DLPFC has been repeatedly observed when the participants faced unfair proposals or unjust treatment (<xref rid=\"B15\" ref-type=\"bibr\">15</xref>, <xref rid=\"B17\" ref-type=\"bibr\">17</xref>).</p></sec></sec><sec sec-type=\"conclusions\" id=\"s5\"><title>Conclusions</title><p>In summary, the current study investigated the hypothesis that the MDD patients have higher victim sensitivity and obtained consistent findings across two clinical samples. Furthermore, we found that the MDD patients differ from the healthy controls in the neural correlates of victim sensitivity. That is, the spontaneous activity in the right DLPFC showed contrary correlation with victim sensitivity in the healthy controls and in the MDD patients. These findings enrich our understanding of personality traits related to depression, and shed light on the link between cognitive control and negative bias about the self among MDD patients.</p></sec><sec sec-type=\"data-availability\" id=\"s6\"><title>Data Availability Statement</title><p>The datasets generated for this study are available on request to the corresponding author.</p></sec><sec id=\"s7\"><title>Ethics Statement</title><p>The studies involving human participants were reviewed and approved by Institutional Review Boards of Beijing Tongren Hospital, Beijing Anding Hospital, Capital Medical University, as well as the Institute of Psychology, Chinese Academy of Sciences. The patients/participants provided their written informed consent to participate in this study.</p></sec><sec id=\"s8\"><title>Author Contributions</title><p>All authors contributed to the article and approved the submitted version. SC and QG collected data. XW, SC, and YW analyzed the data. YZ designed this study. XW, YZ, and MW drafted the manuscript. All authors revised the manuscript.</p></sec><sec sec-type=\"funding-information\" id=\"s9\"><title>Funding </title><p>Funding for this study was provided by the Natural Science Foundation of China (grant number 81771473) and the State High-Tech Development Plan of China (863) (grant number 2015AA020513), which provided support for the data collection.</p></sec><sec id=\"s10\"><title>Conflict of Interest</title><p>The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p></sec></body><back><ack><title>Acknowledgments</title><p>We would like to thank all the participants who contributed to this study. We also gratefully acknowledge the support from the staff in the Beijing Anding Hospital and Beijing Tongren Hospital. 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[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Front Neurosci</journal-id><journal-id journal-id-type=\"iso-abbrev\">Front Neurosci</journal-id><journal-id journal-id-type=\"publisher-id\">Front. Neurosci.</journal-id><journal-title-group><journal-title>Frontiers in Neuroscience</journal-title></journal-title-group><issn pub-type=\"ppub\">1662-4548</issn><issn pub-type=\"epub\">1662-453X</issn><publisher><publisher-name>Frontiers Media S.A.</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32848571</article-id><article-id pub-id-type=\"pmc\">PMC7432151</article-id><article-id pub-id-type=\"doi\">10.3389/fnins.2020.00803</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Neuroscience</subject><subj-group><subject>Original Research</subject></subj-group></subj-group></article-categories><title-group><article-title>Oxytocin Facilitation of Emotional Empathy Is Associated With Increased Eye Gaze Toward the Faces of Individuals in Emotional Contexts</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Le</surname><given-names>Jiao</given-names></name><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/1000510/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Kou</surname><given-names>Juan</given-names></name></contrib><contrib contrib-type=\"author\"><name><surname>Zhao</surname><given-names>Weihua</given-names></name><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/584911/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Fu</surname><given-names>Meina</given-names></name></contrib><contrib contrib-type=\"author\"><name><surname>Zhang</surname><given-names>Yingying</given-names></name></contrib><contrib contrib-type=\"author\"><name><surname>Becker</surname><given-names>Benjamin</given-names></name><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/113198/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Kendrick</surname><given-names>Keith M.</given-names></name><xref ref-type=\"corresp\" rid=\"c001\"><sup>*</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/78919/overview\"/></contrib></contrib-group><aff><institution>The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China</institution>, <addr-line>Chengdu</addr-line>, <country>China</country></aff><author-notes><fn fn-type=\"edited-by\"><p>Edited by: Kerstin Uvnäs Moberg, Swedish University of Agricultural Sciences, Sweden</p></fn><fn fn-type=\"edited-by\"><p>Reviewed by: Gábor B. Makara, Hungarian Academy of Sciences (MTA), Hungary; Ben Nephew, Worcester Polytechnic Institute, United States</p></fn><corresp id=\"c001\">*Correspondence: Keith M. Kendrick, <email>[email protected]</email>; <email>[email protected]</email></corresp><fn fn-type=\"other\" id=\"fn004\"><p>This article was submitted to Neuroendocrine Science, a section of the journal Frontiers in Neuroscience</p></fn></author-notes><pub-date pub-type=\"epub\"><day>11</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><year>2020</year></pub-date><volume>14</volume><elocation-id>803</elocation-id><history><date date-type=\"received\"><day>08</day><month>5</month><year>2020</year></date><date date-type=\"accepted\"><day>08</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>Copyright © 2020 Le, Kou, Zhao, Fu, Zhang, Becker and Kendrick.</copyright-statement><copyright-year>2020</copyright-year><copyright-holder>Le, Kou, Zhao, Fu, Zhang, Becker and Kendrick</copyright-holder><license xlink:href=\"http://creativecommons.org/licenses/by/4.0/\"><license-p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</license-p></license></permissions><abstract><p>One of the most robust effects of intranasal oxytocin treatment is its enhancement of emotional empathy responses across cultures to individuals displaying emotions in realistic contexts in the Multifaceted Empathy Task (MET). However, it is not established if this effect of oxytocin on emotional empathy is due to altered visual attention toward different components of the stimulus pictures or an enhanced empathic response. In the current randomized placebo-controlled within-subject experiment on 40 healthy male individuals, we both attempted a further replication of emotional empathy enhancement by intranasal oxytocin (24 IU) and used eye-tracking measures to determine if this was associated by altered visual attention toward different components of the picture stimuli (background context, human face, and body posture). Results replicated previous findings of enhanced emotional empathy in response to both negative and positive stimuli and that this was associated with an increased proportion of time viewing the faces of humans in the pictures and a corresponding decrease in that toward the rest of the body and/or background context. Overall, our findings suggest that enhanced emotional empathy following oxytocin administration is due to increased attention to the faces of others displaying emotions and away from other contextual and social cues.</p><p><bold>Clinical Trial Registration:</bold>\n<ext-link ext-link-type=\"uri\" xlink:href=\"http://www.ClinicalTrials.gov\">www.ClinicalTrials.gov</ext-link> Oxytocin Modulates Eye Gaze Behavior During Social Processing; registration ID: NCT03293511; URL: <ext-link ext-link-type=\"uri\" xlink:href=\"https://clinicaltrials.gov/ct2/show/NCT03293511\">https://clinicaltrials.gov/ct2/show/NCT03293511</ext-link>.</p></abstract><kwd-group><kwd>autism</kwd><kwd>oxytocin</kwd><kwd>emotional empathy</kwd><kwd>eye gaze</kwd><kwd>face processing</kwd><kwd>social attention</kwd></kwd-group><funding-group><award-group><funding-source id=\"cn001\">National Natural Science Foundation of China<named-content content-type=\"fundref-id\">10.13039/501100001809</named-content></funding-source></award-group><award-group><funding-source id=\"cn002\">China Postdoctoral Science Foundation<named-content content-type=\"fundref-id\">10.13039/501100002858</named-content></funding-source></award-group><award-group><funding-source id=\"cn003\">Government of Guangdong Province<named-content content-type=\"fundref-id\">10.13039/501100002912</named-content></funding-source></award-group></funding-group><counts><fig-count count=\"3\"/><table-count count=\"0\"/><equation-count count=\"0\"/><ref-count count=\"46\"/><page-count count=\"9\"/><word-count count=\"0\"/></counts></article-meta></front><body><sec id=\"S1\"><title>Introduction</title><p>Empathizing with others is of key importance for our social interactions. Impairments in this domain represent a major symptom in several psychiatric disorders such as autism (<xref rid=\"B30\" ref-type=\"bibr\">Lombardo et al., 2007</xref>) and depression (<xref rid=\"B40\" ref-type=\"bibr\">Tully et al., 2016</xref>; <xref rid=\"B43\" ref-type=\"bibr\">Xu et al., 2020</xref>), leading to significant problems in understanding and responding appropriately to others in social contexts. Empathy refers to a multidimensional construct including both cognitive (identifying emotions expressed by others) and emotional (being aroused by – indirect emotional empathy or feeling the same emotion – direct emotional empathy – expressed by others) facets (<xref rid=\"B37\" ref-type=\"bibr\">Shamay-Tsoory, 2011</xref>). Understanding the neurobiological regulation of these multidimensional functional domains is therefore of great importance both in the context of social neuroscience as well as to identify novel therapeutic approaches to facilitate empathy and alleviate deficits in patient populations.</p><p>In recent years, a number of studies have consistently implicated the hypothalamic neuropeptide oxytocin (OT) in the control of emotional empathy in humans (<xref rid=\"B22\" ref-type=\"bibr\">Hurlemann et al., 2010</xref>; <xref rid=\"B20\" ref-type=\"bibr\">Hubble et al., 2017a</xref>; <xref rid=\"B16\" ref-type=\"bibr\">Geng et al., 2018a</xref>, <xref rid=\"B17\" ref-type=\"bibr\">b</xref>). Intranasal OT has been demonstrated to facilitate both direct and indirect emotional empathy but not cognitive empathy as assessed by the Multifaceted Empathy Task (<xref rid=\"B10\" ref-type=\"bibr\">Dziobek et al., 2008</xref>) in male Caucasian (<xref rid=\"B22\" ref-type=\"bibr\">Hurlemann et al., 2010</xref>) as well as in both male and female Chinese subjects (<xref rid=\"B17\" ref-type=\"bibr\">Geng et al., 2018b</xref>). The latter study also found similar effects of OT on increasing emotional empathy in male subjects with both 24 and 48 IU doses. Similarly, another study using dynamic, empathy-inducing video clips reported that OT enhanced emotional but not cognitive empathy for fear (<xref rid=\"B20\" ref-type=\"bibr\">Hubble et al., 2017a</xref>), and several studies have reported that OT can increase empathy for pain experienced by others in both sexes (<xref rid=\"B36\" ref-type=\"bibr\">Shamay-Tsoory et al., 2013</xref>; <xref rid=\"B1\" ref-type=\"bibr\">Abu-Akel et al., 2015</xref>). Studies that employed concomitant functional MRI (fMRI) assessments suggest that the emotional empathy-enhancing effects of OT are neurally mediated by reduced responsiveness of the amygdala to empathy-inducing scenes (<xref rid=\"B16\" ref-type=\"bibr\">Geng et al., 2018a</xref>, <xref rid=\"B17\" ref-type=\"bibr\">b</xref>). The important role of the amygdala in emotional empathy is further supported by studies in patients with focal bilateral amygdala lesions who exhibit impaired emotional but not cognitive empathy (<xref rid=\"B22\" ref-type=\"bibr\">Hurlemann et al., 2010</xref>).</p><p>Accumulating evidence suggests that OT may generally promote face emotion recognition accuracy (<xref rid=\"B35\" ref-type=\"bibr\">Shahrestani et al., 2013</xref>), and initial studies reported increased accuracy only for difficult items in the reading the mind in the eyes test (RMET) (<xref rid=\"B7\" ref-type=\"bibr\">Domes et al., 2007</xref>), which, together, may reflect a facilitatory effect of OT on some aspects of cognitive empathy. On the other hand, more recent studies using the RMET have either only reported effects in individuals with low pretreatment empathy (<xref rid=\"B12\" ref-type=\"bibr\">Feeser et al., 2015</xref>), low social proficiency, or higher maternal love withdrawal (<xref rid=\"B34\" ref-type=\"bibr\">Riem et al., 2014</xref>), or failed to find any effects at all (<xref rid=\"B33\" ref-type=\"bibr\">Radke and de Bruijn, 2015</xref>). Thus, to date the most consistent findings indicate a facilitation of emotional empathy by intranasal OT.</p><p>There is also increasing evidence for OT playing a key role in enhancing attention toward social stimuli in a person- and context-dependent manner, and this forms the basis of the proposed “social salience hypothesis” (see <xref rid=\"B38\" ref-type=\"bibr\">Shamay-Tsoory and Abu-Akel, 2016</xref>). In support of this hypothesis, studies employing selective attention paradigms have demonstrated that OT increases attention toward social stimuli but not non-social ones (<xref rid=\"B42\" ref-type=\"bibr\">Xu et al., 2015</xref>, <xref rid=\"B44\" ref-type=\"bibr\">2019</xref>) and promotes switching attention away from interoceptive information toward external social cues via modulating the anterior insula, a core region of the salience network (<xref rid=\"B45\" ref-type=\"bibr\">Yao et al., 2018</xref>). Studies that acquired eye-tracking indices as a behavioral measure of attentive and salience processing additionally reported that OT increases the time spent viewing social relative to non-social stimuli across a number of different paradigms (<xref rid=\"B11\" ref-type=\"bibr\">Eckstein et al., 2019</xref>; <xref rid=\"B26\" ref-type=\"bibr\">Le et al., 2020</xref> – using the same subjects as in the current study). A number of studies have reported that OT specifically increases gaze toward the eye region of both static and dynamic facial stimuli in either healthy or autistic individuals (<xref rid=\"B19\" ref-type=\"bibr\">Guastella et al., 2008</xref>; <xref rid=\"B2\" ref-type=\"bibr\">Andari et al., 2010</xref>; <xref rid=\"B9\" ref-type=\"bibr\">Domes et al., 2013</xref>; <xref rid=\"B3\" ref-type=\"bibr\">Auyeung et al., 2015</xref>), although others have reported no effects (<xref rid=\"B8\" ref-type=\"bibr\">Domes et al., 2010</xref>; <xref rid=\"B28\" ref-type=\"bibr\">Lischke et al., 2012</xref>; <xref rid=\"B21\" ref-type=\"bibr\">Hubble et al., 2017b</xref>). Saccades from the mouth to the eye region are also increased following OT for fear expression faces and are generally associated with amygdala responses (<xref rid=\"B13\" ref-type=\"bibr\">Gamer et al., 2010</xref>). Inconsistencies between the previous studies may be explained by a number of factors such as subject sex, whether stimuli are presented statically or dynamically and whether participants were asked to passively view or actively evaluate the presented stimuli. In our own previous study in the current cohort of subjects, we found that OT primarily increased the proportion of time spent viewing the eyes relative to the nose for static fearful, but not other emotional facial expressions presented passively (<xref rid=\"B26\" ref-type=\"bibr\">Le et al., 2020</xref>). In the context of visual stimuli-evoked empathic responses, only one previous study has directly investigated the effects of intranasal OT and reported that OT increased eye gaze across dynamic expressions of sadness, happiness, pain, or fear (<xref rid=\"B20\" ref-type=\"bibr\">Hubble et al., 2017a</xref>), and empathic empathy for fearful faces. However, this study found no correlation between the proportion of gaze time toward the eyes and empathy ratings. Another study has also reported no effects of OT on gaze toward positive, negative, and neutral valence scenes depicting both humans and objects despite finding effects on neural responses to them (<xref rid=\"B29\" ref-type=\"bibr\">Lischke et al., 2017</xref>). We have therefore investigated the effects of OT on eye gaze toward different components of negative and positive valence stimuli designed to evoke empathic responses and their association with its effects on enhancing emotional empathy using the Multifaceted Empathy Task (MET).</p><p>The current preregistered pharmacological eye-tracking study employed a randomized within-subject placebo-controlled design to investigate the effects of intranasal OT (24 IU) on patterns of eye gaze in male subjects performing the MET and to replicate previous findings demonstrating OT-induced facilitation of emotional empathy (<xref rid=\"B22\" ref-type=\"bibr\">Hurlemann et al., 2010</xref>; <xref rid=\"B16\" ref-type=\"bibr\">Geng et al., 2018a</xref>, <xref rid=\"B17\" ref-type=\"bibr\">b</xref>). We only included male subjects in the current study due to the main focus of our clinical trial being relevance to autism and, additionally, because we had already established that there are no sex-dependent effects of OT on responses in the MET (<xref rid=\"B17\" ref-type=\"bibr\">Geng et al., 2018b</xref>). Based on the previous studies reporting that OT facilitated facial emotion recognition and eye gaze toward facial stimuli, we hypothesized that the OT-induced facilitation of emotional empathy would be associated with increased gaze time toward the face and away from other less socially salient features of both positive and negative valence picture stimuli. We also hypothesized that the observed effects of OT on emotional empathy ratings and time spent viewing faces would be associated with individual variations in autistic and/or empathic traits.</p></sec><sec sec-type=\"materials|methods\" id=\"S2\"><title>Materials and Methods</title><sec id=\"S2.SS1\"><title>Participants</title><p>Forty healthy male adults (mean age ± SEM = 20.8 ± 0.38) were recruited via university advertisement. Only male subjects were recruited due to the main focus on potential relevance to autism and because the majority of previous studies have also only used male subjects. In a within subject design study, each participant underwent the experimental paradigm twice and with a between assessment and treatment period of approximately 2 weeks (mean ± SEM = 14.86 ± 0.16 days). Subjects were randomly assigned to receive either OXT (24 IU OXT in water, 0.9% sodium chloride, and glycerol supplied by the Sichuan Meike Pharmaceutical Company, China) or placebo (PLC containing all ingredients except for the neuropeptide and supplied by the same company) intranasal administration. The order of receiving OXT or PLC was counterbalanced across subjects. Subject number was determined by an <italic>a priori</italic> power analysis (using G<sup>∗</sup>power version 3.1.9.4 with α = 0.05 and a 0.5 correlation between repeated measures), which showed that a within-subject design would achieve >80% power for a medium effect size both with ANOVAs (<italic>F</italic> = 0.25, partial eta squared, 0.06) and <italic>post hoc</italic> pairwise comparisons (Cohen’s <italic>d</italic> = 0.5) with a total of 36 subjects. Finally, 40 subjects were finally recruited to compensate for potential subject dropout and data collection issues. Subjects were required to abstain from caffeine, alcohol, nicotine, or other psychoactive substances in the 24 h before each experiment, and initial interviews confirmed the self-reported absence of current or previous psychiatric illness, drug, or alcohol abuse. All participants had normal or corrected to normal vision. The study had full approval from the local ethics committee of the University of Electronic Science and Technology of China, and procedures were in accordance with the latest revision of the declaration of Helsinki. The study was preregistered at clinical trials.gov (Trial registration ID: NCT03293511; URL: <ext-link ext-link-type=\"uri\" xlink:href=\"https://clinicaltrials.gov/ct2/show/NCT03293511\">https://clinicaltrials.gov/ct2/show/NCT03293511</ext-link>). The experiment was part of a larger study on the effects of intranasal OT on social attention during which subjects completed seven different tasks. Data from the other tasks involving social versus non-social stimuli and face emotion processing presented immediately prior to the current empathy task have been published elsewhere (<xref rid=\"B26\" ref-type=\"bibr\">Le et al., 2020</xref>). All subjects signed written informed consent and were paid for participation (100RMB).</p></sec><sec id=\"S2.SS2\"><title>Experimental Procedure</title><p>In a double-blind placebo-controlled within-subject design, subjects self-administered the nasal sprays (24 IU, three puffs per nostril, each containing 4 IU of OXT or the same number of puffs with the PLC spray) following a standardized protocol (<xref rid=\"B18\" ref-type=\"bibr\">Guastella et al., 2013</xref>). At the first visit before treatment administration, participants initially completed a set of Chinese version questionnaires to evaluate mood and personality traits as a control for possible confounders due to any pretreatment group differences [State Trait Anxiety Inventory (STAI), <xref rid=\"B39\" ref-type=\"bibr\">Shek, 1993</xref>; Childhood Trauma Questionnaire (CTQ), <xref rid=\"B4\" ref-type=\"bibr\">Bernstein et al., 2003</xref>; <xref rid=\"B46\" ref-type=\"bibr\">Zhao et al., 2005</xref>]. The Positive and Negative Affect Schedule (PANAS; <xref rid=\"B41\" ref-type=\"bibr\">Watson et al., 1988</xref>) was completed three times: before treatment administration (t1) and before (t2) and after the task (t3) in order to assess possible treatment effects on mood. For the assessment of associations with experimental findings in the two groups in relation to autistic traits and empathy Chinese versions of the Autism Spectrum Quotient (AQ; <xref rid=\"B25\" ref-type=\"bibr\">Lau et al., 2013</xref>), social responsivity scale (SRS; <xref rid=\"B15\" ref-type=\"bibr\">Gau et al., 2013</xref>), and interpersonal responsivity index (IRI; <xref rid=\"B6\" ref-type=\"bibr\">Davis, 1980</xref>) were used. All subjects started the eye-tracking task 45 min after receiving the nasal spray.</p><p>The empathy task paradigm used was a Chinese version of the MET originally used in Caucasian subjects (<xref rid=\"B10\" ref-type=\"bibr\">Dziobek et al., 2008</xref>; <xref rid=\"B22\" ref-type=\"bibr\">Hurlemann et al., 2010</xref>) and described previously (<xref rid=\"B17\" ref-type=\"bibr\">Geng et al., 2018b</xref>). An adapted and shortened version of the original task was employed in the current eye-tracking study using identical real-life picture stimuli (30 positive and 30 negative valence) of people (both sexes and variable ages) displaying strong positive or negative emotions via face expressions and body posture in a particular context (displayed in the background of the picture). Stimuli were presented in a randomized order for 3 s followed by a fixation cross for 3 s (total task duration = 360 s). Subjects were required to view the stimuli passively. After the eye-tracking task, subjects were shown the picture stimuli again in a different randomized order and required to rate each one for how much they felt the same feelings as the person in the picture (i.e., direct emotional empathy) on a scale of 1–9 (1 = not at all and 9 = very strongly). Pictures were presented for a maximum of 4 s, and the rating scale was displayed under each picture. Subjects indicated their rating score by using a keyboard to move a cursor across the scale on the display screen. For flow charts showing the whole experimental procedure, see <xref ref-type=\"supplementary-material\" rid=\"FS1\">Supplementary Figure S1</xref>.</p></sec><sec id=\"S2.SS3\"><title>Eye-Tracking Data</title><p>Eye-gaze data were recorded using an eye tracker (Tobii TX300, Tobii, Danderyd, Sweden), which employs infrared (IR) light-emitting diodes and IR camera to measure corneal reflections and calculate the eye-gaze direction. All gaze data were recorded at 300 Hz sampling rate with a gaze accuracy of 0.4°. Recording and stimulus presentation were conducted using Tobii Pro Studio, E-prime 2.0 software, and E-Prime Extensions for Tobii (Psychology Software Tools, Pittsburgh, PA, United States). All raw data with at least 80% gaze weight were analyzed using Tobii studio. The primary measure collected was total fixation duration, although additionally the number and duration of individual fixations were measured in order to help interpret which were contributing to observed significant changes in overall fixation duration.</p></sec><sec id=\"S2.SS4\"><title>Statistical Analyses</title><p>All statistical analyses were performed using SPSS 22 (SPSS Inc., Chicago, IL, United States). Three-way repeated ANOVAs were performed with within-subject factors (treatment – OXT, PLC), AOI (background, body, and face), and valence (positive and negative) and percentage of total fixation duration as dependent variable. Separate analyses were also performed using percentage of fixation counts and individual fixation durations as dependent variables. The wide range of orientations and sizes of the faces of individuals portrayed in the picture stimuli of the MET effectively precluded a finer grained analysis of individual face features as AOIs. Similar analyses were performed with percentage of fixation counts and mean duration of individual fixations as dependent variables in order to determine whether alterations in total fixation duration were associated with altered numbers or durations of individual fixations. Significant (<italic>p</italic> < 0.05) interactions were explored using Bonferroni-corrected <italic>post hoc</italic> tests. Associations between eye-tracking variables and emotional empathy ratings and autistic and empathy traits (AQ, SRS, and IRI scores) were analyzed using Spearman correlation. To correct for multiple comparisons involving behavioral traits (three questionnaires), the threshold <italic>p</italic> value was adjusted to <italic>p</italic> < 0.0167 for the correlation analyses (e.g., <italic>p</italic> = 0.05/3 = 0.0167).</p></sec></sec><sec id=\"S3\"><title>Results</title><sec id=\"S3.SS1\"><title>Effects of Treatment on Mood and Overall Gaze Time</title><p>Independent <italic>t</italic> tests revealed no significant differences in terms of mood and personality traits between subjects with the two different treatment orders (see <xref ref-type=\"supplementary-material\" rid=\"FS1\">Supplementary Table S1</xref>). A two-way repeated ANOVA with two treatment × three time points for PANAS scores revealed no significant main or interaction treatment effects on positive or negative mood (all <italic>p</italic>’s > 0.665). There was no significant difference in the total amount of time subjects spent viewing the screen between treatments (<italic>t</italic> = 0.367, <italic>p</italic> = 0.716). Thus, OT had no overall effect on time spent viewing the stimulus screen.</p></sec><sec id=\"S3.SS2\"><title>Effects of Oxytocin on Emotional Empathy and Patterns of Eye Gaze</title><p>Five subjects were excluded from analysis in this task due to technical problems with data collection on one or both of their experimental sessions. Thus, <italic>n</italic> = 35 subjects were included in the final analysis. We first confirmed that we could replicate previous observations that OT enhanced emotional empathy ratings. Paired <italic>t</italic> tests revealed that OT significantly increased empathy ratings for both negative [<italic>t</italic><sub>(1,34)</sub> = 2.804, <italic>p</italic> = 0.008, Cohen’s <italic>d</italic> = 0.486] and positive valence stimuli [<italic>t</italic><sub>(1,34)</sub> = 2.715, <italic>p</italic> = 0.010, Cohen’s <italic>d</italic> = 0.469] compared to PLC (see <xref ref-type=\"fig\" rid=\"F1\">Figure 1</xref>). There was no treatment difference in rating scores (i.e., OT minus PLC) for positive and negative valence pictures (<italic>p</italic> = 0.549) indicating that OT had a similar effect on both valences.</p><fig id=\"F1\" position=\"float\"><label>FIGURE 1</label><caption><p>The effects of oxytocin (OT) on emotional empathy ratings. Subjects were asked to rate positive and negative valence pictures on: “how much do you feel the same as the person in the picture (i.e., direct emotional empathy)” from 1 to 9 (1 = not at all and 9 = very strongly). Error bars represent SEM. **<italic>p</italic> < 0.01, OXT vs. PLC and positive vs. negative valence.</p></caption><graphic xlink:href=\"fnins-14-00803-g001\"/></fig><p>For the eye-tracking component of the study, we used three-way ANOVAs with treatment (OT and PLC), AOIs (human face, human body, and background), and valence (positive and negative) as factors. For the primary dependent variable (proportion of time spent viewing the three AOIs relative to the whole screen), a main effect of AOI [<italic>F</italic><sub>(2,68)</sub> = 501.836, <italic>p</italic> < 0.001, partial η<sup>2</sup> = 0.937] but not treatment [<italic>F</italic><sub>(1,34)</sub> = 1.250, <italic>p</italic> = 0.271] or valence [<italic>F</italic><sub>(1,34)</sub> = 0.142, <italic>p</italic> = 0.709] was observed. The main effect of AOI was due to subjects spending proportionately more time viewing human faces and bodies compared with the background (<italic>p</italic>’s < 0.001) and on human faces as compared to bodies (<italic>p</italic> < 0.001) (see <xref ref-type=\"fig\" rid=\"F2\">Figure 2A</xref>). There were also significant treatment × AOI [<italic>F</italic><sub>(2,68)</sub> = 5.898, <italic>p</italic> = 0.013, partial η<sup>2</sup> = 0.148] and AOI × valence [<italic>F</italic><sub>(2,68)</sub> = 21.737, <italic>p</italic> < 0.001, partial η<sup>2</sup> = 0.390] interaction effects. <italic>Post hoc</italic> Bonferroni corrected <italic>t</italic> tests revealed that OT increased the percentage of time spent viewing faces [<italic>t</italic><sub>(1,34)</sub> = 2.642, <italic>p</italic> = 0.012, Cohen’s <italic>d</italic> = 0.525] but correspondingly decreased that for viewing the background [<italic>t</italic><sub>(1,34)</sub> = −2.291, <italic>p</italic> = 0.028, Cohen’s <italic>d</italic> = 0.559] (see <xref ref-type=\"fig\" rid=\"F2\">Figure 2B</xref>). For the AOI × valence interaction effect <italic>post hoc</italic> tests also revealed that for negative compared with positive valence stimuli, subjects spent a greater proportion of time viewing the face (<italic>p</italic> = 0.012) and background (<italic>p</italic> < 0.001) but less for the body (<italic>p</italic> < 0.001). There were no treatment × valence [<italic>F</italic><sub>(1,34)</sub> = 0.482, <italic>p</italic> = 0.492] or treatment × regions × valence [<italic>F</italic><sub>(2,68)</sub> = 0.260, <italic>p</italic> = 0.746] interaction effects.</p><fig id=\"F2\" position=\"float\"><label>FIGURE 2</label><caption><p>The effects of oxytocin (OT) treatment on eye gaze directed toward the face, body, or background regions during the emotional empathy (EE) task. <bold>(A)</bold> The mean percentage total fixation duration of face, body, and background across two valence (positive, negative). <bold>(B)</bold> The effects of oxytocin (OT) treatment on mean percentage total fixation duration of face, body, and background across two valence (positive, negative). Error bars represent SEM. ***<italic>p</italic> < 0.001, *<italic>p</italic> < 0.05, OXT vs. PLC.</p></caption><graphic xlink:href=\"fnins-14-00803-g002\"/></fig><p>Identical ANOVA analyses were conducted for proportion of fixation counts and individual fixation durations. For proportion of fixation counts, we found similar results as for proportion of total viewing time, indicating that the latter were mainly due to an increased number of fixations. There was a significant main effect of AOI [<italic>F</italic><sub>(2,68)</sub> = 514.768, <italic>p</italic> < 0.001, partial η<sup>2</sup> = 0.938] but not for either treatment [<italic>F</italic><sub>(1,34)</sub> = 1.830, <italic>p</italic> = 0.185] or valence [<italic>F</italic><sub>(1,34)</sub> = 0.000, <italic>p</italic> = 0.993]. The AOI main effect again reflected a higher proportion of fixation counts to the face and body compared with the background (<italic>p</italic>’s < 0.001) and for the face compared to the body (<italic>p</italic> < 0.001). There were also significant treatment × region [<italic>F</italic><sub>(2,68)</sub> = 4.518, <italic>p</italic> = 0.030, partial η<sup>2</sup> = 0.117] and valence × region [<italic>F</italic><sub>(2,68)</sub> = 28.573, <italic>p</italic> < 0.001, partial η<sup>2</sup> = 0.457] interaction effects. <italic>Post hoc</italic> Bonferroni-corrected tests revealed that OT increased the percentage of fixation counts for the face region [<italic>t</italic><sub>(1,34)</sub> = 2.968, <italic>p</italic> = 0.024, Cohen’s <italic>d</italic> = 0.454]. For the AOI × valence interaction, <italic>post hoc</italic> tests also revealed that for negative compared with positive valence stimuli, subjects showed a higher proportion of fixation counts on the face (<italic>p</italic> = 0.001) and background (<italic>p</italic> < 0.001) but less for the body (<italic>p</italic> < 0.001). There were no significant treatment × valence [<italic>F</italic><sub>(1,34)</sub> = 0.041, <italic>p</italic> = 0.841] or treatment × regions × valence [<italic>F</italic><sub>(2,68)</sub> = 2.005, <italic>p</italic> = 0.147] interaction effects. For individual fixation durations, there was only a significant main effect of AOI [<italic>F</italic><sub>(2,68)</sub> = 65.263, <italic>p</italic> < 0.001, partial η<sup>2</sup> = 0.657] due to subjects exhibiting longer durations of individual fixations toward the face compared to body and background (all <italic>p</italic>’s < 0.001) and to the background compared to the body (<italic>p</italic> = 0.001).</p></sec><sec id=\"S3.SS3\"><title>Associations Between Eye-Gaze Measures and Emotional Empathy Ratings and Autistic and Empathic Traits</title><p>We investigated correlations between treatment difference scores (i.e., OT – PLC) for both ratings and eye-tracking measures using Spearman tests. The results showed that the difference between percentage of total fixation duration for the face was positively correlated with the difference in rating scores for negative valence pictures (<italic>r</italic> = 0.348, <italic>p</italic> = 0.041), although not for positive ones (<italic>r</italic> = 0.211, <italic>p</italic> = 0.225) (see <xref ref-type=\"fig\" rid=\"F3\">Figure 3</xref>). However, the same difference analysis for the percentage of fixation counts revealed significant correlations for both negative (<italic>r</italic> = 0.508, <italic>p</italic> = 0.002) and positive valence pictures (<italic>r</italic> = 0.340, <italic>p</italic> = 0.046) and on the body region for negative valence pictures (<italic>r</italic> = −0.392, <italic>p</italic> = 0.02) but not positive valence ones (<italic>r</italic> = −0.126, <italic>p</italic> = 0.47). Thus overall, the effects of OT on patterns of eye gaze were generally associated with those on emotional empathy ratings, although most strongly for negative valence pictures.</p><fig id=\"F3\" position=\"float\"><label>FIGURE 3</label><caption><p>Correlation (Spearman) between emotional empathy ratings and percentage of total fixation duration using treatment differences scores (i.e., OT minus PLC treatment). Tfd, total fixation duration. Negative face: face region in negative valence pictures. Positive face: face region in positive valence pictures. Emotional empathy ratings for “how much do you feel the same as the person in the picture (i.e., direct emotional empathy)” from 1 to 9 (1 = not at all and 9 = very strongly). OT – PLC, OT minus PLC treatment condition.</p></caption><graphic xlink:href=\"fnins-14-00803-g003\"/></fig><p>Spearman correlation analyses revealed no significant correlations between autistic (AQ and SRS) or empathic (IRI) traits and the percentages of total fixation duration and fixation counts on each region (all <italic>p</italic>’s > 0.113).</p></sec></sec><sec id=\"S4\"><title>Discussion</title><p>In the current study, we used eye tracking to investigate the effects of intranasal OT on emotional empathy and visual attention to different features of real life pictures in the MET. Results first confirmed previous findings that OT increases emotional empathy in both negative and positive emotional contexts (<xref rid=\"B22\" ref-type=\"bibr\">Hurlemann et al., 2010</xref>; <xref rid=\"B16\" ref-type=\"bibr\">Geng et al., 2018a</xref>, <xref rid=\"B17\" ref-type=\"bibr\">b</xref>). Second, results showed that OT increased both the proportion of time spent viewing the faces of the individuals portrayed in the pictures while correspondingly decreasing them to other features in the pictures. Furthermore, there was a significant positive association between the effect of OT on increasing the proportion of viewing time and/or fixation counts on the face and on increasing emotional empathy ratings, particularly for negative valence pictures. Overall, these findings provide a further replication of the finding that OT enhances emotional empathy and that this effect is associated with greater attention toward the faces of individuals displaying both negative and positive emotions and correspondingly reduced attention toward other contextual information.</p><p>Our findings that OT increased time spent viewing the faces of individuals expressing either positive or negative emotions in real-life contexts and that these were associated with parallel increased ratings of emotional empathy for negative, and to a lesser extent positive valence stimuli, support the conclusion that it may increase emotional empathy by enhancing the salience of the social stimuli. Additional support for this conclusion comes from observations in parent–offspring interactions. Mothers with higher plasma OT concentrations have been reported to pay more attention to their baby especially when the baby is distressed, implying that mothers with higher OT concentrations pay more attention to salient cues from their baby and respond more empathically toward them (<xref rid=\"B23\" ref-type=\"bibr\">Kim et al., 2014</xref>). Correspondingly, young children with higher saliva OT concentrations pay greater attention to the eye region of the faces (<xref rid=\"B32\" ref-type=\"bibr\">Nishizato et al., 2017</xref>).</p><p>There were also some indications that reductions in gaze toward other regions of the pictures were negatively associated with emotional empathy ratings. These findings contrast to some extent with a previous study using dynamic (video clip) stimuli reporting that OT increased emotional empathy for fearful faces and time spent viewing the eyes of these faces (and also happy, sad, and pain) but with an inverse relationship between them (<xref rid=\"B20\" ref-type=\"bibr\">Hubble et al., 2017a</xref>). The differences in findings may be explained by the different stimuli used, a static compared to dynamic presentation, and that in our current study, we focused on gaze toward the face as a whole rather than the eyes. We also did not focus on the specific individual emotions expressed but only whether they were in negative or positive valence situations. Another study has also reported no effects of OT on eye-gaze patterns toward positive, neutral, and negative valence pictures despite having effects on neural responses (<xref rid=\"B29\" ref-type=\"bibr\">Lischke et al., 2017</xref>). However, this latter study included both social and non-social pictures and not specifically intended to evoke empathic responses.</p><p>Subjects spent significantly more time viewing the faces and the backgrounds for negative compared with positive valence pictures, suggesting that they may pay greater attention to threatening salient cues, although pictures depicting sadness were also included. However, there was no treatment × valence interaction effect, indicating that OT had similar effects on increasing attention to salient cues for both positive and negative valence pictures. Emotional empathy ratings were also similar for positive and negative valence pictures, and altered ratings following OT were not significantly different.</p><p>We did not find any significant associations between eye-gaze parameters or emotional empathy ratings and scores on either autistic (AQ and SRS) or empathic (IRI) traits. In our previous study, we also found only marginal negative associations between AQ scores and emotional empathy ratings following PLC treatment (<xref rid=\"B16\" ref-type=\"bibr\">Geng et al., 2018a</xref>), and so a tentative conclusion from both studies is that the relationship between trait autism and emotional empathy and associated gaze toward the face in the MET is weak at best in healthy subjects. Given the greater evidence for altered patterns of eye gaze in clinical autism populations when viewing faces or other social stimuli (<xref rid=\"B5\" ref-type=\"bibr\">Chita-Tegmark, 2016</xref>; <xref rid=\"B24\" ref-type=\"bibr\">Kou et al., 2019</xref>), it is possible that they would exhibit stronger associations between symptom severity and eye-gaze and empathy ratings in the MET. Similarly, we did not find any associations with trait empathy scores as measured by the total IRI, possibly suggesting that the latter test is perhaps more sensitive for all aspects of empathic behavior rather than specifically for emotional empathy.</p><p>Several limitations should be acknowledged in the current study. First, we only included male subjects, and there is increasing evidence for sex-dependent effects of OT (<xref rid=\"B14\" ref-type=\"bibr\">Gao et al., 2016</xref>; <xref rid=\"B31\" ref-type=\"bibr\">Luo et al., 2017</xref>; <xref rid=\"B27\" ref-type=\"bibr\">Lieberz et al., 2019</xref>), although we have previously shown no sex-dependent effects of OT on emotional empathy or amygdala responses in the MET (<xref rid=\"B17\" ref-type=\"bibr\">Geng et al., 2018b</xref>). Two other studies showing effects of OT on pain empathy have also not found sex-dependent effects (<xref rid=\"B36\" ref-type=\"bibr\">Shamay-Tsoory et al., 2013</xref>; <xref rid=\"B1\" ref-type=\"bibr\">Abu-Akel et al., 2015</xref>), and so we would predict that similar effects of OT on patterns of eye gaze during the MET would occur in female subjects. Second, we could only investigate effects of OT on gaze toward the whole face region due to the nature of the MET pictures and so were unable to investigate if the eye region was of most importance. Finally, the picture stimuli presented were static, and it is possible that results using dynamic pictures could be different.</p><p>In summary, our findings in the current study demonstrate that the effects of OT in increasing emotional empathy responses to positive and negative expressions of emotions in real-life contexts are associated with an increase in time viewing the face region of protagonists in the pictures and correspondingly less to other salient features. This provides further support for an important general role of OT in shifting attention toward the most salient features of social stimuli in line with the social salience hypothesis (<xref rid=\"B38\" ref-type=\"bibr\">Shamay-Tsoory and Abu-Akel, 2016</xref>) and may contribute to greater attention and empathic responses in more specific contexts such as parent–offspring interactions.</p></sec><sec sec-type=\"data-availability\" id=\"S5\"><title>Data Availability Statement</title><p>The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.</p></sec><sec id=\"S6\"><title>Ethics Statement</title><p>The studies involving human participants were reviewed and approved by Ethics Committee of the University of Electronic Science and Technology of China. The patients/participants provided their written informed consent to participate in this study.</p></sec><sec id=\"S7\"><title>Author Contributions</title><p>JL, JK, and KK designed the experiment. JL, MF, and YZ conducted the experiment. JL and WZ analyzed the data. JL drafted the manuscript. BB and KK interpreted the results, revised it critically for important intellectual content, and finalized the manuscript for submission. All authors contributed to manuscript revision and read and approved the submitted version.</p></sec><sec id=\"conf1\"><title>Conflict of Interest</title><p>The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p></sec></body><back><fn-group><fn fn-type=\"financial-disclosure\"><p><bold>Funding.</bold> This work was supported by the National Natural Science Foundation of China (NSFC) (Grant Number 31530032, KK), Guangdong Provincial Government (Grant Number 2018B030335001, KK), and by the China Postdoctoral Science Foundation (Grant Number 2018M643432, WZ).</p></fn></fn-group><sec id=\"S9\" sec-type=\"supplementary material\"><title>Supplementary Material</title><p>The Supplementary Material for this article can be found online at: <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.frontiersin.org/articles/10.3389/fnins.2020.00803/full#supplementary-material\">https://www.frontiersin.org/articles/10.3389/fnins.2020.00803/full#supplementary-material</ext-link></p><supplementary-material content-type=\"local-data\" id=\"FS1\"><media xlink:href=\"Data_Sheet_1.docx\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"TS2\"><media xlink:href=\"Table_1.doc\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"TS3\"><media xlink:href=\"Table_2.doc\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material></sec><ref-list><title>References</title><ref id=\"B1\"><mixed-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Abu-Akel</surname><given-names>A.</given-names></name><name><surname>Palgi</surname><given-names>S.</given-names></name><name><surname>Klein</surname><given-names>E.</given-names></name><name><surname>Decety</surname><given-names>J.</given-names></name><name><surname>Shamay-Tsoory</surname><given-names>S.</given-names></name></person-group> (<year>2015</year>). <article-title>Oxytocin increases empathy to pain when adopting the other- but not self-perspective.</article-title>\n<source><italic>Soc. 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[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">J Med Internet Res</journal-id><journal-id journal-id-type=\"iso-abbrev\">J. Med. Internet Res</journal-id><journal-id journal-id-type=\"publisher-id\">JMIR</journal-id><journal-title-group><journal-title>Journal of Medical Internet Research</journal-title></journal-title-group><issn pub-type=\"ppub\">1439-4456</issn><issn pub-type=\"epub\">1438-8871</issn><publisher><publisher-name>JMIR Publications</publisher-name><publisher-loc>Toronto, Canada</publisher-loc></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32744508</article-id><article-id pub-id-type=\"pmc\">PMC7432152</article-id><article-id pub-id-type=\"publisher-id\">v22i8e19018</article-id><article-id pub-id-type=\"doi\">10.2196/19018</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Original Paper</subject></subj-group><subj-group subj-group-type=\"article-type\"><subject>Original Paper</subject></subj-group></article-categories><title-group><article-title>Evaluating Smart Assistant Responses for Accuracy and Misinformation Regarding Human Papillomavirus Vaccination: Content Analysis Study</article-title></title-group><contrib-group><contrib contrib-type=\"editor\"><name><surname>Kool</surname><given-names>Tijn</given-names></name></contrib><contrib contrib-type=\"editor\"><name><surname>Eysenbach</surname><given-names>Gunther</given-names></name></contrib></contrib-group><contrib-group><contrib contrib-type=\"reviewer\"><name><surname>Boyd</surname><given-names>Matt</given-names></name></contrib><contrib contrib-type=\"reviewer\"><name><surname>Benis</surname><given-names>Arriel</given-names></name></contrib><contrib contrib-type=\"reviewer\"><name><surname>Rivera</surname><given-names>Yonaira</given-names></name></contrib><contrib contrib-type=\"reviewer\"><name><surname>Dodd</surname><given-names>Rachael</given-names></name></contrib><contrib contrib-type=\"reviewer\"><name><surname>Benis</surname><given-names>Arriel</given-names></name></contrib></contrib-group><contrib-group><contrib id=\"contrib1\" contrib-type=\"author\" equal-contrib=\"yes\"><name><surname>Ferrand</surname><given-names>John</given-names></name><degrees>MPH</degrees><xref ref-type=\"aff\" rid=\"aff1\">1</xref><contrib-id contrib-id-type=\"orcid\">https://orcid.org/0000-0001-8343-5787</contrib-id></contrib><contrib id=\"contrib2\" contrib-type=\"author\" equal-contrib=\"yes\"><name><surname>Hockensmith</surname><given-names>Ryli</given-names></name><xref ref-type=\"aff\" rid=\"aff1\">1</xref><contrib-id contrib-id-type=\"orcid\">https://orcid.org/0000-0002-5613-8901</contrib-id></contrib><contrib id=\"contrib3\" contrib-type=\"author\" equal-contrib=\"yes\"><name><surname>Houghton</surname><given-names>Rebecca Fagen</given-names></name><degrees>MPA</degrees><xref ref-type=\"aff\" rid=\"aff1\">1</xref><contrib-id contrib-id-type=\"orcid\">https://orcid.org/0000-0002-2590-7907</contrib-id></contrib><contrib id=\"contrib4\" contrib-type=\"author\" corresp=\"yes\" equal-contrib=\"yes\"><name><surname>Walsh-Buhi</surname><given-names>Eric R</given-names></name><degrees>MPH, PhD</degrees><contrib-id contrib-id-type=\"orcid\">https://orcid.org/0000-0002-1690-5352</contrib-id><xref ref-type=\"aff\" rid=\"aff1\">1</xref><address><institution>School of Public Health-Bloomington</institution><institution>Indiana University</institution><addr-line>1025 E. 7th Street, Room 116</addr-line><addr-line>Department of Applied Health Science</addr-line><addr-line>Bloomington, IN, 47405</addr-line><country>United States</country><phone>1 8128554867</phone><email>[email protected]</email></address></contrib></contrib-group><aff id=\"aff1\">\n<label>1</label>\n<institution>School of Public Health-Bloomington</institution>\n<institution>Indiana University</institution>\n<addr-line>Bloomington, IN</addr-line>\n<country>United States</country>\n</aff><author-notes><corresp>Corresponding Author: Eric R Walsh-Buhi <email>[email protected]</email></corresp></author-notes><pub-date pub-type=\"collection\"><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"epub\"><day>3</day><month>8</month><year>2020</year></pub-date><volume>22</volume><issue>8</issue><elocation-id>e19018</elocation-id><history><date date-type=\"received\"><day>1</day><month>4</month><year>2020</year></date><date date-type=\"rev-request\"><day>22</day><month>4</month><year>2020</year></date><date date-type=\"rev-recd\"><day>18</day><month>5</month><year>2020</year></date><date date-type=\"accepted\"><day>2</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>©John Ferrand, Ryli Hockensmith, Rebecca Fagen Houghton, Eric R Walsh-Buhi. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 03.08.2020.</copyright-statement><copyright-year>2020</copyright-year><license license-type=\"open-access\" xlink:href=\"https://creativecommons.org/licenses/by/4.0/\"><license-p><!--CREATIVE COMMONS-->This is an open-access article distributed under the terms of the Creative Commons Attribution License (<ext-link ext-link-type=\"uri\" xlink:href=\"https://creativecommons.org/licenses/by/4.0/\">https://creativecommons.org/licenses/by/4.0/</ext-link>), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.jmir.org/\">http://www.jmir.org/</ext-link>, as well as this copyright and license information must be included.</license-p></license></permissions><self-uri xlink:type=\"simple\" xlink:href=\"https://www.jmir.org/2020/8/e19018\"/><abstract><sec sec-type=\"background\"><title>Background</title><p>Almost half (46%) of Americans have used a smart assistant of some kind (eg, Apple Siri), and 25% have used a stand-alone smart assistant (eg, Amazon Echo). This positions smart assistants as potentially useful modalities for retrieving health-related information; however, the accuracy of smart assistant responses lacks rigorous evaluation.</p></sec><sec sec-type=\"objective\"><title>Objective</title><p>This study aimed to evaluate the levels of accuracy, misinformation, and sentiment in smart assistant responses to human papillomavirus (HPV) vaccination–related questions.</p></sec><sec sec-type=\"methods\"><title>Methods</title><p>We systematically examined responses to questions about the HPV vaccine from the following four most popular smart assistants: Apple Siri, Google Assistant, Amazon Alexa, and Microsoft Cortana. One team member posed 10 questions to each smart assistant and recorded all queries and responses. Two raters independently coded all responses (κ=0.85). We then assessed differences among the smart assistants in terms of response accuracy, presence of misinformation, and sentiment regarding the HPV vaccine.</p></sec><sec sec-type=\"results\"><title>Results</title><p>A total of 103 responses were obtained from the 10 questions posed across the smart assistants. Google Assistant data were excluded owing to nonresponse. Over half (n=63, 61%) of the responses of the remaining three smart assistants were accurate. We found statistically significant differences across the smart assistants (N=103, χ<sup>2</sup><sub>2</sub>=7.807, <italic>P</italic>=.02), with Cortana yielding the greatest proportion of misinformation. Siri yielded the greatest proportion of accurate responses (n=26, 72%), whereas Cortana yielded the lowest proportion of accurate responses (n=33, 54%). Most response sentiments across smart assistants were positive (n=65, 64%) or neutral (n=18, 18%), but Cortana’s responses yielded the largest proportion of negative sentiment (n=7, 12%).</p></sec><sec sec-type=\"conclusions\"><title>Conclusions</title><p>Smart assistants appear to be average-quality sources for HPV vaccination information, with Alexa responding most reliably. Cortana returned the largest proportion of inaccurate responses, the most misinformation, and the greatest proportion of results with negative sentiments. More collaboration between technology companies and public health entities is necessary to improve the retrieval of accurate health information via smart assistants.</p></sec></abstract><kwd-group><kwd>digital health</kwd><kwd>human papillomavirus</kwd><kwd>smart assistants</kwd><kwd>chatbots</kwd><kwd>conversational agents</kwd><kwd>misinformation</kwd><kwd>infodemiology</kwd><kwd>vaccination</kwd></kwd-group></article-meta></front><body><sec sec-type=\"introduction\"><title>Introduction</title><sec><title>Background</title><p>Voice assistants, a form of chatbot or conversational agent often referred to colloquially as “smart assistants,” are devices that respond to human voices and can be commanded to do a variety of tasks [<xref rid=\"ref1\" ref-type=\"bibr\">1</xref>]. Smart assistants have existed in their most contemporary form since 2010, with the introduction of Siri, and they are used for numerous tasks such as automation (ie, integration with climate control devices and entertainment devices), retrieval of information about certain topics, and shopping [<xref rid=\"ref1\" ref-type=\"bibr\">1</xref>]. Smart assistants have been integrated into smartphones, laptops, speakers, and other devices, creating a large network of consumers who frequently utilize smart assistants to acquire information on a range of topics [<xref rid=\"ref1\" ref-type=\"bibr\">1</xref>].</p></sec><sec><title>Prevalence of Smart Assistant Use</title><p>According to the Pew Research Center, out of a total sample of 2045 Americans, nearly half (46%) reported using smart assistants in 2017 [<xref rid=\"ref2\" ref-type=\"bibr\">2</xref>]. Of 4272 Americans surveyed in 2019, one-quarter (25%) had a stand-alone smart assistant device (eg, Amazon Echo, Google Home) in their homes [<xref rid=\"ref3\" ref-type=\"bibr\">3</xref>]. Households that reported having a stand-alone smart assistant device (n=1067) also reported higher income (34% earned US $75,000 or more per year; 15% earned below US $30,000) [<xref rid=\"ref3\" ref-type=\"bibr\">3</xref>]. The same report indicated that younger Americans frequently use stand-alone smart assistants, with 32% of users aged between 18 and 29 years and 28% of users aged between 30 and 49 years [<xref rid=\"ref3\" ref-type=\"bibr\">3</xref>]. This difference may be due to varied ways of assessing use (eg, ever use and daily use). In contrast, smartphone smart assistants are more frequently adopted by those aged between 18 and 29 years (81%), whereas those aged between 45 and 60 years report the most daily active use of smartphone smart assistants [<xref rid=\"ref2\" ref-type=\"bibr\">2</xref>]. These usage trends depict nuanced usage patterns wherein younger users are the most common adopters of smartphone smart assistants and older users, once they begin using smartphone smart assistants, use them more frequently than do other age groups. These varying adoption and usage behaviors provide multiple avenues for targeting different age groups through smart assistants.</p></sec><sec><title>Uses of Smart Assistants</title><p>More than half of Americans report that a major reason for using smart assistants is the ability to interact with their devices without using their hands [<xref rid=\"ref2\" ref-type=\"bibr\">2</xref>]. One-quarter of Americans say that they use smart assistants to remotely control other devices such as heating systems, door locks, and lights [<xref rid=\"ref2\" ref-type=\"bibr\">2</xref>]. Given that approximately 35% of Americans report searching online (ie, using Google or another search engine) to self-diagnose a medical condition, it seems logical to assume that individuals may turn to smart assistants for this information as well [<xref rid=\"ref4\" ref-type=\"bibr\">4</xref>]. Unfortunately, smart assistants are relatively new technologies, and their responses have not been rigorously evaluated in many contexts.</p></sec><sec><title>Previous Assessments of Smart Assistants</title><p>In general, smart assistants have been evaluated for their response accuracy [<xref rid=\"ref5\" ref-type=\"bibr\">5</xref>] and their general usability [<xref rid=\"ref6\" ref-type=\"bibr\">6</xref>]. While considerable research has been conducted assessing digital health approaches, such as text message–based approaches and mobile apps for a range of health behaviors [<xref rid=\"ref7\" ref-type=\"bibr\">7</xref>-<xref rid=\"ref9\" ref-type=\"bibr\">9</xref>], few have included smart assistant responses to health-related queries. For instance, a pilot study comparing two smart assistants (Google Assistant and Apple Siri) to a standard Google Search on the topic of smoking cessation resources found that Google Assistant provided a greater number of evidence-based responses [<xref rid=\"ref10\" ref-type=\"bibr\">10</xref>]. Other studies have specifically examined consumer experiences with the natural language processing of smart assistants [<xref rid=\"ref5\" ref-type=\"bibr\">5</xref>].</p></sec><sec><title>Human Papillomavirus Prevention</title><p>To effectively assess smart assistant responses for accuracy and misinformation, human papillomavirus (HPV) has been identified as a controversial content area with a substantial evidence base. The evidence base of HPV is scientifically valid but hotly contested. HPV is the most common sexually transmitted infection and is a known cause of cervical cancer, as well as several other types of cancers [<xref rid=\"ref11\" ref-type=\"bibr\">11</xref>]. While most sexually active people will contract HPV at some point in their lives, how the infection resolves varies from person to person, and a federally approved vaccine has been shown to be effective at reducing both the incidence of HPV transmission and the incidences of genital and anogenital HPV infection and cervical lesions [<xref rid=\"ref12\" ref-type=\"bibr\">12</xref>]. Despite studies affirming the positive effects of the HPV vaccine while debunking inaccurate claims, there continues to be large-scale misinformation efforts (mostly conducted online through social media platforms) surrounding this issue, which are driven in part by the antivaccination movement [<xref rid=\"ref13\" ref-type=\"bibr\">13</xref>-<xref rid=\"ref20\" ref-type=\"bibr\">20</xref>]. These issues of misinformation have contributed to mistrust of medical professionals [<xref rid=\"ref21\" ref-type=\"bibr\">21</xref>-<xref rid=\"ref23\" ref-type=\"bibr\">23</xref>] and misunderstanding of diseases and their risks [<xref rid=\"ref24\" ref-type=\"bibr\">24</xref>,<xref rid=\"ref25\" ref-type=\"bibr\">25</xref>]. In January 2020, the World Health Organization (WHO) released a list of urgent global health challenges for the new decade, including stopping vaccine-preventable diseases and earning the public’s trust [<xref rid=\"ref26\" ref-type=\"bibr\">26</xref>]. These two challenges are closely intertwined, as trust helps to shape whether individuals rely on provider recommendations (eg, whether parents will vaccinate their children) [<xref rid=\"ref22\" ref-type=\"bibr\">22</xref>,<xref rid=\"ref27\" ref-type=\"bibr\">27</xref>] and how misinformation disseminated online and across social media platforms influences vaccine refusal or vaccine delay [<xref rid=\"ref28\" ref-type=\"bibr\">28</xref>]. In fact, the WHO has stated that vaccine hesitancy is one of the top 10 threats to global health [<xref rid=\"ref29\" ref-type=\"bibr\">29</xref>].</p><p>In this study, we attempted to answer the following research question: do responses to smart assistant queries vary between different smart assistants, with regard to accuracy, misinformation, and sentiment toward HPV vaccination?</p></sec></sec><sec sec-type=\"methods\"><title>Methods</title><sec><title>Query Development</title><p>In order to effectively assess smart assistants’ responses for accuracy and misinformation, we utilized questions from the chat-text service of Planned Parenthood (personal communication by Nicole Levitz; March 18, 2019). We chose to focus on questions around the HPV vaccine owing to the previously identified issues of accuracy and misinformation surrounding this topic [<xref rid=\"ref30\" ref-type=\"bibr\">30</xref>,<xref rid=\"ref31\" ref-type=\"bibr\">31</xref>]. We chose variations of 10 evidence-based questions from the Planned Parenthood system, allowing us to better evaluate responses to those questions for accuracy and misinformation. The questions are listed in <xref ref-type=\"boxed-text\" rid=\"box1\">Textbox 1</xref>.</p><boxed-text id=\"box1\" position=\"float\"><caption><title>Queries posed to smart assistants.</title></caption><p>1. Does the HPV vaccine work?</p><p>2. Does the HPV vaccine cause autism?</p><p>3. Does Gardasil work?</p><p>4. Does Gardasil cause autism?</p><p>5. Is the HPV vaccine dangerous?</p><p>6. Is Gardasil dangerous?</p><p>7. Who can get the HPV vaccine?</p><p>8. Where can I get the HPV vaccine?</p><p>9. Does Gardasil kill?</p><p>10. How much does the HPV vaccine cost?</p></boxed-text></sec><sec><title>Search Process</title><p>One member of the research team queried each of the four most popular smart assistants (Apple Siri, Google Assistant, Amazon Alexa, and Microsoft Cortana), which were identified in the 2019 Voice Report by Microsoft [<xref rid=\"ref32\" ref-type=\"bibr\">32</xref>]. In the event that the smart assistant provided a nonresponse to a query (eg, “I don’t know how to answer that”), the team member queried the smart assistant a maximum of three times to ensure that it was not an errored response due to misunderstanding the query, background noise, or some other unrelated reason.</p><p>Smart assistants provided varying numbers of results on their respective first pages for each query. Alexa and Google Assistant provided only one oral result for each query, whereas Siri provided five text results for each query. Cortana provided between three and nine text or video results for each query. <xref ref-type=\"supplementary-material\" rid=\"app1\">Multimedia Appendix 1</xref> displays the number of results each smart assistant provided per query. The team member, responsible for conducting the searches, recorded the first page of the results for data extraction based on previous studies indicating that users more frequently click on the top 10 results, which tend to be concentrated on the first page of most search results [<xref rid=\"ref33\" ref-type=\"bibr\">33</xref>]. There is also evidence suggesting that people do not necessarily only click the first result on a page. In a 2020 study of search engine optimization, 16% of respondents in the study reported clicking on only the first result compared with 17% and 14% of respondents who reported clicking on three and five results, respectively [<xref rid=\"ref34\" ref-type=\"bibr\">34</xref>]. This finding suggests that the remaining results on the first page should be extracted to best replicate actual human search behavior.</p></sec><sec><title>Recording Process</title><p>The team member, who posed questions to the smart assistants, used video and audio recording software to record both the queries and the smart assistant responses. We used these recordings in the data extraction and coding process. <xref rid=\"figure1\" ref-type=\"fig\">Figure 1</xref> depicts a recording of a query posed to Cortana.</p><fig id=\"figure1\" position=\"float\"><label>Figure 1</label><caption><p>Example of a video result from a query to Cortana.</p></caption><graphic xlink:href=\"jmir_v22i8e19018_fig1\"/></fig></sec><sec><title>Data Extraction and Coding Process</title><p>We developed a web-based (Qualtrics) survey to aid in data extraction and coding of the smart assistant responses. Survey items were designed to extract relevant data from the smart assistant responses, including the types of responses (eg, video, web page, blog, and journal article), sources of the responses (eg, business, doctors, and health information provider), sentiments of the responses toward the HPV vaccine (eg, positive, neutral, and negative), accuracy of the responses, any incidence of misinformation in the responses, and topics discussed in the responses (eg, cancer, sexual behavior, and conspiracy theory).</p><p>We defined <italic>accuracy</italic> as a response that reflected the existing evidence. We coded accuracy using a dichotomy approach (0 for not accurate; 1 for accurate), which we applied specifically to the query (ie, accuracy of unrelated tangential content was not considered). If the response did not answer the query with the correct reply or positioned the correct reply as dubious or incorrect, we considered it “not accurate.” We defined <italic>misinformation</italic> as either deliberate or accidental promotion of previously disproved or unproven beliefs, attitudes, and behaviors. We coded misinformation using a dichotomy approach (0 if it did not provide misinformation; 1 if it did provide misinformation), which we applied to the entirety of the response (not just the answer to the question posed). If any misinformation was found in the smart assistant’s response, we coded it as having provided misinformation. <xref rid=\"table1\" ref-type=\"table\">Table 1</xref> depicts examples of accuracy and misinformation in smart assistant responses to several queries. Since accuracy and misinformation were coded based on different aspects of the smart assistant responses, there were some cases with both an accurate response to the query and some misinformation in the result. We coded sentiment toward vaccines as one of the following four potential categories: negative (mostly negative statements), neutral (neither positive nor negative statements), positive (mostly positive statements), and ambiguous (both negative and positive statements). We applied sentiment coding to the entirety of the response.</p><p>Two independent team members extracted and coded the data with an almost perfect agreement (κ=0.85) [<xref rid=\"ref35\" ref-type=\"bibr\">35</xref>]. We resolved discrepancies through discussion.</p><table-wrap id=\"table1\" position=\"float\"><label>Table 1</label><caption><p>Examples of accuracy and misinformation in smart assistant responses.</p></caption><table frame=\"hsides\" rules=\"groups\" width=\"1000\" cellpadding=\"5\" cellspacing=\"0\" border=\"1\"><col width=\"280\" span=\"1\"/><col width=\"410\" span=\"1\"/><col width=\"310\" span=\"1\"/><thead><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">Query</td><td rowspan=\"1\" colspan=\"1\">Accuracy</td><td rowspan=\"1\" colspan=\"1\">Misinformation</td></tr></thead><tbody><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">Does the HPV<sup>a</sup> vaccine work?</td><td rowspan=\"1\" colspan=\"1\">“In the trials that led to the approval of Gardasil and Cervarix, these vaccines were found to provide nearly 100% protection against persistent cervical infections with HPV types 16 and 18 and the cervical cell changes that these persistent infections can cause.”</td><td rowspan=\"1\" colspan=\"1\">N/A<sup>b</sup></td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">Does the HPV vaccine cause autism?</td><td rowspan=\"1\" colspan=\"1\">“There is no link between vaccines and autism.”</td><td rowspan=\"1\" colspan=\"1\">“The scientific literature is now starting to fill up with case reports and studies and articles that irrefutably show that there is a connection between this vaccine (and it’s an ugly vaccine) and neurological damage.”</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">Does Gardasil work?</td><td rowspan=\"1\" colspan=\"1\">“Gardasil works by preventing the infection of the types of HPV that can lead to cervical cancer...”</td><td rowspan=\"1\" colspan=\"1\">“The Gardasil HPV vaccine has been proved to have caused the deaths of 32 women.”</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">Does Gardasil cause autism?</td><td rowspan=\"1\" colspan=\"1\">“There has never been a study that has shown that vaccines cause autism.”</td><td rowspan=\"1\" colspan=\"1\">N/A</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">Is the HPV vaccine dangerous?</td><td rowspan=\"1\" colspan=\"1\">“Findings from many vaccine safety monitoring systems and more than 160 studies have shown that HPV vaccines have a favorable safety profile—the body of scientific evidence overwhelmingly supports their safety.”</td><td rowspan=\"1\" colspan=\"1\">“Aluminum in the vaccines is toxic enough to be harmful.”</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">Is Gardasil dangerous?</td><td rowspan=\"1\" colspan=\"1\">“Although this information is accurate in a strictly literal sense, it is a misleading presentation of raw data that does not in itself establish a causal connection between Gardasil and the posited medical dangers.”</td><td rowspan=\"1\" colspan=\"1\">“The Gardasil HPV vaccine has been proved to have caused the deaths of 32 women.”</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">Who can get the HPV vaccine?</td><td rowspan=\"1\" colspan=\"1\">“All people ages 9 to 45 can get the HPV vaccine to protect against genital warts and/or different types of HPV that can cause cancer.”</td><td rowspan=\"1\" colspan=\"1\">N/A</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">Where can I get the HPV vaccine?</td><td rowspan=\"1\" colspan=\"1\">“The HPV vaccine is available at: Healthcare Clinic for patients aged 11-26. Walgreens Pharmacy. Ages vary by state.”</td><td rowspan=\"1\" colspan=\"1\">N/A</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">Does Gardasil kill?</td><td rowspan=\"1\" colspan=\"1\">“I cannot stress this enough, based on this report alone you can't make a determination that the vaccine caused the deaths.”</td><td rowspan=\"1\" colspan=\"1\">N/A</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">How much does the HPV vaccine cost?</td><td rowspan=\"1\" colspan=\"1\">“Each dose of the vaccine can cost about $250.”</td><td rowspan=\"1\" colspan=\"1\">N/A</td></tr></tbody></table><table-wrap-foot><fn id=\"table1fn1\"><p><sup>a</sup>HPV: human papillomavirus.</p></fn><fn id=\"table1fn2\"><p><sup>b</sup>N/A: not applicable.</p></fn></table-wrap-foot></table-wrap></sec><sec><title>Analyses</title><p>The sample consisted of 128 total data points across all four smart assistants. We excluded any nonresponse data points (eg, “I don’t know how to answer that”) and responses that were categorized as <italic>other</italic> (eg, responded with completely irrelevant content to the query) from the final analyses. Google Assistant provided nonresponses to every query and was removed, thus reducing our final analysis sample to 103 responses across three smart assistants. These nonresponses and other responses were removed because they did not in any way address the query posed. <xref ref-type=\"supplementary-material\" rid=\"app1\">Multimedia Appendix 1</xref> displays the frequencies of responses and nonresponses for each smart assistant. Descriptive frequencies and chi-square difference tests were used to examine the levels of accuracy and misinformation among the smart assistants.</p></sec></sec><sec sec-type=\"results\"><title>Results</title><p><xref rid=\"table2\" ref-type=\"table\">Table 2</xref> displays the source and content characteristics of the smart assistant responses. Smart assistant responses contained content published by an organization in 82 out of 103 (79.6%) responses, whereas 20 out of 103 (19.4%) responses were published by an individual. Content was provided by organizations including some type of health information provider (eg, Planned Parenthood and Mayo Clinic) in 36 out of 103 (35.0%) responses and by a government entity (eg, Centers for Disease Control and Prevention) in 25 out of 103 (24.3%) responses. Cortana provided content published by individuals (eg, physicians and journalists) in 18 out of 61 (29.5%) responses, whereas Siri and Alexa provided content published by individuals in only 2 out of 36 (5.6%) and 0 out of 6 (0.0%) responses, respectively. The most common type of individual who provided content (10 out of 103 responses, 9.7%) was some classification of physician. The type of content provided in the smart assistant responses varied, with videos provided in 30 out of 103 (29.1%) responses, driven entirely by Cortana, which was the only smart assistant to provide video responses. Siri’s responses were classified as frequently asked question (FAQ) pages in 13 out of 36 (36.1%) responses, whereas Cortana’s responses were classified as videos in 30 out of 61 (49.2%) responses and as general web pages in 12 out of 61 (19.7%) responses.</p><table-wrap id=\"table2\" position=\"float\"><label>Table 2</label><caption><p>Source and content characteristics of smart assistant responses.</p></caption><table frame=\"hsides\" rules=\"groups\" width=\"1000\" cellpadding=\"5\" cellspacing=\"0\" border=\"1\"><col width=\"30\" span=\"1\"/><col width=\"30\" span=\"1\"/><col width=\"690\" span=\"1\"/><col width=\"250\" span=\"1\"/><thead><tr valign=\"top\"><td colspan=\"3\" rowspan=\"1\">Variable</td><td rowspan=\"1\" colspan=\"1\">Value (N=103), n (%)</td></tr></thead><tbody><tr valign=\"top\"><td colspan=\"3\" rowspan=\"1\">\n<bold>Source<sup>a</sup></bold>\n</td><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td colspan=\"2\" rowspan=\"1\">Health information provider</td><td rowspan=\"1\" colspan=\"1\">36 (35.0)</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td colspan=\"2\" rowspan=\"1\">Federal/city/state</td><td rowspan=\"1\" colspan=\"1\">25 (24.3)</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td colspan=\"2\" rowspan=\"1\">Nonprofit/advocacy group</td><td rowspan=\"1\" colspan=\"1\">15 (14.6)</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td colspan=\"2\" rowspan=\"1\">Physician</td><td rowspan=\"1\" colspan=\"1\">10 (9.7)</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td colspan=\"2\" rowspan=\"1\">Other</td><td rowspan=\"1\" colspan=\"1\">9 (8.7)</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td colspan=\"2\" rowspan=\"1\">News/media organization</td><td rowspan=\"1\" colspan=\"1\">8 (7.8)</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td colspan=\"2\" rowspan=\"1\">Business</td><td rowspan=\"1\" colspan=\"1\">6 (5.8)</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td colspan=\"2\" rowspan=\"1\">Journalist/press</td><td rowspan=\"1\" colspan=\"1\">2 (1.9)</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td colspan=\"2\" rowspan=\"1\">Politician</td><td rowspan=\"1\" colspan=\"1\">2 (1.9)</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td colspan=\"2\" rowspan=\"1\">Health care organization</td><td rowspan=\"1\" colspan=\"1\">1 (1.0)</td></tr><tr valign=\"top\"><td colspan=\"3\" rowspan=\"1\">\n<bold>Content</bold>\n</td><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td colspan=\"2\" rowspan=\"1\">\n<bold>Format of the smart assistant responses</bold>\n</td><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">Video</td><td rowspan=\"1\" colspan=\"1\">30 (29.1)</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">General web page</td><td rowspan=\"1\" colspan=\"1\">24 (23.3)</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">Article</td><td rowspan=\"1\" colspan=\"1\">14 (13.6)</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">FAQ<sup>b</sup> site</td><td rowspan=\"1\" colspan=\"1\">19 (18.4)</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">Other</td><td rowspan=\"1\" colspan=\"1\">6 (5.8)</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">Blog post</td><td rowspan=\"1\" colspan=\"1\">3 (2.9)</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">Location/map/directions</td><td rowspan=\"1\" colspan=\"1\">1 (1.0)</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td colspan=\"2\" rowspan=\"1\">\n<bold>Topics mentioned in the smart assistant responses<sup>a</sup></bold>\n</td><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">Cancer</td><td rowspan=\"1\" colspan=\"1\">77 (74.8)</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">Vaccines and side effects</td><td rowspan=\"1\" colspan=\"1\">57 (55.3)</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">Sexual behaviors</td><td rowspan=\"1\" colspan=\"1\">26 (25.2)</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">Access to medical care</td><td rowspan=\"1\" colspan=\"1\">20 (19.4)</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">Direct impacts on loved ones</td><td rowspan=\"1\" colspan=\"1\">13 (12.6)</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">Conspiracy theories</td><td rowspan=\"1\" colspan=\"1\">6 (5.8)</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">Vaccines are ineffective</td><td rowspan=\"1\" colspan=\"1\">6 (5.8)</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">Mass media</td><td rowspan=\"1\" colspan=\"1\">6 (5.8)</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">Risk of disease is lower than risk of adverse vaccination events</td><td rowspan=\"1\" colspan=\"1\">2 (1.9)</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td colspan=\"2\" rowspan=\"1\">\n<bold>Sentiment expressed toward vaccines in smart assistant responses</bold>\n</td><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">Positive</td><td rowspan=\"1\" colspan=\"1\">65 (63.1)</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">Neutral</td><td rowspan=\"1\" colspan=\"1\">18 (17.5)</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">Ambiguous</td><td rowspan=\"1\" colspan=\"1\">11 (10.7)</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">Negative</td><td rowspan=\"1\" colspan=\"1\">7 (6.8)</td></tr></tbody></table><table-wrap-foot><fn id=\"table2fn1\"><p><sup>a</sup>Totals may exceed 100% owing to the availability of multiple response options.</p></fn><fn id=\"table2fn2\"><p><sup>b</sup>FAQ: frequently asked question.</p></fn></table-wrap-foot></table-wrap><p><xref rid=\"table3\" ref-type=\"table\">Tables 3</xref> and <xref rid=\"table4\" ref-type=\"table\">4</xref> depict the differences among smart assistants in terms of primary outcomes. Smart assistant responses contained accurate answers in 63 out of 103 (61.2%) responses. Neither response accuracy (<italic>P</italic>=.10) nor response sentiment (<italic>P</italic>=.22) was significantly different among the devices. The number of responses provided for each query varied across the devices, but 4 out of 6 (66.7%) responses by Alexa and 26 out of 36 (72.2%) responses by Siri were accurate. In contrast, 33 out of 61 (54.1%) responses by Cortana were accurate. There were no cases in which responses contained both accurate answers and misinformation. Chi-square tests indicated that the three smart assistants significantly varied in terms of whether they provided misinformation in their responses (N=103, χ<sup>2</sup><sub>2</sub>=7.807, <italic>P</italic>=.02) and whether they provided any evidence to support the claims made in their responses (N=103, χ<sup>2</sup><sub>2</sub>=11.054, <italic>P</italic>=.004). Smart assistant responses contained at least one instance of misinformation in 18 out of 103 (17.5%) responses. Responses by Alexa contained no misinformation, whereas misinformation was present in 2 out of 36 (5.6%) responses by Siri and 16 out of 61 (26.2%) responses by Cortana. Alexa provided evidence to support each of its responses, whereas Siri provided evidence in 21 out of 36 (58.3%) responses and Cortana provided evidence in 23 out of 61 (37.7%) responses.</p><p>In general, smart assistant responses contained positive sentiment toward vaccines, with 65 out of 103 (63.1%) responses containing primarily positive sentiment. The second most common sentiment was “neutral,” which was found in 18 out of 103 (17.5%) responses. The least common sentiment was “negative,” which was found in only 7 out of 103 (6.8%) responses, and all were provided by Cortana.</p><table-wrap id=\"table3\" position=\"float\"><label>Table 3</label><caption><p>Response differences among smart assistants in terms of accuracy, evidence provided, and misinformation.</p></caption><table frame=\"hsides\" rules=\"groups\" width=\"1000\" cellpadding=\"5\" cellspacing=\"0\" border=\"1\"><col width=\"30\" span=\"1\"/><col width=\"320\" span=\"1\"/><col width=\"250\" span=\"1\"/><col width=\"250\" span=\"1\"/><col width=\"150\" span=\"1\"/><thead><tr valign=\"top\"><td colspan=\"2\" rowspan=\"1\">Response quality of the smart assistants</td><td rowspan=\"1\" colspan=\"1\">Value, n/N (%)</td><td rowspan=\"1\" colspan=\"1\">Chi-square effect size (df)</td><td rowspan=\"1\" colspan=\"1\"><italic>P</italic> value</td></tr></thead><tbody><tr valign=\"top\"><td colspan=\"2\" rowspan=\"1\">\n<bold>Accurate response? (Yes)</bold>\n</td><td rowspan=\"1\" colspan=\"1\">63/103 (61.2%)</td><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">Alexa</td><td rowspan=\"1\" colspan=\"1\">4/6 (66.7%)</td><td rowspan=\"1\" colspan=\"1\">4.558 (2)</td><td rowspan=\"1\" colspan=\"1\">.10</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">Siri</td><td rowspan=\"1\" colspan=\"1\">26/36 (72.2%)</td><td rowspan=\"1\" colspan=\"1\">4.558 (2)</td><td rowspan=\"1\" colspan=\"1\">.10</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">Cortana</td><td rowspan=\"1\" colspan=\"1\">33/61 (54.1%)</td><td rowspan=\"1\" colspan=\"1\">4.558 (2)</td><td rowspan=\"1\" colspan=\"1\">.10</td></tr><tr valign=\"top\"><td colspan=\"2\" rowspan=\"1\">\n<bold>Evidence provided? (Yes)</bold>\n</td><td rowspan=\"1\" colspan=\"1\">50/103 (48.5%)</td><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">Alexa</td><td rowspan=\"1\" colspan=\"1\">6/6 (100%)</td><td rowspan=\"1\" colspan=\"1\">11.054 (2)</td><td rowspan=\"1\" colspan=\"1\">.004</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">Siri</td><td rowspan=\"1\" colspan=\"1\">21/36 (58.3%)</td><td rowspan=\"1\" colspan=\"1\">11.054 (2)</td><td rowspan=\"1\" colspan=\"1\">.004</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">Cortana</td><td rowspan=\"1\" colspan=\"1\">23/61 (37.7%)</td><td rowspan=\"1\" colspan=\"1\">11.054 (2)</td><td rowspan=\"1\" colspan=\"1\">.004</td></tr><tr valign=\"top\"><td colspan=\"2\" rowspan=\"1\">\n<bold>Misinformation provided? (Yes)</bold>\n</td><td rowspan=\"1\" colspan=\"1\">18/103 (17.5%)</td><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">Alexa</td><td rowspan=\"1\" colspan=\"1\">0/6 (0%)</td><td rowspan=\"1\" colspan=\"1\">7.807 (2)</td><td rowspan=\"1\" colspan=\"1\">.02</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">Siri</td><td rowspan=\"1\" colspan=\"1\">2/36 (5.6%)</td><td rowspan=\"1\" colspan=\"1\">7.807 (2)</td><td rowspan=\"1\" colspan=\"1\">.02</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">Cortana</td><td rowspan=\"1\" colspan=\"1\">16/61 (26.2%)</td><td rowspan=\"1\" colspan=\"1\">7.807 (2)</td><td rowspan=\"1\" colspan=\"1\">.02</td></tr></tbody></table></table-wrap><table-wrap id=\"table4\" position=\"float\"><label>Table 4</label><caption><p>Sentiment differences among the smart assistants.</p></caption><table frame=\"hsides\" rules=\"groups\" width=\"1000\" cellpadding=\"5\" cellspacing=\"0\" border=\"1\"><col width=\"220\" span=\"1\"/><col width=\"120\" span=\"1\"/><col width=\"120\" span=\"1\"/><col width=\"150\" span=\"1\"/><col width=\"120\" span=\"1\"/><col width=\"0\" span=\"1\"/><col width=\"190\" span=\"1\"/><col width=\"0\" span=\"1\"/><col width=\"80\" span=\"1\"/><thead><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">Smart assistant</td><td colspan=\"7\" rowspan=\"1\">Content sentiment</td><td rowspan=\"1\" colspan=\"1\"><italic>P</italic> value</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">\n<break/>\n</td><td rowspan=\"1\" colspan=\"1\">Neutral (N=18)</td><td rowspan=\"1\" colspan=\"1\">Negative (N=7)</td><td rowspan=\"1\" colspan=\"1\">Ambiguous (N=11)</td><td colspan=\"2\" rowspan=\"1\">Positive (N=65)</td><td colspan=\"3\" rowspan=\"1\">Chi-square effect size (df)</td></tr></thead><tbody><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">Alexa (N=6), n (%)</td><td rowspan=\"1\" colspan=\"1\">1 (16.7%)</td><td rowspan=\"1\" colspan=\"1\">0 (0%)</td><td rowspan=\"1\" colspan=\"1\">0 (0%)</td><td colspan=\"2\" rowspan=\"1\">5 (83.3%)</td><td colspan=\"2\" rowspan=\"1\">8.20 (6)</td><td rowspan=\"1\" colspan=\"1\">.22</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">Siri (N=35), n (%)</td><td rowspan=\"1\" colspan=\"1\">9 (25.7%)</td><td rowspan=\"1\" colspan=\"1\">0 (0%)</td><td rowspan=\"1\" colspan=\"1\">5 (14.3%)</td><td colspan=\"2\" rowspan=\"1\">21 (60%)</td><td colspan=\"2\" rowspan=\"1\">8.20 (6)</td><td rowspan=\"1\" colspan=\"1\">.22</td></tr><tr valign=\"top\"><td rowspan=\"1\" colspan=\"1\">Cortana (N=60), n (%)</td><td rowspan=\"1\" colspan=\"1\">8 (13.3%)</td><td rowspan=\"1\" colspan=\"1\">7 (11.7%)</td><td rowspan=\"1\" colspan=\"1\">6 (10%)</td><td colspan=\"2\" rowspan=\"1\">39 (65%)</td><td colspan=\"2\" rowspan=\"1\">8.20 (6)</td><td rowspan=\"1\" colspan=\"1\">.22</td></tr></tbody></table></table-wrap></sec><sec sec-type=\"discussion\"><title>Discussion</title><sec><title>Principal Results</title><p>This study sought to determine whether responses to queries vary among smart assistants in terms of accuracy, misinformation, and sentiment related to HPV vaccination. Our results indicate that smart assistants responded differently when asked about this topic. Specifically, smart assistants showed variations in the level of misinformation provided in responses, as well as the provision of evidence to support claims made in their responses. Smart assistants did not differ statistically in terms of the accuracy of their responses to queries involving HPV vaccination.</p><p>To our knowledge, this study is the first to report on smart assistants and their responses to HPV vaccine–related queries. Previous studies of HPV vaccination behaviors did not focus on smart assistants, and they indicated that antivaccination messages may influence one’s decisions to vaccinate themselves or their children [<xref rid=\"ref15\" ref-type=\"bibr\">15</xref>]. However, given the growing number of persons and households adopting and utilizing smart assistants and the fact that smart assistants may be able to provide HPV vaccine information, it is necessary to examine whether responses delivered through smart assistants are disseminating these same antivaccination messages. This study is the first step in establishing a foundation for the types of vaccine sentiments that are present in content disseminated by smart assistants.</p><p>Importantly, our study revealed that, in general, smart assistants largely provide accurate and positive information regarding HPV vaccination (just under two-thirds of all smart assistant responses were accurate and positive), with no relevant differences across devices. We should reiterate here, however, that Google Assistant provided nonresponses to every query and, accordingly, was removed from the study. While accurate information on something as beneficial as HPV vaccination is necessary to mitigate HPV transmission and cancer incidence, just over one-third of responses contained <italic>inaccurate</italic> answers, suggesting that work is needed in this area to improve the provision of health information via smart assistants. On the other hand, misinformation was more of a concern across devices, as Cortana yielded the greatest number of responses containing misinformation (one-quarter of Cortana’s responses contained misinformation).</p></sec><sec><title>Comparison With Prior Work</title><p>Previous studies of smart assistant responses have focused on whether the devices responded correctly or whether they understood the query [<xref rid=\"ref5\" ref-type=\"bibr\">5</xref>,<xref rid=\"ref6\" ref-type=\"bibr\">6</xref>]. Only a handful of studies examined smart assistant responses in the context of a health behavior [<xref rid=\"ref10\" ref-type=\"bibr\">10</xref>]. This study further assessed smart assistant responses to determine whether they provide accurate evidence to specific health behavior questions. Evidence is critical in combatting misinformation, at least on social media platforms. For example, in an experimental study that exposed Facebook users to simulated misinformation and different correction mechanisms about the Zika virus, the authors found that correction can work when it provides supporting evidence or appropriate sources to accompany refutation (eg, Centers for Disease Control and Prevention) [<xref rid=\"ref36\" ref-type=\"bibr\">36</xref>]. The authors reported that this finding was maintained even in those who were rated higher in conspiracy beliefs.</p></sec><sec><title>Limitations</title><p>This study has limitations that should be considered when evaluating its results. The devices used their specific locations when responding to queries, which may have produced less generalizable responses. Some of the smart assistants employed in the study were previously used devices and may have been influenced by prior searches of the former user. To the best of our knowledge, no private mode exists in these smart assistants, which would prevent the queries and responses from influencing future searches. The sample size was limited for several reasons, including nonresponses and other unrelated responses to queries. Specifically, Google Assistant provided no response data, which may have influenced the results. Any responses that were entirely unrelated to the queries should be explored in future studies, since this may be an indication of a larger issue with smart assistants not comprehending the queries. Based on our knowledge about natural language processing in these smart assistants, it is suggested that syntax and dictation are some of the many influencers of smart assistant responses [<xref rid=\"ref37\" ref-type=\"bibr\">37</xref>], and the queries posed to each device may not have been appropriately worded to elicit the most effective responses. For example, our use of a single investigator to ask the questions could be viewed as a limitation (ie, we do not know how the smart assistants would have responded to an investigator with a different gender, tone, or accent). The investigator only queried a single example of a smart assistant (ie, only one device was used to query Alexa), which could be a limitation, as smart assistants may respond differently depending on whether a stand-alone smart assistant or a smartphone-based smart assistant is used. The underlying search engine behind each smart assistant determines the responses to queries, which could have limited the information provided by smart assistants that are not connected to large search engines such as Google. The varying number of results provided in the smart assistant responses to queries could have impacted our ability to compare across the devices; however, since we found no relevant differences between the devices with regard to accuracy or sentiment, we do not believe that more results from one device impacted our findings or conclusions.</p><p>Despite these limitations, there are several strengths that provide further evidence of the study’s validity and importance. First, we utilized questions submitted to a Planned Parenthood chat service, which represent real-world issues on which consumers have previously sought additional information. Second, the questions used as queries were chosen because responses to these queries could be scored as either correct or incorrect, limiting “gray areas” in assessing accuracy of the smart assistant responses. Third, the queries were conducted by a single member of the research team to maintain consistency in language processing factors (eg, tone and syntax), and the search and coding processes were systematic to maintain consistency as well. Overall, this research furthers our understanding of how an emerging technology disseminates health information and whether such information can be classified as accurate and evidence-based. This study provides a basic framework for future evaluation of these smart assistants on which more advanced devices may be based.</p><p>Our findings paint a picture of smart assistants as newly emerged potential health promotion tools that are yet to be rigorously evaluated in this area. Smart assistants have only recently been introduced as potential health promotion tools (most notably from an executive perspective [<xref rid=\"ref38\" ref-type=\"bibr\">38</xref>]) despite the growing proportion of households and individuals that use them [<xref rid=\"ref2\" ref-type=\"bibr\">2</xref>,<xref rid=\"ref3\" ref-type=\"bibr\">3</xref>]. The untapped potential of these devices for evidence-based information dissemination to consumers should be further explored. The potential of the devices may also be determined by their manufacturers who have, in some cases, provided platforms on which specialized topical information can be consolidated and further explored. For example, Alexa has an option for developers to create an Alexa Skill, which is essentially an application for Alexa that provides predefined information when queried specifically for that information. For example, an Alexa Skill that specifically searches in predefined evidence-based sources when queried for information on HPV vaccination could be developed. The downside to this potential approach for improving public health is that, as our results show, there is risk of disseminating misinformation through smart assistant responses, potentially reducing the positive impacts of a health promotion intervention. More needs to be done to better understand the susceptibility of these devices and their respective skills to outside influences.</p></sec><sec><title>Conclusions</title><p>Our results suggest that not all smart assistants are created equal with regard to utility, at least when it comes to provided evidence and misinformation in their responses. These findings should spark further research into how the proprietors of smart assistants procure the information that is disseminated to consumers of their products. Specifically, we suggest that manufacturers of these and other smart assistants collaborate with researchers to further evaluate the accuracy of smart assistants as public health tools and determine together how to disseminate information and what fact-checking assessments should be used for such information. With the high rate of HPV transmission globally and in the United States, smart assistants and their manufacturers are well positioned to deliver evidence-based health information to consumers. However, such a practice necessitates strong communication between technology companies, who may not be as focused on the accuracy or source of their most heavily promoted content, and public health entities. The focus on collaboration to address the issues of information accuracy and misinformation is paramount if we are to adequately respond to the WHO’s list of urgent global health challenges for the new decade, namely stopping vaccine-preventable diseases.</p></sec></sec></body><back><fn-group><fn fn-type=\"con\"><p>Authors' Contributions: All authors contributed equally to the development, review, and submission of this manuscript for publication.</p></fn><fn fn-type=\"COI-statement\"><p>Conflicts of Interest: None declared.</p></fn></fn-group><app-group><app><title>Appendix</title><supplementary-material content-type=\"local-data\" id=\"app1\"><label>Multimedia Appendix 1</label><p>Frequencies of responses and nonresponses by each smart assistant.</p><media xlink:href=\"jmir_v22i8e19018_app1.PNG\" xlink:title=\"PNG File , 55 KB\" xlink:type=\"simple\" id=\"d38e1442\" 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[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"review-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Front Neurosci</journal-id><journal-id journal-id-type=\"iso-abbrev\">Front Neurosci</journal-id><journal-id journal-id-type=\"publisher-id\">Front. Neurosci.</journal-id><journal-title-group><journal-title>Frontiers in Neuroscience</journal-title></journal-title-group><issn pub-type=\"ppub\">1662-4548</issn><issn pub-type=\"epub\">1662-453X</issn><publisher><publisher-name>Frontiers Media S.A.</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32848576</article-id><article-id pub-id-type=\"pmc\">PMC7432153</article-id><article-id pub-id-type=\"doi\">10.3389/fnins.2020.00819</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Neuroscience</subject><subj-group><subject>Review</subject></subj-group></subj-group></article-categories><title-group><article-title>Deciphering Brain Function by Miniaturized Fluorescence Microscopy in Freely Behaving Animals</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Malvaut</surname><given-names>Sarah</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><xref ref-type=\"aff\" rid=\"aff2\"><sup>2</sup></xref><xref ref-type=\"author-notes\" rid=\"fn002\"><sup>†</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/958477/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Constantinescu</surname><given-names>Vlad-Stefan</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><xref ref-type=\"aff\" rid=\"aff2\"><sup>2</sup></xref><xref ref-type=\"author-notes\" rid=\"fn002\"><sup>†</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/982296/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Dehez</surname><given-names>Harold</given-names></name><xref ref-type=\"aff\" rid=\"aff3\"><sup>3</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/995751/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Doric</surname><given-names>Sead</given-names></name><xref ref-type=\"aff\" rid=\"aff3\"><sup>3</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Saghatelyan</surname><given-names>Armen</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><xref ref-type=\"aff\" rid=\"aff2\"><sup>2</sup></xref><xref ref-type=\"corresp\" rid=\"c001\"><sup>*</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/3901/overview\"/></contrib></contrib-group><aff id=\"aff1\"><sup>1</sup><institution>CERVO Brain Research Center</institution>, <addr-line>Quebec City, QC</addr-line>, <country>Canada</country></aff><aff id=\"aff2\"><sup>2</sup><institution>Department of Psychiatry and Neuroscience, Universite Laval</institution>, <addr-line>Quebec City, QC</addr-line>, <country>Canada</country></aff><aff id=\"aff3\"><sup>3</sup><institution>Doric Lenses Inc.</institution>, <addr-line>Quebec City, QC</addr-line>, <country>Canada</country></aff><author-notes><fn fn-type=\"edited-by\"><p>Edited by: João O. Malva, University of Coimbra, Portugal</p></fn><fn fn-type=\"edited-by\"><p>Reviewed by: Daniel Benjamin Aharoni, University of California, Los Angeles, United States; Alberto L. Vazquez, University of Pittsburgh, United States</p></fn><corresp id=\"c001\">*Correspondence: Armen Saghatelyan, <email>[email protected]</email>; <email>[email protected]</email></corresp><fn fn-type=\"other\" id=\"fn002\"><p><sup>†</sup>These authors have contributed equally to this work</p></fn><fn fn-type=\"other\" id=\"fn004\"><p>This article was submitted to Brain Imaging Methods, a section of the journal Frontiers in Neuroscience</p></fn></author-notes><pub-date pub-type=\"epub\"><day>11</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><year>2020</year></pub-date><volume>14</volume><elocation-id>819</elocation-id><history><date date-type=\"received\"><day>20</day><month>4</month><year>2020</year></date><date date-type=\"accepted\"><day>14</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>Copyright © 2020 Malvaut, Constantinescu, Dehez, Doric and Saghatelyan.</copyright-statement><copyright-year>2020</copyright-year><copyright-holder>Malvaut, Constantinescu, Dehez, Doric and Saghatelyan</copyright-holder><license xlink:href=\"http://creativecommons.org/licenses/by/4.0/\"><license-p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</license-p></license></permissions><abstract><p>Animal behavior is regulated by environmental stimuli and is shaped by the activity of neural networks, underscoring the importance of assessing the morpho-functional properties of different populations of cells in freely behaving animals. In recent years, a number of optical tools have been developed to monitor and modulate neuronal and glial activity at the protein, cellular, or network level and have opened up new avenues for studying brain function in freely behaving animals. Tools such as genetically encoded sensors and actuators are now commonly used for studying brain activity and function through their expression in different neuronal ensembles. In parallel, microscopy has also made major progress over the last decades. The advent of miniature microscopes (mini-microscopes also called mini-endoscopes) has become a method of choice for studying brain activity at the cellular and network levels in different brain regions of freely behaving mice. This technique also allows for longitudinal investigations while animals carrying the microscope on their head are performing behavioral tasks. In this review, we will discuss mini-endoscopic imaging and the advantages that these devices offer to research. We will also discuss current limitations of and potential future improvements in mini-endoscopic imaging.</p></abstract><kwd-group><kwd>miniature endoscopes</kwd><kwd>mini-endoscopic imaging</kwd><kwd>animal behavior</kwd><kwd>GRIN lenses</kwd><kwd>GCaMP</kwd><kwd>GECI</kwd><kwd>Ca<sup>2+</sup> imaging</kwd></kwd-group><funding-group><award-group><funding-source id=\"cn001\">Mitacs<named-content content-type=\"fundref-id\">10.13039/501100004489</named-content></funding-source></award-group><award-group><funding-source id=\"cn002\">Natural Sciences and Engineering Research Council of Canada<named-content content-type=\"fundref-id\">10.13039/501100000038</named-content></funding-source></award-group></funding-group><counts><fig-count count=\"0\"/><table-count count=\"0\"/><equation-count count=\"0\"/><ref-count count=\"126\"/><page-count count=\"13\"/><word-count count=\"0\"/></counts></article-meta></front><body><sec id=\"S1\"><title>Introduction</title><p>Understanding how brain functions are shaped by experience and, in turn, how neural networks regulate animal behavior has always been of great interest to the scientific community as well as the general public. One of the long-term aims of neuroscience research is to understand how specific spatio-temporal activity patterns emerging from local neural networks regulate specific animal behaviors. Recording neural activity at the microcircuit level from distinct subsets of neurons and glia in freely behaving animals has thus become essential for studying how experience is represented and processed by the brain. The function of these networks can be interrogated using extracellular multi-electrode recordings (<xref rid=\"B78\" ref-type=\"bibr\">Nicolelis and Ribeiro, 2002</xref>), large-scale brain imaging techniques such as functional magnetic resonance imaging (fMRI), voltage-sensitive dye-based imaging, intrinsic optical signal imaging, and positron emission tomography (PET) (<xref rid=\"B42\" ref-type=\"bibr\">Grinvald et al., 1988</xref>; <xref rid=\"B102\" ref-type=\"bibr\">Shoham et al., 1999</xref>; <xref rid=\"B69\" ref-type=\"bibr\">Logothetis et al., 2002</xref>), or <italic>in vivo</italic> Ca<sup>2+</sup> imaging using one- and two-photon mini-endoscopes (<xref rid=\"B26\" ref-type=\"bibr\">Dombeck et al., 2007</xref>; <xref rid=\"B57\" ref-type=\"bibr\">Kerr et al., 2007</xref>; <xref rid=\"B40\" ref-type=\"bibr\">Grewe and Helmchen, 2009</xref>). The technique of choice for these types of experiments has mostly involved taking electrophysiological recordings in either awake head-restrained or freely behaving rodents (<xref rid=\"B120\" ref-type=\"bibr\">Wilson and Mcnaughton, 1993</xref>; <xref rid=\"B51\" ref-type=\"bibr\">Houweling and Brecht, 2008</xref>; <xref rid=\"B48\" ref-type=\"bibr\">Harvey et al., 2009</xref>; <xref rid=\"B79\" ref-type=\"bibr\">Niell and Stryker, 2010</xref>). This approach has provided a great number of important insights into how neuronal ensembles orchestrate behavior such as the discovery of spatial representation by place and grid cells, for instance (<xref rid=\"B84\" ref-type=\"bibr\">O’keefe and Dostrovsky, 1971</xref>; <xref rid=\"B44\" ref-type=\"bibr\">Hafting et al., 2005</xref>). However, despite the tremendous value of this technique, the number of cells that can be recorded at a given time is limited by the small number of electrodes that can be used simultaneously. In theory, increasing the number of electrodes should make it possible to record electrophysiological activity in more cells, but the spacing between the electrodes is limited. Electrophysiological recordings also exclude electrically silent cells from the subsequent analysis of network dynamics (<xref rid=\"B103\" ref-type=\"bibr\">Shoham et al., 2006</xref>). To address some of these caveats, researchers can now take advantage of dense recordings using multi-tetrode arrays or silicone probes that make it possible to take recordings from large neuronal populations rather than from individual cells. Such large population-level recordings can be used to address questions that otherwise could not have been investigated using data from individual cell recordings (<xref rid=\"B21\" ref-type=\"bibr\">Csicsvari et al., 2003</xref>; <xref rid=\"B46\" ref-type=\"bibr\">Harris et al., 2003</xref>; <xref rid=\"B16\" ref-type=\"bibr\">Buzsaki, 2004</xref>; <xref rid=\"B27\" ref-type=\"bibr\">Dupret et al., 2010</xref>; <xref rid=\"B107\" ref-type=\"bibr\">Stensola et al., 2012</xref>). However, even with advances in electrophysiological recordings of large subsets of cells, there remain some important aspects that cannot be studied using such techniques. Although electrophysiology allows for distinct cell populations to be differentiated based on waveform shapes (<xref rid=\"B113\" ref-type=\"bibr\">Trainito et al., 2019</xref>), it is much more challenging to assess the individual contribution of each subset of recorded cells to the observed network dynamics. Recordings from subcellular structures such as dendrites can be also obtained in freely behaving mice using electrophysiological approaches (<xref rid=\"B73\" ref-type=\"bibr\">Moore et al., 2017</xref>). By taking advantage of the immune response that follows a tetrode implant, which allows a dendrite to get trapped between the tetrode tips and the glial encapsulation, it is possible to measure the dendritic membrane voltage in freely behaving rodents (<xref rid=\"B73\" ref-type=\"bibr\">Moore et al., 2017</xref>). This methodology could also provide important insights into the electrophysiological correlates of dendritic action potential integration, but has a low success rate and low yield.</p><p>The study of brain function has taken a step further with the advent of new optical tools designed to investigate neuronal activity at the protein, cellular, or network level. These include genetically encoded Ca<sup>2+</sup> indicators (GECIs). Ca<sup>2+</sup> is an important second messenger involved in the homeostasis of essentially all cells in the brain (<xref rid=\"B20\" ref-type=\"bibr\">Clapham, 1995</xref>; <xref rid=\"B11\" ref-type=\"bibr\">Berridge et al., 2000</xref>) and GECI-recorded Ca<sup>2+</sup> fluctuations can be used to study the activity and coding of neural ensembles in basal conditions and in response to stimuli (<xref rid=\"B110\" ref-type=\"bibr\">Tian et al., 2012</xref>; <xref rid=\"B68\" ref-type=\"bibr\">Lin and Schnitzer, 2016</xref>). Many different versions of GECIs exist, and their repertoire and properties are constantly improving (<xref rid=\"B22\" ref-type=\"bibr\">Dana et al., 2016</xref>, <xref rid=\"B23\" ref-type=\"bibr\">2019</xref>; <xref rid=\"B100\" ref-type=\"bibr\">Shen et al., 2020</xref>). The green and red Ca<sup>2+</sup> indicators (GCaMP and RCaMP, respectively) are the most common (<xref rid=\"B68\" ref-type=\"bibr\">Lin and Schnitzer, 2016</xref>; <xref rid=\"B100\" ref-type=\"bibr\">Shen et al., 2020</xref>). More recently, a quadricolor GECI suite (XCaMP) (<xref rid=\"B53\" ref-type=\"bibr\">Inoue et al., 2019</xref>) and a near-infrared Ca<sup>2+</sup> indicator (<xref rid=\"B92\" ref-type=\"bibr\">Qian et al., 2019</xref>) were designed to further expand the palette of GECIs and to study more complex neural dynamics. Using viral or transgenic labeling, GECIs can be expressed all over the brain or in specific neuronal/glial subtypes and are now widely used in the context of learning, memory formation, and plasticity (<xref rid=\"B55\" ref-type=\"bibr\">Jercog et al., 2016</xref>). They also allow for long-term investigations of brain activity for up to several weeks and even months (<xref rid=\"B55\" ref-type=\"bibr\">Jercog et al., 2016</xref>). In addition to GECIs, current efforts to develop genetically encoded voltage indicators (GEVIs) will eventually make it possible to monitor individual spikes during high-frequency trains of action potentials or subthreshold changes in the membrane potential in selected populations of cells (<xref rid=\"B5\" ref-type=\"bibr\">Bando et al., 2019</xref>). This will further increase the palette of functional assessments of neural networks during animal behavior. In addition to functional changes, several morphological modifications such as spine formation/elimination, the dynamics of glia processes, and microglia migration occur in response to environmental stimuli. Furthermore, several regions of the adult brain retain the capacity to renew their cellular populations during injury (<xref rid=\"B112\" ref-type=\"bibr\">Torper and Gotz, 2017</xref>) or under homeostatic conditions through adult neurogenesis (<xref rid=\"B35\" ref-type=\"bibr\">Gengatharan et al., 2016</xref>; <xref rid=\"B82\" ref-type=\"bibr\">Obernier and Alvarez-Buylla, 2019</xref>; <xref rid=\"B116\" ref-type=\"bibr\">Vicidomini et al., 2020</xref>). All these morphological changes can be also monitored <italic>in vivo</italic> using live imaging approaches (<xref rid=\"B12\" ref-type=\"bibr\">Berry and Nedivi, 2017</xref>; <xref rid=\"B90\" ref-type=\"bibr\">Pilz et al., 2018</xref>; <xref rid=\"B117\" ref-type=\"bibr\">Wallace et al., 2020</xref>).</p><p><italic>In vivo</italic> imaging of morpho-functional changes in zebra fish larvae and nematodes has been performed in whole organisms (<xref rid=\"B1\" ref-type=\"bibr\">Ahrens et al., 2012</xref>; <xref rid=\"B77\" ref-type=\"bibr\">Nguyen et al., 2016</xref>). In rodents, <italic>in vivo</italic> imaging of a given brain region has traditionally been performed by two-photon microscopy in anesthetized or awake head-restrained animals performing specific behavioral tasks. While this allows for long-term and longitudinal recording of brain activity, it is limited to a restricted number of behavioral tasks and cannot be used to study deep brain structures unless the overlying brain regions are removed, which may affect cellular activity and animal behavior. The last two decades saw the advent of miniaturized fluorescent microscopes (also called mini-microscopes or mini-endoscopes) that enable <italic>in vivo</italic> recordings of brain structure and function of freely behaving animals to be taken at the cellular and network levels. These devices, which are similar to benchtop microscopes in their optical designs, contain miniaturized optical parts (mirrors, filters, or even a camera) and can be carried by animals that freely displace in an arena. Displays and recordings of acquired images are then computer assisted, allowing for the modulation of different parameters such as acquisition frame rate, exposure, and illumination power, without any additional manipulation of the recorded animal. Mini-endoscopes have evolved considerably in recent years since the first miniaturized versions were developed (<xref rid=\"B50\" ref-type=\"bibr\">Helmchen et al., 2001</xref>) and are still being refined and improved. They have helped provide answers to several unresolved questions regarding brain activity and structure. In this review, we will first present the various models of currently available mini-endoscopes and provide an overview of their specifications, advantages, and limitations. We will then discuss the <italic>in vivo</italic> applications of mini-endoscopy and the limitations of existing devices as well as future avenues for improving mini-endoscopic microscopy.</p></sec><sec id=\"S2\"><title>Current State of the Art for Recording Brain Activity and Structure Using Mini-Endoscopes</title><sec id=\"S2.SS1\"><title>One-Color Mini-Endoscopes</title><p>The first versions of mini-endoscopes were based on two-photon excitation, and some microscope components such as lasers and photomultipliers (PMT) were converted to transmit the excitation light to the head of the animal or to collect emissions through optic fibers. These systems were used for imaging at the surface of the brain (<xref rid=\"B50\" ref-type=\"bibr\">Helmchen et al., 2001</xref>; <xref rid=\"B49\" ref-type=\"bibr\">Helmchen, 2002</xref>; <xref rid=\"B31\" ref-type=\"bibr\">Flusberg et al., 2005</xref>). They were bulky and heavy (25 g, 70 mm high), limiting applications involving naturalistic behavior. Also, the devices could not compensate very well for strong motion artifacts (<xref rid=\"B50\" ref-type=\"bibr\">Helmchen et al., 2001</xref>; <xref rid=\"B49\" ref-type=\"bibr\">Helmchen, 2002</xref>). Subsequent improvements led to the creation of lighter portable two-photon excitation microscopes (<xref rid=\"B37\" ref-type=\"bibr\">Gobel et al., 2004</xref>; <xref rid=\"B32\" ref-type=\"bibr\">Flusberg et al., 2008</xref>; <xref rid=\"B95\" ref-type=\"bibr\">Sawinski et al., 2009</xref>) that were used to image neural networks in freely moving mice and rats. A promising approach using a portable two-photon excitation-scanning microscope that weighs as little as 2 g was recently introduced to study activity in the entorhinal cortex of freely behaving mice (<xref rid=\"B125\" ref-type=\"bibr\">Zong et al., 2017</xref>; <xref rid=\"B94\" ref-type=\"bibr\">Rowland et al., 2018</xref>). These systems provide the obvious advantages of two-photon excitation microscopy such as a small excitation volume, increased tissue penetration, reduced phototoxicity, and the possibility of imaging subcellular structures. However, the low two-photon absorption cross-section also imposes high spatial (small focal spot) and temporal (short laser pulse) constraints on two-photon microscopy systems. To get sufficient excitation intensity at focus, it requires high numerical aperture diffraction-limited optics as well as expensive table-top femtosecond pulse lasers with proper dispersion compensation. Laser scanning two-photon microscopes should also be properly stabilized to avoid motion artifacts as the pixels of each image are not recorded simultaneously. It is thus more challenging to use two-photon excitation in a context of mini-endoscopes, suggesting that miniaturized epifluorescence microscopy tends to represent a simpler and more affordable method for imaging activity in neural networks at the cellular level, particularly in deep brain regions while animals are freely behaving.</p><p>Several groups have taken advantage of small optical rod lenses [gradient refractive index (GRIN) lenses] (<xref rid=\"B56\" ref-type=\"bibr\">Jung and Schnitzer, 2003</xref>; <xref rid=\"B65\" ref-type=\"bibr\">Levene et al., 2004</xref>) to build mini-endoscopes. These lenses have a short working distance and can be fixed either at the surface of the brain or inserted into deep brain regions to perform imaging in the superficial or deep brain regions, respectively (<xref rid=\"B50\" ref-type=\"bibr\">Helmchen et al., 2001</xref>; <xref rid=\"B49\" ref-type=\"bibr\">Helmchen, 2002</xref>; <xref rid=\"B31\" ref-type=\"bibr\">Flusberg et al., 2005</xref>). GRIN lenses, which can have different diameters and customizable lengths, have been used for <italic>in vivo</italic> imaging of previously unreachable deep brain structures such as the substantia nigra, the hypothalamus, the parabrachial nucleus, and the basal forebrain (<xref rid=\"B14\" ref-type=\"bibr\">Bocarsly et al., 2015</xref>; <xref rid=\"B34\" ref-type=\"bibr\">Fu et al., 2019</xref>; <xref rid=\"B87\" ref-type=\"bibr\">Patel et al., 2019</xref>). GRIN lenses are usually flat at the tip, making it possible to image cells just below the tip. However, they can also be coupled with a prism mirror allowing for “side-view” imaging. This configuration, in our experience, significantly reduces tissue damage and immune responses compared to blunt probes. In line with this, less tissue damage and better neuronal preservation is observed for well-sharpened and small tip angle silicone probes used for <italic>in vivo</italic> electrophysiological recordings (<xref rid=\"B28\" ref-type=\"bibr\">Edell et al., 1992</xref>). The GRIN coupled with a prism has been used for imaging neurons located in different cortical layers (<xref rid=\"B43\" ref-type=\"bibr\">Gulati et al., 2017</xref>).</p><p>Gradient refractive index implantation is minimally invasive, and the presence of the lens in an animal’s brain does not alter the locomotion or behavior of the animal in selected tasks commonly used in neuroscience research (<xref rid=\"B64\" ref-type=\"bibr\">Lee et al., 2016</xref>), although this largely depends on the diameter and type of GRIN lens (<xref rid=\"B93\" ref-type=\"bibr\">Resendez et al., 2016</xref>). Larger diameter (1 mm) GRIN lenses have been described as being more suitable for an implantation in superficial brain regions such as the cortex or the hippocampus, whereas smaller diameter GRINs are more suitable for deeper brain regions such as the hypothalamus or the ventral tegmental area (<xref rid=\"B93\" ref-type=\"bibr\">Resendez et al., 2016</xref>). Slow insertion of probes during implantation or using probes coupled with a prism can minimize possible compression of the brain, restricting it to the front of the GRIN and reducing possible damage (<xref rid=\"B28\" ref-type=\"bibr\">Edell et al., 1992</xref>; <xref rid=\"B65\" ref-type=\"bibr\">Levene et al., 2004</xref>). For deep brain region imaging, a track in the brain is often created using a sharp needle or blade. Brain tissue aspiration is also used in some cases (<xref rid=\"B93\" ref-type=\"bibr\">Resendez et al., 2016</xref>; <xref rid=\"B43\" ref-type=\"bibr\">Gulati et al., 2017</xref>; <xref rid=\"B38\" ref-type=\"bibr\">Gonzalez et al., 2019</xref>; <xref rid=\"B24\" ref-type=\"bibr\">De Groot et al., 2020</xref>). The inflammatory response resulting from implant insertion normally largely clears up after 4 weeks (<xref rid=\"B14\" ref-type=\"bibr\">Bocarsly et al., 2015</xref>), or less for sharp-ended implants (<xref rid=\"B28\" ref-type=\"bibr\">Edell et al., 1992</xref>; <xref rid=\"B114\" ref-type=\"bibr\">Turner et al., 1999</xref>). This observation highlights the need to start recording with miniature endoscopes several weeks post-implantation. A minimal period ranging from 2 to 4 weeks is required before acquiring data using GRIN lenses (<xref rid=\"B7\" ref-type=\"bibr\">Barbera et al., 2019</xref>; <xref rid=\"B34\" ref-type=\"bibr\">Fu et al., 2019</xref>; <xref rid=\"B38\" ref-type=\"bibr\">Gonzalez et al., 2019</xref>). To confirm the correct implantation of a GRIN lens, a <italic>post hoc</italic> analysis in post-mortem brain tissues can be used. This step makes it possible to adapt the stereotaxic coordinates used in different animals (<xref rid=\"B64\" ref-type=\"bibr\">Lee et al., 2016</xref>; <xref rid=\"B93\" ref-type=\"bibr\">Resendez et al., 2016</xref>). When viral vectors are injected in a defined brain region a few weeks before GRIN implantation, changes in background fluorescence may be also used to ascertain the correct positioning.</p><p>As for animals becoming habituated to the imaging device, it is common practice to try to reduce the weight of mini-endoscopes as much possible so as to not interfere with naturalistic behavior of the animals (<xref rid=\"B93\" ref-type=\"bibr\">Resendez et al., 2016</xref>). Chronic GRIN implants do not alter animal performance in standard behavioral procedures such as the rotarod test or the Morris water maze (<xref rid=\"B64\" ref-type=\"bibr\">Lee et al., 2016</xref>). However, no comparative studies have been reported with regard to the adaptation of animals to mini-endoscopes of different weights and configurations. Further behavioral investigations will be required to accurately evaluate the maximal weight that small rodents like mice can carry on their head without an impact on their behavior and to compare different mini-endoscopes, some of which are bulkier than others. Also, not all available versions of mini-endoscopes can reach very lateral or anterior brain regions such as the olfactory bulb. In fact, due to size constraints, placing a mini-endoscope in such regions could potentially alter the normal behavior of the animals or even their vision if placed too close to their eyes. Smaller footprint mini-endoscopes may help to resolve this issue.</p><p>An important step in the conception of mini-endoscopes is thus the miniaturization of microscope parts. <xref rid=\"B36\" ref-type=\"bibr\">Ghosh et al. (2011)</xref>, in their first version of a fully miniaturized epifluorescence mini-endoscope, replaced larger external light sources (e.g., Xenon lamps, mercury lamps, high power LEDs, and lasers) with smaller internal LEDs that are efficient and low-cost alternatives. This approach is now widely used in different open source mini-endoscopes designed to image in freely behaving rodents or songbirds. These include the UCLA Miniscope (<xref rid=\"B17\" ref-type=\"bibr\">Cai et al., 2016</xref>), the National Institute of Drug Abuse MiniScope (<xref rid=\"B6\" ref-type=\"bibr\">Barbera et al., 2016</xref>), the Boston University FinchScope that was developed for imaging in songbirds (<xref rid=\"B66\" ref-type=\"bibr\">Liberti et al., 2017</xref>), and the University of Toronto CHEndoscope (<xref rid=\"B54\" ref-type=\"bibr\">Jacob et al., 2018</xref>). Although very practical in terms of cost, availability, and size, installing LEDs directly in the microscope body can have some drawbacks. Because of its location in the microscope body, maximum output power is limited in order to minimize potential excessive heat on the animal’s head at higher powers normally used for optogenetic stimulations. To alleviate these issues, some commercial one-color mini-endoscopes can be connected to adjustable, exchangeable external light sources such as lasers, laser pumped incoherent sources (Ce:YAG), or LEDs using an optical fiber. However, this has the disadvantage of adding an extra optical fiber above the animal’s head.</p><p>Mini-endoscopes also take advantage, in their design, of off-the-shelf miniature electronic components such as complementary metal oxide semiconductor (CMOS) sensors for image detection. These sensors support high acquisition frame rates (higher than 30 Hz) and can, in some cases, be coupled to a microphone to record songbird vocalizations (<xref rid=\"B66\" ref-type=\"bibr\">Liberti et al., 2017</xref>). Improved versions of mini-endoscopes are currently available from commercial sources (Doric Lenses, Inscopix, and Neurescence) as well as open-source platforms (UCLA Miniscope, NINscope, and FinchScope).</p><p>It should be also mentioned that because of their miniaturization and their use in freely behaving animals, device failure may also occur. Unlike tabletop microscopy systems, miniaturized fluorescence microscopes are operated in challenging environments, with the animal moving in different directions and potentially applying some constraints on the microscope and optical fibers. Several approaches have been investigated to reduce the risks of experiment interruption due to microscope failure, including sturdier bodies in hard plastic (<xref rid=\"B17\" ref-type=\"bibr\">Cai et al., 2016</xref>) and metal frames (<xref rid=\"B125\" ref-type=\"bibr\">Zong et al., 2017</xref>), protected electronics, and connectorized systems with interchangeable cables (<xref rid=\"B6\" ref-type=\"bibr\">Barbera et al., 2016</xref>; <xref rid=\"B17\" ref-type=\"bibr\">Cai et al., 2016</xref>; <xref rid=\"B66\" ref-type=\"bibr\">Liberti et al., 2017</xref>; <xref rid=\"B54\" ref-type=\"bibr\">Jacob et al., 2018</xref>; <xref rid=\"B24\" ref-type=\"bibr\">De Groot et al., 2020</xref>). Optical components used in miniature endoscopes can be costly and, because of their size, failure can happen during surgical implantation or extraction. To counteract this, <xref rid=\"B14\" ref-type=\"bibr\">Bocarsly et al. (2015)</xref> proposed a version of the device, including the implantation in animal’s brain of a guide cannula, allowing for the insertion of a GRIN lens for each imaging session. However, it should be noted that the resulting diameter of the imaging probe is larger, which has an impact on brain tissue, as previously reported (<xref rid=\"B14\" ref-type=\"bibr\">Bocarsly et al., 2015</xref>; <xref rid=\"B93\" ref-type=\"bibr\">Resendez et al., 2016</xref>).</p></sec><sec id=\"S2.SS2\"><title>Two-Color Mini-Endoscopes</title><p>Functional and structural imaging in a given brain region was, for a long time, restricted to a general cell population or a subset of cells expressing a particular Ca<sup>2+</sup> indicator or fluorescent marker. More recently, two-color versions of mini-endoscopes have been developed that allow two different fluorophores with different emission and excitation wavelengths to be imaged. However, GRIN lenses have large chromatic aberrations that induce a shift (sometimes up to several tens of microns) in the imaged focal plane when comparing the fluorescence of two different fluorophores. To counteract these aberrations, one solution is to place two CMOS sensors in the microscope body at a specific location to compensate for the focal shift induced by the GRIN lens when imaging the two spectrally distinct fluorophores. Another solution for counteracting chromatic aberrations is to use achromatic lenses. The availability of several versions and colors of GECIs (<xref rid=\"B22\" ref-type=\"bibr\">Dana et al., 2016</xref>, <xref rid=\"B23\" ref-type=\"bibr\">2019</xref>; <xref rid=\"B100\" ref-type=\"bibr\">Shen et al., 2020</xref>), each with its own emission/excitation spectra, means that it is now possible to image using both green and red Ca<sup>2+</sup> indicators with similar kinetics (<xref rid=\"B100\" ref-type=\"bibr\">Shen et al., 2020</xref>). Two-color mini-endoscopes have a number of advantages. First, when combined with Ca<sup>2+</sup> indicators of spectrally distinguishable colors (GCaMP and RCaMP, for instance), it is possible to compare the activity of two different cell populations in the same brain region of freely behaving animals. In addition, two-color mini-endoscopes can be used for motion correction. In fact, <italic>xy</italic> and <italic>z</italic> drift can occur when animals are moving, which may create some bias in data analysis and interpretation. This issue can be addressed by performing functional Ca<sup>2+</sup> imaging combined with morphological fluorescent marker imaging followed by open-source plugin or software processing for motion and drift correction. These systems can be also used for imaging cell populations that can be defined only based on the combinatory genetic labeling approaches, leading to the expression of two different fluorophores (or sensors) by the same cell. Two-color mini-endoscopes can be also used for assessing the interplay between different cellular populations in a given brain region. It should be noted, however, that because of their optical design, two-color mini-endoscopes are bulkier and a longer habituation period for the animal may be required before starting the experiments.</p></sec><sec id=\"S2.SS3\"><title>Combined Optogenetic and Imaging Mini-Endoscopes for Studying Brain Connectivity and Disease Pathogenesis</title><p>The discovery that neuronal activity can be modulated using light-sensitive ion channels or opsins was a breakthrough in the field of neuroscience (<xref rid=\"B58\" ref-type=\"bibr\">Kim et al., 2017</xref>). <italic>In vivo</italic> optogenetic approaches are now widely used to modulate neuronal activity and to investigate the impact on animal behavior (<xref rid=\"B58\" ref-type=\"bibr\">Kim et al., 2017</xref>). It is possible to modulate neuronal activity by either stimulating or inhibiting the activity of a cell population in a given structure while simultaneously imaging the responses of the same or neighboring populations of cells in the same structure or even in a very different, remote structure (<xref rid=\"B106\" ref-type=\"bibr\">Stamatakis et al., 2018</xref>). However, the opsin and Ca<sup>2+</sup> indicators have to be carefully chosen as crosstalk between the channels may affect the recordings (<xref rid=\"B106\" ref-type=\"bibr\">Stamatakis et al., 2018</xref>). In addition, the imaging light source attenuates the optogenetic response (<xref rid=\"B106\" ref-type=\"bibr\">Stamatakis et al., 2018</xref>), which means that care must be taken in choosing the excitation light source for imaging in order to provide a better signal-to-noise ratio and minimal crosstalk.</p><p><xref rid=\"B24\" ref-type=\"bibr\">De Groot et al. (2020)</xref> developed the NINscope, which can be used to simultaneously record and optogenetically stimulate two distant brain structures. The authors reduced the footprint of the mini-endoscope, making it possible to record the Ca<sup>2+</sup> activity of a subset of cells in a given brain region while optogenetically stimulating cells in many other brain areas using small LEDs installed on the surface of the brain (<xref rid=\"B24\" ref-type=\"bibr\">De Groot et al., 2020</xref>). For experiments requiring optogenetic manipulation of deeper brain regions that cannot be targeted with the NINscope, a micro-LED can be implanted inside the brain. The stimulation can then be synchronized with the endoscope imaging (<xref rid=\"B101\" ref-type=\"bibr\">Shin et al., 2017</xref>). Combined optogenetic and imaging mini-endoscope baseplates composed of a GRIN relay lens for fluorescence imaging and an optical fiber for opsin activation are also commercially available. These systems offer a customizable length and distance between the imaging cannula and the implanted optical fiber. We used one of these mini-endoscope baseplates to optogenetically induce α-synuclein aggregation in the substantia nigra and to monitor changes in striatal neurons using Ca<sup>2+</sup> imaging (<xref rid=\"B9\" ref-type=\"bibr\">Bérard et al., 2019</xref>).</p></sec><sec id=\"S2.SS4\"><title>Multi-Region Imaging With Mini-Endoscopes</title><p>The NINscope developed by <xref rid=\"B24\" ref-type=\"bibr\">De Groot et al. (2020)</xref> also allows the simultaneous and synchronous imaging of two separate brain regions because of its reduced footprint. This is of particular relevance in a context of brain connectivity as the activity of two distinct regions can be correlated at the cellular level depending on the selected behaviors or stimuli. Simultaneous recording using two separate mini-endoscopes can also be performed in both hemispheres of the same structure (<xref rid=\"B38\" ref-type=\"bibr\">Gonzalez et al., 2019</xref>; <xref rid=\"B24\" ref-type=\"bibr\">De Groot et al., 2020</xref>). These recordings can be used to investigate the lateralization of brain activity (<xref rid=\"B38\" ref-type=\"bibr\">Gonzalez et al., 2019</xref>) and to compare, in the same subject, the activity of two hemispheres to which a different treatment may have been applied. A device allowing for simultaneous imaging of different brain regions is now also commercially available. In fact, because of it small footprint, the fiber bundle-based device from Neurescence (quartet mini-microscope) can record neuronal activity from up to four different brain regions as well as from different animals that are socially interacting, for instance.</p></sec><sec id=\"S2.SS5\"><title>Mini-Endoscopes With Dual-Modality Recordings</title><p>Mini-endoscopes also allow for dual-modality recording of neuronal activity. <xref rid=\"B122\" ref-type=\"bibr\">Yashiro et al. (2017)</xref> used a mini-endoscope system coupled with electrophysiological recordings of neural activity to make simultaneous optical recordings from large neuronal subsets. Such simultaneous optical and electrical recordings may prove to be extremely useful in correlating electrically recorded phenomena (multi-unit activity and local field potentials) with optically recorded Ca<sup>2+</sup> fluctuations. Combined electrical and Ca<sup>2+</sup> signal recordings from a single fluorescent neuron <italic>in vivo</italic> have also been made and allow the spiking activity of recorded cells to be correlated with changes in Ca<sup>2+</sup> dynamics (<xref rid=\"B63\" ref-type=\"bibr\">Lechasseur et al., 2011</xref>). In addition to combining imaging and electrophysiological recordings, commercially available dual implant baseplates composed of a GRIN lens and an optical fiber allow Ca<sup>2+</sup> imaging and photometry recordings in two different brain regions located as close together as 1 mm to be performed. Another potential new application for mini-endoscopes involves combining Ca<sup>2+</sup> imaging with intrinsic optical signals (<xref rid=\"B97\" ref-type=\"bibr\">Senarathna et al., 2019</xref>). This device can be used to monitor neuronal activity in large areas of the brain and subtract the hemodynamic signal, providing insights into neurovascular coupling (<xref rid=\"B97\" ref-type=\"bibr\">Senarathna et al., 2019</xref>). Other types of dual-modality recording mini-endoscopes are also available, such as those that allow for combined Ca<sup>2+</sup> imaging and bird vocalization recording (<xref rid=\"B66\" ref-type=\"bibr\">Liberti et al., 2017</xref>). It is likely that in coming years other dual-modality mini-endoscopes will be developed. These systems could allow for combining imaging and local drug delivery via integrated cannula, Ca<sup>2+</sup> activity recordings and respiration/sniffing assessments, or simultaneous Ca<sup>2+</sup> and voltage imaging, for instance. This in turn will make it possible to perform multi-modal assesments of neural networks during unrestricted animal behavior.</p></sec><sec id=\"S2.SS6\"><title>Electronic Modulation of the Field of View: eFocus Mini-Endoscopes</title><p>A non-negligible drawback of traditional mini-endoscopes was, for a long time, the inability to change the focus prior to or during image acquisition, limiting imaging to a single focal plane (<xref rid=\"B8\" ref-type=\"bibr\">Barretto and Schnitzer, 2012</xref>; <xref rid=\"B45\" ref-type=\"bibr\">Hamel et al., 2015</xref>). Indeed over time, following consecutive imaging sessions, the focal plane can change slightly and tracking the same cells can become challenging. To counteract this, the first versions of commercial and open-source mini-endoscopes offered the possibility to manually and mechanically adjust the image focus with a slider, screw, or turret before starting a recording, which resulted in additional manipulations of both the device and the animal (<xref rid=\"B36\" ref-type=\"bibr\">Ghosh et al., 2011</xref>; <xref rid=\"B6\" ref-type=\"bibr\">Barbera et al., 2016</xref>; <xref rid=\"B17\" ref-type=\"bibr\">Cai et al., 2016</xref>; <xref rid=\"B66\" ref-type=\"bibr\">Liberti et al., 2017</xref>; <xref rid=\"B54\" ref-type=\"bibr\">Jacob et al., 2018</xref>). To deal with this issue, updated electronically focused versions of endoscopes have been released by several companies such as Inscopix and Doric Lenses or were built from open-source designs (e.g., UCLA miniscope version 4). These versions make it possible to adjust the focus remotely and thus avoid mechanical manipulations of the endoscope while the animals are freely behaving. To that end, several groups have taken advantage of different strategies. For example, electro-wetting liquid lenses, which are small devices containing two non-miscible liquids, have been used. They allow for the rapid deformation of the lenses and thus the electronic focusing of the field of view by applying a voltage (<xref rid=\"B41\" ref-type=\"bibr\">Grewe et al., 2011</xref>; <xref rid=\"B126\" ref-type=\"bibr\">Zou et al., 2015</xref>; <xref rid=\"B85\" ref-type=\"bibr\">Ozbay et al., 2018</xref>). Electronically tunable liquid crystal lenses have also been used (<xref rid=\"B4\" ref-type=\"bibr\">Bagramyan et al., 2017</xref>). These lenses, which are traditionally used in liquid crystal displays, consume little energy and are lighter than electro-wetting liquid lenses, two important parameters for improving mini-endoscopes without compromising their size and weight.</p></sec></sec><sec id=\"S3\"><title><italic>In Vivo</italic> Applications of Mini-Endoscopic Imaging</title><p>As discussed above, two main approaches for optical brain imaging studies on live animals have emerged—those that require the experimental subjects to be head-fixed, such as two-photon excitation microscopy, and those that allow for unrestrained behavior, such as mini-endoscopes. Each technique has its share of advantages and drawbacks. Depending on the nature of the study, researchers can now use a variety of methods that can accommodate either imaging approach. New advancements in fluorescent reporter proteins, in particular GECIs and GEVIs, mean that neuroscientists can perform two-color fluorescence imaging and can distinguish the activity of a dual-labeled subset within a broad population of indicator-labeled cells (<xref rid=\"B19\" ref-type=\"bibr\">Chen et al., 2013</xref>; <xref rid=\"B52\" ref-type=\"bibr\">Inoue et al., 2015</xref>; <xref rid=\"B71\" ref-type=\"bibr\">Malvaut et al., 2017</xref>). The head-fixed imaging approaches that use high-performance lenses and the optical sectioning provided by two-photon microscopy allow for the interrogation of activity patterns during animal behavior at subcellular levels such as in dendrites, dendritic spines, and even axonal boutons (<xref rid=\"B89\" ref-type=\"bibr\">Petreanu et al., 2012</xref>; <xref rid=\"B121\" ref-type=\"bibr\">Xu et al., 2012</xref>; <xref rid=\"B15\" ref-type=\"bibr\">Boyd et al., 2015</xref>; <xref rid=\"B98\" ref-type=\"bibr\">Sheffield and Dombeck, 2015</xref>). The head-fixed paradigm can also make intracranial drug delivery easier (<xref rid=\"B80\" ref-type=\"bibr\">Nimmerjahn et al., 2009</xref>; <xref rid=\"B70\" ref-type=\"bibr\">Lovett-Barron et al., 2014</xref>). Behavioral assays, including adapted fear conditioning paradigms (<xref rid=\"B70\" ref-type=\"bibr\">Lovett-Barron et al., 2014</xref>), controlled delivery of sensory stimuli (<xref rid=\"B115\" ref-type=\"bibr\">Verhagen et al., 2007</xref>; <xref rid=\"B18\" ref-type=\"bibr\">Carey et al., 2009</xref>; <xref rid=\"B3\" ref-type=\"bibr\">Andermann et al., 2011</xref>; <xref rid=\"B13\" ref-type=\"bibr\">Blauvelt et al., 2013</xref>; <xref rid=\"B88\" ref-type=\"bibr\">Patterson et al., 2013</xref>; <xref rid=\"B72\" ref-type=\"bibr\">Miller et al., 2014</xref>), and perceptual discrimination tasks (<xref rid=\"B2\" ref-type=\"bibr\">Andermann et al., 2010</xref>; <xref rid=\"B61\" ref-type=\"bibr\">Komiyama et al., 2010</xref>; <xref rid=\"B83\" ref-type=\"bibr\">O’connor et al., 2010</xref>) can now be done in a head-restrained configuration to study changes at subcellular levels during complex behavioral tasks. Another exciting avenue for head-restrained optical imaging in live animals involves the use of virtual reality approaches (<xref rid=\"B48\" ref-type=\"bibr\">Harvey et al., 2009</xref>, <xref rid=\"B47\" ref-type=\"bibr\">2012</xref>; <xref rid=\"B25\" ref-type=\"bibr\">Dombeck et al., 2010</xref>), which could provide new information about an animal’s spatial navigation.</p><p>However, even with the enrichment of the behavioral repertoire that can be tested during head-restrained experiments, the array of behavioral manipulations is still restricted and is associated with the stress that the animals may encounter during head fixation and imaging. This stress can have an adverse effect on the outcome of the behavioral analysis and is likely to affect the neuronal activity patterns recorded (<xref rid=\"B109\" ref-type=\"bibr\">Thurley and Ayaz, 2017</xref>). Miniaturized head-mounted imaging devices were designed to circumvent these shortcomings. The main motivation for these devices was first and foremost their ability to record activity from many neurons in an animal that is unrestrained and that can display natural innate behavior as well as a broad array of social behaviors such as fighting, mating, care-giving, and other forms of interaction. The need to accurately monitor cell activity in freely behaving experimental subjects has led to recent advances in mini-endoscopic imaging that make it possible to record neuronal activity simultaneously from different brain regions (<xref rid=\"B24\" ref-type=\"bibr\">De Groot et al., 2020</xref>), to combine Ca<sup>2+</sup> imaging and recordings of other modalities such as electrical recordings of neural activity (<xref rid=\"B122\" ref-type=\"bibr\">Yashiro et al., 2017</xref>) or sound vocalizations (<xref rid=\"B66\" ref-type=\"bibr\">Liberti et al., 2017</xref>), and to image activity in neural networks at different focal planes and in different neuronal populations using efocus and two-color versions of mini-endoscopes. Also, the versatility of mini-endoscopes enables cell activity to be tracked across a large array of behavioral paradigms established over the last decades (<xref rid=\"B74\" ref-type=\"bibr\">Morris, 1984</xref>; <xref rid=\"B39\" ref-type=\"bibr\">Graeff et al., 1998</xref>; <xref rid=\"B75\" ref-type=\"bibr\">Nadler et al., 2004</xref>).</p><p>The main <italic>in vivo</italic> application for mini-endoscopes is the imaging of GECIs in large subsets of neurons in freely behaving animals. As mini-endoscopes have many useful characteristics such as relative ease of use, scalability, and commercial availability, they have seen a significant spread to numerous research fields. The use of mini-endoscopes in neuroscience-related fields such as learning and memory, neuropsychiatric disorders, and drug discovery has enabled scientists to accurately describe temporal changes that neuronal circuits exhibit during memory formation and retention as well as modifications due to a pathology. The field of learning and memory has in particular been directly influenced by the development of mini-endoscopes. Tracking the formation and subsequent modification of cellular ensembles that retain learned information, or engrams, over long periods of time has allowed neuroscientists to answer questions about the coding dynamics underlying the neurobiology of long-term memory (<xref rid=\"B111\" ref-type=\"bibr\">Tian et al., 2009</xref>; <xref rid=\"B17\" ref-type=\"bibr\">Cai et al., 2016</xref>). Established behavioral assays coupled with long-term monitoring of cellular activity has revealed that activity-induced modifications in neuronal CREB (cAMP response element-binding protein) levels are encoded by a shared neural ensemble in the CA1 part of the hippocampus that links distinct contextual memories occurring close in time (<xref rid=\"B17\" ref-type=\"bibr\">Cai et al., 2016</xref>). Neuronal activity in the spinal cord of freely behaving mice has also been recorded using a vertebra-affixed mini-endoscope (<xref rid=\"B96\" ref-type=\"bibr\">Sekiguchi et al., 2016</xref>). Implanting a mini-endoscope in the spinal cord is technically more challenging than anchoring an implant on top of the skull due to the pronounced movement of the spine within the vertebral column. Although fewer cells can be imaged due the curvature of the spinal cord, these experiments showed that distinct cutaneous stimuli activate overlapping ensembles of dorsal horn neurons and that stimulus type and intensity are encoded at the single-cell level (<xref rid=\"B96\" ref-type=\"bibr\">Sekiguchi et al., 2016</xref>).</p><p>Mini-endoscopic applications are not limited to the field of learning and memory. Mouse models of diseases are very important for studying the neurobiological basis of a myriad of brain disorders. As most of these diseases are, to some extent, characterized by a deficit in information processing, imaging neural activity in transgenic and knockout mice could be very advantageous for elucidating the underpinnings of the disease mechanisms (<xref rid=\"B76\" ref-type=\"bibr\">Nestler and Hyman, 2010</xref>). For example, much more information about alterations in coding dynamics in animal disease models can be inferred from large-scale imaging in freely behaving animals than is possible with electrophysiological measurements. These approaches could also narrow the gap between molecular and behavioral analyses. This concept becomes clear in the context of Alzheimer’s disease (AD), where much progress in understanding the molecular pathology underlying the neurodegeneration observed in human patients has led to the emergence of different mouse models that exhibit one or more AD hallmarks (b-amyloid, presenilins, apolipoprotein E, and Tau) (<xref rid=\"B60\" ref-type=\"bibr\">Knowles et al., 2009</xref>; <xref rid=\"B108\" ref-type=\"bibr\">Sterniczuk et al., 2010</xref>; <xref rid=\"B30\" ref-type=\"bibr\">Filali et al., 2012</xref>; <xref rid=\"B123\" ref-type=\"bibr\">Youmans et al., 2012</xref>). However, little is known about how these molecular pathways influence population dynamics during memory encoding and retrieval. With the advent of mini-endoscopy, longitudinal imaging in different AD models during the performance of memory tests is now feasible and could shed new light on memory coding dysfunctions in AD as well as in other neurological and psychiatric disorders (<xref rid=\"B124\" ref-type=\"bibr\">Ziv and Ghosh, 2015</xref>). As in the case of AD, the use of mini-endoscopes in animal models of other neurodegenerative disorders and brain trauma may allow the early hallmarks of disease manifestation to be deciphered. These mini-endoscopes combined with optogenetic stimulation can also be used to induce molecular alterations in cells and study disease progression. For example, it is now possible to optogenetically induce the aggregation of α-synuclein in dopaminergic neurons in the substantia nigra and to study alterations in the nigrostriatal pathway and in the network activity of striatal neurons using longitudinal mini-endoscopic Ca<sup>2+</sup> imaging (<xref rid=\"B9\" ref-type=\"bibr\">Bérard et al., 2019</xref>).</p><p>Drug discovery could also benefit from mini-endoscopic imaging. Although much research has focused on the pharmacological and molecular mechanisms of drugs at the cellular level, much less attention has been directed at understanding the effect of drugs on neuronal coding. <xref rid=\"B10\" ref-type=\"bibr\">Berdyyeva et al. (2014)</xref> showed that a GABA-A agonist used for treating insomnia reduces hippocampal neuronal firing in freely behaving mice. This result, although not surprising, shed light on the need to correlate neuronal dynamics with the effects of chronic drug administration and to identify the effects of the drugs on the overall neuronal network. Addressing these issues would further advance our ability to design future therapies for treating brain diseases (<xref rid=\"B124\" ref-type=\"bibr\">Ziv and Ghosh, 2015</xref>).</p><p>The capability of mini-endoscopes to record neural activity in freely behaving animals can be further enhanced by using optogenetic stimulations (<xref rid=\"B106\" ref-type=\"bibr\">Stamatakis et al., 2018</xref>) or by combining two different recording techniques such as extracellular electrophysiological recordings with Ca<sup>2+</sup> imaging in freely behaving animals (<xref rid=\"B122\" ref-type=\"bibr\">Yashiro et al., 2017</xref>). <xref rid=\"B106\" ref-type=\"bibr\">Stamatakis et al. (2018)</xref> used a mini-endoscope that jointly records optical neural activity and light-induced alterations of cellular ensemble activity in the same field-of-view. Other researchers have used a device to optogenetically stimulate brain regions away from the imaging field-of-view (<xref rid=\"B101\" ref-type=\"bibr\">Shin et al., 2017</xref>; <xref rid=\"B9\" ref-type=\"bibr\">Bérard et al., 2019</xref>; <xref rid=\"B97\" ref-type=\"bibr\">Senarathna et al., 2019</xref>). Engineering advances in the manufacture of mini-endoscopes have led to a reduction in the weight and size of these devices, making it possible to simultaneously record cellular activities in different brain regions as was done using the NINscope (<xref rid=\"B24\" ref-type=\"bibr\">De Groot et al., 2020</xref>). Such types of experiments will yield new data subsets that can be used to analyze the extent to which lateralization occurs during behavior and learning and to track the network stability of multi-region cellular ensembles over long periods of time (<xref rid=\"B38\" ref-type=\"bibr\">Gonzalez et al., 2019</xref>).</p><p>Mini-endoscopes are mostly used to investigate <italic>in vivo</italic> brain function in freely behaving animals by Ca<sup>2+</sup> imaging of cell populations in a given region and are evolving in parallel with improvements in GECIs. The use of such powerful tools will likely expand to the study of other processes underlying brain activity. For instance, an <italic>in vivo</italic> two-photon investigation of adult neurogenesis and, more particularly, stem cell division has been already performed in anesthetized head-restrained animals (<xref rid=\"B90\" ref-type=\"bibr\">Pilz et al., 2018</xref>). Mini-endoscopes could help unravel this process in freely behaving animals and to correlate stem cell division with naturalistic patterns of animal behavior. These experiments can be performed under homeostatic conditions in known neurogenic regions of the brain like the dentate gyrus or the subventricular zone, as well as after injury and neuronal regeneration. In line with this, mini-endoscopic imaging has shown that coupling pre-existing neurons to the cerebrovascular system regulates adult hippocampal neurogenesis (<xref rid=\"B99\" ref-type=\"bibr\">Shen et al., 2019</xref>) and that newborn neurons in the adult dentate gyrus migrate laterally first to increase their dispersion (<xref rid=\"B119\" ref-type=\"bibr\">Wang et al., 2019</xref>). The migration of neuronal progenitors and microglial cells following different neurodegenerative disorders and brain trauma can also be recorded by mini-endoscopic imaging. In addition, the use of miniaturized two-photon microscopes makes it possible to image Ca<sup>2+</sup> activity in dendrites (<xref rid=\"B125\" ref-type=\"bibr\">Zong et al., 2017</xref>) and may be used in the future to image other sub-cellular structures. Future studies will certainly deepen our understanding of the cellular and molecular mechanisms operating in homeostatic and diseased conditions.</p></sec><sec id=\"S4\"><title>Limitations and Future Avenues for Mini-Endoscopic Imaging</title><p>Given the experimentally proven benefits and versatility of mini-endoscopes for neuroscience research, it has become increasingly clear that the impact of these devices will grow exponentially. However, they still have some limitations and drawbacks and further improvements are needed. The weight and size of mini-endoscopes are crucial and should be further improved to enable unrestrained one- and two-photon mini-endoscopic imaging in freely behaving mice and other small animal models. Similarly, the footprint of mini-endoscopes should be reduced for multi-region imaging in order to record activity from adjacent brain areas. Another caveat is that most of the mini-endoscopes used in freely behaving animals need to be tethered to optical components that are too big and too heavy to be mounted on an animal’s head, which means that more complex social behaviors such as social interactions, mating, and mate aggression cannot be fully investigated due to the obvious physical constraints. The poor optical sectioning of one-photon mini-endoscopes and the major aberrations caused by their optical components (GRIN lenses) should also be taken into consideration and be improved. Below we discuss some of the avenues for addressing these issues.</p><sec id=\"S4.SS1\"><title>Optical Sectioning</title><p>Despite the advantages of wide-field epifluorescence mini-endoscopy (large field of view, small dimensions, and technical simplicity), these systems do not provide the optical sectioning of widely used tabletop systems such as confocal or two-photon microscopes or two-photon mini-endoscopes. As a consequence, image contrast in the focal plane is strongly attenuated by the out-of-focus fluorescent background when imaging densely labeled structures, especially in scattering media. The strong background limits neuronal and glial activity recording to the soma, as smaller structures such as dendrites, dendritic spines, and glial processes are too dim to be resolved. One of the key avenues for future developments is thus the integration and miniaturization of non-scanning optical sectioning systems already offered in large-scale microscopes.</p><p>Among non-scanning techniques, the tabletop version of the HiLo microscope has generated a great deal of interest (<xref rid=\"B67\" ref-type=\"bibr\">Lim et al., 2011</xref>; <xref rid=\"B62\" ref-type=\"bibr\">Lauterbach et al., 2015</xref>). The HiLo microscopy technique is based on epifluorescence microscopy and makes it possible to reject the out-of-focus fluorescent background by processing two images, one recorded with a uniform illumination and the other recorded with a high contrast pattern. HiLo microscopy provides high contrast images with optical sectioning similar to confocal microscopy at the expense of half the camera frame rate. The use of speckle illumination for the high contrast illumination has been proposed, as fast switching from uniform to speckle illumination can be obtained with the simple addition of a diffuser and a galvanometric scanner. HiLo microscopy has already been used for endoscopic applications using a flexible fiber bundle to deliver shaped illumination to the sample and to collect the fluorescence (<xref rid=\"B33\" ref-type=\"bibr\">Ford et al., 2012</xref>). Although fiber bundles allow images and illumination patterns to be relayed over long distances (up to meters), they are more rigid than the smaller diameter multi-mode fibers used to deliver light in experiments on freely behaving animals. They also have a small core fill factor, which results in low optical transmission. These properties limit their use for dimmer fluorescent signal recording in freely behaving small animals. Generating both illumination types in the microscope body at the expense of the size of the system is a good alternative to fiber bundles, and the technical simplicity of the HiLo microscopy technique makes it a good candidate for miniaturization.</p><p>More recently, it has been proposed that the light field microscopy (LFM) and seeded iterative demixing (SID) techniques can be integrated into miniaturized microscopes (<xref rid=\"B81\" ref-type=\"bibr\">Nobauer et al., 2017</xref>; <xref rid=\"B105\" ref-type=\"bibr\">Skocek et al., 2018</xref>). These computational methods only require minor hardware modifications to the microscope body by inserting a microlens array in the detection pathway of a miniaturized epifluorescence microscope. A 700 μm × 600 μm × 360 μm volume has been imaged at a frequency of 16 Hz with a discrimination threshold between cells of 15 microns. Despite the complex analytics and lower spatial resolution of this method, the reduced number of optical components and the extraction of a large volumetric amount of data makes it a good candidate for Ca<sup>2+</sup> activity recording in freely behaving animals with improved optical sectioning.</p><p>As mentioned above, the first versions of mini-endoscopes were based on a two-photon excitation principle that provides superior optical sectioning (<xref rid=\"B50\" ref-type=\"bibr\">Helmchen et al., 2001</xref>; <xref rid=\"B49\" ref-type=\"bibr\">Helmchen, 2002</xref>; <xref rid=\"B31\" ref-type=\"bibr\">Flusberg et al., 2005</xref>). However, these systems are bulkier and heavier, which limits the behavioral paradigms that can be studied. In these versions, the laser scanning mechanism is either located proximally on the animal for higher resolution and sensitivity, or remotely and is connected to the animal with a fiber bundle to limit the number of optical components on the animal (<xref rid=\"B86\" ref-type=\"bibr\">Ozbay et al., 2015</xref>, <xref rid=\"B85\" ref-type=\"bibr\">2018</xref>). To reduce the weight of the imaging systems, miniaturized scanning systems were developed based on micro-mechanical systems (MEMS), scanning mirrors (<xref rid=\"B91\" ref-type=\"bibr\">Piyawattanametha et al., 2006</xref>; <xref rid=\"B125\" ref-type=\"bibr\">Zong et al., 2017</xref>), and piezoelectric actuators (<xref rid=\"B29\" ref-type=\"bibr\">Engelbrecht et al., 2008</xref>). High resolution miniaturized two-photon mini-endoscopes capable of imaging a 130 μm × 130 μm-field of view at 40 Hz have been used to record Ca<sup>2+</sup> activity in dendrites and spines in freely behaving animals (<xref rid=\"B125\" ref-type=\"bibr\">Zong et al., 2017</xref>). In this system, a hollow core photonic crystal fiber and an achromatic objective lens are used to minimize light pulse broadening and maintain the high intensity at focus essential for two-photon excitation. However, this work was done in the first layers of the brain (visual cortex). Minimizing pulse broadening remains one of the main challenges of deep brain imaging. To achieve this, the chromatic aberration of the GRIN lenses used for deep brain imaging has to be properly compensated without increasing the outer diameter of the implant. This will require the development of new minimally invasive achromatic optical probes. Another interesting approach to increase the imaging depth without using GRIN lenses is miniaturized three-photon microscopy. <xref rid=\"B59\" ref-type=\"bibr\">Klioutchnikov et al. (2020)</xref> recently designed a three-photon microscope capable of recording calcium transients at multiple cortical depths of up to 1120 microns in freely behaving animals. However, the weight of the device developed by <xref rid=\"B59\" ref-type=\"bibr\">Klioutchnikov et al. (2020)</xref> (around 5 g) restricts its use for now to bigger rodents such as rats.</p><p>Miniaturized confocal microscopes with a scanning system placed on the animal would provide optical sectioning without a complex laser system and non-linear effect management. The main drawback of confocal microscopy compared to two-photon microscopy is its lower excitation wavelength, which creates more scattering and higher phototoxicity. The chromatic aberrations of the optical system, including the GRIN relay lens, will also have to be properly compensated to ensure that the pinhole and object plane remain in conjugated planes.</p></sec><sec id=\"S4.SS2\"><title>Optical Aberration Compensation</title><p>The high numerical aperture and the small outer diameter of GRIN lenses (0.5–1 mm) allow the imaging of structures located several millimeters deep in tissue with minimal invasiveness. In comparison, relay lenses made of optically homogenous materials with a similar numerical aperture are at least five times larger in diameter. Given this, GRIN lenses have become the preferred option for <italic>in vivo</italic> deep structure imaging. However, GRIN lenses also suffer from high intrinsic on-axis and off-axis optical aberrations (astigmatism and chromatic) and have the highest optical aberrations in miniaturized microscopy systems, leading to lower spatial resolutions and smaller effective fields of view, especially with high numerical aperture GRIN lenses. To solve this issue, adaptive optics methods have been used in two-photon mini-endoscopes. One of these methods allows the recovery of diffraction-limited resolution in a large field of view by measuring and correcting off-axis aberrations using pupil segmentation-based adaptive optics (<xref rid=\"B118\" ref-type=\"bibr\">Wang and Ji, 2012</xref>). Future developments in miniature microscopy would also benefit from the use of miniature tunable liquid crystal lenses and miniaturization of the phase modulators used in adaptive optics methods such as phase liquid crystal spatial light modulators. Adding adaptive optics to miniaturized microscopes would also help reduce the impact of light scattering in tissue. The inhomogeneity created by the different components of the sample (water, proteins, and lipids, etc.) distorts the fluorescence wavefront, resulting in lower resolution and contrast.</p></sec><sec id=\"S4.SS3\"><title>Wireless Imaging Systems</title><p>With the development of tethered miniaturized microscopes, it became possible to correlate brain activity and behavior recordings. However, despite several attempts to reduce the impact of the cables on animal behavior (very flexible cables, motor-assisted rotary-joints, etc.), some complex behavioral studies such as social interaction studies with multiple animals cannot be performed with these systems due to cable entanglement. To overcome these issues, several wireless models have been proposed (<xref rid=\"B66\" ref-type=\"bibr\">Liberti et al., 2017</xref>; <xref rid=\"B7\" ref-type=\"bibr\">Barbera et al., 2019</xref>; <xref rid=\"B104\" ref-type=\"bibr\">Shuman et al., 2020</xref>). One of the main challenges in developing wireless systems is minimizing their size and weight by, for instance, reducing the size of the lithium polymer (LiPo) batteries. Several avenues have been explored to reduce power consumption and use lighter batteries: limiting the number of on-board electronic components with on-site recording (<xref rid=\"B7\" ref-type=\"bibr\">Barbera et al., 2019</xref>; <xref rid=\"B104\" ref-type=\"bibr\">Shuman et al., 2020</xref>) and using low resolution sensors to restrict the amount of transferred data (<xref rid=\"B66\" ref-type=\"bibr\">Liberti et al., 2017</xref>). Using a battery backpack for a better distribution of the weight has also been explored (<xref rid=\"B7\" ref-type=\"bibr\">Barbera et al., 2019</xref>). For example, <xref rid=\"B66\" ref-type=\"bibr\">Liberti et al. (2017)</xref> introduced the FinchScope, a wireless mini-endoscope that uses a 2 g wireless transmitter and a LiPo battery. <xref rid=\"B7\" ref-type=\"bibr\">Barbera et al. (2019)</xref> also introduced a wireless mini-endoscope that is powered by a battery backpack anchored on the animal’s back and that is equipped with Micro SD card storage. They were able to record the activity of medium spiny neurons (MSNs) in the dorsal striatum of two freely behaving and interacting mice. In both studies, power was supplied to the microscope by miniature LiPo batteries, which depending on their size, can provide power for 30 min to 1 h at the expense of increasing the weight of the microscope to several grams (<xref rid=\"B66\" ref-type=\"bibr\">Liberti et al., 2017</xref>). For future developments, local recording remains a very good solution for reducing the weight of the system. Transferring data samples at a very low frame rate (0.2–1 Hz) and adding a communication interface for the synchronization with external components such as behavior cameras would improve system feedback. The development of new wireless systems should also take into account future behavioral studies aiming to assess the impact of the weight and its distribution (e.g., backpack battery versus integrated battery) on animal behavior. Further improvements are required to optimize wireless mini-endoscopes, improvements that will largely benefit the neuroscience field by expanding the number of possible behavioral experiments that can be performed while monitoring brain activity. The potential of this technique can also be extended to animals that exhibit more complex social behavior like monkeys and small primates. In addition, more complex social behaviors can be assessed without the constraints of the cable from the imaging device.</p></sec></sec><sec id=\"S5\"><title>General Conclusion</title><p>Imaging neuronal activity using various sensors and actuators has emerged as a powerful tool that allows neuroscientists to record large-scale neuronal activity in freely behaving animals. Traditional tools used for imaging such as two-photon excitation microscopy have opened up the possibility of imaging Ca<sup>2+</sup> activity patterns at the cellular and subcellular levels with unprecedented optical resolution. But these experimental setups are costly and require specific infrastructures that limit their dissemination among large numbers of laboratories. Also, these setups require the animals to be head-restrained during the imaging phase, which markedly limits the behavioral repertoire that can be tested. The development of mini-endoscopes has given rise to a new class of devices that have proven to be very useful in acquiring and correlating functional cellular signals from cortical and deep brain regions to animal behavior. The ease of use coupled with the portability and relative low cost of mini-endoscopes has provided a much-needed technical alternative. The capacity to take recordings from large neuronal populations in freely behaving animals has made it possible to analyze large-scale Ca<sup>2+</sup> imaging data with higher statistical power and to discover different types of coding dynamics related to animal behavior. The versatility of these devices also provides a good platform for improvements to the original design based on research requirements. Designs that incorporate parallel electrophysiological recordings or that allow all-optical circuit-level investigations by combining imaging with optogenetic manipulations can be used to further dissect activity at the microcircuit level and increase our understanding of neural processes underlying animal behavior. The ability to construct lightweight small mini-endoscopes will contribute to animal well-being, will improve the read-out of neural activity under natural conditions, and will expand research to other animal models. By decreasing the size of these devices, mini-endoscopes now also permit the use of multiple devices on the same experimental subject, which allows for the imaging of multiple brain areas in mice and other larger animals such as rats and primates. This multi-site recording approach will open up new avenues for systems neuroscience research, where longitudinal large-scale recordings beyond a single local circuit can be performed and may uncover exciting new information related to circuit activity patterns governing brain-wide functional circuitry and synchronicity. Further improvements to these devices aiming to increase the depth of penetration in living tissue, fine tune spatial and temporal resolution, and add a wireless option for even less restrictive behavioral repertoires are needed to make functional imaging in freely behaving animals more widely accessible. The traditional use of mini-endoscopy is to image Ca<sup>2+</sup> activity at the neuronal level. However, the advances in fluorescent reporters, onsite lens focus technologies, and two- and three-photon miniaturization will enable scientists to image cellular and subcellular compartments such as dendrites and dendritic spines. Further development of GEVIs, which have much more temporal resolution than GECIs, will push the use of mini-endoscopes even further, giving scientists the means to optically assess voltage changes across membranes and dendritic compartments. This will most likely revolutionize neuroscience research, giving scientists a tool to record, stimulate, and differentiate between the fast electrophysiological events associated with synaptic activity and the slow Ca<sup>2+</sup> events associated with the various biomolecular processes that occur during learning and memory. In addition, mini-endoscopic imaging approaches may allow scientists to monitor activity related to spine formation/elimination, the dynamics of glia processes, microglia migration, and adult neurogenesis in freely behaving animals. The flexible methodological approach provided by mini-endoscopes will continue to afford neuroscientists with better tools to image large neuronal populations during increasingly less restrained behavioral paradigms. This, in turn, will lead to new datasets that will more accurately depict the relationship between recorded functional and structural activities and animal behavior.</p></sec><sec id=\"S6\"><title>Author Contributions</title><p>All authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication.</p></sec><sec id=\"conf1\"><title>Conflict of Interest</title><p>SD and HD declare a conflict of interest. SD is the owner of Doric Lenses and HD is an R&D scientist at Doric Lenses. 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"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Int J Environ Res Public Health</journal-id><journal-id journal-id-type=\"iso-abbrev\">Int J Environ Res Public Health</journal-id><journal-id journal-id-type=\"publisher-id\">ijerph</journal-id><journal-title-group><journal-title>International Journal of Environmental Research and Public Health</journal-title></journal-title-group><issn pub-type=\"ppub\">1661-7827</issn><issn pub-type=\"epub\">1660-4601</issn><publisher><publisher-name>MDPI</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32756449</article-id><article-id pub-id-type=\"pmc\">PMC7432154</article-id><article-id pub-id-type=\"doi\">10.3390/ijerph17155591</article-id><article-id pub-id-type=\"publisher-id\">ijerph-17-05591</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Article</subject></subj-group></article-categories><title-group><article-title>Beliefs about the Harmfulness of Heated Tobacco Products Compared with Combustible Cigarettes and Their Effectiveness for Smoking Cessation among Korean Adults</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0002-4370-8442</contrib-id><name><surname>Kim</surname><given-names>Seung Hee</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05591\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Kang</surname><given-names>Seo Young</given-names></name><xref ref-type=\"aff\" rid=\"af2-ijerph-17-05591\">2</xref></contrib><contrib contrib-type=\"author\"><name><surname>Cho</surname><given-names>Hong-Jun</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05591\">1</xref><xref rid=\"c1-ijerph-17-05591\" ref-type=\"corresp\">*</xref></contrib></contrib-group><aff id=\"af1-ijerph-17-05591\"><label>1</label>Department of Family Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul 05505, Korea; <email>[email protected]</email></aff><aff id=\"af2-ijerph-17-05591\"><label>2</label>International Healthcare Center, Asan Medical Center, Seoul 05505, Korea; <email>[email protected]</email></aff><author-notes><corresp id=\"c1-ijerph-17-05591\"><label>*</label>Correspondence: <email>[email protected]</email>; Tel.: +82-2-3010-3812</corresp></author-notes><pub-date pub-type=\"epub\"><day>03</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"ppub\"><month>8</month><year>2020</year></pub-date><volume>17</volume><issue>15</issue><elocation-id>5591</elocation-id><history><date date-type=\"received\"><day>01</day><month>7</month><year>2020</year></date><date date-type=\"accepted\"><day>01</day><month>8</month><year>2020</year></date></history><permissions><copyright-statement>© 2020 by the authors.</copyright-statement><copyright-year>2020</copyright-year><license license-type=\"open-access\"><license-p>Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>).</license-p></license></permissions><abstract><p>Heated tobacco products (HTPs) have been widely used in Korea since their introduction in 2017. In this study, we investigated the perceptions of their relative harmfulness and smoking cessation effects. We performed an online survey in 7000 Koreans in 2018 (2300 males and 4700 females aged 20–69 years) by matching their age, sex, and provincial distribution. To investigate the factors causing HTPs to be perceived as less harmful than combustible cigarettes (CCs) and helpful for smoking cessation, we used multivariable logistic regression analyses. HTPs were less harmful than CCs in 16.8% of participants, particularly among HTP-only users and dual and triple users of HTPs, electronic cigarettes (ECs), or CCs than among CC-only users, those who were aged ≤ 34 years, males, and those with higher incomes. HTPs were reportedly helpful for smoking cessation in 11.2% of participants. Similar perceptions were more likely among HTP-only users, as well as dual and triple users than among CC-only users and adults with higher education/incomes. Although Korean adults generally had negative perceptions of the harmfulness and smoking cessation effects of HTPs compared with CCs, dual and triple users were more likely to have positive perceptions. Monitoring the use of multiple tobacco products and HTPs is a new challenge for Korean policymakers.</p></abstract><kwd-group><kwd>heated tobacco products</kwd><kwd>cigarette smoking</kwd><kwd>risk</kwd><kwd>perception</kwd><kwd>smoking cessation</kwd></kwd-group></article-meta></front><body><sec sec-type=\"intro\" id=\"sec1-ijerph-17-05591\"><title>1. Introduction</title><p>Heated tobacco products (HTPs) comprise small cigarettes, in which the tobacco is heated just enough to release a flavorful nicotine-containing vapor [<xref rid=\"B1-ijerph-17-05591\" ref-type=\"bibr\">1</xref>]. On the other hand, electronic cigarettes (ECs) are battery-powered devices that vaporize a liquid solution containing nicotine and flavorants [<xref rid=\"B2-ijerph-17-05591\" ref-type=\"bibr\">2</xref>]. HTPs electrically heat the tobacco without burning to a lower temperature than combustible cigarettes (CCs) to produce an aerosol with nicotine. This is unlike ECs, in which users inhale the aerosol created by heating a nicotine liquid [<xref rid=\"B3-ijerph-17-05591\" ref-type=\"bibr\">3</xref>]. One brand of HTPs was introduced to South Korea (hereafter, Korea) in June of 2017. The market share of HTPs rapidly increased and reached 10.5% of Korea’s total sales of tobacco products in 2019 [<xref rid=\"B4-ijerph-17-05591\" ref-type=\"bibr\">4</xref>]. Among Korean men, the prevalence of CC use was 61.8% in 2001, but by 2018, it decreased to 36.7%; whereas among Korean women, its prevalence increased from 5.4% in 2001 to 7.5% in 2018 [<xref rid=\"B5-ijerph-17-05591\" ref-type=\"bibr\">5</xref>]; which was still very low compared with that of Korean men.</p><p>Previous studies have been conducted on the correlates of HTP use among Korean adults [<xref rid=\"B6-ijerph-17-05591\" ref-type=\"bibr\">6</xref>,<xref rid=\"B7-ijerph-17-05591\" ref-type=\"bibr\">7</xref>]. Demographic and socioeconomic characteristics associated with using HTPs include being male, young, and having a high education level and economic status. HTPs are marketed as clean, sophisticated, high-tech products that provide all of the benefits of smoking with less ash and odor [<xref rid=\"B8-ijerph-17-05591\" ref-type=\"bibr\">8</xref>]. These characteristics could attract young, male adults. Due to the high maintenance and cost of using HTP devices, people with a higher socioeconomic level were more likely to use them. Moreover, the use of CCs or ECs was the most significant factor related to the use of HTPs [<xref rid=\"B6-ijerph-17-05591\" ref-type=\"bibr\">6</xref>,<xref rid=\"B7-ijerph-17-05591\" ref-type=\"bibr\">7</xref>], which may be because CC or EC users were more likely to have a positive perception of HTPs due to marketing claims that indicate they are less harmful than CCs [<xref rid=\"B8-ijerph-17-05591\" ref-type=\"bibr\">8</xref>].</p><p>Beliefs and perceptions about the health risks associated with smoking play significant roles in reducing its prevalence, as concerns about such risks have been reported as the most common motivation to quit smoking and prevent smoking initiation [<xref rid=\"B9-ijerph-17-05591\" ref-type=\"bibr\">9</xref>,<xref rid=\"B10-ijerph-17-05591\" ref-type=\"bibr\">10</xref>]. With regard to the association between the perceptions of relative harmfulness of HTPs and their usage, as compared to non-HTP users, HTP users were more likely to believe HTPs to be less harmful than CCs, with this belief being more prominent among its frequent users [<xref rid=\"B11-ijerph-17-05591\" ref-type=\"bibr\">11</xref>]. Furthermore, despite understanding that HTPs were not risk-free, most reported that they used HTPs due to their beliefs about its having health benefits over CCs [<xref rid=\"B6-ijerph-17-05591\" ref-type=\"bibr\">6</xref>], with one of the main reasons being their perception of its reduced harm [<xref rid=\"B12-ijerph-17-05591\" ref-type=\"bibr\">12</xref>]. These results suggest that the perception of HTPs being less harmful than CCs may have had a major impact on HTP usage in Korea.</p><p>There is controversy regarding whether HTPs are less harmful than CCs; despite HTPs being marketed with claims of their being less harmful based on assertions that they expose users to lower levels of toxicants [<xref rid=\"B13-ijerph-17-05591\" ref-type=\"bibr\">13</xref>]. While the levels of some harmful and potentially harmful constituents of HTPs were lower than those of CCs [<xref rid=\"B14-ijerph-17-05591\" ref-type=\"bibr\">14</xref>], the reduced exposure of some toxicants does not mean a reduced risk of harm to health [<xref rid=\"B13-ijerph-17-05591\" ref-type=\"bibr\">13</xref>]. Since the health risks of HTPs have not been determined, further studies are needed.</p><p>Even though one of the reasons for using HTPs was to stop smoking [<xref rid=\"B6-ijerph-17-05591\" ref-type=\"bibr\">6</xref>,<xref rid=\"B12-ijerph-17-05591\" ref-type=\"bibr\">12</xref>], several studies have shown no significant association between the use of HTPs and smoking cessation-related behaviors, including attempts or intentions to quit smoking [<xref rid=\"B7-ijerph-17-05591\" ref-type=\"bibr\">7</xref>,<xref rid=\"B15-ijerph-17-05591\" ref-type=\"bibr\">15</xref>,<xref rid=\"B16-ijerph-17-05591\" ref-type=\"bibr\">16</xref>]. Moreover, adolescent HTP users were less likely to become former CC smokers despite multiple attempts at smoking cessation [<xref rid=\"B17-ijerph-17-05591\" ref-type=\"bibr\">17</xref>]. This was probably because although some smokers initiated HTPs for smoking cessation, CC users were more likely to become dual users of HTPs and CCs rather than succeeding in quitting smoking. Regarding perceived effects of HTPs on smoking cessation; a recent study showed that users rather than non-users of ECs were more likely to believe that HTPs are helpful in quitting smoking [<xref rid=\"B18-ijerph-17-05591\" ref-type=\"bibr\">18</xref>].</p><p>Despite the current lack of evidence regarding the long-term health effects of HTPs and their effectiveness for smoking cessation, marketing claims of HTPs’ reduced health risks could have misled consumers [<xref rid=\"B13-ijerph-17-05591\" ref-type=\"bibr\">13</xref>]. To our knowledge, no research has assessed the perceptions of HTPs in a large population of Korean adults. Therefore, this study aimed to investigate the beliefs about the relative harmfulness of HTPs and CCs, as well as their effectiveness for smoking cessation among Korean adults. </p></sec><sec id=\"sec2-ijerph-17-05591\"><title>2. Materials and Methods</title><sec id=\"sec2dot1-ijerph-17-05591\"><title>2.1. Study Sample</title><p>We conducted an online survey using a panel managed by the research company EMBRAIN (Seoul, Korea), with 1.3 million members (as on December 2018). We randomly invited 70,000 individuals by matching their age, sex, and provincial distributions with the 2018 national population statistics. However, only 10,489 responded, from which we included 7000 (2300 men and 4700 women aged 20–69 years), after excluding 2285 who outnumbered our quota, 21 who did not meet the age criteria, 400 who decided not to participate in the survey, and 783 who responded incorrectly. We oversampled twice as many women as men because the prevalence of tobacco product use was much lower among Korean women than men.</p><p>Data were collected from 3–9 November 2018. All participants received financial incentives equivalent to 5000 Korean Republic Won (KRW), equal to approximately 5 US Dollars (USD). All the participants gave their informed consent for inclusion, prior to participating in the study. The study was conducted in accordance with the Declaration of Helsinki, and its protocol was approved by the Asan Medical Center Institutional Review Board (S2018-1662-0001) in Seoul, Korea. </p></sec><sec id=\"sec2dot2-ijerph-17-05591\"><title>2.2. Measures</title><sec id=\"sec2dot2dot1-ijerph-17-05591\"><title>2.2.1. Status of Tobacco Product Use and Beliefs about the Relative Harmfulness and Smoking Cessation Effects of HTPs</title><p>Current CC users were defined as adults who had smoked at least 100 cigarettes during their lifetime and reported currently smoking “every day” or on “some days”. Former CC users were defined as adults who had smoked at least 100 cigarettes but had responded “not at all” to the question about current smoking. Never-CC users were defined as adults who had neither smoked at least 100 cigarettes nor ever smoked CCs in their lifetime. Current HTP users were defined as adults who had continually used HTPs in their lifetime and reported currently using them “every day” or on “some days”. Former HTP users were defined as adults who had frequently used HTPs in their lifetime but were currently not using them. Never-HTP users were defined as adults who had never used HTPs in their lifetime. Current EC users were defined as adults who had repeatedly used ECs in their lifetime, as well as during the past month. Former EC users were defined as adults who had habitually used ECs in their lifetime but had not used them during the past month. Never-EC users were defined as adults who had never used ECs in their lifetime. Since the prevalence of using other tobacco products (e.g., snus, water pipes, cigars, chewing tobacco, pipes, rolling tobacco, snuff) was negligible (less than 0.01%) among Korean adults [<xref rid=\"B7-ijerph-17-05591\" ref-type=\"bibr\">7</xref>], we did not include questions on these in our survey.</p><p>The status of tobacco product use was classified into nine groups. “Never-users of any of the three tobacco products” were defined as adults who had never used CCs, HTPs, or ECs. “Former users of any of the three tobacco products” included former CC, HTP, and EC users who were currently not using any of the three products. “Current users of any of the three tobacco products” included “CC-only users”, “HTP-only users”, “EC-only users”, “dual users of HTPs and CCs”, “dual users of ECs and CCs”, “dual users of HTPs and ECs”, and “triple users of the three products”.</p><p>Perceptions of the relative harmfulness of HTPs and CCs was evaluated with the question: “What do you think is the effect of ‘heated tobacco products’ on your health as compared with CCs?” The response options were: “They are less harmful”, “They are similar”, and “They are more harmful”. Whether HTPs can help with smoking cessation was evaluated with the question: “Do you think ‘heated tobacco products’ are helpful for quitting smoking?” The response options were: “Yes”, “No”, and “I am not sure”.</p></sec><sec id=\"sec2dot2dot2-ijerph-17-05591\"><title>2.2.2. Sociodemographic Characteristics</title><p>The sociodemographic characteristics analyzed included age, sex, education, monthly household income, and marital status.</p></sec></sec><sec id=\"sec2dot3-ijerph-17-05591\"><title>2.3. Data Analysis</title><p>We calculated the proportion of participants in each category of sociodemographic characteristics and the prevalence of tobacco product use. Furthermore, we evaluated the proportion of participants believing in the health benefits of HTPs being relative to CCs, and those perceiving HTPs as effective for smoking cessation. To identify factors associated with the perceptions of HTPs, adjusted odds ratios (AOR) and 95% confidence intervals (CI) were obtained via multivariable logistic regression analysis, after adjusting for potential confounders. Data analyses were conducted using IBM SPSS Statistics for Windows, Version 24.0 (IBM Corp., Armonk, NY, USA).</p></sec></sec><sec sec-type=\"results\" id=\"sec3-ijerph-17-05591\"><title>3. Results</title><p>Around 30% of the participants were aged 20–34 years, while those aged 35–49 and 50–69 years were each around 35%. Approximately 60% had college-level education, and over 50% had monthly household incomes of 3–6.99 million KRW. The overall prevalence of current use of tobacco products, including CCs, HTPs, and ECs was 27.4% (44.1% among men and 11.0% among women) (<xref rid=\"ijerph-17-05591-t001\" ref-type=\"table\">Table 1</xref>).</p><p>Of the 7000 participants, 16.8% reported HTPs to be less harmful than CCs (<xref ref-type=\"fig\" rid=\"ijerph-17-05591-f001\">Figure 1</xref>). Using multivariable logistic regression analysis (<xref rid=\"ijerph-17-05591-t002\" ref-type=\"table\">Table 2</xref>), participants aged ≤ 34 years (AOR = 1.20, 95% CI = 1.00, 1.44), who were male (AOR = 1.26, 95% CI = 1.11, 1.44), and had an income ≥7 million KRW (AOR = 1.30, 95% CI = 1.08, 1.57) were associated with greater odds of perceiving HTPs as less harmful than CCs, as compared with those who were aged ≥ 50 years, female, and had an income <3 million KRW, respectively. As compared with CC-only users, never-users of any of the three tobacco products had lower odds of perceiving HTPs as less harmful than CCs (AOR = 0.64, 95% CI = 0.54, 0.77). However, HTP-only users (AOR = 3.32, 95% CI = 2.25, 4.90), dual users of HTPs and CCs (AOR = 2.25, 95% CI = 1.77, 2.86), dual users of HTPs and ECs (AOR = 3.96, 95% CI = 2.17, 7.24), dual users of ECs and CCs (AOR = 1.96, 95% CI = 1.39, 2.77), and triple users (AOR = 5.12, 95% CI = 3.93, 6.67) had greater odds of perceiving HTPs as less harmful than CCs.</p><p>As for the perceived effectiveness of HTPs for smoking cessation, 11.2% of participants reported HTPs as effective for smoking cessation (<xref ref-type=\"fig\" rid=\"ijerph-17-05591-f002\">Figure 2</xref>). Based on multivariable logistic regression analysis (<xref rid=\"ijerph-17-05591-t002\" ref-type=\"table\">Table 2</xref>), groups with education ≥ college graduate (AOR = 1.35, 95% CI = 1.12, 1.63) and income ≥7 million KRW (AOR = 1.43, 95% CI = 1.15, 1.78) were more likely to agree with the effectiveness of HTPs for smoking cessation. As compared with CC-only users, never-users of any of the three tobacco products had lower odds of perceiving HTPs as effective for smoking cessation (AOR = 0.72, 95% CI = 0.57, 0.90). However, HTP-only users (AOR = 3.61, 95% CI = 2.34, 5.59), dual users of HTPs and CCs (AOR = 2.84, 95% CI = 2.14, 3.75), dual users of HTPs and ECs (AOR = 6.01, 95% CI = 3.23, 11.19), dual users of ECs and CCs (AOR = 2.81, 95% CI = 1.90, 4.14), and triple users (AOR = 7.93, 95% CI = 5.94, 10.57) had greater odds of believing HTPs to be effective for smoking cessation.</p></sec><sec sec-type=\"discussion\" id=\"sec4-ijerph-17-05591\"><title>4. Discussion</title><p>Overall, participants were less likely to believe in the relative health benefits of HTPs and CCs. Only 16.8% of the participants believed that HTPs were less harmful than CCs, which was lower than the results of a Canadian study, in which 25% of the participants perceived HTPs as less harmful than CCs [<xref rid=\"B18-ijerph-17-05591\" ref-type=\"bibr\">18</xref>]. The negative public perceptions of HTPs are understandable, considering they contain real tobacco and there was widespread media coverage when the Korea Ministry of Food and Drug Safety released information on harmful HTP emissions in 2018 [<xref rid=\"B19-ijerph-17-05591\" ref-type=\"bibr\">19</xref>]. These negative perceptions of HTPs can also be ascribed to Korea’s relatively stringent tobacco regulations. While Korean tobacco control policies—which include smoking bans in public places, regulations on advertising, and bans on sales to minors—are applied equally to CCs and HTPs, the individual consumption tax for HTPs is 89% of the tax levied on CCs [<xref rid=\"B20-ijerph-17-05591\" ref-type=\"bibr\">20</xref>]. </p><p>The perception of the relative harmfulness of HTPs compared with CCs differed according to the pattern of tobacco product use. Compared with CC-only users, HTP users were more likely to perceive HTPs to be less harmful than CCs. Consistent with these results, a previous study found that HTP-only users and dual users of HTPs and CCs were more likely to believe that HTPs were less harmful to users than CCs, compared with CC-only users [<xref rid=\"B16-ijerph-17-05591\" ref-type=\"bibr\">16</xref>]. It is common for some tobacco product users to choose specific products which they perceive as less harmful because of their favorable perceptions towards these products [<xref rid=\"B21-ijerph-17-05591\" ref-type=\"bibr\">21</xref>,<xref rid=\"B22-ijerph-17-05591\" ref-type=\"bibr\">22</xref>,<xref rid=\"B23-ijerph-17-05591\" ref-type=\"bibr\">23</xref>,<xref rid=\"B24-ijerph-17-05591\" ref-type=\"bibr\">24</xref>]. Moreover, the odds of perceiving HTPs as less harmful than CCs were highest among triple users of the three products. Since adults with no perception of risk regarding HTPs or ECs were more likely to use multiple tobacco products than those who perceived a risk [<xref rid=\"B25-ijerph-17-05591\" ref-type=\"bibr\">25</xref>], triple users would have been less likely to have risk perceptions of HTPs.</p><p>Intriguingly, both HTP and EC users perceived HTPs to be less harmful than CCs. EC users’ perceptions of the relative harmfulness of HTPs were consistent with the results of a previous study which reported that EC users, rather than non-EC users were more likely to believe that HTPs were less harmful than CCs [<xref rid=\"B18-ijerph-17-05591\" ref-type=\"bibr\">18</xref>]. An explanation could be that EC users considered HTPs to be similar because of the similarity of the Korean terms for ECs and HTPs, whose direct translations were “liquid-type electronic cigarettes” and “cigarette-type electronic cigarettes”, respectively. This could have made people regard the two products as similar and caused confusion in distinguishing them. Furthermore, HTPs and ECs have many similarities in that they are marketed as being less harmful than CCs and as being clean and emitting less odor [<xref rid=\"B8-ijerph-17-05591\" ref-type=\"bibr\">8</xref>,<xref rid=\"B26-ijerph-17-05591\" ref-type=\"bibr\">26</xref>]. </p><p>As for the perceived effects of smoking cessation of HTPs, 11.2% of participants believed HTPs to be effective for smoking cessation, which was much lower than the results of a Canadian study which reported that 35% of participants considered HTPs effective for smoking cessation [<xref rid=\"B18-ijerph-17-05591\" ref-type=\"bibr\">18</xref>]. According to previous Korean studies, about 30% of HTP users agreed that HTPs helped people quit CC smoking, which was much lower than the 79% and 76% of participants who believed the benefits of HTPs to be lack of ash and less odor, respectively [<xref rid=\"B27-ijerph-17-05591\" ref-type=\"bibr\">27</xref>], and cited its being less harmful and having less smelly characteristics as their major reasons for using HTPs [<xref rid=\"B6-ijerph-17-05591\" ref-type=\"bibr\">6</xref>]. Furthermore, in a Korean qualitative interview study, most current or former HTP users did not believe that HTPs had a positive effect on contemplation of smoking cessation [<xref rid=\"B28-ijerph-17-05591\" ref-type=\"bibr\">28</xref>]. Therefore, positive perceptions on the effects of HTPs on smoking cessation were not the main driver, though they could have contributed to the use of HTPs in Korea. It is intriguing that triple users highly rated HTPs as effective for smoking cessation. Perhaps they were working toward quitting, but they became poly-tobacco users instead of quitting smoking.</p><p>The popularity of HTPs in Korea, despite the generally negative perceptions on its health benefits compared with CCs and smoking cessation effects, can be attributed to successful marketing by tobacco industries and consumers being attracted to its other characteristics (e.g., less odor, no ash, fancy appearance) [<xref rid=\"B8-ijerph-17-05591\" ref-type=\"bibr\">8</xref>,<xref rid=\"B26-ijerph-17-05591\" ref-type=\"bibr\">26</xref>]. Among smokers, CC users who had been exposed to HTP advertising were more likely to perceive HTPs as less harmful than CCs [<xref rid=\"B11-ijerph-17-05591\" ref-type=\"bibr\">11</xref>]. The advertisements on reduced exposure claims of HTPs may have resulted in smokers having a positive perception of HTPs and using them [<xref rid=\"B13-ijerph-17-05591\" ref-type=\"bibr\">13</xref>]. Previous research indicates that EC users were more likely to have positive perceptions about HTPs and may be more susceptible to the marketing of these products [<xref rid=\"B7-ijerph-17-05591\" ref-type=\"bibr\">7</xref>], which may lead to HTP use. Presumably, various factors may have interacted with one another and influenced the popularity of HTPs [<xref rid=\"B12-ijerph-17-05591\" ref-type=\"bibr\">12</xref>]. </p><p>Our study had a few limitations. The prevalence of tobacco product use needs to be interpreted cautiously, since our sample was not nationally representative; however, we tried to match sex, age, and provincial distributions in line with national statistics. As this study was cross-sectional, causal relationships between the perceptions of HTPs and their use could not be identified. The participants may not have completely distinguished HTPs from ECs due to their Korean terms being similar, and also because HTPs were new products at the time of data collection. Moreover, a standardized questionnaire for the use of HTPs was not available during the study period. To mitigate these problems in the questionnaire, we included pictures and names of HTPs available in the Korean market and presented the questions on HTPs after those on ECs [<xref rid=\"B29-ijerph-17-05591\" ref-type=\"bibr\">29</xref>]. A longitudinal study is needed to investigate the causal relationships between perceptions of HTPs and their use, including a nationally representative sample and a standardized questionnaire on HTP use.</p></sec><sec sec-type=\"conclusions\" id=\"sec5-ijerph-17-05591\"><title>5. Conclusions</title><p>Perceptions about the relative harmfulness and smoking cessation effects of HTPs were generally negative among Korean adults. However, dual and triple users of HTPs, ECs, or CCs were more likely to have positive perceptions about HTPs, which may have led to their use or otherwise lead people to use them. Therefore, more emphasis on monitoring the use of multiple tobacco products and HTPs is needed to cope with the increasing popularity of HTPs in Korea.</p></sec></body><back><notes><title>Author Contributions</title><p>Conceptualization, H.-J.C. and S.Y.K.; methodology, H.-J.C.; validation, H.-J.C., S.H.K., and S.Y.K.; formal analysis, S.H.K.; investigation, H.-J.C.; resources, H.-J.C.; data curation, H.-J.C. and S.H.K.; writing—original draft preparation, S.H.K.; writing—review and editing, H.-J.C. and S.Y.K.; visualization, S.H.K. and S.Y.K.; supervision, H.-J.C.; project administration, H.-J.C.; funding acquisition, H.-J.C.. All authors have read and agreed to the published version of the manuscript.</p></notes><notes><title>Funding</title><p>This research was funded by the Ministry of Health and Welfare, Republic of Korea, grant number 11-1352000-002406-01. 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Induc. Dis.</source><year>2020</year><volume>18</volume><fpage>43</fpage><pub-id pub-id-type=\"doi\">10.18332/tid/120103</pub-id><pub-id pub-id-type=\"pmid\">32477038</pub-id></element-citation></ref><ref id=\"B29-ijerph-17-05591\"><label>29.</label><element-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Brose</surname><given-names>L.S.</given-names></name><name><surname>Simonavicius</surname><given-names>E.</given-names></name><name><surname>Cheeseman</surname><given-names>H.</given-names></name></person-group><article-title>Awareness and use of ‘Heat-not-burn’ tobacco products in Great Britain</article-title><source>Tob. Regul. Sci.</source><year>2018</year><volume>4</volume><fpage>44</fpage><lpage>50</lpage><pub-id pub-id-type=\"doi\">10.18001/TRS.4.2.4</pub-id></element-citation></ref></ref-list></back><floats-group><fig id=\"ijerph-17-05591-f001\" orientation=\"portrait\" position=\"float\"><label>Figure 1</label><caption><p>Frequency of the belief that HTPs are less harmful than CCs. Abbreviations: CCs = combustible cigarettes; HTPs = heated tobacco products; ECs = electronic cigarettes; KRW = Korean Republic Won (KRW 1000 = US Dollar 1). Values are presented as weighted percentages (the weighted % of women was 50).</p></caption><graphic xlink:href=\"ijerph-17-05591-g001\"/></fig><fig id=\"ijerph-17-05591-f002\" orientation=\"portrait\" position=\"float\"><label>Figure 2</label><caption><p>Frequency of the belief that HTPs aid in smoking cessation. Abbreviations: CCs = combustible cigarettes; HTPs = heated tobacco products; ECs = electronic cigarettes; KRW = Korean Republic Won (KRW 1000 = US Dollar 1). Values are presented as weighted percentages (the weighted % of women was 50).</p></caption><graphic xlink:href=\"ijerph-17-05591-g002\"/></fig><table-wrap id=\"ijerph-17-05591-t001\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05591-t001_Table 1</object-id><label>Table 1</label><caption><p>Basic characteristics of the study participants (<italic>n</italic> = 7000).</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">Characteristic</th><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Men (<italic>n</italic> = 2300)</th><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Women (<italic>n</italic> = 4700)</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Total</th></tr><tr><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>n</italic>\n<sup>a</sup>\n</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">% <sup>a</sup></th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>n</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">%</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">%</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Age (years)</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">20–34</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">690</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">30.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1437</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">30.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">30.3</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">35–49</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">833</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">36.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1612</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">34.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">35.2</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">50–69</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">777</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">33.8</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1651</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">35.1</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">34.5</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Education</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">≤High school graduate</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">416</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">18.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1129</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">24.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">21.1</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">≤Junior college graduate</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">336</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">14.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1005</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">21.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">18.0</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">≥College graduate</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1548</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">67.3</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2566</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">54.6</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">60.9</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Monthly household income (KRW 1000) <sup>b</sup></bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><3000</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">618</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">26.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1156</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">24.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">25.7</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3000–4999</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">817</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">35.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1614</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">34.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">34.9</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5000–6999</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">476</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">20.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1078</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">22.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">21.8</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">≥7000</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">389</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">16.9</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">852</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">18.1</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">17.5</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Marital status</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Never married</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">823</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">35.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1567</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">33.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">34.5</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Married/cohabited</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1399</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">60.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2910</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">61.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">61.4</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Separated/divorced/bereaved</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">78</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3.4</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">223</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">4.7</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">4.1</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Tobacco product use status</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Never-users of any of the three tobacco products</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">760</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">33.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3906</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">83.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">58.3</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Former users of any of the three tobacco products</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">527</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">22.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">277</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">14.3</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Current users of any of the three tobacco products</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1013</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">44.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">517</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">11.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">27.4</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CC-only users</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">541</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">23.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">248</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">14.3</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">HTP-only users</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">44</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">33</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.3</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">EC-only users</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">27</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">29</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.9</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Dual users of HTPs and CCs</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">196</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">74</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.0</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Dual users of HTPs and ECs</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">20</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.5</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Dual users of ECs and CCs</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">72</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">39</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.0</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Triple users</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">120</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">5.2</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">74</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.6</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3.4</td></tr></tbody></table><table-wrap-foot><fn><p>Abbreviations: CCs = combustible cigarettes; HTPs = heated tobacco products; ECs = electronic cigarettes. <sup>a</sup> Values are presented as unweighted numbers and weighted percentages (the weighted % of women was 50). <sup>b</sup> Korean Republic Won (KRW) is Korea’s currency (KRW 1000 = US Dollar 1).</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"ijerph-17-05591-t002\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05591-t002_Table 2</object-id><label>Table 2</label><caption><p>Comparison of beliefs about the harmfulness of HTPs compared with CCs, and the perceived effectiveness of HTPs for smoking cessation (<italic>n</italic> = 7000).</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th rowspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">Factor</th><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">HTPs are Less Harmful<break/>than CCs</th><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">HTPs are Helpful for<break/>Smoking Cessation</th></tr><tr><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Crude</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Multi-Adjusted <sup>a</sup></th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Crude</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Multi-Adjusted <sup>a</sup></th></tr><tr><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">OR (95% CI)</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">AOR (95% CI)</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">OR (95% CI)</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">AOR (95% CI)</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Age (years)</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">50–69</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">35–49</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.99 (0.87, 1.13)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.90 (0.78, 1.04)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>0.83 (0.71, 0.97)</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>0.72 (0.61, 0.86)</bold>\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">20–34</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>1.25 (1.10, 1.43)</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>1.20 (1.00, 1.44)</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.05 (0.90, 1.22)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.94 (0.76, 1.17)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Sex</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Female</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Male</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>1.85 (1.65, 2.06)</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>1.26 (1.11, 1.44)</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>1.52 (1.33, 1.73)</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.98 (0.84, 1.15)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Education</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">≤ High school graduate</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">≤ Junior college graduate</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.99 (0.82, 1.19)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.97 (0.80, 1.18)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.07 (0.85, 1.34)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.09 (0.86, 1.38)</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">≥ College graduate</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>1.34 (1.16, 1.54)</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.15 (0.99, 1.34)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>1.52 (1.27, 1.81)</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>1.35 (1.12, 1.63)</bold>\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Monthly household income (KRW 1000) <sup>b</sup></bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><3000</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3000–4999</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>1.27 (1.09, 1.47)</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>1.27 (1.09, 1.48)</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>1.33 (1.11, 1.59)</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>1.29 (1.07, 1.56)</bold>\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5000–6999</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>1.22 (1.03, 1.43)</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.17 (0.98, 1.41)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>1.44 (1.19, 1.75)</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>1.31 (1.05, 1.62)</bold>\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">≥7000</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>1.37 (1.16, 1.62)</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>1.30 (1.08, 1.57)</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>1.60 (1.31, 1.96)</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>1.43 (1.15, 1.78)</bold>\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Marital status</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Never married</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Married/cohabited</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.92 (0.82, 1.03)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.97 (0.83, 1.14)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.01 (0.88, 1.15)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.95 (0.78, 1.15)</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Separated/divorced/bereaved</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.85 (0.63, 1.13)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.98 (0.71, 1.37)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.02 (0.73, 1.42)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.09 (0.75, 1.59)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Tobacco product use status</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CC-only users</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Never-users of any of the<break/>three tobacco products</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>0.58 (0.49, 0.68)</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>0.64 (0.54, 0.77)</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>0.72 (0.59, 0.88)</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>0.72 (0.57, 0.90)</bold>\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Former users of any of the<break/>three tobacco products</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.85 (0.70, 1.04)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.85 (0.69, 1.04)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.95 (0.74, 1.23)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.92 (0.71, 1.18)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">HTP-only users</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>3.37 (2.29, 4.96)</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>3.32 (2.25, 4.90)</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>3.60 (2.34, 5.55)</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>3.61 (2.34, 5.59)</bold>\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">EC-only users</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.24 (0.72, 2.13)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.18 (0.68, 2.03)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.21 (0.61, 2.40)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.13 (0.57, 2.24)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Dual users of HTPs and CCs</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>2.30 (1.81, 2.91)</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>2.25 (1.77, 2.86)</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>2.80 (2.12, 3.70)</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>2.84 (2.14, 3.75)</bold>\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Dual users of HTPs and ECs</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>4.10 (2.26, 7.43)</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>3.96 (2.17, 7.24)</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>6.23 (3.37, 11.50)</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>6.01 (3.23, 11.19)</bold>\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Dual users of ECs and CCs</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>2.08 (1.48, 2.92)</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>1.96 (1.39, 2.77)</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>2.80 (1.91, 4.11)</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>2.81 (1.90, 4.14)</bold>\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Triple users</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>5.28 (4.06, 6.86)</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>5.12 (3.93, 6.67)</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>8.20 (6.17, 10.89)</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>7.93 (5.94, 10.57)</bold>\n</td></tr></tbody></table><table-wrap-foot><fn><p>Abbreviations: HTPs = heated tobacco products; CCs = combustible cigarettes; ECs = electronic cigarettes; OR = odds ratio; AOR = adjusted odds ratio; CI = confidence interval. <sup>a</sup> Multiple logistic regression analysis adjusted for age, sex, education, income, marital status, and tobacco products use status. <sup>b</sup> Korean Republic Won (KRW) is Korea’s currency (KRW 1000 = USD 1). Bold values were significantly associated (<italic>p</italic> < 0.05).</p></fn></table-wrap-foot></table-wrap></floats-group></article>\n"
]
[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Front Psychol</journal-id><journal-id journal-id-type=\"iso-abbrev\">Front Psychol</journal-id><journal-id journal-id-type=\"publisher-id\">Front. Psychol.</journal-id><journal-title-group><journal-title>Frontiers in Psychology</journal-title></journal-title-group><issn pub-type=\"epub\">1664-1078</issn><publisher><publisher-name>Frontiers Media S.A.</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32849150</article-id><article-id pub-id-type=\"pmc\">PMC7432155</article-id><article-id pub-id-type=\"doi\">10.3389/fpsyg.2020.01961</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Psychology</subject><subj-group><subject>Original Research</subject></subj-group></subj-group></article-categories><title-group><article-title>Body Matters in Emotion: Restricted Body Movement and Posture Affect Expression and Recognition of Status-Related Emotions</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Reed</surname><given-names>Catherine L.</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><xref rid=\"c001\" ref-type=\"corresp\"><sup>*</sup></xref><uri xlink:type=\"simple\" xlink:href=\"https://loop.frontiersin.org/people/7109/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Moody</surname><given-names>Eric J.</given-names></name><xref ref-type=\"aff\" rid=\"aff2\"><sup>2</sup></xref><uri xlink:type=\"simple\" xlink:href=\"https://loop.frontiersin.org/people/616413/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Mgrublian</surname><given-names>Kathryn</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Assaad</surname><given-names>Sarah</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Schey</surname><given-names>Alexis</given-names></name><xref ref-type=\"aff\" rid=\"aff3\"><sup>3</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>McIntosh</surname><given-names>Daniel N.</given-names></name><xref ref-type=\"aff\" rid=\"aff4\"><sup>4</sup></xref></contrib></contrib-group><aff id=\"aff1\"><sup>1</sup>\n<institution>Department of Psychological Science, Claremont McKenna College</institution>, <addr-line>Claremont, CA</addr-line>, <country>United States</country>\n</aff><aff id=\"aff2\"><sup>2</sup>\n<institution>Wyoming Institute for Disabilities (WIND), University of Wyoming</institution>, <addr-line>Laramie, WY</addr-line>, <country>United States</country>\n</aff><aff id=\"aff3\"><sup>3</sup>\n<institution>Department of Psychology, Scripps College</institution>, <addr-line>Claremont, CA</addr-line>, <country>United States</country>\n</aff><aff id=\"aff4\"><sup>4</sup>\n<institution>Department of Psychology, University of Denver</institution>, <addr-line>Denver, CO</addr-line>, <country>United States</country>\n</aff><author-notes><fn id=\"fn1\" fn-type=\"edited-by\"><p>Edited by: Christel Bidet-Ildei, University of Poitiers, France</p></fn><fn id=\"fn2\" fn-type=\"edited-by\"><p>Reviewed by: Matteo Candidi, Sapienza University of Rome, Italy; Wolfgang Tschacher, University of Bern, Switzerland</p></fn><corresp id=\"c001\">*Correspondence: Catherine L. Reed, <email>[email protected]</email></corresp><fn id=\"fn3\" fn-type=\"other\"><p>This article was submitted to Emotion Science, a section of the journal Frontiers in Psychology</p></fn></author-notes><pub-date pub-type=\"epub\"><day>11</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><year>2020</year></pub-date><volume>11</volume><elocation-id>1961</elocation-id><history><date date-type=\"received\"><day>03</day><month>9</month><year>2019</year></date><date date-type=\"accepted\"><day>15</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>Copyright © 2020 Reed, Moody, Mgrublian, Assaad, Schey and McIntosh.</copyright-statement><copyright-year>2020</copyright-year><copyright-holder>Reed, Moody, Mgrublian, Assaad, Schey and McIntosh</copyright-holder><license xlink:href=\"http://creativecommons.org/licenses/by/4.0/\"><license-p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</license-p></license></permissions><abstract><p>Embodiment theory suggests that we use our own body and experiences to simulate information from other people’s bodies and faces to understand their emotions. A natural consequence of embodied theory is that our own current position and state contributes to this emotional processing. Testing non-disabled individuals, we investigated whether restricted body posture and movement influenced the production and recognition of nonverbal, dynamic emotional displays in able-bodied participants. In Experiment 1, participants were randomly assigned to either unrestricted or wheelchair-restricted (sitting, torso restrained) groups and nonverbally expressed six emotions (disgust, happiness, anger, fear, embarrassment, and pride) while being videotaped. After producing each emotion, they rated their confidence regarding how effectively they communicated that emotion. Videotaped emotional displays were coded for face, body, and face + body use. Based on naïve coders’ scores, both unrestricted and wheelchair-restricted groups produced emotionally congruent face and body movements and both groups were equally confident in their communication effectiveness. Using videos from Experiment 1, Experiment 2 tested non-disabled participants’ ability to recognize emotions from unrestricted and wheelchair-restricted displays. Wheelchair-restricted displays showed an overall decline in recognition accuracy, but recognition was selectively impaired for the dominance-related emotions of disgust and anger. Consistent with embodied emotion theory, these results emphasize the importance of the body for emotion communication and have implications for social interactions between individuals with and without physical disabilities. Changes in nonverbal emotion signals from body restrictions may influence social interactions that rely on the communication of dominance-related social emotions.</p></abstract><kwd-group><kwd>embodied emotion</kwd><kwd>nonverbal communication</kwd><kwd>dominance</kwd><kwd>pride</kwd><kwd>anger</kwd></kwd-group><counts><fig-count count=\"5\"/><table-count count=\"1\"/><equation-count count=\"0\"/><ref-count count=\"45\"/><page-count count=\"12\"/><word-count count=\"9516\"/></counts></article-meta></front><body><sec sec-type=\"intro\" id=\"sec1\"><title>Introduction</title><p>Social interactions rely strongly on nonverbal emotional displays to communicate social emotions, inform others about our feelings, and influence social outcomes (<xref rid=\"ref42\" ref-type=\"bibr\">Vosk et al., 1983</xref>; <xref rid=\"ref37\" ref-type=\"bibr\">Tiedens and Leach, 2004</xref>). Such communication can include expressions of basic emotions (e.g., happiness, anger, etc.; <xref rid=\"ref12\" ref-type=\"bibr\">Ekman, 1992</xref>), as well as more complex social emotions that reflect relative social status (e.g., pride and shame; <xref rid=\"ref2\" ref-type=\"bibr\">App et al., 2011</xref>; <xref rid=\"ref33\" ref-type=\"bibr\">Steckler and Tracy, 2014</xref>; <xref rid=\"ref39\" ref-type=\"bibr\">Tracy et al., 2015</xref>). The literature focuses on the role of the face in emotional communication, but a growing number of studies have demonstrated that the body plays a crucial role in emotion communication as well (<xref rid=\"ref9\" ref-type=\"bibr\">Dael et al., 2012</xref>). Although some emotions are often communicated <italic>via</italic> facial expressions (e.g., happiness, anger), others are expressed <italic>via</italic> body posture and movement (e.g., pride, embarrassment; <xref rid=\"ref2\" ref-type=\"bibr\">App et al., 2011</xref>; <xref rid=\"ref39\" ref-type=\"bibr\">Tracy et al., 2015</xref>).</p><p>\n<italic>Emotional embodiment theory</italic> suggests we use information from our own faces and bodies to understand other people’s emotions as well as our own (<xref rid=\"ref25\" ref-type=\"bibr\">Niedenthal et al., 2001</xref>). Growing evidence suggests that when we view an emotion displayed by another, the viewer’s ability to recognize, understand, and respond to the emotion relies on the sensory-motor simulation of the observed emotion (<xref rid=\"ref46\" ref-type=\"bibr\">Wood et al., 2016</xref>). Indeed, over 40 years of research has shown that humans rapidly match the facial expressions of others and that the configuration of their own faces can influence their own emotional states (<xref rid=\"ref19\" ref-type=\"bibr\">McIntosh, 1996</xref>; <xref rid=\"ref20\" ref-type=\"bibr\">Moody and McIntosh, 2006</xref>, <xref rid=\"ref21\" ref-type=\"bibr\">2011</xref>; <xref rid=\"ref46\" ref-type=\"bibr\">Wood et al., 2016</xref>). For example, when judging a word for its emotional meaning, people may use their facial muscles to aid the decision (<xref rid=\"ref26\" ref-type=\"bibr\">Niedenthal et al., 2009</xref>). Further, experimentally manipulating one’s facial expression can affect emotional experience. When facial expressions are restricted (e.g., putting a pen in their mouths), participants are less accurate at identifying others’ emotions (<xref rid=\"ref25\" ref-type=\"bibr\">Niedenthal et al., 2001</xref>). However, embodiment theory also suggests that information from bodies is also simulated when processing other people’s emotions (<xref rid=\"ref25\" ref-type=\"bibr\">Niedenthal et al., 2001</xref>).</p><p>One less explored implication of embodied theory is that emotional perception is an interactive process and that the interaction between emotion experience and the physical display of emotion is reciprocal. That is, a person’s state, which includes current inputs from body position, influences the simulation process and the perception of others’ emotions. Indeed, it is known that people not only physically display their subjective emotional state but also their emotional display affects their subjective emotional state (see <xref rid=\"ref19\" ref-type=\"bibr\">McIntosh, 1996</xref>). This ability to use physiological changes and to re-experience one’s somatic responses plays an important role in emotional processing (<xref rid=\"ref23\" ref-type=\"bibr\">Niedenthal, 2007</xref>; <xref rid=\"ref16\" ref-type=\"bibr\">Halberstadt et al., 2009</xref>). Further, the body conveys emotion information that is different from the face and can even modulate emotional information conveyed by the face (<xref rid=\"ref5\" ref-type=\"bibr\">Aviezer et al., 2008</xref>; <xref rid=\"ref3\" ref-type=\"bibr\">App et al., 2012</xref>; <xref rid=\"ref1\" ref-type=\"bibr\">Abo Foul et al., 2018</xref>). Studies show that people’s own bodies influence the perception of their own as well as others’ emotional states (<xref rid=\"ref19\" ref-type=\"bibr\">McIntosh, 1996</xref>; <xref rid=\"ref24\" ref-type=\"bibr\">Niedenthal et al., 2005</xref>; <xref rid=\"ref27\" ref-type=\"bibr\">Reed and McIntosh, 2008</xref>; <xref rid=\"ref45\" ref-type=\"bibr\">Wilbarger et al., 2011</xref>).</p><p>Thus, a consequence of this theory is that if either face or body use is impeded, it will have implications for their perceptions, experiences, and communications of emotions (<xref rid=\"ref11\" ref-type=\"bibr\">de Gelder, 2006</xref>; <xref rid=\"ref2\" ref-type=\"bibr\">App et al., 2011</xref>; <xref rid=\"ref33\" ref-type=\"bibr\">Steckler and Tracy, 2014</xref>; <xref rid=\"ref39\" ref-type=\"bibr\">Tracy et al., 2015</xref>; <xref rid=\"ref22\" ref-type=\"bibr\">Moody et al., 2018</xref>). A study by <xref rid=\"ref44\" ref-type=\"bibr\">Wallbott and Giessen (1986)</xref> confirmed that viewing whole body videos of actors portraying emotional scenarios lead to more accurate emotion recognition than audio-only recordings of the same scenarios. Importantly, <xref rid=\"ref22\" ref-type=\"bibr\">Moody et al. (2018)</xref> demonstrated rapid, emotionally congruent, whole-body responses from static emotional faces, indicating that the participants simulated the viewed facial emotion using their whole bodies. When judging changes in other people’s body postures, input from one’s own emotional body posture selectively influences perceptions of another person’s emotional body posture but not neutral postures (<xref rid=\"ref45\" ref-type=\"bibr\">Wilbarger et al., 2011</xref>). Nonetheless, little work has explored whether movement restrictions of the body has similar consequences for emotional processing as it does for the face and whether such constraints differentially affect emotions preferentially conveyed <italic>via</italic> the body.</p><p>Although some emotions are communicated primarily <italic>via</italic> the face and others rely on the body, individual emotions are associated with specific social functions (<xref rid=\"ref40\" ref-type=\"bibr\">Tracy and Robins, 2007</xref>; <xref rid=\"ref2\" ref-type=\"bibr\">App et al., 2011</xref>). For instance, emotions conveying status relations between individuals include anger, disgust, and fear that are associated with facial expressions, as well as embarrassment, guilt, pride, and shame that are associated with bodily expressions (<xref rid=\"ref8\" ref-type=\"bibr\">Coulson, 2004</xref>; <xref rid=\"ref40\" ref-type=\"bibr\">Tracy and Robins, 2007</xref>; <xref rid=\"ref2\" ref-type=\"bibr\">App et al., 2011</xref>; see also <xref rid=\"ref4\" ref-type=\"bibr\">Atkinson et al., 2004</xref>). Emotional body-expressions are important in conveying social hierarchical information and the degree to which they signal relative social status (<xref rid=\"ref33\" ref-type=\"bibr\">Steckler and Tracy, 2014</xref>). Generally, more erect postures are associated with higher status and dominance, although context can affect this interpretation (<xref rid=\"ref17\" ref-type=\"bibr\">Hall et al., 2005</xref>). Actors asked to portray various emotions often used a collapsed body posture for shame or sadness and used body expression more than facial movement to display pride (<xref rid=\"ref43\" ref-type=\"bibr\">Wallbott, 1998</xref>). Across studies and cultures, pride appears associated with a low intensity smile and a variety of different body positions including expanded posture, arms akimbo on hips or arms raised straight above the head with the hands (<xref rid=\"ref43\" ref-type=\"bibr\">Wallbott, 1998</xref>; <xref rid=\"ref40\" ref-type=\"bibr\">Tracy and Robins, 2007</xref>; <xref rid=\"ref38\" ref-type=\"bibr\">Tracy and Matsumoto, 2008</xref>). Given that emotions have different social functions and differential reliance on face vs. body expression, the restriction of body movement may not affect the processing of emotions in the same way (<xref rid=\"ref11\" ref-type=\"bibr\">de Gelder, 2006</xref>; <xref rid=\"ref2\" ref-type=\"bibr\">App et al., 2011</xref>; <xref rid=\"ref33\" ref-type=\"bibr\">Steckler and Tracy, 2014</xref>; <xref rid=\"ref39\" ref-type=\"bibr\">Tracy et al., 2015</xref>).</p><sec id=\"sec2\"><title>Current Study</title><p>Relatively little research has explored the role of the body on emotional communication between individuals. Moreover, few studies have considered how the prevention of body movement might differentially disrupt the production and recognition of specific emotions. Here, we examine how restricted body movement and posture influences emotional expression and recognition. Given that experimentally preventing or enhancing emotional facial expressions can influence emotional experience of basic emotions (i.e., anger, fear, happiness, and sadness; <xref rid=\"ref12\" ref-type=\"bibr\">Ekman, 1992</xref>; <xref rid=\"ref18\" ref-type=\"bibr\">Jack et al., 2014</xref>), constraints on body movement and posture may also affect the sensorimotor processing or simulation of emotions, especially more complex, body-related emotions (e.g., embarrassment and pride). Such constraints could influence both an observer’s perception and nonverbal communication of emotions. They may also differentially affect the recognition of emotions by changing the visual cues that distinguish them (<xref rid=\"ref43\" ref-type=\"bibr\">Wallbott, 1998</xref>; <xref rid=\"ref29\" ref-type=\"bibr\">Sawada et al., 2003</xref>; <xref rid=\"ref15\" ref-type=\"bibr\">Gross et al., 2010</xref>). Moreover, body and posture constraints may affect more than just body-related emotions because they change the implied social dominance between individuals. Although disgust is best conveyed by the face and pride is best conveyed by the body, both emotions indicate one person’s superior standing compared to another. In other words, emotions that convey social status may supersede the body-related vs. face-related emotion distinction because body constraints do more than just restrict movement.</p><p>In two studies we extend work documenting the body’s role in emotion displays (e.g., <xref rid=\"ref11\" ref-type=\"bibr\">de Gelder, 2006</xref>; <xref rid=\"ref40\" ref-type=\"bibr\">Tracy and Robins, 2007</xref>; <xref rid=\"ref2\" ref-type=\"bibr\">App et al., 2011</xref>). In Experiment 1, we examined the role of body movement and postures restrictions on nonverbal emotion communication for non-disabled participants. In Experiment 2, we determined whether these constraints affected emotion recognition in non-disabled participants. This research is a first step in understanding the role of embodiment in the communication of social emotions and its implications for how nonverbal emotional displays may influence social interactions between non-disabled and physically disabled individuals.</p></sec></sec><sec id=\"sec3\"><title>Experiment 1: Emotion Production</title><p>The purpose of Experiment 1 was to determine whether current body inputs affected non-disabled participants’ nonverbal expressions of emotions and their confidence in the efficacy of communicating emotions. In addition, the experiment replicated previous studies indicating specific face and body preferences for nonverbal emotional communication (<xref rid=\"ref2\" ref-type=\"bibr\">App et al., 2011</xref>). To examine the effects of body and posture constraints, we compared the expression of six emotions for two groups that differed in their mobility restrictions: (1) an “unrestricted” group in which participants stood while communicating emotions without mobility restrictions and (2) a “wheelchair-restricted” group in which participants sat in a wheelchair with an elastic band tied around the torso, thereby restricting trunk movement. The unrestricted condition provided a reliable baseline to which we could compare performance in the movement-restricted condition. In Part 1, participants communicated six emotions nonverbally to a mannequin while being videotaped; after expressing each emotion, they rated their confidence in successfully communicating the emotion. In Part 2, participants indicated whether they preferred face or body channels to optimally communicate each emotion.</p><p>Extensions of embodiment theory would predict that current physical inputs should affect the communicator’s production of nonverbal, emotional displays as well as the communicator’s confidence in the effectiveness of those displays, especially for those emotions best conveyed by the body. Therefore, compared to the unrestricted group, the wheelchair-restricted group should show reduced use of the body and increased use of the face during emotional production, especially for emotions optimally expressed by the body. The postural and movement differences between groups should also differentially affect status emotions. Further, if current body inputs were used for evaluating one’s own communication, then the wheelchair-restricted group may be less confident in communicating body-based emotions. Of interest is whether confidence in emotion communication was affected by current body constraints.</p><sec id=\"sec4\"><title>Method</title><sec id=\"sec5\"><title>Participants</title><p>Forty-nine participants (33 female, mean age = 19.6 years) received partial course credit in introductory level psychology courses for their participation. They were randomly assigned <italic>via</italic> a computer program to one of two groups: the unrestricted group (<italic>n</italic> = 21) and the wheelchair-restricted group (<italic>n</italic> = 28). In the unrestricted group, participants stood 3 ft from a seated mannequin. In the movement restricted “wheelchair-restricted” group, participants sat in a wheelchair with their chests strapped into the wheelchair by a stretch athletic band around the lower rib cage to stabilize and restrict movement of the trunk. Straps reduced torso motion shown to be important for conveying emotional state (e.g., <xref rid=\"ref43\" ref-type=\"bibr\">Wallbott, 1998</xref>; <xref rid=\"ref4\" ref-type=\"bibr\">Atkinson et al., 2004</xref>). Participants were still able to use their arms and limited torso adjustments to express emotions.</p></sec><sec id=\"sec6\"><title>Stimuli and Apparatus</title><p>Participants directed their emotions toward a life-size mannequin with a soft, gray, fabric exterior and no definitive facial features. The mannequin was seated in a chair in front of the participants and dressed in a casual, gender-neutral outfit, including a sweat suit and baseball hat. Participants were asked to think of someone they knew and to address the mannequin as if it were that person, whether it was a friend, relative, or romantic partner. The mannequin was addressed as the chosen person throughout the experiment. The mannequin’s lack of facial features provided participants with a consistently neutral response to their emotion production.</p><p>To record participant’s nonverbal emotional displays, two video cameras were used to film the facial and body expressions of the participants; one was placed behind the mannequin to provide a face-front view of the participants and another camera was placed to the side of the participants to provide a side view that showed both the expresser and the mannequin.</p><p>To quantify the relative degree of participants’ face and body use during the communication of each emotion, we modified a coding scheme used in <xref rid=\"ref2\" ref-type=\"bibr\">App et al. (2011)</xref> to assess the amount of emotionally congruent movement in the face and the body as recorded from the front and side view videos. Emotion congruence was defined as using non-random, systematic movements to convey each emotion and followed classification procedures described in <xref rid=\"ref2\" ref-type=\"bibr\">App et al. (2011)</xref>. More specifically, coders examined videos for face and body movements that the literature has confirmed to be associated with, or congruent with, each of the emotions used in this study (<xref rid=\"ref4\" ref-type=\"bibr\">Atkinson et al., 2004</xref>; <xref rid=\"ref20\" ref-type=\"bibr\">Moody and McIntosh, 2006</xref>; <xref rid=\"ref2\" ref-type=\"bibr\">App et al., 2011</xref>). For example, for a happy condition trial, a congruent emotional display would indicate the presence of upward movements in the eyes and cheeks resulting from zygomaticus muscle activation in the face (<xref rid=\"ref20\" ref-type=\"bibr\">Moody and McIntosh, 2006</xref>) and/or the lifting of arms or jumping up and down (<xref rid=\"ref4\" ref-type=\"bibr\">Atkinson et al., 2004</xref>). However, if the participant displayed a furrowing of the forehead resulting from corrugator muscle activation that is typically associated with anger displays, then this movement would be considered incongruent with the emotion communicated.</p><p>For each participant, condition, and trial, video data were coded separately from both the front and side-view videos. In both views, the face and the body were visible. Face and body movements were coded independently for each of three categories that are reflected in the levels of the statistical analyses: (1) face alone – emotionally congruent face movement without simultaneous body movement; (2) body alone – emotionally congruent body movement without simultaneous face movement; and (3) face + body – emotionally congruent simultaneous face and body movement. Three scorers, who were naïve to the predictions of the experiment, rated movement for each of these three categories using a scale ranging from 0 [none: no emotionally congruent movement (i.e., no use of muscles specific to of the communicated emotion)] to 1 (some emotionally congruent movement: movement indicated sufficient information for some level of emotion congruence but not a full match) to 2 (full match of expected emotionally congruent movement). Scores from these naïve scorers indicated agreement on 92% of trials overall, with 97% agreement for the unrestricted group and 87% agreement for the restricted wheelchair group. The average use of emotionally congruent face, body, or concurrent face and body movements was calculated for each participant over two views and three trials for each of the six emotions.</p></sec></sec><sec id=\"sec7\"><title>Procedure</title><sec id=\"sec8\"><title>Part 1: Emotion Production</title><p>Tested individually, participants nonverbally communicated six different emotions (anger, disgust, fear, happiness, pride, and embarrassment) to the mannequin as effectively as possible. For each participant, a computer program randomly selected group assignment. The experiment began with practice trials in which participants produced two emotions not used in the experimental trials (surprise and sadness). Experimental trials followed in which the participant pressed a computer key to begin the trials and one of the six emotion words appeared on the computer screen. The participant then expressed the emotion to the mannequin for 4 s. Following each expression trial, participants were prompted to enter number on a scale of 0 (“not confident at all”) to 4 (“very confident”) to indicate their confidence in how well they were able to communicate that emotion. Each of the six emotions was produced three times in random order for a total of 18 production trials and 18 confidence ratings. To reduce any potential influences of experimenter presence on emotion production, the experimenter remained outside the testing room after practice trials.</p></sec><sec id=\"sec9\"><title>Part 2: Preferences for Using the Face or Body to Communicate Specific Emotions</title><p>After completing the emotion production portion of the experiment, participants remained in their unrestricted or wheelchair-restricted states. An emotion word cue appeared on the computer screen and they indicated <italic>via</italic> a key press whether they would <italic>typically</italic> (i.e., in the everyday world) use their face or their body to nonverbally express that emotion. They provided a single face/body preference for each emotion; emotion order was randomized.</p></sec></sec><sec id=\"sec10\"><title>Results and Discussion</title><sec id=\"sec11\"><title>Preferences for Face vs. Body Use When Conveying Emotions</title><p>The purpose of this analysis was to replicate findings by <xref rid=\"ref2\" ref-type=\"bibr\">App et al. (2011)</xref> and to confirm that the participants in our study endorsed similar preferences for using the face or body to express each emotion. The results confirm that preferences to use the face vs. the body to express emotions differs across emotions and show a similar pattern to those reported in <xref rid=\"ref2\" ref-type=\"bibr\">App et al. (2011)</xref>. For each group and emotion, the frequency of preferred face vs. body use was tabulated. To document differential preferences for face or body use when communicating the six emotions, chi-square tests were conducted for face/body preferences for each emotion and group using frequency data. There were no group differences overall in body/face expression preferences for any emotion [<italic>χ</italic><sup>2</sup>(1) < 0.70, <italic>p</italic> > 0.71]. Regardless of group, face/body preference largely replicated previous findings (<xref rid=\"ref2\" ref-type=\"bibr\">App et al., 2011</xref>; <xref rid=\"fig1\" ref-type=\"fig\">Figures 1A</xref>,<xref rid=\"fig1\" ref-type=\"fig\">B</xref>). Participants preferred to use the body to communicate <italic>pride</italic> [unrestricted group: <italic>χ</italic><sup>2</sup>(1) = 13.762, <italic>p</italic> < 0.0001; wheelchair group: <italic>χ</italic><sup>2</sup>(1) = 11.57, <italic>p</italic> < 0.001]. They preferred to use the face for <italic>disgust</italic> [unrestricted group: <italic>χ</italic><sup>2</sup>(1) = 13.76, <italic>p</italic> < 0.0001; wheelchair group: <italic>χ</italic><sup>2</sup>(1) = 20.57, <italic>p</italic> < 0.0001] and <italic>happiness</italic> [unrestricted group: <italic>χ</italic><sup>2</sup>(1) = 17.19, <italic>p</italic> < 0.0001; wheelchair group: <italic>χ</italic><sup>2</sup>(1) = 20.57, <italic>p</italic> < 0.0001]. Preferences were more divided for face and body use when expressing <italic>anger</italic> [unrestricted group: <italic>χ</italic><sup>2</sup>(1) = 1.19, <italic>p</italic> = 0.28; wheelchair group: <italic>χ</italic><sup>2</sup>(1) = 2.29, <italic>p</italic> = 0.13], <italic>fear</italic> [unrestricted group: <italic>χ</italic><sup>2</sup>(1) = 13.76, <italic>p</italic> = 0.83; wheelchair group: <italic>χ</italic><sup>2</sup>(1) = 0.000, <italic>p</italic> = 1.000], and <italic>embarrassment</italic> [unrestricted group: <italic>χ</italic><sup>2</sup>(1) = 1.19, <italic>p</italic> = 0.28; wheelchair group: <italic>χ</italic><sup>2</sup>(1) = 0.57, <italic>p</italic> = 0.45]. These preferences for each emotion (summarized in <xref rid=\"tab1\" ref-type=\"table\">Table 1</xref>) establish a baseline indicating whether people typically rely on the face or body to nonverbally communicate specific emotions: The face is preferred to convey disgust and happiness, the body is preferred to convey pride, and we found no significant difference in preference for face or the body conveys anger, fear, and embarrassment.</p><fig id=\"fig1\" position=\"float\"><label>Figure 1</label><caption><p>Experiment 1 – Proportion preference selection for face vs. body use for emotion production: <bold>(A)</bold> unrestricted group, <bold>(B)</bold> wheelchair-restricted group. There were no group differences. For each emotion, stars indicate significant preference differences for using either the face or the body.</p></caption><graphic xlink:href=\"fpsyg-11-01961-g001\"/></fig><table-wrap id=\"tab1\" position=\"float\"><label>Table 1</label><caption><p>Face vs. body use preferences for emotion production.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th rowspan=\"1\" colspan=\"1\"/><th align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Disgust</th><th align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Happiness</th><th align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Anger</th><th align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Fear</th><th align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Embarrassment</th><th align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Pride</th></tr></thead><tbody><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Face</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">x</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">x</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">x</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">x</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">x</td><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Body</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">x</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">x</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">x</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">x</td></tr></tbody></table></table-wrap><p>Despite the fact that participants were either unrestricted or wheelchair-restricted when providing face‐ or body-use preferences, the similarity of responses for the two groups suggests that these preferences were not influenced by current body inputs and rather were based on functional, and/or experiential mechanisms. These data also suggest that the recognition of emotions that rely most on the body (tested in Experiment 2) – pride followed by embarrassment, fear, and anger – may be most affected by restricting torso movement in a wheelchair.</p></sec><sec id=\"sec12\"><title>Emotion Production: Emotion-Specific Influences of Restricted Body Posture and Movement</title><p>For all analyses of variance (ANOVAs) using ordinal data in both experiments, we report comparisons with Huyhn-Feldt adjustments for sphericity, which is proposed as a sufficient condition for the <italic>F</italic>-tests to be valid for ordinal data (<xref rid=\"ref35\" ref-type=\"bibr\">Stiger et al., 1998</xref>). We applied the Dunn–Šidák correction to all simple effects analyses to correct for multiple comparisons. Alpha was set at the 0.05 level.</p><p>To assess whether posture and body constraints differentially influenced the production of emotions, we conducted a mixed model ANOVA with the between-subject factors of group (2: unrestricted and wheelchair-restricted) and within-subject factors of face/body use [3: face, body, face + body (i.e., concurrent face and body)] and emotion (6: disgust, happiness, anger, fear, embarrassment, and pride) using emotion-congruent movement scores provided by three observers. The unrestricted and restricted groups did not differ in the presence of emotionally congruent movement [group effect: <italic>F</italic>(1,47) = 0.42, <italic>p</italic> = 0.52, <italic>η<sub>p</sub></italic><sup>2</sup> = 0.01; <italic>M</italic>\n<sub>unrestricted</sub> = 1.33, <italic>SE</italic> = 0.08; <italic>M</italic>\n<sub>wheelchair</sub> = 1.27, <italic>SE</italic> = 0.07].</p><p>Also, the presence of emotionally congruent movements did not differ across emotions [emotion effect: <italic>F</italic>(4.98, 234.13) = 1.19, <italic>p</italic> = 0.31, <italic>η<sub>p</sub></italic><sup>2</sup> = 0.03], regardless of group [group × emotion interaction: <italic>F</italic>(4.98, 234.13) = 0.97, <italic>p</italic> = 0.44, <italic>η<sub>p</sub></italic><sup>2</sup> = 0.02]. The interaction between group and face/body use was not significant at the 0.05 level, [<italic>F</italic>(1.26, 59.05) = 2.78, <italic>p</italic> = 0.06, <italic>η<sub>p</sub></italic><sup>2</sup> = 0.06]. However, the interaction between emotion and face/body use indicated differential use of the face, body, and concurrent face and body (face + body) across emotions [<italic>F</italic>(6.35, 298.66) = 14.88, <italic>p</italic> < 0.0001, <italic>η<sub>p</sub></italic><sup>2</sup> = 0.24]. The emotion by face/body use interaction was consistent with the preference data, indicating that the face alone is used to express disgust and happiness more than the body alone or their combination; the face and body alone, more than their combination, are used to express anger, embarrassment, and pride; and the face is used more than the face + body combination to express fear (<xref rid=\"fig2\" ref-type=\"fig\">Figure 2</xref>).</p><fig id=\"fig2\" position=\"float\"><label>Figure 2</label><caption><p>Experiment 1 – Face, body, and concurrent face/body use for emotion production. Error bars indicate standard error. Stars indicate significant differences between face, body, and concurrent face + body use.</p></caption><graphic xlink:href=\"fpsyg-11-01961-g002\"/></fig><p>Direct comparisons showed that for disgust the face was used more than the body or both concurrently (i.e., face + body; <italic>p</italic> < 0.0001) and there was no difference for body vs. face + body use (<italic>p</italic> = 0.31). Similarly, for happiness, the face was used more than the body or face + body (<italic>p</italic> < 0.0001) and there was no difference for body vs. face + body use (<italic>p</italic> = 0.58). However, for anger, face and body use did not differ significantly (<italic>p</italic> = 0.15), but both the face (<italic>p</italic> < 0.0001) and the body (<italic>p</italic> = 0.02) were used more than face + body. Similarly for fear, face and body use did not differ significantly (<italic>p</italic> = 0.16) but the face was used more than face + body (<italic>p</italic> < 0.0001), and body use was similar to face + body (<italic>p</italic> = 0.47). For embarrassment, face and body use did not differ significantly (<italic>p</italic> = 0.55), but both the face (<italic>p</italic> < 0.003) and the body (<italic>p</italic> < 0.0001) were used more than face + body. Finally, for pride, face and body use did not differ (<italic>p</italic> = 1.00) and both the face (<italic>p</italic> < 0.002) and the body (<italic>p</italic> < 0.0001) were used individually more than together (face + body). Further, although concurrent use of the body and face (face + body) did not differ across emotions (<italic>p</italic> > 0.64), the face was used significantly more for disgust and happy than anger, fear, embarrassment, and pride (<italic>p</italic> > 0.02), it was used more for anger than embarrassment (<italic>p</italic> < 0.005); other face comparisons were not significant. Finally, significantly greater body use was observed for anger and embarrassment than disgust and happiness (<italic>p</italic> < 0.03). The group by emotion by face/body use interaction was in the predicted direction but was not significant at the alpha = 0.05 level [<italic>F</italic>(6.35, 298.66) = 1.96, <italic>p</italic> = 0.06, <italic>η<sub>p</sub></italic><sup>2</sup> = 0.04].</p><p>Because the groups were created by random assignment, they had different sizes (unrestricted = 1 vs. wheelchair restricted = 28). To demonstrate that unequal group size did not affect the results, we randomly selected 21 participants from the larger group and reran the above analysis. The results for the equal-sized groups were highly comparable, but the group × emotion × face/body use interaction was now significant (<italic>p</italic> = 0.05)<xref ref-type=\"fn\" rid=\"fn001\"><sup>1</sup></xref>.</p></sec><sec id=\"sec13\"><title>Confidence in Effective Emotion Communication</title><p>To examine the effects of current bodily constraints on confidence in emotional expression, a mixed model ANOVA with the between-subject factors of group (2: unrestricted and wheelchair) and within-subject factors of emotion (6: disgust, happiness, anger, fear, embarrassment, and pride) was conducted on confidence ratings. Ratings should differ by group if current body inputs influenced confidence in emotional communication but not if previous experience were more important because non-disabled participants have similar <italic>prior</italic> experiences. A significant main effect for emotion [<italic>F</italic>(4.31, 202.62) = 13.55, <italic>p</italic> < 0.0001, <italic>η<sub>p</sub></italic><sup>2</sup> = 0.22] indicated greater confidence when communicating disgust and happiness than anger, fear, embarrassment, and pride (<italic>p</italic> < 0.001). Of interest, the posture and body restrictions imposed by the wheelchair did not influence confidence ratings, as indicated the lack of a group effect [<italic>F</italic>(1, 47) = 1.48, <italic>p</italic> = 0.23, <italic>η<sub>p</sub></italic><sup>2</sup> = 0.03] or the group by emotion interaction [<italic>F</italic>(4.31, 202.62) = 0.08, <italic>p</italic> = 1.00, <italic>η<sub>p</sub></italic><sup>2</sup> = 0.002; <xref rid=\"fig3\" ref-type=\"fig\">Figure 3</xref>].</p><fig id=\"fig3\" position=\"float\"><label>Figure 3</label><caption><p>Experiment 1 – Confidence ratings for effective emotion communication overall. Error bars indicate standard error. Stars indicate significant confidence differences across emotions: confidence for disgust and happiness was greater than for the other emotions.</p></caption><graphic xlink:href=\"fpsyg-11-01961-g003\"/></fig><p>In sum, replicating prior research (e.g., <xref rid=\"ref2\" ref-type=\"bibr\">App et al., 2011</xref>), Experiment 1 first established preferences for the selective use of the face and body for specific emotions. Preference data confirm that non-disabled individuals do not prefer to use the face to convey all emotions and that they would use the face and body differentially depending on the emotion. Participants indicated that they preferred to use the face to best convey disgust and happiness; they were divided in their preference to use the face or body to express anger, fear, and embarrassment; and they preferred to use the body to convey pride.</p><p>Next, Experiment 1 examined how constraining body posture and movement might influence the nonverbal communication of these emotions in non-disabled participants. Comparing the nonverbal, physical production of emotions between the unrestrained and wheelchair-restrained groups, movement restriction in non-disabled individuals did not strongly alter the production of emotionally congruent movement. Further studies are needed with physically disabled individuals to more fully explore potential differences in the relative use of the face and body in emotion production. Further, no group differences emerged between the unrestricted and wheelchair group’s confidence in their effective communication of the emotions. Prior emotional experience for our non-disabled participants who are not typically constrained in their movements appears to have more influence on their confidence in emotional communication than current body inputs. Nonetheless, posture and torso constraints had an overall influence emotion production, which may convey differential nonverbal emotion signals to observers. Thus, posture and movement constraints may also influence the reception of emotional displays. We investigate this issue in Experiment 2.</p></sec></sec></sec><sec id=\"sec14\"><title>Experiment 2: Emotion Identification</title><p>Experiment 2 investigated whether the expression of emotions by individuals with physical constraints affects the recognition of nonverbal emotional displays. Using a sample of emotion production videos from each group in Experiment 1, we assessed whether constraints on body movement and posture influenced the accuracy of emotion identification overall or for specific emotions.</p><sec id=\"sec15\"><title>Method</title><sec id=\"sec16\"><title>Participants</title><p>Participants were 22 students (10 females; age: <italic>M</italic> = 19.3 years, <italic>SD</italic> = 1.14) who received partial course credit in lower level psychology courses. None had participated in Experiment 1.</p></sec><sec id=\"sec17\"><title>Stimuli and Apparatus</title><p>A subset of video recordings was selected from Experiment 1 to limit the duration of Experiment 2. From video clips recorded in Experiment 1, two male and two female participant video clips were selected at random for each of the six emotions. Video clips were edited to remove the sound and have a 4-s duration. A total of 48 video clips were used: 2 males × 2 females × 2 conditions (unrestricted and wheelchair) × 6 emotions. All video clips were emotional displays recorded from the side view so that the observer could see the participant interacting with the mannequin. Stimuli were presented on a 21-inch computer monitor with E-prime 2.0 pro software (Psychological Software Tools, Pittsburgh, PA).</p></sec></sec><sec id=\"sec18\"><title>Procedure</title><p>Participants were tested individually and were asked to identify emotions presented in silent video clips. For each emotion recognition trial, a 4-s emotion video was played twice on a computer monitor, with a 500 ms pause between presentations. A numbered list of emotions (1 = <italic>anger</italic>, 2 = <italic>sympathy</italic>, 3 = <italic>embarrassment</italic>, 4 = <italic>love</italic>, 5 = <italic>disgust</italic>, 6 = <italic>fear</italic>, 7 = <italic>happiness</italic>, 8 = <italic>sadness</italic>, 9 = <italic>pride</italic>, plus 0 = <italic>other</italic>) appeared and participants indicated the emotion presented in the video by pressing the number key associated with the corresponding emotion word. The list included three additional emotions (sympathy, sadness, and love) as well as an “other” category to reduce inflated accuracy rates due to forced choice (<xref rid=\"ref28\" ref-type=\"bibr\">Russell, 1993</xref>; <xref rid=\"ref13\" ref-type=\"bibr\">Frank and Stennett, 2001</xref>). Participants then rated their confidence that their response was correct on a scale from 1 (not at all confident) to 5 (extremely confident). After two practice trials, participants completed three blocks of 48 experimental trials (6 emotions × 2 male × 2 female × 2 conditions; each stimulus was presented 3 times) for a total of 144 trials; within each block, trials were presented in random order. A brief break was provided between each block.</p></sec><sec id=\"sec19\"><title>Results</title><p>For participant, emotion, and condition, mean proportion accuracy was calculated for emotion identification; correct responses were recorded when the selected emotion matched the viewed emotion. Mean confidence ratings were also calculated for each participant, emotion, and condition. For all ANOVAs using ordinal data in this paper, we report comparisons with Huyhn-Feldt adjustments for sphericity (<xref rid=\"ref35\" ref-type=\"bibr\">Stiger et al., 1998</xref>). The Dunn–Šidák correction was applied to all simple effects analyses to correct for multiple comparisons. Alpha was set at the 0.05 level.</p><sec id=\"sec20\"><title>Emotion Identification Accuracy</title><p>To determine if emotional displays under restricted posture and body movement conditions affected emotion recognition, we conducted a within-subjects ANOVA with factors viewed condition (2: unrestricted and wheelchair) and emotion (6: anger, disgust, fear, happiness, pride, and embarrassment) using mean proportion accuracy data. Emotions expressed in unrestricted conditions (<italic>M</italic>\n<sub>unrestricted</sub> = 0.46, <italic>SE</italic> = 0.02) were more accurately identified than emotions expressed in wheelchair-restricted conditions (<italic>M</italic>\n<sub>wheelchair</sub> = 0.30, <italic>SE</italic> = 0.02) condition: [<italic>F</italic>(1,20) = 66.77, <italic>p</italic> < 0.0001, <italic>η<sub>p</sub></italic><sup>2</sup> = 0.77]. The main effect of emotion indicated that some emotions were recognized more accurately than others [<italic>F</italic>(5, 100) = 63.62, <italic>p</italic> < 0.0001, <italic>η<sub>p</sub></italic><sup>2</sup> = 0.76]. Simple effects comparisons (all values of <italic>p</italic> > 0.0001) revealed that accuracy was greatest for disgust (<italic>M</italic> = 0.66, <italic>SE</italic> = 0.02), happiness (<italic>M</italic> = 0.68, <italic>SE</italic> = 0.03), and anger (<italic>M</italic> = 0.59, <italic>SE</italic> = 0.04) compared to fear (<italic>M</italic> = 0.18, <italic>SE</italic> = 0.03), embarrassment (<italic>M</italic> = 0.26, <italic>SE</italic> = 0.04), and pride (<italic>M</italic> = 0.15, <italic>SE</italic> = 0.02); accuracy did not significantly differ between anger, happiness, and fear or across fear, embarrassment, and pride (all values of <italic>p</italic> > 0.51). Differences across emotions regarding the impact of posture and body movement on emotion recognition are reflected in the interaction between condition and emotion [<italic>F</italic>(5,100) = 4.23, <italic>p</italic> < 0.002, <italic>η<sub>p</sub></italic><sup>2</sup> = 0.17; <xref rid=\"fig4\" ref-type=\"fig\">Figure 4</xref>]. Simple effect analyses revealed greater accuracy for unrestricted over wheelchair-restricted conditions for disgust (<italic>p</italic> < 0.0001), happiness (<italic>p</italic> = 0.02), anger (<italic>p</italic> < 0.0001), fear (<italic>p</italic> = 0.01), and embarrassment (<italic>p</italic> < 0.003) but not pride (<italic>p</italic> = 0.14). Although the difference between the unrestricted and restricted conditions for pride is not significant, we believe this one lack of difference in condition significance can be explained by the fact that pride was less recognizable in the unrestricted condition. The accuracy for wheelchair conditions for body-related displays of fear, embarrassment, and pride all showed low levels of accuracy.</p><fig id=\"fig4\" position=\"float\"><label>Figure 4</label><caption><p>Experiment 2 – Recognition accuracy of emotion display videos by condition. Error bars indicate standard error. Stars indicate significant differences between conditions and emotions.</p></caption><graphic xlink:href=\"fpsyg-11-01961-g004\"/></fig></sec><sec id=\"sec21\"><title>Confidence for Emotion Recognition</title><p>For confidence data, a within-subjects ANOVA was conducted for the factors condition (2: unrestricted and wheelchair) and emotion (6: disgust, happiness, anger, fear, embarrassment, and pride). Participants indicated greater confidence in emotion identification in unrestricted (<italic>M</italic>\n<sub>unrestricted</sub> = 3.95, <italic>SE</italic> = 0.10) than wheelchair-restricted (<italic>M</italic>\n<sub>wheelchair-restricted</sub> = 3.48, <italic>SE</italic> = 0.13) conditions [<italic>F</italic>(1, 20) = 21.45, <italic>p</italic> < 0.0001, <italic>η<sub>p</sub></italic><sup>2</sup> = 0.52]. The significant emotion effect [<italic>F</italic>(5, 93.59) = 3.82, <italic>p</italic> = 0.004, <italic>η<sub>p</sub></italic><sup>2</sup> = 0.16] showed that participants were equally confident for disgust, happiness, anger, and embarrassment (all values of <italic>p</italic> > 0.54) but significantly more confident in recognizing happiness compared to fear (<italic>p</italic> = 0.007) and pride (<italic>p</italic> = 0.002). This effect was mediated by the significant condition by emotion interaction [<italic>F</italic>(6, 100) = 7.02, <italic>p</italic> < 0.002, <italic>η<sub>p</sub></italic><sup>2</sup> = 0.26; <xref rid=\"fig5\" ref-type=\"fig\">Figure 5</xref>], further indicating that posture and body-movement restrictions differentially affected participant’s confidence in identifying specific emotions. Generally, participants had greater confidence for face-related compared to body-related emotions. Simple effect analyses confirmed that participants were more confident in their identification of unrestricted over wheelchair-restricted displays for disgust (<italic>p</italic> < 0.006), happiness (<italic>p</italic> = 0.02), anger (<italic>p</italic> < 0.0001), and pride (<italic>p</italic> < 0.0001). Unlike accuracy data, confidence between conditions did not differ for fear (<italic>p</italic> = 0.98) or embarrassment (<italic>p</italic> = 0.91). Interpreting this pattern of results in the context of status-related classifications of emotion from the literature, the greatest confidence differences between unrestricted vs. wheelchair-restricted conditions occurred for the dominant status emotions of anger, pride, and disgust, and the least confidence differences in recognizing the low-status emotional displays of fear and embarrassment.</p><fig id=\"fig5\" position=\"float\"><label>Figure 5</label><caption><p>Experiment 2 – Confidence ratings for emotion recognition by condition. Error bars indicate standard error. Stars indicate significant condition differences.</p></caption><graphic xlink:href=\"fpsyg-11-01961-g005\"/></fig><p>In sum, emotion recognition accuracy and confidence were strongly influenced by restricting posture and body movement in nonverbal displays. Recognition accuracy and performance confidence declined disproportionately for dominant emotions expressed by non-disabled people in wheelchairs. Conversely, accuracy and confidence for subordinate emotions of fear and embarrassment were affected less by bodily restrictions. Clearly, the high-confidence ratings for effective emotional communication of Experiment 1’s non-disabled but wheelchair-restricted group were not supported by the emotion recognition data.</p></sec></sec></sec><sec id=\"sec22\"><title>General Discussion</title><p>This study investigated the contributions of body posture and movement to the production and recognition of nonverbal emotion displays. A less evaluated implication of embodied emotion theory is that emotional experience is bidirectional: Another person’s body induces emotional simulation in the observer’s body and, at the same time, the current inputs from the observer’s body or state contribute to the emotional processing. In this view, a person’s current body inputs may affect the production of nonverbal displays of emotion. In Experiment 1, non-disabled participants were randomly assigned to either the unrestricted or the wheelchair-restricted group. Although we expected the wheelchair-restricted group to use the face more to express body-related emotions because body posture and movement was constrained, we investigated whether these constraints had a general effect for all emotions or whether it influenced some emotions more than others. In Experiment 2, we used emotion production videotapes from Experiment 1 to assess emotion recognition performance from unrestricted and wheelchair-restricted emotional displays.</p><p>To confirm that the communication of specific emotions differentially relies on use of the face vs. the body, in Experiment 1 we asked participants to express their preference for face and body use for six different emotions. Consistent with prior literature, we found that preferred use of the body and face differed across emotions, with status-oriented emotions often expressed using the body (e.g., <xref rid=\"ref2\" ref-type=\"bibr\">App et al., 2011</xref>; <xref rid=\"ref39\" ref-type=\"bibr\">Tracy et al., 2015</xref>). Participants preferred to use the face to express happiness and disgust, the body to express pride, and there were no significant differences in preference for use of face or both to express anger, fear, or embarrassment. In addition, we found no differences between unrestricted and wheel-chair restricted groups for stated face vs. body use preferences, suggesting that these preferences are not based on current body input. Instead they appear to rely on past experience, socialization (i.e., what configuration has been taught or is typically displayed for that emotion), or a more evolved response.</p><p>One primary focus of the study was to assess how restrictions of body posture and movement might affect the relative use of the face and body to nonverbally communicate emotions. An examination of emotionally congruent face and body movements revealed that posture and movement restrictions did not strongly change their presence in this non-disabled sample, although coder’s notes indicated that it affected the size of the movements.</p><p>Further, posture and movement restrictions did not affect participants’ confidence ratings as to how well they communicated the emotions. They were equally confident as the unrestricted group that they had successfully communicated each emotion. This was particularly notable for emotions relying on body movement and posture. Current body inputs did not influence non-disabled participants’ perceptions of emotional competency when they were restrained. Instead, like their verbal channel preferences, their confidence ratings appeared to be based on a culmination of past experiences. This suggests that people are not aware of the nonverbal changes that they experience when body posture and movement are constrained to a wheelchair. In light of embodied emotion theory, body restrictions should have altered emotional experience. Contrary to these predictions, we found that despite actual physical emotion production changes, feedback from the expresser’s body was disregarded, ignored, or discounted. Instead of relying on current body inputs, people’s conceptions of emotional competency appeared to be built from experiences/inputs acquired over time.</p><p>Nonetheless, body movement restrictions and their corresponding alterations of emotional visual cues did influence participants’ ability to recognize nonverbal displays of emotions. In Experiment 2, participants recognized emotions based on the videos from Experiment 1. Emotion recognition accuracy was impaired for videos of emotions expressed by the wheelchair-restricted individuals. In addition to a large, overall decrement in recognition for wheelchair-restricted displays of emotion, the emotions of disgust and anger were particularly affected. It is important to note that the recognition of all social status emotions is impeded when the expresser is sitting and restrained. Instead, these constraints specifically affect the displays of social dominance emotions. Following embodiment theory, viewing expressers who are seated and constrained may affect emotional simulation and decrease recognition of the emotion in the viewer because it may be more difficult to feel dominant, which is associated with the expression of disgust and anger.</p><p>This study represents a first step in establishing how the body and its posture influence the perception and communication of emotions in non-disabled individuals. Our goal was to gain insight into emotional communication issues for people with physical disabilities, restricting them to wheelchairs that constrain both body movement and posture. Although the participants in this laboratory study did not have physical disabilities and we simulated disability by constraining participants to wheelchairs, our goal was to investigate some previously untested implications of embodiment theory and to provide baseline data as to how similar kinds of restrictions influence abled-bodied individuals. Future studies will examine emotional perception and communication for individuals with physical disabilities.</p><sec id=\"sec23\"><title>Implications for Embodiment Theory and Social Interactions</title><p>The results of this study have implications for embodiment theory as well as for social interactions. First, current bodily inputs do not affect confidence in emotion displays in a way consistent with actual changes in the efficacy of displays. Generally, people may not be fully aware of situational factors that have an impact on the efficacy of their display. Not surprisingly, in the case of physical restraints that affect body movement and posture, it is the expression of body-involved emotion displays that are most affected.</p><p>Differences in emotion displays associated with constrained body movement also may influence those interacting with that person. Specifically, they influence the perception and recognition of dominant emotions. These results have implications for how people might establish dominance in group interactions – unrestricted displays of dominant emotions are interpreted more accurately. People expect those high in power to show erect and open postures, upward tilt of the head, and touching behavior (<xref rid=\"ref7\" ref-type=\"bibr\">Carney et al., 2005</xref>; <xref rid=\"ref17\" ref-type=\"bibr\">Hall et al., 2005</xref>). However, because body-related emotions are also associated with conveying relative social status, it is important to consider how such constraints may affect social perception between individuals in work and other settings who are establishing relative social power. Another related factor is the lower relative position of people in sitting positions. A number of studies have shown that relative height between individuals involved in social interactions influences social outcomes and perceived social power (<xref rid=\"ref32\" ref-type=\"bibr\">Schwartz et al., 1982</xref>;<xref rid=\"ref30\" ref-type=\"bibr\">Schubert, 2005</xref>; <xref rid=\"ref31\" ref-type=\"bibr\">Schubert et al., 2013</xref>). In a recent study, <xref rid=\"ref36\" ref-type=\"bibr\">Thomas and Pemstein (2015)</xref> found that the outcomes of social cooperation games could be biased in favor of one of the partners merely by providing visual cues from web cameras that showed one player to be above the other.</p><p>Further, these findings have important implications for work interactions, social interactions, and social-emotional development of those with physical disabilities, especially those confined to wheelchairs. Embodiment theory would suggest that those who do not have use of their body may not experience these emotions in the same way as non-disabled people and may not use the same visual cues to communicate social status emotions effectively. This may thereby contribute to well-known social challenges faced by those with disabilities. Such effects would likely have an impact not only on the social dynamic between individuals, influencing both the person producing the emotional display, but also on people observing that person’s display. For example, non-disabled individuals may over or under estimate the emotional experience of those with physical disabilities that may lead to more challenging interactions and affect hierarchical relations within work-place settings. There might be an increased likelihood that social status could be miscommunicated if one individual had a physical disability and the other did not. Also, if those constrained to wheelchairs violate expectations by not showing these nonverbal behaviors, people may make individual rather than situational attributions and perceive the person as less powerful or dominant.</p><p>Correspondingly, our findings support anecdotal reports of physically disabled individuals in wheelchairs feeling marginalized. Individuals with physical disabilities often report that non-disabled persons ignore them or do not afford them appropriate respect or status in social or work environments. Many people with physical disabilities who are confined to wheelchairs believe that they experience and communicate emotions well, but they also report being ignored and disrespected in social interactions as well as begin undervalued and subtly discriminated at work (<xref rid=\"ref14\" ref-type=\"bibr\">Gay, 2004</xref>; <xref rid=\"ref10\" ref-type=\"bibr\">Daley et al., 2018</xref>).</p><p>Given that this study was conducted with non-disabled individuals, a natural next step would be to follow up this study with physically disabled participants. It would be important to assess face vs. body use preferences for the different emotions among those who have experienced a movement restriction since birth as well as those who have acquired the restriction later in life. To the degree that the source of the preference is based on one’s own prior experience instead of a specific evolved response or caused by the immediate situation, then this preference should be less evident in those who have been paraplegic since birth, for example.</p><p>As with the preference measure, an important follow-up question would be to assess emotion production and recognition in physically disabled populations to determine whether the outcomes from this study are applicable to those chronically confined to a sitting position in a wheelchair. That is, for the participants in this study, was it the difference between their normal stature and ability to communicate that affected their display, or is there a more general effect such that those who are always restricted will show the same differences in display? Reports from actual wheelchair users indicate that they feel less able to express dominant emotions, particularly anger, while in a wheelchair (<xref rid=\"ref6\" ref-type=\"bibr\">Cahill and Eggleston, 1994</xref>, p. 304). One woman, relegated to a wheelchair, was so angry she indicated that she wanted to jump out of her chair and shake the person she was directing her anger toward, but all she did was remain seated and “grit her teeth.” New wheelchairs that raise the individual to the eye level of non-disabled individuals (e.g., <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.quantumrehab.com/ilevel-power-chairs/\">https://www.quantumrehab.com/ilevel-power-chairs/</ext-link>) may help reduce at least the relative height differences contributing to implicit status differences.</p><p>These findings suggest additional questions to be addressed with future research. Do people who have physical differences that constrain bodily movement alter their displays, especially for these target emotions (i.e., when the restriction is more chronic, have people learned to adjust displays of body-related emotions)? Do people who have physical differences that constrain bodily movement correspondingly adjust their confidence in expression of these emotions? Finally, do perceivers who have physical differences that constrain bodily movement more accurately perceive emotions (especially status related emotions) of others with similar physical differences? If so, this may be because the embodiment of these displays is consistent across expresser and the perceiver, or that given social-grouping differences, those with these physical differences have more experience interacting with and perceiving these displays. The answer to these questions may provide information on whether people’s display is more influenced by the broader social context, in which personal history would matter less or in sensitivity to changes in their own body.</p><p>Finally, we also want to consider perceiver effects, especially between non-disabled and disabled individuals. Do perceivers’ stereotypes influence what they see regarding emotional expression in people in a wheelchair? In other words, would perceptions be the same if the person were constrained in a wheelchair vs. a kitchen chair or a paraplegic person vs. hostage? It is possible that non-disabled perceivers stereotype people in wheelchairs as less dominant, and this affects the social dynamic between individuals. Studies examining the perception of wheelchair users by non-disabled individuals showed that non-disabled individuals associate more negative emotions (e.g., depression and guilt) with the disabled person (<xref rid=\"ref41\" ref-type=\"bibr\">Vilchinsky et al., 2010</xref>). As with the higher status displays, those perceiving physically disabled people in wheelchairs may fail to account for the situational influences on emotional expression and may attribute the displays to the person’s internal state not situational constraint.</p><p>In conclusion, recent work investigating embodiment theory emphasizes that the current state of the body can have an important influence on psychological and emotional processes. Consistent with this, we found that constraints on body movement and position influence how emotions are produced and how they are perceived. This study’s findings provide only partial support for a pure embodiment perspective. Current body inputs do not appear to influence the expresser’s perception of their own emotional communication: people’s confidence in their displays did not change with body constraints. However, the results emphasize the important contributions of the body in nonverbal emotional communication and how these contributions may affect the social-emotional context of those with physical disabilities.</p></sec></sec><sec sec-type=\"data-availability\" id=\"sec24\"><title>Data Availability Statement</title><p>The datasets generated for this study are available on request to the corresponding author.</p></sec><sec id=\"sec25\"><title>Ethics Statement</title><p>The studies involving human participants were reviewed and approved by Claremont McKenna College IRB. The patients/participants provided their written informed consent to participate in this study.</p></sec><sec id=\"sec26\"><title>Author Contributions</title><p>CR was the project lead. KM, SA, and AS contributed parts of their independent research and thesis projects to the manuscript. EM and DM contributed expertise in the intellectual formation of the project and in writing the manuscript. All authors contributed to the article.</p><sec id=\"sec27\" sec-type=\"coi\"><title>Conflict of Interest</title><p>The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p></sec></sec></body><back><ack><p>This paper honors the memory of AS whose ideas and life inspired this research project investigating how physical disabilities may influence emotion perception and communication. She was an excellent colleague and scholar; we miss her intelligence and optimism, and we appreciate the additional insights she provided from her lived experiences with neuromuscular disease and wheelchair mobility.</p></ack><fn-group><fn id=\"fn001\"><p><sup>1</sup>Consistent with the analyses conducted on the full data set, the group × face/body use × emotion ANOVA conducted with Huynh-Feldt corrected, equal sized groups found no main effects of group <italic>F</italic>(1,40) = 0.002, <italic>p</italic> = 0.96 and emotion <italic>F</italic>(4.95, 198.13) = 1.78, <italic>p</italic> = 0.12; no group × emotion interaction <italic>F</italic>(4.95, 198.13) = 1.03, <italic>p</italic> = 0.40; a main effect of face/body use <italic>F</italic>(1.26, 50.50) = 40.86, <italic>p</italic> < 0.0001; no group × face/body use interaction <italic>F</italic>(1.26, 50.50) = 2.69, <italic>p</italic> = 0.10; a significant emotion × face/body use interaction <italic>F</italic>(6.50, 259.93) = 12.36, <italic>p</italic> < 0.0001; and a significant 3-way interaction <italic>F</italic>(6.50, 259,93) = 2.11, <italic>p</italic> = 0.02.</p></fn></fn-group><ref-list><title>References</title><ref id=\"ref1\"><mixed-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Abo Foul</surname><given-names>Y.</given-names></name><name><surname>Eitan</surname><given-names>R.</given-names></name><name><surname>Aviezer</surname><given-names>H.</given-names></name></person-group> (<year>2018</year>). <article-title>Perceiving emotionally incongruent cues from faces and bodies: older adults get the whole picture</article-title>. <source>Psychol. 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[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"review-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Front Genet</journal-id><journal-id journal-id-type=\"iso-abbrev\">Front Genet</journal-id><journal-id journal-id-type=\"publisher-id\">Front. Genet.</journal-id><journal-title-group><journal-title>Frontiers in Genetics</journal-title></journal-title-group><issn pub-type=\"epub\">1664-8021</issn><publisher><publisher-name>Frontiers Media S.A.</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32849845</article-id><article-id pub-id-type=\"pmc\">PMC7432156</article-id><article-id pub-id-type=\"doi\">10.3389/fgene.2020.00885</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Genetics</subject><subj-group><subject>Review</subject></subj-group></subj-group></article-categories><title-group><article-title>Neurodevelopmental Disorders Caused by Defective Chromatin Remodeling: Phenotypic Complexity Is Highlighted by a Review of ATRX Function</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Timpano</surname><given-names>Sara</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/1030748/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Picketts</surname><given-names>David J.</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><xref ref-type=\"aff\" rid=\"aff2\"><sup>2</sup></xref><xref ref-type=\"aff\" rid=\"aff3\"><sup>3</sup></xref><xref ref-type=\"aff\" rid=\"aff4\"><sup>4</sup></xref><xref ref-type=\"aff\" rid=\"aff5\"><sup>5</sup></xref><xref ref-type=\"corresp\" rid=\"c001\"><sup>*</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/548941/overview\"/></contrib></contrib-group><aff id=\"aff1\"><sup>1</sup><institution>Regenerative Medicine Program, Ottawa Hospital Research Institute</institution>, <addr-line>Ottawa, ON</addr-line>, <country>Canada</country></aff><aff id=\"aff2\"><sup>2</sup><institution>Department of Biochemistry, Microbiology, and Immunology, University of Ottawa</institution>, <addr-line>Ottawa, ON</addr-line>, <country>Canada</country></aff><aff id=\"aff3\"><sup>3</sup><institution>Department of Cellular and Molecular Medicine, University of Ottawa</institution>, <addr-line>Ottawa, ON</addr-line>, <country>Canada</country></aff><aff id=\"aff4\"><sup>4</sup><institution>Department of Medicine, University of Ottawa</institution>, <addr-line>Ottawa, ON</addr-line>, <country>Canada</country></aff><aff id=\"aff5\"><sup>5</sup><institution>University of Ottawa Brain and Mind Research Institute</institution>, <addr-line>Ottawa, ON</addr-line>, <country>Canada</country></aff><author-notes><fn fn-type=\"edited-by\"><p>Edited by: Mojgan Rastegar, University of Manitoba, Canada</p></fn><fn fn-type=\"edited-by\"><p>Reviewed by: Craig Peterson, University of Massachusetts Medical School, United States; Tom Moss, Laval University, Canada</p></fn><corresp id=\"c001\">*Correspondence: David J. Picketts, <email>[email protected]</email></corresp><fn fn-type=\"other\" id=\"fn004\"><p>This article was submitted to Epigenomics and Epigenetics, a section of the journal Frontiers in Genetics</p></fn></author-notes><pub-date pub-type=\"epub\"><day>11</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><year>2020</year></pub-date><volume>11</volume><elocation-id>885</elocation-id><history><date date-type=\"received\"><day>07</day><month>6</month><year>2020</year></date><date date-type=\"accepted\"><day>20</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>Copyright © 2020 Timpano and Picketts.</copyright-statement><copyright-year>2020</copyright-year><copyright-holder>Timpano and Picketts</copyright-holder><license xlink:href=\"http://creativecommons.org/licenses/by/4.0/\"><license-p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</license-p></license></permissions><abstract><p>The ability to determine the genetic etiology of intellectual disability (ID) and neurodevelopmental disorders (NDD) has improved immensely over the last decade. One prevailing metric from these studies is the large percentage of genes encoding epigenetic regulators, including many members of the ATP-dependent chromatin remodeling enzyme family. Chromatin remodeling proteins can be subdivided into five classes that include SWI/SNF, ISWI, CHD, INO80, and ATRX. These proteins utilize the energy from ATP hydrolysis to alter nucleosome positioning and are implicated in many cellular processes. As such, defining their precise roles and contributions to brain development and disease pathogenesis has proven to be complex. In this review, we illustrate that complexity by reviewing the roles of ATRX on genome stability, replication, and transcriptional regulation and how these mechanisms provide key insight into the phenotype of ATR-X patients.</p></abstract><kwd-group><kwd>intellectual disability</kwd><kwd>neurodevelopmental disorder</kwd><kwd><italic>ATRX</italic></kwd><kwd>ATR-X syndrome</kwd><kwd>chromatin remodeling</kwd><kwd>SWI/SNF</kwd></kwd-group><funding-group><award-group><funding-source id=\"cn001\">Canadian Institutes of Health Research<named-content content-type=\"fundref-id\">10.13039/501100000024</named-content></funding-source><award-id rid=\"cn001\">FRN133586</award-id></award-group></funding-group><counts><fig-count count=\"3\"/><table-count count=\"1\"/><equation-count count=\"0\"/><ref-count count=\"159\"/><page-count count=\"14\"/><word-count count=\"0\"/></counts></article-meta></front><body><sec id=\"S1\"><title>Introduction</title><p>Neurodevelopmental disorders (NDD) are highly complex and heterogeneous conditions that have a global prevalence of approximately 2–3% of the population. Despite being aware of these conditions for over a century, it is only within the last decade that the development of exome and whole genome sequencing has dramatically enhanced the discovery of the underlying causes of these disorders. Indeed, the SysID database<sup><xref ref-type=\"fn\" rid=\"footnote1\">1</xref></sup> list 1,334 genes (updated March 26, 2020) that contribute to intellectual disability (ID) (<xref rid=\"B67\" ref-type=\"bibr\">Kochinke et al., 2016</xref>), while approximately 100 genes are associated with autism spectrum disorder (ASD) (<xref rid=\"B117\" ref-type=\"bibr\">Satterstrom et al., 2020</xref>). Interestingly, a substantial proportion of NDD causing genes are involved in chromatin and/or transcriptional regulation including the broad family of ATP-dependent chromatin remodelers.</p><p>Chromatin remodelers utilize energy from ATP hydrolysis to alter nucleosome spacing/density or to facilitate histone variant exchange (<xref rid=\"B18\" ref-type=\"bibr\">Bowman and Poirier, 2015</xref>). There are four main families of ATP-dependent chromatin remodelers characterized by their conserved ATPase domain of the helicase II superfamily (<xref ref-type=\"fig\" rid=\"F1\">Figure 1</xref>). These families are divided into the (1) SWI/SNF group, large complexes made up of ∼15 subunits, (2) ISWI group, heterodimers and four subunit complexes, (3) CHD group, complexes that incorporate up to ∼10 subunits, and (4) INO80 group, ∼15 subunit complexes. In addition, the focus of this review is ATRX which represents one of several orphan families that have been less studied mechanistically. In addition to the ATPase domain that is subdivided into two RecA-like lobes, these chromatin remodeling enzymes are characterized by additional motifs that facilitate protein–protein interactions (e.g., HSA and QLQ domains), DNA interactions (e.g., HAND and SLIDE domains), and chromatin interactions (e.g., SANT, chromodomain, and bromodomain) (<xref ref-type=\"fig\" rid=\"F1\">Figure 1</xref>). The SWI/SNF and INO80 family primarily promote transcription and DNA repair by sliding/ejecting nucleosomes (SWI/SNF/BRG1, BRM) or depositing histone variants (INO80/SRCAP). The ISWI and CHD family primarily mediate nucleosome maturation and spacing to promote chromatin formation post-replication, highly structured chromatin (ISWI), or transcriptional repression (CHD) (<xref rid=\"B25\" ref-type=\"bibr\">Clapier et al., 2017</xref>).</p><fig id=\"F1\" position=\"float\"><label>FIGURE 1</label><caption><p>The ATP-dependent chromatin remodeling family. Representation of the four chromatin remodeling groups: SWI/SNF, ISWI, CHD, and INO80. Each group contains an ATPase domain subdivided into RecA-like lobes 1 and 2 separated by a variable linker region (labeled insertion). SWI/SNF and INO80 share an HSA domain, while ISWI and CHD share a SANT and SLIDE domain.</p></caption><graphic xlink:href=\"fgene-11-00885-g001\"/></fig><p>Mutations in these enzyme families results in aberrant gene expression that impinges on many cellular activities including DNA replication, DNA repair, as well as cell proliferation and differentiation. As indicated above, mutations in many of these family members lead to a wide range of NDD and symptoms (<xref rid=\"T1\" ref-type=\"table\">Table 1</xref>) with some of the more well-studied disorders being Coffin-Siris syndrome (CSS), Nicolaides-Baraitser syndrome (NCBS), CHARGE syndrome, and ATR-X syndrome. Moreover, it is becoming clear that mutations in multiple components of these remodeling complexes cause ID (<xref rid=\"T1\" ref-type=\"table\">Table 1</xref>) and can contribute to a spectrum of clinical phenotypes that is best illustrated by mutations in the SWI/SNF interacting partners (<xref rid=\"B17\" ref-type=\"bibr\">Bögershausen and Wollnik, 2018</xref>; <xref rid=\"B139\" ref-type=\"bibr\">van der Sluijs et al., 2019</xref>). The reader is referred to a number of recent reviews for detailed information of these different remodeler classes (<xref rid=\"B58\" ref-type=\"bibr\">Hota and Bruneau, 2016</xref>; <xref rid=\"B125\" ref-type=\"bibr\">Sokpor et al., 2017</xref>; <xref rid=\"B44\" ref-type=\"bibr\">Goodwin and Picketts, 2018</xref>; <xref rid=\"B3\" ref-type=\"bibr\">Alfert et al., 2019</xref>; <xref rid=\"B53\" ref-type=\"bibr\">Hoffmann and Spengler, 2019</xref>).</p><table-wrap id=\"T1\" position=\"float\"><label>TABLE 1</label><caption><p>ATP-dependent chromatin remodelers are a frequent cause of NDDs. List of NDD implicated genes which are incorporated into ATP-dependent chromatin remodeling complexes.</p></caption><table frame=\"hsides\" rules=\"groups\" cellspacing=\"5\" cellpadding=\"5\"><thead><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Family</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Gene</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Associated disease</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">References</td></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">SWI/SNF</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">ARID1A</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">ID, CSS</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><xref rid=\"B136\" ref-type=\"bibr\">Tsurusaki et al. (2012)</xref>; <xref rid=\"B68\" ref-type=\"bibr\">Kosho et al. (2013)</xref></td></tr><tr><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">ARID1B</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">ID, CSS, ASD</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><xref rid=\"B60\" ref-type=\"bibr\">Hoyer et al. (2012)</xref>; <xref rid=\"B114\" ref-type=\"bibr\">Santen et al. (2012)</xref>; <xref rid=\"B28\" ref-type=\"bibr\">De Rubeis et al. (2014)</xref></td></tr><tr><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">ARID2</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">ID, CSS-like, NCBS-like</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><xref rid=\"B121\" ref-type=\"bibr\">Shang et al. (2015)</xref>; <xref rid=\"B19\" ref-type=\"bibr\">Bramswig et al. (2017)</xref></td></tr><tr><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">DPF2</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">ID, CSS-like</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><xref rid=\"B142\" ref-type=\"bibr\">Vasileiou et al. (2018)</xref></td></tr><tr><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">PBRM</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">ASD</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><xref rid=\"B98\" ref-type=\"bibr\">O’Roak et al. (2012b)</xref></td></tr><tr><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">SMARCA2</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">NCBS</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><xref rid=\"B140\" ref-type=\"bibr\">Van Houdt et al. (2012)</xref>; <xref rid=\"B135\" ref-type=\"bibr\">Tsurusaki et al. (2014b)</xref></td></tr><tr><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">SMARCB1</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">ID, CSS, Kleefstra</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><xref rid=\"B66\" ref-type=\"bibr\">Kleefstra et al. (2012)</xref>; <xref rid=\"B115\" ref-type=\"bibr\">Santen et al. (2013)</xref>; <xref rid=\"B150\" ref-type=\"bibr\">Wieczorek et al. (2013)</xref></td></tr><tr><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">SMARCC1</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">ASD</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><xref rid=\"B57\" ref-type=\"bibr\">Hormozdiari et al. (2015)</xref></td></tr><tr><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">SMARCC2</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">ID, ASD</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><xref rid=\"B85\" ref-type=\"bibr\">Machol et al. (2019)</xref></td></tr><tr><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">SMARCE1</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">CSS-like</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><xref rid=\"B136\" ref-type=\"bibr\">Tsurusaki et al. (2012)</xref>; <xref rid=\"B68\" ref-type=\"bibr\">Kosho et al. (2013)</xref>; <xref rid=\"B150\" ref-type=\"bibr\">Wieczorek et al. (2013)</xref></td></tr><tr><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">SMARCA4</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">CSS</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><xref rid=\"B115\" ref-type=\"bibr\">Santen et al. (2013)</xref>; <xref rid=\"B135\" ref-type=\"bibr\">Tsurusaki et al. (2014b)</xref></td></tr><tr><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">SOX11</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">ID, CSS-like</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><xref rid=\"B134\" ref-type=\"bibr\">Tsurusaki et al. (2014a)</xref></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">ISWI</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">BAZ1A</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">ID</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><xref rid=\"B158\" ref-type=\"bibr\">Zaghlool et al. (2016)</xref></td></tr><tr><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">BAZ1B</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Williams-Beuren syndrome</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><xref rid=\"B84\" ref-type=\"bibr\">Lu et al. (1998)</xref>; <xref rid=\"B100\" ref-type=\"bibr\">Peoples et al. (1998)</xref></td></tr><tr><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">BAZ2B</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">ID, ASD</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><xref rid=\"B120\" ref-type=\"bibr\">Scott et al. (2020)</xref></td></tr><tr><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">BPTF</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">ID</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><xref rid=\"B128\" ref-type=\"bibr\">Stankiewicz et al. (2017)</xref></td></tr><tr><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">SMARCA1</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">CSS-like, Rett syndrome-like, schizophrenia</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><xref rid=\"B65\" ref-type=\"bibr\">Karaca et al. (2015)</xref>; <xref rid=\"B55\" ref-type=\"bibr\">Homann et al. (2016)</xref>; <xref rid=\"B80\" ref-type=\"bibr\">Lopes et al. (2016)</xref></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">CHD</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">CHD2</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Lennox-Gastaut syndrome, Doose Syndrome, Epileptic encephalopathy</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><xref rid=\"B23\" ref-type=\"bibr\">Carvill et al. (2013)</xref></td></tr><tr><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">CHD3</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Macrocephaly, ID, impaired speech/language</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><xref rid=\"B124\" ref-type=\"bibr\">Snijders Blok et al. (2018)</xref></td></tr><tr><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">CHD4</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Sifrim–Hitz–Weiss syndrome</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><xref rid=\"B149\" ref-type=\"bibr\">Weiss et al. (2020)</xref></td></tr><tr><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">CHD5</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">ASD-like</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><xref rid=\"B105\" ref-type=\"bibr\">Pisansky et al. (2017)</xref></td></tr><tr><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">CHD7</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">CHARGE syndrome, ASD</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><xref rid=\"B144\" ref-type=\"bibr\">Vissers et al. (2004)</xref>; <xref rid=\"B113\" ref-type=\"bibr\">Sanlaville et al. (2006)</xref>; <xref rid=\"B98\" ref-type=\"bibr\">O’Roak et al. (2012b)</xref></td></tr><tr><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">CHD8</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">ASD</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><xref rid=\"B97\" ref-type=\"bibr\">O’Roak et al. (2012a</xref>, <xref rid=\"B98\" ref-type=\"bibr\">b)</xref>; <xref rid=\"B28\" ref-type=\"bibr\">De Rubeis et al. (2014)</xref>; <xref rid=\"B92\" ref-type=\"bibr\">Neale et al. (2016)</xref></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">INO80</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">INO80</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Microcephaly, ID</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><xref rid=\"B2\" ref-type=\"bibr\">Alazami et al. (2015)</xref></td></tr><tr><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">SRCAP</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Floating-harbor syndrome</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><xref rid=\"B56\" ref-type=\"bibr\">Hood et al. (2012)</xref></td></tr><tr><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">YY1AP1</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">ID</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><xref rid=\"B47\" ref-type=\"bibr\">Guo et al. (2017)</xref></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">ATRX</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">ATRX</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">ATR-X syndrome</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><xref rid=\"B41\" ref-type=\"bibr\">Gibbons et al. (1995)</xref></td></tr></tbody></table></table-wrap><p>Here, we will review recent studies on ATRX to highlight the multiple biochemical functions chromatin remodeling proteins participate in, and the diverse set of mechanisms that, collectively, contribute to the complexity underlying the pathogenesis of NDD.</p></sec><sec id=\"S2\"><title>Molecular Genetics of the ATR-X Syndrome</title><p>The ATR-X syndrome is a rare human congenital disorder with a wide range of symptoms that primarily affects males. Over 200 cases have been identified worldwide with and an estimated prevalence of <1–9/1,000,000 (<xref rid=\"B39\" ref-type=\"bibr\">Gibbons, 2006</xref>). Affected individuals display cognitive impairment typically described as severe ID, and many are non-verbal, capable of speaking or signing only a few words (<xref rid=\"B118\" ref-type=\"bibr\">Saugier-Veber et al., 1995</xref>; <xref rid=\"B45\" ref-type=\"bibr\">Guerrini et al., 2000</xref>). Originally, the presence of alpha thalassemia was used as a diagnostic tool to identify affected individuals, but there is variability in the hematological symptoms (<xref rid=\"B41\" ref-type=\"bibr\">Gibbons et al., 1995</xref>). The majority of patients are affected with microcephaly and skeletal malformations (<xref rid=\"B54\" ref-type=\"bibr\">Holmes and Gang, 1984</xref>; <xref rid=\"B22\" ref-type=\"bibr\">Carpenter et al., 1999</xref>). Muscle development is also impaired in most, leading to delayed motor development and hypotonia, while approximately one third of patients experience seizures (<xref rid=\"B81\" ref-type=\"bibr\">Lossi et al., 1999</xref>).</p><p>Although ATR-X syndrome patients present with a heterogeneous phenotype, the disease is caused by mutations in a single gene, the <italic>ATRX</italic> locus, which spans over 300 kbp on chromosome Xq13.3-21.1 (<xref rid=\"B41\" ref-type=\"bibr\">Gibbons et al., 1995</xref>, <xref rid=\"B42\" ref-type=\"bibr\">2008</xref>; <xref rid=\"B103\" ref-type=\"bibr\">Picketts et al., 1996</xref>). The <italic>ATRX</italic> gene encodes two major trasncripts (<xref ref-type=\"fig\" rid=\"F2\">Figure 2</xref>), one encoding the full length protein and a truncated isoform generated by an alternative splicing event that retains intron 11 and terminates translation prematurely (<xref rid=\"B36\" ref-type=\"bibr\">Garrick et al., 2004</xref>; <xref rid=\"B90\" ref-type=\"bibr\">Mitson et al., 2011</xref>). The full length transcript encodes a protein of 285 kDa in size while the shorter transcript generates a smaller truncated protein that is 180 kDa and lacks the ATP-dependent remodeling domain (<xref rid=\"B103\" ref-type=\"bibr\">Picketts et al., 1996</xref>; <xref rid=\"B36\" ref-type=\"bibr\">Garrick et al., 2004</xref>).</p><fig id=\"F2\" position=\"float\"><label>FIGURE 2</label><caption><p>ATRX domain structure. Schematic diagram of full-length ATRX (282 kDa), truncated ATRX (ATRXt; 180 kDa), and locations of the key protein interaction domains. The two isoforms share an ADD domain, a HP1α binding motif, an EZH2 binding motif, while the ADD domain is comprised of a GATA-like zinc finger and a PHD-like zinc finger. The full-length polypeptide also contains a DAXX binding motif, a SNF2-ATPase domain comprising RecA-like lobes 1 and 2 separated by a linker region containing a MeCP2 binding motif (MeCP2/Insertion), and a PML targeting motif.</p></caption><graphic xlink:href=\"fgene-11-00885-g002\"/></fig><p>The <italic>N</italic>-terminus of the ATRX protein houses several motifs critical for its interaction with chromatin, including a heterochromatin protein 1 (HP1α) binding motif (PxVxL) (<xref rid=\"B73\" ref-type=\"bibr\">Lechner et al., 2005</xref>) and enhancer of zeste homolog 2 (EZH2) interaction domain (<xref rid=\"B21\" ref-type=\"bibr\">Cardoso et al., 1998</xref>), and the ATRX-DNMT3-DNMT3L (ADD) domain (<xref rid=\"B104\" ref-type=\"bibr\">Picketts et al., 1998</xref>; <xref rid=\"B154\" ref-type=\"bibr\">Xie et al., 1999</xref>). The ADD domain comprises a GATA-like zinc finger and a plant homeodomain (PHD)-like finger that targets the dual histone post translational modification (PTM), H3K9me3 and H3K4me0 (<xref rid=\"B6\" ref-type=\"bibr\">Argentaro et al., 2007</xref>; <xref rid=\"B30\" ref-type=\"bibr\">Dhayalan et al., 2011</xref>; <xref rid=\"B35\" ref-type=\"bibr\">Eustermann et al., 2011</xref>; <xref rid=\"B63\" ref-type=\"bibr\">Iwase et al., 2011</xref>). A region within the center of the polypeptide mediates death domain associated protein (DAXX) binding (<xref rid=\"B155\" ref-type=\"bibr\">Xue et al., 2003</xref>). Toward the <italic>C</italic>-terminus lies the highly conserved RecA-like lobes 1 and 2 that together are required for ATPase activity (<xref rid=\"B103\" ref-type=\"bibr\">Picketts et al., 1996</xref>), as well as mapped regions for interactions with the methyl-CpG-binding protein (MeCP2) (<xref rid=\"B91\" ref-type=\"bibr\">Nan et al., 2007</xref>) and the promyelocytic leukemia protein (PML) (<xref rid=\"B12\" ref-type=\"bibr\">Bérubé et al., 2008</xref>) (<xref ref-type=\"fig\" rid=\"F2\">Figure 2</xref>).</p><p>The majority of ATR-X syndrome causing mutations are missense mutations mapping within the ADD (50%) and SNF2-like/helicase domains (30%) (<xref rid=\"B6\" ref-type=\"bibr\">Argentaro et al., 2007</xref>; <xref rid=\"B42\" ref-type=\"bibr\">Gibbons et al., 2008</xref>). To date, there has been a lack of genotype: phenotype correlations identified, although mutations within the ADD domain typically produce more severe psychomotor phenotypes compared to mutations in the SNF2-like/helicase domain (<xref rid=\"B8\" ref-type=\"bibr\">Badens et al., 2006</xref>).</p><p>It should also be noted that somatic mutations in the <italic>ATRX</italic> gene have been identified in a wide range of cancers that include pancreatic neuroendocrine tumors, gliomas, neuroblastomas, and sarcomas, which will not be discussed here but have been the focus of recent reviews (<xref rid=\"B147\" ref-type=\"bibr\">Watson et al., 2015</xref>; <xref rid=\"B32\" ref-type=\"bibr\">Dyer et al., 2017</xref>).</p></sec><sec id=\"S3\"><title>Interacting Partners and Biochemical Functions</title><p>All functional studies indicate that ATRX is a heterochromatin interacting protein. It localizes to pericentromeric heterochromatin, telomeres, PML nuclear bodies, and physically interacts with the HP1 family (<xref rid=\"B88\" ref-type=\"bibr\">McDowell et al., 1999</xref>; <xref rid=\"B15\" ref-type=\"bibr\">Berube et al., 2000</xref>; <xref rid=\"B131\" ref-type=\"bibr\">Tang et al., 2004</xref>). Later work demonstrated that ATRX could be recruited to the heterochromatin histone mark, H3K9me3, either indirectly by its interaction with HP1 or recruitment by MeCP2, and directly by binding of the ADD domain to H3K9me3 that lies adjacent to unmethylated H3K4 (<xref rid=\"B15\" ref-type=\"bibr\">Berube et al., 2000</xref>; <xref rid=\"B10\" ref-type=\"bibr\">Bannister et al., 2001</xref>; <xref rid=\"B91\" ref-type=\"bibr\">Nan et al., 2007</xref>; <xref rid=\"B35\" ref-type=\"bibr\">Eustermann et al., 2011</xref>; <xref rid=\"B63\" ref-type=\"bibr\">Iwase et al., 2011</xref>). ATRX and DAXX were identified as interacting partners by two separate groups, one using ATRX co-IP experiments and the other a Flag-DAXX pull-down approach (<xref rid=\"B155\" ref-type=\"bibr\">Xue et al., 2003</xref>; <xref rid=\"B131\" ref-type=\"bibr\">Tang et al., 2004</xref>). Further characterization showed that most of the endogenous ATRX protein is in a 1 MDa complex with DAXX, while DAXX also fractionates in a 700 kDa complex independent of ATRX. Deletion mutants were used to demonstrate that the ATRX/DAXX interaction was mediated through the PAH domain of DAXX and a region between the ADD and SNF2 domains within ATRX (<xref rid=\"B131\" ref-type=\"bibr\">Tang et al., 2004</xref>). Both the ATRX/DAXX complex and recombinant ATRX protein had DNA or nucleosome stimulated ATPase activity which was impaired by patient mutations that localized to the ATPase domain (<xref rid=\"B155\" ref-type=\"bibr\">Xue et al., 2003</xref>; <xref rid=\"B131\" ref-type=\"bibr\">Tang et al., 2004</xref>). A mononucleosome disruption assay was used to demonstrate that the ATRX/DAXX complex could alter the DNAse I digestion pattern of the mononucleosome in the presence of ATP. The localization of the altered digestion pattern indicated that ATRX/DAXX disrupts DNA–histone interactions at the entry site of the nucleosome and does not alter nucleosome phasing. In addition, a triple-helix strand displacement assay was used to show that the ATRX/DAXX complex and ATRX alone had a DNA translocase property similar to the RSC and SWI/SNF complexes (<xref rid=\"B155\" ref-type=\"bibr\">Xue et al., 2003</xref>). More recent work has indicated that DAXX is an H3.3-specific histone chaperone that functions with ATRX to deposit the histone variant at pericentric and telomeric repeats, while DAXX functions independently of ATRX to repress retrotransposons (<xref rid=\"B79\" ref-type=\"bibr\">Lewis et al., 2010</xref>; <xref rid=\"B52\" ref-type=\"bibr\">Hoelper et al., 2017</xref>). In this regard, the ATRX/DAXX complex shows some similarities with the ISWI complex ACF and its interactions with the histone chaperone NAP1 (<xref rid=\"B38\" ref-type=\"bibr\">Gemmen et al., 2005</xref>; <xref rid=\"B132\" ref-type=\"bibr\">Torigoe et al., 2011</xref>). These properties could be used to reconstitute H3.3 containing nucleosomal arrays that might guide future <italic>in vitro</italic> biochemical studies to further define ATRX function during transcription or DNA replication (<xref rid=\"B101\" ref-type=\"bibr\">Peterson, 2009</xref>).</p><p>Indeed, in a series of papers ATRX, DAXX, and the histone variant H3.3 were shown to co-localize at telomeres where the ATRX/DAXX complex functions as a histone chaperone to deposit H3.3 into telomeric heterochromatin (<xref rid=\"B152\" ref-type=\"bibr\">Wong et al., 2009</xref>; <xref rid=\"B43\" ref-type=\"bibr\">Goldberg et al., 2010</xref>; <xref rid=\"B79\" ref-type=\"bibr\">Lewis et al., 2010</xref>). Further work showed that DAXX functions as the histone chaperone, that H3.3K9me3 deposition occurs in a replication-independent manner by the complex, and both H3.3 loading and heterochromatin organization by ATRX/DAXX is mediated by SUV39H1 and PML in PML-associated heterochromatin domains (<xref rid=\"B31\" ref-type=\"bibr\">Drané et al., 2010</xref>; <xref rid=\"B43\" ref-type=\"bibr\">Goldberg et al., 2010</xref>; <xref rid=\"B79\" ref-type=\"bibr\">Lewis et al., 2010</xref>; <xref rid=\"B49\" ref-type=\"bibr\">He et al., 2015</xref>; <xref rid=\"B137\" ref-type=\"bibr\">Udugama et al., 2015</xref>; <xref rid=\"B29\" ref-type=\"bibr\">Delbarre et al., 2017</xref>). Additionally, ATRX was shown to be critical for the formation of senescence-induced heterochromatin foci (SAHF) that help drive cancer cells into therapy-induced senescence (<xref rid=\"B69\" ref-type=\"bibr\">Kovatcheva et al., 2017</xref>). Finally, ATRX has been shown to bind to the <italic>Xist</italic> lncRNA to promote recruitment of the PRC2 repressive complex and facilitate stable heterochromatin formation of the silenced X-chromosome (<xref rid=\"B116\" ref-type=\"bibr\">Sarma et al., 2014</xref>). RNA binding remains an understudied role for ATRX, although several reports have shown a range of interactions with multiple lncRNAs including TERRA (telomeric repeat-containing RNA) (<xref rid=\"B24\" ref-type=\"bibr\">Chu et al., 2017</xref>; <xref rid=\"B93\" ref-type=\"bibr\">Nguyen et al., 2017</xref>), <italic>ChRO1</italic> in muscle (<xref rid=\"B99\" ref-type=\"bibr\">Park et al., 2018</xref>), and minor satellite RNAs at centromeric heterochromatin (<xref rid=\"B106\" ref-type=\"bibr\">Ren et al., 2020</xref>). These interactions are mediated through a unique <italic>N</italic>-terminal domain in ATRX to regulate differentiation, gene expression, DNA and histone methylation and chromatin compaction (<xref rid=\"B24\" ref-type=\"bibr\">Chu et al., 2017</xref>; <xref rid=\"B93\" ref-type=\"bibr\">Nguyen et al., 2017</xref>; <xref rid=\"B99\" ref-type=\"bibr\">Park et al., 2018</xref>; <xref rid=\"B106\" ref-type=\"bibr\">Ren et al., 2020</xref>).</p><p>A role for ATRX at heterochromatin was also strengthened by chromatin immunoprecipitation experiments that showed enriched ATRX binding at telomeres and centromeres. Interestingly, ATRX was also enriched at repetitive DNA elements while having a lower frequency of binding within gene bodies (<xref rid=\"B72\" ref-type=\"bibr\">Law et al., 2010</xref>). Further characterization showed that ATRX was prevalent at long terminal repeats of endogenous retrovirus sequences of family K (ERVK), at variable number tandem repeats (VNTRs) and at simple tandem repeats (<xref rid=\"B72\" ref-type=\"bibr\">Law et al., 2010</xref>). Many of the tandem repeats were GC-rich sequences that are predicted to form G-quadruplex secondary DNA structures (G4 DNA) including the telomeric repeats and some CpG islands. The formation of G4 DNA has been proposed to have important roles in the regulation of gene expression, as well as be prohibitive to DNA replication and transcription (<xref rid=\"B107\" ref-type=\"bibr\">Rhodes and Lipps, 2015</xref>; <xref rid=\"B138\" ref-type=\"bibr\">Valton and Prioleau, 2016</xref>; <xref rid=\"B141\" ref-type=\"bibr\">Varshney et al., 2020</xref>). <italic>In vitro</italic> studies confirmed that ATRX can bind to G4 DNA structures (<xref rid=\"B72\" ref-type=\"bibr\">Law et al., 2010</xref>). In addition, ATRX mutations have variable effects on α-globin expression including individuals with the same mutation. <xref rid=\"B72\" ref-type=\"bibr\">Law et al. (2010)</xref> demonstrate that one ATRX binding site lies within a GC-rich VNTR sequence 1 kb upstream of the HBM gene. The authors demonstrate a positive correlation in ATR-X patients such that increasing VNTR repeat size increases the severity of the α-thalassemia as measured by the level of HbH inclusions in red blood cells. Since the sequence is a GC-rich VNTR that is predicted to form G4 quadruplexes, it was inferred that increasing repeat size increases the probability to form G4 DNA that subsequently alters HBM expression.</p><p>The ATRX protein was also shown to co-purify with the MRE11-RAD50-NBS1 (MRN) complex, an active player in the processing of DNA double strand breaks (DSB) that suggested ATRX was critical to maintain genome integrity (<xref rid=\"B76\" ref-type=\"bibr\">Leung et al., 2013</xref>). Consistent with this finding, ATRX knockdown studies in HeLa cells resulted in defects in mitotic progression and micronuclei formation from altered chromosome condensation and centromeric cohesion (<xref rid=\"B109\" ref-type=\"bibr\">Ritchie et al., 2008</xref>). Other studies indicated that ATRX loss impaired replication fork progression during S-phase resulting in telomere fragility, increased DSB, and mitotic catastrophe (<xref rid=\"B62\" ref-type=\"bibr\">Huh et al., 2012</xref>, <xref rid=\"B61\" ref-type=\"bibr\">2016</xref>; <xref rid=\"B76\" ref-type=\"bibr\">Leung et al., 2013</xref>; <xref rid=\"B148\" ref-type=\"bibr\">Watson et al., 2013</xref>).</p><p>The ATRX-DAXX-H3.3 complex is critical for this heterochromatic formation and subsequent maintenance (<xref rid=\"B72\" ref-type=\"bibr\">Law et al., 2010</xref>; <xref rid=\"B33\" ref-type=\"bibr\">Eid et al., 2015</xref>; <xref rid=\"B49\" ref-type=\"bibr\">He et al., 2015</xref>; <xref rid=\"B137\" ref-type=\"bibr\">Udugama et al., 2015</xref>). H3.3 within telomeric regions is targeted for trimethylation on its K9 residue (<xref rid=\"B49\" ref-type=\"bibr\">He et al., 2015</xref>; <xref rid=\"B137\" ref-type=\"bibr\">Udugama et al., 2015</xref>). H3.3K9me3 recruits more ATRX-DAXX-H3.3 complexes, which in turn will deposit H3.3, creating a positive feedback loop required for maintaining telomere structure (<xref rid=\"B137\" ref-type=\"bibr\">Udugama et al., 2015</xref>). Failure to establish proper structure will reduce telomere integrity and result in an increase of non-coding telomeric transcript expression (<xref rid=\"B49\" ref-type=\"bibr\">He et al., 2015</xref>; <xref rid=\"B137\" ref-type=\"bibr\">Udugama et al., 2015</xref>).</p><p>The eclectic properties of the ATRX protein do not make it intuitively obvious how an aberration of these functions can result in a neurodevelopment disorder with cognitive deficits. In the remaining section, we discuss the characterization of mouse models and the insights they have provided into the pathophysiology of ATR-X patients and, more generally, the complex etiology of NDDs caused by defective epigenetic regulators.</p></sec><sec id=\"S4\"><title>Delineating Pathophysiological Mechanisms of the ATR-X Syndrome</title><sec id=\"S4.SS1\"><title>Functional Effects of Patient Mutations and Generation of Animal Models</title><p>One of the first questions addressed was do patient mutations affect protein stability and function? Immunoblots of extracts from patient-derived EBV-transformed B-lymphocytes showed significantly reduced levels of ATRX protein from all patients tested (<xref rid=\"B88\" ref-type=\"bibr\">McDowell et al., 1999</xref>; <xref rid=\"B20\" ref-type=\"bibr\">Cardoso et al., 2000</xref>). Interestingly, in patients with early premature stop codons (e.g., p.Arg37X), translation was initiated from an internal methionine that produced a smaller truncated protein at ∼30% levels leading to a milder phenotype (<xref rid=\"B59\" ref-type=\"bibr\">Howard et al., 2004</xref>; <xref rid=\"B1\" ref-type=\"bibr\">Abidi et al., 2005</xref>; <xref rid=\"B11\" ref-type=\"bibr\">Basehore et al., 2015</xref>). Utilizing recombinant proteins, other studies demonstrated that mutations within the ATPase domain attenuated ATPase activity but did not reduce it, while mutations in the ADD domain or the PML-targeting domain reduced localization to chromocenters and PML nuclear bodies, respectively (<xref rid=\"B20\" ref-type=\"bibr\">Cardoso et al., 2000</xref>; <xref rid=\"B12\" ref-type=\"bibr\">Bérubé et al., 2008</xref>). <italic>Atrx</italic>-null mutations in mice show defective extraembryonic trophoblast development and die embryonically at ∼E9.5 (<xref rid=\"B37\" ref-type=\"bibr\">Garrick et al., 2006</xref>). Collectively, these studies indicate that ATR-X syndrome causing mutations are functional hypomorphs, while more severe mutations are not found and are presumably non-viable.</p><p>Several different ATRX-deficient mouse lines have been generated and used for functional characterization. The most widely used model is a floxed allele in which loxP sites flanked exon 18 which encodes the ATP-binding pocket (<xref rid=\"B14\" ref-type=\"bibr\">Bérubé et al., 2005</xref>; <xref rid=\"B37\" ref-type=\"bibr\">Garrick et al., 2006</xref>). These animals have been crossed with several different tissue-specific Cre driver lines to inactivate ATRX in skeletal muscle progenitors (<xref rid=\"B62\" ref-type=\"bibr\">Huh et al., 2012</xref>), Sertoli cells (<xref rid=\"B9\" ref-type=\"bibr\">Bagheri-fam et al., 2011</xref>), osteobalsts (<xref rid=\"B127\" ref-type=\"bibr\">Solomon et al., 2013</xref>), chondrocytres (<xref rid=\"B126\" ref-type=\"bibr\">Solomon et al., 2009</xref>), the retina (<xref rid=\"B89\" ref-type=\"bibr\">Medina et al., 2009</xref>; <xref rid=\"B70\" ref-type=\"bibr\">Lagali et al., 2016</xref>), and the developing forebrain (<xref rid=\"B14\" ref-type=\"bibr\">Bérubé et al., 2005</xref>) among others. A second transgenic line (<italic>Atrx<sup>Δ<italic>E</italic>2</sup></italic>) was developed by deleting exon 2 and replacing it with a SA-IRES-β-geo cassette (<xref rid=\"B95\" ref-type=\"bibr\">Nogami et al., 2011</xref>; <xref rid=\"B122\" ref-type=\"bibr\">Shioda et al., 2011</xref>). This mutation was meant to mimic the p.Arg37X mutation and make an <italic>N</italic>-terminally truncated ATRX protein by initiating translation from an internal methionine codon (<xref rid=\"B59\" ref-type=\"bibr\">Howard et al., 2004</xref>; <xref rid=\"B1\" ref-type=\"bibr\">Abidi et al., 2005</xref>). Both of these models will be discussed in more detail in the following sections. Finally, an overexpression transgenic line was created with the <italic>ATRX</italic> cDNA under control of a CMV enhancer/β-actin promoter which resulted in growth retardation, neural tube defects and a high incidence of embryonic lethality demonstrating the importance of ATRX dosage to normal development (<xref rid=\"B13\" ref-type=\"bibr\">Berube et al., 2002</xref>).</p><p>While each of these models has provided valuable insight into disease mechanisms (as highlighted below), the field still awaits a model whereby a single nucleotide variant is introduced into the <italic>ATRX</italic> gene to recreate a known patient mutation, such as the common p.Arg246Cys mutation within the ADD domain.</p></sec><sec id=\"S4.SS2\"><title>Replication Stress Impairs Progenitor Expansion Resulting in Microcephaly</title><p>Microcephaly is common to many NDDs and has also been observed in mouse models that deleted other genes encoding chromatin remodeling proteins (<xref rid=\"B111\" ref-type=\"bibr\">Ronan et al., 2013</xref>). Most ATR-X patients develop postnatal microcephaly and, in instances where CT or MRI scans have been performed, mild cerebral atrophy was detected. Similarly, three patient autopsy reports also described that the brains were smaller in size (<xref rid=\"B39\" ref-type=\"bibr\">Gibbons, 2006</xref>).</p><p>The first indication that ATRX may be critical for cell growth came from co-culture experiments of embryonic stem cells (ESC) from control or <italic>Atrx</italic>-null cells. This experiment demonstrated that the <italic>Atrx</italic>-null cells were underrepresented after 4-days of co-culture. Flow cytometry was used to examine cell cycle distribution but no differences were observed suggesting that cells may have transitioned to a slower cycling, differentiated cell type (<xref rid=\"B37\" ref-type=\"bibr\">Garrick et al., 2006</xref>). Given that ATRX has high expression in the developing forebrain, the <italic>Atrx</italic><sup><italic>fl/fl</italic></sup> line was next crossed with the forebrain-specific Foxg1-Cre line (<italic>Atrx<sup><italic>Foxg</italic>1<italic>Cre</italic></sup></italic>) that initiates Cre expression in the developing telencephalon at ∼E8.5 (<xref rid=\"B51\" ref-type=\"bibr\">Hébert and McConnell, 2000</xref>). Loss of ATRX caused a 25–30% reduction in cell number with a noticeably smaller neocortex and hippocampus including almost a complete absence of the dentate gyrus that likely contributed to early postnatal lethality (<xref rid=\"B14\" ref-type=\"bibr\">Bérubé et al., 2005</xref>). Similar to ESC co-culture experiments, BrdU-pulse labeling experiments suggested no differences in the proportion of cycling cells. However, there was a dramatic increase in the number of TUNEL+ cells leading to a reduction in the number of neurons that reached the cortical layers (<xref rid=\"B14\" ref-type=\"bibr\">Bérubé et al., 2005</xref>). Similarily, the <italic>Atrx<sup>Δ<italic>E</italic>2</sup></italic> mice were smaller and also showed brain hypocellularity, although to a milder extent (<xref rid=\"B95\" ref-type=\"bibr\">Nogami et al., 2011</xref>). <italic>Atrx</italic> inactivation in Sertoli and muscle cells, also showed a significant impact on the growth of the tissue (<xref rid=\"B9\" ref-type=\"bibr\">Bagheri-fam et al., 2011</xref>; <xref rid=\"B62\" ref-type=\"bibr\">Huh et al., 2012</xref>). However, a retina progenitor cell cKO only had a limited effect on the size of the mature tissue suggesting that defects in cell cycle progression lead to significant hypocellularity among tissues that require a rapid expansion over a narrow developmental timeframe (<xref rid=\"B89\" ref-type=\"bibr\">Medina et al., 2009</xref>).</p><p>Although not initially observed, delayed cell cycle progression through both S- and G2/M phases was later observed in other studies (<xref rid=\"B109\" ref-type=\"bibr\">Ritchie et al., 2008</xref>; <xref rid=\"B148\" ref-type=\"bibr\">Watson et al., 2013</xref>; <xref rid=\"B61\" ref-type=\"bibr\">Huh et al., 2016</xref>). For G2/M, the progression from prometaphase to metaphase was prolonged and associated with sister chromatid cohesion and congression defects that impaired proper separation at anaphase leading to DNA bridges and micronuclei (<xref rid=\"B109\" ref-type=\"bibr\">Ritchie et al., 2008</xref>). Evidence for DNA bridges and micronuclei in <italic>Atrx<sup><italic>Foxg</italic>1<italic>Cre</italic></sup></italic> mice were also detected by high magnification microscopy at the apical surface on cortical sections of the neuroepithelium where cortical progenitors complete mitosis. Interestingly, a recent study has also demonstrated that ATRX promotes telomere cohesion between sister telomeres to mediate the repair of DNA DSB (<xref rid=\"B83\" ref-type=\"bibr\">Lovejoy et al., 2020</xref>).</p><p>Defects in S-phase were observed using BrdU-pulse chase flow cytometry experiments where a delay from G1 to S-phase and also from G2/M to the following G1 phase was identified (<xref rid=\"B62\" ref-type=\"bibr\">Huh et al., 2012</xref>). Co-labeling experiments demonstrated that ATRX associated with replicating chromatin during mid-late S-phase and cytological analysis showed a high prevalence of genomic instability that was enriched at telomeres and pericentromeric heterochromatin (<xref rid=\"B62\" ref-type=\"bibr\">Huh et al., 2012</xref>; <xref rid=\"B148\" ref-type=\"bibr\">Watson et al., 2013</xref>). Moreover, treatment with a compound that binds and stabilizes G4 DNA increased the number of telomere dysfunction induced foci (TIFs) and decreased cell viability suggesting that G4 DNA formation was the main cause of replicative stress (<xref rid=\"B148\" ref-type=\"bibr\">Watson et al., 2013</xref>). Other studies indicate that replication stress at telomeres may be mediated by increased TERRA transcription (<xref rid=\"B93\" ref-type=\"bibr\">Nguyen et al., 2017</xref>). TERRA levels are tightly regulated and critical for both telomere formation, replication and maintenance (<xref rid=\"B16\" ref-type=\"bibr\">Bettin et al., 2019</xref>). However, when TERRA levels increase, as shown for ATRX-null cells, it enhances R-loop (RNA-DNA hybrid) formation and G4 DNA stabilization, each of which increase replication fork stalling and collapse that then induces homology directed repair (HDR) and TIFs (<xref rid=\"B93\" ref-type=\"bibr\">Nguyen et al., 2017</xref>). The regulation of R-loops has also been proposed for other proteins that interact with G4 DNA during replication and/or at telomeres (<xref rid=\"B159\" ref-type=\"bibr\">Zhou et al., 2014</xref>; <xref rid=\"B108\" ref-type=\"bibr\">Ribeiro de Almeida et al., 2018</xref>; <xref rid=\"B133\" ref-type=\"bibr\">Toubiana and Selig, 2018</xref>; <xref rid=\"B86\" ref-type=\"bibr\">Maffia et al., 2020</xref>). It should also be mentioned here that somatic <italic>ATRX</italic> mutations, and to a lesser extent H3.3 and DAXX mutations, are prevalent in cancers characterized by ALT (alternative lengthening of telomeres), a HDR mechanism to maintain telomere length that is normally suppressed by ATRX (<xref rid=\"B50\" ref-type=\"bibr\">Heaphy et al., 2011</xref>; <xref rid=\"B82\" ref-type=\"bibr\">Lovejoy et al., 2012</xref>; <xref rid=\"B119\" ref-type=\"bibr\">Schwartzentruber et al., 2012</xref>; <xref rid=\"B102\" ref-type=\"bibr\">Pickett and Reddel, 2015</xref>; <xref rid=\"B143\" ref-type=\"bibr\">Verma and Greenberg, 2016</xref>).</p><p>Another indicator of replicative stress as a major impediment to growth of <italic>Atrx</italic>-null cells was demonstrated by studies showing an increased sensitivity to hydroxyurea, enhanced DSBs, and the use of DNA fiber analysis to show increased stalled replication forks and reduced origin firing (<xref rid=\"B76\" ref-type=\"bibr\">Leung et al., 2013</xref>; <xref rid=\"B26\" ref-type=\"bibr\">Clynes et al., 2014</xref>; <xref rid=\"B61\" ref-type=\"bibr\">Huh et al., 2016</xref>). Mechanistically, ATRX physically interacts with the MRN complex where it is thought to block HDR at stalled replication forks to allow for fork restart after the G4 DNA is resolved (<xref rid=\"B26\" ref-type=\"bibr\">Clynes et al., 2014</xref>). Indeed, one group demonstrated that fork protection could be restored by treatment with an Mre11 exonuclease inhibitor (<xref rid=\"B61\" ref-type=\"bibr\">Huh et al., 2016</xref>). This study also suggested that hyperactivation of poly (ADP-ribose) polymerase-1 (Parp-1) during neurogenesis may function as a compensatory mechanism to protect stalled replication forks from collapse and HDR, thus dampening the extent of cell loss during neurogenesis.</p><p>During mouse cortical development, the cortical layers are formed in sequential fashion from a pool of neural progenitor cells (NPC) that must continue to proliferate to maintain the pool size. Alterations in NPC proliferation depletes the pool often resulting in altered cell lamination typically observed as a reduction in upper layer neurons. For <italic>Atrx<sup><italic>Foxg</italic>1<italic>Cre</italic></sup></italic> mice, the most proliferative NPCs that ultimately would become upper layer neurons are more susceptible to incur replication-induced DNA damage. Frequently the resulting genomic instability will occur at telomeres and pericentromeric heterochromatin, but it could also occur at other genomic regions that can form G4 DNA or similar secondary DNA structures that induce replication fork stalling and collapse. Accumulation of sufficient damage further leads to their demise and decreases neuron production and brain size. Indeed, the <italic>Atrx<sup><italic>Foxg</italic>1<italic>Cre</italic></sup></italic> forebrain is reduced in size with a compromised production of upper layer neurons (<xref rid=\"B110\" ref-type=\"bibr\">Ritchie et al., 2014</xref>; <xref rid=\"B61\" ref-type=\"bibr\">Huh et al., 2016</xref>). Similar results have been observed in mice lacking CHD4 and SMARCA5 where the NPCs either fail to progress through the cell cycle or incur significant DNA damage, respectively, prior to undergoing apoptosis (<xref rid=\"B4\" ref-type=\"bibr\">Alvarez-Saavedra et al., 2014</xref>, <xref rid=\"B5\" ref-type=\"bibr\">2019</xref>; <xref rid=\"B94\" ref-type=\"bibr\">Nitarska et al., 2016</xref>).</p><p>The SWI/SNF complex is also critical for brain development but utilizes different mechanisms than ATRX. The SWI/SNF complex is required during early neurogenesis for differentiation from radial glial progenitor cells into intermediate progenitor cells. This switch from a neural stem cell to an NPC is accompanied by the fundamental shift from the npBAF to nBAF complex, which involves the substitution of three subunits (BAF45, BAF53, and BAF55). Failure to switch leads to increased cell death, a small progenitor pool, and failure to further differentiate (<xref rid=\"B75\" ref-type=\"bibr\">Lessard et al., 2007</xref>; <xref rid=\"B153\" ref-type=\"bibr\">Wu et al., 2007</xref>; <xref rid=\"B7\" ref-type=\"bibr\">Bachmann et al., 2016</xref>). Interestingly, while SMARCA5 loss hampers NPC proliferation, loss of its ISWI ortholog SMARCA1 fails to repress expression of proliferation genes resulting in delayed neuronal differentiation and a larger brain (<xref rid=\"B157\" ref-type=\"bibr\">Yip et al., 2016</xref>). Taken together, these examples highlight the importance of chromatin remodeling proteins to NPC homeostasis and provide insight into the multitude of mechanisms at work often resulting in a similar phenotype.</p></sec><sec id=\"S4.SS3\"><title>Transcriptional Deficits Associated With ATRX Mutations</title><p>Chromatin remodeling proteins were first identified as transcriptional coactivators and they continue to be implicated in the regulation of many genes. Since its identification, ATRX has also been presumed to regulate gene transcription. While there is a good level of understanding regarding how ATRX maintains genomic stability through the regulation of tandem repeats, telomeres and pericentromeric heterochromatin, the identification of direct transcriptional targets has proven more challenging. Initial ChIPseq experiments suggested that ATRX was bound at few promoters, gene bodies and regulatory elements (<xref rid=\"B72\" ref-type=\"bibr\">Law et al., 2010</xref>). Further work has suggested that ATRX binding may differ between tissues to ensure proper silencing of repetitive elements located near or within expressed genes in that particular tissue (<xref rid=\"B93\" ref-type=\"bibr\">Nguyen et al., 2017</xref>). Consistent with this idea, ATRX ChIPseq analysis of NPCs demonstrated a higher enrichment of binding sites at gene regulatory elements compared to what was observed in mouse ESCs suggesting that more genes may be under direct ATRX regulation within the brain (<xref rid=\"B72\" ref-type=\"bibr\">Law et al., 2010</xref>; <xref rid=\"B49\" ref-type=\"bibr\">He et al., 2015</xref>; <xref rid=\"B27\" ref-type=\"bibr\">Danussi et al., 2018</xref>). Another contributing factor to differential target gene expression is represented by ATRX effects on α-globin gene expression. Mutational analysis identified >15 ATR-X patients with the identical missense change (p.Arg246Cys), yet they showed a variable degree of hemoglobin H inclusions in blood samples, indicative of differing levels of α-globin expression (<xref rid=\"B40\" ref-type=\"bibr\">Gibbons et al., 1997</xref>). Repression of α-globin expression was dependent on the size of a GC-rich VNTR located within the globin gene cluster (<xref rid=\"B72\" ref-type=\"bibr\">Law et al., 2010</xref>). A second factor driving the tissue specificity and the variable effects was the formation of R-loops caused by the transcription of the GC-rich VNTR sequences (<xref rid=\"B93\" ref-type=\"bibr\">Nguyen et al., 2017</xref>). The larger sequences generate increased R-loops and G4 DNA structures that normally recruit ATRX to re-establish the normal chromatin structure. In the absence of ATRX the R-loop/G4 DNA is not resolved effectively which then impedes both replication and transcription processes (<xref rid=\"B93\" ref-type=\"bibr\">Nguyen et al., 2017</xref>). The slight stochastic nature of these effects likely also dampens readouts of differential expression from RNAseq experiments thereby raising the need for a scRNAseq approach in future studies.</p><p>Gene expression analysis of control and <italic>Atrx<sup><italic>Foxg</italic>1<italic>Cre</italic></sup></italic> cortical samples at two timepoints (E13.5, P0.5) identified 202 and 304 differentially expressed genes (DEGs; ±1.5-fold change), respectively, with almost two-thirds of genes upregulated (<xref rid=\"B77\" ref-type=\"bibr\">Levy et al., 2008</xref>). Among these, 27 were common to both datasets including the downregulation of several ancestral pseudoautosomal region (aPAR) genes (Csfr2a, Dhrsxy, Cd99, and Asmtl) (<xref rid=\"B77\" ref-type=\"bibr\">Levy et al., 2008</xref>, <xref rid=\"B78\" ref-type=\"bibr\">2014</xref>). In mouse, the aPAR genes are located in subtelomeric regions and contain potential G4 DNA sequences. Each gene analyzed had enriched histone H3.3 and ATRX binding within their gene body and showed reduced H3.3 levels when ATRX was absent (<xref rid=\"B78\" ref-type=\"bibr\">Levy et al., 2014</xref>). Interestingly, these intragene G4 DNA sequences also showed increased binding of RNA pol II in <italic>Atrx<sup><italic>Foxg</italic>1<italic>Cre</italic></sup></italic> samples suggesting that transcription becomes impeded at these regions within the gene leading to reduced expression. The authors extended this finding to <italic>Nlgn4</italic>, a gene encoding a post-synaptic cell adhesion molecule implicated in ASDs (<xref rid=\"B64\" ref-type=\"bibr\">Jamain et al., 2003</xref>; <xref rid=\"B71\" ref-type=\"bibr\">Laumonnier et al., 2004</xref>). This result conflicted with the study on R-loop formation which found no differences in RNA pol II loading or histone modifications across genes containing the GC-rich repeats (<xref rid=\"B93\" ref-type=\"bibr\">Nguyen et al., 2017</xref>).</p><p>Other downregulated genes from this analysis include <italic>Gbx2</italic>, <italic>NeuroD4</italic>, <italic>Wif1</italic>, <italic>Nxph1</italic>, <italic>Nxph2</italic>, and <italic>Mbp</italic>, each of which could contribute to cognitive deficits observed in patients but require further analysis to asses their contribution to the phenotype (<xref rid=\"B77\" ref-type=\"bibr\">Levy et al., 2008</xref>, <xref rid=\"B78\" ref-type=\"bibr\">2014</xref>). In a similar experiment in the retina, 173 DEGs were identified with two-thirds upregulated (109 genes) and one-third (64 genes) downregulated (<xref rid=\"B70\" ref-type=\"bibr\">Lagali et al., 2016</xref>). Most of these genes were involved in the regulation of glutamate activity, ion channel regulation or encoded neuroprotective peptides, with four shown to be also dysregulated in the cortex (<italic>Csf2ra</italic>, <italic>Cbln4</italic>, <italic>Syt13</italic>, and <italic>Nlgn4</italic>). Each of these studies showed that the mutant samples had only small numbers of genes with large changes in gene expression and, while some downregulated genes may impede transcriptional elongation, this mechanism may not be universal, particularly as it pertains to upregulated genes.</p><p>However, other indirect mechanisms have been explored to explain transcriptional dysregulation, particularly a loss of repression. The ATRX/DAXX complex is critical for loading H3.3 at telomeres and pericentromeric heterochromatin (<xref rid=\"B43\" ref-type=\"bibr\">Goldberg et al., 2010</xref>; <xref rid=\"B151\" ref-type=\"bibr\">Wong et al., 2010</xref>). Research over the last few years has expanded this regulation to include H3.3 deposition at endogenous retroviral elements, regions associated with imprinted genes, and some CpG islands (<xref rid=\"B34\" ref-type=\"bibr\">Elsässer et al., 2015</xref>; <xref rid=\"B49\" ref-type=\"bibr\">He et al., 2015</xref>; <xref rid=\"B112\" ref-type=\"bibr\">Sadic et al., 2015</xref>; <xref rid=\"B145\" ref-type=\"bibr\">Voon et al., 2015</xref>). At the telomere, the loss of ATRX affected the transcription of telomeric DNA and the non-coding RNA TERRA, although studies conflict on whether levels increase or decrease (<xref rid=\"B33\" ref-type=\"bibr\">Eid et al., 2015</xref>; <xref rid=\"B93\" ref-type=\"bibr\">Nguyen et al., 2017</xref>). Surprisingly, TERRA was also shown to bind to an additional ∼4,000 binding sites aside from the telomere where it co-localized with ATRX (<xref rid=\"B24\" ref-type=\"bibr\">Chu et al., 2017</xref>). Many of these sites were within introns and comprised GA repeats, however, depletion of TERRA usually resulted in downregulation while ATRX depletion increased expression (<xref rid=\"B24\" ref-type=\"bibr\">Chu et al., 2017</xref>). While it remains to be determined how this might affect neuronal gene expression, ATRX has been shown to interact with other lncRNAs including <italic>Xist</italic> to facilitate PRC2 silencing and <italic>ChR01</italic> that is required for heterochromatin reorganization in differentiating muscle cells (<xref rid=\"B116\" ref-type=\"bibr\">Sarma et al., 2014</xref>; <xref rid=\"B99\" ref-type=\"bibr\">Park et al., 2018</xref>). Indeed, ATRX binding to lncRNA or R-loops may be a key mechanism mediating transcriptional repression of specific target genes.</p><p>Histone H3.3 ChIPseq studies have also shown that it is enriched at the intracisternal A-particle endogenous retroviral elements (IAP/ERVs), which account for almost half of all mutation causing ERV insertions (<xref rid=\"B87\" ref-type=\"bibr\">Maksakova et al., 2006</xref>; <xref rid=\"B34\" ref-type=\"bibr\">Elsässer et al., 2015</xref>). Moreover, H3.3 deposition at these sites requires ATRX/DAXX to facilitate H3K9me3 and repression while depletion of ATRX, DAXX, or H3.3 results in reduction of the H3K9me3 mark and IAP/ERV derepression (<xref rid=\"B34\" ref-type=\"bibr\">Elsässer et al., 2015</xref>; <xref rid=\"B49\" ref-type=\"bibr\">He et al., 2015</xref>; <xref rid=\"B145\" ref-type=\"bibr\">Voon et al., 2015</xref>). In mouse ESCs, ERV derepression affected the expression of neighboring genes in a minority of cases with most genes neutral to ERV derepression. It raises the question of whether or not this type of derepression would affect many genes or occur rapidly within a post-mitotic neuron, and thus, function as a major effector in dysregulated gene expression in <italic>Atrx</italic>-null neurons. In this regard, a related study using cultured post-mitotic neurons demonstrated that the ADD domain can also bind the H3K9me3S10ph dual histone mark (<xref rid=\"B96\" ref-type=\"bibr\">Noh et al., 2015</xref>). This histone mark is rapidly induced by neuronal depolarization where it appears at centromeric and pericentromeric heterochromatin co-localized with ATRX to repress transcription of non-coding centromeric minor satellite sequences (<xref rid=\"B96\" ref-type=\"bibr\">Noh et al., 2015</xref>). While it is unclear what the impact of increased centromeric minor satellite transcription would have on disease pathology, it remains to be determined whether this dual mark affects activity-dependent transcription of genes mediating learning or memory.</p><p>It was also demonstrated that ATRX was bound to 56 CpG islands which was unexpected since they are often associated with active chromatin, typically promoters (<xref rid=\"B145\" ref-type=\"bibr\">Voon et al., 2015</xref>). However, these CpG islands were associated with H3K9me3, almost half were methylated and many corresponded to imprinted loci often residing in intragenic regions within a transcriptional unit (<xref rid=\"B145\" ref-type=\"bibr\">Voon et al., 2015</xref>). Indeed, in all cases examined ATRX was bound to the silenced imprinted allele which became reactivated in ATRX KO cells (<xref rid=\"B145\" ref-type=\"bibr\">Voon et al., 2015</xref>). This study contrasted somewhat with an independent report in which ATRX was recruited by MeCP2 to silence the active allele of several imprinted genes in the developing telencephalon (<xref rid=\"B78\" ref-type=\"bibr\">Levy et al., 2014</xref>). The difference in these studies may reflect differential regulation of imprinting loci in ESCs versus differentiating NPCs. Perhaps the most compelling example of derepression came from a study with the <italic>Atrx<sup>Δ<italic>E</italic>2</sup></italic> mice (<xref rid=\"B123\" ref-type=\"bibr\">Shioda et al., 2018</xref>). In this model, the authors identify a small list of 31 DEGs in the adult hippocampus but with most genes (23/31) downregulated (<xref rid=\"B123\" ref-type=\"bibr\">Shioda et al., 2018</xref>). Among the upregulated genes was an imprinted gene from the lymphocyte-regulated gene family, <italic>Xlr3b</italic>. Although <italic>Xlr3b</italic> had widespread expression across many tissues, it was only upregulated in the brain. Further work showed that ATRX bound to a G4 DNA sequence within the CpG island of the <italic>Xlr3b</italic> gene where it normally interacted with DAXX and H3.3 and recruited DNMT1 and DNMT3 to silence the gene. The subsequent overexpression of <italic>Xlr3b</italic> in the <italic>Atrx<sup>Δ<italic>E</italic>2</sup></italic> mice was shown to produce a protein that localized to dendritic RNA granules where it interacted with ribonucleoproteins, dynein proteins and the RNA-binding protein, TIA1, to regulate mRNA transport (<xref rid=\"B123\" ref-type=\"bibr\">Shioda et al., 2018</xref>). One of the targets identified was the mRNA for CAMK II-α which they had previously shown to be deregulated in these animals. Excitingly, they also showed that the G-quadruplex-binding ligand 5-aminolevulinic acid (5-ALA) was able to decrease RNApol II occupancy and <italic>Xlr3b</italic> expression in the <italic>Atrx<sup>Δ<italic>E</italic>2</sup></italic> mice, although methylation of the G4 DNA sequence within the CpG island was not affected. It seems that formation or stabilization of this G4 DNA sequence is required to activate the <italic>Xlr3b</italic> gene and that ATRX normally prevents this by facilitating heterochromatin formation. In this regard, mapping of G4 DNA sequences have shown an enriched number in gene regulatory elements where many function to increase transcription when stabilized (<xref rid=\"B48\" ref-type=\"bibr\">Hänsel-Hertsch et al., 2016</xref>). While G4 DNA stabilization occurs in the <italic>Atrx<sup>Δ<italic>E</italic>2</sup></italic> mice, further work is required to explain how 5-ALA represses Xlr3b transcription when it should stabilize the G4 DNA. Regardless, the derepression of G4 DNA within CpG islands and/or other regulatory elements is an exciting mechanism that can explain DEG upregulation, particularly when coupled with the finding that ATRX binding is increased at regulatory elements in NPCs compared to ESCs. Collectively, the derepression of tandem repeats, retrotransposable elements and G4 quadruplexes can all function to impinge on neuronal function.</p></sec><sec id=\"S4.SS4\"><title>Morphological, Behavioral, and Cell Non-autonomous Deficits</title><p>We have discussed global effects on DNA replication and transcription that occur in the absence of ATRX in the previous two sections. In this section, we review the morphological and functional repercussions of these deficits. Aside from being reduced in size, the <italic>Atrx<sup><italic>Foxg</italic>1<italic>Cre</italic></sup></italic> mice had a normal cortical morphology with proper lamination although a reduction of upper layer neurons (<xref rid=\"B14\" ref-type=\"bibr\">Bérubé et al., 2005</xref>; <xref rid=\"B110\" ref-type=\"bibr\">Ritchie et al., 2014</xref>; <xref rid=\"B61\" ref-type=\"bibr\">Huh et al., 2016</xref>). The reduction in upper layer neurons may also contribute to the partial agenesis of the corpus callosum observed in some patients (<xref rid=\"B39\" ref-type=\"bibr\">Gibbons, 2006</xref>). The hippocampus was also reduced in size while the dentate gyrus consisted of only a few disorganized cells. Behavior analysis was not performed due to the early postnatal lethality, although female heterozygous mice showed impairment in spatial, contextual fear, and novel object recognition memory (<xref rid=\"B130\" ref-type=\"bibr\">Tamming et al., 2017</xref>).</p><p>The <italic>Atrx<sup>Δ<italic>E</italic>2</sup></italic> mice also had smaller brains but no differences in cell density within layers II/III of the prefrontal cortex (PFC) or hippocampus (<xref rid=\"B122\" ref-type=\"bibr\">Shioda et al., 2011</xref>). Examination of dendritic spines in the PFC showed that the <italic>Atrx<sup>Δ<italic>E</italic>2</sup></italic> mice had similar numbers but fewer mature spines and many more, thin, long immature spines (<xref rid=\"B122\" ref-type=\"bibr\">Shioda et al., 2011</xref>). Behavioral analysis indicated that the mice have impaired contextual fear memory (fear conditioning test), spatial memory (Y-maze), but not anxiety behaviors (<xref rid=\"B95\" ref-type=\"bibr\">Nogami et al., 2011</xref>; <xref rid=\"B122\" ref-type=\"bibr\">Shioda et al., 2011</xref>). Electrophysiology studies in hippocampal slices demonstrated reduced NMDAR-dependent long term potentiation (LTP) evoked by high stimulation frequency in hippocampal CA1 neurons which was mediated by increased CAMK2A and GluR1 phosphorylation (<xref rid=\"B95\" ref-type=\"bibr\">Nogami et al., 2011</xref>). This was in contrast to a later article by the same group that showed phosphorylated CAMK2A levels were reduced in the <italic>Atrx<sup>Δ<italic>E</italic>2</sup></italic> mice while 5-ALA restored the levels at the synapse and the phosphorylation levels (<xref rid=\"B123\" ref-type=\"bibr\">Shioda et al., 2018</xref>). A recent article examining hippocampal function using CAMKII-Cre mice to inactivate <italic>Atrx</italic> in postnatal excitatory forebrain neurons demonstrated reduced paired-pulse facilitation and LTP in proximal and distal apical dendrites of CA1 synapses (<xref rid=\"B46\" ref-type=\"bibr\">Gugustea et al., 2019</xref>). This represented the first study of mice in which <italic>Atrx</italic> has been inactivated after neurogenesis and it will be interesting to ascertain the full characterization of these mice.</p><p>Studies of the retina have also provided useful information into ATRX function. Many ATR-X patients have visual problems although this has been an under-appreciated aspect of the phenotype (<xref rid=\"B89\" ref-type=\"bibr\">Medina et al., 2009</xref>). Inactivation of ATRX in retinal progenitors <italic>Atrx<sup><italic>Pax</italic>6<italic>Cre</italic></sup></italic> resulted in a slight reduction in retina size and a specific reduction of interneurons, namely amacrine and horizontal cells (<xref rid=\"B89\" ref-type=\"bibr\">Medina et al., 2009</xref>). Surprisingly, <italic>Atrx<sup><italic>Pitf</italic>1<italic>aCre</italic></sup></italic> mice that ablates ATRX in a bi-potential progenitor that generates amacrine or horizontal cells did not recapitulate the phenotype, while inactivation with a bipolar cell specific Cre driver (<italic>Atrx<sup><italic>Vsx</italic>2<italic>Cre</italic></sup></italic>) did not affect bipolar cell survival but did result in reduced amacrine and horizontal cells suggesting that interneuron survival was a cell non-autonomous effect (<xref rid=\"B70\" ref-type=\"bibr\">Lagali et al., 2016</xref>). Additional characterization of these mice showed that the bipolar axons were mislocalized within the inner plexiform layer and many had axonal swellings or tortuous paths to their targets. Gene expression analysis identified alterations in the glutamate pathway, ion channel regulation and altered expression of neuroprotective peptides. Altered axonal pathfinding was also observed in Drosophila XNP mutants, the homolog to the ATPase domain of ATRX (<xref rid=\"B129\" ref-type=\"bibr\">Sun et al., 2006</xref>). It will be important to further explore in greater detail whether axonal pathfinding is also altered within forebrain or hippocampal neurons.</p></sec></sec><sec id=\"S5\"><title>Perspectives</title><p>Studies to date have indicated that ATRX has multiple roles during forebrain development that can contribute to the phenotype of ATR-X patients. It functions mainly as a heterochromatin interacting protein acting to ensure that repetitive DNA is properly packaged and organized into heterochromatin. We have highlighted how aberrations in heterochromatin maintenance leads to genomic instability and replication stress that impairs NPC expansion leading to a microcephalic brain (<xref ref-type=\"fig\" rid=\"F3\">Figure 3</xref>). The loss of ATRX also affects gene expression typically resulting in increased gene derepression but also downregulation. It remains to be teased apart which targets are direct versus indirect, and when disrupted expression hampers neuronal function. It is likely that inactivation of ATRX in postmitotic neurons, following neurogenesis and lamination, will help define a role for ATRX target genes in altered synaptic activity and/or synaptic plasticity underlying cognitive impairment. Moreover, the contribution of other central nervous system cell types to the phenotype have not been explored. ATRX is expressed in glia and oligodendrocytes which are known to intimately communicate with neurons to mediate function, as shown recently in <italic>Drosophila</italic> glial ATRX dependent ensheathment of sensory neurons, for normal dendritic arborization and stimulus processing (<xref rid=\"B156\" ref-type=\"bibr\">Yadav et al., 2019</xref>). Intriguingly, MRI studies on ATR-X patients showed severe glial defects and white matter disruption, further stressing the need for research in this area (<xref rid=\"B146\" ref-type=\"bibr\">Wada et al., 2013</xref>; <xref rid=\"B74\" ref-type=\"bibr\">Lee et al., 2015</xref>). Importantly, a further understanding of ATRX function and its aberrant molecular pathways are required before potential treatments can be explored. In this regard, treatment with 5-ALA has shown promise in one animal model and it is being investigated in Japanese patients (T. Wada, personal communication). ATRX is but one of many different chromatin remodeling proteins mutated in NDDs but it serves to demonstrate how complex these disorders are and how widely chromatin remodelers impact cellular activities.</p><fig id=\"F3\" position=\"float\"><label>FIGURE 3</label><caption><p>The multiple functions of the ATRX protein. Schematic diagram of ATRX functional influence on brain development and its contribution to NDDs. Normally ATRX utilizes its chromatin remodeling activity to (1) influence transcription and DNA replication in heterochromatic regions to control the rate of proliferation in the neuronal progenitor cell population (bottom arm); and (2) to influence transcription in heterochromatic regions to control differentiation processes (top arm). When ATRX is mutated the cellular proliferation rates in progenitors is slowed resulting in a smaller progenitor population; and the differentiation processes are altered resulting in either dysfunctional cellular morphology or complete absence of specific cell types.</p></caption><graphic xlink:href=\"fgene-11-00885-g003\"/></fig></sec><sec id=\"S6\"><title>Author Contributions</title><p>ST and DP wrote and edited the manuscript together. ST generated the figures and table. Both authors contributed to the article and approved the submitted version.</p></sec><sec id=\"conf1\"><title>Conflict of Interest</title><p>The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p></sec></body><back><fn-group><fn fn-type=\"financial-disclosure\"><p><bold>Funding.</bold> The research supporting this work was funded by two operating grants (FRN133586 and FRN165994) from the Canadian Institute of Health Research awarded to DP.</p></fn></fn-group><ack><p>We are grateful to Dr. Alex Córdova and Valérie Cardin for their valued input and critical reading of the manuscript.</p></ack><fn-group><fn id=\"footnote1\"><label>1</label><p><ext-link ext-link-type=\"uri\" xlink:href=\"https://sysid.cmbi.umcn.nl/\">https://sysid.cmbi.umcn.nl/</ext-link></p></fn></fn-group><ref-list><title>References</title><ref id=\"B1\"><mixed-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Abidi</surname><given-names>F. 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